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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "id": "6a60ccd0-ba9e-4406-a0e2-370d930af671",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: [2024-06-05 08:44:45] indra_cogex.client.neo4j_client - Using configured URL for INDRA neo4j connection\n",
+      "INFO: [2024-06-05 08:44:45] indra_cogex.client.neo4j_client - Using configured credentials for INDRA neo4j connection\n"
+     ]
+    }
+   ],
+   "source": [
+    "from indra_cogex.client import Neo4jClient\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import json\n",
+    "client = Neo4jClient()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee760a68-b717-4d4d-9068-88bc056793de",
+   "metadata": {},
+   "source": [
+    "## **Create a Histogram to Find the Top 20 Journals Researching Cancer**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22561c44-0e63-4ba7-8992-c8db7f7dea1b",
+   "metadata": {},
+   "source": [
+    "This code finds the top 20 journals researching lung cancer by finding the ratio of publications mentioning lung cancer to total publications for each journal, and creating a histogram to visualize it. I selected the second journal because it overall had more publications as compared to the first result despite having a slightly lower ratio. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "913c9451-b3e4-4a93-a98c-de939529573e",
+   "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>name</th>\n",
+       "      <th>id</th>\n",
+       "      <th>Total Pub</th>\n",
+       "      <th>Lung Pub</th>\n",
+       "      <th>Ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Clinical Lung Cancer</td>\n",
+       "      <td>nlm:100893225</td>\n",
+       "      <td>2138</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Lung Cancer</td>\n",
+       "      <td>nlm:8800805</td>\n",
+       "      <td>6770</td>\n",
+       "      <td>6300</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Journal of Thoracic Oncology</td>\n",
+       "      <td>nlm:101274235</td>\n",
+       "      <td>5480</td>\n",
+       "      <td>4801</td>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>None</td>\n",
+       "      <td>nlm:101126433</td>\n",
+       "      <td>1995</td>\n",
+       "      <td>1707</td>\n",
+       "      <td>85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Thoracic Cancer</td>\n",
+       "      <td>nlm:101531441</td>\n",
+       "      <td>2465</td>\n",
+       "      <td>1667</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>None</td>\n",
+       "      <td>nlm:0413533</td>\n",
+       "      <td>12377</td>\n",
+       "      <td>1788</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>None</td>\n",
+       "      <td>nlm:7810034</td>\n",
+       "      <td>21263</td>\n",
+       "      <td>2211</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Journal of Clinical Oncology</td>\n",
+       "      <td>nlm:8309333</td>\n",
+       "      <td>25917</td>\n",
+       "      <td>2446</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>International Journal of Radiation Oncology Bi...</td>\n",
+       "      <td>nlm:7603616</td>\n",
+       "      <td>24193</td>\n",
+       "      <td>2258</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>None</td>\n",
+       "      <td>nlm:8102988</td>\n",
+       "      <td>24442</td>\n",
+       "      <td>2218</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Clinical Cancer Research</td>\n",
+       "      <td>nlm:9502500</td>\n",
+       "      <td>20160</td>\n",
+       "      <td>1877</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>The Annals of Thoracic Surgery</td>\n",
+       "      <td>nlm:15030100R</td>\n",
+       "      <td>39122</td>\n",
+       "      <td>3295</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>British Journal of Cancer</td>\n",
+       "      <td>nlm:0370635</td>\n",
+       "      <td>24814</td>\n",
+       "      <td>2028</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Cancer Research</td>\n",
+       "      <td>nlm:2984705R</td>\n",
+       "      <td>53264</td>\n",
+       "      <td>3828</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Cancer</td>\n",
+       "      <td>nlm:0374236</td>\n",
+       "      <td>43548</td>\n",
+       "      <td>3399</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Chest</td>\n",
+       "      <td>nlm:0231335</td>\n",
+       "      <td>36857</td>\n",
+       "      <td>2908</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>International Journal of Cancer</td>\n",
+       "      <td>nlm:0042124</td>\n",
+       "      <td>26247</td>\n",
+       "      <td>1923</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Journal of the National Cancer Institute</td>\n",
+       "      <td>nlm:7503089</td>\n",
+       "      <td>22678</td>\n",
+       "      <td>1672</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>The Journal of Thoracic and Cardiovascular Sur...</td>\n",
+       "      <td>nlm:0376343</td>\n",
+       "      <td>30671</td>\n",
+       "      <td>2097</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>PLOS One</td>\n",
+       "      <td>nlm:101285081</td>\n",
+       "      <td>278159</td>\n",
+       "      <td>2346</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 name             id  \\\n",
+       "0                                Clinical Lung Cancer  nlm:100893225   \n",
+       "1                                         Lung Cancer    nlm:8800805   \n",
+       "2                        Journal of Thoracic Oncology  nlm:101274235   \n",
+       "3                                                None  nlm:101126433   \n",
+       "4                                     Thoracic Cancer  nlm:101531441   \n",
+       "5                                                None    nlm:0413533   \n",
+       "6                                                None    nlm:7810034   \n",
+       "7                        Journal of Clinical Oncology    nlm:8309333   \n",
+       "8   International Journal of Radiation Oncology Bi...    nlm:7603616   \n",
+       "9                                                None    nlm:8102988   \n",
+       "10                           Clinical Cancer Research    nlm:9502500   \n",
+       "11                     The Annals of Thoracic Surgery  nlm:15030100R   \n",
+       "12                          British Journal of Cancer    nlm:0370635   \n",
+       "13                                    Cancer Research   nlm:2984705R   \n",
+       "14                                             Cancer    nlm:0374236   \n",
+       "15                                              Chest    nlm:0231335   \n",
+       "16                    International Journal of Cancer    nlm:0042124   \n",
+       "17           Journal of the National Cancer Institute    nlm:7503089   \n",
+       "18  The Journal of Thoracic and Cardiovascular Sur...    nlm:0376343   \n",
+       "19                                           PLOS One  nlm:101285081   \n",
+       "\n",
+       "    Total Pub  Lung Pub  Ratio  \n",
+       "0        2138      2012     94  \n",
+       "1        6770      6300     93  \n",
+       "2        5480      4801     87  \n",
+       "3        1995      1707     85  \n",
+       "4        2465      1667     67  \n",
+       "5       12377      1788     14  \n",
+       "6       21263      2211     10  \n",
+       "7       25917      2446      9  \n",
+       "8       24193      2258      9  \n",
+       "9       24442      2218      9  \n",
+       "10      20160      1877      9  \n",
+       "11      39122      3295      8  \n",
+       "12      24814      2028      8  \n",
+       "13      53264      3828      7  \n",
+       "14      43548      3399      7  \n",
+       "15      36857      2908      7  \n",
+       "16      26247      1923      7  \n",
+       "17      22678      1672      7  \n",
+       "18      30671      2097      6  \n",
+       "19     278159      2346      0  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def get_df2():\n",
+    "\n",
+    "    # cypher query to get the journal, name and publications\n",
+    "    # pubmed entries w mesh annotations, rel: annotated_with, find the mesh term for lung cancer and look up the pubmed ids that are \n",
+    "    # annotated w that term and look for the journals \n",
+    "    #  calculate the ratio, # publications about lung cancer/total publications to get who is publishing the most -> normalize the count\n",
+    "    \n",
+    "    cypher = \"\"\"MATCH (n:BioEntity)<-[r:annotated_with]-(pub:Publication)-[:published_in]->(j:Journal) WHERE n.id = \"mesh:D008175\"\n",
+    "    WITH j, count (distinct pub) as LungPubs ORDER BY count (distinct pub) desc LIMIT 20\n",
+    "    MATCH (n1:BioEntity)<-[:annotated_with]-(pub1:Publication)-[:published_in]->(j:Journal) \n",
+    "    RETURN j.name, j.id, count(distinct pub1) as AllPubs, LungPubs, LungPubs*100/count(distinct pub1) as Ratio \n",
+    "    ORDER BY LungPubs*100/count(distinct pub1) DESC \"\"\"\n",
+    "\n",
+    "    results = client.query_tx(cypher)\n",
+    "    # loads the results into a dataframe \n",
+    "    df = pd.DataFrame(results, columns=[\"name\", \"id\", \"Total Pub\", \"Lung Pub\", \"Ratio\"])\n",
+    "    \n",
+    "    return df\n",
+    "    \n",
+    "df = get_df2()\n",
+    "df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "id": "1b7fbf7c-a699-4ce9-b98b-39fa25b2119b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>level_0</th>\n",
+       "      <th>index</th>\n",
+       "      <th>name</th>\n",
+       "      <th>id</th>\n",
+       "      <th>Total Pub</th>\n",
+       "      <th>Lung Pub</th>\n",
+       "      <th>Ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Clinical Lung Cancer</td>\n",
+       "      <td>nlm:100893225</td>\n",
+       "      <td>2138</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Lung Cancer</td>\n",
+       "      <td>nlm:8800805</td>\n",
+       "      <td>6770</td>\n",
+       "      <td>6300</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Journal of Thoracic Oncology</td>\n",
+       "      <td>nlm:101274235</td>\n",
+       "      <td>5480</td>\n",
+       "      <td>4801</td>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Thoracic Cancer</td>\n",
+       "      <td>nlm:101531441</td>\n",
+       "      <td>2465</td>\n",
+       "      <td>1667</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>Clinical Cancer Research</td>\n",
+       "      <td>nlm:9502500</td>\n",
+       "      <td>20160</td>\n",
+       "      <td>1877</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Journal of Clinical Oncology</td>\n",
+       "      <td>nlm:8309333</td>\n",
+       "      <td>25917</td>\n",
+       "      <td>2446</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>International Journal of Radiation Oncology Bi...</td>\n",
+       "      <td>nlm:7603616</td>\n",
+       "      <td>24193</td>\n",
+       "      <td>2258</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>11</td>\n",
+       "      <td>The Annals of Thoracic Surgery</td>\n",
+       "      <td>nlm:15030100R</td>\n",
+       "      <td>39122</td>\n",
+       "      <td>3295</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>12</td>\n",
+       "      <td>British Journal of Cancer</td>\n",
+       "      <td>nlm:0370635</td>\n",
+       "      <td>24814</td>\n",
+       "      <td>2028</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Cancer Research</td>\n",
+       "      <td>nlm:2984705R</td>\n",
+       "      <td>53264</td>\n",
+       "      <td>3828</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>14</td>\n",
+       "      <td>Cancer</td>\n",
+       "      <td>nlm:0374236</td>\n",
+       "      <td>43548</td>\n",
+       "      <td>3399</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Chest</td>\n",
+       "      <td>nlm:0231335</td>\n",
+       "      <td>36857</td>\n",
+       "      <td>2908</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12</td>\n",
+       "      <td>16</td>\n",
+       "      <td>International Journal of Cancer</td>\n",
+       "      <td>nlm:0042124</td>\n",
+       "      <td>26247</td>\n",
+       "      <td>1923</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>17</td>\n",
+       "      <td>Journal of the National Cancer Institute</td>\n",
+       "      <td>nlm:7503089</td>\n",
+       "      <td>22678</td>\n",
+       "      <td>1672</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14</td>\n",
+       "      <td>18</td>\n",
+       "      <td>The Journal of Thoracic and Cardiovascular Sur...</td>\n",
+       "      <td>nlm:0376343</td>\n",
+       "      <td>30671</td>\n",
+       "      <td>2097</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>19</td>\n",
+       "      <td>PLOS One</td>\n",
+       "      <td>nlm:101285081</td>\n",
+       "      <td>278159</td>\n",
+       "      <td>2346</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    level_0  index                                               name  \\\n",
+       "0         0      0                               Clinical Lung Cancer   \n",
+       "1         1      1                                        Lung Cancer   \n",
+       "2         2      2                       Journal of Thoracic Oncology   \n",
+       "3         3      4                                    Thoracic Cancer   \n",
+       "4         4     10                           Clinical Cancer Research   \n",
+       "5         5      7                       Journal of Clinical Oncology   \n",
+       "6         6      8  International Journal of Radiation Oncology Bi...   \n",
+       "7         7     11                     The Annals of Thoracic Surgery   \n",
+       "8         8     12                          British Journal of Cancer   \n",
+       "9         9     13                                    Cancer Research   \n",
+       "10       10     14                                             Cancer   \n",
+       "11       11     15                                              Chest   \n",
+       "12       12     16                    International Journal of Cancer   \n",
+       "13       13     17           Journal of the National Cancer Institute   \n",
+       "14       14     18  The Journal of Thoracic and Cardiovascular Sur...   \n",
+       "15       15     19                                           PLOS One   \n",
+       "\n",
+       "               id  Total Pub  Lung Pub  Ratio  \n",
+       "0   nlm:100893225       2138      2012     94  \n",
+       "1     nlm:8800805       6770      6300     93  \n",
+       "2   nlm:101274235       5480      4801     87  \n",
+       "3   nlm:101531441       2465      1667     67  \n",
+       "4     nlm:9502500      20160      1877      9  \n",
+       "5     nlm:8309333      25917      2446      9  \n",
+       "6     nlm:7603616      24193      2258      9  \n",
+       "7   nlm:15030100R      39122      3295      8  \n",
+       "8     nlm:0370635      24814      2028      8  \n",
+       "9    nlm:2984705R      53264      3828      7  \n",
+       "10    nlm:0374236      43548      3399      7  \n",
+       "11    nlm:0231335      36857      2908      7  \n",
+       "12    nlm:0042124      26247      1923      7  \n",
+       "13    nlm:7503089      22678      1672      7  \n",
+       "14    nlm:0376343      30671      2097      6  \n",
+       "15  nlm:101285081     278159      2346      0  "
+      ]
+     },
+     "execution_count": 199,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sorts the dataframe to get the top publications\n",
+    "# Some of the journals were in another langauge causing the results to be null, and therefore needed to be dropped\n",
+    "df = df.sort_values(by = \"Ratio\", ascending = False)\n",
+    "df = df.dropna(axis = 0)\n",
+    "df = df.reset_index()\n",
+    "\n",
+    "# plots a histogram of the top 16 publications \n",
+    "def plot_histogram2():\n",
+    "    plt.figure(figsize=(8, 8))\n",
+    "    plt.title(\"Top 16 Journals Writing About Lung Cancer\")\n",
+    "    plt.bar(df[\"name\"].values, df[\"Ratio\"].values)\n",
+    "    plt.xlabel(\"Journal Names\")\n",
+    "    plt.ylabel(\"Frequency\")\n",
+    "    plt.xticks(rotation=90)\n",
+    "    plt.show()\n",
+    "    \n",
+    "plot_histogram2()\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04f8da63-32ce-4ade-a8bf-efe002e38767",
+   "metadata": {},
+   "source": [
+    "## **Finding Novel Genes**\n",
+    "**This code finds all of the genes relating to lung cancer in the database, and all the genes in the journal \"Lung Cancer: Journal of the International Association for the Study of Lung Cancer\", and finds the genes listed in the database that are not mentioned in the journal, the genes mentioned in the journal that are not listed in the database, and the common genes. Based off the genes listed in the database that were not mentioned in the journal, I manually looked up the genes and saw how many PubMed articles linked that specific gene to lung cancer, to identify genes that could be further studied to explore their correlation to lung cancer.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "0f8efb86-2e03-4f98-8ad5-9c50794aade0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_gene_lists():\n",
+    "    \n",
+    "    # cypher queries to get genes from the journal and database relating to lung cancer\n",
+    "    journal_cypher = '''MATCH p3= (gene1:BioEntity)-[r:indra_rel]->(gene2:BioEntity) \n",
+    "    MATCH p4=(e:Evidence)-[:has_citation]->(pub:Publication)-[]->(j:Journal) \n",
+    "    WHERE gene1.id <> gene2.id AND e.stmt_hash = r.stmt_hash AND j.id='nlm:8800805' unwind [gene1, gene2] as gene \n",
+    "    RETURN gene.name, gene.id, count(r)\n",
+    "    ORDER BY count(r) desc'''\n",
+    "    database_cypher = \"\"\"MATCH p1=(gene1)-[:gene_disease_association]->(subdisease)-[:isa*0..]->(disease) WHERE disease.id in [\"mesh:D008175\",'doid:3905']\n",
+    "RETURN distinct gene1.name, gene1.id\"\"\"\n",
+    "    \n",
+    "    journal_results = client.query_tx(journal_cypher)\n",
+    "    database_results = client.query_tx(database_cypher)\n",
+    "\n",
+    "    # loads the results into 2 dataframes\n",
+    "    # returns the name, gene id, and number of indra statements for each gene in the journal \n",
+    "    journal_df = pd.DataFrame(journal_results, columns=[\"name\", \"gene_id\", \"# indra statements\"])\n",
+    "    database_df = pd.DataFrame(database_results, columns=[\"name\", \"gene_id\"])\n",
+    "\n",
+    "    return journal_df, database_df\n",
+    "    \n",
+    "journal_df, database_df = get_gene_lists()\n",
+    "\n",
+    "# for the journal, filters for genes starting with hgnc to match with the database\n",
+    "journal_df = journal_df[journal_df[\"gene_id\"].str.startswith(\"hgnc\")]\n",
+    "# creates a \"novel\" dataframe which contains the genes present in the journal but not in the database\n",
+    "novel_df = journal_df[~journal_df[\"gene_id\"].isin(database_df[\"gene_id\"])]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "1ed82441-71cd-47e5-b1ff-a16120f3f4b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Creates a histogram to display the top 50 genes that were present in the journal but not the database\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(novel_df[\"name\"].head(50), novel_df[\"# indra statements\"].head(50))\n",
+    "plt.xlabel(\"Gene Name\")\n",
+    "plt.ylabel(\"Number of Indra Statements\")\n",
+    "plt.title(\"Top 50 Database-Absent Genes Reported in Journal\")\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "id": "47082e3d-e440-4015-8043-2ce8371ca69d",
+   "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>name</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th># indra statements</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>MUC16</td>\n",
+       "      <td>hgnc:15582</td>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>CDH1</td>\n",
+       "      <td>hgnc:1748</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>PDCD1</td>\n",
+       "      <td>hgnc:8760</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>SRC</td>\n",
+       "      <td>hgnc:11283</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>PPARG</td>\n",
+       "      <td>hgnc:9236</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>TACC3</td>\n",
+       "      <td>hgnc:11524</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>EGF</td>\n",
+       "      <td>hgnc:3229</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>CISH</td>\n",
+       "      <td>hgnc:1984</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>BAX</td>\n",
+       "      <td>hgnc:959</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>FGF2</td>\n",
+       "      <td>hgnc:3676</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     name     gene_id  # indra statements\n",
+       "18  MUC16  hgnc:15582                  89\n",
+       "22   CDH1   hgnc:1748                  75\n",
+       "35  PDCD1   hgnc:8760                  51\n",
+       "40    SRC  hgnc:11283                  46\n",
+       "41  PPARG   hgnc:9236                  46\n",
+       "43  TACC3  hgnc:11524                  46\n",
+       "49    EGF   hgnc:3229                  40\n",
+       "50   CISH   hgnc:1984                  40\n",
+       "58    BAX    hgnc:959                  37\n",
+       "59   FGF2   hgnc:3676                  37"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "novel_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f6235a1-778a-4f75-bee2-9ebc38a3088f",
+   "metadata": {},
+   "source": [
+    "## Display Indra Statements\n",
+    "Wrote a cypher query to get the indra statements for a given gene, then use indra.assemblers.html to create an html page with the INDRA statements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "id": "e67a9467-0561-4c5b-a463-3e0c010da118",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_statements():\n",
+    "    # gets a list of the top 6 gene ids and names \n",
+    "    gene_ids = novel_df.head(6)[\"gene_id\"]\n",
+    "    gene_names = novel_df.head(6)[\"name\"]\n",
+    "    \n",
+    "    # initializes empty lists\n",
+    "    string_list =  []\n",
+    "    df_list = []\n",
+    "\n",
+    "    # for loop to iterate through each gene \n",
+    "    for id, name in zip(gene_ids, gene_names):\n",
+    "        # cypher to get the indra statements, uses f-string formatting for query to efficently test each gene\n",
+    "        indra_cypher = f\"MATCH p3= (gene1:BioEntity)-[r:indra_rel]->(gene2:BioEntity) MATCH p4=(e:Evidence)-[:has_citation]->(pub:Publication)-[]->(j:Journal) WHERE gene1.id <> gene2.id AND e.stmt_hash = r.stmt_hash AND j.id='nlm:8800805' AND (gene1.id = '{id}' OR gene2.id ='{id}') unwind [gene1, gene2] as gene RETURN gene.name, gene.id, r.stmt_json\"\n",
+    "        indra_results = client.query_tx(indra_cypher)\n",
+    "        \n",
+    "        # saves result in a dataframe that contains the name, id and indra statements in string json form\n",
+    "        indra_df = pd.DataFrame(indra_results, columns=[\"name\", \"gene_id\", \"indra statements\"])\n",
+    "        # the query returns \"double\" the results due to how it was written, so the dataframe needs to be filtered for the correct gene \n",
+    "        filtered_indra = indra_df[indra_df[\"name\"] == name]\n",
+    "        filtered_indra = filtered_indra.reset_index()\n",
+    "\n",
+    "        # returns a list of data frames and a nested list of indra statements as json strings \n",
+    "        df_list.append(filtered_indra)\n",
+    "        json_strings = list(filtered_indra[\"indra statements\"].values)\n",
+    "        string_list.append(json_strings)\n",
+    "        \n",
+    "    return df_list, string_list\n",
+    "df_list, string_list = get_statements()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "id": "2c52d61c-8421-43db-a325-8cae39423029",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MUC16\n",
+      "CDH1\n",
+      "PDCD1\n",
+      "SRC\n",
+      "PPARG\n",
+      "TACC3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# iterates through the gene name and json strings for each gene \n",
+    "for genes, json_strings in zip(gene_names, string_list):\n",
+    "    stmt_jsons = []\n",
+    "    # iterates through the individual json string within the statements for each gene \n",
+    "    # and converts it to an INDRA statement object\n",
+    "    for stmt_json_string in json_strings:\n",
+    "        stmt_jsons.append(json.loads(stmt_json_string))\n",
+    "    stmts = stmts_from_json(json_in=stmt_jsons)\n",
+    "\n",
+    "    # uses HtmlAssembler to get html pages of INDRA statements for each gene \n",
+    "    ha = HtmlAssembler(stmts, title='Statements for %s' % genes, db_rest_url='https://db.indra.bio')\n",
+    "    ha.save_model('%s_statements.html' % genes)\n",
+    "    print(genes)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/disease_associated_genes.ipynb b/notebooks/disease_associated_genes.ipynb
new file mode 100644
index 000000000..e4e2df949
--- /dev/null
+++ b/notebooks/disease_associated_genes.ipynb
@@ -0,0 +1,934 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6a60ccd0-ba9e-4406-a0e2-370d930af671",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: [2024-06-06 14:59:24] numexpr.utils - NumExpr defaulting to 8 threads.\n",
+      "INFO: [2024-06-06 14:59:25] indra_cogex.client.neo4j_client - Using configured URL for INDRA neo4j connection\n",
+      "INFO: [2024-06-06 14:59:25] indra_cogex.client.neo4j_client - Using configured credentials for INDRA neo4j connection\n"
+     ]
+    }
+   ],
+   "source": [
+    "from indra_cogex.client import Neo4jClient\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "client = Neo4jClient()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee760a68-b717-4d4d-9068-88bc056793de",
+   "metadata": {},
+   "source": [
+    "## **Create a Histogram to Find the Top 20 Journals Researching Cancer**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22561c44-0e63-4ba7-8992-c8db7f7dea1b",
+   "metadata": {},
+   "source": [
+    "This code finds the top 20 journals researching lung cancer by finding the ratio of publications mentioning lung cancer to total publications for each journal, and creating a histogram to visualize it. I selected the second journal because it overall had more publications as compared to the first result despite having a slightly lower ratio."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "913c9451-b3e4-4a93-a98c-de939529573e",
+   "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>name</th>\n",
+       "      <th>id</th>\n",
+       "      <th>Total Pub</th>\n",
+       "      <th>Lung Pub</th>\n",
+       "      <th>Ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Clinical Lung Cancer</td>\n",
+       "      <td>nlm:100893225</td>\n",
+       "      <td>2169</td>\n",
+       "      <td>2043</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Lung Cancer</td>\n",
+       "      <td>nlm:8800805</td>\n",
+       "      <td>6772</td>\n",
+       "      <td>6302</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Journal of Thoracic Oncology</td>\n",
+       "      <td>nlm:101274235</td>\n",
+       "      <td>5522</td>\n",
+       "      <td>4843</td>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Zhongguo fei ai za zhi = Chinese journal of lu...</td>\n",
+       "      <td>nlm:101126433</td>\n",
+       "      <td>1995</td>\n",
+       "      <td>1707</td>\n",
+       "      <td>85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Thoracic Cancer</td>\n",
+       "      <td>nlm:101531441</td>\n",
+       "      <td>2486</td>\n",
+       "      <td>1686</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Kyobu geka. The Japanese journal of thoracic s...</td>\n",
+       "      <td>nlm:0413533</td>\n",
+       "      <td>12377</td>\n",
+       "      <td>1788</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Gan to kagaku ryoho. Cancer &amp; chemotherapy</td>\n",
+       "      <td>nlm:7810034</td>\n",
+       "      <td>21263</td>\n",
+       "      <td>2211</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Journal of Clinical Oncology</td>\n",
+       "      <td>nlm:8309333</td>\n",
+       "      <td>25992</td>\n",
+       "      <td>2454</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>International Journal of Radiation Oncology Bi...</td>\n",
+       "      <td>nlm:7603616</td>\n",
+       "      <td>24320</td>\n",
+       "      <td>2276</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Anticancer research</td>\n",
+       "      <td>nlm:8102988</td>\n",
+       "      <td>24442</td>\n",
+       "      <td>2218</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Clinical Cancer Research</td>\n",
+       "      <td>nlm:9502500</td>\n",
+       "      <td>20263</td>\n",
+       "      <td>1887</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>The Annals of Thoracic Surgery</td>\n",
+       "      <td>nlm:15030100R</td>\n",
+       "      <td>39278</td>\n",
+       "      <td>3314</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>British Journal of Cancer</td>\n",
+       "      <td>nlm:0370635</td>\n",
+       "      <td>24852</td>\n",
+       "      <td>2031</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Cancer Research</td>\n",
+       "      <td>nlm:2984705R</td>\n",
+       "      <td>53303</td>\n",
+       "      <td>3832</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Cancer</td>\n",
+       "      <td>nlm:0374236</td>\n",
+       "      <td>43649</td>\n",
+       "      <td>3414</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Chest</td>\n",
+       "      <td>nlm:0231335</td>\n",
+       "      <td>36945</td>\n",
+       "      <td>2917</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>International Journal of Cancer</td>\n",
+       "      <td>nlm:0042124</td>\n",
+       "      <td>26286</td>\n",
+       "      <td>1925</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Journal of the National Cancer Institute</td>\n",
+       "      <td>nlm:7503089</td>\n",
+       "      <td>22744</td>\n",
+       "      <td>1673</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>The Journal of Thoracic and Cardiovascular Sur...</td>\n",
+       "      <td>nlm:0376343</td>\n",
+       "      <td>30956</td>\n",
+       "      <td>2131</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>PLOS One</td>\n",
+       "      <td>nlm:101285081</td>\n",
+       "      <td>278162</td>\n",
+       "      <td>2346</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 name             id  \\\n",
+       "0                                Clinical Lung Cancer  nlm:100893225   \n",
+       "1                                         Lung Cancer    nlm:8800805   \n",
+       "2                        Journal of Thoracic Oncology  nlm:101274235   \n",
+       "3   Zhongguo fei ai za zhi = Chinese journal of lu...  nlm:101126433   \n",
+       "4                                     Thoracic Cancer  nlm:101531441   \n",
+       "5   Kyobu geka. The Japanese journal of thoracic s...    nlm:0413533   \n",
+       "6          Gan to kagaku ryoho. Cancer & chemotherapy    nlm:7810034   \n",
+       "7                        Journal of Clinical Oncology    nlm:8309333   \n",
+       "8   International Journal of Radiation Oncology Bi...    nlm:7603616   \n",
+       "9                                 Anticancer research    nlm:8102988   \n",
+       "10                           Clinical Cancer Research    nlm:9502500   \n",
+       "11                     The Annals of Thoracic Surgery  nlm:15030100R   \n",
+       "12                          British Journal of Cancer    nlm:0370635   \n",
+       "13                                    Cancer Research   nlm:2984705R   \n",
+       "14                                             Cancer    nlm:0374236   \n",
+       "15                                              Chest    nlm:0231335   \n",
+       "16                    International Journal of Cancer    nlm:0042124   \n",
+       "17           Journal of the National Cancer Institute    nlm:7503089   \n",
+       "18  The Journal of Thoracic and Cardiovascular Sur...    nlm:0376343   \n",
+       "19                                           PLOS One  nlm:101285081   \n",
+       "\n",
+       "    Total Pub  Lung Pub  Ratio  \n",
+       "0        2169      2043     94  \n",
+       "1        6772      6302     93  \n",
+       "2        5522      4843     87  \n",
+       "3        1995      1707     85  \n",
+       "4        2486      1686     67  \n",
+       "5       12377      1788     14  \n",
+       "6       21263      2211     10  \n",
+       "7       25992      2454      9  \n",
+       "8       24320      2276      9  \n",
+       "9       24442      2218      9  \n",
+       "10      20263      1887      9  \n",
+       "11      39278      3314      8  \n",
+       "12      24852      2031      8  \n",
+       "13      53303      3832      7  \n",
+       "14      43649      3414      7  \n",
+       "15      36945      2917      7  \n",
+       "16      26286      1925      7  \n",
+       "17      22744      1673      7  \n",
+       "18      30956      2131      6  \n",
+       "19     278162      2346      0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def get_df2():\n",
+    "\n",
+    "    # cypher query to get the journal, name and publications\n",
+    "    \n",
+    "    # pubmed entries w mesh annotations, rel: annotated_with, find the mesh term for lung cancer and look up the pubmed ids that are \n",
+    "    # annotated w that term and look for the journals \n",
+    "    # could calculate the ratio, like number of pubs about this disease/total#pubs to get who is publishing the most -> normalize the count\n",
+    "    \n",
+    "    cypher = \"\"\"MATCH (n:BioEntity)<-[:annotated_with]-(pub:Publication)-[:published_in]->(j:Journal)\n",
+    "    WHERE n.id = \"mesh:D008175\"\n",
+    "    WITH j, count (distinct pub) as LungPubs ORDER BY count (distinct pub) desc LIMIT 20\n",
+    "    MATCH (n1:BioEntity)<-[:annotated_with]-(pub1:Publication)-[:published_in]->(j:Journal)\n",
+    "    RETURN j.name, j.id, count(distinct pub1) as AllPubs, LungPubs, LungPubs*100/count(distinct pub1) as Ratio \n",
+    "    ORDER BY LungPubs*100/count(distinct pub1) DESC \"\"\"\n",
+    "\n",
+    "    results = client.query_tx(cypher)\n",
+    "    # loads the results into a dataframe \n",
+    "    df = pd.DataFrame(results, columns=[\"name\", \"id\", \"Total Pub\", \"Lung Pub\", \"Ratio\"])\n",
+    "    \n",
+    "    return df\n",
+    "    \n",
+    "df = get_df2()\n",
+    "df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1b7fbf7c-a699-4ce9-b98b-39fa25b2119b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JCwuTMSZZaz53vkbw38Ixr8Bt0qVLp1WrVskY4xJgT548qfj4eKVPn97l/rcf13jrWEr/SNyPIkWKOI8bvZX5/ydj/N0Tv5IkzevYsWPJbjt69KjLYwwKCkp2UpWkO/aM/bvH6KVk5syZio2N1Q8//KDs2bM7xzds2PC3fr527dqqXbu2rl+/rj/++EN9+vRRw4YNlSNHjhT7tSYJDw93BtSkNlhly5Z13l6hQgUtXrxYhQsXlpRyl4cHedwPKunvmdLxlMePH7/n3tcLFy7o+++/l/R/e7BvN2nSJL377rvO6/Hx8Tpz5ozLc/z25326dOkUHx+vU6dOuQRYY4yOHz/uUiswMDDZCZGSdR8A7yYoKCjF2vfbFzlt2rRyOBx3/HvcS8mSJRUREaEff/xRffr0uedz67vvvpO/v79++uknBQUFOcdnzpz5j+eeJEOGDPf85ih9+vQKDg6+4575298rvfkawcOFPa/AbapUqaLLly8ne+OfMGGC8/ZbLVy40OUfqYSEBE2ZMkWPPPKIZS2Gkr6Snjt3rsv4nDlzJCVv/3Uvjz/+uIKDg/XNN9+4jB8+fFiLFi1yeYw5cuTQzp07Xf5xP3PmjFasWPGPav4dSf+43XoSjzFGX3/99T/aTmBgoCpUqKB+/fpJunkowr1UqlRJu3bt0qRJk1SiRAmXr4UrVKigDRs2aObMmfL393cJtv/U7XtNrVC6dGkFBgZqypQpLuN//PGHy1fBdzJp0iRdvXrV2fP49kv69OlTDCjffvttsu1Ich5ykfQ8uv159v333ys2NjbZ82zTpk0u91u0aJEuX77sMuaO31+OHDl08uRJl9fxjRs3NG/evPvaXqpUqVSyZEnNnDlTN27ccI5fvnw5xa4Et/P391f79u31119/3XERkpMnT2r58uWSbr5u/Pz8nId7SDd/PxMnTryv+Us3v9VZvHhxsq4Wt3r66ae1Z88epUuXTiVLlkx2eZj78MK72PMK3ObVV1/VF198ocaNG2v//v0qXLiwli1bpt69e6tmzZqqWrWqy/3Tp0+vypUrq3Pnzs5uA3/99dffape1bds2bdu2TdLNPTJXrlxxrmpUoEABZ3P76tWr65lnnlGPHj2UmJioxx57TGvXrlX37t319NNPu7TMuZukcJgmTRp17txZHTt21KuvvqqXXnpJZ86cUffu3RUUFKSuXbs6f+aVV17RV199pZdffllvvPGGzpw5c98dDu6lWrVqCggI0EsvvaR27drp2rVr+vLLL3Xu3Ll7/myXLl10+PBhValSRVmyZNH58+c1dOhQ+fv7Ow8BuJtKlSrp008/1YwZM/Thhx+63PbEE09Iutn6qEyZMkqVKtX9PUDJufe2X79+qlGjhnx9fVWkSBGXYyP/qYiICL3//vvq06eP0qZNqzp16ujw4cPq3r27oqOj77lnfvTo0UqbNq0+/PBDlz13SV599VUNGjRIGzduVNGiRSVJAQEBGjhwoC5fvqxSpUo5uw3UqFHD+XysVq2annzySbVv314XL15U2bJlnd0GHn30Ub3yyivOGq+88oo6d+6sLl26qEKFCtq2bZuGDRum8PBwl7kUKlRIkjRy5EiFhYUpKChIOXPmvOe3HIsWLUpxVa6aNWuqfv366tKlixo0aKC2bdvq2rVr+uyzz5SQkHDXbd5Njx49VKtWLT355JNq1aqVEhISNGDAAIWGhurs2bP3/Pm2bdtq+/bt6tq1q1avXq2GDRs6Fyn47bffNHLkSHXv3l1ly5ZVrVq1NGjQIDVs2FBvvvmmzpw5o08//fSB+iv36NFDc+fOVfny5dWxY0cVLlxY58+f1y+//KL3339f+fLlU+vWrfX999+rfPnyatOmjYoUKaLExEQdPHhQ8+fP1wcffKDSpUvf9xyAO/Lm2WLAv8Ht3QaMMebMmTPm7bffNtHR0cbPz89kz57ddOjQwVy7ds3lfpJM8+bNzfDhw80jjzxi/P39Tb58+cy33377t2onnc2c0uX2M6+vXLli2rdvb7JmzWr8/PxMtmzZUpxTStq2bWskmUuXLrmMjxo1yhQpUsQEBASY8PBwU7t2bbN169ZkPz9+/HiTP39+ExQUZAoUKGCmTJlyx24DAwYMuOPjTOp2kCTpTPB9+/Y5x2bPnm2KFi1qgoKCTObMmU3btm3N3Llzk51hfnv9n376ydSoUcNkzpzZBAQEmIwZM5qaNWua33///Z6/H2OMuXjxovHz8zOSzE8//ZTs9mLFihlJplOnTi7jSR0Bpk2bluxnUuo2cP36dfP666+bDBkyGIfD4fL479Rt4PZtJ/2ux44d6xxLTEw0vXr1MlmyZDEBAQGmSJEi5qeffjJFixY1derUuePj3rhxo5FkWrdufcf7/PXXX0aSee+994wx//ea2bRpk6lYsaIJDg42ERER5p133jGXL192+dmrV6+a9u3bm+zZsxt/f38THR1t3nnnHXPu3DmX+12/ft20a9fOZM2a1QQHB5sKFSqYDRs2JPudGGPMkCFDTM6cOY2vr2+y38Ptkp5jd7ok/e7nzJljihUrZoKDg02uXLnMsGHD7thtoHnz5snqpDTPGTNmmMKFC5uAgACTLVs207dvX9OyZUuTNm3aO873dj/++KOpVauWyZAhg/Hz8zNp06Y1lSpVMiNGjHDpZDBmzBiTN29eExgYaHLlymX69OljRo8enez1lT17dlOrVq1kdVLqKnLo0CHTtGlTExUVZfz9/U2mTJnMiy++aE6cOOG8z+XLl83HH39s8ubN63wfKVy4sGnTpo1LF5Y7/d6A++Ew5rYOxgD+NofDoebNm2vYsGHenspd1a5dWytXrkx21jEebvv27VO+fPnUtWtXdezY0dvT+c+Li4tTsWLFlDlzZs2fP9/b0wFsi8MGgIfYsmXLtGLFCs2ZM0ctWrTw9nTgRhs3btTkyZNVpkwZpU6dWjt27HAe3tGsWTNvT+8/qVmzZqpWrZqio6N1/PhxjRgxQtu3b9fQoUO9PTXA1givwEOsfPnyypAhg95880317t3b29OBG6VKlUpr167V6NGjnW2uKlasqE8++eRvt8uCtS5duqQPP/xQp06dkr+/v4oXL645c+YkO24ewD/DYQMAAACwDVplAQAAwDYIrwAAALANwisAAABs46E/YSsxMVFHjx5VWFgYS9MBAAD8CxljdOnSJWXKlOmeC6s89OH16NGjypo1q7enAQAAgHs4dOjQPZdWf+jDa9L65IcOHXLLcpYAAAB4MBcvXlTWrFmdue1uHvrwmnSoQOrUqQmvAAAA/2J/5xBPTtgCAACAbRBeAQAAYBuEVwAAANgG4RUAAAC2QXgFAACAbRBeAQAAYBuEVwAAANgG4RUAAAC2QXgFAACAbRBeAQAAYBuEVwAAANgG4RUAAAC2QXgFAACAbRBeAQAAYBuEVwAAANgG4RUAAAC2QXgFAACAbRBeAQAAYBuEVwAAANgG4RUAAAC2QXgFAACAbRBeAQAAYBuEVwAAANgG4RUAAAC2QXgFAACAbRBeAQAAYBt+3p7AwyjHRz+7bdv7+9Zy27YBAAD+7djzCgAAANsgvAIAAMA2CK8AAACwDcIrAAAAbIPwCgAAANsgvAIAAMA2aJX1kHBXey5acwEAgH8T9rwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADb8PP2BGBPOT762W3b3t+3ltu2DQAA7I09rwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDb8vD0B4O/K8dHPbtnu/r613LJdAABgPfa8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANr4bX+Ph4ffzxx8qZM6eCg4OVK1cu9ejRQ4mJic77GGPUrVs3ZcqUScHBwapYsaK2bt3qxVkDAADAW7waXvv166cRI0Zo2LBh2r59u/r3768BAwbo888/d96nf//+GjRokIYNG6Y1a9YoKipK1apV06VLl7w4cwAAAHiDV8PrypUrVbt2bdWqVUs5cuTQCy+8oOrVq2vt2rWSbu51HTJkiDp16qS6deuqUKFCGj9+vK5cuaJJkyZ5c+oAAADwAq+G13LlymnhwoXauXOnJGnjxo1atmyZatasKUnat2+fjh8/rurVqzt/JjAwUBUqVNCKFStS3Ob169d18eJFlwsAAAAeDn7eLN6+fXtduHBB+fLlk6+vrxISEvTJJ5/opZdekiQdP35ckhQZGenyc5GRkTpw4ECK2+zTp4+6d+/u3okDAADAK7y653XKlCn65ptvNGnSJK1bt07jx4/Xp59+qvHjx7vcz+FwuFw3xiQbS9KhQwdduHDBeTl06JDb5g8AAADP8uqe17Zt2+qjjz5SgwYNJEmFCxfWgQMH1KdPHzVu3FhRUVGSbu6BjY6Odv7cyZMnk+2NTRIYGKjAwED3Tx4AAAAe59U9r1euXJGPj+sUfH19na2ycubMqaioKC1YsMB5+40bN7R06VKVKVPGo3MFAACA93l1z+szzzyjTz75RNmyZVPBggW1fv16DRo0SE2bNpV083CB1q1bq3fv3sqdO7dy586t3r17KyQkRA0bNvTm1AEAAOAFXg2vn3/+uTp37qx3331XJ0+eVKZMmfTWW2+pS5cuzvu0a9dOV69e1bvvvqtz586pdOnSmj9/vsLCwrw4cwAAAHiDV8NrWFiYhgwZoiFDhtzxPg6HQ926dVO3bt08Ni8AAAD8O3n1mFcAAADgnyC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDYIrwAAALANwisAAABsg/AKAAAA2yC8AgAAwDb8vD0B4N8qx0c/u23b+/vWctu2AQB4mLHnFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBteD28HjlyRC+//LLSpUunkJAQFStWTH/++afzdmOMunXrpkyZMik4OFgVK1bU1q1bvThjAAAAeItXw+u5c+dUtmxZ+fv7a+7cudq2bZsGDhyoNGnSOO/Tv39/DRo0SMOGDdOaNWsUFRWlatWq6dKlS96bOAAAALzCz5vF+/Xrp6xZs2rs2LHOsRw5cjj/3xijIUOGqFOnTqpbt64kafz48YqMjNSkSZP01ltveXrKAAAA8CKv7nmdNWuWSpYsqXr16iljxox69NFH9fXXXztv37dvn44fP67q1as7xwIDA1WhQgWtWLHCG1MGAACAF3k1vO7du1dffvmlcufOrXnz5untt99Wy5YtNWHCBEnS8ePHJUmRkZEuPxcZGem87XbXr1/XxYsXXS4AAAB4OHj1sIHExESVLFlSvXv3liQ9+uij2rp1q7788ku9+uqrzvs5HA6XnzPGJBtL0qdPH3Xv3t19kwYAAIDXeHXPa3R0tAoUKOAylj9/fh08eFCSFBUVJUnJ9rKePHky2d7YJB06dNCFCxecl0OHDrlh5gAAAPAGr4bXsmXLaseOHS5jO3fuVPbs2SVJOXPmVFRUlBYsWOC8/caNG1q6dKnKlCmT4jYDAwOVOnVqlwsAAAAeDl49bKBNmzYqU6aMevfurRdffFGrV6/WyJEjNXLkSEk3Dxdo3bq1evfurdy5cyt37tzq3bu3QkJC1LBhQ29OHQAAAF7g1fBaqlQpzZgxQx06dFCPHj2UM2dODRkyRI0aNXLep127drp69areffddnTt3TqVLl9b8+fMVFhbmxZkDAADAG7waXiXp6aef1tNPP33H2x0Oh7p166Zu3bp5blIAAAD4V/L68rAAAADA30V4BQAAgG0QXgEAAGAbhFcAAADYBuEVAAAAtkF4BQAAgG0QXgEAAGAbhFcAAADYBuEVAAAAtkF4BQAAgG0QXgEAAGAbhFcAAADYBuEVAAAAtkF4BQAAgG0QXgEAAGAbhFcAAADYBuEVAAAAtkF4BQAAgG0QXgEAAGAbhFcAAADYBuEVAAAAtkF4BQAAgG0QXgEAAGAbhFcAAADYBuEVAAAAtkF4BQAAgG0QXgEAAGAb9xVe9+3bZ/U8AAAAgHu6r/AaExOjSpUq6ZtvvtG1a9esnhMAAACQovsKrxs3btSjjz6qDz74QFFRUXrrrbe0evVqq+cGAAAAuLiv8FqoUCENGjRIR44c0dixY3X8+HGVK1dOBQsW1KBBg3Tq1Cmr5wkAAAA82Albfn5+qlOnjqZOnap+/fppz549+vDDD5UlSxa9+uqrOnbsmFXzBAAAAB4svK5du1bvvvuuoqOjNWjQIH344Yfas2ePFi1apCNHjqh27dpWzRMAAACQ3/380KBBgzR27Fjt2LFDNWvW1IQJE1SzZk35+NzMwjlz5tRXX32lfPnyWTpZAAAA/LfdV3j98ssv1bRpU7322muKiopK8T7ZsmXT6NGjH2hyAAAAwK3uK7zu2rXrnvcJCAhQ48aN72fzAAAAQIru65jXsWPHatq0acnGp02bpvHjxz/wpAAAAICU3Fd47du3r9KnT59sPGPGjOrdu/cDTwoAAABIyX2F1wMHDihnzpzJxrNnz66DBw8+8KQAAACAlNxXeM2YMaM2bdqUbHzjxo1Kly7dA08KAAAASMl9hdcGDRqoZcuWWrx4sRISEpSQkKBFixapVatWatCggdVzBAAAACTdZ7eBXr166cCBA6pSpYr8/G5uIjExUa+++irHvAIAAMBt7iu8BgQEaMqUKerZs6c2btyo4OBgFS5cWNmzZ7d6fgAAAIDTfYXXJHny5FGePHmsmgsAAABwV/cVXhMSEjRu3DgtXLhQJ0+eVGJiosvtixYtsmRyAAAAwK3uK7y2atVK48aNU61atVSoUCE5HA6r5wUAAAAkc1/h9bvvvtPUqVNVs2ZNq+cDAAAA3NF9tcoKCAhQTEyM1XMBAAAA7uq+wusHH3ygoUOHyhhj9XwAAACAO7qvwwaWLVumxYsXa+7cuSpYsKD8/f1dbv/hhx8smRwAAABwq/sKr2nSpFGdOnWsngsAAABwV/cVXseOHWv1PAAAAIB7uq9jXiUpPj5ev/76q7766itdunRJknT06FFdvnzZsskBAAAAt7qvPa8HDhzQU089pYMHD+r69euqVq2awsLC1L9/f127dk0jRoywep4AAADA/e15bdWqlUqWLKlz584pODjYOV6nTh0tXLjQsskBAAAAt7rvbgPLly9XQECAy3j27Nl15MgRSyYGAAAA3O6+9rwmJiYqISEh2fjhw4cVFhb2wJMCAAAAUnJf4bVatWoaMmSI87rD4dDly5fVtWtXlowFAACA29zXYQODBw9WpUqVVKBAAV27dk0NGzbUrl27lD59ek2ePNnqOQIAAACS7jO8ZsqUSRs2bNDkyZO1bt06JSYmqlmzZmrUqJHLCVwAAACAle4rvEpScHCwmjZtqqZNm1o5HwAAAOCO7iu8Tpgw4a63v/rqq/c1GQAAAOBu7iu8tmrVyuV6XFycrly5ooCAAIWEhBBeAQAA4Bb31W3g3LlzLpfLly9rx44dKleuHCdsAQAAwG3uK7ymJHfu3Orbt2+yvbIAAACAVSwLr5Lk6+uro0ePWrlJAAAAwOm+jnmdNWuWy3VjjI4dO6Zhw4apbNmylkwMAAAAuN19hdfnnnvO5brD4VCGDBlUuXJlDRw40Ip5AQAAAMncV3hNTEy0eh4AAADAPVl6zCsAAADgTve15/X999//2/cdNGjQ/ZQAAAAAkrmv8Lp+/XqtW7dO8fHxyps3ryRp586d8vX1VfHixZ33czgc1swSAAAA0H2G12eeeUZhYWEaP3680qZNK+nmwgWvvfaannjiCX3wwQeWThIAAACQ7vOY14EDB6pPnz7O4CpJadOmVa9eveg2AAAAALe5r/B68eJFnThxItn4yZMndenSpQeeFAAAAJCS+wqvderU0Wuvvabp06fr8OHDOnz4sKZPn65mzZqpbt26Vs8RAAAAkHSfx7yOGDFCH374oV5++WXFxcXd3JCfn5o1a6YBAwZYOkEAAAAgyX2F15CQEA0fPlwDBgzQnj17ZIxRTEyMUqVKZfX8AAAAAKcHWqTg2LFjOnbsmPLkyaNUqVLJGGPVvAAAAIBk7iu8njlzRlWqVFGePHlUs2ZNHTt2TJL0+uuv0yYLAAAAbnNf4bVNmzby9/fXwYMHFRIS4hyvX7++fvnlF8smBwAAANzqvo55nT9/vubNm6csWbK4jOfOnVsHDhywZGIAAADA7e5rz2tsbKzLHtckp0+fVmBg4ANPCgAAAEjJfYXX8uXLa8KECc7rDodDiYmJGjBggCpVqmTZ5AAAAIBb3ddhAwMGDFDFihW1du1a3bhxQ+3atdPWrVt19uxZLV++3Oo5AgAAAJLuc89rgQIFtGnTJv3vf/9TtWrVFBsbq7p162r9+vV65JFHrJ4jAAAAIOk+9rzGxcWpevXq+uqrr9S9e3d3zAkAAABI0T/e8+rv768tW7bI4XC4Yz4AAADAHd3XYQOvvvqqRo8ebfVcAAAAgLu6rxO2bty4oVGjRmnBggUqWbKkUqVK5XL7oEGDLJkcAAAAcKt/FF737t2rHDlyaMuWLSpevLgkaefOnS734XACAAAAuMs/Cq+5c+fWsWPHtHjxYkk3l4P97LPPFBkZ6ZbJAQAAALf6R8e8GmNcrs+dO1exsbGWTggAAAC4k/s6YSvJ7WEWAAAAcKd/FF4dDkeyY1o5xhUAAACe8o+OeTXGqEmTJgoMDJQkXbt2TW+//XaybgM//PCDdTMEAAAA/r9/FF4bN27scv3ll1+2dDIAAADA3fyj8Dp27Fh3zQMAAAC4pwc6YQsAAADwJMIrAAAAbIPwCgAAANsgvAIAAMA2CK8AAACwDcIrAAAAbIPwCgAAANsgvAIAAMA2CK8AAACwDcIrAAAAbIPwCgAAANsgvAIAAMA2CK8AAACwDcIrAAAAbIPwCgAAANsgvAIAAMA2/jXhtU+fPnI4HGrdurVzzBijbt26KVOmTAoODlbFihW1detW700SAAAAXvWvCK9r1qzRyJEjVaRIEZfx/v37a9CgQRo2bJjWrFmjqKgoVatWTZcuXfLSTAEAAOBNXg+vly9fVqNGjfT1118rbdq0znFjjIYMGaJOnTqpbt26KlSokMaPH68rV65o0qRJXpwxAAAAvMXr4bV58+aqVauWqlat6jK+b98+HT9+XNWrV3eOBQYGqkKFClqxYsUdt3f9+nVdvHjR5QIAAICHg583i3/33Xdat26d1qxZk+y248ePS5IiIyNdxiMjI3XgwIE7brNPnz7q3r27tRMFAADAv4LX9rweOnRIrVq10jfffKOgoKA73s/hcLhcN8YkG7tVhw4ddOHCBefl0KFDls0ZAAAA3uW1Pa9//vmnTp48qRIlSjjHEhIS9Ntvv2nYsGHasWOHpJt7YKOjo533OXnyZLK9sbcKDAxUYGCg+yYOAAAAr/HantcqVapo8+bN2rBhg/NSsmRJNWrUSBs2bFCuXLkUFRWlBQsWOH/mxo0bWrp0qcqUKeOtaQMAAMCLvLbnNSwsTIUKFXIZS5UqldKlS+ccb926tXr37q3cuXMrd+7c6t27t0JCQtSwYUNvTBkAAABe5tUTtu6lXbt2unr1qt59912dO3dOpUuX1vz58xUWFubtqQEAAMAL/lXhdcmSJS7XHQ6HunXrpm7dunllPgAAAPh38XqfVwAAAODvIrwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANgivAAAAsA3CKwAAAGyD8AoAAADbILwCAADANrwaXvv06aNSpUopLCxMGTNm1HPPPacdO3a43McYo27duilTpkwKDg5WxYoVtXXrVi/NGAAAAN7k1fC6dOlSNW/eXH/88YcWLFig+Ph4Va9eXbGxsc779O/fX4MGDdKwYcO0Zs0aRUVFqVq1arp06ZIXZw4AAABv8PNm8V9++cXl+tixY5UxY0b9+eefKl++vIwxGjJkiDp16qS6detKksaPH6/IyEhNmjRJb731ljemDQAAAC/5Vx3zeuHCBUlSRESEJGnfvn06fvy4qlev7rxPYGCgKlSooBUrVqS4jevXr+vixYsuFwAAADwc/jXh1Rij999/X+XKlVOhQoUkScePH5ckRUZGutw3MjLSedvt+vTpo/DwcOcla9as7p04AAAAPOZfE15btGihTZs2afLkycluczgcLteNMcnGknTo0EEXLlxwXg4dOuSW+QIAAMDzvHrMa5L33ntPs2bN0m+//aYsWbI4x6OioiTd3AMbHR3tHD958mSyvbFJAgMDFRgY6N4JAwAAwCu8uufVGKMWLVrohx9+0KJFi5QzZ06X23PmzKmoqCgtWLDAOXbjxg0tXbpUZcqU8fR0AQAA4GVe3fPavHlzTZo0ST/++KPCwsKcx7GGh4crODhYDodDrVu3Vu/evZU7d27lzp1bvXv3VkhIiBo2bOjNqQMAAMALvBpev/zyS0lSxYoVXcbHjh2rJk2aSJLatWunq1ev6t1339W5c+dUunRpzZ8/X2FhYR6eLQAAALzNq+HVGHPP+zgcDnXr1k3dunVz/4QAAADwr/av6TYAAAAA3AvhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBt+Hl7AgD+T46PfnbLdvf3reWW7QIA4GnseQUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALbh5+0JAPCeHB/97LZt7+9by23bBgD8d7HnFQAAALZBeAUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBtwEAHuON7gaervlfeIwA4E3seQUAAIBtEF4BAABgG4RXAAAA2AbhFQAAALZBeAUAAIBt0G0AAPCP0FEBgDex5xUAAAC2QXgFAACAbRBeAQAAYBuEVwAAANgG4RUAAAC2QbcBAABu81/oqPAwPcZ/y+8UnsGeVwAAANgG4RUAAAC2QXgFAACAbRBeAQAAYBuEVwAAANgG4RUAAAC2QassAAAAi3i6Hdh/EXteAQAAYBuEVwAAANgG4RUAAAC2QXgFAACAbRBeAQAAYBuEVwAAANgG4RUAAAC2QXgFAACAbRBeAQAAYBuEVwAAANgG4RUAAAC2QXgFAACAbRBeAQAAYBuEVwAAANgG4RUAAAC2QXgFAACAbRBeAQAAYBuEVwAAANgG4RUAAAC2QXgFAACAbRBeAQAAYBuEVwAAANgG4RUAAAC2QXgFAACAbRBeAQAAYBuEVwAAANgG4RUAAAC2QXgFAACAbRBeAQAAYBuEVwAAANgG4RUAAAC2QXgFAACAbdgivA4fPlw5c+ZUUFCQSpQood9//93bUwIAAIAX/OvD65QpU9S6dWt16tRJ69ev1xNPPKEaNWro4MGD3p4aAAAAPOxfH14HDRqkZs2a6fXXX1f+/Pk1ZMgQZc2aVV9++aW3pwYAAAAP8/P2BO7mxo0b+vPPP/XRRx+5jFevXl0rVqxI8WeuX7+u69evO69fuHBBknTx4kX3TfQ2idevuG3bd3oc7qrp6XreqMljdI+Uaj5Mv9M71eQxWl/PGzV5jNbXc2fNf8vv1J01PZljvCHp8Rlj7n1n8y925MgRI8ksX77cZfyTTz4xefLkSfFnunbtaiRx4cKFCxcuXLhwsdnl0KFD98yH/+o9r0kcDofLdWNMsrEkHTp00Pvvv++8npiYqLNnzypdunR3/BlvunjxorJmzapDhw4pderUD109b9R82Ot5oyaP8eGoyWN8OGryGB+Omg97vX/KGKNLly4pU6ZM97zvvzq8pk+fXr6+vjp+/LjL+MmTJxUZGZnizwQGBiowMNBlLE2aNO6aomVSp07t0SeTp+t5o+bDXs8bNXmMD0dNHuPDUZPH+HDUfNjr/RPh4eF/637/6hO2AgICVKJECS1YsMBlfMGCBSpTpoyXZgUAAABv+VfveZWk999/X6+88opKliypxx9/XCNHjtTBgwf19ttve3tqAAAA8LB/fXitX7++zpw5ox49eujYsWMqVKiQ5syZo+zZs3t7apYIDAxU165dkx3q8LDU80bNh72eN2ryGB+OmjzGh6Mmj/HhqPmw13MnhzF/pycBAAAA4H3/6mNeAQAAgFsRXgEAAGAbhFcAAADYBuH1IZaQkKClS5fq3Llz3p4KAFjOGKMDBw7o6tWr3p4KHkB8fLzGjx+frKc7cCeEVw+Ki4tTrly5tG3bNo/U8/X11ZNPPqnz5897pJ4kNWnSRL/99pvH6sXFxalSpUrauXPnQ1kvyb59+zxaLzY21qP14B7dunXTgQMHPFrTk+8Bxhjlzp1bhw8f9kg9uIefn5/eeecdXb9+3Sv1r1275pE6FStW1IQJEzz+YevGjRvasWOH4uPjPVrXnQivHuTv76/r1697dJnawoULa+/evR6rd+nSJVWvXl25c+dW7969deTIEbfW8/f315YtWzz2O/V0vSQxMTGqVKmSvvnmG4+80UZGRqpp06ZatmyZ22sliY2NVefOnVWmTBnFxMQoV65cLhd3WLhwoTp27KjXX39dTZs2dbm4w/jx4/Xzzz87r7dr105p0qRRmTJl3BIyZ8+erUceeURVqlTRpEmTPPLc8eR7gI+Pj3Lnzq0zZ864rUZKTpw4oVdeeUWZMmWSn5+ffH19XS5Wi4+Pl5+fn7Zs2WL5tu/kt99+SzHsxMfHu+XDSenSpbVhwwbLt3sniYmJ6tmzpzJnzqzQ0FDnv5OdO3fW6NGj3VKzRIkSateunaKiovTGG2/ojz/+cEudJFeuXFGzZs0UEhKiggUL6uDBg5Kkli1bqm/fvm6t7XYGHtWnTx/TuHFjExcX55F68+bNM8WKFTOzZ882R48eNRcuXHC5uMPp06fNkCFDTLFixYyfn5956qmnzLRp08yNGzfcUu/999837du3d8u2/w31jDFm8+bNpk2bNiZjxowmPDzcvPnmm2bVqlVuqzdr1ixTt25dExAQYHLnzm369Oljjhw54rZ6xhjToEEDEx0dbdq1a2cGDx5shgwZ4nKxWrdu3YyPj4/53//+Z2rXrm2ee+45l4s75MmTxyxcuNAYY8yKFStMcHCw+eqrr8wzzzxj6tSp45aaGzduNK1btzYZM2Y0adKkMW+//bZZvXq1W2ol8eR7wE8//WTKlStnNm/ebPm27+Spp54yBQoUMMOHDzczZswwM2fOdLm4Q65cucyGDRvcsu2U+Pj4mBMnTiQbP336tPHx8bG83tSpU02uXLnM559/blasWGE2btzocrFa9+7dTa5cucw333xjgoODzZ49e4wxxkyZMsU89thjltdLEh8fb2bOnGlq165t/P39Tf78+c2AAQPM8ePHLa/VsmVLU6JECfP777+bVKlSOR/jjz/+aIoVK2Z5PU8ivHrYc889Z8LCwkx0dLSpXr26qVOnjsvFag6Hw3nx8fFxXpKuu9u6detMixYtTFBQkEmfPr1p3bq12blzp6U1WrRoYVKnTm2KFy9u3nzzTdOmTRuXi9U8Xe9WcXFx5ocffjDPPvus8ff3NwUKFDADBw40J0+edEu906dPm0GDBpkiRYoYPz8/U6tWLfP999+75cNXeHi4WbZsmeXbvZOoqCgzYcIEj9Uzxpjg4GBz4MABY4wx7dq1M6+88ooxxpgtW7aY9OnTu7V20nPnmWeeMf7+/qZQoUJmyJAh5vz5826t6+73gDRp0piAgADj4+NjgoKCTNq0aV0u7hAaGmrWr1/vlm3fyZgxY0yNGjXMmTNnPFLP4XCk+L6yY8cOExYW5pZ6t1/c+W/VI488Yn799VdjzM2/Z1Kw2759u0mTJo3l9VJy8uRJ07NnTxMUFGT8/f1N7dq1nR9urZAtWzazcuVKY4zrY9y1a5db/oae9K9fYethkyZNGj3//PMeq7d48WKP1brdsWPHNH/+fM2fP1++vr6qWbOmtm7dqgIFCqh///5q06aNJXW2bNmi4sWLS1KyY1Hd8fW+p+vdys/PT3Xq1FHNmjU1fPhwdejQQR9++KE6dOig+vXrq1+/foqOjrasXrp06dSmTRu1adNGn3/+udq2bas5c+Yoffr0evvtt/XRRx8pJCTEklpp06ZVRESEJdv6O27cuKEyZcp4rJ4khYaG6syZM8qWLZvmz5/vfA0EBQW5/Ti4xMRE3bhxQ9evX5cxRhEREfryyy/VuXNnff3116pfv77lNT3xHjBkyJAHn+g/lDVrVhkPr+/z2Wefaffu3cqUKZOyZ8+uVKlSudy+bt06S+rUrVtX0s33siZNmrisxpSQkKBNmza55XXj6eP6jxw5opiYmGTjiYmJiouLc3v91atXa+zYsZo8ebIyZsyoJk2a6NixY3rmmWf0zjvv6NNPP33gGqdOnVLGjBmTjcfGxnr80DfLeTs94+Fy48YNM336dFOrVi3j7+9vSpQoYb788ktz8eJF530mT57ssU+2D5s1a9aYd955x6RNm9ZkyZLFdOrUyezdu9csW7bMVK5c2ZQqVcrSeseOHTP9+vUz+fLlMyEhIaZRo0Zm0aJF5ptvvjGFChUy1apVs6zWxIkTzQsvvGBiY2Mt2+bdtGvXzvTo0cMjtZI0bNjQFC9e3DRr1syEhISY06dPG2Nufo1XsGBBt9Rcu3atad68uYmIiDDR0dGmffv2ZteuXc7bP/30U5MxY0bL6v0X3gPmzZtnqlevbvbt2+exmt26dbvrxSpNmjQxTZo0MQ6Hw9SvX995vUmTJubNN980vXv3NqdOnbKsnreUKFHCTJw40RjjuleyW7duply5cm6peeLECfPpp5+aggULmoCAAPP888+buXPnmsTEROd9FixYYFKlSmVJvfLly5vPPvvMGHPzMe7du9cYY0zz5s3Nk08+aUkNbyG8ekFcXJxZsGCBGTFihPMN/ciRI+bSpUtuqffbb7+ZRo0amccff9wcPnzYGGPMhAkTzO+//255rXTp0pm0adOad999945fq509e9bkyJHD8tq7du0yv/zyi7ly5Yoxxri8IbiDJ+sNHDjQFCpUyPnV0uzZs01CQkKy+fj6+lpS7/vvvzdPP/208ff3N0WLFjWff/65OXfunMt9tmzZYvz9/R+oTrFixcyjjz7qvISFhZnQ0FBTqFAhl/FHH330geokufXwjlatWpk0adKY8uXLmxYtWnjk8I9z586Z5s2bm2effdbMnTvXOd6lSxfTq1cvy+sVLlzY+Pn5mZo1a5oZM2aY+Pj4ZPc5efKkcTgcltX0xnvA7t27TadOnUyDBg2cx2nOnTvXbNmyxbIaadKkcTkcIelQhdDQUI8cquBp3bp1M5cvX/ZozQkTJpgyZcqY6Ohos3//fmOMMYMHD3bLccSzZs0y4eHhpm/fviYkJMQMGDDAvP766yYgIMDMnz/f8nrGGOPv72/y5ctn+vfvf8dDvS5cuGAqVqxoSb3ly5ebsLAw8/bbb5ugoCDTqlUrU7VqVZMqVSqzdu1aS2p4i8MYD3/38R934MABPfXUUzp48KCuX7+unTt3KleuXGrdurWuXbumESNGWFrv+++/1yuvvKJGjRpp4sSJ2rZtm3LlyqXhw4frp59+0pw5cyytN3HiRNWrV09BQUGWbvduzpw5oxdffFGLFy+Ww+HQrl27lCtXLjVr1kxp0qTRwIEDbV1PknLnzq2mTZvqtddeU1RUVIr3uXHjhiZPnqzGjRs/cL3w8HA1aNBAr7/+ukqVKpXifa5evar+/fura9eu912ne/fuf/u+D1InSaVKlf7W/RwOhxYtWvTA9bytZ8+eatq0qTJnzuyxmhMmTNCLL77osfeApUuXqkaNGipbtqx+++03bd++Xbly5VL//v21evVqTZ8+3ZI648eP/9v3teI1mJLz589r+vTp2rNnj9q2bauIiAitW7dOkZGRlv+Nr169KmOM87CgAwcOaMaMGSpQoICqV69uaS1J+vLLL9WlSxe1bt1an3zyibZs2aJcuXJp3LhxGj9+vFsOgZs3b5569+6tP//8U4mJiSpevLi6dOnilsdnjNHvv/+ukiVLWnao1d+xefNmffrppy6PsX379ipcuLDH5uAW3s3O/z21a9c2L7/8srl+/brLVxVLliwxMTExltcrVqyYGT9+vDHG9auR9evXm8jISMvrecMrr7xinnzySXPo0CGXxzhv3jxToEAB29eLi4szXbt2NQcPHrR823fiqa/u/2vGjBljpk6dmmx86tSpZty4cV6YkbXi4uKMr6+vR8/8f+yxx8zAgQONMa7vcatXrzaZMmXy2DzcbePGjSZDhgwmJibG+Pn5OR/nxx9/7Dzxz0rVqlUzX375pTHm5jcGGTNmNFmyZDFBQUFm+PDhltfLnz+/mTFjhjHG9e+4efNmky5dOsvreVpCQoLx9/e3/ITl/ypO2PKwZcuWafny5QoICHAZz549u1v6Ie7YsUPly5dPNp46dWq3LF6QdLD/7RwOh4KCghQTE6OGDRsqb968ltWcP3++5s2bpyxZsriM586d2y29Mz1dz8/PTwMHDlSTJk0s3/adhISEKCEhQTNmzND27dvlcDiUL18+Pffcc/Lzc8/bxpo1a5SYmKjSpUu7jK9atUq+vr4qWbKkpfUuXLighISEZCeJnT17Vn5+fkqdOrWl9SSpb9++KX67kjFjRr355puW77F7//33Uxy/9fVYu3Zty06U8/PzU/bs2ZWQkGDJ9v6OzZs3a9KkScnGM2TI4Lb+r3PmzHEuAnOr+fPnKyEhQTVq1LC85vvvv68mTZqof//+CgsLc47XqFFDDRs2tLzeunXrNHjwYEnS9OnTFRUVpfXr1+v7779Xly5d9M4771hab9++fXr00UeTjQcGBrpl0ZRcuXJpzZo1Spcuncv4+fPnVbx4ccv7o9/akzh37tyWbvtuEhMTtXv3bp08eVKJiYkut6WUDeyCRQo8LDExMcU39sOHD7u8IVklOjpau3fvTja+bNkytzR+T506tRYtWqR169Y5z2Zcv369Fi1apPj4eE2ZMkVFixbV8uXLLasZGxub4tcwp0+fdjlT1q71JKlKlSpasmSJW7adki1btih37txq3LixZsyYoR9++EFNmjRR7ty5tXnzZrfUbN68uQ4dOpRs/MiRI2revLnl9Ro0aKDvvvsu2fjUqVPVoEEDy+tJN796zZkzZ7Lx7NmzOxuIW2n9+vUaPXq0Ro4cqaVLl2rJkiX6+uuvNXr0aC1cuFDvv/++YmJiLF317+OPP1aHDh109uxZy7Z5N2nSpNGxY8eSja9fv95th0t89NFHKb6PJyYm6qOPPnJLzTVr1uitt95KNp45c2a3LKt65coV579J8+fPV926deXj46PHHnvMLR/Sc+bMmeIiBXPnzlWBAgUsr7d///4U/4bXr19328Ia/fv3V9u2bT222MQff/yhmJgY5c+fX+XLl1fFihWdl797CNW/FXtePaxatWoaMmSIRo4cKenmHpDLly+ra9euqlmzpuX13nrrLbVq1UpjxoyRw+HQ0aNHtXLlSn344Yfq0qWL5fWioqLUsGFDDRs2TD4+Nz8bJSYmqlWrVgoLC9N3332nt99+W+3bt7ds9aby5ctrwoQJ6tmzp6Sbv9PExEQNGDDALS9QT9eTbu5d6dChg7Zs2aISJUoka5Pz7LPPWlrv9ddfV6FChfTnn38qbdq0kqRz586pSZMmevPNN7Vy5UpL60nStm3bnC3IbvXoo4+6ZUnlVatWadCgQcnGK1asqE6dOlleT7q5h3XTpk3KkSOHy/jGjRuT7QGyQtJe1bFjxzr3JF+8eFHNmjVTuXLl9MYbb6hhw4Zq06aN5s2bZ0lNT7V0StKwYUO1b99e06ZNc74Wly9frg8//FCvvvqqpbWS7Nq1K8VAlS9fvhR3FlghKChIFy9eTDa+Y8cOZciQwfJ6MTExmjlzpurUqaN58+Y525qdPHnSLd9KtG3bVs2bN9e1a9dkjNHq1as1efJk9enTR6NGjbKszqxZs5z/P2/ePIWHhzuvJyQkaOHChclen1Z5+eWXdeXKFRUtWlQBAQEKDg52ud3qD3xvv/22SpYsqZ9//lnR0dH2b491K28ft/Bfc+TIEZMnTx6TP39+4+fnZx577DGTLl06kzdv3hRXM7FCx44dTXBwsLPxc1BQkPn444/dUit9+vRmx44dycZ37NjhPG5p06ZNJjw83LKaW7duNRkyZDBPPfWUCQgIMC+88ILJnz+/iYyMNLt377asjrfqGZNyA+9bG3lbLSgoKMUztTdv3myCgoIsr2eMMREREWbFihXJxpcvX+6WtkohISFm06ZNycY3bdpkgoODLa9njDFt27Y12bNnN4sWLTLx8fEmPj7eLFy40GTPnt188MEHltfLlCmT2bp1a7LxLVu2OI8H/fPPPy09ptBTLZ2S3LhxwzRs2NDZ0N7f39/4+PiYl19+OcXuClaIjIxMsZn8ggULTIYMGdxS84033jDPPfecuXHjhrPt0YEDB8yjjz5qWrVqZXm9adOmOX+XVatWdY737t3bPPXUU5bXM8aYkSNHmmzZsjnf27JkyWJGjRplaY3bF0C49RIQEGDy5MljZs+ebWnNJOPGjbvrxWohISEubfEeJoRXL7hy5YoZM2aMad68uXnnnXfM119/7Wy35C6xsbFmzZo1ZtWqVW5ryWXMzXYyP/74Y7LxH3/80RlAdu7caXkYOXbsmOnSpYupVauWqVGjhunUqZM5evSopTW8Wc/TihYtmuI/zgsXLjSFChVyS8369eubChUquKz4dO7cOVOhQgVTr149y+tVqFDBtGjRItn4u+++67Y+j9evXzcvvviiM2T5+/sbX19f89prr5nr169bXi9VqlRm8eLFycYXL15sQkNDjTHG7Nmzx/ar7Rhz83FMmzbNTJkyxe0nxbzxxhumcOHCLh9Wd+3aZYoUKWKaNWvmlpoXLlwwZcuWNWnSpDG+vr4ma9asxt/f35QvX95tLa2OHTtm1q1b59KWb9WqVWb79u1uqZfk1KlTbtuZkyRHjhwPRb/au6lUqZJLS76HCa2yHnKePimlZcuWmjx5sjp27KhSpUrJ4XBo9erV6t27txo2bKihQ4dq1KhRGjdunGWHDfzXXLt2ze1tiObMmaN27dqpW7dueuyxxyTdPH6qR48e6tu3r8qVK+e8r1XPocOHD6tChQo6c+aM88SNDRs2KDIyUgsWLFDWrFktqZNk+fLlqlq1qkqVKqUqVapIkhYuXKg1a9Zo/vz5euKJJyytd6udO3dq48aNCg4OVuHChZU9e3a31GnUqJFWrlypgQMHurweP/zwQ5UpU0YTJ07Ud999p08//VRr1661rK4nWzp5w4ULF/TUU09p7dq1zhM3Dx8+rCeeeEI//PCD0qRJ47baSecUJLU9qlq1qttqSdLu3bu1Z88elS9fXsHBwTLGuOXr53379ik+Pj7ZyUy7du2Sv7+/277K97Q9e/Zo7Nix2rNnj4YOHaqMGTPql19+UdasWVWwYEFLa82YMUMff/yx2rZtq8KFC8vf39/l9iJFilhaz5MIrx7Wp08fRUZGqmnTpi7jY8aM0alTp9S+fXtL69WoUUPPPPOM3n33XZfxESNGaNasWZb3eU1ISFDfvn01bNgwnThxQpIUGRmp9957T+3bt5evr68OHjwoHx+fZGfr36+xY8cqNDRU9erVcxmfNm2arly5YvkZ3J6uJ938vfbu3VsjRozQiRMnnP2BO3furBw5cqhZs2aW1ks6Xln6vyVvk94qbr3ucDgsPbM8NjZW3377rTPYFSlSRC+99FKyN12rbNy4Uf3799eGDRuc9Tp06ODRs4Hd6fLly2rTpo0mTJig+Ph4STc7AjRu3FiDBw9WqlSpnCfJFCtWzJKamzZtUtWqVRUeHq79+/drx44dzufqgQMHNGHCBEvqJHnhhRdUsmTJZCdKDRgwQKtXr9a0adMsrZckMTFRv/76q8tz1c5nb9/O0/2sK1SooKZNmyZ7//zmm280atQoy09Y7dGjx11vd8c5IZ7qSZzk1vfxJA6Hwy3v3Z5GePWwHDlyaNKkScnWhl61apUaNGhg+frOERERWr58ufLnz+8y/tdff6ls2bJuayUjyXlygTsO7r9V3rx5NWLEiGQnSy1dulRvvvmmduzYYet60s032vHjx6tHjx564403nA28p06dqsGDB1t+AtXSpUv/9n0rVKjwwPXi4uKUN29e/fTTT245szilem+++aY6d+7slq4bt3r//ffVs2dPpUqV6o6tq5KkdAKZFS5fvqy9e/fKGKNHHnlEoaGhbqkjSVWrVlXx4sWdLZ02btyoXLlyacWKFWrYsKH2799vab0MGTJo0aJFyZqub968WVWrVnV+iLZKfHy8goKCtGHDBhUqVMjSbd9Ny5YtFRMTo5YtW7qMDxs2TLt379aQIUMsrffqq6/q5MmTGjVqlPLnz+/8O86fP19t2rTR1q1bLa2XOnVqrVu3TjExMS7ju3fvVsmSJS1v7Xh7W664uDjt27dPfn5+euSRRyw/sVCSHn/8cdWrV0/vv/++y2tjzZo1eu655yzvcnCvrhDu+sbHE+g24GHHjx9XdHR0svEMGTKk2O7lQV2/ft25x+VWcXFxunr1quX1kpw6dUo7duyQw+FQ3rx5lT59erfV8nT7IU/Xk26uWjRy5EhVqVJFb7/9tnO8SJEi+uuvvyyvZ0Ug/Sf8/f11/fp1j50N6+/vrxkzZqhz585ur7V+/XrFxcU5//9O3PnYQ0NDFRERIYfD4dbgKt1s6fTVV18lG3dXS6fLly8n65st3fwbp3R2/oPyRi9b6eZqibeeKZ+kTJky6tu3r+Xh1dP9rB0Ohy5dupRsPOnQN6ul9Fq8ePGimjRpojp16lheT/J8T2I7h9N7oc+rh2XNmjXFHqfLly9XpkyZLK9XqlQpZ1uuW40YMUIlSpSwvF5sbKyaNm2q6OholS9fXk888YSio6PVrFkzXblyxfJ60v+1H7qdu9oPebqedLPX6e17JKSbX10mBSN3uHLliv766y9t2rTJ5eIO7733nvr165fihy13qFOnjmbOnOn2OosXL3YeA7l48eI7XtyxHG1iYqJ69Oih8PBwZc+eXdmyZVOaNGnUs2fPZA3LreLplk6FChXSlClTko1/9913btuL7+lettLNr/FvbeuUJHXq1Dp9+rTl9Tzdz/qJJ55Qnz59XIJqQkKC+vTp43KMvTulTp1aPXr0cNuHWk/3JJ42bZrq1q2rQoUKqXDhwqpbt67lhyZ4C3tePez1119X69atFRcXp8qVK0u6eZJIu3bt9MEHH1he75NPPlHVqlW1cePGFE9Ksdr777+vpUuXavbs2SpbtqykmwsitGzZUh988IG+/PJLy2s2aNBALVu2VFhYmPOYs6VLl6pVq1ZuaTbv6XqSVLBgQf3+++/JPklPmzYtxVVpHtSpU6f02muvae7cuSne7o49IatWrdLChQs1f/58FS5cOFl/0B9++MHSejExMerZs6dWrFiRYu/c27+edYeLFy9q0aJFypcvn/Lly2f59jt16qTRo0erb9++Klu2rIwxWr58ubp166Zr167pk08+sbxm7dq11aNHD02dOlXSzT1qBw8e1EcffaTnn3/e8nqdO3fW888/rz179ri8p06ePNltx7t6upetdPP5+ssvv6hFixYu43PnznXLoS+e7mfdv39/lS9fXnnz5nWeLPn77787XyOecv78eV24cMEt2/ZUT+LExES99NJLmjZtmvLkyaN8+fLJGKOtW7eqfv36qlevniZPnmzvvq9e6XHwH5aYmGjatWtngoKCjI+Pj/Hx8TEhISGme/fubqu5fv1607BhQ1OgQAFTokQJ89prr7mtlUy6dOlSbM2zaNEikz59erfU9HT7IU/XM8aYWbNmmfDwcNO3b18TEhJiBgwYYF5//XUTEBBg5s+fb3m9hg0bmjJlypjVq1ebVKlSmfnz55uJEyeavHnzmp9++snyesYY06RJk7terJYjR447XnLmzGl5PWOMqVevnvn888+NMTdb5uXOndv4+/sbPz8/M336dMvrRUdHp9i6bubMmc4+r1bzRkunn376yZQpU8aEhISYdOnSmUqVKpklS5a4pZYxnu9la4wxo0ePNsHBwaZLly5myZIlZsmSJaZz584mJCTEjBw50vJ63uhnfeTIEdOhQwdTs2ZN8/zzz5vu3bubM2fOuKXW0KFDXS5Dhgwx7du3N5kyZTINGjRwS01P9SQeOHCgiYiISLFf7Y8//mgiIiLM4MGDLavnDYRXL7l06ZJZvXq12bx5s7l27Zq3p2OZ4OBgs23btmTjW7ZsMSEhIW6tvWPHDjN16lQze/Zss3//frfW8ka9X375xZQvX96kSpXKBAcHm7Jly5p58+a5pVZUVJRZtWqVMcaYsLAw58ITP/74oylbtqxbav4XREZGmg0bNhhjjPn2229NTEyMiY2NNcOHDzfFihWzvF5gYGCKi4b89ddfbltsIsnChQvNgAEDTL9+/cyCBQvcWuu/Yvjw4SZz5szOpvo5c+Y048ePd1u9h7mf9e0fWHPlymVKly5tOnToYC5evOjW2rt373ZrT+LChQub0aNH3/H2UaNGua1ft6fQbeA/IDExUbt379bJkyeTHedmdWuXKlWqKF26dJowYYKzF+nVq1fVuHFjnT17Vr/++qul9eAeqVOndi5jmiNHDn377bcqW7as9u3bp4IFC7rt+OWHXXBwsHbu3KmsWbPq1VdfVaZMmdS3b18dPHhQBQoU0OXLly2tV7p0aZUuXVqfffaZy/h7772nNWvW6I8//rC0njfduHEjxfe4bNmyeWlG7nPq1CkFBwe7/eQ7Tzt//rxWr16d4t/RXUv9PoyCg4O1Y8eOOz73Dxw4oHz58rn1pG1345hXD4uNjVXfvn21cOHCFF+ge/futbTeH3/8oYYNG+rAgQO6/XOKO/q8DR06VE899ZSyZMmiokWLyuFwaMOGDQoKCrJs7fTbJSQkaNy4cXf8nVp9vJSn63lD3rx5tWPHDuXIkUPFihXTV199pRw5cmjEiBEpdsuwyvTp0zV16lQdPHhQN27ccLnNHccRHj58WLNmzUqxnjvaVmXNmlUrV65URESEfvnlF3333XeSpHPnzrll4Yn+/furVq1a+vXXX/X444/L4XBoxYoVOnTokOU9npPcHpSTOBwOBQUFKSYmRuXLl5evr68l9Xbt2qWmTZtqxYoVLuPGjb0sExISNHjw4Ds+V919Ipc7TnxLiSfD5OzZs9WoUSPFxsYqLCzM5XhMh8Nheb2mTZtq6NChCgsLcxmPjY3Ve++9pzFjxlhaT9IdW+Xd+tqoXbt2skWF/qng4GCdP3/+juH14sWLCg4OfqAa3saeVw976aWXtHTpUr3yyiuKjo5OdsB0q1atLK1XrFgx5cmTR927d0+xXkpnrz6oq1ev6ptvvtFff/0lY4wKFCigRo0aue3F0qJFC40bN061atVK8TEOHjzY1vUkKW3atCkeXH/rm16TJk302muvWVLv22+/VVxcnJo0aaL169frySef1JkzZxQQEKBx48apfv36ltS51WeffaZOnTqpcePG+vrrr/Xaa69pz549WrNmjZo3b275yUULFy7Us88+q5w5c2rHjh0qVKiQ9u/fL2OMihcv7pYPIcOHD1erVq0UGhqq7Nmza926dfLx8dHnn3+uH374QYsXL7a85tGjR/XFF1+4vB7fffddt3Q3kaScOXPq1KlTunLlitKmTStjjM6fP6+QkBCFhobq5MmTypUrlxYvXmzJqmlly5aVn5+fPvrooxRfj0WLFn3gGrfr0qWLRo0apffff1+dO3dWp06dtH//fs2cOVNdunRxy8l+J06c0Icffuj80Hz7P91Wh/R7hUmrA3qePHlUs2ZN9e7dO8UuB1bz9fXVsWPHlDFjRpfx06dPKyoqyi1dTypVqqR169YpISFBefPmlTFGu3btkq+vr/Lly+dsL7ls2bIH6pRRq1YtZcuW7Y4nSL/99ts6dOiQfv755/uu4XXeOVrhvys8PNwsW7bMY/VCQkLMrl27PFbPG9KlS2d+/vnnh7aeMcYMGjTIpEuXzrz88svms88+M0OHDjUvv/yySZ8+vfnkk0/M66+/bgIDA91y4oYxxsTGxpo///zTrWuB582b10yaNMkYY0xoaKjZs2ePMcaYzp07m+bNm1ter1SpUqZz584u9S5dumSeffZZM3z4cMvrJVmzZo354YcfzKVLl5xjP/30k0ffF9xp0qRJpmLFii4n9ezatctUrlzZfPfdd+bQoUOmbNmy5vnnn7ekXkhIiNm+fbsl2/q7cuXK5TxxMTQ01PlYhw4dal566SW31HzqqadMgQIFzPDhw82MGTPMzJkzXS5Wy507t2nVqpWJjY21fNspCQkJcb7m3enChQvm/PnzxuFwmN27d5sLFy44L2fPnjXjx4830dHRbqk9ePBgU7duXXPhwgWX+bzwwgtmyJAhJjY21tSuXdtUr179geosX77c+Pv7m3r16plVq1Y5H/PKlSvNCy+8YPz9/W3/fkN49bAcOXKkeEKTu1SqVMnMnTvXrTV+/PHHv31xh+jo6BRPSnEXT9czxpi6deuaL7/8Mtn4iBEjTN26dY0xxnz22WeWHIR/48YNkzNnTrN169YH3tY/ERwc7DzxLUOGDM4Tm3bu3GkiIiIsr3dr6EiTJo3ZsmWLMcaYDRs2mOzZs1tezxiTYicOq23cuPFvX9whV65cZv369cnG161b5+zisHz5chMVFWVJvZIlS5rff//dkm39XSEhIebAgQPGmJsnN/7555/GGGP27NljUqdO7ZaaoaGhKf5e3cVTYTJJnTp1zJQpU9xex+FwODv9pHTx9fU1vXr1ckvtTJkypfi+umXLFmf3jz///NOkS5fugWv98MMPJn369MkeX7p06dzS2cTTOObVw3r27KkuXbpo/PjxHvlq5L333tMHH3yg48ePq3DhwsnWiC9SpMgD13juuef+1v3cdfzZBx98oKFDh2rYsGEe6Vvn6XqSNG/ePPXr1y/ZeJUqVZz9gWvWrJlsfff74enVrpJERUXpzJkzyp49u7Jnz64//vhDRYsW1b59+5J9RWqFVKlS6fr165KkTJkyac+ePSpYsKAkuaXpuyQ99dRTypw5s1577TU1btzYkq/Nb1esWDHn+uV3467X47Fjx1L8yjU+Pt65wlamTJlSXE3pfvTr10/t2rVT7969U3yPc8fy1FmyZNGxY8eULVs2xcTEaP78+SpevLjWrFnjlgb+0s3jpd3xOriTJ598UmvXrnX78slJatWqpbZt22rbtm0p/h2fffZZS+osXrxYxhhVrlxZ33//vcvxpQEBAcqePbvbDqm5cOGCTp48meyQgFOnTjkX9kiTJk2yY6jvR506dfTkk09q3rx52rVrl6Sbh2ZUr17dI9nD3QivHjZw4EDt2bNHkZGRypEjR7IXqNUnpSQ1BW/atKlzLOkfNqv+8XLXSj1/17Jly7R48WLNnTtXBQsWTPY7tbq5vafrSVJERIRmz56tNm3auIzPnj3b+eabdGyaFZJWuxo1apT8/DzzNlG5cmXNnj1bxYsXV7NmzdSmTRtNnz5da9euVd26dS2v99hjj2n58uUqUKCAatWqpQ8++ECbN2/WDz/8oMcee8zyetLN40+/+eYbjRs3Tt26dVOVKlXUrFkzPffccykucXo/9u3bZ8l27lelSpX01ltvadSoUc4FNNavX6933nnHuYjA5s2bU1xi+X5UrVpVkpyLsCSx8j3udnXq1NHChQtVunRptWrVSi+99JJGjx6tgwcPJnuNWmXIkCH66KOPnCdPusOty896KkwmeeONNyRJPXr0SHablX/HpKWv9+3bp2zZsnn0Q3rt2rXVtGlTDRw4UKVKlZLD4dDq1av14YcfOncCrV69Wnny5LGkXkhIiNuWuvU2TtjysO7du9/19q5du1pa715rUD8Max/f6ySlsWPH2rqeJH399dd65513VLNmTf3vf/9zvunNmTNHI0aMULNmzTRw4ECtXr06xaUy/6mkf5xDQ0M9stqVdPNDUGJiojMsT506VcuWLVNMTIzefvtty8Jdkr179+ry5csqUqSIrly5og8//NBZb/DgwW5/bWzYsEFjxozR5MmTlZiYqEaNGqlZs2ZuOcHIk44fP65XXnlFCxcudAae+Ph4ValSRRMnTlRkZKQWL16suLg4Va9e/YHrLV269K63J4UVd/rjjz+0YsUKxcTEWB7qkqRNm1ZXrlxRfHy8QkJCkoVJK06g8vH5eyvGu+tDgbtt2rRJhQoVko+Pzz2XubbiW8nbXb58WW3atNGECROc3074+fmpcePGGjx4sFKlSqUNGzZIuvkNCu6M8ArLLV26VJ9++qm2b98uh8Oh/Pnzq23bts4l/3B/li9frmHDhmnHjh0yxihfvnx67733VKZMGctreSOg/xcdPXpUI0eOVN++feXn56dr167p8ccf14gRI5yHMDyoPXv2aMiQIS6vx1atWumRRx6xZPu3Msbo4MGDypAhgw4dOuTyXM2bN6/l9f5Lxo8ff9fbGzdu7KGZ2JePj4+OHz+ujBkzysfH546H17gjnCckJGjZsmUqXLiwAgICtHfvXhlj9Mgjjzx0/Xo9gfD6H7Ft27YU+xFavZfgm2++0Wuvvaa6des611JfsWKFZsyYoXHjxqlhw4aW1vsviIuL05tvvqnOnTt77Pgzb/n999/11Vdfac+ePZo+fboyZ86siRMnKmfOnCpXrpzl9c6fP6/p06drz549atu2rSIiIrRu3TpFRkYqc+bMlteTbv49f/zxR40ZM0YLFixQyZIl1axZM7300ks6e/as2rdvrw0bNmjbtm0PXGvevHl69tlnVaxYMZfX48aNGzV79mxVq1bNgkf0fxITExUUFKStW7cqd+7clm77Xq5cuZLie5w79qBJ0sSJEzVixAjt27dPK1euVPbs2TVkyBDlzJlTtWvXdktNT1i0aJFatGihP/74I9nxwhcuXFCZMmU0YsQIt+yMiI2N1dKlS1P8O1rRfuzAgQPOQwW88a1kUFCQtm/fbtkhM/9pHj5B7D8vPj7eDBgwwJQqVcpERkaatGnTulystmfPHlOkSBHnGZZJywomnXlotXz58plBgwYlGx84cKDJly+f5fWSTJs2zdSrV8+ULl3aPProoy6Xh6FeeHi4R8/8NcaYuLg4s2DBAjNixAjncolHjhxxafFkpenTp5vg4GBn26+kx/vFF1+YGjVqWF5v48aNJkOGDCYmJsb4+fk563388cfmlVdesbyeMca0aNHCpEuXzqRLl860atXKbN68Odl9Dhw4YBwOhyX1ihUrZtq3b59svH379m57rhYoUMCsXLnSLdtOycmTJ02tWrXuePa4OwwfPtykT5/e9OrVywQHBzufO2PHjjUVK1Z0S81bXblyxaXF062tlx7UM888k+J7eJKhQ4ea5557zrJ6SdatW2eioqJM6tSpja+vr8mQIYNxOBwmVapUzi4VVlq6dKmJi4tLNh4XF2eWLl1qeT1jbnbG+PXXX92y7f8awquHde7c2URHR5sBAwaYoKAg07NnT9OsWTOTLl06M3ToUMvrPf3006Z27drm5MmTJjQ01Gzbts38/vvv5n//+5/57bffLK8XEBCQYl/ZXbt2mcDAQMvrGXPzzTQ0NNQ0b97cBAQEmLfeestUrVrVhIeHm44dO9q+njHGNGnSxAwcONAt207J/v37Tb58+UxISIjx9fV1/uPcqlUr89Zbb7mlZrFixZzrtN/a53X9+vUmMjLS8npVqlQxbdu2TVZv+fLlbmuVVblyZTNp0iRz/fr1O94nLi7OLFmyxJJ6gYGBKa6dvmPHDre9Hn/66SdTrly5FIO5OzRs2NCUKVPGrF692qRKlcrMnz/fTJw40eTNm9fZi9Vq+fPnNzNmzDDGuD53Nm/ebEmbo5RcvnzZNG/e3GTIkMGtIT1btmx3bee4fft2kzVrVsvqJalQoYJ54403THx8vPN3evDgQVO+fHnz/fffW17Px8fHnDhxItn46dOn3fahZ968eaZYsWJm9uzZ5ujRo277AJKQkJAsmB8/ftx069bNtG3b1uOt5dyB8Ophnm5unS5dOmc/x9SpU5u//vrLGGPMwoULTbFixSyv98gjj5gRI0YkGx8xYoSJiYmxvJ4xnm9u7+l6xhjTq1cvkyZNGvP888+b3r17m6FDh7pcrFa7dm3z8ssvm+vXr7s8xiVLlrjt7xgcHGz27dtnjHH9ve7Zs8ctQSt16tTO19+t9fbv3++2YOdpWbJkMVOnTk02PmXKFLcEEGNu9swNCAgwPj4+JigoyO3fLkVFRZlVq1YZY4wJCwtz9mD+8ccfTdmyZS2vZ4wxQUFBzp7Etz53du7caYKCgtxS89133zX58+c306ZNM8HBwWbMmDGmZ8+eJkuWLOabb76xrE5gYOBdF7bZtWuXWx5jeHi489+n8PBwZ4D+448/TN68eS2v53A4zMmTJ5ON79ixw4SFhVleL6nm7d9+Jn0jamVgbtKkiXnjjTec1y9evGiyZs1qMmTIYIoUKWL8/Pw8vtCO1WiV5WFJ/VYlKTQ0VBcuXJAkPf300+rcubPl9RISEpwHg6dPn15Hjx5V3rx5lT17du3YscPyeh988IFatmypDRs2qEyZMs6l7saNG6ehQ4daXk+SDh486DxpKTg42Nk/8pVXXtFjjz2mYcOG2bqeJI0aNUpp0qTRn3/+qT///NPlNofDYflylMuWLdPy5cuTneGfPXt2HTlyxNJaSaKjo7V79+5kbYCWLVvmlmN9g4KCnL0Vb7Vjxw63rh2/c+dOLVmyJMX14rt06WJprTfeeENvvvmm9u7d6/J67Nevn7M/sNWGDBnilu3eSWxsrHOJz4iICJ06dUp58uRR4cKFLW89mCRnzpzasGFDsuMi586d+0DLet7N7NmzNWHCBFWsWFFNmzbVE088oZiYGGXPnl3ffvutGjVqZEmdzJkza/PmzYqJiUnx9k2bNik6OtqSWrfy9/d3tq2KjIzUwYMHlT9/foWHh+vgwYOW1Ulqu+dwONSkSROXvrwJCQnatGmTW06CleSW5Z9TknRyb5Kk7ga7du1SeHi42rdvrwEDBqhmzZoemY87EF49zNPNrQsVKqRNmzYpV65cKl26tPr376+AgACNHDnSLYHgnXfeUVRUlAYOHKipU6dKkvLnz68pU6a47SQGTze393Q9yfO9OxMTE1M82/bw4cOW9ZK93VtvvaVWrVppzJgxcjgcOnr0qFauXKkPP/zQ8lAn3ey52KNHD+fz1OFw6ODBg/roo4+c/ZGtltTyLH369IqKikq2XrzVj7Nz584KCwvTwIED1aFDB0k3Fwjo1q2b5R94knj6rPe8efNqx44dypEjh4oVK+bsgzpixAi3hCxJatu2rZo3b65r167JGKPVq1dr8uTJ6tOnj0aNGuWWmmfPnnWe6JM6dWpna6xy5crpnXfesaxOzZo11aVLF9WoUUNBQUEut129elVdu3bV008/bVm9JI8++qjWrl2rPHnyqFKlSurSpYtOnz6tiRMnOnf4WCE8PFzSzc4YYWFhCg4Odt4WEBCgxx57zNlz1mqeaNsmSUeOHHE5YXLhwoV6/vnnnY+9cePG9u8Y490dv/897du3N5988okx5uZJP35+fiYmJsYEBASkeGLFg/rll1+cxwvt2bPH5M+f3zgcDpM+fXqzcOFCy+t5Q7NmzUy3bt2MMcZ8+eWXJjg42FStWtWkSZPGNG3a1Pb1vOHFF190fu0UGhpq9u7day5dumQqV65smjRp4ra6HTt2NMHBwc6v1oKCgszHH3/slloXLlwwZcuWNWnSpDG+vr4ma9asxt/f35QvX95cvnzZLTWzZctm+vbt65Zt38vFixedJ96504EDB+56sdo333xjxo4da4y5edJP0jGhQUFB5rvvvrO8XpKRI0eabNmyOZ+rWbJkMaNGjXJbvcKFCzuPha5WrZr54IMPjDE3DznLnDmzZXWOHz9uMmXKZLJmzWr69etnZs6caX788UfTt29fkzVrVpMpUyZz/Phxy+olWbNmjVm0aJEx5uZJeDVq1DBhYWHm0UcfdS4VbaVu3bq57XV+J0uXLr3rxSoREREuy9BGR0e7HFqyZ88eExwcbFk9b6BVlpetWrVKy5cvd2tz69udPXtWadOmdcvKImvWrFFiYqJKly7tMr5q1Sr5+vqqZMmSltf0dHN7T9eTXFdIS8mYMWMsrXf06FFVqlRJvr6+2rVrl0qWLKldu3Ypffr0+u2335xf07rDlStXtG3bNiUmJqpAgQJu74G4aNEirVu3TomJiSpevLhzxSZ3SJ06tTZs2OCxlmf79u1TfHx8srZVu3btkr+/v1tWakrqn3kn7m5uf+XKFf3111/Kli2b0qdP79Za0s2lhBMTE936mpCkwYMHy9fXVy1bttTixYtVq1YtJSQkKC4uToMHD1arVq0sq3XgwAG98847mjdvnvPbJIfDoSeffFLDhw932wpfnnT16lUZY5xLpR44cEAzZsxQgQIFLFk8IyUpLQJx62vFqtdG5cqVVbp0afXp00e///67KlasqMOHDzu/iViwYIHeeecd7d6925J63kB4fUglJCQ4ey3e+rWIdPPNfffu3c6VRqz0v//9T+3atdMLL7zgMv7DDz+oX79+WrVqlaX1/ituX+IvLi5OW7Zs0fnz51W5cmW3rHh19epVTZ482SXYNWrUKNnzyV0uXryoRYsWKW/evMqfP79Hap4/f15p0qRx2/abNWumUqVK6e2333ZbjVtVqFBBTZs2TfZV/jfffKNRo0ZpyZIlltfcuHGjy/W4uDitX79egwYN0ieffGLpUr8XL15UaGhosvexxMREXb58OVmfUqt4I/jc7uDBg1q7dq1iYmLc1sv23Llz2r17t4wxyp07t9KmTWt5jatXr2rBggWqVKlSskOSLl68qCVLlujJJ5+0/LC66tWrq27dunr77bd1/vx55c2bVwEBATp9+rQGDRpk6aEYSZLOcUmS9Nro3LmzPvnkk2RLHN+vxYsXq2bNmsqUKZOOHTvmXL44ybvvvqvY2Nh7Lnzxr+a9nb7/LWvXrjUVK1ZMsR3G+fPnTcWKFS39amTs2LGmRIkSJj4+Ptlt8fHxpkSJEmbixImW1UuSKlWqFPuR7t2714SGhlpezxhjxowZk+IZ1VOnTjXjxo2zrM7OnTtNgwYN7vg3fOmllzzaizUhIcG89dZbpl+/fh6r6U716tUzn3/+uTHmZh/LPHnyGH9/f+Pn52emT59ueb2+ffu6fK1cr1494+PjYzJlymTpa/HWrhC9e/c26dOnN40bNzaffvqp27tGhIWF3bF1XXh4uOX17uann34yFSpUsGx7P/zwg8mdO7eJjY1NdltsbKzJkyePmTVrlmX1blWtWjXz5ZdfGmOMOXfunMmYMaPJkiWLCQoKMsOHD7e01sKFC03+/Pnv+L5ToEABt7Q99JQhQ4aYypUr3/H2KlWqmGHDhlleN126dGbLli3GGGO+/vprU6RIEZOQkGCmTp3q1p7kKVm6dKkpXry4pdvcunWrGTJkiPnuu+9MQkKCy21fffWVWb9+vaX1PI3w6iEvvfSS6dGjxx1v79Wrl2nUqJFl9cqVK2cmT558x9unTJlinnjiCcvqJYmIiDArVqxINr58+XKTJk0ay+sZY0yePHmcx0rdasmSJSZPnjyW1XnjjTecfUFT0q5dO/P2229bVu/v+Ouvv0xUVJRbtr1jxw7z1VdfmZ49e5ru3bu7XNwhMjLSGRq//fZbExMTY2JjY83w4cPd0tYtZ86cZvny5cYYY+bPn2/SpElj5s2bZ5o1a2aqVatmWZ0cOXL8rYs7GrGnTp3arFu3Ltn42rVr3fZh8k527txpQkJCLNtetWrVzNdff33H20ePHm2qV69uWb1beTL4eGvRAE8pVarUXT9kzJ4925QqVcryusHBwc5jsOvVq+c8j+HgwYMePx5027ZtJlWqVB6taXeEVw/JlSuXs99qSjZt2mTpP14ZMmRw9sxMyd69e0369Oktq5ekfv36pkKFCub8+fPOsXPnzpkKFSqYevXqWV7PmJt9CVN6rPv27bO0H2HevHnN6tWr73j72rVrLQ3Lf8fPP//slr/jyJEjja+vr4mMjDRFixY1xYoVc17ctTJTUFCQOXjwoDHGmFdeecV5AuOBAwfc8sZ+a72WLVuaN9980xhzM7S764OWp9WqVcvUq1fP5RuY+Ph48/zzz5unnnrKLTVvb7x+/vx5s337dlO/fn1TtGhRy+pER0ffsx9pdHS0ZfVu5cng461FAzwlTZo0dz2R78CBA255PRYuXNgMHTrUHDx40KROndq502Xt2rVuWRTFmJur+t162bBhg5k7d66pUKGCKVOmjOX1pk6daurUqWMKFixoChUqZOrUqWOmTZtmeR1voFWWhxw5cuSuLYZCQ0N17Ngxy+rFxsam2MMyyaVLl3TlyhXL6iUZOHCgypcvr+zZs+vRRx+VJG3YsEGRkZGaOHGi5fUkKWPGjNq0aVOykwg2btyodOnSWVbnwIEDdz0pI3369Dp06JBl9W71/vvvu1w3xujYsWP6+eef3dKaqFevXvrkk0/Uvn17y7d9J1mzZtXKlSsVERGhX375Rd99952km8fd3d6yxwpp06bVoUOHlDVrVv3yyy/q1auXpJu/W3ecVHTp0iX98ccfio+PV6lSpTxyMlH//v1Vvnx55c2b17kW/e+//+48ntgd0qRJk+yELWOMsmbN6vybWuHcuXOKj4+/4+1xcXE6d+6cZfVuFRMTo5kzZ6pOnTqaN2+e2rRpI0k6efKk5cfZnjhxQv7+/ne83c/PT6dOnbK0pifFx8fr1KlTypYtW4q3nzp16q5/5/vVpUsXNWzYUG3atFGVKlX0+OOPS5Lmz5/v/LfLasWKFZPD4UjWUvGxxx6z9KTbxMREvfTSS5o2bZry5MmjfPnyyRijrVu3qn79+qpXr54mT57slpO2PYXw6iEZMmTQjh07nH36bvfXX39Z+o9Z7ty5tWLFijseyL9s2bJkZyBbIXPmzNq0aZO+/fZbbdy4UcHBwXrttdf00ksv3fUN+EE0aNBALVu2VFhYmMqXLy9JWrp0qVq1aqUGDRpYVic8PFx79uxJ1pg8ye7du912gsj69etdrvv4+ChDhgwaOHDgPTsR3I9z586pXr16lm/3blq3bq1GjRopNDRU2bJlU8WKFSVJv/32m6V9HpPUrVtXDRs2VO7cuXXmzBnVqFFD0s0PW3dq0H6/Nm3apBo1ajg/oKZOnVrTp093a2cDSSpQoIA2bdqkYcOGOV+Pr776qlq0aKGIiAi31Ly9EXvSczUmJsbZocMKOXLk0Nq1a5UvX74Ub1+7du0dX6sP6tbgU7lyZbcGH28tGuApBQsW1K+//qoSJUqkePuCBQtUsGBBy+u+8MILKleunI4dO6aiRYs6x6tUqZLsBFmr3N6vO+m1YfWH8yFDhujXX3/VrFmzkvXknTVrll577TUNHTpUrVu3trSuR3l1v+9/SJMmTUy5cuVSvC0xMdGUK1fO0v6Z/fr1c1ka9lYbNmww6dKle2hO9Ll+/bp58cUXjcPhMP7+/sbf39/4+vqa11577a5ryP9T9erVu+uxZc8++6x54YUXLKvnTU2bNnWekOJJa9asMT/88IO5dOmSc+ynn34yy5Yts7zWjRs3zIABA0zLli1djgsdPHjwXY+lvB81atQwjz32mFm+fLn5888/zbPPPuuWJS//Szp27GiyZcuWYs/RY8eOmWzZspmOHTu6rf6xY8fMunXrXE6GWbVqldm+fbuldVq0aGEKFSpkrl69muy2K1eumEKFCpn33nvP0pqe9NVXX5lUqVKZ2bNnJ7tt1qxZJlWqVOarr77ywszsq3Dhwmb06NF3vH3UqFGmUKFCHpyR9WiV5SF79uxRiRIllDdvXn3wwQfKmzevHA6Htm/froEDB2rnzp3OtidWiIuLU/Xq1bVs2TJVrVpV+fLlc9b79ddfVbZsWS1YsMAte0M9ufzlrXbt2qUNGzYoODhYhQsXtnyvy/r16/X444/r6aefVrt27ZQ3b15JN/ea9+/fXz///LNWrFih4sWLW1pX8kxrns8++8z5/7GxsRo0aJBq1aqlwoULJ3ueuGt1Jkm6ceOG9u3bp0ceecTSPXXelDFjRs2ZM8fZ5/jMmTPKmDGjLly44PY+tufPn9fq1atTfD2++uqrltXZvXu3Lly44LIHbeHCherVq5diY2P13HPPqWPHjpbVu3Tpkh5//HEdPHhQL7/8sst76rfffqusWbPqjz/+cNuKcNLNx7xnzx6VL19ewcHBMsZY/lXsiRMnVLx4cfn6+qpFixYuj/OLL75QQkKC1q1bp8jISEvretLLL7+sSZMmKV++fC6Pb+fOnXrxxRc1efJky2vGxsaqb9++WrhwYYqvjb1791pWa9WqVTp79qzz2x3p5pKtXbt2db42Pv/8c8vagQUHB2vHjh13PBTjwIEDypcvn65evWpJPa/wbnb+b1mzZo0pWLCgcTgcxsfHx/j4+BiHw2EKFix41xOB7teNGzdMv379TNGiRU1ISIgJDg42RYsWNf369bN0j+StvHGijyfNnj3buYLPrZcMGTKYH3/80W11PdGax5tnxRtzs71R06ZNja+vr/H19XW2HXvvvfdMnz593FJzwoQJpmzZsiY6Otrs37/fGHNzz+vMmTMtreNwOMyJEydcxpJWLnOnWbNmmbCwMOPj42PCw8NNmjRpnJe0adNaWuu5555zWQ1t7969Jjg42FSvXt20bNnShIaGmsGDB1ta8/z58+add94xERERzpWuIiIizDvvvGPOnTtnaa1bnT592lSuXNn5Xp70XG3atKl5//33La+3f/9+U6NGDee/GUl1a9SocdcTc+1kypQppnbt2qZAgQImf/78pnbt2mbKlCluq9egQQMTHR1t2rVrZwYPHmyGDBnicrHSU0895bKy3qZNm4yfn595/fXXzcCBA01UVJTp2rWrZfXSpk17zxPErX79exrh1QvWr19vpk6daqZMmWL7Xmu38+byl55y5coV88MPP5j+/fubfv36mRkzZqTYa9JK/6aehO7SsmVLU6JECfP777+79Av+8ccf3dIqa/jw4SZ9+vSmV69eJjg42Flv7NixpmLFipbW8vHxMbt373Y5Az8sLMxs3LjR5cx8q+XOndu0atXK7c9PY4zJkiWLS5u8nj17unQXGDVqlKXdBm6VmJhoTp48aU6cOGESExPdUuNWr7zyinnyySfNoUOHTGhoqPO5M2/ePFOgQAG31T179qxZvXq1WbVqlTl79qzb6vwXhIeHu+VwpJRERUWZNWvWOK937NjRlC1b1nl96tSpJn/+/JbVq1mz5l3bNr711lumZs2altXzBsIrLBUWFubRRv3/FZ7uSdi9e/cUA8+VK1fc1uc1W7ZsZuXKlcYY4xIIdu3aZcLCwiyvlz9/fjNjxoxk9TZv3mzSpUtnaa1bv2259VuX2//faiEhIR57Pd7aeswYYypXruyyJ3b37t0eXxjBXW7tSXzrc2fv3r3067SJHDly3LUFmZUCAwNdXhtly5Y1PXv2dF7ft2+fpX2Xly9fbvz9/U29evXMqlWrnB+YV65caV544QXj7+/vseDuLtauDYr/vHr16mn+/PnensZDJ6k1z6FDhzRv3jznca7uaM0jSd27d9fly5eTjV+5ckXdu3e3vJ50syVOSq3IYmNj3dLSZd++fSmeGR4YGKjY2FhLay1evFiLFi1yudw6lvT/VnvyySe1du1ay7ebkoiICGc3hcTERK1du1alS5d23n7jxo1kLYLsKjY21nn8+a1Onz5t+TKmcI+ePXuqS5cubmkZebvIyEhnp4EbN25o3bp1zg4V0s3jt608/6RMmTKaMmWKFi9erMcff1xp06ZVRESEypYtq8WLF2vy5MkqW7asZfW84eE4GwL/GjExMercubP++OMPj5/o8zDzdE9Cc4cTTzZu3Oi2FkulSpXSzz//rPfee0+SnPW//vprlzd6q+TMmVMbNmxIdmLf3LlzVaBAAUtrVahQwdLt/V21atVS27ZttW3bthRfj88++6xltSpUqKCePXtq+PDhmjZtmhITE1WpUiXn7du2bUvWi9muypcvrwkTJqhnz56Sbj5XExMTNWDAAJfHjH+vgQMHas+ePYqMjFSOHDmSvTbWrVtnWa2nnnpKH330kfr166eZM2cqJCTE2XdZutnu7JFHHrGsniTVqVNHTz75pObNm6ddu3ZJkvLkyaPq1avL399fBw8evOMJXXZAeIWlRo4cqdDQUC1dulRLly51uc3hcLglvG7atCnFcYfDoaCgIGXLls32e0M81ZMwbdq0cjgccjgcypMnj0uATUhI0OXLl/X2229bVu9Wffr00VNPPaVt27YpPj5eQ4cO1datW7Vy5cpkzyUrtG3bVs2bN9e1a9dkjNHq1as1efJk9enTR6NGjbK8nje88cYbkqQePXoku83hcFi6GMMnn3yiatWqKUeOHPLx8dFnn32mVKlSOW+fOHGiKleubFk9bxowYIAqVqyotWvX6saNG2rXrp22bt2qs2fPavny5d6enm1s2rRJhQoVko+P578Efu655zxWq1evXqpbt64qVKig0NBQjR8/XgEBAc7bx4wZY1nXmFuFhISk+O/Dxo0bVbx4cbcsxuIptMqC7fn4+Nz1a2V/f3/Vr19fX331lVtWanqYjB8/XsYYNW3aVEOGDFF4eLjztoCAAOXIkcMte0GTbNmyRQMGDNCff/6pxMREFS9eXO3bt3fLIgXSzb26vXr1cq6MljlzZnXr1k3NmjVzS72HXVxcnLZt26YMGTIoU6ZMLrdt3LhRWbJksWTVu4iICO3cuVPp06dX06ZNNXToULe2xErJ8ePH9eWXX7o8V5s3b27rBQM8zdfXV8eOHVPGjBmVK1curVmzxtJVEf9tklrj+fr6uoyfPXtWoaGhLoHWnQiv+Mc8vZfw9mVFb68XExOj2rVrW/5V8OnTp+VwODzyRvTjjz+qffv2atu2rf73v//JGKM1a9Zo4MCB6tq1q+Lj4/XRRx+pfv36+vTTTx+43pw5c+Tr66snn3zSZXzevHlKTEx06eVnV0uXLlWZMmXctira7eLi4vTmm2+qc+fOypUrl0dq3ur06dNKTEx0HnN75MgRZc6c2ePzwN8TGhqqTZs2KVeuXPL19dXx48eVIUMGb09L165d07Bhw/Thhx96eyq2kC5dOs2ZM0elS5eWj4+PTpw48a/4Oz7sCK/4xzy9l7BSpUpat26dEhISlDdvXhljtGvXLvn6+ipfvnzasWOHHA6Hli1b9sDH+Z0/f16dOnXSlClTnGuKp02bVg0aNFCvXr2UJk2aB348Kfnf//6nnj17phgmO3furNWrV2vmzJn64IMPtGfPngeuV6RIEfXt21c1a9Z0Gf/ll1/Uvn17bdy48YFr/BskJCRo5syZ2r59uxwOhwoUKKBnn3022V4Dq6RJk0br1q3zSnhNcvz4cX3yyScaNWqUvRt46+aa8YMHD9bkyZO1c+dOORwO5c6dWw0bNlSrVq089sHEHapVq6YTJ06oRIkSGj9+vOrXr6/g4OAU72vlmvHSzQ86q1atkr+/v6pUqSJfX1/FxcVp+PDh6tOnj+Lj43X69GlLaz6s3nzzTU2YMEHR0dE6ePCgsmTJcsf3F6sWDUg6NOpezp49a0m9f6OHIbxyzKuHzZgx42/tJfz4448t2UuYtFd17NixzrPSL168qGbNmqlcuXJ64403nCcCzZs3777rnD17Vo8//riOHDmiRo0aKX/+/DLGaPv27Ro3bpwWLlyoFStWKG3atA/8mG63efPmFFfTyp49uzZv3ixJKlasmPNM6Ae1a9euFIN+vnz5tHv3bktqeNvu3btVs2ZNHTlyxPmhZ+fOncqaNat+/vlny08ukG6eYDBz5sw7fltglfPnz6t58+aaP3++/P399dFHH6lFixbq1q2bPv30UxUsWNDywJNk3LhxevHFF1M8U91KV69eVbVq1bRy5UpVrVpV5cuXlzFGf/31l9q3b69Zs2Zp/vz5tj2M5ptvvtH/Y+/M42pM////OqdF+4JCtKe0EllGY8kakW1Gdip8ZBBSGGtGZKvs2RUmGpNtjLWIIkSLpbTLmjVLhZbr94df5+s4ZRnXfR+drufjcR7Tue577teVzvK+r+v9fr2Dg4ORnZ0NgUCAly9f4u3bt5zrXrhwAS4uLnj58iUEAgEcHBywY8cO9O/fHxUVFZg7dy48PDw4n4essHnzZgwcOBBZWVmYMmUKxo0bx3n6R0hICKfX/xGoboe3ktu3b/M0Ew7h352rdtO6dWty/PhxifHjx4+T1q1bE0IIOXDgADExMaGip6enR27evCkxfuPGDaKnp0cIIeTq1avf7Wvp7e1NbGxsqu0zbmtrS6ZOnfpdGtXRokULMnr0aLGuYe/fvyejR48WmdvHxcURIyMjKnoNGjQg0dHREuOnTp0iOjo6VDSkTa9evYizszN59uyZaOzp06fE2dmZM3PrxYsXEy0tLTJo0CCyZMkSsnr1arEHLby8vEiTJk2Ij48Psba2FnUqcnJyImfPnqWmUxUNGzYk6urqxMPDg8THx3OmM2/ePGJgYFBll53k5GRiYGBAtaOPNDEyMiJPnz7lRatLly7Ezc2NXL9+nUybNo0IBAJibGxMwsLCeGmOIMuMGTOGvHr1StrTkAkqfaMru7F9/ODSV5pPWPDKM0pKSiQtLU1iPC0tjSgpKRFCPhgW0zKeV1VVJWfOnJEYP3PmjMgUOTs7+7tN4A0NDasMyis5duwYMTQ0/C6N6oiPjyf16tUjOjo6pGvXrqRbt25EV1eX1KtXT2R6Hx4eTpYvX05Fb9y4ccTW1pZkZWWJxjIzM4mdnR3x9PSkovEpS5YsIdu2bZMY37ZtGycdzVRUVEhqaqrEeHJyMmcm7Hy1pDUwMCCnTp0ihHx47QsEAuLt7U3t+p+jrKyMHDp0iAwYMIAoKioSCwsLEhgYSB4+fEhVp2nTpmT//v3VHo+MjCRNmzalqlkb+LjTXVFREREKhSQyMlLKs5I97t69S+7duyftaVDl/fv3ZMyYMbw0DcnLy/uqR02GBa88w/cq4bBhw4ixsTGJiooSfSBERUURExMTMmLECEIIIREREaRVq1bfpaOoqEju3r1b7fG7d++SOnXqfJfG53j9+jXZuHEjmTZtGpk6dSoJDQ3l7C6+sLCQtGvXjsjLy4uCK3l5eeLk5MRZP3VDQ8MqV+oSEhKovVY+Rltbu0q9uLi4Gt8TW15enty/f1/0XFlZmVy/fp33eRQUFJBVq1YRW1tboqCgQPr27UsOHjxIysvLv/van3b0+ZT8/HzO3o/bt2+vMqCLjIwkO3fu5ETz7NmzpE+fPsTU1JSYmZmRvn37knPnzlHXEQgEpKCgQPRcTU2NZGZmUtepjZSXlxN/f3+ioaEh6jynqalJFi1aROU98SOgqanJOlBSggWvPMP3KuHr16/J2LFjiaKiougDQVFRkYwbN468efOGEEJIUlISSUpK+i4dPT09cv78+WqPnzt3TpSmIAtUVFSQEydOkOXLl5O1a9eS2NhYTvXq1KlDcnJyJMazs7M5CUJGjhxJrK2tSUJCAqmoqCAVFRXk4sWLxMbGhowePZq6Hp8IhULy+PFj0XM1NbUq/235ICEhgYwfP57UqVOHGBkZES0tLWJkZFTlbsm3oKOjQxITE6s9fvnyZc5SXMzNzUlMTIzE+NmzZ4m5uTl1vV27dhF5eXkyePBgsnr1ahISEkIGDx5MFBQUyJ49e6hqCYVCkpWVJWq3qa6uTlJSUsjLly/FHoxvZ9asWURHR4ds2LCBpKSkkOTkZLJ+/Xqio6NDfv/9d2lPjwpjxowhq1at4lynqKiITJw4kejp6REdHR0ydOhQ8uTJE851+YS5DUiBN2/eYPfu3cjIyAAhBM2aNcOwYcM4TVR/8+YNcnJyQAiBqakp1NTUqF7f09MTWVlZOHXqlIRX3bt379CzZ0+Ymppi27ZtVHUrycjIwNmzZ/H48WNUVFSIHZs/fz4nmnzStGlTLFiwACNGjBAb37VrFxYsWECtEreSwsJCjB49GkeOHBFVpZeVlcHV1RU7d+4U83+taQiFQvTq1UtkSXfkyBF06dJFzFAfAKKiojjRLygowK5du7Bjxw7k5OSgf//+8PT0RLdu3VBSUoK5c+di//79uHPnzn/WcHNzQ1lZGf7+++8qjw8aNAhycnKIjIz8zxrVoaSkhPT0dIluWnl5ebC0tKTu4mBpaYnx48dj2rRpYuNBQUHYsmUL0tLSqGl96hZDPulEV/m8JldxSws9PT2EhoZKdH07dOgQJk6ciPv370tpZvQICAjAypUr0bVrV7Rq1UriM4dWEx9fX19s2LABw4cPh5KSEiIiItC5c2f89ddfVK7/I8CCVwYV7t27BwcHB9SpUwe//fYbmjVrBuBDS8gNGzbg3bt3SExMhL6+PnXtLVu2wMvLC/Xr10fDhg3FvkwEAgGVNn9r1qzB+PHjoaSkhDVr1nz2XC66iC1btgwrVqzAihUrRF2KoqOj4efnBx8fH8yePZu6JvDBWSE9PR2EEFhZWcHMzIwTHT5xd3f/qvN27NhBXbtv3744ceIEzM3NMXbsWIwaNUrCY/nBgwdo0qSJxE3Yt3Dr1i20bdsW1tbWmD59utj7MTg4GLdu3UJCQgKsra2/6/epCgMDA6xbt67KIOS3337DvXv3qOrVqVMHN2/elHhtZmVlwcbGhqoLwdd2epNWO+CajJKSElJTU2Fubi42fvv2bbRo0YLqTU9paSksLCzwzz//UG8F/TmMjY2rPSYQCKgtQpiamiIgIABDhgwBAFy+fBmOjo54+/YtZ1aHvCO1Nd9azO3bt8mmTZvIH3/8Qfz9/cUetHnz5g2ZO3cu+emnn4ipqSkxNjYWe9AkJyeHODs7i1U5CoVC0rNnT07zwgwMDDgpWvqYjyua+Sos+piKigri5+dHlJSUROkfKioqnLxmGNzh4eFBLly48NlzKioqqBRTXLx4kVhZWYneh5XvS0tLS06dDnx9fYmhoSGJiYkhZWVlpKysjERHRxNDQ0Pi4+NDXc/U1JSEhoZKjIeGhhIzMzPqegxuaNOmDZk8ebLE+KRJk0jbtm2p6+np6ZFbt25Rv+6PgIKCgkTBm5KS0mfz4GsabOWVZ/hYJfyYoUOHIjY2FiNHjkSjRo0kzJm9vb2p6gHAixcvkJmZCQAwMzOj3r3rUzQ0NJCcnCxVc3u+ePPmDdLS0qCsrIymTZtS7cb2MYQQ7N+/H2fOnKkyFYP2lnpZWRkCAgLg4eHByep8bSY5ORkZGRkAAHNzc7Ro0YJTvffv32PkyJH466+/IC//wUq8oqICo0aNQmhoKPUWmBs3bsTUqVPh4eGB9u3bi5qu7Ny5E6tXr8b//vc/qnoMboiNjYWLiwsMDAzw008/QSAQ4MKFC7h79y7+/fdfdOjQgapeYGAg0tPTsXXrVtHrVFaoquucuro6UlNTP7v6W5NgwSvPGBoaYuLEiZg5cyYvelpaWjh69CgcHR150ZMGnp6eaN26NSZMmCDtqcgMU6ZMwebNm+Hk5IQGDRpI3PRwsaWupqaGGzduSORKyhLVpZx83K65Y8eOMrG1l5GRgZSUFCgrK8PW1rbKRiK0OHDgAFatWiXKb7W0tISvry/69evHmSaDPg8ePMD69evFUpUmTpwIPT096loDBgxAdHQ01NTUYGtry1vO+71793D48GHk5+fj/fv3YseCgoKoaHya1w9UndvP1e/IByx45Rm+VwmNjY3x77//wtLSkhc9abB06VIEBQXBxcUFtra2Em0vaeeg/vLLL3BwcMCsWbPExlesWIHLly9TS4ofOHAgdu7cCQ0NDQwcOPCz59L+EKpbty52794t0QKXS/r374/+/ftjzJgxvGnyjbGxMZ48eYLi4mJoa2uDEILCwkKoqKhATU0Njx8/homJCc6cOcNWoBkMDvlS7jsXN+jR0dFwdXWFsbExbt++DRsbG+Tl5YEQgpYtWyImJoaKjjTz+vmCBa88w/cq4e7du3Ho0CGEhYVx3pJSWvCVBF+Jjo4OYmJiYGtrKzZ+/fp1dOvWDQUFBVR03N3dsWbNGqirq/P+QWtsbIxjx46JCn34YNOmTVi4cCGGDx9eZSXupwVANZGIiAhs3rwZW7duFbXYzcrKwv/+9z+MHz8ejo6OGDJkCBo2bIj9+/dLebZfz/Tp0/HHH39AVVX1i+19aa0uMRg1jTZt2sDZ2RmLFi2Curo6UlJSoKuri+HDh8PZ2RleXl7SnmKNgQWvPMP3KqG9vT2ys7NBCIGRkZGEHu0c29qAsrIykpOTYWFhITaenp4Oe3t76lZA0iAsLAzHjx/H9u3boayszIumUCis9hht+6HS0lKMHz8e8+bN4zVX2tTUFH///bdE3mlSUhIGDRqEnJwcXLhwAYMGDcLDhw95m9f34uTkhAMHDkBLSwtOTk7VnicQCKitLkmLsrIyKCkpITk5GTY2NtKeDuM7KCsrw9mzZ5GdnS2yq3zw4AE0NDSo20kCH/JOk5OTYWpqCm1tbcTFxcHa2hopKSno168f8vLyqGvKKrKVpVwD2Lx5M9TU1BAbGythuyIQCKgHr/3796d6PQZgY2ODffv2SfjH7t27lzPbldzcXJSVlaFp06Zi45mZmVBQUKCeJ/rrr78iIiICurq6vN30fI811LeioKCAAwcOYN68ebxpAsDDhw9RVlYmMV5WVoZHjx4B+OB3+fr1a17n9b2cOXOmyp9lEXl5eRgaGjIv1xrOnTt34OzsjPz8fLx79w7du3eHuro6li9fjrdv3yI0NJS6pqqqKt69ewfgw/s8OztbZFf39OlT6nqyDAteeSY3N5dXvQULFvCqt3TpUjRo0AAeHh5i49u3b8eTJ084KVT7VOtTtm/fTlVv3rx5GDRoELKzs8U8VyMiIjgzgR4zZgw8PDwkgtdLly5h69atOHv2LHW9q1evYsSIEVUWbHHN27dvoaSkxKnGgAEDcPDgwS9uc9PEyckJ//vf/7B161bY29sD+LDq6uXlJXotXb9+/bsqglNTU7/6XDs7u/+sUx0vX75EeXm5hMvI8+fPIS8vDw0NDeqafDN37lzMnj0bu3fv5txNhcEN3t7ecHBwQEpKCurVqycaHzBgAMaOHcuJZrt27RAfHw8rKyu4uLjAx8cH169fR1RUFNq1a8eJpqzC0gYYVDEyMsKff/6J9u3bi41funQJQ4YM4SR4HzBggNjz0tJS3LhxA4WFhejSpQsnFZVHjx7FkiVLkJycDGVlZdjZ2WHBggWcmZNraGjg2rVrVRqxOzg4oLCwkKqeqqoqTpw4gZ9//pnqdT9HeXk5lixZgtDQUBQUFCAjIwMmJiaYN28ejIyM4OnpSVWPr243H/Po0SOMHDkS0dHRYp3Lunbtil27dqFBgwY4c+YMSktL0aNHj/+kUdkFqrqP9spjXHWC6tWrF/r27YuJEyeKjYeGhuLw4cP4999/qWvyjb29PbKyslBaWgpDQ0OJ1w5Lx/rxqV+/PuLj42FhYSHKPzUxMUFeXh6srKxQXFxMXTMnJwdv3ryBnZ0diouLMWPGDMTFxcHMzAzBwcGcOnLIGmzllWf4XiX8tJ3hp9D+8nr06BEaNWokMa6jo8NZDt+BAwckxioqKjBx4kTO8hldXFzg4uLCybWrQiAQVLmVXLnKRRt9fX3eV8gCAgIQFhaG5cuXY9y4caJxW1tbBAcHUw9et27dCi0tLVy9ehVXr14VO8ZFCg8ANGzYEKdOnUJ6erpYe+iP86c/lzP6NfC9u/Mply5dqrIoq3PnzpgzZw4VjW9ZLeeiQIylY9HB3t7+q3d1aN8QVFRUVPnZee/ePc5atX/8faSiooINGzZwolMbYMErz7x48ULs+aerhLT5NLArLS1FUlISwsLC4O/vT11PX18f8fHxEtue8fHxnHj1VYdQKMS0adPQuXNn+Pn58abLFR06dMDSpUsREREh8gAtLy/H0qVLOVkdXbVqFfz8/BAaGsqb72p4eDg2b96Mrl27irlx2NnZIT09nbqeNIO8jwNW2ikZ0l69effuXZV5vaWlpdSKGZOSkr7qPK7SXfhOx5JVpHkT0L17d4SEhGDz5s0APrxW3rx5gwULFvBqEcj4b7DglWf4XiWsyqT7l19+gbW1Nfbt20d9NWvs2LGYOnUqSktLxfJB/fz84OPjQ1XrS2RnZ1f5Jfq9lJeXIzg4GJGRkVUaTT9//py65vLly9GxY0dYWFiIOs2cP38er1694qR6e8SIESguLoapqSlUVFQkCra4+B3v378vkRYBfHh/lJaWUter5P3798jNzYWpqSkvnXbCw8OxYsUKURc6c3Nz+Pr6YuTIkZxp3rp1q8rXKhf2Y61bt8bmzZuxdu1asfHQ0FC0atWKisaPUBRWWFiI/fv3Izs7G76+vqhbty6uXbuGBg0aoHHjxtKeXo1AmjcBwcHBcHJygpWVFd6+fYthw4YhMzMT9evXR0REBDUdbW3tr76J4uJzVVZhwesPgDRWCdu2bSu2NUsLPz8/PH/+HBMnThR9USopKWHmzJmYPXs2dT1AcguREIKHDx/i6NGjGD16NHU9f39/bN26FdOnT8e8efMwZ84c5OXl4eDBgxIOBLSwsrJCamoq1q1bJ+paNGrUKEyaNImTgpGQkBDq1/wS1tbWOH/+vMTK4V9//SUqbqJJcXExJk+ejLCwMAAQ5dhOmTIFenp6Ek0oaBAUFIR58+Zh0qRJcHR0BCEE8fHxmDBhAp4+fYpp06ZR1cvJycGAAQNw/fp1sTzYyi9TLlJOAgIC0K1bN6SkpKBr164APtzAXrlyBSdPnqSuJw1SU1PRrVs3aGpqIi8vD+PGjUPdunVx4MAB3LlzB+Hh4dKeIuML6OnpITk5GXv37sXVq1dRUVEBT09PDB8+nKo9oDQ+S2sFhPFDcPToUVK/fn1etIqLi4m3tzcxNzfnTOP169fk8uXL5Pr16+Tt27ec6RBCSOfOncUeXbp0IW5ubmTTpk2ktLSUup6JiQn5559/CCGEqKmpkaysLEIIIatXryZDhw6lrldbOHz4MNHU1CSBgYFERUWFrFixgowdO5YoKiqSkydPUtebMmUKadWqFTl//jxRVVUl2dnZhBBCDh06RFq0aEFdjxBCjIyMSFhYmMT4zp07iZGREXW9Pn36kH79+pHHjx8TNTU1cuvWLXL+/HnSpk0bcu7cOep6lSQlJZFhw4YRKysr0qpVK+Lu7k4yMjI407t8+TLx9fUlbm5uZMCAAWIPLujatSvx9fUlhHz4DKh87cTHxxNDQ0NONGWdsrIysmLFCtK6dWvSoEEDoq2tLfZgMD6GBa88M23aNLHH1KlTiZubG1FTUyO//fYbdT0tLS2xDwAtLS0iJydH1NXVycGDB6nr1QZUVFTInTt3CCGENGzYkFy9epUQQkh2djbR0NDgVLuoqIikpaWRlJQUsQcXZGVlkTlz5pAhQ4aQgoICQgghx44dIzdu3OBEjxBCjh8/Tjp27EhUVVWJsrIycXR0JCdOnOBEy8DAgFy8eJEQIh6AZGZmEnV1dU4069SpQzIzMyXGMzIySJ06dajr1atXT/T60NDQIOnp6YQQQqKjozkL0PkmIiKCKCgoEBcXF6KoqEj69OlDLCwsiKamJhkzZgwnmhoaGqKb1o9fO3l5eZz8HWsD8+bNI40aNSIrVqwgSkpK5I8//iCenp6kXr16ZPXq1dT1lixZQrZt2yYxvm3bNhIYGEhdjxBC7ty589kH4+thaQM882mhgVAohI6ODlatWvVFJ4L/wqdbFpV6bdu2hba2NhWNgQMHYufOndDQ0MDAgQM/ey4XtlV806RJEzx8+BAGBgYwMzPDyZMn0bJlS1y5cgV16tThRPPJkydwd3fHsWPHqjxOe/s3NjYWvXr1gqOjI86dO4eAgADo6uoiNTUVW7du5ax1ac+ePdGzZ09Orv0pT548ga6ursR4UVERZ4U+ZmZmiIyMxO+//y42vm/fPgkPXxqUl5eLOgXVr18fDx48gIWFBQwNDXH79m3qep9SUlIika9M28ViyZIlCA4Oxm+//QZ1dXWsXr0axsbG+N///lel8wkNlJSU8OrVK4nx27dvQ0dHhxNNWWfPnj3YsmULXFxc4O/vj6FDh8LU1BR2dnZISEig7v6xadMm/PnnnxLj1tbWGDJkCCee5EZGRry6/8gyLHjlmeoKDYqKinDhwgV07NiRql51OZ93796Fj48PFWsuTU1N0RtSU1Pzu6/3NUjTYmXAgAGIjo5G27Zt4e3tjaFDh2Lbtm3Iz8+nnrNYydSpU/HixQskJCSIWnEWFBRg8eLFWLVqFXW9WbNmYfHixZg+fbqYbYyTkxNWr15NXQ8Arly5goqKCrRt21Zs/NKlS5CTk4ODgwNVvdatW+Po0aOYPHkygP/LA92yZQt++uknqlqV+Pv7w83NDefOnYOjoyMEAgHi4uIQHR2NyMhI6no2NjZITU2FiYkJ2rZti+XLl0NRURGbN2/mzEauuLgYfn5+iIyMxLNnzySO0/6Czs7OFtnW1alTR3TzMW3aNHTp0oUTV5V+/fph0aJFor+ZQCBAfn4+Zs2ahUGDBlHXqw08evQItra2AAA1NTW8fPkSANCnTx9OOuFJw9bx08WrSvefoKAgBAQEcKIpq7Dg9QchKysLTk5OvN15PX/+HGFhYVSC1x07dlT5M5dI02IlMDBQ9PMvv/wisgczMzPjpHobAGJiYnDo0CG0bt0aQqEQhoaG6N69OzQ0NLB06VLqnrPXr1+vclVCR0enyoCEBr/99hv8/Pwkgtf79+9j2bJluHTpElW9pUuXwtnZGbdu3UJZWRlWr16Nmzdv4uLFixKtm2kxaNAgXLp0CcHBwTh48CAIIbCyssLly5c5KUqbO3cuioqKAACLFy9Gnz590KFDB9SrVw/79u2jrgcAvr6+OHPmDDZs2IBRo0Zh/fr1uH//PjZt2iT23qFF3bp1RR7IjRs3xo0bN2Bra4vCwkJOjOYBYOXKlejduzd0dXVRUlKCTp064dGjR/jpp59YEPIf4XtHSxq2js2bN5cYc3BwgJ6eHlasWPHFnUvGR0g7b4HxgeTkZCIUCmu8Xk5OTpWFGRkZGSQ3N5e6Ht+8f/+ejBkzRpTjxhfq6uqifz9DQ0MSFxdHCPnw762srExdr3HjxiQ+Pp4QIp7TFxUVRUxMTKjrEULEiqY+Jicnh6ipqXGimZqaSkaNGkWsra2JpaUlGT58OElNTeVE60fh2bNnpKKigrPr6+vrkzNnzhBCPrxuK3N8w8PDSa9evajrDR06lKxatYoQQsjixYuJjo4OGTt2LDE0NOSsYKuS6OhosmLFCrJs2TJy6tQpTrVknZkzZ5KAgABCCCF//fUXkZeXJ2ZmZkRRUZHMnDmTul5gYCCpV68e2b59O8nLyyN5eXlk27ZtpF69emTJkiXU9T5HRkYGUVFR4VWzpsOC1x8EWQleO3bsSHbu3CkxvmvXLtKpUyfqetJAU1OT9+DVwcGBHD9+nBBCSL9+/cjIkSPJvXv3iJ+fHyfBpK+vL/n555/Jw4cPRQFIXFwcMTExIQsXLqSuRwghdevWJRcuXJAYj4+PJ1paWpxo8sHLly+/+kGbwsJC8uzZM4nxZ8+ecaJHyIebkLy8PELIh5ugS5cuEUI+3ISoqqpS13v27Bm5f/8+IYSQ8vJysmzZMtK3b18ybdo08vz5c+p6DH64ePEiWbVqFTl06BAn16+oqCB+fn5ESUmJCIVCIhQKiYqKCvH39+dEjxDJz4LCwkKSlpZG3NzcSPPmzTnTlUVY8PqDICvB68crLR+TmZlJNDU1qetJgzFjxohWevhi9+7dZMeOHYQQQq5du0Z0dHSIUCgkSkpKZO/evdT13r9/T4YNG0aEQiERCAREQUGBCIVCMmLECFJWVkZdjxBC3NzcSKdOnUhhYaFo7MWLF6RTp07k119/pa539OhR0Q3Bxxw/fpz8+++/1HQEAoHoy7G6R+U5tHF2dibr16+XGN+4cSMnq6CEEGJra0vOnj1LCCGke/fuxMfHhxDywUqucePGnGjyzeTJk6usgF+7di3x9vbmf0KM/wyfto5VfRYIBAJiYGBQ5Y07o3oEhPx/12oGpxw+fPizx3NzczF9+nRqOa9fyp0pLCxEbGws9RxbTU1NnD17ViJ/7+rVq+jcubMoN60mExAQgJUrV6Jr165o1aoVVFVVxY7TroqtiuLiYqSnp8PAwAD169fnTCc7OxtJSUmoqKiAvb09JxXxldy/fx8dO3bEs2fPRK+f5ORkNGjQAKdOnYK+vj5VPTs7OwQGBkq0gjx+/DhmzpyJlJQUKjrfkj/bqVMnKpqV1K1bF/Hx8bC0tBQbT09Ph6OjIyf5y8HBwZCTk8OUKVNw5swZuLi4oLy8HGVlZQgKCoK3tzd1zYqKCmRlZeHx48eoqKgQO0a7CBb4kFt7+PBhiY5h165dg6urK+7du0ddszaQkZGBs2fPVvl35KoBDJ+cPXtWrNC40v3HzMyMl+5+sgQLXnlCKBR+8RyBQEAtmHR3d/+q82gXWPXp0wcqKiqIiIiAnJwcgA/VxW5ubigqKqrW6ulbefXqFXXLna/l0wT/jxEIBMjJyaGqV1paCgsLC/zzzz+wsrKieu0fjaKiIuzZs0fURczOzg5Dhw6VaE9LA2VlZaSlpcHIyEhsPC8vD9bW1qJCp5qMqqoqEhISRFXclVy/fh1t27blrKDpY/Lz85GYmAhTU9MqC1a+l4SEBAwbNgx37tzBp19nND9TP0ZJSQk3btyQaGeclZUFGxsbvH37lrqmrLNlyxZ4eXmhfv36aNiwoViQJxAIqLvGFBUVITAwENHR0VUGy7Q/xxl0YaE+T3z6xuAavqr+P2X58uXo2LEjLCws0KFDBwDA+fPn8erVK8TExFDT0dbWxsOHD6Grq4suXbogKioKWlpa1K7/OXJzc3nRqURBQQHv3r3jzHu0KsrLy7Fz585qP9hp/i0/RlVVFePHj+fk2p+iqamJnJwcieA1KytLYjWdJoWFhdi2bRvS0tIgEAhgZWUFDw8PTmzmWrdujc2bN2Pt2rVi46GhoRKrhjQoLS1Fjx49sGnTJpibmwMADAwMYGBgQF2rkgkTJsDBwQFHjx5Fo0aNeHmfmJmZ4fjx45g0aZLY+LFjxzizIJN1Fi9ejICAAE78Vati7NixiI2NxciRI3l73SxduhQNGjSQ8HTfvn07njx5wtvvLguwlVcGdR48eIB169aJrZ5NmjQJdevWpaahqamJhIQEWFpaQigUoqCgQCrm4OSTXvFcERgYiPT0dGzdupWX7aVJkyZh586dcHFxqfKDPTg4mBNdPrcNx48fj4SEBBw4cACmpqYAPgSugwYNQuvWrbF161aqegCQmJiInj17QllZGW3atAEhBImJiSgpKRFZA9EkPj4e3bp1Q+vWrdG1a1cAQHR0NK5cuYKTJ0+KbjBpoqOjgwsXLnCaYvIxqqqqSElJkVgF5ZLt27dj0qRJ8PX1RZcuXQB8+HddtWoVQkJCMG7cON7mIitoaGggOTmZt+BfS0sLR48ehaOjIy96wIcmBX/++Sfat28vNn7p0iUMGTKE94WRmgwLXhk1kkGDBoly+WJjY9G+fXsoKipWeS4Xq4Th4eFYsWIFMjMzAQDm5ubw9fXFyJEjqWsB/9cYQU1NDba2thIrg7Q7l9WvXx/h4eES+aBcwve24cuXL+Hs7IzExEQ0adIEAHDv3j106NCBs5X8Dh06wMzMDFu2bBHdhJSVlWHs2LHIycnBuXPnqGsmJydjxYoVSE5OFt1Mzp49m7Pg0sfHBwoKCpx4ulZFly5d4OfnB2dnZ170Ktm4cSMCAgLw4MEDAB8Ck4ULF2LUqFG8zkNW8PT0ROvWrTFhwgRe9IyNjfHvv/9K5INziZKSEtLS0iRSz3JycmBlZcXSTb4BljbA4ITi4mLk5+fj/fv3YuN2dnZUrr97926EhYUhOzsbsbGxsLa2hoqKCpVrf4mgoCDMmzcPkyZNgqOjIwghiI+Px4QJE/D06VNOumxpaWnx2rlHUVGR15UsgP9tQ01NTVy4cAGnTp0S2yXgosCnksTERLHAFQDk5eXh5+dHvYNYJS1atMCePXs4uXZVvH//Hlu3bsWpU6fg4OAgcaMVFBREVW/y5Mnw8fERdWj6ND+a1mfOp3h5ecHLywtPnjyBsrKyqA0v479hZmaGefPmiXK0P/070i6E/eOPPzB//nyEhYXx9t0hjcYIsgpbeWVQ5cmTJ3B3d6+2MIuL4onKdql85bwaGxvD399fYoUlLCwMCxculImtn1WrViEnJwfr1q3jLdeW721DadCgQQPs2rULPXr0EBs/ceIERo0ahYKCgu/W+LiY8dWrV589l4uiRycnp2qPCQQC6jshVRXDCgQCEEI4K9hi0IfvQlh7e3tkZ2eDEAIjIyOJYJn2Tg8ALFu2DCtWrMCKFSvE0k38/Pzg4+OD2bNnU9eUVdjKK4MqU6dOxYsXL5CQkCAKKgsKCrB48WKsWrWKE80zZ86IfuYjB/Xhw4cSOUsA0L59e856Ylfy5MkT3L59GwKBAObm5lTzfD+1V4uJicGxY8dgbW0t8cFOO00BAH799VecPHmSt21D4MMXR3VFaTRaJ3+Km5sbPD09sXLlSrRv3x4CgQBxcXHw9fXF0KFDqWh8XMyopaVV5XuBy8Du4/cjH0jjZrGgoAAzZswQvXY+XQNiAfO3QQjBmTNnoKury9sqqDRajPv5+eH58+eYOHGiaFdSSUkJM2fOZIHrN8KCVwZVYmJicOjQIbRu3RpCoRCGhobo3r07NDQ0sHTpUri4uHCiy2cOqpmZGSIjI/H777+Lje/bt4+zPMKioiJMnjwZ4eHhoiBLTk4Oo0aNwtq1a6l84H9a7T5gwIDvvua3wPe2ob+/PxYtWgQHBwfeqo1XrlwJgUCAUaNGoaysDMAHNwkvLy9qOaIxMTGi4ki+A8lPuXfvHgQCARo3bszJ9UtLS+Hk5MS7jdyYMWOQn5+PefPm8fbakWUIITA3N8fNmzd5K/RbsGABLzofIxAIsGzZMsybNw9paWlQVlZG06ZNUadOHd7nUtNhaQM8oK2t/dUfbs+fP+d4NtyioaGB1NRUGBkZwcjICHv27IGjoyNyc3NhbW3Nia9kdTmo69evx+LFi6nnoP79999wc3NDt27d4OjoKFo9i46ORmRkJCdB3//+9z+cPn0a69atE1XHxsXFYcqUKejevTs2btxIXZNv+N42bNSoEZYvX85Zkd3nKC4uFm1ZmpmZ8bbaxAcVFRWinZY3b94AANTV1eHj44M5c+Z8lef1t9C4cWOcPn2a18IbdXV1nD9/Hi1atOBNU9axtrbGtm3b0K5dO2lPhTcqLSQtLCx4ff3KAmzllQdCQkKkPQXesLCwwO3bt2FkZIQWLVpg06ZNMDIyQmhoKBo1asSJ5tq1a7Fx40axHNR+/frB2toaCxcupB68Dho0CJcuXUJwcDAOHjwIQgisrKxw+fJlic5itPj777+xf/9+dO7cWTTWu3dvKCsrY/DgwdSD19zcXJSVlUmsgmRmZkJBQUHCG5WWJp+8f/++yvQPPlBRUZFoHMAVhYWFuHz5cpWpEVxUxs+ZMwfbtm1DYGCg2M3kwoUL8fbtWwQEBFDVmzx5MpYtW8abjRzwofCGrfvQZfny5fD19cXGjRthY2PDuV55eTmCg4MRGRlZZXExFwtJgwcPRseOHTFp0iSUlJTAwcEBeXl5IIRg7969vBbl1nh4aULLqDXs3r2b7NixgxBCyLVr14iOjg4RCoVESUmJ7N27lxPNOnXqkMzMTInxjIwMUqdOHU40+UZZWZncunVLYvzGjRtERUWFul7Hjh3Jzp07JcZ37dpFOnXqRF1PGvj5+ZFFixZxrjNgwADy8uVL0c+fe9Dm8OHDRF1dnQiFQqKpqUm0tLRED21tbep6hBDSqFEjcujQIYnxgwcPEj09Pep6/fv3J+rq6qRRo0akR48enP+bEkLIiRMnSI8ePUhubi4n16+NaGlpEUVFRdH3hba2ttiDNvPmzSONGjUiK1asIEpKSuSPP/4gnp6epF69emT16tXU9QghpEGDBiQ5OZkQQsiePXuImZkZKSoqIhs2bCAtWrTgRFNWYSuvUqSkpASlpaViY9JqeUqL4cOHi362t7dHXl4e0tPTYWBggPr163OiKY0cVL57qf/0009YsGABwsPDoaSkBODD68ff3x8//fQTdb2kpKQqzbvbtWsn0VWIJvfu3cPhw4erXAmhbbH09u1bbN68GadPn4adnZ1Eji0tPU1NTVHaEBddtD6Hj48PPDw8sGTJEt5SE54/f45mzZpJjDdr1oyT1Sy+beSAD4V3xcXFMDU1hYqKisRrp6anf0kDvnco9+zZgy1btsDFxQX+/v4YOnQoTE1NYWdnh4SEBOo59sAHb+nKfPTjx49j0KBBUFFRgYuLC3x9fanryTIseOWZoqIizJw5E5GRkXj27JnE8ZpcpVpaWgoLCwux4gkVFRXqXYM+xd/fH25ubjh37lyVOai0kUYv9dWrV8PZ2RlNmjRB8+bNIRAIkJycDCUlJZw4cYK6nkAgwOvXryXGX758ydlrNDo6Gq6urjA2Nsbt27dhY2Mj2lLj4jWUmpoqylm8ceOG2DGaBTgft2rmu23z/fv3MWXKFF5zaps3b45169ZhzZo1YuPr1q1D8+bNqetJoxV2bUoF44vRo0fzqlfpCwwAampqePnyJQCgT58+mDdvHiea+vr6uHjxIurWrYvjx49j7969AIAXL16IFiUYXwcLXnnGz88PZ86cwYYNGzBq1CisX78e9+/fx6ZNm3jrSMMVCgoKePfuHe+Vt3znoEqjl7qNjQ0yMzOxe/dupKengxCCIUOGYPjw4VBWVqau16FDByxduhQRERGQk5MD8OHGaunSpfj555+p6wHA7Nmz4ePjg0WLFkFdXR1///03dHV1MXz4cE66J0m7Eh/4kHf7/v17zgzue/bsicTERF69c5cvXw4XFxecPn0aP/30EwQCAS5cuIC7d+/i33//5W0eXMJ3oFUbyM/P/+xxAwMDqnpNmjTBw4cPYWBgADMzM1F75itXrnBW/T916lQMHz4campqMDQ0FNUwnDt3jrcceFmBuQ3wjIGBAcLDw9G5c2doaGjg2rVrMDMzw65duxAREVHjP9wDAwORnp7Oa/EE30ijlzrf3Lp1Cx07doSWlhY6dOgAADh//ryoOpaLggp1dXUkJyfD1NQU2traiIuLg7W1NVJSUtCvXz/k5eVR1+STHTt24Nq1a2jXrh2GDx+O2bNnIygoCGVlZejSpQv27t2LevXqUdXctm0bFi1aBHd39yrtx1xdXanqVfLgwQOsX79edKNlZWWFiRMnctJFyNjY+LM3kLRdKj5FFtO/pIFQKPzs35H2js+sWbOgoaGB33//Hfv378fQoUNhZGSE/Px8TJs2jbPFpKtXryI/Px/du3cX3bQePXoUWlpaVaZqMaqGBa88o6amhps3b8LQ0BBNmjRBVFQU2rRpg9zcXNja2oqsZWoqAwYMQHR0NNTU1GBrayvRGpILc3u+kVYv9YyMDJw9e7bKPNv58+dT13vw4AHWrVsn1jp10qRJopwt2jRs2BAxMTGwsrKCtbU1li5dCldXV6SkpMDR0ZGT98aVK1fw119/VZljS/O1GhAQgICAALRv3x5JSUkYPHgwDh48iKlTp0IoFGLNmjXo06cPddeIz9lSyUr3qdWrV4s9Ly0tRVJSEo4fPw5fX1/MmjWLuqYsp39Ji5SUFLHnlX/HoKAgBAQESDRRoc2lS5cQHx8PMzMzzm7qGPSQzaWxHxgTExPk5eXB0NAQVlZWiIyMRJs2bXDkyBHe2ptyiTSKJ/ggNTVV9LM0eqlv2bIFXl5eqF+/Pho2bCi2QiEQCDgJXvX09LBkyRLq162Odu3aIT4+HlZWVnBxcYGPjw+uX7+OqKgoTrwf9+7di1GjRqFHjx44deoUevTogczMTDx69Ii6V+/OnTuxbds2DB06FImJiWjbti327duHX375BcCHtBAuOot9epPDF3zac3l7e1c5vn79eiQmJlLVqkSW07+kRVX50A4ODtDT08OKFSs4D17btm2Ltm3bcqoB8FuUKtNIzeeglhIUFCSy4YiJiSHKysoie5CQkBApz45RHQKBgAiFQiIQCKp8VB4TCoWc6BsYGJDAwEBOrv2jkJ2dTVJSUgghhBQVFREvLy9ia2tLBgwYQPLy8qjr2draknXr1hFCCFFTUyPZ2dmkoqKCjBs3jsyfP5+qlqKiIsnPzxd7np6eLnp+7949oqCgQFVTWkjDnqsqsrOzibq6OifX1tfXJ2fOnCGEEKKuri6y6gsPDye9evXiRLO2kpGRQdUOMDExkXTu3FlkX/cxhYWFpHPnziI7K9qcPn2aqKioEGtrayIvL09atGhBtLS0iKamJnFycuJEU1ZhaQNSJj8/H4mJiTA1NeWkEldaPHnyBLdv34ZAIIC5uTl0dHSkPaXv4s6dO199rqGhIXV9DQ0NJCcn81p4I+uoqqri5s2bMDIyQv369XHmzBnY2toiLS0NXbp0wcOHD6lpCYVCPHr0CLq6ugA+5PempKSI/p4FBQXQ09Ojst28Zs0ajB8/HkpKShIV/5/ChR2Qubk5evfuzas9V1UsX74cGzZs4CRXWtbTv6TBq1evxJ4TQvDw4UMsXLgQ6enpSE5OpqIzbNgwWFpaVusoEBAQgLS0NOzevZuK3se0adMGzs7OoqLUlJQUsaJULy8v6pqyCksbkDIGBgbUqyilSVFRESZPnozw8HDRdqGcnBxGjRqFtWvX1tg2mIaGhvDw8MDq1auhrq7Ou/6vv/6KkydPcrK1/KNw5coVVFRUSGzdXbp0CXJycnBwcKCqV7duXZEdWOPGjXHjxg3Y2tqisLCQkzbGt27dwqNHjwB8+GJOT08XBTlPnz6lphMcHIzhw4dDSUkJwcHB1Z4nEAg4CV75tueyt7cXS6MhhODRo0d48uQJNmzYwImmrKd/SQMtLS2Jgi1CCPT19UWWUjS4dOnSZ/OgXV1dsW3bNmp6H5OWloaIiAgAgLy8PEpKSqCmpoZFixahX79+LHj9BljwyjNTpkyBmZmZxJfGunXrkJWVVeP9A6dPn47Y2FgcOXJEVDkZFxeHKVOmwMfHh1pByrfkP9EqvAkLC0NgYCBvwevHq2ZmZmaYN28eEhISqsyz5SII4ZvffvsNfn5+EsHr/fv3sWzZMly6dImqXocOHXDq1CnY2tpi8ODB8Pb2RkxMDE6dOoWuXbtS1QKArl27ivkC9+nTB8CHIJIQQs1y7eM2u3y33AX4t+fq37+/2HOhUAgdHR107ty5ymYJNHB3d0dKSgo6deqE2bNnw8XFBWvXrkVZWRnLW/yPfGpdV/l3NDMzo+pcc//+/c9+hqupqVHddfkYVVVVvHv3DsCHmoLs7GxYW1sDoHsDWxtgaQM807hxYxw+fBitWrUSG7927RpcXV1x7949Kc2MDvXr18f+/ftF/nWVnDlzBoMHD8aTJ0+o6Li7u3/1ubRMzD/d+uUaY2PjrzpPIBBwZgeUlJQEW1tbsS+PAwcOUC9oAj58aaSmpkoEPbm5ubCzs6uyacL38Pz5c7x9+xZ6enqoqKjAypUrERcXJ7pR0NbWpqb1tWknXKSc8MHhw4dFPz958kQq9lzSRFbTv2QRfX19bNmypVq3mGPHjmH8+PG4e/cude3+/fvDxcUF48aNg5+fHw4cOIAxY8YgKioK2traOH36NHVNWYUFrzyjpKSEGzduSHiEZmVlwcbGBm/fvpXSzOigoqKCq1evwtLSUmz85s2baNOmDYqKiqQ0s+9HKBSioKCgxufvfgtCoRAaGhoYMWIE5s2bh/DwcPz+++8SvpY0qFevHv755x+JdrcXLlyAi4sLXrx4QV1TFpk+ffpXn0trlfBzllwfw5U9V3l5OQ4ePIi0tDQIBAJYWVnB1dVV1GCDUTPIzs5GSEiI6O9oaWkJb29vmJqaUtNwd3dHVlYWzp8/L3GMEIKOHTvCzMyMk85tOTk5ePPmDezs7FBcXIwZM2aIbpiDg4Nr7M2rNGDBK89UWuJ82h9+7dq12LhxI27duiWlmdGha9euqFevHsLDw0Xt7kpKSjB69Gg8f/68Rt9ZCoVCsT711cFFX/NFixZhxowZEnmEJSUlWLFiBSdWWcCH3yU1NRWbNm3CoUOHAHyw7Ro+fDh1rSFDhuDRo0c4dOgQNDU1AXywXOrfvz90dXWptfp98OABgoKCMH/+fAkz+ZcvX2Lx4sWYMWMGGjRoQEWPb5ycnL7qPIFAgJiYGI5nwz1ZWVno3bs37t+/DwsLCxBCkJGRAX19fRw9epRq4BMTE4NJkyYhISGhytdO+/btERoaKmrswfh6Tpw4AVdXV7Ro0QKOjo4ghODChQtISUnBkSNH0L17dyo62dnZaNWqFSwsLODj4wMLCwsIBAKkpaVh1apVyMjIQGJiokw3oZEFWPDKM9u3b8ekSZPg6+uLLl26APjQ033VqlUICQnBuHHjpDzD7+PGjRtwdnbG27dv0bx5cwgEAiQnJ0NJSQknTpwQ5ffQZv/+/YiMjKzSO+/atWtUNIRCIUJCQkSBVXVw0TpSTk4ODx8+lEhZePbsGXR1damtZiUkJEBeXl6iOGrChAmIjIxEaWkpTp8+zYkf4v3799GxY0c8e/ZM1NY3OTkZDRo0wKlTp6Cvr09FZ8aMGXj16hU2b95c5fEJEyZAU1MTy5Yto6JXW+jSpQuioqJ4L1jq3bs3CCHYs2ePqIHGs2fPMGLECAiFQhw9epSalqurK5ycnDBt2rQqj69ZswZnzpzBgQMHqGnWFuzt7dGzZ08Jn9xZs2bh5MmT1D7HASAxMRFjxozBrVu3RIsR5P93gtuxYwdat25NTetj3N3dMWLECHTp0oX3NuoyB6/GXAxCCCEbNmwgjRs3FnmEGhsbk7CwMGlPixrFxcVk8+bNZPr06WTatGlky5YtpLi4mDO91atXEzU1NfLbb78RRUVF8r///Y9069aNaGpqkt9//52ajkAgIAUFBdSu963ajx8/lhiPjo4m9evXp6bTtm1b8s8//4iNzZkzhzRs2JCkpKSQxYsXk969e1PT+5Q3b96QTZs2kYkTJxIfHx8SFhZG3r9/T1XD2tqanD9/vtrj8fHxxMrKiqom35SVlZGUlJQq33dFRUUkJSWFlJeXU9WU1vtDRUWFpKamSownJycTVVVVqloGBgbk1q1b1R5PS0sj+vr6VDVrC3Xq1CEZGRkS47dv3yZ16tThRDMpKYlERkaSffv2kaSkJE40PqZv376kTp06RE9Pj0yfPp0XTVmFBa9S5PHjx+T169fSnkaNx8LCgvz555+EkP8zmyeEkHnz5pHffvuNmo5QKOT9y7nS2F0oFIp+rnxoaGgQoVBIJk6cSE1PTU2N5Obmip6HhIQQAwMD0ZfKzZs3iYaGBjU9aaCiokLu3LlT7fE7d+5QNUWXBjt27CCtWrUiZWVlEsfKyspIq1atyK5du6hqSit41dbWJvHx8RLjcXFx1Jsi1KlTR9SQoCoyMzOJkpISVc3aQpMmTUhkZKTE+L59+2TqhuDFixdk06ZNpFOnTkQoFBJLS0sSEBAg9rnL+DLMKkuKyGrhT0ZGBs6ePVtla0gucjPz8/PRvn17AICysrKoKn3kyJFo164d1q1bR0WHSCHDJiQkBIQQeHh4wN/fXyxlQVFREUZGRhIFTt+DsrIysrKyYGRkhB07dmDjxo04f/68yIv43bt3UFRUpKZ3+PBh9OrVCwoKCmIV61VBq0pdWVkZeXl51for5+XlQVlZmYrWpyxcuBDu7u6cF2Zs27YNM2bMqLJgSU5ODn5+fli3bh1GjBhBVff169eiXPfq+DRX9Hvp06cPxo8fj23btqFNmzYAPnh5TpgwgbqzQePGjXH9+vVq8yFTU1PRqFEjqpq1hXHjxmH8+PHIyclB+/btIRAIEBcXh2XLlsHHx0fa06OGlpYWxo8fj/Hjx+PevXuIiIjA9u3bMX/+fJSVlUl7ejUHaUfPtQF7e3vy/PlzQgghLVq0IPb29tU+ajqbN28mcnJypEGDBqR58+akRYsWogdXv5+xsTG5evUqIYQQBwcHEhoaSggh5MSJE7y2o+SSs2fPktLSUs51PD09iba2NnF0dCRycnJkzpw5omMVFRVk8ODBVNtffrxaV13rXdptd3v37k3Gjh1b7XFPT0/OWny2bNmSyMnJkS5dupA9e/aQkpISTnR0dHQ+u5KTk5NDNd2EkP9roVzdg6v2yS9evCCurq5EIBAQRUVFUbvt/v37k8LCQqpakyZNIjY2NlX+3YqLi4mNjQ2ZPHkyVc3aQkVFBQkKChJLqWvcuDEJCQkhFRUV0p4edd6/f08OHDhABg0aRJSUlIienp60p1SjYAVbPODv7w9fX1+oqKjA39//s+cuWLCAp1lxg6GhISZOnIiZM2fypjl27Fjo6+tjwYIFCA0NxfTp0+Ho6IjExEQMHDiQs24pssjbt2+xfPlyyMnJoU+fPujVqxcMDAxgb2+PCxcuICsrCxcuXKjRXpZnzpxB9+7dMXXqVPj6+opcBQoKCrB8+XKsXr0aJ0+eFBVU0iY1NRU7duzAn3/+iffv32PIkCHw8PCgWiSiqqqKixcvws7Orto5/PTTT1St64RCIf7++29R0VR1dOrUiZrmx2RmZiI9PV1UeMNFtXhBQQFatmwJOTk5TJo0SaxSff369SgvL8e1a9dqrFPFj0Ll7pk0uhlyzZkzZ/Dnn3/i77//Rnl5OQYOHIjhw4ejS5cuX205xwBbeWXQRV1dXZRzyhfl5eViq5L79u0jkydPJqtXrybv3r3jdS6yRkFBAfH19SXOzs7E09OTpKSkcKYVFhZG3r59KzH+7t076gWNoaGhpE6dOmK5xEKhkNSpU4ds2LCBqlZ1lJaWkqioKNK3b1+ioKBAbGxsSEhICJXVwubNm5ONGzdWe3z9+vWkefPm363zMdIsaOSTvLw80qtXL9FqcuWKcq9evVjeIuOzNG7cmCgpKZF+/fqRyMhIznZeagNs5VVKvH//vsqc0Ory8GoKnp6eaN26NSZMmCDtqTBqGHzZgVVy//59REZGIisrC4QQmJub45dffkGTJk2o6lTH+/fvceDAAWzfvh0xMTFo3749CgoK8ODBA2zZsgVubm7/+drLly/H8uXLERMTI7H6mpKSgq5du8LPzw9+fn7f+2uI4LsD3dc0Y5CXl0fDhg3RtWtX6rsFL168EL12mjZtSrUjW23C3t7+i7ZRlX/H7t2743//+99/zrtPTU396nOr27X4HjZv3oxff/2VvVYowIJXnsnIyICnpycuXLggNk7+f29zLrrPcM2aNWtEPxcVFSEoKAguLi5VtoacMmUKJ3MoLCzE5cuXq7whGDVqFCeaXJOamgobG5tas5VUXQezlJQUODk5cdL8QRpcvXoVO3bsQEREBOrUqYNRo0Zh7Nixom3uVatWYfny5SgoKPjPGqWlpejRowfi4uLQrVs3NGvWTLS9ffr0aTg6OuLUqVMS78/vwdjYGImJiahXrx61a36Or2nGUFFRgcePHyMjIwNr167FxIkTeZgZ41v4Uiod8H9/x6ioKAwaNAgbNmz4T1pCoRACgaDa4tvKY3x8F9+7dw8CgQCNGzfmVEdWYcErzzg6OkJeXh6zZs1Co0aNJO44a2IuobGx8VedJxAIkJOTQ13/yJEjGD58OIqKiqCuri72byoQCGps0PPxSqSJiQmuXLnCW2DAJ5UrLykpKbC2toa8/P+ZoJSXlyM3NxfOzs7UOmxJEzs7O6SlpaFHjx4YN24c+vbtK+EI8OTJEzRo0EDiJuxbKS0tRXBwMP78809kZmaKVpeHDRuGqVOnUnWN+NEJCwvDokWLkJ2dLe2pML6Dc+fOYfDgwXj06NF/+v/v3Lnz1edy4QhSUVGBxYsXY9WqVXjz5g2AD3m9Pj4+mDNnTq1ZqKABs8rimeTkZFy9ehXNmjWT9lSokZubK1V9Hx8feHh4YMmSJRLtU2syWlpayM3Nha6uLvLy8r47mPlR6d+/P4AP742ePXtCTU1NdKzSDmzQoEFSmh1dfv31V3h4eHx2tUVHR4fK31pBQYF6akBNpXfv3mI7RIyaScuWLTFs2LD//P9zbVH3JebMmYNt27YhMDBQ1AI3Pj4eCxcuxNu3bxEQECDV+dUk2Morz7Ru3RrBwcH4+eefpT0VqkhzZVBVVRXXr1+HiYkJ79pcMn78eISHh6NRo0bIz89HkyZNqvTtBMDJijbfhIWFwc3N7Ys+oQwGg0GLW7duVdlWnLZHMADo6ekhNDRU4tqHDh3CxIkTcf/+feqasgpbeeWZZcuWwc/PD0uWLKkyJ5S2gTdf5OXlSS1ft2fPnkhMTJS54HXz5s0YOHAgsrKyMGXKFIwbN44X65iysjIoKSkhOTkZNjY2nOtVMnr0aACyW8wIVF9kJBAIoKSkBDMzM/Tr1++LdlMMBuP7yMnJwYABA3D9+nWxPNjKtDMuvs+eP39e5a5rs2bNamx6m7RgK688U5nT8mmua00u2AL4rzT+mG3btmHRokVwd3ev8oaAiztovnF3d8eaNWt48z00NTVFVFQUrznYmZmZ8PDwkKlixk9xcnLCtWvXUF5eDgsLCxBCkJmZCTk5OTRr1gy3b98WdRaysrKS9nS/mrp16yIjIwP169eHh4cHVq9eLZMenQzZoTLffMuWLTAxMcHly5fx7Nkz+Pj4YOXKlejQoQN1zbZt26Jt27YSKSyTJ0/GlStXkJCQQF1TVmHBK8/ExsZ+9jhXBt5cIxQKERMT88UVIy7sRz6X5C4rQc/H8FGlumPHDvz111/YvXs3b6uAfBQzamtrf9GWpxIuVkJCQkJw/vx57NixQ7TL8urVK3h6euLnn3/GuHHjMGzYMJSUlODEiRP/SePVq1e87+CoqakhNTUVJiYmkJOTw6NHj2S2/TVDNqhfv77ISk5TUxOXL1+GhYUFYmJi4OPjg6SkJOqasbGxcHFxgYGBAX766ScIBAJcuHABd+/exb///stJwCyrsOCVQYXPWZDwaT8iq/BdpWpvb4+srCyUlpbC0NAQqqqqYsevXbtGVQ/4kLvMdTFjWFjYV59bmcZAk8aNG+PUqVMSq6o3b95Ejx49cP/+fVy7dg09evTA06dP/5PGxy4VXbp0QVRUFLS0tCjMvnq6d++OgoICtGrVSpS7rKysXOW527dvp64vi1Z5tYFvKaKjbbOora2Nq1evwsTEBKampti6dSucnJyQnZ0NW1tbFBcXU9Wr5MGDB1i/fr1YN7iJEydCT0+PEz1ZheW88sDHfp1fMknmYmWSLy5dusT7aou08jP5hu8q1UoHAD6xsrL6zwHb18JFQPotvHz5Eo8fP5YIXp88eYJXr14B+OAy8WnxyLegpqYmauxw9uxZlJaWftecv4bdu3cjODgY2dnZEAgEePnyJd6+fcu5LvBlqzwWvP64BAcHf9V5AoGAevBqY2Mj2i1o27Ytli9fDkVFRWzevJnT+gk9PT3mKkABtvLKAx/ng35phbKmrkxKM+dVGvmZfFMbqlRjYmIwd+5cqRQzlpSUSAR5XOgNHz4cFy9exKpVq9C6dWsIBAJcvnwZM2bMQPv27bFr1y7s3bsXK1euRGJi4n/SGDRoEOLj42FpaYnY2Fi0b9++Wk/XmJiY7/l1qoTvZgXm5ubo3bu3zFnlMbjlxIkTKCoqwsCBA5GTk4M+ffogPT0d9erVw759+9ClSxcqOrVl8YpvWPDKA3fu3IGBgQEEAsEXTZKl7UP3X5Fm8CqN/Ey+UVJSQmpqKszNzcXGb9++jRYtWqCkpIS6ZmFhIfbv34/s7Gz4+vqibt26uHbtGho0aMBJvi3fxYxFRUWYOXMmIiMj8ezZM4njXNxIvnnzBtOmTUN4eDjKysoAfGh9OXr0aAQHB0NVVRXJyckAgBYtWvwnjZKSEoSFhSE7OxurVq3CuHHjqg3qvnbl60dGVq3yGPzz/Pnzb8qL/xpqw+KVNGDBK4MKTk5OOHDgAOe5dVUhjfxMvuG7SjU1NRXdunWDpqYm8vLycPv2bZiYmGDevHm4c+cOwsPDqeoB/Bcz/vbbbzhz5gwWLVqEUaNGYf369bh//z42bdqEwMBADB8+nKrex7x58wY5OTkghMDU1FSsMQNNpPW+jI2NxcqVK5GWlgaBQABLS0v4+vpyUpAycOBADBkyBIMHD6Z+bQa/3Lt3D4cPH67SdzUoKEhKs/o+asPilTRgwSsPHD58+KvPlQVbJ775Um/sBQsW8DQT7uC7SrVbt25o2bIlli9fDnV1daSkpMDExAQXLlzAsGHDkJeXR1XvSyQnJ//nlcjqMDAwQHh4ODp37gwNDQ1cu3YNZmZm2LVrFyIiIvDvv/9S1fsUvnubf+pjyRW7d++Gu7s7Bg4cKMrPvnDhAg4cOICdO3d+V4ekSj7+TH3y5InMW+XVBqKjo+Hq6gpjY2Pcvn0bNjY2yMvLAyEELVu2pJ7iUlRUhMDAQERHR1dZ6CcLjV9kGRa88sDXVoKzbQPG5+CzSlVTUxPXrl2DqampWPB6584dWFhY8FKM8/LlS+zZswdbt25FSkoK9feGmpoabt68CUNDQzRp0gRRUVFo06YNcnNzYWtrK3J1oIk0epuHh4djxYoVyMzMBPAhR9TX1xcjR46krgUAlpaWGD9+PKZNmyY2HhQUhC1btiAtLe27NdhnquzRpk0bODs7Y9GiRaLPHF1dXQwfPhzOzs7w8vKiqjd06FDExsZi5MiRVVrzeXt7U9Fhi1ccQRgMBuMTdHV1ybVr1wghhKipqZHs7GxCCCEnTpwgTZo04VQ7OjqaDB8+nCgrK5NmzZqROXPmiOZCE1tbW3L27FlCCCHdu3cnPj4+hBBCVq9eTRo3bkxdjxBCZs2aRXR0dMiGDRtISkoKSU5OJuvXryc6Ojrk999/p663atUqoqKiQvz8/MihQ4fIwYMHia+vL1FRUSFBQUHU9QghRFFRkWRmZkqMZ2Zmkjp16nCiyaj5qKmpkaysLEIIIVpaWuTGjRuEEEKSk5OJoaEhdT1NTU0SFxdH/bqfIhAIxB5CoVDieeWD8fWw4JXx3bx8+VKq+p9+AHz6YHw748aNI/379yfv378nampqJCcnh9y5c4fY29sTb29v6np3794lf/zxBzE2Nia6urpk0qRJRF5enty8eZO6ViVBQUFk9erVhBBCYmJiiLKyMlFUVCRCoZCEhIRwotmoUSNy6NAhifGDBw8SPT096npGRkYkLCxMYnznzp3EyMiIuh4hhJiampLQ0FCJ8dDQUGJmZsaJ5qe8ePGCFx0GPRo0aCB6v1tZWYneJ8nJyURVVZW6npGREbl16xb1636OU6dOkZYtW5Ljx4+Tly9fklevXpHjx48TBwcHcvLkSV7nUtNhwStPREdHE0tLyyoDvcLCQmJlZUViY2OlMLPvRygUkoKCAkIIIU5OTrx/cRw8eFDs8ddff5Hff/+dNG7cmGzdupXXucgKL1++JI6OjkRLS4vIyckRfX19oqCgQDp27EjevHlDVatXr15EXV2dDB06lPzzzz+krKyMEEI4D14/5c6dO+Tvv/8mycnJnGnUqVOH3L59W2I8PT2dKCkpcaJX1SpoRkYGZ6ugGzZsIIqKimTChAkkPDyc7Nq1i/zvf/8jderUqTKo/V4CAwPJ3r17Rc9/+eUXIhAIiJ6eHqd/SwZd+vXrRzZv3kwIIcTX15eYmZmRxYsXk5YtW5KuXbtS19u1axf55ZdfSFFREfVrV4e1tTU5f/68xPi5c+dIs2bNeJuHLMCCV57o27fvZ7fpVq9eTfr378/jjOihoaEhuoMVCATk8ePHUp7RB/bs2UNcXV2lPY0aTXR0NFmxYgVZtmwZOXXqFCcacnJyZNq0aSQjI0NsnO/glQ/atGlDJk+eLDE+adIk0rZtW+p61tbWJCAgQGL8jz/+IDY2NtT1KomKiiKOjo6kbt26pG7dusTR0ZEcPHiQEy1jY2MSHx9PCCHk5MmTREtLi5w4cYJ4enqS7t27c6LJoE92djZJSUkhhBBSVFREvLy8iK2tLRkwYADJy8ujrteiRQuirq5O1NTUiI2NDbG3txd7cIGSkhJJTU2VGE9JSeHk5lWWYQVbPGFoaIjjx4/D0tKyyuPp6eno0aMH8vPzeZ7Z9yNtU/TqyM7Ohp2dHYqKinjTZHw7Fy9exPbt2xEZGYlmzZph5MiRcHNzg56eHlJSUiS6UdFiypQpMDMzk+jcs27dOmRlZSEkJIS6Jt+uEX///Tfc3NzQrVs3ODo6QiAQIC4uDtHR0YiMjMSAAQOo6kkDZWVlZGRkQF9fH97e3nj79i02bdqEjIwMtG3bFi9evJD2FBk/INJwqenYsSMUFBSwe/duNGrUCADw6NEjjBw5Eu/fv/+iXSDj/2DBK08oKSnhxo0bMDMzq/J4VlYWbG1tOTGb55of0RS9pKQEs2fPxrFjx3D79m1eNGUJaQR2xcXF2Lt3L7Zv347Lly+jvLwcQUFB8PDwgLq6OnW9xo0b4/Dhw2jVqpXY+LVr1+Dq6op79+5R1wT4721+9epVBAcHIy0tTaTn4+MDe3t7TvT4Rk9PD/v370f79u1hYWGBxYsX49dff8Xt27fRunVrUdtdRs3g/fv3VVpXGRgYSGlG9MjMzMTAgQNx+/Zt0e+Tn58Pc3NzHDx4sNr4gCEJC155wtTUFCtXrqx2pSMqKgozZsyo8d5y0jBF/7QjCiEEr1+/hoqKCnbv3l1j7Ufs7e2/2pOTdiMGaQV2ldy+fRvbtm3Drl27UFhYiO7du3+T5czXUN0NZVZWFmxsbHixA2N8P5MmTcI///yDpk2bIikpCXl5eVBTU8O+ffuwbNkymWhSUhvIyMiAp6cnLly4IDZOOOqwV8nVq1dFzTSsrKw4v6mrqKjA6dOnxW5eu3Xrxrn/sqwhL+0J1BZ69+6N+fPno1evXlBSUhI7VlJSggULFqBPnz5Smh09zpw5I/qZ8GSK/ukqoFAohI6ODtq2bQttbW1Otbmkf//+UtN+9uwZNDU1JcY1NDTw9OlTzvUtLCywfPlyLF26FEeOHMH27dupa5iZmeH48eOYNGmS2PixY8eothr9Uj/zj2G9zb+d4OBgGBkZ4e7du1i+fLmoW9nDhw8xceJEKc+O8bW4u7tDXl4e//zzT5W+q7R5/PgxhgwZgrNnz0JLSwuEELx8+RJOTk7Yu3cvdHR0qOqVlZVBSUkJycnJ6NGjB3r06EH1+rUNtvLKEwUFBWjZsiXk5OQwadIkWFhYQCAQIC0tDevXr0d5ebmob3xNh29TdAZ9bGxsMGHCBInAbu3atdi4cSNu3bolpZnRY/v27Zg0aRJ8fX3RpUsXAB+6/KxatQohISEYN24cFZ3P9TP/GGaoz6jNqKqq4urVq2jWrBkvem5ubsjOzsauXbtEtSi3bt3C6NGjYWZmhoiICOqapqamiIqKQvPmzalfu7bBVl55okGDBrhw4QK8vLwwe/ZssVXJnj17YsOGDTIRuAYFBWHevHmYNGmSqDVkfHw8JkyYgKdPn0p03aFFYWEhtm3bJrb94+HhUeXqIePLTJ8+HZMmTcKTJ0+qDOxkAQ8PD7x79w4BAQH4448/AABGRkbYuHEjRo0aRU0nNzeX2rVqIuXl5bh+/ToMDQ2p7YQcPnwYvXr1goKCwhfTSWpq2lBtw8rKipddnUqOHz+O06dPixVRW1lZYf369Zytis6dOxezZ8/G7t27UbduXU40agts5VUKvHjxAllZWSCEoGnTpjV6a/tTjI2N4e/vL/HlHxYWhoULF3LyRZ6YmIiePXtCWVkZbdq0ASEEiYmJKCkpwcmTJ9GyZUvqmnxTXl6O4OBgREZGIj8/H+/fvxc7/vz5c+qaGzduREBAAB48eADgQ2C3cOFCqoHdj8KTJ0+grKws2nJm/HemTp0KW1tbeHp6ory8HJ06dcKFCxegoqKCf/75B507d/5uDaFQiEePHkFXV/ezrWLZanbNISYmBnPnzsWSJUtga2sLBQUFseMaGhpU9dTV1XH+/Hm0aNFCbDwpKQmdOnXipNDP3t4eWVlZKC0thaGhIVRVVcWOs/zsr4cFrwyqVFcEk5mZCVtbW06KYDp06AAzMzNs2bIF8vIfNhPKysowduxY5OTk4Ny5c9Q1+Wb+/PnYunUrpk+fjnnz5mHOnDnIy8vDwYMHMX/+fAlXAJqwwI4u2dnZCAkJEe0SWFpawtvbG6amptKeGhWaNGmCgwcPwsHBAQcPHsRvv/2GM2fOIDw8HGfOnEF8fLy0p8j4Aam8Cfk015Wrgq1+/fqhsLAQERERIqeP+/fvY/jw4dDW1saBAweo6gHSseeSVVjwyqCKjY0Nhg0bht9//11sfPHixdi3bx+uX79OXVNZWRlJSUkSuVK3bt2Cg4MDiouLqWvyjampKdasWQMXFxeoq6sjOTlZNJaQkIA///xT2lOsEbRs2RLR0dHQ1tb+opsDF6sgJ06cgKurK1q0aCFKq7lw4QJSUlJw5MgRdO/e/bs1Bg4c+NXnRkVFfbfepygpKSErKwtNmjTB+PHjoaKigpCQEOTm5qJ58+bUV7TCw8Ph5uaGOnXqiI2/f/8ee/fulcmdAlnkSx6nnTp1oqp39+5d9OvXDzdu3IC+vj4EAgHy8/Nha2uLQ4cOoUmTJlT1GHRhOa8Mqvj7+8PNzQ3nzp2r0hSdCzQ0NJCfny8RvN69e5cTf1Bp8OjRI9ja2gIA1NTU8PLlSwBAnz59MG/ePOp6BQUFmDFjBqKjo/H48WOJYqOauhXbr18/UZAjDTeHWbNmYdq0aQgMDJQYnzlzJpXgVdp53g0aNMCtW7fQqFEjHD9+HBs2bADwwcdXTk6Oup67uzucnZ2hq6srNv769Wu4u7uz4LWGQDs4/RL6+vq4du0aTp06JWFbxfjxYcErgyqDBg3CpUuXEBwcjIMHD4o+EC5fvsyZf56bmxs8PT2xcuVKtG/fXhQw+/r6YujQoZxo8k2TJk3w8OFDGBgYwMzMTJTLe+XKFYkVJxqMGTMG+fn5mDdvHi+2NXzx8bacNLbo0tLSqryJ8/DwoFYIt2PHDirX+a+4u7tj8ODBotdNZUB+6dIlTirJK7eVP+XevXtSD+QZ34Y0Cm+7d+9O5abxa5BG7YKswoJXBnVatWqF3bt386a3cuVKCAQCjBo1CmVlZQAABQUFeHl5Saxw1VQGDBiA6OhotG3bFt7e3hg6dCi2bduG/Px8Thwc4uLiqixmkEX47Oijo6OD5ORkNG3aVGw8OTlZYuWwprJw4ULY2Njg7t27+PXXX0U3V3Jycpg1axY1ncq0D4FAgK5du4ry3YEPQUJubi6cnZ2p6TG4parC26CgIAQEBFArvF2zZg3Gjx8PJSUlrFmz5rPnclFH4O/v/9naBcbXw3JeGTJDcXExsrOzQQiBmZlZtS1qZYGEhARcuHABZmZmnFgBWVlZYc+ePTLTQrQqpNHRZ9GiRQgODsasWbPEdgmWLVsGHx8fzJ07l7rm/v37q13pqcnVzZXFL/7+/vDx8RErKFRUVISRkREGDRoERUVFaU2R8Q3wUXhrbGyMxMRE1KtXD8bGxtWeJxAIOOl2yWoX6MGCVwaDIcHJkyexatUqbNq0CUZGRtKeDic4OjpCXl4es2bNqjI1ggsjcUIIQkJCsGrVKpEFmZ6eHnx9fTFlyhTq6Rlr1qzBnDlzMHr0aGzZsgXu7u7Izs7GlStX8NtvvyEgIICaztdCe0UrLCwMbm5uEp0LGTWL2lB4q6qqirS0NBgYGKBRo0Y4evQoWrZsiZycHNjb24tqGRhfhgWvjBrJwIEDsXPnTmhoaHyxulpNTQ3W1taYMGFCjc6By8jIwNmzZ6vc4qa95aStrY3i4mKUlZVBRUVFwnNRFnKz+O7o8ymvX78GAE6LCps1a4YFCxZg6NChUFdXR0pKCkxMTDB//nw8f/4c69ato6LzuVWsj+FqRYtR82nQoAF27dol0SDgxIkTGDVqFAoKCqjqLVq0CDNmzJDYoSspKcGKFSs42ca3sLBAeHg42rZtiw4dOsDFxQWzZs3Cvn37MHnyZDx+/Ji6pqzCcl4ZNRJNTU3RKtWXAtJ3794hNDQU8fHxX+zG86OyZcsWeHl5oX79+mjYsKHYCp1AIKD+QSsrXbQ+B98dfYAP3bbKysrQtGlTsaA1MzMTCgoK1Fe58/Pz0b59ewAfVrYqA+aRI0eiXbt21IJXvruI1a1bFxkZGahfvz60tbU/u2ItCzdatQG+C2/9/f0xYcIEieC1uLgY/v7+nASvfNcuyDIseGXUSD6uqP6a6upbt26hdevWXE6JUxYvXoyAgADMnDmTF73Ro0fzoiNNli1bBj8/P946+gAfXBw8PDwkCrYuXbqErVu34uzZs1T1GjZsiGfPnsHQ0BCGhoZISEhA8+bNkZubK2F/VpMIDg4WBf+14UarNsB34W11LhUpKSmctW79+Pf45ZdfoK+vj/j4eM5qF2QZljbA+G6kbYr+NZSXl+PGjRuc5DHygYaGBpKTk2FiYsK7dklJCUpLSyXmU9Phu6MP8OHf7dq1axId6LKysuDg4IDCwkKqemPHjoW+vj4WLFiA0NBQTJ8+HY6OjkhMTMTAgQOxbds2qnqV3Lt3D4cPH66ySCwoKIiaTllZGfbs2YOePXuiYcOG1K7LkB5cF95WrtS/fPkSGhoaYu//8vJyvHnzBhMmTMD69eup6jLowlZeGd9NTcgjlZOTq7GBKwD8+uuvOHnyJCZMmMCLXlFREWbOnInIyEg8e/ZM4nhNbVLwMWfOnOFdUyAQiLbuP+bly5ec/Jtu3rxZlB89YcIE1K1bF3Fxcejbty9nr6Xo6Gi4urrC2NgYt2/fho2NDfLy8kAIoWJ39DHy8vLw8vJCWloa1esypIeKioqoIQsXhISEgBACDw8P+Pv7i31/VbpU/PTTT1Q1r169ihkzZuDQoUMSN/4vX75E//79ERISUqO/o/iGrbwyGDWApUuXIigoCC4uLlVucdOu4K7sR79o0SKMGjUK69evx/3797Fp0yYEBgZi+PDhVPVqC3369IGKigoiIiJE3abKy8vh5uaGoqIiHDt2TMoz/H7atGkDZ2dnLFq0SFQkpquri+HDh8PZ2RleXl5U9ZycnODt7S2VjmmM7+NbCm9p79rFxsaiffv2Ep+lXDBs2DBYWlpW2w0xICAAaWlpvPqj13RY8Mpg1AD49iQ0MDBAeHg4OnfuLLbVvWvXLkRERODff/+lqscXqampsLGxgVAoRGpq6mfPtbOzo65/69YtdOzYEVpaWujQoQMA4Pz583j16hViYmJgY2NDXbOwsBCXL1+u0qWCi9apH/tXamtrIy4uDtbW1khJSUG/fv2Ql5dHVe+vv/4Std1t1aoVVFVVxY5z8Xdk0MHd3R1r1qyBuro6xowZ89nCOy47x3GdGmVqaooDBw5U+1q8fv06+vXrx5w4vgEWvDKoI6um6NKCEII7d+5AV1eXt8YLampquHnzJgwNDdGkSRNERUWhTZs2yM3Nha2tLd68ecPLPGgjFArx6NEj6OrqQigUQiAQVFm4xFXOKwA8ePAA69atQ0pKCpSVlWFnZ4dJkyZxUiRy5MgRDB8+HEVFRVBXV5dwqeCiEr9hw4aIiYmBlZUVrK2tsXTpUri6uiIlJQWOjo7UXzuVucsfU/l35fLvyKjZFBcXw8/Pj5fUKCUlJaSlpVW7CJGbmwsrKyuUlJRQ05R1JN/1DMZ3sGbNGri7u0NXVxdJSUlo06YN6tWrh5ycHPTq1Ysz3V27dsHR0RF6enq4c+cOgA+5TYcOHeJMky8IITA3N8f9+/d50zQxMRGtkFlZWSEyMhLAh2BIS0uLt3nQJjc3Fzo6OqKfc3JykJubK/HgcgVET08PS5YswdGjR7F//37Mnz+fs+pmHx8feHh44PXr1ygsLMSLFy9ED64spNq1a4f4+HgAgIuLC3x8fBAQEAAPDw+0a9eOul51fz+u/44MunTp0qXKgsVXr16hS5cu1PV8fX0RExODDRs2oE6dOti6dSv8/f2hp6eH8PBwqlo6Ojq4fft2tcfT09NRv359qpoyD2EwKGJhYUH+/PNPQgghampqJDs7mxBCyLx588hvv/3GieaGDRtI/fr1yeLFi4mysrJIc8eOHaRz586caPKNlZUVuXjxIm96QUFBZPXq1YQQQmJiYoiysjJRVFQkQqGQhISE8DYPWeTFixfkxIkTZNeuXSQsLEzsQRsVFRXR+4EvsrOzSUpKCiGEkKKiIuLl5UVsbW3JgAEDSF5eHq9zYdQcBAIBKSgokBgvKCgg8vLy1PX09fXJmTNnCCGEqKurk8zMTEIIIeHh4aRXr15UtcaMGUN+/vnnKo9VVFSQn3/+mYwZM4aqpqzD0gYYVFFRUUFaWhoMDQ2hq6uLU6dOoXnz5sjMzES7du2q3J75XqysrLBkyRL0799frIvQjRs30LlzZ96N6Lng6NGjCAwMxMaNGznJi/wS+fn5SExMhKmpaY2uiP2WJhVc+C7yvY0/cOBADBkyBIMHD6Z63R+JZ8+eoV69egCAu3fvYsuWLSgpKYGrq6sor5jx41KZe96iRQvExMSI7UKUl5fj+PHj2LRpE/VcaT5To7Kzs9GqVStYWFjAx8cHFhYWEAgESEtLw6pVq5CRkYHExEQJCz1G9TCrLAZVpGGKnpubC3t7e4nxOnXqoKioiBNNvhkxYgSKi4vRvHlzKCoqQllZWew4112EDAwMYGBgwKkGH3xtRTpXuZKV2/hLlizhJX/ZxcUFvr6+uHXrVpUuFVwao79//77KIjFar6Pr16+jb9++uHv3Lpo2bYq9e/fC2dkZRUVFEAqFCA4Oxv79+5kLwQ9OixYtIBAIIBAIqkwPUFZWxtq1a6nrVqZGGRoailKj2rRpw0lqlKmpKU6fPo0xY8ZgyJAhoptWQgisrKxw6tQpFrh+Iyx4ZVClS5cuOHLkCFq2bAlPT09MmzYN+/fvF5mic4GxsTGSk5NhaGgoNn7s2DFYWVlxosk3fHURiomJwaRJk5CQkFClH2H79u0RGhpaY1e0Pg2k+Ob+/fuYMmUKb4V348aNA/Chj/uncBWgZ2RkwNPTExcuXBAbJ5QLqPz8/GBra4vdu3dj9+7d6NOnD3r37o2tW7cCACZPnozAwEAWvP7gVC5smJiY4PLly6KcdOCD76qurq7IVo4m7u7uSElJQadOnTB79my4uLhg7dq1KCsro9pIoxIHBwfcuHEDycnJyMzMFNUytGjRgrpWbYClDTCoUlFRgYqKCsjLf7gvioyMRFxcHMzMzDBhwgQoKipS19yxYwfmzZuHVatWwdPTE1u3bkV2djaWLl2KrVu3YsiQIdQ1ZRVXV1c4OTlV22d7zZo1OHPmDA4cOMDzzGSD2rCN7+joCHl5ecyaNQuNGjWSsD+ilXZSv359xMTEwM7ODm/evIGGhgYuX74MBwcHAB+KYNq1a0e9axlDNpGV1KjaAgteGTLBli1bsHjxYty9excA0LhxYyxcuBCenp5Snhkd8vPzP3uc1lasoaEhjh8/DktLyyqPp6eno0ePHl+cz4/M16wub9y4ER07dqSuvW3bNixatAju7u6cb+OXlZVBSUkJycnJvOZJq6qq4urVq2jWrBmnOh/bngEQy3cHgIKCAujp6TGrrBrGrVu3qrRZ5DLFhVHzYGkDDOrwbYoOfNgeHTduHJ4+fYqKigrRF5qsYGRk9FkDb1pf0AUFBZ/tOCMvL48nT55Q0ZIWISEhGDduXJUm5Jqamvjf//6H4OBgToJXPrfx5eXlYWhoyHvwZmVlxVuR5Kfvic+9Rxg/Njk5ORgwYACuX78u5r9c+Tel9Tr+Whssrr6rGHRgK68MqkjDFL2kpASEEFEe4Z07d3DgwAFYWVmhR48e1PWkQUpKitjz0tJSJCUlISgoCAEBAdTyiU1NTbFy5UoMGDCgyuNRUVGYMWNGjfbPrA2ry5Xs2LEDf/31F3bv3s2Zl+ynxMTEYO7cuViyZEmVq8u0OhcJhUL06tULderUAfDhs6dLly6iDlvv3r3D8ePH2cprDaFv376Qk5PDli1bRPmvz549g4+PD1auXEktz15bW7vaYwKBAEVFRSgrK2Ovmx8cFrwyqGJubo7evXvzVk0NAD169MDAgQMxYcIEFBYWwsLCAoqKinj69CmCgoKo91L/kTh69ChWrFiBs2fPUrne5MmTcfbsWVy5cgVKSkpix0pKStCmTRs4OTlhzZo1VPSkgZKSEm7cuFFtdW9WVhZsbW1lotuNvb09srKyUFpaCkNDQ4nWqVx0vKvsePXpKijtgi13d/evOo/LtqIMenycw6ypqYnLly/DwsICMTEx8PHxQVJSEqf6Dx8+hL+/P7Zv344uXbrg+PHjnOoxvg+WNsCgCt/V1MCHL+Dg4GAAH1rTNmzYEElJSfj7778xf/58mQ5ezc3NceXKFWrXmzt3LqKiomBubo5JkyaJ+RGuX78e5eXlmDNnDjU9adC4cWNcv3692uA1NTUVjRo1oq5bVlaG4OBgREREICMjAwKBAE2bNsWwYcPg7e392XSN/4o0Ku3PnDnDiw4LSmWL8vJyqKmpAfgQyD548AAWFhYwNDT8bHeq7+X169dYtmwZVq9eDWtra5w4cQJOTk7Url/pY/s12NnZUdOVdVjwyqBKz549kZiYKCqa4IPi4mKoq6sDAE6ePImBAwdCKBSiXbt2olaxNZ1Xr16JPSeE4OHDh1i4cCGaNm1KTadBgwa4cOECvLy8MHv2bLG8s549e2LDhg1o0KABNT1p0Lt3b8yfPx+9evWqcnV5wYIF6NOnD1XNkpISdO/eHRcvXkS3bt3QsWNHEEKQnp6OmTNn4vDhwzh58qTEfL6XBQsWUL3e19CpUyfeNRk1HxsbG6SmpsLExARt27bF8uXLoaioiM2bN3PyffL+/XusW7cOS5YsQf369bFjxw788ssv1HUqfWwrdx4+B0tV+HpY8MqgijRM0c3MzHDw4EEMGDAAJ06cENk8PX78mFp+nbTR0tKqchtWX18fe/fupaplaGiIf//9Fy9evEBWVhYIIWjatOlnc8VqEtJYXV66dCnu3r2LpKQkidWVlJQUuLq6IjAwEAsXLqSqKy2kUbTJqNnMnTtX1FRm8eLF6NOnDzp06IB69eph37591HQIIQgPD8f8+fNRVlaGJUuWwNPTkxMvWeCDj20lSUlJmDFjBnx9ffHTTz8BAC5evIhVq1Zh+fLlnOjLKiznlUGVyny3quDKFH3//v0YNmwYysvL0aVLF5w6dQrAh4Dh3LlzOHbsGHVNvomNjRV7LhQKoaOjAzMzM5GnLuPruXPnDry8vHDixIkqV5eNjIyo6pmbm2Pp0qUYNGhQlcf/+usvzJkzBxkZGVR1hUIhLy4VHyONok2GbPL8+XNoa2tTdZGws7NDdnY2Jk+ejKlTp1ab4sbFwkebNm2wcOFC9O7dW2z833//xbx583D16lXqmrIKC14ZMsGjR4/w8OFDNG/eXBRAX758GRoaGpz7TTJqLnytLispKSEzMxP6+vpVHq9scfr27VuquocOHRJ7XulSERYWBn9/f058kKVRtMlgfC0fL7BUFRTTLiz8GGVlZVy7dk3C6SQtLQ0tW7aUiSJRvmDBK4Ma0jJFryQrKwvZ2dno2LEjlJWVvyrHqCaRnZ2NkJAQpKWlQSAQwNLSEt7e3jA1NZX21BhfQFdXF8eOHUOrVq2qPH7lyhW4uLjg8ePHvMznzz//xL59+ySCWxqoqqri+vXrvOa9M2o+RUVFCAwMRHR0dJXpJrTs+T7dxaoOLnK3W7ZsCUtLS2zbtk2U3/7u3Tt4eHggLS2NE/cPWYXtNzKoIS1T9GfPnmHw4ME4c+YMBAIBMjMzYWJigrFjx0JLSwurVq3idT5ccOLECbi6uqJFixZwdHQEIQQXLlyAtbU1jhw5gu7du0t7iozP4OTkhCVLluDvv/+u8nhgYCA6d+7M23zatm0raphAG2kUbTJqPmPHjkVsbCxGjhxZZVthWkizoDA0NBR9+/aFvr6+qAVtSkoKBAIB/vnnH6nNqybCVl4ZVJGGKfqoUaPw+PFjbN26FZaWlqIWkSdPnsS0adNw8+ZNXubBJfb29ujZsycCAwPFxmfNmoWTJ0+yO/YfnFu3bqFt27awtrbG9OnTRakst27dQnBwMG7duoWEhARYW1tzPpeSkhLMnj0bx44d48SCiM8WuAzZQUtLC0ePHoWjo6O0p8IpxcXF2L17N9LT00EIgZWVFYYNGybhwcz4PCx4ZVBFGqboDRs2xIkTJ9C8eXOx/ua5ubmwtbXFmzdvqGvyjZKSEq5fvy5hi5WRkQE7OzvquZLV8fDhQ5SWlsLAwIAXPVkiISEBnp6eorQP4EN+XbNmzbB161a0b9+euuanxS6EELx+/RoqKirYvXs3J4GkNIo2GTUfY2Nj/Pvvv9V2vmMwPoalDTCoIg1T9KKioioLQ54+fSpqHVnT0dHRQXJyskTwmpycDF1dXd7m0aVLF2RkZLAA5D/Qrl073Lx5E8nJySJXAXNzc7Ro0YIzzZCQELHnlS4Vbdu25aw47dNcRQbja/jjjz8wf/58hIWFyXShX0ZGBs6ePVtlXu/8+fOlNKuaB1t5ZdR4XFxc0LJlS/zxxx9QV1dHamoqDA0NMWTIEFRUVGD//v3SnuJ3s2jRIgQHB2PWrFlo3749BAIB4uLisGzZMvj4+GDu3Lm8zOPKlSsoLi5mRvSMb+bZs2fYtWsXpk6dKu2pMH5A7O3tkZ2dDUIIjIyMJNJNZCE1asuWLfDy8kL9+vXRsGFDCRs5Wfgd+YIFr4waz61bt9C5c2e0atUKMTExcHV1xc2bN/H8+XPEx8fLRDU+IQQhISFYtWoVHjx4AADQ09ODr68vpkyZIlOuCgy6FBYWYtu2baJ0BSsrK3h4eEBTU5NzbUIITp48iW3btuHQoUPQ0NDAkydPONdl1Dz8/f0/e5yrbnF8utQYGhpi4sSJmDlzJifXr02w4JVBFWmYogMffF43btyIq1evoqKiAi1btsRvv/3GSY96afP69WsAELXE5YpPgx5LS0t4enryEvQw6JCYmIiePXtCWVkZbdq0ASEEiYmJKCkpwcmTJ9GyZUtOdPPy8rB9+3bs3LkT9+/fx/DhwzFq1Cg4OTlx1smIUXMpKytDQEAAPDw8qvVCps2zZ8/g5uaGmJgYMZcaT09PzlxqNDQ0kJyczJw4KMCCVwZVpGGKzqCPtIIeBl06dOgAMzMzbNmyRdSJraysDGPHjkVOTg7OnTtHTevdu3eIiorC1q1bceHCBfTq1QvDhg3D0KFDkZKSAisrK2paDNlDXV0d169fp97drjqk4VLj6emJ1q1bY8KECdSvXdtgwSuDF7g0RQdks5d6y5YtER0dDW1tbdjb2392RVtNTQ3W1tb4/fffqaxc8Bn0MLhDWVkZSUlJEl3mbt26BQcHBxQXF1PTql+/PqysrDBixAj8+uuvooIwBQUFFrwyvkj//v3Rv39/jBkzhhc9abjULF26FEFBQXBxcanSRm7KlCnUNWUV5jbA4AUuTdG/1Eu9pgav/fr1E7klfMnF4d27d4iOjsaIESO+uoPM50hMTBQLXIEPTSj8/Pzg4ODw3devzfB5o6WhoYH8/HyJ4PXu3bvU007Ky8shEAggEAhYagDjm+nVqxdmz56NGzduoFWrVhI2i7Rt3aThUrN582aoqakhNjZW4nNaIBCw4PVbIAwGxxQXFxNvb29ibm7OyfWbNm1KvL29SVFRESfXrylkZWWROnXqULmWrq4uOXHihMT48ePHia6uLhWN2sjhw4eJuro6EQqFRFNTk2hpaYke2tra1PUmT55MmjRpQvbu3Uvy8/PJ3bt3SUREBGnSpAnx9vamqlVSUkJ2795NnJyciLKyMhk4cCCJiooiCgoK5ObNm1S1GLKHQCCo9iEUCqnr9e7dm8ydO5cQQoiamhrJyckh5eXl5NdffyWDBg2irsegCwteGVSp/BKufGhpaRE5OTmirq5ODh06xImmiooKyc7O5uTaNY3CwkIq1+Ez6KlN8H2j9e7dOzJlyhSiqKhIhEIhEQgEpE6dOmTq1Knk7du3nOlmZWWROXPmkCZNmhCBQECGDRtGTp48ScrKyjjTZDC+hZs3bxIdHR3i7OxMFBUVyS+//EIsLS1JgwYNSFZWlrSnx/gCLOeVQZWwsDCx53yYog8cOBBDhgzB4MGDObl+beT9+/fw9fVFaGgoysrKAHzIXfTy8kJgYKDMNH/gG1VVVVy/fp33auPi4mKRh6aZmRlvJvAVFRU4ceIEtm3bhiNHjkBdXR1Pnz7lRZtRc3n79i2UlJQ415GGS829e/dw+PBh5Ofn4/3792LHgoKCONOVNVjwyqjxsF7q3CGtoEdW4etGa+DAgV88R15eHg0bNkT37t3Rt29fTucDAE+ePMGuXbswffp0zrUYNY/y8nIsWbIEoaGhKCgoQEZGBkxMTDBv3jwYGRnJhFNNdHQ0XF1dYWxsjNu3b8PGxgZ5eXkghKBly5aIiYmR9hRrDCx4ZVCHb1N01kudUVPg60bL3d39i+dUVFTg8ePHiI2NxYwZM7Bo0SIq2gzGf2HRokUICwvDokWLMG7cONy4cQMmJiaIjIxEcHAwLl68SF2Tb5eaNm3awNnZGYsWLRI5HOjq6mL48OFwdnaGl5cXdU1ZhQWvDKowf1Du4WNLraioCIGBgYiOjq7ygz0nJ4dTfVnlR7zROnr0KLy8vJCfn8+7NoNRiZmZGTZt2oSuXbuKWVelp6fjp59+wosXL6jqfcml5vnz51T1gA9etsnJyTA1NYW2tjbi4uJgbW2NlJQU9OvXD3l5edQ1ZRVmlcWgyrRp0+Dq6lqlP+jUqVOZP+h/pKKiAgEBAbxtqY0dOxaxsbEYOXIkGjVqxNrPUuLTm4AfAUdHR2Z/xpA69+/fh5mZmcR4RUUFSktLqev5+PjAw8MDS5Ys4S0dSlVVFe/evQPwob13dnY2rK2tAYDlgn8jLHhlUIUvf9A1a9Zg/PjxUFJSwpo1az57rix45y1evBhhYWFYvny5mF+ura0tgoODqQevx44dw9GjR+Ho6Ej1uowfDy0tLURFRUl7GoxajrW1Nc6fPw9DQ0Ox8b/++gv29vbU9e7fv48pU6bwmsffrl07xMfHw8rKCi4uLvDx8cH169cRFRWFdu3a8TYPWYAFrwyq8GWKHhwcjOHDh0NJSQnBwcHVnicrxs/h4eHYvHkzunbtKtZa0M7ODunp6dT1tLW1UbduXerXZQCxsbFYuXKlKCfc0tISvr6+6NChg7SnRpX3798jNzcXpqamYjezDEZVLFiwACNHjsT9+/dRUVGBqKgo3L59G+Hh4fjnn3+o6/Xs2ROJiYm8On8EBQWJOnctXLgQb968wb59+2BmZvbZ7zGGJCznlUGVKVOm4MCBA1i5ciXat28PgUCAuLg4+Pr6YtCgQQgJCZH2FGskysrKSE9Ph6GhoVg+2K1bt9CmTRvqrQx3796NQ4cOISwsjDkMUGT37t1wd3fHwIED4ejoCEIILly4gAMHDmDnzp0YNmyYtKf43RQXF2Py5Mki27zKFJcpU6ZAT08Ps2bNkvIMGT8qJ06cwJIlS8Ssq+bPn48ePXpQuf7hw4dFPz958oS51NRgWPDKoMqn/qCEECgqKjJ/0O/EwcEBU6dOxYgRI8SCV39/f5w+fRrnz5//bg17e3ux3NasrCwQQmBkZCTxwX7t2rXv1quNWFpaYvz48Zg2bZrYeFBQELZs2YK0tDQpzYwe3t7eiI+PR0hICJydnZGamgoTExMcPnwYCxYsQFJSkrSnyKilfK5g8mOYS82PD9vLYVBFUVERq1evxtKlS3nzBy0vL8fOnTurrYyXBe88PrbU+vfvT+U6jOrJycmp0lPV1dUVv//+uxRmRJ+DBw9i3759aNeundjNkJWVFbKzs6U4M8aPjImJCa5cuYJ69eqJjRcWFqJly5ZUHE5+xIJJxn+DBa8MKkjTFN3b2xs7d+6Ei4sLbGxsZLIyvm/fvti3bx+WLFkCgUCA+fPno2XLljhy5Ai6d+9ORWPBggVUrsOoHn19fURHR0tUVUdHR0NfX19Ks6LLkydPoKurKzFeVFQkk+9NBh3y8vKqXO189+4d7t+/T10vPDwcbm5uEruB79+/x969eznxeWXQgwWvDCp8TQOCiooKZGZmYuvWrVRN0ffu3YvIyEj07t2byvV+VHr27ImePXvyqnn16lWxZhNcVP3WJnx8fDBlyhQkJyeL5YTv3LkTq1evlvb0qNC6dWscPXoUkydPBgBRwLplyxb89NNP0pwa4wfk4zzUEydOiH2XlJeXIzo6GkZGRtR13d3d4ezsLHGj9fr1a7i7u7Pg9UeHMBg8888//xB9fX1q12vUqBG5ffs2tev9iOTn55O7d++Knl+6dIl4e3uTTZs2caJXUFBAnJyciEAgINra2kRLS4sIBALSpUsX8vjxY040awtRUVHE0dGR1K1bl9StW5c4OjqSgwcPSnta1IiPjyfq6upkwoQJRElJiXh7e5Nu3boRVVVVkpiYKO3pMX4wBAIBEQgERCgUin6ufCgqKhJzc3Ny5MgRTnSr+ixLTk4m2tra1PUYdGEFWwzeKSwshIeHBzVvyVWrViEnJwfr1q2T2W3JDh06YPz48Rg5ciQePXoEc3Nz2NjYICMjA1OmTMH8+fOp6rm5uSE7Oxu7du2CpaUlAODWrVsYPXo0zMzMEBERQVWPIVtcv34dK1euFKsanzlzJmxtbaU9NcYPirGxMa5cuYL69etzqlNZmJqSkgJra2sxG7fy8nLk5ubC2dkZkZGR1LV/+eUXODg4SDhurFixApcvX8Zff/1FXVNWYcEro0byaY5tTEwM6tatC2tra4nKeFkwYNfW1kZCQgIsLCywZs0a7Nu3D/Hx8Th58iQmTJhAvV2rpqYmTp8+jdatW4uNX758GT169EBhYSFVPQaDweADf39/0X99fHygpqYmOqaoqAgjIyMMGjQIioqK1LV1dHQQExMjcRN3/fp1dOvWDQUFBdQ1ZRWW88qokXyaYztgwAApzYQfSktLRYUFp0+fFnkQNmvWDA8fPqSuV1FRIXETAAAKCgqsYvcbqVu3LjIyMlC/fn1oa2t/dneAi37q0qCiogJZWVlVun907NhRSrNi/OhER0dX6xqzfft2KhqVhalGRkZwc3ODkpISlet+DW/evKkyKFZQUMCrV694m4cswIJXRo1kx44d0p4Cr1hbWyM0NBQuLi44deoU/vjjDwDAgwcPJKxlaNClSxd4e3sjIiICenp6AD60U5w2bRq6du1KXU+WCQ4OFnWXCw4OltnUlkoSEhIwbNgw3LlzB59u7DH/TEZ1+Pv7Y9GiRXBwcECjxp4VdQAATdhJREFURo04f5+MHj2a0+tXhY2NDfbt2yeR5rV3715YWVnxPp+aDEsbYNRY3r59i5MnT8LJyUmi9eyrV69w9uxZ9OzZUyYaI5w9exYDBgzAq1evMHr0aNEqxO+//4709HTqqRF3795Fv379cOPGDejr60MgECA/Px+2trY4dOgQmjRpQlWPITu0aNEC5ubm8Pf3rzII+RpnEkbto1GjRli+fDlGjhwp7alwxuHDhzFo0CAMGzYMXbp0AfBhtTkiIgJ//fUX89r+BljwyqixrF69GocPH0Z0dHSVx7t164b+/ftj0qRJPM+MG8rLy/Hq1Stoa2uLxvLy8qCiolKlryYNTp06hfT0dBBCYGVlhW7dunGiU1uQk5PDw4cPJf5ez549g66urkysSqqqqiIlJUXCy5bB+Bz16tXD5cuXYWpqKu2pcMrRo0exZMkSJCcnQ1lZGXZ2dliwYAE6deok7anVKFjwyqixtGnTBvPmzau24cE///yDRYsW4fLlyzzPjBvKyspw9uxZZGdnY9iwYVBXV8eDBw+goaEhVnRAg9zcXBgbG1O9JuNDe8pHjx5JBK8PHjyAqakpSkpKpDQzenTp0gV+fn5wdnaW9lQYNYiZM2dCTU0N8+bN40zj1atX0NDQ4Oz6DP5gOa+MGktmZiaaN29e7XE7OztkZmbyOCPuuHPnDpydnZGfn493796he/fuUFdXx/Lly/H27VuEhoZS1TMzM0PHjh3h6emJX375hdeiBllkzZo1AD7kfG7dulXsZqO8vBznzp1Ds2bNpDW97yY1NVX08+TJk+Hj44NHjx7B1tZWovDPzs6O7+kxagBv377F5s2bcfr0adjZ2Um8boKCgr5bQ1tbW7Tz0aVLF0RFRUFLS+u7r8vgH7byyqixqKur4+zZs2jVqlWVx69evYrOnTvj9evXPM+MPv3794e6ujq2bduGevXqISUlBSYmJoiNjcXYsWOpB+k3btzA9u3bsWfPHrx79w5ubm7w8PBA27ZtqerUFipXse/cuYMmTZpATk5OdKzSnmfRokU19t9XKBRCIBBIFGhVUnmMFWwxqsPJyanaYwKBADExMd+toampiYSEBFhaWkIoFKKgoAA6Ojrffd3PURvdRviArbwyaizW1tY4ffp0tcHrqVOnYG1tzfOsuCEuLg7x8fESNiuGhoac9P22sbFBUFAQli9fjiNHjmDnzp3o0KEDmjZtCk9PT4wcOZLzD31ZIjc3F8CHL+ioqCixvGVZoPL3YzD+K2fOnOFco1u3bnBychI1XhkwYEC1fq40gmWg9rmN8AVbeWXUWDZv3ozp06dj79696NOnj9ixI0eOYOjQoQgKCsL48eOlNEN61K1bF3FxcbCysoK6urpo5TUuLg6DBg3i3Nz63bt32LBhA2bPno33799DQUEBbm5uWLZsGRo1asSpNqNm4OHhgdWrV0s4fzAYPwolJSUICwtDdnY2Vq1ahXHjxkFFRaXKc4ODg3meHeNbYMEro0YzYsQI/Pnnn2jWrBksLCwgEAiQlpaGjIwMDB48WGbamLq5uUFTUxObN2+Guro6UlNToaOjg379+sHAwIAz39vExERs374de/fuhaqqKkaPHg1PT088ePAA8+fPx+vXr2WmII4v7t27h8OHDyM/Px/v378XO0Yjr09aVOekwGB8jk+7JVYHbTtAJycnHDhwgNec13///RdycnLo2bOn2PjJkydRXl6OXr168TaXmg4LXhk1nsjISPz555/IzMwEIQTm5uYYNmwYBg8eLO2pUePBgwdwcnKCnJwcMjMz4eDggMzMTNSvXx/nzp2jHjAEBQVhx44duH37Nnr37o2xY8eid+/eEAqFonOysrLQrFkzlJWVUdWWZaKjo+Hq6gpjY2Pcvn0bNjY2yMvLAyEELVu2pLZVKQ2qc1JgMD6Hu7v7V53HZWOayjCI6y19Ozs7BAYGonfv3mLjx48fx8yZM5GSksKpvizBglcGo4ZQUlKCiIgIXLt2DRUVFWjZsiWGDx8OZWVl6lpNmzaFh4cH3N3d0bBhwyrPef/+PSIiIqTSqaam0qZNGzg7O2PRokWi9A9dXV0MHz4czs7O8PLykvYU/zN8FcAwGLQIDw/HihUrRAWv5ubm8PX15axRgrKyMtLS0mBkZCQ2npeXB2traxQVFXGiK4uw4JXBYIhRVlaGxYsXw9PTE/r6+tKejkyhrq6O5ORkmJqaQltbG3FxcbC2tkZKSgr69euHvLw8aU/xPyMUCqGpqfnF1StWUc34EQgKCsK8efMwadIkODo6ghCC+Ph4rF+/HosXL8a0adOoazZs2BB//vmnqLtWJadPn8awYcPw+PFj6pqyCnMbYDBqCBkZGTh79iweP36MiooKsWOf9sr+HuTl5bFq1SqMGTOG2jUZH1BVVcW7d+8AAHp6esjOzhY5Yjx9+lSaU6OCv78/a//KqBGsXbsWGzduxKhRo0Rj/fr1g7W1NRYuXMhJ8Orq6oqpU6fiwIEDok5iWVlZ8PHxgaurK3U9WYatvDIYNYAtW7bAy8sL9evXR8OGDcVWtwQCAa5du0ZVr3///ujfvz8LYCnTv39/uLi4YNy4cfDz88OBAwcwZswYkX3W6dOnpT3F/wzLeWXUJJSUlHDjxg2JNsaZmZmwtbXF27dvqWu+fPkSzs7OSExMRJMmTQB8KODs0KEDa5jwjbCVVwajBrB48WIEBARg5syZvOj16tULs2fPxo0bN9CqVSuoqqqKHWerBP+NoKAgvHnzBgCwcOFCvHnzBvv27YOZmVmNt+Zh/pWMmoSZmRkiIyPx+++/i43v27cPTZs25URTU1MTFy5cwKlTp5CSkgJlZWXY2dmhY8eOnOjJMmzllcGoAWhoaCA5ORkmJia86H3sKvAprEsSoyrYyiujJvH333/Dzc0N3bp1g6OjIwQCAeLi4hAdHY3IyEgMGDBA2lNkfAYWvDJkgsDAQEyYMAFaWlpiP8sKnp6eaN26NSZMmCDtqTAYDIZMcPXqVQQHByMtLQ2EEFhZWcHHxwf29vacaRYVFSE2NrZKn+cpU6ZwpitrsOCVIRN8vDLJ9yolHyxduhRBQUFwcXGBra0tFBQUxI6zD70fF9bbnMFgAEBSUhJ69+6N4uJiFBUVoW7dunj69ClUVFSgq6uLnJwcaU+xxsByXhkywcf3YLJ4P7Z582aoqakhNjYWsbGxYscEAgELXn9gPu5tHhISIt3JMBgMqTFt2jT07dsXGzduhJaWFhISEqCgoIARI0bA29tb2tOrUbCVV4ZMUGn4bmJiIvYzg8FgMBg/AlpaWrh06RIsLCygpaWFixcvwtLSEpcuXcLo0aORnp4u7SnWGNjKK4PBYHDIq1evvvpcDQ0NDmfCYDCkiYKCgihtqEGDBsjPz4elpSU0NTWRn58v5dnVLFjwymDUEO7du4fDhw9XmegfFBQkpVkxvoSWltZX20gxFwcGQ3axt7dHYmIizM3N4eTkhPnz5+Pp06fYtWsXbG1tpT29GgULXhmMGkB0dDRcXV1hbGyM27dvw8bGBnl5eSCEoGXLltT1vrQKYGBgQF1TVjlz5ozo57y8PMyaNQtjxozBTz/9BAC4ePEiwsLCsHTpUmlNkcFg8MCSJUvw+vVrAMAff/yB0aNHw8vLC2ZmZtixY4eUZ1ezYDmvDJlA1nNe27RpA2dnZyxatEj0++nq6mL48OFwdnaGl5cXVT2hUPjZ1UK2Qvjf6Nq1K8aOHYuhQ4eKjf/555/YvHkzzp49K52JMRi1gIEDB371uVFRUVS1CSHIz8+Hrq4ulJWVqV67NsJWXhmMGkBaWhoiIiIAAPLy8igpKYGamhoWLVqEfv36UQ9ek5KSxJ6XlpYiKSkJQUFBCAgIoKpVm7h48SJCQ0Mlxh0cHDB27FgpzIjBqD1oampKTZsQgqZNm+LmzZucdfCqTbDglSETHDt2DI0bN5b4WVZQVVXFu3fvAAB6enrIzs6GtbU1AODp06fU9Zo3by4x5uDgAD09PaxYseKbVjAY/4e+vj5CQ0OxatUqsfFNmzZBX19fSrNiMGoH0tyaFwqFaNq0KZ49e8aCVwqw4JUhE/z8889V/iwrtGvXDvHx8bCysoKLiwt8fHxw/fp1REVFoV27drzNw9zcHFeuXOFNT9YIDg7GoEGDcOLECdHfLSEhAdnZ2fj777+lPDsGg8Ely5cvh6+vLzZu3AgbGxtpT6dGw3JeGYwaQE5ODt68eQM7OzsUFxdjxowZiIuLg5mZGYKDg2FoaEhV71N7J0IIHj58iIULFyI9PR3JyclU9WoT9+7dw4YNG5Ceni5qSTlhwgS28spg8Mz+/fsRGRlZpYPLtWvXqOtpa2ujuLgYZWVlUFRUlMh9ZR32vh4WvDIYDAmqKtgihEBfXx8RERFo3769lGbGYDAY38+aNWswZ84cjB49Glu2bIG7uzuys7Nx5coV/Pbbb5zk9oeFhX32+OjRo6lryioseGUwahCJiYlIS0uDQCCApaUlWrVqxYnOpy1ohUIhdHR0YGZmBnl5lm30vRQXF1e52mNnZyelGTEYtYtmzZphwYIFGDp0qJhDzfz58/H8+XOsW7dO2lNkfAb2LcRg1ADu3buHoUOHIj4+HlpaWgCAwsJCtG/fHhEREdS3nMvKytC1a9cqj61btw6TJk2iqldbePLkCdzd3XHs2LEqjzMLMgaDH/Lz80U7SMrKyiL/1ZEjR6Jdu3acBa/l5eU4ePCgaBHCysoKrq6ukJOT40RPVhFKewIMBuPLeHh4oLS0FGlpaXj+/DmeP3+OtLQ0EELg6elJXW/QoEFVFmaFhITg999/p65XW5g6dSpevHiBhIQEKCsr4/jx4wgLC0PTpk1x+PBhaU+Pwag1NGzYEM+ePQMAGBoaIiEhAQCQm5sLrjaks7KyYGlpiVGjRiEqKgr79+/HiBEjYG1tjezsbE40ZRWWNsCokUyfPh1//PEHVFVVMX369M+eKwutU5WVlXHhwgXY29uLjV+7dg2Ojo4oKSmhqrdjxw74+fkhNjYWVlZWAICVK1fijz/+wD///IMOHTpQ1astNGrUCIcOHUKbNm2goaEhahV5+PBhLF++HHFxcdKeIoNRKxg7diz09fWxYMEChIaGYvr06XB0dERiYiIGDhyIbdu2Udfs3bs3CCHYs2cP6tatCwB49uwZRowYAaFQiKNHj1LXlFVY2gCjRpKUlITS0lLRz9XxtT3lf3QMDAxEv+/HlJWVceJp6+7ujmfPnqFHjx6Ii4vDvn37sGTJEhw7dowVa30HRUVF0NXVBQDUrVsXT548gbm5OWxtbTmpbmYwGFWzefNmVFRUAAAmTJiAunXrIi4uDn379sWECRM40YyNjUVCQoIocAWAevXqITAwEI6OjpxoyioseGXUSD7uF//xz7LK8uXLMXnyZKxfvx6tWrWCQCBAYmIivL29sXLlSk40Z8yYgWfPnsHBwQHl5eU4efIk2rZty4lWbcHCwgK3b9+GkZERWrRogU2bNsHIyAihoaFo1KiRtKfHYNQahEIhhML/y5wcPHgwBg8ezKlmnTp1RLm1H/PmzRsoKipyqi1rsLQBBqMG8LE/YGW1f+XPqqqqYuf+V6/ANWvWVDm+cuVKdOzYEW3atBGNTZky5T9p1Hb27NmD0tJSjBkzBklJSejZsyeePXsGRUVF7Ny5E25ubtKeIoNRaygsLMTly5fx+PFj0SpsJaNGjaKuN2rUKFy7dg3btm0TfZ5eunQJ48aNQ6tWrbBz507qmrIKC14ZNZ6ioiIEBgYiOjq6yg+hnJwcKc2MHl/yB/yY/+oVaGxs/FXnCQQCmfg3/REoLi5Geno6DAwMUL9+fWlPh8GoNRw5cgTDhw9HUVER1NXVxVLMBAIBJw0DCgsLMXr0aBw5cgQKCgoAPixCuLq6YufOndDU1KSuKauw4JVR4xk6dChiY2MxcuRINGrUSCLP1dvbW0ozYzC+jrdv32LdunWYMWOGtKfCYNQKzM3N0bt3byxZsgQqKiq8amdmZop12DMzM+NVXxZgwSujxqOlpYWjR4/WmoT3kpISieItDQ0NKc2G8bU8ffoUly5dgoKCArp27Qo5OTmUlpZiw4YNWLp0KcrKyvD06VNpT5PBqBWoqqri+vXrMDExkfZUGP8BVrDFqPFoa2uLVW/KIkVFRZg5cyYiIyNF3oQfw4W5/b1793D48OEqO0HJgv0Yn1y4cAEuLi54+fIlBAIBHBwcsGPHDvTv3x8VFRWYO3cuPDw8pD1NBqPW0LNnTyQmJvIavJaXl2Pnzp3VprjFxMTwNpeaDgteGTWeP/74A/Pnz0dYWBjv2z984efnhzNnzmDDhg0YNWoU1q9fj/v372PTpk0IDAykrhcdHQ1XV1cYGxvj9u3bsLGxQV5eHgghaNmyJXU9WWfevHno2bMn5s6di+3btyMkJAR9+vTBwoULMXLkSJmxdGMwagouLi7w9fXFrVu3YGtrK8pBrcTV1ZW6pre3N3bu3AkXFxfY2Niw9/13wNIGGDUSe3t7sTd+VlYWCCEwMjKS+BCSBf9MAwMDhIeHo3PnztDQ0MC1a9dgZmaGXbt2ISIiAv/++y9VvTZt2sDZ2RmLFi0S9f3W1dXF8OHD4ezsDC8vL6p6sk79+vURGxsLa2trFBcXQ11dHXv37sWvv/4q7akxGLWSj22yPkUgEHCym1W/fn2Eh4ejd+/e1K9d22Arr4waSf/+/aU9BV55/vy5yA1AQ0NDVAn7888/cxJIpqWlISIiAgAgLy+PkpISqKmpYdGiRejXrx8LXr+R58+fQ0dHBwCgoqICFRUViW5pDAaDPz7dsucDRUVFVpxFCRa8MmokCxYskPYUeMXExAR5eXkwNDSElZUVIiMj0aZNGxw5cgRaWlrU9VRVVfHu3TsAgJ6eHrKzs2FtbQ0ArKjoPyAQCPD69WsoKSmBEAKBQIDi4mK8evVK7DxWeMdgcE9ZWRmUlJSQnJwMGxsb3nR9fHywevVqrFu3jqUMfCcseGXUeDw8PNCpUycJf9NXr15h6tSp2L59u5RmRg93d3ekpKSgU6dOmD17NlxcXLB27VqUlZVxUjzVrl07xMfHw8rKCi4uLvDx8cH169cRFRWFdu3aUdeTdQghMDc3F3v+8cprZUDLxVYlg8EQR15eHoaGhry83wYOHCj2PCYmBseOHYO1tbVEiltUVBTn85EVWM4ro8YjFAqhrKwMT09PhISEiHKZCgoKoKenJ5MBQX5+PhITE2FqaormzZtTv35OTg7evHkDOzs7FBcXY8aMGYiLi4OZmRmCg4NhaGhIXVOWiY2N/arzOnXqxPFMGAwGAOzYsQN//fUXdu/ezalbjbu7+zfNifF1sOCVUeMRCoWIiYnBuHHjYGRkhMjISGhra8t08MpgMBiM/469vT2ysrJQWloKQ0NDiTbbNAt98/Pz0aRJk88WiTG+DZY2wJAJrKyskJCQgEGDBqF169Y4cuSITHi/rlmz5ovnyMvLo2HDhvj555+hq6vLw6wYDAajZsNn0a+xsTEePnzIPp8pwlZeGTUeOTk50QdDWVkZJkyYgL/++gsrV67EhAkTavTKa6XDwOeoqKjAs2fPUFFRgd27d0vkWP0XtLW1qywoEAgEUFJSgpmZGcaMGfNNW2IMBoNRGxEKhXj06BELXinCVl4ZNZ6P77/k5eWxdetWWFlZYeLEiVKcFR1yc3O/6ryKigoEBgZizpw5VILX+fPnIyAgAL169UKbNm1ACMGVK1dw/Phx/Pbbb8jNzYWXlxfKysowbty479ZjMBgMBuNrYSuvjBpPbGwsHB0dIS8vfi92+vRpxMfH1xpbrfv376NFixZ48uTJd19r0KBB6N69OyZMmCA2vmnTJpw8eRJ///031q5di82bN+P69evfrSfLpKamwsbGhuW7MRg/EEKh8LN2VTR37IRCIRYvXgw1NbXPnjdlyhRqmrIOC14ZDIYEampqSE5OljDUzsrKQosWLfDmzRtkZ2fDzs4ORUVFUpplzeDjtBYTExNcuXIF9erVk/a0GIxazaFDh8Sel5aWIikpCWFhYfD394enpyc1LaFQiCZNmkBOTq7acwQCAXJycqhpyjosbYDBYEhQt25dHDlyBNOmTRMb/7gQrqioCOrq6tKYXo1CS0sLubm50NXVRV5enlQ6+zAYDHH69esnMfbLL7/A2toa+/5fe3ceVmP6/wH8/ZwWitZRhFCdsZSQXc3Y913GmC+XKGbIEk0TQmgG3/glux9jKNsMfckYZJmp1MgasoxEJc3XIKShhbbn94fL+c2Zk5no1DPP6f26rq7LuZ/j3O/jIp/ucz+fe+9erRavAJCYmMg9r1rE4pWINAQGBsLb2xuxsbHo2LEjBEHA+fPnERUVhU2bNgEAfvzxR/YlLYeRI0eiW7dusLGxgSAIaN++/RtXYLjyQiStTp06aX0fP0/T0j5uGyCiMiUkJGD9+vVISUmBKIpo3rw5ZsyYAVdXV6mjyc6xY8eQmpoKHx8ffPnll29csZ45c2YVJyOi1woKChAQEICjR48iJSVFa6/LbgPax5VXIplIS0tDWFgY0tLSsGbNGlhbW+PYsWOwtbWFk5OT1udzc3ODm5ub1l+3Ourfvz8A4OLFi5g5cya3WxBJ7M/tAEVRxPPnz2FsbIxdu3Zpda5Fixb97c1a9Ha48ko6Iz8/H5mZmSgsLFQbb9WqlUSJtCcuLg4DBgyAm5sb4uPjkZycDHt7e6xYsQLnz5/Hvn37Km3ugoICFBUVqY2ZmppW2nzVxX//+18IgoAGDRpIHYWo2tm+fbvaY4VCASsrK3Tq1AkWFhYSpaLyYvFKsvfo0SN4enri6NGjZV6X8yEFr3Xp0gWjRo3C559/DhMTE1y5ckV15/rw4cNx7949rc6Xn5+P2bNnIyIiAk+ePNG4rgt/plIoLS3FkiVLsHLlSuTm5gIATExM4Ofnh/nz57OdFhFROXDbAMnerFmz8PTpU5w9exY9evTAgQMH8PDhQ1WRoAuuXbuGb7/9VmPcysqqzOKyovz9/REbG4uNGzfCw8MDGzZswL1797B582YEBwdrfb7qYv78+di6dSuCg4Ph5uYGURSRkJCAxYsX48WLF1i6dKnUEYmqjZycHGzduhXJyckQBAGOjo7w8vKCmZmZ1NHo74hEMlevXj3x3LlzoiiKoomJiZiSkiKKoigePHhQdHNzkzKa1jRo0EBMSEgQRVEUa9euLaalpYmiKIqRkZGivb291ueztbUVY2NjRVF89Wd6+/ZtURRFcceOHeKAAQO0Pl91YWNjIx48eFBj/Pvvvxfr168vQSKi6unChQuipaWl2KBBA3HEiBHi8OHDxYYNG4rvvfeeePHiRa3Nc/DgQbGwsFBrr0ev8DMqkr28vDzVXZyWlpaqE6acnZ1x6dIlKaNpzZgxYzBnzhw8ePAAgiCgtLQUCQkJ+OKLL+Dh4aH1+bKzs2FnZwfg1f7W7OxsAMAHH3yA+Ph4rc9XXWRnZ6N58+Ya482bN1f9GRNR5fP19cXQoUORkZGByMhIHDhwAHfu3MHgwYMxa9Ysrc0zYsQI5OTkAHh1YElWVpbWXrs6Y/FKstesWTNVW5M2bdpg8+bNuHfvHjZt2gQbGxuJ02nH0qVL0ahRIzRo0AC5ublwdHRE165d4erqigULFmh9Pnt7e2RkZAAAHB0dERERAeDVIQXm5uZan6+6aN26NdavX68xvn79erRu3VqCRETVU2JiIubMmaN2rLi+vj5mz56NxMRErc1jZWWFs2fPAnjV0YA9X7WDe15J9mbNmoX79+8DeNWSpF+/fti9ezcMDQ0RHh4ubTgtMTAwwO7du/Hll1/i8uXLKC0thYuLC95///1Kmc/T0xNXrlxBt27dEBAQgEGDBmHdunUoLi5GaGhopcxZHaxYsQKDBg3CTz/9hC5dukAQBJw+fRq//voroqKipI5HVG2YmpoiMzNT45OQX3/9Vaut7KZMmYJhw4ZBEAQIgoB69eq98bm8Ebb82G2AdE5+fj5u3ryJRo0aoU6dOlLH0QmZmZlITEyEg4MDVwgr6LfffsOGDRtw8+ZNiKIIR0dHTJ06FfXr15c6GlG14ePjgwMHDiAkJASurq4QBAGnTp2Cv78/Ro4cidWrV2ttrps3byI1NRVDhw5FWFjYGz+9KuvIWiobi1eSvfj4eDRv3lzj9JKioiKcOXMGXbt2lSiZ9pSUlCA8PBzR0dHIyspCaWmp2vWYmBitzhcdHY1evXqVeW39+vWYPn26VucjIqpKhYWF8Pf3x6ZNm1BcXAxRFGFoaAhvb28EBwejRo0aWp8zKCgI/v7+MDY21vprVzcsXkn2FAoF6tati8jISHTp0kU1/vDhQ9SvX18nPoqZPn06wsPDMWjQINjY2Gjsm1q1apVW5zM3N8ePP/6IDh06qI2vXr0aCxcuxLNnz7Q6HxGRFPLz85GWlgZRFKFUKquksHz06BFSUlIgCAKaNm0KKyurSp9T13DPK+mETz75BL169cLGjRsxYcIE1biu/Gy2Z88eREREYODAgVUy36pVqzBw4EDExcXB0dERABASEoKvvvoKR44cqZIMRETa5u7u/rfP0dfXR7169dCnTx8MGTJEa3Pn5+dj+vTp2Llzp2pRRU9PDx4eHli3bh1XZN8Cuw2Q7AmCgICAAOzatQszZszA559/ripadeXOTkNDQyiVyiqbz9PTE3PmzEHfvn2RkZGB5cuX46uvvsLRo0fx4YcfVlkOIiJtMjMz+9svIyMj3L59G6NHj8bChQu1Nrevry/i4uLwww8/ICcnBzk5OTh48CDi4uLg5+entXmqA24bINlTKBR48OABrK2tcfnyZQwbNgyOjo5Ys2YNHB0ddWLbwMqVK5Geno7169dXaUEeEBCALVu2oKSkBMeOHUOnTp2qbG4iIikdOXIE3t7eyMzM1Mrr1alTB/v27UP37t3VxmNjY/Hxxx+repTT3+O2AdIpLi4uOH/+PIYPH/7GG47k4s8fb8XExODo0aNwcnKCgYGB2rXIyMgKz7d27VqNMRsbGxgbG6Nr1644d+4czp07B+DVnbpERLrMzc0N7du319rr5efno27duhrj1tbWyM/P19o81QFXXkn2PD09sXbtWrXefC9fvsRnn32G+Ph43LlzR8J0787T07Pczw0LC6vwfK9P1Po7giAgPT29wvNVFy4uLuVeLdeVE+GISFOvXr3w3nvvYceOHahZsyYAoKCgAOPHj0d2djZ++ukniRPKB4tXon+wzMxMNGzYEAoFt6fLVVBQULmfu2jRokpMQkRSun79Ovr3748XL16gdevWEAQBSUlJqFmzJo4fPw4nJyepI8oGi1eifzA9PT3cv39fo4ctERHJT0FBAXbt2qV2SMnYsWNhZGQkdTRZYfFK9A/2x5vRiIiIiDdsERFVmZKSEqxatQoRERHIzMxEYWGh2vXs7GyJkhERyQeLV6J/uG+++Qa1a9f+y+fw7n95CAoKwjfffIPPP/8cgYGBmD9/PjIyMvD9999rtZ8kEZEu47YBon8whUKBhg0bQk9P743P4d3/8uHg4IC1a9di0KBBMDExQVJSkmrs7Nmz+Pbbb6WOSET0j8eVV5Kltm3bIjo6GhYWFn/bikju7YcSExMl2fOak5ODrVu3Ijk5GYIgoEWLFpg4cSLMzMyqPIuuePDgAZydnQEAtWvXxu+//w4AGDx4MAIDA6WMRkQkGyxeSZaGDRuGGjVqAACGDx8ubZhKJNXxtomJiejXrx+MjIzQsWNHiKKIVatWYdmyZThx4gTatm0rSS65a9iwIe7fv49GjRpBqVSq/iwvXLig+vtMRER/jdsGiP7BpOo28OGHH0KpVGLLli3Q13/1M25xcTEmTZqE9PR0xMfHV2keXTF37lyYmppi3rx52LdvH/71r3+hSZMmyMzMhK+vL4KDg6WOSERaZGFhUe5FCN6wWX4sXon+wYKCguDv7w9jY+MqndfIyAiXL19G8+bN1cZv3LiB9u3b8yhDLTl79ixOnz4NpVKJoUOHSh2HiLRs+/bt5X7u+PHjKzGJbmHxSrLH9kPaV7duXezcuRN9+/ZVGz9+/Dg8PDzw8OFDiZIREVF1xz2vJHtsP6R9o0ePxsSJExESEgJXV1cIgoBTp07B398f//rXv6SOJ2u3bt3CyZMnkZWVhdLSUrVr/PtKVD0UFBSgqKhIbczU1FSiNPLDlVeSPbYf0r7CwkL4+/tj06ZNKC4uBgAYGBjA29sbwcHBvLnoHW3ZsgXe3t6oU6cO6tWrp7YXThAE2XfGIKI3y8vLw5w5cxAREYEnT55oXC8pKZEglTyxeCXZq1WrFpKTk9GoUSPY2NjgyJEjaNu2LdLT0+Hi4qJqR0RvLz8/H2lpaRBFEUqlssr33uqaxo0bY+rUqZgzZ47UUYioik2bNg2xsbH48ssv4eHhgQ0bNuDevXvYvHkzgoODMXbsWKkjyoZC6gBEFfW6/RAAVfshAGw/VAFeXl54/vw5jI2N4ezsjFatWsHY2Bh5eXnw8vKSOp5sPX36FKNGjZI6BhFJ4NChQ9i4cSM++ugj6Ovr48MPP8SCBQuwbNky7N69W+p4ssLilWRvxIgRiI6OBgDMnDkTgYGBeP/99+Hh4aHzhda8efMq5T1u374dBQUFGuMFBQXYsWOH1uerLkaNGqX64YqIqpfs7GzY2dkBeLW/9fXNxB988AHbD74l3rBFsvfH3pgfffQRbG1tkZCQUC3aD927dw+//vqr1l7v2bNnEEURoiji+fPnqFmzpupaSUkJoqKiJDntS1colUoEBgbi7NmzcHZ2hoGBgdp1Hx8fiZIRUWWzt7dHRkYGGjduDEdHR0RERKBjx444dOgQzM3NpY4nK9zzSkQqCoXiLxtqC4KAoKAgzJ8/vwpT6Y7Xqy5lEQQB6enpVZiGiKrSqlWroKenBx8fH8TGxmLQoEEoKSlBcXExQkNDMXPmTKkjygaLVyJSiYuLgyiK6NmzJ/bv3w9LS0vVNUNDQzRu3Bj169eXMKF8iaKIu3fvwtramje+EREyMzORmJgIBwcHtG7dWuo4ssLilUgm8vLyEBcXV+ZBDNr+uPnu3bto1KhRuY81pL9XWlqKmjVr4pdffsH7778vdRwiItli8UokA5cvX8bAgQORn5+PvLw8WFpa4vHjxzA2Noa1tTU/bpYJJycnbN26FZ07d5Y6ChFJIDo6GtHR0WUeUrJt2zaJUskPuw0QyYCvry+GDBmC7OxsGBkZ4ezZs7h79y7atWuHkJAQqeNROa1YsQL+/v64fv261FGIqIoFBQWhb9++iI6OxuPHj/H06VO1Lyo/rrwSyYC5uTnOnTuHZs2awdzcHGfOnEGLFi1w7tw5jB8/Hjdv3pQ6IpWDhYUF8vPzUVxcDENDQxgZGaldf906h4h0j42NDVasWIFx48ZJHUX22CqLZMnS0hK3bt1CnTp1YGFh8Zd7M3WhIDAwMFC9x7p16yIzMxMtWrSAmZkZMjMzJU5H5bV69WqpIxCRRAoLC+Hq6ip1DJ3A4pVkadWqVTAxMQFQPQoCFxcXJCYmomnTpujRowcWLlyIx48fY+fOnXB2dpY6HpXT+PHjpY5ARBKZNGkSvv32WwQGBkodRfa4bYBIBhITE/H8+XP06NEDjx49wvjx43Hq1CkolUps27YNbdq00ep8Dx8+xBdffKG6seDP3yZKSkq0Ol918Xer5I0aNaqiJERU1WbOnIkdO3agVatWaNWqlcYhJaGhoRIlkx8Wr6QT0tLSEBYWhrS0NKxZswbW1tY4duwYbG1t4eTkJHW8SlVQUKCxd7KiBgwYgMzMTEyfPh02NjYa2zKGDRum1fmqi787BII/FBDprh49erzxmiAIiImJqcI08sbilWQvLi4OAwYMgJubG+Lj45GcnAx7e3usWLEC58+fx759+6SOWGHTpk3Dhg0bNMbz8vIwaNAgnDx5UqvzmZiY4Oeff9b6im51d+XKFbXHRUVFuHz5MkJDQ7F06VK4u7tLlIyIKlNJSQlOnToFZ2dntcNf6N2wVRbJ3ty5c7FkyRL8+OOPMDQ0VI336NEDZ86ckTCZ9pw4cQILFixQG8vNzUX//v0rZbXO1tZWY6sAVVzr1q3Vvtq3b49PP/0UISEhWLt2rdTxiKiS6OnpoV+/fvj999+ljqITWLyS7F27dg0jRozQGLeyssKTJ08kSKR9J06cQFhYGFatWgUAeP78Ofr27QtBEHDs2DGtz7d69WrMnTsXGRkZWn9t0tS0aVNcuHBB6hhEVImcnZ15oIyWsNsAyZ65uTnu378POzs7tfHLly+jQYMGEqXSLjs7Oxw/fhzdu3eHQqHAnj17UKNGDRw5cgS1atXS+nyjR49Gfn4+HBwcYGxsrHFjgS60H5PCs2fP1B6Looj79+9j8eLFPDKWSMctXboUX3zxBb766iu0a9dO43u3qampRMnkh8Uryd6YMWMwZ84c/Oc//4EgCCgtLUVCQgK++OILeHh4SB1Pa1q2bInDhw+jd+/e6NSpEw4fPqz1G7Veqw7tx6Rgbm6uccOWKIqwtbXFnj17JEpFRFWhf//+AIChQ4eqfR8QRRGCIPCGzbfAG7ZI9oqKijBhwgTs2bMHoihCX18fJSUlGDNmDMLDw6Gnpyd1xHfi4uJS5p3pd+/ehbW1tVrheunSpaqMRu8oLi5O7bFCoYCVlRWUSiX09bmWQKTL/vzv/8+6detWRUnkj8UrydKzZ880PmJJT0/HpUuXUFpaChcXF9l/DBsUFFTu5y5atKjSchQUFKCoqEhtjB9vERGRVFi8kizp6enh/v37sLa2Rs+ePREZGQlzc3OpY+mMvLw8zJkzBxEREWXe9MaPt95dWloaVq9ejeTkZAiCgBYtWmDmzJlwcHCQOhoRVaL4+Pi/vN61a9cqSiJ//JyKZKl27dp48uQJrK2tcfLkSY2VQV2Uk5ODffv2IS0tDf7+/rC0tMSlS5dQt25drd+YNnv2bMTGxmLjxo3w8PDAhg0bcO/ePWzevBnBwcFanas6OX78OIYOHYo2bdrAzc0Noiji9OnTcHJywqFDh9CnTx+pIxJRJenevbvG2B+3hnFRoPy48kqyNHLkSCQkJKBFixaIi4uDq6urWo/XP9KFU0uuXr2K3r17w8zMDBkZGUhJSYG9vT0CAwNx9+5d7NixQ6vzNWrUCDt27ED37t1hamqKS5cuQalUYufOnfjuu+8QFRWl1fmqCxcXF/Tr10/jB4C5c+fixIkT3LtMpMP+3OP19SElgYGBWLp0KXr16iVRMvnhyivJ0q5du7B9+3akpaUhLi4OTk5OMDY2ljpWpfn8888xYcIErFixAiYmJqrxAQMGYMyYMVqfLzs7W9V6zNTUVNUa64MPPoC3t7fW56sukpOTERERoTHu5eXFDg9EOs7MzExjrE+fPqhRowZ8fX1x8eJFCVLJE4tXkiUjIyNMmTIFAJCYmIjly5fr9J7XCxcuYPPmzRrjDRo0wIMHD7Q+n729PTIyMtC4cWM4OjoiIiICHTt2xKFDh3T6z7myWVlZISkpSeNmwqSkJFhbW0uUioikZGVlhZSUFKljyAqLV5K92NhYqSNUupo1a2o0uAeAlJQUWFlZaX0+T09PXLlyBd26dUNAQAAGDRqEdevWobi4GKGhoVqfr7r49NNP8dlnnyE9PR2urq4QBAGnTp3C8uXL4efnJ3U8IqpEV69eVXv8+pCS4OBgtG7dWqJU8sQ9r0Qy8Nlnn+HRo0eIiIiApaUlrl69Cj09PQwfPhxdu3at9I+cMzMzkZiYCAcHB36TrQBRFLF69WqsXLkSv/32GwCgfv368Pf3h4+PT5l9fYlINygUCgiCgD+XXZ07d8a2bdvQvHlziZLJD4tXIhl49uwZBg4ciF9++QXPnz9H/fr18eDBA3Tp0gVRUVGVckQsVa7nz58DgNoeZiLSXXfv3lV7/PqQkpo1a0qUSL5YvBLJSExMjOoghrZt26J3795aff2CggJER0dj8ODBAICAgAC8fPlSdV1PTw9fffUVv9kSEZFkWLwSkcrmzZtx+PBhHDp0CMCrVUEnJyfVUbQ3b97E7Nmz4evrK2VM2XnTUb9/pK+vj3r16qFPnz6YPHnyG1u/EZF8xcXFISQkRO2QEn9/f3z44YdSR5MVFq+kE3JycrB161a1bwgTJ04sszWJHK1du7bMcUEQULNmTSiVSnTt2hV6enoVmqdr167w9fXFiBEjALwqXq9cuQJ7e3sAr1qUbdiwAWfOnKnQPNVNeY76LS0tRVZWFiIjIzFy5Ehs3LixCpIRUVXZtWsXPD094e7urnZIyYEDBxAeHl4pbQ91FYtXkr3ExET069cPRkZG6NixI0RRRGJiIgoKCnDixAm0bdtW6ogVZmdnh0ePHiE/Px8WFhYQRRE5OTkwNjZG7dq1kZWVBXt7e8TGxsLW1vad56lXrx6io6Ph5OQE4FULlwsXLqBJkyYAgFu3bqFDhw4azbZJe+Lj4/Hxxx9XSgs0IpJOixYt8Nlnn2l8chUaGootW7YgOTlZomTyo5A6AFFF+fr6YujQocjIyEBkZCQOHDiAO3fuYPDgwZg1a5bU8bRi2bJl6NChA27fvo0nT54gOzsbt27dQqdOnbBmzRpkZmaiXr16Ff44//fff4e+/v930Hv06JGqcAVerQ7+cQ8saV/btm25AkOkg9LT0zFkyBCN8aFDh+LOnTsSJJIvFq8ke4mJiZgzZ45a0aWvr4/Zs2cjMTFRwmTas2DBAqxatQoODg6qMaVSiZCQEAQEBKBhw4ZYsWIFEhISKjRPw4YNcf369Tdev3r1Kho2bFihOeiv1a5dm710iXSQra0toqOjNcajo6Mr9IlZdcRDCkj2TE1NkZmZqdEj79dff9WZNkT3799HcXGxxnhxcbHq4+X69eur2i+9q4EDB2LhwoUYNGiQRkeBgoICBAUFYdCgQRWag4ioOvLz84OPjw+SkpLUDikJDw/HmjVrpI4nK9zzSrLn4+ODAwcOICQkRO0bgr+/P0aOHKkTZ8YPGjQIDx48wDfffAMXFxcAwOXLl/Hpp5+iXr16qg4B8+bNw7Vr1955nocPH6JNmzYwNDTE9OnT0bRpUwiCgJs3b2L9+vUoLi7G5cuXUbduXW29NSKiauPAgQNYuXKlan/r624Dw4YNkziZvLB4JdkrLCyEv78/Nm3apFqdNDAwgLe3N4KDg1GjRg2JE1bcgwcPMG7cOERHR8PAwADAq1XXXr16YefOnahbty5iY2NRVFSEvn37VmiuO3fuwNvbGz/++KPqJBhBENCnTx9s3LhR1XmAiIhICixeSWfk5+cjLS0NoihCqVTC2NhY6khal5KSgpSUFIiiiObNm6NZs2aVNld2djZSU1MBvNpfa2lpWWlz6bI3tTkri4+PTyUmISLSDSxeiYgqkZ2dXbmeJwgC0tPTKzkNEVU1CwuLtzqkJDAwEObm5lUTTqZYvJLs9ejR4y+/McTExFRhGiIiov+3ffv2v33O60NKwsLC4OLigu+++64KkskXuw2Q7LVp00btcVFREZKSknD9+nWMHz9emlBERETAW/0/1KdPH/Tp06cS0+gGFq8ke6tWrSpzfPHixcjNza3iNER/7b///S9++OEHZGZmorCwUO0a+7sSVW8tWrTAwoULpY7xj8dtA6SzUlNT0bFjR2RnZ0sdhQjAq2bkQ4cOhZ2dHVJSUtCyZUtkZGRAFEW0bduWW1yIiMqBK6+ks86cOaPRaF+u4uPj//J6165dqygJVURAQAD8/Pzw5ZdfwsTEBPv374e1tTXGjh2L/v37Sx2PiEgWuPJKsufu7q72WBRF3L9/H4mJiQgMDMSiRYskSqY9CoXmSc5/vEmtpKSkKuPQOzIxMUFSUhIcHBxgYWGBU6dOwcnJCVeuXMGwYcOQkZEhdUQion88zf8RiWTGzMxM7cvS0hLdu3dHVFSUThSuAPD06VO1r6ysLBw7dgwdOnTAiRMnpI5H5VSrVi28fPkSwKvjfNPS0lTXHj9+LFUsIqpChYWFSElJKfPIbyofbhsg2QsLC5M6QqUzMzPTGOvTpw9q1KgBX19fXLx4UYJU9LY6d+6MhIQEODo6YtCgQfDz88O1a9cQGRmJzp07Sx2PiCpRfn4+ZsyYoWqddevWLdjb28PHxwf169fH3LlzJU4oH1x5JZIxKysrpKSkSB2Dyik0NBSdOnUC8KobRp8+fbB37140btwYW7dulTgdEVWmgIAAXLlyBSdPnlS7H6N3797Yu3evhMnkh3teSfbedHqJIAioWbMmlEolJkyYAE9PTwnSacfVq1fVHr/e1xscHIyioiIkJCRIlIyIiMqjcePG2Lt3Lzp37gwTExNcuXIF9vb2SE1NRdu2bfHs2TOpI8oGtw2Q7C1cuBBLly7FgAED0LFjR4iiiAsXLuDYsWOYNm0a7ty5A29vbxQXF+PTTz+VOu47adOmDQRBwJ9/1uzcuTO2bdsmUSp6V4WFhcjKykJpaanaeKNGjSRKRESV7dGjR7C2ttYYz8vL+9vjY0kdi1eSvVOnTmHJkiWYMmWK2vjmzZtx4sQJ7N+/H61atcLatWtlW7zeuXNH7bFCoYCVlZXOtAKrLm7duoWJEyfi9OnTauOiKEIQBHaNINJhHTp0wJEjRzBjxgwA/98xZsuWLejSpYuU0WSH2wZI9mrXro2kpCQolUq18dTUVLRp0wa5ublIS0tDq1atkJeXJ1FKIsDNzQ36+vqYO3cubGxsNFZbWrduLVEyIqpsp0+fRv/+/TF27FiEh4dj8uTJ+OWXX3DmzBnExcWhXbt2UkeUDd6wRbJnaWmJQ4cOaYwfOnQIlpaWAF59LGNiYlLV0bQqOjoagwcPhoODA5RKJQYPHoyffvpJ6lj0FpKSkrB582YMGDAAbdq0QevWrdW+iEh3ubq6IiEhAfn5+XBwcMCJEydQt25dnDlzhoXrW+K2AZK9wMBAeHt7IzY2Fh07doQgCDh//jyioqKwadMmAMCPP/6Ibt26SZz03a1fvx6+vr746KOPMHPmTADA2bNnMXDgQISGhmL69OkSJ6TycHR0ZD9XomrM2dlZ1SqL3h23DZBOSEhIwPr165GSkgJRFNG8eXPMmDEDrq6uUkfTigYNGiAgIECjSN2wYQOWLl2K3377TaJk9DZiYmKwYMECLFu2DM7OzjAwMFC7bmpqKlEyIqoKpaWlSE1NLfOGTR7zXX4sXolkwMTEBJcvX9bY13v79m24uLggNzdXomT0Nl4f8/vnva68YYtI9509exZjxozB3bt3NTrH8N//2+G2AZK9N/XGEwQBNWrUgKGhYRUn0r6hQ4fiwIED8Pf3Vxs/ePAghgwZIlEqeluxsbFSRyAiiUyZMgXt27fHkSNHyrxhk8qPK68kewqF4i+/CTRs2BATJkzAokWLVCtfcrB27VrVr589e4aQkBC4ubmpWqqcPXsWCQkJ8PPzw4IFC6SKSURE5VCrVi1cuXJF4xM0enssXkn2duzYgfnz52PChAlqhxRs374dCxYswKNHjxASEgJ/f3/MmzdP6rjlZmdnV67nCYKA9PT0Sk5D2pKTk4OtW7ciOTkZgiDA0dERXl5eMDMzkzoaEVWinj17Yvbs2ejfv7/UUWSPxSvJXq9evTB58mR8/PHHauMRERHYvHkzoqOjsXPnTixduhQ3b96UKCURkJiYiH79+sHIyEj1g1ZiYiIKCgpw4sQJtG3bVuqIRKRFfzzaOy0tDQsWLIC/v3+ZN2y2atWqquPJFotXkj1jY2NcuXIF77//vtr47du30bp1a+Tn5+POnTtwcnJCfn6+RCm15/U/We6Xkp8PP/wQSqUSW7Zsgb7+q1sOiouLMWnSJKSnpyM+Pl7ihESkTa+3tb2p1Hp9jTdsvR3esEWy17BhQ2zduhXBwcFq41u3boWtrS0A4MmTJ7CwsJAintbs2LED//M//4Pbt28DAJo2bQp/f3+MGzdO4mRUXomJiWqFKwDo6+tj9uzZaN++vYTJiKgy/Plob9IOFq8keyEhIRg1ahSOHj2KDh06QBAEXLhwATdv3sS+ffsAABcuXMDo0aMlTvruQkNDERgYiOnTp8PNzQ2iKCIhIQFTpkzB48eP4evrK3VEKgdTU1NkZmaiefPmauO//vqr7E+AIyJNjRs3hpeXF9asWcN/41rEbQOkEzIyMrBp0ybcunVLdUjB5MmT0aRJE6mjaYWdnR2CgoLg4eGhNr59+3YsXryYP93LhI+PDw4cOICQkBC4urpCEAScOnUK/v7+GDlyJFavXi11RCLSMj09Pdy/fx/W1tZSR9EZLF6JZKBmzZq4fv16mYcUODs748WLFxIlo7dRWFgIf39/bNq0CcXFxQAAAwMDeHt7Izg4GDVq1JA4IRFpm0KhwIMHD1i8ahGLV9IJOTk5OH/+fJlH7v15tVKOWrZsiTFjxmi0+lqyZAn27t2La9euSZSM3kV+fj7S0tIgiiKUSiWMjY2ljkRElUShUODhw4ewsrKSOorOYPFKsnfo0CGMHTsWeXl5MDExUbsLXxAEZGdnS5hOO/bv34/Ro0ejd+/ecHNzU33cHB0djYiICIwYMULqiEREVAaFQgEzM7O/7RCjC/9XVRUWryR7TZs2xcCBA7Fs2TKdXsG6ePEiVq1aheTkZIiiCEdHR/j5+cHFxUXqaPQX3N3dER4eDlNTU7i7u//lcyMjI6soFRFVFYVCgdWrV//tQSTjx4+vokTyx24DJHv37t2Dj4+PTheuANCuXTvs2rVL6hj0lv644mJqasr+vETV0CeffMI9r1rElVeSPXd3d3zyyScaJ2zpku7du8PLywujRo2CkZGR1HGIiKic2G1A+7jySrI3aNAg+Pv748aNG2UeuTd06FCJkmlPu3btMHv2bMyYMQMff/wxJk6ciM6dO0sdi95Sz549ERkZCXNzc7XxZ8+eYfjw4YiJiZEmGBFVGq4Rah9XXkn2FArFG6/p0pF7JSUlOHz4MMLCwhAVFQWlUgkvLy+MGzcOdevWlToelcObWuZkZWWhQYMGKCoqkigZEZF8sHglkqFHjx5h8+bNWLp0KUpKSjBw4ED4+PigZ8+eUkejMly9ehUA0KZNG8TExMDS0lJ1raSkBMeOHcPmzZuRkZEhUUIiIvlg8UokM+fPn0dYWBi+++47mJmZYcKECbh//z52794Nb29vhISESB2R/kShUKhu1CrrW66RkRHWrVsHLy+vqo5GRCQ7LF5JJ8TFxSEkJATJyckQBAEtWrSAv78/PvzwQ6mjaUVWVhZ27tyJsLAw3L59G0OGDMGkSZPQr18/VVH0008/Yfjw4cjNzZU4Lf3Z3bt3IYoi7O3tcf78ebVm5YaGhrC2toaenp6ECYmI5IPFK8nerl274OnpCXd3d7i5uUEURZw+fRoHDhxAeHg4xowZI3XECjM0NISDgwO8vLwwYcIEteLn2bNnMDU1xbNnzzBs2DDExsZKmJSIiKhysXgl2WvRogU+++wz+Pr6qo2HhoZiy5YtSE5OlihZxYWEhOCLL77Azz//XOYq8rNnz9C3b1+cPXtWgnT0rm7cuIHMzEwUFhaqjetCZwwiosrG4pVkr0aNGvjll1+gVCrVxlNTU9GyZUu8ePFComQVZ2RkhI0bN8LT01Pj2vPnz9G3b1/8/vvvuHHjhgTp6G2lp6djxIgRuHbtGgRBUO1/fb31Q1c6YxARVaY39xgikglbW1tER0drjEdHR8PW1laCRNqzc+dOTJ06Fd9//73aeG5uLvr164fs7GxuE5CRmTNnws7ODg8fPoSxsTF++eUXxMfHo3379jh58qTU8YiIZIGHFJDs+fn5wcfHB0lJSXB1dYUgCDh16hTCw8OxZs0aqeNVyEcffYScnByMGTMGR44cQY8ePZCbm4v+/fvj8ePHiIuLY49XGTlz5gxiYmJgZWUFhUIBhUKBDz74AP/+97/h4+ODy5cvSx2RiOgfj8UryZ63tzfq1auHlStXIiIiAsCrfbB79+7FsGHDJE5XcZMmTUJ2djaGDx+OgwcPIjAwEA8ePEBcXBxsbGykjkdvoaSkBLVr1wYA1KlTB7/99huaNWuGxo0bIyUlReJ0RETywOKVdMKIESMwYsQIqWNUmtmzZ+Pp06fo1asXmjRpgri4ODRo0EDqWPSWWrZsiatXr8Le3h6dOnXCihUrYGhoiK+//hr29vZSxyMikgXesEU6o7CwEFlZWSgtLVUbb9SokUSJKs7d3V3tcVRUFFq3bq1RuEZGRlZlLHpHx48fR15eHtzd3ZGeno7Bgwfj5s2beO+997B3716ekEZEVA4sXkn2bt++DS8vL5w+fVptXBRFCIIg6zu4y+oyUJawsLBKTkKVJTs7GxYWFqqOA0RE9NdYvJLsubm5QV9fH3PnzoWNjY1GEdC6dWuJkhEREZG2sXgl2atVqxYuXryI5s2bSx2F6C/l5eUhODgY0dHRZW5xSU9PlygZEZF88IYtkj1HR0c8fvxY6hhEf2vSpEmIi4vDuHHjyvyUgIiI/h5XXkmWnj17pvp1YmIiFixYgGXLlsHZ2RkGBgZqzzU1Na3qeERlMjc3x5EjR+Dm5iZ1FCIi2eLKK8mSubm52qqVKIro1auX2nN04YYt0i0WFhawtLSUOgYRkaxx5ZVkKS4urtzP7datWyUmISq/Xbt24eDBg9i+fTuMjY2ljkNEJEssXomIqoiLiwvS0tIgiiKaNGmiscXl0qVLEiUjIpIPbhsg2erVqxemTZum0cj/tcePH6Njx468g5v+MYYPHy51BCIi2WPxSrIVGxuLuLg4zJ8/H0FBQRrXS0pKcPfuXQmSEWkqLi4GAHh5ecHW1lbiNERE8qWQOgBRRfzv//4v1qxZgxEjRiA3N1fqOERvpK+vj5CQEN5ASERUQSxeSdaGDRuGM2fO4MaNG+jSpQu3CNA/Wq9evXDy5EmpYxARyRq3DZDstWjRAufPn8e//vUvdOjQAXv37kXv3r2ljkWkYcCAAQgICMD169fRrl071KpVS+360KFDJUpGRCQf7DZAsqVQKPDgwQNYW1sDeNXXNSAgAKGhoVi+fDnGjBmD+vXr82Na+sdQKN78YRd7EhMRlQ9XXkm2/ny0piAICA4OhouLCyZOnIiYmBiJkhGVrbS0VOoIRESyxz2vJFtv+tBg9OjROHXqFK5du1bFiYjK78WLF1JHICKSJRavJFuxsbFvPGqzTZs2uHjxIsLCwqo4FdGblZSU4KuvvkKDBg1Qu3Zt1Q2GgYGB2Lp1q8TpiIjkgcUryVa3bt2gr//mnS/vvfcePDw8qjAR0V9bunQpwsPDsWLFChgaGqrGnZ2d8c0330iYjIhIPli8EhFVkR07duDrr7/G2LFjoaenpxpv1aoVbt68KWEyIiL5YPFKRFRF7t27B6VSqTFeWlqKoqIiCRIREckPi1cioiri5OSEn3/+WWP8P//5D1xcXCRIREQkP2yVRURURRYtWoRx48bh3r17KC0tRWRkJFJSUrBjxw4cPnxY6nhERLLAQwqIiKrQ8ePHsWzZMly8eBGlpaVo27YtFi5ciL59+0odjYhIFli8EhEREZFscM8rEVEVsbe3x5MnTzTGc3JyYG9vL0EiIiL5YfFKRFRFMjIyUFJSojH+8uVL3Lt3T4JERETywxu2iIgq2Q8//KD69fHjx2FmZqZ6XFJSgujoaDRp0kSCZERE8sM9r0RElUyhePUhlyAI+PO3XAMDAzRp0gQrV67E4MGDpYhHRCQrLF6JiKqInZ0dLly4gDp16kgdhYhItli8EhEREZFscM8rEVEVio6ORnR0NLKyslBaWqp2bdu2bRKlIiKSDxavRERVJCgoCF9++SXat28PGxsbCIIgdSQiItnhtgEioipiY2ODFStWYNy4cVJHISKSLfZ5JSKqIoWFhXB1dZU6BhGRrLF4JSKqIpMmTcK3334rdQwiIlnjnlcioiry4sULfP311/jpp5/QqlUrGBgYqF0PDQ2VKBkRkXxwzysRURXp0aPHG68JgoCYmJgqTENEJE8sXomIiIhINrjnlYiIiIhkg3teiYgqmbu7e7meFxkZWclJiIjkj8UrEVElMzMzkzoCEZHO4J5XIiIiIpIN7nklIiIiItlg8UpEREREssHilYiIiIhkg8UrEREREckGi1cion+wCRMmYPjw4VLHICL6x2DxSkTVji4VhCdPnoQgCGjZsiVKSkrUrpmbmyM8PFyaYERElYTFKxGRlhUVFVX5nGlpadixY0eVz0tEVNVYvBJRtfby5Uv4+PjA2toaNWvWxAcffIALFy6oroeHh8Pc3Fzt93z//fcQBEH1ePHixWjTpg22bdsGe3t71KhRA6IoQhAEfPPNNxgxYgSMjY3x/vvv44cfflD9vpKSEkycOBF2dnYwMjJCs2bNsGbNmnd6HzNmzMCiRYvw4sWLNz4nNDQUzs7OqFWrFmxtbTF16lTk5uZqvNfDhw+jWbNmMDY2xkcffYS8vDxs374dTZo0gYWFBWbMmKG2yltYWIjZs2ejQYMGqFWrFjp16oSTJ0+qrt+9exdDhgyBhYUFatWqBScnJ0RFRb3T+yQiYvFKRNXa7NmzsX//fmzfvh2XLl2CUqlEv379kJ2d/Vavk5qaioiICOzfvx9JSUmq8aCgIHz88ce4evUqBg4ciLFjx6peu7S0FA0bNkRERARu3LiBhQsXYt68eYiIiHjr9zFr1iwUFxdj/fr1b3yOQqHA2rVrcf36dWzfvh0xMTGYPXu22nPy8/Oxdu1a7NmzB8eOHcPJkyfh7u6OqKgoREVFYefOnfj666+xb98+1e/x9PREQkIC9uzZg6tXr2LUqFHo378/bt++DQCYNm0aXr58ifj4eFy7dg3Lly9H7dq13/o9EhEBAEQiompm/Pjx4rBhw8Tc3FzRwMBA3L17t+paYWGhWL9+fXHFihWiKIpiWFiYaGZmpvb7Dxw4IP7x2+eiRYtEAwMDMSsrS+15AMQFCxaoHufm5oqCIIhHjx59Y7apU6eKI0eO1Mj6JrGxsSIA8enTp+KmTZtES0tLMScnRxRFUTQzMxPDwsLe+HsjIiLE9957T/U4LCxMBCCmpqaqxiZPniwaGxuLz58/V43169dPnDx5siiKopiamioKgiDeu3dP7bV79eolBgQEiKIois7OzuLixYvfmIOI6G1w5ZWIqq20tDQUFRXBzc1NNWZgYICOHTsiOTn5rV6rcePGsLKy0hhv1aqV6te1atWCiYkJsrKyVGObNm1C+/btYWVlhdq1a2PLli3IzMx8h3cDTJw4EXXq1MHy5cvLvB4bG4s+ffqgQYMGMDExgYeHB548eYK8vDzVc4yNjeHg4KB6XLduXTRp0kRtpbRu3bqq93Dp0iWIooimTZuidu3aqq+4uDikpaUBAHx8fLBkyRK4ublh0aJFuHr16ju9PyIigNsGiKgaE0URANT2r74efz2mUChUz3utrBuyatWqVeYcBgYGao8FQUBpaSkAICIiAr6+vvDy8sKJEyeQlJQET09PFBYWvtP70dfXx5IlS7BmzRr89ttvatfu3r2LgQMHomXLlti/fz8uXryIDRs2aLyfsvL+1XsoLS2Fnp4eLl68iKSkJNVXcnKyav/upEmTkJ6ejnHjxuHatWto37491q1b907vkYiIxSsRVVtKpRKGhoY4deqUaqyoqAiJiYlo0aIFAMDKygrPnz9XW538457Wivj555/h6uqKqVOnwsXFBUqlUrVa+a5GjRoFJycnBAUFqY0nJiaiuLgYK1euROfOndG0aVONAvdduLi4oKSkBFlZWVAqlWpf9erVUz3P1tYWU6ZMQWRkJPz8/LBly5YKz01E1ZO+1AGIiKRSq1YteHt7w9/fH5aWlmjUqBFWrFiB/Px8TJw4EQDQqVMnGBsbY968eZgxYwbOnz+vtd6pSqUSO3bswPHjx2FnZ4edO3fiwoULsLOzq9DrBgcHo1+/fmpjDg4OKC4uxrp16zBkyBAkJCRg06ZNFZoHAJo2bYqxY8fCw8MDK1euhIuLCx4/foyYmBg4Oztj4MCBmDVrFgYMGICmTZvi6dOniImJUf1wQET0trjySkTVTmlpKfT1X/3sHhwcjJEjR2LcuHFo27YtUlNTcfz4cVhYWAAALC0tsWvXLkRFRcHZ2RnfffcdFi9erJUcU6ZMgbu7O0aPHo1OnTrhyZMnmDp1aoVft2fPnujZsyeKi4tVY23atEFoaCiWL1+Oli1bYvfu3fj3v/9d4bkAICwsDB4eHvDz80OzZs0wdOhQnDt3Dra2tgBetQSbNm0aWrRogf79+6NZs2bYuHGjVuYmoupHEP+8mYuISMf1798fSqXyL9tKERHRPxNXXomo2nj69CmOHDmCkydPonfv3lLHISKid8A9r0RUbXh5eeHChQvw8/PDsGHDpI5DRETvgNsGiIiIiEg2uG2AiIiIiGSDxSsRERERyQaLVyIiIiKSDRavRERERCQbLF6JiIiISDZYvBIRERGRbLB4JSIiIiLZYPFKRERERLLB4pWIiIiIZOP/AOBDRIhqH7h1AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>index</th>\n",
+       "      <th>name</th>\n",
+       "      <th>id</th>\n",
+       "      <th>Total Pub</th>\n",
+       "      <th>Lung Pub</th>\n",
+       "      <th>Ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Clinical Lung Cancer</td>\n",
+       "      <td>nlm:100893225</td>\n",
+       "      <td>2169</td>\n",
+       "      <td>2043</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Lung Cancer</td>\n",
+       "      <td>nlm:8800805</td>\n",
+       "      <td>6772</td>\n",
+       "      <td>6302</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Journal of Thoracic Oncology</td>\n",
+       "      <td>nlm:101274235</td>\n",
+       "      <td>5522</td>\n",
+       "      <td>4843</td>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Zhongguo fei ai za zhi = Chinese journal of lu...</td>\n",
+       "      <td>nlm:101126433</td>\n",
+       "      <td>1995</td>\n",
+       "      <td>1707</td>\n",
+       "      <td>85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Thoracic Cancer</td>\n",
+       "      <td>nlm:101531441</td>\n",
+       "      <td>2486</td>\n",
+       "      <td>1686</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Kyobu geka. The Japanese journal of thoracic s...</td>\n",
+       "      <td>nlm:0413533</td>\n",
+       "      <td>12377</td>\n",
+       "      <td>1788</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>Gan to kagaku ryoho. Cancer &amp; chemotherapy</td>\n",
+       "      <td>nlm:7810034</td>\n",
+       "      <td>21263</td>\n",
+       "      <td>2211</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>9</td>\n",
+       "      <td>Anticancer research</td>\n",
+       "      <td>nlm:8102988</td>\n",
+       "      <td>24442</td>\n",
+       "      <td>2218</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Clinical Cancer Research</td>\n",
+       "      <td>nlm:9502500</td>\n",
+       "      <td>20263</td>\n",
+       "      <td>1887</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>8</td>\n",
+       "      <td>International Journal of Radiation Oncology Bi...</td>\n",
+       "      <td>nlm:7603616</td>\n",
+       "      <td>24320</td>\n",
+       "      <td>2276</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>7</td>\n",
+       "      <td>Journal of Clinical Oncology</td>\n",
+       "      <td>nlm:8309333</td>\n",
+       "      <td>25992</td>\n",
+       "      <td>2454</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>11</td>\n",
+       "      <td>The Annals of Thoracic Surgery</td>\n",
+       "      <td>nlm:15030100R</td>\n",
+       "      <td>39278</td>\n",
+       "      <td>3314</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12</td>\n",
+       "      <td>British Journal of Cancer</td>\n",
+       "      <td>nlm:0370635</td>\n",
+       "      <td>24852</td>\n",
+       "      <td>2031</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>13</td>\n",
+       "      <td>Cancer Research</td>\n",
+       "      <td>nlm:2984705R</td>\n",
+       "      <td>53303</td>\n",
+       "      <td>3832</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14</td>\n",
+       "      <td>Cancer</td>\n",
+       "      <td>nlm:0374236</td>\n",
+       "      <td>43649</td>\n",
+       "      <td>3414</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>Chest</td>\n",
+       "      <td>nlm:0231335</td>\n",
+       "      <td>36945</td>\n",
+       "      <td>2917</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>16</td>\n",
+       "      <td>International Journal of Cancer</td>\n",
+       "      <td>nlm:0042124</td>\n",
+       "      <td>26286</td>\n",
+       "      <td>1925</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>17</td>\n",
+       "      <td>Journal of the National Cancer Institute</td>\n",
+       "      <td>nlm:7503089</td>\n",
+       "      <td>22744</td>\n",
+       "      <td>1673</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>18</td>\n",
+       "      <td>The Journal of Thoracic and Cardiovascular Sur...</td>\n",
+       "      <td>nlm:0376343</td>\n",
+       "      <td>30956</td>\n",
+       "      <td>2131</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>PLOS One</td>\n",
+       "      <td>nlm:101285081</td>\n",
+       "      <td>278162</td>\n",
+       "      <td>2346</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    index                                               name             id  \\\n",
+       "0       0                               Clinical Lung Cancer  nlm:100893225   \n",
+       "1       1                                        Lung Cancer    nlm:8800805   \n",
+       "2       2                       Journal of Thoracic Oncology  nlm:101274235   \n",
+       "3       3  Zhongguo fei ai za zhi = Chinese journal of lu...  nlm:101126433   \n",
+       "4       4                                    Thoracic Cancer  nlm:101531441   \n",
+       "5       5  Kyobu geka. The Japanese journal of thoracic s...    nlm:0413533   \n",
+       "6       6         Gan to kagaku ryoho. Cancer & chemotherapy    nlm:7810034   \n",
+       "7       9                                Anticancer research    nlm:8102988   \n",
+       "8      10                           Clinical Cancer Research    nlm:9502500   \n",
+       "9       8  International Journal of Radiation Oncology Bi...    nlm:7603616   \n",
+       "10      7                       Journal of Clinical Oncology    nlm:8309333   \n",
+       "11     11                     The Annals of Thoracic Surgery  nlm:15030100R   \n",
+       "12     12                          British Journal of Cancer    nlm:0370635   \n",
+       "13     13                                    Cancer Research   nlm:2984705R   \n",
+       "14     14                                             Cancer    nlm:0374236   \n",
+       "15     15                                              Chest    nlm:0231335   \n",
+       "16     16                    International Journal of Cancer    nlm:0042124   \n",
+       "17     17           Journal of the National Cancer Institute    nlm:7503089   \n",
+       "18     18  The Journal of Thoracic and Cardiovascular Sur...    nlm:0376343   \n",
+       "19     19                                           PLOS One  nlm:101285081   \n",
+       "\n",
+       "    Total Pub  Lung Pub  Ratio  \n",
+       "0        2169      2043     94  \n",
+       "1        6772      6302     93  \n",
+       "2        5522      4843     87  \n",
+       "3        1995      1707     85  \n",
+       "4        2486      1686     67  \n",
+       "5       12377      1788     14  \n",
+       "6       21263      2211     10  \n",
+       "7       24442      2218      9  \n",
+       "8       20263      1887      9  \n",
+       "9       24320      2276      9  \n",
+       "10      25992      2454      9  \n",
+       "11      39278      3314      8  \n",
+       "12      24852      2031      8  \n",
+       "13      53303      3832      7  \n",
+       "14      43649      3414      7  \n",
+       "15      36945      2917      7  \n",
+       "16      26286      1925      7  \n",
+       "17      22744      1673      7  \n",
+       "18      30956      2131      6  \n",
+       "19     278162      2346      0  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sorts the dataframe to get the top publications\n",
+    "df = df.sort_values(by = \"Ratio\", ascending = False)\n",
+    "df = df.dropna(axis = 0)\n",
+    "df = df.reset_index()\n",
+    "\n",
+    "# plots a histogram of the top 30 publications \n",
+    "def plot_histogram2():\n",
+    "    plt.figure(figsize=(8, 8))\n",
+    "    plt.title(\"Top 16 Journals Writing About Lung Cancer\")\n",
+    "    plt.bar(df[\"name\"].values, df[\"Ratio\"].values)\n",
+    "    plt.xlabel(\"Journal Names\")\n",
+    "    plt.ylabel(\"Frequency\")\n",
+    "    plt.xticks(rotation=90)\n",
+    "    plt.show()\n",
+    "    \n",
+    "plot_histogram2()\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04f8da63-32ce-4ade-a8bf-efe002e38767",
+   "metadata": {},
+   "source": [
+    "## **Finding Novel Genes**\n",
+    "**This code finds all of the genes relating to lung cancer in the database, and all the genes in the journal \"Lung Cancer: Journal of the International Association for the Study of Lung Cancer\", and finds the genes listed in the database that are not mentioned in the journal, the genes mentioned in the journal that are not listed in the database, and the common genes. Based off the genes listed in the database that were not mentioned in the journal, I manually looked up the genes and saw how many PubMed articles linked that specific gene to lung cancer, to identify genes that could be further studied to explore their correlation to lung cancer.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0f8efb86-2e03-4f98-8ad5-9c50794aade0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_gene_lists():\n",
+    "    \n",
+    "    # cyphers to get genes from the journal and database relating to lung cancer\n",
+    "    journal_cypher = '''MATCH p3= (gene1:BioEntity)-[r:indra_rel]->(gene2:BioEntity) \n",
+    "    MATCH p4=(e:Evidence)-[:has_citation]->(pub:Publication)-[]->(j:Journal) \n",
+    "    WHERE gene1.id <> gene2.id AND e.stmt_hash = r.stmt_hash AND j.id='nlm:8800805' unwind [gene1, gene2] as gene \n",
+    "    RETURN gene.name, gene.id, count(r)\n",
+    "    ORDER BY count(r) desc'''\n",
+    "    database_cypher = \"\"\"MATCH p1=(gene1)-[:gene_disease_association]->(subdisease)-[:isa*0..]->(disease)\n",
+    "        WHERE disease.id in [\"mesh:D008175\",'doid:3905']\n",
+    "        RETURN distinct gene1.name, gene1.id\"\"\"\n",
+    "    \n",
+    "    journal_results = client.query_tx(journal_cypher)\n",
+    "    database_results = client.query_tx(database_cypher)\n",
+    "\n",
+    "    # loads the results into 2 dataframes\n",
+    "    journal_df = pd.DataFrame(journal_results, columns=[\"name\", \"gene_id\", \"indra statements\"])\n",
+    "    database_df = pd.DataFrame(database_results, columns=[\"name\", \"gene_id\"])\n",
+    "\n",
+    "    return journal_df, database_df\n",
+    "    \n",
+    "journal_df, database_df = get_gene_lists()\n",
+    "\n",
+    "# for the journal, filters for genes starting with hgnc to match with the database\n",
+    "journal_df = journal_df[journal_df[\"gene_id\"].str.startswith(\"hgnc\")]\n",
+    "novel_df = journal_df[~journal_df[\"gene_id\"].isin(database_df[\"gene_id\"])]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1ed82441-71cd-47e5-b1ff-a16120f3f4b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JJ5/0eA+JOS79/az88fTTT3udniVLFj388MMu07zF/+2336p8+fIKDw93nkvHjh2b4PMocC8iUQLuUseOHXN29XyjzJkzKygoSMeOHXOZniVLFo91xE1zXzYhnnvuOUnS8uXLndOioqK8ruv48eOSpAwZMvi9nRvF/bBny5ZNkhQbG6saNWpo2rRp6tatm+bNm6cVK1Y4Y7pw4cJN13n27Fk99thj+vPPP/X2229r4cKFWrlypaZNm+Z1HUFBQYqKinKZ5v457t27V4899pj279+vkSNH6vfff9fKlSs1evRol3VGRkZq0aJFKlmypN58800VLVpU2bJlU58+fZzPMh06dEhmpujoaJcL++DgYC1fvtz5LNbEiRM95scZO3asJOmpp57SyZMndfLkSUVGRurRRx/V999/7/FsRELeY4UKFfTjjz86L7pz5MihYsWK6auvvnKWOXTokDZs2OARV0REhMzM5TkySR7blKSQkJAE7UdvcubMKUleL2jj9vPHH3/sMj3uWZ6GDRt6xD106FCZmfN49hV3XEcjcXEndB8m5HiIT2hoqFauXKmVK1dq+vTpqlSpkr766isNGTLE4/099NBDHrFMnTrVY58k5Fg4duyYgoKClClTJpflHA6HsmTJ4nFOyJo1603fy40OHTqk6dOne8RbtGhRSXLGfOzYMa89cXo79/kjLCxMqVOndpkWEhKiixcv+r0ufz8rf/j6XBPyvZo2bZoaNWqk7Nmza/LkyVq2bJlWrlypVq1aJep9AvcKer0D7lJRUVH6888/ZWYuydLhw4d19epVl17jJHl9oDlumrcf0puJq90JCPi/+y3333+/vvrqK129elVBQf93evnvf/8rSbc8nsjPP/8sSc6HvP/66y+tX79eEyZMUPPmzZ3Lbd++PcHrnD9/vg4cOKCFCxc6a5Ek+Ryn5+rVqzp27JjLZ+b+Of744486d+6cpk2b5qzdkqR169Z5rO/+++/X119/LTPThg0bNGHCBPXv31+hoaHq3r27MmbMKIfDod9//91rT39x05588kmtXLnSY/6pU6ectSVxtUDupkyZorZt2/r1HiWpXr16qlevni5duqTly5dr8ODBatq0qXLnzq2yZcsqY8aMCg0N9Vlr5X6MJrVs2bKpaNGimjt3ri5evOhysVuyZElJ1xNlbzF9+OGHPntA83YxHp+E7kPp5sdDfAICApydN0jXe/UrXbq0+vXrp2bNmilnzpzO9/fdd9+5HJu+JORYiIqK0tWrV3XkyBGXBMDMdPDgQY/jzt+a5YwZM6p48eIuNWM3irtxEhUVpRUrVnjMT2hnDreDP59VSEiI1w4jfCVTt1JjP3nyZOXJk0dTp051WU9iO6wA7hXUKAF3qapVq+rs2bMeAzR+8cUXzvk3mjdvnkvPV9euXdPUqVOVL1++RHURHbedGy8mn3rqKZ09e9Z5YR5n4sSJypYtm8qUKeP3duKsX79egwYNUu7cudWoUSNJ/3dh4H7x+cknn3iUd7/LH8efdcT58ssvXV5PmTJF0v8lcN7WaWb67LPPfK7T4XCoRIkSev/995UuXTqtWbNGkvTEE0/IzLR//349+OCDHn/333+/pOsXYO7z4mK7cOGCcxwu97+MGTN6TWRu9h5vFBISoooVK2ro0KGS5GyO+cQTT2jHjh1eY3vwwQeVO3dun5+HL772oy89e/bU0aNH1blzZ69NN92VL19e6dKl06ZNm7zG/OCDDypVqlR+xZzQfXgjX8eDP0JCQjR69GhdvHhRb7/9tiSpZs2aCgoK0o4dO3y+P3c3OxbizjWTJ092We7777/XuXPnPM5F8cXrbb8+8cQT+uuvv5QvXz6v8cYlSpUrV9aZM2ecN1Tc470T+PNZ5c6dWxs2bHBZbv78+R7JfVKIG6T4xiTp4MGD9HqH/3nUKAF3qRdeeEGjR49W8+bNtXv3bt1///1asmSJBg0apNq1a6tatWouy2fMmFFVqlRRr169lCZNGo0ZM0Zbtmy5aRfhU6ZM0bRp01SnTh3FxMTo5MmT+vbbb/X111+rRYsWLoO/1qpVS9WrV1ebNm10+vRp5c+fX1999ZVmz56tyZMnJ3hAz9WrVysyMlJXrlxxDjg7adIkZc6cWdOnT3deqBYqVEj58uVT9+7dZWbKkCGDpk+frrlz53qsM+5idOTIkWrevLmCg4NVsGBBlStXTunTp9err76qPn36KDg4WF9++aXWr1/vNbZUqVLpvffe09mzZ/XQQw9p6dKlevvtt1WrVi09+uijkq7fyU+VKpWaNGmibt266eLFi/roo4904sQJl3XNmDFDY8aMUf369ZU3b16ZmaZNm6aTJ0+qevXqkq5fuL/yyitq2bKlVq1apQoVKihNmjT6999/tWTJEt1///1q06aNz89y7NixSp8+vbp06eLRfEi6fhwNHz5c69evd+7LhLzH3r17659//lHVqlWVI0cOnTx5UiNHjnR5vqtjx476/vvvVaFCBXXq1EnFixdXbGys9u7dqzlz5uj111/3O3n2tR9vfLbpRk2aNNHGjRs1cOBArV+/Xi1atFCBAgUUGxurffv2adKkSZLkLB8eHq4PP/xQzZs31/Hjx9WwYUNlzpxZR44c0fr163XkyBF99NFHfsWc0H2YkOPBXxUrVlTt2rU1fvx4de/eXXny5FH//v3Vs2dP7dy5U48//rjSp0+vQ4cOacWKFUqTJo369evnLJ/Q471mzZp64403dPr0aZUvX14bNmxQnz59VKpUKT3//PMJivX+++/XwoULNX36dGXNmlUREREqWLCg+vfvr7lz56pcuXJq3769ChYsqIsXL2r37t365Zdf9PHHHytHjhx64YUX9P777+uFF17QwIEDVaBAAf3yyy/69ddfE/XZJRX3sbsS+lk9//zz6tWrl3r37q2KFStq06ZNGjVqlCIjI5M8xrgu29u2bauGDRtq3759GjBggLJmzapt27Yl+faAu8Zt7jwCQCJ56/732LFj9uqrr1rWrFktKCjIYmJirEePHi5dzJpd7z3ptddeszFjxli+fPksODjYChUqZF9++eVNt7ts2TKrWrWqZcmSxYKDgy0sLMweeughGzNmjF27ds1j+TNnzlj79u0tS5YslipVKitevLh99dVXCXqPcb2Ixf2FhIRY1qxZrUaNGjZy5EiXHtHibNq0yapXr24RERGWPn16e+aZZ2zv3r1ee4zq0aOHZcuWzQICAlx6t1q6dKmVLVvWwsLCLFOmTPbSSy/ZmjVrPHrKitsHGzZssEqVKlloaKhlyJDB2rRpY2fPnnXZ1vTp061EiRKWOnVqy549u3Xt2tVmzZrlst0tW7ZYkyZNLF++fBYaGmqRkZH28MMP24QJEzze57hx46xMmTKWJk0aCw0NtXz58tkLL7xgq1at8vl5rl+/3iRZx44dfS4T1+V6XC9oCX2PM2bMsFq1aln27NktVapUljlzZqtdu7b9/vvvLus/e/asvfXWW1awYEFLlSqVRUZG2v3332+dOnVy6YUx7hh1562HLV/7MT6LFy+2Z5991nLkyOE8josUKWJt2rTx+hkuWrTI6tSpYxkyZLDg4GDLnj271alTx6U7fV+9NMb1QubeM9/N9qE/x4M7X92Dm5n997//tYCAAJcuy3/88UerXLmypU2b1kJCQiwmJsYaNmzo0v25P8f7hQsX7I033rCYmBgLDg62rFmzWps2bezEiRMuy8XExFidOnW8xrlu3TorX768hYWFmSSrWLGic96RI0esffv2lidPHgsODrYMGTJY6dKlrWfPni6x/PPPP/b0009beHi4RURE2NNPP21Lly69pV7vvH2ucfv+Zh566CErXbq0y7SEflaXLl2ybt26Wc6cOS00NNQqVqxo69at89nrnXvvkmaeve7d+L5iYmJcpg0ZMsRy585tISEhVrhwYfvss8+8vk96vcP/EodZAtoiALirxQ2qOWrUqJQOBcBdokWLFvruu++SpanX/4IrV64oa9asqlKlir755puUDgdAItD0DgAAIImcPn1aK1eu1Jdffqljx455dK8O4O5BogQAAJBE1qxZo5o1ayp37tz64IMPVK9evZQOCUAi0fQOAAAAANzQPTgAAAAAuCFRAgAAAAA39/wzSrGxsTpw4IAiIiJuadRqAAAAAHc3M9OZM2eULVs2BQTEX2d0zydKBw4cUM6cOVM6DAAAAAB3iH379ilHjhzxLnPPJ0pxo63v27dPadOmTeFoAAAAAKSU06dPK2fOnM4cIT73fKIU19wubdq0JEoAAAAAEvRIDp05AAAAAIAbEiUAAAAAcEOiBAAAAABuSJQAAAAAwA2JEgAAAAC4IVECAAAAADckSgAAAADghkQJAAAAANyQKAEAAACAGxIlAAAAAHBDogQAAAAAbkiUAAAAAMANiRIAAAAAuCFRAgAAAAA3JEoAAAAA4IZECQAAAADckCgBAAAAgBsSJQAAAABwQ6IEAAAAAG6CUjqA/zW5u8/0a/ndQ+okUyQAAAAAfKFGCQAAAADckCgBAAAAgBsSJQAAAABwQ6IEAAAAAG5IlAAAAADADYkSAAAAALghUQIAAAAANyRKAAAAAOCGRAkAAAAA3JAoAQAAAIAbEiUAAAAAcEOiBAAAAABuSJQAAAAAwA2JEgAAAAC4IVECAAAAADckSgAAAADghkQJAAAAANyQKAEAAACAGxIlAAAAAHBDogQAAAAAbkiUAAAAAMANiRIAAAAAuCFRAgAAAAA3JEoAAAAA4IZECQAAAADckCgBAAAAgBsSJQAAAABwQ6IEAAAAAG5IlAAAAADADYkSAAAAALghUQIAAAAANyRKAAAAAOCGRAkAAAAA3JAoAQAAAIAbEiUAAAAAcEOiBAAAAABuSJQAAAAAwA2JEgAAAAC4IVECAAAAADckSgAAAADgJkUTpatXr+qtt95Snjx5FBoaqrx586p///6KjY11LmNm6tu3r7Jly6bQ0FBVqlRJGzduTMGoAQAAANzrUjRRGjp0qD7++GONGjVKmzdv1rBhw/TOO+/oww8/dC4zbNgwDR8+XKNGjdLKlSuVJUsWVa9eXWfOnEnByAEAAADcy1I0UVq2bJnq1aunOnXqKHfu3GrYsKFq1KihVatWSbpemzRixAj17NlTDRo0ULFixTRx4kSdP39eU6ZMScnQAQAAANzDUjRRevTRRzVv3jz9/fffkqT169dryZIlql27tiRp165dOnjwoGrUqOEsExISoooVK2rp0qVe13np0iWdPn3a5Q8AAAAA/BGUkht/4403dOrUKRUqVEiBgYG6du2aBg4cqCZNmkiSDh48KEmKjo52KRcdHa09e/Z4XefgwYPVr1+/5A0cAAAAwD0tRWuUpk6dqsmTJ2vKlClas2aNJk6cqHfffVcTJ050Wc7hcLi8NjOPaXF69OihU6dOOf/27duXbPEDAAAAuDelaI1S165d1b17dzVu3FiSdP/992vPnj0aPHiwmjdvrixZski6XrOUNWtWZ7nDhw971DLFCQkJUUhISPIHDwAAAOCelaI1SufPn1dAgGsIgYGBzu7B8+TJoyxZsmju3LnO+ZcvX9aiRYtUrly52xorAAAAgP8dKVqj9OSTT2rgwIHKlSuXihYtqrVr12r48OFq1aqVpOtN7jp27KhBgwapQIECKlCggAYNGqSwsDA1bdo0JUMHAAAAcA9L0UTpww8/VK9evdS2bVsdPnxY2bJlU+vWrdW7d2/nMt26ddOFCxfUtm1bnThxQmXKlNGcOXMUERGRgpEDAAAAuJc5zMxSOojkdPr0aUVGRurUqVNKmzZtSoej3N1n+rX87iF1kikSAAAA4H+LP7lBij6jBAAAAAB3IhIlAAAAAHBDogQAAAAAbkiUAAAAAMANiRIAAAAAuCFRAgAAAAA3JEoAAAAA4IZECQAAAADckCgBAAAAgBsSJQAAAABwQ6IEAAAAAG5IlAAAAADADYkSAAAAALghUQIAAAAANyRKAAAAAOCGRAkAAAAA3JAoAQAAAIAbEiUAAAAAcBOU0gEgYXJ3n+nX8ruH1EmmSAAAAIB7HzVKAAAAAOCGRAkAAAAA3JAoAQAAAIAbEiUAAAAAcEOiBAAAAABuSJQAAAAAwA2JEgAAAAC4IVECAAAAADckSgAAAADghkQJAAAAANyQKAEAAACAGxIlAAAAAHBDogQAAAAAbkiUAAAAAMANiRIAAAAAuCFRAgAAAAA3JEoAAAAA4IZECQAAAADckCgBAAAAgBsSJQAAAABwQ6IEAAAAAG5IlAAAAADADYkSAAAAALghUQIAAAAANyRKAAAAAOCGRAkAAAAA3JAoAQAAAIAbEiUAAAAAcEOiBAAAAABuSJQAAAAAwA2JEgAAAAC4IVECAAAAADckSgAAAADghkQJAAAAANyQKAEAAACAGxIlAAAAAHBDogQAAAAAbkiUAAAAAMANiRIAAAAAuCFRAgAAAAA3JEoAAAAA4IZECQAAAADc+J0oTZw4UTNnznS+7tatm9KlS6dy5cppz549SRocAAAAAKQEvxOlQYMGKTQ0VJK0bNkyjRo1SsOGDVPGjBnVqVOnJA8QAAAAAG63IH8L7Nu3T/nz55ck/fjjj2rYsKFeeeUVlS9fXpUqVUrq+AAAAADgtvO7Rik8PFzHjh2TJM2ZM0fVqlWTJKVOnVoXLlxI2ugAAAAAIAX4XaNUvXp1vfTSSypVqpT+/vtv1alTR5K0ceNG5c6dO6njAwAAAIDbzu8apdGjR6ts2bI6cuSIvv/+e0VFRUmSVq9erSZNmiR5gAAAAABwu/ldo3T69Gl98MEHCghwzbH69u2rffv2JVlgAAAAAJBS/K5RypMnj44ePeox/fjx48qTJ0+SBAUAAAAAKcnvRMnMvE4/e/asUqdOfcsBAQAAAEBKS3DTu86dO0uSHA6HevfurbCwMOe8a9eu6c8//1TJkiWTPEAAAAAAuN0SnCitXbtW0vUapf/+979KlSqVc16qVKlUokQJdenSJekjBAAAAIDbLMGJ0oIFCyRJLVu21MiRI5U2bdpkCwoAAAAAUpLfvd6NHz8+OeIAAAAAgDuG34nSuXPnNGTIEM2bN0+HDx9WbGysy/ydO3cmWXAAAAAAkBL8TpReeuklLVq0SM8//7yyZs0qh8ORHHEBAAAAQIrxO1GaNWuWZs6cqfLlyydHPAAAAACQ4vweRyl9+vTKkCFDcsQCAAAAAHcEvxOlAQMGqHfv3jp//nxyxAMAAAAAKc7vpnfvvfeeduzYoejoaOXOnVvBwcEu89esWZNkwQEAAABASvA7Uapfv34yhAEAAAAAdw6/E6U+ffokRxwAAAAAcMfw+xklSTp58qQ+//xz9ejRQ8ePH5d0vcnd/v37kzQ4AAAAAEgJftcobdiwQdWqVVNkZKR2796tl19+WRkyZNAPP/ygPXv26IsvvkiOOAEAAADgtvG7Rqlz585q0aKFtm3bptSpUzun16pVS4sXL07S4AAAAAAgJfidKK1cuVKtW7f2mJ49e3YdPHgwSYICAAAAgJTkd6KUOnVqnT592mP61q1blSlTpiQJCgAAAABSkt+JUr169dS/f39duXJFkuRwOLR37151795dTz/9dJIHCAAAAAC3m9+J0rvvvqsjR44oc+bMunDhgipWrKj8+fMrIiJCAwcO9DuA/fv367nnnlNUVJTCwsJUsmRJrV692jnfzNS3b19ly5ZNoaGhqlSpkjZu3Oj3dgAAAAAgofzu9S5t2rRasmSJ5s+frzVr1ig2NlYPPPCAqlWr5vfGT5w4ofLly6ty5cqaNWuWMmfOrB07dihdunTOZYYNG6bhw4drwoQJuu+++/T222+revXq2rp1qyIiIvzeJgAAAADcjN+JUpwqVaqoSpUqt7TxoUOHKmfOnBo/frxzWu7cuZ3/NzONGDFCPXv2VIMGDSRJEydOVHR0tKZMmeK1UwkAAAAAuFWJSpRWrFihhQsX6vDhw4qNjXWZN3z48ASv5+eff1bNmjX1zDPPaNGiRcqePbvatm2rl19+WZK0a9cuHTx4UDVq1HCWCQkJUcWKFbV06VKvidKlS5d06dIl52tvHU8AAAAAQHz8TpQGDRqkt956SwULFlR0dLQcDodz3o3/T4idO3fqo48+UufOnfXmm29qxYoVat++vUJCQvTCCy84uxuPjo52KRcdHa09e/Z4XefgwYPVr18/P98VAAAAAPwfvxOlkSNHaty4cWrRosUtbzw2NlYPPvigBg0aJEkqVaqUNm7cqI8++kgvvPCCczn3BMzMfCZlPXr0UOfOnZ2vT58+rZw5c95yrAAAAAD+d/jd611AQIDKly+fJBvPmjWrihQp4jKtcOHC2rt3ryQpS5YskuQxkO3hw4c9apnihISEKG3atC5/AAAAAOAPvxOlTp06afTo0Umy8fLly2vr1q0u0/7++2/FxMRIkvLkyaMsWbJo7ty5zvmXL1/WokWLVK5cuSSJAQAAAADc+d30rkuXLqpTp47y5cunIkWKKDg42GX+tGnTEryuTp06qVy5cho0aJAaNWqkFStW6NNPP9Wnn34q6XqTu44dO2rQoEEqUKCAChQooEGDBiksLExNmzb1N3QAAAAASBC/E6X//Oc/WrBggSpXrqyoqCi/O3C40UMPPaQffvhBPXr0UP/+/ZUnTx6NGDFCzZo1cy7TrVs3XbhwQW3bttWJEydUpkwZzZkzhzGUAAAAACQbh5mZPwUiIiL09ddfq06dOskVU5I6ffq0IiMjderUqTvieaXc3Wf6tfzuIXVuqRwAAACA6/zJDfx+RilDhgzKly9fooMDAAAAgDud34lS37591adPH50/fz454gEAAACAFOf3M0offPCBduzYoejoaOXOndujM4c1a9YkWXAAAAAAkBL8TpTq16+fDGEAAAAAwJ3D70SpT58+yREHAAAAANwx/H5GSZJOnjypzz//XD169NDx48clXW9yt3///iQNDgAAAABSgt81Shs2bFC1atUUGRmp3bt36+WXX1aGDBn0ww8/aM+ePfriiy+SI04AAAAAuG38rlHq3LmzWrRooW3btil16tTO6bVq1dLixYuTNDgAAAAASAl+J0orV65U69atPaZnz55dBw8eTJKgAAAAACAl+Z0opU6dWqdPn/aYvnXrVmXKlClJggIAAACAlOR3olSvXj31799fV65ckSQ5HA7t3btX3bt319NPP53kAQIAAADA7eZ3ovTuu+/qyJEjypw5sy5cuKCKFSsqf/78ioiI0MCBA5MjRgAAAAC4rfzu9S5t2rRasmSJ5s+frzVr1ig2NlYPPPCAqlWrlhzxAQAAAMBt53ei9MUXX+jZZ59VlSpVVKVKFef0y5cv6+uvv9YLL7yQpAECAAAAwO3md9O7li1b6tSpUx7Tz5w5o5YtWyZJUAAAAACQkvxOlMxMDofDY/o///yjyMjIJAkKAAAAAFJSgpvelSpVSg6HQw6HQ1WrVlVQ0P8VvXbtmnbt2qXHH388WYIEAAAAgNspwYlS/fr1JUnr1q1TzZo1FR4e7pyXKlUq5c6dm+7BAQAAANwTEpwo9enTR5KUO3duPfvss0qdOnWyBQUAAAAAKcnvXu+aN2+eHHEAAAAAwB3D70Tp2rVrev/99/XNN99o7969unz5ssv848ePJ1lwAAAAAJAS/O71rl+/fho+fLgaNWqkU6dOqXPnzmrQoIECAgLUt2/fZAgRAAAAAG4vvxOlL7/8Up999pm6dOmioKAgNWnSRJ9//rl69+6t5cuXJ0eMAAAAAHBb+Z0oHTx4UPfff78kKTw83Dn47BNPPKGZM2cmbXQAAAAAkAL8TpRy5Mihf//9V5KUP39+zZkzR5K0cuVKhYSEJG10AAAAAJAC/E6UnnrqKc2bN0+S1KFDB/Xq1UsFChTQCy+8oFatWiV5gAAAAABwu/nd692QIUOc/2/YsKFy5sypP/74Q/nz51fdunWTNDgAAAAASAl+J0qLFy9WuXLlFBR0vWiZMmVUpkwZXb16VYsXL1aFChWSPEgAAAAAuJ38bnpXuXJlr2MlnTp1SpUrV06SoAAAAAAgJfmdKJmZHA6Hx/Rjx44pTZo0SRIUAAAAAKSkBDe9a9CggSTJ4XCoRYsWLj3cXbt2TRs2bFC5cuWSPkIAAAAAuM0SnChFRkZKul6jFBERodDQUOe8VKlS6ZFHHtHLL7+c9BECAAAAwG2W4ERp/PjxkqTcuXOrS5cuNLMDAAAAcM/yu9e7Pn36uLxetGiRzp07p7Jlyyp9+vRJFhgAAAAApJQEJ0rvvPOOzp49q379+km63gSvVq1amjNnjiQpc+bMmjdvnooWLZo8kQIAAADAbZLgXu+++uorFSlSxPn6u+++0+LFi/X777/r6NGjevDBB51JFAAAAADczRKcKO3atUvFixd3vv7ll1/09NNPq3z58sqQIYPeeustLVu2LFmCBAAAAIDbKcFN765cueLSJfiyZcvUoUMH5+ts2bLp6NGjSRsdkkTu7jMTvOzuIXWSMRIAAADg7pDgGqX8+fNr8eLFkqS9e/fq77//VsWKFZ3z//nnH0VFRSV9hAAAAABwmyW4RqlNmzZq166dfv/9dy1fvlxly5Z1eWZp/vz5KlWqVLIECQAAAAC3U4ITpdatWysoKEgzZsxQhQoVPLoJP3DggFq1apXkAQIAAADA7ebXOEovvviiXnzxRa/zxowZkyQBAQAAAEBKS/AzSgAAAADwv4JECQAAAADckCgBAAAAgBsSJQAAAABwQ6IEAAAAAG786vUuzsqVK/Xtt99q7969unz5ssu8adOmJUlgAAAAAJBS/K5R+vrrr1W+fHlt2rRJP/zwg65cuaJNmzZp/vz5ioyMTI4YAQAAAOC28jtRGjRokN5//33NmDFDqVKl0siRI7V582Y1atRIuXLlSo4YAQAAAOC28jtR2rFjh+rUqSNJCgkJ0blz5+RwONSpUyd9+umnSR4gAAAAANxufidKGTJk0JkzZyRJ2bNn119//SVJOnnypM6fP5+00QEAAABACvC7M4fHHntMc+fO1f33369GjRqpQ4cOmj9/vubOnauqVasmR4wAAAAAcFv5nSiNGjVKFy9elCT16NFDwcHBWrJkiRo0aKBevXoleYAAAAAAcLv5lShdvXpV06dPV82aNSVJAQEB6tatm7p165YswQEAAABASvDrGaWgoCC1adNGly5dSq54AAAAACDF+d2ZQ5kyZbR27drkiAUAAAAA7gh+P6PUtm1bvf766/rnn39UunRppUmTxmV+8eLFkyw4AAAAAEgJfidKzz77rCSpffv2zmkOh0NmJofDoWvXriVddAAAAACQAvxOlHbt2pUccQAAAADAHcPvRCkmJiY54gAAAACAO0aCEqWff/45wSusW7duooMBAAAAgDtBghKl+vXru7yOeybpxtdxeEbp3pG7+8wEL7t7SJ0UKwcAAAAktQR1Dx4bG+v8mzNnjkqWLKlZs2bp5MmTOnXqlH755Rc98MADmj17dnLHCwAAAADJzu9nlDp27KiPP/5Yjz76qHNazZo1FRYWpldeeUWbN29O0gABAAAA4Hbze8DZHTt2KDIy0mN6ZGSkdu/enRQxAQAAAECK8jtReuihh9SxY0f9+++/zmkHDx7U66+/rocffjhJgwMAAACAlOB3ojRu3DgdPnxYMTExyp8/v/Lnz69cuXLp33//1dixY5MjRgAAAAC4rfx+Ril//vzasGGD5s6dqy1btsjMVKRIEVWrVs2l9zsAAAAAuFv5nShJ17sDr1GjhmrUqJHU8QAAAABAiktUojRv3jzNmzdPhw8fVmxsrMu8cePGJUlgAAAAAJBS/E6U+vXrp/79++vBBx9U1qxZaW4HAAAA4J7jd6L08ccfa8KECXr++eeTIx4AAAAASHF+93p3+fJllStXLjliAQAAAIA7gt+J0ksvvaQpU6YkRywAAAAAcEfwu+ndxYsX9emnn+q3335T8eLFFRwc7DJ/+PDhSRYcAAAAAKQEvxOlDRs2qGTJkpKkv/76y2UeHTsAAAAAuBf4nSgtWLAgOeIAAAAAgDuG388oAQAAAMC9LsE1Sg0aNEjQctOmTUt0MAAAAABwJ0hwohQZGZmccQAAAADAHSPBidL48eOTMw4AAAAAuGPwjBIAAAAAuCFRAgAAAAA3JEoAAAAA4IZECQAAAADcJChReuCBB3TixAlJUv/+/XX+/PlkDQoAAAAAUlKCEqXNmzfr3LlzkqR+/frp7NmzyRoUAAAAAKSkBHUPXrJkSbVs2VKPPvqozEzvvvuuwsPDvS7bu3fvRAUyePBgvfnmm+rQoYNGjBghSTIz9evXT59++qlOnDihMmXKaPTo0SpatGiitgEAAAAACZGgRGnChAnq06ePZsyYIYfDoVmzZikoyLOow+FIVKK0cuVKffrppypevLjL9GHDhmn48OGaMGGC7rvvPr399tuqXr26tm7dqoiICL+3AwAAAAAJkaBEqWDBgvr6668lSQEBAZo3b54yZ86cJAGcPXtWzZo102effaa3337bOd3MNGLECPXs2VMNGjSQJE2cOFHR0dGaMmWKWrdunSTbBwAAAAB3fvd6Fxsbm2RJkiS99tprqlOnjqpVq+YyfdeuXTp48KBq1KjhnBYSEqKKFStq6dKlPtd36dIlnT592uUPAAAAAPyRoBoldzt27NCIESO0efNmORwOFS5cWB06dFC+fPn8Ws/XX3+tNWvWaOXKlR7zDh48KEmKjo52mR4dHa09e/b4XOfgwYPVr18/v+LA3S1395kJXnb3kDqJKudeFgAAAPc2v2uUfv31VxUpUkQrVqxQ8eLFVaxYMf35558qWrSo5s6dm+D17Nu3Tx06dNDkyZOVOnVqn8s5HA6X12bmMe1GPXr00KlTp5x/+/btS3BMAAAAACAlokape/fu6tSpk4YMGeIx/Y033lD16tUTtJ7Vq1fr8OHDKl26tHPatWvXtHjxYo0aNUpbt26VdL1mKWvWrM5lDh8+7FHLdKOQkBCFhIT485YAAAAAwIXfNUqbN2/Wiy++6DG9VatW2rRpU4LXU7VqVf33v//VunXrnH8PPvigmjVrpnXr1ilv3rzKkiWLSy3V5cuXtWjRIpUrV87fsAEAAAAgwfyuUcqUKZPWrVunAgUKuExft26dX508REREqFixYi7T0qRJo6ioKOf0jh07atCgQSpQoIAKFCigQYMGKSwsTE2bNvU3bAAAAABIML8TpZdfflmvvPKKdu7cqXLlysnhcGjJkiUaOnSoXn/99SQNrlu3brpw4YLatm3rHHB2zpw5jKEEAAAAIFn5nSj16tVLEREReu+999SjRw9JUrZs2dS3b1+1b9/+loJZuHChy2uHw6G+ffuqb9++t7ReAAAAAPCH34mSw+FQp06d1KlTJ505c0aSqOEBAAAAcE9J1DhKcUiQAAAAANyL/O71DgAAAADudSRKAAAAAOCGRAkAAAAA3PiVKF25ckWVK1fW33//nVzxAAAAAECK8ytRCg4O1l9//SWHw5Fc8QAAAABAivO717sXXnhBY8eO1ZAhQ5IjHuCOl7v7TL+W3z2kTqLK3lgOAAAAt5ffidLly5f1+eefa+7cuXrwwQeVJk0al/nDhw9PsuAAAAAAICX4nSj99ddfeuCBByTJ41klmuQBAAAAuBf4nSgtWLAgOeIAAAAAgDtGorsH3759u3799VdduHBBkmRmSRYUAAAAAKQkv2uUjh07pkaNGmnBggVyOBzatm2b8ubNq5deeknp0qXTe++9lxxxAv/T6AQCAADg9vK7RqlTp04KDg7W3r17FRYW5pz+7LPPavbs2UkaHAAAAACkBL9rlObMmaNff/1VOXLkcJleoEAB7dmzJ8kCAwAAAICU4neN0rlz51xqkuIcPXpUISEhSRIUAAAAAKQkvxOlChUq6IsvvnC+djgcio2N1TvvvKPKlSsnaXAAAAAAkBL8bnr3zjvvqFKlSlq1apUuX76sbt26aePGjTp+/Lj++OOP5IgRQCLRCQQAAEDi+F2jVKRIEW3YsEEPP/ywqlevrnPnzqlBgwZau3at8uXLlxwxAgAAAMBt5XeNkiRlyZJF/fr1S+pYAAAAAOCOkKhE6cSJExo7dqw2b94sh8OhwoULq2XLlsqQIUNSxwcAAAAAt53fTe8WLVqkPHny6IMPPtCJEyd0/PhxffDBB8qTJ48WLVqUHDECAAAAwG3ld43Sa6+9pkaNGumjjz5SYGCgJOnatWtq27atXnvtNf31119JHiQAAAAA3E5+1yjt2LFDr7/+ujNJkqTAwEB17txZO3bsSNLgAAAAACAl+J0oPfDAA9q8ebPH9M2bN6tkyZJJERMAAAAApKgENb3bsGGD8//t27dXhw4dtH37dj3yyCOSpOXLl2v06NEaMmRI8kQJAAAAALdRghKlkiVLyuFwyMyc07p16+axXNOmTfXss88mXXQAAAAAkAISlCjt2rUrueMAAAAAgDtGghKlmJiY5I4DAAAAAO4YiRpwdv/+/frjjz90+PBhxcbGusxr3759kgQGAAAAACnF70Rp/PjxevXVV5UqVSpFRUXJ4XA45zkcDhIlAAAAAHc9vxOl3r17q3fv3urRo4cCAvzuXRwAAAAA7nh+Zzrnz59X48aNSZIAAAAA3LP8znZefPFFffvtt8kRCwAAAADcEfxuejd48GA98cQTmj17tu6//34FBwe7zB8+fHiSBQcAAAAAKcHvRGnQoEH69ddfVbBgQUny6MwBAAAAAO52fidKw4cP17hx49SiRYtkCAcAAAAAUp7fiVJISIjKly+fHLEAuEPk7j4zwcvuHlInGSMBAABIGX535tChQwd9+OGHyRELAAAAANwR/K5RWrFihebPn68ZM2aoaNGiHp05TJs2LcmCAwAAAICU4HeilC5dOjVo0CA5YgEAAACAO4LfidL48eOTIw4AAAAAuGP4nSgBgC90AgEAAO4VfidKefLkiXe8pJ07d95SQAAAAACQ0vxOlDp27Ojy+sqVK1q7dq1mz56trl27JlVcAAAAAJBi/E6UOnTo4HX66NGjtWrVqlsOCAAAAABSmt/jKPlSq1Ytff/990m1OgAAAABIMUmWKH333XfKkCFDUq0OAAAAAFKM303vSpUq5dKZg5np4MGDOnLkiMaMGZOkwQEAAABASvA7Uapfv77L64CAAGXKlEmVKlVSoUKFkiouAAAAAEgxfidKffr0SY44AAAAAOCOkWTPKAEAAADAvSLBNUoBAQHxDjQrSQ6HQ1evXr3loAAAAAAgJSU4Ufrhhx98zlu6dKk+/PBDmVmSBAUAAAAAKSnBiVK9evU8pm3ZskU9evTQ9OnT1axZMw0YMCBJgwMAAACAlJCoZ5QOHDigl19+WcWLF9fVq1e1bt06TZw4Ubly5Urq+AAAAADgtvMrUTp16pTeeOMN5c+fXxs3btS8efM0ffp0FStWLLniAwAAAIDbLsFN74YNG6ahQ4cqS5Ys+uqrr7w2xQMAAACAe0GCE6Xu3bsrNDRU+fPn18SJEzVx4kSvy02bNi3JggMAAACAlJDgROmFF164affgAAAAAHAvSHCiNGHChGQMAwAAAADuHInq9Q4AAAAA7mUJrlECgOSSu/tMv5bfPaTOLZUDAAC4GWqUAAAAAMANiRIAAAAAuCFRAgAAAAA3JEoAAAAA4IbOHAD8T/KnIwg6gQAA4H8PNUoAAAAA4IZECQAAAADckCgBAAAAgBsSJQAAAABwQ2cOAOCHxHYCQecRAADcXahRAgAAAAA3JEoAAAAA4IZECQAAAADckCgBAAAAgBs6cwCAOxidQAAAkDKoUQIAAAAANyRKAAAAAOCGRAkAAAAA3JAoAQAAAIAbOnMAgHuQP51ASP/XEURiywEAcK+hRgkAAAAA3JAoAQAAAIAbEiUAAAAAcEOiBAAAAABu6MwBAJAk/OkI4sZOIBJbDgCA5JSiNUqDBw/WQw89pIiICGXOnFn169fX1q1bXZYxM/Xt21fZsmVTaGioKlWqpI0bN6ZQxAAAAAD+F6RoorRo0SK99tprWr58uebOnaurV6+qRo0aOnfunHOZYcOGafjw4Ro1apRWrlypLFmyqHr16jpz5kwKRg4AAADgXpaiTe9mz57t8nr8+PHKnDmzVq9erQoVKsjMNGLECPXs2VMNGjSQJE2cOFHR0dGaMmWKWrdu7bHOS5cu6dKlS87Xp0+fTt43AQAAAOCec0c9o3Tq1ClJUoYMGSRJu3bt0sGDB1WjRg3nMiEhIapYsaKWLl3qNVEaPHiw+vXrd3sCBgCkmNv9TBTPUgHA/5Y7ptc7M1Pnzp316KOPqlixYpKkgwcPSpKio6Ndlo2OjnbOc9ejRw+dOnXK+bdv377kDRwAAADAPeeOqVFq166dNmzYoCVLlnjMczgcLq/NzGNanJCQEIWEhCRLjAAAAAD+N9wRNUr/+c9/9PPPP2vBggXKkSOHc3qWLFkkyaP26PDhwx61TAAAAACQVFI0UTIztWvXTtOmTdP8+fOVJ08el/l58uRRlixZNHfuXOe0y5cva9GiRSpXrtztDhcAAADA/4gUbXr32muvacqUKfrpp58UERHhrDmKjIxUaGioHA6HOnbsqEGDBqlAgQIqUKCABg0apLCwMDVt2jQlQwcAIEH86QRC+r+OIBJbDgCQNFI0Ufroo48kSZUqVXKZPn78eLVo0UKS1K1bN124cEFt27bViRMnVKZMGc2ZM0cRERG3OVoAAAAA/ytSNFEys5su43A41LdvX/Xt2zf5AwIAAAAA3SGdOQAAAADAnYRECQAAAADc3DHjKAEAgKThT0cQN3YCcbvLAcCdjBolAAAAAHBDogQAAAAAbkiUAAAAAMANiRIAAAAAuKEzBwAAkCLoPALAnYwaJQAAAABwQ6IEAAAAAG5IlAAAAADADYkSAAAAALihMwcAAPA/wZ9OIKT/6wgiseUA3N2oUQIAAAAANyRKAAAAAOCGRAkAAAAA3JAoAQAAAIAbOnMAAABIJv50BHFjJxB3SzngXkaNEgAAAAC4IVECAAAAADckSgAAAADghkQJAAAAANzQmQMAAAAS5XZ0HnFj2cSW87csHVZAokYJAAAAADyQKAEAAACAGxIlAAAAAHBDogQAAAAAbujMAQAAAPDhdnRYcbvL3Vj2VjrIuNdRowQAAAAAbkiUAAAAAMANiRIAAAAAuCFRAgAAAAA3dOYAAAAAwC//C51AUKMEAAAAAG5IlAAAAADADYkSAAAAALghUQIAAAAANyRKAAAAAOCGRAkAAAAA3JAoAQAAAIAbEiUAAAAAcEOiBAAAAABuSJQAAAAAwA2JEgAAAAC4IVECAAAAADckSgAAAADghkQJAAAAANyQKAEAAACAGxIlAAAAAHBDogQAAAAAbkiUAAAAAMANiRIAAAAAuCFRAgAAAAA3JEoAAAAA4IZECQAAAADckCgBAAAAgBsSJQAAAABwQ6IEAAAAAG5IlAAAAADADYkSAAAAALghUQIAAAAANyRKAAAAAOCGRAkAAAAA3JAoAQAAAIAbEiUAAAAAcEOiBAAAAABuSJQAAAAAwA2JEgAAAAC4IVECAAAAADckSgAAAADghkQJAAAAANyQKAEAAACAGxIlAAAAAHBDogQAAAAAbkiUAAAAAMANiRIAAAAAuCFRAgAAAAA3JEoAAAAA4IZECQAAAADckCgBAAAAgBsSJQAAAABwQ6IEAAAAAG5IlAAAAADADYkSAAAAALghUQIAAAAANyRKAAAAAOCGRAkAAAAA3JAoAQAAAICbuyJRGjNmjPLkyaPUqVOrdOnS+v3331M6JAAAAAD3sDs+UZo6dao6duyonj17au3atXrsscdUq1Yt7d27N6VDAwAAAHCPCkrpAG5m+PDhevHFF/XSSy9JkkaMGKFff/1VH330kQYPHuyx/KVLl3Tp0iXn61OnTkmSTp8+fXsCvonYS+f9Wj4u7sSW87cs5W5eln2RsuVuLMu+SJpyN5blM03ZcjeWZV+kbLkby7IvUrbcjWXZF0lT7sayKbEvUlJcHGZ202UdlpClUsjly5cVFhamb7/9Vk899ZRzeocOHbRu3TotWrTIo0zfvn3Vr1+/2xkmAAAAgLvIvn37lCNHjniXuaNrlI4ePapr164pOjraZXp0dLQOHjzotUyPHj3UuXNn5+vY2FgdP35cUVFRcjgcyRpvYp0+fVo5c+bUvn37lDZt2nuu3N0U671e7m6K9V4vdzfFeq+Xu5tivdfL3U2x3i3l7qZY7/Vyd1OsKfHZ3C5mpjNnzihbtmw3XfaOTpTiuCc4ZuYz6QkJCVFISIjLtHTp0iVXaEkqbdq0iTqo7pZyKbFNyt0526TcnbNNyt0526TcnbPNe71cSmyTcnfONu+WcrdLZGRkgpa7oztzyJgxowIDAz1qjw4fPuxRywQAAAAASeWOTpRSpUql0qVLa+7cuS7T586dq3LlyqVQVAAAAADudXd807vOnTvr+eef14MPPqiyZcvq008/1d69e/Xqq6+mdGhJJiQkRH369PFoMnivlEuJbVLuztkm5e6cbVLuztkm5e6cbd7r5VJim5S7c7Z5t5S7U93Rvd7FGTNmjIYNG6Z///1XxYoV0/vvv68KFSqkdFgAAAAA7lF3RaIEAAAAALfTHf2MEgAAAACkBBIlAAAAAHBDogQAAAAAbkiUAAAAAMANiRIAAAAAuCFRQpL7/PPP1bx5c40fP16SNHXqVBUuXFh58+ZVnz59Ujg6JJc///xTs2bNcpn2xRdfKE+ePMqcObNeeeUVXbp0KYWiS5hDhw5p7969KR3GXW/v3r36888/tWrVKh09ejSlw/mfExsbq6tXr7pMO3TokPr166du3bppyZIlKRTZnatfv373xLH6999/68bOjJcsWaL69euraNGiqlatmn766acUjO7Ot27dOr/LXL16ld+NexiJ0j3i0KFD6t+/f5Ktb9u2bWrSpIlOnz7tMe/UqVNq2rSpdu7c6TFvxIgR6tixo86ePauePXtq4MCBeu211/Tcc8+pZcuWGjlypD799NMki/NWdO7cOUF/t9uVK1du+zb94etY69u3rzZs2OB8/d///lcvvviiqlWrpu7du2v69OkaPHjw7QzVpzNnzui5555TTEyMmjdvrsuXL+u1115T1qxZlSdPHlWsWNHrsZ9czp07p8WLF9+27SWXMWPGKCYmRnny5FG5cuVUpkwZRUdH69FHH9Xq1av9Xt/ChQt14cKFZIg06e3bt0+tWrVK6TAkSS+++KLatm3rfH3mzBk99NBDGj16tH799VdVrlxZv/zyi9/r3bFjh6pUqeJXmZvdfJg5c6ZeeukldevWTVu2bHGZd+LECa/bmz9/vooUKeLz96lo0aL6/fffvW7v9OnTHn+nTp3SwIEDtXPnTuc0fyX1b3BiFS5cWEeOHJF0/ftTsWJFxcbGqlmzZkqXLp0aNGigX3/91Wf5f//9V5MnT9Yvv/yiy5cvu8w7d+7cHfEek9qpU6c0ZswYPfDAAypdurTf5Tdu3Kg8efIkaUx79+71+nfq1Kkk3U5yO3HihL744ouUDuPWGG6rffv22ZEjR5yvFy9ebE2bNrVHH33UmjVrZkuXLk3UetetW2cBAQGJKnv8+HGbOHGiy7SXX37Zunbt6rNMt27d7NVXX/WYXqhQIfvyyy/NzGzNmjUWFBRkn3/+uXP+uHHjrHTp0l7XuX37dmvZsqXzdc6cOS19+vTOv4wZM9qWLVs8yj3//PN2+vRp5+t169bZ5cuXfcYep1KlSi5/QUFBVqZMGZdplStX9ln+2rVrPqfv2bPH67znn3/eTp065XOdK1eutKJFi3qdd/ToUZs/f74dO3bMzMyOHDliQ4YMsX79+tmmTZt8rjOp+TrWsmTJYitXrnS+fvPNN618+fLO1998840VLlzY6zpz5sxpR48edb7+8MMP4/2cbjRjxgx78cUXrWvXrrZ582aXecePH/e6D9u1a2eFChWyDz74wCpVqmT16tWzYsWK2ZIlS2zx4sVWrFgxe/PNNxO0fXeJ+S7eyvf34MGD1q9fvyQtt27dOhswYICNHj3a5XxlZnbq1CmX72mcd955x7JmzWojRoywjz/+2AoXLmz9+/e3WbNm2fPPP29hYWEux0dCBAcH+31snz171saOHWujRo2yv//+26+ycbZv3x7vd9+bm+3DxHymN+NrHxYoUMB+/fVX5+tRo0ZZ1qxZ7eTJk2Z2/fxdqVIlv7cX33s8ffq0NWvWzHLlymUvvPCCXbp0ydq2bWsOh8MCAgKsQoUKHt/pL7/80gIDA61OnTr26KOPWurUqW3y5Mku78/b9p588kkbPny4zzhHjhxp9evX9zovICDA619cnHH/+utm+z85zt/efrsdDocdOnTIzMyqVq1qbdu2dZnfvXt3q1Chgtf1rVixwtKlS2dp06a10NBQK1CggP3111/O+b72R7t27Wzx4sWJeg+jR4+2qlWr2jPPPGPz5s1zmXfkyBHLkyeP13KxsbG2c+dOu3LlipmZXbp0yb7++mubOHGix/fLl3nz5lmzZs0sNDTUChUqZD179rQ1a9b4/R6S+vrLzJzHobe/6Ohoe++997yu791337Xdu3cnKpY5c+ZY7969nfth0aJF9vjjj1vlypVt3LhxiVrnrXw2dwoSpdusbNmy9ssvv5iZ2Y8//mgBAQFWt25de+ONN+ypp56y4OBgmz59uke59evXx/s3derURB+M3g7kggUL2ooVK3yWWbVqld13330e00NDQ12ShJCQEJcT7bZt2yxdunRe19mhQwfr0aOH83V4eLgNGzbMJkyYYBMmTLBatWpZ69atPcoFBAQ4fxjMzCIiImzHjh0+Y/clPDw8QeVOnTplzzzzjKVOndoyZ85svXv3tqtXrzrn+/oxMTMrVaqUZc+e3WbPnu0y/fLly9ajRw8LDg72+h7//PNPi4yMNIfDYenTp7dVq1ZZnjx5rECBApY/f34LDQ211atXe5S7fPmyde3a1fLly2cPPfSQx8nOW6yJPdZCQkJs7969ztfly5e3AQMGOF/v2rXLwsPDvX4uN/64myV8Hyb2Qitnzpw2f/58MzPbv3+/ORwO+/nnn53zZ86caQULFrzp9r1Zt26dORwOv8sk5ff3Vsr9+uuvlipVKitatKjlypXLMmbM6PyszHx/prlz53ae28zMtm7dalFRUc4Lmfbt21v16tW9xlKqVCmvfw6HwwoXLux87W7Pnj1WoUIFCw8Pt2rVqtmePXvsvvvuM4fDYQ6Hw8LCwmzRokVJ8tn89NNP8f69//77PvdDYj/TxMRpZhYWFmY7d+50vn7qqaesXbt2ztcbN260TJkyeZQbOXJkvH/dunXzGWdibj6UKlXKPvjgA+frb7/91sLDw50313x9Lrly5Yo3udi8ebPlzJnT67zs2bNbnTp1bP78+bZw4UJbuHChLViwwAIDA238+PHOae5u5Tc4sefvm/G2/288l2bNmtWWL1/uMn/jxo0WFRXldX3VqlWzVq1a2bVr1+z06dPWtm1bi4qKciYPvvZH3EV9gQIFbMiQIfbvv/8mKP6RI0daWFiYvfbaa/bcc89ZSEiIDRo0yDnf1/a2bNliMTExFhAQYPnz57edO3da6dKlLU2aNBYWFmYZM2b0eZNk3759NmDAAMuTJ49lzpzZ2rVrZ0FBQbZx40afcfo6P8X9FSpUKMnP3+vWrfP6t3DhQhs2bJhlyJDBPvroI49yDofDAgMDrVq1avb111/bpUuXEhTHpEmTLCgoyB544AELDw+38ePHW7p06eyll16yF1980VKlSmXffvutR7lTp07F+/f777+TKME/ERERtmvXLjMzK1OmjA0ZMsRl/ocffuj1guDGu13ufze7C5aYAzl16tTx3pXYvXu3hYaGekyPiopy+QHLkSOHy3q2bdvm82K5aNGiLhcP7onLwoULLX/+/B7l3C+yE5rwuEtoufbt29t9991n3377rX322WcWExNjderUcZ6QDh486PNC+cqVK9arVy8LDg62V155xc6cOWMrV660IkWKWExMjM2ZM8druWrVqtlLL71kp0+ftnfeecdy5MhhL730knP+iy++6PUOap8+fSw6Otreeecd69mzp0VGRtorr7zinO8t1sQea7ly5XJelF66dMlCQ0Ptt99+c87fsGGDpU+f3uv7S+w+TOyFlntSFxYWZlu3bnW+3r17t4WFhXnd5lNPPRXvX5UqVTy2eWPNqLe/tGnT+vz+JvYCLbHlypYt67ygjY2NtWHDhll4eLjNmjUr3s80LCzMeW6LKxsUFGQHDhwws+s//L6++0FBQfb4449b3759nX99+vSxgIAAa9u2rXOau2eeecYeeeQRmzRpktWtW9cKFSpkderUsYMHD9rhw4etYcOGXmuGEpMQxPe9uPH74U1iP9PE7sMMGTK4XPhlzZrV5QbCjh07vJ6/HQ6HZcuWzXLnzu31L1u2bD7fY2JuPqRJk8YloTMzW7BggUVERNhHH30U7/d327ZtXuMwu/47kzp1aq/zjh07ZvXr17fKlSvbP//845x+s4vlW/kNTuz5OzG/3Q6Hw7Zv326nTp2yvHnz2tq1az0+G1/ntvTp07ucB83Mhg4daunTp7cVK1bEmyj99ttv1qFDB8uYMaMFBwdb3bp1bfr06T5bXpiZFSlSxNkCxcxs6dKlljlzZuvVq5eZ+f5e1KtXz+rWrWsbNmywjh07WpEiRaxevXp2+fJlu3TpktWrV8+ee+45j3K1atWyiIgIa9Kkic2YMcN5g/Nm+z4kJMSaN2/ucn668a9169ZJev2VEJMmTbISJUp4THc4HDZ+/HirV6+eBQcHW1RUlHXo0MH++9//xru+kiVL2siRI83M7LfffrPQ0FCXWtv33nvPpYXIjdvzVfN1KzW0dxISpdssMjLS1q9fb2ZmmTNndv4/zvbt272exDJmzGhjx4613bt3e/2bOXOmz4MxMQdydHS0RzX4jX777TeLjo72mF6+fHn7+uuvfZabPn26FStWzOu88PBwlwutjh07ujTH2r17t9cfv9udKOXKlcsWLFjgfH306FErU6aM1ahRwy5evJigu8OrVq2yokWLWtasWS04ONhefvlll+aD7tKnT+9MQC9fvmwBAQH2559/OuevWbPGsmfP7lEuf/78LjWU27dvtwIFCliLFi0sNjbWa6yJPdZeeeUVK1u2rC1evNg6d+5sUVFRLnezJk+ebA8++KDX95fYfZjYC61s2bK53MFt0qSJy/b/+usvn0ldUFCQ1apVy1q0aOH1r27duh7bDAsLs9dff91ZO+r+169fv5t+f/29QEtsubRp09r27dtdpk2ZMsXSpEljP//8s8/PtGTJkvbpp586X8+bN8/CwsIsNjbWzK7fBY6IiPD6HpcsWWL58uWz3r17u1xY3ewCJjo62vk9OHbsmDkcDpfmy+vWrfN69zwxCUG2bNnshx9+8BnL2rVrfe7DxH6mid2HlStXtu7du5vZ9ebdAQEBzoTV7HoTm3z58nmUy507t02dOjVR7zExNx+yZs1qy5Yt81jXwoULLTw83Hr27Ol1e3nz5rVp06b5jPP777/32WQrzpgxYyxbtmw2ZcoUM7v5sXYrv8GJPX8n5rf7xjIOh8Ol6bvZ9ZYsBQoU8Bmn+zWJ2fVmtenSpbNp06b5PE7jzp+XL1+2qVOnWs2aNS0wMNCyZctmb775ptfENjQ01OU33+z6uTc6Otq6d+/u83uRKVMmZwJ49uxZczgc9vvvvzvnL1261HLlyuVRLjAw0Dp16uRR23SzfV+6dGkbM2aMz/nxfS+SK5HYsWOH1/Ppjfvi0KFDNnToUGeN10MPPWSffvqp12sN99/S4OBgl2Nhy5YtXs+ladOmtaFDhzprYt3/Pvvss7s+UQpK6Wek/tdUrFhRX331lYoXL65SpUpp4cKFKl68uHP+ggULlD17do9ypUuX1oEDBxQTE+N1vSdPnnTp6eZGERER6tmzp8qUKeN1/rZt29S6dWuXaRUqVNCHH37o88HdDz74QI899pjH9KFDhypNmjRey0jXH1B031acgIAAHT58WLlz55Ykvf/++y7zDx06pODgYK9lN23apIMHD0qSzExbtmzR2bNnXZa58XO+FUePHnXZD1FRUZo7d65q1qyp2rVr6/PPP7/pOkJCQhQcHKxTp04pVapUKl++vCIiInwuf/nyZYWGhkqSgoODFRYWpowZM7rEcOzYMY9y+/fvV7FixZyv8+XLp4ULF6pKlSp6/vnnNWzYMI8yiT3W3n77bTVo0EAVK1ZUeHi4Jk6cqFSpUjnnjxs3TjVq1PD5Hj///HOFh4dLut6L0IQJE1zeoyS1b9/e5XXatGl16NAhlwdpK1WqpOnTp+uJJ57QP//843VbxYsX18qVK/XAAw9IkqZMmeIyf+XKlSpcuLDXsoULF9bTTz+tF1980ev8devWacaMGS7TSpYsqZw5c6p58+Zey6xfv179+vXzOi8qKkpDhw5V1apVvc7fuHGjnnzyySQrFxISopMnT7pMa9KkiQICAtS4cWO99957XtfXo0cPPffcc/rtt9+UOnVqTZs2Te3bt5fD4ZB0/cHyG4/FG5UvX15r1qxR69atVbZsWU2ZMkX58uXzuuyNjhw54jxOM2TIoLCwMEVHRzvnZ8mSRSdOnPAoFxMTo6FDh6pRo0Ze17tu3TqPh7pLly6tNWvWqH79+l7LOBwOn+fgxH6mid2HvXr1Uu3atfXNN9/o33//VYsWLZQ1a1bn/B9++EHly5f3KFe6dGmtXr3a5+cS33uMiorSkSNHlDNnTklSvXr1lC5dOuf8s2fPKiQkxKXMww8/rFmzZumRRx5xmV6xYkXnd9ib2rVrq3fv3qpVq5ZSp07tMu/ChQvq06ePz7Jx2rRpo4oVK6pp06aaPn16vMtKt/YbnNjzd2J+uxcsWODy+sb9Lkm7d+/Wyy+/7HV9xYoV09KlSz1+K7t06SIzU5MmTbyWu1FwcLAaNWqkRo0aae/evRo3bpwmTJigIUOG6Nq1ay7LZsyYUfv27XP+5ktS0aJFNX/+fFWpUkX79+/3uo2zZ88qQ4YMkqQ0adIoTZo0Lu8zR44cOnTokEe533//XePGjdODDz6oQoUK6fnnn9ezzz570/f06KOPauvWrT7nR0REqEKFCj7n+bsPE+LEiRMu3y9vMmfOrG7duqlbt276/fffNXbsWHXq1EmdOnXyuD4KDg526bwjJCTE+XssSalSpfLasU7cb2jFihW9xpAuXTqf34u7RsrlaP+bNm3aZFFRUfbCCy/YgAEDLDw83J577jkbOHCgvfDCCxYSEmLjx4/3KDdt2jSbNGmSz/UeP37cJkyY4HVepUqVbOjQoT7LenumYs2aNRYSEmJPP/20/fnnn3by5Ek7efKkLV++3Bo0aGAhISGJalMdn7Jly9rAgQN9zu/fv7+VLVvWY3pi77q6N2NJkyaNzZw502O6u4IFC9rMmTM9pp85c8bKli1rJUqU8HkHJTY21gYNGmQhISHWokULO3nypI0ePdrCw8Otbt26LrUaNypUqJBLDd+MGTPs/PnzztfLly+3HDlyeJTLkyePS/O3OPv377f77rvPqlWr5hHrrRxrZmYnT550eWYrzrFjx3y2l46JifF5dz/uz9sd4nr16lnv3r29rnPBggWWJk0ar/vi2LFjduLECZ/v4ZdffnGpNbxRixYtPB6QvtGmTZssd+7cLtMGDhzotelYnL1791qLFi28zqtZs6bLs17ufD0Tldhy1atXt3feecdrmSlTplhwcLDP4/uXX36xpk2b2tNPP+1Su2R2veb1xhpiX8aNG2dZsmSxTz75xIKDg2/aHCq+mkhfd6Sffvpp69atm8/1evtsFi9e7Gwq583Zs2e9PttilvjPNLH70Oz6sygjRoywr7/+2qP50yeffGLr1q3zWia+DjcuX77ss0n2448/bh9//LHPsuPHj7dy5cq5TFu4cKHLMynuFixY4PV7cfDgQcuWLZvlzJnThg4daj/++KP99NNPNmTIEMuZM6dly5bNDh486HO9N7p06ZJ16tTJSpYs6VE7faNbOS8m9vydmN/uW/HZZ595bbIWZ+jQoR7nNjPP76G72NhYr83KmzRpYh06dPBa5q+//rJMmTJ5/V7ky5fPpQZpzJgxLrUkq1evtixZsviM59y5czZ27FgrX76887s3YsSIeFt1JFZy7MNLly5Zo0aN7JlnnvGYFxAQEO++OHXqlMe52czswQcftB9//NFlubjWAGZmc+fO9fpc+qeffupssufNwYMH4/3tuxuQKKWA7du3W+PGjS0iIsJ5QR8cHGzlypWLt2lHYiX2QJ4+fbrzRHXjX6ZMmeynn37yuq7jx4/bBx984LXHspMnT/qcFxdnWFiYzZgxw2Pezz//bGFhYV6/4L6aQrj/uUtsgvWf//zHGjZs6PU9nD592sqUKePzQrJMmTKWNWtWl7b7Zter0StUqGBRUVFemy727dvXvvrqK6/rNLvew1yDBg08pr/44ovWqlUrr2X++ecfy58//11dLZ7YC61bcfHiRTt37lySrjM+ib1Au5VyHTt29FluypQpieoxzR9///23PfTQQ+ZwOG6aKLVu3do6depknTp1slSpUlmrVq2cr309O3ArCUFiJPYzvdWbFr5cvXrV62/Nnj17XC6O/HErNx8SY/fu3VarVi2Xc3hAQIDVqlXLozlXSkvs+Tsxv91jx461ixcvJirOPXv2xPtMkS+5c+dO0E0Qd+vXr4+3N7W//vrL67VJ69at7bPPPvNZbvDgwVa7du0ExbBlyxbr2rWrZcmSxVKnTm1PPvlkgsolVGKvv+J7BjZz5syWLVs2r83Tb5a0+jJt2rR4O74ZPHiwvfXWW36v917gMLvb68TuXmamw4cPKzY2VhkzZvTZrCwlXbhwQbNnz9b27dtlZrrvvvtUo0YNhYWFeV1+wIAB2rBhg7799luv8xs1aqQSJUqoZ8+eXuc3adJEU6dOVaFChVSwYEE5HA5t2bJFW7du1dNPP61vvvkmyd7bnj17ErSce1OLEydO6MCBAypatKjX5c+ePavVq1d7rYpu3LixxowZ42w2cCMz04gRI9SrVy+PavGbOX/+vAIDAz2atuzZs0dbtmxRzZo1vZb7999/NWfOHJ9NwvyR0DFkxo0bd8vbulXnz59X165d9eOPP+rKlSuqVq2aPvjgA4+mfknt0qVLunr1arzNU+8VGzdudGlqExgY6PM7401sbKzOnDmjtGnTOpvvuatUqZLPeTdyb46UGNeuXdPGjRtVoEABZzOqOOfPn9f27dtVrFgxBQQkbnjC/fv3e212nZS2bNmicePGaeLEiTpx4oTHODmBgYH6999/lTlz5mSNI86BAwc0fPhw9e7dW2nTpnWZd+rUKb399tvq0qWLS3NKdydOnHD+PhUoUEDp06dP0La3bdumn376Sbt375bD4VDevHlVr1495c2b95beU2KcP39eQUFBLk2VE8t9H2bLlk1Lly51ad6W0LJ3q127dil16tQezQ7jc+3aNU2fPl3jxo3Tzz//7Nf2pk2b5jGO4K1q2bKl1+lp06ZVoUKF1KxZM4/vDJIHidJdIqEDnw4fPjyZI7nO14mhZMmSeu+993y2qZ83b566dOmitWvX+lz3119/ra+//lp///23JKlAgQJq0qSJGjdu7LPM6dOnnSeNX375xWVU+sDAQNWpUyfB7y2lbdu2TQUKFPCYfubMGS1fvlxXrlzRww8/nKwX9du2bdOGDRv0wAMPKE+ePJo5c6aGDh2qCxcuqH79+nrzzTc9LlADAgIUExOjUqVKxdsm+YcffvCYltAB6V544QX/3ogPXbt21ZgxY9SsWTOFhoZqypQpqlSpks8EPyGOHDmidOnSeb3hcfToUTVv3lxz5sxRbGysypQpo8mTJ6fIRVmc//73vxo7dqxGjBjhMv3EiROaPHmymjdv7vXi9YsvvvA67/fff1fnzp21cuVKSdfb5p8/f955LDgcDv3666+qVq2aRyyJ3WZSu3z5si5fvuzSNj/OhAkTNGrUKP35558KDAx0mXft2jWVKVNGHTt21HPPPedRtkOHDho5cqTP7e7fv1+VK1d2nvNudPr0aYWHh3skYNeuXdO5c+du+pmcO3dOU6dO1dixY7V8+XJVrlxZjRs3Vv369T3OIQEBATp48GCyXCifO3dOq1evdnmWo0uXLjp9+rTPQchfffVVRUZGaujQoS7Td+7cqTx58iQoSfZm8ODB6t27t2JjY5U5c2aZmY4cOaLAwEANGjRIXbp08fkehg4dqmnTpjkTrDx58qhhw4bq0qWLzxuIN7Njxw69/PLLmj9/vl/l9u3bpz59+rjcfHLfhxEREVq/fn2CzjVJvf+vXr2qoCDfj8Kn1A2rxPjss880Z84cBQcHq0OHDipTpozmz5+v119/XVu3btXzzz+vTz75JKXDlJS01wqLFi3SuXPnVLZsWa83IS5cuKB58+Y5nwns0aOHLl265JwfGBioAQMGeDxLeFdJoZos+LB3716vAw8mxeCoY8eOtTp16ljRokWtWLFi9uSTT9rEiRN9NrX49NNPrWHDhta0aVPnWAzz5s2zkiVLWmhoqEs303HCw8N9DrZqdr1q31fPV4k1ffp0K1mypEsM7s3ovPX/f+zYMdu3b5/LtL/++statGhhzzzzjEu3pTe6lcFR4xPXbNFbl5/r16939sTlcDgsMjLS5s6de9N1btu2zVatWuUy7bfffrNKlSrZQw895PWZsGnTpllQUJClSpXKQkJCbOLEiRYSEmKPP/641alTx4KCgjy6tTcza9OmjaVPn95KlChhI0eOdA6smBDp0qXz+Zc+fXpLlSqVzyaCiRmwMG/evC5NYf78808LCgry+myVu08++cTZtCU2NtYGDhxo6dKls4CAAAsLC7NOnTp5NF956aWXLDo62gYOHGjvvfeeFShQwKpVq3bTbSWEr3OGN6dOnbKPP/7Y2azN27HWv39/n01Lza53yf322297TG/cuLFLE5Pw8HBbtGiR7d6923bt2mWdOnXy2rzoVraZkKZcbdq08Tp93Lhx1q5dO2e32d27d3ceZ9WqVfNoSvToo4/G23xq6tSp9thjj3mdly5dOp+D++7fv98KFCjgtey0adOsQIECXpt6njt3zu677z6PZrxxli5daq1atbLw8HArVaqUvfvuuxYYGOjXM19Jydt4MUWLFnV51sTdH3/8YUWKFPGY7v4cRqNGjRL8TNL8+fMtICDA+vTpY8ePH3dOP3bsmPXq1csCAwO9NkG6dOmSlS5d2kJCQqx+/frWvXt3e+ONN6xu3bqWKlUqe+SRRxI00Lk3STkW2q30ApvY/T9r1izbsGGDmV2/1hgwYIDz9yp79uw2ePBgr9cZXbp0sbCwMHv55Zetffv2ljFjxnjPA3Fu5Tc4MYOqvvPOOxYcHGylS5e2sLAwCwsLs4EDB1pUVJT17ds33gFuk+N6ITmuFYYNG+byvG9sbKzVrFnTeR0VHR3tMiZmnI8//tieeOIJ5+vw8HCXa9MsWbLEOzj03YBE6Q6T0BOmPye/2NhYq1OnjjkcDitZsqQ1btzYnn32WStevLg5HA6rV6+eR5nEnhgiIyO9dvcaZ9myZRYZGZmguN2tXr3a6tSp4zH9ySefdOkC1f2zGTp0qNWqVcujXOPGja1Tp07O14cOHbL06dNb0aJFrW7duhYcHGxffPGFRzn3H5PEDnAbZ+7cuda4cWNLnTq15ciRw9q3b++xTK1ateyRRx6xP/74w1avXm1169ZN0ICo9evXd2lXvHPnTgsNDbUaNWpY+/btLTw83N5//32XMqVLl7Y333zTYmNjbdy4cRYaGuqyzCeffGKFChXyur2LFy/alClTrFq1ahYWFmbPPPOMzZ49O9HPPRw4cMBat25twcHBVrNmTY/5iR2wMDg42GUMFbPrY4fd2L2xLzdepH388ceWJk0ae++99+yPP/6wDz/80CIjI+3DDz90KZMzZ06XDkA2b95sgYGBib6wulFCzhkLFy60559/3sLCwiwgIMDeeOMNn+PQlChRwmsHIHF+++03lxsTcfLly+fy3Xf/Hq5Zs8ayZs2apNtMmzZtvJ3KtG3b1tKmTesx/e2337bQ0FCrWrWqZciQwV599VXLkiWLDRkyxIYNG2Y5cuSwV1991aVMpkyZ4n32ZefOnZYxY0av8xYvXmxhYWE2atQol+kHDhyw++67z8qVK2dnz571KFe9evV4n8UYO3as1ahRw2N64cKFLSYmxnr06OGSGCVkrKCBAwfedJypxPB2nIaFhd30xpq34TJuJRlo1KiR15t8cV5++WVr3Lixx/QRI0ZYdHS0bdmyxWPe5s2bLTo62mVMN38kZaIUEBBghw8fdr6OiIiIt5OKGyV2/xcpUsT++OMPMzMbNGiQRUVF2fDhw23WrFnOz83bzbXE3rBK7G9wYgdVLVSokI0dO9bMrt+ccTgcVrVq1XifybvVWL1JzmuFUqVKuTwf/c0331hoaKgtWbLEjh07ZnXq1PHaecRjjz3m0lW/+3dx0qRJ9sgjj/j7Vu8oJEq32a2M7n4jf34Yxo0bZxERES6DucaZN2+eRURE2MSJE12mJ/bEUKlSJXvjjTd8zu/WrVu8D4LPmTPHunTpYj169HC+v82bN1u9evUsICDA68VyTEyMy4PZ7p/Nhg0bvI5Cnzt3bpc70u+8847ly5fPrly54nxdpkwZj3JJMW7Tnj17rG/fvhYTE2NRUVEWEBBg3333nc/lM2XK5PIejx49agEBAXbmzJl4t5MjRw6XcWUGDBjgchfq888/97grFR4e7hzz5dq1axYYGOgyWN2uXbu8Dlbpbvfu3da3b1/Lmzev5cyZ86ax3uj06dPWs2dP590pb8euWeIHLHS/mDC7/r4TckFx4/5/6KGHPO6WffbZZ1a8eHGXaYGBgS7j2JhdH0MkIR0GJPacceDAARs4cKDly5fPsmTJYp06dbKVK1fe9GI5sbXC7oNUf//99y41Ibt377ZUqVIl6TY7d+5smTNn9hgk08zstddes/DwcFu8eLHHvPz58zvHz1m5cqVHrfMvv/ziMQ5LWFiY114w46xfv97nQJ5m13s6CwkJcW7333//tYIFC9ojjzzi87uRNWvWmw6s6i35DA4Otueff97mzJnjcpMiIYlSzpw5/e590ixxgypHRUXF+wD5okWLfI6DldhzcO7cueOtxVq8eLHXnt0qVKjgkeje6IMPPrAKFSokKAZ3SV2jFFcTnz59emetgvv+8Cax+//Gm0zFihXzGItrxowZXgeLT+wNq8Tu/8QOqhoaGupyfkqVKpWzlc3N3Or1wu26VkiXLp1zrC+z67273tgD4rJly7z2zOhe05QxY0aXG0pbt271erPqbsI4SrdZ/fr14x2LQlKi21378tVXX+nNN99U5cqVPeZVqVJF3bt315dffuny/MeePXuczxJUqlRJwcHBGjhw4E377W/Xrp0aN26sHDlyqE2bNs62/NeuXdOYMWP0/vvve4xZE2fixIlq2bKlMmTIoOPHj+vzzz/X8OHD1bZtWz399NNav36913FYDh48qKioKOfrBQsWOMfykKTw8HCdOnXKa7kbx9+ZP3++nnrqKWeb6rp162rw4MHxvl9/ffPNN/r888/1xx9/qHbt2ho5cqRq1aqlNGnS+By3R7r+jEuuXLmcr6OiohQWFqYjR454fZ7ixnI5cuRwvl6wYIHLuCuVKlXS66+/7lLm3LlzzjGdAgICFBoa6tL2PjQ01KUNsi8Oh8N5rMfGxt50een6MyKjRo3SoEGDlDFjRo0fP14NGzb0ufyuXbtUrlw55+uyZctq/vz5qlq1qq5cuaKOHTt6LWdmatGihUvnFxcvXtSrr77q0tHCtGnTfL63uO27P49XpUoVderUyWN77m31g4KCEvS5JPackSdPHj3zzDMaPXq0qlevnuBOBgIDA3XgwAGX4+1GBw4c8LquiIgI7dq1y9n5SYMGDVzm79q1y+fzNInd5nvvvacTJ06oevXqWrp0qbMzhPbt22vChAmaOXOm1/He9u7dq0cffVSS9OCDDyooKEj333+/c37x4sX177//upQpUKCA1zFm4ixZssTrs4Vx6tSpo3HjxqlVq1a6dOmShg4dqrRp0+rXX3/1+R0+ceKEy/OW7q5cueJ1nKhdu3ZpwoQJatOmjS5cuKAmTZqoWbNmCfptWbVqVaKeUbl06ZLatGnj8jneaM+ePR5jhZUpU0aTJk3yOQbNF198oYcffthjety5xX1aQhw6dCjejg3y5MnjHJPvRps2bVKlSpV8lqtcubL69++foBiS0/jx42+pfGL2f/r06bV//37lzJlTR44c8fge3HfffV7HRLp27ZpHBxZBQUHxHvO3Ytu2bc7fv6pVq+rq1asu5+86depo0KBBHuUuXrzo8oxNqlSplClTpmSJMc7tvla4cuWKy+/hsmXL1KFDB+frbNmy6ejRox7lTp065fLbduTIEZf5sbGxCbpeuJORKN1mWbNm1ejRo30OWuhtoMNbtWHDBq8Di8apVauWPvjgA5dpiT0xPP300+rWrZvat2+vnj17Km/evHI4HNqxY4fOnj2rrl27+rzwff/99zVo0CB1795d33zzjRo3bqz3339fa9eujXfwyQwZMmjHjh3OpOfBBx90mb9t2zavvcylTZtWJ0+edF7YrVixwmUQUYfD4fMLnpjBUSWpadOm6tatm77//vt4B5h153A4dObMGec+MTPntNOnT7u8pxtlyJBB//77r3LmzKnY2FitWrXK5SL+8uXLHhfg7hch3i5KfLl06ZKmTZumcePGacmSJXriiSc0atQoPf744/FeqJuZvvjiC/Xu3VtXr17VoEGD9OKLL3o8NO8usQMWeuvlz9tD+L7Mnj1bkZGRCg0N9RiE78KFCx7v1cxUtWpVlx+U8+fP68knn3S5UFizZo3HthJ7zoiJidGSJUuUK1cuxcTEqFChQgl6b6VKldKPP/7oMQhonB9++EGlSpXymF6mTBl98cUXPi8mJ0yY4HPQxcRuU7r+XWzYsKGqVaum33//XQMHDtTYsWM1Y8YMn4Mgul8UpEqVyqUTjqCgII/BMZs2baq33npL5cqV80iW1q9fr969e6tbt25et3fjOk6ePKkXX3xRDzzwgObOnRtvZwy5c+fWqlWrfO67VatWeR0ANXv27OrZs6d69uyp+fPna9y4cSpfvrzzXPXSSy/pvvvu8yh3KzfpEjOocpcuXVS9enVFRkaqa9euzt7tDh06pGHDhmnChAmaM2eOx7rcb3R4u8kheb/RcfHixXh7l3MfeDPOyZMnXW7IuYuKivJ6Q066fnzH99meP3/e63T3mw3eYnJ3Kz2YJnb/P/XUUxo4cKB+/PFH1atXT2PGjNGnn37qXN+oUaNUsmRJj3K3csMqMb/BiR1UNbHbu5Wyt/taIX/+/Fq8eLHy5s2rvXv36u+//3Y5f/7zzz9ej/8cOXLor7/+UsGCBb3Gs2HDBpebtXcjEqXbLLGju7v3Lmdm2rJli0c30t7udh4/fjze7lWjo6O93pVM7Ilh4MCBqlevnr788ktnt60VKlRQ06ZNvd4djLNjxw7nKNkNGzZUYGCghg8fHm+SJEkVKlTQBx984LU3LUn64IMPvN6xfPjhh/XBBx/os88+07Rp03TmzBlVqVLFOf/vv/92qZmKkytXLn322WfO11myZNGkSZNclnE4HF4/m1atWmnMmDFatGiRc1TwhHRna/+/a3b3aXEXj3EnQ/eLu4oVK2rAgAEaM2aMvv32W8XGxrrULG7atMnj7mrctuJ+5M6ePatSpUo5L/591Wy0bdtWX3/9tXLlyqWWLVvq66+/jvfC4kYlSpTQjh079J///EcdO3ZUWFiYzp0757Gc+8n90Ucf1ffff+9Ra1CkSBHNmzfPay2qdOt3XW+8GJk3b55LArBs2TKPY7ZPnz4e66hXr16CtpXYc8bWrVv1xx9/aOzYsXrooYd03333OZPB+C6IElsr3LlzZ1WrVk1RUVHq2rWr86704cOHNXToUE2ePNnrRe+tbFO6Xuv59ddfq06dOipSpIjOnTunn3/+2ee+j7Np0yZnzYH7+dTbndNOnTpp1qxZKl26tKpVq6ZChQrJ4XBo8+bN+u2331S+fHmPmsQ47hfKwcHBOnnypEeM7olygwYN1LNnT1WvXt3jHH7w4EG99dZbN03wq1SpoipVqujUqVP68ssvNW7cOL377rsqVqyY19+VxKpTp47XC/c4GTJk8Oi1snLlyho9erQ6dOig999/39kV/KlTpxQcHKwPP/zQ5Zwcxz0Z8Ocmh+T62+buzJkzXqfHxsbGe+MmICDA4/wbx9d392bi6xpfkiIjIxPUE+jFixc1depUnTt3TtWrV/dZ85nY/T9o0CDnd6Js2bL69ttvNXfuXN13333avn27jh075vW7n9gbVon9Dc6fP7+2bNnivKjfv3+/SxKyY8cOrxf1t/Kbn9iyt/taoU2bNmrXrp1+//13LV++XGXLllWRIkWc8+fPn+/1ZlXt2rXVu3dv1alTx6NnuwsXLqhfv353Va/D3tA9+G32+++/69y5c3r88ce9zj937pxWrVrlcSc0ICDA5wVR3HRvB790vVnLwYMHfdYIHTp0SNmyZXMpmzt37pveXXI4HNq5c2e8y/gjsd2arl27VmXLltWTTz6pbt26OU8SW7du1dChQzVz5kwtXbpUDzzwgEu5devWqVq1ajpz5oyuXr2qN998UwMGDHDOf/7555UmTRp9/PHHSfYepesnj2+++Ubjxo3Tn3/+qZo1a2rmzJlat26d16aF0vUuOhPC/bjZtWuXqlevrl27dikgIEAffPCB2rRp45xfv3595cmTR++//75z2sSJExO0LfcfuYCAAOXKleumd0+93R28sQbGW1lfx/eGDRu0evVqn2NObNy4Ud99953XRMWbPXv26Ny5cypUqFCix8OZMWOGgoODfY5d5a/EnjNudPbsWX311VfOY65ixYpq2rSp6tev7/W80LNnTw0ePFgRERFea4WHDBnidTtjxoxRp06ddPXqVZeL3qCgIL333ntq166dzxgTs80ba8LPnDmjAQMGqGbNmh7NId0vQhJ7Pr1y5Yozadu2bZvzoqRp06bq2LGjz5oK95oUX9yP0zNnzqhs2bLau3evnnvuOefYcps3b9aXX36pnDlzavny5X7dcZaun/vGjRvn0ZKgX79+6tq1q89urpNjvBjp+gXrN9984zJeX8OGDZPlTnRCftuk6+fOGwUEBKhYsWI+u7u+evWqx9hhKaFr1666fPmyszv6y5cvq0yZMtq4caPCwsJ09epVzZ07V2XLlvUoeyv7/8qVKxo7dqymT5+unTt3KjY2VlmzZlX58uXVpk2bO6JW4YcfflBUVJTPpp5DhgzRuXPnXK4DUtLtvFaQ5KyJz5Ili/r06aMsWbI457Vt21bVqlXzqOU8dOiQSpYsqVSpUqldu3bOm6xbtmzRqFGjdPXqVa1duzbem/V3OhKlu0RiB0eVrp/ga9Wq5TEYaZxLly5p9uzZSXKC37Ztm3r37q1PPvnE63gobdq00dtvv+01+QkICNDEiRMVGRkp6frgsyNGjPD4gtWtW9ej7E8//aSXXnpJx48fd5mePn16ff755z7v6B05ckRLly5VlixZPJoFzZw5U0WKFHF5jimpbdu2TWPHjtWkSZN09uxZ1alTRw0bNrxpkwt/XLlyRZs2bVKmTJmULVs2l3nr169Xjhw5ElzzE58WLVok6ALEW23OrZzcEyNuwM0bn2F65ZVXNHbsWElSwYIF9euvv3qtUbzbbd682XnMHT9+XFeuXPG63IoVK1xqheMSgvhqhaXrY7t899132rZtm6Trz/Y0bNgwQZ+lv9tMyHfT2w2dWzmfJsbevXuVI0eORCXfp06dUo8ePTR16lRnzX/69On17LPPatCgQTd9btRfyTVeTNyAnomtXUlpiU12pes1OnPmzFHlypU9ktrTp09r4cKFqlmzpsdvdP369fXSSy+pdu3aCT52ihUrpkGDBjl/J8ePH6/XX39da9euVa5cudSqVSsdPnxYM2fO9Fr+bhovyBdv40vdivnz56tdu3Zavny51+uacuXK6eOPP/b6PGRS2bZtm8aNG6cvvvgi2a4VEmvXrl1q06aN5s6d6zJuXvXq1TVmzJgUHS8wKZAo3WbJPbq7N4m5gP3zzz91/Phx1apVyzntiy++UJ8+fXTu3DnVr19fH374oceJ/ZVXXlG6dOl8PhP1xhtv6PTp0/roo4885iXkPfuqNZOuf36//vqrywVajRo1PNqt36rkGGAtNjZWv/zyiz7//HPNmjUrQQ8/mpkWLFigCxcuqFy5cgkekf5mVqxYodKlSzubmcTdXY9z6dIl/fTTT2rUqFGSbO9WJHbAwrJly+qVV15x1kTNnj1bTz75pCZMmKDChQurXbt2KlKkiD7//HOf69i2bZt++uknl4En69ev7/VH4Wa1bHG8PaOUXK5evaqff/75tv3Q+hrgNiXcOEC1L3HPucW5lUFxAwMD9e+//97SQJ5mpqNHj8rMlClTJufxdOTIEY9awcqVKyeoRcC8efNcpr377rt68803Vbx4cW3evFnS9Zq+4cOH6z//+Y9ee+01vwev3LJli8aNG+e8OXHjMyKrV69Wly5d9NNPP3n9TOvXr68RI0aoRIkSLvP+/fdfjRo1SgMHDpR0vQnujc/5BAYG6scff3R27nGjlLjoHTlypH7++WePzztOtWrVVL9+fY8a15o1a2revHnKnDmzWrRooZYtW8bbYYh0vbnemjVrlD9/fknXbzhGREQ4B/Vdt26dateurQMHDniUTY79H59WrVolaDl/E57169frgQceSLLavbp166py5co+m9Z+8MEHWrBggdfB1JP6eiE2NlYzZ87U2LFjb+u1wpo1a9S7d2/NmDHD5zLHjx/X9u3bJV1v5ujt2fC7EYnSbXYro7tL/l2c3YrHH39clStX1htvvCHp+kXOAw88oBYtWqhw4cJ655131Lp1a/Xt29elXKFChTRp0iQ99NBDXte7evVqNW3aVFu3bk2yWGvXrq2vvvrKWRM1cOBAvfbaa847rceOHdNjjz2mTZs2uZRzb3bii3uznU8++UQzZszQ9OnTJV1vIli0aFFn4rtlyxZ169bN50k1Lqa4Wpx9+/bps88+04ULF/Tkk0+qUKFCHhdUJ0+eVIcOHbRmzRo98sgjeu+991S7dm0tXbpUkpQpUybNnTvX4xm1kydP6quvvnI2t2vWrJnLw6pBQUH69NNPXe5Ku1/UpU2bVuvWrXMeY96aakrXO/L49NNPb6l26tSpU5o7d67L8V2tWjWfF7Vdu3bVmDFj1KxZM4WGhmrKlCmqVKmSvv3223i3ExUVpYULFzp76GrTpo0OHz6s77//XpK0cOFCtWzZ0qP5TZzBgwerd+/eio2NVebMmWVmOnLkiAIDAzVo0CB16dLFZflbuRt9Mzt27NDLL7+s+fPnu0w/cOCAhg8frt69e3u9IHz77bfVpUsXjxrbW6kVdnf69Gl99dVXGjt2rFatWqXixYtr3bp1Hssl5TYTokKFCpozZ47Pi5O43iFvfAZ0wIAB2rBhg89jq1GjRipRooR69uzpMc+9WfGtMjPNmjVLn3/+uWbOnOlxsRTfuSdun1y6dMnjO1y4cGF17dpVrVq10sKFC53PN3333Xd+1VydO3dOU6dO1dixY7V8+XJVrlxZjRs3Vv369V0utJs2barChQurV69eXtczaNAgbdq0SZMnT3aZ3qtXLx0/flyjR4+WdP0c3KpVK+eF2axZs/Too4/q3Xff9VjnrVz0JtbDDz+sXr16ufQ4eqMZM2aof//+WrFihce8f/75R+PHj9fEiRO1a9culS9fXi+99JKeeeYZj5utkpQuXTqtXLnSmVDlyZNHvXr1ciYlu3fvVuHChb12WpDY/Z/YG0EBAQGKiYlRqVKl4n0+yt994StRSuwNhJiYGM2ePdtnb3NbtmxRjRo1tHfvXo95SXG94M2VK1e0du1aj9r2xF4rSNLcuXOdtYkvvfSS8ubNqy1btqh79+6aPn26qlevrtmzZ/sV5z0hSTsbx03dyujugwYNsqCgIAsICLAsWbJYdHS0BQQEWHBwsL3zzjs+13n16lVbv369nT9/3mPeuXPnbP369Xbt2jWX6VmyZHHpi//NN990GV/gm2++scKFC3usz30sFXe7d+9O0Bg83ly9etV++OEHj+nuo7S7D+jmayyd+MaKiG/MiFsZYG3Dhg0WExNjAQEBVrBgQVu7dq1FR0dbeHi4pU2b1gIDA72+xxdffNEKFChgAwYMsDJlyljZsmXtkUceseXLl9uKFSusUqVKLqNjxxk2bJg1a9bMJdann37aWrRoYS1atLCCBQtanz59XMrcbNyHgwcPmsPh8NhW2bJlLTo62n7++Wev7/1mJk2aZJGRkc6RwOP+0qVL5zIQ3o0SO2Ch+xhGxYsXtxEjRjhf79mzx1KnTu217Pz58y0gIMD69Oljx48fd04/duyY9erVywIDA+MdGyap+RqD5fXXX7eXX37ZZ7nWrVtbt27dPKa//PLL1rVrV5/lunXr5jEYqzt/Bri91W2ePn3aVq1a5RwnZPXq1fb8889bw4YNbfLkyV7LFC1a1J544gmvx8nChQstTZo01qFDB5fpiR0U18zzO5VYO3bssJ49e1qOHDksXbp01qxZM5dzUXyuXLliI0aMsEyZMln+/Pm9/g7dyngxZtfHMWvVqpWFh4dbqVKl7N1337XAwECfYzflzZs33rGpNmzY4PUcXKJECZszZ47ztfs5avbs2VakSBGv68yVK5fLeDHuNm/ebDlz5vSYXqlSJatcuXK8f1WqVPG6znTp0t10nLB06dL5nB9n/vz59txzz1maNGksbdq09sorr3jsnzJlyth7771nZmZ//fWXBQQEuIwPt3DhQouJifG6/sTu/z59+ljfvn1v+ueuTZs2lj59eitRooSNHDnSjh07dtNtJYSvc2LHjh19/rVq1cpCQ0O9lgsJCbnpeGa+fi+Sa0BWX+8xsdcKEyZMMIfDYVFRUeZwOCxTpkw2adIki4iIsBYtWriMpXgjX9+L+vXr2xtvvJGgQdzvdCRKt1liR3e/lYuz8ePHW+nSpb1eFFy9etVKly5tkyZNcpkeEhLicoCXL1/eBgwY4Hy9a9cuCw8P91hfdHS0zZs3z+f7++233yw6OtrnfG82b95sXbt2tcyZM1twcLDH/IRc2CdmMD9fbmWAtccff9yeeOIJ+/33361169aWPXt2a9mypV27ds2uXbtmbdu29TrIbbZs2WzhwoVmZvbPP/+Yw+FwGSz3zz//9Pq5PvzwwzZz5kzna/fPZtq0aR4Xd4n9PGNjY23YsGEWGhpqrVq1stOnT3v9DLxZvXq1BQUFWfPmzW3dunV28eJFu3DhgvOiNzg42NatW+dRLrEDFhYqVMi+//57MzM7cuSIBQYG2qpVq5zzfX2eZmaNGjWyV155xee6X375ZWvcuHG82/fHyJEj4/3r1q2b1/1RtGjReAfW/OOPP7xeTBYsWNBWrFjhs9yqVavsvvvu85ie2AFub2WbixYtsoiICHM4HJYhQwb79ddfLSIiwgoVKmRFixa1gIAA+/TTTz3K7d+/3/LmzetyEyFufeHh4dauXTuPMokdFNfs+ndq4MCBN92X3ly4cMEmTZpkFStWtJCQEHviiSc8BoG+mcmTJ1vevHkta9asNnr0aOeg2t7iTOzgmIULF7aYmBjr0aOHy/6Ob/+HhITEO8jzzp07vV6ARkZGusT11FNP2cGDB52v4xsUO7EXvYm9yDa7/jneeH5xt2rVKq+/p76cPn3aPv74Y8uQIYMFBga6zPvuu+8sODjYqlSpYtHR0R4Xxd26dbNnnnnG63qTYjB1X06dOuV1+sWLF23KlClWrVo1CwsLs2eeecZmz57tMlCyv/wZwDchNxDy5s0b7w2J77//3udAzMk1IKuv95jYa4USJUrY4MGDzez6DXuHw2EPPPCAc/B5X3x9J1q0aGH333+/pUmTxtauXev3+7uTkCjdZokd3f1WLs4SU4uVK1cuZ+J16dIlCw0NdbmbumHDBq+jez/zzDNWv359n9uqW7euNWzY0Of8OGfPnrWxY8dauXLlLCAgwKpWrWqfffaZHTlyxGPZxF7Yz5s3zwoXLuz1BH7y5EkrUqSILV682GNe6tSpbcuWLT5j37x5s4WEhHidFxUV5dz/Z86cMYfD4VJzt3nzZouMjPQoFxgYaAcOHHC+Dg0NdTmB/fvvv17fY1RUlG3dutX5unTp0rZv3z7n6x07dliaNGlcytxq4rl582Z75JFHLCYmxt57770EXQy2aNEi3uPi6aeftpYtW3pMDwgIsMOHD7tMCw8Pj/fiy+x67WyWLFmsf//+VqlSJStatKjL/Pfff9+qVq3qtWzu3LnjTUAWL15suXPndpl2K3ejHQ6HZcuWzWetZ7Zs2bzuj7CwsJte2Hs71yS2VjgkJMSee+45mz17tksNdUISpcRu87HHHrNWrVrZvn37rH///pYuXTrr0aOHc/6AAQOsRIkSXte5fft2y5o1q/3nP/8xM7Pff//dwsPDrU2bNl6Xj4yMtGXLlvmMcdmyZV6/u2bX92HOnDn9rr2Ou+P+yCOP2KhRo+zo0aNmlrDP1Mxs1qxZVqJECUubNq3179/fzp49G+/y7gld6tSprVevXgn6DgcHB9vzzz9vc+bMcbnIjS/WHDly2KxZs3zG88svv1iOHDk8pqdJk8bWrFnjs9yaNWs8zmtxbuWi111CLrLNrtfyDBkyxOd6Bg8e7PUGmTc7duywXr16Wc6cOS0wMNBq1qzpsczcuXOtY8eONmTIEDt37pzLvL59+7pcON8osfs/vhYtZteTpIS8v927d1vfvn0tb968ljNnTmctsbunnnoq3r/KlSsnKFFK6A2Edu3aWbFixezChQse886fP2/FihVznkfc3cr1Qnx8JUqJvVa48Xfz2rVrFhQU5Ey4bkXbtm2tVq1at7yelESidJuVKFHCPvroI5/zR48e7fWHPTEXZ3ESU4v1yiuvWNmyZW3x4sXWuXNni4qKskuXLjnnT5482R588EGPda1Zs8ZCQkLs6aeftj///NNOnjxpJ0+etOXLl1uDBg0sJCTEVq9e7TMWf5tumHleLLtfKPu6sH/yySdt+PDhPtc7cuRIr0lf/vz57bvvvvNZburUqZYvXz6v8xKbhCS2XGhoaLx3njds2OBxARp3B2r9+vW2fv16S5Mmjc2cOdP5et68eTf9Efrss88sMDDQcuTIcdOLQTOzAgUK2Ny5c32ub+7cuVagQAGP6Q6Hw2rXru3yIxkUFGQ1atRwmebu2rVr9tZbb1nJkiXt8ccf92iK07BhQ/vss8+8xhIaGuqSbLrbt2+fxx3pW7kbnTt3bps69f+1d+dhNeb//8Cf51SUFtlGtsqIiZGlMfYlRNnG1tgKKUPINp+UPYZQH2QfW6cyjGQZZF8ijCWkQnwMspOtspWk9+8Pv3N/O+fc9+l0n9OpxutxXV1X577Pfe73uc/2Xl+vbYLnu3LlimAjWd0UwLi4OFapUiWV7WJHhevVq8dsbW3Z9OnT2Y0bN7jtmlTqxZ6zfPny3Lk+fvzIpFKpwsjjP//8o7anPikpiVWoUIENHz6cm8okxMnJiQUEBAju9/f3Z05OTrz7xE69MzAwYNOnT1cZnS3oml64cIE5OTkxY2NjNmnSJN4OJj42NjaipiMz9qX3ev78+axOnTqsevXq7D//+Q9LSEhgRkZGgmX19PRkbdu25d2Xl5fH2rZtyzw9PVX2OTo6slWrVgk+j+XLl7OmTZvy7tOm0pufppVsxhhbt24dMzU1ZTExMSr79u7dy0xNTdm6desEj//w4QOLjIxkTk5OTCqVMltbWzZ37lydT2sS+/obGxszmUzG+5hv3rxhLVu25J2qr+z+/fts7ty5rHbt2qxGjRqCDaXhw4dz08fV/QkpbAfCs2fPWPXq1VmtWrVYcHAw2717N9uzZw9btGgRq1WrFqtevbrCiGZ+2tQX1BFqKBVVHUOspKQkZmVlpfXjFCdqKOlZcHCwwqhCfomJiaxSpUosODhYZZ+YypmcmFGs58+fs7Zt2zKJRMLMzc1VeuA6derEpk+fzvt4MTExrEqVKkwqlSr8ValShe3Zs0ewHGKmbjCmWllWrih3796d94tB7Fz1CRMmsAYNGgj+0DZo0IBNmDBBsKxiGnUF9fTNnz9fcPpVZGSk4HOUyWQq068kEgmTSqUqa4Xybxeq1D979oz17NmTWVpasoiICMHzKjM1NRU1+qHJj6W6H0x1NJ2epEzTqZ6a9kb379+fdy2RXGJiIu+ase7du7ORI0cKHuft7c3b06fNqPCZM2fYiBEjmJmZGXN0dGRLly5lhoaGaj9n2pxTbKUgMzOT+ztw4AArW7YsGzhwIMvIyFDYl9+OHTuYoaEhW7lypcI05tzcXLZixQpmZGTEtm/fzlt+5XWUmtqyZQtzdnZmpqambMCAASwmJoZ9+vRJo+/EcuXKscmTJxd6qp8uHD9+nLm7uzMTExMmkUjYlClTFEa25W7fvs3Kly/PmjdvzrZt28YSExNZUlISi4qKYj/++CMrX7487zS5kJAQVrFiRcHf0YoVK7KQkBDesmlT6WWs8JVsOXd3dyaRSFj9+vVZnz59WN++fZm9vT2TSCSCs0H+/vtvNnLkSGZhYcGMjY3ZoEGD1HYoqePp6ckeP34s6tiCbN++nRkbG6usr3379i1r1aoVq1evnuA1zT/1ztjYmLm5ubH9+/errJvWBbEdCIx9Ge3q1q2bwm+jVCpl3bp1U9sRLba+IO+YFPrbtm2bTusKEomEbdq0ie3Zs4ft2bOHlStXjq1fv567Lf8rrFu3bmm0/q4ko6h3evbp0yd07doVZ86cEczufvToURgZGSkcV1DUJKFIZADQpEkT+Pj4wMfHh/fYNWvWYP369bzRqDIzM2FmZqYSoe/169cwMzMTTLCYlZWFQ4cOKeRD6dq1q2AiOwAoU6YMBg0ahKFDh8LZ2ZmLTmNkZISkpCSFLNH5CSUbVaacv8fY2BjXrl3jwqgqu337NhwcHFSiA2mTYE05p1VMTAw6derEhTAXymklNknirFmzEBkZifj4eIXkccCXELstWrTAsGHDMH/+fG672BwzUVFR8PX1RdOmTSGTyQqVg0ib97eupaSkICwsDJs3b0ZaWprKfqlUivnz58PMzIz3+Ldv32L27Nlqy7plyxbMnj0bWVlZmDlzJkaNGiWYyDIlJQUfPnxAs2bNePd/+vQJT548UXk9Tpw4gS5dumDSpEmYMmUK935MS0tDSEgIli9fjiNHjiiEwAb+L4Fzz5494e/vz2Wxv3nzJkJCQgQTOCtfg6ioKI0T3Io9p3IybQsLCyQlJXH5lYTeN/KEs3IsX+4P+W2+VARiE/FqG/Xu3r17CA8PR0REBD58+IDXr19j27ZtcHNz471/cSQM55OZmYktW7ZAJpMhISEBDRs2VElWeunSJXh6eiIlJUXh+jdo0ADh4eG8EVTlqQDOnj2LLl26cIl4b968ySVTPX78uMrvqNz9+/cxZswYHD58WOG1d3FxwZo1a2Bra6tyTHx8PAICAnD+/Hn4+PhgxowZhQ6VHR0dzZsnTCjVglQqRePGjeHt7Q13d3fesM7K4eGFkgE3a9YM0dHRXORIvqhn2ti4cSMmTJiA/fv3o2PHjnj37h1cXV3x/PlzxMXFoVq1airHjB07FlFRUbC2tsaIESPg4eGhUdRUTVIaSCQSLoqpnFQqhYmJCUaPHs37GsspR7rNLz09nXv96tatW2CobbH1BbFJsbVJqFwQdelZhKxevRqbNm3ChQsXCnVcSUINpWIgJru7NpWzkJAQhISEIDY2VuXLMSkpCZ07d4a/vz/8/f118wQF5I//v3v3bpX9jx8/RkREBMLDw5GVlYXBgwfD3d0dLVq0QGJiomBDSaw6depg8eLF6Nu3L+/+Xbt2wc/Pj7cyITbBmthGnVhv375FixYt8OjRIwwdOlThS3rz5s2oUaMG4uPjFZIgdu7cGePGjRP8MXr58iWaN2+ucl1MTU0RHByskgtEE8rJhpVlZGRgxIgRGn9J379/H+/fv4e9vb1GPwDv3r1DVFQUwsLCcPHiRbRs2RL9+/fnDdkq9ocI+JKvaerUqUhNTYWfnx9+/fVXnef5ym/dunWYOHEiPn36BAsLC0gkEmRmZsLIyAihoaFc2Hhl+/btg5eXF169eqWwvVKlSti4cSNv0mchmia4FXNOqVSKhg0bco3M5ORk2Nvbc9+hubm5uH79usr75uTJkxq9hnwJjsUk4vX398erV69w6NChQuX7UsYYw+HDhyGTybB3715UrlwZ/fr10zjVQUHEpkwAgNmzZ2Pq1KlcZ1h6erpCJTIxMREymUzwHImJiQq/h02aNFFbhpycHCxduhRRUVG4desWgC+58wYPHozJkycLJljPrzCVXl1UsvOnhXjw4AE2btyIrKws/PTTT7x5mxISEng7JJia8PBiK9iAdq8/8KWuERQUhD179mDWrFl4+vQp4uLiePNZyctqbW1dYHjxXbt2KdwW+ztaXB0Id+/exdixYwtVX9B3Umyx9u7dy7s9MzMTFy9eRFhYGCIiIvDzzz/ruWS6Qw2lUkKbypnYUSxdkWeUliccdHFx4W0o5RcbGwuZTIZdu3YhOzsbfn5+GDlyJOrVq6ezco0fPx4nT57ExYsXVfKpZGVloXnz5ujYsaPKj8fdu3dRu3ZtSCSSUpFgLT09HdOmTUN0dDQyMjIAfMm1MWDAACxYsEClzFKpFFKpFDNmzODN/yPUS+/u7o61a9eqZJ7XhNjeLPl7atKkSdy2UaNGISwsDADw3Xff4fDhw4KjW2fOnMHGjRuxc+dO1K5dGykpKYiLi0ObNm0K/RzU0aY3OjY2Fu3btxcccSrI48ePER0drVCxd3NzQ82aNdUeJ2ZUePv27QrJf0eNGsXt0yTBbWHPWZT5qQrr6dOnCAoKwqpVq1T2ic33pc6rV6/wxx9/IDw8HElJSQr7srOzcezYMcEkl4aGhvjtt99UvvfkI3HqCFUkC8q/Vhg5OTnIyckR7BgsDtpUsq9evYpevXrh4cOHqFu3LqKiouDq6or3799DKpXi/fv32LFjB/r06aP28e/evcv9lr579w49evRA//79FTr7mjRpgpo1a2Lx4sVcrh55Q/DgwYNcfiW+CrY2r7/ctGnTEBISAltbW8TFxan9nvH09NSoXqOrjsPiVpj6QlZWFvz8/AqdTF1bfDkes7Oz0atXL97GvNBvt7m5Oezt7eHn51eqG0kAKI/S1yInJ4cFBwezxo0bs3LlyjETExPWuHFjFhwcrBCkQVc+fPjAIiIiWLt27ZiRkRGTSqVs+fLlgoszhWRkZLDVq1ezH374gUkkEubg4KCzMoqdq6683mDAgAFq57TrQq1atbiIV4wxtnLlSsFwq/ndv3+fi0CVl5fH0tLSWFpamtrQqxKJhK1fv56VL1+e9enTR+U1E1r3IXYdhjZatmypsIj44MGDzNDQkG3evJldvnyZtWrVinl7e6scFxwczL777jtWo0YN5ufnxwUA0DSamDovX75koaGhCtu0WTOifF1btGihEhJdjMuXL7MePXoU+ridO3fyfg7XrVvHJBIJq1evHmvUqBGTSqVs6tSpWpdT3Tk1wfc5mTVrlkI0sPwpF9S5fv06W7VqFVu/fj1LT09njH0JLz9p0iRmbGwsuGBdbL6v/F68eMEuXrzILl26pPBdwGft2rUKYaHNzMxYixYtmJOTE3NycmJWVlZcrh1dEbsYXCaTMV9fXy7n1bRp01iZMmWYVCplzs7OvM/1woULCtdO+fssOztbbQAUfeNLC+Hp6VlgWgjGCh8e/uPHj2zixImsQYMGCpEBdfHdJkQ56lzZsmVZ8+bNVbZ/jQqK0Cd0bfz8/Fi5cuXYL7/8wsaPH88qV66sUcRgsXUFsTkevwbUUNIz+QJA5T9LS0vWokULLreLMrGhrPXtwoUL7JdffmEWFhasWbNmbNmyZezZs2caf0nfu3ePrV+/nq1evVrl/leuXNEoGlFhiFmgWZS5JoQon1M5qa4QMY0X+blSUlJYvXr1WMOGDUVFzREj/xf8gwcP2KxZs9iUKVME39sVK1ZkycnJ3G0fHx/Wr18/7vaJEyd4o0HKo4kpV1TFViby8vLYoUOH2M8//8zKlCmjEkVSm2hi2rzfjhw5wvz8/Nj06dO5Y27cuMF69+7NpFIpb2hhxhhbv349c3NzY0OGDOESTh4/fpw1adKEmZiY8EaHa9iwIZs5cyZ3Ozw8vFC5YcScU2xY4oKSVPOJiYlhZcqU4b4n6tSpw2JjY1nlypWZk5MTb0QzObH5vhj7kjS0Xbt2Kr8ZHTt2FAw7LDbJ5fnz59mBAwcUtkVGRjJbW1tWpUoV9ssvv7Ds7Gzec4p5n86fP5+ZmJiwzp07s4oVKzIfHx9mZWXFFi1axEJCQljNmjV5Ew2LTTJeXMSmhdAmPLw8tPqCBQu4cM8FHSP29RcbVGfEiBEF/nl5eaktc2EUlMesKIKciL02YjtXxNYVxOZ4/BpQQ0nPdu/ezfsXERHBxo4dy0xMTFh0dLTKcWJDWeeXl5fHLl68yLZv38527NjBEhIStErqxsfAwIBNmjRJ5Qdcky/puLg4ZmpqylVEjIyM2J9//qnT8gl5/fo1i4+PZxcuXCiwd7kkNJQ0PaeYxkv+YzIyMli3bt1YxYoVuWhL6hpKyjmNNCW2N8vExEQh/06jRo3YsmXLuNv379/njQYZFBTE6taty2rVqsX8/f253tnCNpRSU1O5nCZSqZQNHTqUHT16tFAjBQUR+9qLzbT+3//+lxkZGbEffviBlStXjpUrV44FBQWxSpUqsTlz5ghGiipXrpxCuXJzc5mRkRF7+vRpgWUVe06xYYnFXNOWLVuyCRMmsLdv37IlS5Zwo2fqQrDLic339fTpU1apUiVmb2/Pli1bxg4dOsQOHjzIlixZwuzt7VmVKlV4P99ik1y6uLgo5PtJTk5mhoaGbOTIkWzJkiXMysqKBQYGCj7H27dvs8zMTJaRkcHMzc1ZUlKSQhRB5Y4+Ozs77jv+4sWLTCqVKkQOPHDgALO2tlY5lybRDvkiQWojMjJSoz8+YqMzig0Pn/9xu3Xrxtq2bavRMdq8/mJIJBJma2vL+vbty/r06SP4pysFdVap67DSN7GdK2J/L8Q25rX5XJQW1FAqYVatWsWaN2+usl1sKGu52NhYVrt2bZWRkzp16mj0Q6+pLl26MHNzczZkyBB28OBBriGmyZd0+/btWc+ePdnjx4/Z69ev2ejRo3mTDRa3gvI2FYXiaigx9qWBHRAQwIyMjNjSpUvVNpQsLS1ZhQoV1P7xEdubZW9vz43CvnjxghkYGLBLly5x+4WykMudPHmSDRs2jJmamrJGjRoxAwMDdubMGbXXRx7OtlOnTszY2Jj17duXbd++Xe17XJueeuX3m7m5uUbvN7GZ1u3t7VlYWBhj7MuInEQiYZ07d+ammgnhe69p+j4Ve06xYYnFfJ7Kly/Phbj+9OkTMzAwUHlNhYjN9+Xv788cHR0FQws7OjryTm8Um+TSyspKoXI0ffp01qZNG+52dHS04PRC5dkSQrfzK1OmjELFr0yZMgrlfvToETMyMuI9lzZJscWwtLQU/KtQoQI3XZCP2LQQYsPDK5N3pKpLMcKY+Ndf7MiQfMSscePGbPny5ezVq1caP6d/O7GdK7qqKxQm/5K5uTmrUKGC2s9HaSZudTApMl27dsXMmTNVtqelpakNtmBoaIgXL17w7rt9+zZ69uyJFi1aIDQ0FPb29mCMISUlBStWrED37t2RnJwsatGtsiNHjuDhw4cIDw/HmDFjkJWVhYEDBwJAgYs2r169ilOnTqF69eoAgCVLlmDDhg0q0ZOKG2MMnp6eXFSl7Oxs+Pj4qEQvU47Uo62NGzdyi5tzc3MRERGhsrCTLxpR/uOE5D9O+XWSSCRYtGgRmjZtCm9vb8TGxgo+zty5cwUj16lz8eJFLipjkyZNsH79eowdO5ZbKDp+/Hi0bNlS5bhhw4Zh3LhxuH79OmJjY2Fvb48ffviB23/27Fk0bNhQ8LwdOnRAhw4dsGrVKmzZsgXh4eHo0KEDmjdvDjc3N/z6668qx9SoUQMNGjSAh4cHduzYwb03Bw8eLHiewMBAdOzYEd26dQPw5b3u7e0NT09P1K9fH//9739RvXp1zJkzR+VYxhg6d+7MBXP48OEDevXqpRIdMyEhQeH2nTt3uM+em5sbDAwMsHTpUtSpU0ewnMCXaEvOzs4AACcnJxgZGSEoKAiWlpZqjwNU32t871O+96jYc7q5uSEjIwNDhgxRCUv88uVLxMXF8Ybpl0gkePv2LYyNjbkoYO/evcObN28U7mdhYcH9/+bNG648hoaGMDEx0Ti4zPDhw1W2eXh4FHjc0aNHMXXqVJWgCwBgYmKCKVOmICQkBAsXLlTYV7NmTVy7do0Ls64sOTmZd5F9enq6wvWKi4uDq6srd/vHH3/Ew4cPeR/zxIkTBT4fZZ8+fVKITlemTBmF3zlDQ0O9pATQRHp6Ou/2p0+fYu7cuZDJZOjSpYvg8ep+M/IH2shvyJAhGDJkCBcefty4cfjw4QPy8vKQkpKicSTYCRMmqI3GJyf29Y+IiICNjQ2aNm3KG21PyJo1axAaGopdu3ZBJpNh2rRp6NGjB7y9vdG1a1eNAj0UxoULF/D69WvuexgANm3ahMDAQLx//x59+vTBypUrNYqYWNSU6xkAf12Dr54htq7A99tfkPr16yMtLQ0eHh7w8vLSedj5koCi3pUwycnJcHFxwdOnTxW2axPK2tfXFzdu3MDx48dV9jHG4OzsjAYNGmDlypW6eRL5HD16FDKZDLt370atWrXg5uYGNzc33pCnfLlGzM3NkZycrFE0Hn3Rd4hvQHzEJalUipo1a6rkwVJ3nLqcL4mJiejTpw8ePnzIm5tGbK4Y5WPNzc2RlJTENd6FIu3l5eUhMDAQ+/btg5WVFZYuXYr69etz+3/++We4urrC29tb47Jcu3YNYWFh2LJlC54/f66yv0KFCmjUqBE8PDwwcOBArjKtLt9XtWrVEBMTw+VCmjFjBuLi4nDmzBkAX6LFBQYGIiUlReVYsZHdCrqmQsQep01UMLHnlBMTllg5jxLf7fzvN6lUitjYWC5SVevWrREdHa3S4NBlRcHS0hKXLl1Sm+utWbNmXDRLuYkTJ+LYsWO4fPkyb0TPZs2awdnZGcuXL1fYZ2Njgz/++APt27dHTk4OLC0tERMTg86dOwP40sDv0KEDXr9+rZPnV9A1ffnyJbp06cL7XSPmOF16+/YtgoODsXz5cnz//fdYuHAhOnbsyHtfXf1mMA3Cw586dUqjc7Vv315lm9jXP38+JC8vL3h4eIiKAHv//n1ERERg06ZN+PTpE1JSUnQa+dDV1RUdO3ZEQEAAgC/Px9HRUaHDavTo0bwdVvqm7xDoYnM8Al8aoDKZDNu2bYOdnR2X9yt/R1NpRg2lEmb8+PG4c+cODhw4oLJdTChrAGjYsCEWLlyIXr168Z4zJiYG06ZNw7Vr17Quv5eXF5YvX64SIjo9PR2bN2+GTCZDcnIy74dN+ccP4K+M/Bt7LIqKmMaLPDy2UDjqV69eYf/+/Rg2bJjCduXwwIUtZ1paGpc4UbmBrOuEs7GxsfD19cX58+dVvswzMzPRqlUrrFixghvlyC87Oxs7d+5EWFgYzp8/j27dunGNJqF8X8bGxvjnn3+4MOVt27aFq6srN3p87949ODg44O3btzp5foBqbqrBgwdj2bJlKiMsyvmJlHO2BQQEYMqUKRr1SGpTVm3PWZiwxHFxcRqVK38eJW3y04hV0GcqLS0NNWrUQG5ursp2MUkuR48ejatXryI4OBi7d+9GZGQknjx5wo1ebtmyBcuWLcPFixdVyvLgwQPeMpYvX15wlFndNZXju6bq0gkU1Wshl5OTg1WrVmHBggWoXLky5s+fL5j4tyi9fv0amzZtQkREhEKy+PydAELXVejaaPP6f/z4kRsZOnv2rKiRoQcPHiAiIgIRERHIycnBzZs3ddpQ0qbD6t9OF435rKwsbN++HeHh4YiPj0efPn0gk8lKxAidNqihpGd8U3mAL5WzS5cu4c6dOzh9+jSaNm2qsD8tLQ2Ojo4wMDCAr68vl4n8xo0bWL16NT5//oyEhATeaSYWFhZITk4WTJKXmpqKRo0a6aSSpkllWSiJXnH++P1badN4KSxtR5TE9GYpjwzIWVhY4LvvvoO/vz9v3p6ffvoJHTt25E0oC3xJvHjixAn89ddfast9584dhIeHIzIyEo8fP8bgwYPh6emJTp06KYzi6bunHhCfm0psj6Q201rEnlP5tT1w4AAaN26sMpKki2mwmiSAZIypTUZaWAYGBrh16xbXgaAsLS0N9vb2vN+JYpJiv3jxAv369cPff/8NMzMzREZGKsxi6Ny5M1q2bImgoCCVY4U+iwBQpUoV+Pv7q/z+aXJN09PTVZLPXrlyRaMRC10m42SMYdOmTZg9ezZyc3MRGBgIb29vtaP1RS09PR0xMTEKnVaVKlWCubk5PD09MXToUMG8O3yNV21e//wKMzKUv4F15swZ9OzZEyNGjICrq6tG32GFURwdVl+jU6dOITAwEKdOncLLly9L1NIJMaihpGdCQ/MWFhawt7fH2LFjBb/c79+/jzFjxuDw4cMKP3wuLi5Ys2aN4A90QRVYXfbWa1NZLi2ZqItDVlYWjh8/LphA0sDAAPPmzVMZbdTm9dAnsb1Ze/bs4b1fRkYG4uPjuUaMcsI7GxsbHDp0SGGaXn43b95E165dBXvJleXl5eHQoUOQyWSIiYnB58+fFXr5temp7dixo0aNCL6ptfpUHNNaxCasVFepl5NIJAqv4ezZszF79mzBkdYHDx7A29sbR48e1aDkmimonJp0HolJip2ZmQkzMzOVRsDr169hZmamsj4OgErSWzn5Z3HRokUICgqCj4+PRuffsmULwsLCkJiYyNtB0rRpU4wcORJDhgwRtS6ysBo1aoQ7d+5g/PjxmDRpkmASZH1ON0pKSoKjo6PC9cnJycFff/0FmUyG06dPo3v37vD29oarq6vGIztiXv/8NB0Zyj9lb8SIEfDw8OCSnRaF4uiw0jexdQVtPX78GJGRkQgPD8f79++5NUv29vY6PU9xoIZSKZSens5lrq9bt26BrXW+KW356XI+t/IUqsIorkzUpcG6deuwb98+xMTEAPgyNe3777/nMq/fvHkT/v7+KiMk/v7+ePXqFQ4dOvRVXtPVq1dj06ZNuHDhgsJ2Y2NjXLt2Te3aDwcHB2RlZRXqfE+fPkVQUBDWr1+PnJwcbrs2PbVCo17AlwADW7duxcePHwU/v4XNtN69e3ds3bqVq3wGBQVh3LhxXCCDV69eoV27dirTU7SZ1iL2nGIJNbCBLwFAVq5cCcaYwutvbW2NSpUqYdOmTXBwcFA4Zv369fDz80ObNm1w8OBBnZQREDdFsKTavHkzFi9erDBNTFlsbCxkMhl27doFGxsb9O/fH/3791eZYXHu3DnIZDJER0fj06dP6NevH7y9vQU7InUh/+gGX4OjOGY88DWU8pMHVoqMjMTHjx8xfPhwzJ07V7Cxrw0xI0NSqRTW1tZo2rSp2kacrgIjadNhVVqIrSuIFR0djfDwcMTFxcHFxQUjRoxAjx49inWkVdeooaRnXl5eGt1PJpPp7Jz6nNImlUpRvnz5Anuu+HpspkyZgjVr1sDd3R3GxsbYunUrnJycsH37dq3LVdq1b98ekydP5irXyovdN2/ejNWrV+PcuXMKx+W/piYmJvjzzz+/qmv6zz//oHnz5ioRq7QJjpKRkYFx48bhyJEjMDIywtSpU+Hr64s5c+ZgyZIl+P777zF58mTeKHja9tTK5ebmYvXq1QgKCkL58uUxb948DBo0SOE+V69eRa9evfDw4UPUrVsXUVFRcHV1xfv37yGVSvH+/Xvs2LEDffr0UThOeRTSwsICiYmJBQbW0GZai9hz8k2rVCaRSLBz584C73fz5k1MmzYNMTExcHd3x7x582Btbc3tf/PmDXx9fREdHY3AwEAEBATg0aNH8PLywqVLl7B48WKMHDmywPMUhnIUPiGlYdH03bt30aRJE5Xn9OjRI0REREAmk+H9+/cYMGAA1q5dKxgYJb+srCyuonb69GnY2trCy8sLw4cPV7tGTYyS2GgtqKEkl5qaCm9vb8TFxeHFixeiAi2oI3ZkSOyIsFi6mlpYkomtK4glb+y6u7vzLv2Q0+WaVn2j8OB6JjaMpjZSU1P1ch45sSGid+3ahbCwMK7C5+HhgTZt2uDz58//qt4JMW7duqUQitjY2FihAdy8eXOMGzdO5Tjla+ru7v5VXdOsrCzeKQbdu3fH7Nmz0a1bN97gKIGBgdzUBWXTp0/HqVOnMHz4cBw6dAiTJ0/GoUOHkJ2djQMHDqitKAl9LgpTcdmyZQtmz56NrKwszJkzB6NGjeLtIfb394eDgwM2b96MzZs3o2fPnujevTs2btwI4EuAmEWLFqk0lJRp+j1VtWpVpKamolatWsjJyUFCQoJCxL63b9+qTXEg5py6mHL15MkTBAYGIjIyEi4uLkhMTOQNKW9hYYFNmzahf//+GD16NLZt24bU1FS0atUKV69e5RqIumRpaalRRbI0rNtMT09XCffevXt3bvRh5cqVcHV1hYGBAdauXavRY5qYmGD48OEYPnw4t15w3bp1mDNnDrp06aISFEkb9+/fx8CBA0vNwvSPHz9i586dkMlkOHfuHHr06IH9+/frvJEEAGvXroW1tTVq166NuLg4wUal8shQRESEzsuiTpUqVXD69GnBDqvt27frNHhEcRBbVxDL2toaEokEf/75p+B9JBIJNZSI5nx8fBAVFYW7d+9qFUazML755hu9TmkbNGiQqDUxDx8+VJgK1Lx5cxgaGuLJkydFUgkpTTIzMxUqw8o5s/Ly8nhzcXzt13TDhg0q03YAYObMmdi1axfq1asnGBxlxowZvI+5f/9+hIeHw9nZGWPHjoWdnR3q1auHZcuWFelzOXToEKZOnYrU1FT4+fnh119/VcndlZ/Y3FRiubq6YurUqdy0lnLlyim895KTkwvM4VRY2vQ0Z2ZmYsGCBVi5ciWaNGmC48eP805FVNaiRQs4ODjg+PHjMDU1hb+/f5F9lvLnJmKMcQ1dobDnJVVOTg5CQkJU3m9HjhzBhAkTMGbMGNStW1erc9SpUwdTp05FrVq1MH36dBw+fFirx1Mmn0amz/WefFFs83v8+LHKNvnazKioKNSuXRuenp6Ijo4u0nrGsGHDdJ7zqCjposOqpBJbVxDr3r17OnuskooaSnqm7wRrwJdFyBEREQpT2saMGVMk06+0Kf/nz59Vph4ZGhqqhL79GolNIPlvv6aaRpFUVrVqVZw9exZjxozBtGnTeIOjCE0jePLkCTcl6Ntvv4WxsbHOp1zlFx8fj4CAAJw/fx4+Pj44duyYRp0cr1+/hpWVFQDAzMwMpqamChWBChUq8E6Dk0gkohIPzp8/H/369UOHDh24aS3533symQxdu3blPVbsOcUKCQlBcHAwrKyssHXrVvTu3Vuj47Zu3QpfX180adIEN27cQFhYGLp16wYfHx8sWrSIWwegK8qjkwYGBmjZsqVOkoPrmtA0yMzMTFy7dg2GhoYqn8XTp09DJpOhWbNmsLe3x9ChQ7kkyYURFxcHmUyGnTt3wsDAAAMGDChU7jRNFMcqhdDQ0ALvk396KAC0bNkS1tbWmDBhApd8W75OMD/ltADa0PfIEBEmtq4gVmlK4isWrVEqZkWdYA340tMWFBTETb+Kj49HmzZtkJ2drfPpV7oMEQ2ohokGdLewszQRm0Dy335NtYkiKVfY4CgGBgZ49uyZYM4nXZNKpTAxMcHo0aPVhp5WntogNjeVNokHAXHrsLQ9Z2HJr6mzs7Pa78D8nws3NzccPnwYCxYswPjx47nt586dg6enJxhjiIyMRKtWrXRSRj6FTcSrT0KRK+WfRXUJKD98+ICoqCjIZDLEx8fj8+fPWLp0Kby8vFRy8sk9fPiQi6yWmpqK1q1bw9vbGwMGDFA70iqWNoGK9ElsWgDy7yC2riBWaUriKxY1lIpZUSdYA4AyZcogNTVVYbqGiYkJbt26VaKmX+kqe/m/kdgEknRNda+gSr2crhqf+s60XhzvGX2fU8wi8jZt2iAyMpI3UmJ2djYCAgLw+++/K0Q71LWS3FDSlf/9738ICwvDH3/8gYyMDHTp0gV79+5VuE+XLl1w4sQJVKlSBcOGDYOXl5dgD7qu8HU68dFlp5O+o0GS0k9sXUGsryGJLzWUioE+E6wBqj3gQNH3ghPdu3v3LsaOHVuoBJJE90pi4/Px48cq61ZKYjlLs7y8vAK/n0+dOoX27dsXWRlKy/f2y5cvce/ePUgkEtja2orKjfP582fExMRAJpOpNJR++ukneHt7o2fPnnoLSiOVSjFgwIACp1fq8vMkJhqkl5cXli9fLjgSR/799FlX+BqS+FJDSc/0nWAN+PdPv/raiEkgSf6dnj17hgULFmDDhg2FzvlESj7ldT9FPXqprevXr2PMmDH4+++/FbZ36NABv//+e5GP+hSl4kjerXxO5RFFvoaSgYEBnj59WuKTjJOip4+6wteQxJeCOeiZ2DCa2hg+fLjKNg8PD509Pil6muSLAUpOhYnojrq8TYsXL8b333+v07xrpORQjs5Vkr+3nz17hg4dOqBKlSpYunQp7O3twRhDSkoKNmzYgHbt2uHatWultgJfWqK6Ud/3103fdYXiiHaqb9RQ0rPiCKNJU2tKP13kiyGlk7q8TQcPHtRrgkuiX6Xpuzs0NBQ2Njb4+++/FRaRu7q6YsyYMWjbti1CQ0OxcOHCYiylePLE7PokNhpkaWnUEd3Td11Bm2inpQVNvSOEkBLMxsYGYWFhcHZ2xt27d2FnZ4cJEyYUed4mQgrD0dERU6dOxYABA3j3R0VFISQkBAkJCXoumW7ExsZi1KhRSEhIUInel5mZidatW2Pt2rUa5eLSlJiALFKpFOXLly+wsVSap0KRkkdMtNPSghpKhBBSghkZGeH+/fuoXr06AKBcuXKIj49Hw4YNi7lkhPwfS0tLXLp0iTcqIADcvn0bzZo1Q0ZGhn4LpiO9e/eGk5MTJk+ezLt/xYoVOHHiBP766y+dnVNMQBapVIply5YVOLLANyWfEKKKGkqEEFKC6TtvEyFiFBREIC0tDTVq1Ci1ya6tra1x+PBh1K9fn3f/zZs30bVrVzx48EDPJVNUHEEnCPk3ozVKhBBSgjHG4OnpyU2/yc7Oho+PT4mNfEa+Xm/fvlVJcin35s2bUh1o4Pnz5zAyMhLcb2hoiBcvXuixRPxofRIhukUNJUIIKcGUp8iU5Mhn5OvFGEO9evXU7i/NlfgaNWrg6tWrglMLk5OTUa1aNT2XSlVpbowSUhLR1DtCCCGEaEUo1YWy0hqlcfz48Th58iQuXryoMmqWlZWF5s2bo2PHjlixYkUxlZAQUhSooUQIIYQQokZaWhocHR1hYGAAX19ffPfdd5BIJLhx4wZWr16Nz58/IyEhAVWrVi3uohJCdIgaSoQQQgjRilQqLXBqnUQiKbXBHADg/v37GDNmDA4fPsxNcZNIJHBxccGaNWtga2tbvAUkhOgcNZQIIYQQopU9e/YI7jt79ixWrlwJxhiysrL0WKqikZ6ejtu3b4Mxhrp166JChQrFXSRCSBGhhhIhhBBCdO7mzZuYNm0aYmJi4O7ujnnz5sHa2rq4i0UIIRqTFncBCCGEEPLv8eTJE/zyyy9o1KgRcnNzkZiYiMjISGokEUJKHWooEUIIIURrmZmZCAgIgJ2dHa5fv47jx48jJiYGDRs2LO6iEUKIKJRHiRBCCCFaCQkJQXBwMKysrLB161b07t27uItECCFaozVKhBBCCNGKVCqFiYkJnJ2dYWBgIHi/Xbt26bFUhBCiHRpRIoQQQohWhg0bVmB4cEIIKW1oRIkQQgghhBBClFAwB0IIIYQQQghRQg0lQgghhBBCCFFCDSVCCCGEEEIIUUINJUIIIYQQQghRQg0lQgghhBBCCFFCDSVCCCE69ezZM0ycOBF2dnYwNjZG1apV0bZtW6xduxYfPnwo7uLByckJEokEUVFRCtuXLVsGW1vb4ikUIYSQEofyKBFCCNGZu3fvok2bNrC0tMSCBQvg4OCA3Nxc3Lp1CzKZDNWrV8dPP/1U3MWEsbExZs6cif79+8PIyKi4i0MIIaQEohElQgghOjN27FgYGhri0qVLGDBgAOrXrw8HBwf0798f+/fvR69evbj7ZmZmYtSoUfjmm29gYWGBTp06ISkpids/Z84cNGnSBH/88QdsbW1Rvnx5DBo0CG/fvuXuwxhDSEgIvv32W5iYmKBx48bYsWNHgeUcPHgwMjMzsWHDBsH73LlzB71790bVqlVhZmaGH3/8EceOHVO4j62tLebPn49hw4bBzMwMNjY22LNnD168eIHevXvDzMwMDg4OuHTpksJxZ8+eRfv27WFiYoJatWphwoQJeP/+fYHlJoQQoj/UUCKEEKITr169wpEjRzBu3DiYmpry3kcikQD40sDp0aMHnj17hgMHDuDy5ctwdHRE586d8fr1a+7+d+7cwe7du7Fv3z7s27cPcXFxWLRoEbd/5syZCA8Px++//47r169j8uTJ8PDwQFxcnNqyWlhYYPr06fjtt98EGyjv3r1D9+7dcezYMVy5cgUuLi7o1asXHjx4oHC/0NBQtGnTBleuXEGPHj0wdOhQDBs2DB4eHkhISICdnR2GDRsGeX73q1evwsXFBf369UNycjK2bduGM2fOwNfXt+CLTAghRH8YIYQQogPnz59nANiuXbsUtleqVImZmpoyU1NT5u/vzxhj7Pjx48zCwoJlZ2cr3LdOnTps3bp1jDHGAgMDWbly5dibN2+4/VOmTGEtWrRgjDH27t07ZmxszM6ePavwGN7e3mzw4MGC5ezQoQObOHEiy87OZjY2Nuy3335jjDEWGhrKbGxs1D7HBg0asJUrV3K3bWxsmIeHB3f76dOnDACbNWsWt+3cuXMMAHv69CljjLGhQ4eyUaNGKTzu6dOnmVQqZVlZWWrPTwghRH9ojRIhhBCdko8aycXHxyMvLw/u7u74+PEjAODy5ct49+4dKlWqpHDfrKws3Llzh7tta2sLc3Nz7na1atXw/PlzAEBKSgqys7PRpUsXhcfIyclB06ZNCyxn2bJl8dtvv8HX1xdjxoxR2f/+/XvMnTsX+/btw5MnT5Cbm4usrCyVEaVGjRpx/1etWhUA4ODgoLLt+fPnsLKywuXLl3H79m1s2bKFuw9jDHl5eUhNTUX9+vULLDshhJCiRw0lQgghOmFnZweJRIKbN28qbP/2228BACYmJty2vLw8VKtWDSdPnlR5HEtLS+5/5UALEokEeXl53GMAwP79+1GjRg2F+5UtW1ajMnt4eGDx4sWYP3++SsS7KVOm4PDhw1i8eDHs7OxgYmICNzc35OTkKNwvfxnljUS+bfnLPXr0aEyYMEGlPNbW1hqVmxBCSNGjhhIhhBCdqFSpErp06YJVq1Zh/PjxguuUAMDR0RHPnj2DoaGh6JDcDRo0QNmyZfHgwQN06NBB1GNIpVIsXLgQ/fr1UxlVOn36NDw9PdG3b18AX9Ys3bt3T9R58nN0dMT169dhZ2en9WMRQggpOhTMgRBCiM6sWbMGubm5aNasGbZt24YbN27gf//7HzZv3oybN2/CwMAAAODs7IxWrVqhT58+OHz4MO7du4ezZ89i5syZKhHihJibm8PPzw+TJ09GZGQk7ty5gytXrmD16tWIjIzUuMw9evRAixYtsG7dOoXtdnZ22LVrFxITE5GUlIQhQ4Zwo0LaCAgIwLlz5zBu3DgkJibin3/+wd69ezF+/HitH5sQQoju0IgSIYQQnalTpw6uXLmCBQsWYNq0aXj06BHKli2LBg0awM/PD2PHjgXwZTragQMHMGPGDHh5eeHFixewsrJC+/btuTU9mpg3bx6++eYbLFy4EHfv3oWlpSUcHR0xffr0QpU7ODgYrVu3VtgWGhoKLy8vtG7dGpUrV0ZAQADevHlTqMfl06hRI8TFxWHGjBlo164dGGOoU6cOBg4cqPVjE0II0R0JY/8/XikhhBBCCCGEEAA09Y4QQgghhBBCVFBDiRBCCCGEEEKUUEOJEEIIIYQQQpRQQ4kQQgghhBBClFBDiRBCCCGEEEKUUEOJEEIIIYQQQpRQQ4kQQgghhBBClFBDiRBCCCGEEEKUUEOJEEIIIYQQQpRQQ4kQQgghhBBClFBDiRBCCCGEEEKU/D8ZCxheeAYxQgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(novel_df[\"name\"].head(50), novel_df[\"indra statements\"].head(50))\n",
+    "plt.xlabel(\"Gene Name\")\n",
+    "plt.ylabel(\"Number of Indra Statements\")\n",
+    "plt.title(\"Top 50 Database-Absent Genes Reported in Journal\")\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "47082e3d-e440-4015-8043-2ce8371ca69d",
+   "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>name</th>\n",
+       "      <th>gene_id</th>\n",
+       "      <th>indra statements</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>MUC16</td>\n",
+       "      <td>hgnc:15582</td>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>CDH1</td>\n",
+       "      <td>hgnc:1748</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>PDCD1</td>\n",
+       "      <td>hgnc:8760</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>SRC</td>\n",
+       "      <td>hgnc:11283</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>TACC3</td>\n",
+       "      <td>hgnc:11524</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>PPARG</td>\n",
+       "      <td>hgnc:9236</td>\n",
+       "      <td>46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>EGF</td>\n",
+       "      <td>hgnc:3229</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>CISH</td>\n",
+       "      <td>hgnc:1984</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>FGF2</td>\n",
+       "      <td>hgnc:3676</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>BAX</td>\n",
+       "      <td>hgnc:959</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     name     gene_id  indra statements\n",
+       "18  MUC16  hgnc:15582                89\n",
+       "22   CDH1   hgnc:1748                75\n",
+       "34  PDCD1   hgnc:8760                51\n",
+       "40    SRC  hgnc:11283                46\n",
+       "41  TACC3  hgnc:11524                46\n",
+       "43  PPARG   hgnc:9236                46\n",
+       "49    EGF   hgnc:3229                40\n",
+       "50   CISH   hgnc:1984                40\n",
+       "58   FGF2   hgnc:3676                37\n",
+       "59    BAX    hgnc:959                37"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "novel_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a1ceedb-ebb3-45a0-b762-581e38541972",
+   "metadata": {},
+   "source": [
+    "## Display INDRA Statements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3b8aa1c-959d-4d06-9113-e7283a70caba",
+   "metadata": {},
+   "source": [
+    "Wrote a cypher query to get the indra statements for a given gene, then use indra.assemblers.html to create an html page with the INDRA statements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e9c49116-95c5-4ca3-b433-55bfe2156ae6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from indra.assemblers.html import HtmlAssembler\n",
+    "import json\n",
+    "from indra.statements import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "20c86f28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_statements():\n",
+    "    # gets a list of the top 6 gene ids and names \n",
+    "    gene_ids = novel_df.head(6)[\"gene_id\"]\n",
+    "    gene_names = novel_df.head(6)[\"name\"]\n",
+    "    \n",
+    "    # initializes empty lists\n",
+    "    string_list =  []\n",
+    "    df_list = []\n",
+    "\n",
+    "    # for loop to iterate through each gene \n",
+    "    for id, name in zip(gene_ids, gene_names):\n",
+    "        # cypher to get the indra statements, uses f-string formatting for query to efficently test each gene\n",
+    "        indra_cypher = f\"MATCH p3= (gene1:BioEntity)-[r:indra_rel]->(gene2:BioEntity) MATCH p4=(e:Evidence)-[:has_citation]->(pub:Publication)-[]->(j:Journal) WHERE gene1.id <> gene2.id AND e.stmt_hash = r.stmt_hash AND j.id='nlm:8800805' AND (gene1.id = '{id}' OR gene2.id ='{id}') unwind [gene1, gene2] as gene RETURN gene.name, gene.id, r.stmt_json\"\n",
+    "        indra_results = client.query_tx(indra_cypher)\n",
+    "        \n",
+    "        # saves result in a dataframe that contains the name, id and indra statements in string json form\n",
+    "        indra_df = pd.DataFrame(indra_results, columns=[\"name\", \"gene_id\", \"indra statements\"])\n",
+    "        # the query returns \"double\" the results due to how it was written, so the dataframe needs to be filtered for the correct gene \n",
+    "        filtered_indra = indra_df[indra_df[\"name\"] == name]\n",
+    "        filtered_indra = filtered_indra.reset_index()\n",
+    "\n",
+    "        # returns a list of data frames and a nested list of indra statements as json strings \n",
+    "        df_list.append(filtered_indra)\n",
+    "        json_strings = list(filtered_indra[\"indra statements\"].values)\n",
+    "        string_list.append(json_strings)\n",
+    "        \n",
+    "    return df_list, string_list, gene_names\n",
+    "df_list, string_list, gene_names = get_statements()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "bdb4c766",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MUC16\n",
+      "CDH1\n",
+      "PDCD1\n",
+      "SRC\n",
+      "TACC3\n",
+      "PPARG\n"
+     ]
+    }
+   ],
+   "source": [
+    "# iterates through the gene name and json strings for each gene \n",
+    "for genes, json_strings in zip(gene_names, string_list):\n",
+    "    stmt_jsons = []\n",
+    "    # iterates through the individual json string within the statements for each gene \n",
+    "    # and converts it to an INDRA statement object\n",
+    "    for stmt_json_string in json_strings:\n",
+    "        stmt_jsons.append(json.loads(stmt_json_string))\n",
+    "    stmts = stmts_from_json(json_in=stmt_jsons)\n",
+    "\n",
+    "    # uses HtmlAssembler to get html pages of INDRA statements for each gene \n",
+    "    ha = HtmlAssembler(stmts, title='Statements for %s' % genes, db_rest_url='https://db.indra.bio')\n",
+    "    ha.save_model('%s_statements.html' % genes)\n",
+    "    print(genes)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}