From f8f1bd864b68809536925d6bdf86af349583a0bb Mon Sep 17 00:00:00 2001 From: 3mmaRand <7593411+3mmaRand@users.noreply.github.com> Date: Thu, 26 Oct 2023 17:00:54 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=203mmaRand?= =?UTF-8?q?/R4BABS@d8b393db7c349513a5ee306a4ac486be479b4674=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../figure-html/unnamed-chunk-10-1.png | Bin 25613 -> 25775 bytes .../figure-html/unnamed-chunk-11-1.png | Bin 26284 -> 26241 bytes .../figure-html/unnamed-chunk-12-1.png | Bin 22221 -> 22307 bytes .../figure-html/unnamed-chunk-5-1.png | Bin 22567 -> 22240 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 25604 -> 25507 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 25123 -> 25537 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 25555 -> 25808 bytes .../figure-html/unnamed-chunk-11-1.png | Bin 15291 -> 15357 bytes .../figure-html/unnamed-chunk-12-1.png | Bin 16140 -> 16167 bytes .../figure-html/unnamed-chunk-14-1.png | Bin 21808 -> 22032 bytes .../figure-html/unnamed-chunk-13-1.png | Bin 23314 -> 22626 bytes .../figure-html/unnamed-chunk-10-1.png | Bin 25736 -> 25415 bytes .../figure-html/unnamed-chunk-11-1.png | Bin 26087 -> 26378 bytes .../figure-html/unnamed-chunk-12-1.png | Bin 22179 -> 22440 bytes .../figure-html/unnamed-chunk-5-1.png | Bin 22016 -> 22033 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 25262 -> 25447 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 25150 -> 25296 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 25281 -> 25593 bytes r4babs2/week-2/data-raw/plant.xlsx | Bin 0 -> 11738 bytes r4babs2/week-2/workshop.html | 4 +- .../figure-html/unnamed-chunk-11-1.png | Bin 15236 -> 15343 bytes .../figure-html/unnamed-chunk-12-1.png | Bin 16201 -> 16250 bytes .../figure-html/unnamed-chunk-14-1.png | Bin 21811 -> 22004 bytes .../figure-html/unnamed-chunk-13-1.png | Bin 23026 -> 23247 bytes .../figure-html/unnamed-chunk-16-1.png | Bin 16022 -> 15987 bytes .../figure-html/unnamed-chunk-17-1.png | Bin 16259 -> 16519 bytes .../figure-html/unnamed-chunk-18-1.png | Bin 14334 -> 14406 bytes .../figure-html/unnamed-chunk-19-1.png | Bin 14835 -> 14930 bytes .../figure-html/unnamed-chunk-20-1.png | Bin 15372 -> 15531 bytes search.json | 1582 ++++++++--------- 30 files changed, 793 insertions(+), 793 deletions(-) create mode 100644 r4babs2/week-2/data-raw/plant.xlsx diff --git a/pgt52m/week-4/workshop_files/figure-html/unnamed-chunk-10-1.png b/pgt52m/week-4/workshop_files/figure-html/unnamed-chunk-10-1.png index 8cca4c27f7ab4644cab267e2c1d9b121cd553cc8..d1828a1d7f5b4d5b463805786890e87208391358 100644 GIT binary patch literal 25775 zcmeFZcTiMa*C%=!1QY~R1eEwFl9OZ!4Xq$KDLD&BPAW-4Ln{h`1j!j%B}d5_l%&LN ziINqBCL=k+J>7usJ9DdM=6-kTo0_Wc52}lE_Fg-j6@F{&wRWJosvIdX4KV-!qzdvf z4*`Gx1_1b<&*OtnnktzV003`KU0L%!_%8$k!NbGD$HzZ+?i>LD0U;qF5fRb(^XD&I zxIj!yOhQ6(@!~~NQc^N9GIDbAOP4NPzI^%0l`9k!6j!farKF^!qN1XvroMLV+V$(# zX=rF@X=&-`=;-O`85kHC85#fi=bsxlZrr?ilZlDx)~#F2%*-q-EUc`oY;0`o?CcyI z9Gsk-TwGk-+}u1oJhyM(=Huhz=jRs?5D*j;6cQ2=78Vu}5xH~cj;N@pn3$NjxVVIb z1QZIDl$4Z`l7hitckkYnmX?;0k-2~WzO1aQoSdAzyu5;ff}*0Ll9H0Lva*VbimIxr znwpxry843$4<0^zsG*^usi~=@rKPQ{t)ru(tE;Q0r>C#4Z(v|xXlQ6;WMph?Y+_>a z=+PrnQ&TfDGjntE$B!RdSXfwET3T6ISzBA%*x1uIyyNyJ$drv z>C>mro;`DRc6M=badmZdb8~ZdcYpr;xrc{`r>CcvmzTG^EIyxpMCN?%UE-o%UK0YBKAu%yADJdyAIXNXIB{ek_iA27C|Ng^=4J#$U0q#$eSJei!{^VRzkK=9*x1Z{>(_7JzO}WrwYRr-baZrfc6N1jb$558P$)DS{r&s*o}QlG-rl~xzW)CHfq{X+ z!NDItehdu_4G#~GjEszqj*g9ujgOB{OicXz`SaJWUz3xQQ&UsFfB&AIo}QVR!C)}6 zv$Jz^bMy1_3kwU2i;GK3OUuj4D=RC1{`^^8U0qvSTVG$_*x1wJUlu&IzB!oPZF2|x&Oiwc|B(Upbx38~{PTuL-?Ko;V%Di*L+;Hpo`K9v|La!{$DLZV-4R0Bk zR(+&@)BRoa5`EzK0^~#B_+>fKcKWYEn}p{pJyMRl?c-OLyT_N{@$sm!?)^gvDsgdV z3~!A%ubX><>E+Ae+Ws5Bn>S*1fTB#=b;Om+HiSqSnFI*i<#5aY^+$RLMEw9=AlE{| z#-$JAlpUVZo)<5#7)$DXbV$YS%Sx+bATcr_EbOBdkLip;dR}c)>>E8~z%Z6+``Ec!I3sdlCQ+jki zm}1htQhYgWXAiUM*t-P@QHftF)BJ**s78c?hAZ1Hkn>Vc$t$ z5KRGq5=H3p5^vUa4sxFtAW-iDG$m-!!~|%?|K>qDF)|>X=xW{Hk#ZG#d_t|G7}PiL zP6l$`!Z(WeU7|JznOl{)eM*T4oskZwhY>BsFsNQVoskj)iLY5(T2b%7pkf$H#Obu2 z3bU-95(j5++8v-IKba#rtmU*fxE=prnxQ}ELNzAlz0W9-mizZNnHNP|5I4{}cSaOH z-%^?DX<22JibpKuls@_&yM#E&E=*1>$r%G%zgu!e2~4cqtABkqMZs^z4-5KM761JS z3-z-imA~WhQ=)}-sqp?%^?Cqtk^A@NZZ$qJGj+N|XCgtU*SjN#HkYF{NJW1i|I}7)E~T#<&`IAI@RkDLfoOEq(69|5!NRfPBCUffNG(}z>_VU>_oWp za&8e=+w1ymJo-R)qTGJ0#f|vPeUTA8(F32diIEGSKAa|oxJ=|-_Unk%4&X}y@YPfC z)xWTCAPS-wl%wou(RnDTMOa)9w;nLF~~Xh_ywUcXh2rj6Au*3shhx50A3N>3WcO7hrHQ z7_+!~f+2fAYAl0d8rXBSWzB*F+bL*k{*G5PSG)!%^qut}3vwEqu{4e5KaeaGB2^Ls__V2qWKA+7mr8yCY4^hFcz}7dd~bcjvGRryj-*1n?t2;}*9HXx6?|WqD?a z88}SdU$PQF?BPuAB&cO^R^eY-v>1SvJl{Zmp&X%YY5i+S_#tB{qod+Q8Y_-J+D;XG z2jJqn$H7P;zNzOcrgLi2uOmX;$UTN`+{t)DaAMIj@$}Q_oV%_s-#fK0Y6LEsc(do* z?%wA-RqK>!2V$f&GZAI}sVdimMJvfQw$@{e~4L{#~pgLh4z5W5b0DbAeVgn#W-u^LOya~I6G^FzNt z%hRbRg(&?0u`_75@>Od0%^-TU(R#ru_b{s&BYgFU3*hHNySX|bZN^8M7K^{6So<^z zdL_)1tN?6*SdDI7lVGtW+22##c@TQI;A11%Yu^vG4F%M%;JR+P?4}fKZlzkxJnRs2 z95-*+wx1nNzW`IXiM1#ry)@2iOO6?0s zARgYq8iL^z20`-FUGg0$od;+49j|EVAaZ^;>Mmq__yK$CGs2d@N2%`6~b;z z!XN3yrhUDn>H~PjiXMww)-upeQ9wJLyOX^-P?#7jj9H={gvKUMCA==Y4bL>LMI2i`X#Pr{UzRGeR zE9Ozc4ri^WlYTN>SXKM(de$z|0&2tH$|4-k9IsS#Ij}J>M1Hd9ayArdv+RCk&tFrn z%jf)7py(SHk$S{=>{f3Hvh0#=#MO;uHPS69H+Mz@idRn7t(vH2Tyaciv9jmUyyz(?Ep0bs1}Dm*z2+Ln8wL&eh(THzZdzoWBVztd-1U<-G=?8c)8VmRI4>^uAHo zihVgN&z9iIo}T-US#5sb<|`)3QJ>oQC2hN0wn| zr5P1JLuN_K*kc?`CmFD8+p>&nyx9-!`hYYl?T{>d^HZD~r7!^p`M z^H|dj?#o`)1U#99qN@?Ds(0M6JI!ItgB0?6xS-Hx8qY^Sff$#krj#-d93;T`4%2wm z%|;Jc+JZqfx#2%w2qgRR&hO~OZY)~6{laH0S;Ub z#%<{+X|yh3=Zb=BMEfwHkL%-$PBt@4`_vjBD9=Z*EM%--%hah%T6(+!q-6}fNG zdQ};icgDFum?EabBS_mAj1#x+v!||Xm^snIDR1qoy;NzIz$Wx$3nk;_o3bgWn;4Zo z;fxCGt4?=(jTk(J7@!R69fE*<6^$pgC3C$|Vyw3pb}2agWIA~JMwy5Evq}5NGxv+e z&29{Ok%IO)

NNF8XJ>-wqCOTL#W>>bu+rYV5P#B*bTQZn!@r*a(J`BZ92(J+1o> z(hK{P!fwrCjg#PSTP;}fCMB%JYtUBJewfqiz9$=m2nTq5rXq<&aYAhIg+Q40HiD50dngBiPDK#vI)q?hh zvd8a&;F;0PE$1~!vQOW5ghzU2a>rGie!Pv=uBdy?O7{0zK4To7OWlU<%YQX1;|W$? zB&dFHwnVIdyP`iJE>7JYr=!ONjX>gX36`(BThINw-z$uy9#Er)`uKz!HI%KN!h0$r z7hgTI{i#tx5T$_L7|!A8$eD{B_A`$G5fRQu26Eyps`f8^-BJk4$g=fxU6`qJb>Kbq zoh|D!=vsv3JOx|&j=pz?zE&DJFgtZ^I>mC5xWbJi1Cd6w%YTNJtd|x%=TD#{3RYJg`g(Wiyf;_EV979X=OX+ zp|^)WIGe5ha=jME@`BCl;YS4MrCq8VQB$eOGjf|g9?zgFiSG9^-9$#~oa_2ZUOSbAJSK`s8K#C@kGl58W!@pI~PSYz+3%JOi%y*flL zAuZsc=90@{(rne|4;u%>Kr{|Tc7t-ILdnj!Prseut2*T7P6K;=;xXdq@a$f$TSQ`#Dxms-yeP*-wfU%@^~el1cztZOYcEk zgc1IIIlouSrZEC5L^yZX;-OSyAtCz7GscYz@rlY1oTDCZ8xsXVYS5c+RMuCM4_FsE#Ud z(pO{dhD;q*3f^3S!GP-=-z$yhi2CEZw~F(qurGGr?nPkTJkZ^Hzj3XRT>`dyUhP-2 z-1F5PP-p(13Ho^84_CrlDt4-Qzv~L&n4SycX1j|I!OPNi5U>Wq)#F6dIdA}@1T0YGFB%(Iw9_1NHg*S zC04_O5=w;Z=Ro#f@e9)a+sk+{v8O~W&#CU;nhnaPtuE7|FZ>&MW5luxWQ_TCTGrXL zs+bNBqEbnk7}Yo02}|9!GxEOdqY-Pzp-n^#qm;zXF1!&(cdGmi#|a+ z97xusDXYk?7&^_;GsocvvD5&Zs9Nob^5s*XfEORll%DO56gU@fwd@Schsg{Z#Qbrl z!h9hs-P`C1OGW4f>H|R`(pagUn6SSt<+gY^T_KcVS&5uHJ@RAOm$0)3G zq**HrflAyd!V8h0?0NH&7&ckF*Hl&K z#OTgY+L<=MI$*{{k89l zdR7x9>W>LeX5*e_5{2iYA%EsYV|Qy`y*qzv=$6^^e$#ceCpZX#MQ0FJ3Y(navXQCB z63N_W<`wj6|3lH;8!o0DjUF9jg43Y(Jq0jdDny@3t{1V^S>c};B8c_Rd=+{^-I+Qj zVGwmuqxUp2#kO~)N^sZ#DXx_uNpb%*1`nJZ1Iz?|<7jj<(c|XE>N#pGgdiN_pMD&X z;&d1Mj83o$mgBjF3W0;m1e7N)WeCvu#v~xNNI@`gDnK35J?iV0Utw2kjSkTU#(tIo z;Ftu~Q*$}PGcz{J>%s>1k9}}h=k8Z=bf^g-Qiz0b*s5S;#>btW_MH=Tsp`qN^b;@K z4m4r8{%I-slFJmaUhDy?I8Z}4rm&sdc@;%a3(42NU2Wey+5{mFO}YxB^>2{?MQD=@ zhVP_e%T4Ff?p)7`%#Ad-J%ceMe0QJ*aWQWdNA(GOm3xkDK54zSbQQ)~JC5k4q)$%8E#`c`qmRv21iZZ_4sO2G|IBO>znTb1O_(t_ z7BM;WI%6sW5aW+UNgn3jm%~`$5UcvUG`y{2D~bpy9@Xz2{TcWegEz}I{a($-3;HN^ z_NO-vBQ-fS>~Hndw^tG&JEF^;XEa}80-KSvOEEhbhhLy^{1UvU?AI%ZLW4RRv7W3QPvKLi|H z!syl|7T-tWw*I~Q!!g){sSDOxdq(P9+A>zfv!AlXdr}n~WcAraPa-~Ow0J(A=NB)L z44XLu*%Bt}KNfWD9#1#l=;lq7O25RqCD_3}CgkT1EM}{z3U0U`()BEo&hB1rBOs*g zZDSL+AF?v9*44;6w#V(^pMKtFTLJ7NNl7V7c-5aoQQ`8`#>KuFE5T;Nc%x6JA{ekj zKxi!OnY+K%OrJ@VCVVq`=&LS9H2&LbxWV?-t{Xq-PeKraf3$0y?nGh;Mb%^CypQ6b zEkkk5C-~*iRxeiKaLFWNf1<7CXo0bQO~-fB{dmX0>VsDpgZuCOA>qC74U=m56K#8F zn|ImAm;Er7C*nk}HsGPVX%XeQ9+t%R56QdcuScV1}J{FeZRP5qy# z7tLi;QXO&~dVJ?@UVx4C%TuCfSd~jMZ6x=}5DzaIEn*yH5`LF`t{^p6`(_?wOFA;6 zrEC)I^-K(K&_Ttq@`{!y+^Q$`}&ob3a}R9B;RB#$?|m z*nrM`e`hdOJyfEdVymFs41wMdPqKE=)T7N%B+u~sO*$K&7%~7x_;^=kw>+E?XW^LT z_fE{7AJcocL)U`X>t^bd51iY-)@Hqa`1J0kdGy8qcpUS%oBQPotY4{AwXNj*#0!7p-l)Dv{?VbD;1!s( zt)-{KY-|}XbmE2OhQ&ua1_1$E!J4wOAP%o2Or!>}40c5Lr+AHhZEk%8kY+o#)$Q#y z8+Rw*BW<};Z)=xN&g^$+-x~kMAdp(RjZcpa^W>$n6T%WLe-Ak{k;0@=WAuE&=y51D ztZ9B!5c7aB!Z}$I(DE9*?N)|ot)%8$==^b%&Pq?N(>QjjVxqppYd|~33@EL7`r7$0 z`EImF*z`Fxn$xw=mM+(p5Pds0$FTgF+VWjyQZH>DeYoKxiT66^kvfh-ETwo^LW7GQ z=whC(`DZJMtJ$du(gk|4sX}rV+*(%=!Xksqh9z#>EeGom!A1V9kWcOfya3vnlZA|N zzvufUS?U%V9hUCxwR`6ZkPpuzwVkFqR?6{a%d#^(oK|>d>bhyazlCH2PBS#_zSCdTg27{s;eCCI)X>mfMK69Q&yVsj}FZ64lD|6efs`%PV5$WYw zFFEtVv5s>bWlRWTkISg?AuDRox1m6f%^f;Xz2g7;rcBE2qwxcsOGr-RgO{t{ediBz zF%e~!Hse6lOPkc1#)6{E5xml;S^M~f{``&BLQU}J`xtSI>UYgc$RTP;0DSc0#>&0tW(3*-%K@zd&Qv(njkWh*+NZo^Ja69xmAhL} z#%0*F9f|t%xAY&u?^>^>XVj28HF$y{0eZxq*5Q13k*>{sA%x-}71HV|OvGO>gRN{3 z{v4we#{389%j{<66}W6bglsIUqV3IV7U*ZCqFsbPxK#bVN9U0C!GTv#1!OjgT|@H8 zQQ~F9Oy3>h!@^~rxuUD^{sohyiVTjGMPW!jeY|&$BKe*Kf3cnJ!%2zSNR(pOt({?@ z?{+Yun>h!Pe)((6m-`JXiH1s3+#I{(?n@~KpZaQ?uC+Exltgg9flD2cq|h)1ek^0o z8L77(U-KT!a}dJ1ne(aO(j1;0eg8+lWI2d_Kk{wYIAyjJDP3IjU_j z;G~&zji={Qxh&8X`Ru@Hl@WeJ@8h!@DL<~G?%!bqQB}fNP$9hYsBeA2odI9HFeEKcXa@rT1U`UL(hCqT%Cv0JjzaHfLSt>1^H>Dw3yH(yr`@ z_U$)vfKV+LA*`$->B~GKP4^H+d2UvwX=lEzxl6-DAoxBoKk&)DznFAOb*$_y;m~mo z!;E_OqmfBGu@SEeAQsnxjYU>PdHTMZRR()X(KKkpRvT@LxB5b?tH)5{g|CSBqtPyV z&$(GCE>yF)2L$+dzgj_-5n4C>vz_|6+gEJhB~aJ(yghj*aCPK6VQJ9-w9ndZ{^dJV z=70r2TK#=_4+FnX296{I3u7}e3nOz@e*ubv2(7?aplEB?1Hz5yK<1)|RRfBN`s9UF zA9iJ>ZXz^71C=h=>5QGB5X_xJ?I-d!5v4ojQRfZ$Iu&M=QajFu2YvvvT`;xVQ4l5FN9(720`wD3OEpWoW_z3nMWjlN%`yC%JF z=v92rgWFz(3xb4X`rvVGj$ULWa+xlAYZ~@8N*`bI@I0ST=Usw_cM$`}F{{=O(FN^~-;`N=3 zN4EHAyTGl3`qh@FUI`{60>4nCA7wgF9{|ldx}o-+jmH+A&R9Ct>}^D~YLlYhY}#V& zVdh0X`;83O(`_lyI{VBOwXH0kE8j-qZ9yK3{4sj8`3O6wbWb-A)94=S5Rh#lXTX&w`IuG^j6OnA&9oooaN_-?MB`leQN-$s1 zJB2Pmk{1u*|4_Q#op;|5W`1~&{z?_-LVF6cE?L=r-JS36&FmbTWJo-RET#v8kpDpA zz7Zmaj1qUhHNbyu*Y7@R>6gd%e0p!56XiYOi=Sn0MG5-~#xRQ^*4l3yl7&A}ob(@?i3P2lKz`_%ivJKaGNfxXsil_2MJv^Xi#-YY*B#VN8amUSqVo3h#AoR+b=g=+>lVAcuk40 z++a7a7U%1zq=c=5A;_m<;;Ud|wH!pZpd!#-b~L)K8yuCgmW9rfbQf=?f5xC?+6XEQWTN@miRs|=?`%9 zc4Ly8CNfh9&}5eodnSCqu%;+ieFT|dUwis_Kr@ZI?QMFie+q&6E(ouows8rDDgnuD zH5UPk4F%j;q#jaCEo zlTZ*Tx-5IsQZJ5)IOipB_<`?R@-_Pd_(8)+@I;^>QsXMDB1-^w#8pTM3#;VpURM8l zRhan9UOQJ~E&it=&@{AZjAAhx7@7pKUNR<;|CSA4HE@HH174d1SR2JJyq zk6a}?ULy;-%2yJdIMq$U*>~iW1ZLweQdz+8MZyi=R>b%DDoT8*h zj)PxVCohIFaNRGaAd*1|v)r61RMT?Xv8`%lkiTX+vSM_t&A?XxV-+TUoIipK>jb&C z3@05WI7EWyDMRv2*OHu5*OoA!i;J4BR1hvjUrY6+B%Upc4?7s4_E||J*ES?TYlAyo zP&>WME$~U(Q!u=fMBRdX@5lGnr)>ssMe#xRlp)IVvy;4;_3wAqnSo(2P&^(@-=0%l zR%QD2EpMl>ud%0QcsmK2dpVN7R3Sc&jSn`#aS?s_si@OojU%*vk9L*zEu=IWjQw%W zGtb1(sd{U38Sz5%*ro}rKYHz~cLzHwBt>6)Zvt}9|4W-+LWp6v!T8}o_w_@KiUIjB z&U+q^VQ)8=$w0QiV*_CskD#p!7ts|UJ3W}97ME3m zve`=|KylTTVxgkFv##shm2hR6eVFoS=OFhVSi@t954L;MK!OxNxuO{D{Kg??qiebc z|E=mcqSdbU+A*rW(_Bn^Q#%cmm?*xnCJnT~cJ@!X61nYD;v zdiJG{dwK)cCO>-@{sWWB#uMw^xr#F^HD*F&ig1KUPF}WoUxiCc{`C_E+S+5 zk>bJkDsnfr^Ju9lChsrUC37t-YI^lAimxAZ5F<5`tE^aV)n7I;x}_tu@UDz-HanPC z*GxcclNNnXUM3h4+Eb0D6GS70qO7BB^YPtn%z^RuQ)~Z zmAOZucY;2w&*5L)lSthcE*66Eatvc@Y4WSyZ~<65UZwS zvE{Gg#z(*iEmCA5V z_8JHEl2CBsk?E)%pf8UF_k%JCO9k8ZuNz(%YXU~e0u`>X#6;f3q0;o6$d%7gS^vPC zDk>P@qeDv2RH^{PghNH**uf9$G7BAj<+n@NN zBJw3rrnDcedAMh@!Vd~o2z0p^$t?rHpK?Q7n8n?EQ`tIp>pdPx4j-fWpk zwEMO{3D~m{2yy?d<7-R1H=?%m{ajSopR1}4Mgu#4)~eP;`d%o}wK$Vu7I z?rob7Hz;I3hp42Ce|r4-F#ir#KmO`-v0f@B9cNuXr|u->4qeBz;YUbrDtk4Ma_hs$ z(!`8^w##V!nqb)`uK8O;{)K2!a11QBmhrGL`*Ywp# zW6#OC2~;nm$MyY3BjcWf2OExRM5N}zh+zj!Apc~9>j+EG)7diF?A_+AOB>w`HMNJR z*Yp!3r%ouj**ifF!%Po8yqgv~{_^NGWUp#GSE{piA%AU6DWyY}OnvjiWnKWcAakaN zveA2A87ZZZnUt`>(^aY;b85p9Gt&?0G)a&k?f}nbN<7`SmDWcA%_Ze|ehcQB1PFH4 zlc0bHCX0trw*<0x7EyOaS$GRPRr7Qf!ai*VAPn;33 z)@BvqG4Z;+T6mg{LEvIEzfjO}BaAHzgFpwqQX-z;MPovj!1L>ZSYz+>{1O`x8+BmAC{#m|aIOl=AC!Jum=*3U&qbADzsH z2(90O($$*%Nc;g*l&@F+K~P=89TH7LN{tCNG*4c@_rpT?hNzDL^%We>;GJ+sF29a4 z!>4Iv(ENw;T$byMdq{(qr;A6d3Fm*B3U2aciIYy09zrWkve@50E`UA|MF)!)~YU%=pA$aIu$1Y-!1jM>Gt?w6gIuWz~ z)z@3(^2V__S73(=hP+)zCQUnDW2sAqtk>DktD6MzAjQY<5ntsmvg0MwqggjEfye$6 zVmF>hq&$_!LK*P(8HSoBB9M)gvrS-8H8C2I=-!AQa4m`s-T5K7x zY_QW?u^4yeSrxTc#O(Q(ES(I9ObMv5Cw;YNOo)_&2+o#=@dmcY>dg2uV*q{VEmwwV z9X|%cO7L(Z9pY+Y#A$wAg*{Z`se4vWX8a<>LWkTA!Y(d;uUd4w*9|ml1t#?`L{(a%OAzTJUj?S@<(sUOj4Zvgn?M0 zFfIDy{hI3lxLk;fio#?WzvR>C(7N>Nh#2Eb4Cbp^0&JTz+e3`x)kehT z2+WV=r0)i}kA*Ozr(<&%Z5kCL#K2hHp7UO-qD^ z8RXPPSY-*6vk4fZ&S_S~8QMEmHj&-|HmmL+1EO>~7W)pTqQylN$v#=z<%q`qctV7H z5krj@Z4ZXVBbL=I^GVz*u?bAnL>m^<{rtkN$A;70#qBI@H>kV-8f)5UooB6E=(hIY zc4gQ?dyli?+uzTU+JL^n1bbM&&X3u?q`LNGxEV?sF!Gdgf|K4y^ijfGj9GY0Njp#Ch z&_MeB+NObV;!|I(z2~1KQ6^o4rMsQQflMr1lGn#?H(h`o&b#kF>|61=8uS z@SsbfbtPuDoQG~zzo?!9G*46#Wrqrv`iH(=trF*4>JX)>*TqM_9NB2n-bXpz0Tsgm z>AFDSaVuyFOnJ(>8fN>R-=&zeLG?9$U`*lfP{NZM#Fp_2J`jpK- zj{DKV@_Q?qQIITMIb%8j`-5Tu1}k%p0ig6J9#E=z@(P=GB+7j#mA{EHQFe)3UFG#z zHK~o=-J>33D1pF@|J7<~(M*A>>@xd+6l!2IBUr$%HIZWZ8q*c58UcpKBfgscKy-hr zQ105fD7DW(>Wm1-CV8p@4o*ZAHLNWV?l#|^CI47fvf0FOmC|oy?XzrHB}I%}ax_|_ zpS3b}H2?CJBP%5(wcvkxqJanDTy_m#bYe$hzQUW1C@Aicc$8T zgP8qf16hE^kd$+=x9Fl_!W~j%vC@w`OIOyB2myctOs%(;U1q;zP7+;%O3Ldpz5>Hv zX#DY8<95%HTwZzMe$qwj?n>L{I{+m%qblF#U5#5zI+;L@f#fYoy?Sov2LNf_^*PL= zXJ9BCdh5o~$`2Qd;tMbfa2vJ63k&kvP=BmsZ)IWxbKc@vt7n@r*g8t4OR}z#Z!$%- znFAcOV9tnsS#>cBPf)*ZO1*h>H5-DkAB;Qd4v!iZCT;s#dvB>*Y~2N-NwMSKtqRq) zF(cYZUWOj_!4m<&EGmwB!v-Oi)sb5*H~J~ck_gm?u$%O-z)Fz$X_)8b#pLz~fJXCe zl-#IfNPjjYoAcwv>_D{w7Ytb~F|rs8(^Qva`YX>4 z>7G2$BZlGU@@<9<1bedq&=&i-b%?k`LKl_!wqaq&-xALN^ zCycC(kz<`8=l)iScLfx0h}DCWY`&2^pDyqi`*BZ7rxz|e1xz9=eaDm3<{^#EGQ=_R zSt^z+J^FH;;QpIc+G;5=l|5dgTiEwBuCNus#+3_m4KA2MpRpf3KmJe_g3(D(0)XM& z9ESKGd>?!byu==Sf>`%K#wzZX)Ooot99eJmGiN9FXQD3XH=484AY|#)mvE(kH%C0T zXFpx&F%NVDNq}P;&Qa8EC?szsJU%(5o_<(hNIAj_jgvpA3Tf{*fw&ZIJi18_a)}%% zk+Al1t`qz^5N)T)NR8$Ib0fiHW?!onZ=R=9J-^4HDe><4JHVL44`TtMd?#B6Flc6{ zkbshuHh@R4{RB$Kj66D(f#HK!Hd}&SRuk;Pu-;ru?SQr_5WN7NDk~ImW9LQy=o^UI zwdb`DtNT+ts)vZRDdX)at;bvDE2Sn%DCcq){ zd;y|S8s%KD1)&&@6r)f77mCzAHjF#Pwuq@J5 zIhqDE%Si&r1)uR>p5uo&BqZrSC1iC+)v3oA#}f9pUHqQujpu@>vhu=1nmj_bgaM+{;UUfgT-c)r4>Skc!KdW&BRJYPuF+<>=B_>D9+74_UK=cdo z^pkzTk{J-Xb)0i4u9=Bq8F%8*)XsQ)&wPW`1jt`l{Wg6Si7f|!W6J>qXieldTD_8M z!k1P3EmnS;bQTu`h_I~9Cs@G$f%?C3A1W@W)R@0)#+vM9{BYSr!8-WpHxY8sr)Tq3 zG*~|{G&}0ixtJVRbG#+vG| z4QxfN0Uyr62PXg4JxuXz4QSv6TdVe|zwRK!q;Ri$6fEZev@usx{1HR%?OOGzti9?*_S3yF5&3>-d)G%Y*7T!S3mI$%9sw@ zqf#S#S0qEhJ_8WgzrU?gQfX?&eq+<=rBGvZ1bL~HgyDBhEy zyY?=ih&nysO1$m1z0Z9pn6H0G4r32it+ImuN^|?ADKg>F$y$~92Ee7TzBc&1EYKTP zfi%?=7u$T!s5o{~!9y$}EBb930m}IIq>U-)txew3lATd7Yj}W%9wzCk?eSTJWd+o} zVCCv-+V^=VFR<$Cb)U0WxBPf4Il7vA-HK;;Xs^gSnG=_4=9p0|nHtTYd{zIW#Lub7 z?P-XM*JQI>6Ip<0Jub^!{nu7??)ufaBAE}01n9Q5{aVOa#CY{!8#zJ;S1C1TnWo(f zVNDCT5=h2|$Z(?r(dP*eueT?f)EwZtoA1ejCQeJt?ima3vp!bOC;2S!Bo0p4Hy5plOW&yUGJP#R0s3XGB3;`+ zLr*+5K~+;i4c42uu-|NUzk~@ahp=WPvN*`ars-pI)&|Iw8zA&!b82Zx#4xAQwxYhd zUPU$G6Hj}6oZLlj;&(#NvF;AGfUNF|D!a)dm)#cO#zsAyj)KA%bQ4pU!rfR@HE-uU zKRAb`Re@f4ZdO8#6i;4}CsNJMi@s(878b9r(UX`on18Re*bkan_Yy#iKbar@@-Yz# z7=@=m5s~<>AKpMP#IJidd=c1s&6mX1Iur+#e(rNeMkY8z#v;f6Tyv`FyvC#rLGY+a znmY&0HN)uHKp=xBo)1*lic+Ij+Yf#qN%;MOwH@%Qc@Bd+$t`5U!Q9bObViy^Yfo>N zC}JSTXDiTwlIC%mEd_e`Xf89&Nh?<=!WgtSuidJ-%J>T^SWAHXAd6C%W$^bL6<7s3 z>RZq|LakaM`*=~uMx}TiH8AIzyE4)|S?KA{j&4mVWF|i(nVY}yO+O2myk_;-Qr?ea zI7gO+;Tv^dVJ3(*NDAjZuT_y$+%l2ULkMql08u1MgxXzm3BU7&Ax0l0n0U`g6@OS( z-oyVPGfkI@HGLLNG6~U;S=ILsD7{n?7n2>DlTMIFAO!Rx9Ze(sWQHy9+@1+ zrkm>tBQxp!Hu%nR0f^Y_7H)4PTuAc|`Yl`ibPIz3vr@Aqn7!^@fArn0s1MG@TJ4Sx zCO5~FFdcr`uu9Tx_#jH8LS*OD%DPtWZmwvu$)s(pEZ1`0MetC5E0FZb3Lz+5T-)m1 z3DZp!nTrhYzIE1FS*&kDDhB46lzog;k}{$1KL1vD^QAm|%r_WZwZYtM5=MzoWo&MD zre)n;StUT5SI_yKh&UwOnEILCUAdb^hGr|o`eKC{A=6nQePbJhHP~6rI9aUG-0rO_ zsBW5DY;-Crrb2Q3d3=*p&W)k^TH}wP+KeyD(uIp3vFNBmu~0`@3?P-Fhqa{+RFc=N zk%BqK*7ZEqnEp@?!#iMVkJ{n+20EB@{tb9`Wq*4exVgK-*?IYIovrY#cd|j{i)URa z#N`@Fc|GE30ujhf-t4J*W*Xq@EEq{I>RYfSKw6WaGf*%QTNNOHB@RlUYIiaz5>H53 zP4WiC{7IMzNGOb@gkfg73DpA=%A>YJT4c4v0?hHV9%0-a$Db0yAc9U-d)0{g2yt52 z>uTL(G5Sl0AXlH55;x60JaE}|#>1%ZAywd+tOO7}---U6!1sv(?4?Gnp)+^QyWN}z zi{>7xs5Vqwa|W@lpc*MBVawtafb#0X6pM8Q92g(g||iIL89ux-)`r|o1gH>mV)6=;?PF_Od{=AO2td;LLaX5CqJ zXcj#&(qIaa;$GOQUaF&U4qFim>XxkPyEeC#Cf?vz4{w&Ph|YZ>xQb1S#m?42!LO^`j$+kejC3>R3Hh_K_(0ye9QZ-QXf5o_uRlb^-B;}28v z!U`)GU<9*?>lX2nde)z*#d2sz_Fh5+dO;0l583mx5_N$r)4|7&)yLb<(6hZ}1# z=TFN;u%8WAdxEtLXEOvL+$jmHkBhwp;y>I>Fio`}Uc_nhLZK+mbH!GfDNu7U7U8Cqokx!`o%Nxllu+^fZ3`U!e?<-^x32qfe9h+s|fyJRk!XTDi>&Q#_Wa(=rTvw7S^ha(e}r*Wcm^{~j)C^ot>@7J@U*Rj2tt7vj!k_pc@ov+-uf z^E|M&3@1m5ph^iSnF042cWa9^8(3J$)ir*R{7hipm-Z647;pXMM$+_*Id-X3saWV= zwrtAwF>XW^Wjb5_C8JAkBAIV?w45<|{cI>~q~t&0wip-hySty-?4V?%8Mja5WW-2g zjlVFRHcjrziCCfCDkR`OL$M$RUc$*4dP{rB__3XOGR|W?2{H|^4gMIuf%9mwr3>X1 zqQuCKbRvqm(;mV`5SRD=)}D$DQ4r3coOU|qNVjgD8Zp|u$1l(YCu-JScGu#;Dz5vU zT;FlG0ss!~TKJJZWKb~s6TD0TH%P;Lws0!*x?LL4=~UH|bKLEHHjTh=0#0#*Dd<+_ zS3r&XKi34ju!VU7!YgB^n&cS(6)NPSqAh;~PXufEaRj(%(K>s~TLp~Xn%3A0WKJHE zpv~ucxyUD>*78^u!J;0B{;!k|Hi3QEx_s>RbC-X(^q&Yj3Fw5L6&% z2?h;GF<9ghyXIVhMtq<~v85(!{%+eMp4x%pim|tK% zwS3dI*TCeH*bUu#xcel)4g8`?9=2Ju%QeIWjd*g_t++ZGyr$-e*Z8NpD$tJ$R{r7{ zsBLPK0W^7WKKhi4+kdFW=D&*v{bxwn|KN6~B|DP|#*OE-2W8F8e`^yWl(7O(o@^oq1yQ~uOtL3^b1By4X z>@}Ry-sQ2y7`x1m(i?cX&ahu+B|XTa^7nQCA4jp&vHZ*~hZrqWGG~?)p;$=PNY#{8 z6NAEItU#7RmxkBs59K=QW*+QQ9I#-zcZX7cF|U}^=4 z4UeQ-&#N)F+61viI-9t^_{-=VQ~D7OpGgZzo50PNRqFnh?-gU zYL#SZUIlpj!Rq8n*7$aHgQ>BJ*L^mnNOWlF!<~ zdl9X+X++Yu28G>K?)(E5Ust~f`@_h!*t4nCQ7v^ywvpX@nCTNY+eOY4Zc+AltfU{_ z-#nmjk=@oPtf4;~yql-SQg2XVnc-K#g~&#Rhp=6uEDM#}9b5|vUP=)bzC`*Fq9Vn# z;Zz)m?km?dn$5sS$T)3yH@n@viJLVu9pCih{kG+p%608Ij>@%Wh z^_R+mtwGf7{D=0lER5nEwz9(14*TeybyXkaiE`Lwf5Xf1_D^Nv`|u6yG(8mVQ${j7 zg9cN)e1N@Q0vR5I`*ynpn7RhJBk(Ny?R|i zCuVO*De~q;>stTy`|A;Ne?U&&4SVhDI-OKyr|K`BfJOYE++#&JDdYm~Zm?5jjs5xG z2>bqv+W!<)bCIB(zp4r241p^3#YG@|RPy!}E3^56$-V?w(a@_@1UP@%UtX)~kG|=cDC)8PULUb}UYsn?mmNUTrlB!G z3)w4VBY*&w0){MKcm&SiRqpu63RVA0&g=g$t%9-V%R9LZKczxfG`C$;yb_OQ$IO zub@O5G+L-3YlAe2c^p}rVh}N==9}6nfW>8{02}noM5ZMGVr?1Ap-i!Cmji7;dHPm^ z$x*{28*pKfY&g0$p2VO>0P-C|* zx%_Mh)q~%4^`aihVQEY1`d%77tL@Tnhb8G^iGgA5bwd!`bgH zF7@7s>QYFlbx1=_=>rL0MAOTW427JJ-v5lmPsGlnd8O`RciY2-mNWybVrFC9FdP-Y zz5z(QhKlcZhbUvUIbG{{8aL@j4l#QV`ia}*R&ZLh@AAycE@tNsbK;Td^hb+xn2`S2 zaNM22D?$LOrv~$8c+A_3;a?u#EPG*x@}X7kQ}(-)te*_&{SZx$6w#EZU8Rs2FP`NV zqSy^c0qq5!Lo_|-=C&(mP@hWo)#5o0WFy8uEv65>Axz1?vHGd7RcMYlugZ}ew?|+J zIf4jZIqVfn_2jT$3H=|UO(m@t5UyeJcovpvw7W!pFnJgBvG~9PwVc#TpKo6-c^+Ni zfTi+XDb-VS{x}~W7!C{jVDV|R%0oq6<+A1xk2)_{*5DM`B1&ma+_t`h7UXXg*;|nm zYMShJ$-7n`QF?`?WEka9UA&q8J72G;Z#Gh!X`KRN+@#e)hqcnn?MbUxL0GohD^1q4 zJ;yhZqEKniaiUCQt@HDeN%c40tNJ67vDU5;jJwY+{261(Sb}xYJMLbE92TcAYPhMrQSzz0lpUW*YQt78MXGb$ zfURs#Qgz}Q+JI@@<~z*~3*ikD>B-3LITHk_V?^=jj6dR>UAI23pc02wfYdbyl9#6t zFIRo2uV7C|tlP<1rFwEobq^jcc(G`gj}(15julvk`ODyVQb(uW2p?0lc?)eI6LHc^ z)lwQ~x^Yw&d;_aci82{p?86Rp;f698#u(@L6)*}t39Wm$5fI;bo5TJ6(O?eSLCy^W z3cV|61a{7#2J@FAqVgDcsq5{Y=(dk5`UKMB73_(ORW>Wo#TB#C6*8lmRM7%#SEGst zGvU$7oE_EWxkj*vPc_${%F%ZrwN3v}e|JbmWEI!En^LOH`QZCYdprKWoNu z?x&Se0_qJ=9W*Io8DKv`&{^xCr6TaZm@)33%;RT zQ-Nt$J3k$Ly1I53)L3(ksuO2;u($)!5NeL&Mi9Lu{Ncq3@#`;g`;)`|0K>81Ex6Kn zY`*{Q$>(gpZFQ?+3I$d3*AU9${ljaMcOtTjv+|eP%9?^D3|)G2Mlq(G?Mmp>SIpY0 zH?SVwP4!8&o;PF-`Ao?TxD8VAoIj3t3X@5}yJx(@V-^))ogVHTg5C?~l0SIlz~)p= ziWwEptAE%@>tZFJO^Ab^W&>~=bSBtM3u(jL0>^MuH1K{8fZ1g_Cr+K6y>}{eCnNXb z?}8_dn+w~P_k+;Z=9^?&pDm7*Up;7}MK`G2X2^U{aQx7<5Gjmk(U)dHxt#8|i!+JT z)MP}f&nvwS^BGEx6h(+K$gqMAO@@T(OVK^rcnE@>7sR2)m?&0q{4NKFBAKFP0viIR zahrS0#%s{Bl1JQG@S>lWEtidgf4+xIqh8kI$vGICEgS;M`1C+|UW(8IRiI~& zN-%M8n^u~Z-7v;JLe#m)wWo&V_J_654vOj1kfl_lPO0|AAW=2<`js0uk{q^=eXM46rb1HE$_sLxTr>o6m~Nl z)y66Vp-Q>6FQ&kpta*V*R9=Qov%6O_o4pGFf{`Ep(E z2brl`mVS;s-wKcQv_G#i0Z=B=@S}BkA9ldis@uOnj3pl`B+ZLOlau(0bbn}N>VN*q zV(CEhVu19Q)RdIl0Etd;l2O8AiVJDl#g66~O|Fm(%oIvOxAe^BReTpIMOsLx7SYif ztiVtM6|5%3fzhq!NUc$c9){xvnc&GR@E0_{ z{(~{gBmrGV*L?V_`a2%wA%mc^L6%a%Rm_?hhNEtNR1>Rn?Ca7b%{EyVV?Z=F6hE)T zgcv>fQ>$BeV|*m8o*Udy5~?#E+jN<~s2U{cH1o};6WJW%Qlf_xt$7TKwHo4V3YJVr z)tH$^1K=ze<-9uaSzA2lnML%`oIdY83mc$?%?ddK&TE_)UxC#%AZr1g)i4F+DFaNY z)=EHg?WBj43lPaYz{~#-RQ)B+`X&u7NLmL1em^Z;?e;{TSbJps*6UmLzuV^&YnuZ~ nfy(LpDsL~pE^Y0G)rPGQRkI!8>YJ#tn`}=zoGSgt^}GKDv^&iB literal 25613 zcmeFZXHZjZ_b;B=Gv@=7VPAdiYxUpSfpB$2Dhg%_005|z?kQ*j04WRr zpr21d!6)^fI2XWw0Jy659q?ZW1VTbW0);|PoH#*BN=imXMovzC^5n@=r%q8&P*74* zo<4n=ii(Pwnwo}&=FFKhXV0FcrKP2#qdRx*96dcf0|NsiBjfq==b4z8n3kiadL8UadB~Tb6>uEnTLnx%9Sg;yu5sTd{?ht<>%)Y z5D*X)6ciE?x_0fFu(0s;>(_7GxFI4UA}T5>CMG5>E`IaoO$iAJNl8g5DJf}bX&D(A zSy@>*IXM^%cI(!y+qZAa%gZY$DBQVo=kDFRii(Q&?%h*TQc_k{R#8z=RaI3}Q&U%0 z*U->_!{PVu-`CXC)Y8(@*4Eb1(b3h_)zi~^@Zf>IzP^EhfuW(Hk&%(Hv9XDXiK(gS z!-o$aJ$m%`@nbVHGjnru3kwTNOG_&&D+B^zZEbC1V`FP;YiDO?Z*TA5;Na-!_~glx zr%#_cIXO8yJG;2JxVpN!xw*N!yL)(eczSwzd3kwzd;9qK`1<<#`T6<#`y-LaXV0EJ zfByW%ix>a=^UuqdFJHZS6%Y{c`t|F;z`!?e-UI~&y?y&OI5;>YBqTI6^xeC6VPRq6 z;o%Vx5s{IRQBhIR(a|w6F|o0+adC0+@$m@>35kh`Nl8h`$;l}xDJT@`{rmSHK72?` zO-)NnOHWVF$jHdd%*@Kl%FfQt$;rvh&CScp%g@g*C@3f_Ed2QKV^L92adB};NeLQ_ zE-fuBD=RB6FaPxEQ$mX>edzJ34xy|uNqt*x!Sy}hHOqqDOUgTZulb#-@l_w@Ai_V)Jm_4W7n z4-5?a`0-zJYinzJ zdwXYRXLomZZ*OmZfB)d%;PCKp{G+ii$OEUI+|zdk0JZ?aA0%{Y#R32>07?qCv^^4W z!{muZR=%_QW}jmp)1IDGb2!Nr!*%AKXN2vi&k5c_G2-9URxgM0>+8O?*FXOcWfRrg zFJy`(cPw;tYg%({T=V;abn-^3bS?VQ`<85iydx~~H>#a8lB}>xi>YyIqcSs_yIlQ= z#jUBlrtNLk(P|0`clEoW7cR}`K(3q(dju#en4d?|o@F9KDJZ=9A9zH10^c_cGanM@ zmOtsGXTR$@Cw!vX-EP--PS}GEIV}Ou)4&>`a6t4l;b#Rn2cshVXiEd6@Or`z*beEv z_W!#5AxQNiQjBfz_v!}aNXX@PkSj=LGSs*QsmtMO_^0uTQg)gv9>))%E{6B7U}sfK zXm}CF&medo>nkHZoz3Sy{vmZSP_?NcBj1pt0GsoKpZ~#brKE`ZZAvwh5N_1*5y{bBx`%KC zX0K!*2l+?vm32%1^Dv@yS*`H0dk|A`S?)kVuJ3M7FkfcXDh@rwzY`*;-RL!pS z`7ap}#zmB~XZ{P-4DyY&1sf!8Q8NFxXNV!i7~nYf9{#(#y@J;lQkQ1~W)u(p9DWdx zq4lT47Rpr#(~JF~U7l38V<7IH+{3zNP70&rjx+FIjbn zc}YYg$&NPOJh#~Jf0QHFMhzXwE)CTR%KU@cd~aVTAoB{jS!-_^#{NRwlN@0?<|lgg zK2H`BS$p>F*JW6Gz!^OFGaAk=|CzE5>TBwCdSsFf#`UHq{Lb9x= zdVt{%?d0$kEP!vDowa(kYL*<51&1J#q43Ul6r@rkfd&+@}{iXimS*>z-ydFTK=6eCTLcUc~Tb!8OcH)_;yghiC1(De{{li2(#Qu3rU^NW`D)8 ztORCT1CwZCUZ4J}mC~VJ-(-**>E5CmmX;0CfrZFMXnz zQ8z5e{;HsEh;b^U@_{A%3&kH*#)hF|M(zHq%k{-35z(=kt;=D;r2K!R5;ODPi^aGkI?mOi0WmC+G4T&XxT%-%GH{WfOz+Fn`&D3iTM%L?y>eo0t`%nsX( z8S@zx>V2iF>3!MKRx{r<+YK#ORM}6tbZJ%ZF49eJe$Z@rKpie4C_xt-7T2sjI;#nA ze|Ttq0ztF>z`T6>krub|j+E^prx`FdwKW+=t8L-hr#_V#*4Yj#iVg z(V6T_oxe5Yr^yzqYNy@!&9>-lV0B>dz44;6*_<-j)Cvc7n!|>;cUamu52avIc=$;o zwL~s&P~&4uhO=%5hp7J3s|;`l66t@>syAXFNlsCs%1<|2mg67CI#;VscgP=F$?q#i z)mZr7%p5YN8VfYdxhSwf-kE)$_bTL=YV+MSIYXgUxm98kbn7*J!<~3-8fUS~;YaUi z9lOP`xi|Q_wJ1=S20v@KVlpoYD36=~IT>*Pg|A*d7IXi%n#hZlqV!tROLmd$ipV3| zzP+;g<~eJZQvAK=EJspZy9W=?`!bW(O#1^AoAH9iZCm=3AoFG!3(M}lyj^nCklG={ zNBNShLpHAV1fkLTy#jY>_lr|Dz`R-jd1Lb2P!9vaJ`-&q6>)jsb?p`vOlI!u=LYYW zX4$QvEDGl91BN+eVo_wMWbHZGzN?xs#O|t4wDM7J>#SXEp#l|sf(_Iwy{RPn@fNY6 zChL}W;Th#&Yl0h+Wp*cUruX_>(FrkN{X?rHibs=yd4IQY zk6$vpk}&96M-vx2(yOQTpveArLO1bfpqUTJQcOxVW~PWzm5C?Pf#?>= z5M3vYoYCSZx>Fr(RoK6plo7`l2aD0^Bvy^@L{+0_^yiA5Xg59oXoQt zibIR?^9HgGS4>jPg&p~N0zL_%Fq$+WGI&M2WfMm%@0Xh#f&?c}z^8OccPoskAcqp= zWbb@v?XB;{pL~L-4-tA#7ZqUXaRmt^C?_=+2-{^D1`QH|#xp^Sdu{ieWh#^+MLiZ} z2-x91AtNNiEJSQf?)~VzQl0L7!^h8&4E{yXdQ?5rc&0vvsDVKTGw;!<_MYLQOGDDI-{=R3J3lN);B#2kkR3U!1WUGRy-Vzly z=)uH~taNV)GT$gEBuE3IMKq`8mZL6#?2@5lI$nD%hB#G^dNzZN zbBZyaHt1Z;)kJ#eM<=&e(jvdREls9>VMa-%H`BF&jMtOP!Qrp(&|XW6q|mMVS)m)g zq4Yj@zdeiKPFZHvUSGwsR@4Q%0@?+XobuVE<6kY4gaA^^+rCAk-S1&yLOKBWYSr$o zIlsZjlKkELV2)R6=?G_-c>aHGB^xw%+02JvVSb_V4^Eb|%FRh=F8D*PBPPd39Mf28b{7Mr%C}brDCTuV6 z`nyGAN%2z+oTBZi7wM1Qa?FM!IYy7*}u2l*|%7B5D!hLxO7V*=RYZAZH3u^0$j7U&5t#hD9n5*uF z3td(pfq3O}pLuzhK?vXEOP}aAF2^l&oBh7L4EQeZgK?dFLcxtildA(me~&-CZSnt8XlH)4X>fw4V|ai|CIogA4TyKilXlBqQ0eA8<#Z$nZ&{Hy z7DkE|Eu`2~Ef6Fus6#|rYjhVZK^cFOsDIHDeKw3$$y_qAz|kyj)j0&dfr5AD7cowS z3331I3uWfBl&^k3oEFiX+Yhku{cA+V7eSPB`tbnx3llfFFDdWXRsN_&Dw|m5(N}17 zuPcyTzr!X0#qr&dBk6rvlz({{khF`z!=CF*%s=ZGHes(CI+DjF^EvKIl3yR)OYXmU zbrOb!Z>=s7y*BVgWDt^Q{cho#gF;-*2F)VrW=TQy4UW=W7l z#Bj|3TR%W0VJZi@CC3kX_V``@)uGRt=S%#rHsCl?*Xi9w>hj_S)e-4H?dM*=zcjN& zX2pE0B5IZ&*ePaw6Ae@PYtGEBUe!_z@q6mN$eMS&J%Xq5UqUx!gD>j?gv?Dyjs>8- z%ahyYDfQ5}=p(@jina~0bB!DE`YDet5(bZ3K=D7vyI#Cf9Ue0d z3SXIa(O&pyZZa42?)SmDeS-JrhF;ql?CHo5AR~k-tKTAZZG{r!NM7*W1F&6!pvR_R zc$&CvCdhDh&6B$nrwL*}(Uy5qmWnLHQT@c9VGgdQH}}7jlIgqy(DedOQpnf3V+ik^ z&*UzQ2C)p|IT{!smEu7uIcs#h&yxswe@0B-(RwR6WQr*#UgTcqKbp18a7>R&_6tqU zil6GsQ!uJZw2$`Fm8>bIO|qliwjsq-gO?9tR<6tnG95=?Tb;NxC*oTMOzHcQX|f(3 z>%8hb9y)93N?X(DwOH5oW3~5Rb=v*>WXDhQhCh?JXH zDm)|DexO++M1^U3(~!>9Ep!pKoww4)JHx^I$6FJDiC0=d40uE`9=f4S6*Ojd(ded(8n>3&a!+?C#ECuhh!Cw0+QL^=>wL_u8O}vhZXxzMs*hB>Ok!n&va1exXA4yqhkf^mldhP!kZb1UlC!5^ z?chefB0JnnTe~P{lMQ4XmuxmQrL8O_9h1L{iz@|pF$(kmN&P>4c%RlRkJEwtZVB?0 z9*#z3SZ`zYv@L7Ma?wrlTaKtHW>imokB;yym9meVGDNT6xRj+-&+VxhmSV|}Ta0h` zo*}x*Gw{a5?Wteztiv`s*6*iK8N}HPa8Yc^a?l=T_eV^IQEj6HYe6Jf}PDa z$I~h&N&ign2v*L6H5zIG$zD%x^0>Be>)Jz4u+a*YivJ)}12WX(1s3|d4)%rW!e*C;jI?~0>-ML_50;nrROa_mln&EoU8~3@Df_C{&W4wp2_>?d%GJqNG2sB7CRdhM#o?7vFNbSzSl>Pclj^<4 zhMDGxoX_udb_5-G!^QchrX_jJw+RYp)<(QDLCoFuOvjCPjIb1)%mM`)DviCnGDCI) zGC6m**fH4k8%=urQc2QuFw%r^5-+$HKcv7~2d}+ePx9o$vua=mSsDK6KS70WGsvaj zKm!VY7;kK1X_W#@QyW4k81mmHXfo zC(8ok&gBawk-U5|g?et=Wkgk+FD^;U(-hu zjKqBqwTly7S?0Gb#NT}WGklAZbB}uBAN)fKV_9H0m5kpjZ$Kj;(e*O>pD2Yd)$Cc^ zKj(G~;Q;;eC`23m4XaYW8Q?WVF*_JD@F|_&Fe7q)MLxY!h)M&m$$06;W{zSiXyj9` zdek)P;V{YEH_+Q-bwxj7uV{+5N(#!nx*%@iZEY9DuKK=}3WGZowRWF~OpoZ3kt!Tq zu0<`~v>zJQG3!<96R*U7#&wOfSvn8cm2B|GU6P6Yg`bZ0yurv(**`7$2&UmB!ulPf zic5cb&+8g{3=_t={DD2h%fTNXya*TlT(+!H4fW-##Q)w*tr?lm-LUQqufXp89(`b^ zTD!cHF{|V8D#g(@{~D^#B=~iAj}YT>iJq^Q1Y`$kFgyRQ1nQrd9mrqOZ&S^=zm6!C zw^1=4=N9cBK#FOo*xB+|rL~h`zPfe~%URm7FuIV#$I6-tL>QUfOuVj;3NgYAf1M?^IzA7$z&wEY(UxA;!b8qrmV1_W+q}TUsab}kU%mNG*Dy52CQDGuz}IUxeWzV z7OnkdD|KY8nSRv}fG#b^GFAxd2AAXb`et7I#+5HEZWq)+>}#)auJSV_`BXE5Q!t_n z)x<1JsbEu;)n+Ttr*;!Yq)V;)c3?&he2lQd5gRo-DBQbX-nXT!u=)pXMEt8(owmHe zB2BVZs%HNx8>+qCO03YtpKGdw3!LsL=m%Ucc=~86ZOrUY3aMP0%fFK`e%i{cd%@Os zKS>O*kIU(;@vX}C=5$n8@XoDDnagIMwo{Fq;S+(Cn;SgxHeYKT;J1w#kmtJ!hx~1Q4?S3(W4(8VhZg3PiezL=S=L~owe-vB1yGdBi~KI2-K9O%w<}on5lqor zR?NnsR^-|W{rW$;fUBYK;njY{L#xqIS&m!*l@c;=fbo;f(751(}*R_^4-#7v&U4?F?NsFsl))f#(v+^2prGzIVsNeG20-KN?g40dtj7 zz95dR_dRPtv?UBL{aBM%xLf1VA_3I*4()v&3{R7m@VSdtLs0Q~}-KbHb-eSuYvx5&QKrgm-MUK{#)pt^n18DyQ zWje+JAkgu%gJwwrD@)rv5HvDLPLqE0ON2WrsX`1=v-QCl`{hF%qcki0%Xcb_6#qi1 z-1$c#sJnLLE{X{5$oh;eq_4KVGFz_U?6(C|hfUcF+T>lK5f zB2%mEgzN%ROifkVVFLRJZKKD_13L^@CwE;La`k_>ZwQ0ro1T%F*hacyCq|y+NN#7{ z0tDbvbcI#jE#DTIGn+DbM7RpUpsrdzf4YtX{8$vt#T_H zX^qkBC&7@2Vtt2o?phj|oY`l_H}+19K(wpH0OR+N%HEz^^l6DeO=J9&dRvjhsmp`} z&A~$#_5Jy<_wd6wogD+fAhyH0ukF;VPN0k0e1D}{B}j{3lGI7_Cnra}ocp*@dDX$rSqF5bsbE)2$(^2Mf-V3keIRQ`8TSnTMc>ew z1i!khAKG-H=HSzMQO&ax_;(wh0}|UAus0sX?!Ey9uXZYKGj9Jwn zckBU!2@`|TNuOxfN;nR{(%LU`==+QLIj-e;^Z4}@zX{%3Tty~h_7<1hFhcKL`HR3! zWbqbNRho7?lz%g3oy$8*hR*6}eShD^9!EZJvSp@LrbC!2evV75+@I$iQtZ>A)2mAZ zKCA_hN>1YNRPMmdU47R?);ExyA3-M6!J4*f_tK)mY9R7=T75sVWSv6YlHr&o8ECcd zaPh2*WC{b&aNL`HO0h-rGSSDQ#W#_!iVGCjdpC-H_XXXhVXC>-TB=j^00&5lnSD8d z&XDey>TQur(lU~hUrJU53_f-UvNH2A*GTSWuBC<88y}dedO+s%Z>4dfRUC_d>;b zm=f+%fyI)aC}7aSjNR+q{6L0C*OUKnniy)bUjF*VT4jP!xIn{5ORzylLh6pQ2#isM z<}wC?un{O>-_9)D8{1^h`^4}NrX_iHw6s>!+w=s=DZd*Sz9JI~#t?V~4!nGz-Fh(q zqMDL25yx!iKDP!&u51`Fw5Ex6#jRwhZ^0*9Jr_pTXV=?DPmMk}jS6JeVAqgHIEkbX zlY`7U&AmS%1*pw8H0O0j)lK~L@}Hc^?;Nl&;(dqAAV)nHZF>!o{7zGK-favJ$yUq{ zbqpFES$Vqf6liBhD)*zEos?#UZZeoif^*pRb5>^fLr{aD0-cv&iSPj8do%NymKuWk z^MQC7qDu?Kq{*g3C0lnykd{Q8%?dcJ+zh8r2Lw@TFx>o6$WWK2o@2ihZ;IuCAT&b_ z$`@acj<^P`pC=4h>-*GZRm_VReRrU;%LSr1+oJ-M=Z_@f$5Ckjhb?er@$K0QA%_h{#L`^_!X}j5o<2LbKrBrIdL<))UZt zuh13eTKVcK=uCyWZOy2}HbQ|ah9L4DF+t(^F%gA}5Y+7`o0u#wHkbtzv*pqRXxloi zmQDfM8Irh1CyhzqgJ3w*Szv0u3Erna z9T0!GN0C*wa><1i)`*>yJ#G7gLWmWNR}@$a{Tx#R_;k(2TW9a<0ope~n%-JorvbDJ zAGwggXI2vdKAqvb+0<)uIw1<-)Ub$|%bVZo=1b7@u!$rVCf_lJLgx%k!2U;t`Ipsw zqw%(g5WzUz`xSM8W@BL9uMBYA0mJFR?w)E}YO|m9n=jFQLoP9~?!!G*EzQ3WFjknf z96kT9m2(^^tdir5hdXB%hEkLLe+Nac_l5X{ZwjHtnCHjK!+~M_b3+QFhgl6-VA%Cd zCe)zBaVD%a`~@!p6mT7;hKDYEUTceTQhK5eaT{lCNsyMx!$4{(3n>9!q%F;e zDe2*Kyb!$;g-HbVqt*wOn;h#l#rqe3b95RUo<_xWLx!_(V=nies6xQ-vm}miY@Ib4xEojga>oC%vZOrTP-oGLA)j z!-45x3tz>fnDz9^EnKU=-SF9i)VlRhAfA^E zLxt^`LH;7Zn%)ecziuy;_|z+m*1c>wg-PNls;%9tbk231GBpslFJB<@{~1qBIBh;t ztjES%^Q+ZXkSv$XfIG?f{o5AIsD(5|!p3^pAM5wW9CU{I?{%6|kDh-#J}Co@Ou4qb%VF$pr;UP z8?(u%dg`EH5VwS=mE3Q>yFX^nv}05tH(VNmZ9NOGa=kUW?7QN8ShM;1WTHxkTVyAa z1Z!08`6cgr5|w7^%2TTt^(!1zvmURwf%rmoOWF5cj}{CZ#hgg-O_Y~W+9{=BQL*s5Qk3(5rsPCoj%n$xPt2gZPQjDVTqm}Z+(2|Ce z`!%EJvePiiA4Rhp?=e|Dg9CBrcEnqEW`1O(`rZhjVlJJHbmzpE8A0sh)?4I!r)cUn zA2x~t_BX)Ho7W~kw9&fI91t*?`F_c8AZdBMgd;)16zWpwZbrwe2xIh@IiPAJ2x>z} z79-Ba_V8=2-bP!Jth-80!{@zZH<#7bysJ282;1a9#Onsok30KF!g0Tfy{2|sYK$ca zf({0HgLG|>9m>WY7j`z#cA*U|d&c$1hT#!MkBvT)d%(^cce4oa;%WG_?HFxKb>wd< z`i<=P7&k=$BD6@aE-=iK&D&bmXgb#D&HZY6p<`Cc&C=i5bFTJ0YH&1tCp{7H?cLcb z+rqJNxT~L}Kn;?>g$y!lLS}e`oHV{-9kWK|t8wa54n5&@0y3Ey=w2d z8xmjCo>{d@aMkh@UDOUqU?pHSEUoUHJC_hPPXNn(L-J5Jb+}@G=u`K=y;i;;dvi&8 zDSqokeaBa`PeJ~*wtXeF@_5q44T$FklaFs27CaQb8zzuqZKZt~c`A#VD%_Xeis@29 zC2xA=S|Y}utxoYqf#!*pB&sA&0}A>Ny%8PFZi=U=RAm^HW5XIIYF%D_6TWNQ~1U^LF(=*km}* zWOBA>2sO&stxYPQg0T#*39nz>G)EGK{Wm(PW`4XtZRmziVVN=1rOxe9FYSqT6~jw< zbLUIT+oRs_FC@R*1rf-d)}eP>LcOP_PaxXu`x%>zo;nya?RxjO^FTzr0luG8@9zuO zAF}oRFj}aEGIwNdRrk91G#AW^iCqvCALw|!L@qgIw5G&{2{NJTst1|V#7@88s&4HC zhtFoo7z=0c-U;|>2DGT>?LRvlY9p8HG`!3q*^h4T-Ag+gV^g|Y*AU7>-$@1c{x&ck z;N;i-8^#52hf1;yKea?VKmN9jrTh`YA}slc<#wRF1mi*i?rIvj?rWzLy`VJxwONNx zUzWQW6%mMLL%wle-}i))&YBF zFhsmJMNhPY3f@{dx7=I@YDzHYIEU=7ZCx~&ZOptM9f_C-r zpqEQY{N_P0u;Xy87)%L)Ss;P3eZR-!4}Ih?-4V~_DYUidkQ*!)Wul*hFL7h8yUykG zzG_O0;aiBkHI>3xT{iyAbCXs1CzrggZM0B6o;YQ|^V1zAmDa5J+X*US)aQ8ED~#Vv z!P=g@!ZG$OK~r$SO0`r?EH9yqvyD)ypOtMz#n zL1KPDysemhaJ-pQyl4IlD!(da-)0e+rXe@fCclg8# zsVUx5OmWBWnTHNKO7sjJHdSW7;IUX`LV-{*fGx+LiUM2#adEdgs@YF($I-bSWc8ni zpZd0kM!Yt^HaKVm@Y}k*gXH&wJW-NVV?Ud_M)BDcl*{jWOx6tg+WOe13Y? zlMSPVB{|wik*@?kC3=0qs9%b2zr14135uum)$@;)UT7~eD>OF}zkn1wXN~N)p(Lb; z=tG0=DcN6>J7_5cgFi0&tf`$KNRH6b?k@{<37|xDds-4Z(!jL5tU8~2Isn+8KSjis zW`dNOFCAqb`B1^lZcDP9qOs4TSx{yUYFIZi(8AE$bl+O6yT0vVN;Fed$k5jH4`{Gno2Of?_T(NZ9}ld~%6GC$U`CLO z1haUw7%i%>L1_Lg-^yt77HDwzE+}!{fJB}EEdUo-!mElQLq>{_>}wz?dGbID*^Kzc zGofCZLuQpmPH<9!ulmz~5+y-|T6jpJc12+-Tt_y;K>v$>jR0|Is7VxsCK#_;=jo5_ z4e|#N(?n8ekmEH$gzt%OMEA3_bDwcxIhkEX%me^xJg^@JOdZAcyLx3lv=A&9>#II5 zDQC&P2-9c$1bmk=P&jfgD_IR}l$pjoVnFy!%n5|6_1@14vpx4P(49?+UsVP(8n_1V zyx$Zrv7JA+?!5PMQLam;^%Q0(KS4#KI%)m!s^1BG`RI+s%L@@1a+0@qgm4jHk}A?{ z%vLut)w1UE9x8rhQP@w*Ma0w>OLG2W* zBopFg{a{hL_elrtm-nMvfrF1jx8gM4cg7-!g-1#i1v%z%4d-whts||v({Q)L#eHjz{>v1 zdSK395G4x2fbNh_jS9}WuV#b&qq3h)0WBQAi|9hh?rK}*@2Su~GBkCj=vbMsKdyVr*i z!&C%XwfB3@Y=*@;d&2iDACI0)0a2Ww{&@6m_pL$H13y2P#gysb>l15+R=drIaeRS? z2`5mrY(2FqOvSx5#$5-YZc||9uL8;5pM0NgB8cTK&onNI2{kHtW{BFf@~SL3jN9wJ zcO@g0t0Uv??t(v&Dns&1A$a+TAn~fsys+y~ys;JIKcn+BiQas|P#9^YQ4R!aTE!-L zQ3m~-xE+@mN<`h*N+Xt@Lf(feJ~LkMGVU4_MZ(tGo`}-e+Eq9M!WN8Rx$;YQ)2vq- zrs?v(OlVbU0iocSf#DDhBipz2r-lWTvwHemE?q+Wus^CtO zFdo14ovw67KdSe&FFTULrOCJa%t5t|)CJl5E(%`eV9kUh+WyoT@t48^+pk>>x2Cg7 zzEuh5hZNQ{C5gWto};8gDyP+m{^;B5n1d9RQzxV~>-+-nWVz09tWW0PPp?oQ5a#mT z4yu75;B=VNs$YeA2t;itD5{J*N@j|Q)|u5w3hjN#GtcRCPbJ4tqgOKHDtD_KY7R!1 z{WdQzM7Q9o!lU;J8+JAieO)>_XT+2aU+#e(8(MB-xM*5GcEI2kpnyM6_P3|H)tNNVjAPK*+`nG}PLK9J9FIJ{9_ zM}2sx*54jBWV#>Vzg1Z^Ib|)WE zVPXLfDTdYdZ4w(--t6nvdLvlO%NYB>Bu!5rwW_*Tt3=fW#M}0*V*4~k^Vntzvt zzJdrOKh42$TRyH=cD+cA$g(G_e68Xlx);n`bvp zwvVL5#ovpZDPwG|budZx^qlB_^X0`|A$v0T9Kr9;;&a2+pT+XHfsxb9p9>0BJ~Re& zQlZO8Gip?DUqFt8f$kNMUK>(U*UAnx5wW}P*0G)GiNW!0i_d79Mk2f}#jNOG&2n-mT)_a)=1pt48&WzKMyiimlPKi>(PTkbAGI)lyf9X9EZOz0G z+PX*W@@!^;3nTz*w@J~GxV=8mH(H?Hf+i_7wBM8Ws_ULV9TZcs`6S!TQO`?O9h|ZK z;-+%Joo3n_B|s%lkwYCk&WaPS&(oUf{_FF8T(pU*(W&eB4`j1sg^Ehx@g+({>VwX~ zC?5>R7jZXoMw<_YJ#zF>+}&1N+QGh7gr;h(bvpQm{B*4<=6_$`eUjQU)A&n03fJG4e|4hiP0-TJWP_`TR4 zkCYQ?nC|}~!Jwd({xy$wH*muHU7X$i)+xP$s9-Z%9^n=bZJA*N!0ZAGF2OO(T|80a z1{q#t*OkJi{s#u{2Fb^$t9~9pfyD+y5JsUfEWkRmpwr`-dF2h%1sm`d$v{R?BPI$+ z>3d?VDx-A<1Ras;y23F3)MWdz1$G@yK*V{0u5jc-YBE?=P+G^iKXy>0_|Qaa=xVsW zH8Po*t)`xF3&;9mo5aPxeB?e%Bg;E+Gns0*N)&>~hzBDaR5i2hu_U_G0Zz%ub4H@p zTMQaabe}aLGU7K6B2zAjrjbPPz((hLtaQ#{D0JB4))EcKP;rv^_FsOjI=#G-84=j4 z!zSIcq-_3x4~4bRG!0|cdi0$R1??Td-3Rw6c6XXzb^kOCs*Bnh`Rd0%_mMZjh%GPZ zW$7tVQ8BP+-}+92vhDSdBAbQg@Ljh}-Qh-B&VQP=$`&V7dhwZ1pp+b$JA#69&P&Y-1|F|iGU05cRP>pO*I7U`>rhglG%lokO$Hs2A*+~zM9~(OM z*H2;UAE)vuYy;>i#$gTYQ&?4QQ%jp!j-$)mv!T=)>($)-AD1>O(28FC3hdgcP%i~9 zk(X^JG5R(kNdW$!K?;-kK?Nd`F8)&>d?3B~_T;UBHzKf3rO=Fbae&u!NM=%f$YUfS z**xu_-PqOWm>0Bx9eQ?U?`&<2PRHBVvJn|#Uc#tfuI*)`Q6O-?>hTcAY|jLGd4Heu z7CTsj^HY83Vk!6aO>R*!Pf@W;+?CbV*jZucjbb|YM~^wfJ+iBW*_=n8*K&zwe2pZB zZOWd@4xQh|$TKcdh)P};Ptzs2q6E6G>1=^`rz>t0U!J<&mgj?e0wTJLoIX+WYqz#S z!8~$Y@iPjdwOWlg8*{(Yaq)rlsFmL|2Yffi=3dbL7iSyo?@j=>qcRD41+ybMBt!w$ zSz6p=(W-w|np?JG%K^{agBpw)4)3dKtY5P-yA}rbbPwF{2X369o4*`V4tM2ozO7xr5Anm08RhIl`OZxKqx?g*P;fDh~>i z=7FAyn1cFOgQarYqY|VQBN$^<6-RV$D>dJPbO=^N=m3_-z^wUTyi%p z^{ZkCMNnytyDv1fF$SuT;v=W6!U_wnk`pl8jXfJsHKQOa0rQX-%&v_zQx(;8@(-^dU1`^QP@qTEs7Sg`QN&uU+m1X`jPJSjj zF^XO70u*z5;HK!(OytiE=(;0lz%G*EcHdicr=Btg41Nm=@r5kX$;q`!6cB`K$)+`u zplmM`fDp_FQf8Z@Sq)E^pcDM%Pu~&&*(YV)sQj!m=e$0~G zzmAI0eLE-j@T-A3`Md=Is>EFbr4nzddHtoMp|R*J`W-jK%lrvmP-y0XIt{V*z~SxU z@EAp_%`LBh#1Tpp89TahX*U#ekVN)MotlIK0XfTVL$w5ULVN zaMAO^lBmF|=Mg+ zpJgqVghHMBddNPJZ zh}Mq#0OyMYonE(b<77E7Trwh~I#VN6X5kD3KpBHf((q0Lovq1ZrTW(m{o7-HQaoxi zbE}kL-k(enw3{e32%d=cxsH|pZC9xfWNC0OtzzsIEe z>Lq8lJA%}5OQ_$UeSo;eQ^#0SfoY%`{9WVXHc= z2Eu0f^WBygKLqWNfR@kCT)|+D3VjoDRIvp~Bt_pHcgKhdB}(vhIy+WY@j7uMzi0UW zW6CRY{xIgITt~~~niyzjOdA;o?#8o`qKcEc#4aTv{9P-*P%G3v5=@S8dPf49ksWLU zBlvUTBw2QpbfKgHtzP%F`d*0|_S8Nm11&uX+>^KPEC|fP_O)q&)%8qxGzjt(ndz|g zqn_rhc2P@c4&Zp*RVY$c$48%IhZJKSDV};=NL>%buxJb=c?crEyRXkL)OrQGeF2Nr zd7V6w7ZYSSG{#6UERI%RJHxdAJ`TnxI-wh=K;Je7Z#e?8*At=SL)R_lmbQEXjAVc? zT%4cg(8B&Z&~TbgO$tkrxGXVq67mOzYh%?E&wnlB>j~8MTw5)a@sc>f`-#fUw%MO= zx4J=MV0un2!K@Bm}cfIzIRw*0m<))Z2n< z#{k@{Ic5}jH>5^{zxHFbSiOe(Ul4Au2Ngzu$t_KeP0G>{&$QgEn{|xOjr4mNIvhtm zW%uK{{bb9Wp-%~o)(zw|F(v%cAji>ih9JXe34YaW9Y&r#)*cFworwQyHfm6G6;4Xe zK#vfrKva&kKK>2g3uM{63C51d8f0ye2Hp?3xWTGt5$n*D^9>LhWp_{Cp^mmiQ z;((*j;PC=S&LuI9NDxT>GoKwG?31uv#FyORVnj#yzM6i_G5j7>EZ46&kpy-14eA~v z?lC0g7yZaepADWM1#d5*z-FZQsk@r@RS4pd6iXGYc^w>aJ6h++eeVqkaW*OErd+_9 zfEsLw4|m&Af8_a5WdScP$Q0`BVxO=8j?BdX!?B+9M4v>YLgmFcqrXy(*DYnh;75}! zvfx`a#gP;K(_quW(5EP$9)kY3LXEx(imv#?gUaEq{x@LPtv^qGp2xXX6%acm38>k< zOo_E1PQ5m2Tj(}**&6BKVB2^NWfIIATSM!Ao-FBm@!j(nacMT%^bLpe@S_TnGKM8R zZ;TTa#$fKti7M!eOQB!4$mX7%N!NwJMwhyip6s)(>l$9gZnzdUo$ECjuJ9On!#T+D z(sry|j-5K7n_ylNs!*$;Xj^}Bth7+k*@K4(3oC3{9zGx*YxEfjLYYeV7WtBm_aCeT zSopNivL{^6Q)sjN(1RG5Cg@7yhe-%`e(<{tRkrg{ncWIltSH#=+NV&|!~46>-z%6C zKenQl%kmez49U`KS6dv?bCR_F&wbNYE2?tGhyUxI-uFh=uQl=f`*M%(3>HfNn;!&m zY_qZd9%Cg6oB7v}Lh!o~_@GcMK`dQ)?OkQkow-ZYh z2ob1_c!oM=Ri!8pEPUL{+A*4vmRKeM#&!@=HL;)AA6;=#CRFJkaT88NxeC=UXfI2+ zv;?*-)AE#K#N=baa{QyO&DduKg2wOuzaLB$q@=I^`vK)X9a6AwhQN#jK1X=t_(HZN z=8FHd6I{!KfX)LImhkDn+o9HR97fj(S`gj${{fLP{lM3El~}(+7&P78*jRu2eet#x z8VuhO=K|v8bu7A!=XRIfKn&%dJ?$EwKPMMkGN}NfC6CW)n(i$dHWwIoJPzw8ZwynR zO$aB*1j5Tj4-48_ouJZbo+0dd{^@#OAFrYalz`%_p+=0WfIw*)m<(G@d0( z7_$HU=fZdTfhOYT8Rjj9ls^bP9~Z!?65de44ETkYND#{il0QHvroj~2i1mgOTBMRZ zmd>#$w-G8+dh0DKmoH^h5E#fAp{I)ZKn3Cu!If%qyK0KnvKnMcZ-P`7)X{LSl}qt? z00!>C`P_|r%hQt8+6Z<;{?7bFsS>e#X}y4%4dWa*cr1wLjn!Tl$?dM4WrsNu%eG|f z3MglTmW+-s_4>yW=nhz5D}(DGJ?3d4scYBfXOO;xQ~co>v~v(yz-IcEW7V!96- z)|BJf!J)UL%V@XzPi3>`@*t+hkPaEHKp>M_nrU{6mHV!BX?xjKZBO%YiPE+iq*!ux zZKN#Y5p$zCaU-kAYl;gs^I2=B{LIX!kKq6K@vqO`!x4K+z1u{Bg9&BPgsY5sIjavv zUh;Dw?^az`m+7||qRGl>nY_E+I=h!!+GXN9tdYdb`ljrr#WQItgK+ejP0gr4zUtr>D8lK!7rvkOyACms z)_mr-fn8NPPBi+H5Nf=6K4vqM8P3XY3#jUR6J>;qM+)0%e9W5$?KLlM!$+GCSl zBsH~#H)VI}cPJ`2N6&V$>F#=U3CHYgi^9EpyfBI$Tkp>g>L7w-DW#dcf}=-wCV+`e zX#ne$B6+?-SV8B98!@o5Vd%c$Gti85P?g@=|DF*3cYzKti`D)_S~-T728q3)Gn!iF z>smJNDj~hDBAV8C@Jm9J&qBZyg8g*>t>pAPUJ4OKIm_?Zq#X;^js$e_xo&DK!QJoF z?&mG%FZpF;di@x+GLw{&KDW(`M!0?_X15z#$L^&y2JQo$-q*ok9SsZ@ZJ{yRvoK?3 zR<~Gs{Cx%G0}^zF!_;qhmdj4q@nwRd)N++C`Ke1!e0xoyV*<4Era+hr4j!BBs1o%> z>)eAa%7vNQPk0o0DzEu*`79V@fc)Po0R9-rAXgH<$@PDu3)SI*^5rj^T67!ZUC zNChg>{r>^{kBYb$~ zXz~rVxy{qBcl6yFdLX1ifA%3pJtbZB@W~YsyOlftIv7?KESs_DB%}TL=TtY7l8!8c z|IUkvAh)^c_RGU;QS8`!`2UK#*03bA?GM_dQ%z>h)X7pRD>Ji;rKXNx$4axjTaH(9 z%)FF1M@38p?5bAgm}VyxCGUzzUWidcrmVEgJBT;LG!tO}Z-{`v|HY;={h#N2Ip@py z!~^espZ!~F?RD9E?X>{*`oY+#?~!MUysTCJK*hmszotVwV{d-q=!R_k5`{%aM2&CT z(T7{9>iwJpY#9oJ;&>7N3We^QFhTx(>+rbGxsIc)pF)!kgs$#(-mpSyPS8A11)mG9 z*HQTvAm$m+{jS?9=d172<2MU-PklAQCs?g}vUAtJu=)fg9_f9#=POi^!usP+YPV<< z>ijP~vqZ>EzSj24SJkPkdU(-HRU4l@Bh&nUJrBE=TkYfK_CR0W1wQ{rMe_ey)5W7o zsv>-Q{6vREF+f-VcD`f9Sp_7_cO*R@rK%s@j+QN2tDa#nGXCZgz>#fYVPsdP52vic zTdd@90YPv1a}VvfvPTCr@4=TS3g|GYay4HM;g6yn;x&&8KMGi0D#xgc5;pI*S)BpO zN@+U;+P%|soo#B$I(65bJ6lvgJxv1n5DMipgI+$kX-2qXwi%`Tu=k!hra%E};x!vy zqEqU87LQ9S#986@Sc$$B^i0;PgaDkmOf4V z5S)d>R=of(2q4R28a6Grv#qW>s>M%wIf^(56n7gYh59oy@f5QDy4pK5&Vyt`HjD~N zb?>rA)Gv4vOsj_iZPAJit2DZ`a4@$2kQ1>!7_3*NKeYAK^Q(6nCr;!V$99Bx+8gi1 zk1!iV9%eRkAr2MR22J#Q&r0)&9KF@_DXrtbgUI~U%7NxUTXve_A7sXby^$4&8vpx- z73}(kODog+fzXtoq~@)(7ie6N4xNO9RWJS_svqKw_*67bH2>{?V0v0(tjLbj{R>-dDMq1p?7n%G%Bja`@|nN$RD6t&Cc3>p8LgzQTk3 z^OG>1XyGQ)fWppOJVAnXA=1Yy<)Kw*?u!9pCReb4tlxoD(b5hokA=Jv3J>8m@|&7m zraL(~LvkCq$({H6Pyv!zM$kXtQvAdQ^%W0rhS}xF9bhUXl&}kg=_6RqBcc5+S zB6o2~hefYrwnQ_Fy1W#ww#=|W#HaN2_axKJ3f%Ne z^jzi86gIny@RmiFqxPD0c)QVP9-y>p{kt-&)=@#}{bc1oE3H25#E7OvQ1*Ml)^rV4HIf>`3j~Bb6-yZ!^aa8q`6^3L6&>&d z4Y3Tf9yVbViJtj0{PoEEH~Oqkeq^>@p){b5uN^*<@QivKNz0ils;ORZ6PxrtbJ#=7 zJX%~WxyKC+OWMQIn3c({mgI-kv$1X!EkSuSfqwU1;mi;+t3W{ zwb&_H-l4f3KkLY@&4g4jg`UD&%C}Tm_{GX&%XtntPq8J1zFO%O_IX|$kBro`8d#a7 z3k5>p4AT28Wt}#}J(O2iWN-SO#@fI!YNtc~XkG-a?K3E9*}_PgLyv4b1`8uyI;ptg ziK^elW*a3zA1)6!kfJZAH&V=&Yr^X50?S`XLTyX|0gFT#fiPFBO6!l#X)CyILl|8? z8@0~!ZV}hoL-8-Q*gHgLV>&EVY&)@3(CRjUdv{K2>IYpGEv`s+qa+$Q&(dl_()1r6 zn|z)~F=B*#Y`ooeuNcC(Onv&izRdk#F==9b#a(ETmCtS9)EB*hMl0|dF5-<4xS2vB zaIA+?eroP~A{MxseQ5gS?mUv?54jENp*={9oRmu2>J6wQ)$-HDlrFgCj%)Sz%373% zM*Eb4nHGxMcXtlJH)8-T@#`CfkZ;1D=P9dw|O=A>(;AJ_c2&U)+ZO3qFc1sJxj34Gc8@I z#ox+C5zG;Iy?Ns!`;GWi)QJ#!PzH=W2$k$8SzodoTJ`O2v)NqQFXjwGVv*Ko=IG*2 zi;piWFai9O>ONm;b|SVFY;sr4%1y}v47P<`+A#t~Fp@*qx4t6zkB*#%HeIS5I(DN{xSGF<#i4!YAfAVHZ-ZJ zLA1;3S~?BWB{)#G(Y&iZl~#QX*;b+&*en>!oqxGzqI zmqzo7rGt5Gr(EPKD`>?;{0@#MM~)jT7&47J?^;jW2bs8`iwqG@bK@%vjCdS7ZedsX{p{7Dz34GD zpmGd><0Qt@g^>w?3m&pT#AhS~8+y^ZK~g`0dN8ql@QeqI5`{(s*X+f~+e4Gg*Sn&} zo6epPqpcS*+l=D~+?r20=;@la{4J+-grkH#)F3##hhKerf0FqE=h;|}eDn~*Vi`7v z*WH3Kr^8CKO93$vva==TweYTv{#H2*pzvaIAy#*N296jq~ZkQ)-TdhF9)`rXlfRBdYi?jt50K%L~W7S zDvWHcXy#NC(l5>p!XTPP72kpe3gy?hi)}XC`w_95Z24px22P9pL;%xmMnBEvDrQ_7 z0e@J$9QwSp^`%R)iWm<0rsCm4I_%AipIk94jD5FBZ<@#V!`x3*y1T zN-m;Ach@BW25QuA@(jLt! zJIsGzW5tmK-HBFdxK3J7{&U`kz>I?}N7Ta$jFsM`0+%=gQ{LG@I~(lMAB~(#D89Xp zKO8PC$`>mV+cE~~o9{zE+xKbT4T5)eOm|0T0Vc-t_8SBf=^oYx%)RVV>B#7AM6$ma>UhGoM{%nc0o^c za>&JRe6YbT%IJ3KOL3Whx(OE5hJvDa>>u6r;L4dBaLFs7oOC!tr;nDqwEk!?u^8@{ zl?=+V3LHk%70O>eUZbvi#|bMWw$0tZevf+|b|FT79FpdY?P%b{sdZaIm-7!pZ@J3@r}Xq7o<(i zGo--dq=ux@s)uuDQs*`#xla*Z*7jRs^X0#Z42Hqz0?9 zRW7&L(E$==otH^m zJl5X$eXb&!haFy|{e<0;o9AT?q;$A{&EGF24_ZY&Aei z#h;|SftDwZX>AiIW`3|%3yrv<&~1+&RAf6BA^A4#`+BkFUk~ngc9M7jGA3x-PFT~D zBl9WB7Ad0l4)&kVy={Pww10s<;1_2SA9OF6`n_AU=athC-UO4ctjEyI%vrrrtY=D! zh&XUj8L9A9>!y?4F@H)I-6Iuq(sZ)T%+xqo>ZZrgggOx{A|W-{B=ta~;$Gd2-so(y zjpXh|IdB z%36d~&@c(zFCC5#X?#^%3e>cIC6lQ3#>v^o1YFP&Wd$&`AyJssk?;wqj8s~-qed>W zMnYcR(Z*^I+Cgg#<=Cs3*(z%jp3ry1Aj7@?3kIDhT%HEClG(dTp=Af8E~y-@K@V Zjj~$@Qo>4a0?_U+p$Dk`d~s%mO#ckbL#S6A22(9qP> z)Y8(@*4Eb1(b3h_y?ghro}Qk*zP^EhfuW({y?ghJjEszpjZI8UOifMA%*@Qq%`Ge} zEG;dqtgP$&)8fpFVYUb#-%db9Z<5@bK{T^z`!b^7i)j@$vEX_4V`f^Y`};2nawR5P^Y# zNF?&vvuDqrKMx8Ddhz1L%a<=-y?PZK92^o75*ix%`t|Fuu(0s(@Q8?r$jHd3sHit@ z-b6=7zkT~QCMG5}Ha0FUEFMbx6zcu^ z_a8od$jHdZ%*@Qn%F52p&dJHi&CSir%gfKtFDNJ|EG#T4Dk?56E-5Mb`0-4h{|v508$HraoE(0C(Wz6Ezbz5QsCF{0EMjUAF;&{sG-l zzG>i{vV`+W;n)h;IRcN}H)kBXP|9>GrMgwpGX>i0xuLbRvsW>_V;lGacKQ6(N3d*4 z>whFGsrXEBc~XPyD9gJdx&K^9^<8a9t1a%ksdmc-AzN;vP$yegMeJLtck!|;QLM(L zO0V}V_2}2hc9(^Qv|rZn1t}|Mu^}1Huu`Lxm0y7`pNW0|QdfTT?gEN`PtLV11H%5$ zt4Cq7*kiAQg;whF#SePzr!Yf8LvUh+f!C7jinv_10ee=OZygH4bJ1ai6-*+fI!x_t zkA7ZgbnEleDcGjl&Qmp*q+o~$$@Q_Nw2LF9ZcgjTG6&Gs8sTQ^iG}dd4~-}upz$wO zu#6?$lR%ZhTVq`BN!4CydTumQS1*3}m3{^lHk6T&q1aPpIF_QbX1Efhzk5nY`Df<| zj9XNm0)MZO>Y&(N)m1}T9JF9ChKiH*%w4fwC&7K$GqZ%ka=N?H21TLj^KYp5A!ujWUAY@vTVGI3O;XUGwy=Q!$G^tP&?-}H4C`xPoZpEhq>51LJ zM*Rv8$<`(hzXEjk3P}AkC29(6r*E2PDpK>v>K$%l7zdn0ej3ZNn@--Ly&L#471MDC z4hFPC3kgDoT_!!L1MtDrCdVu;bwze2q~0}ft~Usa-u5_JmE~G?lk=Rbct)HUc5Y52 z_<&-cpML;g!4 z|5NV5%T@CmWy@V;c9Y<=S5G*9Yl3R5@gMnD_i5-co2$Y9VnrfqQ5R+`!V#xqxRkC@ zK`(Rvyz*}6ga{Y+Pw?P15Hkb72XsLar^ugm&{-HA`C}jlqSF0J{s86w2UY8WVro4G zTDARWjZc3)Y5j+D`G9J3teW#&@BRa!vHJ5{1f;gak#}PM{;RW!Te;ZQN-h#M$|37{qy{5(^k<`q^|8A8*37G$3qY^au z2XvSO3O0jkir>5K=r6b*l9?Vi^f~;>qrbK2@jtmv!U~?V>V5xHt!r8>Q>j$`W?d1@ zBiK*c--jVm5)-;)e9dDxX#gC~7yG;QXS9o6iP|hC-!%PBO2nPtCn)|V-k`PdD~G?F z8{6NzOWjvH^O*2{iw1 zKLWuHG!HFTOcrJSOFs}h$DZkE(N99gJP_dU-{m)Eh;r2Io?n1Axw zS>}I`Mm>yvDgSGjOp<`D5vFq;VVm)nATcPJYLDQf4%q*(boqBB|G!RGCQsWN zsQ#YcMNTf(r))n@|LZ^*Iq_VfLvU`SWYBLaeYOBqWu(i6U_dg}Px>==_sX=Xk4cjX z8~>wRiMQ+wEX=YyHgaW(%j;s-so&%e6S-agn8L2KfIHFnyOP8H9O!qGqz_;jE?5|j z#>Kvum=5_(rgquX__H7(Po;(|T-}5h{coP~&$7_nM^}*NYHA~~Q^nhD5O!0Df!3^j9Ku5)9yM*=n06;-y@iz-h95ehnbcggvPIP z=CUBq-2lV5Q}92fKxjBdBLk``I}Gb6g{<@HrXSz&cK3e@dlg!`A? z_Z5e3zsLFPS#P$1!({I$flQYia@rcHQ@yYMS+?EB?rjBa`qzq-sFu~WDHeW_@pO;t z$4V2M(}Bz*r!&-lX&P2gGU%B%dd}~dp_2J(kQUV4Z|TCn2)yzTtGdHe8vK`-#lQdZ zM$m@>egDz9K6_q#DjKEMR%nJ1|(za@#(JI$Fq!NWK&=jpWpCXb#x=smRZW46m}kh6!I zh<2E~pYE%?MWsuVO|n)cDAAC)0bG4<#ld8Hf2sKK*tVrd!sbMIrA?SG=u#+ox^Q(` zLl)wkqn|c*`@~RN!)9y}a&1ZC-#P{pLOwse$S4CMcGd0yu5-6;8bHF9yTp(0hNW;ymr|OB7o;*F7y^udm&nbYi?vv;6|6N*l(cv3w ztW~tfjJdzt-D3SM3XFwejycW6pJaD*kz~mzV7lChYa{pCMTP&aR&S{a;kb*Z(EXgx z@98>mafvLF1fZzJiyeGW>CBH*DA#2?Tj0xHvi@)dipr3K3F<(lv)@pm9;Pm(`G=Oo zb7@=y+db<7=Mtx0D|=G@E)!a_{MqoOH18~o)|?(DC;^kA`dvUbWI0ur1!YnLnf`DE zVFlewgn`W{WE0Ksrh zggJV?Q4(m&PtZ(n%)|x)GHJW|}Re*(un*vepXoDY#n?*IwhgExI#wxvK(2@^}#_(#5_ z))1cD54tjN&wJG^en*p9w}ze=AukfgPT-u^;Sd;8S_Pp$J(bL!lKKuoH}yUOF)tCm z97d~Ebx2ISPuUWLs-)*mYz8zrU)cpJg6;YW`YH{SBW<;zx}y#{^S0LcS}EX%$XGIZB$0anR@Jf{eE`T9~J2DtSM7#}ti&m*W|?C~_#3pmq^ zF9nl08EH{dKFA)ZE^zTdY_J@@=Hd2I{(Z6vz=bLhhvobkXcHt~>P`3oLC{y5mkxDzVZ+_2%_NSm;aKO(;6$-eA7~J4&_HGh9K0vn{?? z3<4~wO}pe%Nv9=?h(SaAP3e-f&a|4o|E17{!_3)CU9hzqLlNwel{9V zue{~dxtzajH}Z_{qRlcfS|u{$mCrR8 zA0nOm(o$V_&8bmTk>#RyW>OzY>y>6!ZDx#J+>hKV3&_`V4ER#*JBqRiUAL#ez{Mc@ z9xUSzU{=}`B=sLk0(!^&0k(t^0$Mf})mx=N3yUPV=C^()bs{qc`Hg*6EJDyPM8kyZF(799pQ)sL>0(FsS5mKQjzRxEs&1kRDQko5>PF`iJ|yNl(S!DsUw_V`5T72jl>&m=Q! zV2bM#p;D*-Z8e4~21PnoiMb!lGNDXZGN0F2QVT={I>~c=GJ5(km44Oyy4{`9`xHkUgA5jE>xo2{172!l$qV-jOw*XW^HfghDL>>IYospS{Shh$|g&&)MpcC+m`IRD>5q z*7A)f*Q{wwN`%>^9?d+r?*Dqia8Dpc#9>bUGI?(zC~Eb_I!iCV6McELr}eDAbpy-R z3u>VGG#izBJT*S^*xD0cbCC5BSr8^4Kr3LZiPK@89vfzMKeAF9*U3FS1$u({<#nHU zta*VJ1_A71Gz_TO)4W&+Nzf3`2C;ejzkp&WhIX7adoNizq_`9TTzIYY48U&nr;+#g zd)!A3VE^#YSOEI#gtKO9%0SfdMz=+)weiaYU-aPc;|*Dl^{Iev+i-k5iJ~Y93k+GU z|6D_M${AboGf6i-7$s%2aBGYj$>+K8>WRuNHv?j+4>70PBg|x-(?kVqTzI)C)yQ`7 zdQ=3h9v8K4;=+bpqT0c$j<2t@CUm)MpM%KP;h9Ihnd{dUnS-amC1OFTzG_J;e_*rD ze0+uI3Z*CFC&8p?kN~!$V)IvVfS@2MXOBM(eV7B`G2%brxg81GI|M<(#aq9lTQx@IjB3<4}mxu7Tg9!7hdBz??=jv9UdFFV)y6y`C$_8Lj z6voIi2|iRkVDdkOVa58^HV1sKi&cMA+3u~RThue{Icf6BSM1bDZ}*lH$I~B~s7A$g zIaHS5Quc3kQyIwC-QFjyVfhcaQd+XqJYIX(sVr@#T1ODY3+cU|{Au((bhLpdT6o%1 z(@V15C_E~#YMw$y8!{O1HF4utuFOcf`Y$?S{8N^r?zOELCNWLn;3A*nm3-IcDo_;t zr%|O<$7Pm6#%S90<)&TdugQiF*22?hL4;QKb&Z2tUOIikv|XQd09gwV=|s*JctzEX z1=BE*WbQNWgD%$FbMHtre{FC;8+vtCMRuM*;eU~W;NF5O#x zv17y+Ce3{rnVm4M*JrQV187LPT6>m=fK4|`D>ts{iLyrK^lgiP*44x5xl|v8BZkTD z9Wlm=xI>Ku*N*PRXlCFh+iR&c%J}}0Lr}niw8u)Xc_Px&O@~#EOmqr;?N?|d|6$I- zO$=ZTJU~>K*ho7^d&6Q@usB}ZCz!$0y~(PB-mXl@+RtNN-ri^(%JSi{eL=e~1ZSu|>Fum#gK=3**G**7CX zrwpZyq?b>Je)FGf82eJTJ(CicMzpaw{gw8%?F4rm4gS*n87loJ$TXjl*n!B8A>o}8 z8E`9l2hCVKo^XguOGQf|bWDcnk9H%krYSfY9(25((I1_y_|$6L!+C!4Qh#Z3so_g<5g)iJu4XXM6+J3@D)N++&PL{k4G5#-f7Qbx2 z%%~#t8 z9DCqK^UpOf_TzN~U3o{1h+aG=1{+XU9j|ZHf_bSoy*M2t{E`l{4=7X<&2uSt zY@VKLer!RF;y)L0{+bhd}R>z!YpJ^>|2~p<1(#*+af`W3$Ls0ntimM0yNEfD~^s|KbLI5gCc>j3fd%(?gLH% zi>LNT=aQw?8xM@DFTR2mmVQnf?9>e0nK=2r7s`KtfbbX z?GH8@`}R`h^}yoGeS^hydCN;(52y)*9wBHMTvLb2-EFCJ7Wc{lxpL}Z$WMDen|=1` z$i=CsOawGP;DBRZ%xfyM#k_y#HCwuQk`mejh$Z7cQ+j7C;{CBmkN^GJilhu>R@S$ExacN0;xK#F?j(Llyy?y(^Ou}Z5 zt>P&#OrO-SwKelu&9?D@R>+~9%faOoq(hs9`^H#=F4R48`(bjK_kDLdt}a8~;`*7{ zJQA0V{B1S=m)a0kUUU6HAPbg&6{lM_G z#A1ld?PM==B_+;1ILimRiCd}hy|S2BF)=u=LI|5|1b8+Y@pwIRv3livID+_?P|B3y)&>zN~JJwc|OVvF?2zFq=_iy22a#{-}}XJOIDF8}OB}vWzLsvS6838 z8=}GhPY?E(&TC#y>NTxW33RVSoYsc%I~1&Xrq5q7`wj1}v%5oJjCUm<#Bxb*Ko)Z<83tKcQF%fXp?i+IAvPp>eW$ zHPRo!@c|@R-MIyUvAt?3Ki;NjYRpunwRx0URWyPf^cHOAATQKayRNv>bfg-{)X+Cj z=~7nc@`~V(+Un&WnW#Vv=tn7IS@)2mevYsVS|w|F#ky*j=ME#?@2&-SXt`38we^7{ z5B;grnTj5s4{JL}^T}F-a&r*;hUnGGjeA$OQ&T*z`s1=|^dGzYm~qB({lx;D@}$xn za}po$`rSvJTo#V#yfavZC3yTyv3YVg(-n+<@7%K16Kxq&=Jb5#gX?XP8n5r5qk}JG z4^Ves)@QAe@)S@vC-Pjr{bX0aSJUopuXMm}@+7HvlCUVWXf}$dipHuRWp(j_wCuAH zw%v+Jsp4n?xX40&_hp|5XS+NT(DpNx{H`q`z>p+j!3VZe=T1qfHwip4yr%}nagJTd zoBo!PqfbGWyIJhWYz5LY(NgvV>cB5$#kGpD$d%QkHu`3eX|*=zw~23xyk=>lrQ2I0 z1#bJ!#W#%X9#CLn8*MBk#&=l{2+k%_C1M-pGvI&=M0!Ff2nZ`Q=s(;|VZ_(Nn}n}j z-T*3397=U9aPJWg^WJOWj>UJiw|ua_Y>-!PO| zf4Q!)-_uKr0;6>{%dbyg)gW7E*~CN6c#oX<9HJ}8K(W04hev6bue)WmG|;OqoMpA+ zd=e1O$dYMv?r+dNE%2`!Ck;Q7}mxT3oL39_F4TcQK(Ua_?`4yS| zKw2c9?1IM*|FB`uL#eMPay{)+UvqU7IC;uPE3gA`kJ6dPI}Z~5W+`&ja+pR|R;p=W zF~k7cODKNQrQuPQ3Y|^X)j*K=8+!Wi!1L`pEP&5=br9?0mY}0ud^R;7d=@4q!zCMK z8Z|`nsJOmuM}fJYZzlT`zn0gSbCMEszaTKnk+(t`#O4z3@{>}WRB79h;=}I1o^3A@ z7f#DWVfS(9QCjp#SP=X%Z^H$U=}spI<4t<`PfoTDB?crbIyb78xtDp67e!%*f_JTn z_0z(pmnkscD(!&wjxAujC(Db>Po?Gzi#2`D!)S;4Dt!nC$zr60Jud&XLx03bNl<^_ zBHz#n)G$k6)An^__C2Y87(-oxS5u#!KZQJ3N9rC>jYw+haU_DiuiKFwzhe@EG)SSimwKTn%z~2awrGcy_b;} zidt#nZ;(7Xn2g`jm`qefNA(=)=7dTg${j7D#Yfnv#IeatD?9Hza77v4ba?fgk&@y| z#y1>44~J)b1R@OIef-!}(AHlWZO_Yzxf*9V`%b8xYt~D-KOS)@q8Qu zs;{S~X4Q8QGRggf1I7-_1v{_Z5r4?JOc*Jh1cUPohO-h zVR{3g2v1a-S1|=<32b+Ftf(kRzScdYDK)gv1;Y?Bk70m8;KU`s;IeCT>sgtj&25Bu zjQmhFiA_3XhaLbW$%BL(NQtmEmz9*AZ!4M$BcGGqjeS#oI!06xWbf@k76Pc)Lfz>R zFK%BEfjMqjD%#HQBlRvM%@zfXvC~JoHEwfMj;8kCF_RC_xqhp~>3QYO$S%ZJH~G?t z%~SQfdXU!Gz*cQCw#S+D^A1Rz3P1R%S>Q%jjaj*i&eRJ}(v@fGjc6P2my+W>T zK+*5hiKn{h{E;mkl)5=)`Cm`sLwAGf+}SJf?!&AgbbIRg)ncT@xvbTfvdw1Ccob|; zW=%AIxEYy6a=o8QPkh9(~;xMVlU~!*|<;4nZYjKZ#z@-16 zQ(Qm|bwq{Amp8P5b@Lq3wB)4Y_(9@plHLfqqc{mZk@23vKpmdclqH@p<7Dd()4h#ER1tLY=m&Zr1ofAc{vzeT9|H= z2#57r2E}+~s~y3MtLs4r&xg9}2ELL!2~7osy)PJa1GtkWOn!lNYqUk!3|QyIiXxSy z!@j38Z+z1T1eDC0sSOH(sbj5|ZRJ)eK5((n@hdbJ!F!vx>vKIwz)arOTEH2Uq-Tjl zU`bZ1+Alo<2u*%PO?2F-Jr7v9lL|S!i+-0UGK&wh+WD}zIS{|alBlm3_|?ih@F2%! z|2eTeQ2qDR8ot)98%%k2+ZLH+D9V^Ap5R9DS8N`+Z^$NCK*)hg1G&ivwSwtj8G>-K z+o;6MNj$cosN`G2%y_Nbs^hR>!Lq)5aj8l$uqdl4QIp4DU~B_9MVJ?#&SC#zX7jt( z`j$xSe^LP~X)d7HV-TL>aW?0Uoraqb_v$LzF1b)q=A3;{g)J_2A3HJ{I?uP5<09GD zTLwZuaHhaq8vO-29)lYe<)vx)Ip2x?g-iR9lV(B=dT)X*opdj`Q8=ZXUMjBlIvD*R zwXWnRs@uf&Z<{Us2S$%ak-E=kY4LGJTx2dY`Egx067b>x;HxyXeUjL~BrS9TU5pig3AJx0h2zaYZ065n!xK{YHZE%k&Z>vEH8`!-)-B^($kPr6- z!g<(A8ty5hCVap7RpHSAoTOAj*!{|Vhrxg(wV=P%J@1_#*u1sNiPaMSD>!nIc zU~EG@nxr)Im7vHC5Fp)KvWBZHYo$Fq#M$5uN6QoxPAsbq z&xLab{z^9TnH&t*=b!GCO6}dCaG&h7NiO4vjHkD=U|0o(7y3==4`?8=&V_1frc$6@ zE?^WNtvUvJeYHBAjj-B(wc5-swD@XJwUI=$+DezmXdufuGMi`1-rrt1+N&@_kh*#b zgx}#!?jcvs&mdyN^!=1f{AP^z3##2=TL-~bC!V~qnN))}Ee-0^QrNx6ihR5>Bv3?4 z@#~}=Sb!Zia!LF3HSu-(N5t>i(P~6{)zq6%1^qci8gdfW)05FXYCU6hOOgKmX)-}4GJ3X8HS%`-fctG~1DmB*bZ2tFC$4DG1Y6nqSs5u08sxc#3amOt z44&lK{B^!gc%@*IS`WqIo&dhragBX4m&z)3LY>kk}A zeDzQl(|~JLKl1350*+-%fBK?l*UmkM+pW-Ti4Ld=RKDi}6y*a&U19{3LpEFMyE%7; zSeX`H@m3dV{vfIQ4=jdUmp$DGh9&EkEEdSS@c51!3hq?CO*jRX2m;#qQ+51T) z!79hU3v9R5Nmb%B?T7#3?mpLO+SC@+R&t2*P}+DOAhKP&B>51&Yu>?Q(um|j_`El| z0ru#xRM_6Kq?1+QBW6XJ!*Op8H4L#{sLif5iNSGoi2EKPnkUD_!#VZc4+WjxcpS^q z?!wx*3_maK>%?5bjpvsYVMa$5oo&5q6xcUA3;cSCWzu%R)OM{ZnbQ^$H-i)UMyr#x z#&Q%D{rhX1o3ySV(?ZhSA2qMPVme^b`67_`;8l*7)1Jk=C?&w1?^!v5?FwtFQ5GXv z3JSvzacimU*zUDxgh@}3YIZ6IlUZ5Nl##{|xQI2@cm?9f$Dbagy)Srg93-CQC$zXt z<+pu_Run}?SC>OKPuzb_=yF-osqgwW9<6{CIFP?Qi8xnT`ylkv*j)76N-hg=!sCn%^(C)GG%GGs2(>Oau<_de_M7a00u>uRMCDw z9M=8R3!=LN%JC|=}=U`A;`WvUfxf8olCSO6~ zMvD!G1KgO{iWH&qFAG(I`=qlf=#Ufd320a9M|98cx z#FIR#&GGQgXtfGA95FS{EyX^kpq1VSv5<*~OQjalR1CY!d=2n5Slhn@VVDP!2H$xS zoA;1gMkc=!znAFe*tX5>C`pPdT;0e+g!(<6?KD{rOzw|-so3bcGS1xg)=R3b_uYhT z;Kz(phJb;iPKz0%^;BJ+ey0S__AG=G-T zUGt%BNVsF)-JcYgY_FN^oz|>BNSl^^YWE%MSMs}IV~GB#6|2Pkg-3uBJ293Pqd!4k z!+C=v&ko=hvPZeE!PvETdhl4^h2^)@gJ_RZp5nR^NB?^QdNm;29K0;b{gIz#sVhic z+~r%yoiJkTg>UNbmY+@tq-NGbAVZ{8p6k=M1klA1`128|dTPqwz-S6QE@Dn_8Gy`D3=PjtRgiYfbs;)T#?}SM;7m4FT z`J|;T(ru)@zXOiE@~9!kbvxztb%g+J9%LhFp&$XiheKZnFMp6Mjs%m?`L5iFNr$Wo zr;yqsmJ(v*(4s5FLMkz91BL|Ra;x8l!4cunTsrH(YURvwUwwZHBZK^d^9N%}?dNn_ zeYdtYT*|gTBwhu=hua7O)7|YnkxLWjicUH!zhC@@ISUhH&x9JFH=u+s?-PuMc6DTi zNI%_9!psUXR;<~FcuGUfoFlig6T2_Pd4faRyZVDJ9BrTSOiR1VpV@YIcuNZ`zVV9S z{aip0C2}wTO?6RJMC`46vu6n$aBWHjp|f6JsWYnx)1qTS`JqR?S`ukgq(BQIZGL3jrAdKHU-qw>$sLh(iMThG$B((9gJ zC*X&O#Y8YWwJFgU3c!grX&J~R^(dT23$ z6$)+wPxl6G3)3^TaXTn8v!R90&?qd=Ac)u(qF(pb_67)E?QhqC_DR+_m>hZwfIafG zCz4UF6Nz=K9{vMZxWvl)6!y2!IQP*M>B+7R{>SEPL02CEX40L-ldC;0g*tmZXOB)a z0Z9;zEZzW)%Dyh_6{YT2<7EKIl|X4w7pb0Cl9P3J>-^kKx3-wJY}=QS!gSG(8od41 z`MoyvI-c8UiT#IVf+DbzidG*lKGTt36oUx8t$Mq3KlLr0WO@KPbq8kE2jaXamwOGJ z3agCRRN8CEW+O2RVX0x;Nt6v&7{{FZV=zhFUcMO zy3>N{5ugRH`LyIFrdu$bt@c%`Z=|n=A`R%#quObx?Wv_v7gu{1_p2{IAcwh&J=24v zLj$(}Jb?gZ#30CGwEdENHt!sVIra$63S#SA{NgyqBM@nu4mS$)inp!WJ2rELBT~3iQXjs-Ysu-G<>HvXUcBA+)G8XvzETfg#ku<5LkYTc4gUPf zSG1*|>yz`L&;_{OgU2&=S1yt z?##Q+Ql|A)IVfM0dr9i)qx~(K1PSfcsp{asoth@n1cXQU1rsJODG*`xq_(^7aZ_0Sn6oG}BwF2Zf|2?Q zbt!6bW$cS|o)JS1y(N=RJU4a><@7rAa*l=n) z$+6HX_zVUwo46j}cPfcfQEDWtgQZu%B{Ip9lQGJ(_XchE_Zb($l>>NGxAgM!H*O!i zbUed()8l%w8U{Sw@>Mp)HpAUPN&N{$!5&&i3Mt5i`MP?04(CF(!h*!T*J*g}^uaiW zx3}RMZO!@v-J=~((i^kOt@J=`KbflRn%;)o$o0e(UMU|QkqqC6cUTQi1hlazJTkIe zfd)l-&?K_Rr0tR@jnFfU-y+Op&n`w%iq=2zI-F4kAR6|i@`{cXRq73op!ewE%ba^` z4vXhpN*mvbvH)|rt?(shD}@?gAA1Er@7Hfu;&cPVcRl7PSMvmxW74I9CHX5k4V|?S zH0*ITR@^{~5Rc`g=4Fo&(uSfzmGh8aCNpyq_NXh6^p z%=YU$8;I%=xla9FgJvVTXXAgdhY@I;HF^^|K8bFynUfFLSOZ17Ov!M&F21UXV-CK|zjCl(0c2t7>pt)<)^ zPjM?YfO&aFWyT}Q#uw2J}o&#e93qcY*I3Mo-+M+v76Ve08zo|#swkRZ_*%n}YF=jw1AgL|~Jxy(F1;733I!Jn-m1vds3^~42xrZN5y3d5OT$jMSjKC^+cVr5UHFX=BTjF zEcYS_T%FihgRp<9hY-?XXa#qsP7<)hO)RF~CTE~=k?vD&$h~#x(OC>vTLV(bcw1J8jJjEb6xa?qZ=1xC?td0IHVi>n$DY{0uCP@k8DMe4 zmeW}vefAUpqL8qpL4hmP%r@L&08Zjfjt{Y5ZektkIbe__WaUglm-6qdo?R(Rvxq8) z-5V`6;hi%RK_G!Ndb{Nf7GF>9>#Nl>-mi@sk(M09H3mhZ(WKw~W-5@Di-oJu3Z|SM z7PK_@apx^Jswf?31z5fN5L05&2OfcYJ`^&Wf}s&=R!LMS`ymDmn%X4XyXQro4@7Kd z{qt1Fc@&$VqUCgNx4xRC9+lNtZd5>qD`JV;#WA2OUD@RuB?cYI5ZRz2aL$!iZ;cb{ z@ljzG$am<4xR(;QhA)o-{@i*HZtv!ze!#?2=Z2Bh-acfk(5B-bh39uan~5K5HkP}iK$ zxcS(Q|0BR9-8TBJzwXAuv(-uOX(IRejt*tl1SpE1zs`zw%bcm%`_^$P@k<5(c*F7T z`_*0bWAr0A5=$5oRt1UovOx#y=x@&+!pK>8A!_}oJ$0WHcV7i!0tuluj0F~5j&(O!!cA7 zApLO9!}ZO`_vwg-Jjh@9cL97jQi1(%Qv`f}*{Y6ZEBD3;l!@b6Oa`YstZgnWE}VR3 zgH2t6+`A#x_$sJmKnx?Ym3HZpAlC;3xo?r_1y*hpeMQ*)LQpH{q1eA7@c zz(4DD)qlD395qrOj%i77ux!;fW_&mduNmpecC~U?^mDw6% zLCJ-l&3)Kw;u=WC5JH$m+mn|>Q+XY6GHN0?OO;U*kO$5CI~iq^?%~vSq@PRV^3Dn# zAJ*wKz#eB*^8uT_0h0bt0*fl1>_PRvU-)V%F&a!!>& z;WQ@?=aisI@FC!q$W#GU_|wXwf*Uj{JZ0k~kEKzmr-O@-K-Z>#t35dPj>sjeOM?CU z$Ndh|lcOmf;DfcI468eW1mtjU=n+pHuQforP}D=tJ`i17UMmoKE^_0XE;!8Uy~`uk zbq@3DRl@*ui&bXK)`*UL$g^xSU>POftIq~cptkpDxp&;=^!J2t5j_u#Dv^(m=TIZb zx3h$q%_SI9zPhsqn&W^#Wp%AN(=p}QOrdtQ<6t#&8j zpd?xH7?;Lm>MsJhTFVmjvzm^Z16H1=4!&@oDwzi#=lqOy1nf=zcytl5oc~0d(jnj+ zto$#g{fZehy*lXt=0;Ba19o#^a{lIIT@vTK+n?MN2G*>7VV*yUI%`ZW;9F7M3kyEyb|_T899LpW8!aEMEvvd z0&|`UNa+4gu)8y_k4@2fKRF#(z|t93;rLpfdmIQ1K0#UvPjKVDv0*OklJNdEb%--@ z4>ZuUftj=hQ_N>DUSFc&O(hL-7ge*`n7#;q-)5Z`eNpdL86dnjcm_*TCRq(c;(mbW zx>a|G?l2`+%azB>+&^2o4$dG@mCvuzIP2a9kl!hAtEKz~h3_#}b^x_)9H~%J^bhL@ zl}IUoi@pu!oNJJyzQe#D4xC&KIJE$5x{wScEdnJt{b+xm!Wb^?^S~auLVPbbGB+ym zZkRs-G%f)8o5DF?Ak%t~a!Ec(7dU5$GH(2bX7+IBtV3!*M@{v+JQshnr(k-N?98(T z)@`kN(Xb^_nXOtR7tAaKz^2z@LOHP22rE{Od9$~`=n-7Bfe%MxPnJt2d}aeuoPbo2 zP=Le$r2qfy-0*MQ7*fNcB*!WLs~3*1B1qyJ3ud+fC-}+fB;v+{KWUiX7%=udROT5T zg^cHQcm5=10BXPC#`SgPtU%^3j3>yl7HAB`9NvNPFkSj1k$^OV>tOk*UDkQN>*UO~ z;~dTSlXz`9j9NrH>I*ZOl>qR3K9rw?qTqruISCxNV!xYN%!6fMkI3l)zx%f3={IJ; zP%-{HTSNiLwe)taeAPbmk3<2I?VC8~tB4_iNX4b`L`9En)5=TCqU1XZ6iq5g>wM$x zFKXK~te~TVzN(J(v|`_Hzjgc;2r!G4a#2ov+KzbB#gAuR3jc2O5g@vR@sz<`wfp}b zR~n4VS!S%gRt;oD{Hd0L6dnL^vX1|ewm@&%Fcv!`{YmG4E#N9Y$`HsDd(N)k{$~rj zPU6$l_6Ixreo;yNdwZBLq|Y5vsJP&u|69!l_JOpSyQyIic5+VPab}F;Oj&Cksdbye zf^G1RvH%Qg4J2xrNd+v;Txx@`X@szH>G2i*`QzgfI{(tGCELw|dlY1_K zsDZTassBoMzVtgMR!`7OV5m6GLG*LYz@K0X9CRs~eBa>eE|q%nR^=i+PJIvk2onSO z0YL!^XNFi{cX-M4o!oD*SBhvbTv)O|FPHaQ zpYT~7+uj4d*-vPwr!ir%Nmc5DduHbmO z*x*0^%EGqgx_2L3_iU*QMFu^*>-wkqI{VMe1ViB-x_Z+LlX<} zLK(5G!dj*|bG_>!LjL|x_0P>-s43vVh5tS(_TN|5{+FsQ|C=~Ap4okS*RyC$r||@e z_t?7lCGqE(NcKM}RkxpMh08}B`41$D%b{dPRqj*2A&VZrUTnP30{HhMDTCE#@}u@P zuSh^)cP^3@fe1fS!y3h$?7&yQ0|US>bwYrvtAmUSsCK0Pj5>)Bw;8`H$rH9?j`*yEJS?lB*_GP1IhNfAz*| zrKqasPSk~WH1v4+4zK^Kvulq^GJDsMlQ!KvO*)R|rLnTHQfE@LOohtQvE$ur5RFMQ zFQj%6$qVBA981T_%G7=+Vi!{&HM}v3$aK?;1W~~Rfu*J>1SEneh#YJ>^Uqmlt+UQw z-&%WpmuJ8Geco^HckSohHNlV9XgKD2)-Dz~t@~8gy0db0YWLFp)#7Qbm1))6y^ZfQ zS`YNHoz)3tghKC6JrlpkVRLpd-+ay|+2&bU{5^T9*}Kvb@Oe>YSlCUTs0YfJg&5>( zbfn{#q)YNx5PKn7*$X9;1B77nTVK*H{n`J$C4r^@5zM}%N8IJ<+Ylmo6#n`kv&Zc2 zmwTIPMvMj7P|B8s^`CZ^#Y#rfN^Ot6=h1R?kkv#)r%D2U+`Bz&3)4T?H$w1F+0XC( zsRUZ^)j;VUjtkcX5nR^{_Qu6C%#(jDKU@GuiD3M>`QDzY)N}10^Y_&;>KA9bE@)F~ z&-CvfaTV8$+A*6MkM^+b+qX{qRC~V1Ho2)&%izAzj63(X7C<<`N3H^>^9dj=gjoLo zYkg!F&|8~bn+iwPg*BY%^NvgEeu#+*1;#@}Y&WD8`O`q-)_1sn zeFBK9t;PINr?F!TyMWRMwqxHN?;6iUNCZ``<it zFGnM~C-WKsbkIiO@9(B`6;6e(;^=sOugI@yH|3@hnB44J!qnkh1RNH zB<<4Ey%*WJ?g9rDc$t%nNSOBG-VRvBk^^9uABoNM1M(Ha*p$N!H~aBUatpSb`hgDv zgS+o-Yk(L@DK5!pID^z(Z)^$o@apc-A9|VIYn{X=8>`mPHb# zT%h#_7*nb+>YtQ+swenV~5a9RwzODj~%b2bye=I{FmC41WIkz zht?GNh1seSnymfXtxTSLAwuf>J_E*{ypiG(>cp(mC3m%?u$gs9(4FDaKU;=<_A=ud zNBVYB7cNwcL@Z=amT{}wy9^SSe%q3GY+4C%mZe{b!u~lHdHgvsoTY!Vufc1iWFwfa zb6jUygVZmJKqV_s^=9*ruXS#5JR@wl%q5KE zGD(Oie#v0Ul7CB47h}UUia3Tb;d-2_@~S3++vLC+cgm!-=ghobq2Oa!=g6g680% zE>E20ZeX0hZV19zYCFP59nz4J@>LWlea6;i3oj6v} z*O`+(HJqy^%nXqObNF#Iq~bTh}Ue-#Qr-j++k> z<$G$m3(#nZ&us-UtD=M<9S*=->=(^li+E`8zUh`NKSMuTE6S!)l#1{~WYz&O9a;~% zX9;7BaLdnPe7#6Aa9B{1`w)idY*oa7mNOo^Nb0tq%R2#cOkp>_Ds|UZ&wy?q5#6pD zk}fKxdpn+??>Upd=tA_OXFl>m@g)M&So=UFzp z8eF8+jBC;AK1`#`y2V>_H^~B}$=J0LinT!*ctDb;UV5(0|MvBQ!XN0ibZh2z*064g zAr{$2Bts3v+!mGTb!py%@kH~FJH-Pq&7X0EVc~KcM={&7ITH2Gb=!q_Dzat2X%?hbNdxKAHPFErvSj6*gqA zQO4x@_Fs~93Eqyrtrr$qjWTupqTyHj@)m9iGwn6-Gy;!rF%p(AW$@&yEfJJO_9HQr zXp=NQb8HXmbVN8E;1+LrHBT_<%sI=b^Fw9w!sBpUQ}QR^y?88Alonp{Ljo;It6)AN zS5e(mpScB0>;SDjYt-6UoLu=n-o9PVYln5hKLsFoFE%U``SHD7!(?6AWUG~1sM%UW z`L^@G)`j+O!=2wJWDzr1`aUB9Ov%2e33Wd#Jw1s<`}12`SMH+Dv-1hpu?K?R{vGna z7)XpiExoc8+Iq0Oy=#P^_xvVCQ1XKv*g!J%y|9KjTx0g$+N%7nu=2=iiJx)J)QZ{f zCLsx+0USA!P?~cxMKUr9r6Fv7@=Hu*pxs~BI&Vug!b6U-+gRTBD_mf{p=zke0CzkyyGrTiiPY+k9E05X! zd8!Qq8ZtFZ4g}Ij5_5{RmTONk3qM26Y_Rizj?=Y{>%Oju;KUq6oXJ{e#sJ(aeDfn< zm;L^Y;G)8fMj`h0FaHls$=BFkx}m0=MJ8>oc6PL~(YPc=(`C00Mw(Ylg=#aES1rRNyIOHFu|d z+8y24fNFXcXRp`<#&@Xn5Dx2=^zc4->4#qmFB=Y z!ZLhxyMcj~aQ5y=^qaIb8e)i{0MYQ6m5sSfAC%>XPP~oapW--Sd%Wi=%GraNH9i63 z*q5+nJ}xSfyeQ9^R)@nDFGUP&u&qhD!d@;=v3ec)Wf}SL{hOB4j>lW(g|2$1s0eYEkSMOZvISa22?gVB1~2^^ z!H>^=7LP3y>L04)?K&K1WCFPxX;<-;8>-Q{Tos*%Hd%iJ5YXSo*U_@u;$z4|8Ne7z zbSo_$+m3sd>i-TwL{L-Ef@FjEsA~V9;Sdd@)iVOlTMsBZHs=gCr_OeXqqje*3o(q! z4W7=WTxG;t>o#}ha%0?Vuu}t3y1}lsdJyfjIguOPPFYjhLjd%_1@4|PlE$T z?iRUbHD7mvmuyIk6{%=Vb#==b8$B}XNRN^Vs7eSC z@r}Vosn2q)D3p=r6UM5M2hE#T3A2~35%;|B->Nrsl}oajwOi5iDET4RCJyFJ(h6(~ zIG0%gmoGy$;rpH!47TRSIQ5}C-W?eXO0{+{@u2(}nrQL5FZ4y|AxDcSdq-)e9F#Ux zI@2^wRq4hA5;B=eC%3Q2LD_rnQ0~7WC6ycRN{=<|a@ct>1*1MvM3CzscNzk~{P3@n zx+5xLB4`@`=o8>^_maq++a~vRsNVVJzo|mKU7uL|DiS&U(*Bb6RT3EU#KV-6#a^G; zcDMUVk_XPrGMDI6^Z~=Q_-T;0R{I;1G)PV}UA6@lh|Cg<%3k{=wH08^2aZ0=aZo)y zf9nMUyySl9QI0kBCs2yR1m^9*k(=i5-0=F7Uzypalf29hcpBdwEur}~uhFDe%6>Y! zBQxQ{+3yDh9MF|1Yg}FD4cXi#Hcy<9(TH!scZa~wDW07m|JLaT9yiJW>$R=`*p`q* z0IH!G|D%Cww7>P2<3i;DsNoz9z|Y5n Ld>fDc`mg^2|2{zW literal 26284 zcmeFZc{r5s`!{|Mp@<1dS>IBGkdU3J?0fcICHt10u}uq7A%yHxvTxb5wizrtj^p{`_t)?FKF{<0!!d{JzV7qd&h0wS^L0n->#EVyve5znK(C>$ zYybdMFaV(ZaGVls`TU-D3H%4p*EUiC|Ajyx6ciMcl$6Jg9iyV6qNb*%p`kf`{P>9z zCunJD>FDTAo;*oUPtU->0EI$NojS$H$jHRR#LUcm`t)fQ7M3$-&akqwo;`c^+_`gX zY;5f8>>L~%oSd9oTwL7T-2eRZ4-XH|`Sa&_d3i5fxNz~}MLs@0et!N-mo5ni2nY%a z3JD2azI<6&SXe|vL{wDt%9Sf(Vq#aXUKJM?mynQief`WpgqN0+L5)1~re*OB58#iv=ys50LtfHc#s;a7{rlzj0uA!l!si~=@rKPQ{ zt)ru(tE;Q0r>C#4f9uvQ0|Ns?Lqj7YBV%J@6B83tQ&TfDGjnru3kwTNOG_&&D{E_O z8yg#2Tie^WZ{N9d=kDFRc6N66?%lJuw|8)GaCCHZa&mHZc6M=badmZdb8~ZdclYq{ z@bvV&fB(LhmzTGl9H0r($ccBvhwosckkX+R8&+} zR#sJ2RaaNn)YR10*4EY4y?_6{zP`Spp`o#{v8k!4xw*NerKPpC^}~k`A3uKl^y$;* z&!5}c+P-}G^7ZT2_V)H~-@bKpbaZxhc6D`q|NgzZySt~Sr?wJe+=K9SX|8iTmp2J^;WOMgD`t&8^!5z(0V7 z@^z!YjO9t14342p%pS$~JKaWx<*|`#OU*XpCGN{hp+DbYy+X@FLrKfL$8J3Pc!T=h zr@JRhVk$nAScrGZ@Lzb1V7jDr35mFxaH>}TzNx7f;}^1{H^3I;K>sU7eG__-dO|_<2h=ivT`Kk5@Y=+s;(pAb3&XVw{WTRxx*9a9%7YW)6kDZo zLoW#{o1X_mSu=ur3u3qZhj?9J(NW;zCUFYi2x9Zzj5p0($g#;{oVQZ&aJ7fAX@5K; zqvYmuAoDG?TDa=q-EppBqROg#>3ouhnX+=X5-%*JNR2&{n{~X{9mlr+6!U1nJc|F# zefwjWh<2k^L)JLu!K&CCLQ!TPjcEbDmh9E@Zyd5lO?Ijn=Z2>yBL_`5pDEUAC@`hJ zFSjlU(2`I-|7Je*98&ARy5?|YvS?Cu!Qa)GJ=Cpk@l^WzxjZvR&EH)ZkwQb0=^5PR zwa(3-YgaYGC_>L)lZ0T#bJ=(qZ&iIij$&}J-iO*0;5r4o=|XIk*|$6%y*1^T2~ zkF6Q}_?^`^?uX{^lRT@|<6)-olLtl=uql&8tF5mmhVuGi3x@jSss>Smqv#T-3InqL z=J8+#q^Cw4&upe2ki_*!DG5M5t1J3(CtYn&0T+gh&b+}2T+%p(oQ60V!i|KV9%Rgx z)vOw=OvbD`dUz(*j&i~}vH>G`z+KbTXnZbZMa#!>s$smI@OE+Xwr!vVy=FSy-yQ}t z7KPVZT}mx_y;hCt3V2D{$eUP|Pw z9w5ktNLBS1L!q(aS4kegq3-?nFSZ>rQJO@matcfJ0h3@v- zt??fM_AFWZ(Z+%6W)U?1$4R=V6wY)!m?MMu}pKfAu@b>3yBn!p8GxvRxoAbnzxJ=(?(dHq~Ff&pnngTV?px@vG5a z>#+S=XN(_(J7);_qZS6E@XiQ^o>#P>Z9AHhqR~h@rLWC@ypogDu?lPC?tOLqZ=WzD zDFp_nl>(n)?4h^I8aH<{)yf$4wO75KL|xQ74x zx}`8+(8gUhaw3wi?oF#6f0p0QXiFsdc_XmOGKdNZn#m0q@mH=KD(kiyqBZ5ES*z zqT_l*4okHCWHTGseu3QXTR@Fr0-N9DSSQd051L~??l2>grH>xHq{h&aAAN~dnG|4> zxukG5oC3uJ9wrviGYCuy$Q0;HT(=x6&ZU{r11zRNXM_`Fjh2Jo&YVTwh(8EAWcinx zsJ#*j8!hEKXY!FJ;)3D>3QQ6;(tdgN0k2BPb8SfVH4!I5%pCifFTpC5TQ9b5@1KBa zaUhaQw?AhP?91z^;ySeD&V&)3Gd}uyd0zSgx9qTFJv~;Y_7i1+24Io5X!AV08XNHb zLqv_+N+JP=XV1ycUQ+sTD<6`a+XkrnKQK2TWtkK*w6RiXDhm+`xO zoMZkO!vNB#-{A|?`fi8n9(*yc~UXp6MXT(^^fJi+9AFF?vOBXQ!iL|6mK>p3SV%Rg#LYG5MhCv3{ow9a*&bL!T|l_ieAg5 z8H~+g&W_Q3Qr13I?61mg^*ZaAe!_83L80<|^V7njyj}K+eDX_W&xGDjgkQMk*QHZ| zzP6ha{lr3B6;W$aG=n4P#^~2&`mPSDa!-`Xw&hV=VvOMBg?SprsRpF47Q;vJ3LSb# zy|z4@2g_1vr2g!F4B1Gs1<}53bQ@2}ZU3p43v7whTp{|Tz(3S-n>M})Fe!LCSii&Y zazA<%_GLy6mACbNjEQ8PZy`0tSR)H}OlG4+USK@h5@+>&W{gKbW1pF%G@SpWlckCO@m!-aDGE(!zRIf@McC#^u43kok`9+B=<0dpT7G&FM>UnuHdbR z@_*fbET#Si|6g_e|HYZFzTG$<%Pr-jMa&^H44Kg^kVP7O#mlN$T8-aC$SwAzxDU9S z5%UmzpxbuECq9r;FIO8NFXg#$@6xX(%--d)4mnl?h0}qFYC^Y0DnP#K?8=Hs9~5!l zO7gr0P@(qe3Bby&V!fl+LSi&pmh)%R>0uBFPd^qeWc*9d$K+dxu;qA73h9CTCTLCO zS6tF2TIq}$-)U{xOKkcs&VX(KHL^G%ID_*vo55{w zB`M#hB3(hd4&d~Je?&`eE|kb899#k&0|rb*Sx1~suSSf(rJ?7~L^}#=b{)ECoF?OM z4&`#cGCI~&(PE>YvQo-O6t!X{LnM<;) z9bdhkbP*8t0v9cdjB1|oh@zUY)2k9?MTSbkM)nj6Dvu#AGJt#g1L~_WtuXFhj{4Wo z*v!YPcjeG1C{r(Y9?%&rdP9gA)J|7gbReh%KbO(R@piUhbg$Gzd7uKl=;EWo zkALaR#X^bcqspOLIm#locSXQ-fA2O`;MdYTB0`hncpwoR0Z^ z;w$`Kl@Q@|2LHmg7y7&%rH{*Xu%~5>XFIDply8=W+V|#0ecDp*i%%x#DzQxIgu}FN z!h&EIEeTk=7L@PpO1Ryo7{T#6%$0bd#UtA2{!^598+I9}c5W_#bjl1|u#oy-jpq$G z1HrJ5C2m*heWo)7jjWDqJw7YsI{eBVH8^Ru>wl2#Oi>EHquAb^PbOTj8s@81zm}LmmH`dv8$+~!67QpD(oER4 zS=xZ%cq_Xyd62bV;PtFU!)c6@kN8?0tr>Lh&Mkgs8T?qKWQe78nKxyF^My`u0Z; zY5m-A$j&m}F&wp#K!6{oB_7#T|-_3cj$+ zOTaz0pow0_B%veT2bE(A!EC3<-foXRSINpoj_iub}+}^2+L+&qh*L=EyP*Qj;!g(-nbLmrK#&fzO@m}znyALiEytDVQ7Qt4 zfKjbZpJ%>MUfwF_^)S#~Osh+ggh}Or6#7i~vywg@7d%TI8E<-lw44gJRs)ij)l+6S zH{NWSec~YNb_f2cf+Yifpy1wS&63I*qmc+%uMhP+Z`HBYNe7S#F@r4@LDujqJPhDQ z>NnbOOa$yqe8xg43+|C5VWyf;zP8`DgarCNAXMjfM=}O1Tx|CQd%H#Q^5&fG=kV5Hi*6K^wuPs&29kESRF#RSJWErxi@pvRA7G=OPbm z%YnVlZ|a_B!rn@B2SKWQ;UiKxV+}i=-vJ^_L-omqSSA-<7Gme8EC8NAi<7X#jAvou0MdX1zd z6VLAYoNFNWa-wDi8s$9KezA+^qmoP|TlFp&h`AWkYNQrCFqX_Y^RUt_`2-U+3|@eFgA12R111V z_Z+A#E$ulYyer;TZd*lW$A1&UUQd!d65Bo^u8-i7VvNg_Mj9q~{)+e8wyO#7#f#q= zNU9TkmK{&opCjmcG&+(#lX=y|6m=FERZUo%C$e;mXA-G{GYu-|MgfJ33PqLFWIIP% z^NxzUiIMNyz}Y^YYqa(nYzK3h52q&gABjWZ@;gL{YYK! z9{)V=*ghRunF5R9;s&KnPOF95p{%`e-KKsSp_nd<6~yfMa?Jce-S``%sMqg)L{m?3 z4p^_R&feK-HTdH@&XyatWi_7eR!vSf-%0&bPgbcY1Ha{%yXl9oMxINde@Gov3{ta_ z_`M`|h1`7H^oSx$M|YMadKAb)?qz4rYIS+MnhUp0Inb)xDKgdN62Eh+CP22an|Tj^ zR^F}^gX}zuNPe#k3pnX>sDqOg-8;7|U#D)AXx?5x4m@m;1MidXePM9{ zS}l*~kJAgBUJ0>BPp?#1JA^ry$9+u>%vYa%xAy*%IR$umjBLvKXhjL0LJC7cZNz)J zsYl7>)`ok2OX6P7f(Ixin9lJil-*ATj2dYJ?ikVy%}Mli_YFJR>zj5XkDqIo z>OiW!Q?@>UH1$awBIWAPqRYE9m|XHTLdOsXMcLf&JRT#k6U53FUjO=alKq!}V=EPE z@Y_5qmF~9w;fm$W#FVTQE@PPuD9UFw;{3RWcD<5v@CkQDHp8F6U3+ipH=_wJ?cIYz zL$bL(vyRyJ5Eo*697WLV39LD0tK;}RTrVpxJal159a=KGOXH+&`iuXfjY1Eb!|i~` zM2{G}h&86{lV_*oLebfp&w+xS7I#^~Y6wlrt#`0o?>#G?Qf>9)zsv+Pz`cgNK;K;! zSspC<8j<}&>)kFJGI0Zv)>La1_b4n@)b}D#icQ&Il;|&9;=#U7djAHPRQj3Et+(v5 zpG#vCLId+jso=)EZ*$F0V1^_Kh1#t?mPc@H=w7`#HUfz(oee_dHe9-xl)~VS) zv}obWSEDRiz~a+GIt<5(aQ#VG*QoKAd*&v$HbWJdQu#@&v`zwu;pGs?pi%5oB&Q`E z(ljfK)NT$~^k~92ddrp1hf;)fh!;x0^mm2rTe1QYH5zQlMkYoSlk8w+yKSLCeKHCBBrLjd!oge3biDB* zLc2yYj(Z7>F6B>@5?1wlh>1d5;j8Db%U#J!eR-_9M~;0IV|{)9>*z84y7V}}DKvEG zG6A)?e}r7#_*SW$aKb*fHii32SM<>YP@eg?@S@sHFmF z@a(fSGbOF9D^iB-B^H;i(!=o&dM$@d(;N7>VX!#pmJMfSQ+j5A7WDGFA-7lCpR|Ow z^{xh22Vkau+@u0Zxv?bb1xpb<4cIn)$pd*Fr(1|DQr>nBWkq^axEtwfE&Eq%_ZR>r z7Qd{M!eypE&bW7@bkAk=wQ0?qq6l7}2PPHRTJie%BYGbVm@O(REA{uph%dLhXFsYh zwAkzThxt~Aall~#E~2qKnKg15{)O@@njXE|*XBhAlY{p+@A1Hv@;61TQ?)2$Rf6FT zbfETNp~9q;a-lPAh6x#mWi^WIaM*T?8l7oSo!oX3m-&@q_6(~w>$rziLVkD9aRXFx+=1a_2=AC|CItL>E z!7Raq(m%<602C0IyD7liQVx~I1@V9Sp?! zP4nv%SKAcRW8UHPyIbYjSz3yVD>P|lD#!HKhsv7Gt zbgfoR`x5$gzWq~5VZ$Ab>y2}Ak(}g^)E1lV;{C1#rR_<7$~X6W#NJ;r07B;pU{S#D zS^!WO6SUo%P6=NKR_36(>KU$VEd#XG8N)fnC7LWdg%K( zWuj#x^Seg$!ERpo{$u-4DZrk@D2~_!F5QB$H!BNmJ@*nIPMWLx>Gt$bOPj-8lnn`oC%KUW3NV?O`fO~J zV0QwSb^#@7u!n@I9FuWfHC0;6us z=Ptvb!Wq)|FWcr)m2wFD2)bRUvis(oh7xI4tN+mnKOhag7hpD=ndiC+p{(||S^j4S z5?rb63vj+}j+2?Ik1HKI6Y`Iq;^;&Xx_W_xvN+>yZIK-h{#+X7eHT`hxRXoibMLMIF#+F~*7Qn?Bfok?vZFZT+aM9~ zVyT9lFsLctRC^+c+ve4#;=>I{_41Ieb`$iBgEl3qjZ_lBu1}Khj1tdl*n3c>u8r%Z z(GR!eY;-YT-M<_@_Ql9QPmg>N8-3Aa>!k{vvzn5V^}F$p=v`EaT#M7hyXazckQSVS zryx7v;%YQ@0FXdufHYApY{=rPd0%5A%15j3qc<*3y-0vsSFaes^t=on_6J<(7Z7kF zY()bWL<&mGeR|Z0c%Jt)zI6l0q}XfM3s(bAn7$D5A{w9O#cD}Gk$!T?-!0QBdZz9Q z0T>aW`P2kHQV)vW*dqK7h%07ewuru-x!cLhQ2=u9p>;dqpSwY ziE?>Y&6fo%0gHDOh%r1iUg_Y)cC^3v6cy^8Yw$dp-2bAe(B!Hx6vfF{{#@saQNiN5 z`rv7<@-Qs(;pfV*q?MFNm)5PWd{sx%qhi{);&aBQ^z-ZZr`E-mKG(&HE5)mS07{jb z*XLxq2#%S@kXN0@B^-hfWqkl=#?|o22f~d%-uY5pl%kT(UKiI5mP1v-?e{K{+^E1% zoX{M%L%k5Se*WeruCa&s0ZN?>peP^(+A~cA`;liFFjHsGkq2WFHClVpaYnidf?|WW ztLuEcI~epby+e9`L1chTHjUD`Y{56`uuL`+#RHYDhWc?XpqmxZIj9wEnWawApc1?l zm73`-(#o= za282p;~Ow0R2mKqpD#v#M`LY>6>-hZexmKVn!^E6_}kq(7(1n!l1{hQobX&G_j0{Q z!j?LegIHMksx1|=o>D&*?z-A(i1yGf4>W>L1!nbE&lLo1GKg|0IV>)|41YLn>n)3@ z?Z*GwFp5K#ewI3?pYH;6b0Rt=I)V-TJ=cKJ^fTe_Qfe#9xRu4P-+lLB<(@qgircYS z(0C)ne0{QOcu(Pzf$3?aB;3>b$_6v6FvFdoZS~6g(n8wDo}1SBON|-ABLL^(p86xJ zPim|vXY&QKNy#}d(4I|Ww(*og!d-6!I`t#WV*sj0p)cszG3gT7lIVXmex{L-%N6l9 z^NH})Pb1f9)4%-RLhP0A8hK32$ z3VR=3Q{$FhRcxNC+O%D-Y33*;D zoa6vxe_ZDgl%lmKTUU{5fadFW|oT3S^Mx3-I+(XU%* zf+G6{NJl|7o9nPRf_B)l(Xlf1ps1uWK%QcA!J zuOEf)gpOk_ueyt)^Tm?#r-7PAux#fbX7pWDnPdNe|ANiT4C2xfnzst&W;`hn*0Y{UHyI`Y6=x7^Ex z;hZgIu=1u46c)DOw+HfWMvot5W=xNxTsTo@LJjP}uoSna*m9K8{#0J8EQ~11`2qmB zrFgA_5-F!5o4w_%Z(j%?yS(9nI55Ql4;Bbu6{&20d)N;-0UqgbjKnG>Mm_uJn&CoP zjWL&OlHRIn1xpg#laFSuV0P|s|6H`&wzKveNa*unKe6wv5B3ZfW~**EAb^?;HA9m= zu@M%&^O;*V^aHUm@6e^>OtavO8h?ro$L6PvhtJ3ZHSv;&ZGMHxMDaJ?#(;if=k6tZ zh5rnmuNQWR>*PATXU@$%F*pgcJd4y34U;J?E)~^`yHHRXohej0-atW4O-4XUr}mnH zRZHT9_ck5H*yD$5NysYrZghC_ifL^q7 zSxVG;16{VLa_Pd}XjN6ow`QzUGe*lm!0# zo`%&VYs6i?q|DnZ3Q1T2Q} zSZ)UI<$G_!HDi)o?OSufbV$kkk7wqJ4^)W_1FM5EGeIr`BC{f zo(6w}DRsP0=)&-L@tuH&o2oD)Gv+=s2OB!%`ey2XXw zX^6sp=iT!A6c$$8m5M7()xj=nB)8(X&Wm6+Xl=Hv<`^Lh%@%UCN{NCCP-vdXa~ zf#g!cfW*ZO+KtK@yA9)oxfYhpC?^WD(5kd6zb+utg_s8hdeq-ZP<;&{;=4is^yF#G!6@SyhdDGtUTqJt#eDeK@?!hH%<+CfL5 z^_E`2wiXlAv_F3ZYDEFXjOQ{TnUiMPP`UimtkD+Iwej%^mschuFPSd`ER zrcoojDpveH;&IXKWo?Kpo=X>KFiYQmhlgv(otY(94Kx^~%d6~3PEPJG)bf7@Byt&sM|von-r&aeM191|7z$ce7j%`P z&L%Lx7g#annM3Cx7!UXL^1(12%ZMkJ@j>5szzZ6DVSE^h2b`M1z(TC=#TH8llWol& zS^4+vcaJCU93LyZ`wZ_yfoh14&YddEZ&`R079@LMEu;lrD7!?%I*1=y{dQ)m)<4k~ z)H)Yj^@+Q{i6VAKb2bMr_r8iRYidZb%f24`He-puo*VNZYyFGudZc1=gWkpoj*eDW zFrjA1UGpOztu-#k*QLQ=2$W7;`$u-_31v%+$UV+9DTke%+`_|H709GQuHM_dZ$GBF zU*L_mhud0R6`(}v!7EfqfzZpPr+@~yDlV&;AN?rtDt{P?Xw^w#oNV0U$XtL9+)YXA zpQUKH6vl@9^=w{vEWvAd%DUnvMr}d<9JMtVgP56D;ezj#Q`;QC)ZtJ7_ zO;&3%Hg_|{QRz&I<|zclB}DC@$XE)T57J$k8P^Gx!Zhd9wM&U)a!{qZjuRNtEzF;C z?nAISj??#L=i5GS4$qde_{mx@XkBeP0EFtorSE7{`t~-h_x1+z`gulyfmbSfLKFoe z_MG^9GbY@ylN#eA$bbfYEf52CysP&4p`I1@S*3;%RtLcZT}z=d9J)LlVFq~lnf?O96Sl10|gS!YSn?}%7D z`s1XGeOjw^^^^|B%%}j9Ij=&-6;KHFIK&hbox?Wbd1OQcr``wxm~_vUQOxCM)mI@a zdDlvxL5R;S7(?&DiLxQ@ft8|A>bn8w!W*5&s}ktKfy0m!FUd6eB=O1_gCg!#Honhu zL0>|p){MSx27x9Q{8+rsdalzLC^($%2?obomiOyyHpMFq;o3Y)p$oQ4ZE|%ZJlMX+ zl|HqhJ)}b0URG>wKYvV^Kn`u2p(PE_-WWqT79_0%Mmx$XgL@>G5>C zHnm$=g7i|yFTydOY2@8l^W~tqlC99|-+D!svUy{bx%*8PdF>*iOe4K1h#LWln6SOw z_3;2lUDqA=C6Ye{YFIFD*<*<3nq&sT#gPLWY7W2hacQz>OpD5)-pU8lreaHdQ)Ym{ z=}DWFS-!$X(;B8zp82afVV-Z z+UJ%?BA3C>v1^}5y6$lI*p410GuBW}Xs$2r6V-xkofb}KW%jTc{Y-Z-;Ki$pvfulS zIFNM~!!%)vP6yMk3DVg&J+;yUF~d`mfe6Jie-&=)*MbI)bR(*7i78Cw3Gw+6YNy{j zd`)p9aE|5&k3{fKjbD3h`PO@o`1ObCg_ZC2>(BCyTy3AV#IHhKct9unQdh$+YlT+fGXYJ%22+Tp=6DWa2du62j_ zJ#zflOd)u_^5&aIC+_;8u`ldWWV?@pEvq(R!H`&rcHkvkJ z6(SWBV*ARmN^ez+Pb0Idv(|9?!$j@j)al|iIpbhL@B{I>I*oB_315VP`B+Y9^fmb< z4#47Ttd2L-pdce_8}?n$og`mD*evwhTqxC+!8mRH=v2SAwsCtIX?YqsVj&tBkY_eB zPdON7u11wDzRH&Ha$2I-y=%{mLiVvbl5A2bj_-a`6Rl- z0Wh4)U$V-tw;Hr-TjC!CsX3IGMUTrqS4dG*utIR5-#O5wPj&T$t>~nYjQqedsB6x2 zN?nYdbTu7zB<@UQ@DfHWFIAg?fu&!Zcoo|=(o#kzQ>7b_%F&6{@r&=w-#8xPvW7N1imr7TQ*{hO4WMTRQ z)&k@XzkC(?E@<1;3RiV-B*brZV?~! z9}O4E+lnqgw$u-IeAe!siha1=`UouxuPcdqFeKvD`*6swC)moUY>*0y8VCi6pzFLC zkdzWIQ$DT{8~CJ5LsiKfwb3|T)HaaQ6bBiV`YGdTK~sw>T^A9o3+TZxGMiEdSOSQ& z{OnX$pXuu~D4wyIj%|)m)_T9IGxRn!d$r5otm1p=3-HhV=y1Ue8AG~p;fzV7OJm9> zV6F~r4y%bXO`^iBcOmY9zkYFtVtwl5uhgQP&)z(SIoaJdE4A43roYvBDm^g7yEYZ8 zS!6z&==+IDAg22!|%vd-Yu_n@BQ^9-|5ErEN8iEv(&!t@fT(-sB)Q3)A**> zy?+Gv!%hR7Kfmo(gg2G0bcbUnUeO~fZ2Hl$n~Qpv&!UT0-}|MkL=wHC)r4yK9EX+4 zL>M`I?{6eKjYZ6W_fu))K=m9aYBK>;5NN1H_F}+r^EUc}o@I7fr>x-)V4d46f6WcY z*eHgdj3h4Zw0C=q+0#h1^=z;R7Zi)P_@7_MefG>U0{Uq*B;DN$wp|Lj{{EO_G8jl< zTuQ5n9xu1HBT>V0GMD26y?hs|aUGWkI9Zwd3U0FOX71CPl`2tfp9Vk?9@78!S~flPoOh3hTFkVu4_dx zD`CP{J2-b%Hg4qC3EW_(^Jw|a>pE_Qwcph8wL7YKNqrC2r(Si$Ze1*J>HeNF@7Y~> zX$H}=nf0a<(X#xEVvAQ6DQd<|g;F*a%8N}0ftF##c#od21kE}-c2jp+%+j~RE%D;q ztX=-8*BL`}8AF{$$PgY~4?pHQai0_<0kIWszu$*QolZx+mY# zFvq%7yT*EIr5gOacb*@*x$UR{m#o+c=1|Xm`2#8u6xR6BTol5DINIg05vDpRD^~?t zB-WXA5i6k#mhg0ppSXK>@tCgTf|goSTuRQw-kw7YhrGkBWlgV+n=(==b-c#f^vh38 z!5rMqTffN{o4C@=n}=gWR2ZOBUY5d-p#Up?a#b?z=tA7a`Z9n;Eh2V-Ff`H~TOO_w zV7dXuzb6}ff2Hy69%Lk2-$IO433n0O2;m2t;l#&2cF6ebkJJ(s8aaj+`c>eUemN6f zD#qjNY_z7HK#(mGCX~MRq~*45+5j3$C< znE@86p;Y49*V5B5PI7@rR7HA0_kR+X!q+}*W@ujs2F(z=WF+@OQAR(cKr}amV#wd@ zsT&o_`}BR-(vqux+2s8fU2I)6$d+o@A%oF~uK-w81C~s3A&C9II%f$7yh~~>2$4ZR zfpg3H>+g~oU{SA&>8}4&ig$evSjraHv&F*zd zYcdyk`S@0v|%?J66bmW4R?FYDku?in{Hg?|4iru~~xe?s{(_ zb!bpklM1lLx%%UNbYSxQ;>jZzVqSA`a)}&UBNnhI4i#cMf%Cucw?b|KfLqXPz1u=` zn%qJRW19USuuU>9u^^BExf;mt`qU^gghkPvy+6v+O{tC%Rz(JN6a?Z_FSw7OtXQ3o z!}t&llVFe*)Z*j^BFJE-6rC8Og05^C z86Wt_9SYW%P*T{!Z2QEqxlX7@=^g zWz*$WxTFyI!t@xjYsR*B8@hIDjGR%+t~lswG`wRC24Ks#1Aw&A4lM433sIyviA(*J z(8+=TmPJTOdkQb0gJoDE7PYMm*j9V^h56Qm9Y>zyTxOOdY2UlNs#Q-*Df?|)#j9S4 z3};H~Cl;5McegI1U98sVmx}!Sjmuq2)`2veQo`2^VTI&Vrl(MpW7i{dLfAmq5^4}` z3D({m)*l{lAC~&$)VWVNnNPKxL}E(^7;NMO6ke^FoI;IA!h-iC$harx?s_q=}|hsqrKtxkxrY5)Q5)@b;_qN`VwuAtAR5!yG3qSLyewx3NVf;*bKvl_D{gcn{_P^*G)^FIlt7LdFuxR*964nk@d!k-?yyF^{B+-DnA-{?l3JY~< zp~k#=DF7b#Z==&UFbZ*sJt{OTWMM-Z8s-5`n%sSm(2qp0rXH-)rqs`t%|KNd5?^Q>TfN;E?D_bR%}kS*GM?JzGrYF zj$``yC$h3x_!Hv5crn{O#wQnH@frZ>^vdGCnJgBl%5Lmbpx8q&}o^3MJu2?NnP({0B1C-c@_2{!Crd z@Q`Fzmc`xZLu`zWsx=?J2l%C@?eJ9_#u;t{V<$l@${U19W&&3vl!dCGc!3|68f+7c z;q0=YN9wlA1+&kj3{yIZgK!=>*X+#IlGN=Cf8<(|aN{)4T=w}5!H^psj6rRhFVM3i z%bkNCDcVAj-TweAtIHkeiaO<#k+)F>^G_*!>vz}H9?OlKoaR+udJ-pu4 z4~F`-*Eh#>VzP|Enp@EF5}gnZHdW&4EI@U0Fy~D1LH(2%ulQ$(6KS0)he)4#Mk)X5 z(WZ89>~ruaByX4i`g^1=(EXam;V>V)9G@Q%V9=G!s4V40t{dL;mT%h)(6o@n!FSVr zy^WCvah(c)#cwFOTw|;2^WL7Wqs;*Z3)gF=NC6|La?;2(pqu+>l+2nXepA)+EID;r zac?JBXvi62kNfnb4epcWSjZ9jDl7PE{JU__(y#`;Nlb+$27R)l)YM?j#l!U`a}@tr*MM*;plY+ z0x`CD!LQXK1p9RBnEnQ6TA?szLgvBz>s+h;9L&*9L17B8a?(L-9u2a7OtC&Z4r1u9 z4&@~)A~xjS8J%F~QO3T$msOt--Z$pn6oYvZ%I*?b?CZxeU>(p2Ob6@Tk?mK;yz=DC z6w1?)9#*wrt?Bw;OCPLtf0_bR!_B5S#l(==0!Nj2P)vO11!R&MXnH_^V$(4sT@$lV zc_No#5Lnvzb%WH`{dRlI@8mU2Xry}6*A}$gbPxzyG&l!QVBe`;I;#wZ@cG)TD_4BT zux(biy@#7KMjIrQfWdC;7P2!T96=q<5(XB*OOT7;O@4iOR{m+Vzr5$%_rymOPS~{a zW(%8SF6}4aK2IaK`js9~`v@_CFpL#8q}-n=NhAH5n*!P{Va1a80pUA23FXOsAm+md z#4_9!+VA2*y5_bUWdYTXId$B8mjkrnm+pXPqlKf9eMthV^>=hzW^uF@GNiX#up6M) znIaFOHQFv^vxwh>UtmI9t60owWH8l&o}UFbc&uVA_>6WYv6BbfVR6v)15q9z^Tt() zK|mbwZ@qK`ize&9BUmuml>-a&$v|xK^CMjK{{qDBC`4h07^whhzNEc*(%-8~sS=A$)6i$dlfDed>%G3K}hpA8#*lZYo{(t=M-UnD`{Rc%;@qe(6b96_FMDyW;{1Qv2E@e->tsv2+t(%z{B8TM&2j&grQv_4 zxj7BU%KkhU=f+g>`!DQc7~JJ_T>+-3VHCR@biO- zVw?}l3TyLbbsEiI;BR49sN5SRXd(XS!$z4{?9$pF1a5&wqH%;f8PB|0qObRri?rEq z77ph>J&L!-@;q+V>S+W&D8#ec5af(sOHRMr97+`~ z!FX%4mjgw|>|s2RkzH!+zI8xOM?hfFmpIN*HsBS9{40WI{$v-5sN0DH!LPs7vLB(F z-S(Qc&fY-p)skAk&w~_7AhNK9M&&>Fd)tS~Fug1|);DlB<@4cpc@qNNlwAyJ2t~H5 zF;nF48f8}v>H`;}e{anKm&qfv-o&|L>UJJ$;P29%%UWt%$e`wZ>Hh>Z3y>__TRD3F zr{65PL0?y-43!P|8x(IFeWlJvRwd~u=Ol^(Qa%p>J$z@merptX2}1V4Bkia8gA)H= zWhwr5l8ygL7WTjQ?Z1Mb^Z)-TB)2F<@s77#{vYjKYgp1*w@00_d`oN6sWfHMnaWIk zD>cmk*8c6qT6?ec+qS>_DryhIW$QU~#}715J^*YSP`4DY zA`1q{-L~K$^=5X->0Nu>`9Oh#_-~She;CIU(ceE5Y+nP(e>mwB=L(7d639hg;iMzS{m^-mKbp$Xgri@53v(2Z zfFfP_+o9^TEj{aqb%P>qtA6D#$sl{r-FaFh>;Gy7ZjDYwM7&8N`YKOUG<0Bp7$bfxEBd2SYw!EBZJ$yy`Y1fto zlR~S=pT6|{LJz4mc-VW@sU@`?Gg7NKaFxf7xOSBRJ%h%hMlOFkm>M;;mS};kbJuE6 zmAx>dOV;`;93^1eX59JWI}IAin$G)wwZ5Ghu#{4J1LW^oa=7uquaVP-=xX8iLa>7N zu|0YJuCGB%{$EUwqy_uxAHQ6EfRKJ@Yc!k3th_xu(5ZR<>sFe z6=19kmv?5c6#^rVplpn7*Jt;{oQTsexv2UVIp|+}q2GNAfmWTP%q=6AqW0gR&g_b; zJL?cJZs@B+mWeYFHWp7vzlFA{?tI!`wfyNts6@AH;_boFrdj+S;DJVy60K@j=-$^K zT>3I?sUCv2M?;cDPEl6n#&03^>)t6X_2cBL?66%9L?pE<;pgn!l(H&b)atQN-LI*WLaDzKxnv zuBA>vxf@`Wu zS}!%+po>363y5BL!x*1>XCZR3=q#_P2^L+H8}LURdx~0c5?`k3ddC5sHpjN>bqMB0 zp_+O4WcV4*?XJ#=8YhXAG|xNnLaR+GeBQ-q<~=vLMGzAB6Yd(X!{@y*LH(thtJbvf z9fTNo@7#ns-lw>|DIrhLU`3pgmgH|usKiP7p>OrF02w*dd9Ea&p!!kW36jRrUB8ec zfmgKzvTl&s4D_cNP=ZMue{k%9`LU{K`Lzm9Q|lI;+xyW>Q)sbBP_dGi>i;9e4?v25l&_r(+p_MZB4{@I~jv{-nBKD!t_BuzsyuQQyN}1j$(b9fYNvZu_zJ>Wks%m?(d1K{0C?pPq=aqV>eO zJ{@mBq}TP03Ac!HB&W{I^*-0Crd<+UffBo$RXh6)S<8>e$+PdqHnkO3xkzY^@eHG~ zqnj@HZ6_BT%CdX|!?0dgCH*Dhs$+sv{_NHyof64FWV!m^0fPI<2 zj|PTtn}rFU8;&)4Z{O8w+9H9?g(6R}64;3q#UakCLxz<6xzzpuyG z3sp7?y|(BC}%r(Mi>1wIHo?LxIJuE)L>O%S)Z0;-EW_ja*HhRYH8&S zj>$TV&hTaiHUjs;Eyx6`IChdm=u0>wNj?=)qVaUK+V4ZdiZ5fI`JPCBJMqjyZV`oJm_q8-M_gW%hTT7766uWYT0rl_}lNVt+AqfyIXa}on)=2CK^!Y(Yj zT&+L5r?{q7WGVmm1Wn+^{^au{?sd5-+Had7#^fq4OS=#=lRA^5#n-h23MsdduTu%p z{aB1Glkr%PRvXQzFUO#sdrF4cL$}tHWxUNjVdoFIqNbsYUClsV?*tV9*i7o2+~5F5 zS?Xqt3LWk7wT|};i|3t%;YiQY6R?+6sm6H4o+ZYUbs|ck^wVheFZ2DU%2O7X=3Q1x zkB+W^i-$tLk)-6l7A`m|nXRM&_a0{A#W@mN?+nt1QQxWK5vzY~v4vYM&WT;xO&s(- z(~u@=3gnLU+Cw`o{XOL=tzlbRAeJG@^g%~lE? z#NGI;#6Y;F+QUUvwL?za$l~@m!Q$BH(Ud}kuM>%#8q=$=3=5{61t(Xbq2UH-H zXL&;Xu53CI>7ou%ea*9Zx5{A73TN(cV4}XF1pCy*cdt>AJy<@Oa3;c*$B=jw77am@Gs2hOj|Yzu^-s*GB~=1}OoS>AKfLviyEQ{5|KTTkX%%uVXKx9MzWBEC5UFJ}`4K$RA3lU*Vz<4IdXW z<)Hvy*g5#Mfg$hX#;)sY^uvBnFt6DcSO&Oo*+Va9zxmATsZVR$SE zGw=Pt*N;BcZh?_s;py@vVgBx?%l9$pjeQ{8Ck7xM3q+bH)Vbo1>?E^DyW~{X?4|X{no3-1YTe zfpSv2|NXyc%D+^BTlr&D_P(AX=so`U%93gM5#x~4X-~FSMI`!!hPO*sC^x+nRJAsiAo{;VhT4B>M9IXc>Y2gb^7iZwAQr*^dqKDWOvWP+E65vu~Z^$&4KG3LQ!a(SO1 zdde6l?;Z^wHT5_iwN5dZtglgAX8RGOofe))nL53cU5iL%wN=N5X6k`cS(M*>B~ayj zi%|x&Q+{t!yyX1^uZpLRYx6dUDCNa8ur@Mbc|T{`#MFjAd4NEaK#CmR6_xs}N)hn0 zSs~f=vBrG*lMaBcUJJS+bO9^B>Fcxz4rertYL9Pvd_~OjC zJ3CpO>n4C5f#=5i&Q6{K7k+TFj7t$*VjpI&n4Uveb0@Hj#yv4f<$dP###|Cc0};St zixBBIb|!f)hP)K{hcLh^l#z)nyFWqVmIfbHyNlA-Tlv}?Oana6hYk)S;{uKEOtdYX zzd{%EsRf;ovOe9Dqvzw0lBX2&)ykBO<++zM9Z53rGuHu z`O62aCW%W|a_KZ-nZ|i2hw`1OS(pjaPb}irhF~q<-<`y6RSO2@s&Rd=Cl=FxeTXE! zt~uz%clyqUZY0D+Y1f`&A0RZzoL(!r7WBq`eEBxso-+ZQZnsO(`v!rznn=$%04Z#9 zjjab{n>EGyNESnml6v*wRluxVz}am`~s_N9Wt9jXQO3sK+*(GQJ$MarXZ9-A`|r+&pIb%_Zl3 z-{y?GT{H_h?Z|iJ9A=6l7k0|XoD1!E$y`&O5aX?!U!0Fa{7N7U7hw~@%it~UG1IPi zmeHH{zA3R-TeaW^wmvJcBK4b>;QN`$8Up_MvP+*D;EAj!?JkfJuXVb_-tEJENR~QM zTj^%F+sXK#xvYBTQ|1$vGxm_3%SRx(+;NMbnuFEUxX$(92JSF1tER28?Dbid-#91T@-X(@lNaXq?DwlI}!?*w^Q)UmC#XS(#h)k_n#nKPwr zhkgwW($l=JBze^ETb&R4C0KpiN!QIk4)|QC5xYLwv(-Ij=F?5#v0u>$Ubm>;F|OCVOJb>wQ|yW*#8p<3DdxI=+63`*K>6zRHGutB#OtrEfG7_3_cYLar2 ofwCGXbHhH6rT+iDLA?GHGKs{`x#$HvxdA$P+|9A>*u@+F0sfQzwEzGB diff --git a/pgt52m/week-4/workshop_files/figure-html/unnamed-chunk-12-1.png b/pgt52m/week-4/workshop_files/figure-html/unnamed-chunk-12-1.png index 014b26291bc12c60c218a8ac4e2ec84278358ded..63191d429e3c68b5a96a7412d148f41a0a4fd8d7 100644 GIT binary patch delta 19730 zcmY(q2UJtd^FE$X1w<53qzj0M2uQDy2-2nZ5>SvXAT{(GR6v?YM{1-9NRwVdQ4tVn zp-TxxT4({J_y6U6-_Q4V{^#VJ8n!WP=ZZ`6& z^0g)veGa&1zyHRrWa;{ew{4d!rq&;MJV|(n5!o1JU+Z04+iV*9(AV25yB+n62$FEK zfeL~$S40t^IEm;X^uRSV1pO953Q>%J`c-KilV|Zs)3MxT9w{{lQ#B~$qlCBzo1#h` z1x#i8&1LI)m?XDfU3GrrGv}^GMT~lpPmf+Qi=?=2J9ca9$L?2bT6Qbrwy}S|U|Sv( z?yX0OwfQ5q`mrJ7(%LJ+RT{J7OrEr*3{3|c$oqu^BG2@j_JG{ckAOiIj-%hiu5V2W z`KTZjS-L0z+P*_#ng!>!JF5&Q!v6F6L9N7_H}W7&XRq)cRRJwpjn{wnceEM$&kJuk zs__w{?&o{oIE&CSJ~CZ;ZyNr3ug)twaI-DL`Bmwsg2#}bnvT$hP2llFGW#{>5wc7P zhWs_R2}R6IWELPw%~Z#sC`JS~)TPAI{NP$8qER;!pSDVkQQU)DQZ_SP!7^{_8Qg|E z=gNaP^E~{*o*yokx)}N0MblM)?&H_uhUbiKuOFd^UWz3`n(jU>Je~hyzbp}GRS(fG_-^s&aVy|37!Y`a9?Q);1Gj!Z=KsEI-`Yo-r8re~$~ z_7+yuEUsLpEhD)iAi7#o_ zbwVwF(BrEA{q4yE-&XfxAl~PK&)y+UhwQEoBQ8(IKtj@X-V=Ei5v*o{NbXMNs!(E2 z8zO^`uc2SY)AGnsm8}Yp5tLEiHQsqgkl=wQ|B3q*_cG{)8!g*1_?N+nQ^WY7)LF!T zIxLR2`+69rV-ci{!JzncrqQ0!z!Nr{&DRo8DWhHgkHp#B!wu@eGTDL~gxLoDG>ejUwm@gU`V1MZ^y~WlL9Fn@`6W@M=?jKY9qE zz6j1Jl68R1K9*qBXkA|QyQ!8+4fUPBSKaKd{zcQf4&|;xh$rn}yXb4ZNsa1A{wbly zlvsm?$WBSnfNrwxOhIu3UD-DP(MSY5Ti1R zll)+mhVn&bpjG_uMqV<)K5x zW>40Zn^?7p=%t>zJZHB{An0ljhPr%qIskmgD&Sa6TDg#nvi}+(cwZ7F zh-d!1Lvpubn)6C9r%WLken76qWp(0OfQ5K5qUcKe=WHHulETA!3Wrj~PmsK*C;afD z2FfiJzS!E||MQ7lN4hpd^t@Fz#?5_3jh{Xfc!MwNQeoY)acnI>k`HzT4(w|5&qnzc zH{lt?D4-DupX**CkQDIy7oP3qHrV`8KR1urI!6JG*qA6ArD*&_(q!Al5oO zve?9Wu4Tu-HbYamwG=>)Ba;W|VNg#GmS__`c5~cu^|^2gyrg(NtFB-n*F&X=mN|9? zv-Q}nA4PvfN$x^w0nj6lSgpL~L=O*qh4Wo|G0~`5MbmtwI*4Yb(1w(LqDS|H;J=)X z_j6>oU2~dAfVpGr83Ga^q%+iN&~bTLJBgpG{C+8m*0C^b@~bkJzRHthm;~tI%r$u7 z>^HtyFxH0WKiO6+?_o2(lT8$F4f;M!UbrQF*6)~nMGv55nXYR+c>i&^EcL(st?F(S zc>_+`RaE|R6?wp)K;M5H8#o7xEvxEX$*+z_^#)bdrRA6%cvpeU9oh?d>!t^ziu>xe zZEqUMwaCaUuEUDDS6$yib>c5vttLM_Mwat$<_iI~ldnvYQ%Y4qC3!xfQz;OiNLDKV zy+kHA_ILkO^9@-9n`^(zV}~933)AE%`^EQ@T?g!&GK^*2PL46B4PB7;VwbRfpYKxO zjQ1M}YN%q*_21#{II-s}P*b_Ht&cMC$47_~f5)#*do2^4OI=wnb&;%$N~n=OL(3n< z4f$lUwg&dAcTe<@+#eLdyEl2@7=63|*-v4AVrHd|r>`;x5$a0U(7S^@6x!25xHgCh zVEd-hHvwPDodb@%g^VNe%py3V)^zVVv%|a?;&n*uPj?cChMYP&gQe5oi_>J~Yqrs$ zcSSl)punSOEI)VU7gyk;qNkP zRs`CJHR+XlIeb^;>4>VPe%0Ln(DIH-%Hz#>#>>{nmbC*7Jj%rDcq_o{(2sQ(p=t{{ zk-VJPlbyqd1BfW{gq{@^VD?>-IxDlo>jS{elj;p0yt z@=PFDnU{T*toULC`iM%bQ(@9-$D=PKzt%;Sf^t_?M?W0w;V&kp zU8h^Uom0_egKMjsn$SfB6=PDOK;NzzCKFbR-)&HLlaqpv{$m5}x}k4DdwY|+bhr?o zug`e_`2Na2wh=9yl=S+6KkV>Ip0zPBv(ri~C!`XD8=qFjXW^v66KL6Ju+|^F-MFVr zGiEe~zXg#Ot22zOTzkY*_8^XXcw;%|ItwlYu;R1nKI-}n;d)eZ9#S?o1i9J5?~^iRrVAZa>02$XM(0)G0K%;!A8iGs2ztgc*f@Y$@j_U zG06HfMnE*eV46YRYoPx^rmfyzv8mQT`fd;g8ShcBfx=tG3k{TMz?qwR#rOppu=r4O+AnLQOPC2wf)H~G>N^Rd6oma_iCjE zlEbC8t+V~{X~E%k9s~xw@BGua{I;?RH%$YDrV1pB3z*f{sv)nJE@d%J88h5Y|D;Wc z8cB2@_@dIGZaZk-fz z+uV{t`m*=mO7#$e)j%{Pgj~OvaU}1H4~K;EuQ%5jKtMRK|7Puq013*L#J=Jrmf$OgMNmSTE@7XmvTCypA*diF7rz5@qDTMzeLGPN(L>*G z{*Dyl_y~1pId^j6Ss}sk>iT5p0z2!X6E&j(csHE|UAj@sS8JYW58P{GNmKfC6|MT& z-NhK|h9+3u!q7J#+nx!40SzAjikFgL+~*%eP(7dHVt+zI2zb+d3m)%PZ{#DwiVvBx zbq@=8X>$=fT_dp{&&QMM8sd(w?OUH@76t9sj} zK^+?kajvuexEHojch2_4#2I+hp^Xw4K}3Y<1Ntn+e_oIHRsrTjRo?_WX*Le%-E86v zWnO1JKC=>7>`wWMYz@n3s{c!erWVIZ?bMhf`PqzWZ2!pQFk}oPLX)3F7oTu9sQ15u zI2T0pYNCKE1BlD-b$D_`kanI7ImFO$?fhJWyVE)3uVxI`Rh-V%aBX-vN_zhUKHHgF?t~D44B@A+pP#FQV~_no;a-w$dePWh__(~ zSvN%_#gzU-;e;&FTrGYY(gFuV*2%g<$>bKiZh2MhE@@4Y(sL&LvT9AD%N85`!&wl; zQjGNPDbf`@vtA*F9@$Xi_Z=$~*~cA}(fe382BH`m6~PjkmV==;8o#z;^|VkKDeR;O zpg23$lS@f*THFR}~ zliYQmeC$AMY0t(u%)2RR)k64et}yoNMTx6!}R}>>?g!joWMSx$FM@?I+qr zE-o+9Ig4_LhH^4SxRt;dQNM|O94c)BY*&X2EH;VCdCE^M$)p_KLpYdtqHdTEBDB51 zD2|vj!*IXAmu>ECm6z7b%$Nb~Pt1WVtmtN31Jy5qivVs4+s^#EGF^LnST(k(w+)d_ z4a+Je)%)Y=GaDL^jX|%0apAGle)Oi-XW!1n-A!lTPnNOB#I-8}WouJGDF7x<{O4~x z46Efe8jrXLOM?f z)N&x6qD4kT@a@^8H>EF28>N!Bw&0chDsv*~>u_zt@G?Ku1oRX=Uy)5CEU6LHs z5G#{$lNQuVY#1Edd<4*1BDJQZN^gMT+7bi9F9RHZ7BVREDS!XsvFHYE|lJJ;NO&a8m^SefG`1m-+q3D4ffB5}IhhCL@;8QKE#pVYM7I7MM+V)Zp zm~(}R9aemg@^}b;opE5Q@!>BRkdRhWk`@48=n`YS=>`gYEI+n0c?>4X+by$tv|qUt zy|Qp<_+*P{NN$PY8@_Q_(8c+M+~I)waa^Vwf!uzfYQ}URuh(PKrsLhX;R{Cl7Kr(h&TMtfJyzY*<@@K46riE1> z%e2~0uGq(k^qxYfPfq;H>ZUijz19JCmNDKh!qG6pj^`?A+Njcmn!Uo=(UZW8SP!w0 zl(ZYFk)9PN-**Ge_Fp1zSK6Z3Tx~?p+EP+N=llbvg`17jC%wvCz-aVP(vuzd`AVaK z&#mMaHC@={jB=jYtr~6AFSBF(&&XBIIcdwEkIPFd;HJ5BU@)9$MZDAJauhfj*rO;X zZlU4WZb5-nh^=fSbtBvirH0jUa>#=2uU2*ILMv&empw1pX(zr_FDTv z&$cetm-)WeKG1bI^Cx-7d|cbMv$SrRPT%DW zD0KXRU0xF#I}f*>KwbuLX1Z?~@Bgb+V*9S+%&)+}(qWa_)qq;BQf4^-uJx;zPpp4S z1(lghTqSxYN`6Bl&~@Rkdt~9pTCk&G^?)>FAW(<+^@iF(`{y=+)v^0QsP+I4fRI@u z5|0bsS3wy{1#%tv>1T~2bK6hZ1*5AhyR$Z$vjiE-Ezcq%SV&6VvG}v;@LE!}SWgX& zG2?2IbH?hq?3e;xI(h>0Tr6o3aTUr9Ef~E-+@h~6E*^SY%({%Epg!O1Wz)-2Cbqk$#%1q= z{%B#1o(p&oz7BRakFOSZNtWbP8V4F(!oJ{{M&vz43h`c-gIh{8vrCPb9T8gx%zS*k z)s<_z_5mU$*gOq%EUilLOlIw>;-=R6DXoTouldv6bR5$#Srt&=1@$Wc0rW@4>HL%6sP@qnhK- z9VFpsP-ERQPW+9St0TJ=JX1@}1_aG-->WOYq$EVJqb)_w`WSyoxKN#7S`Ugq1*#zk zF6Tk6jQ&JKMt?kAGvn?Z%M8{ZCI>7tJ8Bhmgm4el6G$MoPGmr4BHWd!hlnO4`Zzwd ze3Zx&sI5}%OKR`)P~sW8dU-40YFgHYAuW1L(B0|1i2!CwDhO5T@M01+;Zn5*ock-~ z7%Y~vNigQbUG%Q1P(I1M&rHvD+*~t0IX{CdB9`;xs?+#(JFGhy+l-*drT_Z>{s>{W&<8yC7;wWc~xHc!OXh3Fcj{jB%7 zo=J`MfLZ;moJl1Bak7r-+l((i>N>KNV=4vi@(vCye6mQ{@djj{}Rn z4OcajS2HKwpC!P2&zqJ@o@D)gaX(!BS-S@FI6~iBV709X+Ms3Hsqa@VFH6qjxphcZwwPaM=&HWD_c>KC>%_i4BCua> zL|OUM#^L~%2~ZAg;5QK2b3|DPdKwA~+1EB5*ZDBD&fHBr(!w%6jxD%}3B6dX6112#fQVF=M5;3yW_fLQlgIOP z;}lv(D0_1Y2a8Nu`%jG|9=|~C9XVLf37#XLDZaK_1I8dtHyo5(bdkIQI>Uowg=NPK zy2Hocysx&p`*kSM5s8|J#RO;i94BW9xj2Y41>42In9+;FoqJ@HulXePp0{}7ogFju zw5YYllc$eWi;VS)zIVt6$%ro|=2Op2ZASLd^IwYQ_v`NGm^Ck3Ju717B&g;_&apR+ z9hg_f0@P9=Yd8pXKnzgzba^jy;|G_06Fu5hCZ~@e$6_})vHV0PXR*ls{mR%!5qTLa z%jKsxTRf9azF^ysVR>3E4xb`>q8{7LcG<$*(%f46$k_}t%9DxddAX-4gI?OF15Lv! z4}rh+zr%SX2XC}bv0bP3_Uy%G>vS!MtSsbf0;EaXT{_SeX6AsN4NoeanE|rEYEkRY zM>B1%WZvDFg57rTw4ylgM0bZw`7l2$6w_{DJ871Bpxv&%n@&eZNi-`-z_0d0SEz7p z``Wo0zry@hpQK5T2fO&J`iHt1k%utSp;cm4M*p$~HdBAo=@k=#Sv5Gun#T5rsEj)U z)ck~34Aw-VldoJYH>paVhKvXg77#*rF#acXvEQR8v~z!j#jd+Q{lXhqJv3*~U8=$E zz}LGxh^e2^uk*Mz?P^6N>du}PK|QFfWloqf(;8k?yPe3aPK#DaTygl=Q-Q>j1^(>} zpY0#i^srd|6{7AS&RtOqu!1LK{3Q{f7K}yXE4I~oE??5ljRJDK#Isk^Uk)~u415~r z@f%R+<=Ymq$nVgXsa>ZcvCm{dS9*>n@lv=rv8;RSS7m%y3t=U>tGoQSe6ZJMWFmvV zx*aEdV&%WP`#y*jH7-jWqCypLql*#Q4>aMxy!2GGa zf)Aj63&-t(^0o34`|5MPgFrFU*zcQhL!hA}0b?KPE-iN&w(2oEMZYo#gO(#(DHwU2x^|B&NX+xNMWzbBu4z3FN?>Ub(#2tMqjp59Dd z_i4ET+>ti%kJ_fnvYf6z9x<)$h1 zh`L-k@&9H2&lI{K%VZgj$v-$b`qB%qk&qIN6Ub@m7SIcCb~BrqW^K3Rz7Wl6$)MMQ z`3V_Y_y%B{_G^)(XZ5%T%}Fy0u2Vr1&OlHH@|A*b!+ENHBs-dh38~~_!QXZMHGzJE=V$v-N=4n) zFc)E^e}=8-?(B1!G@va_ybCW%C$l5GFv|jFOb;?cpG_WSX+1pbawW;!5$*B6JQvnk z&ANvqn zuyCebzdmabPBc_sR^Epc9#piE|2~y+qBnlJ_Rj3QP(w~eQhkqG27KS}^EaJv%ZWg$ zSq9M znJ$+O>Pj-vj{QZK-w;Il%U@s|(<3=bv@@$O&QO{!w{n&fJfuR8?#eh>TlkgrtQ_ii z(lK5)n8+}FhPf~zEqXKRF#t6k;iE;b3M2HCevsj31Q}ay8ZpTX6W&cfIODBXxLJP# zZPds2TYG3WCOW!|RoB-Q0y7+GYjN63aicOnoajT)is#A=@F!$>O!_iaw+msdX>PXn zI4cp}p41!3;rHlT5B_|;jr{7t4>!19Z5Y+wp6vf6B)=o% z%THnV3w#=vap~5RhBi!}^62(a292eW=&ik!#ScWQX1z6SEKG;v+a*J~c3{~>?VXfa z4J%r#jMsFPs!UhGd<-Q?+^cptW`GoWaH_z?-jR}$oQ~-_j1W{|pU~zHv89E6ey-TD z$>8z;4jBE=)E;g%i5$Vw70?2%c8r=>#O3ZLU17G!Al0MX0JGdB+@HQq#Y@`UgE{-~ z;}J=0z1G>`nD2latKy>Uvh>$ne`1AS{O=8yX>u$I<3{%k2DSxK)p}Tn^xHb+?N}*D z?6Xp-g+`9E>0bN(Fj@p{dnOYNrr#^U0+l%yTMR0K&dHfH1Nx4OSJ9Lw8;`Gi zFoPZ?N=7H7DYbk?$CBr(dyb-{MyhCWFEq=$sM~O{Wy6+N+mJgBP?JrtAh~$Kt<^(Q{ zVR4a|jp|VEMmir;$zAhB^=?V98Aq1ZOl4Lq7*W^Ji5)(vVr)2PsjWX-j!2Zjf8u?8 z;vwHr`o|MvQEuxq+WY#3B?UELA?2RUnNz(S_W-`npR%EyQcguA`tkJdx1RR)EGS!;7W~3K?QT4L+71ShJ5eOi zhLy6Mpdc`C?SY!Y9^8YsynuO5=z27)>ZNzK+cZC6sBdup{+5ETRFVrgiA|~#alC^s zhwkxZ+;~ZSbKriEIKkEGD!N+I*q93*m{BvFPLNOD96KUvu4aB}ojg+*xf?4qupl7T zumWQxkl-YEatyw|{$n>c7rOPeQ46VbxBK@n)R92rUG#q1AATDi2%BSxnP6UtEqTy; z#=GLAcl$mU?v-@1&FC5s_Oxnye75@rWnVZu8p+(lENzV>FEFXEe}1x4oky1e(=K99 zP3=wg+&^r1(z*Q5+KbQQ=sNy}^pLp#dkDDnIgqwOBHP2?_5P3*oZp!{4ekBC0A;G` z=OMR0@o*ka7VQ6^^7=&*tlcJ)IW%#bjFY8AB~o6q97dkbuY z;*%F;%J_f21PV7(!IcfTjn4`hd5VFm2&QW+hb}#4 z?<}Lum#{Njhw$}3%4!gxBjWe?^JFlNu^iC3aHSl zXt4g&P&>GFE&$e|-9&G}X=JZT*X#d_fw1a*v}B6XJg{aRukG#fF9<~Faody<q1K4Ja{VJDP|s7+n$1ZtyX=yi5z0`>@0_u> zFg)r|g;KlJu}>EhhWvZj(1_#^HNMv1vc@Vn+1Fakhz@<0UTh@tiQ3os6|_M?_Qkq? z=8H_EX%G$pZTLPGXS7_}CgZIFl?H)8Z}|dLjcXP5(182_@v2^Ba4B;k8Gz5Lab6-L z_I!i4j`3CKj^lZ0gi@<*5CBh7$OP3>!!i&9^k-~^YWfdGxlpXJ=kP~kf34;V@l2Ti zh-<2m^-zE%L%EB=1Nbz3*P4)C4$z9Z2QO2_$Uh|LW(rYpQ8i^rxacH#x^)odiUaH<*N7JpC%f3pO9E0#%#+Hl5xe?n)0r7co`*AP!R2W zZ+WbFODrh6y=S1Eay8xlE}T7bEx4IJz^etPax&{G=B^g&Q`+Y%{z1=g3MKdRGrR*}pS14Rb~N)uEQC*9N82tm68RHb zz-9cXuPD|0dNt^!6+JZ0x<=JCdTiV?y5~&^2%o+pH|C_~3H8mvJqE*ThPl0;-=)1V zTdR1ZQJAw6pXtNXokt#TZraD}h8M{AHr$76Y!7s7FP30(F_f-M3u~iKMiG%q=_@QQ zBsf4s)4$z|^cyR-`J=1&xw7}Qphq@y+;%fIb53qNl&5bl zFoRty1H}9}H_BFt=*P|b_7w%56T0LXOW9zL%TFFUDO>=99U#H{ERoTu>ce~b_F!@olvGx_`)6*1R@mhk4lb@ZsW^El(u z0gZ5u{5Zu*9_D756fe2GaI%mkVZPBdaQxRGc-F~0_}u?60X^KuEb-ZMekL2jpYe?K z?uO_G5tRm&u-Qd?X`552s?=}T2ML#?S4j7U&w~Qu@S;~Efw+UTZ>zoOi2Xg}_19P! z)UVL;;PyNtF3)GU@TUM^k975nOVUu1luFU3yHrS}GX2H#dOjkZu;yrgWF5kY0V7If z!`B?cVwSL-Z^!E1BknzlZ5=tsd#u~#{xn23KYB|)ZhTRg6*^O2s;>~?D((_{9epd# z=?zAS605U(PZM)QQc$}=1>A3>^^Eecs~r56xpI8FabavZdnOGaAAmGI>g`)u*xt=x zchU24e)ROgIf5XXOfie-e8sQYh1 zpA3Hd9a6${ye8PZn=I$=_~sA$Mk)k%J8%9MK6xIa)b0!v=-kto@z@vp_CZwB^fT45 znVet;H~x^B_a(PGi{s<;%7SXC<1;kj^v;H+mdO~04yw0KCV*%e!>uObt%0&wWg0W4 z>Q!#3(jR|69lbvl>5ghRjF{=Z7jb{^r%p|8CjQJk#-vyJq?}+{q4^53&Y;qpcK%VZ z$iIc&C)*S7hZ&LB4~Lcq`t3&+KUBA}u;6c-`{A=u7BxAG&JoN?dQzx6U-Z%1$a`}z zcVO)GOE-T_127@c5#3%AxF+`3^t|k*rRv|Ev>s}x-`4t!BVEG{>!3BACx!dQ(|_wc z-XA@S#6(hFp9^%mKY&Oy%ZR(-4kiC<4LYN}c8yq|HL%E4IMe7N?5yIf$k&tnV&|sX zBVcpV7AscdaWavqDuHv~jYW5xvJ1k)@TI>>u5_t?$kGu4lLmLiO>W#Fai=|$0DyvEW|N4uSJgM%6aU}-0$_2AT>p(nLA zgth+*=$Cw@;2lhs+9_bsv7iM$4%fP!QsCazy)`HoF&*)+;0V9iqa6R@ELb{$pD?$5 zJ!<@RMi_e`=FVtbZz7wd&`&Ue(EyM-W1OH@3Cr3HW{>#G*82=Me5-w!D)E!l(BT{V zXYm3CvEH>*T912wg7{Q}Bo$BE!p@b=?U>Y?eZ1Y0`@}D8SA)SuQMbk=ES>F=)8A-ic#1%Cz&De1Ohv;nXBQ%>@QdRDPzjg0hV{#Nukm> zsqcolm>WN8^E~d#Ew4*wsMQA5xf_M}>PtVbxVOpm^!cVkY*-nwI`(}~=(C%(CkG$H zg51?-RDncB(1Kb@-mU>@o};fwdV7|$rhN}g87!SM6z2sV@_a~i4a?f!bCDPg7g)Go z&lkYEgivGPtmlCD#I*AL1n!U15DSe8gw2V5(fjOOU(0-(@Z|Zto?OdzZA$)P77tYXkMue8ZjAu+Whls17*57(w_O#I#k?Uh4<%UCtVy5eB za=Y?C)Kk+$cMOD7TdSkt4V_1mgIbg5VS|RcXJL3(8=3tE=uDmfD56piQ6Gs!7sJYg z@V^2DWna^bgkKtILUs+Ctfvx8m`lyHXpXXdD_Vp=v}s&kKU7);36uU|Yi(doDRKu6 zO`iBIbMH|fzEqi5lM||~U)@qP_@y)7y%SjqBBk!Ga;yh~kd`Piur`sqcH)%?X2?*- zOIss}c6>iX&PG(#v7L=V`+Qj;A1|n1`?vq&+)M7Y5Y8O%{Tq!s_aOTlJwbF15FVV} z151FMaK2;{Bw?-ji#d{mO+w23j!z*$cZMId+M~H%>)9oqW(QF!Sx<3-7FhPagss?g z?u;zM3$zWslXR}9g84tVaxr7E=xB`HM9Y)Y4WQ4y>X+=u+#PFuU6DO`lxFyg+D?Av z+!6U@$D3N>Ad-cmG-Sg8xY?d@emEcBa-uY&sx{cy@gOK=^5a7q61;;01_q`^1k-K@ zUX27RJzA~RP|*L?A0X6DtpmkN^G{F^I&QtCh)|^ zG0N9LCkLNkmMFO_XBikj%wZ1RCv+kRfM;5(@l&MXA`1vzGC}=4|4p>1* z)2ZeLBmseMn;4L>5ZMUAT0$ZXRTkPD1R_B$)^f|LJNl!DH-Hjwm#&m4#oT~M|De%s zwcqJ`%{&4mt`b*$P8wxza+5F#h_{|el0qJe(4U;uB=*4Yz|y+q zrbf`I%QQO)8<0E{aA%EU~G+Dtz&S;zQix4k~! z?jZIg-hTyUY6p5wTY6{_;lT5owX))sPnf*M9>g`w%XAP|6y`9N{g<7(wQJf(d7lp6vD!-e& zg3ZxI9o22iyw8n}?Xgqg=QnX@$Bnglnf~evFe-z?b>6h!F(EdyhSJsI(?hXqlcF?Obwv zQ{u=W>oqly;Z_Q!ySBFKg4^P2-N`6aObzz6{lE;+!x6+2>EQz}3!(GEGSimvjrCG& zSjO!oVtQ1?a4ahpTKbUe#>S@YaK#egi}20{6%$s28Qffw0o%KB{#hdkHB)X$xp~Xe zUzXr1**`B7{{yl+1Sh0#U(LcTnwR+hGB@m^0QRU|r)+HS(|dSnOJLy=aQqOhm)d}$*$ ztp-xpTS#3b$A=qf(Q;Ah&wzFi3C0~OP@l9cK>E|lsg+p`iQI3}r$a70S+ja{8%~7_ z^l(CR2+%9}X|a98Bwgl$)0uJMlAe};%cOWWxGFDw0PzH*9EA5I8AeKhXxxesV+VB* z{Flhcee+KhcPUQf3s-M6m?CSwXH)Es_ ze8dzipd@%aLZxmfjYQ})s6#7~J#Pfn5ln@nASG>iK_5@n023rH{KoO!#{B*sYZ(yR zUQ*gq(QHa(XE}(t^nO^}vwGWOlL8k4qWVoSe~BlqB&-S${q(kBw$(EGGU1;rONaiC zf(aTm-kb~$lw>*i*RJQ=`9X9pVUOyB`KKEs2GWIbArO$HWDiSUzuvGk^OX{7fj%lK3vUpxD|;#Zcz=|pbI*v3Ua7vZKp1(8N@(B#$H28 zCOmoJ3@ZX;@V#5`)f#h3Y&2-8_b^aeMh_4(ZA~Pmd%(l9agy3|aTI!ffxm z_YRB^AVzwZvFf-G{I$vL0G$_=?8)H;U*~Vrf>Wl#>cvD(Z(M@yhcE2S`DYtvz5yv= z-fB?OqvD&|*Vt}ykd4PY+-m+GtIPA#iBn*ay*~_C;uwWM=*lnZ=9@2k!i5FEAazW@b5pgXoH@|zXyZA%{f0_9W{-x!$(t~Y70p46xXh! z7ypmOdM)0Znx*oK0yQznUX=oL?h#eJFvb|N-Z4;&0nZI}2uh;=Wud!ttp9O=ul_Hm ze54o+9*pV`t;C*TKEKEJWyfSUs$^3y=0r_{1vz%72O!5T20YDNwAs-p?+!|5I%UrM zuK~zBK6d-yW5A{XlJGF0`c~7YYe+A-FAvFvgH&3r%uZ{Hn zb}dVk+VQ#1cWQ}AG=`+iDWH{_&19^?kgG3%q*ad<{SVzZ;tCcl=Ie>fv8PStfUS$E;Nu)3H&u7fAf6s4b7ECRci8rRRJVrSxUwl6M8|)Mdm$h(^i3S+fzl99H z13v)70NEIAe`>0})xFTbF)DR&!n@3$`Z5-4aJ~=}qAL*Is2x9t z{E=3z`Qf7f3LV-go(ijye;J$#6X%&dETQ(rZiKNNERA=(Jxh2D;&rS%Qx8Ghws^qB zJ_3Y(w=;XVY{ne0@cKktYahpM%9Bkda3wHrSnqT#D2E|JDlgD9T0A-2L?(-&CyVvk zq%qCTVPKI_XnJjyCCj0FQ4OmhX{&*s(@(!Gef*d}f5W1vuIfLg&_tcy@cFk_3;p@zNy6iOrNU%`^vuO$Gg57 zvgWjxuzw7F`&!=fNb8fg3%Ky6o(pJImAjN7Yf9`7UodlgN)>XY9^*89(;Ro23< zC<*XNj21i}zO3!b_$GPZ%*sB~r})EFt`drc1rXizty95xWY<$lD}c$z$EbUp;d0}2 zL`c7SQRakl^gq@thw!Hj7VlmY*?G2pgI^-WcKF5CxRMiczy(MIJ=)B96!ZIkT5c5tx?6wof!X?Ykl62(>n3@@qOXR0@|~ir2pS;( zGvt3d@p%T*>p6?6x0CB$@$|q zvn75i*t;sjS{}tO64i=^6ZJ+vu%9=JbEcSdYI0Y(47tk!QtX*9xh#mBID>o7dsTC1 zgf9th)c!C&-b%1Hyx!DkO|+ATuLmlNSC_1}-pXxOI-tF*3cemW;N<)qhLOi_2d@m_ z*n^QoLuVA0%|)*d0uAO{|FWA4RiDpsZf-f#mXYSCU%jvxk!1`qnc30X8zVQEURX9h z*{NbzCI^$SWxmz0FB9VdM*m+K*B%Yk+J~7TBvdFVP3e@cTr%b&cjvftBef{RF*Ici z#|(9HjV(@c8BIQyIW>i&uPCO8oS6>8G-y(hRHnvtWQ@xgOvun&&Q|A-v(~r&+iSmj zujluBfA90|wcg+JtnFc_DvQ44R5M@7wQ!pmMrV?`@VEYZ{?~#dz^PkBhu(gN_CSCn z=ZyBea@yk_gH}ZbfAGK~;txF1ri5JefYoZtD>0_c4O2~N?JZU>deR&40 zFka_#2!>>kNHCMhN^=Puu(J$I*ySU5RrS6{SM=*J!JDF=Mrcmlk1HZ0Dt!gRu|$-C z&&peGgb1M39nsRQLnN5q|DG#^``EelVM9c|g@cdScJSK7YHfLZQdwAu9a-k0ul>Sc z_UkZX>O+(vu99*pjY*cNN5@aDQVJ^dw8eReK}=(mOUi}t68vVFjB$k>fLrrz_6k`? zaNfFEH0MePvi@L0pfk=bEEokW;CG_IN;-RpGcxy%Li1ld@vdX{*u=lhE(u;v~;|y{vorca@bX>CFXm=&lDx~k-|_qor z%d{_D!nJ#F+8m(}kTBc@O%5L;B~v`86pFOX5&-bOc30K}C%0aM0)Arq^!Fhij=7p6 zRm*42bRy3mc4+k8UE`hMZ4I?>&q^d6uspqTr!;YgyJQVkq#F+@KKC<`+V20Z-9*f{ zk$%amQH`n{wX&F8KEM9)++vx{>Q{%AUQo3NYF?(@&eiq?pFZx;tyfTz~A z#41vXDNF~HtR@W;c1!JC@Q09O-JY-@Nwlx(&Sl&wOW%J1VQs3YYRqAb>@nmph}3i( zkz?tZzg!|J9G?bCx14|m!Zyr?!fVEbE=hJjW|aL>Xn*&umBhMFQ(08yse0NMCwG7& zM6{h_-8s)o%O0YS2D*Y;e)v1jlH%1Z=O0`Ftau*Fb?5%sgTN^tpqw&2L{>c!_5U2oMO%8_W^t^2(bSfXYZv4b|bJ?q0s85Q#F$ptD zx=-t%5-|9k2EU89Z%poXwYo_|8iLY|M|YD1HzGgP#a2)#fF~e4HGI zSVmo_!B)HdY>&N{yYD--Yrm;JC(xJ>{{))RAeoJsxu=K5x^qSJ7T{wjYnz38FmKWR zM397H#q%=%VhE-VM3}ts#n`v?&zrt6lVE}17{rWH$qikYpyn|HYufyJn9z;4;uqp~ zg}zv_=9qD_On_*XWQ|Cn?+1p*muN~`VWvwoaj7aq$!Mz-;1efl8+!9~%cGV%U?~J0 zy;rM+et4D9Z{kMAKzLETp%`j{3q!N2n6mVY9@&bzk=!__p^-d z@P<-iVezk9o4)RvJCxDdyN_HVwoxA{QeR^Scx`f&iik7H=|EtvKN*#}Kqo)WhC4qq zR+oZm0ml+LFr@ugBK;-UqkC3cJ+F4YJ_4D{Qoa9?K7=>HxP~q*pulnKAx8X2Muc4O zL{ZBsijap!=qrmX=yw@=cG!TAL~Om$aM^NJc3l|G+8s1dIZLH)P4p^b;{X%rE@N8X633TEn!H2x}H1uDS1uALhcr0?cU*fK?Lj=QKo$|P7bJ({^iYWzJkT9=(a@> z3J#d%wDM+s(k0#N?u1OZFxjpbWps$n=}C>K&HxMLfUH&55SP%@W*do<(=$OEmkzY@ zAnkU0w>*Egt%LR{54x)_xL}HmLah_Gya&&cOM~`rd-4j9g@%_uo)&Wluhx#(ufsMN zg25EnlDv3=7AncYPH~U}I=`GP?^)6W@PLxN=j6v-;C@*kcsyut!e0BsCtt25=P<>D zTki9i9AU-KvSj}vM{lgC^LHT*2$@>LDJBlzD8c9*)T)5*fI@F&Oe^P^)WWNkhU_)4Cz*#$8(k2o|vlVS@}mPyva#C zTh~9K$N`zl+(O2(8`rOLXr9tj)$6eu(3_bEwjHVe^*u&Zk`ed@;E=bg2+6A_Kj^lP9Sn_7=CbdI>E{9}3k9Zchm~7dU^Iwt)BiL_nAA*jrii9Aa}YwW8JC!u z<+N^e&`04Ahk_UZe9=<2G*O)CN0w(r_@J}&Cd>b9+~}~3Z3~LLsf2A|ric3;w;_3_ z`pyqna}6ZJ3e9Y|)#?Q6j6M#L@Dm=9b=CA%*G{PZZ^8oA;Qc2m7?73~!HO*%ZIGWK5Fvk}n? zYnxX{pOI82R(+3xqmDi=ALtP>?im>UM#weNepB+wKe@0wSV>w1TuWf|QKXQVIeR14uWLgVZyqfV2pRk|QlC4MV9&C;~%E z3>`xa%}CC9c;D~)|8>?nXRTRF+0Wkh&O7$KuPdhv+EWVsqDu-;1ko>N5VxUB`sqKm zWtsTm5*wwmM0L-8&i?4dE_yY1i==3A6v>*4Rp@*P#xF;)a|+!o;?G8COMaM2`*tG+ z%4(n^o=@OXp}0x*ZmCsrA`{;8urc0`FqU!HWGR}HF*dfXS0!nw^J0m)jSRvs?!yFu zG(NwJFj7W89$_p+!=~4`sjG( zDCs$Eg9&aw^EhQAFJdV;LMXu-5 zquS>f*3=E@4K+x7_(*>ycUu@%VF5`soRX-zLqFBF#E>dN0L0hlMGQ5Qu%l~hjKJ=JNiKtXcc~)Ur*}W z4H&dO?IF@giXn7Dnh+IH>WBKI_Gx!9^5P?iJ5bJx9te%JSR zo>#{@o<7vtR`K!B2Di4(V4Zfa3QzQ&mTacIa6y;GKlerBW4nHXD=5gMh@0ZTHSpiK zs#)c0B*5g-&XGx?gGUJlexV5o3F~%U!&lLVRSu@4jJC^u|YFfEAA%=bV4-_3&r8zaP@E0WNcN$5~vOwiGxYjmjR$>jP- zkrDVM^LR*Dv1>9(Qny+ez%19g3F#rC>g)`(uZK?&pG82BG}}A2HKsM z`6+N#J_`e^ zY%DhI%a1sIMeczW?=cZJ=XL#UNfPN)f`Bd>!(*j0Tva!^7?OPj)kSG`{PzTXyLbqG z@y1P90=3bR+>z@wjYqb0B=oW~8kwTD$#_$RvVgw174Lg3g^e+;_mNx_+O*(~7}wyf z6*d;0ADiKtqRPLif#rKqWQCir@zzc2+I^VOFHaBxW1$e$gW}#45}$aybJIGIu{HV@ zp^XqW0n0DrSkU1Mh^Gi~4!?U|BI7c82=RZ)uQQCfQ#(=@_4%!6SQX-}D@mYHY+pnw zvAVIXTjBs_Lx<`jPw6199dA*0X)z3V&wGd2 z>%JU?oaMigb%DYuSsNTGa;>Z*Zd!V>Rfnl1P*YupLrNe$>#);z^c};+M+;w88@}~4 z^a)_|{M*K+K0)ARjY;b;Bv(Y~<;^QJGaQF8ciA1U^e=K_!)z%@=o{9Y30(pW`{?NH zs8cP6-)4ORi8sJvmw2DY{@Hlz8PL>ur3@(y$!H(|@hvX!la}-725U*c&)MSk@ z6-E<0r%d4B;mL|zP_!oEpK7wMZg-U;o~)FSTja8!x-DF=BjB3T^FGovEq|53{ai$6hJVicftFQRQ7{8T`2{Bv^)l0~Ae~!mw$^ z4L?KUJ~~435yjLM_J=*(dXd?Mnin70b_`cluZD{}ga#aj+WP@#*ml>!$^V+ptNUO6 zQGC!sa`m^Ly;vP=%a?t)S4&~Rj;3jC9GW3zPj0T(6LQ3BsQkX`b9(2{reg4|W%0gkTb#B!1 zAa1-W5pF4$Y3X`sO-OkTy4?{iFaA3- zuY-n%F?NA`KJKw0`+PuAphoOpND@a z5eD%!dVGZvQwN&Nh1Uk5rTIC;RxH}(=M|Env2Pk7M^yiO7)tCa-Zr+2-?S*{hyOBQkYaFa` z>L$mS_`o~>-@F6A37Z$lN^mH>h`ncB&`w^vFldBoVS_(0Mkyb@DqQ{kxl%6w?!cfs+3MQd$27f*kNm%Oep1YdOCKCxez$2@q;TUfqEo@mEVibf*i_10j1LW^JjSK?=i4G2-k2o zU}39imb9a3GXWpC|Jh%to=eY`&q58EKaA;0h*y{U z(DS9JAvABoHx2)AvJ7zUxj{@A!o>RUTnU5d6xg)PoO-Ok%>OyL^-;E5pfv-KC*Mz&&)0eVa4j`D)QVpKe7h(( zQ~pkpyJ`h+oJG18FvUFJ^kgCJdTHbP7%o^swDq`{Z>nKgBbQ#|J0@AKuJW}{nq?1= zTuhpE{Cm?|vm?~#we9-B9ZN~Q{Sg(g+>nOat?cmFXOv|3B^xSp^-mZ?RTnLG`}x;D zFRJhltx|^-xev#>4G2S5QC&L8Yg4P^pQld-(Q5$G?s2Z->$-7qIuvBJxJ1v9q8j%O z)JQOTe&mryN_r@Z%LNrW8vGM59&N?85?|98`;I0&JCCgYC$PSVxs|-}v~Jj-&J^+{ z8lv;?a9wvKucIOlO$~iZei379&UVFhePr=1ULS%}NAlX}{|%|((({!CNWaoSQ(qhC z$5UDa`aqGXprjHoa=MIxUO{QT&9ap?MTk!sG&t->>G?}@R#JkMll76h7=5yGn2J3ZnCU*kaZJw!}UPkLgVEqatpY7B({8 zcn#6%7}-*5f*}L|3rVHQ-RY0Kwq1;5j`|}EWVN#m3I%uDE@&3$9e@GbMJ`yPxBd23 z_P44AT7h}0JjkNB23VKh>p^Q=0tEZH@%oK7;9`GZ``M>3{*ue=3Tj2a;n{=f>{6XCbB804riS}b>uNzIjUS|L!UnVToojIO>Ff$C zsA{?xU=XgIdIjb5ScHN}zU>P|SdW{Z z!I6bau`kmc!y*sgimh1pZOSDFI+~Tl#X|scr#Q}a+#jWnV6dogd6L;TYByqn^}X?{ zO#JYy`3=t^dQF3sgx>v1fR!`h6d1$C5Dmct5UfTKJS{lg}%&#vw2U&G|*keraC{FcG|S zmo2-7ACZ{3ma$d(ahTJuDt}XIG(&M7R25D2&gEm7?%BrmP`EzQZ`k}pnoh!d@9f5; z=m(ts-O0iq!EmPTnigM7S`AO_owgB-&D?ZdxStlQpA5~NfH0r@A9dUn@$0cc)-M90 z2VX%{rHjqqGC96s$8vZ7d|5~X99CC9qa8IU1{9o<`G{FP5d17Fc>wB5cOzsWRqTRu#Bag{pKedy7F6Q|3^oTJUc%(wiA~1zaqeCZ8uGSaGV+Hfccf9{PVUu&op{b}MWgp!X`}vsP$B}r; z@%>-Mm+_OIC%W83HkeW%I=}f;4}4>M(QD5oNruAW_9)kp2Y_BSA;J6slGoM=r!uJk z>g1nyf$Wo~uQnfs2)FKU5;=ml@3j3@FX-c)@x-=PcV|$`CUbsr;$d$_)P`6sv z?oy@^yEHHL+R6Gl<%u z=YKhH)1fE}-x|M?L>F&o3UM|Y{;~NHRUhvcLx%fiR|PDUmX299>dVEWmE7zC`i6&< zgIdZG=ajn2&C$!XUt^oc!1gPwCN$_quRC3CpCF3z9cNK;z?Hy!1N`%ke)Iy~H~&Oi zS)7!;Q&(eblJ{VaW_&H zGH&D%3XnIXr4=2D-OfL}T{&Zzc&kyC0vDI96eKV-^tW+?kI!?>%>Dltr0B zLgM^OsNjAulXt`t1tv#W)fO;r(9>n}kr?^@1=ZX4{OF6^Q`hSgXp%H(&&f z32!2fQe;7WY8+Ld#>65hyk&~_EW1~`VL-qmeq6xy8>zKT$3-{9HFOi;^ID{djRA8& zHl*}2R=5B34PJld))kal?nXnSNrnCQ89MI+@`4YnAqC~Ef&0VCArA&R(b4@p(y&&q z)4{moR4u%?g|2gl2oe8pgVO%V!dzLi0m@rP$m@Q?q}s{UI3_okoK zb2=F{34yMS0SA+e8?ZGW*pae4=#MQ1Z_tH5yCsF)Y@m@zE>lZYAN7B~H`aXIyHY4H ze-Q%)UnJEUV7<;341H4 z5v{MA)q6xx@)G<6shLa$7S8HjNPMDCTzB?0gPy~2${F*BTI7>A_}CTOfoi#jsggBLbz?|#hw4ciK{ zvV5oGn2?Kuz~`bR!iCg7`l_N`zGWzi{Y)p_`yL-Vjla}1xhoIMn-DXPaN!+qbM4pP zL6SX4_Gq-1zRx+YygD8*n^9-((`eT|Xh5e*u(j6z(-7{_u(c&G^;M?Twl zgAfZz8H#~lMUl#dkA~rk-#M@IP79peNB?@oBL}1S$@=AVKe(`5idK$Kl$aV^g^uw? zhD_ranQ_l1H;4ct&r?fZ(oY?k`FzhKWQ^Oq)YPkD zk7Y)auLv1&=gY?`jnvx|`-xCK@(vvR8V>v++U{gEqxZ40T7>5(neM%PVVn%TeIjKZ zu@~WU2OUoo^RVR}%_~f9QR_45$$QEPlUGqoAIVR5sKZOO1Blm{=h;wv=m-+2kF3u_ zZEp=^z>|M%{3xS*Wc%w~;I)I|tm*w|fRZ-2N9?TsSvJrx-}s($(x5wrWAs#uyAF`0 za2!&mF(zu~4Pqu~F;zlS%<{pCftk#bB6%4A?-Cyxa9WUFc5U+0|GOC3$#OmO5{8zf zhcb|tsNzrv22$R0J`RYUKVPGZ?#r;Kz3Qa5PhW!`h18#(gn+3n)%KOwNj@(&YK$$b z_T`iRsL##sTwQIKg#pi`uG^pP0i4aOYV+|=?9=C=0fJug-7t|H2ZlpBp~*S&W^ngT z$}i7T!d-h|^;r$)J-qe@o2fONKaTGW* zC!su<>Jl!cxS&ulG3L2lMhid7@r^oN<`n9?+ezw^RGrKyulsm^C4C#%q))N0z5QXy zXQkMr2+Wn4Pg1WN<0WH8_Hl2Oz`F0MljY7B1St0=(&~^A?)D!hhlvH~==(2wJKAOD`aHv7nVHLb=6iiF3m?TbzY9=dlu+6{M-GB73wU zHsob4hU~1U-kw;7^QCeQVF{?U>KnYg`Ce0xBRzF{yvf}V6>BougQmWpQA7Z~*zSLr zp?g(s)3=bhk#>3!V;yJ1;J!W<#F{?$>7vL`_TsP;v zMIfz~KF&3?M&hlnJgN!tZz;DbQSS58$kIxSPH~Ic-J25%w%#3-;i?+WgS#T#b%tZG z>?no-%Mn=sOi7k<`RDh@q%}V>Wa7}%58U4E2tk}02NobUUD)<$KQYAu2Ht|5iM9>d z9kLkCrlV&MN0bm0g|ebxHdUd5o0ZLUK>j3|eocheAvyo^V6iFtz^U1&x-0VFq%*uj zhUnmVIx*Uy0PCg*t2J`(>?KK=N}#<)J;(MqTLd6;taQ#KRg~UCj55pAezfRI#w4JY%|1Rn3JbB6p~xks(sCaT2dQ}^sdujl73XWa1c zvwT@><a@1b5g=@;k9a$0(O#E*12)=v$lX@o{OWtmx%F82 zaPr%GHZ#tGA;+3=`^VS6-}I3Oo<}oiSf3~sKH+j7OS6JFs4ocpx4PZ&jmP6nE4)(e z`N!6mF2&Gt4jq$LcWoy!8S8M!`|7;7;L=m_8n2>%_(~VaWfQKER?Cp94EQoFNaR%< zuB>tT=Xj~Wp{stq&;WBk)6XBF(5M&_&Z-yk=2z4dON!`F^RG6|sqP);3qx7%NRKaX zx~A=Ic#`o;eyM>-1{hgV%}i);m?Bk$(g(V)&UtTu=NQW)?H2 znfp@h7yskde}N%hh9qH;uR%GkdV)3N;a4k;y%bXiE?TQCfU^aWX1OcvK_d05xFf!; zyKtSagNzt|xnuKPYK0K#USb#L?7~_44N__HV%QSF51R{LCYUZy-gX`#&G|HlbOcoqk4e3$F0%&-ejmLn0s+IUm<@`0*O^9@_Vy} zK8+FTZQcqCe>Qo*8LxQI=y7aGpSqM0zCF0W7zM7Xb={Wcqs#^9Yv7QyI(}$21HnOg zrCc(>1QD)#9MbOm!>3@o^VURoy#%ZW`>DWFcc*vyDN6SY(v#Thp@V_zb^O?f{T%SH z(Q-OqP;{Jr?3=8MUZtd;12VO<(_~G(Z%Cnm{y8OpY!M`P1hD(I+7GZ}pPxG+Qmq=;Ptsj(e+AjOf1J z&Uei+az{6#3ucZ!6U$pmz8wS!c>HJv8_VB&_U8R?%9kYAF*g<*(MYau4->1Q%amYL ziyi^|UjXw;GQ?e>zPiKH`0{Pq$#<6T)??uGD0!|rPI%!Vc+GWMZi8RdM%^)ae{r88 zz@7Na!#8}?1CpgaX~ldeVFDY@>oRjSR#CgBm4K| zF>)Iz<8{78FMdDehLxA#mUkE9$K9JP=#OSz#w4v$M&xbgG~%tz!y!71P}2g^feU7j z00h$F`Z~3gK&F9khT^_k-;;*2I`yM_J9jCIEQfDcmL#BEeqAT2)?Ln1{u_E{I*t;v z`qf*$^wQ~9wvBc}L2R5RtFs=q{i{@+CaVU{vd{btA8d`+H>at3sZkt?R+R8esSL8j zoz=#@m~t{pw>MPeiuRAfTU(`8xvV3PfS_X^repy!N9;kE{OaloW>1k9mZ$Zj#dk-r z=8nN4dTOBSfg-Ar@xzjT1Vjf-s5Mtb@>;YJRz#=k+$jBuX-QC^t3rb~Zi&`QZ$4`qAjePk3Md(ehJ zm(O0+mwu`*sYHP*6R^36`SB#9y#GGzkv6jTtx=BVV<84NMuX+`BSKUid<0JJs_t^3 z5YcN)huU7utFD-$HBahE%mql7vy%UTFLFRh27m$9mJ zRFZ!8k*UVgzpu+Xsj|m&mwF00v`ZA%p`Mv(d%rzNBIP8sjdp*Yi=4N2^ILsySBe#@ z-o1);?RV%)%Eq5AvyH;lmf9&Xh5)PY-_K$;Ok_BVM(!(?1+>l?zktt@3A=fVBk_4& zA%43?GL`~HHFq)B>#T;3D`XmOUlyL-zQr%mBg;yVmbi!^{p9fV_PbJK|=ax_&vi_=q@)9NtMl6UnRN|hsgZ#%5^q9HvYO=bXZmhW4_pYIxS{Z zgNZUjSFPE=5M0G6A0SYss`d;<%M@<^xL&BV9gY1|7~S;vz5tvAuiW^^_5*kWdnQ%9 zc^(fgx=Eu=I!Z9O*^$$Y8o$DzkQSu}yVQK{3(~m(A8my3Tg;2SJCB28Rswf^pNe)d z#z9Sko5fpOBzq4baBjB`^NTXM_f|`+N^{hYR(Bm_?-+XkZP^E~;9{;#^W;u%7X^vm z;xN(c;k#|IiEnqIM=jOQ;@(dC%S*I;iE`kwQ&F-^a!NoHQRi-RG*Mk@W+SbhyI|uO z?2x_-6-4AoJq}U6XMg)9O!C3%V~oVN!%`6`A6U3-7O9)Vg;Zdd4`i;MN1q=jo;Be97{k0Cs}0Z%8Ds^z`}{L>`Wf#k6X{oreFEnBVH%o!uX3M$~xJAc^YMw_6 z&*?5Lo3O8Fcv#Jk9tER%97 zFe|fYKv)_^({?xWk!1e9&Nvx~Pow|Wae~5B$xq6|;Bs=u{kL02z{E=RIg%thDGLie z3*KbNc|{$pG1O)lr97>=tc(`a5*mgmj@E9xgdyXuHgAZRM;FSkG%=zg^6(>3U<;=< zZm1j_Au}JXT1zb~>rAx-?iGH-(o$2DBim}U*^r#+{8+LV(exZnFW|Y?&vq)P( z-=a_5|FaDna?-uo zEe;$t5Q-p-$Nu2_>#nRvUMMTqlF?`p(eL64gLIL8Y!J`P=}iPl*9`5iO1qvw+{ zXPPEQ-sfI62#*|fj9qd@4n3vaa!JCWnBR-~h|L z*7uF;wJph;-JC5qhb%%`Pj9bgasqH{L(ulB4YR-#(q)L7tp?w1UY1xkKB^0ahn4&# zQlhOQN2<+QNUnAf4W?T`1wO{}H;Z!@ThO(XLfu@QmT3<@%VU!fH^S~x;0Q5g38DCo zI(9xPa$LW3_P&sC`!$QrSV|`&UW!7k_={BoY)%b*I$M$jTujL$xixot(gD&v^7+i? zIblVB#Q0rZdN4~r^dErAX;}g3-cYShplpYB%_=@&qh4zx*7@y2$vjAgAz`7mAt8Rx zZ&#$y8_98;y8YxiO=-YLyl7(-P0A>}to6)4 z7yr|3z#AIm9^#*^jPSc7)uR5a!<`EAYO}Ar=EVTs=`@fA&ZZ#baEJY}ba0@kQ$Xu8`3e zIg0hSE9ZP>6&$IOv{&XVlMFV>R(geT8${lvjA3|UXA;fjvVP^OA-yYIUG{G`G3@y+ zKkVmW>R^%{r0F6U{C4zUOivY;mdq&?32Rg-dSi@s=h52XEY=(!KJ&v<;gR&Wg2y7`7^YFQyimhB{P9VJ<-d9p@+R_X6;b0)fT0QE_KD_XbCL+p ztZqtvSf`=;_0i(aFLv(tpw1n%n2(6pA~#OO`tz&$!KA5BZV($@>YJF;OT)8SJ?>Y^ zlIF$KKt|&Gvd#2yg(6;ZrRn4ll|ftA1uZYaWd|BhlIpIkbeWTq7sZV&>wvg{U56|u zD#bW5wZrgTz-qx;1j zPYoX`iV1Lhw~XCe>25O`;NH3MB+CO#ut|U+N0vkxZMk!CxlE?v2Tw5JImQusvgaG) z6NUM?%{IQ6IZJ6z(ecD1YqD;6D^KAMH$%RPv`+hk3_-(;@L`BC0;%_%x<02o1(XJW z;yh$BV=gJ!on(OVgaRz@?Amuq76*bIfRpR|!a^JWpgH5>^tGptilifA!3+>Mqgx(z z)o&F2=B8y~t&TCVmzvxrQDXCxmmjTsDYw~#wO-6nijnb*#6GFs<-*H9jM(_rs+ZyN z;)Y(Sw3b=jFX5YogLW&u1XcUtUZCBZ(QRKt{Yo$Nh+G!d$!huJgQq(MPQWQ=7d|E7 z@V%PW#U;+uej*&c9+I!Q;zP8oDw9lVq?P3gPEOP}x&D1ABIWtF&uyn0Op1oYnMKBB z$Z6;(?QDEEG8XmN?R7J63DC=H&>Pp)@EK!@Ai(JhPm_0sY3{S>r%Wa1}%2Z}h$3 zvnTvq+Zj0uNT-151j>h*YG~0u46vlaFdsR0fAFvK>P0L12rX|sl;K(oK&DbZ*jRQ6 zzI1cOaF~?q@r%0(ehKubxM1~q?UP;=wK`&tTit2?shP!ww(Tnb+xi>kVXj5?&Q~Mq z7x`?(4hPOBcG!*zJJxRK>$5v@wM-5Yw-EAciYh*N^Z4kgjqk??NRmfi9!CATIa^_v zKzi&xa;MQNMJeq)gd8<V(rJ5H?{AH`B|xCj$jSLBZL# zeb7~dx&?K`T&D`?zl`I5TuXfZl~|`Gxp6ELad+CeQcvzjdFL*qij64&!Y|kQVi=v{ z8P_)|Z;^)pF(fb+5Lx8&7H>V!0ip)Gb%pkQGyx=uMXeSUCK4U3$+`@QZ3y2xl%MF; zJ&IsLL zs-kBtpF9z*G85Cz*Oj#K8Xz!Lp;T_@QS~w{J~V8K7|9Ten#VrhG0x_*qf={N83E>#@L;^!H+pF5g; z$MwfYX`Uc<|Bv7UwJw;O&GMW`z^vI!i>Dp|KGJv{=MFMQ4X1J|m-|T%mvCT`kW*R;|G_2eq%@Pro%2m_S0jEcR{VT2G)3#2Q&A~6psd~Iy$0dCD+Ywk z3;h3bc4hf=sQawJpOyoUsD6P#w{Ri|!-{7bxQVxk-MoZ-xyMjUH<;1E*~cqmLn7rE zRn6cr2#=|2;ubz?2Ok)O+W%k4I$qc-H4VUQy9mvvzIr=}v6(6JO62bx`9>dB9=CSR z6_F0JT=n&(jDB!rkr16a6VPuDfKs2dGFa?*`;5-rHy(}MFuWV|;v-&eK=A)|Ve1-U z5Snr3pM4zojU--q_(jDUhu;j~gIM3ko1u1=^?C@N6?##a129F0ezVtR@(|n`<&JPt zpJwn@Q(&!0OZ3{T8pjfB9P)%_mjdBhAZlFg|KX9y9J}%ozA=1$ir}$$0uT6Gjb~AC zvRmj{W5?FyCU_&=TSw|Y6Uf(w^I8DAlVQ5fNN06qxM3-4!seRvAqo%3A%8Q2m=s+R zcj7u5N{@N8{8F<;aP#m@#{n>*&48wcNPI@a(Edf06z{n~Q|CATv+Jut$F#t2P!F1l zAO6yt4#5teAMTL;>#WTF6Zl>c6EgmpXU(|~-)$2|&>Vw^#Q9iJ!SeVjsVD^9=l)g9T09GLAOAn{U4t#Gh=VenJ&KW_rhO}C zk)i!E!I4CJlIn@Q90s0uWWffzGr6M&V*1|If}5w>ZDQ_C%V)>HiyWe=W%7B2&Wy>H zjlymZlIRIe+{Py}Sr210-OoH|fV=WGg*;H7!;TaVap20CATHHTgbbbu%Uz=^{$P`J zUDRh+(-RU29*7@?uaDJ2ED-{b%203QS#V15entf~snkwPWm66ae11!wPC6vUaxyrR zxSv{a3_|417DlLQcE3X=!Iw@}GvoNOYl$MC`~Cz&1r_ZkjdL5XLriY;%3|Q;fKsKz zo|fBm>63J@p-%)oDg(qd-=O}{`lfnObh=7nZ%wqs@LZ@*D?Li_pKn6}3?Um@)0Ld7 z3FTX2QUr2bo^App19dJH6=$zK%&D+XE~LE4@pheuhXCHH+R+sS^lt8Gz`y3rkvkqvsuO=b?CX=sdfv-ov41V+zJ7*8 zgE*|`SI?k&EIeuEnni7xoC=%fOA`D78*1}}Kw}gzg$V#w+cAYY;R!>@*NiVXO7tQq zExk;0L5wErlcAhvFWyHX_})F6{JZ6P=#;xmW1}h|c@TsYi8ikio%xu))wGj$3c{5P z%nx4o-h~EuH-?FKW;jq>)?Qud&7{R7=vO@sv$sMPCoz(be^~%>8{Z{?u#>zZ0;j6~ zJy>)u1Pg$x1}hgz*tow4WipAzuMgP0rAM7JeQ-5`18)M};!X7hq+t%S`X4HmNqtQD z6;Ic6K7*jh<^@Sp_i;9B(xK;|8|tGd93pgyVQV0%`rYQQGB|m zM{}}rxF}uqZuNpx3~)|u0KE!|AX>WDRIy>c=A3=;f z#@a!~P(f`?AGHqZKda{*p_CWVPjqP&Qn)=D@W4aJ>4+bVg5B(E{DVqmSLw4M@bMdd z4dwkE`@FOtPKBB%!i-?%bMg9>BzlV_ZQD)Hk$wj5gXHyXKXqq&o|WKDLE?~UMBYHo z#af)m1`5ONPgjy!u;-En&mehD?|=;ZxqL+}rXlb34}E)|bHKVWyBj?ox4c)t8+W6fCbGrELWREN#B({*zawBOJ*b*^4fO8GjG}O^sAy+4uN< zxR%ik*qZ*hWc~L`e%M`eq;2V?MZOJ~8<}H9uU zs5Qvig4mLhNIkv=qO#BJ-53(0GxS6NHVrNZ5?mYJqNh}t9+2yuCBT7JRHAiO7TO_nYz0Fq1h_rC%y=P`BhefI<6izzk4DVwI$F z4-w-3ND1QsvEQJb`bWt3#pfRT*Y3WgXaXVUkfR`1OyZ*6o&Z>2s#1X)uY80rl+kH8+rks(hYP?EfDw)jp(Iq4x8K z1>%@?(x0{omBz2EAXC8O(Pd0>e3Vgi2@@^wFN?}Mw{`KS87rvl<~riAK3}G6b*sMJ z7Zt;2Cg&WgXbq4;B@ENzpwsi9Z8?Z!IOt=xMk!E2slIN2B-M4z{>_JuR!KMy;$Gz8 zj?_P7P%1Fr*MG#=e^5#6Y+w`?{WW;b13f2VF=jm>Ka8+o3`<^GR2@}qE&P{Ab`_PZ zquMop_ASRvWzE<$iVqh{iL5Im!->-|pq4&EV0U{6%`f|Ao~fKO1cfO;|XLQEXL(2Um3eO^8 z<55I91k&D=9NvWPXBvy38$AuMJpNdsw0jT?LBkN!t|cENBlY`~5ul!C28nwOKK zFFxA%=Vxc8GSPj;xole9^pxo3bp=|){?89@_xP!kMF@Pam{{UP_irfxEy6pp_8%p` zpNrh})mv2~EA%`6IVz^JY-#NJ<^0kYD=@FN@8Lb~KkkmpFJEw+JT4<_gYZ|3PHPDq zAV>JWT=D;5v?yFfMW}wwlDl188_pC(=b3MQ^TH2ijf^`PSuLBNmOD5~Z7Tn&qBTEW z1pJomQkTQ-GCLH>w2Rb?Rh3+ObUT%d6dT$*% zpQ}kx*xgF9Nrq!sTW%lISvNcIz2Hc@4g56)-lW7`(Wx7id*)vD_~%0xWM#>PF3t%Qr8}>wfe>qECdjo`M*JM~$P+0T&#%%7{_UVfww1yx({`+kUXt=zdId zc~S{p2cmzHJJuUqc<=i%{LN?yCxftA^Q{rna{K7oUISiMlsl11feEGc!`uFEw<%z` zW!v2u zl5<|jPkK+V74P8~o*3UzPNb+PxmFE4to~9PufqitU3ZSfxhuKII6LuF2M=9*R0oh} zQe$Qh+LTExN3Pr)Khbl!xpZi0OiQ1AsGPlj!zJSli&$L^L(jMjQ%`A#9#gbQ`5OxR zLsB2BV&1-cqbfdyJMqEgd5#CmyxFn){>_PQs@;4O7sZET4jnP}{R)5i8jenYLzclV zp2a1W_JBy+UdxdAR^uMAz*5zxf5v`$GjmGxr| zmzO+q@R%EhJ!9~{4eMUM;}+a?Da1rxwR=C`MAlegm2BBOeVa5XkjUV9Cl=f4URI=MU9?{``bH2wTRwQK#!5iu+ktr9OWXfBv)_#;TF zu&LDFG1W&^!a23@o!9vRi-p9RUih?lnt!D{=|jpC<6Pp0MfPi`(4ViKjzk7nXcLIX zC3l}-jsB9BtNbO#nwnC&bKW5t+OP~oknDYn;0Wj<*5M<&5;_`7FH8U)wR9=JS3AR+ zr@iVUf%7#j_@geFFZ*@X3#ho59wWr4bMREDF*+=NOa@cnv+q4ZUc^UwZO?yRPP1E$WV@xs^qK$3dBeaofSweOu ze2z){Vm*UKZy8J)fmhw_HvL*c{Tn$;s<%@9(Xh8Kz3ul>e9>Y2yTqSv@MpA($D@&j z&|O2i`ha>J1t+&pq3X*F6hK_9QC+7BN~VU#XrdwI$JQ@QNzOeMqn7HmS4KRd&72-A zjk#>_oOg!2wh7T1q337OBWnAg?`nUwL;$$6IO=|Hz`=xX$!6QChWO=ktM9YIL|;d& zOn?&)I{0oisV~8JCIeDKJ1gYpb4AZMytQQYlv8`-_{|m&oc2Q~RDkd3d|onqe)>EkqGcoYQv|coRTA;kyIH#0t$=F-Ce>QMz;&mdej(D%lV+G5x3*aK3vZoWCCbs=d2TqaRJ7;uNqHR+^@`?h z2}GFk&LKa8+X>MwEuu z|9GC~_x`T?cR$bbUcdVWNY_kV@4xqXn*AdjHxo%`%nxwIXVaVPheEP2pAE#%zuE%+ zd{Ixg^KptQ)EpTcWB3d`sVp=8Cr>g}ub*I(W+jHtG@YFp$Bsk}#x7_HuT>U_xD&AL z^79ikdmG3@In!2H!QkJPI$2d>knakMO7A}+^lD*%kSbs3V*xX+80uj%l`a#7ls z6Co?!|HA&G=3oLD>fbr8UR;dFuI1)mFPlJ-#G(W))2(84cx8)wVd`gHeKSpXKs?JK z?c7NBx4e;S$e8Yu?D$0oSwQ(P`zO3p__UO$Zy;TX-{1UpoC>1Y*ske-#V_+VIt}M= zu-1QIeA2Q!@8ZvvYA=$E@-L740s`gr)*KE=uZf zbB9i`!aGa?&{aSfRJU{+Nh6|M7xT}Ipil4*!~|$icck%aA8#a1^->4CZWFisqX&y*@)PP!C z5)v4QDAz;Og|qR-wey{KP#zYWwB*9O^c9dWTQ7qfWew*VAx6ap?0ie2pxcjmNM7w{ z2o|RKXj_JqQPExkpNE_X_guo3qOI%<0vFMEK{$2)_a#dAN%8$I?XeYB(f?Z3%o&t2 z1YR&3;6g&*Y!^QVp<6pv4xF%u^}bPb`N<4y_K&LX+Lk&wSO4j>(Q(Ucbx_4O@E~Py zF1zchOlMr7cig(yr=d~M#KIbsMHaTE@N&xeHRvPHC~(j_K&4m6 zjh6QMwXWA@|E+j;YAVh>#H z)SDTK}Z>rRSP!5 z6MOAhuk};>m!lJ9K*{O#iS1oGYx?Gn(vU1u?vAfOrPpayiao+~Mw1HBt3;&gR!)x? zsVHz(8N#j~(9!d+h$VmqD`#FVszKNoa1gU=##6^mguetMyHHFp`3R+h%!k!ifQY{~ zYF$W6|Gss0h_=r&UCcJmO~9YXPKuZ^faOhy$C{bpvb{Tkgr1p5c3^?`%wLWBr15W^ zg$DUg5w{+!Zx=EQ`P^<62*3gfF7%g$%k<4*S*9a9TEvLw0IIZ%?YHo9y&9Rllq43N zObCO*ORj_rE^A9Dx^Y72z(XaKKp&{a?Lhv4Oni_h)=yZ%Tc4i}_w)NBKv=_Do%Wh- z7s|^rKUVXCA+&uC66HCJnMy*D-sZ?NWJ;p^uZ& z)ZYD_W%(_N?&>FZJ-!crWN(sC6Co5j843;an;%W8Q78r8m^!HTPEjBo=4(duhr!aY z4~T#$jaxg%=-Q_5=1WxJx-FVj^2ocNDr4r6?5F&jfEFF9us78`UaKxY_`=6osl-^Y zR0`^(Two)h3a{fgFVMF?Bb;yOe;%Y$bdDpCEdI+p}v|f90*kpYpD?R z78(J4E&3V2vy90!MI)9c9ly3fO&)=i(CRV;G?_zSO>!T_*IuOZ1{-fH-xiA9AozM+ zDKsRKH$ow3`OUZ8$fPeNOGZxyY8R-cUlZLnK6_v`oGmMJyrq#!yTGNYsd?LQ Rx}_NST(-5hskidF^B-ZI)JgyV diff --git a/pgt52m/week-4/workshop_files/figure-html/unnamed-chunk-5-1.png b/pgt52m/week-4/workshop_files/figure-html/unnamed-chunk-5-1.png index 4a86cd24faf2f7909f83dfd772241bf2ab5528dd..9ca7fb363b51adaacd240f72155c7fe59a9397f8 100644 GIT binary patch delta 19644 zcmZs@cQ{;K+crKkIzgg`C?N=fh#GYgM52cv%8(#>2}Un_L`3w6=xsvu8l6!>qDAjf zMh&C)UVqztKhOL7-sAg@WBwR>*1FcUS2?e9T}5^YbfyqmqK*d;B{MCgTyB5AEfM&a zSV_Q$xP!Ci{ruw{jSDW4nw{Q-2qS?w>7pZ5A&MCWiN`9Bh2Fn39(6?=HdHgwPx3je zMud~KGBeSKO?Y9~_q~$#M#IKA3d;>-FasPiyPl0yDjeY-rXwsG5_h_UV<{o%UjqmT zAHJFtlmIAEqs9?OBsMQX|G>eX~mj*?u5qFyG%v* zAnkt2&{`wu)qS}yRrFG8GDqyVmV~vRcyH-5A@vt^OF-ROiBjEcZ-3h_0bC3FwpM^H zAw*w&{oWaEWC?jm@9l~kN-3k2A1z^y8;5N(aK6<>(53s$~x(~cmkkTkE zl-u?El(wpteA#KTW^46LKB}f41-0gUbxrJWD1#a&T#)qniu6M+18-lJ?;|(k`9J*T zQ2S1-39-;7>DyUke>=ALdOHV8Au@Cq86`~xB?W#Ul`&hrA)Gz)=HA7=aaN9LVU0t1 zu0z)DFdEb{I7cZ~Wu@bK4~rFdWiGzPDWvgE?~ndM=Qx2vv$SnnMT!!v&M|c9Ek@om z=2HC!3nO#6OH=Xx{JKVo1THRRx9l#>{QX_zS!~C?mWX&=yAnJw!XX1C{ediVQly3f zuj?{8vl{D9!ZvtcvBPLSV5FPORv$DzcIUiL2_-E! z1sL-4hUu$YFd7R(T9Ur=T3f&$e4F?}Q{-Ja+=>9fA}Fl7R3EW+w5I!_5JMFY_S;q# z6EG;<5BXQY)|yJMm!ow}n??f&qAn#=vq(1KAIw^c%rWb75q>KN{B`_5LsRsV6wF{C z^8l#A>sZBcezidanJU=$K0y>@UAG8SC;}3w&$|%%YC6odO^sYoQweZJ5@&`I{D zWgD&?uXYu63^rU^>E|#aiM+-Z?N8_fa7omkV^iW>>7j?W;ZdjR5eG*L-MqsH;>iyX z#U822ZtD9>AsbpV5Ag3gWA__aG;r{D4Vf6aBju6qgtC+J4c-TV5XGX2xnPNG3`13Z z{AkNDyG87B(H&u8&A+ubwNMVjh_Fr_jcdNtW_+%rpKn|VO435nEeo_RR02;LLMm!) zJBVR4H4ddTXeL+sdFKpv6Gi_Gg9qd8CkLqidu`u<)df;) z44Q6==j)kIv>ST=F)AGzb4a;He{$I<@6Mm2*wQSGOG;$Ev?^cT=!3xflkv_Yg&aC2oQx7HkUyxD8InBY+2fxFSGE46Xp# zUkZ7n!O=UVtYDWCp-Ib9%j?`xpG2=Dhxp%uDbi_?_vsW@+Ha20dl-vl$tS4~=A|pK z!TUe;TqFKvN$-vn4!-r34y_{-=usVRwP-}EwNrO}{k;c&?Ir}Al$G(F3l@#44%XEo zyiYj*!IF9ZEP7PRL|Su>)dPX#-A?g%qTr5JMx}p6)GtMJ;zdb;aC#!ouRa!eY?|6z zr6FzZ=$-1&e{W?4?>}geyEH4EI^jKuAF?a$H_SLa?CI#R?xY3J0 zg`E>Do{{kxyyq3K7SlmM+K#(-864tzv4vRKJ0}$108nocY^`7~g{mrYp zAAhvCmbGa9SAXQww`9mj->DqUYi@+nrcwV@^CZgS>$u-7bMf;@&hd%E$l5D#M7$xn zLO%@M1#J}2p(j)^hUYe2DHtrP)390`{OEbcbu>5{9Xd_=UZPC@W9gD{776Y#^>W{u z3XhPfct~}qDS*a(kn2A=B*=b;_rH)a`V~1NFclD*RaY_hZQ}vN{1()}guab%qe=>6 z!Ct*Z;8LrWHr+C8vRiJt9;12}0n;2waKJ9V9SFNMC?SjA`>@*+t z502?`5E>65l*ezN_qB(GsnCw#9&9I!3J>XT-HZVrx=7X)APzN&G@&SK136`j^E#H`+1{yJeU*EiWz7zHJ z(Xt3`b-AJENVr))y#P?U1CLz!R{`F%&1B?SP3pcdUko`}VOOYY+#KC%^YL-)hSop? zq_C09a7ur>NffKY*1#Fn{gtEER_Nz~%QHGR$DG?s)vF1)PdQ*TPx8MMA>%>0Zt^`+^h8MJ(ovbCm@h+plHwt zSNiDPI>!%|ix03B=0sQRxd^OGuq}V<0tSlvb+9ew1mHuyO%{Dzw;Xq}16gJoha_tm zi0Tt{?Yx&?Hw>Jo?(o1vk@V5Dpt)JMscu^xt5xan#kab5W%I6A>)vy;j@eLlP6LK~ zt8sY$NPN+~P@&zbuJ5kselsWZ=ccN+_xeMuPW+7K9XU*Y4JsU>Q_A_)CjHb68wg1d zxqS>Y?FGEE@P(&e{` zCmyv_`^f$FV*cd)Kl2?-Wrpqm^VmCx&h;_K8)^|IJ5qot37mAoH}t&7xKA>~ngq@+ zrt{um-G21!_6y!`J+D0of#}>yvrP51h=h_fV<}> zrB-|Br|00a^8rS0P4JnxEU#S6_}u{ILr1LdBljEN-XhKW;31s?;PI`!HE^+Vy(cL* zcDp^^)+JeICqoGlx#dGbm3lJUBZrc*=nmVRZWciZ{dRI%~OdG7|q-sT)I za(C6X0&UY8BC;Hylxx|~7J0BJqdrM+9o_zho+BK-D+}78&;pPWz1uKFvcsbWV}nNL zX=_!SqARpe>Y(jY2N6C|97%(`jf;Hn)Pb~K%UwwF2D;rTD0_c>!2RR~_D!{l;&l7h zXj5#-5NmnZ;w6EvO*hcX`_DIjtjJv?f`@Cc z-yvlTBKmXcyC+L8N@t;V)#@d_l|jlHi@_z?7@>}HiN-77)aB-`V74cYNbh|Uy=&Y6 znz*l=k@eyGDv$;|Tsg_AdJ<{!WcB;4D%pX=<{Xt{;yz^UofN!;91H_IDI_&Y^mW{Z zDI*m$QC&-}E7h+y6k2|)X+UkUB9L_(RHt0Sz|IS#t$h|{JS2vvD@_~0{#R*4tx zLMX8$VxSLPWxwHij?iupDIw#xUeU`c;^)}gd*m*=8N2hN#F7=1X`wdxiMNcbcHBVD z+IM$=zw2(I&@cgXCC7NM)7a|Ct$xJE)fn0|QJd~=p(%om!%N3bex9GgqNsH^o z@RLDFch`3$s>*g&<+BBrt1^>>i!qM3I^USbgE8PX6!(bl#; z(2x#A#>KdS$1t|)eqB~i$mxZ%W%0nEi9u$YdF@m8oRx)O%gT*f&~7O9NZu*Tvz)JM zl-RQ{-uhnFRwgj7Q`_vMVbqBPM*k5qLS?OkeX+Yv?KH9k`#J7p8A>_dW!nhSYh@`A zDRRv(8@5w8@@(|j=P`FR3aSy7(S~C)?M^9 zIb^l}kY`VQvVRj0+3D`+M7;eYObU_@S{2NMT%B3Os=yuG4Rp=QT>Fm^%e0@hd>s<7 z^3>n*I4?}%^df(E?6ir}<+fT!@5TIup#F$l3-fmNwA+&!9FBy0|J#P#IDU+Lz$zTa zQnV_W3@OZ3Y*K6+tkKKeFffu|Ni9;!>>ceYl?{_KT>yge;~cZrEfej-e3-S7qo1wu zt= zy?$K^G{i+oI;NIW4Y@Jq{{9g`3=*MZxPjZ!nzReASp8V`VSL4#9*`8Y*y4JC!^d6i z18>r#gbgxPu6@#d=TUF^@OQ|WoL9YNf?I4bq%ci;pw!F{7o^X73;jkIk7$lgNpvxG zb4f@?#s5PReQJFQ5f@{&`Kwo9MIW%-Jg-rJu3rh#=K{TKE32Daq5>NdzgOz)2lA*Y zQUWW*BfD0^ouU(C9ypOTZ@(31lyGkF+;`JgR$&4`4-5=zTPXV|$^6cbHMQp*-I9&x z342{z>|&Wqq}hcXR{!+1c^{TU!YeXWlyO5Q0|I=3U+r!jSSJVa!2%on3EG|*L9 zMS~_1*3&s@AzbPZ)15a_4O?bfL*tT0a6wrkq+>ErMqL@<>i2u1>o3e=$q1L8S#$2c zH}r{Z6t8fUc)4p$oEPNNTXTnA=5OpD$a#K}AFI2e%dzi>3Xj^KKxi#j-ACXWQZLT9 zcK8o$U!f8%7d7z*DZYyTgyQCHU$Q3%112VPqs(WkE>AqI#FA53IXjX&pogWG4!pc7 zFELm(wktDAj_6a3?Jch*w5R$D@Uff7H^-_!40c14FQWH2R%>Nx2Qr@{UdHNVZ?p8B zkN&N*Rifmy<1xw4Kc01+4*>p};jY8Vo7(kUDFzW?w`HySdgOq@)7}oJmgx0GWxV-G zKi(RSujj%)9cWqQ;2NDheAuwgFOYo}FU!`oaQfBzi(;VN{8ld@UM=19lg{cuf?@4* zi?Cj%lD~^E=2%YB#v~vvQ|}BCe!OteC9{spllFCMmEX zH^-3r{iGY{xX1QoQuO1NF1uNy1H{z^l0o04TsRYL{mlJF8n!q$!k0bFsZy5j<@?1d zExwVHtRM|$MW4p`0OK~uOEO=wi=+J35e>rn{%wlR6LlN!9Si&=Rciv|#k#%<5G2(+ zAMpm7-?+08mB2clE>~kSvL-5;*mQ@YFR#6ZtoNPB$O@se%D3p+&<@6JyuIIt1t$28 z+b&CSBQ6&NPDW;Y74u$C@Rvk$8;{Wq6=x9zB*s+I4|>#o15tUAnKdj{kKOIPURkND zHh!+yBQl^#RnW^wWVK;Cr}I>m`kX7^hiWkO6-ktS|cCLfWNBDYWSstsV}&OS@RHf`(IN5p-YXd{D|?tb`1 z+1FU*rAX*yCd%HMOO`vM3p5s z!+t7}w%+0mheC3;`v=D62azKPm4lD0Xp_nds+;X)fWz)3ruPK%h09I>WxS~?3=din z_$_MM(`*vk7?_iBr(^SX@JT-D^i9sqoDG9gr89`{kJ?>@rQ_UU!|roRY~iVPV#y8Y zN|K>4JbSsQLsU6q!k?~l`tb|aX?&|EM%TEk4iTUMVF#B5+d6_JORi_xOy4vJ7dukl zYv=%!*Nf^c@U2St-7T$$-z;~;t57FDoGNVBrztKW$kSg~{Epov6!;6txzkj!<$JHQ zc2_RVV=m5QS8jQJ7i*?3_x25dj^id6nSC2E@Jl#4qA*#;QQOMaMf0!wy%V-uP*Mby zE7ya=)IQFqz#DPX+O`ulgaA<-0~K0!x}yRhqjPLLFfLJCVo3C`;lS$Q&U3_xHJ3h@ zh;wa*QOIHDM_8-)-oA5j_$|}VNR58f;zHz6OYgAoLFwd0IN1Wuupa}(zl+ROad#W$ zO({#DLqEkV9CB}OUgDA>STBOk<}^0zTS_l@#w@8lv<_LrC+XzipyQ4S$QiKC83UkR zl~yb7YgkJjj-$E6tZ-?ryS`XJ#M$u7u1;t`7uQ7{eh(&MuwVK^aT8CwUWdm<9`uXd zS?byNK;q9m{PPT>6rQRXU(x}sy^p+C#4SE%4PU@%SDp-)bms!&o1Zu27;E_bP#1q} zM1K42ZPpv=4yy4|i%G!cf|JQvfNTt198-@Tv~Z4SX#uDxc;sJyXsDC}ZsxZr06X1358?ql%xLonfGS6R37$y?-GQ$#r2u~oN9Bvlz-S$q~Fqs zu6Ycx!#eH zqTFS~T+LI#uVtSB25aDjn8co1*k4&*eM?{|*7Rr5vH+*ODV~U*i&-E^5)In$o3EQU zx9p_&;VUt?C`N*7OHFkqsa-OgqlzXz+Gp*SB5I^bng<>?A-F~{3G*AL#l|#Y9MxaX z)&(FR#M|@IEfO4#ugN1?i+H||Nr(&lBXuwnLOzM8a|VF^oo>#=PUoNp{tn&CbbH-!rhZ*CUPh=pc59ZUzv)Z1|@;kg3-TXGXVau4Q!_V5gZ1{di zg79QrZwj#gH2)0rZnU7b`jcNzvWmMLyay^jWY)0)PUl^@WCuI;nOS$#QjCXJsIh>n z?=M2DX~4$LcL9hSzRaVDh7Gw(486_8J;P>Je{_Fk{u8hbVdW<+N8j|~mB~y9fvD+p zo1wFxT?gE=nHIQiJ>snq4ApHxjHiP7k}-l)=K^i%q5>9=M335)YDq^Q0p(4u8G zXGJ#)PG7pyZj?%WPD>$bWzF21=h${|^GH`YidT^l$dXUltHkXU^RLET1iihiUO;4; zGy-SmlB+7k3YN2-i*kNRu0MN;ShZ71UPD8=qI8#%F{88sa6%Rrr2>uC9^0Jf=+g zfLZcZ*GIlx?-R?nsWj-X+W5E~c9S0r8GxYk(*X+P1k-dy0Y;KXH6EfUqjMNs5XfZH z;43@8`N(cw3heq`d*G0oxR2#)bT3n;;H%6L_CbD6`4hbQP1|qxU&CnjD<0NS9YhX( z@q1*>9A3Y9z+iojjgHbn+|r3(RcQpM_c~nm$*h(JpengNf&3biuc;3C}ttfATs zA@Qkt;rz^>cB%0c^u14dpuO!+QyfHO#o$Vc4%aKyz4D)$1mFBLQ`y=t>!NY$nae`L z1@FIJlzKM;p#7}paF=?5?i363F%eAi@E{1?BDyX+A`M4uwG?Pnrn^aeIW6E<{9<#q z+KkKG0j1k;XtA04Gp@|SM(kc`#NzN$iF?qXWWR9mX+w558@Tx+N(x}d-Bmvw)dqD; zEI=E3t|DtMT6-DTKmeTSn5}XLOf7i?z47I)knuFb+k3oa{I{M0SStFppsUoQty*yG zaJ;b)I^r~Imf>VX+vdENb^9;*OH-0M(Qv|fYdIeR>1ke{^-ZdWJV)-L?1b#oS> z)xxKl&;2z_qx!b4GCp*{j*o*1XOGs5X2QueO@=?fE~W5B5~h-DL9^L%Vtk}^G_Yx? zeMb2&gV4r%0Z1M)SYn8XuI(~IAZ(Cl3XfjgL~{KZd#VvoOw$G9<7yHA?wkMV>=`&U z9N#&@Sfp!PgQoh=M@7nypeKjA#qDs%RqhL5)UYaB

)5_t!XdCSUDUsTN8U(tzf z*)IB^6mYC1e^9rlhy~Q-!BpXF`pU3(hieDV4 zRQWFRT^5}dTU&v)9=h7|cC}F3q$=^0wbt^!VC9CJN^`M{=%YP`ynthe~!*+aN31wK?BB zd1pI&`ZCrP2gq*!5^P)q2HJ3#wo<=O;~GJ^P~LrLxtcpaJoYISI-4D1kLa$2aXz0B!_DO&eEYos66m)8&ZSpjkOo?Odz4i#5>g}J0^M0Nz%8hg%ap^`5biK z6Yf{b?Y1smWcYuGx&xn%g6(0$Xk$peLlci~0s70WU$~NOp(mR1V)R8BdajrK@SmXK zL79-k-g>_>4Lg*<<@bjh9t-l0mk4EQE;xy1^^;Ny)%oUQ>z73P-=UIcWFvxHqt)cg z{yipTe#ztO)2Z0)&jS$$??AZO|CLZHQpKPItlPfdqpMZNS z3mW><11~;J*sacXT^fHsoO$op0UOe5kewM`P;^BV`@RknEeY>EKt6G{;vJ?md=hhzLmw715y+DiO~}5k2R;S)}$A` zGKXhuduO(>u9ipPn8u5k#`E9e8@a1Uttp94uHZzWn69J2pO+U{Z(tX*vC0<%aM@|1 zbm{lMI9$%r4~xq|6K@)33ZhbuM0on&@(zd8bG%aj%50^j6D=d;_1lpeogid#0*eJn z(F=U5JmS7@BQ1;B*>PuLU`OF^g0C7jOeYJ|HI13ldvi}Bo-#4!VuK!V-#~Y~IHVA~ z{+0Ro`OYb*_r1>}V=^#?vra;mKv8iGSQOwOEo_PG)(Bv*s?X= ze7pJU?N2Y!ULkMkegVaLsYhPfPc{4#6i{3)cic|Prk8ULqluP|8@>WdnPiJz5{xUq zYq_iZM^~`%wCpF%Ej0sDQX)F>kMOM?=wTED1$<2fkZLSw>Yr)remosHIW60>J*u}- z2Gv%{%fZ_%v98@dl#^BEG!EX`7S^{KED3QJ1Q~f)DgCl?}bWE^EDH@>qAfaj;MgE zX10I_n)s3thE&+O}Y(_XOWM|AOc6h0* z;siNk)>uJbHRM1Z?v*cD=d?}{AuV1~*tF~E$uZixZd7v8#SP$u5z$;EczbT6`#;e3>-zfV_%x zi+|5UuAg{#{_(D-Cc16JB}&DD?oEMewep#rPgkh{{}@J2^>s5BLC)sSoD>g@p>8!B z%Dzr5J$0(PJL|=h~uQG=Td=vk*1lJDvg4C#G=vY=_xrk0yocoLT96niem##0dqe^R6@gBJbmirM+Bi<+;xf zr+j8>3D6(2S=mDkxC#gNUh4~zeLH4t)OlJX@}aH9*!tsMb3>ReS=C1-^n$n{$Ewup zLD=4x@&^Yo(>eTbxzDm9BI}%t>-N^VJ>ha8K6#ryYnB47m)A4}w;r^ExBqwiHmNDsklaUAgKye{=H zLX;ow%g13m|NMA=9JYrpAE8wD4tz4f-TfP%`@L*f*mv12We@6dTtb%)sW^BSC(8x~ zYiCWQ&W^*`z%4+Jrk#zh>#q{Y=ZCB8vajx~A?d9u^O|N2^X{x^JoHIMJ}!yg`|aru z%))BlKK26H%Abh);Qlj0ofP%D%$1C0Es_1Pks*_lX|mwcI(U}D?;uzsw~)^l~*wYa>ds$L;>DPw$bJVTK=zDmB{3oHkR+>G{fCGe{ zc=5_bhD+DByPiXcjk@=Dy(1dz8-!xhFmWGv%88yGPV47j=&H43V0%yVXF4+f&E=x= zbs0=1>&q?9>gCfYr{iZimk%GM=N2TZ$k_n1aAc8%gychbaS%~{|4(9rx(dR_5mPsb6n_8ZW$Z*-1P(AgO@*kWjfvto~f4-X)KfHVr zeAv;?&VVisRp)G=?)~^Kr;4QS6ZLFS@(36)ypLOeEDS{vathaaN_);zbQbIMQ2N~F zFzP?H2Tf|MYpr^Be5EL0OHbVAJ9pcAViy@|@jN_+q>pVgXG?NwKY7EnOY63)XkRz2 z`%K3JBKZlz)l7QJ_YI%@9FEkq$@^5+Cj=?*t-da8*6k#IToDL>kV`HH?~#;)vA_=r z3?zL#wYE-QL!+eplnnl^NBVF(+7|F@o0e^_jg=XvZQ6t-rmfxvD(#5zm%w%Od}FaE zD-C5VR~+yDThvO<=Mm(5X)_sU8~(uYxp@Z*v)^`MdnjUl%BkmG2zOL$$ZW$alq?sF zxtu;Y4z?M?T1D>o7$rIK3@WMYE|fKPJ^4FgY7i03MEf+|!7YRvu1l^sQP^n%d(kLyd_d;i?4v4Dz9KM? z?t5=9&*zYd9@(6DA5MR<^yG92hcjHyJvJU@HS}2`Bd{X7@pMckU48}u6HyK>hdXK4 zoaFGz-oSZfga%P!X3Z7pc9and9?e0Tf4c|FDtsre0VJ@!=EnCLY@&X3t^8J8!MP1@ zQL>CvFkhjqjn~;xNAlBlswhdW_i@=QMvgffJJQ}077SI3?JNNt?yAdF4j-hz+nx*` z$GmP!5(-L%Z{^ZiJKOY#ESAD|sF8X6!D4r0;$=rFa}&h((VCkvKh<;pj%VLKx%k3D zwJkR~0d30c0GEE@31;$gm!_Skx)GF(9@0CeM)d`lvo1ON+Oo+ZnzGT!bH}LzLrVs=UdVb7wHPI)fp6N$cjBk7um~LT;>&h z_mbAj$^7kTg_rJDU={h~wQyGSr76~reedkoIxte4F^%2%mQinY{x@35H;}z=v{p$0 z+Yt?k@hP>K4XKX-Gbz}UI0~)3g*ZfcN$ci;hhQXBh=TI+)-iV)*dP>~Out(zM0$s9}@-B^FP>(NDqbItd_Mvs2dfE}x zYei*5xCy_|ucy{2wD~7d)7PAXnSejxP~{dSzNx6R4$6E%8>V^Db#`E$-miF=ciy%slTC-~(ge z>bBn)eE2FN)yV;%?}seWDiwO8LGbK%`@2Do3#UGpS>am&5$2U*;<~WQS0|*dB^CRv-1pF zj%7L7NkW&{!trciUFR~5oZfyl}6VI$wm&m}` zF=-ul9s!!=SfLoHr>rIq9M;0tV}@ITLc66K6j59p;~LI7a%+ zsljitsyf^h%(yjU!UD>k%ZILV@3bCrZ$Er4kK=5_CqE42ls1Qr3Q>rS$z0?{7l(WcrDW?IZWce zADTYBjwZPHTX!79KQVbqa7mrnNfbt+=~ylQx5!Bl@ZWZeyvLOyPoGaKcx%LaYb{dP zAZPh6B;|$=zq7yAZR~rCl=k7YRDrAmP8K=%A=U~zei?GUHvXfr^1@(&_yc%p;Uhc1 zAF>6`4zm^P{T(Vy449;i%71j>;k6z zhFyG{E0S$#UD$0kD)&+vSok_b`9P)75vyjW;0XE=(qizCpZ6aIl3;k?zEjZ?i)snd z-YTzJVhu;i5JRZC%?U)vH7t#6X`N2upe<6@qMX@3^wSeMZdTFpgrZr;wm)Y200o==#>=fupUcTu`+Mc0eg0Y{J;rW@i57e zecdcTp?S$Bs> zf8QrljfEj?Q)Hbg0FQ0){h{Xy=_(I#Q?#{Tb_FU< zys=WAA%)qi_S*`eEM5_SgQi$%xzu`{T%*QC$;vaT`C3Y}RF6s!%=t8k9d{~|qXA>X*qq!gCqm&l_ z2L9MxNi`?xd8YP1K?tsuvL&ZYJ~E&JPpsf^`S8*O{R!RKwqAH)+BL6?dFI&tl%a#a zB$__}Hrn}sB>k#Q$x6OgY*oDf>PP%+i+Fh7h5EDDob1 zVn5$OEvd`k7j`R*@g2gGV+NAXQ9U(MNUCKiml&?(Qz)#>-yF;}HWg;%X~GD& zo?))q)eDoYNoy33v@H}+Co!u6kalCHOh^hiIn4jt5b!`r*>2b5{(cFj9fB8pIm>5_ zT+t~TYg&pZSgW@QX7C285r&6Udirm;chKiKga(AGP&STy&Q?!(;vfsGk^r1*8ti*P+*}>W%=s55 zMHPT9>&x$LQ77tP9DG0G+AmAX$m}06;UbVBEY|2*?Kc~=v_u<5v$33k!%)5B^Ulv-dS zhJK9tb~D-?E1Xs#yvHrCC6j5cfQmgN2h!wMoD~02W*xeMom`e`BKs^m%2Sc?^$C8C zqegX=Kn&i^De6tf)136CP}nC<8S?0Rv_*QHZ-SuQq_Ijw8#7} z- zPHk#G_MrxBqW+TLkp|FTJ?RPJHu|qpWAFN*iV7|xLss$1^J&ne>sJ12m9ljnk)w!^ zCrHaUf#WE@r_y_YM1x-r&GDM_gyFc>%=@oLiI%F{cVhG+)%8&Y<$B{k6N@nnzNUCX zAs{&}qU-{6 zgAswgts>V@{Hqd>C2h0KI#cU5!7=ak!^P3fAx0=^yq%|noKnq^!GcGScz#G~{m-7h z%R#`k_~bokhs<>b-yJ{PO5e~DQGV;!j@j0jE)m|_Xk61wz7Hj3=*GByl#}%qblDfm zGf*#a^gb7mlE1vr$@KQRjz%pPB}?OW{8(%pzm`k&ZV(Th)HZ`cAz3QpzjkLr!>x%( zAx?KXTBGHGk@ydLkn0-uV;(+RjpNJ5z;TepRn(E-RqcDISHVmg)A8Ug+4^GE5V;k@ zRrv;Z#^*2ODh79B56HeG-MuPzpy!51?wtqwuAGmw(U0OH>aJ=)z!a^oQ*xz)yOq=Y zg*a^bRasc*XbN^(x+2-o3j%-O1S{|Gy`%!ygpgpVq3UFxQM&P^J8%;&CQpj?FJdMSkpVW&H)Nwiyg*Q9UX!?56|KexX58 zNN%?3`T5uN$r$0_NDgf4!J8=mPW#XTid8U|ke0ab4xNg-z}s!CA#`G?eOwcbi7kMm zxP%^&YDT~5i~a?LVo#=*|N zl^q`aheiL@&P&r7=L`u##&EEU^DB?wgklUGTzL83nE_ZWKmZd-&^~S=uu!ep(1LX5 zaR=^z^+gn*Pn>LU-B!@6$lM>wyGT@|keEB7R^miV-6vUn(4T7di~E%n#-V3zIN6^v zx}QK5$U~p}ojS)M>7j5bn8=%{!t?c{<}k^0%ymfO4Rr2K&o0u=&r?Vs<@dEl5At3`w;b$qLk2o>mf zcRq#@0w%BX@UAkF^hJRg%xGFDkeHVn1wnwX1~CAKlN{vuk|xiG9EHL8Ky89f@poK) zwHQ3%#WtAF)(+mxCqaR(uodOdCx;tSuWfbagz8rDl5(|+EsA&{H^?mUnI@T3Q= zyzx}`i>OC~N%GbTsI0c*{j#h^m#6Nukb#isS;BDuEJx)a&?sN|WAFG1ZKoO}mXf8T z7zvW(`zZ#fBHP;vK_ls7G~VdHp0~$Iw#OH8p8r$wNZJ=#sF>!`koT}V(RYT`>px{y zGz#JJS6#JGJv7AA0*uP_ZR9*f zfI5N`)0q_N0DFz;{wp95X@Smeax5sL$hs&-#q6;(w){in*QB{Fz=lT7yTV@1!r zozM%GK(M)FCWPPD;94FrIZa4YPlnRH&uzEi(0FwoIIhEc6{B#vdJ&{jXkZfa-^YNU zY!1Z^W&NF#7so}7T=Im9|IU~)N>`Nrw#EsOMkTeqi35LXx;PhPBOQ$4D!f3Q^pJZN zya{R^Sc)Rv&v|qM0-<)e3YLQ?lcD5Q*01moFM5lB10cVFeq$zCzARG6d!l$Xag1zm zl~nrll{q#pDDGc}}vYlGJHRPaVvy zhxk%yr!94d=ibpud?;|NiLxOr&O0?)Q7-~NfORV z?&#mZz>GE-I?uLfy|nj=+@PA=FjPzO0}H_f4l}dVE}wuEVf{1ElXrjCrRjoagnG$3 z@hrOE2C|BtQvz7dYQ#jAn$mJn(}J<%_}&|=sCnz`2MW!|m#fk}bn>e*g+_bPS>3L2 zGj4#@_?{V7w0d$vElIr}5kN=YPk}7iUAhsv0=7~>*(hCocqcf=O%COZY=%#4&O19E z;pe#h^Qrxx3gHtCUius8@v9GjUQz$g>eVLuyrBNiWAy%8hjuf5DVkWZA77mN7rUj9 z;P|W#Q$|@B!Z^OEy5lb!jO9-aBljS$B>!75{y+8N&d*RR^w7rj^53?Uf;Y^l!fJG{ zJ-grcR?PkK94}H@Zr1#BKNY?8XRRA+Ku;$QbMMy}zFGZkiafPcPXv)-x<2~GT-)O= zlDeU1f7_TvJ5FZ1+Ts(e_w;5`A@-*;0hE;omR90Yk6a0@#`qqWul4DE9O1KJ6%voj zd66j!PZ)J|R{waI%sfr#}L=XJZ3p-1y?}iBRJBm46`bxhe)w8LnCGDx4mf zWig_&0D>a2zJ)nYlcUaYU}SxHM8iXjXi7h2bfW_r6ws+K^rNL8uo?XRV_DoQ;pNNm z#%_F*sQAXqhy8QQ%|Cv7eY@P8IJ5rci92p_>aDV#ww7lz%8Vqmr)3W4m8G$_jo^s+ zlBDt?Zw^04g!1w31S@*N#CD=#0^=`~c`SpdTdy0MHr|!bAQCEG1*`(B-W0P+DNLyf z4fx%jt{HV0n#>zKdCmb>yCgNDM`xrY1ratB+<07mI$inr-k#hc1#S8QNBa7G?}U$x zTkF<>21q*|gYolJ0csZ$I?C>SqNP5u?o{uFgryhjfUBZy|D1cbg4F4Om*qt0h(;B) zX_nw(#v>`k&KZr;LttltalV~%VUa2(FA>Vy8_;*=Cm-g(#bPh;OTt^h)2|KR>eU|G zqAN4Pf$QLeb&6(}k^r_@Wvah7nK%DmHR;fA$dangf6kIJt2OSFH(S)!wb@u+O8We3udE{vkos_WciraoVVlWh>DZ9-^ssEY zd}gm6n^)Y451(S_PJAR`Oa*;p9e+wx7$bMyw@IUPEgcSc#^NI@zsB3YI$IiSqXUoF zEdEDPL_M3u@ujc2&z!yBnZMq>v&$#lg_1?AqOhU8;vX{5lP!)&>AA~7Z{E`EZFL}N zwkK)*muTAIvst^UTaJq+T&@K* zlDup@jy=C~g)SxVA1Ba1Mlw|IsJ^;ybF1dtEkwH30%czi3yH@clD-_z>xt8jAC;1$ znSQqL3GGU8!80Ob9u%+UXd-uydnO(My0085is&>HdJAho9W~f*UeH!^!{uucJBoll z&M;W(Qo~`w+4630BRGdg#y9-Sg1mnX9xyUiyBa^=a!RhRmbne9&*iV0=sABDRz#{M zxsvhv4WGBHo!(BiXmG2O^6!35UY*RxbPt1R;)%WxlH_u< zO1|zEgG(N8wGS_)DHt7A1r5e+tRUusie+@eJwKq{`v^gWQ~yp4DM1C_ z7mG5rXhYlEts}R%wX}c;f#zDF|55GFbq3sPrw3kFtJ%Fc-L)!t*Ippv*mBn3 z^z9NJ?S;&V&cB+~eb{>f5D1WIUVa`hE}-z$-(}gqye~UcUwFC7M^z8o6g(89evTJ2 zaPT|^AZMPf)26Ze@f!49=5#0pTj4I<51IJSG8&G62JkR^R}LckF8BYnajxM|rhNc6 zlO-ET6wNv$y=h}eLk?lOoNYpFVUmy?$26odiy05~mMtV%l#D}OSq+U-&J2>AXS{NV z7)1tSoW?lJ>Dh<<@_u-qPxt-d{$Ky=f4uMOxqkmh)#6?6T}7S$zUwvR(2zq6@I2<`H8{WW zF72e`9F&p>7{UlDAp*H_gVWCE!u6tK6X1sE19@8CgHTlVt$Qa;j*ETJF&C7U4!H1N zvD~~yQV_6UzWY?Ogxb*)lC+YK9|Zk6DFRya!BwBBvi_ss83m&$Fm!(vrlFqg06ph< zE=R+hZWqqYQ_R1&$JeAZrD*U5^7Ouu8H47lZoMXV98K$O3g6hp+4bFbh?81U33sd7 zu zXCp2^nhaWI0KWHFLJel~PniWTqRyw~QJNdZAzx#`85;cubA9`P#gPcFW@>z zgKtVgb4S)w&stSgQ-6wRF1cuBE@El7=Yl75POU{HY%_!Pv-tZ8qD{8;+HQsC9)I@L zJ3a>fX+T&^EJ}OPlF1XZo>L1WuV;w zxDtkyk})(>9*;_Bx?6-Lt$M7jhCstHps5rUeefQ>*pY>aQtsi`wJ9|gxp;?+X2sP) zAmK}}x}+eVCGRopMt&#p4x*kzn}a6~#afO76;cV(Z?DXsly*pxu&u6};0JF-g%>%8 z2<5FyV~EqVP)v=8cpbk&rjV8zc(cpsCbZ$Kv>I)DUSCAhbRJoEfB*5kUq}5YFMvSY z(D3Z4`3t@PQNGpN?mY_{+-Kto{+u*l9W?;o^wWKG&HCbzqY5(6%7xrJMc^zWAa zG?W_{jM*R+=T_d%4xxq50D5ecIJCcldW`_wlBfA&6pz6WD7SnpvPN2d;x~(&dGwF| zkiAGJgvZN@M4!Pdx37&;@(k(^Icl}7{B*Peu0pJ&f-l61KC{?3j81lIxBfMnoOIx# z2OVP^MQ$}#XJ>SwrDq`_$@^1Cb3M)vV@VAbbMg#WD?uHovYh70RhGEid(W&E_pt=} z?1t9XpzX9a7~-6pd=G-$N`vV^<5E01N7g}v=WsyecD96hLe= zn@9>-*yR(~@dQ5=k%7l;@sDgdW;$C6sh-I_*%B7&L~Jp(X2pK|E%**ds78ltQK|hh<0?PZ5WvRL> zW3(K;KWRH1I}us!oA~eobOJb&hGcU(T~R%!A}>d{t~}TnzBtrO^YGq~N}#-XLuv*p zW0h1FW0$G#A%hGL6tLjd*192)W8nT;7^G=L<0BrYg3;2YZ^9Z8!T8DNncWGNSP_M8 zJW??$Wp{GBapL^EQP0?vtQ#;9thgW2VW})uQRPbj2Is#(2jT~I@`LF)jJ_>nE}BWm zFq%*fbO5U+(mUq`x|B{9SKL{Te`jG;SB4iUwRGf|(N z`@>;*lRJWX#7`SnX$`V!TMJZNSxM$%rVz$I!fqA@=`(#XU?{DuJe60xp>_DM@cy9H zWDwv8*5G84(z z+k5VIA>-Z0(#@tzYPAflrcl-|B_9G|?b`Sg2T{Y6{P{_O!8D+hWD|F6A7@cGcrNS9 zaMjh3DP*b;7R_sqVBWgJrk{9VX9{+2HhZmXgM!f|#>$gO?~pYDXw=&^?m&n%@CyZI z5^5%eO&N)2)&p98hhXeDwY>TeiC`#>A%yuiZ3y#*c!9|Ovz?s;0L)r>oRbzbvB>r? ZAJjtTFEL)_pO%0=M_Xr`w^ly){sy~NyH5ZB delta 19979 zcmaHSXH-*77j8lo5m7;UQIw)I5fJG?nt~K5fMJCx!!ea#LN%ekl@(UqTi;^AljD(NDwb{ zdsn~b%yerE{p`*CV14IjM{b1?6NFwW8zSo*&jkFczXy}?5V^k-V(mI0^S71|JKqOE zf4xIac(mcalZmPNxa#kCeb4YHH7MzUj10#nT?s z7ZC0J7!pLP2N&DHvtG5LiikgqV_KRsOdPPUeB^|dT&2j9>uG^M3!~V4VfK}2CVZ(q zwr?QXe+Ei^q)}0+Ypt;8P?wGSImUX(9L`k7J+rFNuAwK{$tyABQ zra;L5Al0CUjn*5cRWBrqm)4SQ>Gp({=r6RyE2n+%{6}^u%;q&+?{TDta)18N&+l2{kdkzm8ev*^M5LwmFP%- zXED27fJL8wGo}!RyNc#}wAdP8A1%CGLiSw+m9ejP>0(P*!4RC9Bpjl=@q)?4ClAX& z#sUq=pd(IG5MJV2Zs0Cx)2Ha(e3DJHz4DrjFQogGB&>%}LZJZ46O!XLE8wyJDbY%+ z)eJJ6b9J@1%iz0Hnv z-_fm2I!PGn$NJq}Kd+}qn!k6V%P4$+XCDgOeQV9V1BiDaB=jv-zLDBWZ7qWsYK8NO zmkNF4pjaJzn{S&7_RM}akd5DM_?mWGyr!r3^i8}zJqakXKqCS3UMb=Jbl0jXlmn97 z4-fVOXI#Td0;b4|V5EahZ){Y_C#-mYFP~<|q@~MZ3aQXL#AQGWDF}>#t=ym1xdWqJ zDd9n>)D|`w8zMz8;M)hKb*yhkx$!MzT6469#*3s6ORy|>a9xwGmxW7|Gi&w2)gka0 zt@PDlc(~(vi?J2dV{0))Ba)A@+RWhB;Oef2SXoP2V8(@H@>)Bm$@WR&h{>c~gZ@<7 zb5A|S=M+c*o}&Dxb_=ql@qv%nbN%9G6a6jTv!bcphBMfq7Xf1qhi&%dN_8Z+-&J?w zYo;IKGUpTAJZij*P(cd%J(JA+X_^@=L?}^`2Mc(rJiQE%f~Om!l>NCvcZT80H_w+o zH;YHY7r{t+b`yN?&VmS19dlc}2KCEF1DHnY*lXza2jGgj^UPHqI*x1oyTFYPI`O0_ zxmbpMTIfZy@j;=~H2Bgf1IjXXBHnsn zYPYOk&s^_a$n*T;+{^!pfrLIFV|jpC=-Vyc7?SN7S&g-;2U4LnId%FI7Hi%dj$?Bl zWx#%YTL(+od%vH0`gHj9vk7^P&v_4CY@wlXgBKa$w z4e>R!AK5u9Oz+)wJ|;B>GEe3nZPfoymypPk=G9(vh1VGYBM$O)YBC(YZW^xLoK;4tqoesj zO_I}5~2Wf2Cl>JIIUt zg4yoW(xF~HTczFJ{}z;ZmOq?6qSQp0SjCY#02e$LBSdjTPm^BtW;E0yHY$9klZMqX$#5-a>R%nNu{6A|qn6%t8`KoiX&G_BIc-^lRU)-cpiNB*bVpMvgP zsD0nZ@Kzfsasa5JhSclW&{hZo(m6br4YgqwHsSadY-K)d3va`Eclz!%f2#s|w5}Ut zG(cv3vhA?q;~ltCLf+oAq=V8NhCe;Mir%UCak6Va0H>DGv-d$R%rsyM|CYVcK?(K$ zTdXVl;B@{A8!ql=fCD_9(EHsPAFw~PagA4A9U!TVqr!O=BcV^c>=|lB1QakC55UjDbqxO z=Ee73+!$v?b0-Sd)8UxVUeKTeU9lwF zc|>!cgfGcPi9 zzf$1%6bI=FD46UvTkM1EYU^t6?u7YWv3OtqWl_oF3d) z6fbAvxmIfMA1tamT{SV&ZxNGs6<=OK*WIAmzM7bYWojx2p{2l;#a)$lS5H%lKOPK3 z-+;MJf2|xX=>yzQg#<-}`!p_3q0;Jl^23miyHkYj3Sy>}onuJ(Jn6-In!2>kiBpp$-~%uBxt4 z4prwIAnPya9}KA8x!?ZZFEP6``hdP*ZL*9@5tU)ho-&E8?9X#! zOJRIpOuS6Eg0BJxbMXf4405vCp-BsQ=Y0bM(7CDc&5?cSIhaAdV|~bwCO3eN&$PAw+rNTU|$#XhWFz}N*^HwA1C=NzX<^SCp$9h z;>#^6Lf&gxM72+Hp6&a=n9z5nSJ742N|&eWjkCiBOC_YthPTNH_wX&>hQUqRO{s&5 z=!IYM35Nu%uf~xe8i~L^ie%Xnto&%onNfd zTJ}z@C@co)&^sG1^6Aj*Hf8X~LIRv;V+7AOMEuo&dJGSFy?+ilVZ)glT|>90_rJd90evFC7M?%s&NwMI zyHmFQd(W3Wo;1dk+|@i88)>$8Q^B9SFKD)z?%dDP^9=6iInv!5rBloppUJwonE!j1 z$TJq8xNq|5Or;@h)Oh}st93C)Dh1s7c~6Le5`CVV_NDu-nfSb9Lbs%bVmkDzirV+G zFAZ+N+e1rr`I@}o9Xa66gh=CP6-}(kM|tUHJPV$qsACq3c92dT&?#IOjMe#D)j5eV z@LCC+(95NRhWtR7j{SYKVH|c>8q*X0b`j6wV8gV$oxhl&ZC04oV~((NqM;2vW4^Sr zIo(wa_FFfA4ZZO_{Iaxkwu9oEzRtEBcd7tP*XuJ;)yt1hfCt?je;e8`t&@h;61-JQ z;M&04kXsc3jVtA9atXM zW$O63_q{N|t@4MK?vh_zmn*m3_)YqvH2=mGG#|Z{JI}~|pYAg)xxHt{b%I)TV{4uM zz8GN1yo}!qfN5hsx|>s7z;z~%JzaFG+r&fx@_i@jjN$5bJdqqt?S<;ue-}1nF_(OX z(;5T^10Y>Lt#)acxt*@2*TGN>*`XF01_{51R!Wp8NyJF!yCEDV#IARqHG|vhXQ}r* zJCG(4QayfM_xz?eCJ{0jNAW^_nr4RF@0gT@sGx~J<1)0H{8Hejev2l=)n5%|nBZ6T z!-jfa9gy--eoz^CHB^oh>wBgZ2I8z^iKFEZ2Ri))&1*8ft7O+Y`aGH)Gd*_0LF zUARP;@I7YmOezwDO(Eqgoa+kje^Ys49AJRl88>s{yvtnOTFMM|T#NXb=aa^R%~D&_ zO5J&?s>$3J#waz_gltBgE;6@1v%KBwfVe4*R%F8i%z|Cxr~5Ge;U>`n*E9)+^=Bqu zX-T(5k*g`1_Mx64l8rwoO^e=?7?FqxxK($?Hkmw}YI)Jb5%h*Sg>kCrAcNC`lCW#q zbfiY{fHx)3@~}5wK_#}0{Smz zt6bj@$MzQC6&z5O)Pogkgz3SsfkY*z&%u|@(wB!Kux3|_Hh(|1w5luNZW2x1`uSFc zYgZbBuphPWIX&pCbHUbRYf{U~rC0TVYTmq0gi4)w2W|ua4x&4{c7OAhEmnf6Pbr(N zN4WZ%IXNk;cgjU%7whdWs+ZPJgXL>dR0q%z5mgki!hr4qXv(|?D^^Nq%iA5|e9 z2vZV*4{lLJBm+M@*R1nhk4PC$Hyu;Ap@$V$m0VD#EwRAn#BegQaKi^sq24t^X(Agh zDxQq-Wa3^oQetGh(n|-}sXXCN?4e@#E_qMBD?rKF9OR_AM9)=oFC#ivZDO4S zX}Z|Pgp5V1t*{Z)tWIfD43M-{Io_oj82G!Ef=`$QxqADe(0xn}$6 za`U~u#C1)4lh!x3aiDIz;oxrBgSOH^pvr4%qH8 zjhs?7<94n;?C793yta1D$)DFr5b5;j*?uq8`M@tunr5^8()TtqLu7GW*HanBpQ{xG z?xrl>4S0O@?`2$8(?nWR{$S-(Trcs=Bwn~-%K9hYQhqcYQignF4` z^0@{f!)?4q28mw2)+bd5xBX_Vu;B*+q92nXvBq7U?oJ}B#vSB*b4s;VQdm|wa1`SU zKHb)D$NTYsm+aAaV5^|7sqem2^S+;@^mHS&wrkl|xW}y)Hg74FvxUIPq6hrmk`+j~ zv|*`}JlpUIE%Xzub$8rE}++jlFnD@bV) zNe%OPr}{vQ;!WktGjeMW%T?;A0*f!y@Raq=qfG~3B7N{cZ~$;uJ!5g3{Bu%HhaPmC zQhz_!d=PNJf4w>~yK3qgQlxH<9WBfvk8o6x+Pl?^2||Z&B!w}oHi0KGyT$KE+|5Hd zQtSe5tAqJEjn?vlrTT|G9J%V;>#fO-(zwli<*9B}p2wUh`SG`mlxStlg-Px zCQ(4Djt(AX%ylLtUeaa#-S3ii#cp?%s+w6MTr@j4_@J;SvH-8yBkdJoe;Xf+m~r!N z6yeQE`ZG$})fC;g|Ed|h7phM}$J*Zp$%y4L8z`R&O0a-KO{1`O@Tzf>7_FktT0>%p zzeC1z`wC2^#T6e}akUKz!R>Z-xu*8N0g||S*5_?j3yhP{L$}$piQsFqiS13s;Z47+ zze6u+dm<8Oq_ct{5mm7o>RI{+al@BV8$`e_t6tMPd9sbtW4C_}6!pq=ZV-L6le;dljJ?SrlMXSIQHl;E7X;F^_|M) zM9iqulA@ojv-$J1&*ka-J`HDIHcj_S0$-V&anj0eCpjr|AjG?pF3q+ntkYI%4nD7u z-!;{ew)383#8a;QqN?>86NG;P_HlNBdhB=2#_p{YOB$beGhW(OANnjDgUacY@g2KPjJ-TRvDN)$(Xc1(jvz?A(- z$4bfRiw;xwx0+oZ+F3U!@?KQgdc4S6th|L;`TK>mtl!3A++ouv1ri{%1R$&oid}@C z-yo&m5m~R+JDm?)H*DOaUXO3GWV&NocDy{41RRC}S{pOBFn|rc#5yE}OH1G^SS>f5 z%=+iMIt#P3vaU4QUzSrVFV>DlWzWy<%vw*Y*?D4vs49moco9^)YmRrOl`Fx1o`Hd` zUlyU8Sl-)9IY`=Q&IHhK=osFEvbd{=uX}<4IOX&irQI&SUuEA{+$ao{8^BYgCyb)> zh5Rkm^}t@krRM@9i1*`O&ILpx|GN|TlPfxIk1phGUB>mFnKSujsID8(>e#8Oq9WXD z@a5{5P^!F49abMRpZ$axe=1yeAJA&tmO$Sg1@E)ElA$bA zE;P849@s^rwK`7_3{`!k5bJmmTtWTH#9ewphqxz6P6oeve z8h90Jb;S*@Gj&E0Yh| zEZ#rnJN=k%$Y2IoJkb{Z1zpBZ7{2=cjs~|RzU9iqI9(+vb>oovX+0YIIRnuy8{H%} z|Flg$xyjlN)-+3IT}=~LVmxB?M=d9psfF%9yTN%A_XFLySN7Xq78g z+udt}KkjbLPo-yI8-c6|u6dP=RK7e_JtanA4fQSODCR#7p>=2E^o2gqa$HQuRl!0{KN`QURvDw?6S198M`$##f<9jKJWK*hW|GT z0+t9!l>BtPVmdc|u+Lc~i3rojibmc(crgpuvmu=}L*s@dq)R1EwH8My{ocrLgkl>e zKd*iuPGRJ1lSxk|Lpfj18#9bRWQmtlnG#L*Cfc)?i$znK{{CD4~x(+Pq z7w;~e7O&irgkeuI(l^4-z8IgW+rFdGc6|f**sV2R&NB@!di_}gVLVC;@5ZJnyHLYni1HoyR%z0k$P*7e_jLr zK*WU|;D3pfm%_5zvQMNVm9!p(N2zRG+K!z$e0b)CXxWkaBWD1Hs~)uJCt79DC<9hZ zP0p>!rh>(PH;f9p)AS6cg`Wa{S!T=4cJPImKQ}1(DkVj;eP0)@HCB#^X$?-CT?&XJ z2z@1dkoWV(p_~inqP>U>T>V_Em=nP2_hp<1e-L-yMNV^hUAOJ8T28O?vYB-UGjf~Q zM@81r?gNS_uCoY4s6$OAf0v*s6r+F)kbB$NAf1nYw&fAO&ygqkc`T^!A|QL;*WBgB z>!GWStDlP^+|h+*C+}WO;8b3HC*#{JDJZR&?iLyLn6p}v#B(2h)3FI?bV>w*4wXr_ zuP$18)=7Un$f+x|Sl!@C(O-Owr&*FGy4aBhkkg^tv2Mf{J$8mfaqXtd zC8y_3iBg9*n+fxsSKJ*nFUGQo_aMqSUvP%e3QuRd@%AbFEBj>g`=R84P-5Q3HeU>R zf0j5)Q-}8_HuS>e?#3{{n7CkqBtoTrcL}aTWCf(t2Rcy@^j2$wYJ|16g%klIoI?1EQ?g;J=JqTsYyNN zR13VRLsn0U7*FnDrg8Mf7?)ZB;y>?;TRT2AD$f0)q?wiLIcN=I zc3x+9mTjvqJgWKiU>$NIof$3N+fvKDxvn!i26{fFkP{`TOXp#bECy4Fa)AMdK0mzM ze*19w=vJSB+u4KB1$K1!rF{Mln^yfOpd(xb?712A@Etw+mK)>yG3ONFLNM0k0ZhLFtC)!$lUbzP;pqO8}5x1j*a=A|Rc*aC&%yMdf^1(oH9`=c5 z8WQD)!__(m%5v#>4AF*BbaVow@Dec<&|w0|3Cv5?Fr(`vPiCNABXz+Q>h})mw48-~ z44UtioJ9t49Cp=k(P9!8Gy*qfI|E0y3{YwsmEH!+CFFc!8xZz&qIG94T-P*f`RVIr zjg9_B;FY*J=GVS&U~|%u0E~a8<^@ttZ?6;f?I%fPMguEa7BL*d2;A|uxY#s$7(Wa@ zsfcS>{ga%yp;079tzF^rZje*?R}%9nJJkOM16no9jyxSUQ4#+f>3;W^0iE-r%BiD! z3lj>t_)Gvc@2B!eS=P`5a{-rdxn`b|CgTrX8ANufo4u{Hf|z)Ids@s*-kR79V=%FY zdTBjKqRw67#y5!ppV&7-AK#i_$JMxj-T4KzJw8(FIkvD3($*wwO8B(EBSaQ>8}!O#&}HuVV&F_gsMN{Cy~Y8F z3QM#yBl>sAy~d<_4U$8jGZ85{{{Hq_THg40fj@rx$1d24BSh^EG>lr!PChd9vAVGd zhh+WnZ+VHMS!%7C<2730(5EKGI;l(k?^)2QYO{+dHNDmuMPIhJfsrrwyN+E*w#}#Yqb%Tri;*jU-kuGYQqyDtI*n%VIXhaQcz1Gf=&2D$ABqIpQ)Gn#mv z@C>5+2Y2)mwRCetWZdOv*GRXECO+?bOsZe17VpDwrehN_lu(u-5RIw3xxp%V*rf1a zsF&ttZ6S)q_=&Y07`|nH61$;)qJY3)f}|nGfr%9c2&Cywe=P@@^`RHm?31>YY6fMe*zf@p{KY51;)`Z?QpPc#7L8m#LNvyyJQwea zxqAydq9dtXu8_F4uv~e&dB`j;D7r7vO0SO1Rd^(TXGE(gcIXt9xD^>~I+^&dWYRI? zP@ikiNhLj6mB;Plv24UzZY$tdui(GXqqNw(^rz`+J9USrjXGb2F}@@&b;)3hX7le~ z{!O?8@e0~TI))yd{#kIS-x1|_$JNX=a#w!9&;^&geBJD5#VvjYIlmeAatc$c>t+75 zOX9J2QF`QmBn$bdxtGlGp-W_hsNG+sGk$`2P;uYy zP`-TYPj7nbc;B)2ztT(I{4t$lSOf{;^U<+kzPL3(6cpAt`0W`gUsB*LH=y-~a*uUV z`Y=Ws;UFB@7$fh#xW1IxZ$xHoyH-9n=1@R1qioOE=XmoP5FMhl^l(|tF9~Xxyw^&F z6FevaMB6Ul1}U2tOwvNx^ia&I6WwL`>kO;?=XYO6fA@zDYK&1?c2Ax3 zXd>iS-Hq(3{N7@a_V76SkHxb$yEV#l86sKbUU`Ode0z8Nx4Tt~m6b@hz>T9E1xn$VZsiF=kZ;@u@ zycZ_DU_ZK`ZTQuU6p@#dM!m`lnhM)>8%L3vmh!4jjILn)PO$=MYiYVF)+^cxhv@uj zW^^^peleFC%f|)YRx^7m8>x3~P#jUfgPGA?x_D*M0nEYt$T<5gZ^h{MB8kr@i?%M} zB8JRz`n!8!TX^7N1{vDVok7G z6+tU2(@urk-8PQrXEb=qbl+?8nP`1yqgnN975k!m{DzPrfW1BW5-wM4g^jrNy#EGC z-9k#Z0rxY*w`JsnR!^Ixfl~FosP$GJ9x<^J%+!#?3HQ%c92lKWqh@97H1qBIB6EeE z&wQ@z!xuS$@RXPZ2CX@!29cJD=)uO7zOBzhGt7R#o*>}VlKyjXVuLHl^<^ZO{j#{A z-E?8kokXk>cmje$>b%GZCDP`M@j{mz@EKh{vm7Czg0Qxxm_Ap)nuqA|=U_lIFR}P2 zewVurPhbVpTs`@e)-7~g;MtOPEhGDiFz@d8b9-pwSS#V>3pjIL(Ziy6k^u`Jm$`4h zxarK&%7xzaNHSjL+^{nv{{@w@ANjhq+eB4ADX9efB-#ESN@V)Y6q$8>*{ku-YZ!F} zskx0`%-6&@e}rZlta*7!wBp4JZqwVI1_tbX+e4f0tOcD@!Zk z@EI5LQyA)%UeRxa4Kh0mIqcvCQ*J}>r#I>u&G2t1WlZ6dLc1BlNSGe!wqD`$3Lq0I zWwQM>&GW_6c&oqasFJ7$ZC#j91s0(v7KOMbbq+S?HtWBHM;5siq&f?2fqlP5Jyu7` zzS^W}=xq72|15aZ3-MJJs7(6;0#erFAt^V>2__(1j6F1&w&+M1Uq$xagqKS({k^y; z`+}o+?Hb=4#&Kn9sjShZc)pf%NuILsE##!jtqt)s(V{VSf#@Za`f%(P@ zxEFnlFB^&GpW^;rwEKzwETK6d%fIaOwK7;h!*2t%ZQdS4I+1k=_eH(|n2H{+I6J3NH<10Z7bRstJ=ewhejoO!;vLS>*8V33g_Kyz! zZV8hZn}!x0mDh}<2U%-vi%$8%1Iz4jFPNGh5mQRHJ zT#j6y$ABM-&D91o$MnY+o;H~%-AA^14Q?iMbJGKZ?2VeugA>h25z{|6smu)3p&T-SWPjU%zyErLjwy$x!qj(Z-wEkE)K;N z2X5(3+5X$kyo(Z5$hnSA7e{ z8ue$HkuzYNVI@_x1pgA1Gstcof!qhdp_=L_iKf6cC430MGTo;B^Zl7-8~kyI8E&9(cVEsz-(-sGm#Na;)#cDu$Y)H8<$cahu^Qd$|34jtKQ6-ewiX8wYIa zeB1d%m(6Zz7_D!YhfFozK;FirQ4PF(=h^t($4xpLanv_;jEds$Qt=>wM?A%emy!U; z;OFPyE~w|q#fLc?NKylK*=pOrd)MECNnZI7poS+3{oDN^t2L(=oq$sFJS3$En^?a^ z=q?Ftt+^{OLJ9SHeXxogPC@b@8wYES{?siYe{t_@R^4ZuC;wSsDE(uyt=_cwq4U^^ z&0LqJB;!~vZLhsNJ4RUx^|PY z>9BJRZRBy(@`JtlHuZMKe0E&|DZT~l@q^vHtgnB}X>ruuziN%vVY1xF zT`5}ALeU?TzAroC>pP-l^!=h!nw0s^fi{z)-lL2^{O;W$$S%jo0;zH`Wg~2<4L?eur;RKb}e}sQkl4t9`)mV>fE)W*;Klw5FjLF*V)W z!MJQ!ydAX-x=3Ym{Eu`=YdK@&sqwLzi$a#aA#2y4bQk*d6}5Dk0TD6@8Rei7oNV8a z4bA+0g+x=cj-vvJi1ks2c9O!`4+RPv5{T`^Pa}!N(Q=>t7QRGi=E69aOFDbJ>iAQt zxh)GIc&|EVH(Kmo%vQr$-@bZm>b+1In6R4f?WgzX+9pfq4TKt-AuX?W6$WgaaA}4xMf3It&!$M&aprILuFxIbiJg1-CfNa3SuIwc{&#K1~ya6HcFq5 zYxA9W%cb*8`sD0U^{)y!jD)2oN-N?AkBw49gOJ^6a^AiU#!t_Um~cB(s&zjDR3``P z`pa7Zv^CWk+8XYUE{IFpo@Me+`Bcib&3_j|hTWwJ@an9Qi+R>JB}DPJliem^oyM)p zm_xR-udceVqy^mPO>H;pujShyt)q$NB@ z1=~R{ZtK96Z08V-wHZ+xAAb0gGNU(RFFDEqzYFi?uKQnOk)6}o_dR03MyMBbg~ZNM zTuy1^A53me>5b?(_KvJaqKGF2&KgbBLz&bkO_nCJX1GQsgY z?6oFr?VDep1)~rnbkgg`fqPFh>q-w>JcUF69kHx5bEZ}U$A%+f8o<4YuK9i52_RSM zA%KV{lyJYjYjByLe@Ql)4(;hOvLy85NCm48IuBj04hPZ8&%5&_emc$9D=evGy`;#i z<%|aa`z~_uZsB zbyxe{MrA76?v1vnxBN?vspzPl1&oyY$3((E zn_Wty9d{sFFp`ZO?Uh_3p@%KZ5e+GC*I%2wmm#Jn<;Me_yRQ3g+%*a&##_cD*Szon(mM>%JqtZ8lXYc=1`Rp zp`SntLTt(71KUW&UeRtDO7j}3li+wHBv}h({?)qH$A%r=Xbes$o){h z4YBQZFg=}~%#u^ic>1$jPZ#0K1f#gH8~Q?(6v@O!!!pjAnVEkQnlb<@ z;6qdm9DHTVIxE2}m;ecIzaEb;xbk#*_9)WIt2gVhCnaHuIb|YPs8uiuh-VqLtb9pA z@8$cT!kcfvV)qW-?ORVhX=RkiXi9=*B(<5N*l#cbcL^7Iw3j*rC+#cG)ZKn+vUsV~ zkAxXaMVGRkEo#Ov4J)1EZI3lnsDWJzR9s|@%BqJ02wc*xv3OI~$fwADGS19h32s;E z@DZ2c>gqaEk??Wuk^)$(@SILmn|>W>-dQ79w+`gDam9hu}1} z1}~QV4Bv!_gqx&GV-Y~WxH=SLfE3SbHr#8z_(O0M_W=yDK#3WGWVT6_8e%@wP{)wdQFT}@G|3N4m}nP=&0yV*^T)Q zMdwH!enxWhBCS0ET%N=k-$^Nax_CAjaeEWp3QI{;7x>m8;$akLVt)-A7*@9+v8)%Gim^-(FAqv*h0_JLE?)Fxtm8c{?Bp1|qAIOp5P4rF> z4-EgtmC9GB*?gCy)vk;FP=X1CqE(9xrVV8GG`zJVKeK-k9$AEXDX=_1hJkW~XR4w( ze~%)8@4s=zhi|-|{91(CYPw*@CbO^%5U&9a^mMF4BPaIeQmDgyY1ZXMh-~9@W-%pJ z^49fzm#+d4H!xLE$RGpQaDJCK#D7ec4>>-z0JpajXbvh%*)#}@T|K`hI;qI?3m^^m zX1_O8_HAu-wWgHk#GkZVe|nbtw2=lU%Wm$S>MQT%y-$#=%%Jq|kcROd@997HGIbly zs@TXKVpdVfTs{zo2}@=+2l1ST>N#Q;3*F*Bk>(SX?t|+G$glZt57;yte*Wcu!h*KT z+Eov8x|Ef@{Qh0K_R;Y5#4Y+-dZ?GiisX~rUCrHf;JfQ2{V@yL=?S!8=n=z(L{~-g zq)ve*xbx0s+@9XLybIWxsu~mn-h5!snC9(8I}q`o-yp2w)15&;J_se%zT{pz{(g6WOG2c4*OKtZ_GXcqW7N;WGq=>& zWsb#H@=g}Icd$w`vk>x6!OPz@Q3Y&W4EIa>lk}<6YP)~q&PppetPVgx^D|!WwoTWJ zDaXa9&c1y0$8}9r_qSWmjB{(jZ3ycOK!Qptrfi$=AN7nC4+l#pUcMnrHDn=xHZE{u zU6u3u8%AVtLGLoCC8-rfz`~vQ&yVsyNVf+Y6B|zz$KH{kf~3kfUY?mf$hs*D@yHis z-P=21v}m}N4(8)@MVBcC6wV1Upl#}(U3~84mpvF3T0qTn z&YV+45trRV-Ih+40ZOZ3tNJJB>=bTFLbs57Ro_;ke4}npWSkc?V6C& z=QfjOP62})q9JB34^Bs-fB3(@f{s0z$@Gq42LZvK0OWkiOv8Y09+n=?6kEX&hducT zLZqobA)C35E|+aEYc7{4gOPTQKoH{K0wNe;sqAqo%Z5c*CJm5eLg4d#w@VPBXs^)k z+XIS-=3+Ntg8}Ng5DCgk4 zVgfQrY7~=6xnV1_9^Q+0=WIcYxu---;a?CDZO<1>1Ojso9KQ;nHG^y@)j$HvLkrHdg z18_1(_yuic)wYrM-vw?wdiRG5Hn_TUg*D>sIWqu)3xMdmlo|WT4CCy|M=CT zz2u2+fq-YC{{3zm9FF0?505Y6Xh8xPjE+R_m-Zc)d>?4%G%jd!>t_E$*?=U1uopS! ztqKDGB)>tU=?dLjyfd%{*bQfC>>OzVJ>K!K?uF&xY8Y1JQ$XM30*~x?l#h+y}`w} z5q|+^FRi~Yw+JiwLE5N-vcv~%91Dzt93qIMGRksl*;*Yp$@Yy@P8GHA?SMA7Uc^;5 zmUtgB#}xgJVhqS)LSwyT5pB(DHA=kyiCz}`l>CQ0%79*%xSrwW+WR_L+#t*~4} zLwqzuU|Mbr8S03hbB@|+o%U@q9>yrheu@z)PX5MerTFf{+i@fE=fSZ5&dVYPov%;} zZ(wNSZqKI#?*9%Zg@>~kr0)u;_$6a94_^F-&-R#{koNO%IiY>vKcuu?-dF!H-SWw- z2iLfWQSEd8CG9SSQ^CIGkr8f2T|qBw_|b~l$sU``JU!?2WvY5r^eS$VMBh-h0Y)$2 zs1=QCtVPT2Zv11G-rR&m%5f{H-!zp=`73ZvT`RwWF8%mGwTm<|)Rd|R9OrS#DHQX6 zd4$Je=e3qO(ap-VM~fZinE$;f{TQ&@^CYUQ0Wlb~-y2Rq=Zy)8!o?zw8lCyneMD zqLv6F84i^6LB-Qm_ zc@hwdAUQ&GD@}#6U{{*BinM><>AgKPw=ce-R_yvu`(WaAw3B3DDdwG9N{+$|_lm0W z7DG?4{!pe(M(kel7mB+NQi+e#g0b?P6#M72$y zJ`%%fO^AOx)V%#PA;~y6hgO2FtH@7lF(hC3qj!!9NhJ`GSi#4)>29Fd)x9a9 zpO5WSJbih|tr7opR8!)rF_*l`$0g~^cYBl~63!0Ub-rLaxd|YHQsbyaoto^@n2)~7 zF?1OuSq@&>csci_C1YvzqN}RiD_`DgJa;*+WZv!k9jrlo_|hT0m~$L(G#VCcnWfZl zwa_h;Ki#y{Xf;T%!iEgR=8UQ%B&`zHgLAGwaSE@)sZH#~A(ZdGO8xLTWQ#R6p9Pxu z_43Thk^W6nK8c>l>*l_Fhng30gjJEE6@;uHkLszf&MI}ywWx3Wj8OpH(DTG`)o{qp zk?NIXNW)&^5i#_**n0qw?enNr^;zVkl<#GVa6q`j>_rt%kCawuC-ek-h_?>xnHd=@&DplmNQuo`2dY|37V9XIN9$ zABN(f0#!kVPzXz?AVXP7Afylyv_fiGvWZj~kyZ8x7uzBrBO+x83K3M46_y4ekd{b5 z3j_p-21v?~9byQEAtb%vmp;$`f4|)izvsO7J->0zJ?H(!9#qUsmu0xMfh+1Phc8hQ z$wA-Egp6iFU%u!qA8*%NetM@Dbj+^gO9-M`m;&CzD=2!NoYq*bRYx4N(`1%1t!g;q zQCA#XlZU-pv{S% zKE;ZdX?RME!8{qUY+1SA`uxoV%!+wq>$8?5c#fR@QGRSJ!^ZU-#4UdPbAF3YAwTipX(vV|{M#p?O<&|3EiBHcDha^n}VEI0Jh#D6En=!OD z*;RpMU5Sg*xCy+y*JJS1fpY2YYHNme-ESLPDXyoo2BL^(i(5PMziPS04vu4Y3UN8Ukj>XExyi|;Q-DjA{hniWIOLB@JAb_8j zl)CF34?ySpy+7M~&42_a|DxU96T=Mp=FmI@(nK@cYOkDy>!)5ejtZgGYy5Ntj_t9#JXq_URiu` z9j_iC?m5If_dCVzUSrw05$#o88boopE>o_*4yegqMQCd!jd8!#q*X}{`Zi_;YKB8k zt~5}*rD}7alXa(^?U&wx+kejtne+VK6Y>jSfdSXR7}ZwsL`sL}K`#`3b<14W9?y(3 z=ShNvhpAVR*^~CRepH&RnDFznH-GGf)tAt35i{Vbdv=#zM2vOTeXi`-4^WIsI-G-7 zlDfj~<{3u{e0j{{&576Yp@%^*)cz8(M{wDap(je1pKj0XB~AV#cg6FKSAd?H{JPQE zpF+#Lwle0g==A6R_){bp^sZWPxzQUcgsBe!v)w$$lI8QBY)FQPCGWw;OX0_3cs`wWVa)Q8(Z}S?L+`28&^vF*}M25Trk|&FB$s-`?OiPYtDAi-*rVa;tdt z^Jgcigf8zGQqbx<>nXt_@H&>+oVjA0RbqQ9;5@@)>QpRL0@pQtpI%7Bj_2uW`VBsI z>-iDr4Ei64Mu&L*&O;->;vpRj&fQ9*1SX1h=GCH7!Ng?o{bf$ksZmEwfkeVyf+uIp z@bT(r#Y5V6h6ZAFO@fMVxnlqmXJlWdGfz2ryR)QW#xIyd%f z&Ca4-6yT|vXi&m=FHoj(NTNemCSI6G-B@I`%@J>i*_VNeMpF~%Y1BauN}eMUDGUse+lB!!7hVqJ#+SYm)&}jioa<(mMiW>={{tbpUZZG#P+h|He2Q z*SNqvE($3T1#hdHpKIhR>B95p7X6j%RyUyPV&Du`_v3_qcGQh+QFghD$#muie&OFG5Z@HX0f%<$i*=vRVp@h1#ivI#gMT= zmt17!>OD+LzT(kcLruE(z3o4rjZTN3>K!9W_<_1xf2LQ@d~T0)Ko&R}jhmO=-X}6o zDf?I!uI=lF-ygKvrT}; zpf)a4SBDI5yH8g&zG^B3=JtWs?Qsyi(pua^~%HAmwxwi{D{I({p-sF)#xoE>}!pE5jQ&NoMG{6IWD%{_D zC}gnD7YHZaCV9OPeX0SRY2z3rk;?~G<{zdEX=>2g%u-aX3Jg$n+GI;CfV4(9!AAZJ zPF{OBk>;ZfpG!Wmb@A^gffb*aML&W=i;zPL5bl{r1Bg4bi6=Y9z0k8eMZX$>eU0EM zOyPDLl406eX=<+hlQx{L7QYJ3sTY1I-Z-V}yFFP%S;HT`eEk-hh~2D10{n7`WpYe= z^A9E1ew@vVt!jvQX+f2rL?Lh`bBb^8GVC{4EqWe6D1nfHPG|DRX?#ZZ#ZlO#iR fcK*?WNChFFHwR(fDc+P81E2HuPKbJ2|6l(F%dy%i diff --git a/pgt52m/week-4/workshop_files/figure-html/unnamed-chunk-7-1.png b/pgt52m/week-4/workshop_files/figure-html/unnamed-chunk-7-1.png index 6339e0caebefa39e534896925fde6c3af5009fc0..9f2ec9cd1aacf77d874d479cc563d2fe5dcdd682 100644 GIT binary patch literal 25507 zcmeFZc{J4j|2O;|6N-jP$o`2?gvw5s7AdmtdnJrL`_8D33Q4l>lk98u>@6e-W6z#_ ztP_S|EOWnSsL%I%|E~L-`}&^y{+)AO=la8$=+FV z4J|G0@#Du~Fc=*j9X&n$i4!MIo;-Q#)F}oAhSR4{GcqzVF)=YSGoLwghJ}TNm6er^ zjg6h1or8melarH+i|eny{yKa1EH^hd4-e0|bLV(@dHMMG`1$$IpFe-$!UX{V0YO2* zix)2n2?+@c3yX+|T)K4W^5x4{u3Qln6%`W`6BiekkdTm+l$4T^l9ra1k&%&=mA!iP zs+^phyu7@Ef&v^4zjp1~_3PJf+_-V`=1oOK#ap*-DJd!4zJ2@7ojc0P$|@=`2H!v_TG&D3aGBP$cHZd_VH8nLe zGcz|gzjyE6{rmSHJa}MXVe#D3@8ICz=;-L= z-S64STH+Oe;4-XGdPfsr|FK=&eA0HoIUtd2zKYxG!fPjF&z(6Dt z859)s`0?W>PoDh!_us+6!B3w)efI3x^XJb)LPA1AL&L(tUc7h_9v=Sk<;#eOh*z&( zMMg%xe*HQsDk?fUIwmG2Ha0dcE-pSkJ|Q6?F)=YIDJeNQIVB|}H8nLYEe(Z2y?OH{ zJw5&H+qW4R8JU@xSy@@x+1WWcIk~yHd3kyH`S}F}1%-u$MMXu$#l`R5y(=jxDJ?B6 zD=RB6FR!SmsI084s;YYb{(W_Ibxlo8ZEbB`U0r>BeM3V-V`Jlo4<9~${P^k9r_Y~1 zH#IdiH#dLz@};GvFMq5 z?d$97@9!TN82Ity$Kc@L(9qEE@bJjU$mr4ZP!otGh;^NZM((>~1%E}5Bi~aTM*Xru(+S=Os`Z^AW+t}FH+}ytj<-A+`RSLw7=x0>i3itK&^w->lRW>C%c-Z z%t&{Z*mHAV`SzL<`8=nA3U%}5Q^@&~k@o@Rn-9+*Pn~4>pD3a{iW$(2{8~A1C*s6L z|IlVtx9H8fzRJC~xZ@K?Q1aBMNl1zP8@e^(*B!MG;4~Zt)mW8cjr#xNogjX+rjNHB1xZ(Y7WF?fcgeso8!P`bvEstUtW`-4Yg{$#q@(n$=S#4vvFpwlv~k7oY@X2dmU z_|2e<5d#Mg?Z0Q~r0e}}Kd=AoCWV#iz8%Ka;zRHSUu#eOrZ}n}{0;?Ph1oO4w7;L> z6T8+Gy;6?ZIYZc=qD2=$)A6hMzoiA@(efn4&;#9-zTXd3>|LyZk*I4iHR^u(gXzn% z_O8Evij)c;AwO*Bq9_O2(*>+Ni<=J$S-hVgS1Lq#P-SOn0Yg((4?xjB1)WlAxD)H+ zEH9He8>>m6``(CTr!BzhC;48#dDV~!N|r_+#lO@Kj3rr%1F!~#T7AVS5j$b3gVC0j zdQ&RxEK@50u`P*yGt~qG*TcacRyod(SV%Tja*rzmtX_;Tad=vOH9Pqo!^&qmkJmeY z@7`Sm5WgJqA%tt~&A-JWtKX=Jg0ZFH@!vA>BVO&T7^=$ZpOUj~#&^0qqsr>j{s_+d zINTUrnlW8_jlz)wKFx`I+Ee>>CE?W+y8rjs|5fD1v7v4JHxUgR3S8gum-*Is$jnWr zE*UvymgqJ0emVUWf2he}|7yTrtYR+Q{q7GQMWMrn>`1*OOn{HoUCa$d!9QhcX!!zNzcezby}Y5aQ_U#*n2POC zAPK|cxDs%#QOE8_oUS+&AT9x?Y?^1o2isDf<>gUWSAY%czu*KWsTTPRhLeaH+(ID z@+sTwobU_DB%&!)@$btH`UYnDxu2mw_$RgWgDD=`?%5+2JVoDdE<}C5_Nljai*10v@xc+}CAx;;t=pyodp!KOJ7- z(rI0fTfXky#vKWaayoG;vbkkatPHK>ix?%$ri#j>`0jk0PB>XFgX7%Oo|OTVL2VaQ zb%~Jk!3@7S-~Xc8?VWtirT)z=NU=)9$wM{a$(Wr(v#d=B?p!1tnPV6FrrA62%4TW# z#GVIrc*Y}`^ue|pXLx@Cy^S))^DA+wyx%}pUIDGy%^SU#zZ$9jR8;*DB*#g}5oGIn zKJlbpeKZ(rUw-U``o--iabTk`q$sU_w)^u{hZ*lEQV7Kr-z`>xC zXj(Wx8Wlpw@zozJ4``gCK{bdQLfzOvWtklrs?UZpt=RcyaUaHb8(1(Fl=O5u&5`*m z#03yAb3k(DL3|txZcv^=5(bbshxCV!aThhg?c~GIPsHKH$Lw<86dFXM*apMnuQ++T z$rJg!ro!Af7|tThSHi{dq>y6_=eZ$rrOdaDvU&Z3TD!E7%U)m0Hg+7E30&lrPd)BNAeYg7lw~v3PZd6ch#c%#$i48Z)voa`IZ=px2)WkHI+X?Pmf0X`} z@<57YLu=MBy|wiYv3EL7mmKm!H@K6#r&OJmOV;y$nx)Yq1Yf>lG&9!){{mIokju?VH)5*SXBbv$3jaIwV^GyuuluBQpAKd6Q177huWlCO(eRAcwXA z1XA}|F+oLXvf$i7-{{pHY;=$PUY3t_3A?AuXDo3<)nDX*pQq9xniaG?7aF5g!1hMf zNKT4_)^pqEy4S{FDYb4-io%ZYhh9&!EtZhnp* zhx+$$fa_wDZ{%{Ea_6b8Lv3a$At_^Xb}J5d@#LWj^nE|6R$+Cj`}1l4uryZr$&Jt# zw1<`llU)m3AFS{eq!M$LSm$c-5}`WK<9#({vpyApRh}~rQNz^_-_N-mO!|&r3|J2CkO0DQ` zkLM@(U6i{UDdD?dtvkl7BGJzbJ+S8o6*yn}*jfCIyQpZmt=mx*w=Ch-w_a`LaJaOx zW$aG$mo!~@UO8pWP0NL(v53SjG$qc7AaH2 zOnCSy{oxhSb9^)c2URMd+4jAJ4jCFpiB{@S8XU|wd6;#NLL&`|h9@l%1}_cpumFrA zw5ZzZfssP)12RWa8MhdveT3%ti9=G-<=nT7H_h|nAUT~7#5*DBE8Zfzo*n#;!I!HF z=jsL=ctv&oB3FJWtZ`2}mH#owQ-9V33bLeQ7$_@h_}g(3A9Dq@(=hAVQAR3q#Z4Ic z#>1yH=Sa&WS$1ICJRws!b=zTMl6NI;#&j#GJHT-x=Ih;$f7~#JK;c!PdFyq3#alGz ze|K*qXm;!vw``5%wA~HzeoXtDDY<}Z)25)42)%#oJz9G7c)w!$527~7XcN~Da$9up ztp`0kr#Vpyd+Z_hwML-L$6Tvf9GQA{a?f_SQJHM>ebe@$;ZOY()&hwkyDa`AUF*O- za7Z`*1EM=ldKLc<6VRt&toKcPI6Pt+F6CrVnW6gNe`Ih!AdOVH&G}|sSo?p#~g2i zti%bE84l<*K>LOF0HXu7BsGn^#`Ow&3ud%y-9J5?<}&x}*Gm3n7KizdcNA1^SzQDl ztt~|jZr*q$`JJ*=;%(4@lGtczE$+NK;%`4o0lq#4wC)p0*P?vAw;5ESW*`HvsQCG2 z(J941bLs(D4Mpo(=EfNpoo2gWd+6M;1bkcx8w;;P9*8PlJXGF~!#!Q_@#h(mE<8AF zVg9P&mSq)t zneXBz{VJUn;&5%wVtoI%+opk(Po`L#nKFI)!%Yi3;QA(A-O?vq@!oH~Gkhdn5NL1P zQo7Agw5Hfr?*Ek>!p)4N8jVh^6(8Skx?HUffP$ew53a6)?XMsB_UKlq5y1LrKumcA zIXpK~%mfOGcSAr*4|?z3cj(!6P^60eSh+interKYJgDP%!!ew$a+`FnuyRYG5gwh! zSPKsFv`{w`73SnYFEr+uLR7^Bj%__0Ebx>Ke2r=gpZVta>3VSCYHB;xc10Z5K;Z}z{8{#kVaUNoTy|mq z+odld4**tsTD%Hj@PTrXE@|K?#9h$-_#hNME)wxy%9?7+$; zv7`UY(S*Kd?4^%&+)HcQIeX+#Zi1&cED;_^Wkt;O?gwdsESk}OvArgo_6W17)~C?u zVp2;fp&>5@_HI6Ne#&519B%{&{D{B7W~8?+F9_~0oB|Cj7ITDN!QapTVg%h`4&tQc zp95g;c*NjJ(yml# zy{i;4MQbnRQmTG+5Cs;yTZcAW5nrQ@QIfpfkgP^3!ZlLYcY3MuBLrD%IVQ~Kp^Y;4 zA7!BVKx!e;?$5mcb;Qn+y8J=!YGLKodml!AW`)658t?5``i5&0zYRKE=Tal{cUWnD z&rZ;60FcOmrc~k!Sf!7klrk&9D>5*Dr7pXemJUf=WQz#9cp$QpBtphz@&5dsGJ`;& z)J69e;S`OEN1xWYD5We`C6uA1DwT{L&T;g#zETM!9aHQF({b$J>PU(E{>9zE5#LSu z7ReWTo&%8|+YQQ1b%&E~(q}PaStGG`kAPwil%NoTdj2grZWm^JiCuX+KCZy-xZ^8> znyITE8Ppn4fKHt~a>al_W6>&PV!@q$k+}cR+;aD_>P5R+$oQJ38Xyiza8TCj83LnO z*B{gnMkcLR`gfd)V!kOPbaur9y<#BKVngj%LwXa>T{f-n@TUvZ8DoYYDrKb*pZgMU zuGsh@GyOr?>Up}a_`+Wx)B6Hy!#kTe0LoK(;qkSu% zdMsr%^6fLWfg^4;U%fW!8P{X`Yska~2#oo!dcIKJ@xsnA(7Ona?s^S$?)^O*20ze= z-QqHlSM3u#KjNOP1mxEF757d)oq9MMcKx9GQE_Yr*8{WH4%^=|P;8?Nn-5POJW9r* zvK{vN=5O|b7i;%FXyy0WcsInn3~)N^Hf)1ERi9wTE8omwe^`{sin7Rf7Get+Wq-+e z*p}jI$B7yB{u%EMD4(i811W#W-PHko4DQe95Pg)&8ydD~dO(?CuYIFTY)2B0#^y|} znguGXd}^&IphUUbqKw}0nYk~0XSBT+V>+fjN{13$S@!xhVO@7#O7%!>CuR9t+{NYr z-1Ifdn%VILPWYmZNClt2PwK+#)lHMWhJq^#$c9>_$uAxh^nYJ3Si@dWs7eIsq)VKC zk7?AI{qDI`E@Y9AY@eJc=#wC_XPCW^LX~wt(~x~Vl@Ya!Ij6B zDqXxTEh*yGfzv9RVph|OyUB)b9+Es`MI=CJ_!^kybIf`+A0bk~_U-l)`3$31V(W9F z7ak>QEt@}2nJ-{!5lJp0@Wyrsso<7tZlB%>OqRN=T3fPr%_Hl%Zb}W!*EzF3#~xTf zTiK+~uo>5oPC6qMMrV0Z*~0Q-y}7$DQF5n6GjEi+a@0)s+=xy+gNk+e9h|9fQds$& zAu7S#68(!Trt}DY>^3%bX_PbXGS`n~V4}zUtho3HD7|N%n02YC=f~ z8EPw)X;>San|HzNcf*BKGSJ7_Z!&1f#QjYf7shl|R6qa4cnCXcCQx=fzId3l{!F0qH-U8?Vg<~|dH6JL3@vm(`2{6BO& z+e|w-?H0LQ)IK@zH3;h~OsSz>U)Z0&4%Dn%16_8eS!y8)CrrC;xL21)$%MVV9GO-R z`YY|m#6>}ND!IzbrRoUt_;B0f#)81$4Nce2OZY9)Uh6d$y1*@w#iCHncpOk`Q+nl7sJl0f>15tU9#D*^YtG z^x^rbH%9*Rm$v^;{UhN3?hq2C0#DvkPi2`zd<9I{7SL||?W zM=K*Lou47%pd@G2-dGk)7C%YNJWv_SjJ7D>@oN0*S=a-mwWFuudqT{pnKThv@7G^v zDa&g;xEl2wb0!NKE1k!CgsDMo>7pzO&(;0a_TBg|-8T^o&2>oI@yLhuLXtYpo7bjr zm2xJHB)#CBJ+kO_J)P&nLUo0C{2XPuUpCIau5jdT^N?M z0zG00#Uk}sWx`^Tv9K%$J3Vg8Ts zTwzNHP8;w2a-*i{uBNSb+E$mavW#Cs1EzkfLjoq-!N8vHl@!=>VYwrKd&-W1r1i8> zV1$OU7DX;=|D7i#Uy(VCa2oRW>U^i^I{g&Xq!YEUi^cgC!`wVTyD1aAvk#b&_InrZ zCh0CzJFJZa9es+-=rVD*dUX7S>l{mt3(IRM@4v47_dRQz(j`;*K$DdcW^glwFZ)0E%Rb#BD z0dT2$9q#3ypBw27qXmznJ!1RbEvAU5vU}SuIU}fnc5MlGTxJ7@&Gf4G_)RM^hi_f< z=37*f)*pQz<{H7-RONeQ^e8jBZHhTVzHoH<@Ds*a%xYOR9p9C?cEkH>jC}(82{q>X(hXb~3~sOd0`@|%ej(EK znX`;oM#AIVzu=Npgj#DV17m28vE@!LY+`GRc9N34qi8qGhuFRSbhy+kq-yC~!}BzU zL;_fcXW$um#-wgou?XvR4X2PZML^dBvNKKOaTVJdA3La!lg|pMq z!j$z2fY3M58dIx3Z5$q;G}IN3D_*nzI<=3^gQ2%&P#c6Bl_1+5m$5fpz!7)~IDte* z?+_0a0}Wk4)O>{P`o^iCOb8*=V>!A(VSkx1{fT1s5KuVsrQXb_fAQHVa7q`Id~+@I z#+-3@78FsVjtf*yv2dBAcB`vhyS3DPT7wmpW|wyV(aVsERu=n>OL-fr838LB-IAn3 zV}cLxCN@0TXz|U`_m|sK@HOZ^!J`m%8r>{|a?Q7e>JSZfNAI_vslY)67R}jMoOb3P zM=53*K}_HIFVGAY1rVe3Xi*803fU6RAUVS;D^CYM4!X)JpjvE(S0~@ydSN7kDvq%l z^D{1vP8QPDbM;A>z_$3l>#->^unl4>pk8zQDDQvcgbU5=>uH~y^G_sPADSPRw4fO3{KK( zTl8xwVB~Hw)H!vIWOOdrnDOK{|LW_=O~d~9G?adI5sW4lBi~-ud}j$QU)Y_tc8u6k zzDIKOrVLvvPZpX%pV=PJHXjg z$5c}GQ6cV}^eUDo_ zKY8;@`L}5sAjYvdlTDh~EjiyQ`89+RwXa`}jHMrv@d$MAzBBn_2oNx7K92vnrOz*tfGdZ4wU z?^R3>#_k~HAFRZb$IaR9lG?^*jz1j_Ou$`?gu&$syLUF3fj(J57(yLft1^CDfkD4T zVOB-`)F{yFn_HE&v+4JS6|T@&sNN|!o@3M{Uv0S0NX+sC7@d}lnK^yGCx|FVoSXr~ zxKL`;{*2CW!D3t1@e_e*+8aO3l?%T-8;#o%yNV2L>56ZHYN)3*mPYEjd=IV~k^gB9 zE4YDWAMx~U8+u#RIURN!?W#qLUSjplGFVk4SPYOKgg)q9a=ME1(p-r#K7q<7KIEwV z%h_eZxuc4;*f%H0-6;c-@-yIPX00_Dd^2eY7)gJZ9(%blxB37|sdo)&X0?WI*BB0z{g$SI~%-(|DL^ATU`r}5O^W>(mw=4|V)AG}e)acwHf6JCj zw`j{A19Gjlx~VetF8ukXOD!Aymm{gl-)^#EuKSZW8Kf`m7xzOba4I!z#$W}4V}YT+ z#7GCcZO}V`!b-j~YAhd<*48MvtUNR&(;t}Y=yBvys`JQFP zKOt^)RG6%@s%MNSA-1x`6jg*d7Hjq@i>0Ra%KBnUj(76i$eL#*cH#rB(j;HxHBTGX zCev?aML!|9^{2up>Sy8)3>c|$p1R zsN?FFqUTlCrk{6&{fs>~*lw_1E~h!tTf{lMgIP9a@1EL}N}giskLgT@mgm{_SC|f^ z5&dQ}0ai70v-JVf&81%kl_FzD&`96wzH0l}0y{5=f31z}a($V?y}}ko8Vt4=1GIzS zgzhk7-j&F}HW#!v0DFI*6}96N`(4UuT*xS83t{<%Kk`fE?E=3K*%NfE*U7GyTiOzL zMuX^0buG>!sYVJ}QPddP6;=7(s~);w=x5H<4<1%X^srvpOI>gXrl?sdDT^3&uYJ4o zavug?r$XJ&sL#_|ZySe*{Y>ovmNQJ{CH5S)0|8yH7hn;wHtK@dmfB9G-*6VCv! zy0+I!u2xe;WS8=~GC10o4v{ zV<{%1v8r5&59KN>=QrnH+sHoq;tlwKeT}Ra6IUon>i!9%zVs~AlO7d_STvL63K69s zvZX|YdYi~AjMcN3eav(O!`IKVlF%{*1`g5e5X2c%Lk2WMHyCEhQdAwWo8l8esb`L- zVfn;O;b27fiwr8lFbP1c0AerncHdH=0`G|Q0()yOd(;>3I+YQEk;DxZa~5OJsxN=Q zjQ2_WOTz3gu;0qnU=b+*mdP0BmesK_pzRWl}wlxMg&rN2`KIDA)bD zoC_>mGri+xGI#A8-~va{nMuY%N(Ihpg)G&XeO~e;;dG;5?I&)pw7T|&Dh)b9rALMS zB-`aHx+d{kZa2d6t@^g~I_$*Z`1Wn)1xpfz$qGv<6xa=XPHIsbxj)U1d`(mxz3N?M zkMif{qr_wmje3Ok{vDa?WUKA81}Z4no}v(oX1?PYpMZ*%#cze2Erw+#T$QQxpi-5uN8x|M?m9RhWT%RJbUn9NNlXyQE zDi{PVWCAG@0avoVZl(+ZJ7H9qoJ3G1Y7fN4)@FykU_wwrSOecQl(4R^V^A8B~dtd392mbLEP{=n0L~Pufy8 zX~D#WB2hINCwt4WzSg)E<>i+0b!%}C3xP=*DzScCnCBx2H0O;50G-SWNqIEe6*y1O zXcxKq_9y7M=9a6ee|w{~(IS8X@iK3Go;ffh0Q0=2G4nJpoH>Sf|MJ)zbly4r4Dy@K zelO#mlaE5cDR|kAw8K4Hr(jj8{*6BLQHas%KyTZ%;f}~u{>R|S0!C%-PN`ghRO8cs zQ#%cdY?jd@t=b@N89Of@>XQ+%AVX#;^q$8FvC}7dcuo4w?e^{!yq4jNzHlAMIYBYx zmWzXTk>POaL^Q}eqCHnSu#jqr(eIi2gG9Szdz z0u@3rgbGpOzgfje2A$x$W9!Yg`+U1Rd6bG;YlM+S`L?-`Z$allv@Yn6SC9Ix5P13y zybT1Itp!`XA?s&E#iQue71yn6-Rn0&Fflz`)M#(w$7M$#v3BbvZcjUaQ`2n6=~XAV z-3q8o;CIX%(=*Y#D9|PL+$t7S|AbdHJB*E_hI7k1uaM7=1inZ+Kc^y}h}#z^2%^aU z3*KVP1nNj@WxrH7Io#%-SA)goNPmiR1qlt=@CPP;hg}nB9suqUPu4)?;=D1y+pcT z(r_YO00-m2aGUzWM$~4017CH3L|JmswT&Jl3j+hJdgJ&bLdVh92&=(A=*7$r6r;8P zlz(^hae9XgB*>ho2O}GPl64Po=lUM%@NUE-1Ouh+HDqN8tGOihS+Y4FJqnCkfH6h= zMeB(c_mSM~)NIC&C7iSr6ownfzqw zjj%dFFpv%`)JN~ZX@&VwF>~kS!!=y)TfS#t)HhEZzETOJ|B62bCmn#Cc-v99YaD|H z9{NH5sjQ~vhGzcst);`7F1fcV*=?xa)y z{*Yds^Z;l`nVFZXnX!fM*Hl}IV+r>sAAmtxd)=Y}_ZSnTJa+J80qtYfKC|Jc>T}@0 zpYxMu9Hv?(nL$)&KPF5CZ-1|1L6nDRR1HSbf4JUYWBq_Ui3#webu>%0;{{-aoorTs zt$0YFItU3xSUEYQy$=@iy^6J!2IXBzN`UNe4L9;+SfegTcu6irM^JCXWqQ=xZ0Ahq zz|d~jMQwzI_R)jKEKW*{G zU|;x`%xVg)L26<=M12L`-3e<5E{mNrr2_{yW`d_k9s{kX`a*lu=*_VgrW(?{-l}C2 zt61xTczQI?6-TSnx50t&eK=R1xpq15oSkL}i*XG3d3w3MD)Ljw%`0Yij=|fo^Il&y zts4?ol^i{YP1nwLaDpMRa?avom}4C?;7zmGs5h$2>gmGN((G2}D|RF+D;HPI9@I;r zoUpM59^G1<*pa#TpRXuzYY(7gih|63+y;kCq8BC?vjkUOWd7 zG{bsJr|M+C2i7=EPO5T7Z#ABX0mSO)T5;?9TO_D0i>fQV68@%cUzm7WjR7ppz26n+ zqw|hDYq>4-OhRyU`K}oNy8^(92PBn8yQ^NL*t&cg4haLDg`#XRr*LaO5`4=5+vUXq zCaBvr9(<`qaP~_uR{FpqsCg) zRWfnPghCMU?Oc9MYQ7$E*QXQ#B~m6JYUt|d@R_Y*BPS%Z`qpuD4==rYrHd#I)CJ|BB zrf<-i`D}jv-bP|SlxP`T-`{fz$|)V5Vti}W7l@C>+J-Iv#vf)*6y#cCtG6qOKGE10 zv~Mq~UM)_|Sfd~`9UGPyFPw{hXC3AN-qiJA95a z!Qf5Tt6y_rkHA)Z)h%&CQJlOKDcF^3l_~otdMVwsjPaL>cuMzSAWVi*z+R8_Xi zosi-Gv#q<)PKFXgU6U5V!>A!e3dD=))YfvQRzGrExZkvqvoyq-4*CTXs7Qr)iXDxFL zcbSr{vA{g_h+8loX$X@)$toHOt}n4y(BIP`W;GSdzSr2l0 z8d*u}pM!`Qiws!vXwBXlcw|!Wh}7ibKv#U0xN74woY_|pTaJI5Y;IDj6w;SRFOu*# z2MGcpO>d2#=4-W%im*0U3z(5+?GfGjKWTuHymSx%3B_o$qKuX4 zLA(VRZU7g<4(I9t9E9aGaHQD^nBZ~p;!9a`-ugUfb~wKsaz&6-g|4Z}nflojB2JH6 zx%%k43{Ys;{Hi_07pGVCOj7-bD^U2OpR&A>tQ#(l46W->pz~;>7vA)uxB#)TZ^F4d zxatYm>H10Rr}4y1*D~*JsG?~tvv_+(9+K+t>oiNthrNB*R-r5@oPHm9@T@yd-RA zdf`fH#5NRj(>)^PNO@c$M_9;RwciuRXNK_WitV5ihD(;9j{`hC6-|8`mO! zuByYpE2DBrIC9nRJ3V{=hENz+gAAGJHi;zX`|!thAfhm`Q3|FeDRX0wilS13&wnuM zdo?0rF&H#dnxvRTE6fAu(pDb=Jz%N|6w7G_G^lw2<4i}%o!xVCw3T>+4Wybj%BDq0Yo&%~pRW>G%I}Nb%IwUI9P9I9xS+TFmB9!* zy8kg04WfbSLe7oZ?j*@+-==Fdk;2m3z+;*oHOxX$J)JKGwB2Razc+6p-1T?6^ZVLB zkOD|*`3}A%0?yAz9z1N%q|a;+x;NMPiK~6i4-hk4yRb_J5gXB#4EQ)6H3_Ot!Y-H{(b1U^*S05A=>95 zmK7RT#OBqabYGR>;Xbq`{n>(x4Zk~+l12JoMri5%Baf=AU5X&6NoOfpSNO8iE!+ay z*DZX{4T$zb?60&Abh2K3Wyc6YPUumhTy@K<+i&O?5c?1#V?bFxT0SGMqBWU4A5@8@ zA$#u%UHimWYW|uIq97wAp$=w*#WA79HTDDWX-f3a6Pq~`BB8H}jI2R(_R2TA+ys$X zI(6*GSnTVDwU;pe-}q6sEJ&hvbeaxmXVAg114Z?o*SQ2<9ds;rQY_g|261DIB7!K< zqCwr-%_8PAg2;nf+!HPQw?kAa%9NX(%C?xh5;=kF7JtzG<`rq*r%15kMRZY3|bX{*#sc?8N{z1D@tn*o&QLQB#eaR z(%VS}DEmZ%p7+VVBW?w>n1m~2aP9DGhn}jaC#T-y?~RMTo!W#B!`RGv~ChKom>y zQNq=FrTgm%g99XgHHb+!s;L*oyiK*FRWA3R5D2C!z^5!bHWmG@hxuj(QDGEQ6$vF5 z3)8pt3f~QX8GV{m^tFZx8JZ0xtRfp%h@Uq3=#g6Qad5L4ZG8*^5yBV~$^?ZcAW)Uap9#?*fwRlWYo!ZT|Ul&x`n9*l* z7xShSN_i+xBMHfS@gCHHW3>fOYNxLz%r68ovQ@K{Or#f(2*L(#U8_I96c+r93`5>g z!aHR6>v_kij*C@1WHX}>SCzejg^;(>jT}SCC7Y-oMkR((f0oMj00S=Y&BGA-Ff{Gqz{6&e`w<~6N`}$No?T|Di7N_HJKE|38H`Eq zJzIxMNwl2_k<*0YUKj=Bno*(T=h!0~CI_$0(Y*{*%W}~fW(u#wUb*9w$Gt$gEbMLH zFAiR}7|I$k&<~$@Lval5-?1LUsq*qp!?1Ja2)w!9bjZ{NUtmYxqLSS}zt>?aXwJ@& zS-R?w(1Md-i-^M(ZJui8h7O@?pFP1FkU@!5du}kbQCbV+ne_s4!i%h+7EsHDX$p@T z=wssg`tq~}xmkV1j?Zjl!q;~(L(J1-3GOg}QGMrJcXZQPx1o*`9OvJG)a(Y2=m%1- zefRo}1*pIEC~I<|5+^Yl*7?Z^6^8M#D5K#VujG8k zpaKm&Y7LZ!fcRbN@+d0IC|G4|5n*d@I847K&;o*kJ0WfzPd;;fb#ChC1~XCAsBw-1 zolnz_b?EpBrLB6e1cRVBTVF-rx%V%5Tc*8YHN?Q@GY_(|SSp;`$H;UhWM~y3zxU)> z36V8*A!tsg_a@Yze|yk#@e&=8#SHAd@OH$Lpr=gFfB^T+3D1|8Yn4v64+|QM@VOQQ zI<8c^&GrKC`ozOQKC(_>q+79-$F&FN#d!X|L;?~yx7Ft$F$q(r~JVElX@xZOIHq< z(=8JW6bX-6s!~0>>dlX;oi;eE4xQUmj%b!+m7%{aS)Ip?cj_JZz2;6Rru2qZaY*V} zDJ?j743HtIRkLep{j?(ZS8p++S!v={~61Ii@(s(K*)>O1$kvtE-^pU8`w zoJYPKP#7|F8+U*n?!Ze1r7fnAq)c~^azOG%frB;V(qhcd{mWqMb+qHdlsLza36DYY zImlnSitHGdo|(JcT*WVUxcfCu5tNAaDw2_H7Kkl%STvGi!;sfd`$ zI`Jmw&_;FT5ol@}OXHD)87|VJ{I&4w^7rXo}uQ4IWMtn=o_Q5gtKp6>Ws`sz=`aC2IEJyvA*}jQK@|9$cyhWhsp^CkP{O? zpO_ll8W9m)5b=p4FUNH}*a@e$3nmAEUN~%0F^}eP^b+mtA~Jw;MMH7)MWL4QMrae| zWjK*vR%?3aGF8q&8b{y9y?Gzajm@I8VMTw7t)a(F`EBhhuQ~tbHb)}&PlVh1`oHAe zGlh#Bch8t~+C>WcuNb|S!5c44L`;j}KC=$*EBZWrL_?3xX{0jRn0a|Y!E^UYFUTf} zgF;*ENS?b?j`?hC$}d;!{6siI^Wj0P`A54pe+^tNX=%Jwbkv#jkqGh!LBQM}Fett1 z0wH{SM=R|A(C|mk5tiR?%aR(m&M?L0gOI+md#I0rUJ+7)PawXxHs{8Xe@$t zK1kdlk)fFc+}e)XahdyH%lbVhh!Yfq#~=PR{RWcK&pk7jOF$kFoASKHj89@1lbezU+~Qp#GQhd3Bet)u=X4|~ghbk`tpN-^euuNR z0Y{fW`hdzo86PUFQauo}F8ziqILYn!Q2Mi1aaTZP>vx+SJEexC3#3#K@`1zJj^OB7 zQJpm8WRt(YWviv8`CcHcHv#Eb0Flu{kaB8*B+KlUJWSW-T3m$4piBmIy%=2K1UWwh zUH_V(hjn!=07xt2LovS(*w;j4=*WN8zsd=50<_~)x zwDROJhd>#qV1eDjIp))xV(SWFuUNou#2`NG8TuM76oNVs*imo&ZE}ySSV_Ta2_hp4 zKj`56P|*wcp97U2(4B7yx~A7IM6E5YT_a^aRDto9DML^J2E<={9t;6XJVC9+_n?I2 z*+e62J2qT957tED1Yp+5u=I>|YZOVBB>Q8aXw(i$_fzMs8wZ;rpL$T$uh&1Gpsg&O zrj0;P`oV{V?w3Cd*!0Itk-T;P`+Q%>s7k&eOMPx#DIi6vY&K{th5Dr-HR>EW=iGha zY^Ds(p;#?(E;+Xbp3A8WBS7GH&XJz$V)t(8rqix1(JM-($NPtOFsp%O9A3K$dOn0i zmIzbU+%HlJ{cf6J4!(cT|EGSZ%y(M~?2n8#7}VPhzwP_Am2hpL`74w#IL#9C_$>%} zB9C8wK|xv}PhHbB+4@)16f@~d1oE~>y|Kz?mWE&&P)lCxS-D0-;Q{3H?f}78YlJhLwV>PKO?TxJi z-91MV0+Bcp*gXH9u({3Vt+(3`0S-YnoBB%Ol8^F+O zTc1;_%NBmOOmf8}%=Laf5B16PfD)3x2|!EMxW3wBhf9du7!kZ+!l7{45azK7trrZK zwcT)uhWNv4At}P(&>-*7!)A@{r^Tf5ef0y%Yibi&6r9Ug9fr$vJIx9xvX7#aeYwdUx#Y=j)pONF8XzSo#xuHx>-zDuSATl)@lL@^L&<`e*qJC*z}*j zBxwqDeVa8wA#!i%lg`SiyxXQgTvMb2f&OdNDwO;nvQ8j%-Ed322s^a;*tt9E2+67O ztc(F<_y0Y@^8b9<>_BDvuRIp$R|wL zb=n{$S}VvwW|9iozeEW`hr1U3Q98tT%9|+N#=(#>Noym;%3|qUARN(lpsVdbH)!t; zgJ}miHl81l`J=lOp3FR^;OWIbN6uO&N$SK3PnoKB1*TMf3BI81e%Q57-#dbr0=0Iq zr~mmkL5DJb87R?tdGP`nD*(o??R`5|JE&tlYTjxcc*977%u0Re+_rP4Fk^SwO)y!G zWDS+K_fWG}U-OB6eyBTpRQNV!#pZ))WLAeZ%rk_NY3g~oyJ82L9BlX&)GZo!sp}w` zC~mI=Lo5AD^A=lVgUCOveLrltI)UI>)&oW5M=^EAuKiZQzkIsDtigOOX-esK#Hl{= z4T6i`$-YnRpwqojPM<-hQL>%|A{hR26YfwAC52KBC;5H%q_5!}Tmva$bGXODW*dza zji4|oisd}JP*}A*PWsf%;)4jD4C|x(zm;mJ265`b)nEqoiDvNke+g}2A#gl^BV->x zO3VO}SmOcsQAv`A->G3om>ZA(1%%&zR%yd8cO5i`RFil~Rn|l0-q(GoA^{x!kW!U@ zztY#gKgkGw{qcalz%M|Osq+HRM=eK^(v-jHjd7iUBd)_V_js0gIdea zIn}QxI=s{!5;8L_iByYms}dj-c>MmAxS)XmL<_Ei5!KmgD<8+>wC*$;Oz%2*X!; zOE8hBz@fOl6dCN1=obws_$FMD8Rz66yP=S84H(onEJlU}i5_K5fGL55sLQ2-6)!t3 z^8#MaZjrb%^5e|DMU!TrjvDV0#9cex4uii4%a{4X-iOh!qxZ!j-(>27oIG$o_;aD# z_-*2gqAmdh?-2MJ@9*&F-q0Y)rs_G?)9x+P{p-8ib&1oC^w_jr}wMw-eWdbGE%gvY_^EjF7 zOTL$f3_Ir^;3?AW^)^Zm(RQ_*yJMOV9Z$Y0wQvO4DTzBTW{y%482+$H? z;e@VP{tN{}fJCdH;#k)S*sTnL%Xe$18g%cnOeNFb^0=XA!ib+eYB%x|Vt!$=eeeQE zeO85uVh*nt6R9{KFVWXj`($j@EL5SBe$sD!{RS~g4tT2t0#iTGxv}4ILWl~!U_~Jr z5?*&Tw@G)S(6dF10L^EJIR~_j=!bU)Y0ahROJuF^4mE$03t zdT*QAN(-3yR^A^#kFcO_W*XT_TrkSl#DH?-xlS?^VmJy0(t2lMhf_dqH5iq&5hvkX ztc-0HtlV3Exa6VJEC1y9TT7N@cwRZFXR8Z1s~#4w*@P-h zW~%i?Mg*F%&2Y-`^iE2xnpEdUP?_JdHjZ}tz~pOy$y_}F8FSx%JQLk~I1aNpaAYDe zVfcZ4FMo7uCrR^xC5pOTUjsa)5^#6OP(Qg!$q+ zKDZ@ppp$EqGh%rtdban>_{g&salB}U0F1qGaZz-X(>J&AwsV<4!f8+5M$`<}=$b^Z z*W|k?6W?>b+s4Kt@XIF^OMq!%{m|0#lR3!03r4T;q82Jm2qP{sB?r{RR#9l=KTD3H zyil{TufsTIM+aSe$+Gzb@)4Ouu+qRz`oZoyE1y#EU?`qtp8*eWz=~#E(e-?ulXqBI zko*N7`83&5$(_KfoQ+`S6_}XFwec6?sb#KbWTq};0GqM;QePz1H2As6E@cF)>T@Y# zppO|Q#Oo%ZCEwN0t?f9EZkxC(R=AViwt-lmgMys4X%)H}^p~-40J!P8)VSDqV@Oi? zCbyYE{(iMnTPU$#ac1BDa{CLeeyvbAZPx5~x<4Yz4O){#~zJuQ~ML^TzPXY2?K zyme>BKB&xDXPGtE>f^*^wE>53PMWnp37IAKfyP8k@j))f{xl7nP)BI}h$qzpe`Coy z`|B!YAIGd^!A26yn=JH=bhCU z!+hJ;d}=PhSL3J{N?4i8!6)g&QChzHCvr^xsU_L&ftl*|g#_7GRnNZ6)OoH6<0V%y z$41(rVec!{6jL{Ed@P0$Sgk5B&D^R9*y84q=wI+1h4h;Da(Q=MS3|os&vAju!8Z#3 zN8~d*Hr3&d0CU2N&HMQlCtEF@PL0H@HSgmbR`hX_FLNoDtP^bjlw(?_DD#YmXml*hpu2RQYJzJdA(v+BT_ zV)-`?+yU$P-$}Y}@Q7v+qvCj>F1kByWmDau;#nIm6t)Q({bMsxmTxDn@40sq_uCP0 zl&_4?!;#hs+7Kep8!Y*j_)?%NN?gI8xeF&~gEM;Gm`DJK0h8&CW-%Di6DUj<8BLnl zUjK2L1hSljPYjp#)j$#{0@tjo3-L0#s9srmmLG1^{Sl##SfMSgsMws-;X55le4_E} zb1J;CYMs5QYC(W^w8j4YZpUp$`3-;crgWsXZX4?2Xx1e}cYRnxMOdMlL`kiU?D(4% z9)(zaK0gbDqZ@%4^5ZI<1NK$vGtO*^;K`tIA`8JTOVFAP;zwtSa}#t2(!(P>Ek5Dv zOM9n4zSU|}Gf;7M_g_J~ckTN>JgSM5AbL%AyBQn@Qsg54 z?D2gQsQ88*sHR^ZYTZnLk=4QI^*2s_D%kDk7OQZX`7C>%0aUO>BNaSgj9rrD=g5jo zW9JayRu-HdujyC6U$jVcCmpA0Il4o1omEEKNK7v~KilGtu4@TVwH=YJ+f%q%Zx1rF zjn#5HCNtT7{WR}|+!JRJ;hvn;F;rw`EZK~_>pUvo!(`k4;9RZwA`k@ zFkH*{XybT{5O7cisb;M#1AC*Mx{lc4^Yit9d zl5CKe$MCioTm2J~~ry)M)0G~B6sH}%wz#rOCU`co+o>Kj7C^L}eiqoJqS_;m-8$$4O}_eViq zX6Gofsn*cZ_jfQ8iOhl&3-9*#LFoH6yeB$xArSUA`5iz${C6_9XeNikh0eoBRjv7k zl=Q{LoDu~g;}^&Sh0wPIbyLSW^?|h2Poz~hzrBJB4(z2&WY(r)3-D*pSH_!5gOglk zLNV~(zrK-vX~8MS_*+{^%j2>%%hIJLuR$R!I22~ZrGzp@o7XOwR~*7<4_iId>pS}| zN|ggSImU8Z$*;~;tktbx_Aia7Vq^i0l$0|WQ10To$B0U*U!Y0Ss4jK?-maATLm99; z)lYTzo;`_cPTo@(kQF>|UfAHyk6S3AT_RJx+MDJQ%R)Dhe&$3Vf;a!?sNN;K_p6=} zN$%z8@u;u+YX^RM)_IoM2GTU@$D+20#4(%ptjm{u)yNItwII)4mq@(k)Wbab zzJDq>EWGHleDxREtaBCRfNzCt!T<7#nb1lFaRE2#x-E0$8*HicYdPmhliE*Ae)Gcv z%@w;cZT+*}h5dUyaJFFFpru_t)|S31?A0x0{kS%afQVN~J6wMlT3KdIWNk!kjxKF1 z5q*;1`bq=f6z$j$U=42p8-xPaf4!Tfz1)j<7`?M+>;J3JevI{%DG&&>`@=s1{`YGA b{56A!s)zgUpW?HC2B1^NA;%hyoV)rDij|1l literal 25604 zcmeFZc{r5s`#*e-wFvJPDQl6GwX%hVX+g+V_BEA#%g)%QRS4Nb7}*KQz6|dYLKr(U zmTVczSjX6T?iuvy`}^lPe(P~O&++@C!_nN=ex28OzRvS{omZfSnj!-oD;)p;3`%$J z+y?+^7ywZIdxR4FrLLT37W@yOaZgJg{4WFop`f6kq@<*xqN1jzrlFysrKLS`C>lKSXj=SIdk^x zSyon7Ha0ePc6JU94o*%^E-tQf=gyr!f1aD0n}>(z!i5XGyu24LUgYEByL9Q&<;$1( z`S}F|1Ox>Ig@lBJg@r{#M6O)9a`o!fYuB!cii(PfiHVDgOGrpaN=jb8eqBmRN?KZ4 zMn>kwjT=xXR9042PEHO6gWbG&^VY3fw{PFRbLWn{yu5;ff}*10-Me>{l$4Z}l~q(! z?%lhms;a7{rlzj0uA!l!si}GY{{06J9%yN4X=`ii=;-L`>gws~>Feto7#J8D8X6fH z855Z;LV#ifq{WRK|#U6!EfKb4G9Se4Gj$o z3w!tOU3hqSL_|bnWMouSRCIK7OiWB{Z0!5@@8ja);^X5J5)wXq_>h>Gn3R;1oSckA zB2!XQK7Ra|nwpxHmX@BLo{^D}nVFfDm6e^Hos*N3o16RT)2F<=y!`z9f`Wq2pFbBC z7Jm8irKqT=xVX5aq@=X8w5+VGyu7@kqN1|0vZ|`8y1E*LLe+9U3S$0sHx zCMPF<|NcESHHE|Brl+T8W@ct*XXobT@Ob?E{QSbg!s6oM($doM^76{c3V}daU0o#- ziEC?X>+90Nyn0N@B9{ey%}E|~+s zIY8;oO)bw4_#s-KQ|80}E*<4XAh{p0K)nmztO^cJ4|#3AJQ2?S5yAcZBR9pPuQVUo zFX=%v3!nDm+!z0mpN!@{*Qt5r-5uzv7VO-LL$al0RYGfB+jdji!rDf4?9hPHaBY!{ zX=$5*#PQ>2&6Sv7ceJ!AHMqI!fT%Y=t^mB;*N(vg-yHkz_{oThuyx<`2^O2Kxae(Y zzoj^R)@OLnZcBIiZ1quC5j!G?7C8pFgkYrs|73Xrbja7FKm42siYznIAMiV1bLRgR z+h33wo0i7mt$$yBB+LSbs};;Vq}51Qyzr*j_Rk7>+ph;RXM0rk09rQyYK=Oe8#N!Y zX+5c`Uc`F1q?I~NkgKq~=VG&es@b+WZ5Okq=`+FVDK!A`V=!aeFEm!eue<*)f(G&u zEua816AaRQ1#rt-n7NLSP%8SfRh7E7wCEIT9ZHQQ;1rC3Qe$n?W+W@w(oy-ZzWW;$ z19qyP$XAy98DXo$jgD=?MO{>qAMau7U?&G70skB{w{%zooZ7)4{=17o9S62oou(j% ztAo`UaeimsbHo36>5;=8q4*X#`OtRP)S_dDKcVdEt`_pVlw~zMawxSX#B!H6ldlUM zl_(AeD6$nm6jzG;NdKq#me3x+SFrM*Kkdw&VL*>|ygHQg8FT+GAn}axe_ECJxl{xZ z;l@kvpA8coSFK~{fXDx7va81G)Y6Ky<=MmSgkys^xAb&YMNKNcFJb}hhuj~n`;eQT&xQlM+FM_i*w>ZVa{aarEA1>c3Ep z>Eqibzc_L2oucd2|3c9t>6VKZ7l27Ajpd6;l+0mMWRd&t-tGUjvh#mi`|sKX5ei%a zUJXhtV9EsQGdN~`)DlGne4yC;zs8pgD*e(LEdqJ9`e&NT|O zAZT8wAw!0gu2BToZZ0>B4}Y@8hE-(QhP&+WD^!ir^$Y1M1g>zx$9b=Lj1JY?-W`A5|$v`&P5D4^?pc@n%!vNb9Bcbr z31{d^VuxZA%#0Wge{~YONU{ijNk2O*v&=@TR&!Kqz-jx;zQHy*g^q9t4|8$P9!qx5 zh9B&`BR|vwg0gUuJ;jMvn{2k`URh!W4~B4mGPUJ7f?Py zvIPcl!~`vm8McPsovB!Ny>o%{*xI-S2q4W#-EowndEq#D3#`*Cy%ZOx9)B(K6TZ(;My0kl_+?QS;0+|N zvzBl7uVlQO$>s7#4}RTq6n|2MN>7Fhn(+d>j^y>cpJ$s%mv5h+zkbQn;x=v1Ch?XV zRPix)9J#uY+zl`L(U3X`g|@f2?vE?)M^5xX4wk%#3lSmt$8`UH6a?#>M+$IOZ@0%X z>dm!^)f_;lPu#G^IWy{9@%~gVA2ON;Z1kuRvZk#;gwMJsH-rJ^9IK9*AD2nmp_8)G zVJ|DW*Foh*LX#yV>+VV6pJkuwHvL&{Fn~u|4-PZ^{DE`X?qV`E@`PL1;HNMXJv#7% z5gg{&1K8sSNXmg4e%-xF*6SpwmYk*`_>YiHrgjTZugv($Gkjnpc7Y0}A)U53nZ`=- z;P(PsNjvKOW-|4h_B0~dAX#hBoT1O^B>f}|Mb`5*I#|&9Pm$Y4EgWY+p9B>`NbWCR zWYLVGgL%nCFY)jN3XO#XBm{nz)G?@Gr@KUNOMV$2UbW)nqCpOq`7zO=kCQqlX;~)D z>gi!?-lSuoYSBV^KlbA+f`Dr6dT7H1P6VA+r|g(zYDGG!G)XM@J8=(|1#K7DbZOKn z4_zjW7K_&48{(vapFzNlmINfl7Op&rE2a8_HSuzduadm5_V0@R18wcy3BD!0i2iGb z2zs^Hd*?7;7SrX!FieiM_c}9pBIj zh_yhnVnezCs0a!3sxaXy+r*#i>V>q;Rww+(+yCsF0)eZzo<{`5o<@iG2b!4s58y`c z%~K)u8DUWT$M?w_E~zb`87)TGG%|9G{pV9KDs}7Q3U4OZs{t%beYx5#nrx)t*biN| zyvLHlq=La}u891OB=t2?x(AWTT7bWLZ( z(TQX_^N4FM9kjo7NBx93YLZiyQpoz@}3d<9>oJ&S|gfhrj{s zgOGg3phs}da)p17^#)?!Wvb7`6)lRw%d9%QU+Z?2pFOnD<5u_nc5{CNn@VeMB0iICGtKRgb!hdi)TyWOAgYOVEI zU4u?`C#Avm{P=A?pV=YOiG*~2K7#bHNnZ2SgkMaKK8h-r(%?zA*+H#f!mmcaNXU z(Xj^XXN?55PJ2s7)sjw+AO$Xx6)Ck8Uc#@%3S;WckI_$JoGc*O(_o-7U(-$zY6?#q zbo|QKQtV<447FQ< zUJ*s>DPvuaE)1X0kD@{QOCffTxva5Y>+Y3mdO2c{Z=? zyVKZW$&32!uUXQ)d3L_gf6L`0gz8dme%9s%4=WhKC`-(Qz%4yBF=5UzZ*9E>!&PbX zR;_OAtzzokKFwIZ#V~^B^#lTrO%{jIzGMNhRfTe;LVhs4@3N*oN&05&b*&MyEGAS) z=b_0mmTB^tDMQVyS72urxdCQdZR4q#_ZOgV`N+cUC?F-dbXfz4wtY+VLR#SlO~8cKo8ggu4c0xx+p|KRiGGs+{^-BP16>I`N=}($(yGlN=xv%~_v7 z9x?b+e+KP~fD|_q&SbAX$^FXkMUx}YN`F2{Ik9LB){*h?lV^zJMRF( zvEd00WbMv;uwB(X&~0QX*8~yv*Qp&dtp8oI+t_^DbNM3Yx8TO5eGK@&DM;`a)ctQm z4&YHtUbS+blPddplH}Cv$719GJlC#!-uBndmGvw8n)UM)q(@_o;q2AvXWK0MuDae4 zw0y~9M6W3?lw>W)Q4M;?=DQVqe{W<${-kb}n& zF6}aqb^T+f8lIm;svHuqa_u;*FlBV!8Bl~!~**%WP_~kh0)JW{R0;Qs2 zu!ShjhO&bS##!KE5U$jan*^fs5@>K2rp~~**$|M7wG1Sj@nhTQE)6;u{dv6%` zmOR;6DACQOOYi$-N(z*muafqAEFCDu1^qB|b(y+E4Hyh0xykbD`8sU`lY&FcBGgEZ zM^$a9NnsQB!H))+awg>X+!!w{foJM*>1rQbE=mAmWiF#mNeEqLp(G8%#2MyoUDf6? z%mlic1_SXnWzVacO^=ZSs7;U^%grrL?Q1}uopG_@*WgJJ8c@WvswWULYfuNG-z8lN zbhAW##qsXF((QV3gf*5OO=Lwfd6Roeue#yRb0z{D4rv)w7;#pO?bQ`Y?GtK%!E{Z? zjT70uG6U&{^%xWzf!4T!4>agz-DSdtTb^%xZ6x8=e%&Qm^nkbG_6@W2DV~Z`hn7xd zRkQ6?YcB2jApZ3Q5l*d+Lq%aS^CD2t-~MN}+c4v}4cQ3>R|woF$p1(r7>1x~;o#2x$F57(dTe;(jFE=Z-}Xt*^!7;-dt-^*i4m}4K# zL=~Naq3mqB;$yVSR`=?KsK~A=^b;CV9J9hJ1I(B)X)!tO4UKfiQjj4Tr~4cCm%~bh z>A;Nf$wwn6%eJ`fuaZS~#;nQ~5gH;yt+8M;X9Vf94(_6?eAN5lJD+N<9cjAVb*xTI zTBW>gEX;-hRxMqSO`Av?u-_pHriPVsWUXx`lsF{muWEN00q@0PY9IELct=|UA%0~` zCXU|MQm|xWz?6WSt7-H*Yec-g3k@rqV;Y zi(+(K@)30+E2;XF=_&fLYTpKVw+)m}yUbK26fGgTY|i zx`{s&zXgv0Lb5{cP6Ba9U8>`9^3CyG=1zm(q7Z%=#>T4EXWH@mmNM}ZK78#JOFblK zXtF6mK=e_5nOR3?#7p^X(>u~_6|(bAzSo$@hHaSW!GP`Ep#Fv+?^1_PQX{g;ucrT?nCZ+ z6_+W7PRKUvFAqLrL{{agY}iJj4t{RtxHn;i z7sfqFN!e!J*Rdv2ur>ZY3vXv}AWv&tJ8ku=OM;@CLyyO{JHQ*)49W3d8>Au^*U1@` zMy5+%?zx%mR|{MU)MC`Yw-S14PO~86vF%Rww>A_TGh*@)5ojmEBjck@oeOsK?*R2Ic&PP{7!f~ z4n`yl>GPf2-fG^yEw`23Bt?%S7<3rQ$2yaY_9VoZ%S&>{jH~(HMAajNyUYBx9RH0- z8FNbXLN~rGZr8>FH)R_bHtKQ&ee$tnQ1#kL5ilqi;hQ~3JJjab`RKyP%&4X&Oj6=0 z05yhR(gnLS&7w73=$LtZKKHq$bhdoTD^cwQBWl+S;zx&yDkP{lN188#QL^?D<8%cz zzXH; z!p*h)m2585GJ7-cnpo;KwT2`gM>guWK6rmtx6nq@VkFh#Raz*W7-h3d|Ou9XM`eI6q!+#bv@S@1!fJ8$;BX!+dx1pk;- zFIHHKbk-hy;+gEEV!V9Lc+9`k$%dzQaL*3PBiYSZEM(=l)sxa9TVA9U=ag{bo1 zml@EK>6R->(#SQb1po4hd-GApkq`EK%e8m5+dLTGM74EBLBR3=Izpi^QNpouCHijT z)GuSC@ih(&{O#+RciSe^&O&T>sgT*=A)-L2^*ZE9R)=#*m2BOc?%0l?!QIE_{AfX0 z-V~)MHn|1FOM{yO;Lh0>pgy}|G*eDywnZfUl(a_peH>ZcRCeW;Y(9eiM1`DCg}@d% z-7yX6>Gh6;TBmxY}B?Z^IyvcmI`cLnIbc?e=YF@syl4~Q*}paRRTns*1ZIY&P`O&nMj`WH8%iNJDW(>uUOo?5ZK9bF=eu3KjabWG$(ulAmPO0=u4#yDZ5G{#|%^;cYel zQ@LVl#6^3eUpPm_1EqR_t~P?_qDuC%VVi)EeVq&P8}yX#*@9>yH3PufCmQ3{u)NsB ztSUIG3NVz;3}E3qwl$@{5`_koby81Xi#l^|_;n99gg2MLew#u^YLFHd`xL(lbD{iKWH z4mqVQNhhz@KGE0`fUEZE02&LJEDmC`b3PRiGJ9n9{M65fv;P1>sP2k^xsz-7hG|ex zl_p|^rxMjNrcFFe?Z?6hhi><=^>&VQ zYS+>gMCN9!L;L&O)@}*k#9w=E>(YMI!0HveN^mrYt9k61B-b9!{@|O6@snGdP8G66 zh9_50TUi_1i-d0FHx?s^eSWi$GR2=g%8X?vFp|4>t-HEu8x_!R;SM*mO9QFfHejhCV7W1Fa>*`VM3Y^d!|Qce<8|BUIFnm8lkkTxTBV~ zMjReD4PXVu_jg#eKYkTI=@{-50LH4+vFj5fj8cn(&vI8m$+Lda2^1HXTf2@xClbo( zJ1O7^rR^h=%P%Vi{SCy6ZMK!L@0~`fkAlvZ6|qGKrcxW?LzU|EE%>H2#J7E5az)nN zqsGY5clre@l5dt}KTyC-ycwnv?+)i(;#isGx04R=Gng zyWg)@#*KEA=wk4CvA!uBT65I2w5kY|0l^s50-*beemNY3(jUbI0`d8DvdtAWBfHN+ zJkdiw|5#@VbTVdaNBBWVp3V{j{h-2ZV(-VEv))u{)znDqob`v?rhZono{L{f%W>LFJsLnu1v;E!`Hw$2RAw|%Sj_<4*czc)QtTOBK4Tzq-O}XC z3`jjXdgkydfb25Sl~;Y{Cp&?1`6H0o~8JQX%j(-;r%kcaCKvTsNxFit}10Q73)X4cO4?@%Y&u`aQ z_mWsIVsKe&AinYm=mwMySyPBk-rYQp%gyvUd-R~)vq%vutb^mTIOjBvlGbwF_*K&4 zh7?2htJS38H@1%cjNfw!q$pGX`m(20#9 zXs@ zcjJ9BPlze2Y2ry4HU5>$NcqQA9@QH?s@b}CcNI1RY+p(ajk*l6{^Vk=b#?2iUERF2 zqfC;z3`(*7v$?)UaqE<~_Er_E6P-g&aZP?0_*i4kG31|T8>uNs7M9K5c#O_)y`R2_z_ z@kh9$0es?qF=KllJFXuIA8a^CihcF9i^YAM_h@jQ!7e4?8Uxz)!6!Q%O9v-;IaHb8@h~bTEhzAZFUvg7Y|co0IOz)#3O_hP7vx~N*A)p+u^#V;{raz@ku48CX)&TjJ|=%d zU1%Ks8G9T_eD|-vS8A=XH-3ca=!Y|puzUu+*)rR-R?|ftVNZUm-z%Xb364W)Va@oI zUpAxCyCb-U@kg~?Lod!k1w~=_Kt6@Nt+it{fzx+pmy`LYEx|yObjD399(IC7vpBiu zl2AtE(EYr~Be){WN=C~dS4Z77s&3%~^v1UrtRDu<^p3Oz=JK~#h5`na#HCQ`KG}rd zW=9Y~Wo{dJnOtz?*uA^$;i*EyGB%@-%R=|4ild32_s*^<54X)U161(lfjn^&7vYeU%h9;UD5DG>#TobjcK76SMAL{6t%; zic;W4TG}9n$BCXvF_)3pMz^YoMM{x5X$v+)1_SJsVMXSOE7oV)Y}{yKa@E7u zLI#P2iod=a0`cG_eST(XQE8~KPnsHT`S zP2ct5-eGwqFz}}V@Ecj$TcU1!vD28lxpS&xz~=!4(%ZVP=|W+O5$Lp48EzD%X3DiP zasG1(_JA7sZM6@Kij2zBCobHU!3)aX?Pw6d& zb4Ik~O&dY@fa-mY`I1{v@QwRGxKa9>GaEJBH8p<~J?NtXL-+f*8u3c~G_Oy7$| z+oN+0I>E9+@kH#;>~9%;K_=9w&b*lUhuk7nGAWJbG+EH4_c;IEtFO84P=M8v(C*({ zI`iI7cp}H^Xz$4FoMf3=xy{?ynE!kxJFH%d6D`}O9b1c;Jc|;#4a4IfS4r4+XIb}6 zU4MUf6X?$4L<3NXp`a$ltm@H8L+Q1I-@{L3r+x9#xHt>c9a!x!lxS;g%-J!1joNjJ zQUjAs@J3E$7>4t7R`sT0+@AWyuQvPgb<&G-*Y;d*y}6k42;7wI7q);HNIuQ0LAwtZ ztN#nhvYk$zmX>OTPu51eY&Lxz$@cYi;0V5a8wSGhYQ9=p^gRlVKIdVk4Dj zR4IaSAbD`t)Ab#L?HyO`E_Ga^=r(cMJNi|%)VtB6jE%Aq^^=8qS0!EX$3V;nPK?W% zr}U1E5%v5eyUxR~km=r)R_%9ZkKaafeGBYu6Vl~0QSf=`8gZa5*tm*XFgl0 zPe`WqVzF1Vc*@$lZtJDx_OC0+AM7Tzmy!2C?+%9N;H+?~ewlNcY}!$9bJRKdRfF2o z-q^0^h!a4LZWaWi2tQ*=#tYk>zf^d1RWYB0HtWAwK(dB;S&@AzOyjepVT71sKCfW? zzl&^IPyA?A>RS~;+Ka;$A~+lR_Ay<+?_{}UCM1s-Y$WF3$M_v*4zwfFn~i~&3nA}F zv;*|Z-L?9cOKL#;C&?8aKZ*A|(4bmSHQ$F+nr*&!V)o%OW)={IDRvhT05R(Yb{32@ zOKUXe_bITCCdiQ3IRYxjvUXtymLCm9CWPo=qFGNieNmjIKfd013APhKQF(q-A$psA zGs{E)VPd5{D{H%o(E|ewWWq&I>?w3jq4&ep)AT<{k&4P>x1#VkK$(KnJRpSW@;a74 z6wuGnItwwtZk~aO?z7<{AUSf!J#)U3^`WBv0>o;c!ftX6rf7|r7r08+#vD4jKr8uD zuwI1*7tD$bxOD<(*{n2l-JEk4LXq8>1v?&j!EvI4U z1wQB8w}8D}PqqKF$mQC5oG>y0n^~mZa+GDr=l%=IT?u}Vd4G!|R?t~t9#KdX6$qa} z!rQQ8%<5&tL091Jw|n)99Zm=Y_HBh2%7b~6e2C9UtH0Fzb7>C2t{nf+ z!yd2zuEQaGVpfE97LaF`!cRe{k}|bhv^kwjgIrSa;<9QOAO^dAGn_84O|x9Go`(1? zMEJDibUiOHb`k~Q6FD-pl{nMkZz&v;RG$fYntCCN(GRAWnfR8Cf%qLC3af#_Ht1?B zc-d_^4J4Wf!6&;n-tToXk=Q(NYY?#1hxMmJT62-YG%%B&kKe$kQ(u zo?iBfeHl7Tj1_?W0O;AcTTY9P1MMST>y13#6awhU9o2xFkXMFSy`|^p^ zQqFWYLVYdN?iWgy=qCu04!ca6!v^nRz8~FGvA1MG9iUF4kTB@{PX!jbW5Cb-qE(!+P3B&U-Z581BWh2rSgHb8?7ZH z<1o(H-5nzIIlajH=tZ`&hTbU#vHTcf2qD$xRly4=AN;!Je2-GqMi`w4CJRL=Z7^RO z&IDt?-mZ$PP7`9{y!T`$%Ca%K%D*U0$8M+kj^X8m^u0?{a{}&VpHMPFiw;RfbKLmf zBXZSu{V7e0bImge=K%3fDej_i{}_FSH@^&%NS9*msx3AxA8JwZ)s?3`IZ*6*S^f6H z6Beto-#;Re#*u*Z9d~6W3XQrpCkI}yXWpVK@zf(9QSC!)4=8e=);1-J?mEf!qjQ4= zFWhZIeH07%7p5FcThyj+0yg~*rz|?tiV1a_;ax2DO4K+v48!bl=lyxgiqnY75tX(O zDSv%W&4guy>?N5r(BU_eFVqcR&Wz2Mx$d4Yok)S!-pp0ALG?Q>y3 z_lwh9%oWTmcjVZv$;r9545PNZ6q9?#={q1$I#`GRtFsPjn$+EV!x))7lNL9$JziphHGmSKl6chK(4e6xzEws?k-Q)XjA8Wk%%c2~*Ww ziHsI@v0Ph(GmM^PpB^F5Gqb{@ZX^3{Eb?w7yO_F%TGFqj_q+RawmhSHb>q*Lt1qx^ z${YIYNt$9Jo83}5cO&Y&y3|OPi{#NR7cJgqrE2u_wzf^;`A|t9+7}l$SYXBux-x=@~TX z$k)(i6J3BAbK8+bX*n~^W9SIBqr)aLU2#d%MVOwm zakE_0iT*KyD74q~s#@yA<4s{wr|BFIi9ehhl}(nWDMFLy6bck;4J(6d#OQEi29uBy z&UbO$mf!;D-uHk0X05v zN>`FDi5ul-_^SsInKKK^t{VSSReW<@(L{2XVN{7!*3Oy0As)M(FVbszR94BR)Cdo; z#3tnkg+eOV)x%ZTggloDR`XuRs)xsQ`IDBYRDG9qyT8SRsevneSDXP&k=@~3vZ{DT z(tcG5^JpsdgFrqb=Q@8aRNV&-vdF9^w>C!}j|{wfdL^72Uv(oXoK?^=f$ey4?_J+) zzboM-kqy-)4O?Hg$%$KmijP^@S5~4_Z}Ch%9T|4JS(K=Z`b>^SC4{`f( z?nM8DRhTGgj-4O{7eErwawFx=+J?B$EZT1*@a=db32cEZod^yMubt=26pQa9z2&Q6C3L zZ(v*!PRTy#shyQEMEL2W&mY6x?~(8vae1)vJi>^w*i&vwb1XVrheD(9cMj__+uvw2 z!}SXh*ww&!+L8=DRP`g6uIXv7_IW#&hLz8PkWH7y(5^Z}D0siDvYFjfL4#|=w49<% zGdK#PHWb}yZI^ogX3JMcw3LTEmf)=>Kvlfn-XZsS;g+rfAqT9EjNei^g9a~}t(r&| z&AGD30$YTR`sz+O3A|ShNV54Xn&joHbdc^p#Vlnh)yUz8$QK~ zT(FW%7!#tz35Mdng=UhS1%OH~9f z#_?qmrWT42`~0BW^s!lOWOn~rWK;5RN#(bSPDQ2mTNh$F*S#AinC7xFc3!O&L6aUT zAVMD$^k0P)1PxK*nQO!X0OOu>@Wj1}!6~XYK3zPl{wg86&xx;3+iJ#SM*gHiN4GlA zJD-3MEK05yyJoPSsadaG2-q5p%^CT0`6Sn6I(W{m z0_#p@2`n+D*gplmxc%~!w3xE@xEdExb*h(kl`yk}4Y2Hljn^U+dMPVu8cY(%C z_|cjumT>oHp2wS3738^|FGz|^PcqlM5rc}*;1V5<;q>(Z*Lk-T>6~w~`G&S(nqdJk z!>JjQ=a9_gi1ikVRd&6Hx@qU z-w2G{VTyqWnAL$C>-RoYbvG;`msyo_>AUwdBR?b30lYoEx#v90wUR>+l+tfPq9bDi zLY2x+9FXmzf;j&du9m0@}x{m>AF*`W$F1AjY;c720b*zb)o^f)uya z-E!CHQV7`D31pb=%|3&!5`)nfNhLKxaBXE@^l=s%c{^q4KqWnRjm_fr<6HN08-F%cg|G4w< zW|k+ccE;#UFRzL-gUP89y1u-=+diG( z3KMb7M?8xV-4Fz^&sza$fy&_Byz6v0+ewJM05sFF6WGa!ccK1pQCh35bTdUoU21AG zZiyZOHs-h*?e&$Tc(Y_yITfKM%!HFmZzQO)BD;zOkogn$q_DOOb%tx0kj?ufSVd?W z!b=aX`KLsh!D6)pXsr|nj{Aa)g6nv?tfr;7FC&D0m!BMjdXjX@o7|BuU`MJessB!R zgpl=)-(BcW`Sy7737-sBcP~PXOsYlHBq#rxdS)G(bXQ-9!pbRO-S(HkbcOS0eGCz0 zU`S$v>k%UFiu49!y;nv~CABDmTw<05%RG_vsphXqXo0}}v05cDegAKKH;nub$>&3Dem;tlQhPA_>#WHOZ( z?j_XUqTKj$1u61-LukEHu{Njh=U?I=3{D2HzJ2P;flrw7`t3pU^P*}~q?`;$g zClQ}XHHX2JK+yYl0aF5Mo?84GolM#3JK8>8E8#2V-mT?t=MJEUZX?{mR71PuJpMT7z#n(fN`r1x;dH<+$+`-IM6ejKT61|kVHGi zJ#`1!5er%Cx;bDt3QTbYMWzv6PZb~XxU^gU(^D;jn!7mqbXa(&b(VnXyaK zzPZU?&1OK&0$Fa4iJTiNAo)8IU$lri$t6O|Y$T8(J%$__ktq{+IK z^=itkB_`W2|K}G5<8KR8(9y1_T;MPx3+oHtO7}-?z7jLl0T09eaQ=6{abbwJ7xA#| z*=W)lk-V9IKmsjuYYR_#VJzmU20wAYz7LYLE&DOryPOokSzkbQjXBZvsa#&m^z9Z) zmIwVgeX6@rGcD0Ef|v+>kruFIOg|f#W^RAhOd7nN4a|EP-5FV|JGwTZ?HLD%ZS5?gMCgF{u?QK^YTig}vTO}S zF%k5qHMcPZ5ZL}h0YWYd>bfDrod8N5{{F*!QR*!=ujIaT+Vi!O+{oH>9_(=VSj3p` zBfsAF3t-lgZ34>oR+vBmo3SADjX%~7YnVoUZ(w!D1(~isB@aQ>w*>mIC_X+xetF=t zYw{g~<}JIS%&AqEg6~W`fBIFlue?eSAL^Z2!Z>@{m$PYBGc<;i6_CT2jay^oY@fA!8m_b}$u! z7hF^_kAx0s&2~yAYA*38iCRo@rYNi`V-`3mi+gp!<@ocHf|Y6J>O*i%!4f17IX<6F z42G>YB|B53h}CqZ0cElN-vwmB47f28-*dljSJf`SR~+1fgXya3HpPK?QT?aDH3nNG z9sLY^%MN&VGrC{_knSLu>1S!Q^R#v!L}7bf{z&B zWv7*V!1sb&-x4YQCCPiLtV1~0uaysF`%}En!GOLD7Pc6uZA&h^1wZ0Xi{$R<0TM|j z;dDUJNhX-Qw30tP9Vqwur9B6L2A{S0#V@oGwB@xApxu1 zuUz0eAGx#7J!H0WYtr2{J-`;wpx7wbLU_@RWVuKsHhp>>zjB%5yT^X z)8{QkdNgE-pT>2(1u8Rqz4S+dlaU}B;x%^r<&35UHnIC7dLS;x!*io|b+l%$gJpmi zdMftR&H$Z9Gru}`apd1mJa~2{XQVTKk!Y4;Jo;}Rl=Vl+7i!$E*@WDx7wlzpvd3Ai z@@F_}o|ue3m5bY*Hp-4<#wT~C{u3yX>;6PH>#96FYR=*q4nb62HTvgz2S~s_EvN}H z@ByoG=AK6ggXFtRw=eW_DK@yj+BrEguUCQ%3|OqK)kJUG9z1&Bxe>(nZD);%^yop8 zh-ddw*UUH`G*eLUO(ucV3VonhuY{jI@qIQ8)d=mTC+;WIh7~cx(CZ*p<|RXVyJLwTD2^=^ zaV6|GZjSHSMhBj^d2287*Xs#A38p;m2~7#3;7q{mMesqASTUHQCyA*7(b$sj@hY@; z^wlrHoVng-$S~mGIGxFcUc^L?TRb+2sP+R6O_^kbLn@LJ>g;wF%7l z$^QKWM$ky-mA74$%ZuE9cMG%ukm4~`DTG1r{-X%wAHGRw_W_Bgjlq14{d+8C9is=7 zghbr^Lp+dI4!$9fD-^iusQX2H_TfKJD)Zj?8teEB*b8We!ElW*X!4- z|Htgd70+ed2H$^>aQSNzX0yHDlMi9gzMn!G);{?Ae+6G+@*`^gJCPA$vE6;hdV{2hJ@IwAP+VHvkjEN$YhA%x`Fudg^NE=fCDe zZ?G`zWV%w;jayQ7gehmb!AqiT5bVM$D_K)a(!zs>#(VFYw@voHYnY-ho#LA8#R4Kh zS)j{d-ve&*@@F;RuCKl=j8XFf^d*=1$N@Aamt*B`m(((0!5g6|gUH#hV}-!yUfgp$ zj^VxZynmogi3c^Jt~E7I-mVvXrL!y7MRxgE`rLbk!o$vgFc4$nPrIm-%glG?FB~Th zzkC&(13%Dv{Ewr^=OPxm$`*I{22B69%GeUEnKt* z&0D9T(*m=u{DDv?vu2lE0#ut|M_PYjHy=jq@mC2!hV_8qiEHeC@+PX>*9WSxW*(6J zj9I3w!0qFZHhincR#8#8nZA#ovu5v{ z$>W8^_W^)=X=g&l*Ci8px1VneznJ;!W+Tqo~>I}7+#OS+bP4$fCR^$+M?y5Os zq%_&ATE-Q=L|c4*?a3VAkVLiAw!4!=VyiA%ngTs>Tv=WbF@Qc68y2){=7k>}Dm^nk z<9P-X!LFOMI(?EFd}jtniX7?L$!?U}^Nt{<*CXC%gGlJq@mW2PeNC_^V~)A9hKQcT zk-0AE*EQzkZkT)ZY`e5rTlWT|-soEOk`&ZgI9pQ`Cn%{v2*uBm`1$b^@4_)WL zkL%vsZbI};pCw84+xUn2j1t`4*v*mJ>|gTC`dq9As(BtdAp3fR6SHuY@1R+78Dt%4 zG-#FYjl;>^gE79>`t7Zy1w|5a7Q@HZV)2K3#h0Uxqi3>6qqd~_Tut9WjkjW`BNR!f zjwtWx72H~w^#JW|j{kuceO=*0VCczXJ&vOcG$tdYC^@I&{CAKC)hgi^Tgvuq+C zZIH8Aq2P&G{5UIxBZTn6k8== zf|y6gK<4S6rCq;o?C=AVOeB;>b@&y_jC1M;WYlJ^ZuOpXIgs)Nw{gD0C- ze}`E)d*5(xv2Ws9T8t`TikVwa*%fuWagc`7ao)2nHb!C@jRx4fylA~>;gb=pu2k90 zQ^5FB{+vu(HYOQ&5sA?)GTG2@{8>?*tWrY0anzSCFz4A^4hlM5a2O1nK;K|zN zy86qD`^^>I7g~)Yr*D9%+8!7AI3J3>G4a+w$yPoF&Wu~LrCGOxpO9n>NP|@spfd*n z-ezwtilRiX7o4d!+6SWI56ri-65DQYo_?VFmCM|+=aNTf5dwwVgWT|_?DXWmrb z!NJOT5|6FtrM6!58{Dg#&lmpYpg%u0RcDo|i?B^e2%lFx-^Ko&#WYk+QhKwlx{bg7 zBKYipBxXDv99$Q;dX5|)5?Rp~T-h`JMYPWn@fPVDa`cNuHf|7>IbCy-%od5cEbF!sY_=Oo;NEyjRfHMS#oBvGZUUeAq z*O!Ui%NXt_-AtAlL&iBu(X0Ch)SP!*>}{*~l=lKNxxx_-S1zWZgPiEZm=61ODZmzE+)M|t7)<%dtI;Atm+)50dr$4lNS?(*&rZ!H>i_DBH?oZ zF+}*n-+4#8aLG`mri8~>gLyp`rRWTJ2uWI0&yU)sbuuxepFJY@1EQT;vr`zR>Z%7; zj6M|Ru1nHOzxT;to{IPFWZj8$JwKKqV;|8+j7gGBCbIp6mxp(a=-dThPJ>7s`3>HZsav);mjQBs`5>n|7h36WrS^W2*^ zY>GPw=)?!eTj!N!r`o_b(>%tJHcu6$^7JG70W)f{yxvqpOzmGDo9`$XYz_J|c~U$t z%xf2jRzq}~Nz$V9Tcvm@yK7x$!`E9Ph%VHq*wiaQ$}&Orf9b)wr2FYHswG0kma1mkYT2GJi@$Y$9OVb9k)hjV|yGy&58B5??&QJ7(a4voRt> ziEblH+A}-VOdf4)H283?#YhZsM_prz$xp13d_3rsaPo}S2=gC}kp8)#cL$Y0uX_7# z*YBRHUL90W&H-)5EnHQvFWW$6C&^LW<$yTvM*IrGp+nbItPxpCdQiM-$um&SmFL*T z$@bJM4vdibrU$NpRy-yoBtW+?qkqj~Id@ao-pZJZbWDk8$3%RrIC1-iHTHi|)By9q z`kY&`(nA4Sj%N*{W>J0($kZ{_qyhc?xFP2K&M}DT!NNJOScMb&=~(7U?59{ARvLA9 z(b{`GQ2Zd}HPj&H%Kfc$?O)cH_c}R>Mgqe6VAWieJobUuIQOJ>4pT0hh1y1XN#xUc zR2-h`8f?7!a-mvyd*Di3jN^j3p~M=d`aTanQn~qlOTx+fp$M? z>qNvh7=hWoRAfLb`dJCKikSd}0hyaOtz^&5t42G)GSn<6^hf+7NR zTfr*EiPH&!5n~x#bg#hxd>a=4RsH{Mqy))a=AUVG#_xJCdM4|rec)GziKZ$=hs78j zGkBVB7;xMo46^S-{RYC#$jx%^l5Hypk3`YGN5{=S^XZJ=zGD0|-+9cR{iusBzL{tD z)77|7pWF6YOCMHwI)}xWMo@356g2p?e@BJtAhlRuE~yDgG{agZ(gpVq?I}VB>_RtH z-DC?&1<#SWx%785TqUNcaZ{}FV5hNx+U^UKwC@D8l>h8UDc->_lShqi4%y@=`Z)}2 zj*~g%NtuF=_|X76hl6&}y><6vUfvjR$301+12wZ%(=-SCA^6WWLz!zsHi625Y8}2< z>?p=7?wYC2h!=*Y&9K@*$~LF07%Qnx4Xoa8UQUdR5}&eI8TowiTWf9h)F{sC3X6Jv z2^>ELVg;wio1@5W*FKcwFZPZ-fL?*MbPtbzRX5WkN-J;&*4Y1Ad1+r05Cq?y;-kgF zSTP6$y1w*}0BzN+P&My*z?xryjs4H|KccQqg?=*nEnEFtD6h#n4T2x~bwkqHHAWi$ c>7m(0p`Wf#xofxvfC%J%*yB*O^Qn~o10o^H!2kdN diff --git a/pgt52m/week-4/workshop_files/figure-html/unnamed-chunk-8-1.png b/pgt52m/week-4/workshop_files/figure-html/unnamed-chunk-8-1.png index fb1b0a4a3954124cd7625d7baaf2cf9a6eacd09b..4a481d4c7a9817767f8ae085936479c7f8dfaf48 100644 GIT binary patch literal 25537 zcmeFZcTiMY)F*nH7Elln0a2oe1c@RblG_U?86-+0pddLZIn(F`1PPKQH#vjk3@S-- zOOz}Unw)cdryKO%nR)Zgd~c>|>Q&7jR2Tc~u+9!^{q|bFb^MeS9}^K!5dZ){BqJ@U z0sy!$0DykJ2n9D8-O$;ima$;l}wD6U<*MoCF|{rdGAH*Qc- zQBhM<)6me+($dn=(b3b>-@JK~fq~)Hty_$YjJI#!W@2JuW@ct#VPR!uy>sUd8yg!t zJ39vl2PY>d7Z=yvyLWkbc<$Z1$IHvh$H&Ld&wu~^eE|W12M-D7e ziHV7ei^E{BhYuf0N=iyeNj-Y>=<(yn($dm0GBUEVvT|~A^78Tu3JQvfib_gK%F4=5 zo;*=eQBhS@RZ~+_S6A22(9qP>eERgMmX;PA4%gP!e)jB{j*gD5uCAV*-t*_rU%Yss zudi=lVDR$gOG86LBO@bYV`CE&6H`-DGcz-Db8`y|3rkB&D=RB&YwK69UcG+(+Q!Dl z*4Ea}&d%Q6-oe4a(b3V#$;sK-*~P`h)z#I_&CT82-NVDf)6)}yKzMn1y?OKI?c2A1 z|NXbOx3`avkFT%qyLa#W{QUg={olWT9}o}_7#J876cijB{NclgkdTnj(9p23u<-Ek zh=_>D$jGRusE;2%Mn^}-#KgqL#>U0P#mC1dBqSszCL)o@q@<+eb&;NakoA3uhM zhK7fSM@B|QM@PrT#>U6TCnhF-{`@&PIr;0?uc@i2>FMd2nVH$ySu`3wH#avwKfkcB zu(-Imw6wImyu7lqvbwsuwzjsuzP_=svAMaqwY9aqy}h%uv%9;yx3{;yzkhIWaCmrl zbaZrle0*|pa(a5o)gV9(?)yuxq_u1TfYulD2ML~8Hw1v2fQ;lrHK(}cF-JF=UH6~I zul2QG=Z*>nZKgixnhBMo(OY0d`-N4vpr>t|LzASx2XOf5zw9j{9||>Wcet%RdF44& z_|gtep`X-J?e6$zJcp|sz(YQnNNE{zsjHja#k|_HcbZFu$v0!cheVq z1F-_m?gN2;+Wkct&P%WGt%~CJ&Xvf7L+qpn^8TseO98_-9wYeO2ZLQg@LIeuc3$`b znw=2&omHg2TQ`{0ms9ta=2An>cRgSD0LxMCg9vx(-&5k#TR3kR$^Y_oe#7LSTDer* z&&*n#p}#Br@0d4mmjCBh;beD3IOJQ|stgIkg8R!6fz*~O>tdglwR;RMW~>30geyv9 zr1o!cE^MK{v+fY9xm1BuBf$Tz z-SVG$>;F5b)9-!G))P^x%Fe^Q5>Ghyxmg&x8gpsuJu~7Jv&k`a{hEtGGl{ z5ohg$Gk_{6*zOVx?Hb%pHP3)cj%h={$9Qrpuy*i<2QB#UH7ph{{b)%5?jjArMT+4g zadd8kIK>k`7$-o+BOf5q5lVO(epZh$T`K!6=sG&4CAu|Ng1Vv!BASQeqTnL`-U0Q80S$5^NV7*;D1;Wv#UH zl-hn;1ovfvSu=l|O>TQubkHPmq5&~MFiXYn((YLK)cxs|T4U{(kI7yF3$~aw@^7(_ z|F5>|479{_q{N?LN>hi(RfP2N*%EP+Afv9jG7)DHyGw=;o|g$A6%4{Q;g=s!pM1P^ z?MzAQTEPTdA2+Fai>JTCJ=4}v)F>G`q~Z1?2^;)eZO`D@eYjWLrgX_2vP$?=}(Z!6o2}rbk2yne&fFwjyqDjHOtH4?|%(w{P;lBaD6v% zfSO69T#Dt=xx5oeaV(>v(T^-zx<8*Iigxj6>E)zjiF}l3!*4Tag{?afvOxuZ14EVRBXI`scfjS=irU$&izhJD}dGS0%!zGd|k$t zT$A!F)AsGr-N$`ymHk#uT{J|<+gCCAmUh0OrAk0ZoV|mh%)QyI!+CY6uU4B75h{sU zMTgYw*_*p)?TmsUhX}!*+7188t1y{cn4Ki75?$F;y`gFwduaP)*4PP1dxZlY#1ckP z%Y+J%Lv{1WL&1|f#mzbuokvfO2B`6W)ov@^E?Rc&k$C4q-;`k2WAM(2{>KjIJkQ@@ z^awFCu7j!@IfDZ~B6Mdd948qzEBEpq)A>j3i%UtW!A&8KCmwh223%ia0e{(P~m?jQ*#|WUWDas zvZV~mc$mqMG8^U_C~8Yza^!-tBCh2ce#D>qQKF6MF(T7S|JU{>BrXh65}*H6HgJIx zGq%)W&E;2zN(#EyngnK{0)x7`nSRgg`-daz;vBI>!$>(l9a-^xeCIJ16U4fTO&O zeU-VkAActn$3nS9iO!zPrqzFb#53S7Bm0G2k}QW& z>fn+_lAt8~;(aL{)(t$XVdsnL42bHEa~DoEzxni-yb^nelsj#p!z%O3LT?1M66RK?b?{cX{jZ)XrgwlDmO-P*~(1!Q};()LKIFJ+}aP?97V9&&*evsK#ceg(cxNzb5%-z_}T*%3QTLueE-@}2PfIm(0{%GrOw6XJ`XJT(Z+eMT1cYPhW11neXDWtIgOV>>xUCZrFRL)H# zf1B@HEe7KAeOmJXNz~d9rn|a9Pgbpq3sFZqEN!TUK{C6+CHIpMj4fA#YYe5<_B8nE z<3h%=03o5qhqza4`IP<2o3KCnJ_HgT>sdmSn~#PpJ)ddyZY<= z2w^rb!D{n#r>AI9yXRj32AG11yXGsR>~;oM<^$^S4>3JKpPx&124_LZ=OKUNq#KUSG@L<>v1;uLq8@ zi-vx~1LZDncfG&d;mRt4Xmd1wvEdrI-6P>=cJ41ZtRZse2faTg=l>h1_>RDf(^{^6 zKLf(^Tz9Hth(LFb6HWrm_+^8cL4O~{JJb`ee#FtuYtD{KeTL3;reGwf^`5tX zdVDVm6OajJN&*>?^6& z@WITZG>C89H_$V+>H!U1acyg#PoEqK_&%9VY{+W7Mm^$QxeaYjV7t;atG0sEJaFrI zGU#l|0N`Wd`!*kS{Z}7?CnEojs+N2>oje~XjR;J;8N?pYqPGOM!Dgyi8FJ$0nP<(= z5Erv&ctmJ;jWJhEz#7iw1fpu3Bn4ZW@6@_}l#Tu#lN%4|!IoDG>wz9ozT&5d06FbGkUFshn#ZLr?ti>+QVG zcG~(gm^n+OZ~f&_4$&BCaN}ZnK?5##?2Rb8a;{1$P;&8ItGIEt9l9ZQ4rT=Ji;8=u z?$ma5-gvGx!>cr-13ACq;mwuyMR#zR`)xVN=0pANEVMb!YD51#WH6+fSO;5{a)oD% zj}1<1ffy6xV&=x1LDR&z5`k#9TlQb|r9c%g|y~Ab9NXo$;xjNz@#c9l5Ni zKZE6Dgli3a-Y=aD;+n<+53tI%8$Q+3!En#GT#+z0Aw<4H?!7&yiYY>YGfOc#LixSex)&e z>V~tJy-Hyg{p9A-Wh$s$T=_uTs}@Q#EPzsp3X1<`8<*~SyT^NPJ`LYOzGf;{F`mQT z$jN}Cb9~LD!%=U%WJ2TqR3MSP(PTz@X( z`0^D_^YQ*bg{hv(%9_^;;c8WyGg ztj;!hz;`(N43X_RphRL2@SO{9ZoV(SqJcWqm+U!qV%%6Li1L;UaBU&O$}7jsKFWAE z=HqDR%UM=7CYeNOuVLmHZ{kK%_sLM=ZQFz%1FZ!CpSyDIKP!E05}e&X7zRGoYODSZ z&0K|;tlhC@ISlNonEza5|5*GR#AGUb+9)H1A@E)s6Em4<7W<6SsugzBzj#C4-~(*- z&h5;O__1EvHb;uRZ4bLFdu0!xhQ@yLO8^G5Cly0gs9E!~+9{bRySqyLD%R)np=qN0 z${ASiyeFJMoGPfuD}%0ZN7zTBz5Wz}v!>!FMP*_rWC zAE>e8Y|ED&pzvdOa_h{ClIvo>i|Xx3@v{SlldgUA%NHE9Qdk^WoWXU3gn!MG!41!o z3I*K{R4F*a|M(=lwbRh(4}U#2Q$@`{w>A!jdeN&Lyo9z<9h5M=``r^Z=I?d(v-WLr z#@6Blg<=qO#qK+Q-&mA8oO|Cj%uIqZ1PzOWGcb1LG}7hiCEE6%gh-|6a+Rp=kHLbg zqOnzu=6<)3u~SMRUczbkSDRv(nU85YCu5R9zmU$x!yr4) zI{o~Bn@DXHYp)$?5+?tC&8F2OQ;WAzoGeJn+S=RAksVpbM?gu}QK92Z#_auFbaF@c z+7n=F;d7sA>KofA8M~L?!&~LLN(|`cn|Vkg5;K8~ZfQK2;WxZo0Fl z*}GJC%ONewxk<QsEPp?`APUdssU^+dyeiT|GqO*tOeZ1A!_EK8hT0fjg^yAgs z<&JE@%`(vRzb%O=s1YIt12sq+sLMPgHyZ|qx(!`41w6y;Po-6_1Ld_-1! zTRS1I=k1+ZI7`#&a0ETYGtQk? zClEg&pj4$<7Hv3R{%BGdb=-=6EZ#1}Ec!8YapTkUm~iz^7tkNC+qDW?#Lqb5i0@UQ zz@3W;>$U}0QW-X7XcgyPVk%B~lwq2%i#H2P+6IBl=%Lq& zQi8FS$vlmT_GP4HBHsY{tIgwx9PmAqq9+`8pm}x$PmP*aB_XJRm}w7r3UsQkicJ>U zJ66j?3RKwLA=3SfqmQlO^Ltw4NCBcL7z8%r66-rgIMwM_rem2U6qhVX>XuNFn|I{Y%Vu4GO}EZqGB#&0Z`7`;bbIT-TdoXrc&a^AyNoQS=VPV> z`Vy>n#EeGDbys)xx(x>ZRK2Ul{rB|f|psEX^XQ$k&ql2rhB@1@%(;vT9+Pq<8a7$0M z8&5nLsOZ@SMNBmG^W7-uLl1CrwcAe5&AQ{O-Xwc)oXjJcli?kr%F|pTlI<#wC zz|8j2bQ}m@DVP~2Eclt!CkR;HSUg@?2YjX12_VDM3fI4PotWq(B?J5cZ-jfGnO6~b z8V!5Xi&pAiwrv0x+5NrxyA`A=$$ufT^_s{>um4~IWyI&jK^;L%Ug^Z5SEL`jl$&(7 zum!4Aoxdv*SRukQuONNjk6~*n>)|L@t#VoE)S2l?2(!-sYVpiD_cFgc$^Hv5Ah}8l z`h+wkbO;e`QBc%z+xs%vm&)I>uOI`VH7)uYAF+5V=YWEd!yFa22(al8!xr0WD3|d< z9!llB(VnDq4sM#f(n2l|ET|e^<<$wN$5EcNS#A#hX!0qs-})lU5nNNHm!pNSJUsd^(5L96BPTxrGr$sHonpRw}foH@<q5`!FUh$Po*tk2j12m7m-8#L^8DjN+7^j$y$Z! zSzB6)xh^en0ukA!rPVyozX}2hi!U1XnydqKN-D!+giQQE0s?y2P zSZ4FH{cKkj6Op$@8S2`uRKYBoYf>h({q(X6C9L+ucRiMcdVU8l9iUvE)9#}^@L_+x zr49|)mG71e(+&T;rMBsnR=_=OUuz$~alk2JEQ;GR-ZwVUO+}2<^?ZJLCtvM)Jt0z& zpJJV*9pS#vLxWJ<(jAnarG8FG0h4>4keEOCaP?$W01sBH%U5S3;JiORK~)Rk8|d!~ zlABowk{d$YXDcwz5$j-hlrdY((pAc#v4Y0!Vf5E8?b7a^t_Lwt|K4J=?WKpSqSkIl zR}jtc*;;tNU{^7uHWkMO(=Kh1Jfr*hnGLBy<;uyP%v=y-s`_v_8R1XXd_% znijtoaF0`5ju;g(9+oCQWX?>D!lO`jFx;rmG05v^`xvgC9J4VkjMB0M7IeG6OXz_L zdw_X++of4MVM-XqGyZymkswhi)Gj7+CZkl|MV@q7`Il>6boq}EF>YiB{zCXpXZi+{ z2{9}pg=p|^!Nzx~D=MG*PLT;KczdgMy$brvpk-E~akVWa#s>azswl3pqFk1>3 zWy?vW{(a_s@a8d+Gay{M{lxI~%H~&ky@O$MxuDq!ua)Y30;F6#F7{kFICd3#o*vd8 z_h;m+04B+AFFic8eiAi{7yd?06fn&x1~74*fJpZ{m5}-1NT^A&ZE-tij>YhLi8nyy zfIa}NWBn2b<}%M$`<+LS{-~X0kPM_ zu+1}uPW`wodl(iUH_1#D9NYHEQ0&gP=7OKB)AqX@@zlyS%n&oZ5l4%eJdh<9{8TWSH z(B7Sh|Aqte9?^?@kYD^Oc}w{g4d~4&EWM2`y8bKS2NWiiBNuNm9K$9Wc@cErz%8^@ z3VysG0_hi{Mus-1V$exp1;m(0b^(1km0w+(gjg8ymA@OYBdkn4?FpCx1zmJ*+_G9q zjebT6PUb7sa9@K`@bV5iI!r>ApXe4c!XauEH`*EswXkFtva!w^2$$te-A~7l%E0>O z6fh-e3~eU!G=deG5us_J&JMndc7h9Ea~bvlXx+5hSxHdKph#l}({G=2Q7c!1^U7RW zE`C9|^!ScAdwdEQ!>9;c-$gbANt4`egP0ujY>91WxNDSvdq=^~K(ATXCf^w`kgHSi z(zy2L=Uc-O%Y;UpKZaOE1Eleh-hlnV1{0{z%({O580p?#kFvOm8qUhke*5DF&lWby zsj#?IrFTzk$8myQ?7;VQkFZ%U*nNAxo4T3aek;sv9)~K2K`=`CDyp3^lzwUOTOeC9 zB-5gk|1h{7ij);N?btiqTz_AB`ZKP7ZzVwQE;Ga=|0<$O#+?`L@W@5_2_cNheEg^4 z{uU2zbEpw5@%O~t2t)C4i(KJA@mkUkw6@|#x* z^z_Jh>+$F^YG9M>mr~Al$0c$HfJ(Qw)-ld@6ql?9x7xeqnyeI#Svlv@VSl&h&gs19 zu(z$V<>UPT8e|@4e>E`OxAB4mxy^_5> zE<{c^cTC9-lgYjX=umo2M!k+(KDO5#nzQP!e7{5DuNSKkRcN&x7b3wH_3WbO2d}d@ zN-0UE<4PCNP(!NkIqXQZLcMK>OFovI*3syV5wspqdH-IoBI^D+U2~n>UmmT ze_+>73vgZs!%SSrZKx{u0mQAvMx38$?I>3{(zv^sPrP(|T>0^JJ1Ly1CAro>Y0nZG z7$GjQhd=QtzxB$29PYr6q#8^8sxOCav3x9+UA`Q!+OWYL>VZD^U*NrL?qeLuiD9s= z-icBnVLMO)hHXZp3)h4Fb2hCTo+blrFNk;h_aUvdkv^HLGsS>T_UKIiBr(qqf1It3 zmcZ_yn)zo3&pQfk)DL{HWdiJ>%0pcwGIfekGdgIZnSK$0#=;2we!sU9>}GvwS_rmk zVXfG|XrF%uPl#TxPB1L)l)gc;^+>`ZsLUi`r})8Fx$cy}m`se#jcdSfdq z>K9_b`s4j7o4P|E+ajB&0FVp*lG-@xGC>rmkZ~F%%^NvH%YwC>QqJ*h}R%2K*Yw$CE%u z9NYG`iTeAhvHVlR`S1H$;K?i^n4_8M1!BYjqPp~Bxmm8EFk6HCDld1*aq(9?f~pUj z_CJHp78+QD((<7;wJQ2z@il3Zu0e2yNcJ!4*#T5ekEghslYDJP(>w59Ds-H(aCJMd zkjAK+7_;OxYS(f?Qzel}Se*wJ!5woL=+9a8prP`?E9aE0A4hqtutkslz0nHo zt~Bi_&#}EmL7?~<3&OhJBRk_=$=b3Ul`|Ua6W-T#PbV+dA!S2H_Rgw>_HE?O*bODmWkS8Z z;obtCXz59S@8>l(?hZl0!OW3bqyD^P0%f@ySI0FpIgq>bh){YEdsCvH zy}dEtRBftxheY*Gw}3J^^3)w=2yos5h$ur;@|V`* zex3UlxyKbo#6HAnym~2<$e(5cGoW>ucN`6K3D*IXOMN(tGiu827v`J=Wdy_gXaeYa z?iQyWFa5NVx2#S$G;BfD(+#dG$TAVmCGFA7%`A>af5!r!?s$@tESKYK&NL4ZOJBfJ zcx{88iCFqztDW}veu)}HR3#y9=T#mE7%_mSsJl%on}TF+EU&vd@hAOW#^9r4=OuJG zq%X_aZlQf+gD?-vR0EMwigr%jzIjkW;sVdE4*DQLTtIB#i%hgEx$^eb6>~VGgbd5} zfp4D9Jbc`ZPp=a`ygdK$MF%eGX)HO+_>2?>V#=+wE(-^x{E9S=su$OVLDWx$i)#Hd ziu*kJApeln{k!l~%S2nHRUa^x4G2|ZSZ^24YZ43EzvXx&K*IIxCoWPp%7+PE_Fx(d9@T0bAePxfj-PI)YjF{yUXxIn0oe|B z#2lACPOV_0D^2G3s9F1!@2;v!zW0%zK^xic13}^nl^gaqPjRldW1*zxbyWJa-S&)c z1GNf9Gie6uIa#68*t39htO-`=&+5c`yZDtF>zuH@J~4QY3?_3EpeVSBR2WxFTKVFt zQ*T)3v@&|Es8#f|c4-)oOzYhi(c9t=!WUw1NZZAt{T z>n)J}oQ)WzDc0u4@3#YQ0<@l}>vj{y+rT-?s247}Mj6O0c5KE5 zbPl!d8~6lhc@V+g=FETHZ+6hcLUH#yhhAlR?Hi3hJ!n{kMf_hb1tHYp=7*MdK%RDKTJ38b5YU_#Bg_OcB z_2=b;NA0rBKZ(#EREKp_z{y~5b4`1H{CJFLJtWMx>$Q?j5VUeKt-pke3hIyZu-Dn3 zsNJvF*bw95ExT%+>4+bt$6ay@f%o*dvC-geedTAkz-o|!=u_1(CiJd__~JL)Bch{U9cLKndG(o7R(f!?zJ5c4U;w=8UsC2vU z_U=S26YlBcBIsRTx0I1j6+JPtY2};Y*Ac$;+EmTd(};9YWx?*(Oa@{8MdX*XxX_9k zu2VCCe(REfat)T<^;qRsi+Y-uVeh+yqklFwW<)I-w)JLJTGegx-O+Q9aQK)u^8L$t zwF^v`r3Zrg2~Yl9A&hBnnJu^AyH$M9#La!#I&<;@vT5Mny~QlsC(e;iHR0L?>Z^Hm zO)qbG*_%#YX{Qodbra439d@0{3G3z?1rDm-4*htmHdb-2MMqHV>`<=viaKDT~y zTDop2hdGCMQ7$##x{h+jjw&5rZhinsx!e)MPh7yOnQ$%3!}<_E!?y3YU7k- z$ocwOZK+wfx|o&3zzJ20Pm?Bm_~fy2RXxC00Z&g&SZOEBb?CJkL5X)Ql?@CCgj346 z+LAhy0wq6U!n+r#!wAjeYOBepua9wm+hy)@5RN5NI zzU2B7ybZn0ecieCBdo&4_~9j9BZHV&x0NVbA}2eL03$UR>PB^0EQ>?EenM+vfi~!+ zrLyO4+^$$ZoP6a|3MB$>bf$QDIUpQjqWUY1H?yzo3)JMX+0e=o0bHzHL+61y#IBd3 zsnu@*zMxA>rhNwmI@xpgVZof4G=hc-UY&|M{J^p@WpI4Q50O-wBujO6y`86T`%25$ z1OhJEetrUG*BJbT!3>5|Zc<@IVZQ45`K5Z5FZ}zIQ?L|NaH)z_?yXpeuJR0VF9*){ zpNw;Q8x=Buo>fd#{{6DcFIZ{Ob|HI;ik=s|y=NdM3NE%*)bSvN49lW`+i|+)_-xVh zoCerBYc3F(_=MS~gq4$jZ18cz>P4X6-1JQ=#Fy79Gj__upFX-W&h0$)C)cQE^R>sL zpyGsnE4|uvR(u=0L_(9}bwu3C7w=o^!U3a!N&(-rf`?M)*4()-q(p zGc->NU+UftYcby(mE5HTyZADYz9ZWvf-eGA(H31j7yx0B88YHDlWYg=Jv4VnFcl z^^AjV#TR?mj{<4$4LiGale%K@UCs9z-v5Q+Gpck8etexg@D-7sP7<$WA_T80uwq9m zmF8X=V$WT-qHV5!`49{X!dbUza5sCb*)GqO^c8Rt*&#w!b~t7x3)zY_m5s`|Dplp} zg1~7aOsnxO)JUIDyMkgIFNn~&Ll4O&dJY2#8#e??e|+A zjuEWNbiAiB?AQ7G}B&b?=CoF#%DK z3B~ue0QR=v-9C(3Ac(uzr%Rl&&FR*442Q)VQX44}c=Rm&5<%`SW;LrxWuKpz{Da(h zkdbv?6J%sf*S;WP@#kTz#bzCqg5+@tO9NI$KMRbG1ACA+X8C_hPcZ~sEOiW>PoKzf z&*&tAyZVfDK#M9~d1!E)`z20Us0Oo(L2%Fm)ZXW)L@y&~GGZ|7No?Mm9Mmo%BBt8P z=jT5>dn#l~ZI*cZW_(iE(Rs?35^hPQe02Rx$#mwebIN=Ifl1h}40T4qY-tQtPRj#U zurgK?wtSr-e23H{6Ni=Txiq9AxK$!gHY{zAHNFBfs!jH;DqC_z<-`tFt{4r+1p zm*erbY?=X)#2#x9jF%a!Xjp}Eb-R? zjFS?app%Hb*1M;H;ASE~`;u;$41WuZ9XqySV#gtPcC{OPF;@=2887DXqr|rZIZtU7 z^wHvod;yl>OJK;C z#^@cyjaeYeR`;t3m7?9jz^*gvxUi6Cm6{p#qUig4IT9b%aG#er@H?W(pL=oaf&{x2 zAqOuPXy@LqYeV|x^%p)a@%KnD5M($*H0Ve){y2wp`*OtS2<^6UA>(e_cn5Ls3jRmUh|kWFR$vG$KC=BN#BJ*t_@!L%L4` zN%!+PQZ9@txmDY}&GPWCf5X1CJi5JpRk(*+X4m$o;r#3UL=ptuL6A0mG1{@twkiMK zl%5?J+coabgv!3aMgC#}EQcOQgHG@|*7h+l%K<&C&?37I z>$(Zlj*qx)wTN~ae+=~dn)@P2*!6EM(Sljnf(j^}&2dd2)2qfS8H$jI@1AhyJJKk`57aav(Hj z*+EYK(itJHht$wI+7Fdety zsIH#FCk)$37@&|5HEyE@Dbg*xK~D6xRA8Y15Mf%iiz7yaOmbj@Dx0Hjobf!;i?dSl z(Vyr|D)_@Q)`z-_m@x!%Jmg>fx9=Nrn1Eu(#_wP2%fJb{QrB>g=-Kq0&vtT=A&V>zFVH%KQhPdm}LJg=6xazavmw zbylyt0)n~Ggbdt4fcaejr&{myw5lPzna>w{_p)nsB@~LzTLa*q0>{P6w0o=}AXZRI zw``4S{IChyH(cs5Zd?$B|Z$Bo1m+XXtLV*_Zrg z7yI`H2j$2igl_!ml8K0ux~fw{fCIqRzda^*Emx%HYvCFYQysW&z~!?lJ7W64GJ*rv zO`?!1P{`02@~CnLlBXx^fkOH%I>X+8_L}eUVwh1R#VK)#vxQj`?u#J)ucd@0ouBrg z_cr9E8U3Gu;tlC+1?_Y0)QtDKhof)bjveR`y#7R*FAC!(k`Z%|So}(*+6Zdt zPOHF(9LsrjL!2s@mc8q{w}G#gF)^rj9GA@HNx>8XzUmwP-YKD!*~3F5C9?Zz>I}s9 zzm`HxifyKMP?0$7Dk1z9(flvkdQ{u4f<|qLnf+RR+{DDOT3sfOL7F^F-G0zpV1N~5 z)-Q~Z5Qk+H{=c*s7y;#A(oYH zJe$w8w=a_q%zS9kcoQ#6>jk<)GNX>Z-)w}?_nfcsNwETpV7HvE{<|;tJWY$Ax9efGS$D5sdmra z^-pBrsq0yIvr%?y`x-fBnhy_H5?Ce7H}k`ALFgLhdCCt74!X(&v80(FU+)^})LB?= z6P#B17Vs&2I0J|oPQ#FJHGQYJTeJ3$J6^LIx1S8M9grkCvY{8IJ!N1584h2T1w<^( zUCn-#cHV<>WI3=Rg5aOTEb7P zA=KWii9WJlP{xmNbyl%qz?@V9WM^yqT-2xX7ay4*w3D=4M$4T~(O;7qaK#=eulKW~ zr{$ODE`!!H8fPxFkL-PccmwDPZ!N!;zA_ETGKc_^JNP$&+Mf!7DjrgH@}%sS=Imw~ zm?mKO1Ep=#@ioGjxmM#PL2b7!I%~0AKh*Ymk`t>F3#Ox+hN?SY-SX8NZ{DYyLeXRF$FC8K%}w9$RNAA zu`<&w-?`RResk{az9s}z5*^TzrdY!l>2qLUh9!cqWOk@EV>_~D5u@|cm+)PjU;gsb zXw40Kfrs=e6Wp#ta;)NGGxz!=1Q#iVRhfw4U3?Bnc*utX_eKwm%slEMFw)B<%BQY3 zCQ;SBB@_gRiZ&iTjeV{}bDzagBvm^sP@?p9a^>Sh*Q=RkJP2>Had6wWa zIK~A+^|I~GwOp?o<)x`R?z-a`>Kpcg@z6mb+TB#8*|NGeRuR;F3s^@RvU4ZALqw+Y z!fN_F*n<$Z2NO!)+aYlu3*Ac?nvBJBPP+{sQ959_dzg!=wt~LDS5|bjKt}xCs)sKv zz{?pn-^1jy&V&lT%TnC27JkEE7NU-crwB_$#Hch@L;6DxgFRw$m_6n?q?R2qPK{dr zJs_KU!^hM_GAl6A2`g3R(Wh4PQ`ycbeDHCCRP66`CC{Cvjt2hm0iB7rV^FSMja2)(Ssg(aGTSEhD+6WcV~ z@X+eS-cLOrnJ9k~c{d;~@iko|_(-1IEt(7X6)Q01oYjpi=`n2RNhNaIg!4v}z>ibMKVu|C;?&(4zt)`{ZB+nj0Z`?VA>_p@*_0RY!O zON=-N*;w0x$r(BG2%gc)F$p=C?Sp-T|J;`V6iZ{TaXfWpMl1Xtfa;NgT>Gft9V4_* zIu*vkVNke4rlEt4M(bY|e-{9}V`k{q8yfdo zgYF!2wWAWVBCE5kX~OH14BiFc03cudyaU-3_LKQs1#faJ`Of)l! z&j)11{W%C<5nR$%UU_B&*5}D+n7(i!qCc0R6?YnWaLKToYLKKraly`I-0yZ3mLUwT zmA~diRHV%eY)ui&}I?#0YF@@ysgQ>YDjJlMNC{bi= zyZmRD>OO>(yCAqoUZ3ZBpUGmG5V~dCQF;Z-N;}KG)^vJ&VD2|`GvgVtwd6x=J~^iD zGA*HAvD>5m(1m}$`hT0M{qO1ePi=Y3d;j+iP#P3o?=hU&R_UwV=tI_o%|cP`>;dxf z-zf?6raZKQeJ4CS67slPeF%?Mvgez0d^P9w)Llflz1@hDt6Zlna zFfa&vl;j~>AJE42q@f1>PtQ*r0rD{15lQi1l(bO5T4XSn&a+K%2h3|?OWQ1Wh(8~y zFK;bJ#e+DL;$LGyEMp?qicjgjkV{K?4@a6a8&_ zVY#}?=DJGe`2alBVZT%P{zDBj17?;z(Z30P_5}Mj9AqflO?D3R={BLHNlBjbQ7^+B zqpHJrv>LjliCA6EM@WOZBy%|5MSpTB#4;E#et=>-ScKrbrXSHAv){EK@WBb$1d6PQ zr7@Hl&B)3N=({3CTyZP{wn>)je7CM z^$V1UgJ)F8D00~2wm|GOdIO?5Ax=^pcf7kX9{`xA4rz8qaFJ4D*NOmead%o^D&+6O zo|b1qNc^#QSp4bbDvtZ9ncyvr0ky7O`VHz;G8n4|pOYSoz~p|)3xGoph57X*inUwZ0?`Ae<5Zlv zapkkDxic3ESe>?On2rDHsKmuVOz^ z5M-CHz1vAYv`WNZKUK4Q^qb^Q1(Ugr*^0tFPPSf~Qv)fp6eD5=VzV{777OEfxW7L) z(4!>3TU>n{S~*gd(-E=~q1h4f1X41&w4dHbfoTrjBZKUoqYt}+%UU|ax>)usSH4O` zCr-*ypbUE3ILtqfzW2+)t~MIS#b)^{lkh^C@6}fx0M+Wig-ut>vg2^ADcv!n6V|$; zol+<3I-O9<73)eOA-Z}i&@$mm(hu!aV_YePj%o_(D~tL+XUjnjz6t0{T9f3?-RZfF(rBr!VsXC6!_F2E0wZfPDzC8PJn%y95OZlrjHA*w_ZYMgpiOWtP zb__S8qi$VWmKt?OBip{mfMJ1;b6fZNU~0=%{oQralebJ`Rf%QGEUF+CHh{Z+&3fJA zvbVOVkA|Kf zrX9zGoiayN_ZMo?!OY*>fRdr5{#o50Ms*8I`0#n%!VZ2?pd`P(pSzOc&It)8HjOc@ z7D~%08gp|Kf+}O_NLlfIVH9DBb-d_!fR2|{DFCHm$k-$62*Fk#J3@AVVt>Gh4P=Ur zEV=fSS$Ffh@;B!nf1`02JXxc#wP=Q?U7GXjBTGx}(!u7{I|(dH(IHtF?LCcpN&{mhaC6yT)`&zPz6v@7Eb9q104>P0buvIBb5`_sD7f%Y_(v#IU1g z-PY3r`T(V)!t!R({uJx}!)SJpSeIvvCN9gJY+tyliOt?zO}EsGo!-!@E0c1$>orS= zlv2+|fZvJh)7i|&CjKY5avXi9DCnMWlX78&oa6fcCXf3+RQ?zpjX`*r4`}@dM)dzy z!c8a5&s^Vu+`2~!P#fFzM4U;IWzs>jE`%^2K zQ=@2dFsvPWc>qmm$#D$33IZM*O87gMx8zvS$phZ|?}8mCj~(ieXBYsFg%!)?nh-lR z&<}w5ng9mVPnd#c$Gp}1_wo*Va59R*9B)%%qT862Hz8eC+SiH}qB;xagLm5ZrUqK0 z-Gxyq@BfIBsn|tFK4t5k@~<~oh7Uix1$rF+`_St@+BIc*$9^f4#h5QBoQdHY@19q0 zsFmVq$nnpYIE0=hJn*-DA~$UFXPQPMypD@zNnc~;5t~u`m2;O_pDJVrbkCemE}>>t z2d;}oE!LTbnQcM=X%M5i{DJA$6a^VF)bqf*%=xr=$A$}0hZ^drSpP{>{%W~1j!;yr z{-={cKIVC2XN6&+KXa)V)!ckcQ^D;n_-DQ~r_%jqjvsSnbw0iPyZ8SOm$?7c!?@sC zF77W`HpYzk9AS6a|5M$SMm2e@>qJy+wIbzG28C2w!3j_U#3W6^ISxS-Vu6|hLW%(b zA|N7RNJ3C=Q54DXDl&u!4xknxLV(DSq^Cd_!>nOW6(kHHF#-u>JVEPf&sz7cb??vn z_uIqw?)Q1$XRp1!{XU<}gSP$6*i8=YAGU`M{(1ORfk3Z!6)bOXwgnkf*8V?86Y5E7 zfyd%PVycs2VM+yD*6^V&{}o(zcyh_sw#amb2}=2KwLHz(1aa{PgZfUa{E&$t+xChF zAcglwK>pV!vNI?=e#{et@Z*O_@cabTpIwG78MQpRx@D=$=HU6`hWVH)zg{{zv;W%K z|3ND9$l9K$@CGU+If@=nVL%b_GZm_~!^$Id&Sb`^xlk2urKq1b5g((7@WRbu!t)Y@L)Vz1#Q`c?!! zee?OX*=rYHVNS5nBD5DvJoVciqJZFs-uNFUrE?EbI$TWZEbh{$N~J&7BLvk@?EJo> z9l9-tjeBSXg|f2g@bZc%e*ls>dcd3I$S_; ziBA!PeBp)ip%C}un(fytiwtF}ds(urJ<2zz7em<2Vh-S*`^bwn zsRaH;j;f)-A*=L#X~t$c#=(gJ1_^@ERz5=V(S)jPX}gtwd#}p^rc7p|)td$_aK5!X z(LRg%8%JSG&E#yx3+9b;w3TZMs)ONo9S=CbH}3AdMdWjS7?}HJV~-DYgPrlTUwjpV z{MtsqOFksf?Z%qJB zwdyr`ZocVC!nnjgdL$8>C(3jj3L)Z1BitV0#Z_ZtP+?sS(IWqz`CIG7=EKUN_!`z& zA6Z%#ExZW93=`vNF$*yxvM<2u@rwE0sUBrC>Bf+j$Y{MNgIb>trhLEb?{CXl{h=+X-D4z(rf{Bb~D=JzzYj7}*^9kiot(B8V*(1vVRu zgxF;-AKGH$e*23gp*QvUcxn?FrNBrZvd5yrKG>#q2q(|I-nHN_Hdj3$g$2G5ZxYWa z9KlHXusuOko74F;@irAUkM$iJKCm1|_||#8m7N8DKem?Bu=9be77zYpp5 zu6MAJE!g%YkK!wV-Xgxudas*~k(k5UIAMWjj5ixDUQRlJ%ED!XR19|*Q8Rl3J)ema zNVFLx$!_sBVIDz?Rxf)ifj`KuOlq~i)Zc0(a!cU1Q>g?1Y?o5ML74#OgqFw7jR{sR z8omh(8k^9|W|XHYn6%L}SoZXxEl~IQkgoL8%6|r&LBi&eB)j?{ig%0?x`%aktG+se z8lTTnUUlOu-+LIjEM-kUSV1Vo0)#IQpWkx8kfe{^*G)EqVD#Dk)g(Q#%8?sC#gp_V zvwwuVN2~K*6JTn5u~Nl0z!!5DTK7tGDL#_xpU|nvVx-2Jt5vOQss=!pr-88cg6yQ{ z+(_Gm95Pt(t~y_IjamkF`-mDF%IzK|x#q+hS*wlYvdNmMV$y!~|odtyzS+I{t`KOhP8QT`g!ABb+73YrE^YCeP@XR6SNx&U--E>wS#@USk&*{n& zF~l*8ezLMNIFNtZUDc{rVwGN$|D-lG%4cGknD(UL&tqN?CX?2h-&~4Z5ygMCOu#( zQqB*u$FT35SS>0zQL7w9fcwfDlrNxfODM4kY$oI_u<9wA{d1y3iO!|q_1|FV_%q$< zb0RP{RCD&hNOvwKCc2iu;2u4$DU{CitVqQM^c`niZReQr}gV5UTFoJ&@uNigOI}liLTSI1)w-|d1+e! z*MfAJm}sA_8oEm4!y*sO=KsZkX8oG*L{>f6{KT-9q9%naK@?v7O5?V)3pfiAxqpb- z)&})x;uMX4VAD@THD;Sy@*5-6UiXuYc4mUo?lumWU%A&syfz<(L8@5!Q)>Coi?6!(ZgS7KW{{e(vl9UBT=M_}cG&g(FCG>9%y~8DIw96y_W*bn zf0gDF0o<7#0PwuOuE_6fwW}-T>yJ&Vn%zDYGgCn`rdz_+7Y$gE9&1Vq^>6h$56x+x ziJPcug6@6@SsDeF?uenr4q}n&-wRPBw_2V2F;$1W)qT31w@iRL z6MiG=e=(I^?W(pcI`u|{6(z)?RB(AIXzMl{wbhx=r1nB*l0IJ@p#M04Ypr*l=y&eK z?VnsouV^O%v;!@f&L+7FZ(^;ECW;kzEq|tYCr`BKhxVl(6cViYvAWRSMQY|}DJ@1W z__9ot=j0)PCxf}Wad1-eO#4XgH3A&ir-F(1#wNAyD`5GcV#(y%c=cOn>%!|Cal;av zpHmJK;`V3y+ZI_xg=xOGGu&dFB^%0QD+Qc)v%W^>EP6!RR!*wU;wCjSWBJ%b@$&V+ zO%sF~MBhHQdaO+Ii=4U39V~2Les~zUwrF6*I@HoPyL7)hdWTbL zGOk8T%|snL9dvTP!@7v604wU9)~kR!Fq{|_ZHcpep>Aw?X<1bNrA3Q1KS6zDVxi3y z98!0UK1Ytt zC^h0~F#qNpRfD`+zLBtL!quWjpVDF|;r(oaI;^9xj4^-E~m zh)CB~sC|rE;T6L28d9GrWyp6VJ>fDu>! zz|gg?8NmB__OsX8-~QIS^$;!oW5!*vLr9R$LIfQ<_#lyqH$HyliAh>Yh0wEzG5fKqFG4aKV z7fDD+NJ&Y_$jB~TxS7gM*Wk^V+p*TwGk&uV3fp=H}tyxpCvh&6_uQ zd3kT$x^?^Z?K^kw@bmKv2nYxY3JM7c2@4C~y?a+gL_|zXOk7-CLPA1PQc_Av3JQhZ zyLV4oT3SX%=HbJKva+&ra&q$W@(KzHii(O#N=nMg$|@=<{P?l1uCAV*p1!`mfq{Xcp`nqHk+HGylP6D}K7IP^*)tOp6H`-D zGc&X2&!3x{n_E~|SXx?ISy@?ITie*!*xK6K+1c6K+dDWoym;}#(a{kGgE=`lIXgSM zxVXUK@Ru)Ny1Kf$xw*N!yL)(eczSwzd3kwzdm|8tSFc{Ze*OB*n>TOYzJ2%Z-TU|N zeSCaAeE8t&>+9#|=kM?T@#DvUfPlcjz@VU@PoF*o2M33QgoK8MhJ}TNhlfW*L_|hL zMny$MM@PrR#KgwN#>K_O$HylmBqSy#B9X|?pFe;3^5yH-uSrQs$;rtnDJiL`scC6x z>FMbi85xloS z6&010l~q+$)z#HCH8r)hwcoyd`~LlVU0q#$ef^IgKN=bu8XFs%nwpxMn_F61T3cJ& z+S=ON+dDcsIy*bNy1Gy()X$$kySux4dU|?$d;9wO`uqC_1_lNP2Zx4+hKGkoMn*dAeCMJIU`ZYN@IW;vkJv}`$Gc!9oJ2y8sKR>^)u&}texU{siyuAGT_wSXJ zmDSbNwY9ZBfBvkmuWxK@Y;JC%(dezMt?ljYot>TC-QB&tz5V_DgM)*^!^5MaqvPY_ zlamwPdSM3O+!H#;>B2xDCLhcX95lOb1_E6H$xGkUd>OYg=IX()0Mwd<80HGLD`VM{6lt%y_izxk~~xI%}2dY<$G;? zBeD*P?3N|7>c5aKA$k3#W=A5-^e)dQ63{YkLT-V+C*IynzLltI%tstWcc&I7N*LU3 zHY6==8pa_3V*N5A2;yM9a}nzM;UYwhm9-ue_M!RK|9DcnEY#SZOr~V)y2RVOfu|$( z3PD$As?A5%6oaltfr^^JX)i(C%m|#cr@v`7|IA3ZV`@844P3&Iko_1`dPmf@&@ z1TR7xmY++Zvan6sc>P-h#_LKX^9AHgx>~wk)_1>2wNZV>T2%!z9Q#o4v z;&T0Kb3>K?-)d5ZAk+xgc?Is>3*HB~*wx+d^`JrF?M33A4o(7y)aDeHFeD&IaS8gg1}gNX}|a^jU=6a0TsjQ@5dlm>#{*r(I#i5O5M zCeP_wy`+Rdh($8`Ngdr8)sdawO-c+d;P3uQXm&=dnC98+ zGLvN~ThC~m=(Yu-GniSK%OA&W=^f$MjEKZYX5R_0MKExfMN2FZl6(>|YTHKh8w(~< z>=f50I3+G8LQt##({X0(1{rc;a>rYHDf4vPZXDwyWk><4WibF6y9#8u@NVsr(e<%P z&>Ejr8T6;%v1h-tAA`2IKSaNxI{0+)`z}3#S_GiW^(-ERoZVS-R$d9ucV zQ>MpM;_3XkkmsnVr`v?kl3x%CEX8?VXNxrN>JXa9r%ti>F~lhq{ltR=>d%PqYZ{9B zF_H>)yXSVR=5$=ZjKo#}ZZpUa(KMFJNlyAb9NYp3f26z*)q{db0I1V$L?ikX?tezZ;Yj4F9@w(esP zH&as&$`@LeYhSr&&i1Di(ND$@GT8F`J`8F?q5R)$yRSy{&*0-P?pY{c9~d>6lyWcow$*+Bi-HPoVkl zj7oC|y+-pnQ@+?jSP>mwH#*C-j#0P*qM6P36V&&+{hxthyFFK z(%t_euCtwId*Zc*Y+l@5Vc;hqdzn%~365L|c7`3uRbEXaKu-!V(?`{}O~Bo25H zA#xabXglql)t~a5oW7;leB&MdEv3pabW{uo$xeZx@e+&m2m3-z$Jv^hHU~u&eB0xN z$@YmtAP!&5maoV&^f}B=2&)(tuh0v2mTzY1ks#U0u@%#mY@OE*m2Bb1hB13|%+Tvm zr!~kjr)lP;Xrkvm0=?z>L?}x$S(@f++^>u?*!!@K)NlYY+|DDk%r%MkpLSl^PJCj1 zaEl6pa_{T5&#-`$$gEvdi;VO2=`~p|mtFOX+>?@#*3_c|59_3xt9Us$$1DxdR`x6@ ziKpW?&Es&9zhh&A9;CC}t?UTW`f79Vy;lsCIt9zU7_>7Npk_K^h+z<=HU|$DZ@;Yy zvN=dW)4oopMU1ERi=8B>k&SGJF1>}BS8^*F`vu5dR-I-WH>I-W4u-<`#; zmc|p&IzbLXoFN!Sk4o5T>|H~j9KW6W5#K_##b5xFfYE`HpJ4cJ(JmO^V(0Aozo*n| zF`i>$K8{atqE8h)rHVDe^nr^e2Q17tY8 zFCyHW8b7kZVX*_S3IMJMhH9Dj@*qQlDg_sAyiYG4|*;2 zknxVnGo`C%!g?13^?W6&uGuX3(L3%_QC*x2%D03_Ag9wxYb9PNmf(rByUYB`;_xDt zZ4Hcxp?Fxo5^3#$#^|I(E!`~Ru3X0E%x^KJxm!(KrDSisT(AME;MkM?SaWFsvw;|X zH;@hwgU9Kb z0fk=(abh*5QxP0CP+>kfvqZC9>eh)-0n91bMH|<=K2-y^%Md9%Ak!V{w2Zm}&Vo3ly^SYj;rNmWSuI&QHwI z!m$YCwQSmWnDe}g0QF=z#)EY3IUx~N;h9^ABTZ|1&JH3;nz`EdQ^>6IY=BOJ#Z#od zVXkqbj8I5={}GQ6_hu9|W?*0e9gR_~fn}#VAbL5I;x6zO-R9{C3|3wYjJ{zm1Pgp> z6@-$Rx3}S0Y?i|e$N-LX84Bx**hiPOTMHrZyPF3IIVhRBqUK2!(p?`p@ywam7;;Df zyuCwrBqcb%uf#EPe$Nyn@Ttf;G+g+3`zEdvd`iN4@AwHm?JCgLrqfE_O?@(9ci{L! zyK^=r`Cz~uNLANgY3st}uTwj;yC3Huhu_=Mx4xHEOtm+`!PI#p2cJ7SBc<_pw;l*{&J94gH$CrRfjimifY8SwA1fBQ%cqyk)uI9-A`dZyOmu`i)8GQ zKghbo|Ae+%_txt5m$IFA#?tC)xBfvcX+x%qST|TV(iysS^a6ZpC2+*hl?}5J7OG-x z+Yy6uFN<>vO==`%q(T!p0y0Na9C0JJ*HN|W_#yUjAKUN z5BkO|#G*d)^LNUD&K0&-E?YCcS(!aJ7VDKmbben>5*5g<{&rr!EC+8Zv6lzzL$4v| zeS*);C6!4&m$tv~EiqtK#i4Ck;6LRTXqHVJkCJ7wVVVsf(*JMRislx(D*!bna=;yX z2e5Z)bj8ou-(kTj*IFFQmN0jzb32t+x~$QJ7t0WUb(+gCLx1WGztCmY_0KJ3a^!o* zq#G&0fKk3NS~slt9>mFL+ve}x?xQC`=?)cQc^dXs0ql>){9l^r187`p0tuE+0S8AU z(|1~goq}#8PJ-rdqb@HDrjE>I;+qUWR7>6CE0tYhrKgv1)X?abz}&o3mx%~iGwBEH z4o`_WcF3TZT>nbpHSuW)Vkk$&Eur=FC%f{X6};0Or|3|y9blNiL2jIWW4-J|>ZoBXunI1a5rH&v$tF9hXf? zOPmV2WS7J38UFK_Y@V%FJ0 zY}!3c#wsRBmO>h&rxrW*0MEzW;DS6cW)Ll;q{=zh3zL1Yd4j{=`+YWfvDKc~PLB%C zYAt@py@a6dl_o(kxngZQnt7tO%DOisqChyP*BRYL)XX$*2Cza@Ui@&(9T~SpqX^lRiu^~c%{^L^<758I} za^i*Or**Wai*~KQcvuS)V-IY!yJyCur-S5|p~P8cO=!R7sD`bvn^@V$^d>*07pWMp zMA#J^ESm+Ue{)&3@i0#3TQpqZ%9&h3G@uUO{p}v7gbP(ZmH+-jrTiZ>^&0t%<->JX zGU_MGQlxXwtpZ=|!eCrVeb*O*-wK^lx~g8nSML)em=!Fn#Cwpnlc%&Xxl&xbbN-W@ zDN*7SWbs?k=U}fK(EyBo0_f+M`I4>68R22X9wTu1Q=?U&$)FB&Wm591E zlknEdV8gHY^n^L_3qS{e;<&}y6>Gbp=504|QH?$G`ku^sAgqM2_>zF$rb&W#>Zb(K zJ>#o?kU@WxcltOBhSP_ynyrYT4rxG1&x#ZwP9D$n;E0kwPOm5JD_;F-*p(Ygo=o2= z_qeSe>|tMuzwq`)(JE+cCS-)-+a=Fasi4$Y|0^5MU}(!cI_OU!WlsgT$n5RG)u*zT zOwgxNp;1%5f^GcvXkf3y@7-{uk5)!;AajUYUMRf_WPyBTFPicP?Gf>*SoYMrthoJ% zcH&d+=7Yix$<>VD|l}zidQM!vGDk zRPFLO@tHTricnQtNIpyJg}RSJTB}(ctkXOVdb~+1KZi(=*h&OM(;)!VA~?41Ky)w_$G5?Ab4r$7>Oic4ut8oY#6(FWhaJZfDa)ic)Qj zVxUie&pTq;*Vo z?AjT{e?MhIll4+k zNM~AIxyTXQNr94N6>zKGyYmy~eM_Hk^b$oGL0Y`PpnU;)6DX`B4)$|8%hmQZ)as~k7al}7 zcfHrxN_Sjv7vE0Vly|NxFEk8K?jmV}BeUs|HY?#=M`9?Qwh(jUVNT&Bbt_eHu6+$e zd6`Fhg7JAuU&g-k5P5M=d7*)p52c#B-Nvk->E-!@U@j6=#EbMkPg?y6|3@RlEd)+> zoy2wEG^2skY-iWesv0-j!+_uNTc$QrDDHU`X|qEm3y`O$($togmo#A8y+MM~DJH|! z$0g9ZU!0TeU^JfTXdFTY-5M{oOG)VH8= zP;cIs^f9>(V&Q&J&csmC|Kf`Ix8hZOH9^bm?KuJHmd33PYDNQjXWRV3>oqpIIXi%K z(EntaXKfZ-1S?VBAzS^r3LpPq#E`anB=5a-!QA8vv@)#h+2}5hD3U<{YKp(t5TloDdO(D<%=TR0$u>SreB*s=@Wmry?jp3MOQ_O%%aH_Cvec_W{&nd3 zm%9O~NAiA3>qa=xo>Vk(gAOSdtTSLFRMtk`hr3j^z_w3t&AC212;(vm-B;zk2sHH$0EQ(_CsBm=QZKjDb z$9i%XKQ8SmNFTuS2W}z*0={2lw+TS?FO}Ynk53G6W?8gaEUk8?41Nl*26hZMFQVrnDPxiB*9c<*oTk5u5^8Yj~ZqgbL&6y3;rEVa?&waYZ-Az{F)Uo0_&54S2j!sm0It z{37On35Py{02M(*g9^5}%AECNlgNJM&gLb8^WYEqa>$D`HJ65XSkFQ|pu|Q?)yz%%L z5ppb?0I-)liJtU$yOZsK;Ozk!(gB635YJ1tb0z5xwl{TFMF^3uZKdRvC6yaBmwtV! z1Nxw`MA&K#l%8V&cu5`AK4&B!53av=sFLBjh$=mnwSsi>c^o_db!bRJRoR7~NVmf5 zza`;V-}S0<=c6Bg)!JXuS5CV07SatN{89(19x3oz*AMNt$@03jFImKbn7h6fh_0ky znQGhHUzsj3SO5JY&b!kD=)71_VS_16s<@An<(G2z1486$kmqG)5~{wDxQ zI?3}YyzW}LD4gw$gBneSxN&$jH_Gk&7#rw}d)ja`SRJA#D{H#esrJhY8OPk4$XSl) zj6AKHtEXBH_cA@gN6hh(pc;G4IE7jR(r~SNIthwIt?i7UBgRZChl8}+Uw2X!ha=hR zlL@B1@xX3A#oww6n=2qtSSmzMn0HH^ds@StJaa(1yG0ka2^k(QFdwQjI~epIPgSw% zQcgnieNn%J^nMcHu%@Y3ON=b<0gY*isPt>z z2j->GKOh=fYJZe1cnJ!d$Eh$r(O`^b7tNqpc367KM3LSeLyQa&B!d~FwYuM|*S-eX z9y>4RzeScZmrA-~4i`?5tp`1l&CEwjZ>5KMa8VZq))ZKI{O*#+<^(|a)=7^7w#O%*^_Ii~rem~|M1I4(qRz8M% zl3*<7PMkkj;;%$meUI7*^Cl0jpUq+ISDm$S<&5=n+O z!J1_$9fH%G?Gto?eK^)&^N5bayrOzFfLE;YD3Z%%(mO z@N$(40%&a(dZ|*Do?fHw`T8tMhl0xkG2%2hYN=J%G*iByhnqjZw7XhO60nN4^78HF zp32bD9j{?GMktH`33hUwEt4}H^<$g}00D;QnEuFYS!d-FT1@8m-jIV#a?Ip<_Ok@L zstZvSl$dlZjwPm_ZyCXxCkdRYGmTVvnQZTZvBQ}?LgJSQ zq1Tb)cMHtMR)4oOXgGi^p2x$Oj~gRv&;dT?usgwr<%p7a30EU0uE{L3cd#0ipL4Re zO!GKkQLg<2cC&T<-F?eM$dSt`)?ZzZVOwGeZMqcC4o?aYClD(W$Qjuyk zd`%+a4?bYtd5nXKHfVp4;`=~7=x`HoaN2`nEV9f&F_m|bO+}$8iJ{EuQ59bd*S>zV zzn6@In(@qO9jc$g*jC3_C6aDwbrD*QKZecP$DR$ zvl~9L+#gb*rXQfAKz3beao~P-2(!{l2x^Lk8#-WtSpCGTo~G9UzgB$(qwY;15*&4= z9doYJm?~?v5O|3u{%XKM73rbJ4=+JC!v+H^9}%JYy4i?yBPrZJb<}c8gj^y(E^71~ zv?r44^-KpufNsS))5j8XbHjvL zjQM1%IFn6-r!K>w4P>DQf7~`ImKnEPSE>hpON;{Kk_WC>luYk{QUCS}yVm@RK-UcY z-evICEI#QI_u>6&2XF=xYqJ(FU#xp0ePKbnHj9pvBzVyhE;>B#frkXkW@;lo(m!UnwJH-7u{iA+kOL{xk`mb#d zDu0!?r}Y7m(2bB{!B8PY%Q;_IxG+#%XOwysdP- zU+@?pO88W2EE;JlSh!knku@c95Ok8*UPJGXMO#qJP^*+O^J{dXiP-I<^sO%Y9aPr{ zGpb+9th3Fe@m-Za#tXP)?PDNytxVR>fObj6iwat}q<3d`+4%7Agx}#cWWM!%!iMVs zgx)its_NzB(m;hS;FI(R4;LjruS>!^5V_p$qdp*UQ|!0OXihbsAwE*l=>Vdq26(kD zqcnWST+C;rUVx)}!|py(Y2dZ};)OptbREhE;3gS`bHh#oPyG|3zXxR52WGFwT`t-! zUP%;tZrbNpv{uk>h3HhosqlX3abI~cK`&1YDn=u|zf*fMV(eWu$BCE&tVqjw)??wW zvA~t<2H2|>Av7qQBek7-Xa|XhQvuN4fK%Fwhpd5{Efd3xN8=B&&6-G7^71Xgq&M0P z(;ZkgD{zsaWAX>dP4hbMz@k^V=i|MH?7!)pXrLR3Kpm8b{jvVt4AJe!Ie4P<8p|u6 zn*Ut!p`H_BXZ|B+`t+F;ihnq^d#}5GGxIe_;z)jZb9&r=Z2!qru5NArO!*~*C^)Ul zRwXBNwmdLxr3{%}ZpLvinIG9#BC!5pK;Rl^ELVbrGTCQd2M@d-wY50bGU6`9Bz6@r zwh{qn{Pn?l6}L|SlkISf7$@rIyNY^2s^Egf%4c1EkhJ6Cg~7Bu9~vFys4d6wdNsRR zA{=yE|JRBX?~52(?LCr~9WIjN-uqLq(=bJ7aPnPMrf;n!DN?p5)k@F3XLL8am95~x z4M(7BUF!sQ^Up9q`|lt}?2m}sW{>`^mh-9ZI>I$(`TeK41zhABb)cDTt!QL+Tm41B zL1L{Z_XU)3KiKezCpCsw918)47v+OJ<>KPyJA7foPbU-%rJiIqUwL5c!Q){yzkii2 z$HMr7N+h%4ZJrA#sTnqp@8Gmr%T%h5Hb&GFKOMaG*B>(dq#1c=uCq^Qa9JzmF7jpz zJbu=vC!zGvJhZGD=&vbDY@5@5t+sw2jF=;Z9@+X=GLyfDMuz%0dJX-7 zvV)SRoVdJFk}b_!i-iUE8CXGEH*E(xs|L)P@twjCLhBV4ary9Ytl*uly9~zr-NMWX z9WBxu1jw2JM~99A66Y1=VfUD#Cc^L?c2!-euVLc|YWxLn7B6xr@kJ;^afBmm4e)|#dG~k=biFn* zPm#)1ZQQ5$@k5|fjHPTj4-rhY^xuid#}y7wUm+)aC2A>nA9@zqjRtAc!a9U87}#KVqLYc>BJR1v4)f8f>=Y%*fpGqPjq9CH&=}P7x$vQ_^K!?Yv;SNRndQv zlsGOdEPimm(Z2CR0MjXe$ta>eX^X!tu~r4Qv?@(CiP*Zo03J(U(T)@E5IXe&SnT%> z4*g~}aDEVr&M{lLD1jVq@nqBTwLI%k^-C3CZE*{hzI zd=62K=v@J!bt1%bwDU3z^N8!DIydHG0v-;WV(sJ-f_6OAN3VLf?q($r$19K~5;FYA zbqf?T&Zk=mD*A@u_EZZTwvja*KpKl)?0K3+4*miWNO^4{+XsABOs-N--IN58veUVy z8_Lgj3`UM)_#rpSjRYluaj;Aj9B(XUESZ90#4uyS6V;E{fc?+{bu0|2=CN43!j}HI zaTz%>7;ydT&~Ld@d03&hg1ysxsL=%|$h)jTl{(SkdfOG$hjl2V3>eW0J9bmdv-SWB z5Z~`j8%>C0zl52e+rUk0J_Pj{h&*jvEHSSieAWvyAd(381HStw=Ms0dORC_ZiE$s~ z3<9NWy^?2KRCD+TmJW#7yvXdz`H$&w1#<-p4s}u2kOAiO!?=wU-5>!^?t@p<21F>H zp6P45^e^O`#ILqPP>`PY4_U+87-SkL>p%k8V*G%b%K^5HkGachBDY64dG%fP4d_S& zM}q%P^S2mOcG2#;izvg@a;gLxX7#~V94GyyblQO%0x6#`pz6mIDEgJ(0yJ-%E*IPZ z+bSDno6E5mXj!RE5@7^zcBF{_lnlPfui1MCERiEYC8!SAhS!GTw?;~tYd9rHPXPE4 zd}}Z>RS(vogd?i1Z@ohxBw*EruX!NEamUC?E!n88A#=ET$B1D*ZqUx8b1O*`Fq^tO z_4?iJ6JBmaFAvQuDvrpOdlf;rV7HPUbwx0(d-YG&2B7}z_lk8!hU!G=t1TS>@&}oi z2x~rfr`38~BCb<%Ww$nG*3G@z#_A*wmPvC(k_@NHBJZZEA3E)nloQ+5LAx$P-}eA~ zTh{ZP%8u{^Vs>z~T5v_@a>`(rZ3eIZ#~d@^7*Ol*2LVy%?_G#5|BB?L-9ZM<) z*2+gE#@tHkt>p)!5@uJ)DX-Tq6+^9L_m5hv8QX(u(&#xkyyn8M5HNHXxR?w_yLEE9 zdfQy-p?@gbJv^(#^jX>R67y#a@Tfa?e5u^FSt6{^u3~e~Q$yd~y)9{`>!O)|)tVWL zZ;{aYLJ;42vB;m{l+**dXqMRFEYsaYVjk-l+zDmBek~_8v)o9uyz+Krdu#!xE;8nF)T%Xz_MCzIl zV41S5F^@xU`EpY{TCPoo^evy)e#6J&9fHs?!g_^6MM}_$u75F2TmCjNaiNRl2Cn8r z=J6AUHzbyEoxFJw&v8TtqN_w+*vql2^N8QC#bC~?mXhz7DlMl6`$qbfD284`h7T*+ zOl|Tpb8p2@n?g|E^nwX3JlHJT!e)*hMZT3?fP;qXpQjq}9K=dk z_ZvX6j1?e(JQuU7yij+MB6jUiLXKk+F2r_Oitc*k!@d4(rePj)xMR&Vq%~VKpMa1# z0NTRWp8oUW+SW^xu4^f^1I`&G8CI)29LIPTVgSynL_(0Qt+AT+s7F+lGXI8aSqgBo z*!b}bDHo#ZsSx-B{WHX(eipN>y)L2{&*7M`%*nDC|Lva#F>+1sE%}Nbo9xo}mVyMn z2{poX=_?A3UvG!ttASQ(isC=`82PHUzj#y*+uZV;{_-N7eF6t{MXb`JwQR|ZZo(Od z3*lmH8}q@Jjn~mAyfED^x8`o;n|&-)3pDKM(*u&APK=r_XzbXxq!A}eN_~clbqZ6X zY?x3#qN_FxX^g}^ri&u4zQ2tjXW$}4Op(S7&DoESIZ*c;={PDZ)Wuf=HGmv zwVHl!6K8nb%lYK}*1=uAPq`f6YLDMs^Cw}1K&L4BAeNrogK-k%jQ0y@kQ9r1p|La6 z?KC0;(98ptQ?<2KCWdcF#NC<$Rq&Q~4eBjUzP){_3-<^39K0_q*gJ%^{ z$mKj?D|7jvutZb>Ne(xY9L9&%0J81vA9P#c^>*`#{Eh~l z7G}oklly)&Y6yOV^Wvc!HdaG1UafqV)vfjP;LsmfTA~|r@5Z1GEqf} zJ>USQ;$DgUV-I^D!ueuf>9vgn^QwS^vx1P<^{oPr~OV&Ls6_vYVitD4yJ55yml zNv0zWQ-~yUQf4iCt`6@YH$e|e* zeh0(1CDx`Sx*F_9qeNbQ<1dc?3_zSE!#oEzZ>cdM^+Z3~hQV*TKUzi=@egf?G_cBx#vEeM!D zjOe1>gf)2pYgN&WJ(7j1wycQ#@pLNw)DG`tk*3kcnB78k%GW)Mmx)pJp7@PmiR@MX z>K&WKk<^=-h??JCEIKZ_fG5k+{`ZuuSz(V#B@t3`pn&~F$sf_Kp^m;5!KaSA&lFQx z!TX)ad&|>^{W>}1$cbP(JM5qLb=sSvKPC%ODO3=-!C5;n+8zPrmSbz zpgi;-L)&~xUhuBuPP5_qxX@l-sjAK>jk0qj`pJhw#gPLcN1&Py>4ML9GF1UKC{3D- z3lWMyV-1?<*F{2z+|HQUk({MT_PIY}!Y^>8%dLH&Gg$EEpYMd`I177~iGfTmVIVZ% zu8giQ3~w;6EfyJPSc`D6=h&9d62B=Ck_Yc8nTTY5aJ{r2ub^toqnjcOp(4lu{0w*RZet0GlET{@ zIBFACdQ%>ILfcT`HjJEXMVfZhIODTVl2e`_!UDLj`vf@3ZJ)^=83~%duNv^#H(L3dSbdjY0t)L9T!lFEv^xjpvPi|Bx;#hp+I4&ab&5~CE zpJpl4z8;;yWaO>D6Qk;VhgtVlI3_P+GBag+{hG`M|eK+7YJq0lXv!Lk#1zZ&C zBLy-T5&K$4e`Z80uAE?Uve|m@vh6T9E>fsxrk#xkN%qPdBNd3=rj3<+=W^Rm{c8Yi z34o}bSdHKIUP7B)tI)p)eY4UUg)T+v13*R~3vdbi>Qs`0I57e2xTdqV>g9TchRAaa z8KHOT7?E8k1wc-jv&p+&_M15`J2B)+VArT0BmpqDn8K3s^z49vsyPUzxJTssNSA;C53D*f9Bsh!u(uTOC(bEVK40d_ReVl`YoR!n>D(d1LJv@0-|Zi8 zf_TPFN&p5cYdbqEY40vU%>_bP^E^w0w?+!>-;%^p`<%04WK=olaHxz2(0sVErI<~7 zn^2xRlp&KsN!8^h7dJwlV$KGYq)sUm?Gsd|1^U^tf4LTm2O4m_52 zgjN9ZyW^c$zL6VMhG5Pc(1KNLBCo|G|cUY2EiQgPGxZo;9w*cC4c4BE1!f-fw~bFp$F!1BS8 z&!+KsoMVxy*emm`_e(E&A7BVBTg3fq>SY+rK6;m| z=>N?7+xy(k!Pf&*Ue%0rjUxw0Yn}m+Cm>k%CJ8|GsX2{QGVfXxmI*SA7)LtO+P*oa z=7OEv*^sc^`Cui)3Ql`uiQtv5MjAyhJbHjIVx!FQV#ktNyToh-RqYYsqcQA{%6S%Eke z|BAY=;c3PH5U+{rL$8O$W(q_{B>Dm(CIup@3UPY(E2^&N_=nH#Qy&2Qh{0Hs^Z+pW zsxap%wT-WZ{_ka3*>oIoyoahl)n)cgHU1QD)&&7@l_+vpyQNw8`|Wob{r>i&Q>LtS zW)QdA$1iM*(vK_w3|T59?#+^wt?tJk`2_st zrZ^di5cd~zb*0W{uz!-YOsy9bKGH@lQK5usH4i2f!GWlgGw?sXnxY0*gm6+<3$AAE zrN?txkL}jZvy_yF_oQ@Ex>|A3t$Dp6^}GZk4<}Axb6|^GO#S29D)Ncp#ViXE>Jsc^ zSePE*R4mefz}V@$Kp^I7%)0XNg33VaGB?#F z8cI5k_Rey^rTNdo4Bjlh#P06+sDcZkiY#^)bu_B-)G`Kk-tI&wlPOIZKLG2j=Ol^2e9?!{1u z6_Wl<#VK-j*;wv=7e@ww@G&xJ+Dly^%tUx_jhg;Elmm!XzX82gj!>ce*CZt&!RjT^pTtUmGyR5Xa8c-%(Y!E!oQQ(m3+VRXU9=Yb#Mxh9iA zHxv2I&EkKYhqVAr{CkMTUz{-}{2m(@gpJO}m>jXp_5VN&|34`PINmMi75%lDd9xK` zxBdy@uYgG(W`(7`VNlyWi*9TqUg)2(?0eWta5pIr!@f#OG{j0-DwYHZpbc#V3-31W zCO%*dUc48G4f~&4w$QTS$a=i^!tOB;AH{-PF1FtMLk%d^z$O*BTRfwR8;=tNMh;Z9 z0VwS~Qf>nbtz5k?W5r|;E=Y;Z5de&v=Q(!am87I?Zo~jWdz}=R79MI;`KNZ>S8NDY z4Fsbq3&6jcj5tuPbjAS?=-DwMpG|st>wJ5v2hUfYgz;PX8XLe00A543x8B#u>{)DxNrV?zotvE~t-aO4V%!h{+=rl%|MG2Dguh_jc^RW7V~!k2S$R(4tn$Am zKzNbD)y_r#^_W69Ws|hVK;P(h!x&iUGl5%ra?DhY+M)6;?uf^jI;< z>M=Fa){Gu0Yl;t`wlD`*t-fI5Rp3vM1kW9e85TSB2$F?o16{Xm6QmxB=jG-dmlRXFXVg|q%QMZ-3Ky`@cL6P_}^f;mjUd|eZil2&Fq zbpizlq@o-!)D|GRjX-Sof2!&KK_5CaUy>0444~b-~+o-xv{FkM}|rK)Ylc1Lutm}Q+ff2Z*miU z@qyR^h*`igmBCkjyREkbRt!q9Jvtr5&75}k-Ff^-&P6l!A|S|)C6l)(%LN}L5Mx|O z^ajPN41V*)EkF$s(U8%PMV;=Fm=S*0T3ijkQ}i<)s1uV3BYu{sm%`GCoj2;6RewqP zLzT)N;10qD47@XL&tu~_A(=7+nqm^Tn0?0%XaL{~jw_7kZwCOqwHZLotwaCb-7?-F zrD||r4GtbavoTwZ0%a;*&Gd?ctof9fuc<{SZRDEOfro zoNN7@ShAf!ywGRZ7Lg^!D@HHHgO6OdQ+gz|P_W{o84rV3?!WMGYwCNMTdV76NXy`> zbMVkA#&&{&kzD4Wxnv@k`9ZUP&DV<6(U)drg|C%ruZUln;HE}~2cfkdJYyvL;7Lx8 zwV%vmjUY#T)g_%R!KPx#P4|0=N5T#6H3HG{VNdShqss}x+zub39~$>9Dc!|HzK8K5 zWi`AWKJ;Lhp#5T`l<{5r$;|UZ;S1qy=!~HWAinl-1;GuSQg3$UI1`I znCNQ_a!~Pn=J#thmD6~FND;TSCM&1Zqsc-m^+xq%0*jH}-b@pBtMh)c;iYvz98OEB zA9z8wi_3WpzNS6lRC81%q&9XCdb|{TDK219U2Cc@zcv0eEU<+wxIbC)OMD~{0!{@{ ztAuU!?2zm}b)Jf>zPy34eIj`nxM2WC{9IbEI0OQZo17x(M)~QStnAm6jmS9F- z23&+xAV*mY@RlZx5diE(Q+(&~E|0!7O^)~|rx@c|_zQ6K;sOXyS^6nE`Ty9pJ`*?o ze>}Dht&zP@5?8E^1IzQ_Hjwh|Rq~ zj=vk0Nl9MnwkGq>cQHMZp^65K-F!e&$fdSM@U9!-VkCTwxKW+a2l8xj#{xhx1e|DAdaBOC1)?CbyBZss{b`4~O3AK7p{J(Fsb2%IL81|@e2RftHh zeLNFbEDbP?0~RgPa>018vHa}@UYZwP*M>(b%V(@khJoed6y>+$6)Cu2 zUL3#{Y9ITc8H$1dzHcKZ%$s4SiFjxy*A0?|LC&B5NMHbh_)UF$<~5nd#BfH8a)cEE zd&p+1@;p3rUANi>aD6Bn&&G>2@M6!$|6`BO=^ctn5&^H_VO~LWm#l;Qw|E(^VXb4}!B2hrTI>@sb0hY(S21tIG>+K&n0k_tqCTMq z;A1y2&&SkPu2$T@^%= z3}{ox{-EmiP|J;XxMP1(Jdlp;xx5tKBp5wz@42jh^!2^=58QBEbfaqclJKrk^1gjb z?XQ(Rza`5WoK_PJws@ zWvV<=Dh`~+@$9>FG*vgL)R>p+_Jsup_JqImN_6rTn}<~_m74~EZ?30aF+~h-J83=l zwt-Fro%H-PyA=l4O#H#y)RmbyYq;^hb#kuJY^7@)4@TS5o8GjgB|4^8ZMAi238J04 zloHY?qHUb2ODS3eMS|WOb;)#8dR!`L6^$0-5>Y`qr${d&?h%5trG$u7*$F$6eWKGd zvu3UH<(&O-zk9FsU(f&dJnMbd-tQw#umfsNzzD3sq$WzkuYTu!yL(t{<%AIV{xW_~ zbV#=DXyYZ6+5w=%ASx;BEQ&|lExOuLs9UY-? zx1aHM?w#Y^ax&v^Vry1nkyWtYWpdm$PK{=nxzLiK=n8a5A%j_(Jt<47k8zqZyBE#2 zv2X+kb(Bd)cV^imqr}J4K02B;akdmTMOawyLi=HT5o=ODWE1RreoiX7iGduJ@1yr{ z<~vWaJy^|@(efBU8`d7)_0_okq)F28FEd2fHesQDgAdK3QWIb!G&<(L>YN&~nZ(Rd z`h)R5Zf{8R$m@casrQlg+d3=uuF#<#H6szsF{Kfl!IWZ_A56w?d(hkA1?+z7rg1eAru zr(Bxaq;j9`Q+c~rBxo3$>^x2HY#2+_2k zcHD{f>qbUxi=_BKq1J>sLB-%6s?2(wgt)w;hHbDAi!9NO+oz1qvqE08) ztUQmjl4=|fml#+jS;9XcI97#Cf}FUj`h#Mu<;)Ik0+>moG(R}EiJ&q#Re3$%)Qo$q z*SPjhtIT+~+#UgO($qnBN)sa=5$N|!5>~X#WpME0X-BzxW4Uw4KG4X_C|)PVkuhOZ zT5|o$$*!ejQREM7#ElX02NW|Lm}$H$TwkPKPh`|@AgqXKZCa}sbLd3P@m2Jl=+mmu zn{Wn~@ayz^Yd|+#xKtK*2uzu2`n$}Y(j*SPl%O123LD2otZ)yETJv!1xBdcNvf;9z zkcbDQXtP70U}HOwaS-frM~pa_Fl{_&;BWplJBErYo)(bTJXLP}0WKNO&)6P46;j_l zL5f*M?QVb^VxKc7F4HSRd?~z=YWOoh_+Z&U_g6jPwtm^dGwUpE#hsOFUU0JLr-u*{ zy3je`>f8k4-eO_>J}}pj=_e437t7PkTV!7xHB5m_VFQ>vD~XQ}NI=p2bdb40*oExT zHh7D9Z^<7-?YHgz7MyLk0BW3}V#ZY+tFt_+Udg=vd{tOMZU>dPl}Y#NRsB0r{Z-7j z)Pz$I1n*;a%2kj*x8e2N%wGA(LDwtjRl08(;T0iMV{iH8mTLupU zpPhq=Q(PTDVaKsYDLVH>DPF7B^iXbes0|!G2CP@=0~fA1MN(8kfPZ53n6m*8@2tJT zwdZvz2gPwqi-1|~xHe7`k{ZjVuU$CDzi1Srs;pR5FNqrTWvp>yn~*zGqVk#^$6+^9 zFO!m232$;K#)q(Z{4`mY1c2=MO%OB4>U;zuA=nH(~ zA_u+9j)BN8zvONun4Oa+W3y)pQ!Vw~wZcM}{y2fc+UzaIYgecvV_ZeDzgn(zYh6-l zAdZ1N*HhB|DV-fp0UxAwvgLwMcPywGocZFCHPWn&t5RZTFlO_pZ>Q zy2c>U6Zw~RDKF_M`V}|dnh#kjUnwia>kx1C8)W17vNwJNI-)F#vN&ew_t5xNqZ-{f zU9L=myT-rEiE8>1uYiD@wwbo+0a9^2z33xfA%Q*!M^feB5%61uA6{93ROx#tGSpa3 zvRsctCpHKQz|TXa5U73OD{7~-p>z5KTlo+0mKx9Sp+)$2xjU_wtvm&DD%zZc_Ep=HjfFWSYf>@6czR8OYA3g}w6xjk3hj!2 z*kx$&cC{_-s5E+^k2U`eQWVV7pOls%>Ep^r{)7+PBExo$v5;U0g*Dp_FGC%K241ls zXOHl0N%k9LEM=w%B$PBuh!;;LEZk#l zWkH{KcI-1r0sFI+&Z&1*bNM9C7N9B{dGkjPLspP?$kbLUW#Jakdsh>sAo~LuZQ*I? zSjnuhaun&qea%z6AOm^G^Ohob;f>)awKlJPBM&xc?|>BzduI5DW-9ofFN^rmtKHt| zZf6Eu?>+|}Z+O&v3-d4ejNZhute^VVhsvPStJ6oQLT8)8C?wmgBgo%=&=Uw=E6>zN`} z9=djPrLerY?Gzc`MtloijCjK=Gv-O*UK#+!6_)z~(`vkP-wBvg<1cNA>-bnATi_CD zUhm12?CfjauLyJ&ZJElW)D-k49#acyMdy4I)M|51+7aGbCl`m-Y6yFq=Q7AZmBQ09 zgWDz|n_-~-@2EP?{z~-Y@4ut=_#hCo@B^Y%6^Nu=F+|dELlBe(9KF0s84U&i-%)d% z{TF|KP+lK3kujg~hD;xHk3?VO7>K$VQ>t^qg+|v{)wASK$XHadAtr?YP|!&M2eW6s z87xZUBH7{jwa@$4QX!x?lJ(&X7pf6wlj%onSPIXJh&|RJ-tqZNhBD2};4fjl4>;pF z8G-vnm;?vV!LMY)<%L}TP=>giRb|66G-v+kO*t0M4^QBLX(5&JPh>Nbzr4=%;_8vw z1?v#zvl8ZZTcjVMtV+5YSMwY)Hcyo7evVp`TO0i^I3oTvBEAdaMjCA%^z%;SPpAfw zRQ?(FMCjzjk%hkDT0PsN2?vRZ@9}|3vhgmdYn)Y60-!e|PrTO2b=~!IB?kOvRi&ZU z;-_)RVl`2ek8rmYgvTw)B=d`XTYea4Ep#L0DAt!m)pzad5bvd+aYUwU-!y*z(e%XB z5werK=%n2WTUy^14NTnXlNVHMfhsgZRQ}tj7}rfgS7_PN7|W17JpX8){!2&ozutn7Bg+h6UUs^CnCd!Z=X$dIgzt@i1Hbh$V*mgE diff --git a/pgt52m/week-4/workshop_files/figure-html/unnamed-chunk-9-1.png b/pgt52m/week-4/workshop_files/figure-html/unnamed-chunk-9-1.png index e53d251c2e18c5b59f53cfb7788f057d9ff9d0fb..2c8a4977dc44522ed3bb726f1bbb046130fcebb4 100644 GIT binary patch literal 25808 zcmeFZcTiOA_a}I}n+7E+QL=)PgJj9Ah$uPdpyZ6?4DG9efQU%Wtr8_kmaIf2wnWKF z&ImL)&22#6@62v(&F<`O)vs#)pt`vCiRU@bIiHhmh^n$25k3t*002Y}?%!1h05}2w zV4p9*z$cBLn3n(my05CJDGUA!fk2>8C=3R}!NGyU;kdZCczAdhE?l^H@ghDxJ^=v% zAt50V5fL#lF$oFDrAwDCU%pIAN=imXMovynK|w)DNl8UTb>+$xYHDg48X8(!S~@y9 zdU|>W1_nk(#;aGaUb}XUiHV7snfdzl>o;!PU}0flWo2b!V`FD$=iuPr_fs;X*g zYU=9h8X6j!nwnZ#TH4y$IyySKy1IILdiwhM1_lO(hK5E)M#jd*CMG5iA3l8a=#ioT6bkkB?b~BO@a- zGczkID?2+oCnx8_hYz{Axp{ec`T6+;1qFqLg&#kDEGjB0E-o%9DJd;2Eh{T4FE6jC zsQC2hQ)OjkRaI4Wb#+ZmO>J#$U0q#$ef{UppTB(h($LV**x1Z{ z>(_7JzO}WrwYRr-baZrfc6N1jb$558(dh5rzxVX?^!E1l_4W1l_YVvV3=R(d`0-MTo1dRwSXfwG zTwGdOT3%jWSy@?KU0qvS`~Ca(`uh6D#>VF6CI*Aq+S=OQ-rm{S+1=gU+uPgU-#<7w zI6OQ&IyyQ&K0Y})q4=5m4&?rekMHZb0swt5_74&FtEXkVUkTCY)`UHn_}YO4pQi`z*DP*n z`-Hugk1t-_=algQ?%qui0&Xz%5F$ce2*OmEm^y*z7xxAJ=O1w)5GGEq?#8-n_*_t1 z3$mzXmkfHbQ(#fcDjD<`s+tBfqp{l&pJSk5c=QE z&I!`Eypg)KHqov8~k}u z3;5qI12=c*53g?7dHtO=Uu%^BZLzooa4;aD*_Hr}ew>XUOJ#lc`Y&-;9xxM2@Y_+GVteo&6AMx9^lXU1pl|21?e6hCbW#& zLC%>@u#~5U|0ZQ_yvMBrpTGOaoq|PagkbdI%KlPAX0Zp=VUMb=wfn=bg6Z1Tz3dSi z1(0(dMZc!WO}@MkZAJ8F?LnFu(0Su-n!f|H11q1;=x|LasNy%h$SLOwrboNFy!!ng z1KD1Qhq%Rg{yR|r_Ycu&cE#GjAEH^O_*U{$=4MO9`~Q20x`!P(oRx!rPW^v@PXEuS zz8L6KPZ7ek6pOj^ZX7Rm>lNk-)A&KGyOIy#FLDMH_m?AErgy}uNaQg;%?;xx@+8+5 z!Jj&AP$tBA!T0MhR~pzQ5PO8t4I$l#<|%>yEd&0`3B`6OX8O(gdlnvPX5nNu+*9Ea zItO>t^6n@wEg40=qdL3asd#9iA~yqgFnQUzfY(HHYnlW3Ro6wct>I_=vY=H`v*j`_ zs^9U?6oU%ftpGaD|Lt~+A(eJGqoDcztIQfs$eD)FL|AbkdEU@z=9Cq3P;x4aW|Q$u z8xZ^%YQGpdZwvO+ZQYN}aO`*9Gkyv^m4%Zz$W={1l1nt3)GpMwq%P4)=zNX%)>FSb z$o%gZx~OLKS4#1qqBisJ&X{s@8PBIQaM4>T+xYSQi~-2ZbAPSWty5u}?KRFEsjTdh zIxEoCMwE>ieh&=ucKe(uxtir~ca`Q`LDz;Sq;6ASUO*Sd^r2>L|n=Val$`Z z@TrqgOLwSW8aM20p%CleLKzXNY!j(Y=SyTj2C4QA%a3#y1#c9<&+iLb)mEZ(t1JpW za3*X1&jhD2CsNokQBzCQLv~#6+1Ir5LkNZWSZZ!8O|>2U3TJaU?@j^7$oqIi76-Ur zd&O@0_n}+9?}g6NOReNZe>y|OTHvLJrZ1j`YM)a=7UJIBLmftc(fRKg7G6OqwJCls zdiv-Uhx(tv{NZA{kZAVUr{@P<$cEY+4dDt7zLojnoEdb;(NPrNde>QoX(r>DD9?__ zc%6R1t}Uui+KfPkRuuz1qk!FvYM0EEJtBDEnQ-cbb0!>IG-ulK^`M`lYrTE`sMTKa ze@VBLmnfZhn-u<7pPsRc^4;+Y7*_&rBw!VUgN_JZ{47kegNJtc`D~U_NtR9BZ(`(| z`&i9Yo;zf@roqkK-m&(hesykkvvVMWwc{5c&xF-*!>I)m6I-E2PBE2z#jlcfMhYcq zt35(sY!7fiO=#__Z?^4u>_4UZ)IuvsCgnL*hXo){de*V6Gkmq78-%*`1SJ!VFt+Dp zSWUFDaJgiAxr53~AbDq%D2UAzu-NQr(^F+1!pm#^?Nuxw zHrs%q4?3Pt@R4o(h}NBdl57v_wKcDx zs1AdWFY6$b?XoO0_%1<3th$qg4^MwUCP4?)%`#{_pSIj-7x}eVKRx);mZRQ)`vTwH z8WOH0nFYRQQH;oOrvjH_NmD6Pz3zCq>Ra%^g_l$3C zejHlzncNuJ@dzYAJYd2q?2B(rXN%4Vj&SZ7g516x!bk$SX{KZIMHgjvR| zc?#8+G#h^!?X6kM8tZJ04-YXv9p1l|Z}ng-@YbQc?ITR%n2(N0=Qu8cJsErcThc%V zh;RREgV(&9sIA;grPQ(o_3ZZ{=I5Xb4ly#iT#`FXyXwVsH{3cl&H4cVD*DbdK1h+B5eHO!{#>yEWYRBqBY{%u=Mm2%7s8%)b@AwrX6F(yj$l5aE`h$XHG25f3wr)@on?sV&4PXO zl$}=cyYQzk^g~xmo1yMCoHP9jcEo|BWB5VQ4+m;%{J%By2g@)jy#|R5PG5=a`blB^ zX$H^DF2j{X@fQ6t5=wyZCkEL8?&ff@dw%3vJAt*O&I|R#*M#TKF^2UWp7*ErD5ail zK=jvA<2^n!ostRLtAx>7X+K9Cx$v=pjSXg%0@+=e&L21jp^KE=NdY>5IC5g`5B@#gQs(LD(| z)Te`i$FTt(1O$`bgo1)c1df0D$mg9z31`#Y5I#5=a-R-+3npjANZhpcx2$F^KDh=b z|NB&$)W+XMJukAgkx}*G88twjdSdpA*FIU?Rm~=a68JN0eE7xxG8dSbcXYVcJRN--Z_V@z8DFVN{#`^N_&WP?Y$S@FAaU zR{KGDA6+Fest0>|u~cZYHZ^+!8Af$;Tm6OPBmcYfNPMgvsAr6*;6T122~@nCXeBL7*AI03rsBu?6t}?t`!Kyg4iyEtey&?We5I6ZML`;w!Vb1RPX#%6TbJKNK)TR1DnWy_*st!>bU%`44te zE%u70DKtM<7jFJ&c1yv00)-jYX5|?Cb?pJam7!#T-&@T)tUni2g~+|d&oBtShqu|* z3pHMPn<5W(BylW*7JGR`l~bPI;lQ1sb1*sfF>h>{jK+K&o-z?kz3NU?^_H8ZmP&R7c2mSE zfArKC0B1l@RG_DDHEJIR6ODN+UpRx064-EroL@wVjMld~6c?r9MD9mq;uvHCV%%$# z=ZjcyWfDrQWs{8Vu`hwnzxz9$vw9^mcWnw<#rwA@K;uThAE5lTo>TISE(}QAv6u%7 zMs!_&*G__f722do8Ys;4D8L8)g48Q1Qhx7pe#!$|^(0RB-Zcg0fcBsatT#yow5c-@`gdqxthL|Fp`# zN-}P;go`qXarN~(%5yDw8zI1pFe^ItBc%DGOrHY|9G;Ny6hdz-K0X`p5-N^d+Dz^a zgJ$XZRp#8KtV@H=4?R9pbf@eM6_acPt%t{01M19~fe-aT`$MX3j9!!FKJz$~G>G@` zsvL-lGc3Mfii24nT6fOY#-#_7_G1w{rwf}^=hdWch+Sd>oeE9~WK6uFF8_erESN9y z^u2Jx{i!eEv^&Pc&NshvK|qPye@u=Oa{vnbu(pqiR8ts zQhCJG;82{l0w~skg!G5zwm)JXzdOHQ<3zdY5Vx%Vd|;OiotIkpb(im4-_Q=&4|Yq< zWdxHopPLqrx@A15{{L>GSoVY*9nrwhyMLzUT59V{PkU;cfk)PP-fdY%bjo+`C5>9` zlc&E<#S7?DG!tg|A8?sItn?VM()>CIB+MBtLt#}8UR;!%Y1&l7*p-940*XgI#1w&s z_#kGCcJc)i6*5%&q%FJc3om_ujt|{0P{ZgDqICqUWETT@ny_aOV{b6gcbSl=hxy_S zV5^b|*%BjD8uv^T3v~Wj;Z%`sVwIch5CRrmWr2K+%uiRSXR(G2zA(S(CRSBYK->zU z*7Y|}PrYEO&CD+r^RVFp2tzKQ4r1_hEaJ^VbwPpUF~cV%?{o7Jb_FDk>e1>>fCOGmziuCnNesBLz-{Y;(^@ zBbc{c`10&Bh@!qTM|z}hN78z;)Iel;0SbHXmhKPt>52)#$qrS!dU;90wO$$o-YCT$ zA4Oh~SZ&%j$imxaAOfQ+;S0e-y%EYUC_YN2jx<|21t=0gO#b@sE}6BP2^WyIAmjtt zrlSStlzq-`&;Rt5SNB`%@eV6D^5FU7P0YS47qz*oU1!Pr$5ob%kUPjC`8+;Mh@wXg zKx$nUH6JKu!?1YZ7Zqbv!k2d^-oOe_hC5$GO;Myfk#OU&j}sOT5Y;!fDfTZxN@0EY zs1c4gp4zf=(hk^tA%iA-EGQ=ufMEy+@qAH7%_j=hhBHVCvEJp@0a9?lxeO7!11Yxh zCaJ6AMkS352@_cDZ@s&YU-~|VXg>9#ziCmx=9|qIBHEukbxyhhHoQ(}m6n37SX)4< zxZLM78Oq}pW9mJNHt#TFI8Waeyx)}54o~(JoZH{Eu6w?w>kS#BR5*F=s=Vopi-43q z1ZXtZF-!3oqD}{|ER`*^d%c@dDHQUSqiZQs=ASRY9dC750*S{W`eH2jZ!&<@1|jW^=PEnT%BXit)!B2o1Mua z-a$hb9x5oN!S6ta&`^U^?z7vn1aegy$S*F;RtOB!99S#;amgP9@x%z2ZPbR~sIFT( z6q!mBy}uBWOfEDJHvTs1WbpuzIFS58^=6-@=$R(hZw` zFq2A~R2ub+gA_-glKNIpcN=b}?xM#k%%J z`&F5)a|)Ba)~Cc{+j{(dMz*4ZWZrOQ&JIe$oUs92tP5t;cS%bHu!n=7R|h1(RURUh zwF3ey5N7F`dg47!Tq<<{y^s1II1Zsmd%f;-XI&(450bA97?Gi`uxo9V8wu~p-}k*J|y`s-hxNFqm_ z>*}+R&7GL0(TntNOA-=px8u$wjgTRpuGlZef7E;K8IA|`{OHZ?y7$Su{%E+Q$S&>{ zQY?i!&aXzP?kYcul*^T}Yr~Hh0TKMcL##4!7?p_c00Z43_u_&_pWC`-Z>|x{@&jen zw*-&8Xd5->rOJ3JS&xu|phpdjFjx*i7FVK$LB9-7ytF%2fTN?wu~GaHgVYBkM?^&# zzYNEhuDnFCfc^cjJsqg^Yay*ll6mLn!cg>f>B#cQMwH~SnQCqA?~mR;DqTwU$N-0J zsquUG;h-nY`gMwWT>~dChJgW5l50}k!fiLk-z!*sRBhv7l=vTR7hgg5*R4q+%xaVM zKS~aRHoU@&$Y?Z67fr9UrnjnA8L2X&*C>9>$6AcxyK9=35usNCr-#OF*{xjPJRX!P z9ZxbHcWo=KN>q9v(mDRE>|jlKTl-p)LN&|Q`gQ@vTE1qfnyOQGcewsa%f*GrXvca@ zhHig@bh+N|Z6xyv;^C&q)S)=PYvMnao%Bd7jGss)8T!tTd>gk9xf^m~+PzWTHkCh_ ziAFK?e4lQ;VZ!%PY~gLRV?|Ik*c)Ea=0h3n=m5n$YW(E6RaB)2$K;opxJj8YI8zEI z&5V~_J|Zj~lB71bFYb8#uBTvwf6nrlKkW%_f(xC@c2@&3+^yK9DTXxDf>!SL4t&3 z+u|XsrZZHvjMkHyEEKTq6!K*#&9f`1miXP?)T7dITO@ypH=enqJd_L7*-0d>Q#jV? z?FC)<(QX#3r0c=rykG%Gf9YxDSm5bin_7lfef+664(Zcyn&NEs_*^O4FUUAQ*6jD$ z`miL58by5T$Eg9Tc!aZ51Ik&TulL8_4fFv*4E;+d_BX=mWP6=7J8P`$~L25sIdKr4stV_9;J4kF`n97l zDh-MfP`Ahjcos_>De};IW!Pv=rjeuetf;S<&@4dp=VPRhK7~S|tM^Gz@&Jt~=UA4D zz2|1pG3fmBO_$SdFw%6y=ssb;4e9GO(t}w40tA~ZIaFr)J3HOYk`+ww&{xItp*}1p z!oXr6IFC-gL`>X}PG5mu@jg((2d61S^n>t09Vu+g_9+Z+Z$M%ur-sj!8;Lven_mjn z31Xi)pvuZccJfOZXiCBybD{Gg9c2KG{nEx!{|45*p$A3pHuN>xaarBQFXxouSDCR{ z#Wn~h>k-*czr%YO5bTd;*BJLa9wb9j@<8SjJ4zB4;-IUK>-)GRG0DCbcF2LkG-)Sy zqtDEoGc=a0PMAu#O8Z6Bf<>dXxk%==xt`8}1rWXx#h{$yY5K-rAg)_YB|?)9-6H!f zMb1hDN3xu>z;Py7G&l5+2rFbeiitTHJ1A0rhxsk`m4_Fm>=($bIk$mUy$`B-5=MwJAV;vuldy(T zz}^1wog=gi_=19>N2MfcJq*Z@<*^o^f=l6)OlH$2M0w0;$ID_cE({bA>JRdU=0u(%R;!n_IZk*o8h zM}(Q{iNsl@zMb-lVpAnWXI4FRuk6(lUdQk2P8<+py+a>UTX7NTQ{8$qV5XWmu8?|D zb<*8h1mISOqBAjcRE^#85iDx8IODLaWJp2Huz+(2ia7vGYru!UH47;{fV!PX-Z&Yl~9e~-T<<2U`0 z-D1~!r9bkP2Q&M|7o&NngS-ZS>@4wcz10S~DP=&3vXI(@Avb9&S8fkK*<7#BNQABYiE%MYeu%^l0_)Sp)n8e@v(b2~3jgdJ5jmwZ_ca~?(8 z?dka-KV&^~vzqmQjOFJsvI{h+e|TiLUoJ7x7JCtq=5bQqAIx86?eWfJkz3b3+0)hn zP~x3iSapJCkI})u>hKR%Ayc&mx}&H~*@ZY=&B}$FwV-Hc?^WuJ`lqm{ij8M^uDpQt zWkdpr;E+B1{*D6EjKS`r?(D}eGEB@|8Om%G9-I}HP_)rPy`$^!?*#S*Pv_2OUu3M7 zzt+V-1pDJ_AlYO5Gwds?^ErUe2mabbUVcaM8AZ#YjAjw{SI;Mm#&`z`eLQbHeV+&P zoyZfby3g)uB-r(DHZanz<}H`R$K9?NW11OA)$;4b*efQJYjFW^^nh#qQ_XE5O^Acz zC|{w^eyB#JwET)g5p+IzGRZ|-c^wd}PAXwIY2`gytzgJGzJ%yCx{~5^M@4fxZyk`Y zE7OCN#<)6Tt(L_8$-?w{#D4D$AaS)#{y^dDwWU7Kd4NXIYHX@%ztlzE6l>qt5~+Ok z9yD=ZS`Rl-Fw`YPB}{I~l~~Te(MXr99LE(-(4|R&kfcA!Uax$+HXJw3xK7*RWY|`% zb{*15gn)fumlSF57^tJedKvn9$y!<~GI-Z^KdqA^DM7?;r{X@L+dMs}pCZSA^-QRk z2s7vi9SH#3AnMl3ZT&KWb&D)6^tKRsJ}P&n!gj(sN5P@8z)&o88ZNp4+X8go`0SI7 zH&loMTerr7#R;r{r&!S((mG&Ow>P$SykbR4b&@BW9rN_PJvhRe66CweDEq)Gd?3N{GQWHs@Uis={HmvI+ajerO z!tWr=;MUt)?XriuUCeVwU zi(FMuQRGL(*gOD}qTpy@Ps{jCbFMI@B9b0l)pdOsW&?#u&^9&Gm*k0d0|mZ=o%0Tk zC9ep~8ZTQ2AuyN3Gi9y?funMQrilmeV$a^g!yv+zMPG@-Nt7{YV;P9kfUeC5_Zq zoLK9X21Og=`*M>3542rPa-jR68*Zb-SYsHFoZxj0Ci>Oa3i`)^;Q?DC=>uaNaQ@tb zCyvU21SWMN=~zd`R$un^tUItJ*ti+PgA5(qkG!9BAhx4(!|5jsA5lB#b1*&V!|f0v zh+6k=r{o%UzESzuM%_n5)wj?dpeQ72Z7|W5c5PEg;(gAKO$ulR$_`o*F2H>JqG6W( zUDKw$xuAE$KX*rz0Hi7{kns>?H}_oWN2BW`-GmIsQ-Q}X)UCnLsmj{qAScTyV+10 z#w9u&>%o&dUHaI&(cBKfe73G&Z-8D}sYoygYHC zY76gXJ>L1W9;^m;lPpAtdz?0V8l9!+zgX2$)o67iXx0^MEZ#!Jo55?c2b)T5l0JGF z_<{o!93$5_oLqKFlsR~5PQ*s^gRaj$9;yCZjbFP?g0Pry`|=cLyVFpdzV>mYbF0Tv z&*uo*-uL*{ewwpi&})Y0JJ^KM6siJ}rY5eX9A$zPC4o8MKJsq|J@Q)*3_LkF!5oAT zJmp%l3tEq>c!zJj8Ckc0CzD#skM*Ka@Gfy5UK#8T4YE8)=SY|tJ<=7^ae(#dZc3n}Ot7BA+vZBYj+5+4cNdylXdHok6qMIl7>@z1i^p6d@ zx*1}Q3mtVFaQbZZ&<6%4yQ6_?qN7h-ANc$3aj_$kXgzrng#s01>a;*w5u?^qO70P- z6{pHfjEsaFl6}B4m=jadGJw<`$zMc+lE({#9LdRk6}PgrZSSU(k)?fKjtW|T;J!B~ zy>&55C1A0@q(UdKVMV7T5cFRLx>sXudN4c(IMAdW*W{l(DB_dNyBx%ac9orq4arGa z;M}>P&1u5n!1i@Ho8xDbDsg}{7}2cZLau_7J}@`_0lpf^VKKIYV>&?W5Oy?(N^XNZtv zOM6e}T3}N}hq>CAt%M91j{n8e;gMMaLqkuA#}TE*GQuic{AQuyCnj8!Ywg#?N+geq z)5(*RLtkbU@HS|0ph{kxl0*6zw_g%?s-Uwj!*YZECj^6B{wmg4^~_855@E|uf=E2{?6C9p zfYy@20+Amrr1rPo)aLubp}2zZJ*6o#R-p7T$f>xhGs{{EqoV#)ZLbQ*#V)rO^i)e* zNgtaJWt6l*>j_Q)JJo>)2~E#0>jyyDo@?TwBUj1%rb9b2!WtSm zpU}7f+?*5nj;|Tf>Uj=GeTBMYsp!r2L_8)ir$LiOwY#_Gk=e#mbi#7ew2YZ6oIbCv z90$GZsO&pEVsbcH!I;oy&V*IsJ;;GvmEXOcapk;5r3K19k2Aj8i6X_STdhDd1Ez|f zby$eVkkiuV?tp8KrGaYNzwAU0a4-Y6Q`e1BXveH}?)~D6F%HM8sL>Z!fw+|wlOiO?90GkYe6HgM6|i~V?_Y}@AAl)> zW2JdJDFjYBS!tsh6PFn@LdrQe>D9SB?o<&Qx+pob#r){(cV*}Sj>?Ng#Rx&D9W>x|pjehTXknj#S`v59PIl>`+ znoB}hLtp*qakTu z5i@*2fT*-(c+exw!Gadp65i*LAwm#j(S>NvB?sLUPSry#f;3bn^Wr5-ow z;-MU{De36X>a1Q@^v%VAElgSq^WU~B_^4)OH+)p;__2(c+x9nUKrj%igK-C(;pidR za+s=jh(M7~lyL5v20;VJo)>5s$7!E+~(EQpd zJwvcx%IYOntlWz_!PWKUS&1Q`)_5R7xA(1&PKyh5fTL1Zg{1~|cE#Z;EGD7I<4J?W zu}6ODJ95DaCvtK30UC3w*jEKK{Qg!o^I*7VfoH0|v}3smX}iNW6@k4a*v)EH+g=d| zO2e^71g`o+t$%P4DPrU__B9FeP`wZX4q6z?ci5v{KpWjV8Ij7xL#p38ePAP7Z^Bo1 z_%$p6lxm>zH4+Hcer4=(&j$=0lAx?q$dmlS4~aprc+W{%b;TaRtD>;VIp~>-v1ZZ} zMuq1FUtfJ0nH09>EhB{s?xzjwW^pco*eXhL~05P3};vfO0 z=V?@Enbj1vuHc>(0)zn(!XI?^=)%!73+s1p@}d-?ea)gqQDPQ4!sIqv9qUoEh3)kP zmTTkr%>m`QvLwZ8k0IG4DI@he=KC&Cv|5{n$NT9|Oo>$JgtpqEyN;?E+Ee&e+L3R? zHw*j+i^n-JPv_)qhwlNkE5s_Lj1L+sZp4uywK-LWOru!TZ&Kj8<&2`_^ICEiM%?`C z`r!~sF@ayJsOF3k4Qk}L<4CW-bsiT=GGu{kS`h`+9wj@b2FarVj4_FTDXaMhUpCey za(j`&3?FiG99#+~;(dwTkTC*P@A#s@sT@NDoOuZqJ zd55A?slRh}5j)x`aaR7M=v@}8lD8v0I@JeRg!UJlOTj$JiXrGuq+o+_`3_7nJ6Td( zn=)67P4cUCOc`y|0Yj?i?*OeViO+a*dTRiU$?E{zy!%G^ezjHnP2X|BrCZ8G2dn$L zE~<7_vGbK3Z}q%?sRZQdr0WXxOjc-0Z;1dL zRiUF%xqUi)xv6MWyAZI+F&A-umfF;S7OA(HKA0}JZ{_f9e`LE=!3)9+ zO*>VFZL-b03P^3XgaAjfP7VjkUxvY16_Rb)nvxO|ti%kydI zP)b>13sa^+;!Z5IVPt&o)TzWeAo^7#%;HjVV`LO#gT==`L(%2sPQysulf}MQ>sALA z3>OzX7cKmkmW8MhNzsLqoIJd1QKMdIeZQH>kwNIlx8H(2+{dRv?GZ`*EiG+9Hwwp7 zDl>~UiRHkN`UR!kySrVax(t^mZ&`b7Oh6vcj!`dIeHziUBK}&C9kb8-LK&dBrz5OH zFY2>8eux)(%Y=!$wBA%ccHG8BX}G)b7m?r(pwxl8`{8^?h;Wbp6oYuxBft94NCgdX zn`$J3Qo!xBAI04RQVIET&erN^P!JHK=I}BYF^#-gI&{fzytFZCCSns80qZ2OrGTX$ zt*IhS{&LIgTeq1D+5b9Jc4*2OfpgyZBp*K(g(@pr3c>TD;gt7tPhV zR9k>UM?&(^?Rr>e+<Eq{AgU4Tk)Wt4T%Ycaz ziM`4@3!?9SN6X7X9Bz3BeT#=Kd2yK#fPgkL3!bRoCTHQ9jvX*DLxMAcV)VlWRCUWv zNHYhO$pv$_bj873?V-L~OeG)h`n9p8H?Zh^{6J2Fnym(R3jxdpX;&Xlqz<+ zHg7g0xDL71uOVm!=9Eq4(I}d@Tr0i2!!|Dq?|jUa_m@EptK9Zd@d!W7qUI7RINHkD zT~+3#S8$tv=WAax72DM8QOr_C>y>cqHmc@REk1q~)gyrDzn2IL#uIG#TEXnj(&t<~ zO6wcA={J)(F|yD5b_7g}6RdzOkWuJR?OiIlgffV+T7QAWy}!jv!o4PLGZ*Y+InI-( zUT+wxwZ87k1}3S^<`fJEkZKpOmrHwGL)cLuvfu&|^0OT+r6UO&KFIhiR#V!>y3K&kYT~E@Y3Jdz;DjNvhwPQ9u80-hEJWF9r8tr}i&z}BW7&;qP<@D6j zcV~6rNXQQ@p*3UGmv{xl+Q%=dM}jiT?P)MhTBmzx8z3 zeuIYtmJWWo$A)x_V^EC7E|d@fr~Q!J^B%+{YQ$i^ME_!i;}0LPInW=ZD7n;jd*RDn zQNSV4YR#X|``7SUh`Xc-lAo5CY)~n+68d+Z9Z{))Qk#rn) zs^7L>9=>7RfMmFz#{7=!0L1bf`bS&`9m&kLzXe_oXx)m~KORZkiOs72*zSp@LY9|Q z_-^UP;FJ#2EgEkAQyW}DJ z*g&^!$zBvGE{0-ey#x(T0)l4b@TK;VO0vVX%iXtw^$llICadpe zE$N~V*u1l(rP%Rec!)aI?8|6XeZ&2gWd7wSccH;R`0rNMNU%`g(0Pzdn+9EA8Ra^o zR^sWha$Fr%o3WWIZmTm^ar0;*hJjFxt+SJ4o^-+49aRdXSacQfBc-Cf;rsS34iBKV zE8isiJmg89vn9uRk9N=r`yzY{>DlNJDifQ@{y)_4(N-PIU1dp~8`Fy>1uEMu;y3zd zr-|t)XiExIYMxV3lUVGdi^roFWFC|o{BkvK`TnJF&e6NGxFA?sD#Q;cZE+K~Xzrky z8`kOI8}F1~R7k(O)z0!V{-faBL$A8Kq!GN4(8adb9ZOy7t-XwvQOp4V?wUy!9 zC)?f-?Fy)>KZ18haP7bb4BPgkhUYv4IKB_rbaB2fB7Te2hl?=VTE6{XjzqUn&Uw3Ga}VhmAaM_Zte0TgT8BnNb7bXr7qEFc52Xs zv&Bgh4M6mEW??LWa>j(@>~Wgdw`N0tsLp?NrB+}K_cT|iU3hf3;qmleY8`UXMSv>f@Swa{K?A)8K-I2K@-L^do?r&*jloSZ>#d` z$mE#U#;uIHCHbRK4lvmbpa}?nDj)#U>VsMT6v{@XeyeD1#spXtQ9L5ynLf=?Lyh@I zQ=tGFuhol@rdmpS!ttqkCdP42{tKshpj?BHx>L1x@kNo?lbUA1eRd#gFXU( z9{Bu%mcRi!BaWyQ&*+SwyXY!%8BmZ0b3BdOKN#>isVP0hAfP1RSdda1J@1a*?WV+- zoOXZV2N6Z&;w9$h6Kx?~gAE`nmGFV+ni$2~T09(=AXCJ3ZC-8RcC2yrn_#Bqdub3f z@g;1^u`sG^C_vlL-ZAwjPggX9`8W6L0$+x+?*TE`408cm^j-p0QqHWPhzQ0x?jUBL z&g8Ag_oJoK%ZM4Qr0>9Wa~mK)Uz-DAN48G%u|&zQOl4J32x3M9;xH=aJNgWMa3h%P zAxJP`luS(_f^|fLB~=^JDIO+b<9XG!J+_$xqA3MOS6%+*YpHJ&z)XUY0!>ooBA1O& z{ki>n7KNE#iesSz>b!53eOji*^-YCBAJW=jWLB`#9B8BY4s;rMPFNkd%6)w z=+Vjpn^)V(znz!%`?P+6$D3`g{CccFS8gs37o*m9&kozV+Z*+}h89_TS;ui1t>iQO zNCwTdDD^{Zy~}*ZH1 zK0L#txDWUotSHmpyVa9=D*-I{rzjk$b$YqsHg;7K#TEw!J7s2URgfzH_aZ5+UpR0OA&bdH4=wS~K^3V!a#U=}0Jg zWaS@z*Rd-Ag_3B(5R$X!X^2Z(bE}x=)1&8y zJ_|*6#k$Rzs5}ac>I*;biHrLKKGpQ90Cxl|+uBMlb2UVE`93c-$d}dz8devn+c;H! z@f6a$*%cRdFT~uEFK>BTIM7#KCBq5=!z9|()rKQ}WSi%;V@Om8j;7yx*Joc}=jVfN zB4Bv7yo&M302ttVv9SCPN8Q!f{ny^!>qKCU=g9cG=rjuPBrqh0gSuVTcvSjf7v~!& z>sG8sZXR3{8WPnxtt7cEGypHHnh(OhS)3k+<#@}SI-|0~7KvH6n#*xc1tC_YZX^%p zb}Ptcmi?{#M2bZn$*ex_sPSQE59IaW^Y%{P!tH;KG9SVssdx$)?lpj?1K$N#c&(RcGIGNSdXyj zET`VFs~WTQ^g553|F$XZP(ato5jet5Wjz?)c`^{4C}D;E(lByf&H%Cg+|QjV=s26% zzR(=B(jMoy%|LUe8o=g{C`~3NL2fdXR1-O5fekoTIaQFOE;tNAx%Jn!} z_PhaR8J$*!fW@{y?08U{WB(4(G}+r5K&&>KbzT+>L*K0C=Utfd+Ipz`cixJsncl3r z8j%9y^HSFTS3hY0Qt1C!#5#mbo%r-{bwfYr``z;@F0ejsy`Q4XoGNOO-~?xn?rcw~ zROk*H&*0Rk(+nc8{DumhIjkeX`00(O89oyUDtPQ-;M|4tvfo(P{J@v(s0v$uiIeSV zUk1cnB~sgvoDN&40CuHC&lD9;_j>aD6BLby{^i|G^We*A;uR$dZ!F6xyzlk9SUlCO zyhw2zJc@iQTeU;o5)Ux+H-yr&n>j(t0DJ_79M^Is0=vVAIP|@Nlw3dD;j6dKD#8<} z(74UrKlqAn%W{&S%2rpTbsl5g&qr(FgUo7BRg0Vla7^hB)H^U?#H30=f&bu%@oR8j zKD?0Zv#Jw=e-8TTKk*f$#fX5q@|(Wx8^-sFfeJp?HTU5c=EboQhn%n_4WHO%)3Ed) z@-ivKnnSg?!alCV`u$IR)=Wb}?NvOdH7e-)0l33{7Uui#`Kxs@`0QXkL_{~pdL_&B z3*E02_C?<*TmP*Q;hIzJ%A^;Ndlg@vp=NnY4SP>Og1tPe_X~ykc+a(2Bq;~qO1ug? z|0YU}DY8!AA~3z*%ELx+1^TDjPZlr&bxY-SV#2=-K{J52d-=+d%O^8@EHF3?Y;zLM zLwCNusf%l*KP`AXb?O>F0z+)A!i*O@|J1{QkDo*G)dBA3dwLLT^&n`o72ZTOw#nNC zX#dApU^&JH!7t*|BF8_C4n~LEErY}}AD)FpLA=0p@WfL2vMOl#{zm~E1*FtjEp0jg zlfhR_{HH380*tXVg)a1OdI~?kl??pZi-e^a^#6x1!GMgqyBc?oIH{)mdKcoy11!Kk zWyaFCVxpddpVe=jD6n2UJEIN)Yd>|L?Z$xCW{@AwjoTWeqo>Nln|k*5)y}JcItb=9 zXXu_;ff0OcDJu5r*C2_tLBhIT@(BI=M4JP-N=|zgQaR;yF?k5);}2}r2Y8Kv4U-2a0JnZd*K#Gvs=$Ih1y z)&q!S@l(>4FTZR0w;qNECDir(zD3a&cP3b<@tM!eQWVL+p~Bc98c)XjobUgr(6yeZg)E-GuN3bo zRFpK{U!kHtt+>PoB~+o`4ORS-X=rx$jA*oLS}mt_^Rfmh7^VQ%b{oOVHj&e!93=7K zDtI^A#S^|Zo4I$ekYbC8ZzE_m5emO2<6P^ryAYXE!>Met{%&ylH2Ie(|(i`YZ)2l)lZ|<2y<>&o;n6 ze(EC6pPOuFA)Kj@;p&0==Yg4+_<#M+ETOl{3cE>sB!p(SrW?w>)%r?EudyKCC}R1s zJl7_3XeF{>*7MZ}!5>ErJ#Yg|lmI`{pu$?^Hun3;);MBQ?Z9HLyK^onUqL0l5fTl0 zMX?_c2dB_7pn^V0S zY`t+_NamnYZCfyWQY!I~p=jcZlShH!S>3?E%g|=i7zG;hAh5tqQvx*Q#w`da>j&7x zl7UTdjKP*fm2%yI#@RQ*^d zq_^H7_aHioia0Kok^?IO<%f}VyC@qe_nXddbBCr4o%@zk49RQ|g*)v4O)l1>@^y?R z$$M9)mRBt8_?={}Vo~?Kb$y2w=#=gc$M2U8Z(hwb)mm0$OInz$BL&#*2Y$ zU~Q*oZ?WU>Nvb--iFgKRCmcg;^~gFn+I?CS>$E5AV|@qjZ78G@wZW`wu(HsEDJ1Tg z)TV4>H;q{`8?gaFdzP=M82@-A<)Ft`(xT_pOb0_TAXr~uYbdh4Y!Gp55hu0_x8ZL{ zkirR)`pE=Iom4AF2su##KGz$dzg>+1;d(Bajh~dikHzbRbv?grS@z#0^;>VoKvrSa zac9yv4%e+7I)CqMP_@|EggC;lSEFCBwAYyiYlii|WPY3m% zAWDfD^qxVe(!o6j0Q+AyGrcEBR{83u{Pw;7&DM`HI*jR?vU0PL3J}A}i`o0>!=Zwx zk!Qi=ssVuQnBn&5kz{}%%U1jNm7~WMe}`}iabk|I$Xr7kXBHf zFrYvLA)y6}GOD!*gu#(8M+tKvp|uWFEK(q75Xh~7fWZg>VgdIK}%M`cXAMqz1zoNa(iwWv20#dNh=;0;S7Ne9f(1EQ6 z$oDk7o8|E=Jla7WyaBP{LgnM#f^*FBDpM6w8L(BPacc3njuP1im7KwD=Y?WMz|@^5uGmGsZoMixYkMk1U~}QUnx6<2ids08E$Y2!HLvbg2RyvU#o&$CSZ>beMgHhwO zsT&;(FFnar?83ES)G%$TFss^=SNnN6b8EFlpx?$d0@VSU>WhC}!TTf*3cKTo@7itm zeQ_+S0+tE5lA4Ec2A}^IXvPKjzrPxPO(XzN;a@}5zpOBYR4|o3o{f^9RpA=3sr&>7 z6|m7gD*uPqVmn)VqxOORq%;*oUD^y&R;$9OW5o{0f11~K1h$tpuC9zs0Pa$)xqH@- zDNn8GEr5opUT=SD5;F}XMF3p9)blK~{qmhSx0)Egbi*4bLC%f^JCe6-4qi`mbR=l( z+ngS#dgSd03OCD;%fvZ8!cpL8VI89T!DF36i#^Y<@RgPu9-J5bO@TMHy`B9m*+rc% zV_lD8DsrMAgeUV@J8JaK-msYXSF~$B!LIrHt-w``?(iEOpZiQOQ5c3D*9qdFmv4Z1()ZW726S` z=`$4jMHEQ>i$cmM^lQ$-A0le9outN}qVmG{=-TmHx#G=T{B>HHee`hoZEM~VG>{;* zsp-r!W8bPpRPSuvQm3}dO6+TKUmUQdc z4}^k=qC&_u1hsrtfa_KEkMoDE(P>G+CESBb{IphOr|7%a8gzbQZ&SawY#GLv%gX)U zSR$G*FdL!kr1_wCTxdJm&Tt2|h54ez_j+_t+K%o(v^VeTz@Ar6Gd#K}4!F0&v{{r* z_5eW=mFCYuc?^}V1eG!bMkOvL&9X@9nOmLK;4{;vy2PgN`%K2xL%Z14CpYjP($SIIbkyG}Q zyqQZd+`;S9w`Kx+z)|KHLw8>+BbzIq%`2j{HZ8ZEs|uuc=8xcG`ER&i8JrzUbgZKK z+IRJRmV~Jba-X~ei%JW2r$Eo4M2GA%a-IxTM_d*~nWybhJPbMf)Z;s;NMIu>>uMZx z+C(hGZg>2oHT2*NP4sgXo#Ia5Nxi$-qXOxcp4yNYzvz0KuD(j}*1VfLb#ppUUf`Cg zNYNhIj$Ya9R=k{1xYTf!Wz*kfd&%34M{lNGWI$gF<^Q<45vuK9MYX^x7!0T%3hD{m3UitXS{jnyN*VSjxcL9IeIpGZ+AHPyatXSpW# zT-mGWL5Eh&E~RS*l?-ULY0Q7C4Nf$hrOud3a~pdNhaY9xv882rC%3U^7l-VI%fC@B zm|o+vy}Ey8!gfctJWWFlle~ zJq{;SpSj(6N+o}dak=hZ%r6?9=$!>9(RQ|WE(F^65YD1AD4+$bXHz(@v6RikFBEO$ zUXzXft|yl95l_oFY5kCN=M~j@n^Y`odfSQp zY8p9CY57giS=S-F-6v&_goR8^CynJDHQ~7`LpW)H^y=%sXi84zVX9iKTJGCS7?m>o zEW3dIf1-Y|EWx%t8eQqT!bf*tFi4GMl9Y-@P|J7%P16*r+hZZ*)&U{4+o+c6iO2`X zOEgWVYd*6{wm}V|9>wuj2HDNIU6Bz}zirmdNvRPLmpVr9=q+g*IA0ENhF@QgabQPC zj#|)gvw`UQVo~ZxL~ztZ0?YeYFg%I+Fp#`?YG>|2TX<=AWJLVHOp*1T*qyN_bLRmg zFqfE8(7%47Pj&*0iOkV%X7T%u>$LXs>S$$stzDRAos^vCjf!LQ$HB(@Hq;#7Cp-x| z(wELG5Pf$?gAi9Re}q~fA{FZHQJC=uM9c;rl}mDc7LXt38m9+4=MUz&+B&>C9%}U@ z=J)3s;m)GDwlIn7iYR@1&0eb{NpjZ0W3Hb8k`T@9+vhM@02md#PH}=-fVmLsL$b^C zVKqUsOUit}$h4yc1bm*KFct|n*Ja|;xI#u%@AArP-D1BKzE(O=BXb3pKGpAiw^vng<2z7vA7PELrpF+VN;nPxqlH*NdbAyacL`!A zNg*w8;S2E@7ACZ6VNK~ZqaIirMI)0qw!iy|84bLiFf4Sbyydk@+wU&|N9f3DMm>pX8nvVA=J)AFkMbW~Y1t1Nf7kgNB?XLO)%L_+3$MuU&jY(vH3)Mbz4-M9 zsaYuHkCmpf1;4LbzK^E3KOe9wrBbWRmYy+~E{67i()8bp5Ns`C1eA5nT^Ra_kzJJV zfhj;9aY-HbAcN%?IY7Muzn1qaf*lbGt2_*w5IzK4fk*>ARMTDdH$P^F7pS!{!cQ+T z$j{)C#C-)G->@q^&cYYl?n_vBToPd$2&>$WWzp%=XVAo~s(Z+jYM1t&Ndg@*qGv2> zA`&(w!LnDVl$1W%hNHT~{_b#*R);V>G1jiZ!cF!Wg!3nxs(l&*Z`H;;2#l4zFdu8_ z4JM>1hhzc>HVjtH4cU?7)x{E4ytZ!t((kz>u}3(!C>PlEk=7;qOwn_su$Z)^*7Sx^ zO~2-6X>#J-C2^mu?F^dX*GG@W(=@dqgWj>A5b;u`IiY2!#HDFVFISkawZ+q zJo8jH|z#8UbgVzHg6vwPjXtSGQQ{d2pIeo%)<|4r2N zPab9Ykasq2n=n5O?A;uw0%d-wV_yOkMSpAxDm?qNuzWUY^_3dK+~$;spJ}LZCI;ke zP)Pf(7ZVkI9TA1yb`OGOrzu}mfcsoF*k3LgMKPvH@OPLuFORP`rjl2(M&`1oGk!#Z zfcCQ0_QTPm$|ErOe1Bn9fLZD&e&e`_$c)(*+fAvvtnW9yF#FL>soNuEM*uX{852*x zxFX@`xi5`%Mm@IOKk)^56tpY@A7h!|UM`M( ze+LZBbBTD-W`@1!?XdCk4a(vm@*a)@A~3$`hU@_n6AOPlkE?%BaA+^c08d}^8_qE8 z_NssJz(=ul80XWiG*{SG9V3T$u5T5VRbcn0>uT(au64)u0#iiwEp%R5*IHI`*J$H# z#R6&e<~&}ipHC&htrAM%x$7)2e^B>)0T+ftV8`I8vCJs&Fca?^fmkNrYIh>(!b56_ zifbRYlFBM@tKBy|gCs0c!)LIu89-sxeLD8uMN*Z*r?74DUFpU7GdL@)pl(lYUFPfC zpiG+(6B8u`5OK72>wJqSqi9^rPgYosp@crqE>zD)Sji|pj2C_ny zLWj`^VHebO_{R?XLL)M@t~Ts~7wQY(uNhvde*{3z>o<;kLgXT@%FpfwuRA>%X9`U{ zo=>3l!mPw##4dYya3Upy?@@O_UzyLqzA|Whm2fY>e1-GKZY0-k0yp;ZC1u7kwXSBu zFeS5UO~{+x;`r&yb1OM0?@h#~@C@IX_%83dL7fstMxJv7T+lKy7m!z#`NpxLdQP!A z?wx7eJ2EV`r>CYYN^eUtGj^f`!ds%D*v@2}7#X0f3Xs=)Mw*UJtxR%;_qx z0m$NxOY8oxKPD(t*5%shfq+YY1Z8)dr?uH^!?p`s_WfdfI{6e4lwq$?1o{#0Ick<# ScSzOh)CqUTXJ20U?mq#OR@5{A literal 25555 zcmeFZXH-;6yDmDrTR=fzDIghCk`g6nXcs652q-x#NRTKwLz_TAK#&|;B}d7UO@QPk zCjpVrWCWU=?wk#}zVD8E$JzVrbI%=TjQzubJ*#G=S@nkJeclRES5+V-rXvObfb^E4 z{9OPbKmq{b<7ou=Q0KuAbPL_~D@^yxEa z&JYt5laP>{J$sgvl$4B&jGUbOufP5}ckbNz^XDljC@3i@si>$fT)03@O-(~XLrY6b zM@L6bPtU->z{tqR#KgqR%zW|U#Y>kiUA}yog@uKcm6eT+jh&sHgM;JBl`B`TUghNE zTYKcI_HJKfi#0fS{nDkdTnDu&{`Th^VNjn3$NjxVVIbgruaT zl$4aTw6u(jjI69I5{bNi{kpuo{EZtoZr;49prD|rsCetvEhQx-Wo2a*6_wk!Z>y@R zs;Q}|tE=C+bLZ~eyBZoAnwpwgT3XuL+B!Nqy1Kf0dV2c$`UVCDhK7blMn=ZQ#wI2v z_wL=hfB(Lzsi~Qn*@FiU%+1X$EG#T7Ev>Aq9zJ|%ZEbC1V`FP;YiDO?Z*TA5;PB|t zBS%L^CnqOoXJ;1|7gtwTH#awTcXtmD4^K}|FE1}|Z*LzTA75WzKR-Wze}5DT_4x7Q zCr_R{efsp-vuDqrKMx292n-B-@!~~LP*8Aia7akV%a<=hLqo&D!otJDU%h%25fKp? z8TtD4>!_%x=;-K}n3&ku*tod3`1tsQgoMPz#H6I8=FOY8Z{NOq z_bx3hEj>LwBO@a-GczkID?2+oCnqO2H#aXYFF!xOprD|zu<-r+_eDiT#l^)XB_*Y$ zrDbJh<>loS6%`*oe5kCftg5Q2uCA`Bsj024t*fi6udo03@#CjYpFV&7{N>A+uV245 zG&D3eHa0aief##Uxw*NerKPpCwXLnKy}iAoqocF46NABYb#--jclY%4^!E1l_4W1l z_YVvV3=R$s4Gj$s508wDeEYFGXO9KLjPdl)2rqHa1ppAe_hif zX=#)wj%v$y_8=m0QTNvOwG6So${@4Mn&xQd$(qWo4OgevHO&|QVlQTue_$5$3jBLOSd3k->Zp6jQ^Et39=OQcsCHXX3)cJD{3DNTM3IB;lGVon} z1J>d^`fj-wUIRP}q;vf8HSXLCIXIdpXOPll$OeS^DnN-8hW=t#tBurBf z`U92&eP{o>+R1|O!DfTnZ7NbCm=s-3R;Jos`1VV-1V>e`D|bkbmu+RY0ao-t&N!M$ zS^jq|34sf_io_D>T3*+>&K_5D@Nb)o!`*vJi-Koa0Gwmz2{5kRPlGG~Vsrv5%fp!x z8LLRn4t{QwH6i+EZ5mLe^v}yH<^-70e`@<2^!R9>wTom@7W&;|9S^Bhy-~MWkkv_p z1NpIBcCtjau}n&uzbo16VPn)1)k|g~af+Aej|ak3!XHG026AXo!9?hvC*6YsI%x*r zWICw_mH%g|#R9nQA&a^D^5VxEt!81xfsQFeOLde(`qeDqaa#itBEqyirgIVUBFD9= zV$L*IA}5b>Uo$D6j5~c~xlKVfj_+@CIDUJkC*~)0O`>r3F4G+kZB&c^(+;lA$<$o{ zPUho+AEUQU8Y3N2A)IwS1_tC zzpu#M@33Y+_gG%^q}JC5m$98fO5`jpC$;#KlmF|h^`BV#tgYG&3-FjHLdTq0w|}JD z?1jgQUcc}h|0#_}sPet7+@Kc{*Fk9zVnyjs#3}tOi`t)PHwH&@*?6CE*1Oy6bjA;0 zT2h^uy$(SYzVhFv|Nl5~s76p)WZyU##K#oHph|giyfpx6DIp8dz2Mmwm#L2rKnlX@ z0HvEln_p}etb1~z$MNriF@XoH$ZvnlNfM(5Ev;rlv ziI|*Ip9<#UZ*Enm2U8CRHI4vEgxm$lr()79 zK+kH?U~Utm-#w*|;)7*1fr6}{S)R_#?z>Cc!U|7M1K2hb0cP$KX-h1AxKb8o=L@f) zV{;jh0|cU1OyKyjg44KM&ClcJ)@gUzR^{*%nh83eSspilcH8f{^J^5OaD3a~Vid7N zBeUQfo~8u@8Ko&OE%%sBq$E%a}ROy?rpai52Nk^Z+T7y7}gC4o{c zb%32{c??P;Gnx6`IQ+LF~4G(s|21A5c0E zjay&Urv33|YGLXJTRq{K(|au?Gfn_o5VRINnMx%cm1}~|B?s8gi=Dp!rBC?o#HAlF ziOcR)4s!1~??0Wnv)9-@6aHFxd@Ol!LzE@Ld^C#b@7}$OhiR{wix-c$F28-0PGIHU<1@4E9m^3fO?O^Le~~^s z(f747Eb7JEhzn@xHs-OfoM_IpnZ8syY0KR ziJom3k$U85{DRTNdJ%a^6^&*!GT4{|#peQ1tZ;C{l_nVrr`E)8JFA!ubiT7l-I|OY zud2ps{a&uz)P$$w#WHaNO9t($T(Tm8WADfO`x#!Bok!ihhF@!K(yjQ>x{=ZS{+O@P zT(#}qe%(Dj&xYa@*&*AsbzviDd}-(4&h2cjNv6fFikZumbq1Me@^UD&AeQMi>xuQQ3`lUq%-FR?qIA`5uSN_f46h9c~C9=QPxZ{y@}!zC?0)Kzxk($V*6h3=@ZdLvoK*h(5R)~sie2FH)%Wp@diBk$IA|dVfoAx>YBNYxW z`gzH$e9dQW73&_4x0PsCga8w9og6Qn&+k1hxGk&D7-()8%1ea`)@f1LPB>|F^G%Wg z(a7F4p3_}qUyQDRovz$W`_v)R?RxTZWYcHSCB0fxWyMXG1t)NWdAgeoPx(Bt6lDk6 z&*6>UpVQ+Jbt2PPjK-(1Z-vL|09??IZ<4AxHbNS5Z0O;P;r_>31eEljKt(d&SxzW+ z!5>^|gnE2fev+vF67Ak)P_nzYL6Tjm_gc@PY5w@zuQ6x-Y{x7|Lod*T)drSM9cx@T zkGosv7#TYGBHpAF+SVtMM5z*DRR6+jZ91Pqi|xD@T4nGoAH2@C`o*Xb_CCyrP<@9O zHvGZ2`LnRXQ-;t~Xk=EuR>?|Bv;a(g^G;*tvniw{uptJJ*7d*XpvPqU5DlMcA)npF zA%qtV-^;CCg$k*xq7k#I;Ao}^a4K3e5yVZjHZVwaqcei$bfyhA5ld&M30&gW>^62# z$Da`3?3%9JR4%r@s&LsgcnvP02g61GDbqNpqNeU=tJj8h6g(&85kI9MC*rq)G)8h< zMJ=E9!0(e|HR=g7$^+9y>i6+b*c>ZFbPlgUslzVadd z?ZK`ghO0y>Ic5iUPj)SIRDyM8b+<$4v7N>>9NZimz=;|M)jRxOy<7s!>ky9cooFj_pnXxmuu{!UK7!;}S#pzF`PR3cqh`^6)1xzC`PrH3+3a9}0hswED z(%I(`*$VfORvlk@b|AZrn>0&eb;5dr{|2`xT2PSu%P;my@c+=p=tgyD1HO9DYXq~j z(~g6jV#gMm9v!CYefvnCs}rp1mM4SpUksY1kZZ(EIy?P|YHnc3cH+|Wy8e<(x~N=U zqz)%amDJDb9OSN+wE3Zly0Od*o_E~j?-$;0{;^#v<)|}5NR76kLRu=F<%@*2=nO-P zr|sWAZ;Y%w4|uZB!{cV*5!@8pO|kz07|jH#{&KlSy*(ola$B`RtHlN_Ek+Jxi$xL^8C|CmNhQ?i4fU%QP|5xiEIGP zWXX$VRE4JZXvnWXrYYv$$nv&cvNOy1GoaV~(GxWj64smwZB3y;{iM!c*@fUxWr#2_ z*U8WbIWc}-lotbv@Ox!f(Uj#^^Em_18X&@uwCHdb{bjEJ*^d}>b+MN`x3;ja{|q5O z)d|BT5nG+EKScm-%E!E^I$MIL)K?jhiMb52xqp8Uy#h4|dEZy*Ba+VK%ZbO6{CR?6 zDpc9i^E7n%7b|o=3}>M6L_1*Z834^2+WlCGsP$Q#*qi8{FP^Hh_)`wTIjspQQ){b} zu?>ws9X`Q!Cqfk|j}n#0FU^;&|E1{OQ*5sU+8$33xY4c45$0veU_nD_??779U#l|j z^T@%!DxY923LeDlGM_`5p0)hD?F!k$|7DN>k_X4W_Wx^Z`ZZjxf{m)W@5g;veq$6D zmcIS!%`T?vGl0xZ2*4`U%_PiFixIBXs-%SuEQE7~Y}7bc^)t4#kbm?CoNE4$GQ6qnvRcvCA<+^#b0s&4I(#Td33Av2dsX707P^4|XpC^(2 z{VX^Y{E9SKr^FDtlz0iEf(JYvt)wV-KYif_uG9@1<9^vs*l*GW;HW=Pyk1lzAmLRe zXN7Qt7|ir2y(qJt<2WI@m@!MqmzSpJqIA^h6Ue1sObh~)cAwyMFEbpwcKXT1-pk^1 zpMK*KrFz6@gLEQ_(PMzGi~TC9Ac9H7<=8pAkyrreb4bVi#$C#FVYv4o8g&8!4Lde< zp5tlI;qX6(Sj-LLHcOoRSEHLVkDD9Pzx#yD=Y|*S9Wwm6jb0usZZ^xvueu`rQt@Pv zpm+0s`~`_WsPV@OF)6&ag`Ef}ZTN4=#+ zDY^N4`Zj-Vy%X9O(Igng_~h!nR4SUgy>RtU+M6Lm6vVvV241Y7wapYug5mF&;14+! zKv@Q87LdkU?mErAIYoi;G9*KGayD6#$I@tgg{v3Q5n80oMQ4J`qX?cDaX>l`&~Jtf z*Abyj?KD#6bN?xLE0vq0&qhd<4`}91w`lkl;O@ae7nPR?O&Z-%Vx9Cg?(zjxaC!&3 zn6lTk$IuvTSRw^l`AX9Em{GxH#;{WDljkIu$vKUs#+!!c@MABUr$TNik+Z)71&hAE41V&Yz5c68@F1dTV9V+2%&DT zyIOY*&fi%lYvOA+tWtPKD>SX%dNMM_+Edty z<-5kOxQZbgOF*(awPTfMR$$_jCml4s-F`N#k>#lMG=aFJjWnon#G^{?@4VN|vkoXu z<}1J9W;=%OyRNLola$OjqvoPbaX;9QVF|x*l7qeEjFh3)o8d$z$iniqdHZDM{PbFg z-U2Aj7&A)}?PN8Bc$6k2I8Vx76yI={+x>e#Bl)-rtTuA&-9zIKWH%t?r#^nSLSZo_ zxl*i392yHBs5p2G8n-~;cZf}Z>FBsl&K*G7_o2)DT~}X&k@O{-{^e$d%cs#lr&A*8 zcJ6N0yBgt@*anZ~_|D*9d3h@dMc~J+MA!0s&urgo`#)~yH*bL3UUd!zq2~_5;mV+r zyy#f#cvLP;Jt=BA7N6mE4OFtn((6bfTru8oRsh4@K_Q;Y=i0kCl|M@Tb#tg-S>1a_8|vcwP$_ zc12){_3vd=C4F$S(;qkP)tIRmz1u$fg0cI?RkBc>%2smVy{Li-}VA&ziG=E zK|Ae9vze^ZFfX6g-doPblUIcj&bYwG&_XfFNrJOFLJf%fEa-uVa5 zxC1No^jn*jyzKZl6F^!O5-%r}$xdEEsGce*e$6+WuepQ}71Og}PcEio-F9v>wKJjh z9eg$k*Ygl_6`-0`u_oHni+w!+>&3F0kF9MVn2(-yQ@_x2L_j zLs#3qgl#TkNVF37F}ZoH&C*%NAr5SdTbiagPYj(|P`B2dH(a}-OwJZ{H52=y{S}i6 z2bU-sYcEO+^Ro9}3thbCj5e2k-w=`=Ut!0!D{IxX8W!9%!+B^HNzYs`RYXik<3 z1c#(d9&0UK-=egAvyjJ$7#)GpJlnmM0tDGQS!TkWdYfK5q^K6bA6 zheH#oJ9jE|ysq7E6Q@TWN$qv7GiK!$(u}Uh&E+&KcG)+y{{7zG0?YgjU%s2V^o)wcU? zK3|=YZ*c4bE2Gs?ux8UShGIYS5&z$^Wb}n5dz4g z#=?a%{35_m0NMty*(2If1>ZrxxBD}i|S2D zDGc|1uP~nAUNQg!K_qi7pIxC_ddbX5ewE?oEU!7l>1VhWMgXDQXg;we zs$wAr5b&cU!tKq)u75~k?xq&=PRS?na(T1(w%Eq%r!ocLI&Mw*J%R}t4WcDtXtfOE zwx_By`qz`6dfH;%k(hfYNeYS7ks?c~Sh#?8P}hPG%Cyy9nN*PsmN+vi)|+i4Ldq)F zIZ*qo143OEu1=is)m9~+sGr-&o3)d8{?qp&5YxHP?Z)Lk`vD4`Ed|59rq8cchj~3auY)uwTg?u*E$q9C+gd!Tf@Ain;HBn< zGp>ma7YM61)?i+aJ)a4AU#RxR&g{ z&@GjJ5tbk-9Ht3NUG1Fu^B!W)>-6hz@P-eb!wr5mSU5~~#ts;%7{kC}=lzB5n4t3v zdv%Ggg>*I&CoTKgFG7@Y5Vv+t1iM3t%INc6D%;%lji5r!6#j*VT)cFvRaS z7XXZ1pg?b49LAXbeT(P`GiXO*7PSvIn$C~=9j=Ww!AkEv2edX*XhFnuyyeX7Jpzml zSWDM>aBr<(y+H?|?q0v08c(Xoyc0H4X$mXNAM&Ixz4Q@`V7y&HiZV@Z-_EDIFz78H zm5SALQU&IhdB1GWRy5`5N*+Gm-or2f`sgR1qE}Uu+%vU##1d>M4oha%$W3zP+;()` z;mtUrYLRY^K98zOFr_h(Os?^mDI_)H2N&Vjnk5d7w>t&P3m*cs78VS zL-1UOKwYPT-Z_bD{pASwjySBg#_7R+Xl60}l~iV5?(w2u)bqxgZmhX96u;hA`Lt3Y zZHNnr(FKPXnkhqC+kIZt3?DT*Gmy){XVK#HOKt(GxX_37yF6B}%!uW3qa_{OCbVF^ zeHx7e4rzSj`;zZO!54_ow^;;Myt=6g70>br38zXqWqkYzM=@!hrJ4Hj?e9_E=?()e z4=up84`w z^R>UtBWgbaLVckRy!}VE9VoWSYs=l&nCWk($E2Pkba5(Ap(^4m|FYob<}q)al;WV1 z@HYjgniJFWjl-9GU+r4IyDJChok7nnh*zyI015RBD6-~5LZJ(Z-2A2LX(~!aiI&lS zqnHX*j&O=Hr=mt1r=oI-ZZyomPGR6E0cNW=PJo11VyCWc?pMs;5owT9=Thret2Vcfq|!r36(*YDhRZjkRdtTemP5|{&3aENDr}VtyP+EzMSmo zXq7iUSf=Ac%g6?z=OMi4j%8-5X7Y9C_}0Ix=ZMjsA_UaZ*@l8gemi)7Qyj2+B=d#O zNK?>kjz7rpc{&{dM#}OTm!G!knmEwD+?b?rG@)F-5gKSMa|-ihdN|_H%5|QqUgaM0 zQm!`4#d28-@>1HZn{~SBUmbc}MP1;;zDdGfIQyc0jANP9M`&W<{Ur9I)c7+1bfV|& zH4Qr?4|5a>3lEg`Kc5G9)Ki+}6V#GxC~@kahw^m2EVq4L--Vp=`T41)mz|*=9FB9f zU8$ln<8jVxKpB@5^OaGj(4Ovb4y*wJ0t9rzX;TMN>h@bWUN(Un5}0?-A7R&NAncHe zawmZ7K_lNG56wQD8UPm3$rEh-gv`g@zRXa)j_ArpMj$~Jss*)Fp9Ys`+7?31;D17u zm{-r$O#!{R{kX10chYx(GvSAsc(XApx9R1G$t{yH*XmUSB<8*Po_!Y_+8)+PKkB}8 z25Drpo45Mhao~JxLFke2|Q6?ymf^Gr!a4U}XGor1LA^%l5fMNXe7?%OHo|`{yJ|Xjb1JbdrREH>qR56pzALc4sSM^~?zA%+e z`Qob>NQM$%)@+oR)1~KYaO#(hU`Ug1D$|+`SEYSGAg^@axTQvdLvXtC-VUrbN<5vx zxn!3P+=40K7VHzB?yFSux^{ z>MJDD=#eNL%dw&E<|AU;=c)5j@(RaA08h2@L9!0^9Eg@w!ci%7QGU;?5sVO`pwn*c zP(zNcG)P``E{7)eh9x45V=JxDup`XEj}X0VFzP$sxyx-sgdr=-a6gqCgMCTSG*F|t z=Op+3Y4AOQ@aGO=MQDqD>&>@Yq5#h+tG9*82u#>gZgw!pbV=A7ZGTQ@FaTTdSf6xw z&Oz}80Q=mqk$L!ib4!hke&e10;fqyKvlQ>ykbu>uX}`#DIC^!gI^I$wLplD-dvU_X zaX=_ef5CmJptJTg(x`SF+bEl)YAm%E9#ld%?s3S{$O^P;jm<4ycn=dp146Id!jz<9 z6;?XR&LWKtdbihe=UP_|pRfMCA8z^5{}je>8c|x*0&}_k5n=?;$;XeT49w`B1_f{8 zoh3tB)BW4~^&_F<08|l&E{>i3h63SkKnS%dsfLwfjD=_Aj)dxCTLkuMe(^Le0SUQ2 zs2qiJWW*p$O@c%yinG4Sn7j+PZY(tVi|cTP@_jw>?%wtV$0&m&7gPH{JoXse8M`JL z=6RP7dFd>~Cpf=BkNP_PNgoba_>qL`l7CAFs z0ByKrX8yY3pB6vw?&Zh|+mGBACfj+&Pl0rfcIT=Qr1e^S^j6QTJxgn>WQy34T*!JZ5#}pl-rj|M?bo|TdW;;57 zdZ9$L_usDm3;HIHqKRkoiO^Fw_%h2Id0O^vG9hc{do9;Ca#J;Tqsc;K&1q8iaeTh> z8mY@4vaF02#H1m#cSF?Df4Ihk4Uu&uoI@Yc;i4-weC_aRrLA9+Z_?jd*{HF&^;ub; zxr(|~_4YtO{~EASHuENalzeYG{u>f z^)A3eiP{~OnGWp?mRb-)i8vTsb2erae7~!nzb-ZvC*$)BVYT?+*He=B-C$rZc0H_q zzH7mz{(SxWQ%I6i=(=)M7;Z;u;Q=5m)@JPRG3G}jbv&=80|J zHQNk|X!=#vpWWW?WF36!__0cWH4(ZlBW2gcI>ogc#ni#DuSNai^t+l%Y0g{<;AL03|$a-6;9P3>UNWyQF34$={dfsa|?p5)8)x{>7bBH&0#1)ylAh#E#Pfi*XmVd_*GOf?bteRL9(!~>gY6sGFc-7GPi?J=KVdc zGZ^Jm;ks+f`aiqNR_iT%_9-FlhFI$UcsA0kC-tWWs$8{=o35=6XRH(ZT_Mj)r|E__ zC6efrY%%zA z(}UYf!L<`(XSus?PTQ|+UvRM_#N>og2tUue3#;7#qG}3%NUE>p?zD{76@TfTNf1Kw zk-X#?RD$Dj5uFIDAh0p2U9u=2n+&H4Y+P*Fjw3yW|J?R+8Y_}?vb;WVn+_Fo9z{6J zhMlq)GMH$tH`WA1DbKhlO@`!SOH z2>bSswtA^KDW0=(x1hhv?Z(-(<`}>)_3L_&=6Q5~{GorsHC*goUiBN&+0f0C$c;YZ z?T%2Hf=kXMipip1Q)Wax&iUG@EoEYd=lm2ga*^2&kdsTkyA?~Xy);u`n3_?sVk@Qg$O2dU zg;xYg>HRX1EAy{UXg?#RM$Co$?R%*X=J z7P>Qb1+cQ0b(dXl%0@)W9H>-|2i$~XN}l$ZkhVuMDS^=lkSSs&Dk5#~|D^uF#Ny?C z$ad;Ds4&~Z8oDz_r?+jg@pj_;aPW$c%o}W`SIsZ&pSxl8*X+5R?`B*9hF^(?>K!`Y zj2wr8E%Vlg^`jA(^)T-X7H-}W4`}#6GfvFMZEmZj>{va2J1YG zsA#WaEqP$-WomRIK_YGcYhs=3{EBK7cM*7Lpk|ni}y`V`sjJ^gkqRw-=O2-viSb@YXUS_kMfY zC%Me}?-L&-0aw?+RTF&+xBJMly$`E_5E~YXPUO_u&=dm46T<&=O)MftDxc8Wd;7-e zwoVoSEUl983FQA3(>V`AQ{sbERr=yT-ZjQDd+}0$ejy1)j_+k2Q}uh9@!=`CAyj#a zWCK6(C&(^*SHYa+#)~vCR=FMfLF9=1)j%c8&M;+A&vDU{Mz((3>xc)dF!pimCkGRV* z#8#~z+T`BrJE<6zXuz8$X#>-b`prRxVJ5ulZv2W(Cce@vR|g9w)&ENiDKZ2E`7_}; zZQ2wDY#;=Z4&QyU_Sa%0gJx{rL$j_6YKcN-Hv$bRmPab`w>xJFYz`!e(8jJ4AM1MT z?2(pN3dhWySVHtU(XXQe?>(JDf7a^R;o3~);20WF#2U5eABLG5k@{(Z#5hsLTqkHUYdPR`SKbc6{f#Az6quFE5+rOI7MWnw}K@#daqlS|Ba0uGbc!n zc{|ZTF~>rSv@x9{7dx<)+ishysdP)LkMm2^9MS)wkTq34jT5xtqoBel_fgG}?~*)a z$~%H(5tV65sHL4n?DtWk+wcXV8(7PdkqF8&1(52RfjS5rNp%gu_ zcM<$jy*WNP%|MR*lkwP^?A2d~uNl;5y%{acs(ixyKS^G2t`G!0%`SaQ z_jF1$cHr#Y6w!sB6-imDM)4gZmq7NCY&F`)BwD<;VBC01i4#o`P5rqaMYI6Ap4S6Y zDvqv(vFLZnRx(>9duvQ{+1Y`p%0P@_xabsURq=F4MP!zx@sbZll!Xz`hP04OcziV5 zkOgX|^<%}dWkJ`Q(e+x;{ow z816mpXs>Y=qxm=sCqp`v5#DGuYi^dG|L)h%T8B$QfB(ok3PQwn?%9VvpQd?{i5g9Q zWM?PoiGbQZw<`j?%J3@WwDaIi?~-bX+#B0qbSwu%X!Fb_$UB%NO^`#Q~+gy^JB2tT3ar?@=;e+r_VCn_k)D-=V_? zO!ci3sd%W_VWaUy0v9Bsj636v#Jy9=*F^9^r_$aI@jx?HizF3bSn6^KNzb#kF#6D* z*A<$@XPU8)ye7azI?pWLwtBci;8nyrmU?>D1W4u29$&s6gyO{i%Qfl|)U}Ll^$BjU$p}Qy%2AN;h zU9QcsU}UT{yX^4eYHxi9x!BL{+pg57AwJP(l(9)R88&RVR1+pzQK_a*Q`hswB`nOM7RFd;d|$)o8Z@gJ z&_P%mPCi#(*IOwOS3q z@rb@o5Ma<8)xf2@PMj>zy1Mh5z8u9A|I zyC84u3d*f77+xhHe7(m-WC_IiZ!%&I=!Hir9Kx5wnIaCA1Jg`V_AQG^l7d+fJQXT| z9@gt8ox3Dodl@BiwMUrotgU(%F`leKRd^abAGHwzr$6S&LBv@2tQP_1;bT%xeP|uP zj4LXbODKGH>LWz`phT{_d%BOtyxVbm43g=Cfhr@Ii}5`$;Q`S1!>;)8dQJ?N*43uy zJz~j2iSR9@dd!6m$9nUFEW}e7h+-s(?+Q~~eTWb|V>LpNF*G8S>MNOq5>dNdzg<3M zNCtK|5}GYCiKUsL>77R|y~M^+CKyp6TkUh=;kZA+-be7N%j@&G60dE`L&ru2N%IRlUIqE1IL-n_@*evD)2+lm*@XG^x{H zyz@N*>{0rBwz=I;6_^&EQ>0lkUDE@RPt#tA@hU0Lq=6q&=E0??Fh-9{U7ftYSv<+|1)wAWK>_^YQ-&(knhMR`SKnY~|O=V%i3h)}3uF z>ZjVuCAhYW2vUhq%f5pJ;X~^afqc5cGrD{Iah05Ds0;YqPk$TZyV1Xjj&foQmEAM9 z%;@|s%|5<01w&`wWu6F6@Er?3S_NaeB_g0~AfNyl1<&(ab!=OmFItYg=_*fk<|C9~ z&6XnS-7ONw1#BFa%*5qQgHg~Z(%ER~g@KiceaFXrUJoXB4+;h2U$CQfyb`ze(=vAy z6)nv;LDCc!jP}*mj(C;_9HyD0rX(brhS6@u3!n0|uiBA)7%!i%HD>$Y8G zK<^&N@oy%WRkbXnvrwy>rlTGn3D4-K);lI}P<0Z_vJzp=JTW6yAT`r)H$S=@Scy13 z(b=FJaHjq`_{t@u_EjZUpz+lYmyfw~Da5nl4V^R;m~nk<^q!ORCt=VPfwE#Xk@T9# zcSV+O{q3}tTdxv`HlIj`$~pw&a?MO&5$$2W{_79aJsLqTWsUCLKF$f0qDF)HL$M@b zS1J3l9k6CkNfz574FD31v2dKy45ih(p#YZ2tq3OR9wCV-R*v9kVBDDpU8)Fs2x2pL@l>fp9~*N+1%+bKjcL3xI-zCOPFZ8 zkQv^wzE!Db%a8@JQRv)qE7YudtpLojOc?F>wE5aXKH}*hzO1N$7`{E(aJ5cmkK8IYcs7b zl__!eE`A>at`(I48qqrnzBL;*Vi~Wfa$bmihY2M2f`M+=0d*rJCbT6OpmXU-A~JpI zi@@7u34I-Y*+GACNrG-sF%wAOy)nJLcx#gfWOFeg4YqjVq~@M85^NzBO<`8qOH8Kq zHx+p1PG?CJQiFBxPd}!yf)c5w331)H-q^lN5*5U#7{eZ}5G>#5h!dIxO%es})+659 zoJGlU6VHn|iy$$$29o%SvZ`PBrnln@Xi+f+>#u@skrkXFAd^O4l%peG(#h8=GhL$v z!QWj+05W#Z%-+O4)~DHr`0lOt-WO7-mi# z52H051|o zqiUD-!>=;5p*9?6XJsvJ{H&P-=56MKE(ux=`3xOW$_gL61%qZze$R9)OUT>LGtmUU@h$J^2bZY#Th^q3{G{u!{TWK+-!{lsNJCJE(y^*D~3QH#?Y&~ zgPp4)qZt9w<8>SJ;xaA5rQ9xo^^W(3|KeK91J&8|ScKoh-H1fS6tf5b*H1rJlaX5h zWECcBh)gqemXM)>W3lQT?VFwrgn4JfQvt69;amgi1+WQgLHCU$pIlHY?XXik9Im*3 zP|$ikys;C&#ZYvIl*mN!Vj~W%N86pf`i!XsECZ8 zYkWB^zPC1vkO{brdog`n>S6#dKZoK9R*9?Kn+eu5UF_ueJcTY&@yM5My^?uYO`Ek_h(>01a z>^<}PzdO|Fi6Z|Y2^C>xR_9Wg8N$S)@i`};w94ftM!P=OZmVZGXlTw(j$3O_?`y!* z1VIR{5aco2^OTxCmJ>H{?_d(l$>60lL6yU@uy`y^C#2g8OdHvFuKhrS{p9X~dWrcO z7^BIf8z;gU*vNnWPkR!Wbe4<7m0q8eZ8dM@mHF7Eq?q$DT zOf^F9!xRI85GM?Kz-#)3cR>?u;WPh%WgtY&1Vt?sh}LLWbY|*L;6K|jjo%zFnzpSN z=aXUw>C_-261;4oMhKlR#{gzn9A`Eize+lLbsCd?z5D4J+i%9cGQvw2Ce+U@%?UDo z{=B+DE6rsJqLwG=upr+*max5^dz5P2R14^m@;TO6>$&ZM<46vHF(uKlN(Of=tBhr6BN4zcyfD2L{_S- zOrSFY8&*$_|L!i7`qLN8bSZr~&%t3F6}`?^2D9S0!i-LgrbuEu?S*#b8G0HqXBvvBva)Hwp!$Nj+sz0=Qym|?z>dC9pd>w5hAp%pv} z86dN83^W;KJulQ2S(4V7?(4^1i-DY{k4eEifs+FPCR6j6nH5c4BBlD0@Z?VxHYm{V zBgr`fQx;F#|4Sy=bC^*&P<8D+{|-pnKR!KFgry*1MElXdoC>b9vrZtX=%1u*P?lap zR~re=x-I=nzqh||cKealQ2xm(2ajIxB2iVQ0MBgy$ABq~-)C88Q8CS^`8DFKhjGq@8RIR;Mkw(T|Dl56`mymZz3r}ntgw?oH2`)raj?>PA0!ml z2NeLwM0>(fPN6=iR!0w*;$FhqDGeY zxsW9iD=5Yf*;svfH0b=-f)2>0vQt7CF9`5w!dlx!7E>ap;isKS2HKmyYi>suUA5KxovcC)*wKz1?l*u6~p=p8SKa zq^AKUM!rh2ffz{8n5;TFu9SE-(mChD@cpp?!(N*nG-Q^9m|}@^`0y(mQhWzo#8y2y zmsWK!FE)()ZYqFssj=m&{I~3U#r#T;dkcwwQC$!~S`Kdz*nmmPH4!XmAw3c^X;ggU+>_Q=_;!=^|W9Z6)(zba{4Zr2KS_Wud z5Hev1bCJ6TofV!6mY$!_wA-cPEkPgxOsoPCjRLrp-Em=kE64o0|HiS=a0BqY4s&tD zmp}=j;39#5h!O$jHolYJC;2p*gHblOYI_X#=iu0iW_XIE1R3ST5(=}X{!vlj^wL3c z%t#;kaSi}9<+p)O<@U+Cw7E%{JYtc>{)2gC;ry) z*M%;|IoTcfh58?D4(MW?|G*yl|ESt@S^fBym$b}b*;KSm&+ev|#V1mLEgm{)Jh|B0 zB~5ap?GDNR4>uHuC(rl$P7?L^U7Gn4RFNzznEJ4Uj#*Fvc5;J(c&I2f>){8taL-T0 zstH@{W8Na}EB(Z1)^pHzp2lhP94^{R+u2v-(K$c!oz`>q^ngjbubI}}I9ZPAQtyT8 zAlcy+!EiR2PY&e{hH(e9GD0Ykd+-1ei=*_tN@b*BY0IyOwJbQ{iN{!ovuayx?&sN@ zp^s>pU1B7Pq&DVHeV-Rs*;iLQ$iS?GwB%Atm==$LWDgjXjT-o@J^->6-(4utsrJ2J zvSVl}iz(WS20@r86t6~#5Fb_-g#KVw$W^jl3gpO&DETmTK!R|LYwzN^-~O6``&JE| z5;9z(Gd{=AyXUyo*r*jSgPsS5aZ0e;l(x_tFuCR}+)#Kh$l&$G>qyU8rCyzgl26=U0oijQ4aXN3g7 z)A{chsSyn@SZB}ezdL3u%aFQj{s)}VAtpiF@gz1jm_}>3O&jAk%e`6&{vGrFVePFY zVBYr)Ovt#WVa6u}+U}$U{fO2X-mHc9{J=4waV&=a`))C4ssDevY^{U=4E_{4#Lz(Z zf#DOQ7J%7;Gex?2M>Wm@knw%2hlfzSL2UFc@>a;M6BW)41~Q6V`bh6Wyt?B`_DBN- z%?GE9*hKzl{~5wknHiz$wtsAdHd5%S9qXb$mV}@vdQZSd`zv8cD0ul0cw)e~P9;qG zT@50c`=54V%BcME5VM5Y5Uh`1t`ctK)|T>~c=0W3#XG;pABP3)izjN*&!c3`^P@vseMk2=KEuJ%=K}iI*1^{0SX6pkAP|E7yi7s;-l5r*&_H` z)9K&j5F~|@Z#gK_x z2Sm}RAjlXj3Nl8)7!yeHUZE&MBr=3KK?6aOkOM@LkntVVzP@)qpZoc|_wN~h=eyV1 zd!5hTYwZoIQ%|m3j5K4l>f$gD=>ymjc);nw631Cn%`7u}j%mn~>cjqPV ze^ECw^azBWXr+?Jx{$4#NDeh%d{FOZ*S8fI^O#ccXu90ckn0VF_}h^}_(0+@IA+|b z#k%#FybN^|7*0bJv13}s$7NS;R8#k$1{?)bW%b{pup{y)PPwTtXP0|6S2}+l-x@o? z)~-LaSC1|W9uU8g?;H?QpDtX7OHEHA8Ed++@E}sDV5jQ$paINlMsigR)qS=uT7Ub} z>H~1M6;fd{Qt&sQZ`oh~gyvtP_&EaZp~-(Vt>pd96`?o7uxr%)5IctN^pzsfMPtF$ zD7RT~LU{G|Usi|kmVR0^jAW&I!EJIVp!6bZ;kRn;L-)Oi8O9vF7iN7<;aaPRQ&^G%q{BcxDHOd3kVkCA&N+_mROEg3w~+_Cc`MLdi)Hv(HR zdZ**$%A226y*WWe{>Ae2a4*xKk772#?x}WR@h|l}7Q`M{CE(jsJHfLrxgjn+x+h{~ zZYVefQ8cniD(MjMZ--LZWo0iSpSb&uAlzY98#2AvB(Px0Uw6T0r;VXWs&b<`=DL#eEZbJlfNm zQ4M;sm+QqZLWrVdm}iu8cF&q|PD5X}cnOKqG73-f&Yy|6#0*C- za2R`=!6Z7eh|f*~EN51Gs66xA%C3)G$MR#6)J#ID;XJnYz(v~_HM0p_-!bfE0BhH7 zK4QRDNtimVf|)@$Ky>0Pm&DnNlNhmNXyO9ag%)If$ z3kORTX-}w~%|}b4jElyJ-iei2PSoFT6P;r;aWrGB?|}Fqt;rcRLFb0q$pDcGwM_*oT1R}Zws86An&uO^luKc~01api0*SirzM}rOYnv+P zl+0+VMT4uvg?CezkM7G*orzYvy`}x48T3VZ{D|}{xp>7h8Zla?l&*9P%(up*x{wWX zv9lE}HC%69{4gPgcFOfVqrFtefCE50%~Y?pifXXzc5!x+ll2k)Z1bhrMoTVC@_py_@&sA7VV_d&#F{f7PPsbx6WFqaQI>a8Egk~EY_EA&hQyKd0sy{;9h7KU zSoq%oZgj+T-jy(8L=H}LNnx$@`y{W)eC{wPZzEgb_=rH=t2t|n-la9-)Ac2}P)sRH zIK5=J2s%u6PPeMcY%%2h9(0#raMO<`w6E8s2T63s647ptq^2Zb(spr9{8I2hF4;dmAwN}1 zB1~jkeKoh**EBxJHKfWeolM|5ijvhf1Fj$}A6*sgdu>@V{Ha7Fdmz2Rv6e3->y@Cq zKGdz~tNJo8_+m$#jG{riKX$UH-vnPU@-uM8%RI;g3wCk_F!(1g#q6c}pO>S!^90kL2_b}vKEpV*$sT5Yy`w>>h0ExUXGVUQ&P-4XPC)zc$MG~A z$6ffD3q(jW+5yXt&AZ4Fk4k7ve)ibGywHGY2x!~Wn~IHW_s3e>mG5Ul`?&9GxnYkO zp6-zCvYcJ1*%3hL$~rt{#s6JlH4;>!aY#eG>o)T+4l^CvmtyHP4s{zUD>7i8sNEOv z2VU$aMXI{UHjD2rfI1$<@TNNqPbJt(z5iK?#S?(jl^Z}1YLaYyaq-Y`TZ2k)EXbti ztlTdiA#viFP@K)vqUx;k_ms-_khl?xUmxp@?PrVgxzW3+!1x1Tlr)tTLm*q6F@!#m z_a?)sSwy!PbCIAM$%}ujAD2cz+GFGB2Ped4Iwuv8 z0DS|tS$z~;waKk8Jebbpl_}Wpn7$#OR6Q{8NPWqgo?TLykJ)1FX%+OnbkEepD@O9e zo42nT&VA{f0VUXkh z__Few=U@un2AEAE+0~V4?XmXy=b$XeWUkz--*Q*?CRnYbEM~}+q3}V_52Ea97dwfe zj=EPrFcw&|=c@}5=E~*1$wd~@ld1{YZL}s(csWVe za7XNwn~(yFsul4r_C(+oDET4h%?iSq%Bn6A!oMv2NBua?842R}^dZRca#FRfNAbZ} zT67Zs=7&?K*TbM0rHf>3T7}ZD{e8u0?)I963LAUvbckVtTMU_1OG|K93Pzvz}(sNGz&>#;eo9I|8-^ zScfz?<`Kg<>)J=+5SoRSPG&5G6|qlLv<0;}J7_kcGSc^CQ>UJJF-?)g51!|I(H`dw z*IDxMVH~I_zJx|QyHK{Vax@|OGbBv8_E{t*=|5!h;1GT`(g(gHYFkJ{O`d)Aa9fY! z*J#ayH({E&Z~n&}my;Cj9h{|c9qumu4Yo}hk^0l`{dngB&tI?#2Y%>sPVg!W%_kU+ zGTi(n=A9`C#U<3GvM88Ba?Z=(AnKjATQCx~X$FaYdf` zt$v3R@$#4%jfyY{OK1aY8SYj-Ij+{#KjX!3=H+F5*?Z?qQ zGBlkxOAD8oH<|u$^93@#3sOS=i>t{^ykEqDbK4vfVY;cqD&;7_OxM#~xV^J~dg(dB zUtIa2AyWK=#8rD#g{)VGV5__G~``&L=r~r zL%~bT()xRNiaDaL+g>L(AUq;j2sNz~ofhi%q-JAAbxY5gp3k;CPz3ooYa^Hs8Yuh# za^Qf^#Db3E40+^lksTCk2&)}1s2mK{2T$8V3(R2P#sOigdjx1yOs18`+TX(eABFk< h#h=+m#wvI*HCD-EM^nc1Xr z&dyn<z$^n} z=C*`!N#G%vxIPMwG7=@kEUe!t^A(GjS<4JB>H2&dhPP~|qZDv31$p_D#Ep<%*aa#} z!DQHbu%bdGZE$K{$>MHG8QS~YSLwR+12FY0IsEGG_2_u|p!=8{%>WI@jgGM@Sj*OB zc;wU*C%>btOjTt#kvGm|OYK81VR%^k1YM()Fhp<-jSze@`a=4rLmdt%JGU~uZv99h&D+ACy)VHggEUx)_sR!n?@i4WJI1g(JsazTwG7?hC0q$5 zh6GBgNx*$OP0_H@CvjL&h5q`9CZb4xEZrHkbM$=J;-BL9ZNI^d(z4)<;`>-cKLV+&GQb(Qsuzx0pq4nzEOJm>mN0HD z^v$L*ONRrRa`A8XP%MFKP#GHzZe{if!JPj7F4B!czzXzPqNTx!+ZtovOgv&^Jl zY}W7F=1R!@8;9`N2J;ntUlk<(AoyJ@vLwJpnC>EnidM4qBPm)BQuyNkR@y{oQ|FcK{QfS89T`?i?p{O=q~Tb3 zxGY;{YdF{$t9H@7yr(1p3DxV4@4_cVmIo_z7= zY^0B8Ymx0Ca~2tx9q{$J`=#MOyWF$13)bYHGthkdQ8X2mU;P#1iyM6Ze8eXXu`^e3 zWG{%mryGs9h0Wh9*GeVEzGS<}$%h^Jx!AB=HkbcW4bY>-PJ@~li>uCyRQvx}{Yadl z<97=F=#BaxD+6l&V5*Yec zb7nh~57YC>M2yJ6MLf|b_p!hkSSP+dwnu$T$C>G!hEQ<+gJNt%$KF6MBj8O4Cev&S zRAb&u51`SqujwEUX(rjESINr~46@qDN+WeCpEL9FzqSBNfu#F4=ibF4m^CQl$*B|e z4j0jaF@QH_%B#@jz&|_bAXu?rE?i7M{!C9`+-xBq2avz)r3JacOivv;VN?&YRP%=X zN6DdU=#vc5Sj2MpXCA_PkO7Xeo;X_yLAc$-!!kL4=SAP;A00A{I8HR#6>wL z3M_zMPljGXZ5W--n!X!03z$NvuCZ@aCi6<>KLnf%t15r%=3h_88Nme9KrUZ7LgiYNX!0-JFcu*aJ1Zak1grk@@o{keur0>dvPQHroE2ca5@m z#8}B+0E5eWy2X6?v2YX^<46FWj3BLwxKv)-T2G7U{*c1#d8k-WaE$Is_H13t&7OTS z&^z$SNpvK}|2FLlv*$QB;k4?93SKg%Hz{hGi||GgA2WZ{6s7hQ$1WU;{UrjIzf47r zX*}k5EP=mih+-RBed2+i@3^f`ue?N~CH&cA^O0udnPdA7QGOlI8Rr9eL2hG zp>X)m7hjS(>FIb0==0S}aqf9DFb}|h2e0MQOWiv?L-X@Wf=p!*+pQ%6oFLV-f!+H# zh!5gYC<8VLXGcQbLD|11r4{*h^$R~Ngm|bQhcmEC;%`E!2MNdp>uKGl2xZLF)oue) zANMnnk{B%F4maVADLXMVZOmueIdI3nZ$e4%>Hj|ajwF?6AS(lLJeDoT5ow`b^GuX4 z3$<*FNATE-fbSk=qKwqg2=4C!Nj%o6pWAY2xI9ujm$#;WB*GbljbDl;9-MY>Jv0oX!~qEc>OL#PiC*e+E?io1E#(Li~-+NNzY>vywi12e>mASaH=9#W?_0 zNR>bAgq~cufMEUc>7Cu-4>D`ioY1DxS<2mQpRuhcvl9W&#qroxm5SZgPw7hLyLZ?O zE(!TpNgV`g(9lDP+^@WW__DDyW1LakXIwa_J~JP9E!EAPqUJ2aEMpnqt>aeh9Uh!n zd|f#YcoFLPR@jBf>{Yl}UoaOJP+128{UaLc;B^N^wG`L=6c3VtHTIR*PGGZLj}>j3dT z!6I&+I6TmeQ8_wz82FhO0V?`3#9@YtdK0GQ+`TB;onH8FppCz1$^loH09IVTfk6=ZuNR8K+j^I&E@>TOz{(%>` zfeLEi5OUP0zdYJuj9KQ9Kd(I8#TpzUfo>G1pklv{zl79HQNB&uMyS;qwH$ zMU)AUrdHqywzMF!))!@A-PiU`@WJ7abPHqpEF`5m?>PVA;`7u`?{NW;*p+gQ=6h#x=7x4!e71jzut zTU)LWXdCg$G_!_ zzF*hN@po-}p6h*mEKW82NiL{Accd_Pl~oTA2F*WrTzMSH>ZUUtuVj-{9J`PVTnXOB z1JO}#CK&04_Xi&j&yjxbXVxDl_8L_vOL@t=5BQqd{MkR0`&OAA6pKz{(l^eaE45k2 zf;+PJl*fW_yh|ES#8@>?DD$xI&GxZT#H_4l?MgIom#R>ue;)RVE)Fbh8FunpZhsR9l`S){MAHHgnUVTbIf~7uyRKasMN!lS>h!#TaFkwZ`KC7a3lt(&RDai0Y**z+GYf`8 z>u~Dx3Sl377NvT3x2w-V04Of0c)S|3&62g$Wv|=7sfLv1!#?}U^hq`^{}$IaR@Lr_ z`#v+fC@b_lWc8Vr){j_KDL=Gppl?zFc z%yxGNi`n)TT>rkcoPgT3DVN6M&iD4-T8+H0#UF~B?GSI*!K)RlHs5ug2#>1&0)2II zN&4DCuY9lYYgW|rCt0yy^YR&H(migrm??B-l8&{lZOY z?fe;#ufiwv<7EcMrUkgSZ`xl(y9PAX%E2=)qfauM{C3&gF)3W#oyZK`ncd!zrA9V& zCD*?>0#sYjnDRvI*7dJR5f5LgA{Esq44-`b5&^ma%ZliGW({(`_vb9phg_;Rqy9{X zXl7nN;DBLv9VVy+{nS{0cxH@a;{h@x~4<<4w4WlS37l+VTsF2ow19m0kvpXMk@Dnzi@BRKD!<6&PxCw{j%UqWS4Cim$ z5_WKBk%`SIgg_21vGsPIAD|De07=uk_*bS*lp(s~)4RxXu*8$5DE_B7vo?1j!pU4Y zrp<+7j0`Bh!)c;7UpBX&HEX|T{1vlK874am;x6ZtH?NRq8YpTc3}B=0%ZG23F;aOm zh)@|JzMDpop6Fxy9z~+utc$F!5WV|dNXi|HS0FG?wj9gxBV4lmUfXlSerz{5v>!65 zkZjCPr0n2x8$uLCZanusG2>}@ljf3Ijq?8Xkyp?s1L7K&zBs&&-?(k|ij#OHwc4^e zz$D?N4XwaF+gh(~X4=;xzKtCym4<(2DEoQ$HID9?A9bhzSbp}Wur^-+>ozw26{I9j z^VrOD<(MZ|557$sd@B^+Ip4?!yVH@BzyHZ9rZgp*D)mQdL%jgq#^$Xwd8z0N7wSa5xsJ)ts|oIdLZ26)fShR zLqncPVJ~JduUA);7_iWtw&|Z?Q=te#$I~w*yt~#byVFy+adIg18jEMVO|#(QjM9swKk#@yg%B%a~L?>gdHGhrjX0A_@v_<{F2 zw|Bphoh_es-rSI$VTL>}E^|C!NUnq=1H<#kYhicfzgxQ28KBtO4s~J3QUaKK_;Mtd z4&TC~=sb__3iMUS=gaB)+AwDAggj!j2ea7eUp#Pm_6dCmR=>*5ClG#Qy?d!4Z|3_k zFg1+nJqB?m9VG0}c25Qsv&}3%*f4uWg-NgTF5d%rpKuMTps#A4?Rd=!m*=uk)W^V% zREWu(toq7BI1dn+lpOe&roZ0<5EyChKcBk~rGC2Tp>x?gOA6nehI>$an1$G&dsi#F zoHikFJJmdWW#{?fP;{ugTRmwGoyp01Cp2Kg6E&fV6tFAzpRW0JAA2A<(<0V&TG-lP9a#`ap6wtJj=$ND!IHi!jPIHr%JVYQ=0P0|ctawJd(OO@-qYyD z4duMr-Jzyz=_$ih>_8cveOlt)(C`j> zEEj6}bWJUfr;*@yf(9ZDF;!LW1Z{VXX}fQnIbc1{5tgR`zWd$t)ec+656)<%iT5)v ztR)Loxiz#GB5bQ!y-dJi#&KOtqI!VxwfbXdLZ5F)%mDy8?2C>>#j=-=ep9;aT*N(0 z+%BR^wV1^`XBgQL2Oj7fn{0S~Cc3k>ssFcD@XNKQwEiED?Pn$a ze&_P^56(>G?ExxknJZau?ZVu5Mp42cRp>!H89)1PR%r`7IfZnf*ZmCXkm%Rk*vORW zIKR8$2E#3MY2O7r$t(rCM?H^p%UGN{7QNOiLcQXc=bYey$2toxMGagT{9I4`GC;Yr zS@}$58G9tOj5WM-W?21=R`os27?JT|A$!}y>j9EL`-R?XXOI~ zn!rOyd3Kfyg`TI=G%NgAeA=$qG+FQ{sFX))r=Fn(&p@~#TLFeXwL=j&_XLj%23MOo zutDJD6$+7{OKQXR{y7h^c{e=g)+@|vJLK?&ckX0C>t9~=dJ>Q69Ne8Rc=keVQBZUn zi?MqcKX$MfBMwOaa=Z#(6A5oTys4e4I4UWr#dK%qXXYo_RON3>Rds0jkm-GUB|XhY zfU$4)FuC%l*iv`zI7{N&n@@Hv3h@HS5A;w$!Mwpt;*9?chLC_J(T-P=SD?sfk<+l9cW|L))@&NcD+~}rqUXlCnzu)_! zx4=O}+Y_z8Aig#=U-aWIy#l6`uh}t?m(j;!#Jy0V+SmJ^JxnS4bx-~uxjrbibEpxq zC0zfNtj~J70;@A5vB~nd%uxk>TKPEqvkKxq<@&YH4YE3^!naD`COib4oiJBtK4xN} z$(W+Rp3n>#9{}_#RA@TRk?FLEwS73q`d3tVemN)6g`_nSIh7P>`^SEa<`6(jdkazv z4R3`qEDvEekNoBh}mc6Pu6q+tx@m=+i-47sUwKGlhB3ga~s%=&%4uyJJP4*|T)z5YMDC zFrr&<{cg&{mI-w|+(gAu{6CS84B2UH1p6Mq&@I?4#r-F*F-AGh*FtwQ)oo{~Iq&9` zzed3J=ICU&8~{YQEnB>pJzD{)(|dRyao}d4J46rW1kF2ZyMH15CJ#u_M)r#82(Q!e z5o}F>IN?YQKYW&=nKEwf;q2?+EcnCAS_E|Iw z2oWSrO4@1VGnG>wF7o&T1;Ac{BUsxi>+Y*sfT1oyToi)g%U*e$O_T$bIC$#=4`Imb zr3H#+aL=KiBvSAY=n3Y?Ci7OCjb1O$?hO!M8uJ$(u{)sqRo;vyS+5;c5*`U@W%jek zOe(L#&9$X(1FJdKQYE9e<{RZoBwcChnjiUgGKywR#-+Tvq1qApTG|NQNU11jHDaEpG&uvec>Z5ivAmJ#TZ z;+?g94$@VnN;B}iIt&+?SM=!Gz1F*U-+#NzEQMXd_?IyZb}-Xe|CBYTBASP8nP03Fo61z0lCY^3+;fK3`d~+-h7vJF=Sfu) zqhZtj7OQT?GY>qf_ilDTy6IK8H}4gBj~HhJrp`&}K5d8MGRt&!{}gK_OkI$GU5INN zf@3Z3W6>LOnYE2)Gyw!N8DdOV|9$vCR46LlrOsaU?FMTU%BE|mG4#cvnx7I~eQ!j?H7y19A^M4zxDrAjp?im`` z@>paR`LJ(;Nq($4eD)EWO`L{dbSDi*fDi!yv~vw{h#U6cYNmDXdusPwK!YnZBTxjm zL-MYGOb8ef=H!Fj{_o1$gq>J$E<2uSSIqD)yZ7KWCXo)?R$+h5K)V(?s5C^H@Ffc;7$U)vSDab+t7BBfH#e>i_24U&_Wf=O#=A zrGrhVD{^~+678*H@SkqS0xC$PeQry|jxYEBC%#tUM8CejbGG0A6ryOETP_scck@4= z@(ff_4RO!#J_6G5U-@^^>(xTCi#1LE?g$Va|6t0m_BDBr0$o}jhK&N~Q7M_S@|)1V zKlRvJJ#cqmk;edZx15yA82j+%+k)V3ibTg>7i96Yxjx-KsGiQ3YPz>tVRmqIunO7b z)_rDX^v$WiK2#Xbd%gzp_9b@Au>6(3KN6qYIaC|@Po=^1Rp?VQO6nOdIwH>6EBy{a zZ-UF1Tdtq4{@UH}v9j@s%ho7Eesd2gyN6Ks=bh#!mu9E`5pc}Rvp2;5ochJS80NOaT@ z(ImAnff-lBz>G_kLcOK=ju3;cas6v1a23R+%W9mfiDqVFreYG{$r*E~vghJ8DPKWj6t6msiJ zDCp?0A_$W8oV;()4}lvrbuAq5;36Av81q1%gw1nV<`07_ee2#N_=Q-k zW=g0hB!>rYA@&H^r=#^L8ehrQux}FhFV*#-1zFRwpr6CaBfPD*nxa)YmXON+mz!IY zRMB((ISqWjJPbVR2>k*-T!`bI3hGV|NeHz zaST>Ae60KgWCzpSFZti%C_Rqkhw*@2e@Z>aa18sSkHGYq3r;miudnD*om>3Ks3-~i znSSA^+v%vOq>GAoAgs^LX6J^GvO%)k=mT)kRqglmqknsT-pte`P=pIZwJ)-PcOH$P z5}zGGo=Z3DeqT-dUG@&439vEeIPiYMdQ9nY_aaVof1by*Em)L$GU5HH-pcE;;MGUZ zbstpo38YYMrvb`to!D18clS0l%`9`F&kEpD15Y8`11Iq8M$vmLFVH8ljq*B|p6|G~ z>*QGXqh|o>a2#R^az8i6kkXV*CA;(wya%JCZ!H9ABhhZG>>5s_=$0(8iLL!4{DF1PJ_I$vuFD){y5v{VAjUjl-+wIUC~#BnIu_4br>ezz z%?NO@?s@RW8qdz=oI%N8OeyAs9KNe=uLbTJv(w1x&mBmQxJwV-4sm!6HGuYMym!MX z_1~MJjoA?A=N1$Evwj0VX%+s8h~<(zWqLAYM{}{DWbezaD6y+P>|wXvN|7J`M$8FX zat2SJer7w?E(K*aT{Irud%1CA`Dn!2gIIs>4Z>Zz6;oQNo4!jeyqf`wM35|HUr@=( z!Y7aduo~t9y@#}i58#;q?3DpZgrs4Lno#*~q@12iM~k00tY)8_DJm4)BhX^=xlY1b zpi;oYziTo{)!;Vf+HvH5*gA88m5n*eTII&aJP(983kY&2S0UBOp<0e-I7aN{%5w@m z@T6DrAE2KS_@9b=wc$aRg8%G2@Bml|-G)6wP8)vf`9?UFc}GQg9Q?6sE3pYG^8!T;b8^7TfUFb%04745%k$RSjRo~( z%x=S?+k)20Q_(c4rBM`CLJ!=A0<9uNg0`Wm{?OF^#$y#IRN^6US6K(CixTbH{Qa%8 zDfS#^PO~!Ez1)0D+uYms<>n{a=Kh8`Ot4zX|NCME%LQkE9YT=Aiy1?_!@JtL=ZoD{ zmfnUw9#b)_%un*U)<;=uUyDcAcB|PWn3ki77te)f_BjpR9l3B%6gM=mj~yQT;P<%W z+`!K6Fl1)hcmnHP?#ui|{qS->=6RH$0a!-nlqf?T12HN#f6*ILp7p?jlsSv^-pLj@#9H zpXb{ZCgy^;Ykjq2MKdv_Omu0E-mefa&F|VDG~ia1)U42}#<=UM2hZe2`!-&j z98(L;a^!Uu-EQF2mAf@(FQyRU6jL(apSzHWTkgvIMxEMsAT{W<4?z`?CI}C{TUYM` zkpl&^vl^Uv!)FMCkond_X9y;YlQK#)y3 z(p+v9yK60d&{?6e($_}2vRsB;6Oo%2xj8)Nr!H06d^0N1`cvIFP#LkZ!KAhvPpJPa zUr+6&SC&GMxTxgS6}I*2qk?WzTEmbVza0Zcf{YB0XzF%k;@A~Km#jZgSgbg49V7^e zD6HtUvI#cl1P1cz3sp9si15YUY~Gzkp!EQhJZGw-Q;ctquN@_#UujLGX>&VT=uNg$ zcj>aP!oc6nIOhab*zVlsuwH1?4ZxEO>OFnHOAQ<^Tfw-q57+?$TTrgf#Yag$EXS^Y z<<$#4ApK|TBSYWsfWm5?)HkdMdn$Y(h5;Y5Bbad#`B;VQ1YvF8@7}E`Nf2o4ctsa!b zEAX%v_z$j8cui3ovm@EE?{@$4X*_!DLxcNl@}XKE2sORPdF_?su4ItAHr)IkR#RV* z>P)5#6tYn{XXBp>F$Kl8Sm+Zk3iA^Zqoh{mxFJh^7J#i8hnC)D2zrQ-sXzT^BGQ?r z+IJ-{@+OL^n+ik2|B*G?*f;jn0%awE=Q!aq!5?{jtxA+9Jfz%UBK0u7oAJrJJ-LMm zq-}CsId-up^97B^eAa^C;=6;v+vQop>s$A!@P`e$)}zZxgjt$}AQBgopngA8PBqCnVLt;)sStcQq&v#J{^RMlRrR-hg9mq_B~u z&X{~dp;9F!rE*MkpAbAI@PQR6mtE};{`fuYTMcFzR(A01Fk=gCtjc)nB z?a6#S^n63&_<(qORC-Ze)8J0F*W^nn!|mN)51!~QRUCpUdfqlbyNyp$LH{$EOmsOd zrm=5j%dyBIYiHt2(a-&gl!#a{>bbbeQnb<=%%An0wfG=S8{Yj>^=Er<{qHQUuc@;u=6X}gxXS%6hDQU5+1;+Qrpy`WL<1}y`s7n zQVR}f{Ym975KCq76Kk^E&!FHAw`rtHZTw>a$;2|0j%N2Usg7(9HXKuLSu^onpU z3zYg2?klW4xV3(jG(~tEV}hJo%~aogbMg5|ZE+WZzZ|<$pczo-TG$nx#U+2v`^Lfo zUAOfhqks1~Z(IUaLW`d7%f)@fl-92Nl;QnNtCxaPg2!~F8LI-9E6ifz>dXu8sgBe_ z?40vX`F+k}aD$LNjSxm|rE(SpN`|`fn`ee@^vsMDy~BpExr**g<|0> z{A1L<(e+yIf%R4dHBF{}o`0bM>TXaf!43@TQ~;YOa9OGwE?{@cD88eDY*Y(v4;-`e z++$l5Z>==G{V0NUra*)M6)qVx1v|L7Os|d=IYtQ74;?MR9`Un4C0YTA&uQU%lhY13 z-k9ubX-Ksz&bs4=RI$H&BS^9Jf`e? zDr0GoRX&rQ84uyzf-eG%j{Ngw61?rB5pCKKEKJgj&rsR?_?$YKe{8LnB}+~aov3M` zf^+({x@P%s+`dxU3vgPtpU}RJrAhpazOQz2bXmv9C0Z@6tyc8e{4V5^<$s2JK>`4v zqdF(>i3@6Q79iFuwt}TOL(KYu6?A<&ZTWNIV8+Csx8E%yRYJ1{zGHS5boiBZL!Iwq mk6g<-xJMp53wPuv@4_jM2pI*ZcWhwb&)DFse(C88xBd@gm0FAd delta 12801 zcmY*Oryd^t?vPY?8ge!X| z``X-VT)xbYwIch8p9={_uU6H5AXT?sEpr`&!Jde>bHebNIv6`=rd_$vdZi9#VJU_#9_;=822!`k26Urk02?H?p*Y14j9*yStK9j#eCwNEsXHo zkDF-Z)-huKHpDx(wy`k&5Nu2}5`hc+$$|?Uqb?u6abLaB%?b8=xRP<|t*UpJavOge zzH9On8kXbFLA+n}alT4&6|?b$aEZbNr@t=GhA5w54I1t{!B+E*K^#_bTZkw+NB$gr z87*|20CR~8!eo@WS8HS7JyM(ap$v0VZKb~x-k2tL;w4|ze@DI!2quEk`0Nuh9T$GC z=!W!uc=UCYA*8+D-5lsxyH%B*aS6j9j{ktx3Obu)7nhKH;fpW^Z0*q!Ty$I* z;!8gbRaQClYt6G5I9<{dxAc_hKU4KTuV~1q2HnOB=pKKt z+`AIg&M9#iVChI+v#TjvE}g#0#U6c0iU?g#TaInFd0qMOCt2>KE(XpY)Hcl1;re>| zX-_yF!QI!_%u~=qHhq5%1K(VKd=4D0e3)$OLL0iQd~a4n^Coa$Kdb8tz=In8`Tx_G z&guI0tCx z9ubr)2v#@wXZ?5eh}EKgvv!k$18{nm;0m{=kz1aC`Bp2ux#4#N@#Qx zy)aZChIjL)4o&z*O6gZd;}IW{Q1GNq14Zf1axeGVDw;eMvQ15-8`_)Et!{XAKd4|7 zhxG$VZNV8V{?Sf&Vb${QA2Xcg)N<^d^Ql0V`)>;nWpX1Q=lyIKNL=H@!it-A4m#)y zsQ>!yH`wK`2*kf)7D;^o3G!Ad`NUPD1AhShp#a=D0Jr=x#b<;5w%@fZhPkWn;}PPl zf-WePPp|VjTuP-_hOMm*kW6W^21C+Mo}jJ=ZPnR=Y$yUYvr_Aim%vfYkZ zIsSK-a@VgT#PL*I$ch_+>9bES%Il+`U}aCHrC8#+MkFBFY85`{r`1jm-!1Q=cK)T% zf01|UXs&z3<(Z5v#ru?w7wosx4$WgVCtFqR%o8gkG6k2?bs9cKU)jaq~j8o z8qY}&7ucQePS3c3LNIZ~&fV9v`RN;+Q%g2*!C*pe7FO7M^%JkAXWT@B>F6^{+o~n; z$zk*i;#h5HRh9L0?aorKC;`K9b&g>Q3rK*$=|k&vBk-$08=2X$geUL4>AXs(8?NB- z2%UF7J(Ay*rHkEy(~vb31pD+lfsXs~s@&?G)n3!qfXX`ge!?NhOT1`Nn`H-w2@kY9s$rV7*?5a|WTz6S5*@cM- zKn^PCVuF4cwx$!NOPD~A8xOO2svg;7Cn7vZ;qmIC-Mj^9-<13TQbxUH&c zw$nP9;EIDzh;zNB;}W6!czSl8YxtLc&2$3+!%j>TB|N+la!cQS)$CmJKf`QMf~=v@ zKKgt3iNo+y`}36%jmg59yoh*Du)1<|&&Zq&5hf_>Vwmr*++nLQ_z##}`a4y*fozHu zXK-lY0O#{^c$XroG;j!VdV-(0)`(BGI5YXs8Yxz}`S8wl1gXGeg=#jjGEG1G@JurlbVx z4ej2%`X`A$FfLQ}k*!am#9s_j%7kIJJ1H~H&5BU&&dav{t=X+q?tIZsy)AbdPQPE2 zplp7B4l`Ue%KGJB5Eg`r@rB}IF#`Suo!lhw0687-LB-UX{H71z#hiTAA#R%X8SnGD zU9uSb<^Edd^0W6^K=;#_rg)N4b<=sh3X3Ogd4U?9xQx-QiwrL<{dcT`VWVnh__7EM zUP9wR8nogh3OPEk_7NJCdyP~mHvkXp?vsP+`xP$-_& zEpaib@H)6xzgmrx#hnuj&^hM=#6(Z`(Vy=Y#^{io*w36Psu9}-%Bk{<4lQLROTO9j zRm=*mPdWA*VBkiTBR$^RbI-bt?n%@_HmV2`c!ftKznW#3>iG9}duOc+>No)}W~YQu zk^GAYMK+G5RDS?m`6i=_(P(rq1X4c_-+t_rFh}Fnw-Gm55b&Zc9^CQ(61R9$Ua5%X zcs6j-irhaC zL2s79zfR7w3}>$U8Kfk)wxF@wPBhg0O7SeyAzP*jBFp|$Bqtugv&o^l5hKhO*EFVOmU>Wv{i)wpYL9G znSBtiZsU!zr>Pnr@xFJ*%-CH$-*eA5~4gi{x*%rC@-B` zomFZTA&lbBkzWtP2Q8WlBk(VUn(wwAD%JB)Bf3#KZo8^}`+K%Z-Ua)GP^XvS`#uKt zez;f-k+7x0=Kpvh(Z zLyDECa^jUiW}*Ye=2?}Ul_~Rzq*P)or})W!8;0T!3%^J<7at+Z&+MevY%W>6vbl7D z)xmpsrt=WMJw~*5E;ip>mSty|GWgMb5$asndHU=q%G8QF6_6Z=)x^NR*1`pVF+;SQ z6dK;eZ0-z~leTIqJ!ED8h1ahr5u}w6)Vyhl-gJ^ZLG{u0h@7`wPQwe)CrThd`)~)7 zp@Jx{CC9joQjh%#iF0#$&o{WZHFOf(4F1LlKJV#Tk-l99-PupZLClZ~6-t`S#C5Y$ zja2~U`K_9h?AYAc zOrcS`pQRLXTSEgdY`X^AJ4K6~;ShEX8L~V%lMTJ$Wii0yObYS#*KX*vd!AiV-mzPx z)7R>)l4YKU2bpf4ESB7gf=r|@TdSq-JO_;Uh)X!ED&a1R=H*J*MvAspnLFQgP%FL5 zHpO^`P(OxmtA$nta@^MO4{@ZTfYwz4LE2q-)ngC#YGNi4k5hU~~nLYI9`5#63eJcSX z^lSKPb8{D)0(6%^DahcS^Mm5JkSnO2X+ieA{2am_9hkQP$XZ5<*+?!6Ws_ZFz+>|-$kbQX)x0}qiwMG)l&$NdyI^bHM z-N#QNH`&f_d|en(U1nA1msd$lqJtZ{zY!$}#RG1(jo6E})^z*tik`!;^zsZdeB@WH zO-)y54&I0@If|O{_Hb&uo|50hoZ>HQ;i1Qg-Ccd?sTk(FEJEmfvhe8RXIdn{s?)Nx z17(|ARtstPHKs;|nl|F4DylCnO1|4?&b)T!D4{d+NpfC-^mDA%kA1qkZ9yS}-78w5 zc)`je!AFU^90H4>cr)kG2N~t++b3*<-=q)y_FM@Z6(ok{#Af@heCG9h^f52LW9|h| z+~2vO>PvW55L7-WMR~;-o4YdD46aM2L3KK=U5*zI)Dr?YG;?2CKT!yPoGm=DI;O!?n5n+^nTn zr!*!L&$xe0f1SJ^hKfMN?`rC@vgvw>Cm139 zAK)iyawFCj6StY`MH}EjlH)IQWmG+w;FOu_6rv-*^u7);og z!67%tuSkDteC(Ch>-XNa-67Wu8Sv~7JlEUgt&jujpG;fK4N<};SFJ5c3UB)4JrS>a zZ1>c(G0y#xOL!j*N1Tkf49XrozCua*l69j}6XR@Y^TTe*5*+;dZO{Bg&io(tAY)@j zSoD`GAsX8ceR6~%>pAmpm7vonZ!&`4$#?0z5==UsTT&6tugXsBD?YPxGqG_D!Rm7-eL6 zH`!(OxeS4SvgIi!&=r%cSKadD{lU{8re70E*DFA@`Ot7mElPwC20g!X?iLVbxldXP zt%=L?K1PrFwnu8AHD5oO1G9=}1c@HBW3DN9NXb_$SPOs59b1bP}MZU z6c-S<&?w;Ntg`Z~q6cZfW*{8s*aH?MzUGv=M9Gs1D%5%ae0IF281);(#uC-G5exJC ziYDk490`K2$M6vFm*!nv6171vluO@K@o>%A`X){U2dfR!p@7pGWgR9ck ze9vTE)fZl(A3LJme#Kf;a+cllkN!Xfg`m#D-2+)! zU6Hx!<*u1-+{${AaC+?F0i?=x{3ukn3U@TZbQ%oX2v!E_t+5UlObeYYxn@kG!6G|o zL_pCji@81gPFV6mYl9N+q2{>rcC#t+AM{H`^pzY>VVRxSi4EB|$~go(nzZ67yiICG z-4IUS;^#-&1NZOrkqz)7*kc5tWRnY7o;O>vQ>88{*8Z-SBz97*CkJ%|A>s}D+w(u` zhS%#9Ta-*b*!?WwPZC@T=gV6voz{>b;4d>y{TefTb%JtUv38RBCG__=W^mZwHwxv2 zZxXs?jjJc8aA7}Z(|I{B!W#9wpUlXix7i~})+nTyjZ~Rq#>$Wgo5Mstru9j+kJ`57 zJaeE)*167S9(J?z4&gTUpk*em=Q&^O37x&T9I3LLtZK*L+A1AE;xmCMK2?>w(rfK4 z5hmIC^7mal630?2);#H+*H^{N_1%`KtF~Wo3bxr5cD zOG2roh~SUT!_n5gtwnXHeD8|$Sx`PZum-Y4bsWUpJpGQNgaTGyE6cgJu)=Xc;#obk z$BDpCl|{x{^hk)4_%ntrg5vpcvs*)+$>I8U!f$rz`OM%8uwD>fR$~=RJ zeSCVB-IJ&E%_NDnQRF+?QCa8De3mc&j*lw*ebr7B`99S3g}t$K(*1!;CO0<5F+&O zD?$I!_JSt6b`uG0U{0=}xoegll<(CX_}~xMioe%X9InmddF{#OkfpNLG)2j8G0CL| zt|FfU>(^SgF;Gn6(LDfy1!E(t9kX@PAIZ+?6xw;LMG1>T4j_L7m2X7MO)aXR9+Z_* zjH_%!Tees2r>n>>PPfd!Pjo=!J#5lk!YR^Pcn#z)yxI6)j&bq+I9RSgOFfg+c=Nu?-~R&Byv$R!?Vb|a z(`@#>dg`Ep!UxxZSl`ba9t?fD-AxCI5KYnDi6-XsCz7|WDg2$63S4plEX?>IouQqL zUv=Q3?vudgk<=uB#Nfj5B4S(p>Q~4TDNy4(28XpqKWmEv`=@yI(89cX^N5W)G{Jc$ zvuTJi#nLU(%-funfB-&Vsy*1I21)%;0x~MvGkLwBTpwHmI1&AFcmO6J8~I3ZKf2Gz z>e!+G;TbmN)MR@kn3Ppb`~ijh2M~z3qt#y-t69 zpd5Py7-m~3`+jm~-+;}4vRpBXytm>r-K{DVm ztAx6X`P@YYaooJ?KnIqC7#J->5YjPnYIpWMWyC0cciy#9D~sPg|0<8@9>&WUHlN5O zZWmM6R)|sD?LU`E`eX{Xno=rSYw1ho!ggFklH3W_t-zPkCW0C`h-=OP#+XZ>c5ox@ zOpreJUzQ9XdE|?338a8U2RY@@}e>HI| z!4#f!*PqP4E@XI+<=Pm0+vV+KpJeB+%}W{z+hX)wAGqLiHdkBfc8A`7zdW2>DWfBE z2e<~z^2_TRq$BN+F$NC8^gb$SHbYC*Q&3=0fFjVa`ViDh*|C@wG+_&9 zTo&8$<-mUDnzbtD{)ma|BCy|~Uwh$rY&@P?BAr-qH87rmwvhy~e3p9dixGzZ8rlX} z6VSNff}{Pkw*1h$p4vcH=&!4;;UTkYxu#VKsFqi<@4SQ3+~W2&1)NEZDR~QZqCh-)Ym71VjL3Zfh{aPhECv4YASrwhH`>lBNB5Ca%pyEN^9c!DX2FhJW@L`uz zdv724ApZ)r|c7(I9hRr#&e5L$#h(>7a+fUl8{BKDu7ncXvlxfX_#ii zf58?_Xko?rteKFplYOWA2+e!Iia}#aIA)-(DqxL@3@JPcL>^GEbnF0(fddO;*JE`+ zng9J)7Wi~bbB`+(V+zTfZNgkF?sQPu)Fg?vyHy!ZA1wd|XpgbQmuhmK43=ITJOb>)9&ysfReuzzSJ<8S(UezOaHU+Jckj5KAOQy%yT8##r_{4JrU?1wG|#wen&Ua>3Po6Z?e2 z$MDv?+4iY+(gIBC=P}M-)pRks$26`CNv~eXNIGtZ5=2-NWDGpaC&GCH!6iP1MVcA~|H_O|+C4Qx^#YD!(-W?}DQ4il+gd>8GD{l&-+ z>EXEoskfd&vsYhk^=x@9EWi)N9wwq&t*yj*h%39)BkI z@piWTwY>}PrF{cz1C^2mf~qqvmZDidzly228@%im8K%XE&fgZPp#?utg=eF>QwED) zB{Si*r&*5|zT5WIVyRlZL92mC>e|d4rtW*1i=p|<^WNj`vB_yliAj!+%};IGMowRV zWxnlGNPZREqzFXa^m4RyMnYhBEQruTEEakoxYxbu2AEDLo;&k)1fCTXQVHilPmWS8N@xXDoIN<| z00;2`Ab?8(VL`Y^2}OCu{<9`~{V_qbhoABEeLVO3vZ!En&w7w#-PFM_cTYNA*hwrzDjO`|{P~N|$=28AXCQbmf(0#T zWB$=?a<~kLsC7WAm*P>GBCwf|Y!Cv_L|Kgc(W`tT+cL%Qz_&9^61e9c&l=KRD?l5r zaiRoH1EX||KRa>qlUr9Y$h=%>?-uGkTkDYJb)emAx(c_l`9;o+4k;X>&flFJq)jHM z=x%Pj9sWBl@b(8vI^t7@Wuhj7bj-czkUbQKTi=yS)V^v7^ z{WF6LvGSEWF^!Nqp^5u2qxHIbXAW&GcqQN@n|`ZxYEyLg*&HNW?`tLioT$?f-N$aU zq4y7T6n|5~gHAEp%LPM>>Oi{prjdvu=vM#v-D$a=8m%9HPT|<56Bb*+Oad9X*@~Na z%Z>85mE$%d@DEm9aP&t%|MkWL+xV4Wns2^(1LYOx#<8;+lsx~tGKFS!0D7LABEI=A^Yc?DEC@lG5>wdFMFc~4MK!Jb0I>rMbO*G;yCnTFvfqNYIt z>V%VlW$R#8>RmwD9!TxqB~hY;g?|UeUGnIz);tugviFI>LH+7s=BdDcb~2yvW?EY@ zR6x=*fjW=0s=Zpfef`I|^b9!R_}(+9qR&##;1GCybxTywQca;+ZRsM7ae{PStw@WK zU(_Y7e;#)T6O5*QtkPANo39z|6<)xj5UU(_#_H99-jB8=|2-Xv30CpiC{3QPOrUXl z6&|G7%4~H}FObb>kJtF2H|CF|CZ}tZ7yfy|WjS>Udeg*4t{PGGsbVJ+%e;e!nPD!4 zwKI5y&I--A9B;?i#VVY)VJrM$nspT}J>z;plr@UH+9OGPd;sJ`)5F=f zTikw)skM7z&Sv$$YZyq!O$TmM3P2ruff8vM$ToehuC48nn_U@%NVXqK|hc zB;SWz75oLTDc0=efre}ixRdYBCl z1c6QezfS;xk&%l$36&eJ^b7j?n9GN43Vu+RLsiKqKCLaF^PDrh= zcB>_Ke!k=>K3x~H^6l`TI;@6Tph1cFskYtyb!|&Uk!q(z;Ncop*SbOv zWqaq;eC)v42`?=XnRbp?C{`Gj1U|y218vrJN2@JGST*Zet8<9YK&kph^tA#g`uDm=;oEQD_4XeOnOmE=bU|Ztv0dZ<_D6eK zqXkFG^n?&SVqBJ-6!PtdmSkm4LW>GV4>p_8!GccCNbPNg*w7i_K!VdQ@(c2EYx^<& z`z5&P-X%PV5W0>sU@PwupS32U{(Gt|clL80Iea?|!H?EfJ5v{d6ScS(ypt}b}( z3sl1D%3PTHV2_M%Xxybg0>0DInsM$V>n}ANBeJV#p}?AhEK^#Sf#cz3r0>{`#2&o` zrzlD?3Ml^4xK*yP^dIJ@)zz1TE(EGUrL|In%i2>TEp&3g2#N{n;plON?;O{WyRG*i z-ic}TH~hB};d7V0Y3#sake2QplAr^hw(#WC^YtPlhRo6F7nNp-P*d#%UGhG%*53abururVr+Qs!p?PYQ!H`6TEgKd7ig=={VdFSm>vY)SaG$ z9D6c;JtwS{vMqs7Iq7G#Cm&Jxm&5rfktnYmH%?1oK(iE~<8}6LrKR6Y0p#Y9Rt)L@jpFJ<*}IizXe#JD&U}9-%HD12 zYnvS0@Cs0xqkP2A4BMYw3*1M9+D-1Qxk8N9K}PGn8O#o~795JCuV2Chj_om#oZDVC z(mGb-b`jGqLfH7TexF)*fxl_navzDvv#gpBYoK;eX*os;ef1c@F!Bd7{=;GTr}HNX zKlDCD(0hfuReu)h*(L}kk&D`nKo_powzMz1`dR9hA6j3telXUYR3b=`%!P~igb#YTK z=i*7I`|brvd-|Y*;p}3v%=WGjTli(Pb9q=+dSq!a`FyLXI)~-Vi-tY%(%1?^sr@RX z-1_RH>*kK5R&yJ$u?-4U#vdZmr#1I|N0Mlk<)|PLqoIpIO5%zgH@YumXtF5QiW5?kB zm=dY&(>c3$i)T_xIk00PpT26&o03{boSmJ^?NHtq&34CKx^+E73H3swF}NdNFEfZ6 zh=2hd7yTo=2y_(nG0^-|&QZEWVnO`2??u6(Z4N*ZN)#>8z&XjJp z>&TNSrNQHOANMD|;oBO^Yi6859-yH!xO&04A)yvLm6SAm>(A=v`Jx!7je!R>kY!f# z7Zgr{l3HzIJwp*qqtd(U4&PHWqja2-joVt0yF97ZX*YilMaq=DH;Fc~t|=(jw%VEf z)$NWYp;{<#Mnve=$bN_IT^07qU)G%|e3b)%ubn=oEia2uMzy~5qomD_eQEN;TrT+y z-R^GWiMfBzeEu>iD2^f-S8-hKbGV)>$V+S=1AZzYEfl@-R+?ElL`MRK%t#HG-|K5q zhqfzC5y zOHnrK>^u*t%^zcU(7qEij=Je)3^`RcQNu-%s4j7NF$^Y3VDIQtDOz!CSF! zp#IvC=F4|6st=@^L>7J{Ej8h}g~7Kj^HrBc;6(xe`@ixM@38HT4V2$DP zw@diH07=;$9gFCI7KUJ)e=gN=J$<+F{{m2UHIvX9e$e;>GjDY^y1wjoEz}`ppA_Rb zRK6G1{vo#k2XTyIg}|A)Zna#O0BpG=PY5~Y-Qk$~xMg;CbkS$gB!)UIDLKJXsU2j} zndB+XKJ|`NS?m-5tZ%-}F+$rvf^dNN*^)=Q_^gvq`BlC$%G9?;n}mIc_FE`U4>kw& zWoiRFJdf=>pqi}&zppx&Y(K0rsxg1%V43oG`GS09DY+f<9c{0GBP{!@h4ERRmNLXd ncNwudcm-kZfrsA~+|=E=5Na0(i%l~Od>3>sYUgQQzxV$Di`yWe diff --git a/pgt52m/week-7/workshop_files/figure-html/unnamed-chunk-12-1.png b/pgt52m/week-7/workshop_files/figure-html/unnamed-chunk-12-1.png index 765fd4bead41d0d4e28a511e1de893480405ffb9..5d98bf07a79fd02029c69c57b77032596b71f8e0 100644 GIT binary patch delta 13776 zcmZvDc|4Tg7qFSJMfNRwQBgu=%Q8&aDp|5eNMy^B7(36XM6%?IHjI&Er?O=iQe+v7 zeNUFgzK(5p@96vez3(5de|(R<$uusi5|SW;fz6kWcj#$C^)cr;@Y`oGSJQ5_hwM zFQZ(Kma6Kc2h8eWqAikxDgVpY&tjR+V3?t)GdQ{?Yv&sg=cL_Tc)y63Wy3I)Q)0NK z;k(7jyjIU>i&drnz_u(@;T5^BY*lI`Hy=k@*`^cEz!~cLR$1|G3jDrOg`1O(Triq( zk8(P1$Rp6M?kiF4G>&RdiiD||587e}OTOiF>w0$1_nwfyQ*&a*rUdYj646;h^MB!H zFzy%V#WBB(Kgx7*7!V#sPC%DRSGKKMTn4dKHNShu1ik7H=9cfdwQ;Cwq9$yIqzyk9 zsp-@rosnAx!S~4#uo4y9t)s9oUlVBGG+UxcB~Zr-z1r?Q6yK*Uu|Xcvimgr$%~^XH zOwnWDgCEG0~fF~t0#6CED5+cL}cDw3Z zq-GE#dIdd9&^gt?4E8aYdgKR*5@*tj=g@=U)bM)`NmJL+#V=2vmAe3KZ2sN|xgPA8 z&tgC%2k8oZivZC3`7yiWN9QI}YE1}1q#U!~9u_)72?56`z%zrT&xhAiujl_5n!ovk zUVL~`nwU2^XLUf3apOND_kOxkEsN0ma!SG(`pmP9N6PZQv1j;v zsP#&8Aikdc1?3)GuO3+mNonxl-1{|hdnW3a7Qj3COIs9ebk;}n^(xY5can4gd#d5zIIaW*0$p!F!(aDyJ`^I)XKgt~nF&`i!Lk`$`PF8FMZlnW} zhnFff7v^${YTK3yVc)_q^>R{%2ZC1foXTC|x~M`UrgzV0a=nZ^^wG5jS+Ryzs|paO zFr1<65*~$)kUB6S7f%Zc84~Ay{37+-%Pz~Tun{-*f3gE9wjR@r#nelVn&o)ddpaV} zj%~_Okf}>e$+}y;uVQ?eSiV`Y$HDFOf|*>IwewyS7L*;s5#r;%8D^-)xan`7=z5qD zV6M}h@b*PO;Ofq`mCQ6X1L#bnG|jh10C9mC@$`LXP4!)gg@mQhr|rgSMa;{t8GQ5C zi;&J_sE6cZ?z?B69nQFV|8m@M+=J(-;wI3TijQ;i8)Agq%0f0`C&BP>Lzzd<%>()| zq2Zz#O!wyd4W0xS6CvxdeF>9;hARi3#~rCTrBtQX}0+jQO%^mOw5b1OV`D>E(devNPAzABH9*Y6G` ztMSl8u%b9X;i}-)dvkQa170aUx)g+MMTTeb^6nSeTB0hgyc%_z`N(#gAIq zJm`+Yx5_G+1fGf+D*I;`u6n`6n_@&P`~~2)@9bDj?cy1#fp{3B!g+$ayZaIQDczCS zTeYG%p)L*BE#9=<3v<5I1a*4x&l)JkC``bQof4F3i?^BsIVOJPd?tjwS>v?2b&Zkq z2TOaGUVNNOB9N{=ASf?TBtis-(at-&!1?i9FOo}x)u4`_d4o}7qdz_H;z!DpDfKT^ zJd)QOe8uE309Vo^+21*V!zffaFwS~ah#?qZG*z|*k~rx(?9p@7oXbzqr5LHse?Lf6 zhr{MBeUQ-$Zb9qQa>E(?8mEQgW>T$sXR!<-6hbVYz9kEMv?gh=YNB8r^zMGUC5pKc zXnjO{977kRz8QRed;8TZp)Mp7g*IhwUzR5uR=I&96xw7|k7{^Z6$YL?&t_03;o}=i zD|`NV zeM0&Cu5i6C1GPL6d$gWH*RD|PhSzy>gTL51wyI=TIg4jZM~p(BVc?^mDpChJY}dO_ z0nig%5)QQ&eUXJ@BaZ;HuR^z%o-1}+@A}m)-oh!t8NLjcrMEJk*f7ru6v=vufiG@a zrOr-`j{$a{6vbgGbt5)q3@UU> zGM6s`)K(AL@^~_(hSLN2vSQ*rnU6!_!mtrMDTcc&T2HC<6$ZXssY1P8GOhJo{%NO! zcD*SGR!a|AW_KLgKA1hNIdaU3L!QHcD&$olN+BX-B=TEi5zw0C_D(Ll6=E&WcTdgliR6#hj53Cn3c_1GM98Fq3rA0`;4 z0t!lo5j+0PiIb&^Rsn^!w>{jBlEPye z7HYrZzBjoiI}SUJ{RpHG1C52H+OK~qWdBR6LjI`RsH%Nyk;lKmHAWuGJeDVCamB3v zKT`Uv2VAYizq~71F|2o>(7U=UH2eIiCmH>;Q|$G}L<(<$f+C7%JLd}g?XA1CC@_~q zk@1}uwV=9P=KK)fub+b}94Ucl-2Zt1U5)y*}lI5VJi6$7CWE1bnIfznONQ{Nr! zLB6`Hj)KJ|v9dVT-f8pI7NV>Kd_(YUX_4$Bj{>D#c#)qP%2eTKWDoFpTDieSasn@E z0$mbP5E<=Qq(kd}_yNQ}sTxHnf{c!zHzD1QWP;JC6g;$FKjJiYam4G4JZ0zL$@~!2 z5-ZY;NM?Z5qpdhj)OzR1(up3Rrc_s`IjI1>RP<~HxJ!wU?6#V8O_>=nq|F`hhog6OAGOo@0zWi1pHVG_K{K{9`&LE$FUzo*a zKcM81oxRvNjL)t7z_Zp`sL*%C3(iHUh`u>&L?$5is#~&}*W%mjBSp*daEACbhBJ`C zQRXVr@N%b&{2cb_7^?w}KJSy%U4hruX@RHrub^NDL;Z3AI~}jk`rm8-OQBEQVsvnR zbjLR{@Dyn2#?qR2H*hd^69}&NmQjw-&a4D zIv|aLkJtPgvn!xXXqaNkC{%R>CZO(w)UXwGOTd(sLWW-H%L;4%H&d(7vzC4dWSa$sE*C`Pb?E7+;gKAEF_O46d_l$< zkNZ)Vpmm# zq2E~#pL@mj1*hv6{+|~^n@88Z{_pcD`tp>qQS_DTk!-^kY|C#SCf9X$u z#dJMs(JE=9j+zr6h2g%vfzeYo$`+OK_ajz}m`Eflzl?EOlgS{K4kt|g2lEg@DU=zb zr4Kr=jnTOX$E(-j8Jx5zjDd$tvEo}a(<^APet#q+ z*H%uz8MaI|FpK;US9xQva)85cWngu=E-0AZOV&-V$)29LbF==bV?pg%Vq9@xO~EeD zJQp?RgIc9_=mO)k-$?D9!mI9+axp*-2FE?k%5|r|GdgJw!%A1hV z-DJj120*;tz~@-ik>-KdE-)BPkOS#?N<(TE2L3a=CjypIg>iM@%m>NA4qa?_<=kyF~tDy05@k6>NtEC)N zl{-eY!J;NgIJ?0UhY8P1>MwLWXijRK@^FZrRom;y58|m=P9D1S7V!thk{zUY$7&_G zG+2V7-tv&VNjTcdx>QW-JkUCqw$}b#>{5RzJN}Uuvy|ai z<+D=EAa)m+HaK2aQ%<0!I|AY`z?HTZ(-1b#u)`$3aLynWO!ZSn3hbgNNp4YdoU)A%RK)&hzk&)KEFJ}C z?L7^MZ-xc`BB6Vl5B$h?+JPi+^7$n+#knQVDAr9vl^xyvX-j;~fBIVcaS#_n2iKz) z9&i2N4We)gR{bu@4dnn$Id7;&n8_>q5-F%3YihW z=lhHay*PRU1%aGL%hAro+j{O*5jlVz)>hXiVH%Pi$V}CEQ8~pbk+JWd!g*MJ=jY4Q zeJ{=Cuyj->fKzP*Lub%PMlJnA+! zHRa0ngKh?xTX{uLV#$|H=^S-&;|m`O_}=!^Q5QqBxxdEvw+%_R$9Z+0nj-ID{nXdX zsaHD`aV*)X-)k6ZNj$`XoTTs`8;S$$y<@C`>-d9ZRC%|W@k8CA&bJ~H9`A*M-UT$7 z37OSq?<^@38}k5D{s_(rY!6x`y4Sz5|C%q+moVjP@t{QU6XYEZ+5n98$%sJ%-N-_y zDzE<4djAJLir3Zc2-z8`b8n{q8oLul3ZBjl(r0o<<1F_s#FrVRit@C>keA8IHHJAc zg*!_R(E+9)9IX2cA}VaoqaIxUea4k%pqXJDnK~pS6Yo-^%m5BymMh$Z?Q>PswYr7w z*0p*G&MaFUDYR0{7<>v5#5x33^Dl*SJ)-lsRv zT}a8?$4<>@z%AmXcg-P=9gp~VH{~cD-c{_-Ej8|5LDseYTW z<@dbp+4qYCfpnb4hV)6Zat|+;JI#RNYO`gnaom&<-WlH>`)lZG!Hdp6^C#(@Y!g>} z%-I<9V)x=$pthB_u|GKAM3eMMm{nGwRF<4X>K}IcqX9Dpx+Do3S^r_3rKSAnGh8LI zFeZ1b+WqRSU1sXyI(c;Trq|W5#c~|C;9%lco(Z!Jd11a098q1oq<t8ZT`+w(9KGz(aww%da)1A(%m4d<^XTk4st?^~Me0i^3BMBV7%9)1`I@}?lRgCW?y5a!j5hvKW zPc+{4;>Mdqj!+co8OZAxQBRW*O8wV-=n~lQZwI8^R78tcR(|a63;4nPsAmv8&c=DE zDP{A|tVFZd8AsOOFWBHY-M#&C?xXZ=zYwSD?*D^OQl3JRYLY01$C0#SSvWXDt&VuN zJU>0f2e+9_JwIMG6v+Ap6Qdc;ZVtm+%3#W4S|uq%lfr3xCg1f(R@o2$&T zFP5@@em7Hoyqcq@W>~u~O9A#_hgaMrxl-A`8w|wRSY;Sb@;9A}wUR@33}b7Jh*_bx zgm70;MeFY+amktj%hcg4aih++Y|fxth2NG1FJmG8f-V2*oJ0MIimTI7pmiC+e6?KE53f-;n-TvxOizQjmPpkYSG1dFiJFJESV z0la~vN8ZyZkbI#-mt4QUr}mk}GL0*@FNK!gocX;YfW=hQg#@DfO=y27d7*wv8N3R? z4%JZ;n0CJJ4!mM92@L|N*E&zT!G@Ikn)!G7uMlN%x)GsdNxDsS;K`h2C~$R&aq4k8 zS)kGJDr!`*?2{)YwIXo&M1S5r#(ygLMDj|ENIJoG8x+%uWaUrHO6i@0t8*<(_7mTcxopUrKC8YU>M-=~9#r zvuhYVEVpl@ymfzaKe{ikl6PS~X`3~h>MT$6qV?d?R3W+pfjRRSqi4v}9sMBoQ-2kC z(8=MT<~XdawmMG2+k4x9bc5=N3FEBxce9(st=Ff~gg<|@jzOIMv=yyi8fwaaAe8d1 zEygGke~8971$hTGu!U;}OS3ds3U!i|Y5dGfFg2+`^hSw7f3(c)hV87qf-o!ZgT3PD zifbD@(j`W6cYPU_Kf1uk7lYvUGL)Lo*q0#@;ZfF=SLjkD$^&wP@5j-}k{of`3X!TK zDqVvQ-WzV%=|h)e^=iGQ{b(6}+*=lM(ygg#7cVb)yC9k0z1+U4<)=JFVQpB6SNCi5vleaL2-fR@cy&7uKAsw~<4SH#`vTnAGDJ80wpILev zr1>icW9@(xWxDZV?-|t(5SE#ZiJd>5H%1y}Jf%P>zGa(pBw0;A&B3UyUUBXT7z=}L zi!eW+nZfK|Cd<`y4cGj00sc09b=LDnM(Qp89QfcV?>h}|3!B9l>^iCrj`Aa)8%f%K zr3CJqD~X?o-zmPNfVus=%{P`?!nYI<44kcE$X4i+a_v{OPF_$#sc#qt-TSO`8)W5e zBQ;Y?(xU=_=Q#{G36bs3JSs#H5XdR5kODUT#wn+PADQNdX`V1ye_lOjT~R$<+3;Hc zFHdZY?wl&rGt8$1?I2N{_C=wwVf=Cd?iYO%)1xd zgYX^0HsA0wQ_UKiB=XonPN81;?@o_<|7}p{2Ai_Cd2>Bb{)2kp)W2`DR^q(+sNtZ2 z-tkSQE45uJ+-slpkf1E9&dJvm1${`arK`MnrdX?oK~`KjL9WrR5XfmfKr^UhmuAgi zCsSb#Yo520Kt;v>^_s?Iz(7^%Ya_K=?yrNchzJy!p+mS(s~5m%O--WM*yGjr}B zyTar=1mvOsdd$(lUmLN!QImxR^{vGJO5zY)JBJ5^YPFPt8cOm1Vf|OO$oI1w$N_BZ z+&&jV6gCb z8#q`{1oao)NW>o=h3tSSl6ppn?;}IBhS}8}UMWGMg6AS!%%TQ31piyi3HQWO{R z(Qj(BL0hNn8XKNQP45!BCD7yi&dk`PnFFCx$)ZXQutW0Dmx44AkYkGn&=gwRIC?{e z{N(pACFby^7DljvGV{JcFVcspF^+cu1+aNC&no8JDgfN#gIde1H09HC#X(cpO#s2? zinDG5%>s+Zplw7x9xq5CD+V@_PpGGK7UYfrwH?0U1FlMjF3}cOed2pL2Z*KZ7=*v= zR0Az*AbApMD9g)d<`N+E>riKa4+C{gi#^?65&thB7Sk$MHeoT$9nh?Kz~8mZ+6PZ_ zd_9xCD{0XKUm@qOHrOG=8%(c{g7tYaJFUu{pqLk5Fri_7rcU?GND2n-J~`afbdB=* z?ZWQz)H9ivW)6NH03DN~-Re=wf7r(V^G~-0V;XfI+U6BI_%xEG{+S{o=uk#9-Ytrq z`r{pM&+>iwfv9k~JBl{QG1=E9o0FC}$8FERckE3R`42w(|75H+D><)u@d0rA&}!Tr zcXT1B?duwn5fxQ6N~ML{_u7NWu}0tTt?M?Nq$cpwiysHYzOwOS-*-QkWR7%x<;vE8J^B-fY*976=%tS3m^Bq0uX zIuNBn_VA|a2_5F``yJNoiOS9?5`Ru5r5ktHREX9ktUTTff3?Z7U!&xjLFwSsErEnW zO%vq19);!8zTb@{*IxmB03kf5@Sz@3847s6G4M`W1dLSFyxOrBu^Y77X%My^be9$5 zfKh2Dw=+{c>7thX;=iA_@y6QY9p52JTJH(2==vp${1?9h*T1_^d-o--tZnR}3n{f( z-O~TRW~01Ux}MEwX8HU+luo!r$$!9j!eN!xH9fofGQKC{e<$@M)@-#PhVfLc&s&R!`yakXxYc|I1q9 z#z`F2i$+D<!~os=X+4kc6T%fX02BVJ6qz^7qeF{gndc zw&YZq=l2-FuLe%~hs`p~*vbiZh;oTS#d@9DNn88hV{NfEhyMNOpCP{>4TMax_LBpz zcDX)^S%6!rZ}C5`h3BS5o0qGtZ_GiaOg0Js9o0;rK#5xPC(2JA5 zboiHzbnCJ`5;a}=DTkdrXjyp%BY&uHCW!w*>=N6zuG694{Url5NNaPNiL$3mVsK`mw5~EF6(d6s}LFrrz@wk6qV}x9c6n5m=1Kc)s{vKhf zZ&>ZV=Uo%z9F!b1!Z2_s;0$H`s{_C8^~QG~{t}|c zl9@W~`0u(FU-U_`RQ4yW_#dw=?uBkB{sZVe`*CjtWgeSRAqGFr0R{s0gEL=m()G8+1>?<{;;cQ7AP~H?KihNo;LNTT-W7c z0jM^gO5(b&2sgc)nZq8Va%_fG*X22;Pj|cwgRY|PcT0dK{l{8ISEzhQYD5Mx{$nC1 zS-UjIR^K}e)B8@r4VF^VSh|W`d;@#%n-yQHFBzN9ink~p>}}O|95DSr#PU*UH^cIU zLujBILuIWxB5ZgI6U9rc5kbsjOE~hzo(2W0xFT*S{Pu0JVSN0-HXAJ8EvZ5~asVCd z1TKeUy!DS_M4|A8*ERFqguRo0THC3MU%Cmo(0bvU3}&A@A#<_u4Ehl?xVmJP_clFK z0(15_$!Sxi(KH8YW05cW3%p)^v<*6O??lK)>#xQ4y$0ItoJmE%_!&O%upg63;~a;huD zU|W2!jcjsv1vNahoumANioqpnLaGl-RB}fR$p_G|_Z+j+z17c^`1FD1DVza)mbBZ% zTP#ZQIIgC+&c|fi42&KuOTvjdphuK=Tb~Mb8i2Hc`uWsIJS+%&++}Eqn)!o0I&Mi# zc(qB2*eLRb!WWyVYBW(~XWMhQ!Z|F%xD7QSb5yEiM-iD7cp3vD3`PD3&EQp_ObBY`_=p$mMFgf|n04sHia_U)FN{v$+E1tyQ_zd#r8+{N^>d?`CV}M_ z%=ZLNN@18Ap8`4s$6)}bEMI@T;VEd{Vl=1;Q_Snpst5C}5`J~}=(C30qB)D#5hF4% zFj!DAI@-5O10S*J&kGcy`iIRxy1zC_=RRC2KJG#|6r0<>HLwbh-h2;I1LwrG0Ggbr zo$DlxNHBF)m3KF&uIpadVm7BJ7~mKfMj6LaZj`}&@$$z=>?aJZ!yNVo8<-;r4`y3( zyz^zTnG@WE`SoXh3i4r40i8`vFwG}aDzZ;qYy^$OpSiM4Kl`rT&C=&sl_;omXc(ZS zq-CRy2uE;@O+R%4XLmH+ztW+p=O;%gy8(_{MG#`xdA3;2nKq25`22og{&5;ImcPt| za$Aq%4(dEL*m2=9g&r_B_wmAsprYEuPhV@^fcp$vf0o?Tj`?n6{N02=l<>v8zC{xM z(&2Ze32j2Xl}lc!D|<%a5J3E3baYvFS!q&c_wR*2`dIgVW>lrf0rg^#=sEm*aA7`v zfBT42;ZPE9N~yLfw?YX)j52ee23zo}w)Hk=zjcB#n^0q2zm#O$HScwQB{$S|K|fmu zL?(;@*?Trq>LH&jDNr0&L6Et66lrj8oqL1>_CgU(WS9~~ zyezm6+El0}`Yeem?mv#W?#W&64jR-4`YitWCH4q^ZIV5eeI)3=MEa>A?0{t*E(P6T zoIMV#>BSj1kydyJu5|l9FtbvO9+(EAdLx2!S6~(3WD);<0fxPdva;26E>7k6cQ56j zt`XwQ8XX!K3D{a^KEQ5^197?ljQvpEI$pGLAprLIq96=Ph{Y@lcYc^^;5f^!a5&Hr zj&ZLYI`4iyO1p&}s0-k3I7Zb)r3$&;BiIyw#FCO<{Y2t^M&EDfK$gi%Y!6aYW4Uq| zq0};ae^mAEjsFdTsJgUX@UXcN!~~L4N;9N-V2eG@HPx!a?RRD1T$HHg91euq&hnL%@YielXSk*Tyn?CC+$`RP;#Yk>|UmZk}YadTGytvhgI3%$?4o zk4000>@sX`L`s#(%ZFVtidu1AbVr`c851L0Gqd}=c0AhRz9gcKva*8|8qU9kOvr`X zgmvFM*@9~uqHv<93C!)2;^*YcsQhpmSiTiCLGrASoQ2+yLAl2i$Ej0{-J|gs?(iH8 zU#!;4HZXPYr%2YfOo+Q@DLzCx^S}n&dB`H+MDEFGdH(#2nJCLgdL;2!*AhC}KYIvj zg0?B2!iZDrXYlEVS9?n?<^=fj3p3&L1 zg%!2k9}V?YvE+$g#b%A~3KlGf`68-@pB<=tZW!+3cb_YZ!FP44C~Bud*-RW_0!KQ>Z_!V(y-NCmD{`lZkWy>{Kp)OUNHWPkIOGIeO9b7jVz&O z)>aDGW_o$&XWeHjOAiFxpv|~&&6Bv5@aGNIZ0bytPod(AQGe1JQG-wQ=AupY z+3>}{B30Z~Qoca8mTUjaw5SIL@VhaG9cUf%b!84yb>mhg*KSxWpwo4C6Prn(o6XKV z8)Mp3IWgsjsug$Mcev-YX4RDSUM+CWmhDK^!i1q)VT6g+bImk^ZxT(Chuc-T$p+@D z6jwxFMs6?1Z{ollXMa_riiiDjqVv~AwcRI} zK_N)0XY=~{f=Sb8V`{i>zeDj^1?cwmzXW@9$>1NKYRgmDGYdPo*m@x#Ny2UcwqQaI z0gBX$d0JD|V(!8?D?d5iFUWo{&jEJ{{Bm-Mi)Z-!!MA+{eiNAPmcL*4siw!hEna^K zwThnjG*x0Wnip`jtkkrWU*QRKpk>Z7`2@qvV9QO4ueyDM}sn;$3nt`^-z>muESh`o*N0BYWwqdqdm%cg`h&yM&R?gz$Q#^E zCS^9x!^c#M)a%D0@FiNi?Vm8Ghk*mQdJdPSS&DgjIswFN1)0SowT-0*1C}h$Ibpi} zKUZsPi=lbb$xjepS;w`l@(tbhnK?oc`O*JeJLHa#^;a75l1Ie4`{!ABvZC9KHOp@y{|+nxOz2y7c^8S z)nbEr_M1#&gDavjA2j1VFUUYr=M7KsW(R74ELr?EN(e7Kb|9X4Fms!v?J0_T5IWUx zqf8tfUs9~nja1H=^xA>i(GVwS_K}Vi5@c>IUO}aCz`_Dq4gA0l2?9^j72matt~^Qo z(G_wyP@^L?NuAI|#W4c#2)g2$x4Z#=_I4(6U(Wv_583e_ydy_KvniuhKo}YMd^4AE4D;rJR*8yDMo{EU7)SjXKuR!)l%fIlBw)fe9|lMV5VH^DI|c z4ZeWh8l72U{#l%Gs*7fU9?KdXU14;H0i>yU7gecHYAR#}Rg~PZY87)nXd;F&<}opi zox=U#0_651_@FG`hq2F+)NK@Q=JYJ{2&q_PV<&{y4T*VTA8st7UmB9@=Q>}FN1Gtg z3+uiI!LM_?{&7*hKRSXY+?<%hiY<+qr=+#>uI}fWo!zk8-N#TL;r%K4l{BO(k(`%d7=JQ7SIKZqs5QgR5!_kV7t*DqOH z=d_oYKIXCK#Q!EA5nqi2S+EQDvH(+*M3~~{qQ`5}$O0`jPtm7qKHSu-0B~G~2^JZ^g zIstrGYk^ANMX-Z8{|Qz+e%?sr`*HMnvmuRMp||E6V-9Kf;{IlhWbh*c=;6PBGK&+O zczhWPc`s0#Oq}iz$u*MNwQcAg3S!TxBEQ!1HOu!^%bz$i*-b6$eDE8iMWQHN_gAvB8qU4y~-@J6e{7$j%$yb zb?s~WJy*Ryzip9Q!oO7PD&ygPz8}e3<6ry;|yoKFaP1#iFma)!q4w{D=>Bn*6LlJhqZ zFa&gq8}p&lSAE5G#jjhpRI&V9=Tnl8muFzX-~S==wzK~%_{f>LAvNbR3?+GsH1Pf# zMk?GY?F6n0h+ebe4es_o{As0*{qYVT6sDU$8vG?Yl)h?9(P9RsbC zYsedl3Fi-#xKQqh9xuP8(4VB&_+2i;sXz96GGMTN=jcmHS67RpQE-piCDOn*apDuu zoz_SyL6X`I6|}oKPZMCtd+Tjj@W(smEOsF;0bqRzsYPH8qfhy+9XMyXf)1w_hJvr= zHKaewKxMNhsT{ZWF4&I!zWZP?F~w|jW8bLz0CKH08C04yBx9zlAqbuQ>0z?BrP(p7 zKR_p%DTYoJIGw_a_9&|uzwGlF!kOXA3cGeiuzNGJx3vx@#%|lMl;=1%fyEep+)@Yw+X+sTvequ7kDy`QYKA?gGvpTTL_l~rFpl&l~9w`GQB9hfwN}W zC3;al0DWB0t6F#dV6AIhTX?Vz}rjgY;*Bg$Ey) zSbM9Njzl)}6=fKz36CF8<@(-S$tZKX)u__%dlbxCm}$)0hY%uTuDLyiD6E{Lz;_mu zvYR#q)A(2g%&g2p3n_(zmTrFcM=IvK&DuM^wtks32hi_q3RCfscoxAe>%BNq_F|(P zzhlcovxTkX5g*E?+p8U|!(Hdb)DdnWjt)`Iyl{|@Hl?Vl}GWXpf1AQ$S2!R)GQ^>CevB5xw+Ob-BT9wr-y}GGxy(i z%^@7;g_!Qr=?hg0coHZU>iQOqT;~ZX>F6U{7RiWtMuVZ&Gr4;=Rzl1)lnr+2a*{0A znh20jY9sZGA(qQL>j$d zPa!_U@LEcisxj2r?oMuV4PnPxE9~RAGgenOmsw@hM6u9paHtf7a`ZBpN2CJDJ1*86 zV;%GOV5mPkOMoYwN57596f#ltUiujg(ezWD;ED$`t3-$e6cR-zqR_6PQst;BC~7We$DxVPBaOv&3aZ={(IO&+~ivs5)ZfQ3|(6 z$?wnH#CDt4@}udlBcAcXQyt0ruF1HQNq8C%yatvN#U=8g2|C63c%bap zzjKX%%ZQC+F!;C|zpDe#<)G(9qiDRP$-OgIzWGza$gL(`_^TpQ7XLtz544l+Ni?l| z@TY>2k4}_2scO8yWkjcd18BdG;m`bVY_E;>`GW_YMsl;H%WuCr$4T+TcM=%rqh?K$ zqT2yd(YqVYNXG0D|G9kc`k3HPWu~WKzj$=yE}weJ?+ltJfC#pYAaQMt>&4#Pt+apyuG#KZ(!J*^-`>Muk)qOi!f#h(5Lu4u+Mk43C*F%5!b!IMGtt?Dj8$wA zPL`%F)&(BVVjE^{A~22huQMHKmSe69{(0xF4eCWUU=t(%|H0}@#(Yk#=8bp^<+86O(uvK2?sSF1+2Zxsa~`-hn^v~>d0T12>v zJXA<7QlbpV>R_P!Sht>rkAB+=AQ=|I{9BJ8{Dv+@nbnVY#*2igCX(f?bRZ8*K>v!J ztlnpm`YmKcX#>PMW%N0ATb|D5*5yx{k};D!{r?ddN|MV`W_LDzgj1Vace_*22+xCX1kyx)aF|g7zhMQGT zDC<`~D@w#-nWp*F@^S7TBsiV-v&}b}COq*L|2J+-ETujVMGnc>&umPqhzuyxz63}y zYK@)F3@+?8t8;uJ2hMHT!R@Nnul!U#`vt60$#~FSPy`HP(vhPObMqNUirKb&h!crkdAJ5IIzR*AieHTH3Gp2T zZo-54WXDnRP3m7!l`nbu0leWUsOtV~NlM^pw&SKvYEv>PznW&wTTbm}O#{mGa;E_f zi>NX@fsmLzZ}S1Y<^S!IB$-r7wC~Cl;Q!?4z5;b0U>XMOKaoK<1Nky=*?UjQMhSF)S-5z)0)K2P%4M;>Dt(aPvI@lZ} z_js1IiKb68Omd%{_sf)rmzXb67m*(PJg#1eT;J}Sh3(H{5j8hkJ#p?4uo0H|j%0?`do zL$v-g1fbX;()K96g$Nt$V>c>8N~{2#tySPek}qtO4l17`Dye0uH2J13Hu13l@Ui_A zd8np>zi<|Jsc|0vEl?XFK5**TT&R5v-6RDr>&AW#%oV>8oDG1JM%5o^iiyF`&3D;Xm>!io@#~B(E zJy{n~8HLhDl&`d5pM{5{I{B|LQ)+WPh^*#{wNY!0y|gV${( z4gAQAG27DnZe2pU=iaYy0{!Z@(N4+~?9giQwN;w}nxe}rtm5E1`~+EV#}p^k4-Hl# zxpLPZ_6{$YCD3wm{*i-M%~4f_W-}B!eCd*&>CCX;x>N0;a5`2>oPMMqUV0(t`2^IG zElwJt^Xubc_v;F`X+gR-6r6ac&Dc$~o$cEhcWU1~Qz+Z`6&7iK!naD-cP@Oif&wtp@CU?iJENbVTa2ldmrA zl|aL%KIb&NJz_i}EI`{QN7z{E!nR&4WW*_&Q+PiJ?w9UR^?*|wr`T(h4y?$MZ+=+F zc&_nAj>jp*Vqt1v`5qA)v;}9dJGy%?cq%?RVKaup+jFbCgQBe|y>fwEvVCwPaC$q=Wk%R`1_K zt`C|m6=kicURErek;CYZK-NWlt@>0%c3h$?U@{3ge;AR`T+B<9=vPD7TT6o}$LnV6 zjgmfLC!2oCKD(l4WI(J3ESHehbu3x-_!to5j^dxgFBhx_)=PDoGj1R@vV#WeXoDo_ z-$oUscZp1V*L_8+gV?YOfVya5LyQc{`V`B(EQ%vVnpgfE$Odn6|=$^iG9rs z)ZC}ouP}%UM9fs13U%`%5?C*kstJPr@VYgR{D+CQ=AxPq=r=Ca`Hng%6K2%%+-{pD z8vUAQuwx(rH?gtxGUOIP>0dsyev$2$CTeIVWugCLr_n$gyT^fLw^i1s&kh>p zUR970LFX zV5QWpfcEKsv(sWJ`Igibr@x|XvpE~vwvN}3t^X@w{Mg;_20RynZw|RB< zEpTE{>*rF6*m-uESIjjUlX^aqNlDHPo=r1)v=JDTNGSGldz;vw=conQf}E*=c|07S zo3z(+JnGeL3vs^h?Z7wd6befyuEe4o9GZS-w$@*+8SbqPp4kY41qDu+&-r;uAa93_ zYWqk$yPR^f`H8pI>iN8mKGkhU89>$#<6hMm%|e-9%pXX)Cftj?oZr4!-wB@x)&}-q z!!RSRFzEVZW|#7^EOd1*yhZ+jeIB6eM}sRh&hm1t!ok?8o3h5$&ZAVwqiiP)O)Af# zRx6a=rS}BoFvo9%ke4I>eFeu5lCjw_m;K-?72KJLLa}{sII8z5Z)txRoN8HmzF;Nb zyfvc3@5<{&^Po03JT)ueMMwl0*^BOFw!nA@#Oy!#ZtKtezdGp3sO=*h6FCeDw7e*5?|+`S7=bytjtXdd{WOSoZB>HwmnHILae z{&HZ14GKlpa#t4efiH8)a9upJYu5J)a`6uQ-n3&*I}Rf8H+65?P2RZ=Cm-o46W#8d z*>5z?hb!oCSG_7fh;7*sDig|2_d#d~_X~NpL_bIN7b;$n)k;9k4W8;%v@7<}Kh^Ji zvm9f)UrgbnPgY3xhR|=Ix>D~kvK@ZdZMNmWP4{gnt(Huxc??7nuGqwDTaZFc;L4BI zdXP9D8N(WvDguk(BEPe4_P{zaG2;^C-uHaEsSyA8AcO}JQO>t3oPfc9yjd5W@KPma zOSk+e3bvY0jk_ysL{!?jkCS$n^~KgdTt;{J{GcV7C#|krf7Vl}zQb}!)%3t>-$@ne zX>$uf4|ld-oD!9&XSoQezRy$JeAn&$gBH<>>eBo$`>d5{CMsoHH|_UQsT0ap7z5FY zhh*NGbOn1cTj5916q)@(Ie>-R>S^%0f~n6pd34z~+6+TND5^B+aG1pYEqmeD*8US3pbX~F^|GWX!AKMfro^tT_xd2aXl-;di zyz9ck!*|u$)bg^=0#901;M63G>_N8a-N};pdPy!|ncRIEGOc$sx!&@0=ug(x)Mu+7 zknANMsnp1A5V886nE;Zwl@BBZg8gvxBLR~_L!X5-@isL` zp$;Y?IRh-6$SaA5U31LzES&)&|90C67kgbNDT+odn5c*b1&%(uTweYjs>N0OBw0^D zEo}L2>UK4~9%c04gctRjt#uCzfXYTg?o(1lPZY}efx^$qUP7G!DPAxT`nP+~wPmnw ze$t=JxHxnTwI{QXaaw75Z!AW zQagf2B^(7v5jqV-b?T!)`Xs{UZQZt)NA;c6zbmdIs(-OSeEJ|1@mE*vGE5XEGcF&h zp4{UZP(JIg&NCL+{UQm5l|!1o20CGJah9h3X-1npsCG~_*uS}q9s!1E*pO@!3d_7; z)pG{~ex=!;dGs@t+$|qH9&D#k?b;a)MO?wJ76OR7?qCiqGY=>dXDg zjPK2F%VRbXQdMcs-~LLnU{e4sP+QQ_Ri9f%%8#9c^L(gAT4RAb6175LEQotL2;+NaAZd zAQkk@S{b_Z-R#J<xrZ*gR$vx8E`<`r)RfFA@(L_WL8FM%SZ95uPhN2KczYrE@wy$_wW@Y|rD!idwt-aJ8nk zN3!8wZ*!PiBl;3?V__4GyTzSc_B@VBQ~sd2Y7=5%NaaS+kJNX{L5%`Ixkz@BFGUO( z%_6rtMsArFfu-Hh*_M>H z&soB^=*0G3nIV#(f-32SGwYz8^B!-8R3PUn53+P(&U&tz zo8IX`IYM7SWn~5`YBW{4T^Ptxtlzbgd!-x!`v7c|#Vgm^bL0dm8Mlngw*%9#(jWf4 z{fcwOO#zrYxe6v$$fXZMa_LRqmN{X@Ep5w))KY@xyxi#G>`zEJRO!UI3(H8e6qH*O@l1 zzhJ~@agA)x#-Fws%?F(_oc3_p$W`v}MP}$=ZN_x9%K>nI>yd&M)l=%4CK>I1{_~$u}k+(v{nL=93ox38m6K)bvHdDq-JcQQNFZaHd>zCMd%wY z@ct$1uIvEJ+EUH>zy{j+<%y&<@v8pIlD8a-sIg+b;iUwt*Ofbt3*1DT2pB=!(Ws+* zE>LXK$<`j<`rg9&lmCp#sb6^by>yg(AeKHv&}_?K>1z`G)Sh{IGQB_3qMl6Lo~q zucPR%sxO>{M!XtV#>FQL2Vlyh2K^yHNHO%Rk)9Fl#d#)P^oO>{TPW-sxqm~(9)%?;g>SB0W&;ihvQHlW%;27 z!Yd91_f0(V_BB6Gd}vrbFFO7j_@Q|Y6IbcjO3W5NBnKb}qOZgc!-98T(V~)IhZ6!+ z8uw^UmL4Ls9ek)W0%||cWCwLpt}6U>p4j_rnDxg!E~>FVroLYx`T5pu8u~wuQ)*D+ zH17M(EC=hHs~)P>oMQ7#zZ|H4IF&tM!#rCNHTkru8 z$UpGG>6ybM6OVN0T83Ye{KxA5&+(sTzpS87@{sdYC+-r( z-!gqbU=w=Kh2PIC(@Wu`QbVMJK9MJobK8D%=Emmt@y^0*L({Os_mOHpdJYB1eRMB0_KnMLFgOXPtn+)9lrrbMT2bRi9EWzfQcNWKO}A-n&rac<4Y0;E{R3)~?`;+mqhj5HmlHy~T^Db^)*&Og@>oHU zgh7i5v(821=q{2-r$dZ7zN17MvuswmFbZbfDZsUWPthpu_u?T<8O>2;(84Y71>`gx zWu`>~S4s0a*+(mC4E>17-U*icFY4A~RqCRG-XMv)FE4LC>RZYyG)}~M9dx~5C z0@H9(Qck2A&**UdJc zo$v*@iaEmxLDrre-TxFYQO{F-vK9F-%kqa=x&DvkfBSE3unzKU;REJHi&ky`8+^h= zZ|qu;D-GZe$5#2PQ{U4|6oOOciN{UX3 zu0(2&5Xua;PQ_hk{_xcYacm~N!xwnm(c3rvzfFZ&hmSOOR(@(HrJfX&J?fd;Lsi%t zAZo|@+c_iFtnZD^pCeLP&kVbIx#Zj-yJKlf4u~;`BDpI~FwyO%?yGE*uPsgdBSUo? zPdTk>6b_~;Z~ejaMW)m5p%K4~Kg_Dvf?Fu$Z#{h7)s@i{3>^#=x_lQJXFz1Uak1Zh zLJ`7t#7X2}CsbbHr?~gz6dv@7Kh_&kcz?^8k619@-}8CUAg`lXK|r{tr%K;nStdTGn41>^5|IGuJ}9L?*gYKHFDj2Vd%9p={;ilIjQ)&4 z{lbV!A=+MOoB|PVZuMg0i^DX%1}kNOE-m4rA{wcMBHSU{*!FJEu1r#@0i|6&Cj?W)w zto$b7PFgR1EqoLlBADe^Yi%q79?&#$wkM>?I2$R1qR3Tz^>wPoF@pWq>TMv6RTe?= za^7r*^_xtX^M|h%wC0&*48*_#b$xHPsqt>aWeby3m^|c&$sY^k1NswP^s|k>{EtHD ztxr{G5fc{vL`HET=$Gy9f$qVO9OM}iAwIOPS&g9gay)D8LIx%3K3U(~cbkOQUQPTd zShA%E)UVNO50#PqAnR?==R(~e>l^#7GdT7dv_(nIFd867-0D2n7_f=3fB!-K8nG}! z)Gub7J^^czBCo1l`9^Z<24dN@`7&Dc@4}oq1X>L`1S^sYd}tYh&;0hYka)pfWmb|X z$^xt_j#1^bcON5G687&?l(WHhXBob@X)B9hgX^E~@Af~W8lr=h`bOsY(KqA;*~P5{ zkbGrPCBQDkjIbz$N!1QKpsjYMGSbn^Ug3ABQQOq#@)t z@B0U4Ajv;3Ojeb;<<3Ko(;|{^7H-jb>ZMI+NFJaL2%Y|M9iKQ!RmDc3eGL7GN3;$hV^>P4z^j<`XhoPL7~kK>~g)C7qSN$e;kouQl$1p zDrubt;SuL0R@h;cTCsyG1zy`q69IE%>r)x%7J=G{yTIH*PejifAN%RKYAP4d>^i+M zC5iwAk(l25JeV!=xOvVEo|T8Amd-XHTXhsTpE@H2P6SNkK|^1TTIT?tW$Z`EM#{ZB|Z*YAk8o zp@g^&CL7Em#ksidDY41F>hQy#+GXzp(+5^pWns&YZS8Xo$G)~ysM}QoTV~dwix|PW zvALSvU?JwaU;7hQ$MuDr8ZHG97DKPOc9jAO-&?EzXFH0svzbx50DJ=og+dtQW$!T}FZ5@+t({}6c7l?lIL5|0ZaodBUo=5qAa{FP5 z%^{T~#^tqK{S?u|}|l7?gscCS=>^kM%nF<)@bIs0xrrFaoCV z^S%*670jG`tfVa9Z+t|JE3?GsV2Nmncf|3OOig$JpTbGzjaQ54X%Wj%dHEK!`G{XO&rKIL9+l%|_b!%PB);K3s!ZCAwrP8`|NY&B z|JddIrbobgze>EnA(gmoiF7YkT+~}pe9h7S{e7U5tzBwS?XCaG zReeSQ?f1ndROLAv=2RV2EaGL6qwctvLCTrykfWZ0%Lp5pQ9{#m6ko61i2aJp7;of| zc?kFXHdxTZUpG&p5zu%$2jcMV{^Tk%tAJLqzlPh^wf7jX=Z&l7kPf^};G7U_jbq0a zA?ZX?Vx|!k5pKb4u_f)-*~ysCf$DZjFhW(SO`$DT zW!KYRLj>PkI~gGJlc&VRv#4(fq_PvZN`j2;hobn@$RH`a=VKL&`zdVy8;_Z1oUu=( zwy#?6RjS#9Gy?U%&6iHbSLk9oltuYgF7S0}G!GKw~iCE6wJeM=vclE(|IaG$e`|#IQssdRu zxU&+|2OetNo~&u^u+l+Q!F%1k#*!nbQ;`9)Nm*bWD@|P+fp2sewRWq%`S^aRJX&*} z=R8`ZV(k3}oxSf)h#>m+#bdu}XlPwC=`uW(8&(|^))`CB=NFT9t~jhVZC?qq^v-Vl z6uwI`ugt|o%1u&&;Rkmp>m_x`W5u>j{@~O&O%VFkeGQz>jES|p4wX-$^k|PCHI{V; z*H&j*u~_gN*C!}ly*~0XQ^XW`Gw9a`-CktSGakRG1P$MpOmuYD$krLRAIyqNT5`Gi#q z_($F}SXpv!2c{fzyU?}FyPUcZ;aUF83+T1{m{uFc&!=-*Rns`yyrHS-EUxbq9O{W9 zadT>vP8MW+mjc;EFF<8O*f$f^_l_rR)a^7^ZJ$0qrfWXlX#3QA90{2s+XWw0sst#) z7x+G*R$Sb!(2XTfY!*Mcy@I4pI=a~VbkD^txp&9b>gUl^2^4;J z?35U7{Nqr0HxaJcm1{vuM{VD{NcDdwPlh+DLopN0|E%`P@uELJaXABYOymq*0o2e_ zC&!I3Y0G&>d**lrM1X;H7w~5{2Dl>ZMt|i7{5kifqc^>1gEpcU1U75QYj>9-i+R~<0y>Cy!}0Nr+mr!Zt}sd24mQ`=y5VRCMQ^W!=&+NrN23;ILyMDp=L1#%+vPI zt#J+T;FuE}@44IC+swEnNS>(h)YF#;L`I(T}5pzgFW65BhZ^I59P1wpGyo|tRaJf^mpp@Susx=`P?Mv$lZNe$l$4kH_mX=Lrf2EO+*d-6nuGu_;A}cw~wwJ zx2M(yIqzhAUC&dYmqL0E`F*O7f>|33z0p-d&k_0bg>(V$0`7P~Fzq~+es^IL^&78p zPz5TLPa9j3RlO`b;_*qr+fKOVp|y7V@$lxH-O5b-CG^9@ENV$8oHo`#QyrJVXmFN{ zS>`-FuI6Iq{jKBI%SWp+aB7Y_ib^wXxhto~EiL<%M+zk?_Mk3b$e#mD+sB$L&Ge1q%gghi?MU{bqZc`cN2L1J#`qE! zhZ{BeqpEMQS*8Mo3aZVHUM)u`GJwbhGH(wwn7$KnXN$LrbIYQL$Fd)yk0O44LHU;Q z*!Vka>`p39#_V2LwzZ9G=4;4axd)Ga1g5Mb<G5X<0?&xhiSNrvh=8J2ucCRdkb)%ZUDmc_!If$>BR|Af?0wKgkO@OW9EGtD)tE0` z0~+FFYw&6|NWMs27C?Wc1B`h|8t8-b{l{>GkKU%B zOx%5+FoUY0!*UxxyXR);{JQmMiytE09~6}C_}`VAU6^J5g;89XY@4B3Q}85>u8AjN zB;1W&BjgFeor=7w9giyZ z^-2i_cRwr!_;honT|#T_3s6Yash%f}om;RRLX6>-^Q4co-;scSnm6@slwH63M4#NRuWA(z~E^1*w6E6qRm6x`;}X-U%g$2!en}l^TkGA|M??Q$VVa z&}--=^iHVZcjDKl-RIu*-rs%iTJIk&Idk^R?6YTPm(Sjlw|Y7n)Re50L_|c?cW$fR zBO)S&5)l!9CnpA4nyUC#fCnNytp{qrBM1ZngTcha#3Upnq@<+h&YdG8BO@m#r=Xyq zq@<*xqB?*6JT)~n4Gj$~EiD}#9X&n$g$oxL7#J8C882SE$i&3N%*=f0(j^ua7FJeP zHa0ePc6JU94o*%^E-o%^Zf+hP9$sEvK0ZEvetrP~fypPtHQ#< zA|fK9qM~AAV&dZB5)u-Ul9Jc1UAun$x|EcZw6ye%8#iQRWMpM!<>ch#<>eI=6ciN| zArOd?l9IBrG878EdGn@*(m{>gww0>FMk1-@AA3{{8z89z1yX@ZqCJj|>b99zT9;XlQ6;WMph?Y+_CcvmzTGg($p8XCTT|Ni60kH*HvrlzLm z=H{P2f3~!=w6?akwY9akw|8`ObariJ3Bi!H#a{&zp${d zxVVVLVwaYdmY0`TR#sM5SJ&3o*4NiJHa0dlH@CL7wzs!;c6N4mclY-8_V@R3INZU( z!QtWI(b3WI@$t#Y$?54S9*;jeJDd4t>Ia+w3a8tKu0%u}A%uURsQInuL`2*~cT{ga z@Jd<3P$V+WryguE>(Qw(Flc8=zLh+?6p@_x?1T1rv-Yo~uWWwNw;s_;abG1tea*8z z-?auG=kE9{bTxz>Od0kHq)$^lcOm-LdrcaaONpQtIe60njfdvfyNn^Hj~)gnqZp9+ zIW;RojlO*v?&T6hM0a1WgNVKtkqZ$C@gj&2p>Qyq7feYM_J3~JWO9^|0fLxgNILV?}tN0xe=QM;Xe~snFEcW z&}mJ9=`&NF%a_!j+jIdhc@UdfC9Z{j_|2l9zv5EGR*hzP^^D1dPOOo1nHGFo5u5G0 zwk9ODRG?t4IIZgAnrX$bqantkug>&&q9ly|d(1q`BELN7^QcJ}m84Dj>+brVJ@E*K z767y6{@nY?cihc9^zcO~c|Q%9G5~3GQZ=3^(){ivQ{d=L=(KK#kto&Kh`;TlaOfKM z|3C5lmjV_V20^^(p!hiKf%_28Fep`!Xl^o2-XG%*%-|9MUx+P)iwT3@2UPwcAcY(K zr{az_f(o|fp}Ibm=&u>MrRmWh>Nl#K2a(c2Z9yD!=dtz2pI(a2TZTl;1D~c&>T=(#ZCH|*Z>hV8Oj_*agLXu%gQg~jQAL>vL$@_cID4F^$vrBx( z#<@}LEO)4G5JrjxBTbcF0>dmC=f-s9u2KBG`N6)=-UL&?O3jFgHQ&%<;l)m!J5)Kd z>B(lML zwfF4hCTm`tk?>Hv#~rH6z{KBxiJu=Tt?XVI>6CB?SH}8E*!)0ZvvT4S`UMa~uUZB) zl%Fnb~7mZ0{Y9~_N|p)5_01bgKcA-O^cdI(&E~G-tIEuFOK-cS;R&- z`mQ8Qf6^&&4A#ddvcKdPZ~on#5gk^1Z(_8txOTvY8<}5TdpNyNaylLR*FrE(sv3>X z4>Hk4$TAwrN|b5$d00l~7>4oy5(?$(B_^EENqO`0?f&$6q$G^>?!}j4PI>jH$c21A z^Pe;bH*Pd7i?{- z4_7<`WKuNr&yr^1q{YDH!SKc+Jg;hM@Cfh_2ye`N6OJ@^yTSF#fj;jd2_wF&s^06_ zO^o-;~LQu{3xy!NC2j zca93QSFZ#-2>*Fd0#i2dAmr4bn8zmP4>})X8l(-yiD z<)vfKv^DmMyBblbqFfvKeq_sa0mN+)j6$^JP%QM-MrhSbO384?|q!Z|rM%eD^AW}xVC2mTDK3X~o8E(P zK#vmWzko!mPxobbK^%ZNu;Yxm{(xJwi$c&dJb&2_CW5}Z4*Gxb8GIo z2XK%*UH^LMs40NxT(1}xOP=(@-LQPe{Owus<7Je`O)8ZCr8R>1)|iyN%z|}NFtE3| z;j+Ate?JZGGUPqK%tpmkL$ayZ*?f1zQIO+aWok^!Q1$j}I9QLFCNJ@V`CV%4rsGc! zN?QqjRMtE(h!oL%9!qN85`PbT^zxv!!Pols+3^{~cOkG=*I)%Wm+mtBtIlccXxf$V z(#fzCKbaFfHxGZSPGT@66hobfM+GM`o8G0dHs-n9(k=c)*gtuwXFCv>&ofBYQVp_o z7O58hf!OC$o)y<4tYD^&x7lBPO=S@+d@70;4DZrlpJ(da@wHg3!(H}k89O6P`T`nt z3hg(x5EURRE2ay`@4se{2KbQ6zu>GJayKoYj-Or`6={NW{{{Lh`OVer>{_!Ko%AwAV9jy~p*Srq#vOZk=8aLG)0IM(tOj zi|4IqIOj>cbyfTAO1{&tE6Z`Prolng1ss_1`o>x7VmDU>7n!4!dCA4P$%--(VoLGk55DJ62S4an}3J2?@wZ-dVxGp!6{>Mf#Oz6eng9vXU zZ#%CpflA2eC(D@9VC#d(8he%&SkLDgF?X1zoC-8h#qH$_lv8M03aoK{O_tc!&*?f3 zLR}44`qX@hN6dWA#n>WU(Qe2<%trR(RwRXO>6oFD{vx!%Er( zSSi3Vd7R^Gk)K>EY1ubKRxB{p>l^dlHH7w3-qZ8$OZ93vq3TLr#+{q~GFpH{zFkjC zM1Q-AX__-KbUx|cQ~WR_%Akj!`&B{AI+_7XG%;SQa@E{MJ_nZe)gpU@VGhs+>vUc; z>s*Pa5Yi)8mt~FF@HDc;Xtky$$Nts%ou|}`@^uRr`}*1Ly6C^!L|mkDRB;m36$8$T zH|E?s0hUNK!fTo@TsOpt$Qejqn?Pk;^u}BRc4cFWLvb_v0Oaoa+6qIRYn|u3XA?kv zKAl>Is?Yu|(Xq`X^Lp#OooYYt2{C|Pbzo6*(wL)aNZE3~$fW8dyw)-9uaK0nilMWG z*3V}q=`P0g+5Bd0agfHZ6BFDeNfu%sm%Wt5a5CMKA*o~D!mi)Zy4m%gzog2__Nrm!CP zs;t&)c=oDjpu9U}XX=HgF%JT+p_-)ZvnshaN>6jg0q^{!3qcxyr;!cR{Ays;7eIEvx97 zZYW-Depv~S5`l&Mpvht36|Z#*FAjyA)#^xj)?6f*!(Ou8Yx?Z?(%QgF97-$#g>HXu z*0S(dI91`f5v=Xn=Po#X83r!VVog6^BCF*LCa)2Bwpwn}#dHk*i z0-n-XBK9fwk~p!7wzd-+7xtg_5GLub`L)qjt)eCIuA`S#oQnJA5!{2ps;hvNr@3tN zP?}oRj=*Euzxwg+DDZ*y5uEer3*BDu!Od#tsW;bEQ{x;eRiF^Ne}EBg1hO|AOUy|P zdRAT1@9d}hx(f+3`Oi8qljZgPQ%O&U&Xg$_leDy@@==f)W1P}^oYXVJ1i)Pa$YlW*Lj&U1B*WUH= zJ5**0BX)XyuM+81K5SLC^fLH1smhCJD=3i1vn+n!Cv~i^xx(E#cGVC+I38zq2rdFf z+6;k;aSUythRf^tRC0b8yU~6i7PRt|+0cxt6n4{n+I|V$<=98we7 z6B-CT5Haa~>=wVm*-~KP0o7Pa`x#pwVgK6t2JE!enXmysrc9$<6o;qZMl?8Z0&e$k zQtCNyGqI#OH{evG=e0Fl&H5qlG|gp+jWS`Ze7B(a%i%+=NW#3^;=s!R;AKcE2$J*r zWfXZae=gxXi6ygl9YS#uVyRB&`l-06G}+Lz5zVD?7(fNOeZIt~t5ei8n6xdu@ypah zZ|AY$(YiRniYid!}VlsuTju4^=w734w69qa1eZp;p?ps{J)cjPlb- z;YHvK0mD5Z*{$zyvM9eAb`(Ze`M`DfD-M`4M?6k*?k|GFm}@@RR+W>S;Q2uc8~^n! zzcpqx|kdzhe?;r&G-Ug@4+>`RuV5~ zm@4Uh%q8xq*@Wt07^$%E6n~@3D8M15#(IRA)TXzC(~R7i=J z_s(;kaeqGsV2D@n1wAH^sX+6h7-4S|aUus59_L{99d~?Knt_Sv>7b&PKV-8NE ztZ`y<(le*&*E?S`GdYw;(E2`#V}c{)D&t8dy<>MU{yt&yDkimuBYD?3Vtqn^{1%3G!K`vIe+MO^9I z^M0K*6C*SA7Cn3e2@$qKnLDq;q^Kl;lQrv^AgG`l>X`fW#2n+Ab~UR09BZj#@2yJw zR+#yOlNAc5lY`%tyOKOHpgK_oAcd#*D&n_`o32nf6Qnqy+QIYb6e@GWVSAqgHX7Mii zv>>>3?%nU0`On(~k#GBq1;~em(&=TW%11gk(klc-hX0Ii;>%ROYUAw~XHga%4O#t- zm;xv(VRd-s;Y^i|yfv#-={6SibgUIx9$H2c1$HlwW*Y{8!yyPP{5={hLqT?}0|Dm3b2nRaq#Q~n{*IWI9kKQ~2DI?$eP<)Lrm2?a#Fmx}OK?g| z-nsTG0rDqwMrX}9**u(KBXRttQNCHIaY|IgJoB8ZLV^;=lAMcXJcH}x^jY30RK!Pf^n8FO6q++zjO?`Fl@nQEFe zQ=d&zW}QgN4c|5k`Bxo=eXDeuwcFPLJUp&er4$cuMTu*3`s#0%K`c%&5+)^+L6$ zV@76v`(K;Yn4m1$SCIq0hNw$6l~$3zh0%+tXGoVHgvleA!`@CS!$KWDQq49G5qz!2 z$@*1>V*$?Dg%z8f)jltuG{vVydj`-5nOV;nxy7acU9w$U3;)=<+hx+s>lc9l9XQwl z$uE`9AL-#la;JhEdi)*SzwR1Bh0WgLL%7nuIacQqk7}n#SK7X&>uX#blA>fH)=451NUE;cotjT|??cr6NYiG8s7D*eneWb-Y7Y~6J3 zkC1>-3@-BHL|G;1Ibxbpk{FI@F4tkF(&6}>p1iY~jUO5@C(zLS_7(Q{d7I|*3ccbd zZ|J5qGTs!6`i_q50h-v?K76)ds?gtyewS9oPy8AU^Rr6`<8cb%=_>p`H<_aHTvpUSv=VJqcco55} zIx>0?gk3*&Lw&GJZ;gT_ZCZTt)%!zNS3(tDnXcP(}A#Wfc(Iwd?_@ z5>nNv@1_C5NT$|?Po=H$ zK+HS14%&J}j! z#JQDjjJEhXBod~X$+L7!uP0E?N7lp#J$W9LB?%&JtQNM$s&*ydqCMGht3Q*((-aIz zcAa9#e%tENHRp3ukGa&7Hma<|=Utl&N!%)A`v_JWk?LxG6i65?iXD_1gQB(~xBdxl zIb(A7Hgzr}oJwdz!fHqDXIT8W{9s?boj2r(+Y$T`Ubhh!?)Lk1U@H~yYrp)WmeT}U znVOH~B#XwhxzL|gOvV>{c-FB_izwBs_Cn9Eya+hi%WsjC7=eCN!=&bin*%F7{*el4 zLu)y-*u9zF3r*IS8>4pe*Xg5>6NKP+YU-DN270P8X#b#Zcj+K4!y(IH!CmG^3v?PS z;mN?p^Gh5nT$7!)QY6U@_)00{gU1^>&%FzCqpu*W4AWnkb2~^fp9`-ch=5mu#$R@$ zPS@{bfV1t>F6i{lMKnidn@OTz{RY9w139~u?2*18zJcW=Nqet^IJQ(1A1VNBA^<+d>UPZnJ9B5KAfW|XilQu-Rkg@c9ssE%qpeVwgBr;7J=d>mutpdri zX|X0eVr)+@;X>0_rT0yE@V~vuK&O6Zbb6&bEirA|=AG<#6_aO|>{i~sF-YP0Dt_Aq z;FRu$tdgXx5l&(gejWtTKdIYzts7QA%UkYRcMP10%^kK+~gdB0~bno+%}U z?vd^}PI`2i%E|6t1wqvEH9V+;--uAIbHbF$TjG z!YW;Wz{Kb7s$kfxThuGnv8S-xZdPaHmp+1z@{TQq1i2Bp*-xMDR_?NRWaE<7ZhdX| z_@i)=8OohaYu*0yskK;Cq%Krfd!WOru^~H%X;WWkr1zs^SSw3AH=VJ`te zs1hQgS^0PD1b_wScJt^q7Jo-s8E$=}YoL*Ytd~2Z1+DJ1(Y*Fh&E?Q>|JU!~;6u-; z=dS4k6OLru8K3TVp%(WN0d(v=|5AFRxq#6;VSu(86ih?;y<`V0YFwx12fnA~+Ck{L z{)JDp*k$!h12r0e)j(ebNp5`f6bsVI`Lpe_lfqg6E>dNQBpWSFJLoahZ6>S*>2}xF z#Y~Lie%0Cozhzm;y=e>nY4iEtohGv`3 z({Z5H{pcCP;rTpg-QvP#K?+m?vY6NX6zFih%h_N7%Xt|DB7J2OpCzxPEP;bX)|8mw zBq+#*=(NUWx|Vvy6OQ|>ozVCXZ_Fm-?;5tiAJq;|>n4~S9M`nWJry(oQ_iKHc4TZc zG}t(t6pHjh?q+I%Qa!v^ISw>)YbFW2uQv-vFSwb1fn6B-@*2mV`7 zvNxS0*hOvQlkJqvk2Zw>(;#Z4G<1X4@XE1!R5aC?8OLKqucS&3u=F)jwbZ9_s>&&?svp`=X&KYcyuH?l>J03etYeAcjrT)-SY=r)kbqI<5C*yieK zx|6zL;yU!0<}#Sb##vV3pYp-&AP$JnuH*`eeH&|o^)fsrLq8<!#`#X?uhxF5%Z@rK9#b47|VQQOB0kZ;#h#H$e zNolq7>$oFgWRK7BhfLC%53oIGbbjsV`~N}4LdhNFYFgG>XG z=mIF+PaS#u1?wvmYcoso>CS!!>QEv9vfUz;cT&2oZ@6A7*%f>~iaQAUbDG%E=5Hs} zx=(I~7DT&7g*y`5sNds33=}!#c650%VG_3wzn@NHw@Gfxj;MvVDNn$6G9jY=8>xgHCbC%OMyn&<~<8z*nTU!IygRzX} z!za(?T6GKmKXRU-Wt-P;K!(GqVPRDxBXM=!WZ* zif?_1XPE}-F>dd@N^RBOH`0LSr<<+Eqy9N!1 z)`*WSc;S>4c2L2p4X(8H4)x9thJVNG4s_-h^B>t4X+56zWw3e0flk;_Vyop9K!o@C zHfC1-Hot+_5j8bGk4EfsyFVfx-i3N9(qC?7s3^QnI0U@p7d*1fFJcBhkKK)6xLGP< zM%)DghjHH~&ntHIJ-BII$*epiLtCb4-a*6RLa^1`x6kKU#BPk3rY)5Sk+0s48r_@U z_NKaVZq9yr4xTwR@E6Wpx8#TsB+C& z3MoibZywb$@x@;k$aO#gihqKY!$KSx6=4&RF3;-;9d+Hzh(KDk2O>zzT%N6-O(1j{GhITjTb?|XTIWHm1EiZ2Dp@EreQ;$UOR=ym-B=w1pN#EdwA`f z+n6%m$;{N^0e-fR{Us2-2E5`zTW+QDt5CV4?$zODg7)RUz~V_hf=6pI_M)2d=-lzs z{(y$Um#$-;)dg1xJfG*qxFWxm8_Y#~MDshYnSRW4ad8WoF*A0L`XF=*3Jab+Y-)J{ zMKR6b`_mmcyfjK{e7;7B#Qrltb(5}4Ci=YPWamQ2&vPPOKlJH7CYjttl zguQ%=V=K!$dLw>JPz~eWu53Q6+Tqdld^X`%updry#Hgc{CYMl_L2`2w%<9kDuoqL8 zhRXeHyCG;CwZzG*b_AagtuGk}^_N@R>TKq?qpZ|#vJZLkK+A4?_$22|>+?mwmhQ-2 zc_Y}PFZp!nvK2iC&Z+rZys_!1}<1S*{hmzN4z@oK|0B);=(435W6O@F7U&=CH$y>S?%LL6ZF9e9v7@ zH&1rkL_jwH{)9n_@auy}Is?D^q4JKoHO(;l=izb-AID~B<+-$szBz=#nGkCGf(Z%O zG&Sw{&EewRnqA?jA^|{90Ga^X$mcrE+h_=QztAf1^WOej#bIBldL+N>r0-2Biysg4 zpR(SM8iG=_(;Z9LG`XWPU*ykjD(`PKQwV){ro`G8pUYJ{ZE$vsT3S;2^sO?Y*6P-- z?`^SfI=LMdK#k^v>Qx~Pjy;OQorh>o4%}4vfe|>2|86TWWb`JD620*yzL3YcJcmAn zdK{+M({8x`XBex1qwM;R&aHBSjy+fQ#iX@;v7!*2y_*EvmGPso#KlCo0XKRge8z zU?N`OlUG3zz%WTdktv7wLakhZQ2@n9wp%%!yGI;Xn}GXb=rc->{scsFg4yTF_ORr~TmC2!NI<5?=oac8SP_UN0(Xl^m;rX(GBE4!j_Xggf*>&cn;d^EBf2 zBXK)6k*Vjr7(jbp;)?zE$1`AzL`y(n)=}Q@^5WDQkOh4Y0__!UU%W2ZWro`E{9a0) zNA}RK!fCw`H9SeTDGy$T)Vwi9VM2CTyuQzp=Pkb6V1eO64U|ESd6>Tg z41ho$>Q0_F*SqM^SCAnfJr3})w4*He=*_wX z(Ss1UPuEoqApZZ;KX&geu2lEqpI6;UUeF;dCn>a?JP-FZlD)kUXNw6U%Hss>AuPiM zBa+Jw64xb(`r-dtx^JfEt%tdTX(A2=MDpWBhQsIe>A*%cXdOUTuL`YuGE_bwd#gWx zy_J2N2K@%^GvWF3BT|FtJrih85lAmw{QG~k+3++)Uy~tzq6_qT8&{7v%kLBw72GDr zwxZl=gWuGly3^szbb2VG(`{0G!kcirfMUbD|2 z(WoM-V5J^;Q==a=*ud%Nv&|qJHjRYyo&*T8rB1t&ex}~#`pjnR{d*4do1VB8cJlxKt z_*UlAgq!8^cf?-ml4A|l;E!~|3fb;ux+;b5?~(@WRw~9^Z5-r zxpYR|yskSgGq+!QdhfU$RB~|5e>Jk1=*ykh!n95oz#UTecM&~~T)mL+4+Zr zNca#8vu9Q5?cZ`wWREA$-i)U3%czg_=@j0TPrSXfeoF~<{jV+P$s#7h z&p4=eyU1)A9{AVW4tNAaMvh(xh1buMV>LgDx%n=2qTVGP8j40nYt{(vQ3fA%-I!$D zafIfZy*({BKRR>X+B$!|9X?L33SE=~%NH|M#u&tfWQBYMXgCz2>JsVD0!Tq(7d|@B zX&XJ-yS$16a>9mYUTuhPy#Pk);hsSqNO%#?w>(2tOoKeXNj97-v$gkB_?Y?&-m>3C z8DZXDk0pN%CFwul#)r%2-;l`)(IxiTj?)vmf}nq-4inv-u73A?Qv|(8O4NhDxJ^!wJp1= z=}t>o#SjZW_jBg#PLE!{rs-kM`ptYbs6ID&Ugi1rYmlgA&N3rAGv6)z3mwms`=L8Y z0=9=U;U6PU!3U}m#6C}VzU<)@m+d9MtT%ofGWGVClvC75s(B<%+4CQm(E*iMh3+SZ z#Wg#X?J0&M@;?n{%KGEdCSzP8`QjYeTGspFK4(>5q1xa}Xa>v7h75>O*Vqla7}`ly z%CXhk^ZqN=S^$Dl1XKN9PIS)AV^yY1)0x|2Xx|E&-_opk_$B%VqEu-#7k2ymrzz61 z@vV8Pz~O?4bddDja~fwC)RQL5F3?yMM-GeOdLg zURE4ZjMZ)n_HEMkIX3h5eW8^lvz_7yYwdAB**0pt}rI|OG{|GC};%W^Cq zn|%AA+(iC(Q5BC4iq zgLAH4Jl+mX108(R+yvXcj11T~eP++_{KwWOuMAH&T4G(wXV8c)U3L5bZXyv?F!%wO z^D)~gQ+~UkJ9TWtVI*7!4R{z=5<${5UA|Wtmv_cvKRf!*90%nbUPf?Dq>k9Dm`s^C zz?uTg_QsSe--)lJK?1fm|*4CEWh3^G&`l6R>NHM`5z35Oc+MG$v47E1trg zB6yI{VeS}?FBV!Z2^XuHdPUU`@Z4?7YO>T}M5zwUQZ&TSWfFujlI8;KIb4smIL0Dz_jIK-h zu4|~2T_39TkSN$TV!dLZ<5o`0Dqjd=mELM~s;gMxRO{L*u#<6agFKr#hO7H7cs01i zmM_F@R(A$ttqToJ^2Z9L%>g^Lw{%nAEaI$u7q@gqN;E)z8>;L=QkGu+!a@XLs=|}b z`S39BI^q*DSKp;=3%8xIZUIlBx(yu~YsfV{XJ~iVM-9xnzDTZI8=R^>K%{^mKJn&y zBnxJRH(emQ1HOP3XK%>s2|@>4f>CZvoV`zxaDRt3#B_|Rf=g~wY#~ceEZN;%%oSd&dX?T5UX$94o@GgwR zt<2mrYXorD*#8i*=4YDADs^l*Xu9$Y4sO42wLBL1=)0J_ICRnd8eaBg`TBJJ{hkI* zMTC-{Fm0at6R<6NqIA`93`*3KGjKu%g!ziMAu4`=+Z-sF#3X-)F>>QxF`&g)vdI?( z+~>LMV05HEsPl?zHL}zDPR}q`poI&QerQB%vcS;>--)5H`LRzRi@@lU@zw5)U1k(4)B>l%% zHGWQnF71C_Hzbm!)7vn%-_Tq-B{TG`(06|E`YoIa|vUvG{;xCL$Pvc>6NZ1=AShA)goL?MC8aPq`RaF2$aBSo^2~x zkzt5?C1r=fD%eR}4^)8V>;#hTl?)Whqla2;OL2-?#N1f$Mf9z1^~=7*V}qO&r|0+P zu16D@P(6S~$+LM6Zv`=&uI-uL^QR=b3lM(EUDerlUZ+A{ja{|oYyr_Z1ybfoD(n+o zXH1$mQHE5f`;826!tH)gTH7e^$RS72xfeVV0EH``v*;6Mjrv+_;=+|F1vYl^w5UPI z`P!T(U`nAwAPM;ch}edJ0(p5mb`S@_tE2{zesKjhjIv*p@b)V>c!1T8%q?Wya7vnl zF>f$!qS(K_5XR_51CUi(BH~bmj?Q$3{6I8Fty7qEbfUSlzrsn(nazpzG+K>J6J{GI zR9mFDXv6fBkXE=yYi$Koz5VS<=gZop{W3uIIS)`@ zNRD79>Hjb8X#S5b*a2rQE4>io!7FYIhC@kr(b3OwNzOd9JT=nJ{u98W1HmNiR?HD}v0{&+f2dmSxA^J=rV=Mn~if zyxM(lZ&Lt-5;r`eCQ2Q2B~8?P95 z7OCxT0jc%}_{mMA2B0%Q>DQ!ZtUekeFFMKBB`u>9D z{loI6^+hyrm4PUf@)0yh7{9HG6Gdu(0QlxKgVzWxC<8=)P{0y~qpTv>k4{vSLP)&G z|NXwXntxo~S#PLhsVA`a+epD-wMYC%b;3mk%V{J|QE=Y$3il|EVYi%heIgvW&~~1! zZ2FSMdpfeGc8+tjp3dul9d_76P(}-tI5&%9N%zRV;Iov;oN! zMPb7_r*@k9VTwSYdirnI=E)n_fFB9CkVg1jfdAnYdO}+A^l57fb8FY|_rOpPEe`^( zr*viep$Z?(ANd7;S+q2NAqN*PB~c85A2m`Quk>4C(@3G`L3_c)K;>*IfFi+#z{^yS zTc$Ilm;wOE&?o7qy{bZE?5<199R@#ADds~Q6BZo!yHVIf`M6k^Q1sf*Tmgjo2*O4p z>E~K#K00J~Y$Q&*2`u1pkYJauN5JXp^*clWcHJOkE_0T8F~nUL^c}#f{=2_F@V|49 z^uKpY=bzG-|Bt41;Y9zr#vfhvfuMFw$lNCXl&ezM5t8Q}?oh6TR2++fLPy}A7H6nT zTl|703Zl&#Fm0kuNRLMn%d78K>TtzePs#t?%NgjkQ_$D`7B&6nnQ$9($#a(}Joc5#c$%#?_z%H$=Tfw>AiUU4083VCTVdSyT9ew><9AjMt>5Qi) zIP&#Wvj=XU*ICAo@9OucA+4M?RJ0o+tM|^r!dS7x4r}LjLDhrusp45h-u`kjPdoUj zjhHS{4;5p^kvUiyq=wVRVP@uKpkI-ve$S*EYHEU5O$`J5Zx)`J87*H?^XU;W>j$c@ zeV<((J2*aWZ&FiB20)&PJ++1b$SW9GSlD_mBnU05F|*S z>Nt?5Sm)OE-uT2fz}I3S=~n>iW9MZNKn?2rbPS;e)fLus7r;mX50FsR4Y<6XAN+*h zZMV)%u}>EMQy+OmjZtSZmxI_CSK(o1c@*5|9B7XQDBG4(=EMeO*e6x6xk?SmEei4(t}af!hww8FB7{td;(&dtpi9tA_T^O{h{` zel@%6z z!UXUG1UY@~QJEs_=w!$0`S^utSWikYNi&AO_^@FelIhxP?93!5z0NaA~9 z<=t8d+Y~EWYOaUsjcQltML!m$?}@8R0~ocPghoY!{*jOFQCREqaF(&FQTD!HoM#09 z=;$I1mSU%Dx4b1yR^?UZBbSEdfII zmC1RCyi@~US+h}Oy$;NJ?w05}t2Ia7si@qF^%>J_@9EQ*YXQ39A!%~fX{q8?zP{-% zdo8qVP98lq(5eE804B0GXH$D~w)rrwdC7bm;fn!3eH;8k(KOh$<_+6DW(7p6@!d&( zk%8+V$oTM4l|r&#ct^=57-d zfC%DwsY9)I$ht-U2uPYUddE#?=8YBDR*^gR^;xa%dLGhrokrO_c3N3Ex~pmS;jdWx zh~b!xS^K%2)`%DeR+%xIb7iI>5$Z|i@9MPK8hpov)`Z7WrHHR8tY3R4t^KR0HFW&$ zY8XWVA7bF>!G!PlNz_PhL(zdUzZI5~hpZ7)SN<^n(RAy@wMyVt4k>``eh^ptJVG+$$nq{-T2t#U%|2F z?`}pDR~uj)>$T;~Z&Xaki^Z3&!PN^Bo#enq1wAg-2dI(-mtr8HSe^i+DR{_~Dm@{q z`kAmx?ve1w?TtRxR)55;J)M#V6GqQ#Ezj8pt~w}rSJE)Pxy86UJ zV%s$CO2Z}3wl*pg*Dk+1iyGp4DJ%<*p`%)pAqL>#^Mm%an!|AXr zo}9$-|5bAB@l5Z1Jkx0+AxUIJrzDjWSypJdoRi#2cF5(RP_((k3~kPd++s)K9Mf#g zEz0ChZs*K3mm!zA)I6KH#f*iv+3flCdj5O@%(*W(8~_RZHK~w`abj~ zoeif2ld+PfHD{~#!S)5$T4acY{?d;KNR!K|0x6bTw`LU5mv7cB8}kv{aoUguiR*g* zfN#{roH*;eeK+_5ITOs-D^pATD;j{1C{r~ybJ{_?T_G{Wpu_`RB}G2?L?n20(U3H9 zDa9}Y%|gw23A7IDgONEnv%waS??O;zl!on=K}7qV0vB#z*rMg>6EQaJX8nzI#xVevB;3ftD9E_<}bgg@`TL zNK|(7@@?Vo7-T{7!oB5tb9LcGn+0`IRyxl!(c1QmkXl&+KW+G;Xlfo-g^rYQ_-?$b zsp|H|T-~?1^rQ_sWmQASShRET$*C7@Djo*7H~j<4KuoWTJ7A!mVU`UkYP2kN8N2SW zt))4@QHj8y{bA%;%3!lCp#s)z{ue*Z_}tWu<<8*;Ai{U&N~eZLZ+u#pW?!hyV8ey7 zRg;_nwcFEs?4_-`I!cR7Z+_HFt2RV2XpDNvtZ!@Qv0?u;fNoh;HXI?w(x6DlBKNeu zdb9c>ydt;t#2m{mzrI(+?pd#UI%;ZGE(o}g{Ni+dqDjV97T2+G_h@tWgqrGEJF5$@ z(I5HR&82NCg%Jl4*R?MwK}W26=PSNw+qgfn9CABD`$ns`vo@A}uzD49OTa5Az~j#> zPAJukfd;CZ^#&w=-k7KI=9l~oVB*>RLcBDKyKuzTe)82D-_uPt7gvXFGJ~SdYYzd` zsweaXa8WPn%T1qyG#j%8YD9q2xdWkEe!u1lHNb6U9wv*g@=!eSTRfh8Xj*BbYpc^+ zpzU#cc=k8u6eb5}nVb1e=k=Bug7IO3`BRdnooqdS{SJz^(Lc#Q>NC=@JLqGM47A?Od+qT8g&s{h7nOeJpi@p*g>{w1RLUZWV zHWHngN>i4|3h2#Sc%HGA7T#vXKc(>cq)buod?+|6QJn_4JbZpDLn2MkA-lcQ-!Ti% zc7SiB`dI3chYhLCC#$L)QOkNM^|SkeDr_?oyX(ERE~yGNiG|B%2UKx~+=u`37?f)A z9UoyRW=GJgvQXwr@rA7SX&YHY|EBEFuU3-c+@cWcG>C9pwm_2dxk*bcr7}8`UNX{qy%?|0| z0Pr%kHm9#G!ezs&_#%6ThRYX}0jtgBDt--LPkfJ84YjS&C;3_E1cBWbE)*0~y2%{d zql*n_xmIJ9{BtS8OsAAuozuP-H}bzf8Qrn17Z;BW@ygGsC)tn?NuTvJ-Etw#=E749 zI~qDq@Lr=Cmpt+fhI;@u3GXaPoKTSrD|PC8{FDJ1YI-};uIJHzW_;V#fPp%g7u2F0 zu@97b5b($v|bEut)FG(kEro zJ=d?Ub0_e~XC-tDEXs=X&e7f<=K8Q2L>ZF=CFM%VdRbg>+aoN35Bs`aI~k!T_V2_UBIHklKpzt!jO zk+C@ei*vogYT0OLfTx47^uHydJ>6%Zt8O?C?uRJ7NsUJR3y9ATo0I260Wc zD|ar_-WFu;o_zeaddjpa%JdRhYDm2Ez|+>Ixi76GVsuxIqA_8UHLO zb|}s2T$zc?XrR3Vi6lLh*=8hU%QQr3(R6&Uy$pLu@%6B)=afs_g{6qEA7qGUJ2rZp zwulc~V{*e`rn^mqqTL5gfu}1eHbJmn)iVjPRQIE(OlGz|$?dq(1}KfN<}3S;&pOVbn;?Ccm+Y4hGY7Q0q?$z2rVpKPqvbj6*Ay5TBoyC)JXZ z2c^bAwK6#OaZBbQLj??mDkZD&l-AHwwppjD_D-EL&KS`lYwnP2=% zskT<6HG1VdD)6!)mIk9QQ3Y5?n20;ruNm!r*HZ9eI>k%(npIW>>A=v$=5Hr~;t%wZ z9Z|3!{>|N~vt5QL8S5d8+YP8~HLYT!3UCu1F%4c^K2S*QqG@vnN7hOH9r#EFkg z#})Zp089xdzKg14t>Vo2VCO{@MLqeNN{GZ`kKcrni(tH_!haZ8so&ki&sY7YYG>|; zhBe`z^=|n*jBcw9c&q&RCbEk0O*1Ywj;5w$!Ox1I@7UzkY9RGv5}(O9yvK%H`7^*2 zpc7y$OOO%l*8|2EG3pPu8e6zp*vdNa2x&=rui-1W1Kqcs)&>+k;sx5ZB* literal 21808 zcmd?Rby$>b*DuTrDGeeh9U>^D0wOUm(jg@R(h4XY(#;?Z0wN011E@4ejnX0wB7<}c z9YYPx(ClmQzMtoLV;|r1etZA7|M1|N^SaJj=eg>))-qB{Ly3ZvkrWRPk3vOR{t+G? zArub}|2qjj(9-;kYZdr`r}gl$0`L-lao_WP*74*Qc+P+Q&ZE>(9qJ-UcP*pj*jlil`Hi0^b8CPSFc{ZcI_G?BO?S%y}g5jgQKIPlarIP zv-8WBFI`+*TwPt=+}zyV-90=!JUuiii(SiOG-*gOH03e`BGL^R$g9SQBhG@ zSy@$8g+L&_e*OCG+qdfK>YAFG+S=N>y1M%M`i6#v#>U3)-@i9CHU0SUqq(`crKP2{ zwY9CSt-Zayqobp&4Gc&WZvvYHE^Yily3k!>ji%UyO zXf%3xd3j}JWp#CRZEbCReI0|rY;0_7Zf^#Rlh%(lc@xG%VaVGqN)p|Lkv=Ih0!!w?3D1_9iEvSUD?COX@fxYN$M< z8|K0l1$&k4(!mkVPNGdss0{^y;^pSw2^*`hcb<5@C>`bMTokK4PE9_oGZU(iOhu(T z!TcsHJQT0u;c?4i@$jZ~3Fz?XXteOO3Fa+6iz&EpPWFu~TDG`_oohV;Vse{o90~JP7t9CoDQlKT>T!_bZ7dAt+Q#z=of7(zWDU$*pt+ zc1aT6x@Fs~92bkX)cySi1m(f7AE}fGQ~Hy!m#T)*P!H<=8S(zNZ=?Y#)y^-6W55Q3R|MuWM(j&L-+NV}OzE>{y zY$^vkc1*_+YE7LzL%2!z}e67Th z|9rJ>X|Q`@=v{c|?5e->!i<~0XG6<5EnxY`sdTisgYoV=Fy<*6a-q-3`W0Ud0LmvC3yd-a&-HCN8OIum(L^@M}T zH$@NSC$|9_G<>2%sqXL0W5@q(>g zmOMSMi_IIfcx*0oNL0B(70ny6;?SEeA7~n><)EO0H>%J$jZ6cFbOWvpF|ku-a{+?+ zqL4pZc?>p0DF#@lU%L^`)OKqWA?4Lj`Mi+p64@PK8mgO+&_G>;fz8$Db15hf_HP3U z`-LK1Z100(T0(jMP{Q!A?%h{+>l;oLtPvP%E~kLJ_iJ9fHXKnf@FqvASiCga!)PX$ zi!zLUpTc1{yBVExlb#b`BR2E6T^wWmsQ7h7?zb61IShCdq|fR9^O&F-d~U+g#-unH zU&9c@kK==K;Zhb?^ssqblpup!>0hjnawEk0_hUa{ukD1t`Q>+~rwQH99BV)It*F1+ z3RXj{zwn|I*qHzpwEbr7_5Pb>pieYZk6O#C|83js3}h*4wcz((%uqrvb4%>Tr8|(n zx#j8V^QKak+>ccxmc;*_METz`#XqIez^Ps=UfqHys!^m`7K$n@1%9ah^&<}U!;ZvS zkw%Lrkq&=yyAETPV$AV>EQkI*SqAO7I1u?W4mh;u;y^AQZ9g@gmr&19nHafpp&Pyd;-<3Z z$!m>38bN?%9sc$9&&U$cAwT^mVuvu%XhDW%( zC&K@awBUcZeIGZvD&k`c-Om*OS`+^EEQ3mv44g8&2dJvd76z zD2N3@4jy3k06;%qh1bbX*ugbG#)rgMg0+rr6lWgQDt z_#Cu_#r@nNpTKR@BoR?QXyWN`N3ok@=o3i1JHiauAT)&}&(88D{Mk(IW})Ty_Oyyk zx1Q1kP2Su9uP};V%Io`E{3p<7lki@&0;W^!z3s(t!_L_7xBt?bSLS40OV6UnrvB$} zsC_>Y{})pXj#1<=Y)jlH?YlWc+DZ)wi@)B-!x65)*zlnU&HT*}V+>@-F0Fo7d97cjM{Jqt^D?NTbDG z`nZnn3tfFed@UKF+qrjkClS|R*sgG?-E~=-^`qn?!`MbzMv2=~oka#vXi(j@E&FWF z!wC~!c-wW3Gp+RPoV2#dPV8xA(3L*Q=LL4&0usBlJk2jyL4@0#i`G$Ke@)BjvmYQ) zM_O~s6>@{Ds8PYw;3_6Y`a2rpaEn*QR5<$1X zbwa(g9k`vw!c2=j7z|{gL4J4JhO%3)eQQ*P_Gs3BRVGK5Yv)k5B`T7>tQb=@MNpg~ zw(~}Th5Fqc8_Es^KD34wmeQNM$U?(Ov}uhbcw2-9^tQ7dtUc>v0PWznol>m8W;K15 z4H6wn=lnHA&4x6Pb&D1#rkj zd4@E1%QemLNgtU<*INjq_6iwUS$tCFB834(=Kb8W(K*WWenw<%1>wK}{MmK5MaV&n zx1qby=36zVz>h{ti&i9k@`rof^_rpKthzH^vw&cO28fG%Qu-o)O0V}~esT>|CH?Ih zgH}#?`*OUTPeeCM-D;qvVed4ul{mIOK9tPzrUuAdN*MG7lexnBNgA;i_471kQ3crE z*ZsYSkUXP6RNTUfg(bUf&g4bwWMnAs7gOx*<#8tR0L!(TK1zU`noN00hL)@eSDCUM zA8Fo;1#Vg0-rHh%S9hv}hB$i*ssTwrr)&?=8AVPGLmnOtjkmFW@}vW%F1~`GT^|6N zx_nyNGD`on&EaP|wZ9&OsQLel*st)+znHlZA%8D#El>-~yQh8j&~j%`Q$!P(M+&hc z&L=-dwaof4pexs?cQ)L1lJp&L4IG!kqxCSu5cyLU(<-m!AzPFVAZ)Z0pg|{9*dFa* z#b0Nt*sD?dJuIT=YB7&h=BzeY#(~$b53@~|Cl5I<+i}2QWCD~9RF8KNm+i9Fc zO1LiKQ%|>2TCK_8H*TSlPZ!O(E9EMrXO{M=#sH+}f+6*81+g zI6}KygL$^HE-=1(v~QDi6kRtF2w+9lH18WGunae;kVia7k0n;F$ z>(Cj@$tGmPOtVm$m_}&G6g?WJb|-M#c()|e^Ifk17%J0$CK#Xi$Y-lUnfNF?H_$w( zfy0&*y}J(yHe*QR3OT6QVKJf6`W~vir`R5up4cKur>drdr(KXaFnD^${bwLJL)AQN8sxpoSRZ`930(wr^!xxk6x`SH3# zjUvdz2sKNth!HfJ@)(Mnkm3BseEcIhZRQihfT-J4-eQl8m)(FZ!>B${qHTVhSPpY? z+#}F@5iOin5(=vsuQrds(BKKFvLMM?LZ*i^W~E%86&-~21BCXhee!w;0-jdL$M>o7 zVn3*YR*ErTXd0bs=)o(di_e~w{;|l@hY6{9+@aAe%R2>1BzHu;0tRnp$KXa7{tuXf zC#nL^G(iGR+)JBXMm@zRwvX~Eu>SI6*$~kE>X&6jY;HANlRAvV7~qxWKa)FBSQHvq z-XXV8+pH{{yBH0JJPgecm)!I?Iagty8raqTm7cS)?tp!;phC-bh>K|aGTTWPz za%|w_$60fkWn4_egb#cDKl#2PKVA`EU1F?M*|;i7YfofCFB>|Iq zO@_z+bR#1;*_A-+t@4mASF6sk=Z}wQL?l+om@q8hD2IHe%D!lVYPLkyA~N~3Dz&HB zflF6yozVvn0veuVpYE-Ly?sq&m=*cG|AKmhXcVaQm?pi-HhT=g{AAQq&3{B_u zzp+VO#aNHq4qIN*K)aI2t#F<#e;)}0(m&Q|5(25bs2qZS(a=N%(x z=9mu>@bG{eC5Ixx7ayKJfI!mSds*j3VZYLx^R3<7!vhs!`Ic}~c5nw0>`of)N4v*U z>(hmpWS*=WJ5=cQKUkQ(;9Zw|%WRYOA9<9QdFV;EzPF_KfArnoH5O_)4>d!vxXMml z1?VOMoHsy})XV!~MRC_a9Br$toz+jFNm5Q{P=ETzw#>~_z82u8@5bLnQngk`e0J)D)(RALcNsr z^h!|SykEpg3nn36eKxQgQg0$ylsyLN` zoEvS3EV(j^_(+CIA1LPGv0-mfQDHdaUP|DuC#tFcM6Etv_XQ#0(iHzS^P6gfaM*os zu?!g4CoS!p=k7qmC*NO&4yqgB)Wh?B<#bM*^+sNgoZnRh49~`fW1MyxWo+n-@0VJC z&B2{Ll^KDXTJZIzu$NaNhY#|OSwgXtWE=Sn_Lkv4 zO6K@<_~n6)aZ@H4Xl9OvbhjBZ_g(`tL4OJn^WGL>r{lRRjdfeGrW+Z`XBe_ah;J3u zC*6jt1z43)#V#c(Zk*ftUBK(|&ER(ke3Pl7L!F8>9SMXsK9%V9{{{x27J{5NU^ zDCjFY{Mtu=p?@Ed@zxqR-g#ZMt+z9?ShCpDpo(*4FNpYv6y5yW2jeKkJ0<$Mue`Bp zGcPUU3l1;*tNSOiVgZFhr-Z^Do^|e_(1f44>o0NMi((iMZ zt6d%p{ljiDSq z=5?xF0|Wc3*wl^d*A}}o6ac`oj))Yk8?fnhkfwxh=zgGF<e5WqksBtH%yy_ z8(RxJjBmd1XyMumzRlpEZ0#&1u?%vwe(ByNf+G!?tM%WTK_~$-xpO|=__);60Ln6Y zu#4Ws-gd?JjoB@usx?K0h}?90kyM*4pF6lGNzjuwdjk#Ay`H(-RsWFa*Nmk+vo3e= z-t_y2`wZ`m4vIJoD#=MxkbzrNd_=J@`X-iW>AI_{v415Quk-Avg^GU+#U-^Vghx{*_m zN!_m0jc+Pvtv*ezN=)86a>bVIaw>m%>+#D3#noR$MzNN$!Oaft>Jz(VQOB@}y`GUJ zH#&}>uDIN2CVtvvM0PH2uW4;Fh4c`RcrBe`&H*@4Vqg_fJyC1*kumVyjTgoBcCvKP1L-i1BUt zsT!%Kjw?F2ODL`5@B5qXk#Kw_>MfN}sCI&>Ptxuy?qe`ba_3CBlt)v z_ujWG$EKoYOa3k|-(wyOFn_R)opQ>`*wXc|(hnq{%h=QVn0Z-vGj&?l=r|;>AqjG5 zP`KVYv(;`pswivY>9MB>oiXXotVBHfy-UqA-j(@U`bS2oFFj#9v3z@Gj>V~JZAGCN zn3PJg>}BWiYL~laJoiScX*Sxw4pUg$Q$BJ4iM4ssIEGnUiH3nOB14GxO+AD;9*5PU!j5kdoLyE zY4_XDITakQTx-Xy;hjq!ER--Jwhu+<;SVy#H0-95%ydVw?qlmWSyENLNJ@U+(EA|d zwx8m`JBTf4m%m&oIP98VXlRcyU5ONb-qzi>rN4YN1T_je(}Rc`e`t`@@jrgz<=G8^ z?9%_j{LT@!Bs*I$wOgn8TvgJ@HS7%ubcHPn3y(-eIcD$0xdK#~&rysa& z{<81;u;*$rZoCGM*~pQ?G&MJ>glohOk~SuQ&kAhgVC=^D@qiF;w!+etr^>>6u|Fn1 z;F%dY7RH2obm*rZBG{I>>vKNRAmw^CE;P7x`ER|{XEouSuW85QcB3Y>0cCIhGUdXI zNf2_V^47)PPTne8SMt%>;1wmPbb(f|g_}%jrLAAsX&-%{U#%Ua0ps!o^Xe5cv>q@x zrS;%>8;jY;go{Cvbo)l6mXb_T1Ye&iUl!GTD*@LD`SuM(A$wBa&mwbKok#pEgaH7# zy~XMVdxdYxM9nJ<3OAoJ8)bRF9RSroFxIoZD$ttRq`g5Oci$ASL3T&)ZAC({R+Gan zKco3aZsv330z$Uo`g{QvMKKj5W;%{I) zk0aA0LK-nM^9WV(T>&uYGN@&k&98b!XRh7zFyT1+^Tob-WhRPfHG@#_Q8Q1x9w?w&*87P$@kmV@5|p{zR+9H}=GX z*KHsIBmA?cx3Fd$T%(D#cBa}VUb#2u6cVTz&O887m6p6GQbWw;>gzDx*MGcyPb!-v zZ=C+#r_W{bNk4Mgqlon`uNG~e9 zN8n$I5lyOh?T`FB7eGldk%+Ay!esn03`Xg|_*F&{M{=9viGA~N0#x&SsR^PnY;uvX zy)BepkrfGHoVf1JWJUS_y7%P8agVGcGP+;cZzRODz>#;S>8J5OLMGEkXK9ufiE4 zbKsk6(uorAxcgJ#F3jMR2K0<6(N;jI#`98xK(cz(gQ=^w!0S&8(OJoUwehf__AzaR zpzk_PTiO~u`*mV=`{Cj4njrncXJnt$m!dOe?pr!SJ(369*Iehnk z<7w3nIQV5yEQ8>J`QuO6H89ot$oPwWUZ4)CQGr&tD+gc-{JDKwV!p(IVVKJkU$Wd*ayRuO zhpyqEiwU@!)`0~h{)85r==0vNz_3}98m0W3a5k|k^?btLaK1p(2Q+c0lD9{@UqX&d z^YlYSRdtGE{PjWQlj&dWITx;g;N~X>GqY4TsvIV4>5hueQMB0o5aSbegp z&8z_s$_H`Z$~xJ*#D1iDc7E@#J`_y9HLu8~?WGzyR<9IICqIbAx#|a6>TC;X`@tPb z;(^mA!OFl5VjHXzj%UEsElN&RFaBB@O(wzn*aB$9e78ogq z$IB-cc*^*=aCoG{M6IV&)iY+5i=%{5Z2`Fm7kM*zKz4@L`Oa(RI>6oC%z6w$NgSU( z+Sx5OBjM0!JXBi(1OafL#o?vt3_llbu!&3+OyQHSoFv8pVfP2WX>2wUJUv7oX7|OI zNS@0_?Ck%Au>tX&T_B=Chdz^$J<;$x&6y_DMTH*7;A8Ys`J4u$14EhU6gGK)7p#$I*411232;H6we9T|3?pH$pew{Y)`P2+;YP;&WG zO6u*UZhwBVfD|AA0PENp@#dFU+sSdsqS&NYcd+J^Kuf05fPEBZG~N}6_&v8||Llvl zMDoeH^+6bn0rtbq{M($W&tlq1q ztN}%PJQ@Sze6Rv*TbH){gUazrSy^bZ24xG{5V)1!Z^dB?byn@x!YTEiskH`p0V$Dj z`Fe}Ewv`w)gCtI4GOQcz^@VM(fKVj7wr}W=+&874^<6-7b#@B7G}gZ0C@&Sz>Gl>o z2W#B2Gzxd0!bp5{@%JBu=Xa80iBnzu0+Nqx3Qa*fa&`G?#jvJZBed$M?CA8GtVqtI8Yt zD1zG18Lc$nC;|YM*NSM1V`_8e2rKrNj~KvtFu@_n>&wz4=&Wiv-+k6C$54#;-50}^AO+3W1_qyO_5j>2 z=mYi1m`<9N;Oi?>IIuWnSk)ecD)PO`Q^$tvjg||f2yXlhQ3>N;ra1~Sh{&!}at{nQceNy=B%`nqK1F!45Nd_AgK@f3nt<@xiB0(-SWN zB5>M3R)l`mccHC;N%IPY-o=cr8zHR0`$na{)K+z@$n6oGAV)bMtaHh1#FROu==m$g zdmqhq)j3|p$x{3cN+bO|r`KQR@c03S4tY4xD;+UO=Y7=r?n|7#43b3P^4}Qsslle( z-x&2KekrkAYOz?*Tup8#-m#KM#?W05fZ08=d-*KeHRL|m@(;F^3}OFp`}*Zv-wzL; zSRDtM4&8mQ4uZsI;T+mtPaS5|jnJ@~rJ`^v^ZMih-^7Q!Us=R;d#xsH${$nUS$i{zR7sfeR#o6J>!u_ zBrnx|hh`kv>t4UFz_yxkUHFz6={Z!>epnbv0a|Byy@X9o20`Q^j^8#mdQVRoO^)vL zBV|WBC__!!^*=v}0C)z!v@w#d^2LYb)TOU08C*IA^fESQ=TEasOx$xR(PKoj#ALGV ziQ`-f&7zT0W&_4ul*N|9tM22Fe?^-Pf~df@SC7T)B_3m|>T-OBt&DaZ*pejRo{^;v z_kIvB6&++9`2P4xg?M|qf9<_as;F=#iPVi+%pHVhBL1HgF_}#TXuJwwjah7c*;8Mw z#cvV1+yGILwudOO3HTDT6?Yl+oM)}uPOcSRWTeUbVU1iPUkg%Vx6k*5XT~)<#ESZ> z%IgjK&W3*cm75pJMy+*SnFU$V>N+G=yg|uergq@byPx?aH?teDn6OW4_w4OqCB%^< z4EBSHgqIdD`2!=2iku9m9E|2zm(3EJ_z?+(MWM8#+|Qak6u>DP8h&@}W`D*(&bA)# z!P|%HM18jeQ90jP9uZfTe(_LoBHUe1up3M5=t{hMpj@ftSeE!Fql4o7cGW=u7rV07hYlQ1 zw#~#HowXm|Y%nq8e?El_?!l#yRJQm%5OHjJY+%cXiWr9O7_4!P1btUUHQ%QuM+Yhf zzQKgM$KjvaPBQg>&Sm?yr5|{Tt$ZBLIEvDktiR_c&CXz$t26%vIJ!ysE+(;(LM>%>Y^nk)k0PjW%xe9wRrS^T<;fN2asUgK|pOg{TEqm%B! z2i>K_y-mhkzNsQa&4z9{jIJo-P`4hZ9edDTFBfvKradU269hRy3TZMhnCg#nq+k-+ z^g{`gYB~bmD<0;!e7OKWcT)Y^XEM>OZ9xaHl`#;t@%5W)Ba-J=s7bkc;vuDTm2VQS1pPTp38lcmJXSxAC z0=~R*OsU10X?FuqVa&*TIkM(UiZL!1uo&9IL%CnaB1KW~^6Wr`-M$j4f@|q5eo7S1 z#Ual#l<_=D8{~0|oAIRjbaqAgZ=1%n%>B!r{dRsYprBpjmEcyQk@5INrZ>*;DHF?B(iyH!?J#84vX8bu6CZQ{S{b+Tf^daCZX4x9f z3tX4R#->nfIUP*wXKcCvUS>3fkPHo4*s&gSXWE=#fQfztd;~|^5g9ep@=N$|4>6v8hqe%IT&LHe?B^a=qeC?oZ=JptS8ydg` zH{D5F7gkvX=ARb>uZZFwYViQkodLUyGD5c)%rn@DBD8+d-3UsBW+m!y0$w26j>Pq_ z{Ll<8c!sd>?_QpR+q7^7&|X@BSZz2WXR5@PmVJswi)zehyKBTI^YIEReuwXW%xrWI zE;wD$K(n%Tm=O$6+#_{o`}-O2sZ@X_4`ZWf9fh2+h91^rJO`4zRg>!){SYwh9qz(C|z?bc4;eevh3}`1E~H!>FH?g=9WxlM=DJ3*M`Gc_r`hIqWFpsGcw0%m!ie{ z!H%)YE!)GEGarrsqj8=i_uh;QcNACPfsR`o*s~)ErWYH1(jTa_!M?{piq=2nl7W8R zw9!Nb54H<(1eE#`wHFYzo?-Vw0qlYFkQUim7I&m#wto$g;x7-KNgpFTL zXL<)9nZ9(?7-;8qU4fZ?u7b%n1*#Z9h9m z0?K&Di=&75udcmT6az3uPYr=@9QB;&3-jU8>q}08{t)$iq{e5gsp_rhRav);a z-W#{RKL6!#;ybzZQ?2B-{JYt+F&T?yO=S6JksNG?+;As=vrP;5));Az7S-CzC6yOL z?|qcHef@;mrvc-=;(XAGWZSii=c%svNqnvc#doqv=jZB@Ikv7+k`@bP-c*B{RS~** zblIgnk)BD8@4sj1`P!Z&@Lff=r4~DK=TlEgZoZoLZr1h(q}=JP)ysKVFiNBp^P6V@ zm(bS%^ZmJ}ZZ-Wlla4|D@l?1s-#(Vwr>t6pn#}t9PVY?6ec-Ec1gzvIrog2~Cww;# zu`X0_&7z%oGO4`~I4GJu zrGbDo)fZx}dS#k{Z5wp8$9Mni7Gqn-r+H>Ei-V&G8#%i4?a}D4-LX4}_TUD5kSnJ# zDd%MG4uccXE_!owstx|g_0Cn~IU}HdvF=ot?$4*f1j|p&9e-`txOAt2L~kwbUpmv> zO}TP(P+P5ZM4>QaQXZ!H`DiuJtwRYoNaJHJra%X^jXoHo;sS!gOfgpbIm>U0`t{)L zAk5j3k!--W3RJ%hBAS*m=)RC*g>TCsapU*aLVrfonE+(r)7%*T%|kBuApRU>inMU* zYL_@0;lORmv9pvBQo4+#I2a}>(DjE|CiSi^RDa|w<%V^}CFh@PAQOh+UQ<(vSwocH z6=W!)k*Ab}b)-HhQn_Nk=u?E)FS+yaAsGnH1??EOt;T0iUcn6Oq!E zSmgbS4TQ1?^c-GSGAR@?BXrAIcj~IGr9EUn68`xcLb|)H1dVxCMhq`OX~y3&Bdq+5 z5Iyzi5x&V6$hqHWw_lh!cS{*T+m~613=eO>!?gWB zw#6ZsS)5KSgi`#oKihp%-$Q(g)(i3ATS$g!D7%_{&PQj|ndb1j-$=?P@_idvtPr_m7gP{qc@Om|iW40Uj^YN}Nx|uiwhC?`5u? zTA!=2l3GL7&FW7oTu^d(;zw==rMa2slNIvy)LIONFMMOx`7(*zhRGH+hj%EPFmLlu zC|r$RWvhIuAr5+xL4^)F6&*Xl=a7(k`_yXfxWxZJxRW0F>5Lq`xY_nF$jWxi@yEig zd!>`k53$t9Upt`U*7EL9G;_poKiLLn{Zl5d1|fVy*72%s5g-9<<%Ah|coTdii)qhm z)VIfPTjcsqG2-l65aGjftqm8yF&?hF()W}dRr;ONJxDbwbKh( zN@XxM&w!|iC?6qWc+6xc7+05|(%twQ+rCiZJ)srswgkm$Nm9&8CK2_dACVOI(Tx)y zz=~Qqxs1f8cuTPJ)^CQb0uu4+gbW?onH8{anD0yb9du6MW|q=)*lSPqs?t8=Zqd1` z6UzXEXgUPYkJIzlo5JBd+$LTY2;xq7R4do{UP|tazWY6$B+r*35~C-M>`$%jA@GZZ z)#&MZj<>seEN2iw?}Aqh+t=CVrat>J;&lR_?o|=KOMd|tl!v11T#7P#S275pq`>JX z#)#HxM2J`Ujjt;ajGNH;GiBt&;XBSB!j87`(rCK5B=z=3p7jU$J%FO@yOo7A?s|wf=O$SO-T8}bx z7v(JN`zPgK@H&X5h}^KH>eo!{ORYA=PtJ-;S)gZI+R*UP6!D2Our>Sb zyQ_S}Z^HfPw2pIr<-gRN_Vr8Ndyn5C2MonNo~X7OHgt$Vat3CiCpJ6Ot89H&O=XoR0Bz%525Ydt+CIh9$93Det)Zxt zzM)F6+(b#{>>7tCAdWv!FI#O*A_)ga&3H}<`8}QtFu3)CleTrBap6-EjCEYctFO-e zpZc<-4?l>1n+YH$<5qFkL~RJ!{FH7yEC_cjFk6{xP?8)Uk!WPnFqjN_X`Bz zKo&0%21sfjm;~(i1l(r;bn6hT6F^+|5GNF8AY=jrW6&i`vNhROt0S;&iI({2u_6eaeOPqTHntSLgU4Ho%tA*$^KHqd27z zi~KA80LINCVMtk~f&W*^4z8g_6(K;C@eX0{ejSHBT&zv>Z_L2?w=7VhHZ6CJA*6uFvKk$oGvu6jH?KG|2vzf0x&K!-IxiZy zv2ejND(g@P{Mb!B{&RkrcBW+o-}m(sHZ6;%{Hp#FXLVjMf&E&*wS-f8QzN~DI##2C z^2frIb*PM>Vt`WsW!YwjxBm=x*ONVF1s0MLl~YVbfB>%W#hW&LIa!63M)fIj8y}8+ zyh^2Hz?UB2U<2DSn{E3wHgGX`!3qT-`riLJ7Z-98?sMe|FMJOI{las81(JPDr3@FX+ zhaorjokQNhoMY{G#KDPay9F`Z) zRt}0<3LT*dYD7ZFV1__a_$b?|)wDYRYUB}u+|xx7FzFls?Wkghm+Zs~!13LIO8~$M z?n9$siAUJmc(xCqCQH`Z{BWSe;^K|}XT{@D*GwQyeBc$VrniqH%g=+1zaK&h8v_g) ziMvgt*5{%%#2_s`@QSMJ0$mgy9!&%8{{`@s@mfvqy#)lp;U51HX85-X_5Yvf@@@;3 z7_+7|;_mnSBor6oe=Y&{#Uzx+yo+Ovss*Y|HnScQ@7@`8k1MOMV7}@JB)j1rA2 zY9F~MY9AH4feV2)gIENPjdJ6jZI6gWZxw>-Ax9*;01}%~JZDv335Q3F> zC0NoB1lA8xH7@9OD9WA6W(0}hH??E``T3T%*9Ig!fzCuqYm1|L-TF!`s8C$1o z#5dpdZC7Qz#XFMcOSI(3dr%B?9@p1$&ILLdJ<+<2>lDMw>!kzyMd@pOQOA-i(j)L$ z%+rbHi+)a)(E`Pf1OpHt9r^iv_&+NnaiaoN6VzH*Al>%ea_by1_S$$op7p;eknI=-t#d7S0R*k8J7+02 zl)3Ns6(c_2kiZeRS_3!4@>5nK0wP{Gu%_!8VBeb**1x_rAg&Z?Pe1m1i|l4@nBdB- z=(&PF>?P=b#Ljb^M!7C8NF@x2+KACU- z4Lq;P_}YnKmHUgVH7{B0?XI!zIJY?JZ7T6L2@2)W@}E)_rdEu6=Rkq-~JL(RB+#}zrL0k3>$LvtVrGOO<1oRONYyay1YEFSI9EXai<5& z$X^NAWtP@RYiLYzxk>1dHnX!jl%YFuu`%j>x%FGWp*r}81bjmYB3X6h`!v6bIUs$1 z+LelaQXDy^eQYegV}1UkEI;7RJ#1s`W|P%^s+HROt~<7CFFgjCO&j>g_@vCRo|_&SW%S8PCUGSN#%lZGltWr5*XiAm zDec*frIa|e=c$1c{0k48y)3@i-x2vRBdN7AT+ela#|smq3{(&tVgRTGXxm2$azicw~-Q35ITkDf6v^GK%M#=j-Gar?DyPfxQ&2!`9G`P(WD($HVf zspR(gZKpj9-x*cca*a|4>?zdA4prm6`BI@>pvLOucX}kx0-prW%k28PYWF8ZD>E(D zqy4w{Q}davrKkOj&QP}}`I#O4OJk(yRi@-ulk1<6i?UtndG8Wj{bo1%4sRtFcIEiz z*PCiUP_%f?TwIbKiKLh-QG(ius!~z}2-tBap)=$SlnzTUGx8qEFmy}x>j$$ll= zy(uIEcJJ0X$Pw!-Rt(&Ej*#m)bCEiDJ?Ufc(Wg6GC3pi=s09ar{}m_~Z(OIjXtCr%az zzNsO}X#AI@K|XsMKNLU|(o~r`sL#oVX&3^=oveZdolIq zOZ9)1a_!+vw{QGia;W7{govr+P@#G!mDJ3kyx2rihKaOQj*(NtO0woHKk1EzoXR>O zhu4mI z^W%6qz{>SI(3yBJRLG|52hQK-32*!L}Y_ zXFuAav#m&D%sU^p6Y;LkEbo_Xms9jwHrF?+CM7dtg zb0~dwEYwmQstpznPXAsx_GzGCmx0?-yRL3{*&H8h0lL><2r74qJ8}$H!3XF81P`-j zbX|UJRdr%_;5gf6q!h$vqw3}+Cm1~ZS#fT3&7_b`Y4OwY5S%s#JM{JzKJX+#)Ha6c z*Z)#8kINgs8eoVUNyytCHTaywnzU*N1-Z-G2m4D{ZJHen^2Kz43&>jhA4-u7LJbqu zIbYGSj3dNd&oW82`Ijw2UJb-#mh5XZz)rM$NAg&9CAlDLN|DTUT3lL_F15f8snyQG z={1de=%O6A(K7Mq_!e{9l?m}LwA|G8k#0wMwO3s0x!8f`1liR31KOvx?$ZBlR+Q8IN!(;dH_}c@PRSGfD2Vzk zO2zCNzpa&g_YFGvN%6AmT37mcn*E&!buN$}+?8wcAI7Gd*s*`u!t3kNPL?X$0Ky3vuy^b(xO6NoPPBxWzL>jE zbzpQEo*zvI7pGn?G#=Hh^iOzjeL8K?AVpqYmcuWE-F1#EjIF8BTiK3948ojaKbEo?TZpXLCoLg2LBl^xjw@`X;Vy(2{qr1RSW=6I4ny)IK?1Q3HR-~AQPptULoU67OB%ZA?H71s{q_`sN&h9~NDBYTz;WAFnbLNM=P+_#QJgP1-I1D`_gebmK{ywBkU@rj zI-p|qK*M9?GwHp&--BTCYSZ4LjFucV?Vxnl`({?f`KB;Nbh)D$t`b~j6#vU^hhS>o z+A|B$;NHhglwp5PN;Sbk0dqX7Q^_0k8gIg;y%1{n`kjPjZ2>vmFJDCNsIY#GcQzE$ z-}oka9=>Y-O8mCkMYyebE3LYWAJBSEdnn)Bc+F&l)*)4|VI&c+R?~BP9VuqHjH#KB ziZ3x)uXW^2=rDB^umS#_p@8eB4Pw*v{z*+Vq2m?W`^xK1B-qDc@ zx9Uow{<0{dWSBI-$EH-2C8snQ*G~%zBJ+UpBPiV7UoW~petfAV^*RaDj6lLp>#QSg zAB-g~Buh%iY& zCt-TGjlRlp`EN^pR1^VX0WMhiH3oOv57+-3cQBheVFQ1tTZiYEBmR!vz1aadK?*bB1<3WuWuV7s|Z<-7X^4Ua35NV1C5dO zwBkB(ouc%=8qU?2gZ|yXIh^eV8=NQ%B>}z6r z%>=?QgN(ofIc8f=(0Xz{_tm#Cy5I8=K=_ZD$N?FKy|2dIYk2#jL3wP>iAS^SqS^C+Fwz`}H*J(S7b!(cpCHoEYG)rF! z&bv?5SSADzN}e`MuCTpmc9v6jmLaQMk1?_m_D{VjQ(UT^0x5F24e$K#P>Bsvg05Uu z*9dZ$zXbZK;eqvR#;=}{9lcSyHfIp}!Uv{vGB#lobpt5PWE_x77H6jHL2yyhtv9#t zD(W*{!IMyb5-V5*N*?yG;;!1OmF~0DT)>yYf5l4>?-W(c{3<64at8X7ER3Z8XVIxd au0BiKntmuNWzS&9|p8j>VaZ|y5;dadF^Usf93t9^l zk2X#lUnlr~#htkCA0spm)eeQR=Lmh@DFIW2zPjsil2qdh`2OIFUw9DfTiK)#q>myJ z56Ovl8zOQG1wjEJ5JHH0BDA)uW8PhUZ1d?W6vDbEyO2vSvzQywN$dqZ@lzOE{u#8h zp}FSIuBkd^+#Ro(Q}Gbe>1qh|s{hIe{JhV^%JN{BtbxslrQ|@C-#e>D1k!0|2sM0Q z`or|Tk;?SlWhKu@-!;!4uYb;#>k&aDHF#ki-HjLek;#CSL5l_XWpj_S(n&WSax#Y+ zwYwN?GTn1b;~aiY);KEfEHpg}tq#*e#>w(|ugNxH*-iHsm+_q&0P_+bxx!c|ECN$^ z9cX8_lU=ucs=lakS*^RX-ApbnN-mL1JpFTx?8qs#FSQT6Vzn-;I5gH|7`UToc3F7r87 zNIXZWLI|8KsnN(LR?}hhRe-~?=UBzB``!izA&Ef{!Wo2DZ-Np{Cnvdh3|YA7?wG!!vjs?^B;f6N_LQ>yfwWM2(O1=6E$`(!VEC_Hd z3mx%u9a_9JLDXh%3$}=d{3wXMzQBez*cLHK^Yo{K@ALDcgx8x(BwEdp5ZB^v=?_Qq zolfM#)YCHB*rU-|>|+rhvCb!0SRX44Yx|L+f2=qo&?CEcyQGCQ@MSz!#h+0TSv%4@ zcS3q04p5kKf4B5m+oI6UX~lyfrGTyncMnqOj#i6(7u^G*zxrfQ6cbIUQ@(jVx2>D) zt!3!I z^=xJ(Y=hz9%B*VOotL`{0@s`#9v$$`MTT_ff8CgFYiZk=Uh!Q8;X39G(qw}|ig5#kzJs@nP@{Scs(FF3{*xb7rzbdW#S2i5}AmoRFBlKG~BpAWBT z<-Fv75;-$L75|az?M3soyKw|lOp&$m!frm_a6X3DwDs!p{OiHZP-MC5ML11!ecQ>y z=U3QMhQQ@J9^wigL#||$AE-C8{xk8#hM!P^7P@l#kYu~6xlVn5&|#dU-`{YWR{pxk z);msqN!?!XTmd|CRRRxZ6T{jv=#Jq?nMX*ew!;tAqtk9Sy@zcUM(p!c#LJMoClke_ zs~<1ROa+o7zHM@j3;-%H`DYv1vSKKbMM+Hy?FP zqfExnhj|;fGV~*ff__2ig&SA4=HjO7)^=Df&tZvgiFc@>D--}_lNcOtMha|iTaP>uz?sXxAF+y3YOkSK_q`xHXZ@gCmo zh#^@@R4P0>%%A1JF$5B5wy%SSh{U~_c}l>|XzP4WUOFa;pD5x9@>hwQ z5*`AGz!}b{f#cYkaLW_NB=QsXfj`yMt4ba1y1WVbSBL`h{Ye8=@~=gB5sFB*Lp%D3 zE{x|H4aNdea!qTYHdH*oN+?GkZ2VCP$u`n^(^NIhq4qW_Jv5Zjz5F2~N-;-`7XQgz zSk!#n{X?q4hwvK|fe^)rq-+9<<#(7ih&I?ubyW-`<#p{H*6U!aDi{g6EjtYQjRMu_ zR2KumI{fDcV{xhEG%B@tyeI6isMWqb(!Wd1F4?NkZ?{mLo))`OCv#<(_oUgR&^KU1 zeCpFGrWrYn1?K31r#`?eC9yv6W1}3$HuI2x+xYdgEpys09*%5M{|pM0V?g1$4Ai$D zoP)+=~j%!$CbxNGJdjNLsM5ryQ_tGUy6GBREP?+X z`!%iV<|iC5{UrW&xErBnYWa47FkFQb?AHIa3bN6v_KyB{V2##fD<#DP8VUAMUqrC% zjA^Ux7PIIo7J;h~T~)1$#d-we-WV^+T6Ad&{#%G|*W&H8sd80Uh6C1-$j^xnj`lMd ztzlf_qsx1&GZ{_v!d7h2nm5Tt`4+4^GyaElXkz-gh{ zcK*@Dn8InK;cwrk26|)i&eRvlxHm#bh@zk5p`jz-bN(Lp{PPFd^?-HQgj6YqEHv~? zh~MXla0`OxX?tqpMa7U^2JW%Ydx)aR7?Why@If{1&F4?u{Zpwz?!GnuERdqfSIuaT z1q!^|o))E)C>M2F{1I@o^Z{^>DZ#=;IJi&={6s~{NVej&um!8xXPCC;eel z4g?R<9?9?X-GEf{hlM^49j9)jS@1Cxlu;=r{MuiuH5y{`G!vvAB4%eQyY!X6tmA?W zPm=1f^B%hyiI%z2VkneZb7ExJL!B1SnrQjum~+^Ow8E4x&?>$a`p&m?{IGhBT9gDD z`iVz+1Fal$ciNiJdQ6s#e6%=a5z#Zwy;%||OD^_6qCi98KXyKaOZvz((zkY`G^mI9 z!av?(u%Qby&N-Ot=*P$Q<}=g=Yd{L)InzH@+YmagQZ%uBlAk(ovAIp&xqlJWw&Uqa zj|W^K3PNjP$zyvQ29fT7`aX$csZo(*KEi=Whn5NjVN~aZF{q4rwbV2Om}kfyG~d1K zBrCO;$pHs*M_)%P?K}h6|8A6qSzuY@@O;I#Fl2<4myO3)U?szba9LK!krvBU8fziT zj^B^FAE)i#C8-6`06|-S=gvP`h6wP*T34^F zvTSvZt%&?Pv%(#^OJR%CQFZx|9vX|r!z<+DW&Yr*)!s%RpzS7S0l#DFc=%|#dAHOy zQ1_s8jS5`VtDo@@{SBov=^!XoJZQf7TRKLnzL2rU)=tn27dge3728VZ)ZCMzU|c?g zWyH0pe#>EnK_!@E|KLZbKcfcn7qKEPYaarGZBr?bq_yNOZDV`LMk$UrdnxmYU3W(?Ty^0xrk)veX zb0L3-dO-msm8 zG{-_h)xlD*&>JkoToop!uRVG3Z{Z>n`|1TBsKJh{<0w&>s}2aPMgJ-epEHA+1AZLj zZplB68yx=7SD{9N=Ex4~g5Z_YTLdWNU%)Y0)z}>D+3KvQ{q-K<4$_w6v4dP3aCIFE z`)aDj$88%i*CTv>8_wHMjgm-5qdYAmfQGSrhq!;HQ#e&Zy*mXrwXD5fB~2yCAp0A^ zx3M7WxmG{L#mh-JGRZ)V`=MJc9!yOow% zm1+D4RO#wkc!)sUo5>}>F>72G#i?nY)MW+N4{$`;o|BYJ!S%2OuW;kmInvHLD#fTA6*FjakT{i4@RKpC3S80C2h9tKYfi4JXmkK((^MgdL{q zwOgi*=Q}N1$6l1aS!p#$`$0kWk}jfOOjg35G62$BPWl3$!8Gk0JR^vhLo%*qdQ8Ov zBc2H6SER`DT5IEN=O<%3g6G7V*tfMef~CfJt4Rbz;LkSh!i3du!&6cHAG%2D-dA;h z;sr$EA;PizTYNL$MyisE*okkU=x98%<8rnNaBmn_9IK0?w39P>K9_N&9KW0!XM+hB z=--3M`n0K4%^OuRRuz;)Dc1KP6dh^tL|UYHp)2T{HlN01I;YG1>?S(dn9XWAt4X1W zug^iFW>b5|N?W>ybaorlc^|B9spPf++@w{bo#&RGU&zS`z~wP+{?1{sm$0b)IQa-^ zIhx)n{2Jl?;)$`@+=5k7V|KoCH75FONtH8M1pc%LTq?e^m%r{N_CJ9cQ!8#qz?hCF zQzo72y&WapW#81sFoJzdN?j`HO9XK7W({?D92>Oyr97K@+CLnyOg9(_>8%HL_*BE> z|CVbfri9a?SQ3l6$J_!nV?AUN$)7sYBitm1<8wA0+D$Cb>#WauTA;P9 z2p&oBrS!R=&7Wa){7;251^A1N^52^qvK|MIFcu3p{`-FZ=lM)vU8}QUu)EElYIL)j z1e(aXdPXk4QW-iy0zG7fVa(F8kHYP;aVijVGH4Z2U7hn zEaW5P-hb95ur}PYq3Y|`C@1V~EKFBMxXHh+ymn7j4GW%Iu$Nb3wbJ@;& zoRqOm3?+&b1Xdx43Cxt8lYHhE4UIKF~on3*r!o-o8zuojt-Dm95+_%^aG z#OyR)__oWP9H9|>bYPv?Oy}@7pKx!GKIxM)=!!oG!LN^yoUg+uQL<{Z0R94LR+4eQ z2Bys%ocIUL*mYFdRowy2F(*0jFj-Qdo{eU_BgO}3yFiMklNOCv{>{Z)Isrsktfsl9 zv+iH{IfbvG&Fg8WZj585?|r_v$o5>EpDn9h8)|s#-X(EF9o**btxrOe(;#q^XMCA^R zugP6Q_4{FB?>s@W>Bp9b8Mk7sL3k*=yZvi!zvTP|;L#DNrx94Lsdi_Kzj_Y7O;_L31xX`^7iuJ%yJ;3jyx@@aUXQbHlP243$8KG&SoKE&&d z=aOO2mH8g%5_=_8;U@7Nla^yM@CcihZS;?}-lnjM%1nlqoNcDnB*HLS0MpmZWl&mD~}V zvC??AC3~cWs{?L6o6~0E&A=O+hI%bdkpXEbzdd4ax<7^&tgvg6(*!9CJk(2Z0*4*V z)>dThZzliZsk5!D4PBA6_nh8*+eB9)Ft00RXfe|HnCbR@^r1pVu;j5`20du_Y^bfi zOiH^e5iehKqkU0|NkT^H7{v|Z-3O5uT~e2UN8iSldT+;3UDJ|Kus9TZi%bm_0p72` zTF~R%WQV-+G6zYpwqc9SLRquSyel)QpFF0=Tb35#k=Gj+&!$k!b%AEaG$EKJMA<&4 zQF&M+z{?(#e6iOOzHu>;Tsoq7fPHfk-$y*fS7yQ@EM8;ftm7k5>pgNe%-F@25}J4` zhqH43yvtrXL?Duf6}CIw^$7T>M(i%ZDkh0b5-e{z^&syH~DeC$&g;i=I1TKPwA6!DV~IneQ12V z?bm;~>rZr?cPQ9x`L-uK=+phhmn4(A4Vv?tcp~_fy!rv|AV46DfdJum=naTj=ySVL zOu`=QvAIvGt0FC%@i<^`pJ=7wRx&hJan==mS?&ML_Zr$tTg0&VjLdpo(CnlZO=! zL{ zew2_@ojwnXY__O5Sf$hy=Bsa=*y}Q};N-Z0-l^DCa^2W%_wEu+E9~xtdT~16&uuXP z2W}4BB23?QVh{y8@$F74VWU?V)U!F<`laS`Iy~D6uoMm%6!fBwpeF{!@_^vr;8BpC z!?Fmmy9@Gup=8`w{x*`SuzRzK9`cbaDDY`&m!t-0`>H^JDqgr`qVG3%^Vfkgeleq>~ncH7Fl&x?AQ*IEpnmY6p=z6FT&FHcCI3BG|^) zS?>f&y|Zl*jTcdkhU0p>6>m>pd51FR8-Gc2!b7wl23D^%8EQ`0Pj|sR5$otUdCG70 z7##)?X=VX9vw6D38w9gBau7(0tA1*PbG`SC-jkAQ@qZ9i;n=b0Qe9f@bG zaQBe>&-vwc&4V^0)xO&dsMDm-+9p8uufT7Brk`vs?EHlhcONNQ_*_Bxk^ROc8krlc z(kguvLa^LNr$8KGWI}W2>|(GcpDrq=NYSO~yQ!Ks z?O-3;v+GNIAl6m=ql}{EB<5VHWPj+Vw`Je=tJ+E<>dREO;Y@nY>k{Zm6;nud@)RJP zMJLS{9D z1KVmd-%3tOmJfDtNbNJq5RbF}!M8pmI?T=5B|f<4r`J)#KbzGg5(bFU#pxi& z?XjujpO~mo6ES@pzM;=+L~ViU7pxm9e+1bkg`Ml@rKq%}GWIh4mWbazt1&Go^R_<= zWkDtV^)nf)+5KwYaIxJ{J9i*gTv@C4ktOp$ed-ysR>l!XbWpyz*8P2N8yCJdyslwh z@OVk8Aam{)0fqoy#}n!4od~R0VjV5iYjV=9&mgk9RNJS${#6qjl$r*5ZJo7j744)q zucNUSYd0>IghgXt$ehM1tKPQCdrW)^8{<>vO5nVM;u;UkQCq(!`sY#2>~PsILePLp z9%g!5o@9Dk+d1sclD7@thKfx)*iT6mSw>+FcG<(7j5FKKPFzfZ5RiIlAZ~HYaYDuX zmbjKvWLE1YtgJY)G+Pd2M25jTyBVSp<Yl)dNt5;xIsw=z7~w zBOo5SnJHqnM6gWf%A#@1KcmncOl$Q`Mp=5r)c%=CV6b+q!sGlryI0>m&!-eB)YQ{cG0mmHq-&AafNFfe_hIJS$deA<*Q<~5M7WYk#3XE% zzVt1h&k-*ZySAwdpBv1koSSJc`P!PstG4FGSgs{QDlDHI%;LTSCr>2+Y&kruOOQjJ zNymnBVdd3I1tm|Hm)yK+>(qe~xVLZ%(wCX>2%mG+nx)VtqNwzW50%mIBeOp$zn13kHtXJP@1H+td84b#CQ?O`yqGtb35=U z5F(R9{{5PYh(p6C|>^0w9?MdY#PZE|gX6DsR; z(hp(8Jg1AN*B*O7?Kf{|t)7cT z5l8le#53h?A30-yo2zzzqk&e*V#6`-&8(xln?JVXF#Z0T(wwjtKTIU?vN)_Yq=kUJ z&m_Fhy(z`>4h?2%k^r+8b;qybOEK?$YgrJrSGeFJDoCkF zSI*y4<9S5OBi);!6hc-vUEN4@cKR5uI}4Sjoj9jAFX?L zaau=dnxB-lCgNaT+qo7-hAx*ykA+-e1J@DZX|VIDxCp>McGxo*r{X;MRhjaBF3UE| zeDD0SU;eNDA;-yNT1@}>-HqEk@;I@09ZBMob6WYQsy*afM7g4p$!YCR)D0jIR>-p_ zk90`VC_;>#E;4xLa|%i|1DI`CYL7;ujOO!Q?vePmh- zftoSDjq`|>Z7{Km`3g}}jxCYL2RC%^_AWlK$dzd|D+6%r${1-Sw3d5jvwlHDFyhcdFYDFt~s#_8LxWC`tN@QSlNk#i7 z60JKTa1wNvS*(A0GQrSoKekaFO-zS%20`kx)9g|a1+rE==!(JX+SUTq6^%^boL-m{_8M)~t#J`D9?=z2MP>Q5R-~gc z{5ra*V)>DTd>c7BAaCBV_r^7?J30g6^^u3Q6s}5$_qabhsa}Z7pC=k`kzU4!&6@;~ zq7iN&9LUb`aswR)&*~`Rry5f;tje2#J9Kc_kL@(Zi*96+1IJcEcS#&xwfG|JF^7|NBpVy zjo`^0o7-BMvpf>vHXX!PaeD(4)#iKJ60nIq1Y9~BM9$xio*l(ieKBg4m{zrQ(pM37 zb3-ymAl{8^gYE_`_RZv(@x)Ge^>@REoT+!ugMy|SFRI$Zf%~m|;Q)j9#vOOVwux*Y z2@%GEnx;x&!-u!7Hv;jYw$Bqw%J9oyN`C)ai;?rRIc?=njVNBl>Mq%vs$2D|>L*^< z{o%VZ@D7SZ*eJsvzkw5Db)i*t_ux+B1hWq2DIkBK2#?ICHrXLbF^3fle zjE70TxDpFdNr_?s{TdJLK2tQr{1gs&AFjhV7MOD159z0+KPiF%LVBf7JHGXu4ma}0 zf7Awn#~3tkipOIVA&9gYfUYo6PDz9Kw^Z(D!xlV{V&r@_J$R#b>M%Rs0{fs<^F>|n z&=^>Yaq2PSQRX97#Q|uoT)lfaZey!%p30R|X#FqRW?En(Ji(p@ko-FSDTN6&T(a?# z&z!X|0l5rG$@F>Ta)jL`@iyDhczc{=TYS?0x8dxkq@7Wou$u?c&?z=U7Q)>iR8AVjEN8K36Nzf zBajg<@CIj@UUm>!&*{x_e8=vkI6%ELrIJXwa2%k${>%)@{O5A5uQ{cU_NwO+=zTxh zCjE=u^I}hlEIKL_0XJaGE(H_VyUBv&7t?>C*Gjb9ED$ms$rqCH)+K4R4JZO%6~{Cv ztH>OC&&_xfFC5#pU3lb)6SRpY;sK+$*Ldx9j776R_TBTJML*AcFu22hJxSu);u4B@ zah4jvv#>PyAa3cgPl-e5dd&_W?8OqV9kOk;2a(Lgbx7<4foJOa!!j7)7sH>3OQFShxHe8m<6%8b9{(qW>W z^YXE}rf?U&69I47mrvGKmDz`dccOR4q#!0^E< zE~$tYL{5XTx51s4z;M*`+IC+beOG(uNz!sieiD0eCl#tIfAVZ&EpS!CYf0IT)(z=Q z5o58-*3#{rQ$ivqHv}iz8ZxF_3xzl?y2QURHNXmk{u6N2EN0aJT)3-#a<)r zy}eC7w0~lvKxyl>%N7fFm*zmbkc?7eB3%s#^;mh}Mr$ihPjt*Fk&$=!q({ z+G#G4#Fsj}bNQV;sbBftHT2;n-^yxE`==Bd6h{h3{RamO5iXCt@{SO+Dl`0k>T~<( z*Qn%*(HonifzQ62zRO!nD$NN;W7Q3*tT40O_}PGIjoOL+`Un!V3rl}e!yf8<hnWfdJ0b;ALv~cok10EYWNxwkoRSl^aM%-ABz#cfHiB(@F zkaw>Qfb1KbW|;%=eiP;5WPa(}uhVPqC{}MWj)*+Vx^GLn4^MAk-(>&b1ey}gfghE^ zZYD^>EZz$U+9DqUVMbn;7y}j)lAh`Jo}Em0<6Cr zI=I!?x@l{G!S``~Mvv_}>FIAd`)oN0{j+!VGC}R1cKKi<{UeByBTIAC)&)vAXCgqD z*nL&Z!}3=oV1Gp>2D*359jnf}#aGt2&y0#tWH3qG3f=oAo{jSUGwu#|HW4~kt%EIA zW|77RlRpUqBVvgA9wUivq53LhoGmR9z@U-`71z*A;Q_SqD#4ZS#=YMB4r%luMQ;lX zO_7p|#f2Nn#P997fX_32L1UZcg+V`_*FoT`2>_lG;q7%|o{PorEMm0f`>G)70et>T z$*!3L&m*+c9+nIt1pBmher66h6n?6a&)l1tbrxPspkhF^1d&NEU5cyJYu=3d3Gr2| zlzX%8ZF~@VQgVuiust$Z%vXiA*2Ucv5vBKA$%@Oq|!-rm*hKBdG=^17TT^S~*ae z+iR{Jpjwgnfx*3%Uwc?G=CQY|JDBbb_Fs_~aIu+EXi747}z*n7x zmz2=@MGZ#YKGsKOt7T{(pWjU%*_iWA7j%0ESQB@Y?-$0&?U5wXIC;IorhmHcj4}QG z0V=gx8Ma=QX8Nu^eWm~Y#lV&@?`;&B1*?U4XPkdptq92Jk$;{M@}ZAqlF%~T)e+?x ztJ$|+U&2co$6x)`oD1*HbtA|HT<7#>NuRp16#QY>x+6Jtop||tmK|+{FOS`DxaZeT z=8C08#o{ZFjAt{PbLI3-2?jE2&MsGr*S6fKARe~!*UpJ;35Htc_Y@I8$6+7CF2=3? z)?XZ+Mkp|}ojKYDEUisMyoa8Ay0M4WdXRyKaixtaI8V2Y4op&ypJTKI-iOU8|bYz^8QBx^UFh5{W%OqmW<1IM;W9KVyijN7}tGAKFTX!B8J%g z_{^h~na6C%;{EgOnd$a{dz!RFxr^8m>*2a2UfblGUC;UTKZ(Lg+1|_kX#Cs6H~VfR z_e@3$6U@Znhcw|y4Zm@w;3K>72w`wSurH%h>f;+dA5}smx<@ z*v6w2j-ON*hy3F)i-xgZrT^e$GdBEr#fEAsLi|B56Su-2-6lYs?>9dchDKP3Y>a>6P@6cN^Gq-z4iCuBPP>EU}Zm5R=N1;PUu#Yh0BzQ3qRFEzmNu|F+T<2Y8M|dY%qB@r|{0|&b<+}EIyzQN57O>C7 zu?)%fIe+)IJZk%k+A=DziE%TIUYTr+l?ylK;t;)A7@6f7evufR*I{=3m`iJ(kQtGdtflm&bgu`g_R>Q1r1doear~ z--H@Uvj2eI4m?#F9eswS3myVQ0!URfAIq5ceR@3rH4LlNdirP(yM0sMfl?mELL2{p ztijYR#6!ib{g@dQS7H0MsR)1Rg9CDm8f)zFB0Nn$6<#fv5y`F;c z%jSm&7L+MP20Ol4+akTNn<0|c;D+9%tFXwfn zZ%fzTdhrc{_06iTO>5F``g37Qorb9i8MOf)!(VHf0q~Xn=U^sftAWvvPa^Dqki}nw zU!yo}<=mS$uD* zH%$la(Q}C#Ua-=Lz|%Aq?fqrva!OP_*=;M>HzUa$?48=!U8O>XsAO6cwON3Xl6J2; z3>xeOuv13qCVk`9pO#gf_Zju4bmtT*9danH+o&p3sxA|blDJQMdUK6Fi7?k9cGK__=Y&y&{9V&yD|LxYYs^2}`{BQG0}ODxr@C%dkSlYb4ebd1 zMSET84v)Yon3>Qeg(9#{-(=k@T>eY8862qrz?~t69I(2MEI~88+m6ZIMXA<`16A~e zYcnj;XwlwwkNaH6Ouh&bEzJo#ZF%{sx9tm_NhyAp+5}foFx17tUxX3S@paxLpL*Lv zLGDxyiFZ?{1f}p>FgKqe+wm3&?7oV)-;Zw@l&GQ&t5`6O2E@qXU@TYW5!6e|6);{^ zdIJtz;J;xj#A12|39ku4{D;JXheHS%Z68UIghs<6Yzr<;k-)2)B(?dL_z3viO~}A z^^K+A>IR37f9WKfOpdCLPL1)yb44-WoY*>IFon*BaEa=wRs>l)NP+#o{6Yy3MSjUk z6Cg4Nlj)niiYx=&HePduoy~|#m)U>(J_(S0<|)_C?f$`T+Ky8qZ;F#<1cxIK^Z!8& z9Y6{_ijy3G^*u3D2A4;kO)PrCC?xiEJ-b1zQ+}!=N zY_kyz*^UOsCtnFovOH<>^Rl{pIp{e(KC(!uy?={)$jKAgNImsBxGu-G7?>1Qsc~u- zh3m4BQcjXi>t1uhvM_rs7p?j_)EW-=K0Xtv*!A4Fi8kxLSy%YCE9$ju?Edz5&{4oj z!eZ;=vP_fte{n!qCR9%>lUot)%_~>^;SMg)BM@+JExb$m`=Ucp!-{;;;STXqc?@~R zBok^khRN+c7=QSR?8Pu~WR}tiIt8xuTiMp(C(o7BEQ_o z!_~*SmL_LhG)r@-5i{wQ^Y|B}@Xqe&ro+)+2SYM}k))D3{JnQ|Pm#=E%=ZIZhulMy zWHzr8H^>0unamWipFM#}?PsGEGg;IyiD2Eq`@qemB^^vwvzxPu_jLE2`kx3U4Qd=K zA_Vum^!_8{!4AjLqP*?k-mt*&l(izV2Y*nSy;?GSMCQG6Z7j>Ucje#jsKqB&WMti2 zo(^(!IBi(IS8~a8%IZ1wHg(D_JwLft&$v)!K#ui-m%DGezbfAgjgz{S2QFt2Ut{A^ zKxEHvjKeJ$#pB|OJV_RZLq0QMu;W{je#j|q=CB(w{C>Ir+{|$C*sFN;gm>VH|BsG7 z$Bt!%LI{oLnK)pyU=Y$3{;_zAjJRcrP5=*eb-Ne`>@eX!<%h@D$ysft(r3>Y;k$!3 zzbvyoZ?H%|>{$guxJ0k+?1odl@8l?IZx%f z3w-m&-|OrS+}uQ+%go?~NK?^`tyGP`4=424m(_rK969aB{RI11`&tSJowB*M28MyP}dh+nmXnZO6RDn7!vT2wiQ@+vs)#}V}YlN8gNGKTqdb;7Wr;4Z#=PiKhd z6~wG6X7<(4C{TXD zMCie`V&?wkSy4*5Dc}}6{0U2k;*Ej$f-deXWz;});+=8a21cp>qHg0G+Tsr#EFpFJ{nJ))x;5EEt^-%ajYnbCEHkbl^ zyM9AHB07f-KSEy}srSIA`;rW3oJv#Pt8Y6zaRg~=8>E+2#eRp?MBwCa5($%U5WbY- z8U3}r`tpJ57YAFXRUf5zT)P+gAp06iklT)tM^LF*K%fe`p`TECYK)%R`h9hMeDksQGwt%tqS!y+Id^y6ln-^ZKr%na z8+57si-U3eXN0DBex9vq*AeF}MiGnz z*Y~8Jic2dvoQ%--UR{UP0;c7#ehrt&-6!L3`F{X=-QjLQew!SHR$de+Bxwp37zEU5 zA$bnzd(YCbw{njvul!384Sh4Je!l+UEGBM9tG0sJ#^z;Gtw<&@(j%{@y}=i-%UPn` z^aPRoRqVX#9aNq7d1-`zY}25Rp$Zlm1ZHw3^ZOw;R<>QFWs9u^%hcTesToFvlCH(t zn|d{^{FJP~4Sc!E`U6U!wS_%)ZeAwmTlG8Ry$M%^+qGq<;~Bf2nZ%I6dm6~$x_SK) zkeUmH$nN(wYrvI@-BdeBkZ~Cdr~Sktj&s1{eoo_T)P|Y72%PkD23yJp zsgigLJe`TIPW%O#jH{r_+^HxC)0^73J$6`yoo*@w(+h^h+e;O$aWXHZtgwNF&&57z zfZ03Fn7g8|6!(f)$WW!}*h3=FJJPAHt&FOum*xWFs>kcGYz*xaR~2=NJMDK8afW{) z;gF%jM;+k!S7&{(u&#)0#(~I_dF5$WHDzNMIsJfBcK$2A|SmeQBeO1S%YGfv%`d!@i}0`;FiF~!4UU7kYHlJn#Mnk zz`H69Zp&vkFbFYx%i}$qkJc$tW=015%CvG@3Ic!k?`VS;7Z*qGntmhhLcp|z-h%FM zm#Ku(vjUtD#2ha={pmQ6Nr#g5k{ztpt<%KE&r&cFvy&}h^STbEtUMaK5ek9a-n#OZ zW56unFG0^M-?5RlkKrq)#+BB@2&6?Mez-dQU?y{E$eP#hyaYq7AH(8%|4$arzey~J zQlEj#K;P=MR|fwVXJ-S#YYhO*eo0wiyj)c}Mx}g{J;N91q5EHsiyL{-Zj*g1-F?gLzLg`86#7vVmeksj|eVFO4~DkhA#9F&uR@ zHdb~N6@s|gQKT0lJ7mz!@6XuDgPuP8$y*%J{TL(EinAc z?ttV@_FXXBCPf3OC-V7j#EBU{*cadbvg3z!U*Tyy|EK^kuY zd~yB%YhiaEa6d8C@9My3zZqUC>#d$L?M?*YGDzBh*m08|n_e-Ex4;b4wzcaAD&yWC)ePPmc!GG0j(*A0sFg)NE&YF09md%vQG8=LwQt`Reo5 z=zrT*d9b)}7jIDF>Z3FL-@K|9rv{7c|CRze)Bh#ia%@uv!Tp)P`k)OZhBy6{_=4jz zUuzs&w}pyc$=R=0z%2?7=wUJ}nM2~}KXty0+=Kj+SZ>V*gTknw*CC1E2BR0gK(M^{ z7sFoZBh0D1!JG-sC2R4oSbg=%fw_&-ROW`Jpe{;U+&vDVQ6MGWA|-Ce`u%s4Sff45 z{!)#{B@eENJf7rO9Bj@VE_IoFW;-f>)+1>7TFQ&MnhTb6zF!R3&Gn$&+}wl5 zdN)}5HAbCANB*^+z4rHRYh-Cbw6j`w?1eZc(`V>1`&&pdmHVF<{K47$W?87$Pd{1d zhGw9ru?w=!#sU~Dl5VprSOkO*X(Xf>>EsE~qaLg-oTV`?0{c(LMyE3mgn16zQ-l^F zu1EJB4UmSzuYh9jiW}$|_~lXqeOB@p!k?TS#a;S&DMIp9xjGzFD61EwXvdrz4_5Dy zeGwQFSV|J{)hH^RR*&bK)BO4tDdkHIa`;O+qJV6)SJLpCv@GIa5ix@K2UBpLZyfwY&*z+TOxj`K~;2%lP;u;ESO zU%JQE=-^V_Ev!|k!H`YNmmQZd72RZrZrmelPtaYl-_vVYe#-hVtF(Ulv%`t<{*S{m z^unVDtH8Ym0fvdw4tfI#gPWDghy!_B@wYY{6@U(jR!%YA2;nltWs3qnukGiU-pk^z zF=alR4d+?!5NYqknx{Cw5fzZ}nYZYLo3qzVW84?YX%}QMKG5+do1ZT5Uo_|aoAGGXJYy!!d=tQ7bhEWpyZia>>+0g`z=_fq&+{0c zvu--DEm25qONuK@ePCp-V<(^&Y(Z z8Czi>cHCKd-?E$7*K8`9Y$>^_vOMNM?3SqG1ypjSURSOJwJqmOZg)It73Yh6E(bJD zmkx`UpU%q<=Ds@fTz#iMRytYg)y2;dw_o(mLk^TQi*Lc#;lBR8jO>v6JXc$+OM=d| zpfH>&RyqE4cXje5&q(`&%>)RU8gpx@#<1`X-*l5^m+TbHA6k##%+2ZCtk{JMZ4OvU z%QZSu>F^>-t&XWuE*A$IeeKheBMpo5)bc){+}+cJRHik z4-b;)OeC7JG*NL7jyOVggUV7#ma%mji6qidF?cO46Gu+gtfL%~Y}qF39EZXX=ljQVJ-_R@fA{@d*Yp1FI|c1h2q>@uteYJ1i3REZmPz9A z5y!Wa5SXmnj&XK)t;?S3L9ee}@$6nYxBy*7?$Sw{REjER&+(_f-{SO45Ju;ZxtDws2IOI zE(LKvX|Z~=tvK7QmS>-TtLUAtI79NR(0zRs-;j02^{#u??YXqxHsqh}BLus4nJ3|` zp?NrkZ-U50{lQTi!&=UuC2!`^hY_I(1yO{MQgGu+7*bo9E*f5Ih0vgdD!o{f`7UQE z_oQ(J-QaQB3iSIXPm)iPj_uH9y3g|BgBY2-;%snGZXp}}K#fj`HBkvW5Y*>3%=T%X z9q|{?cP&s?@JZ(Uh3Q%tD?~&4^Rq!xZY+^0{mE=8@4K7dt3|o>@6j~SbZ4uSkQdh3 zgH{`OISIwEagnH>$)7*rt}%F=bazFrS_~V)05ZHfz5R5P#w~@Czf32exqvZ`k*)UP z>V|u;gPK!@`oM&v#2X>LrXu)Te*RpD??vbY*0;)d%<{lPUvw`6xhzKhGI(LS8P?ED zL4*sm7PLQHTSL>-hTjvo=8}SQ1q|vYSFt-uhxDV=2T6xCEj#T|UbE@I#jDShjzp1u z)kDG+TjM6)Ss z!t2DmgAb}7r+N%IXnhbApWYHUT}v!!s3VhA#2>f#Vnw5$P!%c|RStJ=w$b{Ap#-I( zz=wD!HV)zj$QY?}sulw?k>9%G2`>f87W87PkXh_0bl`cl35aheI3VC-tE!CmLr*gJ! zfb;Hp$qSuLT4{>V*Dg#q!pz84jXQqL*-;HDTYe59I>DSO4pigYy@09&7 z!`(V2CH8Ea@30e3Csn2~YD<{V`_pW82%Lw{7{-_@{JwE6Qut42rd#iOdw+yhKyKL& zt=~fGF)ULT*+6=WkXuahoI>^pCqH^krS=(#kZPT$`dkd*(Jc%e;Hg|mLZeSHkYGL! z`nq{3UL}aNRlmq^TI9XuYXvuDqzYD5z7Ie>_Q~2z7%dgulR)6#Mwf|=mJ>+{-HgZZ zEVQI3IqLbtYy3d5NTGT=2Z`vppK9)X(C-Bfh|)*(7{x%F7EBqG<*7G}%FNijSOmt!huwxM&XMT$S$nqxu?O2+v%|EG% zp!K?kTQ6sy5j|ht$NT)Z8{9wL`1~jFpZiRL6#b{MO`-97?`B;hkLHm#cF$?H*15+! zfV>R1c4q5W*=gtW8#c$F&qZR@{Mph0h?Pa`ucQ=1#~w-P9qpGFSq3ID<5^u_wS?=F z<@#cxa-P<4s&jlBT$J=$q3QJZg}blAZ}$(o18bNA@Ym($SWnc_gp6n}mDrbh63mXo zx%XasWDp`r-mB^=;8lvI-St2%Sfm_;XpBWWOzexbu*J21gyQw()kq?;ASEHVYBx=a zXhCjfVcWAeW&(oA!1BW-9MVd(Fs8B|z0@JjzH)l1G1l1pan(-%3HUz4csO1xC5P@m z1Riu3?NdhVT}2u|>cbcxjXR)M94$hZ^vxFbYY3mD66N<*4D0=3`#f2+^EidJ(FsCO zM&4^BLi*qBvts3qltG>Pr0g)E*~dRj%89S*AH8mvd~$`Tg0jJO{pKfUh+RrSv>!!1 zpT1HRd1IxdvqOoDrBQqV> zgf3wq+@k5q+ERQdC0j7iwR2-P7y~x{c74@$Cbb>aiZVq!S@TiZ2=X+$>oJWyN_63C zD;2#uZ+1jynJeKJQ{-a^DT9!kB=7o|aWg;8m3e|YHL)GGw4dMW_ol=*I8Fwe0Cmz3 z!pok*L$OLq`Qwp}MwXc8^n+c09^Kd#CIYsmkJYqgo@K@}L`P;6#}j=RRc7Q%rBpt*i~(pXumXL~`~> zs<@azfM#E=(1+*Kc0!^6BC&NvAE;VB#<-;@EABU9g`6Xhkww@<%ooY!#)v$HlxM9d zTqFHx0st&C)bWFtv^Rz*{PItQz$DB7_$W&Y1j=TMdMf5DiNLbu7Ut5f1?bH{vH5EM zd>_37mQtX3adpr_>IQ3QZ7)1~bECIX%K`pnmN=UY^5PyyX~^2!B}m{9J*lWvRaRDC zA(SPEOHgUtxv;pm&J8!`Uj2R6LZ#H7&xun6jL^d_JY;6y-_C}5p_oZTk#O(4S0r_= zt6DoaS7umrkJ9&Nla^mEt4wjE-D15A;1Yq0u475WZQGAa>8W}VcPRmTXjP;&4p}MH zI=QN^0!`J3!oMQxT4asyJdk;lHQVOo-Oy7w-6|K+h#q~V)pbTd<0aqSr?6{q{Ka4E z{3ziyFW$gTSi@%6Og{VG=Qq$*Dv{#=wV%YzEu8}m;yN)DIkR(2YFQoIRckVHQ+sciWY!&pQSl$~jGByhs7s|$8V0lz&H;k#1Q5K_W3@ox^C?m8 zW1-%1(TCG?Ee9A_`u!+(L4adm+9%zO&klb4R0(rQcy&;0OdFFw0E4FNnorgx=UipQ zShUbr@CLaBC4OR$KsJi>xX9J!?M0d(G6iIxTnDI0fTjX;`2Wv~l!Dt?qo(qIhJZjJ dyS+kWQbNdy7r%Z0Rstw2}e?3KB{Q3?1*F5|Sby(t}7yO2behDInc7 zG&6K}+!w#^zyG~!v6lFrbN1P%_St9e=V9(A^m7IDrxrfog``>>cp`!@Ki zA4S*L{%WDZ3NCde0cV3gO(^tkxOjoi%?sPq^YF{cTluj}MF)X}-G@UU3;cLA5HTtg z1oaU@2mv$+?m}$)Z}gtar`0b1xM<^`rVp-oGwiM8wG@hn^hMF5HpRYZvc9>uImVDG zbr2pa@ZA?@uS$xKL~HTGv?b!_)e6FjI)X!$b%i>I+w$?XJEj>0$sj8d1rUK)p$~vw z+2`~kRMKMI>}F~(lcY>yUk3ygIdHvKxFqdd6u7-?GW>RIAl36pcbDmNh|b|_LM+Fz z-u=Cd@0q6b3h#uc82He+0WM1s zI7yXOr7g4uW8tkH4%ap1g=*qVT7>@8U+cV*s#U}|(5_m7X z+Q*Uu2K7v_22dvGGmO0U{v=a^RNrMIcR>(6m*4{=-WEE4|FJgsuMA$yP1O+Kmeyr_Bi?(P5K4$as9+|Fz}NZcNtuG*B)Rbj6&ul5~3_+LA3- zsNOR-_xR6%PoV9YY3xUIfXsWyei6IUnfRG&EM<>@xT2T$M@qKEif5=u4%qoB-FDow z(?;N%7oM#h$7y-_Y;k5q8;bI|ZBOfYr@wZ5ONx zR$mT{uvIIC1PlpXyrT@xpit!TPxAK9T)fbidv>>Bct-uQhQqZSJcA!0`{6U?7ovL<$wGuC|!ic-n%rP zO9Pv5DWqLB0Vb3t-4x`?*O!jQp8iTB(dqG23t{OCRt_A!l{96X&{{)Ti(u(R)DK&uLW09Mv55PeF$<4Yk3~_ zV4HN~!xZ%|-%%;mvUnjxX3e@6{?+4s=)9gwv~vr(LhBcZW$`n*-5k7q%qzs09&JggIrVk!2*7hgRO!awekc3|V!T1lrEA-b+MF1X%f*6Q z_12q*970FGi4R`ggIzVD$Ize4_^e|!F6m(Y;LLw3s47`AZLCyqYbqihIwRT-lXt8+ z8Nm20dcNXgMLpT&*A{ZxkKivlnNFjy%5lGKPK9FKR-B{HQiu2kL-s>f2L*-Yp$i1i z6*AcmU+vLEzdT5EoEUhTd9v*O)d;d_Ap%Q0BDs@kVm>W|}aZ5Qh9sT@j9B#)>%B z;6!F*3GVKg9misy_c2bgL``zQq7Rpvi#mlw;MaYFAS(5;W|S1Y?#YZQTA=W31i@|T41%hpS)UWj}W`zYbfV1I|b{Fd1ixA$T z$D{Fb(vwqcOA;hhP)2fWrmV94?FpdQh{sD~hecn8E_+;Wv01KE)1=31V28~TE)sjN z&M9auFvrZQ_yXXlk+Czen`Ba1cy@ULk$G2tjfxxdYP>(JHJVXM{!q>^} z!CrxFxB-d)1fE3JuI>|-&xrvatN^811$^SzVJoZH*P^W>CynLuZ9;|m|m^P&8h3k z;>WtLdq(w-U_ynzZOEaalYFdnB%2)$c%14Q@pJqW6D{Kb8)EQKD8XGh3yZVIJDLNT zN1fef2L_vUod|YrO4L@6az}MlMpoE%{Ep^scK2n~B!W@(>U*XdvfcsQm4wjiPG7SK zuo?p`zl2Bt9nU5N7tAI>kjVc5@;B2LUg*iy;?on{t(^^tNppxw`cDaGa#D#jVW?>z zSuYo0H!bp0dgo}a*WjJ-QmDbHy| z%4;r@^pgY{%A9xfLI=C$w`CmkM5!A~hy@tN-uT>?(w%m_C$?Q;@7E=>^?fbns_G=a z^dE=hb}rB%s;}Hm-|7)`39@qdav0R$!|FJBclLq;j|Mpc`X9byeHUJH^XFg0O%KX= z%syXR-nga%G0Ub!5xNwMZkOCE1GKc{f_ScOcLSsXpW^s=D}TV#1Lw2%A|k*&&2(Qg zco67{xEVvoO||MD1&Nv5Otj@b92GU}^H+wN zfA*uM`&Vp++^Er(@UAMZwR!D1_kxsn>?|;A$JyHwZ$>EbJRp_f=7&emv2td&^E z&QH=?7E2KpDbLE#z7+-gOCz*)bKJ_iPWL7HQx_*}V8`sXqw~Hx{?Gi<^NiE3_Gbf` zub82UUjnp{2PBDtMD4_lv)d`wK{#WPNQma6=P>71gg8uLbaqV?aL`T=-g+1<#U)F@C+K>(oE~D*6Q=ECH9;%yVA%Z=+*{$xYYfjR zNf0Tzopd~n6v4Cv;|;|EqT)lmpe>%T0RElSRyzx(%G31mLS9dH>it3atD<{cFrGxg zR?FeGN_*|@$STmDVWywqA+m55wOpeU)ef%$aTY6g@o0`6O5;r!6WtzplXl zzmrwnoUGSK7-%%UM9rI2XrgNVQ`gJUrv_^_?Vf>qCZ~Gtf@uN}!frI6^{OnWsSER9 zR}@|Qc@xE{@lVWig9`VZpHE5OOkF3?zYCXi{kYVK-6dn)LJ6mOUKglb6z~M7|M_tw+Bku$3q<#EbpXUheU# z%umTtUxvy%{g)SoS&y;R7B8MyQY;jX|6YSW<`QskMr)z4^%6s7nwE)?{KG$PK6qkz*0QG?&I9EObxMnQul^Y@j<9^*Sn*G4ea~$ zCQw7!x{9|g z*t)BWpQKHJh}7U09%W7X7(Z~6wFCp*sR`CeKj#EC{eH$zd~#9piXYz-fomLqyNI54 zJMS@JjmF43Jqcb6jQFb{_bGURMz|_-2;7pnf8jIbc5lXXHOU_Rbq(B|iXDm!eLvtB zX&%jy0KVEKU;HTx^cS!M`l@&Opx6mDuMz(lpWrT1V|<+JbK1PRhT&&?0Q`TrOoRQy zG^mrKBwSSsG_L<13ms??+c2zkWAzXs(S@Py2<` zUOrAur%rdu!W{O9SaW0!pPqi7gy6dB(OkC`o?hp6_jYT_BnA9*knDE*J!#y*fIK#@}sVcW(1?Zj>4~Lq2J3I1_^~+;1*zMSx@nl)I)n@~->Sa7&+s2BALngbYiuU{(?z~9?%=Ngeno;H!BDm5!q>wFk|D0`puJs9 zaCZ^p6qd-d6)HZY&O$pbx4(m-<_BZ4R<~m2D!qf87WZ1>0a82~vx<8sRyn(l196_u zaKzNX^$MFW7J0B5-z!LTzE(>6%V&tP7~4!wn?*;+HZ`y)fxg%MOwBo^fpX8DS*_v- zSuTYlL}!1c!728`HP9F18?QV(yC%*0Ggxp}8Z z2mQsZiyxgH;AwC4DTbH7@FDwRa8eV=-{tN3CioF0tL?42OmX_ z)tqpPHb%znmIj+^bu31X7kDmYWjJ;a9sa>y*^OUnRz3L_MDe?V$4}CP5|){ZD-~b% zVB?1;7Oq_t(^&PG8n5@$4`P`Icqy^vKkpK&oM>&M+PNL6ji?jSYc(Bu5()$iNpygP z04g;O1B{A_j&$sr5vyEVpU)T5WJmLY)jDYz7FqsrZb}BbvY(Ueu&0Om2m`#m^_BRv z#e4FZCz{bb%5o)5a4lX|A*xl_<+yS#%8WlpzOv>}99-QD|sX~B~xtfGP#M7(3 z?bOnCXbImrRM6f0{za&UpbiDtY16!4-PI#`ovtgK1a2YrXa0fT&QUvGi%(W)T;q=3_tq)CtB3 zmid&ju41|$TKnsU@Ly)^@4;u2YWX9J*v40Clm?x?Y_QR=5#+decB7`>FSBay?;WJ4 zYd@>me&q7}a~pw=bj?cgKSf`5_}8=hh@V7x|H#>~1^O~;B<=ULiK+fTy?-X;Tqa3= z?#4M#Ox{rt{gpYSM0-Yw(l@zv9^!`j7*Rj8EnhzMBbF2#?*%A^&xh+IN;J_FrMWVX z2RtV41<+^Og&1-ADZx~2yOw&2UV#7@LZ_YlhligYr<8y~$`f5I4uT!#|o1V^pR$o&Lm@#O%^QWz5HVj+wqZSnMb$-vl?y z{qX?nKa5tJnw#1bH2ZIqNS}Bma3TNKN`EKA_+FIezfgIrLTLlO|FFLHdmA(P>HgYR5vT0EOm$Gv@xkO5T zm7x5`qX0ui`LXyuClrP(eEv&tmyzUcUl+i;T%h?75pHUCU1J9uPjU#+Jn8gn5fHHX zo&-&$=Ou=2zBhb>$qMFLJJiJ$PL( zx(byUJbtxDVmy3wG}u0RlSy{8Z8IRXo^ei`KRGgQA{#CL(*^0zER9$L80!e1ug4z0 zHON!JTg*ECB{R_SD$oa6f481Zal#CdMopA+We^P*^C zymLD9`$k#Bv8@wLMVjT0Ga*x>66pDg398h5o#_-4M~o7xbQRf?!}| zjM?^{POKr-#J2d`S)#(qrnD!AA-11}L*-dfjqcE8>_V)#$uUYp0-m%?<6wMzG0m44 zaECa@#b4$pmz-sp=wInx(`RUwZChQML&$mUQLyOK+l8d(HNf*Tbz}YmPh@U(n-5`u zSBIeN5aM-x3^QuQmftDR<}K~?r_ilTywfDri9upYufOuJO)7H*N*`Zr-dtgC6!}~v zIFx1`^=YvM$crj?2ae;&Os4kUOW!CwqM1MCppdvTvOzv`%GY|WDUYiSE*iHY z9ejG^tkIG7Pn?B(CE1(fFIQ)SH_z0t4tr@`z}DKg_i6^R>0E^5MDFR^a11zooO-vXJ*x_6REA)(rSHC5#vd%{&QBEevD!Pe zzJE}#tQj@EYZA{zZ2RQ!h_K$@R8&bKb1i~b>c^0fQ|$r9WUu1AtM~YOj^o=f>UCy} zc(2vHx*jH-A1v5mBmOCJ^!pAx@nx)32*+7mRc3{5rN8 z6z0`(qMKrW7}ne8bNp%9o_tHnEMT(I0GcW#1b==rXj*P?{ z%E0>=4^nquPG@iLSmx0QOs$kFz9X=0&5uehNYeY>rQ5o3y&uF71e7w7ebyxea;p@PVFu zX~Rua-i^3LLt6Tf$#9u;->)@Ur*pf0mcf^KrB9QDWy}TbnuDP$7F9b5yQ68-Xn`t! z&j+Zf#}!^ge&2k7ulPgEq4ZA1=vNWmY4*N4aj?^v=LI5dpYg>k)1>gk)&!QRN4^-$ zRO`|)X?xrxdpqE~QOs9ni>)fiC1mr--)@*7JwN0uW~9I@)gAz!>AF{81qkxJB%%7G zXihdzz;{uDXD2ohpCV_5rRTYYWT_IqzGS->C1&!0(q5!C!cYR(Ba+*{e$kbzpN#*b@V9yLaj)Cl zXY>7uyc!EUn(C6L>(}}3kCkM3yLpJe*7uD$&_*)?^+)Y}P&Fjc1YZ*H2$8izFIYq| z7Ng%6{=*knYEC;>5y@s(@D-vGc~56vv^OitO5kZ$b%26s2U=!AEWMz6TJkzxfa;25 zZ+FL1l-oDg@4EJpm4d7K1LTOm{21!FrQuur+^}dSCcKX_Cc_sUZJ%;7Sw5QY%uiR= zFrB6Vlv--xE??FtHv`@RY(A=uN>2NWdVb0$4sfTU?BXlEg>Uo1P6L-B!n8FD5B77S z592h?Ks%|u;3-doYKSlwIc|UctomcI?|IqN`Rm#XB}>dXeJPJ+<~44j+CS<(IcEGy zmFUGjEWg@YTimKe98U;B&6(-y|K$5Bw=!@dk-&A^!P4 zK0Y_0p~<6pSvIydg5lj1sZubelN)^NSbJ-mu0&4gGLtO~_xSABJK>_9$+h>>fax0= z)XU0sHK!J{8@(B&C+;f>2bOHU_jVpfkK1zhJ_l%o^!`@MzC`ZUUF6(C`OCrQSWzZR zerkg?^J5!zmpcNcn+b@vy^y>)!8#@n_xCTjNU5i9tusix*ka0{zEQQj@so{YyD2>*ukzQPQ_O1sypzfL#uBi6 z{>&#HfmQ4Uv_66`4_=$`D*2yU(&E|LsHvICNr zeJqCa+XK*lFh%B)eGCRe8c(Wz#)}i!>Vy8y?i^e9!+mA88 zXWk0(sn;z?YVdnc38ZPKp6&~o0aN?tN8^=4UJ?K8;V?vqEks^nPv6-P`-s^2(Z5a{ z_3&cz9wYhEWo1q7OME|EO?70PWkqcc(_<2>FnestyYf#rSyo{qoh_^XdNdyFzSc{j zL61Mk8;3vmT0&-8<4JY&oY|41Qi#yyND~EsIgaan@A$S^n(UPAIB80Agre+_a0+V>2S`=p46qwNwHuROfPtVVo)9)=xEP zuDgg-kIvKTPbHFBGv5swYPZJ$r@jgDZk+4@y!a4rkPl29oQRdBcy=JzVJy1504$yl zD$@!!(aG3YLwMeYDN~_0=Tqo&&2MZafC9;{Zv~})a|wV%tav`!AQL}V$4_|53mZ9E zsE=8I`0+1(R^cBocCIJH)+N{rnkoaJ3-PZ;51kYg-EyT1GipxJuv*YiWG3iQzp*fA zZGKDgNm?ed^z^U;X7!r)q+!JV24~h9Pj{Y8jQgsbor3OZvJ=$%;+1hl91z>gtp!iDm6NGmEC9!-zkK6&YdbT7m_i+wGyXxmhOTyrsekuoH+a2 zfIY!0dRrY*_H8UPy{R(~m|lpcPj%Ry*fhVHSd~8W7|nlxK(a<);`H7kAq8P{ZPO_7 zQ!tl!t!2m;aJkm|iP|U1(~fe!!?fu4rr#p*ezK@r+Hr~&{n{}bioVkCr(g>vg17tH zPBDQk3j!Fd{vh1jX)`0TuBR-CX~4A#`OflVE+j{IIO{o@d(Y2Wh8J#hvz3gmFE}H0P|6J=m0OglGe*Lf% zgAn#w_!^-8gQF;l%9ePg@RiXTgI`SfYftPcpn<7OQM|JpHzf)~4nJ_Z>qObqH3TM) z1n~x^&WE+sK|yqri0aA17M*lyv*y8iBXp?bcrX={kCMfpZLo zxas#R=!|WRj+MQzv9S(b!7rPO8Op2hM(Z}q*yQh26o8o^|A{<%wMzfDou_SS21H+h z1I^$lf}Ff{!DB`e`v`gYzX_7Uys#)5t)v=-*>GIz^z5Pxt@pY2D|m^L*NB#0gVZyV zZ2JyFcq--v#od%HNq8hRs!#zz){867)^-o~vmR&DBlZqHBXKW%a(M;q?PMsC2x7G@>nL@A-${dvj_-sYDOj%!2I73*SqDO zv49+c92vw}Gq!f4qc%hOjL^5p;r}95kOZ?xCBbfn&(oy<9|)FrwX67dL}i3;=1!;ZB%9R>-^F8(37X% zM?TE*;rRwnSystTprcD65m99l6? zuC;*lLHa)uD_jq)1JR3Z2fZ6S%lxu-zYCISH-jen4~=FsN&)Ox)}+2{LGI!b(M{T= z9EGDqJ0y`1z3ZDAiylHOZ}$7FKF(N{{r>OMUj95W7e(T6HQ!}Z0mHRw2+Q}i;g{~X3T8(n{M ze8`qV1%{P2NR%yGQb~2Ja<*T7DX-2xx9wsK1Jg;B7D?xB@z9F*Ow(9qtTb1@W@kcX z2C?e+%E4IlJjbjq_&n9W89}>jf^;;}OG%}8Sf0q7XneoKX5?a)I6hR!;IyBnBIzO% z*ll_vOEdA;?RB%B4EN^(2h#ac@I+lykK9_-f*KSc9+-wl{h6|C5Ncf z3h@1xyN8{9{AjjjDUwkZD4KoaA}9?DCS!7MypRl-;16!MMb_UlVJjt<Iu2k)DTOqNziBPYdK&f^ z>G*%qATN8Lun}Q#$HhTVhfJUwON=Jfrl$Hud+@}b7HqJ$kAcLwrAG}e1}l^p%8(I z;uk8R8&2e6KHsG1P=8-7ymc%071>!B2ZE^LPXu6bjzU<{yN|jxNih(N+w+bG9?|kv zj^;3Ys5#lkL=l+HCANQStfyzP?#GVnBJjF)D3s9FlJUvd6@2$Oh6wA^5uB4-2&88p z^`_5txT+s+NHufGa(2LXOACq$IY5J^s~BYt%e~ae)Lyrh?hYl!;%_^ldH|KwDJ#jF z4To7L{-nL%1h*;r?h~nWl=(T)5a1Cytx3X(nd_$T#M1jRmZNSwBf!FyOm~9oOP44- zZ|}*MJ?W2;-8ixHta*sP_q@6<)#NTU-as&Ybbf?G#_~HfbAkWM?^X_FqWSO2v-lrO z%2k4v^I~sfI|Xn3QU2~;{59YZ;-?uhQK7=>co|KpOPvN6WSJ;HhTmwE*XK6%-?c;< zn{cK~l7YyCby%%vx?Ku_M)S^4bFbO2NH z=FK$t`PYI>`;$>ipZwX9GO2rWQQ6&i={fBSz^w&=UHbaobEX}55S=)&$$HGbtuQt_e zQ@%~GiR;|Kk@K^bo`<9(ffmp={{;iZr~(i6o+48x%N*$?f4|ZXbdeiPFh~pH<@D87 ztJX=siOO&>GK~b=r?0le^br(z8k?exEnDiZ_2M@*u|;cz`3?C>Eyw!DXBjkzmEe+Z zMx^KpmK!{Oqe`CFS-b!dh%!3o+Kvdwi()aCKQc*tfQ^St4GW&IGyaM1y(cp!40sP0 zXlBQNVYV9lm1CNCpC*oOnT$~)Il0y{br1JAv0Augi`n(w9f7drdv2Sk!W`FHwm`x{ z(qy>Y_eTv+Oys7%O3}N!N7^q6*5sP(O#X?L6&IP!`JCpUEBWy&i@EL3U~Tl^9oV^o z;914v-J_97Z-b{=aUO5&gwCAz7Jw-R_Z4Fq`Rqrxdo3Ns<@ICf4z8~c75VAyKg)@D zmQCL)ah_*%Z&>zuk#2RK!1Cu(XC!|Ftq%`OvOFSX)A>m+l@^kyLf_xpV-14oq~~zW z`aW)A+}$ce;y2J_?j4>q=etCjXOwTDg4H%d%`@@yt9jcx|P>Fos2>?9JxnLuD z{>3hJpPzD;3Q398SI2)XY-!SRk91gM)n_vzj9Qmj*q&e|>0QZx#!=#82*Z!F;b)V~ zwF!GBc?8R{f2x&ZdT*j(7uj}brh9M@;;@wN-;l>{h5QobKPM^aw;dlvK4PCbp8!e8 z|L|flKqfXL;n%EMPp*gMksa_IUo?wfTPm~BaXFhzPX!Ks{QA{bnH;q>IB)KS)#G>(|Esk4*EkNC zPw?m6TsDcV@DPDF-_`Id!f$H!Y6&W*8F77V zs_))Cv=KP9*1I5DaknNc*UNP@Hzy0w1EaMHyn^ObKD9e8+hwhx^3*7sWXiS8VF~Uk zD+|m+K3H@&b9`n>Ob}c{fVCgc_tN$XaQWPf;Ypan-y2ZEDO!bB zsadv6U(;EAA9+BWIpP>G@=H?@J-(-GNst8RNnB1H?7*LH}ji9gjX62_2TO|S|eLX zSa03ZW_Q^0Q$uOS`l9T;!@)QRnC9RP-bxTvA4_2XE5d0f6U?*5)|Cd4Gnejs zqxEg8Pbr=6zjY{$D*NPL1SDBes=v%fB*mn7I!77skTZK9> z;f~wVsWLCb^6!loxmBio(XTyTUrc)A&-Cmgh}N7*dVWuMoCkKjWvpj~u ztb2XWg`u~WC)svy`M7r5vrLHhI{bDVu<}$pt5_nB^9jspWnHxU5bxNb?#92omtJ_Xc3V=IV|t6%7(Ss)7U&->3*oHaFQnk92-VOZu!`7 zFakPa@{)XS;&&P!d21s-u z;-%~ouys3JWWQgngPaekmUX6t^5@dx$+*_gYdXK|+{c?#E0akA;ABW3;a(i&<06_A*I0dIe%%b zyVV}@&2gLjeLrg@@#A>V8gSP8-aa_$HEe;Wv#~U@fRf{(#tIVSR`yYdM{=;BdoJTl zks@%?+8%irra-7(v~#0ENNU;V+qCR(Kw*5?2pFXl!biOHU0&~bS3n-o4uJhM9XF$=%I-3tC2)3s2U5_;}BiCz?)8Xe0 z7O&@WC+$}ZWD=g@@CRh-XitS>bwo;bJ8b8*3t1xYaU?Bbp&Caj}+A86-e#a>7Jy zlWw9?SN85lC9k}2x_E0DvJSE&tY`&T`U~1}DCFGnvpN6&+vaQtRN4xj46)i2e`Obp zmHqh}!SyqLs?WL_Upt}jj`}k~Y}sbBAcr4dhaL_ED-W-D`q#fi(_G4kq{DCbx_ge> ztVesb$+S%D$0ZhW$vz{n-PCb0+i&9j>v(F9Z4^tPJr#k?bJ|cG8l=I!vKF?WiOAt= z)N`1-cupWPOcs3c?Pku6ryhuR{pbD|NbOF8zVQ=D*G0X+Chm=Q;ic+Lb_3UX_q?0B zm_X<8dzG9&(d)&*-eKrU;tYO`M|>lQ&x{t}z16Qxtz_zV%6738lc!8^g3bR5A$+0F zJXto@gnyQ~MzOavT97PhHx;t536ZbM+FD^<(P_xJEMoQ&RWMjh_-=#(g&w$Dze6|#^RC)%e=fH-nv8}2Oe)Q%UT*x zF1x!r0TIA~ERZ%~V3*Sz?z9ZPQ-Sl~4iz+!m#tY*{&l$lL~f3Ak;zsFY!s@F z^j|3)!Ki&Jw!jePl$WHO_r0!>vPIwg1t9Cxkdb0=jY2T{*|{avy6->ks_kI7)&MEj zy2miSTweW1BOc1|v~Man9?Hj(65kE%ooqclb%2YV{M0?V4}+SUtTQ*BX7Scd;QsFX z&UUWnBKpUZr+yYh+?7T(5F z$kr>mYqva15qBK()*H(C{vj&ze2S@>SIPG@&M($~Yeq>hKCt6*{Lmy7@)%>>MVZmI%VZ~sN{12P$A2{`Es z9o#3QW%qgc$rh;$7wchJt+bL1kjutUHaJF$s6lk%6WCglIV*@0s$_*h!>=76+yiI9 zuskcCSg8!({O>h!_>C7`yfz`-umBGqkhvhrXM`faY7GoQT5~d*iEDlCgXnDor$t>? zU=rMr!oK(gENMhojb+P$HiQ6DdPTbT<$vXAl~G)_l!K9bP%C!BS-Yle=$ite5Y~#W zy|?p{DC5kcmL1pRU=Kn(u>l1}x{IzoV@ovouY6K!&cDajr#P+e;oA;K1`xcEuG=v2xc!gj0xK=>O{5|fDp7)eJr1PB z5h1Avai}kK41Q zj_8q?3IkE~zew=(Muy)#epkVZ!f5}>Ced*)L61SlBhCmwPUesy+Ht9wkqtzw;v;!M z&gM7}V~yO{INzFzs<@OAadx)Nvo7F?<@v|SmZ^jI)qu3D#12)?IU|vzdS+$=+p-9%d~4L36}Dc>Ytf7*RIp$7A9*xukVdG{K2Nj}v1% zOL{M)rMDSU|6750k?7P0WcK|}1zS;|y6vLcSb;IYd13$OvSZF+k|1P;YgLp56|qQl zy39SsCt~T)6yp7xUa)%M<^b0b{9Ijd`%iE|ckXgh$1{^A z6OH&84|Au{E!`};t?E@Ki8Gr4@dee^f6+~FNivq@v;{&vT|Wz*<;(&=qG01@qNM#% zBJP&&#$phe`F*&u<5~v-NwnZ{x~wOS8eZ*qr0D1wkGpxc(L=`MZ-FLCOX~kQYcwFv z5dF~|b-tDcmCnAZMUdnC@2BE_7p}-p+6zQ&M_&C59oDWHIMO!UIUV-bKmN~G0Q~In zGtmA*N%#0@Y=wTG18-33>ZgoOYY+JOByU!Q(MVUpxcp5D*uUdyu0Fly$c*bFg94dX zXd=#%VNQ97CkymC1O&}NdLJV3kla^)665#|?65OLvBK=;4eg#7W!-z#rU%->(b9P` zXSmeUG)A|di7n6Qaa<~Z$aqS;RZ85BXK1Eb^u)eFPeoyX(%9X8l5J|?$29*$cfB;#;xTq>Gw9ROXFehrZ*csCRu-M6FzydK5=}#?sE9#LjF(_!}68*ySHh{II9#_4AkK- zkEkfzupNd)C(k9iOUv^SY>a&6h_HoDFYagDa6m!&Qx#^gcW&tu8Rw{q`eHp^Km|1i zUrZOag78%&XxG-|ce)dUjrD}NgiBB-_=!T|dQ_oU?}V{VPI3+R;%5cxzAo!u(Q6e= z5sb14SI$4{AbAo;cWSdLDe*DoJHw^?3xz|bFwfoIJ{hUSh6RnQDw3P)moPqLNaTLrmB<(a#^xkG`M# zFHU)V=s-*Pk**x=cR!5P*aQ0``6C|R;`70nTByAy)@d5k5Q&@Z^@=7zR zJ{)i>7j5yE&C`ZEUKB?if72T-8fp+)MckTr|G6f-ugW+J|3FRAJluPvGi!$-+bh}= z<0)j(dn&UV9Pl=^IKNGYBE(!=(Bq_xRZM+|ZM4Zbv>R+}q;{vDOtS8aY|T;<2{rXt zp~O_$wbKi2qr>T^8k`Mb(hpILQ9x(olD9lujT}{jY<|Q6!5kcB(UT4G;$ygc(j!&e z9qH}P8_mHI+W_2EaGK@p}91QLU4pr)3EB zXFgv8nWYH9o^Js!0XCIl`USb|e$6m~JU6|^&&ADJG)5Pc=4hhQ^Zln7#MNe$N$h}J z664YZMuU$0HDZ|pH{s9Y@=LZ7u%I@}Z;FZ=fvyedBt=VnlJh^9>?G|5jElPep=|+v zyetTWIfTt0+~p=O3ylrbS7jeAMz)e4-zRM$3xekiS9RlI<;BM+OD&rK5(k6J=2)P` z!^h{SWQUp~I=y?apv&J&o+8|B5paC&w4$8$FG1Swc*vs%))SH-v`I0sUjKu^dj%{C zej^QSx#Q<%BOS~pY6fxb2T3v5_pc&;#{&U37C-j(sY@+>KV2+hX&FDc3c1=heVt$0 zf3t|Ca{464&zapgq=#5w)u4^g+{+y%3)0g&D($||+be$~mQdL8IN&RoVOzyyqNB3J zS^DEyscJ*KRze;+E&`y;VaJI{hkMzzIAoeVAi>2d&NQhr)b(U)9}cAcUF3w_8!o-Qe$XYskV2H{KE8l2(xw}| zE4Pi7J~fgkq1=Q)*p?aTdG+c%*rw^MX9*(xn76V3K8tB4$&5@ zKlTZq0M<#%l!O*X{vQ5y4SwWoi~A0L+8518Lr~=L0GYC6mV@8D7~I4OhjXO8c%JBa zy?ib8GH0v*ocl=()&!r)}5vSLCuKp0loN?f7oWj|>>i-#r( zvetU{zP}gA=bHM&Mdn=V*HlOBmHG$xJYOh{^ty&+@q~>%Fc&^5`jt?Qe|3nGK*GG= zpDNk(9m_rq27YAg4Bx9yQq7J7>LOu>aYRm%Le6Js?SYOb;j`Hv>OdZ1{jJ^2rXd~F z{a;q*q?4sH9gTH7%E`b^>6$Q259%jMde(Li1@X%Qo>b`%|7%ULQ!i&J zcY2HGj=R=wt?J0;QIFeqHi0y>*uz!_Lu-6S@@(B^oz3-%uRv;YZiB-tz76bk8f`1y z+4y@io!|FY9mdBr-xKP!4hB_Sk!tKi%CG=|FfXoWJ;jUEyKS%y~!K znb{If-Ege0X2=^@G-#f_n50Wslp4Hn-2M)AZxqga`WN@+I^olGR&HxKEgQGSU#OUE z4rL&ST!00be_A^OtB_^8jUkeB3eQdXc2QRUvo<3do=_KAoc~loEgQIF*B}}s^Ga2=R7WM}x~Y^OlA75i{tCtK+U;u<4Mva!^)t^_Uq7Hq zfVBrl9-Gq!#(r!^|MuVaktb|W&ZeyckE z+MTeXyxcSswQfhyt(K}^pqU2HM)sK>NHePRBdP|u=-pky@nW(=)S-V++>QKmAZ}HX z0M2W;!Y29*Zfp?<>&o4o!(PSgYZ_;*Aj~~zOIt&Lwmv57;WH~BPzvZ7`oQ+#n*P19 zW(kW1SF~QqfcTZF((C>{dbGcwXNT0azrkg=b4Y62NI(epVo&u}vwGNgfFakzrC;E~ zwkU=pf|k9ltVxmSsCdh?-;xBUVztHC!glmUNjx|Qx#6!5Hz|KwNMq+ko<&>I>ly;X zsqr(n66Fji8UQrP9I=eq+OsrBdd{ZeT{dSR&uzWY?4+Hs>dHN96ph=su>Y+)?I4q> zQ!tV%$W8uWB;nU&bfT6KhfO4zoO71%peU4=2n0-EJjdeny4Y31%1SyHZ%6E8a!xJp zjJ*sI%r8w0=50FM4UUZp@(WcG$tU6lPi{L2*13qf0j`0xoOwO@RZ{064kO%{1O`Fu z*j`4SPj9EN>gq@1swPJcnP0``6L_;O=LeWBABfK9@4s!hSwG@Duh~aygcr0ZiRbrN z`W@E^5P}}udyDxo$McKKkB?S*qVs%xeB|+v9>+O(I?-l?(5om4ErTYW0a?q!udL3Q z@@@d<1Et4EXSEYpj4#)Qe?IRu|JlKqo){dxfgZ?)cCf9QK2b@ix%%7W`|`NcaRnNk z0lJ}v)NL{B!rIu&5^!?vlPL7iw_JgFcP%h)jbg|j0%OWWrEk4~O1s#j&w$eroQh@G z$E~J#)x)V^6olsQNdouZmYEn_s=g1@1_YW|J_|$c^KTzL3T$j{ZmVG!p2%oU%+*sm zPs!AYxNB5Q z*oJ!#?$`>6_rb=B$)w?C`T8`U;i)eprzv;i3bt9STr3i-{XRNOP3l5fM#NbY8kc-! zB%$=YdUo2$dI?QdK^3r!J7Z0{2}QIo2fUJHM&vJ@F8dNll~@2`uWknEETg~dd^!6IuG?j) z@}fJBk&ANvZ4Vk}lfpaYw4!&6bFj_zk{1liRtz}qHUxjvlgwM?6B^RAl4@4Kq67Tr zdycr%ao{xRHQ0OYmDil#ob#K@Pf`9JIVl4v1VQBY zr6rXhhzJEignykR1W)QeT$+O*f*nOU6)Es90)ZePARr_pJaOU#5fKqFF);}V$;p!^ zPn|kNN=iybMt1u2X>xLM3JMBJO3FX}_=Adyikh05hKAh10y5j`Sa(Qn3$NEnORs^E?l^9@!~~RR@O_GE?vHSnT?I@%9SfuuU=(mXXoJH z;N;}w;^N}w=H}tyxpwW^_3PJf+_-V`=1o36K7M|F0RaI)K|vuQpD$jGRusOaeEn3$N@*x0zZxcK<^goK2|#Kfeeq~zpe3lq)=NA+dynp|`u&}VGsHnKO zxTK_{w6wIWtgO7e{KJP26%`egm6cUhRn^tiH8nN0wY7D1b$|W!*T;_^KYjXCUtjaBy&FXlQtNcw}Vc$B!SQqoZSEW8>rFKY#w5n3$NH zoSd4Pnx3AXnVFfLot>MTo1dRwSXfwGTwGdO`t|GA^78V^%F62M>e|}c`uh6D#>VF6 z=GNBM_V)J9&d%=c?%v+s{{H^K!NK9-A!P#J4A}jro=a=mLJ*TL{D%mcST=y53($SZ zJ1R~I3qy_xXExop4nyO)wf)R$If9iFZUraaYEszXm0xMYS|?bAS=~JO%8&chlRk|i z?wr0NrY1Fw#UDRVcEQ7vG_P4*XwKL8bIyMJ7BU`Ji8_f83Td=5Dr+^`a4^~w>yOuKyPl6dku%k_h!B3_WM2d-ne>j=Z z1eu2L4|oq{)Bo>c#}hJKF{EAABSXpBe>7>Im>3z0<6X2|E9V^ByXdvY?--93sefkWOtL$%&_5p(<;f%Z z9W&MxSeP&Up0mTJF4d_tU#gj-N6Rrm_eGD|7lmjk@uLAh zUygbVewL5RK`Q^XV%7W@`;1rx$ty>dSD(O;=lFhkYA_z7B*}W*APLSkCG2W+d`rc| zr{kuv#3jydF1iz{9piqFk2j_urfSGX0X9> zJKK-K*PK|-9Tn3ZQ*005O7dG3HjH00W~D!V>T)x*xYTWrgXZXe=FUF9|EXt(u1;v= zwPcjNgv-f?OUM=%S(npPOH3`5tVo~dq?EXx9|y*)?w?@?;q>nXC#7I z;&VV9f%`p|)1zmYF>b^%qo0j1W6SjaTPXZ5C;q-5Ec$NicTH=G#HIRDcOUQJe651u zVBsJn4>IcYvAl6yU-_j`M0GZegr@CxseJ!5?Tg45q)G*N>xHHsng! zHlbGW<1P%9Ev2_m0z`;o1u8^G1%bSTjFX-uo1QJ&()QYtD1I;enTm)OO(zV|EWWdY|%2+&1L$_S1gv!m~^-(#sH!4a7Oz z?R5>~nhLt;1`TCbi)+x%VhE0umSd^CwJDkV9T#q@HWFMKDW+J0N#z2e8sE7q!CLwA`(l9E3OaoP)j;#D~%w+NKwGD za@j3JGCN+Id8&9RFzlA}>EAkbB33Td5*roW?kgCBI+`<3b8Ewi|Fgo?l}BL<&WpUa z`O{wF>pya@0z%$mUtU;x-T(gHnTq2Tvw>WSSSxlN%ug0`lN<|rT?&*@h3_BNDBb0# z$c{ufuLy>Gzr$mLp7{Xtj`mnsIiNzg!Zkj}7+(H81U5smbobo%e9ZB6>nJ!gfw^ z0GsW8G34sY-w%`IQY#F5W!auzTM1@4mYHAy{zqhHe|yj2W@63LM(XGSx!Ixdrixxf z#4`e*xk_|&SRThYZeFj-EiNb#HH||)Sj{SjA|#Jqw%v5?Ei1bwP^W)rU3W9tyl|G~ zrY|A8-wA+w(HP;?X;xPLpyWhmGTX!rZeG?iBC6VeVGf{THO9= zXi~yYdvhp?ebrM+Qbo%I(cjIVEV|O}ZEaY7dsimB_}A+`5o<0q^+Wy+f|B^{_r(Ky zf7re;7>f5G+Ho4|S*7*Ut)lkh&QXOmhL1rF7t-hTW|DzUIJ3kpT0&e^Yh*jNesVu* zJ;wQaMbSs@)%-8f(r4t|{4O)=v6f~uwz{`t4U5fkX!(Rh)OD}tKoOVm(}@g{uO&b} z^|a#ua(?2GUo6+(FB17#s-7I4H(ADOLIN0%@eKmPy_(FU@^u>jw|_J8Q8KRkR`^=sZ)-E_kkcj2JFIU-2_@&1|2SB z0y_-jf#NnK>=$K=nvX5UV#QHi(7jLp?K28;C(8s52Zp*K~k2gF%`(isS>jAy^tlUdE4kq{R!2cJtxGs(Bta zSxbu9O(i)~1Mz3dM^-4z>w9n!z}=`L$WwvxLSfK_!(^)^edpR!I|1NVE3sYrE`QE5 zbpYFN7_CfbCt<|@s0X$4Vr^!L@8~Kv|1$*-!-%`Jh;Rl+ zZA?P_kB~Tz;7H!bSFgMP9WbRuQSRoQgnPE4u@xYqer?3IaRr)L z_h2xgTaM$L$1yVHKw%YZ#q#H>(C$s2j!Xes-c!c=nQKA~Z5<>DncitRcKKTK`)bBc?A-ysy`&S&Ly+9&cw`RX^_p zm*$0Nc~@zT1JyBaOKla$P-!s8&I5Y&1g6dF5v*A`I4vG9&(Je|KLKV09FyohI*$sn zY5AFpQmL!7i*=e-vXoa6juhQ}6l22~{!Lz5Ui(^Hc!$@it9V7MJ1d=9sp&_Fihh1s zNxDrD8oG>(d48l%D;hub<|c{M#c66vn7zYWF|TOwNz&GpoZ$u6C31GwN$Z#onkP)qLUf{|7+Dl!(pYa!yPqa6V8|3V% z!!XH{mv+S&Mj^h^5l zP|XU?mJr^IssYDBXS$pf?W2<7y7T%wE0*W@120lct{qJiu2?}N9!2SK3yh6I3r}a6T%`U;gN})&euzU&%ySz5UDU|o2NXQ5pINf zOFD}PyTlcFPtC7{f%5AQhUbo6JLx!Cul#ywyJ2{31KN2>FrBM-?`$kZTjuL^rW*PE zu;VjBiIT7|Sdj?Jwq3k=d?G0^-PPWdf{saz){FPP9iJm+ltN?$dS~gQ>}>&Zhoupw znY80K!$UE2D->d1b=7o7v2wgbN7mjdLI15w70wEk>_Vv>7U2Zb(`!mc^;ZW<2*;NP zFEEaL5aOTMBkHj7BFGfUAUj4%15J_)@~g9am<>*JrSEU6^G2f1Yk|~sg9VxZ3+dl& z5aOA0^_oszJ|1KjC@}Lf4>i%~zT?>N_>GZgbUwlR3VjB%q*90}k}4kvOA&(O(_i7g_S1xMHJ23L z;vX#LlE3p`=}9{ac6~OFeTb{`6|AfxJZ%ydo|>iuF>i(ificqERpA-DU4Z{4lY_Bw zqYD-Ur3l~>JAN9&q+S*@ljRLo!ZfmQ%?MLibC5#-R))b)gcjwSK?kr$_5*38#8`0B zh8``9{;m&XL$@!N{sE3f7$6NMVvfpNJ)4h?uPV5|?@jGXXWGTg#Mlk>eYqpDIqVpx;2nW-3V1|^0w zKd@^-7(Z~HE#%;h?8V>>Q!J@}_t2bG-K5_Up0D9S0OP^RRkCSXkc( zZ~GH}U-kx@%ySWqWndiQv9mX>_jeh1TKLnsjw}4?Iy;2p8x6jZ36H_}c^;3fxvOow zO@0SITj`5n-q6k0iI^wu9o+fS+)Wh5%AUPg|G|(a8Kkh8PTVuO)>D3AQ-&OSHCQsv z{bKbCsp|3Ox#sN8@6PM7w%MpBFwOqqMTRe{T90yE6&WiY_6rKo)o2s?HCn2TuzUOz zxPcW?>!fDY8UHTYea%3GNoA^-wfKhj0Q1C)EIICGU?j^n2`%qC`rfKNatGWgmDwkK zT8Z_C7%WSh$8TOMWS@GQaCPeQ$i34E;fHHK64_ud#-JjriqdbiDL9F_D8#uPvvywr z^@A@~y0gNTZDl_c5535<*O^i36S`C=iFS8N@t;qhp6cvF0DnG%N+~mHY6`rl&%a&a z+4rVPvjUTruB6MP0kq~*!CBB`;?MG3=_NVcil00|S>kO)QN|%>>+Hak!m&ugE z#Ez<9yV?cB6=82R|A*j>2fLb!wwE7M<2wDk!tOGWTDV1!9)11Z+XNN8of`?Qsm!jM zZV%%c?ls}JDh@JBNR+fr8ev>LfeBaY*`WWtJ#mbjdDLX9p3NlJ9dsACtMXz8P|6cXUt8PR1q6^2D*8>L*~ zE}Kp^i}q03X7{pv+8^jINl$w&JPDw$v5k+o@%-ge~nvjEk@H^S#pkqqT+%tB9I zj5M)lY6znRDeO1tk!;9!U)Rg**SCMJ$+6iiraYo0!tJ(l&9pSOR?YV&1?g1r6B<{J zH=Qy|=TmU`C5$ec=aEe`ei?qrdw3vkX57*@DVkXh0}SyTokjZsxf{N>#glJF6BuXn zc_}Mo_@(hjs*~^_j9cOgoQOUbh?4tTx%2Pq%Y?Ycu+jBDOWrf zwiU@dscAH7x%&2V-n}pq=V}Ke^_Rm2izppw@#vk;oF^L26jq*VcnHU#HY40GJk3bG zNAt`ZQMnL;tYS18y#1}(tWjXDs8AkN9}h)HhqtF5+5eZTR6N) z+vqJisECyf8T{&@e;zkJO5uA`xgEn`)?Vk*6v2e+6k_y1Ck@9tUEB@Se4@FTuJ=ML z>BIVj6`M<*z)^n$xUlJW2=qBi^Yi3d&D6W!tWVy;2wV(#ebc4gy+Pbo!sYrMwLdVM z^bJfo!9A%hZ4~5^rT-jGCQ(cFCmNl*M*-N+gfC!uj+nfzQIzwNyr3yQt0bw3{Z?2sY&uJ^(+sY z1P1n}GM6g2%Yh=d(#~V#oF~aNKaiR!JfVFNU2Ac!$_=tuFj=&|rV^{qGxw%%ZfI*l zXldHx6sl9;+O_gz`N(-9tyex=EF4QsHX>-BfxcOyRrOvcy1vFwdf#i^`qINj$jbjf z6%3mOZX&wk^fyEwYpNUCSM08Dl_tk~6=*Ms83ea#aCM-|O1doc*2j`MJcMY}B1tg= zMT1F{xJuPJwkGOtMz{F9=E?pN03uh^`CP?v+tudNblChia-~97T8~c{li% z_M{wF@3l5ae&m-y*y%+LtU{F{6#;FzKIn$Cd6TKk{@JJtQ?!j|rQT&kW`;H&0d5dse9}V3El`**C;0$v zsVunl4MEQPrvQhek-4YK4K$rNl=jZvdTQlMcd4-D8j)Ri(ML-1XXf8z+=*u8t6*cU zlx%7>ahwSCWtGUTyy&CCNh>+Ffis{pe;ENYw0Bzy`}rwtv8kInuzcMRHHwRt%Uqur zm7B^sYee*(M+4wdHuDwpc{SCocti{0Ogb$8Mb^paCRH|$%1UgL8%n$sx$2HEE@}$D zS~tGet9%)eC(8mUZxmroA&2ccQlC-1PPX)<)9p2yqcE1=Sr4b-XwISut(R(UgT=6}^PG z3xv3KDl}c45<%CLgd#*CvdqGQEnf^tI}}UVdYhb$fj&z!}7t5L44XG3XbNfbm z4h1sqO3gbl83c+=PDK4T zR)|V0^H;6Em*g6qm^Y^-o?h@-{Clq-0-6`Xitf`&Grv~ABSfYmzrj55i zw{lXk)@wFvpC_230OcqXa^=%D;x^B|yrt8rgt7DssKfGQk@c*W^*a5%dy)tX6BE&g z;TEKJrF&>*`Gl}{*=G*+7c?w_IMD53%nu?~5h{sq?C)iIk*25$DPGREAL9Ii=S`Jd^hk*%mlT2jq`=NP@)hEsu!4HEFcJzbpd51XjrM9#Aiv2 zbIO>c^#4N4^L6jCC<`V?qO*}N$eh&D?qRNA6mT?$Kk4xDa}Yycdk7^m9DRHE`P>iP zEdB!srLDDbr7nWyjrG$+H#BFbr?q_2SQ(aRZPW$?!$nw7>guhwbn@c z?FR94NJPoRv%{Znm)&=g*9PBE;=G$zjZEHL3VdWgM~mw7z0c2r>Q?`Q?fONtJ7~eZ zsAWYZ9An5imVR=~V&8lH!YNUqpM-X0D|@kwKTlrjdE4Z!@BY+X2^}2DtWdIwSR4Cm zZMAM*5}qAVw5~$Q?q-lUQ&rVO-FZ6J#HRUJm(`~E>RYPNkThdlZ~Mo&t(Z# zW!!dvba;>$Qg?B0e#m_Oi{r|1%=f6JVjX2?oPt@I{tqO+pw%Z)h#{jbC5#z-TMg^+rn=_^aA_e!TW=7!VbX{|onkZ0V)ihgt-(Cy8(x=x_AbwSZ&7z&{rxolsh+E?V!kMbJGzYv3)ayiWszRH!snU;ah8==VJ8PC=lCaq{HEYHY7($ z-PVY`McR}U2f18+7Yy*7}vM22mhVGIsG5 zJhAKC>pqOaW6s%`i{q0ESuG+~g3QFQ0w`n%cfS&k+Vx-4s|4{Sg(-0ZH-|Oa-HyoY zk(**}=TWvKm@lL#5i8(Se7IM!-el#rS>?IewB=2uv2DfPof4K-23-OcJ`*~>|78_9%Z{M3Hin&#H8NdPZ%(M7QR2K&&= zN@X)~dv$*$IPpDry*b&PZD z$EimMJFdU+Y9fN2PA<_dRTI3fJisqH58%64yxT+m9F7*wvt`@~cJoEAWSz)JAtnL{5GL&;7UYe^VP-HvW6Yf#-39de==+0V~$gc$bCQ{oyVL>!WrwcmCE z@Mz`Py%i1gLdHS2SkCgg3Cr~cgmKx*10%<3?tP-pwKqOzO2L$QqT_~DT09tGNviv^<}=`LC9At|#Y1R~vMaciON?!-Obx6&U2eH9<%y%@>X z?mQYF7?0rB-iDacp~{P?H>9}nQE{JxMLOKJ!ds<3Fm)DtA+BkyTjqOpje+6>IP(cY zyFh~J;5Wbw08t8abPo&CZTO%mx}8oj)~=ZZS}m5dzSqdy>mhq#j_;t zpPlGX#gUXa{lzCq$`TB4#AUf3sw@`WPSxuy(s4d@L&@0fOU?yU-bbG&ZU={ZKS&OD zNS4XGc;cVvQ0~!Y-}7=q!mh zVo#PnuSGX_)_#*0NAPQ|&VCNhnsvHG=&uWD<(eKU7K`tEI(wi|b5JK1xmfJEMZeiR zRkXKAHB)C_#rcO~j=k=ZmB(V|p%r$NA|XJbG+F_&7g0wVO}?v1SiO~*uN4%~aGI$q zQ_bZui|tcsmjZ@F|H=AusgIC3Zu2I|fFD;@v$#n=|Ky2vn|e|V0YYbOqU-7z z&$4r$(^LSxtnq^z+Xv$>7UvdueV88!M z57@aX@j4{zB!x38oLMEfEst3HtG4-Poq5`_fzlnc$6(B&ZtR4d5eY^Z3@k3MV55@xK_ilLk-mpr~Z7%Gjq{-Et^7kKc6TmRn9P*z1udJpGeiTy#W zq!#G`^~}E&O{SAN&jeC`+q-LQRYJLiPCED+H3-WTn)ho=d0}7V#5-`Y6r83x|M(l2 zqt)b_Vj?^K?lUsoR`e`l6qpLQVXdIWEQP7mvsl&Ira#W>TvmcCv?5|+ALE+6f_Rde zP$b(2REYp2meSGtEdK{R<=v&)q7~jR?VCY4S*@{XSy_D9_pJ9B)KprXc#-2{nzz~72t%QWKbw;%?NV$fm%kS;%FE5B4{T#EfBR^QKu!`g9xe!X4VK(T+gCXVuN7HhAO}5;y!c<^X_F*Z?gjINj{$Ld2W{!O-Btx zz<7dmb9@tZpOc8?HO%aj1YS0}?xnu>+Zy-J`*bN?z?AomFuQ+G*lpiMtSRvylR0{M z<`TIB;(asfYEHUKKQt?Oa{B%wt zC|UD}@*|_4GsmvQ2dWkHyIzg@b5S4G{9QfJQbqct>7Sq=$LU_rr;IxTAxk|cBFcZA zk*`oNb2HvfaPF4Fo^fmTz%VM=>IdE8y`75YJco0tO5SpxN@X@Eh2r=;QP_MJhfiyz zY}<0Px2$!Ymb)Z`9Zx&#C1}QBcQzmfPot8Fw;_E#2Mg01o@pmAJUnwWBPZ0&$*n}G zK#XbiJlV(xqwbFN(w8ZxiX0IAm2*wvvdnMRmah)%ny{lAM%pr6KR-kPnN44OBl5X7 zp^N+erFN06_(T2K_jUe4!C+VW{aDD(z2Of18988tCg1S?el_(_aH;Agl(?!_ z-tRe}y_AqVyfA})(n^fu-iET7=^jzjaM{;vQO+(Gwyb}I~5{q!%I=BfXF8l$;_ zuuCVHuD;#~^b-Hq;CT+r;f=t>BLi=X2RjbG97< zxX5Go1+xs(Sy9ZDsoQI{;5_z2F4l*^NH%A7r6=g%+V0gMdh{f09;B!SCyy(<5=uG= zVhRS3%QDe!n#9Nq+YOSM@|TcKQsF8BJ$ViLjMca2TAmm~j|M9P+J6dIrz$Iny?^Q3 zNRVcN{hq%3!dEa^ES>^o+X&MiGM#i$Mj4J`^gZlCc)dO%$lQ6826g`eP~(KtCI*`| zVexk}E}+Cs%@H_)qgW6nhLILEbrO){ND0;g8Ui4#$N0G26hcKf$Jf?!D=Zt>qXQpU zP-I7ONKtgY8kER+3oojL4!@pDbY7u^+Kz(m&X9Xn#MN&cCl%+`mKZ!&KWApEP(f>t z@(c@Vdfx5k*mro&Py_zXD%7tyL!f0-^$W;ZWg#gGah!<1u!;H$BAZ$RX<4(T1K@=R9+Et4cdO z=Nwf3z({3XhAfyTnR}{);D~Dy3&yO2a^!1=2xs~<)7e|hSSQULtD$_rm}6495)~V7 z52CJE5?ljfalD+Z>yCXh#BT-l=L>S7gPSUeL6-=pbtjhNsb(>^4an#C2da&*QPo^| zZVrQ~+hzmY`Oyd3~N2{R#aXMm=fq#mFY)sUeG^rMGm@0<`V!mns)r%hPoy(j_3m zGw!K@E$;+E#htB)HT5DfHJYxgzv!oULB`d~EbAUwSj#Jw*b}b3Af$=45=vj6Au*Fv zj?w2_Z|A(wPwraJqPQ-4pXN^neLz&bxF9Y5eVidm^}Be+d4*xSLhhcc_WI5=Xa_VP zkZR3~D!D(sgnf1EU55g4v#Qh16V<)$>&@Q)SgZu5%yEJ@%WBe=Cl+`swDt0Ka)rq- zOcxF%gg51b>oge3vM*LIOTJ}`MK|n)to5ho_GQ(z5#k7x9Fvn{3MUfPlKJhSenhiS ztn|1E#VcM13q;k>bf~f$6hpoI2gQ%&~ zzf>3F5_eSYLt3c#YK0)9F2}2Fwu6jxWloUAprGqss<-ba7OT&MxL+bVQKzOk5A|+o zB)JgX7D=EYK7*#i4F^s&O#eLl;jDVdwE$!%?><47hsi8~79VS&lUlIf5<3P`!4T08 zNSiAp>3t6$Ofi?AO6np)k!2RLjyDyyXg-Qhh~p5hA9|Cr@rfUk(NT3?-e6}~kx=mk zLxz)3Xmd5?ouRmn$*~AB*?n#}p5SG?fRC zivk{lQFrwdqz>kHx^aa8b1T>e z6;_1t+dM*?Jirm^f7v;(fgRNE&2nH;>A5%Mq-R%fI3vDin@xF+#n$5>#bz$~(01VV z=4D-`+jwq4$%DC$k&A2;iRbd&a(*t-$L&yOSz}!1&WA4F6+9|o(GteoEjMZp#n$@DKK|q0&v^wWr7CLoOb@p;K&A***E=VbYRUSCeGBrEa0AWeJF?MCEv*YLGB@94n00fx?Ewl$F` zTQ(pm5z`#@8)wsPPl2QvTv>%{M`QZWM;tjwC=AYU$)JR9+f>XMFWB(L+5>e?n(1-VP-k*+`1_?9aeQC zZyma=>Y@?~Vy7h*S6ezWgqv&hpQwS%744N(MDUw=rF*P37=lXaqcZ%m1JqaR7HQ(+ zcSVMB(PCI%>x)rhbO&E*7l%BgofN-;dib?T5|Ob!=4*XduPkYA34A0ENf>OBjJkaI zG2n|~GX;k!m>a{TuEoSgH%E?E(rN2nY^`ve$2-;ug8pjuby{lI5N`&U3)SbSA6I`5 zGn&>;;_}!U&Sx&a7u5$iU*Y91UPg{cRc!GKsL6ck+NUeMnv z`}T+aT^D=`Q42Em>lbCWKhDa)x)anaXLJ>h1|E{wIq|lMJ>E-SERYY!yS_kyyG%Hd zz+bK^JG?_pro+N%tZKq3ZKp(FMG6=g3qJ(p6&Cw`{n;R73s(n9*8nY4JjFJo*CyP^ zZqSnW_;KkbF?>E!C^8+Q>~&mDew&VQwNn>g@(cKzj+5G@>|nkzcB5&ygCee z!hsfC(z#{dKUV*){?z+-h}+-f2zIzuo9J>5F4{Vsfh_W6tiGhgC$K>bY8k$lm$nPn zhZ(`VoW=2thsQUX^idJ17D7VBH*=00bY9$gqdxBEep5zH0a(& zH-D)na2uu}bbea0xI4B4bHw6KO(ZLT6_Ldfdm&`F8+kYA>zc^jn((Xc3zrM zAGX&^{dC=PE^F&8iV8LNUM*-G`4kZBJ6vg~84dIG@-=#@+P&8~F{PhUNZ+wSm3?+O zrB5dfjb_LUBOOb__NY0Av(m=z1p*qKeI#$_=W*G~%NB;#`z6X{0 z>$$t!&V4Xr0C9f*QWIasM1q3|6gfQ>3ri+ume>K^w;^rggl6H5M!>XMC%|3L;dR^$ z(@fAhiBS@elNO^hxCdU8f)@ih^XWQ~@N*t(N^=jG{d^T7FXoxyDN`_i4mez--8k(2 zR4&8u3!P>z92FpmadwGKe2a-7!tu>bw(3@j?^zSk!vtmWV&A|ikj=H35JltE$u z>aS*9ezCq4cAFK?H1g?}bPOMg*CNLJxu60u4H4tkC}^+=q30f<;7lW&VosLvD#004 zTK0p)r#FUIW6{iZ)`@I4D<9*TOrs9JVn5#%?nRDLpk)Mlq*gSpV47#@n`b5Oo|T5F zQv5#|1Y6IEJQ<}jIE9w6e=oXDYNr;zkGjXcw6tlszaWqACXicS`jOW!{tV2qfK;*A ziU-f0zW{vdc1oPG*_2rMyy9ywKS00h-Hsgh4U8$B$}97=Ss?{l80f;FfJA4nr^@X< zw=vT|*&4*kNp7Dm*?ajFpGft{5|u2A^t>B^Gn!_)0aPj(##aY5^*WFv2GFM_PS}<2 zp3pkC2vSphWxgn)6nAo@zi_YuUVJ>yB7+ujDI*lW_nGm>YyeJfmA`H#}rr&xr4y z4rh8clRB6qp_Czb9O95gXl2y?$SsU+Rf~SlWo#XnGMGUU-U9Le#URvnPQ>G5i!+Gh z;%oA1Hpr_B#Sa>d7*-&nB+oXmIZ`Q4ZrxXS zh``)Y&6sZ$?Y5GFdosv0V;078RyAGB+A1l@Wu@(x`=Nxh>`3?xMCIx%J zCdpCaM635r10MXKGmW>E^fX~99 zQ>^c|-fy|by&)I;L;TWyMVoB2rSREb?`7%?ntiIxecZp!|E2n1MjLLY!m;WwzZGz` zwnbw1Aj`j)4K8M^oqRiW26eWqYhlDb^?n7YJlh)WT8CZLrUDqo3PAU|+FO&?h;VT(;bK+*=TI?qOEjAKy=B7ZwUika&lB*nbwzAm;+EY>oV50V+K7xeHI*&9L=AipFCk>H{&=vwr^jWe+cIfx>ZBtF+DCOi>pWi(h-(o+E)9L2LL2il zio|C$pzNXhn-Vw+Fdq2iw@^+u-D$GOSdqL1AG>ZVR~g*d2q4q|V2TDVc?iDMvRQ&Tze{~lbu31?a(4LNn|2UMH zR>F50JCz53^Z2`|AM*JLVg|$QSCR#OKf}Z4zf)%Zd)UYDLjK>^27E^KEmNX(i*lJM ze$S34Mi_wc6`7JuZS2`*dUCBeW1ZLE(NK0UR6*dh9=P;HtNCK2twSY<~|&!1n}p> z=9-Z`;ZAB<`&3#Z^|B3Asg5YIvYk%xR{?!mFfXmLj{>IYm43iWhI4`(1f}{!9c91+ z+F$h3Yd8|xJTS(AK6WA;d_jXqt@l~r)E&twh_D+!gAxLnO#NRF?#O|aozN^2W8Lo{ z(<^!mEfaDv;S*trzz6ZlOC>6Z5?z<;5vAou!juZzm5d#i-cs|RP$QfR!GOQ-q6~7= z7Pa;!6oTVTdqwLWE4#xJrp`U51E+mDp`*5pFg)0_J#O&}$cWdl;}*`%FDq{lJ|#}V z9LjvZ>u4PYMm|p6qm2JBL*4j^?8sa{%5uwm9OSBYO~>(w&kMHVL_hNL_Ia~eI5S5J ze`b2hYMbaU`czXK<%p4BloD-2q7qO2{cqmG;46M#18*PaK1Z6y5llPGd^}F|qC}-J zmi_D5(>&?zzC(hl z8;DGNvaEl1HK(;@|3#x)zztlAPxgA>nPVuIR5f8A>`kIr1M7a;kS59F!-?1Q|1TxL ztg4o~bi999yvc6_cr36O1sg_m0q{o$1oDQf&ciSRJ`wvO9lXv2^WV=bnc>qL;6ANe zy>wlPW=ewXGrUdNRj%CJSDdttH#vdG0C`^v4*T<0kN116d3P*{0Pt z|Dy2wdjtE5e{yroEjS$~_58`Rnkqd}CBIA~X)XpeAAcx}?xDDE$x|E$@?al9{lRxXyu8*{N`bGm4=orhXT`&n zcJ!(&Bbka2JEaAjQD6ydP=9+nOP2 zOj!L&6fXMuaR9Z{w3P0RS)}?4Pgo)#F7b{Wwa-1Pj(4yf$_B>i#2E zf6@5a?;l+hu#+}Z?O2|4y<7{}=9#t=9JUAkAPEcb{Qx8Z0IxcxlGV91EsCg1Y9XOf1;`k(LAbpyanFZH%H&NjIUt(L7)Z>gm6 zH@W8?09t*)`hH@@c53lPOZ+(V2FcrHhj>ah%RZPqK_g{^^=dh*nR{j5;RdYE{-T2Eb2d7H_v!1_B(&Y{a$94g= zugB`G7r7$p>Bk$|->p5c!FJ7Dh@~_)rlPN6<)tPO2uTM-TbSuR{O`UJ zVF-90YzuFxkfFD(l}u*rtncLI zuLetRU{chglL9yrPg}uZfTPdO;#N5^8J`$oSd2&R;4^2;oRR17Cz3Nq0zdXJTH00z zW#lWEnhE?l0EC!MQch?2Uru)4>Gk>NVU*FmoqdqNM!4Z4fOjpyC8L%U+xf_n{k{}UW`Z^vZfFFUD-p-^jlck%V`OF9!(a*gAoZ17yJn-mckPqt(JsrGI!_FHT@ zyt??=R|rZ`_J~@*R^!uq@u2W__?Z$n*`q{Ue8c4pv)21hB_@r88JO`v0A9(zd+|T* zP?T)xxbsqLs^zTx@ot#BrSb}Jp*tcEZvP1`1p9q&1I!#=rc3(dSN>~k8Qi+?|3NqZ z%X7oyQBXter|UUx4-=_+hn>b%;iNP55vNP61FlZ!yQy_G3l8wR#;*Vf(!v_kZWj`Z$1}%ch7?=2Lb7jJC`9g zR#4jSHK>f0wGE1REzJi%;$}tbUZ-(iYFf|RSXZ4)J#S_ycyH>1QnkbSVUy#cv)#@Y z(VPk9XB_r@{PAIDM{c&YHd!hmEqZ46mj`=^NBNY@xI=&jk#jwblwUNEMJN0+NVGBJcu)1cLQeD=j^q9>$lgp_P5qv zXCNIQIeE+u7H47R zz^o}#AMYqJZ6Qw~-8`g6ZfX*RlTb_ZrEqf7MUcTo*Cv;oB!-b_Bcki0*SoixCG{d( zI3e+yj!{ELruzBuD~?2C`KRCS^U z=a2_q^_}nX4l(*#wyk1X9@~n&Wil|canBguxDwr_Gvtfk^SRn$py-UL>;j)Jz90to zCMaW<0Ji8_Sj}zLC#-zOFI|VZ@>d9crX_)4sl~URnYv)nfazV?`u$EXJsG~}oqWt% zV85`YKVrxq=JmyV5}Xh7YAg0lGj-a=}VC<-k16K=#-w=WF_7;DdL56)k z0AS*;wOp#D&eiyN(^En&Ob@uM@VRN{9XN+kmbG^2fx2S9Z>LY{yLrft+&oOwX1?-U zeyWEcK=8PlD2rP#WP^<|)+|cQX|B*NH)ZFB!pDJ*VkO5m4z_RV%!g!h)BM&K*M6%C z88AWh7Ls|$1X-9)%R=SmoPToh40t7qAZo~hc_s|46i7{;tT#}m>Z>&C;$5qzf`Z(} zfd56~p)p=7;j`F|D6ba(vqIrsH*Jy$x67i5(q43u3%yV9@HYMxD3{z7&mO%BTvEMM z2i>_(^S0Xgh&lXwUD;@%PqXIZDH>fVpu!o^iQ#z z%RgWnty;U_`r{18yJX&yP@f1do@eNa6L%9c#%{;LbfzNayEpM*YhEC>IQA`JU=t4! z`(67JRZU_dC!Pga#q(0%bd3azMAXMec#8~3>!1cTG_6#>lvTzmOXIg`u}9Wk0ii_`^K0Vq;mA=5%deC z^)pqI#~~Sl1eNudYIOh*B{E!KWH--S+6t72QY-S~U^){AS}G!`@Z%0IQ=J|UORp&b z{XgImVCM;v`&CY#Y%^H$*gA6a5OEdZZW$l3{4_8Q zB?Z$vwm3@J9Ew!6DUI1slVkqmuN!ZXpHqfn2hH{KT&_-}IowN-T(ZW}71+%s^6EJ- z2Nfy33@?06fP2n6_y65_$@T%Yb=O&{makYmGFW)HKxRE-EtHjk79X!Dv<19lkT5cm zKlv;*aU)*zs|%&Xd=^U@;(y^nOfcU1^m|R8=qA|-vgm@jov>6MC;fWFMgG>nOIFox zBrkm%+$<2pF=f|7Pp`}=Ye8iHzgR745{dXR+F&cDsxFOiC0HUEeO56E8^f~Cq>rAe zIKywyAeR4uj(7dY2yP6EuTg0@t>PRiYkAVBj%;_5-jVzSyf_@+4-$bGSk!0jDUU#p zM!`QXDtpM3mgAbbP=dq)98~_cwTR=1tGBh;mLx+lT{?l_zo@jG1Z68g8LT%Cm>_oR z?)2;IryE!ttMN--H2GABneD>GZD;Y`Q2K5V@ip~Jue0_w7R>}f#EFM}5b4&uVY4(P zvtNQl@?Gj9W0@0YdZ!oAp_TSd9}xvt68-h?A<=B zl;3wGH^x(PC>+QGF~uIbN|v21{2=t4 zO(GU|ck)u#3%62yA6k<)6!Ufuh_?|(4mqNS$@2w{>p&RUfNz;X|KyaLJ|lb%25 zreqc>CrU`WhyWYC4)zgl3)u$yJb!ar$}*(l%stz_)32XSDM%gId}+`l! zXuu5A0B3~>2{~!tyMX(lmD0xPCMmhLxSro!PE|ATs;`^npt7ingchLx#H7kxm&M~R z;IC6KQcZqdyAFgk`*YhZGfsEm#_>=0xz$y7_~Di92oL-fB>kJX=|4X@+8Sz|)%PNT zcylN5tg^D{mxF1(b-RcM0#)4a`9()?T|NxG@1c#(o9(iI?6^Cia>qFwbq6ns2Lt^E z8S$si>Efdxz=@vC70UgYA7@je-a5Lb@L-UD+kuKXfcAEnv!(dKMSBG1j^9maE?Ceq zKKh-CyMZznX0u;5&~;GFuAo!SbS#?|D^g!Cd#di|X-O}<-YQ(Vol^s11~7p1{hu9C zxtHVLqp)$6FP0jsLJ@};X8J=xNSiJ;YMx7rJ`}>AF5w=Sb$TU%;kvDMPmNdDXK^HR z(msWu&O2qsogS!*|LTk~gR-3DX`R)&!TJeo-G!P=)B6Dhl+LYFbZ?~iEuYNNGH*jc zMaJBM&FDc>8VV;VO_6=wQzY0yX!fIn-Ov6_se7pMZfq3V;#dj{$^gC7nvEw{Y!X+6 zvHPIm$8rOjr>s(A&L7u^D2rd8s>u@G12Kb5xqqnK_&+^GwZYk{&Ssd^4!U^*`%q7< zN2ucNs&~OUiVibs9Be2p-fb7(kOjR{y9e|Yy3-5y(yEJ%=XLavV%;c4mN|A=nQSh7 z!8dvqiDN&M_b%4S6ZQ`cwA#ph?tZ?LI0^~^Og1QUj(BXT=pHs72#hO^S;FhNgPX`H zza_cL=8TBC(UJ1X?@#P;tNUU@&<&(MpBISPL7GIJJchqMU602*noVzI+!*$k&3rt$ zDk1y@O`nc{#j1GnrG~U5`j#bEc+6B@E{w=$z$!@JqH}olsNjXjRc$=>B&M8H-BKKc(bwx-Kue+^#ik4Njo{yLEYFw{Kt0K)E zvYus32c`DRe!KK_&%}H%bv6BmTXr9>7pVt^ya`TvVU_b`LN$J<+`W2L|3>rEhk-OC zF1ad2bZ+GFQgtMC>lC-qa%!BR5#g)%5n`)>@I#JJ8|D4IEAzBR&S|sZxT-jTr7Pl) zV!SH_@T8|>vx8ivtTM=(GRP8MymZ1=XqAP3EHKQt^A{^FC{lh}V~9|lVNPk}t&6Xd zS&TtG&$aNS%TjLd);UKLvSMA=qxiH@gt_n!B83A2Nr^XSYYb^xY8vZiIR;((r35Nt z7S~O~fH{A>$WdMaXx5OyixM?Q#V{>9_WoXUO$#G{A^NZ*G%GY}-yOR7a2Ij(m8#Ej zt&%XCN`hWiZZ22le$h+wP1ka(tJY=!Id#J8d-8V|e*Oor%rM&k literal 25736 zcmeFZcQjpZ*Dk!att12qK}3)c(Ob0WI|XZ!5pK0ZDH0l~R*=gyx$Pe@2e zL_~Dq!UbYtViFP(Qc}{37cXAAbcu|NjGUbO^5x4E6cm({lvGqySFT*4rl!7n^(qYw z&9!USXlZHb=;-L_>91eEe&fas1_lO3M#h^rZ!$44-MV#)nVFe|g@u)sm5q&!ot>S7 zgM*WklZ%Uso12@5hliJ!_xA1Ee0+TT{QLp}0)m2qLPA1!?%WX;78Vf^5fv2`6B82` z7nhKbkd%}}AP{%&-o1D4-u?UcrKF^!rKKM{cpxJqBP%N_CnqN_FR!4Wps1**q@<** ztgNDp{c2Cc{umAAj!$*%E85kHC8X6iI z89jdd_{oze#>U1bCMHjxJ~cHpH8V3aH#fJiu&}hWeD>^_m6esXwY80njjgS%ot@qD z=g;l!?HwE(9335lao;>R7y%pYHDg) zT3UK~dPYV@W@ctqR#tX)c1})CZfgwz38yXrK8yi1;`tgw+9Mx)U`e*Ea^>FMq5?d$97@9!TN z7#JKJ92y!L9v&VU85tcN9UB`PA0MBXn3$ZL{Q2|e)YR1U^z_Wk%&%X+W@l&T=H@UM z%>4ZP!otGh;^NZM((>~1%F4>>>gw9s+V9`L*VorKHa0dlH@CL7wzs!;c6N4mclY-8 z_V@P>4h{|v508$Hj*pK|PEJlwPp3Z`c!Ou){Bv17M*yJphyGw8v+Je+a2=47x~u7y zusrUHq``RqJPtK^7+P?%V&>61jl}6YD@umKcFPJ5!E0ec(F%SH(tIUM??RQ@qfEP4Ayx3f_O)CWZ9Hcd2jnKHXp8-!`Gs9kPN{Cc5H48X7b^fh*W#kgTo zQtGjp%l5@jUgsXiJ-Ku#RNHS0c>A`-3Xqpdr$&-pvcyM8NyWifE`^%?zkj5ML^cdi z25>C-ZCy;A5NP+f?{VSM8f|+GgSg*w9MxpF>N=n)2H>f7LO&Fw2$&`x^wR`a1^O>Q zKkL9{gus6n`!gY5=cj|C1?g;C`N;py(W|1V{Sw_ewZnRIc(xlrh>C+*{4}7WQmE*3 zlt8{CKmobbNc@TEh~(mpO~XytpXuhGzuMM|Y}R(;1W?m37UVO0)ZNO-Z9c&tRv4m@U>qkl1U^^w%6Iv ze$&z={odR+>mCN!=)lKjz}0&}@pFnXb-&z_s{cL%U;P<5`15CY)385%fIok#{@06P zU>4)4!CU36 zR&LGO?}Aty{uZQf)2l4IP01ebf%vyi*?r1TK-IeO?oQ}(Hz zP=^1-ZHbThl^HD;*C$Hz^Lk9qW&H+hNLO{pFFC)!!QC?d!E!>P>2Xet`-EhRbAeZ? zZch}wo8EF!c?#C%in^@qMZiWk`2yNC|MjK+&u*muZtcIa3R=esy?M->B7`V|>u1tF z`}n?zGEw|raip2aI1493HdFo;B-x5H#tU6Qw$s_ckYzTIG-(GkMb2uc+Yq9{VVX6; zumXce;?^B68K;PuKLLFhKr>y55Y-Q`8Tca$*#n!Fx$~pT{O+Wpu*xRXg&o-is4j@$ z^c`2)O2^zMWC6B?0g9Z$4m>Avr2HTZ744s%oL$0~Lc;a|+gv85H*fZ*5TIE@0*t44 zY3tG~qHxY5yh#wIHIEWK9+vtaBbt@~bR;3F`;AC80^3i@RYMlnitDs=H!fKv|6 ze7-6Hg)Fe>2(W377j8ccu5{q=qRNINjI;rRYkymW)SU>x@BRHNQrB7~`xW6$?C3ys zxMU%eNxe!^%lRZ2lw8nfg4(i(5y2fKf1u|s(s2XtGVbzDJ{of@OC@;*^cf+yv);R=&CJy?t#d^wGvv}#zV>57boWJr}cE$kFK7p&ca3&^0*&;MF6A1qP z8=xOa-s7levj6Ap0~Vwp{GWARr4Yw|Z_F5WrpdBpv)aj{+kz@7{^}Fc5$z~3g*673 z^yoimVez*V4LbCpa6~B)?wP1|_L)oyFR%ed@NoVkFe1>pH)L&vN;J00+gblqLWR2y zhPp`(&9=0&$y>{b!ItIX&kemhRhByfo#qqxs$}QE;i6MLx|V%2>%BAfwGIc5zj@Vk zK6CqajTm*44653+bu#EFIP9w($Ih{ozV8g`Z^v%B0zjS&S{QxBrH;>C$wkRk)_MYF zk&YN@M95=j#McYTT=kL^M0&1c-Gy4^QJVW+hp#3)b?xl zj_T53j`m0If@Ci%TT79J1j#mSy>>_Mm-a6Y+ctF+bR}lYxdBW8(01p{v~P7qwXCc> z5HKruWHu{=sfL|{rrB7;O2WSP#4*2q38PjzdAL4mH{N?l_;|Ids@iT z*Um{}qpOO*bGyi0$dek#0MY9@(02+%e1S^+N4DAj9qe27NtvT4w&Jml!T}a8LTHOp zv-e9(@6br!<-cH@GkduBX(T3G16m5=+PT1JK4t(c*pqZK8E#xG+$sl6rQhfjHAV5z zcdgtMP;9^9XOAkZliNATRPnqKhK6Is3{vRGC%#-4*G6^!-x?`@S;a8&`$ z^t*&8PqJ`qC&dG~um!EhCLU}^9E%&Q+(|>B-)=(VxxZYsL1*l7_MFqnNbFU3ve8); z140yIuLcb4V&U7Ttw3{W2W%fVD#$@e*4Rh+edt^BFYHI%U(Uvv@7vM@@X%j8GJ1Sw zBRH*Pl>l&2`M>G=*B;(0Qc=MJNAxjMoV(Ctw$2kmv}hhrrWaZDj20Rm3}>Pc{N|C3 zUk}kw%2)aSnlZ$c-r)qz#`mN9Si}DSMSy>qYcR>GH3q;+s5Te3**YfCNnF+8vjVMM zDslXKautCI3bhLQxZ+VD#_k0NBRdAfcmlsESuW((_rF?{ z+n(d7X`5({W87%OGva@*7y0dqBqWPH1~Qw)93iC#8LIp$L5^X}%i@dTSX&1|)b&;c zaw`eEL!L7XSXB%zq(|kbcwK6U8vaVWOsA4$QtkOdYD#}V2&3ZQZT`q?@p&1z#HUl*4I1uB#eWW?$ zG#cMPHMeb>k28O%2oHRrh**jYY$}SR(JaHh2&fe#vE9)J5KZm4P9YmA6Pnd`rP;yV zx~5#aX0GkQ1)#o^Cs?@|;W?G!%wMT~0=-0wWem2kDkXsiV;K?mg@C3uGq%BBW0`=+ zUE9Zysl#vNnMk}o+g^8!tNJ5Y`&Nz@ulPbb&iuX4Dbj3U{krmN!&N@66Dy!mr|WGx zBJWBS@QezrgV#ctbGGR}54d@cyH|N397e>zO3;WvH^9gBwwCSQg&a@+o0%QEV>T~w zWMO3~(oQ$;5+MTIs8U9r?=k+M6j>L{DH&(pdf^ft1$Jk* zNY8!)JH8gt=ZN6gdH1(Cgb2xMdFx|*;373oT{8&&xJ>GBQJjw9J|vqBxNfYte-A<{ zW2n9nH7&*wo!TRQ@jT*~1X4}wW8nb_lKKxz=qFdcz_$)SWqTSAVS)0^%d)P$AT9{G zkpR9J-(?6jEmr_vG#T|l;9woJ)kyysUovil%o1M$Th2%RU(!-l&!UxclXj+=fEAoG zM?h0wjhOwy%e$h0rUdpxmc>M6aU}^n=l*;6D1YRXY|RqLOn{Id(hqoO27L-gQ6f~& zSqswq3NR;8r2LsJNBM&v53K~S(L&V$eW}gMh+P~Imx6i?$Kq0bDsXW1pQ36rZ*{E^ zU%K^{n{S@ED)bfo*~8W(^SEHyx}E+N*1~-ku(l35RYbUTuGHF)R)!&<+%)F++J(_R zLaa}NQg3BoTa>H9o}*4zL1XeBH~wF5SL{yp=sL+ipjc7E0Fea^1#<3W%XXUP9A7ss zez6A6e*WhXCepy(qzLxU|J!URyMNv)G8#zwSN`kYHdegBkC){BsFBR}v>r^E4>=;^ z48^<=5#pLM-_~47 zR18^>Pfr)v)P^xS=Lz{%usErM20cgJ>TS)Y;wJ$)SMIuQ?5o|E=@O6eg+UUa4rUNL z(O#R&x^+XE6CkdlY9}k2ebc!g1%M!{gBxV5X_s2F#rjW4N#C1x;nXmfY0=jPa(JpM*E8>UL?K5#)rwkL9>=gcxX-tb@ zI>++oVHZ%Z87UIw>8^YKo&H+c>9RV1*8zZ)L(cf~IJ?&OWWHS0D9bhC<)EawV;Qn_ z=N?v+HZ9?z+uJBkDE$PpS)89qZA=-cA54Y4W88ijrnlnY_{ zjn&h2%D{LAAi`gES2|m%Sa%WULga9kUB0M$VcA3^4z{**9h{b81jV2kv;oj-o0r*^ zci$9oa%Z(MJhOn`?U6*VAK{@b@X$7GTT#`m0a}=*<~dKuD8K4PrK5aB&|zn_jvh~R zlu$Cc;P(r`TcoJf?GG)HU2mwNLGaJJkSgcD>D}}uTfTq}TnM_R0KY=lsR@WLD%)m1 zXI_h-Nd%4RZkWX$2!m-8f}TLog_DG= z$durDzcwGXG0uT4Q6-Q=`n&jZ^kqd8{~iUfh`VyO7wXoDisAObGU@KP@L8$eoW!SX zyA?`Kjif;airg^N^sG3ex1)i^_xtDXV2hL;VDN8sCOG96tT62o&tG<>1}H)lJOId( z{M312CVz0$HC|oxT60^Z3I1OX1^@T4JbTSMEp|ytN&Dt#Thkk7GKvq_&WjXxI2e>X z4tMg!DDs}R7#%MpA4#4bpJpp;ul8bA$C5d-*W^w!6mDYoeqYh8D^t77J-!Q|kq_y@ zZFXR#0cN4b$scY|v1X7s(AeEeWCyT}t7i%R;-49{mF!APTwWtKQJ#pUtu#2`Hedit z2dTkqJjeJC-HAlXNNyRY#If@5ntyYZLs+52hs7t%ChPBi!$w! z`5SE;cdb{4)5^aAHUiG9QKD8Npvx}gWZ6!|k ze624=t1G!XhF#$TS}W%(emjZN$ID3kk*P}o%ug%{qM6|6i~7HOD2Jj%sStrVT}LUN z80I-MOtH!T%Dkr(>l0KN;LP22HLyO;mls-|S8Go_46aZRyt!UexKu1ufZ6@_&VeXu( zjqXog$=g3!7)-o!P;{ds9un>#V0(Q634gSsDlA+gluG4&$_G1dLmrG3|o^pS>zibkLFGha0U z{P`}mdB2a|Q-cAQ&TtLV6ZMd4Aa~$I%|wa7wxQO+c{*s>e)BwNI6cECcXS-Yu98@c zzUX7>c#yF67~C&;v0SKG;>8O9aauD~!tnuq(QeI`TgcpclPjJgKv{iJ^<*{gM1qB$ zOsCIUi(R9$${ITpzQyQ|XXZAE&c$hOz!CVdPUbwlHh3O2<>b?z+b_!P+0{mBvGgL6 zn`2Z@gz5nsF51fVYPcxSr~i0I{HdN!@{;{z_w=sMW0J-v^{4NJ1+8*cNi7~O{RpzE zOjvQF28h|WI?Ya zTwaT(BiYWOD^Gj&+5$*$%Xdhsin=v4!aO}1BvwBg9$QUx8sZ~b2j^;iNV=+e+^(US z)oG6AWP`XyW?^N{366NrYH!dc_vMdjUPR?3pXRSS`c9r^b>CY0-ctu-*&n$?g{Gk9 zn?_2+CH4OZFk;Ex06h3t%sDD}x9;Mp-$j}k?E#2Q0#r(*h`6@(y*XA^KEU8Qf0acv z0XjO;&LD}%TlEFlwyPN)Y9Ee9KO4XE&5)dc>-z{<0&MKE393*nC0%-t@d~zOi5J*Bi)p(Xjce3qPbvK(wb>E2{j65F86Gsc!dXvKGYu~SSJkP6xb#2H z#SA9gLBYej<@NljfOJ%w=gpo_HjF!UjrcDZo;EY`s^5wO_ z&33lNeR>y3{VIkmZtCXO4(2=Ld=8iz_XNM7Bi7U6-<`G59o|!s_-nD_JV1S<8LQDd z=5BKs>TcArd|k$g+<~1&jlE!Ighx^R0G%&K2@d+H2?ZjY$`ucN9gbeTggh`}#SwV` z$8)w!U@Ew1>smYsN4dG8E4;# z#bR*EXS^V|=8|`M@O39E$*2l*2U%m|pc1SaD|Q2v!fJ1D<|me80X9QA1ozrxO>J7c zu+vw<`E;Ryzgz(Lmbq{A$@KjFNjG|ghJa3n&P-pcDZu~UVd910HPJ9bK2YJQAZRFs zF`o*B8F^5j*e;-JiDxhkf-e0v>!jZ%{daKCPFa4;gVWME>|X(kE1k~A`4-kzqT($L z5-Qt;-xNVV2@(3B@7CbwB+oUMUP2tFcTc>goTleu8wKBI0C9!30p6>d=T|8RH{c%j zOKtt53cvVEdT5Qfy&j zzhONOh?8785H%chw^6qHXmsV^gM4w6R)AHTQX?wK)ZvrtVKIT~x)?B?3W%sY#U6@E z8pmtC4HkT(~zwGA)k5uMHYL!+HBWe+J$U?QG zj2wnngy}DVV{enUo>@J6Hb7_ZtlaQugnE#?Z`*q%a%%naLQ;AHyX9b%4*L^#})B9T%gOy0=gq>yf#?O(#51?rd zj8g@lgq+ z{6=gjy}@a{sjlJqoUAIWkNZZaFp>7R0uQT zaMd+KRiLwGu6=eZb}ciar?K(2k`|pgM%;X+X0c@GH%5io3}iSY1ht+r0P}{f^<3Qz zF~e_I1E?LjJWp6C)FNwDbPrHb+Pm?lXXq|X+tu@T$yh8wR9}a#i{@ipra_Cr?xEnZ zVaTXAb2VB0db%|{xwmm&U=YUdOGTEqgB((`@w;|m&n8s8Qi-Am4rM&VB?a%7`;BjvW*H>vWD$w9&mTk9~W;K)kxu>-_!Bh zPR7>MAG*w_LH!=|}`_G)-@R*ONoodNABd$Wu(%;E&-D+0<6@lmmy zfsGXs>rF-8D+efk8CeRFPvnb^^hOs3nJVt3_qz9j_O9i2r;|S zSn{qDe2`-)h)3qWFen^_%p|rVCl3mW#Q0x@2RJ&?sE{! zWzjbjckAcV!!cB;Rg<5_%S8H13Q3#K;&*Yd?wV{fYGk28qT%pT0#*88{#L9MzQOND zpaT?gaVX+BHEv3@7EUMp{2-sV+184vuiS^i&T-c1=HT2>sLsBeL`CnSYj)KbGH8$oMq5Drqd6QAc?ZRe{?}Wt^ylh5uV8&o zxv@2ua+>j+-hczlpLwNDy2!Q$I(EU$n60-Gv)B6BcK!E3IAo(jl|7Fshr`O@aid5@7jF|Lv`M#6Jt z-wV6RSI(){a)S}t?%SlZVw+y39NOh26V;B_?JrDcGu~zlRK%Va&DtGI(CEo}z@@at zQ*Qd(CuYl~q~6UuC)q~4UCbb?MD)*`5rgHLzU*K2`AEkgtqD|cPwR50Xt`e37G*g~|y`3ukD zYk&z;m5|e2q)-1|fnVQB<|OZG@G0;2JsE7GJ*3(Lz7||Z6ioR&IT|=U?X)}Dv}>`uP?;~u_r9_2JUF$DY?##-t;aEKq<$h; zM_YvstnbiT*QOeoq_k&Q1TgK;mGK}7W__bXa2KMN6Jt&~_9}$}x5yErE;=Gbsh5~` z$QB(yS&_I;8zQmY>eRR(FuCd~c{n_&v38n!$d}x;%a;?Za9R+Ui$E0YP#yU0^Ccgg z9t6AmeB@K8D*@v-oEe~+}L^3km`Vv^w$rPjo%1>uvFEb*vbt^|SYP*p2ovT%gh*_nk6v9a}FJb@XXf zTWEAv!+Km)Ic2>!wFg>VuLD<~mj^bJ6vU1GDCKG9@ndz3Ctx#5NRm|)eVAA!n(Fx? z)*uBr5p(!k(tAg!;YhX98Cc-~h_lmH+3v7)2Oz>h->@FCLaSQEaNpEi{=U|;ydHEN zhH06E;@o^mcUD8nUfL`$q6S=5T}=5%n|zT6X3?G`U7GZ8@bh_YmZ}n%L1^hxc!}tO zSHPtMttZIf;C6S6(e~oz8>9D=y7(wsTog<|8s_BauONaPxFvBTs$g+%oDEN15as-} z#}1)#D_S-=o7k|J%ZR`G+-*gDu+a#?)6~4SbVVt4&zqdOThcv-uzDAN+Nx^$~i*RJ`Nfg zmy>TVD)OiY-^tH&eP*>jG}SW-Zt-wDSFL#Hw=`yPK#9hn{^#RYgciFan|!4p5=vPA zn7cp^2ZcUAO0bH3?2`g57d1hdvYvP{sU`AP6p=;U+@8!fyj2C?5ud$VwME5zun;m*~!k27zt$sx&_VB=Z&N&QprLn6drL9S(D+trV&4snBsJe_>B zFQVu*V2M^<*C4a!t}~Cb#5?vZN6pF!acNvH|&)cc`uN!)0mVU;i?j{(x4TzR{fh+>myU+`}N5cfP-#EN1;M`RUq*u zPZhv+LDgy>qweqEqr8c@31obbzZQ_*fVa}+U*l9t7QbeVFd=d@6V$HimJ4_kTLB(R z8b11iHoM~i9lE&Btx_UjNXQWoFTfcoP|^8OA2)}yX#aL@V@u=q{L4>2ak5nrcH$tp z!2!^6&A`xFxDngKw(oq&wshE_W*z0^skjar!jjZpFa#0k&R0kXvWz&Sop)QfB%L)7 zgOlA--WV4pnmpa&T-4j;s(KfBwb{m3bX@@81OxZnu=#fDH__SZu(AQG7ww0Fb_JY? z8c)G>FxVML3yC3`tffE{Nil+68u8cJu3t+gY8_?^ELkPZ>p8_!&k1znIq6=e+kFX? zcWEBw*vxZpc`o<~qJ%~m)sF1gz$(#RgoRwr1G6vKi{waIXw{MRXJqr}Zl9e9)i#SL9JNh@ zQH-q~FwwaHdKj%3n4zPFtXOkD2ni#&>3y_d+(^vHgxJmK)(T~0!N#d(MH3AcwC+)JK($A#)-?DhN}VNU-G2ZE0uj<=bKSWbkuP2Om%htbVnN?`pW)xdMqwwTV-9$*nxB)P z!@k0GqL!t8wD-o>7%Dx)ARAI5S^x_fL0k7dxHnJoe5PMe=tRod(KHWy$EkS)E4yWd zjdb00@$!WoM?X2L`Y;{RKF7IxVMvs_fx-w3nCE{s4xKzKkgC9>#-a!p&N$ z%_8k&U54rWJTD%4nVk&6T37&d9yTJvx`O$=JpKkkupnY8^)O!}A{tKxT~D!XeTIrf zX5A3aMzgLCX*g87WFbd8t#<`Av)VD_Lg|he?YgDBt>MjYPI5Mr;KtIk@ylnuagPYc z$$6lQ0J!SQY5l1B4v;Dt z-F%$!Ddr{UDDmxgyVtG=Fuef5mkJ_sMukm&PE!e#HJi+f%pBGqH z%}L7oZ6qZY#!b89i0H$d0zYAWF22@cxF|d*U;}x_wb_xjR97^wgPZ?hqt*NNhCKqL z$KN4HfQV83G1hn;*f$b7SnEDtmR-A)gZY!5~d;ist;HVRYXj#xDk#t}9p=qia*!hr&hfzB9bOXNyERUIE~$?)Z?)fpv|U%c$k$Q+`=MB*7ZTY zq0_XLwv6FQq#xtMb zO^-NUjQF(f*8JoT;BY3K&%X@uo>sR*Y;=@_)z*l1ys?o0eqg&=8AdDc$zXE#HD6{= zVFO2$k8C3M1ZXP731041*u15ZbXwx*xO8$n`tCq410UPuJm;W;_34vcGVVkikuigD z6DB8hylQ2(tl0Mg<*;nU0vdmj)7Lf?Yjq}iI?IwYT>P!UY`=jmN-INwZxeK7*UL53 zvyjS~+PkaUmKlNCL?{E)0F}DR=dg;Ks7{((lj0J&+H`+P3&6ulKvCY?O)N3y0|7c} zt3YdYL?YdZD}-}s_V(^2+9xUi`ni?JURwouTg4C1QMja$*L)PE>?^%XjIb+svk=`m z^xDr5xth$&B52{^P}!~Us+WGs=z?Q<#>1f6)sZzHfWJ?FV1)j&F~w>)j{q$Duq;1} z&IAdY=XAbNPoTt6(Hk2^WT8MfEPUR0H;Q?!`xkLGH?4v=s`y416)SMnn6q?D;QGF~ zUMX?*M!%DL;ugy*B7|LzPDIfprHFc$P1*7s!{FYN` zeXkq#q=>J1&Q%1cZ9$Y{#QD4of9gkoheCmtAroH$E6DCpr_^KT`DW4QP|W3>A_EW? zy*IcXrX@W2qDwn7;&(I(EWDy_$kYxM=F+0nyUz1%9uGC;{0WZMB0;rBvOdH%sjt}* zr5rD2_wblPyupOY(nRGB9u z*rMINjMttnL;V=!GAtB1vvrH?(nKas=Eje@kiI+JzcCZ8mDNYD-pDllJ&gwkeKCHv zp@ym}r!c9scewJ~1Zi2Jh_JT{s~tDUkQ<%x(T!bE94Z>p?R6O&9-|}R7rZ?qo?R>K zQ1ksGBNftbXyJ2?&hAaqN->OTSEc~tvMXg#+1%;EDm{{NY|i&2Z1nzQ(4b80Z_>$Q zW=!|!`_aa&48ut+k9prr%KfO@k>RWalt{nf&|krdy^E0nIpG11vN@-`=M#alpFYb= zdZZZHQ)hW0fVkXmyH_SCu~u}(+l&z1Ly90GLDXXDhd36>>=LI0w!XWK%O^vLzcU(@ zj@;wdLnZfKNggGi=9N~ZK6(rZCPGwo{ZHH5S0jc#R*8x~(O;_mM(oKL=FNd00O>3i z>Sl$Qu%*TZVwZK>#-^VO6)4?8y$uN|Jk^k%!*42zMqko)_H#>a^i~XeK3|rH z>)RVX{{T^OL2_65_^@f(GTz#fVSy};epmUl6eRvGBgCyH8f`l(i1z1J3oBs9cFdcd zS*q{g9t-{VsYH!FfFvQ9X5Oa1#MrR(EX#ahqDz;q_`-)RL@EtPLb2r>welk_F!U+> z`bMOljTvt|>X%SITxccclJA!&m!sEe05o<@A*O1g-mc&%9MuJq%x>Ke`Np+d9~h{> z7F-I#HQ1jH=h1TDqk5FI))mE`5Tv(Yd1a-eQ-vR9#LR?}g=dx@k)pl`M428^2L?sb zY4(9MvQA~;^(V`kI~^m!6zd`H96|DTUGK2RNU@1j71*hnkiH&$De2S6NU`fVUd%{& z*9jstZBH7=svtxS2dNIUCZt=O{Z;8tq4hL-FJod>#o{(V4Jq8!O5@U(g`S_1f0@5ny`tgT0zGy zX=ej4RN(?rt@J%0)&-0|3!Y;qVqW>Q3qTk4AEuQYC8)JUzgZ5$F0JUeAq~4x*G6f9 z^sqHA$RVzE5qs60vQ(1HLkN*#invxMy*o^75-^1Y5KDeA-=I~)D5PlH=L^eEu;aja zMQIP39!r2?EzY#REAM@z(%5$y*A(cLrIEweHB|kwEVt40>$ab)SHMYEOElo&r=+R* z$>Hzd^5PC-J+uL(2OJi{Hce@;$ORl0mdgQd&8yA`ncd#y`_0^oC-aI6jZOhra(5rB zzU5qFOU7x>cxgD%gKuoNZ0v=up6z8|M0_M>4C82Q@Qe^@PK{ve6MpB;+GO5gnoK%a zqxjp4wH-^sTjjTyp&DQp<)!&FpvqP+U>r~TTki~gxPIM;bmDY0 z4zSW97EX!-Cyf-W1aA7B8Hw0-s|; zML^#~TUaBBR|hA1^hwCL&G)RXYhpdwW8hZoK@MnT7;cmEN#@bp5Be{VF>bLZ1GLtw zqoKeS7$`SygNXY-@SU=7=FuM^Qj%sJKT5%&a-GS;Q13>~rmOx5P}7xQ!36V$g5x9B zCjer6rzT#<-tIP-8kC;q>lp5s+T|&{SD#yspibxB zhs_7A?TVRZ04tUicgREHkHKK}MC80uqLG0}r-KnUb9&3Y%gV%r;l+8239N1J^>e27 zlPOV-o%`BX(F8qfh-AeMJk_kZExO6gbpLyo)$Q06NuKFo@--&n8rTKIPJ3Zn%i}7; z{=pG28UUG|$5< zna#~O>~qA#XWT*GN+c=LR$#nKm)9|m&pAg}!m-%GPU_v@#}u>Wy*EXEB5|_K4{oma zJe(4$vEwfdexlq>ZNSZgnB0J&y(e83&38_AD!KK+Yyv)kzu5!|@R6{SRY}b6(3Qxp zE#YC$A=e@=fM0Ke@d%q*AURQ!INw(Srn*6U5`XBTu?`ubr6 z2k1L^A$;VweZn_4l)n$iH@JAs)D)Q4n}Md!Vyo1mYk@&!WpP?Yt>X!Nk1d`|?`5SXUtjVKsw5g*F)Rq84{56GW5^ORq5m0Gn&g85A|KVHdj$&)f99Kp*< zfe5cWdtHy+n79NmfcS7z-aHwz9$KX+1aXV}eo$`TiGm`F`kQ5aBpbCNMpdI&GVK$7 z6_}IB4j8TL6nA!e(?ZpkL3vgKq(VjxgVDs>+kI2QDoP7jvh@@GhcG7tuz2M1hmAhP z4|IyZvEH{U1!@C53GE3u*-u3yvdm((9={}o$gW@K-+QvX%S~t(0pEC1AY^1oZ|z~Z zOA$SGciVR`9dOmwE>qURBDjoy6|;M(Ocp%pCAG{ z`h!TR!^rDcZui7C0)(_iw?J}fpkC`=9=Js~1!55F0b-2Z25iP^_Nu`Nu5eHVlbtL| z-3bU$2RX=9`A2=_Y-9v@u8DwV8EB@e{HbvnQJG=*fa2U$Ftu=`>F$D!Z1@l%DN1?{ zM_=;hG3j~?|~@X>8swwW2+En)lAf2Cu!~ZT`Gt?;y;FGR-=)HCp7VP=b7GO zapE^m)z6^D;>)ui&a+sw087=REYRn;49-#Rirp(NA@L=rn7p(5dHj}m6bnd*jf)L@ z!fY<9x&t2<=5e1bz1ybpNYh8HW3yx~ecW=kIPr#f%(=c2$BA?~qFszFCxjWYolzyv z<`O>ZD2bCv9rZdiJjVxXi#~ik)nT?e{CCHMRQ8iJ}zgw2aje5#hCaoH<4MY$Sen$}C*e@%WdLA?|$w*P3p*mt$6P z%_eN5#`^%hk&8)t?(@dw)w#aET!6>9>e_=x1h2|%ui0|*ylN5t&BdxB>pqaA2=PE? zVOtwzzu0Z=55d_&_1hx0%hR|e^;NEkdcFw{c#YQGJ)hGW2jz|E(ILheV_n{er}kt& zMhMer=-SvPe^@q11CwY3Hu0T&4>ANFIIK{9ZXBP@R5%&SWvNmiu>|MGB6r8jI_1yQV^^w z?IrI$C4K^dcP{8Ftqc$nRH;NnRlVYQi;C0m>duz3`b zy=AN9H0lUY%FT*KY#u%GRQdgcyO_%2C#ZpAk!*)3#Yh?r9_Y1X?DyHPhss~f&%rDn zm(v+Gx>&)?pL76a_K&R*i&x&3s2>7g?v2hTkZv>t@Mn|U@qb0^RoF`&U592Yp zaG_y-VAK_x#R)6tb2k0>BQen$y!&9gOlcTXzqA9%S_r-=f*c9zgu&KSYGeh8Pyx}4Z;nL7P!n%G?lvzg{YkR4h z;I0|Leuyfk0=W-~=+JN&w2`onbj#bbloINTeYzJZmaIhe|0K|ZWI!A9>F=r3R2$ql zXGxv}s25QUHTSemzJ`Kg?5vCLU6EOpP2H`v7tb=Lz_AUt-3xw80PaGFevZB#pL#8% zjD7OnKOH_bxr`8+dw7=V3VI-4DT*`rcs9KGSDR5R$fVZFe`_(|g{5THFN1%oz-^@4 zx^L{0J^+QvSvLqPgTx_nr@r+gER`Hf)BcqBP$+9|Kan<(mKsYO1v|sWqp`er$Ts|s zUvtsAD8#M*aRW?oAUwMUg0Q0Di*RRC*&v`Ph-I^C7WU_m7X*Fx{cJ$U2t83p3et`p zwS`hneU{1_DcQ>LF;@s-!rB5+K-uM!B zJ*;4LesTD+Y(kKnSOXlKDl!>{{$Tk%;n;%DQ)d|@&!WUA8!R_Fc>5h)?0M=Ce{3YE z1{nSzwrGD*&MuU2kxuEupQFrXNQj09e6`9-45w3l{Le!&v;jPPRNX%j{5I8pp5mZA zTz%&)dl(7XAR;uheH4HiLt%y@Z4X)4Ulina9$^kTm&53=QVTj$Y=$`KRzg&UhnfAq z>WOH;oYJ;F;9!%IAZiF1NP7ar*+8n}KMCWwDD(DSAMHwckgoSff>3Z*)6dsLB+q6X z;9vfmU!*R>P#@t&DsrT?0K>!LNAUblXN0_I{Ak;?h4(Q2wLke|h-MGUigzu;F~I+U zJ6_sx%4}Eq)cT1?)l6O@Z@8!+DFy03e3XB$c|;_8oOwsYqSoR`u|bocwlCmjETVnDu}l0Xi5U%6W+l$Qj}1KO)ofj+7FiK-qt9k1h7_Dk?6kXkD8rm$TfDG4cIg~Cb7Z9bNifq8Q1*R} z4av3rr{q3NT)Yyzh$a5uSrwE6%2`4|h@uh0(tX$r4H3Jf@`a+@Z^Xc>9MBA+`G7~W zi3D~Sv+%7W6f7sw+;_-?@D{*Xv0y1l<#9IXIxl2Rw8dMcbDoNyaZycRN==R8qqIS5 z>{!|fEFw3+yN`yTixxO*GPNStvwVNGClN5fz}spd8sS}qCun*TH zHcHjh4!5w1#j&t_G>3ntK2_H@ykFBNPWWA>(WT${b3p`jtTtdZJ)j=sT;|0i|D#Q( zXW?aqx4jtxdiHQhQ9jTDfY}hP>5>v03z*;!U^wRQS&2SX?W&n&G$~FYQhpISL8Jz>{1r9 zW9>b7l6EYy8FPHzrD&7mY{oXO71fz>KtUJ*?pT- zt7jLFd%i)0Zd=>pCm6c6Tikdqs1ow#V4%c=zT^J!TF2t2aD17qOQ^R_H5~iuMOD|u zFBg}=ymx$hy_Qvv9;lQy#XcWfPL3hBM`X416hzSxsXaHK5x%}HPw!~p!J|I@MP_d| zYHQy8%int&jyqh*kC$J8*YvC`jwe_9G(V6JVl^Hw%U|j-dHcACjeGkJt`Ml*l}}*L z^ujyk+daNaggqUed=X{Z6*TuCeWopstvPc~6}-AbRv|*?;Hn@4#l)y6$t~Mue)`J% zKN$(6C>tAQfPV|TrUe5QG6rpA%2JhDN0ZY1{CawevwI!T6%_}!ceS*b1R&wczzJTF zLX3Jd=+n5L>u6G?XY%hE2EbL7;kRXq7LeOl8szJFTtoy~1P`Cam>R^J?^wQ@Jp7j< zCWC=+{>Rosbc*vmFl!B9Q@@{4TfJothDYedY~pr(mTWikx+;@{ygNO8henS`%uVFW z>c!SmbH&*BP+&CY_6n=QYKx{1k}>q%Vsl$o!vsdMh1qv_X@+jOzj1DS`AsUJ#c0m{ zedFRO1#U1;6w!it2oaP+#h-U*IWDtHvf0A|Hy>I3ZaOZRSZeIS=9dcyE}-`lF&RJ-1w=$eh%#wHwNQyb5!)`n99JVld!S;@`^PN}eqwa%?< zA>cX`aHl@(svmiIeC8{OmdkQ$5Kb<~w!#hM|9UMJpiIjf@H#=ul=e%=#n%>KEIyc3s0^t3a<^PB({`@bQ zxA#8piYn0DzWD-NZX#8HjPnXNO8%`*@^7&N>We2v- zM>amo-PEmoo!AT(VGt>l8)(bBoeass$vdb*W9 z3!0jFqOz@p6)!K7zGb~|m#s>vciF8md*uKMthdx)_=QmN)E7R<4_i9; zstSL>e!o1`EV`#$#zmKtY6|6BpF$no4=~i1)kfsVI@8yu019$WTFDyZd4fI6 z_0#MP$C1XVx$ci!4q41a;XWkwogGr2NhGHob{8DXQNymn7Gr{0x^}PL?eW>&TMM?N z1c});6xv>gpqEg>qdTVOTy8#7olowcU`_bNm-IN;#jn@~uUvW+>EbSucK{rL2l@B4 zUkd9~bRdiPgdeBk`pIuRQ<|@=rXSY`#AYu0X%0as{#1-b|69^%6~7=Vh<~YJv$+q z*%K{X2s9zXXzqJdBWu9h@|14i*P9Dr4Qf7)6T;GZCxm@qmjDJGSf7s3XHZMMG4tHs z*}Hs7F=dc_k%x|pv-KTv4(F#g8`U=iJrM+9s*Ug;CtyiTMuxx%Z*Z%2xuTO_s&1I@ zz>-b5d_vnxE=;Kkwkzyj+FM4ZUOJNo(|EJn-59mXWrjw072=uIdpwC@m#-WlLl=0A zd;X2@%Vcb7)-sw|w^}UEK&96UzFl8s)+gyzVOoO`I-Jg?#gx=Cn#M_`;dMd^6f{zY zKpq+5z`}d7)6n?F0KQJED4RK4Oj6LLSChg}tA{ZJZA|OKrcrL76Pe(~XbNUU@L|n$ zQDzMdFzeA>INxQ=G{1>X^*^Dzfj7_$J1@tJv8m=C1~!jdM~taHMM3sp5x?hi{4__# zoygyDz~T2zdJKOPs&hY)-D-dPf>e2Sa1cTdOfbYe>knru6(xriuIH{5_>#-bgK*Rp zyTxHw;)59V65I>_VNHH>^*@7-3Bl=I4NZY1DC>ZszH+8Tcl6v~n8b$Fbqyy??$(J@ zgAO;?mN?a26-PGIipfGvlfeB}MV(rkSk|a~WJBGTQ1=jF&af?YqRMH|VoYO@{&Dnp_yo;`cP;c* z2srA!pw9;+KtCRZlZitelgO(BKP312`qclfAQp%bOGyAq>JBf|Dvl|p5_1tuRD&M- zkc(BD$)|d=mpZt24YZd(?2T*Fod|_lar=yKh~H1<_)N)B%2FSS3fF(eC!*HlMVJX_ zo&9Rp)P~kk7BdAP@NLube^Sc*I!>3}SS&JDn<(Ad7lj3STrJx~g^zjKvNvz{=Gc|7M5Cd!3o>Eg~~pZIPp z3pDEDLsm8khOH8R@2YZc#z6Gi+q0DZCJ)vyW*#4C%{*!)S<$tG-}k!?6WQb?f+RhR z5k$&`aC7KRkA>j3qx+Ub_D9raRh-gjHR;3<0f})GKx@0BG43-U75=6?ZH^V~MwyY`jj~9ik-a6Q~QrEVF z8zc64ERRr$xt*h+1Fu@KYN{-MrlH;;4G{^jP=+TxJSj3%B3P3fcJe22U+$WYLnj(% z-=dVgGtxb}7-fq~5VeuY7qx>|I(?r%15%bI5s&>#@kqRzAUL!7~DUSRD^(8ZBCn)M$mJDn+yS~5?wuP=5}#?C=pBPebU!1PfI-uUC2 z2Uu?T9>#U%y38L8j{dIDIye2V6xCC$oAf@Z?O4;bV}Ie zYdA{Z3|aar!Sry98k*i{34H$3FlwKRyZB&^9=3u!+6iLqW!6#)5;`yhTW$=ZE}2M_ z&-N*)Er{>`=K9KQJ1i&o-rCvt3j2;$+Vr(v=LrcC!>Q<0E-|l)FG8y}6rIl=@CZKt zyDSr*81{!^NpomNOgNj5Bpp$Ef@{CeljWy54sCmU01O6SXAO;pY&y5-~%XpG~&l_#R_aFz-AAdZa8KVJejd zBm}VIrH%IFt^lL3lat&p>N3MJt?K;QJ?uV)*AN5WEm~FOkc7MXmCot0-a(1h!u`&d zQPv*)&hJeaea6?s?>Df`0;1U}X>efwLLAVJ>FEE)gZmK!>le+fQrNMLW1x-*o)z>^ z;xDhxvHgUEaDM6C+vWlt z17!*3gcpY_wU5o0*F6dCRgBUhF!~10z$;@y z^U+Zi#E*6Z8-5>R;DrEh>r)t*TVne-fY7HL{p%#t1lUz`y5lkizqt5qV3%I=YPDOS z7fE0Y2_QOg2HD6PgdMBO0}jDFytCAPE`nN#E2J-pfu41qu_?YV>q*K^4f<>b9j*rc z^=+M5g<8!+r*i4GZYx>n?-U0axc5N2YWvl{H;sPrN)FGzFrLG)`MF>iKaCiBE}yl={@W$ zE=1eqbL)0Ur>d7kce7)>yMeG-99U{14J%6u6`0JiU2sKRYgMk!-(Ux}jZ01$^sO#J z6J5eOg~70Hv~)hfXo+UKzh2afai5TuW%O~ZvHtE?C0_bK#dGs%Hex#l17+@Kv0{}~ z-_@XiXV@}SHIgu*X-W?3*Q7OA-rz-KS_xxj@=Gm<%smBi3&$}P8gw`#mK5)*K-FeLWcQjoNg z1B`OP6?X|XCFbeGYIzsFNpx{DOn_0ED<(^>Xz@CQ@JP|$uZj~Gp7{{Xi&PJ#61>ll zx#98;fT|b6bEv&UP31T^_@QVICR^Z321I(`(Din|^80kYDqrn%j}}pFW{E<#5LABMu-h6Yd6LA4 zYX32BBu^3|^wFmTTO%n&gOXulb>j%G4o!@UFlF&Pwcw+XbaOjo)W7K@;Hp*=*f!#M zMl2Kj`bZ`%B)$InM|E(RmXuW9#f1>iAhwwWor3Kn`EJ(WmStvr(Hw2#uq@` zWOrw5m){Z4_2>N1Tp%?W+LgmO9^DEz7D2mLg#;Fv{uw<@lW{2(0pZXoNpR51f_%i! zriJTUdPw;!X%h@QfYV}flR#>-g;^wLsIZ#gXlEnM1&Y_rCGVudKk5g>$1>nTK3z=z zuFN~DwHntQV9eN5PH~U2PudRGAcX_T>wtpRXu#>q&a&yFlj$Yn=&h+$#twW^Iye7R zUR)$)fP)J4l=6K4^GgJ@GbOTgEkD^U34km2+Iag?G+hDtE(%7s6wfGbyZ_Dh!+-t$ e%I*WdegD_Xg)bkc{^TvMcBt$P!gCHRoUDPm!Nkl{hA$psU=rwxONkmE1 z(HSj>-kCAFZzI=r-`}&|^{)4icdhSP&v*YZYp>a7KlgbY$M0xokd}rbEfp&j006Yg zO7hwOKn4W>(yymU!8hN(TwVbG09tCg3gBM|1VTbWLP|<{>eMMRGBR>pE+~p?Af#D&YfdmU^svNJR>6`6B83NGxLQD7g$(W zSXo)w*x1#vI!FJ8KI>GI{vSFT*)=H}+%;o;@wy?XU3A0Hn- zKfi#0fS{nDkdTnDu&{`T$hB+Nu3x`?Cc{uWw*r@bKY7LqkI&BO_yD<42Dk znV6WEnwpxKnLU2|_{oze=H})W78XyRKDD&8w6e0Ywzh`BU^X^3wzjr*c6Rpm_6`mX zj*gB_PEO9w&d;7bb8&HTb#--fb8~lh_wexW^z?lG{JEExm$$b!91i#K@j)OEzP`SG zett+K^2Li6FJHd&_xFGG>Qz8M!0Xqq0|Nu!ym=E86!iA(+u-2fckkYXgoK2KhK7ZO zy?_5cJUl!iBI3h`50R0PQBhIR(a|w6F|o0+adC0+@$m@>35kh`Nl8h`$;l`b3XMjm zq@;ZO`0>-HPpPS?X=!Qc>FF668JU@xSy@@x+1WWcIk~yHd3kyH`S}F}1)o2EE-Wl8 zDk>^2E-oo4DJ?B6D=RB6FaPr8OGQORWo2bmRaJF$bxlo8ZEY$h*;>gwvg zfB#-zU*FKs(Ae16)YSCj$B&;se>OKax3sjhwzjslwY9gmcXV`MvDnVe&aSSm?(XiM zo}S*`-oC!R{{H@ffq}unK^zV@G&D3kJUlWoGCDdsHa7O_*RS#M@rj9v$;rvzzkg3n zO-)Zv&&uW(KNFBic} zpWj}1uWrply#D(N#n1QtkAD9}L_|oxI^}};0KdWf7v%1_8_I~P?8>U-<+3J{Jh457I!PDLJ?yH)EnoS$- zUT;!nC^KtPQSRSg<;8jloJ=bXmisVE>R^+u_lXXEw=BEeq)gR)J%8A{ra$X8ZTOo^ zcdJvg{O(QDvoVZIILGoOX@&wQ7a>1{bVt9oQAxgs|0YSv2cs$9r2K=&Lt$4vJ-y*+ z{2TcWx2Fi+x85H3rv;wbY#-c2r8b>eA1z(-Olvx$<^|w|(ln~Z*mahKac4_$HLD+4 zd~#iJ0qLB9`gr1gRwy5OG;=1DK@&aoai^Q zqt$SIrH<|1a%F|i)Lf7I-=%vL=<{j-KPQqT^U2ZUEd?5|y>RrHfLukgl7kO$5GwTR zqsM=${bxh|b3*>R(8BhL{jH*<;I4b8;mr2CiTDe5N42#MTd1=3H*2!eN^@U z+MybD@weFhu{X+L7h_+NMwYLhSE~+S4l7^%pGWGF$i7_PCwq}G*=d32`hfFLT2igG zk4VQkj%V|w54;60(n29U*8s+q#F~QKU}{+E0{PsxS2qDNt?z(twd;yO{ocD;a+!-V z@ml03oY2$Jpx!A254+29!Xrm3xLMKNsfrBEe|m3k>jV&{-l8cqf4Uylrp2;!eWJAd z!#8I(ZIJmfoa$FewE!_1XoGbBt*h%PCk44-&&8bqNUURXQw(DfBx5!HijBakTwt+dVc#&=c^lk&MP}GB6am#o=@XXDEax? zKj*QCv@+kWej#rD@$2(tf4?K~{$hM(^c}KMx01u;pe|LiAWiVp$#HC5HCcj;bo3eB-Yi3^^g4A`+=#3ej~Hs6T(FRg+VbZy>(m&#^XyEEPa1rOb zXtYKH@nazM3L^Uioy+Qo*%x#x@bZr>_&Ys3)d8tHW^^Z%+zMK=m^k?2;)R|>uwR=j9rB`P{3*u$HMmHk^ z{_H{}0F$n6S)V)LhVi+U~@cot=Bw zSUT%Z_GgZJj%rbq(3w*bsh`QvLiP<@jtdtCClP*s&^yW1pFQ9*0vq-cA2JWWuVX@% zWi}?OjC7io8czR%-b;%eYVoz4@m zLWvT=8B<%h`^v!}n~GS+U`vOLkUzqPWvMqxhcT&=Z}Ec-Ixax|0WZ5gQO%+JIS-g z)BH>2_7~Tbc9Snh=$=gL5|!e8!-|DT=6KgCW5|?*y$Moft zF)zI_S~7k9fR;=v;`A|u7QM1buTqKud~Q@`h?h|6uWB44WvCL(OLsa6gV*VN-Hw* zD&3=8$DdNT53LMk?Z$-!Jv}b9thv6J?1ACbVyL?IJ3m2MFImHc;?JJ8`2i~5j`WBB zbBsPs>J1y;YUW$8RXCDepmG7qya{BV$W>3xWaGkn&!OYg&<0h&+pT}J%a;;@_7bL> z9Ud5Lx;)}Z9d^|XT+(%@1Wk}Eq*7h#icBI2Iaq^ycC_dgI()qA9+fe`pG-0PZr~<* z#$#^nyfDz_dL3%5u(zoa#`hF#ER4=M*MS^uN~6&I42X6#032D6N+WNzi{PlPK;FuX z92t`medc2bEIAk=^^nvp3mxgAd~){3_T%< zbX2;0{B@$c(8Lj$_43gqT|KrKfSx!esP z^u3{E)XcPVzUIN zYTg*D>mepIdl>;7l?w#HJwEfbONNyax{>H7=(E(aVI@3rSt=H*8}~dvnu|oMVxFEO z*Q3^3x~pI{)GCzb>UbUi#LvWrD5Lq8Cd<44{@%j4n#r|e5 z!FUbaQGg%IN{f8}@b?^SC2xq>7GKwkS+4SwI4J^g$2WjqXQ{g|N%nGhl$tfg5mtrf zh(Q%eGSvhwt#N2xs?h|`OgtHyWUh+6om?x9zf}4OJqQvE0shW6^*N{_1i;@7rNz>M zJjx1^DG!G?qAIBe>2Gqql?1M0JG6@5nYn7b&Ho zdxF(FRP|o$h7j`Y?+z6ET+96{fY@SE;iv`dR^V4vGW4b1ou=9XD+wMzEXV5M-W}1; zM&5!?gqWcVX33&YS7Y_W?!4m_)BPmM;`(T$~LKwq?MZ9qG>}xEbZ?ON30T+JS{z-fxDjC zUVNI2SD23aC^fl!0N?78VJUM{B(Haz$mp(V?x2H7EOiX$zkGM?_tEZ()%jV`g>$OT ziSs|Z9H!#xpbuTZ6DbQAt=^0aQ$QDp`3M`;HOw9bc5fQhB35+YTabImqYcBMUeJC( zRZ_l5O?Tev-MFwcx`6hX;~uALFrrcm{KUl*nyr-fuS9e=L=UIMYNVvm411`RjuBmC z5lLmfpvP{Qu*)5YXotz8$#%O=#GNF(l1e!rD_SSsG>HS7WWpe4Ryh+KDaeM#8DhHk z$OO|tLLA(j3!!Fqd`zkhNyvvtmCp4_E-GQJ*5*0g_M4%4AP%+O^?V#Q;Y&${_6i-0 zr9zdlSwA)G&T*BDbgh98JP8ZfGJIbIk&2_3 zI_@@46`Vi3|4ZLU9J&jG@8;3lYLS67!j2@z4%ZipjJzUkZM{qe(g%$$h^+T zb4(1NDFbY%gFyv-F>b0oi+w?z1vlp zv&0zkqtTB{AFYk2{#W{5d@atwu5ZzPc}ospJdy9}2Kz3aJU`#k#u&LIy-mbQfF+j{oE0>Aj6g!12{sVo2Np?Px&(fS!KUTJ`qH%Z1!^OHLDW4qSp>5=HvecKu z_biLe9N@Ha;5>VadU)%Qt93=my34C6R ziY`zf#!TK%{r2~aRwydvWC4_MEY1t^YY${7VV@EROf6AF@Gh^Nz@Y0*ubhV7KO zr}X@yO6M>(mAFU`rPf=rCyH4U$=bPcEpUlByxRIFDvN*i$6)$d-0YMX>8G(s&0G@f zT|EgNU$|zz6Kpl(k%N>O-Jc#c?U4C)nPDi!{FzNFh)jj;#Ozvd>{NRy;{k1LO3IT; za@f3A#<7xwMQ8UUf2}Ifl!v2i$*~62h%+rQDfmPdDf&H*2r|KsfCi-V3DqNif(yK|3xHxbz`fhKK#L{;Q#iA9%LT7~!F{mS$&%UmJdO1&$9?@ z)n42Wb6j$WdtfMec^4JO4&<4LRXVLap)Ptbj3dP|l~}}X%#`mgPFqcoV13BY4aOc& zdfGM9)=gib*kb~&iD{Wc0liO6L3z@PXLDUT+@`2(w zJ89_4ZFk}7k5o`s$J9-!nW%Sm5Ezgbgos_Yw`Zuw)pOjeIO6Aq5TX8LHEB{--8-P! zZX-k?S)5t@_tZN(g~K#RC|}~z5-WzQv7S?J!CdnPbNhXflEU1PkCUIM%Mcfr7*~>9 zG1X&w@Edw$l0`nu?C6X!f%Otkj&Oq;`IbY>r0hCZrDX;%mra9(q{UsG3*IVAkE}#l zI%sZ(mzyO?Ta1=@l5kkX!pF+SoGS4HJ)_}2esTM~^&Qd#yQGk0~yo4iI@>RVqEPE!r=EpvE-7Yd1yKO|0H#lsJtWG>!7;F7}6UEUcG&-C8%uM<$Pz z!R%M=h*Z0YRwHePyaS(_QbVanr9hNwlkY?_J6$w@n<^l`0ac-x?W-T!Jm6*9jN)Fd z>*;=R-aryk>GV}oj%BQOOMqCr>TwI=pwzmhQ!BHf0vN68+ac58+YwZ=KWl?N_~Avu zi^Iz|yCAHn)wE#gs9&lk7R;uZF&7?>Hq*^Ec*(8LRxgUkxn}LQp=690RiK)oL{5l| zgjmngkYSWBHz4%fSQOgbmK8~rdGOX<=h{6QxDAlkwH)xIv!$SN==@Heprc$dsnm>J zG-1te;r4rb5?u}ql7|<)^qFJ~7sO$E5_sizQW-&fwes6Lm z8^|+Ys^w?r=IYa{d3e_ZCyIdhCKYllbIFWFx5+i2p=WM?;V(t~OrWGe&lcHk=OTn;Dyr5v*uFgHr=C@s(KRSx^EIAEx~n5p#>P9c zYmG({Vw6Li9(+%_lSn@Ma-az3-i<<4?mQ($GL===lI#cJG3oUxtufLyvinajB_!ri zOfk@r%azncF`)VUD&#y&ng*G_Q)kwyoBNl;ay;w$lQ%qiNiwwvK^DgOg}FTr3StXZ zx8*Eq%Dd&w*?ckIzl9aOk{)h5d@9gwucqj^Xk&F_-MsabRt6hg029`s7Bx~+11sD4 z;7WMt#ssxgY1KSS=B7N|>#@rKd1kxivuQrzvD9_wpuwL}?m4cQr|P?j z$z|ME^r8ub=-Lk=EKs&a9!|dCk(Sc^(*D%&i%yjEBBC6|vX*tN2Imxuu1sgVA=z=6 zM#WE$=~;4@T)$WQ;y{1%$625kK4aV$wh158-yhgf$D7Q@s^*QSkd;Vzxb**^S@~Hd z8KL_O^%4EtXu8IdogKv@u-0|MT_XK=NyX}=wp*$~c?llh9VxVGn(~~u>F!JIJJ^}y zRjn7Az&=uz)5a~s9XlLCOcL3Y9Rp{7)oD-A2-IvgMGJ-|6=i!tcNO*wRP0_}fIXbg zk|FbeWR4o-nH~hl_7OiGn%E&ORwM7{^oL&TDd(4Li(=D%{ zLqIF&wVi}LEppz(v29hPwJiV5L3w-S?H=TOn<~UD0*|c23NDdrE;8#>IcyiH^Tx~Z zopw=I?6^)NHCnrCM`fd}Vnw|)h?ixkA z6EI`09buUB`{@M^X4Zydlq)`H^`R6Dxye-!#)~ReHPCaLMG>l3oAlL^M9U-TemJ^A zU@FD=!CWu#Gwm8`{f2VzoB-cwQ8X=~pf!|DY6AgU=0Ex$beeUfJ&Iz~;&%h$aSd5X zj@9!w$Cj>r4RDSArW~%qs+A#R0ax%28SE%r{T4YeJH$)KyLF_5w7HGPMK7?BLu+Ey zu9&G~3b*qQ=M54MmQgyWLtY4orWMP% z%9hC4XqRSO^9l zXc)SDU_YbgA>374Um{P; zU<`SWP0Io*NMyN-m^v_9XR^l)i*d1{4i{iUw@hkAHk&hxQlsZQCFrEl!6n2XWsy6? z&9|5vBk^(zdx$~>xIuAPydUC zNfZaB`jo7x0LbfiULNkvJ?L-sR?uRCTBivuB18Ltqz^75LGG$dwapn$5lO`zO7s^O zEg>a$;Uo?;_F80o)M&5VSD@4yIaJljw-D!(CN4pCjE(dxpt)% z9&bpj>bEP^tXN0lF_j9Ike(gfB%3e^Ooi8*dyhSNhsXf+x8KcuHaWB07n7?pEOz@O)M>1EhQOhaaw z=TD!?J}MGyE;vM)-Tl3-%Y7MAtVIdB?F^byK*T zu1a0=P3cliVFc43ecN~49#)N9-c0~1uRk-9Bf3f83Vj$G5P`o>!%7kz>kqTqi%pAD zbeY}J8MQWs{chygRtQXMsS{PahKSbiAjahN>F2BZn6k$4Umu8YdTid*u5`Gg3nku< zh-Wk@X-(+J^(^FiHgp5}X=!C+UlkB_1(%?BPV-F4dtz$B+8DO3Fe7#D~pm z2_Iz;lD&$MZ1q-o@vWx}&{8?GWv=Rjv|=Fm^tRn^UZi%i@w^;&|E}tSf%Hlr zy!BRcNxm%NTQ#zYB{a0jqnTsMo?8Xt=GiQO{jscuNt{o}Smlype`Uu_FPl7+m56I`E!(%6&)%8KR+$+bvQ-_S!bnXq!sdp_Kn(rA zLm!OL1?zgwQ#ZSO-;<*6=$Gy_#EEdI8^+RMISxLQTR-EN*Nzk$4ENQ)frbY(Mjg74 z;FFvTvjd4d)xN=Agm7`=YXiGqhh6abbGQ_X<@n zW{NZp-a^Q9(S^$TrrLi42{h=yNk4CtY{atd(S%5q#`21@f0q7NfS|9Zx-G~7Wee4G z^&k^M`CeKoJ@W67zxP|(NO(kzenNo{ zlVIKa&UIkc&BgK8ewc7LxC(MRvkeK0^HZs+fyue{K>TYR$zorR5E!7h;w0?ow6oqf6HBwv#dUZC7#S zMmFYz)}F8W_&ru8h-G%nq<4W!XyJpuaU~lp91@?BW>Q0;9`=scV*d@H{0`ll_-H1q z!lcY#U(wQon1~5>jRZL?U}jBRjeP zH`NIT)vyxj7oY|vaFqj1J?Z&qTRiAul$98~j91;tB<4AS9_`(fywlEo!0V3Mn|tLd zG*c`Ja7;US`CR|bpM~Tc{9=pf<_O6`ra|y%x!{Gr(w{b0_#c9b@qTH};krn{PJ&Y< zbADNFPDFmqGS04`WsP|${J@|*^AQlzca7lbxrcHZT`e|qH-nGLgOkE%mMz?mR}XXi ziR46#B03J)Uy(m#h0fR!AHDuy_@H}}(q5(X?SjzVP~>#VIY3vsP$N~4P#snn0p!gy z`!$(K@xHLxPlLF<^XL#_V9|X*jy431H9h9g{UF61tA|PJW~@*}7;)Gq{7#eyymGg^ z!1eYI>9T#tiDrXy2JRz#67?jYe|z?!!C_C|=#zV;#`?JfMmYtaNGo;VIAJP}%qPOM z=*d#JdB6{}N+m9%bf{l)%IX{%XIAj+rN5vWfQz`lLmx2M8BAyy;bZg`=`T~=0(UA4 z^K4Pz`lxd>xz@g#zE6Avv2Ux~k(rpfB8BEHiQyPgail(SRWDhtDc*ImE+EdWsp5Wy znfnn$wr?kTj8t-HNv>}Y$S zB-?&dLt{NFCpwT^UVDgJeD-scqZ~G5O%8z=xOnMMCIPQB7ES|;@-->_eU17ovMjfz zgZM_4(#(y>)h9c|lKk)+S&1WTBEb4%*X~|*Y4oO2s?g}|flu_5G?~BQ8WKgCCO6Qf zGJ6XGM!mdT8(!fcEyGLVCH37$x>K2cmjmC*~L386e(uUpBhSl1tgm&^*P`B*INSH!S^N z)qc)TV~&Xo|06!2rR>?_-N(Het8WC2=S?)oOU4KGE{;^v8|%6lzh`x!D6w6;gmEUp zUGdXi{*hRJmv|E!^b>`Q4trMi7e}mInM-pet`!ru-d)k%J;HK2SwnVku(M%OQS*`E zTg{0+Z1mbboHmh3u0oLt&Hcy`nZKKmT8;wdJuW@lYv?{;kurfwT-lcFy;JNMEfF=_ zz1SYv5h}tWZQlCvcLmv@=oR0V1=lnY;wE%~;PRQ2#v0TnQd)!nbsar>r?eDYZ2(u{ zf%n?GIL3IC{|K{8_XIOsT!xG}uB?4XA>F5<_S!R_d#StQ^>tH)3MS3*$**G8IgmW= zF_t4m&cso9fE5g<-umX>RM0JFzx#$@GvhVCIPrVwx9;5X&Fr>&bt%`p?C$k%g=BO5 z^383<_#(Lc{qtv z>)vEfgd6|9#F{`!8tDWzq0O6Go}4Wk&|TqZQSo!J0Mzr+tLSIt_un4i7`C{xI-`tS zOWE92%;$_8=00!T-GBVOOPW2smkjMalmGcX0Ml4{vxVI3FTl2^pMN6loE)D&l6mlg za<@eCbb2GRMyI}|ZQn}@w6c3qK7s=3I%i3Xb=+Yr-kWhUoa5-=iS$js>~Q~%LtkJ0 zc9_0mmtg8$GPH8dKF0z@6+|?!%m%sHU2y9cipix2JD^g~U(z4Xw%4F|ZYCxc+^7-W z!s*Wu2|eWmuu&U)T%a%TptPF{^_C1Bmn}?$sLSGHYPrmx0^K*Ja=iWepopb~H8XKF4ak4Wi2xNH;8E;4=bb4~lujMZ(YUmN|STk;zt9x&C8!5LFl zIs2a6fgVPRbiGJ-u0r!1i6+A8{-w*bhTy4Tz1?l`&ROmaNsfMMHMa9u&XJuE zhiU7ezi&N_eZwo^8|nJ-^-0rS71z`Q!oJ9CEqe!z??JUH1zKmdSJZ;uMI@ zK5KJCdQU8UBp2=9q;WwdOl?f=HV$}vnnhlv#Kwq0cO{7T@OPw27M#1LK$lZOx?X6K z@yiYDt|i0`Kij!daj4GEf$qq@fY!`zr}ZHC+d?k@BdA+rTe$=007gxU9#mYZYHIm! zw91$GZA6lsrfO+NtPGUCUREy7sBXgyRG5l#afu!lxUO`6ihoNTI0G2P4MbJh*?)X` z)Q@h$eXQKIoPJKxSCNw>Rr_k5VK$_MHgWoMrNA!>91=3u8`)*)P52MW(@ zTeuBH=?aW+cWaM*kGX{2$&yZ9V*=f-%Esd{+{wGUl=DvF%a%!M&7n=3{ikb)D~b=XE)@8^Zw+<}U1oYC2QP}BL5FfKFBi4V z-&twS>xb7^TDj&pmTz8Q;g-3eGSpy@XmP8BC#_Z=y(g*;rxU~$3}l#es@uBVi8+h) zI$V7+)7_ytT9h;A9)fYG>Ifyp>3cOVYbRCPsj^@_D|f=e8@l`{Z9?382d9nfZ$1BA z18qFlh*vgb1`Ryev1H{C;n^F&PKKYdJ+ubwhyTdL*A=ds<*uW}Fjf`9hs z>-$nHg3(*ThzGQ^5HxLIP0}szf>03v3*@Y6ij~%zQK2FF%+e;P;xtEn^DOGwpDAn7 z*7?5x>PH&LgJ43y{1&m-?6YsY&(ienuvE4Oam2GeU)O&m?QE0_Pe<3Ol9VUJk1Vcz zyH#gMVMrmO*(82F%mac2;GGz1o~^)3UW%8JRyR*Am41BH0eR1WMwM|AME3f2d~i|b zPUB#hSo;jgags+BEQ=)m9Fja1L+^QI-PW8BNp7HLI6*gSG$pbLAbukJv6xiN$s`7J zjB&>OOBs(2#VeyIryh&mc*ha;;t!!&_9Q z;BxYMyD`|I9qnRd@*>)C7a{HLcgxDc794Zn@zPusPvLo}zv>vZTQ+6@;^r{%wn)Z> zRM1TlvXe>Jug51d9}Lc81<{-TWQ{DSl40i!`s}45wrv60&(Cn({bN{a^fxhuDvFZs zHYud7-w4kS+RxajQ?Ry~TndYqrduI|@irTSW_=sLNM=N)&a?HKng9Sp<|*G+sKOH^ zbOD&N5Ee*U5eX$R$}#Cybwmb$W~dO;1Plz}J}~{dUm|R)(lTvvjD;T>M*mjkiuZE+ zXL2O@LlbkwxF6nr;I8!xhbS+-LOg)d!}{fIf8T%j3-m=S_sI17$yFm|QO}ihpT=Gh z=Ro(W-rro2vr?zj6cFegAK0zW4=xE%Msns%-0ljnSDRqDe&3jt4F7rB+HfwpqXWR6 zK`|dZ-jNXWwgE;4J9dLo0=xDB#CuyNMare?vU5tzT-eH~cInqIT2zSJ0;7dnib?(i zIo$`)u^y{pwkzV*FJs8CDK^wXXaNEydrtHE=y_~*y^S1s^s+R|H(SkAK_$TGCcnpb zJJ&Rw?uGkt_Jc(9?_B^)9}0GZUN-K^>pF#vcRbLf zp)-GRJW*6&F^Q;#f_x~q zQ>C+$dr&8RzC6PA97D-{t2v}b`@yG3j2kp?n|QaQYv==Lxgq)t?5G96M_&=iefxIo z-EmQOA(^!bS4S$0m>YO9;?ipjxPBkjZ+xQ_P0d?(T3p}8AsVKupxr=TzlZhPMFtMN zi=YvQBCn;d5o6{TM@TqB>Rqik@ePZaL+KojGzTBtJ?u#`@g%#|<($sNfl-sT=rx^t z5ge*a%Q~|k>2cjm$gxzaX2_}Wa!8z-Ki!7 zX_4Zb&8L1}*Xc`ztIY2X@*;)}s}OBC8Ezj_|Gp2hHMksFLQWznEk(i8l|p1UWMqEq za$#K5LX3BT3M#J|ztI4?MSMfbS0&SuUSG4ZC8NqUIOhqCsa>}2>DB86<5!+Yp%1#v z)fVXohqqoHw5jD9Uzs{J>)$uEFgfN_J?woN+MR(KdmN8{d^g@NKGnVJ9$%ZSekjLn z`{urB0~L}I-+-!JxAKa@y!@*D5O{;%8wU*}=WdDn)cc$5p4P8@+joA*0_tWH=ug7o zO3EnZ97fMe;xCj2BNi95TZUMMIz8;?&kHMaLs%7vQw`J-SLp6_ZFUGrn|^*luB(B> z-4trs=^^?ZZ20Zz;uBsR`4X_N3xgCJAIo|$WIMLG%3R)0cV30xk$OMS`Bc{xHJ1Uz zC;IZ_w0Uto`8q|ODYFp2m6F(hf%DCIDrgw(8yR8ms8P=E4GcHCn1vnWhDzIV`vlSZ z6V?mwW@og8x?E26II_F0(C63gP!MIA=YPruFBx?7srIllh&yu0Z7QXngZYf#b zTv($LJq#ehzJ6UyVK(yU<%jn3I)KsVGMi0;qg9O+BqyUEdUpbZZwdE84_hagho2ouQVAct%GnmE34-*Ab0-K*zfKgv?06QhdLj$+e{ zzA)`@nTbn$)1$#oBF@?Ia&7s#ZAS(bn+XN@C;W-GGx}ny*rfbublCPfbY`c(07VN! z-T}E{V+jN{eH0`o^tjY@_`?-_QNL6Z8tk0|I+RJUFC4yeVj<;3`R*g9J9!M{`(bPpn>b@0)sZs>F)8Dah~x zjP+vyOwLQk45H7Cq@;_1>JwsghwJceoVz6+1JA|KaiR4X66xK@#o(2S;HqTM3wtuQ zx6JfXNU^DorTO`xer6k%3+b~l%YF3sC}uY~3$1_kSfz)9k%C<$Mq^`g!tju<=sHFu zPwBY!09NVywi~V3c$$X)5d`Lf5NfI1 z8C$8CX)=53+udJQP@KE+=rkLede8bXs?~`Ea%AJp?*%xnUdwbQL-Vy&3j3b!LP<+s zH?iJd%wyUy+(Xj!C%=@wnGZ&Vr;)jZ_|3R`89hEq!O*F?RoWe5fS9i0Aphdro^8vA z;s#)5OKyE^H%@Bf#g;n8U@z_j2moz<3iL3)CB83wlSU_I>C)nDL52&U7NrF^birt` z2$p^@%&u#tu|)9M3_(+-LwKs{5g=1x{}d&AnaP0SdU9RVe-WRc_(#Z5Sc1VX4XX68 z{m6l&ez@i;7%?viR60QyI|i4b_jNN*L}kd4s02flD4O;cajnCr!AYdwlkAuBd`x5x zJtdQTCYl3lu|K@z)y`G&QTB$Q1e8L4s(NC@$0l;8)yu zdP&i9*-||GFEA1&SIInKvFC9kC|XI}Is)@LbgL(Qh@)RSj8J(RRoZi;IIZHRO8rVJ zKiiVdutBFRq@>r1NO2v9&|K^5Tt*2DXnLcVybwRk!42!XSZ5N}JlUZ~vPY)jw=s-Z z1{~9%k{Pv$PnWv>Q|8bD)B54H_QfO43$lB1tS3d2r?auJOsw-!?lnw%Z1&j&sB3H4 z%$3cKt8#xG$M%Bn*R>ZS{5UtIkkb)}vII=vQ1EdwA^0hH+-r&nvzKj~;Xu6DF52V$ z%9**Nj4^PTAEdEM385*d#V=pZLK8YR=?iDi%?bS76`+>885={N!67T|A0sl0VgfOaa`=4~H96iX8%u37d{S zop#Y;XQ2y$JhIE|ObPn0!EjzutrXj^rZLx}&9xSgaC38qqnc#dNoU}kx>CFc zxl1~U7rT6Hf;)G z7HW})1x%)nn}}}Vt?Or@=Hh3sr`&_^ri_V|UFM z4J~#CqDz7QBoSf~eGbWE^l)0%s+p;g859Npt4~V=Q4xZD$dR;h!E9ZVQSn_X4X85j zn);-h547$FwVZSUp~Wr%^TdOXaJiXkJ4{M>C%Z=)J9k(q=ecM)<{-!S5%i3k1e%XQU^tIxNro3jW=GEABXZ_MTFrm`6jQ_nbN%x_edRZn zO~ev(IvkPE(TVI1`gS-!yZv*>8aRO%;J46`7XO7337X%w=CCStX4L}HkjE64^@;It zc6K`})U|_ko)Ufj;n5H~9Lq!*>+W6aWz$vPZUKm>*fEbF#Xi(S9DM8CXf_j)I(&gY zr9}k>bJIg7V?OOS?5j$qDYpNa(+<+~VBb%f7CPCR?^EX4k>VP1Y9 zEZq*$LlH?Cb<-2}M&iZY0)unG$6)BWSa+p2@B17LKN)1>js_0s$??##Cyy5%*{qK_ z>fLB|o`2G{KO|GViUc{0IS2X|Usv6z1+$7tv3JXx{eFpP92W!!?DlGhtaB-vj74$M zJkSbJyS-dR6UnK%kc~vv>Ua}++ATiwmbI3Nya^2&m2?`)NNdvS+E<1TE82GFJ?ks$ zAeZ5fdr4YyYOF5CF6@&~q2)=+IN9Q|4MAf{Nx;hsX$#|+^dd72R4TEgepj2(b2qn?;^Rt zT-$%%G;Sg9i`%vwxo5%sfrzT!6NAS5`~UA^P=kN}59Tj8{(A>yJHGm0Xwg3%7*8@F z9lMzS8KKO!(c33lPb2Bj7GZwY0)!L;_o^Y%=%bhX0K`8m3gBwm%FBa($xn z{vs;&{n524#~bguSl`Sync=kB(Mk!PrQ?qOa}mmad#d`sa7l~M!YUH(SW!OYxKj7W z;c^;% zxpZ`Ta1rTQO(PPEQQ3k2@7(H>EaM(kA!p3;15fF<`p4HM0g+dZkU|N!dlkHDmw=w1 zt@byYGmZVX+-fnKkNm|;RXudXIu$}}IbKt>X7suA`N2)Hxr?uAHae{fj;;k^8Ilz^ zx`pU6x?tqv#{&5yA43}GV{lsQTc{b`0gf`Q3D$J8gU=+g_&N(5^pfCSG4sDoAZYpX zq3<9j@N!$skL~I9dI79rja@bcyk^bx>YuAeLdp>Ly=gA4AclO+ylK8gazQl-`q^Q` z<_*xSzhBw44b=?`Q97??FMkev3S}<~6Bc9&S1#L@?h+Kl&O?=tQI&Dm97PYBILXZ4 z`|CpL2ITLRxAl$Xf%hVH+{YyTduDZnZ(fc6wi?}SKXJtr!N{B81< zE506ne8W*0nlV*D#vAAFDZAiKWW&ahmfme$Dj6p-gPMd;aUIrrpK`Pf7JC} z+bf#mKfqfZ!0_T45V;;-%vQWa1fOc(Al`+}NBoQtWLEU71<-~`#?0r{ByE2Gy6NG+ zHzxeQAie%`=KtrN`Trk&(^-ny)=Z5hX-%6`S`o*feB-|YuIYcSru+l3Y={53kJ9_k zEiGdC;5C$H|4(Pv8kThStu;$$%%al9$ucX`v5T5{DRn?9EwxNV)0B{D9COsrOpy=; zyO>=@tsJdml9(3+Z+QbniSCvuDIuB&jNMQW$V5>PIMC_*pYwmtbH1Ff``MTGx7J>3 zueE=Bzq>pn?Rz}M?)YUWP$6z(E53XuQK?Nuk*uY|#Uht~HPLa=V0k(&nxEMDvzarz zkUzMVRXRc$D})L->2j&Nm6Cl_AMK#PT(Xx!>@@TW`d~J*$p>cUM?6Zm1zvi9QnE=0 zzf~n3#TRcpu#P2|G;4u`lVc#>R4HZ*72I0$rU#9sI{@*6Py>v$d9GNS4y?#agi?H z6WPSEl|$2~<*!N5(duM^>7nmcKLcx&9;5N)As0dOP1|4ScO7&IRloQDV2<4XEtY+6 zsFQ#GW)yFX^fi4WHq3>R@HR03{J9Mr$zIVU-_NS$FLpRJUzYf<(84qVI0fpZG5!c># zacA|>x{>r4ikbpxir4+zL_V4tPJ;>u0*793f1Iv`xYGUb$7F4Fr_k6AYHV+Q6brTQ zoB>@T^wFZbYAbjf_SqQ%XF4EzP_!UY#_Lje^In@!#|A>qS*Vb`K9D|zbv;ZXj-^;} zNy`!<5iSHftmP$>5Y*XPR6I2m@b31~wnAD;n#J_xP*DxAzwvlkVQoo72)MH$P0U~) zS0wq8pkuH^#Wv&?NvWf8+cVjP8-|Ho+p#DGXcM8iQuC&o*;sm*7=CxesmFGxsJ}5v z-$TiAf{bD|@T|&gjN|EXLj8E*Y$Hr1AoVj*t;c%gNfe6#2xIwvlL^S|1AIp-B^^WH z0dEHJ-RpY2#uW03k=QMhPoQr!jcU5l7`pOyEtwgL)g5+dJtP%$KRVh9+|J#E#T+u^DW4&%yAWSE z>|~iGI~^68JJVO#wDCIEtA|5^mI-^3$frxK#&=Fg?j`M+DV=Z3HD^6@y+5 zc|!?B@vGQB$NQ=0jvBI|z@Mnd0y^)YqyURP+E4oWNL3wuD=X>K{NkTW{v-*OZ8nnk zb>B_)PVSM`tI52dxXER8(gG)RKs)RV6EqOuS$MT-xljs;)t&JpwslsIqAzD;{#GtI zO$`U!Ym-$GIcV1XtEmt2+2OmojW@;ccQoOh1c6q_3t!_-|hr_)#kNA zTXMC#q1&X#6#+kZ+pNk_W{iaTJ+2EI)Y|i+ zJwZOD2GOE#llX%rW8P1m!c%L!^PfxdIwLbeWbZ6xVN!94IpV$^>Rh;j_ZI??S~CW} z-wpT>LH0PNXXmOw0OX(lemMW2^}NV1B^MMNL0tT^n`d1N!5cd@DV*#ZbdvPf*@2_! zgoN>h_H@~Im?U_|`aPFPJz>c*3viLoj!zV3bKp=vMUcK}{Xg9sPE<<+9=4y&#=7yY zgumBg%pdOee22%z)6v(xtkp#ruBQSLC3m@*{up}#4F#Y`CS~!pC{cHDb5m?t9v9eq zHwZy$m5s^Rt@+b%yu9)%bEgcQ=Xy&(|2x4NRjjTbWq^2BE!S1*gK`c#9k2H3G!i0nM9B3F(Cy{%DKZ)YTf;$ zc-XHkr$!%jh0CRwAT=MF2GL{NtQ4jW>>`>XTW8E>=bU3mN*cV!&oG-2#9c1URWskB z8X(ptP7O%Qt?F9-bKA78_OQhCx5&lKYbSL|t$uf9gtyZ{^xo)QHA6h3Vd`+j7_{-TkD(zY7wNEiy~9{=cV<*v`C`H~y#v$VJF;@aN&<2k2C@pzo4}z!Lmo6yWz*<3pVHCE zlYx>Tz5U+Q+iw_nR&T#PpOQa5z!y%=Z58mh<*W?W`ixVUEE)U$b?ec+GUmpxCOWDp z(f^wQt4!D=U)hBL!$bwr<0B2lF+*}Y5QBL~h!Z@jtL6|gX15?exh|@z-bXZcs z7)(yV0B#Qz$;=NkBC~Pcf}n`vCX#CEBXq~G@Q^w9u-fSd8$xhhYO}XEd6wMuQ98jJ zCSax6^{x)eJN~p)YVMSzbxvCFbkZ$37F2dC#l3-lnx(Pw%MDSm}MY)Ey~Hcjqr8mn&ztqAH2lq)*E4KBB@% zeuDT6IuB)gGjEl840g%hUra1W8}H9xMjTY{XTLlz4F=WO#~Gx9r=C;}+F%az-h(auMoh^t_6|2l*DwAW<`5?`W zuK0tjT$C)tR?zI+m!#v6GX^K6B)iUbXVVxW3w8Neo$*1N6~EX(TEnnzoKDpceuv0; zggDG6Do3`7x32gvKl&pffO;NKoB1B8nyT_Z6x59I*RydO3;28h*-gJf^34O=6?~W? zGSJfmZTD~92{~t^_QO<78B}Ot*zx6)e+n<49^$n69F}b0|(-Fn_T5^s8KnmdAXzvY9-^5vV%6jT>CMrUAoUzJX@tIql(GC&jgbzWFk_o68!b0Rm^0QEZKQhweORY+ zoh4nHw&p+rP!E-5@>LFH{|btJ{@-!Z|6Rm3twVo}AI8el1e#AzYFw2->y7P} zK+7CRop|ws4fS{4nSqa%1`f+C^d;01uZS{|@ zE|<9ln&mm983Z;oI3I@}$$}4e&Kw?eKF}bZYKFJg@4)T)ynm_`rNU}mBePWJ>C^61 zo~pPIE5!j)i8Z)AI(Eh0=#*l?xuG6L>UHe3?e_tz^h3ZM>$b#&S`7G~?pn+zj@dO~ z2fNM%1gGT3*nD+@f9O^P8uTqSZ#1N8+4y)DrwNmX7>>;=tTRCFKS;4!f zBLH!%YiK2a0>=@y@OgDPG@@zbP1g8yLM3j^T6s4>H@Z9SWcK^1i#%k(KXa6>k1ESWu> zqct|&rvuXtyQooAm&`U>Q8Y;mmm)) z+T)GZ0duz)R7Z?8;#%r$Mx6-L!?)6`593ZHz!J-~4`wfEVqL&toEAwnPMn2W0OXaM zDsA@LS>-@>B+Xpy<~qq zbG+)QXS3yv3;c!r$;9^kF%4DDHqfBwe$n@Qq`tj(qAi=(9ELJilp~x44}o7WOD$e> zN&wx?puk$kDV8D6lop@4k=>Uuvi?lDYf@(qQaDHK_uXA{i!VNy+=>l-8L-sC*vl$q zB{VL7`W2Po8)zce><*2~jSl0#?Dt0`1RM}tMP-eIojgW)L*ipk$KQ5A4!~@-H%dnP zSaF+#t3&2<%m=g@vqzCjEkI!KN&6+HHk$~$%nfq^t3vKv&3R(-gF~M$c-$yyaN@(8 yFfe6xh{1p_*br=PxXd7EsfB^t|D%AuL@c4~&RTLjS@)oaL4e;;-^TsFUHDIPfZ8ko literal 26087 zcmeFZXH-*Pw=cXyC<+39L_k15K|w)4sZs(FPD&STLLd+l5)vpBN=iyfMn*+a}avczJpG z`1r10zs}FkFCZWwC@6U2#*LddZwd(s2@4C0h=_=aii(MeiHnQhx^+uJLPAnfQc6ln zT3Q+ggUQIq$jZvX;qcqHZ_CNa$;-ybLZ~eyNZg6N=iz~%E~G#DypigYHDig z>gpOA8k(A#T3TB7?%lh8|Gu`iwvLXDuCA`0o}RwGzJY;(p`oFXk&&^nv5AR^si~=% znVGq{`GW@!9zJ~d=+Pq!3kyq2ODij@$B!RdTU*=M*x1_I+S%FJ+uJ)hI5;{wIypHx zJ3Bvl^5p5$r!FoouCA_bZf@@G?j9Zwp?NJvObOiW5jN={BrNl8JWP^qb@ zX=!Qc>FF668JU@xSy@@x+1WWcIk~yHd3kyH`S}F}1%-u$MMXu$#l;^#e*E<5Q%Oll zX=!O$Sy_2`c|}Dgwz38yXtEeEIVA>(_7JzBM*B zHZ?UhH#fJmw6wOiwzajjx3_n6baZxhe*gZxtE;QKySt~S2aQJe_V)Jm_4W7n4-5

A^WMpJ?baZTNYgUg&)6>&4Gc&WZvvYHE^Yily z3k!>jix>lp-m6g@i)wQ*?_4W0Qjg4Qwer;}UZf$Mh@%ZiSZ32O? zv$M0iySulyx4*xCaBy&VczASlbbNd~^U>4~+ykedC>go}09y$02MM3wumXUKfU?4E z9k0aY32)>%eA4=H-{ts5OV{`6Vl9^9i`+M5Mp70=JG(Y7C-5}Py}o!h{NlY=tt{zI zkIubS6>bzACtP_m9)}zcap|}cdhy1E#+%3+UoO}wD8Perv-9_IoXz`T=G9BW{doW5 zX*b>BpK-WVrU}zHD|)(!d$C)<%a=y>fQo|UdE{9-W^$B*LNMeiUBts+q#mu;&jpH* zK}GKFH!I_*oo|8^6kg+$|W2I$x3+#e_?@WZ&^Q`L9W z5mPe4aue=i()bMAeP<2?oVsHZ4R+*l7g?ki;NfqADfs4mn~*b>NJtiS92=N-w*)Io zbHTH7QdfK489g4Gw?j&8yfAv#@Q{wK{NN2>TEy;f=J?jIvZJfa*J2Or?ipceIx;>k zc#lj5xlW4I`TLqdY<1eYpM~}F6*nb?IMW!DNAz~IFU^afM}h}#vw18!>d`8!7A&8~ zPDoQnyv(tBOpc*y&`UbWzH5cgcm;>v>gv|Eo$lGH%4y3C@#pt; z1h2zhpgg-Pj(K6hNJ5cg10Re$paY6>=5rsdd#X_GTqbW-I-|N|IxOUR5SV%|L8ieYE)912mq> zlZ6l2#<+QZ&rS z;q57ajx^8}0H_=h$8idqMuqA^$`6 zLi}+zIoFs|G`k$S=E2w^QQCBmZF^&H?nDJ3lM%B25Jit1nhNS;=5q#8eCgn}n$MY| zIS!QX3!Z=(u39hO7Zh5JL?OUH3x^EcAU>L*T7c*o;$s6i2d5@Jz;d$xd&mS?QSJ$g zYVq6OZl~O(Z~HTe5}?}Cpe@(!UXn8xrO6)&rw%pJWenX;43MWZ`j1zVm(CS`Yq&{v z{r9A6xgoIeE~r~pcp#T5QC29C~<5TJ~Tj{_2S&_uIN?b5_w(7rHmO%Pg7xGQ; z{Hx`~Ri%bo3yb8~Y0{g2+G$HoEdH!7$X=uUulWdi2t@>rRd7VGtHS@VY^RSHH?P~D zN&NftIyue<9TB#N{@B~3Qoq;ovpl_=hQ%eF8^8ZnK>g$*d{$%GRgk|I@lHM~L#XKI zyFNF*=D4)K!FFz6>Hb@C%G)uLUG1N|_QhEWevT{hMzp;83^Vtp_Ap0nYBU!+%K~(SYeC*!!8aj}_JO zqj&#|walV!ws>(hB!fcxKo^H(-(L9La!KLqhUAnub``)U$rA;{PXRigbu(A$iQ*4UUwC@XEndyN`nl(1m%QGK&w0|HHg}fDuzbQV3({n_L?yG1#xS?G9lIBxqky zcglopF|0*BzGnVnp|Y;0=Y3Tz@6$iZlNk%#+K%zLwJENF9X11g-EWFjW+gRk65uN{NhswBdA zjJ~@^NOY>=Uy48Es&e#ctzZ0rqeiEWCSft5Cf~8fg!&0jvKAE(efdNUm59lSnQV%( zE8ln3`(^F*S38xMNcXr9zolc7wl8s{p*!7eQpirG6?qCggU}0ZujAcIDTg2GdZh(Q zE_aU?u)iFy!B;CeCmi%DC=_yY!N+(9t%ebU={hy(-3o83s?5Ez+LMH1AM-a@ zapTd;k8H}^p@}|twFYmVl|nCUeH_Id6=*xMuDml^(vZ#${tx|~h0%|qx^u)yrzp6Z z{!;mQxH2d4C{5^x;Md6(7yq#y?x7sSI_zw>CyX{wp)S)AHR&$gOl14rnFjOKG^RY#;+g~CL&wH;zV3%tj#K zs@a$|foL=2c-@5C^V~7#@FOVG=E5O4-lBCdxMK5T5~7)5b_RWCgQ*S0g1yPW4Tw5B zAB^;h>agsep*^X34<7Cr-T<%dv1w*N##eG>FpRw++ABF;_u%HeW*4I}T6Nw71GUi6 z{&YnL?h`BJ4#YIKPA*tAJog6JnfqaFwxajHiZUUR_kGlYPQwM$GAH|I2Y-MHkRTa) zcvxE8+WV6exl_a)$V3`MQvDB6e6~eTnBj&uIMlPK^4BU8tu}V$KVFjPpVRV_jfu-7 z_P_N=QYOFYki^Z_SJnrJ5j>5?ZqP=w3LjCoKw50hO$j@Px; zZ)?Vq?tRbMw|hzo&%}E5TKX&Z!C9 zG8LCAwC}dZmJy&i3KUFX%+k0jjF}4f-M)owQKboffapHw;?)60Yx7iQkCSPX_Sdc< zDa*Kj<+vGR*q=~?BQ#_3U;4a0+aEA}vufj<0N`k*DraWg&kvxEDlZ+qyY9jbA+Gw; zl06N3SD6hTxXp(zQ=_f0jDXgZ)IG)kC6&ZLQIN(G{#qQYJ^GX%+Y?5Ps;^W=sL3@} z0y(~*(8ng^)#>SH83S-X=ciXw8BVknn>{`I;(@a4EU=}7xKlPcl<**_+WEl=hy;Nb zDOPOZSKVTkZP70Ppy~;7XHiiQow7jsB>9GFb(*FKb71s)Mbowg4sm1U5Wpc2k-&;S z1Wq=_G?q17d7iMZMgvbUZq_1TF=Z<#??1Vow<-5qUMOpjLNiG>kwi= z1kKwGvkIz_mzRHtP@_Ne1zz(#;fSojI*a1bl1f15qqQh}QRy#zDsLK-;8H^Z8}^EE zE@Url>j5PPazO%!!L`k#PSntv8+D~7rm?NE6G|07CEr${I$=EuR2u(lX#BRpBIn;1 zCH$ewXYZBh_+0cSMS0T@ZI@}@#WxqEeQvjyX1?YHx?E437$Ca<|DBN&l|NG9ak#o{ ztqAZa5G8WfkgNP%!!uTwZxkc&>M{{p&7IOxIFNNlW>4|)>> zMY51!g?RKcvrqaz*Ws6fgp$uZoix5@Z^t5RTveS4%6-EVrLrKk#V zOIx&Vr~6AI1T`YyqHiB6@;`E3m5U?6bh4vngIdmg{DwR#=l!S04g4O=5SYxRUuRd_ zE1!@FxXYq9}WG<0L*UdSCXUWj5>`U*u$$I+oTJ~2JTiUla1`+Oe)yUAaI zqgak)zK70BVDc-XmA(p#mZLy<3(%ty>cS)B{n+oLRXnfzHYeUp=PtJ5(F!o4^N;m> zD?MDgTG7`*Z1FTYcQp2=N=oawkGEK(o`{b6TP%Sp=1)-US&_KqkYmV+MW9Z}KkDii z33BBH2yX%i zFY@=r1N8w@6a-Z;}U96oMjTUz84iwiI3KaW^nEcR_vV|C*~`|9>W^9 zWT}vQlTX$nFuK~zW7K7C`CFfVZ+d(#Y%g<04pELMff|)MwR}GPn)aNV@9n$_g@CJf zo1ENNj1Tq`PXthaOH5TH*|VM4?vOBScys&@jjhxfS(?-EO?Kp7 zKkuPvf>&<319BD1yk9O6#AfjXR|I3+EFTljpwL@C8JO%_O)qG*Vgny5D!tBp-0;V3 z@+|8a`?94ki$aHudeJAMEuc?R%QVs}ss1_=$C~n($>ZHKWvQTF9nGVAN!+lH&p`&H zfgdW5t+E=$d+9U6#cdiSN>CPOv|KIh-5wQs;EU0e*ryDjv(**AX=<0nVYi!VK$nEk zeiwv{%>tQcj9rZx^Lwc7r+&PHTX|~WV?stt6nA_5+-t^_^~%|5og|p7B6d_ArDO}- zeI{lx&QTIKK5BB-xG_J2LU)Ha7n^#?13PWU)eWrzLuF50JzYn~8S!D*!Moweru_V2CWCEyBvq}0)igUF>fKjQP+FEiWW35@hRe#O}E!&MXYwaJg zw#VlS@h4{F0^nnS(8IrD*IHMh#eBsy*ws}IAX+=tsXOmvB@IZ5Oo2A&?wYUsMapA! z*1U3SU5qIfR6@jJ%cZOqK0N$1aTjqxw&Ai5!VsPuy}Npl`=gL~RM;<~@(a;KxOwAv zlNQ~-M#eejK?a7X87#HpKE9Vr?ZwG>3pPr0_j& zx#7My_%ss?hSV--Tpvir<=0tFR<00&f)`znAS|fxbx~NY_255P2!nmAwVUhlv-l4# zeUoJz(~4fS%I$QrE&2t6YHx~Ku63Jr7uzB2vYTeOd|ehR1rxNyEEirELv8x2SR=}u zgJYFxbCmL#S&bWKBWd-OsB*TQjcgP<>Y`nRfJ4KsuZAJ ztidefi&NdugxEqR%vlP zsx(QCAQlmNhxHyqo#0{E))Lp$a}VmS8KXp|i2AAf3)C3^%SvU4koT>Ci^!p2j-d+m z+L>;MRsc&PF9wBKY&#R;vid}jTl|4ze9hHG-ZBQW4`6Ry0e4PgMx$v(0HZd6_A8ww zE=C&4$Bsr;lVrDetj(^{p=YHk^ca`M{DHb0Mq6ZTjVn<0ZXQ3EA{%uyR@M>L)EzDS z%U~hM4Nj}>sxf)LqTp7KKr%f_)WV0_T!C3$?j4%a-HI!zh7}AQ+AguyeDZ zVm|q$wKC2NT$6yrZwV3w~7ib>sBRDTgi+10rYn97v_M&;72o##^ zJzR=Rkr{VJa|Ari#W!BkoN$e?@=LEunR|T}{er%udMdAhem~U4=BA9P(S!6_ysV@U z4-92dJMYu|UdUPXlhdFLoi|cr$c%&43bb!rNVWz%3sY8Jj~DD!;X~H52R7o2(g6{Z zi3=;I@4J-M`}_LZJ>UBAJi_zYj@H&$e_Kc)AGPgpAg#D>O2h0K9d=ZDx$aTzGK%uP z22$p+pSb^xKCFFV?lgyKK?*d-SayZEB-6h}tej_Gu)mo}R!P{$@}UnrJSdVcU&Zj{ zuV@NP|0s+3C8?a}(exR zj{|+tEHCpaP}chu+KQp z;1T9m#`F5hwEQTmAGB`wDN**m97bAw08EeFaO6s}w_|W3;D^1xWK1>DKP)P66z1Me zzHgAyS#TO)x#sU5|4b3LL_k6ArS$!xL&cL$St-p_Hhzb_<*twU#~7;oE>&1y9JU7- zdAU#;F_U9x==Q^A2jew%gRfrnZK%pQnQZ>Q_^a8aT3R_N$Bi zo3X~nkqh-2eF0d`({TFud`*V~p32>d!d;9rC>z)1@E7QvMwjNH30V%9nWqO~*X`jVq zuF>vf){GGPCO{cGaD0XowO^)xeLsNGvwNr*FRtx#>-HHqz0{$0 zC+_^wmOmEeeEyf=91=%iB+Czb5602W@?aMdV|6bA>bT|N#Nr$l zUI;P$*gaBH9%50o^(6EobV;wk{+HVT5Px63b*I7$P{)4sRLMejWHG)8kQ%1GO*JU7 ze^|c}#pAi?mF}z0Akok#tiD_Vpn7Q#x`%z$mR@t*$gDY2sr=Ri_3IvG`VOCp{62qn zdM0T-R@`)ao3Eu)sZDQ0s@e{**7f-evL&mZ9CeM2{U7(6W#zetaPt8fqnR)0698lOP?f-5~efcdCW) z;|2IqxQy&Wh22LUZ+Z~MkC=RDkB22Jx+|Wo4!no%$pI{&UjYy?Md%9{lvHJ5EvB<= zkR^dL%^qc9D-KLryjq*(+3Vl-_PRdxLt|Hl8A;Fw`di4mb}U+lyuYY?S+(y~%zs!- zb!ybls?uUcG725$nG1ue$?Lu_k5yRp!;Lg|ML=Xo100?@G;eKH=M9rLzOcHgxBM0_ zzReKm(hrf!+%8?J^&i6Oj}8<|O>DeglM*jF-c2G*B0S@#x^fRkS_hwZkmTU;t!8$l z=*I&hD4vCYm* zLfQjM3?0yQHg0R3>(E`wD52qQ!m*22_39mK!RAQiLi4Omq4 zwUI;YKU|v{J~38bfKU&AT$mO#9>T*sp*Gr(91h0;vx5HeUE%Uws&D=++f~@!>UsnF zJIyjwZq!&S@~yTDi}K$-;*Y{gp3Kwa^v7n`TNq9)lb}98wPYpp>Na|5UnOPKy|f-2 zE`F&Q{WJ+lQZ7A#K)*q*E-~#}7c12_*|DRhxAD8ZSAcj40G~;=F^s$6=Xv8$Ya|F? zK4~_YdgYNk|29o{7aKbM_QsvqU0C|p>HU3seemukI zz}4Ic#sDg9$e}wqyxhKUOM04ciwZnOhcI>SLv5NsrmtS*=!o>dQWfq+RqKAUrrXU7 zvFXpn^>VXk;P!Iuun8V0ioQH3)Izp25l|oP$8!z4#ZMhHMgh^k#!^l9?}sgno|V;l zXzh2%y)rKefAr!jM(b5&y!m@=8K>>o>aWZITNaEn|A5{4wh&|hyB7$=SC{~>qi+~9 znBw(^HFqJQ$)=7uuFpH_Mmx)~RE6l~xg<`W3QZ?&c!+&?n;nOu!DkneG8b$}p{oDh zlZM*?2hEv7v?0>&J-V^@w+sG}6ljB(Ug*re&dyB~O1H*pcSdxKYs@ReM{t-aC`9-B zV6TXRrU;5SEQg+4PDttPzIukiq=u%NcFg!OU0ES*1k0(k*csLz`=1DOYL!PrC-%{#_ z;#rP*oA)G68riI?3(K}Pj8^N@uqHg(;_H1Faz{}|t&90X#ce-wJ8d@prJ=Id6W-bd z4Y#6t9v>faEYT-N$9T>Rc6Q|6;J_N6t5h^Zk2mIpP$_Mo0KSZ4@Y;#eiNBs%xT-gx>S z+M9bG?PvCwd(X>ij9)q=RJDPle7uCS_%y~-?=W%xsJC(N8aUy7?6QxSzlVip76}+E zuo`_i=k8{?CHfJaLdTHzR~r$$Rms8>&{}O7=vUBVHAD)i>u;~Y%jwOY_&&S@#;i(6 zjKsN*I1Og|-&2A*(|@ac2ms*Rpw+_l&gwR$97J#dubCdaT$OUmY0!Ab1v-%IseWK@ zZq1WrUtnH0p(YCvne6zXogEY4W19=Q;SC{!f??x%f|?0jD396FB$iq-5ZG)LEoblnOihDMkR$DRAb4uW}sSy&;;`l+guS3+iHPp8 z#rZox5=WA!LoDL%)V&&e8aCVc6kQ{?QL~#@6d@w!D-1uP?HuEdjK%XWK)U4b&|*xe z(aI5qiWW@sSVoVsp2T4*3WFwFo?Mzba?~y*yp$Z}ZJ$hznQJQmV^6V8RyCH22DSJB zV1g7Sqc3&2{#QCvu7uUt+TAsie`=P3UQ8J-!X6wNZ(0k3C;EPtJyPI_HQQ<|7bgi^h@CL&l-!-bV%3w5+S+{02bo?to@uc=In z+O^4|!RXJH+T(k!31s<^Q5ep#O_g~~+j-KHgKML)#ylzYYnqS7MqO~N{%CLQ!WvUb z&n`R=f|>Q67;}u=GSBvcYBg*Mpg&0pHIBVJH}+*DFu#5)I42U{Cl^3?z!cm-6G@3h=R({(3<)$()c*J0UN^Q=XBRNvh18Kji3Xe#$BPo)i;H z3fJG28sf}<+*o#V-nEpS7O}d5340aQZ*u*SyF=y6ho_7_yTM=ABmyw6Dl$~B-QCt3 z7fej+oK_PTCrX_M`QI$x7(Z;Ly#k4ULbiOn&^v^BgEw8VSgq&R#FYai z!SJC+YKt-u|05~Zy}xfFX5;ypoC?>2A8psJx76%YtR+eLtiQh#g3|^HKNz3g4}wr! zzH4$_w^?i~m|LW7!Xtw4PoZ+XbCIXHZGQcG!%iAF{a)wP>r%hR;s{ z2rRy(=LKybGm!b(F`?pHf;7rL`v`qGK#e19g5UyCWF-=hGYr`PZ*}xpRQl~FNdP01 zS?`c%RQ9`h}$Nlq4A{qPDcn#`^RKW`Y()mM53)4A-U_^3HBi znOM^s%Xe^?Zeaoa$Elw6)`ynj$m+VpRkoZ82@*I}w7*d(v%0`;TIJ7$XsKD}+Xy{I z8x$!@nLmD)VF`LXv@582he`-_I(sTiFU>KS0xieT@L3~gY?YhI*ICZtt6{F^kU~%5 zV1OddFS-~Id0NyQwzXjwS8bglI2hKOSTI423c@KI;Qr~1H(MXye?8%xhY(O5F)#C& z=!ezdmxUerUf%(oCsd$WT+Nva0M#BI3t7~wlYT#=M~+<;%MjT%p8n`PIr@fC%VMSD zOk=Os6n9!>OsPPp%c8@6+iNN&C#AaQ=I&b^x>pMK&P5kq1f6V!ROp0XKC9K^K1!f( zdPb_uW23LjkT#^tODs!rZ3f2LF97$ZgoF4dV|!)@e)|P(RB-;PcZUTqdO$OAZsN1s zV~0f=)5juZPViP~%OA7hvW#`^&LQT9B@;;jZHM{^lND-Jv293eP}?E_;|%n-I>NQVIJu9Ny!+1pIPD^R0eSj7-iJ9RO9{xmu&Ns|8+U{pl@ z*%gHM5Vu?0)y1T(syE1@ncLS)7&iJRV^`Hl%etjP9w_62B8zvv(fF?i=fv32llad0htL!7U(~Rfp3rsZ#NSgOWFE?yF--3r0iI?&CUfmGJ`K%;MCo+}Z0V9-m zrjAjYD~qkpqcJ|_jT~dE_2{d=Cs_z$0^Mh`xh zzphjT@kzJx`#E%pDvr8=>p-T=v@eOm=|DS>0@G-hE#+Cxt88-JfHeZQ*qSUPKQd{Y zW0$Qk{B-KY+ZtwZ&U=vlZ7HnS_TXJV@ht0FO^Z98Rv!}qHVebywb$e0$9|nY0vnU% z9rg`u_w6UDdUB`1v%txFF0C~x{1k1@OXITv4{7>(ZjhkPt_)Tt1ZSlT1BE!&4S`P6k7C%XQJZ+Kh&;~hueb(7yO%_pO4bTCqkho4 zXEGX;#xc#O!?TEZc9LLWD)Iz*|5<<7Hl_N&0yHOOu+VMFJnV;_xoBzS_RGucUu&&* z53UqHhS5FbyUqg_tmH<}_ZM^~kPP_$M+ycgP@|{Hps(c`%fNwHxPQ_%aW~dCZ@7z4 zNRDIwAPd@J`h{kb3Lo&9AdGnO-o&rPZCpJvdQQ-!%p(&G_-x>_q5=aO9|0ZnIH+%*PM?U#mSXH}r0y)lgS#>7aHk*)v*4lIn zdEuo+oA5RrY{V{TrzGzb;Nd!Gs(_C&pCwl0MtHg5OM2T+ley$8GbYJU^}5;%btdAr zVBLmod&})&^CESIsaJ7*poj@pN@%e0bDUe)6Bi3V3bwkkGQ|vs&ra>X(90aBKpoAq zS;AC~Z-8i+%mB)(xntStqJCIx8fZ;m1twf4!k^RR5KO(5t$z7oA%%wfYAI%iEsob) zR^C1550yF7Sf1nm$`YS|NRLAu@Yz#XS5E-hu- z=X=L)wS~&A89hx+Quc({JPQ2gVz!~VB588`YW;21GYYqWVy!JnLwW6ORTZ)_57Tc_ zXk{5{q4O{{^Ws^0ND@D+B(3;7E-&rkU$>LviJQ{85P+n^d4I; z{e>{;Xcv7-REXl0S_pNAGUI{rRB~lP_{MX0Pq!JLH zIqjjC#_n5{-Di#q@Tqktt77e~S>%uHexS*u>$q%;9+~VQ)0&d}?9u*frF=BZ2g*s? z$l6)|RQwsQluic3967p@=M1ShQ=xJ_90!VF#-Xifqw6DRf|WqKk8N zMw_Q}I|KT);72fGvo==duNWT~n**XYd){u(8X+D@MPlmerOrL#`=bUqY^9f@yS>= zG*&#qqt@y#ZWI`lmapA?J-0Ur`pzwW=i4Qf2ripe`D*{VQd@o~Z=u*#xqSjiAp&B9 zOK-j%1WY3rj`R$EyG9w|2}D4&)`k#}mvZcrvNpCW+cu@gE5%`v8w+czDuoNJfNqjpBbCa*?QHT8m4`nsSpe` zYEPB>R|vY4;ugDg-?Fxt+r1@0yMg{IaJVh1J~}-s4CHQKIvz>^TQl$Gp+QpUzc)r+@t5SOUi4@W#OuerOT&{sFwnRG8i% zsC;S()+AWvvH))!dVc7F^P_r%>5kEo3+SaYf`{wrTwc>wBkP2el?2SW^kv>DsZ`^m z0&TZMyzHdHD|f4%21G#v=&}Ui_N@ksCZZOh2j5Hhvu~h84<8vLR1H>0z$n`P5ziDc z%|#ZyO^(lEWs2DZi96*Y#8KVXcr$;;zTyPR>&w@lyXHUL@GMXj`_Z~*@>m@JI{~Vt z{OyXJq%1)Y+czXlc;RMI?~+1fF(`ijyO1T(YY5`<=Ggr12?%GP7|6>)JicE{nLeb2 zN5y-pFaML#@YLf`b<5u1Sl5c>0;jx4&Vwh*j>95r(cTuV zB#p~-qW++X43rA9G^#35W^SYPmG=zhXVIjJ?8TGpnq5H@l6??Tcou~2Q9>oBj2Gok z0(xK)2PpZ9DhWob_pVa_mCN6ddW6v8El!C|?@kQ*`9rJ27U!Ibp*h&J_YphVHN}V~d0cM&q-DLcKJWy_bI9P)e}zjXa`NzcGPWxtj8!H_q~(_q%+A8;M>lUT^+PozDkoVFn3sfG`vgc) zPPR1jzJ5nvv~rGJL#_nVH{D7yeK@+tHp##;wUq{fB%woAOBR*lW($e`oP!I;966|o zI>`JI1o+hi5zg=$8XKQ`fI22&=&(BL_!R2HteXdzt^05kv&)R@KWU(~;MHW)If9!9 z5PyjX1EuPxaSUIeQx^oWnSMGHZYB7eKo^LYra%Sdwv1JYm3;)kp(3fpn{+ZDK3f{# z*HWG3m`2o3ZbhC9XPy-tj;Rw2?2Ndb%ix0=FG-B5B|3@45t@ejG*YwUlEMe2Lm}HF zHVY?B=#=-=CzC9q-`gIN9HxXqJtcz^CBMK48 z^`3ge*0HPJ2#p{CFkhLN1jqUSR3C98$D1_nApBkMgR)hN%o^O0sW|4SOy^Vx%Hdajp0smoxO&d~ZaoC0?q*W6 z)q4T^mGmUVkiNQm#AoZ0=qwEA8n7RI=p>&>K@Yk!q``p)5Y}!5``7kwQiI``w=5{D zzQEHbDactm8*#;w!TzfeT|j)6z9^hRnU?!^KSf;udufP!;gN?x{P?sJJ4!p_Z+qsm z)f6(g^LvdCyQVU9#*ss^vykdLKGz|{3`g{qfBpD8YcDiXW+=02Dwt6M1p*)ilaro) zsD{}OJ4md~qMkDoKU=P7!Cv70>7!rfGQ#Vl>pfGT$fzN|dtmZJp#Q9Kh$Mkv@MRuG z$x5VEPBQcww!Q$9?Q_B@Tl+MY4_&!f%D3?s3(zHfP5Tqn$h_1EhxxV3Bg9VCAh0cp zH6Y~&A)@R^Zjr-b{aZd}Pdf`dElMj4hjajS?UvG;y3K-lY({fy#9W^a9nUR=Wo{&Q zw|nIdIig8`_~`iE>p{OeTy(y-{!oS88#YGWaj8dSLG(5G2Y5+>)Ky@7(I=;ZO) zCCEU626z$*aG^%Acayh0m2Ojk=+HWH%ugQ>NjxhAhE@c&&n$gvow`KE58&&{Sy0d= zhz`Y8qqv4a#5u6$rmjTz+i>Zx9cG*E#4JNU=LB*aZy2&=Y0ji|Co+Kj$&WTQKs{%5 z&{m!z3{gMYLrfXO*Tt9QB!k9b3?9JYk4g|SOTKmu_1`fUJOGt?(1fWJIwE&Ny52gA zv=c4xU*~N_AL~}L620eOPRa2gj1^z1nJIi4^-9#~pkEeb%B6q`h)w^=QP->5*yy{R z=+~44pa&|Xchy_b#V{u?b=s3;rVnAullrW6r%|?JhBJw$2KI#hY65_W_C7~d^dUTf z^5i%MMEqJqrk-4r>JvZVGf=L?=YTLO10`c7d@Xd2Z!3iFPAkbNj5L9?)Ty5==pb%!H0sk6USaWA|6`ICwHCv_Ddw!sI@%9AkPU zJ~famlxo+0n=F5`!p{iIbY3fPOi_;)HA38Upkvm#{fid;)9;0B8njy9MeA#5`1(xd z?kQO<#LWUmuoTQp$%YsW!?s^wS#T|xbG}sIY@tL~oI2<4q?WC>O7yka-)+7U8I?hh z8s#bir+sxM?;?l|?uQhvCkqu*(twG&IbQ0{K^%fYS>M1k1c8T&d-I>NzjIa*^I;hT zC;U;bEd+yQn#;6BKsR999%sw>0I6VgKKdSQQ*=OlTEQfJDJ5bO#PY0#)iLZ-7dER z!*H9RcpoP+HJMhRhqr%lunH~rXX3u3M3L8}K}J=Scjo;%`1R)-9r z0$EVA|Ee9;;)Sfg3XAurBv6MOWh{Jy(h@$so)E{*~Bsntn)f=Lu zKDm}d^pM8wV;eEk06cI2I|E*nqKY)!`sKeUGDj$7+o5hDC&{6|iCEyY+i!Oz6dx%( z`?J8;C#>EQ!k_G3H*Jbg0wMgryWg%}cQ;d{R@(ure z1;+2M@&C?(h!x8RuGV9dS8KTFty?@+(N=e%$VfHf=N8L%cZ|V}o0WgSsSU_x=qP3; zdr`oZK{Np|!*y&$cS?LExXN+VfmRdtHzEN<`aJO#1TtGqO#Fy+m5KIjvv3b)ICBc}(Wyh8`}|nA81f&os7yhn{w&J{N@$ zOYRYq^)F@PU*^nP(_ltkiRUp-r#R{sO4X^lhmAu}3g_OuftUuw;XZz^diuxp9!C9k zYXL!Yy=R8}v&G`D|AyGde#K$^*7<{LvdHA?+IgCDNsFK>ec!cwGbo3hHKHx$2k1g( zjfmG>Nx~5jX_epwLHnB$q8I)nH5j`;_>N#fKW}GquCEzTIZNcF(Zq$Nlp#Ky?9yot z9%s8_0c51Y$tTF3%3onh3(98K^i-lzw&yI{|9a;`S%q5FS<_Vf568QIX)CU|3}0T z9QQMb;s;T2m<)=aKN>PUT{+T=!spWljgIu>v(Q#4^g*j9xZQmzc-g0({reKHf8T2M z`}UQ8-{1B>c`ukL{BNpPy#DdK`)n~jGi<<>2%_C1x~4psIVrP^WFbXe2BDf1sFsy` zl@e<(`_YPA=DK$P4iOp=Uu z$!#L(a(k3@X6sK)Z;Gw39VxWpSqRL#H^40)6EZw;6})tXFg0vSs3yv=<)xwoUKn|V z06RKlP=l}tUb*;TgQ(FpU}URxe_!zd*qQvV!md4>$^QRO(Wsn4o(Ppn2OXXcJS?^f z>EMuKPAw!%D2&3)Ly>Bv!V`vK4%3`B5mpGLHZ-HntRiExQEg_M*>8HD-*tVT-}Sw& z-~H$3^SSTqeZSuC*ZX>3pZooJ-S#**(z+rI$}b+)`}L7Sjzf*SGXR-J`Fd};(K~RZ zL|TEuz0xtb3+y;fvA((5{oqOg`U|r2!_BX)CAajVz9Zej{!uogv`rWc??Xn^^3Gc8 zyF&h1+kR(!b8PTS(zlAA^CtM7i%B;ND{d+&4#0F~n*BUlDqOv=Jy$M7;q-F-w;|idCWPj)sizseji$LbAH1Q2>R6%k z!il96{Y|;?XSn8)39k{0CU1Pk?5)LCmuTb0*2;#adsl8EUT}U74caI;sj(Rn*&?9& z(9>19Jmozq(@%OI!$w3KF|^u{_}5g?eOQE&(qlNFy$n)D?GFZAy-cjs#G|v%hHt3hxTWM0@+wkiN`;$B9 z#~>3XL6`hkqq_tzeopvG)Q~O(XrG_%75uYix&r}7mL>mtHKWq2iSGkl5OF-V7d=nq?Y7Vp%O7Y=w zn|r~Or>=Rbn!b1_Y60775+7FEx&yu`+#dIH)9~030ExkYVOIQDX`9{2sO=vsV19U&?nQ3mjyRX_e2fS|SC2$_7X zhjHtbwc1wMexakfa$rJzc)v2sBKx5K9J`D*{?QR1O-c}_gVXqnO4@$ZHE}&I~6Dg6BJJ2&1VHuQn1Q5i0%ir*Wflojdmw*l$_K! z2qBxce$Oq9<-ev?&?4)(X}8%m^M$(Z^c}2ODLUt8q5WTf#8u>9BUyt>KREOjdWNth zLK-Rdtl-skdQAwYFpEg=D+p!H#!0XwR(Qe#b~KIPi6yWeCLN_cab%k};`cpN>EFJ- z>X(z{8^xS0gpOhtHW|N=;QE+W)YS0^fHE~i!O>yr)fQi5%f}7EXUjT24`b(%T2znB z+AM9zJ?U0uwUTDplsQpF<`Y*eJUt;&GiHE5SmA6>LXM;Z)9SFe0xFo3lwZ&FC+!wO zowE;2cvPFY%eAKP6aw%RX*Xl>LF1q7<-S#jqGP%I>iXC?mosp~wub$Op48p9F7pt} zS>O+elsJ^p{UBP@<5v-2OwvTe*l$&aXT6!HlXoV0zH24bM-4Lw%&0B;lWhjRr94Mp zpOy}57xQg8q7{|}!sGVGR)8-=GRhp&WpCTR6@uJ1Bsp$BH=0$|NA2=sC$V(&T4Ki8 z)8y}@kK70yivn6yFtY_*RiPh%jOncH0W~&^a7p&d{DAVHv90)_V7F)xj^Bo|&xULG zFrBL2^2pxsz>{Caj;r|D-C#5v?NeJx*Y7eaNVC*WJA=-mrfQ9N)YN7jk^H$)xMC9{ zYo4_bd!8712YdmQKJq~?v0^PakxnT#KI^y+Y_efzr|g*8rkNuUeDuYq+0uKS)g3%? zhChTE`EelyR8PJss`12r{QT`G@BS_5aM-c7x`YHw4`%;_YE{k(_485oG|M}cHdMWA;4C8%(2|`A;(N?9}L@{0OHNsAAm2G8K;HS)@z99*A(=8f}Fo zw8xcVoqoA*Xf@ldY=yLViKTuN0~nUs8uWEx-nBe>@e6&Eh`;wfG$};-qPr+Qq-IFs4sxpP_xk(S~#RZYpejo~gCNHWG^>798*0GH?4o=>7 zMlO31c1N4FoB$j z1IVvaI|doniX08Dz=!^f9}*{>mWrKkAiyO<06Fd3O&+r~L@^BL?r)nXbL5#m@t;h8 z4FF$QEp-55XrNwHi#l~Dh9~Q1un!>JggAo!saq>~{{8i=rU8@L56xIJA+tQfH!>JN zNqXAeI@6&)Yyj$NhK7~W#)U7<*l*M1apW$%w5UR(TKrCdl4z&@l{#Oo=Kqf2!K!JN z6)C4LFy&3b;YNrTIC0<#el6yH7Lo%N7Ql=b=6rn41jiU|?2-*ue62v?_DJ51J$i3r z_)R1vviqF zvUHA(lrUqx3_s1*NMUX3_O>#ImltI6-9Ag)fl~Fq6Fb}E2EyP*vYDw0e0D52`0{B4 zf@t-lNZ(zuBl@F)9KaU8JwIBGwVj}MQv*61%_~Mu<>Thx$diDg^By+I2xMZVxc17w z>!JTrBK_AP{42*}qUuP*V&lkiY$$t^GscTrG-JND5pZ$gWuH7Ppym=H+TF-^rK_? z?|m`q!iCMM$TceVA`dN};1|V7mZv{@`X(TQuD<086Tfv8ONIWtsu?B<3CwSz&@tw| zg26~;8T8rR>20f=VWHB3AXWQ(oi#_a8T*l4r{hRr(EH~u4()BQnNznZC(a~_vsTM* z6g%0<9)QJs&!++EkSfg4t)^qLqNFp$N5{l{JE#JN3HV2x{9MA5^Sb5pxxXKlEt%&I zgT$a>xF<_3%y2Xqx5jcX&tjz%5PaImFmZ86QfQ;8q=WT0VK)e#$Ug++B4z}GWKi`h5=PKmlJJH8v zyE)C*A&>IJsc^4Skcfh2NS8Ry-`A8-Hh})%5y&lRNjIwxZ(0*`iC^S{%Q`|cHsyrG zh&fc`=zIk`eXC|_f(P6lTZ?ui&Q6qjJh$L8%8>VbGR?1#9rVeT4;$NfN=7XwFZXTG# z)HEdNJQ)l0A}RcxpdWe4F1+@3j~;)@fe z*Brg_HrH{@!J!DUbPNjf3YuEvO%SU0L$ra{mX7viZyCBUxk2+0CIfu-&7~?E z*pRz%08#bmojY%Poa;FO#^+}O%uhd2@Hhhu2DZ58J8W1|7J9ERnE*qnDj6Os!yJgK(Ts?4*G zlfy@ZrRSu$XpvFo*#WM-XDr5(_TBgr&HhHO1tw)Bcm5%GL+Sl$>=fzG0ya^#igsJG zpSYUqDT7^OJ14#rj5rl!beEqQgf|Mhjn+D3BzHpcI1@D=K^~)tva{R4CF;%NC69`p z+Ze~~eZs35yv>Fq8&jC$Fm4Oa7ny6T<$v0HEt(20yEVKeZHJvXc zCwHDx)ZVzI5|NllRDT06?prRrz0@5&pPL+@tNH<81zC#71MMQEiN*bh*5lV-^=o}#e zqlR?D@ACe{@B53#WB=^lbIyI8*Ez4}t22wBxh2qV8pHqsd1JxIEm|wq+;zFZB_3ri$8$-Z>^)TnQG{(NHxYdiVQ4jvQv1356Zn(*6fGtmrCilq zbim3fAq#_=>2*l7-uK+_Oq-mv_FOzYdRi?kC3Q5t_G{^@^mc?P5#%#-EhPl$CyyjT z-X>yzFwg;L2>Jtr6e1r6^{E*Zrj?yMx9~!{+pI1+a%o$X+=q0K+d*r~LT!$`=r?C| z?PvdNKdE5&T}i3WPX(FM;D+^8T$wLP$|i1|w@gnTm7k1VjIn<9{EI3v1o4srYqnXo zn^{(QY5gtX3a$QeYJ0MXWXy0E}`Ust0sf_E@_P>x5d2U#DDpNg8{08L*u_qEo31eo_e2jvYl@B3rBq|I3 zG7&coOx6Yu8Qu8SUPRyber9U?;o6HYDkKoZ3rZ~MIj6ztvT47oBi@7lxmon&J%f;$ zsj&w@;Y={!@Fhrb76V$fwR!c4+!qo1tr%NbmPulNp%W(&SczbVZVY5rK?OQtz*h-q zFqR}OQZ_khI5UPTz1B^srfTfH34?kisUTD7f0v#9M%yro5St~+JkX)~$i-rP1r4cF zzYFVT4gc$yeB6?$D}}Ym#Ex%0Be@SLc_|6J2Xuc`0#TJw6V)GdM_LcIX+^>@YZ^$R zzmnMyxImI%u}Dde&48Do4gPL_eG{Lr`c7nWFobm0!>%Vm;nCHefSG3^iB8lv9JvkZ zrqrab&9Kr#DSoUn924WlcWJ=`U488}>weyzRK*@35kNu2fE*pxkKVcXJSPlVF8qv+ z2q@v6eBl2(g0|Zu9x8VeHYYS$OU8#otO$_E!jMgdRaMp7!r0>1@q`z%{v`LMvkQH1 zX2AOjYQqDU=+J*Zt3%s$ajieLFKKyoIdH?4Rt3mX6_HIZ@2Ns1;RvC>(nJiY4;3k} zr2m;v^a45X@v=uGBq+ydovj<_Yq<9BS6}LbVouinhW`6YR)C3=+%d-x{E{CM5;Uq! zFc5LuOhpqO&XSYd-_l(Oo?|1H$+k z5nC-L4Uk9P)?AOM)Uz8ce*x0OO~l}Bmp^0?&`2<|nSBY49=gH}8(kJ4lf4*8{e{Rq zl3@0)7hVLP5YLMdl3a39U;Z-$&L2jKzm2Q>QF2M^Pk}k^_Mt?oQ#|@IG_1}eAZ&R1 zMTzLt9t|9PSy~XXmwHDwoi;X|%n#@=w|-PK?QVde`zr(zvAQ;fv=pJExENSKk0UaL ztGdS%HQt8B$|9#T6q>mcsTVIBA+_Jbg80zUU+B23DV-8@?GjIPlN}jbYZiSm2Qg>$W7gZi|&|Vl);W!FvVW@lsE+y=p-t-koWlz91XI z_V2MJBdt{nqP41nsR7_n&p=4D`Z%^^5+9 z0c0=L-&pm3fHpIwc3P^-icNf{08|kC#o$dV5}=82D4^$YkAM4N5SIz9RY7nQz~|X- zIc>N$WVxEfu%7xQ|7>*bg>TBBV-S-E)4SZ+2KkrP&V0l0V8biuH+lP6l#CoJ0%S^I zkRa8_7$Wyjf*CUFl2*;oD{fdg;K4+EM;=_$N)=kGj}U^rAh~a3R}!1XmT#+0Yx4}G zB1K^`g(yfhj|~4w&#;1G?NXxv+eEGRWJ_Bw$$h(Jm7PSkm)>UNMk@R;UP;Q$H*Wnl zF`{p_yl)v$Y)%B#C|}YV(rwK@3xxgrR#dwRdGVYQTbi+(`ufdRFYF2scjVE(m*xHE z`f~;`@UrmTm+{p>T!z`HKn?b^V$DbTip_#;|2JIT(tP?aFhFrhj>~B}%Q+-9%M1F> ziVD3<{RaoYc{drQh)crFj+e3y%)KrooSW2!b+iAh-VynQn|lz*?oZ1a;eHHZwr z>Sv;gpoAUtC0nP;K6?S0G0xFY>!uAS2WC$h@ckh8I`oQTHC#gam57W5_2)6p>d=un z#APkW8JYpD-dgTiewdn({2z!UR4IG`8kZ!&&*pHG;-8vDow^m+m?sH-c=L40jE*TG zg%|`Y73k61F`IMgTS)*(5Au~2t=csg+OT^daH3uy_oCMVoyv<@shLjK0+9*)|Jm{B ztGd^S4eY4h(RcX@9?E*zb6C0RmdHQIiD5ds*1ucl?ctY69vwnvd=Dqdxm*T;90b&ql|R=;F}A!^>fO^ z3Xxp{w0q9J&$kJRXJIHH26Sj1fhY<&XT*~{f(kN4%KwkH7J}O8F_Py#E3@}&@7G>I z1OsGB{;Lt*ir)xB?PAOo#7)>WKAM)oemLbvRmz{w;Uz_a$XbuI`R-RVyxSY>cGvsO!9;-qATq@m zX0e+v9iwFX5=FLn?Umw|_O}}f)qm(jf;tj|Vq(t&e~93+K@iDbJP9VHl*{5S)i6x) zPhY6D-;D0ci?Nj5`?Cxx2p8xD&c3Hd*Gt5Hl9E#JxWrH=TBdqaOFZc-8U&`7-yY10 z1dA77Sb*=YXnU{k)h2lufB^@BVGi%sI+dck-n?KurtQm#0It$T0eM?ahfxXYqks@S zE9=nTdgn2kas2|whQ`Bu-_VHUkco3?o&@Z@zW~Wuhn8@U(ci*s*{E~>+VFl*L!{>4 zV3Uf-a+{gD4Ku$0+%4NVb*~%iE^ZEJ_e+2VLdb^>tq+u+*T-;vb(){fy1~PpD&j&H zz1JJz7u(j>#+d6T>$~PT;+0i> z-$o4}9}tO1wMiL@pk7)jDnGtCVT!AIU!B#vzRa@B`=+j(+ipchQH{+69aTf zY(_qB7**|XYA{)<8ijPWU!9gVVernRM>LJ1A_v5Nuyu8U`X=v8BVLXze@72t`@(lM zlCI)=(bHJaY|6nf$KQs{y-+n{nu~p;BYEDxLaVu0ci-eXh)`E?tu+i#ig=#z)Ij^u zQ1p;l3Im!K6faN_Os!bV{H+2edp6)-m&>#$usHAvgQh~UPJ^pb4h=CQd;W>z@D!M z_+RVUe@$<+Xl)fRznHnRc^OSXR6+Lh8gA{Z&U0CS1VFA{K@S)BiA{X*iX^;%)KEhW z>MKiq$0@F2w25aCBsP_A zWYS<}9e4H_3gS_L$14hK6sYUdyUdPDt>C)-5md#`kh%qh*vr^-@Oj$nLh2SiTsEZh z3VPB;7#~vNU;obL-OBCTNpHl|OAdJbdJMb86MAufycK87|HS zhP3L2Exn1&JSe>ElqTtLu!EcGTsB^)H|IT5thMiy0fjqC8DNUY!3&UeB*w)MmtdoB zpP#04!pbem8PM-QO8@-2WZScEk~2?Y^QACeH?yHO9P0WtzGBgN5U$n>ib0vGL8Jm` ze{@_Ys?a##hjmMe7xW)rj0%2wAURhJ5nWQy$ReNcs4oNo+d3;;$-m~qXVC^qY(X<8 z5JYJUw32XeBiQR|LBr};Jbm_`4fM00wN zc35x0V&oLsdaHUy;1mvVF*N118~yOmdjL40%{ZrC?WZ?r2de!25f)TH!{u$Ru|v81 zSGhObl@yc+1R=KBlPSn z+;Z+u7E7KEQ)|4+;LLVzNoywoEj34T2jX>CWuGR+*XAA^wUYd0^@F*5l(Jz0+n#|x z&JL=lKR3i&GYA_5or`4B@CgYi6gjG>vN?N2mO*M{qqX^?z4MpPpDsHtv@1C~4!lHJ zfl^=iY~l&IeAHZ#65I$EQUyq+B_Sx@5GP?iaZ zF!2@~f2=SWO6Svy^BEOcmQoVm^~+OTLm_q`Zi`FIm^)}tr3cC~H!K&2`wld>FMQV{A) zD5s^EXf(u0*a~vO=Ioqx)UlUE{S^qDr#1p%J2hkTb`+kv{=x^HEBj)po&L`S9wVFh z1qkY9%i(>BGEDxZvg7YR1(rrjpLY>fxMZ@9<9jJrB_|HYhXKvCTsmkH|If^1F~YWO zyvVrFL`S`^$N2zoI5!Y>Idd9rXxB*N1mqy9A8(&v2Cs)edOpGTzN3qI^XsJ}R<58A z(GF@gxAYZ(s$;chprTn*S$K9;d${TNjhcSCq36LFcfc>&MqAB;^D~(R zKF-vkQ`IF@X}@T_%;=IDC~_(kW_tW1_~O>&FG@_oR{@Ut!Hx%>cO!Tvt1SAD%D?mU zDtbzpJe8|u0Y&_oo&8k!aK6B2akd-}WrrJScVL`6kLqys+h|gE_9W4lS#lPgl9zT_ zi9~q+1Pkj(sy}=5D~f;CBh)#Y3QHNIpoOBJ1fIrC`FDi;W1xtzz9k*7tJFh?PHRi> z`)y_bv!^`H*%a8YpZyzI9~V_Bwb^Z7KZF?!%3%#Zf$orgzrI0HjrX^xHQ(x7`^M{` z{zpl9#O|o=Y^svg-_q-UDhPqF##X&2f{M!aK4IJU8lE7nS|3;!@qj)`#6}hccC31! zx5S^+vVD8@uW1cv0m)Y)lhO&cpo6ellR)~MiC57xa5(> zpEV2pQnvG@=U+QW?D2`JqEZbOR|S4ie&bq=STh=+gR$&DX|TOd%no`Nk>Gp87vfgOOV|(_hl*Fin!1X_7KCvj~)GzVqZR#=;YLwW+a* zGY=y)&kZM+j|*wecr+c^CyqF%G6S~NCjGp*V0{{;XNxo{4)rLs>+1V6`Al6G1EqI= zptXIP)Y;RGDM$Mt<{fyhiu<(`N4Uq&6TU-rWttRa69p6{q3 z*~2_*Wx^e?j=lWXC2U&WzU(A+Pp%{GIyLR0Q3@|bI^KM2hCCaN1k`FvzW;4+Q@!3Z z#tY>^iR6qYcNbD}WHFt%Mf##qV}IjNu|yRm1T!NpAsA3CH~d-H$6Ca^1R%jbv|e9+ zEO)E8jQgl{DozBZnjN*A^QS>_gZU3%yF{(M3)@8C2+VHo`mE=dl!ADg<_7~wgvYo> z^?(WX$(W78fxI7;&A!oI`)}!?2E>N~`z*zrw~osu4wp(i&L4T_iY8S%k)N8Oe!x%mT-G&q7K39-uGC*A zu}jAaRj9!Da+5EtcWANi8>hr@gLVpEt>@~mqPOZBdmYCtW1GzH#l;1bZB(>v`DH&G zN!<8m@4Bzn*%xR)!sO`f8d5yX?luOhFYjyXChbgS9s(uFHB|r7PznY%^5{X9UYp01CdUZO*hf&F+Ss2w z%)IV<57x7_K>RdhB{rM$<3I}=7KY}+pvMtHo}|uSMZExJ;k3imJ(&9Tl-Kb> z$N6m--0u5Mr1$nsesAQX2U&(TYXE;61vQKUKaHV|@y8MUdruiHd=_I`|A{dg2K3!xh;jRxMvjYK@u- z5Ax@5InnVVhprg7na{i`CA?Ts*n;?DYqphkc1ugHM?RI4WZ4I=wkCP*5Y&I@I!@HP zkH&V>vrK_xej-rvlGbQ`3d>=emG92&$6F8p4xg#<{o=nMEPgDdP#?D#<0Qu7{Lgfo zg@l$9KUIV>`<;zI#&PrFUWzm_)Q$5gZla-9&*%+CyzH%J-9Lw%t@VF#)Xg>9a%o<_ z<4vgR@lHwm%x(LA6C5@#^k&L62`$BT3~QtC81guTJCTTw#@SHV^5C?fa>7Rl5G0T% zNuL;?xx<*%SjHlRGe5c4+|J@71v2*6%6PdRM_3BgdW}9pB2}dOGS)85Q3?4Z{&T$$ z>5SuPxs=nPY4>d@@f_8i~go#`qPut62k@;lQ;zMB;cs|IN+dUuV9jDKJAlwOfVOy7CW55o1P^7ZRMB; z6?_$a&G2h9OZCBf!o#h0e4*!AV(NwN1P7IG9$hYt=Ss~5gTE9}LD6!PeS1#MQ~IFy zx{-zNCDcBjV~Qh|b7tO6)k-LxA>Prh#vfvAMK!5Z4-+d;YkPp5cJ#|-ppYldS{ot+ z-n$S58H}kp9*=v#!XtBaWCJSMyPVHGPx&UdQy?!ix5@1ul24f(%@knA?BA@oQB9@m z%b481EgWv4M31gY7>+M{${c&`1V{I}CB_=q?!^+CFW< zx=Z%ngy+|o9vJk9{Su?7ti12SbZ5^$aKg>=@i?^S`+!(x!QLZWwoN=Ux-*%T_t%&v z)F&JCZbJKAh%?P~?TUH|MuHHVQchU-hw~d8jE0j^CPVNDClVXRwbd>&W)al$rxjmmvu>4eaOp1G zz*igVqv96f0Tfzp8hmn-9C_Y$xs%ro2Tlj1qXbW1cOBTWcCS|Pm5A#rqKtJ`KN4nY zA6+G`XqOunYd8rZt(0?HDuD1u$}D;15nf0aHm9 z`|D(JLK(ank|dj~Vw~2i;X}7PRYUIl$jMInMI|R)cq7q}h-L|@(Snye%tI|e+fUHi zef+0?=cvwdRQ9w{ZQt8W4Z+B&CLnTd^Br}jlCxz}RxO2zl0Y)3Atn{;Q*= zQL6a{jmbDGHRSE%D5YcqwxdQo5sQK)vyPb+*sY+}rmgY*x|5fGjLJf}d)= zFiP%ACW~8MuqT>z`XI`5YFO8_jpTlr;C<2g06FRW3SWm`VLXGHAFFouU5yG9`CX%+ zP3K16+H_gb912bz0w?#abr#insFx9lV;7mA>-6YcVhyg_GPmm=@FQ-Q*~cyI_kf8o zetaPlwP+(n!9Y6(aY#Z8kMEQT1sRc67U?_2;DPSJ1iilZ6dKk{VJ>YyzK(DfE_W zvg@od_$t|NjP>uHGTIujS_1ULBzVz0`>CkZKDwG*~cvPCUUb82fmHzpC4Ee zxUKF=G~^qN)&SViCCoFCI11p<*xU%8_ox#Af!u^BKYOgLXgD7anv>yB$pL+O&|@Ke z&cvyS=g#7MD_Lrz=9)=PeS?nGYw7{JvJfueaZj@BYx{|lp2J!o*+G>ZJtI;pPK=A- z2s(qCPNnuo$S|XsvfLz^!6ZgP7;UiUrJbaJCkQrx-NW#aukq1a!pyzNPvkbl=3&AQ znrkF>oJ0F>g67Ku*na!(ayYtxV0NY5ZE0lc9i&KO3v!yV3G~7OUF`8QY;7}EBIdMY zcy?RbicB#5G3E7_ATs5|Y!Xp-aun~3*{u>hg*TBPPU}xIWB&CS{JS<(sg+xMgB2h` zbBec_O8bYvyBYyit0frbAM$2QF+7>4)e*bx^9-@%_?4qJT=`lu+1v6)^C3f9_GQn^ zO;6A;d5!Fn(f`#Cr<%Q83V}07v|qcVmN&;a{qeW)0U_9Q>M%`Hdb7JE*w|gH$Cql} zHxU=K@Dmc}6TjxAi{M9wKO+WqGTdjr3KHN-!-*X`z8N|G=UsL!?$MjcZroqheuhI? zQW%$E=~_k6+CMTgpes9Hu3h3Afolt7twnMlIIX7PT)Jru8OMK0$jlcz)G?#2-uOG) z_b_|cfWdK&P%=CbH*9A3V*D@)MWIP%bN@;7`dE*D3aris*&9m)xW6T2xCTNZ@$LYD zy68LKO^#>8?vV`WZFiwjZb54vp%*QyACvv+9>dFDU5#FUZidlbB}+Q z&~>=JF8EcD4ZHMt5Cw1DN=;vw@kkfrAU2Wz6t2XZ1ekD0)}5R*ikzNy3s#&ABT6W# zr;N2eEl0?(p-UApWWgktwZsyyoto8J)U{<8EDRHdx=#biG})x*zFTu-f7;fQSrhrRXO(_O1_9(hSZD8Kza+Vf)s;kai*s<`Hh*{2n6KA4z6 z``tkHIa%KYa*PHS3~mwZPInMNFx$c^Q^&uEd|C^vPN3Or3>FouyT(Vb4{e3nl##U^ z$pd2LItU@}50D-PXi^le*UA=0+GsSPHggAn!Ly=wdcd$&gf*2X*tT%IY_-)fd}% zjrfs0G5Q(4nCc+aPFbgo8-q^Of?(|cU>mRu18)#4Ow??7$xEg`|oP+{?{ zd@pqLQ;S$%$K;c+z27ghm1Xl*?c5^3DHh9kpUB~ z^VCsfX=gj?Vl9}?2P>_bF-`~I|7!y$w)xVkj zIEv$6JF}OIKSOH~XVM`}K!nT{v?+ZGy*OK!TKS8H@$BvhH&A%uvxRs+_0At;xR_fx z?N2OoP)EHtY>7}l&l_O+qRA|3S+9AdJDBNyh;_m#7H(QoerAByZf*Rgk-KKKmI#UK zMNW>6?YAWLYf9M5|EAuO&)Vxeqc90~cM7Ophj(wM+ni?5>y^YwddL!a_0_ek5m^mG2 z^rl;nc$At%7(FOE2e9LcoX#OcJ9az9hy9&2=APnGxv{SL)r!xgN}Tj+W^6U+FJ%qS zpXe{OWvQj*Lb5o$k<*SMdFv7N^yshNE2oI|zC&^5)z)JC+nuk&iAoFP#6?ecc9oqi zSvMSZgN#>I(g`ScNp`$kkN&5?xMjar1jI+U)=*#;l%=j8NPtgX_vMB)tE8xVmx)LY zu)Z8mEMrJpz3bR|yjrJHqJM{9=J3mpiX-`00m#>yI`Scv0Z6?Kb0>#W3Uq@Mf5Y0G z@1qk#VW|{GV?cMUvcYC$n%Q=_6ZIl%m`AO_(cw8QAq+V6u^}#*>XL#4}qG&Zg$$re@eT}aAlt%36I0c`~k@OXW`AWfgNFGUM z+EZ!e@dowU^~Gi^2jFICpr5BoxSIAAIv zzeUOLrc36jzUap|Lw^3DS4D=kL4VtH+;bV^;9oDsFASK_rCU{G>l%V z33M>`iFQ85rp;&d+e*{hqS%Pma|S*r|5WT>%G;?rvqR}WJSy7c9o*paHZeIa=Nzba zy9{(FZ8_9Q1U5x%^TVq73ZKU-a!#nlwi!G7eQ(q3WgE$y{9&u3;0BoXsATxlQpuQX zwEJ&<0MT#eGAJ9laz1@H%YgZszi^0A7>zh;TKfAycLVoc<6)?bNFFuS4wA7=Vp-By&0_WDfUBW_YZu{~~lHX|e(3>}j%Zai) z`P^X7p}{KS@_iz4^(2NF-5gAfF*|4F8s`f6poDA?m`D|gmgfA;!M!jJkzunp1UhIe zP#B&C>)CGvyOWgxUtKYacXD0$r^n6KC6w{LzH*#GFzROUR9)e()R6|k#O@(p9TWS~ z(^=1_^aJ|H@Zw8_Q@21&HbHR1?0wyC45i?&9}<1F{umS@snA&UTaxz|wKnmNUxBV$ z6I2qcervD0XVpdTB?=Q%GTfm8K$ktuony@qOs41A`KnWodvlZTSmwOe)6tfum*y;|9gWhoMdF$`&zrG70{O?%2gkdP-{z+26XIY?){E@tK zn^7U=Y9kAmoKDnW_*-O6GX{p*C*Y6i?t`44JU6-Gx%I<%92%9v&G)=Q{=0UMAd87PcxqCz(FyTjAGm&BsT7Ekq@Tjd zRm@8Db=5q-{kgMGUP24^eyd0%CyUnm$drehsG`)=XdjqtC|{)Z5~<*r5SiJHlOBEFj1%C`k-FY)Ej==(u&l*xx0o>~j`q z<-+x~?gkqB)Cu}{M(w+s#~~7ip0CO0g1uW^I~JWedF5lAP~PnAF#I+f!<+BdVfc^l z5+VFx+UBEsh7vMKFqCl3YTQ1M{5=b#qBuBev zJ@5IrO`>~-4KgX`SZ<_Z_wW0qA+HB8-e|v_emduU{&}b4tabNK1hbonkrjLiCV%R} zS#}am4ZUNt;MTUg`VYwBi~x%w4*!R%LzMd3w|09z|2d=iGm>Z_g$@)F1+c1MH08Df zPA|fl#sIN3R4dk4#QZ*$5*ySnaIJ>hkag+oi|LKaHV9aly0r;dLs;g@tBml}5jiPs zil}EqV^DF~N$l5X)H$$j*tHhj>Fi{tU;3Sns`}sVY+bAKUYG~ti zNPERQ``RLT%&~Fp8UPx;CH3^qE+s zwe;Yj?bRiNQ+I0%a;NiOVROB=QLNWJ0%jWA4D7e^qnmE&Cjb>oDW@@3bni|vYcI)c zS`%`B&0WFs!k#y+#;nqe=*M}a>d_2(NG?_AigX(~O~ey$3frww9k@y@r(iXswYr<^ zE5fw+c+y@6l<}u+la3AR)>acC_c7echK2)*}%^8JP|&W+yz8 zyvr~t{II8Z02nL!o;N(76?;2yZzTJmvcD*WhOB(eu5T>yoTfplKJg0!)PO;yH@R`9 z;?O^hvOeMKL-P_+e74QLQQ&m^OSP@k3eysC32UwDfY|*+D~@H)$a)F>@q(+wJ8zC( z@h17#+HW%-(uFA_9{YoOo=`JglO2zvU5S$g98Y#*fGm2QK6|L$m-kVvlR;I3XWYkI zIDtwo<14nmg(}THl~XXfUYBJRs6>2mOT+&4vc8<-U`lqk^#xL61gzHt<-t0F)m-su zfs;t;%Y@P;qM^E4Pwf)pOj#xW>{3^lgQZ(4r?6W|Ifd)Fr>7FA4T2bb6|KJ^yWRCg zH7T?JO}Pmreb8#@_L{iv)pp<;bRD#J>~#nDYfbK;4y0KyR5Rh_O5-j!M~n+sTi=si z&2b-HnDWfQ6I4duNQSh$@E< zDL0polx=3iZlI@e;EOFea&o4VA4ag8!Fj$5YZIje;qU{P1}Rv zdcUp>_=t^&XbL8a1oS%5mdHyW+@IiU>L>Yp<7LO1^~X^DQ|PY(Bq2fZ_EN3MQ*u-s zVfD*6YsfuXth37{6JC%@QDD?pOeY0%rZO+M)lU^xc+KL1Pgy!B)GZ))n2NV#uV7d{W zP2XC7Q#B|qPuT>lmI=d#w1`A71p>N>N-y(@NKq+xwRdzcQQj zXl&7zO3xtLvnz_7e;EEw;+9R`>>(n2czZZ; zfRo5H7_s$cM;{Y;Do*IW^{EnkC78^Gv+Q`6{oMM+6y*%=L4?%#b|_cC;V?Fw@YOro z)UV{>VHWovZ)B8vRikpP{o}zYn@yHN$4jk8sse6x&UOBvj^lAJ%G!P2`b3Rm3wCAXnG zlctxJOUZ?z|cE#O^{BIfNK!G^Piw94znWBn!p1{o_-o*^iEqAp ztbNRS-kTzVN*TWnQUfCeW>Y2yE1 z7U6^!Wqk5T#vm}>w;|>jFthYT@N3>L@jl|AslmH-#Dnh)W43hu{+TPUwMVvoFG`1N zM$7mM{6`DbFL`7GeI~4c|DEM$!`=H3(Y~H#wo5yP9gci3{+<|t&VWKp!Ww8e^)ZVT ze`qlF$Q-_9)3zlGds^(hR&p2JdY>Dnw<>TC1F8Xd+hFex;kFqt7e@(JN>T=cc%z08 z8jq8P$(cvElC@3X#x27jq_2JTD72z?)j*$Bj-MP4FwYB92!pab_DKA2{Om2ktc=$i z84&)|3!?8@d7lw2>^}O*^8W;9usq#VvG&=P+lNs?xCT3#yz%wa#7sbn{4#Pff>k?} z7p$WHAnv#?IA;4(2Nz^v`2Ryza1kLUgfk#B`~9HsD+oCm)vnXo?wp^ji7Q+;!tSnW ztQHd?S+fXg78c2AGu}&mlM#Qdt#9Y8(WKF&c_|}qL3T2wFcA)6p4v0<0tygNNCA9B zW0mlp1u}Jm!9MC|>?iP{R&(xt-#d+AyS7v+tl7q+OJpCv|A9_mYkl0`C1`~MIZ$uV zL;B*RJeiMShSX+@lh@`nBMPZb9SRQf*{*^w$(5kB3J4*HyQ)8(ZMR8-sU@Dt#3%>M z@RQ|E6-2a4%0oVWw<~EXIo{S=3-MuC>-4+$)_7@DC2=5F0Smzv1zV~8T|23VTAC%AhlU8 zY(Jk+=}S(El5(alsT<9Wf1aZ@O{Fy`{oUr*H-wn>ByZDMR1AO{g08(MN(HfS<}fX} zXs7ObYu1>|Ofr?Z(LLckJLE`V9alB)PfVBiw@+EM^qmi6&uyk|;Ypi2@Ge?s)L4%f zhFqztG@9SVOZ0AH9Ve7=9yJPKkowBqsrQ*fZp363JMry}P|4>M*hsK0vkd=wze9A0 zzn0e}94twy0rqI0?K>m8iq>aF;Zz@Gm@o=@YfUSL=?CQCeZ|@QbHT|vQoVbVWDF4e z0$@mCFcvhf0b=(C2K2Ez6R}0~_M09UH2#xADAevw_5<^CGW>IE{#AG$Sov?ylLdeG z<2i1)h8UXUzac+bx+mokPh`<2Dgl$YY^Qn^K&RQA>}8OEnPwNHZZB1d8S2q$F-gb0 zoFF%}j9BOx=z_%U-kA3}*FG%@TmI{UOj=C8{_dE{L@jOi@0UakB+V#1Tvb~_vQe_6 z$8qHOu|NFU(YM$GNf=a6a~Cw>L`T})FKEgtcA zVEg+AF&@jOU)qa&39%84G(pqTaOtSvuI&SBhAqJ`ut ze}Xvvod=Y&9+-ngR?B7Sa16C!XAcw7Hq}7L{D>m8a~TahU07iwTqDS&_qpKWAhmE* z|1?3xg5{Y0dFc2-eHQ2@MnYs}_$U1JG$lU<_35;w#*3HjEgeX|&2iPW4<3BCW&~D8 z`}of~P#1vx7mOq}9y}QF0yRxg@O-GF9I4iimWx~YFJV_wV$(7_Z> z5LHO#0Dp9X6>t0;{q4zBbCp8@shd-G^NQ~!HS*L=8WVF`k;_CC2AnCPRw)DnNIjVg zO$As!Zs3~&k;%m{!UUIjPE^4DE>f^&+-$-TVj7p3Vc2|WB2eK9T6AoB#=w+peFRR9 zh99~gYh?XkAUq4Z4YPMQTk3sTYY#D+`%`9n>E(=IKRhV>O6(k|Do~{V&7z$~a*j+3 zTr$(~w6Oih&P*|e2IBM-IdW&usCOUK=szqkVOjjvq$z#VK%#0Wp$6}&!A2LbWdK5W zg4Erx3#3N}jk@a*|Ih(JV3s!(53&65+%zbkFUEDC_%{8J5pPx<|0iLw~ekJPF z2kJ$j)*d=5Kw?8Juy;w`8snJ`__&hL-lTyV#G2PPAE)_8fue!z1~+#}V8do<>4$JF zFLd@1IAQTwACd;FxTN@}Mjr9wd$5Qhf*)cO3@Mtv!v&LqUzE>-25!Nqp`9Y21)}Lx z5TwXhksAhfK9V6ZAaSeQq$+qW5F#!p*u4j8qjaNxkDGSlg5`jXh5vjyfu*NUcB=F8 zO?GhOVTW@9af;aM_mLzdHj%x2X%AhXeNTzBNWu2M2_TCZeH@t_%p^GtgK3+q(DTKMiEV`PT=<>AE`FTNr)Ouj&}seLd6f{tfa0d#^ML z)R9g;u=jh&%W?f zhc^DaIiEcJ-=U~~^^;x3hPB;Z>BP?Vf5~X=?rC|s(?;^c zM4M?x{V-P5i!l@Ac$&?Rl^fo5%9-?AHW$kd%fdp!jraO4 zPDkY8KG<=f|BtRdaKJ^xHm&vd-Tw~g0a~HQOBaXmtaI`UlXJ;_crj%5ZgM#rTK!q* zsT;_4_R><*S2E2^#TbGl?(PBnbhUW9vun{{*Khk8Rk!EAPbA>r6-~H$ zLSUt44NyT)%OVV$gMmy;@KlEn^519jCuM0o$n@p(ZV-pa2|#vSHsoV`h*zX$T^0DbaaL{Gtm!^F>lVxU=td5PjK8d(rXuZB z_@>n;IO+prIO^i3Q+3hrcPf6-?^h|vMN5aC_~*Cs-!{LAJ|2#LJq75{D^dRr57ugJ zXBj_3+9_WAMw*uYY3@65WnBCmJyHz%+$4>Ym7b1y1Qx$yi}eZfT6=6puTK{FKb_Dp zIw%Ds?7uBKX(V`4x;;#l>f4dOMnWR3pSz!(GZ2rLSIu*9`29J2u4tdY=MiJ%=JYVVnlixQh>Fj(Yg5 za8Tq`o!#ku=aG{>Gt{nKuuYs_mm2ZVs!8Am*wEoM9Rc|3vr0{$l zHL*Z_U=oE}YeEG1Xl zVIJNn9#G^Q34au$hj|vBKd5wfVTmO!?+%oQ{~dmdh7{j6YEZHAO;S3$qK+%9b1&C; zH%`Ym9I`Wp8eYJWU-hvHtSSP>n3-Fm%^zo?=rng_bFTk-<|m23&{vN`Bx;d|CC zS8?TcJ$7{#aw|#pwgFYeNN5ez4UC>=slV(WTG@9GOgJm^vhleOBNuVrnc9wgHra(8 zlka&JJ0o_Lzo>l`Wp_782KVBFvQ?8Jr|eaAP5}7yj!8f}Q9*$F;^=g!wY;xCi{EE8 zQ{vpTF~6l?K;P9Zbqm%Us|fPaJw0ZO{YdD^uPIiVx|vYYO;z5W^-L9{&nfR;edL#h z(VEsj6IJMtXlaty=;CRXLP2xx@p>*7SX#9FGPn{clX%Sv&g(@nRsF-SKnPx z3>b!4MYHXIyKZ+kl7vV5TzKO0B$c zc;SqQPWHMUAIo}Wfaf(g^>WP`&E?tc5z_peW_owuQ>7-w%&O|EPbvuI2hY47II(=s zl7?qKyhecyVZZDZdsMJGPj!Q^Y|+!dQbJK@;>xR5;BFd!=>aLbheiEU#gqOf6lSj{whG3=tjp~ zN0rfiOfA%Qrhl77uSIaKGJ6jrYL&4D*F#ToXS#K&KLA( z9baki(=*&`4;zcWCjHItMOyna{OaT#V;W5f*$cwnpGohrSuEd8GBC8M@pmc3JDuME zxlj<9q&-25De6H4T`4-^lXi;!aGcBM^;$Q?>IqeWWaP^iKx*;xL={&$WdpMQh11|y z*HafwT|@%B|5p=N;ttjRhfQP2S|~Avx+*1E2VMIu`(6pxz7&H{*}`yap)w}PHZDVc z;Rs8&+q&JpXYPl&-;1LInVdJuc{zoVveTH zgnLJ-OJ&K?b(k>=8Sa;?*X?JD-8w&7H)pMbYJ`-ltkQc{=mFz42OGn7`q?gna#c8- zM~o(Ijv7iE_?v!O!01*B(cP48 zLtr0Oc07#30e*M;omT}HZ`!dD#Ob~aaxu`XF)`?<@^ftUf{0H#IT|Yl#CjI|{J>ZC ztfJ&-6I~$rYK6hDVMMhB*>{ydc^*$>InLPXri4E0N_G`>!GSXqADs$x{W6^BjYOam zjMq2H4-q7>l&?oURcC}R0%iRX6pe^UtTAAINq0ZCz)Rh*jG8xj`{1`oMaEz<{Ao{# za`NxTr)*0=WpLh822qg9(}D>0ed$_@zx_Xfl2-TG|25Loc2DfvY+Fjzt-Nq?_s#O~ znu$X{$ox|nd{T?>d5V2z$2B~I^7O=#lV64q9m1y_beU?^#s`OhSEifbIrb`ec6mmJ z6}S*IPlRp`wGuzHGfZLHJ}r61CW{u)tC#!WcWe_nt!~uYrIOA=Rn3)Kk1)$9Va-|- zP?}xQ>J^*-)o80 z_D-&tVt3=Sou#7V#KI?7aKU~|>efEj?zkcKqqI7*s_1Z1D;vwLn&sx&$W($W)^plo zjYW=iK9Lz);Ql{J0)A=|n5$xq5huUyjC##$rtDu`_p3MvY!}?Ee=i{=(KBHO#{|+s zPaG&=rksPBa33MM`y8Yz=hA~`(rRH3t?^mDzDq3W2C(js+p@Wtfg>Au0)DxnW&Iu9`@ zQ{W@=q&vNbdzgLFoE}Qed&1O!@Cvkl(*aGWib-69cwn(D2{6Eip?O~~+>q~Y_LhDm z0TH8iY!20;ayX`Lr4Y!2V<*h%hJa44FnXgD&U$7f$q*L_ujmDXW_|^|oe{wbq#o-f z_t_;$#=KbcekHS%Q(bxxThP2+#@Ek?{96<8wDpjRfzXRK|BQ>6^eO?r(d+>pofkk@Z~DYyR&Acse`OnY<|`#FW&jPXt2rWhK3T+W)M$?U${snewj| z#zVy^^XXp$Ub23?bNvf4+pm{!4zSJVF6q9}```ifK|m04ZaY3WrlVggcyT^^~Yoi_fs9W<(hWW12-*o~d`0j;kH+F#kdh>!)UzGp_2 zz>;FZJf7qT{1#mqaO{BuFw8a>-_>Lo<-Zj586@t5->!Gc(mt6@fbPZdcttQdxD820 z&G{O|%3+lt4vchp&61I~;EFPSm8Io zNS$Ap$O8RUNkW!3Z8z;0iqtxsMfR<*JPD3X40X2b`}-vdQ&aQbNgUJ}^vmifHUzDg z)Jg_*DOQq6D^dHv~Au0a-8w ztNZ1cWEB-p1p_w>KLn{Quh3Li2Oj}+Qw}P8yGz3bK`<94(xA2dm$;HaVdZ=!78LY5 zN4KY~HjXX)Lw-9+rCx&U#M65Vx2F7e-@$8-lGa&*s%GJOE{P*rZXYrV{d6&ddypFC z(lSNjw%*#8_FnpMiHFt ztJ``JTEXjHHQ}@qMzyj?U@5f#WY|NKk8PU?kV*>3kdb>XHokn?lE`|(29>HnvZvI! zq)uJmZD(c{uW04++pHSuLq3g$v63u-DEuS5*BI~>6&j=jXypP;x0WsJO2Uc);CCa} zy_s2W{RFS_w#;|HvTr(67YAUlvF;GYbF>)cg=Q8*zv&PHY zmxLjZZ3ZX>B#cZCfaobB36MMl^bmS}lBILY=;<@%3!8n|(YGq03Ed(ctH+R!G=l{2 zm*Rb=Uu|dc+Rd+QbjkZn-zW5DRo;dC^45U*XMAP`n(qMGa)z|b{wfuUww%7^pR#Ht zAirERpr!(rWDSEA46iJ(LbFqU8Wv}4wWD$>#UQ^tG@vrTlihO5AnP;-YNq<4R|&1Z zcRv$DYIXTxOz-(lUnO+jvkuw>o3l!PvGIjWWzeG-bU#?02r6!UFea2fMvdA>-eKs? z{nwkL@l5ZrvfjoY$*g$mXi3*%-^p`#C#Q*8iO*z^T6H1V^iiDs&RS5$UQ*ME?d*F= z*K&I&062VLW7g>HP(TaujMYa9(;gIGsk`Iax5hgVc)g7DIO5FVK1@VN%Qyz|G5ixj zMe$SX`i7g9V%@nRLn%{t7``?YDR4-fUV8%}{^KYB_1AbWw%V=G_rvY@r;!&)LAF8-pUMFEgWo#m_Nb#hc%QSs z`=4h{eJdf1vwfpeiDY^Z*W6LArQz9&`V-J(Yp>?cxSXead`;HqiEcxxyPihxf`19B zdCO;1Dxh&N5=$T~5boSDFEu@uiJ|c2god3ONR~G-No+^pHU4-I-QJD@>O`_G{BV^` z6J&bxsgVuutG!1E+<0Lz2vrrr3a3iE^&bbYp3b5@@|b#6m?TXV$un)Jd9*xHjA8X< zg@)A)7*Sv|(c!OhX;HsqZ8DF>>Qxb*7&RXF$CQrj@}gIfPP8cZ;&P=m z@obLi0SofUFi42m5LKjk(R2ZZv4$Q>Q9XXuWqoj@6vJFY2R*~YoMcmL+*gu7K!sZNtgnD3YDt*j z(R+wWkBn4p6Vu(bkNAg3BG9}A;z5B`Isxzmg8QI>3){xF#+nybP4Y2}_Y20v4vNQC z>rYp&qJBMY4}81|j)w1K&E~CZGMp)c1{%h`mqw3@^JXF>J&_3k()ZAseW4JQDAPN{ zOJulKR8$Cq`xH1*fUA~!jSyxsc88vJXAxw1Jb144x=0YtD!SCM0nWv8AdFQg^?3TK7mLFKS zHoC0EKs3h54;xt$R%IffuU%b>r6mmQ2INyZjqg!WJNL*l8NDdDp*&c}*wH&H_HRf( zR2-)$T>8#NDZA^=zVg1MCT85h`dG9A# z=XPAc0Wl3aCoAr)M++|rEAbN0%Rm^uvv z$0kW7ghLP*$8Vn4g<+zJNi1pCqrZ@HGTju7yNbfj{pj$--E0yE!De>Kb{O0(tr1P> zfgWz|(>w0vw?tF#T|TB}*Q!Y1%G^zSXMv7%x%F_4x ztKOb#qH<7*rggBh)IhBa|BKrSqQ>c{jAHXcbvUll5V*6Ff9K)xmM3B0(aSL01{*x_^h6jq5CvP z_f9!UP8f7s;u4t*D6r>W<+0t(tEw$3icU>@3%>@g{%6LBuw19tr9p4B6{W)NHQW74 z1A$WO;>y2P7^ZWT$ARN@cN;oz&1yc1B531e29fZFOPhk`5`rn#>q_|5e5nag{|A(O zmq_{8--h6iP}tckPOd}u@Wyy{iYvRoH@}IQm){L8n?-Rh$BB|K0?QsRw%&d(uin0G z(PTh|h36%`l-C}SIuyr!hKza6HubUMB<W z!Ump&i&%%@O_6ukRVEDNiM5J+5UQzNX%dx#K)Ep&e<+ES_unoWmjze($Z z?5z)3P<`~Uqs(+V1sHLy_A1Dn2Ymdn>J?#k1y&0IIq|Ca3_ze0@#> zEd2DJ!e<(Amlb_**Iu8C2i+_PvZk~shi@Y2d)*@zl-8e0)1?BA9^i~>dQ|yg)8MXo zLS!#HbhXZLJdrEhr_l=Pb5 zvTJ^CKMK~tT7+2=+4~zhn@hNLd*F<&w|#y6%T~b%Ayndde0b!pgr((lJJdq{sI*my z7MD#Mv50UoytBq~f0PyNuEl3HgY~v-WJ`mDI9>T9?ClJ`HcMGyGtC${{cEWU=*B@p z!aES}i@|cuL8`Dj{V&l@9mGVp&7F|+s0ldJ+%AS;Mk3r6(iYB2NEmqk)|(x*GCA|A|c)1wu5IBN8c(B!yL;X>-zuVejSooxK9~@Zz3oweGGRp{ z-g@$D8Q}YBf41m7=c2CXvG`j^chNwIt2gL*yzl@2je^dIgkaKVE`B#xPavX8<92u4>4s95qgb?!Eu>V?j z!Q%cDp^|2>*8z~O(zu_=zXm7b-B)~OgR7jc&u_T@5-qsRd=ci_+&UGlx%r&PKJ;js z%56>DJ0bQ0-Trs+I*!;c0`TwJkfhY!&Uvn_0`J+MOk_OJ(U904ARO0g?yUO(?zY9guMPyJW7v zf8#4A@n7kkD9g88HF3$$%Uq)CPIQyyD8bO>F|9)A8ulm;Zz{Kh`joD+c5VhbwHTYP z4aMm7ev1!GM)WF#aL|!?5<$ZP^=m!t7c-!gy(=Xx$+uJpKly0gBcO+Q>d?R2@w%G} zz+8xKBX6v4xGXNZQ$rtu!Bt1WQ%SV$#fRi2V!;RqF+>Ffq1!w#D2x`G^w_St?n3ko zOX#YCw8f5f5I09T-Ad`VAVWr6hlww~DAmiX#mfOq?l%b}Y))Hvt6ivO+nzT?l3^pp zn4)^A6{e|it&m?3@NN8~ppoALfCUwj3T$c{|Ie$>xhzVFSk+eQBN*Jd!AcA2P#GnJ zs3oJnfYFv?k1{U~?CA(bp(k_EpKEJUAyhMHcI>D5e-OT+xG68DEgLNG({@P$PfuC>X9OpjrRD`MS1%u!Oi ze5ZNR9@$WcVKmN{_70QxCopXO@L+#`bPmq1u7k?_~LZ-Eec^ssw@zmz8Z zLG)7xc$wEb>@B?-D})e;eEF**+}BVR1)yKIzsrlJ6IK0J%wHHg(HfY8VTHZH_i3Xt zb4#flo)W>Vh;QH~%~!vS)@Sp||?H?LOj7AsqML z(s08Js-@7|fms+v*t;)%F3*barXVge+@UO$Er%Nu*webS4Q?U-6&g8LCG{VeYi`)H z9_ez*6ky>BBYv?X zCyqB@G0{5jPQIi!rGIzMvs66VqxITvizt-Vw&MXK?-=0eJZoDZuF|)8CV2lx+WfL0 zUI$$FUtRPCS(9dMCqFzY%oa@%`=9~_X{qA@O&;lLjw*9Iz6jXZ-Ht^{E4WoIxYv}B zJa$35rtUOe{afSd5%B;)g~Y12pSEB7Qa`@)K_C?Yy>zFdY(=-vn8j+jD0po873{!S zE`=ss*{RROBu!ohmGxX>w&70lT3%7~C>5@iydKReP0fjs&w?9bU=-LfEr{+Dj=FF+ zc6WYJ`o0|X#aoYBZ#oy<)xi%&lyRqW2hEiVpLJg!hxIxEdA&-GVSlshjS$k(ArKYU zaUQ-GzAwh2v;#zlrc=0XxeahF2@cRPc2NdI;~hOIpkb5r@>|2rBQ2cXo(~^9`?e$7 zOjgXv?J`jA&A0+J>KC?e_rwj&(}N>fuA%?v_SA+tRBDc!!S=U-Utru`{7NueX4kP0 z2y~3#TmeTtc%}shdr6qwG)_wBZVikATbrcm-S}Zf1sBKX*M1!qBiMrv`#!&%`;elF z^qrkN!`5!71%^?DG(D+NlOf?#oY!-7YC2)`a>U z^$R`;LdHqU2|#uF{4g}n><#bv*{#(x8sJQ_f^1ncwxbD{u zph0w{fhNfv;hSHg!?!+0dTfn(7)=rp*^_j~#s}_(hENE%Pmp2fBV(Ur4}mozc)MiO zWHSRZ5gr#NYIA(tFmd}26~pgJf@`*@Y)g&ILw(*Cz($AZc)$5W*=(h zYAQ^-x95{#wOCn9j$4XCQEWUdN~09lh?c5iWgD4Y9RrmxQbyGFAU)*@)nH)N^QRMBEUGQ>Ck83s*$4+E4zvP_UyS}R@YXR_sCIgG zCScMks#Bj5)ulUa+TH=W^TN?u54FPu3{7_WoMf@l-?xVl`O$v+!;`*6Yc>2P?pdG* z%<=i39*A~7|4Cl%8#f7Kfz>%Z^Wgo4Kle>uL*&KW4x3^$fgal`P(ys*6M6hj`N?-_FeY=#wcaUTg)Vlm6Am_F8=g*nQR~Sb5?&@{ z0Rxh=S;F0344t-gCgNW!*T@p<5Q)9KcZpsoVmi81Lmw)vO3X%3${3G)PJxqsth>>mPg^9z0 z7hH?gl6Z-k`p1tJQ_&aFC4bVdnO0)BN^ClNZ_$(Jnl~;kka<&MTgvD(aHIiZFx}E5 z8HaP7i>LgsgrySx#_yy_Zxq(JY+y3ZI7zsQ?%8+nzB7=gy}wmR`pLEkoYg22(<1 zO9^(Fx8!3fuqZQwx|4}FP#KZBhDW-6_}>N<;=TN^++#<RW<5{< z?TufIAgNZwWzLT&N=Ue_o;YGwspd%YIXVJFVA*DWGl77Fm6A@%S%Tg2mj=0aOxLcW z8V)KyFFkO3L>XL-w-#%YhV`ERJgh$(B=Q>JXp74+k-s*p?n1xA<@Sw)3hVG7bu(W-Cb-@XUvSv%zY!EM9 ze|I>4!~Whcs6eXUYOp;n>`JbWh9b!#to+fj!NP37LMq|zYTJbhDMh3c!3EAbkncb9 zlOIYg7}*q7+rDB{#iN2zOoQjvqB%dDq|~ zaitC}=Bn$^HbsdAltOf%8Y5j__=xSZ>(&%2Ro8{e-pSBA4;yDNrLsAS9A}OcaPL+%FHS216XF%#N7UPD&_uY zMlme9AQ#9x9X=AHPL2=RaAj`WyV%=oK?DkJ0y!9mDQ9NIY(*@(XxZ8&KT}5~ z{d%uS^_`EsuT<0j%&oFC1eDlzO;#@i{!ACkvdZXD&Y5O!AYSfjuHuqn1Bx(g`^=V9 zdgZ4rwdL`68umw_t~c)d(K&SfNC-EOfN4s`6GcB7Ws3v!C#KC2dyQ4X-(P$AchabA zzd))U0*b6-ts^_dhUsqzZi-%6<^!W*W=zVjniuHu!pDBQAF2uIDHtb9mYhPjx_&hQ z=UoUlGW1_WKYim#YjO4YHlni8?}Qh9(f7-(2g*9|=MV=xS(X^ghMMlp3_hMq*sSZ; zVon|IQH=8n#{&}E$zJy?S%WN>``)oJ6WNaf3MVXdA>osgYDHkWXaa7Az=I*>}3%z{71ArBT|i2o({PqR5N2ivM(mJTL5f_GnYmeb<#a(zx*V z=x39F5@O*wc9dPP3v7ieDYE}OWL>HTM^{y8RGQHxttie$;(!64A$vf6`X;~ftvxqgAZy{}=4 zB)Z=(*5pA4AP5tjN|$lm7FgycZh)dSAI3&$Hk!T)NEd<%U9)`F=wAK2qM&_X*PH=m zwr&Uibzl;WwV;bNX$lW%KD?tJW)U<)~#W{hZXGc>?f`FGd8&-1aiPs=4g zKc5B|-+B67q>tMle$g1m#)i9w@)rD@QJ!#Sg^vF%cpjZg4cORra?D<5Azil{qw;ZO zvEa72|1|M$XBoEwTdu5r;`eX@cnLjfE~?1#+Y4E>*GHLP3Y8dNRcc^AuymR~&@P)5 zY4^0ssf*xqYFAdxZ2*>_v5M@RzGiPwZ|nZ5bl~Z}{OaQ5acef0wA9yPyq%TVmDAGn zf#BA6{cQuXK(XoEMJepEDD!eeUJKZjB7$V z@G!USG2nSvm%V$`DBXP)qqdE9D>q$)HD1gXR8)+5K1Lps0&2pXdqsm55xJj>gPl%y z_l(qaxq=!<*Arqr?)?#hT%1O4Cy1~R*%vsb6Us{*Ms-CLn(9g$H?m;(VconwW>x0`SFB@%H$n!! z*&{ry61h_WAQPu7CVVGvJ)e{(E%L&7)XQZ}*Ht{ijB9CabDfxuivlNz0){~^X`jK&FCrN{)l}4j)7_F zwM4}EQ-?O9+xIMK4yaC-(n}PDhVA-mj$0bv*o#bh^_{#kF{6WkL0lGI zS>Nv*OrGz?B79?=k32JO+=AKJR>2YqfIy+T)6k!mO0jMpQERV`W&Bo$O%@^YcVxc~ z{zgJn{8Kj=$IRc|3dmVMHOGihp6HZ`vK04L_PoWbF|#39TtC|&kMFJK>&FpAOGbIO zcDvYS`FkcpRFYiysa!HFqHkPe>pKW?rPq#6x-6zV&YYg&P~7)@WrMth@NX501^xvK zYkK2eP}6$X7uCY0-MN{6e~&%4>Sd077~E$5NCV$b7b_`W_98qR5@d`pG5O1mGFuGp zv3EZikk?2%Q&O6%YACJ3^xO^pI380sLa^(cH@Dt1CG)zYWW^>SFbsM-H1jR!S5@ZtV$5c`Tt(Z6EyvjqSrRtU#7pR< z;3zgsWVhYM+u#I`sqwM_+nL00mSMpDPh%$VlDT^F<(36}`v^c^NG>n1qo&2ts-$R^ zYT|RukD7a+Yrg|i3z-vl*J|MqseRd-j0;RvbJ-r37hB%0gzwkwGT<(f;9i%Z1iVJP zrABOXnpZXRlkM7f$ofr-!qE~8^G{y6AzBm@i2PPk=OQwC_U3el8;oN*WB5|5u*{uyxW>7gm5FJxg3)x zrI2%J#e1wp5;SnkB=eX42zf>nWyaAM&ISkYL!zLjL%oD{mDeUIr*U^9Rxeg(-fbK{ z`J;^(s2jT*oMbBOdk5AF{~(9xF}v~sIv@=*RznC_4m>t^a$`m7#c}t{K!X+aJ!|yX zp-ouzC|J4MMdhpaUm@YAOox+$XjNZ)hz70`&E9WBHrrND}HfgtPRcz#ri$1Qi$&Pt&g4{ioq_ zFk~paLH?x1j$C+#9;K8mulHFvWanWZQdsN$OA|*XwQV8ThPIv8%#Hf26Rs;+?+lQ_ z9~SIHMmWE;lICrtJF({$vb(Qa=>tikPz~e{@e~5X6=* zzNI<>z$Nxa>aa+HnylxowX`LH5Y!S@64Iz22Dy52M8Y)w*6e` zir82wX``ebFD|28c(PhTL=7Hto zss8NG_zy!FXmyPY?5tFcT4#zIRzCC8!}pc$hy?{tMsKtiwcbFTyZ!W+qR*Lc=8O)5 zO3y1E>}uhdHrUO+97kAipn!>t+;-V(sIj|kOb&GAW(Xl+pE3_2#rmU#3*!Lirs#&9 zNZ_;$5&VKCu_lzS|X(W_^?WHV|`}Don-wZqpu^4l(M{5gz(;o zjiR6N@q-ortEY#+lI~(d8YemSr%qA)6BJLa$684uekEj`H}OofKP`EFoE=393p$)@sQgfy2z z^nzJ7hJGXO)5yi>PJ$+#3rl5p+%1muT5Cye7~NCagmjNW^}WqQ$d$Dyok4>FzE9d^ z@t!m~pI$uPSX!Buu9BSNcIdkQ@y`S|Wwtt>-M6RmWO{bzv4;=~F4^YbckoQz=fzFJgv=T_<0^4>Vw0 zHf&h!sEO_*eW$=;Zk%xcuLaMM$x_442vJ`78=~9pS<$J1@{Z|Kr2^};ho}GtTsu+U zyRPnTyr+84#m{MZ%)&Lj!pDAZYD5}j*!{bl(N{*LU59R<)g-mCE00wf$>l^et;y){IiJz6KD6A~~Xb|`~ z2l};JnI=JYDF;y2(dsXXU(+j=Z`Hi@__8&$N$)*LPsPv|&`_jkuocO&uil8Y79#%9 z1wsZ;E1KIN?(+bd^!;pDTCqgpDk>)Y(N<9K*YhalUL~`(wxEV%o^QuGsEpRQ99@ca93kA?r*P->-giEIM;z7d=}+=nnjxEF?U3TSerEG&77Q<}T|L?E zwVhcPw6i|Nq576EY)&@aTrlX#h;|4JEuBv2;xq}bN)z`{Lv~t(-KbIa$frjsu!?0* zqqGT{|;&EoX76p-KtB*!#y;h%>$M%>A_0aV&9=;HC z(!LsG4;%4fU3pV2O}exu<#t#2$a|u(ku~G44T31IR&ox+Q` zW_Ig>Ib^Sx(AL)O|9rGM5X*7w_|{I^Ztk-Oy={sc6z%bVU`K6lWq6}oIbHavd;Zp6 z(ue2gIC! z>Nk5h-YV!5La^t2qXV(gJ1XIMH1RND_9yD_ETHj+b0z1-f3BwZ3F(WAk3^1bCiwWnRnsJtqY{^98U zG{NpH^{7-UlaOu!p~;Ki50;YxxfNC84U1C6z=LAD<>#A=)WHk&bbY`U^!RwSG`;$q ztz|h`N#!=o>i?z;aA5>sG-^q2`mI7NJ*T-Rt3KW|3tNBELym7PcOl!q81>e%;9tGB z1$AU@o5@NNEq3Z3r~W^vSe@x$2k+Dl{G*miS|@ZgkH!oTqMY9=?*@W-wb#eFAprdW zATNc_FeKcCC*O0MfXbNfmbKO0vF)U*;fMX5AYNP*Zkx*rF7eZ{dSbrBuuNU3`6Hk{ zUM=`@@Ra{HJn&xdzR9Na zVV2R7B(BvLC;OEvgSR^9Wjl|;FH3Ebsnf`rPp~Fk=-eEMPLyRJ4z_z z3{_e=>v7UT!#W}Ct&Md3_#`?8Oh4Ck6BbRsvSYXFUJEEZSV^eB zUg0GF^IV=^$WCG$0cHJx2Cz{n@|z?55slH&?PduK7~&7G8qCF{-+6$%CmErMUzT;6 zLXr}keC?i7m>k=!eMVWQhg1-{XJVtNqV){D%oFP;w*A7wEtPv@eGQ4V1~P{SB4UIj z-|&!|o_y`+9>QfpS;@x4ca9GO&lr8b0#6np9M;U$p)!qQTjDK=0PD%J7;RKUAL7KVBT7d@=QP^X7hr?lm1c`A{xXn_zusKG%aFlOlb( zMabD_kM&oZCxj!ch_+z#KCLEO-)xN9e&Qv;qe%CsT*rU~)G_HKq1)u!morq+I0LSj zKhK+o-o)$0`#m-)pynxOXz5U+Pn+D7bl@+m!143V-&NWH=HqgMV0zJ7j*i23_z*p( z{8i~NGDCaw#_}@8^yk`lj1Vj)XeUeDX)jpo&dT%YZA5&K=T-@+PxLDNuTh3qK@t2O zOsP>OG^874*w3^p?5IepWpdbDq-qph2*F^WCA{>Wpe17vV54vo_SlLp8}gU>TgswH zc@(xlRt>0heTBubaR%3yl}OKXgR?B@yD6jR9N6WN;f~~V7Ba2JK9pNom`X&o+yGe? zeHK))H>8etK7Rp8V}xES=blqwn$s>KUCkezeh z2|gy?W7+FEdYz%qQ^Cso<8d9{khk*d{?WbV5+N`RUol>_pWJppQX;=UHr}kLNA-|E z!ddCL{oWVY>zv!ikLFa+nh*L_)#$u39H_zjzg-Jlc6PcF!lzQ=ycSF&v7v@07`si% zWcwJ@FNW36{HZN&q|zunrZ)lD(;TD1Yp+f*=H~VvorcimE*Dy6))UXfAB4Qq+VSRx z8BxFVG8cocEHD0OpKws*3&wJ{>k2+3yeNvNFDgfbk5sk&&-2+9Jg zMEi%BUay(us=Dv!Cd+9`MKY@2AZe`>-`Ovsz(#r>%qeLS!krF%>$)D~Mz<^XBy3Pe zvtBQTk)|}f2``XN@5j%-#%AEXEAS36+?Y4f?zfp8BoFo^*iXu?M^~J)0vrK36N)Z(;{y3?57~0lBB_+on|m&c8w_VbPn_;_YIiE-W|Igg zK>z&kJvmV+asCj07gpFVw|~Ocu$Ao7!Z0C%7Ki1kJJb~Ix0kAOy*uGx0d7Xh8k`8` zC3Fw-aM3ns1~gWDQ*#jdE$PUkYq*v@`ssJ0xAn&CE3fF>>Js*t+pt_5eFJirurkVh zk_F|NSyfx9i02JDePBOH+V^lW1ow4*)=I^5Dq6Uo1-!p`Ku%C` z<70~Pxz|p7buj;?ZBl0h4!DXs7oyWd(^$I8$f&OHHCJctGlgn$ZIDG4vM^3%$Vyw) zzAKC7)x=~5bw8rUDkOz_k>|7M+u?HxTcu&u=8onr8{g75c8ul<1(>2OuA&%hlvcGX znDmw#Djq%5(8x^p3ENH4V_X?3W3z(|Xgg-P9GG-*2aYqShwpyF1J<6=UPuL3b54id zGyLDSI50o?0I52Dg8@$wEi-YmOl+^X8+dz6Nd@B zYvh(%`g0vpUx&vxg=}(Hb1WCxQBthYDx{oJ!FhM*PHFV=we@!&you@B&r3K4aR+xq3~1j;TrwtZAV-v&*ZG7u`ZhR zAZ!|WRQrKhZUp*{=cv32nj=2=>uc;aD|S}l=Qg_0CL!s3zKQqtg$or2)tGN48M1E7EF&UUY(LS(SWXW-fKD%sY48M=U{RQ;&y`)RA-WH zf@}(UHnzA7?G*4@E}sfw9vCROTUt-aA1&cEdh*Q!;aE&BONhxa+Xx zqO6gpV8a>U9aLz$WA=tr`}|i(bz{3bH8#jfv}D<>IQB1LgROa$_CGUdW;$u|Nhx%< zUh!d^P}Ji>Q+J>!C!XBFJ+)0*%r1#tbuhsoo{ECxG-`*M@-JJ( z^|Jl1=#webe+p}y9vM$g+Nu`hijnCx3EorNDbK7%7wQVY-T*P5+I@az2i>)c%C|oZQkKoGkxuJ_BNb~5&7dE_cOKJDNbaUm3 zqQQ;0V9aXNAA`t3H35m}SdjVJxTi7`mc)>6Dags06y^z7ZOROAAwv&v98x(WnpKP^|JYCd{Mr%8JW@{@A zf*);C2rFVlZ_3%bjOPDO zR?jpJ`~Wsg2j~j|FBro;$f|0r#jgM^7S_azyB7I9i4Y4Rm;N`G_1d$t&yqmWJ`3C~ zL3Besc~=k2>{vA}^;@DajJ_lwVQkx^HXj`hcr-@FgjfWY;PYcF)oica#*0h;of|w& z_TNHFFnRAxPAQzeT_%NR`PIR;WIY$wNLAcYsc?4=TV)meKy5PAO}-y5rXr~_wW{3CHc7%)ZiGnhn!PpQbUrg z1(X+-x-X3nnqmo#R9wF;R#N(c;9KKy6t;PS5TWrQOA;d(YzrE5$Gik-?IRXi@!A`b2Y>1bB?y`_Fzi?76a!d%Br(kS36T;y#mTxm$94 zB!6%^ThNrOAaMEBjl9Wv2emJF9*IYw{3CM4Oxn#5P1^xp6#_%EL!{nJ(CY^`@LDgF zG1Hd7ncGDD%wCT&^_8j~Je6V&<|?E%6u-9eP2}`hREZ^~iwR}{ZW2H(6)}PUPv_Wz zh6Ze=WDsF(HV2Gq5h58ua&d37+`oJDCiM9Gpz7!wTq{8h^CHBBz0M#0CGlyr>!|rP zl$$0ZU#K4>F;2$*VPA^M;35^@4R={6oHF?n3rPh<2~?MSN1+cQJT9jvh&L?9dv?PD zmwn2BWbh+!{XsvP3HvhyuX{~baIQSA;oW1w+<^U!vaK7|B|ypy_C<6wo^JJmy;hy) zTk3nNBjde4%-bDM{Ogv9M(-kNUy%&=aW`Z?KlO^YIx=ZPjR0;i`$5Mw5ZL*$Hbit? z%KQebkV(;jqge{Q6FF5)WO)(W9|7KNE|_9&0znB^$aC+D-n;*xIjh{Hhyq6ah#vK~ zob%Z6u6(Jz0v_(}`&UTy%_o_cp4!MHyM)MqpwrHu95>m8sn;|1Wxli)K$gIegYF#u z5^Ufsd1UT8=-zEm_*1*;KqhVB=ANA!nRg$ zN`4vRgL%K33b21SwRT|W&kw*xKsfm&zmu&!uY_yO0v#J`6us@*7l^zOy@@nB2OiJ7 z8A=ZF00%9!K+fh~l|-3mT`7kCI%#AA0sIx-y8kyfOmPH0|KmP|_`VMEn2F(Op=vwA z+n8XUr}k@@>9lLDpaN`yDBWiAPeT3vAfj!GTyH>bd;gU?a@PaT1bL# zDI8ZF$z)8oxR+}MoA`G<3 zbYQ73v!RJ3AOm*N@BiuK^b(nR=wR^t8?WBn#J?S0W09&Yhmafi=!1g%K}9*s-TxzX z#o-YRL`OJ2^5o=9CNa&b4iWYP^b!zP5G)TqpPIda`3L9k!7JJ7E(^g$5fE_3lbB{) z`6c`Gw$-JmOPXj2&sj|@Qg3yJonUeV5+#7k;#Sh75?4jaoJC3Ci*auN$)&<;aWEC9 zD2GO>`9ok9>;x5ky#RwQcuSf$|NHeT3rw`R@r9x$%u>4CcDSPd3|;u^x{P7ZYZr_4 z68q4UVGHt)Dw5gzbF8YEZDAeFzuqkz1fTI5_@D1)2ie2BvvP7Ty}_2NHKi4Cx^9 zWLU#ge>**oUJo{#1I6hL$J`ZlZ51f?6-4{p-06E_zEm^}~lfe;OcOt_|ELjN>FPUDVB`c^&34H|B*zs?Y1z#lc z5-;)pubi6kt&GY3QQOjgdR+c?z3yV{tOl&Tqsn1`6Dm`kM49qo%i83;${fm*o zbP6W&Tm_=b9XF#g4$u?A5glM+S@3w2^Y;HlRR44~pgbo*iY~*2vhRht`=3#RhZT&o zf5rbR-QkDnR$j$LCl3uINsL|ne-x%awUm&TN+QsMSXNMvz6>)4Eb4gd z_fH_nPi$|zRkep!k)ziF`YeZ=x~y~LKpojXMby&Q=%SHSFkf`3Fim6+paR2IcZ@%% zspKPIIReF{S3ZR;u7BraB06g5M_x*y_Q|&^r*26G#}!3Ub}Gu@7G=_*Z`g=E3D0kf{x=nPXkbOdOwq_!I>Z`rrSO_aDxMLge>9T`dmrJ<|I9^b)zUx&M!RwasN6 z;|nlY#b3b(aJml9=gB2qPHv9|JCl*HcU+dp9}UJaH5O-ty9|#V>vf{!L;3#u{NQ_^ zzYs%jSLS~^ap4E{pO!kc&h5YF<$aS@Th+V~4sFuDmn92J{=hrDYTGx=xdjS*30vrd zYv-@wZu(lZXbNxr>WnXE^{&-KjOdfinoi=Mq zC0$Qx&`!UiDoB?GtmF1f1s*QS=?Z_-{xVOl=yaM6pfxwqPa@f9`YhhG*Z0?KQC?Qo z?dL!#eE;pADnK$AV0pAu)uw&fE8sNgTZ(6u!xy(jwl9}F2!~e?uG`fg|4i}k7388t zfmj3nI`4(uV|bM?fT+B0eBrr$6do^|88zzg>AP#ICor?q*JH&$R7{2;UeiQ42h7~Ft0qMhT}g{ z@c%;z?so%rKsk{5{4E{s(8ekup!)UwjCM$BEId06kq2 zy<}S^uk$S#WCv|~%XA**+gnRLoD}>i5nvebc@6gif%dBjUR>1p-aDizjsHU_ACbye}6mIThT@Be6@a>2k`Y@B`-VWRC=P5yn_ zqw^Et;9td8?F54oq-OQ_A#s=z;>`G%K|@+7>1`4_4Q;78yz|sNQ~$FTGEGwm#+xek zttd|7Q5}6?zH{HU^!Iy*uA%mGaxV*@EBon9UQjel?xkvQkvE_cUFkbo-FE&${rRez zKXA>I&zeN%N>Is5%(cSlCB)CQGD{(~FWJ@$!+a{`?V%HFt9VPmSAzfd? zt-C(YDf`w$w3S!?% z=VX>TSAI@F(LRYSnfPQ*vA?e4P-Dj&(O1h z(VKr4Sw9z;LRrhHesrA}aa-QEPU8~%8Cvs~g9){NY0($*LODd|Cs~Js)2c)S8}G?Z zFAZAzUxR|{?*!j?ao#kIR?Adl-7%vEDioZBdXmr8-d0YRHL5?mG%BBont0Bb;^^OV z*&hll#sW!e6b+2wwK-$$c!Ti^!%c!An|&gjrS#Q

Workshop

Add a comment to the script: # Introduction to statistical models: Single linear-regression and load the tidyverse (Wickham et al. 2019) package

Exercises

Linear Regression

-

The data in plant.xlsx is a set of observations of plant growth over two months. The researchers planted the seeds and harvested, dried and weighed a plant each day from day 10 so all the data points are independent of each other.

-

Save a copy of plant.xlsx to your data-raw folder and import it.

+

The data in plant.xlsx is a set of observations of plant growth over two months. The researchers planted the seeds and harvested, dried and weighed a plant each day from day 10 so all the data points are independent of each other.

+

Save a copy of plant.xlsx to your data-raw folder and import it.

What type of variables do you have? Which is the response and which is the explanatory? What is the null hypothesis?

diff --git a/r4babs2/week-3/workshop_files/figure-html/unnamed-chunk-11-1.png b/r4babs2/week-3/workshop_files/figure-html/unnamed-chunk-11-1.png index 07e2a957091fccfd33f059baf82c37baf00bca40..83fbf6cba399319203d3b4021a86afd19e68e777 100644 GIT binary patch delta 12837 zcmY*L)MP(^VA+kC}ziiMnx-zIiJp zHU+-UMMbHeOtpqZ9Rstf3xSMr9}@Bfp5tw7=FhSwirf;|yH`i%t6bDQnFokBSI%6% zm7qJwsIxvT7m&8o9{eouS@O2m$G|@`TLEcFQ-=e#;sZC60=>Or*bp$C=d*Aa?h20y zj9VNBJE((#qaH(81oo*0&b?|>u)Zqzy=#BP68=YW2$oGYOVw+)#9>mro6JfxM{MJk z6ksNoG}uXc{iN1o-^afui?0)v_ro?)_28Nsf1EXsqA?d&9)E~h5#oStw4L7@U$4AcC~9xinu$#>D1e+Hskl-6UovL1%ICXKqM{= zizpQ-50Se9hfK_?zKK%*d|S)TK_TIck*yP~A*FGFhQ<%L5p4Xia{`FV=Qxwo7|(#G zcrJuz$$$SH`QQs?uIT;TCb-AuR)-OS1GmwF^ao+DRmIZ5Ri%SC6i?plPZp9}cQ+P` zXr2nw(Pr*>DawT8mpakbHkrSGiqz~e-nxe)?rRA{M8~SiKaWXp(JxI=D0pC@G1BLS!t7Oj1f22sNyWa- z9Ij;1tixcQxbjME_}1w~-0Vet1~_A(*Lc&_?y=3TRsO68@gd9?AnrYj!Tv)9OTQE} zb-y3N!DJlG9~x(^vgN#KMrLOK(IZpyAgrq z%mr*;K7>Q%6%=1UDwVdZD(Qw{5m}4ajP#JA>>OzsASvJ8R1r9JXm{wy(x8s^)^Byf zH9EmlvPQ^N+5ZU62{=%L)^lgCMzX^hJ$&@1`f(0iNokp_+$OLO<1qmxmr`n*SNV0r z0qX~$)+nqS$=|VHIDIU=E)Hl;DlwUSGF{G{Y7^+`Gl5yQY2(`3ii16eL$b&&7>8Rx;3k3^ z16)2{2DSy$<#-LRt?sg?J9Y1P099pqt%wR*jh?8`X0rwr>n+j9z<6DKNsC?kGTC?Z zFb;Mv5kjdH4GJ^_UFh_7JG%QKy}$F3T;E)kMMt~VKTd`UY`(Gg72Ye5JnTxJMf|XY zYH`6#a#)j#E)MCV-~$~|DslU?(M`Uw-wXyXfEO_mT@3MOn9oD^O4r0zvTse4a$|{>#S{WQ0IvLL_j)v}{pYwvjn^wxI+$4T!lA`jOw(ux8^0zjk80vw1 z61uzT`95$9YY&7xtO2?z3%>Q66ozAZR~52=GBmqIO5jS8MYAiwi4VwnAy*y#eF4>C zMkcAhFax_2b(b{lu!3jljFcLvrjNXMDVo^>lo(M% zpt_|&bvjmazN3cHfsHc8=Bt$EN_0#DCnp6!`Z9H z&!9p~p%d-DUv1iEhrb0!rQcj0gAKlk-mgxdx5p2;is^0|2m#7`^Q8nXJxTO4Cm^@i z{V-+lRcw3U1Nwkf|9wE#Ox_D{#g z;~rVFtk*|J?XCur8cUvRV01sX_?pcX{<3QCi_hN4+-=QwQofMI~^IdZ>266e*HWwquCv?s8fYey;_- z_`!g8e!pZCDH1rnz3xC}+jkUqRlW4KkRIO|IN;5j)v3K=Os6Yg`&Ewe9{bH1YE60o z(D+9@ga-vegggPMwdgjnv^qJT%*44=gY;uNai$zL?q5A`AlUYwN3qK(<7^=7f=7*; zfnS9_x*&)J@sG@j6$)_1JzzJ9f`s_k#V4VAy zSJd_YrbJ;m&7RdSuu5Iu50_O1E=DF9D{8xODe*nl%3m}p;5dj=Y;N-qM$Wx~n9Z)C zFC8u8gfrf=fKbQMAB%j;zDYfC;L9oCb1bNEjJ&66vtw-gr58X;israTD=}u`J%KN7 z_2>d3y<&uxbHvbSYwvrH%7E3T(+E6m7gDTo>f9g@MOD5w&R89RUl`bPJF(|fuxV3YDnJX>7?4XxxB%vRX4g(?B87h z>bfKNZm0S9%WlIRc7dp#Q+7oCQXnXeb0|z?xfg;)?j&AFDeCdOsxkLXu~UD zxOoP!IDKC!JDR;U*If5(T4SZNEHFfEQr~72ZbMh3DozLWb={3ZhYGLd0 z*^XNW$fs!5%5~nOcn;48VL<*@IN?Qwk3}pq&2TGm4~%Z_C1ZnaMiQ4kBKZ}LhhrE0 zpdcx&(gK{>)n-kcWbWps9FRkk7YhmQh$8Rxktuk`yoyL5JKp$5Yc`QYoD{Y^?6L@_ z7C5Kx(n(Z_vol7nR{muGyLj4IGmZ#_v4GX(;F?MbeVuS-_S5mVyMDc{SiH36&c!Lp zD{ge}LL(0PFS_0O!d!y+R3VD!3WFaZ^>(MVE7pdz`eqdUlBCxbLEiYsb z)i%=vRlsE_Hd&*upEMcb=aMORZ|sB-{Seul-F3xr2G_(c3lWz5Sk3o9TaUS_1tJ4c zX2xDJbfJVPE01=DBouQEl=K{!*wV`yyv)C;UoK?Ew!1NjPTj3Rmx3*Z>>eg1ua{F7 zTl~tpK3qK=a?|2;t)8o}dhEzTFu5O!sz-1jt3-ys=%05+B?eT|7cc%3Wvp!wxSXjQ zdjvu0dz+H~5XxjSw_PUGrfq))(a4>452VkV*&D|b1PLuW8L!pmV@rrT>W&Dum&zkN z1Xkdx%tyvT+(anDlReTZhR=o1d;%`&PW>bc>0Wo!m9J}tnI-o4s#nhFwZ}m*g~0un z`>7)E-&Yl0=#%BPU*}b)6${#)*kGTfYv8?08W2wYm1wi&0Y8U)^n8PN0={C)>pK{0 zZO-nAE!pTjO*i_PX;hSe|Mpu`0j=Y78Py!bHRsYqX06CHT?V>XTASBr7(KrOEHN&98{V zm8qxL78&$US1ACaPA#9vNWt7fbG~<8gxj88LH3S+-4YdB&M6SgPv@E&pxw&~emZRY z2icR^{MKQiLqU^O!>T+maA-1od_T~LhV_Jl@ZaB}xYWDm}Ymx#XK zwR(KHrReu+ijBhK~O-`*N%5y=tS!aqGX zvA+Nwxx9j~6goou(Y%7HfxTt={;_ zJ{-Ryyon7&H5Socyu19`sDw&n^R+Vq*F?TOnpzYfpRCufYSh2~X%HJCSo!ns1X~C6 z{!DgK<(zu5Lw|KzL2+?CDZBU^@ECKim47!XrIxE5AB>M^yltx^7K%02Twoz>Wrv@^ zV;hD|Alp@EUy^nEs#pQj5>kb%Wmo@{&fU2tKFH;~M}GQ~bhct`J)!VJE_j_C z#ZiZXS?F>kP<1`2UO9@@yFY%ukyE%Cq>`hHNF(Owtt5rpRNQj)xMGl#1p8gTOfDsp zu?1~tGJBJu)6CFuk*jS{0d)dQ_ufr6q-=ZB+fz5!iFFCDIDjOAJk2bab5^BF@5W$b zPH67IQfinh+%#F8CfZ|YY)Y%K(%Dcr+yn@X(^K&@un1#9*F$FS!g^vSLnxn* z#RRWU<@%y{4&s8}j(qQYua}(ze;U?Z@DC1rg1^$E$J+=JCxGh9o)>>CWP#&`Soc4~d0k4g}4K%cUcM43>h=C(O~ zR+ROXYwv!F+;FSd8k^fR$TAX4nIzt1b&47FLi#k!-hka|nl{X2ED}1FWwdG0<_Fa* zWfuf@<*t7|uy@$o7VzR!Ac3?0OYquQ*v^~DBOJWyg!I+g%MTP&cnQ~{qsS|JyE9;| z`uLHTeoq3IA3;0Yzq1sSm`19tKduAy2EjU6^hO?)NFh~h=e z`CBkdy34I~EIchNZRH6*v1owoS+Z33bGN>S-oO8?BZMA9;g%{!KrSHyMp9bxh*GbQ z+_f!fO>Art?2bRU7rs_I3y$xU=AJzRBNCM9r5@Lu*{e?jqsqwWczbA9 zWsyN)`rc$Zw~MZIx_K^l4@U5@R5z)SMi`;A*e+YCy=*~9_(nA z9{SxhtPYZz)5&@kJu@pyb+lEner%s`Nj9-o0lxPBr4DMVw`a%W%KUOw(XXZ%JrF_V zJm2~f?Ttcdjt_*Xz0cWzjd^mA;_f&*fpCkkZE>0m`*9sD2+M}&?UnI`+iznogG-&uOyenX z%t2i*o4pF_nGiL}8i*U^$`5a`!>*6Gp!dOi-&Mc8N;gD3?96()l+7v5?s!;kTYSce z_Dz@Msu1XT{35T3dts{Db`7i8%IU-JV>MWuEVzo?or>D-NV*=OLmFpLdb59&m6grb zDlcl^;WdF(0jwpw#GPkU_idt)ngbUdrs=_Q3Z43OeIxzi$sz>olK2271tu z2e;67ZF5!0W}{{g{|5ur)9zEp)FGzxpS7Lu-!f}8GXzO_ZB~^hu_IOa!#Iuqm8DZg zi+zDhWg6c|X=Z?q10dzR*%;Atc0OEN^WE&-5wCwB&kqJQ58UME7az(*jP`rpX! zmFQ+mpWVtomlz7I!iY1xTqIPY_IZ2!*lIez$)`D_j{^zzRp0j#Nke&smuuE8@PVGa zT&-WSw@UaV^DFJ7ex6+3@Qc$%NlLBr2jD$T_0J&@wW+et#W`*+zWvze@o7AS zCtCi`-S7Ard$N;ETTW8z(|Ayt8mDn(LWrdATQPs?pN^RQK;S>T{pL~Ph%|}yffosH zh6|iaY;@7QR26;*-tH8V5!88$%wLVQT+=_U*hybW!VuMOpKD%1NH9pC^fG<ijYYFJA3Bq-m(oe6?zckxvngDJ!xaWeclDgMgszDE{ z7%MP>b`rSO_nY6J948+?PwV=1VeFjr+GMimY|y;1ONqu25WS$IvErepffZNKyfk}( ze7QrQBSJ*skNMykNQNc1pGE4Vhp6q1I)Gj24TVMs&ylrveq2e#Y-ZSh`A7eHxCBlN z;8{MmGz872;hVsq0LEWEiNsXN6375g($|}58npN-u*=S^PV9i=a36rdA7*lp9<|76 z*S}`7)Lwse4+O=^h{z}F(Hu+qJHPfp-J#E)`M?*x0~pJ+RA2f2C4j%sx2)+WZ@c`V zk0Qpjl*#Hu9=dO5vX6~)QGevv?tT!#lcI#Z96e#);=~ujuPvI489{_z0^m0H9}MVP zwE&C)+N(oC+r0Xph_GWo@;BE^HaeE;sl8cr;|VqnjjI5SyBy6icA&vOiINS1uB5&V zZyh7Ex#Tj(^;1;$Jy*VJ;&SpGoBSUwVf0h!30QWofrx|3E1C+^@o7;)dfxm;Fy>l7 zvjE3s7==Z=@ZfTC&&oz-z%lcx#P12^=TY55t8ZGx5}c0&&d7A{e8y%0Y->ljcLVB7 zJ_|2;6_li==cT~|WHYLR*go1Gvua>eME$a)u220ltgrl1_P6(5f; z&=ae%(WwDY#%Kg+i+@96g=7_728l`jwc)XHT3*IMd~n9?&-KysNm2;sp5(VHp&UO1 zh5>@r2tXKFeOpEE&98LOK}pO;t);V6^+Xk&BXBrT=21aF9X9VPdqxh)F=q_3NR6w2 z`1Z7&VJAQ%l42!ABsU>DzLE2ES{x*;tbNnKpSIHrEs5u==_h%O0iF3uFbTjcN@C}P z;kF{Xb9Y3XYVc0!^CE*1xXqU-B*$hN=@f!(BRUX8wtG{2C2frUfe;^DL}2(YaV}ED zTVlca#02fW6Lj3Fy{+(y_Es}FQxXD_dB?1@mcmUBk?ovE=L8pt-nBoI>C6ID<;7Y0 z^zKoA`vW^e)-4~9tl+(Hbe^GWH+7qQ-z+n4hFY25zXJ!sIz_^ry^RgCZkvd2eC8+oQX201l zAX>7>bNt#*I90Di{KW8Irvfg;>$#`zJaZ_ouTSuDNnv@%MG1VpQ0u4Y%aO+7aFi{8 zv>qwqD#wgr|^c zOVMBaa6k^|ZTxf7ob8|Z`QQNkg@ll20EB}LXKhIlr`R0=t?i{K2YErTP`}E|ci=lM zaw!NRMy_{yEAYTPnkRn2P*UemE>+(kS4^ePU%28f1D=$h{dx>5x|`b3iLsK>3iw*) zH^&%use_z?axzQMR)y4i_1w@Y%egziz3^ifKvZxo&!AtYxR_dFDo+QG|8m~e`^x4f zNYk^5=|Lt0>NA?SF;lL?ZrfK=<-p|zg)A3T!E?cccgohO2mgCPmg$xCEXbiW0AYiO zW>NbiWKUJ1v3BI!=LY_dz*w?|M53=1)!C+&HLsm{1wk`DQtHh1}aMz2$Q*VWzk~o^6n$O7Ow9)*^#R5^56qinhucy3WkSw>o(5?2{FaH z@4N%}zJx)$`^j_F(45=wP;KO)wCMKR(qm#!ic1nm( z?-Lpnk3Z%)DeC*^pIqToG2gkQfwfK}vqnBTC`~^evjoaSU|d0zoT>BQeEYrM-E{`c zL7#mcF`cob`?nVoPVOFg7qX(P?#3^Nl<2Qq7ypVlaou6D%{uk6O(&D%NU146w?L?R zsfBg0cB;EIs=>30fd zln1Xre62HE)_zC`ACDDOP>O4-oZv79sRd!#115Z}<7>dp`L|mRDlDWEP_-uaV*r)b z@s6JC;vr-`%3&BE=Ov68Yw`pvKwMkC1kFclfpjM1a?+R8-=@2Tju=`H5U6@w=v z?OSq;#k_S*I5wJ6b3N)y9;_!}JtIYox>o0LwzJOhOf-NuWqdbYIcXoWdIWgmYiBqnXd|8a&AW&sTft_a1Z_$Up>flbVV6y~0mKm&Fisffn9@k2ty1 zZWhd*fn@@m!s>v!Y*4>)wjTWH6D(y!=&j;m-?@PItH)u_xJ}a4lT@XHwhyPmwnMO# zAC58IpSV;2GIAK%Lwo^hn|HAQW;lEZr$vsK+)LsHsK$k}D2!XO1a1;kTW}@NbPmO6coUhP_Q%Gg3yV8{8(fA|p0;(a869K-iBUslCIA z5We#BDz_gfdf&aGeAvY0eRwvplnvC3KdOh68il;dmVC5n3lDeaO2$JFr{pl_FRZP&vgC?*$eHz?WNxsesy4ucxe&wx5x3DFC4r;5J4eIEW#1Z$0 zNF!GewOs+-LhJa9u*C`qv;*iN`V2f@Zyl>si%?|)F@wZ`V8bchUz6DW5OG`fgxqjmT>ajZI!yOG(>v;f#`!HG#L^_`{>w z`(Hl{#=MimKE{}KfZmTq?~l=icpFj*c7FY!`$M@MY*n8S^f7UV;D7$qJ?PLBO;K<= zTL3n2E3Y5!`noz?Ub}ZXj>8~HPswE2H1vG5U{V^qIqP!9`E0$?c7+lKwDsQ zvv;*I|F)6K==P;o3OjcnbD z#fs~Jfh_ zm4X%W?I9{}!-drqxO_8ntMm%=Xe&0lDndg=WsnR>VH z-=7+Ov*>VAyG^%?Z)R4~E6^_jAeNrF$$fa;g&k|74ru$mGsYHQ3)Ib-VaVBF6}=B0 z9b^O0q9`myw>Ja7*2B@uY^7;uStT_o!RmB3mm@b=39Ttiu;6>~|IuIA>EA>mk`BPv zqAugNqgpEy*NT+gVpVxZ=Jq&!{ml&MTh40#D725d#>K2u-m_CI>PbENbuR>O2hQcw zXdfk&)GJZ6mh-x8jk@Se9q+aeEAHquvl`8?@!4-D^v$kP{*ur9_&j+IgLh0TuDp70ctmZ(xLhrBlTzLmYZ1 zMF~kSZcj{}@2gX*Ji6%u(7~2!gH*>OThN>!=HMocz*jYYO7Ek;6*u7tIF=vjf7n)? zu12-WZ(D`aPJZjfd{jBF#xPW{NN4sVoWrf|yu2^cr?%k;4LenWF-3`t$>nwG=ME|W zg|_i=Ma3_2X--H`6I-JbS^sj1pL6`#-*1F2U$?=#PJQOYn33FdriLL^X`FF*)nU3m z9p)vBUm2lY5XZHxGW=#f3@yYX<~IB;Vl00=Pp<6i_jfT9B4lozGk#eRc;2n<3`$WP zr*%SiOk`(b?xLo`@426f7=MribGTj%rte(|XW>836M=Q>S@_9yfi~pGmb-HG1fOqm zoBuGnteW#znF^4jH11+&B;EqrU$60$emd%_zHwtAoZ`3xJSL6zt#@v? zP7SqD9t{aFF}xY8nXr$!!{XFMm*Gt~hgQx~{RxOBx7~y`bsBy|h+Vp9zjX<2h_>ue zM%$Bax?KG$#>ROc$0`3m_LaLtRfEzS?raa`Cvk-OY;bJ4%C>#BVB%Wsoz|O2Ry0rO zpyEaKP_h6Ce(hB>3AZh_X)j<64oDBynsyP}th&HwLM(r?$SyWNo8f2eZrlKAZ$I3` zPZxe2|6Ds5Ev<-{6ABb%6^Y0T>nl3?(7ka~-`=~Wmoh_$6M05Ek1XjIQEOpHTBVER zJ}NXy1kMp7-R-JYK_G_(fAR}nkQm!{7m>7D_?TR=xIExWd8<9EK67+LZ1ZqeeC60< zSKmRMzjq=w$)8DUAbY{8>0INJQM>-7;yx~odUYS=)3}wdHhve7J)873bWovGu!h{i zU~zl;XRAj`9zpaaX8$h1C0761wRyf*Oq-h~@As3o;W**$wlW|&3^WeJe2UNnLz8*r zjciFo*%{OX!yl|$&{Aj}Eqw)}r7r{z<0{-Q=vQD0J-%iWx1gw=-@QVCI>?(+u1J2` z3H>|)uOK#f*hTPg26g!si`2G+GR|Un9;0sxBa41r+#!-eHl#CIrTzfgViDL=mgNul zXqBF%!XsN-+dnJr>~ujlZvQ&tKKgs!U8R;pjz5>9v&0?HcK5o%z$L@24Q;{PJBcwY zOk|Di&}_$RTAGxNDPq5tn|*D3&u?wKE2e7&&Wd2$xxhwxI#kxnwA$n?9_K=k2x|F0 z@8I5JZ;>EOSZo(p5w(Tvru3(7X-RjwXMeWi9VFa31lMf<3`0!^2&jy5^pj!U?LX#O zLmG;@n?ye z#+YD5-v&G>kDP5^)I(0`WIhZC1G=Ag)_h4{HS>Q@5vD2Z7Tp|`iNt)dMUIhC?=%^9 z+oOnld>ABG+t+{-7RGz!m@d_7G_HFlh6}tqpI3PX5xLE6vJtL`<7gtP*+X?`g|cbG zEKznQE{K@&A#S)jH|m@~1eS)A@u@kRXYg@}yWPzB+6Xb+E-zTHLDMX+{{fPJ^AK*C z%Mm5dZm*Ts^O=+?klU6TOSN@I4TsB<^PNugL(A|$h zHPN=!JNQ<;Bqpc$PWQH>O+Yan`^hO|& zc}nO})$v-;DSqB9o1OG@IpG3oUZe5q2J_}Yc$nm_Se9+hen>wmax?Sf{$C@|g}lj~ zdc&N4w>BA^Z<<)3Fg#2E%*${??HE%}&hohTH7p0Md62Bb3lT|_AOxE+kI9DK2$p7u zQD4t%%g>*?zI>B_rjTu3=(-!>|EL>w9NSU7 zf@zU9Tq>#`q5e4P82go*@SK>#q_iG{qW892@1yMSmM=e=BP=b$!0)22kxud1>ks}P Dkq?48 delta 12727 zcmY*LLblp&Z@kJB5qbCoEfOW*FZ^17S%~qW zfJoFs0e_}LUz@IOtnK745l{rwtW)5Mr3Iwb#f7cK6241zujR%QB#uy1JvrUYMMYIE zf~Got2@ZolfsoWlb{HMii6Hn4W{9JoUuqs_M8%38VA*bOM9@` zV4c9uKslPDwzt=oI*1>7yq$f^q%aE07sPr7n zAtcI2MKVlO2uH4a!!YI*!H5rcwA~m<`jGPnKJWw)jig?#qNcsqzd#Rk-e;V_}v zONQW=*f9Mx@m7r)V7zvRMztku;}kV>)6yy%z8>M~`fT0j;MZYy9W%kel>dI=4;#W^ z+~Qy3E*ryPn}U@m&Yp%P9%ncQjln(!bUOy^X4U8Ahbz!>-)St5qWz@WRi>c9_2xJl zIkx~wu*C;e$N*GU% z-7l7cEqEj3-in}+gWeUi_ukI7H35Bm0!zH)&}N3l^p;?u`ogDS_hm?XbAr4A zY7?Mq;Qy^H)*qt=!rqy zi7Rr{%-q?&FLn>9+uT4X3F*Sa|5P?};^1kszaAU|Ji5X!X{gbuuAO-y0Dj?+lUz8C z{}?J6ynyX~Cfe6MLsIJ$+;pkSo_O?cOn1smEo$vofK2wsnMX+K7LU=Nyz*M--6?V= zzM{JfHCVp8Me0$RJ0I{)9@xr_e_O{$Q2Bby?9)vy7+uJGrOdlT-}}QZ2#E&Z@B0!< zgJ;6XfA++$8h?og^iIXp`d)_r{x2^D%W_7@g;5C8uQTCYfL@~1%$xo94B&1VsAs|) zCQXf&w$$Xp*|tp%7~Rg?fTGz`x)*!TxZU)h z6Hedyo@WtBqQBSwvW1o#IXWeb>qMAuzOM6blbO5um&o0#UfO@lR^g4`0fit=t~6e! zObRDh+QD?1sC$Gu7!Wd?=b9Y@nSq} zGIXE*g{GVaZ}|x{+|SOF2~P`lnh#K6f|8L9E(keCz~E1HD<`fW_{qTxC2MMO^=#V% zR^YvMI*pgKYUdR-paTvGcB5(HXj6&=#le?We+6B@5wEI80bgEP{$quLJY>p8@9I&4 z?SHYBn)$(}hQKdzs0!>)q5>rp2$`rTW6OWY4;psSg44W=oLzBH3|>&uIAYCi(zILs zW%bt?_6NT}thfM&ZDe?EHIT!)6Fn|)!sv#43r?Z4(@}?B0GA7+IqzAg2K;n4(uNw_ zNpyJ*oOvT7Yu;_9IZyo2Nf28`Q8G`FMhY4{6wWl9Zeje^w-6F4x5MoQi`6N3C+{r8LA8Rc&)a&KIn=NTSkwibR32J&kgd?_W05EWP-r3p*{PRn1uDf{yYoSBy-)gJiNSdE#l5~YFn1;Gp!>vj3LPM!*O|;zSo*`As z5b%7*q8?0H(7n~XWd{DFV9N#*GFL94dMJ6~AB_y8fgZFg!Zt!RQUT+UO{WONk4Y-5 zAfPk@=09o33-lYmHTyHS`PVU-^kQh_4WPy>YY@_g}}BuQL-LY;~8+u~Tf^6#TH;*@;F7%2)_KMllWdG77`R z@H#EiRhnXT<_AVLn8{b{fwvrEDQ2feRSx+pJ;KP9>%s-Wx$8>vV#0s;R(!BwO?PkE zP?K$^dnqT6G52$dl z@kH4|M~*z=TH%sggED`ozqJA7dQ~MgrpBp!B|>kl-e6Y5>?pdKoTKf@JufLf7PgPL z5ipY-*zn!7nB}EvZls0WhfWG9IDUz7zB~8{?_b7TAEPb<7eO-6@oX?dv|4uztCN6) za&$!8_QV%4CW<{FXNj*Z8#irVP}Kf#2o{>>*KXHNS!*nHgR3@NbIW473QU?GJTJW< zuksNX{LVL|(bI=)ptasM&QO4kn#X_xtK-2S6 zECz6zw0M1ExS*Rb&aC%!2m@H$?7lTb{-v1erwB~GjdUzp-II}m(Pf&BDDvWFV>{kP{mfhI zIV&WjaNg+LIlyhrV&7d$PoTYm6F5dBQqwYO7Y za@zkCv;$6&UJF)s-Rz1r$Ct5WuMKk^0{5Yrzlt>9#8SU$4PBr_tla^Er1|b zvp)%EA-<{K45y&`+aQT?;Q1o+4d=P{`>s z=TEU~tUAvaUp1y%WFKmrdpoOgJSh~;wtIC~^FBHy7BUgM_$oRfp!Y5%G_Db^{lQH* zc&TrTL^u8THNJL2qp_W+e%)0Vr`OZP=@g;0<4Z^?iqY#8_UB7QPJ6&tTPJ+eQ7qpE z2enAwc1&;00qe@I{;f~;(kxXQTo-t@gwL}GnaG}B zxnu2ukhxsJ=B}+M8{{0#oc+@|#WNKIUj?lUP)6L|&zxw@aA;RV zvFw`{JitJ#C1khpXZ5OBKxGy@GYaQm-@8(hk@qnU6HUm0;YZNntjU`3$>4g@*H>Tl zU4(J`y&DbDPNRMap0)u$3>_#TUO_8LO&q!r`KxT@=AXS3DR80;xocpv}NBn z6qo;^|8m9s>PNraeZP;t8a;=#R_D}$oXcpqAY7eb`iA|+h^SWU@|;{RCvH|e@aFj~ ziNx~pAi5=yD%H#^T3En9ZAg)c`QgKHp;dv^vuRBak%-=8Ci*-p$V7C zk@7|QWUc)!u537`Eb7AQerNmjuZ+9fldTVts@^i$Cyrz|Ge|AX2n0!!yM+^Hm|7F! zp=U6M`=L3@dY4(t!t9jJ!2)cTzgug$El=S^?){0_Y|Pf)y7fYZml}cgSiOwi7gc=> z!v4s#pK;)V8mj+}PTQ)`hh>$+%plq%SxH(f<%Rop@(txRm}2xP_sgRf3_MK4nNv?> zNie}zJr?}0t(5m{3x6EMpG^M_$mkgCaOCx8q44r+Gp9N~*Gz;2lu|Co^D{_Abgf}m z-UATq)d;U9V;gnizPyyACcLcgnEvK;O4UqaXX3FUIuqY~Y!GRAT2U-$PBEYe%q2X_ z;mSU=-%hk|-*#;AwywzTN=F%RRI=hPTv66xIy>GxlixMbqDh|ogK8U}9SGkR zswG|Z;8`X=LDK}~P9JKVbV@aS0^Ze?%>`ss*Tu;vrbEy)|eYeCj$LY$|utw+)Ju*Mly z)4q?N{Y9nIrHOvDaJAC|By111>2~f8SUrubaFuH$?t){&fL!sJb_wef=Un!J_`s)@(h22AUy9gG;=P_$#_6(31 znrbYZD?x?snEqXI7XJ=u&cQejPOebx3DQ_@NyS6uiOppl+)k>C|G0( z*r*ghexA}*A-6S z%wGQ2%_qYLy|2lPUZQ6HT5;C@dKD-v%_5O9TNm1SW9ewPaOvI-=RAHhxK?-vLLczE zf)QDVqrXE%E?Tt}YV}V-`IQyTX?wntbh7IkKtXkB%r!}y6ZSnsPQk@_^vhrp_QrI2 z042#Qc+{{c7(5U-vnD=y!)bqj#zRUEpA6#gRFO>s4VTFbt@EhxEH~q!vB}KV=7cnj zW;GG0b$j_@HH=lsq(bHz`UH#z1$dBz!@uHxvVI&h51sIt`wwk-myw}!xVX>2_?-*|ijdJjH%GB) zac;@$RCC;dW)~AW>bjf61H|4HmnYyVjo&h(i zIehx$Ul-5b?`sKp^0Kl0$l3J=6-)e?AzXI~ZE5el_DuDD%s^aio$AM?t$&uFRq)kw ziWWNeX6?)@S@ZnMHhgKTZrs?t@NkR&{ z%5)E`tB^jd!f>0yIDKQI@0y=QvHjt=6)OXuArA-JN!a&UUVK{YA7jNvSDW9PJ#T!i zhBXZfbt6JaykF!^;AKab!uwq2q_LAw=PmLVj9K#0AoyK5#h~;*I&Hbl(&CGZ*(U>@ znL0c(f6+!4v4J z>aV8l;*y*(Uu};}#Ic45Xpy#@&hHtaZej342(Y>Pwo{l0H-hDMJoM(ZjhGw*+r z0;jaG6HwblE@9i=*XRel~z4U4!du#Xb^l8esgwy*&8@%Y$!b5VtuXT-MH7V zj46R937kjw1y+EU9_jcaSntf@q{-2s50GDQ9FOM;X>rus{;(;W0gjLRZHGGK-d?%u zD|WBYme}(LuXP=y(GdSOa?fbjcvbiAT&%4qx{4?BSgBinlby%LOK4@Qb2A1(9sR5! zQz-;rd;rm`0M3CuPbOop5f~_oAAV?nh@kl$r~_)t({krIws|%SzvsD`-kYcSZDM$N ziR)%s8mKpvNQ8tiKnHpG!gcp=$aXfvAL zfkYnVgGJQ4bvIN-CW<%55j9?#YI*MtBnIwO5Zc+>z+Hl4JUzcY_wo?_D}{v|6q>Y_ z%QC_6OuaZ8R1UW8wA`df8&tT@IgK=t#FgtJdfR&&jeS-;_G_D(zy8htZb+(>tbK^G zw~7C9@6X$ifAdIz2DJAgXKT1K=A$arP6($`q|u?5{IeU3h*Hrek;wby z(KETH#*wJ9p4}I~{I5WD<_!!{9fzGS#n(*T_p-36xGMvbR`}~JOQxeE0?}ESl{;Sv zMxfTA=s%`Jg4JKt;H;7N_l2|ql0eEt(fbZ?1yr76{19(VV7jU{sGYyN??i~WNd`pT z?ik72kAY=Zu;FbyttUHM>$Q6@lsn}aqA;MSW8OVo2V-C*E)r~^ zwCAOX6xuiYrUur(lmqvyI`-&RjH$GXU77N(6Le_vWhlHu#(aIhT3P@%Hd)yl@nYgB zJMQ9y_Q@u1x%tbDJ0(R{VWVaQ%&0|Cr~1D=98*ebXs;>Oel)=E-iR=!q%9PU4X1Nt zy%D63TDz;zzKTk!Uu+5EUmH~jcgnE+Q=hhn0_!XB(qR1{QprhR76rDc>~HDhzUlYM z#s@>x*hz*n!8cjJCi!O*t$ZrBB&5lBMu3nz}P1c2&e za@8=6H$4>;#K80{fQI8-RVd7r&{MI!xPz}b(jW_}$OLT~=Y1jfLhOzSin1PEGIH_l zU&ai?u_VH8#Vt@L=8^&oiFZ>24wz`7yB!-Z_10pEPvc}Mm-s;NN*HOt1EPTFAX>PS zRZdW3uVKULk+;5CoiP9cDUrNyXX;WMp*ev~EIy;AS}x!O5GgW8Hi(|`7m4^kf5e>O zlDe4v-BUCBdbJ$lF2Fr50`zGR1i&{P6WLAs?iKE1B5ZiK3Ffj#rlrhUgs*w~P#zUq z2Rm-hV(Ruz%(Qg5Y7U|OWLuMt5DqpQ{II6?QQkhF05IflGGV?U;p|-@-etk{tn%Ad z?Quv14UBGbo(2)``SyI+?l67PqwACf80yt;q5!Jf)C|VbJhSWk;>qtQBd3uf*M)K2 zJq44oveCXGFn9n7g6$?NqH}sZfK9*+4i?I-d(41LcOkPEj@Pct8SocvQ|v+&c4Lo2 zpNi_(N&QO~%mFHOw##%z{jNgD0)7m-mS6H0DsYGtoE1Nl+qv`IPYhYG~NDf z!wuk!n+{))GPMoYHU)9?R*lm90KZtSJp|LhDFoG__En^KDIe6fk(Q37=Jgr$4JuxD zGRRkk#s2#qXF@1q{ZY&hmG-z(pbq3HD5v-WMo63Mf-}WlbkKo(K%b?8s29q6d7YFD z2w)!GaLJc^iFNDgsumOwZ7dM@W`Q|ovJ^##`e!PLXlbwm`@xchg`yA!LC-0^cN`?-8^DSs^lP@SwIRa)o%;3C~@<^y7Y)Oq(VNpLBb znPDiAgK7tS;VE=%{LJ$Jv0H|~-tEO;m4k8~sapM&g9O|44d{#9 zT<_|#geaBj0R)vVu|AReqqsxH%JcPKedOjD$R?XonSxFNzgDnClBd9ZK`7DDjR(0en%{NW_3Vk8}Nx$W4~OO(`3UIv;_Izxqf6?Id#^GBBH|W`_8{1^1Z}Xas3ocefY3v z$|Jzo3%ZC`(rUd`W zcCmYhH}1ZfJg-Ja#Vop=K#-VDLgk*%%u*AyufH9!3Rkq)^!Jmmkg~KrIn^7_1Yd-; za&())qL0GX^SEd@#dXrJBV^z|M9?GeKl$5s?oui`0P7fXNyBz=L>J^u9eDlhDFgT@ zD3;OI945Ye&P;d_N-=l@a8OA`mVw&&VBj}u9&*d(`p4j!87Iua10g|SEo8BxtB~!G zoQ`5LsbegtrQKLKT|8Bk@r7-Ecb=#bXT$(kqU|w^>s%Dv^Pm2Dp_>b4!cW_yL;Bj7 zt7c%~MK6F(7SEpk>U$-hLu-Q}u8ro?wWCqKC^AvlkiD zQID0uV~G!?!D1%4`^?>I%b*X-UDXnvYLn;ohtj&fLUOEGZ>v;~>Imm}j{H7#D%gCTMq>+*a4_mtydd?vIyPA;1TJOr&C^iP)nDY)XEBmT8{Flp*r?z zZiH77fMYgTrXoq$+O);@YWJOk5(~(bvi67=TF|!_tG#o_nwA^xce=-5QIqH|NY&5`DN^x{K~|%_lw5ApVFWgu>tLs=73zdm zvrRdOqJi*77(%?L6&cUbH$}N#r}l;L9oTyTr_J}GeQKJq2N(7Z4Pxh7sa=(Kk*|Gf z#la`)5N0RY_3;clO@#-6gafSLyog4kAA=**Q%)*zd^h`HR-bB57rr~&aU)zpgU37- z#W?0h+mq6F@9?Gis~2E1>?T^O0~+jjZfU$--VF8TG1z)eQ21jZ@D7E3`7AX-uS=YU zZvY@gNQlEk3!%D4-Ow%$4$CGFAPi4-LxV>ue+kQ5=4{q4-2-?e)t-(TeAhi-m5Jgm zu#jg+^0npM2U@j0iW)pkUl|L|3Wlou?n2FhHwDYUtBpS@Iy0y%&X;O~j`1en!K05R{q?iYoiEw}X;^EMM_{HqM zFEe&%u$H3&VT(1DHf{ZVEeBQ=#&KYMCs$=PdjGR1`GX^SjX*Y;4@Kb%EL`h&q{be- zf9tM@$=8}vSZsQWqJmSp!$GdYo4LnqvZS5Bs-lvTmso614(dCiy<+RXy^_#g>@FNWw z=_m-6bY2Qt%;s106JS1rd(#G98BrMrmFg$X8A4qjeU*-=qG9#t{I?>7GB>PSZ|WwB zolQm2sOQ${CuSISeVk!_CyYn8y26dSf~8z8z!OA4Lkn2o3P!~;z;7(_sOy$7LmG$4 zB&t}jCA>(epfNqyMfQ9FDsTVI{TD*P{(i;w`@y(M{! z@>xN@4^*NjVl+CELtPPvmtVHKc37sN9(AeQzl5M#Rs#|KA}#mdu;FHcQ3 zY*dGA)`X51vk-d%1n*UnM2jB&DkeOA>cg9!lP|b9=xKRl3mBhUrBRzqS zO_$P<*2`ZWzKc5n0iTmp3E_J4U>T7z2)#C9{h{gal3lrHy8MNq~{z5WFpMO zprD|QVbb!rO8>rAh`+v4VZkk%)1;$0_qU~NrQPAD_SJoQrb}$T>~#iuB)VUg7vYs6 zXc<3@Ve~2Y>lrYv1eb z5-;=3RA?le7#2e~W55Q-KGA$;MoqSVpih7(j`SMFT3?@K$pV^2~wnhi*`hGiww&5^INqxO!Q1=00$p&!fP5aT56KL&b15=83aI(0!gd$wz z&~^cXpP37_wr|6#e2yqz!|+wD!cUvq9>Klb&S{C&pveM;1TT1G-qof#ve(-F5DQt4 z8`!p6YgVgpIqZJm;x}QerDr{`Cpd6ih8cpfQi8at>x3>%JGfbObd5G7P^+Ga0QwG- z^YFoF8YXv3+8$|CH#Bmro>bJ3OL%%=e?U}ub=o}{nOC9*4?QV_!?BR}?6|T&X%&e_ zqMMg8QdD^!{k^&^C#(4DwI}%ONX|$P5$*NLIOJjH`xjLZ6m<@dA8Y7W_#N!bLkM`n=$5dZg#Iv43~x z&L8fdwA@?A(Rboq;B&<`ub8yTg*CnOZL&u*hc`!mL*+5$3j2%un?4gA`wA){SBRt^ z@tO3GFUI!1H{SCV?A$Sa&tyshSXQp2+z?i6+H!d^b z$BHT}=!g?6ISIwxqW)i3GA4w3oSYK)aEq}y%0i5nAtdn(IBqdT&Y6rtv5n;g{W~g#F_P+e6ji;b9z_SSw`a;X?;rwg&Fzv z+g)bnS!?ElzgU*abBx%sH`GJ7zYsVBww~v9XGpBm%#o0^u!vx58r%KG1}3LjG{N|= zlTX7{#owj@K?&rQkIM@*JjE}TUFFb4cAFKDS1(ulg>lJ^)9Vf2NfvVsT)boSxk{0Y zr&qP%<3fGNzSzvUkh?VNckZ=?o zZ;zf=y5k;eS_$s!trO@wY+mqvftnB#2lmEF50QY6-Z7C?CbO`GV>MDS^$*#+pVGen zO^UVfAWo}NNAFNi?(&HNDg zNuvZ78*-AH?Am@p*XZ%Z`nfZ`W6-EwKzgWKDYdQl_9lXsI|2^0?xi6&5-?}HzLg{X zY)!vgt|C=2yOT+@+;lpBM~&BqH!(7j5)#_42F-J_eq?xi9l;S7m`*cCgJnRExxzFt z$KZapP}s(*?Rq!<`&iQd&w+0zS9N<~ImsU?C4>kuSDEGO9zN$!bj+1ubDAyaj4nw% zfu0>4q$QRlt~k+Wivaf}fS_xqm-TbH#$y)}7-kE5VX77CJ@qg=rJ&n<*oHy-mAFNg zvZZg=F(64XokD}gad0SvlwhpiYmZm6nMSpK|2a^q_r+rOmlSt;`uWr+q3~{ux-px3 zNHCmYoq1}$-y5x6o#%H)eVqhrRc5hEigh1|i6&FSZc5lj!u^dHKF4$5R3S*5B!q~Hn ztz?XS-+6b`@4e?AGv9l@=bn4+x#yhwIcJ&!w*x=vQb38UYlO`uzH3YkS9`w2+xw?B z+NaXRuYZH2FY59}{h;G#Wc!8?zEybD`ohym;whf1(SnG_rykQH_#d;Lh{^Cwr+sls zSU=)P1>wLYg^)m~*qgslb1r3L=7g86*Lp0UcWaYqaq)Rd7}G7B5Deye4GBlSz#uS8 za4Oho1U&@1vW#hW*nZ&bU@%(YYEN+ZtjvOdmAr1_jtl)6FD%O9}d+?G7%pJbvge`4E3{JO4KmxHT9HP$em|RNYWkzB^IYZyW#qVJ zhsfIXO^;}OM%$s}FV!1pQsUb98e@cks&WR)^M1gmKb;iZ+2Ot$p|EuK?VQ6*akQT@ z?w3W5dU~vG5J>>*9`2y&GNihhvyif#dAfHXAWVNQ^(ISn{A*wIwjacoULBUdVL}S1 z5M)&42h;~y2C8zZ+w1M4Ejc4q6C7KK#4eq5-nL5RfW36%JZtp6LH6e_ZM-U1k>lLZ zE%s#NnLay9dn4WKuSN^|;^|)B*mRBhAB-eB)Zpij^hV?`JDy@e&^OUu(j$fXj;Bw5 z?v*!M7`?;89x7^?0R<-eR!j!cl!^t+E1`xx6$KeaffS)A&0c>Y-0crN$+LgG}8tU-KaMLvxck2F=zL_7JeZ)CV-aiCWcWzsxSN6;8k3B`-))Gg2eSP%-6WG}Y@!_^=wA^CvAN z)>H9v6315HJ4?;aMT5y-EcZ3i#bzb<8z9*+ZJ*!4n7Y%p!fTxisS%PL=-l|y0goH2 z)OLZakZBl=bO1Oy?W*5++j$RI`JQR0&K2aDa?oS+vQ2iCr!TZr~A?}T(NuvLR z?ofkemlmb83p#ym`Qf!GUZ^jH_osdT?{Nc4R)!@LRLuN2m^`qc7u`QHS1IoVVd^+k z3>;JE{C- zM`u9FngW8ve~k5Y)O*}Qv84vm;wgq-gR-QSHY>z=5cb<9Wq#*le^jJoCR8iy)yAXh zwY`mC3UCQi-pF?%2 zORQhcH~Ub*sdnFcOJalCa6-RMP_ixti)CQ)JC!*K$gix*P8QYc(h+qk3j1cOkys5sJL$2gPx%8oqg;|1@q)khPj2~tZ82>MO+=B8r1%2M-< z3zbi!;8Gu=B#vBw${Oaq1;Y~xPiLVyuk&LuiqYB&Otf5IG&ZzeBav|LxRWoYE37sQ znKY1aliOU^<5JOLF9fg{k;>?!_EY+VsLMy;RCmUwZIBe^p1RZ*DOug0g#*F+l{vlv z8BX}UL*TSGY?#!*>v6AV?fTPjLQhU2=*#LdY>;*&@#G^Dw8CQyQWJar>!0ScPlaO; z^GtW}XS+J+f;~GZ=>WfwC#|+RR;w}drnO|buS5Rl%L3PX-id5I@9+n6qfg}S z?S#_()&`i}97?si#cn@(FMU3{>?BZY*6zD+{Xe{?oKzaXutoyn1|QHKnpv+l;!grX z;l_r|a0mtwDBa46?*z)X1jd9mc6G!Wh6Kp^)w@h=o;(9bnkF((2gL^Imzy%N|A{ z2${e@=B^>mm3nPLIMD-l{B`K(-BamDK}hC!5rKYkuB=XNW(YyE`YEb1UpjdjK+do*~IDkG8tSD2C+!P6;6@V^)m~<>6`YzGr;K9Pd@Ry z!zjv=a&=I8^<}2I%R4tE=aKJDVDk3rjyhdQzjr2d!mxr?RqT)ofv?>EN1WS;*5AO& zb5Dt{`sdve;mcD0iQ-yT-MOph?sDW;OmrK0*#nE5#H;mXYi#wz6IuhB;Kd{!H-1sy z<+lmRaVcLSfm90Z*BPU}UQ~zNGXq4}Dr?H`Y+UdMw&H3iSuUsd{=kpGvLp+vO0Rhg z`9qRmup|S%;AfTssTCGFmDyFn2JJ+90@op7zG1T)3XH5+Y@nl7j}Wya1t>eZY*dFf zdX+g22|r3IBX~ZdB?Hxw@QtlOR(vzC3Oitl@5iBLjBC$den(sujxS8*;xZ?kz))IS zRq9@%Sq56L8oXt4e(&z?BLQ3Ie&u5+V3ST={lCO(SFkK_JMN)dKsK7w5*)FV0V!!O z%6(>n)I(KZaIHbrOKR7fs8JmaYrtB?n+;x4tMn{1` zy!HfAAC5UoQ%Tpx`u%x{K~xuY_jK_7N41>Xj0x}lOI1_ZmVI>&97af9^1?_52arAx zaC%p7@Jhh#tN4W6b|p9#-s}EyPVG%+adzWWl&r9)Am^cF)`KzeW1}K2@<6qFmvng6 zcb}$fpmggYAf#7t)3uKLo|FPV)jsgm<171dySM}OjXS%~F$l3zCEw@2>2OmMvOrJe zx0*3tzqwIOY`|^g2Twaqr~p@yavF zIF6u~tGhILe2Taa-H@jPBtaM)GaOZ{Hi@%?4b9Gotw%T=c)EN$Kxq$*m}VFMPP1JO zoll95V$$p1cGj9g2A>XT_3WV74j5M4rPiO4?N(_uP3@WV6a39Z*^~ZQ%G#}hLHPsv zJifsB7dph4M{86Qc_bWz>#2B2VWK{rOrQ7)!^RCqWkOx9&1wfKK<{#_a8Wq~Nyka6 zNk{kpeGk!s^JJqX-Gvcm?vy=AY;g#MI~z7*dXyz&v{{M)_vwkbI3bE)ek_#+k}$28 zX@fK&B_HMf%RhVIWuQuHQ{>&S!|Ae%Vi9u!d$Sc98-`=lDOvGAj5{*n6aiD=ij@D6^wJU~&dLua|CI!oyU9z$cCS+a zA}JYKujbm4Yz{%d@mZJDv%lY>porRmK!>s!B~J{MY90 zGl92X%liUTsyx4enU_q;N+>|*Q6uPmjOO7MPW1K*by_Xvz*ddQX*`YiQ|2g?k4T+! z17>J4$%}W%(7<-E+v<dm3>HN*dL3F}*6PD7> zstd2xbGzyLt9^3`LPk8n9dx;ZH>Ps$?J*YL)uHtq_^`7uOF!*W+5G12T?GBlKXEjk zyRHQ2f%llXJbY@W;L(|OW0Kt0wswV;AF8DNBNZSQP|C2`(`fZpXOrVHcn^-GLHLhZ z?m)(X?`#Oj@2Cwuai38zRQLE%;AW3$1~-OZId7?rJR+`!r0LW3yWtoSbcsSuXRj?k!@6~ECwS|)#)OdnpgmeFyafjq*4@9urF z#h`@@vUbZphux#K|7uFJ>ht;3C|ES)K1(*?yJWubDrD8W1+b%X>(%rrFT`w*gA*}> z**E{(?hPihmpHRUyJ~PqNkAuap8VlPe)=ihdY75#6HXyoc~nE#Maf`qA`<>&;?NR; zQG~R;ME9o{Hp}N2m0M4K0dJ}x2iY8aEpRx^Z| zXRt>?(irbrshGXs@C`?Capn9{$Q$rJb+|BT7D{Pf?&%MYwVItiQkYro36Yn9HJyhm zcS&st?)Q;x;R1!9UbgoN5823p37|55E!wdH6S&WTuafCm^`{X8_6>~X{W%CL&PE>C zW8E($_5JTa@;R&kJ+Prd2I8yjoSUYBWV=pyBM_KOEjDmJ*&Y3j z0uT39foRr(j|N{%XC`kx&!tAbtJK^631}pvDqqFJ2p+0;N&Sm?iI_~K6#wHY=8HEz z2ozTs50}+HbvtrVcHssiQL};?EQWeCeuSD+SmE9GPSM;;+(pp+k!W5$)K}|;Nnid7 zVe&S0vf}TzHyYYc!vnUxWqM5yd{(ZHP(VPcC&w}{UZ=3njJIU);=#LKyOA?*!jNaj6B5C?sLn%rd;kZ)S}6#^<2gFrr=L z?WoT|?xH7g3xe2ZZzm-(ihLQ9b9+u$7>591kz!xcA^(Q#_D&EApf~5kDJ;6?kEe_I z-60#U$7LiTi$pQqzp<%(jI7u14eig@w;DRcchbY^({VZ*x-B5BcEAVK`{f7 zq1cV#CSVM!%r~>M&hd5#>G#<4ISy}gT-g3>HMsmz9JXz^;OTWD|7YA#Ev0lwUVXjB zyVd>IPZ9Z$Ef4;)RJe&ue!${%^+9ER#y0A}E3d=l|MHf2OlU)ACT z8ReiET)`WAss@lHpT+PZd+(QtCZ(bbze|}xboT0Kg|jN>mxJcZYx}ExKi@p1w>x1= zSaBDh!Kh>ezTFhw{hE?nc!E75x8}FB1iXzH_8YRB^IKtJ-FBYkRGj|K5Ap_4aN=&G zZsxD*)#PtJdaodG$eChD=!jWdcOc*Jr}dEK$Dv0V2S!C7s@^F;i@84F!Q)z1!~6@f z7ft+T4N;Fc<1n^81D&1}Bi4h!$S$>0-5 zl-1tfjKL|D#`e(Kt3P=1YhzI7P4Q8~ajNc`SGse2yd#l5vc(e~H6V#M%!qoaRW7~H z{9)la<9(dro^n%x{8{X~IiBNHqh97Fn6ET(2;2wQuX?Ew%@Kp=)5#&`50oG-1NSSf zs)+$D1@RkUPam~BzXP0uX>`Eql@75dc0&gU`)6M>Fk<504&ap&cPA#7?^_~HXJvA`)|%-zs?=8bve!#s*QwX=mbZuk!qW6R=T2A<0f21eyed0H_?bKzrKhKDad6H zJd)jRv|OrO9kz(VQuaraTP0;ZLT!1aADDHlrJ1f#or&MiSpx&4qH&?yfJ*1oR~YLX6jG!= z3Nz>yat^CpJYT*yDHc@Q?dIO+UTX^E`Q8#uwtb`XbJ0hMKb;lDUIzGTcjPrej*hUL zCo4gY(()X58B?#Jp3~JPjLA(DI#-Y~gZ-IieBY!3j!8C!AavgF;!7pxeHRI2M!k!u z+3VZ=5w-&k(1iUoatGez3^={z)mzJKc)JtBppa%nKB6Sr{De!k!$XS2^VWd{*#a~l zVGMxL*U?)LufcT87iet`$_iY&LC)?Ut*EyS+pF=G#aFuTNKWQu2G0l4wBlW6SwY3C^bXoD2 z#7dz$s4y=lD;CWMEi46mVS6{giHqN*Mnf>+?-$*G_?tvge*)97BTD2ejm3e%i158%0YG!1c-& zJXe2*h~N4lhD^4MP->-1$*_$F$OH!*b#g@jKim#c8II0>yJ1VwP=V5IY@p4mue9?! z7R`cB(_@R@SF6$k-ZT^l6O{ilkYRi>0Ag)1G(cto2qT19FVmE{#vGt@;BsJLpapN6 z8*0D%_9B+cwoQwi9Htwug%0PY4CJHv7eD!CV5-y5l45U9V?zR?m%|!CIL!JP8DXcD zEbX!{sj?mZc)*ojSQqvC<4{>)K0lVzg_E_~$~(>-xp8&O^cTyXzv+hIF!Tc#<}Hk> z5AAhgAs_7W-iE>oFR1tIrs4jffYp3>s!Cnz{+9FT z!(#H@*Wtp}x@p0g@t77lLmzsn;fO!RAZV5os-@9~hhFM!#>mcEqPf*C6HK{!31zEu%+OV*l*1FiLP~OgCTn9W}D_b%kQ_^i+ zHTCIk(M0KdVRpSV@?zc=07`!#?GL*=aVuJxI!~r}yObGWeL2kd8#%6Dn0ck&s*BY~ za=z2$xr$s_-|Fs+__i^T=!KCoq9I&7_ztL_Jc*{nwUqT&4oty$y0C-Q z<_Aw%bd<7}*P~{5_zV_)lOo>c3V9xUfU)PY;y1b(@8#fShG{zr%F(f{@fb{tu~c!*6JHo?*+t95X$N+d#B)e>jHQdDU}~u zO0c{#v7$`CS7u$XqAiH%zTC`k(Ts$~_&3yHiDKA)v&E}%B|6CatYV(jZb)xzbji(!^ueDpZ z(#7O4;T?Sk4V%Nx^>d%ovFtv#dme0JqM!jXA{-asxw-779USwL?3cS5Sn;2;O~N$M zUb;wlW-}9BoBdp`a;5t-{TlXwVh(uT+x@wycf0&+IogG8Z=oZU3RhLm+ytcHt}*^9 z@X|>>OOk)SevL3YuuNjSmtENnUGTSipuT#KIWj95M=;k~kUwtZ= z^$f>nzKM|L&^!QKfj%+V-$XUO-Gr9e?Q+=2zKcLBoU<4{oi%|183$t@+`{sv#jdgE z!_G<4=H&kp6rIDyFp`jP8@bojx%#9xe{?;|R$5*?`}*b|3kd-6$pY8QCm9gE^6b%s zP^dFr10>s@x!~7MSjteXDg&4>I8=z`-1J_}fe~b$6tEUG`WCfa%uvk%8f(nP~pN$a7eWnQJ4LA^MnXPF=F) zkf(pn!zX&DT4UqPu7Db{!tmS5<5YK~u09Fg3<9PrK-@TIF!|Z<#rzbv2_6+kjUL4S z6u=Q+rE*~?l%t{w8S36HV>{J&=zBWzux}5dpHUP5Vf(ErK%m z-SYjjXwL&Mlf62C9cgDU>w2WmdKa)O;Zi9U<-q3DhtxxM)v^VP%sm{hV&|V0pj9VdL4;QK;TR_LSZ$=WfGhR;QIjB&fYW{RHNrr`M!e!7x;K?@OYt>-KTW z^Bk+ns9^;%B!TIFAon`3NT2k3bl9`oO*j8GQHX;~j??0sO!T<5dwnM4D&JLpEKEs! zrTMD*m+C(>s%05#v^s(a*ju?I9vJNdI(zq>$EFETOFGyX(2nr$wntU+@3sdXoyMXI zbdw9F3TVzA(f}lHIX5~7WpL1M$KZI&JbUQOf6vD?EtVWSAJ!MdaEksXxMg1ZFfX{V zUqFQct$`X9*~R8X3VENw@LZ;!=Z!_67>%R*%Reg3) zh?88RYcCqjq9Gd*%o4~EsrgyqxP_8msRF3^>kNQ;f@SPDavOPUtzPBG7guxqf|8M2 zW6+B?b9Y%yGk$@g~;0zBjDSHc<^W~E2 zy@#+K*}!Tt=Wphh7}=5Y&_jJ3U+P~OtCVKO)5yUQ8&x<4^-n!MnX-6nBzI3?3 z1MMtDZqqr$Cmr>FCClBudQ)ShY_H=Q8Jcj5{-w-FzdjlDxm%9$WU4tA1EkgG2j7l0EG=dH zl;p*bG#QDLfW%43%W6{-;aDN71rKNrhd-f+qFBDbE0=MhI%efthB)7g0w%qRHYuuA z8Aa-0M^3%{8N{i(%y_6q_nh`eFyul{Ik8Yj^LF6k*l`17@X*CmWZw33i&C3W=2bjf znr9G>HOqFts9Z*KOGt#=bpR1RnLEjZL;*AHAIo)o%H`Lm3i@uS3agwxdhw_hKd96N zr6ZTt7qFb>35krnDo*v08#pWlT=XfA8h;_zr-9`0b;$cVwP&QV8X}8#27sR4m2!TP zc)^txQSU5Hc$@I%mZH?3K*7DQ#**@!9qU2s%A5Ym?phCag{PLG6!sg$cP{MIz&qfr zC-GyOPKXraDk&J(1I2gWD_nFVq*oJzsmOzW7JI_S1669YH?G2JngcU%MWtq!^b*_K zm(DYMw5)W_oWB9vH+aBxGjqhBA5Mj7$?0x<^6T$u7xHyC6(ygAsP}Xt5MhS+N4Iab zQ<<)|gEq@NrfrT*XA(|#LkaeRNiWQ{!LnhtneH;ogde+oLAx?yB&7c$ zT$Ud@NQoATxM~LGg;COGU9wty=v6yu4?g8;!RONdJ^XB_<5v4=^%f0xUlzK~sbdyUPHv%YPJEK%!$44ysgt%%zDu0r*0 z@xNk%)(H(>*-+cOd|B7Tlp?UQMFp;rXW|Ll-`lkLcuPc3BIM|s7|9!T`JyF6G7Ih# zH!s%RI0Lg1z>1-WpTmRb#peJ%S=$6E%ySETFG$sD8-}n0e(b#TB_`QVS{rxB7M@=s zl?;4BttL26pwkT3g%0|VO+G+ua8$Z!1|}Rd9MzW6W{Fg}&=V>DBhoZLPE98t9W$rw ziDB^8U6m~MiJfnTRbN9A9B%Mqi-8+o6i1NsLtcT>opC-|5Cbvml@7`TxOW6al@gPw z$LxxOE}lWWk@AFQY3-ZwZ?*aOtlpAs1}qbSn}OHcyPnjYz4^EK;}|@k%g5h@7=jL~ zK4*TYGgYipXhMC2vbXg4?H2OJlfe{)VIohte_fp8f6XQVU2$CHiE-Q9poV;iwgfNT z`0bIultnhNL--^cDwOxwdjHVPv%a0Zj#nYAF{Oe66Z`R#A~q~EDYDf_y_NVp{aBi# z@Xu`ZJTnOi)Qu9HvxRGKE)t%!>ZR>$KmIq7U2;lx5xSC;(fxS zMb06LZET^WhkXBwsz2p+UOgY^J{e%|)q_qZc)n!*z4{qXsN<{Cr9mD%=Ax>{R?bD? zg~HbjT*tT=dX;I)G#%=n%g!V}X&QLQO~ibEP#c%Lar^t@!=|$_(5oY)9#VgT9qnfC zaq3|Dloa9>H>lVbj~6dSa4NT62~LsNo>FBkI)(kt42rNotySfnvsT)n=yjtik!2(@ zQUKIpA+JQn)tMk~X;=AO>@k7UA%qZi=slXjJ!ZKfM72|yCPD->x58^@{;%ibzwQ-< z?_DE#@x$BS$C8X@*%DWDGIDKCa!>!}m^o^#?_d6`P?R;^IkdS5z3O_)H)Xc8=(Wd_ zt;X76*EuQ5o{O3aT+chRCLky*(`K}Axa?dvxGSlhM-1Aaoy$4lVI!in#KLRHq}16P zY12Jzlfiw;7KSBqGs%=aeCH94N*w0ll%50;k>Xk2Ds#gTp`E;s`%s(gDJ;k&Gf!HV z3?H}fYc7+r|E%HQoGXuYnBqHy?wa7KihRsVdm%y3ALPnw*hSTTXjZb z>d*TQ2G&NK9tI0L@hZ!+vf~ZqjXj@T!qi=!n@|>`8z@-P9EjT1gI`CqHPYubkjgem zOA*f+wIfHJq_GBR9eMrh)l+ZRqgn-qqH1=268MWEw&w4na^ub>4N8}bFrjf#l`39O zd>h&p=8GdSwi-x?=YB@#rKK15riVL3_Wg5Tc4iqxb}H@Ot1NHCW?XaO6PDx^XRPWR zwO4Y#ehrzq+gj$UNeVnv6(`BYGwcHBSW7GHEO`*Ql;X~w6T=4E%`LolvyvBcuy$>cMr1rl^Uz5M&rjR8u9$#+Q|)9#%C%H ztSWoZYWZM-dh4Z`t_=Y!?{Nm=ylrE}_@oU|Roqc2Q>kPx(AsBkLB+511yzryceZD4 z+?smS+4p(_GmS}y)A=;nuc|u`yWdj7+g<_v3N-fg9NSA3%3)ApQn|HuC(T~XC$9p! zD8Qljt6Ou2S&xy9Mtb#mC;u+?A_rcqag!J$+8wxY4lYBmI8QilYux=cvh`0R;9W0E zJK}ORoNB=MDzca#%kVIwPy$tN(Kr>^I-tI>So4A?yB>B_b@Ua@>iZTl>E4jJdbC+O5=M4eyZ-x#uMcA_!y^J3Gbn)I%g z5{08Y>erA4`HZ7?%a;m>EjKBpK^DT46gqXhl}oTAyXLLbZQl-rUYRZduVL5t^VVn@elo0%ZLmgS%#8%Yy$% zo26E1JDlR4XzD1mn`n7))&U1X^4UP}K_WCxmbzW8-uU=&J=yNY6<`gcZO@18~sRXEc!Y5mApGY9}Kew94k>h-1mm!#yPIk zo8la$hDs7du8JvjOYXMg_~<>_tl)1HoDip@lDpG{#b#4j^SDbwz3!+%;Kl$V-Xk}T zD|Bx>L_Fj?_!98jA`_if&jH)mF$KrzuWf(`#aL(_Qbp5X_Jwf za2HQGj=%iRAk4bDB#y%^T%_Oe9SvB+UgV$i=BqLX_{WyBwh(JlXvcsB_;bB`h#gmQ?UOD^U)ftSc6+)G9ocJwKuF@OSJ(c85v_Yx` zXaVID`*H?Ug?Pj6+}kE-EUgl88j&>3D_Ly zG?jwfG55~5Nf4wEIZrTk@ZWI4_P&Pip6V1DO>UZ}_{eemVWxhmMr@zq#@o3bhyNRT z6ncyFjN9!B@;wXu*(31tf}`Ny2bvORyBOIUbYs)_7jv$ggzKR@S#o*|Rj(wg2IIqkX&gV7#noygou9E7kb~+`>yo+Cv7Xo-gO| zXg6$orUeAw^gz$aX6>nK?5S3NqP%vtNt3NSL3j^SpWtwBp0LJv3~k1wR)^-(j-CpHE*!MuOi9Oi9p`*#53)m+ie3 zakccvEmF1~agni|gUO;s&4RP~I`wK@(c&a$x>!V|nzU6mU5p!F;0N6KA{Di4)6r8=Vkx&Aon~8(^z{A-IC#s8?~7~=IB)zTOf#$$Dht^AS^K_Mv+XFe9=Z8m z{s+8D*<gS5Hz6zA*BcF zYZ6pXSxHqsnB0r)4av8qCt|-}k;cZjTB$By1;78ecjPbO@>@M(OSC~K_pxVwJ^DHA z;!wip6qLzlgxU1^(jKkwEE4E0hGH8cm7YWY$o3iA+8$rjPjIA?-dm?+{X+iLNeg^0 zLP2>yyS1ojS&w+h$W6oZv(aZnd>|S@Zw{LEH6}4Sw;VM+Tl1sp3M?(t#9ro8ix(Ip z0gi^eCj!LC;>M4~kfMFZpXsahLYo&@9rMB?67PQat&%RrAYNi{j9<}~)_!|V6frn< zd$(=u7A0%@EqUyFZ>OwZW=e{A3%aRIUv-U1Zyt*LSy#16%5rQUW~WE#$yi#HD_etk ztj=MVhA57M*X}Xn@#c0-;g=n`OkFt4(au*IQo^ph&wd&8lH9emJ`Gn08|8g{3hU00 zi+Nt{J8L&8KOO7+F*Hx7dYuN<;k5NIYd1M+Mp>_LWO5j17twqiUow{;@*N3xaO*m;dgDq9kYFe-bf>|-qA9>7O&b{Z{bI!;EgQ@tDT z?OcKOJ6B$fmF|xWZFc?$SHt;^4Gq0gxcIth$KzG{4HyObaW?`+;dLHPM{$*kmEs|- zCHACHf#l(g=$Uuqen|IdX*0^661nomz9o5){lAKC9i*+V)P0Z z{y`IH-I0|FUZ#7|aMSqHXuT^8z1ap=+W*KM`yWz-D?TO4BZ@QqcN!bT<;)8hb&tC3 z{^u^p8ZOUAp5~kZ!}8}TykL7Fm`W|m;q^|#FHq%D3|qwyS_*4H61pWuUS~7?Bt_K| zcbJX;1Lh!MuK&hVrjfQRloYw8Vi;{LThEwi$&2nBU8J`MImakEoeYuePFd%p=j2ovi0?Sti0G<;2#8;&J1%+#AD@$nS_&OO`JFf=0^#5gl3 z{3Yne9#ASZ6V}eb=^B?d7#G4?HlB)ZHc_#n2Y&M6Hh|HMn(%;)U!;=XIg6!DYHV7cW*WfekFcN)icvBh>!3dEqZK^CJ9cMR}Sgkohv>3=(xW ztkO-k|8(?uA1+MaS%lbs4On>uc=dF%cbc@;*|&bZQ3giejy0B>cyb|Gv|M?!foZti=hdY5hx|^7@%#erq`g_;UA*v zW@2TtRa6HZV`YcawN@Du1MaQA+D*{nga=0#uvpdV**p&q!~TXx*y^A@cpi$XQn4C% zzl(F@wmK{%xb`Wl6!$80HLvF1?I|&=v=K1!zE6OHesE~UqrZr;o$ph|R@KB|OBQ(_ zoi7*owB_Cn1t^*f~Ye&*VEohE&qc@Ga3vgi1mqJ|10QiUEg+9)3D4oP5IVs zn%$CZml;g@@wErPTG+rk9e>~93UZ3IQXCcH=!&x0Jpa3FHR710ky5a-a^?d%NEZ<*AZ_W|+nH>SVWKI*nh8bKoOb>AEE<0EwFX~*i#)QLiVA2b@so;jKO^ZQd1BbDPNFdz&qZ zjzFYIzd6=Us$zC@21n?j-n?Gb~a@C9f0DgrF8M2krPueY-Ie3LnkHE-Cp-dC{X?tl!$K#~Mm) zwUIXOBH%vK8au1+2bhi6&tSl0iA(TBLGA%c#i88&fc~XKd#PM#(y3;gQ!RK{ykW(a z71-G-^X4DMcHc0bez9`l)=S$OYRY_Fh*xip zs9ADdVq9Py^A$6oV)ZHsa3F@RJX^_U6#?5Aq#64{S(77iL^*|geUIHsJLrujh%1483k=|E)VL0UuhzV}E?mG(-m;c1BNh205h!bXjcxh;Hj~>_bI^q zJ0ln`RppEQ2yJLWGQ9;|m;g5Bi~ zt?R#wRnG_wo;BZ$jp{fb#0cEu(2mXSb*+n=Hvc^5T={-eWjyz0ML$^F++S*l)}359+CM`3yV>2~Rs@0iIEZDo>G_EfmPO;(6- zu{sjA{F>N(;_UvRUY|d)xQ2MuoHm4;(j$KT;9>n?q6qVQ;tl;F2h2G ze%BeGs=wuF{Bl=@Sa|`}p&PzP#-+a}ys<^}#oFrEcQytZTglo4ofhni|X?qm4B zuF8!eUiH1@%h9E6=4#}P9%h$1ON^C-u#qj%-~A_6(+NR=9fW*!yVq;puYjSWgwqAK zPe~;t%4|;*ga~Vbo02ij&#YW$6F-KXpAMs9U3)QjJUMfHPk1pSdIy*>6I-3vS@liK zV~Oi3V6u)vkLFx`H-Zxf_|JWj3di2uaa4&%!{fYM?jtN^^*J1RUS78yAS(Pk3ncQe z1Nwfhv+Lh4D)^!nRqsaz--PBjpGwm7hpoA{A>J2C?Zr1(jx8Rh`J93;>TzoYY?MRc zQuLZD$vX^T=w8>GL-yH{0|?G|Axxa?d4kyD2^tiRO479cur9!yhpztd8BC-WGTr6+ z!Q~gsKt2&6%=r647zR@(CAMIwe5I|ij@IQfvk?tp)K7HlE>?tcM-W4;jpTLtD27o- z86I2FM4pI4E8zuAkaoQY&MJ18#AWy*^1MFmXN)70(9zh1Yq=W8l%}D&romtgIZv&!~Dh>2`I<3BPY$5=ieAz`i112f;lMD zPW4%>Ptj^9svA9OYjny8DFe6k%7+m>()TZ9-t9ar3YN`{I4+9e1pDqqkJn3$C7Wb) zxZZ-(xo9CzNJh^fC#q){J~vXa;$zTk)@(4|g0tEqEPv?$g(>ht5;NnLmzPvM-U2Ve z0;8@KXT{%s`_}G6#^*lBo(?FP?PVA7P!C?x?@|5pBE9&m>4@v-Xp>pp@_G~{~ zv7|)V<9N8u!9OXT! zlZF4GJtgO&Bg6M_1@a^q!Upkj^{Cl72knphU#qRew*WGwpS~u@ea8lF7$k9d32lqn zIF!5qqh9{h%8HlzOI`^>GA`QW1=>= z<}+#L7*?R++h6zZnEh5i;;2dS8J}0d_LvY(izeKv`4LswE-u{@HOk~;)PmcSs-qokBZw% z=M7MCD-B{I`O}y2@xwN6dNm)A@EmfNQi^bvWV)+9^A<=leDd$`Fp$ z^Up0kfpt*#pk%>cc!67DQ!3hhX@eDkBfltN3Um2O+#u`yTR2rxZE+4kzrnu;ni4%ah0sY=RJ*7WjaxM z1GzSDLLh0cdb4!Fh6}vOiy6lq-cu4-`sBQGYY6&l4JZ0I73uYmg^sH0gD#&BmDNZG zRwE+Rt;0i7e@2YITd5k@^Ww2?#?zgwr?Kx^{mm4Bp>|1POM|D5>)93n`RBSx|69Q*b)E$+BW@tmr2Yp0Lod4Q8i5xrC zAemk3yu#EEtGhuIIMmwi_wC#MoMx~%{2CmW8Q}}7(b7^+PNjUkbBJx{>8Pe0JN5|$ ztn&lfC3L;HiOoTCSMP$?F(5mp*}*s_RejXAGkBt_aiQDnbqn zc|W&fZV7imbo12)i}y;qjDfLFe8iuo*g;;@c4ReV{z?T~!lZ4Wh$DzkWy zFZo=`Q-7!Y>f5aDZM!Q3j|IwU-M)#PS@}z_jxYONl&`dY5|aBFHcJzT)UUW-JBgPl zy%zaB^zwkk@^EU|Bz&ya@bc?KXkcCmOlXO7B`WXm)CtZnVOHd6w!F?b9cvxxId@3q zSGZCwAYRxrbe#!*RebgNg7d|(#4B<`c)1Oh)v~KQdQ9rbu{J-w$&+KgS5QNn!Hk(D zONBw<_sdY7gHoPz2hUF>OH=hW-1+3n#>_R0m$c>IPd|uvtlxih_~AI`mDqe{G1jz|O-(o@PFG47BLueknHFtWKqB)^NfYoka9>$_d{>-=wPOW2U2bV&nl0QCl%rDGO72`|tN=viNT zt;lCFG^DU`!o-b;y6b{OmanCN6vklZ-XvWsp#{2AHHbE0GD7@tj+;Nt`RSwR`gs{6 zU{y7d+Z=a;8DA7S3v$nGep|*PI5ohqe&It2y#WM!l{#(>MJw39ka1FwHc!}Ox}@c@ zB(;SUwB~|>B;8$}$zztBJFhXF)I-V?z8sM{xM;r&1NU9G_7>fOJg_zzsp$Isx9`q# zogf?Ep-RW$*?@{+sqRW$_6tK_^EGQdY*jLGTT{8enfl#Fjm(*iDhK(i<3<}Vq711b zQKH@pEz-a`y+CoV8ZAEhb>6&{902z@dS)&_Y6Zrs|E*P(V6~uL8IU(g0h9lq+;`BYCxRz`T-g-R~$Jl4Ur z>G3cbviV6mV=UAZ=+KGkEfNc!vFbA}*shg-ilMzZJfePQiA*F(D$ zLv#LWB{4>Iu1}K>!q>Awv2k0^Kv>w=EKcobvOx7+r(D-XO>UDZo_p-ivA9^VYVWil z_XHZ*6lcu32yj4j|wKw3?KL0$Ys6a|{2)G=6(Kmd|g=ShR6Z_eM zkiK1Aer9p{zZAH$O`{4P4JUFG?lUXT(>C5miH(%C-HCy09;~?e*ku;Aoj>kUagd#Z z_Rwpvk%xs1$oA&ScQT_-TN&iti$psW#pegt^mr!gBkknq<#G#RVY6a3$abyqL!E^` zp88SA(cns1gdmvfV4U*p!k8I7SXfVs82rO?VGQ?0;{q#}DZ1=%QlukD_RBq4JguFA zhtlEg=-ni1I75B7*a3l98pC~u1-~&5kO%vLS&DbJdGQm44ZVdYjgVf4#9 zob}fZ0uMC>xIMKQ-Yk`w0(|bf5(d==oeHouYbBc`|8_}CL}T%tXtg1$g|q| zJ&hgHQLxqde34TD8OHN;N8{u%$O^p)>m_f~9Y<@>D*^R!+BGLPsG5|M`Tebhv{%?v z7`VBR5py1-UKSE4@2%(ytG2~)cETVw7>-4M@krEzmDg5D7Cf6n5+rrOjx&@U`u@J{S1~LCeUY`22nJbbY*_1uY{qip|DTY4(YI}LTP^y z$}Lb*k)nsYDZuG!!oDjEY`j>%r+0_u=RaCS?>%cXn_Po=awsUcXRd2pF(sOL#_33B zb{6ZY0ugb;B7e)H;{KGTtR*>p)aAxs%%~@nrHUJ^Zz&BmQuTUJvHlrRy)cTC3f;ac zY=f-nJptWk6*ZU3U_06rguFpJqGtIaCO6aA6+W!Ufjgmd6^6K;PxFtPMpuyQ6gYS=*FV*Cq&KCAqyoRNDhuIHm^OH(x%B@C7+io z%B1go^@^I*AxfasjL#$lEz z%TUH$KhhIe{3nKm2w&6R+6qzd#mvyBO4>dzC4kiU1z`q>;QX>7`uimZ;h5f41M`9w zp0>Xd^Gfxz`#CyZVX#GCX4fFXb#(A>gT%vodfdlUw|35xRwoi8ovQ^g{s3m|aa2vW z;iLV_B~`B+nORp#Z1`LZ71(I6$7gg!(B?L^6$(d*)cfm6g+7ww(VDt9y^!3L@5IUg zf`x+lpXLfmX-n!UcX{V*^A{O?~A0kb0_5B2;d%k4n&VQVsb^Qt2U!~xvnG(C|gX;Kx01idoDus5z@!u9) zXK?1_#cq3kHKIqnGPc5pchD>O6tn;sS(H0-q!Z2cPh=h9S6)#({_xKy=8b8I^)2>2 zDOG?LGicBpoh`FmuVasUalse404)3OPbz>Zq3n)KoZ1p&QB+bJ`395a{qDrVUG`Sh zhz2A7Ui$=^tVcMDBr_O*%s?M$#vIsiQu`LC1EaBp<$=_(bwZQ@DmS=jHi;Il7H z5byRq>Aa+t(8UEs2ETX@rJ?z(`rHDO1~zc}$G{tv4+uZZ`z$!DY)!SxPm?DSF=@I# z07Oh(^%8{Q#_p#AR?O6ndZ>-4fU3XYdQ~N(v#x)ZBJYP1M&u?!Pr)WkT8o&$4gi#pO8LvqL?``V zB+Y{`*7iBF{y9LMC`xp9%sSiHo8!vruWf0b1%be3ccz}B#?syJwFh@C8Rs1ZDQ-RK z8G@mW9-#@97hYC>ybAJ4um_-tv)JIQ0OXSOcE!CT@ZxXkwz++78U; z?y(yh{oRPyQh&c3(&uHk=UYnDd@zA0-)E zqbLEOGi|mdZ}f%au_A+Uhw2+ncq7n>NNR_3n3W=3Pj}K6gkZ}S~|tn zvSj1**ODVlm<-KWR%d{s6k`y`p_AIk2nKlanWsE@F!@`qVoa&zw}4llA_+4?f2Hn* zOfnu3hv@#D4!N!+-+i>&H&>16^0TXdPc!r})&HgscRv5i#RXN3PXb3LitUxpU^5fp z>14S|s@}~>`zx%JxwfD0X8LfC-YfF#I`wCojy?H%o`BQE88a&g@~bW^sQkA=Kg>Nl z+>W`yw+MR#LXkBHVUE%r4Z4{w^gq4nHxptB(8^c(KZ))1hCNYs@8kTx22#PXySLky z3~oRye}#`i=8A9kG$%BlV?Qr<{VzAJag9bf2f#nBIJRxLgH{Q{h7Y3bem58>+0 z%ils?|IYr1COjIvBtc>Efo>u9x8&nqll+0N0CszS;c4xPWSH_qgcIRZ#fX#)g;@bB zUXC}Mt=QS-_AKO)o$~}3(V<-dzPiYuIN4EFe0IeTdZd{#WpGec#x_^{ID7CjG+TdG z@%jR4IAy}<0eHuA#4S^pex5FPEh5hg3HSp5q_;I^QKHi`FCix@c|=B;!t4X>!h{Ha z?8F%i^_}n;ADwXZ4jKCLk&Gc&-c>i`-6-47nWKwE(Av2KT9=4AkAm@s2akq(#8kUE zLWF5B;`y*Ksf7ZrtVbE!I7R>jzsi(1!1=z955;L5TdZqMu7>43Z?@M<3zjm3 z0=K+n_jQr;-b89Bja8S7O7AtoaZ~ms%3o@$Uf#2g82$0$52F}2D%lfg zDD=)uihK@ulVma?RwT8Q#DEacnqn4>Ufgl31!*V9a48f8Fof-`bD$uClVV5+Gn|x0 z__YsEA_~WNwA=hD$YTev&XfWnRHhB{EX3tNjU7jF*J9-*X1q59fE)oD&~=;Afw+t{ z`Ac1?G2?SW_@8N^vyEo7c`PrWEuYm4^?80e$(0GLHktGjw9*skItkCxRT=jygBE>{ z$%sZQ z|0rK;e&?bV<$Y7Rk)Ag1?R$=!i<=0Ns`+CYYp|@|kZL_GV-+>1(Rc)@d}(lR{_Y+= zRw^n9UdJ98?(f|vl;9EZZ>}O?hA)?*Q*%Rvn;(KVu@C+1mA2q<7nDHM-qH}n2%&J< zk3l&CWn2;=;|X#0W5o@I&?O~l%mW+n?EyQXdbC)u7ncLJbcXhZr6xlB0 z$Es>Z$w_SA0upyisVP%vTPZI@Qu8bc&2XtD(t|-krv6e1fbLPl{R$fgr4#;RGGfAe z0PPmq7$!2`&I$tQZb4ty&ZC`W#nAN|W1FJldo+Gf+rirTV$FMG!cLjKhR0Qm|zoF2~E8Z?;U2d&j;Am7jGSC$@eV2wMqsM{%K7C+waf(r2 z+_g;l(=LkiOx}3_Xu3*AeTff)JoeS<#+g%~VnG?RKW@Wi@t>p0En7pfb03G@8uO5i z4JD}JT%7;cv_0V#%454=e^RDI1l$Zs(hky2-ZGqxmWS6}cFgU6RTqdq5A9{|qUyY7 z4&EJAKMbvjh3@rgdw3Am(7bJT$^LUDW-Bq`xE*A;@Bg!PDQ+=?YSMzLx2Ym_jT)5^ zq_?9%5if`tFRy=l(Sg?RYQ%j3Oa`i|=Au*TN;So}1`=`iMr~$2LljWZwX^U=ixTjk zRaS^JT7=mM$K^=3-~h^7RGy%N`Na-RQ*b%qDB!nQw^v+Qs4sD#9?xKh|^mUc-|!V@uc zFQ9L6Xg*hR zle~Qws^=@T&P}}hUu{biXI)dJA(zSj6_6NmZhom`NwjPFSNyViNtJ%0Q-)6tC=yxn zXn9Dko^~Sq)+o2dM`HG~sLs6gc@K)obM3zwT!e0i?D|J`Nq-_kB3yL-1vd(B>W}%N zYkCXJbaz;5o$|(DbrOdsx3Xr@@_3^5_Dd5kc@>i#o=I^zZu#2(c6e%A{-9M|LfPum zoi!#lM-j>n-Bg2Z10SZ;vGWw>oFgBmP{0;V%5g6<5;-x#)H-&Zyp!YS`jW#X!YYW7)&z`6cGI6#~a47Y2Q}}@pSPKApH_Rz@PGv zAM&Sa*5bJk-zl{-WC=gjSV1Kcx#sLLsAA2o?pppR+=AFI7-tRo+YDV+?xevkRV#B< z^7!K)B|Q7wc}};!A3;;4)BOBg<|0`9^m5D=#U$$gtNUdV#83!mAbA~KS8wWjggN5> zh|6!{!bHZ@d+6i*He%!+Nr_dAei`JUt#yj|{vl{~;LV2otDR5J%9=uFsz?7wf(HsI zh5+f4Z*M?rBre0wTKhWsg_hE7wPbH%$v2$vfcLr59?%oJW`w?P6TuluSz`SBM%H_CIb|C&qwxz1a zv%I-$!O|CL(y4|-A{=AA_uX55eDk@Cd6j)g>U#?%H)w#o4$O%f9Ow;Hzy_A(e-!kq z`M49N&mVL+f}d8oy|`GR^;W*={^w=3PzMKD`XACcyvt*LjvM||z3TlOr2kw-Sr4nV z;7W2E!@;Q45Rag_@~3NruQ*l0UFPwYgC&P@^ImP<5!vc?_OR;2KoxA$NOs3+xIZPV z1ByW_eLeZs{eZKnJIL-BNjdjPSDx9EE|sl2wLvZDoOAB{qEZ=ow6#MxmxIyCTSyT# zr%@HfAZ7UCna}8BN?bNy+H=qbp7&(kEgTM%S+#qPR;72D9%yzb$(yHtyQh}Q*;0nw zn3>`|pgj3@y-2Gxf=X5hzQ{Bd%bp0C-}AJVo^zQ*GQPcIv0(Smdo>w3*`t-!L(;Y_ z?2xvsut>!6!nU6oAp_)P*dmib1<!VK_llNoo#nkq{m1xRha_mxcLNvoXWcNgw zM4_=NnP&%qfl&c5TAqCL@7-OQ@G+p6LB&B&-?mG^=ZzbQGK<9wC!^CJwF04Jazx;6 z?~BU*E<)+aC2`e|iv{-kUQD#gp!84YOje_w65Z^&H%{`rto805JUZbjWe-RmLZKJF z&7A`ECot6GkeJA}^DENzCnq}Pv5|+G8pVSOf?L7o+3-=~p4v#nSso)&!YQmV_F3RTK>wwPuBMF#S+NfW6d0^Iorwb77ALh)0iE&EQY~F^`AZ3>nVI=;M?sk z4Wyb3U)=vsw@R#%^O3i2uG43_^VWSsvE$7^o2I%;9cyVT9Ji*V1TI3(*y> zbh%9UNK3lV$rGM2*QopwyTt3SY~WN)kR2h-deW(}*7XWZkPGKYm+qN_X|2o%gEJUT z@;&JK_LAM^f+w?OtqkF?e-s~a;TL6UE%|(0kd$|IitV@=U52(}8KMUM{Qhup&pA^J zb3H_J%r%s!4)O9J&?SnuML%H&xz9{Kb`urP(Rx+#RXn%*6=S%ipl~qf@(3;LPCgbk zJk-cE6Ja%z`9;mXmd$cUbKz%5H+hSRf1YAL5AtJ&EKia5H)WW<5?d=ma%VX@@TWqI zTnc*Db}OVenaqdCaI2Z;$L~ZusEbK%6{jxiO3*+iRtRDEPBAV_B|ldw>POQFTN(4C@_w?oL6gN-BB`31H77|vb~ScBHVea8hoz1Wj)+n_Hy z&T>*7RrA3!75G=Y&kr@JJmiPV;%Q`E$+vJFeN0uFTJ}=$SC{^rg!Nks=PLG``S;4N zvEbj;39@l(rRAmFVkWu?qv&eB*9{Bw6sG2fY#! zI?iUH8(zDl+ai4N5c^?;lOcpnM>{26IW7w^Tr$n(CH3jl<8s|5Pen@7o)) z;ZFQRa~6CmAS8yNynpA!v8@?j!GyxDa|ewd({A(k+L>qY;5)a40y2gJZ5_|}NM*ht zE)}k)Xy5F$(Lg3#1T0*l>4nUf-b=DnP}RF)OiS5Y)c)9HV;}J?_W;%B6Sel zSpJaXwsw8S3Wio&t23stGG>LZHu>|i4CrijR8Sk9yl>l5=Ek=lI-eS%!3Y_rEJ2i2;^ z`letWMs8tfw4mBwDjjR(oeKRo;x+nzm@WQ7PJUzT{Ka1lMPX(A5@{DAe`bxC>Dcfc zK6I%!W)WZI56-~~hC*0l8A>YFJ@8QigD+r>vZU3 zv={RHZ#%p4m{A*jWCAF=0wR>{ZfZzt5U`Uen=9SFSjb+TCW)c^av}kJbGRXVH!>8B z{=)W`n_Z!-#-!`H9AYP^umi>goh7LV8ECWkJ>1fjH0V#o>JKXFKErs&bz|t$iw3&yyK{tZ!Z9fj0OMMEfWD7+arFYk6R}WoD*U+yh8IU$;dv za{!bnz9~?=Z6|=Y?F-y4m2jm@#X{NWmcMG*B#4Pnbk>f);>KM+A|1TCe*7~@o{lP~ z#ZVZ$uJjU3D7i-XpUDHB84A(I>cbA^Clr5~2ZXZK?GKl$<%QU`8^TZTytZDAm?U=2 u_aj0~AE9%WGr91ChiEF0y;-tvkzk3@Dvp!*EN=?%@4BXeM)B2K_x~TU3Z&d$NX!O6+V#l^+V&CSEZ!^_Ki<;s<-SFiH%@$vKX3kV1Z3JMAd z2?+}eU%PfqL_|bXR8&k%?E3ZVH*VaxdGn^YxVVIbgruaTl$4aTw6u(jjI6AzoSdAz zyu5;ff}*0Ll9H0LvN8k$xpnK7ii(P=s_O0Ax7F0t)Ya8BG&JtqxpVjK-Fx@$X=-X} zX=&ZRfB(UQ2in@&IyySKy1EY^KGf6G)7RHGFfcGQG&C|Ydi3a#v9YmEp+b z&CJZq&CM+=ES@}hVrgml^yyP8D=TYj>u1lNJ%9e(#>U3h*4EC>&febM!NI}N(b37t z$=TW2#l_{tix)3nzI1hUb#rrbcX#*j@bL8X^z!n0_3D+kx3`av4-^XZ_4W1h^Yi!j zhrwWQI6NRA;Pva*Z{ECl`}S>MU|>*CP;hW?NJvO%XlPhiSa^7NL_|bnWMouSRCILo zyLazmVq#)rW8>oD;^X7rzki>QkdT;|_~FBcj~_oKB_)0O^eH(xIVB|}H8nLYEe(M{ zeE$47Jv}`mBO^02Gb=0W%a0t*xu8tFNzbXlVHU{ris}KYsrF z+1S|F)YR16+}zUA(%RbE*4EbE-rmvC(b?Jg>({TYuCDIx?w+0=6bjYb+uPUI*Wcei zFfj1@_wT{M!J(m{;o;$tk&)5S(Xp|y@$qpq8a**FF*!LoH8nLoJv}osGdnvwH#avw zKfkcBu(-I0!C;n_mX?>7S5{V5S6A28*05OY`uh6D#>VF6=GNBM_V)J9&d%=c?%v)W z4u{*{-#<7wz~k|UhlfW;N5{v8T8W`2MSP`tQf>_$Yy9!&TLik{zmMnuF# zbocfx{a49rXtHF6UG)AYC*sL1diwhj+@Uv4S@iC!Y+X?b)qcvct-yy@y+_UyA|OQK zz@gjAl8|9!$KOXP{MwzCahgHrRyzszSF&)vI zwm%r3PD^_Be;N=eks1jD5d)nLkc+8F1$XHSdaa##T2GaPO z%=5exCSmZ`SC~(A3Emc1HXpK4dmj!dpw-#`Z4sDLmQx)L5d|IwUg#dOZlYCxo07P` zhW|`U7+{RXHryW9F;7wBk|q~8S~A(ppl`9}er{+(*LDF+36mSQH{Q27{QBF3#P!X8 z%d!8Sr-UBqPsnz5xQPyo@@9L?f}fDN2N1o38LjNQ4F$l#ZBpPQgKBVf9yk1`My)li zhWZD!0MQ)nf68HdP>Mi?XOd=C#+c-~LeVu>!=EbWL8P=0dl38TdCUn}rlZLGtVYCK zpbVIj3qAlIHFmyMrvH{X^<4_?a*bTaE%hxGwm$=$gf(YdhuRYs8oLxDFRy8u6au>o z%49&rMLL%-w{!#yPS3hN?|U5y2a|JU!JQMMhpDvNcvfB%ACxLu{!oX2NZvh!D8Ey* zs6!qo~EGhN>0o^}Sx2d(gk(L&5-=5+HT|5?|cD_E0L7&fHeC!kAw)jy;Y^ zt1|;?BO4BoX3c^}#fnTSJHmZ|zh47eVWR+fR3%7$Fu8gR<;=6%#u~hki zUyD(99{*X}MU<7ejnN~G!8Wq4`^v#4RNS)sc7IlK46J7)n_|B5xQD^b)y+#4Q&%OB;ZxiL({hzgjw$9V?dUHqp;ZH(j+}5CH)~gpUqwDg2@140HV82Z|5igj z2L=UdUtlgn(oHJr*Qs&w1F~Ru*K8AWqSkgoZ| zT^@TrFlDBJ&-CtW^pW~6lisD`0t3y+t z@SGTOfp+F0}eZ48D^8z;72z& zAE-cLIJ-#gqjEh?tLLdeYW}pSq_^ zE8@G+FT^|zm@8Bkf!$IKB$a;q4-=4kYcWM{~xZ z7@c&ykqzC1Dg@;BF9I;F2vA&nk^5{qZ7ti-;jI0CW(WWMpnY3k4%cBHT0RNxU6JWQws*H0d!H%D&R{V1)qY%(UOaRJiO)l*iN>rX*W*Kj8M z1u<)}Oj|3A1>@aj)*ZSBR_Y138v zNTX=%Hgm&8E4O+Y_vSnb6u1#Y1A)XsUz!ph63ZR+4XfYU4O;Ln_2mNIe*_)3Fc)RT z!=uR4j&p;9sePxEe`;|2I&_x>wmhds&@y~`4(|WR=<&w$In3B}aJt*JyeS88N|v23 zdce4wUSl4*Le^<#-VbkjIfPHnd;Fn`zS3=Mu5r{b8v0Q$##Tvb7ktUW9tuNm1fsqBn)slaYSna|MInpgjnLyJ0Y-mhmXSA zfG_It91?k;ax_y~70-tHs1lN^;agxs_#C8=9d`H;i%v_mc({Aj;1Ng?%aD$&iZ8y@ zuiPcQ{i}>uT#U0S zenfhJE+)it;zJzhyp`U1n<86m=N%geB!EkPRE**GVfZ&OzXcwUBp=mgYgj4gyactD z_moXpUP!rcS2GC|>K;1Ln7wt%rwk%}y;`O-KJa+lUjot6r4sRgQ>XvO63$oTF-qBB z?#b)KbT6^wUQz=KxlVEcTI=IA_?}SbkF{2C^|s39Jo*wbtEbO~tcpV7G3bGN_~Z}K z#$wpE0kB~B`ye*gO*SPaoo&7K`?18;j=w%M?)BMnn^?o(J?vhETL;IGehmNXr%$FyixvpJttj8fa7^yCnS(R(M)!A zk+&qNtT2YL%T^zB(CTnu!lYqI;fvh~Z{Z1#Q`vMHhZT2_lQkQ)xhEBkt8DdZ{MKaXDiU2FfBAyELUQ|sytUJDZ+6&#C!{BCbkjMa1JUPW~vCL4H?ogH-T zxMDdqQ>f;g2A|)EUOqnpazRRv;kQ)C2&f*cI^b5^RUK3(!Htf z@3F>g1p-3gjD;82I(D z!kA;-rV`=uC^~`6+rbJ5_Xcbk#}eyn@Htq)Z?nTmUWtw({fcUTg{QRcV_%fTcSgMK zxktyCW-I#?Z7hXyCN6sAUn2JL`MolGc;0(HrKh-v2~3H(I5z!oy{wwQJ+dhg3819yv{=^5RlnSn%DY5k)Pdjy>|F;9h=Y1w3YkxsIU=ORAvcQ zy8hvqoa?meCv<)w9GoPhq-KuEV*l}Mh|P^SJ0#t=Kw1(Z-XPZG4O><43qNRid=Oxe zF?M_>WVFkx+8A&@B|a|g)6R*a8|UtM`?`GoEULOyH8TgtI*Zz$9#mx}ev%F>pk-#L ze&vw>(2wvW8R$QxdO1tgnsR=ATh`YV(x8u~AuSh9e??#R7@{U^sS(W-N!OCp_0_-m8$4)-`wfhq%jeb`C0iaux{-YNW0(VLBP{|Av(% zI=xGQ31$wac=>W%t*mMOOj<~E?m-em^y|g}{(;F6@o?;`n_40KPG;x^8A6z^BWX_D z1ttIreTl`Y!K-K8xTG@4{%F9fWbVH{+-S{Dqxg%!9GsGS?~yWn<)0n@L5;yf;f`{M z*&&vR_v0US?zIP@~2NbQsuiE_ri5!x76rGXfy*daH{!pchthKnR!d9afD zvy51C;zI-eEmrU$i9A9>iKKWb6X+owmPYjvj~rACk#FTU^~%8YQKI9nw6mbPTxy-& zYM3T&gWU!Eg&4}Ta01Tp)Y0h3gKCYtvD}Dtf9dJaa0165!J$PPZ>wRSp-jx?;ggZ5 zZuiCYRNY~(;hj>O`A2vu=dVCuJmuBw`APvdymmf$*={n@&aOLcQqpze@&W91T79Hb zpsk$O8eXQtfg1rom&*=f&xW#iq>bRxJEsOpn45bS)7OG|LB9Y;cs@WDlW>kA6s& zF@dcT!|S%44%NZBMx$Nzm+AGoJ!%^nl490-`_AoH``zF<`xdp;tlbjc`{yNj<{l_C zZm6N%$J=pxtbQ-pF8hn)r^EQ!jPm2B{MerUkR=OZCw~7|5}$xMLX;wXug%meABlcz zwxXsvYL;7T7*kFVLXV9nB_mH_ii*5hZ}>r?j~+j+kP1H=aICAS$nMeOyzKbk9 zadWDHHP;(;6NLMn_78H*aTNa|tYKRF)KBj+^k7V7{GuX0IOxVHqcZems`{^;EZ5Mp z({rFNAy~4Ya#ZfEg^Vvz?o)*B9vOsm^=1THr5Ulm5Lma$bg??Va&~T!JK%A>(CJFrf(@c$7a%dJi%~WXUwwW z9a#IVvy*xD4LQWF%Q)8OY=z(?wI$-}R_@D_<3NmnYh!c50rq=gLndR;TIrH<@-2yZ`6?Z7RXZ?$V;pie^}zA;G5Yu=Glg z?5d0>xu#ddRUfZ1l)aS^o^NWH_bZMc0%AeNbpI8bA%ms|0^T(^NtMUf2fAuDff##s zb*?TW8|Q&`Gql&-#lEc+U60H1rnh^!UGg)-_W*Yv>ofOb5O!dfvhg#E6lzAW;XWih zh%x&t@}6X$nMm)nYil(dV`)d;B8R7=RL;KB)>~JVmVJ!252-Ss-^AF_1>TFH&+M3< z0+QCc4*RhBl)L_;>&&D_kM|b^Jge=mle;vN_-xM8`gt&B8e+wPc$@c!A}hm^LT zOHV-eOxv7-iXN0pa5`h{#%(Rm&{e|r-4R4Y-&?7w*(`kQ$YM1QahQH7D}EL@6LuyO zz%r=vMbw0y-=}@h9u3c<=l`TVMo)-EHz=wIz?aq{e_kK$Xk*$tgJ1#J_KdJB z!f#YM!E7%$T0+`_{DLW;JfFyiL5Z8kOZ-3-Jw*W=wRmE@wXop2#U-V_N9t0=7QACu zMLr}*N5}y9Y$^m&($=iEKdZ+oY3{&M(aht_VCn)u;PD!b``cA3 z7Mi5>E0-2JQ%UbqNtqLq*796M@Y7`b4xpey_oDb#qZ(U5id)#Nwiw(T@idIO3T5&^ zh$`-E8DUNE1lV;2hGjWdHMIrdSKp_N{Zv1j$FbMUZM=lwIu>G1E5hgE7QUpZ<_Eq@ zjw1lGaHA_EJ9GOwO`3K}#cP*L4{ryp)(iQ^DuaEVELWBZp4xr0y$Zm+`1YcpB9n4i zIwkY*QscXSm>xrmYXAIUgjM64%1h+%J5qV8Y;OgG_&SE;q-yFZchf1`1Ep=*5p3KB ztj0XPOPNN@Blfr>fhN7$8T1kl)g`n+vS$`{n3T=2I6h{l3uMz*&c zf+{fQFw^opbNUOkmCCRCN)3(+jUt~;OJ2aNm!=BK?~g_*COqd3Ivb8*(10xJdtOa> zARlyoEi@Ig1ec<_Q2v&tfO&x<)rGd}k9L@@fv38j+u6p1rcz@J(zW*&Lw%lV?LR54 z93`leBbTA%-o`Z34zAoNTyU~%u&HUQaJ@n!~m6<7u1bwd?J)R<{ zM}H$F`7P;jn>y^>ao2P1+321EGIwf&uOIi~X8|tCAreE4ek#|5Yjy=v%;XYO^|V)! z@{B0D9K+F^^|tGSc~rJtbxX{*4YeN8BHTNr@RWrmxViAJ=wfLGuMbqXPz!>y7}z7r z@r}{vP*vXG9=Yuj22+>x;@<2XgOLI8(VplTe&9p@?!7T6;gV*Xf2J5%jCWpC?l3NY z^h3B3Bb*dhzYf5uvl z-yQkb*6VlmlIXXQ2F?;i{1kDt3=`*JD(xC)r=u}ounM`4(~%T({tWgkV?Tpa%m6q2 z=c2j{7+-+3{I;j@IWGe{;Towsm zu)A$^NSrSPrc5(k9iCXc^P71nf1a_uemmdAEYMBtI0?E|IfpRlsU4vpdqb*u+pTj=yXZM+tM?R8eYCa?m z?kL_d4}?x%0*g*F?Zz*J^1iPH=!WcN&BJDeCYv(Aa*VKOA_3#(oBf&se3y64rQd0mbZc^P^nMG!!3^=5`&BcidE z#S8iYe#q*?rKH#B5fG?ij1GmOK=)gB-dd~^8k8ihDIo?sBgG|0IdY$w7Hyl`L9L<* z9leU73KC{P`hG9d1lFB!C28fyHHGr>Tp07vd2xm$Bs5|5BS)PkcZz(!jh80`#U8S* zU9v)T=uQ(SlD*&=sZ6B!&56>}hKyt)p}wfq{xipApozUTHB>nOW?xZ%V>pGK*7s_W z&E{uOMKtVs3zc~UuB5PZS#$9`FeiJW!_6eG2ET2sRo7Zl(J z7K<8VU3}uA1=;Xhz}ye0XsFBZ`67>f*Ntq zSqE!xjKW8Pj2m2P0R5o0p%vkBr(bjOMBuX#9^Oqq^X+rcOXU8Nxm8bea~dHUN zxcDds%3>Qk9d}10(Dn&S+5p|+$v&H<->q2S3OS&pgATRmPD6bECdrO|+Pbf>FRB&K z^-z8YScA>#4)fJ&4amr{aoiWbcb1*)I5vwxSdu{bv7(Dg! z{aGrN&jH)S+w2o3r6OCRy2$9h)WaBF#B4`ei`?J209y)|YF{4*`1D@xZRq?V=M|?j z>$Fa{GE279J;8pO^jQb9>)UfKnhU{EdX3?5?ArNJ4GrJSqosY~UmVSL-N?d*s~Ibc z-f7em?*tJV${!?p7XW_lXvG76&;#Rt=x;!SOsY8l^})vDgbOLa57hTE6?+)TV2QWt#K@7t=} zSh6JM>N+B9m6`_QLR$UM>#NwLN%w3GG%atg0=7YszLOnR)#y&6=O!zymy`)*d8H3* zxLM^1M->U-gY$y5%&YE4;()_^UO~tY(nA+sT+Iab;p<>?;qfC(CHJYmuYA@-E3gmI zK-|6_E?q+CpsqyTeBvHlbSX{=3f0&e$y(vB)(9{Hj*Ar*_c<93PyJZc+nV-<8UWJN z$Haf)jaUlJaaNIq$?`N=vy5(>s-B8gOJZ4S(Kk3N^v8Y!sX~ z6>vHT+HY=DrLrQ(TxwAqS-&5i6rN~Jj+reRre+`#O018Z9e&3_^ewhN@0RCJmlm7xuUC{b_#1fLk^DP72Xn)|7nK zn&f!-ne#8X7slVcE_wKvi*5VY$1mQ`?pF?MP%rH`3;tR=UbnS=Rp|$tq2e+!IF**l z#E~oaIZ+0?YCVqrA@AT|6E+Of>1`MSmwR2j{xBr_!Mt1Dr9HCIjHvw|Xzdolu8@`% zHeO_9mqG2Wr0$-BSrF)a zj_#l9e%gv=d*8#jou3SW@4Cj%ixWm%Y(@J@r3v!^z;$GK<3rjhw2BJ=IC?4>X+Z*r zpp(>{+toJhpNj$X4cEVGRGAVQ0TO}MH>M#gS82SWyILk_DFhJBOW7I5OMPxgtg-7- z+$&}NMUT3VV7ay6BCjcAGS(kZ4yf4|))n~A^&o$$_EHJF=SWkn^SLBJ#G9YJgTdn@h2!MA}|m-!MNmxso0wh{(?jB@KiCrVJb0lrBD*0uYm4R zuz{p8FA|i_8G?oPOZ`a*&AcR0wBpEeZ z&h?1cKS44HtqTw6PbQEM4F-!-&Tnf0Y!raFF{9SpCdH(HP$!Fa=7>q2;FbXJ&j7sM z$4=(cA{zGdgQ@fulkag=!Jk8Z4|2+mc3px+`Kb<6eRXS1qkOq^7F_~7ltno7T%76o zTyQGfwBBr_M46^DVGwfqm#oAR`_#~8{AzqTP4<~LPfy5SC>^i~g{-?aLRT9RU4_Vk|*60T6=$6H$L&ZLAE@>lu=LXNur##2;( zf5i$1@dO~#*`$7b)$6c=it!2g$T4ztaksML;r_2)exPM;HPhF8j3w}{ z-pjX9qcIS}VEo7qSJFyR=SJuydUu;5+?>E?I+BtZkce;uS?QgXJ1F6+2>I0W^fPx& zhdvU{4;=|`&-8{k<2YHrz~c(WAaV#&0*CXEvwIUVLdn`MJ-Kj~?|9}{I$df^m z5nIPKQJ7Dpso#Tk+RFVj7sVznw(*yT0^Fd)1bm$xd_FBp3CfoZvnVuYWh*wuaREs) zF<0~;HWR&yM{YlwRYFc>R{_MMv}K=XZ{gk#6Lf|(CrB;!{GULLhEKGl{AqQ|t_itq zlC3lP`=Iq|SMQgA*N zb3T7sdrN|F(oOVy0rqKD2e-?C!>%k5`@F}{pLnOIvd}BBh!l99^|Tk%zPn8LadZ8SS}C7CjfN2aiIKHig=f8`;S=N$8yK_c2aFm9(>yY zyg&tplM$V9OVM$D|0Q5UOTXk4dKK#yYU8dOFCM~2k%hnd6vH(8XK{08`2=?}Kn7;s ze24o>U^KFT--Mw3@^6%TL?1sDLn+?dJ5WTl`ZSgFdmyovj}ONE)BCq8yKe!?aw9P? zN4N?1fQ8IOPGi7?Aa|t;!vkVl`y~Q!=*R{XVkBe!M6gdWLU@}UXZ^nW)=kGd4HyTN zx151NoH}o5v^_eN|u>v~{}U;OrV5e_HO z;Phfl`WchC2;g*r8_3)Xc;&?i{Sw)5a~D$H6iV}P)$v`3HM`bCD#&l11?^sj_QbFc zaf%Jtm(Ql!9vcpH5c|k!5~Lt)J@dfxlZ5?BY6*T%wz%Xj?s`F<*LWPU92tNsJDuWZ zo67=qPmtOrg@6liN?{IR7h{jw!$Ex+d1r8Q2KjHSDQ%H%`4-d}S_AmRLqNyx_as%s z?OvQoh^OCm4u17KN|`>tn+tcCGq9rLSP{y*>n~^@1m8Q~x_ub=3vSNUMErHph27nz zzote5xBuv0Q#wcjCMR#-jL_Tm3C!sCOBi(GFPRxW?-o8gv0buzGb4QmWM-s!^yJoD z&HSFGF7*D^K{%`CNWn+Po%E{0Q;?vk=D~hUJdha}#bs4csR_AE}A_>c}t5fRmrtEuqq zl6%a*eqC4&W%BBAw-zA-kpy%mTXlCLiUFeRqV@6ao;Z1>a%$!GPQUoq)CPZAZ`(nC z0$GWFQ-p<>=85OKjaiTL9*5sdZZ3B%E7Q3me^o?A^bW)!FYE$RJGJQ7wM~SBRgk;~uL=jEIz8!S?c2Qkru)5Y&#GQEHrf8m-g1i1|lcWW6y5T7D`6J(8 z$CGOBx$^xt-NCU;d6$8qqr*Bmnwd&yz{6GRl{(ZLLX!(zd${Iq{quJ)V>@7ivM^JTLSj+2376u`)d%l!%2oYhN1 zuv6TQyFB~wnBhc?sE@)GW|LsOXO;jmCLLohusGDDjZ%zA57;xay$gxCV+TXD%C&KC zYQKqmubSWZ7y2eS@Kghm)(iA=CSkagb-}i|w*x7mD(6~hQhoy${P1t6ZBVC^c!RtY zP`c`0L5lb`WH<8#=MN9t-(%nBc4Yk2OtEgbIomYr8gh259CD7M;7m&#>b>E>*Y87O z1g;`9>)HLbF%uC(f;Gl1NR^ZV0|`>ovR5s7%Y#! zEwULIXKV`Sp`yLW($RjKEbOg;lvKv0#T-@NixhVOk0v9h0k}rarOhoV2UNeCf#XNz z*)oHX!{{RFL#EoJndC@DUnY)rpl4Pkuj`y2H{Ww7z>MCarNJzjeZt`nO-J6b0~6KU z31vG_385o=O^x<8v5&83^oBUz-zCw&o0P=N6|&}dQHM+S3c^Bx(NE7VqQT+=gbBFX zug-(Z)yMiKVFM6^tfo#%j1RO1Rp5^eF3oPzRyuaDq>(vyIGf8gqgVrxAT~k+={dd`4RNTPlX{@l zNw8?U&661dJ2)c{*--qxCgS8cUBNnZg)H&63MAL5vXNeG7M; zNJtH$7^H!K!)SG)Kic9-$dCcx33Z6#^Eh^takULdM;5#+*>82s=99NfMT`oC``kaM zbv9JyXIM!kfiRNi;Mn|KMsr>KzCbC740sUrMX;sXbdPKgZP0ghh+q1Ztv%mUgK9Dm zp!$wKTidSIW#K%c!CXObd}HZ;%YF3TN-8OY0kj)9OS-r48M(r7?(FG86wRxs0mbuh z0q`=F@T$M3_dQ^?>Et>0;9wZ0cdqzVzj9{fhF4w{V}fpTkPZPXa>%)PRo(s@=q1u_J z4u{-BZL?tJ)psMx? zmiTQy-YiL?LovqKP<;OSteQW7Y~USSblv5o8z;22GZ8ZB>btuj-`J>p*zk-``36FJ zgjq=7x1^m9zwklR$vJL+qY-|*M~}JFO7#|6zwmbiygT~1uVHombrek&i%BvA>gHlq zjre53&??;wO|?y1kZUXKyC;U@9R7C*+_?m{lxWYEA;kNTn3dHah6UB%E$(G^c{hUe zD#Dcg6xRUU@?wm5!T93ifmeksH@@BEz7J7#h)exR6EgW;XUJ+)UD4_m1?FL^a@Q+# zBcy?q4mA-q=|j^nY8Wqy=#K^DSIHr{{BF-<9dLEq!72oI9VKQ95*e%a)ZBs!XD8np zO-+;4_w7}@4{5zhx)YvimcHnY{^p=V3Xr`AkiAVH`-F67r`CMhV)b=S(tf$mW%-h9#&;um@P&JjX!o*HJKtRz_&B*fL^~qv#2$D5*W8`M#QN;) z29VXWIX!h%c2G*pmTosOa*sT)T5%nTgIj^}A1QkePuVczF~7&I=*^zqBz~}*N{S~H zLiEoxjH}X@yP98AEDImu)s{clToDg1Pr=xcxmzWrgvPH;E>`;eWMU6*6hG0Od+?!WO8BToz9wV64&N8? zJLWWA4QxU;Hc+PN#p(3=J!Xz=C2M&T0EWS0Noa*{bV7bchW3kd(xVLcVEkI#wKrDKWm$#bJKxVXuiI}H;a}pO({!y^c z&KZfxeTrA69=-$nd_x{DE+Hd*h30$UO6#`sWJPc#tF-4zOnyn(n;TVJqyzf`CyTO@ zG!C82uygLEKzP>##;XhHe$KAwApORW17{7V2OE|jP zT$TP{In)KiQTp&VwXFp@4h$6DxLh*n+EMrdMcvEyKRH+&ld$WP_|mzpP4&`XziE-v zc6sNOHlZhpB7$rvZ+s>@|JiFgo&HCypVY!<0L(*l(=0Pvl7^y^eie>owK{pfXEK$h z&6RJPGUNNwa5*&GoyH`~h&|;oI}IkY3GQ?Fo*W?*K#d6+&bDkhJTUX-tPiy|nIC?B z@<;)(F1J0Bm8iV82DZL92T#~pzKG6_K`MWk>xU;Wwx}L-Y`xD-2Pw`B>sjO+s&|Ql zMM-55YEccpnU#7@@g+^!Aj!Sf`qOS-4wPWIh7S~ZXqGma;RX3##zi)8Af=u&)_TCn zC4a;UBps;JY+HWWg~4PH4H3zB%efo2lenWABOfllHV}LAhQFG0gVMyN6Xe3|PJzg_ zY7yL=d&QM2p-O^ZrQt9itL;j-~$j2(lgdegF-%Hho8&RNAf; zy>%4*?m|5^rv4o}Dxf+TR4md4GD~DZz0YAkU1I6b8*3G6xGNi}5VD;ns&4mYr_R|dW=GMu_oO#%-ZvFnN+nGNc8j&; zgrnEuL{#w@f4}sE8WS)9tZ^w#@uEy*A@OLwKJNNP5`t`C7$uA+InldMT)D6gXB{I` zC&ok3aXCxaSB$pVSj#EN0Gga4+{nfo)It{%lwU9P&VJLaR}U3$CvQB z_oV828)-m*+{tt1^!srC@mBnAvGFXsWBaIl~AKPxkNknJ+)3bO?ppODd-Jh^o5xfO|ImAg2R`HyidBq@UKO zxGv1GMa~%-`&O)}Eu_1Et8G%{&?PqINYh$`@L$~=1%e#wfT2fqBnFQdTb`! zz3Qh8FY6SxglrR0T4~OABG`Bksd_iQQ)8|)Hh|Fb6SZXM#blIXh^eVYl9%;VaP=ab z>_7=&!PcQg*^4czRBE?@?ubFt&^@$Nn2xKjev$(*hfN& zu$FMPuYw>+x4F3NSbpfRc(nKtD=IV!UI2J0?|5&~V2I6TU@gbTTYSs$I;)+xRXHyK zq-`}I%*!0-+F}r7?jgrEM@c~x@eH_VWgV7muY|kdlI+4*D5}0~&``7bbZDMjH_KVEPrLBS$_h|6ky%%7- zT0X;cZD#6z_G6lKt`LyOC)On!8l_uNQWsf(6k{@3d2DRx%!$OC6PA-IbO=gP=&-DAE>E7x*!n6 zrMi97VR7KAT)g0AwXC|;NVw7v;M-{dZkFHbFy*lbx{;_2@N{~?8W4VfT@WC3IefyM zWQX+chKCvuo29=RsAa$%WZa;hEk8(wQ}^mQZgb?EBh9*ipKEPePa!|`_Yz-D)Rdoa z_Sc<{w#sB|1B#UJ2=W~16rcWahSD*K)72)1oB*KDmm%R#A2VW#GaOs1o%lUyK!}_5 zGy+5z0LFyqz9X)7Sxf3U-nySm;$qHIT2tEpZNVF_e_L?h7vV1%`5%My~ zvrgG%Q)&Ww`*{HH2|Z6`vNZgYBlw9GlgsL5V~#>$8e**;Lmiz;xYxTE&-eC1PlP-s)1eHn3=6?N#n%UWrn~*XWNtC1M6 zI?Q^BL>SQ$Qxr3KU=uy}%!9n3ht1SA4-k-Ap(h|oAt^0RdFK{&4!56Kfl<;|YygkY zoUs%R9=S0@hhp}iAw~lAUvfZ;sve-k&mLCTL4Fp1CKLk7#oTOfQeb){`CIRwrR-L%1(xZiGNC+* zvkqRkY0s)3p4GDebV%LZa}D!Z)rjQ`sY|Ij6UEQ6a)5@0bE876D1Pf051D_K__X;! z#(-K+%a~Ol6^KwE3B35!=`=X~r>dtednsytfDb|Ry2TH2xwCF?`mCPwAJsmU|19SL zE+W3Wa-Nm%SQ5%Nud|yv_$=3&5Q>5-X>5VI9qR0kpP8hWflCZ>E*ywmIM^ikpH-WO z@B%`uCQaBUDdD?+3Rvfq5bFFR$X_y>m27{NJn1ljt82;}H}8m#oZRX7Q~zpAp7Zj> zqcOg<77oo4S|G2FgckaDLhFB8W%XZ|J(G$d7@>pt%I}cxK{@SUW|YAjRm(={c{>jx z`$7ci&aTct>`@*Kr@&Gms(OmvG1bE78hv-!az%lfWxbd8aq0K%dWT-{2C(y>z-{9s|a3axmIyG8^M zAnt~zcDJ%_uaF*2Fai!Z+D75JSAZ*?|^7Vz@)o*tYJTv&V- z=xwm<3$HW9Nf0QA;hXR`F}H!a89nQUZ`lb$YT%>;!#B8M6D{9eg2ToIIWLcG zG)!6oMvegV%X5x!D*6E_+7N15ow;rq=psM*fJcFZ7NmyVxycDtUn3YbKN1F6z24tN zr+r<(;)|oMB|d^(B&rX-1iB5-lV6344k}U+>e6L*9^|FSn0$ktC%OwD6Lphlyvb*!6(E@5q66*L;?gHO2eY1JV0DSEa3Rc%#(7VE zf7ebKR6I|_4_@|IADztS0PYM}h#rs(teAU{>^K3|=LHYK9QT?RK|m&eYpdOAyC}G5 z-(N#I*+J;fe+_s*jk&n8h@t#5CKYE_VXJ(I9Rh3*3CX~rl5iH~;_r*1&&P6a24m#{cpU82n#Jw8ICYsZrrD;r-NjCX}GGpUEQ*Io}nR z#Yc1Am3dFNa&;uzp&0RQD9X)Xi+>?RxMMb^#>kEI`L0%Fzs&IKj-w#lA-h6a{}kna z*|sJmAQZFx(aW-%vI>93*qT<2%3X_1N#>^~TrSVUr4q;T8lM$g&AqLgy-#CiveM&6 z^nKBAUIGz0Z@%&((vex#(z`LlMCHw>$Fi-li|6mJdKx%#W7-g?ZtjDW(Q2C)Vk|JG zGRb3=$aXu3OSm%FPv8cEd8ra1zkA(9_YWWdEv4YO7RUe z(GKtStKf8~&{C0orOOHO2cN!)Pg-Q&=J}%4hj`W))aa2XT~pw&Z;Ixvl&-gIYyk)$ zjT(ba2?S`Q93LT|=Wt2VjxzUJeJbW85ZqNLxnv=#5@_Q+4!hwOCD=>jHAU0w?$J{d zKY}i-4+5j8E=#fH0i)EV9QT8Ob|}KKF{Rq(x@#djMrY;vl|^e@(0iaPg9|Z1NSp2i znTd=glXEfdv?Z-5oMH1=ijyZ1bqbBNv{BIu<5*AHz3P}_5LR8vaSrONVHY5Dg zJdE3uUssvF9R%yP=l^=2k$c0A@iiMN<9_B*FqV5w3}78Oub_MXmmMw77@?+NaKsCn2gjI!RV=R zM@Ekxbn+Mv*Y*NXS8PhBRmH`cr$6Tb9>^uhdttGmit@tC!Wv5)+*D6NA}M}u;q1U} z*^mvq(icU^hF(sk$k{|BUp(<{7}xDp_vi4h7id(L?sz`E+3~zxK4Lg-Jd#XUha%q`913{7&53gHNFM>|U2!mRlBvtZqAB^=y9INr$@UCuEu| zaoXd!G--6inIP^F{f=}RJ~8^uAa`RnY;VlHX9lob$Mc$U{4ZyzN74=SnU<$p9*v-{ zQ-A!*X00^kYdT@a;sRvVrwo~JL}$6g?*5v%9`rH#?L@q5T-Cd+;2Sc_4&L%pAp{avByuu=dEHh-$nFYzj;-;CO&JS^w-tbbzUx)#_}*Br_ef$y){7( z13|H7((ogNS0~*UtMo>1iwi%FiK>jVMMZA8O`=2ZMNgQ^nbZkF zAY=95aF$D)y)MxO*JyQy8T>);D)i(eNS3G*=Pu==oHK1u2WYDbjHX8M<(L-Oy10IRam z;gOFuT6dc3nr`8KgT0nPy*D8T_8oXrYSb1Cejadr(ohkJSjrA<855 zBDy%kMNsG1lf2n}+sj``1-JAQN*3AkW7#JLL#tE1nsMh2q?5fr4o!ArEi&^u=(g2h zJJ?$1&79Q+e0jWi+z;`E+~=1JD|R!sQcM`R(-ujtnKU^&|BnbAeRl^lsyolmn;D!e zS=c2Ok>=;jhh@vsX{@6qp_zxOU0LuAFxvK1RF}gw>FQEGVb0Z1cCJ;s7)8ms%at5P zXb}ZbWrk6`X7rJ1++9PEo8t9APw|K1o^~dvdgRX>Qr>^_IVfqDD=*$wEr)hHga3=@ z%cMF6-~xO-H&SGl8lpT@8YR0VBV?KdIdZMdHuV%M)6-vKRqblQ!7n_QtoEJ4ubXDq z&##t@%Pbm@-M;o+%L{Ou z9kt+3ipg1nox=t*p+(p+Z-=m()cdv_NJYtDTjE^aZ54WL*S0Mpq&QxdVlll6(_rkUN@685Xfw#|4eiYg+Q${zyQ-CTgT5AVH&0Di*n3gR%4O*Up<6u#|O!Oyed-^^@HwE!= z^=w>#%ayLuu(kwCL-PtA+8vS_R}WAjb|{f!>dqcYhK>%`FWHaBqeR+)@|DCoSp&wz zzc2Q|osm1z*_!fw{trhL8vd1=uc=R{Q=R%(u1HGN+BCk{_Q1psddCthA4t~cd}cSx zU#|@$=CcC*A2&VR5XJTFkj6>N19T#r#Gg-X9)(A*whEK>MM|EjI*mX_>JN{v0Xs}* zgr8;WU&YxiAT{Sv%4WPYmpiVm_nUqT#NpGy<%|vg77fpiy?Sw(e%G=!kA3O)P{B&b zmIhAl<)1nlPVncBf`@8|SI1K-D6Sb!O)l`6HEEkAGBe+E;g}Nnkcyc&Ag0bCE_(Ip zQZXtl#9CA~--VQ@!3?PId{b1!Ug6>EtN@7?|DB@e$#VUf;S@l@rzYt4#D!5zDtc5ivXQxX56xG?*9wIF`aa1} zb^n7?L9iy_ON6E~;5OZgPGuZs6Crv3cHrb>d^=&%MankV7C;;oUEr5*B5u*7kg!i$ zOI5P)4`#p}O!9@k8Khn_p7#{eJ*~T-k8!irU@08)+165mAxQUF=j(anweVZ6FvJP` z#^QtmJL2dCJF*L)o&fp=w&0ArNL}mzzPT_V)Ug@zQ!O(?mKs2)T88(DQ*eiqn>u>E z<-6rey%gYh`Qr`PZ}zy4>R18US4d7W0o=>?|C9AaWjeR-u8%N5RxefmfP(G$(%pqqTK(J4`P@qX~YsC4CT-?k9qYxC0k#-*`>EgxE7Y z&`G9DwbI%PAh&vA?1#P8bp<42YbCOS?-Cv5hdU2SKgOe8dm$;u_O&4D7iRG$U9yVB z+&=x4UF`?&;-9G4GqNmsyA5xY)YA@uteZEqXnU51VKXc1SrXtS^OQY67BJwSFZ3qtK0C zu}4|5z|Tuq@(L*-llJ)2Nre_bwOG8bSvhnnJz?PHIs6Gp!jZy|dJ_gK1;65y0Zj3+ zZ|e(}3OV-U>Tf|CELrqdFUqxR6W9L_p{k}pMwAy#vG?g9kCc;$w$-=^F2;9(@CF=B ziy)qF>nZ{r&P%qmCs)E_Ll8ePdJ-QR{e;>4ezJ^W&hm10%&zosJgxh!iU3h>ooYBD zQiWYQV7%*N+aej_$-l5oWbxF!$?OSVos53V+YfBqRr9@myE2Vvz!Rb?b05CXG%1WyDW6 zFuGc(u$+yZ(CBh?ud>Jf%2xPF_u^KVr+JEwDa2*kLHFeQIn=MKwCK?o*f=@KXk#W( zUtT>2-x&grPD}&2H4wuc1N)T=Rm=_@0HZffAx&GKeC8@Wg)zDXol0vqBI@UKCEx*L zw;)YvE#wyE4T+-Zi9NGHGD1|(V)%0KIe$%#jnX!^Qm7|B_4J3ISUula0AP=Z8`4km zOP!&PH(#7mEW7^&Rxxv^l+g71ePYdj-27j{^WS{)_-#s3xT(SR;v0`$A*}$fE5AGG M?C$gob@~3k08f+z5dZ)H literal 21811 zcmd43XIN8Rw|lF2?#_`iU=rGLR0A= z5{mR9J+#mXowI_z-`@M&?>^_8d-jj}!zawO=9**7F~=Nz7_O@gzd*r4K}1A!;g*J~ zJ`oWqjEIQ%D})$$()fjU85j`hYTi==h9D3K3QQ86(wadGi$*REZ^eqBOBLQ+!l#*G_NQc}{=(lRnKva+&ra&q$W@(KzH zP$*PUQBg@r2?m2HD=Vw0sNB4HQ&m+}O-)T*T^$aGYiMZPx^?UJ?b~f4<9~y^yu;9$Cj3sR#sNl*49s+Jh8E{v9+~*`t+%tot?eCy@P{;qobpflasTv z^Rs8qTwGjSU0vPW+z<$aySuxGhli)9reZ`+goMPz#H6I8DPp^ClxBBQrBID=Q0yLcM+aHaj~z zCnx9KyLa#3zt7Fh&CAQn&(AL?DERQFw?9>+9?9@Bj7d z*TBHQ;NalU(9rPk@W{x>=;-L!*x2~^_{7A-E1O%>02naPFCgksA>adnn-_D0Xhcnuv&t=$5MT zJ+C*bXq$fo+jLO209Q6wn1C0O3uS7~*1;_;ya29|Km9TLy8ZHSmtsPfgv5TW#VTLI zxK%x9*D@wGT%3sLb}$Y^#FY;bAQIp~5uw76U?dNif+*sD{eXfF)2$wTfZ`GYBfkr3 z!uTOQMQ49=|DUb%{{ji}v9CF9j4?VfI2UXqHH4b)5j?86I5&h`{swm1dLfn0Y

}j$P8q54~$wb{rt>3U2!IV=f~ z|EE_EIBsWJh?GchIypTZu}Zi9rVaxI$LYhUrK`W0GYK7<%PW*V>dS5An*y3*4%^T z?@6T@_TN;2@00amg8`q$<@{B5uSc=IqLy3p5HVEpX;?&WEztpA&pb8w8T+F()wBK5 zz<1N(u8M~AkyWW4&zs(gnN7H@Frf>i)`KC~A{@ z_F-dP905&$Y(g!!y!~tVHhq?Zu=4|(X}%_HyS{x4uOI?Q8ht;y#beRIs#=@=%%Sf; z-#VsOu=;W}s(=Z!WsBz^qNmj*8CZO*fs2H(bDL0}J{I-4lQiJ& z5H3QfcD=*V#AH}Xiicd8{a?P%E>H_4i*m|`$r3hBQ9j(CVx zJ^C{C`f^jbDMJvNaDjY{MIr(mm1OhOP)@+%=>mI)(Sk^c)JW(Fd-(tO;MjRsfXmPv zbA0En07?Jp0ffs?wD<1-xaKdB^Orz7kCg>!gl7<^z4R3S*8u-FkuH@tJKye4+Z>7n z4`=YU%-4ra--HGI_|F0>TZUGbE9WM^g;cz z_`O7eM-tUe1K+;3WlB1Nl=ztmn&QQM;i zk#1N22lG!U0Snsb6sq-dLjapAqZ#MKvpJo z7Ddi?X#W+icA)Y8(wp8qD3LjzJqXmG{7MIwOh_Jg%AUi&nBfG|WB$HxPcoJ&=w3qEqp{^PMZG zNT-VOcoC;M_quBjs6LBEw;4qB6ZYY%3iHp20kbd$QZ$^Yg2WrnpC>CG@}gidkbGzQ z%P4jJ;i~TffiNm<0tx>-LblT$_xkuNu{`1q8e=qOySV3xjQ)#8zptUo4YT zQPs~IcE94@D3Xtm~O16L5=oBao^k8F;NB`H5O+`>tL|EvoDi z5dc<{MR!J#8y6{3O?<$sS0Y?3j@unIJM<#w{DafCwMeo%AFp`A(jgnMw?*78LC&7% zUxSBPu-yP4X7B}n!Sa<0x2QDrCK~UzlF{g19rFVspc|FE8)m4!y5%AN8Htm-DS7att(^OKyOTA{ zP*px~$x&T{hD-Vm&IBb1qjj;!z332ZOhh3&y$at zmwG%x6^gE=mrS3EgsGBUI=UtRB8|YTYxL^tNYhj2yHwERNRaoJyDb$>Mlo#b+|L|H zYQDSc?_DVB)F20JL(yb)UNv(UaVH$RMsAr6GJ6u9>xzX5-NK!zH)dyo64!d zm@u_)WgfviyO?|tbOZ0>JWqwqzGk^QbSEd|OG_X6`Y3iURykJAJIv$)n1VhlmaS)T zVKMm?s0ghZ#%Km&=V>zaeHu&Cuw7pKV?Z8vXHdl%aT^viy%oYMXQ?o*e;oyQQQ^*k z$t)OyVC-Wt5xQwto#hUPs+s!p`7cIG7=VrGs1>khNJGb8vFqmK(P-TzM&nttd-&Vm z%LnHt+DT7lCrYi(9|}jiW02tH0=rS0V5$Z9Tkt6*c4?B*!HZYyR9f!c_knYxV6;AcJ*WkD>boXlaxVjKt_c9t zR4HS-;B%WWEy{9_`R}yB9+V(Dw4mr|%+$g%dkAnpY|C<#I}yN_oU$(`^F8SS9CJ24 zpCH0j$!r67Yj$I(O39?7`)V%<0sPa03)l#K25snv-d=UYUgE6J!fAa14n46Wv*Mzp zPb5zHo&&q(g;UK`oHFqEorg$zgG}7Mq@$5S;G*{UO~NUt!-6m)QkL7OYbB)H$U8=k zS&Lb|NjFe%r8lzmh;=t-|M=JnfC1O1TZNZ)D{;!3OGSHiy|tvan)Aqj>+2l}s$%Q6sRceWWi95HQ;c0r$~Z-vaV&+7{JB$(1-7C z-ystBXtTwo#ApAv`FnE22z9W`RBT3G@i?Pd{@BJ8)k*wU3d10`J7|>c z`?}V9(xpTZ1J(3_NmtSDoF2`d^iwKDzTwwqc~DT#08nhCGp4`x128icSK>QIg&ky> zNZ$B7zcLx2y<`uZ&s$mQ1F8FXac_xXmlD5c^z@#v(|XZSoq9=_zX-64@4w{ z3a%WX&M~-0LsayT!tPh_u`tzvMGih^MbELq3Uny2lHvUrO~rex>`gioKRzz?%+~q* zAjf}riNw8DaJtWtdl?1$=D18J*2Swh^i;vQ@^H!Ani5;i>Z{sN?~+C{E#|9G0-SnC zmG5)rv|_pn&N6|L$M~z8AH{I@laVKP!zy3udE{2GA2(+X0y>D@BXIk~)gqX$WLG#a zuo@rg?j0|S+6uPai+dDB^LS|KFl7CxT8o4p2C=5Hm2gSkCmXb$?!XRqs!^5z8z)i` zaDxS2zMW&y8B1rG&7ae(W=>c)Sa%+o$z>i9kRjG&)04nFeVH)(!~@Ng?MuQHcAb1% zbHjAop4#PQ^?LB$%Rzu20_EXRzo1|m)l_1i@GtTQ6{=&lqGO@~i|!&M{S80xwqo>j z7jj$!qm?MR)aH>Mbo@VyS*`?Emz`|RsM-^juXo4JcDAe?M|*ic&6B8E$|QxP{Q!-- zMbKOb*$b%ty!By5t?oSmKY#lVG}(HV8(r9T9rtc9iB|H3#f9_+UH+d^Q9C_-KMAz) zFC-Y7m5Kqsd-c0dGe{z|D$QgYSbX6nIyrgE4ZIe+0B0=O8$k5B8M82?B*(R!H7I-+%xBc@UJ=yNYB1})>{WomQ%B;`Z-oLqV`wXpPDcRfGpKy;{ z@j;d=8!?^ymqk}DhOhKXnzSHsbdMkXnH00#ab!BQP-z$*mAd-HPnYo!hxNH6X$eGX z7-HuEmZx72dg|elyqtN3{LZw=q~hzbq{?H5R`qJnai2Sztlia8L*wQsT8Rd-5pC1B zK@nm=1Zw2qy0I3gTZdmxtNFKd#sW|2$IC{w?@d}eI0UKxV8hfYF{aGBch%iC9tNGsB7wq9&N=)=S)E6`oY+jLv;pMvll%BR zN;KV7M09+0bhOzSfB;>VqAt$t*zq4r4!@vL{7+!f7jnAf_2qOXrHZB$S?GvDJHmKT z$|J0b8JBb$hpO#{eKO$HfU&-TU4>wuj5m5+q|SE&Aal&DB6l~Zro!#N4T8+prq4+C zf_fEM|IwGh*^ga!B82>V{?eic<7obkwn0!vnmF#nP3;)2 z5SR?8K)>liDV4F)r=Jw*1c@!k``^|k&}34N0Wz&23_^=)?6ILsM6%pJ)$v;mYXvUU zS22h!$K3knyo8D$)`f6xl;AYDED0i&h80SnWq>klOCS`feTm=4j1ok8^c*>_*9C(5 zgztqAF7es-)>K%^TM1+*`eE81DG}Zwy zA-tAFnXqlTeANH*nwQFkoSqSY43I>WL^2kLNJ5u&IJ^u9>gNn495Uk6==H_|6}9}# zlbwGcR};%kTWu`l^-)zEJWVNU4iqu40I=NlDvuqtRx!=;c-xS0F!%ZC(m5B+k&r-< zJ*%fBK~8p}sxt$zCE6Tf&rZ{+ErgqH-K?!;1u%ogxO1{Y%bXW8%Z%;aYUrkWpX($~ zX}zm5;h6*(Jkr<+Rj;xi_I|APk`N=sZcH+M$>3}9>=ivq$I04v z=9L8TIRu_$E}tT%N{Z3d3!}`6$J;J~>EpfkPTVVYa{dNWb44!G0+jcn6O2u)BtCw3 zt8Xxt>F}`4qzj%#-SKlYT8n)S_9qNTp!GX?%%k1|N*P47sWVgIocnxZmxxxngPX_@ zMBq0gKVoCR6=x9zuDognhrg7+4KkDI9?>sf@~Qsr*Q+>)e)ej= z9BNGnUlLZIePpqBvvF?q{>z>REtQ|L2*K8WKJ5UBI8NU480LWX_4;v3+QqM6jm3)` z9v}4SumHTn2=JaD`S#lf`V*gpeR2z;9IYDkE8Rq1Cnq+s-GqTG2+L0i9(`j>mo&3x zlD^D|m{>Iy_xCDT#49b(3jC%9kXK-*-qtg+WAaR0yUaY_nfcf2E|)P(uH@B(fPJiV zypez-k#Xg-#9D`ZP9VE$W`(#B)!FuSz2=fL z1P;%f%qNtZNuGrAw$$(w2ECCPym=Idi)#^!F$l*0jkVl5P_%<`!CotVxS z?3hcm@+h%o4n7mu!nvMx?nWl=rcb}{l-C-Mk0YMqO$Rr#8Y^3qJ*XTs<*gJUPuSo@ad%=xlo49pu>KxW{+s+kW5WvF5f_`Ik0=8d7=K)!(YFP=*b|U30zhjTTdJhEyyY$p02zd>HKWTpXS* z7QBCT^}Nd~f|MaK_ddGO3}Qv-yvk|zP&$-Rvg98EL5CWtu>5CuNHn|klt7!9=fBfU zvl3(wE#m$R-3=eFP@Ivg8$#nciZUaGTprc3+>T7nb$n#xMKIIEJ*~xPrPC;@A}t8Q zO%3C3rIeYD@=a!2WuoESOG@k|Q9vbi^Mlc*cmegwe~h-KMC|@tYxuw)I6A|i)yjr> zPJGAZBGkA%1o#b!OjStDuQ72bSe=nHH;@|M7-sG;ga9XKd^3roIINOq0gRpb^ zp2@FF$I!79QhwBFwNhl@xCOzA=IAyWaza;Eya8dTJlIK# zi3MD#93={CcK2>4dA(!vC=pE9u*cky-3oi1VsvKlY0Qtt`4eEW^p{9>Of=w8(Sm>} znK9&P65t3_`V)LvdG zI;9E@0eP#i$(IRGpJ7MOtKL;lf?ROg0#SJZq~ zG3)KnvcenS2%3D;hV}9LA3&>{h?wEoG`@*`@6 zDz3|?)t@=X9zswc(XMbeo^*GenRv^E5sg5zo6ltAQMPREWcOx#2faV{)(e@D4Ia(7 zzi1o=HCx89XJiJ+etODa!hU)U6 znhUU)7u!mfSMR93W|hXf{KyE4#20PwZ`9SaXxE4o z@5|L!2nQezde%H+GZxV(3@-tx*NF|sh9w-0d3>C;?pvqvdZnYX{?G8CoEDq+@w0CG zJ2xx@OJv@#>-uHxd^;4|-+J!vOmVE7!S<6%KrRWoI=cqJb zLPDQ$ePI_>gV34zr5tTiHBW=7(^}gEEOCmxHbooPT25h&vc%&;ilg`fK*7J1`9`>l zzj^P+%sksJ?uSO(#17$eZGtS~+Ad-uKw1G8J(c}kKAdTJo!pW3RKOxxGylBSb41P;_2VlK{;Z?nsld~+qZ5y&;R3r7qnZFmKn z8Q`Sol~_P`|3>t2uiVXyc}=!13(ENzzc(4j%GR_Czsy=(r`KKgd6Vf{b1j3Kr~ z>?7`mc!-9v9lorSXuR<&yp&V3Nv?QeS=!X6^WGXjhl;x!I1Ip|`PP}9pql|7T@}FW z#Q;1=EAh@v*qiBpY60H9$eC#$Kpdn>ze~Mm9?vq{)tEtH|O$ z=y|`(CNO;6>5*zWzz&#^z3(U1tMvwWaq+r&FA`(y%<v;Dzq^JIXM7OFo^c<0mmQC013qN56RizNvkk zb%vLr^J%FEW&4KH3nzovS&Md(@v^TvfVWQ0)1bqC&bCM)>Buny(AZ7Fnn%&wGavyr z;AOyCtFAV%?A-7CA^uv`OzWFI4TcPd=ij<;??I$y_2!Yu8N4s&-K15(#`5q<4*Fwj z_Ud3W+2fh@%|Y`3`xiLD-x5ZZy2gi&8XJqgLPYw95mD8XO2eN>XFy4c^ZcS z7q8Wb8*}K;Pk^sx1pVu)6%9mRm$qd8B=h?mRHkc;|KlNjcgfZHJM($#jFArr2SJK3 z?iC4;tWN=(Vd`GY$OrG>JE=DY6|3;$=%URuRy)M=Rjbzlhyw+lybrj0g zxdHQ)qEV`yd;7<;RKJ@rwRqhSFU;Y9V57H>#yxs`o&4S>b^#iWWA?e>0$(O~=lwN} zY9lF2SHQgnEZe)ky7V&L(Rq6K(itaBAVPRPtK(d-xenYL><3F^j;Xl@M4PSw{%xI} zhZmZ?9~y;jjzQJ~-YbxR37(Mm@Otj2dhEZ;yNFX9juEtn31u#=FKqohY80`Ioqq)A zK@7I-Q&akSUFBkCbL4(I@@Eh9wrM3&yP9i*>S*8F>obrtkXvPDUe=zh06e94b{@4^ zB?nd#7zYqLlZefXg{1v{VM2~gu9rDLrgAy7*AcJ3dKVPsOI0GJ&1@=%Z5Bhs#shvz zVJxl1T0^&j=QE6SleE+sS!q;KiAVG#{p4Lv#$QfT{f%G{M3USPC3|!1+2KLn{s2Ms z6ds8whz!x4BkBd*q;o%!idVF^2{a-c`Zl>j2stv%%9n2O%dCx$u#EN?j^F_j!d2Lm zyokP+9M`+qSv=4(yL5kH0B+UYV4A$6-4S~+#VbBf;s{a_h?O`R{47w?Tg`3u}0@VV>%Ja3B^! z!*5>p?GTWYNE#qBL}{a1=KY<0t|jW_=c?bV-Rrey8wXNDS$oNM0%g}7dwzfT$sTyM ziCLZ?8o>~5PnZEaZ&Mbp*=ZKdsYv~a^7iwNVwIy#g6t*4c(&J66rNC*c!_(=E2%V9J!EMvenS+ed{DoB^RdlK z{xVr0oeLvd1Vz_vF1GQ3NRgWVISZVwX7a`r22pVT4;@rfD?lqY>V;bdc>Q&<{mnwG$i=#F{Li$eC* zrB1@TnoHdO4Cz2>B7m-Y`Yr#Q~_4{lHhz|GfR+YFZ7xm+qo7~h#kQnAb$ z4k=)t5?BcGI?-mmQOJ_?#61NV+82 z1GI0OtHY*VF<>rGtDW13D_WN9CLoaZBDUpF7OmjT3?N5U5;PA2sr_d2FSC%$I$kgs zVx$ES6SWSd+9#OgME6(6%7x~@UPTU|=t)52rOFs>UJ)yPe!zK{Q zG=B480_W%mm#xX3&$P65}qoaw&1=So+Z0bX(+1`t3NrF8LqPYK{MTuBqQF&oKYCX;J`|5kz< zfWjT`39}5=9HJKG5#3XOe6opH$hPnellT$B-z0tZ<@fUWx%9&Ab{-QR7kGukjw)h&YfMZ@g?h92amR9TKKU zapPYx?Z{sv1}#b_z5L!otV4_{bQt*K?hp}0A4St5<=wXU`{>YH9hE{029|5sou}J~ zW6#;n34$+3_VygMF$V6&33$BlV|IzacfPML0|v9zz>08HU$Bhe>UYk?%)8$p@Tht~ zSH-ByKiTb;xA;ZAZJRj&e6KLsxxOgH+&>7-dv4?rc|bn5lO(;GdbqcMH8&zgpG*cS z6xEAgJ!>wsW^=L#s*cs@R3?f-Z*B4nEd`-T z#Ii_J#Z$%uY?Z8M)^1o8_B~30=tfipbcU6x_IFNd8A-W;WTuksW3uhb+&InC6LgUD zjfVH|qqeF;A^qV`g@^I62dP(3h0Ecb^|Xf$ z)vNmb7K%Cu#V7UaMzPFoFD zaVD1-=M~OHKT$h0q5NC!r^W=_b}K4u!Spr4qVTYi>iY4SmZ40dtWK%I)n+kI3I&RtJyu5_)g5I68}6_E*uLw+BUS!izG@^_aU zV_UJ1v!6c`s;`N1VfpsZU zqt@^Bcf+AWwRc(+A6OUfKez38UXcAmvJzAh#83q!6(TtXp_)Hes4HX7i*rg7_O8Uz zH!|gEv9sK8Y-5+pZ)!5yG5Vmj8$PSBj3{LTj%$NY|MO_3uwIHrEJ`7?Wa$yP6Du5N%$u~Rz9@)>|K5ykaxKMtLxg1Hes?43_pHYNY`wX(qPz?JiyXg_|>pTzzYk+6jnY9Fjv(Y2@eH@zEZ3aG}&}K`8a2uH^!zvTGz7N^|(6 z1Gr`)K5+Fb`rFTOdaeekeyjnn_dDo8A?fm{Q(lJXCQ zPyI5rx;iYv8kT5$)zx5A(^S}`?2RYOKXz1vy9sIE^Onn_VpdFR*6X3zA2*hV>4mj;ko7RRPL2SlJjB zTk8~X&uT4oE{*KD6An2t3}jlbdcvqa8PahnjBVz%QF zMkIzkX~3S9LJqJo42qFIJps?G@+}Ieb#k4fz#`x<{-?mp_vGa}ubB?VUSA{VKU#V- z?XKd_G_CTCu~j!>L{D$wR|LLnBcBj*7m`XJ27adQE?uuoEx3b!a&oEd9~%c$3awPy zZ@lA9eens9Cv}&7ueS-Z&AAJ>kBMWBZ(bA3D2(4w{E=Z4DH?*-C5#cw z!gPlnbm6D+c%zsAXM89c1|yy^gTvrU9Arpk7_n72FKWh}n)oXh3RF5yi;+c#f*#Uf zxR@Ek5#c*!huY(1MB)(JI0qpu1tNa~ya16Vi6pG@eAVW#eLPN?$PjpU&tJ~1w_J_f zJr>0E5=r*GSgmZ*uw7Y|D2p2fdS>ftuvOr@c<4}eVUGf|)3B)*E)AHfPzoY`@KRve z%BqqyHj@-d4BDxS2nEtT`Myj& zLLQiVQ_G|1{thN1Jzxflf7WU!4G0fV%=4uu(k784s*H`=*?OOY<_G;&g}p%8{VM0@ zudP?ZGZE?k*TUF->Z_gwGtpOItl%Yb`(QNQotm0n4EP)H&@KZ%>0*T~8`;Y4lP8Aw zb=U?Es@}6wMi~zwx(@sa)KoY8$4?43YR*>(p$X;!3S@ISeo16z9h?LWJ(ouTk@g5x zAH_QJMpGhv8ePs2g?_m`{ch5MS3;;7GC10BGT?5f!>`x(v$gRn$cx}5B@-Y1m{ZwF zQ_Tyu&6wGqB&B{y3#o4Jp|tz>6=pV{!;PSunP%n%zEpcgE+pH9&CY>eOcsbLuJc_C zjACA_Zv?WoE-9)?HQObw}*2`^W@F?_b+ue7o!h+SeLd+M}r+ zykG$9B$utPQ67zAW-opcd#n?@=kM;jy_^Z{s+{dZ>eW69{TRW8TE58HA%)T$JzudA z#2c@}Q~(->4sB>nLPJYh7`RVRzAufvTTU#W*ce$|Ize*aQvB495{sGb=Gfspa27#O z5!>R5ja6!kpuM0i5X9~}vE(QOVlxJN#7lm#s{4a96`z}~ttY78h8euume>sWGx7NP ze(2bhUK1c-QnF6IO$v8mSYpYR@{l3Rw0}p^zX591izUHcm`0HzqG!6R)4L}=zK9K$ zMpF1|EPbngYK~*=$nfdF7v)C2I=W9t7RpzFnDHurK~FACBY)mGBD*XN<)nEnaxl=H zA+?p$_<1V^SzLKIeE6w&*lwXu-+oLR05#_puC&+;NP^f9Y1Ad^8qPou%K2}j?4Tl+ z^dqtFHFEHd@s}KCXp-wF^|?|1AKyX)i+ySKjJ$}^6XV?zos>|O02%3blhQl^Hs

asz{NjM|BWi0wVr_;H%ED6bT&M+m{^0L7%@j5?8EwJY{FIyY@%yFiURz=62 z-kX%EC>454zI_NthfUF1keM|Qa6_q-C}j1GuAMFUQO;vuZ&P11UFf~EGw>~HO&lQKdAYat+`U|Cd4u|eqW%2rb^5xX020ukB0 zc`r7fnDTHWr8|t+_S^ zZM_3ewdq^mSujNrZ>e2ae&xpqoCy%eC-b_04|M#__s9Uh@q49{u1z|g%{=lQxnzKs z@UEuH?@Kpf)K0@rb+iqqQ=;LM<3>$k zqNG?X}N+JkeL+MK4g2WiL-u37R38tnd8E`=r$-?DI{upIB_$G2_!33C)ftc z$na|8yntPMoYg+&=rK1@k+n7eWsakMM4f-k{4F+-+LZ(mp6POrL%w0_H#}OT*;&!o z4M7=G5bPa7KsH1QWl<+P_a6Vl#)6e^H$s)H&L#ira(C8O62wN)qLb4}-sCY(r^~rs zIn3E+&*K$t*eq8TeDE9)G?v^KutDJi4aZIrUBrX;SE(_XYq=jTP%em#CmB*`!m>Iy z40BVlnd8kaL%}e1FR~DZpGXdl)V)C^T;K0H@^$MK@3p4AjgfP6BUo=oP13?fh>inhyP+zmjjWF2(x1 z9PH7UU|Z_DvQcvw<*xh?oL19lpDc{W=Y3z(a!?ZI^6Gx53nS@wW{}B*;v~f&W-y`o zZK<^}0gE+0?`LWIs;I-nV>EeC38^Bg#`C>NK;RGR5L=rJ=FF*3kJYOsIq|irSP1s< z*7HxK+t|Imy5;#b_U|2oANLf#j5?vj!JHUPkEXzzVl>$~?y8ec0ViDADc68t=u>l= zN@P7pjdM>!iut#ZE9W)nI*<=`-AVcbMA^>k0JO~XR&E3Pyi4&v1q_SxzqJI2)4bv= zf$PFOi0g8I!-`(0hX|wW+3~T|QtDqrBv7H8T4en`cMhjIREo#bi^gC1Gdo1Hmm&kO z5L>13Mxu#e3DgEO{#7iDxI*4-S|ia6UGh2kCIjYv43esc078S)3SzaGwmdT&i? zUd?~Mtj5?~FgkGExebiwy?DzBE2oDSM@qlFVB0^R)eIhSzBx5$CU23MGVMF5cwFwB z>FwmV$fUPcYv-5myEo$VBYnLcRF52;a@3rV4j@md!9+nEk9Q<#8zBbv)&DA1XzF|MV0g8eK4kLaP!;1o(5;7PY=t3|M*w+39J&%+Dvp{M9qqN-&tirVpGHc+g z=?r;o)K&EY5tZBC9zaEn`K%QbN9hN-->5P!$Cm*1o1R>c|I34&cD_N zg&^O3B`@E%?#Kl=-2L6-#wB;Q2FQBCS8{qH1fdHgKObydVg2hv5nISbSSI~l7~_t_ zWD7?J`36y2?DRP-`XpRLaIzOlfqW*Ah7^t9j2k+VEOSq@0nt!3U&KV4(d|c_tlg?B zBZ7qUoWo89#bmVejtiAj5p!LR;02f{@*Skte4WUzPzEd~RFM@=EC+0xL5&uZQ+M}p zIMz`aG^?yfvVEr}V?KR7K9vPnm;-qGtAHAIxOJWbrbw>_8_eBy!$?KvaD9@hmp{x9 z^V%R6jNn0$qlfob4wh)66S(p#qbpDLqlWSUwSHTVq~B|R3VSibjVr(Yb}jX=WgSlM zH-9rNCd2>YvEPFQCdh{l+V)d0^(^ywhh|z`%dNPfhLiTAK2Za0O)@|JdVX+WVv&hVcT^8*T>Zx}B^uz>y#iUmr3 z)k8TKUtw+5j{lCI6r%aS2S8QtZy;ya_AMf`$Zyy5BY-ZUQO4!jH@6x7`o=BKce>Qr zH^~;wKqP;>*m@!S(pfzP(1J&8Yr{;9-IaguATme%uf{w;Hh#WNiy^mOSZ&FTaeer= zQN|9G;lE~K&4c=%DtLf(D6!ZFMaDP6$q_*DPKM&&;vE>I0I06J%5l~agZNj?PH-F^ z=!N+_ES$U5Ca3^90|EH4?iXyZ=BHlYJ7>kPth7-c&5Y{zN z9rr$J%W3d=I4~3ehIfiTX)km*0sSyR_j5t~{XkaWdy6lO7wB`5J1eyDqB{qJcvE8y zWt-%HmX?2kMN~W7<$wzQr>;gkk|o1xk|O5_BrD^{O#tv#fdt`Z@7+}=Vs%*eln`Tr zLFTwo|2r)#{}jCcuS(+SJCGdaHIKEtL4)KdnGkUldEQQ#D$jV{O36MP^!3F$DPrxn zhUV#bi18KVG<`i%k@gmo+5LQXhVS3*uZc|1eDEb9F~@?@*B+bPMY%J4jZd9lz8KG) zwZuV!6hVOe0nuPYYHTwue_hHY3#inlK)%C}Zd(KX^(EU3_qCl_|E2o7cwwT8DvTIr zfcZo)a?4{q6T2BcK$`6ADIq%sEIm*y#^!1F1rRxxgGEt?J|o>8H_O=jDBv)_u_or? zMu2Y2^WT-o(=GhymL&_8(;I$EQtib5n*Wdd@W>b}2XVeV3H`o@mA*qlA;@AWlO*2~81HI6&N-bJ z1ybn!BH#!G6UQ}=gLgP1Bw7s?Z$rlpR}=(N&boOJflUWpMtPJU8OL`Zu-APc#}On7 zD7rsyzVM=!0S~xe5Lm(Y7tTgZid5|jt?uk_3=YT!_ChIjtAGU@^|G^ayon;>cu{RN zzh3YvGDh(@K-hHIfCxp-ebCHwu!#xuD6k}G^#&d|;K%kxYbMYD}Udl>?0M*lIbx(%| zZ|nyx#oq#yh^_>9i8E{DIO_ZWpg9OH4RH`#3qVEZGlOuN5mtjh=^#v z5&mC*R=B$khzpQp|GU@bzo*moe|0(k{{kfukL36(zYBom8IW>aLpn9w?HJHk{3JaG z)Uk2}Z?gY~)b4nHCa8e)F;H-6*?m@UX|72qX3r#+d$3!j%^BDCh7sr&=T2NeDxNj5 z>t{9o9TR#L`>IP5wjQ4?%Z5pIt#e0fctn|fa<5&)Nh}>#(-VPljdB-k?^gekGZP$b z7B$qrcvr_(w?Ua$WVx$^>;emxgPU5#MQrJYt*!&>nCXoxZ!JJiY4FNuUz3pe3Whw71=Ms(blqB==a_O;O&SC?uX)-YikPM zy-Npv?Tz#|U&rhe7xf|4|Ads2Cs`l3I^Sw%8JF<#v~FK>kNrLV^VKU81OUDE)hlJR z&AQXQTkSwjY8?kWk4D>wFFlmUa1T6Ht$zi43p6>rKpv0-#l;Hb|Dj0&1Dp-dfp#tc z7YL{h-K-Py`=>GT=ho^~pv^)VcKiY;Vy-7fS69@|PcW1oPoxEw<7jm!xlkvBYUj09 z;&It>$1)E~>e<65Nq*>OQ_28}Rfs8`ab7P^tEunz38>+}hge`Uztu!jGUECHLS+e? z8W#JK<>WFfjX`r?$4G1)miG7a%2y&e_-yZO^RddJ z`K;YS)*|0WPYc~*GW6@6Gg7b4-5f}bVA%YX$XSp$_@p7=X9Lr(F$z%8H_hP_J7%oN zq7X--NM2Ei!d^SCR+=zSv+=+y)q`T*@9B^xkJ(~ z&XEK@bav)ah^8NDOw7{Lg-2-ORDZdjltp@ZUB8GQA4d^MXYr!Vt zySf4|by`*3+SJ>6uD?v|uY~P++pHBIGT?gTJ_3y$@7qic8VDM(XpMfuedZPX7Qqa`6?cG*#t`L$Kz!CyAnMwZ#7ss>3Z^% z6CW5Z*;J4LrI?L_?O$=pOX*0Q6)Mq94`D8A`FT zXf1b0l`e{OSxIcc8p~$;edpZoN*q0|oludgs}6T?49J}`mHlX)Adhq4kvHIT`Ui() zTaK~nP_dYmkNL5Fns|tJ90dDz(ZHfi885!Hf@HzBOCz+WL!7~OV^=Jq0_tu$<=gQ# zL?~J$_LO9GF!XF(HBXji3Fn^-9qrf%jn1Jn<(i8|lFWRiZvd;Zhsh7t$Q z{`2`OUxvk#rEe&4@OlV^3*{cMfrGl` zm;*ILGT?(SKlO#n=3@dSPcNl}`gmP?uqz&A_T(yg8nw)1qBL`hy$IZJ(q4p`uNPmt z^J7B%Wm;_TlUw_#)p5I#*91Jc@E_#6sstB0_y$Z`-@CqK#|&M%8xVthU?0WCa_o~I zVv6#;XuUc&iiWqnh~CW&g?1pQbAHlddcOG^&kRu^@+WRhqzoD`yZu0QC5(6Ruk8kb zJ#VCtUtFxwDXtVX5tM(;9_9bQdPJ;6>-sKu(80~5w|XPXXY9Ni56Uy|6xgV?b^6f4 zXi|qFaL+tE4LO30b*88TdyOU-#-cIopjF0MI4Z!KBJj7}{`;Ouzf?!(ga(dq#hfa0 zh5}=yz+O~kFO{vmtXFVBgToUK@yepQL&h16gxsWoRGlu}pGp2jA^dMZB3h@iVB?G{ z?Wet!kddSt>GchfbYu5E(Io*vm4=S7O)N&hEKYBe0#>wVefs?Tro-zLWQf z9bp5v6ow}5{fy2kvQ1E6l>{uBI2~K|W=eTfNn1l1d%kDqtcX|}|NkiE%D%_$rr96{lJUGMz^Z@u5oI{ULeXPv$G`Ru(9H@c{kPiOgKTRco_T4GA{dBiJo zg`CaqfguGqDZBnx+3=L!P*Y>lGZ}Cy9gkjlZcNq^Ci{p>J;k`E8^i>!-jJ`$&LmKW z`pj7DhoaZADay<0to1v$L4i{ zE;Z-(XO#3QSx#<2Z+x#IicXUo2lU&fD^C)-rXKX4KmWq;NU}6F!JOek?6SH|aK0b(1v$ zFVEo zc_*Km%+ZWD1{&<0Weq<~29%Hu^+5@d&YW1ri>WaBq<0vu^@=&11jp!{8X6P8yU6m> zwM7v>4|J*fsRezidF7fW{MvUF_F#+7hkZz^C~{5n<-23x&^hdVubTj?TwK8f1eEcC zq<5$MTcPu+@8@UY5(URQ7X$5h75qHOSahf-o2DF3nx1_-Vqp|)=RP0BWd1>Yk+Wh+ zRmROG57fgc`7ci$J+kDZ%+aV*d19;1q}l#SiiY4@CH9!QhRbiqZCjA^DjE;U zI7lJy!29x@AEXi_Oh{>Bei1+6)MG5it5dwJ0nErDUfp*;`Eqv4|@7x4goF(SAX`N%fTy@7q z*xcB=KDHgGF#~&EPqj9QON^NAi9%sgsYO3ekoUHSH~`fhl6+`J@hJ zJ@ZLDY^Bf!xnZ{%tEb=~h+hn^fgtKep#`vZ@$3NH@Hw+Ow`2#IyO(k5Hz$vS5ggyu zqZLmCK7pp{8HlRvMyzGIx!oYiqDMi9ZeHA2}kmOX}yq>zQmrB3ecCN~DYuN`e~_T(a39n|=h6uoi~Cz*V$5TD_$-j*UQ8o`<=hud_3^ z!dj8?+vd)?2T(>^r3EYLnO`ew*=2MuqZ8nFo3_Jd^=tPP2M_r@Enb%B^wo=IzZ&c& zhQP`(AgHeFM}cY8h^A^0KFhk|ef`PDH;+wXx7b~a2-(K|w8Vc71%P3I)C8rir1=xC zEk2vhbEbG=odnqd5QiopDX{sZoN1QFNi^5yxgw8L+Mcd|59WvO8&~6euUB1(ZxD_%+mLXC`k0blKU-Lxot9V?UM7Y}-CU4hBJO^L0?5;Zqy6H^*NKG{(W= zJ6D`aRq$i~GVIY$pj&(N7VY8Z&D4kDbQ*Bg6=m=^_EnCS-~}xEn#B#t8$)(&GN!4p zUF(%nFKM$n$@o7$sGa`k+W|PFu`T|vf!a*wxxl@|#OShM34V*lMt+%U>1Rv2lW z%@$`%C5H5Lze@z(!y7{cnqBG0$rHiSbGjp8e&%QjE(|^-ByxaQ1vt{i#Zp+&Qg^M% zt1svcus7&iV~ZVwDg@z`Gz%Uoa5qbpRYcw+T*WddgZDI6VKaZtZ6r$(P>Og@u?i#@ zzBA|zM&+Po7tl4nlDKApAI~lzLk=EO1iRhXRo1{RoAIstyzMZsB0qj8*e22RswHE2 zxB>Bc_jd(Fu)uDqKZO<{JH+_@f67nA@?FDDarlm>Mzg@rkK0^PXcs2((w)Bn+QK38 diff --git a/r4babs2/week-4/workshop_files/figure-html/unnamed-chunk-13-1.png b/r4babs2/week-4/workshop_files/figure-html/unnamed-chunk-13-1.png index 977c1dece570b46a7d9e74f3a092acbdae7b277d..a7faf61010432b174c432ad0c2be54a7c3b9c1d0 100644 GIT binary patch delta 20825 zcmZ_02UJr{7dDzuq)C$|y(u6FiqdNY0hOwNNC_xNZ&E`)K}C8K0jUX~(gdVKLJ{dI zy@Mf23pGgZa1ZbMec$@mfA3uj7Uay#o;`EU*?Z5kpPkK0XkRt-iykT9k7SzYrbGnBxiTZhi zzc7Y7cirv~k9aP;Pb;-DF-Dm9=$y z8VFKN14)7uAYq0uGok?qIs!rt(TIX#8s|bYc$RDXvPC$5Vf-^ZSklC(A=IH}$flvY zGL4my4NFX3)_B23HSabj^4P^MLN>D=A$eT*JF{iJU+7ZQ*Zh>71i093`bN?yY_rf$W z8ptN<5wbU#K1@(mullXnPr-fi(VrV!t)!H!A0f&I5!WX|mMas2RNm$xx{ODEl5cj$ zJiY+&*IN@kE>Do!kt*YtPBs7RuRTw?o8fFe8E0Q`7t*Eg1XUmaw#A+9$>OPZEoBn} zKl>NkIv@4S(*mx+sy_{4AqlCVLOBgSj&q&IcDgDuF;la67;hy$mV7ZyBTe)YWHa#* zvL=P51jRIW`^`o{6=sN&=DE~OvAFkdviT_uAWfexp~F93ueNka?)j#9ugO)nLE;F@ z8Oul}0s#!E`VC`022U;6Z~9j9I(#-<^hOAhFeCBT0Pb(9SM1GHwKBBUil2*ZSsy2I zW?Z%K6^23g!R?iJ+e0wsm^Vz9vgA$+c;s7rQ%^p;d9#`9r$4dJE3p|}LPm?Q`RUz9m$YmI&Rb{v(9Y3k9z*I^&DSDHPlyha0S=DVH0QJ0y50>q|I3JmBr)H6 z14wm=S|0ak5YDPj2GSRVb*-+=cs>?Qh^Op_Od(3LUg8fw93I>s`r}1u@Kp_Y!DN0% z?bv_+z2Zu=J5P~Y$maHA{&XeQk9lrih5`L)2J55p(B_j#lhdQHNhvGAwyoXbvDbd$ zJIA7H*{rE{U!2fLM)c_hFeAom;bt{;;H=TpBd9gEwQ3Uh+V9qRH#g?9v;j=$OAX{< zC#sEJLS?oFDo9i@i3pXJ1s+7;22!mU0X^mB2Ut`TmaPkL|66hf&#a#eeF@w}9%}BZ z@k^LGG^K}p+JD@$W*$}fZ6H;Z#Uwx5>LZS=Eo^u1R{*`21apff@~}gJf4SMV+2u-G z&N8Q#6eC-k<*8Ya?9TDr5%1a1a{}SWNouO|CE2U-L96=4e*+jy~NgG zdHNto@w~r{^H|DMk~`JFmD#h4Xe0x=$)jZ7XVuo6pRUs~|6;#+`$RdYxvHMBsxdaA zxP6{oj8_CkR$!H>WUj;&c*3^PnhyMN_qQ{ zi9|LjdDr-TS+Ke+)FyB=((*b`K8cX;#5V9CBFU$iL&O0Tx6PPqvTd8lz}rs9Z-}VU zjOg7hE>w%rR#x>tY)Q$o(Jd>{MO^#ZOliT~g4Z!$iNYz!>bQ&edqOQU z_(MAQSUmzVt6SabW^_EP9-oTCLiu1PGl6QEjyrI(jbM*yP5Y2#2shtdV2f}ZFvGjh zH`E!A+D=DFDQiUFGjHbCo>*9Xxjl7uoe#x`R`GyEpd!6c9IA6oUTqU)W6LIbC*cKW zb8qRfnZixwKQf7oYnM?>CoeY=@N$}`9W)01F8 zpMH55v4L@hkc2~a!pqfyO0gICUQikUenu9#STa68@ZZH?z#VrL(xRRedRJShs#n31 zuE%U?`T~OynM2lzA*@n$`9B|&5&TlvhkG(ebkd!BO+aq{YuTOvpKntX-lCKw=*HQTr9wP-t_H} z3||VW(mVBh&YK@+Kku(r$$iFNLP9iJCMx%1(|&0Cn1I^-E&ThPF-r|+IaP$E+vkK* zt19exZr@!}!X|Y(;UY@ z!g$x#8GHKvMu%m7Ck9qj4`vVWnP&`8?Q3<8ePK|^n^ za_X&=l7y-wzfq+4(rYj`bB@9(N!~y-{$#L3rQdxikWXpA+^~~VmyB`Vs11%5(lx6y zN3a~c1~-9oiENP%SG_;6RJiszS*b|}arNL8L?frzWu44n@io!uxY*Ok_>*Jj5IR*p37urH|qmWx6zrUK8Jf184 z1TiC$EeibG>A=mtcG2zV6Y;@IaV4P?l==^!vA43626OdE%Ad1F8wPu0)6J6gZ&av6 zjTI|qUAjpoviWOfKh$Q*(pzvl*g0{c2*F+aa`uTsO9x7c*gXiBP z<*()#gb$!rN=fg1VnyfKuFwL1{u=oYHFdRlRrILXbduikKte%z-p4oh>ILbqkuP?e zr$13nkDh04eIn>C*&Il88x;9NNB&O?K)!f=qWjSQ_t4t^A|av-7&bH;QuhnU_oeyi zNqh;3(^q=)pP*n`P<8rvb1Nrt)Z>`jGg;azGzb{Py@8PbMm&H{s>Lra4H| zKDnLxonjS{DqEFxA4&38`Xj_uz&2wA0Pb@EO8s^09mzJUeCy#sWJBjoo@CS$CYtAJPd(|*V;J(q zJixPl$E(ccl8oA>tMiP{1pa^O+&HB5uJ*?{6s$VyzMk_&aQngF>Hjo~!)kG&oVby@ z(^dVM;}ef+{EE`9HpCCShmb{sK@Y`+>55pEb^Tck zKIC8o6*ILu2L_=CNm$lTPnm^NK)RNWheT?26hl&#N3=#NEUiqY(=SU1(KDjiYe%ZP zcLqTjY=7uVOAc=1M&|gzzTXw)Gk&dLqM^$l^2A`Zi~n>^lO39vt63?%`ZP{FL;eI8 zj4A$`bf;Xm-c`iAYGDDiLUz;L`>yy(2^hahb=D)vc~#4SXN8#=Q^ipPST)aWy8VzZjM1}p;m>F z;VB>3CkueH#jsO}C4C=KzFJY2bt37FOGyscq=ZtV3SP_$E4x*B60a^T?>)-|jci!) znX5xQ(VE2d>5E=XCiLHE#$LCnu*4_Q3X6x@G6o1qQ_%3=cr1@y)4^4fO$wPH4}+yB zNtiY?gMs*;G-6`)CXf2Usw+&FFesT^*`?MV*W+xF+|(Dd)SX@YO` zo^;n&y{uR-p(^X0k7LJOkcVPm2ysw;fTHri>G2V95Kd*_Ak(&08IITg#DLEJW;4S* zaap5=Z#7+|4*!>&3rh&!i(YNaKhnb`Kj(xlJNiheOubQ?^&Wef9+sz0>HA>ojVtiH zyQZ95a6^8pj;K%(2iajhBjKQCIXJPTR*g`t&&P4#K7M`dG54N$$uktPV>Cl1LVv3Z z2#m7keLQtZ;)p+-(mIm=@+~buhq1DQfIa1 zy*?+I-EbR$-<#%#HHt^A#jESBNRW?Aq5hIfRaqgFRlEb9eAfP+kJ0XyC3Bz_!)9>m z962gwU&m{Nmqvup25TVXL!^XH3586xqE`f9hS#i`{aUDHQjhoQkB;mC6wF)YXZa9! z7R%}$^zD!aGWRRDJ7IOY{nt(cIVT**xv(N3)CWch-hxZ5@8Dt<&`Cptq(A6IbEYQv zYY=<0QC2daeaRhEaqV;Lpv!WHvjq}(zIK_Kmfr}8lwc0f{kQuc#amdba6R>D!jXMS z!(tFAycnCJ1gIlP3Z_Tt%H7d6-l=ch!K0h>6BDjATkYD#E1)4v=K`Fcte-N;EJ_HT zfp+8H<1uKBMFe2C7xyP^i{lq1NMOATXm+olm(mf7C1pltB`8KO7z32b%duBJIN}2*ho2O*I8(R*;F9tiC-yGI~R26ZlsP*ubZS>md{?)pcbMCz3E!|x}oMaOQ*nINuC zz>szkL$p#-W(e^Jg~rv9)Hi*<3b8RTv2~_K?(58ErP!3~=ezoz39a!FG>x0>6uof+ zt>LX0@XkDPIf6NVE+$&viU)U&u5_&p1?2yLk_THTc7XBFa~^Q-8{tz_ z^co~!q$GWZroXV%Q2EzI`nw*KWkL>6OdtCZK^e65{;#6%IlW{>cZ8Ss%o+2#2_bVg zn>HXP_dp8)YWlMSx}Z7eBYBXj;5b^oKh) zxS>%bQt}~|uJ4GCo(A?cMNM|c3o8_ZgZ^^45G*E7ED(}+JeG&Two*O@S63-Ilvdd4V0h2n^AUs%OXyurbi_eC%gm zMzdUoQAu=^FVh2&5Pv84f+)R#^2B(#y`DF|izT=Ce%Z|X;RaN2JEt!#*z%dcwe0AP zKxiA8gZt#EzB2~td3y#^Ix9_EaR*kqid4yuwJ+U83h4LzrR*QS7n)q&dX!k*I2Y)B zvh1qFao?1F%>K*7Z15J)!acy)Zgp|^>2q)~!=LB=O0{Dh%Q(!Ko%X(kS9AG2jh8Mk z+c3XdkXzoX)!+29xTR(sF9O4A*2*nc%a{>p!$s7SxV!0fBq(Y@V)JY>p0oRgMXT zXf(;yTpK9RDl)6T-%{MWuG%ECQR0d@8wkq{ipoNfzVdl(DM%tU6!+P(W;E~~htaxi zVxY;&sTNxt+)FDyvjX$|KB1yr9pl4$8d9R4zkd;$ZW`Y49{3b3bSY;^5JuAwv zcyFonEiqg#^?SsBqN|caV0EY|r*bq8P#ROLEi>4WS9ONunXhKWe-;H48V%@wIEJ zsRxA6k<&i_v z?-LbY;z!{+Fe5!$IDeaZPJiaRiOX@S84X=1`h|f7G&VYI?_Q3I7Se7|Zfrpcw2Dwk zF=pC+r_<+IF+vWV$@15KcItJO)(cHk1PEAB?H&1R6Z5t>wE z1F{CZ51^+5Zhg|)_-cBM_g?6vqEh1(&jyvPf&J|j$+yobL;ww-3wJJ#S)+a zUG+y_q6rB!U9aUgR#>%45<_gD3fhQg&=+XHaP^j>{nk;#%3*cn?nr1u3Q(oV8Xi%TZ|de?qlUnYMR_&GfQPvhZ% za)iv%`+8@e#6~Zh-)Pz%|5U}c+6qy|1S51`9lXw)GCV@M>B&pBPlQK5u*Z_Ti=zij z3B+0>m#l3IN7~1DpoI8cs*nS1*nFeS|7^l;)4o%HDf!5R&h12ye_0lvz3?)a4UM{Z zOaGm|+!OW6tf31#0Uo2LneUhlNG&?;c=#w->{DXj=DSVXWo)} z+j~7cqrV{x@BPJqu?Q*$#5+r4H_ZWL2gc9$q4KpDoh9OAxV_FIGkyO6hK2_DIu51f zZIVRsmUR1vz3Sot+>5v~(_O{7S_y%(;+-%TwzBa`gR1Y^==wuKSHs`i%yVasOiMPF z8J)i4LIe2BqWO2*ZX=r}$T!-VYj!zA>z8g(CP?2*#xBlho^Dl@lbuVYR{`FB`l`pi z7~io}C$~7ALK9B42(xPJ{k`T6iW;`|-B-Erj#FLpmmx};$kC{k#G5&8m}siEF?6Q~ zCb%;W)hDdF{kicfQf1AxoZP=??g_0!aBIE>OXFQu(Bk zLN45lk^Q-;wYpJR)U?|WabOynzMXpXtJqBU{+_0Z6}f}oCox#KmL1nrLz;GiJVNf& zd((7XKl|n$)1JJ6LfpLG#4fjO$J_Bq_o;*T#FtV<#>n*2gX@jtN1`x$>pUVrl+>VJJ=u(8CSXCqU9!NBr#Y7%E2%f$heF-Y_ z%7XKU=&Z55^?Ty?&w&su3PgThaR!luKI`uK6?|N^W zdVOom2G%Oky~g{waN;Lv<M3ML+IOP@|6r;3C5~p{h?AwML^j;TSyBEF*0~K>>Nu%Kyb_mxaD%ybncLI> zl88C}DeaUs^vZ4PSEONTics|xSdMm4%UvNW7A*PF4?E#c#cg=H{Mx4gdBMY7VBn{t zyN1>S96R?^>55&+)dSp-9&+?W@=HjV#5FA&>gLfyM5^U>bV}pr-WM+DXezzy zT;ojz_+NZ@l1$QML7M}4%$rxdB+Qs$J2~ukC^gNl7g9iMVLD;48FW_j_GiKBEX~ct zbZ3A&;|%m^#?|*reia|_#(o19xY0?!nAT4%Z2SB9$Q_ojWpP1kCun+cbNd_i-hcZh z>&^C5Il_7rIs3ddSMY%*%6E{DR|JoyKS&gNvpxC^B5Ik;trJ<2gCP&)f0OVp<34`* z4ML?_<%T|MDg*Eb&lG*Wq*ww%pL#Hpr+U<(#MTJR7G1wpM8J)7RY5>&T}>*;%C;qx zs-Foy!xoJN&)hnE?-0JFeflHti!XZ=VezMrBUE+=jx=5k^H_4chn)0};E!x@?PT8` z+lSSz6@zH2dDKwGM_I~&Tb|pNXFMa=Mp7m;u|>^(NkWL>SKck3QBacj$WBfYa9h9J z>L_&cCD?(<0=otL?au3H^tp%72-!<>j!gwo(Uz1=lB1=J*A6+p+ip}k$X?2;*V0i$ z(*1;#NA@nM{ZDZ1Xy096d-{QDNcBCOdSR~Zu~|dZSF*+BIS6f--AZXU!P4XLPCmx@ zj_Ydex+rJH!pU!PK6zNJoXm;w@QQo0(UTWXNvawU+X29vN|V~DLaYd&*e_l42}De& zooq}#=RZK;X`7R{(9^hB7FMqJ5NVb5C-Y*9-Z<%lU%e{30oi_Lh^wI&J^{d^fIgXcPejJP(onKv zN@i@5GH~s>91QC8y7qcH*007{EC*pNlQk0(W|6@*Rbgdhx<_33OmhpKZYOFb;e7QT z)ajtDI{9zi>}UB~c~_X1H{P6;YR*-z^?p(o#^IH}cuzydN+rcPcN{?<#E#8Ud;tv@ z-uc-j>%%r{j$)Z|uKS^hOWq$W4^%!WfA;GJ5!iKm`wcEDrL&z{FuM_I>KO?T#AnJ6vOu!Eg=v-$o;!_jE<{o zZ)m@=G%j$9ZC$QpNvR3&Rvgwxu?c%Wo8G zD6Qw!==JS*P#mkuVM1pV=6T>ASyqnC{!!&9n|5OEC& zb-IkjXD9=%UwF6DjXQC{?T;+96e#Ns`#y-S1 z2Dx%Jcu(RO#=IX*9fHuU#m5!kQyHBl zej6XxWV)RwIl_YW^0F*!ZkYZ2J!D!u69QNL02di{e4Cu?3`gBLvtY?Em)<5l`Fp@i zyB_xzP>^YZarFyKa*hM%Oga>Df&p#k+!6+A-x9SuaBM-`uuEI9eA8RH<(oq*Up8cg!Hj-H z%pqK zTH@JmS?kx|5q<;JmV16R5f>IMO}@O>dFSn}4?^nP z!icp88^XaTMVP*8RER09kM-~;1+&Xm#jb&Y6nCUxV^|f)k%uu0uRe$5LQkXob(vZx z7W60QL8WlUZHTf00)P=pTT=(GNrmfSB?kATV#Id~a!4C{Qv|qean@D~hRS|29B58B z)GTx8lFtTRfA#G?#vM1ms=%_&GYcz=F~UKh1j`J?5_dO+5^PMP^EXf1mpk%vm`yho zzG~0b<0TrEtto~k1()coA3D-HdQvcY49WIK3)X^I1d`_nh}>@tIGX>Y8W}EV)fPU+ z_G-}V#TQa(A7FOSKgLDS>cpL;Wk{!&dk{2p5obf3n6h=~C%`{Qh26%hZxb?&)~JJ0kOp z%$7Y6&uQbvNgU}#^qGSp$Nv?y$n`@5evY8tbOz214on0`M_stCl!K+?J#Z4zvcwqd zH465ubw{cew7qX=RXy-d0JA6VGYHw3eI=E; zl!F~BRej`j8;x8=P%KK?t3cBo!&uQ-Ze7ZU<9S~*-PY&qh3WnjUvMaDA-R~hjjLj{ zaDWDA3t?%-25*ChbXdBjFUhv_m@9Z4>?ZU9@*7aovHr-dc}hA1 z63elFbt8PhShE5!#qUnduoZ(_nnS+0@v6GYac-+mX~?d>vwW$tt$A$MB*~-GH)Yl8 zrtjQ`SJ!11 zcU6t@hbg_6@FQjnZ;gAao3HNd1`#Qi9e(`vT~If^6kZ4j!MLL{Q0(#z4Zme|*F%;E zQh?KL_QXi-s0P&QN3^I5i;2A(1SYpC0t5QME+B|26(=pLd8p?yWtk#Jn1hiF+pcV> z785cHk7lg2P!E8X5SsoZ&XkgIZW%i{LVw;^KUKd{CGH^N%~I*delDjc?IWs9%LHQr z=45VD8))9J?$E*>5{^-R%!7cFool0E2b?ZW+Oo*gAGcl4>07XXh@0P{0D{rQGb!Ut7J3d!O$D!m!FR&n>Jw@NQw2{$cE^p(9@r zp7MsnobhjEQzsv$KJox@xY|b-7C%i?a#7PrtU|b%PNJQxde`dv751lZyZ`)Q_*mFo zyRbL(4Gz)vMBxP`%626%W2N4~dJ|TxHO(WtxkGPyVp?gdRyL!O^wKX`N(0%nwN4d| z`bQjmwr#pCSgXX8#eIC}BLwnzchW;^p6Bb3r6Ode`P*V|tHL?+zx*bl@86VNHIIA46V)5x zb2+Z7m@qB)@abjYfX-a^7p`t$S%jd88=kSj9$)MEYpvvV z+)1fHU-Dq!Ln)2KUx;)}FpU+bqL*P%d|*#CLdfE+4kP-H-+ElCkS|r3LM%RG?d6I6 zr^;S@alOws5dr7S2^xz^)jqQlN})aoElT{;m`@8iz>B_rSog+e@^^@!1~Ozu3czl5 zB}$=0!Xh5z2kJN&h)-h6CdGa1;yrrw)(3vUbd^ZA-!n|p%j&(tlMGki4!=a#VMw<4 zBHv%1jw_k#MaiabwcHz^Wn^0V?dHv~)q{y**3db2bbc?}jbWd2;}lYgyZNW+3j+CO z3abe)D9;T*q$iim=5x#}G}C9Ch|8Xez0w)bltj~M0?a&GY(;?qoXWeXlFe7guOl0Q zOsPkl^6kd|fb6}v&h4}zJ}n(I&oH*SXAymEk&wzy4?4^NR3v`P&eTr_c1k{A5);K)zN53`y4xvzr9pO`+C!3J&Bfvy|xFcU=LLc|U%6 z+Du+Bl=0%}%Ei#??+`3ck$cr7zxl-Ot$S+s5!_j}**MKD=kV5pY zbG|&qlNW5{_;45EUVo_cxJEL(Q}7Bh&7aAhyg{>Ay~bP-xEqn$A+FnG?_~q%JM#z2 zA+(_JG!;$>a&$@uRdn{wv!SeZ7Po^EzK#E(@8>XTy((xTa`=37&?xHB)g(REYPCJJ*k&t`0-_uLa6lzGp8t5s1pt+ic^QN2>QSD0>2>c|Jn zqcp$=OvqD6e-C?7pG(0|AU_rOhoE|CS_n2v=N-M)95g@t zv{DQfeL0D5NKaF%hn*vs0d}Cal~&L=P1TjIM`ys_Ml|!WIsEcaTM;(aW&U$L8=E2Z z;IexJB$jhFh6-bh5QlpAv!EM6Jh93sE}~?r>G4_9;CKXC>r0OoJ!x2cm6LQUU4KqR zTl&^#AamIbhV0;SC9Hl;at0AdFItTK$^LO|PR7jMzrji=(!- zpdo4Y2zxFQCiFa~{S7^jt*N+$JyZ<2gO?-Tzrjf5X>)x;&YBL%aK&`B?m(<&wDIV} zMXiOt3Q>LMt&(kTQwB-+|JU zk~IWX|6SoIk{aE4y`?zZe6_Rxi&?vPriTTUaiwiaivio#B7DJmkH{8t@NGBvMw%BG3vx&{y(lu<4<3I2!6G(QqhcMgf7cD?X8KN8X)*XW-Kzq7Jggg?dJg;noq1G zt!gbZPO|OgUG+K?qI*Y-ZAZb=ntR*TjZTBry1U2H8@P{qW@g=Xd(+tV%c4hSUh230 zknuv7Yn(T><%?eqRLYqID7KFD4Z*b&!tliBAs5#2r}DmJ$8ireFJM?4bwqq`bRVS` z{p47OG#V%^=qO1v5hp|fD4T%?HMjKN^3<1`eq#eEN)uIMJ{7KL5G!A6`|j_h8iPOK z$+q5c5j7=9jjsk7!H;*vzbV2vBOdm)JsJ2~`A`ZLD{Na!Jv&!fq5lnJ4pnsHg?2jXZI+SQ`!BlW)F3)G#ArZ54YaKZf_C3vV{G9F*YuP7yAQyihq}SAuf3Db!i32 zThN^sH$gVN)tepP2&vQJpo(!acEMNWeKPpGxcu2&6X5Q*3*3Iuhz}Y2`B&|TQ!`b+ zdL;SkHPR~U;S-?lIHMVBUoc}D{gU3mB>s2*aXLplLOKqYwx)ubmi*CvIMvO*0DVd@ z9l>ANPje2A?|EojP~-SmENu@Dx*kR}-Rl`gA{-IvB zbYE$@SwMcYdaFY-$3vAzXs*^A>9zC(zxZI)$`!aUHrt~nYQL5!O_4}@9Dtif^1xry0d^1f{gIWM|HYl9w#+q2$kYl2hg;4_AO)13*i@NhgEYDi|7-vu_( zwI9W046mFo?L&s|&9m&Wmx45%?(6FixEJuLu3+x{!SdbpvWT+Pzb;0N-{=iy8yE3C zj=j~b-@A7|@2(5Uz}J)xm8%p-YsM#>-prRe;XQyPyrnL-r_`708zI@FG`_=Lwl$Dq zk#uHtf6ZeZ1mzw-`9e=JJg#cM zE8lOZ7;i-93~;p+XUt6=6)E#JOED|bUH)CkK*(m3XhJuiP>&idHTSu_dk0keQu6a58?}IY**dhsDyhOaTGl)x7!__ zK$!vGQ|eW|;iOM2pMl`d)-R~SgP=RlTAD8Qn-nRgD`6H|eY&~BWcxzqM-G&tD)pv4 zO13$&Da%9r9*PWmStfUO3GB)2Rj8C_X9$$(`^H=GU(UgnUIYR7buY^G?j11y(VX?8 z+lnF!+c3#nTIg}W|HiGGCc|1eb<+KX{?6a2mODk!_h@HQ)9~(mJde(o;awpHOCA2j z#r`Me_Fuc6*u>j^e9D^T9DaHBDY||SY*(qQxw=60*>xAGW8-v8^TeTn)O#gtW+r5` zs$VC6)oQx>KX} zTNN?9g@;5J6J&2!ut$B;^5foalQy^c2DK&4E5L<Y^eLcp;Hh=ra-(e7QAzY^HE zj3p+)nLMp5h#z=#p6@F+H?LOU@wcxRyo&Tw+>V-HscQ9>*cKSSphz`Q?j)-`rhBF3 z_=1_e3avq_T#ehj<_y!Fu@;s@V_uG$$sJ-RN6`k|Z?cltOkUE~A$CV9*bJBZ(@fm%l(Z>41Km8$~iEmI;lFuSd-_njs_) z{U{s;r9XI;IZ0o|g@UyEubqz-4kmJRz@}kQnC@6m%Je_tMK83GdS?^O&hfwhe^?Tf zjN%rFTp-kaPv<;JIR_mh_tmSMzkO0o)0YNHFI!q&eBj(dCi2_)f{ulaMH#h&ZF@h$I2+ZAEW!WcOwc)L&HnFMR< zhiVX2RxoN%Er1uyzL)A7Cw2NSK3JYj)BF!S*h1Ccidz4>gM^cRl-dQ*k@6U^JA7cg zG1h)2&!jOk(*=x00NTzicc}XHe|FN=0LfZE=fnQ~-T(N9gNL8~`AYboF#*v`wu8vv63ZpJ(VscX1x3vg0g<+xOVO7|@zY+ebw|h)XNe+dhrj|4!GZIYM&}$q^`wpbxtsG2`aQzYEo|ozz0j@WY`m54#QHjW$5R1mKwe?ADPt(y-Vb6F{%3W0^_~j^E%4?q z`AhKK0)`#E;A~u(5N^)(`Holf$fq0N`EU6o;3iw1*SHKgNxTMgXntKM`i*{)_cD^Eyv158ms1Up61m6AKr%uNr>mQ9gPow@!4T1 z*b2B54xZ;^dFx2LpZD}Y_qD1`{-mmGh_B@EXmAdm3-!i|!3bb}7A7&$AA7A~F?8$a z#l})A3|OkjgnmZ2lb{g@QZ)Hei|G6RzLimpm`>OZlUd55-O)fkc7vb(v6sf(|LY(A z1lg39lna@Qb|R^=J_TAw8G~Lm)2;aX%n@)PO@bbZDN1FJhT}{A2=&C`*49y5^=V9$ zDMn!Iyw+Rxe#zf$NOF`9vTh;1{83sYiJoM;#K8P0V}3>Y%534sGCFo?6v30^&MY^& z?06kSjI;69LC4eSENUM{ew6bI;IWdH&t%U{#H*I5iih!{v*0I5$MPxs z2@g?-YD8*W3;W|vMHr!Q8kiH=hbYf~@9X!)jX?a@pLnc9n* zSIQSCkC0Km_1wFELsbRVO9M?GT-sMo$JM_f8=k9Dgdvtng|w7Nwlg~fr*(3_{5}uq zzzVf8763NnH@$E^ zb9&Njf7qgdohEzl_?GQ{Pfl*xZg>jQ@!B-bqPXwpD{|?ztC+grwer)$ShpG~NpcDV%>^ z&G_m3B4|fYsmFslKp%~nu3=NICRmT67!SeyI#JjfJkmh)7W`0-IWKzArt8;*mq`z8 z&0y_EJScU}=l7?z->W`a*aRmmh}1&L)Sb$9GqZ9fK*R$BUwDl1uahk=(4b|`nSO&o z&G?aSvLGlhC zc-&v(P>x@gxSHW7Y7ei|OvnN&qxj-o5WcZg80T&wk}5h;tep9MdhjjxZ`;A;cJ*o5 zlR0p6`KoG&iO`2aopPF;$DWj7-(MGitvqd3garqGqPVAz7%MG*1lB3sMUJ)<<{C@^ zk zX~+R#{OvU+g4-xuBL%GX1}3d8q4VH^B9vqD^M5`(uz+H~_b{Z_)Lkf`b8!LmXE7|>CAbTRXU%W=O$ zkpeJdZeG`Rdu(C852z9oi4ks8)+*!6i!!1p#6cY_1)Kz~U07VOMBx1N!}Zr9D_Tl2 zYS~Q^$6ub8XE{MJ1(EMcGP*Twentkz-!-9BCj+?#)jy?NY(u-rFAg`}OTZr$3+ zbgAqc-$1S=_y4D|OTkjbmHP{Qa@Iwg*oO9FVmCV*fC-GpL zvkz52i|qF<*?|SmXz6MY`0KNr?)McsF9RDU-Ja{}@U)n-XR7Z^w-h&@0!zLjFYn01 z*zngf`fgeM0(j0Y9KJv4jcfXJbfZN7yzHzUqWtK(t;taa0fgFC2>(KDB>!v&f$rdU z`r=AL(A41pW_8_7?c6MUgormYf-ZEjaf&SfL+vyi;|+ z*b3IHm|nwUmOc)C1Hx}a%su+b@_RR-l~3=5LX)1}D*{BAKo?l&vn4YtddVJ)NRA~) zp_p~XYvvCQRtH|!pBt+mFrvb-`bei$&wI1dayAuU-yj1^pS9+Dk0o%TY!zVYo31N_l?os$)i|QmdHe~2 z>dsytm|q`*tAEvX7=IB%c*+Lw@NNw8%RATd(>=VwB6V)^-8C-@}?++~6C z{#qLkun;T&&IMAP0%;fpzDMq8a|DQ6hJE*C2k3uY%WsznODT|6!X$pdL zL`WzLK?FoY5v12g7YMm1A|idM650wZH3!Rl# zPdqiL%zMK;afC*G9$V{zqmq~HB6(KC4sZ&LnFTU&eo;v&N7$&{AA<)2lt(> zQ!~DuEuGVT{^883&A}^fW)+~d_Aj8ON!NHSgpaS5RKMan?0wZ?{)aJAskJ2Ymd}pR z0o>hiJEa4n8noU<>^{1?o*NX<9o3)DA2<{i@pNCbA5|OLdH%sT0WOLou{BT-+Q>cEj-wGoQ|;K7wphsb z+L6a-C((Te=@0C$4oiDai@}!p((&c2-|g5yF$@V^mO^T-PHXFbxv`sjp&)o$k{l!} z?0ro;;gDav#D{o_O7%CLpDbTGz9*)01kx&_(>v=n?TF<*sv`HghzP*{Rtj}>8bA@d z7C7KN8l< z)`!2@Y23UmO9Go?L?0_9T5Z)q@z?)=dh-}b_sL4zT0)c@gxkrqZF8;K&f7O0^`sk8 z)TMORm+w_lCc`XD;}2;L4YJclaENbG{Gp12_n1Gkl&1j0JolWppnf)Z$~&bsh^C4V zr~$B??`FNEko;f6i+7uy!wb8VQxqIXF2eo%aP5GuBh?YDOZ7U+lTODn8HyZ*^_qVL z?sIQL#*Tuc@Pq3H^sy`?%1{bL%c~#H``v9pu1u%CG*BP1lQHF0|A;la3Z$PA8j20_ zds2W_bB~ZX<2K)54hEAWUHI39f=f<7a~~m!eaSY~v!afH7>hH|I>C5DIFUbAg#1y^ z*?ke|6L+6%anA{Bkfo59u{YK>wuK4ctQEqYfW`g{86aY8qef{A)=I8_ zDH>xE(a(N&z5gy{_5@E1LfZ@gPJe`&QMisr8{zTS_4%@W3Mjy^v-B6#T3cxvLH26KHeQQ`6ER;&^Hi3vTiV8)VB~oUlahl15Ky2#02~cEK2n1VqZWP$nd3s=gVK3HFzLO#|dtQ=q`XBx|W-{k?yZQLV$C4 zwBFj=^4m6#&=m|ZJktnvGowiH%BU}Nj9rUoy}K0WH9XZil5dKY{ycmF?6QJV;og5R zmF%0Z#^94_0{d?E6Q7LR+ zJE0Efh_Y7=cK4^|CT!VS3B71~)430A6&N!eNNkq*gK(|ZFVo^DMdhd6KB@f+GKz6* z$4toNRJDk*dIa$+{tS7@ zmZpbqz=5Cn|3>7LgpO~$6@`6yxG+3IaACc0&m=(u0?rN3lI$L~q6q@|+#Fx1^WI1x z0XMlJVhI_girg831R^CSq{+^oHW+pDKAxO**RTVCv`U?(({7(Z)@@7$dNb?Z0lW|! zTykz`#ze5$$|r3v87In24R@k;k*l_^sEU1iP-r?M;hW!DKO$+1Caif}t2LA?Qvi$J z5<2uE=-;PH(ZQ{L{$7Rfd=a^LvnXxZ#186Nk-YGS<{&!0)H0$Pxo*RUD~ZoCKY{{abe-?P8@MkUr-e)iZ%>MhwnOmb;lQbuztN>|(5~1HwruBan01d&SCcuklK^c{oV2@4NS6kj_^`v z3O@gf>(Uan!aTO=<=E04UnUp*y8! z#4cy0ens2_y6!gRF{aX2(7WGT2P@Uy0^VyE9SvSyhmb(Uruq^xU*au(qLj!2xu6RA zZBRAj^rFklxUfVU%mHGUT-Aoo-yNsHDy-yjBUKPv-Zd?6XD0KPO&!;?_xP6MI_aq5+Fl=w* z9KYYfb&-jA>b#S?FoSPhJ*=SG3wkze<@F98d}mTzIZOZz%z34^#(#zShNb$}KQ83LTUB{p@6~f@&h;t{5OT~)xPJ3+jFW_c%o(P(7R>w?89Po^-K*-Ojo`r z6m&FkGq13<=e2*jm@Z{22XlNhz_k1!27ZY+8haqNSvmJ&_O-S&9&mzt*4#fze5so? zSd6jGQJp|P*@n$ecZCcPeSz5P+A1aaFvlX^0z=%(Uv7-&Ob6DzR~%!NL&YsM zh5>`byc9B-%?%y$1$~q9uN(^*w%7d48~iy`_pn#N=*82|*)pwWhnl|?bmM;GbB)1u zuTWQ$dx7(ZaV+{;bgwR1`}&(Z>b?El29bO5Ede#RRlI)Xazq8v8Nj?Y#{$rvRG7N( z8Ubn=8!RQR*!6sEWn~2|RP1pZ+syCmejCzlzYOUQH$=Sn;;tbsmr&~=Bc~iseVwr} z7k}2xzhc*(Yd$+M4!K+ub{YbzV(aJ+bf+I%vkEZeRQvlzTA`VA_`#d`hctrzDW$S> zhrT2HLHS3HsKiA&AOZnJCftJ_u1LDGz&_8_qq< zV&VaN6W~8DoS8*vRn>N73hfJkZ~FXE1D@1m{|bp?NeIFuk*2*43H#2UsA$DaVwjRn zDr$nvd0fMFkvix#d=50U33YkL#hX(G*IlbNuR#XFuLFL;#h{HCw*IPV_vLs( z6HVdJl=l}o! delta 20595 zcmZ6z2{e@N-#@}xlF^sv%0#G)|8a{&+48><&D17aj;PZ$WO|fCEr?^h-2xZEr}Scjw+^?z>9aDzWji8Uu#RkU3Zt1ThJ}<5TP3V4WrN zX6~L~irV4WawRJ{1g9+skCcs@4SlzG+!ZADAHKaU47u}Q4 z`zgSTD&?2^FyqM$@41H&)6j!3U9{O-ZI5x20~L4^7+ zstJ!i3xM&qcPG=J_q@4=JjK)2K zReo)vnW{bv@aeOo;BaJ?8&_b2Qhh_J2W|8MoppPJD}+4X_YO{ok4k^?TqdkgV5{X% zupM9AT;qVn<_`I;Y(St{Oq#|!HnZ5qnVjozXrgv8`{0IG`DPaFtM|6U-_Bl_FaHXk zXWDF(012#_JIb?U&2{e8D~0z6qKMe_%zF{`f^1Wd0W34LR2X0a|Ei*4=o_%u*J>XC zC1Lg*f_=$}?b-6rb^)5H+b<;zgrdSJpYX`Ldfz2$t}C0_y!68=ff0iahwPRP$OjzE zZj{lFuuk25+JDeGjej8h_WA==Jbd^X{M7y{gP3tCZ@}B8%*~J=)B(>kaO(cIRnSKx zA~WMEdnro6Vl2r2PnX&^s?wC=o+)}R1)nr-V3i*@)b(yoySj?>%w(-P9ipk=2+OoptnuiV-&P6rM*WlVW0C@OQ zk1B={vFge0rsqBtTgxdS8b5D1142$(icJ$p^UH=o`)>9Mzv`YGa6;`qiaZ!HyX7%%QpXNr#@+(#3T9Q=rO7?Il20c zqH#~_Bc)&K4&~PK4ddvoOv_1<~~z0Hyf)3unjyLk}EWm zh}x!nqF2O_X)?DKqR}=O2TW^SpQLY==SGQH&m6oO43K77^`_uJdZA>y-HIRGnATn+H0kxw}*h(VBOsHee!5nlt z@ZET(blAQhSHac*vZ)T+%*bNVw1N_w;acgk5_RWm(!A>tmo}wb4u3HPDY_)pBGu!X zbPt>}_cVkJQMC_zPSG+GG6Nv*`OLggzTT9iG~LbCbh{OZ;yAKLPr%U}FH0DB&LtZX z4)J9ACp|=-pyrL;$h8(5G=GH7)uuPVE7V?c|MHnIdg1|^BeSYD^FVWwUJQGtMDgsD ze0Lk+j9t-C@|v}Idy5rwY4#VrnB(TDaLLK$6kT?{$Hm zA(HPPw`G3Zxz4{*0^Q((4bjYg82y4J|AwM`B6VM^WCQ{Gt3;Z|?xl#+LtR3jGsJ$T z(%>(yu9kgJu~0t(Jz#+vI|597tMvc#Qff}y&9Q7o@z*eRQ?&`WVm;-}#4kEgilsV$ zLaBbFa`JrDx?769oEQlSFbbuhxDgn)R6^Fy3x5&6Pa?tdKmVH*zl4sdqC~{Gp;TGS zq^5VNLpdIyzu)SZfOrJ5)oki>3G1GpvGs*$9tMnf|D+lM zwiZ}Ae~nuv3=-fUUrCoi&ZVvAHFOKcykNwIWl(yA5)|hI8#1Fjk|iDgAZR{<)4FSd zkmc1H`R|LcDO64qB_YlMrLvg4jUrpJz_)#~A-(mnqMrrBgk&ShV#J8|)SW!`s-_|x zUJ)gK1g<_E^->jLc&U*1jy@o%I^2wDvr02*JpXwwpW7Ku=t57Q5 zIIhX2n#U+caqie?RUcr(e*jTv;GhU@a$*o9;PB2q&4%YaP9b==YHgJaVw(yi@p<+W-+JqE24bz zU;#o$08Vm@WQ!#z28(vl1-U20uuwipEGZU-*DQ(hcW?pDOlA?qF#i|z_FYt@w=_p^ zEEsvP{w!;K{QPx%+ba?na}MXc!?q_`eOQ-Ei52_vVr-%V9Q_?hNOkl}B^tZpuNk77 zO8@)OiZ?UGry~pO&3)CM{~EYAv8p|F6uptO&YKE*D?*)D!c3t*tq!h%WH3#fx(QUe z(;KgI*Ep_x^RrYE8qklqr(GE`zxW1U^@(v3^CR5f)GT|$JnR)-#B+J?pC@aplR+T4e)5gzM|9VR z`EP>sunHE2EA@fYRs}gy8390#J`-PB+y`4Htx`SlqWeb+BNVX&PEj-x;p!z`Ycy%S=FRQ!M63kG`X3>WOn0zu<~~L` zi{I7ssGu;5m6{m5kkYx#7|$&Q>%Z=GoqlOo02&=MOgv2&t;j#Jer5NOMYiwN^QZ}n zNK-y@R~=>GKZSSVnAW4OYq18BU+6)d7kW^x{YoH06D&^FMc;`kpU-jgrsxdxF7kiV zi%xkJwvh`eFTfM3zQ8SZEY8OeCwRq2yqe->S;iQdiT=Pe# zuB8V;$SmZ(K=Aw-VpV3|Mp{a;oeOLa)^%j;e1;WhFEk>%{qM~;=;{QgP_zcOWCvhENsgTj}|nrA2pexdQYw+km)ZS}w4ni&RVC%=XBd|Y|KabByY zbp9N2${^}$0?4;fjc>3H0|?4Y*1Pf;UiXn0K~kG;9Dc+sf&6M8A~=K&^^y3ubPz#x zqsKjD!Me#j;_s!tkMCez3JonJ_#E+}OC$%g4oXNgnR$5EW75Wvafi`mld)lHXN|x8485faz56 z{^4sd3SOw>Eb`NH)`focVQNxf`@G+{d|S5%D2cpLwvrzK)&Kc?&(<8f#9#LS1qkM~ z(!r$NNpIN=5!3g`aZYi>2Tzh>OCW3D#V5@R*N<~ZyzkI@bvD^*E+QtpqFo1JJEo7| zrA5M!pNYl;A0hh4yFMAOEntxZh)}gidD+5Mdv@(S2 z(?RZH$wy8eERvAY(A7haG8Nj6w+^l*^sXvYV7L>I}>5zF_^Bj z;6`SX9&-Nm^?S9laYLB@*6DF-imk%K8hk1>hY-mP$|pA|Orc}?CW?havBEuy>?L9AVu}X$gccn*K{tfsa=8ioyD?zmFd5QI>Dzyeg%PxB} zn%p}R!MLMXh-GdBD4(dPxNQ~h=DO8cYGnV)e-2#tuM5f0av@tna?Gl@&$^Ff zvOx=0f7iFIJAY=^q8(%KWO!)txbE007YjpHM~s~~7Ta$oIA5C5yAJ!iVQPK7i zsD`Et|6?qAWs$0H-<>Us1sad5thdcwFoGGSUH zC^KOnR$PB{EGGn|x~A{8x>0#n@f0QK1)5#D`f|dqWb2mrIGaak*kgS9@~Jmn()zKg zI%scUf`R|Fx(D-+BV+E~>h6ZnYkHJ@J4X0F^`bc6G@=~j6(uzJj@u29Fp_O|L)bMb z%047+bz&lZ)K?B7#fVv!9JRX_8y5`GN_gkg@7TEL2{OU=-Hn-uiRpmb579Z@ z(~8qtjZ+f%xGPy$Lc^9Kc+t&+v(#&_(=0W!9O(-bbR64Q;(8?uV+L@)++T=()j&zW zJugUiOF(J+UfIWsZj3ey`W3uBnyD0~AfNN7^&0qpBgI!;D>!HsV1KaOT;)Jkrb5`>pV@dB6}YlnJAH|F6t;QqjwOkLbozPJHGZ*oR(@T;=~*I1a*_r zB%00EpxOj=69<4>REWA!ysUhll!axLT20{y*`dkc))g}L9LZ>+L;!fb3uVT|hvFWp_QrAXNWzWFX?Ki4 z`X}=^wztZtU$ODNtt8K~RC1vn*;e;BsZaS96XkwC^V(t9Z?&(IbWLt%qFr_*wW*II z8r3+_H=YlFh#edwgB1sXns87luEy0PxvMz!1?erZC>BU_J5~2avc+8jt2|USMCXik z?ydgzFc=S}KXrMY4R2uRZ6jzp?Fm zUB+}W{z}dI;rF@4wtubC^2_3#5qVB`#r@tPv|l<@&J<;o1V`Uc1(^5V@U*N4H_%$G zEd6Y{hpW;bgcXq2@+hr6&&C#Mf|rIcohtX;xEe`m@wTcj1|EhVkN*O;{6f1alV*pw z9yWzgCQK*$BI^f_2jh*6y2=KZ=8pJN$KYIYP!Zd6U_Q$Igy5O7rWIJ zAQc_|7?HLh>F*A(_|8ThulQQMpuX9!+8y8*Q$`lM8s^2FZLX*)5#J)zhm;fi#S%8MjB?4L6gZ%ws@`4fm?h%c8P$YI`N<- zJU-)uSVH)4U{#*)RM-Es=ACrVEy(W&X$>O6=tymLMO^Om-$|wDE}GH*Az#y6^$w!& zw;tBJ`OlF%D&G0HV2ALM16Fwyi1l!No9OZ=t}X*9RlNBYvW8K< z1j|87*|*|0)mg2Ml4#Ps+QcKsjOi6vi_PZ)Tm^h4%QX!a*sSqT4#L8bk4mqmBsJ97 zmv{%Cu_W4VXh50?J^CFyr{|c`*Jzo+o{xGN`7^G{z4&BWK--yf@J^KMfn`Re%?)^A zFTOOuocmEC3l;V*bnNIO(eA`HwSqX#bt|Degr?Ec3}wq6Ee#J-N|MdAEUigc)lCJ; zB4NnF3cB49a8o`-(AW2ILb^<9^!BynAJ>J|i6UeLa_k#E=Q`m$;$EcL@8_OAR}O zk)i4-pZS0_!QN3Fs4=rzA6?2?3lQ_(+ZUH}ckrls!~?9T-yDp`Yhgz&owCoij?d8NK1d6 zX1H$hg3bQ%duk{O?Xlk*FjeHXG^j;!BMitFhN~_Y3rSrjK>|!qkN2~B+JVRkj@qX_ zbv1Tz1?XSa+ZStHbv!eVXlXf8P&h2}r`<$Ea7ots;)r%s3xNJfBj%DV8+Cc>)y|@Hbt8yj!YgU z?54d+QzzPT@?;d-Bqcs78Mk5@v!-_=yOnmy&b=voTlnyciR3!u)PZP~IgIE{PdM$d;0pW?hcccTXCM%&)by(-_)R)}+plmE0x9256%fI(Y5` zdgusdruyooNaH6uc5-Ml~qfB*oQ$ux>Q2kMN1?1PJ9d> z_zaxBGIsY689NwO&mC)nA>+M$Ud&;Dle%k?KmG)Y0pd?k66`Zvn9|^k*1ZZUCl-Td zA(1vb*vnT4J{Cz5@tm72gNwh9U!vHwo0$HEjZ^}fo*!6fh-iKcL%E^p?0eYeL zqY1FWBR?qCW0BUh7eG)=F2E+zY|{0~y)2g!N0_o7tzUmC@P(a|6{CkB1TKo$@znAs zMlG-WPB^)0)8{9&co2RmH-|#re5w6U!|z*bV?xz^N+Sr(z_%1jBdzE6NIYl8`T*Im z^&Dx-A24K~FpssD*4Giw273#YoIzl^?htZy5tmZ`JdL$&= zdlcvY`&01Wt?)DPo^QJ8Rh1uS8n&c#_)Wd((d+7E3LB8*=jB;~hR+%vOu>o|dP+ z-n1K}%3XIn{#iEdeT&J5gQVtT5_hNa#Ee&=B-6QW9{AM$`jgqApuybRI}Q_n88I!N zwf*+;4#)l)cF8d%d1&mgW&`CM68IayS?Wv(-yp0~+Kb)RYXErsvsx^5v!~rP;qH^c z2H4OaiJKy>Pc38i+MnI|+~(|DyD*iq`}Mt>@lSdcNfG$_mf~4Ca=D1Ck;&Cr7K~LH zVb6I>hr>=&zrI3vEmz!f8^$c-aE1siVVX%8CX_liuSHBfQau~#b*?ACqUUyAw;&!!Q*$+r_^sy6d~{QS z~n`}Oo zc8qLWX_S5>DCSB1CFXUR{iYB&#Yj&c=AMuU!kfqDj*pRj%L>^%oLoXJ34T-(y(~kcKuQ}qkEa6*q{VDEbL(#_CBX(d7Kguz+P}|Y*Wjwye ze0oID@KPUDDveTNQPA-wDY38-d6J|b@tfz~L2=yuNFh8BI(Dz{q+yH4nP3Y@4rCNN zGgY`G3k0yIFko8#wy+Z_Lgr_sV75+J0xp|l z-=0IB=LRU=>KmI~zMOYGEE5aAy~BZAaG}>Cw4aQNF)2Abk?jE#R%L=h=XcWW>)bF( zZ|8qhR>CA?;E`1!WJ`Sbs)OLcTc|f4lj4jJ$Tf)C!+)Ly6k4~0s?Fy3es?O!#4$2q-3dmLIhZiY8EnLJx#PvO9{L}r?}iUE58k~x(ce?;-cR4B}!wXl!+uUTQz z`t!a1+*jW2-<&CU9b0(lU}i}oU3%fRDe-jNvPQ_j+FJC-Mnc=Xk#DWQhP zQ+#IEHKU3KcoW1>6EG!{Apx9WeKVZT{+gR{EZnPcb*;yLSX9Z5wOp^$H8lg?x@%ub z;RG-(Gu_lvhf(8El6gqb3G~aHGD41p@J*&KmjiX3w?Cp2`fWU5UeWl<*__UU2`;0Z zly_{C9~;tMo|Q(e3Y{FX)0+#69wFB81yXnJ!lRAasO9Unq@qqPA&5kCk5?s%JhdCs zOHCxXYn1e<@^4#hbv&e%B;ZO^ffSqoK&9oRc5xXUG<4^# z(p?5?(*eOL53RO}kM^CzpNmQSJ@j67FC>79$PvD$v@a)F09Ijsa}93TylHve$u3{q z62`nN(Uq3|8J`>(t9@@k<~qV+&{G5SGyiK!Cczv^C1I83?q`LS(n{Zq3@_0| z8#`%iF3^#{kc-()YgnCZwQ4ha#Q5s>pZsSHSOvVrR>kYua*4i{(|>?laPL;~z>b`x zv`s3hZh5#;ig!|;`^YxJ>*O@Ds*S60^6e`5b-%>1XrCSm8>5}auUJ*z%dy-oo+XLf zA2TVRZ^UUV#WdwyMUmM|yl0(-?KmiTzccXfUqGq<<-%C|-SFdCR(X4jpTYHc-D{|R z>_-utkV@YdP$tuMARGeV9Bo&F5=*emG#^ix!EnhJ;-(9fI4zJBPKmuJ-rysCv~ z1j=G}$!bE&J}1P_5I?<6B|yD?F1rh!gL1j}r!1G{= zi62=MyX=x5~gTeu^|N z?r>jT^<1{Y16uW-6gFHEIQB%*5}MQfsJz&Lk8TQZYtY{nNPPMC{reUN6|@1Uf;ID> z^|E&|jRc-yjeKOkurGR{wZZ5XGw5^~n{ac>OgXEX$Rh+C7}rI|b2j_ot44fh+m|d9 z{xbxyq%dQ0oViZ#qdG+2SjmF=B0XzL-=_kqu0ofed_D+!ETLk_o6`48Pre#l0=<1$ ziuD-aid*FgGye7dSPmidDEr?Q3MG&)g14T0=7Y-JWN37s9P*ZnouHmFqTDQ2y1&(l zpQ3AAzDhHBqqSG}Se9>uWav*-?;HFV0Akbw>dkWU*AD{~zg;)7zA@+&lD>B;P{=qa zGSe$qJ>PW*QQ-20`RL3#tCAFS$;0uBEe2Lid76*MIQ4KJSeqNClL6=9A9Ovo<9cZ> zAd_sI+%6QfNl98nvcX=-uS48X^KxR_Dpn8T>MQi3kD(kitZNgM6(@QY5I$gouJ2L_ zBrWzZ6ZDpa+n@{=X!}o9NtUO%Fzd;HtolndzjK*h)Y#?DDoCSPT6aL z-9L-PO(JK$%IZ=*RS*U5`+$l`(E02iLvzlC<;$C5`F=8?zx48j&19|SYTG*86a3$* zfgC;c*pE(T*Z2V_0**N@xmZ<9r&!AJWFRJ{*&g`Rp+aje&l#h0Rw^FL#(A=ZtuL3f zse0^FitX;#-cu~(yVPef?L&|dMkK_)^|gUThCTk3YBC$XTw0EPKXt=$fP7F-*BAQaz5s17BF?%-fMTydZp_*Hl$lbMy8)+p<{ss!M&p*)N-{7VC4fgsG?InS^BEU4Q8`wgV=?&VP!J zXV?8^2~!8eNTC$9cfco9Pq|w8YfV-DhPUUy*+I3E(wIk*v267M>C&Qa5pDJ9Os(6M zG*hToR>@DF#jl5JRJ(GS)4BztS;wU%bYuPoul12(Tm-pDx9f7{`nTZCFE$v}J&ZvA z?dSS13D9*Z1gpFpwEZ$=^|)vBfwgjpNC~%ocB!JdixNMk6aWwK;$y!``!CN!p5S2DCGO{Js6Q7h_<6rx6jhgy^38= z?|L?Qe!K!UF)R7=@|*}A);Kc>dreRI@LZ-ECjDg7ty3c+L%K={_gil5NJ7Z>(fxs< zLi=TJ(z*M>TQ#w}O(!tI$TD>Kx`*QMWh<=PNZd{Z5s z&X^HUapyh}$8J-p7T4jz{kSTFO6s>?Nh;q7#Fs{-K6K1*jYQvCMD}iccVKxA`q!rf z-PRRXh0hNuh{c({3xvV0r11c-z8>mVsPDa(RIt8t*ZYMB7~o>y zSy?RFIh-H+Me$yB!bBzEcS0@&xH_30^?t#>Ut*Ns8bW%1THqGs8eGq2dczx~#8X6E z4CY^qKOD-ldjj^1Wd>Y-94!BbS`8!2pPs zC?0@vVCrtU;`$Y*k*l@c$J_X_JA?0i23I~nw4A=Nyhs?S+ZoUh!_I`tCA?+K9R6a+ z=kDoH-%JX#lWlu|z!B*aN)&UjRF=%XcPo9z{iu8Rj(s$O-uTg}$pyH?kT30_7Va^> z2E*F?V`*=X+=fFZhwt@q3LPx&HMsHWVE~f_^#Di1TKS*X?;FY}Yl~eOE&G*M)r33O zO{3=03fB@9!30@}TkIMX^ik z=g%%X_Xr^HU_;o2>6$=uS~iUQPg)riT7So@XmZ(R;$T3> zIy|lNwwGD)3`Ai?(aP8|?&$XoYamIqz@mJ!#41CFnsSJ=7b1dv_UzaPL8Mr+UF_+e zX3;coL6@K$Ti*|wVw`v1#OE)*e|saazPF~&U%~F3H9DQ~`|)(UVCN~scdF`8e`Cl$ zlw^l@MAIv`@gX4ehQhK=pi}NqUGDJ>x7`-i`y`bc>Jne;rFu>wT#I8T2EfgXq8SK& zF1hP#Wx|q92fpR`KLUo-6NB?R?@>`z4ZZ%=46=qbf(}HAykp8YYFk}GbbV4K+>cqa zFZxGPSdtk=(_!O%t$Ad$9+G=fEG=h*O!*o&URXF0E{-`o-~cg&8`|{oj@sGS2hALF zC*myWhUv^ndHQW^DFDQ35-LYn<7`uDw%v@By}|rC^%z=z>#qaAQVB2MgZH+2eKk!Z z!h8u#0`Nxe9wt3jZ>lYEVx~$sC^}jr#Q$yJ)u81d#r3>JL@bWfW@G2MH!DK+OWl1Q z;V_~xIlrNgm@e+*jlj3GvAO-U^^D_+=zEj6w^pzKc3{yM?UZP)-AlpC?TI}Jl3)vS zezuWsrJJFR2@GE5rGo*KgTTpGMoZ}0C?z=)COP!SbP z;~B_Q1Ac8@(lG*M8`pk8rZutHymZ+-u1QXHpJ$DtWITTCjgr8CrUINgBYGO5u$B6$ zJKIO3sp7LK^%E5~{-B!fe$&iO?Ka}0taP1Bbd27ykYhZ`qNP?ss^;0iqD6!TP2Xqb zp!AA!&JIG#dn-P;`F}G)XPq_nRMGM6K>hJ5@DEC4!?lGA1A3?}LkH-KxQpSI*h0QLbLP9RiuPM|NS(0$lqOdTt9-|~ z82#@1bU!|DPNVX+hcFTq8`<-WWaruTg4=g6|0j?~e=|9CI{h0DRBjY^7n=z9=EYR- z%PI9UfI+O6b`H@I>T=zWtm|}sS=c7UCx)v=a?_Hv;7I42N3=_fx#{{w=5KiaZauJk zYYzHH2u_61`YN;{MOv<|ZwZ!F`Knv-o_ws9^jK1kqYB<^@orn# zZH<9TqfPueX2Ik(OzdZ>HgP^eC)3IGFW6FeFjj~Dqn4okAm44Fb%I`WX_5i}27LCaL5tK8{xz@)8zOYTVdu#n-MrUZu@xjY^4 zj+j?W^G$qEWAorJfVd{(W;R8keEgY~NpZs9T*W@*A#BI9uAdwGxScFVO!P+G5gEse z4fU7+s;uKPUo9uz*e}}ihA$RftsPUKS@=d=R#&*ZAfFY-=2 zLRSgtojjm6kFY>_CCJ7qeE#UlG4Qbu5lU~=u`wNc9vcJbFcUstX6t_I$Gc2IkL+E1 zi>Rl4N5Yiafcg~|6F1z!W(SHqsB7cL08EmomaYgYQyafmJlzak14Sw(Xl<9ih|a2K zQJ7^3)w0;WSY_j|+}M5wx^3(>-$`wtHf4H4ZUk*`zH%e*E$2T9pG^)s;)j3s$6oKg zk8eBZ&jDn&VV>_tp1omQcQ9ELSgHIziq&i&sf@U?M)Q%${k}jbc%R(F)$zTZmca=V z^+0!)bvl?T0qP$a>Qq90nm?1#<9vEdL!cobFeXpJ)mEb*5K6xEKe@k6tKpZ7Yvm5J z6M9#rmAozqo|NmN6l6fV!JGlrE-C~hoT*E1r``hn0hYepcB3z!Kd6C`+6*ms6RHRF z^}OojT+}9`$mQxd!vc{~NH%aBdQ*Y)P(GXGikTVx3)A&S1WrfQNh*v|D|b-e_AyDC zK`VvLwJxpjI#CI@#&-i>J)h?D0$x=^+LO;O87^b0efuYejt1h4WO(ckP`T`Ze})jh90Vs`;e7j!#x=N zC8xFr9k|+u(CN-oM<4CfZKO{|?nH~%*ySQ848y{SiP%TJ5|YT)Nn4K-1=SSQ%1d*! z2>Ydc84}YgGm}uqwSGV#=hEehveFBW4?9NKQn>Y`K(lGGk;?bkU|W)%+AFAs_T6zK zkDkBeW8}At1}2S;WA__U$KMvY2=&PK26dKxr<*t0pXIAHF0#>cxa3hh@cwV~YwJlK z`M1Ak^r)t86PXU@jjI$A_sYvjpvj>+rk)3W^RWN}iq+S)djzn~R_wW98Z*E~jdE1# zWb~^lcC;2dz9Y=ssL`_|(Cg zH;aL+F{<|n1Cw979L+B^CKX6p^1><|22B*|X^a`LYVJw)mg$Iowjb?v{PaG$u>l;K zBJeQpwkGKs@-V3>@{t+YU60=@pw#SU3$w4solm9`4gvkmKfdWq`oBLw6qJM-W-22U zhhG6meLEIPhmwJ1hu6Qfi(Zz)@GsKqiBfxST8C0qep2(cJ}_|H~I@3FLsU{Y5l(1YmHw$ zW~tY2REDB>iB@qs5wBS5SYNU97#~J}fd{pZNb>ez;eTgQFP5pgU2aj}?pU*XAf))? zAnQ$Oxq^p*qF{^>>#|>yJ(c4n{K&duQ5`VwyXJ?xd`<_6mnD^Ax`JgHJ&FmuKB!RW z2v&35L{NoWu|qEzqo}HjCQWyLMOzzca?ATF(5Dv0BP_>AVTgq<1FWwD2{lrcoMFFj zab7VC{>;1<*Px(`+uBI~9xOzBp7=u^4t%@r^B6Vqs8$;#ac1DNrD-P+T_dlA0IbI( z^gBLBM9+_wyyHf`q|Np|D9+a_4}0vc7~Ew3RP}zAxFzVw$rFZqx*A2eBu8yJo9|xb zauRF5KCg{oE@fA;*cj?nJ}+N$zr)`n4GZNuve^WFZs84hR=Lc+XHmTyOcS{|HD`Ur`|&`OUjf5Rf&6;o zKAT~{3sG|U{T4Rl)^L#=aIav*lR7@a~VwRsUBp( zKu5kkBq;MJ2%#ZlQRP`BC#$RXj|-2N|Helz@nkmxf)TmioU|390T(; z(i!P?6{0ZajkH)#ZS>JhPLB{--h4H)cPk$pdhWtawvi*2Jm5{SWbG!#R7Lb^(uiz6 zFRQN28sUt@Uu&h;laCPqD+-1x{;ck`#eaGA0fkOK3^_PmoUR#PuTHl!m29ZK5;gGg z(vxpNfV$FG;?ouBDIVDyH!t-Ww(7jp*$}GLf1WUC9E#OTmVkyLZ`~e1Ky}@H))~#O zHkp8j<2duZRG-=ez@R9uKM6 zwu4f87E9NMu+chf@I62p=cd&QLmNP2VFdX9Md_4CZEzffI%CJ21LBL(B$vCx%bpbR z;^na@^dxUnmDthQH`K$^J`EEWLv^XWf6(z-5e`#a`}&F!x_4S^b?+*pM9>0YwDoIs zt!ECt5KPnhzm76X9@3@q(LH>(K*Ck2g3;ZfG(fwl?w0>V%$(Ul&x2s+)9Gkc;+e;i z)}qA*>vg|)W7*RM3L7;FYm##~N`hRDST8L8w<0&3*O1S*Xj7!$B7eLn|;fGm$ z7$EWFMN@^(xEp0;(LeYY$$i-|e(Jt_Syizcr*D!6fRAA9zd6tS=(LLiU+20BMn~`Z6DL$Qec3}U<;smMs<~%#XzrkO)mXq6W#_5}^9!eq^WB@>KX27a{ zpU2eBoibSx)af5H(mMQ=5=KCea)>XbLESQ?HMZxQ-i$fR>(vAZ*~#lVVK~LI=p!kR z$T4s5#!~IX9#MVpaOdonK)Ap_TlfFDy(OSjiOWTV{**!_TUHVZeTG(k3sL+1ao!r$ zEK928!I%$d7XhDH+*_qCG%2Vu3th;Z^>JgpMnC8ZN12P1thr5kp_Z*tFB zx1KQs5?=S!wrggWtiBKu<<}(-8WN-HO}oWYfWjVsTF5gEoaC`Z?%KP{_&CU8+4_pY z7gCuc!X5q7lolM|LZ4!}{-jAj?T&xjO6*4yjUn0KVyK=H3{N8s?AuB693S3RGU>Nh zxTsugk5H(87*X0@!Eot`MiQY;(SNYM5E^Bs*5mKwxNRw{+&po@^~WAG6ux|m12X(c z{w?hiyWx1yv0p@UI!0}W!d}M2Uj`!=BM4gbPQixINda54tgQmHbSR+{qhSwtc?xf zVg5+BxY*~K0qJz_g6*9A4r;dhvN%uMF^1Lu<;+s0yYi{a9O_l6vGZ^-+BL&h!u}v- zP-(Vb4ebf?Lh*UyfO@ZRiuQd@t}5-|vi#qphEQWx`6u)=e1cFQf%;JfR^3A&hQUzb zF8(wjgY~7Ykkp<&ni!0CVy1K$M9@UZX!ovhP$H8J8nGM|pb^$s-8S)|=}2d9EoGpY zuL;alH7!<{0#PqfA|CEk&diRo%O%-4VBo_0&$@TFAylrL1rrOVp}z_lB2I_q;K655z^s(yynai%WVNe$`!Pn` z5+JjAW1HR>KPS?Upp24{yVrfZMdL`D3|zr<7cX8JIkKur107bqFzL6iCjX|tU*H}_ zkVh$)7V8BPO1=bsl9v-p-XC=zetPW4#d7r3ZPNr4jcHrn}3 zYY=!QeXh65&V)fvIY#i=q?bAB#hCI49sg|u$|QFd&P{Uff|7HiQF&IGFS{mr1x!)`cOZYiWI9ZYAIlne`5h|8(FIaIf#hg#V6fgN1qQbing<+`$3#dk z+Jl!)iBt2}f$f3?sW>qJ1Lg>8E`lzZ`iU0^ujYm6dn)rvMUodl5HL;3J9oTzr(v(( zv+Yj`c$s`dn&EM$+f3V@*%XjxAUumOaY%RwnoAc+WgL@rKfdkv7%1NS%X>3rK|2kb ze04f++roc`F>$G)fRdq#tVNw24Z;Dg&`UAwDU zv><@!HcXnu5h47O`(J)ax5p-CVH1Dffa^U0;R#PYmP*JwFY6)31Jpa%?|p=5smP9m zx`J>l03L5aFMaxvrf<3IPqw}@z6yeS=Z0Cfeud`ls^NBVG%3WgUNd2K4?_g38|VF} z%W_d4)=GVg9HKgU70iA~-mfF-N`jqsms}sV8?X!~%Ap;~MSMIlg;QhwOc+7=GOO(t zNxTXK_vP)6)DX8j&nl>EK=>nwb&VxgQ3a&)Gt)S0SeHa$a8_P;?5*SxQ3lFNZr}mx zI;5Nxy0UhH?zAMA=k7yLp~0w|SIX7kmHB20D`*}EY-lh9J<9|xx>J`lFZT}^N-Dx$ zff$8Cb}KtsFh9UkcTIy4lRgQCB z@cBg~{tKkjfYjzP&s@Ow3jJ&1`K{Zt?_}JpMdTBwlOeCzVA4rHq>epSH}~%J_Wq2o zs@T__l1rJ+guL1)j_HL!SkV`W08FrFZ4FDCzEf`X8c+;9w7??4ExO$e_dkGL;j zB_(Js&n0{5Ut~1YtGsO0=k{~SDziqN2g;?t;#J=rr^hJ#7yK*;7x-zoO0xH&9Pz)P zs`anl9RC4TRlZNPY<7yLHvt64y1CzK^}P8XOj5Py+s4y+tvfXlf7x&(OtX>Gm%zhY zXdsnSCiy9vzKeG68we|ZyBcyB|Lso|c(5N8YNJc+`INmgwdSb z^yvGjc?rKZKaY@aMJY5~fXd-$*apQs1hMzkL9*9t8~6{9T;SNtB)#GD9dKzjp-``a ztWRm*z0-Sth3^q=u(KtaK7?7wfg!@3C?7AkIbRSyZk`Lm$3YYsBu9^`@11~EY39;5 zaoo5cZ6}Us``^#Y#H?M~^230>U!b$8w?6k@RJb5KBR=EPBM^=sa{(8>_rG}Yn{bVl zUwIwM@BbGzb%C0?)JGyI3V+{mbbsh;+t+soAs4-8sd`oTtEodPqSWM}u~BLxz%pgu z|Dzt?1%+%W`~Dv=F8n44NEfAr9@RT|^3|>j;&`2IU)1?S|9_DG_^f|-+7C?^i(efX znWNdaxgb^n52o~&${yju&HCT>3n~z99L9J{|F;uP-&NQU*;vx%Lf?KBMI&6ffymSesq!EfhQ56dZXQhE zSLlA8pS0=h?%a=fH|1OEg#U#c`Rg_UhAMkO=-0%kB1z`H zA)T7!ye>|l3w~7OaJ-{He;<^WH)Ce*gk4x|Bw;%|+AW~t?PDLd(>DEz9YV(s#p zeiV$&PUYbKqwBGgS{bwU!z|(w_25#*HznxU&ZPIcu($0`|d~k7}x2fi&gpAn68&|6%%+)sz?o{ zDP!;iGZ_+T*Ad`F(gVTVSB;NjXaCvSrON%?EhD6_f2pq`pUkF6k38O;g*K|xSS%ns zrQw#L(R$9Wg~|V~jB5>ta&6l~HX&3rR%j#@UL>MPVl0IaN|DVl37PCBV(jO!YzJZ4 zM3K#CMKv}xVU*CAp@o)BGST+&1i(CK9D|7B%z*b*}iWA)ds70P9)n|t4V#*ZJ z>9c5rA3X5B!~GpKxqkzttWz&j5p;-pA-C>T?mJU<*R~a7S%6VnQfEt(6oTvLOTzrr z6kNE)%^DR=n(RWTcyF`KoY4-_h(XfR!~X2a81IWDJzWcLdj5y^q-vq1fzFfV0sI=t zW2$6|1oT&Q*-V~MhY!BI^;r7)hA#dL8$LB2$)Rl9OOa29XtNU_l6Ei(sk*FxZK{Hh z4`)4EocOep77fSUcL@47PB1a6@8;kmGLLqhO1j#K@s#YRoQd8ir%04%t>bj)R~J>L zh`3U=igPDvf;^Ivn@f^#MJK=?;-kJ)n&WbQU0fW(h0n(s8)loKzM~EutUAa zLB3fCy3ZyrRLSjOjgwh;{`Ah9%mEg!Q8!vQFsqV061k{0BNA^!jj_r%@^LGT)V=g` z#o)%NX#KhVY5Pv7(U_qk>``I6n|((0jaTBJvQ|1_1S5(b{)f_i>uu>g&f^4D(ZSM{ z87pv;>s_lC69F<^f2LE1jv+ObQcx}qBgUPmE4sUTD9!5(jy&SO&46bM6M*1C=+NZ| zLg`ur!a4JWIPe;eF6;6!UxE`nhvGUNz`Su?c93H9H<0fXl z^?GwCdgL6K;Lx3Aw5ad8-FOM4PI^LuT$^i`Ps3G`ir97wTfZLQM$b=mO{jlP+^d>i zL}t^r>xYgcI#fOo_3H+_ z@5#3>M?MDxON7yAoBL3|5q66K_DZPT|bj%Sa{yO2)&hb&{0k*?z(K90T74i>8F z4=J3?4>OIGTIL}qx|!MccV?GlB;qu zMJMwe9mtu%0>0aJ<1LWIGZyB!%4C*H)E6qD1e8d@qg^U)ql@qg`?=p01K$ByELDmP zk^JL$wC$1+!`LbMQ${?go34W!zOCZY6;_5>opKfHZ?Nv{13MZ^P%rQ(sBXdCrQKjO zXa+_k3w{ec3ytnp+3VW%w#MfzGf1NWS$3K4v?D+KKOl_|E&oG?37C^TPM%WVB)HMu zb{$9QpH|udx`X+KiJD5((3qm;XYle6mi3FADaue?fuS*I>NlCO<4eTdO3}i&ZU#%qDv2o}QV;>>R zQNw2A>+8Ao8)r;x_+q=z(&V9z>Aur46ZM#RSRFtf-|(hJv{8(D{?t0h6zLw-7A+FX z>wXQY)K3aTzVT=hYoo+flgwuMQ#y=uwdWTAb6dMj-zyU`976fA z7%%P-}P}#bKi}(r#3BUR&a{ zSnqgay7zO{xh_$7(r8VUs=}oew9!_iW$W$dQV1EBpB0>rUFJ@jp7r>T<_TV~oM2HU z0k3^TjVBE(x6McX%U@R|r^bu~bG_X{F_uJbcH0L_8gA0v#bNmW+S1S*qOWhs>-4T? zRScIm9l_44=DT{%U!*ZViWc%bsf5|4=IUQA#}v2Bq*SkWJOK9=u5aK4URE` z|8|?S^6ZuS-sc6$MmHJ>b17Xbv(^b*toph>5p-m9q2!Y)Xa{kGt?|uGa5p9AkcbYD zsu?oAh8T-xgpzsQfm<*xk@@oA<$mO$@kEFjT^+{~KNR%+{Ntmb^!%Wptn&=p^jpNs z85&MI#u`ryyKNj}Af%?705Pk7s!prgSqw|8O57}JRqT|4UCJLVfY^bey+8Q--=5p% z!IX{Z&Jx#^1!cjRa%TCTJL;xCG62bE>U}q)epM7m^?%s3d2fMB$kWPCaNkaKjZ?uL zr`5G6xXW)%`Og^EyywtW5oxwt9iDJ^>1!o6HL>1uNR#XyC_DKe~ zf}@A58u-!kkD_j&f+DnnTnqcOquuZ#bUc-y=6`W^F`sXVm&#%z`JFTZaNrdBtxT!d z&10KBD=%h73q~hIl<(c!t%=Lpf8DE)BwATSv&J2BIvjNiIo6AZIzHcy2~B)cY|X7~ z2rpQpWE9d^gqZGCU`!UyCMBU{?*6tHO;YA=vwj2De&s#BBX&gvI97om4&y z<(0W#c$Q3M8@F8D)yc)@r^-=tnpJ6tI7*qu|WeZiVi^qmp^_1K+n|0x5=dq4x`e9(5<&9O zFn+6?fAA+zlb+|SfA7(`2XlsH>k)<~8|Lj_WSB+ci%V$PHeu6RY|n|qAU}!i$S1qK zYxEtA=9fQ9_N;7v*A1xj{P9FJar2v0FM4vL_xmvl|40jdryI`HCR*%ad()G=djs#6 zSx!c8$_k`vo-@Z=c3(GAmSVhO02;bw?;C3MHUXxech*F9_SZYh2-;#6rJl@H-y>sj zI70K%Hw(PFJ}>N(G??3`O`$aDZVSggiq8K3v7;SKc5%nvlbcpxrL`{uKlrFYTLRT_F8rU=pFhwy9f1fvRs z;a61m0~MH}<>el%kXup8$!lxAAs;RPb_G1 zGX>Ew2_1ywf@nm+&SMLziNW2taOJ;WuQkqBuRgXD_L2w9h|J{r_@ZGirOM$+k3K@c O$I{I9O10^oxc>pDU~J0( diff --git a/r4babs2/week-5/workshop_files/figure-html/unnamed-chunk-16-1.png b/r4babs2/week-5/workshop_files/figure-html/unnamed-chunk-16-1.png index 731f7a4c4159cd37a116215854144abbc6097062..e80a687903084a3971136c8fd68414cb5b93411c 100644 GIT binary patch literal 15987 zcmdVBcUTlnw=YW13|RzZ0FjJJlAuJ%EkO_jMi5joN*E+b&d`D=AVDNaoKZ=NBtej@ z7*OJngdvTR^KfWXOR;iHVDgOGrpaN=lwRd-mM9b5c@L($dm0GBUEVvT|~A^78WM&!1ONP*7A zR#sL~QBhS@RZ~*~LGZ$b3+n3X8X6j!nwnZ#S{E-~)YjJ4(b2hd>5{IluAZLW<;$1# z_4N%53=9noF&NC1D_4w+jEs$quU@@sVq#)yYI^P3wd>cf-?(wZ%*@Q(+}y&#!qU>x z%F4>x+SFMbi85xUii%#ndR1IpTvAd}T3T9GR#skK{`&Rn zii(QL%E~uy-c(gpRaaNPefzeirlz*Gwyv(OzP`Spp`o#{v8k!)-Me@1-@k8eZvOD$ z!^e*wKYjZ2`Sa(NmXl+&z zo12@zfB)Xv+S=aU-r3pN-QC^W+uPsYKR7rTFS87Rb%6Gc&J8aL3hoDoe?Y?2FMA3K zK8j147q0qeEsZkNqkS5F|5{eyQ-^??>DUZQnO>Q^mSNJuQrMf4&21z)8l{N_w6s?4 zzI#>PF4KqkSdg4~Dt}7D;r8}5WQTKx?&-oO>66H>5g9ZH&-1IK^ii)))pjy*kG$C4 z>8`pwK=144>)Yu*-5%OWCaP47BPgz(A_Ekl5i^DYBY>yCYk~k64I!Xo042p~1RaGg z3QdtrBTOOu-xC;pKx~h{8BiT9v`2!{*z2JCu`RdfsSFey(0^q3zsvtWl_2rFc2vF5 z4w^3*g^rfKXlJ=(4E{$lR&^+XWO1>R=)t=eEv8Lno(ogBcSAN-P=?gCf@Yf4eI_KJ2vE+;3t4 zoQQ-3Y)uWn^Q&3ObfKAw+gX2_uU!VOO-{ErDc#OM$e;kN*RiY| z%ll6stXW@aL*w~rgpW-T$6mLYmnfOr-DWU`KJ(#qQtfy41XmAIku#M8Qj}O4M9e*6 z#%9>MF?5mdf8{LygL`e3lE1-zK#HOatZLje8K{4If_Dmnym7Cn)Yu zS8C@70(km=SBkiSkgYOSP6w%R=s; z^1fTshm#r}Nl`-rlr~DPpyXM@eRnyS(Wm`nb9&r|KWwPhRJ~F%>1Q=7+HsYGSt$+V zC2Hl)cWel{ceiVyM%C@y4sL+k)pWZzN2d4nH3{z2{v-%dmLZCFb&L&uzeF>uQ!#}5 z{VF}VAr(UCP@916GHPB*$Uv8wlLQW;vo&r;!Aa)~yaHK$@IZ0=@^V+aC!jJawpkz{ zNBMqI>lD0X)EaZ|l-9txgx|)?XIEDp1@Ie+>XRwtd}>t(T{;mqpJ0x5EBi1iXEZ z3A5R8MvzCr>HX(sXWGIzxwXXDoSvTf>`wYDL@u>hAb_NAs!B z2;Z&@R2NKDQth^KqI+}{g_bqzww5v+a5RC3AOS5`xy3V{UehFQ)_e+)3*eQ^a)F6a zwY5k4XIc#bqj2b%VAn>f<(TIvQNXy(0O)~xsJ&-aiA&60{k_M}9g~o@gjE`wBizCcf5HT?{*j6S&Mt!I- z=Kb5Zn;6Z9{|Uwq8_vbQqU&c&q4@w3{Bk_eXH*|x;znff{dKg^EEB=*y629=fQr7E zv?H#jz8y`$d=mfle>gx2fr}uEtp@McSrPH#vAOzpA{5UKdltrTP*+8{7nIXqqc#AT zWDyz1i-hcM)=aQ0rQ}mY9Ru`ZS@%w_dWV>Pf=6hANjE<<3dlR|k6y8*gLIe)xyR2) zjqPbL&NV&TMN=dS;AwxkxD%(u2eQSb-YlXix&`o;%3YdBcKamH?^zow@R!6uf}g+D zAGE<=@;`Mm|GP&gd!t5(ZiIQ$4;94%6n}jfj-a2|kEieuz{~REwP=Km;{Fp3>R-6Y z|K0Wf-+AJ~iHAXGieth{fRExQm17jqNn}WIYn^*~gcz0pC}DUO{^0mPFG z5q}1TA~3qQzyJz5KD5XY;XM@7QG~7<0EYtGv~ZAMWC%wG$uP~|%=2)Ekpe)8Pi}@% z*<{E7pd$-Dpem$qyDH9nm#Q>g{3p%XR8p`HsFFki0?J+&CeH+nw;TP1`L%AmOnM)xcS7AF~xp`>rbF93> z#&-A(qhpw(?MHH-h~g!W@NRwIA>ZfC5E=W>>DB(~4SAureIz`fhZ%F!l`%J66u;Ub zKK3>LoI~D%s(bt|o}k8ap1(781K^NJ)q++>)&z7saZpnilpoON!~R5XkG`VAa%&Yn zjo^BrM{X}6)n8vOF6V~4xWvb3+;8)banp7%J zy&!Ag$wP}x@8y(J13TQ<3uk+O3p{4ojH1xgWg)B|)X4-*pOaQeVqT33`*BoSEld#h zg}xb3iQMzyJ?V!XHDf#A%oaPBm*M_pSiF{G09Rhe{aIZ4Ew;C0DQR1;>0Kr5xS>aC zEQfBle;EwBEhz3cDlVVieelIX@!ybBIkZQgZ13K8SGyJ` zf2)(nK0p5__VOBwj_hdBPglR}fc9DUcI(WBwP{wCgse)NTR+6+=&>Uau;q%^vF6>T z%{>kjvu~k{{xgj6ztr)#j6Z6r4ua38ykmk+QOsWiPfkrf3_43OuL(Y%gf+5E=Qgs| zM)w|1SE&922w{536>QPF=hhzDF%Bt~y8#sI0{Cvq%3Pd5eI^B%ZNy1vQpr1OYL_$Z z>%MLMS_W@WKTH9J7;#{H>K&qX^T#ChK)^ZoEZ%1l{>(2)jnFrivj{pq!+C_+wfD_O z3qxh@-Pa#7 z`bB2_JCv^hI@bj30X~`AH$1%txUq_;%GAsU#|VPoCKD3wIwWmF-@LurkVBJAc*+ce zR)m=uA;T>@=WtP!I^mVnl~v}$+yq6rBaaN6=&>Fboa@T_fnw)2iWkYX5S3czwz-c1 zQ_a&_1yYm;L&|*6ouj!|wh{AGr*0b0@{XSqZd^Bhxm^7L6wNz}2efmA@Rt-!(S)l; z9E4*i89dHlL<5n*1FSO<@P|{j0-z+1=SujFm51=Cs`qumnN?j;m*?b30Uj&_Pok5_GiD1Trp$MSCio<%R zjH+Zqp+O#m89jUyj6g9ZK>6qa^zE4^7_h+jND6+Pf@rN1y4%N(e^bbW84Jtt3|=;l zhAD! z+7;@f@(!Nmjkx+g&ZAWPL=?oP*UiQUdkNSEYxPu8!t&QI8Ho*yZeAe81-UMy<!0q6Jet5g-Y=c=(_ls6*|ojW zq86-D!*~4dwNmy)2iY)afxU(E+{%xGG%x$TP9FKtjX_lk=q=yu-#DAmg8t?5qfhyw z`blPju2@5Y$>;}9Ks@Z>?Z&|G>@OUnUtW>&s6S) zxPI8X5F$20og7ojqnr^%Z96B=4#!va#6aFNOOCkvj%l6Pr=T zO8uQbS4VG+32@mXYQ3k)p(@o%!S5AE!)b+Me0VWJsZW14M4Dp2v}`3AbHhaIm+T{G zpm>3N0n{+9S3tr#EXxMwox)27FDJoc<>^UmHRcW99cZw!n~zqm^>jTlfi*bwz2>@> zZT2`6I#lY2JLzt=Y?;0YnR@WP@R!{?VqzmXai);-L)tx`7-Yr^M^d*MiQvKi6oSIQfV~1h!CHsP?!C~-O6_4;dO`fGQ9iDtsUu1Lrsm6@_nJIPOCLft z{PynOd{29%kfo9<_k?7X55;@Q}@tDi8 zPgXZ);_aq%Bl(5#h8;nJYL>p(qA!hV1LEg5pxV);&9w?nm*foO?S)ReAEJ@DGWgQq z8+NIF({rlC&G4N5{2O<60iU|zC0eYO3))Z|c;n~(`c8_v(J=yhL)vHqa~+x2q8CFc zuLX{}g`T#bA@dxq(lcE*sZ_xmjC{ae`~);qR|Hw7e4DB7y%zLk6HV`y3Yj|m=;UFO z(~p0RWBbEc^AnKG#~VUYtH?{>dYrvRlF}{zZihloT^|IBMdc&k0B_vm)UR?848OK7 zrk@r_bL{F!?h*}k*!604TUaJ+Y7~Q}l?1x% zyE;;`(ezak@S>2|S81ozgFZJ@G1$0*OXknd!s1mulB%+Lny4T@M>kh|z03sAe}7aH zTu452@r?O%Xlifw^ch@ZU3P4T{&xIg{`E3_K>x$hN?+YZTgv=o=_`{O_NA}BXT2S$ z0$dmhBa6`a>=i6y!`fzP zB5W@DU~#?qRZ|~28Z6HlKeSSq=)J;8Lj}i}%EgAFsNKH0%K56N0z$Xpy<#caZZSg1 zB%~OKe2{C5g31^!pv( zru|Y&Kh@t<2T7As!03Lmp9!oCmsq_EJi;AM<0%)1^==Qc8@*fsQgF&)-OjC)EQDOT z`rh+*D~=J=tCmj5F48&WZM}W|l8JDSt`Y8EJw~|cyCm>tkzPa#tV^ujD`?ODOq-Dg znH8s6@)skJ+xrI&hP=mri1kYhrb1MUTCmp%V8Y~DfM6Q8{GEiwnqke!-UGvGn;_Wa zK^ajUK(-h59yuY%_370UgiJNQ$Lttp?Kd`v41^rD_swMOYg?B_oayr(L;I&zmz2Lh zUfkMXRolNgJuaI;Y0g3z5C5!^<)9AIVq00My(hGDm%CPk#1_RvNLLR}qjs%=D-&5e zeLC{1z<`rmwJP~dq>%?G$yW31POGed7c5=E@SRNT!!-0V>-5UP+ZIR|=99blEA&V5 zM#9%?tn^hP_~2Kj9H>efKtHpmOY?-aa@(AgX1PrrA_n}ccO2RfSNT0-q1uCdPlIJf zn4K0P1NZXI&Y7~?Dun%l3?&UV@h9T; zru*p9jq-6QKG6`ZPpm3Bat-!Hg2W!qqLo{TDn*&ApXJA`@^tR8V%kXO9lHbJbw2$f zNmtO$o?d$WETI27jDD|peDx0v8q;>*{UN^})~3`^d(3-4lV|>9g!~`fi-qRzv0#P| z{0HIpTgIK%)w-ngA|;6iMIWWaOqoLwxd{5uU|C<87579$Tl8v7B9kNtU1nH6t;` zLdzmnZ*XdJdU>>bczHXdU)!@5B||Jxt`~g}9b8tcz!;`XHhF0{tA?RMi#774`cn73 zUKd?Wei9rzwliu?4EbLe7XM2uT+t{QLUA3lj?h@my@ z5n<`taoT=Vr5%5?I`~u@d{}r?@bSlIScF+V?lk=+G=dI}!%m3<;?jMOX-z2M3BEo% zGTY{5c>eavR+qh`ORZba?2YP5*3Jum)^y1a)GI)%MoQK@-88Kdumr>8d!Af+-hSr* z)Anx=vIGAgS)7fh&Ul-f%pnHHf@<373huS8OxsDOge0Z&7>Z=B3 zSHd4~p55<(Qk6%1M>LHo*G#2*!QC*H&+oAkjC3km=LOC}B=lWO(YTWix37XoODf@Xs z9hHldc{NBA7znDWP{4Z~jU()Nl)5tPLS*Lcu19*~R+8Kypu8lPt=reEcp0K$V%WFK ztyUMnB!)f2mj}28AFB+_>56G)B7TegR$nCY^WrY;*I=Jt71B_x3_63Q^n>B{zNKe<20 z?GU;zhLX84)+nq#a5$4E4VK>ol+Uq~2y2>XMo4$5`CNYQUPU8plN}G`gj(3^y(Y78 z)aGjJv*wM(2K9Si1@=t#E7t8Dny1^p{)I^}0+cQ+zvVjVmY3SYc(E&_RSr#7f^j>p zddf~B+kU#TCetyqjeeRR4=N^0Vx7S3C!Ys}GoY%tv)uW168{xY4xTV!#b`4VG92O` z2;vXUQUX4t`kUj4No+1+S#U1x1?I=4;A`MR+{m?>$+DIcit##(cpe6N?6I7maOS~B zxYtlEG#d~P@U&if%z1A7EHj3WD#Myq(9j0V7In{xTG9ZS-$e&eq#CR6>EYv{RMnbg zD=9ffK|7R7p7N^Zh9}fsA}XTLpN^Fc z3!VU$RPoV7WfWTLG2AqhLe@@+6pLICB~7M(bm2k72t*IMC%wvqhN`you2t{6(d%S9 zj)+l@9&}|X_SApe9d`nCh&jk59=RF`Jp-kDhc%1PNx9z71MRox`n)r-_g})OKOaY( z>oXG=Xc!R?yJqfoOAAbtdBq=6=i)PenKD zxatNczLYN!U0Dijo~Mqsex18`k{JUl+p~|0)wITySK3@BP?66)5Ie+5PB?j>chh(H zo(S=!y_|<1$Xzs^r z5o%ZFX|VU6-=hMws1ZYaaDoMeR(QWRscO+QFE7fe=0qd>x^+EC$z$~PV7}Af8XUvX z!1&jRSNCz3L}m+|`vaLV`1iL1Fu41NC@jAw3XLrr3O#c(K+UqwG!A;kCbzWH{UQ%X zJ>fL6cQ@Fugu4$lJ?%+-$9A-std%kmsB)oWLhcAr?VI6R$h)zWqp z7f{RtyovMK2~DbS*HDTNp*)Q*j1SofHLkd$$9{+mgSPOzhdnG6p5|$X{zsD^?um)q z30-{+_iVs4I{)a81>D6E#tvc8(ZqBPBBU0rZ&V=!P z2wjX3&*p=-Phw5_0*x@ijt1+=gu#DhB24n(!HTE2=nLT8d<``iU3`WhH=KKlqlM)R z*dz@;>eA%H3nv7f#V`X}0m@VeQ31U0L!m^_9**LFic%365HX%GNBAM-f0BW~efT^h zaQL*@6+fc>{i6y*brMhW*_!29=(lB9Q|_M6qExrpYlJoRqld8Yyu(2W{BQ30!w_B_ zk!Lf&kEg-(7z&Y@`R^K)Ei++I!VHBhn~xrycN#p@S<1;AZn!>xli{!y0ETyhF6(5; zEGog5=kRT(^#+Q#M=_QUpl5Jin8v6a9*(9b7j$eQWI#nI?28~wfy%8}w=RFuHuIP5?Jwl-q?~Lc0JFq`UuRI`BVO9r1rU?ei~CXCotIE%|_)u5+;NhLop+ zu%~751A6MC-S6TMd}fyzW>W-sk3TuzpylTF{UX3rjG|s!p^|94z=fb2ya!!`9g`_P z{)zy6fug~}c^ey6#0fYPcRljoebr69_7n#2vxmJ$bGs|toX*r|KvaA%pEV%c-V?A? z8h!HXbGw!G9a(?@1c4$fvex779L=nP!#A_dbteb6_b>uT0Z_hrMn8*mV;1y~eC=onrpqyK5I|YBm zM~4Gawvme6VylMinH4Lgf`51%b`8B+GsGR{>t8j#m%cUgEebibCBlo`%-(-6Qt2Zw zAb$t_=5P<@84uI9xd@KpNQ8mYH;BhJ8Jyy*)y{9jQ`68&`TunLK52m!oGjKNDUt!@KLSdLwi?~;x>EY3Sz!whutZlwz-2=7~1aVB~Uu#gv~z* zS}Bc}$CFhAgiPNm0F;=fZDeix<-sh4fSJN8obXEpJh>$=EOWtac9(nhzJm4fKKFwp zQ~w#m)<%N&4P*;@>|g65-*7OfWb?0FwgL~{I?H8KzBzhaoh5T7o@tyw2YvUS1+u&?VIG$tfPgr}~a`mj`UW@xCY z=8#g1Vb(mzo>A~I{cN^?pF`>OVO;e&fF?Q{2-)CcKKT8+2C51(r4s)o;eM`X<=O!K z?7P1O;8O{d;~nz3pQQ52u?0v`+CMf|&PH@=?)U53)7%JIBM0<&wyc;ia9P8-nCmcg zYH+Uh42->TJq}{*b=v<_cm4?tR7*TgU<_TUjAu+)P|O-NmsWeDaS$OW(PioJo;=KW zqwn1>%9F5AiqT;aOqh3~C>gj6tBekagU!k-b?`Y{q*cU3fb+2bzk7Al59Drf`hNVj zHv9?KxiAG9K6>?GyDi&C_IEEy`bnGbFsPmJ>z9>!UdIoAp5O*m-`C!~6}ecZ#3iYN zz`eqiSXU|hhzSa{(1;G&#a8)uNaAU{0Re8XQN zh^$O}qk@g!^fX>YZu|ZaI~=Qbm>n-z>G`&vH0(L|;5~h#am}kVZR>ywj!b^Q6=OQ-m8iZKZ!4Yc4>})YZ%UfwY#j(S*w-bz z`SKw3traM=={kAw|#bb#bAi>(U}IQ zN^D&sF4egfA!?Y~t_|Kz=Evte^>qGmFQL`*!22%O66G4-LG$Hw5-TA>_s_IUn63+2 z^ur@V6&-4;)RM^$?P9}hO+iR=UjA)*1Qe3+{2T-B4jFn~79qaWK2$5Kkv?iHX@eXOI?v|OOF@E^P5P!#ixN~Bv^FwRhc3fH1nm%+M__mU~ z(hg+YNTIm&jr)F8XYtjGW6q@|&~(?~ewNGQvuRzw3*Xj)C3TRCFsou^r0P^hu|0$L=xfa)!pZXuwj06(+kX=M=D-d z7FXi-oZ0~f8h@tj@4O|4wN6APNhW%o5wF<0k4uS-f)H}$=kf8zAs49iNC1+`1s6Tr z7$(`^8%A7menVZUPnCQGjwR&y@s00SY^D#Y9A`F@o|?y$T1ZC7IU0n9b}vNs;@-|C z>b9N2LraScg%f=kf(s~}L((b{ZS>6E)f3kUDpd*61%cr_0lY!=!-XS-L6Jv@li4LX z`&*4c2L7Mdb7~zIN4C)Wn-lksZnvIxP4`N($2I$P%t<_M3vFsz8(Cn2Vs+{^?7;{DMT|cv_5UEbrh7l}sVd;L|4`$~9f0ebPdxRq9sSnAOTmIOH~0K3caA z*JJogbN)>&Mlf-^ z2ob7k@?SbC)u+VMI~*|yCpoIln5q7(ZiZAvL)_f_4npx+DfIA%X#q<$<^FKKr7SuU z5by1Zq2v}*I>Mv;8!7O*OE;`}6q)N&dbel7wE8TbyjA@AgO$qEJALl3=wvm=3*hM8 z*PB1IG+^(lFLtExBH16W#1pL5qNjewQ28fPqeXQlTb!nFhftWX^pi;6K=cfw$H_a| zg6_nI0jo&s`~TZf^Z#C~{eOJar8}VCh_naQG1$4ncLblum}=QO&ncoz;oHRgySveA zpQ<_EUZK!D^8%oR2LJn_@BbIBHUHBoL}DSgf3RNzxNyydT^$@Skaws*S24hT`LJ>r z?&vyK@vXMhP}++=AC4%i;1K)225P0?TBJHSdVXgykL#54n=(ACEWQ>7k9xS*`%f`2 zJcgx1>cAVuRm1)s@NYK>mcJm)Hd}T8(% z)QtNF<6N*tztjM)FavSAG=Qb-Sgb-&a(Zp!&JE_He+pc$HlOF2{U_Gv7o+VDScn#~ ziawm%UG3LL(gcr&qRe8{8ikG0|M-J}OXoWM?RJ*4jOhFKIDl?f?GCu#!W%jnzZ{d#NY!@bK9E?SZDytAgnTzj;;R zSmR0iy({UoGsc9Os{t zGLLJ%o6jE9xU5h5S@eS|lJ8Aw`j2ehyb7njpDth+HeWe?E4_9@QF=UJfU!_PNS+a3 z-~jYvU`J)8(mAr=OWEgBdM?;s*GycxAeOV@bkM%7S3EDlul;A3U4g~IgH=kek@D&N zMKt1s6e0ti4frg}u~bh|uL063Raf|}+S=!?Wsn@ZtZm5a$nE6xthxCXoTliD2v zq?H0D!Xvnjc9w27^;Wyr7O+?TV>zc+l!f5f&ULUD^gRF7Pvmy2g48BWWk`b`6muCI zUF~4p6+y|^p=3mA5%ZlgO-ppM`E}Z{0kVy@{HEzpmErloBtPHsnqQP^_)Nhk9@WM3 z87-N$Zm|LFN`|1uMKDP~63?EA+XA?i+(zD>`+V7wW9BVW0nFRE0!Qo7cq5&maAyQ{ zt39`k$1Ewk1s7PF{++ut4Z0r-32Iq^rK_qcc@rV{vEe6U(Ox!DiR^@#-GUj;1Z83e zW#8x;f$?*o(&u!>ptRi=OQbb5h46nyO2U?Z9oOt0V4JFtSG>DqBQ1$vYTqs$=U~tGY^$X1#}{KIqk#2I z#e}Mm`!#Q*@va39zAS$ooF&lE!gcMZDrQsL!Perrdi^d`o1|S5Mi}WyY+Y3xlU-I3 zYp9XNlj%Qb*X#V*#%I#_QeNw|G#zunUuf0h*8rUQ@vE0|Fb*h4Pu>ZBE&IGf+7j?7 zmF{g}CftEP;|o<~NPoz!_N&XX&pV-##&?(fCBHr8mm@-rE{*4UlsfV~+pc;ox(~Q` z$*U@U+9h<*&*oqH%=BmQamUZGobu$^c1xR~+smHh5d6eNPU-pRx6}`w*dgb0TN-;TVdfT7j%-c&NrXR7PcYj&VOsu80Oc0oK0m1}WH!KGXov2BhAxXU|k zMao{0P<-uYzh7(o0wD)xpZ*KgXqHk?@fU5;u95xEoOd6LbfMNB$FDAjnAlxr{kKUb zDf?6|Pp0Uurl#;>Hs7!ji}pPLxCT5*BNXILBzar*R@JVElSqSO9#wpJGP$Q#VU|kv zi@6-;aEY+~DkqYH3RI?4H>;3=RukD_*m-_la&;H8pWCX0%a^^e*SL{Aa`ax_fRLd- zvB}mJWPHR!WK<%aouQA|UJC1pi9f@0Fr*#GxHnyQMssY|EOntVa9(J8W9Q=wNjpha zNzOtzm%z1x+&0;Ir!?jyh+ow@#X{IjnDckqyR(ecsY7h98afKOA3X7^;lnTOO-}5m ztC2IZ3#O=vXC&O#B}3=sBY_4ljBnMFgjYl=!_^2#;AjA_$D7!@E3NWruDp#gw7k_v z@)&Vp+^@pz-+g77rtponc6X&qd4QefkH3^c)>xG(X z%QD=W1kG%zW}yDoR|ez2=v^r+5tOA79)CnT+dtrFv7RmYF!=OvPfpLO+Ws7F*iyB8 zOew^Xk%K%P!bPn?3h)ybbJ~d$ut8R6jZ3Y>_J2hLt(yv!)3ac)= zq1Cf-YsCc(3zqAik_ys6!-oh{Z4=Yho3LM!;#)w&g~^yjm$aCX<7F=R6DYLD7skRx zPn=qD0hfjHa2PwKjQj^LfiZnL#L!I8{>0!JEvt@E|H9q61%t<AkPwy`TZC}ED$U;kUsn&=L$GIC63ZzOvu35u(& zL#jmT)JR~@bYs2*@@O(@cfxYcT&9q|xI@fAs9@e(t_sUwX35j}vYwx}7)k zrTu#yXq30S~bPK2+YX*ESM_U(cZvLq_1sZL|51qdno+@(SBY@${BA z?qQgXRh~|_-L7!?9`{jm-@xX~rA?()EAxLA0$&om_Y#`c6%s@D&E=Jl+j@y1?)$6b zZJWuBJ%Oa{dBi;VyPBB~uXEy(phd{$h@sAg&w8fj#`%M5?7Vz9_JE=@pVLdbclt8j z6zz9LPMZF;LUes%=*(jGVy#sqE;)$3r`_6apZA76xS5ukH#Qfcp|~$2qO@1gu=lXQ zimW!fr2OQ|NDh%mwG~EZ2tHMZrL6=auep=1r)7U*#cZZMAXn}2B;|Ni>vIzv15*GM zV^8g?q0Z9$c*R}vbKch#<>eI=6ciN| z0RT`^Qc_k{R#8z=RaI3}Q@eTdrn{QUj>0|Ej90|SGCf`WsCLqbA85Pb6FNoZ*3)2C0L zJ$n`w7WVx4^YHNS7cX8!L_|bJMn*+Ny?psHIyyQgCMGsEHZCqMK0ZDnAt5m_@ztwW zNl8h`$;l}xDXFQcX=!Qc>FF668JU@xSy@@x+1WWcIVcqB_3PJf-n@DH_U*fO@7}+E zpPQSTmzS5Hpa0>*hk}BF!otFjA3qiq6%`j3mz0!z`t<4Z=g+03rDbJh<>loS6&010 zl~q+$)z#HCH8r)hwRLrM_4V})4GoQrjZIBWU%q^4ZfoK0ZD%F)=wgIW;vkJv}`$GxPK3&)M18U%!6M&CSiv&o3-2EG{lCEiElCFR!eu ztgf!Et*x!EuWxK@Y;JCDZEgMj{d;?RduL~7cXt;RqDm~Y9Yia5c9KX7k%pr{bdK;K0Zs5yBYl_dq1YKvY3KJdjdy; zymL&pVOabR1F*2-3=q^leNveUIkeY6WCL>nQf zJ6)?g(%bW}oYVk{fP=~E;RD-SIXy!>eT4yp=wJl(XGzoNx^+Iq#D04{^6%!7{PVCj z)By^gLR`(8iYv=hX4#fY)U0p>wOdv2cD=kTXZhGOfs`R>oEKOyHOgxlnYZS0$-KlR zaO_0kk(_sBR7HgPmc*t0ydv&aM0JR#T{u6sBSbqRs0nT;StVR7HHui-bLfNK9H9hSU)T-S3kvQdL7jOPRDD3F}Z$MqK@ikB4BEpSdc!+IxC(32u*lygA=lWs6!*!?)egci0jk=o`H6%P=C;lz;^fafiz zKeh;#+2s3yC9AEVjngmj_x9LOSedy5%QH7wSWd@p3!=WI^0a=4(owqMM@0axiNPBB zG5*sBJwdaB89gnhY54i~_G$fUhN)Sd#9%?b{&uBwvO&jM=jPM4{^so7Eat&rp!r(Z7rztLF#p)me)06Q-SORUGH z_5q4vT!`{obwvJ(ZRLUfU1gw<1KPFUaVziBGw3!<1_L@xRSh{Ul>ZY~?{g)ju_|)0 zK81npoBS7LBXTr}usyi3lz3>VUTrQBq0o4#zKnf5m|n#em7|D1h|8Sy28d7XQ7nc)11I-bqFkaRd?Oy~1r6LZDy4t_KGs(PSW(+s6jm1pO5B$j2M5UvD=Mzep{54N;HFG4 z93;i%+2Q#wKqGYk25DzVg0c8D?*RJgY!J|`NO(b@xUM41000>L&1ApfvD)8ZLY92TtMu^2q6`Oz&qgarhymrldlIi! z!Et5^&0zuF=VLxcl_0Ptc@xghm zd%zj$b)`Di7ZN^VUaWH5|1`V!u#cBNsK3mpa8=uHPw&o+$?$QqonrR!?L%`J|** zY>B#OP8E=TDi87-z|8H6Nd$*qN(vpFi!T2BS+m6O=w?HjVk{ACLG~4v!CS&*Gpa`K zgILLf6G&xy`#x%{;R<2KgLhT=!t*t0(BYf+n9MwNZs_5b{G<)1DiI1K$vFjg#CNa? z9DY60b_jkLv$=D|XLHNffXPKnD|QzJsCAdfI#i3*)`v3qyF z3){wLonRrm(*icja_8LlQy`rrtgy#w^*aCN?&0J{H|%NxSzCbLE%7 zf=&n>XC{N7a4=8(+FvR@ESKgzU0GU7>^Y?RmQJ^s6&(=_&%b#f7`Sx(8;jiLXjpey z)pYIsE|@-Tkzi(94YUa{I6C_hWB6SUD~SS4-@yZF{@-#|(Y1aimEkZ}34FV(dW*_% zI4d{4U65RCD4bUrSo`G%-O&;|IsU>Clf;RB!> zGf*af14|eH3JKw+#SrtKz+S-MriR=o{S{j^NBnn;u%bxNs4W@jg68of2KPTb4Aps3 z&Ve$dK=Y4Y(U6CLk09z9YJU=r^GB2yxWg*sbF1$MU?Ix+;ihyG*sPbJk)y0wm-@x1 zH=NI6yYV!NDWesH-SPZ4&v9+0lZi$Ag7IA>lRRvW5jv+qz3He z9B`xZiJElc?%3noQ8fueUJPC^n?S{#tH_ck;grb1_LA~kV?y|NkB}1;a`5Xd*qv+9 zu)WogYcMq(gjm}j1S-VJLfBR&$?R6nH-;|m!y`@c?TCCU@lZ+WVBcutNU+z$o%yo8 z{wasjjf$IA_;$Mqb-j=XbQWD)dB?BtAfSjME!W}~>#Iqp#b6)^PA}cRj|I^zBl%}^ zaypnTS2sfBbWpmHR4MR5{VCy@qiJZB>0@};Ia8LW(A?~l zV;O>6FrAq-O^1X)F}v@24bLv-64OC;cEi}`?gN=|lkmIA7~3rb^>xEDJB?p#q24=` z7D#wxa)G}X=Qh5QtREyC@N~@~vT#*qcDuK~K>td-L0)*I(IvnRz7j(cM%3*D52i+B zC{Lf@_Y4({ZwL7fVFfHCopQ=(1?%Iq^0TMl8?ORp5(0T;w1oA^${4fZBYe98-wkvB zAehq@YuwcZ?A0-5BbC1mR=j2VppHNOURhCoV(u|MOeb-FGYi6ilZidzs?{$;!hG9+ z`_Zf@`yay%Wyjy+P#m0mbE?GTXqg&UjS3%<0)-rnK9860UQ&GqpxgUZcxaj?e^9Sv zvcZX7MS#-=^#U7iN+ub@7v+F#EO2|3`h@)oz8%w~RIX#OQrOb_?Zj7Z?2rO*ieWUk zVvH)!i6?ul8$rkW>j^l#*e-1CLDGc~TV)&Mfp#?`N2fDN=UyvTstLX+_e_S%I`c(T zExgv~^Zk^|LnVZ^iSthM`#OJpMojhjwk(QeGI%+hhKL*W`Jo31K5wQW2P(@)he7a- zUzQ&5sO=ZO!LseOs)r&sBM$S2+lxqk$ifPCB*vJ7X%;1U!knc~GQ`1`rFrRJ=gkBZ%guMRVn? z#}(yulnfEYH~|cbHxhD~;ii%jl=QbL(Y8$oA3kL>WC+Yq)EGAWOvN?ua^yTi@lW(4 ziq$zsRqX&bK#^(K<0s6xgm}~Q8fqNmte2%Zh8SvW_Bjk$^$WCFmA0EKG4h&m~jk!672tbsVbk8;#_pLZ7)?ST`VlAqzs+u9Y$8?!+wE84? zOC^D|92;vh4;?k0b%n&8*KBPtN*J_Hmnqf!OvNb=RFs=&jnod?J8+B@cs&}|QE*t8 z8FDu4{Xkh!WbItA7b49~&No5G&w_pJ$BOvjQE4oh=LOSYsx*oC1(L=w zlp3GwRCegp0=T?a7)!vNDhpDWb(I}r;-i5Z;FGZ1+I=9AKu~jQCf*6+mOgM4Aheag z^8V%(UeVTI_JE``vy9`EanqOJWx0OGQb@7re zuVGuqrUYJGiw`QLnw~K^zIOYXlmu+S)gEASyA=qGad;kv=Vc~XE@TS)_&hMozO(yt zcujJh)%}@G^EL^j|5UdPYm4d={P68G-B^?KDbai1tY<{Cu1;3-S6B8cibD#9z{;`r(W~i%UW`!gKEJwElG|LD)h`L-0ZjMTsvr z9(=V-R_tSO5Jy-+4)SPrpxEEA<~}@K&YFy}#EzR5`Yb6{aQRux(#L=no-VZzOHpDc z$OG^4v!*wAF!S3nwp#`s>Ac6p8H1oEVMEcpB3Z_}NNlM0C_hN`ZgwNEOKb9<;LwzS z6Jj8xz!gq9AsDcMOL2s`7`UDa%5-gj*|nChoJ$*O?TqY!lA|~c3qVb7DD)B4A3syC zrG^QO%^}~^Ra0q9+sjX|+%FPR5|EkHFkON95hNlhG<;2}61tR`?>JBhYWYa)+G7rp z<&-0;Vn)`d(|4{(DZus!>;#}yBrTA4*-JAP-GuUk)o|SIFrr1(1bH*vKX<^}LLX5h za{?>lCSZnsdIpx`Sa{TUo-Z8_lxANQLtYWhrQk@vb|<1hQzFC~G@GdG?`&WD)?oyR zrU1&04sIF><{G;JcuCo(yNq~^Kd`T>09w`LNXQMKERaH}%ztYu%j9}W4e=_59mSQ2 z5AhYzX~2k%?RIM`3)ELAfOL-+HSGFp61$hdA}omyIQ86sZ{0jmxsIczH~Fq;15`Fg zLaU-$R@zx*3Jw8T>~DzeG8Gspqm(#ry8$KCHN~!7%JE^5kT!XCPg2Gw)I#3OJmYPj;y$X7<4$xr)P;866Q4MEO9@tj3rK= z*rk~+=^53Se|)JfAuw?rAgUkzv*Ljn30eE%RmuX46+7(#%@G=sw7~TeNx{_m&l=xW z3NCkNLp_cJlQ9Dh-@ec@y=)WaG=wB^s?6|Q>4t${*L8UM4Mh21=eOYnyLhFOb^Vc# zDT~RB{opvGs&bO->zxxKpH&4!<$2?NARa>O0rQ1v&_4B%YF3$rOF&loP~O5tu4C3c zcY8qI!C`DusoeUt+wEBad5gx?@NfM`=WpI0<4_^h->-O}5anFmF5i^rT8_3NTX?Dr(-Tjmj%AIXX&B{w~G zyfg55=~aN;Wo%Qk;{E8)@wZu_7km+#vcRvPf#=M611W@3!P%i#DK_zvbst`T>62|JkPbUhq(jGTA zI>bj?N?}Du`SbRxkF%>}OER%E+c$GPTC+vry*=ym-%4#i(T~M~wH<#cx>uH?qyFMK zT>F!<^?MgDRqG0_=dzrQ3#ex}h^9Q!-oH1WBcfmCLymq|y)6^=NUi-bv(Hojr21%v z0<&}g0c7m?d23I#Y^XpuMh-hGvB7qX=85z@3>!*V619h?LeoEHVQz3|jwi`tnix?Y z=Z@LRe$W$J+AkL?toJnPt?BvJQlgD#a`JN?JyVp2(aYql1mB1UIYSDp9`i z$}CMeJ1Y7-O~MX-Or}qcyeJ~dWxV0_eYGVGpEnLm+#+ja*S#V03POyc>8O)#<%w7! zGx|9Ar^!t%`1sEpdu$laosY_jbrXi?)b_qL%&hS@3hL$PAi1N>h z1Gw-=f4@pRa=q1dPT(jfYWVF6$Zho$3M=_w0c9k|?O!d&jQ95JB~Z74=3%=1oMBgCW3hBIb{u+tAojA|dp7wLwc>p$eC2;v{GN{{1+9DCN#49N&o)3i2`U>TWH!{CX;!MF zMJMKCebucAZuiB8NOn2A(d8Ka?0f=xtdTeS!I$n@nj>39YJl#wYmaiP#~!x zgbMyFS^8$W_sg&*89egwpQQu^@OdLCkOumEkQ4c240&MW8g%cHsVoTUeEqYFm(^2x zGi_dXpiFcYy)W~oFBFTaydZ%`?r%Gip(^N)l%BVqVVKOX)BJ6yZFS4mdmsO~)swP| z8+PQ#@@S86lcz8*gY=OG1Wc_?$~X199$w@6hxhor<85uNFN1Rf_aOUI9`QEZWc(z4 zKLm(`W)Xy^Mcug|uE&R|1-NEPN8~(!swZ|tq@q0Af~FMk$jYBLJSbt;m4WZ1j5dZ` zUS&`w`MT<#wTTQR}Z7+g~gU;VtUKduKc>LUlr zPClkAGNInO3o%$G&pm<9YZ}w~@eQX-n){jdAYnN+Os(7PuNjR{QtE-pP2l@uMw=uq z-)@6TKgG-&LY+R`#JY3pM$%;jm}(9s5!&61=qB34p(iLobH7dsGHw;VAB;&d+Jq6* zFGyit#4wyUDgcuT@C5(6UKKf#RTDG7hY(AWo50o(g0E7;({D0XOT_DilcU9cjaEbj z$~*;g$<>9>^iWR0_Z%C_GwZNe0C0!^1K~b5z$hwEK)Tgi890FPRc7^0fgQerwNjDdcSN1JM zVPQy+u?)FD7h$f*TdIcs>gS-za zbDKsg*pQ4+AXTsFH08lBA3pw}5DaQT_|887oir8dNQkscB7%ej36jFp-gWImGRY5w zgY24r@_a~$>;=@27yJc_|qdVCC zHJpPwPLsS?Z;}SVbJcm3R!Up2L;SCipJ4V#ds|fmR1-=eM?)oL?imWgnDzpeb?@#I=U@6TENa>fbNf6#OzA~u; zbhDyB{c@OjLx(5c|EHo4`3}TzLMb&`w5*=o01^8uQ-=a^lYQA6juLUuR*jcGW%eQRYeEouE9em-YP}L~&M<8(NQ7t_5P(Gjf zYlHVke@gi&mjx5qWOIttGPRr+*yFp`Sz5i*&Xy+2Y1mV$+^D13AuQ9$mampws}Jnb z?w#Hq?f4j638$CxG?Xg316ca~j+Y=UW`|IQR%HIMOYEt;PZ|Zw3U+J9;R$(N($V-p zNdN~!P4~UM+3=ExVl3!KJ&6!5iA0N$4A?XD-zkCsmk#r@wM}+)T;tkl-?=k)G9DCG z`UqrIJS=_v5=~M=d@wHT3pj(8+XO4VX3$yn-7;!W3n>*{NN0)e>8B2|rxW$h@X+;8Y!|W?%7>?IUS9j#c>-pB&E3cau)Wok@wA)P zC*etRUNKaiSd{j>dE5dju)AyO0ha4R-)wniuDh4&K}spK3>IWCKOqn6?K#tQJ|^8^ zQGgCc*oP~&OL@MY4fe$s)#k;L4%`A@V*O*_x!L@ZJZwtwVr2g05>){y2K1b2G6sPu zJAx(c{cVKFDsh_ZM~L}8UuFsjx63YsHKogX!oN z6+GTdIq}Lt)|^PjGcU(Qb6a|@N$2zg$4o04$~gP)>`BCw$Uy?rRs<#ozE|(>RNWls zKcLGsneDwNm4*-I)?7uCg*-MoNa@{EMh_0$F_#4OEcu@qv zzjQ7CYnS=|{;bFe*q&TT^dmb)=+gdj1sa_9u5TYCDoD6~U5W|(-0o;@d|LX$oVGjD zR0X=2T9YLCDS7)lM$uQ|qeU+EF5LF$WUcMq*Fok^qz`v+m!5C=Sg7mgZuYX-(a&Zk zrlt=vJdhmq4uSp_DmZ7Ajt8Fm;~z@BjBBK)`*nKBmSw}rAcw)4k;KkTv0+u~p734g z4s&zSyXRNU6cp@JngjYz)m``ar4RJ#w3?V=`w_dSp29%7bxT2_3u*h`^({x2TiX-l z8++NObZpA&$%cdsC8k|mID(Ft&!k5w0jI2}0o=W@pjgtes)43u*Oid3x6{D$Lrd$? zSfVAUpw(4PhyD@lZBSG+w#Cp1w^1m~Ak+&VdM(7P=u}@W*_oN;~YLf`o?#JaIU>m;Is>O8$H%ap4%tW9rr900-T<^FwBA!5lY+@nW zP9-JafY7BUDXalDRp_YzCwxZ?BUv{r|5Ool?DgFEoblNXKOM_-Y6=VbspcQF$kAVT z(}q0GK9?0^EEBGmN?OGChJwr#)4cf0J@++d1EBzlP7-^3x$gH;hqL@vOMe_+L;p3F zq$z#`X8CUbY2}FW;~y5&8pU1aXZMMY!nRxzPsSFL+<^-4N}oFLbPDq5V5}g|>Y7m(dmz)H7CgRFN0&fb8>tyXy@dx-M; zmjrlO2HRs;&D-*sy0PGq;kdn}e&({dZ^^SMawsY~gI|0F7c=g3vNg(pKK--!CT=;; zYbQpvx0m)U1)E;+zhE!_XRp{VU!XyPR0-;N(`7^5zO-bMr1?vJPK+vZmpCtBuU5E` zLq$|jKa(^cvi;@pC$PP7smPm`x=avnuuu^yd!av%(FX4*r3K`ib;V@`T_`Rzw{+Te z@(m@Ad9?{?-GppfiWDQVzpx3#VTDG&R*~#e^-Q_A-r(aiwzw9JSFZm&nHXvt5QI2O z;cmQmk)?N?@tW*Mfkuz_#FiN~heLEFG`b-DwKs`+%- zN3#=g^Z9}bq7R}0s=;E3U;r-tE} zCB%UK6<0f)o{OWW!h-K|dfWRN{_^wlAS1*`U7uo=03`O(B?%2J3?9}{qt0YX zGUz`#J9!}YbTuJz=Cd7t_C5m{(;?|mEVOl7Opcc)D&W|*G6+njL{6q z!kFf;paT{}8_1xB0jVWJcU51gsqPM&IBc&#uvil1@EmWe#brGH!0osmRN!D)%VJi* zzMcxP@53)EpnGqiu(o;{@-i#U=1y`Fxn_%eHvQ^ZQESJ}7z^KTug+$=aLe!OLU%4Q z3U+;>e-&qgVA z>*SfSD_OH5YfeqOp)q7<4L48+-CrDPPcNqQbvrKU{07nj=5ivJd~ODfmTiYZW8!pf zzP!54Mlp8u-nFGj<$Zrl>$|PJ;0M6jz;Oq+?0%yBi01|Oo%2i_*j{+2uB8AyJ;dK% z_W^PDCG08B8KT-5O8=D?@`~lH&+?uYLQq@GC!ci`PFn9*PHwd<^_pBoJ5+5nJ(^{K z!mNM6SN@;9`~R}b{?E|hltN`V=Sa0eS|Z^cE=J^5KErtRZ7fxR53r!1{s$4|{jz@# zCE%dfzi$lxe;N5)_MAOd14jOyQ0AdTVih|m{Uh?whMH*1Mnb`CtHtXFmKpYae5slM zth)FJRCq*6njO$6Y%`c~lz771a87eKb+hbxnJ1J&wSQlP(2UsJfj#=0-TAnook@yOMz8{7Y-pGdu# zqr2j6;8Nd5h7y&vb@?5w%5ZR0)_%UDrt;g*>ZU3gRCbL}eSPOOM}L$2PYVhNj^`Ki#`X#|f%sdV3>R+0(w z{i;ve$=qS)q9du;xiOM9>*me9+Q7HQ?2It5k^pK;jo?09UV5J5!endn@t_q9CfByy z%ebBC!@7{1dRyM|b(zh2z9SG~m8+O}l;U+{ZG#aFzKZm?eA=fO6|Y z49Jk;BNZ25WKzceRAB#k*wqe1xxX~L z`v$atiDzKDu>~MR)F|}+!~@m*Ep@s=`o>*a1ZL&;!qF;r;GHd9P_{Govyl~L9?n!fez4WIPCo_fUG`t8%8M4~ zKHThBA2cQvi={$WLMJu+EsCz&ol!NFADevSVDRE8E9(5sPoB9MaiJwVu}{a!kCMId z1}~D*Dr3M(sByZa-e;DFx{+v0=!RmM81Gl*tLUX(pEE-|#;L5XsKx{LgQ;(6YXR_a zKTjK|O5vmo%oR$as?t=56!2CrM$POm_qc&NFBcZr5*hK%+Zo){>&4U4Dp?`OzgW-z z-cR)x1E|m=qyon}@4WQ=2y&dqKnwfeEgb8f+#MTH@q-2{ml#n!4DMi4PfxZ6F!2;3 z9%S!IW`w7E@TWyr36@I-dgl*{NJ)~dv5((KduzL<=`7UJEygL<*ECxOU3l(tC<>t* za-jm?<>V1n(coWSBk-)f&(pd?Cu;hi=rY>&jD|asks3i93mHHtuCMDZWT@NjCkZ!? z)#_TTE#VA?y!wkGlScqgPn}ngZym(Bx3-zh-{ahC!1EueKdU6`^lN5|2S^!YXGd}L zo>lEkXIu!woT&Z_dA3gvUd}t#9=x)B+*_|azvDizwQTVMGqtJ3(_lElVrFjw;zCJ9 zC=0NBut6_6LTYz^xnl3M89`m73OrEZM$wvOx=o*6B-+oA4A~Y_qCeAy_hmLDb)cGt zw(+Qt<&@CuG_bhWyg_xIW5{;Xe=fR|=Vw0x^Ko-+E5y($kSWCVy)}#;K`k+lGk8(j z(LJg7n{c9ETTq#0Q0e35rEs$8vyS&uK5~b9vBaaZ_qMLn_`UTpX}swAp2H}1n5eFp(O_8b zh+@9P6Ax3k^V;WWw>CfXzJs^7+L-UtP~Ns?p3jn7NH zyt89ithS`e^n5+4W@vCRi?d^65;7w~RexI#2b0HM)*^8Bb**lJtT0~A>Lbm{_h;{BQqJitCX5tnM5+UQ zWa$=`@C?p5GJ{baM8Uy;v4V66YQ2uTT1M-5iqWjGI*~Y#Z}+RG@tE|Xa@WCr;wk7I zeCOt~ez7eU!JO4=Wq&&d)R8q~WSh5_sG#@Uc-XmY?`Z6nzLl|}A}T0{kjK^{`eIt> zm^CQJWDCcqVbn6FUt!-lJ~8M*CVu9*WX>AIK^k^iD_hxn^R(8>kN{yjFSaC&vfG6AVy3> zv%6K=Ux$CaWQ$_3sCKQ!GznCX>yh?myj>eow2(21gv*Er%>2j(7lWx*PNgSaFfvYx zt5>dCwAj(jo><0}%sVqDvY>^!mg;OL^c&4={w8T5wCC}SA|lo6Jb9ja{yWR^xHdU= zq1beOeX0Sr;YVe^V5^pFC3qm@YqE86uXDCWy03?9Sz;%8Q=OT0BE>`kNd_h8cd+SI zx4abbmk&Isf(MP?U0BRD?Vhwo!P5mC)4=1ZNCb<Qx*U_^C2CyE&rQ6T z#OvMH81nI;dxZnbN;%^U(gU{oM`=+d-3mA%=;@uI$%;8b%a((l zeYzv%zmtO%C8`!m#kYsRkkcLB+#jddgCfNFjI<~y2{+XgGd5P!e8AJC?oribgyH^? zzCM2-8E^j&hB%L!rLx>y5CW_t?V^XO|XT`gHFqa2|fPSqwYbnC)#k zfYLd7YFBJ*WhG^wfXbKrtOvb=Bz@Rxvq9ZN*espeFe6uKnPmu|j;D3j@lY!jQxnq%T+*Kt=Cmgu2n+LCS21F5)D%Ors zp)I4<-FxSv`X?-KZ{^4u4bvh+5`T{sTma#YVn=W8^&h>nnlWxNwW=Ek_E==zk&dql zc$9f0ipvB>A8PKi?yuMtw8E>VkBfGcw!2Iw-jUHQX1N3tlVJ}=ypUXh7-l1^Q zQm0Gz;t2e`_d>WJ7&=Es4rFJNYb7I^d7Jdg0&XBr)y0 zN_GC6P6e;8SQPb5Yx8B7OaGKpB3potU+GDVx;hC<`GMg%pU9Vf#jL3xX|kmBZVX2@9P_j2Fw`}6ode*SZvbDis4=eo|h&g-1#gaHvxA)oarfgsHN-xGaL4WY`KP#6hx~3nTuL{Z>E|^WDlp39tt)EE zEL4apr=Z~fPN1OR%THjUy1{wH6EdY3dbAiK%0+R+=LyLux&OE+iH;%^S+RY?4gyqO z!XA1a813l%Xv?$;?k}vExr2l}&rrf9Z;o_hG6Ruo6JIuc`ut@f)QtW+7=2s;Mx`(~ z>ofgKk=FR8AeQ2rJ~!@1hQ}Qm-|8mmrp=&FK6&%+_AeMx*b31tkHM9b1Su8F;%@7% zWV1W;Ax~vXVJQh6rU?-AebdW8Zf{w0gwUy&i#P|=r^K_%8Fdmj3T~>VH*EEOujvvR zXELOCFL-WwwqzflOF3@Lds}xRe@kz=Hpl)RE%YoOUIS8m=)RbJUjqTj1<+7)r~|CA^9{ zK|G$~dB$bzns0WaiAL!i-Hg&6NhkhO)tGTHjm)TJu|^cnXTi0fkp)bRmwg1m#n;E5 zT#v_F{MDJtmht!6OA=$3*b)F|lx#pM;lt`{pT<>CZ#S$@IlUQii0T!O=B*D~i_W?f zukO%8T{1Ws!E2x_WIFNBtXRYVWyOsf>`-7;-7lAxf=`4BZ}TCTEq0ZcL&?K0aV4I@ znF&HnJf7#jieAc)RK7?d%7+ate=jxQ{P)seD+H$mT}5!&ja_?uZTYKxE-e+-j0&$h z{lk(R9o#TXcugS{fgx!Uw1ZXb*FL!F#+^a>!16uUV{6XWJVF^#q^cp&_Dzb|c)%)` zmKw`Jg_niz!j|t1hCy-Xp{wk7mrQSa85Qa=oI?)6@@?=af+X{b;!34Rt`-gU49HDV z9Dg^>@4H|YTzhayx0ho`Vo$)b&1LItZ%n zs3#OkjE3ZtF<-T%6gU?J0|08R&+xbo%RNE==MuQ5!=BXh%bJ7sl)p5Q#|yvyCZcas{ZuS8&DNcK7~ZYTv>YFUS)F_C&G^f!)<(IE&$e5VN%55cC)U zdpe68G={)-{zqV#w7IM&p)5!NhvhRAKcOZVDi5kde?7&JaKE=>LrbKT`6;Yn`MeoC z?rTev`702}pPNYZ%3MXv!TLV)G1U>cHT*;l2y9T7Ss`7@t#)QwH&$b*{~1ZO&iGWq2^`fp!g<+I_I3 zUuCq#Y-lf|XrNu)M|g>O%}h;T$_U)@IM#2rGfN=8Y>QDr-Cb0o!oOWr>%$~v$S4c) zrE5UYXe+-Xev)J(;)K45|AtrWv9c0G+97f6V^=(NZYR}qBQ6>H!l0Woyp^8-ML&l* z$M8@&`QwF#8r6_;9*FeZHnS;`cBt~`Iz_P{{qnDR#kDsV%0#T@d7pQ<;r9i%#s+Q_ zP(q|pD(Wi7B6r_I0P#kuj)F5v+w?+*w$TOhb6H zT&dBLRgzzDE9bI$_lmL69GnF78dKq)Z&*XnLxg;cq03-N`aI!b59<1f-Wyi(c|4nL zlZM2jQd4Ag3Y?Wu=M5$^Sel5kEam7~Ahqnu5$Q=bNsu3+cvvdUmK^rkj5PIyYX z*7HvIiBX6m?q?*|zfr;Ewr@&(*0Ffnzfv^o*JLl4zF6%Bl8g(2@f{^ujX6Exz3*W8 zSEHsN84f62q+Fy5C0cWT($*|FcdbOrzWV<{hdUhV^8KBJljDN*5vVX%VUF-wsWD|BClgI$fv)1XiwD) zmVe{tyS-3-N7c)Gs&f2jm{pKUMV6$CUK}k<1j?yn!Fy-PXPJwej-`to%BgSa-z&~O{Snom9L5n+2M$;fdD+>OY zBzyiZHzaW_=jNv7V6*MlC0IT;g!&aYr1<&j*08SF+VO-IK&uA`_&4en1W)=j{31>V zKlTVEP2>Wtn0_WhUPVdS<3>aBP~~ui4$-H-V(NhP-ek$QIMyqgRQTpS`tQzm2UoW+ zO|z^x(Qw{thS6Z~+qf#)9X*FTlL?suhn2&5MNfxqGA=%_E1k>8geWht;tIbpu%C)3 z%>?{Rw%PhI!wi1=yEpbCXwNLtVxXi6?w)j}VjeC!c28*r6aT&<7we0{o*=8$s6yRJ6_8+7~UdsVPW z#OaR&T?3wLT{Vz4Cc|v*ttNy+#K8U|GKcfGpOunAda1;LvS-npNM?xi3a04I{D9|I z8#gN?JM)nS7kSZM|tyWgqi;q%JPU5b<* zkz}c9Ve zIuiY!R9T>oh<|Tny`7%tvK8wO|?q$|z z#dif;`+$_M?{9x>I|vCL{FhYKDuPY?m*5h&G;wV(?5r46Sav5u{?C+n@ZXY=^qKps zr9RsiDuDf$D>K)!EyV|yB)?a4>5*T=2bU!9wGm{M^MCPUy{T9h zt!gLh4$e61Ga1%iIQ0INp~d^;U|zBIf%gQ@jbkp&T`{*52{~pC0)cdHOx01)bJiyk zlUnud__?($N*DHwl*}pfl&eN&jYmR6X&9N+vjkjkjlravD0dzB!tuQ~HZX6aj0Qzs zZ2XDpgIKpKZxX@5H`)z43lUP(I63 zpmmJPIvSH&J$VvhEoP?a+nG<8cgpA@Qp~DHrSn*({HC0$_i*c5LxM|^zLEwygnU*2 zR5!C&|BpcBx7)GTj+A265;g)tSz~EcI$Jarzzl&qBPmoubsl{=mkqD%4rn*T&u_N` zEY=JoDo%v2)V1w>eq20Be-;e>s_E9Mz$*S=fZ;m3g0)W0)}p`fAFfh+9HGDNITsi2 zl&5jR(rYy~m@}+Kb%^taZAa?{Isi~b#8aB79(~*CWRAPofZ5`nc!7EGb#QZpdFI^n z3H?%xhi5zVgK@CTmP*^WlLZs*m*Nsh7%X$fn+^&t*x*=7pF2>e?&rddP=L_K2od#>&E$}hhart-%7^pnkkUtlP zp_$<<#gr@FtC2S-=9s-GrOAla0K;T-Z5g$GuX9UAxrGj>yK zQ;&@$aVHWwC2ylej=tV~Nc>$w_T1^#vt=@zDM<4Oz7-8T*)m!f|1(Imeb74?&MSY` zE$y&hrG=34>O6#)(H@(u5fo$pqT+Z%@~uWgucSmE%fR_1Ugf>}a_%U@_HYd8hhhnf zm{)Jvcj<^^Jx$FxXyMk@cEFMw74-tz2Be(#OA>IJmyqrSTd?W<0EOU@ckcy>vx;@6c6=LWoy;^K|-;#B7Z zu@d4zi`RGMn1mV}pTLcRu8Ux2_n(ePs`;9@-L7QB)fop^<-J`%t3VRpZ8hGk&GCvw zSnnHDO$lF}a@w@P+$`7~C)ug@#HnvejdU#69iG$!uUy3+T!$nEb<9gMN&dOn%AJ`N zQj8Yx;qm?D5k`ix^s>Y`-Ar6jV@=MbB#nEP7~m^fW9HK)vp;iC-TzRx3O>J@)@~_z z)2^LAc!m{YW<>Skk6IX&O6#*=_PTKeT~bUq(YTNscl#NR19!B?o}F>2n?}l{-?d&P;1W6v!`8LR zHmvH-C1G|*TO(7=8c%kN3;6ep{%T9odP0&y2&TH!SS`Rd`Dpa!tt!^mulYM9iZKGS&QL+S|<|z&2 zIVasHh#&l6Xlig$nC6RE+>;H>hAx2c@~GclGB>PMQLssyq+rC-dY7OmPI_+U{gfIV0&yE(Rn z7fCqmlu$SkD4n_^tdzN)1GpWWmmdiwdV$$};*7&!$LhOh)E98WfBrTDbFSG@LwZUN zOFI+L_aL;<6a~RfQv>a+iWYyBcCEn3{P^UCvkF$azAeH@09!n#5bLeFzw%4cilb{B zT7IuW*pFhY{J<$SZR?9Eaz+$?CG5KhHchknZOx~nS!12!A3o*Ozy+L%ymDI^+3}8( zw%khDu%GL~nJz`mbU&QoyRp}V+#e=%80J^?8j}A>E*7iWoa^$2QHliuMDadz-%fvBWDV2${|9__;B0 zDkR%*q*-1~pGutyqgUJ)8y_b$uv<+$`|?Mgc@omN!;@3j7YFRf8x;U5?1$%$8ePQY zwWuKO#Ug7)B8d(@22PS>>xtTUN2{iW&tCg|v!1_$zgz)6HFBiY_V<&>aYO^TG&$@$ zdzr?S^dNUJO}IxNM$CPAIu37KKbK59Q55>m$wa}&KZkVRsV|>8oDZC@jizm4ZoGbJ z{+-Q00NPsX6*M?oYWKLK#x2frNyqMT`Nwx-$rDcK*xj%%T^u<0s(6ByL!)mDDS6=| z+0cp#0OJGoXD9zT<-<{F_D^+rJmM^UVr=~N+?}x;S-;;WDE9>`)zp?sS@#{u-x({Z zap=RG6r(N&ITYW^elr5UJHvFX1_rHjb%6iiA^)|XcPw#!T*Z1WE3IBC)M0spUeyM- zcth$iL-g#@B!v@AvJIvK`m6oG!%S8uWT}Zyg6WAFT2T6N zbe%cqI-DU?cwQG}{lG@yG}@*OR@^J4xT+dCN|Lm+f>+Rq`l?fbw!rGSXVmO!NFa~+ zJfI1$CVn z5@$}9e4Cn{O0Xw3Q=JoghFR>6Y}w0rqcfwe512?d%4>Yd>J)J>d#@RNWd3`*Gv-l+ z+Oc*itw+ycU{s_e7L6a$nom{*3|#>4?_3+kHmC2Pq3^R@4%KV5Rz^OM#$QAoFL*l~ zQu%eQH0?(O_NOhJq1Odr|EQY-hZxECioYvcRD%X=G5dqFvP$!!ZQU@`PofsGEEE;8J* z)8Und=H(_PcQGu{dZMV|L5&(BtIcz^Sa^T;S->Cp-)EN-X9Sy{Zor$j-Hyih8|arO z_@Tl^YsANnc3Vq}P?s2DAWNtB`N)mUX7}#pbvWtS@!j|?Dj9?CSL0g88vkr%k$d;^ zKANIbyltxLW5$>a0gisX{pskLK{w<`4ex!_`-fqg)9=KqpLiqNvHzGzUX~c`|Quj)1 z`AwulL*EcnkaVa)1*Fp2S0$HsY59DNWQphsyVLQgNDmh;pp+&nP^*bVFQ%9xFVNCF zR>3BB%EJf0!GmGUOLr4HsunS&PA1Wjs*a=lEA`q^KP0iqHhR`24caHSs33{k3S)P7 zo^So2#Vc5N1b3}MW7w&;-)zJw)>R7e=pqCa zmYSIA-)Z9kM5*wF=#p)wu+azY$uh@=tHI>?9=fK3=Ju%Wdb`8dUwLrvfZY3<$i!DK zPfuyuht}6b^@-MV7&32Db@sq-c@jRuWyJ)+oNe6b3kt%2AphoB3@-_nt8-H5t*-7rlkd z5sR(b_jzdSjgo?{s5dbr7lZkb2y>p6r$iMW{>nng=R`pKjDz>NE8WHPkn!Q;loK-{ zij^5xAU_nHkgB z{lmG0$?yx7G}gh;&pTm&Jol9Y=K-v)#a<<5nPz}Kyo1?HPx5KLS9oVLnUuOR!{WV& z&+q*qOLBiKk2O`1voOWBguj)<8YOyj(-d14c(*)@Qo_onO=IOP08f%^xMczQTNA`< zC3BI|yi$p2QfMSybn>ElCkO71&Ha%vdcJZJCsDimq?k(TBFx!YE{@Y2kofB>2`M%+ zOjS8>B^!Twp;PPsJ7iYaZv$bxVnMrg^7`JO02rfNkjU1#+cqO=EY{8n*u0KMeScYK z4H&(7bst#<=bU_6dKqL<)yFkHkI<>);xoSvp`(Fv(ipquCG7_gCGQPCLW)O!>6gD+ zsnJGGv=XJoM$5GoF z<~w^&b5dv!x~ZA4EPM-&a|%uO0rK#s-pzR1T)^n|*rOeCcJa??g6DUGRJoka!+v_w zl=`tyvw+Q$cv>ljQF^IKm@}i7{>QI;*jY_v*h>rz4TMe>fO2Y3NnM9I*D&Gu__0sb zkYS!qrMXkv)yy_WzCLg3I@Z*H4e2e@isN{mv;!?doioYQ^5H*I{(#axU;1ZG9OeZb zgzh{T`h`pvN9QKe{5#(+C6Zo_WSblfDDQjvNMh|KvLjE|yfsd1F;Z;VTr^z%A!ifd zksGA7A9u^RyUN+$Del*Qf_h3mb4d+J8HVA1y#E}}r0Qg1hM2ml%eh@LTxRZUd?qfZVcmJ1pEO|07qYcd>$YkHkW$Mh_O8AkZ2npf=lVyhB8?_ChV zD$ec7@EE!pFZK%LSfr-Vi89gvzT_^ONpDlL)%1Gl3&H*y$~|7XCeTz@DwZU_poZ+X zt&Nm*!YV$JkV=_7*YqIVMED`?^XO|>@qpnvC-$kUUirT-FgT;rhS7avj+d}tD6=&a zu`IZ@dwzNORlr&rUA~`-{_45>a)CES`h03EIEK3A_j#Y{mK6QQ+3JZv{wDgHLP#n6 zaasgskrNW`H;f)#5Ny`u+*yqTBf1%Tb#co6(P~EWSs4-|GqrDQ=;iGf=yLc&vwC9j zwqL*fD~YOeMt~|@rz_KYj zxNWAz^s>jl-8FEVVR2USt+{$>H}pozG^YzhF31WKTDuE`mfDYJ6E z!gw^^>KxXnUtN%?;UYG6cUve3`tf1qZ-G~B5MW5#GsvAtF82R%gb01$X0zNwX|6~V zR2K0cGZX6PZ$2%?-<5()N!4RRi|=QK?y>NWjTGY-Q$c)=OyJ_1!5{ z&gNaNdb!{`>=W!ys93tf!ebj_s+@qy>d~^~0p3KC?z%T_GOXhTm;VRMbYAsPUYm#V zr-)&*N;KihgwpAZx>}B4FHIeH+BY+sFI|PPH+)=lV`LXDLyGIQ^N6pel)Te6_YTEK z9l| zZJf@Ry+;YZzP-diTa2qcR|wk)PM*$xpYLuwr91Hb^i*>(jobMFlObRJ2MaEGE!D&O zF5{6`^%}zC&Wlyq`>{a*2sLzdpf|c-k#=3zV7mI@0~a2`T?-^aNRM9TBkT&JZnDt0 ztoe62>FwVRa93>namNczYbus2DehK2%fU0B57Wrb4O=!Bj64iiSED&X(|fX}pRXnX zXWoP7zVNmD41y-vVCI3FRxur2>}_5dDk-_?Ec~CKk<=d1etI%+32Up4q@1rDh>ol1 zV4b)i&E4nm{hf3pYHFy*_E)YRmy^b(zrKE2LVecs<)x;(wpz%rIo3m28blD6&E)jU z8e*xcLe;Ehg-wZyvx4t(m~GM8kfFpIW1L;pr&>QfPbYJ07lQNe7crh0 z{p3Hb&oDHt=i^2wX(O&Uu31d=dx?rwID);jbhOZh@YiNO7ML@i#$j*+pS6s#@I~J* zLtsO2fIooYar!}lkLZsWIVdM=$Xfhmd#)CBf@WoTmOWshkko~m*nf+NWnZvi@}-hu z)JHY)FM=H_U2ovp^p`icyT2B)B~`a>r*uZSm+CTsBG?*EQSlpnTW^p7D{iY?DoUxS?6q+o`A1j z=5F9!fLl`eN{ZiNcW++nIGx^51)*R=)MLkg%>Kerc=%r}DsBkf3!Y7@-S42qa;?nA zKKpfyqKE6@8{A)-y$_$dsq{|HZ~wA&dpyYx8uzD=mBChqsOnkU0FPEl1c~mNO^wiS zU7gw{jpOmH`5x<{5;wyckdtw1-$$u7uL3|()U!{Waa$?Mrnxf}7o!oo;PYh`i43!= zIIca`C|-fIcotd}VF$}YKWT*^;1y}w;{7si=#(g+g;`mY$8|AGq{F)z&ygoD$ThLHg%=-ZT z#O=!dLQZ6q{_76tkX0Q6(qpgmLvSqdcYa6eDnd+125jRFd8@U^b z#AdQ>sr3zIHmX*IC5ug&EZ^PZ=nI}d92Dn2F&e4{3e3OV$~ITv#VdDp~BOyYcX>LekBAlQbeU6M{6Ygept3df4|A^Wq_I)fWR(}`9rTp zYP|U3c&N;hM{3!`#QwTK0Bg4$8NP7A%M$H`*S`#YhPf^pq#e&~_9Xe?rVL^kUBOOQ z-~5B_trYl$kSpfzRN^Kv(v^wUkSTlA%k~Jcrax~EiP2bm2WL6T^G9_^K3Wh4OC~pz zJLc4pdb{R{7keYjp5z0Sy!R%EJRy@FgJIWHsPJDZtxKQ#fbNkM=~x2P>2JOH26=rh z7L&T@C8J?|=E?YGhHlz>?uF04VEK=G8m!N9)6&ZP?6m4<+Bd(VMZN-dZN`hNAr7hv zqYYW@XPr4F7}((u=^tx3+s{FFa58aX!kq<|0-Jwdmap&M0coSq}mtYI;hxXoYnd&V)+#xYB*`*!@Ewc2=dr zZR7c|i#%Kp0b5;rR~ETz+py)AqvQ~grMC>6gti8rPobP3z~Eu?hOgA%-?MOiMJ4EW z`eqd*5;*>Ke_qS-SmXiu`5kv402z=9X93gE21!{j>&+eG5P(Wt#PT@g8F`RI0U<__ ze6(UmQs7iat8*|goO}1GtAdXKBe#D;q>U2_`4WLe@^N<^6_#x&$fWI4L(fvRzB8k% zw|u*MG9?6FYL*CV?kRC*4izWb8QueG2tfBbb;FfS%(^}~CO2xVU11P`1T*K0d5a(W zGNqoh-x{=sFrJ3onCOIlx+zbT6vZ`=EaE=#nJ{6CQlfx48@llY|E2A zudlhcKc<3xHSq9?CJ*%8I}>yNznh`y|4LEJ>H?3T{bOAy~wIBR9ROpoOd59Rn+o{N)3UV`Mj6S)(^OjwY0i3|I z(&e&0A5sLNf>JG(d2sZsRZDMRT7_wwnKR zndFk@AmuagDTC0spTNej`tktPgienk{UpnyWE;+ulRGM{ z+R8^@v&z6XciyMs=c$Fo<~J;QH@}Oi^Fx|1frY!v+6;;N1Ym#9(;k|3Th4@}1(+*l>x>DfaG261RDZ;1gj^27K1HX3t!~e0B{+Zs8Lnd(Iv| zS)2mlaif*WSS#Yww%?j)&z;!9t6mJ~Md4GV-8dZAS10UMdz$2z{Rh7T2Pe3Ok-R$7 zYDfr6L=`PTtr6j6_TH#PNnU)DAwzp*6O`ts_!zOi>4D!j|4)(OR+Mvw@q|`f`P4sL^ib4-%+$ud6=8}y<_yxS zTMb!kWWU)^yR_hP_>cBp8mNkv>($UV)T}@)%=tfHmBD4uVq&xqbt4;IxaDRhct9NEL@4gZ4;l@A~L=XKp7WAhWfo zfMseF^OCnot`0JD?ih&W5lV$qLVw@ujIBD4Z@CFXFBapK zUF_MNJXgHmZ?vin!mfas@}d>}UO4c8p}4~3aQUvg4GV}D{0TbG*8c--R2kX%D zWx|gQN{#~NpJsCYi*Q_esO{X*Jz}Hk2JZ7MX~d>fO8Ld>o(ZNE2;VyF3Iz;=c{1{2 zDfl#y5ade?$vl8(WTi^w&Kn_R<&O!Q%Z-9AfzE&8b&7%EC+3rW=49cZ4dTiE@yA0- zqqxf+>pxM}s3bVa@Tt~C6Ds^j-CBw2@d{wz87Z*a)W(r#2iagt!wJZ|kAB84%4XPj z18x6M7%Ls;jP~jGFb^c21zUonJ2y~4-e>=vM+d27TT>Kwpz+NK z{W_ZeOE!_zB1CwxK!l(k^JCKpRt3TP%N~*BlU3H^!NJI@R8pp}{8DKe!ay;~)DbB( zYVhpU2exy|{>oBbM7w0b+EV~pXEl>}=lpsNcOKEV8<_y%?ufzq=_ zy0XpSH?Xy~Elzfjn7zN+dPgqdnN>nexKw!E;Upqvw=FMoJ$IzD^a9TE61FLc31^Ga z(9^!Dn?268^_MuxQf^}jkw*m`Eq{vywl#Kg1t#ucbv{c{G1GmR|Gi7A}N>T^`g z|2H$xg-&Xl?PMTxn$%R6%NsGr%-~E|s=jYAygE8~nFp7${FMEBoimSW;v?_X77GF( z15H%~Z-ZOb(~`a;=7PEiYgd>1f6@Yx#ogNm-FJwG?;tTn@FJqc3dX#DrCy8)mwvjz zZPBCt^_g!L>cqhpG}JTNRKBHRm12`2fI_0dUa%^9-;q;ZT4fTkkAO}v4`z|DsBW_ZYb`IoT9l)iCM znh`X-+BCk+CwfbN9V8NyH;1cB<*hx9n+NVNlu1C%fk7kJe<#z^)4!dXIqHX2M#wg>Mc?aKHf`c$Q8dMu2D%IXjVoE7x7Nw%PG!4&tBk!at_Wmo2S(N#&n=V zOk`A3F#O4|E4nYKeS2$C?xIh!9vm*psDh=v4GA&ZQOXe!zYRsRqleH}mi*Z3m)LOQ z_@Qkbbk)JT?9SLAI9inrH@mXN4geqTS6>T#V$=h@3`*;Q<@;sd+me;_Opu^-;c>}p z8sGDR6yHSJ-gHDFbQ(tk4XliJ;~Fe^^lU1r@uUxRg~Mx*;@4$vd|Xs4f{$pvl-}c- zOXk5#xNcS}L`T`!p!b_YNZGPSOV3DuW6l-yi>el~Ws!e(;v<^3qmCnKfpJQb=#Wa# zbiz4x@{%9#aeeT#&dl~W`FMTE=AiYU`mMrn&9xqdK=MfG@$$&lOhUtNOPSYnph+Ys zNeH5o(|mFnCP^g64$>InN-84qcJ|*Izld9E%1A$oCvHzvVKh|>PZYd<|L7N5dMtZA zsPl6Xvn+Y-{)?x87H1NW$j%(}Na5-GxS|KsrzO`nRLSy&=z_4Cpa z!%5-2IJ0+<9;(i3yP2aL^=WNE)2Pe)&^$PUqMOB!a9-+w>v zJT)>Tbip;X)H1K>?$UpU;c=VXKh*u02XCy%Z0ged=iq0zc0i%0`8$Yvv*Zo=J&%yu z-=o0mlR}%UxS2|F>uNxEXNk#bT#~d!0Xwtoj7KsD?y63VcAw16g2(XxnaB}2w3H-} z>t{F<(6`4Mi^+8y&AEScsS0B~C|(9Xe14DCh~eZF$+Ty+pvf?+;h?n_Go7Q7InGTu z(i&3!PfDv3mU!4#wN6Y35gIwAq=i4&@-*;E#FjjM|W5`O00%r{S$QiTq2#eCHqp^X}{ z#mHsTYa5Vb`<=}Wj7&(R?s0e5(BeM`SaA4NVPQ__5mapK6ZC z`Jak*?UduH(zdsQb5xEPrgW0X-T><6R)K4CWplV^%7)@9 zV55{o_b3eWjmiSKWh@-Zm-7NMT~Z>S)UemHPHCQpFSTPQ=cr^%^Hh|SOqbUcHS12D zl+EAyn7XhK1(a;=JowKU%*g?(h6-lhYRpZR`jcs6o3VQZ5z(_zjMfG@ImR!OH6ck<1VkxLKDB$|EPKh!K zc}&hPvEXV(YZv*K5yvHg_e~r&8O0}LH?9GjgH#fJZF@}SNR~Jx@}CV+1|PLAocy)Q zQAr*3t+<5k7a__@RvSl?%Ii$5D%UOjA(baeWmPW4o_T-@e!jaM(OmLorpUl_^^U{2 z2=txfgnM~h_S}aYrZcK&5}H@LxQyBGHltz6!->}?lHJ*3(xK;OBr9155Eb_1jdTYj zS-*0Ex!DrDCnFyh2u>yXlMTRqyPWFThx`b|%TD!TWg<$Da2R+`Vj#zO@?ly+_v08y zWhQwRU{~46$($%A)vJd7pfsZ)1PJ?S{2q)u+Q(h1nMI+XSm4^%ho=m4&u|(Ft@!WpO&5wmuQqLSM&bB4itr;`4+N=F#G<-g;lJi2a zME9=C``I$S5ZIM(8>;vS+6KRn<80G{E&P=<__>=A82(9>zb2~_g{4ids4FmD=&RR7 v!K*V(e`gJ5l6^|{ke#t7Nrkbm zW6LsV5M!IMeBbJMp5Nz>pZ~n)-g}mN&bjx#&MPtu5)OPaqy?DLc;?dA{zQsgw=YYr zneTizQqv*8U4HJIUOeHq$AtR?(W$(S{r!`(QSX_euB2$Po|wGlk^0IQ2IGX) zB9L^oo|-7OFi4z_)191Ul07aLSBT49m~1z;*{6o8>I9gX@v&=kb}y|cy{4n#xS7L1 zL(>+8fnqoybP4US3n$lk@dOd=#?%r=S`6Q;B-Z$~ME6h`q?x6BTIL=Ecy|$s+nyK8 z3XsqaaOr!5Hkc`g@n9x9Xf5}#G4vJh69%jA6Xr}l_x<~-q&q}MW+K#nRtju_r>)6B z=%xZ@FpXpDRW8WtWvkC)8Ie|%Dy~2V2D+wvA8AzO*(A)5FGb&R(B<2Y521%LH?Mwx zkn)G>PqGrZ(T@O!?<~((rNtywK!fz<2QM4dD6=@;o1Nf9J8rs)`Pd?V%Ti2Lul5=4>Ls=wXC`q>uP`%F z6fqFRK;GKnC5rSov$L_*ZAayRhLq?)DHAUNW+8vTgbyFN`Q}^yoYLvH!OvmMtD=w) zgQ2kpbY!=<{PN9*RQ)5vb)dK$d(9vM&b~cl&>SImSsuGE=6nWAvnLgu@VvhvV)Emd zS%in-#5m=}zQvJ|W$byIc`Ii2xBYrFxH8Tjjbwlzd{aHB=58VE&h}80r8fs^nRDlp zvggvJE%$$J_qvWQmytz65|EdNfri|#WEvWWrx>&@Y0x6Pj)4+m`)HhegGTNJ2Hnwh zH%y6OtLlutM8mI+Y!IoDyCYlinH)$XhryuRXONE%GT*{EFl5F&XLlyiM{hh6Xgo!* zwqBtq3-4=_GKh0D_c>7|<_T%t$U8_p8B6m)1KBX6=!~QlN`doWd>Mf}ZCG6$%r|@S zCgesW2L0t^QYihr{7%`D$SSEYPqKN3<|-#D_Ub$@K6%lOdtBjc8DTVF>)q#LTu3N% zi8Ijy5^|QL7xOZvd)UwacGy=YEF7{Kr78unNnfE`6|0}Q?_bBj9D_l(=_g^}da{Q- z6VEr;N-hqgAex|R!0`0%g>Q1@L&tP7g8W;aG7Cpx&{_{GM6j#b$svBiuY6~3nKkM_ znL%%S)kefq6fY3QjTG7Tj@6sz#X+B)!A_J`s+^9p@5)Wy7D+PFr&d7kbaBh?oGlg zRTcr4#*#VkGnE8?b^P@2&oCZ1MDpGO??4Ej8abW6&mN-736XTI6#8h<=#8+-#GFjv zWRM0j0%nR9+Eq8k>(lx4*}ev-vJeA{JaKi22c27M;ZcT`lR17##!TfL__X1XAO5|w&lmLB88GSMSY@j! zOYaG#KC^&hp8l%ul+DZ;R)@m5=TBp6KV<{;KSM`%rxSA+V9nQg@Y87YPEJ|TgtNui zrsVX4?$#p9b7^nlG4svu;5vLUsi))+qBNVDkPvI_i8?Ew*V7zKu1o9K=+F*Tc1UNK zOpG3%r^0{6P%F!)Q>Tj1dOL!9>)*ccaZR>zDN}mw^JK-|f<3ZHr|ffP*g05& zbX=J!i5IV!qYWy-F2fAtC6ypck|6-MG%PCE>nA&y;8b|Y*R3v7Cx z$qa?#45~vuRyIbK%gl`j&FSo)y4klDO~VuQ+iE8z>A+VnRqa^GGgT$k-(9KNN-q$p zS6LvP5m(|Tnt^+0B#rx3k%sC9$4UB^=`H-2D;%g;>K)$?>5gH@ZLQlm479=!$R=(o zW4D`3QgE3mNXXHK3Ns~KgWUmA$Q=T|7+!DlD+Flc!LrB5f%1{CTZHFrkb}U9*guSU zGVPHJ?xvqOnRx`EuHQQ#VK6-dc1ENfra6UXR*?CsP3567%4sTZelNllMKH z{^Tn3O7*GTZT`iWcXA`4Nu60a9eogWKP)woqUDlfd!d`p<1J0^X{^&vZx$D=?<3V) z$G$-M8DFSaG>1WNyu_sZiLyd=XQu?&?~DW@X~TQn3!vhspsrtClR~!){Vw&ktUQOX zzQ~s(-htGJV4X~W8W=f(XM?^S77yh&x5BqTA8I36PsW*amm%*2MOg&U#nW0UU<^>r zw2XPDl?4T`S+^R(Gk-C<2!ATUhSN<&VNx!O*IYh<)ScO(=ct8W;%#`4mw2=+u)DuDlfKb1`yZ;W)*W9DaJg-mY3nn-ot)d zV#Pl7cPDQ+vhb|&LtSr?s4TfJ_^)gTZU<3F?UU<9B`HVX`IvX2pM`@(6F z$cEnrV*&a)5K?z2_DKFJ|NTPeTS)K>QlR05Y<`jr)2zb86v!a1g8z0VC9FC>*lgWY z0wNB!g>?7jD8%OmBhrX-o9D?ia!)Yx?FKp0?Q!`+DF-?btrwVi1_+%l4WyGD%KVxJ znj!?_5r@F!)ELE4P)3)~V>Rc3%z!`Mb=~JTlRf9}ls<>c>?c5@PJnWccTmF)V!^um zNdyb$P0QfHU+xou-T}{o-=9AslR^6l`s|DtLC9uR0K*r`$OaRXIq(={vu4T9nBg@8 z<)xh;`Z9!0xtSI08XCb5blsv3r z%Jt3Wix8Rq0KZEcL!77&#=8?w1FD1+5K-V!!9x0JK4vjb+gBhP3Y>;$xdvrK+d_iY z$|U+U&Yy=>G#6wVsC4i7oU|i6!<#@}SKu5!m~W7yZKA~Ja@W8cCbxF$sq|Uw!|hw^ z5(B?JES^q02DL;b5`Z)aDQP+v3*#x4_%68If2z-3;v}pB>#s;X$>>nC%mj%j)atrj zplsL1#|V+ktMq^n(ns8w#I0G+`xF~n>9ZG@aG+{ihOUgpHciiDUJfEv>V}m60q!Zl zuk3SQr|+uU*M5}0cr%e1DlybU-h8o6q6ag~VXDRoxyw-jAQ|&cL9|Zat`*v*%0T3w zVZPt~eZRscHo#llbMGGW?5oAhOZV8M_$Lr3)rW%$3sM605m%mLc;DD3H6ZU~FUqtm z8}{3sT3$rNI7imPUbiZ;Ji+h|iV+gbHV=|B4Pn{$?bOC7y#rp@`NFPHf9Gro4yfgw z!uX-hqW5hkfJcU`*X$R?Q~|&1H+izd5nMxWXCK1@;OR2m-;jFX>043zypY1&ak*e7 zmxt6b@?&phy!nIDo2E>;_Q$iKTS^ZcK4d?`@LE2Mcwn$vWm@)(nc5NX)N~g8WQ}J* znd}_jsH3DNai88lOZo)VGLYKdZc4tdeYQkxRBXCB8;Er|EzWgY$8)MRzUhq8eKkR2 zi`}v7^p+`%c}&MWe`c(WM|-AoG}L(C=q>5vP-WaK?d%GrcM|J1kuN{!2s;Vqe_<_* z)pEYMPj~H%z^Sli^*#;tgZkfG_-kDwTSl=S`E0;`@N~QRXGWJ&Mba>*1b+YZ;$3db zm-a%t3Hs*#HtR87J-1)nvW6@W-87y)&A>a3Z`YoF2t^Y^p-KW$7>ML^-l zz~7{XHjk+!l55(p^<`;xxj)?VX}W{H@k|8UHqV-b@i)l0^m%{0PF%fZ_RwLg``~?B z3%gzV<$L}8bMD`rqA|Rw_A!>=a|ya^z|O5`oV=57(9K?MJ5#*#F7wyc!W~TZ3k+{M zV8QB%dHMpKPOGy2-n)z>4AF8%KW3o2 z^0iTCsZ?}%KR8^LYbZgh_Qc(eGuSp~qes1^Yx(uF^%%A&L`=2z&mDH?MgIFHhUwe${ zdVx-17~WhBy{Gbf4}yg+?L1x#`)nme_Wmg9sA0ff@Qf3G=5|8;Mm^Q}oq&EU_r!YR znB`}kwJ+cW)H1zg%lX?zPCJvQ#X{8^QM+umlRw0<@dz=nUMd=WpT#jLHIuJS1CkyL zWq)qgTDL+FO;a&VycgeM<_E-*A7JRek0dbpui}c%t|r58|8W(@%ImE(>YwkR218TK zf-WC(!CaVa_NX4y{p@Gc)>|pFGfI1?7a`Amv_j$r4`KzK3Ky@>{ytYz8J32=e$fv( zNohSEhgN91`(aiM1*?#eO1lc6dh(1?svS>3la5CZh(8^ZD9X z&S8@&cLIZNvt=Is8lOKdEp6$C4GXxv5F~oY|GGAWb@C-*@0Ekcy6!rVvdDQ{RvQ`M zPxbhakvYh1B)(Y|*aDN&Smuf`2n}_8MDA)HkiT!Ia5jq-HIa6DBtqu-^0y7p2NAQF z1L0fUdLAj>5__VM&bkL%BA2)nx@wKKNiq)KQ_2<1%@g;@ z@55>r4>ewr<7xQ|7P5f{8EV`?NaV)vUm5w7lZ<4;psKYm8_#Q2wWRm<_~GTU2j`7S zjw@cRyGF7KvHu}Zf1cWwD3^pe*iKv8p@pV6OD%jYrMpDPr7k?hHGVbimC0q^8bZS9 zl7 zAtXF&aMc`piReKq+b^G!>^f{NZL(@^TpmQT!n@Cy&IWe1H|U=%EG?*%e?v9$uRSja zjRD35bK0L=-4+BE1N`dF^`ZvQ5o8A*m>hSvJO}=ORZ=)`3EyJe@;z)XW;gN2V#0Os zWi@jP{=Ig(+f0j#k{CI`5%T6bQdXuo3`2i;uYMWT8@g%zq2GiQVVNxSjfyy;Ol$@iAH060)0T(9Z5fWcMs2SUI#(z>L8 zH}X7q?qsP&&Hd1K8dVEE-^BBnt1Is%FWx+z)1qM7)dWOwO< z3I|a(gmzMB_gkb#Azf18)(id7Kn2puv-2EyeORWU5dM0`%i=A$1*5HGdym34=DsX- zeG10Z15iF=a|zPrG$^D{1nvrlBPLI>*kUDL{|wp;{0^35J+SD2iv{GvZyNGq9mC+$az zD|c_>)bPqnWp*#g7N{4@H(w#0G+HP7-e^Xl7$8a^1&Mn5Hxf^k$*TM)s za@Apmev!w{+*)mHT5hV2Pw*;k;?E&{fYnb^7l@3s1%rdtNp5uV#uj0xbr0N9fw z_tk`4?%-o68VTset}U@~wk<-E_q-O5~1i^Pc97U++kFbG?bIn2>^6`XOw=#v9p`3y+Ezt|H0tzm}JNomlXP#aAHK??1!F%HE zy$RZw0ID0a(qR1SQ}`bPH+*W z(xOqm)$+N-{1PT!YbYeWUq-LG`XS|yP5%5rlkR@r%8HGeQDA3NcCdjzed+hPzoZ@wAl8rR}(}eBGBtsbsg!?#FaVNMw0c3#t0o!-86|!r>K;mPMVC z|<1Y?mjm0^AR<$DA!_UsLY;ey(*h>Faz< z$Blp5u)>Q6^^aj7@6oi&&(ixmvIGhiN!A*-PE9F4#qdvEeGP1QWv2vFb+h|@rnMuT zLiO_NM8Rxj^|AJp`JH9r(@u*Un7ggaqTs^wdIJ%h>9gXax>{y=Oyc()y;mYwzJn9{ z^i?)HEwgJ4nW1MaEYGh^VB7;NZ7Y_VWItE3v-Tds=sNIr(Bjs!`^XA?^+|CC-3Aatof^zwh*t!M9NlX(##f;oTc+WbJl-6=j=C ze<+vi18Fk>Kng+#Zs#kGnn=xmO$l2wOTakI(~b$O-xSJCm4)LG9h(>Rg5tdc+eeo- zmfI)yu8;dQ{oD|mUwW}9A`_&0`p^}Bu|gqUn?!J6ldZcA^}Zt+l2U8ySUeOc%AlH*y8C|$ZoV6 zR`W8-5AjSqg+I_`)l0jqo*#M?gx2MBa#y9_WwHd)vR_Hm-EStJ2$oXB7WLoOvD>0M z1Pu3eKY^ONRbea#Qz#9E)MwkTG2NW_dLLWv#j05gxT)Duf%B0(jU61x?pe)vDy`?` z!GyuggVjaaFJsT=QWAOX$rsGi;PsX*c0+#tV=oFOKsdd)Lb*YU17EMlMqGIP>zFxh zfA(K}d|*6hT~}Wdr}wak4RQipy877-4nN?G8 zT!C~dFLY>Lmor|H6{PR7tiEg7w3TUEpkvenZmkzM`U5UAD>E!3zbQ%#T+v&p?zq`7 z(4wkl1ytRETApIeGdp#t(MmS58ISYcGSm%`3C~uzmAshwI;kcdwL8A%*&f(JoH{wL-g9a<2-lxQ8SQE z+AZ46D^SZ9HRo>i2xAYxyEgc&AG{cAJ%j0(-JH6*o4Lh~}208E3FTHqHU`a>QX8R@gl;8ddq6P|NC}4a)W!JJeE+QT$^yfvSid z^z(2=ve(qtm;nZ}`B7m^#tX2G{eVAocahs{2m74?0h7ETC`Ft`YFD2d*Mcy%_B!&e zg;fx}^~BfQnop6kSU1AD6=&%DIQ~Qah4p>2KXevIJI`#t$U&Ur?8d_Xg_f;Fu#AfG zxv5#0EW#C}4RmRp(?xhKbSGIcrh{Pv0O!))M5^5{q6${K+PaT;+Y@`ZaXt}#YoUm% zci~`W!g${J9c(tft2*oPgiR&!-u!hKId7wHU|}897@TW|kJX;|6gz;BbgOGTySExp zbNffu)+0ujVosF#@ea=ljq4xAA2o1#PTTn<+qx>Z={{; z&NosJF61v(9sUoi2F-1Y)ll3&S4eo9Lu`mP$e^J)C8(o$%>*T~+Mg=I{$^P_%Q&`^ znYuA&z~}-Pa^h#ez&V$S@u;zgtkN7KhQw(ffz=($mYVWnSVb*l^yKKWF;%;d^ur`U z4tT!YXPrHfGz%$83CzlX;~r#ZnZ->OC~IblCu54!Z=%@G4^({uW_0`1rsF>|B}T_x z#d_`;R$H8C^!bNi%W9u{f>@u+31xTD2KwB*8;{^o>sw$paM6<;go>!wt5t*stD>EjXDLsZ*TD8MFN%>s)h*c#Gy0QM{To<#02B{_U`*}R&#V@G!kca@zxShr?#;^+hd7@2sJQ1Z24>3yW=PYq<)c-dL0&`Wz47H#owF_r_yzxRJP$}CU6f$1RK zJo;q>HQ3n+Ib*7WmcV4|$XiVC=#;-Obw*Cw#ROS2I)|9K8;S<1!X-K5F`*y0p{{~L z{tE_zUo#BSo@1c3es@1vU7OV3fBcoMzsyhyEm8l)gL^1x^fc*SS{z)Q*J>PU87l0R zvxHbS^BfmCf5)Dd-`n+&s$G*~Jr6>ba=f+2ed|MFX!~&5z`3pTfLlB$IW`Dg)OC`2 z^<#KJkoW5Qj{T+FEpsj8Ml1%ZR{y2v6I3!UDN1yP$g@WG(xTHM_eidI907KKJDcFmGHCihq5bwgB_)k?j( z6!tXMfaB05yUM!xmnZP6izfvN^Ca0uR)F8C;~eykj-5AjwYRQ{aD7@(oxP1(%|xW zTBWXo-tc{r5$jc#rIINzSbtL2rq`YXNfvQ#R+IGM12CL)@yF)Qhi825C3_!$8J~SU z(#H2-VQgc?_->(G3;FFUfy6AX1Do7_^TDXskdOzHPp=F$@hK|bc&NXgU>}}yDAg7V zU?}lF{z%Ma`?q;a;?~}i?EJg}8KV8p(~e$PKP2QvOcZB zx)H)zPrXUo?wlw)Z$&xBs70C6B~lkxPrA)x%=`tdQhIpB>w2A|F_c??-{GuXVsWH? zEYlA%~38FY^s2(?OQj93;uofWLoHwJwuWK8a=$TyZUFVfqcrJiI_uc)TT-= z$)eJ>i1Z1=KvjM2ZIXLh(aIAo>?)o%=JKMHJKrQ!`#V6`e&_ph3!8+ola;FL+R87v*S zW9rEO`qYrCwnxLNi?hY#%et6Po5|pv;30_PTh=<4-4n^3uXPVykw=KziC`l(3hW8< z1<$_a=UPL*1KHN=Ae#NN10LfSB<}2kWTUIAh56ygL?XLtKi&&_ zo})eI2CKL;-Y6UbcAclhD(9q@hmZATt;<6#FVWhVuH=KQL_065cHU(8>k>}9Iuq`J z;ET%$iJkA!Qp+d!Vt$B$0}|^h9p~mb9B1RlvS*+ElkzxUaNfN75kqVm{|py?B8TBf z$%p=;C8LPgR5;h3;}bn3!@r?m&%DCbM_k3*ElLmB{uY!0aN?oNQlCnO2_Oyw-|0*R zi36_xG;-BA+(GY?8Ne>!q~Zz}O3E(H2v+f_VDc1qqtm_Z4uPwj+}^8n+G?3#0}v3w z(ghq0w;0u=Fs#q~veg-Sy&O7Bezo@!jF~do#rJCh2JGJ!!0wEg6=}hm*w{sWxEF(g z0*&5*!S$J0>~G;ORyk44&J&qeYEc=TX)9}Z_ z*if%PUBby!YVk(}1TR*T0|hV^e+r>05nnA%(`1KXDC(E~Sh67|uONi6q8unY^Elrf z9igrb7!M6(GdwGXW$@({geaDW1EmJMomYwY5(IW+!M@fKt+P$>Ta`YLQNV>nhBT8b zUo?z7=l`Y*{_?{H%766%+5~gRi4uCdz>AO7k7dr_+M62iYNd}&uKV?PF>Z$$vKbcP z0YQL0>9zQVta}%}VmE_v3Cgg3hVkMj+*5&uU+ByROHMe3Ep(^CV*yZep&fj7rieZ} zGra5)S67XfHRowF#yoZyRhbqy4hEUva>7u|qa5v70a!o#g#K_N_gzEo_HvNM5bk<-Ue%x+Rasr71Asfo@fLfL&M5qWyh7g z=_Z9*Jd&)CNhyCnt0@M|+Ozd*a1BY~Z~vXovsP^kHm6+CCaTwvkj$Xbk#6tCh;CM} zE{uCSBgK;833eQ)<^>7rRVZ$f{C4j#22JPyDp{PnY^qYFBZap-yww&`;BlTDC{N`} zr5=f^l^?%@WvXJHfvfAyrKg9d7C`@&P2C$aZABW=K;BK25tU4McPHfztkPs*{jHB> zxh4WOr*5^q8r^Vr7x8VbJC*ka^nL3a(a?vt2UCCeA|-YX9?OK&zesCH?CV+Jf@}iC zvAA=K?t02K*Yf(~1_d0!th8m>wwHzR$y{DQ0&U1%B7jI z!!O}$JkTXas=ZM7^)8Jqs9g)~ggO#InyuKGch0OHK7fJ!QS^8ui1RL4sBWZ+6QKQcro(&H-PkC7TOP0Y`{`y+}IrDCiO4p%~1%ifVP|UKcbv{iDSAO8(e!H zWWBkKFjOR}a@7)!uGi*6X9^Vmef$sEOpFmEpuGw~{^zo6bLqd=dXTRMih%4}&iYq@ z7+;EX9LF!H(wExK|4{y+8@(TUI~;p_94#qrndq!470wqFx}VL>!J{vYv9 zk&*ar%y+EaayjU#05_&LJ{)7&9!Yp)99injop}!AoW;oY(k6t#cFQlpbRh|sV9$W> zTF49kW%_9ziSp+7|NLuw#KZe>4Vj5gzwj}9d%1<_-xpQv|M)xbN~5ENrKp&g0a}r2 z8?xS3$B{EZHyt_h|JRQW|4cff?g8F)*C&P2A3CTHi`aR1YY5S-CA_RRop` z;F;l(E1#=N$=@IRn7=54t&wvZ{7L56bJ8!SMgM)He8GgaknIMXrQCXEF66Xh-uG5$ zMZ4+8ws?BO|5Eu#-ZtCG3(wnbmOBY2K_x9!?xBx~OY~V*>{Kqw06U#jP#m&}w>G^f z(z)T{hf=IobkET`w+QdsBy-xQd}@c;eO+c1unR{&UqklX0CAHt_W!dXWkPJXM6fQR zAWWiomG|Jk&kKhp@&677ovKm2SQT42xSi%2XzCD#c^}85T#2R^8}er^qD`#PA)>4R~f}=7&%ci93aI|SQ80BKEt3bl?4aatE4uv_z(ze z)(WxyqD!F5+N(~>N&cpbD{hvPd=1jefI4Q>=n-%`J zPEUXejHPOH1Pgn&<)e^ePtR4b84wMxadI=~*>VTr3#G7$v2G#8kyVz}wp^hnm|d1N z{6GqrHCQ7C4Luq60aB!Nb265EV8;zJ8BkS%}ptB zZ*vLx<$r+-wXaD~fysfVMy?huf7u~h1JGQ+p6B=yZ!JS7le$Su8a35kE$KJXgraX% z?};lpI*6tzZwgi~YxSJ}H?NJ>wc+FH$0`Qj(iI1HleLsi%5?7L5^+BReN7bWwn^~o zUg>`zatKTcP^yKrbN*>-DuiFMA}a(Twa?nZj-Qp}!5iT0W>;zl0l@%GC{Ab6q?(Rj z<+!SmmN?clfNkVxP zn!bN@(AJGGoeyMdXKiTaNa0DXxumn$TCh}h^5T1@^>V&!(Xy_7_3fQ)o;p8`@rbTR zs0H47U~fVsDR|MtOw*A zaDTG=a{WOb-oKDisrXdXD7Ke=Qw6JyUfNoC!Nv;bVkO${N19HLxkwbQ_EbwNTe_u( zWM!?mjX*6&fNO5h6cJc|-{@i$X|$4R;1bbvcw@(l5Q-&@x)3N2`ohDVHECT5xvfI} zUvE7kx8Tuz62bGuO(qr~4!p{RPXm*|G(Z(^rQ8&Jdi`pZ5b@Cmmeg(z;B1x zX)fWG0xI`|?gWleqVD%;zU4WZ}|EjLFi4MQ1j+>TmScJ_^b3WhIlE z2;6!gyAy6K$?{4Vd#QyNwM?I_s(-~(cMD_5kdjweF2siyhw+>nrsYR1B~qMSV?F?L zyX7szgeMqFJ2QjAaa%lNl_PEP(QG)rSht96Qo)R+D0xU;h_kVYoRJAEmJjW1r;Vz6l+L-DhX3hRjm|cE%+Of%}P$ld0$C-({R`=|R4wZr>dnp*z!*JrP zQ3>9Y$y=tbU!co}EoM^P5<}g^A+X=qH}Bq1W=O zL^6TjC5cOdp(@(>7c$dEHZ1+T)R7AkemC3ya4Xq<=#T!740UmAcMQ{`oZo!9`#}}i zowqmafKaa5ZNJTQuCb2n#C%B`shetr$0kE=$Y*jl5~vUS zIvVj$7b}-i){cuC*_Hf;6z#^~RLZh+!n?Vgb2ED}1S1KLN+r4J<7CGa&XY{7Qb5h3Rvk-f|ostMKoZP0Ejc^JG=QEMORzXM_ia zn7AeK;MsZ>{J+pz28j0JwHtH#C8u-YH_g7hd6U2^9!f zzhQNFjQI?sRi3vhFMMzMECUF}rCG9)f>;dX^K`<|!~#g@$cwh!L!@T#hg#rAjG{51f~|0kG<5|bTG-9MO# zVkyN_4FP8JkfNJQjrh((W4kE97z<-#Hw#k#u{%037k{W_=7_)hA! z0bj=Vi`YRstt!)^+Xnr)qiWRksntAyxV>cj8dpFaKN!jfOqPC8PDCEOh!#q_JQrp( z^q`arbps5WSD!@old_!43lDi4H3+shV!OfnN8vGAO2~kN&5nP^@luV1_kEdlSJ&5C zPRG5R*y+%uzY^Q!Z!wDsgfer2;k!gjX*=AAwC}mU38}QLKXA)zENDGfxCLZ%+x4Dr z#T@7iq~F~A)!wvimPw6NuKtn{SUu;Mbl_07`2javmf7XezHerSUaS^U;Rw-0p1y{> zd1fW1v5mp|8RqlQymb(%;a})Xpe8o1(R%k8&-ZE^P)F#rH@}{aT2C zQ#PBL#+zA|efD9i;HWV%_|QOzmER^cClY#i8oO4T;?=ZA(Xzu$C%a3JFkMZ!2~+3Y Z2QD6%knYWD@za2RI$DOB#p-t+|34t*OaTA@ diff --git a/r4babs2/week-5/workshop_files/figure-html/unnamed-chunk-18-1.png b/r4babs2/week-5/workshop_files/figure-html/unnamed-chunk-18-1.png index fe80f4d771a9759eb866976664ff4d59c486fcc0..2c906dc39e3e37d3eb3c5f31f0f67807649086c2 100644 GIT binary patch literal 14406 zcmeHuXH-*P*QW^`1XK_Z1XL76s?wAiMMP;qs??~o&_y6ffFw5f3ko7lsZj|?7YHC- zML|Fy5Ge@|Y=MMcLJNem6bhmKsZ&ZyO3KR0Dk>_fs;X*gYU=9hr%#_gbLNbOhK8o5rk0kLwzjs8j?US$ zXV0BGr>m>0r>Cc{uWw*rU}$J)WMl+^K+c~(Z)|LQ@!~}j6O&7qE}5E|UcP+!%9Sf- zW@hH*<`xzfmX?-QR#s3b)Y{tm>eZ_@Ha51lwsv-Q_V)G;4i1ivj@PbTb8>QWc6N4g zadCBZb#rrbcXx-uVAro-zj5Qn&6_tpJUl!-J-xiVyuH1Be0<<=xUa9TpP!$g_q*q1L~zIyfQ_3PL9`S~~;uArcxu(0sWn>R&8Ma9L%B_$=LrKM$MW##4N6%`e4 z-@dJ^tgNc4s;;iaDzN|Ni}l z4<9~${Mgpk_UY57&!0cHx3_n6bbR^prL(iMtE;QKyZh_cuRT3Iy}iACeSHK1p})U> zU|@hqB!2t$ZE$dKXlQ77cz9%FghV2d$>h<|(Xp|y@$qpAg)%WQ@%{VvA3uKl{Q2|O zuV24^|E5x@larHEQ&Th=jZUXePfyRx%*@Wt&dtru&(AL`EG#ZAE-fuBFE6jGtS}gi z)z#ItwYBy2^^J{<&CN|Flex9EwY|N~VzG91cGzt8Z+Bj2Kmqt}U3LoHvqw5^_aBr* zUGmzq=g6Kb7th<>$(|dJ9CHnip)JA0z?Uy#@v2y?yR|FiGp=^nzpQ(8NCbTJ$-aO3 z-*(FL-@Hezv^lunMC{-}=eKX({X(wt-waVR8^m5NyI}Iva{uuMkS~$rPPtx_k&{l7 zdK!ISvm;yC*S&ksh35Xs-Zso7_2wVOJpDE!xJSYc%)jqAcF*~LRW)@}I?_2gBy|_a-B$uU3{!KVA}$7b z7TZ-hrDC2j=3oQ^8WOk}<@O~lHUcy*Qe%njkQeL?4Dz==kYqmrj7Xi0pH78p8ODhN z)bD{@dkoW)xEO>=If95^Xs~YM&o^$9 z0w=BE$(WsCl!8MGLh}G&*mKyf&^=j;lbCfG(r*GrdTGyRL@G8`>8@6j>Q@E$PFja0 zW8A|p4i*QH_M479g+0x2%W>{%aczAH?ACC5=fz98A1nGC#vNPU&0xINzx5pi7DN_? zqihh&$?cYKl9OO4m*~j`Ax_4man->JSh_Slz~cQaIs~#ZkynjmYl#QIvz4%r{&R8$ z5P&Er-^u@e{Qs~8@QgG+Wb+}2I4|5Wt$h_~i%AqA1f5C~73H^pG`_L3u|9bQG?zPY zv6*rNTg{1&;uPh#f;5^!UVi7q`^&G|o_~2Ac;OU{2Wl@tUebVqx3qQSL(mDVn?^TA z7xM7#3G6u0&*+3G_hSsAF91AkVk2=KOg2%G#hg01R}?h&DqR?IP?S3!gRt%9!Gihk zUxYOJGu5r;eO9QLmgCrQZ8tG4QSMC4+B3{YhzP;`fVBi1OtuAjT0o-AAUB@!!J%}8 zy#S*spobuNtU*DZ`a4eiFhhhp9kZ4l{SkYB(1OiX|A$lb$YluV-oN)aDM65JroN?i zv>|%3;$<@{9(@{E(WYBfZdki&ExJ;AF{RNgt(ku*4TJsKS=PF zpS8rvml*dtPrBFo)OwSK-mBXqk!H1PYug2{t2Lz3_Y=}LLxd&NUt)5z+-&Mc-W_W^ z+E>vdRD3v!QWe%2xZ2GNFWGK7yf@Pt0_`pC8($Hr4xZZFHB>r(4Amuw3zbLr2H^pi zd0$D#PPO)MwwrAvH`*lpXQ5Y*~Ka7Fme3E3 zNn2@}#p|OjsX6&g%aU+o_2x(4BtZ{9i15+IC+Zsz)JD=0H961Csd^2Y6NP|+i*>1qt!4r15K8|&9@Md3azKukj&;4uKP{W|^`6Ae5 zh)aH+`mGBUcTvY(LM?NHnef`}2&#*MS7C7Pf=fou;M>QT+RKm|-yeXHY_hUT$Owvb zchJ!&*zmZ^64Q&6+}r+5hV|gQNR&8gMchgszUklB+>Dr6pDLxGd$^<|#euH}h?|5@ z{(>Bn@4g9SD?a+;k^TR-4)wVP8~chnFijscS2gh5J(!Q+3nts+kj)ecY#S#&%j^W# zL$C6xFn}Nq!~rK{+t)CAuxb-9-E01ihYF9mczbR2C9shHxx7@W8HEa z&d!Y`*XTrqSIFv0$b-oSxXvzH*>st6kuP%tWC`+s+BpL618rimx9*2lrR8D_6RD%9MM zZQn%*^_nsSKppdCgb|95iWE)1C zm*(of;dbZxQ$Cuji1o?6pd5cN|HGr$P)o>5bWL@L{3YK8!Q;M|fZ}S@H+x@mr z<~Dx+V#j!rr4@4~h)*)_0#rIdIU`WHY8VOX7LH&FY<*nfopV z;phRWxJ-Y%gptV*b9b(9+TrsZmAVa*zCdiIWApA`cL3Fhv`KO>+fpf-VuTrSoQQvl z8kGK|9wYS9H#Q9=6f4H`)u}B%jWsf(_jrI}U}idr787zTOI2)}5BUZzkK;U~(Qm+Z zBLeW~9^kua3|28+lyF0X_4dp>_XPnKUcqHZf{>KYNUp@RluLxqc?9kG zDtZ|dXzT%jWehMYF@_~S+K|G$w70&#*Lu7UAw9{gv%mJABEt95WPG=lga_C$$e>?U zU|m;dNr&od4}e2*4$ez+wAIy@6`*Ai+v*;F9ckM9;0lSeF#pv>kg~%)yI1?t5Q`qE zMx03ooW;xmT+N|6qi-G0W1&iIU02AcZZLqpZ-YA->iE&%fYoTO|4yO>XcA|K~i{H}7lWY`wwx;iPIZQ*$buUY!I z>HG<7prin;EjMh8melnmuXkQp!6hTxb3S^ zNyK(XuXnPuR_7m@?bHKezvz0`S=xlRmHJ_{5b{_tEdJ@tSE?-K(Y5f^hF`9wGV zo)(qEpt-k?I6NIXPS`5zD@c9Kt_)5vh3NS$^S(HR#T}XtdU`N1#{~i=U(tB-^iX^6 ztdQ*?3q;U{7Kc%xBdyflNbuH-}=gL zZ-R2vFM3PrgPq}sz|%eZ2t66nEw*OKQ=4G&S)jqdmO)fk;*Y9*THyaHT`6fZVVb@v z#_6DpCGy}kf0;G3e*StCq>^96!DI~&S%I1tO=O!?0J4D+$Eu|bxvyQN`y930Pbh&> z%AEVk+>sxoD-R08AcfM$h(qDUpC@m=exU?*zSsk9*s+ZIHvM8?>Z6iOI%Y^P&i4Q* zKMzMA^8V-GQ($vaz{qizJiD@~A(BZ%0Ry)fA9oxpD0kE5Xa~1hH732lE$h6)SwtYn zaxaZoygG(pCsEv9C}N3OPa6wfWFy-xW;D9u+jCX2AAwYcLmR1i?|c}s7%P99g#t6$ zEWd76`&oDX630uSCg83}&4>#;7FCNJyJ zu=F^lWa0SSk{BpwNV6v4fi)!I{f?AskI`jzUhsGcnSh_o66SVP#;OSAPqH+@JP9lv z&w}k!Q%MtVqqJTo>Ccb498;3%0v4Qm&?yUa_SS6cQH}A=i!D)OZ6$)^t2K8I3VT6( zG!l+g|BCy{@F^aRCS^*e5J{HjAuqlp%AV1v4IzpPk*y$E2dw2Yn|_%$Q;s+IY6p%j zF1?SBJAzHQ)cq}V@9PhCew#vw?OQJlu~KN1f7oZR-(kU~q4g~3i7On7650YZ;l7=^ zX)05^7fSEU`={)V1UM6FV7K@6v3&%0zs;-L_&G`jc&0xw@p<7i6DCYYMMHO}cHLWkHkTD!FwP^{33ZMpmnkmJez=r!+ z=AP|8DH))$pwF7cI>>hF6po|a8`skD{=|iyF}*W2hS&&g)d3Ww^~_LnM*ilYU_;1x zq%e-X?c)qc8h)ecIQ#3`}XW>v|~Lo1i7%jW+kw$ zUwQZ|hf;;PYHuHOYG>)`XMd>-e*` z_#ADtRF*j--V@c`zTJD!x6U#RQ_zfZN+dhbyZQ?krwYe&7g>+GYh(#IL>@oKoujzq z4AEW-H03Yde|d)GC&AL91fk6B>wu2#km$`%L>vmW(h^j*^4IObx(m}?WmJ085~iR; zUIk#W?oiB$9~DHoOY%7>wZ0*^gJ7};U2J z5v;-RPmj&4vVqTXjtl6E%wc^rgWG+0;#?XOKips+6B3q* z(N-S5i!oemAdl87De0(|yGG5+y14qJVYEj#!MeN!;}2EFDodoC?x+~1OXcR)qB_uH zt<=H_3wjE5wAEd6AuwyXQOK%sTt^Wrp!1jqf1}Z_jtwu@UHv&x`|jgaFxitWKs)jrA>O;wGCua{Xg}Gwo=Pmq zt7ggV44-Bj+}`R?%4^zqVLD+`PnIBMUhvQ^k8 zaw;h32+J6v=O6#UQ8G^n8xvIQc5Qk!wm&YDo3XR-O|`WJMxUm|$7y0U_F12NCJ%CB z$4f_4xNU`*(S6x!wZK_<2@*xciwl)5FqoAdekmNxrMP@MaYCxu%Yy=NjTA6>Mo>~R zZteG&s&t5Ng{)qIwj!2oL-E+kzS`JA6_-T@ZB9le1AT2zOgvl=1mUFQxx?DzhzLO= zW@Nv%Z{q|HaANoH!Gp(`CG+Qw;}RO*F$J4Zv(L$&FfB^BP|3pVi~;)Hg90?sX<#2t^*`=m z#H`#P&1W<`<+&M4^l%aJ$Z({^_0M2kE|5z03y$f)wpW=FgoE@)A_e;I{GAV63)#=X zEJM#>;_{_+D>D~XnIEoyy#vW|9MI{D_rD2IE6#a>@2xLF^1pF7cp4kUV9E`K=MK#9 z+&`_s`AzW(f51(M&Rjae@9f<#)X{>R1-{a2kVbRJR>w6+miEAvocMqN$8)V`jRGT< z%U;(7>EA9%HI#I!#2_*-Yf1eWZD>A5jPyI@QFh%CTwH17x=;Del8+UvCfsa=BIf+B zet?%T*q1#5cltv{1g!g%xs$~MTr)ZG3gw$fB_U~zEL;TCcfweq1lIHET-m#_;Uu>O z2HUZ2cP`o<_*_&5^a}lRN^7N)-`##9WB)W{H=N>d#; zkWQPeS!l%kPkcwgBaH?bc z>YQvp!5PL6CNDTAx@{_MAF6gqyH|^?i7OE!RQb!MQH(++rRiw=a>l~AP2;RHq_sw~ zdq}xE-40q4{XfU^04-Q+6ypAN6fWf))zl8o%u7L#uGKfKZcGKNzq2>$QG$+l%bZ>l z-HB3KAEAHtu}j>bxJ2{);A~nRGgMmmdpMt zWdX#dtj8dk_-ZTArjhmo@d0<=`Fx!>~QHVH|?cOWaRR+Kt=qM$z}l4Eel$iTHOGm{Co=1SGA3#>D{ zmSXOa3tG#(L+5mx+nNPE^^oH^-@)o}EG&?=>M<-*wj&GJ7-2IiZX#W%h;mk0x?h5T zqkrpKT__wk1(Vwyg3-R5M9)6-1t4?h#r;$+0)yEk%* zL4ntnf8t;1`pg~XWOVR&Qsw%oB*8%!M=#T1d02CB&xJoEe&WTo2cfMZ&ohI63bYbu zI?8#R(Sz@PXb}>YEyI&$I95TdI>y%}J$o?u-S5xzx6|44JdBovwQf~Km}-w->oN9j zzUUGN%q)T;`iut>-CUyW?rL1>m$5sSvf`Duo<`HwlRg9?uZ?7bT=#E#^30y=cARC zRC|<5*SmN5!4y~lh#=N64rxL}uJ#)WaV9S4JuloS^A&KtoVOLArNdCSw(zFZXp4yX z)Wj|0md)%);mvRlNNY|lYa@NTH80mnk`LbYOa$C;tDx`cLvX{7H?yUcp_kl%G}xAG zPE0-O#a*G3Z9ms10)8G8vCJHOw>^+bAqE%_%c?r#TL))&Jg{@wwG)w3IkGl8Mz5Ov zDil$^ahm8pY2rO7^$Dm4nv+*M5qbm3Z0`>~HmFA0M~LoVJjk$O`xt{*(X8s#RZ?-+ zBddAc>m&mpbbpe=k7=AI-*LgTFf6jWB>QtW5`X=7G#0(q6nbgmK*Nu>g+`u9_e*^A zfshz#v#KMAT^eC0Cbg*=J^t(osM)j5B)n^6ueL<493{mMt92)aY0Jk0`DT@#cbptR41K~&5Y&T4_fE5h zUDbaKI)J>6Sh8Ik=_v~&ZXiT(Y_FtGejL(G;Ekhx!ez+IUQyQJM=+w#YPB%jxBa1n zI+(v&Oc-80Y;`CaOh%hEQ}z@P7)~)IvL4gZVrM!y@oWi?7{B;o6CWVv@xyD84?c1F z6VpBph-V<2fRxUfv~j6auM?$K!Sc+RUrUMxtvJ2fwTO!8RgaEuocM{em+AaI`Gr!z zk#{G3SQ>1m!wb(_(shb(Ww#-u`!6yQRaigtqF5uRwLo)f6gbUMWCFZ?RAtW|(O(Pyjg{I zET|M;*dAw3Fxhl(wIMM-Mr9#Od%+4VLNL7fd^MwJ%gHbF#5nopByj2px0SY95c^0A zS+yJ%FcHGejpwUrzCe!%mGRM_$!C}~!Plh~7889ojsEnX=^AmI_+Sa|7@P(O2V}@# z5`{4)MlS?Gb0XY+=>68-#*nQC$z&L3Dj-8U*Ynp7u{^aoMKcLdpyegs-$Fms`#ePN z?%Ah%A~On7lvHJia4DK|_RKrT#wN)aV6ZR5^`HZ!uB;LPF~8@ur5rEP;79 z9Q2nvOL^g2d+|Sk=}aJ_-uVZU#33)FL4jGx4*KA(SzdVKWBkt-i(=8f+T9W85}GlX zEZ=eJvqohe&PmlkLCO@ipP}=3fdb9Vp=%PnJ_;nZsnKd9RqnOD0at#s0JMF>DF{cZJb=k}Vg(hK`ZTWwa5Uz}ky(kAe7)B*yu zTzHNV(gQs_%Q}1;%pbUq@IxM(b9H)^n;^$L2Z)l!u5cnv4C7t^E>^$BS4QX^KDdH} zp?WGOUgR+5z4ZZt(4weNYzhW}!F;@RfUp)RE*6`Lsf9vrT;_w1Mkh+freSKk#jnEc z0IKQ{oOrjEE>jLRKaf1B?d&`b{|)<~zPmFg1-F$XPJzjPv!ckat@mCY!_MuumfY}B zcjd&Zq%eJHdv$j#l29sk8Sel5~=V-3BOOVkcSk1+X z>oi{YnCNb8I-1KENUs`ivuTU_t!aCq7h|7dF8JJ0^x@?@8TMfKV_Y%@VP7;X^*(zk z=%@MtLirg0pVa5E_XKE8-WU$oobxFfb}^~rgXjKi z=1H=4Gs>#)q1|eJ8W4+ZDqz`JpOgV!6v58Zs|b*^JXTr<5C(u1AffetlA`_hlezy- z^!y2Gj^13jR35m!m`U|lBaNNi1+XOWgarbymeHiXD&wkv@o~ZGU8n{eqT~AX2{$7( zU~X>H($vRCTlolp&-e^xt`5sS2O9n-xJ&ClyPz2XTAX9KYrW@jthnbwxqEqci}l|I zv|t$ZG6dRL{>B|uymYQ<7c+u_IQq<#WbDFR`gXB3KxkumHg=k7PwEHq2JY8FKFa;U zsm$?&`6B#RmwiZ?lb2S)(#ZV17}zEs$GYSwls1$e+w^o(ix&fb@3$ zETd;LW~QoI(i?!vgaJXvo;Xh>_f}=S0n&5KkAb_G$yOtu)g6D;5yGDx484T>Z@`uH ze{aG6y)Br&2+={^=~=GH2nfmsRG>fn2)0f(&nwQ8)X*qZ!2efF6-ngLm8`_x-hk() z{>rj{_5D*Fxpm5Jw_RACAg=gw7RLQ)b0e~T_v-Yoiri;zSDUx(euduo+hO;v0w|MJ zah~#DBD>d)?vkq4{vtumZ+|{8{TkKp`+ncnLhZ`H0-(18vqRI=zACL6KF0+Y4YmGH z_qYU*&3s`)+gt+M9>LS`|LXS_z9IjZJFLTfp?Qaak6E8zl_xkQks>DI){s(6;=#Zv z+ytP3yMVM^b#`BATA9(x+dOW$J=oH>u-iFqp>d%R*(}JS*ZO|Ex_6ETp9%mwwgDG^ zbVic9Z({vKGlf%k2B6Ja-;H4PKkB=i>(;F71q+IDYlA=pOd)ZO|BK%HqC;ab7_mf* zP=!#m_|AVSKN&U%oHrkVIM?7^3?=$?J zYD%QV>MTDMQ+^V9%^~+z@jao$+mPy#OTkSs$FWk--YV8`>6a?64EvyTj)vLEWD`#O z24|R1=~tBN&cglH&4K0FFFebBkcA41)~OZqY{+#?XuaVs5I2`rp8;=%&^mQYy@d; zq-PU@1<`LMduOT~4`;@dX`4V20D!~$YmgN+uM(X>9>xr5$zb%9bB$qC&sNpKV0o*` zmYEjT=&O+Iizv6+SIh}Vv|iMvY_aWMwjc9^5zmwQW;mEH+w;!gD$+QZ#97AlU{r?m zQi2}@ojGT8o3nvB-$gpo1zf9oDDFAO{xEx8AG_H}(rM>tSTf%#dsi8eWs=YZ8n2pu z|0J!%(UXmI)51zQdMO#jtZbY~D~c%n(&AFD`gfhG*o7`b2SM5gCE$EZ_A;dqy60YL zQ8AOUrK~+|MI^x*%%l-4&5M+|?ryj|b8xT-*6Cnxm8kR|!)4327dvTF5G7fU+md-7 zuJY0PN}zo@v+QXA?z!3CJ%PIA<(Rt>oaQoEHU}btAPD{!SX=$?Q|p-EOP|2dT6u5UU@+7eA1&^&*ATvyaQ>?ZhrM66uTK! zm4K|V#}@-M7jY4=bhk5^U3HI9el54JF_$Valfx>cg$%HBG{9Rs7)ex8h!*iBNWtMF$;jg7BmoyspGhn6&|e z%?9I|&ej67Rg!;(gQ|bgSePe*rV|vUKCw6_R~0cC4jO+a4|t*gf(H%2<@d`N zUFJ~NTyo#ta@Sn1ZWLkWiN}y1s0Wr~?>>9jk{Z~NzCJV1^`eGy2j(tL*fs_0astC2 zXA3jF*T9fiWE23?*^-XT+^-F}1ZGw$mqbT}YNiRhBc_!yq_@dLv~#olI%PX*S6u&n zPcv25ocNDX)Y`PakMl-iYHzSMtK_xP6#)c$)D~q_Vx&LVfUV#=8QuXE4e|1}ss%L&k zl{v@y=n_WLPIPrb!TBCyQj_y-M6I^aHyFK1S{$^*)84?@blvhFDrOYhED@Q>(mya2-r(A^gTZpXEA?{ zr554bw0^Z!B4>%!ivFz~)${|}dYPWS6i(fI(qS0V_!+9d)<7rP`c=IvYpO4cWPh6H0wAVs#~1hf_X-E`W+BK-HO!R+4vq{!JUs%0iL&mgcb zm69^d!vIn->u7fO77j?w&BbC+CvoUvS4ypinUdkgqhTXuT!dW^EOVedn8_H;eOBws zOPjKF4*S&Vdgs%tRKwwbae^afsxB3S`o3MY&Ta#cQz2zI0piD2md7eNi2f+TQFVcA zpwcctqeTrTWjfzv%FNP-rO2?Z&rJ%Ca+f^zup!(W>L~Sm6st>9rQ@@$X#Izh?QE4}!P__Rc$&C1ces^Tf1&~6D zrY=3xW1UosxK5HF=nQ!$@r2D<<~Qc04KSCf-IBROQ=6)EdpYx_5<_f~m6~kx)DAtw z@?XLrOE6;u^(ui_g~@yiu1^PE`IK(NRW$oufSB@+0`@z@ra1Zt-APbEBJ#+DFXPJ@ zqw3RGo+p%$NTl7rc3+b-EO0J`;Q?xHivGU&d3yG>w=5yc#u-R5Y%#`SjTp0xXUYB* z{?YtKwIdqDm8?=JW;~#6N3o`bcIT1@u0ez?M=3Uqh>=+!gMo9*P@`na7&61Z_GHO^ zT}h*#maBv5{;8it)V6H1Qf&>aNN1-8jOM^Yf{eLW`q% zz7#&S+VR|MHTOA0XeKt5C}m{Kona0F(Bz~5+vdnwr@{M_N+5$*|F(9-YOvy?z)6-R zq)0=CV8n)1G{4pk1e}E__YGQE1Z;)4{Xs5bWNpKVoAF8W_9d!O_V%|qS?1%x;m5rj z{m*i0qt-reW({aZvgos@5Jv83;Q9j(v`ga&EJF)~s}rg)_#AisIF|7d2aW8q98}7R zxKe^_QrRaDR8`pMHY2GYQw=A61>RK$;S_dMRgj} Je&PDP{{ig|k)8km delta 11802 zcmb7qdpuO#_kS}82^CSfbw_f)`BjY*-GsgUmr|0wiet+NB@1NiNGiRTD&f4p&z1QArz2B?p>I(MNaS`Cz z?r_5N%#iB-?OMnkGi$_>#N}lzUF4FQo3|u1tH1QSFmUDk1urk1w?__svq>`9w0Qwiu}%d{C#c$#?tCI}VbcD);aAtnjbH$4{nDrn9gMVUF&UA%N<2 zn(|V!uF$&QKfDzoam)t0<}YZI3%DI2mJ}hnTPdRoTv--TTYPt}OzFJ!(<+d-zkrfe z5-##tJn5atzN8HY)RMl7Nd15OivwPwoO`#2&QL0s4*zy5r-TK~okWPW>Znq+oBij( z!D|;&RZ{(wCIdY7LumR(5#GBiMo^L8@2O8%YZHr-fY%_>Pf>;`o2ud_%M7RQ=;3GA z>iFRBBiC1@Y@^oql`h#^L5i2BireXr39qk#t1n11Ju6FuZ&j(?K&4htsP(||7H{;; zN~yJ+5;}jj3oLIp>Amcgo^{d(*Vk9FuU=<^3-sk8f*kove<49E%n#=qJlI2jJ%6`P z*`@Xj`sPU~v9RAkX+Ak{XC%%uc+|Nve7*p=Zhedk8kIq?RFh>6k1U{_i*?CM&x(Dy zXEY%6OB*C_N*u^OC-~ZhLHlHRd1m7MdgYJn?nL-0N-|H=@8pc7OcXP|b^47N%WQIi zCUzpoycZe9^oM!zb@~)Niz1WSCxo}x9>ZVM_p#-Ot=fC+=iYCq>(>_EX-x*7+Xovg zT#dS%Jn^|~NuX3&m6^Iu!v$w0!PJiNEAg(~X~CdBRRo7=x4}`0bp-q1Z=G*n8cxA> z2(^u>dZ*cr0H{As1wI^HxBWOe_T*M~PU@P2NRLPMdCyJP-rq<)bZXzab+?_-QYTKh zXdn>U?|%~A9u98beh3|_w+BjjUMnH7fx1&dBl#Ana6S;FWrN+cVLK$Y!9_zHk>y&( z*MMAPkmhNO%_q^h@}glf)sFe#Ax#P9w}7Fef;%TSP|W~CuRUV&t!VVvXHtaWyqrCJ z)w;ejbXQ@NH$7`H#164Ot-x`FXZzABEHt1ZAXjnO{ zLpIp4RdA3iSiPegksc%Ga4r{LgTw=EGUnGKxs|_*9GfLA$nQfp-fe^r3#HYWz{I+Q6~v2!HTD|I4|!0 zBi@UUJhwdY$Px&jPswlKDPvsqVgxq35qD=}MLF3(VknA7^pGl_c2lotEA?leQQQn5`Rai zHT)QCI`~1=1?zijR?148mw08MI z<=S~}C8H{*RynmSYT)4n#@{!vFvkV=+I_f|IP>$cTjTE028(gm|MCyZBj`}UohPBT zJhS{9mk^|R(NDS7<4K9?XMnKAmtW+`oB!G`!E890?!qZtW4MXBlz;Ms%a-j(^MrrA z=ANy5H$-q`D~-9IPfWGiB~Q0Bd$a@cS4o)Z)swvl%!n8Tu$P z+tEjK>@rnZFNa{)+U48T660PNoEFx~FhP*pzl%oeCCIyMF#?bbY`?Q8iAF-BFHj{g zIj6V5{^79`(mO9oqU>u&lo{#DxopuuGJKL&I|_63TtrC~`?|%#><`A$ZO_-hE%S94 z{Y;6^86dak3+T32yVrS(GLqqLC`!`LwYj27-H2e9Y!xxjpsrxIQqAB7D@R0ZZ7j%j zwE@t%$3;mwqVo1OX#NWWV+i(lsu+Tu_Y$EU6$^$)V8lcC3FQrSm-(Dm?g_CK6Y(OY7*Unu&U%S3*+6-+0LEItqOWa+ODlk zX~ahpnl}@C*B}|{a##V!HcCSp0kC5qf`=p}F%+wvR&`9rHBpDrd`EPyktk^hR_g!V zaKFrNgcl=i19kAJz|%r(^*Q|W8PE8A-LjjhPiBYjU)T+iU0!}}gpeLi*4`$`q9pNs zNOO+)q7)P-)#PMc^E=6TIjt))0w@eSn5$$gf?(flL6jXgJ%et0kpvtH7`fFnXWY0v z$Y{VJ$gKLIsoyo#yP$zgT4!rmo*3zlIS-bU2&#s!+z%a^UbfM+s&J#?5CmTanWEHp z55!>u(OTV)6sVgvQN6cFGW~5UR;~(@<6nByalgwJs<&xDzji=}D}XBT9i-k3)a4Fg zqQdg|jMqr>9P;AbtVO}g!mZ>3!9!gPi=@qjZs~%K70H6QCUDfpiFH4qX&p1V=7n(R z+Lv7pv5l;IPeASwSM(g}sO(V2*g`x?JZdT(AobG(2}@_^dHVAd?t=X5ly*_lW<-G; zk|EcQu+l70GZp0kJ`1J7ySg`mbfCEbUGwWd)x6jF=E89_h0Ug(Arf`MUeQ^U z!}W6Yn7`@VIJr@iTxQPR5&huvRnCED_ReU0t{LQB$mw_hWH>NJq}O28KicM zwQB?+h;6FP*#8-x9S=3L_?NRuor4$|W{@l6fGXABlzevTAVT|P>lZVb5?c2^UDg3( zzynj7&@U=v?hHb^ptoYXS-HI<3ZJhD`8cvZMO#8Zmg?K!uy?&>vcKS^W!y_w6o5olmu(Q)noc?5LWYg&~H&vM4_s6eCezlH9KHFMRi17?rn5J*MeS_ zOXl+PqzB0z$sd{%PZPCy0Vwe}an43(^9vIWzxF!$FJ!vr)Xja!dCO&Vi2+!85Z_8i z^P*O!B&PGYRkZTig1*WeI1YH&xRIKjetr2sWotz^8jb@L1n@i=%uiptl;WMa8Z_we zY|wbW)|nCNzT2z?k9x31fUvhk<1e)ik9^vP;K#8fG1%kn`TV`NJBhw)kqjl*ef07i znUC)-)at|A;)C>^bRaPI@b&CDSv7dq&3?)Xxm9fv>{g+J+qiem1KSMMD8`UvcXS9F(h!LWUP_7W@s@bB*F|VKwypuZJ-QAj5>Kr)1XDKTNNdWk&e8qQp2q6bawC|MaD#+a1_9 zhFa~EWEvMuo-P@~>5`{AzCMiE3sqr$kPul08IkYfFWQzN>t^EkUw)_4#y)7Nj>75~ zHUZbnrs&R}FWI9jfVrf=`ESGAM8n)1!xY2x=nkLHUq|P7myE@W6DG39 z4}hrg|raY-Uk&2S(NF{sTATt1TV zfNs*|McwpTF2*~yR)2ugAre@zn?1J|kwvE4z79wCufObFB^MyDFGR_UK=oo`$M%5w3I zdtY5du)EHWYT*;NI{DYm&xY6UEvm}At0TgwXwQ$Cu)S=ZJDaMjTA2iP97V?@h;RBl zu4QFH*!k+m4k=8(MvdhQQEx286z z1SATd0mWV+(ZlbPvzJ@GJ`@WBqPdUq__ZmYJ4(3^!H%+cEWJX7yQmT&{NNk%^upJg zSnI)%t|eAJ9qZHn1zedG(=4m-UV0Zlzi=zNvid`K?|M(Px|)-8sQwqT@eA$sAlm{g-4`;(b8^LD3wz^}35&S>J! z0@PdePAt*ZgA*&`?qM8_XfK*%Z^bqGh7dE_ca-FWmUeqCRCEub%6#yyjH+M$z@$=E z{}yU}MogF*wc=646tE}YLh$VnkL1B`!}cZj^dQ#+%a~qtMtewi=i43@8$U+lq<58M zrWIn{bb+w1Nom$&Uxmfqk3x3f@ezxVN&uOP~(#~c0JnrC6eMl|Gs`e4kefiB+ zF7=gu&75UkDau*P{9Hhf%i6F_;(dQ)oAt7!mv54Uz~Vqhh1YfVCjixOm@zpcMVlh* zs#weUl%+5!B#E&iJ?9oE2HIvn-XwyJ{DkiEsgPCiIT=o6K1;Ut{(Ti$8QDFs>TAuR zG=Quwqt=%>-~Jw@Kgo2uEg)4qW0 zu4z($SP@CopmCws>9JD>lmwol9vp zEJs+HJ=%XO)t-Nv3RGEM5ethCj}pmA$u?ivx7-!L{@bceEx)Knk~!03);>}v%ha|g zEE)iwj!hkBy`A5>z)n*LnYdbT`hzO923P}!LG9c(o2hSmhuQo#Hf>LyyV{LYJWMtU z$9dZx+kLQGj1+lJ*19z^&|WJ0mA^e}SIni7Mf&rCHR1*Nv?<|Ay%c!RMxGs43 zrS9UtN&Jut&)Zkd(;!>GfQ_@8S7FbG06p2tS$iSJk*Ez+aUV~>EFwJo$CL5uv6YCJ z-=$k;!I}e`wBW4CJfV6h*if21Vk`4N20P@~d9n(Q04!Ms~4G%kO?eL`t=W|bziRL+vrRguMXzsVLQz3VCRnJtlV zKXCbt2xInW=@$h_=CW~nNG35%XmagE7;Y|v|CxdMwsb=H`;AaJYh`#`U6;`Ny4J12 zM4Uq-r%qUGq&8h6%U}S-d*)D??B6`KTVRK0H@k+_w1AaL$BKsONHEdkMiA8il3flJ z%>EX4D}PXa7WIIZDVp3~K8hlr`$4a~C;J{(2}zg5&lm@e6My zn9~_NAnRz1qw4H9$?p6|B*Ru4N`3&!RJ0%l0>)cVo!c5KI8Pmr9_EH(Bw7(067Ch} z46_E3Vj2wD@^C;=vjZwH^hIkC7#nyY*U`f5WZdjy_kw1z)i0L7Jou8+LgaD~^OqtX zILa`KIe#AuRb?1zOO?KPgb<9{;lE!10@A!oM=jti>Jdd=1Nsnj9j#lQ6GwU%`4YG8 zg;RKJgSsF@=xNEKj5dsdWN5 zE*(^0@+rzP1|I2m4PGA`GJ)eJ3jdJ#Po>Ac0__`-5M)&2VDul3AKZ-z*0H@)?=J+s4=5=LSY{xcFt#l9qLGX z_tlgz%zC3s-A+ib7n*wjc7)Ce2BhE&8tHMLf+{XZab|zr6=bU$h&vw8XJ-<)jehZ9 zkTaJ2z)tXr*SN`!c#}K9i3Z|CjIjsAc0VUTe~FwNC~10D#avZzNvGt7grv< zM57*)G7rOd)p}hU75cyVM*+x~XcRiZm@44>uumFIOiI)9oDv?Q%e4X#RDQh8FmUJdc5UbYclP#R$42;sJj$@yNWcBOt7Q=wpl7FO5KT><2 z6L&v%Ug>=4u;82^XzLKEGiDofS8QG#?VTY}!Z~o* z2oc=pES5!<5s6y*1k|qlY$yoG(N0nvOv2RzKYt|ofBR-B#QUE!9xxx%flQyxx86>$ z6co)rXjk*7y$%QpJTvViV6QX3+9W${a_pHh^^j?erFs3a-w2g!1HLr=Ufl*mL4p%; zj?h1z7OFtS>U36yYgPJHyJ`>p7tp(HC%oX9e{DtG@mHi?{71l$wJLS_8>xA8{8zC0 z%CHlKGTIzDpUb~*7l*nd>=-BD@G}-b$-?5GD%3AEr{U5Xz}*Njo%7$kml+c*paO|= z)40SWEy||m@5%WzS0&?Mp*F*K4Q5kzYY*TrM0I)jB7(P=ZKCc1B0Sy*yA|@sg4>6L zt)!p#M+#R5C7N4Qb3wuT3GH1APpb|WFIZu{8cSJ%KqTWHDD(8e;Tvu+J`(ESON+DY zo_+w10#B?Bo^)B!eowX4HblPT3QblvG%8So6m{NWf>!2=3X|+a7)G;ClDYiIY{0iZ ztmm869l(6WoI@9_D^*(qA1x;YW?nk*ouO%dPU1B$`m!Yuk!;_t#UL zVE6kiLi={3#vM>heqtB)SWM5}_@j2l;$e~I`vu|UXlvNYH!%*! z{Y$paC!v+6D?t?S3;xmFo-iy%l7{^t;kr!fOJHHnfAQu$FgrQEbWmD=3;vNAFjNRy zlP(Qs_5AMdi0rJ1LTB1~Kgm9ud7zr-HyJ80DNOL&6?ZN(xYY3+47TUs$NWpYDSRX% z*aj`N1G-(1395RK#d&zv=H^o>si{U{oF-E9-?BZ(RRfTUYc3Jgkh~AKs}k|gwSX$6 zAXj()=3alFQ1rmXe`H*1lJd-O9+G?Nf&C#Rd<%Q-nzHP`>=pHlY!UK-A==e?>#tj= zfPH7iVs0Qh)6V-z{0r@LJ~#->+@u%mN^7g))qWE0_H_4gxdjq=uAyTW0{YC(;ioN5 z#iYvZ>vY!kDb3`n??u){)&k=t)08S`n9Y$!kaetx7l~|DO!ucoF^*@&CpThs)UW9o zJfz-}_6>KU=U9Trfj0=RQ=HfGCLij1pB_Pv6{HGEkMnoQ!MH`#=TS@Hhf_e{OgVP> zg(l>w5WLXOxK$0z{9ZL7R}+fJN%X^349!J{O z^&PkbiSW3FYkTTpENJZ`u){8xcQ$>}e;hHuos*q-#(kNcz$zc0Nm5#Bv+KWK5Oyh3 zu>~rq*V;m$(?8$U7G9dyPofn&IjWT|Xj7G>EGuHi3#qEmabY(-4Th}4&|4KKD@|eE z);4WR&c|6FeI1l+!^bn>V@o7>fiyQ+Keh5I5xCDSK@}|)c+uVyA{=9_vrNT}P=pJN z$f1v{t(h>I@#R3}zky8FO5Z&s?{1SF@LQ?MxJNGQ8)5n(Ua46^L!_cT3u>9m&Z1>> z-K~lwKYgngWlbH59DM7mD>-oEZ$s6Z&fIE4Rbw$D%EgZ(5kzi(@dsO!)R11K8I(3z zeQAgV1ZzOeeyAwSmrq|b>FJ(xeA7$*rg0lx)(%`zvr)~YZEi==Y8~^>CxRh>m7Y@Q zmrp zI4Y}MJvp`*$=+rgFCKO;hL@ZG9&%tKJpz=dTUA-Ehmh-30_`vPO`KkIa?W+yDuKDV z52omL#N91DwQUe++6^7HL&uJ6S2`Z|pp+zyDfk+fj}j#vkg0t4#pTmOP{s_Q{V11^y<}y&gA(%>7l8( zJoBzhpGf!BkV_JCu%TD)X<_3Tqqyg$sb=cX$E(0&>B4wkz*uSysPN&S$y4+7Fm%QC ziD>!f=||A9-_Ix=N4D-pdaP+?oFC8sl`JB?W{rI2u_Fgh z<#5NZZlEF$^sY?;4bnkw&h22m@ZL`Qu1p7%1V(7~b?wO*L8&~M%()G6v+{!fs%GGE ztjEA6YNH$#W_=#PCuk7eIi`P2z}vYA>hNxe&|A2QL&eeq$^W^b0~1uHkIn{GO-*#< z?uMv|IN>5|OmCA5^9bKgmS=WHRNh>_y<3-O7A;CjP(FJbn?dYNQ>;2Ge+13{L`b^X zR}70ZefR^`2-Y-*NCV)G<}QTfC7Bl_`gtJN7~Y8(p?sL^3w#?BBWxHafU;3|&85 z)&AlAV8I>)8$L!Q@oBw)C`l@*quf<93jb7#t5YV4Q4|e3`jt%D0bNVtvo>gH;nPe+ zjjsG*B17L#7N{VkgEms_yZZlDeiaemfX?%n@78y-<>~}WVv_$6$`v(w5sp&v{pCAF z%Ldm1uS`0p>rfoUa>dBiLp3T}o)($dt#c zsO9O0gKMH%`1_w`gWTkil2h;)s)aW}?voGj^+MH2&Sm`s35>2;qkVznDmJP(7Xxx1 z!w)j%;m&*dfCNU?tnvIC3Cz@+8029X6tzQN_#}bpyzylHy*QBj(0%K9rUa(q#_24D zd+{Lmenj10(;KO(u*s}poOJMt^x>mu{QIKQ=eI~=oR7o10@D#W`V8bAiY3AXVGhW> z1-^ILL{-JmoyG3b=)O43Xf^~>}NXKRMD3qK;)C3_h`nL3}FkWn5rEAh*tzExEjYb9XX8Rkj_;ks>b z$NywFkHBnmtQhBTiF<{&$u_D~RYZ@D=_Q{l$~#xt{u~i6t8ITc`m~%7;(FabjCM7> ziSa9it{E3ACa+OHDN<)p{~8w=j=Ms5_aEwUU9!gl!Yg~gt$z7fO?l(@43TjKDGisv z0ddZsQ&>?#Lr)oo4oa$mg3{bC=e=u~t2$jVM7Rke3wjA?E&A!|wpHR?hr4r=w+jw_ z@BPFxhTk6k^^uEkWkER~4Edj2`>HwqZ?1iHxc|kq|8J@tE*$umYTt@XLW|l0<2Gp1 z|5!L%6BaqY9r|xpdGufQK7h`%n9WRR?+DD$`ZMhB%75d^32qa8bq*E~WIen)`}%4a z`0ouX?RproVU0FV|9QX*j=H?O)3ay?JYh9pNJpy*P=Zad8bJLUp-ewF1b1+AyvF<* z;r_O&{=hU;OK*?=IXJ$$bwnywL3>}69JB0htiqBnmepE5XD|fY;6wA&&FoV-`U+I9 z*g^bUcem_U3eyp)RgL%yA*&muG+Lxp-_aHgLvO1!5A_WON`#KN@`^qJ7jPv_H;Y^T z^K`uBcQ8VpEIrrF?!J=;&lF})eKBt7?L$MQ1K=oP-9kfdf};cQl|6g9Y1{M z57ig0eo@Hc;KHO%l#)i%v#dl_XyQ-Nz^(uMk!0QcPq)eRPygb%r>*QO!7uTEZ*J3t zT{q*-;+TtRo!%*a6~#XM!6B7}${uy^Xuu)(2X8-r;_G8$dIhiHE+-S@z8k;t%SX`T zqhk5w^GKk-J4^r;CvMfUz5J{ky6w9gqWAd9VO6A=qpnpGP35)zncJ|5{Rg7e-|Op%_Y1>%KtIf?a;$_+1XDGZLm+=9?XA=tO0(01S8A_1+XBj~y1<*M3-*Gk-jU-}4(CP*IW zsg6{EJE227aJa!5zcftm8xIvFEyE{HlSl@j=~-W(5BYRD?Y~mO{L(bhGu7VcN?A#r z(5eQBD2wZ1ta*Ad1MmUn%O)y_u9>@_U+NG6X2!K+E}??gGsQD0t;vzWyCGCZN2wPX z`|$*-M2yo+KbW_Gt{rf-Qu_NSdak>wws%wlvm}X8J1%O3GUhiw07F)2H%mhHg;X)A zFkV&`Ngo~W(W*-D_K{+a8q>!+zZvKknlE&mrq`DAiIJA!6GV0!k{3TQQVpusMa_?& zK8xeT*@P%&ZI#5V+~g_hJ?MasKBUqz4hn5Qlj> zm-$%dra^HJf>KiV;`;bNG~j{1(a3__f8`{sfAhdPYO=1a{E2KZb$_ z20FU%P<70-XSq*~N^P~EQ=f85q>jB=9NRjlWs4g*gSNX;KcnX4ljx`T6BdRwKfq4} zWnjkncHtPh0u#M4pgUti$tHMnR`FUR$}8S}?$Bk*mGzjO!UaEiXA?35FtE5D3(6qi zH}1I)%LWrSTcZs5R}U)LGzTICKqR+zi7c*yN^M=BET#Fz`eYKKEFsSXHTvs6pC^Y9 zT<6XfF5DcQ>d?^%EMcC~hL#fBrruiChwV?nZo~IQ&xf$o;Ys(QO#GE2_0M#`iO8t? zao%oPYPjlq+4v~5Zbzfr%hcD|t! zXITS?mdsSq=Bi)A$|pfSWL%d4y%lST|Q=(LHF zIPfgG`Iop=cnc~MMaIAO7kB4PiFlC>+T5Zmm~pjl+!sA$_~HS%?T5Izj_RV z$ipiQJFKB4Sd}hZ{;a8Wu=-vD?ozbI0j%O@XIO0h zL`M<0+@W$OQ?amq8b`cZOIYVm_Kc zDe)d`46-k=o^I*0+F4M0x=7a{pwpB%s_lHt0!AXFG_}pfKPakH?=I-9^)UUc1zR)` z02_t3Hcs&~74tbiq8t>9J_6jK>L7f9`k*Vh0Y63Bk6n-9bu*T4yYg%Xw@~rr1Fw9e z2H|~4j`pLiIL*{@2m8jzCzbA^d_l3#*`yW4J@B)a# z<_nV?=Obgg-vuur!Cs3R{CNSe)`apzh#4p6 z?J|&cBbh-9zC)Qb$2?SnGq2myCry>_LYTc9hp;kW8}M+{AsxW6ne?xqnK85)?;{`Q z;Gk3C?e)xiQ14=YMdQ-D%>gITeLZP{E0$1R&Jpvm{N}>2qv2i00uo*uD?i0`&W#l* zCO9?L=|j3l0V9)%tt=buUg^1qAPt&1y*Q<7FMdi_vB)&Oy^gzB#6CmurNh-}U*Niu zuw|Qk01v18O^Iz3Ow^g2F?_yzeEbbNU$k20O5lXLtUYDu;(%U-goZR+8WlIF9cc^! zrsgB;*pnW!pHBmG)$UH~n3xNmqDI1#_C2P%t?F5#yJSb};|8iecLvHZcle^-7+4j4 zth?a7(Pf*FSQtT&#Th}6FaC@lLw!+DGvou{;4|92#8}Gl&S0KR=P})_WG-VgW_sj9 zc1ret2G2l7U9stszg>sGd(BeTwYj3Cf|rdt+v!!=t`2IS%UN z`=qzl{Kl;A#Q&H;H68aN>%^tGYntq|PHRZqt&clF z>NhR!_N*kG|4C$fB^-5rIhZxTp=1so6@HhaO%W^1CoCvQT*9k=4e^MuiFHf1ao9{# z8z>`(hgw28&a9T4#jX^8A(5e}y*zGJC534WWe*0f{C5?~@%g?UR4Q$-2pCpzb^xBz zuzN3B&RXCOtoW)~gz&rYrO4KXz3_$rFX6m#|EkOzn~4L=`o~U!FQ0(4P6LQvB-VFI zS9i#FBJbGzk{UhNByt&`S6h(b0v5BNYlN>M)d}yVH0Clv+WDOjg-`Jg^(PF%5lkm# z`gQ*ld9r!wu-#PU$b+i&%$5MoqgIO9{7Vbea(YmOis$rgzj`+3nZvAUp~`GiH(fCC zl@P&TNj3|Mt@l8yJNWDPQqt@CRT0Dt3eyy|T)9iVdu72bRr2UQ+en4J@8=J3YgIx!|_VFuk0jTMF qV%Nwd3=sX)<&#dRA5dfpMdC$vI_YA5D6N5i$84OAlpi{O>;C{ytGK!V diff --git a/r4babs2/week-5/workshop_files/figure-html/unnamed-chunk-19-1.png b/r4babs2/week-5/workshop_files/figure-html/unnamed-chunk-19-1.png index 453bddc1e284de872c0b5083b7f961627e89b28a..67fcbe343dd9d87d8f52d0446098dfa41eee2c63 100644 GIT binary patch delta 12031 zcmYj%XIK_s`4J36w+GB>>WH#dy{^Bn z*bKDf?JVrgr1hhTgI9Kvxf8VhCVrX=M#wVVKsEUWubH?Qw70{`tS)p{$eGcZS4t4 zGi7MwhpkqKToML;o8ln~unBZtB=z+;q4Y?U!$)>;w$UxCLy}SjK!E*4>Q2Q+M--rQ zO?2|pJ#F6P`Y;6VvMem1-NWx!W%sR(!HJ%mr#B%9{dVx91A`GY8Jm^Cg#BmkDT+hV z-UK!zND^BV6&Gfe)hsE;Bxfx#rR?ABd-?=YcX@>_UwNW?vs|u6CW(B35i-&U22+&! zv^7(uRcqE!i=;mX2CJBE^zWxxiu!r+l{*z=m)W?DiSYKu_opR!BUoUU{sF*}ZcEwQj6`8wE4qr-4CTCFk|jW6)JJ`N_e` z50d!CCt`*%7|MCh@x z&?}Px+utsT;by>Y=L=%ODTPRcpg(kxarOu?AXXaG$yBZhXl<^lzxco58HT2a*B^rA zXGwsnQZ~Es4;Dff(=R&Ma>#s7ez6LVP?HDK#n@4N`S7FW&_2Dn&>xy1Y~;@$)C?r z(Na$J&FO>I6LH;c_ys+giL{eprzjcnDs^d4omk$?&48)aqu4#R``GP0VpCMEDEZn( z$vM~0J&^{#3tR$87`3^uWvv%wW4Dn&-$&*B-JHJmY_O~{;NJib z*z=5fthgV32UYkr1i_oTawM{dbZ>OZTC0z#xH~i?o?u@Na>l4 zO}G`eo6Dp#>(M{)e4hzBl-tnNP&$}C?+?)KO5@*HZ|qB|oh?rR&~&zk2PeAI^K8P* zS-`pT34s4AwMwLh9Cjzqmu5FF*D~12rSW_DskpmCWy-XjORB8|jTqAqhLuHjZHpvS z{2@v;*`a&1gRh5Sqadq5Ah{$jT58~r+ckkBDp1N}v)Oy<#d!6;K5NmQb3=f@VfHVs z2ss2BocKRqrunOl5^FJDeW8yb|53m|T}bOBQ7eBq$MScWaN;XWBH@ee zCya595q+spG|lZY14Q|*`|nZCml}Q^04hA^GMMv&AN#!>rd13yZ0CbxBQYBCAivs?U9cjO zL69#5i}*EOq$p|BgeF`WY`^bmiV#sR|3xvv<&-quqev>!qE$1Lypnkd#uQ zJrbz%S@yq?_{3QtE&w^N$;(sw2=uxndv~l*1%&p?Ff!e&%jD&qbQw(8>e)0hN)tG# z@%o4tCAxbHC-z$65LVGCu8EqBEUpshsi=tqX(k;OPObAA_}!fHgH~>A!;Sak+NjW9 zTmvybYK8%U4$I-XfljjB3e7C^@@OZtxfM!NGh(k79G%7ip^;jsJc-Wg;|U5pt9-jS zx(1WUA7r9fEzQV={S#2^)j-7wGGR^1jIzcx=X@6Lj-0sOLZs+wXzHTe`k zt=Fjw7(D_KG1pqME^c|h>y)pWPYWv5ZEmd9_sXj3UY(tN1MEB-yaO_#`!D zceo&H^49x%IfZOY8t3^XPvnR_n$7DM=AA@wB)-~F=gAZfSls=1?@o8+{87Q z(+AnjM-`#?k8vv)0gr+pSb*M~;vvTD(zdbJk@NN@)FXFr!{}-lVK(khU@jk+0_6Q= znx+K`QCMb=oiKRi0uvue+de?h2#!sFp z!niK<3k6Z4IaoGcc8t@-iI2gQPsZOCGX6K|DfS)|TP)yKt!@rvaT?1GiW3@&vv zRr{k@M+~p#o2n(O77CjZy%5|V3|djbVb)(-)kQKLB7G{ z>eK|5@@eoiRfk>zh5xLhfq7^+MWx+P^CyG!*xj$b=kr-MNG|e8UDOjBYpQ;P1ea&l z(FxFUSN?ItkJhIr-J%mx)Jpgw0=ExVZdc&9Gohz&r4u6DyDElPeMsdEdfx4?7#|<#D=oTz{U%0oyr+;)q zWFzmPFIUOCY^NJaKNP^QaY9;Nv!wyFgDxlhK*d&)N!>s(6*mVNe9khg*;6w+YkBYx zB`0jCh5Gp6ILM$hYb$`Bsa!ZTB9rv!yKoQd$5AfN*DpG;y`Hi{+{FB1h z075p(QsXW&d3iLH&k#DAaDRH=`l4F8Z|(*$Z#pjg3^=}B+Kee`*aolVy;saQLcj`_ zi%h)w9!4C#v^AV;i&hielFA*C;M{18E1wuc$i{k`=EM@;PF?Y%-6@M& zaLRxC;(3JI&l&%BQ}+9&hSi&iI8V<`;GbjRYB=E_#?iW|^Kg*8u9k}U<84Tttmu6o z)JOANx3iP&k@hnc|I4O1abZf*mK0U*Z|OYva(zZ;W|lj*`nQEf_CpnY(?uc|DSv5R z86ViuHv9JWYDr|;Cg(^N#?ZA-yl&NC#ue%|&`KjGV#s+Pl@=I1zgZiM2MTS2l`+N) z*zhl3Dc<=ekk)RE4UMx164P#%Ocq;8Yh`f|*qdm+-0PQ0P=APgDGk zTN!J|mzB0f5!$5!bC#6VC%}u+Ec|2aSjN?&fuRqW;S(Xb*(l3xI{9m)d!JN5SAx@^ zV>Pbalwl}TivY+cbnW8npt&D>n%a&1v%fL9FWVaW!MUNU<*+i~-REU5xT;4y* zvJ6K*=Er=_Dw(*OVC7;r)O7HLu1SPcBB0hC_i=W7Jn*4wM=tqp3qVvKckwnmtbZ^k zy6Q-Xl)Sx}WdG#TM{8KcEkApXD&p1qmg~7IwukeKmmxhIP)&SwDyyc7CT|jtH~I); zpH481Oi!{;FjH}xQ*zl!ir|QE7v2FHApu6C{pEzJtwKCCu+>G>A!$up{3zHsAqjB6 zOr|!}x?+x}YL0<{*A3*HSBNgXL;P+XJo_;!E%&<+JCuu_9kBh1(!V@R(SBnO3 zpwgBO3HII|Rh`tIr(_&UGxxzixWfMauy=p83-{Q3t!7NF7VkPK`h6t!J{q$wW6~Yl z`KdKn85`oJGgF*k>IIZPuQ?)BcvQ4qU0dlJ~9= zBM_huI1nSbd6N)=TCuc?9~{hUnyJte(u;<5Vk@-yN@nFxh%lT0{%*68Rohy2mvtJ4 z(MqTn%JM`f8pC?q`z!?*KvbM)c*mK%Vp0(no$UJQ_L>PxoV@66W#Ns&i<%XkU^XO@ zYLYgc_%*Es1B|?iTyvs?C}j6f_np&?g8)@q#u!`BmJ+ch1c*rmRg-B0Oqk_^YA0WdMQ`)tigJ z!#Nh0&eOfbGAMHRYGK+VnG0u{q?cj@00#$Ru*AEDoy}&QxH~w_M4iqlYq)RhzXO;K z>Fk@@O|XS?E@}G!I?EOHtqBU0Xag&_@3TMN)jwRG>Ylq+)hh1qDf&3J;pltRw#>wo zJYjrcPr6nQ71RY>{aujmt~Tz)BZ5@*bS|v*l#&Z?KQ$dMpK8M)y1e?L} zDs+3(YZUo>oycj2ZzluO9!2D&z5@~g zA)XY2O;0|O8dJbISgg;|fTGrWYj4OBXd!ET?HBfhiEs5ut}$F4o?9dm4Zcv|2iz=AVGft7-6P?-QP?b9IC?G7f-XCe{#?Ws-AY}0 z^|gxjjxHzmqAKzab$)1$v8xrSu>tmL+koR1%~GOGrp)~4KV6uySym@b>wfRO8&(f_ zc#>;sZkIiSl!NYfA0D40t({@&SN*H0T0E_B)KvJ$)q~VNuOLf`HlL_R18}n*ju1_SFRsnI zV}WYx83pj*y+xpEsxT6J0TKN12@1{oeKe%cl;RJJhIkah8C-k_+vwWYJCI?>U0#F!YxV*Fu{Dq-5Z+RwR-!?$Qf$88)@ZcV$|L5rl= zEAPH);i4WLg>F9;+Ah|vPy!1B+s+j}SkUpUubm6Y>rwFD+Bt%h&A;8ch>Byz3QRxz z8))KVZWrI!6c(?5D4$ra8LChK#RD6L0HCR`u1ppQhH0V#U=^hW)N)`?Lvb^~X)Gg$ z0j8}4=LkfZ5ZrEjCswZXrXp8b1i`SRw-}M>VAU&26Nf~pK~n51zdFEv{f$x8;NO`% zHRT%#>H)h5*{xUxEO){QCwh#uN0$yoz2S7@R#Z_}GV31VA@d<08lLK7M~eo6ZCBp! z%lH62v}k>~qMFpxIq*L8GbMW2t>Q}-Kj}(STho+L5TAN)&1U0A8Q&*^kC&ESFi7gE ziyzDnhG7J_Vd6|hx`a=ST?6VHUpBsdc{nkm_KQaard=;fQLa{hx5sg#{Vs)pUmgy& zZbuW@?pbZmPJD_di>Qp|@dtiV@Gms}-E+d1ub;lt42qMCFB2&&TdmD9N?qqYGs{J{ zxje(%ti)|D8$s}))7@$l9OYFS;Op*mAYr|#SIwiiiM0=>>7`P-kMZiQag?hAmkpU0EmRn@`l&o2>EPQ6sho@3T3Yo0GCu$K$Fy8=6hE{Hr;uajE|!(;WB z#?s%_EqUp2I7vCPfnO^4+}_`e&{ELj!hQ&>@O{Lx^k$Y0ox>Bo;bl}%IpYWG@o^Tc zDSf_RvfWoPxkE6WvL1jR5gxR9yp`gc)E6(#u zlQ?Wq>9-xV+q7yTUIVvrpW#Q*QZ^?OUW<~NZ=auB7)!C)FeXGU+uV4{4(&d|bm&M5 z>B5hk4|u_OKI9MU>QQoj;^8 zjym6^11d}GZdUX)I=SiQ1q`|NdvAv&Y#Op)O}aVpRD@^lW(%QX|5txM6KF@5&(#x( zZZj}NMI5mgMiy&CwroPX9Lu4dz{gv2Z}ICr(=HQ7eTR8}ol!=+D}JVVMBvYqoAEX9 zC4W-r+TjN$)#?PL!TP+vssq8LQfC95K5k*< zHGn~xDeF~9aFKJh@yXLu;HQ>H=66X?mw!=#kH|(CuD`qZI`_qm%1?Fsl0%4c167BH zkI#pc{LN9e9n6bYb{X)ko7RtbL6f6>->#Y|&oO>eGZg7PiI}srCtY=6YAsp{_f$GE z-72w8E-`g%YOu=LJzc6ADqX>0kzT06Z@VsmHKp$-!q0O9EBf~D$M{L@6F6_H4KZOq zrt@KeSj%W{t#^$tF-7s0aH@0UvR~&I*;jj0CSGAwJgYl6!_Gj7!ZT1}`yWt3mhrz6 zOINPYk-gIX$0?@@D*-dCa{SE!9GOT9plb^`!JORR-#ob_2!7{JYPa&fze8HPd<4WS zG@i31qU%)N*s_jV}w@Z05PV(e@^> zOR!WEiYpVS!W`Ef)@NO8(n?TRUblGgJa5@r2$cy-YT2 z5nJ)peJ}Ukw%uOdlr8VNo$bo#dZ=jC(!bd%K`1tVH#UYLPZXnsk|gEx4_UA@ZE5#* z7T+2>O6*)4pIkS8gMsHHhz7YkZ3nS%#+4+sk3><47;n^sduM3{S=W6Jq5v-MG({^n zj4*GGoB7fJ7lj&JN`hb&eark4G^>2(qsat;SfB%iT7Oweu=r=inIC2qLWoZWQ%1et zLGs>-HKmZQaf)RDcXa9GAz2pb`c}J`cSv$}|m3$@V?r zcm&OaKf%w<*pvpxB*G^m3tQ*3bIY}H{{K)VR9|O9z0v&4WmqJ$HtN&{{x8G?Axvb& zm5jPzd?wuf;mMXSyfcpY211)qq6r(edy;m;Y}n3KjUGy&KR*DdcI`SX&`vB+|p1U`t(*R8iM%)Y&J#@3wzG z_7EE!h54WhFyl+rC_HZP-RIToEnUmFyjwzj5vKl9Xv12a_;&PQWhasqA^V=rbcs^v z6|yFYUlAI?sVlBJ zx0W%+J@pY9MT$5|A^3-yq=zf_CI@tLvkdh&KgNgxI#=Q6nnE|MAL3>121P|J49gEu z3LSr_O=7?*FyrGwK0Tlm@<-MsCD33*XnsvJ<|4tKTc}^+&3=z-VubtYe~CM$>Jhct z601mH>QzFU*4C{L{n(Lp!96!oX`vXTHv@-R^KylnqMfU-H9Y>i5O)rN)|^P2y_t47zjlx}XMv`7u9%{jhP62g@Bul8?bO z>qEMa&aT{voQ<4eMK)6kDb1fH;a|D4g`hecDT)Ceo?KA)Mv4~guaSyGFuNM*pP!x3 zQM_-dO#=@Jc8Q+;dVQLE&cZr7#QC^@GUkT$E_vucQkrH>3&eEO_%2nogjXJ4LvPhVZD zymF@fO>UTWNclSE&iLeqKx2z&$`=S(mi|f#0ekldQfQW!=jgEpx5sFc^Lxa9>4>gL zT?)bE-WcEicG=1KQ(&uaP;Bsj-%gO{E$FDZ;i+)D30PctjrCa-Q^JQ5eJ&E{SmT&&S$4!A?wr0T6Z-ur&Q6q2o{wqbih|-@D6zYKn1lfGm zj2BRv+dBI?g9$?7xt0DvrPBw-VP`fKR`zMPe1-W?t_Q0Cnz;>(|Aw}a!PM{Y6FN>N zkxGADp`MH|8c3icBv3CmYpqZGJ0JH&1as(xlPne<=*>!dWceBc|JB?jAaxV<^7f49 z;`K`)D*JD*X*xZ&+7f@4RfRw=ijdtHZD7o?I@2iF4FYgV{P^$v>lSNzY%gq+>WA>6s~au^Ag7)YGOBB@?*nT4nj47kICr5Ha=Y^dBy zjoPn~^CY+ZhXz5n^dydEX*{J>>x#`(?Kj6WB0xC|_jPm*?wgxZHymhL`e;*A_#Hul!VCdabT7}#-fbJ`YgrdSo$(;Las)1R|Cb!WZ-j&Lr^j%Ym5APgL)?c@K#vbz z`=4nA|07TQm%3{~bc5I;7-WiWQ->xgoEYW5EhiWi+gACuOqVE*l2+9e$Y!-iq_v|@ z=FEC!ypIIWJOq`h`eg6nx=dd6&72V~3RJ-}T;AMjk= zv@`)s`xmfR7SHpj@U2Oyji;WQx&7vUJ<(h{lHjAtu5#R)nRHWYQq}(;!6F*=-u}lT zO440Y+}#_udRGaaDuj^TgD@*M8CU&s^A88;|PW!yN9`%W?aO9lB`c zwoODU?;DR=qViL{G<->JEBr$_VkU|IUk(S@b8YInhE}q772f-vxm>2@!bzp*-23;& zU6Jx4r?$2{(hG>qam&R_pC1P)BgJkm^-sH_JQFGY6`w#l4QKkt+5DKU7dEP! z@&C^0UVa!U2iX_;uhJiabGx&9pfb3v&6{f?gH|>2hv#-EHjiE`aLa)@$--kblF20- zfR4|o|LW!jGdz_Q0yAr&I!yYDVSoqALvb1<{6Ulno7dxyUm;lLs%(<)+Q}&RA;v3u z<>}M}zKsRxA46Cll&VHH)?-qD$DG8+m~Iqy{h7` z3j?klq+CtL@0C35 z$FzVutY^viaMiu_f_q^GO+ImaWWZ0I2Y&6ob9#A zyoHRG{);=X3iAt6+v@-N4FbJqM_;)hoi=_8`)m2Y8m5k0-8z?YwrK}qh%!$d#(M^E zu!>?SnKmYD{F&j&${mIpEPG@Zl_3DpD9cu$k!J;~D=rBZC+lm-14(8gayl}ar(Qj* zel0EfJ<*QlC^@eSw@~_!`6hg1yp9jHG_CeOEf-`=jP<>Ya*?y?wzQz59omB1XFW}4 zQ*{6I#9eP8Nug7|aJWittPwM=!-yS(Bqu8KEIn}FJi<0W)Vig1yT!7&pX%2@Rk_x~ zeH)1clMU6yQ7Jmj4CzxleBx%qVFVT0m_E*eQW^;E6Y4k`g~esA+Rap{6^UOZPEFp; z#uu3@?yP^_sGh9F+~x!Sc2LNE?DO`0RK18%sBHR_swe=4^25(TyQ`#myz6p3&{MuS z+(~Ujxs%vXP8ICPG=YGz=Ht;9JoyC?Ywzw~)r)m!Czo6kM~q3EZ** z99g`dIGduf8FUX0;PyNs+x9uuUE36cfeqSkhEyeZQ=tKlVHIj8d4Bg7S2|S>zrQJ( z9D*)crr9I8daAml)3q;eUjqMbQS3U#s9sMBm@^{`2N1;Eid3(x|K=iJJM2-cye>Cv zYhK04>)jyt!ep5+Hf!bzT)BV^$f*MGRa(P+r`sTmz7@L3wJ*JtxZj9lG_ z^+a(-x#lU%FMaowcfoTUDw`fslk|w@u)VI=S^%V0zs!WCYn`7mR_#6{e1Kx?aA#_+ zkjK@T0zQ5fr7~-FIIHWDQpw{ZBe66;HAw(o!5S>zRgD@Zjphu~sGWlW!O@c7skn4Y zL%-&iDMjLMwgN|je~bm&@%H{sNZ1oc0_l<6*iNVBCb#gm@S+zlsLCooQu9Pc*J^D@ zF6-n$439$9COy0FaN%OW{Yomtt`RaI?_{p_Q-FHlWBY$KhCq^`3U;GFd;!uNZ#=Gf z=*VT1WPVnd+dV3$uASK66FhwR(#aW!?ziJe29Zk+WX)LYVHA#`b zLQG#`^_9|b4VYShe$2Pz18l322W=roq`AxUfyInY` z6P2iw^hk~#x-qMqU=)6tSS!MmaI86zGCv?GbmwKZB>DX- z+wYZyyshjcDak#aY*0*C=lwS;dTh;<;PMVEfY<%^p@aAS))2)mP0Wjw2)_Am-?+GV zr`ydGuO2X~G>x9PJ^vqbGEpvhFf6LwJ;ZQ}6Q_2Iw2PpcP6krYN}`cZajr3HY^>&I zWm}hESrwhA50l+TxkKW*)!qN~#ZqEGb`w*LKdMQ@?}bsVig^G^m05n=OH~dCk+WE@ z?CnZT5!}F6%b9Pi(=7oJV!Nz$KhjVr20LiRjTo;0Nk3$*KPs>N+6KG&wM?PXJktOa zpsCV`hM+}&OJ$D`EHUKzNkO~-1pk}^_h4(&GG8i=Q`H4OH+guxzIJ= z-eQSSqsGhI_>Bv;8+=7-s?W&Q_@a^K$(K`OLno!AGfC$*BWq@1a0G(w5_?&8$^Ka(X%Z6iP4jR zF=f?&N2e!sS-Z8~&_%SLQmFarPrq=w9}F<{3mXTMi&M{wJ!oUXE=7~+@`02s_mDd{ z(UGuYk+~8Qdj#|Q1IcB-sT~Jy`Ld+7CjPY&5{N+j{-CXK%)8)C1LwXj$yIpxbB`1J th}Uvm%X_0?f}r9{dWN`Hls8z3KtkJA%Z1SaSrX{Eqkdnl=;qT`{|`UcYQF#g delta 11952 zcmY*h|QT%P>%F4>cYqALB zRFOqMv@kgj_!Q5)6TiM;PpLxf@cd19*liu7zVp`@b-YRo5-oHF#t+H9M{$QNJQ5O~ zdV$q$dw!md>&Y*chiTLk_OEN^44+99{M-p)th0Xo?OBM!>z16aeWQl`YW>=&x@hkJ z3`iI^ymqZ|Y4xCI(BKsvnO;;86&cxMRV;uF#Ut?NfM{W)5W5cTf{3xjj#udb9hp9% z3~wpEd1RN*LG}?}`r(Q-0Os;Rg`9qwzINmN$(*HbOEg{l5ZT*1Xab$!d2vW2i$iK6 zZa5E7N-cy>@1@!n6yPAp*$rrt@*V90|B?YH-gqtHl@zV(*a&Q$_vee}N7HWCO{eN& zflJ9U5L!F#Q7RkZ#T>%7&Be141Q0abi)Db~sKS1uC3ZgU`zrX(>{<8kQ{?w3w*x(^ zlj#;zO1~wS4JFNW;!c3zYuVv=gu1i^c5>;uPkqjuLFu64c2!gQzm+G9MK^<_GszcEe&1H6ms!kd<#IBdP7;x0fr7-GiLqkxcW z2pTd?Bs1B&o(qH`H6%A9N{CD-g9oaoW#7e7oYn?6tRrJEM$+-1Glqhmy?%voSI?-fuMCk*K-lr+mNDm7j4 zPWdA4BRIkCe|B;Szd61}W||)nDUn~>kwkDEW$HY)H)ZXaAbT`E%4vVbkyaKm5&`A$ z(s!x0$8yp-_KfVhysnEsw^MM;eCnBUVRaGKV(?PJ4ah!1YVMo8l>O~^meuF;I$5&8 zu!;rWNNFmp5BQeN=0a*YEK?MNN06Lz}u}m zZ;5r3>?pfelPa)^nBHL`7@m+``_4&7DMpKJh2ValYP;UTT9aA+A(5;{or%DbnKlY4 zmJTH?`k&oCxA*tM)g5t_Q?nL;#-}^h#-+A8lt(SC1wM5ro%|yF%sZle!9_4S_8`ki z_oLgk&-Bc3b+4N6I=tq&C@odkZiyK+zj*&; za*|Q+t-RPwdU|Xh0|6lm8L}4pV%x>(xr0y+K}-ZEFj^f0FzBUfV!XDFRv+R=L zFY!VYu*K@29B!`RO*7acjIn7-O57t=b)SUr2G zapKh#S{$F#ZEVn-hCVi*(mz}cH^iYYl9yFPg}g)!=$K&hVf(&>0(88vxV>H)oI3Vz z2Erw(GIh*lA;R^4ga>NsW8Ejq{}xrPC@gAOFOvNzpi0uX8T1lHDbf~@|Ja&A52}VK z>p_@)Ol4+vU`Cj}DZ?WiBoU@qEYJs2%AiMf4QBCQfdK%89@GDZ9LU@3pW)xZ;;g}M zZfJ6s!$KE^{FN1#s=Nsb$TBPwsCV2fcnYb&5To9)y5v;VQ|p<8v76nLM46+LlHLiH zYJt9+hFC1E6TJ{<$K^eyAmI^y(h{YX@00bOW`QFT)8AA4UKvY^a+rEV`lcZO-7Dy* z+LoubXHvy~dO0(JsaG4u`kRBe7Uwcl(@0Mg5Hv_JyUVq_bRAKZ?cW z<|B7}zPxHW|BwLy`5_adA#w)=1iQ!=d>6yhE;hoX`!dnT6;nd(uv~ZK)cZ-p2s^E? zdv}V9aQ9Dl6p0Z6)KD1zp$y`IXNjbVep_eXJRH;rNM@Y zkRGzJdVyhYBbH$q-|RuyK~ZiyMy<_}`^C3#3@^&JHjW^rC8lb8cNs`@LOjh!Wi~fl z>MeDDi?>jDy*$&YgI>&x#tW$OuFg>pJwNxB&>cr-E_$Aj8hUsIO>p1Gob4_PehDCQ6gOLwH1y)L+K?r{Bix}!Pke4 zze``W%fw~YsHC*paP;n}?S%yPjI4tsf`Yfsf zGkgb2^+LLh8xgkT+gG;I`diBx8(A+f9kzziRX~Q?@n5|3XFCI5E~@NJjbGG8{?t24Nk~wX z^8zo^F97ir3o%2jdqMz4*3^`YL!ZLO$7FS_*Y4EYM1u0XM@BZC7sk79fEHTc{nkUuE>TE_lQhy~Dh`Fiwx#YEyTJ=N@Im0lKz9B=CspwC z=h?#0GK+`+luyXc7i|ptc=Y;bImdi4--s~Pb{Z+-Al>>qvW$hjyc)?&$YvlEPzlnr z{#59855V55N|biOwHJO+!AyJZH2cnk)OfhA1kU^cAJKOYsuf#v1yx|5Rmd(Hn;fj3 zcX;0e1RUC~F%SYk`kE!q?_tyqTi2YPF@f(hh!H_Cml+B6c5Z6}p3%?P8uH8}0N0v{ zQRi!aDbjITtE4Xn2C)jW68P;`XZr8W+}R1^JHB~vX+=X7;nd%l(i|bdixNsmGsapS ze_ zgiq?&M(@FwhcBuq`o#2uB#&M%H}pdJ8eJ?Oqr38Nr){5cu$sSc7!w^>|1)4~cApj{o@~Mc5;hsjFI8>hNIGw=pkE+I7?o$`hsPIMeQ;)3%j$wmSUc}I=tO9)fb=ghR8l=B}Lq zM#*>$-*D_5WzrR&MZ%?0DCXxid)M%?5F{z+aJD;IEq2o8ER`OJHM*z8lPRX|@%?=D zHs`e(Y1_qz_|0Mx`tkUNsXF2+((9|eD(G|F=i3}_ud{iUh$&Ex8#}KtVsoyPC)}{} zICeYG^g-muv*i;qXwZVv^3Z%R_s5Tl@}rL{Z*}Mve&Aktjs33Me45rWXf(WQW)-7U zI8b$0?Hj^rujcYpz@D)BM8hOQKQS#AYJPv?BP#(pmJy~oGhx-3Qk!b{-9z-EW$*XEM89#o(f;ft zr%>g??g9>>M(;Z(R|6}9tm1c#=T~^E7kqK*lMk}&{(c&_RWpMnv74;#ZIIhj#O7fj zrkPT7wY|)CEP0*A34fZic@Cc+0P19_tRC;=`R!@>V5VygVIqZx`A&+qj9) zm9$2yx{fVQS||NO>M_^m% zcyD3Vs^ZG@^wJ*lG5c`f{12R%F^COrVVQ_u3$?Yn8z-Jx@HivlI)!wDuhe{?%$-n=*E`)v_HHb*D$+C4ZfY zwmUTjGI=njA*A$xFDU&A&#H=@Zunh3@wa?y{uk=h_Zh2;%5ojl3zn-d;!7X~LcO0j zOo?xd)>tpSY!qKfbc5h%KfKY}2=+Bxg8a(#%X#e_hvzL~?_=9)g;(hMG<~trMKD4< zZr1W~hw0?RAnEYnMDBz6C7UtY(nCP;)1Jo;)i!4K{2sVX8TDdVgQ^{KiV^lgwrWi~ z!=D(`30~2NUBU}kw>#GZYAvDrTir=ua{ZOPGY53@EG?>%t@RA31!;TDQ{mq*lkuGF zKB;MOo_Ke%%x8a!>dHT;h*9cVSm%k8x0e{rESKr7TlsW1(qaq+Sh-A;L~CPF3m|pZ zyI(LdUQfYc&v@5V@sd>82gHJ&hICj9Q4U-1t9ZeFtg^m2{7C?l$vZ{TPoquG(Frlq z$eg!_dNrOh*7=uS2RMIrK6TB&Ynp z4rH{{H__m6~E`MOD$4obB>Od-$|$iRi*o@01^5Yg^}gT zQ1uhf@%BL%fyC85HKpl`iBb%DxHl{QkCdRGS@v^!se50haqB!#?pS=dy*pym_18r2gijYGxnJ+@(MuM@l2yS^ zAfFs?g&lbPLpQLm=6#j%{RfdE#XT1Zyn7WzY6kUpabbqLSv@QSW?o3tNE$*P75W~0 zR$snAgfB{FMm;Dh4C&RX|IBB-Yg~ZW>Hw^PkA0yqXDmIY$Z_Chy=zG61W>=h#M^)m zyPwPQ1CA}wuMxx_leAqmLxn3j03A;cqhn z{E$O>3Sd!Meg@^gIPaa&Ry8(Vl=+_5Z_kL3t{9(1dA#R6fc z%<@8)-@c8_XzG`OQe*G=iFqXX8x>n4*i{+n9d21h3P34+bw%jh6o8Yjb~~=*vDt+W z_ay+juc8?7H5@OM!Tp)}!eE($X3(GaZ@eGMK?GjFd*Y{h0BZ;im2JvMxLPLrD|*)c zrS$IY0D)M1&E4(cw^w)0EW9K>eDsTqmwqRfC-3wEe4u^@E4t%{$`;W{?V{B$#LON( ztUVJ~&Fi}uo|712UI<^ibhloUk8E{2i4wz5*?fj*_p|zI&qB?I)n=H$WVW&6j?&8R z#G)Ma(cxT}x2xY|q51q-BGUG;MXEx%W|xRxf9E9!1l!c4woMsDv$YYlgM zZ*n_|RIA4-e{`y4s(YRgvh0#7IkiJx`hk2@ zPcS}{7YctWalw!q%2CEnv+1dl=hg83L?NXOrpi@@M zqADBxv3gf4W2BzrPmnGap7)MC7n5Yci!e0^b)LBxm1?xXH*)gkBj3R@K`7@O5kE3g zuSf@g<^ItuK)K(Q{^wt0EJgQ%c~+rfO9nLoO3*?x{ObE|d~_*i?Cu{Wk@Tl>|S z-XYe-WO48u!9YU``;|-Y**oFC7V$A~+{p$kwhONgaypak$q-yH&vxcrvdnf;wI*d@ zchZ>us@a!C-6WEFwYQ8oRM?4`BeZ&2t&4rCttqt`(6RW!k8)tXwD(sp z`!BxVjvIn*%ILNRcdI@HI-fk|sIHsu@SR0F?{_-a*7s&oY*(heUcBgfyDHe!Qf?fK z+i32vWV!zwe_LM@!M^*5J$=Te#gE*Sm{#-Xz5k)PffBTvA?cOuSz~=*Oy%#^Z5qBM_E%J5-f{-TYggufU&3fyBolQBqVvzn4&q~_JMfbnR{RVPn zFe7Sw-Y6uk@IbqRGxqVX&%T!$7~u#cR4k&mbbpWI#a!2XU1N{hRy~|#pdfy> z(3OKQS4>@~LQh@R`g|N|te36x=N(n?N8%kazNA9r_a6(aUuNiLOG zaTUo>$L6nmIzX5nrPW*o-yd|RFGiI|y9*;GZ%fX8-y^E5 z+A4RL#~4-t&qyCDv7!@Of$QDB*lO9_=#+v5|5?Osyspvn@G6Y_!+lKtN?k6w>Gr%0 ze0PsphKWN`wk2Cvw5! z2`2EbzW6>T?Y>l7Dc^Oz=Qfrv7~jkTiLWLWyXuo_Dwy^U8(8YrKIPnh?)1cf?V}&U z@A(#L8$GjN!#SN{#SOLFI{XeE!&AnbIy`cvrVIpo`fKrOAK`2SZt*l5aj3n^Z5lxB zF!K$(1_h&sq@BM`R7|Y8egn@Gj`9l|+B(1-^h|rH=L_M`rTJVgLXJ==)sJ_C*I##<-YZ(TXEpV{1urk|!vy zm$LTX%OftBl`|1ystOrv%^q_;a5uYgO#&)c%%u&tE_#8t6d3(fN-PZ9>zN|apYSjc z=mKOmEX(8nJC7CG&LKt{xf zaAC@*wC{_0*Tudjyy*jXXZ?gPegH9WHdoYEaQ=z*8hs4YiJh;-$ZjZxiu@pZyRmrm zxIK2Z%mquXwTp6?yvFh}mj+x&u<881Z*yBzI`#>ND;%`Gg$0tY(Hmbiu{8=cQIkuO zLL2vYhp-wv2yv0HZF!?>d%Qf!(>sNz+BeC7xtttl=6eHw~`D9GN|teES7IQ5MknU>3|5hJGYs zTsqp!Z*+Dnmkf5xuu;YEK-g#lg(Qp6R4uQYUq4Vg45~*<@q0ere0}uk}vU-n>K_|gXhpR5$g2cbaL=U*}QARS3E}iUVSJ`I;|!o$ zZe6*?zZ>ebfj9EAPN`ewL7;?kSt^sotHpN=S@iPyR=~MPaaFjJxi(qc5~%->=2!b_ zrPq0i&9AL{A8mj1xSGrQCH|x4~DWe4J5?w)u2U2UHf&tDUN%o#V7vHI@`K1w@lL{7rUAR}6}VxaNMVl}Vn zYY_jD{R7eez$z&h&$^an=;ZT+T!$SI=JdRLtG{@(x4TIoYa!P`0ud&B+EY53bs1uL zWGGDssIn-J>@SV1<=nPh%Y*%Mb&Z=?eUtn0R#BXdFBu81uN)bMXrhFKX&om;hP#)V zv63b5djckrd7#T!4C;R!WL>pw3~~$eLfMzfk_%n1c^Am*i@dE$BAj4(o}0uA?BLt3 z<*d=DJVi3IJg?k(Pw(K|O!n>;Rv(UXYw|!s;vIhadMXzIRg(|RukgH6D4{l*N=ps8 z(u{GuO0x49w5b6cFUj7McmMbs^PFht^%1&A6=Cjbmx zZc5<*LZ2{)UqmGEu2lWjYF@5t;ei&vL~y`?6BCq!Q_=Ko{6l?$3J;XSn93hKc1G*%&>${2rkAY73@7;0i2t{?VNsj$Y|)6 zasbLf^PBCD@Ybu|a47GASsY9^r*+A6v&}nGr1VpigYdV9|BmiT>6^tVBc)mX9o6JN zfa8^7eb4ZjH_dm)?w`4v2O6AuRWEPAes)NE1jnnJ2nwV$h4mIe@T?6I76A>Mevm^6 z!5I-=%hM+CwD|}_MyE~SX~X*>koOKs=&&i0^+@G4gWeyNkESzB#W%6PZ&l$<8T6Rf zTj;Z?0ac(oIVgcaZ)RhTPLBf-1{+l0OFYOM+)WQ$B?LXQ{;W$^GN1dUpEU`a4PtE@X8 zqu!8zqFLA@k_zv|&~F?r=v>CIubsR^kptRk(CGEFa85DU;e2pvhw&85Ds<(ri^(rJdu)@X-g?Ni~G%Jj$U{1LcNKn0F#!F3FK+fREJJD=E9Wr@Rc9pKo? zze(ORaK!`X68(aPT@J2h$oAR(DltOpWf`3A4>ba@aM=RRFWBT)YGBYE%&)__z#cf_6>1j>vz;^tXIFts z7DoW_YWo=2E7&RR-W2A{DOKSlzhF*wc{jFZ>zp?%?{_A@Ql?$;{F}(ge_5b^>^@_JH zTjGjy${OZZ^%1K<(aAb^7KESIqby&8Bk5q2J9pwAUAlDyly~s0!hX%o@ny|K^57G* z(`Anp{Qo$VddCB4V6pc; z0U#}y5I^{^Fx6G805o*Gg^kAJGw+ilYW(Xicpf-Nd4qJ((?yJnxz0yBdn-0*9&=2St6w>s&gB)v5wYbgJ9v=G*78`8o^h;K4xznVLfZICNHXx-~rvHImt3x9;e)8#j zx6EEO5YVRG)fF)O`tgb4s{R(SvHIS>@EqiQsUE#`4%gM72XT#_Kvs)jY|U?XgG(79nF&&#@vowvypqxCe; zzQUwO5?%vo&(9@1w8RqG1kT&s5cd-z5m{FINFB)eUseCIxR3BF-{Kdl?hd+vwmIPL z2>QS)2#Hteb`M_kNJ^&5u3!Ct=KMY8pLN_^J#&Ys^z|r#@?UcO0YwZ6)fYh1_C=dAF9PZ!CU8s zCAOv+DLS~dWo&*L%bH6L&XAoWcWP9yo)P))ZXXQ%R6IWCcuP?CcUmXyg7=(p%)Q&< z?|tLDR$_zddmp5TAp06dw9#r}SO~B%DMSPJ=--izfIC>Eu zttOXcDJ%#?P&c&{GZ_6FB?UIwzq1Wk5@E9X zRJLBrJII_E2)gn2*aw5DTb726Red#hbv|gll}b8o%~#}#xf~QB>Yh8t#-#V{9PmO{ z0E)GYf4s2*c9pGFY|_TUHY#E9EuFhuklwWi6?LvE6tHDi9Z*~7s&s@(M?T@;-_ zjTvH7jSDH9$>+k=N3l$nyJ9=$vJ97z%u2`jzhaO{PPEK zFv%-eV}B6VZ=Z-BFBz-oX1U-qk?fl!uRWtYiY9co7Q|u#*VLmpliP#v$0{?yU5oP1 zRGQ7bw-!^G<$hHA$F@}1Px$PzJuJmyL7pD>Hr>MH9^IKP>t#yb7x*Ulv=J$2tvbY6 zvF*Pc>}lxPn><-f8MX8=nN0!%i=MqMIs)E<-dSkj&0He=LT*MWsr`64sZ>yNvNoxA z&?n}&U6NkAQKo_1*)PGBq)^Bhik&nzj_4Sw={!_TZg-&!7}o;*g6*&*=`n$xs;yXcfKG)5R?*{Ygz!R3rTYhp3rR!~wf&fdz^zKm z1yM!B$YLiByo@HSSn*m&+Q>&0cYV4bDP8$8O7dF;{Tp{%@st_*g~5lDW7bwN&%!=& zgfJs$&?mL)x;s(7yBw|Lq+}fzsd2k|T=yx-4>;Z7t7Km`%81BUa>?@`|8uY3D3|aL z+t};fXaaF&v^rHmMalSoj4i3SR}+!j!Qe18W|gnUy-;G3)GwikNDy_2!jJ5QTt};q z2tL9`MH0VMtHtad{)PQ97g%(w;&O=__g$2bKLSXe4S1`BCIm~;D}ak#N)-qA27XC- zPmih;oOQmtIv<)D_DAax#Sof_$t5aP+5;g5}cckGUv7cfMF1hJ@T zNx{kY-807*OY@x5TqC2q?ZnYz(>m$i6Eo#zEi=6TYS9lgAsp92Cg`z`547;v>hc0} z4)B3jJkp7QZpUKoO>LdYvDc(@$JRW>kEQe5lhw`~?_*EV_m6`?D&m1|vl|8agtY*`#8VVpJV&Ym38z!uujbmO2LLo8;np9;h z=15$yaarf|R@nMXlo|i9C5M~t6D8>uNdlfA}dQ)bwbOt?^F`F4H+%8};-Go*hs< zZ^;rrpHgi!!^RUyp~nZIzJnT#rB;6P#d5@wz2>4H2nR;J=TmDqyUBd^-T9(mL^cek xEjOM7kOEWX7B>AFOPEjqW3&XtO}3sBs;}!I&Qp@@++^@iL-meIf%0E}|37$G^Y#D$ diff --git a/r4babs2/week-5/workshop_files/figure-html/unnamed-chunk-20-1.png b/r4babs2/week-5/workshop_files/figure-html/unnamed-chunk-20-1.png index 91e153ed19510d7e70559c7e40da92a816eabffe..2fcde0cc514093b1d00d9902f3a55898d6a589a8 100644 GIT binary patch delta 12854 zcmY*=c_39?_rH71Br_rNJZDM>Cz;BeLdHtCWGG`1=O_{~Wu8xFWhnEklrh5%C37;* znTPB0J9?h?{l34y_C05>z0Tfit-aRXYklsk$M2p*^PYkYrWy^-rWBs>*cnl1~z2)5bv}6Sja6V0m zb56`B@+ScSW2i3yLG(=`27)*;Ap#-V*z1x)tb=8cir>L(RF%^>0S&1*<$@f~q|5g@ z6xZ3mnbDXbcg9-iNB@neF&U{drI8bYti~LKvnO z?!bxVE?I4}ZY=8+2Fhp+WqZ2FYh|0(->f|}wuNSRU`)#FbH2Tqr`{dHfIHIvb)-?F zqzCWgQ6LOFJAF2b+1Sm*b-=TC^9aZ_fp2^UuKY?3cSk+c^IjyO`B+dIgk0M_}?~8CeZ;>^BNyaV(TCL3(eU)5uLwhzmwh61{9>%bL(P1OP%ZAvy?Z zBSBI96243a<^TC`C6=3<4=fCXRKPFE6UjTKx~y`zx{_a(XbP^?ZPfVVtPOOX(+jET zh~#pC3#kxVTge{)oieMDnYQ7*r(R#)4;^ih z3qchFB-_R0^lN|6by&+^`pLGgJCeXD{Y7{_Ddc2`a)HJE1E@6n;+Mzc(@(~s8V4+$ z<_Q(QiFpZ%U(A29HPP_(uFL2ZWK4<#EE%O3aFrX*?xye=`D~vezxClwMiUVO@U{>= z;??!iVMPtlN{V0mBG|*{CV?W3rg{qkuIkc=-7Y1 zus4%^79{k2eS1#d${GD>MvsrcF`sh#D1bM1|wST@<WEeAqP{WA4P^4EgrX+85slRO_R}FD4feCzPX=)fIOC#=q8u!uS8D$`m5PZ;eia?$S&nu z-3>dGAh{8Q$i|Rhhd1_(cmuL=N43Uq) zab?h^lmNl%)l+^hC~*l4!&QQ4+Cs_##2-{#J48DT7!a(+v_K;>^ngW<6nu7x9G>lG zH6*|�C;cfj3CDzm-R_$@w2Ut+yPC3Phy%TbVu#Eh34VUO)}Mp)QpcSksrg?nA+S?qU3aI=ncEgz+CpAl+r zAuZEj#2(Bk&fmvl%3G>3FWRR0_nB&)@#|^UB_ZspmI6^M*%S+6(<(mI26B6&yJ{YR zes?2lzc=gdqPv(y2yY|3SNDoN^y}JF?0RMPTi3p@J?onyFJRn(2rA47Ic?2P=soUL zyBNsojE9H!I8JPTmse;~$li7)0e?z|+ZNK%C7_1W(>m5cb;$8D)$dQW}U{69Vm zM4T3(@7yKZJk<;(nU*3?!OM;UPdvzkh>6SI1m^!)ACqj7nLPnI6tT1xJyz}nm*g{tlHp}O>yha_3g>cH4bwr!Bvfe zx<8|pPJYnx4jnRgSpqtgtfz}kT9U(Nre6E%>^CiEKiKS^s4wGDze45F-5O{|h8dhj zVV&MWO-iFl-`c6Jk$xgMVQ-z@>D=YKRfngy@pBQ@7X=ZC)LG-MA7#h&ti;(koYxs z9drY9u3rOCvZhXi<|$nxcti&+9oGSIv^hIyMi7+v14;Y zTzJ>=olFR#DPzVuNwZ=f`p`koz&Mf$-7CwrCUb;N8p;e@M$+`yx{~-F=tWa1!Q*~b z?CWPgymEC(Zaz{)J+$M8cdSAobE7B{?bm}OgA%d9)h9-uIe7X8&1QDA-ELAAcz;J- z>Sx1ByGdjY2CZ-G>&8_VFp>$u33E`wQ6Lpc{MMKO1;wD`I7(a%(23gxW1peDcwwSA z2z{MMb^s+lblNRyjKUvS%Q*{|IYemaM;&U)GNOoCV+x@kag}|63G0Hy#7pV`{?wY) zxZr@>ltR!88beiN)kNqeEdUh+oOVJqWCffkVj47RhZiPL>o5eC_k+$NFVyVO=0hRp z5e3kIWpvm>6#;$V($BE;LRq@0e+KSes#PyOUAvuMcP zqOn;J6KVhRhqZGKifme_8 z>GfF!6TenEEu24sK2fFYVie8ck*a z`U>>fO^%q7x|25%x$0C^Vj!?OsuB6FObq3cD5ba0le4+|@-XjBEgd@+=>{7BU^S`( zmKe1j-C|uBsm)}${IGdCQE5JEuj*rX);T*Gl-_-ycp<uZr9U+f)>l@l;5lD?mJhsid0=?xb}$zU%jvhkx&SZh*NFwU`VU#vNJO zPgvx4z?E1U?8v>c;^492JBc}TYs|7#sAB$$Y!`|~+&Sni99EoEd* zuizgk7Qf36+uf&MySG?9u3D}Og`#kb#V=L4+Y$kN85gkq5`lBi~g|G_j*gFN7Zn$6Hx`Yyw>?#L$+^=9Aoi< zUIgZ+w5QplNGUXDt6x`|QXTuewPRG21XbC}exnw7RI`Uwk0(6*)j7cp<2ZZ|9)vL> z#(zfO@CrY*4@XLY8o-%a8E(-h-cyEE2riO=hBSz>G#f*Gq`*ImrocXlDF zxLq@im2E}gYb^SXLu|_1l9Qi7CSy02Mqk~FEnLF$5xsd>{pMO2VamIC$Y`ik+)~>Y z5EaauIR3(agQ%iVWYTdgo$QS+KwWQV+ZP-}TH$W11`-J|MZEoPaPeU7*Ri@#D{6K5 z^7F~L8dYa+hdfc(WLBZFnX@K1`%Dy6MYbo@B7An?tq%3lL)N;_!clyxa6E79pX2wG zBdlZDWQE=BN!FLa0-S!otAm{|Y|>i!#b7uNnEvo%XX~ zwlr9g2=JO`fXFstdX}N>%3<$Cj)mu^C&#|)Es*9-P8Q!rbeVkpboZFGJPP5da3ZCD za_0~?{$kCu>|QGo1Nmo3VzqYxIdvuVf-a(=%4zHYZ|V(JLk>_6*pp6|}aH}*Zt_yIDy zrrRP+c2vdsufYhlzU| zyt*P7W$Ja8@X%#=M|gegcA4>TGyaL7>UU18{PS>G4y=-I-Jac|C+YS*&I9X?c*T2k z*p;J6UX5e!#ZSbE~|? zs@jg1@`!=L+~_Cip9igO-!D&=P8aR$re@nEM(6`}r4AlOqiSoSNIbjYJv{yC_be;* z0!?kV(iVHnkq!lOmp8ZR5^|lh#ia%$S9HHKn!K2+i5_9vV80FO2Kf`I@-q_)>c(B` zy|#C}NiPVqM=2y~c0QYcx<2!NmE*t`Q&*)WzzK#t9yXVkj3CAhR?+3U6)M8d88_*i}*4RLGb@YdWYm+hQhy-Ka zLT$Ke{DiPE_<}^Q5aSk8YLa2`ySTtD^~M+>>-3veb!4RAN(f zM;b4Ta$H@vwi+E zMW_6P;13fFRy!wUwAf28w|?H^Ss{G zI}QoaC8<`!4kgSus%)0}WYcwzz8!pdB;s?xiC63$#x%>Bcn=%WVVjnH30cibShQ@* zK-{E~3y8h7MoCc%xjtzbA`{J*qZw!Y1@xpv#@p{rZz zBohz5&r8y#Hr~0`g7ZnUhjTA-#&ofZD&0Pvq%nGF>(6kwHAQU;8a)C^*j2Y*f8MPR ztk$U9xyw=ViqdD^bMn&0fQ8!$Xt3{ayg5C+%dsEoxTcl-9M$NW7*M}QGT3ZcPB=n< z9+4obcx|R)ImIFQSwv8tUSyZa_!D?Io)xJHx>>vJ-s)cBxgC&{~&NH&2? z2&-;~uG?O4@!9De7vcdqPry1Ph?-ru2TafROYdp=k49s1QH{IGJWWuT*;~eZL71O7 z!uN87wmp#PJU(?JfN{s=YF0eawnMGWTYTSMuaD%(qJsxBGsYxs0@74YL#`s|FaeRX z3w6{T(8t68>&EyqkF7rZW*{CEfN4kfE%9@qy3`-`M+)57K)T{f@O-ExU0NA%zvN!4 zBSk@a!cze`_^zeoW_m$8)Gc&&foynZ{8!u>b!Q@@sQxWr+8>y`aJrD($xzv*$1$<@ zwOYfBnXnaHFhI{p>Ip1nDS?}|m_iM~@=S8IJ z+jy$A{CQi1@K4nh(J5g*V&(89*fjBJ#p9-$5Qrii(wr*Q+s7Qh1#as2E`LCt5vA9z zrBa4avH8S&3M70vHemAtqvDM4j9!F5^R)^@E@8gs3JPZo6u?noLi=JdOW#LJOguxF z(+@`%Lrk1GAKeA=xM9bG!AB%<9J=?faK`NE*8R$B=V*3Z3li5*>@_M*f;0o6JP~Bm z6J8OwJL^z=U+ZM9qNnFny+S$s@U6{9yPd?D;#0c#w;c}pC3lVe-1I=R-1S%7C0*=> z2k8fX3>Wj#n@SI_>!(7RwzH;A`*4Tg12Jj2CAPb!t6I$hM{FNT#;b+W=7=l4EoUcC z*eq_eWN~IFZs8~smrZpMqeEp9if{GS*&Gs@PoE{Ld~5%T^wytr?cvuIkU4+3F(;O_ zT6N}aiZtI%b)cO87zK*&wME;sp`GMv@&u?T7-_K1q5eQP&XG|vdKdaphret3J?E|l z|C&4m+3RegofyTjIEkl=qkqT)n2+OxpIi;s`_^THM{LRbDw)u4z5`ToRgsL;n@mFX z{vh_{P5thZpql)GqMz$m=@H(3fDX>_dvk?S1jKZ3^H>o4(x5)udVGrvog(gdwm@F| z4a&+rak;>KjiPJ31;75b_~Ggi2WCv?rb*8v)Ip1NzGey-`&_D*mT%tX*iY=3zgqlu z&pkF|)P3iOLOmRr>lgL|UCHmnI3f3E$?Y_PCj zld)l~NCaI@5-*=id)kzC+a-P66kj&tOp5AB7$mMxe@$rqCe!3$Ar@6h=le9@u$l1c z{f`$%oy+YcviwfUJ=X%twuV|rE0BZ9jx9N+icb>0YZ~1KnFr=Bkz+Q#m(v)|GuJP7 z9uV49N3_Zeb^M~Lja}~ZSGa_Kv2$A?DU{pOZG)x^L5)1wm~$jtI@mwis??5tT02_MP6v zE*3D8;+#7AadUmNyQpx0y-A8SQ z^Bu(km3;y($p4LkV0F85+a+8^Y2>-x6p#hYs)MASJBFIV_!Z!t@s$x&aevja1e7Msc=$# zP;~iX%h^$8tki3Z!}Bcb)|d42hzJ$cdcAv9h3e;!K;|CfS!457;W|@=1nj^$-DG*^ zWL)=Q#wST{$uS!6=~)A0+j67CH}8Kv?)|W@)4+SnL7eprW+%Q~W8p3it!ONuRR4y$ zhWS{io)leIYcuaO|8%b*ysi-Y+`Fir_@EPC`uAH!_u0x09XDG7Q&^9HqMp+*g2Y)0IK&Nwh6fS>_G_0gLuE^(!|c zeO@I71VSaz?0%&)B(m>kcvx0@lgD2`Dgi;AtD%w7qGLMLsg4sf?VBM}OFF=`U^2?; zk@nXywjGz{TS6K#k)rO}Ek0L1Q!D*pUHHDMIhp>UlN0MVOW$bfj%KxVk2ycz>xlSe zOXV@kL?w{@Zi8D4*sr^3PlLUP!<8=W*~Pepm_79$tQNoOtM+XZN%u_-jfF}6B+Z@?34ZKeiA+FOx1R|3Jk#2^u^Q#jN?5;QTO_41z~Ws6R(q_ApNcJ z($!vyCQ2~6hLg=)gtKEkkR-v`05H{;6}+6T>c+C(cg^kM7CxK?TXpcYR-8Vc`XC2P zm893BD!6AO;me5R>gpqzv1<;JljhzYk?q@acao1^jC);c_dBLNvw-hE8F9sBl6E*A zAetAWz)YIkWad&V)J^xaD!0Ef$ILyEA3>^hFCg^Jmfi25Y%3j++G{h@j1CgFe_J2y z^nVv9gek7xU*^90qCs#ks@;0xK^R!=!kb?2ChG6{{yD0$Z!NCaXI+%ENb-pPHQ4#@ zvFYZ?2c*#0cH-<`nEdPf!b~x*CW*HxnUG=ig@|_>3fPa8e?YOgbIe!wcVbfUjdz>f|TaTJ642 znEU-vnuwvmBB|pRHO7gO#UKUprjjQ%c4z*dav`KYJFfoDbE*z)KIe-;=>aEpXjG+5 z^Znmbu}n;jBiDgI-&#b(kzpM_%3(;A|~w3=57`(B8Hb{NFK*~ znC1rywjUJDIzYl!=$W64k%l(>aBC?6{GALVa@&X+z8mzh#1lI7B{X-}kmW?_Wq)x~ z3i@a?wQG77vmuHUdeAAFp_7k=0<&9IOO;@ggIugSk!&zs^9Do6$1q=@*k`4Hj&qqd z%~ich)JpLo@yQO8rvJc=2q@36Kh?M&Nn6o^mDVzmTsI~{jV3E@^1_R;`wPhFEro$d z)}M|uy&o}t>cUwLFy|*j^XuB&ytvrZW3@qfab${1e5#7Y)EM+y7S;zy_7k6|zQ9O^ z8Ogab?dV1NnkET(dl2xWiGx)Q7!V+4GYD6Te%TV#L@Gj5F}H)IL8nMN{(L+F4)-NZ zTnoB@lnJ|X?q$C-p?=rmH7M%l=`a4N7$ zX_}oGG4`G>uFz6-`jYz%J_l`p8~}NA;C9@Wc6-TeEYtXe`~gGr{q$J zk5TX`3(8zFOiGSpwrCTG>Z!9MuL&19J%im5x&j#UeP^?JfexGDf`wntK^IpOa(6k@ zkb75y{H^lQQY~*1(p~izEHU1s572`IbS#2NY;?PSGXQ{WB;H9@!1f z7dKDSdPFQ&ftWLTW99m8s;k@MSxLNbK~I=3fp_c)*dda8P%-d0^>+l6RdvZZ9@&iS zQKN&Ng-52`Kgfif!rC>Hm-Yfup4aECo<4SJUQ>PWtI1In9cF&7+^D~Asm5m{3d%}2 z=Mu+|MREKjlmMkc8vjinb2F?Y+W*I?npFB!^=6I{F9%v5f@w#79q_o7XC~V zg21e|=iK5Roj1NOhGr#4hdKSOH)`{^86$l5v=S%%49ZHWagCE=H@SZuDS0Yqy+TIw zl(5-ZwlgOGwGLr6G9i)hRZW(GyBc}As>KEEaa2SM@vUC(UM;@oF)qnWZZ;ly4rOKi z{D(~!DI#dI+W=r>WGUa31fCU@MjQ{4=@dga^Hs6g>4q@=n{q-bwKwTTBcwC3)tG1v zZ_-STr$A3}rD(ilX1p7;gXaci}pZ z4nwP)BML&k_EAeTFq%wgbAObBOAH-$`B+gp_pz+SOQ_}Zl){a$kn(skAgIP^-L>OlW1(2K>ablY~8UIi*+wxNEw9 zcDjHxxNQSdbq=MGyADP9-o%`vc6f)ZenBwRauexbMQOC2?#eR}Q{S8w&=fh0D3{ZQ z!9d773PLY_5tViUfI!dRa;q1S`;f>+$sHvctgLN#=LPi0St8lipZBDTEL9KuG064h zo--)Lq6v&XgYuXybYH};3pP{tXaWP_;sW&|JF$}?0=#fxD-&S_D@sosO>x?YW>lcW zwE*KV(?;kAFD%@OBLS9av7W!vO<^iBj3TY!X(O^Rr3$knE%JyotQ&iRfCU0d5LrcP z9nsKD9$5IM0J33hh0y&kp*(}yfE$J|F@lPqF@*IH-x7CP;z~hK6MBDB-eQ9H%b9Lr%J z9Y7g(ndSXBPhhf3g2d6$JgK zOx*P?iQv8tJ=&=`p!3UTL2*mwv{P&mI})PGH^iCHv0Rds;NUB$r1@Td!SIk5u^`>) zN`s)1l)K%JZRd)}b@>qqmXQB1I_WyX!D<@iKl19)uCfig)Qa56{Zo}73Aodezhe8? zQl{=dG|HdFybavQR6+z#xoklCNM(PZbx!ub1BA+3KxO}*x?2K84P8 zw>OcTLz|_n=fbyBTI+Xok)D{#a6)aLa^rBf+heO_8IJ`la~207Yq{qFQrwr~66x7L zzwlQv@(m^3+;FEBP+qhFN&FY0G}0ClD@P=IK!!<>n{t~n_)l3sqEy6#46{j#6?^a> zOcHVswKDcILLe)Dxv;bo`u|(SFCl->mFB?Vi}4?{1#Bk*7T(MwlgR2Xnb7!N`Gn?oTJTJCBxK5$YeJi@04=J=**F?yC?K|-hA4#}&43KS>??35Zewmu$1jwEA$tNuToaJ% zd0Q}JlXN@()4H5S_YC?lD0w-<-(eI!et6>He(xs=b7K?>QU$CY@DzhreHa`3ED^G#doxP(wMO(kpeM>z$ z(?{@ndlsw+M2!aPy+yozbi`4BV+fd*Deob-iZ2Z*P(ZD!%)Rug@;3n20!4{r^Zi=g z`ZyfBW;(1cB3P!!RNEXUsoKi|N&H5LToq82%!;KJ;84Ra>H^#G+%_>u zqQRyK-3e`={8WGIR{I}5`An_W&NcrEGnz4$AbND-iS!TeW_rq)}?%F5qW(%E&Nnv&u$$qKZ$d= z2`b4%PIP*z=n>cH?gb=DuqTf;cDwD&f-#QN3#QNT_a_u2V{S3U(m$5&fJslhj0n{c!% zwwjU3PuA$y_$U3gR0Uub1kEN3s_^oy`)8)?dlm1WsB6-%>JaNHdp+ueQiJZG{! z(to1y-US$^!?_OD>-^}&cmE2+u{SqdnEq7if9KtJ)4{HLjH%y{-KbRhpO9u({bQdtv}irH=kp1gkp5}m93H;n{?EL@c4q7eZSq>iqNqtGd%&M}rp`Fa znDIW`PS5z4a&SjBuS1uF86plD^PR117%xxAe=Q4)hZ|QF}T%)(+MEI!~)Wv%A zwSjKT{?SbyhoikkfAJ+=>$C?6F5t=and-IHX0Lrh-}nyxiF?d1kFvV!0?oMLKO3Ys zy=w;2f0&*Xv0iznf$EX`6Zg_19sqD)-*^;0v?KgLbX_2`di7e_mi_u~?qf(l!DW+^ z)ZY(2Z)5+DwzeBc>JDmkNOhX))64y5F_U!TlK72$-drI#w&BTFKMS^qs>o8 zv!x7;p%oLUN4-C!YyYba&#eIIkD-k{JV!17JYgVc#_ZZgwcOIyFE92x5gmEU* zlWh66+RP8%Lx(Iqz?OsqWfOFm887^Y{AqN6-#W&-alOT$3#4%TQR;reY2*Ox#YVQL z4b=OH9XY<1$In~TG8;7f9A8{jNa_goZ)_#02Pq5yS~p~)HKNAXPBI!sQh%@+;)X(2 z8fHskGv3P^jOU%aQY7?!I}=rK)K=tQ&Efg$i(N1a0=x$=|1u6=k+BmU1uxal4tR53 zxD!QhvnfGq_yxI+RS5VQYy|T0z%N_GaVuM8et8eHl4&-~2;|UVLYD=lhm<|yJKT6U zC}B;2HnjA5vqwVn?njpN&CnjBC}J!!?o0DtU$aFePBn{z;*=<&LqG-p8Y?{bcvk+ delta 12692 zcmY*IhpXh&X*SYzig{#&g5Cc!l4!9u8`fr_a0=#8Dg6G zu;>(;!^nxaLr>=dVU!1hzI3--JjE{eVLLYIQ68)V8ZV@`n_yv8yBWxRQj4uIzblcxx47d|ZYTw^Ep7S>Xz;pFHxFALscSt6ZBH zxB)Gbb{Wf=9~00jhV)7_oz)HMuL%6bs1F;(QZB{z=&f0!VuiV$_7aMN{h!V@PnuB( zVeSne2_^h`o>M-FAl;I%eW;@M2MT0@U_2~0^AdJ1RjhC9O-)~BK%wbUb~vWBV}?(h zAWecYNyDfpwaEO8%96B8YG@aEl5e)4dwK^ct=k&$Y1NSu^Lb_eYW$JK{=EI4g^7-I zt(d9bO>dz2n|$d3X>guTM1)hHM zj^4Vg_cGw9Fuh#8nxfl_9D{ttdr=SS^Zqcy6p+%0;XB1uZDtxw2_u201^k~T7U}}n>!<;@ z1E8t%xM3dapE;Bd)91mmeTEdVQW*A~KY>9+?<$SSDaY%LqJmR4UMX_)*P&-~4>)#I z`E5x78Xl}83iVS2DgC?mM_kb9g@3ve;z{m-LL6dSYI=sSW-cK5yGgvavJOBWh?J&m zi+sESF?fU@t5dc1rl(5T!pzel`%7*oGpqxJN`IWYCU@XlZil!%L3#bg@ua#GZ3+aG3Zb!;%lXNe&+if;Opj4YWxobpY!)7_#EvKJDv?$sPGg zUEu6>`P!PehdREmXz~sahlJi+!|!}P9nm5!`lLdFVx-5}2bN{85o=xgSW?ZrDKQ#6 zSe@!4jMew1V8iRosa3=7ISAjUOH-amVU^gk@W)Ho?1B76&pJEnF&ykSO(*iptXDuVtGBcx0 zkb_2JdRJBe;3f>da+#`tW#<*FnH%s<+#84pxC#3ci%}pKf`qujgM~4d(c`kxZ=wL9 zTve=$W;_N?F1`a4iAF-3%+HYy9c`MM4g20@f&z;I77>`7M6!zAx&;Cgv`R()C!Gu2 zXXcM(!DV;}Ch(vZVK${#*fyFjB?%Kop#W4k29CK~i(p@o(1($SoxKdh*>^Co+?$;U zv-`M$HUy7mKbL-b2z*KO16_fRDnNKmARD}t4GYjBg|A?@j012j*ADh4^auu{g zaVsCbkQiVs-WGdYn-|=jaY6B!XY(a&&}&YNWe(L6U9`OQmXPP*KyPTJR8;E2?unhJ zrhgdpRc^DPHVjAzsSNM%{%uo#gQZ&J*OUgjO`V&Rdb;VlnIbV;-dwPN_{&(Tp6K3= zyi)rl7gd)SpBQB;Hkm(0r%LYRLRO#<8CLcKTw(mn-sJi~_ib zXI7YEiCrphK)HM{V;-!h^?DlkP12X>5MhIQ$4Q5&_=&9WtUpKyw9hiN9q;OC?y1>! z(*^U+EI3?G|Zv3*IB?i)pvv+s#+840L3uu>v@; zm&2wt>uNv8yb7@<;2B#W5iKPXlHIC-0tlBT%l~p#XZuJJQc6V4S1kh8`)_Ggh>xAm zxJV0BE(>BT6Swqo1tG1`gY@bqdhi`A?#2f?AsFFY0+vm=~XG^Js% z(YWszvyh+E0P0e%IQAUG0FDn-1xDXbP7VUKvWUE>ZB*iKZF}) zKt!-FR zi&7V@OK!GdMqPWaf_)qEou-uElJIjewS6hE~`0|2HoDW_slN}-d;c;87$_1 z5>AD_^AfPVM~QG+J%mod7jdnav6aB8TU2QCR;VA;Mtn>h!Q4=$=Se3ou=<`A8rydh z^Oz4C(hmCUrA2Hz+XXjOADe8g*qk|!qAf!a04XtaA1cY3sd94hCpVEe3_Yz@sLrFZ?hV1EQMfuTm#Fib1wMy=YXTuJPTuN))JZ#KB_ zgOAG$f1l47*vh>(&HAmo+d;R$ zaXJduCwZ$V^l_Q?DYwn=MO*a)gwwRJhnnm*BW{XmqHP!bm6g|M_}ovT9SdNj z-T8(R2@ambQLlVnr)gV$9W3H+x!(SW-rT-mq3( zwN#U(1cq+*wnC5?;>#m|;Kq`S1)WCYxz%gHy2YBqr1r{vUMwZWnAtas%H^$SYm_*X zzl3VA+rcLtR3KZOsV`%l>5_vV#hAIB?~J$zsaM|G{S=jc9YJ8-uH(RAz^enClq5)1 z2)KNHhyn$+-dUsQ_L`2~4_(6Q#JvKg3~qMQ8eM zLI&r@*8;K1@NfltI#MsU(YLFi1gkgd&$b!$H8dL3TSCY*Qs+nsuJIi{64>duJ)eb= zrKJ&jCVdUfrhmQ)cWK|f4ju!+!3BGd_zo~rmpsyaTgI-}M^XIzxuL%~4H)E9WJ@#w z!9eiV!+6Ku)F70is@U^n`Q+;Jjt7P7NU!`?H`p{#Qa2h@FFqvG2%jS%G&_41?+-7I zQ*sxW`w8|VM9U_dui#UfO7;{24j((#c(<$wX+h>?G-vuP<7WgPSWut6*$7+-ey_Fp zLjyl5ddi-+jBgn~xXPe`YEZxUh)hG@8bQR<>4^t@F#pvy+MsgCuSl)n*B^1jvWTxa zcDbckJY` zrsG!%Nd_qopEsxu;zPi4P*LCFB_eIIq0IUH4^=q(x7LW-X_V0@4f^F?=O2Y%RZ}x` zrWY=cP1AY_mIFP1UD-Sux(-JtqFLOVp1RWG*4C4d%xI7vo>ns{ITv8O;$SFy-^We3 z*-8hP{ABW^8^OM5V3f#s4o5wG_f=CCg4k=(*7%_GA)MWi*SOw8^_?hsJ46=Ozy%+3 z@0;%~-I>7OjvQWwX4(ip1w=1+4$cA*mr1-1 zOalXVm=9_KzIB}WZ*gf@57pVmm0Qq7^}PtTFX-@xt8%q$TKwF(HFR4yXLZRvYH2`xu|<@r$6ifOp|=-$;p{HEBeYFb%pm3(-TJsS%x>ZPX7$S}G_4v1RwV?i+% z%T~f4k{^=}uXk0_F?n}eLhP-3x<1qBXLpL{o_1C5d0x2uF4@IUOl}J^ocQXF{R_;w zQU?9(k8blv+iZa~6v5MkLq!>?OIUZK_t~ZpcYvX6x?w9dX4(D%AN7iPSs{4HMF+#wJpSCONm~f&StfQ;V49_*}&p?nuh}4rv zPUbD#j~}% zQ+2#`y??PLj>@~47wc0ruKMFNH{k%(`tki20dvVSP>W|x=ZI!>Rd{1!v@Eb?@1#C6 zdxdW4!Z%as-=YN{Y+yDCG>m99a(cXtF+nDRCCZKqrN*;~c+mX*^K(N5n?rvO)BYAR z7Oh>Xs?7!`Q8n_Q&j9M6!IQZE;+QPNdQ3H={Px@*RSqE+b-gZ#Ql zm?*6s?Yw5GX#&9!y{bc0;*Ow5egc`e|v)r1y19>2l?x@ zhbmaJ=tI{uLh
L8&Nqr!rq#j+f(yU~ntbFW@BM62?$k_p~_1;qb_KijWHe}ptioz0{)Jwb*ukx2?WQx4UBa^MlifxY6@=YON7Bk)e&jaWFrWAP z#aylb&g6?@nY-y;;Q+nR^;%i6`Bfe^r^?z0BACXTs? zA4e{Rkr|~Nt{Rk>X)cpPRtRk^3NiJ`3`1LePm#;i>aWwK7arkJ-s0VxWQ2mP14swJ zu1);1p2oR(!QfjbqH}7To8&^L*03%Qnh-ZOo&sugr zepg;%&D7#aZl}P9qEPwrhR?Q#xN$Z8eZeZHo_u|Qh9@Ihy=QXfICD`l&i^HmPG#{B zkiv#@c-{Lx)x15=-{LTn-ePTc^^X-2^YyH)L0c z(Lqo|;GL?YlFQWSLBz*fl3JWeaTiuY*KKc|tSQ#D$GQZ@9@(Tv&cDLL*)vK2xA_dw z{<7HZPtCCzw+r2-9&9x!=E@AMmm+hdZ1JpSi{wb!ENpey%zdzyCYk-Q5_qb{ep9SE}Y+L*sZjLA9?T z$VGKDWJ6`ka9ud}hOd>wgAu`#cX83X&SKwl;BPSeT*H{1;^&=Tz#CtixKU&4nfPN`I#y2lKM8 zQ_!Hv-6DecRwQ_?Xd@J~cLN0F-ftteG+Um{GU}%Xk<#m9-8-9@9MzUZ^4nb_NZPQ9o zAKsS-8BLX#Tm_;9;VZZ1)n^?aWFv)WmP|&gZ9A(b?Os@aLrOD(NA7f_?X=20IU$Fx zN`s6zuf{K;c7+M#?*WUoVD=ASpPIWW{IUp~Jv>(S_xt(mn1G8bhUzFo;%-D@kuD#E zS67y#+!LgS8^~66{mlEJfSa9tC!O{x(osunNZL*=l6<(E_WeC;zN?@|gZwbl zGzS|&stXyKlWQsJ93KMDg{(6-%-F$=typ(zhj_VUb-j_&Hm|vHSvko#*8bBXfNa=^ zgY}Twh$THepB3GPp*7}4XRc(Hbo&U2`psS};`;&TVS(=9faH!V(W zgN7w@lU4GcFw3PZeiS#oq6 z*)`~mrCB@q^$||&`)NPr?9=m;^%U_RF4;kb|xp7}J zZfooY4p$|+2=47nS?q75)=doL_dsS`Q`}3uTc-MOkry(UIE>0-+KgW@S!^Wrsv}qS zkU>eBngo`G=t$x~Hc{<2&D zF~QH)qLUSuDmf}o9sIT7Zy*~x`BGJ5(Q3waCY2bT0B29}4Ch$0Oj!>~4RmXYS`J8X zo)ajaX!tzXy_Hv7*W%t9cMi_3j+6#~&ohlh{ z=e6xoigc?om!n&i6UVqNX^l@pU{P23K|kYvHwypSMMhdPN(P2M*!HiXg0WZ6K08rQ8u{IF*xHa9!;F zH=rxyT%*LFZ2N-L0-wm`^3E!fCokGcd)ewz>fd;FRRl z_$@amYFti_Tc}y9pYpIf!;6Np;E$Xb*GG^ru!47}$M`@=S=zGdZT~xN)Nhtc#yOL# z>Q~*qVWz~7-Igj&L*lwS$sqJ@p~kQ6UcG08lFdSk35VDZ@|Aym;OLnTa=|Hul*_so zP1?#Z+^>R$ecxNTXZ^a@=H4Nr;d5Tnw!O~d)O&4FInS>xreDEJaM?3~le!>S7c5-6 zTA@QbcF#}nHQ0+nt!LV1i+i6s8v)0mr&B}^shH6wo@C(iC(6& ze~&%rLrfmvZd2(|6e-MLeb<$HkDK2?5CXNphhec`U#jS-rQdW0M8g+{>9>=oW=1GI z$ym3puKzdC4MH9Pa9Ohiibu>zR7nHt=N&2Y=ERD#9=2wwjNO?ZNWs*aB;^?`@d42^ zI;hh}aG=tv&a0ncJyM&?!hq+oDwp^$ga?ojxAB3AJgmKJGU`0_)+>g6qNkvG#p0N z${h6N&Pjy$t2LE8pJdGjSK%8d1A}f)!*^vNW(KhZ?5l~SL71B?ZJG@p|9q z@3TR$uT%S)7-_t|i=}-2X+rPHpQkxUK(l6ly|gnzAQzQHYI+fSzgbv^gJh2+ zB3oa44mF?|ejeZHO}OYR?6>_LU)9CdRU(Ga{>seV>@+IA)fbK$H^r_$xSUG&x z4vnywvw>+PL^>UHB@dQ7Vz!av2QNSikHDC4(X_Z}U?MSm7!U)mgln%ha;Su}%*z0Uo$8x;bS5TZQZgVJ>1)he2*p5~pE+-t&&-3VTT_Y%uyPE^r*a01Sa5pPQ#1)E9L3UeyB)Hc0iQ&C92hw51DRQ*O=R;`r zn*v7YHtcS90nnk|%@SjqU>qVkYASzz%#XeG4L|$iB}$1Nm&=1yc}(7n0CdRl-$*`l z(o-RsZ)u zvF)=B@jBm!br|53blkl^MIKKv^J1Ss=~x%qDpyEfzL2BKKl=~7z3Zl zu)ry`{LgM^iMo#`y?C*ouYijVgErcN*W7BMZFbl@SD=-28bAZ--PgNeMd{a|fabXqP@5A@DNRAPzpRdWdTECDHGvn4^89<=lafIn zEARJ4ER-CbS4t&ISK@h-AB3mbt$ph;EX}?@_)aa89Q_9RqBjo-kdA7H7t7YlHKAm5 z#&5B~FpT~Ax+mNB&H|5L^&vg6So@BGX4tz^BL^cf_UHEao(3@BKK*$lhjm3t7y9ub z3N8RWLRl1~P-aFd;#WTm%EpW~7cx90<-w*lchx+-!E+0GxL}RB!GkruHw7i9c>zdl zOpmj3wk9P?0OKegtx#_BQGDByr{FwZ1klo-bW9abu_lwMITP$}x-4dVY+dze16Zq`V zv%;2R8|>^hpgbVr4eFhxbp^4o{^Apiu4LEbzY?v!Yu)qBNPK;HAyAGf3U+7tj-9ufFd2Pmt1=@>;31J zXif^Dik1o}y0a@HRq|s>>6ba;}q)4wFq|`3qr~T#ZRwT~@c6$B8%G-jRKO50D zfB!*!Un8J=Q>+{)rx#-bz-aQN|ClQW)dk!<1Mpw@&%?`S+@>L4O11AbJDhj45BawT zBEiwK!gKAQvw)$5D0o)JbB1mz)#O0Ug3qb#S-B6ypj7)0fdPhpRg})4?2viAHpOQ_ zx$vWe|3&@GF9Qis+B^O8f8Ln=Ei z59G_NNMJ3Cf{A})8*<)zFJ8n$tNiOPy--8>Mi7T@V=}|&rZ1ii3#8>%xw<0JJkVde z&FK4oT#xl{+`P~ys;aKnC=s*|elUmycG9GS_KynM==Dl`m}0yNRIyMYkaEM2$frQl zRL{e|GpbPm)CIq)U($g}P%wZ!+K+PyzBef8JKXT8oA<1&rM&P2BPPovyQn**wfO4E>|ePn(&{4MXUrw!aT_q=r+*~tMw7~QatASZ87s=63 z*P2uF;?qW-ePQpHb7NOu#*WQ2n`7sh3NJselrpSSV@!PwfSi z+lO6*k9o~T+cdwZ@eQ!DCBEL|P+Y9eWT2y&G;{6#@+Y}(%Awo1!ILg{G51|-pGo~# z!`~>0YG!*d6>aIXMgH^gS&2)!gw;W(aZLBcZGpY=a~=R^diqrb*%=N>c83~FS{M~; zV>;bZ-e@bwPnF@6pL=qwo&aZqf+`iDqk6V{PqruM7B~|lVZU;JoMuU}mq62W{#zSG z^O%QJ&VGYT`iRoGYKt+h@?)UVJl)dJ zH}Pwl-E(l_i`2!e;6OrRk}E3vs@7aAO7WLsHfcOnL2%4L@&C;mEAMfugV!#{#M@X! zxtc5)h+#9CV#nq(ESy4)qAuWcn0ofpjeUD&?^wtVEB($8#4c;0(tBBP+dX;{n3xNy zAx9=QdBqVP=1br$G3>6<6zkOtoBRGiWp)}m%CTvG>ZscIi{XYOQzl*8#s4NdXOvtQ zuBRRJXB$X2pruxR0w*R0?gh;w%!BtT``MKC>{yzZY#&dFvAcRbbD~hG89933AS;xcHR#+_ zmP5$-pICi_EVeJrZS+EnTuof(mBwSWnH9qxofeEwMn;`51)9i!%UcC0mogVdzOJIy zBfI|;+z8F80V)M!&A`~2(zwoYs**Hc+S_hIq=O;$&Fv2V&bdNLn4i_23a@ydS@+j{ zfyArypmIL(=k0lyJmpinhb1u*dR`=hjji41^-zYfm}{A=xZWY(SQ*32#i$6t!m~Ja z-zQgHy}BNw;?@XW>y+QQTVwZ8exbU=wOn>ewIyhJm&D7k4vOmRKr}_m+CYZ{c+Yh& zt*W&q1LR!VmuQU&5nB27_JSp9y@Y-7Td&$>D`k1#+c6$0w^vuwU93>+S`=tfdR$%? z$hGuE-uJP$IIAU@g2#&gnp^1`x+T3R_o)WcoAXtHy$tfMgfAsMf!`bE0$oq|;AtUH zshAWvBc;9NbJCofADHH!*d%Qe0 zhSTlSJS&qvROl0`Cx%V7O!e%TI8An*?wRbH&_QWgEh~Vnu6&3SvmfG@ur~!D$J87p zyVz%WajOyUfDf8air~g(Q!SB%Zo7d7pzULtrY~b<7!$~w-6R=2sc)y6?0Jv`9XGc{ zB`Z$cflUq6%q(5O#jV0(XN6&}(}a!3Ya$sm$iXQ?Gg)KeQ~IDTQtMF6_cm@^x#r35 zjH-^w-JTy~#a_MBiaSypoL{DAs$D$A9=EHy%Io9(R$pB2-}}*~x^eRGD)vV%7`r!g z@xnr|)8DTMJ#*79f4+$~l}t}L)K}h}ODeV--HjC7P<;5iQBZZ(;4k5hH_-$?L1kWgS2z4@#obJH>KM zLk;`!RwRgk@rQ0z>ta<`L@&Hz0U?*s3I*~=PAU&vx}R;)bXY!A6`a+M+Xh2c{na(a z%8r3!?3(YvbKe-|1B|CUJ+4r*aWK@E^8xkXq>if%{n($)(LP$n9dy1VdENV3VuB^}*JK4_0*C^Xf?|{9_>46>O;x3YmOE(S)=uBc&hqa33IS2ssD;_Rz@o6a<_ashI-aap^ z{`gR8EA*(v@CZ202>N(+{QB0xe<+=b9PtO+e(@Z;`PLy zFVIhnjTGt2nOPUC`I|`+-kp+WWJ(sh56+a&z$4{RiPGrgziwmKF6< zLk*(Os@6E$BoJkFE;U{8pp1iYUo$&Se4#Isy`IV{W8G#e^?#8Uq{lh(^^ys*AOfNY zV2h%!+ByiV(;qnD=PqnbeIJ)lRHz!=_45Z$wvx58G{)w1LITNB&HocHqIMm{_FJR) zJg%?fWGb~cc9;uS#Md^0UtMt9zVwN6PU%x>-E2s5;HFzJ#JQ*KYHn4L4HQV`(i_kg zra~`PZ*glFQ!m*rFNWl_=Ot+DmODfB(Kj6&xs$(jWpq7=s+fy$p7yKe_|4JldQ|e2 zYPlNEzv17Wx=yFqg)3rtLjzquL-l4dZ~n48-qCwS?PXPKO1;#lx!W|e`93*FpY-PH zuhBg z91+)I*RC%8zIBDtSV_V9FNo0-DeZe5Qgu4Jc3<~`!o#mBf=zQ4eArhq@CdZdF8&YV z>^>SgPAm7@ZtqT;tE9xAQkWBx*}zZNHY;#JA>K+u=^QE=!^e2aAKYhgu!3-?>TIjh zR6d7-;slJR9B;rXr={r}E=ueT6hm|j_YvfHOdlR}tsZyk@~A;)fZDlJZsuYpRp50o RI}G~W($v)`R(tsDe*jmgXYK$1 diff --git a/search.json b/search.json index 446c5af8..4381ef23 100644 --- a/search.json +++ b/search.json @@ -1,59 +1,31 @@ [ { - "objectID": "r4babs2/r4babs2.html", - "href": "r4babs2/r4babs2.html", - "title": "Data Analysis in R for BABS 2", + "objectID": "r4babs2/week-3/overview.html", + "href": "r4babs2/week-3/overview.html", + "title": "Overview", "section": "", - "text": "This is the second of the four BABS modules. Over six weeks you will learn about the logic of hypothesis testing, confidence intervals, what is meant by a statistical model, two-sample tests and one- and two-way analysis of variance (ANOVA).\n\n\nThe BABS2 Module Learning outcomes that relate to the Data Analysis in R content are:\n\nThink creatively to address a Grand Challenge by designing investigations with testable hypotheses and rigorous controls\nAppropriately select classical univariate statistical tests and some non-parametric equivalents to a given scenario and recognise when these are not suitable\nUse R to perform these analyses, reproducibly, on data in a variety of formats and present the results graphically\nCommunicate research in scientific reports and via oral presentation." + "text": "This week you will how to use and interpret the general linear model when the x variable is categorical and has two groups. Just as with single linear regression, the model puts a line of best through data and the model parameters, the intercept and the slope, have the same in interpretation The intercept is one of the group means and the slope is the difference between that, mean and the other group mean. You will also learn about the non-parametric equivalents - the tests we use when the assumptions of the general linear model are not met.\n\nLearning objectives\nThe successful student will be able to:\n\nunderstand the principles of two-sample tests\nappreciate that two-sample tests with lm() are based on the normal distribution and thus have assumptions\nappropriately select parametric and non-parametric two-sample tests\nappropriately select paired and and unpaired two-sample tests\napply and interpret lm()and wilcox.test()\nevaluate whether the assumptions of lm() are met\nscientifically report a two-sample test result including appropriate figures\n\n\n\nInstructions\n\nPrepare\n\n📖 Read Two-Sample tests\n\nWorkshop\n\n💻 Parametric two-sample test\n💻 Non-parametric two-sample test\n💻 Parametric paired-sample test\n\nConsolidate\n\n💻 Appropriately test whether a genetic modification was successful in increasing omega 3 fatty acids in Cannabis sativa.\n💻 …." }, { - "objectID": "r4babs2/r4babs2.html#module-learning-objectives", - "href": "r4babs2/r4babs2.html#module-learning-objectives", - "title": "Data Analysis in R for BABS 2", + "objectID": "r4babs2/week-3/study_before_workshop.html", + "href": "r4babs2/week-3/study_before_workshop.html", + "title": "Independent Study to prepare for workshop", "section": "", - "text": "The BABS2 Module Learning outcomes that relate to the Data Analysis in R content are:\n\nThink creatively to address a Grand Challenge by designing investigations with testable hypotheses and rigorous controls\nAppropriately select classical univariate statistical tests and some non-parametric equivalents to a given scenario and recognise when these are not suitable\nUse R to perform these analyses, reproducibly, on data in a variety of formats and present the results graphically\nCommunicate research in scientific reports and via oral presentation." - }, - { - "objectID": "r4babs2/r4babs2.html#the-logic-of-hypothesis-testing-and-cis", - "href": "r4babs2/r4babs2.html#the-logic-of-hypothesis-testing-and-cis", - "title": "Data Analysis in R for BABS 2", - "section": "The logic of hypothesis testing and CIs", - "text": "The logic of hypothesis testing and CIs" - }, - { - "objectID": "r4babs2/r4babs2.html#introduction-to-statistical-models-single-regression", - "href": "r4babs2/r4babs2.html#introduction-to-statistical-models-single-regression", - "title": "Data Analysis in R for BABS 2", - "section": "Introduction to statistical models: Single regression", - "text": "Introduction to statistical models: Single regression" - }, - { - "objectID": "r4babs2/r4babs2.html#two-sample-tests", - "href": "r4babs2/r4babs2.html#two-sample-tests", - "title": "Data Analysis in R for BABS 2", - "section": "Two-sample tests", - "text": "Two-sample tests" - }, - { - "objectID": "r4babs2/r4babs2.html#one-way-anova-and-kruskal-wallis", - "href": "r4babs2/r4babs2.html#one-way-anova-and-kruskal-wallis", - "title": "Data Analysis in R for BABS 2", - "section": "One-way ANOVA and Kruskal-Wallis", - "text": "One-way ANOVA and Kruskal-Wallis" + "text": "Prepare\n\n📖 Read Two-Sample tests" }, { - "objectID": "r4babs2/r4babs2.html#two-way-anova", - "href": "r4babs2/r4babs2.html#two-way-anova", - "title": "Data Analysis in R for BABS 2", - "section": "Two-way ANOVA", - "text": "Two-way ANOVA" + "objectID": "r4babs2/week-4/overview.html", + "href": "r4babs2/week-4/overview.html", + "title": "Overview", + "section": "", + "text": "Last week you learnt how to use and interpret the general linear model when the x variable was categorical with two groups. You will now extend that to situations when there are more than two groups. This is often known as the one-way ANOVA (analysis of variance). You will also learn about the Kruskal- Wallis test which can be used when the assumptions of the general linear model are not met.\n\nLearning objectives\nThe successful student will be able to:\n\nexplain the rationale behind ANOVA understand the meaning of the F values\nselect, appropriately, one-way ANOVA and Kruskal-Wallis\nknow what functions are used in R to run these tests and how to interpret them\nevaluate whether the assumptions of lm() are met\nscientifically report the results of these tests including appropriate figures\n\n\n\nInstructions\n\nPrepare\n\n📖 Read One-way ANOVA and Kruskal-Wallis\n\nWorkshop\n\n💻 One-way ANOVA\n💻 Kruskal-Wallis\n\nConsolidate\n\n💻 Appropriately test if fitness and acclimation effect the sodium content of sweat\n💻 Appropriately test if insecticides vary in their effectiveness" }, { - "objectID": "r4babs2/r4babs2.html#chi-squared-tests-and-correlation", - "href": "r4babs2/r4babs2.html#chi-squared-tests-and-correlation", - "title": "Data Analysis in R for BABS 2", - "section": "Chi-squared tests and correlation", - "text": "Chi-squared tests and correlation" + "objectID": "r4babs2/week-4/study_before_workshop.html", + "href": "r4babs2/week-4/study_before_workshop.html", + "title": "Independent Study to prepare for workshop", + "section": "", + "text": "Prepare\n\n📖 Read One-way ANOVA and Kruskal-Wallis" }, { "objectID": "r4babs2/week-1/overview.html", @@ -69,6 +41,20 @@ "section": "", "text": "📖 Read The logic of hyothesis testing\n📖 Read Confidence Intervals" }, + { + "objectID": "r4babs2/week-5/overview.html", + "href": "r4babs2/week-5/overview.html", + "title": "Overview", + "section": "", + "text": "This week we will extend of our understanding by learning how to include two categorical explanatory variables in a general linear model. This model is often known as the two-way ANOVA. It has three null hypotheses\n\nLearning objectives\nThe successful student will be able to:\n\ncombine dataframes of the same structure\nselect, appropriately, two-way ANOVA\napply and interpret lm() for a two-way ANOVA\nevaluate whether the assumptions of lm() are met\nunderstand the meaning of the interaction term\nscientifically report a two-way ANOVA result including appropriate figures\n\n\n\nInstructions\n\nPrepare\n\n📖 Read Two-way ANOVA\n\nWorkshop\n\n💻 Two-way ANOVA Choline deficiency on neuron size\n💻 What to do when the assumptions are not met\n\nConsolidate\n\n💻 Appropriately test if the addition of nitrogen and potassium to a crop influences yield and whether they act independently." + }, + { + "objectID": "r4babs2/week-5/study_before_workshop.html", + "href": "r4babs2/week-5/study_before_workshop.html", + "title": "Independent Study to prepare for workshop", + "section": "", + "text": "Prepare\n\n📖 Read Two-way ANOVA" + }, { "objectID": "r4babs2/week-2/overview.html", "href": "r4babs2/week-2/overview.html", @@ -84,137 +70,305 @@ "text": "📖 Read What is a statistical model\n📖 Read Single linear regression" }, { - "objectID": "r4babs2/week-6/overview.html", - "href": "r4babs2/week-6/overview.html", - "title": "Overview", + "objectID": "r4babs2/week-6/workshop.html", + "href": "r4babs2/week-6/workshop.html", + "title": "Workshop", "section": "", - "text": "This week you will\n\nLearning objectives\nThe successful student will be able to:\n\n\n\n\n\n\n\n\nInstructions\n\nPrepare\n\n📖 Read the book OR 📹 Watch two videos\n\nWorkshop\ni.💻\nConsolidate\n\n💻\n📖 Read" + "text": "Artwork by Horst (2023):\n\n\nIn this session you will\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "r4babs2/week-6/study_before_workshop.html", - "href": "r4babs2/week-6/study_before_workshop.html", - "title": "Independent Study to prepare for workshop", + "objectID": "r4babs2/week-6/workshop.html#session-overview", + "href": "r4babs2/week-6/workshop.html#session-overview", + "title": "Workshop", "section": "", - "text": "Prepare\n\nEither 📖 Read xxxxx in OR 📹 Watch" + "text": "In this session you will" }, { - "objectID": "r4babs2/week-5/overview.html", - "href": "r4babs2/week-5/overview.html", - "title": "Overview", + "objectID": "r4babs2/week-6/workshop.html#philosophy", + "href": "r4babs2/week-6/workshop.html#philosophy", + "title": "Workshop", "section": "", - "text": "This week we will extend of our understanding by learning how to include two categorical explanatory variables in a general linear model. This model is often known as the two-way ANOVA. It has three null hypotheses\n\nLearning objectives\nThe successful student will be able to:\n\ncombine dataframes of the same structure\nselect, appropriately, two-way ANOVA\napply and interpret lm() for a two-way ANOVA\nevaluate whether the assumptions of lm() are met\nunderstand the meaning of the interaction term\nscientifically report a two-way ANOVA result including appropriate figures\n\n\n\nInstructions\n\nPrepare\n\n📖 Read Two-way ANOVA\n\nWorkshop\n\n💻 Two-way ANOVA Choline deficiency on neuron size\n💻 What to do when the assumptions are not met\n\nConsolidate\n\n💻 Appropriately test if the addition of nitrogen and potassium to a crop influences yield and whether they act independently." + "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "r4babs2/week-5/study_before_workshop.html", - "href": "r4babs2/week-5/study_before_workshop.html", - "title": "Independent Study to prepare for workshop", + "objectID": "r4babs2/week-6/study_after_workshop.html", + "href": "r4babs2/week-6/study_after_workshop.html", + "title": "Independent Study to consolidate this week", "section": "", - "text": "Prepare\n\n📖 Read Two-way ANOVA" + "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻\n\n\n📖 Read xxx" }, { - "objectID": "r4babs2/week-4/overview.html", - "href": "r4babs2/week-4/overview.html", - "title": "Overview", + "objectID": "pgt52m/week-3/workshop.html", + "href": "pgt52m/week-3/workshop.html", + "title": "Workshop", "section": "", - "text": "Last week you learnt how to use and interpret the general linear model when the x variable was categorical with two groups. You will now extend that to situations when there are more than two groups. This is often known as the one-way ANOVA (analysis of variance). You will also learn about the Kruskal- Wallis test which can be used when the assumptions of the general linear model are not met.\n\nLearning objectives\nThe successful student will be able to:\n\nexplain the rationale behind ANOVA understand the meaning of the F values\nselect, appropriately, one-way ANOVA and Kruskal-Wallis\nknow what functions are used in R to run these tests and how to interpret them\nevaluate whether the assumptions of lm() are met\nscientifically report the results of these tests including appropriate figures\n\n\n\nInstructions\n\nPrepare\n\n📖 Read One-way ANOVA and Kruskal-Wallis\n\nWorkshop\n\n💻 One-way ANOVA\n💻 Kruskal-Wallis\n\nConsolidate\n\n💻 Appropriately test if fitness and acclimation effect the sodium content of sweat\n💻 Appropriately test if insecticides vary in their effectiveness" + "text": "Artwork by Horst (2023): Continuous and Discrete\n\n\nIn this workshop you will learn how to import data from files and create summaries and plots for it. You will also get more practice with working directories, formatting figures and the pipe.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier workshops\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "r4babs2/week-4/study_before_workshop.html", - "href": "r4babs2/week-4/study_before_workshop.html", - "title": "Independent Study to prepare for workshop", + "objectID": "pgt52m/week-3/workshop.html#session-overview", + "href": "pgt52m/week-3/workshop.html#session-overview", + "title": "Workshop", "section": "", - "text": "Prepare\n\n📖 Read One-way ANOVA and Kruskal-Wallis" + "text": "In this workshop you will learn how to import data from files and create summaries and plots for it. You will also get more practice with working directories, formatting figures and the pipe." }, { - "objectID": "r4babs2/week-3/overview.html", - "href": "r4babs2/week-3/overview.html", - "title": "Overview", + "objectID": "pgt52m/week-3/workshop.html#philosophy", + "href": "pgt52m/week-3/workshop.html#philosophy", + "title": "Workshop", "section": "", - "text": "This week you will how to use and interpret the general linear model when the x variable is categorical and has two groups. Just as with single linear regression, the model puts a line of best through data and the model parameters, the intercept and the slope, have the same in interpretation The intercept is one of the group means and the slope is the difference between that, mean and the other group mean. You will also learn about the non-parametric equivalents - the tests we use when the assumptions of the general linear model are not met.\n\nLearning objectives\nThe successful student will be able to:\n\nunderstand the principles of two-sample tests\nappreciate that two-sample tests with lm() are based on the normal distribution and thus have assumptions\nappropriately select parametric and non-parametric two-sample tests\nappropriately select paired and and unpaired two-sample tests\napply and interpret lm()and wilcox.test()\nevaluate whether the assumptions of lm() are met\nscientifically report a two-sample test result including appropriate figures\n\n\n\nInstructions\n\nPrepare\n\n📖 Read Two-Sample tests\n\nWorkshop\n\n💻 Parametric two-sample test\n💻 Non-parametric two-sample test\n💻 Parametric paired-sample test\n\nConsolidate\n\n💻 Appropriately test whether a genetic modification was successful in increasing omega 3 fatty acids in Cannabis sativa.\n💻 …." + "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier workshops\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "r4babs2/week-3/study_before_workshop.html", - "href": "r4babs2/week-3/study_before_workshop.html", - "title": "Independent Study to prepare for workshop", + "objectID": "pgt52m/week-3/workshop.html#importing-data-from-files", + "href": "pgt52m/week-3/workshop.html#importing-data-from-files", + "title": "Workshop", + "section": "Importing data from files", + "text": "Importing data from files\nLast week we created data by typing the values in to R. This is not practical when you have added a lot of data to a spreadsheet, or you are using data file that has been supplied to you by a person or a machine. Far more commonly, we import data from a file into R. This requires you know two pieces of information.\n\n\nWhat format the data are in\nThe format of the data determines what function you will use to import it and the file extension often indicates format.\n\n\n.txt a plain text file1, where the columns are often separated by a space but might also be separated by a tab, a backslash or forward slash, or some other character\n\n.csv a plain text file where the columns are separated by commas\n\n.xlsx an Excel file\n\n\n\nWhere the file is relative to your working directory\nR can only read in a file if you say where it is, i.e., you give its relative path. If you follow the advice in this course, your data will be in a folder, data-raw which is inside your Project folder (and working directory).\n\n\nWe will save the four files for this workshop to our Project folder (week-8) and read them in. We will then create a new folder inside our Project folder called data-raw and move the data files to there before modifying the file paths as required. This is demonstrate how the relative path to the file will change after we move it.\n Save these four files in to your week-8 folder\n\nThe coat colour and mass of 62 cats: cat-coats.csv\n\nThe relative size of over 5000 cells measure by forward scatter (FSC) in flow cytometry: cell-size.txt\n\nThe number of sternopleural bristles on 96 female Drosophila: bristles.txt\n\nThe number of sternopleural bristles on 96 female Drosophila (with technical replicates): bristles-mean.xlsx\n\n\nThe first three files can be read in with core tidyverse Wickham et al. (2019) functions and the last can be read in with the readxl Wickham and Bryan (2023) package.\n Load the two packages\n\nlibrary(tidyverse)\nlibrary(readxl)\n\nWe will first read in cat-coats.csv. A .csv. extension suggests this is plain text file with comma separated columns. However, before we attempt to read it it, when should take a look at it. We can do this from RStudio\n Go to the Files pane (bottom right), click on the cat-coats.csv file and choose View File2\n\n\nRStudio Files Pane\n\nAny plain text file will open in the top left pane (Excel files will launch Excel).\n Is the file csv?\n\n\n What kind of variables does the file contain?\n\n\n Read in the csv file with:\n\ncats <- read_csv(\"cat-coats.csv\")\n\nThe data from the file a read into a dataframe called cats and you will be able to see it in the Environment.\n Click on each of the remaining files and choose View File.\n In each case, say what the format is and what types of variables it contains.\n\n\n\n\n\n\n\n\nWe use the read_table()3 command to read in plain text files of single columns or where the columns are separated by spaces…\n …so in cell-size.txt can be read into a dataframe called cells like this:\n\ncells <- read_table(\"cell-size.txt\")\n\n Now you try reading bristles.txt in to a dataframe called fly_bristles\nThe readxl package we loaded earlier has two useful functions for working with Excel files: excel_sheets(\"filename.xlsx\") will list the sheets in an Excel workbook; read_excel(\"filename.xlsx\") will read in to top sheet or a specified sheet with a small modification read_excel(\"filename.xlsx\", sheet = \"Sheet1\").\n List the the names of the sheets and read in the sheet with the data like this:\n\nexcel_sheets(\"bristles-mean.xlsx\")\nfly_bristles_means <- read_excel(\"bristles-mean.xlsx\", sheet = \"means\")\n\nWell done! You can now read read in from files in your working directory.\nTo help you understand relative file paths, we will now move the data files.\n First remove the dataframes you just created to make it easier to see whether you can successfully read in the files from a different place:\n\nrm(cat_coats, fly_bristles, cells, flies_bristles_means)\n\n Now make a new folder called data-raw. You can do this on the Files Pane by clicking New Folder and typing into the box that appears.\n Check the boxes next to the file names and choose More | Move… and select the data-raw folder.\n The files will move. To import data from files in the data-raw folder, you need to give the relative path to the file from the working directory. The working directory is the Project folder, week-8 so the relative path is data-raw/cat-coats.csv\n Import the cat-coats.csv data like this:\n\ncats <- read_csv(\"data-raw/cat-coats.csv\")\n\n Now you do the other files.\nFrom this point forward in the course, we will always create a data-raw folder each time we make a new Project.\nThese are the most common forms of data file you will encounter at first. However, data can certainly come to you in other formats particularly when they have come from particular software. Usually, there is an R package specially for that format.\nIn the rest of the workshop we will take each dataset in turn and create summaries and plots appropriate for the data types. Data is summarised using the group_by() and summarise() functions" + }, + { + "objectID": "pgt52m/week-3/workshop.html#summarising-discrete-data-cat-coat", + "href": "pgt52m/week-3/workshop.html#summarising-discrete-data-cat-coat", + "title": "Workshop", + "section": "Summarising discrete data: Cat coat", + "text": "Summarising discrete data: Cat coat\nThe most appropriate way to summarise nominal data like the colour of cat coats is to tabulate the number of cats with each colour.\n Summarise the cats dataframe by counting the number of cats in each category\n\ncats |> \n group_by(coat) |> \n count()\n\n# A tibble: 6 × 2\n# Groups: coat [6]\n coat n\n <chr> <int>\n1 black 23\n2 calico 1\n3 ginger 10\n4 tabby 8\n5 tortoiseshell 5\n6 white 15\n\n\n|> is the pipe and can be produced with Ctrl+Shift+M\nThis sort of data might be represented with a barchart. You have two options for producing that barchart:\n\nplot the summary table using geom_col()\nplot the raw data using geom_bar()\n\nWe did the first of these last week. The geom_col() function uses the numbers in a second column to determine how high the bars are. However, the geom_bar() function will do the tabulating for you.\n Plot the coat data using geom_bar:\n\nggplot(cats, aes(x = coat)) +\n geom_bar()\n\n\n\n\nThe gaps that R put automatically between the bars reflects that the coat colours are discrete categories." + }, + { + "objectID": "pgt52m/week-3/workshop.html#summarising-counts-bristles", + "href": "pgt52m/week-3/workshop.html#summarising-counts-bristles", + "title": "Workshop", + "section": "Summarising Counts: Bristles", + "text": "Summarising Counts: Bristles\nCounts are discrete and can be thought of a categories with an order (ordinal).\n Summarise the fly_bristles dataframe by counting the number of flies in each category of bristle number\nSince counts are numbers, we might also want to calculate some summary statistics such as the median and interquartile range.\n Summarise the fly_bristles dataframe by calculate the median and interquartile range\n\nfly_bristles |> \n summarise(median(number),\n IQR(number))\n\n# A tibble: 1 × 2\n `median(number)` `IQR(number)`\n <dbl> <dbl>\n1 6 4\n\n\nAs the interquartile is 4 and the median is 6 then 25% flies have 4 bristles or fewer and 25% have 8 or more.\nThe distribution of counts4 is not symmetrical for lower counts so the mean is not usually a good way to summarise count data.\n If you want to save the table you created and give the columns better names you can make two adjustments:\n\nfly_bristles_summary <- fly_bristles |> \n summarise(med = median(number),\n interquartile = IQR(number))\n\n Plot the bristles data using geom_bar:\nIf counts have a a high mean and big range, like number of hairs on a person’s head, then you can often treat them as continuous. This means you can use statistics like the mean and standard deviation to summarise them, histograms to plot them and use some standard statistical tests on them." + }, + { + "objectID": "pgt52m/week-3/workshop.html#summarising-continuous-data", + "href": "pgt52m/week-3/workshop.html#summarising-continuous-data", + "title": "Workshop", + "section": "Summarising continuous data", + "text": "Summarising continuous data\nCat mass\nThe variable mass in the cats dataframe is continuous. Very many continuous variables have a normal distribution. e normal distribution is also known as the bell-shaped curve. If we had the mass of all the cats in the world, we would find many cats were near the mean and fewer would be away from the mean, either much lighter or much heavier. In fact 68% would be within one standard deviation of the mean and about 96% would be within two standard deviations.\n\n\n\n\n\n We can find the mean mass with:\n\ncats |> \n summarise(mean = mean(mass))\n\n# A tibble: 1 × 1\n mean\n <dbl>\n1 4.51\n\n\nWe can add any sort of summary by placing it inside the the summarise parentheses. Each one is separated by a comma. We did this to find the median and the interquatrile range for fly bristles.\n For example, another way to calculate the number of values is to use the length() function:\n\ncats |> \n summarise(mean = mean(mass),\n n = length(mass))\n\n# A tibble: 1 × 2\n mean n\n <dbl> <int>\n1 4.51 62\n\n\n Adapt the code to calculate the mean, the sample size and the standard deviation (sd())\nA single continuous variable can be plotted using a histogram to show the shape of the distribution.\n Plots a histogram of cats mass:\n\nggplot(cats, aes(x = mass)) +\n geom_histogram(bins = 15, colour = \"black\") \n\n\n\n\nNotice that there are no gaps between the bars which reflects that mass is continuous. bins determines how many groups the variable is divided up into (i.e., the number of bars) and colour sets the colour for the outline of the bars. A sample of 62 is a relatively small number of values for plotting a distribution and the number of bins used determines how smooth or normally distributed the values look.\n Experiment with the number of bins. Does the number of bins affect how you view the distribution.\nNext week we will practice summarise and plotting data files with several variables but just to give you a taste, we will find summary statistics about mass for each of the coat types. The group_by() function is used before the summarise() to do calculations for each of the coats:\n\ncats |> \n group_by(coat) |> \n summarise(mean = mean(mass),\n standard_dev = sd(mass))\n\n# A tibble: 6 × 3\n coat mean standard_dev\n <chr> <dbl> <dbl>\n1 black 4.63 1.33 \n2 calico 2.19 NA \n3 ginger 4.46 1.12 \n4 tabby 4.86 0.444\n5 tortoiseshell 4.50 0.929\n6 white 4.34 1.34 \n\n\nYou can read this as:\n\ntake cats and then group by coat and then summarise by finding the mean of mass and the standard deviation of mass\n\n Why do we get an NA for the standard deviation of the calico cats?\n\n\n\nCells\n Summarise the cells dataframe by calculating the mean, median, sample size and standard deviation of FSC.\n Add a column for the standard error which is given by \\(\\frac{s.d.}{\\sqrt{n}}\\)\nMeans of counts\nMany things are quite difficult to measure or count and in these cases we often do technical replicates. A technical replicate allows us the measure the exact same thing to check how variable the measurement process is. For example, Drosophila are small and counting their sternopleural bristles is tricky. In addition, where a bristle is short (young) or broken scientists might vary in whether they count it. Or people or machines might vary in measuring the concentration of the same solution.\nWhen we do technical replicates we calculate their mean and use that as the measure. This is what is in our fly_bristles_means dataframe - the bristles of each of the 96 flies was counted by 5 people and the data are those means. These has an impact on how we plot and summarise the dataset because the distribution of mean counts is continuous! We can use means, standard deviations and histograms. This will be an exercise in Consolidate." + }, + { + "objectID": "pgt52m/week-3/workshop.html#look-after-future-you", + "href": "pgt52m/week-3/workshop.html#look-after-future-you", + "title": "Workshop", + "section": "Look after future you!", + "text": "Look after future you!\nFuture you is going to summarise and plot data from the “River practicals”. You can make this much easier by documenting what you have done now. At the moment all of your code from this workshop is in a single file, probably called analysis.R. I recommend making a new script for each of nominal, continuous and count data and copying the code which imports, summarises and plots it. This will make it easier for future you to find the code you need. Here is an example: nominal_data.R. You may wish to comment your version much more.\nYou’re finished!" + }, + { + "objectID": "pgt52m/week-3/workshop.html#footnotes", + "href": "pgt52m/week-3/workshop.html#footnotes", + "title": "Workshop", + "section": "Footnotes", + "text": "Footnotes\n\nPlain text files can be opened in notepad or other similar editor and still be readable.↩︎\nDo not be tempted to import data this way. Unless you are careful, your data import will not be scripted or will not be scripted correctly.↩︎\nnote read_csv() and read_table() are the same functions with some different settings.↩︎\nCount data are usually “Poisson” distributed.↩︎" + }, + { + "objectID": "pgt52m/week-3/study_after_workshop.html", + "href": "pgt52m/week-3/study_after_workshop.html", + "title": "Independent Study to consolidate this week", "section": "", - "text": "Prepare\n\n📖 Read Two-Sample tests" + "text": "Set up\nIf you have just opened RStudio you will want to load the packages and import the data.\n\nlibrary(tidyverse)\nlibrary(readxl)\n\n\nfly_bristles_means <- read_excel(\"data-raw/bristles-mean.xlsx\")\ncats <- read_csv(\"data-raw/cat-coats.csv\")\n\nExercises\n\n💻 Summarise the fly_bristles_means dataframe by calculating the mean, median, sample size, standard deviation and standard error of the mean_count variable.\n\n\nCodefly_bristles_means_summary <- fly_bristles_means |> \n summarise(mean = mean(mean_count),\n median = median(mean_count),\n n = length(mean_count),\n standard_dev = sd(mean_count),\n standard_error = standard_dev / sqrt(n))\n\n\n\n💻 Create an appropriate plot to show the distribution of mean_count in fly_bristles_means\n\n\n\nCodeggplot(fly_bristles_means, aes(x = mean_count)) +\n geom_histogram(bins = 10)\n\n\n\n💻 Can you format the plot 2. by removing the grey background, giving the bars a black outline and the fill colour of your choice and improving the axis format and labelling? You may want to refer to last week’s workshop.\n\n\nCodeggplot(fly_bristles_means, aes(x = mean_count)) +\n geom_histogram(bins = 10, \n colour = \"black\",\n fill = \"skyblue\") +\n scale_x_continuous(name = \"Number of bristles\",\n expand = c(0, 0)) +\n scale_y_continuous(name = \"Frequency\",\n expand = c(0, 0),\n limits = c(0, 35)) +\n theme_classic()\n\n\n\n💻 Amend this code to change the order of the bars by the average mass of each coat colour? Changing the order of bars was covered last week. You may also want to practice formatting the graph nicely.\n\n\nggplot(cats, aes(x = coat, y = mass)) +\n geom_boxplot()\n\n\n\n\n\nCodeggplot(cats, \n aes(x = reorder(coat, mass), y = mass)) +\n geom_boxplot(fill = \"darkcyan\") +\n scale_x_discrete(name = \"Coat colour\") +\n scale_y_continuous(name = \"Mass (kg)\", \n expand = c(0, 0),\n limits = c(0, 8)) +\n theme_classic()\n\n\n\n📖 Read Understanding the pipe |>" + }, + { + "objectID": "pgt52m/week-4/workshop.html", + "href": "pgt52m/week-4/workshop.html", + "title": "Workshop", + "section": "", + "text": "Data data Artwork from the Openscapes blog Tidy Data for reproducibility, efficiency, and collaboration by Julia Lowndes and Allison Horst\n\n\nIn this workshop you will learn to summarise and plot datasets with more than one variable and how to write figures to files. You will also get more practice with working directories, importing data, formatting figures and the pipe.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier workshops\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + }, + { + "objectID": "pgt52m/week-4/workshop.html#session-overview", + "href": "pgt52m/week-4/workshop.html#session-overview", + "title": "Workshop", + "section": "", + "text": "In this workshop you will learn to summarise and plot datasets with more than one variable and how to write figures to files. You will also get more practice with working directories, importing data, formatting figures and the pipe." + }, + { + "objectID": "pgt52m/week-4/workshop.html#philosophy", + "href": "pgt52m/week-4/workshop.html#philosophy", + "title": "Workshop", + "section": "", + "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier workshops\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + }, + { + "objectID": "pgt52m/week-4/workshop.html#myoglobin-in-seal-muscle", + "href": "pgt52m/week-4/workshop.html#myoglobin-in-seal-muscle", + "title": "Workshop", + "section": "Myoglobin in seal muscle", + "text": "Myoglobin in seal muscle\nThe myoglobin concentration of skeletal muscle of three species of seal in grams per kilogram of muscle was determined and the data are given in seal.csv. Each row represents an individual seal. The first column gives the myoglobin concentration and the second column indicates species.\nImport\n Save seal.csv to your data-raw folder\n Read the data into a dataframe called seal. . You might want to look up data import from last week.\n What types of variables do you have in the seal dataframe? What role would you expect them to play in analysis?\n\n\n\n\nThe key point here is that the fundamental structure of:\n\none continuous response and one nominal explanatory variable with two groups (adipocytes), and\none continuous response and one nominal explanatory variable with three groups (seals)\n\nis the same! The only thing that differs is the number of groups (the number of values in the nominal variable). This means the code for summarising and plotting is identical except for the variable names!\n\n\n\n\n\n\nTip\n\n\n\nWhen two datasets have the same number of columns and the response variable and the explanatory variables have the same data types then the code you need is the same.\n\n\nSummarise\nSummarising the data for each species is the next sensible step. The most useful summary statistics for a continuous variable like myoglobin are the means, standard deviations, sample sizes and standard errors. You might remember from last week that we use the group_by() and summarise() functions along with the functions that do the calculations.\n Create a data frame called seal_summary that contains the means, standard deviations, sample sizes and standard errors for the control and nicotinic acid treated samples.\n\nseal_summary <- seal %>%\n group_by(species) %>%\n summarise(mean = mean(myoglobin),\n std = sd(myoglobin),\n n = length(myoglobin),\n se = std/sqrt(n))\n\nYou should get the following numbers:\n\n\n\n\nspecies\nmean\nstd\nn\nse\n\n\n\nBladdernose Seal\n42.31600\n8.020634\n30\n1.464361\n\n\nHarbour Seal\n49.01033\n8.252004\n30\n1.506603\n\n\nWeddell Seal\n44.66033\n7.849816\n30\n1.433174\n\n\n\n\n\nVisualise\nMost commonly, we put the explanatory variable on the x axis and the response variable on the y axis. A continuous response, particularly one that follows the normal distribution, is best summarised with the mean and the standard error. In my opinion, you should also show all the raw data points if possible.\nWe are going to create a figure like this:\n\n\n\n\n\nIn this figure, we have the data points themselves which are in seal dataframe and the means and standard errors which are in the seal_summary dataframe. That is, we have two dataframes we want to plot.\nHere you will learn that dataframes and aesthetics can be specified within a geom_xxxx (rather than in the ggplot()). This is very useful if the geom only applies to some of the data you want to plot.\n\n\n\n\n\n\nTip: ggplot()\n\n\n\nYou put the data argument and aes() inside ggplot() if you want all the geoms to use that dataframe and variables. If you want a different dataframe for a geom, put the data argument and aes() inside the geom_xxxx()\n\n\nI will build the plot up in small steps but you should edit your existing ggplot() command as we go.\n Plot the data points first.\n\nggplot() +\n geom_point(data = seal, \n aes(x = species, y = myoglobin))\n\n\n\n\nNotice how we have given the data argument and the aesthetics inside the geom. The variables species and myoglobin are in the seal dataframe\n So the data points don’t overlap, we can add some random jitter in the x direction (edit your existing code):\n\nggplot() +\n geom_point(data = seal, \n aes(x = species, y = myoglobin),\n position = position_jitter(width = 0.1, height = 0))\n\n\n\n\nNote that position = position_jitter(width = 0.1, height = 0) is inside the geom_point() parentheses, after the aes() and a comma.\nWe’ve set the vertical jitter to 0 because, in contrast to the categorical x-axis, movement on the y-axis has meaning (the myoglobin levels).\n Let’s make the points a light grey (edit your existing code):\n\nggplot() +\n geom_point(data = seal, \n aes(x = species, y = myoglobin),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"grey50\")\n\n\n\n\nNow to add the errorbars. These go from one standard error below the mean to one standard error above the mean.\n Add a geom_errorbar() for errorbars (edit your existing code):\n\nggplot() +\n geom_point(data = seal, aes(x = species, y = myoglobin),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"grey50\") +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean - se, ymax = mean + se),\n width = 0.3) \n\n\n\n\nWe have specified the seal_summary dataframe and the variables species, mean and se are in that.\nThere are several ways you could add the mean. You could use geom_point() but I like to use geom_errorbar() again with the ymin and ymax both set to the mean.\n Add a geom_errorbar() for the mean (edit your existing code):\n\nggplot() +\n geom_point(data = seal, aes(x = species, y = myoglobin),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"grey50\") +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean, ymax = mean),\n width = 0.2)\n\n\n\n\n Alter the axis labels and limits using scale_y_continuous() and scale_x_discrete() (edit your existing code):\n\nggplot() +\n geom_point(data = seal, aes(x = species, y = myoglobin),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"grey50\") +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_y_continuous(name = \"Myoglobin (g/kg)\", \n limits = c(0, 80), \n expand = c(0, 0)) +\n scale_x_discrete(name = \"Species\")\n\n\n\n\nYou only need to use scale_y_continuous() and scale_x_discrete() to use labels that are different from those in the dataset. Often this is to use proper terminology and captialisation.\n Format the figure in a way that is more suitable for including in a report using theme_classic() (edit your existing code):\n\nggplot() +\n geom_point(data = seal, aes(x = species, y = myoglobin),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"grey50\") +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_y_continuous(name = \"Myoglobin (g/kg)\", \n limits = c(0, 80), \n expand = c(0, 0)) +\n scale_x_discrete(name = \"Species\") +\n theme_classic()\n\n\n\n\nWriting figures to file\n Make a new folder called figures.\n Edit you ggplot code so that you assign the figure to a variable.\n\nsealfig <- ggplot() +\n geom_point(data = seal, aes(x = species, y = myoglobin),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"grey50\") +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_y_continuous(name = \"Myoglobin (g/kg)\", \n limits = c(0, 80), \n expand = c(0, 0)) +\n scale_x_discrete(name = \"Species\") +\n theme_classic()\n\nThe figure won’t be shown in the Plots tab - the output has gone into sealfig rather than to the Plots tab. To make it appear in the Plots tab type sealfig\n The ggsave() command will write a ggplot figure to a file:\n\nggsave(\"figures/seal-muscle.png\",\n plot = sealfig,\n device = \"png\",\n width = 4,\n height = 3,\n units = \"in\",\n dpi = 300)\n\nfiguresseal-muscle.png is the name of the file, including the relative path.\n Look up ggsave() in the manual to understand the arguments. You can do this by putting your cursor on the command and pressing F1" + }, + { + "objectID": "pgt52m/week-4/workshop.html#pigeons", + "href": "pgt52m/week-4/workshop.html#pigeons", + "title": "Workshop", + "section": "Pigeons", + "text": "Pigeons\nThe data in pigeon.txt are 40 measurements of interorbital width (in mm) for two populations of domestic pigeons measured to the nearest 0.1mm\n\n\nInterorbital width is the distance between the eyes\n\nImport\n Save pigeon.txt to your data-raw folder\n Read the data into a dataframe called pigeons.\n What variables are there in the pigeons dataframe?\n\n\n\n\nHummmm, these data are not organised like the other data sets we have used. The population is given as the column names and the interorbital distances for one population are given in a different column than those for the other population. The first row has data from two pigeons which have nothing in common, they just happen to be the first individual recorded in each population.\n\n\n\n\n\nA\nB\n\n\n\n12.4\n12.6\n\n\n11.2\n11.3\n\n\n11.6\n12.1\n\n\n12.3\n12.2\n\n\n11.8\n11.8\n\n\n10.7\n11.5\n\n\n11.3\n11.2\n\n\n11.6\n11.9\n\n\n12.3\n11.2\n\n\n10.5\n12.1\n\n\n12.1\n11.9\n\n\n10.4\n10.7\n\n\n10.8\n11.0\n\n\n11.9\n12.2\n\n\n10.9\n12.6\n\n\n10.8\n11.6\n\n\n10.4\n10.7\n\n\n12.0\n12.4\n\n\n11.7\n11.8\n\n\n11.3\n11.1\n\n\n11.5\n12.9\n\n\n11.8\n11.9\n\n\n10.3\n11.1\n\n\n10.3\n12.2\n\n\n11.5\n11.8\n\n\n10.7\n11.5\n\n\n11.3\n11.2\n\n\n11.6\n11.9\n\n\n13.3\n11.2\n\n\n10.7\n11.1\n\n\n12.1\n11.6\n\n\n10.2\n12.7\n\n\n10.8\n11.0\n\n\n11.4\n12.2\n\n\n10.9\n11.3\n\n\n10.3\n11.6\n\n\n10.4\n12.2\n\n\n10.0\n12.4\n\n\n11.2\n11.3\n\n\n11.3\n11.1\n\n\n\n\n\n\n\nThis data is not in ‘tidy’ format (Wickham 2014).\nTidy format has variables in column and observations in rows. All of the distance measurements should be in one column and a second column should give the population.\n\n\n\n\n\npopulation\ndistance\n\n\n\nA\n12.4\n\n\nB\n12.6\n\n\nA\n11.2\n\n\nB\n11.3\n\n\nA\n11.6\n\n\nB\n12.1\n\n\nA\n12.3\n\n\nB\n12.2\n\n\nA\n11.8\n\n\nB\n11.8\n\n\nA\n10.7\n\n\nB\n11.5\n\n\nA\n11.3\n\n\nB\n11.2\n\n\nA\n11.6\n\n\nB\n11.9\n\n\nA\n12.3\n\n\nB\n11.2\n\n\nA\n10.5\n\n\nB\n12.1\n\n\nA\n12.1\n\n\nB\n11.9\n\n\nA\n10.4\n\n\nB\n10.7\n\n\nA\n10.8\n\n\nB\n11.0\n\n\nA\n11.9\n\n\nB\n12.2\n\n\nA\n10.9\n\n\nB\n12.6\n\n\nA\n10.8\n\n\nB\n11.6\n\n\nA\n10.4\n\n\nB\n10.7\n\n\nA\n12.0\n\n\nB\n12.4\n\n\nA\n11.7\n\n\nB\n11.8\n\n\nA\n11.3\n\n\nB\n11.1\n\n\nA\n11.5\n\n\nB\n12.9\n\n\nA\n11.8\n\n\nB\n11.9\n\n\nA\n10.3\n\n\nB\n11.1\n\n\nA\n10.3\n\n\nB\n12.2\n\n\nA\n11.5\n\n\nB\n11.8\n\n\nA\n10.7\n\n\nB\n11.5\n\n\nA\n11.3\n\n\nB\n11.2\n\n\nA\n11.6\n\n\nB\n11.9\n\n\nA\n13.3\n\n\nB\n11.2\n\n\nA\n10.7\n\n\nB\n11.1\n\n\nA\n12.1\n\n\nB\n11.6\n\n\nA\n10.2\n\n\nB\n12.7\n\n\nA\n10.8\n\n\nB\n11.0\n\n\nA\n11.4\n\n\nB\n12.2\n\n\nA\n10.9\n\n\nB\n11.3\n\n\nA\n10.3\n\n\nB\n11.6\n\n\nA\n10.4\n\n\nB\n12.2\n\n\nA\n10.0\n\n\nB\n12.4\n\n\nA\n11.2\n\n\nB\n11.3\n\n\nA\n11.3\n\n\nB\n11.1\n\n\n\n\n\n\n\nData which is in tidy format is easier to summarise, analyses and plot because the organisation matches the conceptual structure of the data:\n\nit is more obvious what the variables are because they columns are named with them - in the untidy format, that the measures are distances is not clear and what A and B are isn’t clear\nit is more obvious that there is no relationship between any of the pigeons except for population\nfunctions are designed to work with variables in columns\nTidying data\nWe can put this data in such a format with the pivot_longer() function from the tidyverse:\npivot_longer() collects the values from specified columns (cols) into a single column (values_to) and creates a column to indicate the group (names_to).\n Put the data in tidy format:\n\npigeons <- pivot_longer(data = pigeons, \n cols = everything(), \n names_to = \"population\", \n values_to = \"distance\")\n\nWe have overwritten the original dataframe. If you wanted to keep the original you would need to give a new name on the left side of the assignment <- Note: the data in the file are unchanged." + }, + { + "objectID": "pgt52m/week-4/workshop.html#ulna-and-height", + "href": "pgt52m/week-4/workshop.html#ulna-and-height", + "title": "Workshop", + "section": "Ulna and height", + "text": "Ulna and height\nThe datasets we have used up to this point, have had a continuous variable and a categorical variable where it makes sense to summarise the response for each of the different groups in the categorical variable and plot the response on the y-axis. We will now summarise a dataset with two continuous variables. The data in height.txt are the ulna length (cm) and height (m) of 30 people. In this case, it is more appropriate to summarise both of thee variables and to plot them as a scatter plot.\nWe will use summarise() again but we do not need the group_by() function this time. We will also need to use each of the summary functions, such as mean(), twice, once for each variable.\nImport\n Save height.txt to your data-raw folder\n Read the data into a dataframe called ulna_heights.\nSummarise\n Create a data frame called ulna_heights_summary that contains the sample size and means, standard deviations and standard errors for both variables.\n\nulna_heights_summary <- ulna_heights %>%\n summarise(n = length(ulna),\n mean_ulna = mean(ulna),\n std_ulna = sd(ulna),\n se_ulna = std_ulna/sqrt(n),\n mean_height = mean(height),\n std_height = sd(height),\n se_height = std_height/sqrt(n))\n\nYou should get the following numbers:\n\n\n\n\nn\nmean_ulna\nstd_ulna\nse_ulna\nmean_height\nstd_height\nse_height\n\n\n30\n24.72\n4.137332\n0.75537\n1.494\n0.2404823\n0.0439059\n\n\n\n\nVisualise\nTo plot make a scatter plot we need to use geom_point() again but without any scatter. In this case, it does not really matter which variable is on the x-axis and which is on the y-axis.\n Make a simple scatter plot\n\nggplot(data = ulna_heights, aes(x = ulna, y = height)) +\n geom_point()\n\n\n\n\nIf you have time, you may want to format the figure more appropriately.\n\n\nYou’re finished!" + }, + { + "objectID": "pgt52m/week-4/study_after_workshop.html", + "href": "pgt52m/week-4/study_after_workshop.html", + "title": "Independent Study to consolidate this week", + "section": "", + "text": "Set up\nIf you have just opened RStudio you will want to load the packages and import the data.\n\nlibrary(tidyverse)\nlibrary(readxl)\n\n\n💻 Summarise and plot the pigeons dataframe appropriately.\n\n\nCode# import\npigeons <- read_table(\"data-raw/pigeon.txt\")\n\n# reformat to tidy\npigeons <- pivot_longer(data = pigeons, \n cols = everything(), \n names_to = \"population\", \n values_to = \"distance\")\n\n# sumnmarise\npigeons_summary <- pigeons %>%\n group_by(population) %>%\n summarise(mean = mean(distance),\n std = sd(distance),\n n = length(distance),\n se = std/sqrt(n))\n# plot\nggplot() +\n geom_point(data = pigeons, aes(x = population, y = distance),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"gray50\") +\n geom_errorbar(data = pigeons_summary, \n aes(x = population, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = pigeons_summary, \n aes(x = population, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_y_continuous(name = \"Interorbital distance (mm)\", \n limits = c(0, 14), \n expand = c(0, 0)) +\n scale_x_discrete(name = \"Population\") +\n theme_classic()\n\n\n\n\n💻 The data in blood.csv are measurements of several blood parameters from fifty people with Crohn’s disease, a lifelong condition where parts of the digestive system become inflamed. Twenty-five of people are in the early stages of diagnosis and 25 have started treatment. The variables in the dataset are:\n\nsodium - Sodium concentration in umol/L, the average of 5 technical replicates\npotassium - Potassium concentration in umol/L, the average of 5 technical replicates\nB12 Vitamin - B12 in pmol/L, the average of 5 technical replicates\nwbc - White blood cell count in 10^9 /L, the average of 5 technical replicates\nrbc count - Red blood cell count in 10^12 /L, the average of 5 technical replicates\nplatlet count - platlet count in 10^9 /L, the average of 5 technical replicates\ninflammation marker - the presence or absence of a marker of inflammation, either 0 or 1\nstatus - whether the individual is before or after treatment.\n\nYour task is to summarise and plot these data in any suitable way. Create a complete RStudio Project for an analysis of these data. You will need to:\n\nMake a new project\nMake folders for data and for figures\nImport the data\nSummarise and plot variables of your choice. It doesn’t matter what you chose - the goal is the practice the project workflow and selecting appropriate plotting and summarising methods for particular data sets." + }, + { + "objectID": "pgt52m/week-1/workshop.html", + "href": "pgt52m/week-1/workshop.html", + "title": "Workshop", + "section": "", + "text": "There is no formal workshop this week but you might want to install R and RStudio on your own machine. This is optional because University computers already have R and RStudio installed.\nInstall R and RStudio.\nNote you need a computer - not a tablet." + }, + { + "objectID": "pgt52m/week-1/study_after_workshop.html", + "href": "pgt52m/week-1/study_after_workshop.html", + "title": "Independent Study to consolidate this week", + "section": "", + "text": "There is no additional study this week but you may want to look ahead to next week." + }, + { + "objectID": "pgt52m/week-5/workshop.html", + "href": "pgt52m/week-5/workshop.html", + "title": "Workshop", + "section": "", + "text": "Artwork by Horst (2023): “love this class”\n\n\nIn this session you will remind yourself how to import files, and calculate confidence intervals on large and small samples.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + }, + { + "objectID": "pgt52m/week-5/workshop.html#session-overview", + "href": "pgt52m/week-5/workshop.html#session-overview", + "title": "Workshop", + "section": "", + "text": "In this session you will remind yourself how to import files, and calculate confidence intervals on large and small samples." + }, + { + "objectID": "pgt52m/week-5/workshop.html#philosophy", + "href": "pgt52m/week-5/workshop.html#philosophy", + "title": "Workshop", + "section": "", + "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + }, + { + "objectID": "pgt52m/week-5/workshop.html#remind-yourself-how-to-import-files", + "href": "pgt52m/week-5/workshop.html#remind-yourself-how-to-import-files", + "title": "Workshop", + "section": "Remind yourself how to import files!", + "text": "Remind yourself how to import files!\nImporting data from files was covered in a previous workshop (Rand 2023) if you need to remind yourself." + }, + { + "objectID": "pgt52m/week-5/workshop.html#confidence-intervals-large-samples", + "href": "pgt52m/week-5/workshop.html#confidence-intervals-large-samples", + "title": "Workshop", + "section": "Confidence intervals (large samples)", + "text": "Confidence intervals (large samples)\nThe data in beewing.txt are left wing widths of 100 honey bees (mm). The confidence interval for large samples is given by:\n\\(\\bar{x} \\pm 1.96 \\times s.e.\\)\nWhere 1.96 is the quantile for 95% confidence.\n Save beewing.txt to your data-raw folder.\n Read in the data and check the structure of the resulting dataframe.\n Calculate and assign to variables: the mean, standard deviation and standard error:\n\n# mean\nm <- mean(bee$wing)\n\n# standard deviation\nsd <- sd(bee$wing)\n\n# sample size (needed for the se)\nn <- length(bee$wing)\n\n# standard error\nse <- sd / sqrt(n)\n\n To calculate the 95% confidence interval we need to look up the quantile (multiplier) using qnorm()\n\nq <- qnorm(0.975)\n\nThis should be about 1.96.\n Now we can use it in our confidence interval calculation\n\nlcl <- m - q * se\nucl <- m + q * se\n\n Print the values\n\nlcl\n\n[1] 4.473176\n\nucl\n\n[1] 4.626824\n\n\nThis means we are 95% confident the population mean lies between 4.47 mm and 4.63 mm. The usual way of expressing this is that the mean is 4.55 +/- 0.07 mm\n Between what values would you be 99% confident of the population mean being?" + }, + { + "objectID": "pgt52m/week-5/workshop.html#confidence-intervals-small-samples", + "href": "pgt52m/week-5/workshop.html#confidence-intervals-small-samples", + "title": "Workshop", + "section": "Confidence intervals (small samples)", + "text": "Confidence intervals (small samples)\nThe confidence interval for small samples is given by:\n\\(\\bar{x} \\pm \\sf t_{[d.f]} \\times s.e.\\)\nThe only difference between the calculation for small and large sample is the multiple. For large samples we use the “the standard normal distribution” accessed with qnorm(); for small samples we use the “t distribution” assessed with qt().The value returned by q(t) is larger than that returned by qnorm() which reflects the greater uncertainty we have on estimations of population means based on small samples.\nThe fatty acid Docosahexaenoic acid (DHA) is a major component of membrane phospholipids in nerve cells and deficiency leads to many behavioural and functional deficits. The cross sectional area of neurons in the CA 1 region of the hippocampus of normal rats is 155 \\(\\mu m^2\\). A DHA deficient diet was fed to 8 animals and the cross sectional area (csa) of neurons is given in neuron.txt\n Save neuron.txt to your data-raw folder\n Read in the data and check the structure of the resulting dataframe\n Assign the mean to m.\n Calculate and assign the standard error to se.\nTo work out the confidence interval for our sample mean we need to use the t distribution because it is a small sample. This means we need to determine the degrees of freedom (the number in the sample minus one).\n We can assign this to a variable, df, using:\n\ndf <- length(neur$csa) - 1\n\n The t value is found by:\n\nt <- qt(0.975, df = df)\n\nNote that we are using qt() rather than qnorm() but that the probability, 0.975, used is the same. Finally, we need to put our mean, standard error and t value in the equation. \\(\\bar{x} \\pm \\sf t_{[d.f]} \\times s.e.\\).\n The upper confidence limit is:\n\n(m + t * se) |> round(2)\n\n[1] 151.95\n\n\nThe first part of the command, (m + t * se) calculates the upper limit. This is ‘piped’ in to the round() function to round the result to two decimal places.\n Calculate the lower confidence limit:\n Given the upper and lower confidence values for the estimate of the population mean, what do you think about the effect of the DHA deficient diet?\n\n\n\n\nYou’re finished!" + }, + { + "objectID": "pgt52m/week-5/study_after_workshop.html", + "href": "pgt52m/week-5/study_after_workshop.html", + "title": "Independent Study to consolidate this week", + "section": "", + "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 Adiponectin is exclusively secreted from adipose tissue and modulates a number of metabolic processes. Nicotinic acid can affect adiponectin secretion. 3T3-L1 adipocytes were treated with nicotinic acid or with a control treatment and adiponectin concentration (pg/mL) measured. The data are in adipocytes.txt. Each row represents an independent sample of adipocytes and the first column gives the concentration adiponectin and the second column indicates whether they were treated with nicotinic acid or not. Estimate the mean Adiponectin concentration in each group - this means calculate the sample mean and construct a confidence interval around it for each group. This exercise forces you to bring together ideas from this workshop and from previous workshops\n\n\nHow to calculate a confidence intervals (this workshop)\n\nHow to summarise variables in more than one group (previous workshop)\n\n\nCode# data import\nadip <- read_table(\"data-raw/adipocytes.txt\")\n\n# examine the structure\nstr(adip)\n\n# summarise\nadip_summary <- adip %>% \n group_by(treatment) %>% \n summarise(mean = mean(adiponectin),\n sd = sd(adiponectin),\n n = length(adiponectin),\n se = sd/sqrt(n),\n dif = qt(0.975, df = n - 1) * se,\n lower_ci = mean - dif,\n uppp_ci = mean + dif)\n\n\n# we conclude we're 95% certain the mean for the control group is \n# between 4.73 and 6.36 and the mean for the nicotinic group is \n# between 6.52 and 8.50. More usually we might put is like this:\n# the mean for the control group is 5.55 +/- 0.82 and that for the nicotinic group is 7.51 +/- 0.99" + }, + { + "objectID": "pgt52m/pgt52m.html", + "href": "pgt52m/pgt52m.html", + "title": "52M Data Analysis in R", + "section": "", + "text": "This module introduces you to data analysis in R. The first 4 weeks covers core concepts about scientific computing, types of variable, the role of variables in analysis and how to use RStudio to organise analysis and import, summarise and plot data. In weeks 5 to 8, you will learn about the logic of hypothesis testing, confidence intervals, what is meant by a statistical model, two-sample tests and one-way analysis of variance (ANOVA). You will learn how to write reproducible reports in Quarto in weeks 9 and 10. Finally, there will be a drop-in for your questions in week 11.\nThis module complement the work you will do in BIO00070M Research, Professional and Team Skills where you will you will learn how to organise reproducible data analyses using a project-oriented workflow and analyses RNA sequence data. It will be important to use the skills and tools you learn in 52M and apply them in 70M.\n\n\nThe Module Learning outcomes are:\n\nExplain the purpose of data analysis and the rationale for scripting analysis in the biosciences\nRecognise when statistics such as t-tests, one-way ANOVA, correlation and regression can be applied, and use R to perform these analyses on data in a variety of formats\nSummarise data in single or multiple groups, recognise tidy data formats, and carry out some typical data tidying tasks\nUse markdown (through Quarto) to produce reproducible analyses, figures and reports" }, { - "objectID": "pgt52m/week-7/study_after_workshop.html", - "href": "pgt52m/week-7/study_after_workshop.html", - "title": "Independent Study to consolidate this week", + "objectID": "pgt52m/pgt52m.html#module-learning-objectives", + "href": "pgt52m/pgt52m.html#module-learning-objectives", + "title": "52M Data Analysis in R", "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 Plant Biotech. Some plant biotechnologists are trying to increase the quantity of omega 3 fatty acids in Cannabis sativa. They have developed a genetically modified line using genes from Linum usitatissimum (linseed). They grow 50 wild type and fifty modified plants to maturity, collect the seeds and determine the amount of omega 3 fatty acids. The data are in csativa.txt. Do you think their modification has been successful?\n\n\nCodecsativa <- read_table(\"data-raw/csativa.txt\")\nstr(csativa)\n\n# First realise that this is a two sample test. You have two independent samples\n# - there are a total of 100 different plants and the values in one \n# group have no relationship to the values in the other.\n\n\n\nCode# create a rough plot of the data \nggplot(data = csativa, aes(x = plant, y = omega)) +\n geom_violin()\nCode# note the modified plants seem to have lower omega!\n\n\n\nCode# create a summary of the data\ncsativa_summary <- csativa %>%\n group_by(plant) %>%\n summarise(mean = mean(omega),\n std = sd(omega),\n n = length(omega),\n se = std/sqrt(n))\n\n\n\nCode# The data seem to be continuous so it is likely that a parametric test will be fine\n# we will check the other assumptions after we have run the lm\n\n# build the statistical model\nmod <- lm(data = csativa, omega ~ plant)\n\n\n# examine it\nsummary(mod)\n# So there is a significant difference but you need to make sure you know the direction!\n# Wild plants have a significantly higher omega 3 content (mean +/- s.e = 56.41 +/- 1.11) \n# than modified plants (49.46 +/- 0.82)(t = 5.03; d.f. = 98; p < 0.0001).\n\n\n\nCode# let's check the assumptions\nplot(mod, which = 1) \nCode# we're looking for the variance in the residuals to be the same in both groups.\n# This looks OK. Maybe a bit higher in the wild plants (with the higher mean)\n \nhist(mod$residuals)\nCodeshapiro.test(mod$residuals)\n# On balance the use of lm() is probably justifiable The variance isn't quite equal \n# and the histogram looks a bit off normal but the normality test is NS and the \n# effect (in the figure) is clear.\n\n\n\nCode# A figure \nfig1 <- ggplot() +\n geom_point(data = csativa, aes(x = plant, y = omega),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"gray50\") +\n geom_errorbar(data = csativa_summary, \n aes(x = plant, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = csativa_summary, \n aes(x = plant, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_x_discrete(name = \"Plant type\", labels = c(\"GMO\", \"WT\")) +\n scale_y_continuous(name = \"Amount of Omega 3 (units)\",\n expand = c(0, 0),\n limits = c(0, 90)) +\n annotate(\"segment\", x = 1, xend = 2, \n y = 80, yend = 80,\n colour = \"black\") +\n annotate(\"text\", x = 1.5, y = 85, \n label = expression(italic(p)~\"< 0.001\")) +\n theme_classic()\n\n# save figure to figures/csativa.png\nggsave(\"figures/csativa.png\",\n plot = fig1,\n width = 3.5,\n height = 3.5,\n units = \"in\",\n dpi = 300)\n\n\n\n💻 another example" + "text": "The Module Learning outcomes are:\n\nExplain the purpose of data analysis and the rationale for scripting analysis in the biosciences\nRecognise when statistics such as t-tests, one-way ANOVA, correlation and regression can be applied, and use R to perform these analyses on data in a variety of formats\nSummarise data in single or multiple groups, recognise tidy data formats, and carry out some typical data tidying tasks\nUse markdown (through Quarto) to produce reproducible analyses, figures and reports" }, { - "objectID": "pgt52m/week-7/workshop.html", - "href": "pgt52m/week-7/workshop.html", - "title": "Workshop", - "section": "", - "text": "Artwork by Horst (2023): “How much I think I know about R”\n\n\nIn this workshop you will get practice in choosing between, performing, and presenting the results of, two-sample tests and their non-parametric equivalents in R.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "objectID": "pgt52m/pgt52m.html#week-1-understanding-file-systems", + "href": "pgt52m/pgt52m.html#week-1-understanding-file-systems", + "title": "52M Data Analysis in R", + "section": "Week 1: Understanding file systems", + "text": "Week 1: Understanding file systems\nYou will learn about operating systems, files and file systems, working directories, absolute and relative paths, what R and RStudio are" }, { - "objectID": "pgt52m/week-7/workshop.html#session-overview", - "href": "pgt52m/week-7/workshop.html#session-overview", - "title": "Workshop", - "section": "", - "text": "In this workshop you will get practice in choosing between, performing, and presenting the results of, two-sample tests and their non-parametric equivalents in R." + "objectID": "pgt52m/pgt52m.html#week-2-introduction-to-r-and-project-organisation", + "href": "pgt52m/pgt52m.html#week-2-introduction-to-r-and-project-organisation", + "title": "52M Data Analysis in R", + "section": "Week 2: Introduction to R and project organisation", + "text": "Week 2: Introduction to R and project organisation\nYou will start writing R code in RStudio and will create your first graph! You will learn about data types such as “numerics” and “characters” and some of the different types of objects in R such as “vectors” and “dataframes”. These are the building blocks for the rest of your R journey. You will also learn a workflow and about the layout of RStudio and using RStudio Projects." }, { - "objectID": "pgt52m/week-7/workshop.html#philosophy", - "href": "pgt52m/week-7/workshop.html#philosophy", - "title": "Workshop", - "section": "", - "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "objectID": "pgt52m/pgt52m.html#week-3-types-of-variable-summarising-and-plotting-data", + "href": "pgt52m/pgt52m.html#week-3-types-of-variable-summarising-and-plotting-data", + "title": "52M Data Analysis in R", + "section": "Week 3: Types of variable, summarising and plotting data", + "text": "Week 3: Types of variable, summarising and plotting data\nThe type of values our data can take is important in how we analyse and visualise it. This week you will learn the difference between continuous and discrete values and how we summarise and visualise them. The focus will be on plotting and summarising single variables. You will also learn how to read in data in to RStudio from plain text files and Excel files." }, { - "objectID": "pgt52m/week-7/workshop.html#adiponectin-secretion", - "href": "pgt52m/week-7/workshop.html#adiponectin-secretion", - "title": "Workshop", - "section": "Adiponectin secretion", - "text": "Adiponectin secretion\nAdiponectin is exclusively secreted from adipose tissue and modulates a number of metabolic processes. Nicotinic acid can affect adiponectin secretion. 3T3-L1 adipocytes were treated with nicotinic acid or with a control treatment and adiponectin concentration (pg/mL) measured. The data are in adipocytes.txt. Each row represents an independent sample of adipocytes and the first column gives the concentration adiponectin and the second column indicates whether they were treated with nicotinic acid or not.\n Save a copy of adipocytes.txt to data-raw\n Read in the data and check the structure. I used the name adip for the dataframe/tibble.\nWe have a tibble containing two variables: adiponectin is the response and is continuous and treatment is explanatory. treatment is categorical with two levels (groups). The first task is visualise the data to get an overview. For continuous response variables with categorical explanatory variables you could use geom_point(), geom_boxplot() or a variety of other geoms. I often use geom_violin() which allows us to see the distribution - the violin is fatter where there are more data points.\n Do a quick plot of the data:\n\nggplot(data = adip, aes(x = treatment, y = adiponectin)) +\n geom_violin()\n\n\n\n\nSummarising the data\nSummarising the data for each treatment group is the next sensible step. The most useful summary statistics are the means, standard deviations, sample sizes and standard errors.\n Create a data frame called adip_summary that contains the means, standard deviations, sample sizes and standard errors for the control and nicotinic acid treated samples. You may need to the Summarise from the Week 4 workshop\nYou should get the following numbers:\n\n\n\n\ntreatment\nmean\nstd\nn\nse\n\n\n\ncontrol\n5.546000\n1.475247\n15\n0.3809072\n\n\nnicotinic\n7.508667\n1.793898\n15\n0.4631824\n\n\n\n\n\nSelecting a test\n Do you think this is a paired-sample test or two-sample test?\n\n\n\n\nApplying, interpreting and reporting\n Create a two-sample model like this:\n\nmod <- lm(data = adip,\n adiponectin ~ treatment)\n\n Examine the model with:\n\nsummary(mod)\n\n\nCall:\nlm(formula = adiponectin ~ treatment, data = adip)\n\nResiduals:\n Min 1Q Median 3Q Max \n-4.3787 -1.0967 0.1927 1.0245 3.1113 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 5.5460 0.4240 13.079 1.9e-13 ***\ntreatmentnicotinic 1.9627 0.5997 3.273 0.00283 ** \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 1.642 on 28 degrees of freedom\nMultiple R-squared: 0.2767, Adjusted R-squared: 0.2509 \nF-statistic: 10.71 on 1 and 28 DF, p-value: 0.00283\n\n\n What do you conclude from the test? Write your conclusion in a form suitable for a report.\n\n\n\n\nCheck assumptions\nThe assumptions of the general linear model are that the residuals – the difference between predicted value (i.e., the group mean) and observed values - are normally distributed and have homogeneous variance. To check these we can examine the mod$residuals variable. You may want to refer to Checking assumptions in the “Single regression” workshop.\n Plot the model residuals against the fitted values.\n What to you conclude?\n\n\n\nTo examine normality of the model residuals we can plot them as a histogram and do a normality test on them.\n Plot a histogram of the residuals.\n Use the shapiro.test() to test the normality of the model residuals\n What to you conclude?\n\n\n\n\nIllustrating\n Create a figure like the one below. You may need to refer to Visualise from the “Summarising data with several variables” workshop (Rand 2023)\n\n\n\n\n\nWe now need to annotate the figure with the results from the statistical test. This most commonly done with a line linking the means being compared and the p-value. The annotate() function can be used to draw the line and then to add the value. The line is a segment and the p-value is a text.\n Add annotation to the figure by adding:\n...... +\n annotate(\"segment\", x = 1, xend = 2, \n y = 11.3, yend = 11.3,\n colour = \"black\") +\n annotate(\"text\", x = 1.5, y = 11.7, \n label = expression(italic(p)~\"= 0.003\")) +\n theme_classic()\n\n\n\n\n\nFor the segment, annotate() needs the x and y coordinates for the start and the finish of the line.\nThe use of expression() allows you to specify formatting or special characters. expression() takes strings or LaTeX formatting. Each string or piece of LaTeX is separated by a * or a ~. The * concatenates the strings without a space, ~ does so with a space. It will generate a warning message “In is.na(x) : is.na() applied to non-(list or vector) of type ‘expression’” which can be ignored.\n Save your figure to your figures folder." + "objectID": "pgt52m/pgt52m.html#week-4-summarising-data-with-several-variables", + "href": "pgt52m/pgt52m.html#week-4-summarising-data-with-several-variables", + "title": "52M Data Analysis in R", + "section": "Week 4: Summarising data with several variables", + "text": "Week 4: Summarising data with several variables\nThis week you will start plotting data sets with more than one variable. This means you need to be able determine which variable is the response and which is the explanatory. You will find out what is meant by “tidy” data and how to perform a simple data tidying task. Finally you will discover how to save your figures and place them in documents." }, { - "objectID": "pgt52m/week-7/workshop.html#grouse-parasites", - "href": "pgt52m/week-7/workshop.html#grouse-parasites", - "title": "Workshop", - "section": "Grouse Parasites", - "text": "Grouse Parasites\nGrouse livers were dissected and the number of individuals of a parasitic nematode were counted for two estates ‘Gordon’ and ‘Moss’. We want to know if the two estates have different infection rates. The data are in grouse.csv\n Save a copy of grouse.csv to data-raw\n Read in the data and check the structure. I used the name grouse for the dataframe/tibble.\nSelecting\n Using your common sense, do these data look normally distributed?\n\n\n\n What test do you suggest?\n\n\nApplying, interpreting and reporting\n Summarise the data by finding the median of each group:\n Carry out a two-sample Wilcoxon test (also known as a Mann-Whitney):\n\nwilcox.test(data = grouse, nematodes ~ estate)\n\n\n Wilcoxon rank sum exact test\n\ndata: nematodes by estate\nW = 78, p-value = 0.03546\nalternative hypothesis: true location shift is not equal to 0\n\n\n What do you conclude from the test? Write your conclusion in a form suitable for a report.\n\n\n\nIllustrating\nA box plot is a usually good choice for illustrating a two-sample Wilcoxon test because it shows the median and interquartile range.\n We can create a simple boxplot with:\n\nggplot(data = grouse, aes(x = estate, y = nematodes) ) +\n geom_boxplot() \n\n\n\n\n Annotate and format the figure so it is more suitable for a report and save it to your figures folder." + "objectID": "pgt52m/pgt52m.html#week-5-the-logic-of-hypothesis-testing-and-ci", + "href": "pgt52m/pgt52m.html#week-5-the-logic-of-hypothesis-testing-and-ci", + "title": "52M Data Analysis in R", + "section": "Week 5: The logic of hypothesis testing and CI", + "text": "Week 5: The logic of hypothesis testing and CI\nThis week we will cover the logic of consider the logic of hypothesis testing and type 1 and type 2 errors. We will also find out what the sampling distribution of the mean and the standard error are, and how to calculate confidence intervals." }, { - "objectID": "pgt52m/week-7/workshop.html#gene-expression", - "href": "pgt52m/week-7/workshop.html#gene-expression", - "title": "Workshop", - "section": "Gene Expression", - "text": "Gene Expression\nBambara groundnut (Vigna subterranea) is an African legume with good nutritional value which can be influenced by low temperature stress. Researchers are interested in the expression levels of a particular set of 35 genes (probe_id) in response to temperature stress. They measure the expression of the genes at 23 and 18 degrees C (high and low temperature). These samples are not independent because we have two measure from one gene. The data are in expr.xlxs.\nSelecting\n What is the null hypothesis?\n\n\n\n Save a copy of expr.xlxs and import the data. I named the dataframe bambara\n What is the appropriate parametric test?\n\n\nApplying, interpreting and reporting\nA paired test requires us to test whether the difference in expression between high and low temperatures is zero on average. One handy way to achieve this is to organise our groups into two columns. The pivot_wider() function will do this for us. We need to tell it what column gives the identifiers (i.e., matches the the pairs) - the probe_ids in this case. We also need to say which variable contains what will become the column names and which contains the values.\n Pivot the data so there is a column for each temperature:\n\nbambara <- bambara |> \n pivot_wider(names_from = temperature, \n values_from = expression, \n id_cols = probe_id)\n\n Click on the bambara dataframe in the environment to open a view of it so that you understand what pivot_wider() has done.\n Create a paired-sample model like this:\n\nmod <- lm(data = bambara, \n highert - lowert ~ 1)\n\nSince we have done highert - lowert, the “(Intercept) Estimate” will be the average of the higher temperature expression minus the lower temperature expression for each gene.\n Examine the model with:\n\nsummary(mod)\n\n\nCall:\nlm(formula = highert - lowert ~ 1, data = bambara)\n\nResiduals:\n Min 1Q Median 3Q Max \n-1.05478 -0.46058 0.09682 0.33342 1.06892 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 0.30728 0.09591 3.204 0.00294 **\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 0.5674 on 34 degrees of freedom\n\n\n State your conclusion from the test in a form suitable for including in a report. Make sure you give the direction of any significant effect." + "objectID": "pgt52m/pgt52m.html#week-6-introduction-to-statistical-models-single-regression", + "href": "pgt52m/pgt52m.html#week-6-introduction-to-statistical-models-single-regression", + "title": "52M Data Analysis in R", + "section": "Week 6: Introduction to statistical models: Single regression", + "text": "Week 6: Introduction to statistical models: Single regression\nThis week you will be introduced to the idea of a statistical “model” in general and to general linear model in particular. Our first general linear model will be single linear regression which puts a line of best fit through data so the response can be predicted from the explanatory variable. We will consider the two “parameters” estimated by the model (the slope and the intercept) and whether these differ from zero" }, { - "objectID": "pgt52m/week-7/workshop.html#look-after-future-you", - "href": "pgt52m/week-7/workshop.html#look-after-future-you", - "title": "Workshop", - "section": "Look after future you!", - "text": "Look after future you!\nThe code required to summarise, test, and plot data for any two-sample test AND for any for any one-way ANOVA is exactly the same except for the names of the dataframe, variables and the axis labels and limits. Take some time to comment it your code so that you can make use of it next week.\n\nYou’re finished!" + "objectID": "pgt52m/pgt52m.html#week-7-two-sample-tests", + "href": "pgt52m/pgt52m.html#week-7-two-sample-tests", + "title": "52M Data Analysis in R", + "section": "Week 7: Two-sample tests", + "text": "Week 7: Two-sample tests\nThis week you will how to use and interpret the general linear model when the x variable is categorical and has two groups. Just as with single linear regression, the model puts a line of best through data and the model parameters, the intercept and the slope, have the same in interpretation The intercept is one of the group means and the slope is the difference between that, mean and the other group mean. You will also learn about the non-parametric equivalents - the tests we use when the assumptions of the general linear model are not met." }, { - "objectID": "pgt52m/week-1/study_after_workshop.html", - "href": "pgt52m/week-1/study_after_workshop.html", - "title": "Independent Study to consolidate this week", - "section": "", - "text": "There is no additional study this week but you may want to look ahead to next week." + "objectID": "pgt52m/pgt52m.html#week-8-one-way-anova-and-kruskal-wallis", + "href": "pgt52m/pgt52m.html#week-8-one-way-anova-and-kruskal-wallis", + "title": "52M Data Analysis in R", + "section": "Week 8: One-way ANOVA and Kruskal-Wallis", + "text": "Week 8: One-way ANOVA and Kruskal-Wallis\nLast week you learnt how to use and interpret the general linear model when the x variable was categorical with two groups. You will now extend that to situations when there are more than two groups. This is often known as the one-way ANOVA (analysis of variance). You will also learn about the Kruskal- Wallis test which can be used when the assumptions of the general linear model are not met." }, { - "objectID": "pgt52m/week-1/workshop.html", - "href": "pgt52m/week-1/workshop.html", - "title": "Workshop", - "section": "", - "text": "There is no formal workshop this week but you might want to install R and RStudio on your own machine. This is optional because University computers already have R and RStudio installed.\nInstall R and RStudio.\nNote you need a computer - not a tablet." + "objectID": "pgt52m/pgt52m.html#week-9-assessment-intro", + "href": "pgt52m/pgt52m.html#week-9-assessment-intro", + "title": "52M Data Analysis in R", + "section": "Week 9: Assessment intro", + "text": "Week 9: Assessment intro\nReproducible analysis of some relevant data." }, { - "objectID": "pgt52m/week-2/study_after_workshop.html", - "href": "pgt52m/week-2/study_after_workshop.html", - "title": "Independent Study to consolidate this week", - "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 In a maternity hospital, the total numbers of births induced on each day of the week over a six week period were recorded (see table below). Create a plot of these data with the days of week in order.\n\n\n\n\nNumber of inductions for each day of the week over six weeks.\n\nDay\nNo. inductions\n\n\n\nMonday\n43\n\n\nTuesday\n36\n\n\nWednesday\n35\n\n\nThursday\n38\n\n\nFriday\n48\n\n\nSaturday\n26\n\n\nSunday\n24\n\n\n\n\n\n\nCode# create a dataframe for the data\nday <- c(\"Monday\", \n \"Tuesday\", \n \"Wednesday\",\n \"Thursday\",\n \"Friday\",\n \"Saturday\",\n \"Sunday\")\nfreq <- c(43, 36, 35, 38, 48, 26, 24) \ninductions <- data.frame(day, freq)\n\n# make the order of the days correct rather than alphabetical\ninductions <- inductions |> \n mutate(day = fct_relevel(day, c(\"Monday\",\n \"Tuesday\",\n \"Wednesday\",\n \"Thursday\",\n \"Friday\",\n \"Saturday\",\n \"Sunday\")))\n\n# plot the data as a barplot with the bars in\nggplot(data = inductions, \n aes(x = day, y = freq)) +\n geom_col(colour = \"black\",\n fill = \"lightseagreen\") +\n scale_x_discrete(expand = c(0, 0),\n name = \"Day of the week\") + \n scale_y_continuous(expand = c(0, 0),\n name = \"Number of inductions\",\n limits = c(0, 55)) +\n theme_classic()\n\n\n\n📖 Read Workflow in RStudio" + "objectID": "pgt52m/pgt52m.html#week-10-reproducible-reporting", + "href": "pgt52m/pgt52m.html#week-10-reproducible-reporting", + "title": "52M Data Analysis in R", + "section": "Week 10: Reproducible Reporting", + "text": "Week 10: Reproducible Reporting\nUsing Quarto" + }, + { + "objectID": "pgt52m/pgt52m.html#week-11-drop-in", + "href": "pgt52m/pgt52m.html#week-11-drop-in", + "title": "52M Data Analysis in R", + "section": "Week 11: Drop-in", + "text": "Week 11: Drop-in" }, { "objectID": "pgt52m/week-2/workshop.html", @@ -294,81 +448,151 @@ "text": "Footnotes\n\nThere are also scale_x_continous() and scale_y_discrete() functions when you have those types of variable↩︎\nModify components of a theme↩︎" }, { - "objectID": "pgt52m/week-2/study_before_workshop.html", - "href": "pgt52m/week-2/study_before_workshop.html", - "title": "Independent Study to prepare for workshop", + "objectID": "pgt52m/week-2/study_after_workshop.html", + "href": "pgt52m/week-2/study_after_workshop.html", + "title": "Independent Study to consolidate this week", "section": "", - "text": "Either\n\n📖 Read First Steps in RStudio in\n\nOR\n\n📹 Watch" + "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 In a maternity hospital, the total numbers of births induced on each day of the week over a six week period were recorded (see table below). Create a plot of these data with the days of week in order.\n\n\n\n\nNumber of inductions for each day of the week over six weeks.\n\nDay\nNo. inductions\n\n\n\nMonday\n43\n\n\nTuesday\n36\n\n\nWednesday\n35\n\n\nThursday\n38\n\n\nFriday\n48\n\n\nSaturday\n26\n\n\nSunday\n24\n\n\n\n\n\n\nCode# create a dataframe for the data\nday <- c(\"Monday\", \n \"Tuesday\", \n \"Wednesday\",\n \"Thursday\",\n \"Friday\",\n \"Saturday\",\n \"Sunday\")\nfreq <- c(43, 36, 35, 38, 48, 26, 24) \ninductions <- data.frame(day, freq)\n\n# make the order of the days correct rather than alphabetical\ninductions <- inductions |> \n mutate(day = fct_relevel(day, c(\"Monday\",\n \"Tuesday\",\n \"Wednesday\",\n \"Thursday\",\n \"Friday\",\n \"Saturday\",\n \"Sunday\")))\n\n# plot the data as a barplot with the bars in\nggplot(data = inductions, \n aes(x = day, y = freq)) +\n geom_col(colour = \"black\",\n fill = \"lightseagreen\") +\n scale_x_discrete(expand = c(0, 0),\n name = \"Day of the week\") + \n scale_y_continuous(expand = c(0, 0),\n name = \"Number of inductions\",\n limits = c(0, 55)) +\n theme_classic()\n\n\n\n📖 Read Workflow in RStudio" }, { - "objectID": "pgt52m/week-6/overview.html", - "href": "pgt52m/week-6/overview.html", - "title": "Overview", + "objectID": "pgt52m/week-8/workshop.html", + "href": "pgt52m/week-8/workshop.html", + "title": "Workshop", "section": "", - "text": "This week you will be introduced to the idea of a statistical “model” in general and to general linear model in particular. Our first general linear model will be single linear regression which puts a line of best fit through data so the response can be predicted from the explanatory variable. We will consider the two “parameters” estimated by the model (the slope and the intercept) and whether these differ from zero\n\nLearning objectives\nThe successful student will be able to:\n\nexplain what is meant by a statistical model and fitting a model\nknow what the general linear model is and how it relates to regression\nexplain the principle of regression and know when it can be applied\napply and interpret a simple linear regression in R\nevaluate whether the assumptions of regression are met\nscientifically report a regression result including appropriate figures\n\n\n\nInstructions\n\nPrepare\n\n📖 Read What is a statistical model\n📖 Read Single linear regression\n\nWorkshop\ni.💻 Carry out a single linear regression\nConsolidate\n\n💻 Appropriately analyse the relationsip between juvenile hormone and mandible size in stage beetles\n💻 Appropriately analyse the relationsip between anxiety and performance" + "text": "Artwork by Horst (2023): “Debugging and feelings”\n\n\nIn this session you will get practice in choosing between, performing, and presenting the results of, one-way ANOVA and Kruskal-Wallis in R.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "pgt52m/week-6/study_before_workshop.html", - "href": "pgt52m/week-6/study_before_workshop.html", - "title": "Independent Study to prepare for workshop", + "objectID": "pgt52m/week-8/workshop.html#session-overview", + "href": "pgt52m/week-8/workshop.html#session-overview", + "title": "Workshop", "section": "", - "text": "📖 Read What is a statistical model\n📖 Read Single linear regression" + "text": "In this session you will get practice in choosing between, performing, and presenting the results of, one-way ANOVA and Kruskal-Wallis in R." }, { - "objectID": "pgt52m/week-5/overview.html", - "href": "pgt52m/week-5/overview.html", - "title": "Overview", + "objectID": "pgt52m/week-8/workshop.html#philosophy", + "href": "pgt52m/week-8/workshop.html#philosophy", + "title": "Workshop", "section": "", - "text": "This week we will cover the logic of consider the logic of hypothesis testing and type 1 and type 2 errors. We will also find out what the sampling distribution of the mean and the standard error are, and how to calculate confidence intervals.\n\n\n\nArtwork by Horst (2023): “type 1 error”\n\n\n\n\n\nArtwork by Horst (2023): “type 2 error”\n\n\n\nLearning objectives\nThe successful student will be able to:\n\ndemonstrate the process of hypothesis testing with an example\nexplain type 1 and type 2 errors\ndefine the sampling distribution of the mean and the standard error\nexplain what a confidence interval is\ncalculate confidence intervals for large and small samples\n\n\n\nInstructions\n\nPrepare\n\n📖 Read The logic of hyothesis testing\n📖 Read Confidence Intervals\n\nWorkshop\n\n💻 Remind yourself how to import files\n💻 Calculate confidence intervals on large\n💻 Calculate confidence intervals on small samples.\n\nConsolidate\n\n💻 Calculate confidence intervals for each group in a data set\n\n\n\n\n\n\n\nReferences\n\nHorst, Allison. 2023. “Data Science Illustrations.” https://allisonhorst.com/allison-horst." + "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "pgt52m/week-5/study_before_workshop.html", - "href": "pgt52m/week-5/study_before_workshop.html", - "title": "Independent Study to prepare for workshop", + "objectID": "pgt52m/week-8/workshop.html#myoglobin-in-seal-muscle", + "href": "pgt52m/week-8/workshop.html#myoglobin-in-seal-muscle", + "title": "Workshop", + "section": "Myoglobin in seal muscle", + "text": "Myoglobin in seal muscle\nThe myoglobin concentration of skeletal muscle of three species of seal in grams per kilogram of muscle was determined and the data are given in seal.csv. We want to know if there is a difference between species. Each row represents an individual seal. The first column gives the myoglobin concentration and the second column indicates species.\n Save a copy of the data file seal.csv to data-raw\n Read in the data and check the structure. I used the name seal for the dataframe/tibble.\n What kind of variables do you have?\n\n\n\nExploring\n Do a quick plot of the data. You may need to refer to a previous workshop\nSummarising the data\nDo you remember Look after future you!\n If you followed that tip you’ll be able to open that script and whizz through summarising,testing and plotting.\n Create a data frame called seal_summary that contains the means, standard deviations, sample sizes and standard errors for each species.\nYou should get the following numbers:\n\n\n\n\nspecies\nmean\nstd\nn\nse\n\n\n\nBladdernose Seal\n42.31600\n8.020634\n30\n1.464361\n\n\nHarbour Seal\n49.01033\n8.252004\n30\n1.506603\n\n\nWeddell Seal\n44.66033\n7.849816\n30\n1.433174\n\n\n\n\n\nApplying, interpreting and reporting\nWe can now carry out a one-way ANOVA using the same lm() function we used for two-sample tests.\n Carry out an ANOVA and examine the results with:\n\nmod <- lm(data = seal, myoglobin ~ species)\nsummary(mod)\n\n\nCall:\nlm(formula = myoglobin ~ species, data = seal)\n\nResiduals:\n Min 1Q Median 3Q Max \n-16.306 -5.578 -0.036 5.240 18.250 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 42.316 1.468 28.819 < 2e-16 ***\nspeciesHarbour Seal 6.694 2.077 3.224 0.00178 ** \nspeciesWeddell Seal 2.344 2.077 1.129 0.26202 \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 8.043 on 87 degrees of freedom\nMultiple R-squared: 0.1096, Adjusted R-squared: 0.08908 \nF-statistic: 5.352 on 2 and 87 DF, p-value: 0.006427\n\n\nRemember: the tilde (~) means test the values in myoglobin when grouped by the values in species. Or explain myoglobin with species\n What do you conclude so far from the test? Write your conclusion in a form suitable for a report.\n\n\n\n Can you relate the values under Estimate to the means?\n\n\n\n\n\n\n\nThe ANOVA is significant but this only tells us that species matters, meaning at least two of the means differ. To find out which means differ, we need a post-hoc test. A post-hoc (“after this”) test is done after a significant ANOVA test. There are several possible post-hoc tests and we will be using Tukey’s HSD (honestly significant difference) test (Tukey 1949) implemented in the emmeans (Lenth 2023) package.\n Load the package\n\nlibrary(emmeans)\n\n Carry out the post-hoc test\n\nemmeans(mod, ~ species) |> pairs()\n\n contrast estimate SE df t.ratio p.value\n Bladdernose Seal - Harbour Seal -6.69 2.08 87 -3.224 0.0050\n Bladdernose Seal - Weddell Seal -2.34 2.08 87 -1.129 0.4990\n Harbour Seal - Weddell Seal 4.35 2.08 87 2.095 0.0968\n\nP value adjustment: tukey method for comparing a family of 3 estimates \n\n\nEach row is a comparison between the two means in the ‘contrast’ column. The ‘estimate’ column is the difference between those means and the ‘p.value’ indicates whether that difference is significant.\nA plot can be used to visualise the result of the post-hoc which can be especially useful when there are very many comparisons.\n Plot the results of the post-hoc test:\n\nemmeans(mod, ~ species) |> plot()\n\n\n\n\nWhere the purple bars overlap, there is no significant difference.\n What do you conclude from the test?\n\n\n\nCheck assumptions\nThe assumptions of the general linear model are that the residuals – the difference between predicted value (i.e., the group mean) and observed values - are normally distributed and have homogeneous variance. To check these we can examine the mod$residuals variable. You may want to refer to Checking assumptions in the “Single regression” workshop.\n Plot the model residuals against the fitted values.\n What to you conclude?\n\n\n\nTo examine normality of the model residuals we can plot them as a histogram and do a normality test on them.\n Plot a histogram of the residuals.\n Use the shapiro.test() to test the normality of the model residuals\n What to you conclude?\n\n\n\n\nIllustrating\n Create a figure like the one below. You may need to refer to Visualise from the “Summarising data with several variables” workshop (Rand 2023)\nWe will again use both our seal and seal_summary dataframes.\n Create the plot:\n\n\n\n\n\n Save your figure to your figures folder." + }, + { + "objectID": "pgt52m/week-8/workshop.html#leafminers-on-birch", + "href": "pgt52m/week-8/workshop.html#leafminers-on-birch", + "title": "Workshop", + "section": "Leafminers on Birch", + "text": "Leafminers on Birch\nLarvae of the Ambermarked birch leafminer, Profenusa thomsoni, feed on the interior leaf tissues of Birch (Betula) species. They do not normally kill the tree but can weaken it making it susceptible to attack from other species. Researchers are interested in whether there is a difference in the rates at which white, grey and yellow birch are attacked. They introduce adult female P.thomsoni to a green house containing 30 young trees (ten of each type) and later count the egg laying events on each tree. The data are in leaf.txt.\nExploring\n Read in the data and check the structure. I used the name leaf for the dataframe/tibble.\n What kind of variables do we have?\n\n\n\n Do a quick plot of the data.\n Using your common sense, do these data look normally distributed?\n\n\n Why is a Kruskal-Wallis appropriate in this case?\n\n\n\n\n\n Calculate the medians, means and sample sizes.\nApplying, interpreting and reporting\n Carry out a Kruskal-Wallis:\n\nkruskal.test(data = leaf, eggs ~ birch)\n\n\n Kruskal-Wallis rank sum test\n\ndata: eggs by birch\nKruskal-Wallis chi-squared = 6.3393, df = 2, p-value = 0.04202\n\n\n What do you conclude from the test?\n\n\n\nA significant Kruskal-Wallis tells us at least two of the groups differ but where do the differences lie? The Dunn test is a post-hoc multiple comparison test for a significant Kruskal-Wallis. It is available in the package FSA\n Load the package using:\n\nlibrary(FSA)\n\n Run the post-hoc test with:\n\ndunnTest(data = leaf, eggs ~ birch)\n\n Comparison Z P.unadj P.adj\n1 Grey - White 1.296845 0.19468465 0.38936930\n2 Grey - Yellow -1.220560 0.22225279 0.22225279\n3 White - Yellow -2.517404 0.01182231 0.03546692\n\n\nThe P.adj column gives p-value for the comparison listed in the first column. Z is the test statistic.\n What do you conclude from the test?\n\n\n\n Write up the result is a form suitable for a report.\n\n\n\n\n\n\nIllustrating\n A box plot is an appropriate choice for illustrating a Kruskal-Wallis. Can you produce a figure like this?\n\n\n\n\n\nYou’re finished!" + }, + { + "objectID": "pgt52m/week-8/study_after_workshop.html", + "href": "pgt52m/week-8/study_after_workshop.html", + "title": "Independent Study to consolidate this week", "section": "", - "text": "📖 Read The logic of hyothesis testing\n📖 Read Confidence Intervals" + "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 Sports scientists were investigating the effects of fitness and heat acclimatisation on the sodium content of sweat. They measured the sodium content of the sweat (μmoll^−1) of three groups of individuals: unfit and unacclimatised (UU); fit and unacclimatised(FU); and fit and acclimatised (FA). The are in sweat.txt. Is there a difference between the groups in the sodium content of their sweat?\n\n\nCode# read in the data and look at structure\nsweat <- read_table(\"data-raw/sweat.txt\")\nstr(sweat)\n\n\n\nCode# quick plot of the data\nggplot(data = sweat, aes(x = gp, y = na)) +\n geom_boxplot()\nCode# Since the sample sizes are small and not the same in each group and the \n# variance in the FA gp looks a bit lower, I'm leaning to a non-parametric test K-W.\n# However, don't panic if you decided to do an anova\n\n\n\nCode# calculate some summary stats \nsweat_summary <- sweat %>% \n group_by(gp) %>% \n summarise(mean = mean(na),\n n = length(na),\n median = median(na))\n\n\n\nCode# Kruskal-Wallis\nkruskal.test(data = sweat, na ~ gp)\n# We can say there is a difference between the groups in the sodium \n# content of their sweat (chi-squared = 11.9802, df = 2, p-value = 0.002503).\n# Unfit and unacclimatised people have most salty sweat, \n# Fit and acclimatised people the least salty.\n\n\n\nCode# a post-hoc test to see where the sig differences lie:\nlibrary(FSA)\ndunnTest(data = sweat, na ~ gp)\n# Fit and acclimatised people (median = 49.5 μmoll^−1) have significantly less sodium in their\n# sweat than the unfit and unacclimatised people (70 μmoll^−1) \n# (Kruskal-Wallis multiple comparison p-values adjusted with the Holm method: p = 0.0026).\n# Fit and unacclimatised (54 μmoll^−1) also have significantly less sodium in their\n# people have sodium concentrations than unfit and unacclimatised people (p = 0.033). \n# There was no difference between the Fit and unacclimatised and the Fit and acclimatised. See figure 1.\n\n\n\nCodeggplot(sweat, aes(x = gp, y = na) ) +\n geom_boxplot() +\n scale_x_discrete(labels = c(\"Fit Acclimatised\", \n \"Fit Unacclimatised\", \n \"Unfit Unacclimatised\"), \n name = \"Group\") +\n scale_y_continuous(limits = c(0, 110), \n expand = c(0, 0),\n name = expression(\"Sodium\"~mu*\"mol\"*l^{-1})) +\n annotate(\"segment\", x = 1, xend = 3, \n y = 100, yend = 100,\n colour = \"black\") +\n annotate(\"text\", x = 2, y = 103, \n label = expression(italic(p)~\"= 0.0026\")) +\n annotate(\"segment\", x = 2, xend = 3, \n y = 90, yend = 90,\n colour = \"black\") +\n annotate(\"text\", x = 2.5, y = 93, \n label = expression(italic(p)~\"= 0.0340\")) +\n theme_classic()\nCode#Figure 1. Sodium content of sweat for three groups: Fit and acclimatised\n#(FA), Fit and unacclimatised (FU) and Unfit and unacclimatised (UU). Heavy lines\n#indicate the median, boxes the interquartile range and whiskers the range. \n\n\n\n💻 The data are given in biomass.txt are taken from an experiment in which the insect pest biomass (g) was measured on plots sprayed with water (control) or one of five different insecticides. Do the insecticides vary in their effectiveness? What advice would you give to a person: - currently using insecticide E? - trying to choose between A and D? - trying to choose between C and B?\n\n\nCodebiom <- read_table(\"data-raw/biomass.txt\")\n# The data are organised with an insecticide treatment group in\n# each column.\n\n\n\nCode#Put the data into tidy format.\n\nbiom <- biom |> \n pivot_longer(cols = everything(),\n names_to = \"spray\",\n values_to = \"biomass\")\n\n\n\nCode# quick plot of the data\nggplot(data = biom, aes(x = spray, y = biomass)) +\n geom_boxplot()\nCode# Looks like there is a difference between sprays. E doesn't look very effective.\n\n\n\nCode# summary statistics\nbiom_summary <- biom %>% \n group_by(spray) %>% \n summarise(mean = mean(biomass),\n median = median(biomass),\n sd = sd(biomass),\n n = length(biomass),\n se = sd / sqrt(n))\n# thoughts so far: the sample sizes are equal, 10 is a smallish but\n# reasonable sample size\n# the means and medians are similar to each other (expected for\n# normally distributed data), A has a smaller variance \n\n# We have one explanatory variable, \"spray\" comprising 6 levels\n# Biomass has decimal places and we would expect such data to be \n# normally distributed therefore one-way ANOVA is the desired test\n# - we will check the assumptions after building the model\n\n\n\nCode# arry out an ANOVA and examine the results \nmod <- lm(data = biom, biomass ~ spray)\nsummary(mod)\n# spray type does have an effect F-statistic: 26.46 on 5 and 54 DF, p-value: 2.081e-13\n\n\n\nCode# Carry out the post-hoc test\nlibrary(emmeans)\n\nemmeans(mod, ~ spray) |> pairs()\n\n# the signifcant comparisons are:\n# contrast estimate SE df t.ratio p.value\n# A - D -76.50 21.9 54 -3.489 0.0119\n# A - E -175.51 21.9 54 -8.005 <.0001\n# A - WaterControl -175.91 21.9 54 -8.024 <.0001\n# B - E -154.32 21.9 54 -7.039 <.0001\n# B - WaterControl -154.72 21.9 54 -7.057 <.0001\n# C - E -155.71 21.9 54 -7.102 <.0001\n# C - WaterControl -156.11 21.9 54 -7.120 <.0001\n# D - E -99.01 21.9 54 -4.516 0.0005\n# D - WaterControl -99.41 21.9 54 -4.534 0.0004\n# All sprays are better than the water control except E. \n# This is probably the most important result.\n# What advice would you give to a person currently using insecticide E?\n# Don't bother!! It's no better than water. Switch to any of \n# the other sprays\n# What advice would you give to a person currently\n# + trying to choose between A and D? Choose A because A has sig lower\n# insect biomass than D \n# + trying to choose between C and B? It doesn't matter because there is \n# no difference in insect biomass. Use other criteria to chose (e.g., price)\n# We might report this like:\n# There is a very highly significant effect of spray type on pest \n# biomass (F = 26.5; d.f., 5, 54; p < 0.001). Post-hoc testing \n# showed E was no more effective than the control; A, C and B were \n# all better than the control but could be equally as good as each\n# other; D would be a better choice than the control or E but \n# worse than A. See figure 1\n\n\n\nCode# I reordered the bars to make is easier for me to annotate with\n# I also used * to indicate significance\n\nggplot() +\n geom_point(data = biom, aes(x = reorder(spray, biomass), y = biomass),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"gray50\") +\n geom_errorbar(data = biom_summary, \n aes(x = spray, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = biom_summary, \n aes(x = spray, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_y_continuous(name = \"Pest Biomass (units)\",\n limits = c(0, 540),\n expand = c(0, 0)) +\n scale_x_discrete(\"Spray treatment\") +\n # E and control are one group\n annotate(\"segment\", x = 4.5, xend = 6.5, \n y = 397, yend = 397,\n colour = \"black\", linewidth = 1) +\n annotate(\"text\", x = 5.5, y = 385, \n label = \"N.S\", size = 4) +\n # WaterControl-D and E-D ***\n annotate(\"segment\", x = 4, xend = 5.5, \n y = 410, yend = 410,\n colour = \"black\") +\n annotate(\"text\", x = 4.5, y = 420, \n label = \"***\", size = 5) +\n # WaterControl-B ***\n annotate(\"segment\", x = 3, xend = 5.5, \n y = 440, yend = 440,\n colour = \"black\") +\n annotate(\"text\", x = 4, y = 450,\n label = \"***\", size = 5) +\n # WaterControl-C ***\n annotate(\"segment\", x = 2, xend = 5.5, \n y = 475, yend = 475,\n colour = \"black\") +\n annotate(\"text\", x = 3.5, y = 485, \n label = \"***\", size = 5) +\n # WaterControl-A ***\n annotate(\"segment\", x = 1, xend = 5.5, \n y = 510, yend = 510,\n colour = \"black\") +\n annotate(\"text\", x = 3.5, y = 520, \n label = \"***\", size = 5) + \n# A-D ***\n annotate(\"segment\", x = 1, xend = 4, \n y = 330, yend = 330,\n colour = \"black\") +\n annotate(\"text\", x = 2.5, y = 335, \n label = \"*\", size = 5) +\n theme_classic()\nCode# Figure 1. The mean pest biomass following various insecticide treatments.\n# Error bars are +/- 1 S.E. Significant comparisons are indicated: * is p < 0.05, ** p < 0.01 and *** is p < 0.001" }, { - "objectID": "pgt52m/week-4/overview.html", - "href": "pgt52m/week-4/overview.html", - "title": "Overview", + "objectID": "pgt52m/week-6/workshop.html", + "href": "pgt52m/week-6/workshop.html", + "title": "Workshop", "section": "", - "text": "Last week you summarised and plotted single variables. This week you will start plotting data sets with more than one variable. This means you need to be able determine which variable is the response and which is the explanatory. You will find out what is meant by “tidy” data and how to perform a simple data tidying task. Finally you will discover how to save your figures and place them in documents.\n\nLearning objectives\n\nsummarise and plot appropriately datasets with more than one variable\nrecognise that variables can be categorised by their role in analysis\nexplain what is meant by ‘tidy’ data and be able to perform some data tidying tasks.\nsave figures to file\ncreate neat reports which include text and figures\n\n\n\nInstructions\n\nPrepare\n\n📖 From importing to reporting\n\nWorkshop\n\n💻 Summarise and plot datasets with more than one variable.\n💻 Practice with working directories, importing data, formatting figures and the pipe\n💻 Lay out text, figures and figure legends in documents\n\nConsolidate\n\n💻 Summarise and plot a dataframe from the workshop\n💻 Practice the complete RStudio Project worklfow for a new dataset" + "text": "In this workshop you will get practice in applying, interpreting and reporting single linear regression.\n\n\nArtwork by Horst (2023): “linear regression dragons”\n\n\nIn this session you will carry out, interpret and report on a single linear regression.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "pgt52m/week-4/study_before_workshop.html", - "href": "pgt52m/week-4/study_before_workshop.html", - "title": "Independent Study to prepare for workshop", + "objectID": "pgt52m/week-6/workshop.html#session-overview", + "href": "pgt52m/week-6/workshop.html#session-overview", + "title": "Workshop", "section": "", - "text": "📖 Read From importing to reporting. The first part of this chapter is about data import which we covered in the last workshop. You may be able to skip that part or you may find it useful to revise. The section on Summarising data will be mainly new." + "text": "In this session you will carry out, interpret and report on a single linear regression." }, { - "objectID": "pgt52m/week-8/overview.html", - "href": "pgt52m/week-8/overview.html", - "title": "Overview", + "objectID": "pgt52m/week-6/workshop.html#philosophy", + "href": "pgt52m/week-6/workshop.html#philosophy", + "title": "Workshop", "section": "", - "text": "Last week you learnt how to use and interpret the general linear model when the x variable was categorical with two groups. You will now extend that to situations when there are more than two groups. This is often known as the one-way ANOVA (analysis of variance). You will also learn about the Kruskal- Wallis test which can be used when the assumptions of the general linear model are not met.\n\nLearning objectives\nThe successful student will be able to:\n\nexplain the rationale behind ANOVA understand the meaning of the F values\nselect, appropriately, one-way ANOVA and Kruskal-Wallis\nknow what functions are used in R to run these tests and how to interpret them\nevaluate whether the assumptions of lm() are met\nscientifically report the results of these tests including appropriate figures\n\n\n\nInstructions\n\nPrepare\n\n📖 Read One-way ANOVA and Kruskal-Wallis\n\nWorkshop\n\n💻 One-way ANOVA\n💻 Kruskal-Wallis\n\nConsolidate\n\n💻 Appropriately test if fitness and acclimation effect the sodium content of sweat\n💻 Appropriately test if insecticides vary in their effectiveness" + "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "pgt52m/week-8/study_before_workshop.html", - "href": "pgt52m/week-8/study_before_workshop.html", - "title": "Independent Study to prepare for workshop", + "objectID": "pgt52m/week-6/workshop.html#linear-regression", + "href": "pgt52m/week-6/workshop.html#linear-regression", + "title": "Workshop", + "section": "Linear Regression", + "text": "Linear Regression\nThe data in plant.xlsx is a set of observations of plant growth over two months. The researchers planted the seeds and harvested, dried and weighed a plant each day from day 10 so all the data points are independent of each other.\n Save a copy of plant.xlsx to your data-raw folder and import it.\n What type of variables do you have? Which is the response and which is the explanatory? What is the null hypothesis?\n\n\n\n\n\n\nExploring\n Do a quick plot of the data:\n\nggplot(plant, aes(x = day, y = mass)) +\n geom_point()\n\n\n\n\n What are the assumptions of linear regression? Do these seem to be met?\n\n\n\n\n\n\n\n\n\n\nApplying, interpreting and reporting\n We now carry out a regression assigning the result of the lm() procedure to a variable and examining it with summary().\n\nmod <- lm(data = plant, mass ~ day)\nsummary(mod)\n\n\nCall:\nlm(formula = mass ~ day, data = plant)\n\nResiduals:\n Min 1Q Median 3Q Max \n-32.810 -11.253 -0.408 9.075 48.869 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) -8.6834 6.4729 -1.342 0.186 \nday 1.6026 0.1705 9.401 1.5e-12 ***\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 17.92 on 49 degrees of freedom\nMultiple R-squared: 0.6433, Adjusted R-squared: 0.636 \nF-statistic: 88.37 on 1 and 49 DF, p-value: 1.503e-12\n\n\nThe Estimates in the Coefficients table give the intercept (first line) and the slope (second line) of the best fitting straight line. The p-values on the same line are tests of whether that coefficient is different from zero.\nThe F value and p-value in the last line are a test of whether the model as a whole explains a significant amount of variation in the dependent variable. For a single linear regression this is exactly equivalent to the test of the slope against zero.\n What is the equation of the line? What do you conclude from the analysis?\n\n\n\n\n\n Does the line go through (0,0)?\n\n\n\n What percentage of variation is explained by the line?\n\n\nIt might be useful to assign the slope and the intercept to variables in case we need them later. The can be accessed in the mod$coefficients variable:\n\nmod$coefficients\n\n(Intercept) day \n -8.683379 1.602606 \n\n\n Assign mod$coefficients[1] to b0 and mod$coefficients[1] to b1:\n\nb0 <- mod$coefficients[1] |> round(2)\nb1 <- mod$coefficients[2] |> round(2)\n\nI also rounded the values to two decimal places.\nChecking assumptions\nWe need to examine the residuals. Very conveniently, the object which is created by lm() contains a variable called $residuals. Also conveniently, the R’s plot() function can used on the output objects of lm(). The assumptions demand that each y is drawn from a normal distribution for each x and these normal distributions have the same variance. Therefore we plot the residuals against the fitted values to see if the variance is the same for all the values of x. The fitted - predicted - values are the values on the line of best fit. Each residual is the difference between the fitted values and the observed value.\n Plot the model residuals against the fitted values like this:\n\nplot(mod, which = 1)\n\n\n\n\n What to you conclude?\n\n\n\nTo examine normality of the model residuals we can plot them as a histogram and do a normality test on them.\n Plot a histogram of the residuals:\n\nggplot(mapping = aes(x = mod$residuals)) + \n geom_histogram(bins = 10)\n\n\n\n\n Use the shapiro.test() to test the normality of the model residuals\n\nshapiro.test(mod$residuals)\n\n\n Shapiro-Wilk normality test\n\ndata: mod$residuals\nW = 0.96377, p-value = 0.1208\n\n\nUsually, when we are doing statistical tests we would like the the test to be significant because it means we have evidence of a biological effect. However, when doing normality tests we hope it will not be significant. A non-significant result means that there is no significant difference between the distribution of the residuals and a normal distribution and that indicates the assumptions are met.\n What to you conclude?\n\n\n\n\nIllustrating\nWe want a figure with the points and the statistical model, i.e., the best fitting straight line.\n Create a scatter plot using geom_point()\n\nggplot(plant, aes(x = day, y = mass)) +\n geom_point() + \n theme_classic()\n\n\n\n\n The geom_smooth() function will had a variety of fitted lines to a plot. We want a line so we need to specify method = \"lm\":\n\nggplot(plant, aes(x = day, y = mass)) +\n geom_point() + \n geom_smooth(method = lm, \n se = FALSE, \n colour = \"black\") +\n theme_classic()\n\n\n\n\n What do the se and colour arguments do? Try changing them.\n Let’s add the equation of the line to the figure using annotate():\n\nggplot(plant, aes(x = day, y = mass)) +\n geom_point() +\n geom_smooth(method = lm, \n se = FALSE, \n colour = \"black\") +\n annotate(\"text\", x = 20, y = 110, \n label = \"mass = 1.61 * day - 8.68\") +\n theme_classic()\n\n\n\n\nWe have to tell annotate() what type of geom we want - text in this case, - where to put it, and the text we want to appear.\n Improve the axes. You may need to refer back Changing the axes from the Week 2 workshop\n Save your figure to your figures folder." + }, + { + "objectID": "pgt52m/week-6/workshop.html#look-after-future-you", + "href": "pgt52m/week-6/workshop.html#look-after-future-you", + "title": "Workshop", + "section": "Look after future you!", + "text": "Look after future you!\nYou’re finished!" + }, + { + "objectID": "pgt52m/week-6/study_after_workshop.html", + "href": "pgt52m/week-6/study_after_workshop.html", + "title": "Independent Study to consolidate this week", "section": "", - "text": "Prepare\n\n📖 Read One-way ANOVA and Kruskal-Wallis" + "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 Effect of anxiety status and sporting performance. The data in sprint.txt are from an investigation of the effect of anxiety status and sporting performance. A group of 40 100m sprinters undertook a psychometric test to measure their anxiety shortly before competing. The data are their anxiety scores and the 100m times achieved. What you do conclude from these data?\n\n\nCode# this example is designed to emphasise the importance of plotting your data first\nsprint <- read_table(\"data-raw/sprint.txt\")\n# Anxiety is discrete but ranges from 16 to 402 meaning the gap between possible measures is small and \n# the variable could be treated as continuous if needed. Time is a continuous measure that has decimal places and which we would expect to follow a normal distribution \n\n# explore with a plot\nggplot(sprint, aes(x = anxiety, y = time) ) +\n geom_point()\nCode# A scatterplot of the data clearly reveals that these data are not linear. There is a good relationship between the two variables but since it is not linear, single linear regression is not appropriate.\n\n\n\n💻 Juvenile hormone in stag beetles. The concentration of juvenile hormone in stag beetles is known to influence mandible growth. Groups of stag beetles were injected with different concentrations of juvenile hormone (arbitrary units) and their average mandible size (mm) determined. The experimenters planned to analyse their data with regression. The data are in stag.txt\n\n\n\nCode# read the data in and check the structure\nstag <- read_table(\"data-raw/stag.txt\")\nstr(stag)\n\n# jh is discrete but ordered and has been chosen by the experimenter - it is the explanatory variable. \n# the response is mandible size which has decimal places and is something we would expect to be \n# normally distributed. So far, common sense suggests the assumptions of regression are met.\n\n\n\nCode# exploratory plot\nggplot(stag, aes(x = jh, y = mand)) +\n geom_point()\nCode# looks linear-ish on the scatter\n# regression still seems appropriate\n# we will check the other assumptions after we have run the lm\n\n\n\nCode# build the statistical model\nmod <- lm(data = stag, mand ~ jh)\n\n# examine it\nsummary(mod)\n# mand = 0.032*jh + 0.419\n# the slope of the line is significantly different from zero / the jh explains a significant amount of the variation in mand (ANOVA: F = 16.63; d.f. = 1,14; p = 0.00113).\n# the intercept is 0.419 and differs significantly from zero \n\n\n\nCode# checking the assumption\nplot(mod, which = 1) \nCode# we're looking for the variance in the residuals to be equal along the x axis.\n# with a small data set there is some apparent heterogeneity but it doesn't look too.\n# \nhist(mod$residuals)\nCode# We have some skew which again might be partly a result of a small sample size.\nshapiro.test(mod$residuals) # the also test not sig diff from normal\n\n# On balance the use of regression is probably justifiable but it is borderline\n# but ideally the experiment would be better if multiple individuals were measure at\n# each of the chosen juvenile hormone levels.\n\n\n\nCode# a better plot\nggplot(stag, aes(x = jh, y = mand) ) +\n geom_point() +\n geom_smooth(method = lm, se = FALSE, colour = \"black\") +\n scale_x_continuous(name = \"Juvenile hormone (arbitrary units)\",\n expand = c(0, 0),\n limits = c(0, 32)) +\n scale_y_continuous(name = \"Mandible size (mm)\",\n expand = c(0, 0),\n limits = c(0, 2)) +\n theme_classic()" + }, + { + "objectID": "pgt52m/week-7/workshop.html", + "href": "pgt52m/week-7/workshop.html", + "title": "Workshop", + "section": "", + "text": "Artwork by Horst (2023): “How much I think I know about R”\n\n\nIn this workshop you will get practice in choosing between, performing, and presenting the results of, two-sample tests and their non-parametric equivalents in R.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + }, + { + "objectID": "pgt52m/week-7/workshop.html#session-overview", + "href": "pgt52m/week-7/workshop.html#session-overview", + "title": "Workshop", + "section": "", + "text": "In this workshop you will get practice in choosing between, performing, and presenting the results of, two-sample tests and their non-parametric equivalents in R." + }, + { + "objectID": "pgt52m/week-7/workshop.html#philosophy", + "href": "pgt52m/week-7/workshop.html#philosophy", + "title": "Workshop", + "section": "", + "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + }, + { + "objectID": "pgt52m/week-7/workshop.html#adiponectin-secretion", + "href": "pgt52m/week-7/workshop.html#adiponectin-secretion", + "title": "Workshop", + "section": "Adiponectin secretion", + "text": "Adiponectin secretion\nAdiponectin is exclusively secreted from adipose tissue and modulates a number of metabolic processes. Nicotinic acid can affect adiponectin secretion. 3T3-L1 adipocytes were treated with nicotinic acid or with a control treatment and adiponectin concentration (pg/mL) measured. The data are in adipocytes.txt. Each row represents an independent sample of adipocytes and the first column gives the concentration adiponectin and the second column indicates whether they were treated with nicotinic acid or not.\n Save a copy of adipocytes.txt to data-raw\n Read in the data and check the structure. I used the name adip for the dataframe/tibble.\nWe have a tibble containing two variables: adiponectin is the response and is continuous and treatment is explanatory. treatment is categorical with two levels (groups). The first task is visualise the data to get an overview. For continuous response variables with categorical explanatory variables you could use geom_point(), geom_boxplot() or a variety of other geoms. I often use geom_violin() which allows us to see the distribution - the violin is fatter where there are more data points.\n Do a quick plot of the data:\n\nggplot(data = adip, aes(x = treatment, y = adiponectin)) +\n geom_violin()\n\n\n\n\nSummarising the data\nSummarising the data for each treatment group is the next sensible step. The most useful summary statistics are the means, standard deviations, sample sizes and standard errors.\n Create a data frame called adip_summary that contains the means, standard deviations, sample sizes and standard errors for the control and nicotinic acid treated samples. You may need to the Summarise from the Week 4 workshop\nYou should get the following numbers:\n\n\n\n\ntreatment\nmean\nstd\nn\nse\n\n\n\ncontrol\n5.546000\n1.475247\n15\n0.3809072\n\n\nnicotinic\n7.508667\n1.793898\n15\n0.4631824\n\n\n\n\n\nSelecting a test\n Do you think this is a paired-sample test or two-sample test?\n\n\n\n\nApplying, interpreting and reporting\n Create a two-sample model like this:\n\nmod <- lm(data = adip,\n adiponectin ~ treatment)\n\n Examine the model with:\n\nsummary(mod)\n\n\nCall:\nlm(formula = adiponectin ~ treatment, data = adip)\n\nResiduals:\n Min 1Q Median 3Q Max \n-4.3787 -1.0967 0.1927 1.0245 3.1113 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 5.5460 0.4240 13.079 1.9e-13 ***\ntreatmentnicotinic 1.9627 0.5997 3.273 0.00283 ** \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 1.642 on 28 degrees of freedom\nMultiple R-squared: 0.2767, Adjusted R-squared: 0.2509 \nF-statistic: 10.71 on 1 and 28 DF, p-value: 0.00283\n\n\n What do you conclude from the test? Write your conclusion in a form suitable for a report.\n\n\n\n\nCheck assumptions\nThe assumptions of the general linear model are that the residuals – the difference between predicted value (i.e., the group mean) and observed values - are normally distributed and have homogeneous variance. To check these we can examine the mod$residuals variable. You may want to refer to Checking assumptions in the “Single regression” workshop.\n Plot the model residuals against the fitted values.\n What to you conclude?\n\n\n\nTo examine normality of the model residuals we can plot them as a histogram and do a normality test on them.\n Plot a histogram of the residuals.\n Use the shapiro.test() to test the normality of the model residuals\n What to you conclude?\n\n\n\n\nIllustrating\n Create a figure like the one below. You may need to refer to Visualise from the “Summarising data with several variables” workshop (Rand 2023)\n\n\n\n\n\nWe now need to annotate the figure with the results from the statistical test. This most commonly done with a line linking the means being compared and the p-value. The annotate() function can be used to draw the line and then to add the value. The line is a segment and the p-value is a text.\n Add annotation to the figure by adding:\n...... +\n annotate(\"segment\", x = 1, xend = 2, \n y = 11.3, yend = 11.3,\n colour = \"black\") +\n annotate(\"text\", x = 1.5, y = 11.7, \n label = expression(italic(p)~\"= 0.003\")) +\n theme_classic()\n\n\n\n\n\nFor the segment, annotate() needs the x and y coordinates for the start and the finish of the line.\nThe use of expression() allows you to specify formatting or special characters. expression() takes strings or LaTeX formatting. Each string or piece of LaTeX is separated by a * or a ~. The * concatenates the strings without a space, ~ does so with a space. It will generate a warning message “In is.na(x) : is.na() applied to non-(list or vector) of type ‘expression’” which can be ignored.\n Save your figure to your figures folder." + }, + { + "objectID": "pgt52m/week-7/workshop.html#grouse-parasites", + "href": "pgt52m/week-7/workshop.html#grouse-parasites", + "title": "Workshop", + "section": "Grouse Parasites", + "text": "Grouse Parasites\nGrouse livers were dissected and the number of individuals of a parasitic nematode were counted for two estates ‘Gordon’ and ‘Moss’. We want to know if the two estates have different infection rates. The data are in grouse.csv\n Save a copy of grouse.csv to data-raw\n Read in the data and check the structure. I used the name grouse for the dataframe/tibble.\nSelecting\n Using your common sense, do these data look normally distributed?\n\n\n\n What test do you suggest?\n\n\nApplying, interpreting and reporting\n Summarise the data by finding the median of each group:\n Carry out a two-sample Wilcoxon test (also known as a Mann-Whitney):\n\nwilcox.test(data = grouse, nematodes ~ estate)\n\n\n Wilcoxon rank sum exact test\n\ndata: nematodes by estate\nW = 78, p-value = 0.03546\nalternative hypothesis: true location shift is not equal to 0\n\n\n What do you conclude from the test? Write your conclusion in a form suitable for a report.\n\n\n\nIllustrating\nA box plot is a usually good choice for illustrating a two-sample Wilcoxon test because it shows the median and interquartile range.\n We can create a simple boxplot with:\n\nggplot(data = grouse, aes(x = estate, y = nematodes) ) +\n geom_boxplot() \n\n\n\n\n Annotate and format the figure so it is more suitable for a report and save it to your figures folder." + }, + { + "objectID": "pgt52m/week-7/workshop.html#gene-expression", + "href": "pgt52m/week-7/workshop.html#gene-expression", + "title": "Workshop", + "section": "Gene Expression", + "text": "Gene Expression\nBambara groundnut (Vigna subterranea) is an African legume with good nutritional value which can be influenced by low temperature stress. Researchers are interested in the expression levels of a particular set of 35 genes (probe_id) in response to temperature stress. They measure the expression of the genes at 23 and 18 degrees C (high and low temperature). These samples are not independent because we have two measure from one gene. The data are in expr.xlxs.\nSelecting\n What is the null hypothesis?\n\n\n\n Save a copy of expr.xlxs and import the data. I named the dataframe bambara\n What is the appropriate parametric test?\n\n\nApplying, interpreting and reporting\nA paired test requires us to test whether the difference in expression between high and low temperatures is zero on average. One handy way to achieve this is to organise our groups into two columns. The pivot_wider() function will do this for us. We need to tell it what column gives the identifiers (i.e., matches the the pairs) - the probe_ids in this case. We also need to say which variable contains what will become the column names and which contains the values.\n Pivot the data so there is a column for each temperature:\n\nbambara <- bambara |> \n pivot_wider(names_from = temperature, \n values_from = expression, \n id_cols = probe_id)\n\n Click on the bambara dataframe in the environment to open a view of it so that you understand what pivot_wider() has done.\n Create a paired-sample model like this:\n\nmod <- lm(data = bambara, \n highert - lowert ~ 1)\n\nSince we have done highert - lowert, the “(Intercept) Estimate” will be the average of the higher temperature expression minus the lower temperature expression for each gene.\n Examine the model with:\n\nsummary(mod)\n\n\nCall:\nlm(formula = highert - lowert ~ 1, data = bambara)\n\nResiduals:\n Min 1Q Median 3Q Max \n-1.05478 -0.46058 0.09682 0.33342 1.06892 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 0.30728 0.09591 3.204 0.00294 **\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 0.5674 on 34 degrees of freedom\n\n\n State your conclusion from the test in a form suitable for including in a report. Make sure you give the direction of any significant effect." }, { - "objectID": "pgt52m/week-3/overview.html", - "href": "pgt52m/week-3/overview.html", - "title": "Overview", - "section": "", - "text": "The type of values our data can take is important in how we analyse and visualise it. This week you will learn the difference between continuous and discrete values and how we summarise and visualise them. You will also learn about the “normal distribution” which is the most important continuous distribution.\n\n\n\nDiscrete variable\n\n\n\nLearning objectives\nThe successful student will be able to:\n\ndistinguish between continuous, discrete, nominal and ordinal variable\nread in data in to RStudio from a plain text file and Excel files\nsummarise and plot variables appropriately for the data type\n\n\n\nInstructions\n\nPrepare\n\n📖 Read: Ideas about data\n\nWorkshop\n\n💻 Importing data\n💻 Summarising discrete data\n💻 Summarising count data\n💻 Summarising continuous data\n\nConsolidate\n\n💻 Summarise some data\n💻 Plot some data\n💻 Format a plot (1)\n💻 Format a plot (2)\n📖 Read Understanding the pipe |>" + "objectID": "pgt52m/week-7/workshop.html#look-after-future-you", + "href": "pgt52m/week-7/workshop.html#look-after-future-you", + "title": "Workshop", + "section": "Look after future you!", + "text": "Look after future you!\nThe code required to summarise, test, and plot data for any two-sample test AND for any for any one-way ANOVA is exactly the same except for the names of the dataframe, variables and the axis labels and limits. Take some time to comment it your code so that you can make use of it next week.\n\nYou’re finished!" }, { - "objectID": "pgt52m/week-3/study_before_workshop.html", - "href": "pgt52m/week-3/study_before_workshop.html", - "title": "Independent Study to prepare for workshop", + "objectID": "pgt52m/week-7/study_after_workshop.html", + "href": "pgt52m/week-7/study_after_workshop.html", + "title": "Independent Study to consolidate this week", "section": "", - "text": "📖 Read Ideas about data" + "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 Plant Biotech. Some plant biotechnologists are trying to increase the quantity of omega 3 fatty acids in Cannabis sativa. They have developed a genetically modified line using genes from Linum usitatissimum (linseed). They grow 50 wild type and fifty modified plants to maturity, collect the seeds and determine the amount of omega 3 fatty acids. The data are in csativa.txt. Do you think their modification has been successful?\n\n\nCodecsativa <- read_table(\"data-raw/csativa.txt\")\nstr(csativa)\n\n# First realise that this is a two sample test. You have two independent samples\n# - there are a total of 100 different plants and the values in one \n# group have no relationship to the values in the other.\n\n\n\nCode# create a rough plot of the data \nggplot(data = csativa, aes(x = plant, y = omega)) +\n geom_violin()\nCode# note the modified plants seem to have lower omega!\n\n\n\nCode# create a summary of the data\ncsativa_summary <- csativa %>%\n group_by(plant) %>%\n summarise(mean = mean(omega),\n std = sd(omega),\n n = length(omega),\n se = std/sqrt(n))\n\n\n\nCode# The data seem to be continuous so it is likely that a parametric test will be fine\n# we will check the other assumptions after we have run the lm\n\n# build the statistical model\nmod <- lm(data = csativa, omega ~ plant)\n\n\n# examine it\nsummary(mod)\n# So there is a significant difference but you need to make sure you know the direction!\n# Wild plants have a significantly higher omega 3 content (mean +/- s.e = 56.41 +/- 1.11) \n# than modified plants (49.46 +/- 0.82)(t = 5.03; d.f. = 98; p < 0.0001).\n\n\n\nCode# let's check the assumptions\nplot(mod, which = 1) \nCode# we're looking for the variance in the residuals to be the same in both groups.\n# This looks OK. Maybe a bit higher in the wild plants (with the higher mean)\n \nhist(mod$residuals)\nCodeshapiro.test(mod$residuals)\n# On balance the use of lm() is probably justifiable The variance isn't quite equal \n# and the histogram looks a bit off normal but the normality test is NS and the \n# effect (in the figure) is clear.\n\n\n\nCode# A figure \nfig1 <- ggplot() +\n geom_point(data = csativa, aes(x = plant, y = omega),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"gray50\") +\n geom_errorbar(data = csativa_summary, \n aes(x = plant, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = csativa_summary, \n aes(x = plant, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_x_discrete(name = \"Plant type\", labels = c(\"GMO\", \"WT\")) +\n scale_y_continuous(name = \"Amount of Omega 3 (units)\",\n expand = c(0, 0),\n limits = c(0, 90)) +\n annotate(\"segment\", x = 1, xend = 2, \n y = 80, yend = 80,\n colour = \"black\") +\n annotate(\"text\", x = 1.5, y = 85, \n label = expression(italic(p)~\"< 0.001\")) +\n theme_classic()\n\n# save figure to figures/csativa.png\nggsave(\"figures/csativa.png\",\n plot = fig1,\n width = 3.5,\n height = 3.5,\n units = \"in\",\n dpi = 300)\n\n\n\n💻 another example" }, { "objectID": "index.html", @@ -405,132 +629,6 @@ "section": "", "text": "All the Data Analysis in R teaching is on the VLE so why is this site useful? Well, perhaps more than any other material, you will want to refer back when applying your skills throughout your degree and this site collects everything together in a searchable way. The search icon is on the top right." }, - { - "objectID": "r4babs1/week-7/overview.html", - "href": "r4babs1/week-7/overview.html", - "title": "Overview", - "section": "", - "text": "This week you will start writing R code in RStudio and will create your first graph! You will learn about data types such as “numerics” and “characters” and some of the different types of objects in R such as “vectors” and “dataframes”. These are the building blocks for the rest of your R journey. You will also learn a workflow and about the layout of RStudio and using RStudio Projects.\n\n\n\nArtwork by Horst (2023): “bless this workflow”\n\n\n\nLearning objectives\nThe successful student will be able to:\n\nuse the R command line as a calculator and to assign variables\ncreate and use the basic data types in R\nfind their way around the RStudio windows\nuse an RStudio Project to organise work\nuse a script to run R commands\ncreate and customise a barplot\nsearch and understand manual pages\n\n\n\nInstructions\n\nPrepare\n\nFirst Steps in RStudio: Either 📖 Read the book OR 📹 Watch two videos\n\nWorkshop\ni.💻 🐈 Coat colour of cats. Type in some data, perform calculations on, and plot it.\nConsolidate\n\n💻 Create a plot\n📖 Read Workflow in RStudio\n\n\n\n\n\n\n\nReferences\n\nHorst, Allison. 2023. “Data Science Illustrations.” https://allisonhorst.com/allison-horst." - }, - { - "objectID": "r4babs1/week-7/rstudio-projects.html#outline", - "href": "r4babs1/week-7/rstudio-projects.html#outline", - "title": "RStudio ProjectsWho, what, why?", - "section": "Outline", - "text": "Outline\n\nWho\nA One-line what\nThe high-level why\n\n\nMight be enough!\n\n\n\nMore detailed why\nMore detailed what" - }, - { - "objectID": "r4babs1/week-7/rstudio-projects.html#audience", - "href": "r4babs1/week-7/rstudio-projects.html#audience", - "title": "RStudio ProjectsWho, what, why?", - "section": "Audience", - "text": "Audience\n\nYou teach using R directly\n\nBecoming a Bioscientist 1 - 4\nIM group project\nPGT\n\nYou teach or supervise students using R\n\nfield courses, practical work\nprojects\n\nYou use R" - }, - { - "objectID": "r4babs1/week-7/rstudio-projects.html#an-rstudio-project", - "href": "r4babs1/week-7/rstudio-projects.html#an-rstudio-project", - "title": "RStudio ProjectsWho, what, why?", - "section": "📁 An RStudio Project", - "text": "📁 An RStudio Project\n\nis a folder!\n\n\n\nhave been part of the stage 1 and IM stage 3 for > 5 years\n\n\n\nStage 1\n\nUse an RStudio project containing the script you used to analyse and plot the data for your report, your figures and and the data itself. The Project should be structured and the script should be well-commented, well-organised and follow good practice in the use of spacing, indentation, and variable naming. It should include all the code required to reproduce data import and formatting as well as the summary information, analyses, and figures in your report." - }, - { - "objectID": "r4babs1/week-7/rstudio-projects.html#y12345678", - "href": "r4babs1/week-7/rstudio-projects.html#y12345678", - "title": "RStudio ProjectsWho, what, why?", - "section": "Y12345678", - "text": "Y12345678\ndemo" - }, - { - "objectID": "r4babs1/week-7/rstudio-projects.html#babs-1-4-lo-progression", - "href": "r4babs1/week-7/rstudio-projects.html#babs-1-4-lo-progression", - "title": "RStudio ProjectsWho, what, why?", - "section": "BABS 1-4 LO progression", - "text": "BABS 1-4 LO progression\nBABS 1-5 LO progression" - }, - { - "objectID": "r4babs1/week-7/rstudio-projects.html#why-use-rstudio-projects", - "href": "r4babs1/week-7/rstudio-projects.html#why-use-rstudio-projects", - "title": "RStudio ProjectsWho, what, why?", - "section": "Why use RStudio Projects", - "text": "Why use RStudio Projects\n\nthe same reason we keep lab books: reproducibility and validation\n\n\nIt’s science!\n\n\n\nvia GIPHY" - }, - { - "objectID": "r4babs1/week-7/rstudio-projects.html#why-use-rstudio-projects-1", - "href": "r4babs1/week-7/rstudio-projects.html#why-use-rstudio-projects-1", - "title": "RStudio ProjectsWho, what, why?", - "section": "Why use RStudio Projects", - "text": "Why use RStudio Projects\n\nTransferable: explicit training in organising work" - }, - { - "objectID": "r4babs1/week-7/rstudio-projects.html#why-use-rstudio-projects-2", - "href": "r4babs1/week-7/rstudio-projects.html#why-use-rstudio-projects-2", - "title": "RStudio ProjectsWho, what, why?", - "section": "Why use RStudio Projects", - "text": "Why use RStudio Projects\n\n\n\nhelp you to work with your most important collaborator\n\n\n\n\n\nfutureself, CC-BY-NC, by Julen Colomb" - }, - { - "objectID": "r4babs1/week-7/rstudio-projects.html#section", - "href": "r4babs1/week-7/rstudio-projects.html#section", - "title": "RStudio ProjectsWho, what, why?", - "section": "", - "text": "via GIPHY" - }, - { - "objectID": "r4babs1/week-7/rstudio-projects.html#working-directories-and-paths", - "href": "r4babs1/week-7/rstudio-projects.html#working-directories-and-paths", - "title": "RStudio ProjectsWho, what, why?", - "section": "Working directories and Paths", - "text": "Working directories and Paths\n\ndirectory means folder\nimportant concepts when you interact with computers without clicking\n\n\nAllison Horst cartoon “code gets the blame”" - }, - { - "objectID": "r4babs1/week-7/rstudio-projects.html#working-directories", - "href": "r4babs1/week-7/rstudio-projects.html#working-directories", - "title": "RStudio ProjectsWho, what, why?", - "section": "Working directories", - "text": "Working directories\n\nDefault folder a program will read and write to.\nYou will have some understanding\n\nWord demo" - }, - { - "objectID": "r4babs1/week-7/rstudio-projects.html#paths", - "href": "r4babs1/week-7/rstudio-projects.html#paths", - "title": "RStudio ProjectsWho, what, why?", - "section": "Paths", - "text": "Paths\n\nlocation of a file/folder\nappear in the address bar of explorer/finder and browsers\n\ndemo\n\n\nwhen you can’t click, you need the path\n\n\nchaffinch <- read_table(\"chaff.txt\")" - }, - { - "objectID": "r4babs1/week-7/rstudio-projects.html#absolute-path", - "href": "r4babs1/week-7/rstudio-projects.html#absolute-path", - "title": "RStudio ProjectsWho, what, why?", - "section": "Absolute path", - "text": "Absolute path\n\nchaffinch <- read_table(\"C:/Users/er13/OneDrive - University of York/Desktop/Desktop/undergrad-teaching-york/BIO00017C/BIO00017C-Data-Analysis-in-R-2020/data/chaff.txt\")\n\n\nOnly exists on my computer!" - }, - { - "objectID": "r4babs1/week-7/rstudio-projects.html#relative-paths", - "href": "r4babs1/week-7/rstudio-projects.html#relative-paths", - "title": "RStudio ProjectsWho, what, why?", - "section": "Relative paths", - "text": "Relative paths\n\nlocation of a file/folder relative to the working directory\nIf my working directory is BIO00017C-Data-Analysis-in-R-2020:\n\n\nchaffinch <- read_table(\"data/chaff.txt\")" - }, - { - "objectID": "r4babs1/week-7/rstudio-projects.html#rstudio-projects", - "href": "r4babs1/week-7/rstudio-projects.html#rstudio-projects", - "title": "RStudio ProjectsWho, what, why?", - "section": "RStudio Projects", - "text": "RStudio Projects\n\nSets the working directory to be the project folder\nCode is portable: you send someone the folder and everything just works!" - }, - { - "objectID": "r4babs1/week-7/rstudio-projects.html#demo", - "href": "r4babs1/week-7/rstudio-projects.html#demo", - "title": "RStudio ProjectsWho, what, why?", - "section": "demo", - "text": "demo" - }, - { - "objectID": "r4babs1/week-9/study_after_workshop.html", - "href": "r4babs1/week-9/study_after_workshop.html", - "title": "Independent Study to consolidate this week", - "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the packages and import the data.\n\nlibrary(tidyverse)\nlibrary(readxl)\n\n\n💻 Summarise and plot the pigeons dataframe appropriately.\n\n\nCode# import\npigeons <- read_table(\"data-raw/pigeon.txt\")\n\n# reformat to tidy\npigeons <- pivot_longer(data = pigeons, \n cols = everything(), \n names_to = \"population\", \n values_to = \"distance\")\n\n# sumnmarise\npigeons_summary <- pigeons %>%\n group_by(population) %>%\n summarise(mean = mean(distance),\n std = sd(distance),\n n = length(distance),\n se = std/sqrt(n))\n# plot\nggplot() +\n geom_point(data = pigeons, aes(x = population, y = distance),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"gray50\") +\n geom_errorbar(data = pigeons_summary, \n aes(x = population, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = pigeons_summary, \n aes(x = population, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_y_continuous(name = \"Interorbital distance (mm)\", \n limits = c(0, 14), \n expand = c(0, 0)) +\n scale_x_discrete(name = \"Population\") +\n theme_classic()\n\n\n\n\n💻 The data in blood.csv are measurements of several blood parameters from fifty people with Crohn’s disease, a lifelong condition where parts of the digestive system become inflamed. Twenty-five of people are in the early stages of diagnosis and 25 have started treatment. The variables in the dataset are:\n\nsodium - Sodium concentration in umol/L, the average of 5 technical replicates\npotassium - Potassium concentration in umol/L, the average of 5 technical replicates\nB12 Vitamin - B12 in pmol/L, the average of 5 technical replicates\nwbc - White blood cell count in 10^9 /L, the average of 5 technical replicates\nrbc count - Red blood cell count in 10^12 /L, the average of 5 technical replicates\nplatlet count - platlet count in 10^9 /L, the average of 5 technical replicates\ninflammation marker - the presence or absence of a marker of inflammation, either 0 or 1\nstatus - whether the individual is before or after treatment.\n\nYour task is to summarise and plot these data in any suitable way. Create a complete RStudio Project for an analysis of these data. You will need to:\n\nMake a new project\nMake folders for data and for figures\nImport the data\nSummarise and plot variables of your choice. It doesn’t matter what you chose - the goal is the practice the project workflow and selecting appropriate plotting and summarising methods for particular data sets." - }, { "objectID": "r4babs1/week-9/workshop.html", "href": "r4babs1/week-9/workshop.html", @@ -574,88 +672,11 @@ "text": "Ulna and height\nThe datasets we have used up to this point, have had a continuous variable and a categorical variable where it makes sense to summarise the response for each of the different groups in the categorical variable and plot the response on the y-axis. We will now summarise a dataset with two continuous variables. The data in height.txt are the ulna length (cm) and height (m) of 30 people. In this case, it is more appropriate to summarise both of thee variables and to plot them as a scatter plot.\nWe will use summarise() again but we do not need the group_by() function this time. We will also need to use each of the summary functions, such as mean(), twice, once for each variable.\nImport\n Save height.txt to your data-raw folder\n Read the data into a dataframe called ulna_heights.\nSummarise\n Create a data frame called ulna_heights_summary that contains the sample size and means, standard deviations and standard errors for both variables.\n\nulna_heights_summary <- ulna_heights %>%\n summarise(n = length(ulna),\n mean_ulna = mean(ulna),\n std_ulna = sd(ulna),\n se_ulna = std_ulna/sqrt(n),\n mean_height = mean(height),\n std_height = sd(height),\n se_height = std_height/sqrt(n))\n\nYou should get the following numbers:\n\n\n\n\nn\nmean_ulna\nstd_ulna\nse_ulna\nmean_height\nstd_height\nse_height\n\n\n30\n24.72\n4.137332\n0.75537\n1.494\n0.2404823\n0.0439059\n\n\n\n\nVisualise\nTo plot make a scatter plot we need to use geom_point() again but without any scatter. In this case, it does not really matter which variable is on the x-axis and which is on the y-axis.\n Make a simple scatter plot\n\nggplot(data = ulna_heights, aes(x = ulna, y = height)) +\n geom_point()\n\n\n\n\nIf you have time, you may want to format the figure more appropriately.\n\n\nYou’re finished!" }, { - "objectID": "r4babs1/week-6/study_after_workshop.html", - "href": "r4babs1/week-6/study_after_workshop.html", - "title": "Independent Study to consolidate this week", - "section": "", - "text": "There is no additional study this week but you may want to look ahead to next week." - }, - { - "objectID": "r4babs1/week-6/workshop.html", - "href": "r4babs1/week-6/workshop.html", - "title": "Workshop", - "section": "", - "text": "There is no formal workshop this week but you might want to install R and RStudio on your own machine. This is optional because University computers already have R and RStudio installed.\nInstall R and RStudio.\nNote you need a computer - not a tablet." - }, - { - "objectID": "r4babs1/week-8/study_after_workshop.html", - "href": "r4babs1/week-8/study_after_workshop.html", + "objectID": "r4babs1/week-9/study_after_workshop.html", + "href": "r4babs1/week-9/study_after_workshop.html", "title": "Independent Study to consolidate this week", "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the packages and import the data.\n\nlibrary(tidyverse)\nlibrary(readxl)\n\n\nfly_bristles_means <- read_excel(\"data-raw/bristles-mean.xlsx\")\ncats <- read_csv(\"data-raw/cat-coats.csv\")\n\nExercises\n\n💻 Summarise the fly_bristles_means dataframe by calculating the mean, median, sample size, standard deviation and standard error of the mean_count variable.\n\n\nCodefly_bristles_means_summary <- fly_bristles_means |> \n summarise(mean = mean(mean_count),\n median = median(mean_count),\n n = length(mean_count),\n standard_dev = sd(mean_count),\n standard_error = standard_dev / sqrt(n))\n\n\n\n💻 Create an appropriate plot to show the distribution of mean_count in fly_bristles_means\n\n\n\nCodeggplot(fly_bristles_means, aes(x = mean_count)) +\n geom_histogram(bins = 10)\n\n\n\n💻 Can you format the plot 2. by removing the grey background, giving the bars a black outline and the fill colour of your choice and improving the axis format and labelling? You may want to refer to last week’s workshop.\n\n\nCodeggplot(fly_bristles_means, aes(x = mean_count)) +\n geom_histogram(bins = 10, \n colour = \"black\",\n fill = \"skyblue\") +\n scale_x_continuous(name = \"Number of bristles\",\n expand = c(0, 0)) +\n scale_y_continuous(name = \"Frequency\",\n expand = c(0, 0),\n limits = c(0, 35)) +\n theme_classic()\n\n\n\n💻 Amend this code to change the order of the bars by the average mass of each coat colour? Changing the order of bars was covered last week. You may also want to practice formatting the graph nicely.\n\n\nggplot(cats, aes(x = coat, y = mass)) +\n geom_boxplot()\n\n\n\n\n\nCodeggplot(cats, \n aes(x = reorder(coat, mass), y = mass)) +\n geom_boxplot(fill = \"darkcyan\") +\n scale_x_discrete(name = \"Coat colour\") +\n scale_y_continuous(name = \"Mass (kg)\", \n expand = c(0, 0),\n limits = c(0, 8)) +\n theme_classic()\n\n\n\n📖 Read Understanding the pipe |>" - }, - { - "objectID": "r4babs1/week-8/workshop.html", - "href": "r4babs1/week-8/workshop.html", - "title": "Workshop", - "section": "", - "text": "Artwork by Horst (2023): Continuous and Discrete\n\n\nIn this workshop you will learn how to import data from files and create summaries and plots for it. You will also get more practice with working directories, formatting figures and the pipe.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier workshops\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." - }, - { - "objectID": "r4babs1/week-8/workshop.html#session-overview", - "href": "r4babs1/week-8/workshop.html#session-overview", - "title": "Workshop", - "section": "", - "text": "In this workshop you will learn how to import data from files and create summaries and plots for it. You will also get more practice with working directories, formatting figures and the pipe." - }, - { - "objectID": "r4babs1/week-8/workshop.html#philosophy", - "href": "r4babs1/week-8/workshop.html#philosophy", - "title": "Workshop", - "section": "", - "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier workshops\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." - }, - { - "objectID": "r4babs1/week-8/workshop.html#importing-data-from-files", - "href": "r4babs1/week-8/workshop.html#importing-data-from-files", - "title": "Workshop", - "section": "Importing data from files", - "text": "Importing data from files\nLast week we created data by typing the values in to R. This is not practical when you have added a lot of data to a spreadsheet, or you are using data file that has been supplied to you by a person or a machine. Far more commonly, we import data from a file into R. This requires you know two pieces of information.\n\n\nWhat format the data are in\nThe format of the data determines what function you will use to import it and the file extension often indicates format.\n\n\n.txt a plain text file1, where the columns are often separated by a space but might also be separated by a tab, a backslash or forward slash, or some other character\n\n.csv a plain text file where the columns are separated by commas\n\n.xlsx an Excel file\n\n\n\nWhere the file is relative to your working directory\nR can only read in a file if you say where it is, i.e., you give its relative path. If you follow the advice in this course, your data will be in a folder, data-raw which is inside your Project folder (and working directory).\n\n\nWe will save the four files for this workshop to our Project folder (week-8) and read them in. We will then create a new folder inside our Project folder called data-raw and move the data files to there before modifying the file paths as required. This is demonstrate how the relative path to the file will change after we move it.\n Save these four files in to your week-8 folder\n\nThe coat colour and mass of 62 cats: cat-coats.csv\n\nThe relative size of over 5000 cells measure by forward scatter (FSC) in flow cytometry: cell-size.txt\n\nThe number of sternopleural bristles on 96 female Drosophila: bristles.txt\n\nThe number of sternopleural bristles on 96 female Drosophila (with technical replicates): bristles-mean.xlsx\n\n\nThe first three files can be read in with core tidyverse Wickham et al. (2019) functions and the last can be read in with the readxl Wickham and Bryan (2023) package.\n Load the two packages\n\nlibrary(tidyverse)\nlibrary(readxl)\n\nWe will first read in cat-coats.csv. A .csv. extension suggests this is plain text file with comma separated columns. However, before we attempt to read it it, when should take a look at it. We can do this from RStudio\n Go to the Files pane (bottom right), click on the cat-coats.csv file and choose View File2\n\n\nRStudio Files Pane\n\nAny plain text file will open in the top left pane (Excel files will launch Excel).\n Is the file csv?\n\n\n What kind of variables does the file contain?\n\n\n Read in the csv file with:\n\ncats <- read_csv(\"cat-coats.csv\")\n\nThe data from the file a read into a dataframe called cats and you will be able to see it in the Environment.\n Click on each of the remaining files and choose View File.\n In each case, say what the format is and what types of variables it contains.\n\n\n\n\n\n\n\n\nWe use the read_table()3 command to read in plain text files of single columns or where the columns are separated by spaces…\n …so in cell-size.txt can be read into a dataframe called cells like this:\n\ncells <- read_table(\"cell-size.txt\")\n\n Now you try reading bristles.txt in to a dataframe called fly_bristles\nThe readxl package we loaded earlier has two useful functions for working with Excel files: excel_sheets(\"filename.xlsx\") will list the sheets in an Excel workbook; read_excel(\"filename.xlsx\") will read in to top sheet or a specified sheet with a small modification read_excel(\"filename.xlsx\", sheet = \"Sheet1\").\n List the the names of the sheets and read in the sheet with the data like this:\n\nexcel_sheets(\"bristles-mean.xlsx\")\nfly_bristles_means <- read_excel(\"bristles-mean.xlsx\", sheet = \"means\")\n\nWell done! You can now read read in from files in your working directory.\nTo help you understand relative file paths, we will now move the data files.\n First remove the dataframes you just created to make it easier to see whether you can successfully read in the files from a different place:\n\nrm(cat_coats, fly_bristles, cells, flies_bristles_means)\n\n Now make a new folder called data-raw. You can do this on the Files Pane by clicking New Folder and typing into the box that appears.\n Check the boxes next to the file names and choose More | Move… and select the data-raw folder.\n The files will move. To import data from files in the data-raw folder, you need to give the relative path to the file from the working directory. The working directory is the Project folder, week-8 so the relative path is data-raw/cat-coats.csv\n Import the cat-coats.csv data like this:\n\ncats <- read_csv(\"data-raw/cat-coats.csv\")\n\n Now you do the other files.\nFrom this point forward in the course, we will always create a data-raw folder each time we make a new Project.\nThese are the most common forms of data file you will encounter at first. However, data can certainly come to you in other formats particularly when they have come from particular software. Usually, there is an R package specially for that format.\nIn the rest of the workshop we will take each dataset in turn and create summaries and plots appropriate for the data types. Data is summarised using the group_by() and summarise() functions" - }, - { - "objectID": "r4babs1/week-8/workshop.html#summarising-discrete-data-cat-coat", - "href": "r4babs1/week-8/workshop.html#summarising-discrete-data-cat-coat", - "title": "Workshop", - "section": "Summarising discrete data: Cat coat", - "text": "Summarising discrete data: Cat coat\nThe most appropriate way to summarise nominal data like the colour of cat coats is to tabulate the number of cats with each colour.\n Summarise the cats dataframe by counting the number of cats in each category\n\ncats |> \n group_by(coat) |> \n count()\n\n# A tibble: 6 × 2\n# Groups: coat [6]\n coat n\n <chr> <int>\n1 black 23\n2 calico 1\n3 ginger 10\n4 tabby 8\n5 tortoiseshell 5\n6 white 15\n\n\n|> is the pipe and can be produced with Ctrl+Shift+M\nThis sort of data might be represented with a barchart. You have two options for producing that barchart:\n\nplot the summary table using geom_col()\nplot the raw data using geom_bar()\n\nWe did the first of these last week. The geom_col() function uses the numbers in a second column to determine how high the bars are. However, the geom_bar() function will do the tabulating for you.\n Plot the coat data using geom_bar:\n\nggplot(cats, aes(x = coat)) +\n geom_bar()\n\n\n\n\nThe gaps that R put automatically between the bars reflects that the coat colours are discrete categories." - }, - { - "objectID": "r4babs1/week-8/workshop.html#summarising-counts-bristles", - "href": "r4babs1/week-8/workshop.html#summarising-counts-bristles", - "title": "Workshop", - "section": "Summarising Counts: Bristles", - "text": "Summarising Counts: Bristles\nCounts are discrete and can be thought of a categories with an order (ordinal).\n Summarise the fly_bristles dataframe by counting the number of flies in each category of bristle number\nSince counts are numbers, we might also want to calculate some summary statistics such as the median and interquartile range.\n Summarise the fly_bristles dataframe by calculate the median and interquartile range\n\nfly_bristles |> \n summarise(median(number),\n IQR(number))\n\n# A tibble: 1 × 2\n `median(number)` `IQR(number)`\n <dbl> <dbl>\n1 6 4\n\n\nAs the interquartile is 4 and the median is 6 then 25% flies have 4 bristles or fewer and 25% have 8 or more.\nThe distribution of counts4 is not symmetrical for lower counts so the mean is not usually a good way to summarise count data.\n If you want to save the table you created and give the columns better names you can make two adjustments:\n\nfly_bristles_summary <- fly_bristles |> \n summarise(med = median(number),\n interquartile = IQR(number))\n\n Plot the bristles data using geom_bar:\nIf counts have a a high mean and big range, like number of hairs on a person’s head, then you can often treat them as continuous. This means you can use statistics like the mean and standard deviation to summarise them, histograms to plot them and use some standard statistical tests on them." - }, - { - "objectID": "r4babs1/week-8/workshop.html#summarising-continuous-data", - "href": "r4babs1/week-8/workshop.html#summarising-continuous-data", - "title": "Workshop", - "section": "Summarising continuous data", - "text": "Summarising continuous data\nCat mass\nThe variable mass in the cats dataframe is continuous. Very many continuous variables have a normal distribution. e normal distribution is also known as the bell-shaped curve. If we had the mass of all the cats in the world, we would find many cats were near the mean and fewer would be away from the mean, either much lighter or much heavier. In fact 68% would be within one standard deviation of the mean and about 96% would be within two standard deviations.\n\n\n\n\n\n We can find the mean mass with:\n\ncats |> \n summarise(mean = mean(mass))\n\n# A tibble: 1 × 1\n mean\n <dbl>\n1 4.51\n\n\nWe can add any sort of summary by placing it inside the the summarise parentheses. Each one is separated by a comma. We did this to find the median and the interquatrile range for fly bristles.\n For example, another way to calculate the number of values is to use the length() function:\n\ncats |> \n summarise(mean = mean(mass),\n n = length(mass))\n\n# A tibble: 1 × 2\n mean n\n <dbl> <int>\n1 4.51 62\n\n\n Adapt the code to calculate the mean, the sample size and the standard deviation (sd())\nA single continuous variable can be plotted using a histogram to show the shape of the distribution.\n Plots a histogram of cats mass:\n\nggplot(cats, aes(x = mass)) +\n geom_histogram(bins = 15, colour = \"black\") \n\n\n\n\nNotice that there are no gaps between the bars which reflects that mass is continuous. bins determines how many groups the variable is divided up into (i.e., the number of bars) and colour sets the colour for the outline of the bars. A sample of 62 is a relatively small number of values for plotting a distribution and the number of bins used determines how smooth or normally distributed the values look.\n Experiment with the number of bins. Does the number of bins affect how you view the distribution.\nNext week we will practice summarise and plotting data files with several variables but just to give you a taste, we will find summary statistics about mass for each of the coat types. The group_by() function is used before the summarise() to do calculations for each of the coats:\n\ncats |> \n group_by(coat) |> \n summarise(mean = mean(mass),\n standard_dev = sd(mass))\n\n# A tibble: 6 × 3\n coat mean standard_dev\n <chr> <dbl> <dbl>\n1 black 4.63 1.33 \n2 calico 2.19 NA \n3 ginger 4.46 1.12 \n4 tabby 4.86 0.444\n5 tortoiseshell 4.50 0.929\n6 white 4.34 1.34 \n\n\nYou can read this as:\n\ntake cats and then group by coat and then summarise by finding the mean of mass and the standard deviation of mass\n\n Why do we get an NA for the standard deviation of the calico cats?\n\n\n\nCells\n Summarise the cells dataframe by calculating the mean, median, sample size and standard deviation of FSC.\n Add a column for the standard error which is given by \\(\\frac{s.d.}{\\sqrt{n}}\\)\nMeans of counts\nMany things are quite difficult to measure or count and in these cases we often do technical replicates. A technical replicate allows us the measure the exact same thing to check how variable the measurement process is. For example, Drosophila are small and counting their sternopleural bristles is tricky. In addition, where a bristle is short (young) or broken scientists might vary in whether they count it. Or people or machines might vary in measuring the concentration of the same solution.\nWhen we do technical replicates we calculate their mean and use that as the measure. This is what is in our fly_bristles_means dataframe - the bristles of each of the 96 flies was counted by 5 people and the data are those means. These has an impact on how we plot and summarise the dataset because the distribution of mean counts is continuous! We can use means, standard deviations and histograms. This will be an exercise in Consolidate." - }, - { - "objectID": "r4babs1/week-8/workshop.html#look-after-future-you", - "href": "r4babs1/week-8/workshop.html#look-after-future-you", - "title": "Workshop", - "section": "Look after future you!", - "text": "Look after future you!\nFuture you is going to summarise and plot data from the “River practicals”. You can make this much easier by documenting what you have done now. At the moment all of your code from this workshop is in a single file, probably called analysis.R. I recommend making a new script for each of nominal, continuous and count data and copying the code which imports, summarises and plots it. This will make it easier for future you to find the code you need. Here is an example: nominal_data.R. You may wish to comment your version much more.\nYou’re finished!" - }, - { - "objectID": "r4babs1/week-8/workshop.html#footnotes", - "href": "r4babs1/week-8/workshop.html#footnotes", - "title": "Workshop", - "section": "Footnotes", - "text": "Footnotes\n\nPlain text files can be opened in notepad or other similar editor and still be readable.↩︎\nDo not be tempted to import data this way. Unless you are careful, your data import will not be scripted or will not be scripted correctly.↩︎\nnote read_csv() and read_table() are the same functions with some different settings.↩︎\nCount data are usually “Poisson” distributed.↩︎" + "text": "Set up\nIf you have just opened RStudio you will want to load the packages and import the data.\n\nlibrary(tidyverse)\nlibrary(readxl)\n\n\n💻 Summarise and plot the pigeons dataframe appropriately.\n\n\nCode# import\npigeons <- read_table(\"data-raw/pigeon.txt\")\n\n# reformat to tidy\npigeons <- pivot_longer(data = pigeons, \n cols = everything(), \n names_to = \"population\", \n values_to = \"distance\")\n\n# sumnmarise\npigeons_summary <- pigeons %>%\n group_by(population) %>%\n summarise(mean = mean(distance),\n std = sd(distance),\n n = length(distance),\n se = std/sqrt(n))\n# plot\nggplot() +\n geom_point(data = pigeons, aes(x = population, y = distance),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"gray50\") +\n geom_errorbar(data = pigeons_summary, \n aes(x = population, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = pigeons_summary, \n aes(x = population, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_y_continuous(name = \"Interorbital distance (mm)\", \n limits = c(0, 14), \n expand = c(0, 0)) +\n scale_x_discrete(name = \"Population\") +\n theme_classic()\n\n\n\n\n💻 The data in blood.csv are measurements of several blood parameters from fifty people with Crohn’s disease, a lifelong condition where parts of the digestive system become inflamed. Twenty-five of people are in the early stages of diagnosis and 25 have started treatment. The variables in the dataset are:\n\nsodium - Sodium concentration in umol/L, the average of 5 technical replicates\npotassium - Potassium concentration in umol/L, the average of 5 technical replicates\nB12 Vitamin - B12 in pmol/L, the average of 5 technical replicates\nwbc - White blood cell count in 10^9 /L, the average of 5 technical replicates\nrbc count - Red blood cell count in 10^12 /L, the average of 5 technical replicates\nplatlet count - platlet count in 10^9 /L, the average of 5 technical replicates\ninflammation marker - the presence or absence of a marker of inflammation, either 0 or 1\nstatus - whether the individual is before or after treatment.\n\nYour task is to summarise and plot these data in any suitable way. Create a complete RStudio Project for an analysis of these data. You will need to:\n\nMake a new project\nMake folders for data and for figures\nImport the data\nSummarise and plot variables of your choice. It doesn’t matter what you chose - the goal is the practice the project workflow and selecting appropriate plotting and summarising methods for particular data sets." }, { "objectID": "r4babs1/r4babs1.html", @@ -699,13 +720,6 @@ "section": "Summarising data with several variables", "text": "Summarising data with several variables\nThis week you will start plotting data sets with more than one variable. This means you need to be able determine which variable is the response and which is the explanatory. You will find out what is meant by “tidy” data and how to perform a simple data tidying task. Finally you will discover how to save your figures and place them in documents." }, - { - "objectID": "r4babs1/week-8/study_before_workshop.html", - "href": "r4babs1/week-8/study_before_workshop.html", - "title": "Independent Study to prepare for workshop", - "section": "", - "text": "📖 Read Ideas about data" - }, { "objectID": "r4babs1/week-8/overview.html", "href": "r4babs1/week-8/overview.html", @@ -714,39 +728,25 @@ "text": "The type of values our data can take is important in how we analyse and visualise it. This week you will learn the difference between continuous and discrete values and how we summarise and visualise them. You will also learn about the “normal distribution” which is the most important continuous distribution.\n\n\n\nDiscrete variable\n\n\n\nLearning objectives\nThe successful student will be able to:\n\ndistinguish between continuous, discrete, nominal and ordinal variable\nread in data in to RStudio from a plain text file and Excel files\nsummarise and plot variables appropriately for the data type\n\n\n\nInstructions\n\nPrepare\n\n📖 Read: Ideas about data\n\nWorkshop\n\n💻 Importing data\n💻 Summarising discrete data\n💻 Summarising count data\n💻 Summarising continuous data\n\nConsolidate\n\n💻 Summarise some data\n💻 Plot some data\n💻 Format a plot (1)\n💻 Format a plot (2)\n📖 Read Understanding the pipe |>" }, { - "objectID": "r4babs1/week-6/study_before_workshop.html", - "href": "r4babs1/week-6/study_before_workshop.html", + "objectID": "r4babs1/week-8/study_before_workshop.html", + "href": "r4babs1/week-8/study_before_workshop.html", "title": "Independent Study to prepare for workshop", "section": "", - "text": "Join the video conference Intro: Data Handling - BIO00027C-A (Lecture) on your timetable\nRead What they forgot to teach you about computers in Computational Analysis for Bioscientists\nRead What are R and Rstudio?. You only need to read this section, you do not need to the read the rest of the chapter (yet!)" + "text": "📖 Read Ideas about data" }, { "objectID": "r4babs1/week-6/overview.html", "href": "r4babs1/week-6/overview.html", "title": "Overview", "section": "", - "text": "This week you will carry out some independent study to ensure you have some understanding of computer file systems. We will introduce you to the concepts of paths and working directories.\n\n\n\nArtwork by Horst (2023): “code gets the blame”\n\n\n\nLearning objectives\nThe parentheses after each learning objective indicate where the content covers that objective.\nThe successful student will be able to:\n\nexplain what an operating system is\nexplain the organisation of files and directories in a file systems\nexplain what a file is and give some common files types\nexplain what is meant by a plain text file\nexplain the relationship between the file extensions, the file format and associations with programs\nuse a file manager\nexplain root, home and working directories\nexplain absolute and relative file paths\nknow what R and RStudio are\nknow how to organise their work\n\n\n\nInstructions\n\nPrepare\n\nJoin the video conference Intro: Data Handling - BIO00027C-A (Lecture) on your timetable\nRead What they forgot to teach you about computers\nRead What are R and Rstudio?\n\nWorkshop\n\nOptional: Install R and RStudio\n\nConsolidate\n\n\n\n\n\n\nReferences\n\nHorst, Allison. 2023. “Data Science Illustrations.” https://allisonhorst.com/allison-horst." - }, - { - "objectID": "r4babs1/week-9/study_before_workshop.html", - "href": "r4babs1/week-9/study_before_workshop.html", - "title": "Independent Study to prepare for workshop", - "section": "", - "text": "📖 Read From importing to reporting. The first part of this chapter is about data import which we covered in the last workshop. You may be able to skip that part or you may find it useful to revise. The section on Summarising data will be mainly new." - }, - { - "objectID": "r4babs1/week-9/overview.html", - "href": "r4babs1/week-9/overview.html", - "title": "Overview", - "section": "", - "text": "Last week you summarised and plotted single variables. This week you will start plotting data sets with more than one variable. This means you need to be able determine which variable is the response and which is the explanatory. You will find out what is meant by “tidy” data and how to perform a simple data tidying task. Finally you will discover how to save your figures and place them in documents.\n\nLearning objectives\n\nsummarise and plot appropriately datasets with more than one variable\nrecognise that variables can be categorised by their role in analysis\nexplain what is meant by ‘tidy’ data and be able to perform some data tidying tasks.\nsave figures to file\ncreate neat reports which include text and figures\n\n\n\nInstructions\n\nPrepare\n\n📖 From importing to reporting\n\nWorkshop\n\n💻 Summarise and plot datasets with more than one variable.\n💻 Practice with working directories, importing data, formatting figures and the pipe\n💻 Lay out text, figures and figure legends in documents\n\nConsolidate\n\n💻 Summarise and plot a dataframe from the workshop\n💻 Practice the complete RStudio Project worklfow for a new dataset" + "text": "This week you will carry out some independent study to ensure you have some understanding of computer file systems. We will introduce you to the concepts of paths and working directories.\n\n\n\nArtwork by Horst (2023): “code gets the blame”\n\n\n\nLearning objectives\nThe parentheses after each learning objective indicate where the content covers that objective.\nThe successful student will be able to:\n\nexplain what an operating system is\nexplain the organisation of files and directories in a file systems\nexplain what a file is and give some common files types\nexplain what is meant by a plain text file\nexplain the relationship between the file extensions, the file format and associations with programs\nuse a file manager\nexplain root, home and working directories\nexplain absolute and relative file paths\nknow what R and RStudio are\nknow how to organise their work\n\n\n\nInstructions\n\nPrepare\n\nJoin the video conference Intro: Data Handling - BIO00027C-A (Lecture) on your timetable\nRead What they forgot to teach you about computers\nRead What are R and Rstudio?\n\nWorkshop\n\nOptional: Install R and RStudio\n\nConsolidate\n\n\n\n\n\n\nReferences\n\nHorst, Allison. 2023. “Data Science Illustrations.” https://allisonhorst.com/allison-horst." }, { - "objectID": "r4babs1/week-7/study_before_workshop.html", - "href": "r4babs1/week-7/study_before_workshop.html", + "objectID": "r4babs1/week-6/study_before_workshop.html", + "href": "r4babs1/week-6/study_before_workshop.html", "title": "Independent Study to prepare for workshop", "section": "", - "text": "Either\n\n📖 Read First Steps in RStudio in\n\nOR\n\n📹 Watch" + "text": "Join the video conference Intro: Data Handling - BIO00027C-A (Lecture) on your timetable\nRead What they forgot to teach you about computers in Computational Analysis for Bioscientists\nRead What are R and Rstudio?. You only need to read this section, you do not need to the read the rest of the chapter (yet!)" }, { "objectID": "r4babs1/week-7/workshop.html", @@ -832,27 +832,6 @@ "section": "", "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 In a maternity hospital, the total numbers of births induced on each day of the week over a six week period were recorded (see table below). Create a plot of these data with the days of week in order.\n\n\n\n\nNumber of inductions for each day of the week over six weeks.\n\nDay\nNo. inductions\n\n\n\nMonday\n43\n\n\nTuesday\n36\n\n\nWednesday\n35\n\n\nThursday\n38\n\n\nFriday\n48\n\n\nSaturday\n26\n\n\nSunday\n24\n\n\n\n\n\n\nCode# create a dataframe for the data\nday <- c(\"Monday\", \n \"Tuesday\", \n \"Wednesday\",\n \"Thursday\",\n \"Friday\",\n \"Saturday\",\n \"Sunday\")\nfreq <- c(43, 36, 35, 38, 48, 26, 24) \ninductions <- data.frame(day, freq)\n\n# make the order of the days correct rather than alphabetical\ninductions <- inductions |> \n mutate(day = fct_relevel(day, c(\"Monday\",\n \"Tuesday\",\n \"Wednesday\",\n \"Thursday\",\n \"Friday\",\n \"Saturday\",\n \"Sunday\")))\n\n# plot the data as a barplot with the bars in\nggplot(data = inductions, \n aes(x = day, y = freq)) +\n geom_col(colour = \"black\",\n fill = \"lightseagreen\") +\n scale_x_discrete(expand = c(0, 0),\n name = \"Day of the week\") + \n scale_y_continuous(expand = c(0, 0),\n name = \"Number of inductions\",\n limits = c(0, 55)) +\n theme_classic()\n\n\n\n📖 Read Workflow in RStudio" }, - { - "objectID": "r4babs4/r4babs4.html", - "href": "r4babs4/r4babs4.html", - "title": "Data Analysis in R for BABS 4", - "section": "", - "text": "This is the last of the four BABS modules.\ncore strand specific: immunology\n\n\nThe BABS4 Module Learning outcomes that relate to the Data Analysis in R content are:" - }, - { - "objectID": "r4babs4/r4babs4.html#module-learning-objectives", - "href": "r4babs4/r4babs4.html#module-learning-objectives", - "title": "Data Analysis in R for BABS 4", - "section": "", - "text": "The BABS4 Module Learning outcomes that relate to the Data Analysis in R content are:" - }, - { - "objectID": "r4babs4/r4babs4.html#all-strands", - "href": "r4babs4/r4babs4.html#all-strands", - "title": "Data Analysis in R for BABS 4", - "section": "All Strands", - "text": "All Strands\nmany var and obs, examples, PCA, log fc transformation, normalisation, QC gating, missing values, excluding proteins id’d from fewer thaan two peptides, FDR, data structures" - }, { "objectID": "about.html", "href": "about.html", @@ -861,256 +840,305 @@ "text": "About this site\nAbout me" }, { - "objectID": "pgt52m/week-3/workshop.html", - "href": "pgt52m/week-3/workshop.html", - "title": "Workshop", + "objectID": "r4babs1/week-7/study_before_workshop.html", + "href": "r4babs1/week-7/study_before_workshop.html", + "title": "Independent Study to prepare for workshop", "section": "", - "text": "Artwork by Horst (2023): Continuous and Discrete\n\n\nIn this workshop you will learn how to import data from files and create summaries and plots for it. You will also get more practice with working directories, formatting figures and the pipe.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier workshops\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "text": "Either\n\n📖 Read First Steps in RStudio in\n\nOR\n\n📹 Watch" }, { - "objectID": "pgt52m/week-3/workshop.html#session-overview", - "href": "pgt52m/week-3/workshop.html#session-overview", - "title": "Workshop", + "objectID": "r4babs1/week-7/overview.html", + "href": "r4babs1/week-7/overview.html", + "title": "Overview", "section": "", - "text": "In this workshop you will learn how to import data from files and create summaries and plots for it. You will also get more practice with working directories, formatting figures and the pipe." + "text": "This week you will start writing R code in RStudio and will create your first graph! You will learn about data types such as “numerics” and “characters” and some of the different types of objects in R such as “vectors” and “dataframes”. These are the building blocks for the rest of your R journey. You will also learn a workflow and about the layout of RStudio and using RStudio Projects.\n\n\n\nArtwork by Horst (2023): “bless this workflow”\n\n\n\nLearning objectives\nThe successful student will be able to:\n\nuse the R command line as a calculator and to assign variables\ncreate and use the basic data types in R\nfind their way around the RStudio windows\nuse an RStudio Project to organise work\nuse a script to run R commands\ncreate and customise a barplot\nsearch and understand manual pages\n\n\n\nInstructions\n\nPrepare\n\nFirst Steps in RStudio: Either 📖 Read the book OR 📹 Watch two videos\n\nWorkshop\ni.💻 🐈 Coat colour of cats. Type in some data, perform calculations on, and plot it.\nConsolidate\n\n💻 Create a plot\n📖 Read Workflow in RStudio\n\n\n\n\n\n\n\nReferences\n\nHorst, Allison. 2023. “Data Science Illustrations.” https://allisonhorst.com/allison-horst." }, { - "objectID": "pgt52m/week-3/workshop.html#philosophy", - "href": "pgt52m/week-3/workshop.html#philosophy", - "title": "Workshop", - "section": "", - "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier workshops\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "objectID": "r4babs1/week-7/rstudio-projects.html#outline", + "href": "r4babs1/week-7/rstudio-projects.html#outline", + "title": "RStudio ProjectsWho, what, why?", + "section": "Outline", + "text": "Outline\n\nWho\nA One-line what\nThe high-level why\n\n\nMight be enough!\n\n\n\nMore detailed why\nMore detailed what" }, { - "objectID": "pgt52m/week-3/workshop.html#importing-data-from-files", - "href": "pgt52m/week-3/workshop.html#importing-data-from-files", - "title": "Workshop", - "section": "Importing data from files", - "text": "Importing data from files\nLast week we created data by typing the values in to R. This is not practical when you have added a lot of data to a spreadsheet, or you are using data file that has been supplied to you by a person or a machine. Far more commonly, we import data from a file into R. This requires you know two pieces of information.\n\n\nWhat format the data are in\nThe format of the data determines what function you will use to import it and the file extension often indicates format.\n\n\n.txt a plain text file1, where the columns are often separated by a space but might also be separated by a tab, a backslash or forward slash, or some other character\n\n.csv a plain text file where the columns are separated by commas\n\n.xlsx an Excel file\n\n\n\nWhere the file is relative to your working directory\nR can only read in a file if you say where it is, i.e., you give its relative path. If you follow the advice in this course, your data will be in a folder, data-raw which is inside your Project folder (and working directory).\n\n\nWe will save the four files for this workshop to our Project folder (week-8) and read them in. We will then create a new folder inside our Project folder called data-raw and move the data files to there before modifying the file paths as required. This is demonstrate how the relative path to the file will change after we move it.\n Save these four files in to your week-8 folder\n\nThe coat colour and mass of 62 cats: cat-coats.csv\n\nThe relative size of over 5000 cells measure by forward scatter (FSC) in flow cytometry: cell-size.txt\n\nThe number of sternopleural bristles on 96 female Drosophila: bristles.txt\n\nThe number of sternopleural bristles on 96 female Drosophila (with technical replicates): bristles-mean.xlsx\n\n\nThe first three files can be read in with core tidyverse Wickham et al. (2019) functions and the last can be read in with the readxl Wickham and Bryan (2023) package.\n Load the two packages\n\nlibrary(tidyverse)\nlibrary(readxl)\n\nWe will first read in cat-coats.csv. A .csv. extension suggests this is plain text file with comma separated columns. However, before we attempt to read it it, when should take a look at it. We can do this from RStudio\n Go to the Files pane (bottom right), click on the cat-coats.csv file and choose View File2\n\n\nRStudio Files Pane\n\nAny plain text file will open in the top left pane (Excel files will launch Excel).\n Is the file csv?\n\n\n What kind of variables does the file contain?\n\n\n Read in the csv file with:\n\ncats <- read_csv(\"cat-coats.csv\")\n\nThe data from the file a read into a dataframe called cats and you will be able to see it in the Environment.\n Click on each of the remaining files and choose View File.\n In each case, say what the format is and what types of variables it contains.\n\n\n\n\n\n\n\n\nWe use the read_table()3 command to read in plain text files of single columns or where the columns are separated by spaces…\n …so in cell-size.txt can be read into a dataframe called cells like this:\n\ncells <- read_table(\"cell-size.txt\")\n\n Now you try reading bristles.txt in to a dataframe called fly_bristles\nThe readxl package we loaded earlier has two useful functions for working with Excel files: excel_sheets(\"filename.xlsx\") will list the sheets in an Excel workbook; read_excel(\"filename.xlsx\") will read in to top sheet or a specified sheet with a small modification read_excel(\"filename.xlsx\", sheet = \"Sheet1\").\n List the the names of the sheets and read in the sheet with the data like this:\n\nexcel_sheets(\"bristles-mean.xlsx\")\nfly_bristles_means <- read_excel(\"bristles-mean.xlsx\", sheet = \"means\")\n\nWell done! You can now read read in from files in your working directory.\nTo help you understand relative file paths, we will now move the data files.\n First remove the dataframes you just created to make it easier to see whether you can successfully read in the files from a different place:\n\nrm(cat_coats, fly_bristles, cells, flies_bristles_means)\n\n Now make a new folder called data-raw. You can do this on the Files Pane by clicking New Folder and typing into the box that appears.\n Check the boxes next to the file names and choose More | Move… and select the data-raw folder.\n The files will move. To import data from files in the data-raw folder, you need to give the relative path to the file from the working directory. The working directory is the Project folder, week-8 so the relative path is data-raw/cat-coats.csv\n Import the cat-coats.csv data like this:\n\ncats <- read_csv(\"data-raw/cat-coats.csv\")\n\n Now you do the other files.\nFrom this point forward in the course, we will always create a data-raw folder each time we make a new Project.\nThese are the most common forms of data file you will encounter at first. However, data can certainly come to you in other formats particularly when they have come from particular software. Usually, there is an R package specially for that format.\nIn the rest of the workshop we will take each dataset in turn and create summaries and plots appropriate for the data types. Data is summarised using the group_by() and summarise() functions" + "objectID": "r4babs1/week-7/rstudio-projects.html#audience", + "href": "r4babs1/week-7/rstudio-projects.html#audience", + "title": "RStudio ProjectsWho, what, why?", + "section": "Audience", + "text": "Audience\n\nYou teach using R directly\n\nBecoming a Bioscientist 1 - 4\nIM group project\nPGT\n\nYou teach or supervise students using R\n\nfield courses, practical work\nprojects\n\nYou use R" }, { - "objectID": "pgt52m/week-3/workshop.html#summarising-discrete-data-cat-coat", - "href": "pgt52m/week-3/workshop.html#summarising-discrete-data-cat-coat", - "title": "Workshop", - "section": "Summarising discrete data: Cat coat", - "text": "Summarising discrete data: Cat coat\nThe most appropriate way to summarise nominal data like the colour of cat coats is to tabulate the number of cats with each colour.\n Summarise the cats dataframe by counting the number of cats in each category\n\ncats |> \n group_by(coat) |> \n count()\n\n# A tibble: 6 × 2\n# Groups: coat [6]\n coat n\n <chr> <int>\n1 black 23\n2 calico 1\n3 ginger 10\n4 tabby 8\n5 tortoiseshell 5\n6 white 15\n\n\n|> is the pipe and can be produced with Ctrl+Shift+M\nThis sort of data might be represented with a barchart. You have two options for producing that barchart:\n\nplot the summary table using geom_col()\nplot the raw data using geom_bar()\n\nWe did the first of these last week. The geom_col() function uses the numbers in a second column to determine how high the bars are. However, the geom_bar() function will do the tabulating for you.\n Plot the coat data using geom_bar:\n\nggplot(cats, aes(x = coat)) +\n geom_bar()\n\n\n\n\nThe gaps that R put automatically between the bars reflects that the coat colours are discrete categories." + "objectID": "r4babs1/week-7/rstudio-projects.html#an-rstudio-project", + "href": "r4babs1/week-7/rstudio-projects.html#an-rstudio-project", + "title": "RStudio ProjectsWho, what, why?", + "section": "📁 An RStudio Project", + "text": "📁 An RStudio Project\n\nis a folder!\n\n\n\nhave been part of the stage 1 and IM stage 3 for > 5 years\n\n\n\nStage 1\n\nUse an RStudio project containing the script you used to analyse and plot the data for your report, your figures and and the data itself. The Project should be structured and the script should be well-commented, well-organised and follow good practice in the use of spacing, indentation, and variable naming. It should include all the code required to reproduce data import and formatting as well as the summary information, analyses, and figures in your report." }, { - "objectID": "pgt52m/week-3/workshop.html#summarising-counts-bristles", - "href": "pgt52m/week-3/workshop.html#summarising-counts-bristles", - "title": "Workshop", - "section": "Summarising Counts: Bristles", - "text": "Summarising Counts: Bristles\nCounts are discrete and can be thought of a categories with an order (ordinal).\n Summarise the fly_bristles dataframe by counting the number of flies in each category of bristle number\nSince counts are numbers, we might also want to calculate some summary statistics such as the median and interquartile range.\n Summarise the fly_bristles dataframe by calculate the median and interquartile range\n\nfly_bristles |> \n summarise(median(number),\n IQR(number))\n\n# A tibble: 1 × 2\n `median(number)` `IQR(number)`\n <dbl> <dbl>\n1 6 4\n\n\nAs the interquartile is 4 and the median is 6 then 25% flies have 4 bristles or fewer and 25% have 8 or more.\nThe distribution of counts4 is not symmetrical for lower counts so the mean is not usually a good way to summarise count data.\n If you want to save the table you created and give the columns better names you can make two adjustments:\n\nfly_bristles_summary <- fly_bristles |> \n summarise(med = median(number),\n interquartile = IQR(number))\n\n Plot the bristles data using geom_bar:\nIf counts have a a high mean and big range, like number of hairs on a person’s head, then you can often treat them as continuous. This means you can use statistics like the mean and standard deviation to summarise them, histograms to plot them and use some standard statistical tests on them." + "objectID": "r4babs1/week-7/rstudio-projects.html#y12345678", + "href": "r4babs1/week-7/rstudio-projects.html#y12345678", + "title": "RStudio ProjectsWho, what, why?", + "section": "Y12345678", + "text": "Y12345678\ndemo" }, { - "objectID": "pgt52m/week-3/workshop.html#summarising-continuous-data", - "href": "pgt52m/week-3/workshop.html#summarising-continuous-data", - "title": "Workshop", - "section": "Summarising continuous data", - "text": "Summarising continuous data\nCat mass\nThe variable mass in the cats dataframe is continuous. Very many continuous variables have a normal distribution. e normal distribution is also known as the bell-shaped curve. If we had the mass of all the cats in the world, we would find many cats were near the mean and fewer would be away from the mean, either much lighter or much heavier. In fact 68% would be within one standard deviation of the mean and about 96% would be within two standard deviations.\n\n\n\n\n\n We can find the mean mass with:\n\ncats |> \n summarise(mean = mean(mass))\n\n# A tibble: 1 × 1\n mean\n <dbl>\n1 4.51\n\n\nWe can add any sort of summary by placing it inside the the summarise parentheses. Each one is separated by a comma. We did this to find the median and the interquatrile range for fly bristles.\n For example, another way to calculate the number of values is to use the length() function:\n\ncats |> \n summarise(mean = mean(mass),\n n = length(mass))\n\n# A tibble: 1 × 2\n mean n\n <dbl> <int>\n1 4.51 62\n\n\n Adapt the code to calculate the mean, the sample size and the standard deviation (sd())\nA single continuous variable can be plotted using a histogram to show the shape of the distribution.\n Plots a histogram of cats mass:\n\nggplot(cats, aes(x = mass)) +\n geom_histogram(bins = 15, colour = \"black\") \n\n\n\n\nNotice that there are no gaps between the bars which reflects that mass is continuous. bins determines how many groups the variable is divided up into (i.e., the number of bars) and colour sets the colour for the outline of the bars. A sample of 62 is a relatively small number of values for plotting a distribution and the number of bins used determines how smooth or normally distributed the values look.\n Experiment with the number of bins. Does the number of bins affect how you view the distribution.\nNext week we will practice summarise and plotting data files with several variables but just to give you a taste, we will find summary statistics about mass for each of the coat types. The group_by() function is used before the summarise() to do calculations for each of the coats:\n\ncats |> \n group_by(coat) |> \n summarise(mean = mean(mass),\n standard_dev = sd(mass))\n\n# A tibble: 6 × 3\n coat mean standard_dev\n <chr> <dbl> <dbl>\n1 black 4.63 1.33 \n2 calico 2.19 NA \n3 ginger 4.46 1.12 \n4 tabby 4.86 0.444\n5 tortoiseshell 4.50 0.929\n6 white 4.34 1.34 \n\n\nYou can read this as:\n\ntake cats and then group by coat and then summarise by finding the mean of mass and the standard deviation of mass\n\n Why do we get an NA for the standard deviation of the calico cats?\n\n\n\nCells\n Summarise the cells dataframe by calculating the mean, median, sample size and standard deviation of FSC.\n Add a column for the standard error which is given by \\(\\frac{s.d.}{\\sqrt{n}}\\)\nMeans of counts\nMany things are quite difficult to measure or count and in these cases we often do technical replicates. A technical replicate allows us the measure the exact same thing to check how variable the measurement process is. For example, Drosophila are small and counting their sternopleural bristles is tricky. In addition, where a bristle is short (young) or broken scientists might vary in whether they count it. Or people or machines might vary in measuring the concentration of the same solution.\nWhen we do technical replicates we calculate their mean and use that as the measure. This is what is in our fly_bristles_means dataframe - the bristles of each of the 96 flies was counted by 5 people and the data are those means. These has an impact on how we plot and summarise the dataset because the distribution of mean counts is continuous! We can use means, standard deviations and histograms. This will be an exercise in Consolidate." + "objectID": "r4babs1/week-7/rstudio-projects.html#babs-1-4-lo-progression", + "href": "r4babs1/week-7/rstudio-projects.html#babs-1-4-lo-progression", + "title": "RStudio ProjectsWho, what, why?", + "section": "BABS 1-4 LO progression", + "text": "BABS 1-4 LO progression\nBABS 1-5 LO progression" }, { - "objectID": "pgt52m/week-3/workshop.html#look-after-future-you", - "href": "pgt52m/week-3/workshop.html#look-after-future-you", - "title": "Workshop", - "section": "Look after future you!", - "text": "Look after future you!\nFuture you is going to summarise and plot data from the “River practicals”. You can make this much easier by documenting what you have done now. At the moment all of your code from this workshop is in a single file, probably called analysis.R. I recommend making a new script for each of nominal, continuous and count data and copying the code which imports, summarises and plots it. This will make it easier for future you to find the code you need. Here is an example: nominal_data.R. You may wish to comment your version much more.\nYou’re finished!" + "objectID": "r4babs1/week-7/rstudio-projects.html#why-use-rstudio-projects", + "href": "r4babs1/week-7/rstudio-projects.html#why-use-rstudio-projects", + "title": "RStudio ProjectsWho, what, why?", + "section": "Why use RStudio Projects", + "text": "Why use RStudio Projects\n\nthe same reason we keep lab books: reproducibility and validation\n\n\nIt’s science!\n\n\n\nvia GIPHY" }, { - "objectID": "pgt52m/week-3/workshop.html#footnotes", - "href": "pgt52m/week-3/workshop.html#footnotes", - "title": "Workshop", - "section": "Footnotes", - "text": "Footnotes\n\nPlain text files can be opened in notepad or other similar editor and still be readable.↩︎\nDo not be tempted to import data this way. Unless you are careful, your data import will not be scripted or will not be scripted correctly.↩︎\nnote read_csv() and read_table() are the same functions with some different settings.↩︎\nCount data are usually “Poisson” distributed.↩︎" + "objectID": "r4babs1/week-7/rstudio-projects.html#why-use-rstudio-projects-1", + "href": "r4babs1/week-7/rstudio-projects.html#why-use-rstudio-projects-1", + "title": "RStudio ProjectsWho, what, why?", + "section": "Why use RStudio Projects", + "text": "Why use RStudio Projects\n\nTransferable: explicit training in organising work" }, { - "objectID": "pgt52m/week-3/study_after_workshop.html", - "href": "pgt52m/week-3/study_after_workshop.html", - "title": "Independent Study to consolidate this week", - "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the packages and import the data.\n\nlibrary(tidyverse)\nlibrary(readxl)\n\n\nfly_bristles_means <- read_excel(\"data-raw/bristles-mean.xlsx\")\ncats <- read_csv(\"data-raw/cat-coats.csv\")\n\nExercises\n\n💻 Summarise the fly_bristles_means dataframe by calculating the mean, median, sample size, standard deviation and standard error of the mean_count variable.\n\n\nCodefly_bristles_means_summary <- fly_bristles_means |> \n summarise(mean = mean(mean_count),\n median = median(mean_count),\n n = length(mean_count),\n standard_dev = sd(mean_count),\n standard_error = standard_dev / sqrt(n))\n\n\n\n💻 Create an appropriate plot to show the distribution of mean_count in fly_bristles_means\n\n\n\nCodeggplot(fly_bristles_means, aes(x = mean_count)) +\n geom_histogram(bins = 10)\n\n\n\n💻 Can you format the plot 2. by removing the grey background, giving the bars a black outline and the fill colour of your choice and improving the axis format and labelling? You may want to refer to last week’s workshop.\n\n\nCodeggplot(fly_bristles_means, aes(x = mean_count)) +\n geom_histogram(bins = 10, \n colour = \"black\",\n fill = \"skyblue\") +\n scale_x_continuous(name = \"Number of bristles\",\n expand = c(0, 0)) +\n scale_y_continuous(name = \"Frequency\",\n expand = c(0, 0),\n limits = c(0, 35)) +\n theme_classic()\n\n\n\n💻 Amend this code to change the order of the bars by the average mass of each coat colour? Changing the order of bars was covered last week. You may also want to practice formatting the graph nicely.\n\n\nggplot(cats, aes(x = coat, y = mass)) +\n geom_boxplot()\n\n\n\n\n\nCodeggplot(cats, \n aes(x = reorder(coat, mass), y = mass)) +\n geom_boxplot(fill = \"darkcyan\") +\n scale_x_discrete(name = \"Coat colour\") +\n scale_y_continuous(name = \"Mass (kg)\", \n expand = c(0, 0),\n limits = c(0, 8)) +\n theme_classic()\n\n\n\n📖 Read Understanding the pipe |>" + "objectID": "r4babs1/week-7/rstudio-projects.html#why-use-rstudio-projects-2", + "href": "r4babs1/week-7/rstudio-projects.html#why-use-rstudio-projects-2", + "title": "RStudio ProjectsWho, what, why?", + "section": "Why use RStudio Projects", + "text": "Why use RStudio Projects\n\n\n\nhelp you to work with your most important collaborator\n\n\n\n\n\nfutureself, CC-BY-NC, by Julen Colomb" }, { - "objectID": "pgt52m/week-8/workshop.html", - "href": "pgt52m/week-8/workshop.html", - "title": "Workshop", + "objectID": "r4babs1/week-7/rstudio-projects.html#section", + "href": "r4babs1/week-7/rstudio-projects.html#section", + "title": "RStudio ProjectsWho, what, why?", "section": "", - "text": "Artwork by Horst (2023): “Debugging and feelings”\n\n\nIn this session you will get practice in choosing between, performing, and presenting the results of, one-way ANOVA and Kruskal-Wallis in R.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "text": "via GIPHY" }, { - "objectID": "pgt52m/week-8/workshop.html#session-overview", - "href": "pgt52m/week-8/workshop.html#session-overview", - "title": "Workshop", - "section": "", - "text": "In this session you will get practice in choosing between, performing, and presenting the results of, one-way ANOVA and Kruskal-Wallis in R." + "objectID": "r4babs1/week-7/rstudio-projects.html#working-directories-and-paths", + "href": "r4babs1/week-7/rstudio-projects.html#working-directories-and-paths", + "title": "RStudio ProjectsWho, what, why?", + "section": "Working directories and Paths", + "text": "Working directories and Paths\n\ndirectory means folder\nimportant concepts when you interact with computers without clicking\n\n\nAllison Horst cartoon “code gets the blame”" }, { - "objectID": "pgt52m/week-8/workshop.html#philosophy", - "href": "pgt52m/week-8/workshop.html#philosophy", - "title": "Workshop", - "section": "", - "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "objectID": "r4babs1/week-7/rstudio-projects.html#working-directories", + "href": "r4babs1/week-7/rstudio-projects.html#working-directories", + "title": "RStudio ProjectsWho, what, why?", + "section": "Working directories", + "text": "Working directories\n\nDefault folder a program will read and write to.\nYou will have some understanding\n\nWord demo" }, { - "objectID": "pgt52m/week-8/workshop.html#myoglobin-in-seal-muscle", - "href": "pgt52m/week-8/workshop.html#myoglobin-in-seal-muscle", - "title": "Workshop", - "section": "Myoglobin in seal muscle", - "text": "Myoglobin in seal muscle\nThe myoglobin concentration of skeletal muscle of three species of seal in grams per kilogram of muscle was determined and the data are given in seal.csv. We want to know if there is a difference between species. Each row represents an individual seal. The first column gives the myoglobin concentration and the second column indicates species.\n Save a copy of the data file seal.csv to data-raw\n Read in the data and check the structure. I used the name seal for the dataframe/tibble.\n What kind of variables do you have?\n\n\n\nExploring\n Do a quick plot of the data. You may need to refer to a previous workshop\nSummarising the data\nDo you remember Look after future you!\n If you followed that tip you’ll be able to open that script and whizz through summarising,testing and plotting.\n Create a data frame called seal_summary that contains the means, standard deviations, sample sizes and standard errors for each species.\nYou should get the following numbers:\n\n\n\n\nspecies\nmean\nstd\nn\nse\n\n\n\nBladdernose Seal\n42.31600\n8.020634\n30\n1.464361\n\n\nHarbour Seal\n49.01033\n8.252004\n30\n1.506603\n\n\nWeddell Seal\n44.66033\n7.849816\n30\n1.433174\n\n\n\n\n\nApplying, interpreting and reporting\nWe can now carry out a one-way ANOVA using the same lm() function we used for two-sample tests.\n Carry out an ANOVA and examine the results with:\n\nmod <- lm(data = seal, myoglobin ~ species)\nsummary(mod)\n\n\nCall:\nlm(formula = myoglobin ~ species, data = seal)\n\nResiduals:\n Min 1Q Median 3Q Max \n-16.306 -5.578 -0.036 5.240 18.250 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 42.316 1.468 28.819 < 2e-16 ***\nspeciesHarbour Seal 6.694 2.077 3.224 0.00178 ** \nspeciesWeddell Seal 2.344 2.077 1.129 0.26202 \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 8.043 on 87 degrees of freedom\nMultiple R-squared: 0.1096, Adjusted R-squared: 0.08908 \nF-statistic: 5.352 on 2 and 87 DF, p-value: 0.006427\n\n\nRemember: the tilde (~) means test the values in myoglobin when grouped by the values in species. Or explain myoglobin with species\n What do you conclude so far from the test? Write your conclusion in a form suitable for a report.\n\n\n\n Can you relate the values under Estimate to the means?\n\n\n\n\n\n\n\nThe ANOVA is significant but this only tells us that species matters, meaning at least two of the means differ. To find out which means differ, we need a post-hoc test. A post-hoc (“after this”) test is done after a significant ANOVA test. There are several possible post-hoc tests and we will be using Tukey’s HSD (honestly significant difference) test (Tukey 1949) implemented in the emmeans (Lenth 2023) package.\n Load the package\n\nlibrary(emmeans)\n\n Carry out the post-hoc test\n\nemmeans(mod, ~ species) |> pairs()\n\n contrast estimate SE df t.ratio p.value\n Bladdernose Seal - Harbour Seal -6.69 2.08 87 -3.224 0.0050\n Bladdernose Seal - Weddell Seal -2.34 2.08 87 -1.129 0.4990\n Harbour Seal - Weddell Seal 4.35 2.08 87 2.095 0.0968\n\nP value adjustment: tukey method for comparing a family of 3 estimates \n\n\nEach row is a comparison between the two means in the ‘contrast’ column. The ‘estimate’ column is the difference between those means and the ‘p.value’ indicates whether that difference is significant.\nA plot can be used to visualise the result of the post-hoc which can be especially useful when there are very many comparisons.\n Plot the results of the post-hoc test:\n\nemmeans(mod, ~ species) |> plot()\n\n\n\n\nWhere the purple bars overlap, there is no significant difference.\n What do you conclude from the test?\n\n\n\nCheck assumptions\nThe assumptions of the general linear model are that the residuals – the difference between predicted value (i.e., the group mean) and observed values - are normally distributed and have homogeneous variance. To check these we can examine the mod$residuals variable. You may want to refer to Checking assumptions in the “Single regression” workshop.\n Plot the model residuals against the fitted values.\n What to you conclude?\n\n\n\nTo examine normality of the model residuals we can plot them as a histogram and do a normality test on them.\n Plot a histogram of the residuals.\n Use the shapiro.test() to test the normality of the model residuals\n What to you conclude?\n\n\n\n\nIllustrating\n Create a figure like the one below. You may need to refer to Visualise from the “Summarising data with several variables” workshop (Rand 2023)\nWe will again use both our seal and seal_summary dataframes.\n Create the plot:\n\n\n\n\n\n Save your figure to your figures folder." + "objectID": "r4babs1/week-7/rstudio-projects.html#paths", + "href": "r4babs1/week-7/rstudio-projects.html#paths", + "title": "RStudio ProjectsWho, what, why?", + "section": "Paths", + "text": "Paths\n\nlocation of a file/folder\nappear in the address bar of explorer/finder and browsers\n\ndemo\n\n\nwhen you can’t click, you need the path\n\n\nchaffinch <- read_table(\"chaff.txt\")" }, { - "objectID": "pgt52m/week-8/workshop.html#leafminers-on-birch", - "href": "pgt52m/week-8/workshop.html#leafminers-on-birch", + "objectID": "r4babs1/week-7/rstudio-projects.html#absolute-path", + "href": "r4babs1/week-7/rstudio-projects.html#absolute-path", + "title": "RStudio ProjectsWho, what, why?", + "section": "Absolute path", + "text": "Absolute path\n\nchaffinch <- read_table(\"C:/Users/er13/OneDrive - University of York/Desktop/Desktop/undergrad-teaching-york/BIO00017C/BIO00017C-Data-Analysis-in-R-2020/data/chaff.txt\")\n\n\nOnly exists on my computer!" + }, + { + "objectID": "r4babs1/week-7/rstudio-projects.html#relative-paths", + "href": "r4babs1/week-7/rstudio-projects.html#relative-paths", + "title": "RStudio ProjectsWho, what, why?", + "section": "Relative paths", + "text": "Relative paths\n\nlocation of a file/folder relative to the working directory\nIf my working directory is BIO00017C-Data-Analysis-in-R-2020:\n\n\nchaffinch <- read_table(\"data/chaff.txt\")" + }, + { + "objectID": "r4babs1/week-7/rstudio-projects.html#rstudio-projects", + "href": "r4babs1/week-7/rstudio-projects.html#rstudio-projects", + "title": "RStudio ProjectsWho, what, why?", + "section": "RStudio Projects", + "text": "RStudio Projects\n\nSets the working directory to be the project folder\nCode is portable: you send someone the folder and everything just works!" + }, + { + "objectID": "r4babs1/week-7/rstudio-projects.html#demo", + "href": "r4babs1/week-7/rstudio-projects.html#demo", + "title": "RStudio ProjectsWho, what, why?", + "section": "demo", + "text": "demo" + }, + { + "objectID": "r4babs1/week-6/study_after_workshop.html", + "href": "r4babs1/week-6/study_after_workshop.html", + "title": "Independent Study to consolidate this week", + "section": "", + "text": "There is no additional study this week but you may want to look ahead to next week." + }, + { + "objectID": "r4babs1/week-6/workshop.html", + "href": "r4babs1/week-6/workshop.html", "title": "Workshop", - "section": "Leafminers on Birch", - "text": "Leafminers on Birch\nLarvae of the Ambermarked birch leafminer, Profenusa thomsoni, feed on the interior leaf tissues of Birch (Betula) species. They do not normally kill the tree but can weaken it making it susceptible to attack from other species. Researchers are interested in whether there is a difference in the rates at which white, grey and yellow birch are attacked. They introduce adult female P.thomsoni to a green house containing 30 young trees (ten of each type) and later count the egg laying events on each tree. The data are in leaf.txt.\nExploring\n Read in the data and check the structure. I used the name leaf for the dataframe/tibble.\n What kind of variables do we have?\n\n\n\n Do a quick plot of the data.\n Using your common sense, do these data look normally distributed?\n\n\n Why is a Kruskal-Wallis appropriate in this case?\n\n\n\n\n\n Calculate the medians, means and sample sizes.\nApplying, interpreting and reporting\n Carry out a Kruskal-Wallis:\n\nkruskal.test(data = leaf, eggs ~ birch)\n\n\n Kruskal-Wallis rank sum test\n\ndata: eggs by birch\nKruskal-Wallis chi-squared = 6.3393, df = 2, p-value = 0.04202\n\n\n What do you conclude from the test?\n\n\n\nA significant Kruskal-Wallis tells us at least two of the groups differ but where do the differences lie? The Dunn test is a post-hoc multiple comparison test for a significant Kruskal-Wallis. It is available in the package FSA\n Load the package using:\n\nlibrary(FSA)\n\n Run the post-hoc test with:\n\ndunnTest(data = leaf, eggs ~ birch)\n\n Comparison Z P.unadj P.adj\n1 Grey - White 1.296845 0.19468465 0.38936930\n2 Grey - Yellow -1.220560 0.22225279 0.22225279\n3 White - Yellow -2.517404 0.01182231 0.03546692\n\n\nThe P.adj column gives p-value for the comparison listed in the first column. Z is the test statistic.\n What do you conclude from the test?\n\n\n\n Write up the result is a form suitable for a report.\n\n\n\n\n\n\nIllustrating\n A box plot is an appropriate choice for illustrating a Kruskal-Wallis. Can you produce a figure like this?\n\n\n\n\n\nYou’re finished!" + "section": "", + "text": "There is no formal workshop this week but you might want to install R and RStudio on your own machine. This is optional because University computers already have R and RStudio installed.\nInstall R and RStudio.\nNote you need a computer - not a tablet." }, { - "objectID": "pgt52m/week-8/study_after_workshop.html", - "href": "pgt52m/week-8/study_after_workshop.html", + "objectID": "r4babs1/week-8/study_after_workshop.html", + "href": "r4babs1/week-8/study_after_workshop.html", "title": "Independent Study to consolidate this week", "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 Sports scientists were investigating the effects of fitness and heat acclimatisation on the sodium content of sweat. They measured the sodium content of the sweat (μmoll^−1) of three groups of individuals: unfit and unacclimatised (UU); fit and unacclimatised(FU); and fit and acclimatised (FA). The are in sweat.txt. Is there a difference between the groups in the sodium content of their sweat?\n\n\nCode# read in the data and look at structure\nsweat <- read_table(\"data-raw/sweat.txt\")\nstr(sweat)\n\n\n\nCode# quick plot of the data\nggplot(data = sweat, aes(x = gp, y = na)) +\n geom_boxplot()\nCode# Since the sample sizes are small and not the same in each group and the \n# variance in the FA gp looks a bit lower, I'm leaning to a non-parametric test K-W.\n# However, don't panic if you decided to do an anova\n\n\n\nCode# calculate some summary stats \nsweat_summary <- sweat %>% \n group_by(gp) %>% \n summarise(mean = mean(na),\n n = length(na),\n median = median(na))\n\n\n\nCode# Kruskal-Wallis\nkruskal.test(data = sweat, na ~ gp)\n# We can say there is a difference between the groups in the sodium \n# content of their sweat (chi-squared = 11.9802, df = 2, p-value = 0.002503).\n# Unfit and unacclimatised people have most salty sweat, \n# Fit and acclimatised people the least salty.\n\n\n\nCode# a post-hoc test to see where the sig differences lie:\nlibrary(FSA)\ndunnTest(data = sweat, na ~ gp)\n# Fit and acclimatised people (median = 49.5 μmoll^−1) have significantly less sodium in their\n# sweat than the unfit and unacclimatised people (70 μmoll^−1) \n# (Kruskal-Wallis multiple comparison p-values adjusted with the Holm method: p = 0.0026).\n# Fit and unacclimatised (54 μmoll^−1) also have significantly less sodium in their\n# people have sodium concentrations than unfit and unacclimatised people (p = 0.033). \n# There was no difference between the Fit and unacclimatised and the Fit and acclimatised. See figure 1.\n\n\n\nCodeggplot(sweat, aes(x = gp, y = na) ) +\n geom_boxplot() +\n scale_x_discrete(labels = c(\"Fit Acclimatised\", \n \"Fit Unacclimatised\", \n \"Unfit Unacclimatised\"), \n name = \"Group\") +\n scale_y_continuous(limits = c(0, 110), \n expand = c(0, 0),\n name = expression(\"Sodium\"~mu*\"mol\"*l^{-1})) +\n annotate(\"segment\", x = 1, xend = 3, \n y = 100, yend = 100,\n colour = \"black\") +\n annotate(\"text\", x = 2, y = 103, \n label = expression(italic(p)~\"= 0.0026\")) +\n annotate(\"segment\", x = 2, xend = 3, \n y = 90, yend = 90,\n colour = \"black\") +\n annotate(\"text\", x = 2.5, y = 93, \n label = expression(italic(p)~\"= 0.0340\")) +\n theme_classic()\nCode#Figure 1. Sodium content of sweat for three groups: Fit and acclimatised\n#(FA), Fit and unacclimatised (FU) and Unfit and unacclimatised (UU). Heavy lines\n#indicate the median, boxes the interquartile range and whiskers the range. \n\n\n\n💻 The data are given in biomass.txt are taken from an experiment in which the insect pest biomass (g) was measured on plots sprayed with water (control) or one of five different insecticides. Do the insecticides vary in their effectiveness? What advice would you give to a person: - currently using insecticide E? - trying to choose between A and D? - trying to choose between C and B?\n\n\nCodebiom <- read_table(\"data-raw/biomass.txt\")\n# The data are organised with an insecticide treatment group in\n# each column.\n\n\n\nCode#Put the data into tidy format.\n\nbiom <- biom |> \n pivot_longer(cols = everything(),\n names_to = \"spray\",\n values_to = \"biomass\")\n\n\n\nCode# quick plot of the data\nggplot(data = biom, aes(x = spray, y = biomass)) +\n geom_boxplot()\nCode# Looks like there is a difference between sprays. E doesn't look very effective.\n\n\n\nCode# summary statistics\nbiom_summary <- biom %>% \n group_by(spray) %>% \n summarise(mean = mean(biomass),\n median = median(biomass),\n sd = sd(biomass),\n n = length(biomass),\n se = sd / sqrt(n))\n# thoughts so far: the sample sizes are equal, 10 is a smallish but\n# reasonable sample size\n# the means and medians are similar to each other (expected for\n# normally distributed data), A has a smaller variance \n\n# We have one explanatory variable, \"spray\" comprising 6 levels\n# Biomass has decimal places and we would expect such data to be \n# normally distributed therefore one-way ANOVA is the desired test\n# - we will check the assumptions after building the model\n\n\n\nCode# arry out an ANOVA and examine the results \nmod <- lm(data = biom, biomass ~ spray)\nsummary(mod)\n# spray type does have an effect F-statistic: 26.46 on 5 and 54 DF, p-value: 2.081e-13\n\n\n\nCode# Carry out the post-hoc test\nlibrary(emmeans)\n\nemmeans(mod, ~ spray) |> pairs()\n\n# the signifcant comparisons are:\n# contrast estimate SE df t.ratio p.value\n# A - D -76.50 21.9 54 -3.489 0.0119\n# A - E -175.51 21.9 54 -8.005 <.0001\n# A - WaterControl -175.91 21.9 54 -8.024 <.0001\n# B - E -154.32 21.9 54 -7.039 <.0001\n# B - WaterControl -154.72 21.9 54 -7.057 <.0001\n# C - E -155.71 21.9 54 -7.102 <.0001\n# C - WaterControl -156.11 21.9 54 -7.120 <.0001\n# D - E -99.01 21.9 54 -4.516 0.0005\n# D - WaterControl -99.41 21.9 54 -4.534 0.0004\n# All sprays are better than the water control except E. \n# This is probably the most important result.\n# What advice would you give to a person currently using insecticide E?\n# Don't bother!! It's no better than water. Switch to any of \n# the other sprays\n# What advice would you give to a person currently\n# + trying to choose between A and D? Choose A because A has sig lower\n# insect biomass than D \n# + trying to choose between C and B? It doesn't matter because there is \n# no difference in insect biomass. Use other criteria to chose (e.g., price)\n# We might report this like:\n# There is a very highly significant effect of spray type on pest \n# biomass (F = 26.5; d.f., 5, 54; p < 0.001). Post-hoc testing \n# showed E was no more effective than the control; A, C and B were \n# all better than the control but could be equally as good as each\n# other; D would be a better choice than the control or E but \n# worse than A. See figure 1\n\n\n\nCode# I reordered the bars to make is easier for me to annotate with\n# I also used * to indicate significance\n\nggplot() +\n geom_point(data = biom, aes(x = reorder(spray, biomass), y = biomass),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"gray50\") +\n geom_errorbar(data = biom_summary, \n aes(x = spray, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = biom_summary, \n aes(x = spray, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_y_continuous(name = \"Pest Biomass (units)\",\n limits = c(0, 540),\n expand = c(0, 0)) +\n scale_x_discrete(\"Spray treatment\") +\n # E and control are one group\n annotate(\"segment\", x = 4.5, xend = 6.5, \n y = 397, yend = 397,\n colour = \"black\", linewidth = 1) +\n annotate(\"text\", x = 5.5, y = 385, \n label = \"N.S\", size = 4) +\n # WaterControl-D and E-D ***\n annotate(\"segment\", x = 4, xend = 5.5, \n y = 410, yend = 410,\n colour = \"black\") +\n annotate(\"text\", x = 4.5, y = 420, \n label = \"***\", size = 5) +\n # WaterControl-B ***\n annotate(\"segment\", x = 3, xend = 5.5, \n y = 440, yend = 440,\n colour = \"black\") +\n annotate(\"text\", x = 4, y = 450,\n label = \"***\", size = 5) +\n # WaterControl-C ***\n annotate(\"segment\", x = 2, xend = 5.5, \n y = 475, yend = 475,\n colour = \"black\") +\n annotate(\"text\", x = 3.5, y = 485, \n label = \"***\", size = 5) +\n # WaterControl-A ***\n annotate(\"segment\", x = 1, xend = 5.5, \n y = 510, yend = 510,\n colour = \"black\") +\n annotate(\"text\", x = 3.5, y = 520, \n label = \"***\", size = 5) + \n# A-D ***\n annotate(\"segment\", x = 1, xend = 4, \n y = 330, yend = 330,\n colour = \"black\") +\n annotate(\"text\", x = 2.5, y = 335, \n label = \"*\", size = 5) +\n theme_classic()\nCode# Figure 1. The mean pest biomass following various insecticide treatments.\n# Error bars are +/- 1 S.E. Significant comparisons are indicated: * is p < 0.05, ** p < 0.01 and *** is p < 0.001" + "text": "Set up\nIf you have just opened RStudio you will want to load the packages and import the data.\n\nlibrary(tidyverse)\nlibrary(readxl)\n\n\nfly_bristles_means <- read_excel(\"data-raw/bristles-mean.xlsx\")\ncats <- read_csv(\"data-raw/cat-coats.csv\")\n\nExercises\n\n💻 Summarise the fly_bristles_means dataframe by calculating the mean, median, sample size, standard deviation and standard error of the mean_count variable.\n\n\nCodefly_bristles_means_summary <- fly_bristles_means |> \n summarise(mean = mean(mean_count),\n median = median(mean_count),\n n = length(mean_count),\n standard_dev = sd(mean_count),\n standard_error = standard_dev / sqrt(n))\n\n\n\n💻 Create an appropriate plot to show the distribution of mean_count in fly_bristles_means\n\n\n\nCodeggplot(fly_bristles_means, aes(x = mean_count)) +\n geom_histogram(bins = 10)\n\n\n\n💻 Can you format the plot 2. by removing the grey background, giving the bars a black outline and the fill colour of your choice and improving the axis format and labelling? You may want to refer to last week’s workshop.\n\n\nCodeggplot(fly_bristles_means, aes(x = mean_count)) +\n geom_histogram(bins = 10, \n colour = \"black\",\n fill = \"skyblue\") +\n scale_x_continuous(name = \"Number of bristles\",\n expand = c(0, 0)) +\n scale_y_continuous(name = \"Frequency\",\n expand = c(0, 0),\n limits = c(0, 35)) +\n theme_classic()\n\n\n\n💻 Amend this code to change the order of the bars by the average mass of each coat colour? Changing the order of bars was covered last week. You may also want to practice formatting the graph nicely.\n\n\nggplot(cats, aes(x = coat, y = mass)) +\n geom_boxplot()\n\n\n\n\n\nCodeggplot(cats, \n aes(x = reorder(coat, mass), y = mass)) +\n geom_boxplot(fill = \"darkcyan\") +\n scale_x_discrete(name = \"Coat colour\") +\n scale_y_continuous(name = \"Mass (kg)\", \n expand = c(0, 0),\n limits = c(0, 8)) +\n theme_classic()\n\n\n\n📖 Read Understanding the pipe |>" }, { - "objectID": "pgt52m/week-4/workshop.html", - "href": "pgt52m/week-4/workshop.html", + "objectID": "r4babs1/week-8/workshop.html", + "href": "r4babs1/week-8/workshop.html", "title": "Workshop", "section": "", - "text": "Data data Artwork from the Openscapes blog Tidy Data for reproducibility, efficiency, and collaboration by Julia Lowndes and Allison Horst\n\n\nIn this workshop you will learn to summarise and plot datasets with more than one variable and how to write figures to files. You will also get more practice with working directories, importing data, formatting figures and the pipe.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier workshops\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "text": "Artwork by Horst (2023): Continuous and Discrete\n\n\nIn this workshop you will learn how to import data from files and create summaries and plots for it. You will also get more practice with working directories, formatting figures and the pipe.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier workshops\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "pgt52m/week-4/workshop.html#session-overview", - "href": "pgt52m/week-4/workshop.html#session-overview", + "objectID": "r4babs1/week-8/workshop.html#session-overview", + "href": "r4babs1/week-8/workshop.html#session-overview", "title": "Workshop", "section": "", - "text": "In this workshop you will learn to summarise and plot datasets with more than one variable and how to write figures to files. You will also get more practice with working directories, importing data, formatting figures and the pipe." + "text": "In this workshop you will learn how to import data from files and create summaries and plots for it. You will also get more practice with working directories, formatting figures and the pipe." }, { - "objectID": "pgt52m/week-4/workshop.html#philosophy", - "href": "pgt52m/week-4/workshop.html#philosophy", + "objectID": "r4babs1/week-8/workshop.html#philosophy", + "href": "r4babs1/week-8/workshop.html#philosophy", "title": "Workshop", "section": "", "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier workshops\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "pgt52m/week-4/workshop.html#myoglobin-in-seal-muscle", - "href": "pgt52m/week-4/workshop.html#myoglobin-in-seal-muscle", + "objectID": "r4babs1/week-8/workshop.html#importing-data-from-files", + "href": "r4babs1/week-8/workshop.html#importing-data-from-files", "title": "Workshop", - "section": "Myoglobin in seal muscle", - "text": "Myoglobin in seal muscle\nThe myoglobin concentration of skeletal muscle of three species of seal in grams per kilogram of muscle was determined and the data are given in seal.csv. Each row represents an individual seal. The first column gives the myoglobin concentration and the second column indicates species.\nImport\n Save seal.csv to your data-raw folder\n Read the data into a dataframe called seal. . You might want to look up data import from last week.\n What types of variables do you have in the seal dataframe? What role would you expect them to play in analysis?\n\n\n\n\nThe key point here is that the fundamental structure of:\n\none continuous response and one nominal explanatory variable with two groups (adipocytes), and\none continuous response and one nominal explanatory variable with three groups (seals)\n\nis the same! The only thing that differs is the number of groups (the number of values in the nominal variable). This means the code for summarising and plotting is identical except for the variable names!\n\n\n\n\n\n\nTip\n\n\n\nWhen two datasets have the same number of columns and the response variable and the explanatory variables have the same data types then the code you need is the same.\n\n\nSummarise\nSummarising the data for each species is the next sensible step. The most useful summary statistics for a continuous variable like myoglobin are the means, standard deviations, sample sizes and standard errors. You might remember from last week that we use the group_by() and summarise() functions along with the functions that do the calculations.\n Create a data frame called seal_summary that contains the means, standard deviations, sample sizes and standard errors for the control and nicotinic acid treated samples.\n\nseal_summary <- seal %>%\n group_by(species) %>%\n summarise(mean = mean(myoglobin),\n std = sd(myoglobin),\n n = length(myoglobin),\n se = std/sqrt(n))\n\nYou should get the following numbers:\n\n\n\n\nspecies\nmean\nstd\nn\nse\n\n\n\nBladdernose Seal\n42.31600\n8.020634\n30\n1.464361\n\n\nHarbour Seal\n49.01033\n8.252004\n30\n1.506603\n\n\nWeddell Seal\n44.66033\n7.849816\n30\n1.433174\n\n\n\n\n\nVisualise\nMost commonly, we put the explanatory variable on the x axis and the response variable on the y axis. A continuous response, particularly one that follows the normal distribution, is best summarised with the mean and the standard error. In my opinion, you should also show all the raw data points if possible.\nWe are going to create a figure like this:\n\n\n\n\n\nIn this figure, we have the data points themselves which are in seal dataframe and the means and standard errors which are in the seal_summary dataframe. That is, we have two dataframes we want to plot.\nHere you will learn that dataframes and aesthetics can be specified within a geom_xxxx (rather than in the ggplot()). This is very useful if the geom only applies to some of the data you want to plot.\n\n\n\n\n\n\nTip: ggplot()\n\n\n\nYou put the data argument and aes() inside ggplot() if you want all the geoms to use that dataframe and variables. If you want a different dataframe for a geom, put the data argument and aes() inside the geom_xxxx()\n\n\nI will build the plot up in small steps but you should edit your existing ggplot() command as we go.\n Plot the data points first.\n\nggplot() +\n geom_point(data = seal, \n aes(x = species, y = myoglobin))\n\n\n\n\nNotice how we have given the data argument and the aesthetics inside the geom. The variables species and myoglobin are in the seal dataframe\n So the data points don’t overlap, we can add some random jitter in the x direction (edit your existing code):\n\nggplot() +\n geom_point(data = seal, \n aes(x = species, y = myoglobin),\n position = position_jitter(width = 0.1, height = 0))\n\n\n\n\nNote that position = position_jitter(width = 0.1, height = 0) is inside the geom_point() parentheses, after the aes() and a comma.\nWe’ve set the vertical jitter to 0 because, in contrast to the categorical x-axis, movement on the y-axis has meaning (the myoglobin levels).\n Let’s make the points a light grey (edit your existing code):\n\nggplot() +\n geom_point(data = seal, \n aes(x = species, y = myoglobin),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"grey50\")\n\n\n\n\nNow to add the errorbars. These go from one standard error below the mean to one standard error above the mean.\n Add a geom_errorbar() for errorbars (edit your existing code):\n\nggplot() +\n geom_point(data = seal, aes(x = species, y = myoglobin),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"grey50\") +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean - se, ymax = mean + se),\n width = 0.3) \n\n\n\n\nWe have specified the seal_summary dataframe and the variables species, mean and se are in that.\nThere are several ways you could add the mean. You could use geom_point() but I like to use geom_errorbar() again with the ymin and ymax both set to the mean.\n Add a geom_errorbar() for the mean (edit your existing code):\n\nggplot() +\n geom_point(data = seal, aes(x = species, y = myoglobin),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"grey50\") +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean, ymax = mean),\n width = 0.2)\n\n\n\n\n Alter the axis labels and limits using scale_y_continuous() and scale_x_discrete() (edit your existing code):\n\nggplot() +\n geom_point(data = seal, aes(x = species, y = myoglobin),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"grey50\") +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_y_continuous(name = \"Myoglobin (g/kg)\", \n limits = c(0, 80), \n expand = c(0, 0)) +\n scale_x_discrete(name = \"Species\")\n\n\n\n\nYou only need to use scale_y_continuous() and scale_x_discrete() to use labels that are different from those in the dataset. Often this is to use proper terminology and captialisation.\n Format the figure in a way that is more suitable for including in a report using theme_classic() (edit your existing code):\n\nggplot() +\n geom_point(data = seal, aes(x = species, y = myoglobin),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"grey50\") +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_y_continuous(name = \"Myoglobin (g/kg)\", \n limits = c(0, 80), \n expand = c(0, 0)) +\n scale_x_discrete(name = \"Species\") +\n theme_classic()\n\n\n\n\nWriting figures to file\n Make a new folder called figures.\n Edit you ggplot code so that you assign the figure to a variable.\n\nsealfig <- ggplot() +\n geom_point(data = seal, aes(x = species, y = myoglobin),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"grey50\") +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = seal_summary, \n aes(x = species, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_y_continuous(name = \"Myoglobin (g/kg)\", \n limits = c(0, 80), \n expand = c(0, 0)) +\n scale_x_discrete(name = \"Species\") +\n theme_classic()\n\nThe figure won’t be shown in the Plots tab - the output has gone into sealfig rather than to the Plots tab. To make it appear in the Plots tab type sealfig\n The ggsave() command will write a ggplot figure to a file:\n\nggsave(\"figures/seal-muscle.png\",\n plot = sealfig,\n device = \"png\",\n width = 4,\n height = 3,\n units = \"in\",\n dpi = 300)\n\nfiguresseal-muscle.png is the name of the file, including the relative path.\n Look up ggsave() in the manual to understand the arguments. You can do this by putting your cursor on the command and pressing F1" + "section": "Importing data from files", + "text": "Importing data from files\nLast week we created data by typing the values in to R. This is not practical when you have added a lot of data to a spreadsheet, or you are using data file that has been supplied to you by a person or a machine. Far more commonly, we import data from a file into R. This requires you know two pieces of information.\n\n\nWhat format the data are in\nThe format of the data determines what function you will use to import it and the file extension often indicates format.\n\n\n.txt a plain text file1, where the columns are often separated by a space but might also be separated by a tab, a backslash or forward slash, or some other character\n\n.csv a plain text file where the columns are separated by commas\n\n.xlsx an Excel file\n\n\n\nWhere the file is relative to your working directory\nR can only read in a file if you say where it is, i.e., you give its relative path. If you follow the advice in this course, your data will be in a folder, data-raw which is inside your Project folder (and working directory).\n\n\nWe will save the four files for this workshop to our Project folder (week-8) and read them in. We will then create a new folder inside our Project folder called data-raw and move the data files to there before modifying the file paths as required. This is demonstrate how the relative path to the file will change after we move it.\n Save these four files in to your week-8 folder\n\nThe coat colour and mass of 62 cats: cat-coats.csv\n\nThe relative size of over 5000 cells measure by forward scatter (FSC) in flow cytometry: cell-size.txt\n\nThe number of sternopleural bristles on 96 female Drosophila: bristles.txt\n\nThe number of sternopleural bristles on 96 female Drosophila (with technical replicates): bristles-mean.xlsx\n\n\nThe first three files can be read in with core tidyverse Wickham et al. (2019) functions and the last can be read in with the readxl Wickham and Bryan (2023) package.\n Load the two packages\n\nlibrary(tidyverse)\nlibrary(readxl)\n\nWe will first read in cat-coats.csv. A .csv. extension suggests this is plain text file with comma separated columns. However, before we attempt to read it it, when should take a look at it. We can do this from RStudio\n Go to the Files pane (bottom right), click on the cat-coats.csv file and choose View File2\n\n\nRStudio Files Pane\n\nAny plain text file will open in the top left pane (Excel files will launch Excel).\n Is the file csv?\n\n\n What kind of variables does the file contain?\n\n\n Read in the csv file with:\n\ncats <- read_csv(\"cat-coats.csv\")\n\nThe data from the file a read into a dataframe called cats and you will be able to see it in the Environment.\n Click on each of the remaining files and choose View File.\n In each case, say what the format is and what types of variables it contains.\n\n\n\n\n\n\n\n\nWe use the read_table()3 command to read in plain text files of single columns or where the columns are separated by spaces…\n …so in cell-size.txt can be read into a dataframe called cells like this:\n\ncells <- read_table(\"cell-size.txt\")\n\n Now you try reading bristles.txt in to a dataframe called fly_bristles\nThe readxl package we loaded earlier has two useful functions for working with Excel files: excel_sheets(\"filename.xlsx\") will list the sheets in an Excel workbook; read_excel(\"filename.xlsx\") will read in to top sheet or a specified sheet with a small modification read_excel(\"filename.xlsx\", sheet = \"Sheet1\").\n List the the names of the sheets and read in the sheet with the data like this:\n\nexcel_sheets(\"bristles-mean.xlsx\")\nfly_bristles_means <- read_excel(\"bristles-mean.xlsx\", sheet = \"means\")\n\nWell done! You can now read read in from files in your working directory.\nTo help you understand relative file paths, we will now move the data files.\n First remove the dataframes you just created to make it easier to see whether you can successfully read in the files from a different place:\n\nrm(cat_coats, fly_bristles, cells, flies_bristles_means)\n\n Now make a new folder called data-raw. You can do this on the Files Pane by clicking New Folder and typing into the box that appears.\n Check the boxes next to the file names and choose More | Move… and select the data-raw folder.\n The files will move. To import data from files in the data-raw folder, you need to give the relative path to the file from the working directory. The working directory is the Project folder, week-8 so the relative path is data-raw/cat-coats.csv\n Import the cat-coats.csv data like this:\n\ncats <- read_csv(\"data-raw/cat-coats.csv\")\n\n Now you do the other files.\nFrom this point forward in the course, we will always create a data-raw folder each time we make a new Project.\nThese are the most common forms of data file you will encounter at first. However, data can certainly come to you in other formats particularly when they have come from particular software. Usually, there is an R package specially for that format.\nIn the rest of the workshop we will take each dataset in turn and create summaries and plots appropriate for the data types. Data is summarised using the group_by() and summarise() functions" }, { - "objectID": "pgt52m/week-4/workshop.html#pigeons", - "href": "pgt52m/week-4/workshop.html#pigeons", + "objectID": "r4babs1/week-8/workshop.html#summarising-discrete-data-cat-coat", + "href": "r4babs1/week-8/workshop.html#summarising-discrete-data-cat-coat", "title": "Workshop", - "section": "Pigeons", - "text": "Pigeons\nThe data in pigeon.txt are 40 measurements of interorbital width (in mm) for two populations of domestic pigeons measured to the nearest 0.1mm\n\n\nInterorbital width is the distance between the eyes\n\nImport\n Save pigeon.txt to your data-raw folder\n Read the data into a dataframe called pigeons.\n What variables are there in the pigeons dataframe?\n\n\n\n\nHummmm, these data are not organised like the other data sets we have used. The population is given as the column names and the interorbital distances for one population are given in a different column than those for the other population. The first row has data from two pigeons which have nothing in common, they just happen to be the first individual recorded in each population.\n\n\n\n\n\nA\nB\n\n\n\n12.4\n12.6\n\n\n11.2\n11.3\n\n\n11.6\n12.1\n\n\n12.3\n12.2\n\n\n11.8\n11.8\n\n\n10.7\n11.5\n\n\n11.3\n11.2\n\n\n11.6\n11.9\n\n\n12.3\n11.2\n\n\n10.5\n12.1\n\n\n12.1\n11.9\n\n\n10.4\n10.7\n\n\n10.8\n11.0\n\n\n11.9\n12.2\n\n\n10.9\n12.6\n\n\n10.8\n11.6\n\n\n10.4\n10.7\n\n\n12.0\n12.4\n\n\n11.7\n11.8\n\n\n11.3\n11.1\n\n\n11.5\n12.9\n\n\n11.8\n11.9\n\n\n10.3\n11.1\n\n\n10.3\n12.2\n\n\n11.5\n11.8\n\n\n10.7\n11.5\n\n\n11.3\n11.2\n\n\n11.6\n11.9\n\n\n13.3\n11.2\n\n\n10.7\n11.1\n\n\n12.1\n11.6\n\n\n10.2\n12.7\n\n\n10.8\n11.0\n\n\n11.4\n12.2\n\n\n10.9\n11.3\n\n\n10.3\n11.6\n\n\n10.4\n12.2\n\n\n10.0\n12.4\n\n\n11.2\n11.3\n\n\n11.3\n11.1\n\n\n\n\n\n\n\nThis data is not in ‘tidy’ format (Wickham 2014).\nTidy format has variables in column and observations in rows. All of the distance measurements should be in one column and a second column should give the population.\n\n\n\n\n\npopulation\ndistance\n\n\n\nA\n12.4\n\n\nB\n12.6\n\n\nA\n11.2\n\n\nB\n11.3\n\n\nA\n11.6\n\n\nB\n12.1\n\n\nA\n12.3\n\n\nB\n12.2\n\n\nA\n11.8\n\n\nB\n11.8\n\n\nA\n10.7\n\n\nB\n11.5\n\n\nA\n11.3\n\n\nB\n11.2\n\n\nA\n11.6\n\n\nB\n11.9\n\n\nA\n12.3\n\n\nB\n11.2\n\n\nA\n10.5\n\n\nB\n12.1\n\n\nA\n12.1\n\n\nB\n11.9\n\n\nA\n10.4\n\n\nB\n10.7\n\n\nA\n10.8\n\n\nB\n11.0\n\n\nA\n11.9\n\n\nB\n12.2\n\n\nA\n10.9\n\n\nB\n12.6\n\n\nA\n10.8\n\n\nB\n11.6\n\n\nA\n10.4\n\n\nB\n10.7\n\n\nA\n12.0\n\n\nB\n12.4\n\n\nA\n11.7\n\n\nB\n11.8\n\n\nA\n11.3\n\n\nB\n11.1\n\n\nA\n11.5\n\n\nB\n12.9\n\n\nA\n11.8\n\n\nB\n11.9\n\n\nA\n10.3\n\n\nB\n11.1\n\n\nA\n10.3\n\n\nB\n12.2\n\n\nA\n11.5\n\n\nB\n11.8\n\n\nA\n10.7\n\n\nB\n11.5\n\n\nA\n11.3\n\n\nB\n11.2\n\n\nA\n11.6\n\n\nB\n11.9\n\n\nA\n13.3\n\n\nB\n11.2\n\n\nA\n10.7\n\n\nB\n11.1\n\n\nA\n12.1\n\n\nB\n11.6\n\n\nA\n10.2\n\n\nB\n12.7\n\n\nA\n10.8\n\n\nB\n11.0\n\n\nA\n11.4\n\n\nB\n12.2\n\n\nA\n10.9\n\n\nB\n11.3\n\n\nA\n10.3\n\n\nB\n11.6\n\n\nA\n10.4\n\n\nB\n12.2\n\n\nA\n10.0\n\n\nB\n12.4\n\n\nA\n11.2\n\n\nB\n11.3\n\n\nA\n11.3\n\n\nB\n11.1\n\n\n\n\n\n\n\nData which is in tidy format is easier to summarise, analyses and plot because the organisation matches the conceptual structure of the data:\n\nit is more obvious what the variables are because they columns are named with them - in the untidy format, that the measures are distances is not clear and what A and B are isn’t clear\nit is more obvious that there is no relationship between any of the pigeons except for population\nfunctions are designed to work with variables in columns\nTidying data\nWe can put this data in such a format with the pivot_longer() function from the tidyverse:\npivot_longer() collects the values from specified columns (cols) into a single column (values_to) and creates a column to indicate the group (names_to).\n Put the data in tidy format:\n\npigeons <- pivot_longer(data = pigeons, \n cols = everything(), \n names_to = \"population\", \n values_to = \"distance\")\n\nWe have overwritten the original dataframe. If you wanted to keep the original you would need to give a new name on the left side of the assignment <- Note: the data in the file are unchanged." + "section": "Summarising discrete data: Cat coat", + "text": "Summarising discrete data: Cat coat\nThe most appropriate way to summarise nominal data like the colour of cat coats is to tabulate the number of cats with each colour.\n Summarise the cats dataframe by counting the number of cats in each category\n\ncats |> \n group_by(coat) |> \n count()\n\n# A tibble: 6 × 2\n# Groups: coat [6]\n coat n\n <chr> <int>\n1 black 23\n2 calico 1\n3 ginger 10\n4 tabby 8\n5 tortoiseshell 5\n6 white 15\n\n\n|> is the pipe and can be produced with Ctrl+Shift+M\nThis sort of data might be represented with a barchart. You have two options for producing that barchart:\n\nplot the summary table using geom_col()\nplot the raw data using geom_bar()\n\nWe did the first of these last week. The geom_col() function uses the numbers in a second column to determine how high the bars are. However, the geom_bar() function will do the tabulating for you.\n Plot the coat data using geom_bar:\n\nggplot(cats, aes(x = coat)) +\n geom_bar()\n\n\n\n\nThe gaps that R put automatically between the bars reflects that the coat colours are discrete categories." + }, + { + "objectID": "r4babs1/week-8/workshop.html#summarising-counts-bristles", + "href": "r4babs1/week-8/workshop.html#summarising-counts-bristles", + "title": "Workshop", + "section": "Summarising Counts: Bristles", + "text": "Summarising Counts: Bristles\nCounts are discrete and can be thought of a categories with an order (ordinal).\n Summarise the fly_bristles dataframe by counting the number of flies in each category of bristle number\nSince counts are numbers, we might also want to calculate some summary statistics such as the median and interquartile range.\n Summarise the fly_bristles dataframe by calculate the median and interquartile range\n\nfly_bristles |> \n summarise(median(number),\n IQR(number))\n\n# A tibble: 1 × 2\n `median(number)` `IQR(number)`\n <dbl> <dbl>\n1 6 4\n\n\nAs the interquartile is 4 and the median is 6 then 25% flies have 4 bristles or fewer and 25% have 8 or more.\nThe distribution of counts4 is not symmetrical for lower counts so the mean is not usually a good way to summarise count data.\n If you want to save the table you created and give the columns better names you can make two adjustments:\n\nfly_bristles_summary <- fly_bristles |> \n summarise(med = median(number),\n interquartile = IQR(number))\n\n Plot the bristles data using geom_bar:\nIf counts have a a high mean and big range, like number of hairs on a person’s head, then you can often treat them as continuous. This means you can use statistics like the mean and standard deviation to summarise them, histograms to plot them and use some standard statistical tests on them." + }, + { + "objectID": "r4babs1/week-8/workshop.html#summarising-continuous-data", + "href": "r4babs1/week-8/workshop.html#summarising-continuous-data", + "title": "Workshop", + "section": "Summarising continuous data", + "text": "Summarising continuous data\nCat mass\nThe variable mass in the cats dataframe is continuous. Very many continuous variables have a normal distribution. e normal distribution is also known as the bell-shaped curve. If we had the mass of all the cats in the world, we would find many cats were near the mean and fewer would be away from the mean, either much lighter or much heavier. In fact 68% would be within one standard deviation of the mean and about 96% would be within two standard deviations.\n\n\n\n\n\n We can find the mean mass with:\n\ncats |> \n summarise(mean = mean(mass))\n\n# A tibble: 1 × 1\n mean\n <dbl>\n1 4.51\n\n\nWe can add any sort of summary by placing it inside the the summarise parentheses. Each one is separated by a comma. We did this to find the median and the interquatrile range for fly bristles.\n For example, another way to calculate the number of values is to use the length() function:\n\ncats |> \n summarise(mean = mean(mass),\n n = length(mass))\n\n# A tibble: 1 × 2\n mean n\n <dbl> <int>\n1 4.51 62\n\n\n Adapt the code to calculate the mean, the sample size and the standard deviation (sd())\nA single continuous variable can be plotted using a histogram to show the shape of the distribution.\n Plots a histogram of cats mass:\n\nggplot(cats, aes(x = mass)) +\n geom_histogram(bins = 15, colour = \"black\") \n\n\n\n\nNotice that there are no gaps between the bars which reflects that mass is continuous. bins determines how many groups the variable is divided up into (i.e., the number of bars) and colour sets the colour for the outline of the bars. A sample of 62 is a relatively small number of values for plotting a distribution and the number of bins used determines how smooth or normally distributed the values look.\n Experiment with the number of bins. Does the number of bins affect how you view the distribution.\nNext week we will practice summarise and plotting data files with several variables but just to give you a taste, we will find summary statistics about mass for each of the coat types. The group_by() function is used before the summarise() to do calculations for each of the coats:\n\ncats |> \n group_by(coat) |> \n summarise(mean = mean(mass),\n standard_dev = sd(mass))\n\n# A tibble: 6 × 3\n coat mean standard_dev\n <chr> <dbl> <dbl>\n1 black 4.63 1.33 \n2 calico 2.19 NA \n3 ginger 4.46 1.12 \n4 tabby 4.86 0.444\n5 tortoiseshell 4.50 0.929\n6 white 4.34 1.34 \n\n\nYou can read this as:\n\ntake cats and then group by coat and then summarise by finding the mean of mass and the standard deviation of mass\n\n Why do we get an NA for the standard deviation of the calico cats?\n\n\n\nCells\n Summarise the cells dataframe by calculating the mean, median, sample size and standard deviation of FSC.\n Add a column for the standard error which is given by \\(\\frac{s.d.}{\\sqrt{n}}\\)\nMeans of counts\nMany things are quite difficult to measure or count and in these cases we often do technical replicates. A technical replicate allows us the measure the exact same thing to check how variable the measurement process is. For example, Drosophila are small and counting their sternopleural bristles is tricky. In addition, where a bristle is short (young) or broken scientists might vary in whether they count it. Or people or machines might vary in measuring the concentration of the same solution.\nWhen we do technical replicates we calculate their mean and use that as the measure. This is what is in our fly_bristles_means dataframe - the bristles of each of the 96 flies was counted by 5 people and the data are those means. These has an impact on how we plot and summarise the dataset because the distribution of mean counts is continuous! We can use means, standard deviations and histograms. This will be an exercise in Consolidate." }, { - "objectID": "pgt52m/week-4/workshop.html#ulna-and-height", - "href": "pgt52m/week-4/workshop.html#ulna-and-height", + "objectID": "r4babs1/week-8/workshop.html#look-after-future-you", + "href": "r4babs1/week-8/workshop.html#look-after-future-you", "title": "Workshop", - "section": "Ulna and height", - "text": "Ulna and height\nThe datasets we have used up to this point, have had a continuous variable and a categorical variable where it makes sense to summarise the response for each of the different groups in the categorical variable and plot the response on the y-axis. We will now summarise a dataset with two continuous variables. The data in height.txt are the ulna length (cm) and height (m) of 30 people. In this case, it is more appropriate to summarise both of thee variables and to plot them as a scatter plot.\nWe will use summarise() again but we do not need the group_by() function this time. We will also need to use each of the summary functions, such as mean(), twice, once for each variable.\nImport\n Save height.txt to your data-raw folder\n Read the data into a dataframe called ulna_heights.\nSummarise\n Create a data frame called ulna_heights_summary that contains the sample size and means, standard deviations and standard errors for both variables.\n\nulna_heights_summary <- ulna_heights %>%\n summarise(n = length(ulna),\n mean_ulna = mean(ulna),\n std_ulna = sd(ulna),\n se_ulna = std_ulna/sqrt(n),\n mean_height = mean(height),\n std_height = sd(height),\n se_height = std_height/sqrt(n))\n\nYou should get the following numbers:\n\n\n\n\nn\nmean_ulna\nstd_ulna\nse_ulna\nmean_height\nstd_height\nse_height\n\n\n30\n24.72\n4.137332\n0.75537\n1.494\n0.2404823\n0.0439059\n\n\n\n\nVisualise\nTo plot make a scatter plot we need to use geom_point() again but without any scatter. In this case, it does not really matter which variable is on the x-axis and which is on the y-axis.\n Make a simple scatter plot\n\nggplot(data = ulna_heights, aes(x = ulna, y = height)) +\n geom_point()\n\n\n\n\nIf you have time, you may want to format the figure more appropriately.\n\n\nYou’re finished!" + "section": "Look after future you!", + "text": "Look after future you!\nFuture you is going to summarise and plot data from the “River practicals”. You can make this much easier by documenting what you have done now. At the moment all of your code from this workshop is in a single file, probably called analysis.R. I recommend making a new script for each of nominal, continuous and count data and copying the code which imports, summarises and plots it. This will make it easier for future you to find the code you need. Here is an example: nominal_data.R. You may wish to comment your version much more.\nYou’re finished!" }, { - "objectID": "pgt52m/week-4/study_after_workshop.html", - "href": "pgt52m/week-4/study_after_workshop.html", - "title": "Independent Study to consolidate this week", - "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the packages and import the data.\n\nlibrary(tidyverse)\nlibrary(readxl)\n\n\n💻 Summarise and plot the pigeons dataframe appropriately.\n\n\nCode# import\npigeons <- read_table(\"data-raw/pigeon.txt\")\n\n# reformat to tidy\npigeons <- pivot_longer(data = pigeons, \n cols = everything(), \n names_to = \"population\", \n values_to = \"distance\")\n\n# sumnmarise\npigeons_summary <- pigeons %>%\n group_by(population) %>%\n summarise(mean = mean(distance),\n std = sd(distance),\n n = length(distance),\n se = std/sqrt(n))\n# plot\nggplot() +\n geom_point(data = pigeons, aes(x = population, y = distance),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"gray50\") +\n geom_errorbar(data = pigeons_summary, \n aes(x = population, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = pigeons_summary, \n aes(x = population, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_y_continuous(name = \"Interorbital distance (mm)\", \n limits = c(0, 14), \n expand = c(0, 0)) +\n scale_x_discrete(name = \"Population\") +\n theme_classic()\n\n\n\n\n💻 The data in blood.csv are measurements of several blood parameters from fifty people with Crohn’s disease, a lifelong condition where parts of the digestive system become inflamed. Twenty-five of people are in the early stages of diagnosis and 25 have started treatment. The variables in the dataset are:\n\nsodium - Sodium concentration in umol/L, the average of 5 technical replicates\npotassium - Potassium concentration in umol/L, the average of 5 technical replicates\nB12 Vitamin - B12 in pmol/L, the average of 5 technical replicates\nwbc - White blood cell count in 10^9 /L, the average of 5 technical replicates\nrbc count - Red blood cell count in 10^12 /L, the average of 5 technical replicates\nplatlet count - platlet count in 10^9 /L, the average of 5 technical replicates\ninflammation marker - the presence or absence of a marker of inflammation, either 0 or 1\nstatus - whether the individual is before or after treatment.\n\nYour task is to summarise and plot these data in any suitable way. Create a complete RStudio Project for an analysis of these data. You will need to:\n\nMake a new project\nMake folders for data and for figures\nImport the data\nSummarise and plot variables of your choice. It doesn’t matter what you chose - the goal is the practice the project workflow and selecting appropriate plotting and summarising methods for particular data sets." + "objectID": "r4babs1/week-8/workshop.html#footnotes", + "href": "r4babs1/week-8/workshop.html#footnotes", + "title": "Workshop", + "section": "Footnotes", + "text": "Footnotes\n\nPlain text files can be opened in notepad or other similar editor and still be readable.↩︎\nDo not be tempted to import data this way. Unless you are careful, your data import will not be scripted or will not be scripted correctly.↩︎\nnote read_csv() and read_table() are the same functions with some different settings.↩︎\nCount data are usually “Poisson” distributed.↩︎" }, { - "objectID": "pgt52m/week-5/workshop.html", - "href": "pgt52m/week-5/workshop.html", - "title": "Workshop", + "objectID": "r4babs1/week-9/study_before_workshop.html", + "href": "r4babs1/week-9/study_before_workshop.html", + "title": "Independent Study to prepare for workshop", "section": "", - "text": "Artwork by Horst (2023): “love this class”\n\n\nIn this session you will remind yourself how to import files, and calculate confidence intervals on large and small samples.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "text": "📖 Read From importing to reporting. The first part of this chapter is about data import which we covered in the last workshop. You may be able to skip that part or you may find it useful to revise. The section on Summarising data will be mainly new." }, { - "objectID": "pgt52m/week-5/workshop.html#session-overview", - "href": "pgt52m/week-5/workshop.html#session-overview", - "title": "Workshop", + "objectID": "r4babs1/week-9/overview.html", + "href": "r4babs1/week-9/overview.html", + "title": "Overview", "section": "", - "text": "In this session you will remind yourself how to import files, and calculate confidence intervals on large and small samples." + "text": "Last week you summarised and plotted single variables. This week you will start plotting data sets with more than one variable. This means you need to be able determine which variable is the response and which is the explanatory. You will find out what is meant by “tidy” data and how to perform a simple data tidying task. Finally you will discover how to save your figures and place them in documents.\n\nLearning objectives\n\nsummarise and plot appropriately datasets with more than one variable\nrecognise that variables can be categorised by their role in analysis\nexplain what is meant by ‘tidy’ data and be able to perform some data tidying tasks.\nsave figures to file\ncreate neat reports which include text and figures\n\n\n\nInstructions\n\nPrepare\n\n📖 From importing to reporting\n\nWorkshop\n\n💻 Summarise and plot datasets with more than one variable.\n💻 Practice with working directories, importing data, formatting figures and the pipe\n💻 Lay out text, figures and figure legends in documents\n\nConsolidate\n\n💻 Summarise and plot a dataframe from the workshop\n💻 Practice the complete RStudio Project worklfow for a new dataset" }, { - "objectID": "pgt52m/week-5/workshop.html#philosophy", - "href": "pgt52m/week-5/workshop.html#philosophy", - "title": "Workshop", + "objectID": "r4babs4/r4babs4.html", + "href": "r4babs4/r4babs4.html", + "title": "Data Analysis in R for BABS 4", "section": "", - "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "text": "This is the last of the four BABS modules.\ncore strand specific: immunology\n\n\nThe BABS4 Module Learning outcomes that relate to the Data Analysis in R content are:" }, { - "objectID": "pgt52m/week-5/workshop.html#remind-yourself-how-to-import-files", - "href": "pgt52m/week-5/workshop.html#remind-yourself-how-to-import-files", - "title": "Workshop", - "section": "Remind yourself how to import files!", - "text": "Remind yourself how to import files!\nImporting data from files was covered in a previous workshop (Rand 2023) if you need to remind yourself." + "objectID": "r4babs4/r4babs4.html#module-learning-objectives", + "href": "r4babs4/r4babs4.html#module-learning-objectives", + "title": "Data Analysis in R for BABS 4", + "section": "", + "text": "The BABS4 Module Learning outcomes that relate to the Data Analysis in R content are:" }, { - "objectID": "pgt52m/week-5/workshop.html#confidence-intervals-large-samples", - "href": "pgt52m/week-5/workshop.html#confidence-intervals-large-samples", - "title": "Workshop", - "section": "Confidence intervals (large samples)", - "text": "Confidence intervals (large samples)\nThe data in beewing.txt are left wing widths of 100 honey bees (mm). The confidence interval for large samples is given by:\n\\(\\bar{x} \\pm 1.96 \\times s.e.\\)\nWhere 1.96 is the quantile for 95% confidence.\n Save beewing.txt to your data-raw folder.\n Read in the data and check the structure of the resulting dataframe.\n Calculate and assign to variables: the mean, standard deviation and standard error:\n\n# mean\nm <- mean(bee$wing)\n\n# standard deviation\nsd <- sd(bee$wing)\n\n# sample size (needed for the se)\nn <- length(bee$wing)\n\n# standard error\nse <- sd / sqrt(n)\n\n To calculate the 95% confidence interval we need to look up the quantile (multiplier) using qnorm()\n\nq <- qnorm(0.975)\n\nThis should be about 1.96.\n Now we can use it in our confidence interval calculation\n\nlcl <- m - q * se\nucl <- m + q * se\n\n Print the values\n\nlcl\n\n[1] 4.473176\n\nucl\n\n[1] 4.626824\n\n\nThis means we are 95% confident the population mean lies between 4.47 mm and 4.63 mm. The usual way of expressing this is that the mean is 4.55 +/- 0.07 mm\n Between what values would you be 99% confident of the population mean being?" + "objectID": "r4babs4/r4babs4.html#all-strands", + "href": "r4babs4/r4babs4.html#all-strands", + "title": "Data Analysis in R for BABS 4", + "section": "All Strands", + "text": "All Strands\nmany var and obs, examples, PCA, log fc transformation, normalisation, QC gating, missing values, excluding proteins id’d from fewer thaan two peptides, FDR, data structures" }, { - "objectID": "pgt52m/week-5/workshop.html#confidence-intervals-small-samples", - "href": "pgt52m/week-5/workshop.html#confidence-intervals-small-samples", - "title": "Workshop", - "section": "Confidence intervals (small samples)", - "text": "Confidence intervals (small samples)\nThe confidence interval for small samples is given by:\n\\(\\bar{x} \\pm \\sf t_{[d.f]} \\times s.e.\\)\nThe only difference between the calculation for small and large sample is the multiple. For large samples we use the “the standard normal distribution” accessed with qnorm(); for small samples we use the “t distribution” assessed with qt().The value returned by q(t) is larger than that returned by qnorm() which reflects the greater uncertainty we have on estimations of population means based on small samples.\nThe fatty acid Docosahexaenoic acid (DHA) is a major component of membrane phospholipids in nerve cells and deficiency leads to many behavioural and functional deficits. The cross sectional area of neurons in the CA 1 region of the hippocampus of normal rats is 155 \\(\\mu m^2\\). A DHA deficient diet was fed to 8 animals and the cross sectional area (csa) of neurons is given in neuron.txt\n Save neuron.txt to your data-raw folder\n Read in the data and check the structure of the resulting dataframe\n Assign the mean to m.\n Calculate and assign the standard error to se.\nTo work out the confidence interval for our sample mean we need to use the t distribution because it is a small sample. This means we need to determine the degrees of freedom (the number in the sample minus one).\n We can assign this to a variable, df, using:\n\ndf <- length(neur$csa) - 1\n\n The t value is found by:\n\nt <- qt(0.975, df = df)\n\nNote that we are using qt() rather than qnorm() but that the probability, 0.975, used is the same. Finally, we need to put our mean, standard error and t value in the equation. \\(\\bar{x} \\pm \\sf t_{[d.f]} \\times s.e.\\).\n The upper confidence limit is:\n\n(m + t * se) |> round(2)\n\n[1] 151.95\n\n\nThe first part of the command, (m + t * se) calculates the upper limit. This is ‘piped’ in to the round() function to round the result to two decimal places.\n Calculate the lower confidence limit:\n Given the upper and lower confidence values for the estimate of the population mean, what do you think about the effect of the DHA deficient diet?\n\n\n\n\nYou’re finished!" + "objectID": "pgt52m/week-7/study_before_workshop.html", + "href": "pgt52m/week-7/study_before_workshop.html", + "title": "Independent Study to prepare for workshop", + "section": "", + "text": "Prepare\n\n📖 Read Two-Sample tests" }, { - "objectID": "pgt52m/week-5/study_after_workshop.html", - "href": "pgt52m/week-5/study_after_workshop.html", - "title": "Independent Study to consolidate this week", + "objectID": "pgt52m/week-7/overview.html", + "href": "pgt52m/week-7/overview.html", + "title": "Overview", "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 Adiponectin is exclusively secreted from adipose tissue and modulates a number of metabolic processes. Nicotinic acid can affect adiponectin secretion. 3T3-L1 adipocytes were treated with nicotinic acid or with a control treatment and adiponectin concentration (pg/mL) measured. The data are in adipocytes.txt. Each row represents an independent sample of adipocytes and the first column gives the concentration adiponectin and the second column indicates whether they were treated with nicotinic acid or not. Estimate the mean Adiponectin concentration in each group - this means calculate the sample mean and construct a confidence interval around it for each group. This exercise forces you to bring together ideas from this workshop and from previous workshops\n\n\nHow to calculate a confidence intervals (this workshop)\n\nHow to summarise variables in more than one group (previous workshop)\n\n\nCode# data import\nadip <- read_table(\"data-raw/adipocytes.txt\")\n\n# examine the structure\nstr(adip)\n\n# summarise\nadip_summary <- adip %>% \n group_by(treatment) %>% \n summarise(mean = mean(adiponectin),\n sd = sd(adiponectin),\n n = length(adiponectin),\n se = sd/sqrt(n),\n dif = qt(0.975, df = n - 1) * se,\n lower_ci = mean - dif,\n uppp_ci = mean + dif)\n\n\n# we conclude we're 95% certain the mean for the control group is \n# between 4.73 and 6.36 and the mean for the nicotinic group is \n# between 6.52 and 8.50. More usually we might put is like this:\n# the mean for the control group is 5.55 +/- 0.82 and that for the nicotinic group is 7.51 +/- 0.99" + "text": "This week you will how to use and interpret the general linear model when the x variable is categorical and has two groups. Just as with single linear regression, the model puts a line of best through data and the model parameters, the intercept and the slope, have the same in interpretation The intercept is one of the group means and the slope is the difference between that, mean and the other group mean. You will also learn about the non-parametric equivalents - the tests we use when the assumptions of the general linear model are not met.\n\nLearning objectives\nThe successful student will be able to:\n\nunderstand the principles of two-sample tests\nappreciate that two-sample tests with lm() are based on the normal distribution and thus have assumptions\nappropriately select parametric and non-parametric two-sample tests\nappropriately select paired and and unpaired two-sample tests\napply and interpret lm()and wilcox.test()\nevaluate whether the assumptions of lm() are met\nscientifically report a two-sample test result including appropriate figures\n\n\n\nInstructions\n\nPrepare\n\n📖 Read Two-Sample tests\n\nWorkshop\n\n💻 Parametric two-sample test\n💻 Non-parametric two-sample test\n💻 Parametric paired-sample test\n\nConsolidate\n\n💻 Appropriately test whether a genetic modification was successful in increasing omega 3 fatty acids in Cannabis sativa.\n💻 …." }, { - "objectID": "pgt52m/week-6/workshop.html", - "href": "pgt52m/week-6/workshop.html", - "title": "Workshop", + "objectID": "pgt52m/week-6/study_before_workshop.html", + "href": "pgt52m/week-6/study_before_workshop.html", + "title": "Independent Study to prepare for workshop", "section": "", - "text": "In this workshop you will get practice in applying, interpreting and reporting single linear regression.\n\n\nArtwork by Horst (2023): “linear regression dragons”\n\n\nIn this session you will carry out, interpret and report on a single linear regression.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "text": "📖 Read What is a statistical model\n📖 Read Single linear regression" }, { - "objectID": "pgt52m/week-6/workshop.html#session-overview", - "href": "pgt52m/week-6/workshop.html#session-overview", - "title": "Workshop", + "objectID": "pgt52m/week-6/overview.html", + "href": "pgt52m/week-6/overview.html", + "title": "Overview", "section": "", - "text": "In this session you will carry out, interpret and report on a single linear regression." + "text": "This week you will be introduced to the idea of a statistical “model” in general and to general linear model in particular. Our first general linear model will be single linear regression which puts a line of best fit through data so the response can be predicted from the explanatory variable. We will consider the two “parameters” estimated by the model (the slope and the intercept) and whether these differ from zero\n\nLearning objectives\nThe successful student will be able to:\n\nexplain what is meant by a statistical model and fitting a model\nknow what the general linear model is and how it relates to regression\nexplain the principle of regression and know when it can be applied\napply and interpret a simple linear regression in R\nevaluate whether the assumptions of regression are met\nscientifically report a regression result including appropriate figures\n\n\n\nInstructions\n\nPrepare\n\n📖 Read What is a statistical model\n📖 Read Single linear regression\n\nWorkshop\ni.💻 Carry out a single linear regression\nConsolidate\n\n💻 Appropriately analyse the relationsip between juvenile hormone and mandible size in stage beetles\n💻 Appropriately analyse the relationsip between anxiety and performance" }, { - "objectID": "pgt52m/week-6/workshop.html#philosophy", - "href": "pgt52m/week-6/workshop.html#philosophy", - "title": "Workshop", + "objectID": "pgt52m/week-8/study_before_workshop.html", + "href": "pgt52m/week-8/study_before_workshop.html", + "title": "Independent Study to prepare for workshop", "section": "", - "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "text": "Prepare\n\n📖 Read One-way ANOVA and Kruskal-Wallis" }, { - "objectID": "pgt52m/week-6/workshop.html#linear-regression", - "href": "pgt52m/week-6/workshop.html#linear-regression", - "title": "Workshop", - "section": "Linear Regression", - "text": "Linear Regression\nThe data in plant.xlsx is a set of observations of plant growth over two months. The researchers planted the seeds and harvested, dried and weighed a plant each day from day 10 so all the data points are independent of each other.\n Save a copy of plant.xlsx to your data-raw folder and import it.\n What type of variables do you have? Which is the response and which is the explanatory? What is the null hypothesis?\n\n\n\n\n\n\nExploring\n Do a quick plot of the data:\n\nggplot(plant, aes(x = day, y = mass)) +\n geom_point()\n\n\n\n\n What are the assumptions of linear regression? Do these seem to be met?\n\n\n\n\n\n\n\n\n\n\nApplying, interpreting and reporting\n We now carry out a regression assigning the result of the lm() procedure to a variable and examining it with summary().\n\nmod <- lm(data = plant, mass ~ day)\nsummary(mod)\n\n\nCall:\nlm(formula = mass ~ day, data = plant)\n\nResiduals:\n Min 1Q Median 3Q Max \n-32.810 -11.253 -0.408 9.075 48.869 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) -8.6834 6.4729 -1.342 0.186 \nday 1.6026 0.1705 9.401 1.5e-12 ***\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 17.92 on 49 degrees of freedom\nMultiple R-squared: 0.6433, Adjusted R-squared: 0.636 \nF-statistic: 88.37 on 1 and 49 DF, p-value: 1.503e-12\n\n\nThe Estimates in the Coefficients table give the intercept (first line) and the slope (second line) of the best fitting straight line. The p-values on the same line are tests of whether that coefficient is different from zero.\nThe F value and p-value in the last line are a test of whether the model as a whole explains a significant amount of variation in the dependent variable. For a single linear regression this is exactly equivalent to the test of the slope against zero.\n What is the equation of the line? What do you conclude from the analysis?\n\n\n\n\n\n Does the line go through (0,0)?\n\n\n\n What percentage of variation is explained by the line?\n\n\nIt might be useful to assign the slope and the intercept to variables in case we need them later. The can be accessed in the mod$coefficients variable:\n\nmod$coefficients\n\n(Intercept) day \n -8.683379 1.602606 \n\n\n Assign mod$coefficients[1] to b0 and mod$coefficients[1] to b1:\n\nb0 <- mod$coefficients[1] |> round(2)\nb1 <- mod$coefficients[2] |> round(2)\n\nI also rounded the values to two decimal places.\nChecking assumptions\nWe need to examine the residuals. Very conveniently, the object which is created by lm() contains a variable called $residuals. Also conveniently, the R’s plot() function can used on the output objects of lm(). The assumptions demand that each y is drawn from a normal distribution for each x and these normal distributions have the same variance. Therefore we plot the residuals against the fitted values to see if the variance is the same for all the values of x. The fitted - predicted - values are the values on the line of best fit. Each residual is the difference between the fitted values and the observed value.\n Plot the model residuals against the fitted values like this:\n\nplot(mod, which = 1)\n\n\n\n\n What to you conclude?\n\n\n\nTo examine normality of the model residuals we can plot them as a histogram and do a normality test on them.\n Plot a histogram of the residuals:\n\nggplot(mapping = aes(x = mod$residuals)) + \n geom_histogram(bins = 10)\n\n\n\n\n Use the shapiro.test() to test the normality of the model residuals\n\nshapiro.test(mod$residuals)\n\n\n Shapiro-Wilk normality test\n\ndata: mod$residuals\nW = 0.96377, p-value = 0.1208\n\n\nUsually, when we are doing statistical tests we would like the the test to be significant because it means we have evidence of a biological effect. However, when doing normality tests we hope it will not be significant. A non-significant result means that there is no significant difference between the distribution of the residuals and a normal distribution and that indicates the assumptions are met.\n What to you conclude?\n\n\n\n\nIllustrating\nWe want a figure with the points and the statistical model, i.e., the best fitting straight line.\n Create a scatter plot using geom_point()\n\nggplot(plant, aes(x = day, y = mass)) +\n geom_point() + \n theme_classic()\n\n\n\n\n The geom_smooth() function will had a variety of fitted lines to a plot. We want a line so we need to specify method = \"lm\":\n\nggplot(plant, aes(x = day, y = mass)) +\n geom_point() + \n geom_smooth(method = lm, \n se = FALSE, \n colour = \"black\") +\n theme_classic()\n\n\n\n\n What do the se and colour arguments do? Try changing them.\n Let’s add the equation of the line to the figure using annotate():\n\nggplot(plant, aes(x = day, y = mass)) +\n geom_point() +\n geom_smooth(method = lm, \n se = FALSE, \n colour = \"black\") +\n annotate(\"text\", x = 20, y = 110, \n label = \"mass = 1.61 * day - 8.68\") +\n theme_classic()\n\n\n\n\nWe have to tell annotate() what type of geom we want - text in this case, - where to put it, and the text we want to appear.\n Improve the axes. You may need to refer back Changing the axes from the Week 2 workshop\n Save your figure to your figures folder." + "objectID": "pgt52m/week-8/overview.html", + "href": "pgt52m/week-8/overview.html", + "title": "Overview", + "section": "", + "text": "Last week you learnt how to use and interpret the general linear model when the x variable was categorical with two groups. You will now extend that to situations when there are more than two groups. This is often known as the one-way ANOVA (analysis of variance). You will also learn about the Kruskal- Wallis test which can be used when the assumptions of the general linear model are not met.\n\nLearning objectives\nThe successful student will be able to:\n\nexplain the rationale behind ANOVA understand the meaning of the F values\nselect, appropriately, one-way ANOVA and Kruskal-Wallis\nknow what functions are used in R to run these tests and how to interpret them\nevaluate whether the assumptions of lm() are met\nscientifically report the results of these tests including appropriate figures\n\n\n\nInstructions\n\nPrepare\n\n📖 Read One-way ANOVA and Kruskal-Wallis\n\nWorkshop\n\n💻 One-way ANOVA\n💻 Kruskal-Wallis\n\nConsolidate\n\n💻 Appropriately test if fitness and acclimation effect the sodium content of sweat\n💻 Appropriately test if insecticides vary in their effectiveness" }, { - "objectID": "pgt52m/week-6/workshop.html#look-after-future-you", - "href": "pgt52m/week-6/workshop.html#look-after-future-you", - "title": "Workshop", - "section": "Look after future you!", - "text": "Look after future you!\nYou’re finished!" + "objectID": "pgt52m/week-2/study_before_workshop.html", + "href": "pgt52m/week-2/study_before_workshop.html", + "title": "Independent Study to prepare for workshop", + "section": "", + "text": "Either\n\n📖 Read First Steps in RStudio in\n\nOR\n\n📹 Watch" }, { - "objectID": "pgt52m/week-6/study_after_workshop.html", - "href": "pgt52m/week-6/study_after_workshop.html", - "title": "Independent Study to consolidate this week", + "objectID": "pgt52m/week-2/overview.html", + "href": "pgt52m/week-2/overview.html", + "title": "Overview", "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 Effect of anxiety status and sporting performance. The data in sprint.txt are from an investigation of the effect of anxiety status and sporting performance. A group of 40 100m sprinters undertook a psychometric test to measure their anxiety shortly before competing. The data are their anxiety scores and the 100m times achieved. What you do conclude from these data?\n\n\nCode# this example is designed to emphasise the importance of plotting your data first\nsprint <- read_table(\"data-raw/sprint.txt\")\n# Anxiety is discrete but ranges from 16 to 402 meaning the gap between possible measures is small and \n# the variable could be treated as continuous if needed. Time is a continuous measure that has decimal places and which we would expect to follow a normal distribution \n\n# explore with a plot\nggplot(sprint, aes(x = anxiety, y = time) ) +\n geom_point()\nCode# A scatterplot of the data clearly reveals that these data are not linear. There is a good relationship between the two variables but since it is not linear, single linear regression is not appropriate.\n\n\n\n💻 Juvenile hormone in stag beetles. The concentration of juvenile hormone in stag beetles is known to influence mandible growth. Groups of stag beetles were injected with different concentrations of juvenile hormone (arbitrary units) and their average mandible size (mm) determined. The experimenters planned to analyse their data with regression. The data are in stag.txt\n\n\n\nCode# read the data in and check the structure\nstag <- read_table(\"data-raw/stag.txt\")\nstr(stag)\n\n# jh is discrete but ordered and has been chosen by the experimenter - it is the explanatory variable. \n# the response is mandible size which has decimal places and is something we would expect to be \n# normally distributed. So far, common sense suggests the assumptions of regression are met.\n\n\n\nCode# exploratory plot\nggplot(stag, aes(x = jh, y = mand)) +\n geom_point()\nCode# looks linear-ish on the scatter\n# regression still seems appropriate\n# we will check the other assumptions after we have run the lm\n\n\n\nCode# build the statistical model\nmod <- lm(data = stag, mand ~ jh)\n\n# examine it\nsummary(mod)\n# mand = 0.032*jh + 0.419\n# the slope of the line is significantly different from zero / the jh explains a significant amount of the variation in mand (ANOVA: F = 16.63; d.f. = 1,14; p = 0.00113).\n# the intercept is 0.419 and differs significantly from zero \n\n\n\nCode# checking the assumption\nplot(mod, which = 1) \nCode# we're looking for the variance in the residuals to be equal along the x axis.\n# with a small data set there is some apparent heterogeneity but it doesn't look too.\n# \nhist(mod$residuals)\nCode# We have some skew which again might be partly a result of a small sample size.\nshapiro.test(mod$residuals) # the also test not sig diff from normal\n\n# On balance the use of regression is probably justifiable but it is borderline\n# but ideally the experiment would be better if multiple individuals were measure at\n# each of the chosen juvenile hormone levels.\n\n\n\nCode# a better plot\nggplot(stag, aes(x = jh, y = mand) ) +\n geom_point() +\n geom_smooth(method = lm, se = FALSE, colour = \"black\") +\n scale_x_continuous(name = \"Juvenile hormone (arbitrary units)\",\n expand = c(0, 0),\n limits = c(0, 32)) +\n scale_y_continuous(name = \"Mandible size (mm)\",\n expand = c(0, 0),\n limits = c(0, 2)) +\n theme_classic()" + "text": "This week you will start writing R code in RStudio and will create your first graph! You will learn about data types such as “numerics” and “characters” and some of the different types of objects in R such as “vectors” and “dataframes”. These are the building blocks for the rest of your R journey. You will also learn a workflow and about the layout of RStudio and using RStudio Projects.\n\n\n\nArtwork by Horst (2023): “bless this workflow”\n\n\n\nLearning objectives\nThe successful student will be able to:\n\nuse the R command line as a calculator and to assign variables\ncreate and use the basic data types in R\nfind their way around the RStudio windows\nuse an RStudio Project to organise work\nuse a script to run R commands\ncreate and customise a barplot\nsearch and understand manual pages\n\n\n\nInstructions\n\nPrepare\n\nFirst Steps in RStudio: Either 📖 Read the book OR 📹 Watch two videos\n\nWorkshop\ni.💻 🐈 Coat colour of cats. Type in some data, perform calculations on, and plot it.\nConsolidate\n\n💻 Create a plot\n📖 Read Workflow in RStudio\n\n\n\n\n\n\n\nReferences\n\nHorst, Allison. 2023. “Data Science Illustrations.” https://allisonhorst.com/allison-horst." }, { "objectID": "pgt52m/week-2/rstudio-projects.html#outline", @@ -1225,11 +1253,18 @@ "text": "demo" }, { - "objectID": "pgt52m/week-2/overview.html", - "href": "pgt52m/week-2/overview.html", + "objectID": "pgt52m/week-5/study_before_workshop.html", + "href": "pgt52m/week-5/study_before_workshop.html", + "title": "Independent Study to prepare for workshop", + "section": "", + "text": "📖 Read The logic of hyothesis testing\n📖 Read Confidence Intervals" + }, + { + "objectID": "pgt52m/week-5/overview.html", + "href": "pgt52m/week-5/overview.html", "title": "Overview", "section": "", - "text": "This week you will start writing R code in RStudio and will create your first graph! You will learn about data types such as “numerics” and “characters” and some of the different types of objects in R such as “vectors” and “dataframes”. These are the building blocks for the rest of your R journey. You will also learn a workflow and about the layout of RStudio and using RStudio Projects.\n\n\n\nArtwork by Horst (2023): “bless this workflow”\n\n\n\nLearning objectives\nThe successful student will be able to:\n\nuse the R command line as a calculator and to assign variables\ncreate and use the basic data types in R\nfind their way around the RStudio windows\nuse an RStudio Project to organise work\nuse a script to run R commands\ncreate and customise a barplot\nsearch and understand manual pages\n\n\n\nInstructions\n\nPrepare\n\nFirst Steps in RStudio: Either 📖 Read the book OR 📹 Watch two videos\n\nWorkshop\ni.💻 🐈 Coat colour of cats. Type in some data, perform calculations on, and plot it.\nConsolidate\n\n💻 Create a plot\n📖 Read Workflow in RStudio\n\n\n\n\n\n\n\nReferences\n\nHorst, Allison. 2023. “Data Science Illustrations.” https://allisonhorst.com/allison-horst." + "text": "This week we will cover the logic of consider the logic of hypothesis testing and type 1 and type 2 errors. We will also find out what the sampling distribution of the mean and the standard error are, and how to calculate confidence intervals.\n\n\n\nArtwork by Horst (2023): “type 1 error”\n\n\n\n\n\nArtwork by Horst (2023): “type 2 error”\n\n\n\nLearning objectives\nThe successful student will be able to:\n\ndemonstrate the process of hypothesis testing with an example\nexplain type 1 and type 2 errors\ndefine the sampling distribution of the mean and the standard error\nexplain what a confidence interval is\ncalculate confidence intervals for large and small samples\n\n\n\nInstructions\n\nPrepare\n\n📖 Read The logic of hyothesis testing\n📖 Read Confidence Intervals\n\nWorkshop\n\n💻 Remind yourself how to import files\n💻 Calculate confidence intervals on large\n💻 Calculate confidence intervals on small samples.\n\nConsolidate\n\n💻 Calculate confidence intervals for each group in a data set\n\n\n\n\n\n\n\nReferences\n\nHorst, Allison. 2023. “Data Science Illustrations.” https://allisonhorst.com/allison-horst." }, { "objectID": "pgt52m/week-1/study_before_workshop.html", @@ -1246,207 +1281,151 @@ "text": "This week you will carry out some independent study to ensure you have some understanding of computer file systems. We will introduce you to the concepts of paths and working directories.\n\n\n\nArtwork by Horst (2023): “code gets the blame”\n\n\n\nLearning objectives\nThe parentheses after each learning objective indicate where the content covers that objective.\nThe successful student will be able to:\n\nexplain what an operating system is\nexplain the organisation of files and directories in a file systems\nexplain what a file is and give some common files types\nexplain what is meant by a plain text file\nexplain the relationship between the file extensions, the file format and associations with programs\nuse a file manager\nexplain root, home and working directories\nexplain absolute and relative file paths\nknow what R and RStudio are\nknow how to organise their work\n\n\n\nInstructions\n\nPrepare\n\nWatch an Introduction to Data Analysis in R for BABS 1 - 4\nRead What they forgot to teach you about computers\nRead What are R and Rstudio?\n\nWorkshop\n\nOptional: Install R and RStudio\n\nConsolidate\n\n\n\n\n\n\nReferences\n\nHorst, Allison. 2023. “Data Science Illustrations.” https://allisonhorst.com/allison-horst." }, { - "objectID": "pgt52m/week-7/study_before_workshop.html", - "href": "pgt52m/week-7/study_before_workshop.html", + "objectID": "pgt52m/week-4/study_before_workshop.html", + "href": "pgt52m/week-4/study_before_workshop.html", "title": "Independent Study to prepare for workshop", "section": "", - "text": "Prepare\n\n📖 Read Two-Sample tests" + "text": "📖 Read From importing to reporting. The first part of this chapter is about data import which we covered in the last workshop. You may be able to skip that part or you may find it useful to revise. The section on Summarising data will be mainly new." }, { - "objectID": "pgt52m/week-7/overview.html", - "href": "pgt52m/week-7/overview.html", + "objectID": "pgt52m/week-4/overview.html", + "href": "pgt52m/week-4/overview.html", "title": "Overview", "section": "", - "text": "This week you will how to use and interpret the general linear model when the x variable is categorical and has two groups. Just as with single linear regression, the model puts a line of best through data and the model parameters, the intercept and the slope, have the same in interpretation The intercept is one of the group means and the slope is the difference between that, mean and the other group mean. You will also learn about the non-parametric equivalents - the tests we use when the assumptions of the general linear model are not met.\n\nLearning objectives\nThe successful student will be able to:\n\nunderstand the principles of two-sample tests\nappreciate that two-sample tests with lm() are based on the normal distribution and thus have assumptions\nappropriately select parametric and non-parametric two-sample tests\nappropriately select paired and and unpaired two-sample tests\napply and interpret lm()and wilcox.test()\nevaluate whether the assumptions of lm() are met\nscientifically report a two-sample test result including appropriate figures\n\n\n\nInstructions\n\nPrepare\n\n📖 Read Two-Sample tests\n\nWorkshop\n\n💻 Parametric two-sample test\n💻 Non-parametric two-sample test\n💻 Parametric paired-sample test\n\nConsolidate\n\n💻 Appropriately test whether a genetic modification was successful in increasing omega 3 fatty acids in Cannabis sativa.\n💻 …." + "text": "Last week you summarised and plotted single variables. This week you will start plotting data sets with more than one variable. This means you need to be able determine which variable is the response and which is the explanatory. You will find out what is meant by “tidy” data and how to perform a simple data tidying task. Finally you will discover how to save your figures and place them in documents.\n\nLearning objectives\n\nsummarise and plot appropriately datasets with more than one variable\nrecognise that variables can be categorised by their role in analysis\nexplain what is meant by ‘tidy’ data and be able to perform some data tidying tasks.\nsave figures to file\ncreate neat reports which include text and figures\n\n\n\nInstructions\n\nPrepare\n\n📖 From importing to reporting\n\nWorkshop\n\n💻 Summarise and plot datasets with more than one variable.\n💻 Practice with working directories, importing data, formatting figures and the pipe\n💻 Lay out text, figures and figure legends in documents\n\nConsolidate\n\n💻 Summarise and plot a dataframe from the workshop\n💻 Practice the complete RStudio Project worklfow for a new dataset" }, { - "objectID": "pgt52m/pgt52m.html", - "href": "pgt52m/pgt52m.html", - "title": "52M Data Analysis in R", + "objectID": "pgt52m/week-3/study_before_workshop.html", + "href": "pgt52m/week-3/study_before_workshop.html", + "title": "Independent Study to prepare for workshop", "section": "", - "text": "This module introduces you to data analysis in R. The first 4 weeks covers core concepts about scientific computing, types of variable, the role of variables in analysis and how to use RStudio to organise analysis and import, summarise and plot data. In weeks 5 to 8, you will learn about the logic of hypothesis testing, confidence intervals, what is meant by a statistical model, two-sample tests and one-way analysis of variance (ANOVA). You will learn how to write reproducible reports in Quarto in weeks 9 and 10. Finally, there will be a drop-in for your questions in week 11.\nThis module complement the work you will do in BIO00070M Research, Professional and Team Skills where you will you will learn how to organise reproducible data analyses using a project-oriented workflow and analyses RNA sequence data. It will be important to use the skills and tools you learn in 52M and apply them in 70M.\n\n\nThe Module Learning outcomes are:\n\nExplain the purpose of data analysis and the rationale for scripting analysis in the biosciences\nRecognise when statistics such as t-tests, one-way ANOVA, correlation and regression can be applied, and use R to perform these analyses on data in a variety of formats\nSummarise data in single or multiple groups, recognise tidy data formats, and carry out some typical data tidying tasks\nUse markdown (through Quarto) to produce reproducible analyses, figures and reports" + "text": "📖 Read Ideas about data" }, { - "objectID": "pgt52m/pgt52m.html#module-learning-objectives", - "href": "pgt52m/pgt52m.html#module-learning-objectives", - "title": "52M Data Analysis in R", + "objectID": "pgt52m/week-3/overview.html", + "href": "pgt52m/week-3/overview.html", + "title": "Overview", "section": "", - "text": "The Module Learning outcomes are:\n\nExplain the purpose of data analysis and the rationale for scripting analysis in the biosciences\nRecognise when statistics such as t-tests, one-way ANOVA, correlation and regression can be applied, and use R to perform these analyses on data in a variety of formats\nSummarise data in single or multiple groups, recognise tidy data formats, and carry out some typical data tidying tasks\nUse markdown (through Quarto) to produce reproducible analyses, figures and reports" - }, - { - "objectID": "pgt52m/pgt52m.html#week-1-understanding-file-systems", - "href": "pgt52m/pgt52m.html#week-1-understanding-file-systems", - "title": "52M Data Analysis in R", - "section": "Week 1: Understanding file systems", - "text": "Week 1: Understanding file systems\nYou will learn about operating systems, files and file systems, working directories, absolute and relative paths, what R and RStudio are" - }, - { - "objectID": "pgt52m/pgt52m.html#week-2-introduction-to-r-and-project-organisation", - "href": "pgt52m/pgt52m.html#week-2-introduction-to-r-and-project-organisation", - "title": "52M Data Analysis in R", - "section": "Week 2: Introduction to R and project organisation", - "text": "Week 2: Introduction to R and project organisation\nYou will start writing R code in RStudio and will create your first graph! You will learn about data types such as “numerics” and “characters” and some of the different types of objects in R such as “vectors” and “dataframes”. These are the building blocks for the rest of your R journey. You will also learn a workflow and about the layout of RStudio and using RStudio Projects." - }, - { - "objectID": "pgt52m/pgt52m.html#week-3-types-of-variable-summarising-and-plotting-data", - "href": "pgt52m/pgt52m.html#week-3-types-of-variable-summarising-and-plotting-data", - "title": "52M Data Analysis in R", - "section": "Week 3: Types of variable, summarising and plotting data", - "text": "Week 3: Types of variable, summarising and plotting data\nThe type of values our data can take is important in how we analyse and visualise it. This week you will learn the difference between continuous and discrete values and how we summarise and visualise them. The focus will be on plotting and summarising single variables. You will also learn how to read in data in to RStudio from plain text files and Excel files." - }, - { - "objectID": "pgt52m/pgt52m.html#week-4-summarising-data-with-several-variables", - "href": "pgt52m/pgt52m.html#week-4-summarising-data-with-several-variables", - "title": "52M Data Analysis in R", - "section": "Week 4: Summarising data with several variables", - "text": "Week 4: Summarising data with several variables\nThis week you will start plotting data sets with more than one variable. This means you need to be able determine which variable is the response and which is the explanatory. You will find out what is meant by “tidy” data and how to perform a simple data tidying task. Finally you will discover how to save your figures and place them in documents." - }, - { - "objectID": "pgt52m/pgt52m.html#week-5-the-logic-of-hypothesis-testing-and-ci", - "href": "pgt52m/pgt52m.html#week-5-the-logic-of-hypothesis-testing-and-ci", - "title": "52M Data Analysis in R", - "section": "Week 5: The logic of hypothesis testing and CI", - "text": "Week 5: The logic of hypothesis testing and CI\nThis week we will cover the logic of consider the logic of hypothesis testing and type 1 and type 2 errors. We will also find out what the sampling distribution of the mean and the standard error are, and how to calculate confidence intervals." - }, - { - "objectID": "pgt52m/pgt52m.html#week-6-introduction-to-statistical-models-single-regression", - "href": "pgt52m/pgt52m.html#week-6-introduction-to-statistical-models-single-regression", - "title": "52M Data Analysis in R", - "section": "Week 6: Introduction to statistical models: Single regression", - "text": "Week 6: Introduction to statistical models: Single regression\nThis week you will be introduced to the idea of a statistical “model” in general and to general linear model in particular. Our first general linear model will be single linear regression which puts a line of best fit through data so the response can be predicted from the explanatory variable. We will consider the two “parameters” estimated by the model (the slope and the intercept) and whether these differ from zero" - }, - { - "objectID": "pgt52m/pgt52m.html#week-7-two-sample-tests", - "href": "pgt52m/pgt52m.html#week-7-two-sample-tests", - "title": "52M Data Analysis in R", - "section": "Week 7: Two-sample tests", - "text": "Week 7: Two-sample tests\nThis week you will how to use and interpret the general linear model when the x variable is categorical and has two groups. Just as with single linear regression, the model puts a line of best through data and the model parameters, the intercept and the slope, have the same in interpretation The intercept is one of the group means and the slope is the difference between that, mean and the other group mean. You will also learn about the non-parametric equivalents - the tests we use when the assumptions of the general linear model are not met." - }, - { - "objectID": "pgt52m/pgt52m.html#week-8-one-way-anova-and-kruskal-wallis", - "href": "pgt52m/pgt52m.html#week-8-one-way-anova-and-kruskal-wallis", - "title": "52M Data Analysis in R", - "section": "Week 8: One-way ANOVA and Kruskal-Wallis", - "text": "Week 8: One-way ANOVA and Kruskal-Wallis\nLast week you learnt how to use and interpret the general linear model when the x variable was categorical with two groups. You will now extend that to situations when there are more than two groups. This is often known as the one-way ANOVA (analysis of variance). You will also learn about the Kruskal- Wallis test which can be used when the assumptions of the general linear model are not met." - }, - { - "objectID": "pgt52m/pgt52m.html#week-9-assessment-intro", - "href": "pgt52m/pgt52m.html#week-9-assessment-intro", - "title": "52M Data Analysis in R", - "section": "Week 9: Assessment intro", - "text": "Week 9: Assessment intro\nReproducible analysis of some relevant data." + "text": "The type of values our data can take is important in how we analyse and visualise it. This week you will learn the difference between continuous and discrete values and how we summarise and visualise them. You will also learn about the “normal distribution” which is the most important continuous distribution.\n\n\n\nDiscrete variable\n\n\n\nLearning objectives\nThe successful student will be able to:\n\ndistinguish between continuous, discrete, nominal and ordinal variable\nread in data in to RStudio from a plain text file and Excel files\nsummarise and plot variables appropriately for the data type\n\n\n\nInstructions\n\nPrepare\n\n📖 Read: Ideas about data\n\nWorkshop\n\n💻 Importing data\n💻 Summarising discrete data\n💻 Summarising count data\n💻 Summarising continuous data\n\nConsolidate\n\n💻 Summarise some data\n💻 Plot some data\n💻 Format a plot (1)\n💻 Format a plot (2)\n📖 Read Understanding the pipe |>" }, { - "objectID": "pgt52m/pgt52m.html#week-10-reproducible-reporting", - "href": "pgt52m/pgt52m.html#week-10-reproducible-reporting", - "title": "52M Data Analysis in R", - "section": "Week 10: Reproducible Reporting", - "text": "Week 10: Reproducible Reporting\nUsing Quarto" + "objectID": "r4babs2/week-6/study_before_workshop.html", + "href": "r4babs2/week-6/study_before_workshop.html", + "title": "Independent Study to prepare for workshop", + "section": "", + "text": "Prepare\n\nEither 📖 Read xxxxx in OR 📹 Watch" }, { - "objectID": "pgt52m/pgt52m.html#week-11-drop-in", - "href": "pgt52m/pgt52m.html#week-11-drop-in", - "title": "52M Data Analysis in R", - "section": "Week 11: Drop-in", - "text": "Week 11: Drop-in" + "objectID": "r4babs2/week-6/overview.html", + "href": "r4babs2/week-6/overview.html", + "title": "Overview", + "section": "", + "text": "This week you will\n\nLearning objectives\nThe successful student will be able to:\n\n\n\n\n\n\n\n\nInstructions\n\nPrepare\n\n📖 Read the book OR 📹 Watch two videos\n\nWorkshop\ni.💻\nConsolidate\n\n💻\n📖 Read" }, { - "objectID": "r4babs2/week-3/workshop.html", - "href": "r4babs2/week-3/workshop.html", - "title": "Workshop", + "objectID": "r4babs2/r4babs2.html", + "href": "r4babs2/r4babs2.html", + "title": "Data Analysis in R for BABS 2", "section": "", - "text": "Artwork by Horst (2023): “How much I think I know about R”\n\n\nIn this workshop you will get practice in choosing between, performing, and presenting the results of, two-sample tests and their non-parametric equivalents in R.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "text": "This is the second of the four BABS modules. Over six weeks you will learn about the logic of hypothesis testing, confidence intervals, what is meant by a statistical model, two-sample tests and one- and two-way analysis of variance (ANOVA).\n\n\nThe BABS2 Module Learning outcomes that relate to the Data Analysis in R content are:\n\nThink creatively to address a Grand Challenge by designing investigations with testable hypotheses and rigorous controls\nAppropriately select classical univariate statistical tests and some non-parametric equivalents to a given scenario and recognise when these are not suitable\nUse R to perform these analyses, reproducibly, on data in a variety of formats and present the results graphically\nCommunicate research in scientific reports and via oral presentation." }, { - "objectID": "r4babs2/week-3/workshop.html#session-overview", - "href": "r4babs2/week-3/workshop.html#session-overview", - "title": "Workshop", + "objectID": "r4babs2/r4babs2.html#module-learning-objectives", + "href": "r4babs2/r4babs2.html#module-learning-objectives", + "title": "Data Analysis in R for BABS 2", "section": "", - "text": "In this workshop you will get practice in choosing between, performing, and presenting the results of, two-sample tests and their non-parametric equivalents in R." + "text": "The BABS2 Module Learning outcomes that relate to the Data Analysis in R content are:\n\nThink creatively to address a Grand Challenge by designing investigations with testable hypotheses and rigorous controls\nAppropriately select classical univariate statistical tests and some non-parametric equivalents to a given scenario and recognise when these are not suitable\nUse R to perform these analyses, reproducibly, on data in a variety of formats and present the results graphically\nCommunicate research in scientific reports and via oral presentation." }, { - "objectID": "r4babs2/week-3/workshop.html#philosophy", - "href": "r4babs2/week-3/workshop.html#philosophy", - "title": "Workshop", - "section": "", - "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "objectID": "r4babs2/r4babs2.html#the-logic-of-hypothesis-testing-and-cis", + "href": "r4babs2/r4babs2.html#the-logic-of-hypothesis-testing-and-cis", + "title": "Data Analysis in R for BABS 2", + "section": "The logic of hypothesis testing and CIs", + "text": "The logic of hypothesis testing and CIs" }, { - "objectID": "r4babs2/week-3/workshop.html#adiponectin-secretion", - "href": "r4babs2/week-3/workshop.html#adiponectin-secretion", - "title": "Workshop", - "section": "Adiponectin secretion", - "text": "Adiponectin secretion\nAdiponectin is exclusively secreted from adipose tissue and modulates a number of metabolic processes. Nicotinic acid can affect adiponectin secretion. 3T3-L1 adipocytes were treated with nicotinic acid or with a control treatment and adiponectin concentration (pg/mL) measured. The data are in adipocytes.txt. Each row represents an independent sample of adipocytes and the first column gives the concentration adiponectin and the second column indicates whether they were treated with nicotinic acid or not.\n Save a copy of adipocytes.txt to data-raw\n Read in the data and check the structure. I used the name adip for the dataframe/tibble.\nWe have a tibble containing two variables: adiponectin is the response and is continuous and treatment is explanatory. treatment is categorical with two levels (groups). The first task is visualise the data to get an overview. For continuous response variables with categorical explanatory variables you could use geom_point(), geom_boxplot() or a variety of other geoms. I often use geom_violin() which allows us to see the distribution - the violin is fatter where there are more data points.\n Do a quick plot of the data:\n\nggplot(data = adip, aes(x = treatment, y = adiponectin)) +\n geom_violin()\n\n\n\n\nSummarising the data\nSummarising the data for each treatment group is the next sensible step. The most useful summary statistics are the means, standard deviations, sample sizes and standard errors.\n Create a data frame called adip_summary that contains the means, standard deviations, sample sizes and standard errors for the control and nicotinic acid treated samples. You may need to the Summarise from the Week 9 workshop of BABS1 (Rand 2023)\nYou should get the following numbers:\n\n\n\n\ntreatment\nmean\nstd\nn\nse\n\n\n\ncontrol\n5.546000\n1.475247\n15\n0.3809072\n\n\nnicotinic\n7.508667\n1.793898\n15\n0.4631824\n\n\n\n\n\nSelecting a test\n Do you think this is a paired-sample test or two-sample test?\n\n\n\n\nApplying, interpreting and reporting\n Create a two-sample model like this:\n\nmod <- lm(data = adip,\n adiponectin ~ treatment)\n\n Examine the model with:\n\nsummary(mod)\n\n\nCall:\nlm(formula = adiponectin ~ treatment, data = adip)\n\nResiduals:\n Min 1Q Median 3Q Max \n-4.3787 -1.0967 0.1927 1.0245 3.1113 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 5.5460 0.4240 13.079 1.9e-13 ***\ntreatmentnicotinic 1.9627 0.5997 3.273 0.00283 ** \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 1.642 on 28 degrees of freedom\nMultiple R-squared: 0.2767, Adjusted R-squared: 0.2509 \nF-statistic: 10.71 on 1 and 28 DF, p-value: 0.00283\n\n\n What do you conclude from the test? Write your conclusion in a form suitable for a report.\n\n\n\n\nCheck assumptions\nThe assumptions of the general linear model are that the residuals – the difference between predicted value (i.e., the group mean) and observed values - are normally distributed and have homogeneous variance. To check these we can examine the mod$residuals variable. You may want to refer to Checking assumptions in the “Single regression” workshop.\n Plot the model residuals against the fitted values.\n What to you conclude?\n\n\n\nTo examine normality of the model residuals we can plot them as a histogram and do a normality test on them.\n Plot a histogram of the residuals.\n Use the shapiro.test() to test the normality of the model residuals\n What to you conclude?\n\n\n\n\nIllustrating\n Create a figure like the one below. You may need to refer to Visualise from the “Summarising data with several variables” workshop (Rand 2023)\n\n\n\n\n\nWe now need to annotate the figure with the results from the statistical test. This most commonly done with a line linking the means being compared and the p-value. The annotate() function can be used to draw the line and then to add the value. The line is a segment and the p-value is a text.\n Add annotation to the figure by adding:\n...... +\n annotate(\"segment\", x = 1, xend = 2, \n y = 11.3, yend = 11.3,\n colour = \"black\") +\n annotate(\"text\", x = 1.5, y = 11.7, \n label = expression(italic(p)~\"= 0.003\")) +\n theme_classic()\n\n\n\n\n\nFor the segment, annotate() needs the x and y coordinates for the start and the finish of the line.\nThe use of expression() allows you to specify formatting or special characters. expression() takes strings or LaTeX formatting. Each string or piece of LaTeX is separated by a * or a ~. The * concatenates the strings without a space, ~ does so with a space. It will generate a warning message “In is.na(x) : is.na() applied to non-(list or vector) of type ‘expression’” which can be ignored.\n Save your figure to your figures folder." + "objectID": "r4babs2/r4babs2.html#introduction-to-statistical-models-single-regression", + "href": "r4babs2/r4babs2.html#introduction-to-statistical-models-single-regression", + "title": "Data Analysis in R for BABS 2", + "section": "Introduction to statistical models: Single regression", + "text": "Introduction to statistical models: Single regression" }, { - "objectID": "r4babs2/week-3/workshop.html#grouse-parasites", - "href": "r4babs2/week-3/workshop.html#grouse-parasites", - "title": "Workshop", - "section": "Grouse Parasites", - "text": "Grouse Parasites\nGrouse livers were dissected and the number of individuals of a parasitic nematode were counted for two estates ‘Gordon’ and ‘Moss’. We want to know if the two estates have different infection rates. The data are in grouse.csv\n Save a copy of grouse.csv to data-raw\n Read in the data and check the structure. I used the name grouse for the dataframe/tibble.\nSelecting\n Using your common sense, do these data look normally distributed?\n\n\n\n What test do you suggest?\n\n\nApplying, interpreting and reporting\n Summarise the data by finding the median of each group:\n Carry out a two-sample Wilcoxon test (also known as a Mann-Whitney):\n\nwilcox.test(data = grouse, nematodes ~ estate)\n\n\n Wilcoxon rank sum exact test\n\ndata: nematodes by estate\nW = 78, p-value = 0.03546\nalternative hypothesis: true location shift is not equal to 0\n\n\n What do you conclude from the test? Write your conclusion in a form suitable for a report.\n\n\n\nIllustrating\nA box plot is a usually good choice for illustrating a two-sample Wilcoxon test because it shows the median and interquartile range.\n We can create a simple boxplot with:\n\nggplot(data = grouse, aes(x = estate, y = nematodes) ) +\n geom_boxplot() \n\n\n\n\n Annotate and format the figure so it is more suitable for a report and save it to your figures folder." + "objectID": "r4babs2/r4babs2.html#two-sample-tests", + "href": "r4babs2/r4babs2.html#two-sample-tests", + "title": "Data Analysis in R for BABS 2", + "section": "Two-sample tests", + "text": "Two-sample tests" }, { - "objectID": "r4babs2/week-3/workshop.html#gene-expression", - "href": "r4babs2/week-3/workshop.html#gene-expression", - "title": "Workshop", - "section": "Gene Expression", - "text": "Gene Expression\nBambara groundnut (Vigna subterranea) is an African legume with good nutritional value which can be influenced by low temperature stress. Researchers are interested in the expression levels of a particular set of 35 genes (probe_id) in response to temperature stress. They measure the expression of the genes at 23 and 18 degrees C (high and low temperature). These samples are not independent because we have two measure from one gene. The data are in expr.xlxs.\nSelecting\n What is the null hypothesis?\n\n\n\n Save a copy of expr.xlxs and import the data. I named the dataframe bambara\n What is the appropriate parametric test?\n\n\nApplying, interpreting and reporting\nA paired test requires us to test whether the difference in expression between high and low temperatures is zero on average. One handy way to achieve this is to organise our groups into two columns. The pivot_wider() function will do this for us. We need to tell it what column gives the identifiers (i.e., matches the the pairs) - the probe_ids in this case. We also need to say which variable contains what will become the column names and which contains the values.\n Pivot the data so there is a column for each temperature:\n\nbambara <- bambara |> \n pivot_wider(names_from = temperature, \n values_from = expression, \n id_cols = probe_id)\n\n Click on the bambara dataframe in the environment to open a view of it so that you understand what pivot_wider() has done.\n Create a paired-sample model like this:\n\nmod <- lm(data = bambara, \n highert - lowert ~ 1)\n\nSince we have done highert - lowert, the “(Intercept) Estimate” will be the average of the higher temperature expression minus the lower temperature expression for each gene.\n Examine the model with:\n\nsummary(mod)\n\n\nCall:\nlm(formula = highert - lowert ~ 1, data = bambara)\n\nResiduals:\n Min 1Q Median 3Q Max \n-1.05478 -0.46058 0.09682 0.33342 1.06892 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 0.30728 0.09591 3.204 0.00294 **\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 0.5674 on 34 degrees of freedom\n\n\n State your conclusion from the test in a form suitable for including in a report. Make sure you give the direction of any significant effect." + "objectID": "r4babs2/r4babs2.html#one-way-anova-and-kruskal-wallis", + "href": "r4babs2/r4babs2.html#one-way-anova-and-kruskal-wallis", + "title": "Data Analysis in R for BABS 2", + "section": "One-way ANOVA and Kruskal-Wallis", + "text": "One-way ANOVA and Kruskal-Wallis" }, { - "objectID": "r4babs2/week-3/workshop.html#look-after-future-you", - "href": "r4babs2/week-3/workshop.html#look-after-future-you", - "title": "Workshop", - "section": "Look after future you!", - "text": "Look after future you!\nThe code required to summarise, test, and plot data for any two-sample test AND for any for any one-way ANOVA is exactly the same except for the names of the dataframe, variables and the axis labels and limits. Take some time to comment it your code so that you can make use of it next week.\n\nYou’re finished!" + "objectID": "r4babs2/r4babs2.html#two-way-anova", + "href": "r4babs2/r4babs2.html#two-way-anova", + "title": "Data Analysis in R for BABS 2", + "section": "Two-way ANOVA", + "text": "Two-way ANOVA" }, { - "objectID": "r4babs2/week-3/study_after_workshop.html", - "href": "r4babs2/week-3/study_after_workshop.html", + "objectID": "r4babs2/r4babs2.html#chi-squared-tests-and-correlation", + "href": "r4babs2/r4babs2.html#chi-squared-tests-and-correlation", + "title": "Data Analysis in R for BABS 2", + "section": "Chi-squared tests and correlation", + "text": "Chi-squared tests and correlation" + }, + { + "objectID": "r4babs2/week-2/study_after_workshop.html", + "href": "r4babs2/week-2/study_after_workshop.html", "title": "Independent Study to consolidate this week", "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 Plant Biotech. Some plant biotechnologists are trying to increase the quantity of omega 3 fatty acids in Cannabis sativa. They have developed a genetically modified line using genes from Linum usitatissimum (linseed). They grow 50 wild type and fifty modified plants to maturity, collect the seeds and determine the amount of omega 3 fatty acids. The data are in csativa.txt. Do you think their modification has been successful?\n\n\nCodecsativa <- read_table(\"data-raw/csativa.txt\")\nstr(csativa)\n\n# First realise that this is a two sample test. You have two independent samples\n# - there are a total of 100 different plants and the values in one \n# group have no relationship to the values in the other.\n\n\n\nCode# create a rough plot of the data \nggplot(data = csativa, aes(x = plant, y = omega)) +\n geom_violin()\nCode# note the modified plants seem to have lower omega!\n\n\n\nCode# create a summary of the data\ncsativa_summary <- csativa %>%\n group_by(plant) %>%\n summarise(mean = mean(omega),\n std = sd(omega),\n n = length(omega),\n se = std/sqrt(n))\n\n\n\nCode# The data seem to be continuous so it is likely that a parametric test will be fine\n# we will check the other assumptions after we have run the lm\n\n# build the statistical model\nmod <- lm(data = csativa, omega ~ plant)\n\n\n# examine it\nsummary(mod)\n# So there is a significant difference but you need to make sure you know the direction!\n# Wild plants have a significantly higher omega 3 content (mean +/- s.e = 56.41 +/- 1.11) \n# than modified plants (49.46 +/- 0.82)(t = 5.03; d.f. = 98; p < 0.0001).\n\n\n\nCode# let's check the assumptions\nplot(mod, which = 1) \nCode# we're looking for the variance in the residuals to be the same in both groups.\n# This looks OK. Maybe a bit higher in the wild plants (with the higher mean)\n \nhist(mod$residuals)\nCodeshapiro.test(mod$residuals)\n# On balance the use of lm() is probably justifiable The variance isn't quite equal \n# and the histogram looks a bit off normal but the normality test is NS and the \n# effect (in the figure) is clear.\n\n\n\nCode# A figure \nfig1 <- ggplot() +\n geom_point(data = csativa, aes(x = plant, y = omega),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"gray50\") +\n geom_errorbar(data = csativa_summary, \n aes(x = plant, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = csativa_summary, \n aes(x = plant, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_x_discrete(name = \"Plant type\", labels = c(\"GMO\", \"WT\")) +\n scale_y_continuous(name = \"Amount of Omega 3 (units)\",\n expand = c(0, 0),\n limits = c(0, 90)) +\n annotate(\"segment\", x = 1, xend = 2, \n y = 80, yend = 80,\n colour = \"black\") +\n annotate(\"text\", x = 1.5, y = 85, \n label = expression(italic(p)~\"< 0.001\")) +\n theme_classic()\n\n# save figure to figures/csativa.png\nggsave(\"figures/csativa.png\",\n plot = fig1,\n width = 3.5,\n height = 3.5,\n units = \"in\",\n dpi = 300)\n\n\n\n💻 another example" + "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 Effect of anxiety status and sporting performance. The data in sprint.txt are from an investigation of the effect of anxiety status and sporting performance. A group of 40 100m sprinters undertook a psychometric test to measure their anxiety shortly before competing. The data are their anxiety scores and the 100m times achieved. What you do conclude from these data?\n\n\nCode# this example is designed to emphasise the importance of plotting your data first\nsprint <- read_table(\"data-raw/sprint.txt\")\n# Anxiety is discrete but ranges from 16 to 402 meaning the gap between possible measures is small and \n# the variable could be treated as continuous if needed. Time is a continuous measure that has decimal places and which we would expect to follow a normal distribution \n\n# explore with a plot\nggplot(sprint, aes(x = anxiety, y = time) ) +\n geom_point()\nCode# A scatterplot of the data clearly reveals that these data are not linear. There is a good relationship between the two variables but since it is not linear, single linear regression is not appropriate.\n\n\n\n💻 Juvenile hormone in stag beetles. The concentration of juvenile hormone in stag beetles is known to influence mandible growth. Groups of stag beetles were injected with different concentrations of juvenile hormone (arbitrary units) and their average mandible size (mm) determined. The experimenters planned to analyse their data with regression. The data are in stag.txt\n\n\n\nCode# read the data in and check the structure\nstag <- read_table(\"data-raw/stag.txt\")\nstr(stag)\n\n# jh is discrete but ordered and has been chosen by the experimenter - it is the explanatory variable. \n# the response is mandible size which has decimal places and is something we would expect to be \n# normally distributed. So far, common sense suggests the assumptions of regression are met.\n\n\n\nCode# exploratory plot\nggplot(stag, aes(x = jh, y = mand)) +\n geom_point()\nCode# looks linear-ish on the scatter\n# regression still seems appropriate\n# we will check the other assumptions after we have run the lm\n\n\n\nCode# build the statistical model\nmod <- lm(data = stag, mand ~ jh)\n\n# examine it\nsummary(mod)\n# mand = 0.032*jh + 0.419\n# the slope of the line is significantly different from zero / the jh explains a significant amount of the variation in mand (ANOVA: F = 16.63; d.f. = 1,14; p = 0.00113).\n# the intercept is 0.419 and differs significantly from zero \n\n\n\nCode# checking the assumption\nplot(mod, which = 1) \nCode# we're looking for the variance in the residuals to be equal along the x axis.\n# with a small data set there is some apparent heterogeneity but it doesn't look too.\n# \nhist(mod$residuals)\nCode# We have some skew which again might be partly a result of a small sample size.\nshapiro.test(mod$residuals) # the also test not sig diff from normal\n\n# On balance the use of regression is probably justifiable but it is borderline\n# but ideally the experiment would be better if multiple individuals were measure at\n# each of the chosen juvenile hormone levels.\n\n\n\nCode# a better plot\nggplot(stag, aes(x = jh, y = mand) ) +\n geom_point() +\n geom_smooth(method = lm, se = FALSE, colour = \"black\") +\n scale_x_continuous(name = \"Juvenile hormone (arbitrary units)\",\n expand = c(0, 0),\n limits = c(0, 32)) +\n scale_y_continuous(name = \"Mandible size (mm)\",\n expand = c(0, 0),\n limits = c(0, 2)) +\n theme_classic()" }, { - "objectID": "r4babs2/week-4/workshop.html", - "href": "r4babs2/week-4/workshop.html", + "objectID": "r4babs2/week-2/workshop.html", + "href": "r4babs2/week-2/workshop.html", "title": "Workshop", "section": "", - "text": "Artwork by Horst (2023): “Debugging and feelings”\n\n\nIn this session you will get practice in choosing between, performing, and presenting the results of, one-way ANOVA and Kruskal-Wallis in R.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "text": "In this workshop you will get practice in applying, interpreting and reporting single linear regression.\n\n\nArtwork by Horst (2023): “linear regression dragons”\n\n\nIn this session you will carry out, interpret and report on a single linear regression.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "r4babs2/week-4/workshop.html#session-overview", - "href": "r4babs2/week-4/workshop.html#session-overview", + "objectID": "r4babs2/week-2/workshop.html#session-overview", + "href": "r4babs2/week-2/workshop.html#session-overview", "title": "Workshop", "section": "", - "text": "In this session you will get practice in choosing between, performing, and presenting the results of, one-way ANOVA and Kruskal-Wallis in R." + "text": "In this session you will carry out, interpret and report on a single linear regression." }, { - "objectID": "r4babs2/week-4/workshop.html#philosophy", - "href": "r4babs2/week-4/workshop.html#philosophy", + "objectID": "r4babs2/week-2/workshop.html#philosophy", + "href": "r4babs2/week-2/workshop.html#philosophy", "title": "Workshop", "section": "", "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "r4babs2/week-4/workshop.html#myoglobin-in-seal-muscle", - "href": "r4babs2/week-4/workshop.html#myoglobin-in-seal-muscle", + "objectID": "r4babs2/week-2/workshop.html#linear-regression", + "href": "r4babs2/week-2/workshop.html#linear-regression", "title": "Workshop", - "section": "Myoglobin in seal muscle", - "text": "Myoglobin in seal muscle\nThe myoglobin concentration of skeletal muscle of three species of seal in grams per kilogram of muscle was determined and the data are given in seal.csv. We want to know if there is a difference between species. Each row represents an individual seal. The first column gives the myoglobin concentration and the second column indicates species.\n Save a copy of the data file seal.csv to data-raw\n Read in the data and check the structure. I used the name seal for the dataframe/tibble.\n What kind of variables do you have?\n\n\n\nExploring\n Do a quick plot of the data. You may need to refer to a previous workshop\nSummarising the data\nDo you remember Look after future you!\n If you followed that tip you’ll be able to open that script and whizz through summarising,testing and plotting.\n Create a data frame called seal_summary that contains the means, standard deviations, sample sizes and standard errors for each species.\nYou should get the following numbers:\n\n\n\n\nspecies\nmean\nstd\nn\nse\n\n\n\nBladdernose Seal\n42.31600\n8.020634\n30\n1.464361\n\n\nHarbour Seal\n49.01033\n8.252004\n30\n1.506603\n\n\nWeddell Seal\n44.66033\n7.849816\n30\n1.433174\n\n\n\n\n\nApplying, interpreting and reporting\nWe can now carry out a one-way ANOVA using the same lm() function we used for two-sample tests.\n Carry out an ANOVA and examine the results with:\n\nmod <- lm(data = seal, myoglobin ~ species)\nsummary(mod)\n\n\nCall:\nlm(formula = myoglobin ~ species, data = seal)\n\nResiduals:\n Min 1Q Median 3Q Max \n-16.306 -5.578 -0.036 5.240 18.250 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 42.316 1.468 28.819 < 2e-16 ***\nspeciesHarbour Seal 6.694 2.077 3.224 0.00178 ** \nspeciesWeddell Seal 2.344 2.077 1.129 0.26202 \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 8.043 on 87 degrees of freedom\nMultiple R-squared: 0.1096, Adjusted R-squared: 0.08908 \nF-statistic: 5.352 on 2 and 87 DF, p-value: 0.006427\n\n\nRemember: the tilde (~) means test the values in myoglobin when grouped by the values in species. Or explain myoglobin with species\n What do you conclude so far from the test? Write your conclusion in a form suitable for a report.\n\n\n\n Can you relate the values under Estimate to the means?\n\n\n\n\n\n\n\nThe ANOVA is significant but this only tells us that species matters, meaning at least two of the means differ. To find out which means differ, we need a post-hoc test. A post-hoc (“after this”) test is done after a significant ANOVA test. There are several possible post-hoc tests and we will be using Tukey’s HSD (honestly significant difference) test (Tukey 1949) implemented in the emmeans (Lenth 2023) package.\n Load the package\n\nlibrary(emmeans)\n\n Carry out the post-hoc test\n\nemmeans(mod, ~ species) |> pairs()\n\n contrast estimate SE df t.ratio p.value\n Bladdernose Seal - Harbour Seal -6.69 2.08 87 -3.224 0.0050\n Bladdernose Seal - Weddell Seal -2.34 2.08 87 -1.129 0.4990\n Harbour Seal - Weddell Seal 4.35 2.08 87 2.095 0.0968\n\nP value adjustment: tukey method for comparing a family of 3 estimates \n\n\nEach row is a comparison between the two means in the ‘contrast’ column. The ‘estimate’ column is the difference between those means and the ‘p.value’ indicates whether that difference is significant.\nA plot can be used to visualise the result of the post-hoc which can be especially useful when there are very many comparisons.\n Plot the results of the post-hoc test:\n\nemmeans(mod, ~ species) |> plot()\n\n\n\n\nWhere the purple bars overlap, there is no significant difference.\n What do you conclude from the test?\n\n\n\nCheck assumptions\nThe assumptions of the general linear model are that the residuals – the difference between predicted value (i.e., the group mean) and observed values - are normally distributed and have homogeneous variance. To check these we can examine the mod$residuals variable. You may want to refer to Checking assumptions in the “Single regression” workshop.\n Plot the model residuals against the fitted values.\n What to you conclude?\n\n\n\nTo examine normality of the model residuals we can plot them as a histogram and do a normality test on them.\n Plot a histogram of the residuals.\n Use the shapiro.test() to test the normality of the model residuals\n What to you conclude?\n\n\n\n\nIllustrating\n Create a figure like the one below. You may need to refer to Visualise from the “Summarising data with several variables” workshop (Rand 2023)\nWe will again use both our seal and seal_summary dataframes.\n Create the plot:\n\n\n\n\n\n Save your figure to your figures folder." + "section": "Linear Regression", + "text": "Linear Regression\nThe data in plant.xlsx is a set of observations of plant growth over two months. The researchers planted the seeds and harvested, dried and weighed a plant each day from day 10 so all the data points are independent of each other.\n Save a copy of plant.xlsx to your data-raw folder and import it.\n What type of variables do you have? Which is the response and which is the explanatory? What is the null hypothesis?\n\n\n\n\n\n\nExploring\n Do a quick plot of the data:\n\nggplot(plant, aes(x = day, y = mass)) +\n geom_point()\n\n\n\n\n What are the assumptions of linear regression? Do these seem to be met?\n\n\n\n\n\n\n\n\n\n\nApplying, interpreting and reporting\n We now carry out a regression assigning the result of the lm() procedure to a variable and examining it with summary().\n\nmod <- lm(data = plant, mass ~ day)\nsummary(mod)\n\n\nCall:\nlm(formula = mass ~ day, data = plant)\n\nResiduals:\n Min 1Q Median 3Q Max \n-32.810 -11.253 -0.408 9.075 48.869 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) -8.6834 6.4729 -1.342 0.186 \nday 1.6026 0.1705 9.401 1.5e-12 ***\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 17.92 on 49 degrees of freedom\nMultiple R-squared: 0.6433, Adjusted R-squared: 0.636 \nF-statistic: 88.37 on 1 and 49 DF, p-value: 1.503e-12\n\n\nThe Estimates in the Coefficients table give the intercept (first line) and the slope (second line) of the best fitting straight line. The p-values on the same line are tests of whether that coefficient is different from zero.\nThe F value and p-value in the last line are a test of whether the model as a whole explains a significant amount of variation in the dependent variable. For a single linear regression this is exactly equivalent to the test of the slope against zero.\n What is the equation of the line? What do you conclude from the analysis?\n\n\n\n\n\n Does the line go through (0,0)?\n\n\n\n What percentage of variation is explained by the line?\n\n\nIt might be useful to assign the slope and the intercept to variables in case we need them later. The can be accessed in the mod$coefficients variable:\n\nmod$coefficients\n\n(Intercept) day \n -8.683379 1.602606 \n\n\n Assign mod$coefficients[1] to b0 and mod$coefficients[1] to b1:\n\nb0 <- mod$coefficients[1] |> round(2)\nb1 <- mod$coefficients[2] |> round(2)\n\nI also rounded the values to two decimal places.\nChecking assumptions\nWe need to examine the residuals. Very conveniently, the object which is created by lm() contains a variable called $residuals. Also conveniently, the R’s plot() function can used on the output objects of lm(). The assumptions demand that each y is drawn from a normal distribution for each x and these normal distributions have the same variance. Therefore we plot the residuals against the fitted values to see if the variance is the same for all the values of x. The fitted - predicted - values are the values on the line of best fit. Each residual is the difference between the fitted values and the observed value.\n Plot the model residuals against the fitted values like this:\n\nplot(mod, which = 1)\n\n\n\n\n What to you conclude?\n\n\n\nTo examine normality of the model residuals we can plot them as a histogram and do a normality test on them.\n Plot a histogram of the residuals:\n\nggplot(mapping = aes(x = mod$residuals)) + \n geom_histogram(bins = 10)\n\n\n\n\n Use the shapiro.test() to test the normality of the model residuals\n\nshapiro.test(mod$residuals)\n\n\n Shapiro-Wilk normality test\n\ndata: mod$residuals\nW = 0.96377, p-value = 0.1208\n\n\nUsually, when we are doing statistical tests we would like the the test to be significant because it means we have evidence of a biological effect. However, when doing normality tests we hope it will not be significant. A non-significant result means that there is no significant difference between the distribution of the residuals and a normal distribution and that indicates the assumptions are met.\n What to you conclude?\n\n\n\n\nIllustrating\nWe want a figure with the points and the statistical model, i.e., the best fitting straight line.\n Create a scatter plot using geom_point()\n\nggplot(plant, aes(x = day, y = mass)) +\n geom_point() + \n theme_classic()\n\n\n\n\n The geom_smooth() function will had a variety of fitted lines to a plot. We want a line so we need to specify method = \"lm\":\n\nggplot(plant, aes(x = day, y = mass)) +\n geom_point() + \n geom_smooth(method = lm, \n se = FALSE, \n colour = \"black\") +\n theme_classic()\n\n\n\n\n What do the se and colour arguments do? Try changing them.\n Let’s add the equation of the line to the figure using annotate():\n\nggplot(plant, aes(x = day, y = mass)) +\n geom_point() +\n geom_smooth(method = lm, \n se = FALSE, \n colour = \"black\") +\n annotate(\"text\", x = 20, y = 110, \n label = \"mass = 1.61 * day - 8.68\") +\n theme_classic()\n\n\n\n\nWe have to tell annotate() what type of geom we want - text in this case, - where to put it, and the text we want to appear.\n Improve the axes. You may need to refer back Changing the axes from the Week 7 workshop in BABS1 (Rand 2023)\n Save your figure to your figures folder." }, { - "objectID": "r4babs2/week-4/workshop.html#leafminers-on-birch", - "href": "r4babs2/week-4/workshop.html#leafminers-on-birch", + "objectID": "r4babs2/week-2/workshop.html#look-after-future-you", + "href": "r4babs2/week-2/workshop.html#look-after-future-you", "title": "Workshop", - "section": "Leafminers on Birch", - "text": "Leafminers on Birch\nLarvae of the Ambermarked birch leafminer, Profenusa thomsoni, feed on the interior leaf tissues of Birch (Betula) species. They do not normally kill the tree but can weaken it making it susceptible to attack from other species. Researchers are interested in whether there is a difference in the rates at which white, grey and yellow birch are attacked. They introduce adult female P.thomsoni to a green house containing 30 young trees (ten of each type) and later count the egg laying events on each tree. The data are in leaf.txt.\nExploring\n Read in the data and check the structure. I used the name leaf for the dataframe/tibble.\n What kind of variables do we have?\n\n\n\n Do a quick plot of the data.\n Using your common sense, do these data look normally distributed?\n\n\n Why is a Kruskal-Wallis appropriate in this case?\n\n\n\n\n\n Calculate the medians, means and sample sizes.\nApplying, interpreting and reporting\n Carry out a Kruskal-Wallis:\n\nkruskal.test(data = leaf, eggs ~ birch)\n\n\n Kruskal-Wallis rank sum test\n\ndata: eggs by birch\nKruskal-Wallis chi-squared = 6.3393, df = 2, p-value = 0.04202\n\n\n What do you conclude from the test?\n\n\n\nA significant Kruskal-Wallis tells us at least two of the groups differ but where do the differences lie? The Dunn test is a post-hoc multiple comparison test for a significant Kruskal-Wallis. It is available in the package FSA\n Load the package using:\n\nlibrary(FSA)\n\n Run the post-hoc test with:\n\ndunnTest(data = leaf, eggs ~ birch)\n\n Comparison Z P.unadj P.adj\n1 Grey - White 1.296845 0.19468465 0.38936930\n2 Grey - Yellow -1.220560 0.22225279 0.22225279\n3 White - Yellow -2.517404 0.01182231 0.03546692\n\n\nThe P.adj column gives p-value for the comparison listed in the first column. Z is the test statistic.\n What do you conclude from the test?\n\n\n\n Write up the result is a form suitable for a report.\n\n\n\n\n\n\nIllustrating\n A box plot is an appropriate choice for illustrating a Kruskal-Wallis. Can you produce a figure like this?\n\n\n\n\n\nYou’re finished!" + "section": "Look after future you!", + "text": "Look after future you!\nYou’re finished!" }, { - "objectID": "r4babs2/week-4/study_after_workshop.html", - "href": "r4babs2/week-4/study_after_workshop.html", + "objectID": "r4babs2/week-5/study_after_workshop.html", + "href": "r4babs2/week-5/study_after_workshop.html", "title": "Independent Study to consolidate this week", "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 Sports scientists were investigating the effects of fitness and heat acclimatisation on the sodium content of sweat. They measured the sodium content of the sweat (μmoll^−1) of three groups of individuals: unfit and unacclimatised (UU); fit and unacclimatised(FU); and fit and acclimatised (FA). The are in sweat.txt. Is there a difference between the groups in the sodium content of their sweat?\n\n\nCode# read in the data and look at structure\nsweat <- read_table(\"data-raw/sweat.txt\")\nstr(sweat)\n\n\n\nCode# quick plot of the data\nggplot(data = sweat, aes(x = gp, y = na)) +\n geom_boxplot()\nCode# Since the sample sizes are small and not the same in each group and the \n# variance in the FA gp looks a bit lower, I'm leaning to a non-parametric test K-W.\n# However, don't panic if you decided to do an anova\n\n\n\nCode# calculate some summary stats \nsweat_summary <- sweat %>% \n group_by(gp) %>% \n summarise(mean = mean(na),\n n = length(na),\n median = median(na))\n\n\n\nCode# Kruskal-Wallis\nkruskal.test(data = sweat, na ~ gp)\n# We can say there is a difference between the groups in the sodium \n# content of their sweat (chi-squared = 11.9802, df = 2, p-value = 0.002503).\n# Unfit and unacclimatised people have most salty sweat, \n# Fit and acclimatised people the least salty.\n\n\n\nCode# a post-hoc test to see where the sig differences lie:\nlibrary(FSA)\ndunnTest(data = sweat, na ~ gp)\n# Fit and acclimatised people (median = 49.5 μmoll^−1) have significantly less sodium in their\n# sweat than the unfit and unacclimatised people (70 μmoll^−1) \n# (Kruskal-Wallis multiple comparison p-values adjusted with the Holm method: p = 0.0026).\n# Fit and unacclimatised (54 μmoll^−1) also have significantly less sodium in their\n# people have sodium concentrations than unfit and unacclimatised people (p = 0.033). \n# There was no difference between the Fit and unacclimatised and the Fit and acclimatised. See figure 1.\n\n\n\nCodeggplot(sweat, aes(x = gp, y = na) ) +\n geom_boxplot() +\n scale_x_discrete(labels = c(\"Fit Acclimatised\", \n \"Fit Unacclimatised\", \n \"Unfit Unacclimatised\"), \n name = \"Group\") +\n scale_y_continuous(limits = c(0, 110), \n expand = c(0, 0),\n name = expression(\"Sodium\"~mu*\"mol\"*l^{-1})) +\n annotate(\"segment\", x = 1, xend = 3, \n y = 100, yend = 100,\n colour = \"black\") +\n annotate(\"text\", x = 2, y = 103, \n label = expression(italic(p)~\"= 0.0026\")) +\n annotate(\"segment\", x = 2, xend = 3, \n y = 90, yend = 90,\n colour = \"black\") +\n annotate(\"text\", x = 2.5, y = 93, \n label = expression(italic(p)~\"= 0.0340\")) +\n theme_classic()\nCode#Figure 1. Sodium content of sweat for three groups: Fit and acclimatised\n#(FA), Fit and unacclimatised (FU) and Unfit and unacclimatised (UU). Heavy lines\n#indicate the median, boxes the interquartile range and whiskers the range. \n\n\n\n💻 The data are given in biomass.txt are taken from an experiment in which the insect pest biomass (g) was measured on plots sprayed with water (control) or one of five different insecticides. Do the insecticides vary in their effectiveness? What advice would you give to a person: - currently using insecticide E? - trying to choose between A and D? - trying to choose between C and B?\n\n\nCodebiom <- read_table(\"data-raw/biomass.txt\")\n# The data are organised with an insecticide treatment group in\n# each column.\n\n\n\nCode#Put the data into tidy format.\n\nbiom <- biom |> \n pivot_longer(cols = everything(),\n names_to = \"spray\",\n values_to = \"biomass\")\n\n\n\nCode# quick plot of the data\nggplot(data = biom, aes(x = spray, y = biomass)) +\n geom_boxplot()\nCode# Looks like there is a difference between sprays. E doesn't look very effective.\n\n\n\nCode# summary statistics\nbiom_summary <- biom %>% \n group_by(spray) %>% \n summarise(mean = mean(biomass),\n median = median(biomass),\n sd = sd(biomass),\n n = length(biomass),\n se = sd / sqrt(n))\n# thoughts so far: the sample sizes are equal, 10 is a smallish but\n# reasonable sample size\n# the means and medians are similar to each other (expected for\n# normally distributed data), A has a smaller variance \n\n# We have one explanatory variable, \"spray\" comprising 6 levels\n# Biomass has decimal places and we would expect such data to be \n# normally distributed therefore one-way ANOVA is the desired test\n# - we will check the assumptions after building the model\n\n\n\nCode# arry out an ANOVA and examine the results \nmod <- lm(data = biom, biomass ~ spray)\nsummary(mod)\n# spray type does have an effect F-statistic: 26.46 on 5 and 54 DF, p-value: 2.081e-13\n\n\n\nCode# Carry out the post-hoc test\nlibrary(emmeans)\n\nemmeans(mod, ~ spray) |> pairs()\n\n# the signifcant comparisons are:\n# contrast estimate SE df t.ratio p.value\n# A - D -76.50 21.9 54 -3.489 0.0119\n# A - E -175.51 21.9 54 -8.005 <.0001\n# A - WaterControl -175.91 21.9 54 -8.024 <.0001\n# B - E -154.32 21.9 54 -7.039 <.0001\n# B - WaterControl -154.72 21.9 54 -7.057 <.0001\n# C - E -155.71 21.9 54 -7.102 <.0001\n# C - WaterControl -156.11 21.9 54 -7.120 <.0001\n# D - E -99.01 21.9 54 -4.516 0.0005\n# D - WaterControl -99.41 21.9 54 -4.534 0.0004\n# All sprays are better than the water control except E. \n# This is probably the most important result.\n# What advice would you give to a person currently using insecticide E?\n# Don't bother!! It's no better than water. Switch to any of \n# the other sprays\n# What advice would you give to a person currently\n# + trying to choose between A and D? Choose A because A has sig lower\n# insect biomass than D \n# + trying to choose between C and B? It doesn't matter because there is \n# no difference in insect biomass. Use other criteria to chose (e.g., price)\n# We might report this like:\n# There is a very highly significant effect of spray type on pest \n# biomass (F = 26.5; d.f., 5, 54; p < 0.001). Post-hoc testing \n# showed E was no more effective than the control; A, C and B were \n# all better than the control but could be equally as good as each\n# other; D would be a better choice than the control or E but \n# worse than A. See figure 1\n\n\n\nCode# I reordered the bars to make is easier for me to annotate with\n# I also used * to indicate significance\n\nggplot() +\n geom_point(data = biom, aes(x = reorder(spray, biomass), y = biomass),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"gray50\") +\n geom_errorbar(data = biom_summary, \n aes(x = spray, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = biom_summary, \n aes(x = spray, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_y_continuous(name = \"Pest Biomass (units)\",\n limits = c(0, 540),\n expand = c(0, 0)) +\n scale_x_discrete(\"Spray treatment\") +\n # E and control are one group\n annotate(\"segment\", x = 4.5, xend = 6.5, \n y = 397, yend = 397,\n colour = \"black\", linewidth = 1) +\n annotate(\"text\", x = 5.5, y = 385, \n label = \"N.S\", size = 4) +\n # WaterControl-D and E-D ***\n annotate(\"segment\", x = 4, xend = 5.5, \n y = 410, yend = 410,\n colour = \"black\") +\n annotate(\"text\", x = 4.5, y = 420, \n label = \"***\", size = 5) +\n # WaterControl-B ***\n annotate(\"segment\", x = 3, xend = 5.5, \n y = 440, yend = 440,\n colour = \"black\") +\n annotate(\"text\", x = 4, y = 450,\n label = \"***\", size = 5) +\n # WaterControl-C ***\n annotate(\"segment\", x = 2, xend = 5.5, \n y = 475, yend = 475,\n colour = \"black\") +\n annotate(\"text\", x = 3.5, y = 485, \n label = \"***\", size = 5) +\n # WaterControl-A ***\n annotate(\"segment\", x = 1, xend = 5.5, \n y = 510, yend = 510,\n colour = \"black\") +\n annotate(\"text\", x = 3.5, y = 520, \n label = \"***\", size = 5) + \n# A-D ***\n annotate(\"segment\", x = 1, xend = 4, \n y = 330, yend = 330,\n colour = \"black\") +\n annotate(\"text\", x = 2.5, y = 335, \n label = \"*\", size = 5) +\n theme_classic()\nCode# Figure 1. The mean pest biomass following various insecticide treatments.\n# Error bars are +/- 1 S.E. Significant comparisons are indicated: * is p < 0.05, ** p < 0.01 and *** is p < 0.001" + "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻\n\n\n📖 Read xxx" }, { "objectID": "r4babs2/week-5/workshop.html", @@ -1477,129 +1456,150 @@ "text": "Effect of brain region and choline deficiency on neuron size\nCognitive performance is influenced by the choline intake in utero. To better understand this phenomenon, pregnant mice were fed a control or choline-deficient diet and their offspring examined. The cross sectional area (CSA) of cholinergic neurons was determined in two brain regions, the MSN and the DB. The data are given in neuron-csa.xlsx\n Save a copy of the data file neuron-csa.xlsx to data-raw\nYou have previously read data from an excel file.\n List the the names of the work sheets in the excel workbook.\nThese data are organised into two worksheets, one for each brain region\n Read in each sheet. I used the names db and msn for the two dataframes/tibble.\n We have the top half and the bottom half of a data set and can combine these togther with bind_rows()\n\nneuron <- bind_rows(db, msn)\n\nYou might want to click on neuron in the environment to open the spreadsheet-like view to check it looks how you expect.\n What kind of variables do you have?\n\n\n\n\n\nExploring\nWhen we have a single explanatory variable, it always goes on the x-axis. Here we have two explanatory variables: brain region and diet. We can map one of the explanatory variables to the x-axis and the other to a aesthetic like colour, shape or fill.\n Do a quick plot of the data:\n\nggplot(data = neuron, aes(x = BrainRegion, y = CSA, fill = Diet)) +\n geom_violin()\n\n\n\n\nWhether we map BrainRegion to the x-axis or the fill does not really matter. It looks as though the cross sectional area of neurons is higher for the control diet than the deficient diet (the average of the read bars is grater than the average of the blue bars). It also looks like there might be a significant interaction between the effects of diet and brain region because the effect of diet seems to be greater in the DB region.\nSummarising the data\nJust as we needed to incorporate the second explanatory variable in the rough plot, we need to incorporate it into our summary. We do this by adding it to the group_by().\n Create a data frame called neuron_summary that contains the means, standard deviations, sample sizes and standard errors for each group:\n\nneuron_summary <- neuron %>%\n group_by(BrainRegion, Diet) %>%\n summarise(mean = mean(CSA),\n std = sd(CSA),\n n = length(CSA),\n se = std/sqrt(n))\n\nYou will get a message that you don’t need to worry about summarise()has grouped output by 'BrainRegion'. You can override using the.groupsargument.>\nYou should get the following numbers:\n\n\n\n\nBrainRegion\nDiet\nmean\nstd\nn\nse\n\n\n\nDB\nControl\n26.6645\n3.633975\n10\n1.1491638\n\n\nDB\nDeficient\n21.2245\n4.213968\n10\n1.3325736\n\n\nMSN\nControl\n20.9695\n2.779860\n10\n0.8790688\n\n\nMSN\nDeficient\n19.9325\n2.560446\n10\n0.8096842\n\n\n\n\n\nApplying, interpreting and reporting\nWe can now carry out a two-way ANOVA using the same lm() function we used for two-sample tests and one-way ANOVA.\n Carry out an ANOVA and examine the results with:\n\nmod <- lm(data = neuron, CSA ~ BrainRegion * Diet)\nsummary(mod)\n\n\nCall:\nlm(formula = CSA ~ BrainRegion * Diet, data = neuron)\n\nResiduals:\n Min 1Q Median 3Q Max \n-6.6045 -2.6308 0.0765 2.4820 5.5505 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 26.664 1.064 25.071 < 2e-16 ***\nBrainRegionMSN -5.695 1.504 -3.786 0.000560 ***\nDietDeficient -5.440 1.504 -3.617 0.000907 ***\nBrainRegionMSN:DietDeficient 4.403 2.127 2.070 0.045692 * \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.363 on 36 degrees of freedom\nMultiple R-squared: 0.4034, Adjusted R-squared: 0.3537 \nF-statistic: 8.115 on 3 and 36 DF, p-value: 0.0002949\n\n\nRemember: the tilde (~) means test the values in CSA when grouped by the values in BrainRegion and Diet Or explain CSA with BrainRegion and Diet\n Can you relate the values under Estimate to the means?\n\n\n\n\n\n\n\n\n\nThe model of brain region and diet overall explains a significant amount of the variation in the cross sectional area of neurons (p-value: 0.0002949). To see which of the three effects are significant we can use the anova() function on our model.\n Determine which effects are significant:\n\nanova(mod)\n\nAnalysis of Variance Table\n\nResponse: CSA\n Df Sum Sq Mean Sq F value Pr(>F) \nBrainRegion 1 122.05 122.045 10.7893 0.002280 **\nDiet 1 104.88 104.879 9.2717 0.004334 **\nBrainRegion:Diet 1 48.47 48.466 4.2846 0.045692 * \nResiduals 36 407.22 11.312 \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n\nThere is a significant effect of brain region (F = 10.8; d.f. = 1, 36; p = 0.002) and diet (F = 9.3; d.f. = 1, 36; p = 0.004) on CSA and these effects interact (F = 4.3; d.f. = 1, 36; p = 0.046)\nWe need a post-hoc test to see which comparisons are significant and can again use then emmeans (Lenth 2023) package.\n Load the package\n\nlibrary(emmeans)\n\n Carry out the post-hoc test\n\nemmeans(mod, ~ BrainRegion * Diet) |> pairs()\n\n contrast estimate SE df t.ratio p.value\n DB Control - MSN Control 5.695 1.5 36 3.786 0.0030\n DB Control - DB Deficient 5.440 1.5 36 3.617 0.0048\n DB Control - MSN Deficient 6.732 1.5 36 4.476 0.0004\n MSN Control - DB Deficient -0.255 1.5 36 -0.170 0.9982\n MSN Control - MSN Deficient 1.037 1.5 36 0.689 0.9005\n DB Deficient - MSN Deficient 1.292 1.5 36 0.859 0.8257\n\nP value adjustment: tukey method for comparing a family of 4 estimates \n\n\nEach row is a comparison between the two means in the ‘contrast’ column. The ‘estimate’ column is the difference between those means and the ‘p.value’ indicates whether that difference is significant.\nA plot can be used to visualise the result of the post hoc which can be especially useful when there are very many comparisons.\n Plot the results of the post-hoc test:\n\nemmeans(mod, ~ BrainRegion * Diet) |> plot()\n\n\n\n\n What do you conclude from the test?\n\n\n\n\n\n\nWe might report this result as:\nA choline-deficient diet in pregnant mice significantly decreases the cross sectional area of cholinergic neurons in the DB region of their offspring (t = 3.62; d.f. = 36; p = 0.0048). The cross sectional area of cholinergic neurons in the MSN region are also significantly smaller than those in the DB region (t = 3.79; d.f. = 36; p = 0.0030) but are not reduces by maternal choline-deficiency.\nCheck assumptions\nThe assumptions of the general linear model are that the residuals – the difference between predicted value (i.e., the group mean) and observed values - are normally distributed and have homogeneous variance. To check these we can examine the mod$residuals variable. You may want to refer to Checking assumptions in the “Single regression” workshop.\n Plot the model residuals against the fitted values.\n What to you conclude?\n\n\n\nTo examine normality of the model residuals we can plot them as a histogram and do a normality test on them.\n Plot a histogram of the residuals.\n Use the shapiro.test() to test the normality of the model residuals\n What to you conclude?\n\n\n\n\nIllustrating\nWe are going to create a figure like this:\n\n\n\n\n\nWe will again use both our neuron and neuron_summary dataframes.\n Try emulating what you did for one-way ANOVA based on Visualise from the “Summarising data with several variables” workshop (Rand 2023).\n\nggplot() +\n geom_point(data = neuron, \n aes(x = BrainRegion, y = CSA),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"gray50\",\n size = 3) +\n geom_errorbar(data = neuron_summary, \n aes(x = BrainRegion, \n ymin = mean - se, \n ymax = mean + se),\n width = 0.4) +\n geom_errorbar(data = neuron_summary, \n aes(x = BrainRegion, \n ymin = mean,\n ymax = mean),\n width = 0.3, \n linewidth = 1) +\n scale_y_continuous(name = \"CSA\",\n expand = c(0, 0),\n limits = c(0, 45)) +\n scale_x_discrete(name = \"BrainRegion\") +\n theme_classic() \n\n\n\n\nHow can we show the two diets separately?\n We can map the Diet variable to the shape aesthetic!\n\nggplot() +\n geom_point(data = neuron, \n aes(x = BrainRegion, y = CSA, shape = Diet),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"gray50\",\n size = 3) +\n geom_errorbar(data = neuron_summary, \n aes(x = BrainRegion, \n ymin = mean - se, \n ymax = mean + se),\n width = 0.4) +\n geom_errorbar(data = neuron_summary, \n aes(x = BrainRegion, \n ymin = mean,\n ymax = mean),\n width = 0.3, \n linewidth = 1) +\n scale_y_continuous(name = \"CSA\",\n expand = c(0, 0),\n limits = c(0, 45)) +\n scale_x_discrete(name = \"BrainRegion\") +\n theme_classic() \n\n\n\n\nOh, that isn’t quite what we want! We want the two diets side-by-side, not on top of each other.\n We can achieve that by using setting the position argument to position_jitterdodge() in the geom_point() and to position_dodge() in the two geom_errorbar(). We also have to specify that the error bars are grouped by Diet since they are not otherwise mapped to a shape, colour or fill.\n\nggplot() +\n geom_point(data = neuron, \n aes(x = BrainRegion, y = CSA, shape = Diet),\n position = position_jitterdodge(dodge.width = 1,\n jitter.width = 0.3,\n jitter.height = 0),\n colour = \"gray50\",\n size = 3) +\n geom_errorbar(data = neuron_summary, \n aes(x = BrainRegion, \n ymin = mean - se, \n ymax = mean + se,\n group = Diet),\n width = 0.4,\n position = position_dodge(width = 1)) +\n geom_errorbar(data = neuron_summary, \n aes(x = BrainRegion, \n ymin = mean,\n ymax = mean,\n group = Diet),\n width = 0.3, \n linewidth = 1,\n position = position_dodge(width = 1)) +\n scale_y_continuous(name = \"CSA\",\n expand = c(0, 0),\n limits = c(0, 45)) +\n scale_x_discrete(name = \"BrainRegion\") +\n theme_classic() \n\n\n\n\n Add the annotation of the statistical results\n Finally, we can move the legend to a space on the plot area which helps you minimise the width needed like this:\n\n...... +\n theme(legend.position = c(0.15, 0.15),\n legend.background = element_rect(colour = \"black\"))\n\n Save your figure to your figures folder.\nYou’re finished!" }, { - "objectID": "r4babs2/week-5/study_after_workshop.html", - "href": "r4babs2/week-5/study_after_workshop.html", + "objectID": "r4babs2/week-1/study_after_workshop.html", + "href": "r4babs2/week-1/study_after_workshop.html", "title": "Independent Study to consolidate this week", "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻\n\n\n📖 Read xxx" + "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 Adiponectin is exclusively secreted from adipose tissue and modulates a number of metabolic processes. Nicotinic acid can affect adiponectin secretion. 3T3-L1 adipocytes were treated with nicotinic acid or with a control treatment and adiponectin concentration (pg/mL) measured. The data are in adipocytes.txt. Each row represents an independent sample of adipocytes and the first column gives the concentration adiponectin and the second column indicates whether they were treated with nicotinic acid or not. Estimate the mean Adiponectin concentration in each group - this means calculate the sample mean and construct a confidence interval around it for each group. This exercise forces you to bring together ideas from this workshop and from previous workshops\n\n\nHow to calculate a confidence intervals (this workshop)\n\nHow to summarise variables in more than one group (previous workshop)\n\n\nCode# data import\nadip <- read_table(\"data-raw/adipocytes.txt\")\n\n# examine the structure\nstr(adip)\n\n# summarise\nadip_summary <- adip %>% \n group_by(treatment) %>% \n summarise(mean = mean(adiponectin),\n sd = sd(adiponectin),\n n = length(adiponectin),\n se = sd/sqrt(n),\n dif = qt(0.975, df = n - 1) * se,\n lower_ci = mean - dif,\n uppp_ci = mean + dif)\n\n\n# we conclude we're 95% certain the mean for the control group is \n# between 4.73 and 6.36 and the mean for the nicotinic group is \n# between 6.52 and 8.50. More usually we might put is like this:\n# the mean for the control group is 5.55 +/- 0.82 and that for the nicotinic group is 7.51 +/- 0.99" }, { - "objectID": "r4babs2/week-6/workshop.html", - "href": "r4babs2/week-6/workshop.html", + "objectID": "r4babs2/week-1/workshop.html", + "href": "r4babs2/week-1/workshop.html", "title": "Workshop", "section": "", - "text": "Artwork by Horst (2023):\n\n\nIn this session you will\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "text": "Artwork by Horst (2023): “love this class”\n\n\nIn this session you will remind yourself how to import files, and calculate confidence intervals on large and small samples.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "r4babs2/week-6/workshop.html#session-overview", - "href": "r4babs2/week-6/workshop.html#session-overview", + "objectID": "r4babs2/week-1/workshop.html#session-overview", + "href": "r4babs2/week-1/workshop.html#session-overview", "title": "Workshop", "section": "", - "text": "In this session you will" + "text": "In this session you will remind yourself how to import files, and calculate confidence intervals on large and small samples." }, { - "objectID": "r4babs2/week-6/workshop.html#philosophy", - "href": "r4babs2/week-6/workshop.html#philosophy", + "objectID": "r4babs2/week-1/workshop.html#philosophy", + "href": "r4babs2/week-1/workshop.html#philosophy", "title": "Workshop", "section": "", "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "r4babs2/week-6/study_after_workshop.html", - "href": "r4babs2/week-6/study_after_workshop.html", + "objectID": "r4babs2/week-1/workshop.html#remind-yourself-how-to-import-files", + "href": "r4babs2/week-1/workshop.html#remind-yourself-how-to-import-files", + "title": "Workshop", + "section": "Remind yourself how to import files!", + "text": "Remind yourself how to import files!\nImporting data from files was covered in BABS 1 (Rand 2023) if you need to remind yourself." + }, + { + "objectID": "r4babs2/week-1/workshop.html#confidence-intervals-large-samples", + "href": "r4babs2/week-1/workshop.html#confidence-intervals-large-samples", + "title": "Workshop", + "section": "Confidence intervals (large samples)", + "text": "Confidence intervals (large samples)\nThe data in beewing.txt are left wing widths of 100 honey bees (mm). The confidence interval for large samples is given by:\n\\(\\bar{x} \\pm 1.96 \\times s.e.\\)\nWhere 1.96 is the quantile for 95% confidence.\n Save beewing.txt to your data-raw folder.\n Read in the data and check the structure of the resulting dataframe.\n Calculate and assign to variables: the mean, standard deviation and standard error:\n\n# mean\nm <- mean(bee$wing)\n\n# standard deviation\nsd <- sd(bee$wing)\n\n# sample size (needed for the se)\nn <- length(bee$wing)\n\n# standard error\nse <- sd / sqrt(n)\n\n To calculate the 95% confidence interval we need to look up the quantile (multiplier) using qnorm()\n\nq <- qnorm(0.975)\n\nThis should be about 1.96.\n Now we can use it in our confidence interval calculation\n\nlcl <- m - q * se\nucl <- m + q * se\n\n Print the values\n\nlcl\n\n[1] 4.473176\n\nucl\n\n[1] 4.626824\n\n\nThis means we are 95% confident the population mean lies between 4.47 mm and 4.63 mm. The usual way of expressing this is that the mean is 4.55 +/- 0.07 mm\n Between what values would you be 99% confident of the population mean being?" + }, + { + "objectID": "r4babs2/week-1/workshop.html#confidence-intervals-small-samples", + "href": "r4babs2/week-1/workshop.html#confidence-intervals-small-samples", + "title": "Workshop", + "section": "Confidence intervals (small samples)", + "text": "Confidence intervals (small samples)\nThe confidence interval for small samples is given by:\n\\(\\bar{x} \\pm \\sf t_{[d.f]} \\times s.e.\\)\nThe only difference between the calculation for small and large sample is the multiple. For large samples we use the “the standard normal distribution” accessed with qnorm(); for small samples we use the “t distribution” assessed with qt().The value returned by q(t) is larger than that returned by qnorm() which reflects the greater uncertainty we have on estimations of population means based on small samples.\nThe fatty acid Docosahexaenoic acid (DHA) is a major component of membrane phospholipids in nerve cells and deficiency leads to many behavioural and functional deficits. The cross sectional area of neurons in the CA 1 region of the hippocampus of normal rats is 155 \\(\\mu m^2\\). A DHA deficient diet was fed to 8 animals and the cross sectional area (csa) of neurons is given in neuron.txt\n Save neuron.txt to your data-raw folder\n Read in the data and check the structure of the resulting dataframe\n Assign the mean to m.\n Calculate and assign the standard error to se.\nTo work out the confidence interval for our sample mean we need to use the t distribution because it is a small sample. This means we need to determine the degrees of freedom (the number in the sample minus one).\n We can assign this to a variable, df, using:\n\ndf <- length(neur$csa) - 1\n\n The t value is found by:\n\nt <- qt(0.975, df = df)\n\nNote that we are using qt() rather than qnorm() but that the probability, 0.975, used is the same. Finally, we need to put our mean, standard error and t value in the equation. \\(\\bar{x} \\pm \\sf t_{[d.f]} \\times s.e.\\).\n The upper confidence limit is:\n\n(m + t * se) |> round(2)\n\n[1] 151.95\n\n\nThe first part of the command, (m + t * se) calculates the upper limit. This is ‘piped’ in to the round() function to round the result to two decimal places.\n Calculate the lower confidence limit:\n Given the upper and lower confidence values for the estimate of the population mean, what do you think about the effect of the DHA deficient diet?\n\n\n\n\nYou’re finished!" + }, + { + "objectID": "r4babs2/week-4/study_after_workshop.html", + "href": "r4babs2/week-4/study_after_workshop.html", "title": "Independent Study to consolidate this week", "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻\n\n\n📖 Read xxx" + "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 Sports scientists were investigating the effects of fitness and heat acclimatisation on the sodium content of sweat. They measured the sodium content of the sweat (μmoll^−1) of three groups of individuals: unfit and unacclimatised (UU); fit and unacclimatised(FU); and fit and acclimatised (FA). The are in sweat.txt. Is there a difference between the groups in the sodium content of their sweat?\n\n\nCode# read in the data and look at structure\nsweat <- read_table(\"data-raw/sweat.txt\")\nstr(sweat)\n\n\n\nCode# quick plot of the data\nggplot(data = sweat, aes(x = gp, y = na)) +\n geom_boxplot()\nCode# Since the sample sizes are small and not the same in each group and the \n# variance in the FA gp looks a bit lower, I'm leaning to a non-parametric test K-W.\n# However, don't panic if you decided to do an anova\n\n\n\nCode# calculate some summary stats \nsweat_summary <- sweat %>% \n group_by(gp) %>% \n summarise(mean = mean(na),\n n = length(na),\n median = median(na))\n\n\n\nCode# Kruskal-Wallis\nkruskal.test(data = sweat, na ~ gp)\n# We can say there is a difference between the groups in the sodium \n# content of their sweat (chi-squared = 11.9802, df = 2, p-value = 0.002503).\n# Unfit and unacclimatised people have most salty sweat, \n# Fit and acclimatised people the least salty.\n\n\n\nCode# a post-hoc test to see where the sig differences lie:\nlibrary(FSA)\ndunnTest(data = sweat, na ~ gp)\n# Fit and acclimatised people (median = 49.5 μmoll^−1) have significantly less sodium in their\n# sweat than the unfit and unacclimatised people (70 μmoll^−1) \n# (Kruskal-Wallis multiple comparison p-values adjusted with the Holm method: p = 0.0026).\n# Fit and unacclimatised (54 μmoll^−1) also have significantly less sodium in their\n# people have sodium concentrations than unfit and unacclimatised people (p = 0.033). \n# There was no difference between the Fit and unacclimatised and the Fit and acclimatised. See figure 1.\n\n\n\nCodeggplot(sweat, aes(x = gp, y = na) ) +\n geom_boxplot() +\n scale_x_discrete(labels = c(\"Fit Acclimatised\", \n \"Fit Unacclimatised\", \n \"Unfit Unacclimatised\"), \n name = \"Group\") +\n scale_y_continuous(limits = c(0, 110), \n expand = c(0, 0),\n name = expression(\"Sodium\"~mu*\"mol\"*l^{-1})) +\n annotate(\"segment\", x = 1, xend = 3, \n y = 100, yend = 100,\n colour = \"black\") +\n annotate(\"text\", x = 2, y = 103, \n label = expression(italic(p)~\"= 0.0026\")) +\n annotate(\"segment\", x = 2, xend = 3, \n y = 90, yend = 90,\n colour = \"black\") +\n annotate(\"text\", x = 2.5, y = 93, \n label = expression(italic(p)~\"= 0.0340\")) +\n theme_classic()\nCode#Figure 1. Sodium content of sweat for three groups: Fit and acclimatised\n#(FA), Fit and unacclimatised (FU) and Unfit and unacclimatised (UU). Heavy lines\n#indicate the median, boxes the interquartile range and whiskers the range. \n\n\n\n💻 The data are given in biomass.txt are taken from an experiment in which the insect pest biomass (g) was measured on plots sprayed with water (control) or one of five different insecticides. Do the insecticides vary in their effectiveness? What advice would you give to a person: - currently using insecticide E? - trying to choose between A and D? - trying to choose between C and B?\n\n\nCodebiom <- read_table(\"data-raw/biomass.txt\")\n# The data are organised with an insecticide treatment group in\n# each column.\n\n\n\nCode#Put the data into tidy format.\n\nbiom <- biom |> \n pivot_longer(cols = everything(),\n names_to = \"spray\",\n values_to = \"biomass\")\n\n\n\nCode# quick plot of the data\nggplot(data = biom, aes(x = spray, y = biomass)) +\n geom_boxplot()\nCode# Looks like there is a difference between sprays. E doesn't look very effective.\n\n\n\nCode# summary statistics\nbiom_summary <- biom %>% \n group_by(spray) %>% \n summarise(mean = mean(biomass),\n median = median(biomass),\n sd = sd(biomass),\n n = length(biomass),\n se = sd / sqrt(n))\n# thoughts so far: the sample sizes are equal, 10 is a smallish but\n# reasonable sample size\n# the means and medians are similar to each other (expected for\n# normally distributed data), A has a smaller variance \n\n# We have one explanatory variable, \"spray\" comprising 6 levels\n# Biomass has decimal places and we would expect such data to be \n# normally distributed therefore one-way ANOVA is the desired test\n# - we will check the assumptions after building the model\n\n\n\nCode# arry out an ANOVA and examine the results \nmod <- lm(data = biom, biomass ~ spray)\nsummary(mod)\n# spray type does have an effect F-statistic: 26.46 on 5 and 54 DF, p-value: 2.081e-13\n\n\n\nCode# Carry out the post-hoc test\nlibrary(emmeans)\n\nemmeans(mod, ~ spray) |> pairs()\n\n# the signifcant comparisons are:\n# contrast estimate SE df t.ratio p.value\n# A - D -76.50 21.9 54 -3.489 0.0119\n# A - E -175.51 21.9 54 -8.005 <.0001\n# A - WaterControl -175.91 21.9 54 -8.024 <.0001\n# B - E -154.32 21.9 54 -7.039 <.0001\n# B - WaterControl -154.72 21.9 54 -7.057 <.0001\n# C - E -155.71 21.9 54 -7.102 <.0001\n# C - WaterControl -156.11 21.9 54 -7.120 <.0001\n# D - E -99.01 21.9 54 -4.516 0.0005\n# D - WaterControl -99.41 21.9 54 -4.534 0.0004\n# All sprays are better than the water control except E. \n# This is probably the most important result.\n# What advice would you give to a person currently using insecticide E?\n# Don't bother!! It's no better than water. Switch to any of \n# the other sprays\n# What advice would you give to a person currently\n# + trying to choose between A and D? Choose A because A has sig lower\n# insect biomass than D \n# + trying to choose between C and B? It doesn't matter because there is \n# no difference in insect biomass. Use other criteria to chose (e.g., price)\n# We might report this like:\n# There is a very highly significant effect of spray type on pest \n# biomass (F = 26.5; d.f., 5, 54; p < 0.001). Post-hoc testing \n# showed E was no more effective than the control; A, C and B were \n# all better than the control but could be equally as good as each\n# other; D would be a better choice than the control or E but \n# worse than A. See figure 1\n\n\n\nCode# I reordered the bars to make is easier for me to annotate with\n# I also used * to indicate significance\n\nggplot() +\n geom_point(data = biom, aes(x = reorder(spray, biomass), y = biomass),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"gray50\") +\n geom_errorbar(data = biom_summary, \n aes(x = spray, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = biom_summary, \n aes(x = spray, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_y_continuous(name = \"Pest Biomass (units)\",\n limits = c(0, 540),\n expand = c(0, 0)) +\n scale_x_discrete(\"Spray treatment\") +\n # E and control are one group\n annotate(\"segment\", x = 4.5, xend = 6.5, \n y = 397, yend = 397,\n colour = \"black\", linewidth = 1) +\n annotate(\"text\", x = 5.5, y = 385, \n label = \"N.S\", size = 4) +\n # WaterControl-D and E-D ***\n annotate(\"segment\", x = 4, xend = 5.5, \n y = 410, yend = 410,\n colour = \"black\") +\n annotate(\"text\", x = 4.5, y = 420, \n label = \"***\", size = 5) +\n # WaterControl-B ***\n annotate(\"segment\", x = 3, xend = 5.5, \n y = 440, yend = 440,\n colour = \"black\") +\n annotate(\"text\", x = 4, y = 450,\n label = \"***\", size = 5) +\n # WaterControl-C ***\n annotate(\"segment\", x = 2, xend = 5.5, \n y = 475, yend = 475,\n colour = \"black\") +\n annotate(\"text\", x = 3.5, y = 485, \n label = \"***\", size = 5) +\n # WaterControl-A ***\n annotate(\"segment\", x = 1, xend = 5.5, \n y = 510, yend = 510,\n colour = \"black\") +\n annotate(\"text\", x = 3.5, y = 520, \n label = \"***\", size = 5) + \n# A-D ***\n annotate(\"segment\", x = 1, xend = 4, \n y = 330, yend = 330,\n colour = \"black\") +\n annotate(\"text\", x = 2.5, y = 335, \n label = \"*\", size = 5) +\n theme_classic()\nCode# Figure 1. The mean pest biomass following various insecticide treatments.\n# Error bars are +/- 1 S.E. Significant comparisons are indicated: * is p < 0.05, ** p < 0.01 and *** is p < 0.001" }, { - "objectID": "r4babs2/week-2/workshop.html", - "href": "r4babs2/week-2/workshop.html", + "objectID": "r4babs2/week-4/workshop.html", + "href": "r4babs2/week-4/workshop.html", "title": "Workshop", "section": "", - "text": "In this workshop you will get practice in applying, interpreting and reporting single linear regression.\n\n\nArtwork by Horst (2023): “linear regression dragons”\n\n\nIn this session you will carry out, interpret and report on a single linear regression.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "text": "Artwork by Horst (2023): “Debugging and feelings”\n\n\nIn this session you will get practice in choosing between, performing, and presenting the results of, one-way ANOVA and Kruskal-Wallis in R.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "r4babs2/week-2/workshop.html#session-overview", - "href": "r4babs2/week-2/workshop.html#session-overview", + "objectID": "r4babs2/week-4/workshop.html#session-overview", + "href": "r4babs2/week-4/workshop.html#session-overview", "title": "Workshop", "section": "", - "text": "In this session you will carry out, interpret and report on a single linear regression." + "text": "In this session you will get practice in choosing between, performing, and presenting the results of, one-way ANOVA and Kruskal-Wallis in R." }, { - "objectID": "r4babs2/week-2/workshop.html#philosophy", - "href": "r4babs2/week-2/workshop.html#philosophy", + "objectID": "r4babs2/week-4/workshop.html#philosophy", + "href": "r4babs2/week-4/workshop.html#philosophy", "title": "Workshop", "section": "", "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "r4babs2/week-2/workshop.html#linear-regression", - "href": "r4babs2/week-2/workshop.html#linear-regression", + "objectID": "r4babs2/week-4/workshop.html#myoglobin-in-seal-muscle", + "href": "r4babs2/week-4/workshop.html#myoglobin-in-seal-muscle", "title": "Workshop", - "section": "Linear Regression", - "text": "Linear Regression\nThe data in plant.xlsx is a set of observations of plant growth over two months. The researchers planted the seeds and harvested, dried and weighed a plant each day from day 10 so all the data points are independent of each other.\n Save a copy of plant.xlsx to your data-raw folder and import it.\n What type of variables do you have? Which is the response and which is the explanatory? What is the null hypothesis?\n\n\n\n\n\n\nExploring\n Do a quick plot of the data:\n\nggplot(plant, aes(x = day, y = mass)) +\n geom_point()\n\n\n\n\n What are the assumptions of linear regression? Do these seem to be met?\n\n\n\n\n\n\n\n\n\n\nApplying, interpreting and reporting\n We now carry out a regression assigning the result of the lm() procedure to a variable and examining it with summary().\n\nmod <- lm(data = plant, mass ~ day)\nsummary(mod)\n\n\nCall:\nlm(formula = mass ~ day, data = plant)\n\nResiduals:\n Min 1Q Median 3Q Max \n-32.810 -11.253 -0.408 9.075 48.869 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) -8.6834 6.4729 -1.342 0.186 \nday 1.6026 0.1705 9.401 1.5e-12 ***\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 17.92 on 49 degrees of freedom\nMultiple R-squared: 0.6433, Adjusted R-squared: 0.636 \nF-statistic: 88.37 on 1 and 49 DF, p-value: 1.503e-12\n\n\nThe Estimates in the Coefficients table give the intercept (first line) and the slope (second line) of the best fitting straight line. The p-values on the same line are tests of whether that coefficient is different from zero.\nThe F value and p-value in the last line are a test of whether the model as a whole explains a significant amount of variation in the dependent variable. For a single linear regression this is exactly equivalent to the test of the slope against zero.\n What is the equation of the line? What do you conclude from the analysis?\n\n\n\n\n\n Does the line go through (0,0)?\n\n\n\n What percentage of variation is explained by the line?\n\n\nIt might be useful to assign the slope and the intercept to variables in case we need them later. The can be accessed in the mod$coefficients variable:\n\nmod$coefficients\n\n(Intercept) day \n -8.683379 1.602606 \n\n\n Assign mod$coefficients[1] to b0 and mod$coefficients[1] to b1:\n\nb0 <- mod$coefficients[1] |> round(2)\nb1 <- mod$coefficients[2] |> round(2)\n\nI also rounded the values to two decimal places.\nChecking assumptions\nWe need to examine the residuals. Very conveniently, the object which is created by lm() contains a variable called $residuals. Also conveniently, the R’s plot() function can used on the output objects of lm(). The assumptions demand that each y is drawn from a normal distribution for each x and these normal distributions have the same variance. Therefore we plot the residuals against the fitted values to see if the variance is the same for all the values of x. The fitted - predicted - values are the values on the line of best fit. Each residual is the difference between the fitted values and the observed value.\n Plot the model residuals against the fitted values like this:\n\nplot(mod, which = 1)\n\n\n\n\n What to you conclude?\n\n\n\nTo examine normality of the model residuals we can plot them as a histogram and do a normality test on them.\n Plot a histogram of the residuals:\n\nggplot(mapping = aes(x = mod$residuals)) + \n geom_histogram(bins = 10)\n\n\n\n\n Use the shapiro.test() to test the normality of the model residuals\n\nshapiro.test(mod$residuals)\n\n\n Shapiro-Wilk normality test\n\ndata: mod$residuals\nW = 0.96377, p-value = 0.1208\n\n\nUsually, when we are doing statistical tests we would like the the test to be significant because it means we have evidence of a biological effect. However, when doing normality tests we hope it will not be significant. A non-significant result means that there is no significant difference between the distribution of the residuals and a normal distribution and that indicates the assumptions are met.\n What to you conclude?\n\n\n\n\nIllustrating\nWe want a figure with the points and the statistical model, i.e., the best fitting straight line.\n Create a scatter plot using geom_point()\n\nggplot(plant, aes(x = day, y = mass)) +\n geom_point() + \n theme_classic()\n\n\n\n\n The geom_smooth() function will had a variety of fitted lines to a plot. We want a line so we need to specify method = \"lm\":\n\nggplot(plant, aes(x = day, y = mass)) +\n geom_point() + \n geom_smooth(method = lm, \n se = FALSE, \n colour = \"black\") +\n theme_classic()\n\n\n\n\n What do the se and colour arguments do? Try changing them.\n Let’s add the equation of the line to the figure using annotate():\n\nggplot(plant, aes(x = day, y = mass)) +\n geom_point() +\n geom_smooth(method = lm, \n se = FALSE, \n colour = \"black\") +\n annotate(\"text\", x = 20, y = 110, \n label = \"mass = 1.61 * day - 8.68\") +\n theme_classic()\n\n\n\n\nWe have to tell annotate() what type of geom we want - text in this case, - where to put it, and the text we want to appear.\n Improve the axes. You may need to refer back Changing the axes from the Week 7 workshop in BABS1 (Rand 2023)\n Save your figure to your figures folder." + "section": "Myoglobin in seal muscle", + "text": "Myoglobin in seal muscle\nThe myoglobin concentration of skeletal muscle of three species of seal in grams per kilogram of muscle was determined and the data are given in seal.csv. We want to know if there is a difference between species. Each row represents an individual seal. The first column gives the myoglobin concentration and the second column indicates species.\n Save a copy of the data file seal.csv to data-raw\n Read in the data and check the structure. I used the name seal for the dataframe/tibble.\n What kind of variables do you have?\n\n\n\nExploring\n Do a quick plot of the data. You may need to refer to a previous workshop\nSummarising the data\nDo you remember Look after future you!\n If you followed that tip you’ll be able to open that script and whizz through summarising,testing and plotting.\n Create a data frame called seal_summary that contains the means, standard deviations, sample sizes and standard errors for each species.\nYou should get the following numbers:\n\n\n\n\nspecies\nmean\nstd\nn\nse\n\n\n\nBladdernose Seal\n42.31600\n8.020634\n30\n1.464361\n\n\nHarbour Seal\n49.01033\n8.252004\n30\n1.506603\n\n\nWeddell Seal\n44.66033\n7.849816\n30\n1.433174\n\n\n\n\n\nApplying, interpreting and reporting\nWe can now carry out a one-way ANOVA using the same lm() function we used for two-sample tests.\n Carry out an ANOVA and examine the results with:\n\nmod <- lm(data = seal, myoglobin ~ species)\nsummary(mod)\n\n\nCall:\nlm(formula = myoglobin ~ species, data = seal)\n\nResiduals:\n Min 1Q Median 3Q Max \n-16.306 -5.578 -0.036 5.240 18.250 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 42.316 1.468 28.819 < 2e-16 ***\nspeciesHarbour Seal 6.694 2.077 3.224 0.00178 ** \nspeciesWeddell Seal 2.344 2.077 1.129 0.26202 \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 8.043 on 87 degrees of freedom\nMultiple R-squared: 0.1096, Adjusted R-squared: 0.08908 \nF-statistic: 5.352 on 2 and 87 DF, p-value: 0.006427\n\n\nRemember: the tilde (~) means test the values in myoglobin when grouped by the values in species. Or explain myoglobin with species\n What do you conclude so far from the test? Write your conclusion in a form suitable for a report.\n\n\n\n Can you relate the values under Estimate to the means?\n\n\n\n\n\n\n\nThe ANOVA is significant but this only tells us that species matters, meaning at least two of the means differ. To find out which means differ, we need a post-hoc test. A post-hoc (“after this”) test is done after a significant ANOVA test. There are several possible post-hoc tests and we will be using Tukey’s HSD (honestly significant difference) test (Tukey 1949) implemented in the emmeans (Lenth 2023) package.\n Load the package\n\nlibrary(emmeans)\n\n Carry out the post-hoc test\n\nemmeans(mod, ~ species) |> pairs()\n\n contrast estimate SE df t.ratio p.value\n Bladdernose Seal - Harbour Seal -6.69 2.08 87 -3.224 0.0050\n Bladdernose Seal - Weddell Seal -2.34 2.08 87 -1.129 0.4990\n Harbour Seal - Weddell Seal 4.35 2.08 87 2.095 0.0968\n\nP value adjustment: tukey method for comparing a family of 3 estimates \n\n\nEach row is a comparison between the two means in the ‘contrast’ column. The ‘estimate’ column is the difference between those means and the ‘p.value’ indicates whether that difference is significant.\nA plot can be used to visualise the result of the post-hoc which can be especially useful when there are very many comparisons.\n Plot the results of the post-hoc test:\n\nemmeans(mod, ~ species) |> plot()\n\n\n\n\nWhere the purple bars overlap, there is no significant difference.\n What do you conclude from the test?\n\n\n\nCheck assumptions\nThe assumptions of the general linear model are that the residuals – the difference between predicted value (i.e., the group mean) and observed values - are normally distributed and have homogeneous variance. To check these we can examine the mod$residuals variable. You may want to refer to Checking assumptions in the “Single regression” workshop.\n Plot the model residuals against the fitted values.\n What to you conclude?\n\n\n\nTo examine normality of the model residuals we can plot them as a histogram and do a normality test on them.\n Plot a histogram of the residuals.\n Use the shapiro.test() to test the normality of the model residuals\n What to you conclude?\n\n\n\n\nIllustrating\n Create a figure like the one below. You may need to refer to Visualise from the “Summarising data with several variables” workshop (Rand 2023)\nWe will again use both our seal and seal_summary dataframes.\n Create the plot:\n\n\n\n\n\n Save your figure to your figures folder." }, { - "objectID": "r4babs2/week-2/workshop.html#look-after-future-you", - "href": "r4babs2/week-2/workshop.html#look-after-future-you", + "objectID": "r4babs2/week-4/workshop.html#leafminers-on-birch", + "href": "r4babs2/week-4/workshop.html#leafminers-on-birch", "title": "Workshop", - "section": "Look after future you!", - "text": "Look after future you!\nYou’re finished!" + "section": "Leafminers on Birch", + "text": "Leafminers on Birch\nLarvae of the Ambermarked birch leafminer, Profenusa thomsoni, feed on the interior leaf tissues of Birch (Betula) species. They do not normally kill the tree but can weaken it making it susceptible to attack from other species. Researchers are interested in whether there is a difference in the rates at which white, grey and yellow birch are attacked. They introduce adult female P.thomsoni to a green house containing 30 young trees (ten of each type) and later count the egg laying events on each tree. The data are in leaf.txt.\nExploring\n Read in the data and check the structure. I used the name leaf for the dataframe/tibble.\n What kind of variables do we have?\n\n\n\n Do a quick plot of the data.\n Using your common sense, do these data look normally distributed?\n\n\n Why is a Kruskal-Wallis appropriate in this case?\n\n\n\n\n\n Calculate the medians, means and sample sizes.\nApplying, interpreting and reporting\n Carry out a Kruskal-Wallis:\n\nkruskal.test(data = leaf, eggs ~ birch)\n\n\n Kruskal-Wallis rank sum test\n\ndata: eggs by birch\nKruskal-Wallis chi-squared = 6.3393, df = 2, p-value = 0.04202\n\n\n What do you conclude from the test?\n\n\n\nA significant Kruskal-Wallis tells us at least two of the groups differ but where do the differences lie? The Dunn test is a post-hoc multiple comparison test for a significant Kruskal-Wallis. It is available in the package FSA\n Load the package using:\n\nlibrary(FSA)\n\n Run the post-hoc test with:\n\ndunnTest(data = leaf, eggs ~ birch)\n\n Comparison Z P.unadj P.adj\n1 Grey - White 1.296845 0.19468465 0.38936930\n2 Grey - Yellow -1.220560 0.22225279 0.22225279\n3 White - Yellow -2.517404 0.01182231 0.03546692\n\n\nThe P.adj column gives p-value for the comparison listed in the first column. Z is the test statistic.\n What do you conclude from the test?\n\n\n\n Write up the result is a form suitable for a report.\n\n\n\n\n\n\nIllustrating\n A box plot is an appropriate choice for illustrating a Kruskal-Wallis. Can you produce a figure like this?\n\n\n\n\n\nYou’re finished!" }, { - "objectID": "r4babs2/week-2/study_after_workshop.html", - "href": "r4babs2/week-2/study_after_workshop.html", + "objectID": "r4babs2/week-3/study_after_workshop.html", + "href": "r4babs2/week-3/study_after_workshop.html", "title": "Independent Study to consolidate this week", "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 Effect of anxiety status and sporting performance. The data in sprint.txt are from an investigation of the effect of anxiety status and sporting performance. A group of 40 100m sprinters undertook a psychometric test to measure their anxiety shortly before competing. The data are their anxiety scores and the 100m times achieved. What you do conclude from these data?\n\n\nCode# this example is designed to emphasise the importance of plotting your data first\nsprint <- read_table(\"data-raw/sprint.txt\")\n# Anxiety is discrete but ranges from 16 to 402 meaning the gap between possible measures is small and \n# the variable could be treated as continuous if needed. Time is a continuous measure that has decimal places and which we would expect to follow a normal distribution \n\n# explore with a plot\nggplot(sprint, aes(x = anxiety, y = time) ) +\n geom_point()\nCode# A scatterplot of the data clearly reveals that these data are not linear. There is a good relationship between the two variables but since it is not linear, single linear regression is not appropriate.\n\n\n\n💻 Juvenile hormone in stag beetles. The concentration of juvenile hormone in stag beetles is known to influence mandible growth. Groups of stag beetles were injected with different concentrations of juvenile hormone (arbitrary units) and their average mandible size (mm) determined. The experimenters planned to analyse their data with regression. The data are in stag.txt\n\n\n\nCode# read the data in and check the structure\nstag <- read_table(\"data-raw/stag.txt\")\nstr(stag)\n\n# jh is discrete but ordered and has been chosen by the experimenter - it is the explanatory variable. \n# the response is mandible size which has decimal places and is something we would expect to be \n# normally distributed. So far, common sense suggests the assumptions of regression are met.\n\n\n\nCode# exploratory plot\nggplot(stag, aes(x = jh, y = mand)) +\n geom_point()\nCode# looks linear-ish on the scatter\n# regression still seems appropriate\n# we will check the other assumptions after we have run the lm\n\n\n\nCode# build the statistical model\nmod <- lm(data = stag, mand ~ jh)\n\n# examine it\nsummary(mod)\n# mand = 0.032*jh + 0.419\n# the slope of the line is significantly different from zero / the jh explains a significant amount of the variation in mand (ANOVA: F = 16.63; d.f. = 1,14; p = 0.00113).\n# the intercept is 0.419 and differs significantly from zero \n\n\n\nCode# checking the assumption\nplot(mod, which = 1) \nCode# we're looking for the variance in the residuals to be equal along the x axis.\n# with a small data set there is some apparent heterogeneity but it doesn't look too.\n# \nhist(mod$residuals)\nCode# We have some skew which again might be partly a result of a small sample size.\nshapiro.test(mod$residuals) # the also test not sig diff from normal\n\n# On balance the use of regression is probably justifiable but it is borderline\n# but ideally the experiment would be better if multiple individuals were measure at\n# each of the chosen juvenile hormone levels.\n\n\n\nCode# a better plot\nggplot(stag, aes(x = jh, y = mand) ) +\n geom_point() +\n geom_smooth(method = lm, se = FALSE, colour = \"black\") +\n scale_x_continuous(name = \"Juvenile hormone (arbitrary units)\",\n expand = c(0, 0),\n limits = c(0, 32)) +\n scale_y_continuous(name = \"Mandible size (mm)\",\n expand = c(0, 0),\n limits = c(0, 2)) +\n theme_classic()" + "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 Plant Biotech. Some plant biotechnologists are trying to increase the quantity of omega 3 fatty acids in Cannabis sativa. They have developed a genetically modified line using genes from Linum usitatissimum (linseed). They grow 50 wild type and fifty modified plants to maturity, collect the seeds and determine the amount of omega 3 fatty acids. The data are in csativa.txt. Do you think their modification has been successful?\n\n\nCodecsativa <- read_table(\"data-raw/csativa.txt\")\nstr(csativa)\n\n# First realise that this is a two sample test. You have two independent samples\n# - there are a total of 100 different plants and the values in one \n# group have no relationship to the values in the other.\n\n\n\nCode# create a rough plot of the data \nggplot(data = csativa, aes(x = plant, y = omega)) +\n geom_violin()\nCode# note the modified plants seem to have lower omega!\n\n\n\nCode# create a summary of the data\ncsativa_summary <- csativa %>%\n group_by(plant) %>%\n summarise(mean = mean(omega),\n std = sd(omega),\n n = length(omega),\n se = std/sqrt(n))\n\n\n\nCode# The data seem to be continuous so it is likely that a parametric test will be fine\n# we will check the other assumptions after we have run the lm\n\n# build the statistical model\nmod <- lm(data = csativa, omega ~ plant)\n\n\n# examine it\nsummary(mod)\n# So there is a significant difference but you need to make sure you know the direction!\n# Wild plants have a significantly higher omega 3 content (mean +/- s.e = 56.41 +/- 1.11) \n# than modified plants (49.46 +/- 0.82)(t = 5.03; d.f. = 98; p < 0.0001).\n\n\n\nCode# let's check the assumptions\nplot(mod, which = 1) \nCode# we're looking for the variance in the residuals to be the same in both groups.\n# This looks OK. Maybe a bit higher in the wild plants (with the higher mean)\n \nhist(mod$residuals)\nCodeshapiro.test(mod$residuals)\n# On balance the use of lm() is probably justifiable The variance isn't quite equal \n# and the histogram looks a bit off normal but the normality test is NS and the \n# effect (in the figure) is clear.\n\n\n\nCode# A figure \nfig1 <- ggplot() +\n geom_point(data = csativa, aes(x = plant, y = omega),\n position = position_jitter(width = 0.1, height = 0),\n colour = \"gray50\") +\n geom_errorbar(data = csativa_summary, \n aes(x = plant, ymin = mean - se, ymax = mean + se),\n width = 0.3) +\n geom_errorbar(data = csativa_summary, \n aes(x = plant, ymin = mean, ymax = mean),\n width = 0.2) +\n scale_x_discrete(name = \"Plant type\", labels = c(\"GMO\", \"WT\")) +\n scale_y_continuous(name = \"Amount of Omega 3 (units)\",\n expand = c(0, 0),\n limits = c(0, 90)) +\n annotate(\"segment\", x = 1, xend = 2, \n y = 80, yend = 80,\n colour = \"black\") +\n annotate(\"text\", x = 1.5, y = 85, \n label = expression(italic(p)~\"< 0.001\")) +\n theme_classic()\n\n# save figure to figures/csativa.png\nggsave(\"figures/csativa.png\",\n plot = fig1,\n width = 3.5,\n height = 3.5,\n units = \"in\",\n dpi = 300)\n\n\n\n💻 another example" }, { - "objectID": "r4babs2/week-1/workshop.html", - "href": "r4babs2/week-1/workshop.html", + "objectID": "r4babs2/week-3/workshop.html", + "href": "r4babs2/week-3/workshop.html", "title": "Workshop", "section": "", - "text": "Artwork by Horst (2023): “love this class”\n\n\nIn this session you will remind yourself how to import files, and calculate confidence intervals on large and small samples.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." + "text": "Artwork by Horst (2023): “How much I think I know about R”\n\n\nIn this workshop you will get practice in choosing between, performing, and presenting the results of, two-sample tests and their non-parametric equivalents in R.\n\nWorkshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "r4babs2/week-1/workshop.html#session-overview", - "href": "r4babs2/week-1/workshop.html#session-overview", + "objectID": "r4babs2/week-3/workshop.html#session-overview", + "href": "r4babs2/week-3/workshop.html#session-overview", "title": "Workshop", "section": "", - "text": "In this session you will remind yourself how to import files, and calculate confidence intervals on large and small samples." + "text": "In this workshop you will get practice in choosing between, performing, and presenting the results of, two-sample tests and their non-parametric equivalents in R." }, { - "objectID": "r4babs2/week-1/workshop.html#philosophy", - "href": "r4babs2/week-1/workshop.html#philosophy", + "objectID": "r4babs2/week-3/workshop.html#philosophy", + "href": "r4babs2/week-3/workshop.html#philosophy", "title": "Workshop", "section": "", "text": "Workshops are not a test. It is expected that you often don’t know how to start, make a lot of mistakes and need help. It is expected that you are familiar with independent study content before the workshop. However, you need not remember or understand every detail as the workshop should build and consolidate your understanding. Tips\n\ndon’t worry about making mistakes\ndon’t let what you can not do interfere with what you can do\ndiscussing code with your neighbours will help\nlook things up in the independent study material\nlook things up in your own code from earlier\nthere are no stupid questions\n\n\n\n\n\n\n\nKey\n\n\n\nThese four symbols are used at the beginning of each instruction so you know where to carry out the instruction.\n Something you need to do on your computer. It may be opening programs or documents or locating a file.\n Something you should do in RStudio. It will often be typing a command or using the menus but might also be creating folders, locating or moving files.\n Something you should do in your browser on the internet. It may be searching for information, going to the VLE or downloading a file.\n A question for you to think about and answer. Record your answers in your script for future reference." }, { - "objectID": "r4babs2/week-1/workshop.html#remind-yourself-how-to-import-files", - "href": "r4babs2/week-1/workshop.html#remind-yourself-how-to-import-files", + "objectID": "r4babs2/week-3/workshop.html#adiponectin-secretion", + "href": "r4babs2/week-3/workshop.html#adiponectin-secretion", "title": "Workshop", - "section": "Remind yourself how to import files!", - "text": "Remind yourself how to import files!\nImporting data from files was covered in BABS 1 (Rand 2023) if you need to remind yourself." + "section": "Adiponectin secretion", + "text": "Adiponectin secretion\nAdiponectin is exclusively secreted from adipose tissue and modulates a number of metabolic processes. Nicotinic acid can affect adiponectin secretion. 3T3-L1 adipocytes were treated with nicotinic acid or with a control treatment and adiponectin concentration (pg/mL) measured. The data are in adipocytes.txt. Each row represents an independent sample of adipocytes and the first column gives the concentration adiponectin and the second column indicates whether they were treated with nicotinic acid or not.\n Save a copy of adipocytes.txt to data-raw\n Read in the data and check the structure. I used the name adip for the dataframe/tibble.\nWe have a tibble containing two variables: adiponectin is the response and is continuous and treatment is explanatory. treatment is categorical with two levels (groups). The first task is visualise the data to get an overview. For continuous response variables with categorical explanatory variables you could use geom_point(), geom_boxplot() or a variety of other geoms. I often use geom_violin() which allows us to see the distribution - the violin is fatter where there are more data points.\n Do a quick plot of the data:\n\nggplot(data = adip, aes(x = treatment, y = adiponectin)) +\n geom_violin()\n\n\n\n\nSummarising the data\nSummarising the data for each treatment group is the next sensible step. The most useful summary statistics are the means, standard deviations, sample sizes and standard errors.\n Create a data frame called adip_summary that contains the means, standard deviations, sample sizes and standard errors for the control and nicotinic acid treated samples. You may need to the Summarise from the Week 9 workshop of BABS1 (Rand 2023)\nYou should get the following numbers:\n\n\n\n\ntreatment\nmean\nstd\nn\nse\n\n\n\ncontrol\n5.546000\n1.475247\n15\n0.3809072\n\n\nnicotinic\n7.508667\n1.793898\n15\n0.4631824\n\n\n\n\n\nSelecting a test\n Do you think this is a paired-sample test or two-sample test?\n\n\n\n\nApplying, interpreting and reporting\n Create a two-sample model like this:\n\nmod <- lm(data = adip,\n adiponectin ~ treatment)\n\n Examine the model with:\n\nsummary(mod)\n\n\nCall:\nlm(formula = adiponectin ~ treatment, data = adip)\n\nResiduals:\n Min 1Q Median 3Q Max \n-4.3787 -1.0967 0.1927 1.0245 3.1113 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 5.5460 0.4240 13.079 1.9e-13 ***\ntreatmentnicotinic 1.9627 0.5997 3.273 0.00283 ** \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 1.642 on 28 degrees of freedom\nMultiple R-squared: 0.2767, Adjusted R-squared: 0.2509 \nF-statistic: 10.71 on 1 and 28 DF, p-value: 0.00283\n\n\n What do you conclude from the test? Write your conclusion in a form suitable for a report.\n\n\n\n\nCheck assumptions\nThe assumptions of the general linear model are that the residuals – the difference between predicted value (i.e., the group mean) and observed values - are normally distributed and have homogeneous variance. To check these we can examine the mod$residuals variable. You may want to refer to Checking assumptions in the “Single regression” workshop.\n Plot the model residuals against the fitted values.\n What to you conclude?\n\n\n\nTo examine normality of the model residuals we can plot them as a histogram and do a normality test on them.\n Plot a histogram of the residuals.\n Use the shapiro.test() to test the normality of the model residuals\n What to you conclude?\n\n\n\n\nIllustrating\n Create a figure like the one below. You may need to refer to Visualise from the “Summarising data with several variables” workshop (Rand 2023)\n\n\n\n\n\nWe now need to annotate the figure with the results from the statistical test. This most commonly done with a line linking the means being compared and the p-value. The annotate() function can be used to draw the line and then to add the value. The line is a segment and the p-value is a text.\n Add annotation to the figure by adding:\n...... +\n annotate(\"segment\", x = 1, xend = 2, \n y = 11.3, yend = 11.3,\n colour = \"black\") +\n annotate(\"text\", x = 1.5, y = 11.7, \n label = expression(italic(p)~\"= 0.003\")) +\n theme_classic()\n\n\n\n\n\nFor the segment, annotate() needs the x and y coordinates for the start and the finish of the line.\nThe use of expression() allows you to specify formatting or special characters. expression() takes strings or LaTeX formatting. Each string or piece of LaTeX is separated by a * or a ~. The * concatenates the strings without a space, ~ does so with a space. It will generate a warning message “In is.na(x) : is.na() applied to non-(list or vector) of type ‘expression’” which can be ignored.\n Save your figure to your figures folder." }, { - "objectID": "r4babs2/week-1/workshop.html#confidence-intervals-large-samples", - "href": "r4babs2/week-1/workshop.html#confidence-intervals-large-samples", + "objectID": "r4babs2/week-3/workshop.html#grouse-parasites", + "href": "r4babs2/week-3/workshop.html#grouse-parasites", "title": "Workshop", - "section": "Confidence intervals (large samples)", - "text": "Confidence intervals (large samples)\nThe data in beewing.txt are left wing widths of 100 honey bees (mm). The confidence interval for large samples is given by:\n\\(\\bar{x} \\pm 1.96 \\times s.e.\\)\nWhere 1.96 is the quantile for 95% confidence.\n Save beewing.txt to your data-raw folder.\n Read in the data and check the structure of the resulting dataframe.\n Calculate and assign to variables: the mean, standard deviation and standard error:\n\n# mean\nm <- mean(bee$wing)\n\n# standard deviation\nsd <- sd(bee$wing)\n\n# sample size (needed for the se)\nn <- length(bee$wing)\n\n# standard error\nse <- sd / sqrt(n)\n\n To calculate the 95% confidence interval we need to look up the quantile (multiplier) using qnorm()\n\nq <- qnorm(0.975)\n\nThis should be about 1.96.\n Now we can use it in our confidence interval calculation\n\nlcl <- m - q * se\nucl <- m + q * se\n\n Print the values\n\nlcl\n\n[1] 4.473176\n\nucl\n\n[1] 4.626824\n\n\nThis means we are 95% confident the population mean lies between 4.47 mm and 4.63 mm. The usual way of expressing this is that the mean is 4.55 +/- 0.07 mm\n Between what values would you be 99% confident of the population mean being?" + "section": "Grouse Parasites", + "text": "Grouse Parasites\nGrouse livers were dissected and the number of individuals of a parasitic nematode were counted for two estates ‘Gordon’ and ‘Moss’. We want to know if the two estates have different infection rates. The data are in grouse.csv\n Save a copy of grouse.csv to data-raw\n Read in the data and check the structure. I used the name grouse for the dataframe/tibble.\nSelecting\n Using your common sense, do these data look normally distributed?\n\n\n\n What test do you suggest?\n\n\nApplying, interpreting and reporting\n Summarise the data by finding the median of each group:\n Carry out a two-sample Wilcoxon test (also known as a Mann-Whitney):\n\nwilcox.test(data = grouse, nematodes ~ estate)\n\n\n Wilcoxon rank sum exact test\n\ndata: nematodes by estate\nW = 78, p-value = 0.03546\nalternative hypothesis: true location shift is not equal to 0\n\n\n What do you conclude from the test? Write your conclusion in a form suitable for a report.\n\n\n\nIllustrating\nA box plot is a usually good choice for illustrating a two-sample Wilcoxon test because it shows the median and interquartile range.\n We can create a simple boxplot with:\n\nggplot(data = grouse, aes(x = estate, y = nematodes) ) +\n geom_boxplot() \n\n\n\n\n Annotate and format the figure so it is more suitable for a report and save it to your figures folder." }, { - "objectID": "r4babs2/week-1/workshop.html#confidence-intervals-small-samples", - "href": "r4babs2/week-1/workshop.html#confidence-intervals-small-samples", + "objectID": "r4babs2/week-3/workshop.html#gene-expression", + "href": "r4babs2/week-3/workshop.html#gene-expression", "title": "Workshop", - "section": "Confidence intervals (small samples)", - "text": "Confidence intervals (small samples)\nThe confidence interval for small samples is given by:\n\\(\\bar{x} \\pm \\sf t_{[d.f]} \\times s.e.\\)\nThe only difference between the calculation for small and large sample is the multiple. For large samples we use the “the standard normal distribution” accessed with qnorm(); for small samples we use the “t distribution” assessed with qt().The value returned by q(t) is larger than that returned by qnorm() which reflects the greater uncertainty we have on estimations of population means based on small samples.\nThe fatty acid Docosahexaenoic acid (DHA) is a major component of membrane phospholipids in nerve cells and deficiency leads to many behavioural and functional deficits. The cross sectional area of neurons in the CA 1 region of the hippocampus of normal rats is 155 \\(\\mu m^2\\). A DHA deficient diet was fed to 8 animals and the cross sectional area (csa) of neurons is given in neuron.txt\n Save neuron.txt to your data-raw folder\n Read in the data and check the structure of the resulting dataframe\n Assign the mean to m.\n Calculate and assign the standard error to se.\nTo work out the confidence interval for our sample mean we need to use the t distribution because it is a small sample. This means we need to determine the degrees of freedom (the number in the sample minus one).\n We can assign this to a variable, df, using:\n\ndf <- length(neur$csa) - 1\n\n The t value is found by:\n\nt <- qt(0.975, df = df)\n\nNote that we are using qt() rather than qnorm() but that the probability, 0.975, used is the same. Finally, we need to put our mean, standard error and t value in the equation. \\(\\bar{x} \\pm \\sf t_{[d.f]} \\times s.e.\\).\n The upper confidence limit is:\n\n(m + t * se) |> round(2)\n\n[1] 151.95\n\n\nThe first part of the command, (m + t * se) calculates the upper limit. This is ‘piped’ in to the round() function to round the result to two decimal places.\n Calculate the lower confidence limit:\n Given the upper and lower confidence values for the estimate of the population mean, what do you think about the effect of the DHA deficient diet?\n\n\n\n\nYou’re finished!" + "section": "Gene Expression", + "text": "Gene Expression\nBambara groundnut (Vigna subterranea) is an African legume with good nutritional value which can be influenced by low temperature stress. Researchers are interested in the expression levels of a particular set of 35 genes (probe_id) in response to temperature stress. They measure the expression of the genes at 23 and 18 degrees C (high and low temperature). These samples are not independent because we have two measure from one gene. The data are in expr.xlxs.\nSelecting\n What is the null hypothesis?\n\n\n\n Save a copy of expr.xlxs and import the data. I named the dataframe bambara\n What is the appropriate parametric test?\n\n\nApplying, interpreting and reporting\nA paired test requires us to test whether the difference in expression between high and low temperatures is zero on average. One handy way to achieve this is to organise our groups into two columns. The pivot_wider() function will do this for us. We need to tell it what column gives the identifiers (i.e., matches the the pairs) - the probe_ids in this case. We also need to say which variable contains what will become the column names and which contains the values.\n Pivot the data so there is a column for each temperature:\n\nbambara <- bambara |> \n pivot_wider(names_from = temperature, \n values_from = expression, \n id_cols = probe_id)\n\n Click on the bambara dataframe in the environment to open a view of it so that you understand what pivot_wider() has done.\n Create a paired-sample model like this:\n\nmod <- lm(data = bambara, \n highert - lowert ~ 1)\n\nSince we have done highert - lowert, the “(Intercept) Estimate” will be the average of the higher temperature expression minus the lower temperature expression for each gene.\n Examine the model with:\n\nsummary(mod)\n\n\nCall:\nlm(formula = highert - lowert ~ 1, data = bambara)\n\nResiduals:\n Min 1Q Median 3Q Max \n-1.05478 -0.46058 0.09682 0.33342 1.06892 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 0.30728 0.09591 3.204 0.00294 **\n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 0.5674 on 34 degrees of freedom\n\n\n State your conclusion from the test in a form suitable for including in a report. Make sure you give the direction of any significant effect." }, { - "objectID": "r4babs2/week-1/study_after_workshop.html", - "href": "r4babs2/week-1/study_after_workshop.html", - "title": "Independent Study to consolidate this week", - "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the tidyverse package\n\nlibrary(tidyverse)\n\nExercises\n\n💻 Adiponectin is exclusively secreted from adipose tissue and modulates a number of metabolic processes. Nicotinic acid can affect adiponectin secretion. 3T3-L1 adipocytes were treated with nicotinic acid or with a control treatment and adiponectin concentration (pg/mL) measured. The data are in adipocytes.txt. Each row represents an independent sample of adipocytes and the first column gives the concentration adiponectin and the second column indicates whether they were treated with nicotinic acid or not. Estimate the mean Adiponectin concentration in each group - this means calculate the sample mean and construct a confidence interval around it for each group. This exercise forces you to bring together ideas from this workshop and from previous workshops\n\n\nHow to calculate a confidence intervals (this workshop)\n\nHow to summarise variables in more than one group (previous workshop)\n\n\nCode# data import\nadip <- read_table(\"data-raw/adipocytes.txt\")\n\n# examine the structure\nstr(adip)\n\n# summarise\nadip_summary <- adip %>% \n group_by(treatment) %>% \n summarise(mean = mean(adiponectin),\n sd = sd(adiponectin),\n n = length(adiponectin),\n se = sd/sqrt(n),\n dif = qt(0.975, df = n - 1) * se,\n lower_ci = mean - dif,\n uppp_ci = mean + dif)\n\n\n# we conclude we're 95% certain the mean for the control group is \n# between 4.73 and 6.36 and the mean for the nicotinic group is \n# between 6.52 and 8.50. More usually we might put is like this:\n# the mean for the control group is 5.55 +/- 0.82 and that for the nicotinic group is 7.51 +/- 0.99" + "objectID": "r4babs2/week-3/workshop.html#look-after-future-you", + "href": "r4babs2/week-3/workshop.html#look-after-future-you", + "title": "Workshop", + "section": "Look after future you!", + "text": "Look after future you!\nThe code required to summarise, test, and plot data for any two-sample test AND for any for any one-way ANOVA is exactly the same except for the names of the dataframe, variables and the axis labels and limits. Take some time to comment it your code so that you can make use of it next week.\n\nYou’re finished!" } ] \ No newline at end of file

NR>WZdKXCh$Sdh%cZWbwN84 z6YNf%fcek=jnbph+rtLsE9&akPHewcs~_(j8ye0tAV*787W)Ul|GBp1zSghdbCMTC z+X1d|1c5}+QU%M2O=^wiC*t$@s)CA83c^+8-^inkZ>5~BjM2o^dMAB;*5B( zn&Dx^a`~Tck~|BAVi##9Dnf64>-Q!4Vz=RufX3$*l^YKKS$Em;-9fX@*0!T4vE$v1 zCd2BtodouY8E4A=EMqYuL{Upj?ZU+`s%dgVcT3L6B{7KPWk#v}&{l~0F#PJm)xXl@S-+iv#2nDT zTI!6tYBrY}qtQ7aDPUAdXlM!U+Oq=hm=>zEoQZ3gx;X;)$DIrEE;M!T2fy=S?bm8{ zz2SR{$ze9%H3i4kTclxc%#Z1Og<;+JX3E;(Xi1d{)=KVgofTJ31CxIMhIT~59}=NE z|5q8;9?xXk$7K$WdB*ePahjKv%2GZdhf$a!dpb~3X_RbblVi;~rUfJ**iXNqT zh{$0?sF$f|Du+!DImED}KFl^W$8GP;)ARoG-v8Zy+}G!Kf4{%q@4D{mzP?|!+4)|> z7F^>(h6eAoNw1eA?Q47tFwMiHT|g`k{Bp#2`3B#`sQDbiCM)Rd{c7*>N4ka9ojaPF zvAUv>zIwe5Tpq>2l`d2=w&30kEvWY{x}3KXC>~R>PH2g3XKZoyM7dvxPFYs2Z#2+d zt2NmU{>U?dZX|THJ4?d~*eTJb_#NWL;fT7J2tmY*60GYv;DT)Y`MPz5mjBfWk%<#0 zXO;MK${j>f;9j$K&s}-99)gXGs7mNMn4wlCYtP`iYXV&hS}J7&!CeV&ce;Wc1zL`@ z?D0_NhwA{wrNzgxVxiqN;AL689|tb=bMDU3N> zA`n$(zy!C7jzYL9?3fcDLaT!4G$TrvpIa%t;m?N{<8X7A%)8$zYXHi-6Z!*;r(XH$ zkVEX!UQ2S+Ot)kK`j8JGa>lI=S(d{SA zbYeyuzXG)l6VpYj&HEPnkU(Xwp*3V?UdOE`->&ZNheU@X$tDrwm(SkzAM^nUWY+5> zDwEn?Aln&Dtn_m(`%Eq*)N?EXB^jUb98Vpevq}FW3SKxh&F#vRc;?S#1&&v=Gh@_q zmjy33L3%^=&&ZYbMxWm4E;eJ`DY^Yo>b>7}Os*3PAYExfR}tCVoj#F>8_anUkkKKR z*Q~79P4%AbQv^&D+ZVn0Y1T_K?s*V3hp;fzQuGCC10FpQ)&D?o9y#Y!V=k7rnH3Ee z17W6@%CpyrL%m4l{e-&UjxzwuN>F;!{IBNz4LbcBv1NY{^yZ*u_&7h8o%w*X;<}Bb zatlDz5tUI(RaNr#+VR&X_rDPqOONf|nzYZH?8DHjyzp~~**3p)sn(Qjc9 zU-l{yn3a1IF5LGx`EBlDLoF?7gk;}VA!)2()Qox}E{+KwsxBu;q_z*0Lj959bu~Gi z5Y77IvF4o0mgC_FEx*al_O#n|l6YQyUv!=l8T=4D2$-rH$4}>=66Z;c8*vdr6WQ;Q zKSG-Cf67!sg1SVn0Xh?PG-9m$AOtjz0A-FdP9%dYlR2B!Co?P*gEdz{T?_c`%w>&j znL#$4RuPVbmI@0i27h1I3t&aOx-_WbVCwoSN#0>4P2=;!4Mek*s!e-A72>D6oUC7$ zePBQ__VUD)C)wAM+d{Tv$)P+D*pvr@1)3m(BQL<2rGH zZ9WkMO4|%uDw0)If8ieMNdZ@zj|AlE^^KhQ-w@ua{SYvkMN->mT@=_>*}Ujg{=EiH z!ip!g=7>HuvpR364W*VN-?a^n8UQj`Y50XJxpMQ!!(dqW%R45MP?MET#N?~TAcDan zYwkvIh^=O}13crUuiJRSWM_J>-BJMwdv9Y6p{x<03*$AFYsqMm8xPI8KF1e?biG|` zg@Wou)AuVP?fKo8r2F~rUKdZPsd&`G>>r_dLsqHWlB9P{*W<;sF%%r|Q;|A$wuXJE zM>%Xf4q;@f5k8!2gW--uM!uisxDCrOFA6|l?t3>5V@8+?(M|ozA5)ZMxw44$JsgDc z&Df`sd(K}>+gAP)COycwF&I4H$3SD0ZId1$vpQvi6BXKep^Hq&GROkmB~CWZ3N&03 zYNyh`-5=w%k0t^D+Paz_k^qOx*%CzQDxZ_%M$@5pxNnZolWuKGSE$7*fp9)~Dr!a9 zGWOf&`z9aI&JvOg)Vu%1n0@JEXIE|AErM4~lhZe!5n`IH=+f{(d;zTTkNx#=q7h!+mk_NMgl z?cuqB+n%9q@%KZzP=Fbx*!w)*Ky|_5D9GFS;8U+|sk2~g1!t;K7BxWxbM=(B*uZ(p zx@~0$%8L{%KOY_Y#AIOG6K0HR^9J0u!O^$F*K5!Q96M%7BO&%D&^5+p&(i4dH0~ z$hz`Z)WEfFEKGnQ2^*R6x{&6u5!e@mX3U;zD>Lcd#C$o^(9crC>x?-=?Xc(K%6ZP| zDuFyZ-P&S%p~Oc+5M@*~+|bygsq_`}5PE-4Bkf~}!pEwRdy;bLp9WlY47jp_3T9sE zV4HfZYMqu=k*Mp{VyrD-i62pa!i&v?@ zO9gdD90+Sq`M-_a;Rxrj=n(zycu)O?aRGV$eHP6JoKj>3+p^{+ne+A~J?>Mb@#ao^ zCiCqYdMo|a6ZWxO!z`qEKFP|6^qvf z`NX9HMN;?MQ356As!w910*y_aKAq2f^9~KE#w43+t3b|CV4JQ43t%#S0IN$I^rG?n q|J&zFqhw1Kb>EIr&~wN`PmcaAx|zCt@UW^9cpX3HIQ72wkHk_x--_zt*e;_nve2sa@x{_eK;#BMYHF)CmDNf_2aD;v(|a{RbX*gKn@` z@Jl{$4m%p533qNE@_0WhxvxU6Ov20bRUpzPjr@59N?T!X;)O^q0!8#lI)L}%HG(Vd zIxjZP+)!0JWAQ>eBlS2mt~;h1-?_6ziWZfTsdMyL$P!3B3VTfe`E~evFuygowOKQra{0hSR3Pkn6E(Dbj6Wnt#;75FoRk^s+ z%u}vWvaQ~uFa{FHk-7j(qnbhccGq^=OjAcdTC8#_?LyksbRbBT5OJDKhs>JsTFuI? zCtG{(Kzm?#lA@9*E|t>r7sA;sK)m$kUYq|O@cCx8l-_P7sp<8d`}z!!IfX2Ui6Hv{ zsj1-TXR1LO-#j@NzSd1how5K-L_y$~y>NJzwuH!K7h2lRZXlS_VQmTdy4YX*%WU(C zs=_L>*6q@6_G)!EGXI)-HS8bC?_`jpr$VsFW5*tR{E1RxWxzq#?|VMu3_bFP(<)y+ z03=1-nqMGaW4{r&1=cv4Oj&w$yiJXJJJ!a&)YqiZ4E45rksU&W@Xe${uKyuFagXur z5lg-9;p$zk^O2zZILUI2o&-w%b3lh+xA2Llrk(K=(5?iE3QSHM(ASvxmcnF6{`b}p~9q=EHjRU z794V&?t~CGu>;_}>Nd`&e&#h%i#aM(-SH+TphJvV7O8k&{-r@ekmZ&n9?)ncNj#t& zAc^u_2&<-EDkk_!gSYQ3dbcgyPymMEbQutB*Ub#+7(O1Q`JxaoTZ_`EB{og`RMBua6LL zng>nkfp+T4|4h><{!H|lFRsNgvsTUp0SQFkp-8@++*T@I76Az?DEwkvlv<3Y^MpXj zCGwW0E8wF!=o>X0;L`{nZo5_!e$PpPfX<=p8Tni6XH;mw2@TRI8lDMZ`=4%{#7nFH zZJzDFP>x^HzVm5r5zCCL{CP&ES@UeZW;0qreMOj<9ftU2lCQsi^|w{5m+bis zDe&3;v=z=#*lR*m7Z4qw=nK%j&o{9f!sqv!5MTQMQ9j=(5!xc&^`0z2U*{x;hMZRa zWW28dzX_uwp!0G}6frNy8-9(_E?B5D^?nJbH&8-wZ+vklaH{}czt0a;-YnI zc^63}I^hl%Eb>5@n%psnu)}@meJ1ff3Qg4X;;Y_?QL#|$L?hZJ>ZB;4SJz4!z!nV0|oUl!3Ji`R1rjv~PB(%N%GOI)}= zKhYptW(G!{CZ&Na6K9JM21?y!Cab1SwtLo%+>_W{Rmh+r@A9w02W2poetYSVTw-f? zI2YdTX2LMl6LnbuB^EBzkOq->h_c?1MA+Ke4%G=V`}}FZyB8GL_VhFLMj)kTQ3-f8 zW!ba!(JS}E5{LK|kYa&k*98Br7CE-#;DL;J{XU(mnrmXs(CC5eXkojv7r0Qw7^pC> z_jLKoRqi2e!s-3i17el>7138TAr3ioNaaVi-Uns3D$T4Ob2QT;A>*KC=3q!&OFj&7 zeDBA4ff#-hafiOUz3)HPB%naUx$1##3Q$1cn`qepx>dOF0$9`D0aull31KF?1&G`*ZP|4u0NI>$&!tFmY#uuQu#N>~!gwJiqHtkx#S zHl@j7g$CU-O3lB@P0>=!+mQXcF7$8heYQ5jqU_W}C3Hu?!J=VB-7O781c8%GrO&k4 zHM#Ei@7jsuu3~pLlWyfb5NYHkd93#6pJs@hQ>yj0rl@l_x!sf#OV9C9%*+dK3xX-3 z5Vo^`6y|-+j>cVu>&!P!VLSU49qBoo;L_RTdI_V`Na%q+opSf{Ih!ngw^%5QkU&hX zHF%_ZaJ?=Ub#4>zNZ)S_D}@uEqbCp(vU>JNga4o&R5F}n!kw!OF9%40xgnD$=bW&< z?F13?SWt_tNl(aB6n7W)m^_;RU%$`;`Hd3y2V$Trez&ZwF#{b__Yje75gVPFOtK_Q z(v}ZU6WV!M+i&RQq2qinn?h-k4eY2;Xqjz7MHP_$pSL9y&!|9k^i{VxtJ1FsZ@x_o zO$ZMu#t4e*!|%cLzJN9@`QNuO#F66pJFjt-o^4jQ7aA+$`<$d6fEzD|3T|%0%Q9ZmW*0Y@t7YKNUxwSM(9DMnB4Y@3k__{<>PNCoA6Fg~+ej;pWRrrt5sRcLm zxD;==O#)3Qj@}S4sH%Mgr)R$}U&aKVFaDK;lH_(n*(!IEo|os;p@^>`Z{6X~6m?FL zXfMhaZ*Mnyf(8`!`A>An4p7)#qjr6K zv(a(mAlX-sm8*tkv{7~eQcuXSRN#u`K&!IwUiLN>#>JmnxR>VyB)scVrO$D8vM}L+R$Prl|7xZ; zNt(2%!2Ft`0Z%&U2^0l#$Hb1h=SU+5v-8ViIh3ss)dUQYZ5dEeza5rhJT?y`A>^;I zmTQn}a_~PC&0fF;MeKX;iCHCssh?_t)KGqr$s=wSJEeB_++Q7m={HXCDZDDuJHl&yhd@j%aqYY& zbis7cy)I*;Nq%wlF(Um{@-ovzv5)Jb&){JwkOh%TM<@6yBGR4K zH`vAtPfAU!ddT<{(7l7aB%m*&rzc%#H$<~B=&dJp-@1|(S?)Fz1qZd3dmb|brrXq5 zF$8q7bNh~Znoid=`bG^en+lHJj7|m6PA(Ez+k=Yl7U%-PXrSbf5Hjq?aG=be-^vfd z`xwFJ^a{vy$CZv5?Q&Ca(h!I>*iWAfwe@rdJXB^MV@2DpSn?lH@`M~MtP77jISf{S`bo@%2g{;w~u(kR|)4dkw7F`19P!TBEYrG$IebDVw5UH?5|zE^5l8B|%^WLPB+ z40A8AM$WDm!?*y-xFV>XKu3dMGlmZ73QDUIcsh{OC(z>nygBIdiSYo!-@=zAJX@|d z;-$w|TuPq!d!Ui%8@vk&K#S2?EDbXLN4<*EY>KY|{H8-?Bpa5%$@vASN@Sf&@X{`y zAYF*=5Wlpy52A-jyP_gcqgx~Mez-u+G@g3Ji?;d-+G>AtNr+xON`? zBFP*yC9hkp#v9S$LI53;#0BevKeC-aOTe2$)bylJXg+{PEJ|d3T=gXO6Dhb2*f$jw zY^;4I&x=7NImTHe=?5;wT5E>&(~+zzAqO;}0jGrsA2gvvUV$b&Whck}bx=nL_gy{* zZ^qB1LBwN8^b>wIHsZQhi;1xxL!ZRYWkHIV84VqQkg8>2V(h%j)vhN4s;#) zp`q(WQU}Y$Ji(xsI$8TL%_PG{mYVki=9@aRl%=jtY^@t#lmO`71p+#AbK^!RJLkxO zKfpfsdesX!mo(@O*RNydr)Tct{?MiTwZA=K3P%kt4NO~(j>yD@*@EiT8sqDc%bg|C z`JMTew>SK0d+SY)XP5>$xntjcDMl<9GpKyZ2o}BJH8hdvD&S^b$H!RTB1rZ(0nin zS_57Vr3a(<*p*c2HvttzJ~=smJHMs`kBYDSZWpsj6MnvHe*n^govNqSH%L7N>|%Ga z9Om~luC`wj;6M1w(HSkjso8w=5;P)hTs8&k3BCn~Q}=xN9UWI}y`QT3MxO!!r^x;@ zRDK`m9zr8eiue5%!b+rSELGSvPHN~J$fQ$^BDKVRE~jIvwa=0Hz}!R147oa;E1Gm) z*If|BS0aiG?5iu7;J2m^@`osQjzU^D+MY%h-)}aUP=0a18lM_CcJF&qzmAbSCpb^_ zHmD&pvjJ^G$_rzCiH7ftHNY3`CJeTWYVDnR=+V-X&DSBccGaZ^*FY7QiKauYKAN5Q z5|$=2*qO*?T644V!!~vlKFBttsm%`h_UIMaR^M=Kt1m_Wkojx^2;j^ueD78&3~)>Ms8(hIrJ-Y|C+beasm%# zQ()3QXv=f6HLkD(xZ_x}BpXl_M?c96qt`uf6DAQEGzp7M27F!~nDfIVmWo@~<)aKA zf|si2;2wTw{W_SDh6>&Kq2cqg5ylH^bukt0nE0l5D&fW$O--hG(q_~i86&wi zp4~pbj{7r5NTAa$Wg|E9OS!m?qwf7ZzHyebKNxjT&JE+O+HkLX&VLv3zzQA+Gflcq z*=^)Lh;xS{5@rn|T!eeK)4JoQK6U%5BRqzT60M%ppG3ThPQhQznwP!*Dl{x+>I<&xJ-zt{Z>m>;2;uekUfJ{evHn0?Hg>VWsYo18`p#e$NZ( zfxw)EzxF-XXIosWy>=s4s@B=<1h^j-RCF~PkhC};UWy+XG+q4RR6QQ67rv_C+2Y~h z>tb0^u}qTsZf;|PXo;X}!0XTBW&Iof%!dUTY{Wzbh~7gj>bT}?=*@;IYRSw_qabov zo{CXyK`lFBmYPI=G%h$2;+KyfGtZZ(d*C~_W`F8pbMo?-o@7OskNP*s(xmZOEKqZj zpt^BCMb)Oofdac7)#kkufH$nO`Bt$Ulv1>uYN4RZeGTcpfkPsr6@BB27NHvSjV{1qA!!!ZeqSms+>annt;QHb8j=tWWodjV#8Y; zwG&G12;73pXWYow4k)KbEim@WtfSL+uHIa7mV$K;u^`V4rfs~gB?ul}&FuHc8W-|M zoPXeQFQ4c*d^*)w5S9q?f*pYl{)5b`6ufucVwo%L%f_h}z9Bxyw_VRpY!_jKf*yrl z09;p&maEjMRVbziyj<{rCtlEUAw{U%LKSe~AbKwR@5d^nOF7`jDM!6C)NCJe-00ck zr({Xq$qF&Cf?u%D#wbo7s5DrLQPsJL19)|tmf|be>WppIYwMFP4%ro_izF(#U#KGD zi~$+VH@`O{SI7OvTivpFRGmb8Kfk_FecGx>eYT$%07%@- z_Cmit#TJ|eG;pm0_#u-p#&*9$>4#ox$r2MU-Fy925szY(l0SFW%K1|CPQLsS=OA(J z^q}e6l^T9nL3ZGSiJsK$h?1-He%!HPBrS3u9)bNo*55m_s<@c%8oE$TJoj+IJgtS6 z`4IH)IwlV{>u)owY@mFZ{l6N-0)Ea4{IE3D`?!$-O5M29Py)Kz(p|ig|2fV`sQQfi zyj6m^r5k^~X97Pzw7N)fVz?rkeiHHcbtnhYv5j!!>`7LiW;bc>MIUiApGexq{G3`R zD5mYTpq=~ji$Q^h;ncpK zG;s$#c6XF0e1b(D@YQl|nYfCj?PW0OENUL%gVWE=w*P5MRvHQxzG#yEb3RM`&1!8O zBFE|2L+Y+S>)o1})ukx&Rq=|*5B&*?8%2FJ=hI-b5UKSd@h&XNM7LM;Lee5iA>RYo z4Cs)dc|QIq52ZF|SV!(inyYXu^$fhg%zEr3S=Ot=g~))dl1iUn!Un4g7Vn24xW(Z! zpNyujQ$~SIKH%K1-WJ;BPB39S4vO6^s9^43j5MuSSS{uaH76AFceMaJ$-)Zwm57&i zj~fHrl2{pxvDzK(`ZH?ti7_H4OTPWN_L!AMWo=y$8-4q9{uj3Z>||NEb+@!fF;|JsW*Pq#%uA?@p+! zo8^1g+KRGrkzR_|i{xMaSjPUvSZa}ByOU1W#6XZYMYr5vC$gkN35D)R*GLxk(|t3? zbVN5O@MXYK{~f{s(;s#Pbk8y5yN}PJd02#j2sAcG4v1r#ZmJ5ECNhd$wN)}E&; zHrjsdd^ym$H}@uWrTy4Ge~`14QAc6hVIbRRklV|bi?S4`*6|v~NQ`>zqTy^p_Nr-^jb`Fp^$m;fx+<`%~O@8dv*1%gG6+vv&QNtPDJ^7fmZrwc3 z{ejx(OsJ=~%IJb>hqQ~&Tdz`DWNAG$+ZuhsaTlw8g6N(^=0(Mj2^HPj=LqiAeWgXK z^kuXB;8q z8%&uwMPjGVh~RM5c$V3$z%%tXYaFuY z;SL6?s1DVljJ9*TrFmh(g(nmzv{t)YX-$x1mXnBnYa8$H^*;!{0#l_eJ;k;4xN)5e zDCp9=muGB?K1`K3j}FGnRoxjH6-*Y6+)&LO*^YZH>ybIQTc3Qc3;JpD=-$peyd!MMFFJOb!5!*jv&-L1l>w|TpY*$WP7!Pox}D5) zGg0CF^Whg({zF*cu>Ge#d&9`xhQ!gxIu*ix4u8(mN3kh8?YCQHRI0kuEkIxU{gd+2 z<$D)~f2gtgSrGT-4O#`5$m!2RwIu;MG+-zDI$>aznYkG>-YSG{25$n>a2{7B*KPHd zg#WMzi&Un#ioNk$!#_NQn9R;CP9=Sd^#Sc5@hoYi(KOJ3zne{DD$U%eX45i3iFGwe zQv6#Ej)_>=wB+?TEMHiE`hqX~hOD=i5H)klS!HXu;}~}#uVjn+`ome$C50PuNdcxU z8Uz<6mgC=g!|Iukc3BoN;vj{O_R**lZ*|ozA~`K53qBvy<1&0BU*8O=HIGG==-r{3 z2b+@?z~hW>Z`ZmYCSdeO7Wc=KSSHoL@NG091i}ek5$sO0kyMxFslT-IWlvDI;U-5JPYX9{X7KhX0qO>$V)J4K`&_*5Hs8%}G zRM-~Eu$js#aE3n<-G7S^jwSep@0()U65$5o-|E&VHW>)pzhO{-4T*_0NKBrGo>4S9 zioLXFUvaOzC!fXSBMQr6$NHr%gNX5WC8%`wB87OiS=4Hj3r|!1=FOnj(hM=fQ7g@3U>%7 zh7`HTG9wq8g}#s`4gIIH$?~Vp`z>>+J?F6!W)~v%$74-WR`7h$eQQL*JVjwP z(qsW$%jm%Q_&wyiNWO5V*NfV(I&crcI%Z_HL9JtlJo^VCji=X;CFlkN5Txo|$B(lV zokWn_=Xr<_jwW!kOzNgEdj}x+8a|-c>c71;wuX8;@+DI6FDrxg_A3)OX9OKm#nn_t z_%})J)`s1zy8gYnY=s;#3xaQuKr3F`7BQ))R8bcbEeG&2@k4~EG2WO#W*{fI!RE9h z`(J`v73l?GjYin@Ka6H;m1}SE&E_9PC%E3u(TLmQT8!U$0;EHPSQ!3xznHHT7Lc$_ zCY0;{b+_1F)WK+uWH$7+_i6v#2FY80WUi14!M=kmcEeDGZ+kd7$x~!_UbkHxgqpZt zAQHZJ6sqL!nobYe^I()Sb&HBASxrq3LKgu4^SlR*Cr$JL0f|~0#e<5_V@tT1Ri{M3 zuTXTHeBAb9;1@)^KqPe-=&e7i|6Nbsc=4U%ffJ_RZ|fkMK0HR27O5`{HLN&q{=$E0 zwylzc^+{2?=eB+rvEupgjmh(jQ40&dzPk>eMUPV*PZow6Ja(!#a(B?gIDNnLx6k?C z%KA(;`dwfYPHR^ME0Z0oiL6IPywCjwVD=f*19E^|FVE!oFJbGTWUQ0GfF$yyM4faU zw(8<#(D*ZQ=A((bnyd12#Bqp}M66n0mU8_w_&o>EaD_mMnwOzzRPHuxw10@K`OR`n zjZM1Kw|ajq)M}4ZN|))u8#Pq~RIjV+ zYO|(fN0;2nuMe*vTpf$qv)$u_(X!k*JLgaVD!A*cym=?smu}caWlg80C0*~zU00sA zKmLZ-GIl^$s2`|G4tRMDL`Z9KaQRJPWTvlRQM^~NYLQ!hm?0GT#!i<+##Mk}2@sg9 z+$WMLVps8hJ0f6{oD%1huezPjF|dx>`S?TqY<%=v3PkO>boJ~Q<6b*#@q-kV9ciw6 zoygwD;Yuxyvtr%y6I_;^KrD%bbTi7Y#oKPv{b#85l-G<R$9RH{BQvT6u|(B-FU0h9ba0B&y7&J8?0b5iKUCL>UL&r8 zM$zYSxtrA*9wL6AwsL)#+EDAQFjX}nn=!+{ZbRy%>>eg8FVq87N$kAO<`(mo-^^-N z>v!psa^^|RnA_X1amb#T{hjCV7GY}5zCvjW`(9GN$e`VwPI8-w_Qe&u2q~_~r#I>z z9mI$kW9y&%5}F|amWZ1_T{z9s)yTs>OgIr#sQ!`i`mi{{zC6l8k7OaV;Cs@{u_R3W zqDm50>UGb1VwfbqTGn&R=FkK;5B-b@sFG-i#na@f z5LAE>>%^`e%)Xos+)|iRp*|yYAmKUw)!nVG5f}z^nRWmLG5_R+b$u|r22DYN%^`F{xZIJO~T<`yQzy2yTSQO|P$m_m<9*q9C_ zg~;d&cf4sanc9G#MzLU@iZ~4DuC%MZbpvUkZ06Ic%DoclYYJ+~UcI`9VB~VUbmwDR zdMGN?IZ?~1(p0zg&6w{rM_TX+nisqxM)PYN6QWn{*#aerFRP_*AVos8d^$UqEIdU> zm#z!I`tXd`@^abf47QEaJMIDsR+a3=t!~Xp?Gv2_&Zt|aN`~A)@jx2H5B9E1<$K8D z`&3=l3QD^<93+{C_!hO&-y(~fX~YT>ER6SDmflwwo?K5&ke+?sZ=-p&qc0;IQZ%}^ zkZ-Z2Al<)G+A$5Wzqvm$JNWr(6SaaZt6P>8p$`54UJ3%y3#Y4t(Xy&~+nn9cWw=O} zMm|Qbf6oSkII-8R5CU#Z5hWEEfB#bDq}8^8Zn3e~L4+0YWZ7$%d20etL3kF%@^Ddq*Sm5Gvh!7f*>JtKMl1+VbAb*&uIK@8f@e zHc=-W)J6+~Pl_f$4vY}@S<2ht39km5m-a<73Io{r@@bn&YFKYs3TNH>(;w<$kA~SaC9sp)M4*z2x_m#ExcB*@_TOKmB0{R zD(%gIh56XuKXWy%XwH2-t;nn~k>Dg0Yz<;?uC6)ztmE$P|1m1Wm@x+gKF_Lpa7;@! zzM`Ownuf@YKd$g^$nyL)5r^#Z0i_maijoMtR zOlo(VyK}47i0!S!^`iWI@ zTZZ<3l|L_f;^Q}s{u&~OecvAQ9N~RjyLqljv5x78PVfz?2;V#8?5Ok_4-Bj)Fk1QO zR8sH52j6pNFRemhHaMwHvcCJMj|=>2HB(MmCXsl+68t;su0=J`Qb?PiwmwH0O%L4s z+uK@64H;H^4L-RoS2fe>0dRC8Q?QRej9#ls$?Ns{6)?__O%owrJSMexHMaPvv3_CL zUk$Ux=8+O?f$+iSI}2SXyRDG*NLrmfT6Ndy@-w{;Nra&-U*c<<&MuHNNcZUOME;%| zY1y}=A1)4D(C2y$8T@rduNRFso{)WMzyHq98ZfuD#P#W7$kipVdw>t91S-6HEgEb0 zRvUdi3(bLd!dEnpq-NV?lNDFCI?`RX37;|v2`!AC`I{x#*{{gO_9ec0n`|muXw_&l zwrcZwCiVKcrFU(;W1$lCxaHs%vHTe}NVsUVY}CQL_#%cM>RmgF}HL0CKRPJ`rPOFn4#ks z<{6sFFH@@YB=t&$nhDf!R(9yQ&AhUAwzPs;WQPykXAb08u~_kjokG-ZFQVjznG_v7 z9aYNnJ#8UF8s!kb?E6^N9hex@KejhMe|HE1@l7?- zz1EBM1z-5--mXPK_99&Li0v9(9@U>92RQaT8Xg7j8kF~r^pxy{;k zo*TI{Mw@`%(}UT2R>%)q5#*vCPZ0?@nUWQ}TVHELBTMeI`TM1~kBoGUFr19=E|dBg zsW`JsXs+mw0Q1zAmwMoCCQArE%aCB_un^QXCAG(3l+~}~=GJ%G=JltcF}-1Yq2WQy z?>&nBfOju&<$rBQ$>fcA!WmI54A4Uw>*eDqAKsQHiQsSJa9@)3Ud z9pi#CV=R%!9{PDmU-gDjFLjl`L777qCH#sE|YSA#dpsCxH@HQ@KY8cWi` zNf*CXkj_Ph`|Uib?IZamk)6VDKrGUuocVYoaI$f-&C~dGivzjlk(O+C=q;iW75|b0 z_t+UZM`@#uH$w`;JpKs$MVWK+tCLjxHkSmm{yON9CK6PXvE=lVtLh#KY#BfLTWg!= zxC#UmZ_r6RR6LfTY_7o(F8rZyN_ShN!s>boQjxC@^iVv3v2 zSv6!L$X9)}TqyAE?Q&aHkTnf1gMG}s;?>oOiME7brYd4R zmkueYk=tqU6d=x>$Izt~s~`x-yoI7t9emV%Pa(L5L)XV2(5Wl9i}*=#G5`CU--8_= zgl%Md@M?e^1*sLrJ$~QBWxaWM3aQ^DoRvrmAM>~v^v-wQs|jk^{00J^1Y~*Mt=>eh zHt36eej8tg+72IP&QATGNZUPVNS!7yy`Qs9M|P3-A;%cb@0$)eBh3gF#r|(J?H-i; z3?odocDQTerVeLwD)?`}tTL4X=(GkQHykr^p7g*BYeE=RA=)o)*-cl1Ab1)A3YGYI zH`A^A4rHhB#4Wf$*Kjgu38LKfH@_6J{=Hk9?YcAwGV5WQ4b45%oCP3@pyAh5N|@baIuek z{UO135w_o0cXSNc^i5vHNTIcpODPB|v?e&cemYMStjwGmn`q1U6GSP(J=zOm2zRZ| z`V`9rFx}!Na3Lpt0x{yVWU4aYWnP5BTz^wrG;c`6Qt_Wq;h1p2Wl`t)u@oN0o39ai z8s2LX)&6I()DgOMJ_BE%$Jw+o z^x*yin+sH6`1&5KOVRJWozejt>h{B^ztkKH??M@RnU)T~v?w_cK_2_wvgfxR;~Goi z`{yv^ufkL@rDplN5zf?-jYkfYL@OTcUf)U`Gx6qS{Qd}!yTzvT{@~?DFBmD{YK0)_ zuQ~K2YWqg075%*!6$-ScfT85XtvFF^)1eA@aen}V_B)o)A(L@qpB(qzoja3tP0-@odHOf&P7-xD21yhzEjus)I7@?-~k6p z08Gr#RDGtnz&G0OgLWNn1UchOzzPnH5{fz|HIgj@1Wq);KwM8$MMm zmrPdO-!0H60Rekpm0(v+0G0zLBg5o#cLYySR*kO}`ZKJpRzy8a5Pxy5-&g)=%3+EI zEQ7m^n2w{H@=Uc{Ob~;VZqh1GHjbGgvAHs^Gdmf72{*-FM(J#i2mS6)TfD&4gYHW1 z_;%1S^*t1bn1zlPf|n-K^N)T+lS>CB`)Hoc<3#{O-y0y}@Lh5|&=B4E`y^?nq5vrym3N5?fipajy&v6vg-yl!(8$PgVgKeY%oFl&Ze39(9h?-{86ushdhdX zfz}zXW>1Olm*LJ>Dg8Ax7_=a1t2pZ;K$CsYa|%~@h?v0oJ9jpUSOrr)UB2q%NV;U5 z*VQL{3H?{)yE@_6-3;M3QDA7qGXH=>s*VkLmPBfMG=2&r^z2<|`zt4TmU-))n^i2m zVkVgS4yH-o|EuuR06l=$c8eH>0LiqmzryO-kfP12`mZ#;=^Z%5HuvJ2lyHBn8CZ~y zjX|8l1K|K4{P6;;!nxle_5Q$pr38JWiwwIK$86e!V(kYpdxJ!fwm&Mg=Z-qUzj*nu z+5*JWW}$}nT2=zX>m#yS08R@&2C3 z!>&R5qmK5a<5r zFTug5Wn5ItP|g%o+13;isO;SdJ;3P2frjem`gM|}^K%8qYfrjGD&wQIB#>aq5u_T7 z!=&z=<$X^Xs!S{ypBKCAIYYEGF`-Zj2-llkOr^WAdtVk4purG0Znag}C1Y7OMPYOeJ>v2{j~FBMD;fHY8#WUTdPEhG#t=qu84#qzlkjIjLC%{VdK6V<*?2 z2LEMpsdh!jvMpsHscCk`pFsmA-om(+9GSTmH%d(UKeP&l?(Of|OsD z%AYkXm}T1c(8gk^2pxBa8z!0)TY+(6x{^|*J$hrDm59iCfiiY5p78Qxb80a)c1mm( zMzF7AKurR145mt%Y0Vy%M6ew7=YY9a{c)5ByyI}2<`LXQ*hhL9z&32Ck=so;~;YeE4zBg5(> zU1j@yaDM0hO6N%^Lp|`=LJd*@&QYtc|ORn9y_t_aNx zseO#do(2(|d9XU}s~wTGG>JQTt5r@DxWuon;G>?5402<+m>7nk0S9BI#dMbr{HM+E zuNBdbE1wk%O3e-HbMrjvjPF=xrQ76;t)fnoX)mNdKx-I}SK|yYJHd zQ+Pvx{oeJ_dQwX)-kd7QQ|r@%M|E!Uw%7ktZZr~;KKISw>~S<73!$P&XgGhTBAe1h z?tg1@ME_9iLr-4zbct1e_9^n~&ge>Djm_b@ENj$4>|YCA8?v8ycpFeY86_0??DapT zKEl-g=8gF4>xDdrT0XDd?%tEVl)GLqY^O|gfBUkk^A>$Kachc!?Ur9H6l@#ly@I6@ zQhKFP@qO5ex`>wTea0n}cJs2SpKy2kW45{XRAs#6TWUv|^$#HLKD*|1GW(fL(Au z82m^Sl=2VqBXtS={JUk>kppVV!#n{^&Gt&Gi$)3;$E12^Que z#(H?#m0TV@sH?pwQ>xt__S~Sv&UTQOM3Y=#J zP-~AZbL}Lv>7B>?aoZ;MB%5Fhh3m($ zK6t?8*R(gEs&QuGx6hhzz%<*-^i%YeY;WZ?okY0~=_^_3Pk+$NSUswvVI6p}8zy5T zHx^vaYQd0Q01#{tS+9bZ_y{3FrqMsoG?Sr*=kKp|ja4(G&yTrlyo|Hc$uDC@-dW8| zx;{gA(#6VUS~{syO&2<&p5TH&o$_z0BlHYQ7#AOuI*tr6A?tthfF2^3zj=i+o_$p;SO5T9~{YKb7uMp5m&@ZMkT#wIULZ+e5Z=aH9 zCDE!qQVUlJiCs-9yE~MtbCVXys7--=l0$iCm6wW9#88A@$Vl$?puxsMh{smWb8#5% zoYqqq78}Cf)fQ3N0CZRgacmb+`In{VcJZ9;e;oXr8&IB+V!!7EpeL8Af4?tlDB_FD zGN0JzS1NC9A(0rY_q*s_8JW&mKd@?*8_73m9cXg;-oU(_<|(=B5NvJY!!(fUck2J*Qor1PwDaIAH~yi z&I)n4S>OBjU)=uBJXJ=hsj1FiHV0boC=><0yym&V!L77&qP&_mC`}{&%E5*|L2lew ztom5aPqov_bXMP3A zbSYl&o^%7LCE{O*p+0g%;%a?Ai&DCN+`?o`e5B4EC_(x&1Tf^sJ zW=}8O&M`L1I)%^G>9KlIV=6@u_Y;k&QizZlwqg$|x@+MlXjm-KwQ@Am^fL~wRIW_F zfZ6|L$e&Ik$bN11c4z*wnjvdh6?Q*j{ch1}OnNi$x^KL{b0y;93-jB z_8IH{K6j0`)}z@O_`2;eb8gT}-=1re{!Fk_Tu`@?K+KluW9-50N3uX?otVL=FZcd; zntR{k*bX;nXAq6aWJDA9Xbv6wd!MaSN{sI?k$QRqYv z^Dpgfr^S^7+1vAfv{raEvNiRpU(DqxG3s%a;5FpI1czHH8}da5XI-5c&A}L>M9^nO z^BDMZ#N*W4mInMbg-hlMIYpeSj_bhH;%n5(B1LbiBSsk`jX~bzhc%;;L^FWGJ|QK8 zsnqd7`hCq8uaZA8-!;3|R1j?R+hUM`+1Jg~X30IN5+%p`zCKT=aug$X`>}4Y72Dpx zr)^8zM!BHZezH>E@J;$9=I{CUxm<<0iDMsndNp$Mfwi+reJe}zY_v!Xm2Myzls4+$ ze&;`5_i?WN(kykimgDrcxrz4sZ9}?(PyHzawdMGwzqWMiT#N0f^z=jz!@7weJN0Gx zz0)L+LvkrDKNTVfgAI;D7Ql(UGcy)^wl;Jn08;d>x3Go%uz%E7|4NU;&)$svPq`(3 zPfr%$HUhunM*AfHnH!KV$rzb=DJCVK2AXW}w80s%;64pHoOhL}KQhoRRro+xbz&l~ef6?Jk07`Lj}{9bn+4W%I{jc9}0aS^@u82-ZSb zv)vOMjCQxfuVAOQF8J@PdduF9IUEx!4acO=vIvXQ^Mf)_HU`F9Dr65!26PrLHwn7az^97|n;@^+{zQ&#WA72(syXw1y$9r~d>p<8 zvibC%lV-=%QsDs=HJ(@nGQ&z99O4Wn5=ili3o6fJ77oMV1gsP(<8yAtXeCv~Wd1r7hB= zw>3hfqm%&oC~ksWipo{k(8;O@xv(p+QWgS6ktzfUppX!0afLvlG%3k``0GB;J^#*+ z(`L>)bDlFZFZbXuDfryS*TkPv!t;}igkMUP0fn+Lb=Xx9hY*Pgbdv5j)d+rn$l;X% z3;pK~H?g&O6EQsGA9*5L2YkfqFLjd7iN~)T{&yqM5X|Pwtb(dXI6@K>G8$=ww^G-) z_8Pl1siR${chY+$K8xYdge3OapMEExd8D=O?uo>sF<0Omj{+r9Gn;c(8nLDtokfzXbqZDf_akzjtpWq6so8 zz@?8vRGj(R{n@&IY&h^Rmv2?5TdUB&0m+hs2v4yZu3&In}1d`%_kgebtkD{uHQY zA6gZAoNEMwU=09$M4ElqG42UBHQ9WctStyBSI9?HER=Ns zVQUQL&;%`hE#-&jcmMK%J*O|I6@Es8=^?~> z^^V*A#hI>s{o6~wQ;M@6{+pYnw)dcjINUY+xf2p`#6R2RM%0x_yOLVrvRK~YNJVs0 zjg4xneHykPNDd9$qvZXyLs>~(?lu=UHpl0SdriY&A!y&j%1YpewCmane(qiKgr@^w zAz_|!MUVLq%$5d~8_?XEG9ZiQ!D{V*gVNcWV5)4?f=0I%MUbFcsyI7-AWn#|B!1s; zMmpwjMGsbT4YvJaWd)U^bZZPUdPnL)$5(QU&)n`J89<#vM^}2=owW`IYas_6yRpYt z@^39|I`OV$#k8Fjo0DUh^{?5tty-?ej<1b#MHB17|7ajO*5a^YMpyj!s+um}I^?mL z&rW|aPU37ZXXhpzrUULq%r@HU&N(1kjlAh6F61wv5P+`fL`8CRwzj7%C8{A$tSb#S zW&<@y3jVW0p7?097RbP1Y;`wUVj{?}AY{cj%FMnoEGQFGgD?%Yno8$`7Vp(tO0Z;> z?rLeVf6>{6gml^e#zHXo8eQN<%7@;txa6sD*q1o5XV(Le?m|PdJLWXw&Jeb%B)Lu9 z?aW&Dpl#XYDl&m$vNrd6W`Ut`4ugshU?xs#)3=_9 zo?J}pc3Qg~(=@G}GM{ePvJ=Pi2ZhTjLv4`QZ-%2%e^!)(GzNSuz`@6X&B01?j1Fc; zYu5Ae*YfTSo6NV#PT8`dOKu#65Vu1TIgr97JEiFwPjVv^HXe=^NcpM5&4-N+6+3HS zDNhK5sX*$q$@`vZg?-?da#EMk@>1)kSf?5a_^|2;@gd=13B~bgVkSTYsXjWRz2CEGR{*VUmULz^`?jIWcch-FVLpn3yS$#bevbvI)dU~a! zZ`1Q_NeC=1L#1T1if#S39`kC1(pXA=sH*(%#M4LIp!=vX7Qi8Eui{EDYBwudU?xn`VquN>A` zySP^Idjuw+h=!8^BXLzySEXy(CTlLFem{>=`vJ_1f5n+ruNw<3P*PR30LA`b!qw&-I4ckUOtkUh=y8kQDsq{B*B309iF6N zD<~}dV;Nww|9B3!mhtgi2LUv;?gz@V4#7vommG?UXAL^*TI?nv5Uyk#0R*W>gM=WX zAjA+rnTQv{%K?MJ5^Tb7*yEoV-2)jW*7|$Ze!CL30{2bmAan#Z5crUiSV&=XeNeNg z=bqDTbMn8xKE^VTK#q0zV7k@R9#)mYSE;GsSFbhmPu;3HRJg5i{|OUhQ8EuANG~ic zoK&jp$mp9`KR$UVJCZw?5Nk{V2>>5eeFL=MTwuMGo7UL{GTSlFg?;RxuyX@)bi<$S zr>l8QNu~?8zNer#JAJNFeY5&Nu=i&;d!=P39s1XtSCx$pJCl(2=k-6cZStQ^hI#IJ zm)#a;Y{Ym8wA0R0N!DGJNy)knWmot@y!!cqrZl5bxB3Z9;GEJe|4ujR(jROubdmmEwlK zIWJLAr>T7Bo`8-_1l$^2&)EFy$bwhAK64v1eJ}iBcfCouqtADgZ0{1#g-sd~R5w*z znX7Fuko{dv7$-rVE~KjzMsN>EAG&>-Xp@hZo|1C5;xU(ET+p2xy^E`qfs%jo)qtL4 zIm)*VwaC_}FI>yYVpOUl$Nv$?)}0Xpw{7@~xGmGHni$@ecJNt@s3$Vd@vn{OU(;X@ zt;op_ZF)Ba82ieBo|2s)&XFC*avJIwmUzJA!y zJ2EN7ur{KU!1f)WO7}I+<~JJ=Z|v8c2k$dC6^8$L(M|t-uidISR?O>fx>xjWWPA5C z9WHxNtP2sgJPOOEHVl<5QABp;zC>Xmf2fhX5Ic`ox}FSzqi`~IPFTXl3lC|@hDI8c z4ni>O6-4=b`i4u+NUcL2&IUpSjXC9+D^wTYTBIRxq&xQbN{7r`6(ZTDMs@*&xsWq8 zgh*H<_zbfkF)vF$=iAHc0!^nI-wYPssb0}rRQOW-ju_y(bx3>6W{RH)beINtPtP-8 z9Hv%B{}7Y(&6SAvs+b`4-xi$4QwajPDTC5L_Qs!rC6BOerr^xgjxJn9*;w1xl^&TI^7o-S-ISI;3dceMZBt zZMe;jivZdP!JC)2uPj2u-BJpuU`VT2S}>Aoq4+s9dgsV~`(Xq8Sly zuju%+<;q$aaV^G@94&}?(+!^L=7JLuqN^4clmibMEx5Mh(BtGgfhH=;mw!Jbpqo?NGj8*Rzet&Y)gs|21itP4#yDJi{*W=E)_vCAwQbXzhDnZ`J906_&2&fJ{(r(RNyxb6$P!2<6xl0 zy`zkJyrbi9BVcg3u=#@Q@pVb?sGRBrEZ+@z!J&C+-kP;3rOV+z4X0=C;QS8csKlLo zl`I~5cmmGr z^rcO@Or>EHyRIT=a?Er#2DjIT)2-gr_ZB>jorE+}S-fUAS!Y5HLjG}(O- zyLgi5{GjykAGoe-*?va`puJGwC3ZCUn>+2=61nO;P4)8tmU#_8WhE>qxHrE5Un`ae zX*PM~ao19qwPMrZXCMM;_>-7>1qMw8jeRCf@ys}9wmO6~hE^X5X0^i2x`9lGl3u0$ac+BBfn-xM#s(g0-F?|?{i}$PEQ!RV` zIgM1WNi3-`{2On|qvX93sgV$^0tRHz7Hu4>I|t5k_3AB0KJ%stEm;*0Ey$^Ig#wys zfsoDppy2(>gz!)*2{*ZtyP@Fu4O?4HU`@PDN)M!|M=rODo}87+hLdiS5s3K+Xd>XkL#UUi+)MR+sh+Qu;a>O08;4T}zI0Mq{L z26(UXkaMLrLIgIOD(rTXZC!}_2l&o4p;B&A@N6%}MGqy5fWx37+xI^35M0yG0;l!R zN+$3P>`P>%BM`Io4|3_2#niC(H|W~ySlLWX*0oirk)xMD{k;c%mA$IT``anLDFo`C zk4X<*0_sYZof3#ur-1gIs?}Va&HY$N-{;?zI{nf^?v-9~81iZSU@3y#DwefhFwhNC z{xlhbEVbLCvvo9B-)BVyJf8vfDj=e(|LP$#In;_{5XIX zfBiPhTH4*SD}JdWZ)%`Z<^WXwacy!v_lmw$^0l$0w|b+v6$`q=B z;NnieJ)s1eN*iy4TtQcV5DdY`4N^=}Q}q5-rtRpT%xfgCGSpyPFV_`LeXZdm$?Iw{mVC?%*i zj>Iu?81?3FF-rnxr&qPwkUPHX4-EM%yu8uzHUM+cb*B0uRLG5&R%#7L_@=x-%eV8j zO1iScT*?i}(PZG0UnHB#pK7+Let&7YZ5tbcE7E&%xSy_+nQsu)BdIThkg(T`h>z7(t9U%IqiLHmBb zu~sJca&_VfdjPzxFbSD#J?&aWmj07;^tF;--O`SM6+OmMrB`h4EiOr#;CS zA8f!qI1&XguOVHMM2^>lsNRYN3rry!=^z0Skkao@wq_|Zh^g2qq2|q+EX|F;%9;=f zEc7<4m`6dtMy)TAaGRE(IxrIqR22~-YMZOSlm!mfga}#`!;C!=>5w6Qj4us!T9!f; zUj~@cd%uB{&i;iV-NayXqwuTx4`$_d=>OImE&>ct^Osq(tg9-Z?RafHFU-4FQ0CBp zGyg3B3UyHX78f4BY(^8{-0GWQR|7_E;AQPve=?7cwnFP=M0|G>2z)F5xg6IYvTd(^ zL1r;|(Q8o!9vRT*9+5+1UexK4qrFgX!of(B_qhcaZ0UgkBRxSiNvAy}urB^|-4bJm zWJ(F@NK30x_|kYoq3A)tRJpDq7p%ChFz5J5#e6 zk~EyzOby0#!<`9=J(RN1Tv&dcyj88-)0_%L)n19?J<#}ZZbt7551pst%!6GV7Oer% z-c29|w3`XDJ3^GYL-04>g-QhAUr)4Rs1E(2LW!4W7uCbs*c}cx@X3dU z7i_5Jr}P6_p{4Fohb$Z2UUrv}{SBG9D{e)bD$>nXYzZFkq{0t$dy~rZPuK_3p%zntK`CoxeQFQa-WsxZv+*@Z zV&1cc(NSuPmcQeH32joB!3(KrKKNR`zOs@_5s-Iz695{NvK=@Up;S)Y z?I1^(1@Dg=TAu!x@w5`DZ{8OEEY6LmU4GMXqd6Fi{72i@bYy;Hlg-;lbuK~tgqZ1r zf^wm_mEMT)X5v|KGlS#G&;;G-(!Kz<1~3zVrDNG9Wg8oV6jEfKfAT>%m{N`m>?J$% zuJI3+0J@TW^&pB`vOkyGeZZC z_Zk(>jVd##f(#FZ;oD6-?K$`==Z zy>%OA@DBgSyb$!0{F#3w-Z@Fu$d1w_!NI7Gq1VnW>cJg1o=uY{-M1KilN@{I+hheZD%N zaMtYR35!t|q7Q(YYq`MXgtUAPByxae@`!F-!?m)~$|r?P$gPmMO8JW|xBLxXoeN`D zyssk`_*!z(m$>%;UfM>bhd!Iq#6HhA$hRS+ypi?>w{kEi=Z5FF+I*L!nI9KeL*VNi&BE$pRNY{oc)N z^)>Lk*RK9Sp@%x{np|>n3P&b>I7PEH4pSQnB z)ZTw621{8)$r~z$!i*epKB%Q$^J{#2zE5W&a5U4=ZX$((>D*~01}9T?V#nN`-OBz75yF{TKO#rgz#jZ;2lzE_-jl8_ zf6S~}#~f=NzFz@CE#^+lGRIjP1}j6~%_cP6grdZ2ukUHrGdo4rh0^WzzTKEyOMgwN zq_2SMhAy-%A;?FssT?y2hJdHY9o`i9Kx3xJz#`Uq>E%Cxbuwgv;iaD@y7T=+aYMlg zxyGmKPc#ImTRxoa573tPzJL0>HKC$Yv!Ia_)&42ie5_d6G!Kz2jUN3Sl<=O^kR4ioxjSJV5X=<5LKn!EhLh_z4m8xnuLs1`kPF-NY%IeZS(gTsrbeS6QWCJZohgRzt3&{BSBt*FNpnSKMwVC_Isi zbze@Ag{6$vxqngGlUbL=(G{e z)C`EMe5^bsjG3moE8fUlMR(YSsJe^IzX(2>p9>b)%m3sy=WM2MU<)8euLO?Yp9O=2 ztq*{7NLxwYpm=2jBxJm2Ku6XBaxDbnA@M7#=sPi-h=NVU1 zz(WL=yoq9%#-VI)ubK9CmMyg|0;%Ytj>3kb%wv?mlcQM543R=}l*{^#%{OJ*C49FyGL5sNkxi!Wvb)z)a|J+Qb4p=+S zdpz;pVIj%Z*5j@0lL0VZQ%nf`I4fCiMT7x^gU@uBNw~=U(|qr5DT2kbKgdNT1YmDf zJ-R5hN&;*O`4k?<(F=EeFqVX;UBxXEG`y3AU33>6qIQ|21M5m6G~_fs(IdBrY)a+p z*2>y5BZ5+fjC8sQ%@Sl`1E-(tfPFHyW-CdxO^&L^(7>{;>EO;ap^g;(JdbaS{ULf! z^*_(vZ5*yNkBFbeDtO;LCKA8T5337e_L!aplfOJ4{ZJChQCYcnv|q}o;HS+#F=qvA zd;=4O5V){gGaR&6nkkDA?cEGYb{Boir>(wqb~S~Hv|c#`zA4rF#v#%Gk2fq(+DP(c zV@j`P!MKox+rcnqG|xTDaPYprpylhwQ1jV`8fZUM@!!1mS;l)72*Go2|Ia}^JNKqw zJc&1ujdC)f4x?DM(BgZxIRqmaLd1@Bdl_YsvGuYKP^Wlr1$&|Nb$T-#%i*dJ7aJFe z{J|(k6K%&{S)vU~&(j%93iQ)y1h_~$icDY|=6H?<0-s`7USVN`N&EKv9mIazd ztnXs(nsb*PCAwrZF;#RHGkhGmsQ=Y(S9*g1S(UdR_2$MD@S$&w<%;hoZ$kvjxrmAR zUza(rqv)hvg^62p0e+sT2>S2Hs)=YQO5Q+J;x0^59Xn``X+IhZXPuXEmO)?Vkm;9C zQxn@eE&5f^EqCgI*pa~{tJZ$79}#okp+x~$RG~NJ9$r|mr^}4{2SIhV?gLrHX%8#u zXG4p@b*pp0*FWZRaG#Z4#H?W6zBW_5{g>flXE0;erzNb$D~#8*{B8AFKRqb76g>2n=Au%9BUf51csT)~tMwGvE_sE$RfQWqvFlTEds)=$EwzQfcfH8t=qGT;}#~TmE}7a(q&>^v0_)ocl+fB8v4pB)$xZ3Qy+&UgQ$YbPkg%d!^#p{ za@=P#E21knU%-N1;$9c#-!y1%b7*lht3%8birEs-edL~_TrR}jZuchKj+ZJ%tJ#U7 z9;}oVHA8jD(WOK0w**!wt4N%;!B3!VN;~|2%b#K z3Z;|z~&MUfdYr1JWP((Rsrun#y`Rz5ayn7ELA+4KHw`HfUDat1v2*oHWQcy zv7~D(-FQJ5Lb<;Gz(pM5U=FRPb4h@^EMCnbu z0PIj5%Jln@>{ahW!Md59L&=LX!OE?Wh2n7l%=55uP2Ve9{51u|hR&v+neRZFr%`-k zo50d{4~sZOVU`k^;k1fYg1y)enQ%Ww!e=IXWY#}yQpOA{mF~lLAaLV3lwDfT9j4Fc zip8_ngMjhk(miS7U6ZgW!e&Ui>Fx*&YQbbNuBxm`gZzs!OZFQENtS>07*y7n6CoZw3Y| z*(vXg*UE^x_>akI2B)8qp_RUSl>%N1eitK^Kd<%kec5`b_T2;OT<$lGpaSc)lw1z|F?AA=OZ=m2&yrU3<{`R>O< z;!;z*e^u^fJkxiC26}LRq8K0QK><$eA$IziC4;1R8sy--71!2odDF!;0F1v|$0S z>U=b~ivrdj8J$<`dZrJffS&E*n8Ad|qP-YDRaltHW@}GnnVVFw!G`664~5LdOl8Z2 zWS>kEg&-^mZWkhH-MjR7uytTh`6K;wTSz&B@E|<4GY<~@!CIr^xS0a@pX8z6|vt2?Hbh`$=nF`vaX$Sre($-BOU7u%gJ{3eky-=40WEQ2Xj=T^iXPN<>(ZoX!SNxak*^c3E;sLq|P^Bg6;3FD;tbIa7u2)h$qX4B`VXWr7ibpl)7 z`%2!cQ?*tpuHvUO~FZ_Ew3 zFqrc*yAP!)GPnH<>nrY{Q>&J^-wXdZ1(xf@?pm{ulRA2uvR_B`M~QSBOE3*P%tUV+ zV;l-_kAk17SNa9&udnH~-}9?3cq8|YL^uVsp>oxn0*=SRfT z$r)uC)2~vV6{_$30=>i)*wfc`eh@Lyrqt^HVbz$rz@O7^lF2~-Gj{-1MeK0b#^VO546(5s!C+eFa`$i^! znQ`-W{eTo_nUm-Ks0o1E9m+LP<%SpH$3^nN(=dO_>E~<%i)amg+P}&2u5#wt;m=1hFF;%awbClnngD zh-ArjyMFkg%X<1kf9Uuj+%tS+&S*oU{q@q;Rp#b*1t+%)z}%sqgaOLr;SssY(5S`4 ze2bL=J=9f5$g*^LOQUY>rX&w8#xqy5v2WgmrTvWVK*=T+2-#;H% zEIQKl3gYM9n<0GdEgH@qeCJpSJvJuj*Btka5gI4~mMD%U0n>s!?SSy*o!;tuw_)i* z%8O^`e^C^#-rk`?iz?KHHdMHa9w>)dCKHMcJ#}(*)x>#fxOnADvyAK9oe@RfcKWG- zA#PIz9U^`!v=jAb&irDj=F01vcrcH9Xc=}8Q*A0u>!dm-F}8C5zDv!BmETkI`u!hK ztDe(1M)4FtN+dIJqMYU0&C)($R=R3RhN*07<^`}OL;cXaFs!aN3fxG_r zMpA@!a zm5nzC=98afY7#W}I_SJSRPbfR=_#Q9$$vl_!~_^Q^viIjTtQzUs19r1N~g`6Hd@qY zA@CiCC%n=87mM)d5O~di^l1J3P!}^@KX@T)x$tae{o~DtYi5h-Yq#m7a#@i}x-p(% ztRd!6$&Z@6xp51RD9~3?Vq8svZyy-!R(FNnZ!n|n+&1$+|DCYiukSfr{n%qb;mHqR zF+%eyT5|UJLKEKO^D_l0B_)DAqyJ(S4l4jcu?Ny9!NW#mBLnb&lXTS!Gn(p#W!@wR zr%cp&<^P#Pndv=z44$YH4Rtmf%Q6?YJ;{CC;~D0D{ZKl{JTgs8nghViL6T6kkJo;& zITgEZ3GQ%6f9=mF9u(zj)CJmE$Ibc-{`3_vAqy;O(r75rm-w({`o9^n;Q*oGx?tfO zg$#-u%ngqO{Jh!XNePt_8-BLEaY5+!N@vj>`-KYI&KI>ILJvnXvAo7YWTdOb*&{5X zAUI;S6*^^!db|ww%5rcAwQtMXb_%n6uiG_PA|q+66!gU^yYYGs3&ovLMFgD5h!&YrH(>es&-Ll^<+oO` zB#u5b=Q*wVvNY$OMnAp^oZUp$&WyKa~@_O{G_j4wA&(ECyNZA~gL7-po_4tDd z@jeY0GUX%KTWswRVjl$xH4Y_meop(}h5d_P^DUTGJDuGcIdlqhKN!24A(TLfC6$qR znD`v&b&&o+-$0sF`U@&kDjI82qCQ@uJ+{KOqx-~4yCUq6~?Rn(?g zrWS;AwudD$w}Lc_6ggrRfHU;yf}3Z91TpK68%g6@aE2DK3df7AmkP> z9pC6R{b+16N=_d&{?RctKXkh-Z&i?MxKA!A39O}zqO5_vvvcN>xpn}x^VmdSaAQyv zG5tfy3gHU-Dc3#R3D%fajQi3mVl8aE*(Zi&-Y-UPPab-CJtwUmx5l?ANoqH~%7p0E z`knRfyo%;I`dtjFyj4ZUK1oJZ3y|!a-F5p^dhpleur!D{sb9`+q10Ra6}nb3qX+Kc z_wRqx3BzZ1`(?ItgT)-a2TF?THe+8oMXq^uJ{dv`XKAUHUe>&U>q<$~d18nP z&t9w=TU!My??vN3K$Iz6W8HGV;xX8LptXw*N;{Z$1WL}9ke>z4yexpkA1B^mS`<3I zzIe^Iq9w^U+cAvw|3NHW4cbF0Qw9i;P@{Paa<5Fx95pF=PuIfB3`|%uYj9CD@`b02 z#dbr{*-&S;t;46oV?VC@4}mR#?|zxMJjAaJX%4sCAD5jEEa5DsN40s-Lvyb0R>-KG z379$^0Y=zpwHL*Bgs@1~BFcOuQi0Ow!t^AapHp62y z$_)z;3P!~@)2JEtLhU7PJugsnyx^|JA3sde{Lz(kJQOH|NcTb^o-N+&{(0#=w?pDWd< z-ex0R{RP`0g6B5g8-8*(-s)S3F|bzFWtiEK>=kpMCewL{8v@d(*t|aT!+7SdH;3&J z)(AL)Dns?2^*brSt{2uK&ZTr}0gRmRa5kY291 zCt9|GMxk9b#)NFj^|EfIVS3L#!c=W{?Ua4 z=S7W3F~6=C#6tubK-NFgw?2*ABT=y!=kJ{ufI0)lf681~80rv~ZJoP>evEe6y3>($ z&RZY4B`{nc9Sf>oNsirfo9rB(vslesG2o&?5A;B#f1SK&iw$8x`nRJFJG4ywabbHY zjj2^WW}c2uI?>s2LJMf){vT3R><=E)2IGwZlgFOwK>hdCE3L9N(x3`vDjvliY01}% z9feAy^Xp`q;RK(tViV{i8a1M-c(4< zLS_yI1ZXW6-JqWCyU;=iMo3G@o*u4!g*W)0BEQp)@oe!0w<1hVU)?e(1Y1RzU3%Mr!_{8g5t*%o1a^xL36<}c6 znpZj#OJ1Wk_%5{4&djQ1AtNaCd%~hD4@y>C5BOUU5 zwME3wf#V0;@shUW*-nDwnpUpR3$}evI@amy-c&!o6 zqH#V7#}TIWK!Et!??SZqiG(A~vA$9`CzhaQ-4O2{9w<2yNEYhY zsc=KCR>CFf5bS8Tn32T&lGPPC17Ayzw4ad81W>2R^u9e%Y=ZYnZW7A1A)fnho4+Mi zxH9(yA7RC$v~8#Zsj2CwK=-ZKntX`K!zcFN&Ae}o(+Qzpz5%|W_Ju)CmJ}vq<@MSX zjGt3tY8<^fAGl@2y|ho(X90$}%^W0Vps}x?CuJt_H^=obu_sfg;Nt_$EMCE#0~n$B zo`{VQh!}kF%U?eXHwcdt0-H1MHk2wqS-OC*ZWSySaiOksc`<0jxt}PamdA{p6oiu@ z#IXl0DVgQiGUZR^#Dxn7Viw%g$gn{jKUZdV{jC)IY$E7|J~;rP{Y8isQH*8=-Yo>{ z5Mmj$sM2%tFi>@ei1%r&;9w+AXkp{^kbm22@u!a`|1z(9>7f6CwRky{CIThDf$N$8 z(PWsfEGR6RHz2I#+1cuoy^u+_u~f7sGgi{6FBPI}Pc*Rmpry$u)Uj0|FhiFMJnRGO z8`pemLIH*oM1`NCIe2su$gq)&q%jd88W9}Aph}S>?-6Bw!THQnSzH@Rt6(Fj4!&T` z+>^@DY(MHUtF5EQ?~_zsx~{$x1rKBwN`7yP8|wj9k&dB(BT`Vh8`X~yEZm=NMp>?; z7lA03=tXE?o!{{JD*okMEmNeWv?(HA9I#y=^bP9l>K}B)B&A|=ee2kpPdKT-*W2xv zTI4tAhEo22E6X@gt}K-m=^)*X(%43V0!?BSsL-NxdglHG2{ZR-b^AG=d*LeKv4-f z7(OT@fH;0#sKaom??rgDxGv?Q`LEg37YpVWe>$+|U+kipKN}3W3%CS^`G2V0bOUP! zqc%>r2S7Ny9zAA~Z~d;@^m#zo!+4eih8;@~1Y|^mjIq%jCujLJd_&BlqjSe67N?JN z#n-Ht&FsKVVqr*$?(Fcd1JTAgZbEo* zm75B!ZDKyYFNKgpcql22MYSawSwz)b-#}egwOY|MLw6 zis0P2f9*308J8Fq`_e&Y>neM!D(up$~weIBRu=*C=1s z*u&l>+D^azQ}v_78^qr|Q;dn&OBC!m*d2oJ1lUk-I^) zx4c^VSyBKKwO9v{J62!&<3^7vhP*TQB13!gyi3>vwU+X>N!@`%Tt;C~N{wu92H8jr zX;4D=-}DTHRXa4k1);a>k)SvVzB5MSH}QP1f_5fnIyPV!P7yW(nu27bEh~~SL5g5H zB;$0@p?U8R{OW%OuB8{NnnYLlt+tP;LQ&c%Gqv~hM5cnP9RbNndiIzADrfRY*{sV9zH` zwO12)&sLFO#L{@946?a=Gl+q-D+VN9iPv%&*o4)3XV^a3$_&DlVPmz)E}=SE%~qNH z+A6R>hM?og9}|swMYqze()PPd(sK@`8+rGvc|-tEVG8RfL>Z85*fK7c_vy1GydgU( zJc<{sf23!EK@!2aKhoung2Q^*ogn=uY~(^A=UbnzHZNU_@%DpGG#J+##G?537}YKL z(8MSFEYZZn|NdT(uz=h!2!IVP-&y^BA0z(EXkYNd7^4(RxcQuM>ZNY^ic9>RIdBaD z6zT|x!#+57br7BbkLSj(twGuI)+@cN<^Z3e-8Y9tzBvy;q5}LE1Y@uxy#HwJI4{{? zXq`mtk|==!fgNT8nKEm~OUA=s7Y&r>De4XsRg53_thqHVC$^wJO63xFdG#j*Gq_)% z6KHwSX_kVO0Q-btYw!IW4gtxxeMhL9UWKb{8><;X8PzyK29rv8T3dC1yO} zjeW_+3S}Hw+h0k|X$i#M_^TDqg@UKo>C;asG>9}cqr1gqai#(ider6|EW?WSoYn}5 z93|2>=*{paLkcluuO02h6f_-4)99dSMs@}|(V1Omn;`IlMh3`7)iC?P zvTVmtN~-lT;>#&UQBBtUL3uJs!yX}>o}uv%GP|=Oi(~}V(w|U`$C9h>*01XH-5km5 zO!eCU2vj&=#TB*i>220$4ogJDPeJBHjZZqKqzwsy;zT5o9@spXs+=g@2>&L3DTo4* zqw_+pzx`*;OBpYykzeB85%^|YnrDzPP=m;Wz_USvdmIVTcB=phXO{@>a}ZW2gAqy2 zB&;W=LXs0yGYRAzPg<9QYqNM86sx!`f8N32u>!s+PO8a>C422s zQ$d8eS+A#+WOU?bI$(2+vWa6hvEbiXrxmKlLNG$+QmWf|xNk>CwjduWY;Aoc)+zRi z-Qpz|Nsr(1%3C?9ahfBIORCS{`gNoW?s}Ho;Id7fOf^_GV{I<7o=5_jiaB3MSKAZf z+9%L1v|bY|v+U-P3LvQd=%sgv(R^JhU4&Hw0_5B|r;Byv=?rP+6M+=gim{%N=W zBNAv!@LQN48mRY^xGM!xi zB^wh7e2519;6EJPKdRS3MpH^SMDC=TTJZJrC9N6LOY!bgBgac@RwPx7AI{~=^M|b3 zmqrysSluuIY_^oq>Aq6E#XA4|AR@38GWYpL^4agQ7tqPux5hz2$ zU(AiR{vppSaSbTW@Mw7y|oD4pjp!KVtSA2ZF zq1Bg|76loNzxw!yBbmI>L`t**`LIEf;GEq|T{#{SvtO zXcKTilPf-!x>H|kKW!pkA}HBU@I#Esyh>CinTV8ZQQYc#-yx=2DufjGU9Fr}f(s(Y zH$k|D0yAF4X&JI2QJ8Sk9i`zh!B>tFzWhHV z4Y;mRjWgUe7Oy5!e1BCFj%=5Ia{#NYFEN&f>o<@Ijpsct$@4xl#BpfHTQQa!%h*|$ zJzgu)4$IIKhS{dy|61bW^zQj&0H4R$X+XtLfTU5ihP)jL>U?7IyAhHukWp)EHO2#M zvUwbMK2<(Se61|Vwpk-usq^#NXQrLtT~&>l(43X1!=6A1Hm8)BLkR`0G+Ft(O%=HH zX%JG=olK9#EU)^j_dxnSBCW2Qg|uOy4@b*DCu91Mb|^|Tr@3#^$%rn zu37V{D@iJrkut^8KR9MSO+6{toY}QeQ}!M6Qeg724lTfc*2uVE9~E;~Cq}8{6>D`h zy-}ODqlV9xM?c14tq1ElLP2R(tAM{g4xqlN{%Lu-`t?h>k9FaMR5gyJwc5Ec9dQi|UIWR^y^yWlAb5g7OPrO4HZeWi3;%Cebdu^$NoB z;S^}AEc?D|WBk>Qb7ggdxMDfj)=MnZY1ALD#)_m=ywui0Okcx0Y^WKn@|%Rr2;+}q zS>+xfdvDiA(9$%LPv^#0D|h?O2yO3fW!D3wJNgCox-J4 z?rcT`BYWlF0O*uvY;3R@RrDw>HS&?w++#1@|10C_uuikVpqd@*1t|oTxOJmoc#p>Qu{c;#S@kr{P#LDUHpx=hpN5 z^L(D`zx$u-{{61+_xHW8>%M-!-%7JTZQYLVq+>?$O{Dk<#s^gcpz^1cs`0eJmtAQC z`B`TiXLq_Scq2!zy&GhCO;=x8mxfD_G<=J=FzQv>&IDh3?dLY)04iJO`N!H~4J$4i z1arN2=d!BEp}n+8l5Og%ZxEHJILJ|pIBF@Y0MX62;5moG_HRy4|HHDE!h3oxk{5(Y z*>$rB=$xP#nN9lZ-mxgki1KhF`HTw~r?so{(U2MUa>1fnZ^Ze%;BMdl#?}Lk=yAJ6 zwJ)mVMi{BUSRf!2ZZ&BUN8dDe$AT>ooq4lyWrZrV46; znqPXhiN-43*rAI#HUHFefv=O66Q%kI>az=a4YU>%pLj`fce`q8-FbRKnTTmzlQah` z<<22O3}G&N?(JTS_FN60&d~J!LBxDkn8zS>+b3ASXCT&mgX)P2yuql+wriqpFlrN* zQn5nA-4)2M9Nu=vS~yDa%AJ{_3ylwLoZa2f_InUlME9Wrl?wQZrF%8$j>Pf%nu?43 za#@7U_tT#^F4l^rs^t117;IzukdJ-k+~j%Ft#46c-F#zCuzgru?ZyC#|GtEg@cd`$ zLJx7U<2FBISvc}jj8}Id29aXdoVh>2`Kpxbh5q?}WHXG~W!~%I=c!kVDsL0u0L5?7 zOWphYdM_SPgFf68@y1&Djh^-+z3E8XM~YnHDkwzNuF9dc{w#ksy_KBO{LW=3VwfzaazZj2AB#NJvt*xmB)c8bsQIAe6#ZoA(h51iOd469+9MF8$Gk`8do}47 z7i+HbY=7=zLP~VT{=B`<+FDpA$GmSmT#STdU{kc~H9Hta57as&0@w0?M*=R7PT5 z^)T6L_TRPdRbGFyP(IAxvK16=zg_bgJ~ef#nyx%W-R%pste}c2T*~NpO=xpiM8sU# zw%|7DgesgT;sX2Go$|UUPCVAHO4s|)@wd~^JTIACN*PBB8=16ER0Hd%@`%S*8G@!gh z=x!b$X7dT1-BN;;ZbR$st;d|9T*k9wt~Rl^(aEbQ(B!OY<$>WqaQG+kV#|iH*`<+; zc4&3!)x>d`gQEwh^OgP<&FF>~mXcGzh=xeDw8hbGEnUbb$T;;T7_C1^`f%-m-uuoW z;IBps*rFrVHLyWFW}g@lzTF8d8h-JWgh)c5i)e&`M9R{`o#@CTFY8W3(ktE;4g*P%s* zIadqu8do7-qfQnWi*%|KtwG96!#gsw@JK8eC#y}g4=LkXKdg4Sei*_HZg4SSI?M5Z znA5l`iUa!vLpAGfCKn8b8?);3(Cb9<(FKLyF!DGlQM7$Dbz3y74EIGN3 z7UB0wDprNgyG2o79UgZ9rp_1MA6-~p4xlPFBw>Mp2Z#= z|Kk-gv@d)em3&a#lXc5l*mqj){G)*?keBw^k}J(AnPO3Zvsm%y)YUJAXxud;b`R5s zfL;j{va@*_g6;>biPl2kowSiMj*r87tWQ<)_#lf{O)!)^Q3;5GI-vAb1RM@q<%^_} z%d6zzQ=m4lE`S;qJ~0^a@w#IO2%wRrU=t(8VNQO7Lk@k>Ve=11F$#P*PR4?q6Uk&V z3txvmrh&^c=sSD0AHN|`A13_x$R!6lQSRfFIX$fEP~i}w}k*)4$l%AP6ipJnE%Vly3&?LN@W#lYGV6igK%$}J&v-{o?sI7UEiGhm&001V{ zTi5Rb01XTPpkEF_!IrwxGqd0iKwDi`1^fwtK&Yswpin3^H8l+l4J|D#9Ua}FLxdH}{DXCwO>xczJpG`1nqqJbCKWsne%VpE+~p?Af#D&Yk1u=NAwV5EK+VfBw9X zkdUyj@P!K(L_|bHMMcHL#4cXEcUi;GK0NL;ycMN(2yN=iyvT3SX%Mpjl< zPEJluuU}VDQMqyB#?6~IZ{513s;YYX_H8va zHFb4$4Gj%VO-(H=Ep2V>J9qBfy?a+jM@LszS5Hq*UtizAz~J7!dxnOFMn*=)#>V&W z-#0NaF*P+cGc$Ye;K9R(4<9{xWNvP5VPRouX=!C;Wo>P3V`F1$Yinm`XK!!s;Nals z=;-9+y@83s8Mn*+NMMp<}`0ybnCMGsEHZCqMK0ZDnAt5m_F)1kt zg+hJ&`0>-HPsz#2DJdzbsi|pcY3b?d85tRwnVDHxS=rgyIXOAGxw(0Hd1y5H^XJd` z`S}F}1sDvbu&}VGsHnKOxTK_{w6wIWtgO7eyrQC_va+(Os;auWx~8V)%a<=-zkaQ) zt*xu8`}XbI_wV29>+2gD8h-rv(b(A7)YR16+}zUA(%RbE*4EbE-j2m$fByXW>({T2 zj*iaG&aSSm?(XiMo}S*`-oC!R{{H@ffq}un!J(m{;o)H%4)^=_?~##_(b3Vdv9UjY z{)~^0PfScqPEJluO-)Zv&&VvLcczycFKX;B$GndL;5fb|JWv95^Yr}J6y}B<(YEHhL&$UAQree&H zcfO@(_wwu4KZI^707riIb0Jxe7}BDyUyp(a9(iX8+`evc{C~fR<0LAnN9A<8aVGDn zncSsHu`coJm6nMcx$swysj!#nP%D5gFOn+55)fj6jY9;HMzsI0=|5wF%VBka2j2Ib zF({7;#LrQf2o!tLSoVpZKzF@CCx;>Z0q!9B9et`E*WMK!pq1-Ea=iKmhbiOV9>wV_ zQ;Q5}ngck$3B4N~wniq8uK-Fbq1U%ZFuT{1;eY+oGW`$uZdX*x$unm2^a>B+<29 zC}90N%yHVpWWBFsVE4OXBRz^h*%~+Nkv>9ANg)T zbAY6a3f8_nzdrtZ%a9#VuDq-V*}vml>LtgJPG-J(e=*%b4aF)4p&1SiUse#2sC(B;nZmA^oAr^R04W?E_WsSjp;py2*Mr=hQqbdxFf0ApAnj?|3^^8e^^ z`CvBisP@hi+}Qq6{|`2eFV3P*znjdx8}`qdKfcnr6Quk_1?Mu5@)=mLU0-**u3xu@ zL8u5go1;F|Ss;J-&im9qH}TXc`9C`GRD3P;eGlw>{1YZ!Ui8!d$0Po4V2xRJKba7Y z+s&dIJyaQr6!OvSOi75pUX#RhK*}1VzE0J(SxX>`nZiuEI=dR(6_8nbjO3h%!#K2@ zdA>vL=7zFlB?0oOF0r z(uYN*%*nt9dmF2iM%Ao99M|*Vs3{r>E}Md(<)JR4C`2Y*km-J>W2hvlD}di!1#LVP zMJ$Ye=X>g83CQaGTMJ}#mFMW5e1!eD8Yvmv~#qTqI-pdbx$? z%H+5ge&O=cNF;DOmW*V{tVn}$rwvi3is(_!nYPD6iFtBU+`u_ivOwYP+I3nCGeGk$ z^74>oMcwJTy}NdYjsUmADMQovLgzo9B~D)4INu&O&&!RND=Sp4Fapl0PzHC@3<|R% z{FlF;Zs(pijV`_TgNFr`LY6f)=3Fz?b7rO3gb$mmKL;n5fZHR~AR!fAT64iAc`d$G zTTqn`Q~H|luGICDkCW@Pluo$cvfkO1O1+sQpN7NoV28!`Gorn zsOv{Va)9}%#3*S8qS1z3yPW9iA$OST^fTdbzcP7N?_s;OLwB4bCwEaG1K*wUR?4(K zl{t25Zuk-0EY2rh+flBs6%-L0HgQ|gy zVHL^1`=jK0?5KW5Ck$r&9Vf*#Km5M*ap3j^WcX>a+zOdy+i1~SR&svBN06zTUsIl& zM9X;)Ibf6;5e-r-2TIb<`#Mj6t=L_6^22k3_rFW%?ET%B~bxf8w5^wWg1pwRM{vp^wPrZ zuvb^^c2a43_aPUwzoK`bHZj7?Hf!rKK6eMs{T()N9I4+Qi~<+JCC%)9ExVJ)b~o^$ z#?dkgP{c(b0kt>Z6})On^oIIi*t)k!3Yj@IHPhl5aP6&8Gp`uV0wQiq{v6ewwODac zbYb+bo(#%(#^P2$_L`K=MTkwzn1ecK-yfT_$&blMYdXt+2J>V%rsbYy)z zLvO<|_KC|pHiqbNa2uJ5n5Rp;5X&wTvIpATt3Czdkyn0~4gPAAEzGMB6D7A=w|{lH zQ6E5UrfNoipo0FBop~AHiLT~=c~nVne)CVB0L4>3E$U%$K4dC;S{S0+g%|RN}DJk<_)gihXJ5wks z_Qr9f6IqnAlX0cNatyfu`TGkKyNO(i~3 z>BOh-Fc2PO1Hv~dtn!8Rn0zRrh81<04)gnGJFID~oqPxCMuqo^MBc3OThuTxpnlm^ zq&i1oI+L%<05sS~BA0jE6I+d_QNNtloudUhG1sNR=1-0`0KusnoV2(CQE?(I@mwv4 zLymbtgwl|x4?R+78@m~hYZ(HModRXAQK9vY*jc`)Dr#8d?trhOrRMHTy_lRU_Cw+KKd?v< zNz?dY8LyG5*|VGyHL914Qa~NRF=M6yUA|MTHEY6z`lY!9;dzg-x!sHpPa}o5aD3d8 zts=6m?xyyzN2p|+Z*ZKEt|>(kh3~U-l1V5sn&hDFrUAnpMq~+k zn9qz)%nKppK;{6F8;z!QPyGf)Wnng1w~e>m_v(&kQl>S%EJ)pe5E?)!_`yhA1od}z z&ImQLoWyfeARhv$mkOzApWqD;7O~brcRSwn-1blA<=_8y%{qmzeO?XvTw>XQZc60p0led~{RcGsEf zRlNyt&}e(TkvzGZv1gT~FCEpCe;M6CA4zrw?B8&T!umg8xBo}*g&&7yR*^%@(qjpO z*#~6kh(btc`PyDxRddUu0~KfTItriEUB<&v2V|}y*9YLg<(wnKAbX@Z=I=qVqJ|*h?o)g}0L6A-qJVt{VN?j7aD$Zy zY4v^G|Tez=rvr~1-yN~974hYiUSDmjR6Kw&wRj;y&@V?G}3RZC(>B;1iK_+7|hZCl@ zafV`tAF{CL?yJ@>fvRo*U;e*!w|hSC-Ul{Nn&CN(_wHjX1eO6xJiW{hAmp`?qmj%kIdX!fOJM?p>gI4w$fB@aSg zgXtyMu7FG1l~bWdwSoQ&+2hE>4_-`rlLE5IO}Ll-oHN=_sZk+K@~MB$d-nk3OMpIv z8hc}66QLadY6*_I&qIt!v6c3e`(i+ir3*2!sk&&*zz=?MmTVo|6tVha0;R0;G*VLn z7&_Pak%}J19gWambz22l5P2O(VD^$5ka_OC$?RPSjwx&mzL!KPm-hSc%bLq$*4Z1i z@#jAPh518>-dAn)H{S>s9;1MLgzQe?doIDpHgOB`SO55wbUmv|X!!yWIjMX?@H2(2 zffBFhOXKyZYKe)b3{MEWEC)(SdiE@btkYxrhoTy(J!7=M(xpaJGBUsv1MRC%g@gVY7GGASUb!>PPIBQwJJ~ z7u(UMpLox{-{Zx#$b|`LxqYd)O1jpDn&SxW#;7Ih9{&ct&s>u$9*XqdSsCuSR*ZbP zyyxERje3Txwx=R^{?O6WPEvPvTY>nP6f}KGIhy0_5JBT3A(aC!M=RRl~K_a{ff5zDHa-w`;Cb(1`hfuW+S) zq~Wl|?cNT0$|?&KW%KnMN*vvYQs-fuPNOxaQibd7(=yO9IHh3a(6P3fm;d{1=ctlifP5TT^#z2otk5lq ze)n0ir|R2?0&7p$e@U3f5oJk2S9%r=$0fE}MT$l=`5DMpCuRgnB;FUqsv7H|u8fyrF-eih%a7GY{J=IwELDnL#_Y|wM&5I7rixd>E=(NEoU)QOggzxP}0RGgial>0Fe3Mekk4Sr@L zJ6HFKr`Tp9+xlAdEk9P<;YN|M#q+!4CSC9EFj3eqQ|b|0QLolRGkjka29;udROXW^ zoujP23GV5QuYMLr9T%xcM;qgNI3;Wo&;1v(tQ`jGa7= zQEcD2p6nOm^=3he^P3X!uebkVe;a@&48A0DEbgv4r!19awdTdCAH95Pc39T{)kK zR-vrt$ThDA2?j$ro;zE}Hg3;9&cwySo`O6m_9EBfw)cZ7aMpIZ!g8+_BxWecZ^ca{ z?*cOMw`lv0{D6WavDeKuz_TcldCEMFpCxYEnmEgV`q-&3Z#|3XF$TjqzOafE=L`*@ z)sDmCfV)b?*bZaz&@3y0*|oty8pU?G;dG+IeMZ#BLg}?HR&n%e(V%{H7*&6Kj>MmM z{zDUFifTiiRQqz_+qa~!3B>@kG;CMua+WC`O@n)!u$yQt#f!$eSCi$OCGik#`w z0iz$OGEX3@=S`P;ZB2-ZzD_%}^b2pkMY0XK33Fou>$i{9D{E7@_+{k6gSm6&-<4_Z zW+L1Ch-UsW&Gi*|1Itf(=9hHmu5o(#k2YmGk^cNLoCh5p1qJ0DrS>~*HL~lZds1xQ zjaxNPPLY$jw|71V=10p5@(R7Z4aKkV@L=LoODY>d8;U>9~!5_cSLDkf+_h)ZNb*L zHW>aoToB9}>*F&`O`FWTlaEO=y(IG?^&^WPnb=C|X*XQhGSn7n(l_N)vn~7IJ4BL!#`dYggRL3T!U4xwcfki%`d6G9x`xj!q`eraWjCA_ z!=0j`Y^|RDU^NWN6JM>--_`|+%v7Mf{N<{{5vdw(lKRTixeFUDn=UA+bE5Z0Cw4Ty zLkJ_9nH4u8D&E+GMx|rDz0jGiy6f^|$?;N!s&i)&g8T!1^&6i@in_(0R{>0hI{XBd z>jXEf+T%vn38&UqdlF<3#v{n?*6FuT+DccYS8oG)YhF-nlW%#cmDJcwL?~Z4;r@p$ z!_D^kc%S;E5|oO^EIq-FiS)Rt3anl4{o1{U@L2aPolp$2RtmsQ9ka`Qu@+Y&GBdJ`~K%7n~8e&S}$*rtMQ5=V1=dm0lc?`>9;n_v5?Jb`zzf&v*)4P4RLT*u7 z?4Z3H6}A?Vk%P#MD@PQ^#&1YtOF}9hS9}i0UXqk7p3eqib!Ov%!pA_??6?A}+!C+z zB*@c->NSb0pRKd!?6TfC$}z*9AA+5qvP? z9@Vl4b%gX}Fgo>8(^{&nw{e5nCxc<}BBCSD3^cZlu6fPjT5MOE?cJ(HK+h68mHSm; zgqV`}+(#k#vGI3`9-!LFRD5>#&gSz8YZDgK3PYmyR%eUm@Ql)_cHL#mw!`_&0vwkUnBlI$g_nAU;p^x6R# zHpil@xtm2*A}uN_%{iv}s;KBr!mXr1C5J!4MGr&S8v%*j;mIU)sADjQzXf+FHV1Un zX3b=@F#U3u70~UbMUA5ifO%&Co>B(DRQw+JDu2lYvY24A8yy1eT6qwxZrx$nb{yzI z==7@>DUd?iL9v60eQ3j-#4`VMLx9UUcKL~QC3*0Wh3e^N)(5!&K)DFgP8^cpI{D+K z@CN>ADujT;SuezCXzoB@=4(w3@02m1L3C&047N%gHmN{raqrVT(n^Q_{_W$D*@XOC za@r3fX>EKOZ2RZy(@9rmN_)q!p#Yct$k)sSk1!d-9%x2}VZGZkq9O{-cLXuh=~?)= z;7}9<{}Dgpzb*Dp*ir3s5P00!-1eQ7hA?vyN}d^&A7xr(KIfl5{v{q^!~`=t2Cx6! z5H&&MrRjV}#H83Bhc%=rRreXl82z~om9?&G3Ay`G3Z{xA4)=YM-E~{zXnP05I!p*J zc2$T%r;Hox%Dw)e=HJ4dSMk-dXWdRrwRFeuJbCY4`Z|&pe~n~#HCU?)WZ>da-Wb`uyA4GjbQ%y%1M6<{(?L&{${Qes;h#=;K%Y?aku}KFlQS=7pWS zdgh&S_hOq!T6b&nZL@4L=cjVFp1e(}y=W~5h}>tua=!6t^MKx+oE`Ryf|uX20Ll|x z#N|)(2pOe9hB|tQQrQ6vDd7Om?-Qk$cUQBX$0q_trAJZ9AwF%+mzCyqI3OM`l)bm)=$)47Bv$vXX-!NfW%NY!d#MdXrH+E>xpmc~ zHS;*n;9!n)QEueIm@`3$nU)DHfnwPnsL6$=J+K3?k&S6iV>>$&nI1`6RUVuT$L+?g zzX3dRe{!G0D;XRJ>zN_j1DzjV(W0UWasFm~$Rig({F{k~ofg8|yr%_P1HLHIQShZ0 z%ZD|~Y{;&(+cmfa=SN=xOg<~a4KdahY&Sk1MkVpQ8{C-q_X^oS=%ReiuFu~GS6^Qxn;+$C@Y!x4pxubFzTOJ^( zI@ckBHhOSE%`Mrp^!A(gFBQsBnh*$ik|mfy2IIi8K+I4sc`L+ zs|E1!->%Tq=!VuZz}$?z&1#h93%kDh-5^n;RGQr+(_-;I9~-Y+FW5Pr)UD2wO^$I& zqzm+?EhbmU)1$sO`N&q>S#B*EX>^+r`$#H6Z21=x1YMXwvkg0UZBJ@}h%VO#u!8Smn6L1)%9Bx3i8C&f<#nW3onn#uV5Ow@*{9WD#RM_kB+A?z- zCt_Dt|8%Cyki8&QT{zoIGP|qXBJmv%kco4_uaM+FmtlsJ#FIS8E|J)-JWVB?jv+FK z7i!$;rZ{d4b0-JK%t~>#OsJRSfFr0~VvKsI@ru?8fXJRh8XPG0gu%>p&mUr=znc){ zoPzMrPIco#a!`EPv;T_sn&P3V{<1eN$hYhWxonK`*#- zcBr)(&Xd2%D8y{R>r6zHUQ?rV(-Ld&Z@!KN`Boey<7QAlblPpIc!0NmQdE=wZmE`O zqBIK(x)Ab*^Twil^>2#d-fZ?ADoaH1hjz?4FH+gO)wqrLX|?*yOy@9o3ZNlBpr;Wo z=NY`+6`|`|0L4w)5;~m>s3Qq z^_tV1F%&8dJ`Ag1zefj}(KZCvVW7}-HFhh`de>-8BGMQ!=Cs*x0I+`TRhggks_z3P z2}!q)Hwk2H>EyO>BfIT`=%?<3{Rismt*Udr$dbP_n^{83Kd081lhhT|X$-0yt;ErC z$$Q@*h3|OAq3}=esM}5s6W=GY8FR^2fJAnxl^GF0dB;|2!W)K5v1iT*-DGhH?`_I` z#d)9XKd@OZ>MQEU^yp+mB!*f>swsC->>I|RuByL%l?4NrzxYH`W{iU(d z9$ZCKn>49bCvfp4+683kCPTl3pxB179`XfXD$CySLwT>5w}7{MqD_VP@?%?CKB>*q z6Tsf%=0a+|F(dnbB>ueV$&asi6m5@-mG)sr5_skvj1!37jq94*y?oJf0Z^M9<=u)l z+CT#=`S!dT6a#Jb>}=jr!?|{ic4HK;@cY|T_*Q<+fpmlK2&3IOUd^sq)3q*<_zH+b z_c2mlhky$W`NF>)Xb14IWi_h1o$JbL_aExAqK21x*L&C71rqSl^N*AcZ&WJqSflj- zF0>yZ5z{OX*i1eo<-ByrR7VU?t&FaM!Ldl%jEz5u)42r?cTT1$BqUecr6ff%*mxwR zHTpES{IMjH-6^TI4YvW+`hl$WEIP)ItdKSRuzA0wZpZaSnbVNT2siF#>+e)X22yLE z%w?HBysZYq%`*_f@^?h9OGz8ciVEavqzt=ZS5@?}up8~V?d;c5sR%U&`A91;(BTT< zDY11$D86Acq3e8BbGGDh^AV3KCYaoW;o6otB7a#_!IZ)88*0yk;tKes>cInu(6Fn} zto7;|$D%s}iaN71nwJ2DG$K5mKWW4u!SKA4N+vR|5{*mp?4S9qn2eypegZAT6EK#Q zji~`kah0@(w5aDZA___vhtCg+PYNBu3iMkIJNUE0g25OfD9Pn}x6$)rzS~Om6UCAn_RFk{h+s<8UQ9RCr991+J8f{hNx|fl*xirAolrk}Zz*O#_yTPQh;O9g&!Dz@)vbq2@7S)ZG&>Ith@oJC=R@i+v3g zjC74Aa(5iz*(K{ZHF$05rNr|qv-Wq+LCW*!r~KOB`xA6ZHwJe|tXW6InaP*fZ#1W6 zP6or8D=T*A5%(r$j7WuNYS=Xm+Q6&MDgROo47hO8xmQA_n61xMyL_7e@W)#?ZVgRZ zpp7iC93H7xdO$SL)*2A<7?SLGNm<|gVxjMu-Bn|egbs>y5@VjSp52sN%J*1pFqLv0 z6^Jp9k;@dwnd8TAkMV8C;yhMg)HrxXTjdMKdc`Gkqbnn+r=Eo*XxtPHBNh5~w#QGn z?EzXA-3g2tDrsbF%--Ji!4LIAgv+(UTwN=0_lZBxlqjwQ+Lf*tu%b+NzT-le=ep-h zmJtR{pI6vm=Tlgb{+0Id4UMXyiry#cM+3$6?2HH%aDUlIx0$yDGObxmER<%C)4c=U zUlZC=O<@VU?fc5@`NPZ(ef7)hQ)@*~eb=6z4NU9ZHEoFgY1DAjB|qjN&jID_!S9XN zgFt%uCBPOVU)XM2Vi7v#-!z!+&3!&&J)33fpEdQX`a&p@518)pp7aJUPsNnn&#|^6 z@0$N(xPxZyv%_W!sPo6{_QbZB2sOoF@mX zaDnbwH3m2~Fpg!b0B!fVX)3oaz6OJU8{hs`a>H$7WRb)Ps|mT0SaMt8TBq@}H*T^F z-pD)6Q(-)HMkT#w)87dLN~%MV)EOJgmVt6OLRF%fEz?v}*E_*=k}fz#*qyDjP;IWK zwcYH<3hd5|3?DYT3-jWREwVoL(v!)}8-2cCbk$(q3|b?)oAx1QGBw6w!b0Q9FYIju z(s$*hSp#I>AGXQKoOEErU932~dkn@18X35BKEgm-@@W?Vqq~^E zg5__~%!bKXCWpD~k3XsKAMk83Or{1}=4lLSvL~jpTCRrT!!S1Y6QO}t5LCC z$_<5~I@t2rpUk#+=88<(=;Bp-Q?MKD)ThK{>xZqOiK&U_dO<2b z-T=X}Hv4i^6W*PgysxAor!E2Ry181a|1&RqqBocnag=x6V(NnF)uMa@d1AvS@O@O#M|0~G5& zWBV-UEDSMH2~ci4cvJ;MTGV`J2eqLp()2eTTnj~Yb?%d)MGfsW`&T^|zr4|`1V|vr zxE>ebb_C#o@K}&`v==)>5~|wEY<0mP@1Ba={01%MM9d?9X2M?8J3(jkXm8YNUaQR| z*xr6jPTbm8f<;y<8Dr1pWXP6%Vu6YsDq<=qxesM($%s$RBnxsa(1bI&CkuCLl zE*&uK$56oAzHrmphD}8j5JB9bac?Upra#+y6?RdGd5SwmOx!E4~ zvAnwN=w+U$vIg)t%zfTfjuD*!Jud+T^@x~~Yj5EKiS>XYLzqdA0Z~;?y;>xXT z2>}FTwawgT_TAkV_Uhl0YMuY!PRu^OXp`)d7gDnuDp;z23Z}#@H=0DsYb~*GWP)+y z@cJ0!Mkn*|2N;8#oy0va4&AjU*vwpNooH|1?519nNA29~WRMjH>p$LkRcvQ(VlB1G#=KoveNhNnPd=gLRz(rm zOtR8HEnMHZq~H2G%=m5UZ@iGaGhXP%ftutn5)ng9TKl6d$K~Gx*!q%07fyIaUKL{G zPdSwU+m`#>e32*gRP0#wokl*b0m-@|gQ^&@nq!>J#$gm~mpVh5z&suVB zdA)$nno2F+NnQbr?$9~ru!C8yYgZQLH5SB5jTB)GTs7r-C}lGAPio<4Ah*VY;Ex^M zD%XnR+6dNP90v$F1NDZJUdhtF=UV`YQY)voWf-)Z+DO+5gy0`plYD~r&zq05s8lc2 ziKJo22t;wUoag*vO90BR0HPr5k+fCgUGVYDF(hp-Tc-K3EN0;ynG~wUTGB91%ZbtH z%UdWmJZ|{1+qSd!7BSa&rxJ(X>Q>j0c4ZHn@Mu46?KnVTCdPJ#ub3<;ffB9*X_YOTO zWa@EUoqzqBc*x|^_6KAviy=wt;$vNqvyj8KvmQAkBi|>e#vD_c)j=mxAI~vj%TtFVnoDGEJW`LwGHJ)7%WPP%)$tL^^?Ow zlcgb7l*mE|w3P*Zg-J*?DzJ?D_<7moFZdhO*F0*Fe>45(O;Uvjy~i&w$%$-^ui|$W z=$TmjOMb}mqk@TVSKQpHeFeVlWnom?3EcN=`>d*B&`3D@D?C^#c)6_21p`eK4&R+@ z6tMndLJot2Nzu(RF}GT0$4r06c~RNSnFiL?ulUJPli4Uxbb$^_W5D}aH4b-p)S=08 zfp7Y`v$12(+E%BBG_FKMNADg6wY zr4;}P6&i2>+~;0ytgnqKiy6^H&DGM-|PjS6h6$MUL+oa-giYx1UUwm(P@> z>(0_fbn#lQ1kH<;gW+C9%ELdUGS#Bj~9c+3~|3 zuI0n`W2x}ZT7Eflyq}Vrnd3-KYIVT^tSB{QIub6sw#~0m|3E%`V|}S zSb?GzYJA7wuDmIR^JRzLEj6i@zKn)AwFso*qYHWQ&TsFjk=gHs+4DPuEow_=B5pPj zqd^zP7=bjkzc8$K#hL=-0wdOQ3vm1@>`HR$kZKxE6N>?R4BK27R+g}`pdgeH8y)wD z4X-ie&ff7NDaIHdX4o8#R5ZWfEEapYfE>w;@;kZPrKhWM$q!qmCSkD$YBD)KY~eWWsaL6o`Pg~+q>%GEe4fF4Ed47f@r#0LJoWCzR>6GQ zs%EtqeQGjxnNc1d&N#$kiqWb`*cplEdKRm^d^_jx%^ag`FMr%>V2>r7`=4-WxS#nHUnNLw}nh zrv5>Bbij*U;+aB6L^i*SxqDJ>n(!eT6O-1Q7`3_+=d#FR7rYrk``Op~+4+=91-j8n zYPUyb{ETFcH$Z>VSqff2&0Jr1F+ujx9O0Eg+DY;}hhgJ>C0>LN1NJZWeYrBCYv?3x`2@J z!POvtYmfuNKiJTsLYz(0Pc`yI;eJ*$e+e+S!HwmX3|_3}HZw|26nM5<2O1-+YZreW zhH(!Cbqt>dJhToij-ue9@WKffkHTbN)}#KQ3lB97-*K zz$@aY@CT9NBM)JGt9;N9U6!L&`jt^c$xibpR41pFTO;_)r?4(HxCHsD3L0M`8L?gi zYwA?8{|XfUq``+Nq8z)RDrolM1}98!8jeWIOJsznrJ8r=4@=SN>RM9aXHBd4&q8>} z+3;3xk}GJb{yAE{LHbE5~!#XA|&NshQMFSt*xC!ah9{O z^~lF=pHRGpY`e6mgw7HOx*s2++D9!=BG-9_#?TwsjrPGrQ{kz-Zj5FHE|k5aDeT|5 zJm%F&)_3$g^%P*F4BqoUny73%qWzUpAoHVbTKA>8=9BKniw$bf{;#?F8T6F6Z{JRry{*CKMj~;> z@OAB6HyoLl%zOS@4p7zZhiwMn_a}}_A^-dmq#_LQPRq3y7crqVbY)tz=%$!w-d$^< z#cK5@v5|W3Fkr=pVgl_2%9SI6N!yPK#Hs_Y=cg~_ll)5&E7cwACSWDUoIv}wzJl@# zxwD*D-kHE>Ifs#&uDgosqAmjG^85yQBCCvE3||k@b2Dfd8sL@5GE&eyI;L^fc5&yz zKzkZGLx7>+G9bJ5{(9A5TYCIu95*=&f1P!sPY`;$9MB!8ptFR)-@y}M!cbJ^U9A!8 z(klPgyUQNwZ8FZs=Fbf#&>D;Di1ObG7iYjF3c_@p6BMrHqqHALj-9$ z^IxP#TA6s#j5`(T+N4gHf&{u#e+4sklnaKG!3`T^FKhYV5s`5fq*x#46qUFn% z>}XMZg-tuPvY2ZzmpUm~NGUy<3R69ZDqp8jYk)~~m3|Ayh_>RTla*LyjDB&6tkjb8 zngFi*CSy+nw+L0+nA*Y z_+nWrZ2V@xIQwW_7Hai{n#vvI0QclR%5|;xrAxT!PpPr&$iaIHCQ3l4kd{ z=t1_IqyY|@C|(#u!uh7ff}#e4ZwDjUX9{o+$oqW*Q*66IX4%@KPI~P)`E4g#g4$-y zjYY}$4V;uG6K1K>sHB8`S>lvOGqcn)n+FgZlpb!V zW;=63RJ5EKOv|ix0#ot8Y$g@RuFXaZ+F4Ta!x7MTB)a1<+wH?dOxdIu@em2K4K5_{ zGP$2Z)8;ORN)q%=qN#e-^ZYe6mHh(REy9;4Ood)VJuvmN@6v#+DojV;PP{)wYKUK@ z<^eglAc&l3MI7qGckS38h2r@Pk+R>1>r)5`*xzzb8uwJBVKDMM{IXr_^NmZeZ7V3; z<)XO0-4(|jjh?jLXl20Wb(K#yX_pYjIgxk{Oq`QIEY2nMKJBUagk|^+D4gNOibG>Q zKZ>(-s(3WH2Ei_sOK-jWmFv`0%>^1u+FD6NYbi(g*zeyzjD6RKH0AjAByvE)Z9pTt zW~VOGEnEq(u`!9QR>*Q{E(cGdv)~c3%{G$8W_ct0e7Z{Ou0~I#to2BEvuCJDRrBXm z4}i;5yt{GmbXS1x{gBKo zzKJXo*=Y)k5s@UtA8{j0ddC$a`=02Hxp5RVJ9ImNK!*;)Fl~8IvVs6jdWk4jo~il+ z9cHlq)HMC+JxVWn;=+fi#RffRDjORfTOiisz65xb!=_UOdL5DhA?H{ARE%IQi5!<( z>QplooV|Bv`s=rw4>vM4{N8dNlUq3la2W}34fwqU1MT3+a;7K-;-g>E7>u}kK3!i| zX3@w+0`*whTww^C@(X$=-pUzy5-qT;LCpeACiMrq&wyeaI8&32)!0Z#NeU~)6HJ_T$A8?&VT43RhWwo^= zBeucww*1${i#IPn`11Fw7#Fg*>ApP`{3hXdWP+6%|6xf=o+20|nd{(CL~UE;T>%oh zWHFUX=bv>sxWPwD#e6(|>Bqq*jMwE!!GUHa>3#tJU`&SS zxJ3#ok~si6zt#Cvz2=JPs083>4;Vc)`@^(X+hbGOS~G)9@N$$&t1oQ=+!0VLH~Sb3 zrJ4scnN|+Nv?PIqd-&+?ru~^BLH+F+qF`1bRh)*6Qn3JbYZ-(2r0+Ux#k-~;51vX$ zq@Vg#-PnLq@==$$qrJa%(0zvBlO`3~4{Mmjy3Z_b5C=Yc{|bCYNxcLuBab43yd>$z z)dT4>s#FIly&irYVm`zP@ioD#bo(Vqr2@OqXxfZ_D=+}#kmbwauMEtx4{8Sn)9UkA z(OCiX()(yA>f*wg&dO&c^8eo74138t$W-vmL#l(S1eXAUe_n;Qg8IQ(2`y;yVH5wY zZo#Dpp|M@&c+Q{cu0BPk@glQ~pWI&=TimW3SSq&%!|iV2hB!vEp9jd;%b_IWnQSkT#npkD{$Fik$~O?3>h_zGD+6nq_n!nPRJ`rKq1h3U zjX{%R>ttQt=APerHeR)Z80Ghifnpom89qmf!bkg$Xo)dfe+XJQK`N{&u@E7LU%>%a z0Gs*9xNjSqB2EnUi9xwm;(S=>UReRz)PNcdoT+M0;_biA-Y-`#6@3MLDVh?yCVzv+ z)pUe0Ii@tt(Ll8)+1x0_3!ul1T%^0PrF@M$^RhVK)8gMprefL28LglYD{XqxUv{?g zqfmZq>#pP0z+QC%$jLt;H6@cg+ZdcPg68fcbcSP#05UCZZI7!suQ~-W^}Bf4k6Na^=iMf6^ls#L*7zwDH7^=ahE=v`wKQ zYa{x$N(e=z!grrI&`+Dju7K7~&Qq<^RrzYa+N*{c?a39$l+(4J!Yp-35xt{L!pbAf zKY&GaWKn8VPjNSw#x{tR{f;&0(s^+}Q~im$9-=?q5!!Z46}U{J(d& z$&E}z^YU*Vfbe_?nR@>39mIQDH_IH0;2-|h=JYQWCbfQADl*##YFCk_SKBVR@ox+M z-pboQva}#|W6U??fG)s7!+$GO1MSNHSiAP$(A3uyAU9vI`q98IE%6MPNJyrH3M-ZDBg%RB;SgdseX5CtGy2rVWLGNBsbeVvr_t2}LYZvXzI07$~RJ zuRBn?c_Y`LtK<6q10*^{Bk8L-@Ysy~{&wa-Q^l=M6g4PAj|Y5rM7N!_Z~v#5WFr8t z`U`x%Tw;Fm=arc|r@8l>LJos%fpt>>qv>FoD%oojQ?kVY|5sa>fMc#ZjUU_{l9_K{u8W_*&#Kp1y@2wdj#i!Z-+|ImK>1m&!{hT zYEa}EOtx+i>-<;Re>fRMl4&yYvX%Jcjrp#H^UDDVoaNBj${FIqp@U}&Is`XHHu;(> z%2ySfi+vn+@+W^W&)Y!`O7JHu0w))SWfm;{zDvLd=A_3;Ski-5HkrH~)phv&I7#Ln zUS3u^$9KMci=?FJKZY&hN&?4naQ3uhv^aKoCBa9%wfs_f&%`%DYzoFn?pnt4-U6lK ztegVnll*A2e8Ij+Gkw=|nMO(P24gVJI0HT+V3uwD`mvTnL@%4?6C;INf}l6)p`TnS zXM@ZFE8>(yw{8i-wXx7I+|P2`0L?Tso-(jvzjonzZK8c*Kbuiy4zJ4FD=1}n>B4k} zxsv;1dsc%MS091s>kzEofED18cb7#uWws($+^3egxvTC4j-*cg!IJkW z^DR@-GA?3wqoK($Xfb`YqHD(X)38wcwyJ`%h0m8>c3vG8wrk_i&Q&Lm0Po?LWv}qc zO?#9wCR%?b?w0u*pVp|#z_X!J?_R83@_eZS&Y*o9*qvCjZR8t%+`d$K@Z!NI=+sbs zm6bifPKT-umGpe+H1qIU#$jZ4sfoUTKlo-sARirEXSDX*0_G>gJ=_H?Lk;^5$@m-* zV)C&D{^1`XC@R~hbn%@3v4zm}%HvLGzDG)kq0H5ipO8W)0g|cP8DtPt`yu%D&~Pd* z|BEId!wWs&lQxw=o~ zR4A+RaUbn!|LdEXbx@C%)EKrc-hQohIuojGxskd~oxXK%P4J;EGps6dtX-zK*)aJo zqL1_7F$G~gNt8!BPrlQecDIR@JDMQ#oqw8iF6>6&i=_B>)xIJLSJ0smfjQs>D}GrL z-@DvNSEC(vMDu__{GSkZ|A_+s@8%!>9ji}WTTpZEu`;0)Mv_Zs=|!ra2sgM$D%d}D z0p-#j_hXVG0Rnwad<4py17wlg|F}VVn%G{$3W|HO(txFny9?G0A5SXd_}fxZyU`(Y z#ZBZdli-V@eqYO(&mwwHKv&W; ztONI7S<8DzJiidzeusta=iV3-o;MLMhVyv&!SpwZ^0h7wRNYcg|Nm;a5~!xHbRSAb zs;FSmQk7*~fGSFgNLd1e3Kc~WS13!MDvGQbmJlI8Xw_n>1QjfZA>oM>5FryWL_z{O zB3mFqkTsj^kQE3?AR)>7gU(FfoO$!!J7-?b$;rw8oO}P@`Q2~3_q)H(`vdYR8h{~| zDF1rXhuYq79|N?o>F1bk?g?=@qW-l?N);S_VX+)ab+2R`G6LTtM=ym*JR^g zJn(yP00vypdgySeBT>G76Y+9KzLj7rP*VEm-5fXRCEtB0TKtjt6dRt&q?f)d%H!Pq zgIzvxPXQq{*#_V{bh2bQqoK)coARTyn~z+iaf=0+`i$;4gRN_J_PGDokI!V zTK7E(sIn5dDV1JWcoKLYb#gd0H#)Wqq>38@X};0uv#HJT3E-PL(xaq;Bu?cV0iTGP z2(Kk&1*rUtT|C2+S+fV6`Wp)5!t@@iGxv^#nGehIg-}1P%`>}ngxUqBx?O0goBM7?wG2%s!Cph`f+B_SW0yh z*1l&|o>Y)y-M49|R#5Rb6){8x?|4mAtkj3o`gtJClrQV8D!&j?Fer>KU)0)j%~chs znB`y2OAukq{=HGU3WF)dFQg!^7c`RM6S#P^R6u1lXNI|VARvlAHE10&32oH( zU4n^}>E5?u;HIbaKm74i7V3!g-KSu~#hHg8$oEeM$zVhZ1jk{DU3MvCQ$S5flSY-4 zhB1l1rSZjuDw|Zf6aQUEt67wo=HpnW$|_?2kxtKkL7^n&#z?6bM{SGP5QFY_Elbe}R6d|GWqO(@j0<@&g6TG_7*Ga2G|^fTzqyDVql! zbt#bJ-;ZQ)*g1Rv+^5d3=%^dY-q&K+bFIp5o@VmuoN_-`d&?bu*Z}$4R9|we^8Gp~ zdn8^;aD!h*$&q;FGxquK0C9tP4Dnpy95$lgHE36Lv9h8iz^MCa+#MdzD^y7X_^xW0 z66*3dVarl}gV-tfz1$mBsCxU^XwD$Iwk`mp_Hp;L)mY!$Si(guOm9+CYKXeyV@q~U zHeV=`o0I{BQELmzzG%}1{|MM@i#5LMD(?vLY`T$OPcZz^lpH}9&wf-2O@G~~|1e|tY=hkUF^+3|aYP@c^)Cu># zCBAV+lG^TQd3(io_2y%!EAxw^7|CoiK?xi|kzm>9Fou?Iko@)mDWK3|kP z@bMiqcq_Gw$ln z1wW%8w{@RC>Irw$#n&O)xntNuA%2T)W(RU1S-BbfL9!PqI z!8MGNdsp>2f%cS9w{O$hod(mt@E(-U7IQAm#r*pG7B{_0-7q$0+9)zZ7rdnzJIw#w z4ChHaz6W)0sx;KU(4@G}-jgtuX4RM!sfm7K!{xP`I9(;fILf4cd!JSPuC^!%V!t$4 zc+q^B?x+>LJ1I^n<#P44NiWmIwTKf9d_)=W+yWv91BdJ^=4?5M#rFLEUBM>>N$IS`th_ ziqMa9QC=e6pBE1*D}eEkGrNMCNEnx~49ltN(B7K6Y zSwk4#=)o34-iBycgO9@E8xk=7!G)%@N-ifwgGI3Y73)9(diRj{9fgkKbPlqlqv@p@)CB{I*Yeq~gV?Ay zQSsSHEYW*>@ye3UxR1BwhW}!!yK`jJAZaxc{LN`qWbn^p@;Z0JpDOJ{yqIajDju{d zX*uI&V7A2oV)k~?LS#mcq2n%=8&yiin7)?j3o_EUl`tlCq6t&wu{FhaYRsUqFrCd^ z>^oi+Min(PxvXv4Ou@5yK;2fOtDJwsi2_L2v9@4Alp*gNFPc5?iM7ZV*Ke|F|>j^mocL@^$jhHg|#YZ?+u(S?zqb-V<|k zpZ>|6I~o6rM^B!6AaRgBhm+o)oP6>epb;2)R`z^ya@~GVnQx4|r;J|}Spv}GR-12E zTkQ|2#Akiw1S)eR5w!`k{Xz+E&l2o(DbAhVfF5yvXyJPKT=WvBK2o=x*31;l9f z4x7+PD-c)G+6<8JU45FpV@Gg#p>vm7w~A5+T$sl&x1ez%GlJehWHqiSzhb@KSJkPo z5ogMF(~DVQ1QqFFZ!}ry-x^d%T{fIDW~r-ceFUSgUrQLEqJ{uwxbNAYLHYeOk6>1U zf}TEaRC&wZGyIt5YU$WM#Tw_3@_|@stRNKvaqr@v(EHtpmth zvR))MsdA}d&kFvUkN6M!AkBb9&cuTbX3U~Qt*JbFCcHL)vsWD_-&|7vJBYQ*;Cs;9 z$+%XNXkKA(4z0d&5w=AJD%d33fs6IkokZlfkm#VVAv2 zuVY*5ZS|(HP%ArDoMicx44;g%$zurnb1h2_wk3r5hlY~ry;lEaU}7=i^6tzpr=2Z# z8$FhqLhrZr=$l7AnyTipqGZwObL$2wRn4--!M358gyngt*%WsD@1+$+wMM0?=&U!; zhfC#sYdu3;3^t+`Y1QUa@VN;%aQg|xo$tKK{oJR`2K$(w*k+Ln|1ZAbfAAn8onBzHbH;6c#2XOsIr`lR@0vqD G{PdrPa~1Re literal 25262 zcmeEucT`i|*X9kOs0h3Yh=70%P!LdQ(xakCN2-E=6+)K|p~Z@VfPnNCdIyEj30Od+ zCUgl9lp0EiNa$^D0>1BWX3eZ`X1?{!H?!9K!OG&EbI&>Vl)az5_p{GyJsmZc13U)+ z0ANwSp{fr63@`wIe%uEIpM0r2H4FX&=xG^V1OJ6UAarzeP$-n1o}Ph$fsv7siHT|7 zzJ2@m?>}(h05dc5!Gi}`SXfwDS=rdw{`%{$Lx&C>K75#+ot=Y&gOii<$dMylTwF(w z9_8ld=HcPt<>ftg>=+*(A3s08fPld9@|DJd%}tEi~JV6e-VFJHNGCecJluY-buf`fzKym=E6 z5)v938WtAz_U+s7@bHL;h{(vuckkXsMMXtNN5{m(#Ky+P#l^+P$0sBtBqk;%B_$;% zC#R&OpirpP)YP=JwDk1!jEs!T%*?E;tnBRUoSdB8+}ympy!`z9f`Wp=!os4WqT=G> z_wV0-`0$~mq@=X86pcogm6es3mseC&R9042RaI42SJ%|k)YjHwFqpc!y88P1j~_pN z`t<4Z=g(ihd}(NCXl!i!`t@s5Q`5I^-+J08 z>gww5?(XU7>Fw?9>+9?9@BjY&`@p~e9*-X!92^=N8Xg`d5C|h9BR_up`1$ka=;-L! z*x2~^_{7A-MTBauk+^Ydgf`PZ*s3kwUsfB#-wT%=Ga zOG``3%gZY(E32!kYin!k>+2gE8=IS(TU%S(+uJ)kJEQN-{lGJ@|M896t^mLnNd1F^ zO)NYDfa8F=>SaT(MAEP)l4D}{;_a-AHEY*0uFU%@gNT$;$}pts=kDrITU^m&`^DN^nd=*k&`G(Lv9@gda(lbC3E1 zmIJm&|9`c;39&eWN*VHRT#_()rOn<7aKKogdJDeD4l?ee26{yG)00uI-I+^fCDvjJ z9Qj-7Sfo8W0=c}17QV%50T1!fqe=RClv3#F7s97OeTrd@3m1sL?c0EUVzIOpUP zW8TixQO>=l7>PzasQSpnvsQZz*&su^00%L7!&*8$K>8r8u^aHyhN6p!dJ6~sG>jfa zzDW^`ycQT_vbU=1_q4FG>QKCzOGYKb?y630obF0*kHo5A&$1_;@z`sAO@+Ch7|*^< z{VS=w*f5vEXFWxJ4}X=A@UExDSgxQu~_Y%HDLAL96Y<4x+}= z;{IM2&d0~MdXfy7x-2=GRX^;laq-(QmyX%Np%R|0>DT+dzJ9Rx{);57jLq(qm_QXx zE$Hqtj=6dKpFND9R!FiRzLm>rg#ESRUrr)tSikaC!7rOmbEln^hvk3X=u}l*Lj48V zvQ*Sighuh7m={cb;xDRnwM|nIx^)xr5+1~=X93NGk4oz;c(Lpr2XGJXZDoLyZFf7e z!5a5I5j-w?+v)#TqaEG<7}|O7ntfeH=h)sVwXzXo+r_dOp(1Gse~X0) z21?yw(O%R(wIH*-J-h~(S83vP^E;VtvT_#hE$nZ|`r8Tj+wcAsX|=vc)?PsA?B}@q z-D+F@6=N2BDb$XbVU4X!L>LS1LE149p%B~nI~C>u)RVoRR^ZNqG6xIzwZGL~>xY6H z=`_G8A%HTFhBc|m8J;u6)Kqu~YqGIx13Zrb5eveXSp37~UrNzxmkbmU*Ego7?Wgah z?T0~G!Pc0G**+N_zuJYbUDDU$$%U*7|5LW3o>7(eq%=UF!&>ecS%U><-1TKoIhqVM zt6GI_^cOm*Noy7GWJB;eh*&rs4hgOrC=Lx>dAyLJ(fmU3fYcA_e&)QNUlm3}3&AI>R@H+2B%1Jskz9Ibp|d0SU5Zr}e% z5fiFklRSr)Wvy#ZDXG2LU8PJjXP4h0Kfd0x7bO<7fa^9I`_oU80X2G>)2EMl2UE#+haJDtr z*!y?e|LM56X@AKK5nH*}euWG6ASzoEdw6hf0EF){6H}?0nQE^c_Vx&~yu9%B5nG(b z^yN|BT@g)?-)POAPiVMIdAcWFon?gnU2&dWXRc z2*p)Xx@|}%3IP_yLilAx_xQ!3(gMGl1AyRb+Pj((`F6u9_3<@b9Mz~HUffPI`XB+k75T%|uxxTLnELtsX!a)iABNT~Ii!1j>>$M>XeNi2*>kZkVnP*U6v zV#1;bP@X%WNhvg`Ke&vbO8tE>U4zIW73=)EawWZXk7BKxr#3YggbrV0cRKmHDdC2@ z4J!WU_U%->$-tLh!1dPbcd1fB4tYAy4f!mY(GB%QOGcdAr}D?e@9ysDPAI{{?E=|FXX z)+r=r;gfnTl745oJ~CqKo#@h;Io9!2b$5L$>Z->Ibf&^gld3OfnFJEX0=`)=!p_E1 z7l}oAuZ{ngHPbmLyfGaUs$7nhEa`5zxT{p(OQBNA%7?ZuT6&>Y8Fn#zG@aXmvT@!s zV~sG53c#@TG-`3GD4PE?1(Z(uNY;{zEHV5b7|rq8593-MXfBB_3(!Qodke)OHR(+2 zf|-bQU8fqw08XcnEdpzEVSV$_>D!-KU{LY>#B-WrXsVTib+|*^2iU@X`9an!4{W0!%Ptn65D za-=iOtdo(-XO*;aAn=bg`2#*&9=FPUpjQ-`EI9N>q&tN;VCCJJTWx!?az-xa(c?Z+ zRkoqx%EOcSgaGSY%JEbt6a>VMnGnB#TQLUW{zi!STVJQ=04l>))ggIl8K@zLYc|5y>s#BhF;!mryebM`c|D^Lhe z^E;f| zQN0$*u&1bp`e8fcYgGnS6xC~eo0#tW<)_CqfxQ=zgjon8mjY|-nYmztd&v8Dq>c% z;%#0$Z&D|wq`e@08S0iF&jr~-zV=!_m)|fFlcgp0`e8!-EPwxj2_E=&9VqUtLL-zi z9UlYiz63r1l@STT9MO{ypY#Yih4?^!|lz+Z2ELP3A% z?}vyiPxJz3$*IOd(0Ey26|2Cs1dHCI8Wy*Ba7Kd&CfLo0#;53mxhcAv`IGaKV%!htz)uT`R^JzS2%In*Zdj{AhG!R~gq`z2ez9%btq)?wrbgXK zWsvn+M)>06$zUI9=iVu*BD~dQrNQ%;G~ANY>}_V>1q4du{v_amq>*n>itY*Rql?t} z>edx1p?tC{@^Q)halX-XvKiysdWpYEj9do@ZB7hnTv%N1ZXY2cJ?D*9w_r-t!#uvJ07d#{~9DhrbF%+cjK$ahO)#Q=%I^QOQJo zkN6je&rb4-y!#`bB!p4x-d}~)6b;XSa*XD@v6%l7tJAXTeII@51;-yJ5BQZopPW_M z(K{xP&OigOpb%6Pn7HFT5V~L4v$EhuhdwA{sarqC%Uf4Se_>mb%+-E-UmDbH>*cL? zs{taQ3n_o}#9PO72d8(*C*sqflt6(8;SMJ1p!Il|cL}r_?-Q`)TcuRg!iU;CpxfC{ z{A<+t%A@}zLx&imT)4c`DxIIv^vZ|k(}E|7&YF$-n|v}}0YUR36(TU;zd1^5J&vhq3gF4Fb#-R+AOQKck7!{(?G{QZP_l(5YJIuRZ`w{|=b?*j}B0!Y3XXLY#S}UoreXIKG zkI(R>X*N1VI~iMb@Si6(QyD3P%CSD&f4@~VRkL%iScRp1#rgK1hg7?1Pv7m%9<$vx zGq&T`FhJX#;0nE}l;tPgUQw^r(^K@F8gpMs+rJTfmkOM`*gg+&Z+6*28`n(?gF?(ah}q zl7Mvai{(#qZ3^nt`3tJ}YVrL{sgwOe73Iyst)Zn$7O?Ljp7$>0D5XO<2=LTYs3F8lCVS8iBp>+N)R!Y!m@9z*t{yJiv#xM z^C16CN zvKEWr7wY+2zIa~@zor|KQmM5I6?Z4M<179DmZ#W)A7-M0BcomCZ*xyyaz40gbV|sg zB+A`XcuTvVb+U5Q@8Ja(y7epT2a8yQRTcGzMCftYEY2_1iV&)$U3&*vgjaC&51wRB z=NGZ)JwR+p^*PP|($Dc+7#xM$HPBrk>t0%QkJ9)Jc~OgOT1u=AGE4pGf8MvqgW$zx zQb&y(klTWbu*5^Xdm?^xf({+VNZfxD)VX;O`NbtH+D-y)`BF07C6blqO%NUDCfZPo zH<8h-!(ty=%--Bes!Oe3zHvw5g5~2~F|)j@|Hz9qed7AE57XZoUBuQGGZ-Yws- zAGI9VPg5qc8=s$f*gJn-y+i&n>C3gWKMelA=f>?-3RQ!clTKyprN83@7pJ!j?jb!ZS z;`qlN8-%gwKw18(X{1LdK#Hm^bofw9e*krv36*NxXEDfESl?fH(E7n)T_$w?GIB|0 zxIDvshOtZo{uxDG0-PRi)`m@4jx%`Lb(ttGBuI^n-`LD7+xvo-fkU@XNbb%RJT}U4 z4C)rh!v&%#H`s`Hv!VryN?-JC`Sha}8W3tj=Y9I7vfb~7P^C}Zm)P2opBwu^IrOQH zA0Dd*TS(RdXHX7dnTSCY;gLbd840hxzo;Ag<$$@eN=2g47%2RbchVCl9} zw6__sJhU;M_c327T?nwV$dQ4G3Yl+Vd9z6dP7~H(@HIf(pxKYZ#eMKhJo2*Lf{9 zgo0=8-+tJYirrQZ%gnm?=;oHO77O=2RfbXxLRG0s3Pr692#uOg!gYe|vBr*bmalh&C z#!xr-bE*@r&>-C%CdzcC_iFBm5y+X>@3{1~dX9vtT{1i-lJFRjz%Gu+J`9`vez)vy zp_$QW%O6LG$bFb*le^;Fx6rh&kw6LfWV1Wm%xLn^Tw30qw-uAi=YY)`OO(mD zdMC@bd(YqQt24jWNw>bNoQqu+FY*^a2F2`L!ll}%@Gj6x;gks{Fjl${oP1f)@H8Q6 zVdlsR_QXupeFjP-U$kkY|@NH?mgC z%Oz)m$h|!uV$dUa?EvvGNz;4VkYoXHhLv7Wr(`V8-(d#Y9XmxOd>#$2BENoE-s6ks(Z5HasFPyr4(kg?TbxmK zdGWU-CY=`prz~zLhbN^o$~QeBOj>Xkm2B%Q0H*8Nl<13<>q!95%_-aB46UVMDC(jZ zk+_}E=PM;JL8$RvFPnUA*d58KaDUQwFm@@r&hi54?*^5Bf6Y=_x1vXMZi`i4bg@sm9dr2F4s4d zY4(Kh&ZWG}F)NK0ISCULRjK#+_NvLa&y5Xvy$8aBGTE4KW_0U(^v~L{Z_Qg-beRf#K-8JP^(M)Rf9lUzdmx0(bZJ~Vt-&Bn- zGwyY+#KI-Vn`r0}oBZuWi&+QbsXO~%etw>Bd#xP#FO%4c=la%0YxLC+{?h;AwfZK$ zw0Ul=!81$%S8*}0|5&>#4&a$CT;X*Ur$^OUc-c$j|LnvBvT8}oP31jdu_mt4;SVt#(?_7gyMLd z$!3Ar?sYP%8DJvPI|Pgfwfqc1OSr*Y>%_o7v1z}t-~2Iv3H5so@KYZgSz$kx2Vd7d zG0P5vpdFPuvvMDiTpJGnQJ|#GbExI33;UsWt=ovOUMmB_QxJFO1+4ou4^U;bxfl6y zwva$eyP;wff4D_Tz?~@96M(im8MCrY-0tT>TDf1f5?1m$h1AG~%I{1jD`fM>M+}}0 z6iGhf>BeS%X1ezD%0(c)&hhr6V_yx;GTSlki${?fQxMY&pA~MoS=K$5%pp|Iy))RB zlO8!t?VPyZVb~3#H?0U`$`pulC_)sjbLHs6A#hWSM16rqL2v=kGosDo^2pFA9SGF_jI@)qW59N{eaRwSY|7LZ}qvw)4>iR6bO8&Pr`R1$pt9PRg;1# z(B-&s!vgY^#MusjI0BHW;bL{I|74dR>I$GuTNAt$W&&s}*VrKY+r^fTPK ztB73Ru$k;D(zoDY(jYTEgEs_K^|`s<(P^r9;`5!1coSUFlqNE7Kb<>7d;Eol_gx%OfKa zHwbSbZY3+?zGj~w!L^TS9`^mtogM^w`cn$bHM(OoJwNEs;ds_J+rtvCve+?E1}tlM z2k()uT`)Rm8#xf5t3~+1$0UE=4_h>lU7P-~dcY@nk<%CA>@$nPpaY zek0lx;#Yao)^2b<>3vp^(fV{W;jvSv@@uz7tLA`$tk!lD%2^WRXhJm$%ul+E|5uTs zy)=Vf#kClrWKGB3dFhdmGDx*oU|U~b5Sh3i6@SgTg(YR@3EW4Y=Aa8?GYmXY$scIE(@*$KPjU)w14cTr^AcnX+i`OF0)vB zv=@g|Q1bzjb%q&Hb;e})lIH&E1-krBH$dC1uwq~?fA|hB$^qR@+SFruqi$mCL-&py zw}O|^IY+74NIO=%>eIM^uN&?j;b24M%A8K93tUQ2$TIPl+v@NWvwI$%a*c@?jt^C6 zuc?xO=@$LuJhjf?hdW=Y%!_^O)XYGPoF3!EzIOYVR&0jYlC4znV@GBc_v_{N0r7Ba zhbYA#)c(Je)qmv{d6uKQEV+IR*)c3qdOT(JU`dxjq@8=InWQWP&JU9l$Qca9A6N_X zbag=bSKpp=+7ekYIjQ%v&^E<1#ausSvJFB6$P4($F#$T#fI&d%;?7daPtdH({n~dF zDP)@ok#DT2>KlVe7AML0UmSvrb#FB}Iy!Vtrc!#3A^A~Ccb(KOnID6K=S&6`=v6bu zk7tjkx`aw{H)1@zPWLR_@6#Z;u)-?3fJeiDV7v+LAV%>I?6=ex{ZabMwaDH_E88r( zuly)?G!#dYf~CAdWW_R|1{}P84Aqm$1~2$E6*J)sC9+q(EiReKfs_Y1K*0j~M9fF*s1~k`hc4S^%y zmVcV8KHW9$fTdon?YiUo?+xw3-S%kqxMgcasmIaMzACiGT&VHb8m%W z)3ir6M*S{^m)JzAbku|RFNWUjn$r~X+XAt`XiY5p7eHz?2*5RNCB~dLy<;A24Vn{J z1~i38A!~u5N3fg&mcL@NZqE_WvFLn+T4=a2pBreqBg$b93ijT-Uv&**Z9|&iyt1M= zeY}w6;rwU(A@IBeyK`U&V2%{QqS9K$fee}g1%7jgr3GFBTmgKo)m-~~u^%PG?EsgavGrJfgQ>qcl>(!kTtYd$d&w2KJU5!~^ zo%@z{-9AvpRhO4;R3eL%WwzMbKjfd{4n_lpd7Kh&0_>ArAuzwfwP#Be49&s8p`LZU zUSFOw_Ep}ZGqn>($$IJb7ROLmBS%YA{QJcZpJEb!9Rihm+)^O9J0{=~4sm9o>F%r- zP{h-J%%)PNNgykrv0rLN0S0~lsF z#%b4qkYlO02hmb>-1kUt zSILU`g0xyE`Sj`xPJ-KEv?Uv}dAX5q zKkJ~maf1zXVMujFv)Ctp@9=a4xkkf?jILIKNk7a!c&Dku#dk#zofeQ+D|h!1@TX4k@iM!O>nY|TZe zdj@w#p}IQ7SO93B85qg#ZK@7r{I`H>Zx6HX+qy13e}x`OON`tz;QzoqY#TRdhI9HFof}&pKJoaxhE+9L-Ed;j+1a zo1=H?2037>DWC(Q7qD@AY9@Z34V1GuT$i!~rt!dtQkb33Z&wh5ZT^4i#l!fMq4nC2h9A6tj036LTXmzLc^@5&qa_!6Lc zh5p9fp4~AcPPR)-RPz8{wzQ0OFfpp}oWxYTNtv){ffDDAOsC*v8!nG^59x#)o-J{% zSnG&7B?WWZ!P+!!Zbh(VOflC#1=qcbik1Af-ST#bTIn29heN z<1hXadUKDNAG5gdqB*#}AJDF|82<6JWHZ30yZAVaWStm5G4muL#>-zbr-y9P>#b)l zsgskLBw8zOIl{PX1d`;W-|T-dY*{$76wWU)cL3VsFXM`z-a4rAiXKJL6s^C{yyC4r zwK?Jz&x^}0Gp~8H5Bjy{kThWWJBVU-(p}ukkRj&vV3`FL*iN)!BBCJY;40#ljCL5;<17v;B zE(U-<0cH6M3?p6;pL)O3t}s!0a6F6$<)e<}0IS&E7E~Kv5JVmUc|kE#=BsVij|w8x z!#g-)vWES;q0B;XB)_=Q&2OCaIU8h+2TR1TdU_mrEF28KP?;O=Ndm4zWtEQ*O?Ah+ z!bf}WlO!L+fLtAF%K^?yqtNP9kt2fJDO8f*ZzcNH2j- zSF6?zPv5>$bRNdu&Qo1cdc}iZYWOcb{}Rd_q#g|o`+U!k}q$vN?apUQm zpNsCo*tP4u*sL?>6h?zD(XDHd)5GMqQcYE^u%Je%k@O?(kLwlgR%~Q4?Z;FD@vH4b zlK*^ao7$V)4Ks}d8PuYIKwm*U~n6-|{5r**5j z?`6{BX77;Ev)1adQJyzx0|pK3;)HNS$Cm4sZTCX`{rE4JT0BhYT zi}E=fbh*s0l8zQN0NKd%xbq)orQ{x@{(z+#{e_aaWjq8aEV$!4@}u=@d$IckT9e%% zU7?AC*N1cL?41L2l%x5V>vMx+tbznl47+4-9@GS(f=@tL-ry1^J+Y$?c1jZRjvj;_ zQZRu&e|J9vw;Vl@eYp053JCfmHL3;DnbF0J#Hh5ZFZ3@X=O17W{L_?!G;HG)E2jo{ zFm&@JkVkeYCgY4&>D^t;f^R9?P{Pd=` zkE7ZPMBlX0>m3GzV`KJO2$frXHNajsOen-i^eqWG+Ciswye{80!}{m5ftlHEtG=@O zRWl{v9Y{q4V?>q`ce-)-V)A<}AU?muS(x;p`!ZL1cyjuy1O>63iggGabRBF}xEzDu z^Yd4AVQ^S{OF4bbbd6=>v@x0g>Y~2{EhtzQ|A+x4ZQ^lbJtbDC_y$)0U7>xXX+4wp zw^9(tGNC?kL19f^x?N#!lQYlE#@((DXx=RVY;hm>QGzKq*bJVx!-<7IxLhQt%MHf( zKp(#RLVDL1j(h!fvlRjJjbNPRAgXkpN6sq#7me&wo>cx!_r~z@nq*j=`3ZTWg&tkA zdIwi(169bP_$`Yj#*PQ>t7ct`&$*C{-ywL`WM3{DTiz*Eq|Xq2>SedOa(^RQyY#5; zp6ECxyHZ7oN_)^yK-ys6#upbA6rAxXu$h@TJJD6jc~usSezjphrww}iL%_&_$9fCT z#{5_Ovqz+IEf@58C=N)d^0o@*R^CbT(=1?&o9(}WH9qKyCHmKDeaX`;`{bWF0mN~Jt#Nc2#+*l7G=?N*BU2wBFQU3`Fr--R2E>%it)GgOf zhHbZx%$!!d3%Y+$7`NEcGJBQ#^UJq+_3Es(4B`2PwhsJNr?FrJ!kZ9mV%FhNByp}8 zzZn*hd>-_<{Wzkufz!-KXKPFg^=zBu_KD2f_u@p>NG$n_U_|w1g}tQH)%tH$_gt1I z18OO+?t$^07)9Rh`Cee6djV3m8JSrIQd(}VRFI&&qfX=##kTPXdlj7w2?+AmvHZgrOzul7&wuK-cJf{nP)`!H~Jiv zLiVL{>5~h|zwXaJ%Ig0`>Bf&H6JMSNLTaDYIt=@L2ng}hoPpt9)|AZ{G^C8vUCNS^w9c0cTtlYS&+(*LQCUx-7!hyAHm+rF~zvVMSAtz&Y~ zu&daj&XE*B6IHnrau7I0sn)^8N9F8VoHod`YR&SCU3pxZwhzW!WY3Fjba3^1Ph_o6 zQBdKZeg(!+=-TTy!|L(Ht_asDU(gX*I8gnj)b*U(GXdP-$)vYKyFqtbzqHoa+03L3 zUw_AK?+o?`Ug}u^Yj+7HztMK_6C1xXavT2gn*4D=swE7Xm7a;hVPC>iL2uC)-A^J&uJE3&ct{ z(aRBulGL=A_1OYa&{TizH}>`jPuW`KuIAApee-W^yd+lCR>9nI%rPliH3NLn_nNPQ z%I%6%HovqQzBHC&-A|0@jLZPly3(*g4OX5CaJjaU(n&!keU+*LPEWp^;ukNEvKvt& z9Ctk(3573kzV%MNKorZLPSA?RvWU{nAERkexCk=8xEPZFMEg`WZIMi+oNcZZYj_ zl#>}(VCyRZNxtV~YBb>mw`kt?Nyk>#?i0_e%flu`di-~9gcIANiXP90P& z57;(NzM`mJgU(bAmdmffvc?Ltv(=eVj50Q;tC9SuvC;zI!@k5ev_e3lt@YLfc`JBk zgNF`Eag3s)xJXxaiIcLgg9d)myMazm)5D!NJ`hQK`uSzrXOS&Z;(tG*ql|edMIbv2vgNQ!t@w(305{{8fid=cU?|y`HejA`x^G(CBx*tZ zblg|_a9@TAd<*DR#rn3lq_n09NMz8_Q3jKhgasjd#g5KS+-b>B+)PLpV05#cBQm;v zbG(K#%J+yxR?S7rxAGU6P=2-AJH}5SU(Ozf6&rPD3qmk=w4I&2Pi}a60q#5n5X7xI zhGsA!0Y1GPM?wTwNz<8Dhg3`ph+nvWh5<@!oncjAfJ;cXVr+t{=|QmN+s7}Bc0K3{Sy}+9A%(`+f2CzXh8AQ@Mr=xJbG?Nx1_i}>{>bI!pN|Mty$*_ zaG0es-?SeI&absirss#`^u1S4XB4%Ybfm=>UX-hrzRC_hcM2w;$2Q0f)5R)6`QFuX z!Td0|^NaL`2#&Om>oH%))}+O6meo`=sGLj|FVO2|(=50h|BAlyfV}c=kDXp;GYB2b z27siz+Pe3F`ML9jy7?cr8@<@i_70s;+sZOZIUBCU-s;7!j{3dRO8j1ep8OK4u@h@C zdhQA2st5qfP9gOfxXW+MWiETUC-}@hUOGvm%y%jbX1WcHPTWaRHSNm}-=HH#O>~BF z8AE`0ktNa1bG?h%4FX``^W737CsJPul5;~qs6J}uZH0(yiq^x)dZs&WQ=eZkF_(RW zlu6u_b$lPdljhi7EdV>45v_dwegl*+l@Bq@$(23GFE@Z`7>I3ynD<_FuR=%42bNyA zc{*yRb?a4YpGii*1*$>Pb4+g|{{lvy!oPEMc?;0EXV@vW)u$TM(_{t$K|El-`Yp#N zOHE3%l7R{C06Fub(~JBgRw1$i$njc(n7c%~j-H$Sc7N5BkxG~dsr;e{W78nep+>>H zq2Q4${^E9_>zDLzP}wnXbRql)#5BV96gAU_86MLHHKw29V4_kz;2WwN^lz4)_<+2w z7&hDhUd8)V`mtTUqZFQEV0g=QH~}1xA~Je9n@{dUSMKM1R94QZq8Akk^U=P%{9uzT z#Muqv&L|%T>DGx|UZ!Vmg#zpDaOf%Rg>EW^D%TZFe$htLAT}(4k)4l3Xo40LNX-;Y zNij}&HFZFl_94|+eWr_i@{;aQa6rsB*)4xliEB!?e$ZYWk5a)+y#JzuQE?3t0R8S< zDA|oIMX{Nxr!+A`IbJED#0~~!z`qF?Ke>< z7(Cz>^SUQ7TTGsdBlDqfrUPOCOUA&L!E(0_=kJx>ExyS4Gz@(MuR4fPM<>?K`8FCn zt6su9u*sk?X*;F0CB^5@i?X4#rC|a=j+rLN@(1`PYyARWXJHeY1jBjvr$u=n(o= z+FY&q4hj08QhMQ8h1Jd1oEJ>_9W5_fy*3&RC6*H62_N<;lS{yF2TZ+oenAlrldt(? za^a_fzkLlhXGLMqOykZVp+1I{*y6vMFrZjedFwc zHcGJJ+FG~Gj!Z551vbk&&EytDQFO*aD33{yDgy*+H3~wxK)!cL!z=4C9nO;Ql;ozO z`_s+ib75(IM<4&&>nWO`$M_=B;}?Hm1ovke=@%EkgGgvr@dxyN_6SxNM$~*962y<7+Vj2knlt61Hu4H!OF6Rqwt{fP^$gxS34h4 z9S9vt4x+Yd)zWM_6Je@suxGaF!IbC{Z)g7ZADYqg$}G68jO^EsxMA5o0n!iTHKOXT@Q;@FGhMI8`L_y*wwM*#*qj{4ZOUS*ZI9d=waoNfvp!N5~AR^ORQMwU4WQuYEB2Io&Sho5wHH41q)hrf(K$ zA&PKdMh^|A9VeZK7Yb)K9Z=fKMr2q&wI6L|#?dOc*|*495I4BH{{0!% zcVL=i(qI)C)3|wd4vgi~Zh8QNnCvw|*zt`@3RNx9ZYPj{5XV9cto7|EITWc6;6t>I zQLX==Hk+7>IJiY-=L9?!>$LBKOlT^R7cH(-R$p4|=)bK2ngfN4eyIbPuCay-RO`b{ z?O9I;9>IWYspzQFgHr6Lokx#?hwEqi_j9EctbpEH-+(n7=@HE@910o|M=SzJ%;*?p@3+z-eMrKE9VE@wYgqU&?!j2TBGZGI^-}^@DJu?5Lb5wfz@)_e7`-q;` z2CpWksQFtD@@7uG_L*hZ4XE4ry}hWTM6S2gz#i`I!!PTC_huNlA&-#lP@GMfTSJ@f ztPdhU7UAnAJ5T`yMsJ^*o?_KqP|rZ@(%?h6+SkCs{j zlvu&#rX3mv`Ch0S-JiM#r0n|9DGs84Y8$4pZNF7KfZk~NXLz}r_15UM4OGQYD=63g zPu?A344_(_R$B=h$!`5KXQD<3OazGXhbwS;BXh~LkEkf3dmX}_zcnO?+vuFF-LfuLjbILBI zQM0YU!wk0Nfb8hnv_H~GOOm7-vju%%3f2;^r6xs7s-&LV_ofiO$c55}`v29V!f(f# zq@?1h`Fy)gky2FiD!(ed^T#fPw}JEqYL<%LmjiNf-&(BI!n~`_J-s)|y=PH_>}whY zldpkP}BZ*bK4k*Z!nQ+SlMHT7O3&) z-KtEeOoG}EVO6Dn(%m4$cEVXzQ)5*nkn^_pE(Fd?I_Kf^Up*oggEwr1&=@wOeig*@ zwcTrH?y$mEZ~#VfhMF3BpkgyE>2QK~aQ@h@5NS}RgM=G>C?zw8%8(HmtcB|HuBSw6 zh8^~&mGRB5Rh^o;cR&`1A`#oN2GHwpu~%4O*A#Sp%514=s$k+~%wl?CN^<$N`@!oko90B2z#BhkC- z%X3f@-vON<@9LDiE#6TtC*dqiVuS~P38f&m5A7a!&4fy0=>oPYb{nFdPI$4mzf7E% zlGfj9bg-El#-y=3dg^9uDL?0T96ojQ*uud*74-N~I;+iH>Fo#SItKUsQA>9I>%Rm3 z^^zWEd4+#tjlpcw|Cz1upIHr8y|hrj?pAkqE~Z)ZZ>P<>htxfU*3s6>yE?Kw^Uk{? zss*C4q-Xoh6VNzG1tX?0lNEL_d)IsFyklwDYU1YY}h}z z44XCPaeIqe{euBgpd3^0uV%dg^EjQyX|U7~n{qg&p7ecs;}(D@t!PG{DW=DPnpHy@WcbTc z-qK#^*)E<@8Z7Tu%8)PCxl{Ac{QOBRQ#+8)}8hJ`Mrm2 z!&a-971Oly7NbXHq>qm$X-VS$w;x+jVSt{B|0q1Vr{8FB=iiTR|JPdk4Kdx`9_Kz3 zdboe@MHgtb+g;bz64N_n@?GBdX>n>$z3afN$qxiKx_`rIj4>LFs{(WJ9WrMREzmA8 zps7_Zl&;k#n&;OQcn)$o2SX#V~BeaVpxOPQyAccy~{YFY_KV0}M);Nx|4U z{-r`f4g_Ia#{i*%XX@OJ%$O^}7Lz;K|7_sWYSFQ&58g3<`o+y%m(p1G9{*e${O6U31y7}P46ZwX*cxz$5>+j=3bRr?b|vQY+s{+cwSl2S>8{*y-((Jvf2&MnE7M< zg2Dc#;$Ixvn+GKL>p{`-|H8F>pH`M)J`e*&Hg&-dTUnAF@f8H`6x*0daJpYouf_z| zl27Yx?mE?gVKyr>6#|QVN{sRYGpzsG4+{XfhM?$VLV?#H?rsY3c0}5C)c9k5MEse8 zf2u5hVv@{Md*!pJvU(T1Rp&zS<})^9P3-Z*|AVU(kFdi`@*Y|r^iOZ8+K#9eZi+DI zjXh@aboQBDhf3xKa>JyzT8s2Zp;#O9V zWOD*z{;b^{boAC2^RU~Zr8xpmR{{Kg>E!CelD@Y%=9=qGwz|v8x2;Vrom#idi77U> zRhFWN5;|q8nTjb(15!e?-BrvvZ{=!fC|a7lNn)ab;LOtNEniYzL`2F|6a+Ly6ap?* z_htXy-=DwV^L)?e`JD4P=RD8vdrnP;q@KL-LbB#P?=@4yk4shkydo&sQ;GaxaHLw5 zP*=dNU#Ie^2|eS#pDx^GcyJ*K1uZTTZ|&bsd~eKts;{?F0XcOi`Cf`0{{T58r10!S zh-y4H*J)&La~*fHH-({o0D_pi%sDMkS9v%}>C?pS^iZ!)y9|D3_v<46$qaJ+l%X3e zj@$anOD5_%Xne^JlRN4W*yBs9|8Uim0b{>lT39W{6c)!?&sdI0sPm6d+?@1k6=MDh zX7#-x_wz$`#hz{_f(fP?sZmCxMX;k@a((w%X@w? z5u`vvV)_XAg(#wSX}p&^er-M&xnG-Mcp13q-80&tvk4pF8dk0GkUlc0jKzf^zjjW4 zO~!@-l=>sSU{gWwmVp|jNat}~C^no>9X8Y^-V6cr&wATHM#j35%_nNN&Har#c>j4G zrik3cSvw4-*(J;X56l4giN)+ep!oBeyql7*s7@QN(>uLIRXkNTuQ76|ekdo8wnzxB zbBxVQ1ripanBm4XVO`iPTzdf)HQV`+c{P~2NEoKNHQIWS87hb@3#WR`Fxtvgdqf}W zA=x6FA};Z3_cv%UVS3EplNB98xkfncl5?CAC=NoYz zcOg&I##uY^7UdH_lIcNrQv49rA)`>W(2$8fn@qmZGT|%A9{`z(M*k`GJO>x+p;kmH zi+C@o3z`bl5VYrHxeBO0F-6umOqX(!Myuo<>KPpx#bhL%n_xpkIaL!M)9HP zw4r&c%j6wwcA>MMe~*zqJ-G;r(7T!Q zzIsVRZzbH0JY&PRCJdskV3e<`L%ZIV7r15SFI6DNBqw^7dpfCd3`XNnqvVrH zzj2t>ha3%gW* zTeFJQ1ajeGm6^)_Po@u8m)o z;5Ji4KrCxFOPJ3~a}(+(M9 z(geQF8DA2U6y z{xP(n(SpK)N2-KM>EpCdGOH6|Np#0Lf#ZN0|GUZaqNep|U|*Y{5#S1+x4x(5Uh|9h zhwIanudrBB+475S+JH?Baxm&$A@$Hoc0nnoS}&_Zk3pz@24dOJ_jz7!0_ijn=H!wSIHDUu}Kp(68Ct}$v=CPX7IgL^4fXKZ4|*9 zY4W;L^6FTx)WI93^oPTALgGF(_n8)cYcq8n`AA9K+#ZpXL(Ke;ON804B)TO#BN{_g zwS!^_W24z8$DY$+Nmu>VBV|K2MQ*h2^D04@^2Jxm>>^(kjAvxC=;|3gJ;{-lbtb>d zU>3owP}Xh#U!zOD{}YmDc4eK(zzaJbX(f7VutB7Hq&O*rj=4Rvcv3)^sfp)z0aUJsTmY`vCIgWlY z2LSTMq(qOg2)967XNhxfuf{~DR>bkIdS%`90nz)|*Pb36{?b+|;x|MqK(l}lc@h%` zY70@RW%=8e6HV3F0Kh-Fek~YR7zN(x!nt(o!Vf&`W473UR|!2xdO1V{ z*rDdmUf3ZDT9)a1B6cdnlLbw2&nz(;`j4-a$GRMsG(&F$COb56GW3|v2czw86!sSh zgJNfBvQ0QWOjD5!POm~X+`+|ZdP5?~w)M;Q(I!z8?M09meK6I_k2nz31ik|a#w=g3 z#ZrAW@wk1a?&cQM6a*{&tKjm!l_D8uFKl+)ALbvcda_94=Xh^X7?UN)rnc|D#baVN ztQ2tF2NN!B*kZ%vFXG6?*%J%i-_4hsmQtm3f9w_jEg`uRiAKUFKAseSS0J zt-!g-OM|@iWvhWrj(J2|};6`!w+@O1Ex*+ZL(Cf`Tt^`Ot*)t?|ldVHI zUw-RK+jXz_G~PnOLMDH;#yy~Q9dZPC;3%M`I*Tw3n*Lx%#s9N%`dtd_6d@g9i0BL3 zy(Wx0WE=eY3{Z1?UD!@Gp!8=Zjm>tiNu~V$&YP9incJDtk3#HXuz(1C1G*k`(%_pf z+r69wJ;7AStkNzvHf_&$CsY<4`jos?TByvgNVW_L@R^(!m0c}fwbs!2ZE0etz&+wG zVYU%HGnf)o*iLeUgLY4I>2%DpOb>g64v zZ*SxfE~UtIc4bX4G0-Cq2E4QKeOvoxqx{lq(d*rt6fHoBQ%o8vVd>l$8MBi2FT?tDw( zdW!T&iaEL(uqv8H^c@;tJMt&Vl~DMm0s)zOmY%|7J0N&>uJ4x)w{r}fyeXpXBG4if z@s^GafLbvitU+^_f)4gp&YG*|FLfg$%HXdf)^3YIOp=v7XkY}NX z{3t%L&26U;tf7n3dlS^eyA0wqF>tlbQ_{BwXROl(J!NB;23GGS%;Lw!Hkx4SLyGgf zWko#1u8jX*at3OU9{#^HFzzG;pd;mjgDCb>!Gq!Z-WWp1g<=kYfjOp91zfBs{Sz^A zCZ~Jd{AcP=r+y?0$MQ#=a?OK0dF{!O7{?{0(fly>-zuq)r{5dgVMPZ){Tzw0NZo;tEeyQzxek2zW}t(4SE0o diff --git a/r4babs1/week-9/workshop_files/figure-html/unnamed-chunk-8-1.png b/r4babs1/week-9/workshop_files/figure-html/unnamed-chunk-8-1.png index 5986918a8dd7cb7b6e2d187ab0a9d356263b5803..cb69855bdfcd7e68bd1ccac8cdcbc13f5a00cec5 100644 GIT binary patch literal 25296 zcmeFZc{tSX|3CT~6DnImLMRnw&Au~55m~bDk}wF_8QYX36_V`RB>SE<%TNhro$SVz zWH*Fi?0&DY^!|LVbN$YBzUTM-oOAuob^a*Vyzc$JpZ9ZpJns-qbtS5k%qIZ=pt`NB zs09F|FaUslIt~TD)PKA<2LL2^%{w|b!JiNagoK0y3WXj!c8rvil#GmwoSgjl@#7~> zoH%*%Bn1TpB_$;l6%{o#H4P2TsZ*zDX=zWNK21kQM^8`Bz`$_k%o#>T#_jg5_+o&Egz^A|2$;Nal6c=6(;OP4NRzRbzV$;HKW<;s<-SFdt& zbMx@<@bdEV@$vEV^IyAmO;Au!NJvOnSXe|vL{wB%OiWB%TwFpzLQ+yvT3T90Mn+av zR!&Y%US1vsgI&LVT~Sf-=FOY8ZrxH+Qc_k{zJ2?)ii(P=s_LCPchuC>)Ya8BG&D3d zHSgZNtEHu-t*x!2qob>-aQKo zi~INQTUuH^c<{i=%F5c>+Q!BP4u{*?+S=LK+1uMceE878!NJkd(aFi_(W6Js&dx3_ zF0QVwZf{)Pd@bl--LqbAcym;~Q<;z#EUWJB+hJ}TNhljs@{W>Bd;?0{kk&%&a z-@c8Cii(boj){qhjg5_qi;ItsPe@2eOiWBlN=i;nPDx2Yp-}JMy-Q6^egFP_T3T9q zdU{4iMrLMaR#sMac6LrqPHt{)US3{)ettngL1AHGQBl!{4i z%PT4>K7RaISy@?CRaISGT~kwoMx$$MYwPOjK7IQ1`Sa&5U%u4W*MI%`wV|P*v9YnK zsp;FdZ{NRvZ*FdGX=!O~ZEb67Yj1Dw=;**;Fh73$=gww5?(XU7>Fw?9>+9?9 z?;jW#z+$n3gM&juL&L+vBO@cDqoY56{u~<{`}OPB`1ttm-@hj&CMG8*r>3T+r>AFT zW@cw+=jP_-=jU-a+`_`b;^N|;KYx~%mX?>7S5{W=c>L<>>e|{Gfk0SaU*Fi+*xcOQ z+S=OQ-rm{S+1=gU+uP%>7iR(K{=_3?LstM`4JQ7BgikD40l<0Sw&HaiuSDFicP#ys z-}IKtePidm#BquCwqFulT#_N&Mxh%#Pg11e0(k9@&yi-7Oe-W(R_mu4}_dM$XA$jIUcl^{A=c%psBFg%QZ}M0#+?!1q?CBX^_0aItAgJo^SXb4KPK9K| z#DAZgU7#5@)patYrln=Fx=n+8o>fhTQdE2fxk4LZ0jMZGIEy?@%k*FPM0$LB&LQHP zv*T@=3$w$I62;S-PA!eNClb<{PN@S%zW^N>fPn@E>Apt%(*V^3L@9}XmH>Jf74Z*P zPO4e||5rQM5DcukW-h%=$!J*siuwh)f@CH`L3-Ekr?cix!bZ}Mc1z?VGgp5|`GoQQ zUJ|!7FqeNO)71My$9;gC4M~z^0WcecMqP#B7`&s8b{q3aYI@67<6Fr&cM)K&`sPYh07{+B@@}v2D7HuQP-%D=zXEN$EDw~q>qyG$j zPf5wkpZ{Td1^wTV2FmhXxzw|zlLIas_57bAh#ELa^4 z9R+s{5^qsn4G{xyBnR6}f{u^pax;J;Jsuy>=3~xvVZK5LhX2&Te)=<(y869>{`0%g z7O4k;GalckZYp^IXLr!kSY}dzE5JTheFHd&dIrgYjr(5LhaOdAn1(fX*m+pao;;|1 z+TmR6pA`d^zvD)Pb?af*FqavJTaM0>TW6(m^UUJuqum6L?~pitL9)CKM)CSvneD&7 zTKXccf+J#lD4Oj?_trP(kq9=0uUns-3sl1|Bz}RG%6^nrQ|u-^c?y%F^uAwObLN5~ zx?dVoL1YH^7h&?R*%Ty#$ghk(){hu>iC4(>`NxYG28>!%x$(rU$B>%>2aLStA=0rt(joe+RyWB3w{Ibtj{1BRMs7~a zq*MCOH?hG*hKTDoc@CTLB71F-{0v}~*<%UJ=TI zN3mmAb<1VXuPL`WoODvWPKvrr4Ql`gv7^H1asnm zdcZN|`EbZt4*yxP#22~H10*p`qDW4_R6#!6^zhEF325oNFXV38e>(&(aEzyTM*3Cx zU{fmRJD-p3uMlM8%OnT~moFVT7cWp>MFFK>iI<<-Z1-YBygs$dPNK6^4TSW}{-~g= zoqH%0VIS-am*L+1jrB&idyrhdi;h@zQ}b1cl^AXe zB2nm+W=-bqPGJZ2vkR*O7BXiC#4?voh%&=&Um}X1moxR`2(e1}+#Z!C$#k|yqdsDU z`5z}ymuQJIY$&}>6}IO){#&;xTUT_6rz2YzP&vIn;vELNl7NX!JNxaaH?19i0F|+0 zpcH@$%I1WbC2rNoa_vg)(z^5dkL#^oTpaOS^F}Y}U2;hr>{NJ;U~fov>$H6C#V*nA zJ|p;4`znAo=i8`y(PN`Jufpzr)qfna`qXprollL>o;fXTl>RN`n$GM19lJ?V9zyh^ zzR@r7WX&D459ICh)CgJ>x|{H;x-DF%{@m19w=^ab?Ndm(hFC#>!+8*S|6th4JN#LW z5v4F!Z3N|*&Pf=%=G-s&@LVO1AF%M`! zyW|&E?KPo$EL$5D4^G#_>CUF!;$oPiiV4%6a^Y^-q76S3MXyejvC(os`lG+f#s8)n zEH5i2P}Qc@0HOTw+K)pm@(wwE*-d)nRm&6T3$sIQf*IA`buk?8{OYKiLSjvZC|v)Sh~WFs>cA>t^W;!&Cd z`AClX>{;oUp>-K{O^U3o~iWU(-Q~k>34PgG><<8QEaMj5BT3@CNAP+OAjUFObp`1x(cZt2ze%a=byR2{o)N>C$AdT*f^T z%xQ8|s)KSZEI6hu`0IiJHEeFXgPg%iNQr5`5#Q3~LHf1#F`zL)SL$T27A%(q+i zbhy^8Av&)sO&dM|xTSgUr_a-ccvK(AIB;*xs9{a^i!egaQ46BtD+!p+Z%G#25vzw1 zXA54%nsZ1t0)#In>C#{6Sg-@rzqE5sk6)MpII3lWEHB7?wIwayo?)_aL%G@o3 z^_K}}T1|KDHWIG^`|=r7v;|q{h-TQ4rhOUW&~G5^=rZg)_CrzVz@R8PhAPzaF=jD; z7|o`MKjaN7fUHrR4BX)z9+}E`Abn!Vvpia{cV}p$%+-R;2K@6bPYoYlG-sHGZPPY? zG~3gn0?AQh>U>!9Q=#>m?mQu-+?d`Ecx@M}u>iHfEG%nv#yS z!y z4T0sB&WqfI>To1BG@r5E1XpzYXy)+xk6%@5?ib>b&3Ps9DGcsC<+6`lCWj^u7Z7qW zqJaNJzzcRyMTE?R-pW08O!L={D*Si9Lqkz3GQ?yA?L4=%99N#(Qu=K;@ap?ETh+ti z1zEH|nKDn-$GvrfV_!iJI|XlOrjoZE^^cJ!Y`u)&BHUmJ%?LSG;*l|XsTtdZGKqV3 zz^O2$k*}axURPmrG1L{fc!r5;o|8n1qssjNV69Y15a&O9FRXf)*$p9*sD-F1R0Owr z0`==MAe%I#@rBKZGxPD{se={{wJG&oljR^iokq5=vZqST9MU{h?xb_J{#010G`J!f zOoj?>RN`-0{^r+7FCX_!9KfASp5@;iJA6g<@J5XFeDp|j(C@8{b{0myn1UdT$V;yy z4%Iq^{IUB@Xi5mN$HtR;s1Fx96;59HOOpN1McM+ORWQVnD!V@|HuVidZ@qv0_C~tX zi7obzYdHS-laoi3n@&9;BO`hX-q0HPW&8yHQKy+|oSl!hAH*$urWcTbIgPwf{IHi! z)Dmzq$2x~SK#jrjc;+9#Z!b{!w*-z@@PTU;R2vj)!p}`zB&rWy$Yt8(FCpQ7ehI!4 z`q#^`uaN$)Xk7aqkE5&hf7;umgg6az=XZ36jZ&mrAm zZPL|$Bh(SRv{VNUs8)H}ce2L+{C*fl18-me^;Y4-+cPGITBH4!-`AN@^cTu<6aGdlt5(R=ce>pe((t_R&&d~E?(49`<1_o zvYhe!=e8K6hsBlIcXByG4*eXM*HZ6s0_Czew!^&usIo!cK+(1EPYt7Vh}6Y@tw6<$ za_#(=3Q{OY73aTJm>h#@t&kpu+ipHn>nGgfDl`tEjTU+DsOt^tBBcX{qG7#Jh263+SNa6c=N zVy{%^&4o9M@sCo^BA;PC1c@pr)xMsqDIsomC32H4UoD1BogRw$WFMNcX^(UeaA-rw z69uOrOv8?xi7kpv;}}@F!o^__wdzlt^?l@Qmq}jq{&!ThNfhFVzS*H-Aqb0sBl%w+ z6#3lrc~!gj8`H6jfFW=BvwAN8hI5dI5oBSo;9k!y!yy#*AEb>~;|c%gI&S zkd&MYLlM{M-SmY$QMQ{+Xby?Lm&$}B8%e)=LSD&w_~i+35+c@?r)gk;i?gQ*FL#@7 z>KBhv9jPD|Zn!_m3X)tckpEThYgJ2iU!H-d34;E|p{1B_l_Vaein|P6LCJk$&?!&< z9$2b0C87LGMQbidSl@M&;$R{k{VAOA4|s#-k39Rbqj}ea0Nuausci&Eeif%|Y%vc1 zM?J&-*#10kvShdEd}q1~&G~NLVq78sIp{`A8S0jTzt>#cyOP=4q;Y~B)FYUjAp7p* zA713l52;$7VQsC6nj_RgHd1f z(PFB)dHf%`Zf<4agcRm8+BCXKiq{?$kRI&7k`#bQ=h%oc-4>oQpnA-A!*V_ux1?{f zn*Oq_lJQ_;8|Hv%b+uMf2M6KOT*=IO6mOZjO8&=lO^-May)$41gj(xW2L}P$lpC(e z1Bu1%wkG!*@|)|<9qf)7IZWIRsqSQNpQUbU%rElQ(YQ<1a#%p#eC~nZ0Ue}L4E8$6 zyiM#&g`&1ZVKI#b>^XN#;;`Lg0(WgptKayM(8})D6xn7rJ3H-S5V?M%1d39_9y_~4 z)s&f8$w*$lIrgMC7oA_P7WK%Z^*wweC;}p211uCKh zIMZ{M6;q!1b~d-$brHA{^uwD9a}z9Len6El)e&1KNqE>pbP6Qv+7vd~hnsF>Bg)%s zeQ$)4VZxHkBrTVR+Rn)vbY^TK*_;cZa3qfHwE2&~k`#)oOkm#^XA!f@S67i+BPEqK z;d($@mQ^J|h`mv9I(=Se)}(R7_B9~#agFj$!CXzI0t9ov>c_ano=SdB$eyoyJ_+WP zR+pBZwR|LHoV$o~!8fVJZyW$s-mL)U$M5u4Ks~&{?dDE@-LiI>=&tMSNgFRf_MOB0 zy_9&Z-cpB=w@f@5Tm=yLJzQt4Q@c2$DDq5~zo^#?VRL)%55+>1Qul}TP~)C(Km;v? z8}Jlr6}-s{_CA5t_OeZ$07ObC1RVafd~}M^%IT})?s2vHS7$>Ao7sWx(NMY2$+awd zYBH8X&u1YRxd0lcm%XE_aS{~@EN|gKkgB;lBLovcU7KdUa;8RHVc4hL8Ig0QL#=1D znt+itbDAiY6jJL<_Nz`O*A^>FF_sSa+<0roLh-#CI1@DfUe)~pNPb;QGdXkv~FdWo?h_mR(@w?P2809LjQB!EXX)&82LjxH$v?o;>!1eU+a z6ZN=9hST3#j<9!YBH>QdBCKokO^31&ZSX}zqVcGXDu9cH8%Q7}^@13J2Xu^;U) zA%kWAE~6$ulHzKXp4C;?n|=Qr<)FV^k{H(F|H2Ybvu}G@=d|-{Z+@?2if*Dr#0F4F z*BBI?@A>Tz%8)H^E7uIjOD2oLBA6cTAoX}O8wh_3^-6)kl z5(iSJZ&k;T8Sb99fOu(us={9N*P=MN!=H-d9gFx?HBX)V@+bNaOi5s>>`1NiJ$1z{ ztLlDDMRC|UpR)d<=8ue}3^j6dr-@9etZuIWe=2q4G;S3QTIv)1)()g~(ct)`n(y2B zbINfR?(@hiasH|6uAPBbn+n#OE`?k1$RMK&(7GdCcGDK12(Bb((Y@+-O>5FhcF-so z&3Qk-2w)V2ty|a6?~axNB0ibiks5iQYsjPK8l!%{V!YT}`w?ExG_&;9qPj-*y1mQ# z2~_r6bWf|}oglHnz3GpNpq9MwtV^iPkCOYAPh#wmfTK*i{ynw$zj=H^S7J!y5=NBTuvIlC~EkH6p>B$Zop->Bm{Q|57!sf9&A1f`gNw zW_Gp%(!|d1d=ZyFhB?QoiYCpM5&-?oZn*UbikXT()FMBb&oqd~v{$`$1&@2ttYx#o z>6Izk;!FJp!aFn|qF;FFSU#3Z_DzPls zXissiZ*R5NLLsj`6LNC4zqNm^(#3ICe->g+RFmYA~ROy^F|-uF>~Bjn@a&iJjIRTmlxe*V*?!is7iKxyBUh$7joE^Xz#FS<$l$Ka}l1$&l4cL@8j8i8R{Vr1TjC1-XDqD48V?u=q-{~W)F3HmUS zlQ)zxoFywn*KkX(H_2Gcf4%=^lZya=3gLV}1sm+tQ2RJkS1Al*v3pae(~^w$H0x?S z!LU{oWE<>1lMvni<`Niss&#?lzrgmaa*`AZx>mmQX{`BA1=(-JyeF}5>p#<%w_!Ww z9i#Y}6a`>y1v1Rig-ea4NlWurKXqaac{JRL>En7+)mrRI;%|~*S{>Vbwig5TD(W`j zI5j}6TSWE)%d~`FacMgw9mz7cXlnjBk&gocztvLrb_j`A;sHeNwq!(n=$kGQRoe^& zxJOiXSuiHS%vwbdIO4sf%g9a)dee79zJZ^Y9HXpNziXL!W@S$5t@1$MGptb zF+1EiCEvRbjsF~LUgDoGICmrBSg9r0S!l-BWa_}!KBFsu+T4k`Na@HJ&wx(EX_zbM zuN!_OXCU5KuEd)k!*=@t6jNFYmo2d0Y$9@K1_3!1Z3;T7<76mXKWzNCd7RTQ6{-?+ zs>HUd%YdE5xWlFlQ^+twhgNFFYOwE90Wge51h0U^TC{*os;MFH&-v_kcks1fJ7=|W zsc%@dk88VXHAMKIB7C%Q^lDt8?>I}qN?d=C;4OcWD?$692&Vk?`^%c`0?cgBkD=(b zl1Q)!xB46^r8u=~A3?>*xP)y7%UJuTOvy$lPI>nv1V8&d@{S9wNfHFnnb72aPFR!} z>5~K0+#cQva?tM<(zeocCz<~7n@K(=?3oymw*qzsnMg;~JNkwnWD_Ab3%8MC;cM-; zShwo#p3A4#BnlmOvgwd+w>k#jr<57n$c4TP2z)^b>Q=tDoW$Ds zI#tgfwi*fH??9g&z753Jv>Qsk%q!_)*54EuRgt?+rsWS~1mm~Iorn&6&?Z&~^7}fx z8r|$4zh%cr;UHNQ`z3V4&CJNj0y>z$4AShRz2*;Yd6aY+`8_2sFmMYPZ35l-Rz-Wu ztevNOQK`EAFqYs9HsrhcP?m$~mebn4qnZBl`Wbzh)Buf}=Pab375h5%3^Jo9k1!bw z#Amhx=xbM;JBJ~G0#8NKvU&z0l&fnrAPOEqqNr(7)J#PNAzL0-QOpYKlB%&@DozS@ zAe~nZfGhu68Ml8&$@|0|!RuQ;xgk91hOlTEu3i5c3K(q>`O&Kd_1$*<0n+G}oJ_Z~CO zs|F9AlL`X{wb_tl12ez1{g4ieEoG-biNw+yu&&RY*U_-+)4VAQsHN3GlZZ|o`0E)b z#^_0-D<|MBGnQ7h6|UEj{==I$ESFun0zNE+?wozF!wSwYS2kUbc&V5lY`P&!T=;%N zu$ohB#n)V1CE1HjPN97KKW}!<4lbmxl?B}Qdl=ItF(29c5Kt5ANf;j*J{bVU_@S8d z#`zE_fxa8gFoEt%aK~ z9De2{E`#&LfdGO8`wNVi=%wt5=pe~Yk9a6GZ5mQIX|8W}EU4cJu-jQNzPV}Gjx0}F zZeNjr3ulThkbn(MSEuN5B_fGU(Q>6x`d`jjWHB7i>Z<*?r!T)Zu$Q?wEs@B>Pv2tW z4aN3W1#HyZG#&~R2E!3UNH#N(0_$09%NTX$5Z2@qiz=h0!-$}d>`JiW?C0N;9*j zJjj+9QEe18`YS(WEyA;EjAA4Ne!Y|wrxsm|Hx@QY*yuH5pIpB|weQ!Bp}>%4hbNQQ zvuSy|&o=x}P->S?T-l`7$ZbPrc3XXHZ%TLDjL0>EeOA=rkQ zH&GnjWq0HJu|DZd^ai9PRoklHLzM$cNnk6as5hdEMj~55NsCsbZXs(=}Hq!4euI+ ze@&8i9+pxhS1Ii6P2d_naUCL}4{+<{?)7GJx6t+rm&J$-js@o4u5d-k!u1yN)vPo@ zQu~20>BEOZL_7iRUO`+?b;0;B%Z?9EvuD!+KSmShEYWjCHXg}y9HU|Ey3MmP-ZcP$ zhXFcyZCDEN77<3O9_SnGGc{*-e{yvD;+$i(+UY_*G8~&0m_xRg$Pr}&XipU*KL2Wy zq`732iGIXoQ@MA~>R0=TiQRJL@3Z-G-iBmTl@rhXltua<4SxAdpuJ+b#1|QLh))nA_c)F|)&x)dyi=fOXT`Z1|2Tel|Rcd&qw|tVM+tAUl*QRzk z)M>lT=00QN*^Lt@HziScH6wp>?YGLWYoico3jyN`{?Iw)4VVE7m*Rbef= zStj5+W*^@EKqz7x0*B2AzoTiZ?EbOpo*O=T|Ay6oSQ=Zl3T|jgwM^+6;p#-SVGs#o z3UX!mc2Es;_5LlyO#IX(_N#uKk8fiy8i)z=G9H@_VE3hkQBcC5Iqy0HHYPwPABdkI z^!BdXerc@98I`yNbt~!qreb;>Y1G@a>(My}7%2nHI!R7J8X!UdJvSs#>)sTCXZ6Um zLq@mh)jk2xO~VNMiL&WHX$T$-|1D9G9;}%mnmIXRnh@~WIBey*ghJ{$ziA0^siElr zVLO_Kv}bkBGaSouEtu6XQ3yyMRjqghLHDqQ+}~3Ln--uYd3JxXldd(mR^IU(&?k7>ac(UBR<5$?nX4Ivt5Myunk`^v@D0wXnmAH7+xEs3& z#oV26O;q@dY?S)by7*ON_O3HE%=HWu-<%<-ZBtlxN%jp0m-%HyUh1&;{9}r1%kjfn$7`;-d7v1)(ap4RY=Y(R)C-wUZM_l;le;hNsi!5LC zSqnc+e#vih;C>xd=t&fpR;0H>1e>ZNs2I6GjQkm>r}OzV8s$Hau4)J??Rd{mRX{|H z*+b91KIW#$GPUP(IKURB2w?*|kG*%H~4us7D`3gC`-)VPfA$Uc}1->CPF)%8U zAGGIEkvla^5V(z7bL%fQ12DYkJV=!$;sA(%!5a?`%(Iz!n^w=TwqU7IhDN!Ncy}YW zlTt*%)#bEfvbV~fK8o*sdZMp{pT!+!{sS=Okd1}!OCzI%ws83u=TM9e(q#;`75koY z5Lc5fz=16HaowzX5%%y6+Z_>bVSvbcPlABiZ*o+nt5%n@e$CuIPt{7J)*aO0@0E=RCRt=-NSy1_YtbP~*vAWI}D0gdLgd=e?u6J*FicM#Rw z)-7w2D<_VjEP7&eyRFgP~4Anxc~^w1VlOiyx`5Wpj~bv_TnoMdjYyp1?-XS$9?Y5W@F787(zh@%^d&Y zK+TR@?LHo*g$%y)Igkeo$secWArZC9?Qh>&&XAs+{c_TiL8IAqWPuV^Xj*e-J?kk5 zhX8&2fpj?hWC&&6yebs`3J-6tcqSO)ZW8AcBg2a6b)X}|Lb)CabgcM7v+nXqF3tec z*3u;o5E}j*TlO)YmNnLJUx7>d%gA^7u6*wySe*m|Uu(mKI?&te317QcZHFzG-%GV7 zLF5Idh^kpo3O(Ul>sxv_^Wm)%qpZyMo@tEz1<;3`O+L}^`qo)H!02JV`}csmB{yPE zOwZquC!gAweZG*k`8iu0<@H_fGS+S-%l6i`d8MgFN3jbuP-~CYi2Sr-q!fz(H?li7iQj_y!W!6#R?9A zN8q}XPw|utr9j{y(6E3;MyJ?Fz}(S7DB`?y{8~`=<~+p)h$211-z0DyQu?hIuJK&b zaG?Vzb&suEe_cF8Ij~BCId9LJ4y_W|x-lUCWeQVnOpdx)uHtipGOnbuo>m}^B_ewL zrN9f>ts5*hPeE;4TB0bD#==1gm01?lvNTNw@D=CVrreudIwN%Vqd2;X0_AV-SJMGQNdt z;~uE{=W5^#@6&w$z=I}(kH1}a4M574@zOW~nES>LGR38KveGDFo`bB$^s`>?8Ifc| zRo&80^j&!0u{RIYWOJ7P&WdqmNB1Q`G54#*Bo;TXvD=ZDC48u!u&3gX=c+6GNR>+@|14*;x55-FzgU6`l~k$Id% zd3VXjWokscu4L&715v#cu30}0n-~Y~HM&@D%Cb&E{j$NZQ6h^RW51y-+}q3d=rz1#6hE zB$hwW>HMNU5m_!;=qyj5$!YOsJI1YMixgv%b$jNyZ9&Y5FmQwoVvW?B3_{EFV~P9=VWk&Sy~4Z6K<@VH}$)HRmmx{hFUfy{)K9q0XH@`gBvXevy1_HU&`2m8SSbWRfmWzn5wjeMbdX|)YAKkm%dvr9D|4g;I*uelI*En>IYJz9z=hT1eok%bzwN3tZo! z*xdMhFl zo898mK*s$2ptCHw_rufWSAx7}n!eEr=ji;>p+xMNDo!d#!YO;1Fza zGES3-Ga%lue@~<;hG6l_X(~Ry?|Y!Bxb{P)Iv{AimuvED_E5yCc-B{9hoFk&eymhd zR7tzxLL4zz2c80GriLLMS7}Hsspqa81%QuFQ?hGTk4LELEbj+NHJ7AFq;5dn>RO4= zO=5l#x0wQANMPt8Ex1~Ch(i2jMR0M zymY*I48yt<9<0#Im8k)0VNxVjjRe=TD?>r2kY|QN7q8O%jioA*U_`&u1h1T4A&_Kw zu|gcq#*@5_^m&_ivBkRG#6x1v6fkn@(jmd5@r*W%~oymRAxK0sFBZQ|1$5bnyXzgNyE_OigeKlv_BG@V~HxI3&L1rJ=^VpOPC9i8d zUcvi)o6Tzu7)u*@8Jt$c;yL!chE#-{?z5*({H<19Ii0tmtTYbMw zURrhoiodhXNmQ5u7jt%d-;FK9_Efg{z(dfrOlgw8LxNQ@eKG0*lN}o#5E`6oBQ#%g zAg{mf)ZpZtIQYf#g>S{VDd>#gTc1!VlzthVC(T*h$j-a*q1SBpC89_$#`5#tjkig# z+GSszWZrH15ZJ1k=J+DzhkFK-e6js&CucsK3hJd|#n6P?>*u5gbH>x6R=P>JNT=(w zJy9AyglJKeI2Tun_(frY&t>lMjLiq?9{jdQ_1A21a86`$9f65Z^!^xXRKIDw!cmuD zWLSRm_iCf`gh7|vqOEYboU_9G#hPj4bW%dtN;3XzbFHjgT=l58kbmJ#C!(wrOW)_dPuUOsVE zo@abeYv8$#B6%%cVan`I(xQorUC+n${Bk3f6ENX@o8VQvzJB9plZjf-k79zAe4nIs z&cI#@EYa@9&Jrxm+80=^*7IBJ1(-oE^-YDOh8-Ux21A#I@+F~%{smm2EyisShE>(Fv8suMm1lk(s zK{R?Z*|bN1Jd64ZdgZp8(A(o0{x|6tWfOQOlU4P=~e*HcE=M%~U@0G*F zHvvWE=Xl~ z&#mM_*7c$JQmnACDNG(CK4==nX8P!0tk}@osZa%3UYp9nS0nBt7zp~j?8l#=56l}swjrK5eU(&S_962l)nV^h zWu(;+5)d%qh93f-%llno!(r^F0D3Q9LUPnbi*Fv4Wv$^2dG=$VzZw8Pn_J7In#S#s zGytg4bD7D$*?F0C45jT03oaQG9|D;_K{?G{pt0_2j)7J$`)8mVHR-+(@i#|LH)9D|?oFLZ>PfSVMp^rY-UXHgD)~!xs_L9(I|Ew z&0VEm2DXTNl^GV)$Cz+x`PBPiDx1{u1@Spg(Z0TzmHArJ3>joHOT6y`>*)Fl9&n7d z-I)vX^{fkOAnDBr=rJC&sU^$uU|ttqAsg`MsS%P_)zH5}gDTkxw${k9Jjn_0)v{*W zd>RV#+$<5~yThmF&Dp(l6U`Fl@gziL5BUKd641%rG8Qyg)2jC)vuPF%Q5xD)DVT}e2E|g{(NHKWF7bFd7ZcQ`ab}K65pD6r0Rb0Ih%=Y zxi2)oF8UTtKuWI)s(nQ+OPGR?klXnRJB(Ik6}L}-!c>rbnqt>dQ~)n{GtXCvL$s&C zGx)3_n{*^(I?2s&&%jI4A9N0YPOTE%?@BLrTmt#`9&qS;)Zv%yt(GqUu*EbADb&c< z6;NX-V45Lt$$izwQTQlEyc~!HN?*s9)r<0=1GFGIHHn#ciHb+h9cawF_SzU&ai=BYbN0K&%Mj@;!pP1PVy9h`~>Oj{s&xt~;o4Ll-pu5BQd z9RS$vi0=Y?)Odb^JhG1n1ZEK6LFLzPci0P@qy%XPAC@ccr72wm>KITAMhb1U6RmCN z*l}{&)*&V;{%6yfqm+B9?2?h|HzTDN^b!~_fy}5Vi&p}#4pSnS)id9$JfW0}^VZjc zg=egvyGKK1_4LAFk|9RZjwy!3`QfC+O+$zShVy;Onf9}RG>4G#cWD6}@~@rE*Yf{h zXI?i?nq(j5_*13pGNW3A7h0&N2f9sAku8&tKwEe}j`XaPg9<~+M3|5`J=1^OX=z6P z%h`KZ4%jas@-`Zb8n%5D==<;|RG=@6O*+7wiItM(AYBooi7kM{Z$xo|bVO7+LARGy zpnSsm>(=n!L~CT<8>KGo;-pP}s1IXaBN`IM>x##R4v>GufRkQKZ%3j*^@3KD^hpwY zb68Jqxz)ymBg4KV0VB9P0A^BmsY_Cae{qCMX+Ix@h(J$*Vx{b?44u zl6K`Zhr#DIj^OltlBh^OSkp7%L%9EbTN12{<{(wwg|xe*w13;uV^rf)Jep3#RcjnQ zjZml!OK^i#uN2(*`( zUGs6fkM@?1!>bNnF!$oX9vRb@mys03auc}s4Rs@v7=q)lb2}P9jfsnkiZ6bObO5lV zpjpC~!9&446tmdf1#n}maFpSySfG>wlqW`tF-n)sYIT($BHL^BQ(P>4WGK_~0H~qI zCn0vi;3OE#V1&^Qbe{I8ry5tRG|B;BMr=xBmi~Us$5O150#s-oFtJQ^@UU+$dl6VB z1it=(p*9ypD=mihwUmW2qe|{0Rl>PkOqnB0cI=091z`dFvT4r=W|W!-=@I4Rgv_9> zI;fu^;#$Of8+N^!y{6hXMjZtEXp zANZkIJGY-d`Q@q%tc4(rDMy2jHlBVO9l2U4L--;LQX1=_;W0WU+bH8nzVu3xdye{Rw19Fd-Y0E$mI za?pga{F%hR5VJRAcz#g~&Gl7~l*d)ee^zz~@fffEO`)%4w=yvhq`ARDTn~iMFNmnO)7{*0`4NXQJ`v zp~Tc5;^k~@Ja)i3h2F|q)_%$9}a8C{}X}Dy8=54()-% zOcc!6eC|OuZ}*vK%J2IZu7P=-*bQSI(kA*3hb{v*n1(H~_juUV`-iTE8E~gpAf~($ z8PAUd5>IXtLb!+Z{I0swpu7ee&j!Y-%D`!D4@QxAp*j}aX;&djLQ&cG0Bfccj3 zX`4HFLkSb=M8ipJ-9Idn7#hQzl9IY6s=q!EMj)n^uC>30gs3U6$$`-V5rFC)(=JO8 zDeEFBD41F$6pt; znA!TMWY?<55~z`Hl!+JdOjjJ6toiOYZ>QMJu&wfy|3fyv?GvNlyC41Qs(wpkc0(6@ zIwq54jUaC?5o4XDvHq#QQihXky4C;5=FjQ#ntbT`Q6a?cp#NIjVzoS$U8iYSpH$uDj(*o!vyH_9=y}l!K}GX7j8)Ta^pa(ie8MvPS48ktDe~ zNIm&pZK_9-3*-K@%yUS@m+C1Zm_BZIMrisMO!^0^RaBim+vpjmIR!bGPX}v6%I&?@ z(qIcSoz4ay1V|)42mo|DTMuOGse}h97c-U1e)73W=?|vXr7u#;eF@O)dp~u%$y;KT z8gIL}=DIhzx)Kh)B2()55r=#6+qJPgnBvvd`(Val0`YAE&?%+2=EJ;AIK9S0w3JlH zv&Qe-?Fn1)^HhB^?oVzM;qUZRoFl0-S;QmG*Lx($^5Pq>N^uT{A7&>@zOd??j{-2B z9<0m!^LiSR|3K@1*s@|c9~0jhVGs~U&6UM``TYod_v129dUMq_+`K<0*^>ZeJ$PFn zKI=ngKbsWNBJxv6sL$!r&W_1ZrUA+5V)c*R-1X^iR^4VX`#lS9Gj(V62Zo#;4cb*QN*qbdiO1Yj zvlLEdX#ulCMyst*KmThNmYPP+9=%sUZwni2=RapRSU=~r$8-aPkqn<9 zp8qA}8J$u{sGmn==cxr{@R5xdF$BqYIIGcF6W|C|0UW!+||Kv3<$*Uyv>E2vK%>0pb!m#zUDL|1^F>30zs?5F6ph*v0c(5MVT*Ezszvnj9hc(usjER zJ%R#txX(95l%Q@a)0Y~rP8BV z`j0fB(%ASZ&TW&ClSfl+D68;zZYoPS-t@)u@xcN8zwitmpP#tg3&pT5e%Ys4o`cSg zD&ZG?4ZlE#6JP8)Qq95FQv7_MWs%XsE7&d;v)zijzYp80K`1{b)bp4?eZU|l3R%KM|H7sigZR0gkyW|>zUwz zgD)ZO6PC)prvaqQH~UTqPzePeN(BG^w<@?nPXom9yNj*0etpD;&y3b^_HWn%mH#Kt zDjFyP=$x9})d4=-!s@CWBJ6TS?viA|+XBD9+O5^#P*)`w6vmPYbjC$#tY(rz$u6nFa#@#Vlmu8fCy9=q=1k{31~tR z#3~{PZ4psH0wTA7#K`@c!<0)ga#08ogrpJ?l8}UuYyt_%o{e;-oquPo^LKyiTkCzF z_qNyG&-;v1qeG2`1KIqC%hy?6Hp&D-ASybvK9}Q}0zY5#;u-nlN4j$w-g2)Kp>NBR zi<|N`DpbfjLN-V)BQOgoU6Pe7t44R7s8FL(6EAU2cu>ixT&CYdsKy5y*?c){Pg%R~ zDH&1*F8Rcn2D_(MNq61c%{?4CMYNKpP05W>7p`u2-fCMz@vq|};>#qNQa|E}s_NkU zgCB11`Dkr!8xET4s*05S6K!2S+(7teZM%>t#6a6Wlqn|+F}qVc(YuPJ7Isf-^)EbA zB`f=Gb4f+MdXuNEQOx<%U+90|g4p=8ZelLs`6+miUbUH2V?(4S4_OegH z#j*Nm)e#rfFGO5vh@9+Absh|C5GrzMv-}2bCJ-@@tUltfGO#=Y(GS@byt=j{XB)!3 z6*R&joFJ=}$+w^Qsdd(Y_WBu&_T<_-RkjXcu0^dziDh?3$CR0|F{|ZYD)Xpkk9;a> z{okZ2^(3e79E#!Ljl)(3I-cpBu$lv2>I`3`e7r|*?vUfeDYu~9JfRw^v~Jj+)Iv)0 z>oD_EA`_`QV$g*`;e6$05ifB0x!Q)E-;BnDVbGGHr% z<`+ep0BN7vs-e4oneE|;qIVWfVxxhjs21mPL9i8vFYLmk5YxqFGZSL!) zz-nx7!gx8Mw(V`9h(0;%F_CAIQ#tNbSddAWaZMPSWv%ldqe$4yuU7~e zzu%B^e_(^<{H(xVxygkCwAtHP;ogZIRKoXFjmPcd@7M3f4@Czs@UTPiru#TbxtFH7 zXXU$2KTrFyq{h*ky@HdE9s!#y2^PJ0eWNpv@{V$S|~|3yQRS>?}4ZI zWp26C$xA5nuf;rHnbHPL>@HdZwMT|@$aV+g#HNtpHO&3A#29W2e zlWJ@?9kp&5^#E4%_7k94y_w`fj*6R}J*X5c*~|=-H}+2@ON<@Gfs4orO)O1RkbYz? z``!Kcmb)1SRRwOPx5T8fS**w6xC&evM$@~r*O&H) zz2>QXjNHlC06V3jP#oTl+L{9v?$A|~n!&Ms^g?`ir)!?j>Ae4?)f@HU>^Nq9E3L`T z!Nr!pZk`S6#J=^I#^yJ@xF!rysRm*7deUigSBennZ#tjs zoouyM3NFrSaK{+l5?*?^UMYS{c2WQAvf9-_v-uFe-?mj^?z=&)Wz<-b@Y`)f5MHQt z_KZN@*Fs2LUNmRtF`z;)ob57m(je+zzG3Z!xG}Deo(l-Rc*I6erYL~D1F^CpW3TURX&_N z@7Uck{`qV%sab7|5Tkc&4Y0GY3#`0u%$hqUEFY?j5W&8ma9Pj+26xI@g;UUSU1Vx+ z^3F>|&o>+pwf?@X+B2WlA}Mnaw&_osz<~zToTyZ{ct*S3W81Ka#^3y{)L+`#bO^W! zPZ1+4QIb3-VjIR7u>NXqUpM8_gOHzehhJt`Bm2l=8+`a`e?t!m2PN|(LmJZwcWq?C z({v!{uZUbVnEO@6cInY5D(Tqh3}uz(o1BBG&uaB0C+vV9FDk3?6Xv%UNKZLr3aG`p{?W-g-f?aA$etezWiUnu30Cffrv!Y(z7==@Pd&Z!}Nc``*svTvW znmX#BQce?CR&AAq0SM?SjV=o37wE%YoRnlq*`mAYg5xD;W~!{1@{R)iKqQFn|N9% zQa?GB!>uOJF-2Q_#_(MaeYmrWuQ!W-KARac#7zq`4{rQtzVg`iPu;6(+Qu&*BYgb% z0PN1q-~IyI1T)a(B49{!d~k!6lmL`a?KU^Y<3k^r*@;~-U|UO!{X zoyv_m2k;$5^={*4%!Spxs585Ud71-c&AeR*xO0arcg%sCR$wEF&i0QCw(FyFS5cyH z|J3ugc{0xoukERRoVce^-XpcWFw?x|<*g!r@t_AQ_T*Xcn26#$2r{($aCl#!{qy4c zr=wPVAAPp5coc9^$FNk3#ShK*L)WH$ZvJ}gS*G+)0a@G_Uis9fxb)_?E>nO!Z2lD*>AD*)Z&w!pR)uagLp6kl&vkim3c&ZBOH&I+k zbp&X&wai|7!1t34K0-ohtKvrTx%McV%xdSuI3z-p+w>58N-l-tG-FR7NlRu8)9b9M zVus?S^PnZ5_m7OA4`IOh(66I{9r&qQPJv8hCe`^s!~fjOFA^D43aLJ3D+u<2HsxW>olL84my4YO-3lJ-Hpu!qd?(NIv7$`MUZDc9< zZ5Gm4h>Xtvvy9w~pdV%1qx9XYSbD{yC|w`nB9x?Ts7h0yB6Eb5*+O~==&jIk)3j78 rX;tjJ`E1kwOQ`<874DZn-D8`ZFkgXJzl9|FVW*Ct@u>agyR?4+Q#7(2 literal 25150 zcmeFZcTkhx_b2{@2q*|B2uQa93Ia+I=}}R75fG#+h}3{mr37L{L_j)Hqkwb}2tm4v zbb$b&g{IU{LhtqWB%trlZ}+>q^O^nb?Ci|<4~H@L-gD1wr@YQNPk^?j5*-aY4FCXi zD$0sF06+-?0O;4lQ1F-PFK4F#fMQu&UGFCNUkC(3K|uk9LJu7}L`g|WMMXtTO?~+A z;Uh!Hm2?>cSSFT7(N?yHsRZ2=qMn* z-o5+x?^{?{SXx>>c<{i=%Ie|6ht}5CHa0f4wzhV5cJ}u64h{}*INZ_E(aFi_(W6I? zA3t_>c6M=badmZdb8~ZdclYq{c=F_lr>CcvmzTG5Z;LV#ifq{W<-@XkB3JMMm4hac)_wHS2XlPhi zSa^7NL_|bnWMouSRCIK7OiaxC_wQq4W8>oD;^X5J5)u*<6O)pXP$<-g4<9~${Ft1a zoRX4~nwt9Q)2FnwwDk1!jEs!T%*?E;tnBRUoSdB8+}ympyw9IM=jZ1a6ciK|7NXJU zqM{-U22)&I{N>A+l9H0r($ccBvhwosii(QL%F3#$s;^(ae*5 z-Q7JsJ-xlXcs#zZudlzqe_&u>aBy&FXz16kU%!9<9v&VZ85tQJ9UU7R8y_E^n3y0C z2$PeOQ&UsZ)6+9EGejbBc6N4dZf<^leqmu@adB~JX=!dk^{W=M;<8~x&Q!|Kj|MNWNg6-08Rlair4g>#1jWR zktezlge_+al^}6K)0~Zl!5uXk6-PZigGBpja96!!xrNV%GonB0ZQdE2eIe$F#KA@`j;3SgqINQJR)5c)} z2k}Ea#z~Fg^o(22s&TvJVb5;()~n@{&yK+4=wUTb?FHZjjE?k2PZprmt|t9qW`IF( zmq>rWc0l6je|0<95D^AcGj*|})FUBm+Y8bC?JAE)V&Hji6|;JJ*ymqgXGKz^-v*?2-4=B;YIK&msx;+gM(EF#?P3mQfU&%Cg02kr*+{P`tGcM8gVoA8G%#6_RJZ#5lhLijpr zlrQx@!8!-^;e+|X2@mE41i1IP=(Y|;=CaVDrc%@me2h6nug0&otob1l}(byotey%an+AqQk) zSBt;6u7&n*nuD|8Ky69D%#WS?dlvsZgi>!>g&Y5L%_|cL#i$+?F!{sEK#;8gXKRn6 zy2igw?2%#~_XMg?D!hLDrSQSLoc_+-iH0lzdby*I)`35}EA?KvV?zxN3E@pSAbB|< z;XiXj|Ch&;zE-j1e!hSx(|+TyH&4%M8A&Y6^{r}5UOuGz2J>4#b;IocmF)hLD<%3Tf|6b8qFiDaeC_k= zC5Fyz;Vo%kUkKiWAg)2tnqIQ0_zxeX6s)QCrDY&R>5ml`y}0Eks%T99>@E4Q{G$i2 znTBAvCb#`?@^^$?5k701m#yi5Qwy1PCl%j7@wA&>S5u9Gs0g^XRtKV@XQUXRDzp!yVv2p<=(<$rsrBVKyir|3m$o zH!_}U8+kSjZX3p}{N#X#viO*>k8eC-4>mQN*C>kv#d?u(p`H1a4!;e^9VInFadxw0L^at1_G`KWGQ7j+fB9q;T$A zF@HCa?|;kY2%PEktMbzxRo`B0HhfMkTQ+(H8KzFsL@b@e${KAHP< z#j3+(Um6`u8sECfp!PV4~5*P6!#MNS;ymJ$}|~BLn|?+azj(n;Ui^ zhCJCPrOp7grKFH@LV1XvVyCp^thT}%5yTp-Tylv1CI+G8b@=|=bHZ~?ucN2u5Pg*w zg2dIz6R?2uoNN%h7NWHm?)#SAs*fSf?5RALIbQqtTuvNl{>h~JA6uR6SrwYR z{Gyz}Ve1Ac=fhc%@~A6U6|^#y;9FZ$DK1w20&3=gBBAGF))`e~D-{Jk&1Ic!X}!`} z+!374KGCQu!S;7MbcUG;89i&aI_*BX@P0IdXonpJZA7d^DXP+sf7Ru>LwBo@1VMj! zQD$wW3W7R!oMau`Tw%Ji5xn#5qrACOfZ0YUJGkzZ_}F*(j7XhJB%NiX7sQY$KQxt9 zSt;h8YKtn}tJ~?c{8_6gN^UiA(%jMCn+vwrGOZmDC#V;^?wO&BB#t(iFXDi(Es{I;?GJ&W9zH45JmtP^HBRA`?W25d zkgAPjYeeR(s6t7$(*>Auol;n2QZyIIs%g*^ImsPR{&_(fY*V=a(^Bm3Y6=D|&GdUR zO(P5&ZKplk02FsLg=-uYfwTAW%ZotdGyywm>8qE#e3t@r4}>WRRCL(M0+M+p(CxZ* ztxDhP;?qOqG5%BpAMzOH>4L2o9?@%Fm-bthv#AK~`z>mFzp7+Uc+Wx7>q^QlH*3m8 z+mm>~BO%Oc;aQ?TfFStP4fqKl^=TY@vTds$Y(WhiG;~A^RU2cvHX6 zh81h;+4a|`9Ln^t-VBHslaht}A%vKYa7VeFYu&yGdJ8nz+Ste@$pjYepd@gKkaQ zo0`CDrIpwT=#^hZrRnf1zquI29coS<=qI72CeJfb$YT>COiyT8wLoWwo%@fQIgGTMAefwzJ5xoW9YXdu%YXQyn!WN<_mQl zz(@1}0*O$<7(!+ZrxtP!O`_0!ga{kDGusTF(+OO?M(ln1;UNEjPD ziW+w7QP65JRTx%Z%L-p3596c5W{^ku%kjIlzHOCpIlpm&G=|s?ButRUm~_s@?tr?O zWRwit$&*tnE|CQ96_*6ww9v#$`~zs%8lag-;6fso-U<NNaPCMD2C?unm zHgpK&xIgnz$m6Sm{}_ASZH5hk+Pw$HlXPy0Fa7(|U^5Hla!%K*f#6f{g@M;9SQl@N z5>W=3=oI2U&L-{YeK^b81?28Ixsz4U{U!(R zCG7fA@l^Tpz3V;n;QkKe4VLY(MCQcJy(Co< z_~Oi}>`{lAQw}*D^9;INv34UA?m=p}$%B(jtvj|T|Cz{;Qg=-K8>G?r2@T)i_rSUr zjbu-L{~nXjfdiF5p6Gwy@BSZB!y|;e$F*Y7kDToBLFC6xx>E3!@Zkxe7@fQ!nm?Qj zbl#1WcIlEJKKz3f5pyMG8}hpCNSAp3tw|OIxkI=*#s}&Dj=~*lvvYN#KJb~aj-@E5 zS6cGynD`8o&btrx+0*hUH$JFo2MgMth66egfi1vNnfc^dMdeQpG6_Aho)=nwM+B}Vss zbM`1%<+rRK&Va9rwBv5BZv%)Nzs11|^ZUTr$Qwan0v!=+)8P|;I$i7p5E}chl?0pR zB^yCqpD){7XFEVN>L}>2IR~$tgw}8wi^D=l&*u*TaRxVQc85vuksM%f<@DY++FL(R zr&2Su{CSNtGJ?at^g}{&QSbgZDl#m)xPl9o*5%HIUgz)A?R!HZy)i!yOzi{xBM4x- zO3(6dIQLYMq%(-}l~SUuZaA=%-&cEY+no33M=1J&r@%W_;X~pdv7o7u3gnneJRT)T zdH0nXR;_{0xtj~6rnB`#%Z|>51Z&l(QIyJDzC4|s)qZwS5Lo%*2viWb&oTA#;9{<` zfEX08{K#-IUp`CE{5eH>og0*3kH3!>p_cgc4kXVDBxrBuv4HQh6yjPzW#gtzHZ;sQ zvC_nEW|?VJphTIb!Uo|j?HvC$Rp6f^p6%7;(f=)WDG3~Be}cGgnqu`4$}e^p<6LW2L^XzDiwL_L@8NMHdmX%Bvlw1 z(n##cG1|fxnXu#cs7_RZtv`zAr*(Ywqo$sc#KQm&t)Im;s}zgXN}Ykrn)2YKg4n1n zF8fvotA^C)f#dgW5E0LFI@7hP*8UaJlaLJ-q!%aoHkX74pu1)j^ct6ppd>|4dM1UJ zAtcQ3-^I{2O{c$gs_D-*yKu7pH9bs-U)J-VmRJ)g{~)`5SsL+H*7ewm5`BpFf-`{k znf1v?XyqVl^_)&QwIB7bIHy6~VhE9wD3)bp!_3EjeX>ovuk~ZD)Qo0Hb%7%bAV#{0 zF?UDZ0*-Gmla{lI2Q}Qy{WicTPWr~gXWBECi~J;$Yo(}@9aW;)NR7JYzTg^Fa=idB zA`U)QqhPxW+WO$S^A>2(N}UA@F9}??i;5&hR&$Y8BFjAb>%Z1c_>rXWl)aJ!ta#!s zHSBrjV`ra`*Y$5dEmN98*fU|YNvW$fK?>+Ij zg#4?BYm!JaI>*=hl72U;pFy0lH{3ol7#dI<7^n(QtLE0vc{sd*<~4=Dy9U1-P+h&X zuV<2EZjZ?x5iIqYjha$c@ilIP8fQUDCEzMiBBA4@)50LpO0xR!?Jo7}{n8_R`K)=v zE=A$k%hMTYQw{h0SqjYB0n<>-Ia-u;n8-DMJ(HtQ;H1pZD$Ie{6jmNv&2x}x1u7$|Tiv@tbx zd6Nky#{k=zMXjQ9S_^!VF+N_ul4*6V`{_)he6>bnKoDaw5_9hr%U$wzdNjFI&ITAH z3z?r>CtQ!*;+7vNWc*YJ1tEXj+_)Vqx1nJBBc_~hzU^h^_~^oWTG7hZ1C!UWl4`Wn zLGX*n?X1P0##-DseK9MW6bG;948wi!qRKc8QYzC&GpDY0G3RPzC2F^b&N%q9OGR;! z_=Db$`#{4`g8L3<;)+d6aBxt|Z&jC$G!1(badG$Q80gctqW$}_u(J3nC1E_fNyedU zWj+_mR+ufJ`F5}6U408sdvbAK6b{+HkDhnAvx=}NXstzy*Eo7&5|Zi$y)OVlBFRCcekFlJVeEu z3!u}Z(iIIXyB%BDWqVt*-Ko#0+u(Yt31!8ksE~{n0b?Pk$4tQY(Dpy~y8KkT`|)$f8U>I*b+>Gs=>8Q++R~I%5?UXPwR?0j}i(G3yIG+q<=wr@bQsL zKVtgxV$uoYb-^mHJ-#$u2ZTORtyr!#YIV|2OceZFo9BfR9O+Vw-wC&O!m!})|Fa+mWF3r5VTLIa-QY!C0cKptC|Cbv(>533Ht!$2r87~Vl zfH`F8JM}y7@k-^CX~+9Otl`eMh9g^3mq6;~PL>X$TQTpbc60&*W>(TdZ6}reD$91cF-Eu%qiHZFkai1oy4HR)T^C~ zIEtE;@?d!&#c~9O012j_+2T$B*p-7sq{OyEel7}7z&c7D4XGCayp%SVv#`3pF1$>i zu9UfAy9Kf16d{0u8!OdvOI4dsmNGFRHwWi&yM3X`-D|=5fH+FHywrVX_tWZX)J|4Z zu3WkT2cVZX;%M*14I|dr{kq&&S@OB{xk8B9)<0;D{v?1O99-TU58BFmXnF{>+x2no zQ0NY7^&Bd?tM#OeRc`~#pS6}g$bI(hWB&d)Ks@JLS9I#BP7t ztH0i}1<1(_#s32kjow@`QBWF_;ctKXp`la%knHzIZbA|n$`5>|#B2c#`<T-v!1=GDw!~p&iOzxB4uNHp9%Sq}eeER;wgM37 zy1Ld8uF`IK#2&yGF^D(}4uGCBGqs+A^>6jb(=W4b!8?>F(b>F5DzP%R_~Y zL!RvpYe%FNyjBL*J=6RvfAXVrQX!>!Q1oc!ZK1>1vTVxP$eUfJr>W4ZHNQja*ELF# zVx>`db@Q_4Cry;?1zd`k<}p3_^Eq)WBE9vOm7P-|(gsl1$X_U*j8m|^uk63gGSI$~ z=i7fj=jN=P0PZIo0yU?kYheOiK}j?d_g5XQmZ?i>5~&Vg?wf4a0dl?9!~##j|RT zz6+p-!!`G7?(QCZ>f5R$z2@H8CIPX6#<&|?E09_%r-@>j=u z69ZXa@ei@-)vn^Gv=`bTaJBe=((=*D=aLlSE7_7EnGi7nmsrK^u9*3UovBv@Pb%FsjfB%Topm5 z5IOfw);7$Yfw1+!-0TiBBC<-@w;uE+KwxdqfC7opFVz}?OLeKph-^px5FT{lpkOnf zl)MgIzPI@?3U^;=-^s^d(h@7AChn?2(G@#{H&arAT8A#7ABpQgmmlRulHAMYnbl`? zd%J6XTUjCcekUKo`l5KNvgIsnVg1`iC#*lA!W9a;-Pz`4QEsW90lWt2V6ZYii<;==s^j*4$Cq#=1%wum+_lkVjZa-(<7K)gK@d7M1Ff?#H7GeCzb zA)}hIfW~yR8;tdC5*11QlVOlgEB@s#MaV$({#3L0xbpHn;c0ImR%RbN!J^rHJ2(4N z;_}rb`S(B&t?-|wWqre_mG^l*BOHE9?+Gs#ZuwH2r#wMoiY;f?Vo|$Agq{2KA=C+N zVHB%Ju5r~`<_rmZch(pl!9{YoT$1N31nazXkkY_ z#DI@KW4|$qW=$AGB}wE44cd{jSYSX{F6MLeI*V_Kcl7A{%Eax%AkXQ-k!7u2_U+G) zcprgrWQDCMOL(_ab0R-=eq8Gk0*H8gL{e5ykjj_QP-Rx8t#M(yFj5K(cG8Kz&aQe(5BGF87*^)%{+5#1e>Qz}_{lJn0DI7?&}L z%2P`iUjLfCsNB0ekigk^fvfOx0C;mc;oH2^mVRqHthGDyJbvFcr;oHMY&Z(lkj1oHA@2}yu zNe;H|%nlFVZ&TX+F+ddBG|p$5r2)rOnt!fD&uY%-ObGf}ARIyva2$V*C#Y}`FSkDT zCXx3+J4xpKHvP(2mna=pY?d8}9kgjGsGP5=TFLQSA0WJJ!|bF*t+0B0KWa0wKAQaS zek~(ur8`7CA>w@>U#V}Dz0yw#Pyp?Tdme803nuaXkEj2p!8utjf*-tiL#z}Lr)k^(U*M*c`+)}flRx4PL+`eRNDqspe4 zoQ9QZDz9gPINXEI?dRZ=s|EM{F>Iiay-Gk1#b}?sO5^I}-4tw?d`okn=cK82cntGe z&BOmj-X2DIg0hJdR&*g(kUdpNW#kjoj^-NfSRJ^-<8nIgPZikr!uK&%MB+zN4x zTfLZP>m>KtXYjj5{3Cq0nM?7^T{z1pjKJEeE_fVY(&Ih$tg0a9yIO6uS%A0}IcUO2 zhrQ#rz%bR=g>m2O!?4hwlDm&^_E=mT)T-q>M2R&4*G`WTEJ|DY z(&K8sV3$g5z$oDqz$oqfdlJpD!l1!7L5;TR4rJo{nbpwmUn;483}uey?v~iG1e&=> zb9rHJiizS74@D+-yEEPymBnW`RZ$TFJs~+-@vZkQj?5$*A44T!%v|#>x`NmUmXkbV z&MdVX6MBb*UzH)()gGrw1Q&xc&>gxGWPA9L56Swf%7&JH-4sWTgDI7PbJRxB@wV_~ zePZhE8s5paNXOZuGRcEKsR^Y&jN1IV^{KAP`UxDyDvS#Gd;=#4xFIFd;x6W}E2lc=XAc&KN-5_8M zf6029zHcyb@9w(VCZcV+>XyJ^ZSP1c!=#Ny$;t)EqyV#%PNV!t9he&gecyo{B|;OF zg;wKA5_dKCg&CtI58cS4lu}d>II8Wv!W!=H?AtaEI|rm9r=)oZVoz%wvU4Lrnn2vehPcpp80UqkM$y-@>wM~tgz#KwaCHQqc_|3(nv{ny>rhz zMw~>Oiv35Qo_xgfR;zhXbj5u}{vEeAjG4*?jZfOS_7gkwWD|87RsJ&VUZb1QNO;~w zUr+Bmh2_*VcZ<8jE7mn^ngY=k&s(>HU35YUmVns8Y$RsJOg!0ZWipqx-v6pdxpLZY zI8GzWo*HZ7HtRJUZTV_voizG&zKw@#TK9 z8p23@4zby5p+H9`f?^G0%F2gofU)-{pwHEpCqG&KylOgW{O!&Xq16d$gDC){TN5~U zP+v*uuOzEvz+|o2JaBR&!&jt&sjPCMH7A5w>VmEf1dip?`o(JlkVwUL4f~bIe3}?o z6Hz0sy8bLMqrhHtqg?)s@i&RACkGIyQIo~B4-37YG;0t)c?e}u!3uHA+B*-7&nniQ zC?MID8dWPU&n11|`F8Y$^AG(j6-GX!@G^nAmj5AO3zt;bJq+uMEAqS;rE>Iw18M3h z#0N?=;!b00fpoy>mqH|oE>^m`+0N?-dY>1OW^rMMTmyfQSK~3@J58v>Uy9#DUxM)u zz$d|%SHKnE3J)mc??@k@7&!tW_k$(9`P4pRQH(M3g>f_#t`$}go2OPu>Z8N<(~?}n zu$Fri+LNo%{A=FA8>}BFA)9xmSrgMso=7nx$Aih?n%}~7YFysMhF%wa4M1^nA2C#~ z4)8t&^uFMvLe7MSrtUJLPr>{e@RTS@awPB((@O7TD)op9NX@^C1rQ?VYro9G0mL1C z;TCi79h9wrW)+wUOpW?pfr5A`dtH(TY0qsZN?cePW>Z6_nZeW%_mq9h zHq}1neN4pyYFXJQ?-Xnrw3Tl*m_v-g|xPf;&7t=`V zr?81;FH$Kz6YY{iiIVqFEn|K7z!VBYyevZe+~qga=DoB8QG`Ls%o zpyZ3q%UI3oOR12XMjg%*@o`Kc6NdZ@D4}SMos*GzfYBqN2A85+uM`_&2l_aoYFYHM zgZ)l{b5o-VOyk0O)~!5SxnJGijSm1o2MR- z-kQ(`Bt*K=0V{=LV3=NZV&bkL0GBDs8n;=>vI^ijuel-H_>}_tU1mz&g(1z+AuhJ` zvHOfP5=1&9y(Jx5Ni6nsI(sZtg}LMiBP-=wPD`$2C994hWRWawuSN%|1}lh<&h2a* zMfnzs6jjt=9+*O4h#A!ClU77!usW>xrl@h!w1KxFV5Gj4;HOkK+u>-GA$PuU3J^zL zp$l@F&0Ok@hNg!Qaolrvq`Dh{WpzVV82_dM4n~3fXqVMjl_w0R4&DH*YGEW*TYyyC zTBv20S#$nL7{VcM^-)rUOU_XktH*+v=+C_mRE$X92>cX%YZnjmST`X4&PaG{<0u`- zQMUJ0vj>k`ibMgf0cPxC`Bvy^B7iIJ5=s{3VX+4EDDS6K$+kd9rbPL=f0W&zSx)U2v?{0I8eDl_(6#oI ziV4Y%Tbx2K))N9ud6+{52O#)T>D@eV#5KArK46TDEBCEJ7pv6}_4f-J)htR%|VQ`_||Vaqt9^ zC82D3``ypFns2i}=fm$YoVX`o_czgC;Btyr>86A5%$sPDH$gwA0W@KC)N6;W7dyy> z%3wv^_FhuA*W%#?@e3EFO~H;4hONs*L=9^Zv?^VbKiGTRBeL+!>bLQT8~Ldlkc8YF z9SztBohhh%qi;y{tv2&0V5H}Ie|T8~My`gHvR)batLv%j?c2zysxqN>ZLdJ76|w^-=YquTSAO5sWiH4La05tb@ zsVi6$sI4ZbmkY|&7Oq#-vEj*~Y;3%#OUO)u&Yde3UAbrx=CWP}SFOc#Onzzs|KryE zRIcWMhU4^4Z=9hmm?uvIJ?Z(*iS&NXe;7u8;3K7|mXmDbP6IK?IR2rQ+-% z&(nL-5owr0B~6p2VhWNx0b+|k;gh8vE?HgS^Rw?;y}rzoS3v0*Yz4+$Kq?y2RTkIB zVqR{kJ;lm+ApBK$KCEPj%-`Y< zv6E7ezh<|n?zb;H7PyqHBy=_9YKtd5Or~Sg<}^ zq9YZVWwI%yoi!qv)9uLae#xDdJdKenKib+ky0FH?lu2f%?gahNw$0?k3r?OY+xROP zzux`MXFIaLe;>2j68m*UqdNJ`6NG=$ehLYz3>x9Dg$JY*edL09!@z^Nxn>v5?~$qR z!SYE%^toWDT@k$eYsr|AG|I9Mf~F>=R=DPQ?Dz=xI(n$wEc{7OJ-g#FzOxW_D+R(X zV=R2YkwMeI#N%3%BwBRZ`nAw+k*Zs|eUBdwS06+4-8xf^KcM83rK>e^n@#4~l=X)` zVX-edw%)@@8MY0k?3&RxUwyGoi^b8Sz=7N_39dzD|d&?)43qzpm8pa!9X+UTgmb1leWCx$P$8 zO3u0LaJDgSr>j=N1J+jVOjvZ2_ei_Cprsafg?ED`++)7d^?omH=m#dr8U-&9uOo#i zo8%J)#*PHcN$FF`qdw!pq!kY`|1Q?~pqe@AZk-t`EZ{t=Wp6Xktsv~Yb}q-Rx^(Ko4v2$4SW~=y8_K2k1&o&p zrEHq^xtl6i9f8%jK;*z=svGX4vYNoTc0_BPNPoPpHt%3!5ke{ zY*eT)D!y6dD=0FgE7OLN7==_EEH%-)7zdFvc>lyMIJ?6U%veA>{Vq<~&Mp^_{?wlL zc=}ndNJZ?`XgVazfO;gboHo_#zJ+J9GYNp=#}m^mKVm{oBj2P!@T)o&?BCw}EX;ma z+^lc>t>ZZX5`q=>JrfyMniIw5F_qq;(AyWk|eb-hLeeO?}KXDp3 zpR;Pu@ZNN`8B85_R6j9|fmeyhy?VFOe#{iBCh6Gg?!O%KtbrEhwD)<=Q09n@dFNFB z6IHrk5jkhA@?wo$SB6=H2T%2z>GtQR!_@b#pJFUIBga|t7BEGz^j?7RXjV`lq$|;S zkRpau?n_%K{@2eL!P!wxstiiPOfZrDE%lkt$%jxw2K))e9_gTvNpcEW8!t=kYFU3! z0s8A4$xgphYi}&bqlp|~b&#QxzOAo*#nP-sUnQ^Hr+bS4mVL~9kd#N>6=~ZsE)5BI$!Yknr6E?W7U2l^+atKiUg*3NHpSLt+is+JO(+>2rf{p`2%B4km$ zq?f6OV#>%nG>!(+RRW6Vi|nvDgb7)g)27#1Dt_aV1RkO~Cx#GSn4@gkqcK?Y? za>5#_*;kzg-LGzyT!H9)8h;)qISY3crbw4UX+S%76lU|d1vh$Q3c#clTtTg8iI~82 zKrrb!N>Jl`UE0s?P#IK3mw;rlj55P{zEp%+T>vdWK|fR86Szq7~NOG@2qkFQ^@mi?vKRk17kBKB~Y)n5Up8WdlJ8I#VjLVofrN z;Do)oNjLIF?@pBNV79KiIUA!!Xs?hdeJnk)Ffm^4VC_w#cZ%Ury~Ex(3b$dJ`2=c}-w1@6gp-e%|k@X~6^{GFrZqb6>x|=60QP zuJb@{7trip4$nA*8g%!nYmAI*RSP)-BJ8b2#3Th@u+})Jr2C7b$T%1`WihSZUbmiZ za^W7iwF?SZ(_#;MWYl8!JTP@%BRrhpy=UrnjinY?-&*EaB_q4S)sEKj6$R4$ZTUT# zrEl|6f+^}A1DG}5B$~6%zMXnBkaUkC+p?2dtz{3Z+itj}ZnqXVfHuyIa*EtS;W-Tf zTLlQNdP>)aJ|LpVbYQoACK_YCh1!{k@A0`O;^43C2VUKH%inSCc_sz3j{+1LA-J-B zDdCTuZy)*U&CC|y+eSnCm(+X`Df8_6ua^~R+s zWXJ^=fE`va)WOQzlIE&+FA6s?_(!&(9-&KUFL>ci;c-(5 zR#eW)O6smxdYWs6i`il&`}@6?DI%CER%2}`9kXRMF6_~u72=Z=2%IfRdQ$ODZQ($IX8z3U`orF~g(k3xQFAdt zl#&1=2f|-#Tbp72{YJv$d^AtBkt z#g{i;^y8b~^a7^8<&z5I>%wJcy-KJErzhLi*}yB>)ROLJ>9P?W8(K+RukDNrj$9A@ z`gC>kM^DG&_TRHzO;k2^6Zxk@KX~#{@Hv&{&e`j<4^=~L@=Y7|=;L+0)_XRd`_nub zNTkHF5H~ob#+rJRr#DqSTtY5RUf==H^+sum2ssGC=myf3(1X>ta&9R~_;-1wdmG#9|4aW`wb zgvHKOB0rMFIMO}B8*}zDm@MRgG~*8498J}?P(Ll`e$l7tcTU_|KK^sXcN5LODG_o1{3G46e5;}+Z&Z1tZ#=6rt!@iY2t94 zt&4nIpj)7Xk54k3xB=Q!&Ki8}?i)G*ey3Pn^Q4kog7Ljp5YVX(W*_jpQ2f9cbj~j) zn8I>Y<(W!@C{Xiam`p?gpB~|@c(npB`v%kl zuTlZH527wt?DW+<`_?8(*CH9IOi|&mGOJbyy987j36R8$H=C zjQ@NUrP0CI+E;*Rhtil|fK8)2mh=uY?7Idm)UH-j0W#YK%df8+&|oduQO7H9(U8l1 zIbm^)1N^hnlVCZT*;A5VY!?G1+$X-5(EtO;fvseBUE(LiHG1m@mkzJoM{MMZ@j8@T zST==lsyzPYBeHU#R-Tk&3DU?tk5ULRw~LJ^XJu)izu++NnnD1qkEEf;YVPg2hGfZY zHi~WFOu?YPb&A-BJAJPg|n7wbw1K@GYz#1^qTP&H&eR(b3)Tcu>Qc2?MW7L3Jrg^`165{k9()%kWE&Tt?b>ByOZg#ud zwEf60bguw0>;0c<+~HjD42o&ycGR?qb*;UifnGTVK-NxyvU^ z_R*5i=I$28aLX`78oqsSq`uY;$^DL7{R=5Y;{+_w!S#VM4R!!b4j|*)6gHt6ZDvoo z!~dm6(DcMS%jCBgx?Y%G4N?jgK%U{^#3F^>l>_`GSmVutVLmZx z8TNfYli$b(?pEf`J$p~|_#~N2?dtu~0MecMpM72h>u7J+=q~tYx5ww&-?W9+6UIy@ zOvx?~Si*rV=i*`;vaK9`G)|6UlL4{rnm4%Rr3o=|Y#x-66gHJnU&Q>&Bl7pH0C43? zxGSPyWgBUmNJko*Mg^a3{kh~pLcgc%;jEgbvyNOKd0EpoNX5HK@~2;T?2QcA5Lg4a zQ*#*$NHo#NzsxskiKP;>Pj}rsuWvVXD7y`+aDcaRDK+7li)Jl3kdMfMdrdv9k{$8_ zZ{J3ypqBjGM$Ly8Nh`n=)0gFOHVTg$f?FZ(RL1lysv@d{l+2(#Wb^^CUM1E_9wZ}? zcCpdg4IWKNVtBJlmJaKawhKNqVy?Y)i%FBS7-R+3mUdlPBqXBCs-Qc&rxAMX_^#4}Q@YoMr_Oway>3X_t|Ce%A zk@%>>u}aW1!5w7t9F+5uuhsvfpV7e%OUo|zK@;P*Z*4dJcO~^Eshe_m1H_K{42aFC z(&t_}(I7h<10zOguGnD6xw06>IiGufQ)CfO1Ia6I5cZ z#@AIoU9)!Z+uS2ZQi;7h8fj)KC(0P0o^8v-Myd^-#A`a5m$_?&FJF3APYW9vi2qpG zZQB=;ZZt7PzmmL8jd$c! z9+2acoyX2N@&q>X9BV_}$+W<$G#jvnnM|4;7!NdU&W}F zH~b^K7Vj=!Cm}A6NQlc3DOAw~g|pR8Dh#jc_Z}*pxxpB+?vYZ*=*sh3XnRX=b6F04 zP@!M7vt3oQwLwiP(eKC=af1ZmXux8ofqWtZSQ0p#YZtQ4)&6`zZVo6SPG+`as(7~e z8iEm2Mp2dGDduN1I=D5BUx0y7uwWm}g+J??yL`{56$%>~a5?9DKhLWXb2hWKq(Dc5 z3Tx9*Pw(HN~Q1vG7?S z)!==qGCVp;3AD*~is0Ru*Yf972TSty=zyTr(k&bIwu|DVlCF;lnh5_B0S*z|Z|6LL zbUAZ6dD}iNkqk(4o4tZgvQuAiqArtY7tI9}eAas%ew}2n2pGkcjSPPFn*6q&$cprx z&(;BR1kG05COTR_J)PR+Su+T^%%NNUN+m$4LzVs}D9|H`>Ngp&#P%Bt3L-#}Qyyac zDMTSAe%;bZ(<^1@*VLM*0gWJvC}hm12x6=Zfk*k@Led|l)ynwjm-ETJ0~QZqpMgQb zw%A--TQ`S=?4a5k`+dIKXS_MrcL=q(Z#=v# zKFi1!2B1HJ8Cd&l(>gi+AYS^TSy=`Z>9AaXl%dg+aB-M9sULa1?{Rq$FV24k=1ote zgC(Q~>c1S5aRKI$&k^pkZ*m+r)$|DxcZDjQSwSA~YuC%j>$)CTL&A|JzOjrP~5wL2$B|q^!y69a1r?epWPDzY0wE-CtkXT;z|1I4g;NE!?HnRr(s; zSyD{GiO9o>HC*`bdo@)2OZoe?I7-(PZ?ZZiSIu{`(P8V0aA3{*zM+6OwRVFU>-mdNw71Ws|2dGo!mrW{#Da8bVi1D{pA2AycN7N@fX| zgxEM!nI@H`lcv~sN0Cs$3YBg~Wq~^2U8$^8E|HfFx?#h?rqeSY&X;q(?)Q22{;%Kq zKkMD^v-etE@&anb5&Li2Y+8(xSDZPA!~F7>h{sP!oP>YMbX&-+-#zC=a}+Bz2gJ;C zLp9LL<)Q+7Quh$VTi`kmTU!_Rn)oz)@7C2FHI0agn?}?u?qcrUT?`5{=0S#PE}Qdl zk61k>oyrCR#6+{L8x;=2<9(-Ti+&#IK_nbvWA`O`A@c4f1`kN^wm7e*i64UELSSOf zwo@ceBgA+=ly+W`!K}n7bH%5I%jn%3k@u39bMWUB{_2)OI3L}!!5Kl=QA)4f$O+yi zq^|!WGn$@27#HM~i5^h6*h-+iwIKV3RDI<>#ar%lmC)kh>;;hPY0?7`{KDH+lMzFwjCC`^bFEVayjeF19bEo6wo{{ZTr6Q#JyfS(4h2) zBYyBzw`!%ct7`UT%EE>nKqVEbM%&;>jd=^X{nKc?g6!0~xx=9$lQa3&rC*|ulDrX1NasBi zhkC9~xw@!MYH}_K%Q(&)W&g&Q4~loR{TeaarGc9C^X6;O;jQefjV0lrftvbyp=MRU zS(}v&>WP?h6LmoYBzH0?8U`kU*yu2;PMP$fV5F}so#lA`MMOMRxA8g47E1whFcy#;2{c!4`g}lD0lTCtR%d?Xw;k6|GpSACsU_> zH$g70@*s-@Q{tC{`E)eqgIn*F*Insdm+zj%N6&c}ZKrE7psfH2YWU z{V|s0y(JhHTt0DIbAj_jJ?>?)3Ai-!D_A%&0T$kr_)8ByWv0@YPZ> z<72&>y-p~H#e-9BCgOYpobQiCE^ZqIB~f8k{TVTbn!s)Hjw3)$AfSpVOLaH<3C@pn zcY59wolcY^QhJ%I7tuG{Ip$QhSpUb{pM{(>x=eF=J4ejhh%;0-JYiuS?S=80zWcAU zDm`f@h_HV;yccZ+~NK$ou+N|xYx$?Lek@A zc7-p^-nx%O19N@a70_>=F?}iwj6rD!fQ6-K`BG;6KPstNTX#y4#T-96MSj<5SkTK9 z4Iwo-V2_13!d))-seM-(nzlf2^+#}l=Cuo&#)tcQVl6=yy zC&%3`r46YBva#fgl^HG&0nopq@Cg-iW_5TfdM#nTA4yh~U zqn3S-suUUf%~}U54KPRjbO(0bn<2=_QUAU4`PECh3d3_i82|Yac0Kt>d@co;;Athw z44O|&El{p%MAlae$+(XNPsVMEdE-4?y>{&WJCF~DAS?*ziPttdL6=3*Mb@#UkpFTE zV?XIRg)Yc`&?2Ji&(z)XyLez$opwa|gqaez^DK0gw~(i6>E6Q+OpU6(q4P}N`W>or zSgfUl&{AT?@~+Yvs_}%`ZNB7};P@R=Uwuv_aM>X~BOpninM={q9Ab~k!L6;}E84nz zi0Z!$nP~Ltp#2ba{+W5H0m7=@TF%pyPD%$@W@{p<{gy8XNl`3my;N`m+#?&RifDBt zw}a?O=iyfpR&(Mi0_577+-|xlf{}^zezy(gjbPd_d_ezE<@&QU1Gw-7InIIHMW%^yZ{NC9`{H{8 z*Ht+sKo$&rWS^c7W`T!2kALL=@859aSJmt`8n|(H%fs3{c{xL9 z*VWhs-ook6Is54q-xS@9jnsbOii6U+8{QM4flb3c>gOM0pW~f;^gSJ*spc^?OgPHB z1vA9*@=GkNF#9FbuND${5M2RFc23Z&ZtacJ-`HOaydmW{qxA#d7*ZCg`}@|F1^ECq z2e{|57?thL^K=Za)wh88^$RHOP%Q9I4QDXT+9>U#m93$p6|T!zN3Q_S-Bpx>y!M-S zQ#2~uJ{`6byQ*fcejHf}dv)k*W@HncNWSAOR<-NgB0aEUI2p35)*tVya)`yBJ)>bh zA^0T=YE@efD799xs99|<*&7VsP0ftf0FBoHAGYB4Xa`y1Sx=B35tv?owxyQPO&G?8 z&<3o-`fpcLF?SR$$Ovdxpgj3r*1hAt?b5%!#@M&|8Y>w5^5E{P7j)xwM12VFz%6@! zRQw^*xLOIy4_K|t5 zO_;%!9)?I3r3O$fP?hE17Kw&T{G^ozHd6-M#Tb^G#S}HsFf0sp-Qnx$>AxJP{>Mvq c^l)3x524u1AD0;h;=}gr+P|}A`!B!#8(QhXE&u=k diff --git a/r4babs1/week-9/workshop_files/figure-html/unnamed-chunk-9-1.png b/r4babs1/week-9/workshop_files/figure-html/unnamed-chunk-9-1.png index 4515a0d373f964939b4d3330b50d47aebdaa9f6a..8a2cb8c34623454876805ec04ac36a018ac027ae 100644 GIT binary patch literal 25593 zcmeFZcTiO8`zLsss34#sA|N10j*=xbv{w`)=O}OmfhH?an$%teLisqbLY;TKYyN# zjEtO|{KAC`6ciMcl$2CdR2MH^ymaZ(<;$0;sj07Axk5ujbM@*~T3Xs`*RIjg(b3b> zGcYh*zkZ#Ok&%gsiJ6)C#*G^+EG(?7tT%7oWMgAvXJ_Z&;NaxsTYy=jRs?5V&>gmXMH;u&}U*h={1DsF;|TxVX54gv9OJwrInSHwY4=I4!5zfv9-0ev$M0ew|8)GaCCHZa&mg|gPo15e zU0hsTU0vPW+}z#WJv=;~J$vTq>4`ufyu7@;y}f;We0+U<{rvopNaXY9&;R-7pBFD) zynOl6-`_tVARsU>@YSnVK|w*UU%w6x4u13IO-M*cXlQ6ySlHXQZ{NLp_x}C+@bK`6 zh=|C@$f&5O=;-K}n3&ku*tod3`1tsQgoMPz#H6I8lw+7ZenH`t+%=u&}78sJOVeq@<*@w6v_O zth~IuqN1X*va+hGs=B(mrlzL0wzjUWuD-th^XJcBzI^%m^=m^zLt|rOQ&UrOb8|~e z%eQaeT3cJ&+S=ON+dDcsIy*bNy1Kf%yU}R$_wV0(dU|?$d;9wO`uqC_1_lNP2Y>wd zF*G!U!C;1mhet+6Mn^}-#>U3S$AA9(`RmuOiHV8H$;qjysp;wInVFf{+1a_dx%v6| zg@uL1#l@wirRC-2m6es%)z#m>f3K~rt*@_dY;0_9Zf?@G;&p%N#bO8XyK-?cBYyl_F} zMgYT8$_cB+&L_dz$O_VnvrjGzc6`46p6@){OJug(SpLWS{7kRCI0cif`1w8G<69>E zVv`C!-Wg#ZGcN?cdQ~EvMhcVfGbYqvW$gqaUMY$I?5sT(U_q}$|0f<15N&RSDn2$W zPY=AfEeZIO^$Z|G#o>Nu8T9vQN8+eX3mG}iC%3eAPfuJ@fi*FCX-{%Fb@NH;wUZ-H^5XLz9`o1E z=v$dpR0}#CGbZGL%xTX64b{oIsbEc~3jr5*`W?9N|EbSX%gAYm-;+e-u|?$E^m>d5wtbu6jG4$v*k?-9 z31MC^Jek6G#dO&qc@-B>&>MJG7CkTx!a9(mnJ(f51if$j6<9O@{3m0Agw<46K9P1k; zVL5#6OrE{St>igBSCq?erV-&NM!KlqE(`s+!@#MMgntt2_=LS~IvD3eeJ1KCs+w!1 zsVx1lL%6MA{BLVZNdQ{w`fxf+a=(bFLAB}p$)JMLDB}@r&L*qb10YP+^;A+ALPiru zUOaXDcK5Z>>7yaQfl-W&d+yyQQkuNeB@{tEc08stzPuj8di7)*OvPd4Tu3!?FOBbH zrvosc1bLBl7q?NVG=IMF;6<+UYoSh;X-T;097{E~{qLSvnoC6IXW{Nd6rv}qtjR^1 zu650dOvwLxQepD8CMK6=UFH&+pZrHdIjeH?AY7e%2BdgQ~+tUvt^sN2M}Wp~R!lx9z$I>U=Pe2sQ#x zDU?V%+~Q5}8^1kdZzyI}TKqfLkQBvs2|s3b$H;`>LF35GK{eYUR0gSa8)r9R5}Aa; zOk?&R+J|n)9zEKKDRz#>-rd1?Y**H<>9G~%O84A-gH2;a$#(i)&HJs>|j@qi*yv9048s(;pxbREo z0<7L9-{lDEC~I!09@moS?_g!PpywSV|En=tabk^K%LCCPGj%ZRxV>CrK0|5!s8aGg z>;WtO5PBOMdC}C{JNLzAh^$$x@`*LxpTlkQiffnmdg$TmGq1sJiy73;Wb}Al!Lb{_ z9)wf2%X)I+Ib&tA20vyO!6h=seQ-2hm9q;~2U(9h;p7Wdsu-WT(ZPc~cduV7X8!NnCwS7s zTY3MRd(#G>b*WbOm&e3gzsn1stf80rB5ohb+*=GZACc*!hyQtWOmv$RK;yj_$((B(iIYDAD8S~@f ztY)qFpdL=Pcp^i8LqZs7nY0@T&_8V%qrG)jI|}J=vf?~!IFJW9piISM1c5(sP?yaL zlo?O*5y44>8hzAF8W_XNmMevTXoVFw)uP3a1kN7pAaqSShUGlgM{5wGf70gwRlNIF zCj&8UF~cS095Yi%QD4OFLquBwQMecE9%%&|OxC^Y%c2TRpvcd;57~rocjAPcG6ANp zw8u9hQqYn!<1N@9=Cblq)0JS|>D|Y80XJ>1%j-3mH}>?7GtMA?v1seIPCQq3Jsv09 zn(b94AaG~iyu3@_Vm4#<#+KaumSjCsZy`|F-N ze{4=N*YAl{>GtWxE!WO8XTbRM)S3|a@_$U#`t+l&QBoukVMfzR|L4Kj z`RU(Z+bDr$$cgYx(Jug`N*F=yONPqxTD)2Sjj&ijk1-;zN&*znEgHBkDRLX>4Wp`1 z5Kz6%E{*z3fMz8{r3@PVB9zL+Y3&EFoeQcRP_3t0&lDlVsC$&K2B%7@ET8Qwpld={ zlHF5lJn>qMr_*B%Fq#5H58^nV`3jWLLUd%P(0OBwB5bppDYdr&erJFOJw4P_T;iQ4 za*rMPdKOXz!#+cN*hCSWc#5vJcb9weXh%VtH;FV$>iMD4{2k)UxQXIe4WzLGpvn}X z@tg=Z2axsFWMC4!NTw(zof{DNJDiVz8&dg*$!bz*V`>aH8&EdT2v#!B@g^ltr(@Y= zkXNNoIt!D>2z^~E-g~u#Bp^TYQ@LBdN;u^tR=axS%snBvaQ0Y;f%;$mtEX*ly!a!8 ziAD6*ng1GB-8sGD5ggxB0N^J95Tg^=IUl@ocu-A(pD0LaE^b5PaupNK3%UT0sUu8% zb)>F>gC1<iW@1QT1y6NYKP3F ztT)z*A2~8G0#qb4j# zU4xzLP0o4XRko=9));v#%ZlN%2z;|HO<1#& z38@Btp|O$>XO>r2vT{&ga{3b8iVqw64x4L4U^3ZO9^+`rq4C zFCmkDALa^lp6~He#4#=eu2HH_#l1w>aee=}z?$zJL!Z;7gYkqrZc3iVQ1-z9Nx;E) z>Bzfj1?=0sbD0rri2++KtFBA3i3@}8!~EsSp7q4o*ll=I+>7h)roYM?zpli9w^rzN z_9OIBADkV&*{l3j>J@lf-H#A7Yy<&{k0iusn@Vvajg$e-)nrewR|Ew9cgTaf7h!I( zEk+bDOIxOuWB=-NAScowNurKf;9LCc$SX#F(C-!k28-2Yr8 z3;y2rC*^Yb#hvhsT#(7>*Z&jB>V%iiBqzxI;GpdYr}o_Q;k5nARBTnJp(I09i;-UX zjxLvV6Ecu_aNm_Htu`8oVA2CGf5&k@s7S8vX1b5EL!(~dLQq`D1vf)U3HOlAzgg)| zz_41i#NXJMfB^_k|^T}nnj$^(M>>Hd(uCX9hG4^_~8G`LyCuDPuZPw&- z5pU0jpvO33Hyl`=@rf^IvI}QYbS;LAt}cg3XWv8I*2G5)tSl}IQo=W0O(}f;cg$x3 z>A1P$&o$|S{(TxN2_}@%mP~q7u=$MjG~!}<9lb_|GEQru{sIyuE>bjstOH$`yZs8W)PCIDYf!KcuLJjx}pdznd?*w(@&6+Nb9Sh~^qCE6=oTGE z9?41A4#!0&=Y7fvcy;(c5?rehg)vC*``X-n@dM6^x70wT1=e%w@6mmu^p;Z)j6jqT)aU zCEq~aCqt>WrOg-i_;T!I<@2vOZ4dRlI4_DH)pUVZL(cShz`$_ZdD?d6y?C~h$?Vb1 z>BtgB&ZT;VQ*j^4Jv^}vXwZzZsKtrk%xKlD>(##%Lzy$obw2pI_E=U;Ymwm%faoIT zx!|$hUH=?L#`w#tb9TdQE&6+mx`)@Bjh_MhAemM3Do`b>8oqO8>*;4-sk}KWmMy3d z9RU3X*Uco5(np9sC&61y%Lm?Gfw2pDZKMe{EnkyI4XWXZwQ@WCZByY0u9jxfA@8Q{ zl+1JmYB=7I>I)4~^fne6^wUpr5YO;w>qWT1Z@w`nk1(z(*)bvHqjJFtq&>PzA$Gsm*2(L(- zjozcQI2nP1i(_R|&kCB}GBQ}@O@j^ZjNML;YoS{Uifh_oO7f5==PAG z_P`GFt)FAwa`EY_4~`Y0Ppyi=IQy65%*y|IK!6@&K)UA`iD(wrx>_{sdF?5ikg+)VK*z(s5n?)>!wZRFOm z{Wa3GW_dLeFdSp$-l^=R{QFZ{DzNv)b0$FB+WQyhj0mG%yT!~8E<(gsWTmzhpBxJ` z=CYF`pJ&fRZJRA<15%&|ELU;|CY4u#0I=mJ&zqju2(0RUskPm>?DBWw%{lmv&7szn z(5AOJg>7CCDP!m(Fw+uEGRL~=`%#8dJPpZ1f|A&mnG)=a>F=uVj&plQT*Bh1HAvjk zz`ac0Tq&{fHD|#H9Et{gH^{o-?d62P(rZXlalfhm#2ma|0*Sx;#PySpITh;%Ovp1j z418tWKtUM|GWMNAw3hUR+-t}c3En7Qzt8m{wk5Po;$f1!<*{;acu=#Egcm%|)BA#Q zg9o*c_JVYpghtVy08{bwg&D@pEyHGepSE|Yny2p8=bE`(nZIRcOubm2zHNywzo~gp zZx9zTL+M@sbqyOf9MH6@2I3B*p{ORGW_VjUKwyKj#{d_$+l;y{JqWAzwy z%>Hw=Cs)m_Fx~R!&{08S3aH`qKGkB0IDwUvjqfCHs zU7oO%j+2tgUokN*FTFAzN<%Q$Ssjus36x%qB@8nd2*LU=?HYQ|2hL;-5A1ixP4?#w-o0fE1D-maa((u9kVy=`ts?Onc6;%05-qE zS1FBdLQ$W2&Y?YyzV2dLum5ATA1HI{=e$i26gnQW`=tJh*lLCoAxfts!pFj#HexH@ zLI5>ipv>L)5H`!Xib}~`KUP`Iy#X7n*{p7EIcoWtEE~-goo+~|!DBTW$ex_!64@Ue z$O&OIMVJ|GMqP&q@@|w0L^5e=&=A4*f~WvdFMwXJ;92-{Ju{sF$1rwChyyi)ai0qS zMb#+8+<>N{z}Z#z5}=nLaQL!YSYc9n-ShIDFRBDaMS@-kZ%UN(dvD61koPBDY7%Jh zIOpvT=8hzYw_dN^K4UONa{Q`{0>CrOLX5uK^CHN8rTn*x4=il+ z6Ox#4d`LhXP7b$lW<~4v5R&D}AiEMIH=whA+OKb*^xy9j&Bkgo!2G4PBY_g0$wI|E zFa17&@dhFDFHyHI&o7a8iDY=+oj>AkC(L?uEpMs$iqi`}lF^amK*B87+2b6-xuuQR zd>O6fk-!g1%N>`Ye5(e#+k254v<}>*8#l5c@JFheb($TLoM292r#nDswf~K``}OS% zTmE6@byTYEp1n%CF%!(p?{KSY*WsNW5hPCw=)^7%gFdq?gSMTUb9duA{*+K|a|nEG zaTKY?gJk+v$^vo#qlQ~|V`8jCia8O=q}t7KGQaE_fz$)fg~YCt_h_kU)2UnO03mvF z;Gn-{SzXTWsE+zOk!#*@2Sd2aT~LHdqk=*A&0i|xeZkY!P{gtJB|vrA`WT8O?op`U z^1T9aaF!z~tq&T`coO1fp|b%uf|Vhg54(4V|IqA)T_U8AyNeg5+lAylK*1qMFd%L!9ef;Zw z;N9Q+-fOmWu{DO&8|468>3DRv|LUr9djb9Nl?O0sDNL7WmbImWvD}E~RY7&hTw)lZ zlgIkJ6yM}nlB61s2Uj8U+{JA|eskl4!L7TagVf+GX5VBU_%mOtzpM%CHJXCLZFTNU zBFn@}gD7~&LUxi1+J#M$7jqK=M+Q+JiX361PND0q-vq2FkQuJgLfvKt@DXCv+V&n5 z{nnJU$3kXQQ(NXL4TFBGhpA_ko;BvitWAjV4qtrzl5VKKpp_uk&bRxx#+zQv=loLI zcKLx2iJRCMx>T8vf{tz33lq9<7^7xVzGbXc_RpH%ESX6+`V(_6z_bxxuPZ zBm{5;1+U8WtTlBWp>B4uX8XG5ZYq_(+IxXo*Ue`l$beDdX(q{uTnIWM|EOrq;4idv z7z48$0Ln4dwig`kJ;q)SJsqRhuZzAn(c_R(Po&EkOIlCQomqv5jzq}W^sg`o0;;aV z%b%UD9Z&lcG8Qn$?2!g&W=vaWLogXKAr>KHMsDiM`fP#vZJw*wCJQzOa~37o298;f z0T0*r`%;1u8Gt_HE;Q5(f)0)NFq!i{*@K5ui0hYOg}-#v!DgD#Xl$|U+E5Vm<{>1z z@Lur%@yel)y@EM0at086zwzANL79U`7p6}}nO`y-e*1U2lw}uoDk2Y{_YI^CWf1e! zbuNDhsETr10Q3cJA9r3duCAKa{{<24ZVgwgcTr9Xqe%QDJN-B7@dr(u-xX{I&is4A zvr5U)W6HY8Pph-&4wqEzr?#r&&*ybiAg<-#V!naFs1jsoevEE$uB>Hby$8HC>3ntI z0E(dP>4hQ)98o?C-P@_(Uo`Vco4JadSy-QHq-_+*SE6D!H{sR?pf{j#uSiigdAm1P z$#{$AYJ9%P7@yvV&|MF0UaqobzW#Pyt<~n zTFyp>5;a7qQAr;SWeyH8ISd1M7(w!4pVZXtl_LTLbrw7?ucNt-5bRghqGOGAhQWgn zzJ2OoY44Sg&B;uP0C7;qZlkMS+PT%%hdp1XNeu{YeGp@&?*`&I* z-D_32jtU$UZ7wFcp(6f#yQSy&JNspjBd{*bv>aof1V>{w+nYJnaO0F1K;WwsFm2;Y ze)c}wyyLsgf%Ju(;1!@y|b zN|teL4!7H=YCA&PN;Kjethf4b;-=Yt=lB8UW^s%Pn};FbAm=e*pCO{y6PWE*De~gm zZ#QlcDggA}jSf;Tz!*JeHYZLQ!uws~S>2Q0yT{io57SioVeOI#?0yFvY}WBlQK zPCqUaiI2nxL!<<;G6k2eY0isVDXs@3=I4@tKC0;Vxi=8_wlJ{J9my2T7z)TL2rFhPg!8NMz7-g^!HQ&agkf6#0ZUv)Z@I zL2r-%+?=>7@ly4}d6aG<5GTouE_>k?tiMuPKLR&?`z%GYT5K>(YbVD{e6cCAMQacj z#EK)=s1{5nMZaN(K*a}yHn1Tw^zc1QmrYVfFIcVA?s zsoC#Sx2^YnQy9HAAgT>!Ufz#8HGYiSIJ`h31_ts;Txa3?-{pRxHVyvCy8-jFKNxab zU9a|Y3z@n($*h|4BI1xA=eo4&&(a&R%E0ZU9n?j~rv9GmOxk>3od4?SKLnAC^h zdLQIQ4k(3z3ODQ7U@rC19dph!1?tDd5JYrghnc~20&QJq1vyM463~I}!OJ#rd85}J zopB3tN3%*{9N)+YHuc{eFA7G`nY}t*<(~AwPkGRJH^Ts+f85EbHg&9VjB%S@GrROP zs}Y<+0Ym-Jgh}1raC7aNUjTz2DJoV8Fe;EimOa4D0{(QEjc>so;i7*3mSIQ$%w!khpDyHy~*^)2YM2RZ|G5sfr+C(&2_yV zgxi(VfOMldtFCtg)o<{UhvTONV!5gxquTir!dG9jdaywBwv=-4vwnE-NY5 zdC+R(e%dqnP9cr7&rbSqD8hGbHp2J2$<{{lQD3I4&yHY*hJAv%+3yI}}33RgpI>{=d&Iym+*qh3w+fHrr>$Bgd zH}xk#c#VDVJFc!j>Q}IJguuH%CZN*(@4&TWiA zw!INrA|rBbV)jhdIQXIjbb`V5hrI*-#k9*ETUE`(CCz?CRJB(yb4t}l$Kr0Gl-Vv@ zCCZ6B*Z&)8fe7`btYK8n>T&1;--j>-ZYK#Ue~G&H`QL{nZ4~c)sFlIg2Pvd2EuBYk zC+=0OVHfPS(|4K=->)h2(@79$Z~_!1jAPVgHL{E10w@|X)c#-BA3g9$YpcD(OEs*_ zzhIpjvfvKdKNiiL9-h%2emzMSufU3lQFq*jtP7IVMSK{u(8w8&wK;A2OiZhw@6_JVI- zYyCrq>9)~#Mb!?puwi{FxAEKdWHu3xbT6Wkg=TiiHxlQU08wR-W7^8E6^adsJ42w0 za@B)@l8szjseVo>4=Pj1QH$XWGm@|C%5kmqAc@xAB(P#q)E#jJh^ygXPJ9`^VZ{P^ zewbc%Pg5)f)9;JA6N$fwK0t-m@#SHDa!2K$5_6&$heVEOItg|dHu z+5^c+a%!g^(T*y6mXp+(5S+*LnE&P|WBKHOg3U2C?kUk`CQ=(L`sZMMOlN9nwzL@;OQCaBcg z`meLqatVpQ-idP|ZGs#ImbMvfU3rwg6>0hiX~bJU#n^^r?<9N&M7;j~%XZfA_L@$# zT?UtWPc6aC56_mnX1gd+mevRT`-c{?77Ek%X<@_!wxhml?+OlQoP3wx8LDu+mj);* z%3jz-b5ge-Kjg7SQ?FdsP0LeD|tp)-oBYSi3-~-fK9iXWzNt25hyk9=i9^aDOZivWXBq z!I;>#)|Mt84|#!U58G%OSh+G4#~X0_q@i}N480wEqtnf2BTd%Wt5XrhSn-&b5!hoc2@r4M8?#iDYa>JXrh z8AAlu$w)-Uo@e?sNMZGf|hGD_x-M;sPCcMg@`S3?fcXSRWeGpq?-YXw)r4(l% z@%z3bc9ky9^=H6+Vq-E~Uok)hD;}glmsKVu3kJR6at3uYF$D9lKC^Bxd9vabjC0H( z3uljA59+Hz#B^FW1@y!PSGRz(paJHljrNxwSOyCO3gd$+VgV2oanw`cckKoFCgquf z$A!!MV1HO}-!buCpFGqx%`T$M#ANq=gl^D=gT3N`zBGbzD3SMO7gXl^ojUNkp0l)r zNaHdH^=T)c6SAD0Gli-e-a#HE6jH;;K_oMn%2+3;QiHhaD!w2~`v*AmnY0A>Z{e+L zu!T7>nwW)x=PlTk+#3a;x-Of{Mi_7i(&eJPOf` z;{KvpFrIT7iw6aCe%Q^I4@@rsn>~h|W8TGgcNAhj)!|TAo{(BmzIkgb_AM@k3E5jx zNG)TY^SQqZWjSOvVZ|-i!vgb`$W`_BU9zlyfW*jc6;cgg`3rs{9Ly3ya7e)zMaeDL zs*tSEQlGS;I)~Y7oufz!5N)q~vEIa}Gs} zBO9EF*<BEsdGl>T zg~w~hd$_h}32D0C^;RlyLz%Xx=}z3CT)^63m7JH}UVW7@7!uP2V}EuBv6rttpyEPU z5|{Yct~(mit;Zm&Gl@f6L`=hv3Rd*M?80?e3*`e@?Z6Eq&ZQy(qt#;*-uf%+D1;>t zCjpus;;6i~hbDz0JvU!JG=!LZ9O0M#i%F+oX<=ri{OM7IO>h~rOojHOb)pf@*r7pQ zEguMDsP{=XB;HKhe9Nk@2eXT)PWR}B^ikW@Ud$};u}I&To__>ZE&q3(TxBSBtn=7+1hZ}YY2wq)8&d{+lyE-1 z6**1Lpb)+yOJdhpg1P*+q`DJ%PaOML+9K0MvoK4d>$hrr}iysRmMb!!Y(`2}9F z1G*Sq$vSux4Z5K%T9N&@gR+#rP@pqrexxf{>EIJF&|l!@I8nI%zS_*)f{zi+QaJt9 z;*I&x4&jy@(KMsCkX_ya*&MpD-a^UCj}+)%O`m#^-eoxU5ipuuDl+Nxs3ou@Et&Uk z1XB+wicKm*a=CWJ=?b)P;N$3gN^6TE_MX?hL^62Gd~LP*x@aU(`#jE{x;>RhZEX-=f-E*|LMHDXcB7?vJ83_`N3x^Dk4tO5+P;?ne#r#_@W? zMn`8QJonvd>;=X0vty;K$wfwp8NtX)|7!tTp1f!#)3=Z;_ne3pFxt~O!-f)UiZ&`& z{VnkplI7=EIkwV{e7W(4-8=0MqJng|;~n=^2%nTlIB$9bQvZbcQ|2l7v~lz2P!l8+we! zzlA2FO-qiac(gMm{bJPj6*zUvr+3pgQwTMlZ~sW%*3VP&hPicO-g;N8Mll+EhGh9h zH*+wthDUab)w-}QtI8rry6;5u#xY%`eGc+G_NH_gCFI6K9n#q$uXp zD4OlSSzWt6ZaFBdzaS=I=Xcw5h;T!Q62LPi)?Vf7z1FzE?Ur6g#fjB6R(W2Nm3}pT=HmUFd!4qBEh2wGMKtP)xbs zZ^$%XK1CjO5Q=O?amAY;Z}0I?ve-PoVep8m!X+{R9WTv zr!z**#PEj3fW{e)I!h_3F+(A8IYK-@3+U%N4{yQxZ`*Z!H-0bOQp!A9VrBQBd%0hv zjbv_mkTQ|niw}fRfanfpbZ97V3uQ9RbtRql3-G%kP`||=iNC@)|G_~v`YO!7w!Aqm z3BD_KZ;@cmj{GC90;1PU@KMy_PaNz4zVpOG_+55FAzuQ9Aym}$&@Pe$zArrc1T&>} zvEZdd-8T@#fH@5PA%ebLETlTEt9Gt5zpy@&p>RP7vnLAE&t&Skm^0IL9t=~US0sdH zAoO0k2);{ML()d>-WX42ZHNP(`Vz&Lrx$*Km;uakSEDzK_oC?U8IF4@(!wk>Xw||^ z$^hvu99ROgyuF8PHe2DF2r>U8#>G|_<*dqs5_E%XYJ9sHf-9C8UD2JUf#)`M6ZH~e zvfjoZFhx2r+KdId<6(D}sdx9m8XL6Zju8*4p|^e*m0G5w`P8>fEE_qHUJoSFufslo zzz%r19pgI!xP9^_Qc2qF_3=IK!88H{TW9zB2No*P*Wj+X*KaYQRwU};(^|AK*&Boz zz7OA%!7;7b0juXd6W)QSl9K(>PBl;BDno=!`Y7=+pU++O%!lEiZu4|9&0T+FX%Qt9 zsf+`f+4gNaR}#!Y=6^*zI|__f)+$hxizv<>C@b@R;6mzu1&2iP0(V4d&-DisRfc() z>|(54FeeqO*!~Nuu&XB62;v1wZIdUKN!t`qUp$@+{QmJN)~i=vgc(#&Ah62hz+i%A zPS`z->chYVJm3S&?aDs#W&x2}zA(h~OQ-a-MBTh9mfXEr_3A4Kh{GMdO5(dDkefhr zYyla{m^P#kVun8aPJ0sg0_~~~l>gDxl}IMDTf*Y@7&Wy5RuHhlh1?#v4wHzelnzp} z-tl~n>tn!aVCmM5?6)wTj}(8#7l@23S&6nfI~drMltFhZL+Aqn0;}` z9Z<_Yh4o7=*{1&+V&kX{B(j29^AtZr3&QXS<{qDwP)#7qn1|~uox(R^sm9mQAm+#g z-@0Vqq+(Ta=W7Vv3sO+=NEacKnC#>n0`vZsl1>3Dv$m{^nJxz}zr|H~p&dOo_A|)g zEz}v3XwhUJ&Qv7Rx+zxWo<{7_XpA1a88Y;d`9H|28qcu~tg-NlH92b3u__kW{F8%* z6YR}=n$j*8I3BnH12w8xkaWt&JBZuqm@cwe5~f1wLT}fhO>T1cO22%dq`H5HZjJ!a zc93z1DK2$N-c}^2`f~i+;dX(KymP3$?XYN@Y?#9Gz4`AEw$inMSPkp@i@X~n=})_u z`G93BCo1}qF3?@ibsvzi8@P8FO&d`l7^+Cc5y#F!Z6oJ0cP%-UNLSl({gJ5~yRXF{ zTQofgY#r8(AvJY_90pZ(8Ww|4cyD=tF&(IqKAzDzhza3->CP3lp)W0Ex>a8iKL|mM zI=sOC6A5hMu<^F>*ak%cDaTjGpQUa2%aBKh~(_}vQiqj^3+RtR)42XZI3E#sKC4xPTb(%TtmC{RuB`(i*ZhwSFGLdU)a0^~Bx8}2ZQ}lxsD*nY4fO@R2B-JOw3gI{Yk&=E@?rm zuU_3&m$U@mqR;%L05Bmn`e-k)6o<86c6QNPu)2i3+*;(woS!IKXXO&SsmRJ;#_IoF zbJb-3_vBhS_1$n}{`^nnG)K3e(rPa{j>fTF_i~yOe&&Y6+I;{QaxoPIIrXfa4~-62 zlT>acRLmF(3$Z)uM(F9Cbu370XxR<$2=Egm5tnlm) z(4Hab+e?vLDBC(Jp4lx~ghaeH*e~Q~a2K_zA%oqs&GYjd%~xKr1o}xg&%pW&{X;Q; z=$p{WRpwdKwkP;1m_4MClZto6_|G_x-U3n$9wX9PtZa^;Ywcy&-TdjFyw%+qV8UjC z6s40J#~V)C(Y1o$M+RW}r@MC6wAHzAAWbns^hGUoPuKkRQh?ryf9$|wdh1B(1Y7cQ zh6m}m>I^m+(sByiV~LCo=Y^a!iAx2l@J-BBr;khuSt_sM)EGd)V!2-OV@6<*0RB1A zv>yJ;$m19r>eFT-BvN?-)S~~(t*v;w6!!(n3i{Ek=TIj$xl$gDte`T*v_qFhqsN z%VgDdB_3uv`vvfVJ#g&*9qJf1PCa>Oc^q~E=Au`#*6WOs@zU1L$N{maMv?jnLi@Kq z-Q8~C?s`hGaqu4``)7e{q|_{OT>Ffw` zjR?!3S}lSsL|)6Gzj?Smo+!{BW9;Ip0Le$2K_V>&wJ+=tLyNX(bGl96sBv?&l5Dli zM|M(E>539=VGdotFN-$JKO+NN^VS*kNQL?oqkk9=%9edn1C0zyaGcVCc@qd@tW84= zvM%i);NG1JVr`|AJ5D3t!Th}5GO2B*nqfstqRBGrxDY@ z<0kKNGq&8z5=30X2aGs8sUkYeQf5g@e1dk)bFH!kk3n`a7%vA$TC(;W6Ioqpc}`Op zVH4F&$c3~j@J!jbr!^hbaX{nDckrw7rp3)!CV}m5Tw@E3#n0x-R0c!0l4lkhm<+k2 zowHm(h%`LE%fX})pD1ryn4lb-7zkb|^Lv2f{5LL6+_BPp(op zC~ETV+pCb$z;eDa>I7vT*kHcnD)USnVLRBc;_u%6S2Tto&<}tV`^cS0M0?G!Tisy= z4wlI$f!c_)e$rfvnrn!*kkznsil;~k&7QIw*P;?^-aNwsSdM?BSxmH$#0i^ria9o7 zG-FB#xA`>C=SO-=s>ORDt+$^r-gGSbhvezRLiK^piew6(UFS~4dLg0MFIo21S5P3V z0r+U>2(!BaqY4nucon%3!K7dv*$s!ZfQH4c>C=}h=hg;(lLxEhg8 zk_&5T(kyJ3N^{ZHy6yA%Kxc>3j*RcKLo9`-Q4?v-kb#BeHAz?NP`tEgFTQywpLdR}L28@fFxo3s5*)qy1hH0j|_N<)9ky)=qCDfL-b=jidC zf)Pr8C%pz%3?P&m*XYV`7dsqAO@hce>>Kq3#`c*;^!)f)$9e1`hq1oIEoRLQ9z@GW;-SBg#Z=Mev ztak`eU%lQ@!Z`*`fjEUq$#(Rq8YE6tWVS1l<*LOIi3^{l$|^NRei#E z_bmZ>VyAiW9Tz3MZ5_;nxcvpjt)IgDqSjt((~Ln2d8b(JDBp`zZf+B{zJAS zV0F=U3%}guv9|;#*lv)Krin_xlu8U{A%}(XUu3TaTa2_&-hO#I3-*T?B_X<*G!L&% zoYkx0Tf08sR^>f+MvBe^RZ0-or>|a+ok@b%(bVW#GSu+h>;I`zt4Tnb(#0kWBIFC? zdRhOlKrQp!SPCc-8W^--ehClr>^D=>S$lNo^@l0I z9l>Aw@xPAy|L?N|p25)hCPP{x?gt}K2T9P}JmEUDKw_4A=Fd21Zm{GN3`m299S~<~ zkuDMRt8oAjJiO`^e&i4#iap2y6&~rImqoO{8I*>p`R#2YiI8pin|sCmlA-8);c_p3rza< zuYm@WM#5~g^Z$HOqP)zIXfofj)Lx0VV{x;wnp^m#@6Oci!zF`kJDK4B=)E;jl<;=l zl;`pi`0;g$ufQ0t>NT5N=t;CvaK6TE02n98My@=`_{?ybVf4mr)ErovhYPJ_0En~> zY4>^T)mD~!{3p#th|%2VWdj?{7uj$k6kwPFsB zpU^*t(r2KLYq+NT2_&Bl2=w942e2$$y#yBG5M#^~9NGDdKVglC7Nj_44NJ;>mPvVB zbuEsGRv4mUj6IYUZ-RAH{LWwudeY~Pz7i$3Ev_{HolTzHeqwN}HaNC{(q>Dj|qWz|5)*u%Dz1kz=bJMFW{ z;S6e5z!O~u5V}}jF?XI4P2x+)tCc0K@&d{Be~Z6+i+_pvNPwvhb0ILLDANv}n zxBYu@G*FW8RF&3vBL#LKi8KQ-|NOahXqs?S%W|CZtGvVRkp0f!D7QaRBKNZ*eKk4p#GRFa-Wh@Rk3u<# z{??sU=KxF}`bSdLX_;(^^z!gp{-OFT;mp&0#shKd>GzvEzuC{t<%F9T8ceTqxZ%(7 zed1Wf7PrO=8SfAHnrYhYyxhH#~UV+(qW|(GAft+@%O{;H3z)>H9few$^b&57Ru4w-pvrl|k?MJ-dtU%cb{gbrg+=$8GZV&IjofbMDh$guk2NP;FT{Es?YGxrPYQ z6dfYia0g_Cw0XE=cIgntd7E6w#leOUVc?nDv87LllhcvMWUh+23Hm7F0W8Y%N9C=y z+``=dT)D8-LsC-OM!Ko%Cx#Q^TBTu|$j zJ%`F!S3Zo$d7 zeAC)uor#}XfQJ_c<)c|_lke`nH<*TR(pP1&vnfG(u%Ff_=w&g{a7Z#(6JMt}55YXG z$N;l)<-a~w;cf29I4fyyN)wmhyOO~V&l6z2e@DzKVd!y`%r0GM^(&PkTJkCrZ0kge zSy>R^@6|e$z_=fLfcYf*{J+J%&5H8l<1k8oxz40d%2hBcXM zQ%;TGt^mQS{%Y3>))~EMJKnRMQeEwN6*IW~f{2)I$ChSNCIv~L8$~8T5D^j324w1xhJYo35VQrMMY%LX z7{o*cWfU+#gpdTU2r3YSFefAmZGZ%b2!zaeh3ISVTJNp5-jDm^{;IW3ol~{z+h_0k z&Z@J&io%sTZJPObU(X)8TO-hfmlQ1>+WAp&Po;7gE%>$9x70~{e633jFK4q=UdbOj zTtn87Y%Jn>Bv|{Rjj}(LF;|3!$G#$uyuUQl=Ll{HU0p#vWV|Zvs^9xX1Imz)FlBJ} z{A$R6>()pg`VngvwAtrtL|)OFjs>fot=7g@p|-Gb$9I{!X6<>rMuE@$?fc!jS)_1nPBg87AQ; z&aX$s1o0dLA%oM~ID#z(6GZ#p8FHsGsvwCmNBW6{_OR zX4IoUM~$T4x>f-0MB|tR1BM%Zwu66ov6@{w)nr0IT*L#n)p2g9R5DMGXgK*G;SQ8O zS8ypfe<|P_1ABq8!t)%DUwdPAV_kIC)q>ZHJOxKyP;^P z3uO_osdG=pIp=UOjA+%f6M#dUio8v+BB_LN&${Zo&gKG|?VAJz7ubo0Q)!gNO@rg> z*5@)?>J&Y<=VbckJ<{6&ksietQ60wQjo}%b^_x@&9zQ4w9lE|@sYTk`bK>URR)o-b zHugx5%$aVSIJK*i3>M2R&qL7T| zM|>@>mB_npH>PCPW9BWHT^o|ay2b6mJ44^r%y?*B4w*eFo#^5g0aa?<^-YEG8?%Zr z(3%*0wQeQN`pwLKqz#UPUdVv_b|+!*xsSNdQLV>G8+W331sFf3=k*Z`jTA%D6(F$_ zVz)Fa|1E7UhIA@gfa@-!S4K(;1&BN8%$Bx5O92rNb?Sb+hkGjVXr3RUZ2atcJ9vxA zSsviOH8*7 zXgYa)z!rLPyygc25AkN4-CM|z7HEw}6`79dSCW8jgn4eplN$Be5*YWT2u|#7Ii}l+ zJrHz)()e{!avY>#KyWo3f)4JXtXTVH*zz++Y3#?*wN|t(L5shS0^{!d+4GEKw^Ack z(#UNLqs50gMb(k&*@*fW`Ep8`cw|eL&Cu|XXg`EpV$>QD01efohWZ8fWDCv?=@11y ztHuWZUHr!Do&L*Qgjd>{t?GpRtKh=Jir|q%b}_Q3wrrdok8Y0eUPOqYS1Jz1!Ifo~|4gW?|! z4*BC;QR&i!~stQZl{_H82~Oqa~r8*&@cPL{m2qsUoAee$ebKKsa1i4 z-D~krGtK@4SWrAEp{?}~bmBwESo{hyn!gUc61W&E+snFu-7ZwP<_Y0srM zbr8hjCGoVGT8DB)gu+Y~!{SF=q93 zZ=2{01~-j<1v3kT!7Z{{f!fh*bkr7!?1e^oKn5F3Gy~g0vDtiR!wAO_YweI2JjaX4jQV!Q-!TRc9NwYSJCd6XGYS;}VbAo!RNyazcwh=5;F-Ss!QG$u}`(9)?}aHfKE z`fhKQNlLyX;2S&p&H|J+FhuHpMlV7>7f>}9_2)ZnCMQ8lQbnAL0KSpmX%e!*LZQy; z{j;fHSGB*dfu@bqs9?c6*`m)AbswnjK9}2c)21{~lFo|`P;qbG2yQboKh_#86u320 zCR1!|GyOb5RKO(u=2k(Lk>%7Fm=QVt(@8TGAN}+>tx*mi66+{ni$xS}BwjQ=EH3a1 zGi@E^DDqDRr0XB1i(k%g|6!DJ%iZY67G*uDTY6h<__Xw8$%p$Hr;C0Y&(N{BIxK?V@<;(`c)Z&W;ND=kD&_N6HUDu^mGwFI); z6Did|>^r5`%9tg+%^zICg!N|Trt-B47?1pbp(QaoO2^(`y6l(Kg$5c9A5vgHq#rg> zgg^O!LHU4=Y7qx4-mRGfssRAgxjmu`k#n$=E&dCnp0FSiJ80x#Z&?E~F-wd!X-5h= zyERf`{dL*I2Z!4*fZdpdyY$=IT>q-JDJNZfSGh^LCPA1?(9$&PQ3zv#=_QTB7Et*N zsRyrUlg(RIp<-o~4j1vE^lTm8)%Z917x7NC2sXLv)w=TKSnMKIz>b2&9Z44d3MC2v zWn-2&hZzCq4Jza!)rMNT0|Oo$Qh$^4r&pCp$^+I07y)k8g9MBE#=M`Fve?!%q)`4g z2h#)n=+XB&g+hhvC85Jk`ugP|!}sF8W!F+|n)*G}at4J{6T7!CMo|3>o^alCvPKe#KAK{@kAD4aA*7M+i$%84Jj8`W-nl_QqUT#Qx9UV}yBjVif6s@qo#@P0vHJl0OFs(zOR z_BS&7cYW)kWnZyRcv|dB6V1S>y>_^P-IE?Qqcr5!Ako+jKzld{x}4X`kxyk=U1rjR zF9Zsfsgkz2Bx9y_A6(U)Y4{GOz2!TVvK=FjdiA2=b-0O?4(+ZW46|AB9{J=3RPSwc zkoW)~b`?5z=A=A8OI!H$P zp3k=af+x1V+wrWf*G2whVIfbrtV<0^GE@{Tb|AN6<6Z_P51DaAt7Zs4n-MdaQ$kOj z+Ogx};S|M?8vR3ImY#B}E_g-8e44+;E+ zlxJfb5i@Xo7ieQNhIRS09Q@E2;a{foy0`N`2m_lFv7^suk*F2Ezp>hDZo6b-T7947 zhrDBxL*%prr{tqwcJ{V4pM-9lRIjy7LEF+gO#|x`Sqs+t^%ZE|Fz;{n43dkq%<cztSh4WjG68+ z{87cluGVy$ayIZ#=vLx(d*z~WSo6Z>4rN_+URr%Wg*rJkXOMfxGSj!@xh)N$as11H zrVLSYFg-O)W3rUIOMYK?YG+N*flNgHpxC!#ip2EgSlrCI4RR4!l!J68Fh# z?MOefF=-1Vl6+Tof79n@Rue*@&PfT1Jd%;x>t%dAPL0YxM-$)3pyccTNccD$S*ELN z6nZebp~efl=GzPd^m|h{b_Kue&o^p^P}m78*P}saNR8Qm& z$mz;Evu=7kk1$-5&#O;}!Sp?`;qRE>j{~l){v%-Kz&D{^+gEg|5eB?fJ$Vb@gc|_o zdlHDPB^Yhc6fcjB;Azqafnpw%-cCQb{{NjV{`Z_BRH`cH_Bg6tAFcuIQE@(c!tv3M H=db=3Jm-0b literal 25281 zcmeFZcT`kMw=cSyEKwyXQ9wZu8kC${Q9wX)kgOuml2emIo4^D}79=zvIcLdAR1j#1 z0s;a;lM!fgx~m)5dw=(i^Tz$&J@>rv#yEd4dazbityxvGX8g^1sj03+Mb1PH007l( z<(t|7Knw!_!cSy`;FHFWT=M`xu%oG_dkg#*0)Y?^5D*d)5)ly*6BCn=kdTs+l97>} zI(3SioScG!f|8Pwii(Pwnwo}&=Je^)XU?2Cd-g0XEiD}#9X&lg0|NsiBjdSq=gyx$ z&&0&U%*@Qf!oteR%ErdV&dz?}!UYZv4o*%^E-tQ%7cXAAbcvgrn}>(z^5x6Cyu5sT zd{?eq;pgWU5DfLPA1UuU@@&?V7N#u!x9=sHmuzn3%Y@xP*j+q@<*jl$5lz zw2X|5tgNh@oSeM8JPZcAe*OB*n>TOWx}~V7sHCK%tgL+d_H7jv6;)MLH8r(6ckZaG zt7~XzXliO|X=!O|YwPIf=<4d~>FMd~>l+vt+`W6((9qDx$jI2(*u=!d)YR0>%*@=} z{NBBL_wV0-@Zf=kg~h{%4=pV%t*or9t*zm3_@hUUY;0_7ZEfxB?CkCB9UL4S9UYyV zoSdDVU0hsTU0vPW+z<$aySuxGhli)9r~85I>39UUDL6Z7ufyV%&+xVX6Y@88GA$0sBtBqk;%B_*LysO042l$4az z)YK0jKBT3krKhK7WMpJ!W@cq&WoKvS%)Y6ciK|78Vs16&Dwml$4Z~ zmX?*3m6w-SR8)NY__4CGvZ|`8y1Kfirlz*Gwyv(OzP|p`r%#_he{N`KXl!h3YHDh3 zZfD!&^5x6duV35R+S=ROzkU1G(b3V_+1b_Ag+`;lfB)Xy-QCmE)7#tI*VotI z-#;)g@Z-mi!NI|yp`qd7;h#T$j*N_qj*gCvjs5!dYkYisVq#))a`N}@-&0dl)6>%! z3}$9#W_EUVZfQ|8aPdO|Ww-g@27=Othqy%5zOeW~4lBegUgw12=CjiUOB7yD4EWUx*NDa&mS6kuQ{m|Mws9!(isN zuWm*oRK3ZxmIEzoIb;K0Y=&5xMzJYKKr*`^nVtY28it1XrDVK`a9!0xj>4k`-$ zG5|dd45BLxP^A)DRn6tbo~$*Kq7EzUN{JQ0@$$mYtvdsvU7pr|Js|6=+!L{_afRs0 z0K}RdfXFkZc2gwVW+(BoaR#R*8dq1+-rvW*1KcjWHH-EvAjDzjlXk0z~~Uv+)9DA7~Q|3H@V# z@o0|uh)%^1kL_-TQ{uNMDhFE03}^W+&~5S3{fOu9wUYi|{}ppP^y2aEhnt#c-jB14 z&~Uw+VRzKVE*PU;+xYe7hvQY6baVf&Jv#@R{qMuR6g#NWaLm;@8ak?ma!l0P8V;nj zo>m`+46*zy;O4X;Bqp12Ox0Ro&BUHgFJtoU`I)a;^L0@|VofWh8m$ zu7@CIBsw|!s5sj=S-a~7^d_fIjyYj=T|sPq_OI~2x@l(IF<5^dO@uD2E_V3&sqH0m zyb|$|RLe|tfZ0+AoI-JU8~eI4r?qdobr$q8h;DfVrX_+Ky7)I68MC znSqi>b-s@mkb%Q2n8$_>GzjqW<*7}?n$(E7({+HVjAWp{R>g-2FD96DgN1bjd(xgW z5}`GvCl6e9TwIRd+@;DnyNn>QbGR=FcA|BF30}-N5(yA@OAo~Q!INsCWYX{zKi#xfm-}IQCYumA2wUBI}}d&VSAOWcjk=)5-Gkp>wezFW$!i zrBp{UDsb$KwZ_sbDZrlaXfERwKM*1t>VN+)K1e*n_m6)+6VGV>{oo z=x7k>B7ifK9c^0uPV%fcs^sr4lDo+u?vI>Vv0VjDwEa!%jLkPIgFeotd(&>w0saq1la9TM6mb zk#pI-uN?_5za#<;5_nEra#fOzgIP))`yrxUUr@v_CP`NTh`dPvvURCw?Ix_XYAcD} zrpS_2ho))Fr{7}%O@_L71~-qqM^SSTTWjm7G5oeEOpzbgQj7>rUS+B2NqhL`iCsL1 zpWmmXLD8w>_NY8vsV~{J$Lv}Lw_K}yip^PC@ysM1OkJ(;;Jl+EXM2@^?6b^G|ApH; zIKwL6(liD%thOOD`!-9Pdb!Yf3y-SIwU;le`y#$p?k?S)-mmB6>8WKyblFa(#++~N{|NoQr8t?n?$K8Rj-+F_k>{aVSmd(mgo`(kmf)hx%& z+TrN+MC>`v(cNf-jyL7_Z&DOZCjCo!nj9s8c|(MRv-MNHc9U1Z%5>^>H_wI!E9cD` zmIWur2r0m)U&It-9@y$csnIn4sS^8geMf zUpH?ZJhx%CuwlH3s3SjFtGSr$34!_j7-`>nId=@k=99pDEn}Zh*9|~r{AiQ1{WK9< zT1xBt!7I`RRoS}%-wkCu2j3~e`BeYjZ61ZW1?fii*dmp7EYpNk{MKM)`^3P=`< zPbOkP+K7*c`22XXoYk!*?m!y=?mK8ONDw_?aec!bXS8g7jy$^baRa1|l^ z>n@y&oDb+*L``N$eu|~U5BVH2GGF5aeJ-AUk3e(8y%JcQQX|3HZ#50b5GOOqD-Fy# zLXb=cQ7DX5G6E;1=nF2OtW68z&u~!ly#;hDcH}yH?nMiq$D4oT1XW$c@TAkE^vXc? z$ON_og*ju*hbM!c$gO|^J$lru`bw&H*ORFHEZhNlWpEnZ1BK<>V9~t*$@Gu0=Gkb; z{IQUDvM{(Pfo$)m951EOp8jO7H5wJC2><4>hfo7#8Y|XtC&$pcNeH_ zdtEL)Lrjm{VL4g^x67D=70dKNOjODJV4jaaF9vVJ6ih|DG?8cKb`cyNQVF20i*XTZN5j+M#z^~rCR_e3Z&IY@gCVd(hB-pAp%8Se@uyG#1%+q!Db z>B!)|iSv^JhZqL51f&-q{ngLL)f5RW%J@qQXWsFENVO)Cpg0f?`+M8->X9s)oT%_j@G-%0^rJtviVDDpFxl<*+wOtK+lX za~k0RdD9%wfxy!?UOrs+Db>(lNc0q}5&nh{ZrQJPpjN9pbW>j-L~-SKf%o!_k+L$oS7uEsbWPMe2zG5ZNWokTq;0!r*$9jn2{f~~VXi(|BQIzth zw!?NFQ3GmLp`jU|O2(G^Liam^JU(*;WF%cijjSXUMMjbn>m2B6BCY`0c7 z_m&u7IZ?s?le3X~3nrM$VlHYmzT%`v`}J7g7!kM`FaI`JeqFMWz?FoPQs69XA?Soz zLWyWPqc?J}<862j$tzGxdYruQ5N^R2#YE#c`cb@f)<^buSLjLH3g&?c%jDp!n?te} z>xOptyW|2K@eVNP2S10HE)uw@fL(*Lb*?yQQ#?8L$-sVD6liC#+eIm@XfxLwa=}wa z%%Oh{ZNrEXR$$h-jeS)z&BsIz{re3q6ej*$EIk~qnM}K7b<9rv4GwA%;RINrj>iIy z>f8oXP}t^gvkxoZUaH;!hDdN0Q7$p8D{WL+asZZ^GKV1$!BkaC$d@a3U9ARm_$X@<*GJ|KvHijwbrqnTexYhoT|MubLI z_cr(*aaAFcM@KG`l(XqlEhEXg6Cg{5x?XN!QfVl+)OX`Gfal3;RPMEevP7uQdh#P? z?LFiyS`N z?titpa0ef!0RB3S|9bS+-ECIhWf!S-mY1HxOIC2c-bQ-SA49HhS20ANbVYybPZvxp zEBf;UFX2IH82A0T*(2C4!^dcST4ecS{0Go)`m6H;YtxQ=37pJ~`_96MqBuGocvJIy zhZ|%^H$L~BB{@zuo!xivnSyO;UW7tS#2cS<&PIn=&|b6jPfUx$}alHxPvSH zC*bs9&%lGR6>LU%6uQDs{b; zwJ45U1i@1?W&`=vsUL#q%8p%xaHNAXL`w5t+E6p16w?0LB+;E!M$Gi@HeI9S@0X|q+Z92QsmbUExD5{M z`|72zOBG*rWmkY0JY*TF^s^(y28>43;5MP(WfHR z%QMW+7zVY0TFRSgUP}B{vh7WrkHMs}PU!R7?-nR2ytCaB1*W43+HumENoCj6k$2jA z8nH55vY~LquM3={D`JVXtX5K0gE5X{rKY<`RL zkhcgl>sXLt@v_c_5BWcnpyWWgRo*KJLFafeg4(hekd?1+TySmtQF~+Z6cktoedfZJ z18UUd8k86{;RTTmuS#Fk&N?=$NPh4RNcy&~`3I6}uE-$G*xq`6nTVku<`cu&l1YSU zAjcVEGrg}!1#5PNcyG*Xr$^0_<|$bww@a2b;tVj6Pw^foSx?fTXjO<&z0AR=t7h8# zSnUl)I@=&z)s+xza`uV=sk4LG24%=qNi!0Uamb1V5aPaY!FU3QI~?jZM4Fk% zWd}_C&ZD{>8lpXjRCn`xpj@WoZFg`qOLt4pIfO*s);?@=XxUiFoXpoZlK<2Uhp zTI;kUFy{&|!OL`L3mS^Io7rEwiJ3ZRuop9bolMK?pmbsVn;DbsXd@KpZ_fhLLC z-r3OeH1Psh=WAYx6Um4bs}e}bwlbs7?vm5N_CdC9CvpqudLSkfR&WWD5Y}wn)z{6n zCXHKEdO^K4|L_jQ!+G1M|am2i#J3sWb&YYu(De zh*C-rHTTcY+Aw*%oHuNbBbU#N1f?^=d`-okLh*gaXz!Dy(gTg^wO;l-Q{q=+Kn!;^&u} zzhG46GTh7}d<{sLB5=Fy`Si7Ce#FaObN=Q6xgPw^NQmlhs@lchjoe@T2DnwMQNioO z{V!Eyef&U*%L4+FZ-bI_@H&5XWx9(E1}x0a=`Dn(9&9sJ8$p-6uii*_0Po)75+U`EEi5zts_=u#csRpaHf?p5GqTSx=8Kx0Djd z8)s@)|BlrK;|PIO5PNUMtbCQ)Eit6_yV@AR>D^0R zzl*h;V2`ILP@$Rgv%{^uge#kg>#Wiu1erM`KCR8KYB^{!MQoLmxGS&CCr}|@VMyFO z4Cm~yX`XVn@<8qP@%xz0o1&LjFt2S+A*OSf~`{RzX{Uq=?Ne(&WZ2I zQkk$-LbPdiS4~FMUMo^~MLIMyuBnM?4g<`Uep(liI=k_X6U<#;2;5@lW93!dG#~5o zqpbA{Tvopsr$o+SmsJoZEY&@?5;W9)<|V3i%06KKz0#7WOt;I7iIe%dE;2+9j-XH4 zD8UB?K|F?w9frkYsuQ<`Z-Jg-Go)&<-g3cVAy!&l_Rrj%`8guQGH?4_CR4aW_7XlQ zvSB6no-53@g4s0(dvC~o+3vkC;&URi06#q;_U7frWX_@A-qV87Y-+!zNU#eDiX5=6 zr8{+DteHD^`x|FHP+gZWY!<|daz&9h{uX&LL9y3abp$B429M|i1g zUr^6B_1uGaQd)DxWbCn6x?py<#V`8r*NL^Ykp3Mr?^?gTVbaa8YS(>F>%owD2AFdz zavbWKy-$;MQ)=u=EjcN@j40@-V@7GPo^x5|b>=S05Iss%2fJ`Iqtn_@xaC*zVx``a zm3>&wSr4_T?N~VBP-*HneN{9Rbp)M0bEN8sVoHL>jKX|sC8~bP*UFD)K4EXE zMXJ00GG(73OYO56kcW?^MnFVdv$dR*ya5C;>TwxQ8J9vmEpf~m;GOl;D>;YN-NDr(;P`2c#(Mb=x1+ z55CVac<^9>xvFP1gTk$Y%gP13HH7vAnq%z{_&joPIjDwyC} zuOGs=oM8z+Y*U?IyyJe(-~qD{8;qG$3K{+D6mPQ>hQ&zwN!}k|PR9(!IMh|d7xIhp z%Ci0zVwTZ+@Mi?%N53?FNJBy{s9%v z3}5JOF-RBu5o^)Nf^)ZGkxK~sPw#le(qBYPKIAmmfT}E0RT(}|J3|4}nXBC}at6FOyrkb6{XgYr}oK}~* zML?v~@2OofR_KV%Qz{nYg21in_mgI+l-YO2eE_2iT@GrqjiTT9LO3b2&Y|XM*s|K+ zwMe7y4ZV|4xsT3C{0hVk)&{POvr`&Z*<4X)96G@QG&L4&CMN);FmOZ6%gxK4N-+A} zZ3g_>MB1&Z2DUir2&q!Gvk+CBu2M)Vk$#~%uWMdwy%m%e*Kb1dhAVw- z|4M^=UO*8BE;D0j!uaJ6e~i}jm^DidEv@Imuod4INau{XSYbSt-vIO*IeQt*&+XWi z-7q2%znklCLD#jf-&sm}d~P=lB5F{b&dwJ8uGjJXk1s0uMgxrPQ|HZB2;qtzOg#zG zg3KO@XV4LyULDpL!{1-9a_NR;<3^d2oEoHeNYQT=7pbZlI^R`nyVND6>SN@zP_Zk` zWJoJ4eQJSgM#Gji9mI4-Mz77Kp4imd-1)&w*4rS(rSd9(^`h*%?xkF2^qZ1W0It|| zeylR8%5Qi1O}-XU?>Zv1QhI=(Q*Q0)yh}%h6V85jde9cD&?>=fds^R*Rzdhf;#v;iIZhd7|mb1m)4V7A}qzE>-ZVwfD zvayZYUm33)c=&3%Oz9_d##5t+f#B(F7>%UA`Zv*l3<~tCLQvC}$9W{3<>M6JVs(&jb%AL!F;TG)ubh_D#eHXSjZr|3a5pF*$xI7MR&eaRGE9wte zCxRAcy$_u-Fc+}MiVnObOIy&$VQ7F>>75yBE4}tRSxa%Ha8v7CG-X+}Q-C_}#~eQHc!x0%efdE(QoZ(LMN8t=cZXw}Ll|Ru^&R?rVBz>v=Qq z@VtE&?{Ro113I?%aG~V}&@s?#!Qknf~c7u~|?t(*|XWf0V5_^5Q$+1=% zThd}Jh%q69(K?H9uUv!977fWMe16ej&2iD35d9`mM6O&E;j`QNCIcc80BXZ&(Xfzj zGuF#JHXV=2qMZqWE7nhv zxK*v30y@}_VhV7@fHtMA6hh5VA$Lp4L4Kh42_6XTVDY_IUT3RE5{ z^dZxdPzt(e9s0Rj;gu0!V-PD0beKb7JW|Wg(sm~ghoW(z3aQ6|X5g{UF6pPTWY-lpi=DD9~BgkSNK z8UED>Hp;Ma9}uq+a3DdOuo_poZFf=_Mr>k>KGM>4WjX#|r)%UF_HD zWEawu>hGNk=pWKpO{0M?7WT$Cths!v)n?p5axx)bF_K~@Mh$494_xam!^XB%C)qW) z9TT@Rw)BD>?;O0n$^?_`@F=HWDbf-G=$BdNJ7QNRV|)ZYa>_;`uaKk7nF72-5xYa? z=Y=f-YVPgPBCYhtS|M(6*n25pK4eo}Az(L3Q*T0Aj*)mKKzv8iV~2SxWZxra#S7?I zD75w1KIne>62t|JzVFncyyU+TlmgWDyj%Wh82=IK`_!2Sj6MP%x_-MD zc?&((r$f~{?q#@UeA_ZvsBe8E4?%yk0UdlX(1D+>GNYI;neJ!J;f>wbdPRWUZDAD5 za2VJxa&}w@9c_^Z;8uuyF0l*{x6=m3dUQ;X-l3_o!ovRU#o3FTgln}afZw`8%RuyK z$e4}U0b>j6aBlm)yp}Lx1Ny1opo3QURw{`HOa#3JJ;|F&8Sj+nv$l$L-pQS?j#r zSC?ON_aaJvEq-Zizrp%IX@KLD1Y^P@fut~?G;8)(cqN6H>xS7N$k zrI&6l(Ponx?@F29EJFJK;RHE~0>th+GBI{_2y^?N~Mer^+Y00A(jiy4Vs;<^ogl@`eoz;iw zIx&v^*W4{QkPKw7<~4$IEG}gkq=STyE0K3{8S1jx~^lf>l2_q_M| z*U!{j`(lZZ(=;%`MbDl2^1W&|Z-}gE;=y_71|!VY6iKb2-bNszI8$d|Ehy60r3w}Q zpga1KfjF}aE%Zji4?`-yHLL+1ufA1U*w-M2k7l{so!(IheqY2K56KFN2(rAIjds_( zj0MTfpkEE9Bu@IG5CzcK`Z+lI z>1&%3ZN81nkCE}=H0ap;f>gn0l1-oZ7|=9q$Zk@NW!B5YLutKY8>@aBk-o^P!~H8S z6~^VtMm;#8tkv?$fwFQho z2opR%?~hmgO+xga>W{XK3#e;nXrkKR$GF=Pfcc`; zw4vZNGqd9QPXxZt7zLO8QMTIt`ljSwV3+IStPwFm)VwfR;ZV)S$`j?!i*N?ia0D(ucbzHg93XS zVxK)$_TXz^4dyD`ql_Fqoglau>j&%RF@rBwBwwPMX}+En${pcAnCS%CXAqcb?gXT^ z>|nNuXG{9fvin#4tNm>Y01=0=GyO@0YQV+&=Vo`h+5N@xF{TMtb@cW1iv@3_jl8te zwMk0r3J7OB0eXCf;f>Y)@QAbQa$SIfcSQza;8j|fPJ+`SyNSP!w}=)l!G->owNf%D zTeDSV+dUju1X}8S9|6&^D7HJjxSW-U>l~)4z*pY2`#qPUNmr%aMF_%NB!`B5{5k%0 zs?GjnXbJ>wkw!S9cJxur!z-4s1VVnbHA*BoE-#Xq;o;s9yGVo@o#RR0GieUNf~#_m z(Y@XqMGcdssvmYDMH%)9s>PovGDip_RY*|}5@OE=&|}Rc$Y3J^!jzyd``S3V;HUF* zi}5K!&8(0Q8$td6Y{YM13mx&kD^Y_KXF7tpxy&7Gd2awj)1%%|CdZN+84f14dl_h9 zu0xy778FPZJV5(K2NC)w@*t~-1oj#a&P{G1fIGkfEM>@1xA4GiOOg{E6&)xAwYzd@wuF^PdMqASils8!6q!AMXPHdFqyYLkA>=d!+bo?@ z&OYO(xFyTA9|H{obh8bCxSqP(i3UCMtrElK{=(GIw_8vwm-J9SNANGj4R-o$awrvR0o2NYV0Nwo^GM z1seohAx2cF>t%z6?WC*xE{{rUKmC|YroIDW4sYe?bS1Lw*I0)Zlv_6e`w_WGIk9H< zqeDlDQXV6Ey)0uAAZQfRbnf(5FNW9&{%hIXy`R4lp_&cGy1ZQYH^14IYFv76tF#lf z!M$_Uq=c)`>H~Z1_CgDtF2;0u%|~?Fq^V5$*`w%XaxWwS1ir%RA}g-wV=b0HpR{GL z$z_OiCiq2bCc6*jAhtZl^+nOTg=aBK`snZ z1ywA|B=}tm+abqfxCdS9RPHal|MAfVOIcZ%f+XR5U>(;r{pHU@R^Zgw9D>BwNO zm!HkYUgh27+*R`XvQ;YSTVdPnJ)jYM%HmxZ4eY6n`f5(5XO6325&9e4>Ek3V8O&HK zYVF&X(Floyw4I$qD;D$*TI=scy;wv(rss>067wJ+k_r8=*`L$4e`*w=Y?d-U9-?sp z+=AUUZH+hI6HUWIIu{(HL(OdB#85@kKQyimkOsIKZCQle1DNje1P-yMbz+PkJRm`p zG_b@g14D7;SQR-zMXz#b{_XZTz@`(l;&dIxmc!=#u57a-NeuRPwp;Ej6LrKi$iDNj;v3#)EL>+_D_X65>$9!!2)}lh6%9q z+4kHIuKh^oTEaWE#Cb_}V+CKNo+UmZgFPSa<=K9gWX2;K{)-SzljuY0BaTv7+z#$r z$+;8RRd+$EZH5TdZE7O?Xy%XMD6L}TLvXa=NK(xKz5D7*@(rzMIu6=sMsCcj zfYHvbQz;&(P7Ll>1Sy)hoA5Op4*-I%n9e59qE|N%UZ1;9;p7RpXq~gZq|n1cDmaa6 zEw(TPbizP5!q6+G^r0ZKP3eBD*5umD+Bwptw`_V3abkxKW*kwQ+TD(su(sG~8|-K| zShh^{(G+DHGjL5q6C8c$igaZ?chRs@|2FLF08@VxjT>*0m)Ifa(c7g?X!hJdz(n+3 zj5q-rmZOo7n_|A>W^iXW-C%=RW*5FAho>U0gmvS(jm4^24ge#xclP!%P4kPt*O>cz zKe1iAosOimJe_+v>)DqKkSJHgWrS5)?%H4)m>yM48sh5@{QY5j_SF2sb8?9H!w0^! zW0~FZs&8?{36)G!HNI;JvH6xT=Fa$uJQMrMPV=-!X3rVHN@T7L&P&Lnj-1*d5t3|rbw|5D{Dg?=sI_P9g_{8tedoO3n9?5*-soKW zB$V`OleVL+_xhmujpa78R4+)LxZyqsPzB<0`ervn{ZV#1H33-~+ofj1EuBPRW zi7#od`ag9&Q_@TCxf<|{{r8rk*&n#(&OIDO+4qg+rYfYzIG;mU0Tg?wMF~lqUyEex zlLQsh##}yxlY&1iVdPH-F9)S)6|O_Bv+2Z?l-waB-1iwxHHBd7SmcJ$+tErfc@$$0 zo>e25lB$dxJ@x?xb2pziIHDP1g$BWxSFFhmc4}z!!!ovZ6UgM0Ve< z+#X?1;6E}1WPo5P)aLo|%{O3(%+K9w^g@YmlMUtz9#IT}p@x+THp(}1WE0{DaHxu2 z$Vjz%iL8(N?7t=0ajq_hgW62>|3Fm8V%`EM+22T@&b%zHc2{pHY(!LQ-6)?W_SH{?? zjAPx1JqRUN*1S!Qjh6+*i4UfuLzw&9sP@2W_@e9>z?xwP3&=Br7enQ<0)fnmh*>{| z4oeQ|tWx8*J7Bigh8(Ff@DaMLwp^C|TTphM5#=N8c89=QP4;xavf)^f0vFQT#q>#q zpyTy@o87`6JN)l|o1_xL(u@=R6KMq9)&sS_@W96A`t5I>MZdJse9(qV6?-$y4NZK$ zuXKRe_>^okU<%MLJ&awGVb`B9kY7KAmb2L`zlf^WXFjVl?JeI(2-h)ZM45c&Dg<;t zrsZQQD^0vbK2Ou2a(XYflVH#2MZ7D$zRv=hv2L;j8-Wt(FNjeMcNWIIQw&r>xqtXV zur4t?qpFZ0Tncy?mjbqX7rqdjgR48jB#3u-8LiuqIjfzSkG8D6A{(fTk&?fjJMbZh z7VZyt&s(!<+~tKyX9qSlab^3sEYh*+vP)SBuk7&l&lMQ=2uSY2xgZe%D=MVg5$DhSgqcWELhh^pIZ3?vcH2-zuxTCn^jeEQ2AD6+-v~15*H~;5DEWa zjzE;t^{NGIwFd1?K;pU#Hf7an8eQUAcpUq*In z-1fc3;xB<$D;Xs{q`lrJxlqsq#D123FBO;LYd&fTM!p$<+8XPg4V`2vZuA~uX&MkI z=Si!>%;Syi+Fb!le6EneN1m9y+6L9C?L!RVIyD}?H#blbX{U*FPx`U`?nFopRaZG&ssUDg^n z)Y{zYn~PBvU81H;kG!uu<{B7M@B&7}?O_Fvg=wD4#b3tNYU(s@MMf#un0Ky5c%a53 zY0-fO3#9k2XPNU9gPRUy_i&T84-WdTvce^%h}`b)Xx-%i6AL&1WzRX%cC$hM4LKg! zI(Q@nx~KDzZ@Zu}C7%&a#`oS$oV3sA;htT&^}z(0?=DO2C76EGUk^Y}zHSMQFD`!= z(oU$!UakG2%ilOHh%w3qGIyY8`M3#{qGtwUUMSG!a}Scnv9^5sN~P>5_EO2TVDiEUz z8UKTwGcP?3UYjEG!&%Mf+DK=#tSe^jrxl5V0N+6ej9%4H<%VkiV+T687>ZF_iK!F* zMP_s8%q%3@e;Y8J@jhqMz=kV&DCoE?E8Q|6E>PyEe&u-&TX{w}QY=KhUN@#?Nm5!_ zEu77qsErivE&r%Qw|ok0FPf|3>mrzBz*wy#?0T=uKf>d7D6h*U2*Qu)a)G^#l=PQ6 zVQdO^i&Ik$Hll>dqxR8!OoXa)cUgK{Y!+3bt{TeK4dT)!z4IJi-^oj$9s8O)@T9%F z@%4>uAYs8+6(1sx$tvV8bPth0jD6@uy4-V1(c z3@Nz^fzP|LFJ9BfeFJgXdO!O!=;2xgVLdqw8j}q+6a93&=|HpZKyO)UFJUro$|XVV z!v^K9!1K|KEnc#3ugbzaCuvY}<<}OiZ+(qlE@W-FHFeWCwu*8w_c9oMmg^Vl*eDT? zfzmETPj~|WyB_Xd07}@U9A~*edoWl)2FXkk)u(wO>)4i_7|x~)76X4xmqaSwH=qO7 zD_VAFt$)mGiUCwbf%~=w4$kHa?PVOF-h8_C2}I=a&qw3KAb|tdF*4l|fFF|CDG+GZ;{#y1B8;x4Lo z2Ka}aCIpfp&smNOdr}fulYDimRJ|`GAFoB`Lu-6M^HZ^cJk7|v6 zX!WQir98^Q-n&6|w6dC?nUp?^A)^x_|IutP%#0Ui@DFZtTh7d zV%)YcDI!v}F>3F---*)Von-;t{l~$~G`~HUdx$`&XG5h!)AaGp1@OmW8=>_sy9HhT zYlNueUI{eBO|9PqZ;p!utmzbtcu86jz{wI2rj@z5pQRb`19l8UG0oLnK2T&JS_{;g zc^=Rs$G4*BxttsU{M-t4DF{6W;tF=IVXE?E3m$fmpijF5kV;9}9=8Ww3n^rz;Js@JWa-}(8Q5!LRf68h{5FAO#Yjf$2=LOT*|4J#ViwdtfwtU7;E zl~qnRsns%Vz%QrH<4Z0Zcx7O!Q_|>k)!7PEl%Ws$4=bGc#WJiJga-m0W3l7l-3l%V z6rg!{6holUt+$*jyssmMkvYe9^6<0?;_CH&- z{~(v=UbOB?ePTh*ebTiZzpMr09f%10c5jy2 zi^OvtNV?axu)sKh_tb?nnQF&f@Pn^?Tt~t_nKYh^JDA+^l z#2gvnqSC+ zZCqI#ej$mO=LpK)2sFcSQChUVs9~0RsJrf%JVSjC z_Kmh_l=q4~a48JOQTBIuq})t4>kwu2-uwsUYXfu7x#w3SY{-z|$~X=yPd{Vhz85;b znX?v+_(xo27RvoC2uN)LXow9iT(Gicr;%(U%_d>p%O z&}7mt@r&G@>;8d)dN7Ty1c8vHE7s(7E!MG#=dOMU_Fc-TeR~zJY@qfMJ%)E|*J$aMgJFN3 z6X~U>0Hjg>IlcRaT`d(C4Of<7?bk5TzZE~UTw_E9P3Zi1agmKV>`7ktZhTh=c*VfX zqWx~TczKRB{|OXc3n*oQ4VwT=cD1Hm+X%GDRWe{m zM5K%=D`;@-fiD40RD7QHC-oNEaqQZ=wBjV0FQfHRt7;jm_xt@43KSveOCSX7 zSa(2gY?zL^gOQ)qj8Vq>>L@2}r44AFI64`x+4S zbfls3W|~S}dKM(z&G*FqQhj4fW>&>|MCXTc5 z;q%$mc4NS?YX0|GpyUR!pMB7yl%uKW=5ge`WDjW;H#RKNQTKZO2CD|q(QHK9DQ31g zHXuoQjE$^Xzi~PY^zy)mAF;ej>CdE7zTg%YjPgX&pr7WK1hP?h;lfx)#uhs&KVmfY ziUONX(@96t8L)1}wUJf3_wOg5&$;%mZNO5U`G0-Fiy3mA!u<1DHFM9N2&%o|pH#sc z+sl*InEpZO6(|0^rLX(nUS;hzu-v(LJSot>J?4o2vRm*n4@W%Z&l-Z_`Sgi+e&KOt zL+%E6*~^JP3xa0?e4k}FO>otC`aUE5_qX-`0|BtV96Z4J-EnL$4>vJp$GYt*+%-1y zj&xaKr&I~r4t`B$r_|g=W>ncgDE~^6D0%%8&}Hq>f$uurJU_Q=7z&RJB0uX~ZVj9$;@778RK8Kk z8^yauiLOs8A18T|e(v^hXvn#f+xlVUM5FgW&;B|54%e&+UADEFnEYxmD#_DEzeDR= z=Zaz=Nd4l!It+VMpKJmLz(RS){I)TFyhp|G2J)Zr`j62-=J*hx1E^>z4i=SbEid1PSw1 z@4R6~EO9=5JJi~2kkSoJ_%_aH8a(uDV)|O+3hJ57*vvsP&D3Cah^wFOjLJqup063A zPU6OA$EVY1ZrHdMQ3u1HF;MLG`E2J_sgx@on!432*W{F!{`&1+ZI3@Vflar6 z5HIRfiiKHr6%5SeC6f3M;z;)UIH0F#w~#$?!`K~maTzsH?>ntFrO$2^{dnvE%sXZ7!{R|J&KGUbK|IuHE8Mszs)@kI zHdTn!ULkew!m6xkZndr>y00M&O)A$(vXeFt8ywgBIi~|atFm!!&Q4cG)$L(#O`#@& zh}|#d&+v{dr5XbDyVzgTroP+!6c-Zb#5-L`E!Yq9)vT@xuau9$HN`;B*q)l{C_%-M zR(`nLvu7yjg|RpoZHn_dKmJ5^qx>q9ez={PSt_IsO8oEKpBw`Z6@~1l+^ccD9BN$Y z`ZR+{cZH!CmqL#Q>*7jIHMvfFP&e@938K4c&EQ++z2}WcnI1J^;aiC2;vKY3M4)J^ zF}M9-vFmK8U^}cRcdVs3cRth^x+JJS6`u?-HK2OF&{njx=451^*I!-s6urz3ck$bE zG~tTJ3qH}P(vyU;$f5Sr3(aPlr;zO$|D66?Wr$|hOJ#gDy@jmgZi)k0i# zF~+A($;SEyx*!4Wg%Ly@)d7S3cIb5ioFoBmU=^VcmL!2lm818sGvd0K+wn7I%6C7npZ}m8^ zQ|Qfiu11Vf9tN5`C{4P~@mtjm_VKTIt|ywLrJU9&ClGmGnRv|L;*vxFND@7jN;eDt zxk8A*V|AKg+ZB0P9A%j2-E1}IyRyo^mRLB1c0hg>j6Jjj5*lW&Oiyih*>3QVzI73$`tZmIuH0hsELyg)ShiDB}y_=@(`&a23Znou2^GHjDC5`#%-{oki2W(qZ0bh z2oAtZ-10&Pru2$Yktv3pngz}yWs`}>*1^^#o&L!vYZ|eQ2%*vATmjICJ zVBbmf#OyXf&upj3!K)W1ZP_FsTh+Czb0OkAdm>n2Qxzc=I!N-YY2meWTmSL1&Z#Hy zM|vOQ*ZA@U;9mdKTe)(MmQTQ?15y>2@G(eLw(+oDOypMnO+S&3sesBwV)v%*>ny#8 zi8odKuBr|Cql4Zk-+8CPamD;U3QVZ~1e4#AD^AY`5!??tNg^@>Zf3IS9gR$d7)Uo2 zkeGKWxHR~X`x5SBgM|ggB_a(DUIK*aCVPk}woDA?wR+rJ39pKAy3Y9&0M~$#qcZx^ zrPCX&?5}YZ6p^6+1y@mIXGm-mPhw7z?7RJWx8WqEXOwvy`rt zR{NAPR%x~xIb#xkMpFTR;rm`S^HtV< z_H$8!#QgNdsG+nM&m*!PH0Mfgz_IoPO@kXrv|nZK!6JEK^uCNs(9bsM7sKflrHLm% zKLY2X|9gM*KekMNepJVeyMDkk-RP@Ay<#{R_)oYlz>cO@{I@S)Bo4BfiVIn5TCHGR zm-@!7t=1bhWeg*Mmo6-$86Vsb42Lu@X%h?5_zezeR;`+t8e39R3_2Ie+I1p=kTebQ zSeY)+P3YW?B%NEK%t>MHzA@^+UDoRYklFE?M}_ZWWtao7?^Rl~0RBp#^X_g(FMe+? zkTSbfh?|I%BgPG}q*n;|)KNWnJe96PW=xia=BhHEIhKSa!K8Gi@&a{kK=b1dDR~2l zc_**whC{G2gi#Gbt^JCrR6ET0DQA8qLacyk)3DzZg7o*&I`&wwMwQjE9v9SuIR;2G zzL2+{kx}qN7Zt8~Br7~7Tb}7ej+BzN_OsF5pq?ceFf6TpN#yZ5~n-R0; zE1-@NWb1K@<4u`5|C_p)P#s(Og-Y^1XYRiS_+g(rc8*}gm?dCT`1=Z`kuXPoNA@%4 zmcpX?VI$Qnk`IkH5JmPZ_!2Vy0wWo>F71;wO{2BU*{g3z0NWDA>FtE{mp=b4RD#Qu9dgEK($RYg5<5=;EW$h zrkh*0$7||ks=g&ncw0g^SLf6jBbe9p;e7stdtSa?h$w>Oo3m;^BkJZaOlr#tEO3dT zxkk|^uVUXyS?GZD*N&}#O|{WAz7_$}z^dO_;SiJYbWf6SJ@Zicc%hb{BzcJ#(PozW zDa+xkSQl2TAb@364;IV)&2gDFSjM;`t6pDjKfPS% zrI*Sob4D1-pMFr|WXoexM~~6?#RofaRl`R;T5L7ePaj=b$-FK@x82!Yc;r9p`V5Q0 zcgWOkiD4UHAy~UKhM3l+Mj`Gi{L)>YsBgFxDRe7$rd`W!{6XfHOpvB8R-*0o>oq~2 z%ZlnyFP2Kxuh8}N?-;UPhI&e>6%=gq<3k7KzG!W?&b;hUOhdA(+d!Z>qxdCWuXr>g z30B9374OrlbFt0}3PJXP{Z|8%~KAzlq+_WBUK8#P8o) ie-;j}+7TVdr|MnSjxWIlKsA0npJRSr&3`-l)4u`EXJJtQ diff --git a/r4babs2/week-2/data-raw/plant.xlsx b/r4babs2/week-2/data-raw/plant.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..954f190e08ea3896fa61a2e736b3bd8647b3207f GIT binary patch literal 11738 zcmeHtWmFv767JycWN?=N!8H&FFt|f-_aMRDA?V-|+?@b{;4Z=49fE}5?hpv@COP-M zoRf3zTJP8Ub8oLT)4jWQeJ%T|UA3!9Q3eJU7XS}H1ONaO0BOg`d21*DfDRr2zyTma zYm3<0IGNfw>8rTgnL6q+yIEV2=fXnMWdoog{r|W97k`1_c-=Rhthm9)2zMAX^?8v^ zY`qm8mC-|T$#+nij+Oxy*bccgcF0caWCQ7veVl(#R}U(4V%3H~B| z*+;Q#b-^$4xXNr$EBj7I?uK#zoXK(X3gJfrX|^c}cg^wbC!q+DjP6=bxb=8(MlDpq zLit`c8)QjNhcQZKA3CIYTPRR_wMkMlmf`o?TnQu9vEyP-zQ|1ncHpJ@Y75?1xuhQA zS>)8179+>hCC_laMa@8fTIc)_j*a_*s?N16IO*2j8G(V;AyM4^ z!mzm3%s}u=ocRH8WXok{;_c<~Ht5o7sr=pdZW6bJ=NH_(=!9anOw_3e&nkz;( zxy?0}2zH^C%yJ&)7UfF}mlqda;MrI4;Uj0@Sm){6gmCboADvUB6 z_$b}=O{_khi>9^X+lcw=O1`{su+`?tSZykwA z52DqjQqJX|B}{%(b42u}d6ALj6vQ6+HS)rji=4@haT*W38^{Ebo!7 zkIlpx8p^lL@1dxa2ug>~OyH7m2Xd;ypxOGDSQQ*QV-CIj)Nzhly1;g7zw71`J^?)QN(;O|r_ z$iicUfy}kC5Gr9ndO)c3XAbpV3!Dj|nU}N6E$u~@&QXcmy#N~I7ZJB zP!&kxIhZCdM>P<%#Vp9)EriNWq0}UHYT6F5(yB}&xX0RJOGr%?rL)KspXAhM2{}*-6pe7T8T5i1DXP?O-kBP0pe?25XHJkKFSRi}$ zKW_~w>i9lxEjUQ2vaxT(mUr%ROf{(cguSgnZ@8%$VW)h1H_XPQu=H~7iiG;2VbNZ0 zGu>|Ml@v}rbCVI;Of57HywUAfO@51y%|bXw5p9I;KR%Su_Gk)_yf4yaD7t~86PBAr zx9-+G7c0ULen9;vzW8*6Z5lwP6DJ5~@PF~e(cIM3$&uyfj`PRt6A|}I;Z9ra7t59i zzcpPg<4p_E$J@RXT`;^?-cMLNoD!oJ2bsaNxb(zC$#R}RvISI2r8&^ z?rm?ctHvAkim8id((1n<;Md>2cHPBVUf}_}#p<|?;Gu8|#nj_6$d9gokx4+#h<*D? z9c7g_W|P%Ks}lV6sbPMd7d7<}5MC`otwPwD)u0Q}etb~q);sTo;6y|doLT-7SyKvV z3M+TK41F7FFhyygyl%&2VsfSETb>Gzr)2qw__GkwQcNuQ2+&7Yz0d^mSDA^a+&k80 z=p_;DqmBJj9qD9f5|1peTj$``+gJ_;Bl^5c8}^pb^?aYJfoL$SZ@Rw{?KB}d&C^&M zt4j`^!?#c#BjPY6CQ0zHOXj*%1-tHAS_{0%A#BRzb)=$=qC=m*tR4$wWQFBao*j37 zTaGV*k**3hl=i4idW?JgM}xri=}BJ$M1!~R{^O{y{}`3{J)4iTdYaO5pV%A*kn<3;5AwykY`#5d+}WJdU#4)g4zA?7 zXTVj$hL_V%lUq7g`ywdV?n~ckkY3`WOd`Abq4WARYJ0iwqxHAf!Q1w$oXP-#Tl*(R zMY|2)uazr2x?JL0^A~nzZ-UEH*Bs`1zS^6YMBXd8f=%O0?A2?%!34lA0p4MkrP-0Z zV*RGVEY^W>;u54x-_P}Zr}1T_$5L~uUFSomGS*F0`EHgNhZi>Qf`!#td=5M~R1^^2 z-zAN!dJkH&*i5=w3nWii6(LUc++^2tCD{{*6ka+^f#A+75JakVgI2rODH04@^cJCR z>#bLM!;d@oR}^~r$9R0K)C}1}dU+~W58WQQVAEb}w-Oh&k1UbNH7_Z!Hq!Gy>hv0& zKg>sPMUdr(n%o-RYRJua@Fv#-wm_e{$x7W{_keDF8uex!4|ouRO{n58o8%KupYdzS zU6V2&hems{&3m~CKiswEJ7blj4a7ct2H7*>YNcn*s(ll3ARHJI;OrC0>*Iam5kqX6 zrTvL`Mer597M&SLkvZ=}%WZmB`4;Wh=dX9%GGQ3QHqOpd9il{{(2JFE2DNSqU-!;0 zmo^GTpW_-lp?kK9{EQb!qqQMVKqoUs2oy5w&6FL_v%99+)ufIaiZ-)FCPjoz%`779 zL$4aPQq+vsPw@a|7+^u=Zf~3g^LGcHBkGh{br?a5YUAJ`=Rd>1fki>aw3gFhh$%OT z*FRi1zilj!2tsmW(@;*r$%mnPxmxcZ1P`ubW+swU7lS3~=;^Cx*QmklhudBEccP(O zEir=9P;D1hIkY`2w=0g96^j;4iUO`_}&d`!#_(Txkj0@oEX zi_3dBm=(12Lfhxq@S#kKZ7kTt!C`~z=$wQ>O~C?3yKx$HvEVoqW{Oa$q{VB}fZ&*^ zNDBUV6pVg$btGF8{jt8>5K#}hB+`5lt~O!gQxGLf{1D1y?O31~id1Bv3SO zay$li#@oWnH-;*vw2N0I#A7YG}UM3@rOBug%Ur{q3| zS%sJri&-p#I0?sj*QQJdq~y#ccW#EA+*vHouL4TJzSJ<$UcuK5=T9z`LML-nAU2@4zUv5&M_L zBZe3+Q!Ln*cP%ltchS<@q5HZF-V8f6C65IQ%J0nm^KAG3V1qdRXM_HK+n_Dg_g~E+ z)+z8mdj@QOjLHzi6~`324tfX}f0_91CC+*v2yrA=@pf_OY#tcPDW1wXvg&#e^Qxcr zMI@(V!*$fw5ZQ4zPqzshu0#6fDl@ob7KLIc*TOQ z6d`{kiYHYxd#|Nwru^sVyU?NAr}0~9nT_*a;R&mE#kg9=ZC?+*A=-##4NqdNC@*{a zBsC*MNJ0{}DD<*u)@4`pd<#2$&c1nH9NxPRJjo{$0>u!-Q!R(Xx;I&QoON2p{q#yP z{Yxts7S3!un>~#{U z2{V04o4Z~#{muLLd}+EE3+>`c!ey>eD|U~lbrzu%t_KD6oTcc`B!jC>WVsvfa4IMB zQxI!<;rVuL7QroJ7ALP zs}1AjCU$40(LAHrjnc}8C6RHWJuR_LFRVf_(&$TFon1lswnrP4SY1M2?+D{uC{ex0 zO9{~IT(FW@v^`Kwy@zk~3(rvQFbFznbMXDFDKRs{BApD{66c*bkhW2eO2;e~RT7w} zkmk(7P~|Kc=N?CE(pTZc6kaY^c9x5}O|Q7DWrJ^_U8=w6iE=ReHkL7TFqa{516e52 zs8KfeCZ^Q(MjYRs+RvLPZj+!;*VT(%3&>ks^L~B<_A|!lYut}nXX_vNv)cojJ??7< z-rs890SoQ=CL;&v7$$S45#5ORhK@E2fpe?LK^E*RIJdQ?$uzs=ZPrsnj6x&VRj{AM zYzgDR>@T_k9-d!}3j5ZK4eZ6XtdyRNQMIN{wH*uWNNIWHA;4*WU;))cU6-S1honpt zml+dily4aeAR2IL7rJv=krWyl4`-zq92@oCuJm|p!OFAl)R=*obHAJGNTKlwCzX5k z7}#+%v?jl3=5Z-;jl|TqgdK;PE5pP25FO&qj<_>MbGFf`tR;bE%wZ0vY#vv57;*I6 z=`8rgJFE|Ck~&*ZI_4wWARp*~Vb%Nr|0<1H0orh73KVZC|5&D4pMyoo@i|lv^+2(#On(&twe>W|iR9n+TkI6qC|K z2ZMl!%5S@)*>>1TMms?^mi;EnqiOFwC45+J7(QJXgSH_d37s#;tJ}f6pKYUTx{`pX zCvMsNCE&NYKl=@qP?1EbzGtV;RL6o5QxZeP6|1f8G0XeZcJ?ovmKP;fu5fpa?n=zr zFIQ&G<>qB(u4(f)^|0p~6KWWc9^;JAe8*5g*-`uiOmTLq8-~)?E_Xm|rkoY@g3C&I z(sw&Z5l9JL9+A3fH*_dvd<{-Bhv+(vYaTA`EAvh!eBpQlv@Z^H-`tsh{}ef0|IwBB z8>?+3=L>zkepd7EsV7o(205=|6RiNel?q4$K;4U2yi1ciuvY&i7FWyxmF0vIkSPp} zf6twCjT}Ffc8Me-gQX8j|7xq$8vXv8xQ3Ie$n8bvjg8dU1bX^-JNhjBOm15G+B`d7-bgKy9amy_-sPPF+UaUB+zYA6 z4Afh$5dr~EFJ-)%h9&1a4he)XRapI&do!W(i;&?f*jG6GU^h|yMo~0RGwltO4BrGI zIryc)-jSJNb8fvKz&O~X{}#loZ_=PGX-hHDnD%8o@3WV*k*X9b@{0)0Bj^SL`_7oc^ zkkDzqpmK@@1RpnQ_>CJVwF+slI*s)}pK}3+xTD|-_Nyy}x`-G{wh&R`FLm_B^E)1c_qB5k(qydC-GG>0qdI#yE+>%Ixo32%B0+gkyL_B-6F~Kdk{oKEW?h*8QS>t zc^Kap={?xqb$i?&_kC#04I{9XdF>V1Y5a7Huf>;T^O!J5LT=bWf_>->E-4FJyY9gE zawWHXQB`{y)dKM^sq`Lm2^UA4ItQxiviRDQGsF}reLhEkXQ}2i8o0iI_STUrOj%q^OWVD{M?POAaRuG%Gl@zU@3HoaG92aHV zy4J;>VynWLR`vx@^m=?TI>PhHYO#ZSi>xdu9cC&DUP)#5RtkPgxoxEe3%fR6ewiIJ z>)m&HJ#Y$@$9$s7)jR2z~pH^Vb9ixQ8 z1=yC!;Ie)Gte!o26-j`5bY;%`8fI-ptOb>a-rd}nclO)1{=BwTjO<8#8}%x3CwVX5 z<1ji?7hQ1Db0M3T>L-H)-%G6rO+Gw2@{ruryS`l}N}Vzsb2j?8?K!oSxjkA~U;oy2 zaqB?e$>*M=Xl_B%bN;2~k#j;P!FAQHmB_=hcAnV@F}bW(9M~E**VUsmo=np#=)M{c z8$sf$VDG+txJ=X4tL5DcJU4Ljb?^CEp`qs8`I+`mGp!V`NUAM*<|XKc85W6Iefse_#*}TGuY^4Zfb4H^7H?vcX*(w9lj)v>%(#(h}im~ zO{%K3o5*TGe(`{oVn2Vw-YU9mAc|9*VIhihz%l((HNA{6 z^%{3Z!c%%a_;xm?IfjI6Vyd_Xd4D52Pj5aN;{((J8CTcM7KJa|SUTJh79Qlf#$6!8 zry;7LYciyh)4ZUXLy#bj3xD@@;J#uv9KU(ubJ2^ra?bW2V-!bL`oNJbD_3Ci^Wri5bcDK zO?S+8>A+|ZyRvN7$SJ7+1|Uf;=1(1^F?xy{nG+@= zIzUG=N}3|3=;j#DiawH{WvW=*<@@b?Iw1^ku|u*tvBeK*)jZCN_@(@SPR+7HM`@{W zrl6AbG+fNZ_zw=Q*G@9W0%ZI;z`ukX&YmlphUrz}3V z>piyZ?Sldl4TwGGwwWz%w;SK;^&d{Z8i!xYhl%rfNg54jyt$cg95BMq4iY3Suzk%I zfyu_G2;0U~en-jfH2Fox07`^?oqD!L28lo?acn+F_mh&MCV!mpJa z$b_HR#SR~26rqr~;^a_iuTn@yz+U4y#;6-P5{S=dy9eZoW)sL)*llpQfGbU%ToKvU zn-qHKzgjV*zv$kjG(sDQiHHC(M3l$ipHyy5!6ODg+$9zw*VoQ9)pdm z(~W=$Hq4qigs#xz({&8kiW(zvSEKK;(NA`a)l}ZspSONNN5QZ~Yx1Li=loQgLQ-X@ zEF5pmx6dLGyZxjuP^oao%F?B;;y#oqQm4WEu18cI$%{u(EbrN#iWXe;R<8^>{ZA76F%7*VyvbxX{-d~~Bb-SQ55 z|Aq5re6VfTJwifr2yg%mm1Ssa_)@q*5V8KQ9q-8c`0U$|bIv&VN{^Oq+2^liDA)8k z7eb8mD_<=SM-BB-QL~k-8Ky@aT4U2B>{4uTP(L$@4faZ<+(dwP$U%-8Yw9!HPQ8wE z97n?hAx+|v4->XdM|W-U2kvi3ESzQTrvdy_e~ckrOS4@`kGz zNvFh?AwzI1xg5{=iNZNOzpRRDZqefjbmuXyNjtG~fkQtPi%5NT**uw>5yB~;2-B{G z_doeaGY75ttv)s&KTWE4ik=ij;7}_(!pQ{%Im;sGvY6f1v!ccXMeXcag=X~Cg)SwQI0u~+VpC1D1DOdy^zw3;%w9oKj>RTZn!(VI%QK~-q9@E z_|X}gh!Bs+^V9*Ps|>T{>LN=5UOCQi-YLZ=yl)nJF7D;d2uqEyxim~C%j(ct#+zoP z_p=qts=EoQkKV{464vH&1f-g@(09G6K399U$jgjaBMGa$UYAMrA=Ygvh3r6Hy)2zv zZC_gTX0I#c0m8l`f47S@TsL7j_2NJl40?YteV3^80Z8!sTC`0(@R?a`T%Bk8x@$|t zR!D*L)NxWvlR7=uy?pER>AgqovHG)Cn?opBM#`{KpQ{N|0D_WJim!*#AyJn8`i$mM z5L@_=thXM-SKB+6sja&|4;K+~Izp%wE?wD!FHmdcpmT4vsh&m-uqnn1j9kgayB(Qb zd%I6eUolr4qBm}v=x>Ri*pf}T%cETotX}HeB3`s`&4+PjiWRsn_%7+V&r9 z*l@-&MK0t@ALNJohYfQyH*_#HQFd~$urd3&o)(P~M(+F(;_!DxtgbKC1xv;;!zyGW zARf-cr%H{;_N=Uc!9&+CvmWK{!rplpZN>C=wv`A|I2U7ZYBcDwG*i(56QP6VaF}lzxASkB3j?YW-cOU0ByAX&5V?SM@=-jT<2FEFmHXz{iFbYFAK%GA5wk@E61p*lgl@1P`D7DYV?_sB zJ4Y5{TL;r00Rl+r#sAXCkWUv8r?1dOj@z>;_Z=PWn5kx@kit6Y#!P{kjW6#s^3$?7 zV?1E-a7kvVY<3udta1C~YdTR_kKW7#p2kxm-CRxzI_ME1ipx4tmVHyNNXqRL;0GJd z_#FT`Q^`=cKhYo&N^~8YK`SgCT%1dM8unoe53X^aYj(1XzX*c;ltz!&^F=1^2;T~( zwTKvAwY3z3%rEHpX|doa$qtG9Z#B*sn8YGz$ThCd6YJ{yo*q3=AczF$#(v1H z!LhAwxIl!ozQxLw8UA)255Iyu7c&$cPJZx>k7vDJE&~-os1)hD1-=pUmwACLuC|X& zt763Pd&z5A-Mk|Xe9ftt3VtP@%vN)6A%5TA!&2B@j;=)z<~hOu0GNMfo}r!H|A+@+ z+`qQ;cO5oMKWbiJuF!*zBGWnPf`!zS@`*g=D*#e_2k!@+7*!HsL%G%k?#+p$GU}QS z#7~QSyr^mHJ*})}L9)Hg{v&3*L!T`430>*teXbApP=>G>)EV|ka6bfe+iY$t7~y#P zf#F|?1DT(E5J!DB`mQ`j$wIt}s^F|rNwUvvt*s;?*z==4pGc#rdt}BH`-ji2JXCV_4PxTG2F5h<`zkC8WVJ+M)8jvvzmqps6 z_6+3>dtyQr60ENr7(wxVoO`gFS}eR2$a$Mga~{%LXRz>2di*Qy2h|C>kieOw$PkJ; z3d8tzFE4p60BuLHfh_u$8W+N}0v&WXTpi#QJQX5R68J>`aB|EtkJ3k|B^?_BmFgH= zefSNsqT{frm|hAQw6i$1-M!Uq??Mdyj<&zZBitx|ltGZm?2_`aYL%0xLm~CX=Ck8s zNJhIlVo{JTZBLyVmh|GOd(L*CTehy)Qrd?%o9K#}mMbXt=N`n5G3l$rXZz`Tqts7t z_HAQ*Ar$_D+)&U=5VP>lHK+d`%zy9yp#oJ==C1&MErMi_|2LOm@0D!;w4!?{4b;|#< fc*4^^iT}$0D9XS=`1&Jffd+U5p}YgbkJkSIcoKPW literal 0 HcmV?d00001 diff --git a/r4babs2/week-2/workshop.html b/r4babs2/week-2/workshop.html index fa3efdb9..fa8e7796 100644 --- a/r4babs2/week-2/workshop.html +++ b/r4babs2/week-2/workshop.html @@ -479,8 +479,8 @@