{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "5178af69-b066-4729-a89c-2633cae8cded",
      "metadata": {
        "id": "5178af69-b066-4729-a89c-2633cae8cded"
      },
      "source": [
        "# CS640 Project Writeup\n",
        "\n",
        "In this project, you will work on a Kaggle competition titled [ISIC 2024 - Skin Cancer Detection with 3D-TBP](https://www.kaggle.com/competitions/isic-2024-challenge/overview). This is a binary classification task in which you need to predict if the patient has skin cancer. The competition is already close, but we will explore the rich (training) dataset it provides in this class.\n",
        "\n",
        "***"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bb7ed497-90a5-4d68-8210-ac90d630e2fd",
      "metadata": {
        "id": "bb7ed497-90a5-4d68-8210-ac90d630e2fd"
      },
      "source": [
        "## Data\n",
        "\n",
        "We will be using the training dataset from the original competition for this class project. The dataset has been downloaded and preprocessed. You can find it on SCC at */projectnb/cs640grp/materials/ISIC-2024_CS640*. **You should use this downloaded dataset only, not the original one on the website.**\n",
        "\n",
        "The directory looks like the following."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "579e20cd-7ee7-4df8-8e01-e37d5e5969d1",
      "metadata": {
        "id": "579e20cd-7ee7-4df8-8e01-e37d5e5969d1",
        "outputId": "7017652e-c18f-4023-c45d-c8fa99d7fa1e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['test_metadata.csv',\n",
              " 'submission.csv',\n",
              " 'train_metadata.csv',\n",
              " 'train_image',\n",
              " 'test_image']"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "project_dir = os.path.join(os.sep, 'projectnb', 'cs640grp', 'materials', 'ISIC-2024_CS640')\n",
        "os.listdir(project_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8f03792d-b675-4c2f-a424-54b3a4cb1921",
      "metadata": {
        "id": "8f03792d-b675-4c2f-a424-54b3a4cb1921"
      },
      "source": [
        "The CSV files store a list of attributes for each sample, and the image folders store a JPEG image per sample. The image names are sample IDs which can be found in the correpsonding CSV files. The submission.csv file is a template of your submission.\n",
        "\n",
        "Let's first take a peek into the CSV files and a few sample images.\n",
        "\n",
        "***"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2358a162-9760-4798-85ec-9630c819b87b",
      "metadata": {
        "id": "2358a162-9760-4798-85ec-9630c819b87b"
      },
      "source": [
        "### Training Metadata\n",
        "\n",
        "Note that in the metadata file, the **target** column is the label column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b6262145-3a06-4397-ac0c-ddb1d4ebf0d1",
      "metadata": {
        "id": "b6262145-3a06-4397-ac0c-ddb1d4ebf0d1",
        "outputId": "8e47a84c-220d-4714-8914-1c05bc39fba6"
      },
      "outputs": [
        {
          "data": {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>target</th>\n",
              "      <th>age_approx</th>\n",
              "      <th>sex</th>\n",
              "      <th>anatom_site_general</th>\n",
              "      <th>clin_size_long_diam_mm</th>\n",
              "      <th>tbp_tile_type</th>\n",
              "      <th>tbp_lv_A</th>\n",
              "      <th>tbp_lv_Aext</th>\n",
              "      <th>tbp_lv_B</th>\n",
              "      <th>...</th>\n",
              "      <th>tbp_lv_norm_color</th>\n",
              "      <th>tbp_lv_perimeterMM</th>\n",
              "      <th>tbp_lv_radial_color_std_max</th>\n",
              "      <th>tbp_lv_stdL</th>\n",
              "      <th>tbp_lv_stdLExt</th>\n",
              "      <th>tbp_lv_symm_2axis</th>\n",
              "      <th>tbp_lv_symm_2axis_angle</th>\n",
              "      <th>tbp_lv_x</th>\n",
              "      <th>tbp_lv_y</th>\n",
              "      <th>tbp_lv_z</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>male</td>\n",
              "      <td>upper extremity</td>\n",
              "      <td>2.58</td>\n",
              "      <td>3D: white</td>\n",
              "      <td>21.989610</td>\n",
              "      <td>18.149720</td>\n",
              "      <td>26.138980</td>\n",
              "      <td>...</td>\n",
              "      <td>3.207238</td>\n",
              "      <td>7.162229</td>\n",
              "      <td>1.181736</td>\n",
              "      <td>2.552678</td>\n",
              "      <td>2.169827</td>\n",
              "      <td>0.230000</td>\n",
              "      <td>45</td>\n",
              "      <td>-439.338600</td>\n",
              "      <td>1230.412000</td>\n",
              "      <td>22.647890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>female</td>\n",
              "      <td>posterior torso</td>\n",
              "      <td>2.90</td>\n",
              "      <td>3D: XP</td>\n",
              "      <td>21.153528</td>\n",
              "      <td>17.243578</td>\n",
              "      <td>28.471102</td>\n",
              "      <td>...</td>\n",
              "      <td>2.749542</td>\n",
              "      <td>7.242474</td>\n",
              "      <td>1.014255</td>\n",
              "      <td>2.979940</td>\n",
              "      <td>1.937938</td>\n",
              "      <td>0.292453</td>\n",
              "      <td>65</td>\n",
              "      <td>-59.504822</td>\n",
              "      <td>1047.626465</td>\n",
              "      <td>109.244873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>female</td>\n",
              "      <td>lower extremity</td>\n",
              "      <td>4.38</td>\n",
              "      <td>3D: XP</td>\n",
              "      <td>20.569130</td>\n",
              "      <td>14.896040</td>\n",
              "      <td>24.978840</td>\n",
              "      <td>...</td>\n",
              "      <td>4.339059</td>\n",
              "      <td>14.451710</td>\n",
              "      <td>1.233737</td>\n",
              "      <td>5.317332</td>\n",
              "      <td>1.839798</td>\n",
              "      <td>0.158025</td>\n",
              "      <td>0</td>\n",
              "      <td>-223.811100</td>\n",
              "      <td>770.993000</td>\n",
              "      <td>29.067170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>female</td>\n",
              "      <td>upper extremity</td>\n",
              "      <td>2.76</td>\n",
              "      <td>3D: white</td>\n",
              "      <td>23.365559</td>\n",
              "      <td>18.483379</td>\n",
              "      <td>30.853418</td>\n",
              "      <td>...</td>\n",
              "      <td>1.650849</td>\n",
              "      <td>7.870664</td>\n",
              "      <td>0.496438</td>\n",
              "      <td>2.770145</td>\n",
              "      <td>2.381648</td>\n",
              "      <td>0.254237</td>\n",
              "      <td>90</td>\n",
              "      <td>-440.008942</td>\n",
              "      <td>1140.614502</td>\n",
              "      <td>-14.935974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>male</td>\n",
              "      <td>posterior torso</td>\n",
              "      <td>3.31</td>\n",
              "      <td>3D: XP</td>\n",
              "      <td>23.061540</td>\n",
              "      <td>18.730060</td>\n",
              "      <td>29.790280</td>\n",
              "      <td>...</td>\n",
              "      <td>4.174303</td>\n",
              "      <td>10.950840</td>\n",
              "      <td>1.521283</td>\n",
              "      <td>1.608716</td>\n",
              "      <td>1.997881</td>\n",
              "      <td>0.461111</td>\n",
              "      <td>0</td>\n",
              "      <td>-108.822000</td>\n",
              "      <td>1215.113000</td>\n",
              "      <td>-101.404500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>320842</th>\n",
              "      <td>320842</td>\n",
              "      <td>0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>posterior torso</td>\n",
              "      <td>3.60</td>\n",
              "      <td>3D: XP</td>\n",
              "      <td>19.985520</td>\n",
              "      <td>15.393206</td>\n",
              "      <td>35.482277</td>\n",
              "      <td>...</td>\n",
              "      <td>1.317773</td>\n",
              "      <td>9.854949</td>\n",
              "      <td>0.359343</td>\n",
              "      <td>1.018512</td>\n",
              "      <td>2.205429</td>\n",
              "      <td>0.307692</td>\n",
              "      <td>115</td>\n",
              "      <td>30.655060</td>\n",
              "      <td>1204.034302</td>\n",
              "      <td>165.979797</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>320843</th>\n",
              "      <td>320843</td>\n",
              "      <td>0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>male</td>\n",
              "      <td>posterior torso</td>\n",
              "      <td>5.88</td>\n",
              "      <td>3D: white</td>\n",
              "      <td>17.846150</td>\n",
              "      <td>11.566220</td>\n",
              "      <td>24.022090</td>\n",
              "      <td>...</td>\n",
              "      <td>6.996178</td>\n",
              "      <td>16.388990</td>\n",
              "      <td>1.827318</td>\n",
              "      <td>8.247600</td>\n",
              "      <td>2.282445</td>\n",
              "      <td>0.208081</td>\n",
              "      <td>140</td>\n",
              "      <td>-109.284200</td>\n",
              "      <td>1228.212000</td>\n",
              "      <td>155.480600</td>\n",
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              "    <tr>\n",
              "      <th>320844</th>\n",
              "      <td>320844</td>\n",
              "      <td>0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>male</td>\n",
              "      <td>anterior torso</td>\n",
              "      <td>11.41</td>\n",
              "      <td>3D: XP</td>\n",
              "      <td>16.364410</td>\n",
              "      <td>6.870663</td>\n",
              "      <td>20.882192</td>\n",
              "      <td>...</td>\n",
              "      <td>3.671125</td>\n",
              "      <td>28.208751</td>\n",
              "      <td>1.136926</td>\n",
              "      <td>3.310037</td>\n",
              "      <td>1.509960</td>\n",
              "      <td>0.181329</td>\n",
              "      <td>0</td>\n",
              "      <td>-170.062561</td>\n",
              "      <td>1129.213257</td>\n",
              "      <td>28.841248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>320845</th>\n",
              "      <td>320845</td>\n",
              "      <td>0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>male</td>\n",
              "      <td>lower extremity</td>\n",
              "      <td>4.02</td>\n",
              "      <td>3D: XP</td>\n",
              "      <td>13.500010</td>\n",
              "      <td>10.076300</td>\n",
              "      <td>23.654770</td>\n",
              "      <td>...</td>\n",
              "      <td>2.443795</td>\n",
              "      <td>11.177810</td>\n",
              "      <td>0.847317</td>\n",
              "      <td>2.623507</td>\n",
              "      <td>3.329334</td>\n",
              "      <td>0.401914</td>\n",
              "      <td>10</td>\n",
              "      <td>249.819500</td>\n",
              "      <td>254.294600</td>\n",
              "      <td>55.758790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>320846</th>\n",
              "      <td>320846</td>\n",
              "      <td>0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>male</td>\n",
              "      <td>anterior torso</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3D: XP</td>\n",
              "      <td>17.482757</td>\n",
              "      <td>12.255344</td>\n",
              "      <td>34.196833</td>\n",
              "      <td>...</td>\n",
              "      <td>2.113237</td>\n",
              "      <td>8.872541</td>\n",
              "      <td>0.645124</td>\n",
              "      <td>3.542404</td>\n",
              "      <td>2.282705</td>\n",
              "      <td>0.525926</td>\n",
              "      <td>20</td>\n",
              "      <td>-128.891724</td>\n",
              "      <td>1038.359863</td>\n",
              "      <td>-63.770935</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>320847 rows × 41 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            id  target  age_approx     sex anatom_site_general  \\\n",
              "0            0       0        55.0    male     upper extremity   \n",
              "1            1       0        50.0  female     posterior torso   \n",
              "2            2       0        40.0  female     lower extremity   \n",
              "3            3       0        50.0  female     upper extremity   \n",
              "4            4       0        60.0    male     posterior torso   \n",
              "...        ...     ...         ...     ...                 ...   \n",
              "320842  320842       0        70.0     NaN     posterior torso   \n",
              "320843  320843       0        45.0    male     posterior torso   \n",
              "320844  320844       0        40.0    male      anterior torso   \n",
              "320845  320845       0        40.0    male     lower extremity   \n",
              "320846  320846       0        50.0    male      anterior torso   \n",
              "\n",
              "        clin_size_long_diam_mm tbp_tile_type   tbp_lv_A  tbp_lv_Aext  \\\n",
              "0                         2.58     3D: white  21.989610    18.149720   \n",
              "1                         2.90        3D: XP  21.153528    17.243578   \n",
              "2                         4.38        3D: XP  20.569130    14.896040   \n",
              "3                         2.76     3D: white  23.365559    18.483379   \n",
              "4                         3.31        3D: XP  23.061540    18.730060   \n",
              "...                        ...           ...        ...          ...   \n",
              "320842                    3.60        3D: XP  19.985520    15.393206   \n",
              "320843                    5.88     3D: white  17.846150    11.566220   \n",
              "320844                   11.41        3D: XP  16.364410     6.870663   \n",
              "320845                    4.02        3D: XP  13.500010    10.076300   \n",
              "320846                    3.15        3D: XP  17.482757    12.255344   \n",
              "\n",
              "         tbp_lv_B  ...  tbp_lv_norm_color  tbp_lv_perimeterMM  \\\n",
              "0       26.138980  ...           3.207238            7.162229   \n",
              "1       28.471102  ...           2.749542            7.242474   \n",
              "2       24.978840  ...           4.339059           14.451710   \n",
              "3       30.853418  ...           1.650849            7.870664   \n",
              "4       29.790280  ...           4.174303           10.950840   \n",
              "...           ...  ...                ...                 ...   \n",
              "320842  35.482277  ...           1.317773            9.854949   \n",
              "320843  24.022090  ...           6.996178           16.388990   \n",
              "320844  20.882192  ...           3.671125           28.208751   \n",
              "320845  23.654770  ...           2.443795           11.177810   \n",
              "320846  34.196833  ...           2.113237            8.872541   \n",
              "\n",
              "        tbp_lv_radial_color_std_max  tbp_lv_stdL  tbp_lv_stdLExt  \\\n",
              "0                          1.181736     2.552678        2.169827   \n",
              "1                          1.014255     2.979940        1.937938   \n",
              "2                          1.233737     5.317332        1.839798   \n",
              "3                          0.496438     2.770145        2.381648   \n",
              "4                          1.521283     1.608716        1.997881   \n",
              "...                             ...          ...             ...   \n",
              "320842                     0.359343     1.018512        2.205429   \n",
              "320843                     1.827318     8.247600        2.282445   \n",
              "320844                     1.136926     3.310037        1.509960   \n",
              "320845                     0.847317     2.623507        3.329334   \n",
              "320846                     0.645124     3.542404        2.282705   \n",
              "\n",
              "        tbp_lv_symm_2axis  tbp_lv_symm_2axis_angle    tbp_lv_x     tbp_lv_y  \\\n",
              "0                0.230000                       45 -439.338600  1230.412000   \n",
              "1                0.292453                       65  -59.504822  1047.626465   \n",
              "2                0.158025                        0 -223.811100   770.993000   \n",
              "3                0.254237                       90 -440.008942  1140.614502   \n",
              "4                0.461111                        0 -108.822000  1215.113000   \n",
              "...                   ...                      ...         ...          ...   \n",
              "320842           0.307692                      115   30.655060  1204.034302   \n",
              "320843           0.208081                      140 -109.284200  1228.212000   \n",
              "320844           0.181329                        0 -170.062561  1129.213257   \n",
              "320845           0.401914                       10  249.819500   254.294600   \n",
              "320846           0.525926                       20 -128.891724  1038.359863   \n",
              "\n",
              "          tbp_lv_z  \n",
              "0        22.647890  \n",
              "1       109.244873  \n",
              "2        29.067170  \n",
              "3       -14.935974  \n",
              "4      -101.404500  \n",
              "...            ...  \n",
              "320842  165.979797  \n",
              "320843  155.480600  \n",
              "320844   28.841248  \n",
              "320845   55.758790  \n",
              "320846  -63.770935  \n",
              "\n",
              "[320847 rows x 41 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas\n",
        "\n",
        "df_train = pandas.read_csv(os.path.join(project_dir, \"train_metadata.csv\"))\n",
        "df_train"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "85967c0c-3092-4352-aea1-6e1bdb70b197",
      "metadata": {
        "id": "85967c0c-3092-4352-aea1-6e1bdb70b197"
      },
      "source": [
        "### Test Metadata\n",
        "\n",
        "The test metadata file contains the same headers as the training one. Note that in this file, the **target** column is empty by design."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4948a611-c166-4f0e-8f3d-c6a2cb15d7d5",
      "metadata": {
        "id": "4948a611-c166-4f0e-8f3d-c6a2cb15d7d5",
        "outputId": "0c5947df-5a8d-4bb8-9092-8dfa5a5eed88"
      },
      "outputs": [
        {
          "data": {
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              "<div>\n",
              "<style scoped>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>id</th>\n",
              "      <th>target</th>\n",
              "      <th>age_approx</th>\n",
              "      <th>sex</th>\n",
              "      <th>anatom_site_general</th>\n",
              "      <th>clin_size_long_diam_mm</th>\n",
              "      <th>tbp_tile_type</th>\n",
              "      <th>tbp_lv_A</th>\n",
              "      <th>tbp_lv_Aext</th>\n",
              "      <th>tbp_lv_B</th>\n",
              "      <th>...</th>\n",
              "      <th>tbp_lv_norm_color</th>\n",
              "      <th>tbp_lv_perimeterMM</th>\n",
              "      <th>tbp_lv_radial_color_std_max</th>\n",
              "      <th>tbp_lv_stdL</th>\n",
              "      <th>tbp_lv_stdLExt</th>\n",
              "      <th>tbp_lv_symm_2axis</th>\n",
              "      <th>tbp_lv_symm_2axis_angle</th>\n",
              "      <th>tbp_lv_x</th>\n",
              "      <th>tbp_lv_y</th>\n",
              "      <th>tbp_lv_z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.0</td>\n",
              "      <td>male</td>\n",
              "      <td>upper extremity</td>\n",
              "      <td>2.52</td>\n",
              "      <td>3D: white</td>\n",
              "      <td>20.739760</td>\n",
              "      <td>17.346250</td>\n",
              "      <td>23.604410</td>\n",
              "      <td>...</td>\n",
              "      <td>2.013941</td>\n",
              "      <td>9.113276</td>\n",
              "      <td>0.793600</td>\n",
              "      <td>1.368380</td>\n",
              "      <td>3.130576</td>\n",
              "      <td>0.392593</td>\n",
              "      <td>85</td>\n",
              "      <td>-352.631000</td>\n",
              "      <td>1024.501000</td>\n",
              "      <td>21.431270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.0</td>\n",
              "      <td>male</td>\n",
              "      <td>upper extremity</td>\n",
              "      <td>2.63</td>\n",
              "      <td>3D: white</td>\n",
              "      <td>21.498600</td>\n",
              "      <td>17.128050</td>\n",
              "      <td>26.919320</td>\n",
              "      <td>...</td>\n",
              "      <td>3.554856</td>\n",
              "      <td>6.968501</td>\n",
              "      <td>1.322546</td>\n",
              "      <td>2.980941</td>\n",
              "      <td>2.610491</td>\n",
              "      <td>0.342857</td>\n",
              "      <td>150</td>\n",
              "      <td>317.008100</td>\n",
              "      <td>1296.112000</td>\n",
              "      <td>85.410520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.0</td>\n",
              "      <td>male</td>\n",
              "      <td>lower extremity</td>\n",
              "      <td>18.31</td>\n",
              "      <td>3D: XP</td>\n",
              "      <td>21.261867</td>\n",
              "      <td>15.949655</td>\n",
              "      <td>36.927874</td>\n",
              "      <td>...</td>\n",
              "      <td>3.685572</td>\n",
              "      <td>67.921989</td>\n",
              "      <td>1.323685</td>\n",
              "      <td>1.912243</td>\n",
              "      <td>3.394053</td>\n",
              "      <td>0.385400</td>\n",
              "      <td>145</td>\n",
              "      <td>-185.792664</td>\n",
              "      <td>680.623718</td>\n",
              "      <td>-21.791901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45.0</td>\n",
              "      <td>female</td>\n",
              "      <td>upper extremity</td>\n",
              "      <td>3.55</td>\n",
              "      <td>3D: XP</td>\n",
              "      <td>21.087236</td>\n",
              "      <td>15.657230</td>\n",
              "      <td>31.419333</td>\n",
              "      <td>...</td>\n",
              "      <td>2.082827</td>\n",
              "      <td>10.582854</td>\n",
              "      <td>0.691356</td>\n",
              "      <td>1.349557</td>\n",
              "      <td>1.570233</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>155</td>\n",
              "      <td>443.583984</td>\n",
              "      <td>1213.412598</td>\n",
              "      <td>39.409851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55.0</td>\n",
              "      <td>male</td>\n",
              "      <td>anterior torso</td>\n",
              "      <td>7.06</td>\n",
              "      <td>3D: white</td>\n",
              "      <td>22.121790</td>\n",
              "      <td>14.444030</td>\n",
              "      <td>30.308130</td>\n",
              "      <td>...</td>\n",
              "      <td>3.691011</td>\n",
              "      <td>19.856620</td>\n",
              "      <td>0.989644</td>\n",
              "      <td>3.126280</td>\n",
              "      <td>2.467318</td>\n",
              "      <td>0.227068</td>\n",
              "      <td>70</td>\n",
              "      <td>-162.127900</td>\n",
              "      <td>1043.082000</td>\n",
              "      <td>-44.661830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80207</th>\n",
              "      <td>80207</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.0</td>\n",
              "      <td>male</td>\n",
              "      <td>posterior torso</td>\n",
              "      <td>2.88</td>\n",
              "      <td>3D: white</td>\n",
              "      <td>20.565030</td>\n",
              "      <td>15.228920</td>\n",
              "      <td>30.234170</td>\n",
              "      <td>...</td>\n",
              "      <td>1.458585</td>\n",
              "      <td>8.111398</td>\n",
              "      <td>0.618510</td>\n",
              "      <td>2.274461</td>\n",
              "      <td>1.914292</td>\n",
              "      <td>0.232000</td>\n",
              "      <td>10</td>\n",
              "      <td>-72.637900</td>\n",
              "      <td>1487.536000</td>\n",
              "      <td>138.852900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80208</th>\n",
              "      <td>80208</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50.0</td>\n",
              "      <td>male</td>\n",
              "      <td>upper extremity</td>\n",
              "      <td>4.20</td>\n",
              "      <td>3D: white</td>\n",
              "      <td>16.314590</td>\n",
              "      <td>14.611030</td>\n",
              "      <td>25.403000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.941789</td>\n",
              "      <td>11.952720</td>\n",
              "      <td>0.599103</td>\n",
              "      <td>1.422653</td>\n",
              "      <td>2.196585</td>\n",
              "      <td>0.331522</td>\n",
              "      <td>45</td>\n",
              "      <td>-477.687100</td>\n",
              "      <td>1121.040000</td>\n",
              "      <td>-38.915830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80209</th>\n",
              "      <td>80209</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.0</td>\n",
              "      <td>female</td>\n",
              "      <td>upper extremity</td>\n",
              "      <td>2.90</td>\n",
              "      <td>3D: XP</td>\n",
              "      <td>21.597580</td>\n",
              "      <td>17.705739</td>\n",
              "      <td>27.266721</td>\n",
              "      <td>...</td>\n",
              "      <td>3.355798</td>\n",
              "      <td>8.872541</td>\n",
              "      <td>1.076741</td>\n",
              "      <td>3.248064</td>\n",
              "      <td>1.624508</td>\n",
              "      <td>0.359477</td>\n",
              "      <td>25</td>\n",
              "      <td>442.464355</td>\n",
              "      <td>1128.834351</td>\n",
              "      <td>31.510681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80210</th>\n",
              "      <td>80210</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.0</td>\n",
              "      <td>male</td>\n",
              "      <td>posterior torso</td>\n",
              "      <td>3.32</td>\n",
              "      <td>3D: white</td>\n",
              "      <td>22.596327</td>\n",
              "      <td>20.186998</td>\n",
              "      <td>30.480790</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.033031</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.321416</td>\n",
              "      <td>2.082772</td>\n",
              "      <td>0.495050</td>\n",
              "      <td>10</td>\n",
              "      <td>-110.265747</td>\n",
              "      <td>1429.494385</td>\n",
              "      <td>156.874146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80211</th>\n",
              "      <td>80211</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70.0</td>\n",
              "      <td>male</td>\n",
              "      <td>posterior torso</td>\n",
              "      <td>3.14</td>\n",
              "      <td>3D: white</td>\n",
              "      <td>19.856977</td>\n",
              "      <td>16.932049</td>\n",
              "      <td>22.454427</td>\n",
              "      <td>...</td>\n",
              "      <td>3.738181</td>\n",
              "      <td>9.340242</td>\n",
              "      <td>1.366712</td>\n",
              "      <td>3.948254</td>\n",
              "      <td>2.571864</td>\n",
              "      <td>0.313609</td>\n",
              "      <td>55</td>\n",
              "      <td>-126.982124</td>\n",
              "      <td>1117.368408</td>\n",
              "      <td>166.916687</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80212 rows × 41 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id  target  age_approx     sex anatom_site_general  \\\n",
              "0          0     NaN        30.0    male     upper extremity   \n",
              "1          1     NaN        75.0    male     upper extremity   \n",
              "2          2     NaN        30.0    male     lower extremity   \n",
              "3          3     NaN        45.0  female     upper extremity   \n",
              "4          4     NaN        55.0    male      anterior torso   \n",
              "...      ...     ...         ...     ...                 ...   \n",
              "80207  80207     NaN        75.0    male     posterior torso   \n",
              "80208  80208     NaN        50.0    male     upper extremity   \n",
              "80209  80209     NaN        40.0  female     upper extremity   \n",
              "80210  80210     NaN        75.0    male     posterior torso   \n",
              "80211  80211     NaN        70.0    male     posterior torso   \n",
              "\n",
              "       clin_size_long_diam_mm tbp_tile_type   tbp_lv_A  tbp_lv_Aext  \\\n",
              "0                        2.52     3D: white  20.739760    17.346250   \n",
              "1                        2.63     3D: white  21.498600    17.128050   \n",
              "2                       18.31        3D: XP  21.261867    15.949655   \n",
              "3                        3.55        3D: XP  21.087236    15.657230   \n",
              "4                        7.06     3D: white  22.121790    14.444030   \n",
              "...                       ...           ...        ...          ...   \n",
              "80207                    2.88     3D: white  20.565030    15.228920   \n",
              "80208                    4.20     3D: white  16.314590    14.611030   \n",
              "80209                    2.90        3D: XP  21.597580    17.705739   \n",
              "80210                    3.32     3D: white  22.596327    20.186998   \n",
              "80211                    3.14     3D: white  19.856977    16.932049   \n",
              "\n",
              "        tbp_lv_B  ...  tbp_lv_norm_color  tbp_lv_perimeterMM  \\\n",
              "0      23.604410  ...           2.013941            9.113276   \n",
              "1      26.919320  ...           3.554856            6.968501   \n",
              "2      36.927874  ...           3.685572           67.921989   \n",
              "3      31.419333  ...           2.082827           10.582854   \n",
              "4      30.308130  ...           3.691011           19.856620   \n",
              "...          ...  ...                ...                 ...   \n",
              "80207  30.234170  ...           1.458585            8.111398   \n",
              "80208  25.403000  ...           1.941789           11.952720   \n",
              "80209  27.266721  ...           3.355798            8.872541   \n",
              "80210  30.480790  ...           0.000000            9.033031   \n",
              "80211  22.454427  ...           3.738181            9.340242   \n",
              "\n",
              "       tbp_lv_radial_color_std_max  tbp_lv_stdL  tbp_lv_stdLExt  \\\n",
              "0                         0.793600     1.368380        3.130576   \n",
              "1                         1.322546     2.980941        2.610491   \n",
              "2                         1.323685     1.912243        3.394053   \n",
              "3                         0.691356     1.349557        1.570233   \n",
              "4                         0.989644     3.126280        2.467318   \n",
              "...                            ...          ...             ...   \n",
              "80207                     0.618510     2.274461        1.914292   \n",
              "80208                     0.599103     1.422653        2.196585   \n",
              "80209                     1.076741     3.248064        1.624508   \n",
              "80210                     0.000000     1.321416        2.082772   \n",
              "80211                     1.366712     3.948254        2.571864   \n",
              "\n",
              "       tbp_lv_symm_2axis  tbp_lv_symm_2axis_angle    tbp_lv_x     tbp_lv_y  \\\n",
              "0               0.392593                       85 -352.631000  1024.501000   \n",
              "1               0.342857                      150  317.008100  1296.112000   \n",
              "2               0.385400                      145 -185.792664   680.623718   \n",
              "3               0.250000                      155  443.583984  1213.412598   \n",
              "4               0.227068                       70 -162.127900  1043.082000   \n",
              "...                  ...                      ...         ...          ...   \n",
              "80207           0.232000                       10  -72.637900  1487.536000   \n",
              "80208           0.331522                       45 -477.687100  1121.040000   \n",
              "80209           0.359477                       25  442.464355  1128.834351   \n",
              "80210           0.495050                       10 -110.265747  1429.494385   \n",
              "80211           0.313609                       55 -126.982124  1117.368408   \n",
              "\n",
              "         tbp_lv_z  \n",
              "0       21.431270  \n",
              "1       85.410520  \n",
              "2      -21.791901  \n",
              "3       39.409851  \n",
              "4      -44.661830  \n",
              "...           ...  \n",
              "80207  138.852900  \n",
              "80208  -38.915830  \n",
              "80209   31.510681  \n",
              "80210  156.874146  \n",
              "80211  166.916687  \n",
              "\n",
              "[80212 rows x 41 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_test = pandas.read_csv(os.path.join(project_dir, \"test_metadata.csv\"))\n",
        "df_test"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b919ba32-8e36-480c-85c7-7ae7e3933981",
      "metadata": {
        "id": "b919ba32-8e36-480c-85c7-7ae7e3933981"
      },
      "source": [
        "### Submission Template\n",
        "\n",
        "This template file simply contains the first two columns of the test metadata file. You will need to fill the **target** column and submit for evaluation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3658e058-ec68-4e5b-aff2-707abf7fccb7",
      "metadata": {
        "id": "3658e058-ec68-4e5b-aff2-707abf7fccb7",
        "outputId": "2f517eff-85cf-4fa9-d4c2-2df8f5cedd41"
      },
      "outputs": [
        {
          "data": {
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              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
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              "      <th>4</th>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>80207</th>\n",
              "      <td>80207</td>\n",
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              "    <tr>\n",
              "      <th>80208</th>\n",
              "      <td>80208</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80209</th>\n",
              "      <td>80209</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80210</th>\n",
              "      <td>80210</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80211</th>\n",
              "      <td>80211</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80212 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id  target\n",
              "0          0     NaN\n",
              "1          1     NaN\n",
              "2          2     NaN\n",
              "3          3     NaN\n",
              "4          4     NaN\n",
              "...      ...     ...\n",
              "80207  80207     NaN\n",
              "80208  80208     NaN\n",
              "80209  80209     NaN\n",
              "80210  80210     NaN\n",
              "80211  80211     NaN\n",
              "\n",
              "[80212 rows x 2 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_submission = pandas.read_csv(os.path.join(project_dir, \"submission.csv\"))\n",
        "df_submission"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8becf9b2-0602-42f2-9c2b-095b906a2985",
      "metadata": {
        "id": "8becf9b2-0602-42f2-9c2b-095b906a2985"
      },
      "source": [
        "### Sample Images\n",
        "\n",
        "We will view a few images from the training set. The image files are named after the corresponding sample IDs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c4088b07-6ad5-4f1a-859e-7c720014218f",
      "metadata": {
        "id": "c4088b07-6ad5-4f1a-859e-7c720014218f",
        "outputId": "1c554fa8-c10f-44c3-e660-d75fbfe29d27"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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yNn4HQN+7gmYyw9o+Kz/KHl8mEaFwhd0a6xv1gEheEfTtqbWi6nhFYOXr4sGFr/NM2QEwKHe+zVZu1EVinWf9+xjI9CSvAPr+sn40fWAGyPxcKE7n8WMYyfrQ+tueYTK4lILz5YICAimwqFxRq+g/yAtySrCiGVqHRMiUkUE7+c/MqNx5Qx9jAsBalrxL2+26ilK2AWxYu+S7cZ11fsC7660epsf5/ojzPpFwBF+ON2QMOi2NoKyBg7AO7DtmbrwqpQRODAKDWFpjI8bMqMpr7PmV7b4+R8yoXJhRzTiiXZsUCDWw6fqBXL9EvmV1ewg9GGyYJXnmQbAH+gGMTHR2j/cWXLPyzCgyYL/oZuQnSAMATUHuFqk2+Gr9MNRYefzeFnUrWxXu2fPldxFKxrVt4G0B+zb7tvnfPbNIKeHm5qZ5TszKZFaM119/vX1n5RhAMgTvF4QxDv9M++yZkiH97plJYIjwtElqY+ont58HUXjENl4TQKRMy57jgdrzlJaDDjrocZHnT1Eo+3f77C2uxoujHKi1gqMn3fEdARaM6n42Qb1TuozvoIOLWgoIyu/VswEIeCklABgWjYC9suEMWfVursh4+ekVMJM7Xhn2RjkxxqWrIMYU7aZwOkXK96u/VxQcoNY98InRDbHeHhj4+4zHb9y9CYPypu3xcskrfh7AxLljRiybLzOgFvt0BjCvKd8vi7xu4uvoQebz9Cjfv5fSDbz2TmGdXHt2jJC4nM+48KgoVxaDatb10+YrbL5kMAgL7fta2lEm4wQ1bBJyFiOn/A4IwC6oas3vbVNPDo9zxdc1jr/XfaJ+a6+cU5gndeh/e48AnrDv51oruHLb0NDWjeNjBmooEYilXXDPYflG+JgaGwx6pNTHS74HqoINu9eAYjcMJ4DmRtwI1uIauo8eDDZ8yMvsAbMJ6xF167RgtYgMwE/umQI5+z52gF0XyTO1fp0TVKEeDIDrXnnmWgFXRl9EeybmAQJcnTwwucaoge5a9ozGlH0rJ6XUQqKsnszc3J4AGjDxngo/Xv55M8bjn91c+co4PNgjosGz4eeAFxLacgBilWCuqHUEG4NQpQRKGBa5FyAH2DjooI8dspDRqGx6sr+9xc14iLfaNl5rgpotTLYbs2qt2Gpp1r/GN4BmIoxKXGUJuxrqRaY4QACJlQEMFt9SCkBqT9X7FwshWhbcBbAB7JV9q7cZi7yiGS24Era7oFYJfdm2bTDIWH9a2+7u7nZhSF5B9UY5H5o1U0aj8n8NaPg2lRAe43m9vSxsy49hDJXx91wul2EueYu8r5fV236LwOyxkQdOEXAAXWafz2cAo/XZAxJ7PTtvw7zwusu1MfNzx8bk7u4O5/MZ5xrWDtn1piskMBMSTM9KYNQhzMrPKw+EfVtN71nXFcuyNHDp9R5/X3z35djfs3nr302/suuXZcGyrkjZeSjKHuj5vo/zLvarTbtd30/n5Vg3BD3LwqLEW2FraxnKF/7ovDQ5Yck9ZFF0MtcPUI9uAGMvum4eDDaePHnSCo7MxA+OD4vynZ5SanGSfsANIUeL+kyB9Ep3vM6jSFO2Zy5EY2wibNAYq7eeWFsqM7aytRhVs8at6uXxoMTCeoi6BcqHEJ1y7nF4jml6weknvvey2O8Wa7ssS4t1TCk1IHE6nXA+n/Hs2TMwM549e4bz+dz63sKyfCia9evMfez7bFhsbg9IqYxlSVjXDKDiciHU2hd9zgnLkpHzAnF1FshEH92W8nxG1Rhhm79EjgG4MY2g8bEKioMOOmhPUfhHo5O9eyNHVBgjpZzFCIRuVLF7tloaCGnlCGIY5UIzopjXAz1egcj94cCBGkn8PjxALI2+PZ7M0xzlwTWaKfj+O1MSWl9ciUDwZTzkeV4ZGr8b9wz476Pi5q2+vc/G2Hov16OB0gOHWeTD7L7YVz56we65pgw+NuOVD1EGxvAgP39m3sKZp8Z7uTz5vonlmI5kupCFpYs870BgWRbkRXSSm3VFTrYfY3P7CiqYC8iF5/nnRuDpjZy+LRalYbqZleN1vFk513QFa0+cW1H3MaBhfWZRKnZ9SgkIa6MZNkKf1lqRl2UHUlKYn5t6o6rqQRzG3s9Z20/S93fltjZKraBaAYieSskib7w+RYORhbmCkYb5ZG1/XhSSpweDjaiI+k6bPdAr+54Z2H0ecUdAEBf8jIHM6uLv8x1h6DS6n73XwNfFnkHuWptsALqHw7dHN9XETWmz/vLPt/6bIXGrx+zl2+E3TBsj8czA2ujr5hed3ecBSBT81j+7esofUmZKqLpQ+sKSaxIB1dC0/mAAAq1fpAz7WysgZXHVeNCKRBrSQOYtIq8DHHTQQY+cPNDwfCcq3ZFvG3klOiopM+W2BFnUgUYvLwW+XUoBUw+FSqShEDBZU4f3qCBb6KzxQq9om1X1IYLatzXKtwHcTIx098lR+/2hFOWO967P6un35ezkF3dv9+wZ8VmD0jZpo/bUUA+vENqzrK6+zNhXjwloAH3PqbXDz/MZaIxAA3DzxulZMx3DyPonApO471V/bPekPAcG9rvUO1jO3Vgvy4KE+T5fb0ROKQ0h4R5oeE/ZrJzB26n3z7ylEbDb63K+iNdSyyoKdPx+4UQ0gD9rn+/PVmfuHiOrIzmex1WMJYlII6iEpxSNCBFPRB87H25pno3m4UupgY32d0pItN/P1Ay6ZQyl98YB018fQi8MNvwEsQkZGec1BuMr6AcmTnTv+pwJFd8hkbwlw0/CiFZtst7d3e2e48v2yrnV7ayTq6HQlHA6STaHm5ubXXaFthEHfdM40ZhNJIKXSLF+flxsE7WVZS7Gp0+f4tmzZ21RGtjwG+ytTPN4WF9FS1KsV9UJ2+qHboHxY+prLKAjMDj9zdpwOp0Gr4XVZdsuqJcNzBWVGJtjCAcddNDHFvlwTB+e4Y1U/tqoEEV5RKSbNZ2CEHmMXU9ETUGye81gsrMao4fagqhlZmmApnYrvQc5AASUkBQgMqkCtAEMLNiHOM3kTyzXPvvwosb7g5z1ss8Sk3hFdAZ29kpor0NUCs2j7uXXNcBn31noh/eWR1nvr/dgY3adL9/H0kcLtlfMfft8X3t585gAh0+SY33v56gfU+A6gDSgS4RdX18DpRHURWu2hE3tMyyVUlAogYkBMErZsGhihFo1jK6M3q02F3gWWrWBOak+JZvlt+3iAEIVY2WW16g/MFLaz0lvkPVgw9fJ95PV/VzO7fdaa1v/Ucc0auOWxuxWDVy466LOVGttm73tWtZ//l7Pz4SnjmvF+GyttgWgP4MoOBPSCFYLNr2GQLZ3nCVLFQIvvY8eDDb8IERwEEHDzoqBcSH7wTOk7JXumPIuWjc8NbR5uQxKsVek/YK0tkRFPzJZLah1uL1qrbj73/+7TdQmoJbcnmvP856FerlIykMFIxZ2Ze0yJX0Wmub3t1jf+cwbUQh7hd2Ah3ljrM/tN6vLNSvEbLw7UwIU4nfvhpbnQYoXkr6frQ1eQPrxHpA+7bOJDAv2EQmHgw466Pk04yuz73xIqfGOWdgnuCv+RATKqQnjenHAI3U+zXJxe5Yn4VVOwc5ZU90CXCrKVoT36W0WQtzlB9qmUGlHASxSN8jPazLO80u/Wdr3R1PyS23hD15embyzfvPWSetbr5xbHaQ9BMIIIGYGnijf7Ttf//YMHq3Acc9GnAfRQBnlXpefYxIT36YYZRH7NrblMZFvhzd82r4nryPM+sb3mfwmU/4a0PDyd3aNUZPXqUdMgPs82YCWya2UC2ix/ToFpWxA6WDSv5fLqBd08FkGXc2nqZW+McDZ559tHGfu88qDC68HRhAVvUJ27XbZhjAj8ISHhbCqnDNOy7pbkwJWHJB25ex0QaDBDB+SJTys/y3zZe/9bcBJwUbnV2O0EYha0iMZ06K/mydYsoExsRqZf4mzUc0mm2+Eke+gmQUigo3Y8f6eCADiopk9d1aniNg9w47XxGttoli8ns/aZK+UEhY3cT2z7psFCbWUAUjZa9GYPWAUKFaPGSCKVjTf7mjByjm3TdtWtrdseRAzm6BxDsyYjgcL0aoU58PzvBGze20s7huzgw466GODfPIKI7+OPY/0IONeHrNtkgKSAFLrngcSpSpvVh5Z1Mre7nd8CkmVbBbRnnOWMCsGUBkFTs5BrHwzw4mAG0ub3irbDD4eGAxtCXwvhsx4OdtCcpOk6Z0p5tZ3Xr5Gz7VXrvp3CxLx8B27+gMis2xzclTS/Fj6eqfQ9ui9iHL4muzwOkVyCUS8B9/66FqZce7N/n6ZZMY7P/ejIipJB+524zST6RZ243UzT37t+fLsfj8GKSUs62kAjKzZmQpIPRtd56q1YivbADZi2M/d02dTsOHnb6y7zXmvywBdT4vgegY2Zvqj11sB0+k2CT1KaeeFaM8M4H5dV6xLj0AxRd+8MpYW19Z6BFLj+ImijyRhVQQaUuGm7FJyu3HTD0iQUDbhGdRCtlqIV+qeXiICpwxgvx1iKPcB9GCwMaMZo/STILojvYXFJoF5NnwqWKCHL83cnn4BeYFkCN/q4WNKvaLt63p7eztVohvaXvIQy0skh9Y1FF4KGH1jvL/XKOeMum2oypRnVhbzSMSwKs8UYl97NO7Ls4XnvQx2gJOBjsvlgmfPnu0yOHig4sfIPvvNnLKQlqHvUpK0vN564IWi9+p4gWft8dbBvdWiZ+XwffQiE/6ggw56+RSt68ConHo+4xODjMplsIjWCkA2eEdPauUK3nrMuoEEyyEv2fVEyDbLIZHuNfNhn2LhSymBEwFlL3ibvHLfp5RQ0S2RnmPN+HckryxHedUMXMRYlj3I8KFhM7ASlVIvKwkVnPZGLl8nL9d9X8QN5L4u2ckQLy/3fH9UbGP7u7JKsExcMw+IN9gZRSXaX/eYwIbtyfTrIfb3tm340Ic+1BVbPcPKj6e0iZATDXPT90nUSWbRBf7wRgvzIXIh2tRT7KONscuAJitq2ARtOqA9qwGTyeZr7w2LESIW4RKBiJ9Xvvyoo1gCHiJqiXbsmAHzhmaXGY6IQLWHZLU1xv3QYivbG5/9GNpZalaG9bX1TQzv8p4NEHWDiX5lz/c6V3tmlbQVyyLzg9VTwdwPhQbJRvSuq8/TT/v19RB6IbAxs5T4d/85dli8dvZ+38toxhyvMekZc471t0kay8yalYTyiPKBnkZ2AD+hftFqszG33Mm11pamDxjTxll/eUbu6xqterGNsU99OBUwZrbwoVh+MfmxjhYAi/01YCdCjMHu8Cd5rrjqer2idQKT8Y1hUXZPatfHeTYb94MOOuhx0+3tLYA5bzZPsvEqL2gBDHzIeNC6rqAqaTVTklTgQ+jQsqDUigzhF5u7v51ITD1UAXCeYlKFvlY5OTyLUKbKKNwtkH4Pw7Ztkj9fChK+7thU9GREcOQNPvZ9vDYaa4AKvjnt+H+TQarMRSu//W4WZhuLy+UCQoUd+OUVXTM8WR1tPHxYlvWrV6JMft2oYc3XIcbOe9DpZZnJz6KRAuu64vb2Fnd3T3d95w1yVp4HsB7UmIyMOsfLJn96uCev8EdgVWsdskaOsnyU8TNgNtPfotfKriulIme7BxKGQzQkipFC5K1WBtcNlffzHYBkWqp7Y3UcF28Y9e2bGWBnyrGfU/Z73ADtgU2bN8RA0pTDIBCPBpFFy/Dh7ObBmIVr7cKqePRGMauRBNq36EYS3z/M6tWlMfSLyIF+Fu5m6wsEUMphXLkn49nNO27v/riCh9ALbxCfMUX7+9o9fuL6+2xwvRs3KtUxQ5Jntv4Z9wGNiHQ77etvTHJdV3Evpc5MjWG++uqr7YTv5o53yrC1wY6IP51OeB3AxXk1Zoq+zyg1q68Xjr7dsX3xvgaetH5mRTAvBwB80id90m7vhvXd+XxuKeasbQ0Fu+s8UOvjYG5LIGeGxFMyWKIZ2su+k41go/tdrHVyjT3Pu91njPiggw56vBSVlmvyZGah9C+vUBLXll7cyAxGyQ74koKGZ2/bhoK98p1zRgXjohs0RUgzKlXdHCnpdhNJBr5ZOEZKqXnGOTmj2Xl/CJi138tBz3f99daHXln0J/2a8hNDlHwYzul0mlp7jYe/8sorSJThDwaLCp+XB/Z3HEMv971Mj8qSr5/vB19GBAjW1mfPnmFdl+H5XpGbzTE/TlFGPiYi6pEbXheJYUDACDZs/Id1A5v+fYyi3hCNrh70AcErhlHHgktrX8mHc6FlcWuH75W93kdEAAvYYBZvHTDqCWIOEJ0LFuLHthdET89mrxPu2+p1UCPrL9/v3qBhehsWgCvZTUDdG6pzSoNRumylpav1dQDkAL8YCTLMU+tDa4n9lggJzmgBu4aH8NA49y3EiohaGNXgReF+Dk6vR/OFwXS1YSwfQC90qJ9X8mZMwlsUfId51+0MTXvBY+UZ87O/7X6/kOy6FJiskd+EFvdoiPttflpoe25KSEvPBNEQrl5nzy614OwO0rMwK3u+7ZGIudVtgp3P553701yVxjCt7rMJ6d2JsQ98PePistCqbdtwe3s7WL6sLlYPsyR58JFTRt2KpLtLGh6VqC1AVAaZg6IyyrbhwgwokFtyBlK3NFWuuJwv+5CtIhk0QOPGwpkAPuiggx4/RcXRvvN8KoYPeIqAo5SC09LPyABBQIZTtlN2+eZtb1ytKMwg7kq2HIilnm0wyB85DhPeqQtrkix7FiLb2oJw1kXgV9cMRGZRnFlBvSLt997VWgEaz6qy3695772hxseRWz+s64pEGUT9GnteHDNvBIxKvcnC2Aa71lMERvbZeyTsOT5M6+7uDjnf7nSRa8ZI/12s72OjGFlgnh2fXMc8S8AecAxgo1bYIW92rb3H9egNvt5L5fWwWgo0FsdKRHNhELnzILiF9tRaUbYNdeuex8GLBT0BmxRPNEMuO4BR4c/zavqUS0NNbUMzgXi+D8i/+3k9C7trc5xpUOY9aLJ2rGoQtn7atm1IIevfy1ZQ6h5stGsc3yAi4WvWJw0csWTxYpY9GQ5MeS8kpSQHkTqd0OtRtSpoqzWspXkY5yyE8ho9GGxQqHxU8CJziAvXK8sRPUawESe+L3sGQiLDs/vi/gOjMRRp3EznO49qFfTokLegbbnWrDmpJGy17z8xROsnsTFM7/b2IUomPGKf+DJ8H1o77H1Arq69XlAxz7NWmKAxYeOZS0r9MEZf/7bwahWQsQJYlo70q6SEa7OAWTK4sGSoyBktPrD9vtWG8i1sQZiPMpwkQj2p5cDaGoHkQQcd9PhppgAbRWXJk5dFtu5LKeCcmnZCKSHxGDpjwt9bDNv/1f2etOwWUKV8OMo8O/wqtKvx4ZB+slkog1LXyg99sytvosQP4RcoQ8iHBwD+3RtzosHG7vObRS2jkA/JivWK+zPieEZZdZ83/j6l388LG1dv7W/AcmJc833rgZG9+2sfkzyxQ+t81k4zbHrAcXt7u/MazfZAnly4n9d3IoiMBk1vzPTPzTdPRg9KjXN8HGeuAvLlrKw+l+25RN1ef238DGDOgIOfp4kSAGrhPqZ/xnnq10HUjfzckLnrOAPzbv0b+Xl5uVzE+EqjQYWZdcP8uC+itcnzD5YwqaY9ExrzEbVL9mMY74vjSaReDQcYSY7zAye3p0QPQM3NOKO8DiOf8LznIfRgsOE3EkcGMVOebaNODK8xy701ZEgPq/efz+ed1QG47kq9Nlm8BcSfMWEbrhriVvJxnH5SWhuaazL3MzeiFwdAO83Snyq+pjScFOnbZBuJPCO3/mibqhyA8husZ4vM4qFnLnJLl2fgxgTOs2fP8PTp07YZ6tVXX8XNzU07f8MzGr8Ay2VrZ2f4DCtGnvkZxT7z4M+DH7vPBMvppm96i4D1MQmHgw466PkU5Yi3wjZroFN8AVzl9QB6iBRhUDp9KJMvgw2Y+PIg8dGNt8OFVzC3VLakz4GBksrD/oJmgUzJxVc7Iw2PHoaZgh1lnLXFPnvlSeo6bqz3hqpoMfV9Y9/5CIAupyrIKWcevPm6R49HVObi37PY+muAK7bD5I+XAczcZJuXKbPybG75vo3y46EK1EeDrL5RsYtgr9bawptNZvtxN13j5uam9aUdPOfbPyjrbn5YPWJSnyfriqyGVwm/UZBTTG/RvRxRb8vjQcgtKxXt9UsP0u17f/RA1Av8Gs1pkXns5p6VH4GFJ5tTpidZPbd6Htcu9/Fo5Tg9stba9m9lp+O1ZxcN++J9WJuFTenNo7474xlhLA2NNH0Wo+cLREi5grnPIQMbVhfR7/aOgBfVuV4o9a20e24J4dC53trsmZwvIzKrWO7snvj5vvrGDrFFaROnVgbRfD+HTVS6Up63FETG64XlYGkCpvXxzCCWMRMU0Z0eKYa0ecDnhUV2oMksJ1ZXD5S84B6ez4wNgJ1qGd3o0ePix8a/x7rH62ZlRJfnATYOOuhji2ZyABitZpHXR3kBuHSqaa+8N6WJx7ABWvTMDP9cAHYycNu/kBKQXUw5gMSjNbzWinLZdnvpLLkIzDCkMevgMSw3BQXEPz8q8b4PvOd+27bh9Gbj/V5O+ROYLd26D5VJKQ3Gv7u7O+RUkHPZyQA/fkYN9ExkuH+3unhjnf0WlVsvD+PmdA8mJMpgfs6Hnye+T6zcOA8/EkXqjaTYV1HmW5+ZXuM3hvtoCduLGnWZqFfEOWFKtwc9g87iZXaV/RQGMoREWbW65yVLlMKyDs9pz6e9QhuBbDRWzvhHiwA57cPGZp410388+bli824g6nsghvLC3AYBi9uIHeuTyLwYaaybm7/ZDBeJWsQNkoET8aQmFgMwtfEZs8Pu2kqWHW/vwfF9bePHbOeWaEYxyw7+AHphsHGNrCGeWc0mwH3I6Bpj9czYl+UZR9xIHt2DHgHbopRrx5jIaEHCpP7kLCylyNbCnBI4bKIy5r4sCwrzkELR2hvb6S38vh2zSeAXamTU/trYx/YcS1G7LAtee+01nM/n4XBEc//5rAqeMYCB07K26/yGc8/4ojD1nrA45kP5cIsxjwkKvBXiMQmGgw466PkUs77MLNszQ4q/3382XjIzbAyyhiChCGm0EjLtPcFitRSrbLsW6DqUPKBd7+ss96Zx03bth3gl7JOAeL4f5USUn/F6u85HDZiMNBkys1h7vu7HoclPLmDeb/L2StsMLM6UYq/Y+Zf30Pg+jPLBnuXlg/0txrHrIGGngKZ5yPdjlSV+jK3uRjbup9NpmvLW7rfX+SJnovgIAx+B4OeIB777Q/RmSR4AtjMZGojTNmj5S16AnLE4wO3nDlDcGhsWG5qXBFbPceykfj4zpiWdwW5NRH3UJ0jwfRvn9vPGyDwG8Syd9lsYDz+XQUDVOV894KAxs5cBHNvxbzVuPBXdmF3Kvs69jez6TdcDUuv3Wqt6msbrbBz83HkevXA2qgge7PPMSmXZjSIT99fHMmaMxFvj/WD7ieEPiIqMxS88AwA9m9QyMMwhE4a+InM01NqsBgDSsgCeSTsiklSLcHXyfTnLvmG/m4fBLwQPQHaT1d0bBbm3YBj5fRjejW6M5e7uDqfTqTEzAxGNyacFpGnQtou4UUstAFeAdWM3J0j6NWonTqqtA1wlRpAhC6ePQ2oHP0n7N5yf3ck9DCAsvef7ug466KDHQsY/ovCOyrR9F++195Hn7cup9jIZJExmBBvu3IGoDMBndpEfhK8ykKHWSBeq4Osum8trC9USBcLqsd9XYc/1ipD1kzc4ecU8AhPP671c8KnP/X682Me+HDMo3QcoZuPq2xHrGcfT90EEmxGQWP2BfVIZacs8msF/9jLQ6yGzej0WitZ7k9P++3Vd8eTJuHeiBn3DQsfuzv1ARl+mf5b1r4Va+axo0TBqQFa+l/UEyN4BXTRBFwFSJpzyMiiqY8gRw8IO5T2GjwEWd0KEdm0MNUsp4XIZ962Y/hQBh5+D1icxDLyUAuz4zJxHxTGydLLX1my7Dm4/hK0Z9bjKPtjuiSUkUHLr1c3nvsadtxFwXo/kdqX5Nd/3pNRaUUmARfSUWv8+lD4isDGzrMRFbZNxWZa2hyFa6T1dY5Iza9WMQczKnIEOb7UnSliWvTI+MGIilAA2apiclHp8qM845anqhue4ECKDje3yZ1/4d98mAIMnyS+Y+B772JiugRbbdHZ3d9eAmc90YYADkDW3LBmSGaLoPFcwJjVFLgmEjFTVggggp34+hy3AsU9Si21sC7CWNo9i/77IhD/ooINePkVB6wX+EH6g5PmdXX9t3bdyWDKzDIqFlDZY8lnDZWe802sWLXVlkdS3KWkoVup7FgZFlyvg5FdRhQjMoDwClOip9Tw5ejbsPt9vvt2+X+yzGf7M2+z7z57v+8kAipyJcP8Yjv21b4NvJ4lmuLP02j1eNsWyvC7ggWpXZPs9UXHscqR7Nnx/zkDfYyEbO2tnzrntmbB5fHNzg1deeaWNpc8ead9Zuvu7i2SQXHIHnaKoYtiTNNyj+kD3IvW+3bYLcpLDj4lkTaSUNBOl6kkGzNWQmJeE1YENbzz1c93rb8zjWRNV15LpYzOgIYbTSwMEvvxSSzvPQ7wRgCouslZWCTsDSVatFv2ymqvG5va4DqJOZlTqnr8JUDyNHke/P6IZSUSHlDrqfF4yEhgJdl9jZMgpYVlX1aPMyI+W9GJZWAEQN4+TBxuVaw8F01Ap6bd5Wx9CL5yNqnUC7zfU+Ar72MGo8AOjq9QrwXOGvy/f7vX0EAZhk/ru7g7Mtli2gRk2pK8o0y9WZsYSmJa115h/ZNrbtrUsStGS5NvhJ6CV7b0elhVqFpZkbs6cMz75kz+59bsxWL+ArZ4+g4htPBdLwGVIrQegnQPi25bCeBsTsuf4TXjRje/nkR/fmat/Jsgeq2A46KCDnk9xzUa+GQ1adk0EKePvo6W8BOXcwMaQ8pzG+nhek5Kc+m3KjAENrhViTkzNS2L8c+BpBLAvz9JHMlwY794w5dvmw6KiNT6+1xpDHXpZlsbcjEVPnz5199XduUvGz2vpCkbkxV6me4u6HwM/hr6tXvn34VH+GdF7Ye0wueVDfqU+ff7Y/V6Z9vLOrol7S2fj8dKpMm7W09Du7XwR7xpp6JymobexFOOmeOAsO5Sth0JABaFyQWbn6aLUEiRwZTx9+hS1VBQuoCzPAgAmRkWBROdkLDUjg5FQgVIligMxJJpwOj3p/bsBl8LYSOtcNmwXBVDp0jyIgI6/Aoz+DiSQei0LLLWu1RGA1L1W3JK0yVK6blybble44sIFFy5yXk7OsvdpTaAFwGJ7wTISLUgA8jM9pVwNGlodASWl4FwtJbAo8lvYUhA/y9YDC4HUulfAAkSKZr+yMco5y74XHy5XGKVuqKXilFZwyiicASQgAbWSnkMiIWaUFjUi9835zDyk7845yyGm9gwkgGQ+CAgaeezz6CPas+EXZbQw+060SkeLj7fWRABz7TlG3qpxLVYsAhevuEYl3Vt0PCPUGjTmFV1uvi4pSchPpREsmQBlZtwqypz1XbRQ2b0eVNnfHoCY9SHWzZiwByLXBPnY3n46ur0DnWkYWXliXehTyDN3/3y7x8iAx0xx8GPl50C3tNWh/EcnGA466KAXoqio2nemkEdjiQcmXkHlnFGJUBngpBZTljz2zAzKLvxzPaGlsGQJ28jG41RJYGZRoojARTZVpyx1utydsdUePlKz8icXdrK4/YFEhMwWEgIUEo+IJJ6UMBADC0D3ltC6IC8rCgGMilrkDIKkLwajEqMSkAiDdfeaccc8xEbWJzEblezZgLPgVouK0fK734drUUVQY7wTsGR3GByJ8ldrAVcgYcVWLqCakQGkvGBdFuS0aLpSgHLG5XKGnFCdkXgBCvV+qoQEDc8lwrmKUkXIWLOcdQBm8KVg2+5AIJxWsbxvpYBLkTA3CxpJYnWnnIdNzy+bzHBnRlHTWYCuO5Rtw1OX0TPqBAMIdRummywPBr3mGZoarscDIpe0Tg2IbS2TbHz2+kgMX/IG07RaGJWT7Qo4XBX6M3SORv2p9VVK3TNR1YOy9PPSBm+arstMcnaYb0cDOx74hDpKffbhkdGQ7uvJ3EOdGgi+omu2qJvZwLR1KWfjyNzY6+tWZZs71u/GV319xdAxep16auMXM/Q+GGyMFoR9xikjDzbMlWfXxHhRUyJnLrNoufaWCQ8c7HnWWb6TvNchKszyDEmzG+toz2Tum6TtBaCleTOrU1bmlIIS7xfVq7e3eHJ7257jF5hnDv4eQ73RymWHBgJo9fcM6EMf+tCQacreo3JvQtD3l1mPiKhtGLc+NwubeXlySjjlDqCsvHVd23j4CewtM34MY0YK63/vWiUiPHnSLSOx7w7QcdBBHzsUPQjAGCbq+b23kHtPhr/HPre/iMTzYEoLUdusLc+WXWO65xS2X8zuFUOKekIs5tzVlYjarf15/SyNWis47MUjZlQ7E0xP7u2VTyLUXR9Z+YWrHXMGCwRjQk/dy/0sIuuHWbSAVxii0uP718oQgxJdVZ4SeeOi1Vh/610CyxIm5RAoARkZlSpy4qY4Jcr6PH0GSL7L3XqfqBsCE3Vdg4iQGCCqYn1OchZT3QTccKliuWYZ+VQZGaoES/4kMJE+kx4V2JjpCgCGsbIoggjMjdraQscP3nA3ixK5piBHub2QT1vvgUICiNs8kGUnvV25KPjU+rb9DAZo5/J8ZqB9nqHah0BZWleAwbZXS8PHKsu5YOY9GPgSM6rOef/8WDdfjwbYwvWxnrI0QjipuyalpOfdhO0Dk3bLeI19dZ+x3P9uv8XIklpngGUPKp9HDwYbfnPQfYPrO9eHHnkruh+EGc2eYwvIW7IiI/Xvdq1/mUfAOhi4nubOBry47FpG3nIk1q7U2uWfFV21NpBA35htjMKfLWHP98+y/rT3KDA6CuV2Tkmc4L4Ofpy8p8SnTbT2eTe31amUAk6yx8PX2YMY6xN7jt1rQHM2lr6unnGaW9+PgdXD5thBBx30sUFeaZ+BDc8/ItiI1BSlZBrN6AH35D3CMzkWeao3bFxT1GcgaBYOarwPADiPVtP4bPvNy4ZrYcuAJhkp25CVJvJVLx99xirfh77PDWwQsGu/f3nPlH+PbXE1H+Wjxs5bJAEp3uDKWPRUeIk9z6FcVuVZvjslAZcECbERi+wYFUDUY+KXnCV1KATEXaqdv3DFoP+SyCzPPu2sN9jaOM50s6h3WZ4hu95n7PTXG/kxtr/9mWXLsiCTWNpljgB2wrfk3xTAAcX+CaK8Ah109PqyXHtFlPv5ZX/bewQh/h5m8Qi2A4O5ApzENdccGvJ8Imr7RTMlWIgWgyHhl4AH1Z5HzOoye496rYDxNLSPMa7hnCSsH2Gs40T14+fnRvRy2ZywsffZ9OKYXwN0L0oPBhvxIJ2HPNQ3wjP/PRLrgzATEnExRUYHjKkUo3DySNyTPWLGIBsinggaP1gppbbh2T8rIj9vafBCaFZue36YxBFs+H7yn6NQ8uMVQVvsP/v+dDq1v/0YDuFRzAMIHQSfu3cGTP0CfB7QsD7wYNG3w+baQQcd9LFJXgH2Hmi/zmNMPxDT1dYWfgTME3DYtd7zatf6urR3vq7U2Hurk+PlSRVkz7Mav5cbd0rTjA/eZ0jxz5Ln16sW4Vl/RxkTFVMPNmYyzD/bgwdfN9++JusApAyc0oJSSF9OTqgORVkzF5qSxduwgVkLdVZgQs4ShpUAbJcut3LOSKCWfriy7L20fZQVjFrsULrHRfHkcKCPh395Y9yMmn5UugfNg+/dPA1jDYwp7e2FwqNnUCkqqT4yxWcX9fMmZ4ND93s2rq2dqD8uywJskLVZKuq2YasFXAiU+zkTBYy0ZCQWQJSTzKFaGaz7XxJVlJQkC92kTjF6xGdIjX3ivVTrmjA7I4iIWvKhlOQMHPNwtr5O3WPb+YB4Zf3z/BzwY2l6tR97H2Eiut9+LKIu/hB6YbBhFfKKqlU8MkvbzOWVVfvNezf8JDQU75Vqo1nHWYPNAg/gXgbtO8YQ5ez3qPT6enpLf9tckyQm14Me34678xlgbidy39zcDGnVrH9jKJlnNH7Ttg978nVJKTWgYGUw95Mw/cFOvn5Wht+4aHWbpRvMOYO1Tv75dr23nMVwJx82ZfW0OeL7Pc43P0b+uwNoHHTQxxbNwmSjm/9aeOROIA/GIgnhSRpzbUksWvpb3QBbLt2LLMo0ZHModUs5s+7XUKPJAEIwWgHFyjha+n1CkLEtBE771KSRH0ZgYzzV96H1V0oJS0oN8Pi+nXnlfVu8BzrKvGt0zdJ5zVjo3zHI4Ot8PFruvXJjCpE3St1dimyEV2t70QyQAJApA2Bsm3g7iDQkzYyYWqWcxOL92MKoiKiBCRtTf9gfMEZQzMKi2rguuVvEiaS9kK+yjQcRqgPtXtdpCmmW9fX6s9d3CqfXo2ztWOSGB8nRKOvvjbSbR+5Z9vL6lxk701Zaqv0GzgoDhcBEqDoXbnMWRsAMquJlEYBim70JyEANOmKsu9Xfgw0PCuO9koBhzJAXeU27BzJQbS0oOPKgRMDfqCdH4BP72uvR3hMrPHAchwhKH0oPBhuRIti4ds3susiodh06mUz+98bcQ128teaa0BqRmP8sezTkd/dsyAYnntQh6YuI1HXbvHLiQCTCkjNO6wpi2ahYc0Ulces1Kw2oCUf7DDAKUUs/RoBY7gjYNL2sBxiUBB0vTolvoMCBJQMX0eMQ+zQKnTiONhnLBW0PoWZ6bFaOlJLE9SZdqCQbBGP4mrTP2IEb//Zs7+bs9bOzPOT7j9y9d9BBB330aVj/RFPeD+yNDV7pjAauJnPsZffW6vgUNyXUFCtTtqhBDbTfc8o7K158tr/ev7wVc+C1hYZMLrtyemMbiCEAHPuJpS0MiKVWySs1vt88OPH1BfbpR7tsmmcEu0+Oe9pZVsN3vh72/czAFMvx5ZsxDiobMxEu5zu1SBNoWUDELfBKDom1kBWRTynpOQX2eiRkSrS3Nsf9jMCYzvca4JA+5MFD5MGbH4cYgj47n8XqEwHj84wE/m/TJVqUBY2RGb4tcQ7P9MChfADcUk5LlBYqD+FHxOJty5SQICl7uVRJic1yng7bXqlasWGMXprpSV7B92PkgZb9LYcDUNcfI9DgeA6N1UaBIMmG9kQS+sVJN4b5a4IO7nmSr7Pnb9eAxLV1/jx6IbDhJ7WvfLRSRZqBDe99mFlB7Hkzhh4BRymlpW31i8HSeJkF39IT+nhHsfrkYRJLe2RRLimhKMNv1hUokEhJXLE6kbkyFgY2TY+4QE7YfnU94XzeUEpFuUhOs4QNdanNkiDoNIt/WVoplottQ2JBv9UsA1XQdUoJrAxgyYScVixZ4gwFoHcvwu3tLQDZUP7s2bOWN9uPIfOYqWQGFq1v13VFZeBplo1eZStqLQAyV6zLglNekJJaC5e+92XbNqBUJCpIqbu4SSU+ASDilqdbmERFomoTYLQU8NzVd9BBBz1e8rw+CuyZkSPKi9meCLtnptjYvVHeXJMzgIKPNAphX6fZfe03dEVp2LtIY5YY+w4hfMyMLcNGaPQgk2ty81o7o9IWoxG819uDNoNfHhBGmX8NcFxTWJ6nM8z0AK8QzcZK6l+xpQuIGedndwBkbwbxDdY1A5SQMmHVNO5F9wyAgKzZymBej0dCLQlNUOgjsPXrZDb327oK/ejXkN0/WxfRANmv7ftfRSYbsE46rUmV7xyeUVGrYTs5EBAAUuj7YY+Cq999+mOLqKgVKJry1foN6sGyMCr1FGRV3EkKAsBN+Wfu+4HqJFLH97XXQ2cGFM87pL0CFIYxwAgkZRzkF7/Lv/e9AAx7VBz/aIyfgQkbP6t3G3suu/5+noFhRg8GG4P7zIENjzBj59h7nBSzsJdoqfAU74tZqbwFycqywW4HsbiF4xfvzIUcXV7RrdT2PGBcmD4MKQW3b84yQ+z+8/mMlTM4y8ncfjIA/SC925tb5JQboOrwfOwfAwnbcsF6c4sYHub3Yvh22dh6l6zvB6CHOO3Qb8pY1ifiqVG3NZhBLNalbSuQg9U7Q1vXFXXbNE9zacCBSV2CyS1iQBh/ZeR2cGCfV34Mrwm1gw466PHRTDHya9nWtxd+Mwsn0OXBLAOfF+Ce30ewEF+tfOoRCZ73WxsGxdfAQBDKPvkHkYRXzWRgVKqMvHJwnwEOkz7aXeOeFflnlN055+7tIRoOrJ2FRj9E8RDAtA+Pii8r01v0fbstLNfexaDIOJ/FiHa5u8PTp0+RQFiXFa8+eYJP+qRPkkyLSVPb1gpUARdNRmWT0Y9HnsTEKBGI+9dsjuxAQ067+6Pi7OfiffNpDzYKSjEjZm5gQzwj/pwT0XPsQGAhrQ9G0O3l+2yuXKuzHW5I24ZS+llqKWl6Y00+gCT3LFkymKWmYlkESwKS7TciXIokxYlGdL+9IIY8Rv3Y7x+2bF6uZ+Hz0hERcqJ2iGGL+yQCs2WLkkxeKSXkdL9ab+t3BiBmc2u7lKt9/iL0QmDj2uT08ZRWUV/h+yo6Y/LXNjjPEJlfZH6heUFgE8++L6W0zU1+wszq6RepPceno41tmR0cBNjk6nWR+sgzbI+F7Tuxe/3ehu72qgD1PjeQYWdrbNuGvJ6Q0uhu931izCtO/LjXZSbg/aLIy4o33b6KSoQiF8iCqBu4shwGRBpPrJ4nIomB5lqwFbES1M3mD0Ds5hCZY4NbzHW04lw7a+Wggw56vOQNHJGveKFtf3sly4MJ44tmyPDKqOfPvjxTmo28cSqSWUeNz0Z+MygJ7ln9q2648UphZctCI/sGiQCz9Ha+zcNv2jtNeWthQFzFa16xAxzROOP7MMpjM5bF/SUgIC1ZM+KoAqhKPsHxavRbrDvYXDGAniui1vmtoGX6cW1NCWCmpjxJM2rrJ2a5p5TSgEWx8zsYuLuT066fPn2Ku7s7jUBYcHe+w6VsuLm9xc3phJvtpsmclEnT4EojWGXYYyEbEx8Z4A2cXgcC9h6IAWzYOnBydAiLYtlcXEtp+1aiQuo/E4Cb9TToCj7kx+adrU27xg6RtNT9Xklf17x77gxge4rA1Z5xuVxAl00PtNPEOjkhLwvyssi+qpzAFDyl1m8gIKmSX8e1BewBx6CkOwPubDyaATivQxtqrcgMMEm4n+mBUp6FAkIPF6VBLwKAm3XUqf14DCCMaHhm1K+MH2yXux2/iDzuQfP4QVcBbbI85CERXfuOnoGNGTKP5cWJ5BV7YNy0bcLEOtZSwfrrvGvb183qNHODzTrbe1t8rJ6BJqtDvB8ANj3x89mzZ63eMeZwD6YAEA/peG3yNA/Ecmr3eFDlzwUB+kLxE8tvRvdxfVNEWzac7+4ag65la4zKrluX1Cxk3roBEEphlK3gsl2AKn16OmldqeexZwgQevq0SFiW66tu9ZpOxYMOOugR0mBpvWIpi9bZeG2UB8uygMzCqpmpCAAn3TtGkMwtIBSLCye0fQmkoZxsCqe+F957x4nn9fN0zbCWkpwJEUGVGZNGABDBlgEQk62sr9rCQaKRyT9jVsfowSdy2aEsuawqPr693lvjGbB2HVrMV3P8mELfz1XwoCi5TGK1jv1pv4vzvKKUfv6VTz5iIcLPnj1VTzthow2XbUNlxu1lw83NDW710MVllXMisiaFtfC0hypQHw0yeWzJYogINzc3bZy2bcPdnYSM+Th8OzHeG1xLKUicWphQLRUVQJnMZUuicE13a7Sehn0kcZOxN6Bafa09QNedejTK/ggAP4ejUdrrmT6hTvNk5ARi4QfMAHLCejrhdHuDvK5yGjeNnjswy1ktmginXDaZW8+eyTPT3GtUa9X59wzruk71JuubZuBgtNPI/TkqANo6aW2F4Z4xuYSVRwpyrE5mYPAhkn4u+L61rQjNIFLrkG3U1yNmBn0IPRhsxIG2h8yU9UjXfh+YN1FQqvegxgMOq0d8eYZp5fnYVM90I5qLE8eDnBma9cIuKvBWfhSSo4CS6+yQOytHBnsvXKVtgI+Qsuf6SVqN+YqZTCbb5QKwHoTE5pqzOpOcuKuLjABwzs1bwSnDLGsuEhBECWXbmuWqar70as8FYE4qS6FGYM2BznoSpY2vCfcOMEzYicCXayVhxDzm8KCDDvrYoKmVdGJkArrMuWbAMro93QCZxAJZJYa5MpCoyn445V6J0E8L1/L9AXWdEbGmP9+DjeQ5ocmVpmWPbYzKf6IEgvN02MFykxAxwpgP3+qYJn0V01165dHLPl8nb6Tz1vGZbBws4KGeJr/seX789jQqoXa/D282T1AM4bXneMXVFMxSCp49E4/G+XwHWLhWZTBfcL5suLu74ObmBnd3t7h9csIT3IISUDlL/7H3Ij0O8npPJD8+vv/836aY+/lwH4CIOsvMGOuf7fWk+L0924CIAYJrnhfzKMQ5OlPa47NnHgT5wJIiVjSPZoCVva4ZKcsZFpSShIC3tdf1FNNp9IdBj/Pk+8pn35pd08CQHRSoe0KADvJtv6oZFZirzufadTKSYEfjW/55kaf4/rrWhtnYX6Nr98/ohcHGDGhEZfzadb6CnsHF06M9agJGb4SftN5jEF1As/fYuYawbeLZ5nHvMTFm5idyTOtqdY6WKj/ZDAzY/gyJR94aErZyTqcT1nXB7e1tsx555gFdkPasm5ubFkbQ+uWyDZ9TktzQy7KAb7ll0Eq+TxIDVWNWkwCCmjJKKlhSGfrY5kOtjNcV6ftx9+NdUFGrZsZKkp7Ru1TbwrYQK+qHLFXup4uCawulsrZ6YXTQQQd97JAZWPxhnd5QY4ksAEzDXT0f90rwzGrnZYXd78NH4v2eh1FKWPParHwmL165fdLDQrU+tze3gCoZl8sFpVYUpwx7Er4nwGPRGPq8EAqlof6ZhHcTnHGIUgM1xMK6WxgQsHu3583q4GXL5XLB3d1ds2be3t4OB7jd3d3h7u6uhSF7A6TJNCv33peGo0SZGb369myvsPqQNGYJH3727JkeLluxbX38Xn/9wy30JJEkSSml4u7ujNdffx2vvPIKaqkAXsG6Lto/kvEx58fjKn+e0hcB4RAORGMYTbTCR2XeXzcDv8B+r5KEsElSHbnMzIUC3Gwfh4yf7OuQZ3qdURVoA/0B9E5BBMbQo1noYK1VNvsTAVl2g+ScJSxwWeRsDRdWNtzP+vzSz2YBOgjxa9/q4vvrWp193bXA1l9QwCG/sfxVxXNo3g9ZQ1p28xra+LEbhznYsJdfv/59By6x9+DM7nsePRhsxAGOv82sGTMkHO/xVptrjfDXAeOZDMaA4gLy6N0zNY+2ryHmWdt9XfwBRtesAr4POnOMB/olEHWG3+tWB5Ax9KfLauYZsj8k5+7ZZRCwxpStXyIzsvhCf/3MGjbzGlV2cX+loALukCUGsYQmoDIqF2wOFFaIxSHT0idzklwUlYFq8b0sedKXvA89eAj6Puiggx4feQuf/R15rVlCI//19xs/KLW0PQFxfwKw9857fufr5HkKJwmZiN5xr2gYHwaCF8PVzV9LJBs+UyKAhC8SERiyH89ezGrhJAmfFeXCLJhVFQ25hxINXpVZPxtZfWebvO3vVm/IQXcpZ7Drw6zWYVLj17KuuJzPjfeP3un+GQo2ZnV8nsHSxtD6fl1XrOvaZPn5/Ayyp6OH/pbK2IpkLCzMeHY+Iy8LTuuKSkBaEjgByMCruMGyjAlwHgM9VL55GR3DrwewQCQZNuGiN2rPv2VjKvqRqLxQcCwGwQhKeviffKchadw3ghNhCFs3ZdrKaGBj4hn0QGLWH94IHYEQoGf6kdQrpYS8LrpnQ7wabazdfcmASuHm2TQ9kyEeMA96Z2MU63xVTw7tlXXS+51NX6rd4NquE8vzdK3Ev+NciLw26q7NkJ72+vpDAYanFz5nY9apU6X4nt9jI+0ev1EoXhtfvuNmymcUTrb4ooVkhvji/RHVzSxhvr4z8kIqkgdctrjs4D7vZbHJVWtPJ2e/+3jDWuTwIl+mz/Pu+yP2SxynyHTjpANyW+xnZs1n38SL2OQYfTMT9Q32nKys/ebM2sZSS0mpbRx73nw76KCDHjfNBKApCzFToGXmiwajMd4/qRLL3Wjhy1ZlSvZsoFsI7TfjIcbfTMAn8c7GrEjAPgVpU9xmSntQhFjPfIjGsT0v66CDLfUmC1+1PSWmuKk6uOtjb3gz8sYpf63vT9+mBt60b72xyu6pobwGNHbPkXBawNfL/rbPfm6gjSt0X03OCcuS9bWglA13d9wUXwMcppzVWrEVy3KWsN3cIC8L1nUBZZJ0uJmQ0g2WpadhfQw0Awwzr8NMCY9zMucMS2jZzpyootxai1vLZfCal4FIfmtnOuieBvNUyDX7MHSvg1xrn78eE/Ab+8HWU/TK+HXalH1nyExLRr45YTmtAjRyhp1ax/qsRKR9IwbUrRQUS+dLhLJ1kO6fHV/XEh3tdNkAsoe2ArDsow1sBN4Xzxiz9RJBh3+3e2c6aZxrFnLqwyxjWQ+hFwIbsXCvIHrmNWOasWH2u18Ibc9BmLi+M2ZxpwBaeJKvqz3TxzL6xbcsS9tAdQ08RAXd6hAXQsxm4n+XOsiksLM/BCULaPAbc3rs6TOcTic8efIkgKwKxv5Ucz/xb25usCx1F8/qsxD41yycbdb3vj3yynj1yQ0ul7PEyNaCy6XiUooKdwZnv6Fb7n3t9Q9jUwUiW7aFVX5vigYRKEv2kwxgoQ6w7puTBx100OOn6I32+96ioccsiPE7b/xJKaFctpaIIpGEHpiAbkqBKgzsFDG/PyElORnZwoQ2VGxuvwaAZvX11lRRuvYGKy+vWhnkjGua7anxW3Qeu1MWmdvhsjMFzAON51FUMmYAKaUkG6vrmCjk2v1RdnoadAKWU4kt5MNARv95X5bUy/YyEgABGxL+tGHbJMMht7CX0vuMIR6O7dLmUlH5n5YEpgqg4mZNOJ1WATOPEGz4v428Mu9T6Pux8adHExFef/314e+oz1wz5Jky77NkpqT7Nt01/t4YATIztt4Hev1zvZE4ekOtPqZLMXMLf0fuG7LNq5Fylg2gieCXTTOTqqPQh3NbXbxHcLYe7N0f6jcDJc2DdtETynn0WvQ+QeMZfh9uq3N16DHJAYT3Gb2NvE58H/lf4/Uvon89GGxci9+LlvDoCrXvPdiIVhwPLvyg+AnoF0EL4XF1msWMzp4b6xP3h1zrvJnibXXxdB1oiavQGLd4LuTMyJglyk+6PfrsB7fMxoDZ6rTv4zkD92EAfQx9v8Qxbv1HYiWq1TEfFZjNQMXWT+LByNkfpti9FyCAlSmQxlCKXNEwrDY2KpzhxjIBu055SfTCrsIr9NFqzfNEqv/9oXV6yHVtjvwi6b76c7jml7JPP5IyX7TNj0fd+aWn+9KbP1f4BeENGF/CYO2PBhMPLvwmVa8w2HtWwIG64a7050IegYSelMOAQQYNcskUHF/HlCQbFuqoWDSDmPFQVawZjLr5vup1iApaemAacFOYZsbDeNDfUH5QSqNxMYZCx987KLJQm73yObse2O/jNMXSjGliRGuqotybZM9NygBp7P5WKogZeSu4u5xBrxNKuWC73OH2dsWrb3oCwumqsvYyyDJDzuZqNGradyUCZPce74nfxbGPoH7n1cJcN4pj6EHP7LpW97rffzsDK7HO0eBr9VzWBWRG52VBXhdN8yagAmoAgKaVNd29WmgZUdtgDtBQH18PX79oWJ/xpGZs4SJeFGaAO5iQcuN6lxe1YyfHrJ0EQMLRRlAzo/uAZRig547ZQ+jBupE/bdpo1hgbiJiWzE9ST37Dt2dakYHF/QJ2r13rvRf3dYAxzJaNYII+Z/f6gblcLrvvfbtjOdbuWnm4jtRab9kRfHphyaaxtwgQpX4SZmhrT3uX4eWOFy4xE4T97tO0+ZAF32exvTKzq2SYIkJOCTXLwThgySAlNqixjevTp83aWGpFYXlJHwF5ycir5r5WCwMqg0tBSgYs+3zIeDxnbfyK5XpdzCbnMNj0GkDDyCa/8+Ta51E/Mgm7nrLVqMdFtr1qxuLsd3Z1mtXf2laIUN090Pv885JeW93vvsxrSjm5l/2dwu9Gvjy7Jj4n3ncfgPC/+TrEZ83IX/+8az3ZWPl+uVZW8KQjgcf5QaQxwA98+BtM27btjEDXjFFeiTCZMAMrOfV9Gp6/+U3GBjZsD9sMtPjsOQUdTHhlOqXxROdSCii7UNZaeyKOFE7kBaaGJU9NCWBogFSna9deM4jN6Hw+w4x0Xj7HCACrq531YONg+zWsX6JX34/D8+Sp/T1T2uK9cXx93UWGSU6ftTI2VsULhFIZtBW1/BI2BSiAKnuo7cwHBl9V0F4G+bkKjDJ4tl78nI7K+mzuXOvr+IyZ14SZQQmgqqyF+nxl9w/QtP2BczKjfW9hglGRj/Pbt9nXddaGlCQpjXksk+7ToEzCE0mfb/wRQEtG4/s7yT4UK9PXJ0aCzADefWCs1u7ZgL0caPb91cFGu0J6LoATe26MVLnWr/b3bK32MdrXfdbn1+gjAhu+k+eV20+WODFmSNuHURkDjzGw10KxIijx5fnOvC9EK5Y7W8ARSM1Cp2J75LqMlHq4lynMFkbUULgy8VKeICXC+XweNoEjuMg8ozEBsWTNX+IOYjThx7WikjuNUgoB14rL+dzq3+KMrU/kB9mMpIi/MnCpEhaG2rN0nU4973ZaxJtxWlcsmvf7//6/fxkueqjPViQHeuszZtmUUgpqFWslMyMzY3FzydoVLV4vm/7c/+f/AbD3Bnny+3CuMSZj3EZxTUVmEanNCTBqUlczzLIKmAUYOnZ1E29b3TbUUsFFNg36dfj0buub/4lASWKdU85Y1gTkhOW06JigoYqKMb3mmm+w0k2zKDG6hcnc10DfUJisvqU2ZsxFvZr1rKDdMdWUNNWpUCIPsQh73ytQtguqU2A9T/FzzVt+Pb+IBoZrPMv4mU8AEYWAvU7Lk0GIkHr0kl5XWfLrl600AdXmhhQGQDLr5DwqkI+NouD230cj0lWlS/Ji70CEXycRNNj3vuwhbCh1Y0vkPbvxRzCWUQ9sGttkfycN3/KQPioXzmuDLk9t4y10D0OURfdRN3aN2XR8GUM4B3SPi6ljBI1zV4MTUXtJu6HjMM5nQNa5Hdy675f+2esWvo4zfcDWpoREJ6QsjKcSiRZMBYWBtFWkam0BtlKRUkEphFrHqIhHg8oB3N3d7cZoFuocgWsEIhGk38d7YhnRUDxck4Vp21TofedMUzZdBpnWgQbYwgD7nghgPBXcKHp1rJ5+XvtrbRO4HeBnHkSN5etzl6CJbFgzPzn+T9T2dvgoED8mUR57XhH5hQ8Hs9Co2K8DMVrIZe/ha7r3mGlqdmDpfUBhDzoY/s/ZPHkIvXAYlVUmTuT7FL4Z4PCdMVPSY6pCu3bG5H18qy87MqtY3xkKjc/07mYPNjzYim2beQ5EYPazNGQC7JmHxQrLhCy7TCymKV4TorVW2fBm/QvNrFDKEGucgmLEzG0/B5hRUtKYxiDAFKzIdQUXB0JzSsCSwbyilIRSUlvc67rKQTp6mE5lxoc//GHcnc94+uxpTy8MBUFu/EutWICmVHoGOFtIL5PWqntiGC2dL4BmoWRmEOthWLAxnbhWCe303WjxIe62TuHV7l4TLAYkmLEsyvQoITGp21ut4aq8FxRQvcP5cga2DaiMxJDxvlzAlwuopO5JUB6dOSOnFSsWZDD4sgEb5EA1tR7JXhsATEicJW5eLcakr55h3LqLQewsPgZ+SgEXVRiZQXwnc5QE7MhayiBneZaUQmrFgp1REMIQ+KKx21KJiooC9RSyZGhPlJEpt/5u825gvmiWJilX7u3KksQAExckJGTrA7VVVfX0Va7IFr5TuY9z4gYSU6lYecNCmsWoVlQuLca4ZZipBNTU5s5joKhk3vfy/N3z+AhQslNIYxhJJOOxUbECujIAoG2GjgAmAkj5LGNo9zejwfT5cr3JBFMSYkaeyOdNge+bQsc0vlF59O+eTG7G8fByjVk8zxWMzWV4Ioixiki9hcarEvWzk4DRst2qpWEfrc8BA0z2OxSQjP1Nu4MAc+5AI+esqXo3rKVgWU6gZUVKF6S0oTLhvGmITK0AJdkXzQbIc/dWsYzDYyHzwPhxjoossB9n/5vfszkYTJKlUR4NlhGERlDT1wi3KAOjvbLaKX5v93VeCnAqbQ6Z3NsbGBK27TI8L+qXzRi5LKCckXS+SJg2GsgY1j3Xdk6PZU2zfrK/LVV3pJlOFkMzgW64Nw9rqhXGKazdvVDtE/1s5otrfNyveT9fdnozMBjX/L3xu93X7K5nG6Xn04PBxnC6IvbK+YxiNiXrXCvPLPlWcXMXWvkWevM8S0xM4WefZ2FVfnJb/WKI0TVg5dsSf4/f+WfaZ+aK29vbFjN8d/cMpWx4/fXXsa4rXnnlFZfGtqLWziys74iAvIyCZYY0PUPwXh/v/vZAyrwnNik9w5kxHbZVsZVB6ZdTOh3Icy7um9MJN7e3uLtcAC7i5ckJeckSe1srLpvkp5dc9WJlv5QNCxh3YNze3g6bwIjmWR9eFp1wEcU0JRDVob+MmQ1MzP0DqIXDFC4KvHzpPDBHKwMsCql5KxiMpIyAAdDmlLekyq8yiKRjdSLgDhXgDaVu+igCMmMrjDNVlHLW8DdViBIBJQG8giBxsDlnpCz295wSiPSgJBASkoT3lAoqZ9CSkZO4ttEEprYTwClpewqjkqj/lTcULkhVQAelisQKpGpGQkZCBfGma4dQimUlSb3PbUwgz1hS7QoPKdhgCalofccFqXYjSWIVXABa6kc15A3CsXpGLy76Rd3fxBVUbTxkjmQdw5XkIEy2PUnEktlN51HlDafcLYuy3gVjypzQehBAqb6IXHjDaeaRjGDimjXO+Fj8PeXUxtYrzH6jNTAqJ5Fn+mcvy4KaIOCQ3L42YFiXTai7r5lZhHnw1NgYbdt51yfRk3ZNxkY5fE1JuI+Mh94nxw0AjXWXOpoRzIdReaV3dnCbvRNBPKcMgE2W2boShcpADetn1TpR9GwqIsKS5ARw0IrKBYnP4JsTKuSk8SeXDa8/vcPTZ2esr9+B8tMmX5gZmYAntye8+uQG/9f/9Sa86U2vir4BnoaNvyxalls3VwnMGUQriBbtNxtDO/ytqr6ygDnpHlFGrYSUTmIsTHpAMGSzvvBz0T0WktBnO08lLxKBgcp61szWgAaIUIuEPtscKKW0RDjACJK80cC+j8D9Us2bMCZr8Ptda9mw3r7iADDavgwDsFbek9vTbg1yBVC6jAQA2rbBCGtlmmyyub7QaoJqmP9e3y2ltDPQaq0tQoVZkgRZ8h4AyC0sajAher8QGITzWUEnSTRBprHvBEEAFyIQZWTOqJyxUga303qkeOaCjdEPM+WCct4GEGvrOAHtsMPGS73B+4Ey5YUP9Zu54Z/H+Owa//LusREpd6U2WpSuoeKZ0Lqm8N93TxROdp0tCD8Qs3rEZ/TvMC3P+tP6wntMrln1KBEouBb3fbN3Ycc+9ffZ8yOAif0XyxChW1vwPaeQ/tHNw1olq0u6XOSAn9bHY39slJpluNYqlrVSsHEFaj+8cD2t7jwPPB4larsTSz7304FlcSuD5W4N2IEN35y6dTcu0K3pKpjlXjRluVR3arzeROp+KLUDm1SpbeLvgENzhm93oMsdSE+FTzbHLmfw5Q64bJLa2FzMBFDOyHUF8wpeFqRlQeYMYkLGAsp26r2CHBbvCphAJSGTXq8xV129EaCKyuDMYEj6wUobNhQU3sBckQE92EzmTWJG0kOQJFwjNS+Z9Ye5ow3YiKAtgOUTB0CVAbaMNnJvygmJuwUuU0ZuXhMFYZC6dLFBLeFJs/Yy6/UMbOyHXQ6d0jpmXJrVsXs0LW1rQeFL93rA2qLZ6sDtnWynzCNZI8CYqcXzwOj+B0bvsn1v+9sAH/JGDRSYgcobkoxSSnj69OnOW2L3eSWIMabebXXeylDnnLPElcIlUsF1a+Frr702Vbasfp7Pds9J5yM+E5bVKxrDouwzIqLBOhtlnl1jfUIaomSKnpdxMdww6gAD6G6gndtBtKaoeRk1k9ODwcaBG/t9XVfUs3g0kTIoLVi3imW5wbLeIaUVTLmDjVqRCHjl9oRXX32CT/7kN+GVV17Bkhcwj/syXzbFsfF6iDcQns/nQYbbfT0cXfWPBtTV4AFoZjTAC6HT6dSNRwT0w/hsvuhhy2qU8YcdGw3rw5HXJby3JiXZ0N2MXRiTE/V758l9rhmK4zUz/SbqmqJITxIduPXo2xB5hD27hUs5gOING1RrlwvMKObVQ+chFaP3Mqkhb5wnolvQGgwcEJnRd51o/wz9JMDOt62FUFMP3fVAzPfxQ+jBYON0OrUKxMk/sxgBfQO3Z5R2bd/MvD8zI272nrnQ7Xuv+Md6eJoxPX9Sq010q8MsZru5618g68dMsY9129yeBUlb29PE7vqo9PKi9a+Dk9EiFS1l1j4vzMxzYp4Pn8rYxtH6vrkndRJudUO9VBS1VrWFlpMoRqXgF157rdV7vblByrrpkkXZa8KTCRVmGZec4JwytvOGu2ev47yJG/jucm6pi2dWzpdFVM5iravUrCKi2HcFRDy4ogx7rwaAZjEhVaZHpVXs9kRoe2nM00C8YdOUwwY05KAvGhh3IvdEEk8KEZArI/OGBQWA7gGoDK4F9XKHej6DtiJAV1+cANQMSkBagJwYqTJSqVJXPbkXpsCzeAOSejmAisxAZgY1pd2JPO5buoXdFhQUgDcQb2LNrMp8UUHESFwFkJO2HYTUBJABOu8aVtCWC5AZstuRQFyR1AvFUItrzf30ZgISVyQsUHiMqimpMxbHlAmJBSSY4DB4UVGbp0tAgbYlK0gtG9wE0DETK1OtG5i3to+F9EdqQqqDDnCSMCoyePkIqA2AWlYNPNeKWjYV8hJeQNTDjppQ198biNffmc1ABbCCTSJGSnIekAn1pPvfioVMQA4orZxkTOR4UjAXcClNYOsgCjR2wh+1ylqCvCwRglZE5oMqL2DJirRQctZ7Z6iqcrBrZS/MzZAjHhPL2NMMPWBkqhLW1Iw9fZ3H51/O59YHtTJKETAmIYgyB0tVkM4ScsG1diOFyWIzfujaYu1PM0pIuIfUIxGpJRVI68jvmNFCXBJJOVuVg/iqlrPVikqQ8N6cUEhCIysTsGTc3r4ivU+iiC2JsVDGKWecKCGDcXcmtYxXrEvGK6/c4pVXnuCV0y1WyqAKYEPjQ4+BBCizKtiprQdJFa9ho9TNGcY0LP2v17VMtstnSLlO/wJ0msAZHis30G0WeStbdIm5wm4UDZijwrxXVpe8DPffB2C83PdnqAE9scQ1HWxmCPDX+TrH+yIY9oYD6fu93hv3nzSgiKqyLtQR6CILPOwVMZ4wq68lphh1axf+ruX1suRBOSV3vlnf41MdgGp95o4h+CUHG97qM2xumUyu+yiCi1kIjJ+EHsX7gY4vj34jkr1GMVRoZk2abTiydlxr3/65ptzs0875mErbCMbMOJ3Wdt3YRkYpkTmEQWdTaY1pAKAkiocZOBmGm5vwYEPGlJpVmxmopY93A0FVBDwnt5isPsoIbaMeA8OcoZxbBikThmlZml611Ip1KbhsF9RSsW0FFyI8K5cWGrNtpfVprY8ne0jSmM/G/FkOKTKLHoCmFIqQ1vGoPTmCKLdFeq7NI1GaibJY701t1H5PXJC5iMJbdfx1I2bqVwPDvKX23ZKypB5eMzKJQlM3tV7VAqpSPmoFlwJSjxZlBnFGrgmZEzJXZA2XymAI+5K6iMdAEhWkBNk/wgVUgZRY9hbopj0GetY1BhhVmDK6IkiQE15FMRWlUjbxWWhB36dgyhzrIVZt7ehGcoZa9hooFy+J9zskkHiBuApQJIkbJ4hnAyRKa+IygERibgDPoIDNB1KQAq1FIum7RIyyXcBt/ti8IaTKqHwR0KdlWP3IDqiq3NempUJUheUxUK26b4m8p1g8Nts2hsXmJMq4T6QhoaiXxqfXdQVqEsCg+2KEVIEFgVFQy9ZCHLZCoG3DRqpstzEsqHxpii4cbzMn07quWNUww8zYzhcZBR2TJQr75nGkVq+UlqYomkIAiLGFC4P1YFfxQgi4KoWxbf1cEsoKVqrMYVMQZgchonlrgGd3dzjdnACVK5eL8Nb1BCxJvUYbRC6Ugi14L0gVzIVSS5tWStHkHpJgAs1jDmQQsnpkEyWcllNXZqqmMkoJuSVIKShlw6XW5pUqcF6nZcGW1CaSCLwueFM6qXdX13cCbvOCsi54dc24oYLXn1bItrQNT57c4pVXXsHt7S1uTyfkClAxXvJ4wEZRuSdYMSnAIPduc80ZZ9hAZDf4ynzwelOXv123AgCRL10/0/2jlwsul4szjsp4FJLQ2EJ9jx0ryDYrS/sbaPLQlGIACsrl45LzcBhjK8cp9kSEJeee7tldb+fXEPaRITGkEhgBQwQmnvx9pgvb/dHT5vs0Gq7tWT38skiASK3axm6IYTU4MGjiyRnBXQRzWoKO/35/lqeoZ/q+9LFMXR6O+vdD6MFgw6fq8/snZhZ7X4n4XbTUR7R3rQF+EOOgjovl4UzC2uI3THlvgl0zQ+nx8+za2fM84vXuxapMtYUJrcuuH5pVlMeF4q8zy+ugUlJqG0uFl0gYRwvS4dosdnZvs4Fy1c1SFWXbxKXWwEZCWvabs0zI5Swbfy2OtJ0vsi5iGVZXYEqaKcKNpfTHiqJg446Acrlrz+leFDwusJHsYDI0htf+OaUKUKt3Ywod3DFXUJJNoGaJBUsK28pVLBBkMMXGVYQPVesbid9lAEu+kWewWVkxThDIPiAsCxIRlrygbhu2pBvZ1pM8f3vWhEktshEZXHA5kyiEANa8qFKRkWgRdd/WanhJY8WCy9XAFzVvkNxKKq/EUwbOoCrWuArgWQsd0OdwkrAq25QO86CpElK8Z1bAA1FfA+Lp0fh76ht8yXAQ+lZ20r8T9W974Bu3T8mNlTVbypO2CrCXS2xOENBPvR6sUAQQwyAS5W4tbgpEZTCRbhYHqHYFih7JOgGw42/XyNa8VxCisUsyqTGYFbzQ/n7hNwtytn1+3kJKg3Iuxgz1N3keq3yyKTUTpaJ5AIJssjoAI3Ay5dqHT0WlJcq7rjx2GXK5XFD03AkfS976WMc/5ywhQ2rQ8tct69IUuJSSN5RfHasom2djHMd6Fnrl+7R50qkf6JbVAxTLl3pyT21tvACynnIi3CTCJxFw+8qTpiyfNGGJpWT34/9Y5IlRSqnV03vz/fzyafdNn4hW/qxGY7g5Ae4hUKbTJxDu7u7UWCGHJG7nyxCF0b2MqjyrGV4MLIotKqOqp7wZkaEGpGoe2NFzkNOpZ2hyXpMBnBB65IOtvU328bHEDaNCZOaZe/TKNT01rrdIDzGoez7lN4HPDMy2fuV5BUy9jo2XGxNLAtSzjbd7VtRVY12ILYrFtxEA5PwZuVZ0Ds/D/J4SNo8iyOyX+EhWx0ecjSq6jLyyH5X+GGt2jXnatfF5kVFHRGnXNGvPFcAy83rMAEpkNr5uL4LkZt4H/7eFS3XXZGe2m1q1rj1rhmYBQZ455cENasw7IvPBEkDUmJgtBM+ofL/He3PKyEvGaZUN38uyNFlfK6un48Z3sPSjhstZIoCUMnJaYAr0pnHR27bhcnuDV5+cWj/ZQUf+9Rio8UYG7JApWSvKiHWhky7eyn1eiBFVrRmQ+7t1yeYOwVun+jokgCQEQsIwTPj403B9WF+3agHA07tLq3dlQkVqL04ZWFbc3Ej4CWnWslKrRGmJMROpyp67xEBWz3AhDSmDnvch+jUW9XjUWpBKlb0dmpqQuIdTsjJdC7uysLAMiEA5nxW8ZJgVm1LWEBdZP1WzfzFXlIrmGUza3pRIHEvE4pExxuq8GioikcisXwxGkvhwTbkqdoACi45lmw/c3d5VN4hvl+DRJbKLUXQSWTYyUlDSrFOUwEliy2upmlq4K4fCFza310E2kooh4nEoUZ6HP4+fzmSKF7b2LmE/AhTtzA2viHW5kNoG587T9x518bBI3y8WugU0IL9txXQ13fBc2/rNkOd0gNJ5MQiN5/nnWb/E8FjfXzF02NruefxDlKJo+LLvzbpq1yQas3H5Po/Kmregz+R7u7/yDlx5+WQKVZTDzCzhG+0+3SOAKnyJ0c5DMF6pd4KWjFeWvqdg24p4BrKERloqVCb1Dj6iE8Q9yPAACRhBt9+X5Oe+l7FLzuLDJfNs7K33ANTRpKmZXapir4NZNrD15jSsY1tr/gw1o6jY++MNWn2TRYL0Z874hNdBTIfyfKIpzmVsY5y3Xr+z+XcNdMzIy2h7rgd6tiZsHPz6KGq0K7WigFEwrl/hF5poRR4mBj/92K/phs6JLXHSdgCcWnp75oK6leYV9aF3GpswGACHcXhgP70w2LgGEmaDc02R9/fEsmaCJ14/+y6WEQWH1SeWO9vzEX+777nXaGTO/d7IqM06YMzCW3iA8dRNUslmjDg+r12XqIGNWT/F+nuGFK1qM1DZ2gcV8CQIGoQeLw81iJHYfNPSAVDRGGgi0jhekqxEOWMZFowKMACJGDnVdmKsATL7+6GM4Y2mvHQvQpzTFgaRWorI0boHQGLGa0UtEiok42EhdfIMcgqJgUKuyrAKQ6IZNJ0sAc82KWehcWzb+DrlgplBiZEXAEtBTQsu/BS1ApwYeV1A+YS0bThvF1y2C84VoALQIukladGwuKSbgEHIi3g5LEyMlbEXVBDrRnHd3ImU1DLVQVJKJHtDEoNzAdMFlTakVcqTY7kSKidsTFgqIDH3spG7EmHDhkstKFtXdtY1Y00Zd5c7lEtRwZ6wLFmebkqHKvwgQlrMw0C4FODiQ0FplQ0sMuCobGGPnrckYBbOtFNwNL58q0jEOC22fi7izaMFnLamGHBK4FJReEOpBNCioYwZtWbxujyOZXIvnzfyCrhfT/76GF7ry6E2r8cNps3rpMCDmUN4iVsPYAkLSuI1sXJ2hipmBRqubAWnoJhYRUNAAq/1bcrJJRmgbr1PJOdHmOIxKHMToDHwH9d32yb7Yix7YGtPHdVP49kRbPi+98+x766BSbPcxvEy+N6eQaNMb3I6de9MmxesMfKJhrAgP1ZJZZx9l9RgMow5ZJk7Z86jIO99GVPj192ry5q+dgxo5Cz75+IeD3Z7HSwUyzIqen3EP8NOqWZm3NzcDAq+vdu9/j3qZB4sGdUygpsZrzCF3hTj2WHFbc4GL9rMIO3n2LQMR3GO+X4nGsOQDGSMRtUeOs/MOG9bixQwY8XueRh5iPCmkRe2vg3zWvb5TACb4x+1zjN65pwlY6J7ThsDBT7P04WNHgw2nj17NiifY2PyviEYB8oUIw8EjLxbaXZvZFaeZg21Z3nm4q050YrUTosNB96IMrJOn/siJMy1L0ars+x/yLi5udnV4+7ubmAsVl/V6KftbtdRGpi9vZtVzMCNFwxmOfHl2Pu6ru1eYzwtywtvGgcPXOpF4/yhlhMnLPKCnBIyJTx79kzSQpYqbWHo5uEeVqQNlnCSlJDzCbe3S2MwOWdcLhcp6zF5Nmhp1mu0+Su/Gd+7yYuCsq7wNOa1bWCqKJsyZ9cnbN4IJqQquqkYwZOEzRABOYHSIpubdcpyFUt/6yGS/xIIrCFPtpdAPOGyt6EigxcASwWq+BeIWTaHLwuWSwZnYSFpXYF8g7TcgpYbIEv2lwrb+Lyo9VBc55ta/sV2TGAWLw9Vna9g3ShNWLR9xJbylVAogzPh9KZPkvjeopvaNcHAWfu/VvEzlFJRLgXbViUNJANUGJeyIZ0rSmYgk4ZiSEjGogBZOwWleKVK3ym1/iR9ZztEkDooZ/U42KD0fRrS/rEMoYRF9l2ABSxRRiZCkcAEJE46/qz7XwiyDZbF22JzEhk1L1Nl9GWR52tGUdG372YhLR5kdMV/zNAiZXTVsZerZcsDBplkazJaW6Mi5y3zAIZwD1/vCHy0gLbJ3b43BcaML2ZYGsox2es9nQrewV716HWIz7bvtm0D0+itlnbVMUQiFOqVxjhmUcn3/TUaXbrPsBmyCIA3GkX5YZ/1vATTJWqV1NGoZgsQngGiZoCBemskZWjqezrsOdjT41glQqfTqvqMeMQNFESvQLdp91Acv7eDCBLil/teoz7upSudLClaLHytlA1b2VA2SyKQGtCwv4H7daS4ZiIoMRLQ17MH1toTRFTWdMgmBzZT0NWr2NKPW7i51onmhojnKcmRv9h3hD6nPQCL5TXDgQMaPXSq9/tl28Qj3vpBZM4493viC61dn6P2bNMryXv/Ugtx6+tPDJ8xMYCF9Vp72r3OjTLTxR8qUx4MNuzAt34gXWek3h3qH+yVUs/sTMGN9w/ojPepcD3jt0Zax0ThMBMQceL4zotgIwKoGcN+CPVnCSMY0fAYXmZWalPoo/u6PZfmVsBWtm3enSwwK9MWgAeMBibGeo8C3/cXGFjzqU30bdtQk8Tgt3niFIBaGUy2qHo5ZplnItRi5x2o9d8Cce1Nn5VyRqoVeVmw1PpoYtHT6SQbqL0XSH9jJIAqLqZcKAOww8gYwIXtIC0IELPNztzZS2LZI1Dr6MJkdY+L7qqWeSZwSo1Jm+KrIgnsDrzzmgUnsZzUJICCc8WiOjEzI9WKtKxIm5wqnpeMvKzI6wkpr+CU5cUVTAmJMhJJ5ipmPS+jhZGR7LOoAEGVB9JNpZY+F9D5IEABSc7owKLx90WzBul8YWbdKF9BeZUDApPucYAcLGh7kc6XTdV3xsbyOkEswT5FqzF+tr5mAUWjoknInBvjF76hlkDJHQBmxuWydUGSSJUkSMihZsSSwGaSZ5DUZwOwQazeltgWUH2KVdAsp7bBU8Y8CQBifrDL+6NBgwI+oVnIEDBabccQ3n6f8DFTVPq+iKoeReFjEio3yiG0zyklXC4XnO2wU3u+U5TIlFXeKzCRRzeDSBvz0Qpt90SZZfWZWVljv0TFaCcvXb1mMtciZqJSYeV4sOGfMzMUxjCSoV8SgZbclSIXdoJYP/W+mpU+L7mBab+HYAOQwGBVspEyeJPUEtY2hqyxfHO6Ok5S98ezTtZ1aXNTQvXK1KMvorH/nZIksFgWASqAbPTOWFXh5BZGYzoHc99z1A2LcvZSTPlqr6rnGLVDF8vobfGKNaustnLaHAHa96ZbsGQGQaksB8EzDffVWptcpAQxRhkAawaFfX1jWNY1ULGL5Gh8Zq+Pxu0B3pA7ixqx+9o41tKAL7k0t72uKudMhsP0ITXWwWT6eEClhXCTWL5cO0qPNGiyzfU/3F6u6gwHOrdmuvTz6MFg43w+D8zdp6f1ICOCjVipmWdkNglmDNgPbhz8a0BjNnm88u6BjbeOG3OcAYtrqPi+AbDF7fvIkKr1ybIsDdR516WvB5FZQffPNZK42xFc+D7298XF4CdafK73viRVYPNpaUxm02wkCzNAp92mbwZLbDOgjDOMFwgEy/5FTbm04PXKrMJCYm0zM5aq2ZgemI74jaa05LaPwbIeDYwNCWUrzVKQqvMoVEZhOUHaYuzbvaLly/ioMsoMFAfcrN8aJqXOhEDCrIVbWEpdmX+VJU2igUaCdHlFlfMgEgOZsKSlKduZK/JSkbeMTTfoy74Lgs8oRRL7BDSGKN4U9uc+kGbUYe035ZrSLywWS1awWgFmatYb1ty9TAKqTEFPTOBUOyixRyVJySueioLtfAZvRT05Fdgqapa5uORVDx+XuZhy0j1FQgZo2uZzHYtyJSTHhxSct7OE2eSEjIyUWOsqseNJz5tJnJsXpHdNalnHTJiKgGbZTIjU3F4tm1ULOXkcSpQ3ENnfnjwfioaXeI8pMiaqLfW3DzWUsdBkSS19OMHvq5qNGdGoIMQ2ALKkOAIj6gB1Jg+uGax8G70y6c8wsHJj6k9oAg8fguutzgY07N6cemKOpmzWiuKUJlmmtOv7KHe9Yc+3w//df99blwc+p56+Ns56V7O0knjIpU3ioQWpvpGSslarv3hkbR1YeFZO2duGez1VnhR+PAfFeoPHmH62W9S9cmrXAhj0tKZTqKFD+H9tqZLbmCn/9alkuXYvm4RzdaPz3bNnra4+8sHv2WjgU5XXNm9tjDXqIZEaUlzacbCEhg5rFEC5bF2fTBJCulVJZNN0lOBdjP3k+8q3YWa8btcFvdLI8w8/dnEd+n7atvE8Lcmcp3xLX4k0kXaRtdmABlT2QUIJ4ebCEEUkODPwlTK0g4iGkH3fpmQRJhMewGDno7+fHgw22mRR8q6W+8KobJJbxePLyvIMy4Mauy52RAQUnunF+kam5+tin314kb92Jgxng+Kvn05QiLVktPDAfe7WH3NnXrfq3Y8oL5dLU1SvLa4I7mYAz1/rP5tHBAngG8s2JbnfwYzcPBkVGxXdb2EhYBp2BzRBbpbBpjDZ/g8mS14qmSWKxUVX9YwnLMsJWQ9iehQkMWGqsLvsHoMls5+twOxy5kMsU0kFvPSRjQ3QWAyPc2d4fPue2t+sbnEikixkVesAsRox3CnAyuzl1a3DOQMrclMImBNKrsiZkIvlBHcCjPW08JQaU+NawUmusfpQAx3U6mzzsFQ5sbtZsNQTYQ0TBfqi7nN1aRjWAQGsZ4AA4CweBkBCuUg9BsVAnIZbMATUVCrgtcrZGxAlnRLa3GZW8FtZYvprT49ak/EUqaOBxKrXsK5v6a4sbmoAWlNRgBM376S0s6pVS+eBltfWlfOfdTLPRh/Ra/Pmo01RMfXv9nk0yux5avS2Sna0a9lkqq7FqJDpnJkARC1crYTSz9bTlgGMYbLG9S1Ljh0Dn30t9vqye06UiTOw4fvLPnvDnT3Xl+HlclXebAoJ0Ovuy7aUo1Z+BExRTvgxmdVxpoDBeZP8SFEa94sMnqAg5z0QIYhHz+9JGeaA8oMOaEh57x48PhYxEimeIWGHWja+4iI8TJc4nU7Dd0SEJ0+e4PWzheFJFg/zbgBoimVy95VKcv5I7XOqQ0C0lP3AODcBDPWKeyWGeeeUbzEYdJ2xbW4PCRW8XuKvj14G2+Noz7HrvTHCG5etnnd3d8NekOTKmSneNj7Rc2Lf28nq5s1ooWTo89jAntdnmRhJ9SOVYM6zQZoUhSTBil/fWieoUcr0CenHLCm9a4UBzmfPnk3567qeANY9xS5LlSg2IfT9Hnow2PCD6hmYn1wzVAegVS4quL7M2Ej7266/JnxmdYzAKJJ/tmf2szjiGbO9DxX7Z3gwZEpnZLr+GaPw5CmzHyzcV9plbkTvyvO/39cf8bt4nX2fcwYnSI7thHZ4GyD6ttXDFNzk+phMxpNYpZoItxWk1mlrLqlUNwFhGzHZykgvNI3fUOIi8ZdgtSIT1BKHtieYM6GS21Ba0TYHi2E79TJgqqSmd2IDCuIeTSl4dIhbf7Z5U826TapbyWFprPs6Kovwld+cskWMSoxFrYNydoC2gW3fgihdRcELcQVXiQdHXhyTLxKXXantAWeW8yQkdyJgp6KRjXVloCbY3jWuyjhVeZCTYzYwiijobIqHtdHPqw2SYwvSN5prSzxptZ2p0dZ5KcC2iSVOAQPV2uLlwXoOR5UDDpP2s0xhPZROa9mBdNWTyRlL1jYkSX4gV9thilkBYuqqKgugZwUaduCieTC1UtpW2WPTAJSeYdKAyiMiL/gjRe+0p2gsAqBhc3sFV2TBuFfPnm3XJx2PKOgJhGVZu5Jla9LJQMtC5UNYxACjTj15Koi6rJG9TDBrgHzvFDhW5USNl6Py5HipGQgABodQplmkgO8T4t4P7Zp7ZMs1mTD7zsucCKJMSWrGJVJfK9FgQW3Gx4lSaWVbXTMl1OTAlvahH2ffN0NdFITlUOfHQrNw8jFDYSf7e6anmRG3A1RVW10YkPBGyUJmzyASOS9eZpszVY1Wc9Dm5yiAYX3Hvt0bG/qek5Ya3+1xtGuaMYXNAClhYFZne2/r5EpETaxDbJNvS7bMg+63GNnT+23c4zx4MxzAI0Dlf6+TpbkldHlo6YQ7H6dWl1bHeLaOXWomp7bWRRbKnIAY4DC2Y9Ana8j6qeetJQfUnkcfkZZmHerduDYQnpHb5D6fz41R+D0fVk7sfLsfwLCpJoIEI7+Q/O+DBSRMfl+et/5fAzX+mvviiKOlxzNOOxDPyC8OoLs85fr5AEru/XFTlbXByIRUrHNcWGO889gfvi1+XHyZhSuebh9GThlLlo3uALBdBP1ul0sLv6ObW6zrCeu66oFPZpnSEJsK2RgNSeGW1CJtC1BCfXqM4+Y2q+3CCV4ikUbUMxnQILGI576BU06a3kQRtL5V4NQUDrVENgVDlRphLgSuYsnIac/EqQluqccFG4jlHpiA1/KFx1GL98/t5HrBQJwAWoCFE06qzDJLuYW5AZdUzQ9SdBO5MMfMGUyyR4NtH5F6tHLrJwUxBA3fEqAm0XMFvPVsOxJalGQDfNmQkuzFADOgCiOp8DFviAyFHjCoHJvByNiwEAMLoXBPQwxIal3SvTdm6Uu1opzP2sf9uraPIyVUZpxZrHOovkyTEiIk1yU1hw6RAY0KroRND7xb00k9NFVOcteYXXleQUNhTbCqACosVl4u2u4s10+suY+FZjzTgwOvONl3npe3uGdInLr3Esu5M3tjkZcJo0JcUYskE5CwkWXgncSj/PCWZaOaroeiigHGTuOGhBVy0fNV5J9sN9JwqZTbgXgMbmEXaYAn2CkEvk616mZpVw8v26TfssxH/VtSmFNb81FJvKaw+f4dwriqk1ttnddWvidrhw8BMs+mKXzJjADU93N4mcUYy2UeN9F72WaZmmze5CvRCy+DKnelnxLARQ6hrKx7klpCGG7GCGHzpJ4q9bRD05CTnSmiCjyz2wNSG9joa5Chu2HcWIf9Fr7CNg+oWViH7+WjW+PuegOAZpxse06SbfxHB8QOHFsomMkI6y9LuBABh69H3JtR3TqK+qMZHOw7/x71TgrPjeFZjdqZUAquNMnBYH5gX37t+/VS0tBYMSamHJJSGDTZ7UHibhw24esGceBlpfa9MArMWyj7GwI2jGmhW3YkL7V8dykbijGHJMiMVXlImt+aWEI0tlpwKZrmtXRX6XJaAZaFlGAgRgaicmn5ni3rQHSdGVMaUxACfTmkhpR7KM+oVFfm5kpuzFPbnpW51VLapjXSxbQDOF64EYGW3GyKrM+QyunJ3iTsgBma61tc2pvPR54z5OwAsSxwlc3EBKBS0uw9Cdju5Dn6fHaKnHa21AG93SmnYaNyexdNS++y9gKkWaSWLUu6RA2lEiUygdKKZc0otnmsVBRsODM16/S6rM067xVxU8rEpavoHgxsm5zQTZCUniTW9SXPAeDLoIVE6W0ylSxLinkSGFQvoFqQuLrMqtTH1QnF3i+SVamyWZ8UoLk0gXDzsYUNgLGQAAs7UbZUtyksy3nfVYFM62swiCoSFTBJn2e1wNveCeKKQoyUgQsUYKTOtEiQo/xZJaY+p4xEXYiJ5UbPvCB1KfvGW1YtzSol/SThMgLgqyjZBjS0PIszrurNSOCW2anFKaPidl1Qc8ZlO8NYEaDekVqA7YK8riJ8y5jHvfW5VVXbVOvdvXNExkg9gaxraVigauEt6iJnUcrIvF0JAvLqhlI2nWM2lgBzT4crG2UZNZmguLdqHzXyYSH+cLxolbO+jtkFY+KR29tbvF6eYtv66dnmWU8ptXN57G/LAOgVhagIMGtq70J9g6daE7dtk0P0rB2lDAfDLS4rlgcAzSKdRmVkXdYBGFg9fX/NjEdWH+sr//KJXEopqBrLbv1X0dOamlIvqS41S87lgnVZseRlJ29bKK3ru2iB9yAm59xCSGqtbQ+RkY19DP2y+vt9CwCGc1LMwvrs7q5d77NIeuXRj4mdzu7nQQSIj4HafjoAAKNywVYunRfl7JZ1D5q0ZBQNdFZGrSQJJFLfOE5u/kgKVG7ZiwzECIP1a7LrOssV2RvnAbDXtdq1cPqKB5A5Y1WjmjdENKChRjNFP02GAWY8S4NSHSNl/Lo3AO73ncyM1R7ER6Dh59o14/Wuj3Jfy/LuPNoVCqjqAKqqaeKV0JIfpuQST7iN9qbRtWfLDGEyMDMa7mMdB91WB8uDKEmv/nx6ONho+bjRQITFYxdnNVlYPRFq5WNg2LxrDPpyubRGmWBYTxIbhnN3AQ5KmPYXVx47gLmlbe0x/T3jiHkUiPwu/UX3se7LqmGisTEjXTSlFCRmScNnkxdo8djsmNo4IGMOZpBtXDPG24EBkMCl4LydkbNmGMi5PbOh5MulKRbIC5AT+HwBquQdtw1xPveyWX1kfGRTMml6UT+5jMzSFcOxEiesWFr2ibL1NLpLIlAmbKReq1JxKRfZHK1lreuqhwDmtuBr89wkSdFnjKoyqBZkghy65id7sFa8TCrlMvYdEWrtgsyY0LJI+kBvGfTXADwcpqSyAmZYITBQCy5328AIBwavQiGTbCSv2wXbdml7RCxb2LokvP7sDktlSKxT6sy7bjBmtGoYVtXwKjnhXOq1JMIGlqwnZGkKJXwFAMCiLC0a8lZqUasumjef7Og+nX+JFARV8YSxMWJKatVn1HSBWISAlBboaS7gRCi8gRgol4tj3ADXgrKZV5GwpL7Pone49jEXgPUsEl0rKhWlWY4XARJOVst5EDA2D+ydiEDVJU9IupGxKVu6ZssGsB0YWAEu6qVJoFrU21EFzMI2p7TO1JN9RZhVsn0/j4OG9a4V84peDION/eivBzqwoiRdZQLVQgeNonLQyzKBbs9D58kTJcHKiPU3y2dN43VD/anXwys6M8+Bv/daCHPrA/T5Fcux9T7UJ408w+QlwYX/ejV2MgbxN19ebL/xpbY27D7TE6y+KQH1+gG9w3iYUUd+GNobFcPZWPiXX7OPRZ4AfZ5b3X0GLv/9NfDn+8AUVsAZQqknf5GLREH1lngyA6MbC1OAzbvuAamVP+vjwcPlyNdxAJIOIFid/LkfszLbM3UO8ESJ9gaPWqtEXTCPG+Ndv7Z1pIcWR70xgpcZ77f5779jFyKWNIkLsxhouVZwkZA1G8vCmpSESNi9yuzEEl1ge9esL1ufxnnVsq+afBnnU+snVsO0t4e1Ox+e4fDBYMNPMm8xucbYjLkZI7VGABiUZXsZ+JC80J3JzJiwxbt7oSQDPAqswVpDptWg1aVCLZ5XGNAojPoEt9+9RaiVGRbbi/Qv0A2PTFLe+XzGsiyaaYOQdVHYPaNFX8JP1mVxKHYcFx9+EJmx37Tk7/OnikcrkNXN/rZ+8td5QUdE01PAI6PfWQTq9kL9+bLoDD3FG0Cz/rRY8gVpJdCyNqZfHLiQixbQIpZPy8gh4yZnnWyXi4QA5azejdpCDblyP7l00+wdKYE4gZBBpxWnFeZsF8YKYLswLlhB+QQitSAqoOAlo1bGVjZ8ON2It4Aq2F5s4U6yke2i+0+I5NyPrchBgMvNDZiACyDKGHerZ1oyFsv9DlvL1i+6YV2tahVo2XIIhLW+qYURNM8bC8MlKqBcUeiZgli17C8npFXTY0K7/tmHgeLmmHB/VCLcAThvmxw6ua5q3aHugqm1GSiqnnVRWIBAUY+OWG2TeHZSxmlduwe2qrcoVbQkOARQuhWQBQBIoCwGla0WbJzA+SSAj4FLZWxnwpIki1ZKCaVK/4MzaF2GEJyXTdHSDIxW2Cjw4m8xxIFZgIWcRVAb8JDpsD9kzsdVAz5Dlb1DjAGT59vWDK88MNBCURIlcI65973RS7xNtvYtpWhelq78UUIlF2LE3A1G8nCrWMNSN+ve29D6huQgQbuv1trW6ShHuM1XrfagSPnx83J9phzOFEGTAz4pBrt2CLzby7aoKHbPDnSN8NSC7WWcl+kza3uUOY+FTAGOstL3v3l64rqw372XibFh2yyVbtJziEzB1lAqlgykUpAmCdnpANJf2a1Jv7aiB2AWbhMBgrUrggzf/hiOFPU3m4/XQLHvp/vAj9XFH8xn6ygCHQ9OfFsiTedkRgv9svVeNvGW1iJgAwVyNICBQDBqSuLZqBJVYM8upQ5GgsawXL1k+B42x21eeMNIVWM2WhKf59MLgQ3/+dpgxnv8dZ45DxYO7q5dcMKy7rNbDc9ycj4KnzjxunKtVhtfDpvaNa+zryPQF48t3Og2jhaGEbgEq0p43r7zrIouq1atmtrvyqZ2RcR+Q11v6n4B+v651n8RpETw51MyxvpGT0h8vu+/54EN8N7a9BjBR+UeZqjVbmtajKzULAHsXjbgrX+pQnQN9egRnGWBmsdHzm7QPQ4BOJcqB/fUrez6XjwSjuGSWHcLyWZyK4sogZKkYKW0iODRo/hs06soTGYt7bHlXFlCsGqVEI7UkyU0QQntI6tXm7POEu/ms1loSD0mKyszUJdgWwqu7ynlFm7SylcgZuUu6wrO+43DkXe1uqCvYRtDCcHse3BkzDuwTwo0jB8R2bPM0ODWprhWBnnQlDK2Dcg9GQA3aySDc+8/7hNsmGePgaLy4fmnVxjtN29Y8mDFeKCP77fvjVIWy6zJgL7nK8P6JCo+KSXtU7HiEom3LauXfFkIKVWkVFBow3YRLyAnDXsb2gFICKyuj3oBWwpsYdpIyrsJsn4S27xqHQQ4S2Lk15lGxdTvq8xZsjJkZ2zyJ9Z7kGXrM6Uke70CL95ZZqfyFm2sTN5bf8heEN1HUOehJ7FsLzctxMfX3ZTt+/SSuJ59+b5t9vtjIa8U29z2h8P5731Ehpe/Xq5CIywulw21UgMbXYFWT7LXVaiPr3lfjSxs1Vvt/bhFOX+f5d9AjMwjAND9KQPIqG2etjqQBJ50+wUDGpJNeR3GM4KC6G3wxtj4spL92EQQaOV4QOH5WNR1yDpY5QcX1nOjan+1+GLhASYLeltFU5BkJRXsoyISWmpctLewTjQsPuq7ANSAOYatWujUi6yTB4MN74K1jp9NFt/5UWBYOYZ+4+STQ+FIsrMEgeOfV7YOTuzefmDT3soide+KyLUOimEoDQA5iswpWsiiBW0Ulk6YQQRLBaZ9aae7jgJ3rghZPawvzdLg+9Yvllg3+352AKP9bvGtEaz4FHz+BHTP8OJYeotArbW5hX09Y1KARBDLHEbg9NjCqKJw9P0ZlaOZYOtj0wFyv0aVS1LgLL/I3h1Ux3wIPSREhTpbv2k923fQujFqsYw6HYTb3iROvd4EqAuXmyZUSa1Y7gFWipyBwOBKzcpsl4mHUjbxdiWFwrvVGK1NJjQ57cFtFABeCZ157ogIeV3BdX6WwzUlZjbGXPv8n4H1qOTM5u2158DNI//MAZFgFHK7Mh4JeX7g+YeR5wGxn4e2D2XaHr+eWcdftu/qDj68sgHLc58JpW5jMoqUAO4nAjc5B2paCFHPoAR0L4CfF3d3TwdetzsTw3mZY2rJOH86r5dQsGi1ba0lz0t6p3j+xEBTLI18li9PcQy8JwXATna2+wAs6wqCWGFlL1TVs3T2hqr2HIsjdfWzdpZSgDwaAf3vMeJgHO9930Y+8TLJ5vJ+TVe0tPNlDzaY+8HBQ7hcM5YWMGv0pR9L7nqeyQwKc2LgYw5g+O+jUu0zaM30mMaLcxq+F+C8P8TQ1q5VS+ZMD4XsfTfOg/g58vdrfNvKqcHLEnXC2XPvH1uIEamyhuGzhg67PSj2/NZyaFhx1wc4juPuGb7fZC1Rv2ior39Vrvs15Yt7IL0w2PAWbE/Rim2hNP47Pwn9QPpG2r1x8P3gmVXPD7YprNUdGOPdeL4e7fNk0scFE599DaXGdu7L8L3lrDRkm2LJCmmx7EvOuL29cWMg2YL64lejbkP51wWxZ8oD46ExbM0rZRHszcYqIv9ryly0bPkFHxVD6zMfemVpJa8t3McCNgAM8+bexX8vWWrZUWHknMVao8oNGYhu2b3MYi6AA0A7cyVS8n269YQNPi5T0nWmZqFvwsrWhbloLeMPqVfDAIFfK7QHyYC6fWn0Etrn3nb77EE7D2vcKxC+j32on79uWANpBdjCO0WA93GwuesB/3wMGRiMCj09sI0B6Xq1/vQKcf9MZPsIxnnvXdbCw9zfrn27vniO0PtoU7T6eQMEMOHV2AtyT/G7vv7cfSFUwXee1GG02ItnQzL49Ofs62H3NHnnnm/XjS8xsPhzA2IfREXoeX1hSqHMrZGvx5ChxqOJALdnMYKNmXIZ+ziOwUzmGqAy+cPuXkqkMef7FTUAjSCDrZ99mFl7rimErs/B3A94c98PMtuP1aMCG7bHTmSCnyPeyCj95UOSe7rlpCGGOSckBcmmONt42FhZ8pp+7pjj87A+EuOX/R1pGDu9ZsaD/fX28pv7rW0W5uWvjXzcnhcVY7jvfZmzZ8/4iJE3bPv1PPZLf/l1d42IqJ/JY4YCTY9ufETCfd2eGpXxKcmG8JalC+JdX6gbKQHle9z5n/Sbhr/5ygw69ug99n1jcme23u+jB4MNUxBnyrdXSI1mSqfd4yfJkNpOJ0a0zEUFRZKyjB0irp5x8kfXXqyLvyY+x4d7zdx+A4N2ircpzEAHTfpUZ1rrFmQAw2QZ6rdmvEJPwjPIDmh2LxM0plSgTRxfnm+Htxx6T5WNhYESuy6ie1+Of8V+ikAFQGMmtmfDZ35g5jbX4ph0BRDD2MRwt5dNUVmOSsm16+1aa9tsbBL85lnetd02VzbmqSdf7xQDOOULaOdHtHk9YeRtXeo9iQhkmSg0Rr2C5aBA1+aUkkY57bOB2PhFr0/sl1kfAd2o4fvMyH/v74tzS14aMoIi6TgVMJEGfJAuNq+suMq15W3gwZfb69W9Vf58lMhLSJW9guC6pp7ooa0ns/ZaVUKYFKlAkax/vFPoXhb5+RSVbADtez9fIkWQeU34e/Bn4X57i2R/t/4XsJGRsw+RkOuGujtgEOs3U4AADPzVFJhrcd9RqfK/eeopUvdGpKhk1Vr72nXjweDpOvXlXnu+7xe7xntuIp8fyiI1Uri+J/SQOdZ1Z3UVZUz7V9NsJx1AtvsUcGiT9FwBd6guS/ip8FUZ3GLPeERgA1CjnvaT9aM/cA7YA0KbX/5QvPV0QlpWPYV8Fk4EGJ/o8lxClzwY87KYgpLdvqcO+Jsh1H7T/22t+ZdlHotgysuIqMg3sOjmu1EtZeCRg7fPeS7iied2rdefSq1gd25cJH+/6U1eLsUxavWxwxV1o7YBjUwkB8m6VMSJICHJKbkkRd7QsV+7DEh0gY2Brb+mkspzvdfG+iPnvN9gT8Fw8QB6MNg4nU47xdArQ+YGjoJzaHD4bJ3vw2tgjCRMwIEx8Qh0jFkDdSjf6tvv74OekqQt6YfA7DNzeCbvrU0zC5Q90wMXXx5oVANae/RvU8aGwVkWrOs6nDhJFNWJMTYZzKAygjU/DjOg6D0NAHaLetYnnkyw2RwwJcG/evV4+tmu8WdmjNY9OSfBe9asThav+xioBTe5xUkaf0/2L9SVVSACXR2stZ9JY+OTc0ahra9BALbJufUHRgthxT50SCvVLFqMMelDcfGZFv4BAAWaSYyoCT5jis17wCGloc3BWlvs+QDCnHLQeACcANJyGiBh17fcU2D6ZBR+vnmg4es0A1+SBw0oYJQWqmWntybN106G1Fx5SSAJyXb2vHUQP+MrngdZm30IotVRVb++5pLVwfFLB+xgCpdrr/QhdeG+nwkvjSKY9GM1G6N476BQ1KoesnF+df5sgFnCBeV3OH6IForixyvnhLzcjAanOobw1qqbOJUEpKDVwZOBSVOovNLox/50OrXsONELdK1vWnII9L10wwtoh35BemTXVzXAUbGy9j6f8a6ZccD+Nv5lXhzpq4LN0rmG+y3Zg6WmH0ChAxyo7E5p1zEz8KFjZGEpADRRRlfGvJcLlfv+lSr3PhpUDuAma0rWIuFSdZMEICcGiBZJW58XXC5nbCDNVsTSrDWD0grQgsoJT88b8vn/aF8T8kp6vgXAvDVgIcBP5JZ4tYX/FtZ00OhrdkmSMY+reMUZcgaTDYVQN7Yy274jSbErFvrU5ma5bNhKQdl66NSaF5xCeug4HysMdFL30Km87V4uiEEbZliVsa7MWNcFImCkrVut4M14iuzTIizIWbKIdmO316t6FEvOSwsJMxKZrrxIQ51Pm626BOQkfZekjzYAhcTIVGiRkCbbs5KAlcRwlXPGkhbV5/y61jpV7n1QVYBlB+qqC1ETVI606EbwuqEWiXowfTqxAJyUHi5RXiiMyqOlaJnwiv/MFe6/n6Hf9lmtic8TNPH+rqDu4wajtaVTnXpSfJuisLD6zwCU/zuWda3+7TugpZ7110WhIQ9BU9IiACAitdiMIORa/Gysz0yoPU+R96BzVkbsm9h3/tprXpREDKZ5v88A0MukJtSU6RkTVN4NLm4+GleMQj7MK2A/HyTtaWg3O+uRKXDB4yd/oAlYwmgtMmUpjlcxOQ80pbcdi27PI0nBl2qSdqtyftk2UTCS5E5fUg+Rg2bTAGoXSBDFiK2PmNvp2z37hqbQdTzAg47Ir6I125P8Nj90KSp8Nt9mBhMCkG5upmvKf4480D/Hvtulqw1rJaUkmwfdOrDDQgY+nR62jj+aFNc5MAIO/9s1LzkQvUKlAQRMcqR0hc2sji50JD6jHykKQM6HqXY+C417BKX7NeWwea5a8ntuY9hBn3h3zWjgDUnR2xG9Pffx5LvzXZsLOefdvkhKCUmNGLMQLu2kwVgg6Tc7iI982b/8nj5vuPL1sDaWNo6kWcP8aexjfH/npQq+q6agdvMhrq9duzCCyMWHEoU6XuMRL4uikbPXz2YnmqeHzFsAiDEorpVasS5jRENfZ30tXNNjBiNG0Klav6d+doc915O/1xsYmkWdNzVO6YhbI5udRz1giZqVvqo8NKXZW/g90GArs9XLknqQ05FIMg86LzurnGboPiPehxn1fhiTH9l7WxvoRkVqon80LJkBq8lDyno+lo2rGg6ynJWypOhFGeWe9yh6z4uvI+nE8Z6RlBISE8pEP/by6iH0QsdkzjwY9ymY1nC/AXgmZO9b5J4Jx+d4evJEwo22rTOpcSKP5kixhFg406i0RyF4n7Li9xhEhdkYekoJW5lvTo2ffbBDbKNn4MSksfOjsk9E4LvzoJj4vvSgw+oTN9pHhd673n09/GcfDuefMetHf6K83Wvzyme38p6jZZEsZb6cWL9HQU7ZHwAResjeTHmIgkEYC6FWNHd3rXrqMLmzGJjVepKa1VBOCicQZCN2hVmiuuIDEsGUVOBLCEbfV2AMy/62DefNna7tYcu9rnuokEYhlFMWr93WhV2lZKdvICVhbrCj99iPr9+PAe0PyQ4kXdgtkDE8Y6Z0XAtRMaED91uPV8Zw/X2CWNqTZLN54HfxvY/dxJhgc0M9YjO+2J7tBLuEweyVcxBQ8fgAhzcCAXsDwjXjVLy+C/2eAGHWTubuRRPF2Htf58YPdtZzU+24CXKZMoSElJ380IxVvo5+vhBEUYheWkthbfLVPBVzhWYvG7wnOILj3dwiWfuUAqjjXp6s1+tx2VEu+r6z8rxs6UCSJI27W2NWz1IZwLgR2BToPhd6n3q9QD8Ma3G25gHs5Iz/Pa7tl00jyHDzcyKHiXpIGCJAVB4X5bvdZ+R1ofh97Jf4uw/59fWK5fnPVg9LxIC66XhTM2Z5mQr4OWO2155GnlnrwmhHCMx46FCepo1v/IBSOx/OvBe2T5hqxS47U+vrhg+0fPl7eH6YWtSibLo8M8Bh1xa2PrQCus6Xkx16OAmXDrpSXG9+7BpfCJEoiakfYA1Mx/ch9GCwEU/sBDC4/o3B+X0AMwBi5JmEZ2bM44LwzLKVFQRnf5eYaD8BOsNtukQn2gsvAFNGfY1h+7/jZz94ZB4X93hfncYg1bczU1gRru+iizS0RcIsqkjO6T2ewRuztTNOvODwYxJP0oz18v3sgeiM4vcRoJmA2I+tMh4C0BTfaTNfKtk8nc2bMeRvH6bmP9fLNoyJ7W9Zl6Vt+CayzcjOqzgoHdjVBdC+ZFZPmuVb720YPYu2Dq4ohXoAWGIb23HPxbIsQCJcyiaJEJLM0z7Go7UUuK7QzxinWPXQ7pvNHW/F9N8PY9Zbjqa0RiGh7ug299RzQ93kphZxljladf+KWaTkEa1/l6WvYW55jTX0inKLU/eKVCTS7309Y1sZjFK7MvAYyPZRULczujGVbytXDE4//WyWcDUIanc7oIG9jAA8L7G115VYWyumLJD+JmdKuvAs+71dL9fl5skY14+ljo3nm/Rn9XW/rms7KXnbNgkjqeZBqQ18tjb5SSUPa38yVNHSTmOGAotef9+W1heEZjBpITWen3djq1bhunyMcqSfOk4tBa/Je8uUJak/gYTaPK5eiZrxVgvD4WFMOuCwevi6XTModmXu8dAYlUBu7sk3HoQkghiaZFAHDyIzwLXifBaP+LUw5wjkZnSf/uPlXNTf/L1e4fXGIOLq1ic1/labwLdnm+wSwxyzT31ry8MzEJN9+nvjOdXfACLdSK/n0kj/lw424ppwZUukwfj9NfBq7U85oVYIiFEDGmUH2ifz13Qmb6DIbg0MAp0DcLS/lc/68ci6lqyfGAI2qqt/lLEPBRwPBhvn87mlP22x3S6ft1ltb25udhM4drDvMGm7C0sIg+g/txholxnHx0VbCNVMCa6qJEQrkbdoRVDj6xC9Gl5B9HRNme7bd4xZ97hCvVPf3GRlbtYIr3T4dlkRCbKZKCVZJFbnmVXMtzVafAdF0n32ityMadiE9x6J+0K3Yh+aV8O+9+9iaTBJKKCDNbr4oadXfrQowcVHqitYJD21cyfafG//qm7sZjm8zo1TBPkEL2xHoc76HO89sWt9qEYTQE4QrzenXhZ3S6QvX8JTroQWwgwF2r4kaZ2fPXsGJughe3ItESEvfayjUmL0PA+ZeHLieQa0u35zG/riOrB3mV5mte5WQBPubUw07APcs0XJAXJJU3cW1IsJBmXYjJ2wkCQJ4+FSYjToCsBWx9CalBJyWH/slNiU9CA4jJlT0pKR1qWHXDwG8sxP53LfMyGbInNSb6nL0kIaVkFJ+J0cEKOZIhctUw+MXNLS9iwVVWDWlXA6rVjXBaVccLmEDbZJ46ah8zVpWlutF0GemdfUFHEDvNbfWym4K6WFGuaUdSpwE+CoG25OC25Osh/rfL6A6wWlasIQLiA+I3FFLRvuXn+Ky+UCyypERLicz21+3N7ejv2bs5zJ1MAcsJxOSHRCTSvOFwJV3Ty8LsjrgpwJ9cKom4ZvgXDKJ9zqYZJkhxwaS2NCsfNtAFzuzthSQsrdq78si4Z6uJANZqTKQC2a6rOCKiNrH4rqbOHUCojQQWQFtTAsLwOp7V/r4L6P7Tj5mKtmZzoN/MRvEn4sNMxP+aB6B7f1PMplAEigJQtfMr5IIi/Pl/PAM6Pcted1cDjWYwB5oOlBoV3vCmGeoSxfXmunhgcZPxc+W5qst3XKFHUgjBuZ9ZqxbV0Z93NEQA6BKQGZW5lRV2Lmtl9xNk4cwLgHhb0e/SIiwkJpMOia4UU/4VIKtkuXq9K0fmbOEMLOAHyIp0ZDZIy6GrA3StlvHeRIeBmj69pRxl4DUjN6MNh49uyZWF7cBjNjrnd3d7hcLkP4UFY38W4iTRpllFKSTfHPOeXWLxI/qSlYNryXJYINQELWUxoFs5HvxNnCsHL9QooKuQGw1tZh1nXBH3Cw/dwsd6SWJ5CIPVKlz64Z9HACTjc3YGZcLpfdmRee6dvvFubm2+fHy4eyeaaWUsLpdGqT0PrFlLsIzuzdb470/eo9ZEAPzVqWBbUUXC7nHZB5kcn+0SIPmn2mGrE2equn/q7+LLZTvEy4una1fq+jULSDeDrjcBZtVw8bZxunnHNzw+/AJfdn2joWpY939TIliyDzobgx5yobCqtOYjavMPeN7frgtv+EyNQKuLqo6qGMuucjvw6Ao7UO2KfnHuZ66s804dlACKgBRoCRUlahP85T638O4xbrEZ89M2L4vy1RQEppyIlPpAysS81BMDZeyM7LjMdB3lpth4q1kNRQy+aZcmE+TQZR39KcUkZaRk9Z608e+3bbtqa02di1vtO6xLNubD527OnXcs+uZ/VblgXLoDSZ17jg8vSM6oS+hUwlsnkoh66x7RNp9XPKlzMoeB7eeLaT0xWyz6FwwbJtEu+e5NTyrICjKp8o+ry8ZDBXVOwNAU3euX4jIt2snMc1Visq7SMF+lygptj6+dtPMxQl2UI1fbiYf21XjBMzivXwPMIU6cdIjT8aAgv9KIc6y146SglZPcuAtXFiKLoiQ2fhZb7vmVVmUeq6zISv+flxn05o6+TJkycqH6itA+FnfT+X6DEM80LkDCwL7zJ0Wfnyt3or7bkwO+7Il8Xo0ZMtFP3OXzcbl85f5LoO4q7eJv3sdEeRPbLvEYDzvsyf3WRW7Vk5S5lEAAz1G2Wh/y7KxlrF2OFTKsd191B6MNiwVH327pVWH3rjz3Gw34fOuVLB9n1UygPFBdPBxD4O3ns9YK5kItdpgOWhHhaQ++zbOPMSGMVFREQ7Rb/dowJlp7T1RrqOce3Uv0149jtsgssrMkyvePgwKn/qau/bsa8j6PL9OmP4szhsT/H+2TO94thDq8ogaHz9HhPNmGn87IWxf/cMQ862QBtiNqW7StadbuklAHYfhpfcQS3Mp1Y9jbVU1EWUCUsFeblsAzjv7tWk2TOA7XInGUxILEvCiCxDVUJeMmqRFNTm3anmGk49FIvRMwJJ2+XFlRpgFjNcbzspSmGGlF323saWKSMw0qhQzN7HtNnd6uXXWJ+bNodTW1Nyr2bLUjAdx9/GEEADflIG3PX7jefDK8ynpGBD+nm/Ju5b3y+TIviJ4ZPAfpwsjCiU1O7JOTUFe/ecwLO9AWXwyHL3Bdj8x+CB0gTGk35ta0fniQ8NtWeKjLQzoerwfa21yQZ5Vh1+63KWUKtk67G+Iw2hbdg45yEzWWUGpYq6FRSQrEfIBvDqvJ5V3RY5yaZTqtQO9ZuOi/tsHuhZXyP01wCY0UNKrV/s1GL30B1fjWNcyxje4d+jQnlNKba6PDaKfdr6vtVdQti9RtD6ADOI0cnrO55nRrARw1vtupR6+bMx8mvC61J2jV8nOWcs66l5TEQm2N7aZgUDkFC5DvONoBb8Upt3HoCcqA2geZmdzmrqpKV6720I65kh3tZMkvrWXevrPoZm9p4fZIH57vS7WjZJXOGMZ23gIEAKrIZtmvAe5mbYA3MLwb02x+Ohs36M7RrT5Usp4FqG+tr1Lwo4Hgw2TBm6u7trClPv4NSs5Gah8BuwPCPxjeFh0HWwE4GwP23XbzS78IaceEgNKlalMbexZxpyGM7Yplq2IV2gHxRfL8/siQhvetOb2jVjGMSoTHuA8ezZM9Ram9fHh6P5PvEol0iyIiSQnXskfeHi+UlDOMwCDQAb93SgpggZ+fb5MfQeg1r7Po7IcLy3IefcPBt+0lomFD+x7fp4OGQkO4PD5hsz4+7uDkA/f8OXuz9V9HFQrNN1Rr1P02qgm8zjwdzAqCjZar2orEYrp6Cys3zJL6C8ImcGXYqANgCJASCJlZ4IW61ICU1gcWOSYoUWvC5aP6XUwhra3M/jerUwslKKKjDSHiQREhuXYc4QASnZPhO0Z8d3AwbWByn1teOfb317H0P0QCSe0dMUTpC4pvX2lqNIQULjbQC4ivFiWU+ObzBSMqDRBeC2FaTEkLBgD+YlJEjkqe8f6sqko0SpKWKxbYPbm10jHgENc53HZB7gPqZm4ALgzxUbqPPafRy+f94M2BtPMgXXTrLuHmnzbrgkBJgof7XunhE3VZp3sWwbMvY8wte5AxEeUp/b76UUlEsHG1yBdUlNyfTyiLV+XIrs56MCPZKjyzAob9c9VTlnrDnDQtJ83QblZaeExAxHHVDNvvdy0usMzBLO5QqGt67avbPx9gDGX+v7L8rtWOZDFaiPJjVFtVraa/P0kfKNqin0IWDSvFvcTZHAXrH07/F58d3rSt6w0+rHc6U0fufL8N/LXFCdhbsBBpTgE5yIR80MWPYMANCkOb4duCiPhO65G/U2ZgXeuhZSyqgVSHkM17e9MJYta9D5/JkaiXv9paGtztAQPjbkA8i6dB4jUB8rsQsk0OJkLY192PpWP4sO3rEo90e1HpF2Sv/ZGFlKXylTNsHbkRJxNfixe6j+9cKpb/3k8IjO79+4XC549uxZ+23KpNzE9MCDgLZJblRGPJKTeFIL1+lhJR1FG8XJ7CdIrXsrx6zzmrCfeGx8G+K9NiAmQMkFCMRF2fqh9vCQdh3QFMBYV6+I+PMPIs3GYMaUo0IcxyB+P2uH9ZUPLzGw4TfrRsXQK0e+/GTKrTJQY0DSR6MQeSwUvW1GpnTYmF0TeJIGds/gwQ5464FuQ3/F58E8BxTSXe73GjWATmhWFv9sD+Cb4FMyZa1Uz6xGsAEwEquVtY5WyDj+Vua+X8Y1a+3w30UeEMH/jCdcyrYbL1PU4JdUItmDAlv3EgRn9WW2E1wxtCmuETNy7Hibq7M13wMaq4avo79mCuaVgdDMMfCIqPeDnMlCPBoTEmg3jsZRzcPjl1LrT5J7k5NhohSMyRlMoUiEBkIEjHfPhMQtxvrKFzE0mJmH/vZAfM3jvPWf7WyIWsUDdj6fsW3bYAAqpWA7X1r9a60ol86n13WVlNIp6UZxAJt0BteKoo1IWUJtBGjk1sdLyljyAsuId3XMVNFt89g12K/JKNOjrPW/E9EuQoKAdoDZTIb56w24+LGIxs2hDRNw8ZjkSeQdXDQEubrEFtavBjZsXGoPP/Jlzdq7A6nhcxyz5wGyGRh8nn4BdMOPePDVm5j6aeYiQ7s3X+7phigRky7aoq0rNVSVoudVGL+Q527NM25GIICNryoIErm97oyHjZ+kvgaawQ7qTXGKPPtQXSd3iAiV9BDcVjMa5G7rXwvvFGbpgl1qG/82TgY4G/IQY92o03V9TWR/Adetres4ljP95j56oRPEgX3mIbPSm1DwmzFfffVVnE6nVrmomPtKN1RJhGWyn6Nfr8Kodgu3MVthUnsFWv7uKM7IW2ajUhcVEp/iNgKUGDrTXNJOmbB0iCYe/XWAd7GX5uGw/iQFEG5dtHpJBqqk5Vtc93yBz5hF7Gdv8YsUGYRdbwrerL/sWd6zEcMCfNkzIWHfeY8JKAkjrRV4RN6NbGPllN6Krjg1pdFxSQOUjUisJ5WvbMaeKCj3kV+THfz3cAUQDSkCZ0LZvHLtN30ZFQP81JlaK4ur7MWqcpJrpoysrGcOIPqasM9+E+MM9M6MDLPrrvVltFRFZd6e4VN/R35RReINY2l8Mpbb0jyGtvvvCnXw2OqTxhBG/5ycM/KpJ+7wXtvHRoPSOeFRUKV8mNthrgBqxDXPwyYW+x2g1LChCP7Mi2z9ZfWitA+HsrVr+5Z8PcyD5+daA746/rbfcV1XXC5nXJ6+1p49nLdRSjM2Mcu+uvP5rJu2+8bPWpUHkkUXLCiXs+1uQt3M85cAVbhkP4WcdVMkGTLSkrGsEnqRkIAi24BK3WQjPK3TfPyDouW9G9yvazzErVGvxA5rHvt1540EgISxxL4fQtDyfp3bdc2rFEKbZ8Ytb2R5LNT7Ci0dsZ2pI/W3PUwEUG3GBZSCRA48EE95qZGfv79YiuVHPcnz7t5OtP1HM/3A60mN5zryf/cxRZNDpdRePrPur1OefBHwnhfdC8I+eicjJzXWLQRvTPPgyTwZvf/2uqKkze31zENUgISC+b5q8z+Pqf+lwQw0z7m0+XK+ND1xJsfa+lOPpvG4VgiP7pC+lxJDGS9q5H0w2Hjy5El7QERZftH6EJxZqJDRDNXOJv9cWQYAUcS90PZI1wOia4pG62RcD+uJSjQRDSe72vdWRmSkds9WLuA6hn5JzHwPZfHMWA6pKbiYomjPkQ/Tfot1niml/u9Zv9i9fjyNYoiaB1Rx0hH1jeDWz94yHsf4mlIYhY9dH9v/WMDGbFED4zyK88S+822tqiD4/o9K6wzgIjCAphCowtOei255ATNK7el1vRt3PG+iM9laigg0e44JgJwa2DIQxczYapEQMGYsacFNGgUcJwZT30gK9H0NspndAQ5GOzgx9vs1QDzjMZ5hFo3z9QJwNhf9eTJRaEL7tJS6G59Yt6jgxOv89024Qqy7fi14RZ3QwchwXo5Z2h7JGgFGy+kMbPnrGi+fGFH8XreNWaIoAt8fyvDjGqzdUTDbbxbq1sYsrK/K41qWoiuKO91+sMhzv8YrT5fLRWK3VSkmIpzPZ1wuF/iT4u0968nBBmK2sjUDgIAg1r1XutIry2ZvlZ8MUbZqKpLIvzAsyx8XQqWCvCwtA07ks9YX1fWxHxvjXYn26y6Ov/WBn9f++pRSP0jU9fEgz9y+Ki+Xmidosnl4BjZ83R8rSX94g6oCZIKefSR8eK8kwlnYez/YPLI1Er3MNj/9nl3rt3hWkJdpPuLFrzVmHsKw/TNyGWWc/TYrP+oH67q255lH8HYRMObHtumNREAaebqd5ZRSwsrcTiWX/mOcz8+G9vg+k8ssHKun7416ppczOUUDSdq1cyZr/G9WB2ZGXpZdeKNfc3GOe/01ykVAEl94/unH1YOh59GDwYYfRN+AKEjte29JiEzcaCZ8o7IWw5OI0LLDRuTLDKQ0Ws17J08adUUBmdUtfhfbMAdFfXKUrQDs6+QUJPV4eOssqZV0Vw/aA47deFD32vjvY918G+K42KTyCu4s1OnaIoj9Fj1iVvZs/syEWpxDUYF4LIqU7xNgDziiUPOfI/OUthH6fgJ1nDJkP4d6+LrGxaqU9NSPRMCSl+E5piAx2jQSq3AFNpujpPXVzdKlFHBSBbbVu3sYE6N5baRdOj76dy0Fmx68xBCrrO8zImpnVngBJwKEwassfNJQF3AaDrUaygl97L10s/4HVPFzYMPGy8gz3ui1G9bXxODg74+AIl43Kqc9zr0pTe5eCfsZrUyJsVsX5iGIa/RlUl/j1ndOuaNxnZvlz8LBTL5UloxFtn+5ZAZ4BBTeOu55ULSyxv7pgrjszxlq4K0O68GIIRmnTKERGditttt2GeahBxzb5YJNlToiwuVykZS3qu6YqYAZLfPTsqwSIpkXVD0Qj3XtyYuQ9XPfR6IpS1kM4agazkEEJp2PpQBZUjsLe5F1bfLL+sMUMeub3qdjBiF5LrdWDH3GI+izDbsDMKH9Gh/ksVRvWGu7iAH3jMgDYl0eC/l+kKaq120AoDpnk+wloyQp8HvaW+qgsc750TXdZhoyFOT/LFoh6hy+fA9Y4rVmcZelVXWfBUsbYCoQtfAhkTPyD5qchC3xwVZwqRK2VPQ7n90NIKCiefYbtbqYXK4t8+NWi3rrBbBLyt02/Xp7FXwY60LtMk76w4yvtYdN2bx24Mb016v6Gwzj+DkiNEYRod1rvNc1eHiGrd+4Pu7Tj59HL+TZ8GjR0KExfgA7xXTbNrz++uttcnrrtlXSQExf4NIJ1W0Ajo3M7jl+ohMxas2hvL0ybUQ0pq301qeZBd7ofO4pWA1Q+TSyte4tB6QMelkWDUc56QAsWBe1KGSZNjLBZFNuWlRc0FiX2pSIqkKsu/FXzes+I1+vcSLuAYkPJYgC2/pn5krz18+YkvdweCY2AyxWD6C7V68997GQb2tUZHx/eFAVlR4iOdhN+st9z4FFVHEPG3NCCAEgkLNsA3YwEcPAhjzLvFC1OkCQgYLurcypINn5BkDbVMfMbVNpiYqjkk+rWSuDJxN0xrz6fMuolNr+iFZu8FrEMQCgp0XPjQr+eh/3PeMdflxnY2fhZSvt92jM5oHVP/aBhWvVOsahW39fA1W1jBb61qeYqXcvl+R8TlEKmMeUpQQCKXAQcgYliOJQWUNiau0bx1NWtVr/DLwK6HIkHqxpZHzVSIDGnf0KMrca0OUVMShj2BAutiKz6sq9to7KtuGT33Tj5FafkyZDLHy2h42qSsE9c1olU8jkZSmZzTu5JjmvgBSspaWnUC7s946lZsRIDKQK5CwgIhm4UmV/N5O8UWPJ4tlzyr1EcekJ65ogobi11V5ufGrtKY79uik8Gq5MYWWWELHnbUi6phh5I8/MGPAYaARYmrkNowW61qob/J2sTkmS7hjgMOU3KK0zQ5iX29dASQR0MSTR19+eY397fWsAeFaegZcqG91z0pS+Ol9qLSBKesgn98P2SmkZnrhWnLdzU5yb/BtATVf2G7HIVm8sN5Bn7XWXaiP1s+fxhL7Xtm2c09+TAkD293u+1wueyaH2jd5iG9MNSLRwO0VBFO43vVK/1KI6Q2F9bx7KYPjy9XkIPRhsREUoCuyoXEa30Sym2mj8jYFahvLjfWYtMaUgKnJRoLMq+rFPdoAg3BsXlb17b8sM2fuXkRy4R8NvtlGo1trQceVx4WUNS9m1BwJgYlwzEWErGxKNC9nTtb71v3swECc6MGYZiWXM3Gr+eQ9FxJ5BRW9VbNtjAR5xPni6pmj6v4fwD65NUWST+d28C9g8APr+iaEfdL641HUyd7j/akxHFRnUAlSz4kh8qSghFVwLGO4wpSaA0BgtqdWJtG5LSgAyuC5qaZIsKpXMmwBI7Iu9mp1GwkQWAhUglQpKVS1bDEJFIsZWe1a1KADH9T8HGJ2viRAXj5F8Zk69b0BgEGoluF2YsqbRs3NBe1ceTUjtGIz4fBXkziosewUyQHIuUCa3oVmFrKVdBQhqmO6HSOpcMeWbiSQAv0qfSzUexzqZ7qFzfCExNSEth9mlllDBKzZcGcT9gDEr5z6gAYjhYllXLBrSSiQJRzZN9WgGJ2a3bw5o4ay+LAMZXnGzUFkr53Q6tfW9NWsza9jSuG/DNoVLmxIyLcgpD7xFrLQVtTDKwihbxcKXwbOWk1i2E4kxYV1XCd3NuYVNWkKHuhXUVCQ8MQOgClqvhUe4OeSUmGVZUCzNKPY82cCUB8VNZ8A+Q5RZkruiq+1yFnHrawB6EOPIY71O4hWleBbUYOR5YFjIR4tiWxLUoFr2qZMzp5ad0k7ANndASgnICbzNZT6wN6j476wO3kDr+zNGs/iwrNl4GEWPSC0XbFtBKbbHRuZWWhakzEiscqIWIDEIGUnBRgtD3DawruVNz+eStrj+4r6hHADSkpuuBYhMraVggxjpLBSrsBnFZzqn4vKBR/B+bg9zEq2fWyXbnfZ/0JVY/iMx2bT6MhHUuiFtnKS5tXrIPg2zJShA8fqUXldceTM5+lBg/mCwYVZlY55GVoE4oUxBnCm8MwHTFYK6b7S7VoROt17FUCof6uOf5ftnBhys3vYeF4RvR0y3GkFHdNkyM2hdQDzGDUbXY61y0q2VYQICaRzMWmtT5mb9e7lcBsHrycZlRrM2e5o9a1TY+r0+dOVFAcEMADaLuxvvOH6PgQZFiMcMTva7ef2s7pbut1sDJYTGGIGfwwz0swBIMuzIb6wYZAxFEkBx6cICEn7hBa/ls2eWUIpkfVzQgMWaEgpDTid188QOXxLGLWd1JGWgBGDVFJoLCLhs4HpWC5xYoLatomIB5VXix7UsMHA66YnZmQRsUAHlgpwYSTdtXOp4poH1vxeOfp54fmP9bd8tWYJNCAnESXqLs/VaC3esxbw3DM4JWdNPpwRQruByhgCNpHtYJJnBIHBIx1gqNfCtWisupeB0knzzLQmE47FtPrC5v6kpFmzzoQkihuXOZjxsDb7R1OVGxrp2C7710UL7zcTe49BDxGrjg7X2zbDAqJRuta8t8xz5pBVRcfIA1daxKcWG17yRjINyllLCejqJ8qOe70YGtHWjqCg+8rkBBd2U2lNJdUObgKMKLnbYmJxtAyrdIgk9ZJCBzKZQSUhmyhLeVLbuNbF5pnAWAEk2KgUlMY26t2AznOykBGS0/ZrWHwYYAYi1vfbIBQHTo9JmFm1P0cjkx4eZW5hQ76tell//cfztOw7z67GQB0EpCW9KKaFiG8abVB7knCXJjqYx9+sG0P4P8hrYGxJ9qnkPDqP312SZnxMz8vLQezV8O5kZl/NdixRpe0RcrBKr1R669pVja/jihlouqEUBh+PzrOBi5DVoYZi3Cs6sV2qtKJskZaBUYInPQUVAtgfLA+BwSn2VKAG/56RBCJ6AEIHdA573wEKuGft0GEdmbJet7ctpacODju4peil2el/b+7U3Dr0IPRhsyFkH+4lvQiOiU6ArnHZtjNGLjRPXoKTB80zeL6bYUPu9n++x7Cyd/nmeSqntYCTftob6sLdi+TrHyRKBhJ9gF64oqhRtl0tboN76lXNuKdns/k1PeyWQpN0MzyDmhqSslheXEtGPl32exZxf66cIMOLimI3LYJ13492YyeUyHY9ZWe1++QKsjLJWPcQM8wXysijmw4+CEeh9NGPMrb/V4jJa27pF3ZRMcwuXUlC2eu+4XBvnGdOYKeiFt4HJzkC5fCYQ7U8d9mVvzYpbsNUKThWMDRUkSVZUkV5qBnEFagKXhJoJS7bzRRjpdDMAJ+trv4HRz8WoVPp72jXm5YHtffGffeiNnnmTLFNVBuU5APegeebxmq3VaBmMlqn75rw34FDKzUr9WNZJU/zdQXzbdukKC/VTggE0UEHq+m8hnlIYAMKl1EGx8uvMLNmmaIqVf2l9G7NCeSEtxDBVIEG81HYgpllJrU5mKEor4aL8zpIdWP/b3Ouv/QGwOa2tPDlYT54pKSortrq1e2uFnKXj4OTlUrBVIKsnxNKFysFpK7ZLabIu6T8DNCllLHnFXS3YyhlEN1jXBWbdbXJAOqn1EaWEjPHsrVortnPBuq6azU3i7b3BJav3x89xG0siagpPnPv+XfNitfH3gCHyKMsOZroC0EPr4vUvmwYecCVWzPiFJQ0wr4bMzdr4KRFaqlW7DxgVX3ufAa5oPAObd2AMPb+mf83kjte7AGA7n1G2TZOQ6JpgSZtu+4pQKzJJ0GQCLG4R9XKR16bJElj2sTEDtVRcNESxj7VAFWjUSSJqAK6B4Wp7j1Sm5doy1nk5OCOvn8x0K6m6D8liAGnngJ6Ou13j9D+RMWUX8TLz9Fod4suPCWDhka4uV3S859ELnSA+E3weoXoFyv6+b+LFxrbBxjgJfSNjw3w9rl1jilkky6Djy4pIdSbgPZjwAsNnXohgA1VCqSz+0ICVCd0GAKjXuQMKq8d8IzrpAhFFSRaV9clsgsXFHZWjCBD8mPq6+fGPjGNGdn/0DM3qFsdUPwgjgFtn2FtuXiZ59/y1OT/zvvm+LaUAbAkPxhDD8XyMvRvbTubuYy+hPJ5i3eJvfg77ehd09zic0uctmH2Oess7C6jYSlN6QGJtLoVRahEgQWJlKoUb4yxbBpcNKAt4TeCFUBdCzqL0ZYweIevP2Tz2zD2GCXgPB2koFDMArirHSK3IkmVPvFEVtYr1bFkyQAsWyMb1aEmNfezPmzGaAVJ/zwzwR4Y/5ZfEapQYDWYvk5ale+SkPcZPxHpZIWEig+IPVQ284WTJyCSewbpd2inqzVAzAXjWf2XbUChYx2mWOQbtvZk+iFqOA9J619rlQWobQVnXCSn7koVR0fn7nv92sDkz7rDyhtoSQUjP5DVL2G27jptXxVLomnK9aGY6m2PmeTjRCTkTTnnBQgklKb/lgvP5LqwpAxsKBFCboibj1g8jFOtABhEPewp82+PZGoPnCBpyGfba2DrJE4UoyqT7eF6UhY+JouHTNky3jdPYy3q29R7mFgpaeK6VaRR1C88jo5y3e8VLMvI6vz5n+so13gV0jxa5M0SS3OQs7LrmbK+RGh7F4CabwquF69WKUp0+VuQwzGJ1VjlGlLpekhckSDKOXs/a1qXszxKAgpQk/ErXushkN8+IxKucXEITPQ1dTVga+jq6Mhid3w1kghVqjLSnet1No8xkf2cARegyoDr9tDhZZLomiWWx1WG2Lu5bV5EeDDbG9u4X5mzS2vdeMb0GBtp9QF9Mk8mNKw2PyppXiPVuACMDKnXMNOKfN0OrVubTp0+7xUaVvPP53Bi7fefBxs2S28Z2K//Z6YS8LHj11VexLAtubm66pYUkK4gJTJlE+0GNwIiIcK6jt8b3j2+TV868UPfl2ucIEGeMPB78KEpmV76tDEuxOgMo9jl6tGbfewXxsQmIawLOt2NMKyvUmHwLZ0CYy7XxG5lvW7MsMbNyGNcfPJZ9H8XQvh3Y4DKAC++tvAasjMn7tjNLQoPLRTbPlsoAaSYcc5VXYDsX8FKRmLEBWFIG50VjdKkrpi78ZOZFLaXszm+Jc253JoUqcmWTNIjSHyICpF/0zBTd3FwqQBsATljIWcWI2tkcfixvbm6coj1XcGx++/XmFQFTFh/C7B9XsGEP52Q31+x7IhLLpLbbwgzFUgk31iNfyNwz+HlvRq0V2/+fuj/bkiTHsUXBDVJUzaOqh7Xu//9iP9y+VRluqkKiH4ANghAxD8/TfcqtmWlhbqoycACBjZEp2b4K2Dr/NcTDwAgVUURo46acylVB53UcazwXMNCTksFNZnAejM4CPCpMIQ8FfmIOQGeGDnYoGbBognPAXBSzfLc43PD5fIZn/efPn9s6xH557Ces5/kOmsbae5D9fCkRC+1pfYUuibCS3l7Ipa5D5fcY10TuDIJPHRu/u5NR/7RX6vO/Q6t7X50frXLIy/AjAlNmp4X9bEYKVegYaLrLXv7OmIvr8isjbsxpkdXVAHWvMF/DcigrBIA0oE3z0wng5zoxfMkVqxireRYtV+ON4cnhkxgtJa5b1UX39sCe1UyI4DzPFCooKZfBr/MStV26J3ebZ7ZFpEiMbk2Sh7eKMRF/tGIlrE+IYMu1u1v/5mtJJmDrv97ENWsikL7krMDyfjc5EXOW+V+W9WbwsH4jMDnXLa8f1+132r91gjgbJ6lavGvLsa93Wu/dxldVqxdewPH6zQv3e9aAx8aAVlvKRgY9eZNtguKLvlUAnP9dQXFeBH6+uSphLm5aOTdg0gQHjh0kWYcvzwR2Re44ehD+ncJ09zk3+h1zyXOU353BTv2pTKbSSqWBu/nLwPEcI6xadfy79f/Pt7tx1X+zz5VGAILMVQKyoSol63nmuRBkwbKUdQQdVMvSHaO/A165X9UsnoVTHhefxz1AumfS7BgT82WxsBZGJYAMs42qeTamK1PP2XACOLvgPBoeEV7W0aWHJTuPrbYqRGu/sxCCx8zqtPj2MdTOB5m0Ngl0WrWTx3GgH16aFYCOaQnrj2VN5/P57m0+cVXk6x7iHqtr8Ts0HyCFSxeW+T/fYh1wv2aZdmJfFJ5iAnwJUR5MlffRVzybAKXSzrI58voFoADcKhto9zH+FXit9xsYpLKxDFSrNGcTSZVw7Cf2dJQuXTKtScd5fto+c4XCzo4BNO43zyETi02Hs+/GmHi/T/R+hAfy/T7RukA8RPl2fMBlD9bxK2qi9lrjZRS54T1VRuAqZ/ievL9z+2r98/e5v/n3d2mhmKtGUQCLeFBAlxGWcx0yRTX2Pf+eTkCZxwBXpaO1FkaiaqipfWuyy/UMbLe9dbM2+d/cD1ZYrGHKdMCsmDowVezMCzGPpsAPk53m0TjHad60OdbPGPj8fIexaPNySurHnDjP7IURryBKWrO915qFCVaMsvBOVVjvZSnXyUIu28qjSQtG/0JrFuL4fr+vyoYWwy8E8rg/AJd9ylE3NXSOa8rnttYiUubCK7/AC1+1f+ucDRJE/skdrIMiE82gkJb7eoDMEigTr9dru2efsHuiX5P2lbXPtLXtWbpbDHNYFD9jy//OpTSz4pU3b7XW9G5VQcKFPwbOMdDc+sZT2BlLKt0WPE5gj1HEaMD4+TqPksDpnUVhm5U0Bq5nFt55nAHMxtgERRWqfE8Ox8mgOicbboL8hk7CAwAAKcny3wFd/5PtLmQwb9wKHLNSmmlx3QM/5bR6BRPAind5gnSU3LTyiMdxeC7Qyk/g/whiIA4amiCqLcmqsiQCtGGlBlk1beiEuLWJTJbhMLpSk63eDssLCtD6gcezA/KGzrdf0wxEDa9fPhRDJt7O9JsoegMOsdLXlohegeHOf/i5ln1OI0hrLcAe44NNFivGZGEuVzSmzbntAfFcrwlpD/ewaOzROa97rlp7R1Gc8xi4P+48jVkJsXd9XQI6ABhoYJOqL/6xVg07F0UQN3tb170ANhkCePUl3AOcyiNp7IlH+7V2qvCukLeWQtpS3/JezqAjjyn3bx//CjNa3ofp9O9VZbCA5CrZibBs27alXOs4zwkZitEaeuQ4mFW2d4IciXm0sqGM7dcIcbRTlr0K0KOj3wDS4EOCCLMImZfGabymGlKusf16I3NFEedE2DNwmdNNvpU996XRpKx7Xa+vrv1TjfkCS6Y6H1fdFDDAeNeUAa8MEPMXM0E+lWRM5ZGc23xgcsY2VUFrhefufb9GI2zGgujWCgf+aB0qijlTLvCYUJmQ1tEgOFpf3p0xcXqooClk7r04rTLV58+fWMo5O+b/UUQuUC6LC1YYTMoGKf1ub1flDZqrbJVCB9iPTXg8PqyoikhYhgReNIFho8oQunlRNuL9jgm71uJIu1JBL3724P7KO2G5afu6/UoB/ar9fulbj00G3VnKH7jGbO4gDrgJ0GAlKul2OrwWtui0EpgATiDuk2aM8/n8EYyD1pxxZgtw2xabgweA3g93r5PAyXCvYOQ8B86Rktg8Sc/iAmmB8dJkLohEBMfjYcSkCnGil9Yxx8DxeNpijonX+71tlnMqFAcgcx1gpgI9rW78qYCcwyrc9I73643//nzhcAXk+fFhmy0O66Hw9JKdk5bCqwcg/2ar1uxalaUqe5nQFl2sZ5zvN6bPK93k3gN7TrPEwx9//WWlHH1tND1n6sSYwGsMAGscc5wYr0+01tGaVUqRsOp/n5aBRWWsWzztTavrs9ZmWjw0BNCsrOyeBE1u0fgbnNNk5VWWCgUIDcx6shivwJLy2C8RQcfD97aH+U0vciRqpxsIoLKYrfJnrn8Ddqrq4zDGOocpIzy+QMXi9QU27jmAIYLzLXi3hqMJOsGfwqvl7nObgWym1Tyn2RgwxrCOUOAM82iMoV5e1hSwzEeaCMZpfWsw7xPPM6CycQei2adQBApAynu1CuRKK/kddy3e6UjtuygabEGDrV8q2jQgwnfC+JQE+Do5+8CjLzFGFYLrGsLYw3iysiGJt60zkgZGWqfer4VNpMy7iABtFSmhIGf58cu+V1o17YC/UDbeXoFpKgYmxjhineep8KJxTr8AvBxn4P5u4UkDijEsIb25kQtYllRV6yu6n8HQrJZPpaUAJm9sxh/2QdW4EjEci8igPIf8fRkfG5ocQQP+jx306LL+MjxL0vV5H8d+77ul/iuDG8cWNCh3Rs3v0yYE5xS8T7WkfxpLxNduKkSmpfiLeblaB1pDKJkAABEc2vCeiTaojASPAkImdIE0uAwZaU/Z/HZ/xunKD5zVLL7k1zkQ7r1hwEs2z4HxNu/2mGbsaefAEwP6fOJ9vnHqaXmCHi45YeGAn3jjwQqL0wD4bBPosD0Bp7fxwt9//w05jZ+QLhUNMqzy5+le7aGK/8f/8/+5cp6mH8LsY+wN6G3i6A3z/QK6oOlhGHf6eT8AWM8QUMgEph9Ai6A3u0q146MfmDIxf77dqLoMFqxAyLUZ44SMk1Y9dLFSzywsIeKyrh2Y5/D9ck/PHVZI4BwnoIgzsgyjyVJcAIjeG5L/V9rv52wEWrCKF8vH4DZMao5UHMQtQvzdZEteC2HvFiYTJmLWy8fTX+fMR3d3ezDXG0sLFyxbjO+EslkHZrjbCOr4fG4UA3fqWucOGjF1E0TTAbVIx+zTVC1qsOrnF4jbiptbbuDWCExMnCbopicTjWGVFZ5POyVWOuCHXTF0Q3pzG/VSrtaS7RaI/Dt/n+cna8AXbT09KxgUn5WAwnkuF52tBUuTdkg/8Hg8A3DNqXbAFBzsuSuT1psQFGNAhidpqfi5JavG+ncRENmClwFPtu5kBSQLx9zqtXMypEKTZZJWI96/6n8vD4umZyzPRg1RybXTcx8y4H08npfxKs3mywfineM+FbQ20aSjtwOzu3CAW4+kgaezNo/T7b4BDdjB650TnDWc/UR7UdlYIC/3Oc8h/12vyeCCViMdE+OcoWzY2Cx/RlUxfM+3o5sBAW/YmR9wfmeA724978BMtTrxupzvVUOK7vZt3aMb/ZS5+A6ttZVzAXXFTocZXcYJP54OIoKPj6cllE8N/tJaw/NpB6IevVmlJbfyA1dF/3g+NmAF7N4OgNWIfI9FPxuOx1Ii4V4uI1FN16sDO66tQpor0c2TXRVoHUDbQxA2gCxeTyoUKztDw2LWAfW9ZkALaC3t927VmPaSoStE432eON36+/HxEZ69x/FA6w2P9rDcljnQxomjdzMyDvW1OGDnDSRDCGyLGB+aS9FwJSj4ocumOQGxju9RDbpynGI+sBdziMPsqIDNPb/s0T0KoOz/KgfznOfnXRTIb9NWn7IH7c6AGvSDfQzbvPIwOW+VDonHjhI+t+SNGxfHafbAmTxYCeCKZF/Cdb2IVySe7by2Tr3LEvUwW8yJc5yeZ52VzhEH2TGEavGSNUe7JWz9rV4MJHvrzGnt9AEa01yGTfOckE43vj6YgF7WMNYSUB+rYs0dvRqVP9wVbGH5dV+x+EzQ8kfb2q45QGZzqZdXJSVfdidH7jy3d+3fDqPK7pe9dvBuYWRnWW+7tXYh3ixgF4OxaafFmicb3wnv6hoSWfX2uVlqy+87zxM/f/4MoPXx8VEACMBk2yuIcWIsln97/7Lqz+nlbac6mB6Y0zwq0+PECSYHvMKHLoH5fr+jZODPnz8hzQ63OR6W3PfxH3/hOA48Hz/CMwRtYaG+W5+7+eE8hisygeV8qAuvJQD6+fff8QyuUbYWsZb5R3hlyjvVBWoKt6rCgVaUx3NZJu8Sk79Dy3OUx1KZRxVw+bOsVFOwr/jtETTZ+4Hn88AYSwlmroeqlQ1VVYzz8yJMgAWTBHDrJizZDebVmMGMrXVW3dDliseNMIpSzSLm0qagcWV+jJfF1865VcGgZczCAzQYsPi7aJkdww4la2MAZ2Hyhd6B5UnY+pgEZ2vNZOZcp9YyfAoxRIkEXhGBTgOGA2+IWJjk8TggkPCAftUyn8z8qIZeZgGWAUamozvPTW3KtcSVj/7Jpkoh7wYrB6dRZQY2bvI/USb7W3hHdw8vYGFJUxpyuGE1RmVQVnkH16QDLK6zeE8YNOhfnEH2jpPjN59vp5yvsEICbxvfNU+ntQbtSpuoKzQthRdtWxHcvYsWCMaU5j8Lm5yI8uhjjHWI35z4fL0iD+bj48PmUYuykvaL9Zl5TU5LIjEX3Psocw8gjfnK2ytNc092aWHFh402ze91/u6U7vye+u/ME77TvrhrWXYzqZ+fs1V5w9/b2BKQztfVMBrig4p7KPczNmDJ5N7284CobOR35IptO08WwHMhRiL2SiNQM86qzJW0zFyMUSqD+t8dGXMAed+sd6Q5EPJ9gUyxcvtwyk20wvdwjJvSN2bwjYT7L2vGsefw64oTsse3Ksp5jvJ61r8rDrlr9fPoZ8j7/Pydn/1O+21lI29WElzWcu8YO7+/qxdfrRNL2H4dWlDj3fPE/VNNejYqSJnxZmBcY28tuuLOWnq/MHl8d0yMgj+HvUyXVlPoxrw2VbOqNkzoGBDxuvFxZoVbiZsAjVaka3zk/Vh2ZlaJOvpdGATSsxeAWiFTFNTLRbdq2nMNTLhZYiTnpa67ETTQBbdzmuf/T7fPz8+939l6B4uD5WnCWbCy5RrgtPza3JsFn7Xox7kOKDvPF1SXhf/1ekXFL9VV0awV7wWZoMIPgtzWEXYCa6KBc6azEbBo6dQJVUGHYOo0N22ntVgjF0RFrLwtAJUG6cDzR8NzAOfbD1MbVpZQdFmGRNUiNk9gHA0DaqUEjwfGXJXh2P9V9Ua2+SVNsSADQ3Q+Pz+B02qzx8ndHXj//MScgt6dRj0fbJ5vC7k8DrN609L1Pi0stC0hAawDzmgUAVaIEH9svfdzGDL9ZM9TVkK+Eh67kjK3Q+e+Q8sCFWlMrTXgMBYWCkDvlienuwzIAHNOK3/JZO0KJOt8VQU1lIomwJRNkLfEt3jy+e+MS2/4bwByb3nPmeW0uSePygYg4tZKsbApwEP1Nhlkey8+FUqoNcf5fYoVky9YB4sC2GRy87nPHgbSZGstLOmxz2RJxqpw8N9383cnX5i3lL0PtW2YAPdKRe0Ln30FvPfVMr9Dy3sh0zdbNVb88llF2Yh7XSA094Td8Sg2AuD3+42jHUG/8ouwZlvLERXo8tgYDthawzjf3k/7Ye6CemitTIkQO4ErH+wnK095voaeA3IcmzXAdZb1/mYVqXiyPRxHSbMIE572Qw9eb3ulQz43h2gOP1eHL7wofdvYr5g4Xx/hjGlfannnRre6K+Z3v3lvbdUQYwrXjeE6OxUurqj79vs5GwWYZmUif55/Z+tztiZWLW4vEwvQnVUZTwZCwC5UKNCzZyOD2rypCMDGOLfnP5/PbTOrKsa5T7IqD6W7zsndJo93TtPEWX/cgJWXagSALhgKHNiT57IChCZQsY11OjhHE8s9eVstcxHB4+PAUkqvTGljMN5oPcyfVZfnbUGA1qJu+qbwNcvPyGvN98eZENkKoUA9kCv/HM1+6pi4Of6Jwf5PtQwm89gzM6ntjs4ZisN76LGI/dSlVAGJpxXms+aH3pHcJwryXOM+77Nt7SpTi+/Xvs57LQNu0vKcEx8/GlozOni9XlAdBtpbhwjQ/YClx8PY04zCrZb89/n56fvwhfaYm9d07U+NQg6xl0sFjtzPwwWSWaqaJdvCFK4xGdYHU3J40qwOE3zngErDEPM8acNW7pZrTEBGhZFW+2xs4X7gPsn3V2F1B4zCSJI9bLjy6D/dNktdAvaPxyPQAAHE33//DVVFl1qSli5/lyO6BD9wb+XjPJu3y5TkDEAbOpqukrHAEsqtNU8gv4JoYOeXwG7Vjf6KRCw2Pwv+rhLASZkEBfOwa+x1fuahpwJE6NiYgKxTmut7Rfe5CG+pWBncLP9CJj6e+Pj4wOv12uiRByNGsQiP5ddIZE58yXlEnq6KAbZ5S/Ik5zfRsrrxwYQn3vOETPeslPWp92SDw1d9+i6NY66ylN9lRfB3WqNBt2CiZZgxXME8OlFECCPge3dYEnqXHA5XZT0AaHi16DHO54OQ/qhY5Oa+DnTPJoTCKnG58cQfYC/yRPE5GHLruQz+DjO/LkXD9F0JuurdPIGiHh2AYr1P2LIfSbarXjwXF3ylHg6csBOv498VI2VFmPlkFcN+pYDOtLZ3reLziqeyXFT1IgNFka3Oht9pv61svL0S0Ga5uQHb+XcWhnmS2NGqyYV2jXZxtbFWPstnhnBoe2IZhTRBR2ViWbDYAUZtO020aojVg1FbnnwCBSsdOIJBnx7XB60nTGegePWGEATxtNvHxw/f0C0YOBzQfc5XPPd4rUSf7HrNRF7nnTWmX6/XZoHNLQswqwa04jozUObmpnDgWkBOCASfr9clJ0OVFrkrQ72jocys7pjwn2xVEFbmcreH8prbOgzkRH9VRe+K81zWk6uVdNHTeue6n/ugJrzm/Vj7mxXMd1RlWjTrf8FK7wrsFUbblAOmjBw4Druu94nnA/j58xNznlYuFkBDwyHA7FbleOrYYlzRSNcK1YE5BefrhePo4fLfrLJt5fR8BdZDgPBskujvus6ea8mMFp5mOQdzHjjPN8yOZEUe5DQPJcMHqciR17FPr9frQh/ACoWo534sIPC1d6+24M9u6frd+/4n2vPwc0Z0GWNsfslHTRZIO/B4HFYyQN0C+rB5PYHI5zkh+OvxhIA81g5alOZnP7xPi/f2ggKv9xvt6HaKeLdiFa9J3gz0x4Eff1lY7c/TvNAsASkiOKRjMKTQcykejx84+sQ8hhX9GBPQiaYNMs2I1qbgoQcOOfA8nmja7FlD48TtMQYgDc/nB061qjrSGw434gCC1+uNU+1MAR2Kpg1dm+8XN+jMid6NR//48QNzTrzfL4xx4q+//sJxPPyAygMK4PV+o8+Jx3Hg48cPPD8+MIfi9d9vkz/HEyvU7cDRnhBPQB5t4u/Pn1bwQecK22sCiOXtgYY0BdrnGxCBH6sDAfDRVkjgfL/x8/UC4HWBVM2jqcv7f3S3WHsi9IGHnTt0Gk+hcQRq3lXyOYAwwytEqhkxmM9SweN3aVVBzaAw85lq5NqaLuNNxgHV0wCsowv4fVbAiU2Mx62y0IRLYWQKZXPvxAZQ/Z+8qqkHZ0BcKXC+7PxBp4bizPDfLoKZlApLlE9YBOSbtlcBhNEgGxCCvlszmn880B8P9Mex8fSQnzaRrsjshtyMoeiprW0pREsW59D1/FlWDCq+yDL7LF4otmpEzAUtMtbmO6uCXpWomkbxT+3fOkGcHcmdqtaD+tmmcZUBVI0KWIynDixvqqzV5cnJG6O6nrJFIzakJ61na85XQDf3sY63/uQa6hHXKMkVmKPmw2J9JcYM4o/e8TgOy3todo6GAhi6rLZ0U55zxQFmJYrP3CwUc8UD9t43BS0zrqoE5ZNoWyLIq2KAC0EOrynPDboPeh973tQXxRQ7A/4OLXui8txxjb7qd1XMgRmeN9X1/Xm+IcnCu5hHrVJlwj1CfxLD4vqxVQZWT1Becz626/M91c3O65aykXhCh1d8Gni/DzsRrykwG8apVtKPLvMmOESsJC9okSJTHVCs0qF1P5KG75SpfG3vHeLJwSHwIGZJbza3osCUGZ48KCznqgGqAzo75nDjx4HttGZ6XfO8VOFeBf9XBpvK++qBibx28zrrtDMT4cn336DR8h3oYM8gsnFwnOLWfQebeWxUWMg43NeB1iSSox2nxPODDqStFU/AxTZc6p4zW8WyxEIYPuDKb8tW2hYWflW4t2KFVXEMvXXMNr/YPyYlSD9S5qJ1ASuMhOdvLAq2qmrT+zc2kEgPZKa7lROG6M+cE20qtK0Sqz5Zi6dPJKCV5bZbbYUejUR3PgfwtdNYgnXN0FV1LAoJFBXAFBmsHBXG7hf+BJGoIEaaGUWRn0nx5Rx8x5b3OXlb5WtV/tSWMdUdWORn+UDejKsylrJc3rVHkJ4ndDpsXpelLMZ1dV35P13GMvgewjC5AFdAuRftPt67+HdvC1zzN4/MJd3mf1OBaW7U7seBfqxQ8LxXv+LpF4xi3b8oiDu4343Wm2eae7EYm77CGbId0LfoJtNHVTwqHs/3AVeTO6/NmOaf2m8rGy+3MuQO3AGl/G8C+5rcksFNVR6MqfUNIPHaO6WA//7Xv/4VoSCcjEwUZsF5hBfDXOIDc66+ZYUqM9baFtHsQn0t1vWeox8rTnQOzHmEJ6e5FbinGP2clB3zqRY/KNRkW8MBY7LHXArHv37+VwBRKht8zlY9K3ljMlHzOoIkVi7Jc09AeqZY/98F/E1gIA1XCwyFCi0NsTmhFu+f+syWBeV3bJmBUAmuwiFfu9aO1VcM8N55SEjzOUlrrYeDpiZm2bObwOTrZXUSq2p2WJWlFY8JAFZBRqHB39XBWISwuMBn7pHlDUkANMCAQRMPP7KSIp6D8rCKhXNaxbFAL35wk5h1a6hboObEe1qVEWmCx1MidrgKWwrMO2Gc+Y6IhaSRMI2WrLqQqoUDAkDXvq+VThd+ZmHvc/ppyauUa/bEcs3mnNtn+btaXa3uq8xDs/DKwKICpaY2hzR4fId2Z0ljy/3PoIotK82bwWpqnCeCZjlKgXjsaaCysWTJek+XZelmH3OLfcX3weuUiSWRxtogGZ8IPOxwjE0JzrRYDSs2LytEkPNi+P4KJNjfvL7iezAOS4szrtqFJ1XlPMvsyls5rjGGlW6/rGeShYpL+BYSDU8Y0NpoHAv4bzLnVgFICkQczLlbfqGwKkLWkU252vozV94CVG/e9WfaAqhAxhYiHKd5lO8MKpzWzDfqGT93ILPeUw0dxBWWV7PCXFWz8QQuM/IzsO0P8lrSbWsNLSkDorq8B6SxuYwBhuLj9aFwNDesdFlGteAL/lk+1FNFcCbPUKMROika2z5LvDbnZrEfl3nV/e/936voQlU28rWbvPpCmWytoU1Fhf9VntzdW3F5KFdLO1zUl9cFv9d+37Ph9YshKx6SsadSGSaJXGiNsM9c8Y3OQmGAiKBEdiFTJySDhKpREWhnMJw3H11g1cpflYW8CUywLIGU+wYHWpnwCBaOw55HsG7PSpZGSVY7VS/ZyIRgn+MMHGRp79yo8JltvaM1i22MPAg9o/JPnh8KkrxpvhJ+ZCict+yqW4JRrc+b1WvXxjUBTt67qsiwzF25f5FHYgbr/k0wXxS9P9uyazEL6qxsV2s0250ivkBxvoYWS/vu8XjE1slWJRGsPZvKHSqoPPh6uiWnuSVYuCe4Z2llTeewRB8TXVxCdYT2Wf/TS8NCXLg8DwAfGL1hvE/ME2iYEEy0ZuAxLJfTQBbPDtEpLpL6Nqf10NBcWacCu7w+TGhfZ/iwmsiESCeD2rw+6nkc05Un8QlvqU93CgWVjbzuec0zn6v3LkG+G1O+uldd4neO+5vsk3c6oBO4F4a5feU9I/82ob/miPRMHhLv8Xdw/rOf+Wh9y0GgwshnWk6H9fOE5fQ0TdVYVJEPBos9T14YRp0RfV9jTca4aerEKXs0wbImzs1rTrlD4xUBaG9ejtbnu7XmeRkNf3sVwQ0klTbGwCPt37C2+kye5+n7s0Q+TA91kWy93vn5eqbGYaNc/+BPWPvlqxLn6uCBibqSaL2750qxSg/r3BWovJ82wfONzm+Kg1IVaU4l/W2/ycN41ACVkTXfNrOW25Pm8s5GmgCzureI+8YiLfYwUd6+r48pchuov/kxSwgCT9IoIiSu7VqupT23UbXIOBE0BAiO1vwsN1M0eD4ZJ1R1VerLGKM1C6PK2Ie0SXokT7gYRhP/ooJVqanKA36W+caVDq7KBq+/yIB0zx2mQPm+etP5/N47xutFC4Lj1WW4/Hdw17+lbAAWq9yYN+CnjxKgVK8DF1Kl+R7WAMFzGOHnSbDmwPsmdp8Tk2MJM4DjZLEaTbXE59ARWoyzyzg/bwFrbH3cFZNFILtLq+M4lpXoPE+8Xy+MM3lO/OEiBpmsRj/ioLqWAHg/7MAwQHfiAnC4JbW1I5798eOBMdPJtP7zFQEDK0Hc1rhFwvzz+fyyZLEAZm3yZ8xEuFU7p8IpIiWsRFxs789m0vgCC+Jzcxeq9etSo/+TrVokqjci971u9P0aKdda0YR8YBywlELtluzKcoCZsWTGsbu0d8BTGVFtvXcrM5j6yQM0BVjhLGndWJffrm/mVWmAihov6YKzC4YIBhQnFE2AMZolj2MZJXhvIy2130tQ+1LIpXmyCiPNBbEJuuM4/J09BF/rzNmwREILB2QpVAV0X8safnBntfqqT9l4wnsZAsN7axGOXcg5L4NE6Bf+Ya7+p9pXilSlv2wQ4tiqEWqNdWACcehj8JI5vay4RC5b8HHgigTSuwEDslk5gbqyOdXiywkuVFed/0QHIaNcWeb+qXvSVFbr01QFxglVXMCHnb1xxmnCAuuD3WsKcEOPClMrDENwHPY5FXALhTmC/5uBqeN4+Pki7WrNhezedyoH29qpVc5iKdQmZiGVJfaDr2WQxnmvRpo5J+Su3Hlb+yOXhOVz8r6K5xTQxb31neTIV419vFOYqjHwbiyqlp9ihhEJhRzAlhAu6bPpZfu700vO/5znyi9o0tDbVYnTWbxoFUyLyZCrMXP1mYrGBnCnWsGc9Kym5gXfaCgw5W4QZ3l30Rk0yf5Y5Egy3gBhySdP4lwg0VfwXJGVsyEIz00dm4iXSseiz7zO23VFIdjWNO3P7uXr8+f1eRV31H2TvVd3GOV/pf22ssFwBCsFuYBUJuys0YlPdmaUWWjWwa0JVKhKUHvW9ghCcy4GG8OjCI4vp75m4leGnwj6sfqbk0mBRSC1LTCIbTGX4J/IMXjHcWCcAyfo5tWlcNgDAVD4yLbYTA63vw9UrZyzaB4Q+/dH/4E5xxb6lkOlKhEDiLnjXG0laxPj3xQyETwPEraHY0077GcG0PTxeEI5hcKyhnu4vl4tAzrt5E/hqdFUFlWXld2tLt8FROVzYfIm/0pA1E28wL+5xndhOSOxPj9z/dAdewVs+ayJ3EhnudXrVp/txNNVllbAA/0U8BPs1UNZvDyil+hVtyqZnuASTR2I6MT7PO1EY3QcXTBGwxhvAGK0MYaDKSufKx4OYtdgtz4lOsp0XOeb3z0eDzQdEB3ubDOQ9TgOdAFaOzDa2Oe+mWCd3cs52oEGUJgxJFfCoqIvaZ/nUIbK8HNYZ/UoZl7GeysP5jv4LLcR+hr9Xnzt/+52x4Pqb/77Tsbc8aR00+Lzk2GiBlbZKM/swDtAdIVqKTTK7AadtOaWcv8e4kB5r6KH7FHmpKe+K4HOYYY6Jkifw3jaQdkzJ6zowq/Xa5uTNqCD1tpFB2ZQGxBZtEb6JGDiTz9M2fgqdGQVpMgKm4YME10VgOLH6S9zpS2Mi/Ik44m2FO4s5yuN06t+B7CrspfHU/FBvue7tVymN/MR/k1cRPmdQSuwSqfyBPcczbHKrO8Fe2pOD99XQ+z4tyktq4roAsaWvP14PLy8upos2LcrgIVNns9n0C3LuOcSs2wsejDn3IzNPRmEeu9b6Hnzs75MgVhel1MVMk5XRLDK3PYVVsxx/fWff22VnAS4KVBiZ9dQueIB0Nlwy35DEd697OWoPznhPHszsjGxtYaRjEpc67w3+Hc1rkcxoiQvc9GBTAf5799t/1aCeGb0lRDvhAc7lj/P99bv/apwoeZ3cMNw01Tg/OPHDzwejyjfl8FV1hbzBgGsSghwTfaJ/s312T6+3bOxC3mxTaXLC9G6W4lUtmelp8azGc7SeseRFp/PDjYSjDT1SwQdvslLvsadEOe/67XVeljnT23wUE1VCphwfrqyoQkE2qyEFXCzwK4V3+azSa4usVftaUia+xduxz/Rfvz4cWHQS0hwo+a8mQVicwnhhVT2MnOt7VbGCrYyLfJvYDHO3Ddg0Vu2lt/dW+dXp1V7aQ7YeDijo6u4v4a+NJHkrjaaGLHOnp/SrEqQuNfxHCfwemO8T5zTDRiz2emtbdHv4fPG9/KzVsInM8+KcKbPv5eA9BCE4ziAbkr+mIrPn594vT7R+2EnWku3foyGMU9XOhSfn59hKWY+SS6jWg/lyopBBQm85g6I52vyd9w7EXo01ZLwVfEP2PV/rH0lL+r48ue797h6mXUpAf4ZFY0xZ9Acr48cBgGgzWLDVbewq9bEvPfAlmyqqnFCcPZ0GGZKMhHLaIa0zvaZgxg/BK+fMzwRBNlRNWfbk75vCk1wzWeajzxv67DPnDOEUCwiZPZou7KRxpjfn70TNlkAfRymbCyzhyxXp/FAZO/1UjZiPNjHF+NJ19Vr77xdeT9V+Vc95JmffhdZwqY6XELS5eBluAWu8FpG3phnmiubZwLGMSzPbYwTqrtB7Cvl/upRm7frEvIfADaPnucQbXtaLhFqVkVRONgNz2wKb5mX6Uour5tzYmKC3jLy92kP26JvwM+w1j+w4+OJw/N7D69WF2PG7k3O87YZfnKkgDQQaNb7+Bnl1qZIF2Ujl4GuP1tkQqL5igeqDPwKq2ScfIcZ2P6dvfLbysbn52cwetao5gRwUFWLujAGWWEEd5b2AKW6BsTrqeFmwZ1L4Nbk7zp5eaKWJXeBktZaMHsKozkV529MdlWc8mbhtX/99Ve4r3VOjPNtpyuTibqFa6oGsVQ3FxMDJ8wFCII5GZDpyXqq+Pnzvy+Vprh2+UT3rBmf5xlxvJyjzGx4bbZ46Zz4/PyM6+hxylbb/rTkcnqcjuPYvBjx41bxjE035aJswgyAv5OAeByyxVSzkaGIKJqsdRExCzpgCWpzvDCbRmwoaeg8a0LnAstz2iFgZoXZrYYKQHqzeHGBWVSxlEAV4BynW71HJNgGXTcLMeq94/zv/xPdcyYmNM6iwPG0ilHA+nHPtLmjATuVTwA5PETQ+MWpE1OA9jjwOPqywp0n5PGAnta3x0NwHA/85d+zrPTHf/wFvE68xr8wHDA9Hg/0BhwqaBPoR4vQAJ5wrLrO4wCAdnxAup2ibPQ4cSTvGaZGqeenKyhjDExMMyTMA+cbmGPg+XjGHJM/ZsvRcRxR+rYqQRkkPtNeBbIlbCmmvA9qoTVQjTNDBLZvhgCndAN22JND/1TLe5vtXgjfG6z4OedkjGGHT/kjcpx4Bq80iqz7FRYOl3IOZCWqUnneFOeZ+H06f6C7lySSlFsLowi6HVDXlB5If9Sc6P2ATus4FVTbBzNKkWflqs4Vx1npiXwfwCbbzAr+iHVoraEfYj+9AjKFZ0tBlflIOzAXEYiKh4lZHy1vQgGMKOwAhBkivH1jzgjBuuPhWcl0LWsHWkmWnW8vBiEtgDafsZVlv5Hn1eOP7yFOAABjphw/wJQMzDivgoc5ruqFewRH5HL6XM85Q56GYYJzmcFwUvAAAM0PnESSEVj7sEkLjHIxTsVaAFDZ9I+lnHJfu9ySZXiN52DfywKJw/fYn+m/pTUcvbuyYf+hHLZnBVCDAHiEsrGwJKNKzKNnGnTeY3x0VRTA6BURAtovQTr7RIWpKgfVuLLuuTdEca/mz3K7UxaBhRvyHtkqaPr4C5f+bez128pGbHYQEA8D4ozVFEAY/kRtqxMQpslrZAYwS0cQ0rqOi0fgkUEz+0IGQosMQ6c4aWQg27SkSbR+GePPSlK+VpIFabcuXYUf7zemD6heF4Gg3uJtXdGY0+fEErx6263/VcOFCBpPC/cx2gbbQ6ayIKiKRf53FkzVZZ0VFP5dFYvJ2uR6n2RkY1qgaHiJUW6IvBbgvWDIzdoUTVbll7y5spL7HVpNKv0Vg+F3DOlwDcIVyCvw4pzSTSuyEvR+jp8bnQT9iOCtw8J+cK8cE3z/DmNaO5T8e/VblZY3v8cTpyHLc9JEvdIHtv0MB/YbCGU/gaCVDNynWvnbnd6SldZ/v7NXlrSVxjTn9Aooy9LFsWd+0VrD4/kwS9fcDSUx56qYZR7vLEa0rHP8VXiISBgjqkEmr031hrBtigh2r+B3bBuPS5/Vf2dL9cVgoRZ61DQdbCcSygPLqc458Xw+l0JyDgvNIx1hN3SFZ5yJtf59g8QBY3NOCEMkXEeR3la+DJDinwfe5xmleSGC4z22k+YtNGVcDBZNxGLVW4f2w3MgBUfrwPPYQILqwBiAiOLx+Eg5eSncuYmnXt7nbalbpiNUbM7Ilcp5gAGUIglXYs50MARrlY3m9a21AH137897WyrIc94ThsvzxNEPH495c9S9AR0Ld8w5cXrCfPBZVxybOq/5N2j3f3djmFI2smV5m9c88+uvQs7h+4TKC5WNVuaeykbw4TE2g1JuZgydsXes3RkPZPvGPtv516QHpzc7ZHMUrxWcfogLmkV8tCTPeJ5O7x0PejtDEbK/6VARCJoCx+Nhe5bJ7xknJbrOxT04T1WBxdyNWXeGE/4tSWbWcOe8L2u4M7+vzzbD7a6Q3LXKP4klMpYdww5vDDyM9D6sHOzfab+tbBzPlGym0w/v+YxklIcrGV0OZ2B2GivvWZYhlhZb+RH8bs5JJRDvT/Ni2AnDa8FyEjMthY/HAx8fdgBT3VzZqsH3Leu7AsmqlbU6W+QVi5eVCuZCVMa4xkP8tQvEOSd+/vwJATBOKxmL1nB0A+UP/92cCVCJEif21jrMJWebS+GKxXliZAtbXwSWqyZUy1fuc7YQZs8TYKFBvJeeJSs1fJoC4M9paTMS6IXAnxNvp4PlfVohD8si5kpXP6waAdzqpIqGfXN8tQZ/suV5qwAquyT5eVbwyEzGGCs/xZ4E1pqngK6hU0gCe8tTAPA+V+x/tlZURphbVZKqAiVCJX3R+XQgkvtkh6FlwD7RYUUUspKYwXEoVUnIDgDDx6Gq4dX8+X5tNJcrzl2YsK4SixegKgzvkjjxNu5xCzX72NwtXhlzO5icvPpzp9jneb0IKexCYutH4mVVweB1VQEyBdCs9yIXHfaPtTzmAL5lv3wlLOva8RorKEErrFnbezJ+6VAMp//H44G3ex/DA3gsL9Q6c4E04+/BCllqHZhihwvWca1kVAcqKUQUAuhbE2DzhRExPg8avxhO2sIr19CAxN8zXcxz0YR5/3a+wNApjhHQ7e9lpKDleX0fdOgAvxqn6Alv2twotHvdVGBo4AKc1jxsykZZ87s9ZDzH/KwLO+whJ3fKaZZvWa7fgsZv0HL1QrYqo9kyv8hRJEDyZp/XqkN1XvPc5T2W542yPcrTlkba+Uo+x+ciK9QOpmwIi+Q0wTEG3v0Nustjj7qXBepGsCbojyNOu17Gg2Wssh9/X9qfgIVRoa0zNmJu+m74zV7C4MMZtANAu1bcq4ajxcOXp4dzm/vPv7O8zOteP9O0Hnc8lNdtXguRC01tSk96Xn5Wa3v+4a/abysb//Ef/+mVNsbGoKGwJFCZAAaaDLTuLteN0Ta0o69yuW4QHXNYlSYdmDBB8Pn5GfkZ2fpZLf05EZwKQNXUKvPYwJ4z1QzCcrP7dtBigGlVUqrNCMomJlshxpxAW2FMVsMfEFX03iBiQJt2Awqlcww0nRC1E5pF1ONjJco0buMDtmTsPFcEosCV+TJBHFgJZQxf++///u+4d/OaAMBMia6+gVXsNM+NobcW4nrNrc9X2rRL6E334CC8YU0YR2oVZhJ/+jYKh7M28/hFHGqoY84w1+c6gfNNYeLWOALc8LjZkyvjUNVI+Ottefg24eFrTOCbrWFVmOR/3wF13w7xffQH9D6oW83Sfkl9WIzNTwhPCkwAwqyIYSlg4oc6baeyNgvP+0qZzoA89gLWnsh7XniNAz+BbgfgZTrjuBZoW/9uqtCibLCv+b3sa16rqohm/rGNQZJyJ1f+dAVMGr+/yTbZxlk/A/axZl6Vx1UVZtJRrE+uXieA6F6dcDC0hM9wQ1hvDZipapRI0L5TELo0TBcD3Sqm3A/UlR2jKZY6TzTvX7fe0I8OnMDEDMDEZzS3zNt4Sr4BrMrUKWcCJuLx+ctQwANsbY8AgHkZs2xdP0vBoi5k829V73ZlY4XzHPCwancRMHTHKshZgQg7zsb3jVpI5gX4tq+9rZleLGTG3v/A12fUZIBVQ7jrNd+t1X4C18PUqgGiGim2+T125TLfU70lud3tT8oUABtvJcaTcr/di5CN9jwKFjeYCJV1WGlpP/+pQaHT80AcVyrsYGPy78MxTMibyUpTwcEd91m+VORWtI6Pjw//zJLI41C/3i1UNniyPS/mp8hSek2Ct+iahzq39nsVC7qj97oPvsKzvJ+yKa9RxcJVseC1WfmIeRzDQvfbXhUw08vvtN9WNj6eH9FBkVXjG0iCbwhGGzHB6HDAuBM7Bx8eD0+e0bmUjRo2VYVznkQRuVR8CWsF7pMozTXmSYA+cbnUZL1+0/LENOFKGHkz5SayypvZGAS9r7ANO6tCITBlTnV5ZsYcZNVAlNi0zeJqybIq+MZl+EyeowzG2O68Pzl0gHPy8+fPjTEF0Sp8u3MCDGhOVcicEU5C6wJkeTL8BqcdvayRCVGLTUcaC8T4hyQh851a78c2f8jWVqWFhWP0OvMee9uHjVlVoYev7aZoOEO2Gsn2Do8DF/E4/bZiXC35cgkCU/7NGrsqTxfGxQOTUtKeRuJ37sv+7415CsdMzcTesysB+7pVRaq3FoDkOA5grupMeZ/Tw1EtdBsj7L8OISIYjb76nKMBMl2g2kPXs7En6vE5XaxaVuZNFTQD1++vAghWPal4MOr46nOrZ0NEjO5kr3byHdqdogRgs0rTQ/rz588Qjll+bDLiaXviPQfkNbfDSIeHy358fODHjx/B8zKdHK3hcKX91P2AU1UFxrXUenjkZXndw0M8JtpsO98VAL3hr//b/w1juDGtNfzVzTtPb/6//utMzxlQTV5LL/98tBRyoeaB5lwSBP78+ROv1wtjnPj4eOL//n//v+M//uM/8Pn6O+aaOY8fH4/NM2iPdWuyH17APr/f7w2YYvjffubPmCvUVkTwcTzQpPs5Os3KSYsAOhdQTPHx5A58z1TFfL9DuAYvyfwL+xlGd0CI9BK5bmk/kFY4h9+lmaLHSk/M59y9GiZLWHEsg1D7vq2Ul8uBonc8M/MXXnftl+OLpKxnzEGBl59r56q58r89Mxu11A5hVET5e/CgWJiBLk6en0suNXo2sLDkUA3PyzJI8FDkJS/Nw7GXuw25kLppe38p2KpLqc79Fy14UPdn5N/0nN7hyXpt/v2VEpIVlzuF8isl4W7dAVg5eKwcsIoD/3+ubDyfz6Rs7BWFolPOhDjQXEWpKgsc1Ov12n7GeeL982c8gy6rHIe9KQyyJ3teLLvYLWDsgyVRGoi6cxfadR1Hf8Rz2OfPz0//5GqRXBt8J4LW3BXuViWBrvJvHvc75gnBBOYOQBhGBC/J2ePt4sqSOAu39jj22vt5Le4UKc4l15PExoR8JoFnYjuOA00EP44bhWu8MaZgDot/lGRxFvHyj+lddhrtCrGqgiIzRPYjn7Vyxwj/VGOC592Gzv/eFJL0GbDiUcOkKMA6tc8YZDs6MHwfqeUutHFCekOXZeFwqgmeabrA+huKiMmEqoFSFCHk/WwbTQM0NFCixb9pbJCls+Q9sRi2NRFBT2prpdHWLNFPvVznBrILmK8KB98ae8kV4bwmZo1iHtEeWrGtHxYPyGuV+ykiOG8ER34f91HuYzVmxP0im/CIPpd7MwDmPoowhzS27wSicn9yqc0sIClTckGF2riPWE3GEvcNjMxkNczJ+oDRRa7alYWnqMX5C4w27XlXQ1ZrLcBG9hqLSCjpEwqdI5X7VLSeK9es58Vn7ul4PB6h6FifJc7yqDHhHAcroFlyuYULqyp+/vyJ/+v/+n/jfX6aAvDxER7t5/OB4+gXWY1ue6zSPdcsjIJ+6G9T4D0V8/CTpdW8tLP1FDngp32rzSk9UDH/0xSHusc07Yv4ncOw9P6sr7xPsgKZ92wFZd9LKQfghki2O75h67yPOc5ByjyynGF2/6xfK1zbnIWCt3/OqlA5lMstlNge7XIi778xZ/Dt4RETCJlGDwd1AYF0iaqDPlsmQ1szo6UJPh+3Y6HeNs9GOnHhMj/k2QoFK27VuduU73zeFTZ95TqP5dt/WodK05e1lKuhq/Y133d33QWXuIEUql7sge/836BsZHC0iGkXkNExEFDs5XKrVkTQyDyA8zyjUkgm2ho+dbdRarsAjiTIgjHrftozrw3CirJy+yKYpWu9PwOTW02zMDJ6MdQPy5m6yrWRc1JwbETT8r9bVNaBW63ZGN9fW+7f3dqFUiS7h4NCLVe2UlUcvUGPx+Yi3N9jol7KvNtmrEnQxbJR1iSvTXjEklXxu7S8bsBOA/nv7M6n5TRbbivwzfe21vA4BHBFz9ZlPTODhTpfmU7rurMP7G8G3XNOP/iOc51p1jSKDJAJ38FiEH5P3kub5dhcQBt/qUCeFULynP79+rzMc2tLiAAl1FD1UkPRwPsCLRvQ8PFtyi7fRY/J1O29SFVhuF8yQM2Ke/Y0Vp5X14LCNpJ1099nAn5UVhfPNMtdBYt/slWFq+71yo+BfX4qPwfga+5cJ1todd3Pe/PcdhqC8j6rz3ZYS/BAAK7umciNND3lei4Rlb9zLCUAQKxjlrHdab6JYBKcezjhnNNLjRv8aq2FJ4GKBsOoOIb3+cbPn39j6ogDW3tveDwOPJ+PHZD6AYDa9nK6WTGb08qcj/PEHEvZmnOinc0OPWw+F2NiNoa02HldPrEg7wj+wf2YZbvsSvfiQxZuYxjwho5m8n0UPhDjddppaf2/o0z5in8DOw4BrrwkN/H/3e25uEZ2o05uldfS8GHFBO6BOsN9W+OSb+4C5/8a/JXVy7JMAox8hp/p1XTlIvV+RIi6qvpZHoJ+dOjJ6nDcf66U+N9mlLHKiHWMlEkAooS26NzUAye/NY/TKoUt5W4vOpHn2O53i1x+xs26/A5NVgWiGpgzbqrGkfy+7b2OSwVIIjwZ1/+xV9Z+W9nIidr8nQUlO5fLrH2VbFLv2Sy8aocb9dY2gJETdu6ecafJAWlD6KpQEhM7Fe/zjbrRyLhFBOO8AvSVIHQ/huk5G7kf3V2XixhPTN8043z7w2aEDuVxRL/dS9C6xRTGfIhAU3UJniWb+0zGUZlSZTC1Qo6IhLIRAsarXIze8dfDD6f6Yp3tcKwe9NJag85hzKYt+mkOUJEUrwwy+GzSVE1y/i6tVqPaz7YwtkRGThqj9fLvv39CVS12VJe1VKRttNbayn9SVS+zu95Xww2z8ggswZTpgJ/XBMBtL0EutKNkRBQyhtrSM6cr1FkZvhGAcj0h9fl8Xtb57r4sVG95gAvBUIzSKbf8zZhcVbUjaKOzlpQrqIUu1h4ZGM6DFT19zmvqemSGX8eSjThvr5hzVx5XRMKLxpb7t8+B7anvpGzcCdQ7gZdbNlrxb14P0O6yr39VVPjvTONsJkN9/nAVwhVgiFhuWl5XKX9ncL76hpTMnZRcekES7xBpmMeB+TZQP8bEUD8IbK4yvgJxY8PpHulVnXDxgBOvt72RyeIG1vrl9G3jU7hUbqu8Y4yB833GQZbkR+IA6zgO98ZYyOecVp0Owspd1/1K9LbxJ3UvSKEjkdBfQgEjmL2cVJ3W+ALKKz/8RnKlyj8AF94M7If/kbZzlU673yIn7sJ66vsyH8l77S6iBcBmILNrseEFow/3siZ8REUDicaqQVHLd2MMnKr4aHYA4PE4zNuvWGWWA8/wfBs32LpnI0KzxEpWH617rmSPilTNo0QgCWfPlGPhv/OcDNXYW4bJZLvuoth5nmee/9/FNl/h3vxdbRuOSP2+m28AOIQ5XPfv/d32+yeIzxKntr86tNPpzHMCcaiSaatJ+VC3JHgi5fEwzfQ4DkueG/u5EJnYK/CvrWbY89oqfEyQ74CqaoB8XhaKrbVIpK79WP/e+5bnzBZyblasmEUxi1ITs2rFxuaG717Fh+DFQadZLqmlAnamwR6fXQXFtnrlswzs6Gbn/Gfwp6r4/Pm3C6QreAWAPhXa561i2v2+zAxjHXSuc0jKfZmZfhfwxDZGroTWNrpjjhNgZ3GMMf0ArSPRhxrAcJBegRNpovcOnZpOiV+nAYtInPORvSx1rjYghF1xzmB57Y/mFrFpoQ7WxYjwInggZ43nAtDkRQSOi0U/N67vx8cHPj8/zUo7R2yrLKSyESL6WuiwP45tfJqewXsYb69qpWtVNarbtGanR5MeKTCyJVdVoxTj0VcFuxwDnxm7naZ7tTjRM0UL9d29ue+8l+O/8wLktfguLYMm4Co0M88kaKrtLmSGrT6jznVVqDfe4opBy8+4lXt7v6sSWr3TmUbfXufATjBXS4RtK4Tp8XjgaOlsjNcbr88Xzve5rIxlvOM88T7f4dHY5qjdKXbmFckK9PrMLdutQXqHJo/2Ja/DQTA9GNBdkWsi0DEx4JEOXfDx15Mdv42TZ7n0XCSGvcvrqW7DUVWc73eUIa6griqafGaWPZkPfie5cr4BBsM2D0f7/DwhGBDxPJ7jsAqhM1Uscgyrc4VTNenQ7jxQ1coojxHFEqAr2RpIPHwYLfTmppdhBtP3OfDXgzxmYs6B96eF7y15orAaCm+oNkwH12OcGOdIeYW2Hx7SMGDe2knPmeeszGFeA0zgaAcwG863Anri7IpQLOBzZqgf3ZWHflhw5OBhkjDjkxn0LNqClQmH2lk3KPtXWZTlONDdA/3y6Jzsrc+GxnPOnTc7zpPWLArN5ZIZWhmF5fK0GwZ8n2+48Ep7ZYUfD1WowjAhHHNoOhekdT//ZlXBtLNZ7IWZB7QUfqrNz8OaGnPUe48CPr8rWX5b2aDWerU6CUQSIwMscUfMUjN0YswRwpOC2RbDsv1b73g8H/j4+GFl88Lltqx69b2/0tjuGAx/52sejwPPj8fFYkbmyepbqr8W1pVRqS/6Nl9zLte7ahzEg9RX22wkxCWENSx2bYVSUXC15kwkgcKLcnMVlFlI380P/6agz5a6EAA6HTSlhD/czdXVve3/iPrVee7CHqsTGMVa4vfFGFxY62+T/P/eVue7uk1ba5HzQwD1er3Qe8d//ud/LvBiWaCeiKxo/cCHhxvN6dW4ADw+nhbTfk7M9wsKmJWnWUxqb4KuVq2N1ZWY72Iew4cdBjcVDWbdUXIQhXE9XxDpcqGNOFCNgEv3ZNnTD5rLYVpZmPP+RzfGnUHMz5/m6TmOA4fs4Rs0WjCXDEigu1SkCkurPx/t6qFhlas7pdyMJeaN6x5u8nq94qwEGkTi+i+AbA2DqOC47kO0Bm12WBrmiPMN4r5h+U7aBO1x4BCvXpf3rLv0CQDvQPufaJk31nC5W+tf4i3ZegsshWVMBeTeoMJ7s+euPjv3K/+77ukqK+7WMdaytYg9X8moq2IgW0QVuSA/zgNQwfQDQmWqnVkxLd8h3gcD80dreOta+zyHV7loyv/n52coSXNOPB5HVKxa9KkAVpl4aUDvgsej4/HomGeHjoYxBOKAhAa15+MBnQ1zNpzjFd4M6cub6ybMvX9eOr/OJYpyGWO5WcO6Npl+vsIQea2/k7JhTeK/gh0fAADUw3w2/pLxTuI3ZWsYyE3zgmXwkYQmA3yqGjaSCVFBO7wCoxoYzdZxvkEkv8NAsKoGNuRYGgBtK7yniVihELoVmj9nJUABEMdbbusSWfMlgtb8FHDHSpYj4pUDgTjItvm5Vfn075zzxbGnP77EVzFPF7y8njEBO5cpya+NNi88TMAwtcve4BVyT9c0LuTn38kgu5Z0Bu+n0gQBBRbfIi69jPy+/bbk6f0RljdqrWtD5rwMe+RxAMfxhKpGgh9P/Z2TuRwdrR3o/YHeHziOB44meLglPTMIAqS8CbKQ4juqRZfMm88iUD7PE4/ngY/XM5gzBTQZsKpGZde8sH///fdFmcjJmZyWzPTG++VAmgRjpTWtpnP3xTWGbkRha9m7gz8BxA/zyy0EQjBdRHgN2x3greAmLG4eMsXfDOXh/HBuz/PE+X7hX//n/xVz3Hr32OYrsX/ME9CrB6M1t8rcMXlXInTauccEcXAmxM0XFSu+QcsHK+UNnZWPWoY2gyw2UxYWQGYoGudnBq2VKl7Y6VEAPI7nl8plby1ON3aps8U+b6CZ0b5xZsPc+mTXIA7zXEzPpYDTt+Ka/F8ZJ0NZNK35YobrujyX8Ry+lWPIFefmjHCw3O7oh/vJDD8aVlPN72qpzAtwWeva3218N+PeFDHf18Hvypw5oezAvex7WitbjYv/Bq2ufaY1fg8sGqi8tsoB5tflvbQJ7yL474BoCGClpTaFFXi/M71VYZ29awDl8e594S2Re+NgkM+GKqTDCoUoXOl041vjORj7nLXWcDgf3kNQLLTqjLNuVoGN1S/mVvwVSeKhcLgsyod3hUJ0HBjHgclcMWkAzDiHGJoZ7OZpFd1a69Ce8sigW06ViECm8ZGaxI0S3QCsPaqq4fGv2AFYnjTurTgDis8pisZ3Ujbu6Kx6hnP/2TKdZ2/14lVXZTnft8mM9B3XLcBoqox1B74z3664pHrWAUBdIYCsnBDLhUvjUQVo+RcJI2z8nfdH7zEC9fuIw/Lglhdw37/wV20KGJIsnld5lvl4XZM8D1fFzFqNBKG3Ls9pxXFxfZEnG06oCk1YErE3ufnoBi/+O/Lk9z0bZTJCx74RFqtDa3IJwjIzzIyBEzJUcDS5LBAXhZNeBfWdNlf/zgKLnhbVuSkbO6MCoFcwRMaVN+u+aTg/+/ut/K1DNgHEz47o3SomWIk2z7eYJjgvhL92vY1JqZyk+Qqecu37V+CuEn+1wub7g3Dn4D5cFOHgjNaS3aV93Xwk9yYSm6R+z78sTEUiJjfW4NuoGgyDIuPiXkCy6NOSawJ+zW8LhcJuQlRpaa1fmI+dx7HmKocS8RoC9rv4XuvXTgd1n/EaPn+Oue+NZP8I+oECk0qVWXF5no2qvxPXErA99bG+O+bjhrlVus5zFN9nulI1Z00Ft7wGSS8S2SireknqibvEWHdgtu6hvK/q/IfnsN+HGFVQnpWNaiUzEAj3PCrkSgZ/pH3Ft/P3mV/nOY1xFbqeapZ3zk8V7Hc8Kb/DXwByHZ1qic3k7c7gdOoereGt9ifvnZ0m9rFAFbpcG2ZZ5f5SBVrH7BOzd4w20JsrBGJW36DH3gIUAVY2/TzfkHNXFlRnePLY3cfjgOoqCUx+M1A8cljz291QNsdhCcCQUMTzntNpieQCAB3Qsd6flQ2uR2stvI8ZT+gNzWw5LqGMGe8dGKE0si9jTj8kUZYimGljTC/Y8n1kCttljsrnvzJuAAsUdzcIK65KARKdrj0Kn8dMt6m0/eBt2ZKPdS/uFG7FLAn9ga9Iq00g0tHV949YCM8E/KA+/FCjRAAApidJREFUU0AtCqABnftPYh8Zn845KyPC9tg/9bk5wii9ywsV2592Av3cwivvZCXnusqjjLE2vpSVASwlO+PNfT12GVBlXy7okHklAGSJto0Vaw30Yi7e1zBHAv1vUTbuTj/Og717MQfIhOrlGZnb8/j8nz9/GgPtLUIqeF9OvM0eiAArKT7ujoDvhNvr84V//esdz7g7hbi3x2V82aNzp8XDY9n4t4hApqZ61LRO2GFsj+TZmGqeo3CUN9YxSFooLAxL3LVo7xqh1bdQx/d1+JVixrmlhyjnquS4eF7buxUrPfqyhG2JaHOBYa536/MmjIEAaBfMeU67xwz//0Nb3a5AfLl6WfZ1zYMpG5JD59pMyoZEqJnF2c4Atpbw1ryE53IBm2XCOOlOOauTIWwluULLHmIFHajifZ4bwM4KR1rOAFSqFjrSwRA8Sddfld99HnUp2cLdcRWklVZi9tNzz1yqMNajgv60Y9TH43Oo7pmJOfFQrUN+He5zp2xkfpX/roBhzgnIsqJncJyfVechGz2yRV5TJbDv0PKBh6EYywrV5BwF7/iFJS1byQks+Ny6PvUQ1M0qCQQIpZDerNwkyZZNLKuFfPCNXtdprZEZ3sKoMzWMWo2eMth5Rb11tMPH7Ztep+LxeGC495myqz338xOAidMTxlkSmNNHsM+qU7yn9pmVEvMc5DXsx4FjTDyOw21ki+YIXnVMnDPth/PE33//bd/7mRp5rzRWCkIqJpGUAqTr02KjwQ5bZIhRB9djAUeMCViEquVkeYlSndPyAwou+S7tzviX8Ui1jld8lhtpL/P64FvApnDwT8qKxZtM7kCAeVaDH1dK4m/S6A7QFZZreu8x2PbOBNAn6E0H9qI/VDAz/QYollbmKBl9sKrAzTGSdrSRurUOgHmL0MBJeV6rElHpKPPmX7WrYrbywfL3d/KT/CsriCvUGYFDU6dK/3RbvTq+r2T2P7XfVjZ4sBuFQPYEZKtrjsmuZ1/kxGoufmbotDz8/flzA/xk/Jy4GnJCQs79y5uvuhzZ/x8/PqwQQWZ0dFcTRLuywUZAzvWpAN4I/FrD+8GEpNhIiqN1j/m2yiACYM4Tc7YNXCziTSFK6iThxDHd2iaqGHLVTO8IJGu+XIc4RGlew9Bq+ICIxMFZGTyMMfwArBHv4Wb+cpPJEvJ3hNzFLFtZq96e/c2UkQqgMtO5o+W6PuqMUAXrwPEMnoSKRoc0C90TWZ6wJgaIURiLpN/GiNe8tunWKgrmBPgB4P06QSXZ+skxdLCerOp+EFTz61StPG/vDYAnKhYm90/M605ojpkttnscP3/O92v1yee2rpXkHWOT4zTpnzeEpaz3jqMfaMCtsKneBfYrC4rn83krlDY6KILvDjzneWHf8vulyQU8fIdWPTP0GJHv5zyxCirY8v1zTlOn2vJcZNmyAX43ltT5z1Tx1VzftQzCVDXCozJfzEaUOcXya851cF/zcpxRFekcGG/PyRl+vhXEvQembJyPRyRu87Rj0oyFta65PY4Dn5+f0KQ8cL7r+VWZZvM8cJ+0bX8z9KQB04t+qGKkuR1z53eQE+Pz097pXoy8rijv5GeRvprXjfHkqot/YecVFQRyj+Y14dkkmTd/l/ZVqE1te1TJvdV757k7z6RyCFknxNuF6x1BE4BbwMWKd5S25MMyRFUgvl+7j3fHVYwEsPEZvUoYIej9z+O/U5wBl735Gp+IiaQAZHrhMzq9JA3AjPLTmT/Vec6y4VeN+7AaQfJzcuGQzO/vwquCz/j5bZf1KO/PCjz3FBieAgBydSrU/fU77beVjVyGkdbsx+NhbksHNWTkM23gsILL8hw0BwkEt8F0PZ/j/Pxc17Za/x+Y87iAyzwJdXMyZpuhEVyw43FYkptbl5jcWpWN3FYoBbA20k7YrVmFqAWM7SA+VXPTqlqRw0NyErwziWkVJWgdyAx3REXbtLiFOExTX6Ubgevmy2OhkMz5LnfKF/9dmdPj8UTzpEJzL7JbgtnADkN1WRByTXN+SStDbrmvneCvEHvu63doYREs9JmF7d1ZFrX/tEwEc0mK26IJMkWAtELmEz8QS+JLFvRc3phrmoFqtaLXMcy5vBTZFS8CO4ujSeqDQESjNK9d1yHo2z6dOjc6gxagJzuTDHqZ49LHrOjVtRGRsJrmuZx6VXbsbwNXc06rZNLMeqpQvKeDwbn4Bt9TwVJdZ1r5MiDOYRDHcWAIoha8QkF/lpUTXntN6ClqYkUBPPxKBehQKzX9zRrztMKiCCReKFYdxYFx978tPk/tZ0ye4uPV+zr8NEvInDg/P020quJIckHnBOb0KCjbQ6HEOB2e0z27UAMZIhiL+RrNp6IIIoLHcUQxBFAOTjuMVWD8S+ChXmrFGM5zoqmCpTbZFLDCAM2VrEfHPO2Mgt6AowFPHZgNASaO47BQKcqv48Dj+QAEeKqiP5+Qf/3L8ibHafmCzyfk+QQeH5jHE6MdONsBaQfAgh/SccyJc544WWEIgHlhO0RWJMDQ02jQlSJpLATTIvTknJ8YY+DHXz8ghxndRADFCcCVlgYABFh2Te9HVNIBqFMZLz3HwPs8MUUxYfRiuOThyeuGLURYmc5DcZonNk8rZKOCKJ8KrdLoz7WqZGQ+z5Z5OQDcyRR+Hs+5iX4g/V4/u8rYkAeaAe31Xfydwfmd3F6KpfFdswTvzzBcxrzgY8NZS5mR+F/jaeEiUYxGk+JiBpmGVozHnIOwOzlN99YwS4W0ytvvjEj12vyu3Jc8FzWk+atnV+UsKp0mo/Hd+qyHXhV4kzPstFXIzMpG7cvvtN9WNv7z8REl+Xj2Re9W/3++T7zfp5cAe8WJ0M/nE8fziefjiePoeKTTuDsapgBHf+BUwYDgPd2i+58g1vYKCxNTmSBugz9Hh2JYpZp+kCLQD0HTBtV0Kuz0fHqxShr96Hh+HFG+s7cHpDuxurdgqmKciiEvNFlhQlSycqta9Pq3bRoRy0MBgOFyc7KW/2ZlEkg70BuMUYbAtc3eEpCa04liWvw+mkLmNVSjEmQeB61edgDU2CwF+bpcwYZCGwBG79CxwNq0uBPI44njUHQ1Rg4B5DiAdgDo+PHxl/UNijEF+p4Ra219XW5D67MniQk8l4V/WLsJn/4W7atNmOnnbgOblXZuQP7qt8kMLlkRkyID0OtwtXZR6cjWkayo1P6rKh7PhzHd4lVif1QVx+Gx4zG+ZUsxC5FV/WCxgF95uqrAr0xTATzailPnfj/HiEPdLkIh5UBlZim01iUla1m/JIBOE1mHqs0ZVr2miMMr7yxOfM9mAXclfzuJObW7wzmrxZmf5T1f2zwVSJ7K79Bab6FM3IVJabKsttZccPqaUcmL+TYDGJpVj8kV0pobNrwK6CUUR2QBNVN8vEy7ujLRLNQV2Gl1TlP9mhvbDg/1nNMMPcPHMOHVWxjm656K1hEn2TNvb4ShDjYWUcAPxTPWav09uuCpVoVMxzoY9PX6jDH35wPH84l+2DlI7fGwIhutYb7fVlr3ONAfD7THB3A8oMcD2g/M1jEd0HXxfEJX+MzaC8CDSWzuDvTuc97JH6YpGw+rXHUcHefrjddrmMLl4sCWW8ND05qiuZHK9tiJOX1MbRnljNYPvH2e33NiiIQXuDUxhVYA1YkTasa9VBocDOukAkUaBFh79Fu1BaZ371M2BPK73LKBMN8bgHrjdbsCS+yBm2fHv2X9/RV/yTzrV14NwwvrfAo+fHkjrPIljQNWXWpFRdjPwl324zmh6GhdL/NkNh0z3mzdcSOeqNE6ezNzSGBRGPJ8XuapfF8Vj4wBqhHqrtW14BzPZPD/LWOs7LT15WWpz/+uogH8G8rGgyFTrYXraZzpgLUxjGFAoNLQjoaH1/0/uh1AhqKNkeCbCNC8/rOK4xC/dlhZwKZ7fHQW1pyI3DaiLoAnAIGoKRspIScACq04KRmwElEllLsf+375IkSAVbXNBMud9rk2zldGluT294vILO6AZbYaXwR7IsgMWnIIQCXG7pWnzsdjJVHCLQHtutF66wZC3W2OJtDpmxcG9hbgS6c0J+HGspbfTwyslpWkupb8dwWhbBmM8rTdmQBJpr3qJp/zBgxxf+leran29y7e946hPI8fAQ7y58r6+c0tg0jXAEtpEovRFVlW+fCsYPUt5jD1kzpV7lcGjtt4LkKU8tL2O2TvP7CS6gBsVZ9a8Dt7lhUBnaH9MdyH/RFceUCeR4LgXF3vbs6jf0lo3AmtO96R13nOaUnE+BoI/ImWhSKwPMYcZ8c+JnpAKFSDZ0MinE2R4q9LuAF/c03HGFHy0pTkI96Xlba8JjlMg4Yc/ru1FqFxHAtDw7juS6kceHqFuAziptfYF7fwr5zFpYjS+PPXX3+Zh216tbHWttyL4/ATlXuPSj2WN9fxMQY+Pj7Wdc8H/vrrLzyfz1BCqHxpOsCMngDAwiHt1R1NJ/o4cBwDUwWqXuYdu9exPw4cZlfewtsiDCzC5xp0rnBTOwtgFZlZ879opB+Hi4lF+7kyJdIYskGA8jIfamiA93uF5WZv82YkkRWJUT0HVe4Ay5tReUnIeZjBd1NgXNnYZEV6vnTZmTWAmw9AgxSweGs1XKno7d6zD1octvd4PIIn5/vHuPLUiRlGB/ZrRXAooH7yR+qy6Rm2H7UpMAGVAZ2r+tadwpDf/RXfzut2F+mQjU/52trunquqft7GrlB+1YRzIllu3Bv6Kqb5ndC+3H5b2fjx4wcAROgTgX5mxK01/PjxI4g/A1XeW0vT5hAr2/ATinVidQ7FyloeF0XTJribyLyoFWj3LoDu1kA+J1sf2ddc1rQuwB2Q4zUANsLLChd/V6LN9+T21eLGxhev7FPAaJ6L+izOSc17yaFs+Z5tPXyNCBSAlfxZ5yMYGFbZRzceYOrXiXnjnBD5Ojkqf/anW7XSkulwv1QFubq+6xyE8ivzZj2zIijbO4LW5oRMCeAD7Emx3M/VMpPfz9/m1VgQn3SwvGGmCI50oJOtrZ1TYU/tJfTKhSX2vqny0MfolSkNuoPmMc5tjslgSf+tNQ8/SgwSV4uedZXGieseN6JN3oq2H2625lvD+/bVPHKcbJleMi9TF/KZb2QA/VXYROVF/8Sj/kQjv2mtQVu/8MDq7Tv9cC9+x1wxqGKewwuI6KZs5LCsW2CTQg+rsp3b3d6IfYm15+tzspeYEQFUNpp7cjdupzTXrPnh4Z8bf+8d/aPH2TNQ5s+tPKDW7MBX9oFKxPs8MeZcylVr6EfHx8cHPj4+TEFp2bBnXnReC6GtiEVQHEK4h13jtPqB8T7dY03vh49Jl1KYjVsfHx84jgeaGD2sA0tx2QM27GR8EYFQjutujOQ63cky0hwVxfM8TRbdk+0fa3lvZDrLsjoX0WHLc5x5kP3szw7DSsEK6h4/FhXIuQjL7BM9TfuH4L755xZWq0rFyWiDfRKZEBmop1Wvfqa9RGU9rmGo7g7Srb9r/TOvrniSRuAA6VSqpmK2ZRC9M2TcrddXrWLSVRluHw/HkSMQfvXsLFsoe5eRe3murn1cxVdM1onRAJ97qzj+++23lY0sCHPFIn7H0JDMXCuD34j0hikbIcwAK3mj8P13YDQvTAZrNZyoAjk7EXlZDDKYrUQUm1GuFVPyOHltVqSWlWhXYO6syvn+ylT+CURE/7ArH3dKX1X8MvHz2VmgVkbHfj2fTyt7mDZD5LukMK669txEiyGML8dq996Dqe8EoACg4TCjmKYqK6qQCbQpEF3x9CIW8sf4+ymMlmw4RrN682pJvv14oDU75AsiK+xPGTqQksFcaIh4Muf7jcejm3JtqBkGIiy++eiC83wDquhip2D3bmGOmW4HTiiSEtlpARFicbeQ2L4iczskhXMNwYRgqLvADS/i5Nr3jp4OycuKrNiglgIz516dS3UDCUMndJxxoFOP/eGWZo0siLCQq678jbwXpAm0icXHCDxZ3EJ4RFLo35zo8sDr9cIcA80PLzvd+wuYN+QkPwJCwMMV8eEg6fl4QNTeqVMxu8X6SzfhrQr04wGIRPEJCg2oYopgNkvIh6gVG/gmKGoZRjpwU9UL2IH9Pz1rzomhK+y0KnNZmQkgn+drvRVA8ij2dpEF/D6Dmtz/DOzyWBbPAlTPeFvw7un5AtFPzyngeAIcN7QukEO3ohvH8eT2MJ6RjD7qCnj3cefPOMYsy8Y0z0+O5wYc1HtOlk6FoqPrxDw6uhww16FijEVsYVDoB47DIpQ+xx4+auPqaNI32br4PuXR7gnL6y83Se55Tfg5+2PeLc/5SZUvdVoJ5e/S6HXJPxnwAldjBtutbAxQ7ZXQUAybugy9C3gvxUZE3MtImbM8BnxWXlfSlh0AvHtNKsY5jgfO8+XK6YquWHm9A4KGo6+zwGolR+IO2xaK5/MjBl1xUVU+7uavGj7yetyW4HbarIWKiInz3ADrYNrsEc2eTD7z/X7HZ7X/vIb7QCkPpiYvrL97M3h6yOEWcm8RRiLknbtxLCq1xjz/nhfw3zpOlpPLCclxsbSKk7Cy1W+biET7klw3ZHKe+xeLwftrqUQ+s27CdQbGrqzkMfC3nXPRQ3nIrRJZ/ru6f3+lbMS4isU5x+TVssL5GfmzrHhV63kOBXu9l2dojXV3gWW3Pp+d16vOWX5W3UABDKe5pXOVsnyveUJYGlG3MWSCDUHjwu4rz08FhN+hSZSO8l+qkdAKMm7AcwcQYHOqWgy23y7TS+zNGUAZEDvYyEG3gcdpSZmS6tWTLto6CyLe56DdujIxIX52iT1XIKEAiHfQ7ewY84QmMEAQQMBEkMTk/82K2HaBScBuT14AX1qDpL1Ou9i21109mCIR8rQxPNI5DLj344giBpk568zCh29b7Q4Aqz9fvP8RMii+Lk3sTItuvhoC3RWAArTjWNXGksLB+UBrHpLatvlSTGjrQUaq8NrvNoesAERQOv1ZPoPxnO/Qgic1iXFmvnKnYNT9HwKVYEFNecheacCMFbXUcO/d988ORrM2pqqxhnd9yu+ohpJ8TQaHBLORi8d92jpa22nuDjg39y42aXEOC685PD+DIRFV2YBYWdlORcnfQWUj+u58iXsN/jvvce5VRYP2jjan7QFlSdNVXpUKUo+TzYET5bwsvnRb51XOdM2xWeSr3ASsMqndB2RCv5P/la5y0QxtE9lr8p1a5UkZA/Dv3KrCPudEO3qsibRV6c3W4SrvrTTzNSSrtrt3Z7xCg3TGhHfjaW1XGtd1wdrT9a2Mn3sVEOE+ck9V0M8q0uMUHnS+uLQ/P+QbVSq9WPq3uSp4p+7lOkdUeuucZa81n8XvyTvz97f8J/GCeL9/np/DHm20JMvT0zo9U/t9d+v3q/ZvlL79F1QV52kardXnfqA1uogPt1xY1Yw5z7D8tahO84AI0PuJMcwiaERuwGcMC7fIVu5gJAm4Z+afmTk1w8yIeC8bPz/P062vqfJUCf/Jwikz7c/Pzw2cVyXhrp1eEYONfc6W2+ryZDm+POYM4jNx5D4evWPIHjqT224tli3pP8/5XasKTDs6uudgSFI2jJEJ5mkJgcO39QRw6kRXgfTmgLjBjmo3K7X0hvZYsfATOzPi73+a8z/RqsUJzqwJjNb3vqGhZlMXT0Fz8MqqNUHbYwAEnwCaJ95PPzXZQhiStTz3ISpHIRQVEYY8zVSJSVYoUWZkZMhzrcSucFz/rrS5Cw230Kc+1us5l6Gsz7kpx4Bb/FIc/8bY07OixCaSIC2MszLrbFHi+Lb7v/g3f/Jeynszjze3/N4oyCAS62D0sBT2ps0+c+tsLjmM8p4sHH9TLvxvb/SG9taND8huYTxxzbvIQpFWbahCedhkscJvzzvPqAiYefoVDNi9GXzu3++e7Uw7NfcvG3Qyj9c58fFwo1UU4XhY4ZAAKNeys8sN5rJJGnjYsnV2p/ksN4bvOaTvJ/aYfxHzkGGuvpvl2/fLen14ZgUdDcABK6hikF+9gtcIumSsfWsNCsFT9j2PlMfBdlU09n1zycfQpBCltcvXXPhzaQuEf3nJ/3irIBa48suKQ3hNvT+D1tYEDCMP/lT2D5VjncUQsCXQByTPrAfiZ56wPPLjeIDpAFZRD5iYyIffCsxYA7gBbvBMGioFSxkWN6TEHJgFi50PhXnqGQoz/7d5QF2RQSgcHBVp3Mw1zftX16IaSjjH2fNZ1wJYMrMqJfXzLdSr0O4d9gyFonVAcgTJjmtjX8hS9NdzlhHRvM9yGUcdzz+1f6v0LZCF7ypl+3w+tvj+NSHUFBcjzzF7e+OCXct85UmoIUFVk6TLjYtlfd0tU+searoL6FcregYjFE75+gq+v2oZaN5ZCL6yuPyqsS9ZUPuNF0GY56jGKmYl7k5rrnPBzwhY1d8Zgrm1kNrc3NEvzrouCzzLewKLYkg1CsSJoV8pF/8093+iMScFZBg6N2AcykZKclSPaxW4UEwkYSDb17iZx8OY7rI6zfg+0SmABgMRlJ9cHxlWdWcMOz0VoIJjTN2Un25Ge1WMm3ln3/m7Mtg7ZQNtP5vnjtbYoqxmypeKcL3WMEqitT0ElzNZFOlg0kWR8X7uzWxAoILDPvDdHF+2ai++oDjnGe/Oh1jm0IE67jz+AK3DQF6sqxZL+XYj0r5vYYlWNVVDw7H2PbSN8IAX4xGbTL18lsFjrANg9CnuUSuyZVMcVaMCFBPKIXs1Kt4TIRw34ILrmJOK+e+8znxm5U8Ks63MyYpju6LuPd9kIejRyFbofhiYaNan93wht6+AgTTBkazMImY0Mq/Q7nFn4QZT0G1fwfm+gHtsKWni/GhIwzEVcGXDckYeDiyt0tZxHIgQUp8nU0I7Wuvx99qfe8x9Dq/tzQ9+5Z5P4Ds8rHMVaAhPl+yedYLw7+TZqHyG7U4OVkyRaaDKfWnmfd2U5HRvYIWxyqjGu4GkcBCc+v/8nS08UxYqTJqz5wpmUBGfYs9prW95fzEGmJI7YSGy0la/qfjWeVFVjPcLRNOZ/25gfqY9uuAK6Bf2wGA0AO+ZPH1lrrJMvAsvytfcGbD4ecZrWZH8CgNtn6c5aVhy0L5DrLk9E7fPiYNtYZ4NSdp35Sf/P1c2fv78GRNBK/iPHz/weDzwfD63cqpkBHeTXQEIUBYdHsuNedk4bNWKCixL1Pv93s7uuJuYrxYtV7vI77zrRx5rJrD8k9+byzHeEUoWUJkZVo9O3Ug1NEtEwl2dwR8tgYz9q/k22YJXlYrcT76X/X2nxPB1erWDQtX44Qagxaue5OsvMEHMzesfn6fVhe/HTq7/pKz9idYehx9o6GvjjFrnfvbJnApW1zKM1Ey5mGrhVHO6xmXXU9kwproUObYA0arAGOax8PArdaQ5da86IgBkNmBOvE4zJiybBhDhGOLJpJA42M46vd45x0SbO7iq9FoV3Lp38m/SGFsOg8nhEw/PiYi9CoSFN/bNsYpLTF0lS/m+rGxkAVALUOQ+8n05bJPXnucZ/SJYIrgGEPG8vI8t84bjOKAyMQaA8XWKXsxpS2sDsypSu5zzCpj/dHs+nwkk7MK0teb2h+UhYLsTcPEcVTS5+TzRoiS+CA9Hqrx6lxGyldm9ezb5NA1yd/TPsYQCcr6XIVXzuwHVZZmndbi3VZHOnmUGLIve4zzufeNJ5prGFjEhSLygmRc6j3/NQQuFKxsOTUtbELM1CfkNmGHD+LV5OfrxcIs1PaziJUwtT8VkgvN81cg/mm6FtlyNsdZElwe39QPH44GBvYrdFZgna/dNFEFeO0m85zu2zJ/4dw1FznyK1+xriziHIpS4pGgCK1xtjr0YEODKPd9RUWvpa+5jjui4A99cwF3ZWODXngMcB+measDOQ/PfU5fhsvYJ6d/0nwoQ5B4GDf9b/TybPL6aZ5H3ffYwVu9dDTWveLWO46u2fUe5JxJlu72n27XcE9fnNIgsY2fz8sK1n/+uPPltZeP1el2EMZkrmXcWoFnhyJp5LetV3dTsv7oaO8l0hv1IG+j9dODaqK8GeLGqJfs77Ap76HDgN6YxQW0LaHy1mHeEkMESE4HuQkli/j4/IzEvmCHnKrt51YFF8kBkMISYp7VJwGf6uxmGUTczn0MmdZcHc0fgdyED6krEiwf6QFwDFhzi4TgKRO0fkUgsnFnoiVjYlJ9+O+1hpowQyKqmDVsVtdsl+2OttWZlVGP9RpSPbAks526rqoWPiZ/47VboBfoNhMiYkMexM0TSg10WJ2qLKwOnTvT0MhGsEAjpFnqQBJWL8nU9uS3cqumJ0uJ/z+ROt0MxYXkCIus356a756xYdKrCkcEasKr95J/zPHGmhENVXfkupFkHUYcfIBe0KzvDJf/6CiDmdcoGAfaz7rGsTNx7P+bmqflK4bHDpnR7Tj6QEBk4JIoSsRVsoJfj+7VaavQSSpDWBdjnbxdy2bK5r2nmxaQHpM8E+3UZDGVFl63SQm65r8AyflU6I8gbJ5+x04WB6D3viYfEIgGvOU3Bt6M4JAwAAciw83SCc27mGGOTyDuq82uAxX40nfcUfMctFiINTRuknTA4Z7lUrR/OBrw0bWuuTLmi0LoVb3C+Mt8nFC4DwqtjHtbpcxVmFrW/WrNqWo/nA/P9WjIyKXAxatnHmUPzcu6icGzfuNX9UA07wOKD/Dd/V37L55kXg4crL4t6VVLs3rV/vjoytBqbSd93pV3ZByoPq49G//zeaD8bbHjvLM9psVfq+CsPMBrLMg8LgzvNE4swEiHPyd1YgN1jlPuWeV02nN39XPp+0y6Yk+8seJRGx71pbLV4RjOFg8nTtvfveQTwdUWu2n5b2WBoUgaouWQXJ/HONVQ9HtnrkQFEDN8VDXsmGfHyCtx5NvjOXF70TvvaFrN9rUXWe/J1OeSKShY/28exg4u7TX7RwtMmn3Pu4LE88258IqsyWN4Eq2b56gc9UhSU1TJSvSyVOQ1VvOcI4u7wMLq2AAKVBlrJxQWcLb5ECFXrXlbRKX+mMTJTQH4RSPsrrf9/sjHpdOgMxRa4AhsDip6PMlNCL2NQ1ao2tdYwh81ha82qLwHBNAQrp6XSwvreGnNr4jArAeTR8WglVGejLRdmiFusl+0arlLBOT+PuUnf3YXzbfOoenlHBXAZUA1Xbvd3WZWp4DlqCslm/KCwTiE9mXFnz2Led3U/rvnecwE2UK17onAGA3l+ltBFrFU+wTXPYfRFBB3LG/jdWwCZOcM4lL/jPLKp6sW62ppZ3SXdx+/u5IQUPlbDR8kfI5+kdy+esPrA37W4Ri6YUkFajImK6byG6hqvZrUlBdDDo4EjKZfTrLpjnDjfr/BsqALylDDghCEr9UEFu+xugmNelS21QbnS17xKW6kYZrFrWxEKvN07q4A2Huhu1lUawOacYBJ5zHF4m/a9ad2wU8qhNJztdMRnHMeB91jhi7nyo+2PFjKo0mG25N/hiu/S7owfwJqzevhu5hfkEzb/uhmBeD89GBeFW3DdMyIRYjfIr77AZFwHvqviIfY3jykbEdb+pNhL8k4BxZIhDNPjyfSmd65kcJOGLhs8z5F/R7kSKtQArKS8KfTT73epuI3nTu7lUOGqMGRcWOertq9o8o6W/+n6zBfsfT6f6bUxdl7r+aG/UoR+p/22spGFMAnn58+faK3h58+fW9hPJhBgaXjAtcTpVSu6L0HG994xcBH5UkOs7Z8WJF931785J/7rv/5rAQIC7QJKVDXOI8lnUGSgku/NwjFmIm3S+tmv5oIVovL1r9criLv29U7ZyAL7KyGqAow0Xt6Tz+uoFsucmM7SdRQYnAcKfLbjONBk5dPkdfxu7f1+xynEAEwg+/hPnehj0WTMIzSuay4IBrzijLcxT2jv6LND2yqJCcDCrja75dpbrTUMAXrQsiRA6t6NwyrU6HA3urTINYF6zD9M8DO0A+7WVgcO3GpmHDCnxibsY8z2H5VlFKiMOcacrP8EZPaO5RWl57M2dcUo9hCBlvPVsyaXlv1S9xW/q59drk2elF1Q7lZzGinq8/O1ed4qvzpTLkjwQFXYYaR+T1/WPYt3vxdmf6L9/Plz7WXsRTwIIjKfizC0ArDUFUrAC1O0xbOyMvF+vzE8vO35fDo/HLFPOe+9H2huPBpjYEwzBmSwXNeHhTyy4aby5lyEBADm6xOqZoE/x4S+LVG2NV//YdWlPj6Ml79eL7x+vsL49nw+8dfzw5SPYQff8QDaLFNo8BlzxKnsIoIff/0Vh/3FvItw48ZnnM/sZRznSJ7bpTzHeRwCSOs4sA5KVIZbHQ0CxXwPl40DrZkl+vl8Bu9/v994v8Z27sfn58vXqNuhhqn05s+fP7dQMPje4Lwfx4GmC3/kfQmsqnGfn5/bWTnfoWX+sCnPBStUflH/HTwr+OXVgHkLnLGs5UvZaOhUQJLyXHkln5uNNXf8dRuLd00gK8Rv0JtlvRaXZcH71d9Jo6auPkf1KWW0xPp7KR4xkcjyDB6yN0VgmXDNq7DdG8wyjsvK4R3uA67en1+tYQX4X2Gg+FydP0ZffMb0GprKabxTFik0q2y86+ev2m8rG1kw5jACJpRl0MBENvbJXFw7EL8Q2KZprZaVlruFqdovn/UrZeMrhSMTRCaYOpnZWsnnZHCehU315Hy1yTif9V13C3nHcO4IPCsTtWVBfKdY1fHfElSs701cu8dD81RcJjSLB+k0EbzdW0YFKCwo+VXpnhp6cvf7T7fX286rYHynNAGmMzwPHM37IE5Lb7INm4dmkR0aYJaobAXVZcdWgcp0gG1MlrG4rbWwaHJP8r3sCxk3xBQXyypfITzsWa6BD2TL2tUzqLvxbGvqzDzTTFU4uHdycnY1KLRmZ4EIyt5phen3RctzTpzDvHGbV0U1wFh+/r5Wv2PRMSWM48oAlAp0tXhVHpT5GLAZnexZienn+7l/w2usHoKDqyfqT7fod6ET1T0/hWPKIS/b/KQxtbaHDmR5MacfQJrlhD2k9IuGX1nPTzIrvz+PJfr4CxCx8djWQd1JycOw1jrTm/VF0t4FePAWYiRGdxd+LR4ows+E4H8paQBW2exC31RWBIkty7LsMuYk+upr0FsPYGNnwCh6Zw4ZAHEvjjhPJNuP/jdATGZYbo2FXKlOK9XbO1o/0h7ejYJ3cpZJ4/w8+lwBodyZL/5cuwOZeT9nXsfr5xcKQKxR+i4rGxU3Bc0mGq/KxrjBCRWUkp/m5/J5+RpbAwDJk6tA5O4AYmcx9W4UzP4nz3z0xQjV+5JKZGPtaa505TvRd1WoLL6qqlZyWpeMqjKshq5lOXPHhzMfv5uPrT/e7gzTcU3ZuwDxO6BYOUotGS11LuU8v+luXr6KRvin9m+cIP5xS1CmaJyXCSXoBoCPjw/woL/8vf1bfG52Zldd4NmidLfJ6gLna+8s9nmT5c3G9hXI7r3j//g//o/t+RuwkeUl+Pvvv3GeJz4/PyMpOysDdb4qoR3HgR8/flyEXNWWK5HT88JwKl5Pxef5fOLxeODHjx8bmKvtjlllbX4CmG4IO+dSqM45worHOTpT1SCO+ZwD58cHJjROBHZjXzg+AYv1n+fA5+d76xfXM6/7n26KpDDZQGwkFNhZyIFAXjHOAciIkABaJXLVHMDXwEtKWkKlM/EONykigEELOp9R4pbPsL6kveNClqajEPwleHnBtJXomfvGZDICEgIbVY/d7gcUq7DAVwC4CkE+n9/FD62bYbnyYfh9rbdVyUwMCHlkyu7KN4R5MYjEfKV9xr1G69CWqzYVLAfO+zLP4t55Pp/xdw514D1hAVdFmzPoKgOOO/61PWvMEMjZsvgd2uYxrjQgO9XFadf+N4W7zWm2/i25kvPVgm8l/mXv3W6/bwXAL/B/b8SpfOlOOTXlz89Lcbhj+8Q6ZbfsFuEMjDhldedcFY3yXZMwRrBvfK+m68hvBBL8OJQg/haBCPmA7R0zIpmCG4mvk7yKEbMWzqTq2R266B7R7+b5HHMpH81yM1Q7em+p9j/zmzoU15CUvF8khTjG5/bldt/ElSS/S8ugtGIP4Br9kQ2A/Ozx8XTl/Wvi3+jI6vXEu1iljXdXwyQVi6pw5AiOMIgkPhDGp4L9CI7HVPS2PFWabA3rPbqt3eK/a15oRNh+f9FUmYu4Y8wcpsfx5nWoCl5VKCoO5D05CojPr6FW+flV4Yj+3SoqsgwAibcgXRfKXn4eZf02LzuW+53228rGX3/9dQHfOf7uLkeCg84njldgnoH2r4BjZuJVY87X5OvuhEL1QGTh+5UWmZ+dF/kuJi8vOKt2PR4PfL5eO9EkIdZbLj23+tBb9yRbYIN9/jdDQ+IZngvx4/wRmyDHFhPIPp9PPJ9P/PjxY3Ptb5sxjTl/zvc9Hg+oAGezw/x4knjeVHmOCL4AKzZQwVwuJnBnFRFY1ZEFHug5uk/m/FOt9R5gX/3cmDktTpbCLpdDFRGIGvM6xwCeflAiSpm8o4VlTt8nJhSnDsw5LAwKTIAFQIDi8d8sIxh9pMCQxLTccpwFv9sAANBKMiGim1vdYrYX0KaipY5YzJLiDFkOdPcKnjexqnlO8ueZb2yMWhXyeJjyNTzULDNOmAEYIlbSdw3HmlhH55wWg5ze/U9hisDKY+Mhlud54pz22cfHB1Q1wheBvWLe4/HYKublfRbC1Dri52jcM/ZsAGCicCj50xI+pf+at/6JFnufcW3YhVsozYXP6g2/XqDnWk2MYGO6wsbPv+oTgf4Co4LWC9DQPSFXVaM8c1b4qqzMQv5gP6EpijwpEQE0rHi10cYa7VcyMvrFvDjOURM0XJPW4T8CbFZNSffbPJsvxPYU93+DePGKu+IMhtQsudSe0YC2ey+vVlLz6HIP0HTSmoVZWSGghibHtjfNAu57fy61rOKDS3RFW4n3nLsp5in+Lu3x+Ah5txRsDUOtOB+3Ij52D+nU6G6sfSaC/mhkjMYC51LOl8yxsKHWeMJ8Dw/yhCubzXDHfHvBHkYgzIHzXCHSj9bxeKRDE9WL8ygp3/bZ0RrQO8732/uWcBvEPdaAnG+c/205Tq0JWu/4YBh2M+04lIs5Md7MXZ1eAt7X2j0Us9l1rTfoNHlJmSnW6WVAE2D8/BtQQHSi01wwTpjfoOPwoyBMhlt0hxn7rDDR2k827omOBi/EIB3iIciUsZ2yOWTStLNDADRdclqgjiNcnneJohjDq87QSIyj4z2HR2A0pyUPsfZlmbAzccYcSGx621tzrpPN/6n9G56NH0EsfFHWvEis543FMh9sdKcB8oyOXIKV7+A1+fqqtd0pHFkQZC2yhg9VZSQ/s77/EqbwhbuMn+UTM9vRcf4iDKiOOd4bJxTvilRWmqpX5MePHzjPc8vT4POezyc+Pj5C4SA44Xiz0piVjTw2Ki3SGnpvXppwbgyrrgUBRKaHqmxs70qocM6JLnbw1SoAkA9CvLcy/pEm/h9dCWqZZu8qV0j64ZjoAg46HxPaNcCWuitY/F1mxU55CTGP0+nO3tTacqPqVAwvJ3mxXlA2RZ8MUbi8MiEVoMyYYevrhGTArLe9edlZndApGOcMRfsrl3JMZdq3/Ps61zBgNX/xPRDll/N30jyWXFaiceZrtArW/ZUVaQrUAEeKOJmc1+Zx5mdl/pFphP9+jTPWICoO4cqfcr+Y0CxYoTGAhbve3fOnWoAaXTQGYN8TRfE0Et5lQZWCfE5ev/D6tpUbZt8V5RPuDURS/CABTnKTsh7AqtgI7GtbjVhaaVGpbCzlyRQPcbzu8kY6pqzzVVpr7knjByCxbH3NY8xzyoND2y/2jXqiLStENE3zkBUVmRh9QqVhKIBBbLCUpDFhZ8dgp/M51RWMNXdm4V3ew5DXXq6w7heR5eVQGK+5GC6wewKFeyWtY2t2IrqO76NsUCkArpgoy5i1L3Y+qusx8E2yzoGCnflSq3dWzLVO5k7UpM5XnePoenx4vbP3JPAWrvuO0kbivwgl0GcgZJ712Q0G6kVT/IeVFTM2nSPlaKiHkfE9gjBEzXE1dlLxBxBlmenpEJEIJQPI2wn+dQtN1MykNg+lcnLiJ2wKhSfet6V8Ir9vFiNsUkLN8NCsgM26O+ac/Yrw6OEh3JwHn0edV8Pwr9pvKxs56Xd1ZpV7pUBleACwGMSd1yN7Rd7vN16vFz4/P9F7x8fHj3gXrRs1nntZqMg47nMQyKzX+xiXvTZptmJmgHwXF8d2pyxkUFE3K5psyka+proh+V2O896Ae7qn/gaAQ9bBhhk4cc5yZbHs2szuzaxgZACWgZK0hqN3TG1QmXF43CwKS+3/f/z4K55lp4db/kGACQKQyXjfidYO9HSmQmYmtbLFn2xLuVrJuQoAb70yoDw/3n0dE+N94vBTO3PLSiPEmbZ4Gcw5wEONuoiVMPTcBLOMU5h7zLTawUict4/jAdWWJAVSf+1joZCHWY6qIMm/tzlxZWDMEX9nCyNbBmMVKOQW96SukoZDKU7lPM+ZSk4bigWwyouqKno6TLHSE/dGVSCYCF4NKK+f7wsv2XnSnmB855kbY+D1fi9jQBNMgogbBp9nalNGFBbC0plD9z2U8vDoYIGLACMe7pONOFnusFXFZOq0MP9CN+G9FSvBSiURInWLuZ2yhDrelH2s76hrXMMhKj2Mk0YD57Wqm3eARRjSGzYDWZfm5xwAKlYXf8oZ4GhXcHQDd3xf7IkYOwJ5bPvPu2Lb2MfoJXHZJvacoYkZntWsHNSzIcgj8zwG6Nc9Fl5VoLKU9kUXgta623gU8ET76/ksS67Fu5PsC8VQBN/pCPFqXLgo28jK7zV8RoLxWcuGwxkFBvZKb/s77hXmio2M1BS5DO3SG3T/7AtxXZ+blfV8TZ6HPD+5GmnGB3levnpvzQn8qk8snd5Eomob+5N5VuAnXHnW3i67MF1/8404fwTNfAsvisBzW66FewCBtDV38xxb36iMaf2xL3ca+CIK5Vft36pGxU5mxsl/3wHtDKKrgM0dvcTWJlCbY8vuGHxulUjqtewHmQ5bjQnneKur/J+IML+Hzwhmqep1xq+AKleHyoR7Vx7ud5QN7UDXdY4GXfx5zvJ6ZE9EHecdo8tMQ8S0eyY5NrXI2UqEue9MsOf3c05IAZgZSMzy7gpEv0scOgC0o4c1FK2ht4kuVl1qvN5rM9NyJrISmgED+HMCzU8vxgIhvJdC0oDQspzImFDSmFudpk6ABgB/L+8HECUM2Sj6l76x+tuej9QPLO+KiP0mICJYE4K3VQFlqDuN5apwZBq7EzDX7/fktbDqpudNAMjhmyk5coEaDaNf3l85N2BTDMs1d0CA77/jk601/Otf/0oAYR9jPkyw0nnECGMBLt5L7+DRLPO4QaKk9PdQxVcLfoSlxAL3wvgrsMEWPG0qFLtFL4N/ejaCf4Tied/EJPfGp+p65z5l3vSrQxubSCjAN2/1vu1ec5Fm1Vt9bxvYB8zbaMa2CQNVd8/kaeTZvmrfEKDbrq/ic+uidysrh9zrIhbOohsIwSU80cLCstyUhbU2Ol3vsD3TARVTYmIuGxQ7P8t93ubX+VVeQxEJmVzX7xdk8T/eKj7J4zK5j6C5Ocd2LYBlwY81WNfMOTHOZVj82gh0NQrxWcaN1Pbf1BXiE5jkLnTxa45UPYa1sX93BUWyghFVLXVhn6/Gx/vrZ3XMqor+OKKfzUNo457iPfMXxxrlZ68cyn1eVNeW4NaUmBc+s6EXI43xf4XZMfa5a04DyucotmIZDopNViceR2UjnubXVfz+O+3fVjYqCM2tKhi8Zx2Nfk2AOY6jnPZ9Bb9La7u3dubv70J48nX7/buVnOAjg3/2PW9Q/v5KCeIc5Bri0rvF7KXchKps5CRvPqeGZt0pclXZgAJwr0X2NNU1qhsM+LoUW1VsAAcJ6b15rmoVrnw/8zder1eUOeQ1DKX71VpnS3MGhN+hUQADZPJezrI1TGe8Iu5t4NyoAyL1CNBMw84kZsRauPVJ6LZmlfHVdgYCr4K10iebiFnym1kEqSzw2QTeUEDUrV9zAucJlLnOnq68LtXjdBXme1hiVToyj6jj2vag6FJsOGxclRMRVuBpAdSDvpqYEubvySWYM9AM0Jr6k4UcgMhbuaPHqkDU/V4NAa1bXPH0MCjMxdh7TsLlPlY1r+CxwEfv3WOozy/A7Z9puZw2QxFizzswrOCK9M6mPt5sPaVUzPTIecgKdH7eJiNA8L5bc/M6V3CaBW7lW/m+CKHbwhvEvZqraENr3WgyxuqFVBJJNXRzOCjC2n/OTyDxRwBROIFKwZwWEpGLtYhIvK/O+cRdhSJEWBXnDLATw/NZKK4VGA/00luqPMejr4fx/f5EOwvHQI40u1daD3wggBloeodMu1+Blc8TGC8rsAqraLsfMhzKFpZsMYUP375VBZ1g9CLD/XNex2gJW9crj808z+jcQvXuDQGAgt7guWRI5mW6Y5hF60lRFTqjzfzAUFx+n39f+ELaf+d5bj9jDAg8t6MYzOu9d6d5Vwyz4cqCMSvPj70lsgyGaV6Zg4EkwVWv1R7z3KnPHQ2ESwGxdeL8bm0q9CbUPPBl4odV0bDbaYy7GqHnnOY6/43228rGqlRDUGKMg/H64Y7xA0BsQT2Maq48jgaWsTPtShrQjxbxcj4NF4LP4UQZ9NYwrvyzb8K7zbIW5g603ykqu/v2Xvu+KCAC9N4sEbiMKys6/Lxuqvrsu/fmv8/zjJhGhnvkOahgPisHeZxVuahzoqpWj1wYK+7VS4QmMPuOFmNas5lIhqmewHXGPDWbLBxHR/fKJe2wRLQKcvO6f5cmoQQghCetldIbdEw/L2DF1DeYpRJ9lbjN1l57rv3WMDMsgchrRQQtTYWIeUvG6e5hOJMQgbZUVaTZqe46Zigo3BmN9yiiVDHHQ8Fs1WcaWN2K8bE8oEySEMnhIVXhqK0Klqygxz5vX6y9rpA1wEGLKxp8HtwqyFnMAjkrs+wL78sWNH639rMpAjnxu/KmOSeO49jOEKj7XlWBY6/UQqXxK0U8V1tqzcITsyX6O7XgMSJRplkSkTNeuIYkQXcwRZoGADSJfDKuRzb4nGNAPWyX1Wzq1Lhku/T1CrivxqxsNCK9MhS2nvtxSCrMAWAM23SteaUlYXhv0q/ENOpszBBZ/WutrWc0L3ltyP3iJY958UNVgasBUUQwx8TQaWWwVcOTipinVdRkK48vy8AYP644qCrG3K3fWdG0A31nzBkBMXMCAcFx7AVeWmsY5xthoEn7NYAhdgURWHkrnL85p5UdzwrhN2xrzvbPLgQNLu/6fJfzAsHV2r8DbDtDpUZf8NlUNHQjVmDOwUJWiQ9LKBU0iLiuucJap2DKle/eGZ5yX8mTs6IxxkATk2R3BoT8nI+Pj6CpjIkqHcc8oPJ+pWgGkEqcSyzCJZrH+p5CAPNziE29zPU5XnENDQ/ilkFVjQMZtZshQVlauqe9rbrK53NebUL2OdFkuImeXJvo19/V9tvKRrhh1V1F5pQ0IsMCyyMfG9+Ys3HuzxFLLAaMGfaewhGm4dcs0KlhctFpqWJJxLwBSCxZIN1tEnuWT1gSGlXhyN9XRSM/i9fFHGUArIB0qwWSXbjs46p5fLXixXMoaL5QRHK/zvMMK09rLRLV+Xe+h89i3gyFUD4Y8G4uQtEYVqpOXaEyTdOI3KqXiH3koUWdgJTjGzM2iYhgeOy8gWG30PUecdPsA60Q9VTpP92W8uPWG2fmohZioF0DCC7LBpWxnDBcHjytFHADoH3F5irv7SvHw3i5xDyfp9MKgYrXs9RpJ2+rWr7NMkxJeDfs4DFXD6lAFQFHxUIkl+rM1yx2xe9U7wHbGv81l2pzV/PzG1anWMA7rnO+BV+TCYUXLnEgJGABmjuDReYLmb/k/UBwmEs+h1W99CfnS+W9mz1DnUpg8ADfR20dKpXR6DafHlI0x/CEPpup9k1i0QN4F2VDdeVp5XUPvlj4L6+3cV+LZuT1yZX5CMyRwoCAXdkwQ8Di+5n/Zy8XgSqNOuShBMcXuaDwCj+mVKgqmhoP5flE2dNQFQBXM/jJUkBTuAyVjWzEYh+AlDOjnIcr3YtYJRoaFLcQFLUy1zlshHy5ScNs04pWZP6WPdLvcmJ8WqcLgNMZAJLKBnDdh+NcJ4gvMJaMiFM94T3xG11GmJhfwcVb/B3anTwGKv7Y6cXmY9Fz5VtNmh0Kq0WJ3/hfMmyVtap9q/2acG+rLKv8Mjax0JNEuB3gpWZdLoXV3vvguqjhzjli7SxqZCkJcwzHQJa/UGVN3vPsP8O7M9as82/0HZOx7Rk1oRbXLgPS8hBmJWMwmqZ7RTRko9sy+k1JBqtpgjsb8rgfI7SJxvqmcMffGsumbPiPpuMMkqdDVk+SErn2JOIZ/z8Oo8onOn/VstbIhcknr3KicyUifreIu4XFb1uYsU4PzoSeXeVfWbkzgeVnmoDoGyFWgrxTWu6ssHWjss8r/KK5ZccYtGgLQiE44kFr/pAdRpEAHO2Z1pq0bdktVBx31KhPmyAzkjy3dS2rVl89HVMVw5P6ebqvyG5VYrhPxO4CfrCTWnJ57zg91lfnxDjPuObxeODwKmVzKOZ7umUMmG06GNY7Y84fa/04YiyicE+egvZIdaVptn3ORfbKFsBuvYGDZTKcYBZquUAtWSj5PIIZGQMUGGT2zTn3HBOYAmkdcQIwnwGBxY6bgOc7vgIHwDrwsobP7eDBn55oMu/pCs4yYKoKB3Tf79GfAlrEOXeAHdXIEwLg1sxdcFQeU0Hr3QFOWamiByNfk3OnKq/Je80v2sI8Wi5RqMkThbXXwgugqZw0gEGB/01KeuYQA0khDiG+OB8OJIRx4WbaWuc/0Grrp8LxXloTFcCY6ZTr7SRhe+MC6St7KWjA+2cVyzSqA9LAwuv7YWU3TYkgsHbL8XaWlJWCdbwcY0Z6ryW6r7ykRRc+Vs6DOghrNOZ0QEyGps2+lA/fL6qmtE1Yjh21bsWyaIpvfVkL4gqgP1NXz2ndVPYnyYjgVQlQKoAuOV7cutogzml8D83pUEYgQuu5X6Ezdc7KXjMR2A5EXUoUQADrYyRtMCIjbDHTfJ8yraLWN2l3oJctl4+1v3feJeQhRSkMPiMekaB7uBKV8CWDFmbKcmlTJoOXLTCcxTNBL6/Nf2/A399HIAxgVeW0njkwXwndNXQ7xgozgt7lxcb83Mi07BGN8bVlIKgGj4x9eX2WRRM3ucm8Z6rJnrTGqjOwHp8bK6l8Jyty6f5cnGZE0IbhCkmuOLdCK31/zok5R1SojDW4kWneYZc7E1W5/VX7t5SNqlnn8J81STvRfiVU77RotmV9Z6KdoHcNIl5EsYT6cjmnTRb9zY4gAbD3OYPpPL76rLtNz75vVp80Lv6eTjxkj/V5men9qlHL1ATqAcLBfTx5LPXfdRzVGsh+Z5crsFfc4txTbmaFR/K8kRby55CIOz9ah7YRYNclV9xHIEhrn4gNWOsm+AatHQ06fI1d2RBZsZWk0TY62uzAcEbqQIJM1QBRLs+pXj5WLcdjSlSJCbAW9IgEGqgk+vORLBZqjGcOj+l2zwmQUBRAKYHDvScL9Cy6Zd985K4E5XC85W6fapkToYJR6GX65FjWqwCs+NGvlA0OLCsaRh+yvnPmTppliICgRfhheBSibzbZqsxhsdE3j103JTKt8QSgDlLjbAHnWY7mCNCmLhDMNctLsPYMwn0/8t6Tpcyv0Bw3IohFtg5fl+zZ/JPtOI5V/QWwmvcAMKzG/anGd8Y88fr7THSciKEbNbOW/DkH3uMN8RAdNBPa7yjGAMjR3VqImG9A0WGhm+RvDMOYbWIeFsojR7ea9640vl4vwHn/4+MRHmVFR7fNj3Y0PMynbd9DcU4FzoZWvB5m6UzhRbFWClZbpLI1pj2L82LgaFXre8/pIN68nkfvELf26jSvg6gr0sq9Zypd3rc/9Y3PeULPvfqkSHOsr5xqnK9XKA7RL/decYzTjZZPDzWcYwIYaBAchymNAybPTlpa20RrwNEbtDFHbKIdHYAZMuwsrxNAhxxiZww4DznnCR1JlqczBcZYIeANDZ1V3+7Puf0j7Su5XQGu/fvmPscK5Il7NIAicvLKO5cB6AqmM45rHkK71plHIVDnFbS+DCHGq1KYURlD/mwpP7j8zvmv+fiFij15tEIOcax4M+OavKdWmezdmwksQE5DRn5mxbcD1+qedZyUHfbhwtIWMTLQm0DRvIrkWOV/0zPIu4SKEefrhu/PMaOgwxhjhbOn9RfKqaSswsN5aUjQ+XsGrN9WNn7+/Algj7HkIGvLi12vz56FO/BuGi9P6VX0fiRlwpSNVaJshtD/++/PDTTnk36zNVTkDBBNF94F+BdCyGOguzx/nxWeDHDy2RHjtcByJur8vvr5r1rehPWz5/O5AbL8s1uY1/uez2eUN+Y61c3LTbs2LDDO00Dm1CW8mp/aDA8JIM+DeAihQHrDj+cTXSzPoMtKyG2AWdKGCRp0s2wy9I79IbNZ7vU/387Tkt1ZypdgUKHAQUWzoT8+8DEOzPPEOIcBjcQ8mNpAK600s8SxxrXxDzto6Tg8ARPuQRzDwhEx7CyUdJL8RjNqczzMVhxMiR4MoxcH3FPwpAKQE8K6x3172d3P1wtTTaD1duA4BL1b/hbAQ8pgJXt9biCM8W5h8VGd7gKWAOsbk/b7ssJJSx1aizhsnu3z7EfQ9xwT5kG1+XudL7Qfj1DYhqof9uV7QS3EzKy7gGqHouN9Kl7vEYqIyAMCwXkqBB8WBigHVN0QATsFGa3hHG+8p4U4KdSBM8ISZcLBDuqzEK+VjCet4WjASBnD0slrnffoWEmBAawnBq5ezD/VvuK5X1kbs1eoJtfTiwRZXvR8nQQ9r0Igmf9VWUFQ1vo6JDH3zTu98fn6PeWZ+nNzOCvBxDYf4WFxOVYqWk239Gu6JvNrGgwyL8xz2NMcDCzjYd5XkQzq7e1elHzdndxZY9jl2mRCeGpN7KCy2rJBi+duARZy1t1DmPuQ1z/Ofkq0slUew04X1WiW+1Bp6zu3K2i9VoHMVkzSPpsBzev5GjvG29+ZcyRUFf3R0DsPn3P+l2hEGhJuYH5GMRy418/vuIzzzoCaPRkMs+M4suE0Kxv5GZwL/rzf733vJuWk0lv0KQH6wIAV3+Fmrycl3PLTzFwsvGHNChQT8DN3xjjth8YQDxXLz38r8yizcpfmz99EZX6OiXlez1m5VNTTZdwLDPlv4K5/I2djMbxfgeFMqBkIZtD61X13BJEZQxYUFdhXzTILqa8EWXYDXTfoztDviP1u3Hmu+G8yAZY4rUK09j0ra7/TMoGoKlii5VcK1G71tZZD0bL2fxciVjpwq8Dczf/Sjhc9ZEGR6YsWKwBWVKA9tmfUn+/SMiAWMhC4wtGtGAI3rR2M6F6hdLouxKzToqvUY6xN71EFLAvMSp8Ks+DCgVNPCgkZFZlxVtQzU90YrWX6pT5eleXeLQ270jefVWPJM8CovCXT4R0t20dLmN5Z5zhmgj0RCaFERYTgZAcyJhg3a9T0sAARD5GhIFpJ71CFpnh0WoSMzwxfWI4LAeTyXN/R8h0wynyi3nPhad9sn9CrxH+bcQSAmjfQwOiivZNGDVsA2wPuNeTaZCU581iCBiord2GjtdXQudsxlP3H0FYqEqT9rUJT3HfD3xMIo1dAsYcgqz3gYkSrZTszyIixeJ+aSORTGH6YuQtrPGr8qoYqx9zahWusYy7DUvD+GcaJCPHLQPSGh0mZ/8yX7ngEn/M4Hht/EqcjRjaM6C/CQKZzFcVgJqzoHnbyp1s+ofk69gXS14nru2LINC3yQ4Jqm2sJZZr3mGywex+PB3o/0Fq/YDiuRxzGbKh6w01NLCTa5E8KoWKYFkYYUbh288XzYoxUeuvmB58KnWbRn+cbOk6crxfe7xNzDhzHgcMNrWBEQe9xIHXGPaQj/q5KCPk+Dz7mta/XC88fH3HdSJW9/LXOlxKGheGXzfuCpNDIUmiooENW6OQ8J97nCUvm18hne79fABRH63Eu1xgnnkdHi7xmyjFXxHRFW5gxyw1V0gGZON8jrlNMSCokIWIeXitAZEoPlZTfab9fjcpbJuKLtlaAdM7R+IphZ+3Skm+6CQ3sQjQz71joVI8+5xdUgJMJigzaQIZpihmwkkiyq7EC/39SNoBriJl2sw5nd14eF/9dn/dP4KCuBUHMph7f3FMVjpzcyP7fjTF/R+D1FejPlqivxsnrcrIsW3aTHv2Bx+Pe7fqdQNRdW2u5gMMWbnB0QL16C4fRYNbpgUiKDSWgFeYIgrVFA9meSBqhR64Kr9ba5UCjqgirKg60WwUDWOPqx9XifGm6519k4cV77hTXPB77jQU2b3hRdplnTyeAZBWydubkUjHXNQGhnYJsCX1zzgj/28MOfd+pQg7mZqx9RhuVgS24sGU+zFJgMt/8p6YC45UJ3N4YBW/35p9ukjoqplnb3IEegVQ9rwBbALF/Ii7VnxTNNwWfRwDAv7e50PVbVaNvTTw3Zi46DJqExEm8q08aygGQipUkxZ60Ummavc9ySmQppvZ4V7Sw74nYDwQx7GFWuBzki+6lsg1Y/NoIKG1XNsJKmnjACqdNhjS3muZ9fFc6OsujzDtqVERdAxS67r3tJVO9P83n3LzNwoVeaz7X84LGvs9WKbL9PnEZQPAZfrau+4VRLjk+dhk9NyPUV3gk06BcPs9K/6LMNDI7l6MoG1lxzs/PynsOn2LuBoeTMV7FWVWmXfZQwZsV025yqMyZ7YmbYd4138uqarlRqR9zDs/Darupkp5NpQeLeR2ZftN+dFkSRkp2WgAJz5KPl0YC7L+jxP42N6agc+1+V678trKRtcOsAddFuQ9d2j0VXzE2AJAm6P3Y3gPs4J3Pzozr+XzuBOlaKAmS7zVNnZb0HSjdlX7dAcVaSI6nvrP+e/X5QOstXHqPdBo2td28yWrN57uWQWEwAgVe5+dlDJyr/I7qdcqWJCofObY6rwMB68fzh1l5fWtsZziIWbGhinOcBjJV8U6H27HyjMDua03iszlHEPTxGGAp5Y3Byfdye98z9AUaCDbdtBEACAAkMVzLU9zH2VoLJaLuhaABgefz8NWy3pNAfQX6dX3rPBvQWZVNKDyGM8E5gDE9T2OZN2nI9+fYz1Q7miuv35dzhwU6MiBZ1+nlXl5PBWOvK6/b3sif74BwL/hnnxujnTN7Rnfjg2BVZaN1sDb7iHNrYxBh6WCE8lctZLkvsV+bVzLRVckpX5OFsNvFLv35E60qkjX0Z4uNTuuV6bTSdLj0pwNNV0R0TIyILzaFPg63BIxGx7TkeV2V8kxwm5LJd4Tyo2rluFPtx4G90qA6MKplkEV0o3m2zjCqwstyZUHr7lXxNu/P4IThQnY3INHu3Wk8FDt/9mzNQyvLj1/DdWu4AWSKW7kK1VDcq5yseCGPczvrSgCMlVulquFR5d6IMWTv5D/I04xtvkurPGQDt6nVUL/Fv/d53sPLVu7Y/rwcpvfrCIL8mSnCi8fVNcx0p0hHJ1DhwNVQG3SDRU+5LG0F/BXD3Zcov+4HVgXNSlWWOQDCg0OPNedum486QULZt4+pzKI/y/IxMMWNinDlACtHQg0bZR5lMmn9u8kK66LxAUAYCmh82BQmvSqMIma45PlRNArFz+2A79tvKxuLOXJCDGwsAb6IgoKTORbWp90iI4iaE6FlkfiF2hTHRBEpa8LWEvlAvAzfnBMYg/q991lXLKoIhp/pYdVD4Ak3vtjQdXKzX6+xeeyZ08erPvG8Vrf/XbVliR9a2gDFymtQWR4DLqoJGon55b95XXQrmIVtYlXEkShKbdsnNfcY8aw1AluTtZ51c4bwDCsRgnFgGphsaqVMQnDAteepYYnIgoKEG/+NzbUqJYxzRPjQogGJEozfvZl+Ye5INE0GCV3z40yJpX6vzN0BI9c7K8H2dewlE8qy5XNkZTKHU2VmWg0CochNUm90BQ0WnsBwidVdG+cCiWSEDhBkKeXVasbfNWyDfdnByag4au8zVoWs7L3gsxfINQWDQsGey/cs4WVV1+hZPWHeieQxFas4l8M9jFfc0yd5Evc2N6rpaXvIgiV8u/KIvM4JKGEJjG0NldWcvl+7E/z5O4KUWumMdLuBCrmWQmfYXFUy6/vuaJF9yMYceqi2a6fu5cKxG3bqORsZUH8FHnNMuiTvL2C8cZSTkWtr1nnoGDg5z7qsv2Ka7ea5ofGDPZnYIxMOVz40K7OuxC1laF+7ux/o2v9ssWcSzefnbE2X3PaLcI7zArRr3uHdPOd1zoa379KqQah+x+9NqbsaZ2ZSNgA7T2LNgeWj7Qcr2+eZvwC7FTvPYewbJIt4sqbnqQxFETnP1QsF+Onnc8ydVsocqO4G3apcRK6C/12NTXfPpLJR1z6/D6AReBUjmij8IT0zG0cyHlxGDv4OtAmFK11hLTF50ETw5lyxctSYhk1DATAZC6wzyQxLUGwsh0BvtUKkXkpVL6OBLgOM59LmvM373XRtv3+CuBzx5OjElu/GwWp8cp6lrnNrHia110DfrHZWwgM6BoYOq9wBgzgteU0o7Kcn1z4eD9fWJqQdaH2iH08w7p8JRP/6+zNi5x4/Hvj4z2dUGOlPt5CLYPSGKQrFwIGOw0GEiKQywM5oXchHVWgRs0yjBSjsEDQrsQHogMLnA8CjNSgEQxHMQU+LTTz9AKrWOx7PI2rS500QypTTpi2VQvXEmBPn4HW7ht263bNAXTPGIIqpnrzKQi5FADweDygUQ99rjYat5cf8wPE4LN6RCeLdhN7EwGu8thJ10z0bEIvPtH8KVIYf2mbXfP48rbSkZIYisNIh3wxGUcMj0KRSOmvOk6412RQ+awEk2qoKYXchQGowFlkx31m4ZqtQ9l7xs3zOy531Zd+fsvEAA15kbGvcSwD0jV7NOkJAvQRiZfx871fAYF1//bzel0+2n3Plr1QhLiIbTfL5uT9mQV/K+RjMITOjifJEc+ylOUMvT0CrCqdby1ca/jauSkPJ+ggRLyjglyoVEPk2u2QT0NjX4M66WGmoAq/WmlX8E4sRb83KaUOsLKo6YIfyfAgXwA6RImAh8UdTP9fZQOzLOtdj3RN7C/dgtkECFFhOAN+gfgjqvpfnnJv1/7YsNvdaw+aRyGcWQJf3mKBRIJF8rVOBVgqHIFlAbVHWWNNacb04WbT6wi2h+ZoAotGXtd4V4FAZymPNntD1RdqbzUyEWaHL/cy8j3/znQGMC4D+7s3ma2cFVVEnHZHfttasgEyi53Hua8FpW89KMgo7H+I7oiw6OY2IhwJpsEDyzK1vY5jRxisjAYhohqqM3o2RUSocV+SPYPX3/X7/UlGrSu1XtED6mWmfBNotz88ymDwj9z1fP2YqJjOzIQOAzjRXby9Pawnda23cs+GPJF4UERzl7CGRVZwhlLYxMN8ryiXG7ocCxr6tyhpYrvr3pMq/nbPBTlaGegEhaaKrxvmV5cimbl+IzAiyIAJg4LjvMLMCLABb9ajsUlOdeH1+bgCFz2e4E2Dg/diqMCkY4iDpuWMMDBHzrOgCYfn52/yoXoi1EvxWSQE7I6mWIuUEyirnt2vyCzzta/hFcjCt6DdNnLEcj2uy/jnOAEm5QoiI2AE27/SMtNnz/AgBE+dCHxA9bq2DqnObmz/elAx2ebgECHcxD5BTLMa+7QcsMEKvhLiiK9O2dlbWqezmpRLZKzXxs+2eRBtkMnmv/Qrox3NQylhfpuL67tZaVNS5uya/h+2rPI5f/YRS3XYapVKR54H9UGVp2+zV2M/22cdm1xoN27+HIg4ybVEaN+3ruUqJR1nuUHCYfN7MVe5zGnpc+veiHQ95nsZzem8Ay0Xb5CEC9b+PtgEq4xy7ADZhSwcDQDqQpcgFz0s0ozlEwPIKZoBcSZNoz9yqswQwB+oEiZiyYV1eBh6RFeol+TuYR2EHxUtgi0paPNIXQnYKjRNTN9DF8KONzSlMxiQlhlNK5UPVnzOuVmnyWKRxBY23VbZW5Ou9Gf/tyZOjCN5j6yExfp0Ts/CfC7+p7xFYaFzZ23kcNRme+TYCYJIW/HMaHrgu9o7EOy5U8D3aV/w4zyEbgfx5noDsOasZUJLXZeOfvSsre1cFkzKbuWlzaoRNL9k84wH2TiNQkrHR3Mq/AL1Kjo1ErUDKlpPh68iKfL0f6F5p8PF8BJA2j+bA+33i/d5D4u7kxJ3Mo5zI8oERAVXZ2PZDeuZ0eU1jUH42/5pvT/6WbITXTb6+Xi+rGjZn8kBwfoEs/h9dljfJD1gOTKg0lgvO823FK1zhi8O3PVfRem71ixWUiZYrAi/LT/n2O+1/SdkA7q0b9Tfj+rmIOQ4XuMYY0jJS3XX3isYOUnOfssJxtwl536knXq/XrVbKUrDGuBrwuMa6d1l/03MS80NQiQV2fjWHVXnIykZVAr6KOwzwUw76YX9VLaE0K4LioJbzub0nvr/vszSzkDFUgW5Ihi1kl3QOX2GrVvba3+3f84Do85Yp3M3FH2u+t8mM4mPOh1vWbWKNQ8yErijsrPqDArrmKMZY10qW55XPWCFZwBwjlFauewXfOa66Cq8q5GINmKuT9iX3L+B7LVkk+bt7KEZ9Zn52pp/c13zN3f3136S//DeATbhyb5BrEwvaz+51iX2s+/uDl2HgLWOVQ237eCCADBPuq9S0x9eO6ULGTmEeHiJGZYLgSdyzMhR++Jt5MdxIDRoPthAVpZEF36LpmGhdEJ6E4cnN6iWdXYDSqAOwRKQBoXzIlz1vmOc5zTew01VevzjsFLuh59JSobFsbbWSxO61c6vxjDVPnhlXAiovyCE/TZopNG3Rgl1oJymLwEMnYIfB8qBArOo1rHAn7kFh3pQmYo7xqcmro+3evRxGE33r7Be2zwGExyf221ylbvNez2PPvCGvD/uQDWd3+/yuHzRkfkjfrm+qe7iXtzEGQGtyAZ4VS3zHdp2zZXnP+RmLv+0Yq3qSl2FlPT9/XzFX9QqrDoSLj0ZYGlkmAJjC0OqedeCqiT6DXsF+X+W7qpXHZzjw4/GIXFwCfIAK12mKNnAZR35elrF5zGuMSXmIfbTLeCoH1fBpZ0td8TJbRJD0ZXCgEUKnhvdHVCO0XAn2XX6w0SixsJ2ti7pBwoxSzse8ktTMstA6GOOe06qocg4iTybll36xRS/tt5WNX23CSpB1oe6A7F1yOV3h4/Xa3nv3PH7HEIkK5u8ABz0EFPTv+cJ7LgLKIPnz8xPv99v+/fjE6+dPfHx8JFdUx+M4QhnYLAPK8KQFsHM84J3yRELPieEZZGUwuClnaYz2e+L1+twsS/bTLmto7zGa2av1GJMQMatFndfsbWIf8jrQqpKtcpynvC53p3regUoLXenQ0zCqyfOA1n6Ow4Us/0i781b5HzY/UKt3D9/4xOjOWCN/idFnsluRGMJBoDrG2KvLyHqmAR6ro549dbUgwebNuhG8/BxLl17XlL+nTuhIHog5Upie0/rRvNpTccvm9d5odA/zqF6dNdarhS9/n93lOY7faJe8c9k1a/9oUKheQHUD0MJy9gwRgcyibPgVMZY0Bu7H7gcErlAuxl5T2UjVjhzsEuDZEHaewlLDVYj+6ZaV0CpXNuU08c/82QqnNeXu+Vh5OBm4tPQ+dbo/ercF43zc0KJ/7JfJxtdaMgJxH9DYFKBMBCoN0w+wy3vg8/N94Z9HawZKine5wQDJGANNLRG6pVLhnAOU9RUswyaAyEWEKl4/P6EeclKV+1AmeocK1knHeS9BMFvas8nwthRvX8e0n7Os/UqWcV1iLIl+o7/JUszf2YNZDZl8d04s5j0ZRLN9pzCqyh/3PbP4CvfDV3OwKwicu6vhOHj5zB7d3SC5yetrj/1744M8N8ny29Y1VJKMb13XM/eprkfGH71EneQ5OM/T886rAqDbc+54UVY+8nOhu1ej9in/vTDWeqYP6heWH4Yb5/cOdOzrbgcptsv61Tk0JWJXGMYYwJhXWkmqUvBbmJeQ+0d15ZverddX7d9WNn718DrZ/yuCTVUx5rhsrAws7pSbuz7le9m2mDQ1k2EGAhngk1iNYa+Dgnq3E1l1zq1vsRjDjpCuwCn3/Q5kXRnJzvS+up7f2fcthaJRS+Za/Ho9FhMRv/TX1mORr8Ecf9fQlTofeTz5HXWNKTnXuAEywH8a1/9k+/nz53ZmCMefD7F6vV8rvAPYjORTgEN2wEtgnJWYz/c7LKqPx8OSZwG8z3cA4sfjgefzA58vU5r//vvvjW4ej8dWAvbW04gFEI7Zg7nRSs+TrKswaO7q3k5E57oPhbTdmkogcefJq6A/W+wobC/MHGsv//3335cqeXnPWgiPeRQsdM/c1vTMPp/Prd76HsKX9rEriLnS0cZvfFXzQUh1LzOs4fV6hxLJk8EDTLk6QwVSCZRSnLxyntyCdc0V+rMtW0drGGVVPmqZ4uDL5dpx2onf+TmbgpCUxPP93sqQZyVlt166sUoajkb6t/LHAb5EIMex9zuVYCWdm+eDJW3dwliENhWi1ho+Pj7i88/XC2OuEroM+RJdoN5AvwF/Aba8id6SMUjnBqQ49/W8BVW1PElZOSTBh1JpYoFAMa8GN2DjWXPs3tS7Axe5TuIVG6tBIa+55eSse1/jdeEF2x4vezfzu9qP71SNivN1J/MzWGSITx1v60j85apEzXmV4yJtW6ssEzKwtzkuOKEt/qpuVd9lCwDse5x4iUD+Doh/hZ3qXGRAbSfEXzEp56fSV5Zlucpl8BgvQhHAKo9bMlRffa78LPrsf7eNLtf+yfdvz/TfOZeMjZErW59EwmORjcH17KVaUW4pOrsRmfKt7qN/av92NarKjGICikJQAQuJIQuY0JyycFDF67Wsh1ko1QnMFs7j2Mvl5n5wU+T3HMcBlSdm+7EJBP68Xi8/PGbgdX7i009QFxH89fEj+kaGGBslxcay75fDkNKmzb/zAvJdX51RwufmBbc5Xgzf5n5VB6sASRlr829EqBLcNweLr9drGw8TtrKQB3ar30Vo3GwojolzYMzIDqiRph4uQW/A9wFRd8LAwEpV1NJaq25AccqK3tB1U4AKss/M2CZ2YBXvPQaAbFFafcgKHeklKxjZ+m8Kn8KqjrmlZCqGK0Nxivs0xTAE3bG8ZVngnZ/7Hs+KBGmK/bqjDU7b9bNr+BUVhQxqKx87eIL36RWAhkLgVjzItqfulA315GSoYurp4Sy0nieglNaUjNzW4joe8nANAS3LQkVvxpkVl2aFKIK3DqgAx6Pj8bz3AP/Jlte1ej353a94Q21jDjS1PCeCbXoyrLw20LoV2XidLw+FchpsKQRDEaGo6qGOAkRBD1poJe1J0nEkcCarsE6GP1hOjTCnhIvu9DOnKbpUDPvRI8zrPAUjhsx7ssVUHOA7Vyg18FkvXwv/zUpYLseb5VTrDeP0MuQMewnRoe6l3b2P/Jo8yvDlGi8PHcv4IJ//s4Xq4MrjQ+lJ9BE8CNh4DfliVjapXLJd9/Pvy8Q/1RYY3C3UVYHkWbH8Poe3qxFFoiPOT9/kQ5Xxd4pLxS+1n1Xe5PtUkqX/BpjXZ+W/s9zLZ6/9zhrma5jjQkWTVbv+v6WFf7rfFPodMwC2Z2wcq+rc5d6kUN99zzWhsQFg1TIJQ7u9y2RMlW0A9vk9lwf1bi1/1f6tnI1fPbQqFxU8E8xX6+BlgpQuN/IlgZWk3DcRAQyQB05QwPjeq2tugVNWm1p94Gaac1ppsOGAq2zirJxwAzWRYL5sJIDzPEtt6/UuWnPJGOp831mg6iLvDMdOKeY4609aMQA7s5hzxmFaWZDlNd7X23MFivLHucnPvdPsN+FU+pjfZevRcfSssMUsfSsAlecg/8ykcNxa2MXjcEQsRjrTkiTXZpOr6tB2a4+KwyS3XjyOZ1yaGT4b+5BDUwBsFuFcZGHz/M0VmjDnjMRvEYv3Ft2rPxngVkzsrv8qRP6JyS+FXjDTeST5h/3KoYlfgwoTvFngrLVcya87fZrqoASmugsL7i+J5L++FA5VU2javMwB+ZPmRBzCtibAWNbCfAaDAcOj9FEwx8TnuJ7l8ycbT94F+Yb/+44H/BNdxHVOf4qJAc6PK/IKz2vwylRK1W1VovJaUp47QjALJsLcvzPxXlXPz5gr7NUv2mKhRYDj6FhLoV59cc/F0yYrJLIBvVF+ASIpnAKIuvoQt+xi7VMR87yIoYnodwYTmzdRBA+Phe+HnQ9FDhTPpDqh2Og25o3r6HyM8pH0z8M/+c5tvrB7MbJCUa+Ja9dUhoI4sAwvTJCnnPqVzOA7v02bL6+kZlRqtGyGRTs3xkMFB9DgfJaeZQjgRzKIKjqAOf7btWeETJrOK8gHVdWNWsa7IIIJ4D0mBs5Iyv6Pjx/4r//z/+W8p6O70nu+To/wsLX/j+dfGOfbjbGwPaFW0PSAlVK2wgET+rB9OxzPjPnGeb4BEGMcEOkhk2jsovGGicxWuKPjGMRFu5E2R6awFOwDwIOVLlXRkvIK2By+1cLLifuMtx++I9RyIVSBZsaCQ00STBaHcBl5iK3POd9+aji5vF03fCxzCAQPDFVI/8AjofaT+8M3wJyCh5rX30pf+3EESWY3AA80nOpFOYi1vSz9qkTXbTxTIcpcEKM9G9F9KfGv2m8rG9ycXwGD+pMtCLwvx4iHxpWAAJnn9Wdi5w3cENwg66CaMVaFhKygLEZDwNdxqnq5xOKenBPzeEDdCpA1O9XlvYgxqWEAgVxA5AJFC8jxPfyM5dqqF+duIfP31bKxPAlXpr0rKQRK2J811Y5SKDHred4Jsmws05nRNXEsKxTXPsPXZ4Ha7AXi8zYABmwniK9n7Yrgn24MqxipPzGPSdngeSF+QcRlby0LUThNlSQ7JiAb0HDra1YUSz8qWLtT+O+UdMB3mi4L2pgD76xoTNsITF61w9VYhSR5F9tSTLPCybkhDVCA/NN85z5Xr4aqed+ycr9PcfI4KBUGBJiP+YnpXLRPRXejP817h0nP5gm0Z1kf3u83pkyrLhagbz1C5EbZZ9+bQE8fp+8N8ufheVaiu4ABQdc32SdsAXGyQk0BymsSTwm6TPezNdg6qSdLG6BRi3XmTcOTqQEMCJoCE4J2HFx0O1zTvSLaEAnSG9BN7w7LOVZuxRgjhDZ5ZF7PHGoZYx4e3+5AZp6nVaPz/bJ50WXPUWrNT7xJRgodk5MLwPPyAHRVvBmamfpELwbEvEOaBrkMfN7XmRRef06FHA2yhVFyrgCExzPLyTB0JNmhyoIne+O8xrqIW76CdtTAk9P8HAMyBacugK2xv9N6JuX0uzQaX0UbkAuNBs9BeF9pYAieJYBOieuAtJ/oKb3BAjb30w6ttJvsWQSfMjGGoLXkHfJn2v5d+3gpguqJxavvXEV1Op0O3idmnA8BuhaxjJzkj1cj8NXQHDThtKVp/5SBR18l3beUCvt5R9Umzo3SRJHeNf2sLCopPtJKV1T4sgxWbGeQKZV0XfuQfanykw/IPFW5Hyjf0vzw30EbWPt1yVRd8j1N6Z2M+lX7bWXjziL2lYJRFYkcl50tClUZmXOiQ/B47M+OKSzAKf/O4OVX9+UWmp4zOVrYGgTSu5X/S67l5hv6v/7rvwJkRPKdJ8jlUrW8L2+K+m/2s7V2a925A0d5nrNyYxbkhjlfv+SV9opl4VmbFGit5sO0uKeGoYh0PI+Ha/Zrw8xpMfBNOkxWKnpfG8DAKWlhUW9rgsPP0WAtfBUHrM1LhIaAAQAqLt+nSfm90Z4Ex7wI7wlE4m8oGWTI+b7tOeXfLtybZm/YVZG4syByD/wqEdfKIFnZO1U7XGjOEyxN6H5CMMxkTrcbe5aqqJdkRQ83/d1eqNbt/N0FgKe+b6Er6brMc2Kfp33XWsM41cE+aZ6xu8b3DukuS+5CLOjdgAun9S4uY02QjP7OGXtMlesTT4X/38N3dAHN/LfuntbZmikbftCi/fbDD4ti+ada9nRVXr2FATrI5ndsmxLuv1tblViyUpUNF3zueZ44ZsrqTy0LWgK3zQh0029MswPHGRKKjU8Dy1NYQ11yy3yD9MyzqfJ8ZToMWSssG2yhlhFlIKuMLWnp+XjgTCG6O79Y+YrZQDF9vji+DejogqxK0FbHVvZ1npvtHWn86s+lArbNmRZF9Is11LkDMk1zGEqg7vd9J285245/0j/cdmg8yAAucc3WBF4em7L09i0AVkh7I435f8l3ttPcURTxtD6UbTWfZ1kLrvwcsuPKkJW6R1DcrdFM+Sd3vHqjA92V3Iovq3E43j0kjNRB++WZxFRJw4s5zO8A9jzDuz7fyb4aDrjzg1n2CsM515poMpRZL1e0SOWVY175Vp3T32n/y6Vv10CuSgdbjY2861RlIAoFo4k2S1ZhrpVo6jMrE8/tIrRCg8b+eRlTkxbJe/4gCxvBrgTU6jsUblULz/35p42Rx1bB2T5mCeZTx6tpnFWhEFzni1aE/fk7g+ketpG/oyvQYpPVD+Zbz6BlhNp6ZaL2pw1CzFwPoFiak9KR/vzjbaNTFFB0s194ocDjwfP168b1Qfq38TJKSWwWqmy9yn2rNMPP7phWjaMHLEwjfixYw39WwvZSAm0Nlf2JtdqNAlWhqAaDzAfulIkL/RUhVT9jywDOLD90idv15zmxcir2g+b2dbRxUVFYe8aXZyeC6GcAIUnLqBp9QBZm2YoLmLV9SlSzYkhVzB04Jlju2RhmSPlNl/f/7vaVF5Rj+SpXLV9fGy2EtYQrf2eAVI1nBOyX93CNkWQClmzL4InWQzjYzsrGBqRLOFul3fwsPo/5JYuH3hQZAGkx7R1ghdNg8dqD1QMTqDAvmRHsCsudoaRsXpMiswQ774Ni4/vRVxQeR9maPJoBcmh8KzKo0s1X9HCHGUKBK/vgDix+10YgS9AI57UWKmQH87ZGHtwWtnH5nw91I8111KDquCWtqfH2Ge+78un93sTjYHRPAR/0mP5r9y8Fn/tr5fRJHFoLXXmg1hvSiQC6vDUSQufrNf5qrXNURlWO4z5n8nNS7q0iDMDVM05azu9/Po/bfuWWZWTGmvm7pTwRF+W+7tfsOVZZmdQYe+Qwp/DOyld/NX+1/S9Vo6qgKb+UTJDf5cTyCi5yTCnA8BIAskKrvkr4qUrMnSZfNdYK8AWyao1LcuNDL9c2CNTjYbsn9CgE2nQjAo69ySpPKiIYx4GRFzCVsmUFnlq/ek5Llv1qPXhvPkWzNQlrJjepzfcC6QRGLPnJyJy1Xln47eAqz2Ve30pwOXenEmhm+HVz5fXOczrnxGt+xoZf1YUkDr77Dq1aWzJt12bWOj/NuHt8NudqLsG8FftIc0VXa2stnauxmILINYyhMozcv3pvtbjUcbXW0P3fy0KilH0OfhYAWsYHxXu8cHjp6MwjaFGt4O9OyfiqX/nffE4WFpWGrbvZyrq+t/rwzWLMl5EqPVsA7Ocq8OhuuyZrG+4B0RZrxSThmHeYVXpAIRGuZXQwlRVI4MpItsotT4aE0scxaJRM/i4g6k521L7d8fS7McRzRLb98dVYq7JxRyMR3msF/fPNAV6ohDDvj5Z/geUrbaF7qh637V6D18ve6ciASgH7J6o4o/oUAMgKo+PYSMfJQ5P3bWsSZ/ioKnRYHDwE690+D/QyR/6Mv2OOdR5FnX8B4sA+ac1klS6+tRRfp0YeSib22iwj2M7zjHvnnLcFYi7rntY6y898XfZ+3j0nK7nZ4vsdWjaKAEvZCLA4jceoLsDLfwN57QAI3KtsoenZE9BdqWQe5iEdrS28EO8O3mLPYhUj+w6Ln9EaPpkbQGXDYK34++ryNitVCL/KeF4/fOt5fiwUMxXHWIqA8P9hcJGEQSpd5N93eKQq9ZdrVP0sl7Hj2oIhQxECb1u0eBf18ivv56KBKx9c+383LKpoKI5xXxM67pf30Okp92H4nqzv+nfbv12N6quX/dPgvxIuFCi8zghdL9fcuXDy86r35I4w8gLHxvRFqQx138h2gJbfiEc/oG1C2963WBgfExUKi4Vd5RWrtZVCqZ6wnp+7LX6y3DGRlYfacA7JfJZMyqAHsTEFhn3qmq37r9qspudXpnw3/xUoVq9P3WRVMe1eZtgYKd2HgjntLJNKC3+yneeZDmvbrXF5XXfGuFv27T7+A1cAtWMqm89easyLW3RaQ+UU9V3xDNktmvVsF4AWW/eGCcyKlMK2DDwlpcefb1Y3Wz8TcrKtuV2zl8bMSemZhtY9GmV+6/e8J98f85d+x/g3q+qaI4Y4WVlVE7xzcg6ovCe+lp4ZQEs5BxOKEzpZ3IL9WEUt7AGujPe2nZpdAeVMZ5WEkEnLyu8ejwd6KqP6Hdr7/b4Ie2A3YpAvsv2TssSDADF15dXle6kMuNLWpRlQMimMhv2gVlXFEMWJXbaoOihX27uz0FlWWHoB7/z9ev3E4/HEcXQ06UZnbfHl6Ym/FqZIQD4hnlNh+/PEojiG9MrG6uOA0YbLGRNIe8YUJbtpCwOecyuhe5lzYJPf+bAvU55XF2N/O+vLYcPbM1QxtnfIktMZRwTeWwr7pX9Jxt7xPSlz8E9etT/RMh9kvzKftvFznMabFg0yOmEZPI5mPBjCnIwCul2+0IBp22Mgl7g1MnXDR8ZhqqmkaioooAqJA/wW3eT0Ud5ToxQu6+5tzBEGrYUj/fmyaAbYC7fk/c3vfhX2nn+I4ZhHRJmX5fsF1PuQaEysz8xV2L7CuZSHeR7uFCGTATwfbeE4YEL9hHdTHieO42EYdqQqXr4+SPiUn9+tQZ7Hf2r/S8pGZvp3C5KFHAUGN3oG+wTOfL6IgIakMa4KRx5UfX+2rAL7RPG6O2CsCR7cg5N9UwuA52ExxNkSwkTymSxmWWD++Ouv2Lx8dwbT9E5sRJuJ29+RPSMErb13nOcZ871cYcvYp65wxMZlYEAR4KyYk4Hnvu7xF1T3w5l4Tf53BYB3buwMNnNS8O62VMDj3k3hELRmVtvsPfnTLYO/Ohf8rWolY5cPabWYs1/oTsYPHJQdB47nI5Rbrgcy8/mHAkR8J+lwjBH0rKrL+zC5t6jLCACGX5nUeHw8oy9jDLxZBWmOKN3Z+hPH8QPAcoVXxl/5Tf69+ryYcxUW+ToC22r0yHRz9I6ZaH49c4XwYcJryK/3sSpQ7JM70JNAL88GWI39XoKYzztah6SylBRim/KahCIVQI6PYG7MiTPHTH+Ddr5eoKJma2NEJfBkTD9Nu/kp9UAyZNRdQ+zcdh6UeU+modZWBcAVErsbSvLDybN2PtksR2Lrl/+XWNjlm8Cqb2UaX0VMSC/m8crgOIc2hpdnKqQvS7Xqes8DnVwdQEoyhUJmC4Ma+XbeY7TENrT9/WNC5ZoLNWmtzryjmXKc57LRr5HpLsns7MnOc3kJf0zreqecKhAWdIErntPyAC1nxfNH/f0i4sDYgeCYUeqehq3v0q6GksUj1p4WPDoLCNDOdA2rsz0X6N5lajI+OlEpdJPRoQh4f5q0VegDO6ZDNQ7z88wbSSOKTemtP9c5SJhzLmNxNuSK7Hs5g3PiJf59/44dZ2aM2VrDwJ7rdKHNGx5rgH1engvsSvedosFWQ0/zGLPBeizABwasRVEgIwyXOg0qluez42M1s6IAguV5reP5d9tvKxt///13WNAZ/kBAeqbKFhmcZsYELA2d2hw/305/jMFaEqoFFDD+DY5yjMjOMYHTuEXrgqN39yCsw5bIhMZ0opi6ntcEXXr0IzwRTIrWndjgG46hTZoWNRJpC/AlY//Xf/83pK1Dy6gYZFd7PiskV+4iQ+Xmfz6fm5XqTtusLQsQKhp39EKrdQZmHKa4mUqEyp/NWQ6FqQoIkzvP88T7/cb7/b4cepetCFcGZ9WEem94HHsuDNt3cnk/ns+dccjayHlOm1glHIbmuGwM6+toVjVjco0JpmFr2ZsVMJgAXqdVfGoieP74C0y+N+VjbuUzWRIR4iLCQ2x674BiU2Ifj8dipABwPAO8KK0fAPphoNAskNbH1h9oXSHnifNkCV1Bax1QwXwzzMsnLsoCqnkNh6A/Dytr6BYszqnC6PTRD4QGo245gqDpqikOAZ79hwlGP3VvAkG/Ig9TsJti6gvn/IlTX7Ag5onzbQnwYRTQjvepGO83no+nVR4bwxIGnUGrgxyR7qdI7wJrYvi+boCyYhAgjdzvjQnB+DTw8Ggt7V2b9wbg0ZqVHvacGV8sKIy/Bt/AhOiI93yHNs5VitIs4vTMiFsK2wI1+cZfyLisRBJ8fPV9FZYV5MQeht5+N+eMnIR4flI0gAXIde7J1rWt52I7B6OJeIjGDmw4hmpMk9Yipr0CNhHEOSHAqnwXQB6CXL1xkxVtKdzZ+t9g/SXP7q1vgDKAmO45JqF4q34pwzYDQ1E26oMW+EyVGCm01O7tjHKYlj9goTqp/JuvE3++U9vxhP1cDQfkOTl0df+pzzTjqFfMa80BT3pRfnfQSypO47meG73NlTt22WPlH1mB+UrZqEpHvnZFCKyQS6PhnU7yvs+/69zezXs1to4xMNsyUpPGN+86rv21YZc5LeP5Jxx3h33Yr+3HS9aaULQ323fmHTUmYOtHr6zqwm+Ba1WBhltemvv+O/gT+F+oRpWtDcu6d6+V5c5v1rhEyLeLzc0h+2f2LgDY77e65qczVAvraB6pICAIsckP4YHFbPMEt9ainGjtGZWPWj5SsJITs3DI1s7pILKGo7EPdxab+l2dbxJ/ttpaGJWNNa/LvjlXWMxl4iEX4l2Kxvq9NvaKT/9V3xmOki1YtUTdZmkE7PRPnQFUc/uKEf3pltexWibDOgmgzQnte615Wgr9QQGsFWsrBG2pVQrJczfTuxeD88e1PaRxvQfBLDH28LUq4JdlRkLxzwrz/d42mqIHY4VrcMwxstUnXUor72s9eS7mxJiIw9LYlrECofjE5CmBYy4eSWMJ0LrVYl98gWNPtK0KHSP2v/YJbfs4mpjlK/aYLC8hfNTKvnrcj9uetr3V2jX8LQ10p5U039kTyr/VlZPvomgAy7BDmuHvZZkkD9ZEI79uuYTknDO8dMCi4xwumHkIy2/X81QUpgxmAxDge7v1SNBXXefo5D1Ab/dwazmrFjZXqJbRR8Pr1Vx4Sfp3AIa2PmttrwhkFafMuzHm2LCjNMHRGrR3jDnwer9DuUPM/5WvhhHB+ziGbkpHNmI9/3rE3rXLNXlDE19R7gEDhrbuXOO1hpk38rOge12y3CdhO5cmy4WdJ4t7/K/l1e+Uze/QFtC7FlTZsIMsPk/eZS0BYDXayBESqgr07jzC3zEZpmfvNeUC6TTy5NVzLKFqOUE6VnXC3E9oZWm6jScwCnbFNjSSZIHP+3cZLCaG7vKLxjm26jWvfID92BW8gjWOxcP9AojkQxB3jxKVqi306kYZYv/u3lsxdL1vmz8xxTH6J2JhbJguj6YZCZBph+ecKKzS5IQdk5Lmt8wR+/C7GOzfCqOqCcw5mYobIjP1bMn91ebdFlOuZy3U8Jrc6nW1Ekn+PP9e/cUKffINAexa5DwHxtQvE3Xr4gegxPp8JObIMBW2j4+PvYa6z3cF7TmWlocPUmjnJHHxXJL3+x1VsF6vF5o0HMfzkh+y3rHm0X6WQsB3ZuuArYuFQ8yZiVDiZ3rCKj9vrWOMrCVbRat8zgZpYZ3maVY3npcgsuIX6zj+dPtKqc4WQWDNX08KR743CtKl/VSZYbYe5XXM1wmwxZRu+9H3204D+/skPbuCX15Hesp7poKzzC+WEZ5lcV0QkYZgCr2eI8LkWCJRMWFx7D7uxFABeHU4WcBaBIKVRK2AlVOWRZ9zDjRtIdh4ABkPKGwLwSxFIfEQDS8JcBffvvEtkQClvxI4vXfMFP6WjQX52i0Upgik4MMK6Pk9DvNjO30vhxdC4HX7BWM0PJ9PSLeQ0Pf5wsfHB47j4cakZbyJhOvzNC938uoB9xWhMsiqwpO0HDLoEHwcHnZx9Fj38X7hX6/PbZ7fw7zvAWgUGBQp05PDMQAVPPoP97SJV+jgnhSMYfRrgZZ2cGV3rz3pfIwT0APNvZVW+tbCM6cq5mm5JmGcEMGzNxy94yEHjkdfBjABnh/uLZ8T+npBx0B7PiEfAnTF589PvF9vA7SH4HE0nKf145wKzAa8u53E3jq6WF/nmDjHxHueGHoAAvQOfDwmePgaGYsEaDXr+DlG5CIKBA9p6N1PXxcAQ1bIyJyQZ8fr84VzGFb4z//4Tzweh3sfTZZ0sbNEVKfn+7mH/VS03vBxfIQy813aMipXRfC+0p4IvNAOUEvLGgY5MSZDsnfLPWQdVzLeb+OfzQ4kbe45M60ky55+UVSV68LnfjGfoasgAdip8UPPdbBzfj51TQwf0OitElqLtufev/+qZPyjshHK8o4z11pclRzKn/z3ndzN9+TwcuKhuzDj3L85J/rjgVWFzPdYAwTN82kEjA6yaUsenznisNGM6f8/7X3ZdiQ3ru0GGamqe7r//1fbpQwS9wEDAQRTJbuPLa2zAl5lSZkxcASwMTG+q47fZ3WvT4ONqkjEzlWUGS1IsYG7ia1KmDHOqGDb+1/F2EVBshPgO6Ah1yJtkug58AlQYcDBleTvDeMT29I8BCAIfQUb6bRlEwRBANr10WMRxzXOR7KUh0XYek0gMwtEVVjW/Mj9cX7sGTm3xPrH0Io4wSIbxzG2NzLH4zgSIEyhRcGSFb+XMRqY4+l9jnMdrQFfTQYgYv9trisQtvkxoJjzFOAuamAJlrjXAGx/JvBbwISRt6N8Hul3LGR3n4HAWlo0rnEwoTe1oK0AKQ1vhFpH1726ErTd0y1zzGY5DX1thWkTudWUDfAjzo3sxW6hVww0K52ooIgh72EVJLqhRPEEPBlZ1rAJ9BcCbDMflaet9i/+UAGEXRvXWnR1R6MERrQxfg+qAJtVKM65cgqYkCsw6ZxUmeN/B54bLXE1BMDCWOu472RW3IMo77yCO6S15150W3dxz1mBARIjT/QOAtByk1aJKpMDZ1bDTlKGYunN1TcLsatkfCaNV5TzGnY851QdjlTxlGKphIZJIe7f9kjYf5f2ex+ysaTK7ghGfX5q+/V5BIB6l9PO5xp/sKx9Xy8E83mmZ0jT87ko34VaaxcdBriu2VyYBDD+wZgaMSbzeyjiawycBDz1mRb+LAbLMB8skSPROx//mWwT3s4eLj9OMeQyxOt2HMZjZV0J2B3JUMksxoXKP4dHCKy91/uBKqUIV7l78sRxHJ4CYONpYwsIn/z165ePa9LnaoQCc5Iztp5tXrquw8xf4OH58R3ynquXJnqeAODxeHjOrr2rrlV73/HjJ6TKJYNUh7McaCiPXPfqPvQ1wIAeSEi9WTTx8swXb2B87+/oL4GNLaMPTGMHTOw+o3hPVDBb74rMstJv11YlprrFd52Pik52mwHGnO26etgUtY7BkNN+TfErjNHboop4Yo6mqJO069evX2nh2kYlIkeuABKK3S1+34TB7e+5H4/mfT7PAShTnxRqlysx57wLe94YASmHnBJCQ2uW6M5o7boGaptjux+PxxY07hB7BBXP5zvG+Z7WkYzD5xf7P0ERAAKVqVyBdxW2fq0dlKgUQaddXyk+M4HlzXX6C8yiakIgXefXaF+ixyC8z+aImZOHLoLUaF0W5WgTwjBnUgMSiDQFS092NsF2Pld+EDVKXhwLE/H3Wi8CX7E58zmAAUHlCxqqxWNICUXbe1PLgtr30PLYzJiE9M40z/KitR7I+raEjY+nT8J17iLg+B0R0fXk+S+mC48E0lqacwKaF+CFM7DfPwvIZZlRy6ZHBcloty/TepikYUQfWDqxQD6F9bYL0QDWlDKzh+ZG4AjAT79O/ZVfykjKvpA2L7AR+Ur8GftKJCbYul+T7B6S2BrXnexnyb0iNA13XVUdI5OQ/S4KPoEcAFQZsGurjcmrAyBrXwjXfJ1YHUlHS9YIrcI1jDUO3xVs1MiRql8ozvN/ci1gAGOto3nhK8SMCcZkoKkXuKn128CsPinx+vjPdbuw/zyst8gtDg0wRR0IfLI892I0ha2RPE9zzqTQr37vDW7191fzbmt77fHdPQZ54X1YfV3r06756F07nvHq2vispWczyAzHLCaB9V4D1dYJiBBiyeda+ofysbDOdjz3z+he/9WhflVZisqQfb+8CNeFUQHEnHqibu86oTKxzeNUI2hZE08UrT2WuLw+X+5a+D1rjOgyadp4t5qs++TfI1gQ1lPgNtq6cGVcxFoQrQ/2XmOmFl7FLAmehsSrApk3bhZ4Yi04tJ1vIHQ8jnFR0AHG83m6ReOpMbwiuLKgFCBygJnwOCaOYyooEKuXxdZHZG/M0ENNwhhXq6OBwOitiR6b4zjUGjLS5tVpwhjLu/LVZH2KQisq3aZIVbBo1gy3tIQxqIp73E9x/ncgjprVMqfLdUSETsuAYCVAd0xfGgnfODsFZadkGUXlcp5TvQHumLisE1Fq1Do0GVOtP3JA3fQb4/iRMsydwrDmAlKNwz1myhd4ZZEYAJhDKtPM88QYE40moGWYxXIk4MZ4xbIGhmp4QGQ20rYYM4wsWInI91KjK3uOY2xryaqQxTGMOXZHy+UzvwPtDBJR6RhjgAlowetHfVlRq6IgoTZh3mk9x/J+PMyUYn7IWrex0ImDlg6vCPYRyJiTcRxZKEdFqdLaY+Qqk/NGADzOtE/rvXa9tUTeYRXNcht9nDkrizIP5NZg48u2XueUBOI5h1d6aiQhUr11tKOj0UQPig0RNLRRx2pozD2rXjNZzquqyfVFGTQgcPX4XpPfVx83ZUynxKnnaxvetEALADVeSI7UR+d6fCXF+bF127udg7E0lQg04n5aoGOqgr/OwIB9r2DQ5Us4kVwU0Axg1rrOxpLYZlNYmUOua5AvCG30Z2DJ0THkQFIvaGB5BtEIrbqfvGt6QQN79s5jae0zqoAifl6vl+jHvF6tLHCUnTY3/gzeP9fGb//d+mxnJKnXMzPOMSTcjOyzpJ16eXrblwBJnjJLFABziByhqzejAszPAo7/WgJ9hBgrU6iDEifYQQkkDjSjdguFMNdURGpxQtd7pCQl0ueriVdFJC0ynYQ9Q9tbP6qDNzJFwMJy80FldYNG4RSV8PScEjIU35PmQPMjjiMr/es9SItGvre5uMbbz8no7YCFuMhiBEAzlUq16+2dsf1mFYvroX4f+1M3V6O+SisCZc7xLSgJuqJkAnDvUZ3bCLYAIJYR2DHx5A0sXo+ojJg1Me+TwKRbZlofiVkR3HnPy5wecl5AmPM4BnX+iQk8MtiqYSlLiVuV4gwYx1CCSEQZBCHsK9+3jYCwzlxRnauqCDPjZEVC0xebtPHUWP+uh1mpEt+anNtAaLBawzL+AIfx6r2jhVDCncLtoHGjkMfrbC63ILPM0fHNPBuRbJxiiXTLUUhr+oWS6aG2lrQ6JezinHL67VSrulhvJURgqhzpzZKkATTS6jzipUKT687TwH1zOQQsAUxqGJNk58hXWfkSgaiv9QkAU7zXEwwaLfHGzJMXVTnae4ca5RP/tnFJ64slNCsCG/tp4+ZgA/CQsTknJoXw4cCrAJOFBkyMDzOYT4yhCeWeFyAVJvFire7WRe8dj8fjEtHwSmmM/WCWHBYq17be0I6ObjmKAKD7sQLZ70CvdBADG2alXmN01bnswE9AEsSrriH8hjFJvFZjSB4bNYKcp9H8AEewGBmZyfWAqtslI5R+NvRQP8UrycMXlXTYO6YWV7CQWQ4hXnyNsCEisNgH0rsjgLTrqnEsfh7Hra5PATpIclD233XerKKeAS3zkF6BRdRRc5ROfPdOt9gp+uc50Jq1XU1ojZJhw0zpDt5IqjRaJcfWmhSZIJJS2xzWUuBNNZLjI/rTYVQ1hjIqO7sBejUgVSC6kjklsTmG7kTXaI35/yzZu6p3IAIeZhUyvJLGoxIUczD8eS/cYnExMDOIw0FPoUxsDfGKLn8A+OOPPxLQiNb/qIzFd9rGiWd3WDtOLUUKjOSefn9/4vlcHorIdMfJaG1gnDMnAzdC7+KF+fXrV4ovtLbGuap938UfmnW/WqgMgUdXvAn2neL5VbQ7mLG6wKsSvl/L1XqEl3ushgSltYcsoOLz6uekBrK0n5MiS/ATuUDgKVYrNMjvIAmpgIZvsTKpOcGTAG44+huGJigCi2GtdhmDJMx5gjn2X5LDz/OJOW1/Dm3PBKOpEGwAOprWCD8H4+iSnEqtw7Q9efYUQ9iUvWxV4y2vsrWGRz9EMbH9OaaHv9g5CpgTmBPt6BhYwNEUwQieYlgH4QoefS5VMd7xrErVCGLrrLXmJ+5+J6pyZJbwn2h4sGIaNJtbXisAZ2b3ZkRDTXyXeQ1GUTji2Ns7jVcxrcRM2dvNDTg1ybyC7LXvW7BAGxfTtqoCZoeBzrmqXyXlv1AEz7bGrESzWayT8gROB+75eDDcGBGfm9tPCqKXoSkCML+GoIpvVKpYLMEIYwyG2dyqXE7zuaGPFK1YnOECGGjFqjMznu9PDFp5c8ubeM0L+WpKgEApKfNpz0vUh4295dssZR7gp+obPDGC4VHOEZqYPNBIvYDu9SoeAn26tO8qw67zx15SmIGUt/5KwY/6jvUn9uU8c5iitG8py9beeqSA7eX4rshPIh/YGYNaz0YgOfBO9kb83AoQmByttUNWf/M42O/ViFjvrfvAr9Mz/VpbwAiqNzEAElQh9/jX+r7eQTzRplR0ZWbwyDquHx4dDBKfof8KbFhHIxqri2yHDO159neqPoUlMOI7FnOsqPCqhH1kKYkbM/bNlNeIxIFcvtbiwR1A0HJj794pE3ktoVvbFPsUw4eiQm7vi2NVF+PvULm1yu5foRVLwbM+y30autNyzL0DIjaLiiUhmXBf8ZUxnleUyxxmABhogG/YOY2xyK6REBbyWulxXj5C+F9B8SyZHTOoCn+kyGAMeNd9Ved7N9dpX6gCnBS7ML9JQQWhSloKTIVKPCyw4uLHyOeIgFZsqwkj/643ZWB0mcf1jHzmR7bqrOp0KyykgXhicj5EVJLcdM9wM767FZDeZ6y9dFADDlUsqYn1rwXBP0VgD5uD0fBsaz5m2MPet9mTRSgChcj35lgKdPWmxj0Ule8I5u37k3Ap1/3VFEOahOdeebn9bWVprfuRH+6oyoMqs+I1u2sBuKHmOU/JzQkKUHxO9d5W/lTv873G5inIcyWySJ4deUHc22mvtOZ7Vs4/0nYVxZSVyXJolz4wySeTf+lUdEHpdrm2weaEYQ2wM5psHcvzbFw72lT+wnou0AZEV55ZP6vjHMmVUiqyz8ZcvVjMjOd4qqVZQsWMn3QtZ/y9EIeAPUYMH7/KduYVOinhUlV+6OdjeIL2nFNzcvR5EwBPjHbiR/she6c3ULtWG3QFPqwp26/yLL0GSEC35ouk+SQtyVrydKxyloT1GTiM0StZt1yen1fnztClL5U/xHUc77GQeN/buHo2qNtYRYP6a31lpyfXEr32MwKn+Bx7Rn80X+v2fmbzssoaaCxV7nSD6rqyW8RDBNdvZY/XdRd1wc/QnwYbrxhu/LmbvHivPc9+xhr9k1krcSwGXKv7RHfzK8Zj7Yi/RyUttq2TlQnL90bUFr0azkhhSlVWEuQ7qHLGOmnXesS7PtgGeXt7c49BnNBdLGsFUa8EcRWqZqnr/YCcHL68EWKxbdhV6FhtJkwWsGGAQ9490Fq2zBmzA+Z2k8iIWT4JYKFczHbabcMPLRxQx+y7AA0ghyKYddQslnaoIRH5wZgALusK0JO1XyhT1c1aQWgdj+gizt/l2H9zskaGlizvlE/Yrvvoo72YrKUsHsTn8x1jTBxdymXOMcRrQA1H6xjniaPbeQSa+MYsjFIZ4XMMvL3JqeWT2Q9BfNPDFWMejLdxhoPJmpWZzWGDNkedmgtJK1tqe9D6EvMhWK27LgzC+JG22ayoc04NU8iGjziG0etZD8+sP22NXRRcnevvROb9tPVjPH/nqY28DcjhVnadlZq9KOOUS4ZHAVmBrlFa17yXHTvBu5NHmdeukCZ0VYrU2pjWKBb/sHVihjgr9bvmmHxfSoWoPT+UvX+tGglqHmbk3mQidMArxwG0DshlU/CsDQA0GmDJERsrTU1tDY+HWJfp2cBzoPdr2MqOf0QvlbX71bWDpx/sKaesZ/3DrjaZQkQaqhPySglAozQfX02NmuS4uFFuhb5EEbHGRby2U39WGczxfCtmZQ0LNBq/WN4MDaGibFTd6TNA8ByGXA7jlWDLyVAjSjh9XPaG6EuEa+4dT/YDWUU3yPNrPyPQiIA2tiUbr/bGi6uOop9j1VBcbS1t8XyN/Zx+hnYe19qPSOsaAYcGHOB71lpv4dIKSARrIEYReN9awzxXeHXU4WN7PkN/KWejbvQa+1cVwfp7bHBEn601TVhaQqEymsgEEmDYCOGIAOM9/hyWjWeHQSXhF0N8yvfR25FOYrVnkzjKTRAQkdZbz27AukGAfSnY2JcY4sTMaeyiNe0KANeYxndaG6yKkOUUPJ8jHUAVxzaNPUm9crvP2lQ3erwvJrPaZ7YOolCOVnhhds14ESwnB7gKna+knSWkKk3JohuYB5A3cFzzdV3HULm4xuNndn2jKwAhXaNJkWNsx9LbaeFTvNZT0xK28TyV2naZV/OcDT18a8ByIUwhluqM/nAVhoDNsYRjqWJkpf2K8LOSn54gXfJVTFmLORPx/qqsttaAGYBzGE/WxRitZ4OnXzu0glVS/lGBAvkY2OdrDbVLe+p6qfyhKpNr/7wOyfkqispmwwYo06pg93g80I5+6T8Qqg2ew8fSwOYOFADA+/u7/11LUhvfmlPyPWJRj8hXo4yIfbLnVgtrpdYauioEVgxE5Oiyzsew1TOAZJ/bUNbZlMHKb32Pq4xMYCsY8RMvsHGD7DnqBxoaGml1KjC6ZZaxWHapMYjFaEHMwDhFUWXxEDIY3FY4yau2xrmonv06j/b7bv4W/1jXnVrFzmVra2htuvfKAP23AhsqBxsILAcm6DiYDMwgWoCGjdvrkHPfG2zrLXwGWtZ7v4P9na+eycxuuOnWRjOO2d5hDl6Pq5EWYS6rngPEsNtdAZVrNdCJqx4ajxnY9WWnx/p3E5gaKksqA9I+I7IkXZeJ9vv+fQvo2btinyrQju2pukWUuc4rNb8GOs96IdLBjGmMsgfNNlHlpTte/BH9JbBRQ5wi0NiFU9nfNezHKHUg3BNjbsW6/foAt4pU7ff6M91vrr1wloe1JyVI79paJiiDjaaLbU3+4KsbrCrj9Sew9+KszZaF204IrznI45UX/bLkesLuyO7IOo4+3libNioLF2uet2MpVLGt8V1140TFuG5eE7LfRZGKtcqB65hFcB37uQOhRru9VBVKe0614MZTSy9AKHzeWpMyroXRx9K565DHBTaAWPUirj/yf8bUpGmanDtITwXPlcRY3bxzSkwxJsIpzdOvb03Aj1WsmnN4eyzMCuDgzpbPx1iAgyGlHs3aI7eLIiVKGGnVE7Gcy9fTz5mxPvseRBwX/X4u8OH7Ju1zgoVKVV569Lc0b7FakoxnS3O+5mnxmsWXvldJz8jbbRwiDxGB3VyZBrKcqPxQjFKSPxPLpbrZTsk81FIhJxwsx4BrXE2SycEA9ewZmdOUleXJXe3rMIVstVeSyWHrzObPwkt1jeWytwJyonVWQG/2yMw5gSFKoXjpcghdHDcxrq1KPc5vkPlTXKNEpIoV4+iRv5vRRA0PAKy6kYwiBW+B8WetbBTeGdu3+zuC9tSPQJVfVqV07evM23rv6KrES47W95AfO1oyxGwvtu6BZWwzvjG8VLH8LTwTWOPa29WIwaaIErl3SkCnN0KKejSZf+NzVTbNOTG0+hu6GFcslJSUh1v58jknOmWjFIiS6r2T6xWgZlDfL3wiGtHsp3m1dzpX7A+XdWP3EledbbXHwPpn2m/XRB2mhthWfai2p46DGLUorRv9H6BG8BVmZf2L8ufKO5b+dY3w+V8HGzslvnoyIiOMDTAFrCqdWytF+D5acWwx1brb9l6zzEdhbW3cKXJmsQSH6idBCUwelTDwdi0z+2Fesd9EBOorSWmBk2wNs2tjCJlZ/K3iDhHh58+ffphLPHW8utDiQoubaL0rK7jGxC3Xwiw6U93KdZNVYCTvQLCgcBoza0e1CsY5SOEkYWx31sA5Bs7nmdZPXJffBWxUJgZksF2tA0AG73b9ZPYQnFcAO256Dus47gvitSeqgkdEnuj88+dPTL6W/jRFv+716jl75XHc8Y3WCHQ0HLNhkHhHxMoqrnYpAkWAu9hNF2T1Fq53nbP7OjaLn4AKSf5+O36IAJ6q8BB5uVlp5wSoqYCGJLJD2bJ1oxOIukdNU1uVWUxIswrjidLfICj8XxgrZfNr3sNctTbTZ5XPxHFPwJyyBWxqG1+dD/QVFI05AuTyuDUFGhT3El+tiGnf6+etd/RQGKMaNuSaJtZ4TJ/zqCgYgLVTvq38uq1n410V+EHzAAQr5VAO5iXzo+XR+pH2lwLmWOCj8gJ25S33M8qoSNQIDbWwyFLtYlsIJiPFyHCew5O6WYGZ5ePZZ/mgtBryMRSMydsiz7ext59VN4g86SO+313WcEoWZxgDgd87mdHjeNIKFRtzfrscp7guIsBYY2DgMQKNUZ8CAAmUxjHVmDfsDBN+7TSwmBVdb2OQMxNISrQB0jknplX1I3YZAwDVK5/+IRjBmgD4i5K94Q1ReY6fR93S+vEZfWLMDDZaCetcguNza8gOM41AYs7pRl5rc9wjde8kwMYLJMrzVrtIDXXmZY/P3/VbngsNqb+mUPwZ+jTYOGeOaQZDK2lcFwUgG9wqO1Q0ZPF7rTVRzNXKCIQlTPI//eHxotlaF1yudguzH7hldfKhCcdSAUHj7aaJ+AaJHGyq4MAVHlBDP9YhZQyKlTAhW1x/8qpIMJgBYrWOGWqUPkt/CpreTbKN9bT65oRDravxGXFx2t+/3t9d0Z9uSRDAJ7Gz8mwPe3IFTfrQj5i4CYBXdROzQNq7hq0BsMTLNhtb1moXa0OT13SHClxS/UElMAMrDlOVKRUEYMLRg/DysdWh+CZKFJAVv6iQmBJgvwOZ8UWaKvAqeLR7K4Azir+LpWixvAhQXXFSC+TPnz89adCUvfhM+TyHTO7aUOvUR5Bj13ViPFoH9yOUjJY8Dnexay6HvZ8guVWtE4BQyhBSsc1DSmgBdwOs6SwOW4dFKEm+x5SDqNZAe9lUak2ECoB2HF5NxffdnLGM+qIN8361VhMgoWUdrsYS2ccPD6/Y3W/9srk7dR9GAfaVFK138bMdcLa1OieDsM7hiWv98XiA2wotrJ7uaB03g0BV0CMAMKMPn3LQbGvNc4Ps7A47EwnIlQKNomEsngbcSMK85AyBbHDxd49V4vkRDrmt5zQJyJb2W9Ws4zjQHg83WtkYdGqgY71DiFLfff9r+9ts4HHijz/+ALAMfgD8/BKAcBz5IN6mobX2PNmPDCJWT0xOurcxrEa01c99ARp7JxHh57/+Ry3rA3M807NE/1oGAjtXyvQT69dHpbW/it5//bFkr3phpUSzeZLEmDjGu95BaOhqlAljpnJ6UF8J4izeK2FLDZ26r3fY+TKo+p140xo1LyPMzFLliBoIh3qaRwAn7OCnCTPHGFJ8QSJ8pNhAa8CpXj0iAJPxfH/XvSNrHWCc51PPSuloHWidQB0YJJUKieElxyX8rBi7qLnRSrUJ1f90fCFFEgRXZ6X+fx7/Et2W1HDUCFBPOwgY6k2zc8FYdRXLlXE9ENK/g6RCGywKQQ1DmCcYYth6+3Hg/dcytqIRTGXoTUMt/dTydzCTe8EIDdQJVhFuDsY4JwaRpG1oeN6c3auTzQlMSX1XHU9ySJuVHyYBSVyOSfiIPg023ke2Kkf0eanppUSNvJa1bejn84mhng5jnLa5e+/qNidXRv1ZtBZMLMu6lGKx0mKyHMJ1nphqfXIgAqjmpYoqdXA7MKkDaHhaJY85ARZr5vEIlnnA++oKCvtWUsXZkh0hJcQIoKnXmbJgfZIHeXiGLcIIpKzSg5diDODCQJu1x659/voD/Tjw73//W+rNnyeev97RWwPR//P+DJ3Tt8cDY5wYc2qVmA56i8lzT3mNbS4QWEPun2YUUSWKAXBTIDoZg4crR703qWuvaK01s4hNG10/9AmN5UEkLrzegHbYyc4sFTVgITXfJ0SkxvzuQEL97GJhguQk2OFBOytTtYCsOc0hB4/jAbSliCQLDANTFZVaetnbosqVCN+PQxheUVUSiCif4BuUvfrsqlzUubYT6SU/QtagjUFUjNxb1ruHOFpfn88nxlSRM9kPBjRLXbdwniYMW2qWA8qpdQ9LKWAUI8hvidykkhTo1hrO5yqLbeMULeWvlLP6fsX232aPRHKFk3IlLfm5ynK21tCYXaDGMXD+bHlAyEAr/m5kOR02htVqx6xJ54RUhbAaD+oe2O3X+J0pa16WvDUwQfljXwaC5+qb83pTfsLz1u+A7U+eUh2t8gJmeAEF509Y8qiS9K2j9wdiMm4EvaSWcJft4fljWm4eiV6nOgljjVk0RkQ+Ea3MH1G0Zj/fn+u082beSKQ1AZb98Hg88Pb2tj4P4Pe7gY1o2El6D65y4RWtPQUP6XYzrRrriFYRAjCD1Egb26FPA3ETkKuP8M+JFLhocnQwLM5JqqTCDb92CLM8n5SPI3lP4ppo4Z+/CwaW7D4xEVnbZ7S4aZ8pKYWr/dbjtdyLnkmETjHMzAALBaM41nPMa42yX/3DNRNgfU8ok21GLAv7NOPA4jMG7k0H8MFNxlhSOSNTK7rqhOimsa8c/mmDEJsa1wfUgH5JLH9BnwYb//nPf7ThV8baLgMQrAXhwKC3tzf01jAUZNiBPTIuazNEIBOZ/wVghL/P8wlwYRS0rGIVIDEzJgnKs/YlZQ8yujU0Yc6J8/15eeaubdFKXJW43wkkY3zVUhzfu1PiAOD5x38clNnp3ud5YgaAtxOUl7EtgtP674JABugyF96u0lfbhAghIT5fIbY0WsnNAkpY+QM7hf27KFK2ngF8ONe7dR2pHz2Nf1SYYnJzrcyTrPgUrBthzuP8WBuHegEvYZDh+ta7eiLknylO65/MsSSLxz2aQWHjAR7vL5W9KjTrONZ9ZMm14k1buTGmvJkXQIwZYokya6uHLDapPDXVy8oqMC2eW4Tq2hdQoenVb0hCczgwaIR7Xq3RCgSiRb6Gv63wqlWQoXoH6lqxt/R2vGzDV1D02DVVuKO3wQQpUIBDsLLWsEQiUyrC+jCAqMJxqqVVLHhjGYEEeXuOyDR1R6v1EZrKCqusAxAaDHeyLhoV/8t6CoJVdCKQr40UhlXWdTQkRKUcl/FZyonwAPa+R4+NA/khZZqZJeyYbV0XHdUMafb8o3cQxypDYjBq1N26fbSOJz89SmG6Yk+FQ+jPUNGt7ufK2229VAtq3Vfn+fSxJNIDG6G6V9gPDFzey8wgk+/KF74LRYNMXvfX4hBXqtWocAkbrOO4826/bJs8eKuHRB2Dwucmj+r9HtZkMoDXteuZmzy1DUtbUCO3KfYbYT8Cuk053r+AU74vDgD7CenWH+alwPsYKBhJe4AAYsIYez3B5xXKj4xVELR4z9UDnMMjS1MDgLPny3vz+qhU+y3Xqf5QIis+os+HUak3Ir68qZWa6TqZgDIaWvkWViIyKtImNNf9V0XjI8XcflqeQQ0rqBRjnycJuqt5FABCyM8VPFxKt+r/6qaNQqNSZCBxTPmyuV6HRtRJjvfYtUlhoWXhtv58hqHEvsQ2sAqeqljO+VqhiUpuVnxzDkCN6bXY6e9OcZ3s5ninSG83a+YL6RnRQ2HP9OpLQbDMOcHPZ8ojSEJWmaC12Rh7WrehbXZ6cu3TZ9eQ94Mjo1uf996wkm4tl4PDNet6S+zlTjgOCW8acwiQBXAcD/FigP10c1fMSCvFQVz1kgdyyBIcynNUwWLlE+JwNcUxKyxEfoG7qn3tUoilxVr/cU7t890eq38noaxA6ioIwlj7F/rjhRf6n6YaZ23W7lcKkJBa5OYq1xy9EamYxgxFE0hDI7jBqitNng403CsNRtZQZG30tqo42U9mG98WrCiiDJkSYP/JoxR8cAD3vg8zr/ZwC+u1XTdnKn0rOVVRdkSLdejGhm+6l1wVusg36hpt1KXuvkIGfx6VXEHtszXAciDkGXb6uvaJAo8qQGMLygOvi7wv8h+PYtg9x3ibKkmTrx6VFvbYd/JsAFd9IYayRIOEXbt+2j97hni3ItCPRp64DmsOxMu2mYHGZIrJqMm+5gnLeMhlXoC1t8Tw01fbOci8sO5j2fholFjzfKW6Xsh4+rrCZa6I36u+S0QWhOHviEBDfnIUm4hX+9pcLwGf+Xt7ZpwPKaLekjcr6tM172QNbYRd5OO92sx6vt9qfaUku5TmHB6N878ONszluKNXyg4/3sQqQstt3IuitMr9Waxo8LcW2q15m5zd2Q0W8w1cY8cByGE+gXnHf2/Hgd6zW91CGt5//fJ3GKpMyWZhXCqIqX9Xikzgz5I98/F4XAbLXOvvms8RmXdk+NmKksFNJLO0tZDTEjdxFPjruRPMLYENf14QcLEt7plSl21khh+N41dSBaSRGXyWhllPw9i+WhtxPKrwmFMq28RnRM9fD2erNFyZnXkK1t7Ilo665neA91ISGQONQ0EFBxHkoXVAzi+ogJsIqngxjjc9SBFSVpNA6AowzsKXVKuEnAQdiiJwwxjsQtMMKQhjBcAORfexiVKljoWExGS+YpY870uYx4txILY7zLPtz2jRihTnGhA+99m42n+KzNtUcyuA1X7JkwnlvwFXBuygv6qAAdiOXZQTRjtAZ9daGyweOl5feU9d/zslLq2NIEuYAOLl3YttqvfNuTwTgAEG+MnY55nLX8f2xN/tewEw13AzpLmwMClCa9fxjM+vymn8mYq6gDEvyctXkFANh5XXx3GuACTJYbAH1kD34Kle/3Rdkd3fhXa6g6z1Bb5lXUfF0uhqkGy9pbL2UVGt3rQq23ckmNXRNoBggLGPAYmC4JCvAKS5i6GR69mrzwalP2zPRvll5pVj8Ul9gRCAcpC7AjZK2J9VZSNbawA2r3n1ZhkD1Ul9T07Xvyaz53wzWMBOy3usyok4BC6zZXjSXifFH/vh1HfIUkv8iRleVeyz9JfBRkResSpTii8GeelHV3LKhqkLnudMWGPHuGo7dkwoIcjwWVJkAT8FO/aJiDB5gmaeGItJleS2ipT37au02wg7YVAZ7nUxXV2XzAyQADgog3cQ17tvxASucE1OXmORyxnXMY5tXoIGYBYrBIcQttpfKvfyNrs2tydWu/pI4H8lTQxQk6T7OSFjTtPtS5ZjwdMsmnrj0rhlXJLFLwYvkVsFg2ll7SvRpSXciQDJJRiacCY10FeiagOsWueEWnqLYGcA0P3LU0CfpSpYcqp6AaUJcigfAA/LsmvHOGVNEAN+0re57EVxmlqMAOohI+paIncRYVWlGjzQ+yGCgYGuISxND3nrXfN+Woede8OQ1CsRuJDvJoNIytpKqLIBLUKt5CbW24nWyUN7bPAbAZjmOYFb7OPZfUPXAMisk2Zhl0Q9jbrB0QFixrsqE5N1XWEpGM/nc1Ug0/F5+/Em42kWzSHK3XeqRvXjxw8AC2DF3BRX9I9eKnftFcDI2yu/iopwzHmLOXAVZFTAaM/e8Z4oUyrIqG100DSnHnT3ECWitQQcW2toc43Frn+A8pLQDuPrVtY1hu9aG+IBlEQUC9dsSe7N4HZ3DbOcRxKVGKKcU0NaTY5BmCMqzbg8fxcyFQ/TtL9juGgPyfPYAHeJ9W/r3qOnEHArAEC9Z+v7F1OU45lWH8cY6IdJiahXXNcyuEMMO1cFvG3WzYfEjCi6DdDJ71a/Dylcb4k85dPIeyeCe9OzzEhjTbL9EHU8id5oINOZ9F5oumFdu2IsChEUitVIAVMEKOleXmGGJkskrwIu52u0j13MYA3R1zFiAxuBF84hssDa4s9hvw4Q2ej9xuIDZpBYY2UhcZKLnI2TIps4xpBhjUF8rtFfNVx9Gmz861//8sUXw4nmmBg4U9lWt9CdAz24vM7zTALPhUoIscIYaONqqdPfACC1Iy26qgAXpT26320RWm5Ltd6bkmQK7hgD7+/vcmANrzCGqkzH91eqSncFGqntL55dBU78vDVRSt/efqTDngBgBg9StIL7vERLqHt+5KCseiiiWxXCZszWe8Ik8sN9FvoOKJuW9c3DuYqCUPt3hiTm2Icd4PwqYho4HgcOkoornniNKTXreaKhYcyhifGUCyNAlOimCq0DM1WAI8g3Isaq6qSWEerw8A8eQ/LtmTHen2rRa0A/QEyi/AMeO5oUJjZm23HyH8L3GiAcfLr1h7CAg4GiOaUinCghwDiHJv83EHUcxxsAqQv/OCSGfJ4nxNrAOIdVJcl9Jmroh5xyfJ7vaP2h6x9oHUkpkdyXjn48NKci5HU1BVwAqJ14NIB7h5T6PHUcFfC0FfoJnaneJA5e+KEaINDQR1ZQCZazQmAmtwZZTfhzDnhtmdZALEL60ScaIyXePn78AM1VmtNiy/vRMXSMfjSpEPN+Sm6ZlKceoH7o4YpfT8mjjeydW/xz8W4BY7y1Ntq/aIwwnhCNS1V531EFKaYg7Aw7r4xEFw9ipcDfUcr72v1H4d9EEqBX938FH9E6HK3U3rZLXz8TBpErfMXxcVkfZLEowexn4JjiwmwK4WrTzgNj7bc+xfftxlkvVAC/QNlQuW1x5dKHYtDTdRbBTHzvd6DdepX2fVyhCxYMVNangY3ds20OY3h7HPsqa03xXe8EyEqZy18BKMQQvZVM3ih7DurecbAhbwRQcmxp7RO3tIs1UwdrPSeu4altd0N3HDsSI1Psl/MQ7YPwJKk+BQMb2lbLQct8xPGMGwjZvQohTH9O60CZI4KFXHI8aDa0OwLFCMykDStaJc6PpQD8jqwdImsJkwmZo3xMnwYbu0XnnyNMdrA8zHOk6hc7sGE/3UJBQB/XGuHy8Pye2Kaj1Iau7Y3uev+cCS2c0huV6oeGYdhBd34fUQSAifnvAEQEO39mnO2+6i58xVxSqEtffW2tScnctzeM80wn5+ovF9epfT/OIVW95qqIlZg8NcnbKONuDO5qWQt9DH0VoCEK1m48rT0RaFSPz3cBGxaaI9bwBW4BgOfrg3D2gPUaYrK+u4ZcxGftAKndGz1xdc1+RCYc7Pfc9rCHJB4rzZW1p7WGOSZAnL77SGC+1IUoW+IsUbbSq+en9/DmMxcPu3FogB7iJnMh1xMINIvhwwQ+TIhZvP36m4jk0KsX7aug2sZu7XvJR7D+xnH9hH3yH6fIj/HB/KQ1Hj6rRJQtdq+eYde+uiYqVERa+rhUW4nyz/7OoYbZG3HZK0QAhwMeS3uIVjGMKE+jASor8evMpCswDzKw7Pml+JW+AUGmk3sEmpdSNV1OLdfUxIigcyTPnW6o2G3g3/Gbvfy4grrIB9M4h7EzsNFag6lxeT6AKLN2yv1XU21PlY+77+lVv3jx//r8j0D07h1wvuZPUuPK4omV1G7mV+/2jr4g988UbmzmWq+3MzUYoYsBbBjPlLGTdvtn4bmNyGOLqpxtbvzQNQZ2rwYMcFDR3QheJMrHrv6dfnovvBvNzoNKHo6rIWWtjTyfhogynzQZtKq/ri275i/uOdlLXcK96G8Io9ptPh/Mlk8Ht88Hn56M5B1sbWuhcQEMTkw10UcKWsubYQdorkq8/Nu9K1p302aQh13asXvGK4D2inYg5RXTq5/Hdlr7fGxbE4sm90s7zKqYhD+uyn0UnAtsmHoTxxcwBpcsBamtWApYuO/j4XmtnH5WWf4naK2bZZkTMAVwKLkKLIaya7+N0as1Q2WOjSLQiMpT3WciJ7LR4HdkAHEPlMrBc5TL6VYGx8DWEh3X/k5BTNdi1ccHIGcC0HUsq+Jd3/VKMMpeymNgSktrQMOB1qyfWgd+Xvev/2uE5SlaFm1iCeGSTbAZ940gNgUzWvbj4VXMvGrc/4bvfAXt+Fckma+qGtbvr4aSDMBy9bW4z+rnNWxk8VNGLIJh98b2Rgt75I9VgVjKHicYW/lZbH8sX9tLv+RnrMC4+hKrKQmwbRrOmPMWLWcj9T2MgYGUriXIWTVFgtVEaLp2VX7Q8HA3K1Eax8Df0fJ4VlBRvVVZbmd5Fz07F97BHPgte9vi9aaYf2T4+Era8ei1P3Rt9A7G2LY/g7MGOzKt6hg7mRrv3RHZ/7xtokSbHmLrBXHP/GaML7ohsHil8s0MRNmV/sk2z1i6sr7K9uRaL3Kdr6nWlqeeliEoRvI4SND2DZ5+1pqHUtm4PsK4FV4Qac7r/l/Kvg+KnjHV5CT3oDdcwHYAGnE8TZ/InyO9a8nC9GqY98PW2pwDaO1PHX5J/N121k033XTTTTfddNNNN930f4K+RwDvTTfddNNNN91000033fR/jm6wcdNNN91000033XTTTTf9LXSDjZtuuummm2666aabbrrpb6EbbNx000033XTTTTfddNNNfwvdYOOmm2666aabbrrppptu+lvoBhs33XTTTTfddNNNN910099CN9i46aabbrrppptuuummm/4WusHGTTfddNNNN91000033fS30A02brrppptuuummm2666aa/hf4/nzZBsfv/9NgAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1000x2000 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as img\n",
        "\n",
        "fig, axes = plt.subplots(1, 4, figsize = (10, 20))\n",
        "for i in range(4):\n",
        "    id = str(df_train[\"id\"][i])\n",
        "    image = img.imread(os.path.join(project_dir, \"train_image\", id + \".jpg\"))\n",
        "    axes[i].imshow(image)\n",
        "    axes[i].set_title(id + \".jpg\")\n",
        "    axes[i].set_axis_off()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fced5464-4188-4941-a748-05a7998a917a",
      "metadata": {
        "id": "fced5464-4188-4941-a748-05a7998a917a"
      },
      "source": [
        "***"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "63fa3bb0-ddfc-4473-bd18-28acd60dcb22",
      "metadata": {
        "id": "63fa3bb0-ddfc-4473-bd18-28acd60dcb22"
      },
      "source": [
        "## Tasks\n",
        "\n",
        "In this project, you need to work in a team of at most **four** members to build AI models to classify the cancer status (0: negative, 1: positive). The team signup sheet can be found [here](https://docs.google.com/spreadsheets/d/19vqrE4G5LN05zADdiHAZP7pu0W9k9Rob2n2KmxCKlA8/edit?usp=sharing) (you need to use your BU account to access it).\n",
        "\n",
        "Additionally, before you start the project, you must register for a Kaggle account, join the competition on its [website](https://www.kaggle.com/competitions/isic-2024-challenge/overview), and submit a screenshot of the team tab to Gradescope as a proof.\n",
        "\n",
        "Since both tabular and image data are provided, you are expected to explore multimodal learning. To be more specific, we expect you to do the following.\n",
        "\n",
        "- Examine and perform some statistical analysis to the data.\n",
        "    - For example, you can check the distributions of the features in the metadata and find if there is a simple relation (say, linear relation) between some features and the target variable.\n",
        "- Design and implement at least one model for each of the following categories:\n",
        "    - Tabular data model\n",
        "    - Computer vision model\n",
        "    - Fusion model\n",
        "        - A fusion model should combine two models from the previous two categories (one from each category, as \"fusion\" is defined in class).\n",
        "- Perform stratified K-fold cross validation to demonstrate the performance of your models and choose your best model to predict the classes of the test samples.\n",
        "    - While evaluating the performance during cross validation, consider the methods introduced at the beginning of the semester. For example, you should consider ROC analysis and perhaps even AUC analysis (check the ROC wiki page).\n",
        "- Write a report to record your methods and findings.\n",
        "    - You are free to choose any tool (Word, LaTex, Notebook, etc.) to write the report. The template is available on our course website."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "15452e2e-e27f-43e9-b31f-6bac2daf2d9f",
      "metadata": {
        "id": "15452e2e-e27f-43e9-b31f-6bac2daf2d9f"
      },
      "source": [
        "### Challenges and Tips\n",
        "\n",
        "We will provide some insights into the data and some ideas to get you started."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dc4c0cd1-8c83-44b5-baf9-6b6b7558a769",
      "metadata": {
        "id": "dc4c0cd1-8c83-44b5-baf9-6b6b7558a769"
      },
      "source": [
        "#### An (Extremely) Unbalance Dataset\n",
        "\n",
        "If we compare the numbers of training samples in the negative and positive classes, we will have an astonishing finding."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dc8664f2-02fd-4c9c-9950-1d5db7bfc292",
      "metadata": {
        "id": "dc8664f2-02fd-4c9c-9950-1d5db7bfc292",
        "outputId": "3bbbf08e-4215-479b-b0e7-d09431401c85"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of negative training samples: 320533\n",
            "Number of positive training samples: 314\n"
          ]
        }
      ],
      "source": [
        "counts = [df_train[\"target\"].values.tolist().count(0), df_train[\"target\"].values.tolist().count(1)]\n",
        "print(\"Number of negative training samples: \" + str(counts[0]))\n",
        "print(\"Number of positive training samples: \" + str(counts[1]))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7b5d7cba-b6fa-47be-9db3-abc815e71666",
      "metadata": {
        "id": "7b5d7cba-b6fa-47be-9db3-abc815e71666"
      },
      "source": [
        "How to tackle this nature of the training set is one of the major problems you need to address during the cross validation. Furthermore, note that we do **NOT** know the label distribution in the test set."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4141bff8-5783-469b-a892-fb7f8ca780f9",
      "metadata": {
        "id": "4141bff8-5783-469b-a892-fb7f8ca780f9"
      },
      "source": [
        "#### Too Many Features\n",
        "\n",
        "There are about 40 features in the metadata. Some of these features are categorical while some are numerical. The numerical features even have different ranges. Therefore, what features to choose and how to choose the right features are two problems you will face during feature engineering."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "346a0381-29dc-43b1-a059-53a3a3c55d55",
      "metadata": {
        "id": "346a0381-29dc-43b1-a059-53a3a3c55d55"
      },
      "source": [
        "#### What Models to Use\n",
        "\n",
        "We do not put any restriction on model selection on you. In fact, you are encouraged to explore pretrained models (e.g., from PyTorch online library) or even models shared by other competitors (check the code tab, models tab, discussion tab and the leaderboard tab on the competition website).\n",
        "\n",
        "If you use any of the existing approaches,\n",
        "Do\n",
        "\n",
        "- cite the original approach by including links to its post;\n",
        "- read and understand the approach (you need to explain the approach in your own words while writing the report); and\n",
        "- verify the author's claims by applying their approach and try improving their method.\n",
        "    - direct improvemnt of a single approach\n",
        "    - fusion of different approaches\n",
        "    - demonstrate how unreliable the approach is by showing, as an example, that their approach is sensitive to fluctuation or outliers in the data\n",
        "\n",
        "But don't\n",
        "- load the trained parameters;\n",
        "    - the parameters here mean the weights trained by the authors\n",
        "    - you can load model parameters that are not trained on this skin cancer detection task\n",
        "- copy and paste the entire post to the report.\n",
        "    - intead, summarize the approach in your own words (this is part of the study process)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "22740f5d-06a1-4025-9a7e-16fad249ae59",
      "metadata": {
        "id": "22740f5d-06a1-4025-9a7e-16fad249ae59"
      },
      "source": [
        "## Evaluation\n",
        "\n",
        "You will be graded mainly based on how much effort you put into this project. We do not ask you to find the solution that yields the perfect predictions (but your solution should at least beat random guessing). Instead, we expect you to explore the data and appcoaches, which should be reflected both in your presentation and your report.\n",
        "\n",
        "Here is a general breakdown of the grading:\n",
        "\n",
        "- Performance (10%)\n",
        "    - Need to at least beat the random guessing\n",
        "    - Otherwise you will receive a low project grade\n",
        "- Presentation (20%)\n",
        "    - The presentation is like a short version of your report:\n",
        "        - Briefly talk about your approaches\n",
        "        - Summarize your results and observations\n",
        "        - Your goal is to let us understand the main parts of your work with little confusion\n",
        "- Report (70%)\n",
        "    - Your report doesn't have to be long, but must be complete (30%)\n",
        "        - You need to complete the sections listed in the template\n",
        "        - You need to demonstrate your effort into the project\n",
        "    - Your description should be clear (20%)\n",
        "        - Do not build a wall of text\n",
        "        - Do not build a wall of numbers/figures\n",
        "            - Only choose what matters to the point you are trying to make\n",
        "            - Always describe the numbers/figures, don't let the readers guess\n",
        "        - When explaining your approaches and results, using a combination of words and figures can be very helpful\n",
        "    - Statements should be backed by evidence and reasoning (20%)\n",
        "        - For example, if you find some interesting relation between A and B, you should show some evidence (say, a correlation plot)"
      ]
    }
  ],
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