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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/VU-CSP/github-classroom-intro-handalls/blob/main/PopGrowthLecture_assignment_with_answers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "03994108-9a10-4b4c-b4bf-0a48f6d1c023",
+      "metadata": {
+        "id": "03994108-9a10-4b4c-b4bf-0a48f6d1c023"
+      },
+      "source": [
+        "# 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>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7ac8861e-0e5d-4d2f-b442-238f48bafd7d",
+      "metadata": {
+        "id": "7ac8861e-0e5d-4d2f-b442-238f48bafd7d"
+      },
+      "source": [
+        "### 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."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "25b229c6-432b-4d6c-9979-0f67c3246d34",
+      "metadata": {
+        "id": "25b229c6-432b-4d6c-9979-0f67c3246d34"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f5d5336f-8a1c-4beb-adc0-6ba4e0b63804",
+      "metadata": {
+        "id": "f5d5336f-8a1c-4beb-adc0-6ba4e0b63804"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "095e56de",
+      "metadata": {
+        "id": "095e56de"
+      },
+      "source": [
+        "<img src=\"https://github.com/VU-CSP/quantbio-assignments/blob/main/img/contact_inhibition.png?raw=1\" style=\"width: 300px;\"/>  "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bcafe3ca",
+      "metadata": {
+        "id": "bcafe3ca"
+      },
+      "source": [
+        "There are some specific models that have been used to model growth inhibition, including the\n",
+        "**Logistic** and **Gompertz** functions.  \n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fa4c4a18-c3cc-47ea-9e68-5f468497ff94",
+      "metadata": {
+        "id": "fa4c4a18-c3cc-47ea-9e68-5f468497ff94"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ec305d36-2c3b-4d52-b806-054b615a84f7",
+      "metadata": {
+        "id": "ec305d36-2c3b-4d52-b806-054b615a84f7"
+      },
+      "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",
+        "-----"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "40e0a719-37e3-4a84-8668-769a294dd293",
+      "metadata": {
+        "id": "40e0a719-37e3-4a84-8668-769a294dd293"
+      },
+      "source": [
+        "### First, import necessary Python packages"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "549f8f33-de74-4ddd-9645-22a758ea2814",
+      "metadata": {
+        "id": "549f8f33-de74-4ddd-9645-22a758ea2814"
+      },
+      "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",
+      "id": "ad299e4b-bd09-40c9-93ce-a8959d76a375",
+      "metadata": {
+        "id": "ad299e4b-bd09-40c9-93ce-a8959d76a375"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c72e3dc2-f48c-447a-95de-f428c2f6b3da",
+      "metadata": {
+        "id": "c72e3dc2-f48c-447a-95de-f428c2f6b3da",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f02836d1-bb57-4adb-878d-68d2a7fa9d7e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2024-03-11 23:20:26--  https://raw.githubusercontent.com/VU-CSP/quantbio-assignments/main/growth_fxns.py\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: 2307 (2.3K) [text/plain]\n",
+            "Saving to: ‘growth_fxns.py’\n",
+            "\n",
+            "growth_fxns.py      100%[===================>]   2.25K  --.-KB/s    in 0s      \n",
+            "\n",
+            "2024-03-11 23:20:27 (27.1 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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "5ffdbde1-987c-4817-9487-288cce2d3509",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 403
+        },
+        "id": "5ffdbde1-987c-4817-9487-288cce2d3509",
+        "outputId": "8cf24f3d-e868-4fe9-beb5-67cc148fc7fd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2024-03-11 23:20:37--  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",
+            "MCF10A_trametinib.c 100%[===================>]  13.17K  --.-KB/s    in 0.002s  \n",
+            "\n",
+            "2024-03-11 23:20:38 (6.73 MB/s) - ‘MCF10A_trametinib.csv’ saved [13490/13490]\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "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"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5d5412dd-24f8-4994-853c-74f0a1d77b98\" 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>cell.count</th>\n",
+              "      <th>uid</th>\n",
+              "      <th>drug1</th>\n",
+              "      <th>drug1.conc</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>A01</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>32</td>\n",
+              "      <td>10ACh-28A_H03</td>\n",
+              "      <td>trametinib</td>\n",
+              "      <td>0.000004</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>A01</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>25</td>\n",
+              "      <td>10ACh-28A_H03</td>\n",
+              "      <td>trametinib</td>\n",
+              "      <td>0.000004</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>A01</td>\n",
+              "      <td>10.5</td>\n",
+              "      <td>31</td>\n",
+              "      <td>10ACh-28A_H03</td>\n",
+              "      <td>trametinib</td>\n",
+              "      <td>0.000004</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>A01</td>\n",
+              "      <td>15.3</td>\n",
+              "      <td>53</td>\n",
+              "      <td>10ACh-28A_H03</td>\n",
+              "      <td>trametinib</td>\n",
+              "      <td>0.000004</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>A01</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>39</td>\n",
+              "      <td>10ACh-28A_H03</td>\n",
+              "      <td>trametinib</td>\n",
+              "      <td>0.000004</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
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+              "    .colab-df-container {\n",
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+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\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",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-5d5412dd-24f8-4994-853c-74f0a1d77b98');\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-d89c5645-3fd7-4e66-b86b-4e90cdb53311\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d89c5645-3fd7-4e66-b86b-4e90cdb53311')\"\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-d89c5645-3fd7-4e66-b86b-4e90cdb53311 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\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": 206
+        },
+        "id": "e1348e04-bfe8-44f4-9069-bc4a277a6e27",
+        "outputId": "0fde7bc4-b33d-46ae-eb06-55ce89960afc"
+      },
+      "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"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a64c738b-faef-4259-9b69-f3972576aa64\" 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",
+              "    </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",
+              "    </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",
+              "    </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",
+              "    </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",
+              "    </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",
+              "    </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-a64c738b-faef-4259-9b69-f3972576aa64')\"\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-a64c738b-faef-4259-9b69-f3972576aa64 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-a64c738b-faef-4259-9b69-f3972576aa64');\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-1282fc50-1f6f-4d12-b3b2-901c98077b0e\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1282fc50-1f6f-4d12-b3b2-901c98077b0e')\"\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-1282fc50-1f6f-4d12-b3b2-901c98077b0e button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\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}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ],
+      "source": [
+        "d.head()"
+      ]
+    },
+    {
+      "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/"
+        },
+        "outputId": "8610a199-4e50-48d9-e3ca-1bc4c2786e88"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0      0.000\n",
+            "1     -0.356\n",
+            "2     -0.046\n",
+            "3      0.728\n",
+            "4      0.285\n",
+            "       ...  \n",
+            "295    0.778\n",
+            "296    0.778\n",
+            "297    0.802\n",
+            "298    0.871\n",
+            "299    0.729\n",
+            "Name: pd, Length: 300, dtype: float64\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Add your code here\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()])\n",
+        "\n",
+        "print(d[\"pd\"])\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ae735ec1-1f4c-45ff-9290-56c9def398ed",
+      "metadata": {
+        "id": "ae735ec1-1f4c-45ff-9290-56c9def398ed"
+      },
+      "source": [
+        "I copied the code that populates the values to d[\"pd\"] above. Pd is the subtraction of the log2 of the current well minus the log2 of the previous well, the resulting value being the extent to which the population has doubled.\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": {
+        "id": "7adfe491-1870-4265-97e7-24551c6c1569",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 466
+        },
+        "outputId": "b62670f8-a1b6-411d-e616-ca2d94809e6c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-10-315d59414379>: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": {
+        "id": "72a45a3e-b307-43cd-af53-1ac27c02fec0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 410
+        },
+        "outputId": "af931bd5-952d-45b8-c3e5-708c98890f93"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 3 Axes>"
+            ],
+            "image/png": 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WrFjBuHHj2Lp1KzfccAOJiYkA5OXl0adPH5599ll++OEHHnzwQa6++mrefvttli1bxpYtW4iIiCi5xq5du9i6dSuhoaF8//33vPfee2zbto2IiAimT5/OY489xltvvVVPfwNC1JDFCNknwDfAvV8utcBqtZKVlVVmu8Vicdqu1+tLvjYaHTf5O3bscHnu9u3be2iUDZ/Mj6LZKs6B9KNQlF7zp3OuaDZ1c560BY5vgt7Xw9UvVusUhw8fBmDgwIFO2//73/+i0+mYN28ezz33HAADBgzgvvvu47///a9HAg3fffcd77zzDr///jtZWVlERkbSuXNnRo4cyTPPPNPkn8DL/CgEqsvYuYOqA09oK5X5VVU2C5zeqTIXTm1VWRGuBEapedmDmvYMJRqEbdu20b17d4YNGwaAl5cXaWlpeHl5MW7cOACuuOIK4uLi2Lt3L23atCEgIIDrr78egMsuu4zjx49XeI2bbrqJ0FD1JGT9+vX87W9/K/lFcu+993LTTTfV0ncnRB3IP6sm//D4+h5JiQ4dOpSkC1fV5s2ba2cwjZjMj6LZ0TQ1p2UcA0ux56ulW82QuhdO/gJJv0JxtuO9hHWgvVCtpRkZGRkEBQURGRnptH3Tpk0A3HPPPSXbbr/9du677z727dtXo2/BYrHw97//nY8//hiAtm3b0rdvX7Kysti5cye//fYbjz32GCEhDaOORW2R+VE0e4Y8FWQwZKtMhqrOlRlH4MhaOPGzylRwxS8YOg6Hln2gz03Qootnxn2eBBqasrheKiWtLq7jAaXXM/r7+5e89vb2xmq1VnhsRb9sG/U6SSFMRarVW0BYg2xd1GjJ/FjmvELUOqtFZWdlH1f93sNaeea8FgOk/KGCC8m/qXmzPDlJ6gY8tmeVT11UVERgYKDTtqSkJDIyMmjXrh0dO3Ys2R4cHExERATZ2dkXnqZa7r33Xj7++GMGDx7MsmXLnJap6fV61q9fj79/Ldbqqav50X6tGpL5UTRJxTlw7oAKFFS1do0+G3Ysg2PrXO/j7Q/tL4cuo6HtEDUX552plYxZCTQ0ZQHhla4NrguXX345CQkJbNmypST1LS4uDpvNxk8//cTYsWP57bffOHfuHP369SMzM7PC84WGhpKXl+eU+lbamDFjePjhh5kzZw5hYWEsW7aMq666qha+MyHqQG4ymAshvE19j6RpkflR5kdRt0xFquBjboqqlO4XVLPzGQvg1Da1LCJlh2r7W5EWXdTSiWpWaY+KiiIjI4Pc3NySn6uNGzcC6uf3QhaLpUaZBps2beKdd96hQ4cObNiwoeRpu11QUBDXXXed2+evEpkfZX4U9asoS3WWMOnVconKggw2Cxz6Gna+W36g1csH2gxRwYX2l4FvDeffKpJAg6h1kZGRfPHFFzz88MMUFBTg5eXFwoULWbNmDQ888AAPP/wwAQEBrF69mpCQkEp/UTz88MOMHTuWoKAgfvzxxzLvT5gwgQMHDnDZZZc5FfMRotEx5EHeabVuTjRJMj+KZqEoCzIOgz4HQluCt6/75zIVwb5VsP+zioMLOi9o3Q86XAkdhqpaN+0ug+DqtQceMGAA69at49133+Xhhx/GZrPx7rvvotPpGDlypNO+GRkZFBYW0rNn1TMmLvTyyy8D6mf5wiBDcyPzo2iWCjNUJoPNCGEtK9//3J/w61LIKmeZUOsB0GWUmgcDwjw+1MrotOoushUNQqdOnQA4ceJEyTaDwcDJkyfp2LFjua17RN2R/xfCI3KSIPVPiKhCNoO5WP1pP1QVjaxEeXNIU+Hqe5Ofy4ZB/j80I5qmWrFlHFG1E0Li3G9dabPA4W9h1wdgyC1/H29f9dSu4zAVVCjdNjHvdLUCDfZ5ZPHixdx88814e3szZswYMjIy2L17N6GhoZw6dcrp6fiaNWu48cYbuf7661m9enW1v0WDwUBYWBhms5nDhw9js9lYvnw5hw4dwt/fn/79+3P33XfXqGCuzI8Nm/x/aOYKzqlMBs0GwTEV71vRMokWnWHobGh5cdWum3cGWl4EkR2qtHtV7yElo0EIIRoiTVO/cKoQNBBCiAbHXo8hKxF8AyG0eksWSmiaqpa+fTnkpZR93zcY2l2qggtth3g8Jfhvf/sb69at44MPPihpdRkQEMDbb79dJgX///7v/8rNdKiqffv2YTarlnNbtmzh/vvvx2Qylbz/7bff8uKLL/L+++9z8803u/cNCSEaprwzkH5IBWMrCjJUtEzCNxgG3w29rmsQXcrqfwRCCCHKMuarpRMB0r9bCNHImItVV4m8ZLX0y916DOmH4Pe3VGrwhULi1A11pxGqmFkteu+997j77rv57bffiIiIYPTo0SVP9OxMJhPh4eHccccdXH311W5dJzU1teTrf/zjHwwYMIDXX3+dvn37kpyczJNPPsn//vc/pk6dSo8ePZyKRJZ24dhKS0lJoW3btm6NTwhRCzRNBVHTDoGPHwRGut63omUS3cbBkBkQ1HCW20qgoQmy2Wz1PYRmT1YkiRorzgWrqXZ6yzdz8vNZv+Tvv4kz5EH6YShMh9A494IA+WdgxztwYlPZ9/xCoP9t0PsvdTo/Dh06lKFDh7p838/Pj+XLl9foGoWFhSVfBwUF8f3335e01ezSpQurVq3i2LFj7N27l+eee86t5RmVkZ/P+iV//82MzQa5p9Sc6Rfk+uGSuRi2vaFaVl6oussk6pAEGpoQPz8/vLy8OHv2LDExMfj5+UlrnnqgaRoZGRnodDp8fWtQ8Eo0X5oGhWkSZPAwX19fdDodGRkZxMTEyPxYD2R+bOIK0tQNs7kIwuOr35LXkAe7P4JDX6n04NK8fFRwof9tTTbTq/Sa/DvvvLMkyGDn5eXFQw89xNSpU/nxxx+x2Wx4eZX9O65o3bSrbAeZH+ufzI/NjNUCWQlqiZl/qPpTnuyTsP4ZFZAozS8YBjWcZRLlaZijEm7x8vKiY8eOpKamcvbs2foeTrOm0+lo06YN3t7e9T0U0RgZC9QNt6tfOsIt3t7etGnThtOnT5OUlFTfw2m2ZH5sguxP5TKPqeBCWOvqnyNxg0oJNhaUfa/TSBgyzb3zNiKlAwuuOlfYtxcUFJCVlUVMTCUF46pI5seGQebHZsJsgIyjat4MjlZ1bMpz9HvYurRsh50GuEyiPBJoaGL8/Pxo164dFosFq9Va38Nptnx9feWXhHCfIU+lyVWzDZuoXEhICF27di0puCbqnsyPTYzFBJkJkHNStU+rboDUkA9bXy1/mUTLPnDpvRDrfrvImho1alS19g8ICCAiIoLevXszfvx4Bg4cWOVje/ToUfK1n1/5S05KZz14eqmszI/1T+bHZsBYoOrPFKS5bvdrMagAw7EfnLcHR8PIp1Tr3kZAAg1NkD3lStKuhGiE7N0mZNlErfH29pYbOSE8wVQE6UdUTYWQGPCpZpeclB3w84ugz3TeHt4WLpmh2vXWcwr/5s2bS762Lye4cB19edt1Oh0LFixg9OjRfPjhh7Rq1arSa8XHx9O+fXtOnTrlcvnD8eOqCFxAQAAtWrjZyaMCMj8KUYv02ZB2EIx5KkPLq5yftZxTsP5p1eK8tLZDYOQTEBBRFyP1CAk0CCFEQ2IqVD3i3Vg2cfRECj//9C7bduzk7NmzZGRkYDAYaNGiBTExMfTs2ZPhw4dXWNBMCCGqRJ+tnsoV50JYq+qtEbYY4Pe34dCXztt1XjDgDlWHoYGsOX766acxm8289dZb5OTk0K5dO4YPH058fDwAZ86c4ZdffuHUqVNERUUxc+ZMCgsL2blzJ7/99hsbNmxg3Lhx7NixwykbwZW//e1vvPjii3zyySc8/fTT+Pg4/z289957AAwfPrzMe0KIBiw/VdWwsRohtHX5QdRjP8LWV9QcaafzUh12+t5c/bo39UxmKCGEaEgMeeoXTBWXTaRlZPHep1+x4pM1nDpzrmR7eZWrv/rqK5YsWYKPjw8+Pj6EhYV5bNhCiGZEnw2p+8GiV0/lqpN1kH4YNj2v2rmVFt4WRj4JsT3KP66ePPnkk4wZMwaDwcAHH3zAHXfcUe5+K1euZObMmezYsYMffvgBLy8vfv75ZyZPnszBgwdZsWIFs2bNqvR6jzzyCMuWLePkyZPcf//9LF26lICAADRN4/XXX+ebb75Bp9Px2GOPefpbFULUBnsNm4yj4O2jlktcyGKEX1+Do985bw+KhtELoFWfuhmrh0mgQQghGpKCtCq1g0tNy2D+S2+x8vO1WCzWksBC69at6N9/ANHR0URFRREYGEh2djbZ2dmcPHmSffv2YTKZMJlM6PV6unfvzqJFi7jppptq+zsTQjQFxTmql7ulGEIrXw5QwmaB3Sthz0rQLqgt0Pt6uGR69Zde1IFXX32VLVu2sHz5cpdBBoDbb78dk8nE9OnTee2113jooYcYPnw4S5Ys4d577+Xzzz+vUqAhJiaG1atXc91117Fs2TI+/fRTunXrxunTp0lNTUWn0/Hiiy8yYsQID36XQohaYbVAViJkH3fdWSI3WXWVyL5guVSbwWqpRGBk2WMaCZ0mDVsbJXt7oopaGAkhGhlTESRvU9WHfYNc7vb0v97ileWfUKQvxsfHm3HDL+OWa0dz5YAexF8yCXxd36ybTCb27NnDuHHjKCoqwmq1otPpuOSSS1i2bBkXX9zw+jBXl8yPQtSS4lwVZDAWQmhc1TMZck+pLIaMo87bg6Nh+GPQZpDHh1pG3mlod1mVs8Xs80hYWBiHDx+moKDAZYFGO6PRSFhYGL169WLPnj3qsnl5REVFERUVRUZGRpWHm5CQwPPPP8/69etJS0sjPDycyy+/nDlz5jB8+PAqn8fV9yXzoxC1rCqdJRI3wC8vlV0qMfDv0P/WulsqkXcGWl4EkR2qtHtV5xHJaBBCiIaiOBdMxRBYcbuihUvfIToqgqcevId7bp5Mi6gI1aXCXFzpJfz8/LjkkktKbnw/+OADFi9ezLp16/jiiy+aRKBBCFELDPmQdkBVTA9tWbUgg6bBwS9g+9tgNTm/12UMDH2wwbfxPX78OCEhIZUGGQD8/f0JCQkhMTGxZFt4eDgRERHk5+dX67pdu3bl/fffr/Z4hRANgLHwfGeJcyooe2GmqmaDXR/A7o+ctwe1gFHzG01XicpIoEEIIRqKonTw8a30Bn7x47O4/+9/IzjIRd/larjyyiu58sor2bFjR7WetgkhmhFjAZw7cL7wYxVrMpj08PMLcPJn5+3+oXDFQ9C5em0j64uPjw+5ubmkpqZW2jkiNTWV3NzcMvVv9Ho94eHhtTlMIURDYTaoIENhWvmdJSxG2LykbEvf+IGqTk1QxQ+bGpPGVbpSCCGaKpNeFVjzq/zp3rx/3OmRIENpQ4YM4ZprrvHoOYUQTYCxUAUZDNmqu0RVggw5SfDFjLJBhjaD4cb3Gk2QAWDAgAEAzJs3r9J9H3vsMTRNKzkGIC0tDaPRSFxcXK2NUQjRQFjNkHFE1dsKbVU2yKDPgm9mlw0y9L8dJrzYpIIMIIEGIYRoGAy5YNaXv4avCpLPnCP5bBoGg6HynYUQoipMRWq5hD5L3TRXZb1w4gb4YqZzVwkvX7VMYsKLEBxTe+OtBQ888ACapvHJJ58wYcIEtmzZgsViKXnfYrHwyy+/cPXVV/Pxxx+j0+l44IEHSt7/4YcfALjkkkvqfOxCiDpks6nCj7nJarnEhUGGrOPw5X2QcdixzcsXRj2l2ldeuH+tjtWiMitMRer+02ap9BB3NOpAw3fffYdOp0On09GhQweX+xUWFvLUU0/Ro0cPAgMDiYmJYeLEiWzevLnSa2zatImJEycSExNDYGAgPXr0YP78+RQVFVV4XE2uKYRohgozVN/46rSJK6XDFTfSadTtZGdne3hgQohmyVwMaQfV3BRWhSCD1Qy/vQ4bFzoXNguJg+teh95/cXt+q0+TJk1izpw5aJrGjz/+yIgRIwgJCSE+Pp42bdoQEhLCyJEjWbduHZqmMXv2bCZNmlRy/B9//EHfvn2ZPHly/X0TQojapWkqkyvrOITEgLev8/vJ2+Dr+9VyCruACJj4qqpXUxvjMRWqIHFhGuSfUQUf7X8KM9WSOKsZdD4QFg9+IR4fRqPtOlFYWEjv3r1JTk4GoH379iQlJZXZLzMzkyuuuIKjR4/i7+9Pr169yMjI4PTp0+h0Ot544w3uu+++cq/x+uuv8+CDD6JpGm3atCEmJoZDhw5hNBrp2bMnW7duJSqqbIpLTa5ZVVI1WIgmxFwMp7ap+gxuTvRh3a/A18ebrPT0CrtO2DXlOaQpf29C1AmzQWUyFKSWn/57oaIMWP+sOqa0NoPV07qABlCfwM2uE/Z55NNPP2XBggVOhR5L69KlC88++yw333yzZ8ZbS2R+FKIW5J2B1P0QEOJ8H6dpcOBz+P0/zm19IzvAuMUqiOtJNisY8lSmgl8I+IeAT6DKlvX2Pf/HTz3Y8vZVGRXeldcGu1CT7zrxxBNPkJyczKRJk/jqq69c7nf33Xdz9OhRBg4cyNdff03r1q3RNI0VK1YwY8YMHnjgAS6//HL69evndNyuXbuYPXs2AMuWLWPatGnodDrOnj3Lddddx65du5g2bRqff/65x64phGiminPVL4XAeLdP0aFNKxJOpmC1WssE0oUQososRpXJkH9W3QRXFmQ4uwc2/BOKc5y3D5gKA+6o23TgWjRlyhSmTJnC3r172b17d0nx3JiYGAYMGCD3dEI0V4UZqvijb4BzkMFmUVlehy74nNpmMIx52rMZBFaTupe0mlVgt1VnCI4FP9et0utCo1w68fvvv/Pmm28yadKkClPR9uzZw9dff42XlxeffvoprVu3BkCn0zF9+nRuv/12rFYrCxcuLHPswoULsdls3H777UyfPh3d+UhP69atWbVqFV5eXqxZs4b9+/d77JpCiGaqKBO8vWuUVjz5qmGYzBa++/4HDw5MCNGsWIzqhjn/zPkgQwXPozQN9n0Kax92DjL4h8L4JTDo77UfZNBs6mbealJjNxeroK2xQLXjNOSqsemzPHbJfv36cddddzFv3jzmzZvHXXfdJUEGIZorQ56aMzUbBEY4thsL4PvHygYZek2G8Ys9F2Qw61VQuChLtUaPHwDtLlUZE/UcZIBGGGgwm81MmzaNoKAg3njjjQr3Xb16NQCjRo2iS5cuZd6fMWMGoGo9lK65UFhYWFK8Z/r06WWO69q1K6NGqYrJn332mUeuKYRopswGFWio4S+deTNvo0v71sy8f1aZAKgQQlTKpFfdJXJTKg8ymArhpwWw/W3ndOAWXeEvy9WNbm0yF0P+OchPVfNnca66sTcXg8WsirKBWnvsEwj+YRDeFnwqX1YmhBBVYtJD2iE1H4bEOrbnn4Wv7oczOx3bdF5w+QNwxeyK59aq0DQVSM07rcYQ3g7aDlHtMcNal60PUY8a3dKJxYsXc+DAAV599VXatGlT4b6///47oPrEl2fIkCH4+/tjMBjYu3cvQ4cOBVRWgtFoxN/fnyFDhpR77LBhw1i/fn3JNWp6TSFEM2XIBXMhBLau0Wk+/34zM6ZM5Jk3PmHQoEGMHz+eoUOHEhsbi7d32aeKBQUFNbqeEKIJKc6BtMPqyX9lQYa80/DD485dJQC6X606S/j4184YNQ2M+Sqg4BOgbqjDWqnr6bxK/fF2fO3V6J6nCSEaA4sJ0g+rQGd4qfu3zAT4fq5zlpdvEIx+GtrVsPOMzaruGU16lTkW3QNCYxtGDRwXGlWg4fDhwzz//PMMGDCAWbNmVbr/sWPHAOjcuXO57/v6+tK2bVsSExM5evRoyYd++3Ht2rXD17f8qJD9nEePHvXINctjL7RRnpSUFNq2bevyfSFEI1GU5bgxroE7H3muZImXpmmsXbuWtWvXutzfZrO5fE8I0YwUnFNBBotB3TBXNBel7oMf56sP/HbevjB0NvS4pnbGZ7OojAVzscpMqIeba03T+PDDD1m1ahX79+8nOzvbqcXlhXQ6XYXvCyEaMZsVMo85lpjZ58xz+1UQ1lQqYz0kTi2ViHL9ma5SVrMKXFhNEBgJ0d1Um2A326HXpUYTaNA0jWnTpmE2m1m2bFm5T+guZG/zVl5nCDv7ezk5jsiTu8fV9FghRDNjMUJRuqoKXEPtWsepEg8+/kDltR5Onz5d42sKIRoxmw1yT0HGUfD2gbCWFe9/bB388pJzv/XQljD2n+rG19PMxVCcB9jU2uOYHudvrut2+YPRaOSaa65h06ZNNNJGbUIIT9E01cIy+6RaLmHP/kr+HX56GqxGx74xPWDc8xDk+jNhhSxGKM5W1wyOVsu/gstpndmANZpAw1tvvcWvv/7KAw88wKBBg6p0jMGg+jj7+fm53MffX6X4FRcX1/i4mh57oYpahlSU7SCEaCSKc1Uhn9BKbvCrIOnXz9WNefuh1WpvKYRohqxmyEqE7BMqBdc/1PW+mg12vgd7PnbeHncRjFukesF7SrnLI1qrG/V66l7xwgsvsHHjRgCuv/56Jk2aROvWrfHxaTS30EIIT8lNhqwENSfZl4klrodNi0GzOvaLHwBjF7lXkNGsB32OmvNCWkJ4Gwhq0Sg7+DSKWfLMmTM8/vjjxMfHs2jRoiofFxAQgF6vx2QyudzHaFSRp8BAR/pJQIC6Sa/ucTW5phCiGbJXQq/hsgkhhKgykx4yjqhaCyExFRdItBhh8xI4scl5e5cxMHyu6sfuCTbL+d7v+npbHuHKp59+ik6nY8GCBTz99NP1PRwhRH3JO63mTr8QRwDh4Bfw67+BUtlOHa6E0U9Vb37UNFVUsjhPPSyK7KCCrIGRNepIVt8aRaBh1qxZ5Ofn8/777xMaWkHU/QKRkZHo9fqS5Qzlsb8XGRnpdFzp96p6XE2uKYRoZiwmKEz3bB9lIYSoSHHu+QJmGZUXfdRnw49PqdZtpQ38Owy4wzM3vxajWntss6rlEdHd62V5REVOnjyJTqfj4Ycfru+hCCHqg32ZWfoRNTcFhKnAwJ6VKturtO5Xw7A5Ve8sYc/iMuSr+8GY7hAa1yCCrJ7QKAINu3fvBuC+++7jvvvuc3rPvvwgJSWFli1V+vGaNWu4/PLL6datG2fOnCExMbHc85rNZpKTkwHo1s2xvtD+dXJyMmazudyCkMePHy9znP21O9cUQjQzhlwVvfbAsokLZWZmsmnTJk6dOoVer2fBggUev4YQopEpSFNBBrMewuMrzqTKPgk/PAaFaY5tXr4wYp7KZqgpU6HKYND5QHCsIzXYu+HdloaGhmK1WgkJkaCwEM2OzQqZiZCd6Fhmptlg25tw4HPnfftMgUtmVD0IaypSAV2/ULUULTQO/II9/z3Uo4Y3o1cgLS3N5Xs2m63kffuyhUsvvZRNmzaxZcuWco/ZsWMHJpOJgIAA+vXrV7K9f//++Pn5YTQa2bFjR7mdIeznvOyyy5y2u3tNIUQzoz+f9eTBNXcWi5V5c+fxn7eXOy3fKh1oyMnJoVOnTuTl5REfH++xawshGij707jMYyq4ENaq4v1TdsD6Z8FcqnJ6QDhctQhaXuz+ODSbempnKlTt3iI7qUBrA08NHjx4MOvWrSM7O7vCQt9CiCbGYlLFcnOTVE0G3yC1zOvnlyBhnfO+Q6ZDv1uqeF6jaovp468K6Ua0bXIBBrtGsTA4KSkJTdPK/fP+++8D0L59+5JtI0aMAODGG28EYNOmTeVmGCxbtgyACRMmOEWqQ0NDGTduHADLly8vc1xCQkJJYSD7NezcvaYQohmxmtWTQg90mwBU6t32t7lp5jyW/vsNTCYTvXv3LrdYWWRkJLfccguaplFUVFTOyYQQTYa9QnraQVWLITi64v0PfaUyGUoHGSLaweT/uB9k0DR1U513VgU64i6CtpdAXC91896AgwwAc+bMQdM0Xn311foeihCirpiL4dyfkHPy/HKuIBUg+OnpC4IMOhj2cNWCDDaLuvfTZ6sOEm0GQWyPJhtkgEYSaHDXgAEDmDhxIlarlSlTppCamgqoVpnLly9n5cqVeHl58dRTT5U5dv78+eh0OlauXMny5ctLWhqlpqZy8803Y7PZmDx5Mn379vXYNYUQzYQhTz3V81R9hiPf8un/VvPVL3uJjQhm5/bf2b9/v8unbzfddJMaxvkuOUKIJkjT1BKIrGMqayAgzPW+NqtKBd76qso8sGs9ACa9CWFuZj9ZTaqAmk+AqsLe7jKI6ui5IGsdGD16NC+88AJLlixh4cKF6PX6+h6SEKI2GQsgdR8UnFUZYD7+apnD9/Pg1K+O/bx8YPQC6HltxefTbKr4d8E5NRe3GQSt+qivm7hGtXTCHe+99x5Dhw5l165ddOzYkV69epGZmUlKSgo6nY6lS5cyYMCAMscNHjyYV155hTlz5jBjxgwWLVpEdHQ0hw4dwmg00r17d1asWOHRawohmgl9NqB5ZtlE/lnY9ibv7zWhA14aCf07VpzeO2TIEKDizjpCiEZM0yAnCTIOqwBDRS3WTIWw4Z9qyURp1S1qdiHD+TaVEe0huqt7bd4agFGjRgEq2/WZZ55h8eLF9O7du8Li5Dqdjg0bNtTVEIUQnqLPVhlgxjwIbaXu0/TZKtMr85hjP58AGPtPaDuk4vMZ8tRcGBgJMT0hJK5B1qKpLU3+O42JiWHXrl0sWbKE1atXc+jQIYKDg5kwYQKPPvooI0eOdHns7Nmzufjii3n55ZfZvn076enptG/fnhtvvJHHH3/c5dKHmlxTCNHEWS1QeM4zqXKaDX5+ASwG9qSqp5A33PsUtOhc4WFBQUF4eXlhs9kq3E8I0UjlpajCj/6hFWdO5Z+BH55QNRxK6FRBsz5/c29Zg2ZT6cFevhDXWy29aIT93+02b97s9NpgMLBr164Kj9E18OUgQohyFKSpLjuWYghtrea//DPw3aPqoY6dfyiMX6LmN1fMxSqLwTdELTsLa60yI5oZnWZfEyAalU6dOgFw4sSJeh6JEKJairIg5XcIiXX/SaHd/v/B7/8BwH9RPsH+3mTn5Jc8OWzVqhXp6elYrdYyh3p7e6NpWpMMNsj8KJq1vDNqbbFvQMUt0s7uUeuNjfmObT4BMPJJ6DjMvWtbDFCYodY0x3RXNRgaKfs8MnXqVLeOf/rppz05HI+R+VGIC2iaWuKVcUS9tteyyUyA7+eqFrx2QS3g6pcgqpPr8+mzVCHJyA4q0NqIlopVVVXnkSaf0SCEEA2KPlv9UqtpkCHnFPzhWL4VGehFht6GwWgioJIU5dTUVGw2W7nFIoUQjVh+6vnCj/4VBxkOfwNbl4JWKggZHAvjn4cWXdy7dnEOmPQQ1VllVfkGuHeeBqahBgyEEB5gs51fZnYE/AIhIEJtP7MbfnxKtQO2C28LV7+ollSUe67zxR59g6F1P7VfM89uatLFIIUQokHx1LIJmwU2P6+6V5w3oGdHQMemzT9Xevh7770HgL9/80vjE6LJKkyH9IPg5QWBEeXvY7PAr/+GLS87BxniesNf3nYvyGCzqLRiTadurmN7NpkggxCiics/c76WTagjyHB8k8pkKB1kiOkJ173uOshg0qtAb0hLiB+olko08yADSKBBCCHqjiFPpSnXNI1uzyeqt7Nd20u49ZZb0TSN+c/8k8LCQpeH/vDDDyxcuBBAWuwK0VQUZcK5AypbKqhF+fsYC+CHx+HgGuftXcfBxFfdW+ZgPn9zHRwL8f0hvI0KdAghRENXnKuWR/gGOWrZHFijiuPaLI792gyGiS+XH8C1t+815ENMD2jVt+IOP82M5M0KIURd8cSyicxjsPsjx2v/ULjyUW7xDWL5f79ky849XHrppcycObOkq8RPP/1EUlIS33zzDd999x02m42goCCCghpnFXghRCn6bBVksJlV7Zfy5J1WQYa8lFIbdTBkOvSd4t6TN/s65Ohuar2yj59bwxdCiDpnNasgg6VYZR9oGvzxLuz92Hm/LmNhxLzy79usZrVUwj9MFXwMiZMshgtIoEEIIeqCJ5ZNWIyw6XnnlOehD0JwNDpzMV++9Sx/mfMKv2zZyoMPPliyy/jx40u+1jSNMWPGcOzYMYQQjVxxjir8aCmG0Jbl73NmF6x/RmU02PkGwqj50P7y6l9T01R1dp8AaN1fXbeJ3FzbC5x16dKlzLbq0Ol0HD9+3GPjEkJ4WHYSFKRCWCuVvbDlFTj6nfM+ff6mOvDoysnSMhWqjIiweBVsbYIFHz1BAg1CCFEXjOd7yofEuH+One+rokV2HYdD59ElLyPDQ9n44w988r/Peffdd9m+fTtGoxEAHx8fhgwZwvTp07ntttucbqSFEI2QIU9lMpj0EBpX/j5HvlU30Fqp7jKhLWHc8xVXTXfFZoGCc6onfGyvRt1VojxJSUkABAQElNlWHdLeUogGrDAdso+r+ctmgfX/hOTfnPe59F4VaLiQpkFRhvo6thdEtAdv+TjtivzNCCFEXSjOUTf77i6bOPcn7P8/x+vASBj2UJkniV5eXtx+++3cfvvt2Gw2srOzsVqttGjRQrpMCNFUGAvg3EEVbHBVdOzkL/DLy0CpLuYt+8DYf7ouFlkRq0llMoS1VmuRm+ATvPfffx+A8PBw5syZ47RNCNEEmPSqxpVOBzYrfD8P0g443td5q6USXa8qe6zVrObAwAjVvtfVUjVRQu46hRCittmsqip7JW0nXTLrYfNinD4wDHvEUSHZBS8vL6Kjo927phCiYTLpVQvL4myV9ltekCHjCGx8Dqc5o/vVcMVD4O1b/Wua9aoWRGRHiOmm2mc2QVOnTi352h5oKL1NCNGI2WyQdRwMuaqA7do5zkEGnwAY+yy0vaTssSa9qksT0R6iu9S8e1gzIYEGIYSobYa8mi2b2L5MBSrsuo2HDkMrPcxqtZKdnQ1AVFQU3t7e7l1fCNEwmA0qyFCYAeGty187XJgGPzwBVqNj20U3wmX/cK+WgiFfrUeO7g4tOoOXzCNCiEYo/wzkJUNwDGx/yznIEBAO45eo9rwXKs4Fc7HK5IrqJEslqkF6EAkhRG2rybKJ03/Aoa8cr0Pi4PL7Xe5eVFTEyy+/zODBgwkKCqJly5a0bNmSoKAgBg8ezMsvv1xh+0shRANlMaogQ0EqhLV0UaCsSHWXKM52bGs/VK03difIUJQJFgPEXQTRXSXIIIRonEq3sjz5Mxz8wvGefyhc9++yQQZNU4FbzQqt+qg5UIIM1eL239ZHH6n2auPGjSMuzkURIiGEaO5sVvXBwJ1lE8YC+PkF523D5zr6PV9g76FE/vLg3SQnp6BpmtN7ZrOZXbt2sXv3bt544w1MJhP+/k0z/VmIJsdqhvQj6olcWKvyg5Y2i+r/nn3Csa1FVxj1ZPUDBPbOEr4BquCZq2KTQgjR0FnNkJWouvOY9edr19jpYNRTaklEaTaLmgMDwtUcGNyiTofcVLgdaLjzzjvx8fEhNzfXg8MRQogmxpAHhgL3fkn99rp6omjX+y8QP7DcXVPTMxlz51yycwvw8/PjxhtvZNSoUcTHxwNw5swZNm3axOrVqzl16hQ6na7kPSFEA2a1qOJluadUxwhXmVHb3oSU7Y7XwdEw/nn1BK867DfYgREQ11sVnm0mSreyTElJKbOtqqS9pRANSHaSWn7qHwJrH3FeVjbo72VrMliMqjNFEy58W1fcDjRERamWRkFBbhY3E0KI5qA4V924V7cA28ktkPCj43V4G9XP2YV/vvY+2bkFtG/fju+//4EePXqU2eeuu+7iqaeeYvz48SQlJUmgWIiGzmaFrASVpRAS63oeObDGORXYJwDGLVZrkaujmd9gl25lac8Kk/aWQjRi9laWgRGw6TkoKFXvqt1l0P825/2NBeoBUVQniO4GPn51Otymxu1AQ48ePdi+fTuFhYWEhDSvX0RCCFElNqv6pVbdZRM2K/z+luO1zgtGPK4+PLjw3eZt6HQ6Vrz1n3KDDHbdu3dnxYoVjB07Fr1eX71xCSHqjr1CemaiKiTrqtND8jbY9objtc4LRi9Q64mrw1gAxXlNvrNERUq3snzkkUcA+Ne//lVfwxFC1IRJD5nHVH2aA2sg+XfHe2GtYeQTzrVu9FlqmUVsb4jsAF5SyrCmarR04rfffuOdd95h9uzZHhySEEI0EfZuE0HVXDZx+g/nqPvFf1UpzBVIy8ghMMCPMaNHVXr6MWPGoNPpsNls1RuXEKJuaBrknFTZDMFRroOMWYmw/llVbNbu0vug/eXVu1ZRhvq65UUQ0a7ZFn0s3cry2WefLbNNCNFI2AO1+mxV22bXB473vP1h7EJVBBLU/FmQBj6B0Kq3qoMjPMLtQMM999zDunXrmDdvHn5+fkyfPh0fH6nEKYQQJYpzVXZCdZdNHPrS8bWXD/T9W6WHxLSIIL+get0kvCRaL0TDlJMEGUdUITJXNRaKMlWHCYvBsa3XZLjohqpfp3TBs5ge7rfgFUKIhsTeytJmVUsmKFUg+8pHVKteOD8HnoOgaIjt0axq0tQFtyMDd911FyEhIfj7+zNr1iwWLFjA4MGDiY2NddmrXafT8e6777o9WCGEaDRsNvXLyzewesflp0JyqYJunUZU6Rff6MsHsvKLdezavZuBl1T8NHPnzp1omkZgYDXHJoSofbkpqsOEfyj4BZe/j7kY1j3hyEQAVdDs8vur3sbSVKSe9kW0Vd0pmlk9BiFEE6XPVq0sdd6waZHKLLXrfT10Hau+1mzqnissXrW2dKc7mKiQ24GGDz74AJ1OV1IsJzs7m3Xr1pW7r30/CTQIIZoNY576ExRVveMOf41T5L3X5Cod9tSsO1nzw89Mm/kPflq/nhYtyl+ukZ2dzfTp0/Hy8iIiIqJ6YxNC1K78s5B+CPwCHGm9F7JZYeMitfbYLqqTqsvgqiPFhfRZYDFDTE+I6ii94ato165dfPrpp+zcuZP09HQAYmNjGTRoEH/7298YNGhQPY9QiGbOWABpB1Ury90fquVldnEXwaX3qq81TQUZgmMlyFCL3P7Ncscdd0hVXSGEcKWk20Q1KhZbjHBkreN1i87l1mZIPpNaZpufrw/vPD+HGU+/Qc+ePbn33nsZOXJkmfaWb7/9NmazmejoaJnDhWgIbDYw5Krq6LnJKlgQEFH+vpqm2lie+tWxLTAKxi92nf3gdC0LFKaBbwjEXwQhcVXPgGjG8vLyuPvuu/niC9XZw/6QDeDw4cP88ssvvPLKK0yePJl33nmHyEhJvxaizpmLVaDWmAcpO+FYqQfggVEw5hnHUtaiDBXMjZMgQ22qUUaDEEKIcri7bOLEZjDmO173mlzuh4COl15b6akWLVrEokWLKhhizQpBnjt3jp9++ok//viDP/74g71792IwGBg+fDibN2+u8Fiz2czSpUv5+OOPSUxMxM/Pj379+jFr1iyuv/76Go1LiEbDXKwyC/LPqFRfmxUCwlxnMmg22Lr0fNbTed7+MO55FTCoyvWKMlW19ehu6lqiUkajkVGjRrF37140TaNNmzaMGDHCKYj7888/k5KSwpdffklSUhK//fYb/v7Nr2uHEPXGYoK0QypgW5wD2153vKfzUkGG4Gj1ujhHFbyN663q04haI7lyQgjhacY81XEiqJpPtUoXgfQNhi5jyt2t9NO0+vLpp5/y0EMPVfs4g8HA2LFj2bp1K97e3vTu3ZuioiI2b97M5s2bmTdvHkuWLKmFEQvRAJRkL6SpYKSxAHwD1BKrirKfbBb4+UVI+LHURh2MelIVMKtMcY4KNER3hajO0hu+Gv71r3+xZ88eAgICeOONN/j73/9ebjbYBx98wH333cfevXt5+eWXeeKJJ+phtEI0QzYrZBxVQVvfANjwrJoz7S69F1r1UV8bC8BsVK/tgQdRayTQIIQQnlacCzZz9ZZNZByF9MOO193Hu8yIOPn7N2U3mo2q+nz8QPWLthJXXHFF1cdWjrCwMMaMGcPgwYMZPHgwe/bsYeHChZUeN2/ePLZu3UrHjh35/vvv6d69OwBff/01f/3rX3nhhRcYOnQo115bedaGEI2K1ayeuOWfBazgHwbhbSpfumA1w8aFcPIXxzadF4x4DDpeWfl1C9PAyw9a9VVFz2SpRLWsWrUKnU7H0qVLueuuu1zud+edd2IymZg5cyaffPKJBBqEqAs2G2QmQm6SWj72w2Mqc8uu8yi46Eb1tcUAhnyI7QXh8fUy3ObGI4GGr7/+mnXr1nHq1CmKi4vZsGFDyXtFRUXs27cPnU7HZZdd5onLCSFEw+XusolDXzm/7jXJ5a7t27Quu9FcrP60b1+lQIOvbzVbbl7grrvucrrpPnPmTKXHpKWl8fbbbwPw7rvvlgQZAK677jrmzp3LwoULeeaZZyTQIJoW+xO33FMQEgs+VUyrtxjhpwWQUqoTjZePKvxYlSCDPhu8fFWQIbj8ArGiYidOnMDHx4epU6dWuu/UqVOZNWsWJ0+erIORCSHIPQXZieqe66cFzkVyIzvClY+q4KrVDIUZKqsrskO9Dbe5qVGgISUlheuvv57du3cDlHSWKM3Pz4+bb76Z06dP89tvv3HJJZfU5JJCCNGwGfPVsonAiGocUwCJ6x2v4wdARDuPD62+ff3115hMJrp27crIkSPLvD9jxgwWLlzI7t27OX78OJ07d66HUQrhYZoGWcch+2T1ggwmvWphmbrXsc3bD65aqFpZVsZYoG6uW/WRIEMNhISEYLVaq1Rzwd/fn5CQEJdt3oUQHpR3RrUC9vaDDQsh7YDjvcAoGLdIBSBsVvUAKKIttOgCXl71N+Zmxu2/6aKiIq666ip27dpFfHw8//jHPwgOLlvx2NfXl7vvvhtN00qq9QohRJNlyFU391X9MAFw9Huwmhyve/3F48NqCH7//XcAhg0bVu778fHxdOzY0WlfIRo1TYOcJPWULTiq6vOCsQC+e8Q5yOAbCBNerFqQwVyszhHTXRV/FG4bOHAgubm5nD17ttJ9z5w5Q05ODoMHD66DkQnRjBVmqA4TXl7w8wvOc2VAOEx8RS0V0zQoPAehcaqdr3fNsjlF9bid0fDmm29y9OhRBgwYwM8//0xwcDCfffYZer2+zL6TJk3imWee4ddffy3nTEII0UTYbJB/DvyqsWxCszkvmwiOgfY1W2b222+/sX//fnJycjCbzeXuk5OTU6NruOPYMZXSWFGmQufOnTl58iRHjx6tq2EJUXvyzz9xCwgD3yq2UCvOVUGG0v3f/ULg6hfV2uLKWE1qjXJ0N4ho79awhcOcOXP48ccfefjhh1m1alWF+z7yyCPodDrmzJlTR6MTohkqzoG0g6oW1m9vwOk/HO/5hcDV/3IsjyhMA/9wNXdWYVmp8Cy3Aw2ff/45Op2OV155pdxMhtIuuugivL29S24yhRCiSTLmq44T1WmXdHqn+jBi1/NatQbbDd//vIP7Ft1NcnJKpfvWtL2lO7KzswGIiopyuY/9vcoCIZ06dXL5XkpKCm3btnVjhEJ4UEGaKvDqF+C6ZeWFijJg7SNq3bFdQARc8y+V8lsZm0VdN6K9pAh7yNixY3njjTeYPXs2o0eP5qmnnuKKK64oqXNjsVjYsmULzz33HFu3buWNN95g9OjR9TxqIZooY6EKMhiLYOcKOFXqIbZvEFz9kqrDAKp9sLe/amNZ1TlYeJTbgYajR4/i7e3N0KFDK93X29ubiIgIcnNz3b2cEEI0fIZc1cu5OssmSmcz6LyhxzVuXXrjb7u4buZ8rFYVQOjSpQtxcXH4+JQ/zdfH0gSDwQCo2j2u2NdBFxcX18mYhKgV+mzVYUKnU4GCqihIhW8fhoJSKfpB0TDx5aplJmiaCjKEtlItL72lsVh1lQ5gpqSkOG3z9fUtacPr4+NDdLRqjZeZmYnFolrpBQUF8dJLL/Gvf/2L48eP1/HohWjizMWQflDNr3tWwolNjvd8AmDCEojtqV4bC8BqUYVwg1w/3BC1y+3fQkajkcDAwCoXvNHr9QQESMqKEKKJ0rTz3SaqMc8VnIPkbY7XHa+EIPeKtj279D2sVhuDBw1k1af/V+ETf6g4I6C22H8HmEwml/sYjUYAAgMrXn5y4sQJl+/Vx/cmRAlDHpw7AFYDhLaseF9NUxlNqXth14cqo8EutCVc80rVaywUpqkitLE9qxfsFCWSkpJKvtY0rcw2O7PZTGpqapntRUVFFBUVlSmMLoSoAZtNLZfIPaWWp/75P0j40fG+ty+Mew5a9lGvTUWqjWVcbwhrVT9jFkANAg1xcXGkpKSQm5tLREREhfsePHiQ4uJievWqwtpCIYRojOzdJqqzbOLwN6pGg11v94tA7j54FJ1Ox38/+rDBftCOjIwEHEsoymN/z76vEI2KqQjOHQRTgcosuJBmU8UhU/dD6j71p7icn4fwtnDNy6pLRVUUZaonerG9wD+kRt9Cc/b++++XfP3II48A8K9//au+hiNE82Y2gD5TBWOLskGzwuEv4ci3jn28fGDsQogfqF4bC9Sf2J7SxrIBcDvQcMUVV7Bq1Sr+7//+jxkzZlS474svvohOpyu3nZkQQjQJxbmqCFtVnyRaTXBkreN1ZEdoebHbl/f18SE02IvOnRtmkAGgW7du/PrrryQmJrrcx55u3K1bt7oalhCeYTao5RLF2eopWumn2lYz7HxP/cwb8ys+T1Rntc64qum+hjyVGRHbS1KEa2jq1KklXz/77LNltgkhapmmqWWohRmQf1YFbX38ISgSdn8EB9Y49tV5wegF0O5S9dqQCyaDmgsjOzjPwaJeuF0l6L777kPTNJ555hkOHDhQ7j4mk4nHH3+clStXotPpuPfee90eqBBCNFj29kk+1Vg2ceJn9UvRrvfkGv1S7NmlA8UGU0kdhIbo0kvVzcDWrVvLff/MmTOcPHnSaV8hGgWrGTIOq+VQoS3VDbCdzQIb/gn7VlUcZPALhm4TYOKrVQ8YmPQqiyK2h2rfJoQQjZU+G87shuTtqiWwTqdaVAZGqvlz7yeldtbByCfUklP7sRYTtLwIojpKkKGBcDvQcPnllzNr1izS0tK49NJLufHGGyksLATgiSee4NZbb6Vt27a8+OKLADz11FOydEII0TQZ8lRGQ3WqGpcuAukbBF3G1mgIM2+djNli4eP/Vtx+rT5NmjQJX19fEhIS2LRpU5n3ly1bBkD//v3p0qUKFfaFaAhsVsg4Crkp6sO+V6naVTYLrP8nJG0pe1xAOHS4Ei6fBdevgDu+hhHzVCvMqrAY1c11i25qqYUQQjRWhjw496d6aBMQDpoFjm+An+bDR5Ng1wfO+1/5KHQZo74uygA0lRUaIXNhQ1KjksRLly4lLCyMJUuWsGaNSmXR6XS88MILgCqk4+Pjw/z585k/f37NRyuEEA1RcU71uk1kJkBaqUywbuPAL6hGQ7j9+vFs2LKd2Q8/Skh4JFOmTKnR+WpDXFwcM2bM4I033uDuu+/m+++/p3v37gB88803JYHpp59+uj6HKUT15CRB9klVT8Hb17HdnsmQ9Itjm7c/XDId4gdBRDv3n7rZLFCYDlGdoEUneXpXi5KTk906rl27dh4eiRBNlKkIzu6FYz9A1nE4u0vVnXFl6IPQ42r1dWEaePlDy95Vr2kj6kyNAg06nY6FCxdyzz338MEHH/Drr79y9uxZrFYrLVu2ZOjQodx1110NtjCZEELUmM0K+angV3GXBCelsxkAek3yyFA+eHEuHXpv4tZbb+Xxxx9n0KBBhIaWn2WRkZFR7vaqSklJoX///iWv7Us2fv3115K2bwBz585l7ty5Ja9ffPFFdu3axbZt2+jduzcXXXQRhYWFJbUZHn74YSZN8szfhxC1Lj8VMo5BUIRzoNFmgQ0L4eQFQYbxiyF+QM2uaTGqIENYPER3c86gEB7XsWPHah+j0+lKWl4KISpgMcKpbfDtg5B3uuJ9vXzh0pmqcLa9na9fEMRdBMHudewStcsjTZbbt28vT6CEEM2TIU/VWgiOrnRXQFVDTlzveN2qn8cqI6/4v7Usff09AE6dOsWpU6fK3U+n02Gz2cp9r6qsVitZWVlltlssFqfter3e6f3AwEA2b97Mq6++yieffMKxY8fw8/Nj+PDhzJo1ixtuuKFG4xKizhTnQPoR8PEFv1KdHmwW2LgITv7s2ObtB+Ofr3mQwaxXyyUiO0JMN/Dxq9n5RKXsbS5r+xghmh2rBU7vhu8ecR1kCIxUHSXiB0KbwepeS7OpIG9AuKrJEChdqhoqjwQahBCi2SrKVL/0SqdMV+TYOrCUKtjYe7JHhvHVj1uYMX8pAMHBwVx22WXExcXh41P+NL969eoaXa9Dhw5u30z7+fkxb9485s2bV6MxCFFvTHoVZLAYIKylY7vNAhufgxObHdu8/c5nMgys2TWNBao3fHR3aNFZMhnqiL1ArSt5eXls376dV199lYyMDFauXEnPnj3raHRCNFI2G6QfhO8fhpxSP2M+AdC6nyO4EHlBYUebFQpSISga4npXvaaNqBceCzQUFBSwe/du0tPTAYiNjaV///6Ehck/ACFEE2U1q194/sFV21/TnJdNBEVDhys8MpQXl6lqzOPHXcX//e8zl0sm7H7++ecK3xdCuGC1QOZR1d89rLVje0mQoVShU28/GPd8zYMM+ix13bje0ratjrVv377Sffr06cPtt9/O6NGjufvuu9mzZ08djEyIRkrTICsRvpsLaQcd2wMjYdKbzvNqaTaL6uwTEguxvcE/pPz9RIPhdtcJu71793LttdcSFRXFqFGjmDJlClOmTGHUqFG0aNGCa6+9ViZcIUTTVJyjnjL6VbHbxNndkJfieN1zInh5Jt574NgJdDod7y57q9IggxDCTfYb5NzTEBLnaGNps8Cm5y8IMvjCuOegzaCaXa/gHOAFrfpK27YGLCAggH//+9+kpqby3HPP1fdwhGi4cpPhxych5XfHNt9gmPCiCjLYrKp2g6lILU0tylTzYN5ZCG0NLftIkKGRqFGg4f333+eSSy7hu+++w2q1omma0x+r1cratWu55JJLeO+99zw1ZiGEaBgK09UHjaqmMB8slc2g84YeEz02FF8fH8JDg2nVqpXHzimEuEDeacg+DsFRjuVSNgtsXgLHNzr28/aFq55Ta4rdZbNCwVlV/6F1PwiTn+2GbuDAgQQHB/PNN9/U91CEaJgKzqkaNgk/OrbZg7KBEZB3Rt1bGQvAYgadj8p0iGgPrfqomgy+1Si+LeqV24GGHTt2MG3aNMxmM126dGH58uUkJiZSXFxMcXExiYmJLF++nB49emCxWJg+fTo7duzw5NiFEKL+mIuhMKPqUXV9Fpza6njd4YqqF5Csgr49u1BQpKegoMBj5xRClFKUCRlHVJVz3/PtaG1WFWQoXeDV2xeuWgRth7h/LasZ8s9CcIzKZAiKqtnYRZ2w2WxYrVZSU1PreyhCNDz6bNjyCvz5P8c2nReMXqAyGbx8oXV/aHcptLsMOgxV90ptBkFcL5XRVdU24qJBcDvQsHjxYmw2GyNGjGDfvn3cc889dOrUCX9/f/z9/enUqRP33HMPe/bsYeTIkdhsNhYvXuz2QD/77DOmT5/OoEGDaN26Nf7+/oSGhjJgwADmz59fbvVzu8LCQp566il69OhBYGAgMTExTJw4kc2bN1d63U2bNjFx4kRiYmIIDAykR48ezJ8/n6KiogqPq8k1hRCNQHEOmIucq81X5Oj3qmikXa/rPDqcB/9+E1arjf+8vcyj5xVCoJ6upR9WP8MBEY7tO5Y5Bxm8fGHsImh7ifvXshjVU7+ItipFWIqdNRqbNm3CYDAQERFR30MRomEx5MP2ZbBjufP2YXMgpqeaO+MugvB4FVj1D1FddWSpWKPmdqBh69at6HQ63nrrLQICAlzu5+/vz5tvvllyjLuee+45VqxYwYEDB/D396dPnz5ERUWxZ88eFi1aRK9evdi3b1+Z4zIzMxk0aBDPPfccSUlJ9OzZk4CAANauXcuoUaP4z3/+4/Kar7/+OqNHj2bt2rUEBATQs2dPkpKSWLRoEYMHDyY7O7vc42pyTSFEI1GYBt4+VfslqNngyLeO12GtVdTeg64bO4wF99/G/Gf+yZIlSyguLvbo+YVotiwmyDiqWtkGxzi2n9gM+0s9mfPyhasWQrsaBBlMRVCUAVGd1U23pAg3Cmazmf/9739MnToVnU7HqFGjPHr+7777Dp1Oh06no0OHDh49txC1zqSHPR/DlpeAUt2qBk9TmQs6nSp0GxLj8hSicdJpbvYnCwoKwt/fn5ycnCrtHxkZiclkqjQTwJUVK1bQvXt3LrvsMnx9HW3k/vzzT2655RYOHDhAr169OHjwoNNxkyZN4uuvv2bgwIF8/fXXtG7dGk3TWLFiBTNmzMDb25udO3fSr18/p+N27drFkCFD0DSNt99+m2nTpqHT6Th79izXXXcdu3bt4vrrr+fzzz8vM1Z3r1kdnTp1AuDEiRNun0MI4SZjAST/7pxCXZGUHfD9XMfrITOg380eHdKoG+8BzcaeI0nk5+cTEBBAr169XBaG3LZtG0CTDEjI/Cg8xmaD9EOQfULVSLAXb81Nhi9mglnv2HfsQug4zPW5NE11qrCanO61AXWjrQHe3tCiq2rp5lXjet2iBuzzSGUMBgPp6ekl9cnCw8PZvn073bp188g4CgsL6d27N8nJyYDqgpGUlOT2+WR+FHXKaoE/P4NvZzu39r7oRuh3i3oQE3eR1KBpZKo6j7gdaOjRowenTp2iqKgIr0p+GVqtVkJCQmjfvj1Hjhxx53IV2rFjB5dcop4gHDp0qKR/8Z49exgwYABeXl4cPXqULl26OB13xx13sHLlynIDBpMnT+arr77ijjvu4MMPP3R6LyEhgR49emCz2di3bx99+vQpea8m16wO+UUhRD3KTYaze1Vqc1X8OB+StqivvXzg1s9UcSMP8mpTvfZ59qnfzV8BDZrMj8Jjsk+q9mvB0Y61wWY9fHkf5CQ59ut3Gwy5x/V57N0j/EMhvI0KLOi8AN35rKjz//X2g6AWki7cANjnkep8qL/iiit4/fXX6du3r8fG8cADD/D6668zadIkvvrqKwk0iMYlYT2svlM9oLHrMgYuvU8V0m15set2lqLBquo84nZftb/85S+8+OKLfPnll1x//fUV7vvll19iNBq54YYb3L1cheyBBQC93vF0YfXq1QCMGjWqzAd+gBkzZrBy5Uq+++47ioqKCA4OBlT0+IcffgBg+vTpZY7r2rUro0aNYv369Xz22WdOgQZ3rymEaCRsNihIrXpKsz4LTv3qeN1hmMeDDABPP3gX2MwQ3k4t6ajE0qVLPT4GIZqUgnNqyURAmCPIoGnwy8vOQYb4ATDo767Po2lqzvAPUzfVgRG1OWrhYe+//36F7/v4+BAZGUnfvn2Jj4/36LV///133nzzTSZNmlTyAEyIRiP9MHw50znI0HYIXHqvKnjb8iIJMjRxbgcannzySb744gumT59OVFQUI0aMKHe/X375hRkzZtCjRw8ef/xxdy9XIXvth5CQELp3716y/fffVX/WK6+8stzjhgwZgr+/PwaDgb179zJ06FBAZSUYjUb8/f0ZMqT8qtHDhg1j/fr1Jdeo6TWFEI2EMR/0OVX/sHBhEcienmtpWdrTs+9SnTDaDwVf13Vz7C7M1BJClFKcq26SvX1UFoLdwS/g+AbH6+AYGLXAdYtb7XxgMiBS3VQHhNfqsIXnTZ06tV6uazabmTZtGkFBQbzxxhusX7++8oOEaCiMhfD1A6rmjF1sTxj2qGpb2bK3yu4STVqVAg0fffRRudtnzpzJs88+y+jRoxk6dCijRo0qieaeOXOGTZs2sXXrVsLDw5kxYwZr1qzhjjvu8MjAbTYb586d48cff2TevHkALFmyhJAQRwX4Y8eOAdC5c+dyz+Hr60vbtm1JTEzk6NGjJR/67ce1a9fOqR5EafZzHj161Gm7u9csT0XrA1NSUmjbtopp20IIz9Fnq8yBqrRYqoMikEIIDzPpVZDBXOy8bjjtIPxeqpizlw+MecZ10FGzQX6qWgrR8iLngIUQlVi8eDEHDhzg1VdfpU0b+UAmGhGbFbb8C07vcGwLbwsjn1LLJeJ6QUS7+hufqDNVCjTceeed6CpYL6hpGr/++iu//vprue/l5eUxZ84cdDpdjQMNS5cu5aGHHnLaNmTIED788EPGjx/vtN3eFSIqynX/aft7pYtauntcTY8VQjRwNisUnK1aAUiA0ztV+rVdj2vPr8sWQjRIVjNkHlVFG0un9BbnwPqn1U2y3WX/UJXSy2Ozqp/9oOjzQYYqtsEVjYLVanW63/P2dpHR4qbDhw/z/PPPM2DAAGbNmlXt4+VBlahXxzfBtjcdr719Yfg8VXsmtidEtK+/sYk6VaVAQ7t27SoMNNSl+Ph4hg4disViITk5mXPnzrF3714++ugjLr30UqfexQaDqm7q5+fn8nz+/uqpZOnK6+4eV9NjL1RRgY2qVkMWQnhQca7qBV3VFkyHv3F87eUD3ce73lcIUb80DbKOQ26KymSwBwVtFtiwEIoyHft2GQO9Jpd/HptVZTKExqpq6n5Si6kp0Ov1vP3226xatYr9+/djsaigk4+PD3379uWWW25h+vTpBAVVMRDtgqZpTJs2DbPZzLJlyzwexBCiVuWdge8eVt117Ab+HYKiIKYnRHaQYrfNSJUCDTWpbutpN910EzfddFPJ6/3793P//fezatUqDh8+zM6dO0sm5YCAAPR6PSaTydXpMBqNAAQGOgq7BQSo9c3VPa4m1xRCNAJFGYDN0eKuInVUBNLOu1MFbfXKYbPZKt9JiOYkNxmyElWHidI/4zvfh7O7Ha8jO8Cwh8u/WbZZVCZDaEuV7eBXsw+domE4evQo1157LcePHy/TqcdsNrNz50527drFW2+9xTfffFOj1pZvvfUWv/76Kw888ACDBg1y6xzyoErUC5MefnzKuVhum0HQ/gpo0Q2iOkqQoZlxuxhkQ9GnTx/Wrl1Lp06d2Lt3L59++im33norAJGRkej1+pL0tvLY34uMdHwAsH9d3eNqck0hRANnMUFhGvhVMQW6jopA2jXFNpVC1JnCdNVhwj/UuaNM0lbY+4njtW8QjF1YftcZm0VlMoS1VkGGqnamEQ1aQUEBV111FSkpKfj4+HD99dczduzYkroJp0+fZv369Xz++eckJCQwbtw4/vzzT6eaYVV15swZHn/8ceLj41m0aJGnvxUhao/NBvs+hYNrHNsCwlU2Q3gbaNFJggzNUKMPNACEhoYyfPhwPv/8c3bt2lUSaOjWrRtnzpwhMTGx3OPMZjPJyckl+9rZv05OTsZsNpdbEPL48eNljqvJNYUQDVxxDpgKISSu8n3roQjkplWvg8Wo2uf5lF26lZeXx/bt21mxYgWapqHT6SQlVwgAQ54q/qhDtbK0yzsNmxc77zviMYgoZ317SZAh/nyQofLOL6JxWLp0KSkpKbRu3Zpvv/2Wfv36ldnn7rvvZt++fVxzzTUkJyfz2muv8eSTT1b7WrNmzSI/P5/333+f0FApHioakXP7YeM/nbdd9g8IbQ3RXVx35hFNWpMINAAla+Xs/wW49NJL2bRpE1u2bCn3mB07dmAymQgICHD6xdG/f3/8/PwwGo3s2LGj3M4Q9nNedtllTtvdvaYQooErTAd0VftlWQ9FIIdf2r/S9pbXXXcdDz74ICNHjiQhIYHWraV/tWjmzAYVZDAVOhd/tBjgp6fBVOTY1udv0LGc1tX2IENEW4jtVbWONKLR+PLLL9HpdCxbtqzC+7a+ffuyfPlyJk6cyJo1a9wKNOzerZbo3Hfffdx3331O79nreqWkpNCyZUsA1qxZw+WXX17t6wjhUUVZ8P2j6oGMXY9rIaYXtOgsbX2bsRoHGhITE/nf//7H/v37ycnJwWw2u9xXp9OxYcMGl++7Kzs7m82bNwMqSGB34403snjxYjZt2kRiYiJdunRxOm7ZsmUATJgwwSnFLTQ0lHHjxvHNN9+wfPnyMoGGhIQENm7cWHKN0ty9phCiATPpVX2GgCo+YWrARSBjY2N58803GTlyJLm5ufU9HCHqj9WilksUpjsHGTQNfvkXZB93bGvVF4ZMK/88BWkQ2kqCDE1UYmIi/v7+XHPNNZXuO2HCBAICAlxmtVZVWlqay/dsNlvJ+xXVAxOiTpgN8NtrkFKqlWVEO7joL2peDZfWrM1ZjQINTzzxBC+99BI2m61K64Pd7Vzx888/s2XLFm677TY6dOjg9N7u3buZMWMGeXl5xMfHOxWKHDBgABMnTuTbb79lypQpfPPNN7Rq1QpN01ixYgUrV67Ey8uLp556qsw158+fz7fffsvKlSsZOnQo06ZNQ6fTkZqays0334zNZmPy5Mn07dvX6biaXFMI0UAV54C5CALjK9+3jotAumP48OHodDr0en19D0WI+qFpkH0Cck+pwo2lM5V2vgeJ6x2vg1rA6AXlF4EtzgHfYIjpJkGGJspsNlfYSaw0nU6Hn5+f2wGAioqvf/DBB/z973+nffv2DapIu2jGbDY4vhF+f8uxzcsHhs6GgEhZMiHcDzT85z//YcmSJQB07NiR0aNHExcXh4+P51dj5OTkMH/+fObPn0/Lli2Jj4/H29ublJQUUlNTAdX28ttvvy2TJfDee+8xdOhQdu3aRceOHenVqxeZmZmkpKSg0+lYunQpAwYMKHPNwYMH88orrzBnzhxmzJjBokWLiI6O5tChQxiNRrp3786KFSvKHa+71xRCNECappZBePtVrZBRHReBdIc96Ft6qZkQzUreachKgOAWqse73ZHvYM9Kx2svHxjztAo2XMhiUNlOrftJanAT1qZNG44fP86hQ4fo1atXhfseOHCA/Pz8MtmsQjRJOUmw7gnnVpaD7lK1rGTJhKAGgYa33noLnU7HnXfeyYoVK/Dyqr31x5dffjmvvPIKmzdv5uDBgxw7dgyDwUBkZCQjR47k2muv5Z577im3cE5MTAy7du1iyZIlrF69mkOHDhEcHMyECRN49NFHGTlypMvrzp49m4svvpiXX36Z7du3k56eTvv27bnxxht5/PHHXS59qMk1hRANjKkQirNVNfrK1EMRyBI2C6qaXeV27dqFpmm1Om8L0WAVZkDGEdV60rdU+8nTf8CWfznvO3wetOxT9hyaTZ0nqpMqdiaarNGjR5OYmMi9997LunXrSlqgX8hgMHDfffeh0+kYM2ZMHY9SiDpWnAMbF0LOSce2+AHQcZgsmRAldJqbPdECAwMxmUxkZ2cTHi4Rq7pm74NcUa9kIYQH5JyC1H3lV5q/UMoO+H6u4/WQGdDv5tobm53VrLIuYnpAdNcKMy/++OMPpk6dyuHDhwkKCqKoqMjlvo2VzI/CpeIcSN2vshFCYh3bs47D17PAXGo50aC7YcDt5Z+nIFWlBrfuLx0mmij7PLJ+/Xp69+6NyWSiS5cuzJ07l9GjRxMfr5bSnT59mg0bNvDSSy+RkJCAv78/Bw8eLDneUzy1dELmR1FjZgPs+hB+mAec/xjpHwbXvgoBLaDNQOcOPqLJqeo84nZGQ2RkJAaDQYIMQoimy2ZTHyh8A6u2f30UgbRZoeAco6YtAr9gl0EGg8FASkoKZ8+eLWlvGRERUfvjE6KhMBZA2kG13CGspWN7UYa6YS4dZOh+NfS/rfzzGPLByxdiukuQoRno1KkTH374IbfffjsJCQlMnz693P00TcPX15cPP/zQ40EGgDvvvJM777zT4+cVolpsNji7G35eQkmQAeCKh8DbH6I7S5BBlHA70DBkyBC+/fZbMjMziY6O9uSYhBCiYTDmqSegQVGV71sfRSDt9SNCYtm8bWeVD2vfvj3FxcX4+0vxOtFMmIsh/RAYcp2XOpiK4IfHoCjTsa3NYBg2p/ygndWkAhZxvas2L4gm4a9//StdunThySef5McffyxTAN3Ly4vx48ezaNEip+5nQjQ5uadUXYbibMe2HhMhuhuEx0NYFYpmi2bD7UDDo48+ytq1a3nuued49dVXPTkmIYRoGPTZqgWedxUqjtdHEUh9pqodEduTp59+usJdfXx8iIyMpG/fvlx++eV07ty59scnRENgMUHaIdXGMrSVI4Bgs8D6Z9SyCbuozjDmmfI7TGgaFKSr1m0R7epi5KIBGTBgAN9//z15eXns3r2b9PR0QLUMHjBggGT4iqavKAs2PQdn9zi2hbeFfreBT4CaP6XLhCjF7UDD0KFDeeutt/jHP/5BcXExjz32WJnWk0II0WhZLZB/FvyDK9+3PopAGvJU1mJsTwgIrzTQIESzZLVAxlHIPwNhrRw3wZoGW19VBSDtgqJh/GK1BKk8RRkQGCEt25q58PBwKeotmh9zMez6AP78zLHN2x9GPgGaRZZMiHLVqBflPffcQ1ZWFo8//jgrVqwgKiqq3M4PdjqdjuPHj7t8XwghGoziHDDmOxeMc+X0TrWEwa7HtaCrxY4OZj0YC6FVn6qNT4jmyGZTLSxzTkJonHOWwt7/wpG1jte+gSrI4OrnyVSkghMx3V0HIoQQoimyWSHhJ/jlReftw+eqIpCyZEK44HagwWq1cscdd/Dpp58CqghOVlYWWVlZLo/RVaUHvRBCNASFKi223BTqC9VlEUirWaUvRndTKYtCiLI0TQUYso5DcLTz8qfE9fDHCsdrnReMeVZ1bCmPzaKWUcX0lMCeEKL5STsI3z+quvXY9fkrxA9UmZWyZEK44HagYenSpaxatQqAYcOGMW7cOOLi4vDxqVGShBBC1D9zsQo0+LvO0CpRl0UgNZvKnIhoBy26uOwwYbPZSEhIIDs7G7PZXO4+xcXFtTNGIRqCvBS1ZCIwwrlrTOo+2PyC877D5kDbIa7PVZimntZFdaiNkYoGxNvb8WHJZrOV2VZVOp0Oi8XisXEJUW8K0uDb2c5Zm/EDYMCdKvOzVV9ZMiFccjsq8O6776LT6Xj88cdZtGiRJ8ckhBD1S58N5kIIrEIq4JHv6q4IZME5CI5V6dveZafv1NRUHn/8cVavXl1pIMF+Ey1Ek5OfCumHwS/IeZlD9glY9yTYSgXf+t2mKqa7os8C32CV7eDtW3tjFg3Chd0kXG0TolkwFcH6p+HMLse20JYw8kk1N0Z1kiUTokJuBxqSkpJKAg1CCNFk2FtGevu5zBgoYTXDoa8cr2uzCGRRBviFQFxP5ye05509e5ZLLrmEs2fPyo2xaL6KMlUbSy8fCCjVBSA/Fb57FEyFjm2dR8Pgu1yfy6xXHSta95Mnds3Epk2bSr6eMmUKAJ9++iknT55kzpw5FBUVceONNzJq1CjatGkDwJkzZ9i4cSOrV68mODiYV155RYqji8bPaoGd78O+VY5t3v4wdiGYDaqDT3RX8KrFelSi0XM70BAZGUlxcTHBwVIUSQjRhBjzVaS+Kh8sTmxWLSbtev2ldopAGvKdOkyU55lnnuHMmTOEhoby3HPPMWnSJFq3bu0y7bdTp06eH6cQ9ak4R60ltlmcaykU58B3j6ifa7tW/WDEPNc/rxYjFGWr7KHQVrU6bNFwDB8+vOTrwEAV0O3Zsye33HILoaGhbNmyhd69e5c57q677uKJJ57g6quv5qmnnmL37t11NmYhasWJTbBxofO24Y+qexDfIIjtAT7+9TM20Wi4fUd85ZVXkpeXx5kzZzw5HiGEqF/6bFXwyCeg4v00zbnNk28Q9Lja8+MxF4OxQH3gqaAQ3ffff49Op+Pdd9/l/vvvp23btm6tLRaiUSrOhXMHwKR3/jkxFcF3c1V7S7sWXWHcc84FIkuzmlSNlqhO0KJz5ZlNoklbtGgR586dY8WKFeUGGex69erF8uXLOXPmDM8991wdjlAID8tMgK8fcC7+ePFfoc0gFZyN7Vm1Glai2XM70PD444/j7+/P3LlzPTkeIYSoP1aL+kBSlfZ15/ZD5jHH6+5Xe77tnb3DRIsuqgBkBTIyMvDx8WHy5MmeHYMQDZ0+G1L3q5avoXGO7RajqsmQleDYFhYPE15w/bNqs6jiZxHtVXBPKqk3e2vXriUgIICrrrqq0n2vuuoqAgMD+eabbyrdV4gGyZAHX94HBWcd21oPgAG3g6kYYnqoTj5CVIHbgYY+ffqwZs0avv/+eyZMmMCmTZsoKiry5NiEEKJuGXLVL9mqLJsonc2g84KLbvD8eArTIaJNhR0m7GJjYwkMDJTOP6J50WfDuT9V8dbQOMfPic0CGxdB6l7HvkEt4Jp/QVBU+eeyWVUth7B49cSunIKrovk5e/ZsteZVHx8fUlNTa3FEQtQSqxl+nA+ndzi2hcTByCdU1liLLhDept6GJxoftwMN3t7eXHPNNeTl5fHjjz8yZswYwsLC8Pb2dvlHboCFEA1aQZpaEuFVyVyVfwaSSrW0bH8FhHl4Hbc+WxV/bNGlSh94xowZQ0FBAQkJCZXuK0STUJSp2lVailUdBXuQQdNgyyuQtMWxr18IXP2S63oL9taxoS0hrhf4uFhWIZqdiIgICgsL2bVrV6X77tq1i4KCAsLDy6+lI0SDpWmw8wPY/aFjm7cfjP2nWsIZ0V4tJ5OlZKIa3A40aJrm1h8hhGiQzMUqg6Aq2QwH1qCqM57X50bPjsViUH9iulV5HeQTTzxBcHAw8+bN8+xYhGiICtPVcgmrST1xK+2PFXD0O8drb38Yv1jdJJdH01QmQ1ALiOtdblcX0XyNGjUKTdOYNm0aWVlZLvfLzs5m2rRp6HQ6Ro0aVYcjFMIDTmyGn55y3nblo+oeJCRW3Y9IlpeoJrf/xZRuASSEEI2ePlulXwdW0hPaVOj8ISamO8Rd7LlxaDYozICoztWqdt+lSxe+/vprbrjhBsaOHcsTTzzBkCFDpDOQaHoK0iDtAGjWsgVS9/8P9v7X8VrnBWOfgZYV/IwWpKlK6nG9PV9nRTR6CxYsYM2aNezbt4+ePXty7733MnLkSOLj1e+KM2fOsGnTJt5++20yMjIICAhg/vz59TxqIaohMwHWTLug+ONNql23l69aSiYBWOEGtwMNpVsACSFEo6ZpKm3a26/ytMAja1X2g93FN3k2lbAoA4Kiq13tvnSHiY0bN7Jx48YK97fZbG4PUYh6k5+qggw6HQTHOL93bB38/h/nbSMeg3aXuT5fYbq6gW55UdWymUSz0717d7744gumTJlCZmYmixYtYtGiRWX20zSN0NBQVq1aRY8ePephpEK4oTgX/u92de9h12Yw9LtVZYxV0FZbiMrUQsN3IYRoZIz5oM+q/IOGzXJ+2cR5wdHQaYTnxmEqBHQqRdG3kvaaF5BlbKLJyzujCj/qvNQyh9JO/QY/v+C87bJ/QNcKOgXos1Q9lpYXQWCk58crmozx48dz8OBBZs6cSWRkZJn5NDIykpkzZ3LgwAGuvroW2hwLURusVvj8Hsg47NgW2UEFaM16iO7m3MlHiGqSxTZCCKHPVq3wfGIq3i9pKxSmOV73/kvlhSOrymYBfQ7E9nKrdVR1l7NNmTKl2tcQot7kpkDaQVWk8cKgQOp+WP+MWnZk1+9WlW3kSnEu2GzQqo+0ahNVEh8fz3/+8x/+85//cPLkSdLT0wHV8adjx471PDoh3PDTfEj8yfE6IFwVfzTpIbqrKgApRA24fYf8yy+/uHXclVde6e4lhRDC82xW1UXCL6jyfUu3tPQJgB7Xem4cBWkQ1hoi3fvFXt3lbIGBst5SNBK5ySrI4BsAARHO72UmwA+PqxRfux4TYfA9rs9nyFdrkVterLpMCFFNHTt2lOCCaNx2fQi/v+l47eULY54FdKrFb4su4CWJ76Jm3A40jBgxAl011yXrdDosFou7lxRCCM8rzlEfPEIqyWZIP6Q+7Nh1G+e5Nd3FueAbpJ4gePt65pxCNAW5yXDuIPgFll0nnJsM3z0K5iLHtg5XwhUPua5vYsgDUzHE9ZR+8EKI5inpV/juYedtwx5WdW9CYlWLX7kXER5Qo5zf6q7zlXXBQogGpyhDpVxXtgRi/2fOry+6wTPXtxjVWsiWfaTgkhCl5Z1Wwb3yggyF6bD2ETDkOrbFD4TRT4GXN+Uy5ILZAC17Q0S72hq1EEI0XDmn4P9uA6vZsa3fLWoZWWCUtPgVHuV2TozNZqvwT25uLuvWrWP48OG0aNGCn3/+WaqcCyEaFrNBdZuoLDOhMA1O/ux43e5Sz3xQ0TQV6Ahvp1IVhRBK3hk4d+D8cokLggzFObD2YShKd2yL7QVXLVSdY8pTnAMWk1ouIUEGIURzZCyAT26E4mzHtg5XQveJ4B8mLX6Fx9Xa4puwsDDGjh3Lxo0bufzyy7nuuus4efJkbV1OCCGqrzgbjIWV/2I98IVzobmKisxVhz5TrTlv0VnWQgphl39WtbD08S9bk8FUCN/NhbwUx7bIjjB+iVp+VB59NlgtEHeRLJcQQjRPNiv87w7IPObYFt0NhtwD/iHS4lfUilq/s9XpdLz44ovk5eWxcOHC2r6cEEJUjaZBfir4+Kp2ea6Y9XDkG8frqM7QekDNr2/Wq9ZS0d2qVohSiOYgP1VlMnj7QmCE83sWI6x7ErISHNtCW8M1/3J9g6zPUkHClhdDuGQNCSGaqXWPw/GNjtdB0aoug1+oCsJKi19RC+rkEVr37t0JCwvjp59+qnxnIYSoC8YCldHgX0kE/+gPYCpVbO7iG10XmqsqmxWKsiCqoyq8JIRQy5jSDqgaCxfe9NossP5pSN3n2BbUQgUZglqUf76iDECn6p+Etaq1YQshRIO24x3Yvszx2icARjym5s643hDsYg4VooY81AC+YmazmeLiYgwGQ11cTgghKqfPAnMxBEe73kezwYHPHa8DI6HzqJpfuzADQuMgqlPNgxZCNAUFaSrIoNNBUJTze5oNNi+B5N8d2/xD4eqXVEvY8hSmq3ZtLS+SYJ4Qovk69iN8P9d529DZEN5W1bYJjauXYYnmoU4yGr788kvMZjOxsfLLXgjRANisKkW7siULp7ZB/hnH616T1LrxmjAWgLcPtOgKPi4K1wnRnBSmqyCDppXNTtA0+PXfkLjesc0nACa8oAJ15Z4vTRWFbNVHggxCiObr7F5YfSdoVse2AVOhVV/VwlKWk4laVmsZDSaTiZSUFD7//HOef/55dDodEyZMqK3LCSFE1RXnqlZ3ITEV7/dnqZaW3r4q0FATmqauHdOz7FNbIZqjokxVk0Gzqh7uF9r5Lhz60vHayxfGPaeexF1I01SQwSdQ1WSQdGAhRHOVmwL//Zvz0s8uY6DrVRDTQ2U0CFHL3A40eHu76FNdDk3TiI+P5+mnn3b3ckII4TlFGYANvCqYAjMTIHWv43WXsTUvllSco6roR0jleyEoyoLUP8FmLpt5oNnUmuL9/+fYpvOC0QsgfmD55ytMU50nWl4sgTxRK2w2GwkJCWRnZ2M2myvc98orr6yjUQlxAX0O/PevUHjOsa1lH+h7mypAHdlBlm2KOuF2oEHTtCrtFxgYyA033MDixYtp3drFWkohhKgrFqMqOucXWvF+f652fn3xjTW7rs2iakK06ga+gTU6VXJycs3GAlgslhqfQwi3FefAuT/BZiobZLCa4ecXIfGCAtLD50LHYeWfryjDkckgQQbhYampqTz++OOsXr2a4uLiSvfX6XQyx4r6YdLDZ3dC+iHHtvC2cMm9ENtd2mmLOuV2oGHTpk0Vn9jHh8jISLp164aPT53UnBRCiMrps1WdhPAKAp/6LDi+wfE6fqDr9eBVVZSpPlCF1rz6fceOHWt8DpvNVuNzCOEWYyGkHVSBt7CWzu+Z9fDT03D6j1IbdTD0Qeg2vvzzGXJBQ605liCD8LCzZ89yySWXcPbs2So/ZKvqfkJ4lNkAax+Bk5sd2wLC4YqHIKYbtOiiuvoIUUfcjgAMHz7ck+MQQojaZ7NB/llVjFFXQUT/wBqVgWB38U01u67FoD4IRXZU164huYkVjZbZAOmHVa2SCztGFOfAD49BxlHHNi9fGPkEdB5Z/vlMejAVq0wGKfwoasEzzzzDmTNnCA0N5bnnnmPSpEm0bt26WkuIhah1ZgNs+Rfs+8SxzcsXhj2sik/HdJcC1KLOSaqBEKL5KExTaxYramlpyIeDXzheh7eFtkNqdt2iLIhoX/F1q+HkyZM1PscVV1zhgZEIUQ1WiwoiFKSqIEPpNcL5Z+G7R527vPgGwVWLIH5A+eezGFWGUkwPCJe6J6J2fP/99+h0Ot59911uvLGGS+iEqA0WI+z5GLa84rx96IOq+HRsD9USWIg6JoEGIUTzYDFC9gnVPcK7gqj+gc9V+rZd/1srzn6ojLEAfAMgsr3Hii+1b9++xufw9fX1wEiEqCKbDbISIPcUhLZ0Tt/NTFB93otzHNsCo1QLy+iuLs5nUW0xozpBi05S2EzUmoyMDHx8fJg8eXJ9D0WIsixGOPId/DT/gjaWd0Dr/moOlWwvUU88EmjYtm0b+/fvr1IV3gULFnjikkIIUT15Z1TthYr6RpuKVKDBLrSVagflLs2mUsRje0FAmPvnaYKysrJ4+eWX+fbbbzlx4gQmk4nY2Fguv/xyHnjgAcm4aEo0DXKSIOu4ainrXSrIdXon/LTAObgX3gYmvARhLuqZaDbIPwdh8aqCuqw5FrUoNjaW/Px8qTcmGh6LCU79pgK1pefQrmOh8xjVXSKi5g8mhHBXjWbN9evXM336dE6dOlXlYyTQIISoc4Z8yDmpiiJVlJ1w8EswFTpe97u14haYlSnOUS0xJa3bSUJCAsOHDyc1NRUvLy86dOhAWFgYx48f57PPPmP16tW8/PLLPPTQQ/U9VOEJ+Wcg4wgEhoNPgGN74gbYvNi5HkpMDxi/BAIjXJ+v4Pzyp9iesuZY1LoxY8bw4YcfkpCQQNeuLjJshKhrVguk7ldLzorSHdtb9YV+t6tCu9FdpcOEqFdu30Hv2LGDiRMnYjKZAFUFvXXr1hLxFUI0LJoGOadUhfuKshnMxfDn/xyvg2Og2zj3r2tvZxnTQy2d8KDG3t5y5syZpKam0rVrV7788kt69eoFgMFg4KmnnuLll19m7ty5TJw4UW7sG7vCDFX80TcQ/EIc2//8DLa96bxv2yEw5hlVm8Hl+dLVeWJ7gl8F+wnhIU888QSrV69m3rx5rFmzpr6HIwTYrJB2CH58Qi1JswtvqzpMBISpew8f//oboxDUINCwcOFCTCYTPXr04H//+x8XXXSRJ8clhBCeUZQBeSmVF2I8/DUY8hyv+97snOLtznVDWnqkneWFGnN7y4KCgpL2yC+99FJJkAEgICCAl156ia+++orExETWrVsngYbGrDhXtbFEc85Q2PcpbH/bed+u42D4oxVnEBXnqoyk2F4VZzwI4UFdunTh66+/5oYbbmDs2LE88cQTDBkyhODg4PoemmiObDYVvP1hLqRsd2z3D4PRC1TWWEwPlcEpRD1zO9Cwbds2dDodK1eulCCDEKJhsppVAUgv74oj+xaj+vBjFxgFPa5x/7oWA6CDqI61sn68Mbe3NBqNJePv3Llzmfd1Oh2dO3cmMTGx0po/ogEzFaknbma9c62F4xvLBhn63gxDpldc0NFUpH6uWl6s6jwIUUdKt7HcuHEjGzdurPQYnU5Xr1ljoonSNNW5Z+0c5yCDl6/KBvMJVK0sXdW3EaKOuR1o0Ov1BAUFMXDgQE+ORwghPCf/rMosCG1Z8X5H1jpXvO87pWYph0VZqghTUAv3z1GBxtzeMjo6mjZt2nD69Gl+++23MoHqoqIi9u7dC8CQITVsKyrqh8WonrgVZzvf8J7br2oylHbpvdDnb5WfrzhXLZcIq2D5kxC1oDEHdkUTommqQ883s+D0H47tXr4w9lnVWSK8nXrAIUQD4XagoX379h5ZJyyEELXCVKSyGfxDKk7Htppg3yrH64Bw6Hmt+9c1Fqj16BHtaq3lXmNvb7lkyRJuv/12Hn30Uby8vJg4cSJhYWEcOHCAxx9/nLS0NG677TaGDh3q8hydOnVy+V5KSgpt27atjaGLylgtkH5EBfnCWjmKr+adhnVPqSwju743Vy3IUJgOUZ0hsqO0sRR1zr7US4h6lXUCvpwJZ3Y5tnn7wlXPqfsN/3CIkS48omFxO9Bwww03sHjxYn755ReuvPJKT46pDE3T2LZtG19//TVbt27l8OHD5OfnExERQf/+/Zk6dSq33HILOhc3IIWFhSxZsoTVq1dz6tQpQkJCuOSSS3jkkUcYMWJEhdfetGkTL7/8Mtu3b6ewsJD27dtz00038dhjj1W4Pq8m1xRC1JCmQXaS6iBR2RPQY+tU1oNdn7+qQIFb1z3fzjLuImlnWYFbb72V8PBwFi1axLRp05zea9WqFW+99RYzZsyop9EJt9lsqjBZ7ikIjXME+IpzVfs1Y75j304jYci0ck9Twh5kiOx4/gZaqqeLujd8+PD6HoJo7rJPwpq74ewexzZvfxj3HESdD7rH9nD/3kWIWqLT3MwJKywspH///gQEBLB582ZatKidFGGADRs2MGaMo5d9p06diIyM5OTJk2RnZwNwzTXX8Pnnn+Pv75zunJmZyRVXXMHRo0fx9/enV69eZGRkcPr0aXQ6HW+88Qb33Xdfudd9/fXXefDBB9E0jTZt2hATE8OhQ4cwGo307NmTrVu3EhUVVea4mlyzquxP806cOFGj8wjRJBVlqdTCgLCKf/HaLPB/t6l2eQD+oXDzp+DnZpEvfZb65d92SL1Xe9Y0jaysLPR6Pe3atSvzfn3PIUuXLuXf//43p06dol27doSFhZGYmIher+eSSy7hnXfecbv+T31/b82SpkFmImQeheAWjjaWFqNaT5x20LFv3EVwzcsV/4yYi9XPcVQniOkO3tLRStStpjqPNNXvq8nKPgWf3Q6p+xzbfAJg/GL1IEXnBXG9Iax1/Y1RNDtVnUfcfjywe/duFi5cyNmzZ+nduzcLFixg7dq1/PLLLxX+cYemaXTs2JHXXnuNtLQ0jh8/zs6dO8nKyuKjjz7C39+ftWvXsmDBgjLH3n333Rw9epSBAwdy4sQJdu/eTXJyMsuWLUPTNB544IGS9cCl7dq1i9mzZwOwbNkykpOT2b17NydOnGDgwIEcPny4zJO4ml5TCOEBNqtaMoGt8uh+wk+OIAPARTe4H2SwWcBsUB+M6jHIsHv3bq6//nrCw8OJi4srs8QgJyeHGTNmkJmZWW+dJ/7xj3/w0EMPER0dzeHDhzl58iT79u0jMzOTRx99lO3btzN06FBOnTpVL+MT1aRpkJOkggyBkY4gg2aDTc87BxnC28C4RZUEGfQqaBfdVT2lkyCDaGCsVisZGRlkZGRgtVrreziiqcpNgf/d5hxk8A2E8UsgJE7Nta36SZBBNFhuZzR4eXm5XKrg8mJuVuHNz88nMDDQ5Zri559/nieffJKoqCgyMjLwOp9euWfPHgYMGICXlxdHjx6lS5cuTsfdcccdrFy5kuuvv57PP//c6b3Jkyfz1Vdfcccdd/Dhhx86vZeQkECPHj2w2Wzs27ePPn36lLxXk2tWh0SkhXAh7zSc3eucul0emxU+m6r2B/ANglv+T2U1uKPgHATHQOv+9bZGcuXKldxzzz1O3Rp0Ol2ZG+ExY8awYcMGYmJiSE9Pr9Mx7t+/n379+uHj40NCQkK59Sbs45sxYwZvv/12OWepmMyPdSw3Bc79qeqhlP75+f0t2P9/jtcB4TDpTRVscMVUpNrMtugKLbrIcglRby6cR/R6PW+//TarVq1i//79JfezPj4+9O3bl1tuuYXp06cTFBRUb2OuCpkfG4ncFFg1BdIOOLb5BqlMhsAWqmV3XC9pYynqRa1nNIDKNKjOH3efnoWFhVVYuGzChAkAZGdnk5HhWGu9evVqAEaNGlXmAz9Qsgb4u+++o6ioqGR7YWEhP/zwAwDTp08vc1zXrl0ZNWoUAJ999pnTe+5eUwjhAeZiyDquIv4VBRkATmx2BBkAel/vfpDBXKz+G1k77Syr4tChQ0ybNg2z2cwDDzzAzp07iY6OLnffqVOnAlBcXFyXQwRg69ataJpG165dXRa1vOqqqwDYuXNnXQ5NuCM/FdIPgV+g88/PwS+dgwzevjDu+YqDDMYCFWSI6SFBBtGgHD16lH79+vHoo4+ya9cuzGZzyb2t2Wxm586dPPzww/Tv359jx47V93BFY1eQDv/9m3OQwS/4fJAhSmUwtOorQQbR4Lmdj1hfKbflKX2zHBjoSJX+/fffAVwWqxwyZAj+/v4YDAb27t1bUuF8z549GI1G/P39XbZXGzZsGOvXry+5Rk2vKYTwgNwU9UGlog8zoFK696x0vPYJgD43un/doizVUiqobM2WuvLKK69gMpn4xz/+wdKlSwHn/u+ljR49GgCj0VhXwytRUFBQ5X0NBkMtjkTUWGGGCjJ4eUNAhGN78jb47d/O+458Uq0jdsWQr5ZMxPZSrWGlu4RoIAoKCrjqqqtISUnBx8eH66+/nrFjx9Kmjfo9c/r0adavX8/nn39OQkIC48aN488//yQkJKSeRy4aJUMefDwZ0kstOfMPhbELVSZDVEeI7gY+fvU2RCGqqkksfFy1SrWm69u3L2Fhjkrv9qhy586dyz3O19eXtm3bkpiYyNGjR0s+9NuPa9eunctMCvs5jx496rTd3WuWR9q3CVENxTmq2n1QVOUfUk5uUWvK7XpNcv6gVB2GfPWkIbJ9vX442rRpEzqdjnnz5lW6b+vWrd1eylZT3bp1A9QStFOnTpWb1fDjjz8C0L179zodm6gGfbaqvWCzqP7tdhlHYf2zKphnd8lM6DTC9bkMuaq+SWyvWm0LK4Q7li5dSkpKCq1bt+bbb7+lX79+Zfa5++672bdvH9dccw3Jycm89tprPPnkk3U/WNG4aRqsvsu5ro1/GIx9RtVkiO6mAg3SwlI0Eo0+L3HXrl0la3gfe+wxp/fsHSnK6wxhZ38vJyenxsfV9FghhJtsNtX+yWqqvJijpjlnM3j7qZaW7tBs6ulDZAf3l114yNmzZwkODi55ylYZnU6HmyV6auSqq64iNjYWs9nMTTfd5JRmXFxczNy5c9mwYQOgatqIBsiQp26ELXrnIENhGvzwOFhKZaL0mgR9/ub6XMU5YDGpThT1HKwTojxffvklOp2OZcuWlRtksOvbty/Lly9H0zTWrFlTdwMUTcfGRZC43vE6IAJGL4DwdtDyYmjRWYIMolFp1BkNaWlpXH/99VgsFv7yl78wZcoUp/ftabd+fq7Ti+ztMEsvv3D3uJoee6GKCmxUlO0gRLNTmAYFqao4UmWSt0FWouN1j4kQ5GZ73uIcVWU/PN694z3IviRL07RKC/UajUZsNltJ4dy6FBwczCeffMLkyZP5448/6NmzJ+3btyc0NLSkvSWozhSTJk2q8/GJSpiKIO2QCjaUrnSuz4K1j0BxtmNbu0vh8lmugwf6LBX4a3mxVE0XDVZiYiL+/v5cc801le47YcIEAgICSExMrHRfIZwc/ga2vOx47eULwx5WhXHjejkHdYVoJBptRkNeXh4TJkwgOTmZgQMH8sEHH5TZJyBAtdgymUwuz2Nfo1y6toO7x9X0WCGEG2w2tWTC21dlJ1RE02B3qWwGL1/oO8X1/hVet2G0s7Tr1KkTZrO5SoXI1q1bB1QcEK1NY8aMYf/+/dx///1069aNc+fOcfjwYcLDw5k0aRLffvstb7zxRr2MTVTAXKwyGfRZENbKEUDQZ8E3syEvxbFvi67qSZyroqz6LPVfCTKIBs5sNld5rtTpdPj5+Tl1/hGiUhnH4Mt7gVJZhgPvhPhB0LqfBBlEo9UoAw2FhYWMHz+ePXv20Lt3b9atW+dUm8EuMjIScCxnKI/9Pfu+NTmupscKIdxgyAV9TtVqLJzZCRmHHa+7j3f/F3hRhlozGdrSveM97Oqrr0bTtJJCkK4UFBSULDOrzzZsnTp14vXXX+fw4cPo9XpMJhNnz57lyy+/rNKTQ1HHLCaVyVBwTv2b152/fdBnw7cPOQcZQlur6ui+Lv59GXJVgDC2d4P5+RHClTZt2lBQUMChQ4cq3ffAgQPk5+dXeQmbEBgL4dObVdcdu65jofdkaNUHAiPqa2RC1FijCzTo9XquueYafv/9d7p27cr69etp0aL8tGd70TFXKWxms5nk5GSnfUt/nZyc7DIqffz48TLH1eSaQgg3FaYBVpXRUBFNg90fOV7rvKHfLe5d02IAdA2qKNPs2bMJDw9n+fLlzJ8/n9zcXKf3i4uLWbNmDUOGDOHIkSN4e3sTGlq/dSVEI6FpkJkA+WdUJoP937w+G9bOgdxkx76hreHapa6XMRkLwGRQqcBhrWp96ELU1OjRo9E0jXvvvbfCTjgGg4H77rsPnU7HmDFj6nCEotHSNFhzj/NyzpieMPAu9V9/6VwiGrdGFWgwGAxcd911/PLLL7Rv354NGzbQsqXrpyGXXnopAFu2bCn3/R07dmAymQgICHAq8NO/f3/8/PwwGo3s2LGj3GPt57zssss8ck0hhBvMxZB/TlVlrsyZXXDuT8frrldBqJsfdIqyILyt+7UdakF0dDSfffYZAQEBPP/888TFxZGZmQmoLhPh4eHcdNNNHD16lJCQEGJjY+ulRoNohApSITcJQmIcSyGKc1SQoXT3ltBWcO2rrrOETEUq0BDbo/IWtEI0EI8++ij+/v5s3bqVvn378u6775KUlITZbMZsNnPy5Eneeecd+vbty9atW/Hz8+ORRx6p72GLxuCXl+Do947XQS1UXZuWF0Fww7m/EMJdjeYu02w2c8MNN7Bhwwbi4+PZuHFjpe0db7zxRkC1fSsvw2DZsmWAKt5Tut9xaGgo48aNA2D58uVljktISGDjxo1O16jpNYUQbtBngakA/Cr5WbKa4NfXHK91XtD/VveuaSwA38AG2YZvzJgx/P7774wYMQKz2YzVakXTNM6dO4fFYkHTNEaMGMG2bdtK6skIUSFjocpm8PEHn/P/Zopz4dsLgwwtYeKrajlRecx6dVxMD9WlRYhGolOnTnz44Yf4+PiQkJDA9OnT6dy5MwEBAQQEBNClSxdmzJhBQkICvr6+fPjhh1KwW1Tu2E+webHjtZcPXHY/tOoHYRKIFU1Dowg0WK1WbrnlFr777jtatmzJxo0bqzSJDxgwgIkTJ2K1WpkyZQqpqakAaJrG8uXLWblyJV5eXjz11FNljp0/fz46nY6VK1eWtCsCSE1N5eabb8ZmszF58mT69u3rsWsKIarBZoP8s+AbUPkH/r3/dV5D3v0a956oajb1YSmyAwRUIYuiHlx88cVs2LCBkydP8vHHH/Piiy+yePFi3n//fY4dO8bGjRvp3bt3fQ9TNAY2q0rpNRZA4PmWzYbc85kMJx37hcSpIIOregsWIxRlqwKRkR0bXIBOiMr89a9/Zdu2bSUPoTRNc/qj0+mYMGECv//+O3/9q5vtkkXzkX0SPr9L3VPYDZgKHa6A6C4g2YaiidBp9dFIvZpWrVrFLbeotdQdOnQgPt51K7nXX3+d/v37l7zOyMhg6NChJCQk4O/vT69evcjMzCQlJQWdTsdrr73GrFmzyj3X0qVLmTNnDpqm0bZtW6Kjozl06BBGo5Hu3buzdetWoqPLrkOtyTWryh5oqagFphBNmj4bUrar9pIVdX3ITYHVd4HtfL2VwEj460fg70Z9An0WePtDm8EqwNGINeU5pCl/b3Uq5xSc26+WQnj7qZaW386B7OOOfULiYOJS1/UWrCYoSFfdWWJ7NJiaJkJUxtU8kpeXx+7du0lPTwcgNjaWAQMGEB4eXudjdIfMj/XMpIcVIyHjiGNbl7EqmyG+PwQ0jn9Honmr6jziou9Uw2JvBwmQlJREUlKSy33z8vKcXsfExLBr1y6WLFnC6tWrOXToEMHBwUyYMIFHH32UkSNHujzX7Nmzufjii3n55ZfZvn076enptG/fnhtvvJHHH3/c5dKHmlxTCFFFRZnqiWtFQQZNg62vOoIMAJf9w70gg72dZUyPRh9kEKJSxbkqm8E/1BFkWPuwc5AhOFZlMrgKMtgsUJCmshhiukuQQTQJ4eHhch8n3KNpqo1l6SBDTA/of4eaIyXIIJqYKmU0fPTRR5XtUmV33HGHx87VnElEWjRrFiOc2qZSsCtawnDsR9j8vON1/EC4+l/upW4XnFOFmloPAO+GF6NNSkri3//+N+3bt+fBBx+scN+XX36Zf/7zn4SHh5d0wWlKZH6sIasZzu6FonQIaw2GfBVkyEpw7BMco7pLhLnIMLRZVKHWiDaqjaWPX12MXAiPaarzSFP9vhqFrUth/dOO14FRMPZZaHe5WjIhRCPh0YyGO++8E50H1lTqdDoJNAghak6fpdaNh7d2vY8hD35/0/Ha2xeueMi9IIPFABrqyWwDDDIArFy5ktdee42XX3650n31ej35+fl4e8sTZlGO3GQVWAtrqYJ638+9IMgQfT6TwUWQQbOpIENYK9WiTYIMQojm7tg62PCs47WXj8qwbHmxFMgVTVaV7pjbtWvnkUCDEELUmKZBfqr68KKroGDS9mUq2GDX/3b3W+oVZUFEe/UBq4H6/nvVImvy5MmV7nvrrbeyYMEC9Hp9LY9KNDpFmZB1HIIi1I3w1qXOab5B0aomg6ufJU1T7TBDYiC2lywzEo2K/Sldly5dymyrDp1Ox/HjxyvfUTQPKX/AZ1PLFn9sdylEd2+wDzCEqKkq/cuuqCaCEELUKUMe6DMrXsuYuh+Ofud4HdEe+k5x73rGAvVhKbLhtbMsLSkpiaCgIDp06FDpvp06dUKn02GxWGp/YKLxMBtUK0tsqmVswo9w5FvH+4GRKpPBZZDBpoIMAZEQ1xv8gupk2EJ4iv1+t3T7X3fugeXhnCiRfhg+uRHMxY5tXcZAt/Eq40vmSdGESQhNCNG4FGaAxeS6CKTVDFsuWD4wbI4qaFddmqaK4sX0bPBFmrKzs10WqC2PTqfDZrNVvqNoHjRNtawsyoDweNVxYssrjvd1XjB6AUS0Lf94m9VRxySut3sFV4WoZ++//z6gCj7OmTPHaZsQ1ZabAisnq7bAdvEDVfHH2B4Q3KK+RiZEnZBAgxCi8bCYoOAsBFTwgXrfp5B7yvG6+9XQqq971yvOgYAIVdCugYuIiCAzM5OCggJCQyv+kFdQUIDNZsNLenULu8I0yElSy4MsRlWwzGJwvD/wTmjdv/xjbVa1nCk0VhV+9K96wEuIhmTq1KklX9sDDaW31TZN09i2bRtff/01W7du5fDhw+Tn5xMREUH//v2ZOnUqt9xyi2RMNAZFWSrIUHDOsS2mBwyZCS26QFjDv68QoqbkLlMI0Xjos8CYD/4uOk3knYY9Kx2vA8LhkhnuXctmUamOUR3BN9C9c9Sh/v37o2kan332WaX7/t///R8Afn5SpE8ApiLIOKZqMvgGwq9LVdDBrs1g6H9b+cfaLGq5RGhLiLtYggxC1MDGjRsZOnQoL7zwAr/++isRERH07dsXm83GTz/9xG233ca1117r1PZdNEDGQvj4etUi2C6iHVz+ILToBNFdQQL9ohmoUkaDtLcUQtQ7e5E5L5/yi0BqmipcZzU5tl16n/tLHgrTILSV+tMI3HDDDaxbt465c+cyaNAg+vTpU+5++/btY968eQAEBwfX5RBFQ2SzqeKPhjy1ZOLo96o6ul1QNIx8svyfOZtFZTKExUNcr0YRkBOiuu666y4iIiJ45ZVXKt8ZmDt3LllZWbz77rvVvpamaXTs2JHZs2czZcoUYmNjS95buXIl06ZNY+3atSxYsIAXXnih2ucXdcBigk9vhtS9jm3BMXDFw+rBRWwv6cQjmg2dpmlaZTt5eXl5rL2lFB/zDOmDLJodQx4kb4eAUPApp5J94nrYuMjxunV/uOYV9wo4GnLBpkGbgQ2+NoOd2WxmwIABHDx4kICAAKZNm8bEiRNp3749AKdOneKbb77hnXfewWAw4OPjQ3x8PCdPnqznkXuezI9VpGmqlWXaAVVbIf8MfDHTEazTeakOE63KCVpZzSolOKKtqmEi3SVEE2OfR5KSkmjZsiVnz56t0nEdO3YkOTkZq9Va7Wvm5+cTGBiIr69vue8///zzPPnkk0RFRZGRkeHW8jeZH2uR1Qqr/w6Hv3Js8w+F/2fvvMOjqrY+/M5MJr2HFBISeq+hI1IFwY4Fe+/tU6/litferv1ar4J6bagoCCoWUBCQovTeAwQSIAnpPZOZzPn+OJlypiQhnbDe55knmXP2OWfPnJk1e6+91m+d9bSaNtF+IPh7icgUhFOIutoRKW8pCMKpQWkuVJnAJ9p9n6kY/v6v47neCGf+o35OhiqzGvbYfuAp42QAMBqNLFy4kClTpnDgwAHee+893nvvPbd2iqLQvXt3ysrKxK6fzpiKIe8wFKarqufWKljytDYiaNitXpwMlVCcpVZziektq3OC4EQd1u+8Ehpa8yT0nHPO4fHHHycvL4/s7GxiY2PrfS2hkVEUWPSw1slg8INxMyCymyqSK04G4TRDylsKgtD6qTKrq62+XvK/132oCjfaSL5GzYesDyUn1PJ9oQn1O74F6dy5M5s2beLVV1/l008/dVuBS0hI4JZbbuHhhx/2mlohtHGqzKqWSV4qmMtU8UeDrxoNVJjuaJc00nNJWItJ/Y5EdFZV0w2eV14F4XQlJyeHwMCmKVlYXu4okRgQIKlKrQZFgeUvwsZPHNt0ehj3CET3VFPLAiNbrn+C0EJI1QlBEFo/ZXmqCGSwh9WbzJ2w9yfH87BEGHR1/a5Tng++QaoitN5Qv3O0MCEhITz//PM8//zzpKWlkZmZiU6nIy4ujsREL6UJhbaPoqilK3MPqX/9Q1RNBoDdC+HgH462QTEw/jF3XQZLBZTmQGQXNQzYIEMIQbBRWFjIxx9/TFlZWZM5cufMmQPAwIEDa4x+sIU1eyI9PV1+Cxqb9R/Byte020bfr1bhiekNIRJ5IpyeyChBEITWT3GmmgbhOvm3WmDVG9ptYx5UV2hPFotJrTLRfqCaU9kGSEpKIimpnpEdQtvBOU1Cb4DQ9o7vUk4K/P2uo63OAJOedk8bMper6UvtukG7HqesI04QauPZZ5/lueeew2q1Aqq+WFZWFgZD3T7zOp2OSy+9tNH7tWnTJmbOnAnAjBkzGv38Qj3Z+yssdrkfw25Tq/XE9D4loyMFobFoFEeD1Wpl06ZNHDlyhLKyMqksIQhC42EqhtITnvUS9vwM+U5iht2nqCKQJ4uiQEm2mm4REl//vgpCa8JSqaYcOadJ+Pg59leWwNKn1XQKGyPuVHOJnamqVEvLtuuhOhrEySC0cZx1FnQ6XZ11F3x9fbnuuusa3RGQlZXFJZdcgsVi4eKLL+bKKz2kNTlRk0BbTdEOwklSeAy+vx0UJ+HP/tOhyzhVlyG8Y/20ogShjdBgR8O7777LCy+8QE5Ojn2bs6MhPz+fMWPGYLFY+PPPP0W4RhCEk6M0R11NDWqn3W4uh81OpXf9QmDkXfW7RnmeKtIU1fWUqG197NgxEhIaf5UkIyOD9u1PjXKeQi2UnFDLVpbmaNMkbFir4M9XochJx6PTmdD/Mvd2NuHHqK7iZBDaPDfeeCPjx4+3T+azsrKIjIxk/vz5Xo/R6/WEhobSo0ePRtdOKCws5JxzziEtLY0hQ4bw2WefNer5hXqiKLDgdnUxxEb3KdDrAlXDpl23U2I8IQhNSYMcDffccw8zZ85EURRCQ0MpKSlx8/pGREQwePBgvvrqK+bNm8e9997boA4LgnAaUWWpFoEMct+3a4HqILAx8GoICD/5a1hMYK6A+N7g50VsspXRrVs3brvtNmbMmEF8fMMjML777juee+45LrvsMp566qlG6KHQopTmQMY2UKzaNAkb+Ufgz1fgxG7HtpD2MO5R99W3kiwIjoHoHqLJIJwWdOzYkY4dO9odBklJScTGxjJu3Lhm70tJSQlTp05ly5Yt9O3bl99++63WyhRCM7H2Aziy2vG8XQ9VQDciSdLLBKGaervaFi9ezAcffEBwcDDff/89BQUFREd7KDsHXH311SiKwtKlS+vdUUEQTkPK86C8wL0klKkYts5xPA+Mgn4Xn/z5bQJ54R3VidYpQnx8PO+99x7dunXjyiuv5Keffjrpmu1ms5n8/Hx69OjBFVdcwe7du+nUqVPTdFhoPqrMkHdQ1S8JjtEOdq0W2PoVLLhV62TQG1VdBldtktIcMAapecZGUbgXTk8OHz7MunXrmv26ZWVlnHfeeaxdu5bu3buzdOlSoqKimr0fggey98MfzzieG3xVXYawBFUoV0r+CgLQgIiGmTNnotPpeO6557joootqbDtq1CgAduzYUd/LCYJwOlKcpSrf611M1bY5an65jcE3gI//yZ+/PA/8wk6ZlAkbe/fu5Z133uHFF19k7ty5zJs3j/DwcEaMGMHw4cMZOHAg0dHRREZG4ufnR35+Pnl5eRw6dIj169ezbt06jh49CqhhuWeffTavv/46/fr1a+FXJjSYwqPq9ybUxXGWd0iNYsjep91uDFQjGaJ7abebitWIiJjenvVRBEFoMioqKrjwwgtZuXIlHTt25I8//iAuLq6luyWA6sydf4saDWlj0DVqCcuYvuKUFQQn6u1osHl3b7755lrbhoWFERoaSmZmZn0vJwjC6YapBEqz3Cc5ZbmwwylXNjQBep178ue3VKhiefF9wbdpap43FUajkYceeoibb76ZWbNm8dFHH5GamsrixYv57bffaj3eluIWFBTEokWLGDNmTFN3WWgOKgpVh4J/mMM5Z7XA1q9VPROrRds+cTiMeViNfHDGUgEVRaoopJRlEwQNJ06c4OjRo5SWltYoEjl27Nh6nd9sNnPppZfyxx9/kJCQwLJly6QcZWtixcuQud3xvP0g6HkORJ866ZeC0FzU29GQl5dHWFgYISF1KwOn1+vtpYIEQRBqpTQbKsvVtAhnNn8BVU4rCUNvdo94qA1FgZIciOwCIafuKlFERAQzZsxgxowZLF26lMWLF7Ny5Uq2bNniMZUiLi6OsWPHMn78eF566SUMBoM4GdoKVqvqZLCUO8qp5aSoUQy5B7RtfYNg1L3QY6q7JoPVolZgieyqphQJggDAe++9xzvvvMPBgwdrbavT6bBYLLW2c6Wqqoqrr76aX3/9lbi4OJYtWyZVIloTRzfB6jcdz31DYOgtqiZDkKS1CIIr9XY0hIaGkp+fj9lsxmg01tg2Ly+PwsLCRhEtEwThNKA8v3pl1sWRWXRMLWlpI6ordJ1w8ucvy1GFI6O6tJnSU5MmTWLSpEmAuiJ24sQJsrOzqaioICoqiujoaMLDw+3tX3311RbqqdAkFGeoFSSCotXQ3i2zYctX2rJrAEmjYMxD7lVcQHXAFWepjop23U+pdCJBaEquvPJK5s2bV+cyl3Vt58rcuXP57rvvAPD3968xavjdd98lObke5ZyF+lFZCvNv1trUITdA/CC1NLYgCG7U29HQv39//vzzT9atW8eZZ55ZY9s5c+agKApDhw6t7+UEQThdsFSqK7EWk3ue+cZPtT/yw25TNRxOBnM5VFVBXI82m0tpNBpJSEhokhKYQiukskyNWvDxV510ix9THXXO+IXAGf8H3SZ7d66VVKcqRfcUMTNBqOabb75h7ty5hIWF8b///Y9zzjmHoKAg4uLiOHr0KJmZmSxZsoQXX3yRgoICvv32WyZMqIcDHDCZHNF6hw8f5vDhw17bFhYW1usaQj357XHIP+x43nG0mjLRrrtUmBAEL9R7ueKyyy5DURSeeeaZGlMitm3bxhNPPIFOp+Oqq66q7+UEQThdyD8MxZkQ7FLFJvcgHPjD8TyuPySOOLlzWy2qkn5kJ/e8dEE4FVEUyEtV9Rn8w2HZC+5Ohk5jYPpn0P1s706G8gIwGCGmj+QZC4ITn332GTqdjueff55LLrnEXvYS1LTg+Ph4brjhBjZv3kxiYiLTpk3jwIEDNZzROzfeeCOKotTpMX78+EZ6hUKtpCyFTZ86ngdGqQsd0b3a7IKFIDQG9XY03HbbbfTp04fly5czefJkfv75Z3tOcEpKCkuWLOG+++7jjDPOoLCwkJEjRzJ9+vRG67ggCG2QkhNqab7AKHfdhQ0fA07hqMNvO7m0B8VaHRYer2oztJGUCeE0pzQbCtLUVIiDSyHTqbqTfxic9TRMfs5d68SZyjI10ie6l+QZC4ILW7ZsAeDaa6/VbHddZAsODua9996juLiYV155pdn6JzQxZXnww13abcNuV1MmPKWgCYJgp96pE0ajkV9++YWpU6eyfPlyVqxYYd/Xq5ejTJaiKPTv35/58+ejk4G9IAjeqCxTS+/p9O5VIDJ3QNrfjudJIyFuQN3Pbcs9D4hUy/X5+DVOn09h6iNUJrQyLJVqpI9erzrS1s507DP4wrQPVMdaTVRVqmVe2/VyiEgKgmCnoKCAkJAQjcaN0WiktLTUre2oUaMIDAxk6dKlzdhDoclQFPjpfig94djWfYqaMiG6DIJQKw1SeurYsSObNm3i2WefJSkpyS2sKz4+nmeeeYa//vpL6v8KguAdq1WdMFUUqGJ2zigKrP9Iu23YrSd3/tJsVWk/to/6tw2xbNkyzj77bCIiIggNDWX06NF8/vnntR53/Phx0tPTm6GHQpNRkKZ+tgOjYNPnqsPAxqBrancyWEyqAy4sCSI7S5SPIHggKirKbaEsPDycsrIyCgoKPB4j5dzbCNvnwp6FjuehCTDiDtFlEIQ60mBJ6cDAQJ588klSU1M5evQo69ev5++//yY1NZX09HSeeuopgoLa1sBeEIRGpvg4FKapTgbXyU76em3N6m6TIKpb3c9dnq8OCGL7qpUm2hAffvghZ599Nn/88QeFhYWUlJTw999/c/PNNzN+/HiOHz/e0l0UmoryfMhPVT/TBWmw8zvHvpD2MPDKmo83FatOiojOENMLDPUOcBSENk1CQgJFRUWUlJTYt/Xu3RuA5cuXa9pu3ryZsrIyAgNdovKEU4+CdPj1IcdznUEV1G0/0D3qUhAEjzRq7ar4+HiGDh3KiBEj6NhR6m8LglAHKgohO0UVVHJNaVCssMEpmkFngKE31f3cpmJ11Ta6d5vLpUxJSeG+++7DarUSGxvL3XffzYMPPsjgwYNRFIVVq1YxcuRIUlJSWrqrQmNjrYLcQ2oZS99gWPO2+l2xccb/eU8PUhRVC8VcAbH9VAecpBIJglcGDx4MwIYNG+zbzjvvPBRF4eGHH2bDhg2YzWY2btzIDTfcgE6nY/To0S3VXaExKD4Bc69TxxA2+l4CPaa2ubGEIDQlJ+VosFgsFBUVUVRUVOdjbO1tQpGCIAh2qiyQcwAsZRAQ4b7/0Aq1bJ+NXufVPY/cXAYVRarAXVjbyz1/7733qKyspH///uzZs4f33nuP119/nY0bNzJnzhzCwsI4evQo48ePZ9++fS3dXaExKToGxRlqZZZDyyFjq2Nf4khIGuX5uCozFB4DYyAkDFbTJfSNut4gCG0Om1Nh3rx59m133XUXCQkJpKamMnLkSPz9/RkxYgS7du3Cx8eHxx9/vAV7LDSIvb/AB6Pg+BbHtqhuMPIu0WUQhJPkpEYYV155JREREdx44411Pubmm28+6WMEQThNKDiiTpo8lZq0WmDjJ47nBj8YfH3dzmsxqUrRUd0holOjdLW1sWzZMnQ6HW+99RZhYWGafVdccQVr166la9euZGRkMGHCBPbu3dtCPRUaFVOJqmfiG6QKOa5937FPb4Qz7vWstVBZouoxhHeAhCGyKicIdeTcc89l+fLl3HSTI5ouODiYZcuWMWrUKI02WVJSEgsWLGDEiJMsvSy0PKZS+OkB+OYaKMtxbPcJgLH/VIWkRZdBEE4KnaIoSu3NYNeuXfTv35+wsDBSU1M16rs1kZ+fT5cuXSguLmbPnj107969If0VqunSpQsAhw4dqqWlILRSSnPg2GbwDVDDv13Z8xOsesPxfOBVqghTbVgtUJSh5p7H9mmzA4PQ0FAqKiooKyvDx8dzfn1mZiZnnXUWe/bsIS4ujmXLltmrAvn4+FBVVUUdfwJOKdqsfVQUyNwJ+YdVh8G6WbBtjmN/8nUw7Bb3Y8pyVMHVdt0hvKPoMQhCHairHTl69Cjp6emEhYXRu3fvVl9hrc3ax4ZwdKNawjJnv3Z7WAc44z41bSI42vOxgnAaUlc7UueIhq+++gqAu+++u85OBoCIiAj+7//+D6vVypdfflnn4wRBaMOYKyAnBbB6djJYTKqKvg3fIBh0de3nVayqkyE0AaJ7tlknA4DJZCI4ONirkwEgLi6OFStW0KdPHzIzM5k4caKkUZyqKIoq+liYpg54C47ADkcoN8GxkHyN9hirBYqOq9FA8ckQ1VWcDILQyHTo0IFRo0bRp0+fVu9kEFyoLIOVr8Nn57k7GbqfDWe/qP4VJ4Mg1Is6OxpWrVqFTqfj0ksvPemLXHLJJQCsWLHipI8VBKGNoShq6HdptnspSxu7vteGLg68GvxCaj9vUQYExaghjj6+jdfnVkh0dLS90kRt7ZYvX07v3r3F2XCqoihqFMOJ3apjzuALa95VHQk2Rt0DPv6O55Vl6vchpL2qxxAS2+zdFgRBaJUoCpzYB19fAcueB0uFY59fKJz5IIy4CzoMa7Ppl4LQHNTZ0bB//370ej3JycknfZEBAwag1+slR1gQBFXEruCIukKg82CCSnNg69eO5wER0O+S2s9begL8w9R0idOg9FS/fv0AWL16da1to6OjWbFiBb179yYjI4OJEyditVprPU5oBdidDHvUyB7/UDi8Co5tdLTpMAw6jXE8N5dDRYEqhNp+QO1OOkEQhNMFRYGt38Bn58Lhldp97QfC2c9Dt8kQP0gVkpYoFUGoN3WOoSwoKCA8PLxeYWF6vZ7w8HAKCwtP+lhBENoQFUVqyoSPr3b11YbFBL8/CSanyjaDr1dLX9ZEWS7ofSGmjzoROw0YO3Ysv//+O9988w1Tp06ttX10dDTLli1j4sSJ7Nmzp01qM7Q5FAXyUiF7j+os8AtRV97+/q+jjd5HLWfp/NtcmguRXVRNBhkkC0KdseUdA6Snp7ttqys6nY6DBw82Wr+ERkJRYOmz8Ndb6v82dAYYfB10ngCRHaFdz9NiwUIQmpo6OxoCAwMpLi6uvaEXSkpKCAioZbIgCELbxGpVIxlyD0BlMYTEu7dRFFX8MXuPY1tUV+h1fs3nNpeDpVJdfQiKatRut2YuuOACnnjiCebOncvLL79MXFxcrcfExsayYsUKJkyYwK5du5qhl0K9URTIO6RGMgSEObRMtnwJJVmOdv2na0uuVRSqbSM6ipNBEE6Sw4cP2/+3OWOdt9UV0WpopWz+Ata8qd0WGg+jH4CwRLWMZWQX0bIRhEaizt+kmJgYDhw4wMGDB+natetJXeTgwYNUVlbSsWPHk+6gIAinOKZiyD0ERUfBx091MngahO2YCym/O577h6lCTAaj93Nbq9RUi3bd1Vz004j+/fvz5ZdfYjabKS4urpOjARxpFFIBqBVjtao6Jrn7ISBcTZkAKDwK2751tAtqp67C2Y+rUqOG4vqDnweRVUEQauTTTz+1///www8D8Prrr7dUd4TGpLJU1WNwpvsUGHilOt6I7qkKSYuTSBAajTo7GkaOHMmBAwdYsGABjzzyyEldZP78+QBSV1gQTiesVVB0DHIOgrlEFX708fPcNm2dWqrPhs4Ak5+DkFomz6XZqvhjZJfTcnBw9dV1qMThgXbt2hEREdHIvREaBatVjfzJ2a/qk9jCdxUF/noPrGZH25F3g9EpvLcsV/2ehXVo3j4LQhvhhhtusP//7LPPum0TTmHWvKOOGWwkXwvdp0JghJp2GRjZcn0ThDZKncUgzz//fBRF4bXXXiMjI6POFzh+/Divv/46Op2O88+vJQRaEIS2QUUhHN8KGdtAhzrx8eZkKDgCfzynlqa0Mfp+VZSpJkzFan56dA/v5xaEUwm7k2GfOvh1zhE+8hekr3U8j0+GLhMczy0m1bkX2aXmKCBBEITTjZIT8Nc7jueBUdB5vCr2GJ8sTgZBaCLq7Gi49NJL6d69O7m5uUyZMqVOIjcHDhxg6tSp5OTk0K1bN6ZPn96gzgqC0Mqxidcd3QglmRAcq4Z+e8NUDL89AeZSx7Y+06DPhTVfx2pRnRmRXWWAILQNrFVqFEPOfvUz7RypkL4elr/oeK4zwBn3aaN4ynLUsF9vJWMFQRBOV5a9AOYyx/O+l6gpZnH9HalpgiA0OnVOndDr9Xz++ed2EbEBAwZw7bXXMm3aNJKTk4mMVAf7eXl5bNmyhe+//56vv/6asrIy/Pz8+Oyzz0QcRxDaOkXHq8vwBaoCSzVhrVIjGQrTHdvaD4Iz7q39OiVZqtaDswjeac4XX3xxUu1LSkrQ6/UcO3aMhISEJuqVUCfsToYDqqCpc5WVXT+oK3HOET/9LoXIzo7npmK1iktkJ9DXef1AEASh7ZOTooro2ghLVEtmR3UT0UdBaGJO6hs2cuRI5s6dy3XXXUdRUREff/wxH3/8sdf2iqIQHBzM7NmzGTVqVIM7KwhCK6aiUJ0s+fipwkq1sX4WHN3geB4SB5OfUdMhaqI8H3yCVAFIGSTYufHGG0/KmWu1qhPXpKQk+vbty4wZM+qt+SA0AHM5ZO+HwjQIbuco+2qtgrXvw8752vYJg2HYzY7nihXKCyC6d92+d4IgnBQGg+Gkj9HpdFgslibojXDS/P4EKFWO5wOvVqMhZfwgCE3OSS99XHDBBWzcuJHp06ej0+lQFMXjQ6fTMX36dDZt2sRFF13U4I5mZmYye/Zs7rvvPkaNGkVAQAA6nY7x48fXeqzZbOa1115j4MCBBAUFERERwYQJE1iwYEGtx27ZsoUrrriCuLg4/P396dKlC/fffz/Z2dk1HteQawrCKYelUp0sVZbWLZVh/2LYPtfx3Mcfpvwb/MNruY5JnZhFdwf/0AZ1ua2RlJREUlISAQEBdjtsMBiIjY0lNjYWg8Fg3x4YGIiPjw96vR5FUdi5cyfXXXcdDz74YEu/jNOLsjw4tqXayRDjcDJUlsHvj7s7GXpdAOe86mgHquMtIALCE5uv34JwGuFtnFvbQ2gFHPlbHW/YiO0HPadKipkgNBP1cud169aNb7/9lhMnTrB8+XJ27dpFbm4uAFFRUfTt25cJEyYQExPTaB395ptv+Mc//nHSx1VUVDB58mRWr16NwWCgb9++lJaWsmLFClasWMGjjz7Kyy+/7PHYBQsWcOWVV2I2m4mJiaFv377s27ePd955h3nz5rF69Wq6dOnSqNcUhFMOmy5DcQaE1qHEZNYuWPmGdtvEx1URu9quU5oN4Z3UtAlBw+HDh5k1axb3338/EyZM4IknnmD06NH4+voCUFlZyZo1a3jxxRdZs2YN4eHhhISE8Pvvv/Pvf/+bzz77jLfffpsLLriACRMm1HI1oUFYrWq51+x9qt5IaDzoqv3+JVmw+F+Q56yDpIORd0H/6Vpdhiqz6nyL7g1GfwRBaHyWL19e4/7CwkLWrVvHRx99hKIo/Pe//yU2NraZeid4RVFg8WPabcnXQkQnSTEThGZCp5wibtdPPvmEOXPmMGzYMIYNG8aWLVt4/vnnGTduHCtWrPB63P33388777xD586dWbRoET179gRg4cKFXH755ZhMJhYuXMgFF1ygOe7YsWP06NGDsrIynnzySZ566il8fHwoLCzkyiuvZPHixQwdOpT169e7hSvX95ong83BcejQoXqfQxAahaIMOL4FAsK0AnaeKM2GBXdAeZ5j29CbYfD1tV+nNBt8AiBhiFaNXwBg2bJlnH322Vx++eV89dVXNaZRXH311cyZM4f27dtz/PhxAG666SY+//xzrrzySr7++uvm6naT0Krto8WkajEUHFZFyJzTHU7shd8eU6MUbPj4w8QnodNo93MVZaiCq/GDQH/y4d2CIHjnZO3IiRMnmDBhAlarlY0bNxIU1DpFBlu1fWxMdn4P393oeN5xtBoRFtv3tCyHLQiNSV3tyCnj0rv55ptZsmQJ//73v7n44ovrFC2RlZXFzJkzAfjf//5nn/ADXHjhhfzzn/8E4JlnnnE79rXXXqOsrIyxY8fy3HPP4eOjBn+EhYXx9ddfExYWxsaNG/n5558b7ZqCcMphKlZXZX18a3cyWExqrqSzk6HLeEi+rvbrVJapOevRPcXJ4IU33njDXoK4Nq2GV199FVBX4mzMmDEDgL/++qvpOnm6U14AGVvVaIXAKK2T4dAK+Ol+rZMhqB1c+K5nJ0NlmepciOwsTgZBaAXExMTw3//+l3379vHSSy+1dHdOb6rMsPRpx3O9jxrNEJ4kTgZBaEZOGUdDfVi4cCGVlZV0797dYyjwHXfcAcDmzZvdynV+9913ANx+++1ux0VERNhLdc6dO1ezryHXFIRTiiqzqstgLoGAGnQZLCY48Af88pDqlLAR1Q3GPVr7j761CspyVfEmyav0ysaNGwkPD69TBYkOHTqg1+upqKiwb+vZsyeBgYGcOHGiKbt5eqIoakWWY1ugNEdNlfDxc+zb8hUsfQaqTI5j2vWAaR+ooqeezleWqw6apbyrILQaxo0bh7+/v30MKbQQ6z+CgiOO590mQ8JQ0XYShGamTTsa1q5dC8CYMWM87k9ISKBz586atgDp6ekcO3YMgLFjx3o81nZO5+Mack1BOOXIOwxFx9TQbVdngaJAxnZY+RrMvgSWPQ9ZOx37/cPh7Be0Zfy8UZINIbFqXqWsRHiluLiY0tJSzGZzrW0rKyuxWq1ugmVGo9EevSU0ElVmtbxaxjbAqjoZbBEIlgpY8RJs+Eh7TKcz4YK3vTvWKgrBL1TKuwpCK0On06HX60lLS2vprpy+mIph5auO575BMOgaCOvQcn0ShNOUNj2i3L9/PwBdu3b12qZr166kpqayb59jpdV2nK+vLx06eDZMtnMeOnQIs9mM0Whs0DU94Ulo0kZ6ejqJiaIyLrQQxVmQd0BdTXUuR1mcqSo87/8dio97PtZghMnPqeUsa6OiSC1BFdVdTc8QvNKpUyf27dvH119/zQ033FBj2zlz5gBonAolJSUUFhbWaHeEk8RUokbxFB2DoChtelHRcVjyJOS6RLYNuBJG3O4Qh3TFalHPGz9QHUALgtBq2LRpE2VlZURGSqRRi7HiFW0KWu8LIa6fpF0KQgvQpiMa8vLUXPCaDL5tX36+wyjZjouIiPCa62w7zmq1UlRU1OBrCsIpg6kEsveqDgbnic7BZTD3Otj0mWcngzEAepwDF8+C9gNqv06VWXU0RHWT8PA6MH36dBRF4Z577rE7EjzxzTffcM899wBoxMq2bNkCoNGVERqAqRgyd6jfhdD2WidD2jr4/g6tk0FngLEPw8g7vTsZQE2ZCIk5bSqvHC8o568DOViqrC3dFUGokQ0bNnDdddeh0+kYPdqDrorQ9BQdh/UfOp4HRavVekLbrr2sMFe1dBcEwSttOqLBln9sK+/mCT8/NU+2vLy8Xsc15Fjn4zxRk5KnrDoKLUKVBXL2g6kIQp20AA79CcteAMV1MqCDhMHQYwp0GlO3VAlQdRmKMyE8UcLD68ijjz7Kd999x549e7j22mt54oknGDt2LPHx8eh0Oo4fP86ff/7J4cOHURQFo9FIWJhDjPCLL74AYNKkSc3a719//ZWPP/6YtWvXkpubS0REBF27dmXChAk888wzp2Yqh6kEMndCea62dKVihc1fwKbPAae0lYBImPwMxNXigDOXg1WBiC5qpE8b53BOKee/u5oSk4URnSP54pbh+Pl4Fr78+2Aum9PymZacQEJ4He2MINSBiRMn1ri/oqKC9PR0jh8/jqIo+Pr68sQTTzRT7wQNS57Wat30m66KSPv4eT/mFEVRFO6ds4VftmdwRtco3r4ymegQz6+zyqqQcqKYpMhAAn3b/m+H0Hpo0582f3+1rnhlZaXXNiaTapACAhwDk5M5riHHOh8nCKcEBUfcdRkOr4E/ntM6GcISocdU6D4ZgmuvEKPBalGdDCHtoV1PUdSvI4GBgaxYsYLrr7+e3377jdTUVA4fPqxpY9NkmDx5Mnv37kXvVEv84Ycf5t57760x7asxsVgs3HTTTXz55ZcAJCYmMnDgQHJzc9m4cSN//fUXM2bMIDg4uFn602jYnAxluWokg83JYCqG5S9Cmos2T2w/mPysWoWixvMWq4/ILmo1itOAlSnZlJgsAKxLzePFX/bw3EX93Nrtzyrmqo/U9/XrdWkseXCsDKaFRqOmEuqudOzYkVmzZjFs2LCm65DgmYztsNNJhDOiM/S9EILrkKZ5CnIop5RftmcA8NfBXC75YA2f3zScLtHa30xFUbj+k3WsOZBL1+ggvr1jFO2C257jRWidtOlf4oiICMCRzuAJ5zQJ1+Py8/NRFMVj+oTtOL1eT2hoqNuxJ3tNQWj1lJxQRe0CwlWdBVBDwJc+A4pT6F7vC+DMB+sn3OjsZIjtW/cICAGA6OhoFi1axJo1a5g3bx6bN28mOzvbvm/w4MFcdtllnHnmmW5RUc2dMnHXXXfx5ZdfMmzYMGbNmkVycrJ9X1lZGUuXLtVEjp0SVJaqoqdl2dpIhtyDqh5DkUtKUd9LWBB+A//+upw+7XKZdV4EAUYPaROlOfx4wMLzGwx0icnnvWtMxIT4N/3rOUl+3n6cZxbuplNUIP+9ZjCxoQ3ro2tI8Bd/H2FIxwguGqStrLLhsOP39lhBOZ+uOcw9E7o16NqtnfS8MqJD/PA3iiO2qXn66adr3O/j40NERAQDBw7kjDPOqLW8sFB3Ki1Wvtt0FKNBx6WDO6DXe35vLeUlZH77IB2cFzwGXtWqor8sVVbmbjxKldXK9KGJDf7uFpZrhZ/T88q59IO/+PiGoQzpGKnZvuZALgAHs0t59qfdvHtVMoLQHLSOb18T0aNHD9asWcOBAwe8trGVmOzRo4fmOFCjEtLT00lKcg/dth3XuXNnuxBkQ64pCK0am6idTge+1d7yoxthyRNgdfqx63EOnPmP+jsZijLUCZo4GRrE6NGjW3WO8PLly/n444/p1KkTf/zxByEhIZr9gYGBXHjhhS3Uu3pSWaY6GWzlK21OhpQlsPJ1bTivwQ/GPoTSbTIvfpxFbrmVlWkm/re1lHuHOb0XilV1vBmDeHUL5JRVknM4nwe/3cYXNw/3OuiuK/uziknJKmFirxgCfBs+YX1l8V5ySkzklJi4/5stfH3ryAb10aq4b5sxfwe924fSI9bxPpkt2pStmX8e5JoRSYQHtk0B2Rd/2c1Hq1JpF+zHwntHEy+pIk1KbY4Goel4a+l+3l+hjpmPF1Rw/yQP5X4rivjh24+5rGCDfVNV3EAM3Sa1qpLY7684yH+WqILxP2/P4LObhjfI7lo9GMj8MjNXf7SOt68cxNR+7QEoqtA6JH7adpxpg+I5q3dsva/dmskrreS/yw9gNOh5YFJ3cca2MG1aDHLkyJEArF692uP+Y8eOkZqaqmkLkJSURHy8KhyzatUqj8fato8aNapRrikIrZbKUjUUvKLQ8aN9fCv89rgq2Gij22RVzK4mITtvWC1QlKnqPsT2EydDG+eNN94A4KGHHnJzMpyS2JwMxSfUaio6vfrdWPOOmi7h7GQIjYdp70P3s7FYIbfcMUn+OcVJt6fKDIXHICAKEpLJLrPYd60+kMNnfx1uUJd3Hy/i/HdWc8/Xmzn3nVUcyS1t0PkAThQ5XufaQ3l8vNq7zlBdqPIwkC43V3Hn7E0UOw2eLS7tiisszPyzYddurVitCrPXHgEgp8TErD8P1nKEIJy6fLrmsP3/N5fux+wqCluWB5u/4LzDr9g3WRUdz1RcRWVYR9DXf5ozb2M6V3+0lhd+3k1ablm9z2PDOfJqXWoet8/e2CAhR0/2EcBksXLXV5v5ZHWq/bkrj3+/U2ND2xL/WbKP/61OZeafB3n+590t3Z3TnjbtaLjoooswGo2kpKSwfPlyt/2zZs0CIDk5mW7dtGGWl156KQAffvih23H5+fnMmzcPUJXeG+uagtDqsE2gynKq8811qpL+4hnayVOXCTD+0frpKbhFMrS+kPBTkeLiYv7880/mzZvHvHnz+PPPPykuLm7pblFRUcHvv/8OqMKTu3fv5oEHHuDss8/mggsu4KmnnuLIkSMt3MuTwFwOWbvUkq+h7dXvQGUJ/Pow7FqgbZs0Uq26EqXqYFS5jBP35lg4lG8Bc1m1GGpHtYylf5hdX8PGy4v3sj+r/vfzr4M5VFYP2lNzSrnk/b/YktawSkiuE/7Xf9vPnowiL61rx9OKHai5yY/O325/T1yvC/DZX6mcKKqo97UPnCjhnq838/zPu6n0MFBvKSxWhQqzoz8Ltx1vVf1rLgrLzTz1407u/2YLGYU1C2sLpy7lLhPxVSnZjifFmbB2Jix9mgAc3/Vvq8YzOzOJ//vhsLtjoo6cKK7g0fnb+etgLh+vTmXc68u57YuN/HUwx80W1xXXCf+qlBzu/XpzvftY5dKPXnEOp72iwHM/7+b5n3dj8uDMyCyq4OVFe+t13dbOzmOO35zvNh2lsKxtOlRqo76f08amTTsaYmNjueOOOwC45ZZb2Ldvn33fTz/9xKuvvgp4Dot75JFHCAgIYOXKlTz11FNUValf1MLCQq6++moKCwtJTk7mggsuaLRrCkKrwj6BclqlPbEbFj0KFqcBfKcxMPFxtdzlyWJ3MiSoda7FydBgduzYwYUXXkhkZCQTJ07kyiuv5Morr2TixIlERkYybdo0duzY0WL927ZtG2az+sO/atUqkpOTefvtt1myZAk///wzzz//PD179qyxRCeolXe8PdLT05vjpTi+IyWZDidDRSH8/CBkbHNqqIMhN8GUf4OfYzDoaYK8aG8+lBdAdC9NCpFr00qLlQe+2VrvSabroDe3tJKrPlrL77sy63U+RVHcVtgqq9Q+1nfVzvl0Oh2aahK/7sjkk+rVTk+lLyvMVt5d5j2FsTaeXriTX7Zn8L/VqfZw5xr76sUp0thYXQaP+WVmlu870SzXbk3M/vswX/x9hB+3HufurzY3y6BaURTmz5/P9OnT6dy5M0FBQQQFBdG5c2emT5/O/PnzsVpPP6dPUxLnovOyYPMxdRZdkAar34RVr6njiGpWVA3kOct1APy2K4uH5m7zuvJfE0dyyzT2R1Fgye4srv5oHee8vYq5G9JP2q55iixYuucED3yztV7le10/av++pD+3nNlZs+1/q1N58sedHo//al0a6w7lnvR1AQ6cKGbC6ysY+sJS1hzIqdc5mgrn+22yWJm/+WgL9qZlKK+s4vpP1tP/6d+Y2cJRb6eMoyE9PZ127drZHzNmzABgzZo1mu22ibyNV199lVGjRpGamkrfvn0ZNGgQ3bp148ILL8RkMvHQQw9x0UUXuV0vMTGRL774Ah8fH55//nni4+MZOnQoCQkJLF68mNjYWObOnetR9Ke+1xSEVoO5ArJ2Q3EGhMapE6ic/fDrI+pqq42kUXDWU/VzMlSZVSdDeKLqZGiD5aeamwULFjBixAh++eUXqqqqUBRF86iqquKnn35ixIgRlJY2PFS+PmRkZNj/v+eee0hOTmb9+vWYTCZSUlK4/PLLMZlM3HDDDWzZsqVF+lgnzBWqk6HouOqI0xugPB9+/of6XbHhGwxTX4IhN7ilFXkaW/5y0KKWuWzXXSNi5jrBBNidUVSnSbAnPE2MK8xW7vhyE5+7pGVYrQpHcktrHAx7cpoA7Msq5vXf9nncVxvOK3ZGvZ73rxmMr8HxHr706x42HM7zeu0569PcQp6tVoXUnNJaJwkpWSX2/79ae8Re/cKV/VnFjHrpDwY8+zs/bDlW62tqKJ4mTQtqGEhbrQqHc0opr6x/iHZzUlRhJrOwolbHQcoJx/3ZklbA1vSCJu1XWloao0aN4vLLL2fBggUcOXKE8vJyysvLOXLkCAsWLODyyy9n5MiRp1ZEVisn0E8bJblkdxbFx/bBipdh3UxNtauFVaO4zfwQ5TicEwu3HWfG/O0n7Qi0uIabObE3s5h/zt/OGS8v4/Xf9pFVx8gpb07hX3Zk8M/vTr6PrhENRr2eJ8/vw1Pn99HIZB3M9v5bP2PBjno5gr/4+wipOaXklJh44NutNdqXKqtCTonJ6/7GxvX34Ov1aa1mdb+5WLHvBKtScig2WXh50V72ZtY/srChnDKOhqqqKnJzc+0P2yDZYrFotpeVaQcVAQEBrFixgpdffpk+ffqwf/9+cnJyGDduHN999x2vv/6612tedtllrFu3jssuuwxQVwqjo6O599572b59u9fUh4ZcUxBaHIsJsveoZSxD26tOhNwD8MvDql6DjQ7DYNIzjgoUJ0OVuTo0PBFi+oiToRFITU3lmmuuoaKigo4dO/L++++TkpJiHwynpKTw/vvv06lTJyoqKsjOzrZHFjQnJSWOCUJgYCCLFi1i2LBh+Pr60q1bN+bMmcOgQYMwm828+OKLXs9z6NAhr4/ExMSmfRHOTgbbd6Q0G366H/KctAECIuHCd9WUCQ94miDvzlM4bIl0E1T1Nk6atfJgvValvE3OFQWeXriLF3/ZjdWqOqiu+2Qd415bwRkvL+PbDWkeJ7s1Dcw/Xp3KX/VY9XIeHOr1MDAxnKcu6KN5Dfd8tZnMQs8DfYtV4c2lWkfM7bM3MeH1FZz/7mpOFHufIDg7dopNFuZu8Bwl8/W6NDIKKygxWXho3rYmjy5wnVwALNt7gvxSzyW1/2/OFsa/voJz31nFsYLGSzEoqjCz7lAuZZWeHTDOZBZW8PfB3FpXbQ9mlzD+tRWMfOkP3vmj5mgU14+gq3OsMSksLGTcuHFs2LABRVEYNWoUTzzxBB988AEffPABTzzxBGeccQaKorBx40YmTJhAYWFhk/XndMI1rcBkqeLoTy/A1q802xf7TeF+8z2Y8SE+zE/jkJy36ShPLdx5UpNNi0u4wJCOEbjq2uaVVvLe8gOMfnkZ93+zhUU7MjiaX+Z10m2yOLb3iA3G18fRxwVbjvH4DztOqo+ujglDdQdvPrMz7189GD8fz1O8EZ0dFSlSc0p5+48Uzf4DJ0pqdQw4788uNvH534c9tis1WZjy1kqGvrCU6/63zqutbkyqXO7dgRMlrE/1XgnwUHZJjb8FrQmTpYr9WcW1RjK63r/3l7dcVMMp42jo1KmT2+qcp8czzzzjdqyvry+PPvoo27dvp6ysjIKCAlasWGHXYaiJwYMHM2/ePLKysjCZTKSmpvLuu+8SExNT43ENuaYgtBiWSjixBwrSHROovFT45SEwOXlE4wfD2S/Uz0FQVenIP4/pK06GRuK1117DZDIxatQotm/fzp133knXrl3x8/PDz8+Prl27cuedd7J9+3ZGjRqFoigtMhj293esNt14441uZX71ej3/+Mc/APj9999bXyhyZVm1k8HJEVecqToZCtIc7YKi4cJ3ILKz11N5m5v/ujND89x18Dm0Y4TTPnhw7jY3ZfHacJ5I+/roufGMTpr9H61K5f/mbGHHsUJ7abQTxSYenb+Dc99exZ/7szXtzS73aUJPrdr7Q/O2aXJlV+7PZsnurBpX8ZwdGvpqx8s1I5K4JNlR3vJEsYlvnJwAwX4+mvfnh63H2JepallkFJazdE8WoA4+H5q7zev1XR0xn6xJ9ehgyS9zTPCrqh0fO495/l6l5ZaxcNtxr06BulDl4UNjrlL4aftxt+25JSZ+2aF+llJzSrnnq82NoudQarJw1ht/csWHa7nuf+trzDFPyy1j8pt/ctVHa7nyw7WUeokMAfh5WwZ51e/NO8tSanSMuEb4/LIjo8kmCy+++CJHjhwhIiKCJUuWsHr1ap577jnuuOMO7rjjDp577jlWrVrFH3/8QWRkJEeOHOHf//53k/TldMNscbIBWHnJ52N6Z/2kbTT4ej7yvR6lekrTu30Y718zGB8nz8CXa9N48Zc9dZ7Iu37/n7uoL6sencgd47oQFmB0a/vj1uPc9dVmznxlOb2fWkyfpxYz5tVlXPTfNdzy2Qb++d02sp3Ecod0jGDWdUMwGhx9nLM+nWd/2l3nPrraI4PT6z2nf3u+vm0E4YHuC0FPX9CXmBDHuOvDlYfsNuvtpSlM+s+fjHllOb/uyHA71v6aXezQzD8PevwNWroniwPV0UerUnI45+2VLN2dVYdXV388OdG/WpfmoSW8v+IAE9/4kzNfWc53mxovxcJqVdhxtLBO+hBVVoWdxwprtc0mSxXnvr2Ks99cyfWfrKuxvetb8PP246TmtEwU6ynjaBAEoYmpMlc7GdKqQ8FtToYH1bxzG3EDYMqLdXcQKFb13JYKVSSvOKvaydAbfNpm+bmWYOnSpeh0OmbOnElwcLDXdkFBQcycOROA8vLmF1Fzdiz07t3bYxvb9uLiYnJz65dD2iSU5akVV4qdIhkKj6pOhiKniV5Ie9LH/YdPU8PZmullUmmpwFLgeSDnOsBzHTRM7hPL5UM72J8fKyjnmR93qW2tCt9vOcrcjemaFTS3yzud1KjX8fQFfXjiPO39+GVHBrd/scnt2H1ZxdzwyXqu+986dh9XHZCuA8/JfeK4eoSjNHRGYYU9V/jLtUe4/pP13PbFRu77ZovXHGrn1XtDtaNBp9Px4sX9NcJnzvgYdPxzai/7c0WB139XUzeKK7ST3FUpOXyyJtXztV36dDS/3KN+hWu7ssoqbvpsg9skObOwgkn/+ZP75mzhxk/Xu63ul1dWMXvtEZbszqpxouEpogFgvodBcpnLyurW9AL+/eser+dOzSnl41WHOJhd4rUNwF8Hc8kuVidNm47k8/su7xOHP1Oy7e/7xiP53FOD+J2zCn6VVakxSsH1PTJXKcxZ5znq5M/92by0aA+v/7aP95aleGxTE99//73dtp511lle202YMIGZM2fatRyEhmP7rPhi5j3jO1zl4yKyPupe6DEVq1P6pk6nY1KfWN65KlkThfDx6lTe+L1uqWau9sxHrychPIDHzunN349N5IVp/egW4/13tqyyivS8cralF/DH3hPM3XiUYicnm5+PgQk9Y3j3qsEaB8Fnfx3mlcX7yC+tZGt6QY1RQK62wOAyoxvSMZL5d51BYqRD28ZHryMpKpDnLurnOI9V4dH527FUWfl2gzohLzdX8X9ztvDLds+/Ua6T+YIys73KhTNF5dqJdn6ZmVu/2MgzC3e5pWyYq6xsPJzXYPFGT78ni3ZmeIzSmLdRtZuVFisPz9vm8TXUh7u/2swF761m6tsraxSrVRSFK2b9zfnvruaMl/9gRQ0RcetT8+xpMGsP5dXoGHF1xFoV+GBF/TWLGoI4GgRBgCoLnNgLBUcgJFZNh8hLVfPNy52U6GP7wtSXvZeftOkuFB5zPIoz1XNUlqrXiegsToYm4OjRo4SEhNC/f/9a2/bv3x+9Xm8XuW1OevVyTAJ9fT1/BpyjHlpFRINNfOz4ZjAVqhVS9D6Qf0R1MpQ4TbTCEimd+hZTf/Lh2ZVFTJubw1ULcliTblInR9YqKDkBZflUhXbweLmdx4o02gKugwa9TsdTF/QlKTLQvm3BlmP8sj2DmSsP8o9vt/HP77ZzzUfrKCjz7OhwXsk36HXodDpuHdNF1UFwCrnNrCH/eFVKDue9u4pH5m3jaL42bdHHoOOJ83rTuV2QfdvCbcf5cesxVuxzREP8vD2DZ3/a5XFy7bxJ7zQYD/A18P41gwn2c9eG8dHrGd45kvFOERVLdmexOS3fY3rHK4v3eoxA8BTp8LGHQaineX92sYmbPl1PodMge8nuTHuVj21HC/nNZXL+8LxtPPnDTm77YiOP/7DTa6SFt+3bjhZy4IS2Comnvn3212F+9hD9UF5ZxUXvreaFX/Zw7tur2HTEewUS10oAs9ce9trWVfF+xb5sHlvgOUTcdfIyZ12aV20MT2bhq3VHPDox1qfmMuvPQ7y3/AD/rUcI8dGjR/H19eWSSy6pte3FF1+Mn58fx441vV7HqYy5ysq6Q7n8viuTJbuzWLo7iz/2ZLFsbxbL955g+T71UV5pIZAK/md8jXMN6+3HW9HD+Meg4yjwj8Dq47CFNlNxbv/2/OfyQZostPeWH+CZhbvYnJZfoyPWNfzexynyINDXh2tHdmTJP8byxc3DGdO93Um//sgg9bdvar843rxikMYhMvPPgyQ/v4Rp/13Due+s8hoh5WoL9B4047pGB7PgrtFM7BVDiL8P/5jcg2A/H6b2i+OcfnH2druOF/HRqlSNM7bKqnDfN1v4aZu7vfAUNfDxqlS3aC1vTuTP/jrMxe//ZY92UBSFqz9ay2Uz/+bcd1bZHZnOHMkt5cCJ4lojPjzZeXOV4nFi7pr69dzPu3lzyX4vv0dq5EFhec2OkMIyM4urndIZhRX8d7n3Cf7B7BI2VtvanJJKbvx0Ay/+4rnSUYmLo/y9ZSleP8Oe3vYFm495jBI7UVzBT9uO89uuTJbvPVHr6ztZxNEgCKc71irI3gf5hyE4Bgy+ap75z/+AigJHu5jecM4r4Bvo+Ty2aIXQeGg/EBKGQOIIVTAyaRQknQEdR1cLP4qTobExGo111lywpZq1BAkJCXTs2BFQdRY8cfCgOhnw9/cnKiqq2frmEUslZO+FjO2q4KOtAkvuAdXJUOYUcRHRCS54i20l4ZSaHe/v30crueb7XC7+Noulu46j+IZAh6FYIrt7vaxz+oSro0GnU1MEXAeo//p+Bz9tcxy38Ug+02f+zXEPgwuLi6PBxrn92/P1rZ5DbtX9cZr2iqLmQF/y/l+adkaDjkBftY/O7Z/4YSdHcrUhnF/8fYT3PFSI0KZOaPd1iQ7m9ekD3I6xhSI/fHZPzfbXFu9zy7sGdQB63zdb3AacngbSm47ks9mlBKi3gfT+rBLunL3JPmDMd1mlcxVwXOeUQ/z1ujQenOtZid5bRIN6Tu3k1pOAKMCj3213i1rYnVFEUfVA1mSxctsXG93uk70PLu/j2kN5bk4OG5668N2mox5Xll3vT03aGJ5e24liE4t3ukedOE88nCeMdSUiIgJ/f3/0+tqHzAaDAX9/f7eUMEHLA99s5YoP13L77E3c9sVGbv1iI7d8vpGbP9vITZ9t4KZP1YevuZCvfV9kjMFROaFCMfKM3yMoMX0hIAra90dxEtp1nnBPS07g5Uu0zvfP/jrMJe//xdhXl3sN+Te7RTS4f250Oh1je0Qz+5YRrHxkAp/dNIz/XD6Qx8/tzZ3jujJ9SAcm9ophYGI4HSICCDCqwpbdYoK5dIjDyXzhwHheudTdloFqR6b9dw1vLd3v5kRzj2jw/NmODvHjkxuHseOZKdwzwaEt9+xFfQn1dzhr31y6n1IXO1hlVbj/my0sdHE2uNoAgBKThZkrtY68GqR72JNRxAXvrmbuhnTS8srYcFi1rccKynnKpVLGsr1ZTHzjTyb9ZyWXzfybjYe9ay54s8lfr0tzc854avr2Hyk8+9Nut7bPLNzF+e+uZtgLS/ni78Nex1Cu7+G8jUe9al64Rp2BmrZ42cy/OOyS6lDpcv+PF1bwzXov9tGTjpJVYZaHChR7M4r5vzlbuGP2Jm76bINXu19fxNEgCKcz1ipVIT//EARHq+kQeYfU8nwaJ0MfOPc1VUHfFUWB0hwwlUBsH4jtp4o8hrZXzxkYCf6hqoPCx9dN5E5oHLp160ZFRQW//fZbrW1/++03FEXBaKyHkGcjcMUVVwDw1VdfYbG4r1h+8sknAIwbNw4fn3pUNGksKorUMpU5KRAUBf7h6vbsve6OuKjucMFbEBjldTK49YSVW5cbOOcHMz8dtFBZwyhskVP6hOvpbNWOhnSM0AwcC8vN7MnQqkunnCjhkvf/Yn+WdiJY5cXRADC0UyQL7jpDEzFh49kL+/HbA2OZ1DtWs911Ym6onpQNSgzn/yY6+lhcYdFUDLDxxpL9fO2SR+s8mfQ0iJ7arz23j+3icl21Xb+EMM4f0N6+/e9Dufy5T6srYeNQdinP/bTb67Wd+Z9LVIPzvU6KDNSU4Pz7UC4z5m9HURS36Itl+05o9AoqXVamfqgu2+i6YlVTqb7vtxzT7Pf2OSytrOKuLzdpnCuuk5i80kpu+myDx4gYTyuGX671nAPt7X18b/kBZq/VVmfw9Nq8aWN4exs8pVs4Txp9XePL68AZZ5xBUVER+/fXHna/f/9+CgsLOfPMM0/6OqcL5iori3Z6z/+30VmXwfe+TzFI75gcFSkBXF85gy8KB7KnIgLi+oNfiOZz5uoPumJYEs9e2Nft/FlFJl5etJczXlrGv3/dowlxd/3M+dTyuUmKCmR8zxguGdyB28Z2YcY5vXht+kA+uXEYP94zmtWPTmTP81PZ98JUlj44TmMnAKYPTeT5af08nttiVXhraYqbHXfto6eIhpqICfHnifMd4rqVFqvH75VVgQe+2cKPWx2OTFdHjI3P/zrMCacoONcJ78fXD6VdsGOhqdxcxT/nb+eBb7dq2i3amakR1fxhy3H76910JJ/LZv7N7V9stEdEOOP8W+QcnZeWV8aag1pRYm8RYp/9dZhHvtuucfZ+u1Gd1FdWWXnqx13849utHsVwXe2jyWLls+pSzK54s+fbjxZy3jurNJWMPEU5/Hf5AY9VQ7zZ3W82pLtp2bie19eLiGh9EUeDIJyuVFlUJ0POAQhq5+RkcI1k6APnvurZyWC1qPnqBl9VIDKqq6Ykn9B8XHTRRSiKwm233caePd7zsHfv3s3tt98OqFUfWoKHH36YsLAwUlNTuffee6moUH/4FEXhnXfe4aeffkKn09nLGLcIxZlwfAuUZqlOM5/qdI7MHfDzQ2BymrjH9IHz/2N3RNQWLLI3s4T/m7OFaz5ep9ne3Snnd9vRQtLz1HQE99QJx//3ndWdAR3CarxeZlEFl33wl0Z5uyZHA6gRAwvuPoOBTucO9fchPNBIt5hgPr5hKHNuG0n/BM/XNjqd894J3RiUGF5jHwGe+GGHZkXaeRDoqZQ0wD+n9GS4k4q6s3Pkwck9NK9t1kptBI3zKuU3G9I12hjeBoCLdmTY7wtotQKign359KZhhDitEi7Ycow3l7ivRioK/Oak+eBp4P777ixu/XyjZjDrupDo/L5mFFaw1qkCieuKm/Pq5f6sEp743qHE78l5cCi7lNtnb3JzdngaxM7fdNSj0GNNFfue+nGn5n576oM3bQxvq4kbj+S7hZo7v/f1iWiYMWMGRqORu+++G5PJuxp/ZWUld999N0ajsWVtVyunyqrU+LkAGKHbw/e+T9FZ70gxqvQN58rKJ1mvqFoy3x8NAT/VZjp/LzzZihvO6MRj5/Ry2w7qSvyHKw8x9tXlPDR3G/syi92+r54iGuqDn4/B677rRnZ008lxZsexQs5/ZzWz/jxY/R7WLaKhJqYP6cCZ3TynfsSFOqUwKvCPb7faJ77O9tEWqQFqiWTnVAFXZ+eEXjH8ev8Yt3STLWkFbtef7xT15SmF6vfdWUx5ayX/+n6H1rnhdM2pfeM09+6rtd6d2V2jgzTrYPM3H+XurzZTYa7CUmWlwqz9TPyw9TgX//cvDrlEh7lGHgB88fdhj6/B9XvgfA9LK6t44NutPDh3K6Umi8ffiBPFJr50cdh6Oq+9bxYr/1uldZa79rc+ztiaEEeDIJyOVJnVVdncAxDcTp1E2Z0MToO0miIZzOWqHkNwHCQMVrUdhBbjgQceICEhgaNHj5KcnMx1113HZ599xpIlS1iyZAmffvop1157LYMHD+bo0aMYDAbCwmqeoDYV0dHRfPfddwQEBDBr1izi4uIYPnw4CQkJ3H///eh0Ol599VXGjx/f/J2zVkHuQdXJUFUJIdV6DADp6+DXR8DsFFrYfiCc+zr4OcQJXX/kP7i0C1cPT3T7AXcNp7xwYLzmuW0S5no+55Uro0HPm1cMwt9Y8895UYWFa/+3zn5OT0KLrrQL9uOb20dx0+hODOkYwevTB2J0eg2jukbx4z2jeeuKQW4rdM5CaT7VfXQekNpwnpRbFbjvmy32yXJd+uhj0DPz2iFcnJzAmO7teNJpha5LdDDTnUKUXQd6D57dQzOwnDF/O8cLylEU7UTIeVBsVbSr5hqHjU5Hj9gQZl2rVZN/Z9kBftjqnuf8/RbnFULPWiSrUnK44ZP19vBu14H7JYMTNINTZ1FI18/Nv87tTWyoQ8R3wZZjzKkOvXWtGmJjfWoej83Xaip4SispNlncwqvVPmjbPuoi1HnfN1vYUB0G7c2540kbw/l9aBesFSb+wqXUnnNKhrEeg+ihQ4cyd+5cNm3axKBBg/j00085fPgwZrMZs9nM4cOH+fTTT0lOTmbz5s189913DB48+KSvc7rg6iO6c1xXfntgLIvvGMCi6cGsGbKSOf4vEa5z2FlrSDy+F72FMaqjfduPO7LtnxlNRIMXW3HHuK48fHYPr/0yVynM33yUKW+t5K2lWtHQxnI01MatY7rwz6k97XZpYIcwTZWLyiorLy3ay+Wz/uZQtjbEvT6OBp1Ox78v7u/RNt85rgvXj3K831YFHpy7lQWbj2pswOhuURpx3q/Xp9k1e9yjLtRIis9vGs5j5/Sq8X39ZE2q3dnsrcJClVXh63VpjHttBf9Zsp8Sk0UThdA+zJ8pfR1aFEv2ZJHl5JRw7t9ZvWN576rBGtv9++4sbvl8A3letI72ZRVz4XtrWOwUoeMpRa+owsI3692jvlzt45tXDNKUHwU1Je78d1ez/WiBxz58sOKgm5PX9bw9Yh2/x7PXHtFoabj+9tTHRtaEOBoE4XTDYoKs3apjIShadTLkHnR3MsT2rXYyBLmfozxffbTroVahcJpkCS1DaGgoixcvplOnTlRWVvL1119zyy23MHXqVKZOncqtt97KnDlzqKyspHPnzsTFxdUp57ipmDRpEtu2bePGG28kJCSErVu3YjabufDCC1m+fDkPP/xw83fKXA6ZO9Xvh1+wGumj06kj4+3zYPFjavUUGwlDPeqWKFXanN+khHj+fckAVv5zArec2dnjoA4gOSlCM2G3lSasKaIBVMGvx891Xwk7t38cI7s4Bi2VFit3f7WJL9ce0ZRJNNSwyhvga+DpC/oy/64zONtpwGbvi17HtOQE/nhoHP86txfjekTz7IV96R6rtQmd2wVpnAA2pvSN05TWrLRYue3zjew+XqSZKNc0iI4M8uXNKwYx+5YR9G4fqtl3/6TuXkNBz+zWjnvGO9I6iiosPPDtVrcVntHd2tE33nHebzak2yskOC8y2QQrz+jWjlcv0+Zcu6a0AKw5kENWUQVWq6IZuE/qHatJtdhwOJ9rPlpHXmml28A9JsSPcT0cwpeLdmbaB51ubUP9eO9qrcr9Mwt3seNooQeVfUebBVuOaSZe3hwCs/8+4hZp4Pxcp1MnL673+9bPN5KSVYzZy3k9aWM4N+0SHcQZXR1aLj9u1ZYQrXQqkVifQbTBYODiiy+2p0/ceuutdO3aFX9/f/z9/enatSu33nore/fupbCwkGnTpmEwGNweLZoG1opwtWdxoX70DCikl2UfvQ99SsKumegVp4lTXH/0F70LOh8u7u34Hp4oNvFXdSi8Rji2hvn2vRO7s+Lh8fzx0DhSXjyH/1w+0GMFG1fRPJ9m/K28e3w3fn9gLAvvHc2P957J7/8Yy8ReMZo2m47k866Lrs3Jpk7YSIoK5CEPDhg/o4FnL+yr+b5aFbVUcYpTCoevj56HnDRxzFUK7/yh2gtXnR1btIler+OOcV2Zf9cZdIzyHFl5KLvUXkbZOaqqV1yIW0REubmKd/5IYfxryyl1Srkw6HVc41T9qMqq8K2T7otV87nRcd6A9nx0/VCN437NgVyu+9ghRAraDOASk4U7v9zMv3/dg6XK6jEyC1SxTFeHiWvqRnyYP1/fNpIHJ/fQfI5Tc0o1ZZydyS2t5HMX56o2GhBNimVZZRWfOjnLTS598pPUCUEQ6o25Qp1IFRxRRe18/Lw7Gc551d3JoFjVKAYFdSU3uqcIO7Yi+vbty/bt23nppZcYNGgQer3eLvyo1+sZNGgQr7zyCtu2bfNa8aE56d69O59++inp6elUVlaSnZ3Njz/+yLhx45q/M1VmOL4NCtMgJMYRxVNVCX++Cmv/q37+bSSNqi7z6q89j7kcxfm7hGMAGBfmz5Pn92H1oxO4d0I3QpwmkzqdGgVwbn/HZH5regHHCsrdUzE8DCivHdlRU2kBIDrYj89vHs55/R1aBVZFFWRcsscRkuwtWuBk8DcauH1sVz6/eTg3OA1MnblqeCJnuQyY/Y16njq/j0ZPodhk4YZP12sqb9S3i+3DArh+ZEeP+3z0eu6f1F2TfrA+Nc9NmNJHr+PWMZ3tz0tMFvtgVVG0A2kbFyd34KHJ3ldPQb0XC7ced4smGNklkq9v0wpy7jhWyBWz/iazUJtfq9fpuHSwI2qj3FzlFAmj/eDodDqGdYpkhlNUQWWVlbu/3kReqTa65rmL+mlW9t7+I8UeLeFtIL07o4gt6QVur9G5rzqdjifP76P5nBeWm7nhk/VkOE3uIoN8NffcNdzX9X13/syZLFbNoFwb0XDyHySbDW2Mh+Dhc1maDUc3wpq3YNf32sbdJquVrirLIDSeC0ZrxWW/rxZArUtEg41O7YLoGh2M0aDnksEdWHT/GD67aZjGWeVKfVJuGkL32BAGdAgHIDbUn//dMJRXLx3gsdKOjYZEXdw0ujMDXdLbfA16dDq19LGzs0FRtCKGBr2eSb1jNMfP33yMQ9kltaboDUwM5+f/O5Npg+Ld9oFDE8d5gp4YGcjsW0bwxc3D3RzLOSVaZ6yPXseorlF0cap+NGd9mj3qwerBfo/vGcPsW0Zoou32uWgc/euc3ozqov28fLjyEFd/vM6j+DKoKYzOOhfq9bVt9HodBr2O+87qztw7RrlFCjrjnNry4cpDmvLAGie9Tsd5/dtrHDqfrUm1txeNBkEQGofKMjW/vOiYmnNuMKqpEz//A0xOq22x/Tw7GSwmtVxlQCQkJENYBxF2bIUEBwfz6KOPsmnTJsrKysjIyCAjI4OysjI2bdrEI488QnCw9/rfpy1VlVBZrEb5GKqdMOX5qjDq/kXatr0vhLOfVx11zlSWQHk+1mDtoMn1axIV7MfDU3qy5rGJ/HNqTyb3ieXNywcRF+bPOU5OAVA1AVwnKJ7GkzqdjlcvHUCE0+S0a0wwfj4G3r0qWTNQBFXsz0Z9Qm7rg06n4+VLBxAV5HBydWkXjF6v443LB2pyhbOLTaw+4BDuakgf757QzXMpTIMOo0HPO1cma/a/t9x9pfC8/vGatINP1xzGUmWtcSB978RuXDE0sca+LdhyzC331mjQM6BDON/ePoroEMc1U06UcNdXmzRtDXodZ/WO0egv2HKbXee1tgnYrWM6M6WvI9UtPa+cVxbv07Qd3S3KTQl/xoLt/H0wt0ZByi//1uYLexrIG/Q6/nO5NkT4eGGFvcwbQGJEAGf1cvRx0U6tNobrxHJS71jNoPzLtUfskwmNRkM9VqaXL1/eKI9ly5ad9LXbIq6fnoCCFFjxMhxepd0x5EYY8xCU5akC03H9iAoP1UTwLN6VSVmlRfN5ONlhiU6nY3zPGL6+bSQ/3Xsm5w9or7GxCeEBXqPQmgudTsflwxJZdP8YtwmuDX0DbKRBr+OVS/trHHE9qqPSbM6Gm0Z38nisT3V55EecohqqqgUs6+IACvE38uYVg/j85uHcf1Z3hnVyVGxZfSCHfZnFmlV320R4bI9ofvm/M3nzioFeJ+T66r5d7RTVkFFYYS+x7M1+D+sUyTe3j9T8VjkTHx7A7FuGc9f4rprt61PzuHfOFo/HgKoT5Bxt4Kl0tY2hnSL59b4xTPUQSWg06LjHSWS5oMzMJ6sPezyvXqfDx6DnrnGOvhZVWOwCvpI6IQhCwzEVQ+Z2VeAutDrnPPeAOomqi5PBVKxWlojsAvGDIEBKd50KGI1GYmNjiY2N1VSYKCws5NixY1LrvSZyD8D3d0KWU5ktnR5GPwBjHnToNtioKISKYojujRKqdTR4G2CF+hu5e3w3Prp+KNOSEwBITgwnPsyxUrFoZ2aNGg3OxIT6M/uWEUzsFcP1ozpyWbU+gV6vDhRneBFDay5HA6il1r68Ve3jtSOTuGKYOhH38zEw87ohXsUlGxJ1ERnkq4lIsGFbAUyKCuQFJ8V31wm6Qa/D10evWTU/VlDOb7uyahxI63Q6Xri4H2N7aCNNnNmTUcSOo9oIGNtAr2dcCPNcVrWKXWqp6/U6/I0GznfS9/j7UC7HCsrdxeKq+6fT6Xht+kDNCpez4wlU7YtLBnfg/rMcJVjNVQp3zN7oVsHEOYz55x0ZmnM5f3adRfr8jQY+vH6oJndY01eXKBKroirB2587jY31OnUV8DqnfPJjBeUs3XPC3m8bxnqs1o0bN67RHoI2MKyr7hjnHHwWsp0EjPVGmPA4DLwSSrMhorM6Nql27F5cbStBXVn/fVeWS+pE/W1F/w5hvHf1YFY8PIHbx3bh3P5xvHt1coMm8Y1JYmQgX906gmcu6KMJ7w8LMBLo2zBnSK+4UD69cThT+8bx7IV96ZfgiBbQ6XQ8dX4fbjnT3Y7afj9Gd4vSpOr9tP04u487xpc1RVzodDrG9YjmH5N78IxLhZBPVqdq0tmcQ/v1eh0XJ3fgj4fG8fi5vTV6FqBqNABcOriDZqX+62q9hJo+N33jw5h35yjN77FzH3wMeh6d2osPrxuiiU50jRBwjpQ5cKKEP/aesD+vLS0yLNDIB9cO5oVp/TSvOz48gCuGJmp+Gz5efYjC6jLKnqqwXDK4g/39APh41SHKK6skokEQhAZSUQgZO6AsB8LiQW+A/b/Dj/d6cTK45MuVF4CpVBWGjOkDRnejK5xaWCwWKisrqaz0LHB02pO6Sv1+lDjSC/ALUUUf+05zb1+Wq0b8xPWDyM5YcZlwnsSldTodU/s5oho2Hcl3C8WsaczbLyGMT24cxnMX9SPQ1zk1Q8ed47ryxvSBbgM+QzNrdfRuH8onNw7jhWn9CXIaoAX7+fDpTcPo5CFnt6HBU7eO6UKky+qU88rNtOQEzQTGGdsk4+rhSZqVzY9XH3Kb8LpiNOj579XJmhBf18HwvI3pLsc4ztOpXRDz7hylCf11xuY8uHSwo++KAj+4lLpU++f4P9TfyPvXDPaaj2urGvLApO6a96WowsI8J8FJgBtGdbL/X2mxal6P8+qd2yA6wMjnNw/XDH5t+Bj0jOgcqZnsfLsh3S6K6WkF+4qhiZrXYxOFdF6xM7aSCePpjNWspv+cod/J975PE2JytrOhcP4b0HE0lOZCu+4Q01uNwKxmch+thsn3W455jJxpCElRgfzr3N68f80QBie1roUVvV7HjaM78+t9Y+yRPE+c17tRVqLP7N6OmdcN4YYzOrlV79DpdDxxXm9uc3Ha2u6FTqfjYaeoBkVBM6muq7Omb3yYxmHx/dZj5JY4xiqebJa/0cBtY7uw8pEJ3DG2Cx2jApnUO4ZzqyMEI4J8NSmEy/ed4Gh+mUZU1pP97hIdzHd3nUGXaK39DXWy4Wf3jeOn/zvTo9YHwG1jumgm7zP/dJRrrUuJUp1Ox7UjO7Lw3jMZ2yOajlGBPH5ub3x99Nx3lrZ09Eer1MpKnhzgvj567nAqB51bWsk3G9I0jgadrvGFT8XRIAhtmbI8yNgOFfmqer6lAla8BCv+rRW18+ZkKM1RS1i27w+Rnd0LVAtCW0JRYOtXsORJ7fcjvCNM+0CtruJKyQlAp4qihieBTueW6uCtNKM3zhugDZX8xansYn3O58ylQzrw8Q1DNatfzuH5LU27YD9m3zLCrU8NjboI9vPhbqcwV18fPREujofnLuqrKY9pwzbwCg/0ZfpQhx7ClrQCjcijtz6G+Bv5/KZhTOkby+CkcD67aZgmcuPXndr767qiFB8ewLd3jPI4kLVdc3BShMZBM3/zUY8aDc70jQ/j+Yv64Qmf6kmLmu7SX1NCVNNOr2NCrxjNytpX69LsDobaQqfbhwXw+c3DNakftvPqdDpuPdMxMC4xWZhr18ZwtLWdNyLIl4uccr3/OpjL/qxijaZEY4UFV1VVkZ2dTXZ2NlVV7nXsBS+YK7Ce2Msl+pV8bnyFUJ0jHYawRJj2vhrBYBObbtfTrWS2v9HAOf0cNnJVSja5TlE0DYloOJXoEq2WGV4zYyLTa0nRaix0Oh3/Orc3/zexG3qdWjXIWSNmaKdIN60gGydjw29x+t5XWqwUljv0B2oqvxgWaOSxc3vz5yMT+PiGYYT4OxwCzqKQigLfrE93SZ3wfM748ADm3jGKIR1Vh9PADmEkJ4Vr2nRqF8T3d4/mEg/O6g4RAZr3aNORfHulHW/pbZ7oGRfCFzcP589HJtiFmS8Z3EETmfbpmlTySivdtHFsXDk8iXbBjt+9D1ceosSpfLKxWpejMZFZgyC0VUpzVCdDZTGEtIe8g7DgDtj/m7ZdwhB3J4OiQHEG6HzUCZToMQhtHXMZrHkTNn+h3Z44Eqb9V/0OOKMoUJQJBj9VGDW0vWaXMyc7R05OjNAIPf2yXTsRbehAenzPGL65fSQDOoTRLSaYByZ1r/2gZiQxMpDPbxquCUcND2i4eOkNZ3Ti8qEdSAgP4PFze7vpNoT4G3n7ykFuA2LntI2bRnfWmMJip7JiNd3nmFB/Zl03lAV3jyY5KcKeKgO41Wf3NBmODvHjm9tHaoQrwREdodPpuMRpMHsou9StNr2n/l0+LFFTAtSGs/idn4+BD68b4raqB+rkwaDX5kCn5ZWxMkXNgfY24HWmR2wIH98wTONgiQhU7/e5/dtrvgs2bQxvK9jXO0VXgFqK1DnsuiGifmVlZfznP/9h2LBhBAYGEhcXR1xcHIGBgQwfPpy33nqLsrKy2k90umIxQdYufHfP53XjLIw6JwdN+0Fw0X/VyLHKUlWQul13r4sbFztF8FgVbUpRY0+UBC06nY6Hzu7J+scn8fdjZ9G/gzbdzTmqwZmTSX87q1eM12oUfvXUyxjSMYKeTtWQXKs41BRx0S7Yj+/uHMWaGROZf9cZHm10gK+BNy4fyIsX97M7Q7pUC4/ePraL5ndj5go1qsEtdeIkZ+VGg16T3lZaWcWsPw96jSTzNxo0TpyMwgp+cCqz7NfI+gwgjgZBaHsoiqrFkLEdqkwQHAe7f4Qf7oJCJ8Oq08PQWzw4Gayqk8E3BOIHQkis+zUEoS1ReAxmXwJH/tJuH3CFWlnC1yWP3FoFxcfBv/o7EqQtteWqqXCyA1+9XsdUpxW7tDzt5KUxhtEDOoSz8N4zWfrguFYXGgzQJz6UT24aRvswf4J8Ddw5vkvtB9WC0aDn1csGsmbGRK+VMZKTInjQpVpEsNNqe+d2QUzq7dkmnowD6MKB8V5X+LytuocH+vLlrSPs4mBT+8bRxyklwzX14zuXFAdv13vuon6aaAmjQYe/j3YwHx7oy6c3DnNLP7FFe1w+NFGT8vHlWlUU0ltVDleGd47kg2sGExnkS0SgkZvP7ATgURtj8a5Mr5ES/RLCGNrR8XlesPmYRjOiptXQmti3bx+DBg3ikUceYdOmTZjNZnslCbPZzMaNG3nooYdITk5m//799bpGm6baycCeHwneOgu9znH/UmMmqaW0lSq1MlZsX4joVOPixsjOUR5TbqBxUieE2mkX7OdRZLdfQpgm4sTGyehc6PU6bvJio+v7HdbpdFwz0uEQzSnRVtqpzX7rdDoSwgPs0V5erzGiIyseGc87VyXz472j0et1dG4XpHlP/th7gn2ZxW6pE/XRIrpoUAJdnZzAn/99mKwiR0Sm6/t+7cgkTfpeVpHjfaiPhk1tiKNBENoSFhNk74fjWwArGANg6dNq2agqR+gZQdFwwVsw+DpVs8GG1aJOugKj1FXaQM/hsoLQZsjaDR9NUMVSbeiNMH4GjLxL+/0A9TtSnAGB7dTviAdh1NoEnurCeQPae913umQwDesUyZpHJ7Lz2SlM7NV8Ds87x3W1v//dY4Ldarbf6kEQDU7O0RAd4ud2Xhs1rboH+/kw87ohpLx4DjOvG6IZRCZGBmoqOezN1Io2enN4Bfga+ODaIfa0ketGdvIoCNYxKoiPrh+i2edfvboYHeLHOU7aIsv2qjnQWlGymt+fs3rHsuHxSWx+cjJDOjpeh6s2xkerUnEu1OH6upwdE+XmKg6cKLE/r09EQ3FxMWeffTYHDhzAYDBw+eWX89FHH7Fo0SIWLVrERx99xBVXXIHBYCAlJYUpU6ZQUlJS+4lPFywmOLEb9v0Kf7+PzkkN8n+Wc9jY/R+qlpS1Cto7UtBqQq/XcdEgL5oqEtHQ4jw4uYfbLTzZSfT0oYmaEpM2GiJWOC05wWsFkcYo82wjPjyACwfGa1I37hynrVAxa+XBBi9KgOpAfmCSwzleYbbys1MUpOv3IcTf6FaFykZ9nTg1cZoMVwThNKA0F45tgZx9qqBScSbMvxVSV2rbdRwNl36spkQ4U1UJRRkQmqDu89fWJxaENklYAvg7hX4GRKhOuB5T3dvaviMh8ep3xM+z+JNr+bb6DHyHJEUQ40U74XQaSNvKkzUnBr2O965KZu1jZ/HzfWdqBougrr57qo5xsjoS3sQn6zLY8xb14JwL7EpN3evcLojf/zGWdf86i6cu6OO13ZCOkbx5+SB7H50dYteOdFR9sCpqvfq6pE44Y/Bwv8MCjVzupI2xLb2AFKfKF66va2q/OE0pUmfqo9Hw1ltvkZ6eTnx8POvXr+ebb77hlltuYcqUKUyZMoVbbrmFOXPmsGHDBuLj40lLS+Ptt98+6eu0SSyVqpMhZQmseQesjgWPry0Ted5yLfrKIkeapkvFnprwKt56+pjHVkv32BAudnEEnax9DPLz4arhSW7bG+JoCPU3cuFAz5+xpq4sMqBDuKYCxcKtxzmar41WrG8XzuvfXpMWYrF6jviycdPoTgR5qFLS2BUnQBwNgnDqU2WG3INwbFO16GN1qsTC+7Sq+XofGHUvnP2CdmIFYC6H4ixVhCmuv7sopCC0VfzD4KpvwD9c/fxf8I4auuuKpQKlKItFOdH8khONYvRctxtwE4OsD3q9zmP4KUgOcnOg0+mIC/PHz8d9MKbT6TyWyzzZ23J2nziPg72GCBae0z9OU/bOmdom+v5GA7GhtVcVOm9Ae5Y8OJa5d4zimQsc35VhnbQ50N9uSMdkceThN2Qc76qNUVbpfF7tiY0GPdeM6Ign6vPe/vDDD+h0OmbNmsWgQYO8ths4cCAffvghiqKwYMGCk75Om8PmZDi4Ala/qaZyVvN91WiesNwM6ND7+KqRDCeZptkzLkSTOmRD7GPr4P5J3TUVDOoTiXfDGZ3cHBTeKuXUFef0CWeaw0HlHNVgsSp8uuawZn99RY/1eh3/mOxZa8nTKcMDfbl2lLuNNDZAw8Yb4mgQhFOZ8nw4vlUN//YNUH/IF8+A9bPUfEcboQmq0FL/y9xHw5UlanUKWxkpn4aLrgnCKUVUV7hmLpz9PATHuO+vLIOSHD5Mbcddv+Zxzzc7ePGXPe7tqnETg6zn4OGc/p7TJ2TFruVxFSmEkx8kBvgaNKVMbTRksBfib2RKX88OqoZW7nCmY1QQwztHaj7bahk25xzoSn7flaXZX186tQtisjdtDA8j2SuHJ3p8H+vz3h44cAA/Pz/OO++8Wtuec845+Pv7c+DAgZO+TpvCUgkn9sDh1bDqDVVst5ryDmfysPlOrNVTEH1EEgRFeTtTjXiKajidIr5aMx2jgrhimKMaRvcYzxGANZEQHqDRK4KGr7oP6BDeKBFp9WFM93b0jXc4x465la6ufx+m9I3TnLu2c956Zhc3p42vB8d6QxFHgyCcilirIC9VjWIozVadDBs+gXk3qduc6TYJLvkQol2UgBUrlOWCqQRi+ngsIyW0XgwGQ70fMTEeJtOnO7H9wMfDaq6pWHXoRfdidZbjJ/OTNansOl7o8VSNodEAqkZBu2D3EHBdo8hBCg3BaNBz4+hOmm31yfH1NFFqaAnGS7ykTzTH/GtacoImSuNEsWMVu6Hj+NucasA748mBERPiz3keHHU1Cbl5w2w24+tbNwe8TqfD19cXs9lce+O2SpVZdTKkrYWVr6mVr2x0GEbOyH9RhdOExte9mklduWhQvNvnShyxrYcnz+/DzaM7M21QPE/XkJJVEzeP1kaPRQY2fDHMudSljeZwUOl0Ou5w0WrQ7m/YuV2FjMG7AyU6xI8rh2nLokrqhCAIUFGkRjFk7gR0kPYXzLsRdi1QnQc2fPxh3KMw4XH3H/KKIlX00eCn5kVGdj59FObaCDbF8/o+hDpQXgAmW6m1bjhVycOqwLMLd3t8L90EnurpGDDodUzt576KKwPp1sFVw7QihYF+J78aNKprlJueQEMHe2d2a+dR36M5BtIh/kZN6c7GvP7QjhEM7OC+EuntvJ4qi9RH7KxDhw4UFxeze/fuWtvu3LmToqIiOnTwrpXRVimvrOKx+duY/t8/2bRlA6x8FSoKHA3aD4Szn0epdM1Lr//nIibUn9HdtKKqTZ1rL9Qdf6OBpy7ow1tXJtMxqn4OpSEdI7i+Osy/f0IY43s2fKHkgoHxmvLJ0HyRMOf2iyMx0nPqZUOjKib2inErgVzTy7p9XFdNeotvE6ROyPKlIJwqWCpVzYWcA2AugYLDsG6WtmSljQ7D4Ix7IdwlB6uyVF2dNQarK7ih8WCsPSdXaH08/fTTDTr+rbfeapyOtFVKc9S/7ftDmDppcI1UWH84j5+2Z7iJS7k6Hxoydji3f3u+XJum2SY5yK2DsEAjj53bi6d+3EWwnw8XJ5/85NKg1zFtUAKzVh6yb2uo8rdBr+PiZO05bdubg2tHduSrdWlu2xs6kNfpdNwypgv3zdnicl7P7ZOTVMfEtqOOyCOferwHZ511FgcOHOCuu+7it99+w9/f829mRUUFd999NzqdjkmTJp30dU51ft2RwZwNR4klj+jcV0CX49gZ3RumvASWCqzWKs1xDf1cXJycwKoUx7XEPLY9nruoH/+c2otAo6FRHElBfj5MS05gdnUZXoAmKLjgER+DntvHdOHJH3e57WsMG/ng5B5c/8n6Op0zITyAa0Yk8fnf6vvgXO2nsRBHgyC0dipLoeQEFB5VV1jLcmHLbDi20b1tWCKMugcSR2h/bS0mKMtRIxiiuqnt/IKb7SUIjU9DHQ2ff/55I/WkDVKSBXo/iO2jEShzjVQA+Pcve5jUO4ZAX8fPqWuQQ0McAyM6RxEV5EtuaaV9myzYtR6uH9WJKX3j8NHriPKQ5lIXrh6RxFfr0igxWegYFWgvM9kQLhncwc3R0Fyfm97tQxnWKYINh/M12xtjAnhOvzjiw/w5XuhUJ76GE18/qhMPzdtmf16fOvGPPPIIn332GatXr2bgwIH885//5KyzziIhQY3cOHr0KH/88QevvfYaKSkp+Pn58fDDD5/0dU510rNyiaSIr3z/TZLuhGNHZFc45xXACuYKrJHdgX323Q39XE7pG0eAcSflZtWBEdEIofVC6yPYr3GnrFePSNI4GprzczN9aCJvLU3R/K5D49jIMd3baexvRKCxxvZPnt+HTu2CsCqeU0oaisRKC0JrRFHUyIMTeyFtHWTuUB0FO+bCL/9wdzL4hcAZ98H0TyFppMNaVZmhOAPK8iE0UY10iOktTgZB8EZJFvgEelRB95QmkVlUwfvLD2q2uUY+NGTwYNDrmOIihiViZ62L2FD/ejsZQBVN++6uUbwwrR8L7jqjUVbsesaF0C9BKwzWnJEwzqUubTTG59aTNkZNpz1/YHuindJIurQ7+fDtLl268Pnnn+Pj40NKSgq33347Xbt2xd/fH39/f7p168Ydd9xBSkoKRqORzz//nC5dPOtJtFnM5fgWHmK270t00x+3bz5hTIDzXgeDUU3ZbNcTa5DWrjb0YxHk58OzF/UlwGigZ2wI0wZ5Tt0RBGd6tw/lngldMRp0jOgcydge0c12bX+jwWNqV310flzR6XS8dMkAEsIDCPI1cNd475oQoEZY3DS6M7ec2Rl/Y+OLQUpEgyC0JqxVasRC0TE1iqHKrOY4piyBlN/UMpTO6AzQdxoMvgH8Q7XnKc9Tjw+OhYhOEBglMYWCUBM6PQRGQ1xf9xKwuEcq2Phw5SGmD+1gz0F1bdbQCdZ5/dvztVMoelAjr+wILU+vuFB6xbkrhjeES5I7sPOYQ1egsVcEa2Jqvzi3SJzGSt24YlgSby9NobS6xKVrrrUzfj4GPr5+KG8u3U+nqCAucElzqiuXX3453bp14/HHH+f33393T4/S65k6dSovvPACycnJ9brGKYvVCqmrufLQY0TqM+yb06zRTC9+jH+lGrgoIReiuqtjkZxSzeGN4QC7fGgilw3ugELzpQgJpz6PTOnFA5N64KPXNXtK4vWjOjLzz4M1luqtL91iglkzYyKWKmu9BHAbExmtCEJrwGJSq0cUpKuOBqyQvQ/2/gLHt3g+JmkkjLxLq8NgtaiREGYTBLVTf9SDY0Df+F5KQWhTGHxVLYbQBK8RP86RCsF+PpSYLABUVll54Zc9fHT9ULd20PDQ4FFdopjUO4ale04woEMYwzpFNOyEwmnBFcMSmbfpKHsyijhvQHtiQ5tPj8fPx8DlwxL5YIUj2qexxvFhAUaeOL8Pjy3YQViA0WuVDRsDE8P57KbhDb7u4MGDWbRoEYWFhWzevJkTJ9T0gJiYGAYPHkxYmLtz8rTg4B+w4FYizQX2TRlKJFebHyeLSP61opgB13Sgc7vuoNe7paA11uRKRCCF+tDQKj/1JTzQlyuHJfHJmtTqfugaPaKgpZ0MII4GQWhZTMUO/YWKQrBUqHWn9/6ipkp4IqKTqsPQYZhjW1Wl6mCoskBgO4hJVB0MhppzswRBqMZgdC8B64LzAHlkl0iKKiysT80DYMnuLFbuz2Zsj2h3jYYGlqPU63V8dP1QMgoriAv1lwG1UCeC/Hz4+f/OJLOogviw5hf9vXp4EjP/PGj/PjRmys9Vw5OY2jcOvV5HWEDT/c4pisLu3bspKioiMjKSHj16EBYWxoQJE5rsmqcUe36BBbeC2VFJ4og1huvNMziqqNUBSi067v29mAXdwU/vIbWsWTssCK2Heyd2Y1VKNiknSrjxjE4E+La9RcGWd3UIwumG1QqluWp5yrS16t+sHbDxE/j+Ttj8uWcnQ2xftVTlpR87nAyWCijKUM8X2E7dnjgMwhLEySAIjYxzuLRep+OZC/pqohWe/WkX5iqrW1i1rhF+aXU6HfHhAeJkEE4Kg15HQnhAi1QqSYwMZKJTKbp2wY0rthYR5NtkTgaz2UxeXh5paWn079+f0aNH07t3b6Kjo3nxxRebpUTw8uXLOf/884mOjiYgIIBevXrx5JNPUlpaWvvBzcH2uTDvBo2TYYe1E5dWPkuuT3tGxTneo10Zxbz0617APQVNKmsLpyuRQb789sBYtj41mcfP69PS3WkSJKJBEJqLKrNaMq/waPXfNMjYDkdWQ/5hz8cY/KD7JOgzDdp1d2yvLFUrUBiMaqh3WAIERMovtiDUA6tV4bddmfgY9EzqHeN1UuYc0aDX6egTH8o1IzralasPZpfy+V+H3UIxRbxROF155sK+HCsop7DczH0Tu9d+QCth2rRpFBaqZTGd7UFubi5PPfUUKSkpfPbZZ012/XfffZf7778fRVHo0KEDiYmJ7N69mxdeeIH58+ezevVqIiMbvxRdnVn7ASx+DGdFmkNBA7ky9z5KCSBMp/DW5HDOXVBGbqkZgM/+OsyorlF0iAjQnErK9QqnM3q9jvA2XClFHA2C0NRUlqr6C/lpkLkdjm2CoxugMN37MaEJqnOh51S1ogSAYgVTCZiKwBgAEZ0hLB78w0XkURAawLvLDvDm0v0APDi5B/ed5XlC5Bzya/PpPTi5Bz9tP05BmTqYfntpipuatHw7hdOVxMhAFj8wtqW7cVLMmzePRYsWAeDj48M///lPOnTowOHDh/nqq684fvw4s2fP5qabbmLcuHGNfv1NmzbxwAMPADBr1ixuu+02dDodx48f58ILL2TTpk3cdtttzJ8/v9GvXSuKAkufgTVvabd3m8Qcw52U5qrin3qdjtiu/XnjchM3frrB3uyRedt49bKBmkPFESsIbRdxNAhCU2C1qtUiirMgdaX6OL4RijO9H6PTQ9IotYpEwhBHvHVlmepcsFaBbxC06wWhcQ4HhCAIDWLpniz7//9Zsp9Lh3QgITzArZ1zRINtFS4iyJeHzu7Jkz/sBKDYZOHTanEnGzKQFoRThy+//BKAgIAAYmNjefHFF+37Hn/8cSZOnMiWLVv46quvmsTR8Pzzz2O1Wrn++uu5/fbb7dvj4+OZM2cOvXr1YsGCBWzfvp0BAwY0+vW9UmWGhf8H2+Zotw+4HAZfj+XPIvsmg48P+IcxvifcOa4rM/9URUGLKiw8+eNOzeFiHQWh7SKOBkFoTCrL1OiFg3+ofNY3rAAAKAJJREFUJSnT11VXkaiB8CToMh56ngshceo2i0l1LpgrwDdQ3R4cBwERYGx+US9BaMvsOFaoef7G7/v4z+WD3No552U7D46vHp7E1+vS2JOhDrRLncpVgQQcCcKpxObNm9HpdERGRrqF9YeGhvLKK68wefJktmzxUhGqAZSUlLB48WIAjZPBRvfu3Zk4cSJLly5l3rx5zedoMJWoegwHlmq3D7kRup4F5goU32CgBACdUxrnQ2f3YH1qLpvTCgDILjZpTiGOWEFou4ijQRAairVKrRyxbzHsXwTpa9UKEjUR2RW6jIPOY9UqEqCWpizLU4WVDL4QEA7RvVXngpdye4IgNAxFUTAadJirHE6E77cc49Yzu9AnPlTT1uoiBmnDoNfxzAV9uOLDtR6vIeNoQTh1yMnJwd/fH19fz3nTQ4cOtbdrbLZs2YLJZMLPz4/hwz2X5BwzZgxLly5l7VrP9qaxuez1hTxX9ix9rAccG/UGGHaHmt4Z1gGCorHuTsPmaHDWrDUa9LxzVTLnvr2KogqL2/lF31YQ2i7iaBCE+lJyQi1Duecn1blQWYsSdLse1c6FceoPs6KApVyNeLBUADrwD1O1FwIj1f9lhiIITUpRuUXjZAD1q/ny4r18cfNwt+02XAfHI7pEcf6A9vy8PcPtGrJiJwinDiaTibi4OK/7w8LC7O0am/37Va2YpKQkjEbPFTW6du0KwL59+7yep0uXLl73paenk5iYWLcOlRfwRvEjdNQ50j6tBn/0k5+FPheq1a58VIeMN0csQIeIQF6bPpA7Zm9yv4aYR0Fos4ijQRDqiqJAQZqaErH3Z0j7u9pBUAMxfVTnQqexEByjtjeXQeExdb/RH4xBENZRdS4EhKsrBYIgNAs5pZ4nCyv3Z7MqJZsx3aPt22oaSAP869zeLN2TRYXZqtkujgZBEOpCXl4eQI0VJWz78vPzm75D/mGssA7iBoOazpGthPJWxPM8P/Q69D7asYprVR5XpvSN48YzOvHZX4c128U+CkLbRRwNguANqxWydqpCjulr4dhmKDpW8zE6PbQfBJ3HqMKOvkFgLldFlEqywCcQ/CMgqB34Bqv7jQESuSAILUROsfdVyZcX7WV013boq8MXFA9ikM7Ehwdwz/huvLFkv2a7fLsFQagLFRXq4oW3tA0APz8/AMrLy722OXTokNd9NUU7uKHTsT/5X/y8pYC+ulRuMM8g7Wg0Pdanu1XX0WjYeDF6j53biw2H89h13CEc6SO5E4LQZhFHgyDYMJdD+gY4vFIVcTy+VRVkrA29EToMhU6jVSeDwag6FhRUZ0VwrKqzYHcsiJijILQWAn19mNQ7huySSnKKTXSICGBdqrqquOt4EQu3HWdacgLgGtHg+Xy3je3C3E3ppOepkwCdTvyIgnCqkZWVhdWqRiYZDO5RhjqdjqysLI/7bPstFnc9gtrw91fHB5WVlV7b2FI2AgLcK+M0BS9eMpDU0XO4+r0lZCiqXtRLi/Ywpns7ukQ79KOsToFc3qIU/HwMvHf1YKb9dw2F5WYiAo30Swhr0v4LgtByiKNBOH2pKIK0tZC6Ag6vhqxdqiBjXfDxh8ThkDgSYnurzgadjxqdEBjhcCz4haiOB0EQWiX9O4Tx8Q3D7M+ziioY/9oKys1q5YjXftvH1H5x+BsNtYYGA/gbDTx3YT9u/WIjVVaF0V3beYx+EASh9eK8Ou/8f1MTEREBOFIoPGHbZ2vbHHSOjeTOc0bw9MJdAFSYrTw0bxvz7hiFj0GtMFFVB0csQOd2QSy6fwyrD+RwZrd2+BslXVQQ2iriaBBOH8rz4cjfasRC6mo4sQsUa+3HgaqjENMLontAZDeI6FKtrxAojgVBaEPEhvpz25jOvLNMVVg/VlDO7L+PcNvYLpqIhpp8BxN6xTDvzlGkZBVzwcD4pu6yIAiNyNNPPw3AW2+9BcADDzzQbNfu0aMHAGlpaZjNZo+CkAcPHtS0bS6uG9mR33dnsuaAWrJ7S1oBs1Ye4p4J3YDaNWyciQ8P4PKhdRSkFAThlEUcDULbwmpVHQolWeqjOBOOb4bDq+DEXtR8hjoQ0h6ie0JUd4jsAqEdqh0L/qrGQkCYOBYEoY1y+7iufLUujdxSNXz5veUHuHxoYq0aDc4MTopgcFLzrTgKgtA42BwNn3/+ueZ5c5CcnIyvry8mk4n169czevRotzarVq0CYNSoUc3WLwC9Xsdrlw1kypsrKTap0Z9vLd3PhJ4x9IkPdbGPzdo1QRBaKeJoEE4tKsvUFIesHVB0XHUkFGeqpSZLT0Bpdt3TH2zo9BDZGaJ7Q1Q3iOwKwdHgE6A6EgIiVG0F30A1gkGqQghCmybYz4f7J3XnqR/VMOHCcjPvrzhQJ40GQRCE+hISEsKUKVP46aef+PDDD90cDSkpKSxbtgyAyy67rNn7Fx8ewNMX9uXhedsAMFcpPDh3KwvvPfOkIhoEQTg9EEeD0HqpKILMHZCxrfqxFXL21z3dwRs6vepMiO4B7XpBbD81/cEYAAGR4BesOhR8gyRaQRBOU64ansSnaw6TmlMKwKd/Hdaoo8tAWhCEpuDJJ5/k559/Zvbs2YwePZrbbrsNnU5HRkYGV111FVarlWnTpjFw4MAW6d+lgxP4bVcmS3ZnAbA3s5i3/9hfJw0bQRBOL8TR0MQsX76cN954g3Xr1lFSUkLHjh2ZPn06M2bMICgoqKW717JYKqEsB0pz1EiEslwoPOpwLuQdbJzr6A2qYyGmN8QNUB8hseAXqjoXjAFq9IKP93JSgiCcXhgNev45pSd3fbUZgEqLFWcdeIloEAShKRg2bBj/+c9/ePDBB7njjjt44YUXaNeuHbt378ZkMtGzZ08++uijFuufTqfjpUv6s+lIPnnV6WUfrDhIp6ggpzYt1TtBEFoT4mhoQt59913uv/9+FEWhQ4cOJCYmsnv3bl544QXmz5/P6tWriYyMbOluNhxLJVQUQkWB42+57X/1oZTnoyvLVR0KNsdCXUpH1oTeAP7h4B9WrZsQDoFREBip/g2OheA4COugair4BKgaCz4BoNfX+7JVVgW9rvYc7bqSXWziRHEF7YL9iAzyxWjw3Lcqq2Jv562NIAiNx9R+cSQnhbMlrcBtn1SSEAShqXjggQfo37+/faHqxIkTdOzYkcsuu4zHHnuM4ODg2k/ShLQL9uPfF/fjzi9VR6xVgUPV0V8ABvHECoKAOBqajE2bNtmVimfNmmUPfTt+/DgXXnghmzZt4rbbbmP+/Pkt29EqM1SWOj1K1L+mompnQYHqLPDwv7k0H11FIT7Wilov09CfnDJDKAVBXTCFdcXcrieblV6YQxIJCvAj2N9IsL8vwf5+BAdUPwL9KCw388/vtrM3M4MQ/2xC/I2E+vsQ4u9DiJ9R/euv/t10JJ9AXwO+Pnr8jQb8jXoCjAb8jQb8jAb8q7e/tXQ/B7NL8TfqiQ7xIzrYj+gQP2JC/NXn1dsCfQ1sOJyP0UdHqL+RsAD1ERrg+D/E34e/D+Zy6+cbqaxypINEBBppV33edsHqo6CskgVbjgHqSmr7sAASIgLoEBFAh4hAOoQ7/t+TWcTqlBwCfA1EBfkSGeRLVLAvkUF+RFX/H+jrw687Mvj8r8ME+hpIigwkMTKQDhGBJEYGkBgZSKi/mjaSVVTBJ2tSySioIDzQSHiAkbBAXyICjYQHGgkL8CU80EhEoC+bjuRzOKeUxMgAOrcLpmNUoL10VXaxieV7T2C2Wgn287G//8F+PvbnwX4+GPQ6nvxxJwu3Hicq2I+u0UF0jQ5WHzHq/+GBjuiT33dl8uf+bAJ9DUQE+RIZ6Kv+DfIlIlD9GxZgxKDXsSejiI2H8/A3Guz3ISxQ/Rse4Iu/UW+fQC7emcHOY0XEhPqRWP2+dIgIdCvFVWVV+GZDGmm5ZYT4+9jvc3igr+MaAepnr7LKypdrj3C8oIL4cH8SwgPt9zEqyFczed2XWcwri/dSYa4iKTKQpKhA9W/1IyzAaG+/63ghT/6wk6tHdOS8/u0J8BUdkcZAp9Pxr3N7M33m3x72tUCHBEE4bTjrrLM466yzWrobXpnarz0XJyfwffXYxBlJnRAEAcTR0GQ8//zzWK1Wrr/+em6//Xb79vj4eObMmUOvXr1YsGAB27dvZ8CAAU3bGVMJfHMVVpPqRNCZS9HZHAtVlbUf74WmUi84qrRjl7UTO62d2KV0Yqe1MycIh1IdnABSbC0z6nzO7GJTo/axwmwlPa+c9LzyRj0vQH6ZmfwyMyknSjzutypqyb1jBeWsT230y9sJDzSSGBHIjmOF9T6HXgcJEQEkhAew9pD3uuDeKKqwkJpTytI9JzTbo4J86RodjKnKyrb0glrPo9NBXUqh+xr0hAYYySnx/nlpF+ynOmOqnQ8/b8/gSG5Z7SevBX+jnvhw1ZmREB7AnPVp9n1/Hcx1ax/i70PHaufDrzsyAdicVsBzP+1ixSMTiAySVKDGYFinSM7uE8vv1fnINmQgLQjC6c4zF/Tl74O5ZBZpF5wkoEEQBBBHQ5NQUlLC4sWLATROBhvdu3dn4sSJLF26lHnz5jW5o2HFgQLGp66kpYLtSxR/iggkTwklVwklF/VvnhJKjsv/eUooZfi3UE8FZwrKzBSU1d/JAKpTpCkcMrmlleSW1t1xURcnA0BllbVGJwNATomJnBKTx3D6hlBhtnIou5RD2aW1NwaKKyzsPFbEzmPaFKRe7UPFydDI/HNqL/7Ye4Iqq1SdEARBsBEWaOSVywZwwyfrNdsltUwQBBBHQ5OwZcsWTCYTfn5+DB8+3GObMWPGsHTpUtauXdvk/fl03XHOUAz46qrqdXylYqCQIIqUIIoIolAJoohAl7+O7UVO24sJpApHGHef9qEcyC6h0lJz5YgpfWOpMFur9QtM5Jaa6jxZ9IS/Uc/oru0orrBQVGGmuMJCcYWZEpNFo5QMaupCubmKCnPNfbxsSAeyi03qo8REbonJ7VwnQ/+EMKb2i7OfL8fpb1GFo2TnhQPj8fXRczS/jKP55WQUVmgmQPXB16DXpG/UhNGgw1zVsOudDD1jQ0jNLa31MyOoXD08qaW70OboFhPMFcMS+XqdI8pEIhoEQRBgXI9orhmRxFca+9iCHRIEodUgjoYmYP/+/QAkJSVhNHpOMOjatSsA+/bta/L+XD+qI3+mDgKgFD/KFH9K8acMf0oVP8rwp0zxs28rU/woIogiJZBCgqjAl5pUFs4f0J6nL+iLyVJFpcWKyWK1/7VtCw80MigxAoNeh6XKyuHcUvZmFrMvs5i9mcXszyomLa8MRYGbRnfi6Qv6aq5hqbKSV1Zpn9gXV1joFRdCkJ8PJSYLxRUWSk0WSmyPCvVvWWUVyUnhnN0n1qOHXVEUSiurKCo3Exqg6gU47zNZrJjMViosVVRUOx+C/Ax0iAh0O1eVVSG31GTvY6mpioGJYQT6+lBUbqaw+lFU4fR/uerw6BkXwjUjOnoVUDJZqigoMxMd7IfepY2lykpWsYmjearj4Wh+OZFBRi5KTsDXoCevtJK80kpySyvJKzWRW1L9f0klPgYd04cmMigxnKIKM+l5ZdURCGWk55eRnldGWl4ZOSWVjOoSxdtXDcLXoKessoqCcjP5pZUUlpvV6Idy9f+E8AC6RgdzMLuE1Bx1hf5QTgmHskvx89Ez45xenNG1HUUVZvt9Kq6wUFx932wOoLAAI5cPTSQxMpAqq8Kx/HIOZpc4HidKOZhdQm5pJTodPH1+H64Z2ZGCMjP5Zeprzi+tJK+s+m+puj08UD2vTgeFZY574fooKjfTLSaYa0d2JK+0kvR89X2xOXjS88o4VlBud7r4+uj54ubh9IoL0ZynoEx7zrLKKkZ0iWRCzxg1BSa/XD2n/X81LcaW7tMtJpgFd5+BUa8nPb+MtFz1njg/0vPKqKyycnafWM4f0N7rd1WoPw9M6s4PW45RVqk6bKND/Fq4R4IgCK2Df53bm1UpOaTlqWmEYh8FQQBxNDQJeXlqSHdNFSVs+/Lz87226dKli9d96enpJCYm1qk/43vGMDn8KfrGh9E3PpTyyir7yn5RuVmzyl9UYcZqVbhkcAcendpLnSiVV1aH0ZsprP6/sFzVEYgJ8eP2sV0I8qv7R8nHoKdbTAjdYkI43ylrpNRkocJcRVSw+w+Uj0FPTIg/MSGNm1ah0+nsYoSe9vlXC0KG1UGRwqDXee1jQ0PZ/XwMxIZ6FvjzMehJCFd1EEZ42B8fHkB8eECt1wj1N1Z/RsJqbRvk50OQnw8JNZy3X4L2PIqi1Duc0qDXqWKIUYFM6BWj2VdQVokOHWGB6j2yiXI2Jh2jgkhOinDbXmVVyCqqIKOwnITwQOLC1HvvLFZZEz1iQ+gRG+JxX4W5ivyySqKD/fCprjLirb3VqlBZZXUTqhQaj5gQf96+Mplnf9pFUmQgFycntHSXBEEQWgVBfj58eP0Q/vHtNixVVu6d0L2luyQIQitAHA1NQEWFKorj6+t9suHnp06EyssbX0zQFYNex28PjK1XScQAX4N98tTU2CavQtukqXI26zqpbwoMel2dHTkni7/RQPuwup1Xr9fhrxcnQ1MzuU8sk/vEtnQ3BEEQWh294kJZdP+Ylu6GIAitCJnVNQH+/urEvLLSe0UHk0kNiw4I8D6ROHTokNd9NUU7eKI+TgZBEARBEARBEARBOFlk9tkERESoIda2FApP2PbZ2gqCIAiCIAiCIAhCW0AcDU1Ajx49AEhLS8NsNntsc/DgQU1bQRAEQRAEQRAEQWgLiKOhCUhOTsbX1xeTycT69es9tlm1ahUAo0aNas6uCYIgCIIgCIIgCEKTIo6GJiAkJIQpU6YA8OGHH7rtT0lJYdmyZQBcdtllzdo3QRAEQRAEQRAEQWhKxNHQRDz55JPodDpmz57Nhx9+iKIoAGRkZHDVVVdhtVqZNm0aAwcObOGeCoIgCIIgCIIgCELjIY6GJmLYsGH85z//AeCOO+6gY8eODB48mM6dO7Np0yZ69uzJRx991MK9FARBEARBEARBEITGRRwNTcgDDzzAkiVLOOeccygtLWX37t107NiRf/3rX2zcuJF27dq1dBcFQRAEQRAEQRAEoVHxaekOtHXOOusszjrrrJbuhiAIgiAIgiAIgiA0CxLRIAiCIAiCIAiCIAhCo6FTbCqFwilFQEAAFouFxMTElu6KIAinIOnp6fj4+FBeXt7SXWl0xD4KgtBQ2qqNFPsoCEJDqat9lNSJUxQ/P786t01PTweQH5VWjtynU4e2cK98fHxOyo6cSoh9bJvIvTo1aCv3qa3aSLGPbRO5V6cObeFe1dU+SkTDaUCXLl0AOHToUAv3RKgJuU+nDnKv2g5yL08d5F6dGsh9ajvIvTx1kHt16nA63SvRaBAEQRAEQRAEQRAEodEQR4MgCIIgCIIgCIIgCI2GOBoEQRAEQRAEQRAEQWg0xNEgCIIgCIIgCIIgCEKjIY4GQRAEQRAEQRAEQRAaDXE0CIIgCIIgCIIgCILQaEh5S0EQBEEQBEEQBEEQGg2JaBAEQRAEQRAEQRAEodEQR4MgCIIgCIIgCIIgCI2GOBoEQRAEQRAEQRAEQWg0xNEgCIIgCIIgCIIgCEKjIY4GQRAEQRAEQRAEQRAaDXE0CIIgCIIgCIIgCILQaIijoQ2zfPlyzj//fKKjowkICKBXr148+eSTlJaWtnTXTiueeeYZdDpdjY+ZM2d6PNZsNvPaa68xcOBAgoKCiIiIYMKECSxYsKCZX0XbIDMzk9mzZ3PfffcxatQoAgIC0Ol0jB8/vtZjG3IvtmzZwhVXXEFcXBz+/v506dKF+++/n+zs7EZ4VUJ9EPvYOhD72HoQ+yjYEPvYOhD72HoQ+1hPFKFN8s477yg6nU4BlA4dOijJycmKn5+fAii9e/dWcnNzW7qLpw1PP/20AigxMTHK6NGjPT5++OEHt+PKy8uVM888UwEUg8GgDBgwQOnatasCKIDy6KOPtsCrObV588037e+f82PcuHE1HteQezF//nzFaDTaPwODBw9WgoKCFEBp3769cvDgwUZ+lUJtiH1sPYh9bD2IfRQURexja0LsY+tB7GP9EEdDG2Tjxo2KXq9XdDqdMmvWLMVqtSqKoijHjh1ThgwZogDKJZdc0sK9PH2w/VDccMMNJ3XcfffdpwBK586dlb1799q3//jjj/Yf/YULFzZyb9s2//vf/5RJkyYpjz32mLJgwQLlySefrNMPRX3vxdGjR5XAwEAFUJ588knFbDYriqIoBQUFytSpUxVAGTp0qP07KjQ9Yh9bF2IfWw9iHwWxj60LsY+tB7GP9UMcDW2Qiy66SAGU66+/3m3f/v37Fb1erwDKtm3bWqB3px/1+aHIzMxUfH19FUBZtmyZ236bgRs8eHAj9vT049133631h6Ih9+L+++9XAGXs2LFu+/Ly8pSwsDD5wW9mxD62LsQ+tl7EPp5+iH1sXYh9bL2IfawbotHQxigpKWHx4sUA3H777W77u3fvzsSJEwGYN29es/ZNqDsLFy6ksrKS7t27M2HCBLf9d9xxBwCbN2/m4MGDzd2904qG3IvvvvsO8PxdjIiIYPr06QDMnTu3sbsteEDsY9tA7GPrQexj20HsY9tA7GPrQeyjiEG2ObZs2YLJZMLPz4/hw4d7bDNmzBgA1q5d25xdO+3Ztm0bV199NRMnTuSiiy7iySefZNeuXR7b2u6N7V65kpCQQOfOnTVthaahvvciPT2dY8eOATB27FiPx8p3sXkR+9h6Eft4aiL2se0g9rH1Ivbx1ETsozga2hz79+8HICkpCaPR6LFN165dAdi3b1+z9UuArVu3MmfOHJYvX87ChQt54YUX6N+/P//4xz+oqqrStLXdR9u98oTcx+ahvvfCdpyvry8dOnSo8bhDhw5hNpsbpb+Cd8Q+tl7EPp6aiH1sO4h9bL2IfTw1EfsojoY2R15eHgCRkZFe29j25efnN0ufTnfi4+N57rnnWLduHdnZ2VRUVLB9+3buvPNOFEXhrbfe4rHHHtMcI/ex9VDfe2E7LiIiAp1OV+NxVquVoqKiRumv4B35XrU+xD6e2oh9bDvI96r1Ifbx1EbsI/i0dAeExqWiogJQvWDe8PPzA6C8vLxZ+nS64ym/qn///nzwwQd07tyZRx99lDfffJO7776bTp06AXIfWxP1vRcnc5zrsULTIN+r1ofYx1MbsY9tB/letT7EPp7aiH2UiIY2h7+/PwCVlZVe25hMJgACAgKapU+Cdx566CHi4+OxWCwsXLjQvl3uY+uhvvfiZI5zPVZoGuR7dWoh9rH1I/ax7SDfq1MLsY+tH7GP4mhoc0RERACOsBtPOIfkCC2LwWBgxIgRAKSkpNi3y31sPdT3Xtj+z8/PR1GUGo/T6/WEhoY2Sn8F78j36tRC7GPrR+xj20G+V6cWYh9bP2IfxdHQ5ujRowcAaWlpXsVBbCVUbG2FlsUWGmWxWOzbbPfmwIEDXo+T+9g81Pde2P6vrKwkPT29xuM6d+7sVXxLaDzEPp56iH1s3Yh9bDuIfTz1EPvYuhH7KI6GNkdycjK+vr6YTCbWr1/vsc2qVasAGDVqVHN2TfDCzp07ATTKsiNHjgRg9erVHo85duwYqampmrZC01Dfe5GUlER8fDzg+M65It/F5kXs46mH2MfWjdjHtoPYx1MPsY+tG7GP4mhoc4SEhDBlyhQAPvzwQ7f9KSkpLFu2DIDLLrusWfsmuPPLL7/YayGfffbZ9u0XXXQRRqORlJQUli9f7nbcrFmzAHVg0K1bt+bp7GlKQ+7FpZdeCnj+Lubn5zNv3jwApk+f3tjdFjwg9vHUQuxj60fsY9tB7OOphdjH1o/YR0AR2hzr169XdDqdotPplFmzZilWq1VRFEU5fvy4MmTIEAVQpk2b1sK9PD3YuXOncvvttytbt27VbK+qqlK+/vprJTQ0VAGU888/3+3Ye++9VwGUzp07K3v37rVvX7hwoeLn56cAyg8//NDkr6Et8+677yqAMm7cuBrb1fdepKWlKQEBAQqgPPnkk4rFYlEURVEKCgqUqVOnKoCSnJxs/44KTY/Yx9aD2MfWjdjH0w+xj60HsY+tG7GPdUMcDW2UN998U9HpdAqgJCYmKsnJyfYPdM+ePZXs7OyW7uJpwZYtWxRAAZTIyEglOTlZGTZsmBIREWHfPmbMGCU/P9/t2LKyMmXUqFEKoBgMBmXgwIFK165d7cc99NBDzf+CTnHS0tKUqKgo+yMoKEgBFB8fH832V155RXNcQ+7FvHnzFB8fHwVQYmJilCFDhtivGxsbq6SkpDT1yxZcEPvYOhD72LoQ+ygoitjH1oLYx9aF2Mf6IY6GNszSpUuVc845R4mMjFT8/PyUHj16KP/617+U4uLilu7aaUN+fr7ywgsvKOedd57SpUsXJSQkRDEajUpsbKxyzjnnKLNnz7Z7KT1hMpmUl19+Wenfv78SEBCghIWFKePGjVO+++67ZnwVbYfU1FS7ca/p8fTTT7sd25B7sWnTJuWyyy5TYmJiFF9fX6VTp07Kvffeq2RlZTXBqxTqgtjHlkfsY+tC7KNgQ+xjyyP2sXUh9rF+6BTFS90MQRAEQRAEQRAEQRCEk0TEIAVBEARBEARBEARBaDTE0SAIgiAIgiAIgiAIQqMhjgZBEARBEARBEARBEBoNcTQIgiAIgiAIgiAIgtBoiKNBEARBEARBEARBEIRGQxwNgiAIgiAIgiAIgiA0GuJoEARBEARBEARBEASh0RBHgyAIgiAIgiAIgiAIjYY4GgRBEARBEARBEARBaDTE0SAIgiAIgiAIgiAIQqMhjgZBaER0Oh06nY4VK1a0dFeajL1792I0GunXrx9Wq1Wz78Ybb0Sn03HjjTfW69xTp05Fp9OxbNmyRuipIAitDbGRYiMFQfCM2Eexj20NcTQIQjU2A1+fx2effdbS3W82HnnkESwWC08//TR6feOakGeeeQaAhx9+2O0HSBCElkVsZN0QGykIpx9iH+uG2MfTC5+W7oAgtBZiY2M9bi8pKaG0tLTGNgEBAQD07NkTgMDAwCboYcuzfPlyfv75Z/r168dll13W6OcfOXIkU6ZM4bfffuPLL7/k+uuvb/RrCIJQP8RG1o7YSEE4PRH7WDtiH08/xNEgCNVkZmZ63P7MM8/w7LPP1tjGxt69exu9X62JV155BYA777wTnU7XJNe48847+e2333j11VflR0IQWhFiI2tHbKQgnJ6IfawdsY+nH5I6IQhCnUhNTeX333/HaDRyxRVXNNl1zj33XCIjI9m1axdr1qxpsusIgiA0JmIjBUEQPCP28fREHA2C0Ih4E/I5fPiwfd/hw4c5cuQIt912G0lJSfj7+9O1a1eeeOIJe3gdwM6dO7n22mtJTEzE39+f7t2788ILL2A2m2vsw+HDh3nggQfo27cvwcHBBAYG0qtXL+6//37S0tLq/do+/vhjFEVh8uTJtGvXrk7HfPfdd4wfP57IyEgCAwMZNGgQb7/9do25c76+vlx66aUAfPjhh/XuryAIrQ+xkVrERgqCYEPsoxaxj20ARRCEGnn66acVQKnL18XWbvny5Zrtqamp9n3z589XwsPDFUAJDQ1VDAaDfd+YMWOUyspK5eeff1YCAwMVQAkLC1N0Op29zRVXXOH1+l9++aXi5+dnb+vn56cEBATYn4eEhCi//fZbvd6HwYMHK4Dy0ksveW1zww03KIByww03KPfcc48CKHq93v56bY/rr7++xmvNnj1bAZTY2Nh69VUQhOZDbKSK2EhBEFwR+6gi9vH0RBwNglALjf0jER4erpx11lnKrl27FEVRlLKyMuWdd96x/1g88cQTSlhYmPL/7d3PS1RfHMbx5zteNBmV/BGIFAYp/dolY4vIEguhQtpEFFREA9EmbSOCC8WFS1chttE/wF0QkTKLIghlVm1MLW0IomgwpyESw3u+C7lDjnd0Ru4dcOb9AmG4Hs/nXBfPgQ9zz71586b5/PmzMcaYZDJp+vv7U3NMT09vqz01NWUCgYCxLMv09vaa5eVlY9u2sW3bfPjwwdy4cSO1McVisZz+B4lEIrW+SCSScZyzSVRXV5vS0lIzMjJiEomEMcaYeDxuwuFw6h52mmdhYSE1bm5uLqe1AsgvMpKMBOCOfCQfixmNBmAXXm8Sp0+fNmtra9v+9s6dO6kxly9fNrZtbxtz/vx5I8k8ePBgy/WNjQ3T3NxsJJlnz55lXF9XV5eRZLq7u3e9l39FIpHU2uLxeMZxziYhyUxMTLiOaWlpMZJMOBzesWZFRYWRZMbHx3NaK4D8IiPJSADuyEfysZhxRgOQZ0+ePFFZWdm2652dnanPfX19rifyOmPev3+/5fqbN2+0uLiouro6hcPhjLWdE3hfvXqV05q/fv0qSSopKVFNTc2u448cOaJ79+65/q6rq0vS9ntIV1tbu6U2gOJARpKRANyRj+TjfsLrLYE8a21tdb3+7/uVQ6HQjmN+/vy55bpzsm4ikVBDQ0PG2uvr65KkWCyW/YIl/fjxQ5J08ODBrF5JFAqFMo5z1reysrLjHDU1NYrFYqnaAIoDGUlGAnBHPpKP+wmNBiDPKisrXa9blpX1mPRTg52O7d+/f/X9+/dd1/Dnz5+s1upYW1uTJNcuuptM65cy30O68vLyLbUBFAcykowE4I58JB/3Ex6dAArAxsaGJOns2bMym2ev7PqTC+craOldcD853WqnNgDsFRkJAO7IR/iFRgNQAOrr6yXl/nW2bB06dEjSZhc7X91hZ5NwagPAXpGRAOCOfIRfaDQABeDcuXOSpG/fvikajXo+/6lTp1Kfl5aWPJ8/XTKZVDwelySdPHnS93oAChsZCQDuyEf4hUYDUADa29vV1NQkafNEYufAnkx2O0Qn3fHjx1OHCM3Ozu5tkTmIRqOybVuWZaU2QADYKzISANyRj/ALjQagAFiWpbGxMVmWpbdv36qtrU2RSGTLYTlLS0saGxtTKBTS6OhozjUuXLggSZqZmfFs3Zk4Nc6cOaOKigrf6wEobGQkALgjH+EXGg1Agejo6NDk5KQqKys1MzOjS5cuKRgMqq6uTgcOHNCxY8f06NEjRaPRrF4vlO7WrVuSpBcvXuR8EFCunj9/Lkm6ffu2r3UAFA8yEgDckY/wA6+3BArI9evX9fHjR42Ojurly5daXFzU6uqqgsGgTpw4oVAopKtXr+rKlSs5z33t2jU1NDToy5cvev36tS5evOj9DWiza/7u3TuVl5fr7t27vtQAUJzISABwRz7Ca/8Zv9tKAArG0NCQBgYGdP/+fY2Pj+/bGgDgBzISANyRj8WHRgOArP369UtNTU1KJBL69OmTDh8+7On8v3//1tGjR5VMJjU/P6/GxkZP5wcAP5GRAOCOfCw+nNEAIGtVVVUaGBjQ+vq6hoeHPZ//6dOnisfjevz4MRsEgH2HjAQAd+Rj8eGMBgA5efjwoVZXVxUIBGTbtgIB7/qVwWBQg4OD6unp8WxOAMgnMhIA3JGPxYVHJwAAAAAAgGd4dAIAAAAAAHiGRgMAAAAAAPAMjQYAAAAAAOAZGg0AAAAAAMAzNBoAAAAAAIBnaDQAAAAAAADP0GgAAAAAAACeodEAAAAAAAA8Q6MBAAAAAAB45n/Ehqwy7u9lOgAAAABJRU5ErkJggg==\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.\n",
+        "\n",
+        "The confidence interval is a lot smaller for the normalized log2 plots, which indicates a lower amount of uncertainty in the data for the normalized plots."
+      ]
+    },
+    {
+      "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": {
+        "id": "21c868af-fb1b-4f07-8ef1-6bd912932254",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a4fb9319-42dd-4496-de10-b91bbb0b499b"
+      },
+      "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": "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": {
+        "id": "fe669618-97e3-4dd6-8b98-74d85ccc42b1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 410
+        },
+        "outputId": "19b5b04b-e65f-4d4d-c556-ae079be70dd7"
+      },
+      "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": {
+        "id": "b0458a96-3200-4988-a61c-0c44aec6b0d4",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 490
+        },
+        "outputId": "b5955aad-fc56-4341-85b3-14d4d491a510"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7df3f0b95930>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        },
+        {
+          "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": 517
+        },
+        "outputId": "933354a2-426f-4111-d7a3-d0cff411697c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Population Doublings over Time')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        },
+        {
+          "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 = d[(d[\"well\"] == \"A04\") | (d[\"well\"] == \"A05\") | (d[\"well\"] == \"A07\")]\n",
+        "\n",
+        "sns.scatterplot(data=dtp, x=\"time\", y=\"pd\", hue=\"well\")\n",
+        "plt.ylabel('Population doublings')\n",
+        "plt.xlabel('Time (h)')\n",
+        "plt.legend(loc='upper left', fontsize='8')\n",
+        "plt.title('Population Doublings over Time')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cad14622-98de-4521-9cc7-8c6b01c7c583",
+      "metadata": {
+        "id": "cad14622-98de-4521-9cc7-8c6b01c7c583"
+      },
+      "source": [
+        "The log2 plot is simply plotting the log2 of the cell count over time. The normalized plot is showing the population growth rate over time, showing periods of a doubling through each point. The y axis has a smaller range for this graph. They show different things as one just shows the total number of cells and the other one shows the rate at which the cells are growing.\n"
+      ]
+    },
+    {
+      "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": {
+        "id": "e5f82ff0-829a-44cb-8548-afd68b4fab4d",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "62d75350-7ae5-4855-ee4a-4ccee0c36424"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Example linregress output for well A03\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinregressResult(slope=0.060846424497371744, intercept=5.212788495165553, rvalue=0.9990947611147231, pvalue=4.776167619926461e-33, stderr=0.0005402101680293283, intercept_stderr=0.036687154857080205)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ],
+      "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": {
+        "id": "ca448f0e-839c-4bf6-bd73-1102897d1196",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "86ee27a3-d3c9-4d80-ec09-7b1e9cfd6628"
+      },
+      "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": 22
+        }
+      ],
+      "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": {
+        "id": "f286e02f-4282-431c-bd00-5782b1ec398d",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5b79c8e9-36c8-46f6-98a0-c926ee8aba8e"
+      },
+      "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": 23
+        }
+      ],
+      "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",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "da9e7c94-2dcf-4fa6-ab6f-eaf90c6b9dd7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Proliferation rates for trametinib-treated wells:\n",
+            "A01    0.0013\n",
+            "A12    0.0018\n",
+            "dtype: float64\n"
+          ]
+        }
+      ],
+      "source": [
+        "trametinib = d[d[\"drug1.conc\"]>0]\n",
+        "trametinib_res = [linregress(trametinib[trametinib[\"well\"] == well][[\"time\", \"l2\"]]) for well in trametinib[\"well\"].unique()]\n",
+        "trametinib_proliferation= pd.Series(\n",
+        "    data=[round(r.slope, 4) for r in trametinib_res],\n",
+        "    index=trametinib[\"well\"].unique())\n",
+        "index=[well for well in trametinib[\"well\"].unique()]\n",
+        "print(\"Proliferation rates for trametinib-treated wells:\")\n",
+        "print(trametinib_proliferation)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "68348d8a-9f8c-44ba-bc41-0a9548286b76",
+      "metadata": {
+        "id": "68348d8a-9f8c-44ba-bc41-0a9548286b76"
+      },
+      "source": [
+        "The proliferation rates for the trametinib-treated wells is much lower than for the control wells. This makes sense given all the previous plots in which there is a low/stable cell count that does not proliferate exponentially in the presence of trametinib.\n"
+      ]
+    },
+    {
+      "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": {
+        "id": "65b0b21d-ffc3-42c7-b1d7-913d281e39ac",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 492
+        },
+        "outputId": "85e10070-397a-4c64-a37b-3b516c51620f"
+      },
+      "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": {
+        "id": "cdeaa57f-6ae9-4f2b-a254-08bdfbc8da22",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 483
+        },
+        "outputId": "4b716887-56e7-4f11-a9ad-f26febc50908"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 702.125x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "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": {
+        "id": "1dd0cd18-72e6-4d70-ab38-b0bc3109352a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "8eb04ba4-c2d3-4f01-c0a2-52187f43f056"
+      },
+      "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"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-24397ea6-49fe-4654-970f-52243b158afb\" 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>time</th>\n",
+              "      <th>cell.count</th>\n",
+              "      <th>pd</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>108.452629</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>3</td>\n",
+              "      <td>114.671082</td>\n",
+              "      <td>0.080437</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>6</td>\n",
+              "      <td>137.605309</td>\n",
+              "      <td>0.343471</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>9</td>\n",
+              "      <td>163.397796</td>\n",
+              "      <td>0.591324</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>12</td>\n",
+              "      <td>178.516048</td>\n",
+              "      <td>0.718989</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-24397ea6-49fe-4654-970f-52243b158afb')\"\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",
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+              "  </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-24397ea6-49fe-4654-970f-52243b158afb 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-24397ea6-49fe-4654-970f-52243b158afb');\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-38baf542-22a5-4b73-893a-09b19a7fb23c\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-38baf542-22a5-4b73-893a-09b19a7fb23c')\"\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-38baf542-22a5-4b73-893a-09b19a7fb23c button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\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": 29
+        }
+      ],
+      "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": {
+        "id": "f9d9c4a9-903e-4ae1-9dac-7dae6b90f08d"
+      },
+      "outputs": [],
+      "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": {
+        "id": "411cf5b6-294e-44fb-9627-176ba593a8be"
+      },
+      "outputs": [],
+      "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": {
+        "id": "2c0cd14d-55a1-4c7a-ad11-f868d49addfa"
+      },
+      "outputs": [],
+      "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": {
+        "id": "644dd92a-ca26-4129-aa05-4f8c2014d5df"
+      },
+      "outputs": [],
+      "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": {
+        "id": "59b4da50-59c3-4eb8-8445-d2c09b9a4081"
+      },
+      "outputs": [],
+      "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",
+        "    return()"
+      ]
+    },
+    {
+      "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"
+      },
+      "outputs": [],
+      "source": [
+        "# Execute your function with the correct input argument values.\n",
+        "# timeToEgg(P0=100,egg=<egg_val>,DT=<DT_val>)\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"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file