diff --git a/20220419-new_MBC.ipynb b/20220419-new_MBC.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..aa608c2ccc9be05dc17bedbc8bcfbe8747a966cd
--- /dev/null
+++ b/20220419-new_MBC.ipynb
@@ -0,0 +1,696 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Inoculum_size</th>\n",
+       "      <th>MIC</th>\n",
+       "      <th>MBC</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>20220111</td>\n",
+       "      <td>500</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20220111</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>20220111</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20220111</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20211222</td>\n",
+       "      <td>500</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>20211222</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>20211222</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>20211222</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>20220304</td>\n",
+       "      <td>500</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>20220304</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>20220304</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>20220304</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>20220303</td>\n",
+       "      <td>500</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>20220303</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>20220303</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>20220303</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Date  Inoculum_size  MIC  MBC\n",
+       "0   20220111            500    2    8\n",
+       "1   20220111           1000    4    6\n",
+       "2   20220111           5000    4   16\n",
+       "3   20220111          10000    3   12\n",
+       "4   20211222            500    2    8\n",
+       "5   20211222           1000    2   12\n",
+       "6   20211222           5000    2   12\n",
+       "7   20211222          10000    4   14\n",
+       "8   20220304            500    4   12\n",
+       "9   20220304           1000    2   14\n",
+       "10  20220304           5000    2   20\n",
+       "11  20220304          10000    4   18\n",
+       "12  20220303            500    4   12\n",
+       "13  20220303           1000    4   20\n",
+       "14  20220303           5000    4   16\n",
+       "15  20220303          10000    4   18"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv('20220419-New-table_MIC-MBC.csv')\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chip_data=pd.DataFrame(\n",
+    "    {\n",
+    "        'Date': '20220419',\n",
+    "        'Inoculum_size': np.array([.7, 1.3, 5.4, 4.1, 1.3])*500,\n",
+    "        'measurement': 'chip',\n",
+    "        'lethal_concentration': [10, 10, 15, 15, 12]\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Inoculum_size</th>\n",
+       "      <th>measurement</th>\n",
+       "      <th>lethal_concentration</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>20220111</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>MIC</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20220111</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>MIC</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>20220111</td>\n",
+       "      <td>5000.0</td>\n",
+       "      <td>MIC</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20220111</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>MIC</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20211222</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>MIC</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>20211222</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>MIC</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>20211222</td>\n",
+       "      <td>5000.0</td>\n",
+       "      <td>MIC</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>20211222</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>MIC</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>20220304</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>MIC</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>20220304</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>MIC</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>20220304</td>\n",
+       "      <td>5000.0</td>\n",
+       "      <td>MIC</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>20220304</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>MIC</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>20220303</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>MIC</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>20220303</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>MIC</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>20220303</td>\n",
+       "      <td>5000.0</td>\n",
+       "      <td>MIC</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>20220303</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>MIC</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>20220111</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>20220111</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>20220111</td>\n",
+       "      <td>5000.0</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20220111</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>20211222</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>20211222</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>20211222</td>\n",
+       "      <td>5000.0</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>20211222</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>20220304</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>20220304</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>20220304</td>\n",
+       "      <td>5000.0</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>20220304</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>20220303</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>20220303</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>20220303</td>\n",
+       "      <td>5000.0</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>20220303</td>\n",
+       "      <td>10000.0</td>\n",
+       "      <td>MBC</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>20220419</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>chip</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>20220419</td>\n",
+       "      <td>650.0</td>\n",
+       "      <td>chip</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>20220419</td>\n",
+       "      <td>2700.0</td>\n",
+       "      <td>chip</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>20220419</td>\n",
+       "      <td>2050.0</td>\n",
+       "      <td>chip</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>20220419</td>\n",
+       "      <td>650.0</td>\n",
+       "      <td>chip</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Date  Inoculum_size measurement  lethal_concentration\n",
+       "0   20220111          500.0         MIC                     2\n",
+       "1   20220111         1000.0         MIC                     4\n",
+       "2   20220111         5000.0         MIC                     4\n",
+       "3   20220111        10000.0         MIC                     3\n",
+       "4   20211222          500.0         MIC                     2\n",
+       "5   20211222         1000.0         MIC                     2\n",
+       "6   20211222         5000.0         MIC                     2\n",
+       "7   20211222        10000.0         MIC                     4\n",
+       "8   20220304          500.0         MIC                     4\n",
+       "9   20220304         1000.0         MIC                     2\n",
+       "10  20220304         5000.0         MIC                     2\n",
+       "11  20220304        10000.0         MIC                     4\n",
+       "12  20220303          500.0         MIC                     4\n",
+       "13  20220303         1000.0         MIC                     4\n",
+       "14  20220303         5000.0         MIC                     4\n",
+       "15  20220303        10000.0         MIC                     4\n",
+       "16  20220111          500.0         MBC                     8\n",
+       "17  20220111         1000.0         MBC                     6\n",
+       "18  20220111         5000.0         MBC                    16\n",
+       "19  20220111        10000.0         MBC                    12\n",
+       "20  20211222          500.0         MBC                     8\n",
+       "21  20211222         1000.0         MBC                    12\n",
+       "22  20211222         5000.0         MBC                    12\n",
+       "23  20211222        10000.0         MBC                    14\n",
+       "24  20220304          500.0         MBC                    12\n",
+       "25  20220304         1000.0         MBC                    14\n",
+       "26  20220304         5000.0         MBC                    20\n",
+       "27  20220304        10000.0         MBC                    18\n",
+       "28  20220303          500.0         MBC                    12\n",
+       "29  20220303         1000.0         MBC                    20\n",
+       "30  20220303         5000.0         MBC                    16\n",
+       "31  20220303        10000.0         MBC                    18\n",
+       "32  20220419          350.0        chip                    10\n",
+       "33  20220419          650.0        chip                    10\n",
+       "34  20220419         2700.0        chip                    15\n",
+       "35  20220419         2050.0        chip                    15\n",
+       "36  20220419          650.0        chip                    12"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.concat(\n",
+    "    [\n",
+    "        raw_data.melt(\n",
+    "            id_vars = [\"Date\",\"Inoculum_size\"], \n",
+    "            value_vars=[\"MIC\",\"MBC\"],\n",
+    "            var_name=\"measurement\",\n",
+    "            value_name='lethal_concentration',\n",
+    "        ),\n",
+    "        chip_data\n",
+    "    ], \n",
+    "    ignore_index=True\n",
+    ")\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sns.lineplot(data=raw_data, x='Inoculum_size', y='MIC', label='MIC')\n",
+    "sns.lineplot(ax=ax, data=raw_data, x='Inoculum_size', y='MBC', label='MBC')\n",
+    "sns.scatterplot(ax=ax, data=raw_data, x='Inoculum_size', y='MIC',)\n",
+    "sns.scatterplot(ax=ax, data=raw_data, x='Inoculum_size', y='MBC',)\n",
+    "sns.scatterplot(ax=ax, data=chip_data, x='Inoculum_size', y='lethal_concentration', markers='+')\n",
+    "sns.lineplot(ax=ax, data=chip_data, x='Inoculum_size', y='lethal_concentration', markers='+', label='chip')\n",
+    "ax.set_xscale('log')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DY8A1M90jhNCSP6f4XcM3xRj/bqnqKrrXLwF/XXToR8DVMcaRGc65jNSGsil/KAdcGWO8b6nrm6sQwuXAPYX9e+65h8svv3yGMyRJK0LhDbX7PgldB+DZ/31u540Nwm1vgWf+viGcJEkrQf8x+Nzb4EX/u3wIV6zw+uBb/wcaWgzhJE1x7733csUVVxQfuiLGeG+16pEkVZ9B3DILIXSQZsLdUHS4B3hGjPHOCtzvw6QZbgW3A8+KMfaVGRuAvwF+sejwQ8ClMcbxWe5zK/C6okPvjTHeMss5TcADwLlFh/8WeEss8xczhNBJ+rPbV3T4/THGn53pPpVmECdJq5hvrEmSpFLFrw98rSCphEGcJKlUw+xDtMQ+xeQQDuAvgC0hhGfN81oHYozds4z5feDFTKypdgPw9RDCr8UYv1oYFEJ4DPAnwEtLzv/t2UK4hYoxjoUQfhv4YNHhN5H+LH43xvhAUX3PBP43cFXR2AHg/61EbZIkARNtqHyDTZIkFRReHxQeS5IkSTMwiFt+Ty9zbI69rqZ4BvDVmQbEGH8cQvg5UthV+A3hauArIYSTwKPANtJadaW/QfyfGONHF1jbnMQYPxRCeArw5qLDLwdeFkI4RGrZeR6wpeTUHPD6GOPBStYnSZJvsEmSpCl8fSBJkqQ5MohbA2KMH863nfxHYF3RU1vzWzl/DvxmpWvL+2VgBPh/io4FUsvKc8uMHyKFcB9bhtokSZIkSZIkSZIWpK7aBWh5xBg/BFxBmhk3U6vJrwNPjzH+Rrl12ipUWy7G+OvAM4H/mGHoGPABUm/tf16O2iRJkiRJkiRJkhbKGXHLLMZYtf4VMcaHgJ8JIbwZeDJwMdBBmo32KPDNGGPXAq99C3DLIuv7Cqll5h7gRtJsuBagH3gA+EaMsW8x95AkSZIkSZIkSVouBnFrUD7M+ly165hOjPEw4Iw3SZIkSZIkSZK0otmaUpIkSZIkSZIkSaoAgzhJkiRJkiRJkiSpAgziJEmSJEmSJEmSpAowiJMkSZIkSZIkSZIqwCBOkiRJkiRJkiRJqgCDOEmSJEmSJEmSJKkCDOIkSZIkSZIkSZKkCjCIkyRJkiRJkiRJkirAIE6SJEmSJEmSJEmqAIM4SZIkSZIkSZIkqQIM4iRJkiRJkiRJkqQKMIiTJEmSJEmSJEmSKsAgTpIkSZIkSZIkSaoAgzhJkiRJkiRJkiSpAgziJEmSJEmSJEmSpAowiJMkSZIkSZIkSZIqwCBOkiRJkiRJkiRJqgCDOEmSJEmSJEmSJKkCDOIkSZIkSZIkSZKkCjCIkyRJkiRJkiRJkirAIE6SJEmSJEmSJEmqAIM4SZIkSZIkSZIkqQIM4iRJkiRJkiRJkqQKMIiTJEmSJEmSJEmSKsAgTpIkSZIkSZIkSaoAgzhJkiRJkiRJkiSpAhqqXYAkSZIkSdKyihGy45DLQG4cspmJx7lMfj//OEZoXAeNrdDUmr42NFf7O5AkSdIKYRAnSZIkSZJWvlxu+jAtOw65bD50G4eYnd+1xwbSNpjfr2tIgdzZcK4N6n2LRZIkSVP5KlGSJEmSJNWmXG76MC2XmdgWEq5FIOYDu5gpCvIy+ftk02OAhpa0Nea/5jIw2pe2gvqmfDDXNjGDrq5+yf4oJEmStDIZxEmSJEmSpOVTCNfKhWm5fNhWeG4h4VrhejED2Wy6xtn75Caey83j2tlRGO3N74TUmrKhJQVuDS3Q0ATZsbSN9Eyc17AujWlqy8+gWwchzO97kiRJ0opmECdJkiRJkhanMHusXJhWugZbzM3z2rEkTMtOBGmls9fmG9yFAKE+tZqsq89vDUXHGoAcZEZhfBgyI+lemZG0FUK3UF80Y645BXD1DZAZTtvwmcINJ68119iazpEkSdKqZRAnSZIkSZKmmilMK12DbUHhWnGYlg/RsvmvxSHbQsK1s2FaccjWUD50m4um9onH2fEUwhWCucxoqnF8MG0FdQ0plDs7a64l3bN0XKifHM41tUF94/y+Z0mSJNUsgzhJkiRJktaKmcK00jXYiPO7diFcOxumlc5gK1qDbb7BXSFcq6ufPFttUsBWFLJVUn1j2po70n6MqSVlZjg/c24ktbLMZWAsA2NFoVt900Qodzacy8JYf9oK6hrzwVzbRGtL15uTJElakQziJEmSJElayWYK0yatv5Zh/uFajvLtIAsz2IrWYJt3uFY3fTvIwvGwTOHaYoTCmnHNE8divp1lZiQFc5mRiTXksmMw2lc4OYVzjS359eRaoL45/fcb6U1bQX3zRDjX1JrG19Ut67cqSZKk+TOIkyRJkiSplsRYPkwrDtSWJFwrDtOK7pHLTYRuCw7XZmkHWZjBtlqFujSTrXEdrMsfK15brhDQ5TJp9lx2FOidOLehsNZcS7pGfWMaMzwKw92Fm+Tv0TrR2rKhJQWDkiRJqhkGcZIkSZIkVVqMM4dpkx6Pz//6uezk2Wpnw7Ti9pD5NdjmG9xNCtdmaAdZ15DGqry6+tRisqlt4lguM7HWXGHmXMzB+FDais8ttLJsLLS0bJg6LtRNDuYaWyfP1JMkSdKyM4iTJEmSJGkhCuHadGFaLpvfL8xcm6ezwVp2apgWS44vKFxrKBOwlVmDzXCtcuoa0lpzhfXmIN/SMt/WsrDuXC6b1pqbtN5cY5n15oCxgbQNFt1jUjjXBvW+HSRJkrRcfOUlSZIkSVJBjDOHacXrsS0oXCsJ0CaFbEsVrjWUzFQr0yLScK12nV1vrjPtx5jaUo4XBXOZ0fT3MjsOo/0T59Y3T54119CS/j6N9hWtS0d+XbrWNDuvENK53pwkSVJFGMRJkiRJkla3s+FacZiWKVqDrWg9tpid57VJAVo2v95aaYvIszPYsgsL12ZaZ630OdcGW51CmAjV2JCO5bJFs+byW3Z8Yr250d7CyRPrzTWuyz9uguxY2kZ6Ju7TsG6inWVjaxrv3ylJkqRFM4iTJEmSJK08uVz5MK1cq8hFhWuZonuVtIoszGCbr+J2kGfXWZtmDTaDEJVTV59Cs6bWiWO5zOS15jIj+cBuOG2F0C3UT501V98wMY7T+QuGyWvNNbamcyRJkjQvBnGSJEmSpNqQy00fppU+Xki4dnaWWgay+fXWSltELjhcK2kHGUpmrRUfN1xTJdQ1QFN72gqyY2nm3PhwPpwbTX/vxwfTVnzupFlz61KrytJxoT7fznLdRGvL+sbl+x4lSZJWIIM4SZIkSVLlFAKussHa+OQ2kTE3z2vHiTDtbLhWPHMtx6QWkfMRQvl2kFMCNsM11bD6prQ1d6T9GPPh3HB+5ly+lWUuA2MZGBucfG4hlGvIrz1Xl5263lxdY37WXD6ga2pL/19IkiQJMIiTJEmSJM1XLjs5TDu7X7wGW2Hm2kLCtZI2kFPWYCscX0C4Nl07yHKhm7TahJAP1Zqh0GUy5ibWmxsfSSFddnxiHbmzoVvRuQ3rUpvK+ub0//pIb9oKGlrys+baUkhXmGEnSZK0BvmbhSRJkiRpmjCtzBps2XFSn8f5XDuWhGn5r1PWYFtAcFcI16ZrB1kaukmaLNTlQ7N1sC5/LJedWGeuENAV1qDLjAC9E+cW1pkrtLasb5wYN9xduMnkdpaFFpjOJJUkSWuAv4VIkiRJ0mo1XZhWrlXkvMO1XEmYVjqDrWgNtnmHa3VzawdZeCxpadXl14Jraps4lh2fHMxlRtL/2+NDaSs+txDONRbWm6ufGDd0Ko0LdUXtLPOtLRualvf7lCRJWgYGcZIkSZK0khS3fSy77lrR44WEa1PCtKI11orXYFtsuDZdO8jCDDZJtaW+MW2F9eZgoqVlZjS1tMyMpn8fxgZL1ptrLFlvbh3U5WCsP22FoXUNk2fNNbZBvW9dSZKklc1XM5IkSZJUTTHOHKaVPp53uFa0rtqkMC3DpLXWsgu49tlwbZZ2kHUNaayk1aWwZlxBjJAdnVhrbnwkv9ZcflbuaP/E2Prmollz+S2XSWvSnV2XLj+ucV0+nGtNm+vNSZKkFcQgTpIkSZKWWiFcKw7Qzu6PT12Pbb6K20Dm8musnV2DraRN5ILCtYYyAVuZNdgM1yQVC2EiVGNDOpbLTp4xlxnJB3OjaRvtLZw8NZhraJoYN9IzcZ+GonaWhfXtXG9OkiTVKIM4SZIkSZqLSeFamTCteD22RYVrhSAtO/0abAsO10oDtjJrsBmuSVpKdfUpNGtqnTiWy0xeay4zkg/shtNWEOqnhnP1DUXjTufH1RWFc/nWlsUz9SRJkqrIIE6SJEnS2hVj0ay18clhWjYftuWK20LO59pMDdCKH5+dwZZdWLg2aYZacbBWZg02Z4pIqiV1DdDUnraC7NjkYC4zmv59HB9MW/G5k2bNrYM6po4L9UXtLPOtLesbl+1blCRJKjCIkyRJkrS65HKTZ6Zlix+XrLsWs/O7diFcKw7QcqVhW9EMtvkqN0NtujXYDNckrSb1TWmjM+3HmMK5wlpzmXyLylwGRgfSVnxuIZRrbEnrytVly6w315RvZdk2MXuurn5Zv01JkrT2GMRJkiRJqn25XEmYli2atZaZHLItJFwrXCNmioK8kllsCw7XGkoCtjJrrxmuSdJkIaT2kg3N0JI/FnMTs+XGR1JIlx1PgV12rCh0K5zbMtHasr55YtxI78R9Glrys+ZaJ8I5/y2WJElLyCBOkiRJUnUUwrVyYVrpGmwxN79rl4Zr2fx6a2cDtqKwbb7BXQhTZ6xNaRHZMHHcN3QlaWmEuonQbF3+WC6TgrnMCIwPp8eFNegyI5PPLQ7mGlpSq8rCuOEzhYH5WXOtE60tG5r9t1ySJC2YQZwkSZKkpVOYPVYI0IrDtLNtIjMLC9dysSRMy06/BttShGuTWkSWHJck1Ya6BmhqSKFZQXZ8ImArrDsXczA+lLbhwrn1JeHcunSsMG7oVBoX6oraWeZbWzY0Lfu3KkmSViZ/g5QkSZI0s7NhWj5AKw7TittE5jILDNeKw7R8iJYtbROZXVi4Nl07yHKhmyRpdahvTFtzx8Sxwqy5s9to+tkyNpi24nML6801NOfDuRyM9aetoK5h8qy5xlao92eJJEmaylcIkiRJ0lo0U5hW2iqSOL9r53JFYVqZGWzFbSLnG9yFupnbQZaGbpIkwcR6c6xP+zFOBHKZ4TRzLjuWX3NuHEYLoVuA+qbJLS0bWtLPsNG+onXpSOvQFdaZK2x1dcv9nUqSpBpjECdJkiStFtnM9GFa6eOFhGtl20EWZrAVHV9MuDZdO8hguCZJWkKhsBbcOmBDOpbLTgRzhRl02XHIjqZttDd/bl0K3YqDuYamNGZ4FIa7CzdJzzW1pnaWhfu53pwkSWuKQZwkSZJUq2KcmKk2XbC2JOFaaZhW2HJLEK7N0g6yriGNlSSp2urqU2jW1DpxLJeZvNbc+HD6mZgZTltBqJ8czDW2pJ9xZ8edzo+ry8+WWzfR2rKheVm/TUmStLwM4iRJkqTlFOMMM9XGp67HNl+57OTZaoUw7eyaa9mJtpHzDe4mhWsztIM0XJMkrRZ1DdDUnraC7NhEMFdobxnLrDdX15AP54rXmwPGBtI2aVy+lWWhtWV947J9i5IkqbIM4iRJkqTFKoRr04Vpxeux5TLzv/7ZYK3o69k12EqOLyhcaygTsJVZg81wTZKktGZcfRPQmfZjTG0pi2fOZUbz68gNpO3suc0ToVzjTOvNNU0N52zPLEnSimQQJ0mSJJUT48xhWvF6bPMN1yJTA7RJIVthBlt2YeFa6Zpqpe0gi58zXJMkaXFCmGhJ2ZI/FnNFwVx+3blJ680VQreQD+aKWlo2NKdZd9kxGOmZuE9Dy0Q7y0Jry5W03lzMv56ZT80xrqzvUZKkMgziJEmStHacDdfKhGnZ8cnrscXsPK/N1ACteBZb6Qy2+ZpxnbWS53zDSpKk6jq7FlzpenOjE2vNZUbSa4JCi8vicycFc+ugvmFi3PCZwsCJ9eaa2vKPW6hJxYHafMI1X9NIklYBgzhJkiStbLlc0ay1kjDt7DppmSUI1zJF9yqZxbbYcG3SOmvTrMHmG1GSJK1sdQ3Q1JBCs4Ls+ETAVmhrGXMwPpS24cK59flgrni9uXoYH0zb0Kk0LtQXtbNcB41t0NC07N/qJIXg7aGvppDxgqfOLYzLjsG33wlP/GXXzJMkrWgGcZIkSao9udz0YVrpGmwLCdcK14sZyObXWzt7n9wiw7WSdpBTWkQWHTdckyRpbatvTFtzR9qPMQVQxeFcdjS9JhkbTNvZc5smQrnCDLq6LIz1p62grrFk1lxrmmG3HIpDuA/9NBDg1R+ZPYzLjsFHXgM/+gIc+i684n2GcZKkFcsgTpIkScujMHusXJhWugZbzM3z2rEkTMtOvwbbfIO7EMq3gyy3BpvhmiRJWoxQWDOuGVifjsXcREvLs+Hc2MQ2WgjdQgrnGlsmgrmGlvTaanS8aF06oL45P2uuaL25uiVeN7Y0hBvPT+/74CtnDuOKQziA+z8H//xawzhJ0oplECdJkqSFmylMK12DbUHhWnGYVrzOWskabAsJ16ZrB1kudJMkSaqWUJcPy9ZNHMtl8+Fcfq25zGh6TZYdTRu9E+fWN0+Ec43rUpiVHYXhURjuLtxk4h6NbSmka2hZ+AeMpgvhILXcnC6MKw3hCgzjJEkrmO8qSJIkabKZwrTSNdiI87t2IVw7G6aVzmArWoNtvsFdIVybrh1kqJ88e02SJGmlqqtPYVlT68SxXCYFXoXZc+PD+dl0w2krCPWTZ801tqTXR4V16TidH1c30cqy0NqyoXn22mYK4QpKwziYPoQrMIyTJK1QBnGSJElrwUxhWukabPMO13JFM9XKzWArWoNt3uFa3dzaQRquSZKkta6uIa01V1hvDlK4NZ5vaZkZhsxYel1Wut5cXUM+nCtebw4YG0jbpHH5cK6psN7cNKHY8XvLh3AFhTDule+Hcx8PH3vD9CFcwekHYKQX2rbM+schSVKtMIiTJElaqQrhWrkwrfTxQsK1KWFa0T1yuYnQbbHh2nTtIAsz2CRJkrQw9U1pozPtx5jaUp4N5/JtLXMZGB1I29lzmyfPmmtoyY/rK1lvrmlqOEcdPPGX0mvFL/3X6esbH4L3vzTNtMuMzvy9bLkYbvmsIZwkacUxiJMkSaoVMc4cpk16PD7/6+eyk2ernQ3TittD5me2zTe4OxuuTdcOsvhY3fxrlyRJ0uKFMBGuFcRcvpVlUThXvN7caG/h5BSYFdaaa2hOW3YsbSM9E9fs3JMCsyf9atqfKYyDuYdw7dvn+x1LklR1BnGSJEmVVAjXpgvTctn8fmHm2jydDdayU8O0WHJ8QeFaQ5mArcwabIZrkiRJK1PxWnAFuczEbLnx4fQ4l50I6gqhW6jPB3vNE60t6xug73B6Tdq+fe5h3HQM4SRJK5xBnCRJ0kKNj6RPCZeGadlMUdC2mHCtEKQVh2xLFa6VBmxl1mAzXJMkSVqb6hqgqT1tBdnx/My54RTQZUZS2DY+mLbhonMbmmHodJopt/6chYdxhnCSpFXAIE6SJGkhuh+G4e65jY2kAC2bX2+ttEXk2Rls2YWFa5NmqBUHa2XWYAthnt+oJEmSBNQ3pq25I+3HmIK2wiy5sx9Sy8BYBsYGYfAU7LgStj02hXGZEfjKH8/tfhvONYSTJK0KBnGSJEnz1Xc0hXCR/CeB8+utlbaIzBW1h5yvcjPUpluDzXBNkiRJyy2EiXXiWJ+OxdzEbLnCmnPH7oZNF6Z15a551dyDuL3PTCFcjL7elSStaAZxkiRJ8zHcAwPH0uP+ozDaN/dzJ62pNk2wVpjd5psNkiRJWmlCXQrcGtfBuvyxLY9J+9kx+Oxb536tu/4ZrngZXPBUwzhJ0opmECdJkjRX4yPQ82h6PNSTQrgQoL55YsbalBaRhmuSJElao7ZcAk2tKYT7yGvgR1+Y+7njQ/DBV8KrP2IYJ0la0QziJEmS5iKXhe6DaR23sSEYPJGOt22FdRurW5skSZJUaxYTwhUYxkmSVoG6ahcgSZK0IvQ8kta4yGag/wgQobnTEE6SJEkqtRQhXEEhjDv49RTCxbh0dUqStAwM4iRJkmbTfwxGeiECfUfS7LiGFujYXu3KJEmSpNoy3xCufVua7TYTwzhJ0gpmECdJkjSTkV7oP5oe9x+DzHBa861zV1qMXpIkSVLS1JFCOIAv/O7sIdyWi+FN34DXfRqe/T9mHlsI4/qO2J5SkrSi+O6RJEnSdDKj0P1IejzcA6O96XHHTqhvrFpZkiRJUk0a64dcJj1+ylth897px265GG75LLTnu0w86VdnD+Oe+fvpA3HOiJMkrSAGcZIkSeXkcnDmIMQsjA3DwIl0vG0rNLVVtzZJkiSpVp34QQrjOnamoK1cGFccwmVG4fRDKVybKYx77v+EJ/5SGueMOEnSCmIQJ0mSVE7PI6kNZS4D/UeACM0d0Lqp2pVJkiRJtSuXmTmMKw7hsuPwg8/C8Xth4Pj0YZwhnCRpBTOIq7IQwu4Qws0hhD8NIfx7CKEvhBCLtocXcM3zS66x2O2WCnzftyyypq8udU2SJJ3VfxxGeiACvUfSmwgNzdCxo9qVSZIkSbVvujCuOITLZeDw7ZAbS+f0H4XB01PDOEM4SdIK11DtAtaiEMKTgLcCjwd2VbmcuRirdgGSJC2bkb78DDhg4FiaFRfq0loUwc8wSZIkSXNSCOO2XTYRxoW6iRDu+L0T68kVDJ2C9i0TYdwFT4Vd1xjCSZJWNIO46rgBuLnaRczROPDFahchSdKyyIxC98Pp8XAvjPSmx527oL6pamVJkiRJK1JpGFc4dvxeiLnpzwshhW+GcJKkVcAgrvYMAO2LvMYx4NkLPPd/AE8o2v9MjPHUIuuZi//F/AK/7koVIklao3I5OHMQYhbGR9IaFQBtW6Cprbq1SZIkSStVIYzb8hggwsn7Zw7hCgrhmyGcJGmFM4irrn7gAHA78N381wuAryzmojHGEeDL8z0vhLAeuKbk8K2LqWUe7osxzrtmSZKWTO+jqQ1lLgN9XUCE5nZo3VztyiRJkqSVLZeBE/dVuwpJkqrCIK46Pk2a/fXDGCd/BCiEcEF1SgLglUBL0f4J4HNVqkWSpOUzcAKGuyECfUfTGwX1TdC+o9qVSZIkSZIkaQUziKuCGOOD1a5hGreU7H8gxpgpN1CSpFVjtB/6jqTHgydgfCgtIt+5C+rqq1ubJEmSJEmSVrS6aheg2hBCeAzwxJLDt1ahFEmSlk9mDLofBiKM9KVZcQCdO6GhuZqVSZIkSZIkaRUwiFPB60r2vxdjvKsqlUiStBxyOeg+mNpQjo/AwPF0vG0LNLVXtzZJkiRJkiStCgZxIoRQB/xsyeFbq1CKJEnLp/dQakOZy0L/EYg5aGqD1s3VrkySJEmSJEmrhGvECeCZwDlF++PAB5e7iBBCPXAhsBWIwGngaIyxf7lrkSStcgMnYfhM+mnTdxSy41DfBB07q12ZJEmSJEmSVhGDOAHcUrL/6RjjqWWu4XeBvwI6So7nQgh3A18C/jrG+EgliwghbCMFgfOxtxK1SJIqZHQA+rrS48GTMD4IoQ46d0FdfXVrkyRJkiRJ0qpiELfGhRA6gJtLDt9ahVIunuZ4HXB1fvu1EMK7gV+PMQ5XqI63AH9QoWtLkqotM5bWhSPCSF+aFQfQsQMamqtamiRJkiRJklYf14jTK4DWov3jwOerVMtsGoA3Ad8JIdg7TJI0PzFC98OQy8D4CAwcT8dbN0Nz6YRsSZIkSZIkafEM4nRLyf4HYoyZZbz/ncAfAS8AzgfagSZgB/Bs4M+B3pJzrgQ+HUJoW7YqJUkrX++h1IYyl4X+oxBz0NSWgjhJkiRJkiSpAmxNuYaFEPYCTy45fOsy3f4O4PoY4x3TPH88v305hPA/gfcANxU9fz3w34G3LnFd7wI+Os9z9gKfXOI6JElLafAUDJ2GSArhsmNQ3wgdOyGEalcnSZIkSZKkVcogbm17Xcn+HTHGu5fjxjHGu+YxtjuE8DJSQPbSoqfeEkL4ixhj1xLWdQI4MZ9zgm/gSlJtGxuE3sPp8dCptB/qoHM31NVXtzZJkiRJkiStaramXKNCSo9+tuTwrVUoZU5ijDngjUBP0eEW4KeqUpAkaWXIjsOZg0CE0f40Kw6gYwc0NFe1NEmSJEmSJK1+BnFr19NJa7IVjAEfrEolcxRj7Ca1qCz2nGrUIklaAWKE7ochNw6ZUeg/lo63boLmjqqWJkmSJEmSpLXBIG7tuqVk/zMxxtPVKGSe/q1k/5KqVCFJqn29h2FsAHI56DsCMQdNbdC6pdqVSZIkSZIkaY0wiFuDQgjtwMtKDt9ahVIW4lDJ/taqVCFJqm2Dp9N6cAD9RyA7BvWNqSWla3tKkiRJkiRpmRjErU0vB9qK9o8Dn69SLfM1XrLfWJUqJEm1a2wQevOf2xg8lfZDHXTugrqG6tYmSZIkSZKkNcUgbm26pWT//THGTDUKWYAdJfsnq1KFJKk2ZTNpXTgijA7AUL7rcvt2aGipZmWSJEmSJElagwzi1pgQwvnAU0sO37r8lSzYk0v2S1tVSpLWqhhTCJcdg8wY9B9Lx9dthJbOqpYmSZIkSZKktckgbu15HVC8OM6BGOM91SpmPkIIDaT6i/1bNWqRJNWgvi4Y64dcDvqOQMxCYyu0uZyoJEmSJEmSqsMgbg0JIQTgtSWHb61CKQv128BFJcc+WY1CJEk1ZugMDOa7FQ8chewo1DdC504IYeZzJUmSJEmSpAoxiFtbngJcWLQ/BnxwsRcNIdwaQohF262zjH9zCOGZ87zHrwP/veTwp2KMB+ZZriRptRkbgt58p+Kh02ltuBCgcxfUNVS3NkmSJEmSJK1pvjtVJSGEJwHryjx1dcl+SwjhWdNc5kiM8b553PaWkv1PxxjPzOP8pfJ44F0hhO8D/wz8K3BvjHG0eFAIoRX4CeBtTF3X7jTw68tQqySplmUz0H0QYg7GBmDwVDrevh0aWqpbmyRJkiRJktY8g7jq+QBw3hzGbQe+NM1z72VquFZWPtR6ecnhW+dybgVdnd/+GMiEEA4BvaSZehuB84HGMuf1Ay+OMT64THVKkmpRjND9MGTHIDsOfcfS8XUboGV9NSuTJEmSJEmSAIO4teRlQEfR/jHSTLRa0QBcMIdx3wF+xhBOkkTfERjrh1yE3i6IWWhcB23bql2ZJEmSJEmSBBjErSWvK9n/QIwxU5VK4G+AblK7ySspP+ut2AjwVeCdwGdjjLGi1UmSat/QGRg8kR4PHIXsaFoPrnNXWh9OkiRJkiRJqgEGcVUSYzx/me833TpzS3HtW5hji8z8+O+QZrYRQmgCLiPNhttFmrXXCPSRwrofAd+LMY4tadGSpJVrfBh6D6XHQ2dgtD+Fb527UhgnSZIkSZIk1QjfrVJV5QO27+c3SZJmls3AmYMQczA2CIMn0/H2baktpSRJkiRJklRD6qpdgCRJ0pzECD2PpDaU2XHoO5qOt6yHlg1VLU2SJEmSJEkqxyBOkiStDP1HYbQPchF6uyBmobEF2rdXuzJJkiRJkiSpLIM4SZJU+4a7YeB4ejxwLM2Kq2uAzt1pfThJkiRJkiSpBhnESZKk2jY+DD2PpsdD3WlWXAjQuTOFcZIkSZIkSVKNMoiTJEm1K5eFMwch5mBsCAZPpuNt26Cxtbq1SZIkSZIkSbMwiJMkSbWr++HUhjKbgf4jQISWTli3ocqFSZIkSZIkSbMziJMkSbWp72hqQ5mL0NeVZsc1tED79mpXJkmSJEmSJM2JQZwkSao9wz0wcCw9HjgGmRGoq4fOXRB8+SJJkiRJkqSVwXeyJElSbRkfgZ5H0+OhnjQrLgTo2AX1jVUtTZIkSZIkSZoPgzhJklQ7clnoPggxC2PDMHgiHW/bCk2t1a1NkiRJkiRJmieDOEmSVDt6HkltKLMZ6O8CIjR3wrqN1a5MkiRJkiRJmjeDOEmSVBv6j8FIL0Sg70iaHdfQAh3bq12ZJEmSJEmStCAGcZIkqfpGeqH/aHo8cAwyw1BXD527IPhyRZIkSZIkSSuT72xJkqTqyoxC9yPp8XBPCuUAOnZCfWPVypIkSZIkSZIWyyBOkiRVTy4HZw5CzMLYMAycSMfbtkJTW3VrkyRJkiRJkhbJIE6SJFVPzyOpDWUuA/1HgAjNHdC6qdqVSZIkSZIkSYtmECdJkqqj/ziM9EAEeo+kMK6hGTp2VLsySZIkSZIkaUkYxEmSpOU30pefAQcMHE+z4kIddO5KXyVJkiRJkqRVwHe6JEnS8sqMppaUAMO9aVYcpBCuvqlqZUmSJEmSJElLzSBOkiQtn1wOzhxMbSjHR9JsOIC2LdDUVt3aJEmSJEmSpCVmECdJkpZP76OpDWUuA31dQITmdmjdXO3KJEmSJEmSpCVnECdJkpbHwAkY7oYI9B1NYVx9E7TvqHZlkiRJkiRJUkUYxEmSpMob7Ye+I+nx4AkYH4JQl9aFq6uvbm2SJEmSJElShRjESZKkysqMQffDQISRvjQrDqBzJzQ0V7MySZIkSZIkqaIM4iRJUuXkctB9MLWhHB+BgePpeNsWaGqvbm2SJEmSJElShRnESZKkyuk9lNpQ5rLQfwRiDpraoHVztSuTJEmSJEmSKs4gTpIkVcbgKRg+AxHoOwrZcahvgo6d1a5MkiRJkiRJWhYGcZIkaemNDkDv4fR48CSMD0Kog85dUFdf3dokSZIkSZKkZWIQJ0mSllZmLK0LR4SRvjQrDqBjBzQ0V7U0SZIkSZIkaTkZxEmSpKUTI3Q/DLkMZEZh4Hg63roZmjuqWpokSZIkSZK03AziJEnS0uk9lNpQ5rLQdwRiDpraUhAnSZIkSZIkrTEGcZIkaWkMnoah0xCB/qOQHYP6RujYCSFUuzpJkiRJkiRp2RnESZKkxRsbTLPhAIZOpf1QB527oa6+urVJkiRJkiRJVWIQJ0mSFic7DmcOAhFG+9OsOICOHdDQXNXSJEmSJEmSpGoyiJMkSQsXI3Q/DLlxyIxC/7F0fN1GaO6oammSJEmSJElStRnESZKkhes9DGMDkMtB3xGIOWhqg7at1a5MkiRpbpraYfPF0Ng293M2nAvrz6lcTZIkSVo1DOIkSdLCDJ5O68EB9B+B7BjUN6aWlCFUtzZJkqS5aOqAzRdBcztsuTh9oGg2Wy6B1s3QtiUFeJIkSdIMDOIkSdL8jQ1C76H0ePBU2g910LkL6hqqW5skSdJcNHXA5r3pA0TjQ+nr5lnCuC2XQFNr+gBSLpMCPMM4SZIkzcAgTpIkzU82k9aFI8LoAAydTsfbt0NDSzUrkyRJmpviEO7g1+Evr4JHvjVzGFccwv3za+FjbzCMkyRJ0qwM4iRJ0tzFmEK47BhkxqD/WDq+biO0dFa1NEmSpDkpDeE++AoYPAkf+Knpw7jSEO7+z8N9nzSMkyRJ0qwM4iRJ0tz1dcFYP+Ry0HcEYhYaW6Fta7UrkyRJml25EG58OD03NlA+jCsXwhUYxkmSJGkWBnGSJGluhs6kT4sDDByF7CjUN0LnzvRmlSRJUi2bKYQrKBvGTRPCFRjGSZIkaQYGcZIkaXZjQ9B7KD0eOp3WhgsBOndBXUN1a5MkSZqLDeem1y89j5YP4QpKw7iZQriC+z4JX/qD9Li5HRrLrDEnSZKkNckgTpIkzSybge6DEHPpjanBU+l4+3ZoaKlubZIkSXM1dDqtd7vhXHjK22YeWwjjvvqn8L6bZg7hADZfBDf+Snqcy8D44JKULEmSpJXPj7BLkqTpxQjdD6dPgmfHoe9YOr5uA7Ssr2ZlkiRJ8zNwDEIdtG+Dp+aDuH//H9OPHxuAr/7J7NfdfBHc8lno2JFCuOP3Lk29kiRJWhUM4iRJ0vT6jsBYP+Qi9HZBzELjOmjbVu3KJEmS5q//SPo61zBuNuVCuJhbfJ2SJElaNQziJElSecPdMHgiPR44CtnRtB5c5660XookSdJKtFRh3Ka9cPlL4eFvQNvWNIOudTM0NC9drZIkSVrxDOIkSdJU48PQ82h6PHQGRvtT+Na5K4VxkiRJK1khjBsbhHUbYeulcPKHcz9/80Xw0n+Af3jG1OeaO6B1C7RtmfjatiWFdYVj6zakNpmSJEla9XwnTZIkTZbNwJmDqa3S2CAMnkzH27eltpSSJEkr1Wg/dN0BXfvh8IGJQG6+XvUhGO6Z/h6j/dB9cPrzQ32aPVcc0rVugbbNRY+3+NpLkiRpFTCIkyRJE2KEnkdSG8rsOPQdTcdb1kPLhqqWJkmSNG/ZcThxHxzen8K3k/cvzRpu3Q/D2NDCz4/Z1AK80AZ8OtuvgJ/864XfR5IkSVVnECdJkib0H4XRPshF6O1KbxI1tkD79mpXJkmSNLsYU3vtrv0pfDt6Z2q5vdQ++nq45meW/rqlmtrnNu6Tvwy5TFE7zEIbzM0Tj5vaKlurJEmSyjKIkyRJyXA3DBxPjweOpVlxdQ3QuTutDydJklSLhnug68DErLdCW+1KGhuA/e+p/H3atsw+JkY4dX+a/TfTt964rszadSWBXesm1wOWJElaYr66kiRJ6ZPiPY+mx0PdaVZcCNC50zdjJElSbcmMwrG7J8K30w9Up47cOIS6FHCNDVbmHq2bZx8z2ptCuNmMD0PvobRNK8C6jSVhXT6wu/g5UFc/59IlSZKU+M6aJElrXS4LZw6m9VLGhiY+Rd62DRpbq1ubJElSqR/9K3zjf1e7iiTmgACv+QRsOBf6j8CR76UW30On0uuqodMweCptMTu/67dtnX3M4KkFlV5ehOEzaeNHE4cbWuAxz5v99BM/TAFlIcirb1zC2iRJklYmgzhJkta67odTG8psJr15RISWTli3ocqFSZIklbFn3+Kv0dACmZGZx2y+CF70v+HH/wbf/Mvpx40NwD//LPzMR+GCp8L5T4Hj96Q124rFXGqjOXRqIpg7+/jkxP5o/8Q5c2lNuaRB3DTatsytVfn+f4TDt0/st2woM7tuC7RuhbbN6XHzetugS5KkVc0gTpKktazvaGpDmYvQ15VmxzW0QPv2alcmSZLWkvFhOPr9NBt/51Uzj+3cDR07of/o3K/fuC4FZBc9C3ZcCZ94M/Q8PP34zRfBLZ+Fjh0pXGvugH//H9OPHxtI9Z93Y9qva5gaxIW6tAZb6ybY8pjpr5UZnQjoNl4w+/e2HEHcXFpkwtT1+UZ60nb6x9OfU99YZu26Mo8bmhdavSRJUlUZxEmStFYN98DAsfR44Hj6VHhdPXTuSm8USZIkVUouC6cegK79aZ23wgyy8540exAHsHsf/PDT0z8f6mDrpWn23J59sO2x6cNG2x6bXu+8/rNw64ug++DUc4tDuELryae+LT03XRj3nD+CJ7wZYoT+Y7PPtptJQ3MKGzt3z238zqvhKW/Lt8E8BYOnYSg/w26kd+F1FJtLi0xYWCiYHU+h6mzBanNnCuWe9d9gwznzv48kSVKVGMRJkrQWjY9Az6Pp8VAPjPamlkAdu1zLQ5IkVUb/UTh8IIVvXXekWfmljnwvBXJ1s7xdsadMENe5KwV0e/bBrmvTLLZiuQycuC+Fcev3wC2fmRrGFYdwuQwcvxfad0D7tunDuOf8Edz4KxMhXOGDTstlwznTB1PZsclr1A2eyod0pyfWsBs8lcbNpHUOLTIzI2lmYKWM9qWtcd3sY3/0RXjkGylAnNQWc2ua3TeXa0iSJC0RgzhJktaaXDa94RSzMDYMgyfS8bat0NRa3dokSdLqMTaQgrXDB9K6YX1ds58zPpTCsh2zzIrbfR20rE+zwfbckAK4zp2zX3+mMK5cCBdz+TV0KR/GVTuEm019U2rj2THDn02M6b9VIZSbtHZd/vFcZqAtR4vMUAfrNs4+7sR9cPDr0z/f1DY1pCsN7Fo2pNmTkiRJi2QQJ0nSWtPzSPrEcjYD/V1ATK1+5vKmhiRJ0nRyGTjxg9Rqsmt/ehxz87/O4QOzB3HNHfCzn1hYO+1yYdxnfg1e8tdTQ7iC0jAuhBRy1XIIN1chpD/P5g7YdOHCrzPan9p/LqYt52xaN80tHBuaJRQcG0xb98PTjwl1afbcpIBuC7RuhbbNsHlvCuskSZJmYRAnSdJa0n8srRUSgb4jaXZcQwt0bK92ZZIkaaWJEXoPT6zzduR7aUbbYh2+Hfa9fvZxi1nTtjSM+5mPTRwvDeEKisO4p7w1PV7pIdxS2nYZvP7zMD5YfmZd8Rp2w2cWFtLOpUUmLM3svJjLzxI8CSfLPP/M34eLnjXzNcaHYHQgHyD6FpwkSWuVrwIkSVorRnrT2iyQ3izKDKdPFHfuWtwbWZIkae0Y6U3rux2+HboOwMDxpb1+y4YUjMVc5V+fFIdxdfUzh3AFxWEcGMKVCgGa2tO28fzpx+UyMNxdEtKdLlrDLn+8NNhtW8YgbjZzCQUPfRe+/IcTLTWLW2C2bpm8bl3blvTnFkLFS5ckScvLIE6SpLUgMwrdj6THwz3pTTRI64XUN1atLEmStMLsfw/c98mlu159Y2pDuXsf7NmX2v0t5weEchk4dg+0b4WBk8AcZmn1H0ltDYkw2lfpClenuoYUQLVtnXnc2NDkWXWtc2ilnsumGXeVNpdQsBAIxlwKGodOA/dPP76hZZqwriiwa93s63dJklYYgzhJkla7XA7OHISYhbFhGDiRjrdtTQvVS5IkzdXufYsP4jbvzQdvN8COK6GheWlqW7Dc/Gf2jfZWphRN1tQKTefChnPnfk4uA9e8Oj+77nS+veQpGBtY2trmE8TNVWYktXvtPTzzuHUb8yHdZtj7TLj4OfO7jyRJWlYGcZIkrXY9j6Q2lLlMvp1ShOaOtFaFJEnS0OnUZnLLY2ZuJwiw65o0Y20+63u1boE916fwbff1vgZRZTU0ww1vnHp8fLio/WVRS8ziwG7odHrNPJum9jR7bTZDFWqROdydttMPwNZLZx8/2g93/FPR7Lot0Lo1/b9Y9SBckqTVzyBOkqTVbOAEjPRABPqOpjcWGpqhY0e1K5MkSdWSGYGjd0HXfji8H848lI5f8xp4XJkAo1hzB2y7LK2lNp2GFth5TQrf9twAG85z3StVX+O6tP7g+j3Tj4m51MJ9UlhXsobd4MnUHnIuBk8uTe0zmcvMvP5jcPc/l3+uuTPfJrRMO8zWfEvMlvX+PyxJ0iIYxEmStFqN9EFfV3o8cDwtdh/qoHPX8q69IkmSqivm4PSPU+jWtR+O3Q3Z8anjuvYDswRxkGa2TQriAmy9ZGKdt+2Xu4aVVqZQl9o+rtsIXDz9uLnMmoP5t6ZciNZZ1tmDmWfmjfal7cyD04+pa0xtMKesXZdft67w2Nl1kiSVZRAnSdJqlBlNLSkBhnvTrDiAzp1Q31S1siRJ0jIZODERvHUdSLN8ZnPy/vRBnpbOmcftuR4e+MJE8Lbr2jRjRlor6ub4dtpN7yozu+4UDJ2EwdPp+HD34mqZ01p1pxd3j9x4mlXXf2z6MTuvgRf/5ezXitHZdZKkNccgTpKk1SaXgzMH0yd1x0fSbDhIv6Q3tVe3NkmSVBljQ3D0zonwrefRBVwkwpE74MKnzzxs+5Xw0x/yzXRpNi3r07Z57/RjsuOp7WVhnbqzYV3J18xI+fPnFMTVSItMgH+6KX0wsLBOXUtnmnG38xrYMEPbUEmSVjCDOEmSVpveRyEznIK4/iNAhOb2ua9lIUmSal8uk2awdR1I4dvxeyFmF3/dwwdmD+IM4KSlU9+Y1m+eaQ3nGGFsIB/MFQV2Q6fTGm+zmak15VJpm0OLzPHhidm5gyeBH0w8V9cAe59WkdIkSao2gzhJklaTgZOpvU0E+o6mT9jWN0H7DL/YS5KklaHvCBy+PYVlR+5Ib8wvpfXnQPsc3kyXtLxCgOaOtG26YP7nbzgXdl03MbtufGjpa5zTzLwZAsHWjUtXiyRJNcYgTpKk1WK0H/q60uPBE+kX7FAHnbugrr66tUmSpMX76p/AsbuX7nrNnbD7+rTm2+59M8/IkbRyXfXKtBWMDeXXqTs1/Rp2Q2cg5uZ+j9Y5BHEzzcxrMYiTJK1eBnGSJK0GmTHofhiIMNI3seh7505oaK5mZZIkaansuWFxQVxdI+y4IoVue66HzRf7YR1pLWpqhabzYMN504/JZWGkZ/K6dYWQbvB0Prw7CWODafxi16pbt2le34IkSSuJQZwkSStdLgfdB9NaMeMjMHA8HW/bAk3t1a1NkiRNL0boeTS1m7z4WdCyYebxu6+H/e+Z3z02XgB79qVtx1XQuG7B5UpaQ+rq0xrTrZthpo6148MpoGvfNvs1B09P/9w6Z8RJklYvgzhJkla63kOpDWUuC/1HUguZprb0S7MkSaotw93QdQAO74eu/RNrJq3bCBf9xMznbr0k/YwvzEApZ93G/Iy3G9KsN18PSKqkxnWw4Zy5jb3ombDxvMntMPuPwdBpPyQgSVrVDOIkSVrJBk/B8BmIQN9RyI5DfRN07Kx2ZZIkCSAzmtpJFoK30z8uP67rwOxBXF0D7LoeHv76xLH6Zth5VX7W2w1pBlwIS1e/JC2V9u1pKzbaD31H/HdLkrSqGcRJkrRSjQ5A7+H0ePAkjA9CqIPOXa73IklStcQcnHkoBW+H98OxuyA7Nvt5h/enVpWzvRl9zg0wcCwFb7v3wfbLXQ9WkiRJqmEGcZIkrUTZceh+GIgw0pdmxQF07PDNOEmSltvgyfyMtwNpG+5ewDVOpHbTG86dedylL4LLXrywOiVJkiQtu6oEcSGEbDXuWyTGGA0hJUkrU4xw5iDkxlO7q4Hj6XjrJmjuqG5tkiStBePDcPT7cPj2FLx1P7w01z28f/YgzvZtkiRJ0opSrTDK3xxUVghhM/AkYC/QBgwCDwLfjDGermZtklQzeg+lNpS5bFpPIeagqQ1at1S7MkmSVqeYg5M/ygdv++H4vZDLLN31Qx1svRRa1i/dNSVJkiTVhGrOCossfyBXjXvOKISwG3gc8Pj8131A8XSGR2KM5y/w2nGR5V0QY3x4kdeYkxDC1cB/B14E1JUZkg0hfBb4rzHGu5ajJkmqSYOnYSj/uYT+o2nNmfpG6NjpJ+QlSaqUGOHzvwGj/Ut3zc5dsOeGtM7brmuc1S5JkiStUtUK4r5OCsXWpBDCk4C3ksK3XVUup+pCCP8F+HNm/vtYD7wEeEEI4ddjjP9nWYqTpFoyNphmwwEMnkr7oQ46d0NdfXVrkyRpNaurh13XwcGvLfwaTe2w+3rYsy+Fb507l64+SZIkSTWrKkFcjPHp1bhvDbkBuLnaRdSCEMKvA+8o89RR4AgpqCz+DbUB+KsQQogx/tUylChJtSE7ntaFI6ZP4xdmxXXsgIbmqpYmSdKKlMvAiR/Asbvh6lfNPrN8zw3zC+LqGmD75Sl027MPtjzGD85IkiRJa1A1W1OqvAGgvQLXvYs0C28+jlWgjrNCCDcCby85/FXgrTHGO4rG7SPNmHta0bh3hBD+M8b43UrWKEk1IUbofhhy45AZhf78P8/rNtrGSpKkuYoReg+nNd4O74cj34PxofTceTfCxvNnPn/P9bPfY+P5E7Pedl4Nja2LrVqSJEnSCmcQV139wAHgduC7+a8XAF+pwL26Y4xfrsB1F+N/kVpOFnwaeHmMcax4UIxxfwjhOcDHgRfmDzfkzy8O5yRpdeo9DGMDkMtB3xGIOWhqg7at1a5MkqTaNtIDXXek4K3rAAwcLz/u8P7Zg7iOnakddF/XxLF1G1PwVtjaty1V5ZIkSZJWCYO46vg08EXghzHGXPETIYQLqlPS8gohPB+4sejQaeDnSkO4ghjjWAjhDcB9wOb84aeGEJ4dY/xSZauVpCoaOgNDp9Lj/iOQHYP6xtSScrYWWpK00m26EFrWL821RnrhzENLc63VYKn+bGvtzzU7BsfumZj1duoB5rQ8+eH9cOXLZx93/pPg9ENpxtuefenPMdQtumxJkiRJq5dBXBXEGB+sdg014I0l+++MMZ6c6YQY44kQwruA/1pyHYM4SavT2CD0PJoeD55K+6EOOneldWckabVbqhBuqa+1GizVn0e1/1xjhO6D+Rlv++HoXZAZmf91jt6Z1mOtb5x53BPesqAyJUmSJK1dvounZRdCaAaeW3L4PXM8/T1MDuKeH0Jomm4mnSStWNlMWheOCKMDMHQ6HW/fDg0t1axMkpbdle+9clHn3/26u5eoktVnMX+2VftzHToNhw+k4K3rwMTPyMXIjMDxe2HXNYu/liRJkiQVMYhTNTwdaCvavz/G+MhcTowxPhxCeAC4OH+og7ROnLPiJK0eMaYQLjsGmTHoP5aOr9sILZ1VLU2SpGWXGU0z1grh21K3wmxoSQGcs80lSZIkVcCK+k0jhHAV8HzgWuBcoBNYB8x3kZwYY9y7xOVp7q4p2f/WPM//JhNBXOF6BnGSVo++Lhjrh1wO+o5AzEJjK7RtrXZlkiQtv5Fe+PxvLd31Qh1suQT2XA+798H2y2dvSSlJkiRJC7QigrgQwo3A/wdcV/rUAi85h9W6V6cQwk5gF2lGWjdwKsZ4dJnLuKxk/755nl86vvR6krRyDZ2BwfySmQNHITua3hzs3AlhoT/2JElawdq3wYZzJ9ZNXYiOHbD7hhS+7brOGeaSJEmSlk3NB3EhhF8F/oIUuhXegYwlX+d1yaWoawW6MoTwEHBB6RMhhGPA14BbY4z/ugy1XFKyf2ie55eOL72eJK1MY0PQm/8nbuh0WhsuBOjYabssSVqEv/3+36YH/enzZy/e+SR2r5s6y/j2Mz/kQM8Ppxzf1bKFl+x6cvlrP3Rb2eMr7R7LbmwQjtwJgyfg8ptnH7973/yCuKa2FLjt2Qe7r4fO3X6gRZIkSVJV1PS7eiGElwB/md+N+a0QyI0DPcBQNWpbgTblt3J2AK8EXhlC+B7wuhhjJVde31ayf3ie53fNcj1JWnmyGeg+CDEHYwMweCodb98OjeuqW5skrXDvvPOdk/av33BJ+QCr+z7+pkzotW/jpdMGWO988ONlj6+0e1RcLgMn74fD+9M6b8fvS62X65vgkhdAQ/PM5+/ZB/eW/3MAUrvJ7ZenwG7PPth6iR9ikSRJklQTav03k7/Mfy0EcCeB/w18Erg/xpirUl2r2bXAd0IIr4sxfrRC92gv2R+c5/ml40uvt2AhhG3AfBdhcr1BSYsTI3Q/DNkxyI5D37F0fN0GaFlfzcokSVq4vq4UvB3eD0fuSLPgSmXH4Pg9adbaTHZeA6E+hXcF68/Jz3jbB7uuSbPgJEmSJKnG1GwQF0J4PHA+EyHc3cBPxBhPVbOuFegU8Bngy8BdpNln/aTw6lzgKcDPA1cXnbMOeH8I4XiM8esVqKk0OBuZ5/nDs1xvMd4C/MESXk+SZtd3BMb6IRehtyu9ydi4Dtqc8CtJWkFG+lLgdvhAmvXWP8elqA/vnz2Ia2qF854IdY0pfNuzL80alyRJkqQaV7NBHHBV/msghXE/Zwg3b68BPhpjHCvzXE9+uwt4ZwjhF4H/Dyj0hGkCPhhCuCjGON+gbDYtJfvl6pvJaMm+PdskrVzD3Wl9HICBo5AdTa20One5lo0kqbZlxuDwd+H7H0rh26n7U4vl+eo6MLdxz/mj+V9bkiRJkqqsloO4LUWPD8cY91etkhUqxviBeYz9uxDCSeCjQF3+8G7gl4B3LHFpI0Br0X7TPM8vXUBiqYNCSVoe48PQ82h6PHQGRvtT+Na5y3VtJEm1J0YuGM9w4/AwTxwegT87H8bn22W+jFM/guGe1JJZkiRJklaZWn6Xbyj/NQJz7GmixYgxfjyE8E/A64oO/yxLH8QNMDmIK50hN5vSGXADiytnkneRwsj52Etat1CS5i6bgTMH08yBsUEYPJmOt29LbSklaQ26q/dBWuubuah9z5Je97pt16UHY+llY3tD+X9nd7Zs5roNj5ly/OIZ6ik3fiXeYzqd2RxPGh7mxuERnjg8wvZsdvaT5mPdptRmMjMMbFjaa0uSJElSDajlIO5HRY/XV62KtecdTA7irgohbI8xHl/CewwAxQsfzXdV9dLxSxbExRhPACfmc06wdZyk+YoReh5JbSiz49CX/7xJy3po2VDV0iSpGu7pfYh3PvhxvnH6Lp6y5Wrede1bl/T6733+e9ODI9+bcdzNu5/GzbufNr9r3/D78xpfq/eYztWjo7z95OkluRYA9c2w62rYnV/nbeMFtmKWJEmStKrVchD3DVLLwRZgbwhhfYyxt8o1rXoxxrtDCCeYCMoC8BhgKYO4E8CFRfvz/Wju7jLXk6SVo/8ojPZBLkJvF8QsNLZA+/ZqVyZJy+revoP8zYOf4Gun7jx77D9OfZ+7eh/kqvV7q1eYztrf0sw40LjgKwTYcnEK3fbcANsvh/r5dqaXJEmSpJWrZoO4GGN/COH9wBuBeuC1wP+pblVrxmEmz1jbusTXvx94QtH+ufM8v3T8DxdXjiQto+EeGMh/tmHgWJoVV9cAnbudESBpTbm39yF++rt/WPa5dz34cf72ut9Y3oLWksGT8L0PQEPp0stTDdfV8b2WZh43Mjr367dvh93Xp+Bt97XO9pYkSZK0ptVsEJf3+8CLSaHQH4YQvhBj/NEs52jxxkv2F/4B2PJKg7PHzvP8y2a5niTVpvGR1JISYKg7zYoLATp3pjBOktaQx3ZewFXr93JX74NTnvvm6bu5s+cBrtl1bRUqW4XGh+Do9+Hw/rQVfhZtv3Lyys3T+Pa6lpmDuMZW2HXtRPi2fo8fLpEkSZKkvJp+1y/GeCKE8BLgi8BG4CshhNfGGP+tyqWtdjtK9k8u8fXvLNm/cZ7nP2mW60lS7cll4cxDEHMwNpRmIwC0bUtvYErSGhNC4C0X3sybvvfnZZ9/54Mf5x8e+woA7n7d3ctZ2sqXy8KpH6XQrWs/HL8Xcpmp447fzd1vewDat019rljXAfiHZ07sh/oUuu19Bux9JtTV+4ESSZIkSZpGzf+2FGO8PYTwROCjwOXAF0MI3wA+BtxBColGFnDdR5e00FUihLAHOK/k8KElvs1XgUGgLb//mBDCeTHGR2Y7MYRwPnBx0aH+/PUkqbZ1P5zaUGYz0H8EiNDSCes2VLkwSaqchwa6+JuHbuPV5z6bazc8ZsrzN26+kmvWX8ydvQ9MOn7V+r28/vwXwEgvtKxfmmJGVvly031HU+h2+HY48j0Y7Z/beQ99Fa56xcxjdl4Du65Ls972PgPOf8rEz6+R3vRBE0mSJElSWTUfxAHEGH8YQngu8CVSW8In57cFX5IV8r1Xwc+V7B+KMT5QduQCxRhHQghfBG4uOvwG4A/mcPobSvb/NcY4tmTFSVIl9B1NbShzEfq60kyFhpa0ho4krUIHB4/ytw/dxueP/SeRSPd4P+++/renjAsh8Ja9N/MLd7wdgCs6L+Qte2/myZuvIoRgwDOT0X44cmcK3rr2Q9+RhV3nno/BlotnH/eiv5h43H0Quhd2O0mSJElaa1ZEGBVC+G3g90grGETABQcqIIRwGfDWksO3Veh2/8jkIO6XQgh/HWOctg1mCGEb8JYy15Gk2jXcAwPH0uOB45AZSS28OndBqKtqaZK01A4NneBvHvoEnz36LXLEs8e/c+Y+9nf/kH0bL51yzhM2Xc4r9zyTp2y5mqduuSYFcJoql4ET98HhAyl8O/nD1O54sQ4fgBhd002SJEmSKqSmg7iQfgv/IPAKUvhW+G3eMG4GIYRrgGcAfxdjHJrHOZ8EOooODwN/Oodz/5DJs9m+FmN8+kznxBg/G0L4T+AJ+UObgX8MIbwsxjhe5h5NpNBtc9Hh/4gxfmG2+iSpasZHoCffCXmoB0Z70xudHbugvrGqpUlSJXQNn+TTR79Z9rl3PfgJ3rPvd6YcDyHw+5fdUuHKVqj+o/DIt9MabUe+B+Nzemk/dxvPT2u9ZcegoXlpry1JkiRJAmo8iAPeBrwy/7g4fDsO3AucBpb4t9HlEUJ4ErCuzFNXl+y3hBCeNc1ljsQY7ytzfAPwF8DvhRA+DnwCuD3GeKqkhgBcAfw88AtA6W/fvxNjXGCPmzn5DeBrQGFKyItJawC+NcZ4R1Gd1wPvAJ5WdG4W+M0K1iZJi5PLptZdMQtjwzB4Ih1v2wpNrdWtTZIq5PGbHsv1Gy7hQM/9U567vfsHfPfMfTxu02OrUNkK9eBX4Lt/v3TXW7cxBW+7r4c9+9LPJEmSJElSRdVsEBdCaAF+h4kALgBfBn4/xvjdata2RD4AnDeHcdtJa+OV817glhnO3UwK2X4eIIRwHDgF9APtwG5g4zTnviPG+P/Nob4FizF+I4TwO8CfFR1+OnAghHAEOArsAnaWOf03Y4z/Wcn6JGlReh5JbSizGejvAiI0d6Y3QSVphTs12sOW5g1TjocQ+KW9L+UNB/6k7HmfPvpNg7j52LNvcUFcfRPsuCpdZ88+2HShbZElSZIkaZnVbBBHCmQ2kIK4CPwL8MoYY5zhHM1se36bSR/wlhjjB5ahHmKMbw8hZElhXH3RU7vyW6ks8LYY418uQ3mStDD9x2GkN/306juSZsc1tEDHbP8ES1JtOzZyhncf/BT/0vU1/u663ygbqt2w6TJu2HgZt3f/4OyxC9t28eYLb+I52x+3nOXWnhjTbOnD+2Hz3jQzbSabL4KW9elnylxtvgj23JCuveNKW05KkiRJUpXVchBXWMk9ADng1wzh5uxu4LdI68Q9Dtg0h3N+CLwHeHeMsbuCtU0RY3xHCOHfgD8Cns9Eq8piOeBzpBmR31/O+iRpXkZ6oT/f1XfgGGSGoa4eOnc5C0HSinV85Azvfvgz/MvhrzIeMwC888FPcMPGy0jdzid7y96bef3+H3B+6w7edOFNPG/HE6hfq/8GDp2Gwwega39a623odDp+0bNmD+JCXRrz4L9PP6ZtC+y+Ic14232dM68lSZIkqcbUchBX+OhmBH5c4bXKll2M8fwKXvs08Pb8RgjhPOBi4FxSK8p1wAjQTWr/+J38OQu93x8Cf7jImu8EXhRC2AI8GbgQaAMGgQeBb5aucSdJNSczCt2PpMfDPRMzGDp2Qn1j1cqSpMX450P/zp/96AOM5cYnHb+j537+88y9PHHzFVPO2bfxUv7m2rfxhE2X01BXP+X5VS0zAke/n8K3w7enGXDldB1IM+TKBJmT7N43OYhraIFd1+aDt32w4dzZryFJkiRJqppaDuKKg7eeahWxGsQYHwEeqXYdc5EP226rdh2SNG+5HJw5CDELY8MwcCIdb9sKTW3VrU2SFmFv++4pIVzBux78BE/YdHnZWXFP3nJVpUurDTEHpx5IM94OH4Bjd8M0f16TDHfDmYdSi8qZ7NkHWy+bWOdt22P9cIckSZIkrSC1HMQ9VPR4W9WqkCRpLnoeSW0oc5l8a8oIzR3QOpfuwJJUu67feAlP2HQ5/3nm3inP3dn7AN86fTdPWiuhW8HA8bTO2+F8u8nRvoVdp2v/7EFc+za4+W8Wdn1JkiRJUtXVchD3baAL2A2cF0I4J8Z4qMo1SZI01cAJGOlJzZT7jqYwrqEZOnZUuzJJmpPusX4+d+zbvPqcZ5ed3fZLe19aNojb2bKZsVxmOUqsrrFBOHJnajXZdQB6l+jXksP74apXLs21JEmSJEk1qWaDuBhjLoTwbuAP8ofeCvxa9SqSJKmMkT7o60qPB47D+BCEOujclb5KUg3rGevnvY/8Kx849EWGs6Oc17qjbEvJazZczJM2X8k3T98NwPbmTfzCBS/h5t1PpbGuZn+lWJyT98Oj/5nCtxP3pRaUSyXUw/bHwu7rl+6akiRJkqSaVOu/Nf8p8JPANcAvhRC+HmP8eHVLkiQpLzOaWlICDPemWXEAnTuhvqlqZUnSbHrHB3nfI5/nA49+kcHsyNnj73rw4zxp85VlZ8W9Ze9LeWDgMD9/wYt56e6n0VS3ytcpu/uj8OMvL9311p+TX+ftBth5DTS1Lt21JUmSJEk1q6aDuBjjaAjhecBngeuBj4QQ3g78WYxxgQsxSJK0BHI56H44taEcH0mz4QDatkBTe1VLk6TZ/Lf73sOXTtw+5fjdfQ/xH6e+z1O3XjPluavW7+Vfn/yO1TsDrtSefYsL4po704y3PTfAnuuhffvS1SZJkiRJWjFq+rfoEMJr8w/fDWwGzgd+G/iVEMKXgQPASWCk7AVmEGN83xKVKUlai3ofTW0ocxnoPwJEaG6H1s3VrkySZvX6819QNogDeOeDH+cpW64uOytuRYdw2XE4fm9al+3iZ8PG82Yev3vf/K5f1wg7rszPetsHmy+yRbEkSZIkqbaDOOBWIBbtRyAA7aSWlT+5iGsbxEmSFmbgJAx3p59KfUfTm7v1TdC+o9qVSdKcXLl+L0/bcg1fO3XnlOfu63+Yr526k6dvvXb5C1tKMaaZy1374fABOHonZPKf32tqmz2Ia9sCG89P15jOpgtTYLdnH+y8ChpalqZ2SZIkSdKqUetBXEFgIpCLZZ6bq0KQV3oNSZLmZrQf+rrS48ETaVZcqIPOXVBXX93aJClvKDPCBw99idu7f8DfXvsbZWe3vXnvzVOCuA2N7bz+/BfyuE2PXaZKl9jQGeg6MBG+DZ0qP65rP1zzqtmvt3vf5CBu3aaJGW+7r3cWtCRJkiRpVishiAslX5fiWpIkzV9mLP+GbISRvjQrDqBjBzQ0V7MySQJgKDvKRw59mf/78OfoHu8H4Csn7+CZ266fMvbyzgt4+tZr+erJ77G+sY1bznsBrzrnWbQ1rFvushcuMwrH7krtJrv2w+kH53besbvSubP9233uE1Ir4sKst40XQJlQU5IkSZKk6dR6EPf6ahcgSRIAuRx0H0xrwo2PwMDxdLx1MzR3VLc2SWtejJF/evQL/OPDn+HMWN+k59714Cd4+tZrqSuzXtkv7X0pV3bu5dXnPpv2lRDAxVwK2wrB27G7Unvg+cqOp3P33DDzuMLsN0mSJEmSFqimg7gY43urXYMkSQD0HkptKHNZ6D+S3gxuaktrCElSlYUQ2N/9wykhHMD9A4/y7ycO8KztU0OnSzvO49KOWdZKq7aBE6nd5OH96etIz9Jc9/D+2YM4SZIkSZIWqaaDOEmSasLgKRg+k1YY7TuaZlLUN0HHzmpXJklnvWXvzXzl5B1ln3vXQ7fxzG3Xl50VV5Me/XYKyg7fDj2PLu21G9tg1zWw7bKlva4kSZIkSWUYxEmSNJPRAeg9nB4PnoTxQQh10LkL6uqrW5ukNWcsN87xkTOc07p9ynOXdpzHs7bt48sn9k95rmv4JAcHj7K3ffdylLl43303nJnjem+zCXWw7bGw+/rUZnLbZVDnr0GSJEmSpOVR1d9AQwhfAT4JfCbG+ONq1iJJ0hTZceh+GIgw0pdmxQF07ICG5mpWpmUXSFMiKzVemtl4LsMnjnydfzj4KdobWvmXJ/xR2dltb7rwpklB3Lr6Zl59zrN53XnPZ2PTClrPcs/1iwvi1u+B3fvSdXZdC03tS1ebJEmSJEnzUO2Pgj4NeCrwjhDC/cCn89s3Y4y+eyVJqp4Y4cxByI1DZhQGjqfjrZugeQW9ma3FqWuATXuhvhHOPJTWCZxN6+YUAgydSWsLSoswnsvwqSPf4O8PfoojI6fyR8/wxeO387wdj58y/pKOc3n2thv4xqnv89PnPItbzn8Bm5o6l7fockb74cj30hpvj/9FaGydefzufXDXP8/9+s2dsPu6ifDN1sGSJNWW+ibYfBEQ4fSDkB2b23kxQggVLU2SpEqrdhBXEIBL8tvbgDMhhM+RQrl/jTEOVLM4SdIa1HsotaHMZaHvCMQcNLVB65ZqV6blUteQ3ixoXJf2N+9NbxrMFMa1boYN56bHbVtSS7yeRypfq1aloewoL//273Fo+MSU5/7moU/w7O03UF9mVtxvPOZV/O6lr2VL8/rlKLO8XAaO3wdd+9Nabyd/mP4dBTj3iXDuE2Y+f+dVKQDPjpd/vq4Btl+RWk3u3gdbLrZdsCRJtaq+Kf2srm9K+5svhtMPzC2MM4STJK0C1Q7ivgA8HWhmoodTADYDr8lv4yGErwGfAj4dY1zi1dolSSoxeBqGTqfH/UfTL4j1jWmGhb8Irg3FIdzJ+9MbBZe+aOYwrjiE+87fwbWvSTMowTBOC9Ja38zlnReUDeIeGjzCF459hxfsfOKU53auq8IHBmKE3kfh8IEUvB29c/rQ+vD+2YO4hhbYcSV03TFxbOP5+Rlv+1JQN9usOkmSVH3FIdwj34RQn14HFMK4mWTH4fZ/gMf9guu7SpJWtKr+FIsxPj+E0Ao8B3gJ8EJga/7pQjDXBDwrv/1VCOEeJkK57y5/1ZKkVW1scKKd4OCptB/qoHO3sy3WitIQ7r0vSm0mX/4eeOxPlg/jikO4z/8WfOdv4d5PwGv+xTBOi/KmC2/iC8e/Syyz5uDfPHQbz93x+LKz4pbFSE8K3rry4dvg1MCwrK79s48BuODpsG5Tftbb9dC2dbYzJElSLSkN4T7wU0BIr5ELYdzY4ORzCj/vs+PwsTfADz4Fh74LL3u3YZwkacWq+k+wGOMQcBtwWwghAI8HXkwK5i7PDyueLXdFfvvdEMIJ4DOkFpZfjDGOLG/1kqRVJTue1oUjpvWMCrPiOnZAQ3NVS9MyKRfCDeTDhY+9oXwYVy6EA3j02/D+lxnGaUbZmOMLx75De8M6nrr1minP723fzfN3PIHPHfv2pOONoYHHb3osI9lR2hrWLU+xmVE4fk8K3boOwKkHoExAOKvuh9MHHdpmmbn32JekTZIkrTzlQrhC6FZ4jXzuE9Is94Hj6SVFx05o2zw5hIP0ATcwjJMkrVghxgX88rxMQgjnk0K5FwNPAxrzTxVCucJjgBHg30mh3KdjjEeXr1KtZSGEy4F7Cvv33HMPl19++QxnSKpJMcLpH8PYQHqzuefRtJ7Ruo3Qvq3a1Wk5zBTCFY8phHG5TApr27en54pDuGLnPjG90dDUlmbWGcYJyMUcXzx+O3/70G08ONjF+a07+MQT/4SGMjNvDw4e5aZv/TY5Ig2hnpt3PZWfv+DFlW9BGSOceWhinbejd0F2dGmu/fTfgcc8d2muJUmSastMIVxBU/tEGDc2CAMnYdP5U0O4YpffvCLCuHvvvZcrrrii+NAVMcZ7q1WPJKn6ajqIKxZC6ACeRwrlXgBsKjOs+Ju5gxTKfSrGeGfFC9SaZRAnrRI9h2DoFORyKSjJjqX1h9bvcV24tWLLJdDUOn0IV1AcxhVMF8IVFIdxAyegr2tpa9eKkYs5/u3EAd710Cf48cDhSc/9z8t/kRfvelLZ8/7rvf9AfajnFy54CbsqGcBlRuGhr8Hh29Ost+EzS3v9ti2w+wa49IWw44rZx0uSpJUl1MG2y2YO4QqKwziYOYQrWAFhnEGcJKlU7f7UKhFj7Ac+Cnw0hFAHPImJ2XKX5IcVt7C8Lr/9QQihi/xMOeDfY4xjy1y+JKmWDZ1JIRxA/5EUwtU3QudOQ7g1I0BDU3p47O7UNm86uUx6g+Clf5/CuC/83swhHMCpH6XwbctjbHO6xnUNn+Rtd/01uTItHf/2odt4/o4nlJ0V998f+0bCcv179B9/nv4dXAoNLbDr2vw6b/tSG1f/XZUkafUK9VCXb2h16PbpQzhI3Uje/zJ49UfSerAf/4WZQziAo99Pv7/ZtUSStIKsmCCuWIwxB/xHfvvNEMJFpDXlXkwK6BqYHMrtAd6U3wZDCF8ihXKfjTGeXP7vQJJUM8aGUhtKSOHL2GD6FGfnrpr+lKWWWr4F36a9cOXLITcOt70ltSctpxDGNbWnNxBm0roZXvepFMKNj0z8fdOadE7rdl6480Y+ffSbU557dPg4nzn2LW7a9ZQpzy1bCNfQDDuvTjPiFiLUwdZLUui2Zx9se2z6YIMkSVobcuNpPdiN58GTfy19uOcrfzz9+LEBuPWFc3tdvelCuOUzhnCSpBVnVbzDGGP8MfAXwF+EEDYAzycFc88D1ueHFd69aAduym+5EMJ3Se0r/2wZS5Yk1YJsBroPAhFGB9J6X5DW/GpoqWppqoKxQTjzYArjrn5VOjZTGAdzD+G2X5FCuNMPpBBPq16MkUikLtRNee4XL/hJPnfs22TL/N1698FP8ZKdTyp73oL1H0vrvJ05CDf+yuzj9+ybXxDXsQv2XJ/Ct93XQXPHwmuVJEkr30gPdJPCuKf9Zjo2UxgHcw/hOncvRYWSJC2rVRHEFYsx9gAfAj4UQmgAnsJEC8u9+WGF2XL1wBOBJwAGcZK0lsSYPqmZHYPMWHqjGmDdRmjprGppqqKFhHHTMYRbk2KMfPP03bzrwY/zU3ueyc27nzplzHltO3jRzifxySP/cfZYIPC8HY/nTRfetPgQbmwQjnwPDu9P67z1Hpp47qpXzv4p8t37Zn6+qR12XZfCtz37fENMkiRNtZAwbjqGcJKkFW7VBXHFYowZ4Cv57ddDCJcxEco9kYlZcpKktaavC8b6IZeDviMQs9DYCm1bq12Zqm0pwjhDuDUnxsi3z9zDOx/8OHf1PgjAmYP9vGjnjTSWaXP7ixf8JJ85+k2yMcdztz+ON194M3vbF/jmUi4DJ36YZr0d3g8n7pv+72vXAbjk+TNfb9OF6UMJw91pP9TD9svz67xdn1pP2rpXkiTNZinCOEM4SdIqsKZ+g44x/gD4AfD2EMJm4IWkFpbPrmphkqTlNXQGBvNLhA4chexoWsOocycs1zpMqm2lYVw2A5/65bmd29xhCLfG/Kj/EH/0w1v5Xs8Dk453DZ/k00e/yUt3P23KOee0buN3L30t16y/mMd0nDO/G8aYPkxweH8K37q+B+ODczv38O2zB3EhwCUvhPGhFL7tvAaaWudXoyRJEkwN47Kj8PU/n9u5nbsN4SRJq8KaCuKKxRhPA+8D3hdCcAV5SVorxoYm2rQNnU5rw4UAHTud4aHJxgbT35H2bXD1K+Fzb4PMyOzn7bkhhXCQwhJDuFVvXX0zd/c+VPa5v3vok7x455PKzop7xZ5nzv0mI31w5I6J8K3QTne+ug6k2XKztb983BsXdn1JkqRSIz0wshHWbYDrXz/3IO7i56QQLkY/MClJWtGWcBX4lSvGOF7tGiRJyyCbge6D6U3osQEYPJWOt2+HxnXVrU21p3XzxFpaX/z9uYVwAAe/Bvd9Mj3eeF5qeapV7ZzWbfzkrieXfe7IyCluK1oLbs6y42mdt+++Gz7xJnjfT8KX/xB++JmFh3AAI71w+sGFny9JkjRfHTtTCJcdh3/97bmfd/dH4dH/TCFcjBUrT5KkSvOj/5KktSFG6H4YsmPpF8C+/BvZ6zZAy/pqVqZa1LoZNpybHn/+t+A7fzf3c3NZ+Ngb4OXvgcf+JGzem4KP8aHK1Kplc2fPA1zeeUHZ2W0/f8FL+OSRb5CJ2SnP3df38NxuMNwNP/4yHD4AR++ce/g7V5v2wp7rbTMpSZKWT8dO6NiRfgf72BvgB5+e+7ljA/D+l8Fr/gXOfYIz4yRJK1ZNB3EhhHOX+JIZoDfGOMdFNCRJq0bfERjrh1yE3i6I2TQLrm1btStTrZkSwv3t/K+RyxjGrSLf7/kx73ro43zr9D3810tv4RXnTG0puXvdVm7e9VQ+2vWVs8eetPkqfmnvzVy5fu/cbjTcA99+5xJVTfq7vHtfCt92X5/2JUmSlsuUEO5T87+GYZwkaRWo6SAOeBhY8rnnIYQc8CiwH/gm8IH8mnGSpNVouBsGT6THA0fTAuF1DdC5y1/iNNlShHAFhnEr3j29D/HOBz/ON07fdfbY3x/8FDftfgpNdVOXGP75C17MbUf+gxs2XcqbL7yZazZcPL8bbjwfWrfA0KmFFdzQAjuvzodv+9L1/DdOkiRVw1KEcAWGcZKkFS7EGu6xnA/MIlCpn66Fb34M+CfgbTHGvgrdS6tUCOFy4J7C/j333MPll19exYokTTI+DKd+lNaFGzoDgyfTL23rz3FdOE023xCuqQ12XQuPfCv9/ZpOXcNEGJfLGMatEB8+9GX++IfvK/vc7176Wl51zrPKPtc1fJLd67amnZhL/70P70//7a/72dlv/JU/gQe+MMcqA2x9zETwtv1yqG+a47mSJEkVMt8QrnUTbL4IDn135nFN7SsijLv33nu54oorig9dEWO8t1r1SJKqr9ZnxJUqlxqW/tSdLlmcblwAmoGfA54TQnhRjPEeJEkrXzYDZw6mN8PHBlMIB9C+zRBOk4U6WL8nPf72O2cP4Vo3w+s+BduvgO9/CG57y/RhXGFm3Bu/nIK7zt1w+oGlrV9L7ulbr+Xt93+Q8ZiZ8ty7D36al+56Ks1lQq/d2Qj3fz6Fb10HYKQnPdHcCde8GurqZ77xnn0zB3Ht29OY3ftg93WucSlJkmpLfXMK4QC++Huzh3CbLoRbPpNeI3/t7fCVP55+bGFm3C/fnrqbSJK0QtR6EPc+JgKzpwIXMHmGXBfwA6AHGAU6gV3A5UBLfkzMb58EeoF2YBNwJbClaEwAzgU+G0J4QozxaKW+KUnSMogReh5JbSiz49Cf/2e9ZT20bKhqaapBMQfDvdC6Ea59Ddz9MThyR/mxxSEcwNWvSl9nCuOue20K4QCGzyxt7aqIHS2bedmep/PhQ1+e8tyJ0W4+1vVVfubc58DYEBy9Mx+87YeeR8tfcLQvBbBbL535xruvn7zf2Jr+7uzZB3tuSG9S1einvyVJksiOwugANLfDjb8CP/oCdD9cfmxxCAfwtN9MX2cK457y1hTC1fCMOEmSStV0EBdjvCWE0Aj8NSmEgxS6/Tnw/hjjoXLnhRDqgecCvww8L3/48cDLY4zfLhp3FfBfgFuYCOP2AO8AXr3E344kaTn1H0tvfOci9HZBLguNLWk2iVROz8PplcC6jfDa2+B9N00N44pDuPGRFPBuOHfmMG7fG+BF/zs97j0MQy5LWyseGDjMBx79Ir9zyWvKzm574/kv4uNdX2MsNz7p+LWte7jk6A/gzs/A8XshZud2w8MHZg/iWjfBY54L7TtS+LbtstTeVJIkaaU482BaH3n9OSlou/VFU8O44hBu4AT0HoFdV80cxv3EH8BTft0QTpK04tRVu4A5+CfgjaSg7GvAY2KMfzJdCAcQY8zGGD8XY3wBKVAbA3YAXwwh3FA07q4Y488BL8yPKYRxPxVCeEzFviNJUmUN98DAsfR44Fj6VGZdgzNJNLvuh2G4O82cfO1tsOu6iedKQ7jTD6S2g2ceTEHv1a+Cm96V2lwWlIZwhfaoqqqHBrr4jbveycu+/Xv8S9dX+VjXV8uO296yiZ/a/Yyz+1fFZv7uVB/vvfdb7Pv+J+DYXXMP4QAO3z63cU//Hdj3ethxpSGcJElaeQrr5I4NTIRxG8+feL44hBvphYNfh95D0P1ICtme9pvwjN+bfE1DOEnSClbTQVwI4Q3AK0jh2L3A82KM8/oYeYzxw6T13wLQBnwohNBUMuZfgV/Lj4mkP5ebF1u/JKkKxkdSS0qAoe40Ky4E6NzpG9qam3JhXLkQLpdfO2xssHwYZwhXcw4OHuW37v4bbvr27/Kvx79DzHdAf/fBTzOSHZsYONoPD30N/uMd/Nydn+P64RHedewE73/4AW7s75my8PCcHb8HxocX/X1IkiTVvOnCuOIQbnQAjt098bp6pAf6jkwN4wzhJEkrXIgxzj6qSkIIDwHnk8Kx58cYv7iIa30TeGL+Wm+MMf7fkufrgAfy9wP4Yozx+Qu9n9aOEMLlwD2F/XvuuYfLL7+8ihVJa1guCyfvTzPgxoZS+EFM7SjXbah2dVppNp6f2lSO9KYWlFsvnRrCFWtqg017oa4eDn0XznlcOm4IVzP+9Ifv5wOHyr+c/I1zns9r+4fSOm8n759+vb+FqGtIs9t274PHvgSaO5bu2pIkSbUs1KU2lU3tadZbqJsI4c48mF5r9x2BxjbYsAca1sG2SydCt0f/E859wooK4e69916uuOKK4kNXxBjvrVY9kqTqq9mpASGE65kIxXqBLy3ykv9CCuIAXgpMCuJijLkQwqeBX80funiR95MkLbfuh1MIl81A/xEgQkunIZwWprCOxbqNaXbcTCEcTMyM27TXEK5G/dwFL+JjXV9htGTNN4B/7PoKL3/oAVqX6kNqGy9Ia7zt3gc7r4LGdUtzXUmSpJWkMDOusGYcTIRwM33wKYQUvq2wEE6SpHJqNogDrsl/jcAjcfFT9x4sc+1SdxQ93rTI+0mSllP/sdSGMhehryvNjmtoSbPhpIXqfjgFb/XNqeXpdCFcQSGM69wNQ6fTpmU3kh2jpb5pyvGtzRt4xZ5n8k+PfmHKc2dyI3yuvZWX9w8u7KbrNqbQbc8+2H09tG1Z2HUkSZJWm0IYt+HctN/z6Ny6DxTCN0M4SdIKV8tB3Naix0vx0eTCNULJtYsVv1tmzyBJWimGe1LrQICB45AZSe0BO3el1ifSYvQent/4sUE49aPK1KIZHRk+xd8f/CT/ceouPv34/0HrqQfg8P706etLXwDAG85/ER89/BVGchNrwl3Utps3772ZZ33j74Efz+1m9U2w8+qJWW+bLvRNIkmSpOnE3ETHCUmS1phaDuIKK9kH4LwQQljkrLi9RY9H5jB+LmMkSdU2PpI+UQkw1AOjvenN8I5dUN9Y1dIkLY9jI6f5+4c+xSeOfI1M/tPVH/nka3l9T08asOOqs0Hclub1/PQ5P8Gtj3yeC9t28eYLb+I52x9HXaiDPd+D0zMEcZsvTsHbnn2w/QpoaK7wdyZJkiRJkla6Wg7ijhQ93gD8BPDlRVzvZUWPu6YZU9xDqHsR95IkLYdcFroPQszC2DAMnkjH27ZCU2t1a5NUeYOn+It7/oH39/2Q8ZIGCv+3s41X9vamNd+O3wtjQ2f/XXj9+S/k0o7zeN6OJ1BfPGt2zz74/ocm9tu2wp4bUqvJ3de73qQkSZIkSZq3Wg7ivsnkdpJ/HkJ4XIxxbIZzygohvBK4seh635hm6JX5rxF4eL73kSQts55HUhvKbAb6u4AIzZ1prSZJq8/4MBy9Cw7fDl37U3ujjRsY39A5ZWh3fT0f7Ozgjb19Kaw/eiecdyMAm5o6eeHOG6def/sVcP6TYdd1sOd6WH+u7SYlSZIkSdKi1GwQF2M8EkL4BvBkUjB2JfCZEMIrYow9c71OCOFm4Nb8NUL+6wenGf7Uosf3LKBsSdJy6T8OI73pX/W+I2l2XEMzdGyvdmWSlkouC6cfgMMHUvh2/F7IjU8acktvHx/ubGe4bup6kLeu7+Cn+/ppjzGtFXdemfCtWEMzPOePlvI7kCRJkiRJa1zNBnF5bwO+TQrQAqk95Y9CCG8HPhRjLNtiMoRQlx/7S8CL8+dCerv2MzHGr5U550JgHxOz5r65hN+HJGkpjfRCf76D8cAxyAxDXT107oIw9c14SStI/7E02+3wAeg6AKN9nK6r48dNjTy+JIQD2JTL8eq+fv5xw/opz7XnIocbG7h0bDxdU5IkSZIkaZnVdBAXY7w9hPD7wP9kIiDbAvwZ8GchhEPAD4BeYAxoB3YBVwDr8uMLs+ACcBB40zS3+y9F4zPAvy7pNyNJWhqZUeh+JD0e7kmhHEDHTqhvqlpZkhbh6Pfhwa+ksKz38NnD3XV1/N+NG/hwZztNMfKvh46k2W0lbunt50OdHQzlZ8XtzGT4xe5eXjIwSGNTO5z/xNRqMuYM6yVJkiRJ0rKq6SAOIMb4pyGELCmMq2MiVAM4Fzin5JTihTxi0fj7gOfFGI9Oc6su4L/lHx+LMZ5egvIlSUspl4MzB9N6T2PDMHAiHW/bAk1t1a1N0sIduh3uu+3sbk9dHe9d38EHi8K1YeAD6zv4xZ6+KadvyOX4mb5+Pt3exs/39HNz2/k0Xvo82LMPtjwG6mr+Ja8kSZIkSVqlVsS7EjHG/xVC+Crwt8C1hcPTDc9/LbSzHAbeAfxRjHFshnu8fWmqlSRVTM8jqQ1lLpNvTRmhuQNaN1e7MkmLsed6uPP9AIwDL9u9gxMNU1+mvrezk1f39tNROituw7m8cee1vGn3Ppp2Xw9NrctQtCRJkiRJ0uxWRBAHqU0lcH0I4UbgtcCTgUtJs+RKdQP7gU8B748x9i5boZKkyhg4ASM96eMWfUdTGNfQDB07ql2ZpFIjvdB1R2o1eeEz0sy0mWy/HBpaIDNCI3Bz/yB/t3Hqmm/99XW8f30Hbx4JsPt62HND+tq+DaM3SZIkSZJUi1ZMEFcQY/wW8C2AEEIbsAPYADST1oo7M0P7SUnSSjTSB31d6fHAcRgfSus8de5yvSepFmTH4Pi9cHg/HL4dTj3A2SYFdY2zB3H1TbDzGjj0nwD8bF8fH+zsoL9+6v/f/7RlOz/z5L+gs6ljab8HSZIkSZKkClhxQVyxGOMg8GC165AkVVBmNLWkBBjuTbPiADp3pjfvJS2/GKH7IBw+kGa9Hf0+ZEbKj+3aP+XQYGaYDzz6JZrrGnnd+c9PB/dcfzaIW5+LvGa8gb+pz006b1NjB284/0U01Tcv6bcjSZIkSZJUKSs6iJMkrXK5HHQ/nNpQjo+k2XAAbVugqb2qpUlrztBp6DowEb4NnZ7beb2Hof8odOxkKDPCBw99ifc+8nl6xgdob1jHTbufyvrGNjjn8XD6x7B7H+y+ntc0NvP+b7yV/swQGxrbef35L+Snz3kWrYZwkiRJkiRpBTGIkyTVrt5HUxvKXBb6jwARmtuhdXO1K5NWv8wIHL0rhW6H98OZhxZ8qeFHv8OH2xr5vw9/ju7x/rPHBzLDvO+Rz/MrF70cNpwLT/+ds891Am/ZezMj2TFedc6zaGtYt5jvRpIkSZIkqSoM4iRJtWngJAx3p2Wm+o5Adjy1omzfUe3KpNUp5tKMtMP7U/h27O70/90SyBy5g39oOEF/ZmjKcx949Iu89rznsb5x6izX15z73CW5vyRJkiRJUrUYxEmSas9oP/R1pceDJ9KsuFAHnbugrr66tUmrzY//DR75Zmo7OdK7tNdu3w57bqDjvBu5JXuK//Pgx6YMGcyO8N5HPs+vXvRTS3tvSZIkSZKkGrBigrgQQh3wQuD5wHXAOcB6YCF9imKMccV875K0pmTG0rpwRBjpS7PiADp2QINrQ0lL7sdfhke/vSSXGm1sI7v7alp3Pw727IPO3RACAK/ODPO+Rz9P7/jglPM+eeQbvPnCm2ms8+WZJEmSJElaXVbEux0hhJuAvwJ2Fw5VrxpJUsXkctB9EHIZGB+BgePpeOtmaO6obm3SarXnhoUHcaEOtj2Wsd3X8fF1jfzD6f28eOc1/Nr/z959xzle1fsff51kUmYyvW3v7FJ2KbtLZ+kISAe7iDQRwY7t+rNf7716vaLoBcQLKiCKUqWKIAosVdjCsrSF7b1Nr5kk5/fHSUh2kmk7mUlm5v18POYxyfme7zefwR0nyTufc2aflza1uKCQS6adwS/evfu9sSJvkAunnsrF005XCCciIiIiIiIio1Lev+NhjPk28AOS4ZuNf8HAAzm7F+eIiMhwadzolqGMRaF5i9uzyh+CUHWuKxMZOZq2wKaXYdMSOPIzbknX3kxeOLDrl02BSQth8qF0jZ/H/buWcvPaB9lWXwfAHzc+wSennU6lvzTt1I9NOYXb1v+VzlgXF05xAVy5XyG7iIiIiIiIiIxeeR3EGWPOAv49frd7+LYB2AGkr28kIiIjT+suaK9z/2/ftBWiXeD1QcmEXFcmkt86m2HzUtj8igvfmrckj01eCAec2/v5ZVMhVAOtOzMfD5S+F7wx+VC37xvwdvMGvvDyv7OlY9ce09ujndy67lGumfPRtEuFCgr52UGfZ1bxpIxBnYiIiIiIiIjIaJPXQRzw3/HviU62LcAPgbustfU5q0pERLKrswUaN7nbrTuhq9UteVc6CTze3NYmkm+iXbDjDdj0igvfdr7tukcz2fRK30GcMW55yrcfdfc9Phg/z4Vukw6F6tnu97GbKYW1tEc7M17yTxv/zsXTz6AqQ9h2WOX+vdcjIiIiIiIiIjKK5G0QZ4zZD9ifZAj3JnC8tXZXryeKiMjIEu2C+nWAhY4m1xUHUDIeCgK5rEwkP1gLDRviHW+vwNbl0NXev3O3LHV7Lva1/9rME8Bf7Droxh8EvsI+L11UEOTS6Wfws3f+nHasPRbmd+se4atzPta/OkVERERERERERqm8DeKAw+PfDS6Mu0ohnIjIKGMt1K2FWBdEOqFluxsvqoSA9o2SMay9ATYvSXa99bRsZF/Cra5jbtzc3udNOdx9dRO1MR7d9gJHVc6lOlCedvwjU07h1vV/pS7ctMe43+PDm6GLTkRERERERERkrMnnIK425fZOa+0zOatERESGRuNGtwxlLApNW9zyev4QFFXnujKR4RXphG2vxcO3l2H3u9m79qZX+g7iuonaGH/b9hI3rf0La1u3ctHU0/j6vhemzSvyBrhs+pn8dNWdAPhMAR+YfAKfmn4W44KVWSlfRERERERERGQky+cgLrEpkAXW57IQEREZAq27oW23u928FaJh8PrckpTG5LY2keEQi8Br97jwbeur7ncgm6rnwKSFMPWI/pdkYzy+/WVuWvMXVrdufm/8rk3/4NLpZ1KToSvuw5NP4g8bHmdR1UFcMeNsJhQqSBcRERERERERScjnIG5Lyu2+NyoREZGRI9zquuEAWne5+8YDpZP63stKZLQwXnjjL9C8LTvXC9XC5ENd+DZpIRSWD/gSD2xZzHff+E3aeGesi9+sfZh/2+8TaccKvQEeOvq/CXj9e1O1iIiIiIiIiMiols/vdr4U/26AqcYYj7U2lsuCREQkC6Jdbl84LHQ2J7viSsZDQSCnpYkMK2Ng0qHw1sN7d76vECbMd+Hb5EOhbMqgu0nfP/4ofvnuPewKN6Ydu3vzP7l0+hkZl5xUCCciIiIiIiIikpkn1wX0xFq7ClgSv1sCnJC7akREJCushfp1EOtye2IlOoEKKyBQktPSRAYtFoUdb8GyO+Cv33BLT/Zl8qH9v77xQO0BsOCTcM4v4eKH4PT/gnkXQPnUrCzpGvT6+dSMszMeC8e6uGXdXoaGIiIiIiIiIiJjVD53xAF8C3gsfvuHxph/WmttLgsSEZFBaNwE4RaIxaBpC9gY+IogVJPrykT2TvNW2LQENr8Cm5dCZ1Py2I43YfyBvZ8/cQGu+b+HpzelE13X3ORDYeL8QQfW1lqe2bWcW9Y+zP8c9FnGZ+hu++CkE/jtukfY0Vn/3pgHwxkTjuLCKe8b1OOLiIiIiIiIiIw1eR3EWWsfN8b8FPgqcCRwszHmSmttNMeliYjIQLXVQdsud7t5C0TD4PVB6YSsdPKIDBtr4d0nYPmdUL+253mbXuk7iAuWQs2+sPMtd99fDJMWJMO30olZKtny7O4V3Lj6flY2rQHglrUP8e39L06bG4h3xf3XW7djMLx//JFcOfNcZoayU4uIiIiIiIiIyFiS10EcgLX268aYKPAN4FJgnjHmu9bax3NcmoiI9Fe4DRo2uNutuyDc6pbZK50Inrz/UySS1LgJFv8Mtizte+7mJXDopX3P2/8cmHa0C96q9wWPd/B1pni57i2ue/fPrGhcvcf4fZuf5vLpZzKhsDrtnA9MOp7VLZv42JT3Mat4UlbrERERGVHCbRDtzHUVIqNXpCPXFYiIiAy5vH730xjz25S7a4EZwGHAX40xjcBSYAcw0L/a1lp7eXaqFBGRXkUj8a4hC50t0LbbjRePg4JgTksT6bdoF7x6Jyz7vbvdHzvecEux+ot7n7ffGYOvrxerWzelhXAAXTbCzWsf4rsHpIeFfo+Pb+9/yZDWJSIikrdsDDqaoL3ereIgIiIiIjIIeR3EAZew56YpFreRCkA5cOJeXDOxEYuCOBGRoWYt1K9zb2BEwtC8zY0XVrgl+URGgm0rXBdc/bqBnWdjsGUZTD92SMrqrwsmHc9v1j3Mto66tGP3b3mGy2ecxaRC7dMoIiJCLALtDdDRALH4jhjGC/4ikm9FiMiQKPC779q2QERERqF8D+IysX1PERGRvNC0GcLNEItB0xawUfAVQUhv+ssI0NkML/0fvPXQwM7z+mD8QW6ft6p9hqa2DDa2bWdK0bi0cb/HxxUzzuGHb96adixiozyz61U+NuWUYahQREQkT0U6XfdbZ5P7IBmA1w/BCgiWgUfBgMiw8YdyXYGIiEjWjYQgTs94RURGorY6aN3pbrdsdXtreH1QOkGfcpT8Zi2s/ge8cL17U64/qma54G3yoS6EKwgMbY0pljWs4obV97Gk/m0ePuYnGbvbzp94HL9Z+zBbOna9N3Z89SFcNet85pbOGLZaRURE8kq4Ddrr3P7FCQWFUFQB/pLkuxEFQfD4clKiyJhhjPvQZnH6B8tERERGunwP4vTOkIjISBRug8aN7nbbbrc3nDFQMgE8+f6nR8a0pq3w3M9h47/6N3/8QXDsNVAxfUjLyuTVhne5cc19PL975XtjN699kO8fkL76ts9TwKdnnMP33/wti6oO4upZ53Ng2azhLFdERCQ/WOu63tvrIZKy3XygGIKV4C9MGSt1KzloSXURERERGYS8fjfUWrs+1zWIiMgARSNQv9btjxVugdZ4B07xOPAV9n6uSK7EIrDiblhyq+ve7Iu/GI78DOx7BhjPkJfX3b+/8Tvu3vzPtPG/bFnM5dPPZkpRbdqxcyYuYk7JFAVwIiIyNsWi0NHo9n+Ldrkx43FhW1GlW7nBDbr9jItr9dxVRERERLIir4M4EREZYayFhvUQDbs3OJq2ufHCcre/hkg+2vEmPPNTqFvdv/n7nAJHXu3etMuReWUzMwZxURvj/9Y+wA/nXpF2zOcpUAgnIiJjT7TLdb91NLoPigF4vC5sC5a72wDGC6Fq1wHn1TKUIiIiIpI9CuJERCR7mre6Te5jFho3g426TxKH0rtzRHLOWrcP3Mr7ANv3/JIJsOjLMOXwIS+tL2dPOIab1z7EpvYdacce2vocV8w4h6lF2l9DRETGsEgHtNVDuNn9zQfwBlwAFygFj0mOhWqgqAo8w9/lLiIiIiKjn4K4McIYUwzMBfYDqoAg0ADsAF6x1q7LWXEiMjq010PLdne7Zatb3s9TAKUT3f5wIvnGGNe92VcIZ7xw8EdgwSehIDgspQG83byBpQ2r+NiUU9KO+TwFXDnzXL7z+s1pxw4um01HNDwcJYqIiOSfcIsL4LrakmO+EBSVu6WlU8eKa1xXnJ6rioiIiMgQUhCXY8aYScDhwBHx74cCJSlT1ltrp+/ltY8AzgNOBhYCPX68zxizHrgJ+LW1tn5vHm+AtV0C/G4Ql3jaWntCdqoRkUHraoeGDe52Wx10Nrs3NEonujBOJF8d/mlY96wLkjOpPQCO+ypUzhy2kt5p2cSvVt/PEztexoPh6Mp5TAuNT5t31vijuXnNg2xodwH4/PLZXD3zAo6oPACjNxRFRGQssRY6G10A996HUQwESlwHnC/lgzTBMrdaQ6A446VERERERLJN747mgDHmGOAruPBt4hBc/xDgXmAg7xpOA34EfNEYc6m19rFs1yUio1QsCnVr3Z4b4VZo3enGtcG9jASBEjj68/Dkv+857gvB4VfA/mcn944ZYqtbNvOrNffz+PaXsfEuvRiWX699gP+ad2Xa/AKPlytnnsufNz3JZ2ddwFGV8xTAiYjI2BKLuL3f2hvcbXCd7MEyF8B54295GA8UVrrnpwWBnJUrIiIiImOTgrjcOAw4fwivP5meQ7hGYFv8ezUwA0h912488Igx5kJr7Z+GsEYRGQ2shfp1bhnKaJfbIw7cmx/B8lxWJtJ/M0+EVX+DjS+5+zOOd+FcqHrYSrDW8qVXf8m6tq1pxx7Z+jxXzDiHGaEJacfOmnA0Z084RgGciIiMLdGw62bvaHIfBgPw+iBY4Z6HJvZ68/jc3/Oi6mQoJyIiIiIyzPRMNP+0ANleI+NF4A7gn9baN1IPGGNqgCuAbwFF8WEPcLsx5m1r7bIs19KT/wEeH8D8IV8+U0T6oXkbdDZBzELjZtcd5wtC8bhcVybilkz1FLg35npjDCz6Evz13+CIK2Ha0cNS3p4lGD498xz+38pfpx2LYblpzV/47wOvSjvmMT2uOi0iIjL6dLW5AK6zJTlWEHTdb4HS5EdMC4Ju+cmiSu3/JiIiIiI5pyAut5qBJcDLwL/i32cA/8zCtWPAH4EfW2tf72mStXYn8F/GmIfjj1sZP+QDrgOOz0It/fGGtfbvw/RYIpIN7Q3Qss3dbtnmuuI8BVA6SW94SO5teAGevQ72OwsWXNT3/JIJ8KHfuaWrhpi1NmMH2xnjj+L/1jyYsSvur9te5KqZ5zE9Q1eciIjIqGYthFugvQ66OpLj/pBbbtJflDJW4pafDJYOf50iIiIiIj1QEJcbD+G6v96yNrGOhmOMmZGF668CDuotgOvOWrvCGHMp8EDK8HHGmH2ste9moSYRGU26OqBhvbvdVu+64oyB0gkujBPJlbbd8Pz/wpqn3P1lt8OsE6Fsct/nDnEIt6l9J/+35gEiNppxzzev8XDVzPP4xspf7TE+p3gKV8+6gGlF44e0PhERkbxiY/H93+rdEugAGBeyFVZCgT85VljuOuBSQzkRERERkTyhd0tzwFq7eoivv2ovz3vQGPMGcEDK8OnA9VkpTERGh1gU6te6N0fCbdC6042HasGnNz8kR2wM3nwI/vV/EG5Njke74NmfwRnX5qxTc0v7Lv5v7QM8sOVZIjaKwXDZ9DPZpzg9HDxt/BH8eu0DrGndwj7Fk7l65vmcXLtQS1CKiMjYEYu48K29Ibn/m8fr9h8uLE9+6Mt4oagKQjUpoZyIiIiISP5RECfdLWbPIG5qrgoRkTxVvw4iHRCNQPMWwMY/mVye48JkzKpbA4t/BttXZj6+eSm88wTMOXVYy2qLdHDtO3/ivs1PE7HR98ZtfM+3nx70ubRzvMbD1+d8nOZIO6eOO0wBnIiIjB2RTrf8ZGezW44SwOuP7/9WBh6THAvVuBDO481dvSIiIiIi/aQgTrqr73a/LCdViEh+at7mlqGMWWja7LrjCoJQPC7XlclYFOmEpb+HV++ElKAroxdvgKmHu0/TD5OA18+S+rf3COESHt/+MquaNzKnZErasWOqDxqO8kRERPJDuNV1wKV2tPuK4gFc8Z5joRo3rv2IRURERGQE0cespbtJ3e7vzkkVIpJ/Ohqheau73bLddcV5vFA6ccj31hJJs+kVuOdSWH5H3yEcBmaeCB7fsJSW4DUePjPzvIzHEl1xIiIiY5K17rll/Tpo3BQP4QwESqB8GpRPSYZwgVKomg01+0JRpUI4ERERERlx1BEn7zHGGGBRt+G92m9uLx/fC8wEagCLCwG3Wmubh6sGEelBVwfUr3e32xqgs9G9CVIyEbzDG27IGNfeAC/cAO8+0b/5lTPh2K/AuLlDVtKuzgbCsQgTC6vTjp067jB+vXYy77ZsSjv2dvMG2iIdFBUEh6w2ERGRvBKLQkeD+3sei7gx44Fgmet0SzyvNB53P1QLPv2dFBEREZGRTUGcpDoBmJFy3wKPDdNj/z/gl0BJt/GYMeY14Angemvt+mGqR0QSYlGoX+u6jsLt0LrDjRdVg78ot7XJ2GEtrHoMXvyVWx61L94ALLwYDvoweIbm6c7ucBO/W/cIf974JIuqD+LnB38hbY7HeLh65vlcs+J/3xubFKzmypnncdaEo/ENUW0iIiJ5Jdrllp/saAQbc2OeAhe2BcvB40mOhWrc80yv/kaKiIiIyOigZ7YCgDHGA/yo2/Bj1tptw1TC7B7GPcDB8a8vGWNuAa6x1rYPU10i0rDeLUMZi0DzFsC6JYKKKnNdmYwVDRtg8c9g6/L+zZ98GCz6sls2dQjUhZu4dd2j/Gnj32mPhQH4+45XeKt5PfuVTEubf3LtQvYtnkpTpJUrZ5zLORMXKYATEZGxoavdBXCdKYuceAPueWSgFBKrTBYE4/u/VSZDORERERGRUULvAknCV4EjUu7HgG/lqJaeFACfAY4xxpxmrd2a7QcwxtTilsYciFnZrkMkbzRvj39yGWjc4sK4ggCUjMt1ZTIWRMOw/E5YdgfEuvqeX1gBR30WZp08ZPvHrG7ZzMf+9X3ao51px361+i/84pAvpo17jIfrDvki4wIVCuBERGRs6Gx2AVxXyucn/SH3t9ofShkrhuJatzSliIiIiMgopXeDBGPMscB/dhu+zlq7bBgefjnwMPA88AawCwgDlcCBwGnAFUDqK7MDgYeMMcdba1uzXM/VwPeyfE2RkamjKd4BB7Rsg0g7eLyuy8jok8oyxLa+Couvdd1w/bHfmXD4lRAsHdKyZoQmMK1oHG81p9f1j51LeKNpHQeUTk87NrlwoJ/xEBERGWFszD1/bK93H6YBwMRXUqhwH+ZKjAXLXACXGsqJiIiIiIxSCuLGOGPMTOA+9vy3sBy3Z9tQWgostNYu7eH49vjX340x/wX8Fjgv5fhC4N+BrwxlkSJjVqQT6te52+0NrisOoGQCeP25qkrGgo4meOkmePvR/s0vnwrHfgUmHDy0dcV5jIerZp7PF1/9Rcbjv1p9P/87/8vDUouIiEheiEXizxcb3N7CAMYLheXuK9ENbjxQVAWhWijQ80kRERERGTvGRBBnjPlHyt164GfW2udyVU++MMZUA38FqlOGtwMXWGvT19zKImvtigHMrTfGfAC4G7gg5dDVxpifWWs3Z71AkbEsFoO6tWCj0NUBLTvceKhan1qWoWMtrH4SXrjBfZK+Lx4fLLgIDv5o1sPh5q427tjwOMfVHMzc0hlpx0+sWcD+JdN5s3ndHuNV/jKOqDoAay1miJbGFBERyRuRzvj+b03u7zi4v8nBCtfx5on/LfT4XPdbUZVbXUFEREREZIwZE0EccAJuh6OE84wxfwe+a619KTcl5ZYxpgQXws1JGW4ETrPWrs1NVT2z1saMMZ8CTgLK48NB4EPAdVl8qBtxgd9AzAIeyGINIrnVsN4tQxmLQNNmwEKgxL15IjJUYl2w5Nb+hXATDnFdcOVTslpCa6SdP2x4gtvW/5WmSCsrm1Zzw/z0xmtjDJ+ddT6fW/5zACp9JVw24yw+PPkkCr2BtPkiIiKjSrgN2usgnLJLQEGhW37SXwImZay41u0Lpw+oiIiIiMgYNlaCuO4McApwijHmMVwgtyTHNQ0bY0wQeBA4NGW4DTjTWvtqbqrqW7wz7rfANSnDp5LFIM5auwPYMZBz1PUgo0rLDreskAWatrowriAAJeNzXZmMdl4/LLoGHrmm5zmBUjjyKphzelbf0GuLdPDHjU9w6/pHaexKvqn4zK5XWdG4moPKZqWdc1z1IRxXfTCHVuzHR6acQpECOBERGc2shc5m94GZSEdyPFAMwUrwF6aMlboALlAy/HWKiIiIiOShsRTEZXrHzgCnA6cbYx621p47zDUNO2OMD7gL1yWYEMYtRzkSlut8kj2DuH1zVYjIqNPRFO+AA1q2Q1eb28ujdKL7LjLUJi2AOafBqr+lH5t9Khx5tdtrJsuWNLzNL97N3Ax94+r7uWnBV9PGjTEZu+VERERGlVjU7RXc0QDRLjdmPC5sK6oEry8+0bjOt+Ja8BX2dDURERERkTFprARxP0i5HQSOBo4A/CQDurOGu6jhZozxALcDZ6cMR4GPW2szvOuZlzZ2u1+TkypERptI2C1JCdAef7MFoHRC1vffEunVkVfB+hfcfjMApZPg2Gtg0sIhe8hFVQdxUNksVjSuTjv23O4VLG94h0PKZw/Z44uIiOSdaJfrfutoBBtzY54C94GYYHlyrzfjhVCN20v4vVBORERERERSjYkgzlr7g+5jxphCYBFuz7GTgfnDXddwMm79xP8DPpoybIFPWWvvzU1Ve6Wr23292hMZrFgM6te6ZSi7Olw3HLg3VPzFua1Nxp5gORz1WXj6J3DIx2D+RW551CzoikXwedKf+hhjuGrm+Vy17KcZz7t1/aNcV/7FrNQgIiKS1yId0FYP4Wa3HCWAN+C63YJlyY+xegPx/d8qwaOVE0REREREejMmgrhMrLXtwBPxL4wxo30B+58Dl3cb+4K19tYc1DIY3Teq2pmTKkRGk8YNbhnKWBSatwDW7fdRVJXrymS0aNkOz/3ShWq1+/U9f/apMH6e64bLgs5omHs2P8Vv1j7Mzw7+fMbutmOqDkzriisuKOSiqafxiamnZaUOERGRvBVucQFcV1tyzBeCovI9P5jlC7kALliW1f1aRURERERGszEbxHVnrW3OdQ1DxRjzQ6D7R/n/n7X2+lzUM0iLut3vvlSliAxEy0637JAFmra4ZYi8fijunnmL7IVYBFbeB6/81n3CvmU7nH+TW9qqN8ZkJYQLx7q4b/PT3Lz2IXZ01gNww+r7uHnhNzI8pOGzsz7AlUt/Qsgb5MKpp/LJae+nzBcadB0iIiJ5ycbcUtBt9RANxwcNBEpcB5wvmJwbLHcBnF9/F0VEREREBkpB3ChnjPka8O1uwz+y1v4oF/UMhjGmALi42/CTuahFZFTobIamze526w73CWjjgdKJyX0/RPbWzrdh8bWwa1VybPe7sPJeOOgjQ/7wT+1cxn++dRvbOur2GH+x7nWW1r/Ngop90845qnIu39rvk5w+7gjK/SmN8oFSF1C37erfgxuvW9q1o9EFkCIiIvkkFnF/o9rr3YoI4P52BctcAOeNv01gPG6FhFBN1paJFhEREREZixTEjWLGmCuBn3Qbvt5a+/9yUU8W/BuwT7exB3JRiEhesXbgSwPZGNSvAyx0NLk3YgBKxuuNFhmcrjZ4+bfw+n3u31l3r/wOZhwHJROGtIyAx5cWwiXcuOZ+bln4b2njxhg+OuWUPQeLqqB8qrvtL4KGDb0/sPFC9T7gK3KdA7vfha72vfkRREREsisads/5OpqSf6O9PgjG939L7PXm8bnwragqGcqNdnvzfFpEhp5+N0VEZJTQrsqjlDHm48CN3YZ/B3xhCB7rVmOMTfm6tY/5VxljThrgY1wD/Hu34QettUsGWK7I6BLpdC9MrB3gOR6omg1d8eUCwb3ZEhjt22XKkFr/PNx1Cay8J3MIB65D7NlfDOzf7F44snIuC8rnZDz2Ut0bvFz3Vt8XSQ3hIp173s8kNYSLdLolOKv2AV/hXvwEIiIiWdLV5lZBqFsL7Q3ub3RB0H0opmImFFW4EK6gEMqnwbi5UDJu7IVwkc6BnReLJDsKRaR/BvJ7s7e/myIiInlojDyzzj/GmGOATO/MHdztftAYc0qGeQBbrLVvZLj2KcBt7Bm0vgX8GTjZDOzTRPVDEHYdAdxojHkVuAt4DHjdWrvHsytjTBFwMvBV4Lhu19gNXJPlukRGll2r4PcXwPv/G/Y7s3+fFnzsm7DtNbjwbhcOjJ8HDevdfh+h6uGpW0af1p3w3P/Cumf6N7+jAcItgw5+I7Eoz+1ewfE189OOuT3fLuDyJT9OOxbw+FjbuoXDKvfr+eKpodvfvgVbl8PH73bjkN4ZlxrC7XoHfn8+nP4j2P9sF8apM05ERIaTte5vbXud++BVgj8EhZWuy/u9sRLXxR0sHf46cy3x/Hn983DPZfCRO2DyoX2fF4u4+V4/nP9rLesu0h+xCNz7Kfeh0Atu7v33JvG7ueFFuPsS+PDtMOXwYStVREQk20ZsEGeM8QFluDBrwH3q1to+1pYacn8ApvVj3jjgiR6O3QZckmF8Een/2+6HC7wG6mnghL04rz8Ojn/9JxAxxmwEGoEwUAFMB3wZzmsGzrbWrh6iukTy365VcOtZrpvt7ovhQ7f1HcY99k14Md4o+4cPuTAuUOLO2/n28NWeTypnuqWYBqujEerWDP46I00sCm8+CP+62X3avi++QjjsCjjg3EG9YRW1MR7d+gI3rfkLG9q385uF/8bhlQekzTu88gAOrdiPV+pd95vf4+NDk07k8hlnURMo7/kBuodwL1zvbv/xQ5nDuO4h3G1nQfM2uOdS+ODvFMaJiMjwsbHk/m/RLjdmPO45X2ElFPjjEw0UlkOods9QbixJDeH+8CEXXP7+fLjo/t7DuEQI90bKLgkK40R6lwjhXr8/OdZTGJcawt3xAfe7eccH4BP3KowTEZERa8QEccaYCuATwPuBBUDNIC5nGUE/+xhRAMzox7yXgAsVwsmYlhrCgXuTpa8wLjWEA1i3eM8wbtw82Pnm8P0M+SIbIVw2rzOS7F4Ni38KO/r572b6Ijj6C+4T93spamM8tu1FblrzAOvatr43fsPq+zmsYn8ydXxfPfMCrlz6Ez4w+QQ+Nf0sxgUre3+QnkI4gHXPpodxTVugalZ6CAfud1NhnIiIDIdYxIVviaUnwb3BHSyHworkm93G61ZBKKpOCeXGoEwhHEBnU+9hXKYQ7rW73XeFcSKZZQrhVt7rvncP4zKFcOB+NxXGiYjICDYiwihjzJeAHwKJj+ppp9aR7VdAPW65yQPJ3PWWqgN4CrgBeMTaId5YSCSfdQ/hEnoL47qHcAmpYZyvEGr2H5thHHDgbQfu9bmvXfxaFisZASIdsOQ2WHEX2H7s7xCqhmO+CNOPHfRD/+CN33L/lvTlL5c2vM2Lda9zVNW8tGOHVe7H48f+nOpAP8LS3kK4hO5hXLDcvXnQPYRLUBgnIiJDKdLplp/sbE7uv+oNuG63QBl44s8HvX7X/VZUqbCopxAuoacwLlMIl6AwTiSzTCFcQvcwrqcQLkFhnIiIjGAm3zMNY8xNwBUkw7dEwYMJ46y1Vs+O84Axxg/sj+uGmwiU4IK5JlxYtwpYZq0N56zIPhhj5gIrE/dXrlzJ3Llzc1iRjFrhFvjfQ6F5a89zvL5kGAc9h3CpDjjXrbkP0LjJ7fc1Vkx0e4tlJYjbsiwbFeW3jf+CZ3/e+7/B9xiYez4cdrnbjyYLltS/zSWv/GfGY4eUzeb2w76dsSuuX4wHxh/ovr94o/vd6c30RS6M82fohMvE64PL/w4TD4HOFtj9zt7VKSIiAhBudR1w4dbkmK/Idb8FivccK651HxzZ27+Ro1HDBrjxqPQ3+lMFSpNhXG8hXKqjvwCn/jC7tYqMZH/9Brx0U+9z5n0gGcb1FMKlCpTCZ1+C0onZrTWLXn/9debN2+NDgvOsta/nqh4REcm9vO6IM8ZcDHw6ftfiwjeDC2heA3YArZnPlpEgHrC9Gv8SkZ5YC/5iOPFb8NDnk5947i7RGbfoGtd1k/iUYU+KKuG4r8UfI+be0BHprq0OXrgBVj/Zv/lVs+DYr0Lt/lktY2HFvhxZOZcX69Jfwy5vfIcX6lZydNVehqqJPXUKK+Dgj8Grf4KtvfxpWvesC9/mng/P/296l2p3B3/MhXCg3zMREdk71rqOkPZ61wkHgHHBW2El+ILJucEy1wGXGsqJY63rgD/qanj6Jz3PS3TGHfsVWPMUrPln79etmAFHXJnVUkVGvKOuhrcfTe6tnMnKe93z8OmL4Jmf9h7CARz9eRfC9bY/uoiISJ7J6yAO+Pf490QI9yrwb8AT1iYWvhcRGQOMcS80Flzk7vcVxj39331fs6gSPvmg6wKyMdj5tvu0r0iCjbkXzi/e1PcLYoCCICy8BA78IHgG/hTDWss/dy7lmV3L+d7+l2XsbvvsrAvSgjgPhrMnLmJq0fgBP+Ye6tcDxoVxn3wAbj+39zBu8xL31ZcFn4Rz/tfdbtwMbbsGV6eIiIwtsSh0NLj93xLP1YzHhW2FleAtSI4VVkKoZs9QTvaUeF594rfc/b7CuL9/r+9rVsyASx6GssnZqVFktCif5n43bj2r9zDu3b+7r76c+C04/usK4UREZMTJ2yDOGDMfmEJyKcrngfdZa7WpioiMTQMJ4/qSKYSLdGSvVhn56tfD4mth24r+zZ9yBCz6EpRMGPBDWWt5Ztdyblx9P280rwPgfbWHcUz1QWlzDymfzTFVB/Lc7tfwYDhzwtFcOeNcpoUGGcK5SqB+HTC9/2FcX7qHcK07slCniIiMCdGwC986Gt3zNXAfdCmsiO9R6kmOhWqgqDoZyknvBhLG9UUhnEjv+hvG9UUhnIiIjGD5/Cz9kPh3gwvjPqcQTkTGvGyEcQrhpC8dDXDfpyHa2edUCivc8jAzT9yrF8TP7lrBDavvY2XTmj3Gb1h9H0dXHZixK+7qWRdQ5ivmMzPPY0Zo4MFf77IYximEExGRvdHV7paf7GxOjnkD7jlcoDS5W3pB0C0/WViRDOWk/7IRximEE+mfwYZxCuFERGSEy+dn6zUpt7dYa5fnqhARkbySGsad/b8DeyGiEE76I1gOc8/re97+Z8OHb4dZJ+31C+I7NvwtLYQDeK1pDYt3Ze7GO6hsFv994FVDEMIlxMO49oZkGDfh4IFdQiGciIgMVGeze4O6YUMyhPOHXMhTOR2C8RDOXwKVM91erKEqhXCDkRrGHf/1gZ2rEE5kYBJhXPnUgZ2nEE5EREaBfH7GblO+b85lISIieSc1jDv80/0/77xfKYST/ll4CRSPy3ysYroLmY79CgRKBvUwV8+6oMdjN665D7u3y68OWrcw7sJ7wOvv36kT5yuEExGR/rEx97embg00bXHdcBgIlLm/t2WTXRiX2MO0el+o3sftDyfZkXhj/8RvwZzT+3/ex+5UCCcyUOXT4CN/6P/8/c9RCCciIqNCPgdxqb3qxTmrQkQkXyVeiDRv6/857801bjkjkZ74CmHRNXuOeX1w6OVwwc0u0B2A5q62jOMHlc3i2OrM3WavN63N2C03bIwHCuLhW9suiEX6d157A4TjP69Pv2ciIpJBLAKtu1wA17Idol1gvFBUBVUzoXQ8FATc36JQLdQe4II5f1GuKx+9YhFo3dn/+QN5Di4iyS0Vmrf2/5zWnRCLKoQTEZERL5+DuOfj3w0w3RjTz4+hi4iMIY99E954oP/zH/0qvPWIeyFTMd0tQSjSk6lHuL3fACYugA/+znVhen39vsQr9W9x2Ss/4lNLftxjd9tnZ56fNnZizQLuPvKHHFg2a69KHzTjdR0HviLY9Q78/nzXtdAf9Wvhjx9yYVxR1cCX3xERkdEr0ukCnLo10LbbvcHs9UNoHFTOglA1eArA44PSSTBuHpRNSn4wRIZGLAL3XAabl/T/nLs+CZteGbqaREaTREfbhhfd71p/bXgB7rvC/X+liIjICFaQ6wJ6Yq3daIz5J3AiUAi8HxjAu80iIqPcY9+EF28c2DnRLrj7YvjQbbDfmS6Mq18HHQ1DUKDkpWiXC2/3P6t/XZFHfw6mHgWz3zegT6Iua1jFDavv46W6N94be2rnMk6sXZA2d27ZTE6ons9Tu5ZxfPUhXDXrfOaWzuj3Y2Vd9xDutrMG/qn3dc+6MO7jd7swDvZuY3oRERkdwm3QXgfh1uRYQSEUVey5zLOvCEI1bhlKdYAMj0QIN5APtwF0NrkP6lx0P0w+dGhqExkNUkO4Oz4A4ZaBnb/yXvf9gpvB481+fSIiIsMgb4O4uH/DdcZ5gP80xvzNWqsNjURE9iaES1AYN3Ztew0WX+v+927bDUdc2fc5RVUw59R+P0Q41sXnl/+c53evTDt245r7OKFmPibDG4tfnv1hPj3znNx1wCVkI4RLUBgnIjK2WQudzdBev+e+vIFiCFaCvzBlrBSKawe996oM0N6GcAkK40R6N9gQLkFhnIiIjHD5vDQl1tqXga/glqfcH7jXGKNXJiIytg0mhEtIhHFapnJs6GyGZ66FBz/vQjiAFX+G3auz/lB+j4+AJ/PyWW81b+AfOzMv+TSzeNLoCuESEmGclqkUERk7YlFoq3NLFTdvdSGc8bjnWpUz3ZKT/kLAuL8NNftB1SyFcMNtsCFcQiKM0zKVInvKVgiXsPJeLVMpIiIjVl4HcQDW2l8CVwFdwOnACmPMp40xFbmtTEQkB/oTwhVVwod/Dyd/t/cljTKFcQWFPc+XkcdaWP0Pt4fJWw91OxaDxT8dkheyV89K3/Mt4cbV9xPr715rw61q1sBCuIM+DJ98ACYc3Pu87mFc6cTs1SwiIvkj2gUtO9z+b6073X1Pgdv3rXImlIxz+6x6CqB4PIyb6z6g4dPzr2HX3xCuYgZceA8c8Zne5ymME9nTQEO4oz4HF97d94fWFMaJiMgIlddLUxpj/pFydycwCZgG/Aq40RizDtgBDHS5SmutPTkrRYqIDJdwi3tDvzdFlfDJB2H8ge5+qBYe+rx7IZRJtMsFNfud6V4o+Qoh0p7duiU3mrfCs9fBxpd6nrPjTXjzQZjbc3DWkzeb1tEZ6+KQ8tlpx/YrmcbJtQt5ckd691uZr5jGrlYq/Hn2qX9joCDgbm9ZBi3be5+/4JNwzv+62598AG4/F7a+2vP87a9D4wbX9aDAW0RkdIl0QFs9hJuTz7m8AbfPW7DMre+SGCuuhcJK8OT9Z2JHt9adsHlp73MqZsAlD0PZZLdXbrAUnv5Jz/M7m2DDC26JykQIITLWrX+u7xDuxG/B8V93ty95GG49q/fl3DcvccvsF9dmr04REZEhltdBHHACkPruceK2iX/NBGYM8Jqm2zVFREYGfzFc/CDcdg5sW5F+PDWES7wJtOAi972nMG7hJXDmte52ewO01w1F5TKcYhF47R5Ycuue+9H05NU/wf5nu0/n98PbzRu4cfX9/GPnEmYXT+GeI3+Ix6S/mXjVzPP3COIWlu/LZ2ddwGGV+/f3Jxle1kLdWtexcNCHIdrplvLM9HuTGsJ1tbs3WnsL4wor3O9uzX7uf5OG9UP7s4iIyPAIt7gArqstOeYLQVG5e96W4C+GUE08lFM4kxdKJsAlj8CtZ0LjxvTjqSEcuOcDJ37L3e4pjDvpO3D05xXCiYD7HbAWjv0KRMPw1I8zz0uEcInfm/JpvYdxFdPdcYVwIiIywuR7ENcTBWkiMjYl3tDvHsZ1D+F2vuW+1+7fcxi38BI4+xfudnuD28dkjHrt4tdyXUJ27HgLFv9P//d+2+cUOPLqfoVw77Rs4ler7+eJHS+njG3kiR2vcNq4w9Pm71sylffVHsaucANXz7yAIyoPwOT7m1LhFrecWOVMmB//vekexqWGcI2b3SfqK6b3HMYlfmfHH+RCuF3vuLBURERGJhtznU9t9e7NZQCM29+tsAJ8weTcYLl7s9gfykWl0peKaZnDuO4hHCRDhZ7CuJO+A8d9VSGcSKrE780J33T3u4dx3UO4hJ7CuEQIVzZlyEsXERHJNmN7Wq4sDxhjhmoTGWut9Q7RtWWMMcbMBVYm7q9cuZK5c+fmsCIZE9rrk2Fc9+Uo6zdC+y5o2gol42HifPfCZunvk2GcQjincqb7dPpgdTS6ACdXwq3w8m/g9fvp12dVSibAoi/DlPQALZNtHbs5bfE1xDJce1ZoEvce9Z94M3TFtUU7KfT48z+A685f7P5teLyw7PfJMC4thNsRPyG+x2JhufvdTIRxCuFEREaPWMT9vW+vT+5NZLzueURhBXjjH2oxHrcfaKgmueSx5Lf69ckwLlMIlyoRGPzzP5NhnEI4kd4lfjee+lEyjOsphEvVsD4Zxo2wEO71119n3rx5qUPzrLWv56oeERHJvbzuiLPWauF8EZFMEm/w330pnPrDZAgHEAsnb+9a5UKF6tnJzrgtS+Gsn7vbYzmEg17Ds0gsytstey6Hsm/xVAo8efY5jnWL4blfQOuuvucaLxz8ERcoFQT7nh83PljFKeMO4/Ht/0o7trp1M49v/xfvH39k2rEi7wh9AzJTZ9ymV5Lh9R4hHICF+nXA9GRn3N2Xwqn/rhBORGSki4Zd+NbR5LrhALw+CMb3f0vs9ebxufCtqCoZysnIkOiMe/BzcN6veg7hIL0zzhtQCCfSl+6dccbbdwgHyc64Bz4H5904YkI4ERGRTPK6I05kJFBHnOSd3avdkklNW933UA3U7Aulk/Z8oTPWQ7g+5H0Q17IDnv8lrHu2f/NrD3BvFFXO3KuHe7dlExe88C1shq64GaEJ3H/UjzJ2xY1oqZ1xCWkhXKqUzrgEhXAiIiNTV5sL4DpbkmMFQfeBi0Cp23kcoKDQLT9ZWKEgZixJDRAUwon0zxj6vVFHnIiIdKeP6omIjAWtO933RBinEG7kikXhjb/Ay7dAV3vf830hOPwK2P/sPQOlbja2beemNQ9wbPXBnD7+iLTj+xRP5vRxR/DX7S/uMb5/yTSumnk+HkbhC+nUzjiPt48QDvbsjCtXCCciMtJY6/6/v70OujqS4/4QFFaCvyg5Fih1H3YKlg5/nZJ7iQ6fxG0R6Zt+b0REZAxTECciMla07nRvKhUEoK0fyxhK/tn1Diy+Fna+1b/5M46Do78Aoeoep2xq38n/rXmAB7c+S9TGeK1pNe8bd1jG7rbPzDyXx7a/hMUyp3gKV8+6gJNqFoy8PeAGItwCO992y4yFW/txgnUhd1upm2+jQ16iiIgMko0l93+Ldrkx44FAiQvgCvzxicZ1vhXXgq8wZ+VKnhjNz39Ehop+b0REZIxSECciMpaEm92XjCxd7bDkVnjt7uT+NL0J1cKiL8G0o3ucsr2jjl+t+QsPbFlMJCUsWtu6lb9ue5GzJqSfO7N4Ep+ecQ77lkzl5NqFeEbbUpQ9iXa6r4HobBqaWkREJHtiERe+tTck/756vBAsd4FbopPceN2HWoqqU0I5ERERERGR/lEQJyIiks+aNsPD10DL9r7nGg/M+wAcein4inqd2hrt4P7NTxPLsOfbTWvu5/RxR2TcD+9z+3yg36WLiIjkpUinW36yszm5TJo34JYVDpSBJ96x4fW7D7cUVfa6vLOIiIiIiEhvFMSJiIjks+JxECjuO4irngPHfgVq9u3XZWeGJnLGhKN4eOvzacfWt23n0W0vcM7ERXtTsYiISH4Kt7oOuNSlhn1FrvstUJwyFoLiGtcZp2XURERERERkkHISxBljPtl9zFp7e3/mZUumxxMREck7ngI49qvwl6shQ/caBUE49HKYd76b282Ojnoq/aUZu9uunHEuj259IWNX3KPbXlQQJyIiI5+1brng9nrXCQeAccFbYSX4gsm5wTLXAZcayomIiIiIiAxSrjribiX93cRMwVimedmiIE5EREaG2v1h7vnw+n17jk89Co75IpSMTztlV2cDv1n3MHdt+iff3f8Szp14bNqc6aEJnDXhGB7c+ux7Y1MKa/nMzPM4Y/xRWf8xREREhk0sCh0Nbv+3WMSNGY8L2worwVuQHCushFDNnqGciIiIiIhIluTD0pSGvsO2bK0HYvv5eCIiIvnlsMth3TPQuguKquDoz8OM49OWzNrV2cjv1j3CXZv+QUcsDMBNax7gjPFH4cvQMfeZmefyyLbnGR+s4soZ53L2hGMyds+JiIiMCNGwC986GsHG3JinwC0/GSwHjyc5FqqBoupkKCciIiIiIjIEcvmKo7/hWjYX5dcC/yIikl/q10PFtL7n+UOu+23TK3D4FeBPXzbrb9v/xXdW/h/t8QAuYVP7Dh7e+hznTzo+7ZwpReO4ZeG/cXDZPhmDOhERkRGhq90tP9nZnBzzBqCoEgKlyVeCBUG3/GRhRTKUExERERERGUK5esft0izPExERGVnaG+CFG+DdJ+Cs62DiIX2fM/1Y99WDA0qmEbaRjMd+veYBzppwTMaw7dCK/fpXs4iISD6xFsItLoDrak+O+0MuaPOHUsZKoLjGLU0pIiIiIiIyjHISxFlrb8vmPBERkRHDWlj1GLz4K+hscmOLr4UP/ga8/kFdekrROM6dcCz3bXk67djmjl08sGUxH5x84qAeQ0REJOdsDDqaoL0Ool3xQeM634oqoCCQHCssdx1w/qIcFSsiIiIiImOd1uIQEREZLg0b4OEvwdP/nQzhABo3wrI/9OsSjV2t/HbdI3TFMne+fXrmORSY9D3eavzlBAYZ9ImIiORULOL2Sq1bAy3bXQjn8bq9U6tmQul4F8IZjwvfag+AiukK4UREREREJKe0GYyIiMhQi4Zh+R9d2Bbryjxn+R9hn5OgPPN+cc1dbdyx4XF+v+ExmiNtlBQU8aEM3W2TCms4d+Kx3Lv5KQCq/WVcPuMsPjjpRIIK4kREZCSKdMb3f2tyneXgusiDFW6pSU98AziPD4prXTDnSf9QioiIiIiISC4oiBMRERlKW191S082bOh9XqwLFv/M7RdnzHvDrZF2/rDhCW5b/1eaIq3vjd+89kHOnbgIv8eXdqlPzziHZ3et4KJpp/HhySdR6A2kzREREcl74Ta3/GQ4+fePgkK3/GSgJDnmK4JQjdsXLuVvqIiIiIiISD5QECciIjIUOprgpZvg7Uf7N79sChx6WdobiNevvpc7NjyeNn1rx27u3/wMH5lyctqxiYXVPLboWgrUDSAiIiONtdDZ7DrgIh3J8UAxBCvBX5gyVuo64FJDORERERERkTyT90GcMWZqyt16a23zXlyjBKhI3LfW9tGWICIispeshXf/Di/cAB0Nfc/3+GD+hXDIx90yW918curp/Gnjk0RsNO3YzWsf4ryJx2bc+00hnIiIjCixKHQ0ur+d0fgyzsbjwraiSvAmOsCNux+qBV8wV9WKiIiIiIj0W14HccaY9wMPpwwdCizbi0vNAV4GbPy6J1trnxp0gSIiIqmaNsOz18Gml/s3f8LBcOxXoHwq1loyLaY1obCaD0w6gT9vejLt2PbOOu7f8gwfnXLKoMoWERHJmWiX637raAQbc2OeAigsh2B5cq83TwEUVUOoOiWUExERERERyX95HcQBV8B770s+aa3dmxAOa+0SY8zTwPHxoU8BTw2+PBERESAWgRV/hiW3QTTc9/xAKRx5Fcw5nY5YF3evf4yHtz7P7Yd9O2N326dmnM19m5+my0beGyspKOKT007nzPFHZ/MnERERGR6RDmirh3Cz6yYH8AbcPm/BsuSrQG/ALT9ZWAkeT87KFRERERER2Vt5G8QZY3zA+4h3sQF3DvKSd5AM4t5vjDHWJl7xiYiI7KXtr8Pia6FuTf/mzz4VjryaTn8R92x8gt+sfZid4QYA7tn8FBdOPTXtlPHBSj44+QTu3Ph3igsKuWjqaXxi6mmU+kJZ/EFERESGQbjFBXBdbckxXwiKysFfnBzzF0OoJh7KZeoZFxERERERGRnyNogDDgYS7zBa4G+DvN5jKbfLgXnAa4O8poiIjFXhFvjXzfDGgyQ/M9KL0omw6BqYfCj3bnqKG9fcz47O+j2m3LL2IT4w6QSCmbripp8d74J7P2UK4EREZCSxMehscgHce53jBgIlrgMuda+3YLnrgPPrb52IiIiIiIwO+RzE7Z9ye6e1dvNgLmat3WyM2QnUxIcOQEGciIgMlLWw9ml4/n+hbXff840XDv4YLLgICgIAvNOyKS2EA9gVbuTuTf/gommnpx2rDVbw+X0+OOjyRUREhk0s4vZ+a6+HWNSNGa/rciusAG/85ajxQFGV64CL/60UEREREREZLfI5iEsEZhbYlqVrbk257vgsXVNERMaK1p3w3C9gwwv9mz9uHhx7DVTO3GP48hlncc/mf9IZ60o75TfrHuGDk0+k0Ks3IkVEZISKhl341tHkuuEAvD4Ixvd/S+z15vG58K2oKhnKiYiIiIiIjDL5/GonZX0SOrJ0zc6U21rrREREBibSAZtf6XueP0TXYVewfupC9imZkna4JlDOhyefxO83pK+63BppZ2XjWg6r3C8bFYuIiAyfrjYXwHW2JMcKgq77LVAKia3eCgrd8pOFFdr/TURERERERj1PrgvoRV3K7eosXbMq5XZzlq4pIiJjRdkUmH9Rr1MiM07gL8d/jnPqn+Xypf9NW7Qz47zLpp9F0JPcCy7g8fHJqafz10XXKoQTEZGRw1robIaG9dCwMRnC+UPu72bFNAjGQ7hAKVTOgtr9oKhSIZyIiIiIiIwJ+dwRtyv+3QBTjTFl1trGvb2YMaYcmIZb6hJg5+DKExGRMengj8G7T7o3HFNEi8fx6IFncFPjSja8e+d743dtfJJLpp+RdpnqQBkfmXIyd278Ox+efCKXTT+LmkD5UFcvIiKSHTaW3P8tGl9q2XggUAKFlVCQ+LCJcZ1vxbXgK8xZuSIiIiIiIrmSz0Hcivh3C3iBs4E7BnG9c+LXSVzzzUFcS0RExiqvD477Cjz4BXffeIjN+yAfYwtvbnsybfrv1j3ChyefRFFBMO3Yp6afxUVTT2NcsHKoqxYREcmOWMSFb+0Nyf3fPF4IlrvAzRN/yWW8EKp2e8B5fbmqVkREREREJOfydmlKa+0qYGP8rgG+Z4zZq1dwxhg/8B2S3XDbrbWvDr5KEREZVWLR/s0bfxDsdxbU7Afn/xrPUVdzZNW8jFPrupq5c+PfMx4r95cohBMRkZEh0gnNW6FuDbTVuRDOG4DicVAxy4VuHi94/VA6GcbNhdKJCuFERERERGTMy9sgLu5PuBDOAjOB3+/ldX4PzEq51p+zUp2IiIwOsSi8fj/cfQmEW/p3ztGfg3NvgOrZAFwy/QwKvYGMU29d/yitkfYsFSsiIjKMwq3QuAnq10FHk9sTzlcEpZOgcjoUloPHgC8EFdOh9gAorkl2xomIiIiIiIxx+R7E/QRIvCNqgA8ZY54yxszsz8nGmFnGmKeAD5LshmsDfpTtQkVEZITavRoe/Bw89wto3Aj/ujnjNGst/9ixhAe2LHYDBcE93mSs9Jfy8SnvSzvPazycVLOQzljXkJQvIiKSdda6/d/q17kQLtwKGLf/W/k0KJ8CgWI3N1gGVbOhZo5bmtKYXFYuIiIiIiKSd/J5jzistbuNMV8DfoUL0gxwHPC2MeavwKPAK8AOXGBXDNQChwJnAO/HhY2JV4MW+Jq1dsdw/hwiIpKHIh2w5DZYcRfYlCUp33gQZp/qlp3EBXBL6t/mns3/ZF3bNsp8IU6uPZTigsK0S14y7f3cufHvtEU78BoPZ004hitnnMOUonHD9VOJiIjsvVgUOhrc/m+xiBszHhe2FVaCtyA5Vljp9n/zpe+BKiIiIiIiIkl5HcQBWGt/bYzZH/gCya42L3Bm/Ks3iaUoEyHez621Nw1VrSIiMkJs/Bc8+3O3100aC8/8FM6/iVXNG7l1/aOsad3y3tHGrlb+uOEJPj3znLQzy/0lXDT1NLZ07OLKGecyLTR+CH8IERGRLImGXfjW0ej2fgPwFLgOt2A5eDzJsVANFFUnQzkRERERERHp1Yh49WSt/ZIxZi1uqUofyUCut3VPUgO4TuCr1tobhrRQERHJb2118MINsPrJ3ufVr8W8djebSoJ7hHAJt63/Kx+bcgolvqK0Y5+ddQFGy3KJiMhI0NUO7fXQ2ZwcKwi6AC5Qmny1VRCEUK0b9+T77gYiIiIiIiL5ZUQEcQDW2l8YY/4OfAv4EK4r7r3DKbdNyvcI8CfgR9baN4elUBERyT82Bm8/Ci/9GjqbiQLv+n0sDQRYFgxwbHs7Z7e0Jed7A+ANsG/J1IyXa4q0cseGx7lq1nlpxxTCiYhIXrMWwi0ugOtqT477Q265SX/Kh0z8JVBc45amFBERERERkb0yYoI4AGvt68DHjTHXACcDxwKzgEqgBGgG6oB3gMXAk9oPTkRkjKtfD4uvhW0reChUxCPlNbwaDNCS8on+GCSDuClHwKIvYUO1TGxeT0lBEc2RtrTL3rHhb1w6/QyCXv8w/SAiIiKDYGPQ0QTtdRDtig8a1/lWVAEFgeRYYbnrgPOnd36LiIiIiIjIwIyoIC7BWrsN+EP8S0REJF2kE5b/EZb/AWIRAFb7fTxXVJg2dVkwgC2swBz9eZh5IhgDsSjGGPYtmcor9W/tMf+U2kP5zMzzFMKJiEj+i0Xi+781QCzqxjxet/dbYbnb9w3AeKGoyu0BV6C/byIiIiIiItkyIoM4ERGRBGstm9p3sLRhFcsaVrG0YRVfrTqC45bfD40b95i7oKOT32S4xo6CAjaf/TMml89IO5YaxJ1QPZ/P7nMB+5VMG4ofRUREJHsinfH935rccpQAXj8EK9xSkx6THAvVuBDO4+35eiIiIiIiIrJXFMSJiMiI9Z9v3c6T219hZ7hhj/Flm2/juMbGtPkHd3b2eK1lbVsyBnHzSmdwXPXBnD7+SN4/7kgK9CaliIjks3CbW34y3JocKyh0y08GSpJjviIXwBVWuE5wERERERERGRIK4kREJK+1RTsp9PgxGd4krAs3pYVwAEuDgbQxgLKYZZ9wmHf96UtuLW1YxdkTj0kbnxGayNWzLhh44SIiIsPFWuhsdh1wkY7keKAYCqvAF0wZK4Xi2j1DORERERERERkyCuJERCSv7OpsZHl8icllDe/wZvM67j/qR8wITUibu6B8Do9v/1fa+Ep/gDCQaYebBd5S3sW9SekzBcwrm8H88jksqjo4yz+JiIjIEItFoaPR7f8W7XJjxuOWniysAK8vPtFAUSWEavcM5URERERERGTIjeggzhgTAMqBzK0PvbDWbsh6QSIistcisSgXvPj/WNu6Ne3YsoZV6UFctIsF297JeK2wx/BGwM8hneHkYKAUjvosp1dNYULTahaUz2Fu6QwC3kxxnYiISB6Ldrnut45GsDE35imAwnIIlif3evMUQFE1hKpTQjkREREREREZTiMqiDPG7AdcAhwNzAeK9vJSlhH2s4uIjAZdsQjr2rYxu3hy2rECj5egJ/PnKpY2rOKCScfvObjmKWYvv5vQtMm0ejzp5wQDySBuzmlw5FUQLOcw4LCqAwb7o4iIiAy/SAe01UO42S1HCeANuG63QCkkVnH2Btzyk4WVkOFvpIiIiIiIiAyfERFGGWOqgZuBc1KHc1SOiIj0U0uknRWN77K0fhXLGlaxonE1ERvluRNvosibHrotrJjDm83r0saX1r+dfvF9TqbgjQc4uGM7zxcVYqxlTriLQzo7WdDRyWEdHVA2GY79CkycPwQ/nYiIyDAJt7gArqstOeYLQVEF+EPJMX8xhGrc0pQZ9lYVERERERGR4Zf3QZwxZhbwDDAeF75ZkiGcTZ2acjt1PNNxEREZYhvatnP2c18nluH/klc2rubwyvSutPnlc7hjw+Np4xvbd7Czs4GaQHly0Hjg2Gu4/JHPclFTMwd1dlIaiz+WpwAO+QQcciEUDHj1YhERkdyzMehscgFcNLHUsoFAieuAS/37Fix3HXCpoZyIiIiIiIjkhbwO4owxQeABYALJcM0CLwFrgY+ljD0O1AGVwFxgUsoxgLfi54mISBbEbIy1rVvZ2dnAkVVz045PLqyhuKCIpkhr2rGlDat6DOJ6srzhHd437rA9Bytncvi+H4DldyTHxh/ouuAqpvf7ZxEREckbsYjb+629HmJRN2a8rsutqMJ92ATcB1KKqlwHnD50IiIiIiIikrfyOogDrgAOIBmmLQE+Zq19F8AY87GUYz+x1v4jcaIxZjbwWeAqwAfMAe611n5nmGoXERlVwrEu3mhax9KGVSyrX8WyxlU0drUyPljJE8delzbfYzzML5/N07uWpx1b1rAq42PUBMqZUljLxvYdTC6sZUH5bOaXz2FB+RymhyZkLmzBRbDmn+5NyyM+A/ud4d6cFBERGUkindDRAB1NrhsOwOuDYCUES5N7vXl8LnwrqgJvvr+cExERERERkXx/5fZFkktRbgZOsdY29udEa+07wJeMMbcCfwGmAv/PGBOw1n59aMoVERm9ntv1Gl949bq08W0ddWxt38WEwuq0Y/PL52QM4pY3vEskFqXA40079t8HXsW4lt3Ubn4V5l3Rd2EFATjl+26ZrqKqfvwkIiIieaSrzXW/dbYkxwoK4/u/lSQX2C8odMtPFlZo/zcREREREZERJG+DOGPMVGAmLoizwA/7G8KlstYuN8acArwMlAFfMcb8zVr7ZFYLFhEZ4ba272JpwypCBYWcUDM/7fgh5bN7PHdZwzsZg7gFFZmXmrQ2xub2nUwLjd/zQLiVA1c+Aq/fD1gYNxemHd138dU91yYiIpJ3rIVwC7TXQVdHctwfgsIq8BcmxwKlrgMuWDr8dYqIiIiIiMig5W0QByQ2AjK4IO7uPub3+LFQa+27xpj/AP4nPvQDQEGciIxpm9p38tyuFSxtWMXShrfZ1lEHwGEV+2cM4ir8JcwMTWRN65a0Y0sbVnHGhKPSxueWzsDv8VFcUMiC8jnvLTO5b8lUfJ5uf4LWLYbnfgGtu5Jjz/0CJh4CvqJB/awiIiJ5IRaFzibXARftcmPG48K2wgoo8McnGne/uBZ8hT1eTkRERERERPJfPgdxtSm3N1pr6/uY39cr1FuBHwNe4ChjzGRr7aZB1CciMqL9c8cSfrLqj2njrzWupisWSQ/KcEtNZgrietrzze/x8cgx/8O4QAWmp2W0Wna4wG39cxmObYdXfgdHfbb3H0ZERCSfxSIufGtvSO7/5vG6sC1Y7m4DGC+Eql0HnNeXq2pFREREREQkizy5LqAX5fHvFtjZw5yUdVwI9XYxa+1uYG3K0JF7XZmISJ5rCDfzVP0b/GzLP1hc/0bGOQvKMy8b2REL82bz+ozHFpbvmzbmNR6CXj9dsUjGc8YHKzOHcLEorLwX7r44cwiXsPJe2Pl2z8dFRETyVaQTmrdC3Rpoq3MhnDcAxeOhYpbb29TjBa8fSie7JZlLJyqEExERERERGUXyuSMunHI787u70ITrnLPApH5cczewT/z2lL0vTUQk/zyz6Rn+ufGfLNvyEqtbNr433ojl2EmL0ubvWzKNQm+A9mhn2rFl9as4qGxW2vj8ijkUeYMcXLaPW2ayYg4Hls2iyBsYWLG73oHF18LOt/qea2Nu2cqa9BBQREQkL4VbXQdcuDU55iuCogrwF6eMhaC4xnXF9dQ5LiIiIiIiIiNaPgdxqUtRlvUwZwfJJSz78w5tecptbbYgIqPKs5uf5Z5V96SNL21ek3F+gcfLQWWzeKkuvWNuacMqLub9aeOTgtU8d8KvKEgsoTVQXe2w5FZ47e7k0ly9CdXCoi/BtKP37vFERESGi7XJ/d8iiQ+5GAiUuCUofcHk3GCZ+xsXKM54KRERERERERk98jmIS6xDZoCJPcxZAcyLzzm+t4sZY2qA2bjuOYDGLNQoIjLk2iPtvLbzNZbuWMqyHcu46uCrOKT2kLR5C2oXcOdbd6aNr2vfQV24iUp/afo55XPSgriZoYlMC43PWIsxhgKzlyHchhfh2Z+7fd/6Yjww9wI47DLXQSAiIpKvYhHoaHT7vyWWaTZeF7YVVoA3/pLLeKCw0u3/lhrKiYiIiIiIyKiWz0HcSlxoZoASY8xka+2mbnNeBD4evz3bGPM+a+0TPVzv6yT3xLPAu9kuWEQkm361/Fcs3ryYN3e/ScQmV+g9dNyhGYO4TGMJyxre4eTahWnjh1bsx8Fl+7CgfA7zy+dwSPlsKvwl2Sg/qW03PH89rPln/+ZXz4Fjv6KlKEVEJL9Fwy5862hMdnl7fRCscCGcJ/7Sw1Pgwrei6mQoJyIiIiIiImNG3r4StNY2GmNeBQ6JDx0P/KHbtLuAn+MCNgPcZow5z1r7r8QEY4wH+ApwDclgrwNYPKQ/gIhIP1hrMT3sCbNi1wpe2/Va2vjSHUszzh8fGs+k4klsbtmcdmxZw6qMQdzhlQdwx+HfHWDV/WRj8ObD8K9f77lHTk8KgnDo5TDvfPempYiISD7qanfLT3Y2J8cKgq77LVDqXm0kxkK1bjwRyomIiIiIiMiYk+/vdD5OMog7k25BnLV2hzHm98AluJBtPPCCMWY5bmlLP3AkMIHkS2IL3GKtbRvi2kVE0nTFuni77m2WbnfLTC7dsZQ/n/VnxmdYCnJB7QKe3fxs2virO14lGovizbBP2/za+XsEceN8JSwom80hZftk9wfpS91aWHwtbF/Zv/lTj4JjvgglmZfEFBERySlrIdziAriu9uS4P+SWm/SnLKPsL4HiGtcVJyIiIiIiImNevgdxf8ItKWmA84wx1dbaXd3mfA14H24fuUTH23ySAV5qAGeAd4BvD23ZIiJ7CkfDXP33q1mxawXtkfY9ji3fsZzTZ5yeds782vkZr9Xc1cy7De+yb2X60o0nTT2JomiE+SXTWVhQzgS88eWwKrPzg/Ql0glLfw+v3gk22vf8oio4+vMw43jooTNQREQkZ2wMOpqgvQ6iXfFBA8FS1+lWEEiOFZa7Dji/9jYVERERERGRpLwO4qy1y40xnwcSu5nXALu6zdltjDkBeBSYjQvc9piCC+AMsBw411rbjIjIEGjsbKQskP4JeL/Xz/a27WkhHLilJjMFcfOq51HgKSASi6QdW7ZjWcYg7n3T3sf7imdCZxM0bXXfh8vmJbD4Z9CUvjRmOgMHnAuHfwr8xUNemoiIyIDEIvH93xogFv9giccLwXIXuCWWUDZe96GSUA0U+HNTq4iIiIiIiOS1vA7iAKy1N/RjzmpjzDzgKuAjwGEkf7YI8BJwO/A7a236O9oiInvBWsvaprUs2+6WmFy6fSm7O3bz3Meew+fxpc2fXzufdU3r0saX7ViW8frBgiBzq+by6s5XASj1l7KgdgHzx83n8AmHZ/VnGbSV98Hzv+zf3IoZcNxXYdzcoa1JRERkoCKd8f3fmtxylABef3z/tzLwmORYqMaFcBmWihYRERERERFJyPsgrr+stV3AL4FfGmMMUIXrgtttrY3ltDgRGXXWN63nokcvor6zPu3Y23VvM696Xtr4/Nr53P/u/Wnjq+pX0RxupsRfknbsE/t/gnP3OZcFtQuYUTYDj/Fk5wfItumL4OWb99w3pzuvHxZeDAd9JNlJICIikg/CbW75yXBrcsxXFA/givccC9W4cS2pLCIiIiIiIv0wKt8JtdZaui1hKSIyUM3hZra3bmefin3Sjk0MTcy4zCTA0u1LMwZxC8ctzDg/ZmOs2LmCYyYdk3Ys05KVeam4Fg69HF64PvPxSYfCsV+G0knDW5eIiEhPrIXOZtcBF+lIjgdKoLASfMGUsVL3ty6Q/qEZERERERERkd6MyiBORGRvbGvdxrIdy1i6fSnLdixjVf0qZpbN5C/n/SVtrs/r48CaA3l528tpx5bvXM4n+WTa+JSSKVQFq9jdsZugN8iBNQcyv3a+W26ydv5Q/EjDa+758M7jsGtVcixYDkd9FvY5RZ0DIiKSH2JR6Gh0+79Fu9yY8UCwzHW6eRPLSxsoqoRQ7Z6hnIiIiIiIiMgAKIgTEQGeWP8E1zx1Tdr46sbVNHQ0UB4sTzs2v3Z+xiBu6falWGsx3YInYwzfPeq7VBdWs3/l/vi86fvIjWger9v77f7PgI3BvmfAEZ+BYGmuKxMREXGhW3u9C+ESK9d7Clz4FixL7vXmKYCiaghVp4RyIiIiIiIiInsnr4M4Y8wx1trnsnzNYuBX1tqLsnldEclvndFOXt/1Ol6Pl4NrDk47Pq8qfSnJhOU7l3PClBPSxhfULsg4f3fHbja3bGZyyeS0YydNPan/ReeLzmaoXwvjD+p7bvUcOOIqqJkDE9L/O4uIiAy7SAe01UO42S1HCeANuG63QKnbVToxVlzrlqX05OmerCIiIiIiIjLi5HUQB/zTGPN9a+1/ZeNixpgFwJ+BmcCYDOKMMUHgaGA/oAIIA5uAl6y1a3JZG4Axpgo4BpgFhIBWYDXwnLV2dy5rk5GlsbORV3e+ypLtS1i2Yxkrd62kK9bF8ZOP5/qT0/cxm1A8gfGh8Wxr3ZZ2bOmOpRmDuINrDsZjPBgM+1Xu55aZHOeWmawurB6KH2t4WQur/+H2fYtF4MO3u66Bvhz0oaGvTUREpC/hFhfAdbUlx/wh97fMH0oZK4ZQjeuK0zLKIiIiIiIikmX5HsQVAD80xpwAXGSt3b63FzLGfBn4EeAHbFaqywJjzCTgcOCI+PdDgdRd4Ndba6dn4XFqgO8Bl+ACrkxzlgA/tNY+MNjHGyhjzMHAvwNnAZk+ghw1xjwCfMdau2JYi5MR6c637uSG5TekjS/bsYyYjeEx6f/M5tfO569r/5p+zvZlGR+j2F/MbaffxpyKORT5igZfdD5p2gLP/hw2pSy9+cINcNK3c1eTiIhIX2wMOptcABcNxweN63wrqoCCQHJusNx1wPkzPjUWERERERERyYqRsubKKcCrxpj3DfREY0yVMeZh4Ke4EC7njDHHGGPuM8ZsxnWj3Qd8AziRPUO4bD3eCcAbwGfpIYSLWwj8xRhzmzFm2P5bGWO+CLwCnEPP/ya98eNLjDGfH67aJD9FY1HernubO9+6k1d3vppxTk/LRjaFm1jTkLn5s6dztrdtJxqLZjx2SO0hoyuEi0Vg+R/h7kv3DOEA3v07bHolN3WJiIj0JhaBtt1Qtwaat7sQznihqAqqZkLpeBfCGY/rfqs9ACpnKIQTERERERGRIZfvHXFduBotUAv81RjzU+Bb1trM74qniAdQdwATcLs/2Pj3p4am3H47DDh/OB7IGLMIeBQo7HaoAViLW55yCi7oSvgkUGyM+aC1dki7B40x1wDXZji0FdgCTMT975dQAPzSGGOstb8cytokv7y28zVe2PoCS3cs5dUdr9LS1QLAJ/b/ROY936rnUWAKiNhI2rGlO5ayT8U+aePza+cDML10+ntLTC6oXcCUkimYsbBU1fbXYfG17k3Mniz+GXzot1AQHL66REREehLphI4G6Ghy3XAAXh8EK91Sk57432+PzwVwoWrweHu8nIiIiIiIiEi25XsQdwzwJ9yebhbXLfU14DhjzEettRsynWSM8QDfB76JC5gSYVIMt/zhfwxt2YPSAhRn40LGmArcnnipIdx64IvAg4mQzRgzGfg2cGXKvAuALwM/y0YtPdR3NPCTbsNPAV+x1i5NmXcorqPx+JR51xpjXrTW/muo6pP88qe3/8SDqx9MG1+6Y2mG2VDkK2L/qv15bddraceW7VjGh/f9cNr47IrZPP2Rp6kMVg6+4JEk3AL/ugXeeIA+V+5t3gIr74VDLhyW0kRERDLqaoP2euhsSY4VFLrlJ/0l7qN3ibHiWrcv3Fj4UI2IiIiIiIjknbxemtJa+wowHxcmpXa0HQksN8Z8oPs58VDpKeBbJEM4g1sC8kRr7Q+HustrAJpxtf4P8CFgOnB2Fq//NVxHWcJa4Ghr7QOp/w2stZustZ/B/TdL9d14mDdU/oc9O/EeAk5LDeHi9b0CnAo8kjJcED9fRjhrLRubNvLAuw/w/ee/z7v172ac19OykW/VvUVrV2vGY4kOt1QhXwifx5dxvsd4xlYIZy2seRruugTe+At9hnDGC4d8AuZ9cBiKExER6cZa6GyGhvXQsDEZwgWKoWwqVEyFQDyEC5RC5Syo3Q+KKhXCiYiIiIiISM7ke0cc1tpm4GPGmCeB60h2d5UDdxljfg182VrbaYw5D7gFt9xianD3IHCptbZ+eKvv0UPA48Bb1ibW0HGMMTOy8QDGmBqg+15qV1hrt/Ry2o+A04Dj4vfLgK+SHtBlo773A0enDO0GLrfWhjPNt9aGjTGX4fa6q4oPH2eMeZ+19ols1ydD76637+LFrS+ybMcydrXvem98dsXsjMtGLhiXOYiL2Rgrdq7gqIlHpZ9Tu4DH1j3GwtqFzB83n/m185ldPhuvlqSClu3w7C9gw/P9mz9uHhx7DVTOHNq6hlvlTLd0WQYFwFwO7d91Ohp7X9JTRET2XiwKnU2uAy7a5caMx4VthRVQkNja2Lj7xbXg674qu4iIiIiIiEhu5H0Ql2CtvcUY8zyuO24uyZDtSuAYY8y/gMtIBnAAYeBr1trrc1Byj6y1q4fhYT7KnktcPmOtfbK3E6y11hjzAyB13mXGmG8PQRfhp7rdv8Fau7OP+nYYY24EvtPtOgriRqBH1jyScVnJpduXcuH+6cseTi+dTkWggvrO9Dx92Y5lGYO4E6eeyElTTxob+7v1VywCK++HV34DkY6+5/tDcPiVsP9Z7k3P0aaHEC5n1xERkaRYxIVv7Q3J/d88Xhe2BcuTe70Zr9v7LVTj9ocTERERERERySMjJogDsNa+YYw5DPglLoBJhHEHAvPYswtuFfARa+2rOSo3187tdv83/Tzvn7glLBOdeeNxS4G+kKW6MMYEcJ13qX7bz9N/y55B3PuNMf6eOumkdwfeduBen/vaxel7rwHsat/Fsh3LWLp9Kct3LOem991EWSA9pFgwbkHGIG7ZjmVYa9PCM2MM82vn84+N/3hvzOfxMa96HuND4zPW4hmNwdFg7HwbFl8Lu1b1b/7ME+Hoz0FRVd9zR7ih+F0QEZG9FOmE9jq3DGXis2DegAvgAqXgiT9H8PohVOv+Tnn0N19ERERERETy04gK4gCstR3Ap40xfwduB3y44C31XfvbgM9ZazNvHDXKGWOKSS4vmfB4f86Nd8X9HbgiZfgsshjEAScAoZT7b1tr1/fnRGvtOmPMO8Ds+FAJcDzqisupjkgH//Hif7BsxzI2NG/Y49irO1/luMnd/zlm3r8NYGf7Tja1bGJKyZS0Y8dMOoaIjTC/dj4Lahcwt3ouAW8gOz/EaNbVDs9fD6/fl+wo6E3xOFj0ZZh65NDXJiIikhBudR1w4ZSn8L4QFJWDv3jPseIa1xWnrncRERERERHJcyMuiAMwxoSAcwA/rgMusWxi4pV4O9CVg9LyxVxcQJmw1lq7bQDnP8eeQdwh2Siql+v1c5Oq9zxHMohLXE9BXA4FvAFe2PICO9p3pB1btmNZxiDu4JqDMRgs6aueLtuxLGMQ9+F9P8yH9/1wdooeK7a+CivugrZdfc81Hjjwg7DwUu2tIyIiw8Pa5P5vkc74oIFAieuA8wWTc4NlrgMuUJzxUiIiIiIiIiL5aMQFccaYQ3D7xO1DegCXum/c0caYj1hr3x72InNv/2733xjg+d3nd7/eYOV7fTJAxhjmj5vP39b9Le3Y0u3py08ClAXK2KdiH96pf8ddA8OcijnMr53PjNIZGc+RAWjdBc/+HDb9q3/za/aFY78K1bP7nisiIjJYsQh0NLr932IRN2a8LmwrrABv/GWK8UBhJRTXQoG64EVERERERGTkGVFBnDHmc8D/4DrhUsO3a4FiXACXCOMOAl4xxnzBWvu7HJSbS/t2u79xgOd3nz/NGBOMLwuaDdmur/v1JAfm12YO4lbuWkk4Gsbv9acdu2CfC6jrqGPBuAUcXHMwJf6S4Sh19HvzYXj6x9DV1vdcXyEc9ik44DzweIe8NBERGeOiYRe+dTQml0v2+iBY4UK4xF5vngII1UBRdTKUExERERERERmBRsSrWmNMOfBb4FxcyJbohNsFXGyt/Wt83pPAzUBp/HgIuMUYcwrwGWtt83DWnUO13e5vGuD524EIyX8fHqAK2DzIuhIGW1/3Orpfb68ZY2qBmgGeNitbjz+SLahdkHE8HAvzxu43OKT2kLRjnzjgE0Nc1Rjl8fYvhJt2DBzzRddlICIiMpS62t3yk50pT8cLgq77LVCa/IhdQdAtP1lYkQzlREREREREREawvA/ijDFHAXcCU0iGcAZ4GrjQWrslMddae48x5pX4/CNS5n4UONwY81Fr7ZJh/hFyofvGGa0ZZ/XAWmuNMe1AantSNjfjGFR9GeZns7arge9l8XpjxpyKOYR8IVq73P88k4sns2DcAubXzmdyyeQcVzfGzDkd3vgLbHo58/FQNRz9RZhx7LCWJSIiY4y1EG5xAVxXe3LcH3LLTfqLUsZKoLjGdcWJiIiIiIiIjCJ5HcQZY/4N+AHgI9kFZ4F/B35obWI9myRr7TpjzCLgv4Cvppw3C3jOGPP/rLU/G/Lic6t7MLU3S0oOZxA30Prau93PZm2yl7weL19a8CUqghXMr51PbZG6rHLGGFj0Zbj7ErcEWPIAzD3PLUXpD+WoOBERGfVsDDqaoL0Ool3xQQPBUhfAFfiTY4XlrgMuNZQTERERERERGUXyOojDhWmWZGfbFlwX3NO9nWStjQLfiC9VeTtu6UKL21vuf4wxJ1trzxzSynMr2O1+OOOs3nV2u1+4l7VkMtj6hrI2GYSP7vfRXJcgCaUTYd4H4NU73f3KWXDcV6D2gNzWJSIio1csEt//rQFiUTfm8UKw3AVunvhLD+OFoiq3B1xB+h6yIiIiIiIiIqNJvgdxCQb4K24/uF39Pcla+7gx5mDgDuBkkoHe6UNSZf7o3mG2N+9wBPq45mB0AKkfex5ofUNZ243A3QM8ZxbwQBZrEMmOfc+ADS/C9GNh4SeTb4CKiIhkU6Qzvv9bk1uOEsDrj+//VgYekxwL1bgQzuPNXb0iIiIiIiIiw2gkvCsbAf6ftfbavTnZWrvdGHMq8E3g+4yMn3mwWrrd796B1h/du8y6X3MwWtgziBtofUNWm7V2B7BjIOcYY7L18CLZ5SmA478OxeMUwomISPaF29zyk+GU7Xt9RfEArnjPsVCNG9fzJhERERERERlj8v2d2bXAR621Lw/mItZaC/yXMeYp4I/AlCzUls+6B1MD2gzKuGRpqIO41A3EBrpZVff52axNZHQxnlxXICIio4m10NnsOuAiiUUJjAveCivBl/L5qkApFNdCoCTjpURERERERETGgnwP4uZba5uydTFr7fPGmEOAW7J1zTzVvaNr8gDPH8ee/zZiQL+XBO2HHcDMlPsDrW9ShuuJiIiIyFCJRaGj0e3/Fu1yY8YDwTLX6eb1xScaKKqEUO2eoZyIiIiIiIjIGJXXQVw2Q7iUazYAH8z2dfPM293uTx3g+d3nr7fWZnMftreBI3t5vL50n//W4MoRERERkYyiXa77raMRbMyNeQpc+BYsB48nOVZUDaHqlFBORERERERERPI6iJO91j2YOmCA5+/fx/UGK9/rExERERnbIh3QVg/hZrccJYA34LrdAqWQ2OrNG3DLTxZWJkM5EREREREREXnPiAzijDEluI6qI3HdURVACdAM1APrgReBl6y1zbmqM4deB7qAxMeRpxtjJlhrt/bz/GO63V+ercJ6uN7RAzx/qOsbM167+LVclyCSF/S7ICISF25xAVxXW3LMH3IdcP6UbXr9xRCqcUtTGpN+HREREREREREBRlgQF9/f7cvAhwF/P07pNMb8GbjOWvvqUNaWT6y1zcaYZ4CTU4bfB9ze17nGGAOc0m34oSyWB/AU0Aok3s2ZY4yZZq1d39eJxpjpwOyUoeb49UREBq6j0b2JnI3riIiMVDYGnU0ugIuG44PGdb4VVUBBIDk3WO464FJDORERERERERHp0YgI4owxBcC/A18DPCQXwwGwmU6Jfw8CnwQ+YYz5CfA9a21kKGvNIw+yZxB3Of0I4oATgRkp97cDL2WxLqy1HcaYx4HzU4YvA77Xj9Mv63b/MWttOONMEZG+1K3JdQUiIrkTi7gPErTXQyzqxowXCsvdlyf+UsF4oKjKdcClhnIiIiIiIiIi0qe838ghHsI9AHwD8OJCttTwzWT4SkjM8wL/BvzFGOMd6przxJ9wXWcJxxljTurthHg3XPcw7HfW2li2iwN+0+3+Z40xNb2dYIypBa7u4zoiIiIi0ptIJ7Rsh7q10LrLhXBeH4TGQeUsCFW7EM7jg5KJMG4elE1WCCciIiIiIiKyF/I+iAN+DbyfPQM4AzwP/BD4IHACcFj8+wfj48+RDOZs/Pv749cb9ay1O4Druw3fYoyZ2Mtp3wSOS7nfCPxPX49ljPm+McamfD3Vj/oewe3jl1AF/MYY48s03xjjx4VuVSnDi621f+vrsUREREQEt+9b02aoXwftDW5JyoJCKJ0IFTOhqBw8xo2VT4Nxc6FkHHjGyufYRERERERERLIvr5emNMYcA1zKngHcX4FrrLVv93LqfcD3jDH7AtcCZ5AM4y41xvzGWvvC0FXet/jPVpjh0MHd7geNMd33bEvYYq19o5eH+QlwMTA+fn8G8Lwx5gvAQ9ZaG69lMvBt4Mpu5/+ntbaul+sP1teAp0kGwmcDjxtjvmKtXZqYZIxZiPvf8fiUc6PA14ewNhEREZGRz1oIt0BbHUQ6kuOBYghWgj/l6Wig1C0/GSwd/jpFRERERERERqm8DuKA78e/J7ra/sNa+93+nhwP684yxvwA+A7JMO77wGlZrXTg/gBM68e8ccATPRy7DbikpxOttXXGmI8Af8Ptl0f8MR8AGowxa4FyYCpu+c5UDwA/7Ud9e81a+6wx5pvAf6cMnwAsMcZsAbYCE4EJGU7/urX2xQzjIiIiIhKLQmeT2/8t2uXGjMeFbYUVUOCPTzTufnEt+DJ9RkxEREREREREBiNvgzhjTCmuAyrRDfeXgYRwqay13zPGzAPOjw+dYIwpsdY2Z6HUvGatfcYYcyZwN1CZcqgcmN/DaX8ELkt0zA0la+1PjDFRXBiXGgZOjH91FwW+aq29bqhrExERERlxol3Q0ZBcehLc0pKFFRAsTy4zabxuL7hQjdsfTkRERERE9sqSJUsMEAL0xFok98JA28KFC4c82xiIvA3igEUk67PA9wZ5ve+RDOIK4tf/6yCvOSJYa/9hjDkA99/gYqCoh6nLcF2H9w1bcYC19lpjzJPAf+D28cu0d2EMeBT4trX21eGsT0RERCTvRTqgrR7CzW45SgBvwAVwgVK391tiLFQDRVXgGQnbRYuIiIiI5JclS5Z4gYXAKcCxQBWZ388UkdyILFmyZCMu/3lk4cKF23NdUD4HcandUFustSsHczFr7UpjzGZgUnxoUm/zh5q1dvowP9524GpjzFeAo4H9cV1xYWAz8JK19t29vPb3SS4jurf1LcctI1qNC0ln4j5J0gqsBp6z1u4azGOIiIiIjDrhVrf/W1dbcswXgqJy8BfvOVZc47rijBnuKkVERERERrx4APc54CygIsfliEjPCoAZwNXAVUuWLPkX8NOFCxeuzWVB+ao6/t0CW7J0za0kA7iqLF1zRLHWtgNPxr/yTjxs+0uu6xARERHJW9Ym93+LdMYHDQRKXAecL5icGyyDUC0EijNeSkRERERE+hYP4X4InBof8nq93hKv11tqjAkYY7yAPvEmknvWWhuJxWJt0Wi0IRaLtQFHANcvWbLk0oULF+7IRVH5HMS1ptwuy9I1S1Nut/U4S0REREQk38Qi0NHo9n+LRdyY8bqwrbACvPGn9sYDhZVQXAsFgZyVKyIiIiIyGnQL4YzP55vg9XpLUfAmko+MMcbn9XrLvF5vmbU2HA6HN1prxwG/WLJkyRULFy5sGe6i8jmISySTBphujCm31jbs7cWMMeW4dsTEJn05ST5FRERERAYkGnbdbx1NYGNuzOuDYIUL4RJ7vXkK4vu/VSdDORERERERGazPEQ/h/H7/JI/HUwIQCAQ6SktLm0KhUGtBQUHU4/HEclumiMRiMU84HPY1NjaWNjc3l8ViMb/f75/a2dm5FpgNfA/42nDXlc+v0JfFv1vAB1wG/GwQ17ssfp3ENZf1MldEREREJLe62qG9DjpTPqxXEHTdb4HS5OdvC4Ju+cnCimQoJyIiIiIigxbvhjsbwOfzTfB4PCXGGDt58uSNpaWlrX2cLiLDLxoIBLpKSkraurq6dq5du3Z6V1eXPxAITOns7FwHHL9kyZKShQsXNg9nUXn7St1auwpYHb9rgB8YYw7am2sZYw4EfkCyG25N/PoiIiIiIvnDWuhshob10LAhGcL5Q1A2BSqmQTAewvlLoHIm1O4PoSqFcCIiIiIi2bcQKMftCVcGoBBOZGTw+XzRadOmbTDGWGNMoTHGj8vEjhruWvL91fqvcW8zWCAEPGWMOW8gFzDGnAP8EyhKudavslumiIiIiMgg2JhbfrJ+LTRtga4OwLilJytmQNlk8MefzhZWQPW+UL2POy4iIiIiIkPlFACv15u6HKVCOJERIhAIdBUVFbVC8vcYWDTcdeTz0pQAvwSuAqbjArRy4F5jzHPAb4F/WGs3dD/JGDMVOAm4BDiWZAAHsAb43yGuW0RERESkb7GIC+DaG5L7v3m8ECyHwnK37xuA8UJRldsDrsCfo2JFRERERMacYwG8Xm8pQGlpaVNuyxGRgSouLm5pbW0t9ng8xcBu4MjhriGvgzhrbTje0fYMLoSzuFDtmPgXxpgm3H+8VlzXXBVQmnIZk3JeHXCutbZrmH4EEREREZF0kc74/m/NbjlKAK8/vv9bGXhMcixU40I4jzd39YqIiIiIjDFLlizx4N5rxhgTAAiFQuqGExlhioqK2gDiS1MCVCxZssSzcOHC2HDVkNdBHIC19nVjzGnA3cA0kp1tie3py+JfGU8nGcKtAz5krX1j6KoVEREREelFuNV1wIVTXr/7iuIBXPGeY6EaN25M+nVERERERGSoFRHf2skY4wUoKCiI5rQiERmwxO9t4vcYlxcV4pq7hqeG4XqgwbDWvmKMORD4EXAp7v8EIRnKZWLiX624ZSy/Za1tGdJCRURERES6sxY6m1wAF+mMDxoXvBVWgi+YnBsoheJaCJRkvJSIiIiIiAwbX8ptA+DxeIatg0ZEssMY0725C8CPgrh08RDt88aYbwMfB44HjgCmEP9kQlwM2Ai8CDwN3GmtbRzmckVERERkrItFoaPB7f8Wi7gx44Fgmet08yZe1xsoqoRQ7Z6hnIiIiIiIiIiMeCMmiEuIh2q/in8BYIwpA4qBFoVuIiIiIpJT0S7X/dbRCDb+gVlPgQvfguXg8STHiqohVJ0SyomIiIiIiIjIaDLigrhM4uGbAjgRERERyZ2u9vj+by1uOUoAb8B1uwVKk4tgeANu+cnCymQoJyIiIiIiIiKj0qgI4kREREREcibcAm11LohL8IdcB5w/lDJWDKEaKCwf9hJFREREREREJDcUxImIiIiIDJSNQUeT64CLhuODxnW+FVVAQSA5N1juOuBSQzkRERERERERGRMUxImIiIiI9FcsAu0N0NEAsagbM17X5VZY7vZ9AzAeKKpyHXCpoZyIiIiIiIiIjCkK4kRERERE+hLpdN1vnc2uGw7A64dgBQTLwBPfAM7jc+FbqBo83tzVKyIiIiIiIiJ5ISdBnDHmH7l43BTWWntyjmsQERERkXwXboP2Ogi3JscKCt3yk/4SMCljxbVuXzhjMl5KRERERERERMaeXHXEnQDYHD22yeFji4iIiEi+s9Z1vrXXQ6QjOR4ohmAl+AtTxkpdB1ywdPjrFBERERERERkmdXV1nhdeeKHo3XffDezevbugo6PDU1JSEq2oqIhOmTIlvGjRoraqqqporuvMR1qaUkREREQE3J5vHY1u/7dolxszHhe2FVWC1xefaFznW3Et+Ap7upqIiIiIiIiMIZMmTTpwy5Yt/u7jP/nJT9Z/7Wtf27U31/zlL39Z9cUvfnF69/GJEyeGN2/e/Fp/a3nooYdWnXXWWc0Dffy6ujrP9ddfX33vvfdWrly5MhSLxXqca4xh5syZHWeffXb9pz71qd1z587tHOjjjVaeHD62ydGXiIiIiEhStAtadkDdGmjd6e57vG6ft8qZUDLOhXDGC8XjYNxcqJimEE5ERERERET6dOedd1bv7bl/+MMfqrJZS391dXXx3e9+d9y0adMO+t73vjdlxYoVvYZwANZaVq9eHbzuuusmzJs3b96pp54667XXXgsMU8l5LScdcdbaXAaAIiIiIiJu2cm2egg3u+UoAbwB1+0WKAWPSY6FaqCoCjx6GisiIiIiIiL9t2zZstDrr78eGGiH2KpVq/wvv/xyyVDV1ZMNGzYUnH/++bOWLl1anOl4IBCwlZWVXZWVlZHOzk7Pzp07fY2Njd7u85544onyBQsWlD311FNvHnPMMe1DX3n+0tKUIiIiIjK2hFtcANfVlhzzhaCoHPzFe44V10CwHIwWVhAREREREZH+83g8JLrIbrnllqqf//znWwZy/s0331xl4x8aTb3WUFq9erXvpJNO2nfDhg17dLKVlJREL7roop3nnntuw0knndRaULBntLR7927vPffcU/bwww+XPfbYYxWRSMQARCIRU19fP+ZzKH2kV0RERERGP2vd3m/166BxczyEM67zrXwalE9OhnDBMqiaDTVzXHecQjgREREREREZoCOOOOK9PdnuvvvuqoEGaXfdddd7y1IeccQRTVksLaPOzk5zwQUXzOoewn30ox/dtWrVqtduuOGGzaeeempaCAdQVVUVvfLKK+seeuihtcuXL3/9zDPPrDd6Lf2enARxxpg18a/VxpijclGDiIiIiIwBsQi07Xb7vzVvh0in2+utsNLt/1Y6AXxBMB4oqobaA9x4IOMKHCIiIiIiIiL98vGPf3x34vbmzZv9f/vb3/r9QvPxxx8PpQZiF1544e7e5mfD5z//+UkrVqwIpY594xvf2HznnXeuHz9+fLS/15k7d27nww8/vOb2229fHQqFhr6NbwTIVUfc9JSvHne5V2AnIiIiInslGoaW7VC3Flp3uUDO64NQrQvaimvAWwAeH5RMgNq5UD4FCrSPtIiIiIiIiAzeBRdc0FhRURFJ3L/11lurepuf6ne/+1114nZlZWXkggsuGNKOuBdeeKHwlltuGZc69vGPf3znj3/84217e81PfOITDU8//fSbU6ZMCQ++wpEtl2tz2n7MmZ4yt8fATkREREQEcEtOttdDZ0tyrCDolpgMlIJJGQvVunGPVmsXERERERGR7PL7/fa8886r+93vflcL8Oijj1a0tbVtKCoq6jUbaWtrMw8//HBF4v55551X5/P5+pOn7LUf//jH4xP70QFMnDgxfOONN24a7HUXLlzYMdhrjAa5etdhIP9ohvQfmIiIiIiMcNZCZzM0rIeGjckQzh+CsilQMQ2C8RDOX+I64mr3h1CVQjgREREREREZMpdddtl7S0q2tLR477jjjvK+zrnjjjvKW1pavCnX2DVE5QGwevVq36OPPlqROnb11VdvLysr07KSWZKrdx6aSH4euaK3iSIiIiIiGdmY636rXwtNW6CrAzAQLIOKGVA2GfxFbqywAqr3hep93HERERERERGRIbZo0aK22bNntyfu33HHHdW9ze8+Z/bs2e3HHHNMe2/zB+uee+4pj0QiibyGQCBgP/3pTw/5nnRjSa6CuK0pt4/LUQ0iIiIiMhLFItC6E3avhpYdEO0CjxeKqqBqJpSMhwI/GK9bfrL2AKiYHg/lRERERERERIbPRz7ykfdCreeff750w4YNPW4Ztn79et/zzz9fmrj/0Y9+dMgDsWeffbY49f6hhx7aXFNTEx3qxx1LchXEvRD/boArjTGfNMZ4eztBRERERMa4SCc0b4W6NdBW5zrivH4oHgcVsyBUDZ4CN1Y6CcbNhbJJLpQTERERERERyYFPfepTdV6viz+i0Si/+c1vqnqae8stt1RGoy4D83q9XH755XVDXd/LL79cknp//vz5bUP9mGNNj8nrELsVuBS3/5sf+B1wvTHmHdyylZn2hbvWGFOfpce31tqTs3QtERERERlK4Va3BGW4NTnmK3LLTQaK9xwL1bhxY9KvIyIiIiIiMky6ojE21LXpU4FDbGplUdjnze+9v6dNm9Z19NFHNy1evLgU4E9/+lPVD37wg+2Z5v7pT396b1nKY445pnHatGldQ1lbU1OTZ/v27b7UsQULFiiIy7KcBHHW2sXGmDuBj+FCNwMUA/NJD+ES76IclKWHNxkeQ0RERETyibXQ2eQCuEhnfNC44K2wEnzB5NxAKRTXQqAk46VERERERESG24a6Nv/J1z59YK7rGO2e/Mrxr82qKQ7nuo6+XHjhhbsTQdyqVasKn3/++cKjjz56j73fFi9eXPTuu+8GU88Z6rp27NiRtlLh+PHjhzT8G4tyGRVfCtxCMmgDBWQiIiIiY1ssCm273fKTzdtcCGc8rsutcgaUToyHcMbtCVezP1TNUggnIiIiIiIieevCCy+sLy4ufm/ftd/+9rdpy1OmLllZXFwc/cQnPtEw1HXt3LkzrVmrsrJS+8NlWc6COGtt2Fr7aWAe8N/AM8B2oBMXznVfT8hk6UtERERE8k20C1p2uACudRfEIm6/t1ANVM5yHW9enxsrHg/j5kH51D0740RERERERETyUHFxsT3zzDPf23rrL3/5S1UkEnnveGdnp3nggQcqE/fPOuus+qKioiFvXGpqakrriCsuLo4N9eOONTlfPNVa+4a19pvW2hOstROttUXWWo+1NlGbjX+dkhjPwlfaPy4RERERyYGudmja4gK49nqwMfAGoGSCC+CKKsHjcWNlU6B2LpROAG+utjoWERERERERGbhLLrnkvaUmd+/eXXDvvfeWJe7fddddZQ0NDQWZ5g6l0tLStO63lpaWnOdGo43+g4qIiIjI8OtshoYN7quz2Y35Q1A2GSqnQ7DUrWXgL4aKGTDuAAhVu1BOREREREREZIQ59dRTWyZPnpzYBJ3bb7+9KtPtKVOmdJ522mktw1FTTU1NpPtYfX29GpmybCS8k6HlJEVERERGAxuD9gaoW+u64LraAQOBMqiY7kI4f8jNDZZD9Ryong2F5TkrWURERERERCQbPB4PH/rQh+oS95988sny3bt3e7du3Vrw1FNPvdcd96EPfWhYuuEAamtr0zritm3bpiVosizf/4PelnJ7S86qEBEREZG9F4u4AK6jAWLx5/jG6wK2wnK37xuA8UBRldsXriCQm1pFRERERESyYGplUfjJrxz/Wq7rGO2mVhaFc13DQHzqU5/add11102w1tLZ2WluvfXWio6ODk8kEjEAxhiuuOKKYQviSktLY+PGjevavn27LzG2dOnSoksuuaRhuGoYC/I6iLPWXprrGkRERERkL0U63b5vnU1g43tMe/0QrIBgGXjiCx94fC58C1WDRytgiIiIiIjIyOfzephVUzyiQiIZegcccEB4wYIFLUuWLCkG+OMf/1jV2dn53sqFCxcubNlvv/2G9d/NYYcd1vzwww9XJu4vW7YsNJyPPxaMhKUpRURERGQkCbdB4yaoXwcdjS6EKyiE0oluv7eichfCFRRC+TQYNxdKximEExERERERkVHv4x//+Hsdb0uXLi1+/fXXizIdGy6LFi3aYz+6V155pXjXrl16gZ5FoyKIM8YUG2MmGGOKc12LiIiIyJhkLXQ0Qf16aNwI4VY3HiiGsqlQMRUCJW7330ApVM6C2v2gqBKMtgQWERERERGRseGSSy6pCwaDse7jwWAwdvHFF9dlOmcoffCDH2zwer02cb+jo8Nz8803V/Z2jgzMiAvijDElxpgrjTF/MMasNsaEgUZgE9BojAkbY941xtxhjLlC4ZyIiIjIEIpFoa0O6tdC81aIdLi93oLlUDkTSieBvxAwUFgJNftB1SwIlua6chEREREREZFhV1lZGTvllFMauo+fcsopDZWVlWkB3VCbNWtW1xlnnFGfOnbDDTeMb2pqGnH5Ub4aMf8hjTFFxpifAZuBG4GPAjNw+9yZlK8CYCbwMeAmYLMx5qfGmMKcFC4iIiIyGkW7oGUH1K2B1p3uvqfA7fNWOdMtNen1gfFC8Ti3/GTFNPDpKZmIiIiIiIiMbRdffHHaEpSXXHLJsC9LmfCNb3xjW+r9zZs3+z/3uc9NHux1X3/99cCKFSsCg73OSDcigjhjzMHAcuCLQDEucAOwvXwRn1cCfBlYHr+OiIiIiOytSAc0bXUdcO31YGPgDUDxeLfcZFGV2+vNG4DSyTBuntsbzuvLdeUiIiIiIiIieeGCCy5oamhoWJb6df755zflqp5jjjmm/bLLLtuROvb73/++5jvf+c64vb3mfffdV3rsscfut2HDBv/gKxzZCnJdQF+MMfsCfweq4kOWZPcbQDOwG2gFQvF5JSmXSMyfDTxhjDnGWvvOMJQuIiIiMnqEW6CtHrrakmO+EBSVg794z7HiGrc0pfZ+ExEREREREUnj8XgoKysb9mUoe3PDDTdseumll4pff/31osTYf/zHf0xes2ZN4LrrrttcU1MT7c91Nm7cWPD1r3990p///Odqa23fJ4wBed0RZ4zxAQ/iwrVEp5sBXgQ+Dcyy1pZZa2daaw+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\n",
+      "text/plain": [
+       "<Figure size 1500x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(5,4), dpi=300)\n",
+    "ax = sns.lineplot(data=data, x='Inoculum_size', y='lethal_concentration', hue='measurement', style='measurement')\n",
+    "sns.scatterplot(\n",
+    "    ax=ax, \n",
+    "    data=data, \n",
+    "    x='Inoculum_size', \n",
+    "    y='lethal_concentration', \n",
+    "    hue='measurement', \n",
+    "    style='measurement', \n",
+    ")\n",
+    "ax.set_ylabel('ciprofloxacin, ng/ml')\n",
+    "ax.set_xscale('log')\n",
+    "ax.legend(loc=(1.1,0))\n",
+    "fig.savefig('MBC.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "l = ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0, 0, 'MIC'),\n",
+       " Text(0, 0, 'MBC'),\n",
+       " Text(0, 0, 'chip'),\n",
+       " Text(0, 0, 'MIC'),\n",
+       " Text(0, 0, 'MBC'),\n",
+       " Text(0, 0, 'chip')]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(l.get_texts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "l.set_"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/droplet_growth/register.py b/droplet_growth/register.py
index 4bbffd2414e455dd44a303d423f80d9127752e5c..6d2d927b4468838b5db6c7e2af393c9f93a3c45e 100644
--- a/droplet_growth/register.py
+++ b/droplet_growth/register.py
@@ -25,7 +25,7 @@ META_ALIGNED = {'ImageJ': '1.53c',
  'LUTs': [grey, green, blue]
  }
 
-def align_stack(data_or_path, template16, mask2, plot=False, path=None, binnings=(1,16,2), suffix='.aligned.tif'):
+def align_stack(data_or_path, template16, mask2, plot=False, path_to_save=None, binnings=(1,16,2), suffix='.aligned.tif'):
     '''
     stack should contain two channels: bright field and fluorescence.
     BF will be binned 8 times and registered with template8 (aligned BF).
@@ -44,9 +44,11 @@ def align_stack(data_or_path, template16, mask2, plot=False, path=None, binnings
         print(path, stack.shape)
     else:
         assert data_or_path.ndim == 3 and data_or_path.shape[0] == 2
-        assert path is not None
+        assert path_to_save is not None, "Please provide the path to save the data"
         stack = data_or_path
 
+    print(f'Aligned stack will be saved to {path_to_save}')
+
     bf, tritc = stack[:2]
     stack_temp_scale = binnings[1] // binnings[0]
     mask_temp_scale = binnings[1] // binnings[2]
@@ -64,7 +66,7 @@ def align_stack(data_or_path, template16, mask2, plot=False, path=None, binnings
     tvec8 = get_transform(f_bf, template16, plot=plot)
     plt.show()
     tvec = scale_tvec(tvec8, mask_temp_scale)
-    print(path, tvec)
+    print(tvec)
     try:
         aligned_tritc = unpad(transform(tritc[::stack_mask_scale, ::stack_mask_scale], tvec), mask2.shape)
         aligned_bf = unpad(transform(bf[::stack_mask_scale, ::stack_mask_scale], tvec), mask2.shape)
@@ -87,12 +89,12 @@ def align_stack(data_or_path, template16, mask2, plot=False, path=None, binnings
 
     aligned_stack = np.stack((aligned_bf, aligned_tritc, mask2)).astype('uint16')
 
-    imwrite((p:=path.replace('.nd2', suffix)), aligned_stack, imagej=True, metadata=META_ALIGNED)
-    print(f'Saved aligned stack {p}')
-    return aligned_stack
+    imwrite(path_to_save, aligned_stack, imagej=True, metadata=META_ALIGNED)
+    print(f'Saved aligned stack {path_to_save}')
+    return aligned_stack, tvec
 
 
-def align_stack_nd(stack, template16, mask2, path=None, plot=False, binnings=(1,16,2), suffix='.aligned.tif')  -> Tuple[np.ndarray, dict]:
+def align_stack_nd(stack, template16, mask2, path=None, plot=False, binnings=(1,16,2), suffix='.aligned.tif'):
     '''
     stack should contain two channels: bright field and fluorescence.
     BF will be binned 8 times and registered with template8 (aligned BF).
@@ -277,7 +279,7 @@ def register(image, template):
     '''
     assert np.array_equal(image.shape, template.shape), \
         f'unequal shapes {(image.shape, template.shape)}'
-    return reg.similarity(template, image, constraints={'scale': [1,0.2], 'tx': [0, 500], 'ty': [0, 50], 'angle': [0, 30]})
+    return reg.similarity(template, image, constraints={'scale': [1,0.2], 'tx': [0, 500], 'ty': [0, 500], 'angle': [0, 30]})
 
 
 def filter_by_fft(