Copy_of_PopGrowthLecture_assignment.ipynb

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        "<a href=\"https://colab.research.google.com/github/VU-CSP/github-classroom-intro-Deepanshu-s1/blob/main/Copy_of_PopGrowthLecture_assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Analysis of Cell Proliferation\n",
        "## Objectives\n",
        "This notebook has sections designed to:\n",
        "1) Provide background on cell proliferation  \n",
        "2) Describe basic mathematical models of population growth  \n",
        "3) Analyze dynamic cell count data  \n",
        "4) Perform linear and nonlinear regression of models fit to cell count data  \n",
        "5) Create functions in Python to perform calcuations\n",
        "\n",
        "## Cell proliferation\n",
        "### Nomenclature\n",
        "* _Growth_ can mean an increase in mass or volume (at the individual level)\n",
        "* _Growth_ can also indicate an increase in population size (e.g. the number of cells in a tumor)\n",
        "* _Proliferation_ generally refers to change in the number of individuals within a group (e.g. exponential population growth)\n",
        "* _Tumor growth_ generally refers to the size of the tumor, but an increase in the number of tumor cells is implicit\n",
        "\n",
        "### Typical assays of proliferation, survival and death\n",
        "* Usually performed to test effects of perturbation (e.g. a drug)  \n",
        "* Most are static assays (take a snapshot)  \n",
        "* Multiple measurements require multiple samples (since they are destructive)  \n",
        "* Typically reported as fraction of control or percent change (relative, not absolute metrics)  \n",
        "* Biomarkers of the processes that change cell population size  \n",
        "\n",
        "Images below are representatives of 1) fluorescence microscopy of calcein/propidium iodide stained cells, 2) flow cytometry of cells stained with FxCycle violet and phospho-histone H3, 3) fluorescence microscopy of annexin A5-fluorescein-stained cells and 4) fluorescence microscopy of cells after addition of caspase 3 substrate that becomes fluorogenic upon cleavage by caspase 3 (pink).  \n",
        "\n",
        "\n",
        "\n",
        "<table>\n",
        "    <tr>\n",
        "      <td>\n",
        "      <img src=\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/calcein-PI.png?raw=1\" style=\"width: 200px;\" />\n",
        "      </td>\n",
        "      <td>\n",
        "      <img src=\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/flow-mitosis.png?raw=1\" style=\"width: 200px;\" />\n",
        "      </td>\n",
        "      <td>\n",
        "      <img src=\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/Anx5.png?raw=1\" style=\"width: 200px;\" />\n",
        "      </td>\n",
        "      <td>\n",
        "      <img src=\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/activeCasp3.png?raw=1\" style=\"width: 200px;\" />\n",
        "      </td>\n",
        "     </tr>\n",
        "</table>"
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        "### Exponential growth\n",
        "#### _A math refresher_\n",
        "<img src=\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/Math-ugh.png?raw=1\" style=\"width: 500px;\"/>\n",
        "\n",
        "* Definition of Logarithm: $log_e e^x = x$, where $e > 0$ and $e ≠ 1$   \n",
        "* Product Rule: $log(xy) = log(x) + log(y)$\n",
        "* Base change rule: $log_a(n) = log_b(n)/log_a(b)$  \n",
        "\n",
        "#### **Exponential growth equation**\n",
        "$P(t) = P_0e^{at}$, where  \n",
        "$P(t)$ is the population size at time = $t$,  \n",
        "$P_0$ is initial population size, and  \n",
        "$a$ is the growth rate constant.\n",
        "\n",
        "_The logarithm of the growth equation is linear_  \n",
        "$log_e P(t) = log_e P_0e^{at} = log_e P_0 + log_e e^{at} = log_e P_0 + at$  \n",
        "\n",
        "_(reorganizing to the form of $y = ax + b$)_  \n",
        "$log_e P(t) = at + log_e P_0$, where,  \n",
        "growth rate = $a$ = _slope of the line_ and log of initial population size = _y intercept_   \n",
        "\n",
        "##### **This means that you can fit exponentially growing population data with linear models**\n",
        "\n",
        "This also works in log2 scale using a population doubling function:  \n",
        "$P(t) = P_02^{at}$,  \n",
        "which allows for an easier biological interpretation."
      ]
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      "source": [
        "## Plotting and interpreting cell population growth data\n",
        "These graphs were previously generated and are shown for reference. Data are shown in linear, log2 and normalized log2 scales. Lines shown on log2 and normalized log2 plots represent linear model fits and the slope (proliferation rate) and doubling time (1/proliferation rate) parameters of the optimal model fit are shown.  \n",
        "\n",
        "<img src=\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/pop_growth_difft_scales.png?raw=1\" style=\"width: 800px;\" />\n"
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      "source": [
        "### Divergence from exponential growth\n",
        "There are numerous conditions that result in non-exponential cell population growth, for example, when cells fill in their available space (a.k.a. contact inhibition).  \n"
      ]
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      "source": [
        "<img src=\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/contact_inhibition.png?raw=1\" style=\"width: 300px;\"/>  "
      ]
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        "There are some specific models that have been used to model growth inhibition, including the\n",
        "**Logistic** and **Gompertz** functions.  \n"
      ]
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      "source": [
        "<img src=\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/evidence_for_Gompertz_tumors.png?raw=1\" style=\"width: 800px;\"/>  \n",
        "<img src=\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/logistic_growth_model.jpeg?raw=1\" style=\"width: 800px;\"/>  \n",
        "<img src=\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/gompertz_growth_model.jpeg?raw=1\" style=\"width: 800px;\"/>  \n"
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      "source": [
        "_We will do some exploratory data analysis of a small cell count data set to interpret how populations of cells grow in culture._\n",
        "-----"
      ]
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    {
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      },
      "source": [
        "### First, import necessary Python packages"
      ]
    },
    {
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        "id": "549f8f33-de74-4ddd-9645-22a758ea2814"
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      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import os"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Sfrp0w8cysS1",
      "metadata": {
        "id": "Sfrp0w8cysS1"
      },
      "source": [
        "See whether we are running in Google Colab or not."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "CLSZT8xMyphI",
      "metadata": {
        "id": "CLSZT8xMyphI"
      },
      "outputs": [],
      "source": [
        "try:\n",
        "  import google.colab\n",
        "  IN_COLAB = True\n",
        "except:\n",
        "  IN_COLAB = False"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "### Load custom functions\n",
        "File name `growth_fxns.py` includes functions for:  \n",
        "* flattening a matrix (nested lists): `flatten_matrix`  \n",
        "* stochastic simulation of exponential growth: `gillespie_growth`  \n",
        "* deterministic solution of exponential growth: `exp_growth`  \n",
        "    \n",
        "* deterministic solution of logistic growth: `mylogistic`\n",
        "* deterministic solution of Gompertzian growth: `gompertz`\n"
      ]
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            "--2024-03-12 00:45:48--  https://raw.githubusercontent.com/VU-CSP/quantbio-assignments/main/growth_fxns.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2307 (2.3K) [text/plain]\n",
            "Saving to: ‘growth_fxns.py’\n",
            "\n",
            "\rgrowth_fxns.py        0%[                    ]       0  --.-KB/s               \rgrowth_fxns.py      100%[===================>]   2.25K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-03-12 00:45:48 (29.2 MB/s) - ‘growth_fxns.py’ saved [2307/2307]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "if IN_COLAB and not os.path.exists(\"growth_fxns.py\"):\n",
        "    !wget \"https://raw.githubusercontent.com/VU-CSP/quantbio-assignments/main/growth_fxns.py\"\n",
        "from growth_fxns import *"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "95fd8d3f-61c2-463d-8b01-1e961269af9c",
      "metadata": {
        "id": "95fd8d3f-61c2-463d-8b01-1e961269af9c"
      },
      "source": [
        "### Adjust seaborn plot settings\n",
        "_To make the graphics prettier._ You can learn more about seaborn at the [seaborn website]()  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f28ff5a6-dc57-4fe3-a49f-d448f8bbc226",
      "metadata": {
        "id": "f28ff5a6-dc57-4fe3-a49f-d448f8bbc226"
      },
      "outputs": [],
      "source": [
        "sns.set_context(\"notebook\", font_scale=1.5, rc={\"lines.linewidth\": 2.5})"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fb6587a1-2b3d-4aaf-ad96-c50e6eb7547a",
      "metadata": {
        "id": "fb6587a1-2b3d-4aaf-ad96-c50e6eb7547a"
      },
      "source": [
        "#### Load a small dataset\n",
        "Data from multiwell plate of cells, some of which have been treated with a drug."
      ]
    },
    {
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      "execution_count": null,
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            "--2024-03-12 00:46:03--  https://raw.githubusercontent.com/VU-CSP/quantbio-assignments/main/MCF10A_trametinib.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 13490 (13K) [text/plain]\n",
            "Saving to: ‘MCF10A_trametinib.csv’\n",
            "\n",
            "\rMCF10A_trametinib.c   0%[                    ]       0  --.-KB/s               \rMCF10A_trametinib.c 100%[===================>]  13.17K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-03-12 00:46:04 (39.4 MB/s) - ‘MCF10A_trametinib.csv’ saved [13490/13490]\n",
            "\n"
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            "text/plain": [
              "  well  time  cell.count            uid       drug1  drug1.conc\n",
              "0  A01   0.8          32  10ACh-28A_H03  trametinib    0.000004\n",
              "1  A01   5.6          25  10ACh-28A_H03  trametinib    0.000004\n",
              "2  A01  10.5          31  10ACh-28A_H03  trametinib    0.000004\n",
              "3  A01  15.3          53  10ACh-28A_H03  trametinib    0.000004\n",
              "4  A01  20.0          39  10ACh-28A_H03  trametinib    0.000004"
            ],
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-654f502b-818f-4e22-9a2c-0f3def069535 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "d",
              "summary": "{\n  \"name\": \"d\",\n  \"rows\": 300,\n  \"fields\": [\n    {\n      \"column\": \"well\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"A11\",\n          \"A10\",\n          \"A01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.68103948685099,\n        \"min\": 0.8,\n        \"max\": 117.2,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          63.1,\n          68.0,\n          24.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cell.count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1255,\n        \"min\": 24,\n        \"max\": 5411,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          1357,\n          49,\n          44\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"uid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"10ACh-28B_H02\",\n          \"10ACh-28B_G23\",\n          \"10ACh-28A_H03\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drug1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"control\",\n          \"trametinib\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drug1.conc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4885364750364486e-06,\n        \"min\": 0.0,\n        \"max\": 3.9875e-06,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          3.9875e-06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "if IN_COLAB and not os.path.exists(\"MCF10A_trametinib.csv\"):\n",
        "    !wget \"https://raw.githubusercontent.com/VU-CSP/quantbio-assignments/main/MCF10A_trametinib.csv\"\n",
        "d = pd.read_csv(\"MCF10A_trametinib.csv\")\n",
        "d.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4ee5278b-1d1e-4427-a596-8151c61ead1a",
      "metadata": {
        "id": "4ee5278b-1d1e-4427-a596-8151c61ead1a"
      },
      "source": [
        "### Add new columns\n",
        "Add values for log2(cell.count) (`l2`) and population doublings (`pd`). Calculating log2 values are easy since it is calculated directly from the `cell.count` values. Population doublings must be normalized for each well independently. To better understand each step, try to pull out the code in smaller chunks and run them in different cells."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4312e53f-babd-475b-91c0-a20cc04b03c1",
      "metadata": {
        "id": "4312e53f-babd-475b-91c0-a20cc04b03c1"
      },
      "outputs": [],
      "source": [
        "d.insert(2,\"l2\",np.empty_like(d[\"cell.count\"]))\n",
        "d.insert(3,\"pd\",np.empty_like(d[\"cell.count\"]))\n",
        "d[\"l2\"] = np.round(np.log2(d[\"cell.count\"]),3)\n",
        "d[\"pd\"] = flatten_matrix([np.round(d[d[\"well\"]==w][\"l2\"] - d[d[\"well\"]==w][\"l2\"].to_list()[0],3) for w in d[\"well\"].unique()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e1348e04-bfe8-44f4-9069-bc4a277a6e27",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "e1348e04-bfe8-44f4-9069-bc4a277a6e27",
        "outputId": "71a5d681-e6ff-46a2-92c4-1d49c51ddd8d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   well   time      l2     pd  cell.count            uid       drug1  \\\n",
              "0   A01    0.8   5.000  0.000          32  10ACh-28A_H03  trametinib   \n",
              "1   A01    5.6   4.644 -0.356          25  10ACh-28A_H03  trametinib   \n",
              "2   A01   10.5   4.954 -0.046          31  10ACh-28A_H03  trametinib   \n",
              "3   A01   15.3   5.728  0.728          53  10ACh-28A_H03  trametinib   \n",
              "4   A01   20.0   5.285  0.285          39  10ACh-28A_H03  trametinib   \n",
              "..  ...    ...     ...    ...         ...            ...         ...   \n",
              "95  A04   96.7  11.628  5.651        3164  10ACh-28B_C23     control   \n",
              "96  A04  101.8  11.930  5.953        3903  10ACh-28B_C23     control   \n",
              "97  A04  106.9  12.134  6.157        4494  10ACh-28B_C23     control   \n",
              "98  A04  112.0  12.219  6.242        4767  10ACh-28B_C23     control   \n",
              "99  A04  117.2  12.305  6.328        5060  10ACh-28B_C23     control   \n",
              "\n",
              "    drug1.conc                    x  \n",
              "0     0.000004      137615995405040  \n",
              "1     0.000004       93945591407952  \n",
              "2     0.000004       93945591407952  \n",
              "3     0.000004       93945591407952  \n",
              "4     0.000004                    0  \n",
              "..         ...                  ...  \n",
              "95    0.000000  8387200179420031329  \n",
              "96    0.000000  7307218078133023349  \n",
              "97    0.000000  7953392626460355616  \n",
              "98    0.000000   733650746116022368  \n",
              "99    0.000000  2314885530818453514  \n",
              "\n",
              "[100 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bcc5c7cb-d068-4820-8e60-3016540c2c6a\" class=\"colab-df-container\">\n",
              "    <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>well</th>\n",
              "      <th>time</th>\n",
              "      <th>l2</th>\n",
              "      <th>pd</th>\n",
              "      <th>cell.count</th>\n",
              "      <th>uid</th>\n",
              "      <th>drug1</th>\n",
              "      <th>drug1.conc</th>\n",
              "      <th>x</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A01</td>\n",
              "      <td>0.8</td>\n",
              "      <td>5.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>32</td>\n",
              "      <td>10ACh-28A_H03</td>\n",
              "      <td>trametinib</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>137615995405040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A01</td>\n",
              "      <td>5.6</td>\n",
              "      <td>4.644</td>\n",
              "      <td>-0.356</td>\n",
              "      <td>25</td>\n",
              "      <td>10ACh-28A_H03</td>\n",
              "      <td>trametinib</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>93945591407952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A01</td>\n",
              "      <td>10.5</td>\n",
              "      <td>4.954</td>\n",
              "      <td>-0.046</td>\n",
              "      <td>31</td>\n",
              "      <td>10ACh-28A_H03</td>\n",
              "      <td>trametinib</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>93945591407952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A01</td>\n",
              "      <td>15.3</td>\n",
              "      <td>5.728</td>\n",
              "      <td>0.728</td>\n",
              "      <td>53</td>\n",
              "      <td>10ACh-28A_H03</td>\n",
              "      <td>trametinib</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>93945591407952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A01</td>\n",
              "      <td>20.0</td>\n",
              "      <td>5.285</td>\n",
              "      <td>0.285</td>\n",
              "      <td>39</td>\n",
              "      <td>10ACh-28A_H03</td>\n",
              "      <td>trametinib</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>A04</td>\n",
              "      <td>96.7</td>\n",
              "      <td>11.628</td>\n",
              "      <td>5.651</td>\n",
              "      <td>3164</td>\n",
              "      <td>10ACh-28B_C23</td>\n",
              "      <td>control</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8387200179420031329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>A04</td>\n",
              "      <td>101.8</td>\n",
              "      <td>11.930</td>\n",
              "      <td>5.953</td>\n",
              "      <td>3903</td>\n",
              "      <td>10ACh-28B_C23</td>\n",
              "      <td>control</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7307218078133023349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>A04</td>\n",
              "      <td>106.9</td>\n",
              "      <td>12.134</td>\n",
              "      <td>6.157</td>\n",
              "      <td>4494</td>\n",
              "      <td>10ACh-28B_C23</td>\n",
              "      <td>control</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7953392626460355616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>A04</td>\n",
              "      <td>112.0</td>\n",
              "      <td>12.219</td>\n",
              "      <td>6.242</td>\n",
              "      <td>4767</td>\n",
              "      <td>10ACh-28B_C23</td>\n",
              "      <td>control</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>733650746116022368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>A04</td>\n",
              "      <td>117.2</td>\n",
              "      <td>12.305</td>\n",
              "      <td>6.328</td>\n",
              "      <td>5060</td>\n",
              "      <td>10ACh-28B_C23</td>\n",
              "      <td>control</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2314885530818453514</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 9 columns</p>\n",
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              "\n",
              "<div id=\"df-4dafb03b-5bcb-4f13-99be-a614247c3050\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4dafb03b-5bcb-4f13-99be-a614247c3050')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4dafb03b-5bcb-4f13-99be-a614247c3050 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "d",
              "summary": "{\n  \"name\": \"d\",\n  \"rows\": 300,\n  \"fields\": [\n    {\n      \"column\": \"well\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"A11\",\n          \"A10\",\n          \"A01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.68103948685099,\n        \"min\": 0.8,\n        \"max\": 117.2,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          63.1,\n          68.0,\n          24.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"l2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.2019001223910015,\n        \"min\": 4.585,\n        \"max\": 12.402,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          10.406,\n          5.615,\n          5.459\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0783555644217295,\n        \"min\": -0.356,\n        \"max\": 6.901,\n        \"num_unique_values\": 259,\n        \"samples\": [\n          0.466,\n          1.246,\n          0.585\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cell.count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1255,\n        \"min\": 24,\n        \"max\": 5411,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          1357,\n          49,\n          44\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"uid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"10ACh-28B_H02\",\n          \"10ACh-28B_G23\",\n          \"10ACh-28A_H03\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drug1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"control\",\n          \"trametinib\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drug1.conc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4885364750364486e-06,\n        \"min\": 0.0,\n        \"max\": 3.9875e-06,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          3.9875e-06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3057812771808076800,\n        \"min\": -1,\n        \"max\": 8751655595886605088,\n        \"num_unique_values\": 198,\n        \"samples\": [\n          8317711410589422112,\n          7306353860595949600\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "d.head(100)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "56245c23-c8a9-4d8e-ace1-a372b934377f",
      "metadata": {
        "id": "56245c23-c8a9-4d8e-ace1-a372b934377f"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "d1222d88-bd97-494f-83c7-658156b50806",
      "metadata": {
        "id": "d1222d88-bd97-494f-83c7-658156b50806"
      },
      "source": [
        "## Assignment task #1\n",
        "Copy and execute a chunk of code from the line assigning values to `d[\"pd\"]`. Describe what you did and the output in the following markup block."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5c2bb7c4-bb47-4c3b-99b2-70a002d3a163",
      "metadata": {
        "id": "5c2bb7c4-bb47-4c3b-99b2-70a002d3a163",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9ce42c8b-55dd-41c9-9bcb-a2e8d993604e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  well  time     l2     pd  cell.count            uid       drug1  drug1.conc  \\\n",
              "0  A01   0.8  5.000  0.000          32  10ACh-28A_H03  trametinib    0.000004   \n",
              "1  A01   5.6  4.644 -0.356          25  10ACh-28A_H03  trametinib    0.000004   \n",
              "2  A01  10.5  4.954 -0.046          31  10ACh-28A_H03  trametinib    0.000004   \n",
              "3  A01  15.3  5.728  0.728          53  10ACh-28A_H03  trametinib    0.000004   \n",
              "4  A01  20.0  5.285  0.285          39  10ACh-28A_H03  trametinib    0.000004   \n",
              "\n",
              "       x  \n",
              "0  45.00  \n",
              "1  41.44  \n",
              "2  44.54  \n",
              "3  52.28  \n",
              "4  47.85  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-da7b75b6-aa1a-4ed7-af0e-98f739a1080a\" class=\"colab-df-container\">\n",
              "    <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>well</th>\n",
              "      <th>time</th>\n",
              "      <th>l2</th>\n",
              "      <th>pd</th>\n",
              "      <th>cell.count</th>\n",
              "      <th>uid</th>\n",
              "      <th>drug1</th>\n",
              "      <th>drug1.conc</th>\n",
              "      <th>x</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A01</td>\n",
              "      <td>0.8</td>\n",
              "      <td>5.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>32</td>\n",
              "      <td>10ACh-28A_H03</td>\n",
              "      <td>trametinib</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>45.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A01</td>\n",
              "      <td>5.6</td>\n",
              "      <td>4.644</td>\n",
              "      <td>-0.356</td>\n",
              "      <td>25</td>\n",
              "      <td>10ACh-28A_H03</td>\n",
              "      <td>trametinib</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>41.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A01</td>\n",
              "      <td>10.5</td>\n",
              "      <td>4.954</td>\n",
              "      <td>-0.046</td>\n",
              "      <td>31</td>\n",
              "      <td>10ACh-28A_H03</td>\n",
              "      <td>trametinib</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>44.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A01</td>\n",
              "      <td>15.3</td>\n",
              "      <td>5.728</td>\n",
              "      <td>0.728</td>\n",
              "      <td>53</td>\n",
              "      <td>10ACh-28A_H03</td>\n",
              "      <td>trametinib</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>52.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A01</td>\n",
              "      <td>20.0</td>\n",
              "      <td>5.285</td>\n",
              "      <td>0.285</td>\n",
              "      <td>39</td>\n",
              "      <td>10ACh-28A_H03</td>\n",
              "      <td>trametinib</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>47.85</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da7b75b6-aa1a-4ed7-af0e-98f739a1080a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-da7b75b6-aa1a-4ed7-af0e-98f739a1080a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-da7b75b6-aa1a-4ed7-af0e-98f739a1080a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-78941c0e-e8ef-4511-8099-b6fc41973fab\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-78941c0e-e8ef-4511-8099-b6fc41973fab')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-78941c0e-e8ef-4511-8099-b6fc41973fab button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "d",
              "summary": "{\n  \"name\": \"d\",\n  \"rows\": 300,\n  \"fields\": [\n    {\n      \"column\": \"well\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"A11\",\n          \"A10\",\n          \"A01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.68103948685099,\n        \"min\": 0.8,\n        \"max\": 117.2,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          63.1,\n          68.0,\n          24.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"l2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.2019001223910015,\n        \"min\": 4.585,\n        \"max\": 12.402,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          10.406,\n          5.615,\n          5.459\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0783555644217295,\n        \"min\": -0.356,\n        \"max\": 6.901,\n        \"num_unique_values\": 259,\n        \"samples\": [\n          0.466,\n          1.246,\n          0.585\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cell.count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1255,\n        \"min\": 24,\n        \"max\": 5411,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          1357,\n          49,\n          44\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"uid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"10ACh-28B_H02\",\n          \"10ACh-28B_G23\",\n          \"10ACh-28A_H03\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drug1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"control\",\n          \"trametinib\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drug1.conc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4885364750364486e-06,\n        \"min\": 0.0,\n        \"max\": 3.9875e-06,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          3.9875e-06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.869547919203924,\n        \"min\": 41.265,\n        \"max\": 118.137,\n        \"num_unique_values\": 279,\n        \"samples\": [\n          91.625,\n          73.008\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ],
      "source": [
        "# Add your code here\n",
        "d[\"x\"] = flatten_matrix([np.round(d[d[\"well\"]==w][\"l2\"]*10 - d[d[\"well\"]==w][\"l2\"].to_list()[0],3) for w in d[\"well\"].unique()])\n",
        "d.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ae735ec1-1f4c-45ff-9290-56c9def398ed",
      "metadata": {
        "id": "ae735ec1-1f4c-45ff-9290-56c9def398ed"
      },
      "source": [
        "*Put your description of the code in the previous block here.*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5b56a147-be3b-4a8d-b833-37357a76d1c5",
      "metadata": {
        "id": "5b56a147-be3b-4a8d-b833-37357a76d1c5"
      },
      "source": [
        "#### Plot data in different scales\n",
        "We will look at a single well (`A02`) of data in linear scale (direct cell counts), log2 scale and as population doublings (log2 scale normalized to 1 cell at the first time point)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7adfe491-1870-4265-97e7-24551c6c1569",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "7adfe491-1870-4265-97e7-24551c6c1569",
        "outputId": "99cf3e42-d7f1-449a-8bd1-5b050dbf896c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-32-315d59414379>:7: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n",
            "  plt.subplot(131)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ss = d[d[\"well\"]==\"A02\"]\n",
        "# names = ['Linear', 'Log2', 'Normalized log2']\n",
        "if IN_COLAB:\n",
        "    fig, axs = plt.subplots(figsize=(12, 4))\n",
        "else:\n",
        "    fig, axs = plt.subplots(figsize=(12, 4), layout=\"constrained\")\n",
        "plt.subplot(131)\n",
        "plt.ylabel('Cell number')\n",
        "sns.scatterplot(data=ss, x=\"time\", y=\"cell.count\")\n",
        "plt.subplot(132)\n",
        "plt.ylabel('Log2(cell number)')\n",
        "sns.scatterplot(data=ss, x=\"time\", y=\"l2\")\n",
        "plt.subplot(133)\n",
        "plt.ylabel('Population doublings')\n",
        "sns.scatterplot(data=ss, x=\"time\", y=\"pd\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "95a0731e-59a7-4cfb-9087-7f0232c3b0da",
      "metadata": {
        "id": "95a0731e-59a7-4cfb-9087-7f0232c3b0da"
      },
      "source": [
        "#### Look at aggregated data\n",
        "Many seaborn functions will automatically perform the statistical estimation. In the plots below, data will be grouped by `drug1` using the seaborn `hue` argument."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "72a45a3e-b307-43cd-af53-1ac27c02fec0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "72a45a3e-b307-43cd-af53-1ac27c02fec0",
        "outputId": "c63e15b6-ebd7-4d17-b3a6-f2d1c05c1b3e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 3 Axes>"
            ],
            "image/png": 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zo2hETAbQp6unc/pMQFPzYUD5c44oX32ZH0HmSCGqRdPAkA356ZB3DorywMNTzY+BzSo9vLp0mqZpNX4V4XYdOnQA4OTJk8XbDAYDp06don379g5pXKL2yb+FqDVFeZC4GzQL6Dyh7VDwrvz/ubLmkOpYvHgxM2fOrLAYZGWmTZvG8uXLuf766/nqq69cHkt5fzf5uawf5N9BuI0hB/JSITcJjHng5Qt+YeBZRheDnLPQZojTN9funiPrC5kf6zf5dxBuoWmQnwLZiWqJhMUEfkHgGwI6j9L75yRBzCUQ3s6p0zs7P0pGgxBCNGQ5Z9XSicBI9VSvgdLr9Xz33XeAerokhBDlKsxWc19espr3/IIhJLbsG2ghhGhKrBbIPAUZcWpO9A9TQdg6IIEGIYRoqAw56mY7IAJo2EWlnn32WVJTU4mKinKo/VAeWzS9LImJibRu3dqdwxNC1AeF2erJW24SWIwQEF4r6b9CCNEgmI2QflwFGgLCVQ2GOiSBBiGEaKiyz4K5SN1om5xrw1Ufff7558XFH5cvX05ISEjdDkgIUb+UFWDwjqrrUQkhRP1hLIDUv9Q8GRRdZ1kMJUmgQQghGqLCLPXLJCC8rkdSLT/99BN33303AC+99BITJ0506riK1gVWlO0ghGhAbAGGvHMqqCoBBiGEKK0wC1KOqq4SIS3Ao358xK8foxBCCOE8TVPZDFZjg77p/uWXX5g0aRJGo5Gnn36aZ555pq6HJISoDy4OMPjLEgkhhChT3nkVZDAbVJChHtWqkUCDEEI0NIVZ6gbcv360H3PFzp07GT9+PHq9npkzZ/LKK6/U9ZCEEHXNYoKMeFV7RgIMQghRPk2DrNOqJoOHJ4TE1PWISpFAgxBCNCSaptoVWc3g7V/Xo3HJnj17GDduHPn5+UyZMoV///vfdT0kIURds5jU+uKsUxAQKQEGIYQoj8WsgrIZ8eAbDH71s7ZV/cmtEMJJL7zwAgaDa238XnjhBR555BH3DkiI2qTPVNkMAZF1PRKX/Pnnn1x99dXk5ORw++23s2zZMnS6ht0xoz6R+VE0SCWDDEHR4BNQ1yMqZjQaSUhI4Pz586Xey8/P54knnqB379707duXOXPmUFjYcAvzNnYyP4pGwWSAlMMqkyEgot4GGUACDaIBevHFF8v9RWE2m2t5NELUIqsVchJAo15UE66quLg4xowZQ2ZmJjfddBMff/wxHh7ya8idZH4UDY7ZCClHVJAhuHm9m9vee+892rdvX2YNmfHjx/PWW2/x559/cuDAAV5++WXGjRuHpml1MFJRGZkfRYNnyIXzByA7AYJj3JPZaiqEMzvg0FfVP9dFZOlEY2bIUb+8a1rzHuAXWuEuO3fu5MknnyQvLw9N05g3bx4tW7bkoYceIj8/Hz8/P9566y2GDh3K6dOn6dOnDw8//DBr1qwhJyeHt99+m7/97W/cd999AAwbNgxPT09+/PFHnnrqKTw8PIiPjyc1NZW//vqL1157jY8++ggPDw969erFO++8Q2hoxWMUot7TZ0Busopg17HExET69u1b/Np28/brr7/SrJk95fmpp57iqaeeAuDBBx8kJSUFgDNnzjB8+PAyz92iRQtWrlxZU0O/MGCZH2V+FHXObITUo5B9RgUZPH3qekSlbNiwAYDbbrvNYfv333/Ptm3b8PDw4LbbbsPf359PPvmEbdu2sWLFCu688866GK571Nb8CJXOkTI/CnFBYTac/xOKci50lvB0/VwWIyTuhvhNkLBTFZL0D4fLHwZP94UHJNDQmKUcgQ+vqfnr/OMHaDuk3LczMzOZNGkSq1atYtiwYVitVtLT0xkwYADLly9n7NixbN++nRtuuIH4+HgAcnJy6NWrFy+++CI//PADDz/8MH/7299YsmQJS5cuZdu2bYSFhRVfY8+ePWzfvp3g4GDWr1/PBx98wM6dOwkLC2PatGk8/fTTvPvuuzX9X0KImmO1qgi2jnrxxM9isZCRkVFqu9lsdtiu1+uLvy8qKir+fvfu3eWeu23btm4aZQVkfpT5UdQtcxGkHlE1Z4JjwNO7Zq9XlKeqs1ex9sPRo0cB6N+/v8P2zz77DJ1Ox6xZs3jppZcA6NevHzNmzOCzzz5r2IGG2pofocI5UuZHIS7QZ8L5Q2DMh+BYcGXJqdUM5/ar4MLpX8BY4Ph+YRac2gqdRrtjxIAEGkQt2LlzJ127dmXYsGEAeHh4kJKSgoeHB2PHjgXgiiuuoHnz5uzfv59WrVrh5+fH9ddfD8CQIUM4ceJEhde46aabCA4OBmDjxo3ccsstxb9I7r//fm666aYa+tsJUUsK0iD/PATWj3aW7dq1q3J68NatW2tmMA2YzI+iSaqtIEN+Kpzerr6S90OvW+DvS6p0irS0NAICAggPD3fYvmXLFgDuvffe4m133HEHM2bM4MCBA9UeOsC6det477332LVrFxkZGYSHh9OxY0dGjBjBCy+8gJdX476Nl/lRCKAgA1IOgalAzZdVCTJoVlXP4cRmOLlVBRPK4x2gOv64UeOeoUSDUrIgnK+vb/FrT09PLBZLhccGBQU5dV4hGiSrRaUWe3jW/FM/US/J/CgaDVshs9ykC+m/br4VzU6E09vg1DZIO+r4XtxPaj6tQspxQUEB/v6O66BPnz5NWloabdq0oX379sXbAwMDCQsLIzMzs1p/BbPZzD/+8Q/++9//AtC6dWt69+5NRkYGf/zxBzt27ODpp5+u8Ge7KZH5UTRa+Wkqk8FaBMEtnD8u+wwc+0EFGPJTyt/P0xtaD4HYPtD3dojqVu0hlySBhsaseQ+VklYb16nA5ZdfTlxcHNu2bStOfWvevDlWq5WffvqJMWPGsGPHDs6fP0+fPn1IT0+v8HzBwcHk5OQ4pL6VNHr0aB5//HEee+wxQkJCWLp0KVdffbWrfzsh6l5+qspoCIqu65E0HjI/yvwoal9NBBk0TbV4O70NTv2i+sqXR5+u1iVXsJzpYhEREaSlpZGdnV38c7V582ZA/fxezGw2VzsAcP/99/Pf//6XgQMHsnTpUod6OHq9no0bN+LrW4NL6GprfrRdqxwyP4omLS9FZTJYzRDU3LljjPnwx4dw+BuVzVAWnQe0GgAdR0G7K8AnEHKSwMvPfWO/QAINjZlfaJV+mdaU8PBwvvnmGx5//HHy8vLw8PBg3rx5fP311zz00EM8/vjj+Pn5sWrVKoKCgir9RfH4448zZswYAgIC+PHHH0u9P27cOA4dOsSQIUMcivkI0SBZzBeyGbzc/+SvKZP5UeZHUbtMhReCDOfcE2RIPQIntqgAQ17p1pMOAiKhRR/od5e6wa6Cfv36sWHDBt5//30ef/xxrFYr77//PjqdjhEjRjjsm5aWRn5+Pt27d6/iX8Zuy5YtvPfee7Rr145NmzYVp/UX/1UCArjuuutcPr9TZH6U+VHUrdxkFWQA5x4yaZrK2Prt3fKXR7ToDR1HQvvh4B/mtqFWRKdJD54GqUOHDgCcPHmyeJvBYODUqVO0b98ePz/3R6WE8+TfQrhN7jlI2qsqsld0Y24qVF9th4J35f/PlTWHNBbl/d3k57J+kH+HJsidQYbMU/DbUkjcVfF+wbHQfhi0vxKiu6trtxnidDFI2zzyyiuvcOutt+Lp6cno0aNJS0tj7969BAcHc+bMGYen419//TU33ngj119/PatWrXLprzdhwgTWrl3LokWLePDBB106R0Vkfqzf5N9BkHNWFWT18HSuy1jGCfh1oepIcbFmXaDTKOgwouKARU4SxFwC4e2cGqKz95DyeEwIIeoriwkyT6suE5LNIIRoiIryVfZBfkr1ggz6DPjjAzi2vvyU4IiOKhW4/ZUQ0cG1yuwXueWWW9iwYQMfffRRcatLPz8/lixZUioF/3//+1+ZmQ7OMhgMxU/aR48ezZEjR1i2bBlHjhzB19eXvn37MmXKlNrpzCOEqH3ZCSrI4OVbedZBRcskQmJVq8o2g2tsqM6QO1chhKiv9JlgyHJ+bZ4QQtQnhVkqk6EwWxUyc6Xvu0kPB/4HB/+ner1fLLqHCiy0uwJCW1V7yGX54IMPmDJlCjt27CAsLIxRo0YVP9GzMRqNhIaGcuedd/K3v/3NpescOHAAk8kEwLZt23jwwQcxGo3F769Zs4ZXX32VDz/8kFtvvdX1v5AQon7RNFVfJvUv8PFXy5cq2jfuR/htSellEp4+0Pf/VIedetAKXQINQghRXxVeqFzuys25EELUpfxUFWQwGdTTtapmF1jNKnvhjw/tc2FJrQfB4PtU5kItGDp0KEOHDi33fR8fH5YtW1atayQnJxd//8ADD9CvXz8WLVpE7969SUhI4Nlnn+XLL7/krrvuolu3bg5FIku6OAhSUmJiIq1bt67WOIUQblQcZDgKvkHgG1z+vhUtk2h3BQx5oGrdKWqYBBoaIau1nJRCUWuk9ImoNotJ3aj7BNb1SBod+fmsW/Lfv5HTNLXGOO0v9TokpurHJ+xUdRiyz5R+P7KTCjBUsahjQ5Cfn1/8fUBAAOvXryc8PByATp068fnnn3P8+HH279/PSy+95HIdiIrIz2fdkv/+TYzVCpkn1XzpF1J+kMFihN/fgz9X1dtlEmWRQEMj4uPjg4eHB+fOnSMqKgofHx/pAVwHNE0jLS0NnU6Ht7d3XQ9HNFSGHLX+Tlpauo23tzc6nY60tDSioqJkfqwDMj82clYrZJ2C9OOqKK1fWNWOTzsGu96F5P2l3wuMgoH3Qucxqj1bI1Sy+N/dd99dHGSw8fDw4NFHH+Wuu+7ixx9/xGq14uFR+r9FRQXayst2kPmx7sn82MRYrao1b/pxVY+hvAdLuedg04tqfizJ0xf63l5vlkmURQINjYiHhwft27cnOTmZc+fO1fVwmjSdTkerVq3w9JSUd+EifRagSRFIN/L09KRVq1acPXuW06dP1/VwmiyZHxspi0ndNGecAP9Q8AmqwrFGtUTiwBfARU90vQOh721wyY11cjM9cuTIKu3v5+dHWFgYPXv25JprrqF///5OH1sysFBei0zb9ry8PDIyMoiKiqrS+Moj82P9IPNjE2G1qABDxgkICAfvgLL3O7UNfl4AxgLH7fVwmURZ5A62kfHx8aFNmzaYzWYsFktdD6fJ8vb2ll8SwnUWM+SfVzfYwq2CgoLo3LlzccE1UftkfmyETAb1tC37DARFgVcV2vJlnoTNL0HmCcftOk/oMRH63VlrPd/LsnXrVvuQLjzlvzi9vaztOp2OuXPnMmrUKD7++GNatKj8A0G3bt2Kv/fx8Slzn5JZD+5eKivzY92T+bEJsJjVfJl5EgIjwdu/jH1Mqtjjoa8ct/uFwvBZ0Pby2hlrNUmgoRGypVxJ2pUQDVRRrmoJF+Rcv3dRNZ6ennIjJ4S7FOWrImZ5yRAcA55O3ntYLfDnSvj9fbBe9MG23ZUweFqNdZGoiueffx6TycS7775LVlYWbdq0Yfjw4bRs2RKApKQkfvnlF86cOUNERAT33Xcf+fn5/PHHH+zYsYNNmzYxduxYdu/e7RAkKEvLli1p27YtZ86cKXf5w4kTKiDj5+dHZGSke/+yyPwoRI2ymFRniaxTamlsWVlaecmw8Z+QdtRxe8ylMHJOg1pSK4EGIYSobwqzVcX1Ki6bOHYykZ9/ep+du//g3LlzpKWlYTAYiIyMJCoqiu7duzN8+PAKK6cLIYTTSravDIl1vkNOXjJsXQDJBxy3+4XCsCeg/TC3D9VVzz77LKNHj8ZgMPDRRx9x5513lrnfihUruO+++9i9ezc//PADHh4e/Pzzz0yaNInDhw+zfPlyZs6cWen1brnlFl599VU+/fRTnn/+eby8HH8PfPDBBwAMHz681HtCiHrMbFRB2ewz5QcZTm9Xc6Mx33F7n9tgwD0NbjltwxqtEEI0dlYr5J1XfZSdkJKWwQdffMfyT7/mTNL54u1lVa7+7rvvWLBgAV5eXnh5eRESEuK2YQshmpi8FHXTbCp0vn2lpsHxH2DHIjDpHd9rczlc+QQERNTMeF301ltvsW3bNpYtW1ZukAHgjjvuwGg0Mm3aNP7973/z6KOPMnz4cBYsWMD999/PV1995VSg4YknnmDp0qWcOnWKBx98kIULF+Ln54emaSxatIjVq1ej0+l4+umn3fnXFELUJHMRpB6B7EQIbg6eFy2Nsprht2Xw55eO231DYMQz0Oay2hurG+k06aPSINmqBldUWVgI0QAVZkPCLlUc6OJfRCUkp6Qx57V3WfHVWsxmS3FgITa2BX379qNZs2ZERETg7+9PZmYmmZmZnDp1igMHDmA0Gov379KlC/Pnz+emm26qjb9drZD5UYgaZDGrJ3IZ8Sq4EOhkMcLCbNj2unpiV5K3vypq1nW8c8EKV+WchTZDINC5JWm2eSQkJISjR4+Sl5dXbt0Em6KiIkJCQujRowf79u1Tl83JISIigoiICNLS0py69saNG7nuuusoLCwkNDSULl26cPbsWZKTk9HpdLz66qs88cQTTp2rvL+XzI9C1BJTIaQcgdykspeX5afAxhdVIKKk5pfAqLm1s1QiJwliLoHwdk7t7uw8IhkNQghRnxiy1Rq+CoIMz7/+Lm8u+5QCfSFeXp78beRQbrt2FFf260bLwRNVW7lyGI1G9u3bx9ixYykoKCAuLo7Jkyfz1ltvsXTpUi699NIa+EsJIRqFojxIOw555y60Y3Oys8SZHfDLa2qpRUnNL1FP60Ji3T5Udzlx4gRBQUGVBhkAfH19CQoKIj4+vnhbaGgoYWFh5ObmOn3N0aNHc+DAAV5++WU2btzI/v37CQ0N5brrruOxxx5j+PDhLv1dhBC1zKhXy8vykiGkRemlDwk7YcvLam4tqddkGHRvg1sqcbGGPXohhGhMNE1FtisIFADMW/gezSLCeO7he7n31klERoSpiLmpsNJL+Pj4MHjw4OInbB999BGvvPIKGzZs4JtvvpFAgxCiNE1TS7rSj6sb4uAY526AzQa1TOKvtY7bPbzUeuNetzhf16GOeHl5kZ2dTXJycqWdI5KTk8nOzi61LE2v1xMaGlql63bu3JkPP/ywyuMVQtQTxgJIOQR5qaWDDJqmlknsWoJDS1/fYLhqdoPpKlEZCTQIIUR9UZQHhTngF1zhbq/MnsmD/7iFwADn6jhU5Morr+TKK69k9+7dTqf1CiGaELMRMk+pVmxe3s7XY8hNgh/nlm5bGdFBZTFEdqqZ8bpZv3792Lp1K7NmzeKTTz6pcN+nn34aTdPo169f8baUlBSKioro1Klh/H2FEG5QlAfnD4E+40KQoURA1WqG7QvhrzWOx0T3gNHPQ1DzWh1qTZJAgxBC1BeGHPUE0KviNc+zHrjb7ZceNGiQ288phGjgDDkXlkokX+j3HuDccWd2wJaX1BO9YjqVwTDwngqXhtU3Dz30EFu2bOHTTz8lLS2NZ555hiFDhhR3fDCbzezYsYMFCxawYcMGdDodDz30UPHxP/zwAwCDBw+uk/ELIWpZUb5jkEHnUeK9PPjpeTi31/GYntfDZfc73x64gZBAgxBC1Bf5qWW3O3JCQtJ5MBuIbm7Ar5KlF0IIUSFNUxkJaXFgKih7bXFZrBb440PY/1/H7f4RMGoOxPatmfHWoIkTJ/LYY4/x5ptv8uOPP/Ljjz/i7e1NZGQkOp2O9PR0TCYToLr9PProo0ycOLH4+N9//53evXszadKkOvobCCFqjVEPqYdBn34h+6tEkCHnLPwwG3IS7dt0HjD0YegxsfS5GgEJNAghRH1QlA+GLPB1srjaRdpdcSMeHjoSTsQRGxzm3rEJIZoOcxGkx0P2adURIrSlc8cVZsPmeZC0x3F7TC+VDhwQ6e6RVs5iUm00jXrw8AZc62rx+uuvM2DAAObOnUt8fDxGo5Hk5GSHfTp16sSLL77Irbfe6rB98eLFro5eCNGQmAovFH5MLZ3JcG4//DQXikoUhfUJhNEvQqsBNT82zaoCyGgXfW/7stTIZRt0oGHdunWMHz8egLZt23L69Oky98vPz2fBggWsWrWKM2fOEBQUxODBg3niiSe46qqrKrzGli1beOONN/jtt9/Iz8+nbdu23HTTTTz99NMEBgaWe1x1rimEaIIMOeqXlL9rPeSDAv3x9vIkNrb+Vm8XQtRzhdmQ+hcUpEJQFHg5mR2VekSlAxdcVOfl0pth8LTaq5yuWe2BBYtRXdcnULVs8w8Hv6oVZCxp8uTJTJ48mf3797N3797imjZRUVH069ePPn36uOfvIIRoeMxFqoVlcXeJEjUZjq2HbW+o2gw2wS3gmlecbifpNM2q7iWN+WA2gYeH2qbzADxUrFXnYQ+C2Lb7hl4IxrpXgw005Ofnc//991e6X3p6OldccQXHjh3D19eXHj16kJaWxtq1a1m3bh2LFy9mxowZZR67aNEiHn74YTRNo1WrVrRu3ZojR44wf/58vvrqK7Zv305EROkPBdW5phCiiSpIUzfFLvaRb9eqBXGnErFYLI1tiZ8QojYUZkHyn+oGNSTWuW4QmgZHvoOdix1vor39Yfgs6HBVjQ23mNmgakGYDBeuHQiBURDYTFVw9wkGT/fd7vbp00eCCkIIO7NRBVtzkxyXmWlW2L0cDnzuuH/MpTBmnmoR7A5Ws5q3jXo1J3sHqIKSgVHg6XshsKC78GULMuhKbL+wrQZuHj0q36V+euaZZ0hISHBYB1eWKVOmcOzYMfr378/JkyfZu3cvCQkJLF26FE3TeOihh9i/f3+p4/bs2cMjjzwCwNKlS0lISGDv3r2cPHmS/v37c/ToUaZOnerWawohmihTIegz1U2xiyZdPQyjycy69T+4cWBCiCahMFsVLzMWXGhd6USQwWxQ/d9/XegYZAhrC39fUrNBBk1TgZHsRLXszDtQVWxvPRjaDoGW/SCsjcpicGOQQQghHFhMkHpUzUUlgwymQpXldXGQofPVMP6N6gcZzAYoSFd1H/LTQecJER2h1UA1B8b2hdBWKjMtMBICIuxZXb7BapmuT4AKCnv51lgRygYZaNi1axf/+c9/mDhxYoXFdfbt28f333+Ph4cHX3zxRXFKsU6nY9q0adxxxx1YLBbmzZtX6th58+ZhtVq54447mDZtGroLTxljY2P5/PPP8fDw4Ouvv+bgwYNuu6YQooky5Kh0X2crupdh1n3/R6e2sdz34MxS85IQQpTLkAPn/1TV0IObO5dVlXMWvp0B8T85bu8wAv7+rgo21BRDrrq+zhNi+0CbIdDmMojsoG6qpRiuEKI2WMxqqVn2mQsB2gtBhoI0WP0wnN7muP+AKXDVbNe77mhWde7ssyrA6hussiPaDIY2l0N0NzWHe1e/9bm7NLgwr8lkYurUqQQEBLB48WI2btxY7r6rVq0CYOTIkWX2L54+fTorVqxg3bp1FBQUFNdcyM/PL25HNG3atFLHde7cmZEjR7Jx40ZWrlxJr169qn1NIUQTVpBuT2Fz0VfrtzJ98gReWPwpAwYM4JprrmHo0KFER0fj6Vn66WReXl51RiyEaAwMuReCDLlqzbAzc1DSHlXUrGTrSp2nas12yQ3VmscqZCxQWQzeQdD8ErW8o5aCCpqm8fHHH/P5559z8OBBMjMzMZvN5e6v0+kqfF8I0cBZLZD2F2SdhqBoe0ZAdgKsfULVubHx9IERz1Qvy8tUqO4VAyJU9pZfKPgE1dx86yYNLtDwyiuvcOjQId566y1atWpV4b67du0C4Morryzz/UGDBuHr64vBYGD//v0MHToUUFkJRUVF+Pr6lttbftiwYWzcuLH4GtW9phCiiTIXqV8eLnabsLn7iZeKM680TWPt2rWsXbu23P2tVmu1rieEaOCK8tRyicLsC23YnLhh/WsNbHvLsUJ5QKTqKhHTq/zjqsNsUP3oPX0hsrNKB67mfFkVRUVFjB8/ni1btqBpWq1dVwhRT1ktkH4csk5dKJp7oS152jFY/5TKErPxD4exL0N0d9eupWlq/rOYILITRHRoUFlbDSrQcPToUV5++WX69evHzJkzK93/+PHjAHTs2LHM9729vWndujXx8fEcO3as+EO/7bg2bdrg7V32mhXbOY8dO+aWawohmihDzoXiay2qdZo2sc3V5wQvX5xp4Xb27NlqXU8I0YAV5asggyHzQhu2SuYMzQq/LYODXzhur8nWlRYjFGSq6Sy0LYS1dl/xtCr417/+xebNmwG4/vrrmThxIrGxsXh5NahbaCGEO1itqv1vejwENbMHGc7tgw3PqmWwNuHtYdwCVZjRFeYiyE9VwYqYS9V56nkGw8UazCypaRpTp07FZDKxdOnSMlOBL5aZmQlQZmcIG9t7WVlZ1T6uusderEOHDuW+l5iYSOvWrSs8XgjRAOjVnOHQb9kFp3/9SqXWtR3qVLS7ovlFCNGIGQtUkEGfUbrXe1nMBtj8Uun1xl2ugWGPu7+ImNWs5kWrGYJiVPu3gIg6u8H+4osv0Ol0zJ07l+eff75OxiCEqAc0DTJOQEYcBEbY2/+e/hU2vaCyDmya94RrFrhW5FvToDBTBRoiOqgvH9dreNWlBhNoePfdd/n111956KGHGDBggFPHGAyq1ZGPT/lFN3x9VSSqsLCw2sdV91ghRBNjMUF+Sq2mAQshmjBjgarJoE+7sFyikiCDPgM2PKNSgksaNBV63+beD/9Wi6rBYDaoNc9h7VR7No+6rVt+6tQpdDodjz/+eJ2OQwhRh6xWFWRIP6YyDGzFu49vgJ//pbK+bFoNhDH/dK0oo7kI8tPALwRiezhfO6eeahCBhqSkJGbPnk3Lli2ZP3++08f5+fmh1+sxGo3l7lNUVASAv7/9fwY/PxWhqupx1blmWU6ePFnue/I0UohGwJCjbvyDoup6JEKIxs6oh5RD6ibWmUyGjHj44Rn3FzW7mNUChmwwFqolGNE9VIpwPWlLGRwcjMViIShIAsJCNEkWs8piyIgH/wh7dsGfq2DnYsd9O4xQc6QrmV76TBVoDW+nuuj4NPyGAfVjFq/EzJkzyc3N5cMPPyQ42PkUlPDwcPR6ffFyhrLY3gsPD3c4ruR7zh5XnWsKIZogfZaKgnu4dypOT09ny5YtnDlzBr1ez9y5c916fiFEA2MqhJTDkJeiMhk8Kll+mrALNr2ojrPxD4exL6lAgDtoVpXBYCpU547qdiHAUDP93F01cOBANmzYQGZmZoXLYoUQjZDFpFpYFhd+9FNLG/Z8CHs/cdy3+7Uw9JHK59dS1zCqWgw+QdCiNwTH1nkml7s0iL/F3r17AZgxYwYxMTEOXw8//DCgahbYtu3YsQOALl26ABAfH1/meU0mEwkJCQ77lvw+ISEBk8lU5rEnTpwodVx1rimEaGKsFsg/79Z1d2azhcefmkXr1q2ZPHkys2bN4sUXX3TYJysri/DwcE6fPl3u/CaEaERMBhVkyD/vXJDh0NdquUTJIEN4O5j0jnuCDJpVPbnLSVKdJFr0gVaDVDeJehZkAHjsscfQNI233nqrrocihKhNJoPKAss6pZZzefmp+evXf5cOMvT5P7jisaoFGaxmtXy2IF3Nf60GqD8bSZABGkigwSYlJaXUV25uLqBatdm22ZYtXHbZZQBs27atzPPt3r0bo9GIn58fffr0Kd7et29ffHx8KCoqYvfu3WUeazvnkCFDHLa7ek0hRBNjyFGV333cl45700P/ZOHbizEajfTs2bPMqujh4eHcdtttaJpGQUGB264thKiHzEZIOwq55yA4puKbYKtZ3UDveLv0euOJi9Va4erQNNVKMydJLcGI7QOtB6tuEl7l17Wqa6NGjeJf//oXCxYsYN68eej1+soPEkI0bLalZtmJau708lVz5OaX4Mi3jvtedj8Mutf5WgpWCxSk2TtKtBwAMb1dKxxZzzWIQMPp06fRNK3Mrw8//BCAtm3bFm+76qqrALjxxhsB2LJlS5kZBkuXLgVg3LhxDmvvgoODGTt2LADLli0rdVxcXFxxqyPbNWxcvaYQookx5Khe9G56gvfF9xv5buMOoqOj+eOPPzh48GC5ab433XSTGsKF4rVCiEbI1us9O1HVZKhoiZaxADY8B4e/cdzeYyJc80r1A6KGHMg5q+pCtOgFrQdBWJt6HWCwGTlyJOvWrSM4OJgXXniBZs2aMXDgQEaOHFnu16hRo+p62EIIVxXlwfmDkJus5k5Pb1U7YcNzcGKTfT+dB1z5FPS6xbnzalZVYDc3GXyCIbafCjIEN29UWQwlNYgaDa7q168fEyZMYM2aNUyePJnVq1fTokULNE1j+fLlrFixAg8PD5577rlSx86ZM4c1a9awYsUKhg4dytSpU9HpdCQnJ3PrrbditVqZNGkSvXv3dts1hRBNhNUKeeedakPpFM3Kh8v+g04Hr73yMn379q1w90GDBgEVF7wVQjRgtjZsmRdSfisKMuSeU0slsk6X2KiDITPgkhurV/Fcs6rUYE8f1Qc+uIX75r1asnXrVofXBoOBPXv2VHiMrgFXiReiSSvMVpkMhmwIvdCZx5Cr5siUQ/b9PLxh1Bxof2Xl59Q0db6i/AsZDPWzHk1NaNSBBoAPPviAoUOHsmfPHtq3b0+PHj1IT08nMTERnU7HwoUL6devX6njBg4cyJtvvsljjz3G9OnTmT9/Ps2aNePIkSMUFRXRtWtXli9f7tZrCiGaiKJc9eUX6p7z/bmKfSfTQNO4IfSgWldYwc18QEAAHh4eWK3WcvcRQjRgWadVNkNghEr5Lc+5/fDTXDUf2Xj5wai50Pby6o3B1r7XPxKiu0FAwyyk+Pzzz9f1EIQQtUGfqdr/GgtUQUadTi1vWP+UYyDW2x+ung8t+1d8Pk1Tc6vhwv1ei94q2NoAMrncpdEHGqKiotizZw8LFixg1apVHDlyhMDAQMaNG8eTTz7JiBEjyj32kUce4dJLL+WNN97gt99+IzU1lbZt23LjjTcye/bscpc+VOeaQogmwJCjqgxX9AHAWZmn4Pfl5BRphPqC/5Ev4KqHVfE2IUTTk5OkqqT7hdh7vZfl6BrY/pZawmUTGK06SzTrXL0xGAtUR4nQ1tCsi1uL3tY2CTQI0QTkp8L5w2AtUjUZdDrIOgPrnnRs8esbAuP+BdHdKz6fsUAFLnyDofklqhBvA8vmcgedpmlaXQ9CVF2HDh0AOHnyZB2PRAhRJZoGZ39XaXSBUdU7l8UE386AjDhiXs8jTa9R8PN/8Bs2A4AWLVqQmpqKxWJxOCw5OZnY2Fi8vLwaZecJmR9Fk5WfCskHVNFH/3JaaFvNsOtdOPSV4/bmPWHMvOpnHugzwGyCyE4Q0R48G+YzrcY6jzTWv5cQLss9pzrzAAQ2U3+mHIYfZjtmewU1h7+9purLlMdqhvx0NQeHt4PQluATWGNDryvOziMNc/YXQoiGqihPZTS4o7rw3k8gIw6Afi082XDCzJasWMZVctgHH3wAgK+vGzIqhBD1Q2HWhZtlrfwggzEfNr6ogp0ldb4ahj1evSwrq0UFOrz8IbaHShGWWgVCiPosLwXOH1KBAVuQ9cwONU9aiuz7hbeHv71a8QMiQ666vwuOUYHWBrpczJ0aZ4lLIYSorwzZqoaCVzVT6FIOw/5Pi1/e3i8EDR1zXpxHfn5+uYf98MMPzJs3D0A63wjRWBTlqZtlk6H8G+GcsyoDyiHIoINB0+Cq2dULMpiL1FPBgAho2VelCUuQQQhRnxkLVC0bHfagwLH18ONzjkGGmF5w3dvlz60Wk1qyZjWporexfSXIcIFkNAghRG3RNMhLBu9qZhKYDbD1FYde97fd/yTLTn3Otj/2cdlll3HfffcVd5X46aefOH36NKtXr2bdunVYrVYCAgIICGi466aFEBeYCiH1CBTlqAJmZUnaCxufVwEJGy8/GDkH2g2t3vWL8tSTvPD2qrZDA16HbEsH7tSpU6ltVaHT6Thx4oTbxiWEcDOrBdLjVJeJ0Jbq/uzAZ7D7okL/7a5Q82R5gdjC7AvFI1tAZEfwD6vhgTcsEmgQQojaUpQLhTmqSFt17Fqink7adBqNrv2VfPtuN/7+2Jv8sm07Dz/8cPHb11xzTfH3mqYxevRojh8/Xr0xCCHqntkIKUdU+m95WQRHvoNf33Ys+hjUHMa+rG6MXaVpqh6D1QLRPSC8rUo/bsBOnz4NgJ+fX6ltVSHtLYWo57ITIDsRgqMBDXb+p3Tdmm7XwhWPlD2vmYugIF3VX2jRC0JaNvj5ryZIoEEIIWpLYbZKx6tOivLZ3+HIt/bXgc1gqAoqhIcGs/nHH/j0y694//33+e233ygqUul/Xl5eDBo0iGnTpvF///d/Dk/shBANkNWi0n5zkyCkRembXE2D35bAwf85bm9+CVw9r/w6Ds6w3WT7BkNMNwhu7vq56pEPP/wQgNDQUB577DGHbUKIRqIgAzLiwf9Ci/HN8+HEZsd9+t0F/e8uHbzVNCjMVMvUQltDZAf31NxqpCTQIIQQtcFqVcsmqlOboSgPfv6X47bhT6tfcqZCADw8PLjjjju44447sFqtZGZmYrFYiIyMxMtLpnwhGgWrFTJOQNYpCIoGjzJ+tv9cWTrI0OUaGPYYeLrYx91qVlkMmqZusiPaVz9Dqx656667ir+3BRpKbhNCNHAmgwrQWs2gC1CdJZL2lNhBp7IYekwsfay5SBW89QuDlj0gKAY8pNxhReSuUwghakPRhWrEfqGun+PXf6uniDY9/w6tBpS7u4eHB82aNXP9ekKI+sdqVQGGjDhVcKysDKnT21ULy2I6GDwdet3iWpFGTVOFbIsKVKpxeHtVGE2WCAghGgqrVWUyFKSpzhDrZ8G5vfb3Pbxh5HPQYXjpY40FoM+CiA7qy0dqXDlDAg1CCFEbCrPUempXl02c3ArxG+2vQ1urDw4VsFgsZGZmAhAREYGnp6wfFKJBs2UyZBxXQUvvMm5204+rVGA0+7ahD6nApCuM+eoG2y8UYnurgpOecvsohGhg8s5B1mmVBfbHB45BBu9AGPsSxPYpfZwhG4yFEN1dZXFJLQanSb6HEELUNKsV8s6Dj79rx+szYNub9tc6Dxgxu8xlGAUFBbzxxhsMHDiQgIAAYmJiiImJISAggIEDB/LGG29U2P5SCFFP2Z7GpR9Tqbs+gaX3yU+FH55RnWlsLrnBtSCDrWWlyQBR3aD1IAhrI0EGIUTDY8iBtDiViZCwCw58bn/POwCuXVh2kKEgDSxm1bYysqMEGarI5d8Wn3zyCQBjx46lefPGUQRICCFqRFGOioi70ldZ0+Dn19TSC5u+/6eqvF9k/5F4/v7wFBISEtE0zeE9k8nEnj172Lt3L4sXL8ZoNOLrW802m0KI2mFrxWZbLlFWJoNJDxueAX2J5VVtLoPLZlTxWmbQZ6prhrRS3SSaSMu2kq0sExMTS21zlrS3FKIesZgg7TiY9WA1wc8LHN+/arZqzVuSZlXdfLwDoHkPlQUhqszlQMPdd9+Nl5cX2dnZbhyOEEI0QvosdfPuSgG2v9ZC4i7762ZdoO8dpXZLTk1n9N1PkZmdh4+PDzfeeCMjR46kZcuWACQlJbFlyxZWrVrFmTNn0Ol0xe8JIeoxW3eJ9HgIjATvMjKjrBbYNE9lPNhEdoSRc6v2BM6Qq4KaAVEqRTgwqkkVOyvZytIWrJX2lkI0YJoGmadUMW6/UPj+weLi2QD0uQ3aD3M8xmpWQQb/CGjevXodepo4lwMNERHqyVxAgBTDEEKIclkt6hdcWU8gK5ObBDsX2197eqvIu6d3qV3/+e8PyczOo23bNqxf/wPdunUrtc8999zDc889xzXXXMPp06clUCxEfWcxQ9oxyDwJQc3K71qz611I2Gl/HRAJY1+pWsGygjRAp1KEQ1qWOc80diVbWT7xxBMAvP7663U1HCFEdeWnqLo2/hHwy6uQnWB/r2V/GDDFcX9bZ4mQWFWToawlasJpLgcaunXrxm+//UZ+fj5BQUHuHJMQQjQehhz1lDAwsmrHWS2w5RXHtdYDp6qnjGVYt3UnOp2O5e++U2aQwaZr164sX76cMWPGoNfrqzYmIUTtsZgh7a8LQYbo8gvJHv4WDq2yv/byg7EvO5/qKynCxUq2snzxxRdLbRNCNCBF+WrJhKcX/LUaTv1sfy8wGkbOccz4MunVsrHw9hDVxfXi3aKYy/lwd999NxaLhffee8+d4xFCiMalMAs0S9WfDv65ElIO2V+36A2X3lju7ilpWfj7+TB61MhKTz169Gh0Oh1Wq7VqYxJC1A6L6UKQ4VTFQYbE32DH2yU26GDksxDV1bnrWM2Qm6yKS8b2adJBBiFEI2K1qJo2Rbkqi2H3Mvt7Ht4w5p+OtWcMuWqZa7OuKuAqQQa3cDmj4d5772XDhg3MmjULHx8fpk2bhpeXVCIWQohiFrOq2l7VfstmA+z/1P7aOwCuelp1myhHVGQYuXlV6ybh0YTWXgvRYFhMkPoXZFUSZMg8CRtfVBkJNpfdB+2Glb3/xSRFWAjRWGUnQPZZVWNm0z8d58mhD0N0icxPfYYKTMRcAmFtQWqsuI3LkYF77rmHoKAgfH19mTlzJnPnzmXgwIFER0eX26tdp9Px/vvvuzxYIYRoUAw5UJQHQVFVOy5+szrOZtBUCG5R4SGjLu/Pim82sGfvXvoPvrzCff/44w80TcPf38V2m0KImmE2QupRyD4Dwc3LLyCrz4AfZqtUX5tu18KlNzt3HWPJFOGu4OVCodomaM+ePXzxxRf88ccfpKamAhAdHc2AAQO45ZZbGDBgQB2PUAhBXorq0uMTAD8+q7p+2XT9G3SfYH9dkA46T2hxCQTH1PpQGzuddnEPNCd5eHig0+lKtVAr8yIX9tPpdFgsFlcuJy5ia7d08uTJOh6JEKJcaXGq531oFbo7aBp8M11VmQfwDYbbV5ZfBO6C+Lg4+o3/B506d+GnjRuJjCy7JkRmZiajR4/mwIEDxMbGFrdwa0xkfhQNkrFA3RxnJ6gb3vKWW5kNsPpRSDtq39ayP4z7F3g48fzIkKOuFdlZ+sJXoOQ8kpOTw5QpU/jmm28ASt372rpMTJo0iffee4/w8PpbpV7mR9Go6TMh+YBaFrZvBRz5zv5esy5w3SJ7llhhttqvRW9ZNlZFzs4jLmc03HnnndK+RwghymMxQ/75qqcjpx6xBxkAuo4rFWRISEoudZiPtxfvvfwY059fTPfu3bn//vsZMWJEqfaWS5YswWQy0axZM5nDhagPivJUnYScs2DMrzjIYDGqNpYlgwxhbWH0C84FGQrSVTAz5lIIbS0pwk4oKipi5MiR7N+/H03TaNWqFVdddZXD3Przzz+TmJjIt99+y+nTp9mxYwe+vrLGW4haVZSn7qHMBkje7xhk8A1RdRlsQYaiPNXmskUvCTLUIJcDDR999JEbhyGEEI2MIdu1ZROHvynxQgc9Jpbapf1l11Z6mvnz5zN//vxy369uIcjz58/z008/8fvvv/P777+zf/9+DAYDw4cPZ+vWrRUeazKZWLhwIf/973+Jj4/Hx8eHPn36MHPmTK6//vpqjUuIBsOQqwIMuWdVhoF/GIS1Ln9/swF+nANnf7dv8wuDaxaozKeKaNqFzhJ+EN1DLcsQTnn99dfZt28ffn5+LF68mH/84x9lBmk/+ugjZsyYwf79+3njjTd45pln6mC0QjRRpkIVZDBkQ1EBbHvT/p7OA0bNtS+NMBWq+7Oo7qqVr6gxUr1RCCFqgj4TsDr3lNGmMAtOlmi/1Hpwmb8EXVzx5lZffPEFjz76aJWPMxgMjBkzhu3bt+Pp6UnPnj0pKChg69atbN26lVmzZrFgwYIaGLEQ9YQhRxWJzU1SN7x+oRUHGEDVVNgwW6UE23j6wtiXIKTi+i1YzZB3HvzDVZAhIKL6f4cm5PPPP0en07Fw4ULuueeecve7++67MRqN3HfffXz66acSaBCitti69OSlgE8QrH1CZX/ZDJgCrS7UT7EYVWZXsy4Q3k6yumqYBBqEEMLdLCbIS1a/8Krir7VgNdlf95xU5m6ndq0uvdFUpJ54tuyvnlpW4oorrqja2C4SEhLC6NGjGThwIAMHDmTfvn3Mmzev0uNmzZrF9u3bad++PevXr6drV9WG7/vvv+fmm2/mX//6F0OHDuXaayvP2hCiQbFaVbu17AQwGSAgDALKrqXioCgP1j3luFzCO0BlMjTvWck1LSrIEBSjOkv4VnFOEpw8eRIvLy/uuuuuSve96667mDlzJqdOnaqFkQkhsFog7ThkJ6qg7Y/Pqvsvm7ZDoc+tF/Y1q2BEeHuI7KQ6Uoga5ZZAw/fff8+GDRs4c+YMhYWFbNq0qfi9goICDhw4gE6nY8iQIe64nBBC1G+F2erDQVUqGFvNjusJg2Oh9aAyd23bKrb0RlOh+mrb1qlAg7d3OWvAnXTPPfc4PN1LSkqq9JiUlBSWLFkCwPvvv18cZAC47rrreOqpp5g3bx4vvPCCBBpE46JpkHFC1V/xD4PAZs4dV5gF655Qx9r4BsO4V1XgoLJr5p+HwCgVkKhqm10BQFBQEBaLxamaC76+vgQFBZXbfU0I4UaaBhknVatfv1DY+AKc/9P+fmhrGDFbLZ3QrJB7XmWJRnUFT3nWXhuqFcpJTExk4MCB/P3vf2fJkiWsW7eu1NpcHx8fbr31Vq644gp+++236lxOCCEaBn26+rMq1dzP7ISCNPvrHtepX46NyPfff4/RaKRz586MGDGi1PvTp08HYO/evZw4caLU+0I0SJoGWadVBxr/COcznQrS4PuHHIMM/uEwYWHlQQZQT+58QyXIUE39+/cnOzubc+fOVbpvUlISWVlZDBw4sBZGJkQTl50AGcdV8HXLS3Bur/093xC4er6abzVN1cMJjFJzp7TzrTUu38UWFBRw9dVXs2fPHlq2bMkDDzxAYGDp6ure3t5MmTIFTdOK2wIJIUSjZTZCfmrlxdkudqTE/Ojpo3o9NzK7du0CYNiwYWW+37JlS9q3b++wrxANXs5ZSP0L/EKc/8Cfm6yCDDkl2s8GNoNr/61aUlYmPxW8/SHmkqrPRcLBY489BsDjjz9e6b5PPPEEOp2u+BghRA3JTVZ1Gbz8YNvrkFjiYbZPIIx/A8Lbqtf5KSrjoXkPCbrWMpfzRv7zn/9w7Ngx+vXrx88//0xgYCArV65Er9eX2nfixIm88MIL/Prrr9UarBBC1HuGbNWiLqgKVd2zzkBSiUh8p9HqQ4mLduzYwcGDB8nKysJkMpW5T1ZWlsvnd9Xx46ptZ8eO5X9Q6tixI6dOneLYsWMVnsvWw7ksiYmJtG5dSXE9IWpDbrKqhO7j5/wH/uwzsPZxVbDMJrgFjH+z8sKPoArReniqTAb/cNfGLYqNGTOGxYsX88gjjzBq1Ciee+45rrjiiuLlZ2azmW3btvHSSy+xfft2Fi9ezKhRo+p41EI0YgUZkHqhZs3OxXB6m/09b3+1tKxZZ/Van6EK5zbvWa37KuEalwMNX331FTqdjjfffLPMTIaSLrnkEjw9PYtvMoUQotHKTwN0VVs2ceRbx9flFIGszPqfdzNj/hQSEhIr3be67S1dkZmZCUBERPlV723v1UUgRAi3yk+D1MNqLvALc+6YjBMqyGDItm8La6OezgU60SrXkKuqqrfoXfXWusIhgJmYmOiwzdvbu7g7jpeXF82aqTob6enpmM1mAAICAnjttdd4/fXXZfmXEDXBkKuCtxYD7PkYTmy2v+fp61gk15CjikW2uES67dQRlwMNx44dw9PTk6FDh1a6r6enJ2FhYWRnZ7t6OSGEqP/MRWpddVUquxv1cHyD/XXznqrtUhVt3rGH6+6bg8WiAgidOnWiefPmeHmVPc3XxdIEg8EAqNo95bEVXCssLKzwXCdPniz3vYqyHYSoFfpMSDmkCpA5EyAA9YRu/VOqkKxNZEf42+vOZSYY9SqbKroHhJRRMFZU6vTp08Xf29oIl9xmYzKZSE5OLrW9oKCAgoICdNIyTwj3M+oh5YgquP3nl3D8B/t7nt6q3W+L3hf2LVD7x1xStcLcwq1cDjQUFRXh7+/vdGVdvV6Pn1/lldCFEKLBKsxSN/rOpDfbxP8EphJLznpMcunSLy78AIvFysAB/fn8i/9V+mG7Lj6M234HGI3GcvcpKioCwN/fv1bGJITbGXLg/CHVbrayG1xjvto3+YDqOlNyLojurlKAnVlyYTao+adZV9UbXrjkww8/LP7+iSeeAOD111+vq+EIIQAsZjW/ZZ9R9Rb+Wg1Hv7e/r/OE0S9CqwHqtalQ7R/dQ3WeEHXG5UBD8+bNSUxMJDs7m7CwsAr3PXz4MIWFhfTo0cPVywkhRP1XkK46RTjbLULT4HCJIpB+YdBhuEuX3nv4GDqdjs8++bjePtEPD1dPZW1LKMpie8+2rxANSlE+nD8MxjxVV+Fihhw4fxDOHVB/ZsSrrIeLtegNY19xrnCZxaiWaUR2gsgOIE/TXXbXXXcVf//iiy+W2iaEqEVF+aqLV85ZNXeig7gf4M+V9n10HjBqLrS9XL02FaqMssjOEN5e5sM65nLXiSuuuAKA//3vf5Xu++qrr6LT6cpsZyaEEI2CyaBu9quybCJ5v2p7Z9N9guo44QJvLy+CA/3p2LF+BhkAunRRS0Li4+PL3ce2rtm2rxANhlGvajIUZqogQ8kb3KI8+HEufDIRfpwDh1ZB+vGygwytBsK4fzkXZLCaVRvLsLaq+FlVasMIIUR9YzGrrjnJByFxl/rTXARB0RC/EfZ/VmJnHVw12/6AxqS/EGTopJagejSuFuENkcv/AjNmzEDTNF544QUOHTpU5j5Go5HZs2ezYsUKdDod999/v8sDFUKIeq0wC0wFqmezsw5/a/9e5wHdr3P58t07taPQYCyug1AfXXbZZQBs3769zPeTkpI4deqUw75CNAjmIlWgLC9VLZcoGWQw5sO6p+D0L+Ufr/NQN8YDp6p1xl5OLDXVrJB7XtVjiO6m1igLIURDlXdeBRcSd0NOEnj5Q1hrVaPm0Ffwx/uO+1/5BHQeo74vDjJ0liBDPeLy0onLL7+cmTNnsmjRIi677DKuueYa8vPzAXjmmWc4c+YMGzduJD1dtWd67rnnZOmEEKLxKkhTv9icTdPLT3VsydR2qIrYu+i+2ydx1975/Pezz7l3ev0M6k6cOJEHH3yQuLg4tmzZUirLbenSpQD07duXTp061cUQhag6i0kFGXLPqfosJbMKjAWw7klIO+p4jM4TorqqJRItequCZVUJUmqaap0ZGKXWIXv5uufvIkpJSEhw6bg2bdq4eSRCNGIF6ZByWGVpBTdXQYNTP0PSHtX+u/CiJZeXPwTdxqvvbUGGZl1VNoMEGeoNlwMNAAsXLiQkJIQFCxbw9ddfA6DT6fjXv/4FqIq9Xl5ezJkzhzlz5lR/tEIIUR8Z9eqXpDNF22z+WuOYNt3z79Uawh3XX8Ombb/xyONPEhQazuTJk6t1vprQvHlzpk+fzuLFi5kyZQrr16+na9euAKxevZpXX30VgOeff74uhymE8zRNtaTMTlSZDB4lbqtsQYbUEkEG/3AYPksFF7xdLHiqaerJn18oNO/h3BIL4bL27dtX+RidTlfc8lIIUYnCbLVE4swOVbcmaQ/kVNCme/B9cMn16nsJMtRr1Qo06HQ65s2bx7333stHH33Er7/+yrlz57BYLMTExDB06FDuueeeeluYTAgh3MKQrX7Z+TvZUs5igqOr7a/D2kJs32oP46NXn6Jdzy3cfvvtzJ49mwEDBhAcXHbwIy0trVrXSkxMpG9f+5htSzZ+/fXX4v7yAE899RRPPfVU8etXX32VPXv2sHPnTnr27Mkll1xCfn5+cW2Gxx9/nIkTJ1ZrbELUmpxEdWMc2Mxx6YJRr1pVph6xb/MLg/FvQkTVP7gW06yQl6zO1fwS8Atx/VzCKbY2lzV9jBBNUlE+JPwGG56GzPLbVgNqidmAKdD7woMUkx70WRJkqMeqFWiwadu2rTyBEkI0XfkpKl3a2WUTp35RNR1sekx0S2Xk5f9by8JFHwBw5swZzpw5U+Z+Op0Oq7WMInRVYLFYyMjIKLXdbDY7bNfr9Q7v+/v7s3XrVt566y0+/fRTjh8/jo+PD8OHD2fmzJnccMMN1RqXELUmPw3SjqkCsCWzE2xBhpTD9m1+YTChmkEGq0VlMgQ0g5ieVcugEi6z1Y0pT05ODr/99htvvfUWaWlprFixgu7du9fS6IRowEwGOLcPfnqu/CCDdyDE9oGW/aHNYAhpqbYb9Rda+naRIEM9ptMk7Nog2bJETp6sJPonhKhZxgJI2KWKtzmbwvzdg5ByoYiutz/cvgp8Aqs1jO/W/sjfp88GIDAwkCFDhtC8eXO8vMqOJ69atQqAvLy8al23PpL5UdQ4Q666QTYbHGurGPXwwyw4/6d9m18oTHgLIqqR3Wk1q5oMwTHQvGe15wtRuarOIwaDgVGjRnH69Gn27dtHdLTrNXdqksyPol6wmFTnrbVPqD9tPLxUtlbLfiq4ENXVcUkalAgydIXIjhJkqAPOziNuyWgAdbO6d+9eUlNTAYiOjqZv376EhEhanxCiESvMVsEG/3Dn9k+PswcZADqPdcuHhleXfgrANWOv5n9frix3yYTNzz//XO1rCtEkmYsg7S8w5kFwieVSJj388LRjkME3pPpBBotJZTKEtFQ1GVyt7SBqlJ+fH2+//TYDBw7kpZde4t///nddD0mI+slqgdRjsPEFxyCDbwhMXKSWk5ZHggwNSrX/dfbv38+1115LREQEI0eOZPLkyUyePJmRI0cSGRnJtddey759+9wxViGEqH/yU8DLx/mlD0e+dXzdwz31CA4dP4lOp+P9pe9WGmQQQrjIaoG04+qDf1CJNpYmPfwwG84ftO/rG3JhuUQ1ggzmInWtsLaqM4UEGeq1/v37ExgYyOrVqyvfWYimSNMg4yRsfRlOl2h17eUH4/5VeZDBkC1BhgakWv9CH374IYMHD2bdunVYLBY0TXP4slgsrF27lsGDB/PBBx+4a8xCCFE/FOWrlkvOrpUuyoO4jfbXLfpUb812Cd5eXoQGB9KiRQu3nE8IcRFNg6zT6iso2t7G0lSoggzJB+z72oIMkdVo02o2qLa5ER1UJoO0sKz3rFYrFouF5OTkuh6KEPVT9hnY/gYcW2vf5uEFV8+D0FaQmwQ55XwV5UJkFwkyNCAu/yvt3r2bqVOnYjKZ6NSpE8uWLSM+Pp7CwkIKCwuJj49n2bJldOvWDbPZzLRp09i9e7c7xy6EEHXLkK0i7N5O1mY49gNYiuyvq9nSsqTe3TuRV6BvlDUXhKgX8pJVNkNAmP1Dv9lQRpAhGMa/Ub0gg1EPBRnqHFHdHDtaiHpry5YtGAwGwsLC6nooQtQ/uedg1xI48HmJjTq4arbKUigqgMjOqg5NzKUXikD2g1YDoPVg9SVBhgbF5X+pV155BavVylVXXcWBAwe499576dChA76+vvj6+tKhQwfuvfde9u3bx4gRI7BarbzyyivuHLsQQtQdTYO8FOefMmoaHP3O/jqwGbQb6rbhPPyPm7BYrLyzZKnbzimEuECfCal/qWVSPkFqm6bBz69etMb4QpChWWfXr2XMv5Ae3EXdfHu6rZyWqCEmk4kvv/ySu+66C51Ox8iRI916/nXr1qHT6dDpdLRr186t5xaiVhSkw96PYfdF9yiXz4RWA1Wtq+Y9VPHHiPYQ3lZlOITEqiK4QVEQECFBhgbG5X+t7du3o9PpePfdd/Hz8yt3P19fX/7zn/8UH+OqlStXMm3aNAYMGEBsbCy+vr4EBwfTr18/5syZU2abNZv8/Hyee+45unXrhr+/P1FRUUyYMIGtW7dWet0tW7YwYcIEoqKi8Pf3p1u3bsyZM4eCgoIKj6vONYUQDYCxissmzu2DnLP2192uLV1JuRquGzOMuQ/+H3Ne+CcLFiygsLDQbecWokkz6lWQwVKkbnRtDn8DJzbbX/sEXQgydHH9WoZcMORBVHcVrJCb6jrXoUOHCr9iY2Px9/fn1ltv5dy5c4SEhLi15Xt+fj7333+/284nRK0rzIaDK2HbG6CVaK3d9w7oPEbVuGneA8La1NkQRc1wub1lQEAAvr6+ZGVlVb4zEB4ejtForPQDenn69OnDgQMH8PX1pUWLFjRr1ozU1FQSEhIA1eXixx9/pHfv3g7Hpaenc8UVV3Ds2DF8fX3p0aMHaWlpnD17Fp1Ox+LFi5kxY0aZ11y0aBEPP/wwmqbRqlUroqKiOHLkCEVFRXTv3p3t27cTERFR6rjqXNNZ0p5IiDqWnQDn9kNYa+f23/ginNyivtd5wG1fqqwGNxl5472gWdn312lyc3Px8/OjR48e5RaG3LlzJ0CjDEjI/CjcxmKC5IMq5Tc0Vv3sAqQchu8fAs2iXnt4wYSFqmBjVWhWVePBVKgKP3p6q6USYW2cLzAraoRtHjl9+rTTx1xxxRUsWrSo1L1odTz00EMsWrSIiRMn8t1339G2bdsqjeliMj+KWmUywKGvYd0TYCrxGbDbtTBoqspkiO4O4e1kzmtAary9ZZs2bThz5gxWqxWPSiLuFosFg8FA27YVVBKtxAMPPEDXrl0ZMmQI3t72tYp//vknt912G4cOHeK2227j8OHDDsdNmTKFY8eO0b9/f77//ntiY2PRNI3ly5czffp0HnroIS6//HL69OnjcNyePXt45JFHAFi6dClTp05Fp9Nx7tw5rrvuOvbs2cPUqVP56quvSo3V1WsKIRoI27IJ7/KzuRzoM+H0Nvvrtpe7NcgAsHWXY3efwsJC9uzZU+7+LsaYhWg6rFbIiFfFyUJa2IMMhVmw8Xl7kAFgyAPOBRmsFvX0zlSoghg6HXj5g18YBESCXygERtbIX0e45sMPP6zwfS8vL8LDw+nduzctW7Z067V37drFf/7zHyZOnMikSZP47rvvKj9IiPpC0+DUdtgw2zHI0O5KGHSvygyN7iFBhkbM5UDD3//+d1599VW+/fZbrr/++gr3/fbbbykqKuKGG25w9XJMnTq1zO2XXnop77//PoMHD+bIkSMcPXqU7t27A7Bv3z6+//57PDw8+OKLL4iNVf2udTod06ZNY/v27axYsYJ58+aVChjMmzcPq9XKnXfeybRp04q3x8bG8vnnn9OtWze+/vprDh48SK9evYrfr841hRANRFGe+rDh5+SyiePrwWq2v+5+rduH9PzD94DVBKFtnFrTvXDhQrePQYhGJfsMZJxQQUHbMierGTb9U603tuk0GnpMqvx8pgsFHn2DIfDCemOfYPAJdD5oKWrdXXfdVSfXNZlMTJ06lYCAABYvXszGjRsrP0iI+iT1KKx+QNWcsWnRB654RHXtkiBDo+dyoOHZZ5/lm2++Ydq0aURERHDVVVeVud8vv/zC9OnT6datG7Nnz3b1chWyBRYA9Hp98ferVq0CYOTIkXTqVLr68/Tp01mxYgXr1q2joKCAwMBAQK2H++GHHwAcggw2nTt3ZuTIkWzcuJGVK1c6BBpcvaYQogEpzFLV5r2iKt9Xs8LREm2cgmNU4SM3e/6Re9RT0rZDnfrQ8vHHH7t9DEI0GnnnIf24Cgp4+9u3//GhqrdiE94Ohj1e+Y2yIUelCEd1U0XOpFWlqMQrr7zCoUOHeOutt2jVqlVdD0eIqinMgbWPq7nUJrIzjHhW3atE91BFHyXI0Kg5FWj45JNPytx+33338eKLLzJq1CiGDh3KyJEji9PGkpKS2LJlC9u3byc0NJTp06fz9ddfc+edd7pv9BfYikwGBQXRtWvX4u27du0C4MorryzzuEGDBuHr64vBYGD//v0MHaoqwO/bt4+ioiJ8fX0ZNGhQmccOGzaMjRs3Fl+jutcUQjQQmgb55x0/fFQkaQ/knbO/7natPQVbCFH/FGapJ3EenuAXYt9+ejvs/9T+2jsAxvyz8rmgIA3QqXZtoa3lxroRsFgsZGZmAhAREYGnp6dbz3/06FFefvll+vXrx8yZM916biFqnMUEOxdBwg77tuBYGP2CyryM6iZBhibCqUDD3Xffja6C/xk0TePXX3/l119/LfO9nJwcHnvsMXQ6ndsCDVarlfPnz/Pjjz8ya9YsABYsWEBQUFDxPsePHwegY8eOZZ7D29ub1q1bEx8fz7Fjx4o/9NuOa9OmjUM9iJJs5zx27JjDdlevKYRoIIpyVQVlv1Dn9j+y2v69zhO6XlMjwxJCuIGxAFKOqgJmITH27TlnYetFLbqHz6q4SrpmVU/zfIJUsbOg6JoZs6gVer2eJUuW8Pnnn3Pw4EHMZrUczsvLi969e3Pbbbcxbdo0AgICqnUdTdOYOnUqJpOJpUuXuhTEsBVqK0tiYiKtWztZxFgIV5zcBjvetr/WecLwp9T3kV0gooMEGZoIpwINbdq0qTDQUJsWLlzIo48+6rBt0KBBfPzxx1xzjeMNfMloc3ls75XsnuHqcdU99mLyi0KIeqgwS1WHdyb1uSAdzpRo69vuClXwTQhR/5iNqo2lPkN1mCjeboCfnldBCJteN0OH4eWfy2KC/BQIiILm3Z0PTIp66dixY1x77bWcOHGiVCFdk8nEH3/8wZ49e3j33XdZvXo1Xbq43uL03Xff5ddff+Whhx5iwIAB1R26ELUrJwl+eFLdJ9n0u1PVpWnWBSI7StveJsSpQEN12ui4W8uWLRk6dChms5mEhATOnz/P/v37+eSTT7jssssICwsr3tdgMADg4+NT7vl8fdWHhZIt3lw9rrrHCiHqOasVcs+Dj5NPrI6tc+wZ3eO6mhkX4NlhWJX2t1qtle8kRFNhtaqaDBd3mNA02P4WZJ6w79uiNwwqXb+pmKlQBRnDWqsUYWeXWYl6KS8vj6uvvprExES8vLy4/vrrGTNmTHHdhLNnz7Jx40a++uor4uLiGDt2LH/++adDhq2zkpKSmD17Ni1btmT+/Pkuj7milnMVPcQSolqMetj0ourWY9OiN3QYcSHI0EmCDE2My8Ug68pNN93ETTfdVPz64MGDPPjgg3z++eccPXqUP/74ozjNzM/PD71ej9FoLPd8RUUq4ubvb78R8PNThdSqelx1rlkW+UUhRD1TlKuqJ/uHVb6v1QJ/lSgCGdISYvvW1MikXaUQrtI0yDqtvoKi7R0mAP5aA8c32F/7R8CouY77lFSUp76adVY31Z5lL78UDcfChQtJTEwkNjaWNWvWlNmafMqUKRw4cIDx48eTkJDAv//9b5599tkqX2vmzJnk5uby4YcfEhzsZFcjIeoDqxUOfQ1/fmnf5hsMA6ZAaCu1XEKCDE1Ogws0XKxXr16sXbuWDh06sH//fr744gtuv/12AMLDw9Hr9cXLGcpiey88PLx4m+37qh5XnWsKIRqAwixVyMiZZRNnd6vUaZvuNVsEcsvni1SqYsyl4FU6oyonJ4fffvuN5cuXo2kaOp3O7QXMhGiQcs9B2jHwD3X82U77C34tuc7YA0Y/X/7yJ32GCjBG94CwtnJT3Uh8++236HQ6li5dWmaQwaZ3794sW7aMCRMm8PXXX7sUaNi7dy8AM2bMYMaMGQ7v2bJgExMTiYlR9UO+/vprLr/88ipfRwi3Sz0CG+eqwK3N4PtVkCGyo1Ntt0Xj0yj+1YODgxk+fDhfffUVe/bsKQ40dOnShaSkJOLj48s8zmQykZCQULyvje37hIQETCZTmQUhT5w4Ueq46lxTCFHPWa2Qm+x8GnTJIpAe3tClZotADr+sb6XtLa+77joefvhhRowYQVxcHLGxsWXuJ0STUZChAgrevqpoo40hR9VlsJrs2wZPV2nAF9M0yEtRP3ctLlEtbEWjER8fj6+vL+PHj69033HjxuHn51fuPaCzUlJSyn3ParUWv19R9qwQtUafCetnqWCrTeexENNLBRmcyQIVjVK1Aw3x8fF8+eWXHDx4kKysLEwmU7n76nQ6Nm3aVN1LlslW/df2J8Bll13Gli1b2LZtW5nH7N69G6PRiJ+fn0OUum/fvvj4+FBUVMTu3bvL7AxhO+eQIUMctrt6TSFEPWfIhqIcCCi/0Gux/FRILNH6tv2V9eYXbXR0NP/5z38YMWIE2dnZdT0cIepOUZ56Cmc1QVBz+3arWa0zLpmR1P5KuPTmss9TmKnqtrToBf6SqdjYmEymCutulaTT6fDx8XE5AFBRTbSPPvqIf/zjH7Rt27Ze1U4TTZzZCLvedSx8HRILvW5Sf4ZK4fqmrFqBhmeeeYbXXnsNq9Xq1PrgmupckZmZydatWwEVJLC58cYbeeWVV9iyZQvx8fF06tTJ4bilS5cCKgJdsmhPcHAwY8eOZfXq1SxbtqxUoCEuLo7NmzcXX6MkV68phKjnCrPBYgZPJ244/1rrWASy+7U1NixXDB8+HJ1Oh16vr+uhCFE3TAZIPaqCh8ElMns0DX55HZL22reFtlatLMu6hzHp1Y12bE8JMjRSrVq14sSJExw5coQePXpUuO+hQ4fIzc0tde8nRKOkaXDq4laWHjD0EfBvpmrVeMgSzabM5UDDO++8w4IFCwBo3749o0aNonnz5nh5uX81xs8//8y2bdv4v//7P9q1a+fw3t69e5k+fTo5OTm0bNnSoVBkv379mDBhAmvWrGHy5MmsXr2aFi1aoGkay5cvZ8WKFXh4ePDcc8+VuuacOXNYs2YNK1asYOjQoUydOhWdTkdycjK33norVquVSZMm0bu3Yxplda4phKinrBbIO+dctwmr2bEIZFibstOt65At6FsyA0yIJsNiVjUZ8s6rJ24lAwj7VsDxH+yvvQNhzD/BJ7D0eawWtfSiWRdZLtGIjRo1ivj4eO6//342bNhQXDD8YgaDgRkzZqDT6Rg9enQtj1KIOpBzFn6YpVoA2/S7UxW/btYR/ELqbmyiXnA5KvDuu++i0+m4++67Wb58OR41WPQoKyuLOXPmMGfOHGJiYmjZsiWenp4kJiaSnJwMqLaXa9asKZUl8MEHHzB06FD27NlD+/bt6dGjB+np6SQmJqLT6Vi4cCH9+vUrdc2BAwfy5ptv8thjjzF9+nTmz59Ps2bNOHLkCEVFRXTt2pXly5eXOV5XrymEqKcMOWDIg8ByisCVlLAL9On2192vLftJaB3as2cPmqbV6LwtRL2kaZB5EnISILi549O24z/CHx/YX+s8YcyLENG+7HPlp6pzRHSodz/jwn2efPJJPvroI7Zv307v3r156qmnGDVqFC1btgRUe8tNmzbx2muvERcXh6+vL0888UQdj1qIGlaUB5vnQ0acfVtML+gwCkJbqmCDaPJ0mos90fz9/TEajWRmZhIaGurucTlITU3l008/ZevWrRw+fJjU1FQMBgPh4eH07NmTa6+9lnvvvbfcVkB5eXksWLCAVatWcebMGQIDAxk8eDBPPvkkI0aMqPDamzZt4o033uC3336joKCAtm3bcuONNzJ79uwKlz5U55rOsLW3rKgFphDCTTJOqDTrUCd+ca6fBYm/qe89veH2r2onqm8qrLQYJMDvv//OXXfdxdGjRwkICKCgoKDmx1bLZH4U5co6AymHVM0U7xIZSkl7Yf1TKiPJZvgs6Dqu7PMYslWB2Jb9ZMlEI1VyHvnyyy+54447MJlM5S4D1jQNb29vVqxYwc03l1PPox6Q+VFUm8UE+z+DNQ/bu0z4BML4N1W9m1YDVGtL0Wg5O4+4HGiIjY3FYDBU2MZR1Bz5RSFELbGYIWGnKhhX2QeKvGT4/DbgwrTa+WoY8UyNDxFg5I33qroQfqFlttE0GAwkJiZy7ty54po6LVq0ICkpqVbGV5tkfhRlykuB5AOqhWXJ4F/WafjuATCWCLr1uxMG3FP2ecxFUJCuij+GtanRIYu6c/E8snfvXp599ll+/PHHUnXJPDw8GDt2LPPnz3eoFVYfyfwoqkXTVGD285vVPGhz1WzVXrtFb9XSUjRqzs4jLi+dGDRoEGvWrCE9PZ1mzZq5ehohhKjfDDkqRTAoqvJ9j66lOMgA0P26GhvWxbbu2uf0vm3btqWwsBBfX98aHJEQ9UhhluowodM5Bhn0GSoLqWSQofPV0P8fZZ9H06AgDcLaQYjcTDcl/fr1Y/369eTk5LB3715SU1MB1cmnX79+NZ7dK0S9kHsOfnjaMcjQ+WqI7qnmxGBpmy3sXA40PPnkk6xdu5aXXnqJt956y51jEkKI+kOfqTIFPCqZLq1mOFaiCGR4e2jes2bHVsLzD9+jsi5C24Bn6bF6eXkRHh5O7969ufzyy+nYsWOtjU2IOmUsgJSjqtNESImijaZC+GG2YxvL2L5w5ZPl11woSAO/MNUbXmqcNEmhoaFuWQIrRINTmA1bXoKzu+3bgltA3ztUAFfmRXERlwMNQ4cO5d133+WBBx6gsLCQp59+ulRHCCGEaNAsZshPLrvi/MVOb1dPTW1quQjk84/c41SNBiGaFLMRUv+CwkwIaWHfbrXApnmQfty+Layt6jDh6V32uYwFKqMhqqtzHWiEEKKxMBeprjz7P7Nv8/RWSyZ0HhDZCXzLr10nmqZq9aK89957ycjIYPbs2SxfvpyIiIhyCzKCaql24sSJ6lxSCCFqjyH7wrKJ6Mr3Pbra/r2nL3QeU2PDEkI4wWpRgYTcJBVksNUu0TTYsQgSdtj39Q+HcQvKL2BmNavspqjuzs0HQgjRWFitcGIrbHkZh+WhVzwGAREqSBvcoryjRRPmcqDBYrFw55138sUXXwCq2m5GRgYZGRnlHlNepV4hhKiXCtIBrfJlEzlnIWmP/XWnUbVbcVnTwGKsvesJUd9pGmSchMxTKjBQ8mf4z5Vw5Fv7ay8/uOaVim+U81MgJBYi2tXUiEU94Olpb3dqtVpLbXOWTqfDbDZXvqMQDUF6HKx9DEx6+7aef4fWg8DDByI7yJIJUSaXAw0LFy7k888/B2DYsGGMHTuW5s2b4+VVrSQJIYSoH8xF6sOFjxOpgEfXOL7ufm3NjKkkixGMevWL32pVyyUCIsDDC6vVSlxcHJmZmZhMpjIPLywsrPkxClFXcs6q/u4BEarLhM3Jn2HXu/bXOg8YNReiupV/rsIs8A6EZl3KX1YhGoWyGrG52JxNiMahIEO1scw9a98W0wsG/AOKCiC2p3PLS0WT5HJU4P3330en0zF79mzmz5/vzjEJIUTd02eAIRdCK6mgbDHC8fX215GdKv7Q4iqrWdVgMBao7z281S/3iI7gHwY+QSRn5DJ7yr2sWrWq0kCC7WmdEI1Ofhqk/QU+/o61FM7thy3zcUj9vXwmtL28/HOZDSqgF9vHsVuFaJS2bNlS/P3kyZMB+OKLLzh16hSPPfYYBQUF3HjjjYwcOZJWrVTXkaSkJDZv3syqVasIDAzkzTfflJplonEwGWDzPNXi2yawGYx8DgpzVfA1OKb840WT53Kg4fTp08WBBiGEaFQ0DXKSwMvHvq67PKe2qRaYNt2vc18RSKtFFbEzF4HOE7wDIKSlekrrG6yyLTxUWu+5c+cYPHgw586dkydwouky5Kg2lppVdYewSY+DDc+CpUSGT6+bVfpveTSrClpEdFQ/d6LRGz58ePH3/v7+AHTv3p3bbruN4OBgtm3bRs+epbsJ3XPPPTzzzDP87W9/47nnnmPv3r21NmYhaoTVCntXwJ6P7Ns8vWH0i+oBS1gr1WVClsWLCri8oCY8PJyQkBACAyVdRgjRyBRmgT4d/Jzoi374G/v33v7QabT7xlGQCt5BEHMptLkM2g2FFr0gtJUam4d97fALL7xAUlISQUFBvP3225w5cwaTyYTVai3zq3379rRv3959YxWirhn1kHIEjPmOBRtzzsL6p8BUYN/WYQQMvq/8c2ka5J2HwCi5mW7i5s+fz/nz51m+fHmZQQabHj16sGzZMpKSknjppZdqcYRC1IDT22DjXEoVf/SPVF/NupbZSluIklwONFx55ZXk5OSQlJTkzvEIIUTdy09V2QQl13aXJeUwpByyv+40xn1t74x6lcUQ1RXC26kshgrWh69fvx6dTsf777/Pgw8+SOvWrV0qYiZEg2QqhNSjKkBYMpVXnwHrnnRsPdtyAIx4puJspfwU8A2B5j2kXWwTt3btWvz8/Lj66qsr3ffqq6/G39+f1atXV7qvEPVW1hn47kHH4o89JkHrweq+qHl3afErnOJyoGH27Nn4+vry1FNPuXM8QghRt4x6yE12bj32n6scX196o3vGoGnqA1JYWwiMdOqQtLQ0vLy8mDRpknvGIERDYSyA84dUG8vgGHsAoSgP1j0Fecn2faO6wdX/rLioY0E6ePpB857OZTWJRu3cuXNVKnTu5eVFcnJy5TsKUR8VFcC390NOgn1bzKXQ/24wGyG6m2oHLIQTXA409OrVi6+//pr169czbtw4tmzZQkFBQeUHCiFEfaZPB2Ne5d0m8pLh1M/2122GQFgb94yhMFP9Ig9v6/Qh0dHR+Pv7S+cf0bQU5UHyn2qZQ0isvY2luUjVZMg8Yd83tDWMW6BqnZSnMFv92byHyiISTV5YWBj5+fns2bOn0n337NlDXl4eoaESoBINkMUMm16AM7/atwU2gxHPQlE+RHWpuA2wEBdxOdDg6enJ+PHjycnJ4ccff2T06NGEhITg6elZ7pfcAAsh6jWrRRWB9AmofE32oW9UsTibXje7ZwwWo/qQFNlR1Xxw0ujRo8nLyyMuLs494xCivjPkqCBDYbrqDmOrWWI1w8YX4fxB+76BUTD+dccCkRcrylNV1qO7Q3DzGh26aDhGjhyJpmlMnTqVjIyMcvfLzMxk6tSp6HQ6Ro4cWYsjFMJN9n8Gu5fbX3t4w6gXVBHd8HbqS+rViCpwOdCgaZpLX0IIUW/pM9Va7srSpY0F8Nca++vITtCij3vGUJCmnswGVa1l1DPPPENgYCCzZs1yzziEqM8KsyD5IBiy1BM223IJTYNfXoOEHfZ9fUPgb69DUAXBA5NetbON7qaKrQpxwdy5c/H19eXAgQN0796d559/nq1btxIXF0dcXBxbt27l+eefp3v37uzfvx9fX1/mzJlT18MWompO/wo/zMKx+OOjKrsyuDk06+xQgFoIZ7icYlCy17AQQjQKeefVnx6VTI1/rXMsknTpze6J8hflgZcfRHQAj6rFgTt16sT333/PDTfcwJgxY3jmmWcYNGiQdAYSjU9BhirCatRfCDKU+Nn7bQkc32B/7eWnlktUtAzJXAQFmfbCq0KU0LVrV7755hsmT55Meno68+fPZ/78+aX20zSN4OBgPv/8c7p161YHIxXCRZmnYdU9ZRR/HASePqq2jRTFFS5wOdBQstewEEI0eEV5kH++8mwGqxkOlSgCGRAJHUdU//pWi1of3vwSlwrQlewwsXnzZjZv3lzx5azWCt8Xol7KT4Xzh8FaBCEXZf0c+AIO/s/+2sMLxvwTonuUfz6LSZ0zooO0sRTluuaaazh8+DAvvfQSX375JZmZmQ7vR0REcPPNNzN79mxat25dR6MUwgWGPPjy/9T9j01sX+h3p7rfieomRXGFy6RoghBCgKo0bypUhY8qcnq7an1n0/P6iivYO0ufodaRu5i2LUvTRKOXmwyph1VtlIuXQRxbr7IZiungqtnqiVx5rGaVxRTWRmUzSFqwqEDLli155513eOeddzh16hSpqamAKsTbvn37Oh6dEC6wWuGbaXD+T/u20Faq/a+pUHXekXo1ohok0CCEEBYT5JwF30o6TQAc/NL+vZcfdL+2+tc3G9Qv/MiO4OXj0imqupxt8uTJLl1HiDqRcxZSjqglRYFRju+d3qbqMpR0+UzoNKr882lWyL3QqSKqm3uChaLJaN++vQQXRMO3+Z9wbJ39tW8wXD1fFcWN6KhabAtRDS4HGn755ReXjrvyyitdvaQQQtQMfYaqYB9cSQHGlMOQesT+uss14BdSvWtrmsqmCG9f+gNUFVR1OZu/v/MdLYSoU9kJ6mfPyw/8wxzfS9oDG//p2AGm311wyfXln0/TVHZEYDPVYULWHgshmpoDn8P2hfbXOk8Y9bxachYce6H4o8s9A4QAqhFouOqqq9BVcS2jTqfDbDa7ekkhhHA/TVMtLT08K0+dLpnNgA4uuaH61zdkg08wRLSX9eFCXCw7UdVk8PEvvU449ShseBasJvu2HhOh/93ln0/T7LVYmvcEHymWKoRoYhJ2w+qHcegwMfRh1e0qKAaa93A5u1KIkqq1dKKqa4JlDbEQot4x5IA+vfST0ovlJqsUbZu2l0NYNYt+Wc2qcn6L3vKBR4iL5ZxVmQw+fqWDDJmnYP0stezIpuModbNcXsDOFmTwDlRBhupmIwkhREOTkwRf3Kq67dhcehO07K+yvJr3BG/JeBTu4XJOjNVqrfArOzubDRs2MHz4cCIjI/n555+lyrkQov7JTwOzUaVlV+TQV47p2Zfe5J5rB8eodeJCCLvcZFWTwcsX/MJKv7fuSSjKtW9rcxmMmA26cm5rSgYZYi6FgIgaG7oQQtRLRj18eqN6uGLTZgh0n6g6aDW/BHwC6m58otGpscU3ISEhjBkzhs2bN3P55Zdz3XXXcerUqZq6nBBCVJ3JALlJlT/ZNObDsbX21826qCyE6jAWqLWQER2k2r0QJeWdh5RD6ufi4kwjfQasfdzxRjmmF4x+Qf08lUWCDEKIps5qhVX/cKwzFdEBBt4LAeEQc4lzBbGFqIIar/Kh0+l49dVXycnJYd68eTV9OSGEcJ4+HYx5lf9y/WudavVkc+lN1aunoFlBnwnh7eRDjxAl5aeq5RI6XemfDUOuymTIO2ffFtkZrnm5/IwkTVPtaCXIIIRoyjY+D8d/sL/2D4ehj6pWwTGXqo4TQrhZrZQT7dq1KyEhIfz000+1cTkhhKic1aqyGbx8y0+3BlVH4dBX9teBzaDDVdW7tj5T/ZIPa1O98wjRmBSkq8KPmkWl8ZZkKoQfnobMk/Ztoa3hb6+CTzmBwuIgQ4AEGYQQTdeej2HH2/bXnt5wxWMQ3lZlMlxcA0cIN6lWMUhnmUwmCgsLMRgMle8shBC1oTALCjIgMLLi/U5tUx9WbHper35Ju8piVF/NL5G2ekLY6DPh/J9gNUJQtON7FiP8OMcx5TcwGsa/rgJ2ZZEggxBCQPxmtdyspMEz1LwYc2n5c6gQblArGQ3ffvstJpOJ6OjoyncWQojakHdeLWHwrKCFk6Y5trT08oPu11bvuvqMCy2kmlfvPEI0FoVZKshgNpQOMlgtsHk+JP1h3+YXpoIM5f0MlQwytOglQQYhRNN0/k9YeadjC+BLb1ZZmRKAFbWgxjIajEYjiYmJfPXVV7z88svodDrGjRtXU5cTQgjnGQtUcTj/StIFUw5D2lH7667jqreO0WJUbavD2oBHrcR5hajfCrPh/CFVDT34osCBZoVtb8CpX+zbvAPVconylh1pGuSlqHaxLeRpnXAvq9VKXFwcmZmZmEymCve98sora2lUQpQhKwE+uwWK8uzb2l0BvW5WyyUCm9Xd2EST4XKgwdPT+SrpmqbRsmVLnn/+eVcvJ4QQ7lOQroINlX0I+bNENgM6uOSG6l1Xn6mewl68/lyIpsiQq564FeWpNq8lC6xaTPDzvyB+o32bp48q/NisS9nnkyCDqCHJycnMnj2bVatWUVhYWOn+Op0Os9lcCyMTogwF6fDFraoOlU1Udxh8v1q2eXHmmBA1xOVAg6ZpTu3n7+/PDTfcwCuvvEJsrPSKF0LUMYsZchJVr+iKOkfknoPT2+2v2w2F0FbVuK5RfRAKa13tbIaEhIRqHQ/ITbCoW0X5KpPBkAMhsY4/i0a9qpB+9nf7Np0njHmx4ray+RJkEO537tw5Bg8ezLlz55y+93V2PyHcrjAHvpqqWgTbBLeAKx5W82dIi7obm2hyXA40bNmypeITe3kRHh5Oly5d8PKqlZqTQghRucJMla59cZr2xQ59pVK3bS69uXrX1WeqpwgB1U9XbN++fbXPYbVaK99JiJpgMkDqUSjMKB1kKMyC9bMg/bh9m4c3jHwW2gwp/5z5qeDlr1KCJcgg3OiFF14gKSmJ4OBgXnrpJSZOnEhsbGyVMnuFqBWGHNgwG05utm/zDVZtLFv0rd7DEiFc4HIEYPjw4e4chxBC1DxNg9xk1c7So4LprygPjq2zv47qqgonucpiUkELN9VmkKdlosGymCHtGOQlqydrJVvL5p6DdU86pvt6B8DYlyC2b/nn1Geon+eYS6S4mXC79evXo9PpeP/997nxxhvrejhClK0wG3Ysgv2f2rd5eMHlD0Gr/hDRoeIsTiFqgKQaCCGajsIsyDsHAZU88Ty6Gkwl1uFeenP1fkEXZqp2fG7IZgA4depUtc9xxRVXuGEkQlSB1QoZcZB9RtVkKBnsS4+D9U+pn1Eb/wgY9y9o1rn8cxqy1Xlb9JLiZqJGpKWl4eXlxaRJk+p6KEKUrTALDvwPfl3ouH3wfdB6sKpr41VBhy0haogEGoQQTYOmQXaiyizw8it/P1OhY0vLwCjoUI0MLotJtehzY6eJtm3bVvsc3t7ebhiJ6zIyMnjjjTdYs2YNJ0+exGg0Eh0dzeWXX85DDz0kgZDGRtMg6zRknICgKPAs8f9f0h74cQ6Y9PZtoa1g3GsVrycuygOjQWUyBMfU2NBF0xYdHU1ubq4sAxb1U2EWHP9RtQG2lqi91Guy6jIR1RX8QupufKJJc8usuXPnTg4ePOhUu5+5c+e645JCCFE1+swL2QyVdHw4ulo9JbXpfWvFyywqY8tmkKetxeLi4hg+fDjJycl4eHjQrl07QkJCOHHiBCtXrmTVqlW88cYbPProo3U9VOEuOWch/ZhqKVsy0Be/Cba+4niDHNUNrlkA/mHln8+oV10rmvdUBVaFqCGjR4/m448/Ji4ujs6dK8iuEaK2GXLhzE5VPNdYoo1lh6ug23iI7CxBWFGnqhVo2LhxI9OmTePMmTNOHyOBBiFErdM0yElQf3r5lr+fuQgOfGF/7R+hflm7ympWa9LD2oCHFA6zue+++0hOTqZz5858++239OjRAwCDwcBzzz3HG2+8wVNPPcWECRPkxr4xyEuBtL/A2w98guzb/1wFOxc77ttqoOou4R1Q/vnMBvUUr1lXCG9XI0MWwuaZZ55h1apVzJo1i6+//rquhyOEYtTDuQOw6Z+q5o1N857Q904VgJX5UdQxlwMNu3fvZsKECRiNRkBVQY+NjZXUMiFE/aPPgNzzlReK+2utykCw6X1rxYGJSq+bqdLE3ZzN0JDbW+bl5RV3LXrttdeKgwwAfn5+vPbaa3z33XfEx8ezYcMGCTQ0dPpMSD2ivvcLU39qGuxeBgc+d9y389Uw/KmKM4gsRshPU3UbIqW4mah5nTp14vvvv+eGG25gzJgxPPPMMwwaNIjAwMC6HppoqsxFkHIYfl4AaUft24NjVYeJoGhVl8FTPpOJuuXy/4Hz5s3DaDTSrVs3vvzySy655BJ3jksIIdxD0yD7wgfzyrIZ9n9mf+0fDj2udf26VrOqzxDW1u3ZDA25vWVRUVFx14yOHTuWel+n09GxY0fi4+MrXYon6rmiPHUzbDY4pu/+tgQO/s9x316TYfA0xy4UF7OaVXZEeHuVEixZQqIWlGxjuXnzZjZv3lzB3opOp6uzYK5o5CxmOH9YZTKc2W7f7hMEo54Dv1CI7g4+EggTdc/lymQ7d+5Ep9OxYsUKCTIIIeovfQbkna+808TxH0Cfbn/d65aKi0ZWel1bNkOU6+coh6Zp1f6qK82aNaNVK9XLe8eOHaXeLygoYP/+/QAMGjSoNocm3MlkUJkMhhwIam7ffvjb0kGGyx6Ay+6rJMhgUa1pQ1up4mbypE7UkoY2x4pGzGpV8+pPz8HpX+zbdZ4wcg74hqhsL6kJJeoJl39T6/V6AgIC6N+/vzvHI4QQ7mO1QtYZ0FFxNoPFBPtK9J72DYEe11Xjumb1FVoztRkaenvLBQsWcMcdd/Dkk0/i4eHBhAkTCAkJ4dChQ8yePZuUlBT+7//+j6FDh9bZGEU1WEwqnTcvBUJi7csbEnbCjrdL7KiDq2ZDl6srPp9mVcHC4Bj1pE7atIlaZFvqJUSd0jRIO6baACfstG/XecKoOWquDWunsiiFqCdcDjS0bdvWLeuEhRCixugzID8FAivpNHF8AxSk2l/3urniYnSVXjdTdbcIinb9HBVo6O0tb7/9dkJDQ5k/fz5Tp051eK9Fixa8++67TJ8+vcJzdOjQodz3EhMTad1aOhHUCasV0o6rVrLBMfZAW/px2PiiChrYXHafc0GG3GT189S8J3j719zYhSjD8OHVaG8shLukx8Hqh+Dsbvs2Dy8Y/SJEdFRdepp1dlsbbSHcweX/G2+44QYMBgO//PJL5TtXk6Zp7Nixg6effporrriCyMhIvL29iYqK4uqrr+bTTz+tME0tPz+f5557jm7duuHv709UVBQTJkxg69atlV57y5YtTJgwgaioKPz9/enWrRtz5syhoKCgwuOqc00hhBtYrao2gw7wrOAJqNUM+/9rf+0TBD3/Xo3r1lxthsYkPj6e1NTU4vaWvXr1IiAggOTkZD766CMOHz5c10MUVaVpkHECsk6pIJvnhWBWfgr8MFvVarDpMQkuvbni81nNkHPOHmSQNcdCiKYo6zR8N8MxyODpDVfPt2d5RXVVnX2EqEd0mosLyfLz8+nbty9+fn5s3bqVyMhKnhhWw6ZNmxg9enTx6w4dOhAeHs6pU6fIzFQV4sePH89XX32Fr69jenR6ejpXXHEFx44dw9fXlx49epCWlsbZs2fR6XQsXryYGTNmlHndRYsW8fDDD6NpGq1atSIqKoojR45QVFRE9+7d2b59OxERpavYV+eazrI9zTt58mS1ziNEo5WfCmf/UNkMFQUajq2Hn/9lf93/H9D/LtevW5AGPiHQakCdriPXNI2MjAz0ej1t2rQp9X5dziEPPPAA77zzDgMHDuS///0vXbp0AaCwsJDnn3+e1157jZCQEA4ePOhS9obMj3Uk85Qq/ugfDj4XMoKM+fDdTBV8sGlzmbpBrqi7hLlI/QyHtLxQ2KwaGUZCuKCiecRisRTff0ZERDgUjKzvZH5sYLLPwso7IWmPfZunD4x96UL7Sp0KxIbE1tUIRRPk7DzickbD3r17mTdvHufOnaNnz57MnTuXtWvX8ssvv1T45QpN02jfvj3//ve/SUlJ4cSJE/zxxx9kZGTwySef4Ovry9q1a5k7d26pY6dMmcKxY8fo378/J0+eZO/evSQkJLB06VI0TeOhhx4qLjxW0p49e3jkkUcAWLp0KQkJCezdu5eTJ0/Sv39/jh49Wirlt7rXFEK4SXE2g67ybIZ9JbIZvAPhkuurcV0zmI0Q3qbOggx79+7l+uuvJzQ0lObNm5daYpCVlcX06dNJT0+vk84TBw8e5N1338Xb25uVK1cWBxkA/P39efXVVxk1ahS5ubm88sortT4+4aKcJEj7C/xC7EEBqxl+et4xyBDZGUbNrTjIYNSrFpYRHSDmUgkyiHpBr9fz5ptvMnDgQAICAoiJiSEmJoaAgAAGDRrEwoUL0ev1dT1M0ZjknIMv77goyOALY1+GkFbg4QMtekuQQdRbLmc0eHh4oKti/2pX2/3k5ubi7+9f7pril19+mWeffZaIiAjS0tLwuLA+ad++ffTr1w8PDw+OHTtGp06dHI678847WbFiBddffz1fffWVw3uTJk3iu+++48477+Tjjz92eC8uLo5u3bphtVo5cOAAvXr1Kn6vOtesColIC1GBvBRI+kN1fPCsoBZB3I+w5WX76353woB7XL9uQRr4BEOrgXUSaFixYgX33nuvQ1tInU6HxWJx2G/06NFs2rSJqKgoUlNTLz5NjXrnnXd44IEH6NGjR7nLI1599VVmzZpF//79+eOPP6p8DZkfa1l+KiQfUEuF/C90d9E0+PlVOL7evl9gNEx6p+KK6IYcFWiI7AyRHWT5kagzJeeRY8eOce2113LixIlyl+rqdDo6derE6tWrHQKo9Y3Mjw1EXgp8djMk77dv8/JTmQyBzS8sKeuhajMIUctqPKMBqt7yx9WnZyEhIRUWLhs3bhwAmZmZpKWlFW9ftWoVACNHjiz1gR8oLja2bt06h5oL+fn5/PDDDwBMmzat1HGdO3dm5MiRAKxcudLhPVevKYRwE6tFZTN4eFYcZLBaYO8K+2tvf7jkxmpc16zSvcPb1kmQ4ciRI0ydOhWTycRDDz3EH3/8QbNmZX+gu+sutTSksLCwNocIQF5entP7GgyGyncSdUufqZZLoNmDDAD7VjgGGbwDYdyCioMMBekqIyjmEmjWSYIMol7Iy8vj6quvJj4+Hk9PT26++WaWL1/O+vXrWb9+PcuXL+eWW27B09OTuLg4xo4dS35+fl0PWzRk+kz47CbHIIO3P1z9kgrYhsRCbG8JMoh6z+W74bpIuS1PyZtlf397Repdu3YBcOWVV5Z53KBBg/D19cVgMLB///7iVmr79u2jqKgIX1/fcvu4Dxs2jI0bNxZfo7rXFEK4SUG6Kj5XWceHk1shJ9H+uuf1Ku3bVYXZ4B+pbgLqwJtvvonRaOSBBx5g4cKFAOWuGx41ahQARUVFtTW8YrYnfXFxcZw5c6bMGgw//vgjAF27dq3VsYkqMuSqIIO5EIJb2LfH/Qh/fGB/rfOEMS+qpRBl0TT19M7bD6J7QHDzmh23EFWwcOFCEhMTiY2NZc2aNfTp06fUPlOmTOHAgQOMHz+ehIQE/v3vf/Pss8/W/mBFw1eUDysmqSwxG+9AGDNP3deEt1OFH6XNr2gAGkUPlM8//xyA3r17ExJi/6Bw/PhxADp27Fjmcd7e3sUt0I4dO1bquDZt2pSbSWE7Z8njqnNNIYQbWC2QfVplMlSUzaBZ1RNXGy8/6HVTNa5bt9kMoDrk6HQ6Zs2aVem+sbGxLi9lq66rr76a6OhoTCYTN910U/GcCSpo/NRTT7Fp0yZALTUT9ZRRr4IMhhwIirFvP7dfLZkoadjjqjhqWaxmyD0HfsEQ20eCDKLe+fbbb9HpdCxdurTMIINN7969WbZsGZqm8fXXX9feAEXjoWnw1RTHIINPkArUhsRAVDe1XEKCDKKBqLuS6G6yZ88elixZAsDTTz/t8F7JisDlsb2XlZVV7eOqe+zFpE+8EFVUkKaKyFX2YeXUL6pdlE2PieAX5vp189MhqLn6qiPnzp0jMDCQVq1aObW/Tqerk8y0wMBAPv30UyZNmsTvv/9O9+7dadu2LcHBwcTHxxcXU3vggQeYOHFirY9POMFcBKlHVPZQaKwqugqQfQZ+fE4FD2z63gHd/lb2eSxGlckQEnuhs4S0rxT1T3x8PL6+vowfP77SfceNG4efnx/x8fG1MDLR6Pz8Khz/wf7aNwRGPgdhbdQcGdraPt8K0QA06IyGlJQUrr/+esxmM3//+9+ZPHmyw/u29b0+PuVH/mztMEsuv3D1uOoeK4SoBqtFfdDx9K64or1mdazN4OkDvW5x/brGfJXFENmxTttZ+vr6YjQayy1UVlJRURFWq7W4cG5tGz16NAcPHuTBBx+kS5cunD9/nqNHjxIaGsrEiRNZs2YNixcvrpOxiUpYTJD6l8pCCIkB3YX/h3KTYO0T6ufBptPo8ourmotUkCG8/YXOEhJkEPWTyWSq8J6uJJ1Oh4+Pj0NBXiGccnwD/LzA/trDS2WDRXaBFn1UsEGCDKKBabAZDTk5OYwbN46EhAT69+/PRx99VGofPz8/9Ho9RqOx3PPY1iiXrO3g5+cHUOXjqnPNslRUybOibAchmqT8VPWEtbLaDGd2QOYJ++vu10FA+RlIFbJaQJ8FUd1dP4ebdOjQgQMHDnD8+PFKaxts2LABqDggWtM6dOjAokWL6uz6wgVWC6QdVwG94Bh7QC/3HKx+VGUU2bToDcOfKvvG2GJSP68RHVQqcB0G6ISoTKtWrThx4gRHjhyhR48eFe576NAhcnNzyywGLkS5Mk7BV/eqByE2fe9US86ie9T5/YUQrmqQGQ35+flcc8017Nu3j549e7JhwwaH2gw24eGqArZtOUNZbO/Z9q3OcdU9VgjhIotZffjx8Kokm0GDvZ/YX3t6Q+/J5e9fGX26aqEZXrqgYW3729/+hqZpxYUgy5OXl1e8zCwgIKAWRiYaBU2DjBOQeVIF82w1UHKTYc2jUFCiTWpER1W4zLOMQJbVojIZwlqrgmYSZBD13KhRo9A0jfvvv7/CTjgGg4EZM2ag0+kYPXp0LY5QNGhGPXx+CxTl2rd1HAk9/64CthJkEA1Ygws06PV6xo8fz65du+jcuTMbN24kMjKyzH1t1c3LWytnMplISEhw2Lfk9wkJCeWmv504caLUcdW5phCiGgouZDNU9gs5cRek24sP0m1Cxe32KmLSgwZEdqoXhZkeeeQRQkNDWbZsGXPmzCE7O9vh/cLCQr7++msGDRrEX3/9haenJ8HBwXUzWNHwZJ9RPzuBEeCllv+RlwxrHlFdXmwiOsCEN8ru4KJZ1THBzVUWUEUFW4WoJ5588kl8fX3Zvn07vXv35v333+f06dOYTCZMJhOnTp3ivffeo3fv3mzfvh0fHx+eeOKJuh62aAg0Db6ZDuklisM36wIDp6qij77yO1o0bA0q0GAwGLjuuuv45ZdfaNu2LZs2bSImJqbc/S+77DIAtm3bVub7u3fvxmg04ufn51BJuG/fvvj4+FBUVMTu3bvLPNZ2ziFDhrjlmkIIF1ktkJ3gRG0GDfaUyGbw8Ibet7p2Tc0KBRmqzVRQlGvncLNmzZqxcuVK/Pz8ePnll2nevDnp6emA6jIRGhrKTTfdxLFjxwgKCiI6OrrOajSIBkafCelx4BsE3heyYPLOq0yGkkGG8PYw/s3yC6vmpagWsNE9VCtLIRqADh068PHHH+Pl5UVcXBzTpk2jY8eO+Pn54efnR6dOnZg+fTpxcXF4e3vz8ccfy/JW4Zztb8HR7+2v/cNhyEwVZHD1IYgQ9UiDucs0mUzccMMNbNq0iZYtW7J58+ZKuy7ceOONgGr7VlaGwdKlSwFVJTgoKKh4e3BwMGPHjgVg2bJlpY6Li4tj8+bNDteo7jWFEC7SZ6ivyrIZEnZC2lH7667jKq/nUNE1AyJUoKEeGT16NLt27eKqq67CZDJhsVjQNI3z589jNpvRNI2rrrqKnTt3FteiEaJC5iJVl8FqBr9QtS0/RQUZ8s7b9wtvBxPeBP+wss+Tn6oKPjbvoQIWQjQgN998Mzt37iy+N9Q0zeFLp9Mxbtw4du3axc0331zHoxUNQvxm2DzP/lrnCZc9AC37QWibuhuXEG7UIBZHWiwWbrvtNtatW0dMTAybN292Klrcr18/JkyYwJo1a5g8eTKrV6+mRYsWaJrG8uXLWbFiBR4eHjz33HOljp0zZw5r1qxhxYoVDB06lKlTp6LT6UhOTubWW2/FarUyadIkevfu7bZrCiGqSNNUtXudruJsBlMh/Ppv+2udJ/S5zbVrmg0qiyKyc718KnvppZeyadMmzpw5w6+//sq5c+ewWCzExMQwdOhQKVImnKdpkB6vliaFxKpt+amq8GNesn2/8HYw4S31NK4s+kz189m8Z/mBCCHquX79+rF+/XpycnLYu3cvqamqLkl0dDT9+vUjNDS0jkcoGoysM7DqH47FH/vdAR2uhGadQbINRSPRIAINX375JatWrQJUV4d77imnXRawaNEi+vbtW/z6gw8+YOjQoezZs4f27dvTo0cP0tPTSUxMRKfTsXDhQvr161fqPAMHDuTNN9/kscceY/r06cyfP59mzZpx5MgRioqK6Nq1K8uXLy9zDK5eUwhRRYVZF9KxK8lm2POhY4p3z7+rqvlVpWmqFkR4e9ezIWpJ27Ztadu27otUigYs9xxkn1bLgzw8VVeJNY9A3jn7PmFt1XKJ8oIMhlzVZaJFb0kFFo3C/7N31uFRXN8ffnc3G3cnCQkJ7u7F2wIVSoVClTq1X6m7Unf/tlRpaUtbpF7aQnGHQHEJkEASEuKerM7vj8nKrMRDhPs+zz7J3Lkzc3dn5+69557zOUFBQUyYMKGlmyFoqxgqYdEsqCqylSVNgJ6XyFl4LBo4AkE7oE0YGizpIAHS0tJIS0tzW7e4uFixHRERQXJyMq+88gpLlizhwIED+Pn5MXXqVB566KEafyzuvfde+vbty5tvvsnWrVvJyckhISGBK664gscee8xt6ENjrikQCOpBSRZIxpp/mPNSYO8S27ZfJAx1b6yskcoC8AqUBe9EPmtBe6aqRBZ/9PCWX+W5crhEib2RIV4Ol3AXtqQvB30ZRPWBwA5npt0CgUDQWpEk+PkuyDlgKwvrCoNvhMietvA0gaCdoJIkSaqt0tdff11blTpz/fXXN9m5zmYsoSPHjx9v4ZYIBC2ErhTSt8qTIE8/13XMJvjlTsi1U3Se/BIkjKr/9Yw62ZshdpDNjbwVkZaWxnvvvUdCQgJz586tse6bb77JvHnzCAoKsmbBaU+I/rGRmAyQtRvKsiEwVv7e/34fFKfb6gR1hIvfAV/XWZ8wVsnHRfSEsM7CMCdoc7TXfqS9vq82wcb3YYVd6LR3MJz7LCScA+FdRD8paDPUtR+pk0fDDTfcgKoJvvwqlUoYGgQCQdNQlgP6SvcTHYD9PymNDJ3GNszIAPKkKagj+Dcg5OIMsHDhQt59913efPPNWutWVFRQUlKCRqM5Ay0TtDkK0mRvocBo2SvhjwccjAxxsiaDu2fPpIeyXDn1q/D+EbQhLINney2bhmSQUKlU1jToAgEAKStg5TO2bZVaFn+MGQChiaKfFLRL6mRoiI+PbxJDg0AgEDQJhiooSgfvGnJMl+XAjs9t21pfGP1/DbteZZHsNRGW1GpFmpYvXw7A9OnTa617zTXX8PTTT1NRUdHMrRK0OcpyoOAY+IbIoqnr3oCiE7b9gbGykcGd3oLZKOumBCfI+eBb6fMiELjCEpprn5WnpnBdd4gxs0BBxg5YPBskk61s0PXQaRSEdwdNm4hkFwjqTZ2+2Q3pZAUCgaDZKM8BXYm8suoKSZKzTBgqbWXDbgW/iPpfy2SQ48w7DACvGgwbLUxaWhq+vr506tSp1rpJSUmoVCqMRmPzN0zQdtCXyx5AKpVsWDvwCxxfbdvvH1VtZHDzHJmN1Z4QMRDZQwyeBW2OL7/8EpAFH++//35FmUDQIHIOwbcz5P7VQtJ46H6hHFrm6dtiTRMImhsxChAIBG0LkxGKTso/zu5WjdLWw4mNtu3IntBzWsOuV54rGzRaoS6DPQUFBW4Fal2hUqkwm821VxScHZhNcirLyiIIipUNDps+sO1Xe8C5z7nPtmIyQGm1pkNUb6GcLmiTzJ492/q/xdBgX9bcSJLE5s2b+fXXX9mwYQMHDx6kpKSE4OBgBg4cyOzZs7n66quFx0RboTgDvrlMFpK20GFAtfhjD/CrIfRTIGgHCEODQCBoW5TnypOhgCjX+/XlsPE927ZKA2MelNPz1ZeqEllsMjSpYcefQYKDg8nLy6O0tJSAgJo9L0pLSzGbzaiFW7vAQtFJWYchIFL24Fn5HJgNtv0j7pAHxq6wGBmCO0JkL2FkEAgayKpVqzj33HOt20lJSSQmJpKamsqKFStYsWIFixYtYunSpXh5ieesVVOeDwsvhZJMW1lYVxhxp/w30I1HpkDQjhCjTIFA0HYwm+UVAo1WXmF1xfbPoCLPtt3vSln1vr6YDHJ4RmhSm0g5NXDgQCRJYvHixbXW/eGHHwDw9PRs7mYJ2gIVBZB/VA4NUmth7WtQapfGMnEs9L7M9bEmfbWRIQEihSeDoP1x0003Wb0b6sLDDz/MzTff3KBrSZJEYmIi7777LqdPn+bYsWPs2LGD/Px8vv76a7y8vPjjjz94+umnG3R+wRlCVyaHS+QdsZUFxsLoufJ4JLyr0K8RnBWI9JZtFJGeSHBWUp4H6dtkd0ONi0lyzgE5RzXV3VpAB5jxpeyVUB8kCYozITgOovq2iVjzzz77jNtuu43Q0FBWrVpFv379XNbbvXs3EydOpKCggLCwMPLy8lzWa8uI/rEeGHWQuROqCuXnZe8S2GwXMhEYA5d9Ap4uwnKMOjm7REinak0G7RlrtkDQ3Fj6kbS0NKKjozl16lQtR8gkJiZy8uRJTCZT7ZUdKCkpwcfHB63W9bP00ksv8cQTTxAaGkpubm6DvNJE/9jMGHSwaKZS38YnFCY9JXt8RfcTugyCNo9IbykQCNofJVmA5NrIYDbCujexGhkAxtxffyMDyOEZPsGyan4bMDKAHEf87rvvsn//fkaMGMGtt97KRRddREJCAgAnTpzgt99+47PPPqOqqgqtVltriIWgnWOokj0ZynNlg0LOAdj6sW2/WivneHdpZKiSDX+hiRAhhB8FAgt1WL9zS2BgYI37p06dyhNPPEFBQQG5ublERbkJIRS0DCYj/DxHaWTw9INxD8vjiajewsggOKsQ6S0FAkHboKoYSrPAJ8T1/j2L5bR8FrqcC3FD638dXSmgkidPnn4NampLoNVq+fXXX5k8eTJHjx7lgw8+4IMPPnCqJ0kSXbt2paKiQvTrZyuSJKexzE+Rwyb8I2Vtk5XPyQY7C6PulgfHjhgq5fjjsM5tyhgnEJwJ8vLy8PVtnslkZaUtk5KPj0+zXEPQQMxmWP4I7P/JVqbxhLEPyZ4MUX1adeYqgaA5EOktBQJB26A0W3bV9neRWq8kC5IX2La9AmDkXfW/hkkPlcUQ3cf1dVo5iYmJJCcn89prr/Hll186ufrGxsZy88038+CDD7oNrRC0cwxVUJgKBWlyjHBQrFy+8lkoO22r13mi60wthgrZOBHetTrOuHWLpAoEZ4ri4mI+++wzKioqmq1/XbRoEQD9+/ev1ftBcAaRJFj7Kuz4zFamUsM590N0f9nI4BPcYs0TCFoKsQwhEAhaP/oKKD4FPi5EGSUJNrwFJp2tbPjt7j0f3CGZ5VXe4Hj51UYJCAjg+eef5/nnn+fkyZNkZ2ejUqmIjo6mY8eOLd08QUshSXKIRN4R2VDgFwba6lXX3d/Dyc22ukEd5Uwtjh4v+nLZsyi8O4R1EWJmgnbJc889x7x586zpf1UqFadPn0ajqZtRTaVScfnllzd5u5KTk/n4Yzm06dFHH62xriV+2hXp6enit6Cp2fGFbGiwZ8SdEDdEDpcQaSwFZynC0CAQCFo/ZadBXwpBLtJBHVsFGdtt2x36Q/cLGnCNHPAJg4hu7WaVNj4+nvj4tms0ETQRrrwYVNVGguy9sO0TW12NZ7Uug4Prt75M9vaJ7AkhicLIIGjX2OssqFSqOusueHp6ct1119VqCKgvp0+f5rLLLsNoNHLppZcya9asJj2/oBEc+RuWP4xCH2rQ9dDpHLm/dJeKWyA4C2gSQ4PZbCY5OZkTJ05QUVEhBB8FAkHTYdTLKS29/J1XWHWlSoV8tVYWgKyv9kBVsZwuM6I7aEXcq6CdUJMXA0BlEfz7nOzNY8GSfs0eo85mZAhNqv/zJRC0IW644QbGjx9vncyfPn2a0NBQli5d6vYYtVpNYGAg3bp1a3LthOLiYqZOncrJkycZPHgwCxYsqPWYmpTga/J2ENSTkixYdqtS26bXJdB1sqzz5GpxRCA4i2i0oeH999/nhRdeUKRIszc0FBYWMmbMGIxGI2vXrhUKuQKBoH6U58oToqAY533/fQeVhbbtAVdDcEL9zm/UyS7hUX3ajHtjZmYmsbGxTX7erKwsOnTo0OTnFbQANXkxgKxHsvpFOXOEha7nO3sDmY2yt09oZ9mTQRgZBO2chIQEEhISrAaD+Ph4oqKiGDdu3BlvS1lZGVOmTGHXrl307t2bv//+W2gztBYkCX6aIy9UWEiaAH0ul40MIZ1arGkCQWuhUb6Pd911F/feey+5ubkEBAS4VDAPCQlh0KBBpKSksHjx4sZcTiAQnG2YTVCcDlov5SQJZAPEPrsVpsAY2dBQ3/OX5cjGiaC2E7PapUsX7rnnnjrnda+NJUuW0K9fPz799NMmOZ+ghdFXwKldcupKn0A5q4T985NzCJbdpgw5CukE59ynNCRIZijJhsDYauFHES4hOPtIS0tj69atZ/y6FRUVXHjhhWzZsoWuXbuycuVKwsLahjH8rGDbJ5C61rYd2hkGXi9n4hGeXwIB0AhDw19//cVHH32Ev78/P/30E0VFRUREuFZpv/rqq5EkiZUrVza4oQKB4CykIl9+uRJ23Pm1vCprYcjN4OFVv/OX5YJfZJsTtouJieGDDz6gS5cuzJo1i99++w2TyVSvcxgMBgoLC+nWrRszZ87kwIEDdOrUqXkaLDhzSBIUpMqGuMAYZaiEUQdb58Mvd0Jhmq3cw1vWZXAMGyrNAd8wiOwBHp5novUCgQCoqqpi2rRprFu3joSEBP7991+io6NbulkCC3lHYcXTtm2NFobeIhtkw4RRViCw0ODQiY8//hiVSsW8efO45JJLaqw7cuRIAPbu3dvQywkEgrMNSZK1GVQqWT/BnqJ0OPSHbTusK3SeUL/zVxbJhonI7qD1bnRzzySHDh3ivffe48UXX+THH39k8eLFBAcHM3z4cIYNG0b//v2JiIggNDQULy8vCgsLKSgo4Pjx42zbto2tW7eSkZEByPG/559/Pm+88QZ9+vRp4XcmaDTluVB0EvzClV4Mp/fLquhFJ5X1vYNh4hPObr4V+bIBIrIHePo1d6sFgjZDTk4OGRkZlJeX1ygSOXbs2Aad32AwcPnll/Pvv/8SGxvLqlWrRJaI1oTJCEtvAmOVrazvTIgfIes8aYTOvkBgocFPg8WN7Kabbqq1blBQEIGBgWRnZzf0cgKB4GyjsrA6E0So874dXygF7Ibd6hxaURPGKjBUyBkq6psGsxWg1Wp54IEHuOmmm5g/fz6ffvopqamp/PXXX/z999+1Hm8ZHPv5+bF8+XLGjBnT3E0WnAmMOsg/Jq+mWbwTjFWw/QvYuxiFKjpA54kw+h7Z2GCPrhRMBujQG3xdPH8CwVnIBx98wHvvvcexY8dqratSqTAajbXWc8RkMnH11Vfz559/Eh0dzapVq4R4Y2tj3euQtdu2HdUH+s6QQyaE55dAoKDBhoaCggKCgoIICAioU321Wm3NSSwQCAQ1IklQnAmS0TkcIvcwHF9t244ZCHFD635us0kOmQjvKseet2FCQkJ49NFHefTRR1m5ciV//fUX69atY9euXS5DKaKjoxk7dizjx4/n5ZdfRqPRCCNDe6LwpC1kAiBrj+zFUJKprOcTImdn6eTi3ht1UFUi534PFMKgAgHArFmzWLx4cZ3TXNa1niM//vgjS5YsAcDb27vGxbz333+fgQMHNug6ggaSuRPWv27b1vrB8NvljDxe/i3XLoGgldJgQ0NgYCCFhYUYDAa0Wm2NdQsKCiguLiYmxoVqvEAgEDhSdhqKT4JvuPO+bQ6ChcNurZ/oUtlpCIiWhZvakVjTueeey7nnngvIrrc5OTnk5uZSVVVFWFgYERERBAcHW+u/9tprLdRSQbNQUQBFaeAbImuXbP4U9v+EkxdD1/Nh5N3g7UK53j7DRH2ztwgE7ZTvv/+eH3/8kaCgID7//HOmTp2Kn58f0dHRZGRkkJ2dzYoVK3jxxRcpKirihx9+YMKEeobyVaPT6az/p6WlkZaW5rZucXGx232CZkBfAUtvkRcrLAy6DjqOgACRUU8gcEWD1Ur69u2LJEl1UuJdtGgRkiQxZMiQhl5OIBCcLRiqIC8FNJ7O3gyZyZC5w7bdaSxE9qr7uSuLwMNXjqNsxy6OWq2W2NhYBgwYwIgRI+jatavCyCBoZ5iMcsiEySB/x5fcDPuXoTAy+IbD5JdgwuOujQwiw4RA4JIFCxagUql4/vnnueyyy6xpL0H21o2JiWH27Nns3LmTjh07Mn36dI4ePdqga91www1IklSn1/jx45voHQrqxIqnocAubKbjcOgzA0I7tViTBILWToNHEldccQWSJPHss8/WGBKxe/dunnzySVQqFVdddVVDLycQCM4Wik7K+gyOseGSJKeTsqBSw9Cb635eo07WZQjvAt5BTdNWgaA1UJIBpdnyM7PqeSh1SHva/QKY8SUkjHJ/jrIc+XiRYUIgULBr1y4Arr32WkW549jX39+fDz74gNLSUl599dUz1j7BGeDYKthu503pEwKj7pHFpDU1e3ULBGczDTY03HrrrfTq1YvVq1dz3nnn8fvvv1tjglNSUlixYgX33HMPo0aNori4mBEjRjBjxowma7hAIGiHVBTIafd8Q53FHVPXyfoMFrpNgZA6undLkhy7HhTf5nUZBAIFVSWQfxy8AyBlhfIZ8YuEqa/CuIfBqwY9pYp80HjLccYiw4RAoKCoqIiAgACFV5hWq6W8vNyp7siRI/H19RXp3NsTFQXw0+3KsmG3Q+zgmvtVgUDQcI0GrVbLH3/8wZQpU1i9ejVr1qyx7uvRo4f1f0mS6Nu3L0uXLkXVjuKhBQJBE2Nx/zabnCc7ZiNs/8y2rdHC4Bvqfu7yXFlZP6yzcAmvpiGK6IJWhtkMBamyp45XgNLjR+sD0z8Ev4iazyEyTAgENRIWFkZlZaWiLDg4mLy8PIqKilyGpYksa+2IPx6QtZ0sdDkP+lwmaz0JBIIaadSIOyEhgeTkZJ577jni4+Od4sdiYmJ49tln2bRpE9HR4oEUCAQ1UJIpu3/7uxCAPPIXFKfbtntfBv6RdTuvvkKOP4/oDp6+TdPWVsSqVas4//zzCQkJITAwkNGjR/PVV1/VetypU6dIT0+vtZ6gFVOaJYdN+EfAjs9BV2LbN2h2zUYGSZI9GXRlEN5dZJgQCNwQGxtLSUkJZWVl1rKePXsCsHr1akXdnTt3UlFRga9v+/utOSvZu6Ra76aagA5wzr0QmtiuxKQFguai0Ut7vr6+PPXUU6SmppKRkcG2bdvYvHkzqamppKen8/TTT+PnJ1wxBQJBDehKq92//UHt4Ghl1EHyAtu21g8GXF2385qN8mQqtHPdDRNtiE8++YTzzz+ff//9l+LiYsrKyti8eTM33XQT48eP59SpU7WfRNA20VdA/lFZMLUwDQ78atsXHA99Lnd/rLEKijNA4yWnhw1NbPbmCgRtlUGDBgGwfft2a9mFF16IJEk8+OCDbN++HYPBwI4dO5g9ezYqlYrRo0e3VHMFTUVxJvxxv21bpYZz7ofovkKXQSCoI03qQxwTE8OQIUMYPnw4CQkiNZZAIKgDkgQFaWAol8MbHNn/E5Tn2bYHXFV3MceyXNm9MaRTEzS0dZGSksI999yD2WwmKiqKO++8k/vvv59BgwYhSRLr169nxIgRpKSktHRTBU2NJMkhE1XF8jOz8V0UGSZG3eN6ICyZ5TCiyiIITZJjjAM7iJU5gaAGLEaFxYsXW8vuuOMOYmNjSU1NZcSIEXh7ezN8+HD279+Ph4cHTzzxRAu2WNBoKgph8Wy5j7XQ+zLoeZEQkxYI6kG9NBqMRiMVFRUABAa6SI/lgpIS2ZXTz88PjUZTz+YJBIJ2T9lpKD4Jfi5CJnSl8N+3tm2fkJpXau2pKpZXe8O7tksV/Q8++AC9Xk/fvn1Zt24dQUG2wc8PP/zAHXfcQUZGBuPHj2fVqlV07969BVsraFLKc+XsLH7hkPIPnN5v25c4DuJcpJI2VMoGO58Q+ZnwjxIGBoGgDlxwwQWsXr1aEQ7h7+/PqlWruOGGG9i8ebO1PD4+ng8//JDhw4e3RFMFTUHKCvjlTjkTj4XQJDlkIkCEmAkE9aFeHg2zZs0iJCSEG264oc7H3HTTTfU+RiAQnCUYqmT3b41WNgo4svt72dhgYdBsWeSuNkx6+biwLvLEqh2yatUqVCoV77zzjsLIADBz5ky2bNlC586dycrKYsKECRw6dKiFWipoUow6yDsqi5qajUoBSA9vGHmnsr5klgfMlcWygSFuiOzlI4wMAkGd8PDwYNy4cQwdOlRR3rVrVzZu3MjJkyfZuHEj+/btIzU1lQsvvLCFWipoFPoK+ONB+O5KpZHBwxsmPilr2Yh+UyCoF3U2NOzfv59ly5YRGBjIF198UecLfPrppwQGBrJo0SLhwisQCJQUnZRTR/mGOe+ryJeFmCwExshui7UhSVCaI6eyDIprura2Mk6cOIFGo2HMmDEu93fr1o3169fTs2dPsrOzmThxojA2tAcKT0JFnuzNkLwAKgtt+wZeK3sqWDBUyHHGnv4QNxgietTNUCcQCOpMXFwcI0eOpFevXiK7WltEkiAjGT6dCNs/lY2zFgI6wHkvQOdJ7dIzUiBobupsaPj2W9l9+c4773SZyscdISEh/N///R9ms5lvvvmm3g0UCATtlIoCWcTON1QWWXIk+Ssw6WzbQ252Fop0ed58OYYyvAuo22+4lk6nw9/fHw8P959JdHQ0a9asoVevXlZjw+HDh89gKwVNSulpKEyVn5mCVFm/xEJgLPS7Uv5fkuQVuapSCO8mazH4R4rVOIFAILBHXw7r34IFF0LuQeW+pAlwwevQ80LwCW6R5gkEbZ06GxrWr1+PSqXi8svrGB9tx2WXXQbAmjVr6n2sQCBoh5iMkH8MzCbwdJGVpjgDDv1u2w7rAp0n1H5eQwWYDBDRzfV52xERERHWTBO11Vu9erXCs0EYG9ogJVlwei+oAK2vLABpv/I26h7QVK+46Yrl/2MHQ2QP0Hq3SJMFAoGgVWIywukDsOgqWDUPjJW2fZ7+MOr/YOzDkDBa9qYUCAQNos5ikEeOHEGtVjNw4MB6X6Rfv36o1WrhtisQCGRKMqE0GwKjXe/f8YVyEjXsVtdeD/aYTVCebxO6a+f06dOHrKwsNmzYwJQpU2qsGxERwZo1axg/fjwHDx5k4sSJmM3mGo8RtCKKM+RBsVoNfhFwdCVk77HtTxgN8dXic5IkezJE9wX/iJZpr0DQRklKSrL+n56e7lRWV1QqFceOHWuydgmaELMZ9nwPK5+VhXXtieoNQ2+FmEHyWMLT1+UpBAJB3aizoaGoqIjg4OAGxZ+p1WqCg4MpLi6uvbJAIGjf6Eoh/zh4+7sOhUhdD8dW2bY7DIC4YbWftzwX/CJldeizwEV87Nix/PPPP3z//fe1GhpANjasWrWKiRMncvDgQSRJqvUYQQsjSbKOSc4BWZDMJ1h29d3yka2ORgsj77ZtVxXLoUNCHV0gqDdpaWnW/y19pH1ZXRFaDa0USYJVL8DGt5WLGSo19JsJPS6GiO4QmtiuQy8FgjNFnQ0Nvr6+lJaW1l7RDWVlZfj4CBEqgeCsRpKgIA0M5RAU67w/LwVWv6gsG3Zr7YYDXalstIjo5jp7RTvk4osv5sknn+THH3/klVdeITrajXeIHVFRUaxZs4YJEyawf//+WusLWhBJknUYcg/JYUDe1Smld34t65BY6H81BFYbFSSz/CxE9xXhEgJBA/jyyy+t/z/44IMAvPHGGy3VHEFTs+tb2PCmssw/GobfDjED5VCzgNp/SwUCQd2os6EhMjKSo0ePcuzYMTp37lyvixw7dgy9Xk9CQkK9GygQCNoJZpPsAl6cLivmO1JZCP88CcYqW1m/WbIrY02Y9HLqvug+skjeWULfvn355ptvMBgMlJaW1snQALYwiq5duzZzCwUNxmyWNUzyDoNPkBwzDLJ4qn0mloBoGHC1bbuqGLyDRUyxQNBAZs+ebf3/ueeecyoTtGH0FfDvc8qypAnyOCMsUc7K4x3k+liBQNAg6mxoGDFiBEePHmXZsmU89NBD9brI0qVLARg+fHj9WicQCNoHlYVyuETpKfAKcPY6MOnhn6eg7LStLH6E7M1QE5ZUlsHx8uss4+qrr669kgvCw8MJCQlp4tYImgSzCfKOQP5R8Am1xQhLEmx6DySTre7Iu23PkmQGXRnEDDhrvHoEAoGgzmx6D8pzbNt9Z0DvSyE4QdZjEP2mQNDk1DnrxEUXXYQkSbz++utkZWXV+QKnTp3ijTfeQKVScdFFFzWokQKBoI1i1EFuCmTskI0I/lHOKwaSBOvfhtP7bGXBCTDxqdpjJMtz5bj1dp7KUnCWYDJAzkHZyOAbphQiS10LmTtt2x2HyyKQFioLwSdEdgMWCAQCgY3yPNj4nm3bJwR6TZc9JiN7CSODQNBM1NnQcPnll9O1a1fy8/OZPHlyndR0jx49ypQpU8jLy6NLly7MmDGjUY0VCARtBEmSs0pkbJdzU2u95Thyjda57t4lcGS5bdsrECa/WHt6Sn11WseIHu0+laXgLMColzNLFByXM0to7TSNUtfD6pdt22qtnH7Nol1iNoGhEkI6gYfnGW22QCAQtHpWvSBrQ1noNxPihsp9prrOUyGBQFBP6hw6oVar+eqrr6wiYv369ePaa69l+vTpDBw4kNBQOTa6oKCAXbt28dNPP/Hdd99RUVGBl5cXCxYsECq8AsHZgK5UFnwsTgeNhyz66C415cmtsNVOQV+lhnOfhaC4mq9hMkBFkbwaIVL4AfD111/Xq35ZWRlqtZrMzExiY10IcwrOHIZKWfSxKB0CokBTbSyQJNj9PWz7BLDLEtJvpvIZqSqq9mZo/2ldBYIzjUZTf285lUqF0WhshtYI6k3+Mdhl9/sYGCeL6AaI/lIgaG7qbGgAWafhxx9/5LrrrqOkpITPPvuMzz77zG19SZLw9/dn4cKFjBw5stGNFQgErZzKIsjaLRsb/MJrdkcsOgH/zlOmmBo9F2IH1XwNSYKyHAiOOyt1Gdxxww031MuYazbLn3t8fDy9e/fm0UcfbbDmg6ARVBZCzmE5djiwgy3lq8kAG96Gw38q68ePgsHX27bNJtBXyp49wptBIGhyRCrgNs4/T8j9pIVB18vpKwUCQbNTb3+hiy++mB07djBjxgxUKhWSJLl8qVQqZsyYQXJyMpdcckmjG5qdnc3ChQu55557GDlyJD4+PqhUKsaPH1/rsQaDgddff53+/fvj5+dHSEgIEyZMYNmyZbUeu2vXLmbOnEl0dDTe3t4kJSUxd+5ccnNzm+2aAkGbRF8uu37rq1NX1mRkqCqBvx5XujL2ukR+1UZFnqzzENZV9pgQALLBID4+Hh8fH2s/rNFoiIqKIioqCo1GYy339fXFw8MDtVqNJEns27eP6667jvvvv7+l38bZgyRBcSZk7pKNDUGxNiODrhSWP+xsZOg7A85/3ubxAPKxvmHCm0EgaCZWr15d4+vnn3/mscceIzw8nLCwML7//ntWrVrV0s0WgOw1edguNDOip6zNYEkXLBAImhWV1AhTbU5ODqtXr2b//v3k58t5vcPCwujduzcTJkwgMjKyyRr6zjvvcN999zmVjxs3jjVr1rg9rqqqivPOO48NGzag0Wjo3bs35eXlVo2JRx55hFdeecXlscuWLWPWrFkYDAYiIyOJi4vj8OHDlJeX06FDBzZs2EBSUlKTXrOuWK57/PjxRp1HIGgSjHrI3gMlWRAU4z5UAsBslCdR9sJ2MQPhgtdtEy136MurlfUHCrdHF8yfP5+5c+cyevRonnzySUaPHo2npzwp1ev1bNy4kRdffJGNGzfi7+9PQEAA//zzDy+99JI1vG3lypVMmDChhd9J42j1/aPJKGsxFByTjQb2aVmLM+Cvx+TQIwsqNYy+F3pNU57HbITS0/LzECTCXwSCpqS+/UhOTg4TJkzAbDazY8cO/Pxap3ZQq+8fmwpJgs8mQWayrWzqGzDwaqHrJBA0krr2I41SQImMjGTmzJnMmzePDz/8kA8//JB58+Yxc+bMJjUyAAQGBnLuuefy2GOPsWzZMp566qk6HffII4+wYcMGEhMT2b9/P7t37+bo0aP88ssveHl58eqrr/Lbb785HZeZmcl1112HwWDgqaeeIjMzk+TkZDIzM5kyZQpZWVnMnDnTpUtdQ68pELRJzCbIPQwlpyAwumYjA8DmD5VGhsAYWZehNiOD2QgVBRDWRRgZXLBq1SruuusuLrvsMquxwGJkAPD09GTChAmsXLmSSy+9lLy8PKqqqujSpQtffPEFs2fPRpIkPv300xZ8F2cB+nI4vVfWZPAKVBoZsnbDz3cqjQyefjD1NWcjAwhvBoGgFREZGcmHH37I4cOHefnll2s/QNC8HPxNaWSIHwldJgkjg0BwBmkzUqs33XQTK1as4KWXXuLSSy+tkyHj9OnTfPzxxwB8/vnndO/e3bpv2rRpPPzwwwA8++yzTse+/vrrVFRUMHbsWObNm4eHhzwJCgoK4rvvviMoKIgdO3bw+++/N9k1BYI2hyTJQkuFaeAfWbux4MCvsP8n27bWFya/5Jzy0tV1SrMhMFZWiRY48eabb1pTENem1fDaa68BUFxcbC179NFHAdi0aVPzNfJspzwfTu2CogxZj8E+feWRv+GPB0BXYisL6ACXfAhxQ5zPZTbKnkQhCSKESCBoJYwbNw5vb2+WLFnS0k05uzEZYMXTtm21Bwy+Ue53BQLBGaPNGBoawq+//oper6dr164uXYHnzJkDwM6dO53SdVp+JG677Tan40JCQqypOn/88ccmu6ZA0OYoOgn5KeAXWrMmgyTBiU2w8V27QhVMeqpuhoOKfHn1N6K7mFS5YceOHQQHB9cpg0RcXBxqtZqqqiprWffu3fH19SUnJ6c5m3l2YjbLz8qpnXJaVns9BskM2z+DNS/LxgMLUX1g+kfun4+KAjnjil/Teg8KBIKGo1KpUKvVnDx5sqWbcnaz4wsoTLVtdzkX4kco0wYLBIJmp10bGrZs2QLAmDFjXO6PjY0lMTFRURcgPT2dzMxMAMaOHevyWMs57Y9rzDUFgjZHabbs/u3pL3smuKuzcyH8eD38/ThIdsrPw+fIroy1oa+QY9ojuoOXf9O0vR1SWlpKeXk5BoOh1rp6vR6z2ewU+qXVaq3eW4ImwqiXn5PsvaDRymEOFo8TfTmseBZ2faM8psu5cOGb4BPs+pxmo/wKFt4MAkFrIjk5mYqKCnx93fwmCpofXSmssdNB8/SDQbMhILrl2iQQnKW0a0PDkSNHAOjcubPbOpZ9hw8fdjrO09OTuLi4Go87fvy4YmDf0GsKBG2KigI4fRDUGmf1Zn25rPL8272waBbs+FwZcw7QdTL0m1n7dcxG2ZshrIuIQ6+FTp06YTAY+O6772qtu2jRIgCFUaGsrIzi4uIm19c5q9GVyroL+UdlLQb7EKHCE/DzHZC2TnnM4BtgwhM1ewhV5INfhPwSCAStgu3bt3PdddehUqkYPXp0Szfn7GX9W1BZYNvucTF06FdznyoQCJqFdr0UUlAgdzShoaFu61j2FRYWOh0XEhLiNtbZcpzZbKakpISwsLBGXdMVrjJaWEhPT6djx441Hi8QNAu6Mji9H0yVcgy5tbwUtn4MKSvBpHN/fJdzYcz9tlVdd0iSrKgfGAuhnWqvf5YzY8YMnn/+ee666y48PT256qqrXNb7/vvvueuuuwAUqui7du0CUOjKnAn+/PNPPvvsM7Zs2UJ+fj4hISF07tyZCRMm8Oyzz7ZdD4uqEjkTS1WRHBdsr19yfC2sfQUMlbYyjRbGPSI/HzVhMsgCrMEJsqGvnZOWV87RnDLGdAvHy6P9v19B62PixIk17q+qqiI9PZ1Tp04hSRKenp48+eSTZ6h1AgWl2bDlf7Zt33Dof3W7XqjQGU2ibxS0WtroCK5uWOKP7ZXXHfHyki2clZW2AV99jmvMsfbHCQRtAkMV5ByAqmI5W4QFfRn8+ZDsIu6KoDjoej50PU9pnKiJinw5LCOimzwJE9TII488wpIlSzh48CDXXnstTz75JGPHjiUmJgaVSsWpU6dYu3YtaWlpSJKEVqslKMi2wv71118DcO65tUx0mwij0ciNN97IN9/IYQMdO3akf//+5Ofns2PHDjZt2sSjjz6Kv38bDJepKoHsfVBZJD8nFiOZ2QjbPoM93yvr+0fBec9BRI/az12RL9c/C7wZjuWWceF766kymBnQMZjvbxuBt9b1gHr1oRx2nizk8kFxdAoXqvKCpqOmFOqOJCQkMH/+fIYOHdp8DRK4Z+WzYLRpD9F3RrsdQ0iSxNzv/+PX3acY0zWcd2YOIMzftdeGySxxPLeMuBBffDyFUUJw5mjXhgZvb29Ajkd2h04nr7z6+NgEYupzXGOOtT/OFTXlJq3J20EgaBZMRjmNZWm2cvKkL4c/H3Y2MngFQOeJsoEhslf9PBIqq719InvK5xHUiq+vL2vWrOH666/n77//JjU1lbS0NEUdiybDeeedx6FDh1CrbdFzDz74IHfffXeNYV9NyR133ME333zD0KFDmT9/PgMHDrTuq6ioYOXKlQqDbptBVyobGaoKlc9JZRH8O08WhLQndjDSxKdQudNjsMeokz19guNB3a4jHwFYfySXKoMZgP/Si3jml/28ekU/p3oHTpVw44LtAPy4I52V948jwLv9TSwELcMzzzxT434PDw9CQkLo378/o0aNqjXrj6CZOH0Q9vxg2w7pBL0vlTNitUNS88r5dfcpANan5HHZR5tYcOMwEl0YWm9csJ11R3LpGunP97eNcGuQEAiamnZtaAgJCQFs4QyusA+TcDyusLAQSZJc/mhYjlOr1QQGBjodW99rCgStGrNZzi5RdFIWVLK4bBsqYPkjspeDBZ8QGD0XEkaBxr1nj1uqimT38Kg+ENB+3R2bg4iICJYvX87GjRtZvHgxO3fuJDc317pv0KBBXHHFFZxzzjlOxsozGTKxevVqPvvsMzp16sS///5LQIDSmOTr68u0adPOWHuaDKuRoUD23LH8duQclFOtlecq6w+4mu99ZvHiV+X0DDfy5bRQ/DxdGxB+3J3Hcxt1dA71ZP6NfrTGJG3Ldmbw9C/7SQjz5ZPrhxAb3DiFd73JrNj+YUc6gxNCuHKoMmxw50lbGOLpEh2fb0jl3nO7NerarRlJkkjJKSM6yJtAYVBpdmozNAiaD4PJzLKdGWg1aqYPiEWtdm3EMRkMZP1wH3GSXZ/R/2oITWw1IWYms8TS5AwMZjNXDI5rdLhDUaVS+PlEfgWXf7SJz2YPYVC8bX6RXlDBuiPyb09KThnzfj/Au7MGIhCcCdr1kki3bvJA4+jRo27rWFJMWura/6/X60lPT6/xuMTERLRardOx9b2mQNCqKToBBcfBL8zmgmiohL8eg9P7bPW8g+HCtyBpfAONDCVyeEZkLzkFoKBBjB49mnfeeYd169Zx8OBBDh48yLp163jnnXc455xzWrp5vPnmmwA88MADTkaGNouurDpcIr/ayFD983rwd/j1HqWRQesL5z2PNPRWXt1cQaleYtspPZ/uKnc+r9kIJad4a7uecqOKPTkG5v64B5NZcq5bT/ZmFLMkOYPSqtozldSFN/85QpnOyP5TJdyzaBdGB0NBfXH1Fp/6ZR/7MosVZY7X+Wx9KgXl7r0K2zrP/Lqf899ex7jXVpNeUNHSzREImo13V6bwyNK93P/jbj5c7WZcbdTx06KPiSvYbC0yRfWTQzV9w89QS2vnf6uP8vDSPTzx0z5u+GI7VQZT7QfVgNlFB1lQrueqT7bw9/5sa1mxg0Hil/9OserQ6UZduzVTVKHn5T8P8sbfhxv9GQsaT7s2NIwYMQKADRs2uNyfmZlJamqqoi5AfHw8MTFy/Pn69etdHmspHzlSmZ6vodcUCFotJVlyyIRXgC0HtbFKTleZtdtWzysQLnpLXkFoCLpS2UMishcEC6HT9kpVVRX//PMPIOtBHDhwgHvvvZfzzz+fiy++mKeffpoTJ060cCvricXIUGFnZDDqYO1rsP4NMNsN9IIT4NKPIHEMRjMUVtkmyX+kOOj2VBVDSTb4R1Ogs63kbUst4NP17kPr6sLejGKm/28jDy7ezdR313M0p6xR5wPIK7OFFCafKOTjtccadT5XxhSd0cyd3+5UDJ6NDvXKdEb+525S0sYxmyW+3y4vgBRWGBr9GQsErZkvN6Za/39zxREMjsZLfTns+oYLj79gLTJLKubpr0If0LFRIWbLdmZw3edbeXn5QTIKG2/Q25Zm83TefDyfOQuT0RkbPhF2Z2zWGc3c/k0yX21Ks2478sRP+5rMwNzaePOfI8xfd5wPVh/lxT8OtnRzznrataHhkksuQavVkpKSwurVq532z58/H4CBAwfSpUsXxb7LL78cgE8++cTpuMLCQhYvXgzISu9NdU2BoNVRUQA5h0DjYdNKMOrg7yfg1C5bPauRoYHaIfoy2dAQ0UOOPxc0mtLSUtauXcvixYtZvHgxa9eupbS0tKWbxe7du60pgdevX8/AgQN59913WbFiBb///jvPP/883bt3t6bgbPXoy2Wvnoo8ObuESi175vx+Lxz+U1k3cRxM/0g2NgAmh3FiSoGRlHyD1YsBsxk69IWYATgOKd/85zD7TxXTULYcz7cOVDMKK7n8o01sS3Uf8lcXHCf876xMYU9GUYPP52rFDuBkQQUP/Pifdb/jdQG+3nKCrOKGCy4fyi7hlq+288RPe1vVqpjRLKG3mzj8tvtUoyYrbZWiCj0PL9nNnIU7zphXhyRJLF26lBkzZpCYmIifnx9+fn4kJiYyY8YMli5ditncOC8egZJyvfK7vSElz7ZRWQRb5sPyR/DBJgC52DSOr7ISmPvryQZ7VeWUVvHg4t2sT8lj/trjjH1tNbcvTGZbaoFV66i+OE741x7J5f++2+VsPKkjJod2dI+yeQdKkuz59NKfB132D1nFVbz21+EGXbe1s9fO421xcrqTR4fgzNKuDQ1RUVHMmTMHgJtvvpnDh20P1W+//cZrr70GuI6/e+ihh/Dx8WHdunU8/fTTmEzyg1pcXMzVV19NcXExAwcO5OKLL26yawoErQp9uay9YKoEXzl9K0Yd/PMkZCbb6nn6w4VvQFgDDWf6CnliFtFTFm8SQlqNYu/evUybNo3Q0FAmTpzIrFmzmDVrFhMnTiQ0NJTp06ezd+/eFmtfVlaW9f+77rqLgQMHsm3bNnQ6HSkpKVx55ZXodDpmz55tTbnpiqSkJLcvdyFvTY6+HLL3QrmdkaGyEH6/T9ZlsKBSw/Db4dxnwdPXWuxqgvznoSKrFwNxg+VnQq1xCiMwmCTu++G/Bk+CHfUPiisNXPvZVn6rFherL5IkOa2wGc1yGyv1DWuj/enUKkgIs312Kw/m8PE6eTXf1WRCbzTz3r8pDbouwLO/7mflwRy+3XqS1/+ueUBuMktnzBhhdphclFQZWXUw54xcuzXx1aYT/Lgjg7/3n+bOb3c2ePJXV06ePMnIkSO58sorWbZsGSdOnKCyspLKykpOnDjBsmXLuPLKKxkxYkTb88hqxUQHeiu2f9qVKf9Tlgsb3obVzys8xtaZ+vKs8XoAlu/L5sHFuxsUZnYiv0LR/5gl+Gt/NlfO38xF729gSXJGvQ18rjwL/jlwmvt/bFgbHW1aL1/elxtGdVKUfbLuOM/8st/l8Qu3nGiwcfloThmT3lzD8JdWsulYXu0HnEHsP8sqg5mfdma0YGtahiqDidlfbKP/c//w6brGeT82ljZjaEhPTyc8PNz6evTRRwHYuHGjotwykbfw2muvMXLkSFJTU+nduzcDBgygS5cuTJs2DZ1OxwMPPMAll1zidL2OHTvy9ddf4+HhwfPPP09MTAxDhgwhNjaWv/76i6ioKH788UeXQpENvaZA0Gow6uWJUmWRPOEBMOlhxTOQsd1Wz9NPNjKEN1BvxFAhT8zCuskhF8LI0CiWLVvG8OHD+eOPPzCZTEiSpHiZTCZ+++03hg8fTnm5Cz2AM0BZmc1F39fXl+XLlzN06FA8PT3p0qULixYtYsCAARgMBl588cUWaWOd0FfIngzlebJAqkot///bXCiwc2f3DoYL34T+s5y+364WspYfN0J0H+jQH7xt6UcdJ5gAR06XNXhVytXAVm8y83+LdvHJumOKiZvJLHE4u7TGybTB0T2jmmO55byyvGHuq/Yrdh5qNR9dMxgvD9uw5Y2/D7PpWJ5Lgw3AjzsyOJ6rDAkxmswczCqhXGes8drHc23Px/fbTrpdFTtwqoThL61kwLx/+GH7yVrfU2Nxdd+W1jCQNpklDmWXUFbL+20tFJTrScsrr9VwcDzPdl/3ZhaTfKKwhtqNo7i4mHHjxrF9+3YkSWLkyJE8+eSTfPTRR3z00Uc8+eSTjBo1CkmS2LFjBxMmTKC4uOHeRgIbvl5KwcR/DmRTlnMSVj0PG98BO/HH303DucXwIJXYjBM//3eKJ37a69Y7yh1GN/0ZwP5TJTy4eDejX1nFWyuOkFNa5bauPXoXhgaQvZIeXrKn3m109GjQqtU8c3Evnrywp6I8pYawuEeX7mmQkXTh5jSO5ZZzukTHvd/XbEw2myUKz6BmjuPvwbdbTza7IbK1seZwDmuP5FJcaeDFPw9yOLvlvFnbjKHBZDKRn59vfVkGyUajUVFeUaF0ofPx8WHNmjW88sor9OrViyNHjpCXl8e4ceNYsmQJb7zxhttrXnHFFWzdupUrrrgCkFcKIyIiuPvuu9mzZ4/b0IfGXFMgaHHMJsg7IrtuB0TJkyOTAVY+B+lbbPW0vnDB63K4Q0MwVkF5AYR1hbDOwsjQSFJTU7nmmmuoqqoiISGB//3vf6SkpFhX3VJSUvjf//5Hp06dqKqqIjc31xrCcCaxpAAGuOGGG5yy76jVau677z4A/vnnH7euyMePH3f76tixmTU+9BVwei+U5tiysJRmw2/3yJlZLPhFwiUfQIxrhW9XE+RDhRLHTBFyuJId7sZJX2xMVboT15GaVtBe+vMQz/y6H5NZNlDNnL+Zye+sY9Qrq/hqU5pLV9+azvfV5hOsPZLrdr877AeHajX0ignkhel9rGVmCe5ZtIvMQtchEiazxNsrlV4NNy7YztR313PBe+trDK2wN+yU601ujQjfbz9JXpmeKoOZR5ftVYiwNQeuvjNrDucq9DHsmbNwB1PeWc/kt9c1aYhBYbmetUdy6xTnnV5QwerDObW6iKecLmX866sZ/8aaWr1IHD+GBdXx6M3Biy++yIkTJwgJCWHFihVs2LCBefPmMWfOHObMmcO8efNYv349//77L6GhoZw4cYKXXnqp2dpzNuH4nakymDi57CnY+ZWi/B+v87jH8H/o0dIh0BNPjW168/32dJ77bX+9JptGh9+dAR2DcUx4kVem571/Uxj9yiru/+E//tmfTVZxpVtPB/vyrpH+eNoZTZfuzOCpX/bVq42OhgmNWoVKpeKWMUl8ePUgxfntGZYYav3/eF45769S9pGpeeXku+lPLOTa7c8p1bFwS5rLeuU6I1PeXcfA51cw+4ttnC6pm1GmMZgc7l1KThnb09wbItPyyt32n60NndHE0Zwyt0YrC7mlyvfzvzUtp1nUZgwNnTp1clqdc/V69tlnnY719PTkkUceYc+ePVRUVFBUVMSaNWusOgw1MWjQIBYvXszp06fR6XSkpqby/vvvExlZc17exlxTIGgxJAkKUuWXfySoPeR48X/nwYmNtnpaH5j6mizc2BCMOnn1N7yL/GqEYJNA5vXXX0en0zFy5Ej27NnD7bffTufOnfHy8sLLy4vOnTtz++23s2fPHkaOHIkkSS2y6mZvWOjZs6fLOpby0tJS8vPzz0i76oyuTPZkKM2RwyXUGijJlD0ZSuzCDgI6wLR3ISjO7alMetcT3eV7sxTbjoPPEUmhiu0HF++mqKJ+K0b2E2kvDzW3j+us2P/15hPMWZjMrvQidlSvFheU660ZD/7en61ol8FhcHd+L2Vq2ocW77auakmSxD/7s/lt96kaDRT2+9TVhsgZQzoyyy69ZV6ZnsXJthV9fy8Pxefz2+5TVi2LzKJK1lcbZU7kV3D/D+5dlh0n9As2ujaw2Hs6SBLM/X4X/6UXuTzn0ZwyftyR3qhBratVT6NZchnyklemY2V1WEVmUSV3fJvcJCEepVUGJr21ltlfbOOaz7bW6EKemlfO5HfWceOX27n8o001GiZ+35NFSZXsefHx2mM1GkYcPXz+2pfdbJOYn376CZVKxccff8ykSZPc1pswYQIff/yxVctB0HgMRrs+ADMveHxBr+yflZUGXMN8zxswV09pesUE88HVA/Gwswx8tfkEryw/VOeJvOPz/+KlfVj70ARuHZNIgLfSCGwwSSzblcltC5MZ+fIquj/5F32f+Zvxr6/msv9t5Navd/DYsj3kltie+yGdQvj42kFoNbY2frv1JC/8cbDObXTsuzR27/fCfh349pbhBPk4p7999uLeRAR4Wbfnrz1u7SPf/zeFCW+sYcxrq/lrn3ujqaPHx0drjrl8tv89lMOR07JHxdojuUx9dz2rDzVvqJcrY+y3W12HM3289hjj31jD6FdWsawJQyzMZon9p4opqYMh1myWOHCqpFZDrM5o4qL3NnDuW2u54cttNdZ3/Ah+232KtLyW8WIVo3uBQGCjJBPyU8A3BDy8bEaGNLvsKx7eMOVV2b27IRh1cnxlSJIcctFKcly3dVauXGkdDPv7+7ut5+fnx8cffwxAZWXDxfIaSo8eNg8YT0/XKVDtvR5albhaeb4sglpmZ2QoOgG/zoUyu3RhQR1JPedNPj7iz9ZMnfPA0WyEkmyMla5dWv/cqxzgOQ4aJnSP5NoRNtHU7JIqnvx5n1Un4fttJ1m45USNk0r7wZiHWsWjU3vw/PQ+ilW7lQdPc9vXyU7HpuaVM2dhMjPnb7FOqh0HnuO7RyrihXNKdTz+014kSeKrTWnctjCZ/1u0i9u/SXYr2GbvGqyx83h6dlpv+sQGujzGQ6PioclKL6s3qlfHHcMlNh/PZ/4611kbHAfxp4qrWO5i4O1Yr8pg5uYF2zmZr5wkZxZVMuWddTy8ZA/Xf+48SCzTGfls/XF+232qRhdqR3dpC67CJxzdmfdllvD87wfcnvvI6VI+XH2Ug1klbusAbD6Wb00fuiejuMYJyYaUXCqq27Eno5jbv0l2uxpnf3/MEny5Mc3teR2fKaNZ4tutrr1O/j14mmd/3c+LfxzgzX/qH2qUkZGBp6cnl112Wa11L730Ury8vMjMzKz3dQTOWJ4TLUbe1X7AtR7/KisMvwN6XIBZbZv8q1Qqzu8dzTuzBij6s/nrjjt5OLnDsT/zUKvpGOrLExf2Ystjk5h3SW+Swv3cHl+qM5KWX8HOk0WsOHCaRdvSKbX7fnt5aJjYI4r3Zg1UGAg+35DKG/8cprjCwN6M4poNsZKjoUG5f2inUJbeMYq4EB+796EiPsyXedN6296rWeKRpXswmsws2iY/QxV6E3d/t9PJ6G1/jD2FFQa+2JDmVK/YwQBeUK7nxgXbmffbAScDpdFkJvlEYaPFG119Zsv3Zrv00vixOoOPzmjm/h93s8Auy0ljuHvRTi58bwNT3l5HdrF7A6gkScz6ZAsXvLeeUa+sYl0Nnn/bUwutYTCbjuWzJNm9YcTREGuWZGNQSyAMDQKBQKY8D3IPyYYET7/qcIlnIXWdrY7GC6a8DB36uT6H2QilWfLKbmmWPCErz5OzV1QVyaKPZTmywF1Ed2FkaEIyMjIICAigb9++tdbt27cvarXaKnJ7JomNjSUhQc66cPy4a5GiY8fkH0Rvb2/CwsLOWNvcIklQlA5Zu8BQDoEx8ne34Dj8dq+cccJCSCdKJ7/N1N9UvLKxlJlL87liSR6rUquQzGY5BWbpafCPkHO9u+BAVoli9cFx0KBWqXjigl6Kge7ve7L45b9TfLj6KI8u28tTP+9j5idbrBNCR+wns5aB7nUjEvj0+iH4aG3PZU2r79vSCpj+4UbuWbSLVIfVEg+NbLzoEmkzei3fl83SnZlWrwKAFQdO88RPrl2G7YvUdoNxb62Gj64ZTKDDyiLIE4LBCSGc29Pmdbj6cC7b0wpcxl2/9c8Rlx4Irib7n68/7tROV/P+/HI9N3y5TRGXvGJ/tnVwfiCrxMlo8cCP//HCHwf5v0W7eLh60O8Kd0aIfZklTnG4rtr27daT/LTLeYBarjNy6Ycbef3vw0z7YANbjrv3JKpyMBR8u8W9NoWjAN7Go/k8tGS3W88Me37YftLtiqAr++N3W0+6NGLsPFnIgk1pfLo+lc/W138iERISgre3N+o6eN5pNBq8vb2dQsIESowmM9vTClh54DT/HjzNqkOnWX0oh9WHc6zx5euO5FKpN+JDFZ9rX+dijS1004waxj4MCaPBNxyzh00o1tJVXNQvhtev6K+Iynzv3xRe+P0Au9OLanQ/d3S/97DzPPDz8uD6kZ1Yef84vrxxKKO71P83KsRXNrJP7duBt65UtvHD1cfoP+8fLv5gAxe+t54Dp1wb/hyfIbWL8NMukf4su3MU47pF4O/lwdxJXfH38mBq3w5M6R1trbcvs4TPN6RSWmUzhhjNEncv2sUfe5yNDa68Bj5bf9zJs86doeSLjalc9r9NVg0dSZK4+rOtXP7RJi58b73L3530ggqO5ZbV6vHhqp/Xm8wuJ+bleqXx+dnfDvDevylufo/q5qVQXGGwLhacKq7iwxpSLR/LLbemPc0t1XH9F9t4eflBl99NR4+R9/9NcetN5upjX7Yrg1NFzotLuaU6/tybxcoDp1l7JLdOXhj1QRgaBAKBbAA4fUDWZ/AJthkZ0jbY6mg8YcpLbuPNMVZBSRb4RUF4dwjuJMev+4TKhgvLikNIJ4js4RSDLmgcWq22zpoLllCzlmLmzJkAfPvttxiNziJ1X3zxBQDjxo3Dw6OFvycmI+SlyNklVBrwr9YtyT0sGxkq7WI/w7rCxe+wtzSAKjuX3+QsAzf9VsCF32bxR5oaU3R/iBmI0TvY7WX/3Gcb3DkaGlQq8PHU8PbMAQr34Kd+2acYFO5OL+KKjza5dEE3ujA0AEzqGcX3t40g3N+1t8n0ATEKd1+AX3efYsbHmxRlWo0Kb62Gdxza+Oyv+0nLVxolftiRzpv/HHG6ljJ0QrmvY6gvb88c4HSMpW0PTu6uGLy//tdhp7hrkD+Hud/vchJLdDWQ3p3hLDrobiB9PK+c2xbusHqVOJ7fUQnd/rxLkjOY+/1/Lgeb7jwaACfXX3d1H1+2jyOnlUaJQ9kl1lSCBpPEnIXJHMt17XHjOAnbllbAoWzXkyFXTfjlv1O8+tchp3LH+1OuN/HDNtcZZFyJo+aVyQNmp/PaTTw8NM6TsdoYNWoUJSUlHDni/B115MiRIxQXF3POOefU+zpnE/f+8B8zPt7MLV/v4OavdnDTgh3cuGA7N365nRu+3M7sL7Zx/Rfb0BpK+NbzJcZqbJmSdJKWeV4PQHRf2bMsuh+SyjadsZ9wXz44jhenK43vn21I5ZIPNzLu9dV8uu64S5d/R3FbD8cOCNn4OaF7JN/eMoLVD47nixuG8PoV/Xh0ag9uG5vEZYNiGdctgr6xQcQEeVs1E5LC/bh8cKz1PJcMiOXVy1wbnQ9ll3LJhxv4YFWKk/HR2aPB9Xc7MsCbr24axr7nJvN/k7pay+dd0lthrH1rxREqHLzgTGaJe77fxe97TjmUu5gI64x8vFa5eFCDpib7T5Vw0fsbWLwjnfSCSmsGjIzCSqdMGasP5TDhjTVMenMtMz7eXKP4q7s++bttJ52MM66qvrXiiMsQlud+O8CF721gyPMrWbjlhNsxlKPxoqZwuQq98/hn/trjzJi/2ckrzjFT1KniKn7Y7qZ/dPHGDCaJT1xkoDiYVcKd3+7klq93MPuLbU0eYiEMDQLB2Y6hCnIPgr4E/CKqs0s8rdRk0HjBlFcgdrDrc+hKZbfy8K6yt0N4F4jqJSvnxw2G+BHQaQx0OkceHGic4wYFjaNLly5UVVXx999/11r377//RpIktNqWuQ8PPvggQUFBpKamcvfdd1NVJbsWSpLEe++9x2+//YZKpbJmF2oxDJVwer/s6eMTJBvhQC77/X7Q2U2uInvCRW+Bd7DbCd6BQhV3razkvC+OsWRXVo2hDfYTJsfTWbId9e8YzD12A8fSKiOHHSaQx/PKueyjTdYYXAsmhaFBORTo3zGYn+4cTVKEs2vwkxf14t/7x3Nhvw6KcsdxjUf1OfvEBnHfebasNGU6I8dynQcyH6w+6uS2aj+ZdDWIntQzirsmKLUlLPV6RAdySf8Ya/m2tAKrXoEjJ/IrnAa2riaygNOKuP297hTmS8dQm5vy9rRCHlgsr977eCoNZqsP5yqMD44r/3/szWKOnaHCer0aXKl/2pWp2O/uPVQaTNz+TbLi+o4Tq+JKAzd+ud2lu7GrFUN3Xg3u2jB/3XG+2ODwWbp4b19uTHXp3eHuY/hqc5pTmf0A3dPRv7wOPProo2i1Wu688050OvcePnq9njvvvBOtVtvyfVcrxmAyuzQIOZKgyuYnz6cZpLatCJdKPlyvf5QFxQM5YOggjyc8fRXfM0fHk6uHx/P0Rc56UlnFVbz450FGvbKKV5YfUmh8OH4XPWr53iSG+zGxRxQzhnTk9nGdefyCnrx15QC+umkYv/3fOWx6bBKHn5/CwXlTWPXgeOJCfBXHXzm0I/Mu6e3y3AaTxBv/HOHyjzZxNMfWvzu20ZVHQ01EBnrzhF2GCp3R7PIZNJkl5n7/n0IHxl1WjgWbUhVZOBwnvJ9cN5gwP5sRu0Jv4qEle7jvx/8U9f7Ym6UI/fppV6bV+LvjRCGXf7SJ2xcmO2UVAmWfbP+8n8ivYNMxpaeWOw+xzzek8vASpWeZZVKvN5l56ud9PPDjbpfZNhw/G53RzFduxGrd9ee704u44L31/PKfLQTLleH5g1VHXY4j3PW7i7addBKKdDyvOxHRhiIMDQLB2YzJKK/MlubIaSxNevjnaTi52VbHwxumvgKxg1yfoyIf9OUQ1Vv2ZKjJiKDWiOwSzcQll1yCJEnceuutHDzoPp3ggQMHuO222wA5vWRLEBERwZIlS/Dx8WH+/PlER0czbNgwYmNjmTt3LiqVitdee43x48e3SPsAObXrqd2yBkNAtJxlBWSNhj8ekEMoLET3gwveBK8AwH2GCAvHc8t5cPFurvp0i6K8R3SA9f99mSXWFQ3n0Anb/3eO78zA+OAar5dbqmPm/C1sOmoLWVAaGpyP6Rjqy7I7RjG0k80FPNhXS7CPlvgwXz68ehDL7hzF4ATXLuL2K4C3j+usOI87nvv9AL/aDWbNCo8G1/3G/ed1V7guJ9qFk9x3XjdFOxzzidt7ZizdmaEY1LlLmfn3gWxO2Hlk2K9qhfp5suDGYQT72vrAP/Zk8erfh1wKd9nHP7sauK8+nMsNX25TGAQcFxLtP/+cUh0b7e6x44pbiF27jueW89iyvdY6rq5/sqCCW7+um7Fj2c4Mlyk0a8rY9/wfBxQrpa7a4E4bw91AetfJIvZkFCnKGuvRMGTIEH788UeSk5MZMGAAX375JWlpaRgMBgwGA2lpaXz55ZcMHDiQnTt3smTJEgYNcvN7KcBklmr8XgAMVR3iZ8+nSVLb7r1eG8Qs/ZNsleTJ8c/pPrKWFMrnwlXa+ZvOSeThKd1dXqu0ysjHa49xzqureGjxblJOlzo9r648GuqLSqXCx9N9uOj1Izvx+AXus3jtzijmgvc28Nn649WfYd08GmriyiEdGdXZdehHVKBNMNJU7fll6SPt+0f7ULsqg5n/rbZpATga3Sf1jGL53DFO4SauPBSW2YV4uepb/tqfzXlvr+PJn/cqJs/2/dPkPtGKe+coCmn/GSZF+CmGp4uTM7j7u13ojCaMJjOVDv3gsl2ZXPq/jU5hg46eByCLK7tKqez4HNjfwjKdkbnf/8dDi3dTrjO6TCGdU6rjmy3OQpfuni+d0cxnG5S/g47tbYgxtiaEoUEgOFsx6uWV2qITEBAp6yuseEqZwtLDG6a+6jpcQpJkHQbU0GEAhCaK7BEtyL333ktsbCwZGRkMHDiQ6667jgULFrBixQpWrFjBl19+ybXXXsugQYPIyMhAo9EQFBTUYu0999xz2b17NzfccAMBAQH8999/GAwGpk2bxurVq3nwwQdbrG2UZssGhcoCCIq1Gc9ObILlj8phQhZiB8vPiKfNaOM4APz0ukHcOLoT3lrl81FUoXTZvdhuBR5s4RPOgxHbaMRDo+admQPwrWEAC/KgZfaX26yrUu6EFu0J9vVk4c3DuWN8Z0Z1DpNDNewGIYPiQ1hy+0g+umYQCWFKo1U3O6OJRq3irSsH4O/lHAZjPymXJFmrYH1KrlMb3RkaNGoV/7t6MLOGduTcnlE8c7FtVTAhzI+ZdhkqHAeKD0/uoRjYPfnTPtILKqpDi2zlE7pHKNpoL1LomBmjc4Q/n1w3RDFYm7/2OEtdxAf/bGfYcKcgvuV4Add+tpXi6u+K48B9+sBYxUDaXhTS8Xvz2AU9iQmyCa3+tvsUC6sHqY5ZQyzsPFlk9cqw4Mpjp1xv4uddzgKIjs+C/cqyJMH9P+xm0zHZOOLOuPOZC20M+/NG2inog3OqS/vPVtuAQbRGo+HSSy+1hk/ccsstdO7cGW9vb7y9vencuTO33HILhw4dori4mOnTp6PRaJxeLR4G1kpw/PrMGZvEn/eM4Y85A/j9ymDWD1rHDz4vEaKyrVabAzrgedGreIR2spb9sifb+vyZ69BX3Dm+C/ed283lPpA9BxYnZ3De2+t491+laGRTGBrqwm1jO/Pg+bY29uoQqAhv0BvNvPDHQa76ZAupeUrX+oYYGlQqFS9f1tfptwngjnGduWa4TXTYLMF9P/zHL/9lKp7V0V3C6B5l6++/23qSzGotAGevC9mTYuFNw3l4Svca2/zFhlRrv+NOT8Nklvhmy0nGvb6ad1YeoVxnVHghxAR5c35vWwakfw6cduu5cm5PWZzT/l7/tT+bW77aQYGbrE6HskuZ9v4GRVpjVyF6xZUGq9CmPY7949szByjSj4Js8Lj4gw3szSxy2YaP1x5zCsFwPG9XO62kbzafUGhpOP72NKSPrAkxKxAIzkYMlXKKvoLjchpLSYK/n4D0bbY6Wh+44DU5/MERs1HOUOEVCDED5DhJQYsSGBjIX3/9RadOndDr9Xz33XfcfPPNTJkyhSlTpnDLLbewaNEi9Ho9iYmJREdH10ncrDnp2rUrX375Jenp6ej1enJzc/nll18YN25cyzTIbJZTu576DyST/L1WqeXn479v5WfEZOd2GD8CJr8kPysWJAmpQrk6ExPiyzMX92bDIxO5c3xnlxNugP5xwQrXe8uKd00eDSBPqF25Bl/UrwNjuoZbtw0mif9btIsvNqRislsd0dSwyuut1fDIlB58d+sIJnR3TuusUqmY2rcDK+4bx7xLenN+ryheuawvnSOUmU86hvryzMXObTy/VxS3jU1StPH2hcnsyShSTJRrGpAG+Wp55fJ+fDZ7iEJ8EuCeSV1dDqIBhieFKkNPdEbmfr/LKYxheFIY/eNsRrkfd6RbldHtF5ksgpXDEkN580plv2lRC7dn07F8soorMZslxcB9ap9oxeTiv/QiZn6ymdxSndPAPcLfk/F29+Xv/dnWmHPHuuH+nnxwjTKl3vO/H+C/9CInbwL7On/syeINu2wN7tx9v3ERt2y/rVLJK8tz7O633mRmztfJHMwqcWtocKWNYV+1U7if4nv+++4sRciH/UpgQwbRdUmtXteXwLk/6xDkTa+ACnqbjtDn2Gd0PPAxarPdxCmqD+qpr4FXENP72wQMT5fo2FztCq8Qjq1hvj333K6semAcK+4by5EXpvL6Ff3oFuWcpSmjUCma53EGfyvvntiVv+4dw893jebPuWP4575xjOsWoaizLa2A9xyMIfUNnbCQEObHA+c5e3t4aTW8ML0P141IsJZZjA1H7UL0tBo199sZR/QmM+9Xt81RZ8fibaJWq7hzfBcW3z5S8Ztnz7HcctZWG53tRQ+7RwU4eWFU6E28szKFca+vsWrNgPy7cc1wW/tNZsmaacLyfmztU3Fx/xg+nT1E8ZuxPiWP6z+3Gxs7UKozMmdhMi8vP4jRZHYbVvL5hlQng4lj6EZssA+Lbh3Bved2VXyPj+eWs8iNXk1emZ6vNjl4apiV/e5dE7pYt8v1JoWx3PH3zkuETggEgkahK4XsPbKhILADIMHfj0PmDlsdrS9MfV12CXfEqJNFHwM6yJ4MvqHOdQQtQu/evdmzZw8vv/wyAwYMQK1WWwe4arWaAQMG8Oqrr7J79263qSXPWsxmyDko6y94+oJv9UDGqIPVL8K2TwH72c1YOO95q+uufA4TlJxC0ipX9y0DwHB/Lx6e0oONj07kgfO6KVzZ1SroGuXPBX1sRrvdGcXVK+zKprpyDZ5ZvaJvT5ifJ5/PHsqlA2MV5fN+P8Bfdisw7jwa6oOnh5rrR3bik+uHMGtYvMs6VwyOUyidg5zm7dEpPbhskK2N5XoTN365nVQ7LYeGNjEq0JvZdmk27fFQq7l7QheG2IUf7DxZxNsrjzjUU3HzGNvkuEJv4vvq1Sn7yaP9wPDi/jE8OtW9G7R8rCyM6OhNMDghhB/mjFQIch7KLmXm/M3WlULbNVVcYScsV2UwW0MNnEVEVQyKD+GJC2xx2QaTxF3f7nQSK3vx0r6KWN3/rTlmfc/uBtKHsktrNAhYnoNHpvRg+gCb906pzsgNX24jo9C2Qhvm56m4547aGI6f++yRnazbepOZ7+0mE/YrjI5CpnVh9erVTfJatWpVva/dHnH6Xlbky4scG96E/cuUlTtPgklPy2LU0X24eGQfhdHR4l5fF48GC0kR/nSNCsDTQ82MIR35+96xfHnDUIYnuh/LNCTkpjH0iA5kQMdgAKKDvFlw41BevqwvfjV4rjXG6+LG0Z3oZ2dMBdmFXqVSMe+S3lw/UmlssJ/Me2jUnN8rSmGMXZycQWpeuUOInnP7BsWH8Mc9Y5y8+SxYdFzsJ+jxYb58e8twFtw4VBFuCLIgrP01PdQqRiaFKULqFm076cYTRv47oXskX980nAC7BYFDDhl9Hpvaw+n7Mn/tca79fKvLzA4ga4LYhwbK11fWUatVaNQq7j23G9/fNpIOdh5ojth7cs1fd0whaqow0qtUXNSvA/GhtnHJgk1p1nAUR+OH8GgQCAQNp6JAXq0tz5UNBSYD/PU4ZCbb6mj94ILXIbqP8/H6cijLhdAk2Qjh5bwSIGhZ/P39eeSRR0hOTqaiooKsrCyysrKoqKggOTmZhx56CH9/cd+cMFZC6SlZ8LFaa4GKfDmzxNGVyrp9Lodzn1bqkZiNclpXv3DMIYmK6o7j3iAfLf83qSsbHpnIUxf14sJ+Hfjg6kFEBXpzQV+ld9Bf+7KdVkJdjaNVKhWvXN5XMTm1DKbfnNFfsYoMKHKVN8TltiGoVCpeuqwvEXYDpM4RfqjVKl69vB/j7UIU8sv1bLZLsdiYNt4xrrNi0GhBq1HJoSezBhBg50Ew30E5Xa1SMbVPtGLQt2BTGgaTucaB9JyxSQrXY1f8vCvTKfZWq1HTs0MgP8xRDjSP55Vz17c7FXU1ahUTekQS5GP7LlrCNBwNVJYJ2OxRnRRinplFlbyyXJkFYkRiGG/OUHplPPHzPtan5NYoSLnQIV7Y1UBerVbx2hX9OaeLzQvhdImOXSeLrNtxIT6c38tmOHPUxnCcWE7oEalYGf1mywmrC7W9a3BDVqbHjRvXZC+BwlwLgE/REVjzEqStV+4YNBtG3iX/H90HguMJD/BWrO7/vS+bCr1R8X2or1FSVf39+WHOSH65azQX9u2gMBrGBvsodAhaApVKxVXD4vnr3rFuDSLqRvSRHho1r17eT2Gs6Frt6aFSqXhuWm9ucGuwVaFSqXjgfJtXhMks8c7KI3UyAAV6a3lv1gC+vHEo90zswiA7zaH1KXkcOV2qWHX39JANIOO7R/LHPWN4Y0Z/txNytVqFWq3iqmG2ELpTxVWsOZxjbacF+/57WGIoi24boRCutCcuRDZ2OP6ubjlewN2Ldrk8BmD+2mMKbwNXqavt27B87hhFP2jBQ63i7ok2L4WiCoPCS8Hxc/fQqLljvE04ubjSYNV2cAydEGKQAoGgYZRmy+J2+lIIiJGFH/96DE7ZDVw9/eDCN2RhR0cqi2RviMie8stDrIi3drRaLVFRUURFRSkyTBQXF5OZmUlmpnNM9VmPJQ1r7iH4aY6ckcV+39iHYNT/2eqBbLAryYLAGIjuh1mjHPS4G2D5eXlw8zmJfHj1IKuBoV9cELHBtgnTH3uzatRosCfc34vvbh3BlN7R3HJOIlcMjpPrq1U8dkFPnrRTGLfnTBkaQBZMXHTrcKb2ieam0YlcWa2hoNWo+d81g9wKWzbG6yLY11MRnmE9Z/X7jgvx5eXL+jrtt6+n1agVA+2s4ir+3JvlpNFgj2WAPrGHc8iJhUPZpexJL1KUWVaUOkf48+OckYqVKEdRNLVahZeHhov72wwHW1MLSC+ocDIIWD5DlUo27NhnFbE3PIG8gntx/xgemqycPNz5zU4OZClTWdobiJbvzVZ4RyhdeG2fj6eHmo+uHUSvDoG4QqNWcYudF4mjNoa9E4haJa8CXj+ik7Usq7iKFQdOAw6hE008iBbUH8nu3nVWZTL16HOyN5kFtQeMfxx6T5e9xKL6QFCcdff0gUrvpxUHTjuETjS8r+jfMZgPrxnE6gfHc8s5iUzuHcV7Vw1o1CS+KekY6suiW0fw9EW9FC7ugd4eter01EbPDoF8ccNQzusVxTMX96JvrM1DQaVS8czFvbhxdCen4yz96Jiu4Qp9gV93n+KgXV9R0++MSiWnCr3//O7Mu0S5yPXFhlSFYKGX3Yq7Rq3iisFxrH5wPI9O7aEwGANEB8q/xVcM7qjQzfl2q8UjzVbX8XvTJzaIH28fqdC1sbbBQ42HRs1jF/Tk42sHKcIhHT0ERibZwjxScspYdciW/ai2sMhgX0/mXzeY5y/prTAAxIb4MHNoR0XbPl1/3Krl48rwdtmgWIVB5rP1x6kymETWCYFA0EgkCYpOQtYewCR7MpRly+n5sv6z1fP0hwvflI0IjseXnZZj1qP7QlhnOXuEoM1iNBrR6/Xo9a4Fjs56jq2CX++BcpuCP95BcOFb0ONCZV2jTjbiBcdbU605rtjVZ4yqUqm4oK8tvOC/9CKFS3lt5+sWFcDH1w3myYt64e2wCnfLmCTeu2qgk/u4Y3rL5qZLZAAfXTuYpy/uha9d2kdfTw++mD2Uzi7SajY2uuOmcxIV3h6gdBG9qF8MMwbHOR4G2AbIs4bFKwbzn29IrXXFzkOj5v2rBtIn1jahdlwl+3GHMvbW/v50DPVl8e0jFWJeirZVX/PyQcq2/7wrs8ZBrL+XBx9dM9itfoVlZfPO8Z2ZOcS2GliqM/KTg+ijY9iC/ftRhk4orxHgrWXBjUOJC3GO0fbQqBmSEOJWG8PVQHrGkDjF+7GIQirEIJtowmgymcjNzSU3NxeTyX2aWoEzZoMsxjdSvZ9lns8QoLPLKuIVKI9D4obIWbGi+8iCvHac1zNKMbFbtjPTpedMY0gI8+PJi3ox/7ohDE5oXeGharWKm85J5M+5Y5jUI5KYIG+evLBXk7i8j+0WwafXD+HG0YlOIXoqlYqnL+rFTaOVHnuWe6FSqRSGSUlCkVK4rgbtPrFBCq+NZbsyyS+zjVW8XPRZ3loNt4/rzLqHJnDLOYl0DPVhYo9ILqj23Ar181T8rq4+nENGYUWtgsOdI/xZfMcoksKVv0mBdh5kU/p04Ne7RysEMe25bWySwsjx8Vq7rBx1SFGqUqm4bmQnfr17NGO6htMx1IfHpvbEy0PD/zmkt7ZklHCVGtrLQ6MwuOeV6fl+20knQ0NTC58KQ4NA0J4xm2XBx+x9sgeCbxgcXwNLb1Gu1HoFyD/uEQ4xxRZ3cK2frMcQFCfSUwraL5IZkr+Cf+fJHj8WQpPg0o+hg4NmiaFSDkMKTZK9gKr1GpxF3+r3zEx1CJ/4wyHnvCuNhroyrX8MC24cphioO6r2tyQhfp58ffNwJ1fYxnpd+Hl5KASxvDzUhDhM+J+d1lsRy+t47SAfLVfaTbr3ZBRzMKvUqZ6ray+4cRgX949hRFIoC24cpphAO6ZvdFxRigr05oc5IxXGCsdrDugYrBgML3NhaHD83nSPDuClS117cliyi6hUKl64tI9TOjprPbWKsd0iFGEL3211FwPt/PlEBnrz1U3DFHollvOqVDVpY9jqWs4b7Oup0CPZmlogi0w2UgzSev2KCt566y2GDh2Kr68v0dHRREdH4+vry7Bhw3jnnXeoqKio/URnM4ZKzDkHma7ewNfaVwhS2X1eQXEw/X/yX8ksGxkCnWP3fTw1TOljmzSuT8mloNzWXzfGo6Et0TnCn89vGMqmxyZZPcOaG5VKxVMX9eTO8Z1RqyDAy0OhrzO0U6iTcKWF+vThN59jM2bojWaFx1VN6RdD/Dx58qJerH94Il/cMJRAb1u/co2dqKUkwQ/b02tN8wxy6MyPt4+0etv1jQ2y6mdYSIrw56e7RjnpIYEcBna5nY7OjhOF7EgrsLbDnpq+uz2iA1l483DWPzzR+v2/YnCcwuPtiw2pFJTrXWrjAMwaGq8wdM9fd1yht2EJS2lKhKFBIGivmAyy+3fOAfAOlCdB696Alc/KWgsWvALlldoIB9Vhi+ijf5ScWcIvHIGg3aIvh/VvwO7vlOUJo+GSD2RPIMf6FQUQ1lX2ArLTa3AePNSvKQM7BitcIv/YozQ0NHYgPbpLOD/MGcGQhBB6dgjkgfPdp3xrCWKDffjqpmEK3YEQ38aHal03IoFrR8STGO7HMxf3dsr+4eflwbuzBjit6NiHbdw0OlFha7UPZajpPof7e/H+VQP5/raR9I0LUgxIHVW/XU2GQ/08+e7WEQrhSrB9LiqVSjHgT80rdxJmdNW+ywbFcZUL8U578Ts5rGWwS68KTbV42dXDbIP4jMJK1h6RVzLdDXjtsUyY7L0RLEagqX2iFc+CRRvD3Qr29XbeFSDnr7d3u26oqN/hw4cZMGAADz30EMnJyRgMBqvQrsFgYMeOHTzwwAMMHDiQI0eO1H7CsxFDJZzej/bgT7yl/Qitys4TpEN/uORDOYOPJMnhEi6MDBYus3t+zBKUVNmew6aeKAmUqFQqHp7Sgy2PT2LTYxPpFxes2P/g+c4ZLKB+4W+TekY5pUy20FDX/iEJIYoMI/aCsVCzxkW4vxfL7hjF+ocnsOzOUS7b4OvpwVtX9uf56X2sXmmJ4X50jvDn1jFJit8Ni1eDk9dZPd+aVqNWZE4q15uYv06pA2H/tnw8NYqQtKziKn7aZUuJ7NXEQpAgDA0CQfvEUCWr5+cflQ0EZafhp9vh0O/KelF94LJPILyrslxfZhN97NDPJo4nELRHik7C15co07sCDLwWzn9ezsJij64UqoplD6Dwbk6hRI6aCvUd+KpUKqbYZZ9wTLXWFMPo3jFBLLljFMvnjnEaKLYGukUF8OWNQ+kY6kOIr5Y77bwRGoqHRs0L0/uy+sHxXO1GpLFfXDAPTlYOlO3jfuPDfJncK9rxMKB+BqCL+se4XeFzt+oe6K3l65uHMa1/DB5qFdP6x9Czg61vvtQhfGLxjgzFtrvrPXNxL3rH2LwlPDVqvD2U3+kgHy1f3DCUcH+l94vFKHPlkDjFSuM3W2rOyuHIoPgQ5l83hIgAL6ICvbi1ejCs1ai5wS4u3KKN4c5TomeHQEWc+E+7MhSpLmtaDXVHaWkp559/PkePHkWj0XDllVfy6aefsnz5cpYvX86nn37KzJkz0Wg0pKSkMHnyZMrKnNOZntXoK2TPykO/E7DrY9Qq2/1Li5wkC1CbjbKRIbpvrSmzhyeFWePvHWklcgrtnsgAbwK8tU7lfeOCnLILQf3EKjVqlVvxSS+PhoXuqlTKVJe5pcpMO7X13yqVio6hvjV6RalUKq4bkcDqB8fz7qwB/HL3aNRqFUkR/orPZOXBHI6cLnWro1Mfpg+IUXizfb3pBDl2783xc792RLwiffLpElvd5tCwEYYGgaC9UZ4vZ5YoSpe9EY78LRsZCtPsKqlg4HVw8TsQ4PCDUFEAVWUQ1ata9LH1uFULBE1O9j74ZILs+WNB4wkTn4Kht4DK4Weyqgh05RDZq1qvxPlntDaBp7pwYT/Xk1mo/6pHW2VQfAjrHprArqfPd+uO2xzcNibJ6nHQIzqAMQ7XvmVMoqvD6mVoCPf3cvueakrB6OvpwXtXDeTIC1N576qBCiNWbLCPk/CYPe4MXt5aDR9dM9gaNnLj6E4uV+w6hvryuUOOeYsOSJi/l1MMdHpBhdIgUMuDMK5bBNsen8TWx89VuCbPHBqvSOv3+YZU7BN1OL4v+wlKlcHMMbs0qQ3xaHjnnXdIT08nJiaGbdu28f3333PzzTczefJkJk+ezM0338yiRYvYvn07MTExnDx5knfffbfe12m36Mvh9F5IWQGbPkBlpwb5pXEy27veLxtvQTYyOI5JXKBRq7hkoGuPh7MldKI1c//53ZyibOs7iZ4xpKPLbEGNESu8dFCs2wwiTZHm2UJciC+XDIhVhG7cPq6zos78tccbvSgBsgF97rm2xcJKg4nf7NJoOj4PAd5abhjt+jesIYbY2jhLhisCwVmAyQD5x+RUlVWFcurJ1S/AhreV8ea+4XDRWzD0ZqVyviRBaRaggpj+sjeDEH0UtHdCEsDfLiuAbxhc/B50meRct6oIDDo5djg00a1eibMYZP0HDwM7htSwYnf2DKRbwg1arVbx1pX9SX7yXH77v3OcQiwGJ4TQ3yFGF+qvI+EqnhfqpiPgbtJuHz7hdEwNzYsP8+Wve8ew86nzeOwC19lJQFblf2/WQKvi/bQBtsnedSOVMdDfbTtZp9AJe1zd7yAfrSIGfU9GMYezbWr2ju/rvF5Rbp+dhmg0/Pzzz6hUKubPn8+AAQPc1uvfvz+ffPIJkiSxbNmyel+nXaIvh9P74Pg62PgOmG2x9ouME3jOeD1qQ7VBLKpuRgYL7p4f4dHQ8nSLCmD6AOX9qW//6O/lwaxhztoTjTE0BHprFRl67GnuzCL9OwYrDMG//JdJZpHSW7GhTbi4X4wiLMRYQzYkgBtHdXKZpaSpM06AMDQIBO2DigI4tUsOl/D0kQXqfpoDqeuU9eJHwOWfQcxAZblF9NErUNZjCIwRoo+CswOvALhqEfiEQFgXmPY+RPZwrldZhFlfxS/5sfycqlbEQDriLAZZf9RqlULwzB4Rg9z8qFQqwvy9XE5MVSoVt5zjvCJU39tyXq8oJyMGNE6wcGrfDu5X7GoZxXp5aAh1kzfenvN7R7PqwfH8dOconr6ol7V8UHwIPaJtoRw/bE+nymCLw2/MOP7GUYmK46sMtlVxx4G0VqPm2hGuQ2Ma8tkePXoULy8vLrzwwlrrTp06FW9vb44ePVrv67Q7dGWQvRdOboUNb4HJ5qL9s2kUTxhvBlSoVepqI0NUvU7fIzqQni7So4r+sXUwd1JXRZ/TEE+82aM6OfUbXo2cDNuHT9hzJgxUt4+3eTUYzRJfbEhV7G+o6LFareK+c11rLbk6ZYifJ9eNcP4cavKmayjC0CAQtGVMRihIlb0YyvPk1di9S+C3ubIugwW1B4y8Cya/DD7BynMYq2TRx4AOsgHCt3WlchIImp2QTnDNEjj3OfkZcqSyEEx6Pk6LYu6vJ7n3h/+Y9/sB53rVOIlBNnDwcEFfNysvYhzd4jiKFEL9B4neWg1TXRiTGjPY8/fycGugakpPmNhgHwbGhygmdXIaNtvgtaBczz8HTiv2N5T4MF8mu4j7BtcTmFnD4l26ATfkszUYDHh61k2MVKVS4enpicFgqL1ye8ZiZMjcCeteB4Mtu0Rl7GgeNNyOuXoKog7pWG8jg4XLXHg1nE0eX62ZTuF+zLTzROoaWX+tr7gQX6b2Uf4ONnbVvV9cUI0ZfJqTsV3D6WVnHHP2aGh4Gyb3jlacu7Zz3jwm0emz9Gyg/kVNCEODQNBWqSqGrN2yW6LGU84wsfQmSF4gp4ayYEkZ1XeG85JbVZGs6RDaWY6N9HRO7SZonWg0mga/IiMja7/A2YY7PZKKgup87n3ZnGmbPHy1OY19mcUuT9UUGg0gq2S7Sj2pahI5SEFj8HAQKYSGxfhe6iLUoTEeDeA+fOJMzL+mD4hVeGnYC641dhzvThvDlQEj3N+Li/o5G+o8GvDZxsXFUVpayoED7o2LFvbt20dJSQlxcXG11m236EplI8PpfbDuNdDZwlyIG0reyCcwYufJ4+08Oaor0wbEOH2vhCG29fD0Rb24YVQnpvWPUXg/1Yebzumk2A5tZAYiR1FIC2fCQKVSqZgzLqmG/Q0/t1qt4v7znL0a3BlQIgO8meWQFlWETggEAjCboPAEZCTLmgqVRfD34/Dvc1CqzMdO1/Ph0k9kZXx7DBVQnAFm5HjzyJ7g0fj0cYIzhyW1WkNfgjpQkV+dz70vBMYoFKIlCZ75db/Lz9JJ4KmBhgF34RNiIN06cBQp9HMRBlEbIxLD6ODgGdHYwd6ozuEuNQrOxEDaz8vDraGjsdcfFB/ilL++pvPOdqFa3xCxs0mTJiFJEnfccQdVVVVu61VVVXHnnXeiUqk499xz632dtk6VwcQTS//jyo83kXzgEKx9VfYGsxDdD85/HslYP7X/mogK9GZ0F2Xq7eaOtRfUHW+thmen9ea9qwbSKbxhC1mD4kOsoVB9YgMZ373xCyXT+sc4ha2dKU+YC/t2oGOoj8t9jfWqmNQz0kk/qKa3NWdcZ0U6Z89mCJ2o/6+iQCBoOapKoOAYFGfKxoI9P8DRlc71fEJgxJ3Q9TxluUkvezCoNXI8elBHWTRS0OZ45plnGnX8O++80zQNaa9U5NtSrVULlDl6KiSfKOSX/04x3cF919H40JixwwV9O/D15hOKMhGD3DoI8tHyxIW9ePynvQR4u59g14RareKSAbHWvOrQeOVvjVrF9IHKc1rKzwTXjkhw+s5C4wfyKpWKW8Ykcvd3uxzO67p+/47BDOgYzH/pRdYyjwZ8Bg899BALFixgw4YN9O/fn4cffphJkyYRGyvf74yMDP79919ef/11UlJS8PLy4sEHH6z3ddo6f+w5xbfbM4mkkIiCl0CVa9sZ0QOmvAwmA2aD0ljT2O/F9AGxrE/Js26L7rF9oVKpeGF6Xx6Z0gNfT48m6cf8vDy4dGAsC7fY+qlmSLjgEg+NmlvHJPH0L/ud9jVFH3n/ed2Y/YUtVXdN54wN9uHq4fHW/npwQtOHTgtDg0DQ2jGb5VWBstOyB0NFARxbBfuWKLNJAGi00HcmDLgaPH3tzmGSJ04mg6zFEJootBjaOI01NHz11VdN1JJ2SHmePFqN7qeIHXal//jy8oOc1ytKsZrt6OTQGMPA0E6hhPt7kVfWdC7ogqbj6uHxTO4dhUatIriBLr3XDI/n260nKK0ykhjuR0KYb+0H1cLlg5wNDWfqe9MtKoBhiaFsSy1QlDfFBHBK72hig30Usc01DaRnj0rgvx+KrNsNyROflJTEV199xXXXXUdKSgq33Xaby3qSJKHVavnqq69ISnLvHt1eSc/KIZQSvvV8iXhVjm1HaBJMfQ1QQVUJ5uDOwHHr7sZ+L6f0iebJn/dRWS08GtJI13pB6yTALlVkU3D18HiFoaGh/XdDmDG4I++sTKGgXDmGb4o+cmzXcIYkhLDjhOxNFOJb8+f21EW96BTmh1mSuNaFQGRjEaETAkFrxVAphzdkbIP0bXLqyuOrYfnDsPs7ZyND54lw5UIYdovNyCBJcmiFJaNE3BAh+CgQ1ERFHqjUTkYGcJ1N4nSJjg9XKxXmHT0fGjN40KhVTOmjbIcQO2tdhPl7NWqQ2jHUl5/uHMWrl/dl2R2jmsRjpWtUAP3ighRlZ9ITxtWAtSm+tx4aNTc6aGPUdN4L+nYgKtCmc9I5omEefFdeeSWbN29m8uTJgHPomkqlYurUqWzZsoUrr7yyQddo01SV4FmSyteer9BVnWktztXGwAVvyKGZlYUQ3g1zoFK/orFfCz8vD56b1htvrZpuUf5OaRUFAlf07BDInePl0IFhnUIZ1y3ijF3bx1PDDS5Cuxqi8+OISqXilcv70iHIG19PDbeP61xjfa1GzU3nJHLLmCS83WQsagzCo0EgaE1IUrX3Qo7svaArlX+FT+2E/T9DwXHnYyJ7yRklonory/XlsveDVyB06C+nrNQ0rUVYIGh3ePjK4RL+zoMOdxktP1ufypVDOlpjUB2rNXaCdUGfDnyz5aR1299b/HS3N7pEBtClAarsNXHZwFj2ZNgESwMaoCHRUKb0jibc35O8MptBvKlCN64cKq8GlumMAATU8Dx4eWj47PqhvPtvCp3CfF0KRNaVQYMGsXz5coqLi9m5cyc5OfKqfWRkJIMGDSIoKKiWM7RTTAZIXces1CcJVWdZi9PNEcwofYzHTnhwSYdcCO8KYZ0hr0JxeFMYwK4c2pHLB8sGjDMVIiRo+zw8pQf3ndcND7XqjIckXj8ygY/XHqNCb58CuGna0CUygM2PTcJgMjdaXLixiNGKQNAaMOpkd+2SU/KKqtkkKzUf/ReO/AX6Mudj/KNg2G2yJ4N952SokI0VGi9ZBDI4XhlGqBrUnQAALX9JREFUIRAInFFp5NSvIYngF+6yir1HQ4CXB6XVEx29ycwLfxzgs9lDgabLOmFhRFIYk3tH8ff+0wyMD2ZoJ+GRJKidK4d2ZMnODPZlljB9QAyRLgQimwtPDzUzh3bkw9W28I2mGscHemt5+uJePLxkDyG+Wi4fVHOGh75xQXw2e0iDriVJEgcOHKCkpITQ0FC6detGUFAQEyZMaND52iVH/oFf7iLUUGQtypZCuNrwONmE8fjqUvpfE0ensK6g1jgZbJtqciUMDIKG0FIT8WBfT2YNjeeLjanV7VA1uUdBSxsZQBgaBIKWRVcmey8Up8tCj2oN5ByAw39CZrLrY7S+MPAa6HOFLR2fJMnGiMpi0HpDcCcIipVFIQUCQe1ovSFmkPwMusF+gDw8KZQynZEtx+U49JUHc1h9OIcJ3SOdNRoamY5SrVbx8bWDyS3VEe7vJVTVBXXC19ODX+86h7wy3Rk1Mli4alg8H605Zn1umjLk58ohHZnSJxoVTR+7DWAwGCgoKKC0tJS+fftay0NDQ7n33nt5/PHHm30FdPXq1bz55pts3bqVsrIyEhISmDFjBo8++ih+fq0kFfWBX+Gn2+RQz2pOmiO43vAo6ZIc8lVuVHH3ijKWdlXhhYvQsjPZXoGgFXHXhM6sS8nlaE4Zs0d2wsez6UMXWhphaBAIWoLKIjkVZUmmHOJgll0POfS77NngCq9A6D4F+s2yaSxIZtnzQVcGWj+I6C7HlXufpS6cAkFjqMHIAEqPBrVKxbPTenPBu+utE6nnfzvA6M7hTloOqiZYVFCpVC0yWRS0bdTqlvvexIX4MrFHFCsPngYgwt+rliPqR2AzGBgsTJ8+neJiOezE3qCQn5/P008/TUpKCgsWLGi267///vvMnTsXSZKIi4ujY8eOHDhwgBdeeIGlS5eyYcMGQkNb2LPpv+/h17vAbLQW7TN34kb9w1RqQxgZZmZztvzZ7TtVyst/HuLZab2dDLHqll90FQhahDB/L/65dyxFlQZC/dqniKkwNAgEZwpJkjUTSk7J+gtVxZB3BE5ugRMbQTK5Pi6iJ/S+BJIm2DwYzCaoKgJ9JXgHQlQf8I8Ez1ayyiEQtCFMZonl+7LwUKuZ3DvK7UqlvUeDWqWiR3Qg141I4Kvq1FDH88pZsCkVLw+lwUKINwrOVp67pDdZxZUUVxq4Z1LXlm5OnVi8eDHLly8HwMPDg4cffpi4uDjS0tL49ttvOXXqFAsXLuTGG29k3LhxTX795ORk7r33XgDmz5/Prbfeikql4tSpU0ybNo3k5GRuvfVWli5d2uTXrjNbPoK/HsNekea4X39m5t9DOT4Eqcy8MzmUC5aUkV9uAGDBpjRGdg4jLsRHcSqRrldwNqNWq9qtkQGEoUEgaH7MJtlLoThDFnPM3AGn/oNTybKIkis0ntDlXOg1Tc4/bcFkkPUXTHo5LCK8G/hFym7fAoGgQbz3bwrv/psCwNxJXbnvvG4u69m7/FpW4e47rxu/7j5FYYX8LL+7MoXZDmrSYhgtOFuJDfbhj3vGtHQz6sU333wDgI+PD1FRUbz44ovWfU888QQTJ05k165dfPvtt81iaHj++ecxm81cf/31ilSaMTExLFq0iB49erBs2TL27NlDv379mvz6NSJJ8O882PCWsrzzJBZ53EF5viz+qVariUrqy5tXVnLDl9ut1R5avJvXruivOFQYYgWC9otwWBIImgujDoozIWUFrH0Vfr8XfpoDWz+G9C2ujQxBcXIGiWuXwriHZSODZJb1G0oyZYOFTzDEDoaOw2WhR2FkEAgaxapDtpzv7/6bQkZhhct69h4NllW4YF9PHpzc3VperjexYFOa4jgxkBYI2g47d+5EpVIRGhrqtNoeGBjIq6++iiRJ7Nq1q8mvXVZWxl9//QWgMDJY6Nq1KxMnTgRkz4sziskAP9/pbGToOwPOmYtRr7MWaTQe4BXA+O6RzBmXZC0vqTLy1C/7FIeL3lEgaL8IjwaBoCkxm2T9hex9cOg3SFsPeYdxCkq0x8Mb4kdAj4sgdpAc0G0Rd6wqlQ0NXgEQ2kVWw/cJqTWWXCAQ1J29mcWK7bf+OcJbMwc41bPXXrAfHM8aGs+3W05yIKsEQJGuCppObV8gEDQ/eXl5eHt74+np2p15yJAh1npNza5du9DpdHh5eTFs2DCXdcaMGcPKlSvZsmVLk1/fLfpy+HE2HF2hLB80W/a+NJmQvIIAuQ9U2QkvPHh+d7alFrDrZBEAuaU6xSmEIVYgaL8IQ4NA0FgkSRZkzNwJ+3+WjQsFx2o+xtMP4kdB0liIG2bTXjBUgK5UXjnQ+kFQR1l7wScEPNpvDJdA0FJIkoRWo8JgshkRfvovk5vHJNI7RimqanYQg7SgUcvCkFfO3+zyGmIcLRC0HXQ6HdHR0W73BwUFWes1NUeOHAEgPj4erda12GXnzp0BOHz4cJNf3xUz3vyVeeXz6GlOsRWqNTB0DvS4AII7gl8E5v2pWAwN9olxtBo17181kAveXU9JlRFHRBIdgaD9IgwNAkFD0VdA2gY48DMcXyOHNtSEVyB0OgcSx8meCxqtrNZsrILyEjBUgdZH1lzwj5IzS2h9aj6nQCBoFCWVRoWRAWTb4SvLD7Hw5uFO5RYcB8fDEkOZ1j+GX3efcrqGWLETCAR1oaBATpdbU0YJy77CwkK3dZKSktzuS09Pp2PHjnVrUGURr5c8TCdVlrXIrPFGfd48WaTaN0wey+DeEAtyBpLXrujP7d+4SNstukeBoN0iDA0CQX2oLJbTUB78FY6vhvLcmuv7hkGnMZA4FqL7yZklDJVQkSeHWag04OED3iEQESV7LngFnJn3IhAIyC1zvSq5PiWPdUdyGdstwlpW00Aa4LELerDiwGkqDcrQCWFoEAgEdaGqqgrAbdgGgJeX7AFZWVnZ/A3yDmKtuT+dNLKhIVcK5J2Q53l+6PWoNUqZN8esPI5M6RPN7JG2LD011RUIBO0DYWgQCGqiJBvS1sHJzZCxHXIPyxkfaiIwBjqNhYRRENpJFoU06qHstBwi4eENQQlyWkpPP/nl0bT5xQUCQd3Ic2NoANmr4Zwu4air3RckF2KQ9nQI8uHuiV14/W+lS7MYRgsEbYvTp09jNpsB0GicNZFUKhWnT592uc+y32h0DhOoDW9vWdxZr3c/zrCEbPj4uPd4PH78uNt9NXk7OKFScXjA4/z+XxG9VanMNjzKyYwIum096ZRdR6Fh46bTe+yCnmxPK7Tq2QB4iNgJgaDdIgwNAoEFsxlyD8keC+lbICMZik/W7diwzrLnQtxQOfTBqJPz35nN4BMqvyxGBa2vLTeeQCBoUfw8PTi/VxR5ZTryyvTEBvuw+Xg+AAeySvhldyaXDowDHD0aXJ/v5nMS+WF7OicL5MwVKpXQaBAI2hr2k2apJjHnJiYkJASwhVC4wrLPUre5eeny/qSes4irP1hBluQPwMvLDzKmazhJEf7WetV2GcC9l4K3VsOH1wxi+ocbKa40EOKrpU9skMu6AoGg7SMMDYKzF5MBsvbI+gpp6yEzWRZ1rBMqiOotey3EDJK9EyRJTjXpFSAbG7wCwctfeCsIBK2YvnFBfHL9EOt2TkkV415fYw1/eOPvI0zt0wFvraZW12CQB9LPXdKbW77agckscU6XcJfeDwKBoHXyzDPPAPDOO+8AcO+9956xa3fr1g2AkydPYjAYXApCHjt2TFH3TJAYFcrtU4fzzK/7AagymHlg8W4WzxmJR3UIhakOhliAxHA//pw7ho0peYzuGo63VmTREgjaK8LQIDh7MFTBqZ2ygGPqOsjcIesl1AWVGkKTIKInhHeB8O6yEUHjKXsp+EaAT5BsZND6iiVMgaCNEhnoza1jk3jvX1lhPbOokq83p3Hb2M4Kj4aaHvEJ3SNZcvtIjuaUcVG/mOZuskAgaEIshoavvvpKsX0mGDhwIJ6enuh0OrZt28bo0aOd6qxfvx6AkSNHnrF2AVw3IoF/DmSz8ajs8bXrZBGfrD/OneO7ALVr2NgTG+zDlUPrKEgpEAjaLMLQIGh/GPVQniNrIpRmw6n/ZONC5o7a9RUsaH0hogeEd5MNDKGJcpmHl/zyDpazQngFgGeACIUQCNoRt41N4rutJ8grk/uLD1Yd5cohHWvVaLBnYHwIA+PPjGuzQCBoHwQEBDB58mR+++03PvnkEydDQ0pKCqtWrQLgiiuuOKNtU6tVvH5Ffya/vY5Snaw/8faKI0zoHknPDoEO/eMZbZpAIGilCEODoG1hMkJ+CmTvg9JTsiGhNFs2KpTlyFkgqorqf17fcIis9lYI6yaLNXr6yqEQ3hZPBR9ZyFHrI+eQFggE7RJ/Lw/mTurKU7/IbsIlVUb+t+ZYnTQaBAKBoDE89dRT/P777yxcuJDRo0dz6623olKpyMrK4qqrrsJsNjN9+nT69+9/xtsWE+zDM9N68+Di3QAYTBL3/7ibX+4aXS+PBoFAcHYgDA2C1ouhCnIOQPYeyNotv07vk4UWG0tABzn8IbKHnHYyKE42JngHy4YErbfNg0EgEJx1zBoWzxcb00jNKwdgwcY0PDS2wbMYSAsEguZg6NChvPXWW9x///3MmTOHF154gfDwcA4cOIBOp6N79+58+umnLda+ywfF8vf+bFYcOA3AwawS3v33SJ00bAQCwdmFMDQ0M6tXr+bNN99k69atlJWVkZCQwIwZM3j00Ufx8/Nr6ea1LGaz7H1QngcVebI3QskpyN4LWf/JqSTN9U8P5ZKgOFlfIbovdOgvh0J4BcrGBK2P/FcjHgeBQCCj1ah5ZEp3bv9mJwB6kxm9ybZfeDQIBILm4t5776Vv377W8WNOTg4JCQlcccUVPPbYY/j7+9d+kmZCpVLx8mV9ST5RSEG5HF720ZpjdArzs6vTUq0TCAStCTGzakbef/995s6diyRJxMXF0bFjRw4cOMALL7zA0qVL2bBhA6GhoS3dzMZjNsvZGqqKq19Fdv9XvyoL7QwK1a+KfJBMtZ6+RrS+sheCT1D131DwDQHfMDkcwj8KguMhMMbOqNC40AdJkppURb6oQk9uqY5QP09CfD1Ru5nBmM0SBRV6QmuoIxAImo7JvaMZFB/MzpNFTvtEJgmBQNCcTJo0iUmTJrV0M1wS7u/FS5f2sRpizRIcr/b+AtCIMYpAIEAYGpqN5ORka0qk+fPnW2PsTp06xbRp00hOTubWW29l6dKlLdtQswn05WCokP/qy+S/VSVKo0FlkYMRoQhDeRFSZRFaYxkqmjfPtF7tTbFvJ6qCkjCGdGEP3akKTMTbz58AH0/8vDzx9/HE38cLfx9v/Ly9KNZLPLp0D4ezS/H3TiPAW0uAtwcB3loCq/8GeHsQ6O3BzpNFeGvVeGrUeGs1eGk1+Gg1eGvlbW+tGm8PDR+sPsr+UyX4e3kQEeBle/l7KbZ9tRqSTxbioVYR6K0lyEdLoE/13+rtAG8PNh/P58YF29Eb5QTUGrWKUD9Pwv29CPf3JMLfi/AAL4orDPywIx0AT42aDsHexIX4EBvsQ1yIr+3/UF8OZ5ewPiUPH62GUD9Pwvw9CfXzIszPk9Dql7dWw9/7s1m4+QQ+nho6hvjSMdSn+q/8v6+n3D3klFTx9eYTnCquJNjHk2BfLcG+8nsI9vUk2EfeDvbxZGd6Iam55cSF+JAU4U98qC+eHrJQZn6ZjjWHczGazfh7afH39sDfy6P6nsj/+3l6oFLBvN8P8NvuU4T6eZIU7k/nSL/qv/4kRfgR6G1L+bXq0GnWHM7F19ODUD8tIb6ywSbE8n59PQnw9kCtVnHkdCk70grx8VQTVH0/7O+Nl4fNALXywGn2nSomMsCbjqHy5xwT7K2oA2AySyxJTudEfgUB3lrFee1fAd4e6Ixmvtt2klNFlXQIstxDX2JDfAjx1Somr0dzSnntr8NUGc3Eh/oQH+pLfKh8f+JDfQmw+wwOZpXwzC/7uXp4PFP6RIt0YU2ESqXisQt6MuPjzS72tUCDBAKBoJUwpU8HLh0Yy0+7Mp32idAJgUAAwtDQbDz//POYzWauv/56brvtNmt5TEwMixYtokePHixbtow9e/bQr1+/5m2Mrgx+uAazTjYiqAzlqPTVhgVjHdM7usA5u3PTkC8FsN/cif1SJ/aZE9kvJXBCikKqUEOefc2i6lcdKG7aNpbpjJTpjNb47YagUqFQaQZ50ppbqiO31L0Ohd5k5kR+BSfyKxp87boQ7u9JXIgv/6UXNfgcGrWKjiE+xIb4WFNi1Ye8Mj1HTpfBfmV5RIAXSeF+GM0SyScK69QOk7l2Y5i3VjZAnC5x/fmrVBBlZ3joGOLDn/uyOZpTVuu5Xd1ve3w9NcQGy59VbLAP3249WeP5Qny1xIf6Ehfqyx97sgDYllZAyG9a/n1gPKF+nrW2SVA7QzuFcn6vKP6pjke2IAbSAoHgbOfZab3ZfCyf7JIqRblwaBAIBCAMDc1CWVkZf/31F4DCyGCha9euTJw4kZUrV7J48eJmNzSsOVrE+ONraKkEjHpJQzF+FEiBFEiB5BNAvvX/QAqkAPKlIGt5IQFA+/+VqmnS2RrIK9Nb0/s1FJNZIi2/grQmNorUZoxx1Y66UGUwU2Vwf15JguySKrJLqtieVruBw/HYmqjQm0jJKSOlDkYLgMIKA4UVxezOUFrROkf4CyNDE/PwlB78eyhH8T0SA2mBQHC2E+Sj5bUr+nH9F9sU5SK0TCAQgDA0NAu7du1Cp9Ph5eXFsGHDXNYZM2YMK1euZMuWLc3eni+3nmK0pEGrapgegllSUYIvxZIfJfhSIvlRjB8lki8l+FnL7ffbtv3QocViOOgeFcCx3DKMtUz8JvWIpMposk4oCysMDWq7BbUKxnSNoLTKQGmVkZLqvxV6588kwMuDKqMJg6nmNl46MJbcUh05pVVN0sZuUf5M7dOBvDJd9Usv/y3VUW7Xzsm9o/DRasgorCSzqJLskqozarRQq6CO8/YmoXOEHyfyK2r9zghkrh4e39JNaHd0ifRn5tCOfGfnZSI8GgQCgQDGdovg2hHxfLPFvn9swQYJBIJWgzA0NANHjhwBID4+Hq3WdYBB586dATh8+HCzt+e6EQmsTZW9JirwpkLyogJvyvGmUvKiHG8q8KJckv9WSt7VBgPZkFCGN1IN/hBT+0TzzMW90RlN6I1mdNaXCZ3RjN5oJthHy+CEEDw0avRGM8fzyjicXcrh7FKOnC7lUHYpGYVyGMcNozrx7LTeimvojWbyy3VWw0NJlYGukQEEemsp1Rko15ko0xko05koqzJSrjNSqjNSqTcyMD6EqX2iXVrYjSYzZTojpVVGa5y+/T6d0UyVwUSV5a/BhL+XBwlhzhlDHNtYpjPSPy4YXy8NJZVGiisNlFQZKKmUX/K2kdIqA92iArh+ZCe3AkoVeiMF5Xo6BPk41dEbzWQXV5FRWEFGUSUZhZWE+mq5dFAcHmoVBeV68sv1FJTryC/TU1Cut5ZpNSpmDOnIwI7BFFUYSC+sIL2gsvpvBemFlWQUVJBbpmNkUhjvXz0QrVpNqc5IcYWBoko9RRUGiioNFFfoKa40EB3kQ9dIf47nlZGaW86xvHJSc8tJzSvHQ63i4SndGd0lXA4/qZLvU1n151Cms20H+WiZNTSe+DBfDCYz6QUVHMst53huGcdyyzieW86x3DKrgeeZi3tx1bB4Civk91dUYaCgXO+0HeDtwayh8ajVyPeg+l4oX0ZKKg0kRfhxzfAEiir0pBdWkFH92WQUyn9PFVVZV7k9PdQsuGEo3aIDFOeynr9C/lthMDE8MZTx3SPJrDYWZRZWkFl97+TtSvKr1bw7R/ix7M7RqFWQXlDJyYIKMgorOFlge2UUVKI3mTmvVxQX949x+6wKGs6953bl512ZVuNkRIBIfSsQCAQAj1/Qk/UpedaQTtE/CgQCEIaGZqGgoACgxowSln2Fhe7dr5OSktzuS09Pp2PHjnVqz4QekZwX9Cy9Y4Po1SGQSr2xeoJrWdlXrvKbzRKXDYrjwcndKa40UFQ9gbRMlIoqDRRVGCiu1BMZ4M1tY5Pw86r7V8nTQ02P6EB6RAcqyst0Rir1Jpc/UJ4eajoE+dAhyKfO16kLHhq1LGjo6+xq7qFR46FR1/m91dTGyIDGtdPX08MqzujquvFhvsSH+brc7+flQcdQ1/vsCfGTBRT7xQXXWtcicBiP+/P276g8j1TtdtEQl0qtRk1ShD9JEf5AlGJfQbkeFXL7gWb5ngAM6eT8PBtNZjmUoriK2BDbdcP96zbICvLR0ism0OW+Sr2Jggo9kQFeaDWyoa9XjOv6ZrOEzmjGx1OIQDYXkQHevDtrIM/9tp/4UF8uHRjb0k0SCASCVoGvpwefXDeEe3/4D6PJzN0TurZ0kwQCQStAGBqagaoqWRTH09N9nLSXlzwRqaxsuBhjXdGoVfx931jrZKU++Ht5EBvc9JM2d9fyr4fBQtC2aK6YzZbUI/DQqKuzftRuyKkvPp4aYj3r9uyp1SphZDgDnNcrivN6RdVeUSAQCM4yukcHsHzumJZuhkAgaEWIWV0z4O3tDYBe715IT6eTBed8fNxPJI4fP+52X03eDq5oiJFBIBAIBAKBQCAQCASC+iJmn81ASEgIYAuhcIVln6WuQCAQCAQCgUAgEAgE7QFhaGgGunXrBsDJkycxGFxnIjh27JiirkAgEAgEAoFAIBAIBO0BYWhoBgYOHIinpyc6nY5t27a5rLN+/XoARo4ceSabJhAIBAKBQCAQCAQCQbMiDA3NQEBAAJMnTwbgk08+cdqfkpLCqlWrALjiiivOaNsEAoFAIBAIBAKBQCBoToShoZl46qmnUKlULFy4kE8++cSa2i8rK4urrroKs9nM9OnT6d+/fwu3VCAQCAQCgUAgEAgEgqZDGBqaiaFDh/LWW28BMGfOHBISEhg0aBCJiYkkJyfTvXt3Pv300xZupUAgEAgEAoFAIBAIBE2LMDQ0I/feey8rVqxg6tSplJeXc+DAARISEnj88cfZsWMH4eHhLd1EgUAgEAgEAoFAIBAImhSPlm5Ae2fSpElMmjSppZshEAgEAoFAIBAIBALBGUF4NAgEAoFAIBAIBAKBQCBoMlSSRaVQ0Kbw8fHBaDTSsWPHlm6KQCBog6Snp+Ph4UFlZWVLN6XJEf2jQCBoLO21jxT9o0AgaCx17R9F6EQbxcvLq85109PTAcSPSitH3Ke2Q3u4Vx4eHvXqR9oSon9sn4h71TZoL/epvfaRon9sn4h71XZoD/eqrv2j8Gg4C0hKSgLg+PHjLdwSQU2I+9R2EPeq/SDuZdtB3Ku2gbhP7QdxL9sO4l61Hc6meyU0GgQCgUAgEAgEAoFAIBA0GcLQIBAIBAKBQCAQCAQCgaDJEIYGgUAgEAgEAoFAIBAIBE2GMDQIBAKBQCAQCAQCgUAgaDKEoUEgEAgEAoFAIBAIBAJBkyEMDQKBQCAQCAQCgUAgEAiaDJHeUiAQCAQCgUAgEAgEAkGTITwaBAKBQCAQCAQCgUAgEDQZwtAgEAgEAoFAIBAIBAKBoMkQhgaBQCAQCAQCgUAgEAgETYYwNAgEAoFAIBAIBAKBQCBoMoShQSAQCAQCgUAgEAgEAkGTIQwNAoFAIBAIBAKBQCAQCJoMYWhox6xevZqLLrqIiIgIfHx86NGjB0899RTl5eUt3bSzimeffRaVSlXj6+OPP3Z5rMFg4PXXX6d///74+fkREhLChAkTWLZs2Rl+F+2D7OxsFi5cyD333MPIkSPx8fFBpVIxfvz4Wo9tzL3YtWsXM2fOJDo6Gm9vb5KSkpg7dy65ublN8K4EDUH0j60D0T+2HkT/KLAg+sfWgegfWw+if2wgkqBd8t5770kqlUoCpLi4OGngwIGSl5eXBEg9e/aU8vPzW7qJZw3PPPOMBEiRkZHS6NGjXb5+/vlnp+MqKyulc845RwIkjUYj9evXT+rcubMESID0yCOPtMC7adu8/fbb1s/P/jVu3Lgaj2vMvVi6dKmk1Wqt34FBgwZJfn5+EiB16NBBOnbsWBO/S0FtiP6x9SD6x9aD6B8FkiT6x9aE6B9bD6J/bBjC0NAO2bFjh6RWqyWVSiXNnz9fMpvNkiRJUmZmpjR48GAJkC677LIWbuXZg+WHYvbs2fU67p577pEAKTExUTp06JC1/JdffrH+6P/6669N3Nr2zeeffy6de+650mOPPSYtW7ZMeuqpp+r0Q9HQe5GRkSH5+vpKgPTUU09JBoNBkiRJKioqkqZMmSIB0pAhQ6zPqKD5Ef1j60L0j60H0T8KRP/YuhD9Y+tB9I8NQxga2iGXXHKJBEjXX3+9074jR45IarVaAqTdu3e3QOvOPhryQ5GdnS15enpKgLRq1Sqn/ZYObtCgQU3Y0rOP999/v9Yfisbci7lz50qANHbsWKd9BQUFUlBQkPjBP8OI/rF1IfrH1ovoH88+RP/YuhD9Y+tF9I91Q2g0tDPKysr466+/ALjtttuc9nft2pWJEycCsHjx4jPaNkHd+fXXX9Hr9XTt2pUJEyY47Z8zZw4AO3fu5NixY2e6eWcVjbkXS5YsAVw/iyEhIcyYMQOAH3/8sambLXCB6B/bB6J/bD2I/rH9IPrH9oHoH1sPon8UYpDtjl27dqHT6fDy8mLYsGEu64wZMwaALVu2nMmmnfXs3r2bq6++mokTJ3LJJZfw1FNPsX//fpd1LffGcq8ciY2NJTExUVFX0Dw09F6kp6eTmZkJwNixY10eK57FM4voH1svon9sm4j+sf0g+sfWi+gf2yaifxSGhnbHkSNHAIiPj0er1bqs07lzZwAOHz58xtolgP/++49FixaxevVqfv31V1544QX69u3Lfffdh8lkUtS13EfLvXKFuI9nhobeC8txnp6exMXF1Xjc8ePHMRgMTdJegXtE/9h6Ef1j20T0j+0H0T+2XkT/2DYR/aMwNLQ7CgoKAAgNDXVbx7KvsLDwjLTpbCcmJoZ58+axdetWcnNzqaqqYs+ePdx+++1IksQ777zDY489pjhG3MfWQ0PvheW4kJAQVCpVjceZzWZKSkqapL0C94jnqvUh+se2jegf2w/iuWp9iP6xbSP6R/Bo6QYImpaqqipAtoK5w8vLC4DKysoz0qazHVfxVX379uWjjz4iMTGRRx55hLfffps777yTTp06AeI+tiYaei/qc5zjsYLmQTxXrQ/RP7ZtRP/YfhDPVetD9I9tG9E/Co+Gdoe3tzcAer3ebR2dTgeAj4/PGWmTwD0PPPAAMTExGI1Gfv31V2u5uI+th4bei/oc53isoHkQz1XbQvSPrR/RP7YfxHPVthD9Y+tH9I/C0NDuCAkJAWxuN66wd8kRtCwajYbhw4cDkJKSYi0X97H10NB7Yfm/sLAQSZJqPE6tVhMYGNgk7RW4RzxXbQvRP7Z+RP/YfhDPVdtC9I+tH9E/CkNDu6Nbt24AnDx50q04iCWFiqWuoGWxuEYZjUZrmeXeHD161O1x4j6eGRp6Lyz/6/V60tPTazwuMTHRrfiWoOkQ/WPbQ/SPrRvRP7YfRP/Y9hD9Y+tG9I/C0NDuGDhwIJ6enuh0OrZt2+ayzvr16wEYOXLkmWyawA379u0DUCjLjhgxAoANGza4PCYzM5PU1FRFXUHz0NB7ER8fT0xMDGB75hwRz+KZRfSPbQ/RP7ZuRP/YfhD9Y9tD9I+tG9E/CkNDuyMgIIDJkycD8MknnzjtT0lJYdWqVQBcccUVZ7Rt/9/e/YfEXcdxHH+dnj/O37vZjJG10uVqi3JiPwhzsdZ+sgbbkKJ0kdWi0Rb9IGqhxajWH0U/GDNiBRv944IYG+HKrChiwyhiI8uaP4KxMUnNtpab9+4Puy+pp572PT3vng84cJ/7fL+f99ebr6+88T6HkQ4dOuR8FvKdd97pjN91111KSkpSa2urmpqaRhxXV1cnafAXg8LCwqkpNk79n9di/fr1kkL/LHZ3d6u+vl6StHHjRrfLRgjk48xCPkY/8jF2kI8zC/kY/chHSYaYc/ToUfN4PObxeKyurs4CgYCZmZ08edJKSkpMkq1bt26aq4wPx44ds4ceesi+//77IeMDAwP2wQcfWFZWlkmyNWvWjDh2y5YtJsmuvPJKa2lpccYPHDhgKSkpJsk++uijiF9DLHvrrbdMkpWXl485b7KvRWdnp/l8PpNkzz//vF28eNHMzHp6emzFihUmyYqLi52fUUQe+Rg9yMfoRj7GH/IxepCP0Y18DA+Nhhj1+uuvm8fjMUmWn59vxcXFzn/ooqIiO3PmzHSXGBe+++47k2SSzO/3W3FxsZWWltqsWbOc8bKyMuvu7h5x7Llz5+yWW24xSZaYmGjXX3+9FRQUOMc98cQTU39BM1xnZ6fNnj3beaSnp5sk83q9Q8Z37tw55Lj/81rU19eb1+s1STZnzhwrKSlx1s3Ly7PW1tZIXzaGIR+jA/kYXchHmJGP0YJ8jC7k4+TQaIhhn376qa1cudL8fr+lpKTY1Vdfbc8++6z19fVNd2lxo7u723bs2GGrV6+2q666yjIzMy0pKcny8vJs5cqVtnfvXqdLGcrff/9tr7zyil133XXm8/ksOzvbysvLbf/+/VN4FbGjra3NCfexHjU1NSOO/T+vxbfffmsbNmywOXPmWHJyss2bN8+2bNlip0+fjsBVIhzk4/QjH6ML+Ygg8nH6kY/RhXycHI/ZKJ+bAQAAAAAAMEFsBgkAAAAAAFxDowEAAAAAALiGRgMAAAAAAHANjQYAAAAAAOAaGg0AAAAAAMA1NBoAAAAAAIBraDQAAAAAAADX0GgAAAAAAACuodEAAAAAAABcQ6MBAAAAAAC4hkYD4CKPxyOPx6PPP/98ukuJmJaWFiUlJWnRokUKBAJDntu0aZM8Ho82bdo0qXOvWLFCHo9Hn332mQuVAog2ZCQZCSA08pF8jDU0GoB/BQN+Mo/3339/usufMk899ZQuXryompoaJSS4GyG1tbWSpCeffHLEDQjA9CIjw0NGAvGHfAwP+RhfvNNdABAt8vLyQo7/+eefOnv27JhzfD6fJKmoqEiSlJaWFoEKp19TU5MOHjyoRYsWacOGDa6f/+abb9by5cvV0NCgffv2qbKy0vU1AEwOGTk+MhKIT+Tj+MjH+EOjAfjXqVOnQo7X1tbqhRdeGHNOUEtLi+t1RZOdO3dKkjZv3iyPxxORNTZv3qyGhga9+uqr3CSAKEJGjo+MBOIT+Tg+8jH+8NYJAGFpa2vT4cOHlZSUpIqKioits2rVKvn9fh0/flxff/11xNYBADeRkQAQGvkYn2g0AC4abSOf9vZ257n29nZ1dHTowQcf1OWXX67U1FQVFBRo+/btzp/XSdKxY8d07733Kj8/X6mpqZo/f7527NihCxcujFlDe3u7tm3bpoULFyojI0NpaWlasGCBtm7dqs7Ozklf27vvvisz07Jly5SbmxvWMfv379eSJUvk9/uVlpamG264QW+88caY751LTk7W+vXrJUnvvPPOpOsFEH3IyKHISABB5ONQ5GMMMABjqqmpMUkWzo9LcF5TU9OQ8ba2Nue5Dz/80HJyckySZWVlWWJiovNcWVmZ9ff328GDBy0tLc0kWXZ2tnk8HmdORUXFqOvv27fPUlJSnLkpKSnm8/mcf2dmZlpDQ8Okvg+LFy82Sfbyyy+POqeqqsokWVVVlT366KMmyRISEpzrDT4qKyvHXGvv3r0myfLy8iZVK4CpQ0YOIiMBDEc+DiIf4xONBmAcbt8kcnJybOnSpXb8+HEzMzt37py9+eabzs1i+/btlp2dbRUVFdbe3m5mZn19ffbcc8855/jkk09GrH348GFLSEgwr9drTz/9tLW1tVkgELBAIGAtLS22ceNG58bU0dExoe9Bb2+vU19jY+Oo84I3iVmzZllycrK99tpr1tvba2ZmXV1dVl1d7VzDWOf5+eefnXk//vjjhGoFMLXISDISQGjkI/kYz2g0AONw+yaxcOFCO3/+/Ihj77vvPmfOsmXLLBAIjJhTVlZmkuyBBx4YMj4wMGDz5883SVZXVzdqfWvXrjVJtnXr1nGv5b8aGxud2rq6ukadF7xJSLL33nsv5JySkhKTZNXV1WOumZGRYZJsz549E6oVwNQiI8lIAKGRj+RjPGOPBmCKPf7440pJSRkxvnz5cufrZ555JuSOvME5P/zww5DxL7/8Uq2trcrNzVV1dfWoawd34G1oaJhQzSdPnpQkJSYmyu/3jzs/Pz9fVVVVIZ9bu3atpJHXMNzs2bOHrA0gPpCRZCSA0MhH8nEm4eMtgSl24403hhz/7+crl5aWjjmnu7t7yHhwZ93e3l7NnTt31LX7+/slSR0dHeEXLOnMmTOSpJycnLA+kqi0tHTUecH6fv/99zHP4ff71dHR4awNID6QkWQkgNDIR/JxJqHRAEyxzMzMkONerzfsOcN3DQ52bC9cuKDTp0+PW8Nff/0VVq1B58+fl6SQXfRQRqtfGv0ahvP5fEPWBhAfyEgyEkBo5CP5OJPw1gkgBgwMDEiSbrrpJtng3ivjPiYi+Cdow7vgkRTsVgfXBoDJIiMBIDTyEZFCowGIAZdeeqmkif85W7guueQSSYNd7KnqDgdvEsG1AWCyyEgACI18RKTQaABiwK233ipJOnXqlJqbm10//7XXXut8feLECdfPP1xfX5+6urokSddcc03E1wMQ28hIAAiNfESk0GgAYsDtt9+uwsJCSYM7Egc37BnNeJvoDFdUVORsInT06NHJFTkBzc3NCgQC8nq9zg0QACaLjASA0MhHRAqNBiAGeL1e7d69W16vV1999ZVuu+02NTY2Dtks58SJE9q9e7dKS0u1a9euCa9RXl4uSTpy5IhrdY8muMbixYuVkZER8fUAxDYyEgBCIx8RKTQagBixdOlS1dfXKzMzU0eOHNEdd9yh9PR05ebmKjU1VQUFBXrkkUfU3Nwc1scLDXf33XdLkg4dOjThjYAm6sCBA5Kke+65J6LrAIgfZCQAhEY+IhL4eEsghqxbt06//PKLdu3apY8//litra3q6elRenq6FixYoNLSUq1evVqrVq2a8LnXrFmjuXPn6rffftMXX3yhJUuWuH8BGuyaf/PNN/L5fKqsrIzIGgDiExkJAKGRj3CbxyLdVgIQM1588UXV1NTo/vvv1549e2bsGgAQCWQkAIRGPsYfGg0AwvbHH3+osLBQvb29+vXXX3XZZZe5ev6zZ89q3rx56uvr008//aQrrrjC1fMDQCSRkQAQGvkYf9ijAUDYsrKyVFNTo/7+fr300kuun//tt99WV1eXHnvsMW4QAGYcMhIAQiMf4w97NACYkIcfflg9PT1KSEhQIBBQQoJ7/cr09HTV1tZq27Ztrp0TAKYSGQkAoZGP8YW3TgAAAAAAANfw1gkAAAAAAOAaGg0AAAAAAMA1NBoAAAAAAIBraDQAAAAAAADX0GgAAAAAAACuodEAAAAAAABcQ6MBAAAAAAC4hkYDAAAAAABwDY0GAAAAAADgmn8A+gfeONTPe7AAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "if IN_COLAB:\n",
        "    fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(12, 4))\n",
        "else:\n",
        "    fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(12, 4), layout=\"constrained\")\n",
        "\n",
        "plt.subplot(131)\n",
        "plt.xlabel('Time (h)')\n",
        "plt.ylabel('Cell number')\n",
        "sns.lineplot(data=d, x=\"time\", y=\"cell.count\", hue=\"drug1\")\n",
        "plt.legend(loc='upper left', fontsize='8')\n",
        "plt.subplot(132)\n",
        "plt.xlabel('Time (h)')\n",
        "plt.ylabel('Log2(cell number)')\n",
        "sns.lineplot(data=d, x=\"time\", y=\"l2\", hue=\"drug1\")\n",
        "plt.legend(loc='upper left', fontsize='8')\n",
        "plt.subplot(133)\n",
        "plt.xlabel('Time (h)')\n",
        "plt.ylabel('Population doublings')\n",
        "sns.lineplot(data=d, x=\"time\", y=\"pd\", hue=\"drug1\")\n",
        "plt.legend(loc='upper left', fontsize='8')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aaf8399f-9670-46fc-af7e-fe2cc85ebb88",
      "metadata": {
        "id": "aaf8399f-9670-46fc-af7e-fe2cc85ebb88"
      },
      "source": [
        "The shaded areas represent confidence intervals. Compare the confidence interval between the log2 and normalized log2 plots, especially at time < 100 h."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "154dba49-a823-4eb6-a004-63a9698bb955",
      "metadata": {
        "id": "154dba49-a823-4eb6-a004-63a9698bb955"
      },
      "source": [
        "How many samples of each type are there? Calculate this by counting the number of unique `well`s there are in each group (`drug1`==trametinib or control)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "21c868af-fb1b-4f07-8ef1-6bd912932254",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "21c868af-fb1b-4f07-8ef1-6bd912932254",
        "outputId": "12314a61-716c-4a70-c427-e456601a101b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wells with trametinib treatment: n = 2\n",
            "Control wells: n = 10\n"
          ]
        }
      ],
      "source": [
        "n_tram = len(d[d[\"drug1\"]==\"trametinib\"][\"well\"].unique())\n",
        "n_ctrl = len(d[d[\"drug1\"]!=\"trametinib\"][\"well\"].unique())\n",
        "\n",
        "print(f\"Wells with trametinib treatment: n = {n_tram}\\nControl wells: n = {n_ctrl}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_tram"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FWMFst_fRAhB",
        "outputId": "fd802549-83b7-4907-a552-67cd46ca521d"
      },
      "id": "FWMFst_fRAhB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "893b067d-b9d0-47bb-b4e1-7051aa6a3421",
      "metadata": {
        "id": "893b067d-b9d0-47bb-b4e1-7051aa6a3421"
      },
      "source": [
        "#### Look at data by well\n",
        "To see each well of data individually we will set `hue` to color data by `well`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fe669618-97e3-4dd6-8b98-74d85ccc42b1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "fe669618-97e3-4dd6-8b98-74d85ccc42b1",
        "outputId": "01457187-09d7-43fc-ee2f-1b978ccb7645"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "if IN_COLAB:\n",
        "    fig, axs = plt.subplots(nrows=1, ncols=3, figsize=(12, 4))\n",
        "else:\n",
        "    fig, axs = plt.subplots(nrows=1, ncols=3, figsize=(12, 4), layout=\"constrained\")\n",
        "plt.subplot(131)\n",
        "plt.xlabel('Time (h)')\n",
        "plt.ylabel('Cell number')\n",
        "sns.lineplot(data=d, x=\"time\", y=\"cell.count\", hue=\"well\")\n",
        "plt.legend(loc='upper left', fontsize='8')\n",
        "plt.subplot(132)\n",
        "plt.xlabel('Time (h)')\n",
        "plt.ylabel('Log2(cell number)')\n",
        "sns.lineplot(data=d, x=\"time\", y=\"l2\", hue=\"well\")\n",
        "plt.legend(loc='upper left', fontsize='8')\n",
        "plt.subplot(133)\n",
        "plt.xlabel('Time (h)')\n",
        "plt.ylabel('Population doublings')\n",
        "sns.lineplot(data=d, x=\"time\", y=\"pd\", hue=\"well\")\n",
        "plt.legend(loc='upper left', fontsize='8')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b70a0be3-611d-425c-8f75-14e1b9c5588d",
      "metadata": {
        "id": "b70a0be3-611d-425c-8f75-14e1b9c5588d"
      },
      "source": [
        "How well do the individual lines reflect your expectations from the aggregated data with confidence intervals? Do any wells clearly stand out? Let's look only at wells A04, A05 and A07 in log2 scale and visualize each individual data point using `scatterplot`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b0458a96-3200-4988-a61c-0c44aec6b0d4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "b0458a96-3200-4988-a61c-0c44aec6b0d4",
        "outputId": "c058a8e3-9d48-44f1-cd55-2af429fc5c65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7d28de0a7a30>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# dtp = data to plot\n",
        "dtp = d[(d[\"well\"] == \"A04\") | (d[\"well\"] == \"A05\") | (d[\"well\"] == \"A07\")]\n",
        "sns.scatterplot(data=dtp, x=\"time\", y=\"l2\", hue=\"well\")\n",
        "plt.legend(loc='upper left', fontsize='8')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "759f0e12-f434-48d4-a846-598badd68e64",
      "metadata": {
        "id": "759f0e12-f434-48d4-a846-598badd68e64"
      },
      "source": [
        "## Assignment task #2\n",
        "Generate a scatterplot of population doublings over time for the same wells as the block above (wells A04, A05 and A07). Describe the difference you see between the new graph and the graph of data in log2 scale."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b22f7170-76bc-42c9-af8e-fd623469b30c",
      "metadata": {
        "id": "b22f7170-76bc-42c9-af8e-fd623469b30c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "50cafd6b-2d82-456e-b5bb-85d1afd85f4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7d28e61ffa90>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Add your code for scatterplot of population doublings here\n",
        "# dtp = data to plot\n",
        "dtp = d[(d[\"well\"] == \"A04\") | (d[\"well\"] == \"A05\") | (d[\"well\"] == \"A07\")]\n",
        "sns.scatterplot(data=dtp, x=\"time\", y=\"pd\", hue=\"well\")\n",
        "plt.legend(loc='upper left', fontsize='8')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cad14622-98de-4521-9cc7-8c6b01c7c583",
      "metadata": {
        "id": "cad14622-98de-4521-9cc7-8c6b01c7c583"
      },
      "source": [
        "*Describe your comparison of the data shown in log2 (`l2`) and normalized log2 (`pd`).*\n",
        "We see a differnece in log2 vs time because the initial cell density is different. However there is no difference in the doubling time between these cells so all these wells align with eac other."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b2ad573a-5ba5-4e3a-a6c3-eebc3730e83d",
      "metadata": {
        "id": "b2ad573a-5ba5-4e3a-a6c3-eebc3730e83d"
      },
      "source": [
        "Apart from visually inspecting the data, we should use model fitting to extract parameter values that can help us interpret the data quantitatively."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "52ab9eb0-8019-4f34-8a94-f095019df421",
      "metadata": {
        "id": "52ab9eb0-8019-4f34-8a94-f095019df421"
      },
      "source": [
        "## Model fitting\n",
        "### Use SciPy's `linregress` function or Seaborn's `lmplot` function\n",
        "Because an exponential growth rate is directly proportional to the log of the number of components (i.e., cells), we can fit each well of data independently with a linear model to help interpret the data. Linear models are easy to fit and fitting functions are commonly provided by many different Python packages. We will find optimum parameters using two different packages: [`scipy.stats.lingress`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html) and [`seaborn.lmplot`](). You can learn more about these algorithms by clicking on their names to get a link to online documentation.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8fea67ea-f287-431b-b895-174fc71940d2",
      "metadata": {
        "id": "8fea67ea-f287-431b-b895-174fc71940d2"
      },
      "outputs": [],
      "source": [
        "from scipy.stats import linregress"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f800f6e5-54be-4420-8395-1f2a235cb829",
      "metadata": {
        "id": "f800f6e5-54be-4420-8395-1f2a235cb829"
      },
      "source": [
        "Let's fit only the control data (`drug1.conc` == 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9178cdf4-ea40-4404-9968-d2b4e8d8916f",
      "metadata": {
        "id": "9178cdf4-ea40-4404-9968-d2b4e8d8916f"
      },
      "outputs": [],
      "source": [
        "# dtf = data to fit\n",
        "dtf = d[d[\"drug1.conc\"]==0]\n",
        "ctrl_wells = dtf[\"well\"].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "64346a18-877b-48df-ac54-5dc39d364c04",
      "metadata": {
        "id": "64346a18-877b-48df-ac54-5dc39d364c04"
      },
      "source": [
        "We will perform a list comprehension to fit data for each well independently."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0cbfeb0c-2bbd-445f-a35f-457fe69937f3",
      "metadata": {
        "id": "0cbfeb0c-2bbd-445f-a35f-457fe69937f3"
      },
      "outputs": [],
      "source": [
        "res = [linregress(dtf[dtf[\"well\"]==ctrl_wells[i]][[\"time\",\"l2\"]]) for i in np.arange(len(ctrl_wells))]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b35b0afe-3e3b-4b70-ae4f-dc651f50d9ca",
      "metadata": {
        "id": "b35b0afe-3e3b-4b70-ae4f-dc651f50d9ca"
      },
      "source": [
        "The `linregress` function returns a `namedtuple` of length 5. Let's look at an example output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e5f82ff0-829a-44cb-8548-afd68b4fab4d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5f82ff0-829a-44cb-8548-afd68b4fab4d",
        "outputId": "6b69b4b7-8acc-4bcc-f782-e122a1ddd8b0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Example linregress output for well A12\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinregressResult(slope=0.0018027181675447268, intercept=5.804797993779366, rvalue=0.2947633020923504, pvalue=0.1526106083686435, stderr=0.001218577477198253, intercept_stderr=0.08275694027457718)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "example_well = dtf[\"well\"].unique()[1]\n",
        "print(f\"Example linregress output for well {example_well}\")\n",
        "res[1]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4bf15674-73c4-407e-9774-4e1fd6d7ca08",
      "metadata": {
        "id": "4bf15674-73c4-407e-9774-4e1fd6d7ca08"
      },
      "source": [
        "Each value can be pulled out independently using the respective names. We will pull out all `slope` values, which represent the population doubling (proliferation) rate. To make it a bit easier to read we will also round the values to 4 decimal places."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ca448f0e-839c-4bf6-bd73-1102897d1196",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ca448f0e-839c-4bf6-bd73-1102897d1196",
        "outputId": "d9589491-3135-4493-cec4-c43d629c5c29"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A02    0.0602\n",
              "A03    0.0608\n",
              "A04    0.0596\n",
              "A05    0.0421\n",
              "A06    0.0632\n",
              "A07    0.0528\n",
              "A08    0.0612\n",
              "A09    0.0515\n",
              "A10    0.0592\n",
              "A11    0.0557\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "prates = pd.Series(data=[np.round(r.slope,4) for r in res], index=ctrl_wells)\n",
        "prates"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0866a88c-9647-4c9f-bc19-11cdf04e3af4",
      "metadata": {
        "id": "0866a88c-9647-4c9f-bc19-11cdf04e3af4"
      },
      "source": [
        "Most values are above 0.05 and most are close to 0.06, but one value looks low (A05: 0.0421). We can check for goodness of fit for linear regression by calculating $R^2$. (Only `rvalue` is provided, so we need to square it using `**2`.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f286e02f-4282-431c-bd00-5782b1ec398d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f286e02f-4282-431c-bd00-5782b1ec398d",
        "outputId": "0be7ccdf-cd35-4871-d7ed-4bbfb5aa5f81"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A02    0.9940\n",
              "A03    0.9982\n",
              "A04    0.9916\n",
              "A05    0.8989\n",
              "A06    0.9976\n",
              "A07    0.9948\n",
              "A08    0.9951\n",
              "A09    0.9906\n",
              "A10    0.9963\n",
              "A11    0.9961\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "r2_vals = pd.Series(data=[np.round(r.rvalue**2,4) for r in res], index=ctrl_wells)\n",
        "r2_vals"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "56c980c4-7a5e-4584-bb46-929e993244df",
      "metadata": {
        "id": "56c980c4-7a5e-4584-bb46-929e993244df"
      },
      "source": [
        "Only well A05 has an $R^2$ value < 0.99."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aebafc13-a5dd-4cf8-a932-e44d057a416d",
      "metadata": {
        "id": "aebafc13-a5dd-4cf8-a932-e44d057a416d"
      },
      "source": [
        "So, 9 of 10 control wells are explained well by linear models. This fits with the visual evidence that something anomalous happens to the cell counts in well A05 after ~75. This is consistent with a possible technical problem when medium is changed in the experiment @ ~ 72h.  "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "14240290-81c5-4de3-bef8-274eb4338c04",
      "metadata": {
        "id": "14240290-81c5-4de3-bef8-274eb4338c04"
      },
      "source": [
        "## Assignment task #3\n",
        "Perform linear regression using the `linregress` function on the trametinib-treated wells. Describe how the proliferation rates compare to the rates of the control wells in the subsequent markdown block."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3a6d7961-96e5-4f57-aa99-9a808a3dbe27",
      "metadata": {
        "id": "3a6d7961-96e5-4f57-aa99-9a808a3dbe27"
      },
      "outputs": [],
      "source": [
        "# perform linear regression on the trametinib-treated wells\n",
        "# dtf = data to fit\n",
        "dtf = d[d[\"drug1\"]=='trametinib']\n",
        "drug_wells = dtf[\"well\"].unique()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res = [linregress(dtf[dtf[\"well\"]==drug_wells[i]][[\"time\",\"l2\"]]) for i in np.arange(len(drug_wells))]"
      ],
      "metadata": {
        "id": "sw9ymmJGRl-4"
      },
      "id": "sw9ymmJGRl-4",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prates = pd.Series(data=[np.round(r.slope,4) for r in res], index=drug_wells)\n",
        "prates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZSHWNYPuRvNZ",
        "outputId": "86f254c0-a5e7-4273-cdb4-1c4be0590462"
      },
      "id": "ZSHWNYPuRvNZ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A01    0.0013\n",
              "A12    0.0018\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_vals = pd.Series(data=[np.round(r.rvalue**2,4) for r in res], index=drug_wells)\n",
        "r2_vals"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lkfuM9rMSKey",
        "outputId": "178d4494-74b1-46c1-8abf-fa073f7d9d92"
      },
      "id": "lkfuM9rMSKey",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A01    0.0234\n",
              "A12    0.0869\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "68348d8a-9f8c-44ba-bc41-0a9548286b76",
      "metadata": {
        "id": "68348d8a-9f8c-44ba-bc41-0a9548286b76"
      },
      "source": [
        "*Describe here your comparison of the rates between trametinib-treated wells and the control wells.*\n",
        "\n",
        "The growth rate for trametinib cells is nearly 0 and thus is there is no correlation between time and cell number thus the r2 values are also not significant. This indicates that trametinib is a potent inhibitor of cell growth."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a543b1dd-6136-4c6b-81a8-f4beec76df7c",
      "metadata": {
        "id": "a543b1dd-6136-4c6b-81a8-f4beec76df7c"
      },
      "source": [
        "We will visualize the linear model fitting using the seaborn `lmplot` function, which uses SciPy `linregress` function itself. This is a simple way to visualize the fits and their confidence intervals."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "65b0b21d-ffc3-42c7-b1d7-913d281e39ac",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "65b0b21d-ffc3-42c7-b1d7-913d281e39ac",
        "outputId": "52edbdac-3e90-43a0-ce05-a7bbf85a9b9b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 627.375x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "p = sns.lmplot(data=d, x=\"time\", y=\"pd\", hue=\"well\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dbbd32cc-aa59-4839-922f-8443c38ea42b",
      "metadata": {
        "id": "dbbd32cc-aa59-4839-922f-8443c38ea42b"
      },
      "source": [
        "We can also get fit parameter values from models fitting to all data from each condition (control or trametinib-treated)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cdeaa57f-6ae9-4f2b-a254-08bdfbc8da22",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "cdeaa57f-6ae9-4f2b-a254-08bdfbc8da22",
        "outputId": "584ab7dc-9667-48df-9157-7bbc5c5e0fa5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 702.125x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "p = sns.lmplot(data=d, x=\"time\", y=\"pd\", hue=\"drug1\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "86f8611a-f42f-4b12-b162-f7ebd8e26466",
      "metadata": {
        "id": "86f8611a-f42f-4b12-b162-f7ebd8e26466"
      },
      "source": [
        "## Non-loglinear data\n",
        "When cells are in conditions that limit their proliferation or increase cell death, such as when contact inhibited or treated with drugs, their growth may appear nonlinear. To analyze data like this we can interpret the data using nonlinear model fitting. For this exercise we will use simulated data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1dd0cd18-72e6-4d70-ab38-b0bc3109352a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1dd0cd18-72e6-4d70-ab38-b0bc3109352a",
        "outputId": "0005978b-2381-45e0-ba7f-91c3b0ea4b8f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   time  cell.count        pd\n",
              "0     0  108.452629  0.000000\n",
              "1     3  114.671082  0.080437\n",
              "2     6  137.605309  0.343471\n",
              "3     9  163.397796  0.591324\n",
              "4    12  178.516048  0.718989"
            ],
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-02c688be-f6de-4a14-a92e-a33675b58491 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sim_data",
              "summary": "{\n  \"name\": \"sim_data\",\n  \"rows\": 42,\n  \"fields\": [\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36,\n        \"min\": 0,\n        \"max\": 123,\n        \"num_unique_values\": 42,\n        \"samples\": [\n          75,\n          39,\n          24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cell.count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.7311321037736,\n        \"min\": 108.45262851900178,\n        \"max\": 1077.9266075507483,\n        \"num_unique_values\": 42,\n        \"samples\": [\n          843.1655558706785,\n          528.6337840676507,\n          335.49346590942423\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pd\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9812842706555597,\n        \"min\": 0.0,\n        \"max\": 3.313122028595346,\n        \"num_unique_values\": 42,\n        \"samples\": [\n          2.9587509132859466,\n          2.2852036086582492,\n          1.6292196492814919\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "np.random.seed(7)\n",
        "times_by3 = np.arange(0,126,3)\n",
        "mycounts = mylogistic(t=times_by3, P0=100, rate=0.06, K=1000)\n",
        "sim_data = pd.DataFrame.from_dict({\"time\":times_by3,\"cell.count\":flatten_matrix([np.random.normal(x,0.05*x,1) for x in mycounts])})\n",
        "sim_data[\"pd\"] = np.log2(sim_data[\"cell.count\"]/sim_data[\"cell.count\"][0])\n",
        "sim_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f9d9c4a9-903e-4ae1-9dac-7dae6b90f08d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "f9d9c4a9-903e-4ae1-9dac-7dae6b90f08d",
        "outputId": "bdecde11-36ef-4dd3-80ff-3cf80c72923e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='time', ylabel='pd'>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.scatterplot(data=sim_data, x=\"time\", y=\"pd\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5725808c-f3b2-47ed-b7a8-514a63b8deb9",
      "metadata": {
        "id": "5725808c-f3b2-47ed-b7a8-514a63b8deb9"
      },
      "source": [
        "## Nonlinear model fitting with SciPy's `curve_fit` function\n",
        "Nonlinear model fitting is more complicated and there aremany ways that optimal parameter values can be found. There is an entire field of research around parameter optimization! We will use a specific method employed by SciPy (the [`scipy.optimize.curve_fit`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html) function) that has the objective to minimize the sum of the squared residuals of model to data.  It returns optimized coefficient values (`popt`) and the estimated covariance of `popt`. The diagonals provide the variance of the parameter estimates. To compute one standard deviation of parameter errors, we will use `perr = np.sqrt(np.diag(pcov))`.  \n",
        "\n",
        "This approach to identify optimal parameter values is referred to as least squares regression and attempts to minimize the function\n",
        "$\\sum_i(f(x_i,\\beta)-y_i)^2$\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "112958e8-43f8-489b-b32b-234f1b34852c",
      "metadata": {
        "id": "112958e8-43f8-489b-b32b-234f1b34852c"
      },
      "outputs": [],
      "source": [
        "from scipy.optimize import curve_fit"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "46860fe6-0035-4c59-989e-a5c123672c2c",
      "metadata": {
        "id": "46860fe6-0035-4c59-989e-a5c123672c2c"
      },
      "source": [
        "The function `{0:.4g}\".format(x)` is for limiting the number of digits of the calculated values (making viewing the values easier)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "889c0d0b-4721-438a-9017-344f56ab858c",
      "metadata": {
        "id": "889c0d0b-4721-438a-9017-344f56ab858c"
      },
      "outputs": [],
      "source": [
        "logistic_popt, logistic_pcov = curve_fit(mylogistic, sim_data[\"time\"], sim_data[\"cell.count\"])\n",
        "logistic_popt_str = [\"{0:.4g}\".format(x) for x in logistic_popt]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c95a0c96-8b81-4455-ac57-3b0a78a5fd7f",
      "metadata": {
        "id": "c95a0c96-8b81-4455-ac57-3b0a78a5fd7f"
      },
      "source": [
        "Printing the optimal parameter values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "411cf5b6-294e-44fb-9627-176ba593a8be",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "411cf5b6-294e-44fb-9627-176ba593a8be",
        "outputId": "90e15fc2-724d-4d9f-e8b9-ad8aa9ddf6d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal parameter values for P0: 100.3, rate: 0.05983, and K: 994\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(f\"Optimal parameter values for P0: {logistic_popt_str[0]}, rate: {logistic_popt_str[1]}, and K: {logistic_popt_str[2]}\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "09903214-aaff-458e-9e84-2429f5136a45",
      "metadata": {
        "id": "09903214-aaff-458e-9e84-2429f5136a45"
      },
      "source": [
        "### Plot the data and overlay the model fit\n",
        "To visualize on the plot we must convert to normalized log2 scale. We will also include a linear model fit for comparison (standard output of seaborn's `regplot`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2c0cd14d-55a1-4c7a-ad11-f868d49addfa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "2c0cd14d-55a1-4c7a-ad11-f868d49addfa",
        "outputId": "cd08ce22-e35b-44be-bf98-792872992f4f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='time', ylabel='pd'>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x_pred = np.linspace(min(times_by3),max(times_by3),100)\n",
        "y_pred = mylogistic(x_pred, *logistic_popt)\n",
        "y_pred = np.log2(y_pred/y_pred[0])\n",
        "sns.regplot(x=\"time\", y=\"pd\", data=sim_data)\n",
        "sns.lineplot(x=x_pred,\n",
        "             y=y_pred,\n",
        "             color=\"red\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f2014d0a-cbb7-4e39-9a71-cd77c70535e6",
      "metadata": {
        "id": "f2014d0a-cbb7-4e39-9a71-cd77c70535e6"
      },
      "source": [
        "Do the same for a Gompertz model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f57f9d8e-5782-4ec0-aa60-7115c5c86c65",
      "metadata": {
        "id": "f57f9d8e-5782-4ec0-aa60-7115c5c86c65"
      },
      "outputs": [],
      "source": [
        "gompertz_popt, gompertz_pcov = curve_fit(gompertz, sim_data[\"time\"], sim_data[\"cell.count\"])\n",
        "gompertz_popt_str = [\"{0:.4g}\".format(x) for x in gompertz_popt]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "644dd92a-ca26-4129-aa05-4f8c2014d5df",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "644dd92a-ca26-4129-aa05-4f8c2014d5df",
        "outputId": "87852395-d8bb-4f55-e170-b239945d28e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal parameter values for P0: 61.38, rate: 0.03833, and K: 1037\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(f\"Optimal parameter values for P0: {gompertz_popt_str[0]}, rate: {gompertz_popt_str[1]}, and K: {gompertz_popt_str[2]}\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "59b4da50-59c3-4eb8-8445-d2c09b9a4081",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "59b4da50-59c3-4eb8-8445-d2c09b9a4081",
        "outputId": "d582f8ed-c759-498a-b0bb-ea51a25a3d88"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='time', ylabel='pd'>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x_pred = np.linspace(min(times_by3),max(times_by3),100)\n",
        "y_pred = gompertz(x_pred, *gompertz_popt)\n",
        "y_pred = np.log2(y_pred/y_pred[0])\n",
        "sns.regplot(x=\"time\", y=\"pd\", data=sim_data)\n",
        "sns.lineplot(x=x_pred,\n",
        "             y=y_pred,\n",
        "             color=\"red\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5de323d7-c662-4a00-b5ae-7fe19b3074e2",
      "metadata": {
        "id": "5de323d7-c662-4a00-b5ae-7fe19b3074e2"
      },
      "source": [
        "### Limitations of these nonlinear growth models\n",
        "#### Both logistic and Gompertz models:  \n",
        "* Are phenomenological (they describe the result, not the cause)\n",
        "* Use a carrying capacity parameter ($K$); this may be relevant to space available in a culture well and/or average cell size, but how would you interpret different values in response to drug?  \n",
        "\n",
        "#### Gompertzian model:\n",
        "* Has initial assumptions that do not correspond to a stable, exponentially dividing population (infinite rate at time=0, rate is continually changing)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "411ea7d9-fcf1-44aa-92bd-4ce8fdcb1ddb",
      "metadata": {
        "id": "411ea7d9-fcf1-44aa-92bd-4ce8fdcb1ddb"
      },
      "source": [
        "## Assignment task (extra credit)\n",
        "### How many days would it take for a single tumor cell to grow to a tumor the size of an egg?\n",
        "\n",
        "\n",
        "**Assumptions:**\n",
        "* There are ~ $10^9$ tumor cells  in 1 cm$^3$ (~1 g)\n",
        "* Tumor Cell ≈ 1ng\n",
        "* Egg ≈ 35g\n",
        "* Average time per division (doubling time) ≈ 18h\n",
        "* Doubling rate = 1/doubling time\n",
        "* Population doubling equation: $P(t) = P_02^{rate*t}$\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4cb40054",
      "metadata": {
        "id": "4cb40054"
      },
      "source": [
        "<table>\n",
        "    <tr>\n",
        "        <td>\n",
        "        <img src=\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/cell_division.png?raw=1\" style=\"width: 150px;\" align=\"middle\" />\n",
        "        <td>\n",
        "            $\\Longrightarrow$ $\\Longrightarrow$\n",
        "        <td>\n",
        "            <img src=\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/tumor.png?raw=1\" style=\"width: 150px;\"  align=\"middle\" />\n",
        "    </tr>\n",
        "</table>\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c2f24f5b-7c9d-4a8a-b616-2721a4bc434c",
      "metadata": {
        "id": "c2f24f5b-7c9d-4a8a-b616-2721a4bc434c"
      },
      "source": [
        "_Try to write a function that calculates this. Start of function definition is provided in code block below._  \n",
        "NOTE: to calculate log2, use the numpy function `np.log2()`  \n",
        "NOTE: check the units!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "00a748b9-312d-455b-8e74-516d22d00687",
      "metadata": {
        "id": "00a748b9-312d-455b-8e74-516d22d00687"
      },
      "outputs": [],
      "source": [
        "def timeToEgg(P0, egg, DT):\n",
        "    '''\n",
        "    P0=initial cell number\n",
        "    egg=number of tumor cells in an egg-size tumor\n",
        "    DT=time to double the population size (i.e., the average cell cycle time)\n",
        "    '''\n",
        "    # add your code for the function here\n",
        "    rate = 1/DT\n",
        "    time = (np.log2(egg/P0))/rate\n",
        "    days = time/24\n",
        "    return(\"days:\", days)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9e33a969-ddac-42c0-b07a-80a45c04dcca",
      "metadata": {
        "id": "9e33a969-ddac-42c0-b07a-80a45c04dcca"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "id": "fc218c88-2a9f-40d0-9995-1c50e4543890",
      "metadata": {
        "id": "fc218c88-2a9f-40d0-9995-1c50e4543890"
      },
      "source": [
        "### Calculate time to egg-sized tumor from 100 cells\n",
        "Do the calculation using the function you made.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eac88800-2715-41ee-a061-953435113623",
      "metadata": {
        "id": "eac88800-2715-41ee-a061-953435113623",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e1aa395-d181-4e84-94d8-9aeeb69166b0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('days:', 21.287084760867376)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "# Execute your function with the correct input argument values.\n",
        "timeToEgg(P0=100,egg=35000000000,DT= 18)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6aef3847-ab37-42cd-9f62-fd6a512d36a7",
      "metadata": {
        "id": "6aef3847-ab37-42cd-9f62-fd6a512d36a7"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    }
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