18-Principal Component Analysis with Breast Cancer data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "317a183b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "83c94162",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e2b9ab33",
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1956fed5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b3834db8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _breast_cancer_dataset:\n",
      "\n",
      "Breast cancer wisconsin (diagnostic) dataset\n",
      "--------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 569\n",
      "\n",
      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      "    :Attribute Information:\n",
      "        - radius (mean of distances from center to points on the perimeter)\n",
      "        - texture (standard deviation of gray-scale values)\n",
      "        - perimeter\n",
      "        - area\n",
      "        - smoothness (local variation in radius lengths)\n",
      "        - compactness (perimeter^2 / area - 1.0)\n",
      "        - concavity (severity of concave portions of the contour)\n",
      "        - concave points (number of concave portions of the contour)\n",
      "        - symmetry\n",
      "        - fractal dimension (\"coastline approximation\" - 1)\n",
      "\n",
      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
      "        worst/largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
      "        10 is Radius SE, field 20 is Worst Radius.\n",
      "\n",
      "        - class:\n",
      "                - WDBC-Malignant\n",
      "                - WDBC-Benign\n",
      "\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ===================================== ====== ======\n",
      "                                           Min    Max\n",
      "    ===================================== ====== ======\n",
      "    radius (mean):                        6.981  28.11\n",
      "    texture (mean):                       9.71   39.28\n",
      "    perimeter (mean):                     43.79  188.5\n",
      "    area (mean):                          143.5  2501.0\n",
      "    smoothness (mean):                    0.053  0.163\n",
      "    compactness (mean):                   0.019  0.345\n",
      "    concavity (mean):                     0.0    0.427\n",
      "    concave points (mean):                0.0    0.201\n",
      "    symmetry (mean):                      0.106  0.304\n",
      "    fractal dimension (mean):             0.05   0.097\n",
      "    radius (standard error):              0.112  2.873\n",
      "    texture (standard error):             0.36   4.885\n",
      "    perimeter (standard error):           0.757  21.98\n",
      "    area (standard error):                6.802  542.2\n",
      "    smoothness (standard error):          0.002  0.031\n",
      "    compactness (standard error):         0.002  0.135\n",
      "    concavity (standard error):           0.0    0.396\n",
      "    concave points (standard error):      0.0    0.053\n",
      "    symmetry (standard error):            0.008  0.079\n",
      "    fractal dimension (standard error):   0.001  0.03\n",
      "    radius (worst):                       7.93   36.04\n",
      "    texture (worst):                      12.02  49.54\n",
      "    perimeter (worst):                    50.41  251.2\n",
      "    area (worst):                         185.2  4254.0\n",
      "    smoothness (worst):                   0.071  0.223\n",
      "    compactness (worst):                  0.027  1.058\n",
      "    concavity (worst):                    0.0    1.252\n",
      "    concave points (worst):               0.0    0.291\n",
      "    symmetry (worst):                     0.156  0.664\n",
      "    fractal dimension (worst):            0.055  0.208\n",
      "    ===================================== ====== ======\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      "    :Donor: Nick Street\n",
      "\n",
      "    :Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "     San Jose, CA, 1993.\n",
      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
      "     July-August 1995.\n",
      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
      "     163-171.\n"
     ]
    }
   ],
   "source": [
    "print(cancer['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c44b3747",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(cancer['data'], columns=cancer['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e8075972",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a0a4f94f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['malignant', 'benign'], dtype='<U9')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer['target_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8999dd79",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6a8527a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e4c3d098",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f005882",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_data = scaler.transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e2b9b916",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "89600d91",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "338acded",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=2)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7a54af6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pca = pca.transform(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "61e75409",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e4fdbd79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 2)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "87e52dc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'First Principal Component')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4H9M0CIw7tOtb6mHhXx1iDbD6DRFxhUKh6MMowVb0Lv7RcM7L4N1qZmL0gNOfgbD+h3a9xirIW+56vCr3UFeoUCgUxwRq+Iei90meBDctln5tn1AISQXjIf5o+oRA8hTY/q3z8ZDkw12lQqFQ9CpKsBXHBkEJ8nW4mL0lX122Hcp3gWaAiXeL65lCoVD0YZRgK44/IjPhmu+gMgc8fCAkDcyevb0qhUKhOCyUYCuOT/wi5KsrmmuhpVEqxzXt6K1LoVAoDhEl2IoTC7sNcn6FhX+D6n0w4ioxaQmK7+2VKRQKxX5Rgq04sSjaCO+cA3arPF78OLQ0wSkPi8sagNUCeStg/Yey+x56McSNAuNRGFSiUCgUXaDauhQnFiVbHGLdxqpXoLbQ8ThvBbx1Oqx9G9a8BW+eBnkrj+46FQqFohNKsBUnFh6+rsd8QsHUoSht1RsykrMN3Q5r3z3ya1MoFIr9oARbcWIRPUyqxjsy8xFn29LOO3AAu5ogplAoeheVw1acWAQnwuUfQ94qqCuFmGEQO8L5nKxrYMsXzseGX3m0VqhQKBRuUYKtOPEISZGvrogfC1d8AStfBTQYfQPEjT5aq1MoFAq3KMFWKDpj9oLUqZAypdd6tEtqmthdWofZaCAtwo8gH49eWYdCoTh2UIKtUHRFL4n1juJabn5nNdll9QBMzQjnH2cPJjbYu1fWo1Aojg1U0ZlCcQxht+u8tzynXawBftpeyvI95b24KoVCcSygBFuhOIZosFj5dZerOK/Pqzr6i1EoFMcUSrAVimMIX08Tpwxw9UDPSgzuhdUoFIpjCSXYCsUxhKZpXDgqnpEdBPrCrHhGJ4f24qoUCsWxgCo6U3SPlibIXwXb5oJ3MPSbAdFDe3tVxyXJYX68dlUWe8sb8DAaSA7zxdvD2NvLUigUvYwSbEX32LMY3r/A8fjXZ2TmdPSQXlvS8UyQjwfDVCuXQqHogAqJKw6MpQEWP9HpWB3sWdQ761EoFIoTECXYigOj20SgO9PSePTXolAoFCcoSrAVB8bTHybc6XxMM4gTmEKhUCiOCkqwFd2j3yw47zWIGQFpp8CVX8m/FQqFQnFUUEVniu7hHQSDz4f+p4FmBJMqiFIoFIqjiRJsxcFhVn7WCleaW2xsLqxhd0kdQT4eDIoNIDpQ/awoFD1Jjwi2pml7gVrABlh1Xc/q9LwG/AeYDTQAV+u6vqYn3luhUPQ+P2wr4Zb3HL/SY5JD+O/Fw4kM9OrFVSkUxxc9ucOequt6WRfPnQqkt36NAV5o/a/iWKcmH4o2g80CEQMgNLW3V6Q4xiiuaeKhLzc7HVu+p4IthdVKsBWKHuRohcTPAt7WdV0HlmmaFqRpWrSu64VH6f0Vh0J5Nnx4GZRskcdeQXDllxAzrDdXpeiE1WanqqEFfy8Tnuaj74jW1GKjrL7Z5XhNo/Wor0WhOJ7pqSpxHZivadpqTdNudPN8LJDb4XFe6zHFsczexQ6xBmiqgqXPga2P/SHWdWisAmvLYV+qqsHCou2lvLx4N99vLqK4pnd70bNL6/jr15uZ/d9fuH3OWjbmVx/1NUQFeHH64GinYyaDRmqE31Ffi0JxPNNTO+wJuq4XaJoWASzQNG2bruuLOzyvuXmN7u5CrYJ/I0BCQkIPLU9xSJTucD1WuB5aGsAYcPTXcyiUZ8Pat2HLlxA/BsbdClGDD+lSLVY7r/+6l//+sLP92KzMKB47bzBBvWAjWtvYwp8/28jyPRUAzN9SzKqcSr68dQLxIT5HbR2eZiN/mJGBl9nIl+sKiA/x5qEzMhkQ3Ud+Ro5jrDY72WX1lNY2Ex3oRVKoLwaDuz/Hir5Ajwi2rusFrf8t0TTtc2A00FGw84D4Do/jgIIurvUy8DJAVlaWW1FXHCWSJsGy552PDbkIvPrIH+KmWvjuPti1QB5XZMPuH+H6hRB08DeDe8vref6nXU7H5m0u4rpJyYxKCumJFR8U+yob2sW6jYp6C7tL6o6qYAMkhfnyz3MGcdfJ6fh4mAj2VW1/vU2Lzc4Xa/O5//ONtNh0vMwGnr1kBKcMjOztpSkOkcMOiWua5qtpmn/bv4EZwKZOp30FXKkJY4Fqlb/uAySMhZMfllYuzQDDLoMhF/b2qrpP1V6HWLdRV+w+ctANmqw2bHbXe8hGi+2Qrne4eJqMmNzslnprspeHyUhssI8S62OE3aV1/OkzEWuAphY7d3+4jn3l9b28MsWh0hM77Ejgc+ncwgS8r+v6PE3TbgbQdf1FYC7S0rULaeu6pgfeV3Gk8QmG8XfCwLPBbpVdqcmzt1fVfYweYDDJ2jtyiN9DQrAPw+ODWJtb1X4s1NeDlHDfw1jkoZMY6sNtU9N4pkOIfmpGOOmR/r2yHsWxRXFNk8sNZm2zlbI6CwmhvfMzqzg8DluwdV3PBlwGI7cKddu/deDWw30vRS9gMEBIcm+v4tAIToFxt8ko0DYSJ0JE/0O6XKCPB09cMJRXFu9mwdYSRiYEc+fJ6cQFH93wcxtmo4GrJyQxLCGIzQU1JIf5MjwhiBC1w1UA0YHemI1a+w4bINDbTLh/H7rpVjihiZYem2RlZemrVq3q7WX0DHab5FAtdRCYAL6hvb2iE4P6MshdLl8RAyBhAgQfYjGjrkN1Hja7nSpzBL5eHnj1QhuVQtEdrDY7czcWct+nG2hqsePvaeLZS4czJSOit5em2A+apq3ubD7W/pwS7KNAcy2seRt++CtYmyEiE857BSIze3tliu7SUAFr34NFj0mIffztMOoG8FcFPIpjF7tdZ295PWV1zUQGeJGoQuHHPPsTbDWt62hQtAG+v1/EGqBkMyz8K1hU8UefYe8SWPB/EiGxNsHiJ2DHvO6/vr4ccn6DnfOhYs+RW6dC0QGDQSMl3I/RyaFKrI8D1PCPo4G7P9C7FkBDOXioX6I+wdavXY+tfUcq540H+DWqKYRv74btc+WxdzBc/inEjuz5dSoUiuMWtcM+GvhHux6LHgaegUd9KYeEpQHKdkJ1Xm+vpPcId1OoFjUYDN3IYeevdog1QGMl/PhPFWFRKBQHhRLso0H0UBh2ueOxZwDMegy8+4Bgl+2CT66D57LgxYmw/kNo6V07zl6h/2znGy+vIBhxNWjdcI2q2ud6rGANNNf01OoUCsUJgAqJHw18w2DWIzDySmiqgZCUvjH1ymppzdW27g4bK+HzGyE4UUxVTiQiBsA1c6FoE+g2KRgM69fl6btKatlRVIen2cCkkH50brSqTZrJjlIDw3x1jMoqUqFQdAMl2EcLr0Dxsu5L1JXA5s9cj5ftPPEEGyAkhZ0t4SzNLqc4r5kJqWUMTwjC28P512h9bhWXvbqcumYxbLluZDB/nvoApsX/ApsFS8wYfom4lDteXc1Xt01kYEyr1WtFNlTmgE+I3AyYvY/at6brOo0tNrzNRrTuRA0UCsVRRwm2oms8fCG0H5R0cpr1Deud9fQy2aV1XPbackpqpNr/+Z928fylwzltSEz7ORarjRcX7W4Xa4DXVldyynVXkD9mJL6GFn4o8uaT+TXoOuwpqxPB3rsE5lwsLYCaBif9GcbdAp5H3rUsu7SOz9bks3BrMZP7hXFhVjxpEcotTaE41lCCregan2A49VF49zywWeRYylQpmDsB2Zhf3S7WbTw2bxvjUkMJ8RX3qKYWO9uLal1eW1jXwj+XW6mobwEcYz6DfDwkkvHFLSLWIAYtPz8CKScd8UhGVYOFez/ZwOqcSgC2FdWyeEcZ71w3RjliKRTHGKroTLF/kibBjT/DBW/C5Z/DOS9BgJuqd4DGamlha+pQTFVXIscsDQf/3rYWKN4MO+ZDwXpoaTqU76DHaGpxHfJR32xzsn4M8DZz3sg4l/PC/Tz4+1mZTjVqpw+JZkC0v5iyVOW4vmHNkZ+Ps7esvl2s29hWVMuesroj/t4KheLgUDvsE4HKfVC4Tkw/IgZA1JDutSOBhGcjMw/sypa3CubeK9XPcaNg9r+hrkj6j6tzod9sOOVhCM/o3vvqusyw/vxGsXXVNJj1L6nMNvfOzm9gTKCLN/P1E5OJDPByOu/sYTEUVjUyZ2Uu3mYjd5/SjyFxQTIv+tYJZJfWE+rrwYCYgNadeTiEZUDZduc3DIrnSGMyur9nNxnUvbxCcayhrEmPdyr2wPsXOcTAYBLTjpQp3Xt9U61URXsHdX1OVS68MkV8u9uY8Q9Y+JCIbRtpM+DCN7tnFlO+G14+CdAc7U+aAW76BaIGdW/tPYzdrrMqp5Lnf9pJXmUTV4xLZPagKCI6CTZAi9VOflUjJqPWveEgeavgw8ugtkimjM18FIZfdsQLz+qbrfzl8418sc4xnn5qRjjPXDSMQB81REShONrsz5pU7bAPhuZ6KFovIugXKf3VfuG9var9k7/aeedmt8LCh+HKr8AroOvXWRpg9w/w87/A7AVjboHUqZLX7kzlHmexBmiqdhZrgF3zoaYAwtIPvO6GcvHrbigH33DIXyPmI3XFQO8ItsGgMTo5hKFxWTRb7QR4m7s812wykBR2EC52cVlww09y8+MdBKFp3Y+CHAa+nib+eGp/TuoXzoq9FQxPCGZCami3xLqpxYbJoHW5S1coFD2LEuzuYrfD+vdh7j2OY0MuglmPg09Qry3LidoSEWdNk7YgvwgRvM5U50JLw/4FO3cFfHOXjKeszoO9i0S402e4WnF6Bcl7dozWGN2Erf0iwMPvwN9HYxX89l9nO9ARV0FsFgQniejrOgTEdM+4pIfxNBvxdDOlK7u0jjX7KqlptDIsPohBsYF4mA5CzAJi5OsoEx3ozTkj4jhnhGvu3R3ldc38uK2Et5fmEB/szXWTkhmREKzawRSKI4y6Ne4ulXtgwQPOxzZ8CKVbe2c9nSnbBe+eA2+dDm+eBu9fLGHlqCGu5w6/SiIE+2PLlzDudvj5MVj5Kqx+U0K2+35zPTcsHSbe7XwsKAEyZjseaxrMfqrrgrWOlG5z9e5e9y5MfxB2/QgvjIf/jYElT0tR2zFAdmkdv3tvNbkVjVQ1WvhlVxlr9zkXc20vquXLtfl8s76A7NK+W9T11foC7v1kAxvzq5m7qYhLX1nO5gLl2qZQHGnUDru7WBrcW3I2VR31pbhl8+dQ3KFfumA1bP8ORl0PF74D8/8iYeus6yDr6gPvTENSoHynTKZqQ7fDshcgYbzzLtvsDRPulJavmjyZ9x01WPLkWddBYwWEpMqx7uDuc7bbJCrwXYcIxw9/lRuP4Zd177pHkNU5lVw8KoHH522nsbWafFh8IM9cNIykMD/W51Zx6SvLqLfIc2F+Hrx3/RgyovYT5egBymqb2Vtej5fZSEqYLz6eh/crX1bbzAs/73Y61my1szG/mkGxfcBqdz/UN1sBHV/PrlMdCkVvogS7uwTFQ/RwKFzrOObhK7nG3qa5HrJ/cj2e/TPEjYbU6XD9OLA1ix92d3Kj6SfLWNDONFaJcHfGKxCSJ3U6FnBo86JD0yR83nH3HDMCyrNdz13ztqQmDjQx6wjjZTYwZ0Vhu1gDrMutZmN+DQkhvryzLKddrAHK6iws3FpyRAV7R3ENt7y3ll0lspu/bEwCvz+5H2GH0V9tMGh4uUkHePThPHaDxcqSnWU89+NOrDrcMiWVKf3C8fNSwq04tui7v2VHG+8gOOcF6HeqVCtHDYbLPuleAdWRxNoC696BmOGuz0Vmwnvnwa6FUhwXGNf9QqaIATD4fNfjY28GUxcFSfXlULn30HquOxIUL59t/zOk4Gz4FXDOi+6nW4VnHJXirAMRH+zjtne5pLYZq93O7hLX5/aUHblpXS1WOy8vym4Xa4D3lu9jbW7lfl51YEJ8PbhnprOHerCPmaHxfXd3vWpvJTe+s5oN+TVsKajhtvfXsjTbTe2HQtHLKME+GCIGwPmvwx3r4apvIHF8b68I8lfC93+R6u/UaY7j/U+XvuvmGnHNaqo++GsnnwSXfQqJE2SHe/6bEvbujK5D9mJ4fSb8dxh8ci2UbDvU70iIHgrnvQo3/wanPS3CPOB08Al1nOPpD1nX9ErhWWcyovyZNSjK5fiAKH88TEYuHu3aUz0z8xCiD92gqsHCzpI6vD1MeHYqetvhxoXtYJneP5K3rx3NleMS+eOs/sy5cWyftjL9fG2+y7F3l+Vgtx+7La+KExMVEj9YPHzAI6G3VyEUb4HsRSLWy1+EtJPhpD+KgPlFwzd3ynk2i/sw9oEwe0loPGmivN6ji37i0m3w/vlgbbXt3PGd2GxeMmf/lejdeX9zhx7nyIFw7XwJ1dttEuWIcDOnuhfw9jBx0+RUyusszN9SjJ+niT/OymBIfBAA0/pHcO/MDF74eTdmo8bdM/oxJjmkx9exPreS+z/fxOaCGtLC/fjTqf3ZmF9NkLeZ5XsqGBATQE1jCyajho/Hof36+3qamNwvnMn9jvGWxm4S7OMa+g728cCgpqgpjjGUYPdlijdDY7nkpWsLJfS9ayF4B0sYuY2Jf5Bjh4rZ1RjEifJdDrFuI2cJ1OQfnmC7IyxNvo5BksJ8+c/Fw8mvasTTZCA+xHGDE+7vxS1TUjl3RCwGTXNxR+sOzS02thbVsresnjA/cUoL9XXkowurGrnxndUUt/qd7yqt45mFO7n5pBSe+3EXt09LY195A//4ZishvmbuPLkfY1NCMffh/HNPcOawWN5bvo9mq9zUmo0al49N7OVVKRSuKMHuyxiMsPY9mP6A/Ld4E4T3h5n/hE2fQ8I4qRJPnX5k1+EV5OZYYPcczY4zvD2MpEW47zXXNI3owEN3Lpu3uYg7P1jX/vjsYTE8dEYmwb5SU5Bf1dgu1m1UN7bQYLHhYTJQ02TlsXliopNdBle9voKPbx7HyMSe3+n3JYbFB/HJzeP5bXcZNrvOhLQwBvfxinfF8YkS7L5M9BDw9IMFD0LGaZAxC6JHSmg8ZapYihqPgr1kxEAYeDZs+cJxbNZj0out6BHyKht48MvNBHiZmNY/Eh2dH7aWcPGoeMamyrjTAG8zJoOGtUPuVdPAZNCYlB7O3I3Ow0TsOqzYU3HCCzbA4LhABscpkVYc2yjB7suEponF6I7voSIbYkdJTnfvr9IbHdbv6Ai2byjMfgKGXw71pRCa6t6w5RhlV0kdm/KraLHpDIwJIDPm2PvD3WCxMSQukBEJwXyxLh8NuH5SMk02R21Ccpgvf5zVn3/OdZj5XDIqgR+3lRDm7+nWStVftS4pFH0GJdjHCjar5HwNJgiM7f7rIgbIF0hO+62zoGqvPB5xJUz7vwO7mvUEfhGQfsqRf58eZntRDZe8spyKepn37WkyMOeGsYxIPIyc/xEgOsCLk/qF849vHWL89MKdPH/piPbHZqOBS8ckMCIxiLzKRsL8PJi/pZj1edV4GA3cOyuDjfnV7Q6yQT5mRicd37vrncW17Cypw8tsYEBUANFBR3aYikJxJFGC3VvUl0urlV+4zI9e9gKseAnMPmLBOegC8D6Igi1rs+ys40ZCfYm4gq15GzJOdbYIVTjx07bSdrEGce167dc9DIkLPOBQi6LqRopqmgn2MZMYemj5epvNjrEbRV/+3mZ+3VXmcvyrdfmcNsRh9+rraWJkYggjW2umUsL9GJ8aRnldMxlR/nxww1hW7Kkg0MfMmORQ+kX13XasA7Emp5LLXl3ebmaTGRPAi5ePID7kxKutUBwfKMHuDfb+KoM1ynZI7jkuC5Y+K8/ZLDJDOiih+zvWmkJY+w6seFkMXqb8SXzOizdD6Q5nwbZaJHyu26U1ytMfghO77mVuaZIq8KZqOS+wewMi+gr5Va4mL/vKG7DadUz78WNZubeCW99bQ0ltM36eJh47bzCzMqO6Pbkqr7KB7zcX8+2GAsakhHLu8FjSI/cvnjFudoexwfvfMUYHersUuo1JCe3i7OOHRouNZxbudHKe21xQw6q9lUqwFX2Ww+7n0DQtXtO0nzRN26pp2mZN0+50c84UTdOqNU1b1/r14OG+b5+lbCe8f4GINUil94YPXc/L/vnA16orgR3zYeOHIrjRQ+T6Cx6EwRfKOeEZHc4vhR//Dtu+hrl/gJcmwosTYOVr0OxmGEVjtQzYeGkSvDkbXpkm4zrbsDRA4UYZfdlY1d1P4MhTXw6bv4CPrpbxoMVbujz15IGu6YLLxya4td9so7imidvfX0tJrVRk1zVbufODdezu5kCPRouNJ77fzt+/2cKafVW88PNurn1zJQVVbjzUO3BhVjxeZsevrLfZyNnDDyJ9cgLRYLGys8TVJCavcv+fsUJxLNMTO2wr8Add19domuYPrNY0bYGu653/Sv6i6/rpPfB+xzbVBdBUCf5Rzq5cbVTscbbYrC0UK87STs5gIcn7f5+mWvjhb7KzbiPrOjETKdoo1x17G8SOdDyfuxz2LpERjjmtU7csdTDvj2JtGpwkBWRtFG+ERY85HtcVw9z74PLPJOT+0yOw9m15LnECnPmsFJz1JroO695zTFbb8jmsehWumed2bSMTQ/j3hUN58vvtNFvt3HxSKtMH7D/nX1zTRFFNk9Mxm10nr7KxW97gORX1fLmuwOlYbmUju0pq3e6i2xgaH8SnvxvP2pwq0GBEQhADD6FAzmKVXafH/kIIfZwQXw/OGR7L/zoNKhmWENQ7C1IoeoDDFmxd1wuBwtZ/12qathWIBbre1hyPlGfDnkUSbvYJhfy1MOn3EDPM+TzvIOfHeSulBSrnN4eQByeLLej+KNsG+5ZCv1lQuF4Ees2bMOkPIthh6TD0Umd3stzlkDBGctttBCfDmBth3p8k9z3qBhh0PgREydzszuSvkulb+WscYg2Q86sI5bQHes8qtGKPrMtSD1PvF/e3hgqJRBRvchFsq81OXkUDIT5mXr4yi2BfM7FBrZ+XtRk0japm2FfRgJfZSHKoL2aTgWAfDwK9zVQ3tjhdL8K/e2YoRk3DoElbVUcM2oEDXpkxgYdcxd5gsbJ0dzkvLcrGaIAbJ6cyNjUEb/PxlxnTNI1LRidQUtvMZ2vy8PUwcd+sDIa3Os8pegaL1YbVrh+ya57i4OjRT1nTtCRgOLDczdPjNE1bDxQA9+i6vrmLa9wI3AiQkNBH+ngrc2DOhRKObmP6g/DtPXDph8671vD+IoorX3Ec8/CH63+Akq3ShhU1WPLF7ijbKbOiwzMgdoS8pv9pUqz2239lhxkQK4Lf2Uo0MhO2fiXtXgWtU8dGXAnz/0/y2SBjOG0WmHQ3BLr6XxObBT5hcrPQmW3fwsTfS168I7UlULELTF5yI+HpD41VtBRtxWZpxBiejjnEzXsdDKU74N1zHTcZZh+pkP/+fnlsd7Vmnb+lmNvnrMXWqpx/OKUfN4yNwGvfIvjteewmL6r6X8/9v3mzrdTCLVPTuHZCEvEhPjxx/hBue38tFpsdTYM/ndqf9Mju5UYTQn24dHQC7y7f136sf5Qf6ZEOwxWbXWdDXhULtxSjaRrTB0QwNC7osOwyl2dXcN1bq9ofL82u4J3rRjMp/fiwGO1MfIgP/zxnELdOTcPDqBEb3IW1ruKgsdt1Vu+r5KVFuymsbuKq8UlMHxDh5Lyn6Hl6TLA1TfMDPgXu0nW98zT7NUCirut1mqbNBr4A3I650nX9ZeBlgKysrL7hvl+43lmsQfLCaSdDTYGzYHsFyO5vwBkSYg5OhMjBIq5t7VldUVMEH14BKZNh1RtQ3foHv2ijzJ4ecbW4mw292P3Yz8TxsP5Deb50u3iQN1U7xLqN5S/AsMshcgic9CdY/LgUqflFwOzHwcvfvYd30kQwdxKtkq3w4ZXSshaXBQNOx54yHfuChzFv/wozgH80DRd8gE/CsP1///sj+yfniEBLgxyLy4Ly3RA1yOn03IoG/vTphnaxBnj2x11cH7EVPhVbVwOQtOdn/nnye5z1jcZ/f9hJVmIQY5JDqWps4ZapqVjtOl4mA0NiA/Dq5k7V02Tk9ulpDE8M5setJYxICGLagEgnu9I1+yq5+OVl7et7cdFuPrxp7GGZnLy3PMfl2Merco9bwQb5rJPDVJFZT7OpoJpLX1lGi01+Pu/7ZAN/PTOTq8Yn9e7CjnN6RLA1TTMjYv2eruufdX6+o4Druj5X07T/aZoWpuu6a59KX8Td2MeGchE4LzfhS58QSDlAyNsdZdugdCsMPNMh1m1k/wxXfwdJ+5kgFpQA570iVd+XftjaWuZm3KJ3iIzQ9A6QHfOAM6CpSnLcgXFSaW5plJuDtp12cLK4qxk6hHVtLbD1Gxh5lfSWr3kbFj+BVrQRU/RA2P6VnFdbiLbkaRrO/h8+Ph3+uDbXyg2Ph++Bq9Mr97oeq86ncfJf2GAaQlWhieTGIqLDgvH39qSqoYWaJqvT6aMTA/Bc9ajLZRKLFpAUejZ7yxvYUVxHiK8nf/x0Q3s/M0Corwdf3z5xvznojkQGeHPeiDjOGxHH5oJq5qzYR1F1I2cPi2VsSihzlu9zupmw2nU+XZ1/WILtziRFGacoDoX1uVXtYt3GS4t2c/qQaEL91C77SHHYgq1pmga8BmzVdf3fXZwTBRTruq5rmjYa2bwcPwNnIwaI4Ym9gwAMOhfiR0NwD4b1234/3OWINYNzfrxsF1TslvBzxADH8A/fMPnqeF7b8JA2pj/kuJbZy2V3it0Kmz6Rwrgpf5IwfEO55OMHdKgrbK4Fews0NsLiJ0T0AW3N27Lb7zdTXNoA78LllNVUOgS7YK2MDc35VW5wZv9bQv9dzeJOnQbL/ue8zOFX8F7FQCJashlf8z1hRYupTzoZa9blRAbEExvkTX6HymyrXRer107YTD5YWgdDJIT6UFTd5CTWAOX1FsrrLN0W7Da2FNZwwYtLabBIlOOr9YW8c+1oaptbXM6tbXI9djBcOiaBr9YXtN8ImI0a545QVeaKg8fD5Fpv4e1pxKQmnB1RemKHPQG4Atioadq61mP3AwkAuq6/CJwP/E7TNCvQCFys653/5PVhoobAFZ/DD3+HqhwYcjFknuMqdIdLeIbkwKv2QfQwKFzneC7rOkdRVe5yePc8EUyAzHPh1MfcO56FpcFVX0vRW0OFFKXFjHQ9ryMePjDuFvjsRufjV891ftxcK4YwY29uF+t2cn6Dk+5rF+yahGnYPYPkuZKtMP8BEWuQdX16DVz/o+Tt3RE/Bk7/D/zwMLQ0wrhbKU84lfKleVxVdD/mMimZ8C3ehD3vVyIu+4hnLx3OHXPWklfZSKC3mWsmpqH53gI7v3eMIzV5sjN0KgXVTZw2OIqhcUGU1zW7FI1F+HsS7n/wNrBr9la2i3Ubf/92C3+c1Z8FW0qcjl8w6vDy/CMSgvn4pnEs3FqMpsHJAyIZGhd0WNfsTFltM4t3lvL52nwGxQRy5rAYBkR3zwDIYrWxuaCG7NJ6gn3NZMYEEBnQu85k9c1WNhdUs6+igQh/LzJjA1SeFvlZCvIxU9XguIn8wykZBPocBSvkExjtWNbNrKwsfdWqVQc+8VihuVZCxX7hR65SumwnbPlSvMLtVunnTpkq+WP/KHFNe+ccqebuyKUfQ78ZPbeOhkrYMQ9++w94+EnOGw2SJzjy52W74PksyYP/3CnUbPKEcbfBL0/REjmUkpP/S2z6MLFo3fgxfHGz63ue+woMuXD/66opkJx8QCwrcqpozv6VSb9c7nredQshfhSltU0U1zQT5GMmLthHwvh5q2D7XDB50pQ6k832VGotVhosNlLCfEkM8eHbTUX85fONNFvtBPmYeeGyEYxLDXN9nwPw1m97eegr5/rLpFAfPrppHBvzq3lpUTYGA9w0OZVxqSHdzpP3Bna7zn9/3MkzCx31HKG+Hnz6u/EkdSOPPHdjIbe+v6Y9ejEpLYynLhxKxCGMIu0J7Hadt5ft5eGvHA0vZw2L4YqxiaRH+BPoZo72icS2whp+3l5KSW0z0/pHMCIxSFWL9wCapq3WdT3L3XPq0+1JPP1dK6R7mrB0mHxP18831Uj/dGcayiBnKWz8CKrzpTo8afLB2Z92xCdYogjNNbBrAXx3j4hdRCZc8ZncPATGwsBzJGceP0Z2/q3YJ91HZcJMmqOnYwtMJCKi1V6zpRHKd0iFeue2Mp9uCGJATPs/40O82buviz+qRvnRD/f3IrxjO5bRDInj5AuwNrUw/8ddvLQ4G5DJVy9cPoJzh8cyPD6IioZmogO9RewPgZGJwXiaDO2zmAFum5ZGRIAX0wO8mJgWhqb1jZ7p/KpGXujU91xeb2FbUc0BBbu4pomHvtzslGr4ZVcZWwprek2w95bX8+hcZ3+EL9cVkBLux4Itxdx5cvoJLVD9owPo383oiaJnOHF/2o4FGiqkWtvaBKHpENRFYVXJVnHrMnnuv+ULJD+dPgu2fuk4FjlIjr97jggiSNj3nJdh6EUHt+baYil8s7WAbzgsfEiumTJFitB0m+xy25zTpv0FVrwm5/Y/TaxO47IwxI4ktHNPOkgO2eQLY26ChQ876gL6ny7f+0EQHeiNNX0IjXtPwXvPAscTmedAiJsqejdsK6ptF2uQPPcfP93IN7cHkhrhRyruZ193l8yYAD64cSzvLcuhqKaJS8ckMiHN0VXguR/HtWMR94GlA0ebGi02yuqbXY7XNB5e3v5waLDYnG6k2rDa7Ly0OJvThkQzpIdTCgrF/lCC3VtU58E3v4ed8+Wxfwxc9rFr3jtvFbx9ZgdTlRQ5L6wLwTF7SdtYdR4UrIawDBh2mTictTQ6n7v4X5A+Q3bL7RXZfl1PC6vYC59e5wi3+0XBlD9LW5utRcLe4++E3T+JN3pzLYy5GSbeJed7+Lot6nJC02DwebD433DSH+W6QQmQNElSDQdJfEwMTac9SUvOEox5yzEkTZBreXUvElJW5yoiFfUWqhtbDrrAzB2apjE8IZjhCcHY7fph9Vn3NrFB3tx8UqpTSDzMz4P+0Qf+rCMDvJiZGcW8TUXtx4wGjdTww7shOhxig7wZGB3AlkJHl6qvh7G9dqGm0drFKxWKI4MS7N5i768OsQaoLYDfnoUznwNTaxjX2gy//kd2p2knS/66ZAvsWdy1YIP0SF/xmeS3awrg67tg5JVSbY1B+rA9/cBglvetL4Fv7oacJRA3Slq58laKoUvadIdb257FzrnxuiIo2gSJE+H7P8MpfxcTmbwVMPJqmY299DkxbBl2afc/m5BkOPVRadUymCAk1fGZHAJeYUkQlgQj3eSyD0BCiI9LgVl6pJ9Tz3RP0ZfFGmT9l49NJCnUly/W5pMZG8CZQ2NI6sYkM28PI/fNzMDTZOCbDYXEh3jz1zMG9WrINdjXg6cvGsqj321j0Y5SMiL9uWhUPP/9YSdBPmYSQw/fiKWmsYVtRTUUVjcRH+xD/2j/EzrMrtg/quisNyjZCmvekgrqjgQlwo0/tworMnxj9ZviErZvmYS2o4fIoI1T/nrg97HUw6o3Yfn/YMY/pDLbM0DC4788CfVl4BUEM/4OX90ug0hO+Zs4n7X9XHgFwRVfSL92VR7sXSxFYW2E94cJd0mYfP0csQFtY+jFEsr3j4HL3Aw46QO02Ows3FrMnz/bSFVDC6nhvjxz0TAGdwiF6rpOg8WGj4cRrYtiQ7tdp7SuGW+zkQDvE7tYaX80W22U1srndKz08zZYrOwqruO7TUW8uiSbfpH+/P2sQYc9M73BYuX5n3bz/E+72o89dMZArhib2O2pb4rjD1V0diyx+yf45BoY+zvX55JPkl1vG5omuea29qayHbLzPeel7r2Xh68UTwXEwKfXO/LBJi+Y+hcZkNFUBbt+kJsBnxDYNlfEOiRF8sYxw+HDy8SpDMQ5bPSNMsoTpGUsIAYq9ziLNcDGT0TMTR12o41VsnM2e8t7GLsQr4YK6cWuzJGcfcxwx41MD1FW18zGvGqKqptICPVhcGwAAd7ObSlmo4FTB0UzODaQmsYWogK9COnQ1rO7tI6PV+Xy07ZSpvYP54KseJcwbl5lA28vzeGjVbnEBnnz51MHMC41FGMf31EfCTxNxkMu4DtS+HiYGBIfRHK4L5eMjifQ29wj7Uu7SuqcxBrg0bnbmJgWdsBRq4oTEyXYB0P+Gtj8udh5DjpXKp/NB5HHrM6Dz26AxkrZeQ69REZr6naZqhUQDZXZED1Uzq/Y4xDr9mvkgrU1F125Dyy14h3uroDLbpO1bvzI2dTF2iTTwYISpW+8cq+jurq+VFqnPAMk712d6xBrkJx68hQR4fQZref6O8aFdkQzyOfTZqZSugO+vEVuOgwmmHwvjL5JcugdsTSI0UpHI5Sxt4o3eGd/9EOktqmFf83bxser8tqP3X1KP353UipmN6YQccE+0GmZ5XXN3DlnLZsKJMe5vbiWX3eV8eY1o9t3h1abnVcX7+HNpXsBqGpo4eo3VvD5rRMYHHtoQzwUvYO/l7lHneE6D48BsNjs1Dap3LjCPSru0l0K1sEbp8qAjTVvwdtnSU73YKgrEUEE2PKFVIhPvkeqtQPjxLPb1uGX1WhyX3brGQBr3pE51S+Mh3fOlsKvzpTvgl/+7TBQ6UhzrUP8Bp0HBWtg7y8w8CwR8pWvytjP4k3uX3vTL3DOi5Jv9g2TXHdgJ1e30TfJTUl4f7C2wK/PiFiD3ED8/Ki8r8u6d7q4lrH8f/L99BA7S+qcxBrgvz/sZE+5G5vZLthTVt8u1m1szK9hb5njGsU1TcxZ6Wwja7Xr7Ch28//kELFYbSzdXc6dH6zlzg/WsnR3ebszm+LYJSHYB39P5z1TdKAXscG9axajOHZRgt1ddv8kO9OO/PKUDJnoLr7hzragBWtg8ZOSOw7rB8OvEreylibZHYekQtb1ztdIniLv+dVtDvewgrXw3Z9chbkmH3KXQupU17XEj5KhGMMug4iBcNW3cPaLkHqyDM0AuUlJ6ORN7hch7Vl+kRJybyMkBS7/BKY9CAPOhPNegwm3OyrOGytgx3eu6yhxM4XVnTe7rsvs7sOhcq/cJFnqqW923cVY7bqL69j+MHeRZ+yYf/QyGwn1dQ2f+nn2XHBrdU4ll766jC/XFfDlugIufXUZq3PceMQrjikSw3x57eosUsPl92hwbAAvXTHyiBQ0Ko4PVEi8u9gsrseszW7HNnZJULzspj+6UsTHaJa2qF+ekhD1rEdh02ew9h0R7jE3i31n0kQpOoseIv/e/r3rtff9JpalkZmOY0ZPEbrdP0nR2YYPJRQ95mbxJR93m+TIA+OlsjzzbGiogsBECX1X5cgNRuJ4KVgbdJ4Yt3x5qww1mfaAuKyZW3O64RnyBRL+z1spueiwVkvV2CznyniQoSGdCU52NU4JTJCbgkOhuU6+94UPyU1Nv9mMmPIwwT5mKjtYK2ZE+RN/ELub5DBfZmZG8v3m4vZjpw6KosFi5aOVuSSH+TIoNoAHzhjI7951RBL6R/mRGdNz1c9zlu9zMhzRdfhw5T7GpYZ2/aIeoLS2CbuOEpjDYHRyKB/dNI7qxhZCfT2UtadivyjB7i5p06RvueMoyol3dd1XXFciO8WWRtnV+YZKYVfadAkn1+TL8/P/IqHewHioyIblrQVlBWtkvvT1C0VIM892XNvbTe4zMF7asNoo2gTf3SctWtvnySjPwHgxNvn6Dhh6KRStl7nRFbsk3wzQXA2xw8W9rLlGbiaGXAyX3yNh9x9aq9Orc2HORe4nhNUUwCfXQe4yx7Hz35D+8PxVIuIgU8Bi3fiWB0TDJXPEmz1nibSNTX9A3NMOhfw18O3djsc75uLrH8kH1z/IQ19vZ11eFZPTw7l7Rr+DqkwO8Dbz0BmZzMyMYtXeSoYnBFHVYOGSVxyObo+dO5hzhsfyyc3j2FJYQ4iPB0Pjgw67sKreYmVPaT1Wm53BcYF8vbHQZSDJkaKmsYXvNhXy5PwdtNjs3DIljXOHxxLmf2xUdfc1Qv08j5mKeMWxjWrr6i52m+xyl78kLU6jb5Sqbq9OO6WWZtg5D777o/Q3DzhTztuzWCZmTfs/R7Xzu+eLMIJUja97X4rEOjL7CagrheTJUqFt9obCTdKWteVzOcdohnNegUHnOF63bo74cYelw5T74bPrnW82NA1m/FNC3E21MOpaOW5pkB10WLrjPKtFXMy+/7NrHnnK/TDlj87Hts8TMe+Ibzjc/KukFar3yWcREOuYIuYOS4O0npl9wK8btqR2G9QUykQvvwjH8eUvw3f3Op/rFQS3LKPeI5zqphZCfM0uPt1F1U1sLayhqcVGWoTfASt3l+4ucxJrAH9PE9/dNalHK5+Lqxt5/PvtfLpGigFHJAQxIS2MZ3+U/zeaBnNuGMvYlCOzw164tZjr33L+vXzygiGcP/LwhpMoFArV1uWK1SI7XKNH165enTEYIWlCq/2mDsYuLCOL1knIG2R+dNwoCcfWl0rut3R7u0818WMcgm21yHo601AuxWiLH4eL3pXdZuEa8PSVMLeHr9hstpmbtNHWLtVmtmLvlJvVdRFRnwip9m7Dw0dGZn59l4TZU6eL6Ul1npzfWbDdtVpZ3BRUNZRL7t3WDNmL5OZn0PkykKSD/7cT5Tvht+ck2jDofClgC0lyf25VrtxMrXxF1jTjEciYBZpJ1t2Z8AHg4YevlwlfL9dfg9yKBm59fw0b8uQGysfDyLvXj2FEQtc3GO6qfmubrQeVFy+tbWLNviq2FFTTPyqAEYnBLiHnpdkV7WINsGZfFSMTg7lmfCKVDS1cPDphv+s8XOZtLHQ59v6KfZw5LAaPrn4vFArFYXPiCXZljlROr3tHqq1n/EMGVHge2I0JAMMB6vRKWocFRAyESX+Az290iOWv/wHPQIdgDzxLdtWV2VI1PuH3sOD/HNfyDXeuGv/hbzDrX2Jy0pHLPpWdfksTFG2QFqvAeMk/15fJrt0/Cmodto/4hEiI3t4iFefWZslVG4wSsj7935Lv3rcMvv2D5NKHXSYuZm3fj2+Y3MB0Jqyfm/ng58vW790LZIcNUpWedZ3k7k2dQoLl2VKJ39haPLXoMfm+zvofeHTKM+s69jVvY1j6rDyuKYBProbLPpFecP8oiB/rCNGbfeCUh/drT7o6p7JdrEF8pZ+ev4OXr8zC28O9KKWG+7kM8hifGkpsYPfy4g0WK08v2Mn7KxxV5WcPi+HvZw9yaida5aagbNGOUj6/ZTy+nkfelCXBjXNZcqgvJk3VsJ6olNU2s724lkaLjZRwX1J60VL2eObEEmxdF+ewNW/K48ZKCf8GJkDK5J55D58Q2ZGaPCXn23lnu+o1mZRl9hYBu/QDqCkCu0WKxKY/BEUbpV3K5AVL/u14bXMN5K92fc8tX0D6yTJ28/PWGdVmbzj/TenjriuB0/4Nix6XGdqRg2DmIyLSb53mqMo2mFqdzv4CZl+46iv48e8Srv/pEbmBmPaA3AT4Rsj3+u3dMOQiKYZrKziLHAyXfwrz7hdDlcEXwoQ7pQ+72rnFidVvyKCPtte2UbrdIdbt3+fn4i8e0R8QT+9Gi5VQqvFa+47Lx6Jn/4y27Rsp8Bt6CfrAs9ACYiBigOv7dSK/qtHl2I6SWjblVzEq2X2oOT3Sn7evHc3fvtnCzuI6ZmZGctfJ/dzu4N2xp6zeSawBvlhXwNUTkhkWH9R+bHh8IO8uc37tpPRwvHto9Kau66zbV8VHq/Moq2vi4lEJjEkJba9snzEwkjd+3dNesOdtNnLFuESarDa2FNSQU95AmJ8ng2IDVG72BKCwqpF7Pl7Pr7vLAUkDvXP9aIbFH7koz4nKiSXY9aVin9mZgjU9J9jRw6Xga8lTIlKd8QqUnPSCB2H3AhHB056CfqdK8dfPj8qwi/gxIpwdd6njboPyva7X9I+G3JUw/37HsZSp4kZWuE5MVTZ/JrvZaQ+IsH91G6TPlNnVXkGyc25phD2/iHvZpk/l35Y6MXax1MnXwoclDG8ww/g7pDL927th+oNg8obgBIlCpEyBq7+FlnppATOaoWK369o1DbfTnExu0gNGDzCasNrsLNlVxl+/3kx+ZRNvXT6Qkf7xeNQ6h2o1s7ejFW/9HLT1c6i58gcCDiDWAEPdTGGa3C+cv369hRcuH0l8iPuc9JiUUN6/YQx1TVbC/D3xPIixmE0t7jsOSmqaaGqx4dU6uWtsShhTM8L5abv09KdH+HHxqPge8yLfmF/Nxa8sa48ULNhSwvOXjuC0ITICtX90AJ/cPJ5NBdXY7DoDYwLIiPRnzop93P+5o2//3OGxPHjGQIJU5fNxzdrcqnaxBkkD/Xv+Dl66outolOLQOLFiWB6+IlCd6SqHeigERMOen2XKVJtvd0eGXgwFqyB6kBSgJYyFj6+C/JVg9IJZj4mwLX1OPL6TJkuIefaTslMdeqFzrtvTHzJOba2+Lpfd88kPy1zqmOEw8hrJ7/pHyXvlLpP2skEXSBFbYJzklSfcKddpLJdcu8EEhRtlZ945B9y2I/cOgn6z4OS/ir1p5V7n83yC5fpt+fSIAa5tXFk3SK6/MxED5eanI5PugeBkthTWcO2bK9lT1oDFZmdZXhM7B93lZHNqD0mTVryONzyagexKC1bbgVvxhsUH8pfZA/DzNGHQ4JSBkYT4eLCpoMbtBK+OBHp7EBvsg6fJSEFVI1sKqimr3f9rAJJCfegf5Rymjw/xZvGOUn7e7rB99fU0cvOUVN64ehSf3zKe924Yc1BWlg0WK+tzK1mwpYgtBdUuJitLd5e7jJV8/qed1DU5cvSpEX6cNSyWc0fE0T8qgH3lDfzj261Or/lsbX6PGsQojk3yKl29KLYU1lDb5FrToTg8TqwdtoevzGd++2zHzityEMSP7rn3MBgheoTkfu128dKuK5HdaVACbPhIeqVzfpPWqMHnSx44e5HYcU64C66ZJ57bAbEw7ApAd1Sj+0XAdQsgd7kUiIWkwtavQDNCxpkQP1J2720ExLa2nwXIaMw2ATv9GWnvsrYKyb5lMPFuCXOveh1GXis75c9vktawzjnwrGukEK62SEZynvI3Z3F0R2AcXPqRrDd3JQw8E1Knud9NB0TDBW+KPWrZNsmvx48Fg5FdJXVO07OsdrjzN2/+MmkOybZ9NBm8qA9IY+iWJ51+wCsGX8/rWw38faCNQG/HvWpNYwvrcqtYnVNJYqgPWUkhJIT4MDUjnJLaJjzNRpbtLuelLcUEeJsI70b7kt2us2JPBStzKrDbdeotVmZlRu93YESonyfPXjKclxbv5rdd5QyOC2J4fBBPLdjOr7vLGJMcSk1TC3/6dCNLs2VHMyopmCfPHwrd1OumFitv/baXf83bDoBBg39fOIyzhsV0ObgEZAzofp6m3uK+uK5G2Wwe9wxwM1Ft9qBoQtwYBikOjxNLsEHE8YYfZWKW2QeihkBQXM++x/DLZKqVwSg5553zJadcXybP95vlsCjd+ImYp2iahJ6XtBZ7Tb3fvS2ppklFeMww8SN/fabktgEungM7F4gwVrfabtbkg1ewWH22CarRQ0LZ1k67vo0fSdi8sVJ232358GX/EzFvrBJ3tYhM2POTQ8AtddBUI6kFW5Pkt+tLJRQe3t/Z/zu8H4Tf073PMSTJqSq8qcXK9twqrHadP8zox/zNxWzMr+aDFfu48+R0Hvolm30VUWTGBPDouRnkBv0dW8xsAmt3UhY4mPcLokiODCGw07Ssz9bk8fDXDse1zOgAXr06i8QwXzKiArj/s41YbHZ8PIw8c9GwbrVordlXya3vr6G8Xgx3BkYH4GE0EBvkTWRg10Yj6ZH+TM2IwK7DjuJavt8sn3F1oxWr3c7CrSXtYg2wcm8l320u4uaTUrvzibKzuJ7Hv9/e/tiuw18+38iw+CCSwqSYbHxqqEvx3K1T0/Zb0BYb5M3QuEDWdyjU8zYbSe7GaE1F32ZoXBD3zx7Avxdsp6nFzpSMcK6ZkKQmjh0BTjzBbttZh6ZJeLajCUlljnhn21okJBve79DeI2oQnPsqvDHTMVu6TUATJ4o5SUebU88Ax5zpwDgJNa95R8LdMcOkAM0d+atErNsc03Z8L7notFPkGkuelvM0DZzyu7r7mwGDWRzXsq4Xg5U2rM2SW/cJgdOegZ/+4Trso6EUdsyHYZfAT5c7iu1O/qsUlR3MkBQ3tFjtfLAi10lYb5qcQlOLjV2ldcQGevPZLROoamghzM+DIB8PKut9eGevgQ+3JVJU08iszABuGOF8c5ZX2cATHQQMYHNhDdsKa5na35uzh8UwNC6Q0rpmogO82kWtI7tKatlaWIOmaTL7WbfzztKcdrEGCRFOyQinqLZpv4INEB/iw1frCrB2CCNcPzGZAG8zi3eUuJy/aHtJtwW7vL7ZxWCl3mKjqsECyPc2KDaQD24cyyer8yirbeaiUQmMTtn/pLRAHw8eP38oj323lZ+2l9Iv0o+/nzWI1IiDrxa22/VjYjb47pI6NuZX0WS1kxkTyKCYgP1GIU5UArzNXD8xmRkDI2m22ogL9sG3B613FQ5OrE+1rkRaula8JLvZxIlwxn8gLE0E6N3zxY4TpDjsyi8lD3woeAXIeyx6XMxPdF12zn7h8O55jvMC4iQkvPVraX3KOFUMT2yt+Z+gRLjiM9fce9U+x4595DWw+i3H2ku2QOIEyJjtsB5NGCdtVCDXNnnKjULb7hxg5FXyfe9aKPlms3erYctomRDmGSj2oBPukqlbbRjNsqvuP1tuEjpWxv/wsBSgde4TP0iyy+pdcqSvLdnDS1eMJNTPg4HRgXiYDIR1qEoO9vXg1qnpnDEkBqtdJy7Yx6UIpsVqp7HFNZTb1HrMZDSQHunfZY54U341l76yrD30G+LrwZ9P7c/G/GqXc4trmgjpRgFWZkwg7143hmd+2EFhdRNXjE1kVFIw9328gYyoABbtKHM6f1r/yANes424YG+X3XNkgCdRQY6bCE3TGJ4QzPCD7OXOiPLn+ctGUFZnwd/LRPBBFptll9bx9foCftlZxszMKGYOiiKhi+K+I82OoloueWVZ+02Xh9HA+zeMISupZ0e8Hi8YDJrbm1lFz3JiCfa+ZbD8BcfjnCXSVnTK32HXjw7BA+ldXv4SnPmcTM06WAJixLPb6CH2nhXZcjxhnPRS//QPGawx+R746VHJAZduh+UvOsQaZE37ljkLts0KS/8n4m/ykl15x7WDtHPNfFSqxatzJQwPItq+YRKqvuprsT+tyZeq9NpCcT0LSZWbgTP+C9k/S2tXG5Pule/r3FekRc0nXDzGFz8B/U91HUCi62KLepg0t1gZFBtIfLA36ZH+WGx2vExGgnzM+20fMRo0kvfTExoT7M05w2OdjEh8PYykR3ZvZ/jxqlynPG1FvYX1eVXMyIzkxUXZTueOSwklrhte5UaDxtjUUN6IH43FaqO6qYUznv2V6sYWrpuYzJjkEJbvEXvXiWmhzMjsvmCnhPnxwuUjuPfjDZTXW4gL9uaZi4YRFdAzE6J8PEwkhBz870tpbTO3vr+GrYXy87Mqp5Kl2eX85+JhPTrSsrss3lnqFCGx2Oy8tGg3Q+IC8TiIyn+Foic5sQTbXQ/z9rlSfVy63fW54s1SaXwogt1YKQM2agsdYg2wb6nMkL5lhYStPVpNPN44VWxMOxZ2tWGpg21zJVztHSyh+rXvioHI9IecbTjb0DQJqVfsBpMP+IaAT6jcIDTXSvg8brTctKSdIus1eYq/+LZvpUAudRrsWeR83SVPQfop8Ouzcs2afPjyd7KO8l1yo1JT4Djf6AHBiQf/+bWSW1HPgi3FmAwGJqaFYbXb8TAaeG95DsU1zTx27mBGJAS7DVXuK2/gt91lbC+qZWxKCFnJIYT6OheMeZqM3HVyP2KCvPl8bT4Zkf7cNi2NtIgDV3HZ7Trb3VRBF1Q1MSEtjCn9wvl5Rylmo8b1k1KY3C/8oEKq3h5GvD2MrMqpbHdRe23JHmZmRnHXyelkJQUzODaQQG/nnWx9s5WqBguB3mb8OomdwaAxrX8kX98+kYp6CxEBnkT49/7wjt2lde1i3caP20rYW1bPYDctdkeaouoml2N5VY202HQ8Tqy/mopjiBPrR6/jJKs2EifIAI/0U2D1687PDbvMuWDqYDB6iBVnnhsv9Pw1kHWt43FNobRK7VkE/U+X/uk2zN4Siv7wUtqTj4HxEpb+6e+w+VNInyVFYh3fK/NcWPs25K6Q/Paun2D7N87rCEmWavWc38QTvHijhOZBUgSfXidGJT/+w/Ea3Q41eVC8wflaO+ZJWHzagxI9qM6Tm4uznofQ9IP++AAaLTYe/W4bw+KD+de8re1hXA+jgftmZfCPb7fy92+2MC41lMROxU0lNU3cNsdhLfrGb3u5fVoad05PdymGiQ/x4e5T+nHNhGR8PYx4tvY7Vze2sL2ohrI6C0mhPqRH+juN1DQYNC4YGc+y7Aqn6w2ND+Kx77YyNjmET24eR4ivBwkhPodchONtdt7Rfb+5iJ+3lzDvrskuYr2loJpH5m7jt91ljEgI5oHTBzK0g+lKGzFB3sQEHTtzl7tKWRt6KWc8JSOCV5fscTp2xdhElZtV9ConVhlfwjjZTbYRGC8uXkazPDfjn9KiZPQQk5KBZxz6e3n6ifjGjXJ9LjJTXL/a8GotfLNbZYTm2S9A1FDZ5Z73uliadqwUqs4F7wCZsNXW6z3oAgnfz3xERNZgErEG2Y37ufHTbq6TSvaUqRJW39ZJ0G0trk5tgfG4HQtVsUf6ygedC9f/ADcukqlk/U+T9zgYWpqhYB3atq+5NrWebYXVTjlXi83O2n1V9Iv0o95io85N69CO4lona1GAFxftZl+F+/nlmqYR4uvhJNZPfb+dC19axi3vreH0Z5fww1bX0P5JGeHcNysDXw8jAV4mbpyUwsa8KnQdTh8Sw5C4IFLC/Q6rYrZfpD/jO43KvH1amkt+t6SmiZveXc2SXWXYdQkrX/vmSrd9sscaqeF+jEgIcjp22uDoXsuLDk8I4tlLhhEf4k2orwd/nt2fUwZ2P/WgUBwJTqzbxaB4OPdlCX9bm2UiVdvwD59gGH+b+HvrNikG624ovCIbtn4ju8z0GZB5jriHxQwXsSrfJbtnTZOwd9FGWPaCOIEFxUs+ecr9Yk+64EGZUjXqehG7XQuhocz1PRur4eS/Q2CMFKm1tWilTgP/GBk40oZvuPuwuU+ICHLSBHFE8wl1FLK1ERgvNx0FayBujBi7NFW5Xmv45Y7Qt3+UyyhMu81Gs10/sH2m3S7tZV/fjpeukxU9lNe8n3A5rbapBV8PE/2j/Il2s1O0uDFHabHpTpXXbVQ3WtiUX0N+VSOxQV5kxgSyo7iWt5c56gLsOtz/+SaGxAU57UzD/Dz53UmpnD1Mfo4sVhu5lY3cMzOD1HA/px35oRLm78mTFwxlTU4le8vrGRwbyLD4YIydtqX7KhrIrXC2VC2vt5BT3tCj08KOBKF+njx90TB+2FrC8j3lTOkXweR+4b22o/X1NHHG0FgmpIVhtevHRNpAoTixBBtEpNqGb7gj6CBHBDZUwJe3SZEXiEf1yldFvMP7i4PY6c/Apo9FHPcuEWtQkGruoHi5WQjPEMezNpY+J6K/43u5iVj5quM5zSDFZgWrYf27zv3Uu38Up7OO5w6/XPLW0UPF37wtgpC3Us6xNokV6dhbnAvMYkdKzt0nBMbdLk5pMUPlWmc8Cz/+TSZoBcWL93jRJmlpA7npyF8NjZXoe5dgKNqAZ8o0GqJG0hA2mLAwNzcQIDn3ufc4dvFFG7lwSj3fbXE+bWxKKD9sLebpi4a5NWhIj/An1NfDqXDotMFRxHcSrmarjVcW7+G5nxxTyG6anMKQONeZ4xX1FmqaWojB+QZB0zQMBmiy2IkO9CYprOcHH3QnhO3nZcJo0LB1uinx76aXeW+TGOrLtROTuXZiMk0tNrfV+0ebkE41DwpFb9Ijv8maps0C/gMYgVd1XX+s0/Na6/OzgQbgal3X1/TEe/cqdaUigG1iHZkpftyrWnPhZTtg9w/SHrbocVcnML11F1hfJgLf1krVNjRk82fSRmWzSGGctUlC3bEjYfGTcNK98Mk1rusyesE5L4sxTEOpiHZLA4QPhH4zRQwTJ4nLW2A8xI6S6235QjzBWxrF9MQzAL79vawzehjEjpDre/rDyCslgvDRFTLgA+Qm4sovRdh3zJPJYeveQ6uT3mHDvqX4DLuU5vhqmgPPag8/O9FY4dyjrtsZs/Npnj3vaV5cWoiuw0Wj4tGAhFAfFxOUNuJDfHjnutG88ete1uyr4syh0ZwzPM6lrSu7tJ7nf3YeGfryL9l8cMNYF/HLjAkgutOoy+YWG/O3FPPwV5upaLAwe1AU987s3yuh3JQwX+6YlsbTC3e2H7tmQhJpfWxy0uqcCv77wy72ltVzyZgEzhwac0zl2xWK3uKwBVvTNCPwPHAKkAes1DTtK13XO+6JTgXSW7/GAC+0/rfvUrQRPr4GBnfoqU6fCW0jHtuw1IshS9b1sOJFx3H/aDFnAdmxpp8iQmmpkwEd2+dKEVdDOQQlyYSqij2yKw5JEYOSujIpONv2tfN7+kfK6769W64Hsks++a9yc5E+SyrNA06S9bc0w9kvys72h7/J9Wc+IpXgWdfKFLHo4eDdqQe1YI1DrEGEdvETcNYLsPAhGHqp9L53ZOPHBIekUVpZTHiEGw/3gDi5WejQCuZTvIpRYc18H+aLxWrn0e+2tg/KuGWKG2/4VgbGBPLouYNpbLF12RpU22R1ScnrOpgMGi9ePoI/fbqR8noLg2ICePz8IQR26i3eVFDD7XPWtj/+dmMRvp4m/nn2YMymo1si4mEycs2EJEYlh5Bf2UhUoBeDYwPx6UOFUlsLa7j81eVcOiaREQlBVNZbWLa7nJmZkfj2QnuXQnEs0RO/yaOBXbquZwNomvYBcBbQUbDPAt7WdV0HlmmaFqRpWrSu64Wul+sDNNfCvD9D+U6Zf50wVnqlrY0SGrZVOZ9v8oaJd0LkAPESjxslIynbcr41+eLZ3dZ/vWuhiGvsCHntpk/hi1sAXQaA5C6DsAzwi5ZRno0VIsRegWJp2tD6uE2sQY4VbZIe7tBUyavPb529PfIqqSqPGiwtXb5hkksv2wH7WqMHmgFuWizntNHm3taRimwo2y6fkVtrVQOgY0KXwrvmGimua8uxB8bCRe/AZzfKMBH/aDj7BWq84/lu0xKnHW9WUjBRB3ANMxkN+O8nj5wQ6k10oBeFHdp4Ivw9iQnyZmRSCINiA6lptBIV4Oki1iBuWJ35cl1Be6tYZ2x2nT1l9ZTUNhEV4EVymG+PumcFeHswPjXswCd2QYPFSn5lI2ajgYQQn/06ju0uqWPuxkKW76ng1EFRTO0fcdg74e1FtVw0KoFFO0rZ1eGz/Y9hGGcNjz2saysUfZ2eEOxYILfD4zxcd8/uzokF+pZgW+plt9lQ7nAN2/IFjLsVYrNkNznp97DgIcdrglNE5AJiYOTVMOIqVyHbudDZLAWkvWroRbBjgbPZy7r3YNIfZAeaebaEq5NPgtE3yq5/yTOQeV4nK9IODLlQhLa2UELtW7+S8PUVX4pYBsbLxK7O1qO6XQTfK1AEFqQlrjPpM2DBA2JHGpIsIXZrk+TNV7wMGadR75uAz94FkPeb5PlrCiXPHjNUrhE/Rgac1JVIIVxANKl2nVeuHMn9n22iqKaJcakh/PWMQYdtqhEV4M0rV2bxz7lbWLGnkqzEYP7vtAHthWzRgd5Eu6az2wnycX3/pDBffN2MFbTZdb7dUMC9n2yg2WrH22zkmYuHMWNg5BGxvGyx2skuq6O2yUp8iDeRBzBH2VtezyPfbmX+lmI8TQbuOjmdS0cnEujmeyyqaeTGd1axu1Qmty3ZVcYFI+P4x9mD3Kc6uom32Uiwj9lJrAEe+W4r49NCCVfFX4oTmJ4QbHd/aTqX4nbnHDlR024EbgRISEg4vJX1FNV5Iszr5siwkFHXyS65rWhr6fPSc3zZp1J5Hpou07cCYkR8OlZMu/vDbHM1aQCgYINruBtkNz/8CilSM/lIAdnCB2Q3Pup60K2yY963zPGaQeeJaH54OaDLDj9ulFiohvWDL26UXfjIa2TNPqFyY+L0OeTCq9Ph8k/lc4jLgjOfhR/+KjvqwRdIAVzBWpj+sOS/20xjTF7o575MA97UGwOJqF4PBesk9D/wbIkyBMaBb2v7kl+EU2W7sdXw46vbAqlrthLp74VvDxVT9Y/y5+HTM6lttpIQ6k2Ef/d3iYPjAhmbEtLei202ajx4+kC3u/Hs0jru+XhDewV7Y4uNuz9cx7d3TOrxnHdds5V3l+Xw5Pfbsdp1ogK8eOnKkW7nfIOYwLy3LIf5WyQV0Wy186952xkQHcCUDNcCwZ3Fde1i3cYna/K4flIyGVGu05u6y8CYALdmNBX1FpeRnwrFiUZP/MXLAzqWVscBBYdwDgC6rr8MvAyQlZXlVtSPKtYW6YNuMzMp3iQic8kHYiiSOkV2kKHpsjutL4V5rS1aTdVS7HXpx7KTLN8pBWHhGQ5hAhFO46vOu+wRV8m1wvvLtK+OhKRIa1XBOnmcu0wMSkyeMoWsJh9atsPke2HFK+DpKyL83X2Oa6x9V3bqKVPhs+sdVdlL/i2iP/X/YN4fZRcOcmzXQtn1/vpfOOt/0mueea4UzTVVw47vpGXO5CVh8Y4Ob9YmrCte517u5cmRJTDvT44e77XvyGfo4Qcpk/f7vyMiwIsu6ssPiepGC+8szeG/P+zCYrOTlRjMY+cNIa3T0AqrzU5xTROeJiNhHcZrRgd685+Lh7O1sIaaphb6R/oT5OOBza67tF0V1zS5tJvVW2yU1jX1uGBvKajhse+2tT8uqmnigS828s51Y90W6lU2WPhmg2tUZn1ulVvB7prDixTEh/gwNiUEk0FzasG7eFQ8kQFqd604sekJwV4JpGualgzkAxcDl3Y65yvgttb89higus/kr6v3yeSshHGSEy7aKGLZXC8V1wsecJw7+9+yY63a63yNqhz4+k4RUpBhGGc+6wgtR2SKaUvucnmP5JNkp/7eedIW1tHu0y9CfME/u95x/dRpYqO6/AURwbB+UlVekw9jb5YBIlu+cv3e8tdKzr1z1dXWr6TCfOLvpSfdJ1xy3rnL5fnc5WCpBVOoiHbSRHj3XIeP+OALXPu5AXP1XoalmzDWF7sasmz9SuZdJ02UOdxHiQ151Tw53xH+X5VTyYuLdvPIOYPaPaNzKxp47Zc9zFm5jxBfDx44fSDT+0e0h34jA7yIDPBifW4Vj3y3jc35NcweEsXV45NJ7iDEkQFeeBgNTqLt52kiwq/7QrS7pI51eVU0WWwMjgtkUEyg2zxzflWjy7ENeTVU1lvcCrafp4lBMYFOuXzAxUGujfRIP9LC/dhV6ghdXzgyjqTQw+/3zkoM4e1rR/OvedvIrWzkgpFxXD42sUd62hWKvsxhC7au61ZN024Dvkfaul7XdX2zpmk3tz7/IjAXaenahbR1uelFOoJY6mW6ldEsIzUPxnlL16V9avdPInxDL4bfnhMvxYUPOZ+74P/glH9IuNovQnquWxohe7FDrEEGauxb6hDskFQJV4ekys1AxW7JK1vqpd1qzE1SMe4bBsFJML+19aqNhLHO9qFlO+TGIige6uul0CxigOyAO9LWA94Zv0ip/m6zKR12KVTkyExsg1E+Q68Or4sfDTf+DOW7peUrfIB4ki/udN30Uzg9MJtaQyAu3a1+URKN+O05aCiBAWdJ+9oRFu+dxa5FYwu2FHPvjAwiA43Y7TrvLsvhzaV7ASisbuKW99bwyc3jnCY37Smr54rXlrcPAnnrtxz2lTfw3KUj2s0/UsL9eOrCodzz8XqarTJf++mLhpHYzd31juJaLn1lGWV1EvUwGTTevX4MY1NCXc6NdlOMNyDa323OHcDTbOT26Wks31tOTaN8D1mJwYxKcj9YJSrAm5euGMm8TVJ0NmtQFFMyIg4rf92GwaAxPi2Md68fQ6PFRpif5zExblOh6G16JAmo6/pcRJQ7Hnuxw7914NaeeK+DpmIPzH9AcsEmT7HtHHmte6FyR94qaXVqY+d8EWW7zdFH3YZ/lLRPle9q7XWeKBXZvzzpet2iTRJOzvlNTFJiRwF2uX5jpRSxgYSkNaOEnNe8KTcNE++UHXtDuQhoS6fdVGSm5LE7hsDPfRUCYh03Dr7h6APPwhqQgDk42dGeZTDCiKvh+z/L48AEGHA25K+UXH1Lg+zgowaLjWoboWnOE8Vis2R06Y9/h6YaGHw+6DoxP/+B+iu+Q4/MRCveLOdqmtQFGD3g+z/JZ7f8RWov+Qo9dhQBXfRa9wTupmcNjQvE31t+NUrrmvloVa7LOVsKa5wEe3dJndPULoCftpeSV9nQntM1GjROGxxNZkwAJbXNRAZ4HdSO9LfdZe1iDWC16zz/0y6Gxwe5COXA6ABum5rG8z/vQtelOO4fZw8maD8jL4fEBfHlrRPZVVKHt9lARlQA4f4ut1btpEb4ceu09CP2i+3vZe6VSV0KxbFK32nQPBR0Hda85SjcsjaL+EYOknD2gSjcAL8943zMZoGWeuljdpknfbUUooVnwM4FsoMOS4Pxd8A3dzlfJ360OIG9cxZM+L0YnOycL68ddC5gFIvOyEwR69VvyOvKd0sx2TkvSR+0h6/siDvS/3TXm4SvboWzX5LBHSYv0AxkV2skFM6VanOzd+tNxoRW8U2Vm5LwDKkoX9zBHrRsB3zze7j8M/DuooTa00/6xzPPkRY03Sb578BYfA02uOh9yb3XFLSOCfWW92gLz9taaNn4BS9sD+Lmk1IJ9etaOA6HofGBzBgY2V5sFeht5g8zM/BpHcnkbTYSG+RNZYNzFX/nudadDVkAPE0GPDuNYjQYNFLC/Ug5BDOT0ppml2OF1U1YrHYXwQ7wNnPbtFRmDYqiurGFhBAf4rsxWzo5zNcpjH+isDGvim83FlJU3cSZw2IYnRyKXx/qX1ecGBzfP5GNldLDDI5wtm6ToRdWC5i63m1QVyqOZZqbEJ/JS0LTF78vPt7VeXL9oEQpBNv8mZxXky8V01d8AUmTYe9i6UMefaPkhS0NUiy26TMp6ALpPc5bJSJ+9guy1upcKSAzmGDn9zLtq3iTTPGyNkq4vv/pjuEdBpNrjtjaTIsOlpJsPMxm9LixxNrLMFfnyFxrkLUNOENc0lKnOl7bsdq8jfxVUhTXlWCD5N69QyU/vWexRDiyrqPJJwL8Y/AamiTn2azw7nkyPrQDBlszC7YUMzEtjJMOqvCp+0QGePOv84Zw/aRaGpptJIf7OuVtA7zN/OnUAVz9xor2Iqj+UX4MjXf+vvtH+jMuJYSlHSZ33X1KP5cBHYfDhPQwnv95t9OxK8cm4t9FBMLLbGJQ7H7+/ygAMWu5+OVl1Fvkd+aLdQU8e8lwpvWPYGN+NdmldUT4ezE4LuCArXEKxZHk+BZsD1+x1LRbxRzk50elEttghOZ/S262K9GuK4INH8Dk+yRcnD5Tcsi7fpBhGSBFUld8Ibtun3CpjN76pfN1rE0iuBe/J1XiVblSdb78RbneaU87xLqNhnLZ8Zo8JXe96DE5pmlSPe4ZIN9DeIa0TnkGwPArRbTN3hL6XvWa82xtnxA22JJJCC7Gd+3zsPIlOR43Sm5k1n8gIf6d82XYSGCc47X+bqYUBSc5pox1RUiK9HTvaU1mW5th2f/I8x/N/ZsiuWVKGuNSQvE0m2Rq2p6fHa/VNHKiZrJ3VQMlta47y54k2NeD0cmueeA2xqWG8vmtE9hRXIufp4nMmACXYRqh/p48deEw1uyrJLeigcyYQIbFuy8IO1RGJATx4uUjeOL77dQ2WblhUgqnDo468AuPMBX1zVQ3Wgn19Tii6Ysjxeq9le1i3cYzP+zAy2zghrdXtx+b2j+cJ88fesSiPQrFgTi+BdvkKa1L2+fCL0852qbsNph7N8SPcj8jG8SG08NXKrxnPirTtbC3juP0hj2/SNjbbpXQd3h/yWF7BsjOviOe/lI9brU4D/ioL5MdtdHsapzSXA37fpM2rbZ+aF2H1W9KDj0yU14/5GIxUVnyFATGYx/zO/L2ZRM56yk8Fz8mNwujroPYLDL9zXh986DzsJC8lZB2suNx1DDZuXckcjCMuVluMkBuCs581v3Izvb110nFe3W+uK/ZLDKJa82b+FdsZEexL1e/sZI5N4xhXGoYJE3CfvEH6MtewGrwZHfqlfxpmQfQ3KO71EPBaNAYHBvI4APsVo/0jGkvs4lZg6IZmxJKi83epYlITnk9FfUWogK83E4y60mW7ynn/s82sru0nhEJQfztrEF9bldvczMu1mrTWbDFeZzqT9tK2V5Uy/g0JdiK3uH4FmyAmGEioIv+5XzcboPa4q4FOzBWeo3LdsLXdziOz71Hwtjf3u0QPg9fOOk+8J0O0x9yzldHZorg6boUbPlHO7uQbfkCxt4Kvz7jOJZ5rlRNe/qL3/h2b8l3t+EVIDv39e/JeRs+kOONlRi+uYNdkz7grE/tvHvuswxs3oC26F+w4mW8sq6V9rFfn3G2FdVbdxdeQTDzH+Dl7/xZ+ATD1L/AoPPFBjUkRdrO3NFYjb5zAfz2HzSztziYrXhZqtY9/WHq/dQ2BrTPsJ63qUgE29MXQ/9T2Rc6nmd/3M2n3xRiNrbwwGkDyOxjAnCk6apwzGqz8/3mYu77ZD31Fhuhvh48f+kIxqZ2HT04HPaW1XPdm6uoa5b/l2v2VXHr+2v49ObxTr3qR4LC6kY25FVTWttMWoQfg2MDD3kU58jEIDxNBidjlpsmp/DYvG0u57Z9rwpFb3D8CzZIAZV3sPPO1+TpmIXdFWmniMFIZzZ+Ik5fbU5nlnqo2Av+W6UHOSRF8tCBcWJYYqkVM5WcJTLj2uwDv/1XXpsyVW4cZj4qu+m4LGnt+upWRwHW2Ftkh2qztIplPwlfx40Wo5OO2FoYbMzljUkBpNlq0DrebPz6H7FRHXKhuJqtflNEOmUaxI2F0GS5vju8AiQicQCadizE6/PrHAdyl8Opj0vFenMt+vKXKR3/Ela79Gl3DqEmhAfylzMGceXEFDyMBgK9zJhVS0+32F1ax9+/2dw+lrK83sJtc9bw0U1jSQn3P8CrD56cigYXAcspbyCvqvGICnZpbTP3fryBJbscvf6PnjOYS8YcmjNiZkwgc24Yy7vLcyiqbuKyMQlkJYbw3cYifunwHt5mIynhJ15BnuLY4cQQ7KAEOP8N+PRaESrPADj7f+JOtj8MBmdb0TZ8Qly9ui21EgZuroGUk+QLJCT8zlkS+gYZOZl8knhuF6wRo5B9S6XgKjhR8sLf3etsZrL8RTj/dSkqK9kKu+ZD1HCpNPcJcZmIFWy2EL7sPhhwpuvat38nQ0U8A2WQSOxwaXu79APJqTdUyu7dP8q9jep+yC2tJKYtN96RwvVw6r9g3p/QKvdQVCmWll5mA6cMdM2PB/l4sKesnr9/vZkN+VVMy4jg96f0Iz3y8EXHZrNj7EUDDrtdZ31eFYt3lGI0aEzuF87g2EC3XuIlNY2U1bUQ7u9xQA/twupG1uZWcVKGDODYWVzLNxsKKauzMHdjESnhdUztH463ued+5QO9Xa/laTIc8fnb24pqnMQa4JG5W5mYHtatSvjOaJrGiMRgRiQGY7fr7XUHD5+VyfM/7WLepiL6R/nz59kDSIvo+RsfhaK7nBiCDVL1fONiKcTyDZPBFN1hyEVindnW62zyFCFsqz5vI34sFG8Ug5KADmMjy3Y4xLqNPYukWC0wARY+6KjuBpj9lHOOGWQ3XbZTdvYZp0qVetVeiBwiU72++F37qfbIIZjKd8iu311RmH+U5L53/SBGKN/fLzcHVbkSdl/woOTEs66DrGuci88OQHZ5I+EeQbjU1RvN7ZXs9vJs/EOjuW+mmcn9wt3mO3PK67nqjRXtBh5zNxWxr6KBd68fs98+4v1RUNXIgi1FfLmukFHJwZw3PI5+UT37x7e2qYXtRbVUNlhICvUlLcLPRYjX7Kvk4peXtVecP/vjLj68aSzD4sUXwGbXWZ9bxcKtxdQ1W4kJ8ua7jQU8cHqmU993R6obLDz4xSYWbHXcuJ09LJYRCUHsKqmjutHKLe+t4YMbx7o1WekONY0WthXVUlorNQUZUf6kRfhz/cRkXl3iGLF6/+wBJHXhjtZT1LsJS9c2W2m22tycfXB0LBJMDffjsXMHc8+MDPw8TX2yoE5xfHHiCDaIs1dQ/IHP60jMcPEN37dMRDssAwKT4MznYNn/RDxHXw9V+bDmbWnZ6ojBzUesadI73VjuLNYAFbvAN1xaptrw8JVd78CzJCz+0yNy3OQFF74NJz8MlnrswclUeUQR8sl5IsIGowhuW77a6CFV5lW5YrXqHSz59eJNEoafc5FjZ//Lk7LOqX/p9k47WKtHH34F7F3oaCsz+4gz2uo3YfqDrI2/mlqDP7dM7fpGYE9ZfbtYt7GpoIbcioZDEuzKBgu/7CylvN7CwGh/Xl+yh6/XF/DxTeOJdWOccihUNVh4av4O3lmWA8hO89WrspiU7lyY986yHCeP7GarnS/W5rcL9uqcSi55ZVn7GFEPo4E/zOjH795dw5e3TXBb1LazpM5JrAG+XJ/PndPTmTEwitdaBXXl3goGRvuzLrearYU1xIf4MDw+6ICFaXVNVv774y5e/UWuo2nw9IXDOHt4LLdPT2f6gEiKa5qID/FhQLS/i4d6T5Ma7oeX2dA+Ex1geg+M9nSHh8l4RAsJFYqD4cQS7EOheJP4ZAfEQvRw2LVAKsJPf0bEeMsX8PVdIqQz/iGmI3a77GI9fOTc+LFiEgIiojMekUprW4vYmBZvlvA4wNr3pKht/v9Jhbp/FJzyN/j+L3Iz8NM/HWuzNskOOXYkbP4cg8mLoPNelSEazbWSs571mAh1baF8D1u/cZ4ANvV+sQFtc2fryKrXZOiHu7RAZ2qLyNz8FMbcZXDuK7D3VzCapCjut/+AwcgOv9Fc/lk9985s2e+l3BlWeBgNeHsc/I9rSW0Tj83dxmdrxeEtIcSHP8zI4LHvtrGjuLbHBHtrYU27WIMI8Z8+3ciXt04gzN+T2sYWSmqbGBjtzw9bTU65347uZe8u2+s089tis7OtqBYfTyOF1U1uxcPdFCtdh8QQH56cv4PSOonYhPl58sHKXB6Z6yimOik9jH9fNGy/rUo7imvbxbrt2g98sYkRCUEkhPoy7ggVtXVFeqQ/71w7hke+28rO4jpOGxLFzSeltZvdKBTHK+on/ECU7pDdYtU++QLJI097QArIgpOkejooQRzUavJlN7nuffEGn/4AnPOi+IcXb4H0k+GXp0Xgc1dC3nIpNBv4V7HxDE4Sz++UKdI2FRALZbvk+jY3/cjlu8SprLUozVC0SXbFmz6FpEniIFa2Q/L2uu46rnPZCzJ7Om+F67X9omQX30ZDheyYzR2O1RSKUYxux7hhjhzb+o2E9ff91l7oVz7sVu79xUaLzc7IxP3bwqZH+HP2sBi+WOcY6Hb3jH6HNFhifW4VcSHe3HWyo15ha0ENQ+ICMfTgDOqOottGflUjNU0tlNQ188Dnm1i9r5KYQC/uPDmd15fsaR+0MTQukPzKRqIDvVwiCwANFiuBXmaCu/ABTwn3JTbIi/wqx+COoXGBLNpR2j4EJCbIi4HRAVz40lL8vUyE+HqQW9HAop1l7CiuZdx+BLui3vV7q222Ut3UexXTo5JlQEhdk5UwP0/MJjUYRHH8owT7QHi5me3rGy7C5RUAieMcx60tkhuOGAizHhcL07oS8AyWfHDhRnj7DJn/vOp1h3/3tm/lJuDUJ8Si9OdHHbtd72Cpsh5yofiPdyZxokzdakO3yi787BdlV161V8R6wp2Sm+5MYyWgg8FDQtdta9IMMP428A6SG5W178H69yGsv7SwxY+SSMKq12Hx4+LE1kpdbRUFI+/FM+ZU/Or2oMUM48faBMKqW3jv9MEH7GcO9DHzl9MGcOawWIqqG0kM9WVIbCCmQygWs9p03l6aQ1WrtWiAl4m7TulHVKAX/SIP3h60K9z1io9ICMLHw8it769ha6FMMiuobuKJedv506n9+WpdPueNjMPbbMRqt2MwaFw+NpGfd5Q6XWd4QjAnD/DoMjccHejNa1eN4tUle1iWXc4pAyK5bGwijRYr41ND8fYwMTg2kCarjacuGMre8nr2VTRw8+QUlmVX0GjZf+43MdTHZcpYargvsW4GjIDcYHibjW4L6XoS5TWuONFQgn0gooaIKOYskceaBrOfcHX/stth90IRt8L1kP2THA+MkyEYDVHS4tRYKTvnyj3Or6/IlmK42nwJk2/6TIxHAuIgcRIERoN3GFzyoUzwyl8pIfqMWY4RnwPPgn3L5b8LHnCM+WyuERE//w1Xk5a0GbKLn/8gDDxTbkRsFgmr7/0VBl0Ai5+SXu9xt0mYe1erYUxwIix7Xq5ja5Gbi+YalqfewXVzKjEZQvH3iqRqSQuPnhPCjZN9GBzbPfevcH8vpvU//PnHq3Iq28UaoKbJyrbCGu6YltajpiL9o/x56oKhPPTVZuqarfSP8ucfZw+ior6lXazbsNjsWO12gnw9+Ps3W7HrOh/dNI7EUF/Gpoby0hUjeHFRNkZN48pxiaRF+JEa7rffz61/dACPnjOYumYrAd7m9jzy4Lig9nM25Fbx1IId7CmTKv2PVuVx+7Q0l9nfnUkN9+PlK0fyx083UFzTzMDoAB4/fwghnXble8vq+WxtHgu3lDAhLZSLRsWrqmqFogdRgn0gAqLhvFfEI7yxUnqgo4e6nleyBT6+Bqb9n0OsQQq+1r0vhiltoyLd+ZODhNMXPylmK+e8JNcMS5e8d95qEd2i9dD/DDj1MVlPbSFc8Lb0cu/5GXb/ILvdimzna+t2mVw245+w8jUpbhtwluSwPXxlmtanrf3TBqOkAc55WYaFrHtHLFpXve7Ypf/6Hxn+4eErFemrXpdrFW3g42wPoAGrXW8fmvHdpiKqG1u4Y3oa0/q7sTo9QuyrqHc5ll/ZSFyIY7da09SCyaAdVA60ssHC3rJ6zEYDyWG++HqaOG9kHKOSgqlrthId6E1wa9jZ39NEbafK5sYWOz9vd+ykf9lZyojEYPw8TczMjGZyvwg0HbzcDBXpCrPJQHCr1W5+ZQObC2owGzXsOu3tSjWNzvUDr/ySzQUj998JYDBoTMmI4OvbJlLV2EKEv6dL8V91o4U/frqB5XvES31LYQ0/by/lvevHEBHgRVltE1sKa6lutJAS5kf/6IAjXpymUBxvKMHuDgExzq1a7ijfKYMwqnJcn8tfAyXbpKVs6CWQ86vsgrd08B0fdJ5UjGua7LC/+J1jEtjMx+DnRxyPV78hM7NNnmKPCjD7SfAJlZaz6KGyW6937lXFN0y81QdfIMLbXCe7aV2XvvHpD0l1uH+sjCFNmigh9sjBIuAdQ+rWJukPP+M/MOcSWdv398P4OwnXfYEq54/Qy0R+VSNv/rqXyenhXYa3m1ts7Cqto6reQlyIj9MgjkPh7GGxLNjiXEF9YZZ0ClTUNzN/czGv/LKHYB8Td0xPZ2xKGB4HyIdml9bxh4/Xs3ZfFQBnDYvhz6cOICrQi4RO640P8eFvZ2Xy+4/Wtx87c2g0Gjq3TUujtsnKl+vyXfLp3ocxVzq3ooEb3l5JRlQAFfUWftlZ1n7N+2Zl8MjcrbTYJOXSYtOdQt37IyLAi4gA91GPvWUN7WLdxs6SOrJL69E0jT9+uoEft8kNitGg8dpVWUw5QgNdFIrjFSXYPYVXkLRihQ9wfS5hDNSXiD3q0IulSM1gFCe1imwpKNv7C+xdIoVi279zHtvZVOH8GGSgxkn3OQT7x79Le1f5brFPnXg3LHzIEf6efJ/kzr38Yc8S+PwGGW1p9pHq9sxzIHE8RLwuofsf/wohaTD5bqlO3/uL6/dVkQ1xY6SnfPcP7QVq50UU8VEHq0dPk4Eh8UF8vaGQpFDfLnOb9c1W3vxtL0/O346ug7+niVeuynLbO7ytsIZfd5dRWd/CpPQwhrmZCQ0wIS2Mh88YyNMLd6Kjc/vUdCb1CwNgweZi/vTZxvZzr3pjJR/dNI5RXfQ7A+i6zqer89rFGuDLdQVMzQjn7OHud6qzB0eTGuFHTnkDYX6eGDR48MvNbC+uJczPgzumpbm0fx0Oa/dVsq2ojtmDY/hy3Y72440tNj5alcvJAyL5bpMMhjl3eGyPeLV3tVs2GTU2F1S3izVIr/n/fbGJL2+doAZpKBQHgRLsniJyEKTPkh3ykIth44eyc40ZIRaiO+bJees/kB7ohgopTpv/Fxh5Lez4Xp4PipeK8o5obv43mbycc9FN1VBfLi1engGyuz73FcdwkohB4OkrofOvbxexBvE3n/sHOWfhwxA5EDZ8JM9V7ZNK70l/kBuJ9XOcltA49Cq+2dpApH8mQyIbCPr2RrDUMdQnnE9nPMFSazrFDQZC/Tx4eXE2mgbXTkjq8o/7tqJanvh+e/vj2mYr936yns9+N4HwDlaX24tquOjlZVS3hnef+2kXb1w9iqn9XXdsQT4eXD0hmVmDogGIai2Uqm1scTL8APnf9euusv0KdoPFxsJOPc8AK/dWdinYnmYjQ+KCGBIXRFltE5e9upztxXUAzBoUTVmdhf8s3MG41DAmZ4QTH3x4AtpWse5u57yrpI7LxySQXVrPKQMjGJsSiofp0HfzbaSE+3LeiFg+XZPffmxSWihpEX78tM3188qvaqS+2aoEW6E4CJRgd4fmOkCX4RV2u8yC3vSpTN8afJ4Isl84nP609G2HpYvANZaD0Uvy2DvnO65n9JTng+JkLOamT6Qi++fHIHeF+Iuve89xfkiycy83SH90x5B61BDxRv/mLhFvkP7rq76G2FGO/HnVPjf5bV3c2NJnwNJnnZ+zNknUYPPnMOVPsPotaGlEH34F31sGc+9XGwC4PiuYP0QMxzvvF7SGUgb9eDWZY25m64j7+XRNPjMHRnLG0BiGJwR1+TEXVje6HMutaKSyweIk2Mv3VLSLdRvPLNzBqORg/Dydq4Z1XWdveT0V9ZJ7bcNkNBDkpk3qQFXHPh5GJvcLY3uxcyHZ/r6vjuwtrWf6gEhmZEbh7WFg6e6K9pD1NxuLmJQexnOXDCfwEB3dADJjpLPBXVh9QmoYmqZx7cQk6pqs1DZZabHaD7stysfDxL0zM5iYFsayPRWMTAhmfFooQT4epIT7oWnObf4zBkR2GV5XKBTuUYK9PywNUkC2+EnJC6dNlx1r0SbZKTdVwZo34MqvIHmyVI63VY/HjxXzkjVvOw/oiB4mrmnlO2HUDdKnPfxSsNlEqCv3QmC8jNTcMU/yx3uWyM550DmSBw/LkGrvrV/JNRPGw4y/y01Bm1iD5Kd/+69Ul8cMkZneZi9xWevc4uUdJGvy9JfdP0hOfOzvpHI9LA0MZsmRaxra1i/xGTyp/eWvra7k7FMuZ1CeI3SuRQ1hYEwgA2O6N20rLsh1Z5ka7kuor7N4NbhpQ6ppsmKzORu/WG125m0q4t5PNtDYYiPQ28zzlw5nYno43h5G7piezpWvr2gXkgBvExMOYAKiaRoXjYpn8Y7S9l3ytIwIxqWEHfD7q2u2smxvBa8t2UOz1c79swe0i3Ubv+wsk1GViYcu2EPjA3n2kuF8uiaPW6ek8sZve2mw2BiREMSYlBB+2FZCYogPc1bk0mKzc9mYBG46KfWwHb2iAr05Z0Qc54yIo77Zyqb8an7dVUZssDfPXjycv369hdK6Zqb1D+e+Wf3xOow8vUJxIqIEe3/kLocPLpUCsYYy+PByOe4ZIBXR8/9PBHzZi5A4QfLSbRhNMpZy+GUidtk/ixi2NLTO1kYK2UZdL6HxdjT44W9yrZP+CHGjpEWrvhQGXyTX/OJmabsadC74RchNwJe3iKh3pq5YLFXXvoU+bDVaU62I+1d3yO4ZZNZ1c720ko26wTGKdNLd8OM/HD7q3sEw/nZZH2DHCEj1s65Ds3cEJIyF0u0SdUiefFAfd/9oP/565kD+8a0URYX7efLE+UNdwqajk0IwaNDBEIwbJiW77EqzS+rZW17PdZOSMRsNfLwql9vnrOXr2ycSF+zDmORQPr5pHL/tLsffy8S41FD6R7npu+9EWoQ/714/lj1ldZgMBlIifAnydi+wDRYrK/dW8MmqPKb1j+DJ+Y6csjtPbJAcb1WDhR3FtdQ0WkkO8yX1AK1XHfEymzhjaAxjUkKw2uycnxVHU4udQC8T328uIisxmH/Nc6Qe3lqaQ3yID9dP6mJS20Fit+t8sjqXh77a0n7soqw4Prl5LDZd0hLKlUyhOHjUb83+aBvwETVYhKuN5hpp1cqYLeYoqdMklB2aJqHxjviGiSNazjJY+rxDJEFGdw673OEcZrfBxo8dPd97FkFQorzP8hfE13zFi/KcpU527yDzs3Vddt6dSTsZfpVRntr6DyQfveJVaRtrrJR2s/ABYK2HqKEyTezMZ6GuVFrJWjqEqRsrJfftF4klaQofZ5uBZjQN7j65H8sadZ60PswpY4M5ZUAk8UH7dzTrjJfZxGVjEhmfGkZVo4W4IB+3vdJD4gJ597oxPPfzLsprLVw7Mcll6lddUwsfrc5tz1ObjRr3zMjghUW72VlcS5C3GT8vM1lJIV0O1dgf4f6e7WF6m11n1d4KPliZS01jC5eOSWB0cgg+HiaW7CzjxndklnnnCvLdpXUMiw9iXW5V+7FBMQGU1DaxtaiGX3eV8/3mIrzNRv513mDW51UzMzOSYfHBB6xkB4hwM+Hrmokp3P+5FNp5GA1EBnpSUtPMp2vyuHRMQo8I6b6KBh79znmW9Ier8jh3ZBxjko+ujalCcTyhBHt/+LT+cekosm0Ub4KxN8OWr6RoC6R/+vw3ILxVOEu2QNFm2S0njIZVrzhfI2Kg5JnbaKiA7d/Kv5Mni6HJ9rkQM0zC3uvek5YrVnVaZ4iE57d/C2f8F357VmxMB18AhRtkVw+tQzx0sSH9uNWK9MZFEJIo/77obdnJ562G/HniutaZxkqY+HvMNflcHefJr/sauH1aOvM3F7E+T8LxS7Mr+CW7lv9eMuygnahMRsMBx2iajAbGp4UxIjEYm92Or6fre2wtqnUqKmux6by8OJsLR8azNLuCtblV/O6k1EPyJ+/Mhrwqpwlc87cU8+qVWYxPC+V/P+9uP8/L7Cyy32wo5P7Z/ZnWP5wftpYwMjEYX08Tt81Zi67DOcNjGZEQzJp9lfz3x10MjA7gopeX8f71YxiXeuAQfFf0i/Dj3BFSHb6nrJ74EB+iArzwOIyxoxvzq1m4pZiapham9Y9wGszRRm03rUx1Xaeu2YqPh0n1aisUHVCC3RXWZnH+ammQlq3OJE+S6u226m+QIR6rXoeZj4jb2VtnyE4YxM506l8kjA4SVh99oxSD1bUKo6c/JE+DAadCwXqxNZ35KHiHwJQ/g2frUI+d86RwzdoIZl8R9roSyV+nzxAf8qxrsH95G4a2oSIg7WQ7Fzoe+4bJVxsevvJl8oLsxRCVKZGDjsSOlBYysx8Rp1/Ikxck4Gky8MT31U6n/bS9hOzSeobGu/nsegjJgbrmQXVdJ7ukzuV4eb2FIXGB/PXrLQT6mBmTHEpZXTNhfp4MiPYnxPfQKpZ/3FbiNIEL4KVFuxmVHIzd7hCuzQU1zMyM4vvN0lLl52liZGIwIxNDmJUZxdVvrKSg2nFz+MW6fO6Yls6afZXsLq1jZmYUug6vL9nL6ORQJzErq22muKaJIB+PAw40mdwvjFV7K3lm4c72Y5PSwjhtcDTBvgefO9+UX82FLy6lsUVqC4prmhgeH8TaDpEDXw8jKWEH7qnPLq3jo1W5LNxawoTUUC4bk9jjY1AVir6KEmx3lGfDosckPO0XKYM+Tn1ccsnWZmmRmvEorH/P9bW7FsCU+8VNzNJBNOpLJbx8yUcSRo/MlClYa96R99J16asecRl8er24ngGseVMmbqVMhYj+Ugh34btSqe4TCv4x8NVtss7xt0vYvbYQUqZQOO0ZDNvnElyxnvK4k/EPDCFg0ydyXQ9fcTJzN+/aPwpm/UMGi5j9pHLc6AHDL5cbFLuV5RNe5qr38mm25joN1uhIcU0TLTY75sPYuR0MzVYbTRYbtU1WyuqbXfLcqRG+rMqpxK7rnDM8lstfW95ecHb2sBgeOiPzkATLLRp4mYz8bkoav3tPbprmbSpiWkY4b10zinqLFQ2NuRuLqGu2YjIYnMQa5EdCRxY4LiWUdbmV7d+nruvyJkjf9Z0frGVfRSPBPmYeP38o0/pHdLk7bWyx883GQqdjv+wqY3dpHVm+B58eWLKzrF2s277Pf549mMhAL37YWszA6AAeOH0gKeH7z8NXN1i475MNrMqR73NXSR2/7CzjgxvHqopyhQIl2K5YW2DJ07DhQ3lcWwhf3QrXzoebf5UWr+BECUOXjnB9ferJ4kBWts31uYo9IsptbP1GxLaNb+8Wx7I2sW5jydOyy7c2SUvWJ9c4ngtJhdP/DfuWSjV7U5UctzWjR6VzX+E0qhonsWtHHelhnrxx+Q+EUiNCHbKfIiNPf8mZxwwXs5d9S6VlrLGS6swreXiZ3m6MklPewKDYADblO8xdpvQL5aNVuSSE+nSrkOtwWZNTyf9+3sXu0nrOGR5Di9XOH2Zk8NLi3dQ0WkkK9eGeGRnc9cE6LhmdwKu/ZDu1GX2xroCLRsUfUqh5Wv8IXly0u909DODmk1LxNBuZ1C+c167K4r3l+wjx9eCSUfH4e5m54KWl7a1pry3ZwxtXjyIm0MtJtFPCfCmqbiYzJoCT+oW354WvHp/U7hRXUtPEbe+vbZ/KVdnQwi3vrebbOybRr4vUgrVTNX0b3XU860yT1blq367DO8tzeP/6MdQ3D8Dfy0yA94FTI3vKG9rFuo3ssnp2l9adsIJdUd9MXkUjPp4mkkJ9DmkAjuL4QQl2Z+qKxPSkI7ouIyqHX+58PHG85Ik3fiyPIzNh1HVSRDbiKsjrlGsecIbz405GJOh2acXqjKUO7C3SYrbiZefnKnZL7nvFKyLqPqFyjchM4oJ9ePyCIWwpqKHBYiMtwo+QKP/WXPZB4B8p1eojroTlL1EXNoydax3FaJ+vzefmk1I4JSOMtfsqmBptZUicB+fMKeTysYlHVLC3FdZQVNPEze+ubs+b/nvBTi4YGUduRSUXZsXjZTZSXttMYVUTzVY7Ef6e7R7nHXF3rDsMjQvigxvH8snqfGoaW7hwVByjWwvZ/DxNTB8QyfQBjqK4T1bnUtPUwpSMcAbHBlLXbOW5H3fyt7MG8eHKfazeV8XIxGBuPikFL5ORphYb//t5FxNSQ7l2YjJjOji/Fdc0tYt1Gy02nT2ldZTWNot3d7gf/SL824eHJIX5MCopmJV7HeKYEuZL6gF2wF0xKT2M//6w0ymacdOkFIJ8PFw8x/dHVzn0w8mt92W2F9Vw5wfr2FZUi9mocfcp/bh8TCL+3bj5URyfKMHujIevVGaX7XA+7u2m4jkgBk57GsbeIiIZkuqoEu83U0LpS56WnulpD4jAdyQ4yfmxrst7m7ycC92yrhVBTj+lfb60E2ZvuOg9aUPT7dIvHpIGyOjF6MAemEoVGCte46NuIMzgzSn5OcxrtbcEeHFRNl+caeROnoCNu/jF5wWMBq3dWexIsGpvBZe9upybT0p1KXL6cl0Bd0xP48n5Owj19eDx84cwIiGISelh+Hqa+HFbidNuzmTQSA33ZWthDfXNVhJDfQh3U2XtDoNBY2RiCCMTuxdObrHp3DMjg8U7Snn2x10E+5i5YVIKG/Kq8PU08fuT0/lsTT4tNp2RidLD/tIVWejgkl4I9PFwO1ykvN7CTe9KKN5s1HjtqlFM7ic/m4HeHjx+3hC+XFeAp9mAr6eJ2CBvjIc4DnNoXBDv3zCWVxZnU9Vo4doJyUxKP/hIRXKYLxdlxfPhqtz2Y1Mzwg+qpe14odFi46nvd7CtSAx6Wmw6/5q3nWHxQYdVcKjo2yjB7oyHv8yO/voOabMCCQv7Rbk/38sfYt2Exv0ipYVq6CUyWzog2vWcwRfCmrekkAzkZqGuWFqu1r4jrmSDzoOITKn8jh4GI6929HGD3AwEJcL7FzqqwVe+AlfPlcI0Tz9xSrO1iOOa0STGLIeCwQhB8XgC98zwpKrewrI9Ffh7mnhidgwtthJ+SLybyBFBfLEFHjs3gZSwI/PHttFi45mFO2i22nGXqvXxNDJ7cDQjE4PJq5QdaLNVb69A/9d5g1m0o4zC6ibW5FRwz8wMvlxXwIuLdmPXZQb0i5ePZEB0z0cHRiQE8c9vt7YPy6hsaOHx77dz/+z+fLGugIRQH9bmVjk5lXUVCk0I8eGx8wZzxwfrsLVucW+dksonq/Paz2mx6fzfFxv5/BaHd3dyuB/TB0Rwxesr2sePjk0J4akLhh2waK0zJqOBsSmhjEwIRkc/ZKtTbw8jd8/ox8T0MFbnVDA4NpCxKWEHtUs/XqhosLBop2uXxr7yBsal9sKCFMcEhyXYmqY9AZwBWIDdwDW6rle5OW8vUAvYAKuu61mH875HlLpCmf980p9k12w0S+9x+Q6Ic2NM0hUNFVK17RchQuyOqMFS+FWwGnTE0nTZ/6QXOu0UsS/d/Jn0fYemSsg9+STwDBRBD0qUnPi+ZR1atwyym//6Tmk98/CTkZoNZfDzozLsY/qDYsLifehilBbhz6tXZZFf1YTZAO/8toc3lklUwNNUw/8uG8FJ/bqeynW4NLZY2VMm33NNk5X4EG9yKxyh4T/O6k9RdRNXvL6iXchGJQXz34uH42k2sHBrSXsY93dTUmmy2p1asHLKG3hq/naevWQE3l2MuMyrbKCgqpFgHw+Swny7XVznbTbyy64yl+M1rW1PBk3jgpFx3Q5Rz8iM4pvbJ5JX2UC4nxcltU083+F7AdhX0UhdB+/uZquN53/a7TQrfFl2BatzKogNju3W+3bmcO1NASIDvDhjaAxnDD3AdLzjnEAvE4NjA11y+kcyYqU49jncHfYC4M+6rls1TfsX8Gfgj12cO1XXdde/UscaJm/JF//0T+fj6TO7f409S+Db30tYPWUqzPyniG1nKnZLAZm99Y+mwSRi6h0Mu3+UvPHJD4vwZp4vQ0NKtsCWL6R1KzAOshfJzOo20k6GrV/La0Dy39/cKSJut4r4z71HCufSZxzEB+OKn5eZjCgzS3f/f3t3Hh5leS5+/HvPTNbJvpIFkhDWgBAgIJu4VNwVrVoXbF2rtkWP2vbYfbGn53T9Vdtje1zq0loX1KJorfuKihAEZBGEQAIhLNn3fZ7fH+8kZJgJScgymeT+XNdczDzzZt4nQ5J73me57zIeXXt0GLO5zcVPV2/jhW8uIiHyxLZK9STOGcLleenc++YuHvuokFuWjMcYaHW5WDIxkUnJEVzx4NrOYA1WgY7tB6tpbHV5JPa4980v+P65U7zO8eHuciobWggL9r7iXF9YwS1/30BFfQtBduFH5+fwlbnphAX1/CvlDHEwLi6covIGj/Zgu41LZ6cxLzOOiSdHEBHau1/PILuNqSlRnaMBG/d5T5ucOTXJI5FKXVMbnxVXeR23p8y7frgaehGhQfzw/Klc++g6ahqtD3KXzUlnWlrv0vyqkalfH4mNMa8bYzomz9YCvssVBZKIRGsLV9f5vPS5Vi7x3ijdCU9ednQOfM87Vm3rBh9zzzUl1ly1q926tTVb+7SX3A23b4Llz8K4RTD9cljwTWtIetXXoXSHuziIgY/utQp/dEiZYc1lH6vtmMIahWt69/30wqEa78QyxZWNXgU6Btplc9L56vwMBHhy3T6ykyK468xJLJmUSJvLcKDSu5gIwIubPFfhu4z33DBAXkYs0e4CIS6XoaK+hZa2dspqm/nOs5upqLcWCLa2G366ehtfHPLe++1LfEQIv1g2nSD70Z+xUyclMmtcNN87ZwoLJyT0ev7cl5yUKO67MrezuMnC7HjuPneKx0hBbHgw553kPU0zMz3mhM+rBtascbG8tGIxj18/l+e/sYCfXJBDglY3G9UGcg77BuCZbp4zwOsiYoAHjDEPdnMcInIzcDPAuHHjBrB7fTDhTLjxLSswhsVYwTraPUzY0mANOwd18we1Yo9nOk+wkqhUF1t5wDtUFllJTGKzoLJLmccgJ0QmWWU2OzTXWFfjVfuPzqt3tWUlLP2FdeXtclnbtY6tyGU/5hc9Nut470CvFZbVU+NjdXXu2JhBu7rukB4bzk8uzOHmJVkE2e0ew4VJkSFcPCvNYy5XBMZEhTI5OYo3tnuWfHTYhGvmZ/DE2iLASj1697lTcAY72FNWx5Nr9/HqtkPMyYhl+cnjvK6OwSoZ2dtEMYsnJPDSisUUlNURHRrElJSoAftjHBJkZ1luGnMz46hvaSM1KgznMVfrNpuwfH4Ge0rreHtnKcF2GyvOyO511TE1NDLinWTEdzOlpkadHgO2iLwJ+Fpx9UNjzIvuY36IVQXCRyYRABYZY0pEJAl4Q0R2GGPe93WgO5g/CJCXl+d7w+hgcwRb89Vd56wba2D369bir5BIWHynNcfsOGZBjK+saMFOa/FXV4e3wvoHrCHvzU9bQT1uPJz/eysneYddb8Cqm6058TN/7vkahWtg8vmw42Xryn78qdBSD+f9Hp6+6uhK85lXWx8+OsRPtDK1DYB3dx7hmXwrecpD7++hvqWd7AQnv7xkOtEDsP2kvL6Z7QdqKK9vITM+nKkpUYR0WYwVZLcxNs77D1qww86K0yfQ7nLx4qYSkiJD+flF05iYHIXdZuPJdfs6r5Cjw4KYlRHLxbPTuTwvnYbmNjLinaTGhFHb1MoTHxdR3diK3Sa8uKmEhIhg0mLCvLZTpfZhftFmE6akRDHFx6K22qZWisobCLILmQlOQk5wEVdP1beyEpz86erZ7K9sINhuIyPeqalAlRrGxJj+xUQRuRa4FfiSMcb7ssP7+J8BdcaY3/V0bF5ensnPz+/psKGxdRU8d93RxyJw7b8gc5HncY1V8NoPPOtZX3iftS+76zD79pdg5TXWvPWU860gWnfYWlke5776LS+AB5YczZiWnmelBv3kAeuxPRiWP2dlJNu2yvoAMe0Sa/Fa2RfW14fFWDnL6w5b89/2EGuxW8wJrhQ/xu1PfcrqzQdJjbYWC3UUpbjtSxMItvevfGJlfQs/f2kbL2wq6Wy794pcLp7V+0VRzW3tHK5pJizI5jHMvKe0js8P1WKMYWpKVLcLvD4uKONPb++msKyexRMTiQ0P4om1Rdx3ZS53rdxMTVMbNoG7z53CV+dn9Lt4RmFZPT9+YSsf7C7DJnDtwky+eVp2v4bIOzS1trF5fzVbDlSTEBHC7HGxjIv3LmmqlPIfEdnQ3cLs/q4SPwdrkdmp3QVrEXECNmNMrfv+WcA9/TnvkGttgrX3e7YZYxXmODZgh8XA0nushCp1h63gm3ySd7KS5BxrcVljJWx/0WpbcJvnlquaEs/0psX51j7rS/8KTTWQOtPa6jX+VCthS1eJk48WIenoV9fHA+TUSYms3nyQkuomHnjfGob/wXlT+h2sAXYcqvEI1gA/Xb2NuZlxvd56FOKwMy7OOyiNT4zoMVVmwZE6vv63DdS59zivzN/Pl6YmMS0tmtjwYF6+bTH7KxuJcwYzPsHpceXfk+bWdjbur+LtHYeJDg3itClJ5KRE8dyG4s4V5C4Dj35YyLzMOM49KYXS2iaKyhuICHWQFd+38wG8sf0Itz21sfPxpOQIHrluLumxGrSVCgT9ncP+XyAEa5gbYK0x5lYRSQUeNsacByQDq9zPO4AnjTGvdveCw5LYINTH6syQbrZFORMg+/Tjv2Z8NnxtNWx4DA5ugtyrrTKa9i7/Jc4Ea1tZe5c54sPbrOHt939r1a4eMwNfBTCGyqIJCVwxdywr8/djDJw9LZlzp3ezZ72PqnwsWqtubKW+pXdVn/pr15G6zmDd4e0dR7jnomlkJjiJjwjxKpnZWx/vKee6R9d3Pv7zuwX88xsL+XeXZDQd1hdWkpng5NYnNlBU3oBNYMXpE7hhcVav9ygXVzTwi5e3e7R9cbiObQdqNGArFSD6FbCNMRO6aS8BznPf3wP0con1MGWzW5W1Ct62rnDB2s88qQ9bvXxJmWHNWbe3es+FgzVMft7/s7ZlGZfVj0V3wGcrrec/fcwq+BGb0b9+9MOY6DB+flEOdy6MJ7JmN6FShd1WAfR/ocz4BCdBdvHI0Z2XEUvKEOWVDgvyXjkeFmRnbmZc537mE9HY2s797+z2aKtvaeez4mrmZcVSUOq52nxhdhy/fXVH50I3l4E/vr2beVlxLJ54TP31Y7S7DGv3lLO9pNpjz3WHhiH68DPQWttd7Cmto6yuhdSYMDLjw5ETzNSmVKDQTGe9UfA2vPYja4FY+W7ranvy+VYGsv4SORqsq/ZZc8/2EKsylzPRKryRNgsOb7dWk2997uiq8tBoz3ravVDX1Mrm4mo2768iNSaMOZmxjO3nFVZowyHGvHk7FLxlNUQkwzXPW3PlJ6i5tZ12F9x35Sx2H7FqW89Mi+HHF+QMWS7lKSlRzEiP5rPio6VDv33WJCb3s9yjy+XyWRu6pLqRaxdk8t4XpZRUWQsG54+PY2xcOB8WlHsdv7+bbWtdbS+p5tpH1jEhKYLzZ6SwauPRLW1BdgnI0pXNre08/2kxP3lxG20uQ3iwnT8vn81pk5P83TWlBpUG7J7UHrGqVFUXwxs/gcgUKwNaeDx8+jdYdDskTe3/eQ5tgScutea9ATJOgUv+DDHjrMAXGm3lJS87WsOYs/7bd8rT41i18QA/fnFb5+MZ6dE8+NW8/mVQ2rf2aLAG63tYcy9c/BffIwc9qG9u428fF/Lb13biMhATHsRfls8hd2wMzpCh+5FNjgrl/qtnk19Ywb6KBmaNiyV3bEy/r+ScIUHcvGQ8d63c3NlmE2t6YUpKFM/dupCC0jqC7DYmJkUQ6rAzOyOGjwsqPF6nN6vSvzhcR5vLsONQLfPHx3PVvLG8+fkRxsWF8d2zpjB1CCqpDbSC0jp+9MLWzmIjDS3t3LVyMy+tWNzntKpKBRIN2D1pqbWCdYdadx3hljrY/CQUr7Pydkcm+/763mhrtepYdwRrgKIPoOhjK2CD9e9VT1vnqz1orRZPndWn0xRXNvDrV3d6tH1WXM2OgzX9C9hdt4x1nmydlSPdEe/9XA92HKr16GdVQys/fmELz966cEgDNsDYuHDG+li01l9nTEni3ityeeTDvcSEBXHradmde7hTY8K8tmT94Nyp3PBYPqV1zQB8bUEGJ6X3nPXKGXJ0fcNjHxUyNi6Mi3NTuXFxFmMGoiiMHxysbvKoDAZQUd9CeX2zBmw1omnA7klEMmR/yfMKUsRKYQrWEHlFQf8Cdmu97+xkR7Z5Po7LtG4nqKXN5XPBVmOrj2QsPSgqq+dgTRMJEcFkp87G65pzyoXWyvQTUFLlPdS7p6yBivqWfs0dDycx4cFcMCOFORkxRIQ4iHUe//s6KT2GF761iKLyeiJCHWQnReDsxRay6WnRTE6OYOdha158f0UjszNiAzZYg/WBxiZ4BO2EiGDNAqZGPA3YXTVWQ+EHVp3q2Exra1ZqLpz93/Cvb0PRGgiPgwUrji78AqscZn+ERkPOMmvIu6uxJ/fvdY+RFhPGRTNTebHLVqmwIDsT+li+8N2dR1jx5EbqmtsIcdh4cvkkZi++E/noj1YmtswlVklQ24mtXk/zkfAjO9FJfMTIqdpUVF7Po2sK+efGYsbFhfOD86cyPyu+s2a1L2mxYX2+gkyPDefha+eyaX8VFfUt5Ljn5QNZdmIEv7lsJj9ctYXmNhdRYQ7uvWJWj4lilAp0/U6cMpiGPHHKxifgxW8dfRwcATe+Ye2Zbq61FoUV58NbP4cG9yKg3Kut3OMh/Vy8U14Ar/wnFLxpBboFt8HCFdbCswFUVF7PPz7ZxwsbDzA5OZI7l05idoaPWt/d2F/RwAV/WtOZJzw0yMaFM1JZfnI6Y0MaiHdVWmlPQ3v3fhSV1/Ph7jK2Hqhh4YR4Ts6KIzzYwZPr9vHrf++gzWWIdwbz4Nfm9Lre9HDX3NbOD1dt9UibGmQXXvzWInJSAzuYDpV2l2FvWT0V9c2kRIed8LTF1gPVvL3jCNWNrSzNSWbW2Jg+729XaiAdL3GKBuwO9WXw4GlQvd+zfdn/wqyvHn1cexiK11v7oZNzIH1e/4bDu2qqhapCa+91bDY4Bmc1tMtlqGxoITzY0W3pyO5s3FfJJX/+CICoUAd3Lp3E4x8VUljeQHaik99cNqPXgfVIbRM3PZ7vsQr7psVZ/Oc5kxGEgtI6jtQ2kxoTyoSkwV/N3NDSxtYD1RSU1pMQEcKMtCiSB2HouKi8ntN/967XPOx9V+ayLPdoFre2dhf1Le1EhToCdstSQ0sb1Q2txIQH9/lnbbBtK6nm8v/7mIaWo1NCj10/V1ebK78atExnI4oxWDVKfLV3EZkMUy+wbgMtNLJfW6F6y2aTE54LTogIITosiOrGVq6YO4773trVub+3oLSem/+2gdW9XK2763CdR7AGePSjQq6YNxZnsIPnPz3A39cWEhMWzI/On8qZOUmE9qJ85Yl6afNB7n7+s87Hp05M4PdfyR3wIiahQXbinMGU1bV4tEd0WVC342ANj35USH5hBedMG8PleWPJTAisIhBbD1Tz29d2sr6wgsXZCdx51qTOEqC91dTaTuggXfGu2VXmEawB7n9nNyePjydMr7LVMNT/ivMjRUQiLPmuZ1tQOKTO9k9/hqmxceHce0UuzmA7IUE2r2Qc5fUtFFf1mFIesJJfHKvdZWh3GVbm7+ehD/bQ1OriUE0TK57ayOb91T5eZWDsr2jgv47JBPberjJ2HKoZ8HMlR4XykwtyPNpmpkeTk2oFswOVjVz32DqeWb+fgtJ67n+3gJ+u3kZd8+CWKx1IJVWN3Pj4et77opSGlnZe//wwtz6xgSO13qVYfSk4UsevX93BpX/5iD+8sZO9g1Cnu8nHYsu65jZcLu+fy+GgvrmNnYdqKSyrx3Xs8IwaFfQKu6uci6383hset+ZhZy2HMdP93ath57TJifzr9lM4UNXIn9/Z7TG067AJMb1MbDIhKYIx0SEcqm7ubDtn+hhiw4N4et1+r+M37q/i5PF93ybWG01t7dQ2e6+gPzY1qTGGovIGqhtbSY0JPeGiHGdNG8Oztyxg5+Fa4p3BzEyPIcU9/F5QWufxngC890Up+8obAmaOu6i8gcM1zV5t+ysaSOrhPSurbWbFU5/y+cFaALaV1PDh7nIevjav16lYe2PxxATue2uXx8/vzUuycYYMTWKevthbVsc9L23nnZ2lhAbZuGvpZK6cm05U2MhZiKl6pgG7q7AYa7V2zjJ/92RYE7HKPqZEh/Ldsyd77Jm++5wpjE/o3arz9NhwHrt+Hk9+so/1hRVccFIqF+amEhESRHpsGIdqPK/GEgdx205qTBinT07knZ2lnW0hDptHgZDm1nZe+uwgP3lxKw0t7aTHhnH/1bOYObb3i/Y6hAbZmZsVx9wsa76/tc1FTWMrUWFBBNu9B77sNiHIR/tw1XX/dwcRCO/FPPaesrrOYN0hv6iSwrJ6cscNXICamR7DP26azwPvF1DV0MINi7I4dfLALvIcCG3tLh5dU9j5s9nU6uK/X/mcqSmRnNJDalo1smjAVicsJMjO1xZkcnJWPCXVjaRGhzF5TCRBjt4HliljovjZhdNoamv3KE35nbMn89W/ftKZRzwrwUleH1az95Uz2MGPL8gh1rmbV7YcZGJSBD86P4dJyUcXu+08XMt3n9vcuayhuLKRbz+7mZW3LCCuh33Ux7OluIqHPtjLtpIavjwrjXNOSmb++DjW7jma2ez6hZlkBFApzOykCK6Zn8ETa4s6225eMp6sXnyYs9t8//zYB/gDi8NuY0F2PHMyYnAZBm2uvL8qGlp4ZetBr/btJTUasEcZDdiqX5whDmZnxDKbEw+mNpt41ZGelxnHqm8uYufhWsKD7ExPix6UjGNdjU+M4FdfPonvnDUZZ4iD6GOG9osrG73WIO4ta6C4spHN+6tpam2noLSOpKgQ8jLiui3febimkY8LKvi4oIyzp43hjmc2UePOLf7b13eyr7KeX186g/zCSraV1DAvK5a8zDiCHcMzoPjiDHZw59KJLJ2axP7KRjLiwpmeHt2roJid6OTUSYm898XR0Y4LZ6Yw/gQro/VkuL+vkSFB5KRG8f4XZR7tWmVt9NGArYYlm02YnhbN9LShnbMNdti7TcCR5GO1+O1nTOD7z2/hwpmp/OrVoyla02NCeeKm+V4ru5ta2/jDG7t4er01R58cHdoZrDs8m1/MLUuyuXROOpfO6bnPpbVNVDe2kRgZTPQwmtOMd4Zw6glskYoJD+aXl0znw93lrC+sYMH4OBZkJ+AMHZ1/rsKC7dx15iQ2FlV1rrNYmB1PrjuVrRo9RudvgFInYMqYKG47YwJ/etsqjekMtpMYGcKkMZE8tX6fx7HFVU1sOVDtFbALyxp4Jv/ogjqbj/3VIQ47juNkPOvgchnW7C7j7uc/42B1EzPSo/ifL89g2nEWptU3tbG/qoEQh51xceHYe3Eef0iPDeeKueFcMXesv7syLOSOi2X1bYvZfaSO8GA7k5MjB3y7oRr+NGAr1UsRoQ5uPTWbpTnJlNe1kBEfzpufHyYy1OG71rSPVedtLuMxrF5R30J2opOC0qPblv7jzIm9Gv4vKK3j63/Lp7nN2ob0WXENtz+1kWdv9T2nvresjp+t3s57X5QS4rBxx5kTufrkcT6vyosrG9h1uBabzcbk5IiAzj3ewRjDtpIathyoJthhIzc9huw+puX1p6wEJ1kBthdfDSwN2Er1gTPEwYz0mM7HuXXN/H1tERfOSOGJT45eZTtswtRU7yQhmfHhHvOzT6wt4q6lk4gJD6KwrMG9CCq2V5nNisrrO4N1h4LSekqqmrwCdrvL8NhHhZ3nbW5z8etXdzItNYolkzyHrXcequHaR9d1bi2blBzB/10zp9s5+UCRX1TJ8oc+ocW9/z/OGcxTX5/f7/rmSg0VDdhKYQW0fRUNtLlcjI0N63VGtRnpMdx9zhQ2FlVx/cJMXtt2iNSYUO46a7LPoemI0CDuWTaN1ZtKeHXbIRaMj+fsaWOYmNz3oBHr9L4yjghxEOVjrreivoVXthzyat9yoMYrYK/ML/bYB/7F4Tre2Vka0AG7pa2dB94r6AzWYL0nH+wq1YCtAoYGbDXqVdQ387ePi/jzOwW0ulwsm5nKt8+axNi4nocfQ4PsXDAjlflZ8bS5XHzrjAmEBdmPW7c7I97JbV+ayE1LxhPqsJ1wnvDJyZFcvzCTRz8q7Gy7Z9k0xvlYTR0Z6uCktCje3lHq0Z55zFaxlvZ28gsrONbm/ZVA1gn1czhobTcc8FG29VB17zKvKTUcaMBWo15+YSX3vrmr8/ELm0rITorgtjMm9vo1DNaVaG1TKxOSIpiUHNljIO5vvuqI0CDuWDqJs6ePobS2mYz4cCZ3c6UeGmTnP86cRH5RJTWNXVcae27HC7bbuSg3jc3H5Hj/0tQBKnDjJ84QB1+dn8EPVm31aD9tGCZKUao7GrDVqPdxQblX2+pNJVy3MJPI0J7TVB6qbuLbKzfxoft1Qhw2Hr9hHvMHKY1qV9FhQb0+z8z0GF5aYa00DnOvNPZVBOacaWP4/GANz39ajF2E6xdlsjB78L+XwXZWzhgaWtp54P09hAXZ+c+zJ/eptKxS/qYBW1m1uHe9AQc2wMSlkLUEIsf4u1dDxtf88cyxMb2+Av6suKozWIO1oOsXL2/nya/P90q+4m8Z8U4yekhAkhYbxi8vns4tS8ZjE2FcXHifstcNVwmRIdx0yniW5aZit9mI87EGQKnhTAP2aFd7CJ69Fg5tsR5vWQnzboGzfgGO0bHPc9GEeKalRLHtoFWZK84ZzPWLMnH0MhVmeX2LV1tBaR31zW2DHrDrW9o4WNVIaJB9QDNfhQTZT2ghXCA40YItSvmbBuzR7sjnR4N1h/UPwdwbIHGKf/o0xDLinTxy/Vx2Hqqlpd3FxKSIHq9Cu8pO9D72ghmpg1qsBGBPaR2/eNmq4BQVauVCv2BGCmHB+mut1EgU+ONcqn9c3jWBMS4YpjWBB0tyVChLJiVy5tTkPgVrgJPSovnd5TOICrMC5dKpyXzr9OxBHUZubmvnf9/e3VnBqaapje8+9xlbDgxezXCllH/pR/HRLnEKRI+F6i71p6dfatUDV70SFuzgsjljWZidQFNrOynRYYT1ooxkf5TXtfCvLd4VnPaU1jMvK/AXiCmlvGnAHu1i0mH5s7DxCSj6CKZ/2aoHHhz4qSiHWndFQwaDM8TBxKQItpbUeLT7WvWtlBoZ+jVmJyI/E5EDIrLJfTuvm+POEZGdIrJbRL7Xn3OqQZA0Fc7+JdzwGiy8DWLG+btHqgfRYUH8+IIcQroMu586MYEZad7pUJVSI8NAXGH/wRjzu+6eFBE7cD+wFCgG1ovIamPM9gE4txpIDt3mEkjmZcXx0m2LKThSR2SogyljorSCk1Ij2FAMic8Ddhtj9gCIyNPAMkADtlInqKyumV2Ha2luc3FSevSAbulSSg1PAxGwV4jI14B84NvGmMpjnk8Duqxoohg4eQDOq9SoVFzRwJ0rN7G+0PpVS4wI4fEb5pJznDrYSqnA1+Mctoi8KSJbfdyWAX8BsoFc4CDwe18v4aPN+GjrON/NIpIvIvmlpaXdHabUqLV2b3lnsAYorWvmkQ8LaW0feVvx9pU38I+1RXzn2c28sPEAh2u8C3goNVr0eIVtjDmzNy8kIg8BL/t4qhgY2+VxOlBynPM9CDwIkJeX121gV2q02nmo1qttQ1ElDS1tRIeNnHUI5XXN3LlyIxuKqgB4bkMxV80by08vzOl1+VOlRpL+rhJP6fLwEmCrj8PWAxNFJEtEgoErgdX9Oa9So1leZpxX2/knpRDVi0IlgWTXkbrOYN3h6fX7KSxr8E+HlPKz/qZi+o2IbBGRz4DTgTsBRCRVRF4BMMa0ASuA14DPgZXGmG39PK9So1ZeZizfOC0bh82abTpjSiKX5aWfcF3t4SrIJnzztGwuzk0jNMj6U2UMtLt04E2NTmLM8P3hz8vLM/n5+f7uhlLDTmubi8LyetpchnFx4ThDRtYQcUFpHfe+8QWvbD1ERlw4y+eP4+EP9pKTEsV9V+YSMcJGE5TqICIbjDF5vp4bWb/lSo0SQQ7biK2m1dDSxi//tZ23d1iLTveU1fOrf+/g3itymZ4WPayCdV1TG58fquFQdRNpMWFMSYkkXIuvqEGiP1lKqWHlUHVTZ7Du0NpuMNDnwiyDqam1nb+u2cMf3tzV2faTC3L42oKMXpdmVaov9KdKKTWshAXZiQ33voqOHkZX1gAFR+q4961dHm3/8+/P2VtW76ceqZFOA7ZSalhJiQnjZxdN82g7dWICU1KGV570qoYWjl0C1NpuqG5s9U+H1IinQ+JKqWHnnGlj+Oc3FrKnrI44ZzDTUqNJHGZ50sfGhRMV6qCmqa2zLSkyhLRYrXSnBodeYSulhp2QIDuzM2K5bM5YzpiSTHJUqL+75GVcvJOHr81jfIKVx33KmEge+OocUqI1YKvBoVfYSqlB19zazmfF1awvrCAmPIh5WXFMSAr8Ve7zsuJ59taFVDa0Eh8RTGz4yMk0p4YfDdhKqUG3ZncZNz5+NKdCYmQIT988n+zECD/2amDER4QQHzG8huvVyKRD4kqpQVXd2MpvX9vh0VZa28zGfccW9lNKHY8GbKXUoGprd1HV0ObVXtfc7ofeKBW4NGArpQZVfEQI1y/O9Giz24SZ6Vq/W6m+0DlspdSguyQ3DYfNxmMf7SUpMoQ7z5zMSWkasJXqCw3YSqlBlxQVyo2Ls7h0dhpBdtuIK1ai1FDQ3xql1JCJ0W1PSp0wncNWSimlAoAGbKWUUioA6JC4UgqAIzVNlNe3kBARTGLk8EsFqtRopwFbKcXHBWXctXIzB6ubSI8N4/99ZSbzsuL93S2lVBc6JK7UKFdUXs8tf9/AweomAIorG7nl7xs4UNng554ppbrSgK3UKHegqtGjRCRAZUMrxVWNfuqRUsoXDdhKjXJx4cHYbeLRFmQX4nQLllLDigZspUa58YlOvn/uFI+2n1yQQ1aC0089Ukr5oovOlBrlgh12lp88jryMWA7WNJEWHcakMZE47Pp5XqnhRAO2UoqwYAe542LJ9XdHlFLd0o/QSimlVADQgK2UUkoFAA3YSimlVADQgK2UUkoFgH4tOhORZ4DJ7ocxQJUxJtfHcYVALdAOtBlj8vpzXqWUUgOvtqmVfRUNBNttZMSHE+yw+7tLqot+BWxjzBUd90Xk90D1cQ4/3RhT1p/zKaWUGhx7y+r58QtbWbO7DLtNuGlxFjcvGU98RIi/u6bcBmRIXEQE+Arw1EC8nlJKqaHjchmeWrePNbuta6p2l+GB9/ewYV+ln3umuhqoOexTgMPGmF3dPG+A10Vkg4jcfLwXEpGbRSRfRPJLS0sHqHtKKaW6U9PUyqtbD3m1byzSgH08LpcZ0vP1OCQuIm8CY3w89UNjzIvu+1dx/KvrRcaYEhFJAt4QkR3GmPd9HWiMeRB4ECAvL29o3w2llBqFnMEO8jJj2VfhWaFt0phIP/VoeDtc08Q7O47wz43FzEiL4dI56UxNiRr08/YYsI0xZx7veRFxAF8G5hznNUrc/x4RkVXAPMBnwFZKKTW0ghw2bjoliw92lVFa2wzAoux45mVqTfRjtbW7eOTDvTzw3h4A1u2tZNXGA/zzmwvJiB/c/PsDkZr0TGCHMabY15Mi4gRsxpha9/2zgHsG4LxKKaUGSE5KNKu+uZA9pfWEOGxMSIrQBWc+HKhq5NE1hR5t5fUt7DhYGxAB+0qOGQ4XkVTgYWPMeUAysMpal4YDeNIY8+oAnFcppdQASo8NJz023N/dGNYEEPFutw1BVpN+B2xjzHU+2kqA89z39wAz+3sepZRSyt/SY8P5xqnZ3PvW0TXWKdGhTBkzDOawlVJKKWWx2YRrFmQwPtHJK1sOMi01mnOmj2Fs3OCPTGjAVkoppfogISKEi3LTuCg3bUjPq7nElVJKqQCgAVsppZQKABqwlVJKqQCgAVsppZQKABqwlVJKqQCgAVsppZQKABqwlVJKqQCgAVsppZQKABqwlVJKqQCgAVsppZQKABqwlVJKqQAgxhh/96FbIlIKFPnh1AlAmR/OO5rpez709D0fevqeD61AfL8zjDGJvp4Y1gHbX0Qk3xiT5+9+jCb6ng89fc+Hnr7nQ2ukvd86JK6UUkoFAA3YSimlVADQgO3bg/7uwCik7/nQ0/d86Ol7PrRG1Putc9hKKaVUANArbKWUUioAaMDuhoj8TEQOiMgm9+08f/dppBKRc0Rkp4jsFpHv+bs/I52IFIrIFvfPdb6/+zMSicgjInJERLZ2aYsTkTdEZJf731h/9nGk6eY9H1F/xzVgH98fjDG57tsr/u7MSCQiduB+4FwgB7hKRHL826tR4XT3z/WI2fIyzDwGnHNM2/eAt4wxE4G33I/VwHkM7/ccRtDfcQ3Yyt/mAbuNMXuMMS3A08AyP/dJqX4xxrwPVBzTvAx43H3/ceDioezTSNfNez6iaMA+vhUi8pl7qEWHrwZHGrC/y+Nid5saPAZ4XUQ2iMjN/u7MKJJsjDkI4P43yc/9GS1GzN/xUR2wReRNEdnq47YM+AuQDeQCB4Hf+7OvI5j4aNOtC4NrkTFmNtY0xLdEZIm/O6TUIBlRf8cd/u6APxljzuzNcSLyEPDyIHdntCoGxnZ5nA6U+Kkvo4IxpsT97xERWYU1LfG+f3s1KhwWkRRjzEERSQGO+LtDI50x5nDH/ZHwd3xUX2Efj/sXqsMlwNbujlX9sh6YKCJZIhIMXAms9nOfRiwRcYpIZMd94Cz0Z3uorAaudd+/FnjRj30ZFUba3/FRfYXdg9+ISC7W8GwhcItfezNCGWPaRGQF8BpgBx4xxmzzc7dGsmRglYiA9fv/pDHmVf92aeQRkaeA04AEESkGfgr8ClgpIjcC+4DL/dfDkaeb9/y0kfR3XDOdKaWUUgFAh8SVUkqpAKABWymllAoAGrCVUkqpAKABWymllAoAGrCVUkqpAKABW6luiEh7lyo/m0QkU0Q+6uNr3CEi4d089667StlmEflQRCZ3c9zDJ1oQpa/99dE/r+IgIhIkIr9yV53aKiLrROTcEz3PcOD+v73a3/1Q6ng0YCvVvcYuVX5yjTGFxpiFxx7krjjWnTsAnwHbbbkxZiZWMYjf+nptY8xNxpjtfe08gK/+DoBfACnAdGPMdOBCIHIQzjOUMgEN2GpY04CtVB+ISJ3739NE5B0ReRLY4s4g9i/31fJWEblCRG4HUoF3ROSdHl76fWBCxzlE5B4R+QRY0PVK1/3cL93nWSsiye72ZBFZ5W7fLCILffT3ffcx20Xk/0TE5n7uLyKSLyLbROTnPXz/4cDXgduMMc1gpX80xqx0P3+VWLW2t4rIr7u+byLya3fBkTdFZJ77+9ojIhe5j7lORF4UkVfdIw8/7fL1d3XJ9X+Huy1TRD4XkYfcfX9dRMLcz2W7X2eDiHwgIlPc7Y+JyB9F5CP3uS9zn+JXwCnukZQ7e/i/Uso/jDF605vefNyAdmCT+7bK3Vbn/vc0oB7Icj++FHioy9dGu/8tBBK6ef13gTz3/e8Cz7jvG+Ar3RxngAvd938D/Mh9/xngDvd9e5fzd+1vEzDe/fwbwGXu5+K6fN27wIxjz9ulLzOAjd18P6lYGbwSsbKovQ1c3KXf57rvrwJeB4KAmcAmd/t1WAUa4oEwrDSSecAcYAvgBCKAbcAsrKviNiDX/fUrgWvc998CJrrvnwy87b7/GPAs1sVKDlZp147352V//8zpTW/Hu2lqUqW612iMyT3O8+uMMXvd97cAv3NfVb5sjPmgl+f4h4g0YgX229xt7cDz3RzfwtECBhuApe77ZwBfAzDGtAPV3fR3D3SmcVwMPAd8Rawymw6soe4c4LNe9r+rucC7xphS9zn+ASwBXnD3uyMF6hag2RjTKiJbsAJvhzeMMeXur/+nu48G6wNTfZf2U7Byc+81xmzq8n5kikgEsBB41p2CFSCkyzleMMa4gO0dIxRKBQIN2EqduPqOO8aYL0RkDnAe8D8i8rox5p5evMZyY0z+MW1N7qDrS6sxpiOfcDt9+x0+Ng+xEZEs4DvAXGNMpYg8BoQe5zV2A+NEJNIYU3vMc75KpfrqtwvoGE53iUjX78Grjz28bnOX++1YV+Y2oOo4H7a6fs3xXlupYUXnsJUaACKSCjQYY54AfgfMdj9Vy9AsyHoL+Ia7L3YRifJxzDyxqqLZgCuANUAU1gePavfV5nFXextjGoC/An8Uq7oaIpIiItcAnwCnikiCeyHeVcB7ffw+lopInHsu+mLgQ6z5/YtFJFysCmOXAN2OYBhjaoC9InK5u38iIjN7OO9Q/T8pdcI0YCs1ME4C1onIJuCHwH+52x8E/t2LRWf99R/A6e4h5g3ANB/HfIy1uGorsBdrmHkzsBFrXvgRrADZkx8BpVhDyluxhrxLjTEHge8D7wCbgU+NMX0tIbkG+DvWuoHnjTH5xphPseae12F9KHjYGLOxh9dZDtwoIpuxvrdlPRz/GdDmXrCni87UsKTVupQaBUTkNOA7xpgL/NyVbonIdViL3Fb4uy9KDUd6ha2UUkoFAL3CVkoppQKAXmErpZRSAUADtlJKKRUANGArpZRSAUADtlJKKRUANGArpZRSAUADtlJKKRUA/j8dIdP+cT08TgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.scatterplot(x=x_pca[:,0], y=x_pca[:,1], hue=cancer['target'],data=x_pca)\n",
    "plt.xlabel('First Principal Component')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4e438e44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.21890244,  0.10372458,  0.22753729,  0.22099499,  0.14258969,\n",
       "         0.23928535,  0.25840048,  0.26085376,  0.13816696,  0.06436335,\n",
       "         0.20597878,  0.01742803,  0.21132592,  0.20286964,  0.01453145,\n",
       "         0.17039345,  0.15358979,  0.1834174 ,  0.04249842,  0.10256832,\n",
       "         0.22799663,  0.10446933,  0.23663968,  0.22487053,  0.12795256,\n",
       "         0.21009588,  0.22876753,  0.25088597,  0.12290456,  0.13178394],\n",
       "       [-0.23385713, -0.05970609, -0.21518136, -0.23107671,  0.18611302,\n",
       "         0.15189161,  0.06016536, -0.0347675 ,  0.19034877,  0.36657547,\n",
       "        -0.10555215,  0.08997968, -0.08945723, -0.15229263,  0.20443045,\n",
       "         0.2327159 ,  0.19720728,  0.13032156,  0.183848  ,  0.28009203,\n",
       "        -0.21986638, -0.0454673 , -0.19987843, -0.21935186,  0.17230435,\n",
       "         0.14359317,  0.09796411, -0.00825724,  0.14188335,  0.27533947]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7da4e901",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_comp = pd.DataFrame(pca.components_, columns=cancer['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8bba052b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.heatmap(df_comp, cmap='plasma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2202ca28",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}