notebooks/plot_shap.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2e184043-88d5-45a2-8c00-c7fc2e5f1baf",
   "metadata": {},
   "source": [
    "# Plot SHAP values for RNA modality\n",
    "\n",
    "This jupyter notebook shows how to plot the SHAP values collected for several unimodal models with a repeated cross-validation scheme. It also shows how to combine the SHAP values associated to different predictive models to obtain a consensus ranking of features with respect to their importance for the prediction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "26cc60fa-2c3b-45a1-909b-e0dc9cf95495",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd \n",
    "from tqdm import tqdm\n",
    "import seaborn as sns\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "from sksurv.util import Surv\n",
    "from sksurv.metrics import concordance_index_ipcw\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Setup for local running - please delete this block\n",
    "import sys\n",
    "sys.path.append('C:\\\\Users\\\\ncaptier\\\\Documents\\\\GitHub\\\\multipit\\\\')\n",
    "\n",
    "from multipit.result_analysis.plot import plot_shap_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f223bcd-e9c1-4b74-85a6-fc6956e127db",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_omics = pd.read_csv(\"omics.csv\", sep=\";\", index_col=0)\n",
    "df_clinicals = pd.read_csv(\"clinicals.csv\", index_col = 0, sep=\";\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5cd5833-8ade-4157-af63-aabc7aa9ea13",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 1. Compute SHAP values for each predictor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56d733bc-214b-4782-a3d9-e501896ba658",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 1.1 XGBoost (1-year death prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "814358d0-0130-4e9d-9b94-61240597a0fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B lineage</th>\n",
       "      <th>CD8 T cells</th>\n",
       "      <th>Cytotoxic lymphocytes</th>\n",
       "      <th>Endothelial cells</th>\n",
       "      <th>Fibroblasts</th>\n",
       "      <th>Monocytic lineage</th>\n",
       "      <th>Myeloid dendritic cells</th>\n",
       "      <th>NK cells</th>\n",
       "      <th>Neutrophils</th>\n",
       "      <th>T cells</th>\n",
       "      <th>...</th>\n",
       "      <th>NTRK2</th>\n",
       "      <th>NTRK3</th>\n",
       "      <th>LTK</th>\n",
       "      <th>RET</th>\n",
       "      <th>NRG1</th>\n",
       "      <th>NRAS</th>\n",
       "      <th>MAP2K1</th>\n",
       "      <th>RIT1</th>\n",
       "      <th>TMB_RNA</th>\n",
       "      <th>Biopsy site</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.000046</td>\n",
       "      <td>-0.000061</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>-0.002639</td>\n",
       "      <td>-0.001950</td>\n",
       "      <td>-0.010122</td>\n",
       "      <td>-0.053596</td>\n",
       "      <td>-0.004653</td>\n",
       "      <td>0.000722</td>\n",
       "      <td>-0.004441</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000372</td>\n",
       "      <td>-0.000442</td>\n",
       "      <td>-0.002161</td>\n",
       "      <td>-0.004190</td>\n",
       "      <td>-0.011121</td>\n",
       "      <td>0.020717</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>-0.001189</td>\n",
       "      <td>-0.004268</td>\n",
       "      <td>-0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.001117</td>\n",
       "      <td>-0.001009</td>\n",
       "      <td>-0.000900</td>\n",
       "      <td>-0.003275</td>\n",
       "      <td>0.001616</td>\n",
       "      <td>-0.033531</td>\n",
       "      <td>-0.045218</td>\n",
       "      <td>0.011911</td>\n",
       "      <td>0.000892</td>\n",
       "      <td>-0.003142</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002513</td>\n",
       "      <td>-0.002942</td>\n",
       "      <td>-0.003462</td>\n",
       "      <td>-0.007488</td>\n",
       "      <td>0.014353</td>\n",
       "      <td>0.034734</td>\n",
       "      <td>-0.003734</td>\n",
       "      <td>0.001358</td>\n",
       "      <td>-0.001913</td>\n",
       "      <td>-0.000310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.010676</td>\n",
       "      <td>0.000682</td>\n",
       "      <td>-0.000398</td>\n",
       "      <td>-0.003216</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>0.010414</td>\n",
       "      <td>0.082396</td>\n",
       "      <td>0.005641</td>\n",
       "      <td>0.000511</td>\n",
       "      <td>0.004766</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000520</td>\n",
       "      <td>-0.001892</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>-0.002984</td>\n",
       "      <td>0.007779</td>\n",
       "      <td>0.071337</td>\n",
       "      <td>-0.005638</td>\n",
       "      <td>0.003498</td>\n",
       "      <td>-0.000204</td>\n",
       "      <td>-0.000015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001231</td>\n",
       "      <td>-0.000245</td>\n",
       "      <td>-0.001655</td>\n",
       "      <td>0.006247</td>\n",
       "      <td>-0.001482</td>\n",
       "      <td>-0.022117</td>\n",
       "      <td>-0.042654</td>\n",
       "      <td>-0.001476</td>\n",
       "      <td>0.000555</td>\n",
       "      <td>-0.003165</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000557</td>\n",
       "      <td>0.002593</td>\n",
       "      <td>-0.001563</td>\n",
       "      <td>-0.004521</td>\n",
       "      <td>-0.000437</td>\n",
       "      <td>0.023812</td>\n",
       "      <td>-0.002893</td>\n",
       "      <td>-0.001532</td>\n",
       "      <td>-0.006263</td>\n",
       "      <td>-0.000066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.015261</td>\n",
       "      <td>0.001102</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>-0.004468</td>\n",
       "      <td>-0.006288</td>\n",
       "      <td>0.023625</td>\n",
       "      <td>-0.040676</td>\n",
       "      <td>-0.001834</td>\n",
       "      <td>0.002985</td>\n",
       "      <td>0.021671</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002667</td>\n",
       "      <td>-0.002989</td>\n",
       "      <td>0.001666</td>\n",
       "      <td>-0.005040</td>\n",
       "      <td>0.010429</td>\n",
       "      <td>0.053614</td>\n",
       "      <td>-0.005092</td>\n",
       "      <td>-0.001537</td>\n",
       "      <td>-0.003849</td>\n",
       "      <td>-0.000351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
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      ],
      "text/plain": [
       "   B lineage  CD8 T cells  Cytotoxic lymphocytes  Endothelial cells  \\\n",
       "0  -0.000046    -0.000061               0.000170          -0.002639   \n",
       "1  -0.001117    -0.001009              -0.000900          -0.003275   \n",
       "2   0.010676     0.000682              -0.000398          -0.003216   \n",
       "3   0.001231    -0.000245              -0.001655           0.006247   \n",
       "4   0.015261     0.001102              -0.000611          -0.004468   \n",
       "\n",
       "   Fibroblasts  Monocytic lineage  Myeloid dendritic cells  NK cells  \\\n",
       "0    -0.001950          -0.010122                -0.053596 -0.004653   \n",
       "1     0.001616          -0.033531                -0.045218  0.011911   \n",
       "2     0.001873           0.010414                 0.082396  0.005641   \n",
       "3    -0.001482          -0.022117                -0.042654 -0.001476   \n",
       "4    -0.006288           0.023625                -0.040676 -0.001834   \n",
       "\n",
       "   Neutrophils   T cells  ...     NTRK2     NTRK3       LTK       RET  \\\n",
       "0     0.000722 -0.004441  ... -0.000372 -0.000442 -0.002161 -0.004190   \n",
       "1     0.000892 -0.003142  ...  0.002513 -0.002942 -0.003462 -0.007488   \n",
       "2     0.000511  0.004766  ... -0.000520 -0.001892  0.000274 -0.002984   \n",
       "3     0.000555 -0.003165  ... -0.000557  0.002593 -0.001563 -0.004521   \n",
       "4     0.002985  0.021671  ... -0.002667 -0.002989  0.001666 -0.005040   \n",
       "\n",
       "       NRG1      NRAS    MAP2K1      RIT1   TMB_RNA  Biopsy site  \n",
       "0 -0.011121  0.020717  0.000535 -0.001189 -0.004268    -0.000012  \n",
       "1  0.014353  0.034734 -0.003734  0.001358 -0.001913    -0.000310  \n",
       "2  0.007779  0.071337 -0.005638  0.003498 -0.000204    -0.000015  \n",
       "3 -0.000437  0.023812 -0.002893 -0.001532 -0.006263    -0.000066  \n",
       "4  0.010429  0.053614 -0.005092 -0.001537 -0.003849    -0.000351  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_omics_xgboost_raw = pd.read_csv(\"classification\\\\shap_xgboost_100cv_OS\\\\Shap_RNA.csv\", index_col=0)\n",
    "\n",
    "# Compute the mean SHAP value for each sample and each feature over the 100 repeats\n",
    "shap_omics_xgboost = shap_omics_xgboost_raw.groupby(level=0).mean().iloc[:, :-2]\n",
    "shap_omics_xgboost.reset_index(drop=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01e15aee-21ee-49a7-89a8-31b7ed525751",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 648x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_shap_values(df_omics.loc[shap_omics_xgboost.index], shap_omics_xgboost, n_best=10, figsize=(9, 10), title=\"XGBoost - 1-year death\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c72c803-c16d-4eb0-8cc9-543b902eeede",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 1.2 Logistic Regression (1-year death prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "570fd606-b1ee-424b-b40d-9bf7a80f5485",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B lineage</th>\n",
       "      <th>CD8 T cells</th>\n",
       "      <th>Cytotoxic lymphocytes</th>\n",
       "      <th>Endothelial cells</th>\n",
       "      <th>Fibroblasts</th>\n",
       "      <th>Monocytic lineage</th>\n",
       "      <th>Myeloid dendritic cells</th>\n",
       "      <th>NK cells</th>\n",
       "      <th>Neutrophils</th>\n",
       "      <th>T cells</th>\n",
       "      <th>...</th>\n",
       "      <th>NTRK2</th>\n",
       "      <th>NTRK3</th>\n",
       "      <th>LTK</th>\n",
       "      <th>RET</th>\n",
       "      <th>NRG1</th>\n",
       "      <th>NRAS</th>\n",
       "      <th>MAP2K1</th>\n",
       "      <th>RIT1</th>\n",
       "      <th>TMB_RNA</th>\n",
       "      <th>Biopsy site</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.002828</td>\n",
       "      <td>-3.667885e-07</td>\n",
       "      <td>-0.002032</td>\n",
       "      <td>-4.689850e-06</td>\n",
       "      <td>-5.965425e-07</td>\n",
       "      <td>-0.018154</td>\n",
       "      <td>-0.002821</td>\n",
       "      <td>-0.009878</td>\n",
       "      <td>-1.246004e-06</td>\n",
       "      <td>-0.003684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.007261</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>-0.000025</td>\n",
       "      <td>0.000752</td>\n",
       "      <td>0.012900</td>\n",
       "      <td>-2.955667e-07</td>\n",
       "      <td>0.004495</td>\n",
       "      <td>-0.001061</td>\n",
       "      <td>-0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.005328</td>\n",
       "      <td>-1.428501e-06</td>\n",
       "      <td>-0.000547</td>\n",
       "      <td>-9.802448e-05</td>\n",
       "      <td>3.678220e-06</td>\n",
       "      <td>-0.013815</td>\n",
       "      <td>-0.017609</td>\n",
       "      <td>0.007297</td>\n",
       "      <td>9.415263e-07</td>\n",
       "      <td>-0.004148</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>-0.012053</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000215</td>\n",
       "      <td>0.007772</td>\n",
       "      <td>0.123016</td>\n",
       "      <td>-9.270523e-07</td>\n",
       "      <td>0.020890</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>-0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.020997</td>\n",
       "      <td>2.348326e-05</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>9.212514e-05</td>\n",
       "      <td>-4.506698e-06</td>\n",
       "      <td>0.010897</td>\n",
       "      <td>0.099408</td>\n",
       "      <td>0.006653</td>\n",
       "      <td>-8.989653e-07</td>\n",
       "      <td>0.004826</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>-0.010863</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.004909</td>\n",
       "      <td>0.006776</td>\n",
       "      <td>1.016818e-05</td>\n",
       "      <td>0.040526</td>\n",
       "      <td>-0.000040</td>\n",
       "      <td>-0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.006743</td>\n",
       "      <td>-1.103222e-06</td>\n",
       "      <td>-0.000347</td>\n",
       "      <td>4.468009e-07</td>\n",
       "      <td>-1.886775e-06</td>\n",
       "      <td>-0.009859</td>\n",
       "      <td>-0.121407</td>\n",
       "      <td>-0.000054</td>\n",
       "      <td>4.672998e-07</td>\n",
       "      <td>-0.005929</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.020360</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>-0.001260</td>\n",
       "      <td>0.020229</td>\n",
       "      <td>2.861908e-06</td>\n",
       "      <td>0.002501</td>\n",
       "      <td>-0.001003</td>\n",
       "      <td>-0.000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.019245</td>\n",
       "      <td>-5.830081e-06</td>\n",
       "      <td>0.001965</td>\n",
       "      <td>-2.966224e-06</td>\n",
       "      <td>-6.512199e-05</td>\n",
       "      <td>0.006009</td>\n",
       "      <td>0.000378</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>9.122563e-07</td>\n",
       "      <td>0.009283</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.005518</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000212</td>\n",
       "      <td>0.007125</td>\n",
       "      <td>0.062623</td>\n",
       "      <td>-3.101210e-06</td>\n",
       "      <td>0.004117</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>-0.000003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   B lineage   CD8 T cells  Cytotoxic lymphocytes  Endothelial cells  \\\n",
       "0   0.002828 -3.667885e-07              -0.002032      -4.689850e-06   \n",
       "1  -0.005328 -1.428501e-06              -0.000547      -9.802448e-05   \n",
       "2   0.020997  2.348326e-05               0.000233       9.212514e-05   \n",
       "3   0.006743 -1.103222e-06              -0.000347       4.468009e-07   \n",
       "4   0.019245 -5.830081e-06               0.001965      -2.966224e-06   \n",
       "\n",
       "    Fibroblasts  Monocytic lineage  Myeloid dendritic cells  NK cells  \\\n",
       "0 -5.965425e-07          -0.018154                -0.002821 -0.009878   \n",
       "1  3.678220e-06          -0.013815                -0.017609  0.007297   \n",
       "2 -4.506698e-06           0.010897                 0.099408  0.006653   \n",
       "3 -1.886775e-06          -0.009859                -0.121407 -0.000054   \n",
       "4 -6.512199e-05           0.006009                 0.000378  0.001834   \n",
       "\n",
       "    Neutrophils   T cells  ...     NTRK2     NTRK3       LTK       RET  \\\n",
       "0 -1.246004e-06 -0.003684  ...  0.000009  0.007261 -0.000002 -0.000025   \n",
       "1  9.415263e-07 -0.004148  ...  0.000026 -0.012053 -0.000009 -0.000215   \n",
       "2 -8.989653e-07  0.004826  ...  0.000128 -0.010863  0.000044  0.000004   \n",
       "3  4.672998e-07 -0.005929  ...  0.000009  0.020360 -0.000006  0.000030   \n",
       "4  9.122563e-07  0.009283  ...  0.000028  0.005518 -0.000001 -0.000212   \n",
       "\n",
       "       NRG1      NRAS        MAP2K1      RIT1   TMB_RNA  Biopsy site  \n",
       "0  0.000752  0.012900 -2.955667e-07  0.004495 -0.001061    -0.000001  \n",
       "1  0.007772  0.123016 -9.270523e-07  0.020890  0.000028    -0.000001  \n",
       "2  0.004909  0.006776  1.016818e-05  0.040526 -0.000040    -0.000003  \n",
       "3 -0.001260  0.020229  2.861908e-06  0.002501 -0.001003    -0.000009  \n",
       "4  0.007125  0.062623 -3.101210e-06  0.004117 -0.000038    -0.000003  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_omics_LR_raw = pd.read_csv(\"classification\\\\Shap\\\\shap_LR_100cv_OS\\\\Shap_RNA.csv\", index_col=0)\n",
    "  \n",
    "# Compute the mean SHAP value for each sample and each feature over the 100 repeats                    \n",
    "shap_omics_LR = shap_omics_LR_raw.groupby(level=0).mean().iloc[:, :-2]\n",
    "shap_omics_LR.reset_index(drop=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e22644eb-467c-4870-a9eb-a62908e31924",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bFglsgfLy2H3WZ8aMuuEseHyicBbYsGHH267P73+fuAU7J8e6S0orlpJS/1eSa0goWgb0Anp57zdF3f8xMATo571f1kzHF8N7f2tL7Ke5ee8fAR4JbjvnPLCf935JePkbwP676fB2n7feiq1wgPV/Ouus3XI4gDUVBuFn3jwYO9Y60W+vEnLmmfbv/L/+ZVet669P3BwHdpV97TWrUh188M4fZ6IK17Jl9vXhhxZ0778/Nrzk5tr+33vP2qSijRhhX7ti61b43e8iaaOysu46p55qV+3o6lN2duTnjh1tO4GMjNgOWM5Zuvjf/yJX/yBA5edbaeTII2HaNLvq5+REwlZqqqWSqip7Xvbe25YPGBCbJAYMiJSC4hNWcE6pqbGvcVQ6qaQd1WRALVTX2q4yMuwQEl1DojMjWFiK7uKWnW27y8iw7/n59nNQVauttQpWbq6FlvR0exsuXhz59aqpsSLpXnvZ44NBq2Ad40ePtu/Rqqvh73+3t3/79tYKHf3rmp5uObWsDIYMiS3CBuprMi0vt+23bx8bSKOLjvG5O1p6unX7nDTJMvO3v924gvV3vgPHHmtdHg87zP4P27ABzj4bevZs+HZEmlJDmziXAhcAfwFwzh0ENE0P2D2Mcy7Ne5+gLCAJ/xLu7r+O0R3YAb780tqSunXb/uPOP79uJS3ea69ZlSq4mP/2t3DjjTt3nAUF8MYb9hXf3LZunY1+7NIlce/y9u13bp87smBB3UADFmSCFHD00RbSXnjBlmVmwpgxkXXPPBP+9jdLH2DzHkS339XUwFNPRfqVRRs50gLY9OmRkkxpqYW2Qw+174nKUz16RAYQ5ObCokWRx2dmWmgMUkN9wTs9Haqr8UA1sc95VZU9LUFAiy6+gYWu6MJhfGUneOvts49tK3g62rWzQ9u0ye5/553YrBpf0QLLvvn5VlQNKlDFxVbAvvrq2HXvuSfyNG/ZYpk4Xl6etTSXl9vggNWrrZU6Pd3+t0k0WOAf/7BxINXVVmj++c+twrd8uYWysjJ7ruJbnrt1s1DlPVx0EfzqV5Gunw88YP8jHXVU3f3Vp08f+wI7h+awapVVHTdsgEsusVApu6gNVMrq09Azexi4OOr2JcBDwQ3n3DDn3ProZkrn3LnhKhvOuRTn3C+cc1865zY75x53zsV1jvn6cb2cc88657Y455Y45y6LWnazc25K1O2LnHPLw9u8YXsn4JzrHN5ukXPufWDfuOXfcM69Et7vIufc+VHLJjvn7nXOPe+cK3bOveec2zdq+cnOuc+dc9ucc38FXNSyAufcW865u5xzW4Cbo5tXnXNBL+h5zrkS59zY+GZG59zezrmnnXMbw+eaYLwROOdSw83AX4aPc65zbu8dnd8OnrefO+dWh7e3yDk3siGP2yn772/NYYF99oHrrmu23TXIscfG3h44sOlGM958c+xV85Zb7Oq4M3Jy7Kr23ntWDoiX6ApXXm7lmPjm0aZS3/QiBQUWur71Lbt92WVw++1Wrbz//th51wYMsCF6qamWZKKbD4uLLSUkCmcpKTbY4KWX6raLbd1q6WF7Peqzsuyfg8LC2Pa2iorYSmD79layWbrUvteGS2S1tdsdahnk51AotjoG9rDgobW1FoaCSlLv3pFW2qysuqcQhLWUlNiQV1hYd/yDc9Z5H6zlNlpZmQ2SDVRV1X2ava+7/9RUe3pXrLCXp31728fFF8O550bWKy+Hhx+Ga6+1MBV0EZw50/4E3H23/TrcfrsVh0tKIs/R8cfDrbdagfqUU+Cmm2y/QTgDO/eHH7afi4rgBz+w7okXXWQv0+5QWWkjUe+6y8byjBpVt7ukSLSGVtDeBS5yzh0ALAbGAscCvwPw3s9xzm0GTgZeDD9mPBbsAK4CzgJGABuBe4B7sapcvP8AC7Fm1W8ArzjnvvLez4xeyTl3IPA34DTgPeA2YHtjju4FKoCeQD9gBlYZxDmXA7wC3AScChwMvOycW+i9D9o6LgC+BXwI/Bu4BRjnnOsCPAV8F5gG/Aj4QdS5AxwJTAW6Aenh54/wczc83MQ5JGjidM4dH3WeqcB04H/ARUAtcHg953ht+DhPw16ng4GyBp5fHc65/cPnM8x7v8Y5tw+QcK4B59zlwOUAEydOpG/44nzEEUfQtWtXnn/+eQD69OnDKaecwqRJ1pUwIyODgoICnnnmGTZs2ACDBnHeO++w7sMPedM5/IsvcvTRR5OXl8eMcJDo378/w4cPZ/LkyQBkZ2czfvx4nnzySbaEO9+MHTuWBQsWsDDcVDVixAjS0tKYOdPeRgMHDmTYsGE88oi1NLdv355x48YxdepUisLNWRdeeCHzTz+dg599luziYnxKCluOP56n/vEPAAYNGsTgwYN57LHHAOjUqRNjxoxhypQplJWVAVBQUMDs2bP5Knx1GzVqFMXFxbz99tuc+9VXRI8h9JWVvPj005x2ySVMnjyZqnDVaMKECbz88susWLECgNNPP52NGzfy/vvvA3DYYYfRu3dvpk2bRo8vvuCQDz9k76wsKCmhNj2d1KBtqKzMrlx5eZE2suxsNsyezZpTT23c6wSce+65LFmyhHnz5gEkfJ2OHzcO99hjpHhv/7UccQRPbd7M5gceSPw69ehB2qZNX79OwzMy+MacOXb85eXw+OOw336888ILHLlxY8L/MGvT00nNy7PkU1FBiNj/RNc6R0+o8zq9M3s2Xy5dSo33X79OtYsWMSR+otqoNshNn39Ol6CKVlFB8bp1pPXsSVY4RTmgHZVUEu6Fn2CgQG0tfPrpl7zzzmt897sTePHFF1i92v4/+/a3R1NYWMqyZSvYvHkphx12SMzrdPjh55CV1eXrbQWFxqKibaSmdojZT3wToffw6KMvs88+KeTnn8imTbHP5gsvFPHmm1MBOPfcsTiXh/fR63h6936GbdsGUFOzD4WFcW2iYamp8MorZZxwgmfOnDksXryYl176FitX9km4/sqVcMstSzjxxI8ZM2YMS5Y8hXOD2LChI+ed15mDDprF449nMXHiNwFHRkaIk056CzguZjvz5q3gmWc+5KmnzmJK+N/6RYtg7VrPT386o0G/TwC9evXijDPOYNKkSYRCIVJSUpgwYQLTp09nTXh07ujRo1m1ahVzw31UE/3dg1NYtizy/HkPv/vdSpYufTH8HO/496ml/u4FrxPAyJEjqampYdasWUDj/+5dHkxxI43mfPzw8PgVrA/aBOAoIAeYBfwEu9BXE+6D5pz7OXCw9/7CcHVsFbCv936tc+4z4EdByHLO9QRWYM2kvbGglI6Fp2VAvve+OLzubUBP732Bc+5mYID3frxz7ibgQO/9uPB6OcBW4DTv/atx55CKhbODvPefh++7FRjuvT/WOTc2fHzHRT3m78Aa7/2vnXOTgRrv/YTwstOAO73333DOXQz80Ht/VHiZA1YCN3vvJznnCoDfeO/7RG27AJjgvT82fDumD1o4oE3x3vd2zn0TeDb8HGy3aTQ8sOA67/20uPsbcn6rvPc3xu17APA28B1glve+AV2cAesX3TbcdJM1PQZSUqxa0ifxxaVR7r3X5h4InH02PP30zm/vtddsmFtwJe7f3+YAO+usSNUn6AgfXU3LyLB1r702Mr1FU6qosHa3zMzITP8NNXWqNdtG23dfu7oFaSRaWhoccEDdqlm3blZK6d7d+qRFDxaorbXm2E2b7PXt3z9S/Ssqss5Uwd9J52xb2/t0goyMOk3gtaRQSyqpaY6KULvt9qcKRnFmZFgFanvdHaurLdBUVMT2Qevb16pYwWFXVyeuHO2zj3U1XLrUnup4V14ZaQF/7jkrSAaOP97GmIBVgrY3nqdjR+uOCNa8d/rp9a+bl2dvlQsusO3HjzCtrLTumtEDfIcOtfy+KDy0KhSy5cuX2xzD8YMmBg603gW33BLb5bE5ffxx3V+vH/84uaZI3EnN+9EaPXvWf71ZuzapP9ajMdNsPAzMxqpPDyVYPgX4zDmXC5wPvOG9DwrnfYH/Ouei/yzVAt3jttEL2BKEs7DlJK4Y9cKCEADe+9JwFS+Rrti5roy6L3ouhL7Akc65wqj70oitgkWPmSoDgsHi8cfhnXPR+yFuv421N7C8gf3W9ga+THB/Q86vDu/9EufcNcDNwCDn3AzgWu/9zk/qlGyi5zEA+8v/2WdNE9CuvNKa2l54wQYJ/OAH9a+7fn1kFOIrr1gnmSOOiF3nwQdjyyRffWUdXVJSrLlun31sHoJ+/eyq8PnndlWrqrKff/Yz66+VaM6FeJWV9tWQ/muZmTs/Lf1++9UNaF9+aZ2ZopNLSooFzyOPtD538QHt4IPrny115UoLZ2DP35IlFiRzcuz8hg61zkMpKbYN7639LlwtqCOYByOqr1wKIcBTVZNJWkakD1oiztlD1661p3h73R3T0y1PLltmwScUsu5z0TO9hEKJR3VCZGBAv36RgQbR1qyJvMRDhthL6b09nT162DG+/ba9Lbt1ix1JGczfW10d2+qelfV1F72E515cbF/33GOd9f/859h1qqrqPvWFhfYW37zZsnNFhZ33O+/AgQfCm1Hj9VNTLbw9/rh1Q9yZbp/BwOfGzEU8dKhNh/ivf9ntffax/4lE6tPggOa9X+6cW4o1n9WZbcZ7v9o59w5wNtYU97eoxSuB73rv34p/XLjZLLAG6OScy4sKaX2ABLNHshY4IGo72RDTYhRtI1CDBZjPo7YbfXyzvPc702VzbXi7wXG46Nthu1JRWgn0cQ0bXLAS61u3IMH9O3V+3vtHgUedc+2BvwO/x17fPcOpp1oVKtChg3Vsbypnn21f9fHe/s1/9ln7uaTEygTeW8CLnv68Q4e6j1+92q6G5eU2FcWpp9r9DzxgM/J/+GFk3W3bLPzU97k2y5bBn/5kobWiwko1I0ZYhbEhoa6x1q+3K2t6up1vdKeqsrLYDvrf+IZ1fnriCQt17dtb9SsUsvN64gkLbyUlFooPOCDSuTjRvGelpZHtd+pUdz65+KlJ4sWVvTyOsuB/uvA8ZPGjNaOlpFgxcMuWHY9HAbvY7723HdZ779XdVnZ2/VNXBPr2tRGa0YI5y55/3mZMAQtyBx9sVbfrr48UZ/fbzz4hbM0aG+0Jkf8XgsD50EP2ljziCBu0HS8+tL71loW+6OcgL8/GjkyLaif4znfs5X7qqch9qal2nPfeC+ecY2/tlJTYj1Z99926x7Aj110XmXnmyittRpqGforBP/9pfew2bLAK5Pbmq5MG0iCBr30PONF7n+AvGmCVteuAg4Coqxr3A7c45/oCOOe6OufqzEDqvV+JNand5pzLdM4dHN7nI/HrAk8CZzjnjnXOZQC/qe98vPe1wNNYB/3scP+1S6JWmQ4MdDboID38NSzc525HnseqS+c4GyRxFdCjAY+Lth7oX8+y97EQeLtzLif8vBxTz7qTgN865/Zz5mDnXGd28vycc/s75050zrXDmojLscrnnmPCBJsI6YAD4MQTrY0nfv6B5vTee3YlCq5cubmR2e/vu8/asQInnxz7xyooVURvK9qBB8bezs62K30i3luF7d13LeTU1Nh9r79upYjm8OCDNjdEdXXd+dC2brUr8gEHWLPu0qWWACoqbPLazZvt9rp1ken3P/nE0sMHH0TSBtT9NIFg7ort6dix/mU5OXUGCKTgif4/rba27piHaDU1kSpbotGXiaSmbn+esESiB0mPHBnb6X+ffazwuXVr7NNVVWVvgxdeiB0vEcw+U1YW+SCFwOLF9n/Gu+9awXL58oYVX9PTE2f/u+6yMTbnnWf/o1x5pY0xOf98+xXo1MlC4t57W0hbvNj+r9l//9hzHDp0x8cQ7cUXbTq/oIB8550WXhtj6FAb3KBwJjvSqIDmvf/Se59gdpuv/Zdwc2ZciLsb60f1snOuGBt0cGQ927gA2Aerpv0X+JX3vs5Yl3Dn9iuBR7EAsxXr91afH2HNkuuAycC/orZVDJwCjAvvdx1WKdrhB6eE54Y7D7gd2AzsByT433C7bgb+7ZwrjB9dGQ6XZwIDsH57q4gaZBDnTuBx4GWgCHgQyNqF82sXPq9N4cd0AyY28tySm3Pwi1/YkLaZMxs3br8prEzQOh5cYbyPLYvsv7+VGjp2tNARHyLiO8B873s2r0FKirVX/fa39U8bsX69VdASCSbCbUplZYk/dinafvtZ/7qgdBQtfmgkxPYbi57Aq0cP21ZOjgWzIUN2fPUMwmyHDrHlk/bt7XjiklctKUR3xXHOVsnKig0gtbV2KkGLKyT+MIX61DdLf6Iw1L69FRUDHTtaBez00y34jA3/lUnUFFlVlbhw4VziAmww5Ua0RMErPT02QF1ySeJjb9fOBv/++c+RAnRWlk2tUVRkb9foTxVISbF+Z/ffb5XClBQbRfmzn9Xd9vZEjxTd3n3SgtrwRLU7HCTQ6A069yXw/fiO+rJHaTuDBHa3NWvsahl0DvLe+ouVltrcZm+/Hbv+lVdGemvn5NgVe/NmaxO6447EnWZqauov5USvc8YZiUs0v/619bhuSt7bZwNFd2rad187ji1bLGyed579Ea6qsjJKdDln6FCroAU94/PyYiufPXtaGaMpBJN1ZWREgl0oZMdTW8uqtWtZuWEzgw+OhPvMzNgiW/BxniUldYNM796NK9q+915sqAsKpS/HfXbJ0UdbLm2Ihx+O/SikMWPsVH/xi0iF78ADbSCAc9ZN8qmnLHCOHm3F3e9+N7bvWDB2BGxbxx8PF15oL82cOZZ/oz/doCk15C2fyPvvx4ZasH5uLf1/W5Jp3o76e+9d//Vm5cqkHiTQpAHNOXcuVpkZ6L3fzjglaeMU0JrS3Ll2hayqsirZp59ar+4f/jBxeeGdd6xp78QTrcITCjXNf5PvvWdVtvXrrYLUsaNVsAoKdn3biSxfbp2W1q2zcHbppfU3LS5ZYglk2zZLHWecYWWW9estlG7YYO1roZClo5EjGz+idCcF0yJcfHHB1y9FfS9H8GlSQWG0QwcLLA3t4wR2iuvXWyWua9dIuHvvvUj3xX32sTm5Gvq2qKqyStG2bdbdb+9wL9uNG+3tFnTN3M7Ub7z1llWwSkps/xdcYJ8+1q6dBb5evRp+jrvTQw/BH/4QafVvrrd/G6KAtpOaLKA5514HDgQu8t4308yXkiQU0NqqUMiqdy3ZD6+6evtX/mi1tfVPgVFRYe1fnTrtXPlkJwUBraARV/Kgy11TH2YwurGlppaIF0wF0pCBD9JmNG9I6tu3/uvN8uVJHdCa7Nffe398U21LRFqplJSWDWfQ8HAG25+fLDOzeUabNoPmyo+7+/QTTBEnIvVouX8jRURERJpSGxgMUJ+2e2YiIiIiSUoVNBEREUlOqqCJiIiISEtRQBMRERFpZdTEKSIiIslJTZwiIiIi0lJUQRMREZHkpAqaiIiIiLQUVdBEREQkOamCJiIiIiItRQFNREREpJVRE6eIiIgkJzVxioiIiEhLUQVNREREkpMqaCIiIiLSUlRBExERkeSkCpqIiIiItBQFNBEREZFWRk2cIiIikpzUxCkiIiIiLUUVNBEREUlOqqCJiIiISEtRBU1ERESSkypoIiIiItJSFNBEREREWhk1cYqIiEhyUhOniIiIiLQUVdBEREQkOamCJiIiIiItRRU0ERERSU6qoImIiIhIS1FAExEREWll1MQpIiIiyUlNnCIiIiLSUlRBExERkeSkCpqIiIiItBRV0ERERCQ5qYImIiIiIi1FAU1ERESklVETp4iIiCQnNXGKiEgi3kNtrX01qfJyqKlp4o2KSLJQBU1Edovycrj1Vpg9Gw48EC69FPbay76ayqJFtv30dBg5Evbeu/HbqKqC6mrIyam7zHsoLgbnIrczMiAUgjRXQ3pNBeeMGsXCxYsbvsPqapg3D7Ztsw23a2f3de4M3/iGnUzgyy9h9Wro0wf22afxJzd/Pjz5JBQWwuGHw7hxsduPPtH//AdefRVyc+HCC+HIIxu/v0Q2bYJZs6BLFzj2WEhNrbvOli2wdCkcfHDi45M9VxuuoDnv/e4+Bml7WtWbassWuPJKeOUVGDIE7rsP9t9/dx/VnqW4GEpKoGfPyH2/+AU8/LD93KkTZGbaz6edBjffHAk9RUXwyCOwfDkMH27LG2LFCrjjDgtLYDnnppsgP7/hx/3JJ7Bxo/2clmaZJDPT8pJzdl5FRbaP7Gz7itaOCjKothtZWbaRaMHBRV9kvvjCDj6Rnj1t3Q0b7GvTJquylZTAiSfCEUc0/OTKyuCGGyyBBs44A049te66//sf/OUvkdspKdCrF/TrBwUF9gLGq62FVavsmDMyEh/DkiUwYQKUltrtY4+Fu+6KXedf/4If/hAqKixhv/SSJfqGqqiAP/4RXn8dDjgAJk6MfSNKrK1bLbSnp8OYMRbId41risOq18kn13+9eeWV5t13M1MFTdq8a66BqVPt5//9D847zy680jL+9je7PlZUwHHHwT/+AXl59lqAVaaCcAbwwgvwrW/BN79pt3/yE/j4Y/t55ky7lp9+OkyeDAsWQI8eMHSoXbP79o0UWD76KJJ/ACor7XXv2xc6dLBjWL7cckrv3lYkysuLZKgNGyLhDCwHLVhg1/aiorrnWVFhoS0rK3JfdW0KGYWbLR2mp8cGtM2bLWCFQnZAPXvaBsrK6n8yv/jCql2BYGfp6fDuu40LaKtWxYYzgK++SrzuggWxt0Mhq/K9+669OA89FLt80SL46U9h3TpLxL/7naXb0lILbu3b23qPPhoJZwBvvgmff26VQrDn4uqr7ckFWLkSrr8epk1r+HnedhtMmmQ/f/aZVeKmT2/44/ckGzbAYYfZewPg97+HDz5IXD5uLdpwBU0BTdq8116LvT1/vl0bO3fePceTbIJrY3SIaqilS+GWWyz8ALzxhl0rf/xjGDjQAlBwrY62Zo19X7UqEs4Czz9vy19/3W6vXWthLHDSSVYxTVQpe+stO4bycth338jf9rlz7XtGhlXpOnSARK2StbWJw1mgrCw2oKX4kFWXQiErtwVPYkWFXQwD27ZZ+S0/35r6Nm2yJ62qykJd0OxX384zMyMvVEP17m0nHB3S9t038nNtrQWsrl1hwIDYX6Tg2MBCz/LllnwDf/yjPRYsUN56K5xzjgWjUMiS+v/9n5Ui40Ufz6ZN9rwFMjIsZf/613DZZVbFi7dpEzz4IDz3XCQAR/voI3vjde263adnj/TQQ5FwBhaWn3oKLr549x3THqztRk+RsMMPj73dvz907Lh7jiUZvP463HuvFWt+/nN7rjp2tCbJaBUV1kJ2+OFw/vnw6ad1t7VkSSScBYLgc/PN0K1bpCkzkJEBxxxjYeraa+tus1MnmDOn/uN/9VU7loMOssJVICfHjmXTJttGon+8q6rgww/hvfes4pZo+dq1to1E2SK6+5QL1ZCRVmsnmJpqBxOEhURhKrhvr70sta5bZ0Fi7VprwuzcOTb9RQuFGt95Lzvbmhe7d7cn/eij4YQTrF/bvHlwxRXwox/ZOt26WfINwmJRUeSFy8iwYLl+vSVysMAWbc0a+O9/rQwZClmfs1mzrAktuqo4eLC9cIE+fSJVwYwMew6qquCBB+Css+o+j0uWWMK+5x47luLi2AodWADeUTv35MlWpr3ySuvjt6dINCgl0RtdWoQqaEnKObcMyAL6e+9Lw/dNAMZ77493znmgDOsPtg14DPiZ9742ahv9gC+B+733P4zb/mjg10B/oAqYB3zPe7+smU+tyd1zj11Q33zT+p79+99tuiq+S37yE7jzTvs5NTV2ZOLvfw8jRkS6KN15p13HwDJEQQG8805sSDnySGs2jC6CnHSSfR8wwL7iK1W33WbX4uuuq9sCl5FhOWB7rYBgRZYlS2JDVmmpZaSqqrp9xaJtr1IYHE9VlXXV6d49EkDT0uxcAfCe7MotpMQHqi1b7ODj+6JBpBlp1Spraox+8ktKYNAgCxazZ9tOvbeQFIxUOPNMW7e62t7giTrbxxs0yL7AwuC110bKl4HiYgtEf/+7hbXf/c6aPDt0sIA0bpw1Vb78sq0/cKC1T7/wQmQb++5rT1h0G/CKFdZv7uGHrYNo587WBy4+sU+bZhWz116LfSOtXg1vv21vyk8/tQreP/9p1ch4QaUwP986Jm5voMGkSVadC7z2mp3vnvBH46KL4E9/sj+YYAF5zJjde0w70oZfl7Z7ZnuGNODq7Swf4r3PBUYAY4Hvxi2/GNgKjHPOfV1rcM4NAB4CfgJ0APoB9wFxbQXJYe+9rVmrrMwq9k01+Ky5VVZaBWvwYBg7tm5RItH6N91khZArr7Rm3MYoLLQwG0g0bUR0U+Ls2bHLVq+u24WpfXu7do8YYTng17+Gc8+NLB89Onb9Y46BQw+1oBgfzsAywY7CGdhrvGmT5aD8fKsABqMrYfvNlEGGCqSk2Hsovp97TY19ZWTY9rt2DTKRJ91V46LLd2Aha+NGS4rbttkDg75p3bpZuquutmQZPPlpadZJOy0N3n/fnuTMTDvIDRsi/dhOPdXKgi+9ZMn5z3+2/mFgAW7OHHjsMdtGfQPDHn+8bjgLbNxoL8jcubH90TIz7TyCcAaWuA84wMqq3btbRaykxB43f74t995eaLCUfsUVtn6i5Nyjh3VkvPDCusu2bbPHH3ywNXfGt4cHzjvPSrJz58IppyReJ/Cf/8Te/uwzqyjuCfbay8719tvtfTR3rv3SyW6hClpyuwO4zjl3n/e+sL6VvPdLnHNvAUPjFl0M3AjcDJwJPBm+fyiw1Hs/M3y7GHiqyY66iW3eDM88Y2Fg9Oj6B4zV1zrUWt1wg/0zC7BwoV3XogNSvJ/9LDLQ7p13rIky+rq5I9XVO57La+TIyM+DB9v1NtChg3VrinfooRbSEhkzxppU33nHssqIEVbAiO+THogvPDlnGaG8PHJf9+42uHD9essbQSEpO9vC6xtvWJEqPd2CVfB982YLcBUVkZaz7GybvaJfPwvA0d3GUlMtvKWnW/GnsjI8zQZVtKPq6//sQ6EQ24qK6BjfVFRVZQdbWmo/V1XZgdXW2ps1NdU2HFSUNm60sBBUzSDygnXvbk9a8AapqrLKT2Gh/VcSNJPOnm1vpPHj6z650ScXb8gQe4ESrRPdZymwaZO1Fa9fb1+1tfZ8hEJ2TOvWxQ52aIgJE6wD4sqVdvucc2yE57Jldru01MrkHTvGNtWdeKINLGhov4b4uVjS0vasUZ+9etl/hsmiDVfQFNCS2wfA68BPsaCVkHPuG8BxwB+i7jsO6A1MBQ7EwloQ0D4EvuGcuwt4FpjjvS/Z3oE45y4HLgeYOHEifcMdho844gi6du3K888/D0CfPn045ZRTmBQeVZWRkUFBQQHPPPMMG8J//M8991yWLFnCvPB/rUcffTR5eXnMmDEDgP79+zN8+HAmT57M5s253H772RQVWfrq12891133LBdccD4LFixg4cKFAIwYMYK0tDRmzrTMOXDgQIYNG8YjjzwCQPv27Rk3bhxTp06lKFxeufDCC5kzZw6Lw21wI0eOpKamhlmzZgEwaNAgBg8ezGOPPQZAp06dGDNmDFOmTKEsXIIpKChg9uzZfBUuLY0aNYri4mLefvttAIYMGcKAAQN46inLv926deOss85i8uTJTJlyFpD/9XP88cfw+98/wvjxx7Nx40bef/99AA477DB69+7Nww/nApHRVq+8Avfc8y+ys2uZMGEC06dPZ024QjJ69GhWrVrF3HDv+OB1Gjq0io8+6vf1No455jOWLOlJSgrccks+a9c+wwMP2Ot0+eVj+OSTNBYubE9ubinXXruJDRtI+DoBZGdnM378eJ588km2bNkCQGrqeKZNi1RNrrnGc+SRFVjrfV2ZmUVUV0dGFXTuvJJNm2IvqOXlNbz33gpeeCGHrKzuX9/vHMydu5ADDviMb397DDNnvkNFxSbgBDZurDuVQHl5DUVFr7N5cx7Llg0iLy+d8vKtZGX1+Loy5xxs3VrOSy89zKWXTqC0pJa0tNh+PCkpKTw+fTpjjz2W/KiEWRsKkbp+/de3i9etIy9YnpdnoSa6uS8vzy6ecVUun5/PP/7zH75ZVsZBxPnoI0veJZFfX//OO6w69FBefPNNIPI6vVdby3HRj3UOUlIozcmh9nvfY/5bb7Fy6VLOc47UcECsdY7nams5IyODtHDJ0wMLv/ySwUuWRLaVmmppNziObdsITZzIl/fcw2sffABs//ep3/TpHPDGG+T36cOXZ53FJ926sblnTwpmzCDm/7HiYrZNmcJXjzxCeU4OW088kdPHj2fy5MlUhY9vwoQJvPzyy6wIT2Vy+umnx/w+ffPsszlw5kxSV60i5ByLx43jGz16MGnSJEKhECkpKQ3+fYr/uzfj6qsZ+O67VHTqxKB//pNn5szZqb97kPj3aezYsa3u797ll18e/66UBtI8aEkq3AdtArAOeAsYAIwmtg9aMZAKZGNBrMB7Xxl+/CSgi/f+LOfcN4HZwF7e+w3h5UcB1wInAHnhx/9oR0EtrMXeVNdfb9X4aC+8kHgqp2RzzjnWrzrQpYsVK+JbzgLDh1t1KNCzpxUbGtIVCSwPHHecFWlqamw/d9+duGUpWlGRdZ9q6H6iXXWVDRKLNmaMdS0KtG8P999v1/euXeGJJ6xYtO++cMEF1mctfD0CrNtMVpYVkeIH6vXubXll/XqrtHbrVneQXyConkU3h2ZnWyEm+s9mWpoNPKmqsuctk3LSiYS06poaHn/+eS4cM8b6XUU3X8Z3ys7NTTx8NFBaaifbpYuVDTt0sPLeoYdalezJJ+s+Ztu22L5bYL8gZ5xRd92XXrKvTZvsRe3Y0eYg69Mnss6nn8Kzz9oTd+aZ1oS5dCk8/bQd08kn2+Nvvjl220VFkYDWrZud//3327QO2/P007Ht4u3a2f569oQbb7RhwoEjjrARHruqutqag/v02bnZjRN57jn49rcjt/v3t+pm2594t3nnIjvzzPqvN889p3nQZPfx3i9wzk0HfgF8Frf4UGwQwHnA7Vh5pdI5lxW+b0J4G+8451YA3wH+HL7vXeB8AOfcMGyQwQ3A9c18So2SaKRdovuS0R132N/vzz6z7kX/+Ef94Qxsfs8zz7TrdF6eTcjbmNA0f771c4vuBvTcc3Yt/fGPLRxecIGFtuhm5ETTZCSyZo1dOwcNsn7kYLkiOqClpNi0Vz172mjMnj1tOq399ousEx8Yf/pTaxZ9//1IX/Ag95SVRc6nutpa1YKMEHxCQKLnKDXVziu+r1pZmV2zg/tTU611MbrPWgWZpFBCKvafytZt2/DeW1vsgAGRCdOCprlo3btboFq/PpICo6towYy4J59saTwUsrCybh2cfba1Qc+daxf9lBTbTqImoMJCC4vdu8e+qb71LfsqLbXmzN69rd/bokWR2Z0PPLDuRLH9+lnHwUBpaWy1LzvbntBlyyJ96rKyGjZj9Isvxt6urLTm2+98x0JgZqY1fQ4aBL/5zY631xDp6dYhsikFszIHvvrK+sUdf3zT7kfaDAW0tuFXWLPkn+IXeCuRPh4elXkTcA1wNtAeuM85F0wPno81c/45wTbmOOeeBgY3w7HvkgkTbIBZMJJ+4MC2UT0DqxAtXGjXtJ49dzwP2WGHWcBauNByQGMnAO/Zs25Rp1s3K14EBZj777eCwsSJjdv2zJn2WgUd/3/1K7j8cusK9fnn1j+9fXvbbv/+di5vvGEzPjz7rOWB6EJDWZld33r2tG5aw4ZFZniItmWLHXtqqoXckrj6b0VF7Byc6elWlGrXznJRdHcvsKzTvbttq7zcAmGiRgiHAzwO6Na5M6OOO86qSuXlFlYSJe30dNtg8B9GTY2tn5dnB1JbayczcqSl5bIyWz8lxdZbvdoqSMG0FJs22RMbCtm60X3gPvrIOv5lZdlnbAUjOQM5OVY+vOaaSFXu6KNtnqyGdOZcuxa+//1I0DztNHvS7rgDZsyIpP7CQvtPorjYqkvDhtXd1kF1Gm4j96WlWRXtxnp7eLSc6mrr9JmSYgE6vsNk9+51H5PoPpEwNXEmqaCJ03v/avj2P4BzgPlRTZz7ee+XhJcfBLyHTZvxb2AVVhEL7AXMAYZgIzcPAKZ57zeE+7A9C/zbex/VnlCvFn1TffEFTJliF/hLL038qTPSMPfeC3/4g2WDfv2smnX++bHrjBplrWCNceqpsZ/ekJtrFbugEhcKRQo9mzbZtTo6T1x1lc0AADbI8W9/ixSixoyxlrgHHojdZ9AnvWNH2199f+qCCfzBQnCXLpFlffva8ZSW2vYGDoydbqy0tG4TaQq15JBgqGkoFOkw37GjleGiR2X06GH3z59v1SvvLeiAXeyDdJuWZgEt2HHHjhaaTjmlbmf2UMgGF7zxhn2lpdlJRnfQ79w5ceVp9mwrmUa75RabT2V77rorMiqkY0ebnqNfv7rrlZVZO34w3DglxdYdOjR2vcpKe/GffNKO/Ze/tL4NrUlJiX1MVTDa89BD7fmOLkevWGH9EILh2D/8of3CtX3N28w4enT915tp09TEKa3Cb4CL6lvovZ/vnJsF3AaMBA7x3q+LWmWdc+4l4BJgMvBt4HfOuRxgE9bE+Qdaof32s+kbZNddeaUFnnXrbJRmUZFdY6Kb8HZmmpL4psLycgtgQUCLboVbtKju3JjRo0WffTYyytJ7a4b93e+sqBKst+++liPKy20+1PqmKMnIiG1BrKiw8+3TxwJ/Xp5tq6zMil7xRZHo3BQIkYInwVUpJcWCVEmJPSF7723hqabGynbB5Kn9+lmAqqqy8LR5c+xO1q+PTYVFRVYy7dGj7gkGJb8xYywMOWfDfaNt2WJBMb6tN9HozGAEZX3WrYudpmLrVhtpmSgAvvtu7FwwoZA1Z8YHtHbtrBK4ZYv93Bo/dug//4mdiuPDD+2Yo8Nsnz7Wv3D2bAvS8VVLkTgKaEnKe79P3O2VQGbU7TrXB+990Ph3aT3bPC3q5pm7fpSSjLp3j7S8dOxoH1R+9dXWnWjs2J0bgX/hhbF9uc86q/7r7IEH2nU4ui9h9DU7UTNlbS384AeRWSqiP3Uo0UTo6ek2NdbmzXbNDPYVVMmiPwbMufqPNQh41dXRmclRm55FWk1F3bJd9Ky2WVmxne8DubnWnLhtm63z6aeReUdSUhLPhRLM/rs9QQo+5JDYURgHH5y4I96JJ8am85SUHX9S/bZtdc9569bE6yb6rLXtlb9bc2k80ZQhie7LyGjYayUN14an2VATpzQHvanaoOhmyJ0xbZq1+gwaZIGtvvnqwIoM99xjRZPTTrOuUEH1asYMq6IFBg+2eU7rM39+7Gdjd+1qzbbB4IbycgtpVVVWLdvZHOB95DlyLnxHaWkksFRXR+YR6927cZ0EN2ywC36vXjbXV/SEeEOG7Dg4RauqsjbqL76wfmannVZ/v7KPPrI+YhUVVg2KnggvEe+tOXLRosh9t95qfbISueGGyGR9ffvaSJhk/By2FSushBuUivPzLVQ39uO32qbmbWY8++z6rzf//W9SN3EqoElz0JtKmo331r/900/t+nfCCTseQLF0qeWRzp0tzyT6pKVm4T3/e+UVnHOccPzxVqoLJqHdWbW19skAK1da0Bs2rAVPqAEKC63Jb+1aqxYNH7799RctsmBzyCGt6zwaa9Ei60PnnJVzo4ce79maNySde27915unnlJAE4mjN5VIWDCxaMGOOteLtE0KaDup7TbeioiIiCSpJK4ni4iIyB6tDQ8SaLtnJiIiIpKkVEETERGR5KQKmoiIiIi0FFXQREREJDmpgiYiIiIiLUUBTURERKSVUROniIiIJCc1cYqIiIhIS1EFTURERJKTKmgiIiIi0lJUQRMREZHkpAqaiIiIiLQUBTQRERGRVkZNnCIiIpKc1MQpIiIiIi1FFTQRERFJTqqgiYiIiEhLUQVNREREkpMqaCIiIiLSUhTQRERERFoZNXGKiIhIclITp4iIiIi0FFXQREREJDmpgiYiIiIiLUUVNBEREUlOqqCJiIiISEtRQBMRERFpZdTEKSIiIslJTZwiIiIi0lJUQRMREZHkpAqaiIiIiLQUVdBEREQkOamCJiIiIiItRQFNREREpJVRE6eIiIgkJzVxioiIiEhLUQVNREREkpMqaCIiIiLSUlRBExERkeSkCpqIiIiItBRV0ESkcT7+GK6+Gr74As46C/70J8jKarn9L1wIkyZBu3ZwxRXQt2/L7Xt7tm61rx49IDu77nLvoboaQiFIS7MvoLAQVqywxb17Q+fOtvqaNTBvnm1y0SK779vfhsMOs2U1NdCnT8ucmoi0PAU0EWm4mhpLCStX2u2//Q3y8uD3v2+Z/S9aBEccAWVldvtf/4LPPoNOnSLrrF0Lt94Kn34Kw4fD//0f3H03vPMODBkC118fuz5YcPrsM+jYEfbe20LUxo12OyPD1tmwATIzoX37usc1fz58+KH9nJICBx0EBxxgITJQUUFNjSdECqnVlaS2C1FWk8GHH1o4A9tlTg7ssw88/LAdRrDJ+fNhwQLo2hWmT7fHnHIK3H9/7G5E9ihtuIlTAU1EGm7x4kg4C8yc2XL7f+SRSDgDC03//S9873uR+77/fZg7137+/HP43/9gyRK7vXChlZ8eeQSefx6mTbMSVU1N5PHHHw+bN9tXdjaMGwdvvmnnnpICJ5wA55wTWb+mxkpdgVDIwl5xMey3H4Py8thQU0NlTQpVRJJUu8pKNm6JhDMA5yxfrl0bCWdgGTE/H778Et59N3L/yy/DiSfCH/4Axxyz/afutdcs9PXoAVddZd9FpPVSQBORhuvXz6pKW7dG7jv00Jbbf4cOde/Lz4/8vGVLJJwFvvoq9vbrr8NDD1mwCxQXW/jKybFSVVCSKiuDRx+Figq7HQpZID388Ej7Ym2tfUULhaCqChYuZFh+PrXOUeYzwEVWqSSD9PS6pxO/qUBNDVRW1r1/0yaYONHyZm5u4sc+84xlyiAMPvGEFRgT7V8kqbThClrbPbMk45x73Tk3Ifzzhc65l3f3MYnUkZUFU6bAXnvZ7ZEj4ZZbWm7/l14KAwdGbh9wgAWtG2+09sH27aF799jHxKeWPn3gxRdj78vJgZIS+zk1NXZZEM6ibdhgyaiiwsLcPvvU3V6UFA/gY9fBkZsbe3jl5ZYJDz449jAKC2HbNjjqqLrNmZmZ9pj774/NzYGaGigoiK3ULVkCb7xRd10RaT0U0JqJc26Zc67cOVfinNvqnHveObd3Qx7rvX/Ee39Kcx+jyE457TTr1V5cDK++ap2iWkqnTvDJJ1YSuvFGa0p85BELicOHW6q5445IH7N+/WLb8zp2tI5b1dWx2/U+UgmL/488LmyRnW1Vun/8A/7yF+t7duyxVlVr3972nZcX8xCHJ2P9qjqnU1trq3ftagW34mIrCL74oi0LhSycBa3KS5fCGWfY+hkZkS5yoRD8+c/W+rpmTew+Xn/dwl28YDCCSFJLSan/K8kl/xm0bmd673OBnsB64C+7+XhEmkZKSv3taU1p3TpLKNHatYPRo+Gjj2Lv//xzG6wweDB88IFV1lJT4Xe/s+raxRfbYydNsqQTXVIqKrKUU1pqVbojj4wsKy21MlW/ftb5/7DDrCkVrDz16quWrGproVs3q+AlaDtsV7SRtFJLSuHWTz78EFavtoeWltr9X3xhlTSwp9m5yG2wsNaxI3TpYnnQOauceW+n+eijsftNNMB2//1tvISItF4KaC3Ae18BPAkc2JD1nXMFzrk3o25759wPnHNfhKtx9zrnXNTy7zrnPgsvm+Gc6xu17G7n3ErnXJFzbq5z7rioZVnOuX+HH/eZc+4659yqqOW9nHNPOec2OueWOueu2tXnQqRBKipsKo8zz4RvfcsqVQ1x/fU2V8Udd8Dtt8OcORa+qquth/y6dbbeunWwfLkFrXXrIiEsNxduu81KWFVVscdz3HHwgx/Urb7V1lpv/XnzbDvFxXXXSUmB7GwySzdRWlTNihWWFTt3tgrY1q320GBz0aIPI5CebkXB7t2hVy8LbPWtf/TRcPLJkdsdOsBzz+34qRSR3UsBrQU457KBscC7O1p3O84AhgFDgPOBUeFtnwVMBM4BugJvAP+JetwcYCjQCXgUeMI5lxle9itgH6A/cDIwPuqYU4DngHnAXsBI4Brn3KhdOAeRhnn6aXj7bfu5ttY69X/2Wew648fXfRxYVeumm6yCVl5ufcs2b46tmIH1I+vVKzKCMz8/0ixSUVG3XTAYPbr//nX3uXKllari+6+F+exs6NUL160bnbMrycz0ZGZaSAuaKYNdx1e8MjOtShYtNfXradRwzroEBq06y5fD+vWRdZ2DF16A666zLoN/+AMMGJDwMEWSTxtu4tQozub1jHOuBsgFNhAOVTvpdu99IVDonHsNC10vAd8HbvPefwbgnLsVmOic6+u9X+69nxK1jT85524E9seC1/nAFd77rcBW59w9wM3hdYcBXb33vwnf/so59w9gHDAj/uCcc5cDlwNMnDiRvuHJQ4844gi6du3K888/D0CfPn045ZRTmDRpEgAZGRkUFBTwzDPPsGHDBgDOPfdclixZwrzw1AVHH300eXl5zJhhu+3fvz/Dhw9n8uTJAGRnZzN+/HiefPJJtoSbnsaOHcuCBQtYuHAhACNGjCAtLY2Z4SkhBg4cyLBhw3jkkUcAaN++PePGjuXJf/2LLeEL9oUXXsicOXNYvHgxACNHjqSmpoZZs2YBMGjQIAYPHsxjjz0GQKdOnRgzZgxTpkyhLHwxLygoYPbs2XwVHkk4atQoiouLeTscPoYMGcKAAQN46qmnAOjWrRtnnXUWkydPpipcCpkwYQIvv/wyK1asAOD0009n48aNvP/++wAcdthh9O7dm2nTpgHQq1cvzjjjDCZNmkQoFCIlJYUJEyYwffp01oQ7KI0ePZpVq1YxNzzisbW9TpUvvlin3Py/f/2LDYceyrhx45g6dSpF27Yxul8/ui9dSh3xYSx6gtjw/BU17drx9EEHcdrateSWltofdO8jaSiulLUtN5fHHniAw0pLOSy40znro+acpaLoUlYU17//1xeMsqpUYoZzYt3aqqrs8IJp1qqqLLht22aVtpISO/QVK+oOZk1NtapaZaXNkTZv3laOOeZNamtTuP76o7jhhnIefLA3YINQP/xwG4ceau/bZvt9Cl6noiJAv0+t9u9eM79Ol19+ObJznI//QyZNwjm3DJjgvX/VOZcKjAYmAQd679clWP91YIr3fpJzriD82GPDyzywn/d+Sfj2ZGCV9/5G59ynQB8gaiIn2gEjvfdvO+d+AkwAemHDyNoDJ3vvZzrnKoBDvfefhrc7CnjQe9/bOXc+VnEridpuKvCG9/60HZx+8r2p3nwTLrnEpmQ48kh47LHWM0P9nuitt+CaayK3MzNtYEB8z/ZgGoyXXoLwRRmwMBQ9kZhz1ukqukPXxIlw0UU26dhPfmKdv1JTLWRFl7ecswT1299aYrrzzkjVrVu3SCkrFLL14wYIANZvLVxdK69KYdmG2IEHNTXw/vsWysrLI9NpZGVZ8+fKlZYd33vPdrfPPrHFuurqSJe8YKxDSYkVAg8+GFatinSbg9hBqyLNzO14lV3w61/Xf7351a+ad9/NLPlrgEnAe1/rvX8aqAWObeLNrwS+773Pj/rKCoez44CfY5Wyjt77fGAbkV+YtUDvqG3tHbfdpXHbzWtAOEs+oZA1lwXzZb33nvV/kt3nmGOsmfKgg2xuib/8JfGww+xsmDABnnwSHngAhg2zVNOhQ2y7YE6ODSw48EDo399GdgZNpA8+aOEMLNls2WLb6NQp0lRSUWHthKtXR8JZZmYknIGtV1hooXG//az5NBB0MAOyMkJ0yq0k+v+Y/Hx7WGFh7Fxn5eU2Hcby5XZIJ5xgT030wNLNmyPjKIqLrZC3aVPkMD/5pG5BMdEnUYlI66ImzhYQ7tD/baAj8NkOVm+s+4HfOuc+9t4vdM51AE7x3j8B5GGVtY1AmnPuF1gFLfA4cL1zbg6QDfwoatn7QJFz7ufAPUAVcACQ5b2f08TnsHtt2WJXwGjBx/bI7nPmmfbVUJddZunmttvsdrt2VpIK2v9eeil2Gv7A55/H3g6FIh2/OnSIjFZdvDg2PSVqfXDO3k9btlgYXLPGtlFUBBkZ+IwMPClUVqcQXViorLSmzURVrf32s+5y69fbyM20tMgmg0C3zz42MLW4OPK46A9HGD4cnn02csi/+Q0ibUMb6GtWn7Z7Zq3Dc865EqAIuAW4xHu/sCl34L3/L/B7YKpzrghYAJwaXjwDeBFYDCwHKrDKWOA3wCpgKfAqNtK0MrzdWuBMrK/bUmAT1kSbYCr3JNeli7UDRRs5cvcci+ya6N7vKSkWtIIpL+rpwM/hh8feTkuzx2VmWnOlc5FqXPSUH5WViaf2BxsZmp5uITEoV23ahFuzhsoNhZRWxk7DUVFhBbf4vmXBPGjBz9Fz6W7ZYvnTOfv/on//xIeSlWWDWhcssCLjvHk2GFVEWjf1QZOvOeeuAMZ570fs4qaS7031xRf2odoffwyjRsE99yT+WCFp3UIhG6742GNWLor++3bnnTB2bN3HVFTAXXfZfGYVFdaPrGdPa1KNfw/su699IGa0du2sjTI6AB54oDW3rl+P37yZqFlx8M6xPGMg5ZX2/7H3kU8AyMiwucwWLbLdbNsWW1XLzLSW17IyC1zRBg+2j3vauDFy34gR9jnxmvNMdqPm7Qf229/Wf7355S+Tug+aAtoezDnXE5ti4x1gP+B54K/e+z/v4qb1ppLda9Mm+/7FF1YyOuYY68+2Ix99BFOnWtgaOrRuGLvkErvvrbci4e/AAy0YhkfBkZkJ3/721/NlLJk1iwHdukW20aEDodw8tlZms35jKs5FBo9WV0eKuW++aVNiROe+Ll0sD9bU2KFGj4MYNw7OPdfGMixbBuefb58bL7KbNW9IuuWW+q83N9yggCbJKTyh7fNAP6AQmApc771PMDVmo+hNJW3Dp59G+q0dcYSVqcDC2JIlVmHr398GF6xYYd/79rVSWNjkyZPZq0MHTj76aGv2DL4yMykstEGkYAFtwIDYcQeFhTBtmvUvGzTIxjmUllp3uOXL4Z//tEEC3/ymfWBC++gepiKtgwLaTlJAk+agN5VIWDBvVUH0J5ZHN3l6q4TV10Vue2prrRucRmVKK9a8Iem22+q/3lx/fVIHNI3iFBFpKfEfCRC+a2fCGdjjFM5E2iYFNBEREUlOmmZDRERERFqKApqIiIhIK6MmThEREUlOauIUERERkZaiCpqIiIgkJ1XQRERERKSlqIImIiIiyUkVNBERERFpKQpoIiIiIq2MmjhFREQkOamJU0RERERaiipoIiIikpxUQRMRERGRlqIKmoiIiCQnVdBEREREpKUooImIiIi0MmriFBERkeSkJk4RERERaSmqoImIiEhyUgVNRERERFqKKmgiIiKSnFRBExEREZGWooAmIiIi0sqoiVNERESSk5o4RURERKSlqIImIiIiyUkVNBERERFpKaqgiYiISHJSBU1EREREWooCmoiIiEgroyZOERERSU5q4hQRERGRlqIKmoiIiCQnVdBEREREpKWogiYiIiLJSRU0EREREWkpCmgiIiIirYyaOEVERCQ5qYlTRERERFqKKmgiIiKSnNpwBU0BTUSkKYVCsHWrXTjy8xv0kJISWLYMMjKgXz9IT9/++i+9BG++Cd/8Jpx++i4fsYi0QgpoIpJ81q6Fv/0NVq6E4cPh4oshNbX+9V99FaZOteRzySVw1FE7v++qKtvel19Cly5w8sn2HaC6GubOhdJSyMuDnByO7tePD1eurHdz27bB9OlQU2O3P/8czjij/sLA738Pv/hF5PbNN8OvfmU/f/EF/O539vRcdJF9ibRpbbiC5rz3u/sYpO3Rm0qaj/cwdiwsXRq57wc/gO99L/H68+bBZZfZ48D+oD/2GOyzT+P3vXYtvP66lbsCHTvC+eeDc5a2vvwSOnSAnJzIw7Zto2dUKKyqgg0boLISNm2CRYsihwdw7LGwYoUt69QJcnPhgAOgfXvo3t0eG8jKgrIy29a++8Lq1ZFlgwfDrbfCmWc2/lRFmohr1q0/9VT915tzz23efTczBTRpDnpTSfNZtgzOO89+3rjRqmihkFXQLr7YKmuhEMyZA/37w7RpMHmyrd+unQWp/v3h3/9u+D5ra+Guu+Ctt+Dww2P/a8/MtBQFsNdetv3u3etW9AYO/Pq+r76yQBVYu9ZOI5CSAqtWWUDbuhWKi+0QBg600ysqiqybmgq33ALDhsHIkXUPPSUF3n3XlovsBgpoO0lNnCKSXLp0sVC0dSssXx65v7YW/vUva1r83/9gyxZLJ0GY69rVAhpYsFu/3oJUfcrLLUn16QPvv28pJyurbpNKba19z8mxcAbWXhkV0EqrqsgJhSAUotqnUlkZu438/NiAtmGDZcwuXeyrttaqbIsWWR+1efMi6/bsCU8+abmxXTursqWmWlWtqMi2M2WKApq0UW24iVMBTURav5oauPNOePllGDIErrjCOl8l8vTTlkrAvk+bZikmCGfl5RbuzjgDjj7aOm3l5cVu45NPrLNXeTl07my994MwV1sbWx0LevSXllo7ZceO1tTZqROkpVETCpHZoQNUVAD2RzczI5uKqsg2yspsk97bV21tJOuBLdtrLwto++5r16SNG63pMzfX1lmzxg41kJtr2ykthXvusWx5++0Nf8pFZPdqu9GzDXDOLXPOrXfO5UTdN8E595VzriTqyzvnSqNuH+ecm+ycqwrf3uKce8U5942o7RQ4596Mut3eOfeWc+4p51y6c+6a8H6KnHNrnHN3OecU6GX3+OUv4ec/h5kzLahNnmxlo0T/PWdnx96uqIi0/YVCVlkLAtzbb1tqCYcnwMLZH/9oQytzciycRdu6NdJhLCMjNtyVldm2amosQdXUkJaWRmrQBIq193TJi7RvVlZaq21trR2W95YFXVzjjHO23qJFlgl79IiEM4C//73uUxFkUrC8+cUXddcRSWopKfV/JbnkP4O2Lw24Ou6+Fd773OArfN+QqPveCN/3h/DyvYDVwIOJduCc6wi8CiwHxnrvq4HngEO99+2BwcAQ4KomPTORhnrssdjbr7wCe+9tbXc9e1qJKT3dBgNce23susOGwc9+ZkGqpia2Nz5YVW7kSLj/frjxRhts8NlnsG5d4j/yFRVQWGhNpl261O1rVl1t3/PzIS3NthGXtkK1dgxBXgxaSQOVlbGZEeDjj63pc+tW659WWGi7Ch67aVPdQ62qir39/vt11xGR1kkVkdbvDuA659x93vvCndmA977cOfc48ET8MudcF+AV4GPge977UPgxX0avBoSAATuzf2mjQiHrDNWli4Wl5hRdCgILPB98ABdcYF/x8vMteA0YYCM8c3OtJ/3NN8cOgQxUVcGDcf+/pKdbRSy6TBUoLbVklagPW2amfS8stOOuqbG0Fb7fe8/qjRns1dd2262bNU9Gh7TCQti82Qp46ek2iOCTT2J3E4zc7NrVbufl2dMSDCCorbUiYCDIiVGHIiKtmAJa6/cB8DrwU+DGndlAuIn0AmBJ3KJOwCzgDeAKHzek1zn3HeB+IA/YBPxkO/u4HLgcYOLEifTt2xeAI444gq5du/L8888D0KdPH0455RQmTZoEQEZGBgUFBTzzzDNsCF84zz33XJYsWcK8cE/oo48+mry8PGbMmAFA//79GT58OJPDI/Oys7MZP348Tz75JFu2bAFg7NixLFiwgIULFwIwYsQI0tLSmDlzJgADBw5k2LBhPPLIIwC0b9+ecePGMXXqVIrCV7gLL7yQOXPmsHjxYgBGjhxJTU0Ns2bNAmDQoEEMHjyYx8LVnU6dOjFmzBimTJlCWVkZAAUFBcyePZuvvvoKgFGjRlFcXMzbb78NwJAhQxgwYABPPfUUAN26deOss85i8uTJVIXLHxMmTODll19mxYoVAJx++uls3LiR98PlkMMOO4zevXszbdo0AHr16sUZZ5zBpEmTCIVCpKSkMGHCBKZPn86aNWsAGD16NKtWrWLu3Lk79TpllZRwwTPPkPbll3jgk29+k9Sbb26W1+morVvJjJ5SA8B7ii64gKm33MKF48fXfZ2GDmXWtm32On36KYMHD2bF1Vdz0HvvWS/6RKErJSXS9JmWZsGzpsZKVnl5dl+0qGGY3ntIS8Pl50eaRL23Pmw5OZa2srMhLY0vlqXRrX8WKSmezEyrrB1+uLW2pqZa97XVq+3hQcCKr4QFMjNji3y5udC3L6SlraJPny947LHhFBWlhptPPRde6OjUqZqpUzeydOn0Jn2d9Pu0879P0Hb/7l1++eU0qzbQlFkfTbPRijnnlgETgHXAW1gFazQw3nt/fNR6HtjPe78k6r7JwDigAmiPNV+O9t5/El5eAPwFyASO9d6/t53j2A+4GLjXe7+uAYeuN1Vb99vfWpNgtFmzrGLV1K66Cv7yl8TLzjoLjjwSrrlmx2WhE0+0DlzORTr+B/LzLUBt2GChLDc39lMAUlKsVOVc5ILQp481rwZ/Q3NybIBAtE6dvj6uNVvbUezz2P+gdtS4NCppR4gUwFFba/OVVVbaJvPzLdutWBEJZwsWwJL4f7Gw0+jYMdLSOmgQ/PrXFtQWLIBDD420ugbOOw8ef3z7T5dIE2neqS6ee67+682ZZyb1NBttN3q2Id77BcB04Bc7WjfOH733+cA+QDmwf9zyeVhl7kXn3CHb2f8XwELgvkbuX9qqcOUgRvQMqU1pv/3q9pgPPPMMXH+9laA6dLD2wnvvTbzugQfad++tw1ZFBYwfb0ErNzcSwvLybC6LaKGQTcuxfr09rnPnSHthoKwsUoEDC2bt2rF840ZqUlLIbp9B/wMzqXLplJNFiFSCa1dNDRxyCPTqZQ8tLrbTGTgwMgfafvvBMcfYPGjRObCqyqpugYUL4eqr7TErVtQNZ5D45RNJShokIK3Ar4DLsA7/jeK9X4ENNLjbOZcVt+xu4HbgFefc4O1sJg3Yt7H7ljbq29+Ovd2jh1WymsP3vmcBLFr8yMqFC63z1caN8KMfwYcf1t3ODTdYWSlQXW2B6sgjI1Ww1FRLRiNG1K2Gga23ZYvNWdGtW2zfuPbtrW/a+vV2HLW1UF7O6199xYLCQvL7dqA2rR2VZBFfVKittevJ6tU2H9qyZfDppxbcuna1wt68efbhCY8+aq2u0eKbQFevtinchg5N/AlY48fXvU9EWhcFtCQRbr58jJ0cSem9fwVYQ7ifWNyyPwB3A6865/aHr6fz6Bb++UDgemDmzh29tDmnnmqlnJEjYdw4m/KiuXqeZ2fb8MM337SJaNeujVTD6vPOO3Xv69gxkmxSU61J8tFH4YknrKIW/VFQRx1lVbTu3ROfl/exVb2g6XPbNgt+wdwZAwbggUVffUUIqCbxp6Bv3WrNkdEDBcrLLed16WL5NBiP8OWX1gwaLT6vZmbaOm+/HVvUy8mBf/zDxk2ItAltuIKmQQLJ5TfArnz88R3Anc65++MXeO9/65xrB8x0zo0AjgFucc7lAhuxEaC/3IV9S1vz7W/XraQ1p2OOsS+wSb3OOstSTGpq3XkqgvWilZZGhjjm5MT+Aa+stM/m7NgRTjsN9t/fQujkyZZ2opsyU1NjZ4SFus2dYCEu3PbYuWPHhB1xgnEEmzdv/3rinOXF9evt9gknWFCbP9/CWXQhr107uO46a7X9059iZxWpqICzz65/PyLSeiigtWLe+33ibq/EOvXHr1fnb7/3viDBfY9hVTiAyeGv6OU3Ehkpemnjj1ikhZxyirUFvveefbLAgw/C3XdbmLrpJmvbi9e5Mxx3HLzxRt1lAGPG2KeUBw4/HHr3hsWLrQn1tdcsKeXnW9iLrqztv7+lrPLy2G2GBxpsLSrCARlUUUWQpjzFxY6iImtV7dTJKmlB1kxPj91FTU3soQYtyhs2WCFw82Y44gg46SRrga1Pfd35RKR10ShOaQ56U0nrtG2bjQidN886agU96Pfd1ya9jZ9KI9qcOTB9emRS3MMPtzJV1672+IoKm0B361Yrhw0dCgcd9PW0CAXf+Q6+spJaUql1aaS2S8fjKCmxKtjSpfZZ7IWFkRbYwkLbdVqabXbbNquAnXRSw073qafg/PMjzZzf/37dwbcizax5/yWYMaP+682oUUn974gCmjQHvamk9Vu1yiazzc21Zs1Ec6PFW7PGHterl1XXEikrs0QV7hj2dUArKIi0N9ZTxqqpsdzYubMdzsqV1irbv//2q2Lb8/HH8OKL1m3vzDPbRNccSS4KaDtJTZwismfq3Ru++93GPaZXr8hcGPWJ/yzQaDtoX0xLix1o2hQf0DB0aOIWX5E2oQ3/x9F2z0xEREQkSamCJiIiIslJFTQRERERaSkKaCIiIiKtjJo4RUREJDmpiVNEREREWooqaCIiIpKcVEETERERkZaiCpqIiIgkJ1XQRERERKSlKKCJiIiItDJq4hQREZHkpCZOEREREWkpqqCJiIhIclIFTURERERaiipoIiIikpxUQRMRERGRlqKAJiIiItLKqIlTREREkpOaOEVERESkpaiCJiIiIslJFTQRERERaSmqoImIiEhyUgVNRERERFqKApqIiIhIK6MmThEREUlOauIUERERkZaiCpqIiIgkJ1XQRERERKSlqIImIiIiyUkVNBERERFpKQpoIiIiIq2MmjhFREQkOamJU0RERERaiipoIiIikpxUQRMRERGRlqIKmoiIiCQnVdBEREREpKUooImIiIi0MmriFBERkeSkJk4RERERaSmqoImItLRQCMrLITsbnNvdRyOSvNpwBU0BTUTavC1bYOJEeOstOPhguPVW6Nt357cXCkFREXTosIN8VVvLsIMPJhQKwYYNdiDl5bBoEVRW2oM7doRBg6BdO1i6FPLy4JNPID8fhg+3nQDMmQMvvgjV1ZHtjxwJxx+/8yciIs3COXcyMA7o5r0/0zl3ONDee/+/Bm/De99sByh7LL2pZLfatg2eeQZWr4YBA+Cll2DGjMjyQw6B6dN3bttLlsC//gWFhdC9O1x2GfToYfmrsND+oe/RAzIzQqRVleIAiovtoIKfP/ssdqMrVsC6dXV3lp4O119vG77+ekj093riRBg2rO79RUVw553wwQdw4IHwk5/YAbeUW2+FO+6wEPrzn9uX7Imat0S8fHn915u+fXdLedo593/A1cAk4HrvfQfn3CDgH977oxu8HQU0aQZ6U0mL2brVwlj79nDmmbB5Mxx+OKxZE1knNRU6d4a0qDaDL76wFsbG8B5uvhk2bYrc160bXHKJFcai9e1ZQXZquNpVWWmBqbLSUuPq1bEbXb3aEl5NTd2drl9vlbTXXkt8UCecAFddVff+iRPhlVcitw85BB54oEHnuctefRVOPjn2vtdeU7Vvz7QnBrQvgZHe+2XOua3e+47OuVRgg/e+c0O3oyZOEUkqn31mBaUjj4RVq+CIIyzDABx1FIwaFRvOAGproaTEWg3BqmoNDWcffgizZtlj997bQl63blYIKy+3sLZpE+TkxD6uvAKyg/vatYMuXexAo1esrbUHB2W3khI7uWilpZYm69Ojh6XSP/7RmkYPPhh++lN4773Y9d55B+691wLdgQc27OR31ttv173vnXcU0GRPkQesDP8cBMh0oKoxG1FAE5GkceGF8PjjVmg6+GC73gfhDODdd61/WCLB/QMGwD33bH8/Tz0FkyZZAEtNtepcfj4MHBipwnXqZK2S7dpZcSw+oGWkxf1j7xxkZVmfstxcS3glJRbSArm5FsiCfmY1NVYiXLDA+qqFQrGd3tLTYd48q5R9/LHd9+qrduD77Qdz59p927bZdm+9FW6/He67z8qNzeXYYxt2n8iuap2DBGYDvwBuibrvKqCeMnhiCmhtjHNuGdAdqAVKgJeAH3nvS5xzk4FVwAzgxeAhQDZQGrWZA4GjgGuAocD73vvjm/3gRbbjJz+xVrJu3aCqyjJLVYL/RzduTPz47Gz4xz/gtNO2v5/PPrMMEy09Hfr0ib3POQtphx5qt2tqIuEtJQVS01OBuCbLmhp7YPfu1qxZWkod6emRgLZkiQW4tDRrCg3CWUUFLF5s3996y1Jkt26R5e++C//5j/Vb++ILKCuLbD8Usnba5gxoJ55o/c+CPmi/+AUcd1zz7U+kdfk/4Dnn3GVAnnNuEVAENOqXrlVGT9llZ3rvc7FwdQhwffRC7/0b3vvc8DqDwnfnB/d571cAW4A/A3GXKpGWt3w5PPZYJH9kZFixqX37uusOHGjrtWtn67VrZ0Wrbt12nBFWr4Z//tOKVRkZkfurqmIzTmDQIKuceW8tlevX2/cePaA2JZ0aUiMrl5VFOqqlpNTfxlpZad9raqw0F5xwtFWrLJwFamsjadV7u3311fDrX8N119UtK65caSNJm9NPf2pPyLp1cM01zbsv2XOlpNT/tZt479cCw4CxwHeAS4AjvfcJRgLVTwGtDQu/GWZgQa2xj33Ve/84sGaHK4s0s+XL6w5gTE2Fm26Crl0j92VkWPXrqqsspKWmWr+xUaNgyhSbwaI+RUVW6FmyxEJX165WzAq2u2mT9eP3PjLNxvz5kceHQpbBgpGc4KioTcevXWud4rZsiaxYURGpnsXP0xHsNDU18nMQ2gK5uXVPoHfv2CbQtWvhBz+wJyM+XZaUwJ/+lLgEKSK7zJv3vPdPeO/f9d7X0/mifmribMOcc72BU4EGz7uyC/u6HLgcYOLEifQNTzJ1xBFH0LVrV55//nkA+vTpwymnnMKkSZMAyMjIoKCggGeeeYYNGzYAcO6557JkyRLmzZsHwNFHH01eXh4zwvMk9O/fn+HDhzN58mQAsrOzGT9+PE8++SRbwhfBsWPHsmDBAhYuXAjAiBEjSEtLY+bMmQAMHDiQYcOG8cgjjwDQvn17xo0bx9SpUykKVy0uvPBC5syZw+LFiwEYOXIkNTU1zJo1C4BBgwYxePBgHnvsMQA6derEmDFjmDJlCmXhC2JBQQGzZ8/mq6++AmDUqFEUFxfzdrgT9ZAhQxgwYABPPfUUAN26deOss85i8uTJVIUvnhMmTODll19mxYoVAJx++uls3LiR999/H4DDDjuM3r17M23aNAB69erFGWecwaRJkwiFQqSkpDBhwgSmT5/OmnDv+dGjR7Nq1SrmhvsotfbXaf78qeTkXEBpaaTqNHz4x6xe/T6zZ4/nN7/ZxNq1xRxzzOfk5Q3liitq2H//2dTUOA4++ECGDh3EY489xty59jqdfvoYLr74cz77rBddu27l/vt78vLLX1BSMujr7TtnwSwrK0QolPJ1dezggy0L5eVZ97CSkkheSkuLdC/Ly4PUyjJc0MespsaaLktLLeUNGgS1tVQvX0765s1f75f8fAq3biW/spLQwIH4L78ktbISn5uLc45QRgaF+fl0WrXq68rYlq5deebUU7n0oYdih8wVFVmqBAt56en2vaoKnn2WRatXs/LHP26dv0/5+Yw55BAeffNNSsJNvvp9aoWv0w7+7l1++eU0q1bYB805t5J6ZjPw3vdJdH/C7WiajbYl3AetC/bmyMXC2bne+8KgD5r3/sao9fcBlgLp3vs6Y/ydcxOA8Y3sg6Y3lTS5RYtsWq+NG+Hcc23AQH1CIWvhe+ABa+KcONGqY4Ff/coGAQSysqCgINKnPrBqlTWNBhkHLLgdfjjstZfdPuAAC2PR14mUFBg6FDJ8BTnrl8ZuNOiwHygvtwfMn28B6sADbULaYMqNzEw7kOhqV1kZLFtm6TBw0kk20W30fB9bt9pXUL0Lmn7y8uyk09KsL1u7dnaSN91kx/Gtb9kTFlTwWtr8+TB6tJ1Ply7w8MN2TJKMmneqi7Vr67/e9Oy5u6bZGBF/JNi8aFO993c3dDuqoLVNZ3nvXw2/SR7FAlvh7j0kkV2z//7w9783bN2pU+Gvf7Wfq6qsr/yJJ9qUHACvvx67fnk5PPGEdfoPpuIoK7PZK3r2jF3Xe+talZdn66anW5ev6IAWCgXTemRSlJpJXo2FJlddbQuCZshQyMLa2LE2b1hKCjz3XOx8aKWldZsiMzLqTrz26qvwy1/CH/5gZbySEmtaDZpHU1Mt7KRG9YvLyYmMbDjnHBtwADZNRmlp3dESLeXqqy2cgZUtL7vM2rlbYbVEJJ73flb8fc6517FBew0OaHq3t2HhN8lk4I+7+VBEWlR8JSz6vsJC65/fpYsNMnDOrvvp6RbKFi60kZwLFlj/taBSFi0723JPXp79nKjQlJ0N1NaQ56twzuEibaaRlVJSrL0UbCOpqVYxi5aaWrefWk1NbNACO5hLL4XbbrMhpzk5sX3XamvrduT74Q9tOxs2WDiLDkDhZsLd4vPPY2+vWhVbLRQJtMJBAvWoBPo15gGt7gykyf0ZONk5N7QxD3LOpTrnMrEqa4pzLtM5t5vaO0Qa58QTY2+npETmSP3Vr6woE4wE7dDBKmHORUaAVldbeBs61PJTly722GCGjPbtbaTnfvtF9uG9faWmwj77QEaGJ5syXM+eVoYLgld8ANuyJXYutOLiugcfH6wyM6Ffv0hwc87acdu1s2rc9OkweHDdJya6MjdsGPzoR/ZzYaENPe3Rw04wIwP23bfu41tK/BQgI0YkHrIr0go5534T9/VH4C0i01s1iJo42zjv/Ubn3EPAL4HiHa0f5SLgX1G3y4F/AwVNd3QizeP0022Q4l/+YgWrm26yvmJVVTahfbRgtotjj7XqWXQO2LAhEshycux7ZqYNwhw2LPajo7y3/DNsmGWqdlSSmhoOUKmp1n66dm1sSCovt1LdXntFJlRbsCD2AEMhS3zLlsXe36EDfOMb1t569dVw8cWxyy+6yNp6A3362Ay9r71ms/Veemlk2W9/G6k4BE2hu6t5E+Cuuyw9v/qqfUTV73+/+45FWrfWVykD2DvudilwJ/BwYzaiQQLSHPSmklbJezjrrNiPgho8GP78Z8s7F15ofeYDOTlWqIqe3B8sw3z/+7HXhpoay0sdOlhBKyeljDSiKmNg84+tWxepiC1daqMeDjkk0gn+r3+N/TD1nBz7BICFC22C2Wg5OfZBoPGfexmYPh0eeshS5k9/CuHR1XUMG1b387Hmz7dQKbJrmrej/saN9V9vunbdLYMEmooqaCKyxwhaAm+4wQZTdusGP/uZhSqwQtTVV0eKXEErYfzHYwYjN6MHY3btan3YKivt59qU1NiA5r01ZwbhLBiFAJYCA2efbZW2wsJIk2VaGgwZYm2q0Z/LeemlMHJk/Sd8xhn2tSMnnADhqRcA25fCmSSDVlJBc86duOO1wHvf4GmvVEGT5qA3lbRqlZVWMAo+/Dza1q3w0UfWCtinj637xhvW8lhcbMWu006zbmMrV1pzZ/fu1iJXXW1Nps6Bw0NlJdVlZaQ6R0plpQWuigprO/3oI9v4IYdEhpcGamstpHXpEttnrbgYnn3WqnBHHQXHHNM0T0hpqTVzzp5t87P96leRwQsiu6Z5q1ibN9d/vencucUqaM65pTteC++979/gbSqgSTPQm0okLJhYtKCgYLceh8huskcEtOagJk4RERFJTq2kibM5KKCJiIiINBHnXHvgZmAENlH815W8xnzUU9uNniIiItK2tc6Jau8DDgV+A3QC/g9YAdzVmI2ogiYiIiLSdE4BDvDeb3bO1XrvpznnPgCeoxEhTQFNREREklPr7IOWAmwL/1zinMsH1gIDGrMRBTQRERGRpjMP6382E3gDuBcoARZv70HxWmX0FBEREUlSlwHLwj9fhX1UYj5wcT3rJ6QKmoiIiCSn1tnEudx7Xwv2edjAhJ3ZSKs8MxEREZEktc45d59z7thd2YgCmoiIiCSn1jnNxilYn7NHnXPLnHO3OecOauxGFNBEREREmoj3/iPv/XXhSWkvAToCM51znzRmO+qDJiIiIsmpdfZBi7YI+AxYCezXmAe2+jMTERERSRbOuXzn3PecczOBL4Hjgd8D3RqzHVXQRERERJrOGuBt4FHgHO/9th2sn5ACmoiIiCSn1tnEua/3fu2ubqRVnpmIiIhIMmqKcAaqoImIiEiyap0VtCbRds9MREREJEmpgiYiIiLJSRU0EREREdkRZy5zzv0vmJzWOTfcOXd+Y7ajgCYiIiLSdH4DfA94AOgTvm8V8PPGbEQBTURERJKSx9X7tRsVAGd476cCPnzfUqB/YzaigCYiIiLSdFKxD0uHSEDLjbqvQRTQREREJCmFQvV/7UYvAnc659qB9UkDfgs815iNKKCJiIiINJ0fAz2BbUAHrHLWl0b2QdM0GyIiIpKUtlcpS01tueMIOOdSgTHABUB7LJit9N6va+y2VEETERERaQLe+1rgTu99hfd+g/d+zs6EM1BAExEREWlKzznnztzVjaiJU0RERJLSbh4MUJ9M4Enn3DvASiIjOfHeX9zQjSigiYiIiDSdBeGvXaKAJiIiIkmpNVbQvPe/bortKKCJiIiINBHn3In1LfPe/6+h21FAExERkaTUGitowINxt7sCGdjncTb4454U0ERERESaiPe+X/Tt8NxoNwLFjdmOptkQERERaSbhudFuAa5rzONUQRMREZGk1EqbOBM5GWjU0SqgiYiIiDQR51zM3GdANjY32pWN2Y4CmoiIiCSlVlpBGx93uxRY7L0vasxGFNBEREREms4w7/0f4+90zl3rvb+zoRvRIAERERFJSqFQ/V+70U313H9jYzaiCpqIiIjILoqaoDbVOXcC4KIW96eR02wooImIiIjsumCC2kzgn1H3e2Ad8H+N2ZgCmojsWSor4fbbYeVKGDoUCgogN7dhj/34Y5g5E3JyYPRo6NkzdrubN1vbSn4+5ObiPXTs2Jf09EyqqyE9PfHhVFVBXt6un5rInqY1DRIIJqh1zj3kvb94V7fnvPc7XkukcfSmkt1j8WK44gr46CM46ST429+gc+fI8i++gKOOgi1b7Hbv3nD22XDPPTve9v/+B//8J6SlQVERdOgAt94KK1bYfbm5MVcL36cvKzZlU1YWvu0tv+XkwAEHQFYWvPMOvPkm1NTAPvvAOedAu3axu62pgdmzobAQhg+303GO3aK0FKqrLX+KNFCzvls3b67/etO5c/Puu7kpoElz0JtKdo+hQ2HevMjt73wHHnkkcvvCC+HRRyO3nYODD4ZZsyxw1WfrVrjtNujY0W5XV9tXhw4WzLKyoEsX6NsXUlOpzspjW85ebNwUuT54DyUlUFxsD+vfH/7+98guQiE45hg4/nhiHnP55TBnjt1OT7dK2xVXwHnnNfrZ2SW//CX88Y922uPHw6RJlkuTTXk5PPGEBd7zzostgkqzaNaQtHFj/debrl13T0BzzrUHbgZGAF2Ieg68930avB0FNGkGelNJyyssjASoQM+esGZN5Pbw4fDGG7HrDBtmZaziYnjmGejUCZYuhQcftAQybpyVur75zfr33bWrpaeePfF7701Jz/0or0pj82ZbXF4O27bFNseUlVlBr7LSDvGrr2y9jh0hJQW6d4eBA+G++6yKFn9q//kPDBjQ2Cdp57z9toXHaP/6l7UOJ5OaGjjySPjwQ7vdsaOF33333b3H1cbtiQFtCtAbuAuYgs2L9jPgKe/9XQ3dThL+/yMikkCHDrDffpZ6AsOGxa4zblxsQMvKghtugFWr7Mq9aZPdn5ERaUv861+t3LU91dW2fmUlNelZkJJKZqZltqoqy47R/wtXVVkra16etZYuXw4bNljOC6YIWL0aliyxqlv79nV3uXBhJKBt3gyvvmp59Ljjmr4J9JNP6t43f37d+1asgGXLrBU5I6Npj6EpvPxyJJyBFUbvvx/uuGP3HZPsmtbUBy3KKcAB3vvNzrla7/0059wHwHNYaGsQzYMmIm2Dc9ac+Y1v2O1jjoG//CV2nR/+EB54wPqnnXcevP++dfa/995IOIO66WLFiu2nnpoaS1HZ2bitW8A5nLO7QqHYcBYoKrKQtWkT7L039OtXd7eZmfbYysrY+1NSYMgQ+/mzzywQXXstXHCBVbVOPhlSUy2fLlhQ/2E31Ekn1W3OHDUq9vZtt9k5jBhheXbRol3fb1NLdDFvpRd4SW4pwLbwzyXOuXxgLdComrcCWpJxzi1zzpU754qdc4XOubedcz9wzqWEl092zlWFlxc75xY4525zznWI2kaBc67WOVfinCtyzs1zzp0RtXygc26ac26jc26Lc26Gc27/3XG+Io0ybJgllvJya7bsk6C7x2WXwSuvwOOPw+DBdl9FRWR5SoqVtqIDWU2NbXflSuspX1Zmycl7qK21tBWWVrSVlLISwCpnKSkWlqJVVFjgad8e9trLKl99+tQd5VlVZSGtosJ2UVNjXd5+9SsbVADwk5/w9UAEsEra669b8PjgAwttu2rAAHj6aXt6Bw+2vnOnnBJZvmGDHVMQdlavht/8Ztf329ROOQUOOihyu3176+Mn0sTmYf3PAN4A7gX+BixuzEYU0JLTmd77PKAvcDvwcyLzrwD8Iby8K3ApcBTwlnMuJ2qdd7z3uUA+cB8wNZzyCd/3LLA/0B14H5jWXCcj0uQyMxu3/qhRNrTy4IMtMdXUxJa9nLOSUFGRtYtt2WJf3bpZ+WuvvWJCmneOigrLbs5Zt7Z27SyoFRdHwlZWlq2fmmr9oeKrVJWVcOihFixycmyXK1fGVsUWJ/iTH50tFyywZtJddeaZVnCcP79uqNmwwVp5o61atev7bGoZGfDWW9asefvtdi7761/PpNZKP0ngMmBZ+OergHLsutqoqTcU0JKY936b9/5ZYCxwiXNucNzyCu/9HODbQGcsrMVvIwQ8DOQA+4Xve997/6D3fov3vhprM9/fOdc5/vEiSa+mxkpCOTmWnlavjiSrgPe2XmWltUlu2xYZGAC2blGRrderFznlm3ClkcCWnm5d1NLS7KHp6XVDU0pK3YB24onWGf+CC2D9ejuE8nK4+26begPqDh6A2LA0ZEjDp3nbWYMGRZpcA9/5TvPuc2fl5cH3vw8//3niAqvIrvLef+W9/zL880bv/QTv/Vjv/aeN2Y4GCbQB3vv3nXOrgOPqWV7snHslvPyv0cucc6lYcKsGltezi+HAOu/95vqOwTl3OXA5wMSJE+nbty8ARxxxBF27duX5558HoE+fPpxyyilMmjQJgIyMDAoKCnjmmWfYsGEDAOeeey5LlixhXni6hKOPPpq8vDxmzJgBQP/+/Rk+fDiTJ08GIDs7m/Hjx/Pkk0+yJTy/1dixY1mwYAELFy4EYMSIEaSlpTFz5kwABg4cyLBhw3gkPAVD+/btGTduHFOnTqUoXAm58MILmTNnDovDJYqRI0dSU1PDrFmzABg0aBCDBw/mscceA6BTp06MGTOGKVOmUBZucyooKGD27Nl89dVXAIwaNYri4mLefvttAIYMGcKAAQN46qmnAOjWrRtnnXUWkydPpqqqCoAJEybw8ssvs2LFCgBOP/10Nm7cyPvvvw/AYYcdRu/evZk2zYqcvXr14owzzmDSpEmEQiFSUlKYMGEC06dPZ014ROPo0aNZtWoVc+fO3eNfp45btnDehg32L/eKFfY9vk3STsASEljb49KlVgKrrIy0V4bnbHBlZXSmjNo0WF/enlDIHlJRYblu/vxaIHYf1dXWehrtG9/YxqJFS/jb3zKAg2KW3X33PMrK1vKLX5zCVVdB8P/2ZZfBc8+t45NPutKnz0buuy+T995b1Oyv09NPH8Fll33Jxo3tOeaYdXz/+0fy6KNTWbQolU6dSpgw4Xz9Pu0Bv0/xr9PlzdyG3Br7EDrnHDABuADo4r0/2Dk3HOjhvX+8wdvRNBvJxTm3DJjgvX817v53sREi+wGrvPc3xi2/HTjMe3+yc64AmASUYJWzGuCSRG8c51xv4D3gp977/zTwMPWmktavrAweftgqZrNmWYexYLhiXl7dqf0POsjaKVNSIreDNkqAHj2st34Un5XNu2v6UFYeO8Bg8WLbHVjRbcUKaz4M/1/ztdRUuPFGm/Hj6afrnsI559gcu198YVNhHHQQHHZY456G5vLFF3DqqfDll5Zt77sPLrlkdx+V7AbNOtXF6tX1X2/22mu3TbPxW+Bk4M/A/d77fOdcf+AJ732Df0NVQWs79gK2NGL5u977Y51zuVj/teOAmIDmnOsKvAzc14hwJtL6eW9DE995x2536WL9z3JyrIxVXGz/mrdvH+lEFkxqlpdnU3rED7ksL6+zG5fiyMqspaw88qe2psYyXn6+ZcSqKpv+oUsX22xZmbWiem9Nk//5j3VxS+Tpp20w6vDhNmdaazJxooUzsHP6v/+DMWPsKQ7U1NisJ/n5cMghu+UwJcm1xgoaUAAc4r3f5Jz7W/i+pdgHpjeY+qC1Ac65YVgAe7Oe5bnASdhokhje+xLgh8BFzrlDoh7TEQtnz3rvb2mO4xbZbd59NxLOwBJRr17Wa/yKK2wysaFDrckyNzd20EFxsaWqkhJrrzzwQEtHoVBsG2V4NGhW+rqvLyK1tbBunW0uO9v6paWnw8UX2wjJnBzbZG6ubX7DBhuT8J3v1P/xSq2xMz7YHG7RioutH11g0ybLxCeeaAMhxo1r2eMTaUapWAsVRFqUcqPuaxAFtCTmnGsfnh5jKjDFez8/bnk759xhwDPAVuBfibYT7ls2Cbgp2C4wA3jLe/+L5jsDkd0k0WcUpafbRF733Wc98F99Fa680oJbvKAXfr9+lqby82044LZtljyqq63JMy2NnEwLWevW2Vc05yyotWsX+xmc3brZ91AoMgXHzJnwve/Fjl3IybFCYGt09tmxt4cMiZ3v9777bOaSwGOP1f2QB5Ek9QJwp3OuHXzdJ+23WDekBlMTZ3J6zjlXA4SAT4E7gfujll/nnLsaC+DLgOnAGO99afyGovwZ+NI5dzBwCDAMGBTurxY40Hu/oqlOQmS3GTbM5q54+WW7nZsLV18du05mJnz3uxa8rrkmcr9ztqx379j1g75pVVWWusK3N2xeQ0pKN2pqEk+tX1sbGSQafV/gxBPte48eNrfYiSfCv/9t3d9+8INImGttbrjBcvBzz9ncwb/9bezy+LBa330i29NKmzivBR7CJqtNxypnL9PIaTY0SECag95U0vpVV8Ozz8LatXDWWXUDV7TnnrMOX95HptM47LDEn8/ZubNV1sIz1D785JOEfB7t2p1d5xMBqquta9tee0VaXL23DvaFhXD44fZhCMn4oeQ78tZb1ncuuMB262bNovFjMyTpNWtH/eXL67/e9O3bsoMEnHM9vPfrom53w+YrXRl9f4O3p4AmzUBvKmm7Kipsio2uXe2DHVNTLVVlZlow69kz0g7p3NfTIlx0UQFr1lhT5rZt9gHpWVlWXWrf3qpHK1bYJr780tYbOrTpP1ezNXntNZg0yVqIr71WH1reRjXrO3jp0vqvN/36tXhAK/Let4+6/bT3/pyd3p4CmjQDvalkz1FRYSEt/nOawoKAVlBQ0HLHJNJ67EkBrTj8KT7B7S3e+047u702WDgXEWlBjf1YKRFpMq2sD1qTFicU0ERERER2XZpz7gQiVcP423jv/9fgjTXxwYmIiIjsiTYA/4y6vTnutqcRk9UqoImIiEhSak1NnN77fZpye5qoVkRERKSVUQVNREREklJrqqA1NVXQRERERFoZVdBEREQkKamCJiIiIiItRgFNREREpJVRE6eIiIgkJTVxioiIiEiLUQVNREREkpIqaCIiIiLSYlRBExERkaSkCpqIiIiItBgFNBEREZFWRk2cIiIikpTUxCkiIiIiLUYVNBEREUlKqqCJiIiISItRBU1ERESSkipoIiIiItJiFNBEREREWhk1cYqIiEhSUhOniIiIiLQYVdBEREQkKamCJiIiIiItRhU0ERERSUqqoImIiIhIi1FAExEREWll1MQpIiIiSUlNnCIiIiLSYlRBExERkaSkCpqIiIiItBhV0ERERCQpqYImIiIiIi1GAU1ERESklVETp4iIiCQlNXGKiIiISItRBU1ERESSkipoIiIiItJiFNBERHZBWRksWQIlJTu5gYoKqKnZ+QNYuhRefhm2bdv5bYgkqVCo/q9kpyZOEdljffwxOAdDhjT+sTU18Pzz8Nln4D2kpMCZZ8KBB8audPigQdSUlUFVFaxZAx9+CKWlkJNjG/nyS8jIgOHDYdiw2J1UV8ODD8L778N++8Fll0GXLpHl998Pv/udHUBuLkyZUncbIpKUnPd+dx+DtD16U0mr4j387W/w3/9Cjx7w4x/DL34Br7xiy7/1LZg2zXJSYOFC+Pe/7eeCgtjg9fHHMGcObNpkt9u1g7Q0+z52LOTlQaiiivSybbB5sx2A97BsGWzcGNlQKATr10eWV1XBwIHw4osW3rKz4ZNPYk9mr71gwgQ4/nhLlhUVkWXHHguPPWY/f/EFvPsuHHWUhTuR3cM158ZnzKj/ejNqVPPuu7kpoElz0JtKWpV//xsmTozcTkmBVati13n0UbjgAvv5oYfgzjstI5WUQHo6XHklzJwJn34ayVH5+Vawiv4z2qsXnHAC7JVfQuqGdVYFC9TUWAUt2qZNtkGAF16ADRsiaXDtWqitTXxSw4ZZqox2wAHw6qt2ApdeagHQOfjnPy1lir2g995rpc8zz4Rzz93dR9TWNWtIevHF+q83p56a3AFNTZwi0ua9+mrs7UTdtYLANns2/OUvFsrS060qVloKjzxiyw84AA47DDIz7Xb8/7iFhbBuHfTMg9T4jjCpqZYOg/u9jwS46mprAq2osMDmnK1bX0CbMweOOMKaPwNjx9r3G26I3ccNNyigBcaMgRkz7Od//9tKqz/4we49JpEEFNBEpM0bOBD+97/I7dxcy0CVlXY7MxPOOcd+fv312MempFh3sWC9Xr0i4SyRlBTb/pqt7eibnW3Bq6bGvsrL7cFlZbby1q1QXGxf775r4Sw11W6Xl9s63ltYS+SSS6ypc/FiGDkychJFRbHrxd/eU61aFQlngUmTFNCSWFsYDFAfjeJMYs65Zc65k6Juj3PObXXOjXDOeedcSfhrmXPuFwkef3x4vesSLPuec+5z51yxc269c+5551xec5+TSHO4+GLo2tVyT1oa3HKLBbaxY2HcOHjtNdh3X1t3773rPj4t/K+sc/VnpUDv3rZOZXUqtXkdrFN/jx7Wwa2wEDp1sn5kXbpYCFu8GJ59FlavtgcOHBgJZ8FOE+nYEU46Ca6+2prsgnAGdQOHAojJzY3taAjQufPuORaRHVAFrY1wzl0C3AmcDqwJ353vva9xzh0OzHLOzfXevxL1sEuALeHvf4ja1gjgVuBb3vuPnHOdgDNb4jxEmlp1tXU1mj8/ct83vwnf+AYcfXTd9c8/34pZH34YqZ5t3WohLS3NQl5FRaSKFgrZegA9e8L++9vPmem1pEb/C9ylC6xcaU2WztkGgpGcQ4ZYO2pWFrRvH2mD9T4yf0durj1u5Ejb1rhxdl8it98OgwbBW2/ZSV500U4/f0mhutpe4H33hQ4d6l8vP9+ae3/1K7udmxv5WZJSW66gaZBAEnPOLQMmAP2JBKoPnHP7AEuBdO99TXjd94EnvPd3hG9nA+uBy4CHgKO99x+El/0UONZ7f9ZOHpreVNJqPP88nHFG7H3BKM5//ANWrIARIyzvpEQFqg8+sLC2dKnlotNPhz59bJn3FtaCYoxztm6Ql7yHbnnldMmPm9/s448jAwLAhorOmmVBLS3Nkl9JSWRwwOLFFtzANj5ihFXbUtT48bV58+C006z/Xna2TUsybtz2H7NwIXz+uY3m6NSpZY5zz9WsHfWfe67+682ZZ2qQgOxeVwDHAiO99/MSreCcOwoYDNwWdfe5QAnwBHAhcDHwQXjZe8BvnXO/Bl4GPvDeV27vIJxzlwOXA0ycOJG+ffsCcMQRR9C1a1eef/55APr06cMpp5zCpEmTAMjIyKCgoIBnnnmGDRs22IGdey5Llixh3jw7naOPPpq8vDxmhPuO9O/fn+HDhzN58mQAsrOzGT9+PE8++SRbtmwBYOzYsSxYsICFCxcCMGLECNLS0pg5cyYAAwcOZNiwYTwS7vndvn17xo0bx9SpUykK99e58MILmTNnDosXLwZg5MiR1NTUMGvWLAAGDRrE4MGDeSw8rUGnTp0YM2YMU6ZMoSzcx6igoIDZs2fz1VdfATBq1CiKi4t5++23ARgyZAgDBgzgqaeeAqBbt26cddZZTJ48marwhXzChAm8/PLLrFixAoDTTz+djRs38n64c/hhhx1G7969mRYe0derVy/OOOMMJk2aRCgUIiUlhQkTJjB9+nTWrLHi6ujRo1m1ahVz585t869TUdExwCCipabCJZdsYdMmuzh/+CG8+eYyMjIWst9+6zjjjJP44x9zWbrUmr/y8yPhDCyQ1dZasSZo/pw7Fw491LJTWRlQnRoJaNXVVhWLnpC2tNSm3OjUKZL0srIioxW2bImEM4CSEipWrKB4xgz+u3Jlm3uddvb3qfMVV5Abfl9TVkboyiv555YthNLS6v99WrmSFZs3w5NP6vepmV+nyy+/HNk5qqAlsXAFrRPwGnC29z4Uvn8frIK2DWgHZAJ/An7mwy+4c+5VYIH3/hrn3AXAPUAv7311ePmpwA+B47Ag/0D48fUMKYuhN5W0GjU1NhVY+NpJx442O8W110bWWbXKRl6CNVO+/rr1W3vvPbuve/e6szG0a2f92qIddJB1N6uttQEIRw6tIL10KymFhbaC91aSW7PG5kcrL7e+ZwsWWP+0Ll0spBUWWhVt9erYHbRvD926wTvvxE5YuycbMMAm+422caOen9ajWatY06bVf70ZPTq5K2iqkye/HwADgUnO1elN3AXIBX4KHA+kAzjn9gZOAMITBzANC3GnBw/03r/ovT8TC4CjgQKsOVUkqaSlwRtv2IwKd99trVuHHGIBC6zFMQhnYLno0ktjr+/r10cmpQ0kuv5nZVlrZfv20LWrJ8NXkRI9gtI5S4CFhRbW0tOtLXXlSuvctnSpBbfUVEuS0U2ZztkOysqs3XZnzZ0LZ59toz+DuUOS2ZgxsbdHjFA4kzZBTZzJbwMwEpgF3Ic1eX4tXPH6k3PubKwi9mfgIiycPxeV6TKxZs5n4h4fAmY65/6HNZOKtBoVFXDXXTYl2PHH22Syqal118vKsopYtGuvhT/9KbYVMRBugSYlxb7S0uDNN21u2AEDoG9f6+MfHdratbNKW4SjuCqDvFAo9t/41NTI5GklJbGfLAAW0A45xEpw+fmWKCsrLfkFJxdfMWqorVttkEEwCGHWLAuCp522c9trDX77W/vohpdegoMPhptv3t1HJC2oLQ8SUEBrA7z3a5xzJwKznXN3AXcnWO124AHn3P1YEPs1cH/U8iOAJ5xznbE+bVnADKAQGAaMAK5prnMQ2RmXXWYfPwn2MU4rVsAf/9iwx55zDpx4olXMzj/fWhnjhULQv78Fwe7drcWxtNQyVI8elpuysixvHXBA3XBYk5pJbU570kqjqmibNkUmnw0+kzM6JWZnR0Z5ZmbasNBw/8OvxbetNtTMmXVn6X3qqeQOaOnpNjLzhht295GINCk1cbYR3vuVwInAGGIHAwSeB7YCPwP2Ae713q+L+noWWAJcEF7vMuALoAiYAtzhvW8D7SHSVlRXw9Spsfc99FDjtpGfH/l0pJ/+1D4hIN5ll8Hf/x6ZjqyoyJo816yxgFZYaJln/frYSf+zsy07lOf3xOfn2w2IHSjgfWx7a3Z2ZJ6OQOfOsWWC2lor4+2MYLK3aDu7LZFWIBSq/yvZaZCANAe9qaTZeW/dudavj9x30EF1P1u8MQoLYehQWL7cbnfrZjNjbN1qk/ZH++EPra9Z8AkFnTtbM+uAAZazgsxFKETuhq9wNeGPdCoqijxo2zbrexYK2YCB7Gw7sS1bItNxrFplw0yzsiwlVlXZAfbosXMn+dOfWrtwKATHHQfTp9uJiDSPZu2o/9RT9V9vzj03uQcJqIlTRJKSc/aB5gUFVk3LyoI77ti1bebnWx/6hx+2QteFF1oI7NnT9jNlit1/7LH2weq1tdb/rbjYmj2//NK2EV2oStu2IRLOUlIsAa5fb33LOnSwTw7Iy4t81MGyZXZyn31mZbrOnW35tm02Hcdtt+18OANrA/7JTywoxlfrRKTVUAVNmoPeVNJi1q61uUqPOKL55xzdts2aNbt1i9y3ebONEq2psUn7e/Wy4lRtreWxZ5+Yyv7dunHgoEHWIT+Y86ykxFbIzk68s/Jy++T20lI45hgbTNCjh0YoSrJp1irWE0/Uf7057zxV0EREdpugwtUSEn2KUOfOcNZZsfcFoz8BtlVU8P6KFRx44omxK9X3MU2BrCwYNSpyO3aIqIi0cQpoIiIikpTawmCA+mgUp4iIiEgrowqaiIiIJCVV0ERERESkxSigiYiIiLQyauIUERGRpKQmThERERFpMaqgiYiISFJSBU1EREREWowqaCIiIpKUVEETERERkRajgCYiIiLSyqiJU0RERJKSmjhFREREpMWogiYiIiJJSRU0EREREWkxqqCJiIhIUlIFTURERERajAKaiIiISCujJk4RERFJSmriFBEREZEWowqaiIiIJCVV0ERERESkxaiCJiIiIklJFTQRERERaTEKaCIiIiKtjJo4RUREJCmpiVNEREREWowqaCIiIpKUVEETERERkRajCpqIiIgkJVXQRERERKTFKKCJiIiItDJq4hQREZGkpCZOEREREWkxqqCJiIhIUlIFTURERERajCpoIiIikpRUQRMRERGRFqOAJiIiItLKqIlTREREkpKaOEVERESkxaiCJrInqq2FefOgb1/o3Hl3H01yKS+H9HRIi/rzuW4dfPml3b9iBWzcCL16Qe/e5KenU1hdXWczZWWwciVUVkKnTrDXXuDcjncfCsGiRfDrX8PmzTBiBNx4YxOen0gSacsVNAU0kT3N4sUwahQsWwbt2sHdd8P3v7+7j6p1qKiAN96A3r3hgAPsvk8/hZkzIScHnn0WioosnH3ve3DOObB0KTz9tIXedesiV4xPP4UTTmBUj55s7Lc/JcUenCMjw7Lx1q22alERZGbCW2/BggWWl885B/r0sc1s3gwzZkB+PvzjHzB9Ohx8MKSE2z+mTbOwd+utLf5siUgzct773X0M0vboTdWajR0Ljz8euZ2dDWvXQvv2u++Ydof334df/tJC1cUXwxFH/H97dx4nZXXlf/xzqhd6ZWn2ZkdAVMS4IAbXBIPrBPKSBFRiWmWYSX7q6GSbMJpkJJlEYzJJnMwYhyQE0aDRUQwmipIEosaATMSIKyA7sjRb0yy93d8f5ymreoNm6e6q4vt+vepVVU89y32qurtOn3PvfeDGGz0DVl0N//zPMHw43H1343/T8/M9Yho0yAO5/HyorPSoK9nQoewbP5nabr3qLV65Etau9UxYXR2E4Mvim2dnw4wZ3owLL/SPpksX2L7dDzN0aP3DZGfDn/98fN8ekeOkBXnhozdjRvPfN3fd1brHbm3KoImcaFavrv983z7YsuXECtD27PEs4q5d/vzeez1l1aULdOrk0dJ//Af06tV03XH/fr/l50NNDYwYAVlZjVYLObnUdu3ZaHnHjrBpUyLuaxjb1dTA734Hy5ZBv34wZIgvHzwY3nuvcXPy8o7w/EUk5WmQgMiJZuLE+s/POKNxSibTvfxyIjgz8/5icbGYdwgLwW+Hsnu332/a5NskB7l5edCzR5ObZWd7RTSuqRjwnXc8yzZoUP3l/frBjh2JptXUwG23wfTpnsy7+mqvropIelMGLYWZ2RogHxgcQqiMlk0FpgPJf/kLgX0kSotXADcD1wFV0W0ZcGsI4e1oP2XA1BDCBdHzjsDvgA+AycAFwNeBs4CdIYSBrXSa0ta+/GXIzYWnnoKTT4ZvfKO9W9T2hg/3QKyuzqOjhtmv7Gy/Ha7Xfk6O3+/Y4emu0aM9I1lQAP36UdeppNE+qqrg7bd9tbjCQv9IqqoSy15/3UucTTlwwKvSI0d6wu+WWzxQ273b971ihScBm0jqiWSUTB4koAxa6ssG/qnBsnUhhKL4LVp2RtKyP0XL7o1e7wNsBH7W1AHMrAvwArAWmBRCqAYqgZ8DXz7O5yPtLRaDO+6ARYvgwQc983OiGTgQ7rvPA6m6Oo+Oku3e7euYeTS0fz8UF9dfLzsbeiaVL8vLYe/eRJm0upq6vHxyOUgOVYS6Onbvhq1bPYiKf7GE4IMDXnnFs2qXX+4JuRB8V8mZthBg48bE84MHfUxDvDlduniT16xRFk0k3SlAS33fA75kZp2PdgchhP3AY8BHGr5mZt2A3wMrgCkhhJpomyUhhIeA1Q23OWFVV8MvfuEZqD/8ob1bI8fqjju8793GjfDHP3pJct8+7wxWU+OPb73V+6r17u0BWrduPswyPx9OP91HwcaNHu2BWU2NB2q7dpHdqZAOm9eSt20D+VW7qNwb2L69fvbMzOO+/HxvzqxZ/rh790RmDXyw6N/+5nFg3Nq19U8pFvOsWVGRz6Aikunq6pq/pTsFaKnvVeCPwJeOdgdmVghcC6xs8FIJsAj4C3BTCCEDfqRb0ec+Bzfd5JmXj38cZs9u7xbJsSoq8v5nubke0ezc6dky8Ojottvg/vs90xbXoYOP4Bw7NrEsPx96NOhvlp2N7dvnKbCDB8nZsY3ivKoPq6LJdu70AQClpR4DVlc3/oIpLPTBBHFmjSuwtbUeRz744Ik15kMkE6kPWnr4OvCSmf3oCLf7kpndAnTEy5fjG7zeD8jDg7NjmhrDzKYB0wCmT5/OgOjf93PPPZfu3bvzzDPPANC/f3/GjRvHzJkzAcjNzaWsrIynnnqKrVu3AnDNNdewcuVKli9fDsCYMWMoLi7mueeeA2Dw4MFcdNFFzJo1C4CCggKmTJnC448/zo4dOwCYNGkSb7zxBitWrADg4osvJjs7m4ULFwIwbNgwRo0axcMPPwxAx44dmTx5MnPnzmXPnj0AXH/99SxdupR3332XvIoKPjt3br0x23v//d+pHT+eRx99FICSkhImTpzInDlz2BelSMrKyli8eDGro5GTl112GRUVFbz88ssAnHHGGQwZMoQnnngCgB49ejBhwgRmzZpFVdQhaerUqSxYsIB169YBcNVVV7Ft2zaWLFkCwNlnn03fvn2ZN28eAKWlpVx99dXMnDmTuro6YrEYU6dOZf78+WzatAmA8ePHs2HDBpYtW5ZRnxPA2LFjqampYdGiRQCcdtppjBgx4pCf002xGHW/+Q05FRVYfj6hpITNN97I/Dlz6FVezifj/dXiCgvhxRf9cSzmtcUQ6kdM2Q3+vNbVURg7QE5Ohw9XravzbFpeXmLT7GxP0h04QL1gLi/PE3lr10LfvnvJyioiKytRhe3SZTeXX76Eu+/+BM8++xQPPph5n5N+n9Lvc5o2bRqtKRMyZc3RPGgpLBokMDWE8IKZPYx34H8LL0VekrReAIaGEFYmLZsFbAgh3Glm/YFngW+EEH4dvV4GTAV+DXwDGBtC+GsTbbgUmHmEgwQy74dq507vb5Tca/uCCxIdgCR9rV7to1iT/9Lfcw985Sv++Pe/h29/2wcCxGuIvXsnZooFz56deaZn0uJ69/Yhl3FmfNChP0/8toA33/SkXXW1x3VbttTvawb+WjxAq6ryixOE4PfTpsGTT9Zf/+c/96qrSIpp1bnI7rqr+e+bGTPSex40lTjTxzeAv8c7/B+REMI6fKDBj8wsv8FrPwK+CzxvZiOOR0MzUpcuXu6Ky8nxeQ0k/T33XON/w996K/E4J8c7gG3d6pPaHjhQPzgDqKjwVNjWrT7AoLISNm0ixLNoUZYtxLLZts0XVVUlpspI7soG3pwhQzyTtmWL7za+7rhxPmozuaJ6+eUKzkQyjUqcaSKEsNLMHgVuA/52FNs/b2ab8DLkjxq8dq+ZdQBeMLOLQwjvmFkMyAVyADOzPKAuhFDVaOcnivvug09+0ucwGDcOTjqpvVskx8Nf/tJ42amnJh7/6Ec+XDKuvNwntU2uP8an5Kip8RtAbS3Ws2ei9GlGVWWMvn35MEiL69jRVztwwLu7jR8PX/uaX97zpJP8/wPwoGzGDB9I8NprftgQ/DJRDSusIieCTC5xKoOWXu7G5zw7Wt8DvhIFY/WEEGYAM4GFZnYScBGwH/gt0D96vOAYjp0ZLroIPv95BWeZJHlYZNy55yYeV1Q0fj0/39NeWVneH62oyCceq6lJREkXX+yPY7EPl1XXxBg92i88UFjoN/BViou9inrvvR6QrVrlr61a5dNylJbCE094pf3ZZ/21mhovjb7zTmJ9EckMyqClsIb9vkII6/FO/Q3Xa/R/cwihrIlljwKPRk9nRbfk1+8E7oyerqKV+w6IpIRPf9qvQB7Xvz+MGZN4PmmST1QWl5XlIzsfeKB+KiwW81sIHrBV1U82B4CYkZ0Nl1zit9pav+SnmVdGa2v9vmvX+k3cu9enZYtf0mngQL/gelxBQf0p2UROFMqgiYhkqhtugJ/9zKdOueEGWLiwfvnyppvgzjv9mkvnnANPP+3X6LzppvojNUtKvFZZUOBzZuzfD5s3U7d/P5s2bcIKCujatf7/PKtW+fiTHTs8OBs2zC9ycOaZMGVKYr0+fTyQi/vqV/0Q4Mm8GTM8AycimUOjOKU16IdKTgzl5X5Npk6d/JqmWVmwYYPXJHNyYOhQHl64kOraWsrKygAfP7B3r2fD8vI8HnzlFS9hTp7sU6zFLVnigwQuvbT+AFHwzMHKlR4ras4zSWGtWon56leb/7655570rgKpxCkicrS6doWPfaz+sr59/RapbjB/RnLfM4Arr/RbU5K7wjUUi3nGTUQyk0qcIiIiIilGGTQRERFJSxokICIiIiJtRhk0ERERSUvKoImIiIhIm1EGTURERNKSMmgiIiIi0mYUoImIiIikGJU4RUREJC2pxCkiIiIibUYZNBEREUlLyqCJiIiISJtRBk1ERETSkjJoIiIiItJmFKCJiIiIpBiVOEVERCQtqcQpIiIiIm1GGTQRERFJS8qgiYiIiEibUQZNRERE0pIyaCIiIiLSZhSgiYiIiKQYlThFREQkLanEKSIiIiJtRhk0ERERSUvKoImIiIhIm1EGTURERNKSMmgiIiIi0mYUoImIiIikGJU4RUREJC2pxCkiIiIibUYZNBEREUlLyqCJiIiISJtRBk1ERETSkjJoIiIiItJmFKCJiIiIpBiVOEVERCQtqcQpIiIiIm1GGTQRERFJS8qgiYiIiEibUQZNRERE0pIyaCIiIiLSZpRBExFpZVmxGGzfDjt3+oLSUigsbN9GiUhKU4AmItKM99+HP/wBevSAceMgNxdqa+H55+HNN6FrV+jZE/r08XV37ICOHaGkBGIxOGlQLRecfTYDu3eHDRugvNx3vG4dDB/uGyarqYGHHoJXXvGDjRgBl14Kf/kLFBfDFVdAdiv/2T54EDp0aN1jiBwnmVzitBBCe7dBMo9+qCRt1dV5cPXaa/CFL0B1tS879VSYPRvmzfMALQSoqIADBzyOys1N7KNfP+jXNzDmjL3kxOOpEODtt30jgJwcuOgiWLsWfvc7z6ht2QKPPprYUUUFrFrlgRvABRfAH/8IWVnH/8SXL4fPfhb+9jcYPRoeeQQGDz7+x5ETjbXmzj/1qea/b558snWP3doUoElr0A+VpJ2aGrjtNvj5z6FzZzjnHI+X9u6FPXt8nUGD4MwzPYu2d69n1kpKwBp8DRQVwSUXVjOgdw0FHWrJzop+JbZsgffe80xadbVHgv/zP561Ag/i1q/3YK2kBDZtgs2b6+/8mWfgyiuP/xswcqQHZ3Gf+AQsWHD8jyMnmlYNksaPb/77Zt689A7QNEhARDLOBx/AzJnw61/D/v0t2+Y//xP++789VtqyBX77W982HpyBVybffx+2bfMgrmvXxsEZeOIpKzebjTvyWbm5kL0HoozXgQO+cW2tB2cAp5+e2NAMqqr8IBs2JDJn4Ac77TT48Y/h6ae9kddfD/37w1VXwVtvHdF7VM/Bg/WDM4ClS49+fyJyzBSgiUhGWb0aPv5x+MY34PbbYeJEj4cO54EH6j8PIZHYiquthb/+1SuPnTo1vZ/cXM+0WRS5BYxtezr4Duvq/D5Zv371n8c71Wzb5n3BzKCgwHean+/Ztxkz4NZbvdxZW+v12BtvPPxJNqdDBzjvvPrLLrnk6Pcn0kbq6pq/pTsFaBnKzNaYWZWZdWuw/DUzC2Y20MxmRevsTbotN7MLk55XRusnr9O/vc5L5HBmz4bduxPPX3sN/vSnw2+3Y0fjZT/8oXcVa6iiAvbta7w8O9tLoA1VVZsHWt27N35x167E45ycRMouLw/+4R9g/ny4+urG2736av3n69d7jTbZ4sXwzW/Cb37TePuGHnnEy5qdO8OECY0jVhFpUxrFmdneB64F7gcws9OB/Abr3BtCuLOJbYuibQZG++kcQqhpYj2RdldbC7/4Bbz4os9m0VBL/pseM8YHAMR17uxJq6a66ebleTyUm+ujNuvqvA0jR3qM1bDsmRWLdrJ+feOdFRX5gbKy4I03EsuHD4dly7zzfpcujberafDrWF0NP/kJfO1r0Lu3B1if/3zi9a98Be65p/k3YNAg9TkTSSHKoGW2h4Abkp5/DpjdTm0RaTX33OMlzeef9xJk8iDHU0/1wZLNqavz4C4nB/r29WVdunjS6v77G8dBnTp5Bm3nTli5MtHnPzvbu5U1LH2aQbdOVf7kwIHGDdi3zwcFVFZ6hu3KK71PWf8oUV1T4+XOvDyPFkPwEQrxnYfg/dZ27vSTiWfg7ruv/nF+/GNfTySDZHKJUxm0zPYK8FkzOwV4F5gEXAB863gfyMymAdMApk+fzoABAwA499xz6d69O8888wwA/fv3Z9y4ccycOROA3NxcysrKeOqpp9i6dSsA11xzDStXrmT58uUAjBkzhuLiYp577jkABg8ezEUXXcSsWbMAKCgoYMqUKTz++OPsiOpUkyZN4o033mDFihUAXHzxxWRnZ7Nw4UIAhg0bxqhRo3j44YcB6NixI5MnT2bu3Lnsib7grr/+epYuXcq7774LwNixY6mpqWHRokUAnHbaaYwYMYJHo2kRSkpKmDhxInPmzGFfVP8qKytj8eLFrF69GoDLLruMiooKXn75ZQDOOOMMhgwZwhNPPAFAjx49mDBhArNmzaIq+jKdOnUqCxYsYN26dQBcddVVbNu2jSVLlgBw9tln07dvX+ZF6Z/S0lKuvvpqZs6cSV1dHbFYjKlTpzJ//nw2bdoEwPjx49mwYQPLli3LiM/pkUeygcTcXXV1gUsueZWiov185jM5VFae2uTn9NZbBcydO4bVq3t9uG3fvtvZsKEbc+Z43NQw4OrRw2OlujpPWuXkeF81M79vWBLNyoJYXgdq66qJlZbC++8nhpZVV/tG+fl+31zHNoBu3RId90OAzp05cOAAedHPFsC2QYPoPmMGO5YuJX/Llnrp8uq6Omb97Gd89MIL9fuk36c2+5ymTZuGHB1Ns5GhzGwNMBU4DygEFgFfBK4AqoFBwDeByUDyv/XzQgifS9rPQLzEmXMEJU79UEmbGjPGpxOLy8726cOam9O1stIzbj/4QdMlzDgzn0s2/t94cbFXHouKGsdSffrAySd7dbFz50SZMyvLl+VUlJP3zus+FBS8JvrWW55BW7HCBwIcaoLYgQNh40Yvg3bo4Gm8/fs9K3fggNdbS0p8lERT7roL7r67+f2LtI5Wneriiiua/7753e/Se5oNZdAy30PAYjwga6q8eV8zfdBE0kZRUf3nNTUeBzU1z+quXTBqlJcnmxKvGsbLlZ07+/JYDAYM8NcbBn45OX5Fgb17veQZD+RqaxPdx0JWTv0BAR984MFZVZXvPHmmW0iULjt08Ck2br/d03e/+Y0Haj/4ga+Xl+e3+D6TnX46TJkCZ58NY8c2fcIikpIUoGW4EMJaM3sfuBK4ub3bI9Ia+vTxJFRcLOYJqaY8/PChg7MJEzwGOuWUREIrO9uDvvJyT1Lt2eNJq6IiX6dnz/pBW0WFjwfo1s2rmADZlbu8ZhqLJQKz+EE7dqw/siAEfz56NFx3HQwd6svffttHda5d6/tp2NGm4RUGzjzTBweIZKhM6GvWHA0SODHcDHw8hFDZ3g0RaQ233FI/ILvhBujVq+l1m5u49sILPfv1v/8L06fXrzbGBwoUFXkgFp8jrbzcY6uPfKTx/goLvU379wfytq8nZ/37HkAVFflggPh1OOPBWLJ43bW0NBGc1dT4pZgWLvQIs+HohXPOge99z/uzgUeYKmmKpC1l0E4AIYRVh3j5K2Z2e9LzAyGEbs2tLJKKzj4bXnoJFi3yrlqjRjW/7rnnNl7Wv7/P+Rqf3L+0tOltm5rw9oILPNGVPNVYbm6iNJoTqyVnz476AZWZ1087dvRJ27Ky6u88FvPb+ecnlr31ll/6KdnIkT7i86yzvCMewKRJvt4ppzR9mQMRSQsK0DJUCGFgM8trSHTaLItuh9rPGlq5k6fI8dCjB3z604dfb9Qoj4uSL+H0sY8lgjPwmS4eeSQxGW23bt7/rKmrKfXq5WXP88/3ILFDBxg2LBq9GYOK8rchv4lfodpab0hRkddICwt9ttwQPHj7xCd8jpC4/v29r1nyVB1nnOHpw2SdOyeiQ5EMl8klTgVoInJCyc/3qw1MmwZbt8JHPwrf+U79dfr183WeftpjogkTPAh78UWYMycxGe4llySuHDBkiJc0333XY6gBA3ybX/7yVboMG0a//PxEfbWmJtGhv7jYL9NUXHzohnfqBN/9Lnz96x5dnnUWfPGLx+ttEZEUo2k2pDXoh0pSXnW1D6ps6upLh7N5s2fIevQ4/LrxeavKPvMZn0w2FvOhnWvWeKA2dOihp9doaP9+b3jv3kfecJG216oVmLFjm/++Wbgwvas/yqCJyAkpJ+fogjM4ytiooKD+SIbhw4/u4Pn5iYEAIpKxFKCJiIhIWsrkPmiaZkNEREQkxShAExEREUkxKnGKiIhIWlKJU0RERETajDJoIiIikpaUQRMRERGRNqMATURERCTFqMQpIiIiaUklThERERFpM8qgiYiISFpSBk1ERERE2owyaCIiIpKWlEETERERkTajAE1EREQkxajEKSIiImlJJU4RERERaTPKoImIiEhaUgZNRERERNqMMmgiIiKSlpRBExEREZE2owBNREREJMWoxCkiIiJpSSVOEREREWkzyqCJiIhIWlIGTURERETajDJoIiIikpaUQRMRERGRNqMATURERCTFqMQpIiIiaUklThERERFpM8qgiYiISFpSBk1ERERE2owyaCIiIpKWlEETERERkTajAE1EREQkxajEKSIiImlJJU4RERERaTPKoImIiEhaUgZNRERERNqMMmgiIiKSlpRBExE5kZWXw/797d0KETmBKEATEQHYvh3mzIEXX0ws27sXbr0VrroKPvlJ+MlPmt9+92545RV47TWoqoKDB6G29pCHrKiAV1+FdeuOzymISOawEEJ7t0Eyj36oJD1s3AjLl4MZTJ4Me/b48ptvhpkz4cEHYfZs6NbN1wG48kr4+7+vv5/t2+EXv4CaGn9eXAzDhxNyctlUOoicTn2orY1RXAxFRfDee7BgAezaBZWVHtuNHg033OCbhwALF8KGDX64Hj18+ezZ8OST0KsXfPGLMGRIq79DIsfKWnPn/fo1/32zfn3rHru1qQ+aiJyYnn8e7rorkeXKzU3cv/46/PCHsGKFd3Kpq4OsLH/9uedgyhTIz4cXXoBlyyA7OxGcFRTAyJHQrRt1sWxihb3ZsSNGCB6QVVfDn/4E+/ZBLOaxXE0N/OUv0KcPjB0L110Hc+f67jp29PXfegu+9rVE85csgT//OdFsEcksCtBE5MT0wAP1S5B9+ngm7PLLoW9fT3Pl5nrwtXw5nHKKB2U1NbB4Maxd68vj28YzbGeeCd27U9OhkP1d+1AUy6Kg0LuxHTzoSbvycuja1bu1VVdDhw6eSXv5ZU/aPfZYoll79sB99yV2H/fBB/B//wfnnde6b1NDlZXw05/62/OpT8G4cW17fJFkGiQgLWZm/c1sr5llHeF2ZWb2YtLzvWY2+Pi3UEQA71+WLCsLCgs9OEvWr58HZevWeaexvXvhBz/wvmZxdXWeDgOPvICDnXtCzP8MxGLQubNnwh58EP76Vy9hxiuqVVUepGVne/ly2DB/Hrd2LQwYUL9ZsZg37Wjt2uXHPVLjx3t59YEH4LLL4Fe/Ovo2iEjzUiZAM7M1ZlZlZt0aLH/NzIKZDWynph1S1O5L489DCOtCCEUhhEP3Dj6MaB+rj72FIq0s3iE+VSxbBnfe6X3IDtWuT32q/vPzzoOyssapqvg+du/2OuPKlbB1a/11Kipg8GDo399rl0Bddk6jQ/7+94nHIfiu4ruvqPAy57p1MHy4J/JGjvTXFi/2AO200/x5Tg589aueuDtSFRXwd38HXbpAz57ws5+1fNuVKz2wTPbTnx55G0SOl3gPhKZu6S5lArTI+8C18SdmdjqQ337NEZFD+ta3oKQEOnWCO+7wqKM9Pfus97b/9re9I/+ECc2ve9ZZnsKqrPR00po1cP/9HrjFg7SqKg/KklVX+y2ExPnu3evb9+zp+zt4kOx9e+ptVlmZ6KYWV1vrmbCNGz0ht3GjDwwoL/cmDB4MAwf6GIWZM+Hzn/fnpaXeNw18sMEXvgAzZjSOG5ty330wf74/3rUL/vEfYdOmw28HPsAh1uBbI94OETm+Ui1Aewi4Ien554DZySuYWSczm21m28xsrZndaWax6LUyM3vRzO4zs51m9r6ZXZG0bamZPW1mO8xspZn9fdJrWWY23cxWmVmFmS0zs35m9hMz+36DNvzGzG43s4eA/sBvopLkV8xsYJTxy47WLTGzX5jZpqhNT7XkjYj2MSR6PCtqxzNR2/5iZiclrTvczJ6PzusdM/tM0mtXmdlfzWyPma03s282OM4N0ftYbmZ3JWcEzSxmZv8SvSflZvaYmZW0pP1yAnjpJe9kHwUk/PCH8Pjj7dum+++v36/s2WfhnXeaXvehhzyw2r3bs16vveZzXowdC7fd5n3JVqxoXAoF//d8+XIP8swSfdP27/f77dvJW/oiOavfoqa67sM48Kyz6u+mZ09ffcuW+ssPHvQgbeNGz3SVlMC2bT7jx5o1XvL82tfg+9+HG2+EefO85PiZzxw+cxDvNhdXU+On2RK9evlbE1dYCP/6ry3bVkSOTKoNEngF+KyZnQK8C0wCLgC+lbTO/UAnYDDQFVgAbAbiifrRwC+BbsA04Gdm1if4fCK/AlYApcBw4HkzWx1CWAj8M569uzI69khgX7Svp8zsyyGEuqgEOxaYGkL4oZldGD1+AaCJUuxDwF7gtOh+zFG+N9cClwP/F7Xp28BkMysEnge+DlwRtXuBma0IIawAKvGgdwUwIjrn10IIT5nZqcB/RftdAvw7kFw0uQ2YAFwMbAN+DPyEpCxnnJlNw99vpk+fzoCow8y5555L9+7deeaZZwDo378/48aNY+bMmQDk5uZSVlbGU089xdbo3/9rrrmGlStXsjz6JhkzZgzFxcU899xzAAwePJiLLrqIWbNmAVBQUMCUKVN4/PHH2bFjBwCTJk3ijTfeYEX0zXPxxReTnZ3Nwqg+M2zYMEaNGsXDDz8MQMeOHZk8eTJz585lT9Qx6Prrr2fp0qW8++67AIwdO5aamhoWLVoEwGmnncaIESN49NFHASgpKWHixInMmTOHfVGZq6ysjMWLF7N6tVerL7vsMioqKnj55ZcBOOOMMxgyZAhPPPEEAD169GDChAnMmjWLqqiD0NSpU1mwYAHrosmyrrrqKrZt28aSJUsAOPvss+nbty/z5s0DoLS0lKuvvpqZM2dSV1dHLBZj6tSpzJ8/n01RqmT8+PFs2LCBZcuWHfXndH15OYUNfg52//73PLpzZ7t9Tp/YtIlBDdr06vLlDO3Vq9HntHLdOhrOUvG/zzxDpwcf5OOzZ2NVVYSsLGzQoPqpLzPvZ1ZSAs884ymkXr08i1hQkFitIJ+c9e+zu+fJH/Y1Gz/e+40tW+ZlypISOHAgMUA0WXxe3KwsH+lZXl5NCPXLpnPmVELSp/Dee96/bf365n+fRoy4mnnzSj/cpqCghrPOquPBB1v2OZ1yCjz22GWsX59HCM+xfPkB9u/X79Ox/j5l6t+9adOm0ZoyoZTZnJSZB83M1gBTgfPwvziLgC/iQUc1MAhYjwdNZ4YQ3oy2+wfg2hDCJWZWBtwZQohnngrwAKU3kAOsATqHECqi178D9A4hlJnZO8BXQgjzmmjbW8BtIYTnzewW4MoQwpXJ7W4QoL0fHa87sBHoGkLYeZjzL4v2c0H0PABDQwgrzWwWUBNCmBq9diXwgxDCcDObBNwSQrgwaV8/BTaFEP6tieP8EAghhDvM7OvAKSGEa5Per13R+b0QnfctUQCLmfUG1gH5IYSahvtOkho/VNK6Xn8dPvKR+mXNBQvgE59o/WNv2+Yd9desgUmTEqXMl16CSy/1qAd8voroy6iRN9/0bNn27f589Gjv7NW9O0RfeIAHXUOGfNi3jI4dPcpK7qs2ciQMGtSo1/7BPoM4WDq43oUIKiu9n9m2bbBzp79969Z5Ai+e/DNrXC2uq/ORm8kuuKD+vLpm/hY0HFDQcD933+0JxN694bvf9f2ItJJWnYusZ8/mv2+2bNE8aMfbQ8BiPCCb3eC1bkAusDZp2VrqZ30+/BMWQthn/ke0CM+27YgHZ0nbnhM97gesaqZNvwSm4JmqKcCPWngu/aJjHjI4a6HkP8378HMCGACMNrNdSa9n4+8jZjYa+C6ePcsFOgC/jtYrxYNe4MP3qzxpPwOAJ80s+X+UWqAnHnjKiWzkSJ859dvf9prcHXe0TXAWgh8nXqubO9eHEk6eDOef733G5s/3gOmKK5rfT2Wl1xbjkdDLL3sNMTk4g8QVAbKyvCaZn++Ps7O9j1qvXj7kcvduHwEaBW4BqOnUlb17PV6Mx3N5eV62jGfVzHwi2pNP9phxzx7v51VUlGhCCPX7l2Vl+XiGG2+EiRMTgdvNNx86OAPf9ze/6TeRdJfJGbSUC9BCCGvN7H281Hhzg5e349m0AcCb0bL+tCxY2ASUmFlxUpCWvO164CTgjSa2nQO8YWZnAKcATyU3+RDHXB8ds3MIYVcL2ng01gOLQgjNfTM+AvwncEUI4UCUQYuPlN0MnBxf0czy8UA2ed83hRBeOu6tlswwZYrf2tJf/9q4I9WsWR6ggfeiv+WWw+9nwYL6aaoQ/GoADeXleaoLvCY5ZIgHZGYeuPXs6Y/37YNVq6B3b0KHPA4MG0l1fid2b66/u+xs3+WuXYnAqrraM2FR5Y6iIhg6NFH63LOn/hfRRz/qWTDwjNkrr3g27OSTEZEMkWqDBOJuBj4eQqhMXhhNXfEY8G0zKzazAXjfsTmH22EIYT3wMvAdM8szs5HRceL1j5nADDMbam6kmXWNtt0ALMWzUk+EEJKvmrwF7w/X1DE3A78D/svMuphZjpld1NI3oYXmA8PM7LPR/nPMbFTUjw+gGM/iHTCzc4HrkrZ9HPg7MxtjZrnAv1E/Hf0A/l4PADCz7mY2/ji3X+TIJF92KS5+LaQjEZ/DIlnyEEUz7/wVH6ZYXOxBXG5u4vhZWYlBBDk5Hsi99x4UFlDbrUejUZtxBw54wFVd7d3b1q5NBGfguyxPymU3zBKUJrqQkZcHl1yi4ExOTJpmo42FEFaFEF5t5uVb8X5lq4EX8QzRz1u462uBgXg27UngGyGE56PXfoAHfwuAPfigg+QpPn4JnE5UOkzyHeBOM9tlZl9q4pifxbN+bwNbgdtb2NYWibKB44DJ+Hl9ANyDlzIBvgDcbWYV+ECCx5K2XYG/n3PxbFpF1Mb45FE/Ap7GBx1U4IM4Rh/P9oscsf794UtJv2o9e8L06Ue+n6uv9iGJOTl+u/XW+tdSCgH+3//zgLBHDw/QsrMbzzMRj8JKSjyN1a0bNnAgBeyjILcK706aUFfncVxVVWLTpgaKJk8imzxpbWkp/NM/Hfnpikh6SZlBAqkuynzNAQaGEDIgNm/MzIrwQQJDQwjvH8Ou9EMlre/NNz31dPHF9UZPHrGKqMdDcbHfL1jgF7k8/3wP0hoGfyNG1D+emfd3Kyz0/mkAF17ogw2ALTuy2bwzn+xsz5jt2uWB2bJlPo1GZaUHY48+mgjYzPzKUgUFiWbdfrvHiWedpetvSlpp1Y76Xbs2/31TXp7egwQUoLWAmeXgWablIYS727s9x5OZ/R2wEP8l+j6eITsrHNsPhn6oJDPs2OHT7iens667zgcGbNqUmLT25JO9P1p8vZNO8hKqGSEElr6xh6q6vmRneyzXs6f3HVu0yEdz7tnj8dzq1T5RbSzmCTkzv1rA977n8Z9IGmrVIKlLl+a/b3buVICW0aK+XK8Cy4HLQwh7DrNJWjGzmcBE/JfoVeALIYRmZvZsMf1QSeZ49VW/gObu3T6R2XXXNb9udbWXS8Gzb7W1PPHkk1RUVlJWVtZo9fJyv/Xv733J4ps9+ywsWeLlzIkTfdSnSJpSgHaUFKBJa9APlUgkPrFoUwGayAmgVYOkTp2a/77ZvTu9A7SUHCQgIiIiciJLuXnQRERERFoiE6bTaI4yaCIiIiIpRgGaiIiISIpRiVNERETSkkqcIiIiItJmlEETERGRtKQMmoiIiIi0GWXQREREJC0pgyYiIiIibUYBmoiIiEiKUYlTRERE0pJKnCIiIiLSZpRBExERkbSkDJqIiIhImjOzYGZF7d2OllAGTURERNKSMmgiIiIiGcTMRpnZn83s9eh+VLT8O2b25ejxZ8yszsx6RM9/a2bj2qJ9CtBERETkhGJmucATwF0hhJHAncAT0fKFwNho1bHAK8DHzSwHGA282BZtVIlTRERE0lII2FFuejJQFUJ4wfcTFppZVbT8JeCxKFg7H/gSMBHYCPwthLDv2Ft+eMqgiYiIyInGgNDE8hBC2A8sB64FNgN/AD6KZ9N+31YNVIAmIiIiJ5q3gQ5m9jGA6D4HeDd6fSHwb8DCEMJBYANQFi1vEypxioiIyAklhFBlZtcAPzazQqASmBhCqIpWWQjMIBGQLcTLnUvaqo0K0EREROSEEEKwpMdL8dJlU+v9GUhe917g3lZvYBKVOEVERERSjAI0ERERkRSjAE1EREQkxShAExEREUkxCtBEREREUowCNBEREZEUowBNREREJMUoQBMRERFJMQrQRERERFKMAjQRERGRFKMATURERCTFKEATERERSTEK0ERERERSjAI0ERERkRSjAE1EREQkxShAExEREUkxCtBEREREUowCNBEREZEUowBNREREJMUoQBMRERFJMQrQRERERFKMAjQRERGRFKMATURERCTFKEATERERSTEK0ERERERSjAI0ERERkRSjAE1EREQkxShAExEREUkxFkJo7zZIhjGzZ4Fu7d2OJN2A7e3diFaQieeViecEOq90konnBO13XttDCJe3w3HTngI0yXhm9moI4Zz2bsfxlonnlYnnBDqvdJKJ5wSZe16ZTCVOERERkRSjAE1EREQkxShAkxPBg+3dgFaSieeViecEOq90konnBJl7XhlLfdBEREREUowyaCIiIiIpRgGaiIiISIpRgCYZwcxKzOx5M3svuu/SzHo/N7OtZvZGg+XfNLONZvZadLuybVp+aMfhvFq0fVs6gnO63MzeMbOVZvYvSctT5rNqro1Jr5uZ/Th6/XUzO6ul27anYzyvNWb2t+izebVtW35oLTiv4Wb2ZzM7aGZfOpJt28sxnlPKflaiAE0yx78AC0MIQ4GF0fOmzAKamzTxP0IIH4luv22FNh6NYz2vlm7flg7bJjPLAn4CXAGcClxrZqcmrdLun1UL2kj02tDoNg347yPYtl0cy3kl+Vj02aTMvFstPK8dwG3AfUexbZs7lnNKknKflTgFaJIpxgO/jB7/EpjQ1EohhMX4H6x0cazn1aLt21hL2nQusDKEsDqEUAXMjbZLJS1p43hgdnCvAJ3NrHcLt20vx3Jeqeyw5xVC2BpCWApUH+m27eRYzklSnAI0yRQ9QwibAaL7Hkexj1uics3PU6EUGDnW8zoe78vx1pI29QHWJz3fEC2LS4XP6nBtPNQ6Ldm2vRzLeQEEYIGZLTOzaa3WyiN3LO95qn5ex9quVP2sBMhu7waItJSZvQD0auKlfz0Ou/9vYAb+B2sG8H3gpuOw38Nq5fNqF8fhnKyJZfE5gdrts2rgUG083Dot2ba9HMt5AZwfQthkZj2A583s7SjD296O5T1P1c/rWNuVqp+VoABN0kgI4dLmXjOzLWbWO4SwOSq1bD3CfW9J2tf/APOPvqVHpjXPCzjW7Y/KcTinDUC/pOd9gU3Rvtvts2qg2Ta2YJ3cFmzbXo7lvAghxO+3mtmTeBkuFb70W3JerbFtazqmdqXwZyWoxCmZ42ngc9HjzwHzjmTjBv1nPgW80dy6beyYzus4bN8aWtKmpcBQMxtkZrnA5Gi7VPqsmm1jkqeBG6JRj+cBu6Oybku2bS9HfV5mVmhmxQBmVgiMI3V+l47lPU/Vz+uo25Xin5UAhBB00y3tb0BXfETge9F9SbS8FPht0nq/AjbjHWY3ADdHyx8C/ga8jv+B693e53SczqvJ7dPknK4E3gVWAf+atDxlPqum2gj8I/CP0WPDR9mtitp8zuHOLxVuR3tewGBgeXRbkYbn1Sv6/dkD7Ioed0zlz+tozynVPyvdgi71JCIiIpJqVOIUERERSTEK0ERERERSjAI0ERERkRSjAE1EREQkxShAExEREUkxCtBEREREUowCNBEREZEU8/8Bdgho7Ibr02oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_shap_values(df_omics.loc[shap_omics_LR.index], shap_omics_LR, n_best=10, figsize=(9, 10), title=\"LR - 1-year death\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81463468-8b58-4fab-9445-457c8d18b159",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 1.3 Cox model (OS prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "22f818eb-b560-4156-91b4-f21e8d53fad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B lineage</th>\n",
       "      <th>CD8 T cells</th>\n",
       "      <th>Cytotoxic lymphocytes</th>\n",
       "      <th>Endothelial cells</th>\n",
       "      <th>Fibroblasts</th>\n",
       "      <th>Monocytic lineage</th>\n",
       "      <th>Myeloid dendritic cells</th>\n",
       "      <th>NK cells</th>\n",
       "      <th>Neutrophils</th>\n",
       "      <th>T cells</th>\n",
       "      <th>...</th>\n",
       "      <th>NTRK2</th>\n",
       "      <th>NTRK3</th>\n",
       "      <th>LTK</th>\n",
       "      <th>RET</th>\n",
       "      <th>NRG1</th>\n",
       "      <th>NRAS</th>\n",
       "      <th>MAP2K1</th>\n",
       "      <th>RIT1</th>\n",
       "      <th>TMB_RNA</th>\n",
       "      <th>Biopsy site</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.029504</td>\n",
       "      <td>-0.005880</td>\n",
       "      <td>-0.214433</td>\n",
       "      <td>-0.002618</td>\n",
       "      <td>-0.273590</td>\n",
       "      <td>-0.041863</td>\n",
       "      <td>-0.006018</td>\n",
       "      <td>0.197270</td>\n",
       "      <td>0.096681</td>\n",
       "      <td>0.183944</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.078544</td>\n",
       "      <td>0.034580</td>\n",
       "      <td>0.009224</td>\n",
       "      <td>-0.001835</td>\n",
       "      <td>0.029217</td>\n",
       "      <td>0.077512</td>\n",
       "      <td>0.058155</td>\n",
       "      <td>-0.008655</td>\n",
       "      <td>-0.315771</td>\n",
       "      <td>-0.030933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.046102</td>\n",
       "      <td>0.007573</td>\n",
       "      <td>-0.075461</td>\n",
       "      <td>0.016567</td>\n",
       "      <td>0.083877</td>\n",
       "      <td>-0.038155</td>\n",
       "      <td>-0.120068</td>\n",
       "      <td>-0.010685</td>\n",
       "      <td>0.052770</td>\n",
       "      <td>0.357678</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015010</td>\n",
       "      <td>-0.074523</td>\n",
       "      <td>-0.062527</td>\n",
       "      <td>0.068768</td>\n",
       "      <td>0.181040</td>\n",
       "      <td>0.646756</td>\n",
       "      <td>-0.108230</td>\n",
       "      <td>-0.036537</td>\n",
       "      <td>-0.007983</td>\n",
       "      <td>-0.018468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.188247</td>\n",
       "      <td>-0.110234</td>\n",
       "      <td>0.027370</td>\n",
       "      <td>-0.029127</td>\n",
       "      <td>0.241383</td>\n",
       "      <td>-0.015412</td>\n",
       "      <td>0.613600</td>\n",
       "      <td>-0.080147</td>\n",
       "      <td>-0.012392</td>\n",
       "      <td>-0.171486</td>\n",
       "      <td>...</td>\n",
       "      <td>0.450466</td>\n",
       "      <td>-0.063682</td>\n",
       "      <td>0.065507</td>\n",
       "      <td>-0.001285</td>\n",
       "      <td>0.194799</td>\n",
       "      <td>0.032672</td>\n",
       "      <td>-0.259777</td>\n",
       "      <td>-0.271165</td>\n",
       "      <td>0.007257</td>\n",
       "      <td>-0.000151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.064481</td>\n",
       "      <td>-0.076735</td>\n",
       "      <td>-0.037774</td>\n",
       "      <td>0.209951</td>\n",
       "      <td>-0.457779</td>\n",
       "      <td>-0.034053</td>\n",
       "      <td>-0.805480</td>\n",
       "      <td>-0.000836</td>\n",
       "      <td>0.017162</td>\n",
       "      <td>0.471569</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.115966</td>\n",
       "      <td>0.104293</td>\n",
       "      <td>-0.045336</td>\n",
       "      <td>-0.006514</td>\n",
       "      <td>-0.033336</td>\n",
       "      <td>0.123748</td>\n",
       "      <td>-0.203117</td>\n",
       "      <td>-0.004284</td>\n",
       "      <td>-0.165638</td>\n",
       "      <td>0.005106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.134374</td>\n",
       "      <td>-0.143456</td>\n",
       "      <td>0.158070</td>\n",
       "      <td>0.009675</td>\n",
       "      <td>-0.427093</td>\n",
       "      <td>0.010889</td>\n",
       "      <td>0.005631</td>\n",
       "      <td>-0.017799</td>\n",
       "      <td>0.036893</td>\n",
       "      <td>-0.372375</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059103</td>\n",
       "      <td>0.023040</td>\n",
       "      <td>0.034998</td>\n",
       "      <td>0.034686</td>\n",
       "      <td>0.154052</td>\n",
       "      <td>0.301338</td>\n",
       "      <td>-0.190051</td>\n",
       "      <td>-0.006641</td>\n",
       "      <td>-0.011417</td>\n",
       "      <td>-0.000022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   B lineage  CD8 T cells  Cytotoxic lymphocytes  Endothelial cells  \\\n",
       "0   0.029504    -0.005880              -0.214433          -0.002618   \n",
       "1  -0.046102     0.007573              -0.075461           0.016567   \n",
       "2   0.188247    -0.110234               0.027370          -0.029127   \n",
       "3   0.064481    -0.076735              -0.037774           0.209951   \n",
       "4   0.134374    -0.143456               0.158070           0.009675   \n",
       "\n",
       "   Fibroblasts  Monocytic lineage  Myeloid dendritic cells  NK cells  \\\n",
       "0    -0.273590          -0.041863                -0.006018  0.197270   \n",
       "1     0.083877          -0.038155                -0.120068 -0.010685   \n",
       "2     0.241383          -0.015412                 0.613600 -0.080147   \n",
       "3    -0.457779          -0.034053                -0.805480 -0.000836   \n",
       "4    -0.427093           0.010889                 0.005631 -0.017799   \n",
       "\n",
       "   Neutrophils   T cells  ...     NTRK2     NTRK3       LTK       RET  \\\n",
       "0     0.096681  0.183944  ... -0.078544  0.034580  0.009224 -0.001835   \n",
       "1     0.052770  0.357678  ... -0.015010 -0.074523 -0.062527  0.068768   \n",
       "2    -0.012392 -0.171486  ...  0.450466 -0.063682  0.065507 -0.001285   \n",
       "3     0.017162  0.471569  ... -0.115966  0.104293 -0.045336 -0.006514   \n",
       "4     0.036893 -0.372375  ...  0.059103  0.023040  0.034998  0.034686   \n",
       "\n",
       "       NRG1      NRAS    MAP2K1      RIT1   TMB_RNA  Biopsy site  \n",
       "0  0.029217  0.077512  0.058155 -0.008655 -0.315771    -0.030933  \n",
       "1  0.181040  0.646756 -0.108230 -0.036537 -0.007983    -0.018468  \n",
       "2  0.194799  0.032672 -0.259777 -0.271165  0.007257    -0.000151  \n",
       "3 -0.033336  0.123748 -0.203117 -0.004284 -0.165638     0.005106  \n",
       "4  0.154052  0.301338 -0.190051 -0.006641 -0.011417    -0.000022  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_omics_Cox_raw = pd.read_csv(\"survival\\\\Shap\\\\shap_Cox_100cv_OS\\\\Shap_RNA.csv\", index_col=0)\n",
    "\n",
    "# Compute the mean SHAP value for each sample and each feature over the 100 repeats   \n",
    "shap_omics_Cox = shap_omics_Cox_raw.groupby(level=0).mean().iloc[:, :-2]\n",
    "shap_omics_Cox.reset_index(drop=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c16059b7-9947-4cbc-b96e-6237b93c8a02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_shap_values(df_omics.loc[shap_omics_Cox.index], shap_omics_Cox, n_best=10, figsize=(9, 10), title=\"Cox - OS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17d5ea7a-980a-4a85-901a-b4fd484b289e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 1.4 Random Survival Forest (OS prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ef1f6e65-c018-4221-807c-3687fb96cf4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B lineage</th>\n",
       "      <th>CD8 T cells</th>\n",
       "      <th>Cytotoxic lymphocytes</th>\n",
       "      <th>Endothelial cells</th>\n",
       "      <th>Fibroblasts</th>\n",
       "      <th>Monocytic lineage</th>\n",
       "      <th>Myeloid dendritic cells</th>\n",
       "      <th>NK cells</th>\n",
       "      <th>Neutrophils</th>\n",
       "      <th>T cells</th>\n",
       "      <th>...</th>\n",
       "      <th>NTRK2</th>\n",
       "      <th>NTRK3</th>\n",
       "      <th>LTK</th>\n",
       "      <th>RET</th>\n",
       "      <th>NRG1</th>\n",
       "      <th>NRAS</th>\n",
       "      <th>MAP2K1</th>\n",
       "      <th>RIT1</th>\n",
       "      <th>TMB_RNA</th>\n",
       "      <th>Biopsy site</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.005355</td>\n",
       "      <td>-0.027795</td>\n",
       "      <td>-0.034235</td>\n",
       "      <td>-0.035611</td>\n",
       "      <td>-0.043940</td>\n",
       "      <td>-0.050742</td>\n",
       "      <td>-0.182080</td>\n",
       "      <td>-0.005250</td>\n",
       "      <td>-0.035786</td>\n",
       "      <td>-0.131692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005594</td>\n",
       "      <td>-0.013593</td>\n",
       "      <td>-0.027019</td>\n",
       "      <td>-0.093556</td>\n",
       "      <td>-0.105872</td>\n",
       "      <td>0.095691</td>\n",
       "      <td>-0.013038</td>\n",
       "      <td>-0.057836</td>\n",
       "      <td>-0.012119</td>\n",
       "      <td>-0.018183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.038669</td>\n",
       "      <td>-0.033319</td>\n",
       "      <td>-0.046348</td>\n",
       "      <td>-0.042761</td>\n",
       "      <td>-0.011249</td>\n",
       "      <td>-0.064499</td>\n",
       "      <td>-0.234602</td>\n",
       "      <td>0.071307</td>\n",
       "      <td>-0.026414</td>\n",
       "      <td>-0.108473</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007632</td>\n",
       "      <td>-0.030154</td>\n",
       "      <td>-0.030774</td>\n",
       "      <td>-0.007364</td>\n",
       "      <td>0.293084</td>\n",
       "      <td>0.194181</td>\n",
       "      <td>-0.000657</td>\n",
       "      <td>-0.016292</td>\n",
       "      <td>0.002404</td>\n",
       "      <td>-0.000352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.109010</td>\n",
       "      <td>-0.015317</td>\n",
       "      <td>-0.032350</td>\n",
       "      <td>-0.025712</td>\n",
       "      <td>0.099228</td>\n",
       "      <td>0.226979</td>\n",
       "      <td>0.678402</td>\n",
       "      <td>0.050274</td>\n",
       "      <td>-0.004743</td>\n",
       "      <td>-0.061566</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070832</td>\n",
       "      <td>-0.039695</td>\n",
       "      <td>0.003904</td>\n",
       "      <td>-0.102953</td>\n",
       "      <td>0.232170</td>\n",
       "      <td>0.056428</td>\n",
       "      <td>0.019598</td>\n",
       "      <td>0.463725</td>\n",
       "      <td>0.004231</td>\n",
       "      <td>-0.001555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.010208</td>\n",
       "      <td>-0.017782</td>\n",
       "      <td>-0.041158</td>\n",
       "      <td>0.023670</td>\n",
       "      <td>-0.016281</td>\n",
       "      <td>-0.074280</td>\n",
       "      <td>-0.184134</td>\n",
       "      <td>-0.027880</td>\n",
       "      <td>-0.017612</td>\n",
       "      <td>-0.067255</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012320</td>\n",
       "      <td>0.126303</td>\n",
       "      <td>-0.036546</td>\n",
       "      <td>-0.127917</td>\n",
       "      <td>-0.096383</td>\n",
       "      <td>0.138117</td>\n",
       "      <td>0.029479</td>\n",
       "      <td>-0.057557</td>\n",
       "      <td>-0.041169</td>\n",
       "      <td>-0.010118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.142377</td>\n",
       "      <td>0.354802</td>\n",
       "      <td>0.027465</td>\n",
       "      <td>-0.035471</td>\n",
       "      <td>-0.031780</td>\n",
       "      <td>-0.011180</td>\n",
       "      <td>-0.178201</td>\n",
       "      <td>-0.018674</td>\n",
       "      <td>-0.015085</td>\n",
       "      <td>0.514970</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011940</td>\n",
       "      <td>-0.026638</td>\n",
       "      <td>0.007909</td>\n",
       "      <td>0.013291</td>\n",
       "      <td>0.247359</td>\n",
       "      <td>0.123876</td>\n",
       "      <td>0.003482</td>\n",
       "      <td>-0.037832</td>\n",
       "      <td>-0.004156</td>\n",
       "      <td>-0.001593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   B lineage  CD8 T cells  Cytotoxic lymphocytes  Endothelial cells  \\\n",
       "0  -0.005355    -0.027795              -0.034235          -0.035611   \n",
       "1  -0.038669    -0.033319              -0.046348          -0.042761   \n",
       "2   0.109010    -0.015317              -0.032350          -0.025712   \n",
       "3   0.010208    -0.017782              -0.041158           0.023670   \n",
       "4   0.142377     0.354802               0.027465          -0.035471   \n",
       "\n",
       "   Fibroblasts  Monocytic lineage  Myeloid dendritic cells  NK cells  \\\n",
       "0    -0.043940          -0.050742                -0.182080 -0.005250   \n",
       "1    -0.011249          -0.064499                -0.234602  0.071307   \n",
       "2     0.099228           0.226979                 0.678402  0.050274   \n",
       "3    -0.016281          -0.074280                -0.184134 -0.027880   \n",
       "4    -0.031780          -0.011180                -0.178201 -0.018674   \n",
       "\n",
       "   Neutrophils   T cells  ...     NTRK2     NTRK3       LTK       RET  \\\n",
       "0    -0.035786 -0.131692  ... -0.005594 -0.013593 -0.027019 -0.093556   \n",
       "1    -0.026414 -0.108473  ... -0.007632 -0.030154 -0.030774 -0.007364   \n",
       "2    -0.004743 -0.061566  ...  0.070832 -0.039695  0.003904 -0.102953   \n",
       "3    -0.017612 -0.067255  ... -0.012320  0.126303 -0.036546 -0.127917   \n",
       "4    -0.015085  0.514970  ... -0.011940 -0.026638  0.007909  0.013291   \n",
       "\n",
       "       NRG1      NRAS    MAP2K1      RIT1   TMB_RNA  Biopsy site  \n",
       "0 -0.105872  0.095691 -0.013038 -0.057836 -0.012119    -0.018183  \n",
       "1  0.293084  0.194181 -0.000657 -0.016292  0.002404    -0.000352  \n",
       "2  0.232170  0.056428  0.019598  0.463725  0.004231    -0.001555  \n",
       "3 -0.096383  0.138117  0.029479 -0.057557 -0.041169    -0.010118  \n",
       "4  0.247359  0.123876  0.003482 -0.037832 -0.004156    -0.001593  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_omics_RF_raw = pd.read_csv(\"survival\\\\Shap\\\\shap_RF_100cv_OS\\\\Shap_RNA.csv\", index_col=0)\n",
    "\n",
    "# Compute the mean SHAP value for each sample and each feature over the 100 repeats\n",
    "shap_omics_RF = shap_omics_RF_raw.groupby(level=0).mean().iloc[:, :-2]\n",
    "shap_omics_RF.reset_index(drop=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c3b3cbc9-a79f-42df-8d72-5db1221cb26a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_shap_values(df_omics.loc[shap_omics_RF.index], shap_omics_RF, n_best=10, figsize=(9, 10), title=\"Random Forest - OS\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5ec86fc-faa7-4f3f-bd6b-a33292371e49",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 2. Combine SHAP values over the 4 models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ffcf729-0eda-4f1d-9f73-f355442f2f4b",
   "metadata": {},
   "source": [
    "### 2.1 Compute correlations between features and associated SHAP values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd39850-7409-4fb8-88f0-7eb72d407b4d",
   "metadata": {},
   "source": [
    "We only keep features that have a constant correlation sign (either -1 or +1) across the 4 predictive models. For instance *Myeloid dendritic cells* feature is negatively correlated with its associated SHAP values for XGboost, LR, Cox, and Random Survival Forest models. It is thus considered as a \"robust important feature\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b5e8fbb0-cc94-4f9d-8cde-cf4690b6602f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_correlation_signs(data, shap_values):\n",
    "    corr_matrix = data.join(shap_values, rsuffix = \"_shap\").corr(method=\"spearman\").iloc[33:, :33].values\n",
    "    return np.sign(np.diagonal(corr_matrix)).reshape(-1, 1)\n",
    "\n",
    "xgboost_sign = extract_correlation_signs(df_omics.loc[shap_omics_xgboost.index].iloc[:, :-1], shap_omics_xgboost.iloc[:, :-1])\n",
    "LR_sign = extract_correlation_signs(df_omics.loc[shap_omics_LR.index].iloc[:, :-1], shap_omics_LR.iloc[:, :-1])\n",
    "RF_sign = extract_correlation_signs(df_omics.loc[shap_omics_RF.index].iloc[:, :-1], shap_omics_RF.iloc[:, :-1])\n",
    "Cox_sign = extract_correlation_signs(df_omics.loc[shap_omics_Cox.index].iloc[:, :-1], shap_omics_Cox.iloc[:, :-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dfd752b5-d654-4662-9c96-2c4ff14679b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_signs = pd.DataFrame(np.hstack([xgboost_sign, LR_sign, RF_sign, Cox_sign]), columns = [\"XGboost\", \"LR\", \"RF\", \"Cox\"], index = shap_omics_xgboost.columns[:-1])\n",
    "bool_mask = (df_signs.sum(axis=1) == -4) | (df_signs.sum(axis=1) == 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93a1aee7-162f-49ef-8c32-66ad8a96b8ed",
   "metadata": {},
   "source": [
    "### 2.2 Rank features with respect to their absolute mean SHAP value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c76eb0e-c783-42fd-8967-536f6f19d710",
   "metadata": {},
   "source": [
    "**Note:** Here we only rank the \"robust important features\", filtering out the ones whose correlation sign was not consistent across the four models (see code above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "692227f6-6213-4eaa-adfb-769684096759",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>XGBoost</th>\n",
       "      <th>LR</th>\n",
       "      <th>RF</th>\n",
       "      <th>Cox</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B lineage</th>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fibroblasts</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monocytic lineage</th>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Myeloid dendritic cells</th>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KRAS</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MET</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROS1</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGFR1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGFR2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTRK1</th>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTRK3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NRAS</th>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMB_RNA</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         XGBoost    LR    RF   Cox\n",
       "B lineage                    7.0  11.0   6.0   8.0\n",
       "Fibroblasts                  2.0   1.0   7.0  11.0\n",
       "Monocytic lineage           10.0   6.0   8.0   3.0\n",
       "Myeloid dendritic cells     12.0  13.0  13.0  13.0\n",
       "KRAS                         9.0   9.0   9.0   9.0\n",
       "MET                          4.0   7.0   1.0   5.0\n",
       "ROS1                         6.0   4.0  11.0   1.0\n",
       "FGFR1                        8.0   2.0   5.0   4.0\n",
       "FGFR2                        5.0   3.0   3.0   2.0\n",
       "NTRK1                       11.0   8.0  12.0  10.0\n",
       "NTRK3                        1.0  10.0   4.0   6.0\n",
       "NRAS                        13.0  12.0  10.0  12.0\n",
       "TMB_RNA                      3.0   5.0   2.0   7.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rank = pd.concat([np.abs((shap_omics_xgboost.iloc[:, :-1].T)[bool_mask]).mean(axis=1).rank(ascending=True).rename(\"XGBoost\"),\n",
    "                     np.abs((shap_omics_LR.iloc[:, :-1].T)[bool_mask]).mean(axis=1).rank(ascending=True).rename(\"LR\"),\n",
    "                     np.abs((shap_omics_RF.iloc[:, :-1].T)[bool_mask]).mean(axis=1).rank(ascending=True).rename(\"RF\"),\n",
    "                     np.abs((shap_omics_Cox.iloc[:, :-1].T)[bool_mask]).mean(axis=1).rank(ascending=True).rename(\"Cox\")],\n",
    "                    axis=1\n",
    "                   )\n",
    "df_rank"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9beafc-fd85-4ab6-a877-98ad198b54f0",
   "metadata": {},
   "source": [
    "### 2.3 Aggregate the ranks (weighted average of the ranks obtained with the different models)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52d75d03-77c8-444d-ba59-3bd75700e7a6",
   "metadata": {},
   "source": [
    "We define the aggregated rank of each feature with the following formula:\n",
    "\n",
    "\\begin{equation}\n",
    "r_f^{cons} = \\dfrac{1}{s_1 + s_2 + s_3 + s_4} \\sum_{i=1}^{4} s_i r_i^f\n",
    "\\end{equation}\n",
    "\n",
    "Where $s_i$ corresponds to the score (i.e., AUC or C-index) of the model i and is equal to max(0, $score_i - 0.5$) and $r_i^f$ corresponds to the rank of feature $f$ for model i.   \n",
    "\n",
    "**Note 1:** Performance scores were computed with all the patients with the modality of interest available (e.g. The AUC associated with the 1-year death prediction of an XGboost model and estimated with a repeated cross-validation scheme was 0.663 (for 134 patients with the RNA modality available)).\n",
    "\n",
    "**Note 2:** The aggregated ranks are normalized with respect to the total number of consensus features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a5bc62a8-4cf6-4c63-ac49-665b25b17cfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Consensus importance</th>\n",
       "      <th>Impact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Myeloid dendritic cells</th>\n",
       "      <td>-0.972503</td>\n",
       "      <td>Lower risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NRAS</th>\n",
       "      <td>0.908738</td>\n",
       "      <td>Increase risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTRK1</th>\n",
       "      <td>-0.795715</td>\n",
       "      <td>Lower risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KRAS</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>Increase risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B lineage</th>\n",
       "      <td>-0.610324</td>\n",
       "      <td>Lower risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monocytic lineage</th>\n",
       "      <td>-0.592105</td>\n",
       "      <td>Lower risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROS1</th>\n",
       "      <td>-0.487854</td>\n",
       "      <td>Lower risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGFR1</th>\n",
       "      <td>-0.395749</td>\n",
       "      <td>Lower risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTRK3</th>\n",
       "      <td>-0.367915</td>\n",
       "      <td>Lower risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MET</th>\n",
       "      <td>0.316296</td>\n",
       "      <td>Increase risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fibroblasts</th>\n",
       "      <td>-0.300776</td>\n",
       "      <td>Lower risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMB_RNA</th>\n",
       "      <td>-0.281039</td>\n",
       "      <td>Lower risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGFR2</th>\n",
       "      <td>0.278677</td>\n",
       "      <td>Increase risk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Consensus importance         Impact\n",
       "Myeloid dendritic cells             -0.972503     Lower risk\n",
       "NRAS                                 0.908738  Increase risk\n",
       "NTRK1                               -0.795715     Lower risk\n",
       "KRAS                                 0.692308  Increase risk\n",
       "B lineage                           -0.610324     Lower risk\n",
       "Monocytic lineage                   -0.592105     Lower risk\n",
       "ROS1                                -0.487854     Lower risk\n",
       "FGFR1                               -0.395749     Lower risk\n",
       "NTRK3                               -0.367915     Lower risk\n",
       "MET                                  0.316296  Increase risk\n",
       "Fibroblasts                         -0.300776     Lower risk\n",
       "TMB_RNA                             -0.281039     Lower risk\n",
       "FGFR2                                0.278677  Increase risk"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Weighted average\n",
    "xgboost_per = 0.663 - 0.5\n",
    "lr_perf = 0.627 - 0.5\n",
    "rf_perf = 0.624 - 0.5\n",
    "cox_perf = 0.542 - 0.5\n",
    "final_importance = (df_rank.apply(lambda row: (1/(xgboost_per+lr_perf+rf_perf+cox_perf))*(xgboost_per*row[\"XGBoost\"] + lr_perf*row[\"LR\"] + rf_perf*row[\"RF\"] + cox_perf*row[\"Cox\"]),\n",
    "                                  axis=1)\n",
    "                           .sort_values(ascending=False)\n",
    "                   )\n",
    "\n",
    "# 2. Normalize by the number of consensus features\n",
    "final_importance = (final_importance/df_rank.shape[0]) * df_signs[bool_mask][\"XGboost\"].loc[final_importance.index]\n",
    "\n",
    "# 3. Add annotations\n",
    "final_importance = final_importance.to_frame().rename(columns={0: \"Consensus importance\"})\n",
    "final_importance[\"Impact\"] = 1*(final_importance[\"Consensus importance\"] > 0)\n",
    "final_importance = final_importance.replace(to_replace = {\"Impact\": {0: \"Lower risk\", 1: \"Increase risk\"}})\n",
    "final_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d7226f8d-6b58-45a1-bd4d-8cdc5ad11fa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "sns.barplot(data=final_importance.reset_index(), orient=\"h\", x=\"Consensus importance\", y=\"index\", hue=\"Impact\", palette=[\"blue\", \"red\"], dodge=False, ax=ax)\n",
    "\n",
    "ax.set(xlabel=None, ylabel=None)\n",
    "ax.set_axisbelow(True)\n",
    "ax.yaxis.grid(color=\"gray\", linestyle=\"dashed\")\n",
    "ax.legend(fontsize=12)\n",
    "ax.axvline(x=0, color=\"k\")\n",
    "ax.xaxis.set_tick_params(labelsize=12)\n",
    "ax.yaxis.set_tick_params(labelsize=12)\n",
    "ax.set_title(\"Consensus importance\", fontsize=14)\n",
    "ax.set_xlim(-1.05, 1.05)\n",
    "plt.tight_layout()\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da4b96a7-e15a-4678-87f9-287ab5f7e155",
   "metadata": {},
   "source": [
    "## 3. Compute correlations between consensus features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7d53f17b-5f10-493b-8245-1422504b679b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(226.82954545454544, 0.5, 'Correlation')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cg = sns.clustermap(df_omics[final_importance.index].corr(method=\"spearman\"), vmin=-1, vmax=1, cmap=\"bwr\",  cbar_kws={'shrink': 1.3})\n",
    "cg.ax_row_dendrogram.set_visible(False)\n",
    "cg.ax_col_dendrogram.set_visible(False)\n",
    "cg.ax_heatmap.yaxis.tick_left()\n",
    "cg.ax_heatmap.xaxis.tick_top()\n",
    "cg.ax_heatmap.xaxis.set_tick_params(labelsize=12, rotation=90)\n",
    "cg.ax_heatmap.yaxis.set_tick_params(labelsize=12)\n",
    "cg.ax_cbar.set_position([0.86, 0.25, 0.025, 0.5])\n",
    "cg.ax_cbar.set_ylabel(\"Correlation\", labelpad=-70, fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9521a5aa-89e1-44f0-8323-f98ae14a008f",
   "metadata": {},
   "source": [
    "## 4. Univariate tests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d391c296-f500-4490-b431-397f57e259b3",
   "metadata": {},
   "source": [
    "### 4.1 Univariate test for OS prediction (C-index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f2ac6ede-5115-4f9d-9bd2-c3724dd83623",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bootstrap_surv(data, data_test, risk_score, n_b = 1000, disable=True):\n",
    "    \"\"\"\n",
    "    data: Structured array of shape (n_samples_train,)\n",
    "        Data to estimate the censoring distribution from (see sksruv.metrics.concordance_index_ipcw)\n",
    "    \n",
    "    data_test: Structured array of shape (n_samples,)\n",
    "        Survival time and event indicator of test data\n",
    "        \n",
    "    risk_score: array of shape (n_samples,)\n",
    "        risk_score\n",
    "        \n",
    "    n_b: int\n",
    "        Number of bootstrap samples. Default is 1000\n",
    "    \n",
    "    disable: bool. Default is True\n",
    "        Disable tqdm\n",
    "    \"\"\"\n",
    "    n_patients = len(data_test)\n",
    "    c_unos = []\n",
    "    for i in tqdm(range(n_b), disable=disable):\n",
    "        boot_sample = np.random.choice(np.arange(n_patients) , size=n_patients)\n",
    "        c_unos.append(concordance_index_ipcw(data, data_test[boot_sample], risk_score[boot_sample])[0])\n",
    "    return c_unos\n",
    "\n",
    "def permutation_test_surv(data, data_test, risk_score, n_p = 1000, disable=True):\n",
    "    \"\"\"\n",
    "    data: Structured array of shape (n_samples_train,)\n",
    "        Data to estimate the censoring distribution from (see sksruv.metrics.concordance_index_ipcw)\n",
    "    \n",
    "    data_test: Structured array of shape (n_samples,)\n",
    "        Survival time and event indicator of test data\n",
    "        \n",
    "    risk_score: array of shape (n_samples,)\n",
    "        risk_score\n",
    "        \n",
    "    n_p: int\n",
    "        Number of permutation samples. Default is 1000\n",
    "    \n",
    "    disable: bool. Default is True\n",
    "        Disable tqdm\n",
    "    \"\"\"\n",
    "    c_unos = []\n",
    "    for i in tqdm(range(n_p), disable=disable):\n",
    "        np.random.shuffle(risk_score)\n",
    "        c_unos.append(concordance_index_ipcw(data, data_test, risk_score)[0])\n",
    "    return c_unos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "93bb2049-71ab-4cd0-9590-e4059593f8c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [01:31<00:00,  7.07s/it]\n"
     ]
    }
   ],
   "source": [
    "bool_mask = df_clinicals['OS'].isnull()\n",
    "target = Surv.from_arrays(event = 1*(df_clinicals.loc[~bool_mask, \"Statut Vital\"]==\"Decede\").values,\n",
    "                          time = df_clinicals.loc[~bool_mask, \"OS\"].values)\n",
    "\n",
    "dic = {'cindex': [], 'pval_left': [], 'pval_right': [], 'boot_left': [], 'boot_right': []}\n",
    "\n",
    "for feature in tqdm(final_importance.index, total=final_importance.shape[0]):\n",
    "    \n",
    "    temp = pd.concat([df_omics[feature], df_clinicals.loc[~bool_mask, [\"OS\", \"Statut Vital\"]]], axis=1).dropna()\n",
    "    \n",
    "    target_test = Surv.from_arrays(event = 1*(temp[\"Statut Vital\"]==\"Decede\").values,\n",
    "                                   time = temp[\"OS\"].values)\n",
    "    \n",
    "    c_index = concordance_index_ipcw(target, target_test, temp[feature].values)[0]\n",
    "    p_cindex = permutation_test_surv(target, target_test, risk_score = np.copy(temp[feature].values))\n",
    "    boot_cindex = bootstrap_surv(target, target_test, risk_score = np.copy(temp[feature].values))\n",
    "    \n",
    "    dic['cindex'].append(c_index)\n",
    "    dic['pval_left'].append((np.sum(np.array(p_cindex) <= c_index) + 1)/(len(p_cindex)+1))\n",
    "    dic['pval_right'].append((np.sum(np.array(p_cindex) >= c_index) + 1)/(len(p_cindex)+1))\n",
    "    dic['boot_left'].append(np.quantile(np.array(boot_cindex), 0.025))\n",
    "    dic['boot_right'].append(np.quantile(np.array(boot_cindex), 0.975))\n",
    "\n",
    "df_tests = pd.DataFrame(dic, index =final_importance.index) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cfb1f920-0ac4-492e-9df4-8d0e8f56b834",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cindex</th>\n",
       "      <th>pval_left</th>\n",
       "      <th>pval_right</th>\n",
       "      <th>boot_left</th>\n",
       "      <th>boot_right</th>\n",
       "      <th>adjusted_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Myeloid dendritic cells</th>\n",
       "      <td>0.401703</td>\n",
       "      <td>0.001998</td>\n",
       "      <td>0.999001</td>\n",
       "      <td>0.336083</td>\n",
       "      <td>0.469753</td>\n",
       "      <td>0.025974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NRAS</th>\n",
       "      <td>0.585145</td>\n",
       "      <td>0.992008</td>\n",
       "      <td>0.008991</td>\n",
       "      <td>0.513537</td>\n",
       "      <td>0.655507</td>\n",
       "      <td>0.049351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTRK1</th>\n",
       "      <td>0.417541</td>\n",
       "      <td>0.014985</td>\n",
       "      <td>0.986014</td>\n",
       "      <td>0.348820</td>\n",
       "      <td>0.492976</td>\n",
       "      <td>0.049351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KRAS</th>\n",
       "      <td>0.516243</td>\n",
       "      <td>0.693307</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.424479</td>\n",
       "      <td>0.592642</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B lineage</th>\n",
       "      <td>0.422202</td>\n",
       "      <td>0.018981</td>\n",
       "      <td>0.982018</td>\n",
       "      <td>0.344191</td>\n",
       "      <td>0.502500</td>\n",
       "      <td>0.049351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monocytic lineage</th>\n",
       "      <td>0.435446</td>\n",
       "      <td>0.050949</td>\n",
       "      <td>0.950050</td>\n",
       "      <td>0.361497</td>\n",
       "      <td>0.518247</td>\n",
       "      <td>0.094620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROS1</th>\n",
       "      <td>0.449236</td>\n",
       "      <td>0.097902</td>\n",
       "      <td>0.903097</td>\n",
       "      <td>0.377599</td>\n",
       "      <td>0.528620</td>\n",
       "      <td>0.159091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGFR1</th>\n",
       "      <td>0.461426</td>\n",
       "      <td>0.138861</td>\n",
       "      <td>0.862138</td>\n",
       "      <td>0.383403</td>\n",
       "      <td>0.544979</td>\n",
       "      <td>0.200577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTRK3</th>\n",
       "      <td>0.471907</td>\n",
       "      <td>0.206793</td>\n",
       "      <td>0.794206</td>\n",
       "      <td>0.394614</td>\n",
       "      <td>0.544144</td>\n",
       "      <td>0.246753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MET</th>\n",
       "      <td>0.494086</td>\n",
       "      <td>0.415584</td>\n",
       "      <td>0.585415</td>\n",
       "      <td>0.398247</td>\n",
       "      <td>0.576490</td>\n",
       "      <td>0.415584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fibroblasts</th>\n",
       "      <td>0.424034</td>\n",
       "      <td>0.018981</td>\n",
       "      <td>0.982018</td>\n",
       "      <td>0.345698</td>\n",
       "      <td>0.510861</td>\n",
       "      <td>0.049351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMB_RNA</th>\n",
       "      <td>0.465814</td>\n",
       "      <td>0.208791</td>\n",
       "      <td>0.792208</td>\n",
       "      <td>0.383805</td>\n",
       "      <td>0.553036</td>\n",
       "      <td>0.246753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGFR2</th>\n",
       "      <td>0.570165</td>\n",
       "      <td>0.974026</td>\n",
       "      <td>0.026973</td>\n",
       "      <td>0.488042</td>\n",
       "      <td>0.650655</td>\n",
       "      <td>0.058442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           cindex  pval_left  pval_right  boot_left  \\\n",
       "Myeloid dendritic cells  0.401703   0.001998    0.999001   0.336083   \n",
       "NRAS                     0.585145   0.992008    0.008991   0.513537   \n",
       "NTRK1                    0.417541   0.014985    0.986014   0.348820   \n",
       "KRAS                     0.516243   0.693307    0.307692   0.424479   \n",
       "B lineage                0.422202   0.018981    0.982018   0.344191   \n",
       "Monocytic lineage        0.435446   0.050949    0.950050   0.361497   \n",
       "ROS1                     0.449236   0.097902    0.903097   0.377599   \n",
       "FGFR1                    0.461426   0.138861    0.862138   0.383403   \n",
       "NTRK3                    0.471907   0.206793    0.794206   0.394614   \n",
       "MET                      0.494086   0.415584    0.585415   0.398247   \n",
       "Fibroblasts              0.424034   0.018981    0.982018   0.345698   \n",
       "TMB_RNA                  0.465814   0.208791    0.792208   0.383805   \n",
       "FGFR2                    0.570165   0.974026    0.026973   0.488042   \n",
       "\n",
       "                         boot_right  adjusted_pvalue  \n",
       "Myeloid dendritic cells    0.469753         0.025974  \n",
       "NRAS                       0.655507         0.049351  \n",
       "NTRK1                      0.492976         0.049351  \n",
       "KRAS                       0.592642         0.333333  \n",
       "B lineage                  0.502500         0.049351  \n",
       "Monocytic lineage          0.518247         0.094620  \n",
       "ROS1                       0.528620         0.159091  \n",
       "FGFR1                      0.544979         0.200577  \n",
       "NTRK3                      0.544144         0.246753  \n",
       "MET                        0.576490         0.415584  \n",
       "Fibroblasts                0.510861         0.049351  \n",
       "TMB_RNA                    0.553036         0.246753  \n",
       "FGFR2                      0.650655         0.058442  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tests[\"adjusted_pvalue\"] = multipletests(df_tests[['pval_left', 'pval_right']].min(axis=1).values,\n",
    "                                            method='fdr_bh')[1]\n",
    "df_tests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9e688cb-e615-4d0f-a13d-6cffed19a914",
   "metadata": {},
   "source": [
    "### 4.2 Univariate tests for 1-year death prediction (AUC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3fddadfd-9b4d-4f1e-a312-d744916cbf5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bootstrap_clf(y_true, y_score, n_b = 1000, disable=True):\n",
    "    \"\"\"\n",
    "    y_true: array of shape (n_samples,)\n",
    "        True binary labels\n",
    "    \n",
    "    y_score: array of shape (n_samples,)\n",
    "        Prediction score\n",
    "        \n",
    "    n_b: int\n",
    "        Number of bootstrap samples. Default is 1000\n",
    "    \n",
    "    disable: bool. Default is True\n",
    "        Disable tqdm\n",
    "    \"\"\"\n",
    "    n_patients = len(y_true)\n",
    "    aucs = []\n",
    "    for i in tqdm(range(n_b), disable=disable):\n",
    "        boot_sample = np.random.choice(np.arange(n_patients) , size=n_patients)\n",
    "        aucs.append(roc_auc_score(y_true[boot_sample], y_score[boot_sample]))\n",
    "    return aucs\n",
    "\n",
    "\n",
    "def permutation_test_clf(y_true, y_score, n_p = 1000, disable=True):\n",
    "    \"\"\"\n",
    "    y_true: array of shape (n_samples,)\n",
    "        True binary labels\n",
    "    \n",
    "    y_score: array of shape (n_samples,)\n",
    "        Prediction score\n",
    "        \n",
    "    n_p: int\n",
    "        Number of permutation samples. Default is 1000\n",
    "    \n",
    "    disable: bool. Default is True\n",
    "        Disable tqdm\n",
    "    \"\"\"\n",
    "    aucs = []\n",
    "    for i in tqdm(range(n_p), disable=disable):\n",
    "        np.random.shuffle(y_true)\n",
    "        aucs.append(roc_auc_score(y_true, y_score))\n",
    "    return aucs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a992988e-08db-44b2-907b-6a6090193866",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:26<00:00,  2.03s/it]\n"
     ]
    }
   ],
   "source": [
    "bool_mask = (df_clinicals['OS'].isnull()) | ((df_clinicals['OS'] <= 365) & (df_clinicals['Statut Vital'] == \"Vivant\"))\n",
    "\n",
    "dic_aucs = {'auc': [], 'pval_left': [], 'pval_right': [], 'boot_left': [], 'boot_right': []}\n",
    "\n",
    "for feature in tqdm(final_importance.index, total=final_importance.shape[0]):\n",
    "    \n",
    "    temp = pd.concat([df_omics[feature], df_clinicals.loc[~bool_mask, [\"OS\", \"Statut Vital\"]]], axis=1).dropna()\n",
    "    y_true = 1*(temp['OS'] <= 365).values\n",
    "    \n",
    "    auc = roc_auc_score(y_true = y_true, y_score = temp[feature].values)\n",
    "    p_auc = permutation_test_clf(y_true, np.copy(temp[feature].values), n_p = 1000)\n",
    "    boot_auc = bootstrap_clf(y_true, np.copy(temp[feature].values), n_b = 1000)\n",
    "    \n",
    "    dic_aucs['auc'].append(auc)\n",
    "    dic_aucs['pval_left'].append((np.sum(np.array(p_auc) <= auc) + 1)/(len(p_auc)+1))\n",
    "    dic_aucs['pval_right'].append((np.sum(np.array(p_auc) >= auc) + 1)/(len(p_auc)+1))\n",
    "    dic_aucs['boot_left'].append(np.quantile(np.array(boot_auc), 0.025))\n",
    "    dic_aucs['boot_right'].append(np.quantile(np.array(boot_auc), 0.975))\n",
    "\n",
    "df_tests_aucs = pd.DataFrame(dic_aucs, index =final_importance.index) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "62ae9924-0928-47d9-83f9-09431659450b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>auc</th>\n",
       "      <th>pval_left</th>\n",
       "      <th>pval_right</th>\n",
       "      <th>boot_left</th>\n",
       "      <th>boot_right</th>\n",
       "      <th>adjusted_pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Myeloid dendritic cells</th>\n",
       "      <td>0.299060</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.427348</td>\n",
       "      <td>0.643591</td>\n",
       "      <td>0.006494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NRAS</th>\n",
       "      <td>0.674207</td>\n",
       "      <td>0.998002</td>\n",
       "      <td>0.002997</td>\n",
       "      <td>0.380286</td>\n",
       "      <td>0.605504</td>\n",
       "      <td>0.012987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTRK1</th>\n",
       "      <td>0.365893</td>\n",
       "      <td>0.008991</td>\n",
       "      <td>0.992008</td>\n",
       "      <td>0.388981</td>\n",
       "      <td>0.604827</td>\n",
       "      <td>0.019481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KRAS</th>\n",
       "      <td>0.619859</td>\n",
       "      <td>0.989011</td>\n",
       "      <td>0.011988</td>\n",
       "      <td>0.339284</td>\n",
       "      <td>0.545669</td>\n",
       "      <td>0.022263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B lineage</th>\n",
       "      <td>0.314336</td>\n",
       "      <td>0.000999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.429191</td>\n",
       "      <td>0.644988</td>\n",
       "      <td>0.006494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monocytic lineage</th>\n",
       "      <td>0.364865</td>\n",
       "      <td>0.007992</td>\n",
       "      <td>0.993007</td>\n",
       "      <td>0.417118</td>\n",
       "      <td>0.621728</td>\n",
       "      <td>0.019481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROS1</th>\n",
       "      <td>0.410987</td>\n",
       "      <td>0.061938</td>\n",
       "      <td>0.939061</td>\n",
       "      <td>0.305947</td>\n",
       "      <td>0.523812</td>\n",
       "      <td>0.100649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGFR1</th>\n",
       "      <td>0.450059</td>\n",
       "      <td>0.190809</td>\n",
       "      <td>0.810190</td>\n",
       "      <td>0.394007</td>\n",
       "      <td>0.636226</td>\n",
       "      <td>0.206710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTRK3</th>\n",
       "      <td>0.372650</td>\n",
       "      <td>0.008991</td>\n",
       "      <td>0.992008</td>\n",
       "      <td>0.333093</td>\n",
       "      <td>0.523875</td>\n",
       "      <td>0.019481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MET</th>\n",
       "      <td>0.564924</td>\n",
       "      <td>0.880120</td>\n",
       "      <td>0.122877</td>\n",
       "      <td>0.371033</td>\n",
       "      <td>0.601713</td>\n",
       "      <td>0.159740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fibroblasts</th>\n",
       "      <td>0.417450</td>\n",
       "      <td>0.074925</td>\n",
       "      <td>0.927073</td>\n",
       "      <td>0.337889</td>\n",
       "      <td>0.555326</td>\n",
       "      <td>0.108225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMB_RNA</th>\n",
       "      <td>0.466222</td>\n",
       "      <td>0.297702</td>\n",
       "      <td>0.704296</td>\n",
       "      <td>0.432989</td>\n",
       "      <td>0.694823</td>\n",
       "      <td>0.297702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGFR2</th>\n",
       "      <td>0.561692</td>\n",
       "      <td>0.851149</td>\n",
       "      <td>0.150849</td>\n",
       "      <td>0.373617</td>\n",
       "      <td>0.604652</td>\n",
       "      <td>0.178276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              auc  pval_left  pval_right  boot_left  \\\n",
       "Myeloid dendritic cells  0.299060   0.000999    1.000000   0.427348   \n",
       "NRAS                     0.674207   0.998002    0.002997   0.380286   \n",
       "NTRK1                    0.365893   0.008991    0.992008   0.388981   \n",
       "KRAS                     0.619859   0.989011    0.011988   0.339284   \n",
       "B lineage                0.314336   0.000999    1.000000   0.429191   \n",
       "Monocytic lineage        0.364865   0.007992    0.993007   0.417118   \n",
       "ROS1                     0.410987   0.061938    0.939061   0.305947   \n",
       "FGFR1                    0.450059   0.190809    0.810190   0.394007   \n",
       "NTRK3                    0.372650   0.008991    0.992008   0.333093   \n",
       "MET                      0.564924   0.880120    0.122877   0.371033   \n",
       "Fibroblasts              0.417450   0.074925    0.927073   0.337889   \n",
       "TMB_RNA                  0.466222   0.297702    0.704296   0.432989   \n",
       "FGFR2                    0.561692   0.851149    0.150849   0.373617   \n",
       "\n",
       "                         boot_right  adjusted_pvalue  \n",
       "Myeloid dendritic cells    0.643591         0.006494  \n",
       "NRAS                       0.605504         0.012987  \n",
       "NTRK1                      0.604827         0.019481  \n",
       "KRAS                       0.545669         0.022263  \n",
       "B lineage                  0.644988         0.006494  \n",
       "Monocytic lineage          0.621728         0.019481  \n",
       "ROS1                       0.523812         0.100649  \n",
       "FGFR1                      0.636226         0.206710  \n",
       "NTRK3                      0.523875         0.019481  \n",
       "MET                        0.601713         0.159740  \n",
       "Fibroblasts                0.555326         0.108225  \n",
       "TMB_RNA                    0.694823         0.297702  \n",
       "FGFR2                      0.604652         0.178276  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tests_aucs[\"adjusted_pvalue\"] = multipletests(df_tests_aucs[['pval_left', 'pval_right']].min(axis=1).values, method='fdr_bh')[1]\n",
    "df_tests_aucs"
   ]
  }
 ],
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