anatomical_changes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from patsy import dmatrices, Poly\n",
    "\n",
    "from neuroCombat import neuroCombat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>site</th>\n",
       "      <th>participant_id</th>\n",
       "      <th>DX_GROUP</th>\n",
       "      <th>DSM_IV_TR</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>HANDEDNESS_CATEGORY</th>\n",
       "      <th>HANDEDNESS_SCORES</th>\n",
       "      <th>FIQ</th>\n",
       "      <th>VIQ</th>\n",
       "      <th>...</th>\n",
       "      <th>WISC_IV_BLK_DSN_SCALED</th>\n",
       "      <th>WISC_IV_PIC_CON_SCALED</th>\n",
       "      <th>WISC_IV_MATRIX_SCALED</th>\n",
       "      <th>WISC_IV_DIGIT_SPAN_SCALED</th>\n",
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       "      <th>WISC_IV_CODING_SCALED</th>\n",
       "      <th>WISC_IV_SYM_SCALED</th>\n",
       "      <th>EYE_STATUS_AT_SCAN</th>\n",
       "      <th>AGE_AT_MPRAGE</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CALTECH</td>\n",
       "      <td>51456</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>55.40</td>\n",
       "      <td>Male</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CALTECH</td>\n",
       "      <td>51457</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>22.90</td>\n",
       "      <td>Male</td>\n",
       "      <td>Ambi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CALTECH</td>\n",
       "      <td>51458</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>39.20</td>\n",
       "      <td>Male</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CALTECH</td>\n",
       "      <td>51459</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.80</td>\n",
       "      <td>Male</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CALTECH</td>\n",
       "      <td>51460</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>34.60</td>\n",
       "      <td>Female</td>\n",
       "      <td>Ambi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1107</th>\n",
       "      <td>YALE</td>\n",
       "      <td>50624</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>11.08</td>\n",
       "      <td>Female</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1108</th>\n",
       "      <td>YALE</td>\n",
       "      <td>50625</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>YALE</td>\n",
       "      <td>50626</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>11.08</td>\n",
       "      <td>Male</td>\n",
       "      <td>L</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>YALE</td>\n",
       "      <td>50627</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9.50</td>\n",
       "      <td>Female</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>YALE</td>\n",
       "      <td>50628</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>14.42</td>\n",
       "      <td>Male</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1112 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         site  participant_id  DX_GROUP  DSM_IV_TR    age     sex  \\\n",
       "0     CALTECH           51456         1          4  55.40    Male   \n",
       "1     CALTECH           51457         1          4  22.90    Male   \n",
       "2     CALTECH           51458         1          1  39.20    Male   \n",
       "3     CALTECH           51459         1          1  22.80    Male   \n",
       "4     CALTECH           51460         1          1  34.60  Female   \n",
       "...       ...             ...       ...        ...    ...     ...   \n",
       "1107     YALE           50624         1          3  11.08  Female   \n",
       "1108     YALE           50625         1          3   7.00    Male   \n",
       "1109     YALE           50626         1          3  11.08    Male   \n",
       "1110     YALE           50627         1          3   9.50  Female   \n",
       "1111     YALE           50628         1          3  14.42    Male   \n",
       "\n",
       "     HANDEDNESS_CATEGORY  HANDEDNESS_SCORES    FIQ    VIQ  ...  \\\n",
       "0                      R                NaN  126.0  118.0  ...   \n",
       "1                   Ambi                NaN  107.0  119.0  ...   \n",
       "2                      R                NaN   93.0   80.0  ...   \n",
       "3                      R                NaN  106.0   94.0  ...   \n",
       "4                   Ambi                NaN  133.0  135.0  ...   \n",
       "...                  ...                ...    ...    ...  ...   \n",
       "1107                   R                NaN   90.0   91.0  ...   \n",
       "1108                   L                NaN   99.0   90.0  ...   \n",
       "1109                   L                NaN   61.0   66.0  ...   \n",
       "1110                   R                NaN   88.0  103.0  ...   \n",
       "1111                   R                NaN   77.0   72.0  ...   \n",
       "\n",
       "      WISC_IV_BLK_DSN_SCALED WISC_IV_PIC_CON_SCALED WISC_IV_MATRIX_SCALED  \\\n",
       "0                        NaN                    NaN                   NaN   \n",
       "1                        NaN                    NaN                   NaN   \n",
       "2                        NaN                    NaN                   NaN   \n",
       "3                        NaN                    NaN                   NaN   \n",
       "4                        NaN                    NaN                   NaN   \n",
       "...                      ...                    ...                   ...   \n",
       "1107                     NaN                    NaN                   NaN   \n",
       "1108                     NaN                    NaN                   NaN   \n",
       "1109                     NaN                    NaN                   NaN   \n",
       "1110                     NaN                    NaN                   NaN   \n",
       "1111                     NaN                    NaN                   NaN   \n",
       "\n",
       "     WISC_IV_DIGIT_SPAN_SCALED  WISC_IV_LET_NUM_SCALED  WISC_IV_CODING_SCALED  \\\n",
       "0                          NaN                     NaN                    NaN   \n",
       "1                          NaN                     NaN                    NaN   \n",
       "2                          NaN                     NaN                    NaN   \n",
       "3                          NaN                     NaN                    NaN   \n",
       "4                          NaN                     NaN                    NaN   \n",
       "...                        ...                     ...                    ...   \n",
       "1107                       NaN                     NaN                    NaN   \n",
       "1108                       NaN                     NaN                    NaN   \n",
       "1109                       NaN                     NaN                    NaN   \n",
       "1110                       NaN                     NaN                    NaN   \n",
       "1111                       NaN                     NaN                    NaN   \n",
       "\n",
       "      WISC_IV_SYM_SCALED  EYE_STATUS_AT_SCAN  AGE_AT_MPRAGE  BMI  \n",
       "0                    NaN                   2            NaN  NaN  \n",
       "1                    NaN                   2            NaN  NaN  \n",
       "2                    NaN                   2            NaN  NaN  \n",
       "3                    NaN                   2            NaN  NaN  \n",
       "4                    NaN                   2            NaN  NaN  \n",
       "...                  ...                 ...            ...  ...  \n",
       "1107                 NaN                   1            NaN  NaN  \n",
       "1108                 NaN                   1            NaN  NaN  \n",
       "1109                 NaN                   1            NaN  NaN  \n",
       "1110                 NaN                   1            NaN  NaN  \n",
       "1111                 NaN                   1            NaN  NaN  \n",
       "\n",
       "[1112 rows x 74 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pheno_file = \"data/abide1/Phenotypic_V1_0b.csv\"\n",
    "pheno_table = pd.read_csv(pheno_file)\n",
    "\n",
    "# rename and recode columns in phenotypic table\n",
    "rename_dict = {\n",
    "  'SUB_ID': 'participant_id',\n",
    "  'SITE_ID': 'site',\n",
    "  'SEX': 'sex',\n",
    "  'AGE_AT_SCAN': 'age'\n",
    "}\n",
    "sex_dict = {\n",
    "  1: 'Male',\n",
    "  2: 'Female'\n",
    "}\n",
    "\n",
    "pheno_table.rename(columns=rename_dict, inplace=True)\n",
    "pheno_table['sex'] = pheno_table['sex'].map(sex_dict)\n",
    "pheno_table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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       "      <th></th>\n",
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       "      <th>Left-Lateral-Ventricle</th>\n",
       "      <th>Left-Inf-Lat-Vent</th>\n",
       "      <th>Left-Cerebellum-White-Matter</th>\n",
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       "      <th>BrainSegVol-to-eTIV</th>\n",
       "      <th>MaskVol-to-eTIV</th>\n",
       "      <th>lhSurfaceHoles</th>\n",
       "      <th>rhSurfaceHoles</th>\n",
       "      <th>SurfaceHoles</th>\n",
       "      <th>EstimatedTotalIntraCranialVol</th>\n",
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       "      <th>0</th>\n",
       "      <td>50002</td>\n",
       "      <td>6795.0</td>\n",
       "      <td>128.9</td>\n",
       "      <td>16091.9</td>\n",
       "      <td>69823.4</td>\n",
       "      <td>8246.9</td>\n",
       "      <td>4585.2</td>\n",
       "      <td>6206.6</td>\n",
       "      <td>2433.7</td>\n",
       "      <td>1111.8</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.162577e+06</td>\n",
       "      <td>1159704.0</td>\n",
       "      <td>1873287.0</td>\n",
       "      <td>0.739239</td>\n",
       "      <td>1.026209</td>\n",
       "      <td>46.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.825444e+06</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50003</td>\n",
       "      <td>7522.3</td>\n",
       "      <td>178.7</td>\n",
       "      <td>16025.3</td>\n",
       "      <td>67429.7</td>\n",
       "      <td>9688.5</td>\n",
       "      <td>3914.4</td>\n",
       "      <td>5586.8</td>\n",
       "      <td>2367.0</td>\n",
       "      <td>776.2</td>\n",
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       "      <td>1.103638e+06</td>\n",
       "      <td>1100432.0</td>\n",
       "      <td>1769263.0</td>\n",
       "      <td>0.731629</td>\n",
       "      <td>1.007296</td>\n",
       "      <td>107.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>1.756448e+06</td>\n",
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       "      <th>2</th>\n",
       "      <td>50004</td>\n",
       "      <td>7489.0</td>\n",
       "      <td>607.8</td>\n",
       "      <td>15851.3</td>\n",
       "      <td>70463.5</td>\n",
       "      <td>8066.5</td>\n",
       "      <td>3940.7</td>\n",
       "      <td>5397.6</td>\n",
       "      <td>2154.5</td>\n",
       "      <td>797.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1.162371e+06</td>\n",
       "      <td>1.146088e+06</td>\n",
       "      <td>1143487.0</td>\n",
       "      <td>1826136.0</td>\n",
       "      <td>0.783234</td>\n",
       "      <td>1.073058</td>\n",
       "      <td>31.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.701806e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50005</td>\n",
       "      <td>7643.5</td>\n",
       "      <td>361.1</td>\n",
       "      <td>16423.0</td>\n",
       "      <td>64430.1</td>\n",
       "      <td>7888.1</td>\n",
       "      <td>4108.0</td>\n",
       "      <td>5784.7</td>\n",
       "      <td>2331.7</td>\n",
       "      <td>790.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1.025767e+06</td>\n",
       "      <td>1.008729e+06</td>\n",
       "      <td>1005784.0</td>\n",
       "      <td>1636027.0</td>\n",
       "      <td>0.759772</td>\n",
       "      <td>1.045904</td>\n",
       "      <td>83.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1.564223e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50006</td>\n",
       "      <td>4173.8</td>\n",
       "      <td>268.8</td>\n",
       "      <td>12938.5</td>\n",
       "      <td>71976.6</td>\n",
       "      <td>8383.8</td>\n",
       "      <td>3776.1</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>2097.0</td>\n",
       "      <td>704.9</td>\n",
       "      <td>...</td>\n",
       "      <td>1.150942e+06</td>\n",
       "      <td>1.141858e+06</td>\n",
       "      <td>1138346.0</td>\n",
       "      <td>1699011.0</td>\n",
       "      <td>0.815125</td>\n",
       "      <td>1.051112</td>\n",
       "      <td>55.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.616394e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>51583</td>\n",
       "      <td>15427.7</td>\n",
       "      <td>565.5</td>\n",
       "      <td>14439.5</td>\n",
       "      <td>61665.9</td>\n",
       "      <td>8794.4</td>\n",
       "      <td>4750.6</td>\n",
       "      <td>6426.3</td>\n",
       "      <td>2361.2</td>\n",
       "      <td>1041.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.244557e+06</td>\n",
       "      <td>1.213413e+06</td>\n",
       "      <td>1210360.0</td>\n",
       "      <td>1935309.0</td>\n",
       "      <td>0.825391</td>\n",
       "      <td>1.144279</td>\n",
       "      <td>312.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>1.691291e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>51584</td>\n",
       "      <td>14100.3</td>\n",
       "      <td>378.8</td>\n",
       "      <td>15046.1</td>\n",
       "      <td>52082.5</td>\n",
       "      <td>8821.2</td>\n",
       "      <td>4304.7</td>\n",
       "      <td>5837.0</td>\n",
       "      <td>2129.6</td>\n",
       "      <td>1088.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.193655e+06</td>\n",
       "      <td>1.165903e+06</td>\n",
       "      <td>1162228.0</td>\n",
       "      <td>1754362.0</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>1.039918</td>\n",
       "      <td>260.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>1.687019e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>51585</td>\n",
       "      <td>11869.0</td>\n",
       "      <td>388.5</td>\n",
       "      <td>12212.7</td>\n",
       "      <td>58642.5</td>\n",
       "      <td>7883.2</td>\n",
       "      <td>3670.3</td>\n",
       "      <td>5239.3</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>814.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.100216e+06</td>\n",
       "      <td>1.075217e+06</td>\n",
       "      <td>1071602.0</td>\n",
       "      <td>1674198.0</td>\n",
       "      <td>0.780945</td>\n",
       "      <td>1.052622</td>\n",
       "      <td>193.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>1.590503e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>51606</td>\n",
       "      <td>8365.4</td>\n",
       "      <td>556.4</td>\n",
       "      <td>12518.6</td>\n",
       "      <td>46659.6</td>\n",
       "      <td>7313.2</td>\n",
       "      <td>3464.6</td>\n",
       "      <td>4685.3</td>\n",
       "      <td>1993.7</td>\n",
       "      <td>1321.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.056425e+06</td>\n",
       "      <td>1.037762e+06</td>\n",
       "      <td>1035171.0</td>\n",
       "      <td>1580106.0</td>\n",
       "      <td>0.809950</td>\n",
       "      <td>1.087467</td>\n",
       "      <td>42.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.453015e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>51607</td>\n",
       "      <td>9687.9</td>\n",
       "      <td>578.6</td>\n",
       "      <td>18671.6</td>\n",
       "      <td>64465.8</td>\n",
       "      <td>9048.9</td>\n",
       "      <td>4534.6</td>\n",
       "      <td>5714.0</td>\n",
       "      <td>2638.8</td>\n",
       "      <td>908.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.126065e+06</td>\n",
       "      <td>1.107100e+06</td>\n",
       "      <td>1103858.0</td>\n",
       "      <td>1775053.0</td>\n",
       "      <td>0.772302</td>\n",
       "      <td>1.060869</td>\n",
       "      <td>88.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>1.673207e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1085 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      participant_id  Left-Lateral-Ventricle  Left-Inf-Lat-Vent  \\\n",
       "0              50002                  6795.0              128.9   \n",
       "1              50003                  7522.3              178.7   \n",
       "2              50004                  7489.0              607.8   \n",
       "3              50005                  7643.5              361.1   \n",
       "4              50006                  4173.8              268.8   \n",
       "...              ...                     ...                ...   \n",
       "1080           51583                 15427.7              565.5   \n",
       "1081           51584                 14100.3              378.8   \n",
       "1082           51585                 11869.0              388.5   \n",
       "1083           51606                  8365.4              556.4   \n",
       "1084           51607                  9687.9              578.6   \n",
       "\n",
       "      Left-Cerebellum-White-Matter  Left-Cerebellum-Cortex  \\\n",
       "0                          16091.9                 69823.4   \n",
       "1                          16025.3                 67429.7   \n",
       "2                          15851.3                 70463.5   \n",
       "3                          16423.0                 64430.1   \n",
       "4                          12938.5                 71976.6   \n",
       "...                            ...                     ...   \n",
       "1080                       14439.5                 61665.9   \n",
       "1081                       15046.1                 52082.5   \n",
       "1082                       12212.7                 58642.5   \n",
       "1083                       12518.6                 46659.6   \n",
       "1084                       18671.6                 64465.8   \n",
       "\n",
       "      Left-Thalamus-Proper  Left-Caudate  Left-Putamen  Left-Pallidum  \\\n",
       "0                   8246.9        4585.2        6206.6         2433.7   \n",
       "1                   9688.5        3914.4        5586.8         2367.0   \n",
       "2                   8066.5        3940.7        5397.6         2154.5   \n",
       "3                   7888.1        4108.0        5784.7         2331.7   \n",
       "4                   8383.8        3776.1        5400.0         2097.0   \n",
       "...                    ...           ...           ...            ...   \n",
       "1080                8794.4        4750.6        6426.3         2361.2   \n",
       "1081                8821.2        4304.7        5837.0         2129.6   \n",
       "1082                7883.2        3670.3        5239.3         1890.0   \n",
       "1083                7313.2        3464.6        4685.3         1993.7   \n",
       "1084                9048.9        4534.6        5714.0         2638.8   \n",
       "\n",
       "      3rd-Ventricle  ...  SupraTentorialVol  SupraTentorialVolNotVent  \\\n",
       "0            1111.8  ...       1.178499e+06              1.162577e+06   \n",
       "1             776.2  ...       1.116107e+06              1.103638e+06   \n",
       "2             797.4  ...       1.162371e+06              1.146088e+06   \n",
       "3             790.4  ...       1.025767e+06              1.008729e+06   \n",
       "4             704.9  ...       1.150942e+06              1.141858e+06   \n",
       "...             ...  ...                ...                       ...   \n",
       "1080         1041.0  ...       1.244557e+06              1.213413e+06   \n",
       "1081         1088.6  ...       1.193655e+06              1.165903e+06   \n",
       "1082          814.2  ...       1.100216e+06              1.075217e+06   \n",
       "1083         1321.6  ...       1.056425e+06              1.037762e+06   \n",
       "1084          908.8  ...       1.126065e+06              1.107100e+06   \n",
       "\n",
       "      SupraTentorialVolNotVentVox    MaskVol  BrainSegVol-to-eTIV  \\\n",
       "0                       1159704.0  1873287.0             0.739239   \n",
       "1                       1100432.0  1769263.0             0.731629   \n",
       "2                       1143487.0  1826136.0             0.783234   \n",
       "3                       1005784.0  1636027.0             0.759772   \n",
       "4                       1138346.0  1699011.0             0.815125   \n",
       "...                           ...        ...                  ...   \n",
       "1080                    1210360.0  1935309.0             0.825391   \n",
       "1081                    1162228.0  1754362.0             0.785714   \n",
       "1082                    1071602.0  1674198.0             0.780945   \n",
       "1083                    1035171.0  1580106.0             0.809950   \n",
       "1084                    1103858.0  1775053.0             0.772302   \n",
       "\n",
       "      MaskVol-to-eTIV  lhSurfaceHoles  rhSurfaceHoles  SurfaceHoles  \\\n",
       "0            1.026209            46.0            30.0          76.0   \n",
       "1            1.007296           107.0           127.0         234.0   \n",
       "2            1.073058            31.0            38.0          69.0   \n",
       "3            1.045904            83.0            77.0         160.0   \n",
       "4            1.051112            55.0            47.0         102.0   \n",
       "...               ...             ...             ...           ...   \n",
       "1080         1.144279           312.0           334.0         646.0   \n",
       "1081         1.039918           260.0           254.0         514.0   \n",
       "1082         1.052622           193.0           228.0         421.0   \n",
       "1083         1.087467            42.0            34.0          76.0   \n",
       "1084         1.060869            88.0            69.0         157.0   \n",
       "\n",
       "      EstimatedTotalIntraCranialVol  \n",
       "0                      1.825444e+06  \n",
       "1                      1.756448e+06  \n",
       "2                      1.701806e+06  \n",
       "3                      1.564223e+06  \n",
       "4                      1.616394e+06  \n",
       "...                             ...  \n",
       "1080                   1.691291e+06  \n",
       "1081                   1.687019e+06  \n",
       "1082                   1.590503e+06  \n",
       "1083                   1.453015e+06  \n",
       "1084                   1.673207e+06  \n",
       "\n",
       "[1085 rows x 67 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aseg_file = \"data/abide1/fs-6.0.0.abide.aseg.csv\"\n",
    "aseg_table = pd.read_csv(aseg_file, sep='\\t')\n",
    "aseg_table.rename(columns = {aseg_table.columns[0]: 'participant_id'}, inplace=True)\n",
    "aseg_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "aseg_and_pheno = pheno_table.merge(aseg_table, on=\"participant_id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "make basic test on sex effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>BrainSegVolNotVent</td> <th>  R-squared:         </th> <td>   0.097</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                    <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.097</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>              <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   116.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Fri, 21 Jun 2024</td>  <th>  Prob (F-statistic):</th> <td>6.21e-26</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>16:24:03</td>      <th>  Log-Likelihood:    </th> <td> -14263.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>  1085</td>       <th>  AIC:               </th> <td>2.853e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>           <td>  1083</td>       <th>  BIC:               </th> <td>2.854e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>               <td>     1</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>nonrobust</td>     <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td> 1.124e+06</td> <td> 9766.284</td> <td>  115.114</td> <td> 0.000</td> <td> 1.11e+06</td> <td> 1.14e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sex[T.Male]</th> <td> 1.144e+05</td> <td> 1.06e+04</td> <td>   10.809</td> <td> 0.000</td> <td> 9.36e+04</td> <td> 1.35e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>61.080</td> <th>  Durbin-Watson:     </th> <td>   1.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 180.905</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.223</td> <th>  Prob(JB):          </th> <td>5.21e-40</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.950</td> <th>  Cond. No.          </th> <td>    5.01</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    & BrainSegVolNotVent & \\textbf{  R-squared:         } &     0.097   \\\\\n",
       "\\textbf{Model:}            &        OLS         & \\textbf{  Adj. R-squared:    } &     0.097   \\\\\n",
       "\\textbf{Method:}           &   Least Squares    & \\textbf{  F-statistic:       } &     116.8   \\\\\n",
       "\\textbf{Date:}             &  Fri, 21 Jun 2024  & \\textbf{  Prob (F-statistic):} &  6.21e-26   \\\\\n",
       "\\textbf{Time:}             &      16:24:03      & \\textbf{  Log-Likelihood:    } &   -14263.   \\\\\n",
       "\\textbf{No. Observations:} &         1085       & \\textbf{  AIC:               } & 2.853e+04   \\\\\n",
       "\\textbf{Df Residuals:}     &         1083       & \\textbf{  BIC:               } & 2.854e+04   \\\\\n",
       "\\textbf{Df Model:}         &            1       & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &     nonrobust      & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                     & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}   &    1.124e+06  &     9766.284     &   115.114  &         0.000        &     1.11e+06    &     1.14e+06     \\\\\n",
       "\\textbf{sex[T.Male]} &    1.144e+05  &     1.06e+04     &    10.809  &         0.000        &     9.36e+04    &     1.35e+05     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 61.080 & \\textbf{  Durbin-Watson:     } &    1.784  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &  180.905  \\\\\n",
       "\\textbf{Skew:}          & -0.223 & \\textbf{  Prob(JB):          } & 5.21e-40  \\\\\n",
       "\\textbf{Kurtosis:}      &  4.950 & \\textbf{  Cond. No.          } &     5.01  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:     BrainSegVolNotVent   R-squared:                       0.097\n",
       "Model:                            OLS   Adj. R-squared:                  0.097\n",
       "Method:                 Least Squares   F-statistic:                     116.8\n",
       "Date:                Fri, 21 Jun 2024   Prob (F-statistic):           6.21e-26\n",
       "Time:                        16:24:03   Log-Likelihood:                -14263.\n",
       "No. Observations:                1085   AIC:                         2.853e+04\n",
       "Df Residuals:                    1083   BIC:                         2.854e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "Intercept    1.124e+06   9766.284    115.114      0.000    1.11e+06    1.14e+06\n",
       "sex[T.Male]  1.144e+05   1.06e+04     10.809      0.000    9.36e+04    1.35e+05\n",
       "==============================================================================\n",
       "Omnibus:                       61.080   Durbin-Watson:                   1.784\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              180.905\n",
       "Skew:                          -0.223   Prob(JB):                     5.21e-40\n",
       "Kurtosis:                       4.950   Cond. No.                         5.01\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y, X = dmatrices('BrainSegVolNotVent ~ sex', data=aseg_and_pheno, return_type='dataframe')\n",
    "mod = sm.OLS(y, X)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# Create a box plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x='sex', y='BrainSegVolNotVent', data=aseg_and_pheno, palette=\"Set2\")\n",
    "\n",
    "plt.xlabel('Sex')\n",
    "plt.ylabel('BrainSegVolNotVent')\n",
    "plt.title('BrainSegVolNotVent by Sex with Predicted Values')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "use neuroCombat to harmonize across centers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[neuroCombat] Creating design matrix\n",
      "[neuroCombat] Standardizing data across features\n",
      "[neuroCombat] Fitting L/S model and finding priors\n",
      "[neuroCombat] Finding parametric adjustments\n",
      "[neuroCombat] Final adjustment of data\n"
     ]
    }
   ],
   "source": [
    "covars = aseg_and_pheno[['age']].copy()\n",
    "covars['sex'] = pd.factorize(aseg_and_pheno['sex'])[0]\n",
    "covars['center'] = pd.factorize(aseg_and_pheno['site'])[0]\n",
    "covars['age_2'] = covars['age']**2\n",
    "\n",
    "# include ICV as with only one column combat generates nan\n",
    "dat = aseg_and_pheno[['BrainSegVolNotVent', 'EstimatedTotalIntraCranialVol']].T\n",
    "\n",
    "res_combat = neuroCombat(dat=dat,\n",
    "                         covars=covars,\n",
    "                         batch_col='center',\n",
    "                         categorical_cols='sex',\n",
    "                         continuous_cols=['age', 'age_2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>BrainSegVolNotVent2</td> <th>  R-squared:         </th> <td>   0.108</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                    <td>OLS</td>         <th>  Adj. R-squared:    </th> <td>   0.106</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>              <td>Least Squares</td>    <th>  F-statistic:       </th> <td>   43.70</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Fri, 21 Jun 2024</td>   <th>  Prob (F-statistic):</th> <td>1.18e-26</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>16:39:52</td>       <th>  Log-Likelihood:    </th> <td> -14155.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>  1085</td>        <th>  AIC:               </th> <td>2.832e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>           <td>  1081</td>        <th>  BIC:               </th> <td>2.834e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>               <td>     3</td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>nonrobust</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td> 1.093e+06</td> <td>  1.8e+04</td> <td>   60.693</td> <td> 0.000</td> <td> 1.06e+06</td> <td> 1.13e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sex[T.Male]</th> <td> 1.015e+05</td> <td> 9615.059</td> <td>   10.558</td> <td> 0.000</td> <td> 8.27e+04</td> <td>  1.2e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>         <td> 4959.2147</td> <td> 1544.669</td> <td>    3.211</td> <td> 0.001</td> <td> 1928.325</td> <td> 7990.105</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(age ** 2)</th> <td> -121.4166</td> <td>   30.758</td> <td>   -3.947</td> <td> 0.000</td> <td> -181.770</td> <td>  -61.064</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>24.627</td> <th>  Durbin-Watson:     </th> <td>   2.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  48.753</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.079</td> <th>  Prob(JB):          </th> <td>2.59e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.026</td> <th>  Cond. No.          </th> <td>2.96e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.96e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    & BrainSegVolNotVent2 & \\textbf{  R-squared:         } &     0.108   \\\\\n",
       "\\textbf{Model:}            &         OLS         & \\textbf{  Adj. R-squared:    } &     0.106   \\\\\n",
       "\\textbf{Method:}           &    Least Squares    & \\textbf{  F-statistic:       } &     43.70   \\\\\n",
       "\\textbf{Date:}             &   Fri, 21 Jun 2024  & \\textbf{  Prob (F-statistic):} &  1.18e-26   \\\\\n",
       "\\textbf{Time:}             &       16:39:52      & \\textbf{  Log-Likelihood:    } &   -14155.   \\\\\n",
       "\\textbf{No. Observations:} &          1085       & \\textbf{  AIC:               } & 2.832e+04   \\\\\n",
       "\\textbf{Df Residuals:}     &          1081       & \\textbf{  BIC:               } & 2.834e+04   \\\\\n",
       "\\textbf{Df Model:}         &             3       & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &      nonrobust      & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                     & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}   &    1.093e+06  &      1.8e+04     &    60.693  &         0.000        &     1.06e+06    &     1.13e+06     \\\\\n",
       "\\textbf{sex[T.Male]} &    1.015e+05  &     9615.059     &    10.558  &         0.000        &     8.27e+04    &      1.2e+05     \\\\\n",
       "\\textbf{age}         &    4959.2147  &     1544.669     &     3.211  &         0.001        &     1928.325    &     7990.105     \\\\\n",
       "\\textbf{I(age ** 2)} &    -121.4166  &       30.758     &    -3.947  &         0.000        &     -181.770    &      -61.064     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 24.627 & \\textbf{  Durbin-Watson:     } &    2.038  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &   48.753  \\\\\n",
       "\\textbf{Skew:}          & -0.079 & \\textbf{  Prob(JB):          } & 2.59e-11  \\\\\n",
       "\\textbf{Kurtosis:}      &  4.026 & \\textbf{  Cond. No.          } & 2.96e+03  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 2.96e+03. This might indicate that there are \\newline\n",
       " strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             OLS Regression Results                            \n",
       "===============================================================================\n",
       "Dep. Variable:     BrainSegVolNotVent2   R-squared:                       0.108\n",
       "Model:                             OLS   Adj. R-squared:                  0.106\n",
       "Method:                  Least Squares   F-statistic:                     43.70\n",
       "Date:                 Fri, 21 Jun 2024   Prob (F-statistic):           1.18e-26\n",
       "Time:                         16:39:52   Log-Likelihood:                -14155.\n",
       "No. Observations:                 1085   AIC:                         2.832e+04\n",
       "Df Residuals:                     1081   BIC:                         2.834e+04\n",
       "Df Model:                            3                                         \n",
       "Covariance Type:             nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "Intercept    1.093e+06    1.8e+04     60.693      0.000    1.06e+06    1.13e+06\n",
       "sex[T.Male]  1.015e+05   9615.059     10.558      0.000    8.27e+04     1.2e+05\n",
       "age          4959.2147   1544.669      3.211      0.001    1928.325    7990.105\n",
       "I(age ** 2)  -121.4166     30.758     -3.947      0.000    -181.770     -61.064\n",
       "==============================================================================\n",
       "Omnibus:                       24.627   Durbin-Watson:                   2.038\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               48.753\n",
       "Skew:                          -0.079   Prob(JB):                     2.59e-11\n",
       "Kurtosis:                       4.026   Cond. No.                     2.96e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.96e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aseg_and_pheno['BrainSegVolNotVent2'] = res_combat['data'][0]\n",
    "y, X = dmatrices('BrainSegVolNotVent2 ~ sex + age + I(age**2)', data=aseg_and_pheno, return_type='dataframe')\n",
    "mod = sm.OLS(y, X)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vvvtob2+nvLx83jo0TeOee+7B4/Hw8ssv43K5+OEPf8gtt9xCR0cHHo/ngt/rmYizB+FDJUnShzIIr/1QN5lUZsGWtjanlclBH+2HukVgcQa5xR4++ScfY7B9BN+QH0mWKarO7vqI4XiCIAiCcHYvvvgi9ncFQnfddRfj4+N84xvf4HOf+xwA1dXV/Pmf/zl//Md/PCewePTRR3nkkUcA+JM/+RM2bdrEn/7pn3LHHXcA8PWvf51HH3109vGrVq1i1apVs19/5zvf4dlnn+X555/nq1/96ry17dy5k6amJiYmJjCZTAB897vf5bnnnuOXv/zlbLDzYRCBhXBFCvnCmMxnLjA2mo2EfOFLuKIrjyzLVDSUUtFQuthL+dApioIsyyJoEgRBuFxZrdmdg8V67XO0detWfvCDH8x+bbPZqK2t5eDBg/zv//2/Z29XFIVEIkEsFsM68zorV66cvb+goACAFStWzLktkUgQCoVwOp1Eo1Eee+wxXnzxRUZGRshkMsTjcQYGBhZc2+HDh4lEIni93jm3x+PxOSlWHwYRWAhXJIfXTiqZPuP9qUR6wTkNwtVDURQ6D/fQtKeN0Z5x9Hod9RtqWXH9UvLLRYGdIAjCZUWSLko60qVyKpB4N1VVeeyxx3jooYfmPd5sPt3m3WA43Vzm1AWvhW5TVRWAP/qjP+LVV1/lu9/9LrW1tVgsFh5++GFSqdSCa1NVlaKiInbt2jXvPrfb/cHe4HkSgYVwRapfV8Ox7U3EwnGsDsuc+2LhOLJepu4idJ4SrkyKorDzqb0cePkIiqLicNuIx9Ps/uV+mve2cd+Xb6dqheh8JQiCIFw8a9eupb29fV7AcaF2797N5z//eR588EEgW3PR19d31nWMjY2h1+uprKy8qGt5P6IrlHBFqlpRTuP1SxnrncA/MkUmrZBJK/hHphjrm2DF9UupWjG/oEm4OnQc6uGdl4/g9DqoaCjFU5RDXpmXqhXlhP0RXn1iF3Ex/V0QBEG4iP6//+//48knn+Tb3/42zc3NtLa28p//+Z9861vfuqDj1tbW8utf/5pjx45x/PhxPvWpT83uZizk1ltvZdOmTXzsYx/j1Vdfpa+vj7fffptvfetbHDp06ILW8n5EYCFckXR6HXd+4Wa2/NZ16Aw6hrtGGe4aRWfQseUTm7jzCzeLzkZXsabdLWiKitM7t3GAJEkU1RQwMeCj+2jvIq1OEARB+Ci64447ePHFF3n99ddZv3491157Ld/73veoqLiwHfK/+7u/Iycnh+uuu4777ruPO+64g7Vr157x8ZIk8fLLL3PjjTfyhS98gbq6Oj75yU/S19c3W9PxYZE07Tya917BQqEQLpeLYDCI0+lc7OVcNRRFYbBthL6TAyRjKdwFLpasrcJTmHPBx44Go0wM+ADIL8/F5rpycjSFi0/JKHz/6z9BVTQ8he4FH9N7coCtn9zMjQ9vurSLEwRBEABIJBL09vZSVVU1p/5AWBxn+zzO5dxZXNIVPnTxSJxXfrKDtnc6ScXTyDoZVVF5O8/BjQ9vYu2tKy+oW4/NZaNqhQgmhCxJltDpdaSTyTM+RtO0izZTRRAEQRCErEVNhXrrrbe47777KC4uRpIknnvuufd9TjKZ5H/+z/9JRUUFJpOJmpoafvKTn3z4ixXOi6ZpbP/5bo7vasZTmEP1ygoql5dR2ViOklF549/fovNIz2IvU/gIkWWZ+mtqCE9HWGhDNhaOY7IYKa0vXoTVCYIgCMJH16IGFtFolFWrVvFP//RPH/g5jzzyCNu3b+fHP/4x7e3tPPXUUyxduvRDXKVwIXzDU7S900leiXdO9yZZlsgvyyWVTHN0x8kFTwAF4XytuHEZOQUuhjpGyaSV2dtj4TijPePUrqmitK5oEVcoCIIgCB89i5oKddddd3HXXXd94Mdv27aNN998k56entlx5Je6jZZwbka6xogGY+SV5S54f06+i+GOESKBKI4cMXdCuDgKKvK490u389oTuxhqH0FDQ9M0jGYjjdcv5a4v3oJOJ1KhBEEQFpu4sHh5uFifwxVVY/H8889zzTXX8H/+z//h3//937HZbNx///38+Z//ORaLZcHnJJNJku/KtQ6FQpdquQKgKCpI0hlrKGSdjKpqqMqZ26YJwvmoWVXJ5//8t+g62sfUWACdXqasvpjS+mIRVAiCICyyUwPhYrHYGc/hhEsnFosBcwf1nY8rKrDo6elhz549mM1mnn32WXw+H1/5yleYmpo6Y53FX/7lX/LYY49d4pUKp3iLczCaDcQjCSz2+V0fgr4whVV52N2i+Fq4+Cx2CytuaFjsZQiCIAjvodPpcLvdTExMAGC1Wi+okYtwfjRNIxaLMTExgdvtvuALb1dUYKGqKpIk8fOf/xyXywXA9773PR5++GG+//3vLxjxfvOb3+QP/uAPZr8OhUKUlZVdsjVf7crqiylvKKXrSC/lDSVzOvFEgzEyqQyrbmoUHXoEQRAE4SpTWFgIMBtcCIvH7XbPfh4X4ooKLIqKiigpKZkNKgAaGhrQNI2hoSGWLFky7zkmkwmTyXQplym8iyzL3P65m0hEEvS3DGG2mTGY9MTDcSRZYt3tK1l5o7iiLAiCIAhXG0mSKCoqIj8/n3Q6vdjLuWoZDIaLliJ8RQUWmzdv5plnniESiWC3Zwt9Ozo6kGWZ0tLSRV6dcCZ5pV4e+eMHaN3fSev+DhLRBFUrylm2qY4la6sv290KJaPQ1zxIX/MgqXgKT6GbJeuqL8pQP0EQBEEQsnQ6nah9+4hY1MnbkUiErq4uANasWcP3vvc9tm7disfjoby8nG9+85sMDw/z5JNPzj6+oaGBa6+9lsceewyfz8fv/u7vsmXLFn70ox99oNcUk7eFDyIajPLyv22n41A36VQGnU6HklFw5TnY8shm1tzcKHJBBUEQBEH4yLtiJm8fOnSIrVu3zn59qhbic5/7HE888QSjo6MMDAzM3m+323n99df52te+xjXXXIPX6+WRRx7hO9/5ziVfu/DRpWkarz35Jif3tlFcU4jFZp69fXLQz+tP7sLptVO7umqRVyoIgiAIgnD5WNQdi8UgdiyE9zPaM86//69nsLttC3ar6m8ZYtl1dTz09XvEroUgCIIgCB9p53LuvKiTtwXhcjTSPUY8ksDmsi54vyvPyUDrEPFI4hKvTBAEQRAE4fIlAgtBeA9V1ZDONtRPltBUDU0VQ/0EQRAEQRBOEYGFILxHbokHvUFHIppc8P6gL0xeWS4WhwVN0/CNTNHfMsho7ziqCDYEQRAEQbhKXVHtZgXhUihvKKG8oYSuY31UNJTOaYcbnoqgaRqrblqOb8jPnucO0HOsj0QsicFooGRJIdfeew21a7KF3aqqMtI1Rl/zIOlkBqfXzpK11Ti9jsV6e4IgCIIgCB8KEVgIwnvodDpu//xW4t/fRn/rEGaLCf3MUD+dQcfGu9eSX5HLr/7+Jcb7Jskt9eIp8pBKpOg7OcRY3yT3ffl2KpaX8fpPd9G8r51kNAmSBJqGO9/FTZ/czKotyxf7rQqCIAiCIFw0oiuUIJxBeDpC6/5O2g50kogmKajIY9mmOqpXVbDtJzs4+MoxKhvLkOXTGYWapjHUMUpBRR4FVXkcfvU4+eW5s92lVFVlYsAHwMf/+73UrKpcjLcmCIIgCILwgVwxcywE4XLmyLGz4a41bLhrzZzbw9MR2g92k1PonhNUAEiSREFFLgOtQwy2D5Nb4pnTslaWZQor8+lvGeLI9iaqV1aIlrWCIAiCIHwkiOJtQThH8XCcVCI1OzjvvYxmI5FglEggesZaCne+i8G2YSKB6Ie5VEEQBEEQhEtGBBaCcI7MdjNGs5FEbOGuUalkGkmS0Ol1Z9yN0OllVEVFVUQXKUEQBEEQPhpEKpQgfEChqTD9zUOkEimcXgcDLUM4vQ5keW7wMDngo6gqn0QsSTyaWHBnI+SPkF/uXXCytyAIgiAIwpVIBBaC8D4URWHfC4c4tO0YQV8YJEjF00yPTROPJKhaUY7VaSEVTzM55Mdg1nPHF27m2I6T9Jzop6KhFFl3enMwNpNKtXLL8jmtbAVBEARBEK5kIrAQhPex/8XD7PyPvdjd1tkg4VS6U2AyyHj/JDq9DoNJT8mSQjbddw3162vJL8vlN//0Cn3Ng1idFgwmA7FgDFXTWHNzI6tuWrbYb00QBEEQBOGiEe1mBeEsIoEoP/4f/0EmlSGv1DvnPk3T6D05QPXKCq57YD1mq4mi6oI5uxCBySDNe9tp2ddOIpYkrzSXxuuXsnRDLXqDiOuFiyfoCxEJRDFZjHiLPaLbmCAIgnBRiHazgnCRDLQOEZwMUba0ZN59kiThLcrBN+QnvzwXp2d+Byh3novNH9vAdQ+sR9O0ee1pP6o0TWOsd4K2A52MD/gwmQ1Ur6qkbl01FrtlsZf3keIbmWL/i4fpONRNIprEYNRTvqyEjXevo3J52WIvTxAEQbiKiMBCEM4incwAoNMtHBAYTAbi0cTs485EkqSr5gqypmkceOUou3+1n8h0FJPVhJJRaHqrjfJlJdz3X+/AW5Sz2Mv8SPCPTvPsP7zEUOco3sIc8stySSVStB/oZrhzjPu/cge1q6sWe5mCIAjCVUIEFsIVR9M0/CNT9LcMkUllcOY6qV5Zjsliuuiv5fTa0ellkrEkJuv840cCUWxOK3a39aK/9pWq+1gfO5/eg9FkpGpF+WxAlU5l6Ds5yCs/3s5vf+PBq7ZwPTwdYbxvEoC8Mi+u3PNPyTzw8hGGOkapbCyfDX6NZgM2l5Wh9hHe+uV+KpeXibQ7QRAE4ZIQ/9oIV5RUMs2up/dw4q1WooFodidAlimoyOWWT99IzarKi/p65Q2llCwpYqBtmIqG0jm7DqlkmvB0hA13rflQgporkaZpHH+rmVQiTXF14Zz7DEY9xTWF9LcMMdA6RNWKikVa5eJIxpPsffYATXtaCU6G0LRs4Lr8unquf+harI5zSxGLBKK0HejCU+iet6MmSRL5FXmMdo8x2D5CVWP5xXwrgiAIgrCgqyPhW/jI2PWfe9n3wmGMZiOVjeVUNpZTXFvI5NAUL/zLa4x0j13U19PpdWz97evJyXfR2zTA1FiASCDKeP8kg+0j1K2rYd3tqy7qa17J0sk0Q20juM4wcdxsM5FJZhibuWJ/tVAyCtse38lbv9qPqmiULS2lYlkpsiyz9zcHeelHb5BKps/pmLFQjGQ8ecaaFZPFSCajEg3GLsZbEARBEIT3JXYshCuGb2SKprdayMl34c47nT5iMOoprSui7+QgR3eepLim8CxHOXcVDaV84g/v58gbJ+g43EMsHMeeY2PT/dew5pYV2JyLlwalqirhqQiapuHw2NHpLpf0oiuzniSdStNzYoDBtiHSqQy5JV7q1lVfULoSQH/LECd3t1JYkY/VeToQyClwY3FYaNvfQePmpTRsXPKBj2mymjCYDCTjKcy2+Ttm6WQanU5a8D5BEARB+DCIwEK4Ygy0DhOZjlHZOL/TjSRJuPKcdB3uIfGpJOYF6iEuRGFlPnf/7q1s/WScVCKNxWHBaDJc1Nc4F6qq0vJ2O8d2npy9+p9XlsvqrctpvH7pogUYBpOB0qXFNO9tJ6fANe/+RCyJ3qSnoCJvEVZ3doHJIC/+8HV6mwZQFRVZllAUlX2Fbm79zBaWXVt33sfuPNJDJq3MCSpOMVtNIEm0Heg8p8DCleukZlUlx3aexOm1z2sOMDnkJ6/US/kCHc0EQRAE4cMgAgvhipFJZZBkzthdyWDUk4wnyaQycJEDi1Msdsuc1BNVVRlsH8E/PIUkS5TUFpJXlvuhdoDSNI09z77D7l/uByTc+S4kCYY7RhlsG8Y3PMXWT25elNa2kiSx6sbldB7qwT8yhacoZ/Z7kUlnGOkeo3ZNFRXLSi/52s5GySi8/G/b6TrSQ8mSYkwWI5D9fMd6J9j24+24ch2U1Bad1/EjgSgG45kDUaPZSGQ6cs7H3XDXGgbbhulvGSK/PBeL3Uw6mWFyyI8kw3UPbMBoNp7XmgVBEAThXInAQrhiuPKcSHJ26vVCuwXh6Sh5pR4sdvMlWY9v2M9rP91Ff8sQqUSadDJNKpnG5rRSv76WmtWV1K+vmTPfQtM0psYCxCMJLHYznkL3OQchI91j7H/hMHa3fc6ugN1tI+QPc+CVo9Ssqly0GQY1qyvZ+snN7P7VfnqbBjBbTWQyCkpGoWJZKXd98ZbLriPUQOsQvU0DFNcUzQYVALIsU1RdQN/JAZp2t553YOHyOkinzlxDkYoncefP3+F5P8U1hXzsa3fx5jP7GGwfmZ0CX1CRy6b7rmH55qXntV5B+DBEQzE6D/cw3jeJpJMorimkdk3VRd9hFgRh8YjAQpiladplPWuhakU5RVUFjPVOUFZfPGetiWiSZDzJypuWX5KT1vB0hN98fxuDbSMUVRcQjyboPNxNyB8hncow2j1O6/4ODr6Sxz2/dysVy8oY7hpl/wuH6G0aIDQVIRaKoTcaKF1SxIobG2i4to78stz3fe32g13Ew/EF04mcXgf+0WnaDnQu6nC0soYSatdV03m4h0Q0SUFFHuvvXM3SDbWX5YC84a4xMsnMgvUIkiTh8DjoPNLDbZ/dcl5pZkuuqeHga8cJT0VweOxz7ouF4sg6maUbPnga1LuV1Zfwqf/xECPd40SmI5isJkrris66QyIIl1p/yyCv/HgH4/2T2b/dGiBByZIi7v3SbZdleqQgCOdOBBZXOUVR6DrSy8m9bYz2TmAyG1i6sY7l19XhKby8hpgZTQZu+fQNvPCDV+ltGsCV58Rg1BMJREnGUzTesJRVW5ZdkrW0vdPJYNsIFctKSSXTdB3pIRlPk1fqJZPKEI8kcBe4mBoL8NK/vsHNv3MDbzy5C/9IAEkvMd4/SSwUR8kosx2mjm5v4q4v3kL9+tqzvvbUaACj2XjGINBsNeEbnvow3jZBX4jepgGSsSQ2t43qlRVz2qSqqsre5w6w/4XDRIMxDCY9qqIyOeijp6mfpRvO/t4Wi6ZqZ603l2Up+xjt/I5fVl/M2ltW8M5LR4iF4rjznSBJBCdDRIIx1t26guqV599+V5ZlSpec326KIHzYpiey9UvTYwHKl5bMXvxJpzIMdYzwwr+8xu/8z4cuy4sOgiCcGxFYXMUURWHHz/dwYNtRVEXF7rISD8bZ/rO3aNrdyv1fueOcTlZC/jDdx/uIheJY7GaqV1Xgzjv39I6zqWos5xN/eD/HdzXTfrCbVDJNfnkuK7csZ+WNDZcsn7z1nU7MVhM6vQ5fzzjRUJycfBeSJGEwGQj4wvSfHCS31EPXsV7C02ES0RT5Fbk07W5FUzXyyrygQWAyhIZGLJzg1Sd2klfmPWtQZ7aZSKfOPOk7ncqc80yE95NKptj2kx0c3dFMMprAZDUhSRLeIjc3fuI6VtzQAEDz3nbeemYfNpdtznC8eCTBiTdbsNgt3P3FWy7q2i4Gb4kHWZZJpzIYjPP/LIanoyy/vv68d8NkWeaW37kBd76LYzua8I1MoWka7jwXm+6/hvV3rbns0sME4WJp3d/B5KCfysZyZPl0BG8w6imrL2GoY4TOI72svPHSXBgSBOHDIwKLq1jr/k4OvHIEd55rTnqGqqoMtA2z7fEdfPbPHnnf7keapnHglaPse/4gQV8YSZJm259ee886rr1v3UUtJC6qKqCoqoCbf/t60qls+srF6oKUiCXpbxnKXpF3WSlvKFkwpSQeSWCY+b4EJ4LoDTokSUJVNYKTQUK+EMlogvB0hMh0lMG2YVbf1Ih/ZIrEzG6GhAQS2JwWghMhKpeVMTHgo+1AF9fdv/6Ma6xdU8XRHSdJxlNz6gEg22JUVVTq1lVflO8HZFOvfvV3L3Ly7XZ0sozVaSGn0E3pkiIigRiv/Hg7JquRmtWVHNnehCTJeArdc45hsZvJLfLQuq+DjXevxVt0ee2G1ayqoLA6n8H2YRw5NkL+KKqqYnVY0Bt06I06VlzfcEGvoTfo2Xj3Wtbc3MjkkB8Ab7FH5JcLH3ndx/sw20xzgopTTv3tHGgdEoGFIHwEiMDiKqVpGifeagGkeTnfsixTXF3IaPc4vU0D1F9Tc9ZjnXirhTf+/WVMxiDlNQlknYxKLtOTMjue2oPZZmLtrSsv+nswmo0XbYdC0zSO72rm7ecP4R/2o2kg62WKKvPZ8sh11K6pmvP4/PJcJvp92ecCp/Jogr4QQV8YyAZWrlwn6USa0FSEgfZhjGYjOoMuG1TM0Bv1xCMJ0qkMRrORoY7RBdeYTqUZ651A1usoqS1koHWYvDIvdrcNgGgwxsSAj9q1VdSuvTiBRcu+dn7z/W20H+7Garfg8NhJJ9NMDPiIBWM0bKrDPzzNwW3HyCl0M9E/uWCbWQBnroO+5kFGe8Yvu8DCZDGx+WMb+Jf/96e0vdOFrJOyOxjpDBabmds/f9O8n4HzZTQbz7sIXBCuRGpGPevFJWmmtbMgCFc+EVhcpZLxFBP9k/OCilOMZgOaouIfmQLOHFikU2kOvPgCstJFXhGAEVQNmS68uVbSyWIObjtK4/VLL+u2lyfeauGVH29H1ukoWVKE3qAnlUgx1jfJ8/+8jQe/fg9VjeWzj1+2qZ6WfR1EAlFcuU78w1Ok02kigSiapmG0mLA6LWiaBpKEzWUlGU2SjKfQvyflRUkryDoZvUGPpmnIurlX9U4FgQdeOcrkoA8lo2Iw6dEZZKbHA/hn6inMdjMrbljKrZ/ZclGugqcSKfb8+h3CUxH0eh0Ojw1ZljBZjBjNBgITQUa6xiiuKWS4a5TpscAHawCgnWehwodIySgc23kSh9uGp9BN0BfKpge6bUiSxGj3OMNdY6KOQRDOQ2l9Mb1NAwv+fVBVlUw6Q3F1wSKtThCEi0kEFlcpWScjyTJqRlnwfk3T0DTeN+97vOcok/3teAsNIL9rOrGmgRbEkzvC+JCJ0Z5xKpadvUtRNBQjEU1idZgvaRFfKpFi/wuHkCSZoqr82duNZiOldUX0twxx4JUjVC4vm/1HsXZNJetuW8nBV46hKAp6o47JQT+JaAKj2YA7z4neoCc0FcHhtaM36AmMB9AyKmlVm/0HVtM0ouFsfYbVYWZyyDfv+3ToteO8/uQuZFkmrzQXvUFHJBDFNzxFbomHdbetwuq0UFCRR2FV/kXr7DXQOszEoA9XnpOxvkmkd6UxSJKE2W7GNzJFYVUBSlrBYjeTU+hmamR6wUFwkUAUi8NM3gfofHWp9bcM0Xuin4plZfM6Q2maRl/zICf3tIrAQhDOw7Jr6zi2o4nJQT/55ad//zVNY7Q7u4NZ9z4744IgXBlEYHGVMpoM1K6p5NC24+QsMEshFopjshkpeZ8TqUysGVVJoze5594hSYATvd6Pmp4mk144gIHshOBDrx6j/WAXqWQak8XEsmvrWHf7ykvSmWqoY5SJQf+CV8wkScJb7GGgdRj/6DS5xR4AdDodt31mCwUVeRzf1YySUYkE+gAJq9OKpmpMTwSxOS2zaUkng1FioTg2t5mAL4TNaSUejmM0GSiuLWSwY4TCynzq15/+BzY8HeHt3xzEaDbOaUXrynVid9voax5EUVRWb2286N+XeCSBklHJKbChN+hIJzNz6m30Bj2JWJKgL4TdbcVTlMPqrY288m/biQSisylakC0onxz0s2JLw5wTi8vFSPcYmZRy5nazOfYLajcrfLSkkmnSiRRGi1G09f0AiqoLuOV3buSNn71Fb9MANlf2b2Q0FMNd4OKOR7fiynW+/4EEQbjsicDiKrZqy3LaDnQx2j1OYVU+si6bAxuPJBjrm2DlTcsorjn79rTbO47VYSQ8nSEn/z2pTpJMeFrCYk+fcfjXWN8Ez/7jy4z3TpBT6MbpcZCIJNjz7Dv0NQ/w4NfvmT2Z/7Ak4ylURZ0txn4vo9lAyJ8hnZg74Eyn17Hm5hWs3LKMaCBG055Wfva/niEaihOLxLE6LOSWerE5LZgsJoqqCgj4QjjcNoa7xvANT2F1mHF6HYSnIhRUZGdevHugXs+JfgITQSoa5k+q1ul1OHLsnNzdyqb71qE3nP51Dk2F6TjUw+SQD71BT1l9MdWrKt+3EP/dLHYzOr2M0WrEne/CNzyFIc85Z5K2JElEg1E23L0Gm9OaLUwe9HF0x0n8I1OYbWZSiTSZdIaa1ZXc+js3XpazUj7sdrPCR8P0RJBjO0/SsreNZDyFxWGh8foGVm9djiNn4bRSIWv11kbyyry0vN1BX/MAsk5m/d2rWbap/gPN7xEE4cogAourWGldMXd98Ra2//wt+lqGspsMmobBbKDx+qWsu3UVfScHMZoNFFUXLJgW5fSYWbrOyP7XUjjcevTG0wV6mbSKf1Rhza3eBYt1NU3jrV/uY7x/ksoV5bPFfVaHBVe+i/6TA+x97gAPfOXOs74PVVUZ6R4nGoxhtpkoqS2cc5L9fhw5NowWI7FwNhjQ0FAVFUnKFvDGQnHMNhM2t3XB5+t0Ouw5NlKJNEpaIZVI4/I60DSNgdYhpscCVDaWozPo+OQff4yypSVMj00z1j9JMpZEp9dRXFNI3bpqbC7bnGPHwwlAmg363stsMxGPJrK1GzPvuf1gF689uQv/yDQ6nYyqasg6mcrlZdz7pdtAkmh5u532Q12kE2kKqwtYvqme6lUVcwosyxtKyC/LxTfgp7KxjEQ0wfREELPVhKyTCU4EsXsdLN2whI13rwWyuxh3PLqVJWuraX2nA9/QFDaXlaUbllB3TfVl26c+t9SLJEtnbDcbmo7QeP1S0RL2KuYb9vPsP77MUMcoTq8Dk9VENBBj+8/eoud4Hx/72l3iqvv7KKktEo0LBOEjTgQWV7ll19ZRVl9M5+EeAhNB9EY9NpeVnhN9PPPd54lHE+gNeoqq89l4zzoaNi6Ze8VZv5zr7m5nctRKz8kYVocOi01HIqYQDaYpr9dz48M3LPjaEwM++k4Okl+WO69jiE4n4y3x0HWkl+nxADkF7gWP0d86xO5f7WewfYRUPIXBqKewKp/rHlj/gScZF9cWUlZfTPexPuxuKxMDPsLTUSRJwp3vJJ3MsPWTm+fsJLxXx6Fu9j1/kKqVFYx0jRENxjBZjOiNZiaH/ISnItz++ZtYd/sqzFYT5UtLWPUB1mZxmGEm0FkouEhEs21xT7WdHe0d5+V/e4NEJEnlsrLZ56QSabqO9fLUXz2LpsHkgA/LTCvV4zubaXm7nWvvXceWR66b/SyMZiPXP7SRF/81O9iqZlUlU2PTjPZMEJ6O4Mpzct9/vZMbH944JyDS6XQsWVvNkovUmepSqFlVQXF1tgi9fGnJnJ/H6fEgBpPhgtvNClcuTdN48xdvM9w5lr1IMPN75fTY8RS56WkaYN/zB7nzC5ffjBZBEIRLSQQWAo4c+2w7WN+wn19+70XG+ibIK/WSV+ollUgz2j3OCz94FTWj0PiuEyzJuBpn3j4e+rKPpv15NL0dJhZWcObIXHenQuMNy3GVXLPg64anIiSiCfLL8xa83+q0EvKPE56OLhhYDHWM8Nz/fYXgZIj88lws9mzazWjPOE//9XOsv2M1NasrKa0rnpPv/16yLHPjw9dycncrbQc6MZgMmG0mMskMvuEpXHkOPMVnrvU41bVJzaiUN5SSk+9mrH8S35AfRVHILfGgNxpYcUPDOXdrql5ZkU1DGpmaly6QSSuEpyNsvPd0GlTznjaCvjBVjeVzAkCj2UBRVQEHtx3FW+xh2aa62ZPnvFIvQV+YfS8corCqgIaNpwOyZZuyQ+H2v3SYke5x9EYDNasrKV9awpZHrqO4pvCc3s/lymg2cucXb+aFH7xK38nBmTQwHdFQDJPVyA0PbZzTbjaTzjDSPU4qnsLmtlJYefGK5oXLz+Sgj54TA+SVeWeDilP0Bj2eAhet73Ry3cc2nPUChCAIwkedCCyEOQ69eozR3nGqGk+nJpltJkrrihnpHmP3r99hybpqTJbsCbKkywPLb2PjGa69bZT1N6ukkxoGkw6daQWS9ZNIknnB1zKYDegNetLJ9LxBb5Ad9qY36DGa59cFaJrGOy8dJjARnNOtKZNKE5gMMdo9Tn/zIBXLy8gpcLPutpVce++6M6ZIpRJpjBYjRdUFxMMJFEXFbDfjLfWQiiZ59h9eRkkprLpp2bx0pXQyzUj3OA5v9oTC6rRQvaKcymWlKIqK3qCjr3lwZr7FuXHk2LnugfW8/uQuRrrG8BbnoDfqiUxH8Y1MUbG8jFU3LZv9nnQc7sHhti94khsLxYmG4pTUmebtELlyHQQng5x4q4WlG2rnPL9+fS21a6sY650gFU9hz7GTW+L5yJ1Ily4p4re/+SCt+ztpP9RNJpmmYdMSlm2qn/0Z0zSNtgNd7Hv+IGN9E2RSGUxWExXLyrjx4Ws/MoGWMFfQFyYWjp+x8YDdbWOsf5KQLywCC0EQrmoisBBmRUMx2g50kZPvXnCYUV5ZLiPdY/S3DFG37nTnIsmwBHT/DdLN6JRRdFYZSV8BhqVI0pmv0JfUFlJQkcfEgI+SJUUoisL0eJDIdHYWRCQQZdmm+pnUrH5UVSW/LBen18HUWIDek4PkFp8+wY1HE7Qe6CIyHcWV6yCZSOPKc6GkFXY8tYd0Ms3WT16/4Fqa325Hb9Cz+qZGkvEUiViSoY4RpscCpFNpxgd8/OK7z3P4jePc8qkbWLap/vSTJSl70qnOreyVdfLp9CVNO+/p49fcvgqj2cDBbceYGJhEyWQnQl9zx2quf2jjnBMZVVXnzcE4JRaOg6rNm6NxisPjYKxnPDuo7z1F3jqd7qrIjXbnudh03zVsum/hXbaWfR289K+vk0llyCv1ZmtzQnHaD3bhH5ni4//9Xgor8xd8rnDl0hv16PQ6MqnMgk0e0qkMer0O/QL1OYIgCFcT8VdQmBWPJEgl0ji9C19xMxj1aKo2U1A8lyTbwbTxbI115tEb9Gy8dx0v/str9DUPMDUWIDwdRUlnSMZTSJJEJp1htHccJZVB08DhsbP8ujpqVleRSqRx550ulhzvnyQyFcGd70KSJOKRBJlkmvzyXHR6HUfeOMGKG5ct2GVqtHscmytbnG20GOhvGWRywIfNZcWeY0OWQjg8dqKBGC//23ZsLuvsvAmjyUD1inKObG/CU+ied+xYOD6zG3J+J5ySJLFqy3KWbapjvG+SdCqDO885Lz1MkiTKlpZwfGcz3pn3qKGAFgVAVTMgZdvhLkRVVXR6GVn+aO1EXCypZJq9zx1ASSuU1hXP3m5zWalwlNF3coCD245x35dvX8RVCh+GktpC8ko9+Eam58y6OcU/Mk1pfTF5Zd5FWJ0gCMLl4/wuoQofSRa7GaPFSCKaXPD+dDKNJEszBcUXx/Lr6rnl0zcyMeBnrG8SNaOgN+jJLfHiLc5htGeCnuP95JZ6KV9agiRJ7P3NQfY+9w4Gk554JBvkqKrK5KAPo8VIOpnGN+wnMBmi/WAXJ95qIRFLEPSH6Ts5uOA6DEY9ysysjVgwxuSQH6vTgtFsREJCA/QGHUXVBcSCMY7tap7z/JVblmF1Whjrm0B9185FMp5irHeC6pUVlC0tuaDvlcFooLSumKrG8jMWszduXorRbMA/5kdT+iC5D5L7UeP7iE+3Y3VomG3z0840TSPkD1OzuvKcOmpdTQbbhpkY8JG3QDqMLEt4i3LoPNJDyH/uKW8XQyKWZGpsmmgwuiiv/1FmNBtZf+caMqk0k0N+VEUFshPbx/om0Blk1t+5Wsw4EQThqifOIIRZNqeVho1LePv5Q7jznKiaiqpoGIx6JEliYtBPfpmXyuVnn6B9LiRJwmIz4S3OoXxZKajaTKqBRsv+DnKLPcRCMabHg9iWWPEUurE5LfQ3D5Nf7mW0ZwKHx46qqChphUxaYXo8QDySwGw1YTQbCfnCBCdD6I16osHYguuo31DL6//+JqqqEvJHSCczszsYmXQGWSfhys3OcHDlu+g90U88Ep9tn1qxrIzbP3cTO5/aQ9/JAfQGHcpMJ6e6a6q58ws3n3cq1LmoXlnBDQ9vYPcvnqFvZByr04CqGolFVIqrM1Q2pBnpb8ZgXoPFmg0QVVVlrHcSu9vGyhuXfehrvFLFI9namzPNAjFZTUTHYySiiTPu+n0YQlNhDr9+guY9bcQjCfRGPUvWVbPutpUUVZ19Do3wwa2+uZF0Ms3+lw7T3zrTnhsJT6Gb6x/aOKfpgSAIwtVKBBYfAZqm4Ruewj8yhayTKa4pPGsXpLNZd/sqmna3sO/FwygZJTsgzWLEaDaQX5bL9Q9di9E8/4r3hehvHUKn11FYcbo7VPeJPpSMisliJBlPMTU6TenMFHCT1YQkgclmpqAyj76Tg3iKcpB0Mv6RadRMBpPFiMNrR9M0dEYd6UQK39AUvU393PjwtfMKj5dfV8+JN1sYbBsBSUOSQCI71yAyHSG3xDObdqXT60jFMygZdc4xVm1ZTvnSEjoOdWeHyZkMVCwvo3pl+SWbzitJEpvuclKUn6TlYB4jvRp6o0zdajsNG5zodCFeeWKU7tZ+MhkzsiyjqiqeQje3/M6NlNVf2K7KR5nVYUGnk0klUgv+DsSjCUwWIxbHxZnVMTU2zUDrMOlUhpx8FxXLS+f9HIX8YX79Dy/R2zSAK9eJI8dOMpHi0LZj9DX188BX7xKf6UUiy3K25famOnqbBrKtnp0WqlZWYDtDeqEgCMLVRgQWV7igL8TOp/fSdaSHaCievaKe52T11uVsun/9OU1aBvAPT5GMp0DTUDIK6USKaCCGyWqi4do66tfXvP9BzlEmpcyb0ZBKpGfbOsqyhKrOPYk3WUygaXz8/7mHt58/RPexXtSMSjqRwmzPXokPjIdIJVIoigozQ+L2PncAp9fBrZ++cc5V5ZwCN/d/5Q5e+ckOuo70EAvHUTIqBpOe3DIvtWuqiIZjBMaD2XSYMi+RQBSbyzonSMkpcLPxnnWkkmnSyTRmm+nSp0dkmqhcKlHZWLXAnbl8/Cs++ntKGOytI51K4ynMoW5d9SW9yn4lKltaTFFVPmN9k5TWFc353FVVZXp0mvX3rL3gCcypZJo3f/E2J95sIRLIzlORdTJF1QXc+ukbZmt7AA68fITepgEqlpXOprBZnRbceU76W4fY+fRefudbHxcpOheR0+Ng1Zbli70MQRCEy5IILK5gsXCc33x/W7YGocRDXlkuqqoRGA+w6z/fJh6Oc8ejN3/gtqDRUIw3fvYWsiSz6f5rSESTpOIpdHodkiwx1j1O6/5OVtxwcQeFFVbmcXR7U3ZC9EzhsMliQlFUNDTSqTROz9yTtUQ8iSvPSX55Hh/76l1MjwfY9Z97+cV3XyAajCLrZDQ1GxydqnjQmwykkxmObG8iFo7z8f9+Lxbb6XqR0rpiPvftR+g43MOz//ASvuEpqldW4PA6GGgZZLR3gng4W+AuyRI/+/NfsuHuNVz/4MbZNKfJIT/Hd56k9Z1O0qkMDo+dVVuWs+LGhjmv9aFSfHCGFr8AeoOZmkao3biZ47uaOfzGcV758XZsTgtLNy6hcfNSyhtKP3LtZC+UwWjghoev5YUfvMpg2zDeEg9Gs5F4OI5veIqimgI23rXmgl9n13/uZd9vDuIucM+2uU0lUox0j/H8P7/KI3/0AAUVeURDMZr3deDOd82ri5EkiYKKPIY6RhnqGKWiofSC1yUIgiAI70cUb1/BWvZ10HtigLKlJTi9DiRJyk6sLvbgLfZw/M0WRnvGP/Dxuo724hueorAqH1mWsTosuPNdODx27G4bkizTtLsFTdPe/2DnoO6aGnIKXIz1Tswe21vkRm/QEfSFMZgM5Jae7rYSjySQpOzU8FNyCtwz3Z9k7G4beoOeVDKNLMuYzEYcHhuappJOZSifmbLdcah73lqMZiONm5fyhb/4FPUbljA9HqR1fwfdx/tIRJMYzAZqV1eyasty9AY9b/5iH8d2Zgu5hzpH+cXf/Ibdzx4gM9OydWpkmpf/7Q2e//424pH4Rf2+nZHsAFJneUCagM/A97/+E/7uSz/k9Z++SfvBLpp2t/Hiv7zOz//3rzn06rGL/jl/FNStq+FjX7ubqpUVhPxhxnrHScSSrNq6nAe/fg+5JRfWFcg37OfEmy3kFOaQM9PdDLI/l+VLS/CPTHN810kAooEo8Uj8jGk4FpuZdCpDeCpyQWsSBEEQhA9K7FhcwZrfbsdoMWJYoHe6I8eGb8hPz4n+Dzy0KzQzwE13hjkHdrcV3/AUykznposlp8DNbZ/dwraf7KDv5GC2PkQCvVFHdDJGSV0RRouRVDJNcDJEeDrC6q3LqVld+Z4jZQeYufNdxEIxlIyC2WrK7l5oGql4OjtPQq9Db9DRfqDrjCkNJbVFfPJPHuDw68f51d+9hMFkwFvsoaA8j9wSD7JOxlPoJhlLcvi1YyzbtITtP3sL/8j0zHDB7Amh0+sgGU/Rur+T0rpiNn9sw0X7vp1JWl1G37F9hALj6I1GTFYd4wNJQlNpbA6FksoMO5+fYP+L3dmJ3DUFSJJEMpYiHo4z2j3Gzv/cS1FN4Wxdi3BazapKqlaUMznoJxFL4sixkVPgvig7PP0tQ0QDUfIa5wcokpRtINB2oIubPnk9BrNxNoA22+bPi8mkM8iytOCASUEQBEH4MIjA4goWDUQWnFgNzOZln2rH+kHoDDo0VUPTNGRZw2yJIEkaibgNVdWTTmWwOizz6iHOl6Zp+EenScVTlCwp4pPfeJDmve10He1F01Tu/OItSBoMdY3iG/KjaeDyOlh/1/VsvHvtvODG6rTg9DhIRJOkU9lOTqdSolLJNAaTHpPFiJJWMJqNRM7QIeoUV56T6pWVFNcWUlxTgNE0/3udU+jGNzzF8V3NDHeOzuz2zD3BNFmM2FxWTrzVwvo7V1/04vd3623q542fNzPWqUNJ9zI5IhMJgN2tp6BMRs0kiUYcjA10o9fpcOedviputprQ6XVEQ1EmB/207e8QgcUZyLJMwbuaDVwsmVQGSZbPGKQYTHrSqTSZmVkmFcvLaHm7HUeObd5z/CPTeArdF9zmWBAEQRA+KBFYXMFyCt30HB9Y8D5N01AU5Zy6Q1UsK8XiMOF0tlFVP4LNMQ1AIm5ndKiK3mYD625fdVHapva3DvHOS4fpbxkincxgtpmou6aGa+9dx62fuRFg9kQpGU8yOTSFpqrklnhmW7y+l91to7Aqj0QkyUDbMOlEGmZG9hlNBix2MxaHBb1RTyKawFucM+8YiqLQfrCbpt0tjPdNEgnEmBiYJLc4Z8HAQqeTUVWNoC9MJp3dIZl/TBVN0xjpGqPv5ABL1tV8KPULI91jPP+DVwn7oxTWrsM/eJJ4ZBS9QSEWTqNpVqpWLuHIriThqRHMdhOqohILx0nGUmiAyWIgk8qQTqYZ6hq76GsUzs7hdYCUneS80E5kJBAlvzwXk9WIJElsuHM1g23DDHWMUlCRh9FsQMko+EemSSVS3PTJzZeutkcQBEG46onA4grWuLmBzsO9JKLJeakQ02MBnB47S9Yu1BloYUXVBdz0YAyLfh9Gs4lkwg1IGI0h8rx7qK6vRElv5PibzVQ1lp93F6GeE/385vvbCPnD5JZ4MOUbiYXiHHrlKCOdozz03++dMx3bZDF9oCvnFctKKaouZHLQT+P1Szn+ZjOSJGFzWrG5rISnIuSV5ZKKp5D1ujk1GpANKrb/7C0ObDsGWjZQ0TSN6fEgR3ecZPnmpagZlckhP/FwHIPJgNFswFucg6fADWioM7MrIBvcTQz4GO4cZXoiiJJReOZ7L1J/TQ03fmIT+WXzB61diKM7mpgeC1K1ohxFVRnpM6E35mHP0ROPpBgbNOAqLCI81YKizMzrSGRA00CWkIBoADRVRW8yYDCITkKXWvXKCvLLchnvm6RkSeGcADQRTZKMp1i5Zflsl6eKZWXc/1/vYOd/7mWsbxxFUZGQ8BS6uPETm1h328rFeiuCIAjCVUgEFlew+g21LL+ujqbdbThy7DhzHSgZhenxIIqisPW3NuN91wn6+5HUEdZe72ews4iR7gzpZBJJkhjp1dPdnEMmFaa3dSea5MTpdbD+ztVsuv+aea0s06k0EwM+VEXNFn+/q/2mklF485m3iQaisx1vIFuc6vQ66G0e4MDLR7j7d2895++H0Wzk+oc28OS3n2G0dxxN1YiF40SDMQzjBvLKPOj0MpODfq65cxXVKyuAbKvQ4c5Rdj69l30vHMKd56K4phCn146n0E0sFKPraC8HXz2G2WIEJAxGPaGpCNFgFIPZQEFlHk6vg+mJIN6i7E7IWO8EXcd6gWzL3MK6Itx5Tk7uacM/Os3Df3Df7GMvVDwSp/NwD85cB+P9kwy0DTPcOYrBqCeTsWFz2fEPT3N0RxOxUAI1kyGdzMzWoVidFmQ5W4sSCUQJTYYoqMy/KGubt9ZognQyjdVhEVO+38NsNXHrp2/khX95ld6TA+Tku9Eb9USmIyRiSRo317PihqVznlO7poryZaUMtAwRCUQxWoxULi/DepHmaQiCIAjCByX+Vb+CGU0G7vnS7eSV59L0Viu+YT+yLFNYlcfaW1ey4oaGc0q50dLN6HQxKpevJq8sTsgfZqA9Tm9rGFmvp7wyjt6ioOlKmR4LsPOpvciyzHUPrAeyJ+gn3mzh0GvHmRzMBhb2HBvLrq3nugeuweayMdQxwmj3OPkVefPWJutkcvLdHH7tOIqikklmsLtt1K6toryh5AP14g+MhzCa0qy6Lkx59RQ6fYLxIZn2Yw5CIQfuPCcrtyxj1U3LkXUymXSG7T/fzdHtTXQd7SUWyqYF+Uey3bGqV1RQubyM8f5JRrrHycl34fQ6srnwEpQuLSGTVjjw8hFW39zIW7/YjyxL2FxWBtuHkaRsQbnJaqKkphBHjh2b00pf8wBH3zjBrZ/ZMv9z0DQG20do3ttGf0t2wm/N6iqWb64/4yTldCpDKplmvHeCwGRodnifqqoEJkOE/BGS8SQ5BW5ySzwMx5NIKQVZksikMsQjCWxOK+lUBkmSMJqNqIq64Gudr9GecY7uaKLzcA+ZtIIjx8bKLctZtXX5VZGuEwlE6TjUzfjAJLJOpqyumJrVldmZLO9Su6aKT/y/93N0x0m6j/WSiCbwFOewastyVt20bN7jIfu3oHbNB9+dFARBEIQPgwgsrnBmq4kbP76J9XeuITAeRNbJ5JZ4ztjZ6ay0KUAPUvbE2Oq0sOfFIdIZmbwSA9FwCkM6hNUj4S32oCoah18/zqqty7E5rex/8TA7n9qDTq/DW+RBZ9AR9ofZ8+w7TA75ePC/3U00GCOTzixYi6AqKhP9kwy0DxMLJ7C7bWRSGQ6+eoyVW5Zx++duOuvAv8BkkOM7d3PHI70UV4ZIpSQyKTsrrstw04NJ3nljnP7eEg69eozDrx2npK4YWSfRsrednEJ3dsK4xYjFZiYZTzHcMYrJYqS4tgiL3YzVacHiMKMz6LC5rOSX55Jb6iERSdJ9vJ9r770GTdU4sr2J1v2d+Ib8mG1m7Dk2qldW4M5zAdkAypXromV/B5sf2jjvpPrIGyfY8R97iIXis+lYe559h6bdLdz5hVto2Lhk3nu3OiwkIgnG+ibxFnvQ6WQS0QSqomK0GAhNhlAUFasj+x70Bl22wF8vo6nZNBslnR1U6C3KobS+mPH+yXP/GXoXJaPQeaSH5rfb6T7WR3/zIHqjntK6YqxOK4HJENse30F/yyD3//6dHyi4OJVeFgvFMM9MXr8YNT8ftu7jfWz7yQ4mh6aQ5Wyw+Y58hLK6Yu798u3klc7tAlVaV0xpXTHxSJx0KoPNaT2/32lBEARBuIREYPERYbGZsVRf6FVfG6DMfhX0pWl5J8z0RJqRngRqJonOoJJT1ElZfTGeIjeD7SMMtY+QV+Zl/4uHsTosc9KvPEU52D12Og730vx2BzkFLmSdjlQyPS9IGO4aZagzezJf2Vg2e6IZDcY49OpxHB47Wx7edMbV9xzvo6ruKMWVIULBPDQ1eyKWSkE8NE7diiHGB3UYrVuRNInW/R30twxRUJ5LxcwckFNX6U0WI5lUhrHeCVx5TpLxFDaHheoVFZTWFc/9rrl0TA75mRoLsPWT11PZWMZTf/kcQx0jMLNjEQnGcHjss1ebzTYT0VCMZCw554R6uCubkiXrZKpWlM/enqd5Ge0e5/Und1FYlU9OvmvOGrK7PxKqps12BLO5bAQmgkg6BVVVyT5EQs2o6PQ6bE4LJpuJRCxJKp7G4bFTtaKC6lUV+Ab9aOrCcyxSiRTJeAqzzYTBuHCgl0lneO2nuzi6vQklozDUOTozwd3ISNcY9RtqKazMz7bifaeT8oZSNt13zRk/W4ChjhH2PneA/pYhkvEUBlM2SLnu/vWzaW2XI9+wn5f+9XXCU1EqlpXOTpRPpzL0tw7x4r+8xqf+50ML7kRY7BZEQpMgCIJwpbj8L/UJl0QqkcI3Xoh/VIeSzs6z6D4Zpa8lQsifxmiWsLsk9CY7E/2TtO7vIBbODnxLJ9N0H+sj5A+TUzi/ZsBoMmC2GGna3UJpXRH5ZV58g/45j8mksyfxSkYhvzx3TjG6zWXF6bFzYlcz0dCZW8Sq6QEKS6aIRd2zQQVkB+pNDqdBM7B8fRC704TT68BTmEMmmWF6PEgimsRbnEMimiASjDI55CfoDzE+4GOsd4JMSkGn1y1YsH5qjpwkS/hGpnjjZ7vxDfkxmA1YnRaUjEpv0wAt+zqIRxOzazJZjJjfc5W+dX8nkUCU3JK5tTGSJFFYXcDUaGDBwX7RUPakPb/cS3g6TCQQxWzL7sDEQ3E0TUOSZcL+MOHpKJ4iD85cJ4UV+ZTVl5BX6qFx81Lqr6lBr9cRjyQoXza3TenEoI+n/upZvnXfX/E/7/1Lvv3Q32S7UE3PH8B2KiXOU5iD0+tAzajklXpx5ToJ+sN0H+vLpohZjNgcVk682Uw6lT7jZzvcNcqz//gybQe6cOTYKa0rxp3novfEAM/935fpPt53xucutpZ9HUyNBiitK5oNKgAMRj1l9SUMdYzSfaxv8RYoCIIgCBfJogYWb731Fvfddx/FxcVIksRzzz33gZ+7d+9e9Ho9q1ev/tDWdzVIJVLse+EQj3/rKR7/s/08/pcy//E3HTTtHuDEW9NoGpisGkZjEllvxWR1zQygi9Pb1I/eqMOZ6yQWTmSvlMsL13SY7WbCUxH0Bj3XPbAeSZYY7hojlcieTE6NBvCNTGG2mbA4LAQnQyiZ07sn7nwXgckQE+9Kz0nEkrS+08mBV45yfFczBoMPvSFDKjm3LWwsFEPTIBwy4slPYrEns3doGkaLkWQ8iX90Gk9RDvFogrHeCaLBGOlkhkQ0Qc+JfiLTYQwWIw6PnfcK+cPYc2wUVRfw1i/3MdwxSsO1deTku8jMpLG485wEJ0MMtg2jZBSCvhDLN9fPSwkb6hzFarcsWBsjyxJ6o56x3ol59+n0OoxmIxVLS6ldXYXZZkLJqLjznFSvrMBbnIOsk3HmOlm6oZbVWxtx5NgIByKgaRiMBmwzaVfZXRrHnK5ZA21D/M3nv8/z39/GSPcYQV+IvpODPP1Xz/K9//IvTE8GZx+rZBSO7TyJ0WjA5rJmW9lqGjq9DlmWsbusBGfqPgBsbivh6Six0MKTyTVNY9/zh5ganaZyeRk2lxWdLjsZvryhhFgowZ5nD6AoyoLPX2xdx3qxOhb+TA1GPaqqMtgxsggrEwRBEISLa1FToaLRKKtWreLRRx/l4x//+Ad+XjAY5LOf/Sy33HIL4+PjH+IKP9rSqTSv/Hg7x3Y2Y7VbcOe7UTLLGOpuo+Ooj3gkRdkSiZFeFYvNgWzMRUIHElgcFkZ7J6lbX0tpXRGjPeNkUhn8Y9OggdVhnjNvIhlL4p2ZWL1881KQJPY9f5Dx/uxuwPT4NIloklQshX90eqaA2IArz4nL68TmtpJOpmd3B9oPdrHz6b1MDPpA09A0qG300bg2QzQcx+46Pb8jlUxnAx5VxWyzZN8D2YF6JquR8FSKRCRBMppAp9dhtpnIpBQyqQyyTofRbEBn0GEyG5keC5BTeHrKciwcZ2p0mmvvXYumqvQc6yO31IvJYqS8oZTOo70EfWGsDjMmi5HR7jFUVaV6ZSVrbpnfCtRg0J21aFpTNfTG+bn2VoeFqhXlnNjVTMXyMopqCskk08h6HQajnt6TA3Qd66VyeensYLfaNdW0H+pivN+HK89JLBxjeiyAK8/J7Z+7ifzy7ONUVeWnf/YLBtqGyS/zzg740zSNWCRB6/4OfvW3L/C7f/VpAMLTEabGAjhzs7s72cnn2cdLkoTBZCAajBELx3HlOkinMugNOvQLzG0A8I9O03dygNwS77yTc0mSyCv3MtI1ykjXGGX1l98wODWjnnWo5LtT8ARBEAThSraogcVdd93FXXfddc7P+9KXvsSnPvUpdDrdOe1yCHN1HOqh6a1WCiry3tWa0ozdvZHu4x2Mt/Wy4c5qkqkI/tEYRnMKg0lDSSvEI3EMRj1rb1mBqqj4R6cY6Rqlr3nwXfMdPFQuL0XW6YhHE3O6VDVuXkr9+hqGOkY58WYL2//jLdSMioqGwWIkEUkQC8cJ+cJMuQMYLUbMFiNDHSNIssSLP3ydZCxFSW3R7FXfWBRiER1KaorQlIbVbkHWZU/akokUJdUaGaWQZCL7Xo1mI/llufhHpkgmUoT9EVy5ToxmA5FAlMh0lLzSXOquqSYeSRDyh0kmUvQ2DaA36MhkFIwmA6u2LmfLb21mpGuMWDhB7kwhbn55Ljq9jqHOUSLTEZSMQiqZoX59Lfd9+fZ5dRKQ7f7UcaQXVVXnFSWnUxk0TaOioXTe8yRJYs0tK+g+1sdI9ziFlXmYrCY0TSPkD6OpGuvvWE08kqD35ABmm5lMKoMr14nVacFsM5GKp6lZXcmW37qO8nedoHcd6aXraC9Or2PO1HBJkrA5LMTDMQ69dpxP/OF9uHJdyLrs5Gh1pkbDlefEZDGSiCaw2C1oM9GhJDEzJyTAuttWYXNaF/w5TUQSJBNpcgrcC95vtppIJdLnNGX+UipbWsJw5ygwf26JoqhoqkpBxYfT2lcQBEEQLqUrrnj78ccfp7u7m5/97Gd85zvfed/HJ5NJksnk7NehUOjDXN6iigajhKejGM0Gcgrc79tqtvntNkBasN+9t7iQjkODTE0aWbqxkfG+Ccb7J0kl0+h0Ogoq8rHn2KhYVsbOp/Zy4OWj5BTlEJyYxGJPYDQl8Q2FCUwGySlws2RNFY2b6+e8hsFoIL/MS09TP7FQHKvLQjKeIhVPz+bfZ9IKyVgSg1GPu8DF2785iHt/B9PjAcx2M71N/egMOtx5LnIKSgiHqymp6CaVkgkH4miqhtlqxOGO48ix09VeB8jEInEmB/3ZHZN0hpHOsWw71sIcUvEUmgaVy8tZsq4ag1GP2WYmncpw1xdvJuSPEJwMYbabqV5ZMdsKV2/UozPoyKQyGM3Zycg5BS4cHjvJWIp4NE4sHOe2z2zBs0AtCsDSjbUc3dHEQNswpUuK0c8MqUsn0wx2jFDeUErNGdqKFlbmcdNvbebt3xxgsH0YNFBVDavLwvq713DLp25gYsBH2zsdjPVNkkllmBjwEY8mkCUJNOhtGiAeiXPXF2+Zvfrf0zRAMpbEU+SefS0NDWlmqrndbSfoCzHQNsKK610zNRBF2bkaHjtmq4mi6nz6m4cACUkCw8wk9KGOUdz5LtbcsuKMP6dmuxmT2UAimsSwQFewRCyJ0Wy4bOc2LNtUx4k3W5gc8s/p/nRqGntuqZe6ddWLuEJBEARBuDiuqMCis7OTb3zjG+zevRu9/oMt/S//8i957LHHPuSVLa6gL8Q7Lx2hZX8HiUgCvUFH+bJS1t+5hqrG8jM+zz8yjcWxcCcpp9eOxWHBPzRFdWM5ZfUlFNcWkkkryDqJofZRlm5cQjqV5uiOJgrL7Sz/+DiTAz46jqYJ+iWcLplUKkBx9Y187Gt3YXtXetIpPScGGOkcRVM18kpz8Q37CUSCSPJMeoimzbymzLKNdfS1DNJxuBtJktHUbIqJqmoMd4ziKcpB2bAOVYmx4TY9JrOMqsrodAqDHTKHd+cRCueRSoRoO9hF2Bcmk1EprCxAUzVGuiOk4kkKKvNxeh0oaYWm3S0oaQWjxYjeqMOd72LljcsX/J4VVRdkC9OHp3HlORjrm8A/NIWiqJisRnR6HUs31s6mIi3Enefi3i/dxiv/tp2hjpGZzkwasl5H5fIy7vm92+a1ZZ0YmOTojpO0HeiabeW7ZF0NBeW52FxWKpaXUTAzN6R8aQnlS0uIRxM89ZfPkoglKasvme3QlUkrDHeO8uIPX+e3v/kg7jwXer0MM/MuEtEk0WAMRVExGPXYnFYkWUKSpOzjyO5krL55Bb1NA0wO+skt9VDeUIoGDLWPEPJH8BS6mZ4IUlSVz82fuuGsk9W9RTlUrSjn5J527Dm2OQGzpmlMDvgpayihqGbhGR+LrbSumK2/fT07n9pDb1M/NpcNVVGJheN4i3O464u3YHfP/90QBEEQhCvNFRNYKIrCpz71KR577DHq6ure/wkzvvnNb/IHf/AHs1+HQiHKyso+jCUuipA/zK//4SX6Tg6Sk+/CW5RDKpmm7Z0uBttHuO/Lt1O3rmbB59pcVgITC+/gyLKMp9CNJMHgTDtZk8WEkkoxOujDne/i2nvW0ntigHgkypZ7hyko7ic330HpEidTYyqyLonL7UNvOYnTu3Bb0lgoRjqVLbo1GPVYHRZioRiSJKOq2baoSNldlZkEGnzDU+SWenEXuGavmmfSGXxDfmSdxEhnI9XrllG7IgVaDGQveUvL0Pa34RsepPNIL4loEmeug9wSD9UrK9BUjVQyRSqRxpFjZ7xvgvB0FJ1OJhlPEfKHQZL4wR88weYHNqBqGqNdYwDUrKpk+ealFFUXsOGutTzzty/QeaQHVVEw28xIsoR/ZBolo1C5vIxUMo3lLDMJyupL+My3H6HrSC8TAz4kCYpqCqlZVTEnFQlgsH2Y3/zTNnzDU7jynJitJqLBGJNDftA0HvjqXQteye860stQ+whl9SUY3lXboDfoKK4toGVfB8/931do2LgEd5Ebk9XIcOcYkpStmZBkmUQ0STySQM2olC0tpnLF6ZavdeuqufUzW3jrl/vobRpAp5fRFI2SJUWs3OJhzS0ryCv1UrWi/KyzSSAbqFx73zWMdI/T1zxIXqkXiz07a2Ry0I/NbeX6Bzd8oAGKH4SSUZgc8qNkFNz5rjOmaH1QkiSx7raVFFTk0rKvg8G2YWS9zJI11TRsqiO32PP+BxEEQRCEK8AVE1iEw2EOHTrE0aNH+epXvwpki0o1TUOv1/Paa69x8803z3ueyWTCZJrfH/5Kpmka/pEpEtEkR3c00XdykIplZbNpMyarCbvbxnDnKG/+4m0qGxc+eVu2qZ7eEwNk0hn0htM/CoqiEJ6O4C3xcMNDG2nZ10H7wS4Ck0FkWaZ0STHr71xFaCrC7l/tR022YtSPMDHsQW+yYjRJFFbIgJ5kVMXpGoT0STCun7cGsz174q2p2mxxr06vn203m06mUVUVvUmPrJMI+oJISBgM+tmgAkBv0GNz25gc9KOvKcTkWIVsPR1AeizwyW8sY9cv3sY3Mk1eiRd3vhOzzcT0eJBYOI7FbiEwHqR5XztWmxmH24Z/dJrYTItbh9uarTc4kq03qF1bjcGgZ89zBzjxVgt3PHozyzfX89KP3iCdTKEz6EklUqCBK9dBYVU+/pFpjm5v4rr7538v3s1iM7PihoazPkbJKGz/+W6Gu0Yxmo2M9U1gMOnxFOZQVF1A28FuSl8/wQ0PbZz33IG2oWwh9XsKpoO+EF3H+vAN+glOhuhvHsJkNaJmlOx0bpcVo8kIM/UR8UiCVCJFaV3RnJ0USZK45vZVVK0op+tID4GJEEaLkaoV5ZQtLT7nIKCktogHv34Pe597h/7mIfyj0xhNBqpXVXDd/ddQteLC51homsbJPW0ceu0Y4/2T2cnxbhvLNy/l2nvXXVCAIUkSZfUls7stA61DdBzuRlEUll+3dN6APEEQBEG4El0xgYXT6aSpqWnObf/8z//Mjh07+OUvf0lV1cJ55x81A23D7H/xEP3NQyRiSfpODmB32yioyJuTTiFJEvkVeYz1jtPfPMiStfNzuBuuXULz3jZ6mwbIK8tFVRXGeycZ6xsnGUtRvaqSdCJNOplGb9BRWJ6P0WIkk8nw8//9awxGPZqq0bBqmnQywUh/GJszg6coZ7ZffyIOeoMRLdWEtEBgUbWinMKqfALjAeLRBHqjHkliZqCbRDqdwWQ2zpx4SQQmQrgLXaSTaZSMMmcasd6oIxqMY3NZKa0vnvdasixjMBpw5zoprSsi5A9z4s0WwoFoNrBRs2lX0VAQfYlMuD9KPBLHZDHi9jiwOS0Md46iN+pRVY1EJE7JqiryNC9jvRO8+sQOwoEoqqqy8Z51xMJxMqkMBpMBd74Lo8nAeN8kJ95sobyhhMh0FFknU1xTeF6pMH3Ngxx54wTRYLadrk4voyoqYz0TeEs85BS4aNrdysa718zb6cikFKT3tAaOBKK0vdNJIprE6rTg8NqpbCzDN+Qnk1KwuSykU2nSiRTMBIN6g57imkLMdjPxaGJempbp1DRzhxmDyYDJYjzvSdmlS4p45A8fYHLQRzQUx2wzUVAxf/J2JBCl41A3w12jSJJEyZIi6q6ped/A4MArR9n+891IZAc76vQy4akIu3+5n4kBHx/72l0faDr4mRx+/Tg7ntpDLBjD5rKhqRqDbW9zfFcL9/yXW6k9Q+2MIAiCIFwpFjWwiEQidHV1zX7d29vLsWPH8Hg8lJeX881vfpPh4WGefPJJZFmmsbFxzvPz8/Mxm83zbv+o6mse5Ll/eoXgZIjcEi8Wu4keTSMwEaT1nU4aNi6Zc4JqNBlQFJVIILrg8WxOK/f//p3s+PlbHNneRH/LEJlUBqvDQmFVNl/9yf/1DGabiWvuWIXBkN316D05QHAiiMlqYsm6atz5rYAOo8lANBBDb9CTU+Aik1bQVA1Hrhe08IJrsLttNF6/lJa32xlqHwEJlIwKkQRGox7ZkK1rKKjIY3osgCRLVCwrJR5K4B+dxmDUYzAZUDLZIm+TxcCqLcvPeEX8VMATjyZoP9RNLBTH4bHP3p5KpGba08rIsoQ734XT68BkMRKcCKEhgSRhshiYHPRTWl9COpkmGorSvbufln0dpOIpGq6to7Aqf846VFUlHIhydGcT+144iCTJOL12SuuL2Xj3Wq57YP0ZJ1kv5Mj2JiYG/XiLcubMw8ikM0wO+NBUDVknE5oK4C3KQZJOH7uwMo+j25tQVY1oMMp4/yR9JwdnZ3JomkZxbWH2CrssYTAbsbmsFFTmMz0eIBVPYXFYKF9agtNjxzcWoGl3C/FwknQyPfP5Zzjw8lGmxgLZF9XA4jCz/Pql3PbpG+cFOx+EJEmzbXAX0tc8yMv/9gYTA350ehk0OPz6CQqr8rnnv9w6b2r6KYHJIG//5gAmi3HO7oG32IPDY6fjUDctb7ez7rZV57xmyM4n2fnUHmRZnrO7cqqAe9vjO/hsxSM4PfMHMArCYlBVlYHWYToP9xCejuDIsbNkXfV57TgKgnD1WNTA4tChQ2zdunX261O1EJ/73Od44oknGB0dZWBgYLGWd1lRFIXdv9pH2B+hcnkZkiSRSqZnTyijgShDnaMsXV97+jkZBQkJk/XMqWA5+S5u//xWuo71UbKkiLxS7+x06c4jPUyPBUCCd148QsmSQiRZpudEP3qTAVXVCPsj6JcVIsuTZFJpZJ1MOBBFb9QTj6YBN6M9GqGwhYrVp3cYgr4QTbtbefs3B2k72ElwIkgqmZ5NiUKDTCpDToGLvFIvvqEpzA4TDRuXoCoqFUtLGeubzHaqSqSQdTKFlfnojHoaNtae8f0W1xZiMBsY7hwlGojiznfNpqdoaKiqhs1lzaZfGfXkvWt2QjKeQpazg+qMFhOxUIzJQR+DbSNMjWV3XCKBKEpa4cC2o5TUFLL21pXoDdl2uO2Humnb30E6mSGvPBeDSU94OkrX4V6mxwLEIwnu+PzW9+3mBdngoed4H7IsYXrPCbreoMfiMOMbGsXl8aNP/SNaSI+mX5LdNdIvoW59Le+8fISOQ91Mj08TjySIBrM7KKcC0Xg0gapm09P0Bh2KomKxm6lc1pgdiChL2N1WItMxhtqHefFfXs+ecEjZn0ff8BQFlfnUXVODTiejaRrh6SiHXjmGwajn9s/e9L7v81wEJoO89K+vMz0WpGJZ6WywqGQUhjqyBemf/tOHF9wd6jraR9AXpnL5/GYHRrMRk9nIibdaWHvryg/0+bxX674OosE4VSvmHl+SJIqqC7JNCQ71cM3t5xe4CMLFlE6lee2nuzjxZgupeBqDSU86meHgq8dYddMybv/cTed0EUQQhKvHogYWN91002xP+4U88cQTZ33+t7/9bb797W9f3EVdpka7xxnqGCW/PHf2xMZoMuAtzmGkcwyLw8zUaPYE0WLPpmv4hqfwFLqoXH72YvWuo9li5qUblqDTycTCcdre6WR0ZsKzhMTkkJ+JAd/MjoKCwWRAliRGJVi7dTlGyxDuvBTTEzpS0RQDHRlG++zEIhk0NYXeMkrxkl9ww8evxZ3n5Ln/u42B1iGGu0bxD0+TiGVbAusNOnR6HZI005bUbMCV62T1zY0s3biEwHiA576/jXgkQVl9MSW1hWTSGTQNRrrGqF5VQfkCcx5OKVtaTM3qSrb9ZAfyTDcjYOakN4LNaUFVVOLRJJqqkppJBdNULZsGlFZwzF5VlhhoHcY/MoWqqMiyhM1pJRVPkU5lGGgbxmQ1sWrLciYGfPQ1D5JOK+QUusmZCWg0VSPgCxHyhzm64ySrtiynqHp+d6N0Ko2SUTFZsm1sAxNBMqkMdreNeDQxp0BbQ0OnixKc9JMIG+k6JlO93Iw7dz9a+gSS5QFy8q/j+oc28ne/90NC/jCSLJGMp5DI1ui4ch1MjQaYGp3G6bVjtpkITIQY6R5juHOUVDKNJIHZZiboC6HX6yiqKcDmsKKh0bK3nUQsyfR4gMh0dj6IJEk4PXbUjELT7lauuX3VGdvuno/2A11MDvqpbCyfMwFep9dRWl9Mf8sQHYe6WXvr/MGE2YYBZ54cb3FkJ8crGWW2HimdStPfPER4OoLJYqRiWemCnc8gW2R/pna4sk5Gp9cx3j9/ovpiSafSdBzqoeXtNvyjAWxuK8uuraPh2rrLtq2vcPG889IRDr16nLxS75xAPBKIcujV47hynVz/4PzaLUEQhCumxuJqFw3FScXTs0XNpxRXFxIYDxINZk+M0sk0eqMe/8gU6VSGLY9smp1dMN7vw2Q1UXdNDfXrT+ecT8+kquh0Mhoafc0DhPxhbC4LmVQGVVVJxVNYnRbSyQySlL1KnoglCPrC9HdIePNXUVbTTG6JwvHdBnqazej0GYoqJYzWGpLpSkY6x3j++9uwuqyM9U7g8NqJHIpmJy/r9ch6GVVRMJgN6PU6Uok0Op2MM9fOlkc2YTAaKK4pYHzAx4GXjzA1No3VYSWdypCMJylZUsQdj26dU3fxXjqdjru+eDPvvHiYiSEf0+OB7KQ2sieP1Ssr8A9P0XW0N9t6tWt0Noc/GU/NnFjqiIcT6Gdy8JWMitFihEQau9tGxmoiMBlCTWv0twxRs7qSke4xYsEYBqMep9cxG9BIsoQjx0Y0GGNqZJqeE/1zAovRnnGOv9lMx6FuVEUlt8TDihuXkV+eh96oJ78ij9HusdmTfEmSCE9N4x+aIJnQmJow8OLjcRw5aa65NYfr7kyji7+Epquk42A3qUQKs82EqmooGSWbhsbMtGxVZWJgktwSD97iHIY6RsikM+SW5GQDBFXFPzxNYDJIZWP57AlnKpEmNBXGne8iEU0y3j+JK9c5+56cuU76WwYZbB+5qIFF78mBmRqO+cGBTiejN+jobx1aMLAwWU1zGgi8VzKWIqfQNfuz1X28j51P7WWsbwJFye4MuvOdbLhrLRvuXjOv7kPW61DVs09UP9t07ksplUjx0o/e4OSeNiRJwmI3Mz0WoPtYPyf3tvPA79+BO2/+cMfzoWQUpieCALjznHOaSAiLIx6Jc3xXM3aXbd7unt1tIx5OcHxXM+tuW4nFLoJMQRDmEn/FrxBmqxG9SU8qkcZkOZ36YnfbWLphCW0HuwhOhhjpGcdiM+Mt8bDhrjWEpyM8/VfPkoynsMwMeWs/0MmxhlLu/8od5BZ70Bv1MLNzFAvFmR4PYnVZUdIKSGGS8SSyLGdz4iWJeDhOKpkGTULNKBzd3sx4Xz4rNy2htHKAgc4UkqRSUusBXQnoSjEbsleN2w91MXWkh6rGcvpODhCeiqAqanYHRJZAk8kks7MY0qkM8XCCsb4JxvsmKa0rRpZltn5yM5XLy2jZ18HEwCQmi5H6DUtYuqEWR479fb+XTo+Drb99Pbt/tR+724aqZlN8vEU5GM1GnB47vScHSMRTqDMF4jqDDovNRCqZYnLIjzvfRWFVPpPDU0hyNqAzmvXo9DJ6oxmnphHyhUlEEjTvbSPoC6M36HB6nXPqISCbuqSpGunk6enRSkahZX8Hrz/5JiF/GJfXic6go79lmN6Tg6y6aRk5BW5URaNiWRmjPeMEJkOkk2nC/mmQNLzFJho3OTHbdExPpHnz1z50+jyuu8PHePfbHHrtJAaTgYKZmoVoKIZv2I8kSYT8EZy5DsLT2WL0WDiB0WzE4baRiCRJRFOAhqzLtgJORBJMjUzjznehqWq2sNuow2w1MTUWmFNon90pYjaIuVhURZ1XkP5ukjQzG2UB1SsrsOfYCEyEyCmYe9KcSStEw3Fu/MQmJElisH2Y5//5VWLBGIWVeRjNRpSMgn90mjd+9haSLLHx7rVzjlG7upK+poEFA5d0KgMwO5BwsR169TjHd7VQVJU/u/sJ2XX2nuhjx1N7ePBrd59XStgpiqJwcncbR3c0MTHoAy07r2T1zY2s3tq44MWBqbFpepsGSMZT2N02alZXXnAr4AvlG5mi/UAXY73j6Ax6qhqzQzWv5F2diQEfgYngbJ3de7kLXIz2jDMx4KNi2UendbsgCBeHCCyuECVLiiiqyme0Z4Ky93Q8cnjs5JZ4WHXTcq67/xpMFhOl9cV0H+vjtSd2Ys+xU1xTOPv4TFphoHWIVx/fySe/8THKlpZgMBmIRxLEIwnSyQw2lxWMBvR6OTsgzm7IDqqTZSRZyp4AaxoGk4HwVIRxg45Xe1U01Yk7z8aKG+vA6ELi9AmCJGXrPcZ6xglOhLJdk9JKNh0umZ49gVcVNZtapJPJpBVS8TSZtDJ7HFmWqVlVSc2qyvP+fjZurqd5bxsWh4Wc/NMnkpqmMdI9gcVmZsmaKgKTYfyjU2iqhtFsxJwxEfJHAEjFUyRjSfSG7G5LOqUwMeifnSxtdVmQ9TL162vxDU/hG/aTTmTmrUXTNE4lBBqMOt7+zUGO7jjBsZ3NpJMZKleUYXVZsNjMuPOcRIMxTuxqoX7jEsZ6J/AW51BYmc/0RIDeE/0omSBGo47iGitWR/ZX3FtoRFU0juwMsGqTkY5D7SRjyTkF1FaHBUeOnfBUlFQyRXgqgk4n03dykHgoRs2aSsqXlhAYD5JJKxjNBgKTIXpO9DM+MEnQF8LutlNQmYveqCcZT2Ew6NE0dU7K46nuX55C93l/fgspqy+h41DPgifv6kzgdqZBfLklHtbdtpI9v36HdCqNp3CmK9R0lMkhP5XLS1m+uR5N0zj46jHC/jAVM7VOkE23yi/LZWLAx8FXjrDihoY5J5fLNtVxbMdJBttHKKktnD1xTqcy2XkiDSWXRVeoVCLF8TebsTktc4IKyM6ZySvNpftoH5ODvrMW0Z+Npmm89cw+9j53AJ1ONzuPZnJoipd/9Ab+kSlu/cyW2V0fJaPw1q/2c+SNE0SmotkNRgk8hTnc+IlNrLxx2YW+7fNycm8bb/z7m9lWymYDqqJyfFczJbVF3Pdfbz/rIMzLmaYx8zu08P3Z2zXOksUsCMJVTAQWlxlFURhsG6HnRB/xSAJXrpO6ddXkleWy+WMbeP4HrzLYPkJ+eS5GsyGbajIwSU6Bizsf3Tp71VPTNI6/2YymZVMM3k1v0FFUVUB/yxBD7SOUN5SwZF01zXvbMdtMp+dKqNliZk2DRDRJLJIALfsPi6zXoTfokHUSqqKhKmq2C5WqIzydRmdwzAkqIHvS0n28j1g4gaqcKtLOFmprWvYEQoPZEwo1k51fYXVZ511FhmyxbsehHkZ7x9EbdFQ0lFKzpuoDtQStbCxn84Mb2PPrd5ganc7WVcSSZFLZHaGcIjfVq6qQZYl4JM7UeLamwWgykFEUpkcDuHLt9DbpSCXTGDQ9eosuu/ugaaQSaZLTEdx5LtbdvoqJAR97fvUOiWhg3tyQVCKNqqi48p20HexmtHs8+/xYCrPdxEDzENOjAZZuXILNacXmsjI1FiARSbDh7jUceaOJdDKNJElEgjEMepm8Eh1Vy+amMXgKjAx2xhjqlpgay9aKJGLZLk4GkwFJksgpcGOymPCPTpNKpPEU5bDxnrU07W5BbzDgcNtxuLO7QkFfmLZ3uohMR7I/J6pGKpHBNzKF0WSY3T1w5zlnu29pqsZY7wRVK8px5Ng4uacVRVHxFuVQXFt43q1oAZZuXMLh148z2jNBUXX+nPqZka4xPMU51K1fuLBfkiRufDibbrf71/tp3d8OkoSn0E1xTQGldcW07OvAleug50Q/OYU5C16x9xbnMNg+wkDrEEs3LDn9vS/M4Z4v3ca2H+9goG04W1+jaciyTHlDCfd86bZ5O1mLITARJOgLkVPgXvB+e44N38gUvuGp8w4shrvGOPDKUZxe55y/TVanhfB0hEOvnaB2TTXVK7Pds/a9eIjdv9yP0+ugsjEbzCmZbBC/7Sc7sNjNC7bT/jANd43y6hM7yaQUqlaUz/4sKBmFgbZhXvrRG3z6Wx8/r85niy2v1IPT6yA4GcK7wPDG4GQIp9dBXqkY7CgIwnwisLiMJGJJXn18B81vd5BOpJFndgv2v3iYTfddw6b7r+H+/3oHe587wFjvBOlUJjt0rLGcGx++dk4qRSwcZ6x3YrbD03tZ7GbSyfTsdvZdX7wFWZZoO9g9e7ukk0mnMtldCgl0koSiqGhqNifcne9GU1RUVaW8oYT88jzUjMrBbUcZ65mcs7OiqiptB7tmW8aqM+kyp656qYqKpJNQ0xksVnN2h0LKFqivuL5hTo4+QNuBTl776S6mxwLoDHpUReXwaycorS/mvi/f/r4DxyRJYvPHNjDeN8nLP34D/0g2uNAb9Thy7Dg8diT51PfKQslMLrGiKDS/3c5Y3wSZdAadQUcsHEfNKNmCc1kik1bIpDKgZrs3eYs9lNQW0XGom3g0TmgqjMliwmQxkkqkCExmT+QKKvIY6RqjvKGU0Z5xDCY9DrcdVdMITgTpPTnA8uvqkZCw59iYGp3mE390P/XX1NB2oIv+1iHG+ycoqfFQWDKI7j1NW3R6CVSVTErC4izFaMm2qp0Y8GF322aDC4vdjN6op7Ayj//nB/+FoupCpsYC9DcPvuvz1Og80kNoKlv4LWnZOgWj2UAkECU8FQZZQpZllIzCodeO4S3yYM+xkV+ei9lm4ok/+08i09kOVEaLkcrlZdz2mRvJLTm/YXF5pV5u/9xNvPrETnqb+mfzv+ORBDmFbu58dOuc3an3GuoYoftE/8zPnkQ6kaa/ZQjf8BSjPeMzAxk1Jgb9c4KGud9jHcwElu9V1VjOZx97hM7DPYz3TSLpJEqXFFGzuhKT5dIFFZqmgToOysjMokuRdPkASLKMLGenpJ/puZIkXVA9SMehLuKRBIWV+fPuc+TY8Y9M03qgk+qVFUSDUY68fgKbyzpnh0un11FUlc9A6xCHXjtG7ZqqC0rNOlfNe9sJT0Woaiyf87o6vY7SumKGO0boOtbHsmvrLtmaLhaby8aKGxp48xf7sDqtc3au4tFsXd2WRzadsVGBIAhXNxFYXCaUjMIzf/Mb9j53AKPFhN1tnR1yFpoMs+sXb+PKddB4fQM1qysZ7hojEU1ic1kprimYd6U321VJ4kz71adaup7a77a7bTz49XsY6Rpjx1N7OfTaMQITwdmiV1VTkWUdOoOGqqqoGY3AeACb24an0JXtbjQexFPoxmQzMdo7Tmld0ew/usHJUPZkPKUgSZw+8cqeq2XXNHMyk8lkkDMysk6mtL6YVCLJr//hJXJLPSxdX4uqarzy4+0koikqlp/uAJROZRhoHeKlf32dT/2Ph973auGhV4+z7Sc7CPnDmMxGNFVDURR8w1NMDvqxOsysuH5umsVw5xgDrcPZLlL+CMxcdVYyKtFgjFgkfjoQ0+tIxdMcfOUoWz+5mbwSD93H+oiG4oSnomiahk4nk1vi4bbPbqGveRB3vguDUY8sZ69oa5qGLEnYXFYCE0EigSgOtz2bnmYxYDDqqVpRQdWKCuLRBD/+xs9IxmPojBFQpwEXSNlf81gogdEcw1WwHKNnI4e2v0rZ0hKQJKZGpokEo0hkX9doMXDPl26jqDqbQrf8unq6j/USC8eJRxIzn+c4mgZWu4V4NImSUQhOnkpvA1QNo82IrJOJRxL4RqZYcdMyLFYTLfs68BS4qVhWhixLxMJxOg52Ew3EeOSP7z/veQ7LNtWTW+Kh9Z1O+k4OIskSVY3lNFy75KwBy1DnKM/90zaCvhAFFXnkl+dy/M0WQv4w6WSGwop88ityCU1F6DrWR/vBLq65Y9W837tELInOqMfhWbjWx+a0snrr4s3d0dQgWvwFSJ8ELUp2C9KJZlyNZL4bT6Gb/PJcRrvHsTrndhpDg8BECIdnbmrluZoaDWA0Gc8YCJisJqZGpgEY6hglMBGal/55iqcoh6GOUabHAxe1EcD76TnRj91tW/A9GGYGaI71TlyRgQXApvvXE5gI0fx2G6qqYbaYSMSTSJLEii0NbLp//rBTQRAEEIHFZSGdSvPsP77Ca//+JmjMDC2LMT7gI7fEQ926amLhOIffaKJhUx16g56K97RUjQSidBzuYbgzexWypLaIvPJceo/343pPKhRkdzRMNiNFVaevGsqyTGldMb/zrYewOsz88nsvkE5m0Jv0pBNpVEXBZDWRiqdQlAxKdObKPNlUKZCyk6l1OmxOK71NA3iLPZitRkZ6xglNhrMnrWYjqUQ6e/Vz5v2eCi4kWUJv0ONw23B47RhNepr3dqA36Ei9mT1Jd+Q4CEyGqV4x92qhwainrK6YwfYRuo/307Bx7lXlWDhOx6FuOg93MzUW4M1n9hGYCIKU3YHRGfSYzEZsLgshX4Tmve0U1RSSW5Td8k+nM/S3DpKIJLDnZNcXCcVw6LO7FulEti2twaLH6rDMtIJN8uYv3qbtQCcGk5GqFeX4RqYY7hglncrgzHWQU+Dm6BsnmBj003j9UgBcudkOOdmicCMGs4FoKE4ymsTushGaCnP9gxvnDKqy2Mw03tDAzqf24sxtwCi3gxoATUVVNcYHVJasLaKk8VFUzcuStZ207OugrL6Y4ppCwlMRErEE0WCMhmvruPHhTbPHrt9Qi+NpO2//5iCQ3YkJ+SPZoM5iJLfEQyKaIBaKI8kSOr0OTVXJyXfi8DiIBmO4810ExgOMhOPkl+XO6ThjdVgobyihv2WIlrc7uPbedR/od2ch+eV55JfnseUTH+zxmqZx4JUjBCaCszNiBtqGSITjFFbmEwlEGewYxlviweV1UL6slO5jffhHpufsjGmaxljfBOVLSyhfenkUYr+bpiXQYv8B6SaQC0GeqTfRpiG5C02NINs+w7rbVvF816v4R6exOs1MDk4xOeQjHk6gqirXP7gRi+P8J5BbnRYy6fk7Oqekk+lsjRfZiwWapp2x05veoEfNqHNqsC4FTdNmdrDO9ADOeFHnSmC2mrj3y7fRcO0SWt/pJOyP4PDaadi4hJrVlWKGhSAIZyQCi8vAwW3HeOelw8iyPDMhOfsPViadYWLAh8lipKiqgPH+CabHg+S+J++1/WAnT//VbxhoG0JVVUwWE44cOzmFLlKJFL7hKbzFp4+bSqQY65ukcXM9xbXzrzzqdDrq19eSU+hG1slYnRY0VSMWSRCaDM10hMo+VkMjlUih08l4inNIJ7OtRjc/tAGn10HP8X4iwSjJWBJZL2PQsp2tZJ2MTpZRZ+osMhkFNO3/Z++/w+RKz/NO+Hdi5dDVOXejG0AjA4MBJmFy4swwkyJFikGUZcuSLX27srW+dr2+vGtZu15/9vqTd1eyJMqSqEQuJYYhOTkPBphBzkA3Gp1jdXXldPL3x1tdQBPADDCcGQ7Nvq9rBkB116m3qk7VeZ73uQP+kJ/uoU76tvWwNLlEY0eCcEMY3afheR6p2TRvPXWUzvXtuI5Lfrkg9B21KY/m03Bth/lLC6sai/RChif/4Fkmz82A5zF2ZorluTQgpjuqTzhjGRWDQMhPqCFEKVPi8NPH2fv4Leg+neRUktRMGq2WH1IpVLENkW0hSxKaTxQ58eYojR2NyIqEZaTJLxdQVIU7Pn4rruOyNLNcp5KkF7JIkoTu01ieSzN8eJRdD24j3BCiqauRhTExFVB1BUkSlLLp4Tka2xvYfs+mq967PR/ZxdzoAiNHxghE+giGq5jlPPl0lfaBTu7/yudRtDYU4Il/9DCaX2P02DiVQgUkiWA0yOY7NvLoL9+3Snw8cuQSxWyZhrY4RtmglCsD1AP0AhE/pWwJVVNQdVVoZiwbENbEtmlTKVSYPDuDP+ijc+BqEbWiKgTCfs4dHP6JGoubRS6VZ/zU1KrPXmo2XaeGBaNBipki6cUMElI97O/swWE27V1PuCGEWbXILGRJtMe5/wv73tby+KcG6yxY50HpB+kK6pWUACkA9kmwb2Prvs3klwu8/M39nHz5DNWygazIaD6NcCzE5LkZnvr6izz2Dx5E9918gTm4q59jL5yiWjau0pWs6I023DoAQLwlhi+oU85XVk1QVlDIFAnFQ9edEL1f6N/Ww1s/OkZz99VTMMu0kWSJ1mtQvX6WoOkaQ3vXX5f2t4Y1rGEN18JaY/FThlExOPnKWXxBH6q2uhhRNZVA2M/SzDKNHYm6oPpKTJyd5j//0z9leS5NOBbC5/dhGRbphSzlQpnG9gRGxWD89BSaTxNp3LLExlvX8egv339dsWwg7EfTNXwBnWgiQiaZo5KviERqTcE2xQ6h5wp9RCFdoFKqil08Cc4dHOFX//df4r7P34VRNvjRHz/PpRMTwvHJdsSfsoSsKKi6UtNwe/Tv6EXyPM6+fgFJEYW37tdo7m6ia0MHzV2N2JbD9PDsFWvyUHWVeHO0FgYordosdF2XZ//sZSbOTtMz1Mn82CJW1UJSZDzHFb/riQbDthxK+TKBSABVV1FUhVK2hBt2iDZG8IfE+7Q0vYxl2tiWg1E2hL2qprBiWaMoMtWyoAfpfk1YtuYrlHJl5i6Jx3ddB9t0BBWrlqMwMyI0Fq09zQzs6ENCFLmZxSyKplDIlOgcbOPhr9x3lXjW8zxK2RJb7tpEIBJgfmyRaklHDzdy7yMb2HHfVhrbL9NFwvEQn/rNx1mYSDI/lsRzXVp6muhc377qvDCrJm/+4AiarrH7oe0YVZPCcoG3njpGpSSsiHNLeSzLQVoR3jsOsiLX6WgiSC9X//uKANe2nHooIoiU63Khct08ifcDRtnEMq1VxaljO/XmT1FkTMNi5PAoji3OF891sU2bibNTtPW1EG4Is/uRHdz6yI5rBhy+L+uuGFw6McH08Byu49La28z63euua7nsWafF+SldQ88hBcBz8axzyNoWbvvoLZx54wLJqWW6miLoPo2G1jjRxjCVYpUTL52hY6CNPY/uvOl192/rYeOeQc7sv0BTZyPRRrHeQqZEaibFhj2DrL9FOGR1DLTSu7mLkcNj9G7uWqXtWMlLeeDRHTdk2PBeYsudGznz+nmSUymauxvr56rruMxenKN9oI2BnX0f6JrWsIY1rOHDgJ+osTAMA5/vp+9k8rOM5bkM2WSOlu4mlmfTWFVLhK3V4A/6yKXyLE0v0TPUtYrW5Lou3/6PT5KaSdPe37Jql9SsmpTyZfLpInd/5jb84QCp2WV8AZ1123tZt6PvbXcbezZ10tbfTGo2TTFTorBcwHFc8Ri1AhxPXEjLeVEIUjTQAxqJtgamL8zyl//rt7n/i3ex+XZh7WoZ9mVrVdfD8Twcx8W2bGRZ7AxrmsrE2WkkCdp6W5AUGbNiMnVuhlKuzNDeQXx+jfnxJIqmEm4II8sSlmGTmk1TzleINUdp6WmqP5fp4Tkmzk7T3t+KUTFqx5eQkHARMg+zaiF6IqExWAkcjDSGeexXH2L9rn4kGc4eGGF5Tuxm6wFNNFmWg1W1sC0bSRIhg8vzacyKhSRLaLXi2nVd5i4tUClU8DxRfAttRgXHdnE9MKsGF4+N0dSRQNNVBm/pJxgJkJpLs+P+rdz2+C2s295zldh3eT7DK996g0snJ6jkq8iKRLxVOFLd9sQt+K8jDpYkifb+Vtqv41m/8volp5fpqBXMPr+Or7OR/m09nH/zIq7rUi0bdXtK2xTC+0DYX99lliSwqjbNXU1IEoyfnSY1nRLhiJpKS3cjrf0tlPNlBgY+WCFuKBbEH/RRKVTqO+jhhjDJyRTBSACjYlDKlvBcj8aOBHJtbeu292IZJon2Bj7/P3ziXYvO3w1Ss8v88I+eZ/rCHK7rIklQLlSJJELc+wt3cvtHd18dNucWgbf7vtbALQAwOzLP8lyGLXduvMp2NhgJ4A/6OPXKWXY9sPWmQ+00XePxf/gQgbCf4cOjTJwVeopgNMDOB7bx4C/dXT+/ZVnmgS/cTT5VYOLsFJGGCHpAo5yvUClVGdq7nj0f2XVTj/9eoHtjJw9+6R5e+pv9jJ+ewh/y4zoOlmHR1t/CE//woQ+Fy9ca1rCGNXzQuKkrwrPPPsvf/u3f8vrrrzM1NYXrugSDQW655RYeeeQRvva1r9HRcW2R3RqujZUCKhjx09AWJzm5hKqrq3bmbMvBqFjsuH/rqmZgbnSB8VOTBKP+q6gXul+nWjYpZIqkF7J8/n+476bWpft1Hv/Vhxg/PcXi1JKwoPWE16xt2jXht5iqOFc4IjmWQ2puGUmSGTl6idlLC4TjQeYuLaIHNMyqCSt06BU6lechqTLhhhCLEyLwTvdpaLXnqkYC+AI6y3Np5kYXMGpNQLVYxCwJOpOiBfGFQqRml4k2hRnc1Vd/LsmpFOV8hWJWZBIs1bImQAjGPTyQBAVMkmqOVZ4If6vkq2JqU3PXijaGmR9bxHVcirVi02PFOhc8ySWzkBWZF0EfRkW8Bw0tcXxBH7lUQVjDyhKKouApoPlUNJ+KUTYAqBSqTA/PgSdcs6JNYT7zix/ljo/dKu7jeSSnU8xfWsR1XfwhH2987xAzw/M0dzfS2tMsAtvmMrz27YPIssTdn779mu9zuVDh4rExZkbm8FyP9nWtbLh1YNWut1Ex6yGGV2LjnvWUcmWmL8xhGYLetuL2FQj7aepMoNbOy2KmhCRL3PqRnbz016+LAL7GsDh/LJuJs9MsTqVoaIuz9a6NN3Wu/qQIx0MM3baeg08eIdoURVFkmrsaSc0sUy0bZJM5PKCxUzQVxWyJQNhPa08TiqYyPTzL7OjCOzYW1bLB6PFxlmuhih0DbfRt7b7pwtysmvzwj55n8uwMXRvaqZQMps5Pk03mmTw3zciRMU68dIYHfuluNt22/nKTprSAPXztg3oeYIIiGvLl+Sy2aV3VVKwgkgiTqZkJvJsU7lA0yEd/7RFu/9itLIwnwfNo6W2muavxqqaytbeZz/3OJzj56lnOvjGMWbVo7Eiw/d7NbLt76KeW/rzrgW20r2vlwqGLzF5cQNUUBnb2s3HPwA0Fda5hDWtYw3+LuKEr2ve+9z3+xb/4F+RyOR5//HF+53d+h87OTgKBAOl0mjNnzvDCCy/wu7/7u/zyL/8yv/u7v0tz889mONAHjcaOBhJtcXJLBdZt68WqWmRqbkyKqlDOl0GCvY/t4paHtq2679LMMqZh49oumWQOz/PQdFXQeFQFf1Anm8rX05xvFjvu28IX/sdP8wf/3Z9RqYXZSTLgCbqTLAs6i+d6dQG0JIFng6oJm89itsSc7RJtipBoj7MwlrzqcSRZHM+o5JElmcFdgyyMp7FNW6SCI/j3qqYyNTxLtVSkockgn6ni2CDLHqpWgGUFXzAu3FquoPJYhsXc2CKu7aAH9FrwnrCKXRGfr4gtPc/DczwUTSaaiOB5Xr3gNypGvXCuloxVjlZ11P7d1NVItEEUX8nJJNWKQWo2jW1YOI6LT788lVI1BVmW8Yf8VMsmiY44n/qtxynlyvhDPvq39dTtdsuFCi/+9etcOHSRcr4CknDqyafy7H54O+FaxoSqqbT2NrM8n+Hw08fZetfQVdkE82OL/PCPnmPu0qIQYUsSR587RVPXUR7/1Qfp3yZyBCINIXS/CFC8stBUFJndj+wg2hhh/tIiqqaSWcriC/mQqdHKciWqFROjVGX3Izvx1RquFUteVVNRfRq27ZKaTdPa08SGPQM3cZa+N9j72C6mh+cYOzmBospUSga2ZZOez+A4LsFwALNsUi0b6H6N/m09+Go70nItRHDHvVuue/ypC7M8819fZGF8SSSTe6Kh7N3SzRP/8KHr5kZcC6MnJpi+MEvXhnaMismFt0YoF6oi4yQeJD2f5eJx4eDluR5b7hSNmqRtwzPeBDcP8o8ZOngZkAJImviOUVT5iqC0q6dHruMiyz+Z7SxAU0fiKs3YtdDQGue+z93Fvk/dJmy2/doq44KfFKZhsTSdwnM9Eu0NN5yc3dbXck3b3DWsYQ1r+HnFDTUW/9v/9r/xH/7Df+CJJ564Jif/c5/7HACzs7P8/u//Pt/4xjf4Z//sn723K/1vFLpfZ+cD23j2z14mGAuw+Y4N9ZTmUqGCP+xn36dv49P/nyeumkqUCxVK+RKlbBlVU5BqwQuqr0CiVQivXcela8O7myJJkkTX+jYa2mKAR6VQxbEdXHeFBnVFVV2fPlCj/2gEowFyyTyO41AtVnFr/HRJFtqMy/f10HWH9q4cQ7cY3PaYxNGXVQ69aBFS4vXiRdNVcqk0uDkc2yMQVnEtCdP0cGwIhG1C4QyXTozzX/7ZX9A52Mam29aTTeapFCroAY1MLejOrJpX9QSO7dY8+iViTVHC8ZAomscWAZi5uEB6PoOiyUgyOJYrph0ySJ5UT5f2PEjPZTArBq7jEWmMIiFyEiS1pkFwRR6Iqsr15smxHNFQKgrb7r5amO3YDk9//QVOv36Bps4ELd1NeHgsjB+jWqpy8dg4W+4cQvdfniw0tMaZPDvN2KlJdj8cr99eLlT44R89x/yYcDGSVVlMXFyP2dEFYdn7Lz9Dok0E13VtaGf89DS9m7tWFZqe46FqKp/95x/nzk/s4dk/f4njL5wmvZCjWjIwXJdQLMDtH93Nx379Ub71775L/45eHMthfmyRQrooqDyyaLJzywVOvHSGXQ9u+0CdZxJtDTz4xX381//5m1w6OVnXWASjQarFam1aJzj/rb3Nq3JVFEXBMq7vcpRZzPLD//Icy3MZuja01ycURsXk4rExfvhHz/P5f/HJGxZCz43O4zoi9X7s1CTlfIV4S6z+vviDPiRJnM9vfO8Q63evE8dWN4DvLjBeBS8HUk1v4y4DNvgfAqUPgK4NHYTjQfKpwjVd5dKLWTbdvuED35lXNfWmJzxvB8dxOPbCaY6/cIrUXAbP84gmwmy7ZxO3PbH7A9durGENa1jDzzpu6Bv60KFDN3Swzs5O/v2///c/0YJ+HrH74e1kkzmOvXCK1Ewa3a8RSYSJt8TYfOdGHvsHD17VVDi2w/k3R/AcwVmWZAndL1xsjIpJciaF53hEmyL10LIr7T1vBFMXZnnq6y+iaSrheIh4a4z50UUco9YVXGvHHsATxeaKkFuSJGzbwcqVUFQZRdOFgNt10TQbSfEIRaF/s0TXoEUwVOC+T5jEEkGe/5aEh4osyxRzJULhHKm8hy+g4NdBUkCWFTzXI5N0WZh08Jin+IMKmq7yyjf3owV8KJpCZiGH5tPwh3yYhoVrX2FRKV3O/gg3hGjqTNC5vl3srNesLMdOTuBYLpIs4fP5qRQryJJcfxmc2u+5tksxV8KsmkQbI8RbY5gVk/RcBlmVhXDZcpEVCT2g4zoeVtXAq4XMXSvtFmDy3AwXDo3Svq61PjlwaloXPehn7tIClmnT3t9CY0cDwUiwNolYsQO+jItHx5i7tEi8Jcr4mSnSC1nwPGLNUZq7G0nNZjj/5kXu+uReFEXh3s/dRWbxqZqFcAN6QKdSqJJZzNK9sZPbn7gFWZLwXOo5Hpqm4A8F2bh3kE/95mNUyyblQpWOgVZUTaWxo4HMQpZLJycp58tYhpgQfOf3n+L8Wxd5/Fcfesegw/cKtmVz4MkjqKrCnR+/FdcVkyRfUOfky2dZnFxi/a3raGxdnZXgeR5G1aCt//q71ucOjpCcStG39XLmCoAvoNO9oZOJs9OMnZy4Yfcdx3bFhK9skF7IEogEVjV7kiThuR7N3Y0sjCeZvjArjAAkBQKfAKUVz3wTnCSCz9iFpN8B+p76cRrbG9hy50be/OExZFUhHA/WaYLJqRT+oI9dD2z9QLUw7zU8z+PV//cA+797CH/AR3NnAkmRyacKvPLNAyzPpvnYb3zkXTlffRAwqyZT52cpFyoEwn56NnV+oEGLa1jDGtZwLay5Qn0IoGoqD3/lXob2DjJyZIxcKk84HmTwlnX0b+25pnXl1PkZFieW6NrYztzoApZhY5RNZEXCNEyMkoGiiHTal7/5BidePsM9n72DHfdtuWYxYFQMRo9PMHF2CrNq0dzVyKnXzjI/nqRvaw+XTk5QyBTFzvYKs+oaTYWsSDULUlixeZdlCVwPtyb+lmUJWVdRVIdAwEDzy8SbdKZHXWTZplrxMbgjxK57clSNCGfe8mFWLUzTIhSxyKddqmWp5iUvJiSW6VEtSzi2REOLQ0tfC5ZhU8qVKE4tI8li4uF5HuV8tU7xWJky4IHiU/D5dRo7Euy4byuaT2PizBSJ9jgHf3CE/d89hKxItcLNFUF2ioxtObhX+Og7tYbFRFBn7Lk0gYifYCxI/5ZuTu8/j+d6IlndFsnlekBHkiUCIR9DewaveZ6Mn5nCNp1VdCTHcSikixQyRRzbpVyYZn5s8bJIf7ugM63kAqxgeniWQqbI/NgiZtVE94vAsoXxJMkpIVo+d3CYOz8his2eoU4++9sf49DTxzn/1gjGfIZQLMhdn9zDno/sIhQP8Xf/8UmGj4zRvbGTjXsGkSSJSrHK/PgiT339Re757B0omlIXbFuGzcSZaUq5MpFEGKNsYNb0HGdeu4BZtfjS//yZqzj0nucxP7bIyNFLpOezBMJ+1u3oY932nnc95Rg/M8Xw4VFUVWFxMoXm02hoFVOAwV39JKdTTJ6ZXtVYeJ7H4uQS8eYoQ3uv/Z4BjBy9RCDsX9VUrED3a7iOy9SF2RtuLFaMCYyKWXcduxKmYdHS04ju03AcdxUVUpJU8O0DfS+4Qp+E3CRu/zHc94v7sEyHswcusDSdEuc9Hg0tMe77/F0M7Oi7ofV+WLEwnuTIsyeJN8eIXzGVaeoUCfHnDl5k4971bL1r6Ke4ymvj3Jsj7P/OWyxOLuE5LpIi09LdyF2f3MuWu4Z+phu+NaxhDT/buKnG4vz587z55pvccccdDA0NceHCBX7/938fwzD40pe+xAMPPPB+rfO/eciyTO/mbno3d9/Q75967RzTw3Nit9wTRTt4GBULq2qhaioNbTH2PL4Lz4XU9DLP/tnL+EO+qwqY9EKGH/zhcyLjAaiUKsxeXCCzmCUUDZJbyqP5NIyyiQTIqoxri6mFotYEuzXLVq/2XGRFxjaFjmElfM6uTSkcxxXHkS0U1cMfVFmadTENl0I2yOm3LI6+4vDIL0oM7cqRz+9mZngWTVOwbZfGNpulOZlqCVRN7N5WijKOLaFqHrpfCJdXMjByqQLVkkGsOYJjOViWfdlmtgZJRqQByxK5VIFKqUJqdpl8usA3/4/vkU3mKCwXBYXKo65JkF0Px7Sv7rEkQRMyKia2pWBbNsFIkIe+fA96QGPy7AyWaYtMEED3aQSiQRKtMbbfu/nHj4ZjO0wPz7E0k8I0TPwhP4n2ODPDc9iWjW3ZgveuiGlBMVvi5KvnmB2dZ+vdm66yviykiyQnU/hDPkGjQcJ1hIVqdjHHwkSSQqZEtCnC7od2MLirv54Ejne5aHFdcdulExNcOjlJ94b2VYnngbCfnqFOps7Pkl3K09bXzPylJF0b2lmeS5NPFwnHg2QWMiJwT5FJzSyDJHHge4dYt72XB7949xWPJ+g9B39whHKugu7XsC2bo8+fZP3uAT76jx4iFLu5yZzjOLz01/sZOzmJL6DXC2jNp9G+ro2+LV30belmbmyRsdOTde1NsZah8OAv3fO2wm3bct4212JFq3QlFieXOP/WCDMj8yiKzLodfQztHSTWFGXD7nW0dDexOLmEoip1rQoIipvmU2nubsKomKiqTOga+Q+SpIPy9unZ/qCPvY/vwrEdhg+PAtC3pZt7P3fnfxO6govHxijnK7T2Xq0H9Ad9SLLE+YPDH7rGYuToJX70x89jGzbt61rRfRqWabM0leJHf/ICsqr8zCZ+r2ENa/jZxw03Fs888wyf+MQnCIfDlMtlvvvd7/KVr3yFHTt24Hkejz76KM8+++xac/EB4PxbF9n/3UNkkzkaWuM0tMQpZksYFaEbiDZGCIb9aH5N7MQrMq19zUwPz3HkuZNsuHWgrpVxbIenvv4i46en6NrYzvSFOcZOTtaTpAtOEVUXvGbP9fAFdKwrrGaBeh4EniimZVXYv+oBnUhDGMu0sE2HYDSI6zgiIE+WCMdc4k2wnIRy0UVRwDQ0LNMlc8phcUri/s/OUcoPEW2KkJpdxjIV4gkTWXbIpCSqRQnHBcuU0P0e/qCL62hkUzlK2RKO62KUDRzLZXkuU6eJwGXRODVml+e6SKpKMVNk5PAYqk9lYXxRUHtkCVmVhca7ZpUL4jWQZAlFlurZHvVAXgmRxq27mBUXX9CPbTk88tX7efbPXyaXzKFoKpIsinpf0McdH919VRNQKVV55k9f4uQrZ0jNpKkUq3iux8SZKRFepsoiT8T1UGqvvWu7WKbN/KVF4s0xxk9Pse3uTVeELzoYFUMEJyIoLsvzGWGzqwhKk6LKjBwZY/LsDDsf2MqFty6SWcjR2NFQCwiscPDJI0wPzxFriuC53qqmAkTRbls2eB6Xjo+z97FbePIPn2VxYonleeGOlF7IUkgXUTWFps5GwvEQju2wOLnEU19/gVse2k5Di3AeOndgmNe+fZBwPERrT3P9+VTLBucODhMI+/jYP370pnZsj794hnMHh5Fkua5VEKJ9k+kLM2g+labOBMFYkO33bGby/AyyBFvu2sjWu4beUcPUMdjG3OjCNX/mOqLRvrIxOfXaOV74y1fJLxcJhP24jsvIkUsce+EUT/yjh+kZ6uQjv3I/P/rj51kYT5JJ5Yk0OFimhebT6NvSTaQxzNzFBdr6W+h+lyngJ189y4t//Tr5VAFfQMdxXC4cGiWfLvLxX3/0qoLccRyyyTye5xFrinzo05mL2RKKplz3XPGHfGSS+eve3/M8lufS5FIFVF2tF/nvJxzH4a0fHcUom6vS3TVdpWOwjZmROd784VE27F73nmpR1rCGNazhRnHD3zz/5t/8G37nd36Hf/tv/y3f/OY3+eIXv8iv//qv83u/93sA/Mt/+S/5d//u3601Fu8zCpkiL/zlqyiqSMT2BX0oikwg7CebypOaWSYcC2KZDolEZJXYPtEWZ25UCJBXCpmJs9NMnp2mfbCV+bEkZw+IvIlA2I/netiWI+xTg4JTv+L8JMmgKkrNWckRtbQEsqqg+zUS7fF6xkOiLY5RtSjnyji1XXXXdmlo9ijmXIyyi2WAHhNOWJIkhOjZlMeh5/z8o//0IC9841WqJZNQNEgxZ2LZLkZFxbJkJMmrmTp5OA7k0iqO6WBWLGRFurzDXnO5WYFXS42WFAkJCEREwrhl2DR2NrAwnkRRFfxBH9lkHn9AhNgZNVH2yjEUTca7csO59hCyItfyKkTTZZs2uVSefZ+6jWhjhBOvnGHq3Ayu69Ha28z2ezaz+c4NVxkkvPKtNzjxyll6hjqpFKpirdEA6XmRgULtueOJFHSQUGvvg4QQ2j75B89imza3PLQdAM2v4Q/5xVQiEaacq1DOl2sTAAfNp+IL+ujd1MX8+CLf/7+fIdHewODOvlUi4WhTVFjFTiZXBTzats3CeJLFiSWMikmlWMUom+x9fBcf+dr9vPHdt0jP5ygsF6iWTfxBnXhzrE7ZUlSFUDxILlXg/MER7vzEHlzX5fhLZwDpKhclf9BHc2cjI4cvsfTRZVq6m7gRmFWTo8+fJBwPUcyVcV23bjvsD/lwbIeF8SSJtji7HtzGx/7xI/Vz6Eably13iDC19EKWRNvldXuex/x4ksb2BjbWkqbnLi3w/DdexbVd+rf1XA5ec12mL8zx1Ndf4Cv/+nP0b+vlC//TZ3jzh0d55r++SCFdpLm7iY7BVnxBH9Pn5whGA9z9mdvfVYE5PTzL8994Fc/1Vq3DsR2mLszyoz9+ni/9q8+i+3WRQv7GBY6/dFq4XnkeDa0xdt6/lZ0PbP3QNhihaPCqSdGVMMom0fXXFqen5tK8/vdvcun4OOVCFUVTaOluZO9jt7D93s3vGxUpOZmqWRtfW4fV1CV0NfNji3RvfHcN5RrWsIY1/CS44SvO2bNn+cY3vgEIF6gvf/nLfOYzn6n//Atf+AJ/+qd/+t6vcA2rcPHoGOmFLIM7+6kUqhTSRWJNESRJqmsIcqkC4YYQrb2riytVU2qBdJcvpgvjSWzTwTEdxk9N4tguwWgAWZbR/Q62WcYybRynJkx2XJABFxRNqbvC5JcLuI6LqqvoPo1SroymqzS0N9C3pZstd25k8vwMF94cAUTRlluap5BdxDLBF5CRZYXMooXjeDi2h+fC2DmLp/74eS6eGCe7lKOQkQAdsyIk06LJkcADsyJjGYKGhQye5SLrGs4VVb+sSrj2SmEISOJxVF0jGPYjqwrx5ii+gE4+VUAP6izNLIu8Ck+r78gb5cvNhWO6IF3Jq6oJaGv/9AV9SEiYZaNecPRv66F/Ww/VUhW3lvtwLce19EKGcwdGaGxvIN4cpWdTlfHT0+RTBWzTxjLENEBWVqYpNdctqCeHq6qCYzsc+P4hNu4dJBQNEmkI0drbTDFbYnE6RTFTxLFEWKHu1wnHQ/WAL3/QR2YxS1tfy1UFk6LIJFrjzI7OodWKTNdxGTl8iZmLC9imhed5mFULSZL41r9/ko//xqN87fe+yN///37ES3/9Gqqu0tLdtIou5OHh2i7x1hgXDl3kzk/sIZcqsDi5REPrtXMTIokwy/MZFsaTN9xYJKdSLM+l6dnUSblQqbsg1ZunkI/0QoZYU4St+4Zq583NFY29m7u4+9O389rfHWTi7DSRhhCu41HIis/uI1+9r56Tcu7AMIVMkf6tPaseR5Zluja0Mz08y8WjY+y4bwuN7Q088Q8fYs9HdnLg+4cZPT5OOVfBrFis29HL7R/d/a51EGf2X6CUK9O/tWfV7Yqq0LW+ndmL81w6OcnQ3kEOfP8wr/y/B5CAeHOMYrbE+bdGOfHyGTY/vZEv/I+fItHWcO0H+iliYGcfB394hEK6uCp1HURgpmM5bLrjakpRdinHd//zU8yOzNPU1UhTVyO2aZOazfCjP3ke07DeVSI5iIlEZiGLY7vEmqNXheyZVRPbtK+aDK5A92s117vru5StYQ1rWMP7iXc1K5VlGb/fTzwer98WiUTI5XLv1brWcB1kFnNISOh+ncFd/QwfuUQ2KdyObMvBdT2RSHv7ehra4uSXCxSzZUDkOcSaI/UiBqjbaKbm0hgVU+gjagWuWmtU8GpONCuVci2u2rEd8qkCiqagKArBSJB9n95Lx2C7EOTGgyxOpsgtF3jrR8cAiCQiKJqCP+RDkoSeQVGreK6EUXVxbA/HErx91xOWri9/6wChWBDHdfAsGctQkSQHRQVZFvaurivhuBKSC67n1BqBWqJ2LcxPkqWanaywyhVaCfFcJUWilCvT2JEg2hjBMm2KuTJysYJZMZFkoRmRFBl/QEeOBKgWDRRV0KQ0XaWYK8OK/gKpXvAHQn6qZQMPSE4v81e/+20yizlCsSBb7hpiy50bkWW5LgbOLxfQdJXO9e1MnJ1hdnQeX8DH7Og8oWiQvi3dZJeyjJ3K1fJDas/Pk5BV0dA4tkO5UCGaCCMrMg1tMdLzOcZPT7H1riF6N3ej+4+iaAauLZLDxdDDRZJlJEWu24xWilUs08a+zu5uMBrAH/ajaSrp+QzVssGlU5M1CppTDxFUVIXhI6P86E8U7vnMbZRyJcqFCkbZpBQrE4oF69kE5VwFPajT2N6AUTFXTtbr5ipciSunUu8Ex3FxHQ9V1y5/npZyNZMBGdOwcGyX3Y/seNdFuiRJ3PHxW2nta+bMGxeYvjCLLEtsv3czW/cN0b7ucur5xNlpQlExtcmm8uSW8niuoMk1tjcAQmC/477LmRkt3U188p8+RnohQzFbxhfQae5uvGajeiPwPI/x05PXdZHTaqLwxYkkjTVjg1AkQCQR5uLxcZbn0jiWi+M47P/OW6Rmlvnkbz3+tjkf74RKqcrI4UtcOHSRUq5Mor2BTbdvYHBn39vqV94OHYNt7LhvC4eeOk61bNDQGkdWxHdSeiHD0N71bLyGkcKJl88wMzxH35bu+mPrfp2OgVaSUykOPnmYTbetvykXPs/zOHdwhGMvnGR+LInrukQTEbbds5k9H9m5KhHeH/JTzpdXfY+voJSr4A/7iDTcnM5oDWtYwxreK9xwY9HX18fo6CiDg+KL9uDBg/T0XN7Nmp6epr29/b1f4RpWoV7sA7GmKNv2DZGcXiY1m6ZSqOALaEg1Ksy5N4bJLuVxLAfHcTEqBuu29/LyN/ejqirxlii+oAiMy6fytd8xMatmPTtCkmRklXpRKcmg+XQkSVB7XNcjGgvS2tuMoio8/OV72XzHRiqlKn/3H3/A/LhwKNIDGqqmoigy+eUC3Rs7eeQr9/IX/7rM1Pnx+rEcGxRFuEtJnorrOLVAMQ/PAdtzaqMGFctwUVSxJsemLh737MshGZIkXdY8IHZcXdfFvXICU2uSIo1h+rf3YJQMKsUqjmXj2DWXK0/8nmfaFIzLzQqSh2t6OLZbF3SvPK7reHiuR7lYwbFEON+Z/edJtMYJRAIsTS/z9J++yLmDw+z71G0cf+k0E2emqRSrKKpMOB5ifjzJ7MV5wvEwsiKxNJ1C9+u09DQRa45iGhbVQhVZUXBrYhFZknBx8RwXoyqE3pF4mOzi5bBEsUNfJjm5RKI9RrVoCGG661HOi2J//PQkSzPLFDJFyvkK46cmwPPoGGxb5e9vVkwi8RC7HtzGoadPcPr1cxTSRSQkJLmWa6JrOLZDZj7LWz84wtyleZraEzR3NTF5bpr58STBiJ94c6xOxVq3tRezYtYL72hjhKbOBAvjS1e5XAGUcmUCYX/dNelGkGiLE24IUVgukGhvYNu+IVKzaZbnM7i2S0SViTVFuO/zd/1E9BZJkhjY0cfAjr63pVJJsoRt2Vw4dJHUbLpG1RG/F4j48Yd8q87nKxFrjhII+9F82rtuKm4WI0fHKGZK9G3t5uLRMZKTS4TjoXpKe2YpR3Jmmef+/BWiiXA9dPFmkF8u8OQfPlsLL1TR/RqzFxc488YFdt6/lUd/+b53RbeSZZmHvnQP4XiIky+fYfbiHJ7nEW4Ic/sTu7nns7dfPTEwLM7uv0C0MVJvKkzDIj2foZgt4XlQKZQZPjzK7od33PBajjx7guf/8rVaQF8cRVHIpwu8+FevsTiR5OO/8Si6XyfRFmdgZx8nXz5LOB5aFVDoui5LMym23LmR5huc2K1hDWtYw3uNG24sfv3Xf71OhwHYunXrqp8//fTTa/qKDwA9Qx2oPrWeghwIB2jra6aQLlLKlZBlBSR466ljIInCSVFEMa2oCpdOTFzm30oQaQgjSTB7cYFStoTnubiO2EGzDBvP81A1Rbg/OR7+kI9wQwg84UBjGWLsnk3msAyb5//qNXxBH4V0kZOvnCW3lKeQKYrU7lpGRNeGdsZOThCOhyikS5TzDh7g1kKwxR8eYAmbfU1BqTlNrQSRSYoMkoRtiamAqoswQPeKpkIEnAWwa1Quq2pfnmBciZr2IhAOUMqW6d7YzuS5WRRVwaia9amOjIRbm+CASE72h3yUshVsyxZ6jdohReibmFyYZZNQQwhNV+kZ6iTefJnKY1s2I0fHOPvGMOGGEC3dTbT2NmOUDQ7+4AjphRyyApZpEU1E0Grv/dT5WZEersgomoLruHVRuicJvYVU07JEG8N1B6wVh6CZkXmC0SDt61pqOghDuEqpIrsBIDWTJj2XIdocxRfQcByPqQuz5JbybLp9g9DheB7L82k23bGRB754N83dTRx6+iie66H6VFRNaD1WQgDzy0XKxQqaptK1oYOmzgRGxaCQLlItGxSzJfq39dLa24QsCzveleRoRVXYef9Wfjj8PPl0kWgijG3bGCVDJHfPLLP9nk035VgUaQiz9c6NvP6dtwg3hPAFfHQOttM52I5pWExfmOXWR3bWxePvBd6uQenf1suhp45jVk3CDeG6GNj1PArLBXJL+as0E5VihVOvnefUa+coZkrofo3Nd25kx72b3xUFSZIksY6nj18zR8QyLBRFpqW3mbGTE6iaQjlfITWbJhgN1psKAF3X0DSVaqnK8ZfP0PdjFK93gud5vPS3+7l4bJyejR2rjl3Klzn63Emauxq57fFbbvp5gsgAuvvTt7P74R0sjItJQVNnYtVn9EqYFZNqxaw3HNmlPKPHxyhmy/U8m3KhwlNff5GuDR3XdJz6cWSSOfZ/9y0xabri9faHfEQbI5w7OML6W9bVrcL3feo2klMpxs9M0dAaJxD2Uy0ZZBYytPa3cPdnbv+p2M2aVZNitoSqq7XryofX8tYyLcZOTZGcFJqg1r4W+rf1fGgzS9awhp8l3HBj8Y//8T9+25+viLjX8JPDMi0qhSqaX7sq+bVncxcbdq/j7BvDtPY1Ewj7GTk6RnJK+My39bfQ2Bbn/FsXKRXKLE0v4w/5hRZBEqJXo2KSS+bwJImlqRSVklGzPZXQNBXTtEUKtSzhOS62bQMigE9RlTo1yqxRVBxbTETiLTFyyTzf/7+fplo2mDgrchdkpdYUmBZL0ynSCxniTVEWJ1N0DrYxN7aIY9jXfjFEjYzmU9FtDUWVqZaMKwp3MbFQFCEscHFRNJloIlJvpmRZolx06ser44qAP8/xSC9muO2JW+jf1suJV86KzA7Xq7tdOT/WkWi6iusIpyxkoaEAQYuSFAlZllFUufanQkt301UFi6qpuLbD3KUF7vrkXsLxEJZpcfbAMOmFrGjwqi7phSzlfIVYU4RoU4RyvkKllmbe2B6nlKtQLlREPoYiI8kysizhD/mJNkZYmlmmsT1O/zYxZZw4M4Xu09i4d5BcqoCkyCId3XHQgz7KNVqXpChUChW6hzop5yvgiWJq5uI8PRs7WJxMEUlE2PvYLmRZFk2M6+EP+wjWgttWCgzP8/BwawJ40SD6Q36G9q5n9Pg4pXwFRRHHyC8XkSS4/aO3rnLJ2n7vZlIzad566hgjRy5RzJYwKyYeHu3rWt+VA9Idn9hDai7NyJFLqJpGIOyjWvtMbLh1gLs/c/tNH/Pdorm7EaNi1DVTdXgeruuh6iq51GWnolK+zJP/zzMMH7lEIBwgGAlQzlf44R8+xyvfeoO7PrmXbfs20dZ/tT7m7bB13xBn37jA4uQSLT1Nq8TbMxfn6d7YweDOPubHFrFtkaVimfZVkyTHdtD8GrHmGJNnZ6gUqwQjV9vfXg9LM8tcPDZGc1fjqqYChPg6EPJz8mWR1P6TFIXBSKCe+fJ28AV1AiE/5UIF1acycvQS1WKVWJMwyhDW2y7ZxSw//KPn+OL/9Omrclh+HKPHxsinCvRu6bnqZ/6gD0VVOP36uTr9rbmrkc/89x/lyLMnuPDWRbJLOXS/zt4nbuHWR3fesL7ovUK5UOHo86c4/fp5itkiqqrQt62H3Q/voHdT1we6lhtBcjrFU3/yAjMjczhWbcqrynRv7ODxX32Qlp53bgbXsIY1XB9rfnQfIlSKFY6/eIbTr5+rWSGqbNi9jl0PbqO9X9BBFEXhsX/wILIscfHYOGMnJ5i5OI/u19F0hUgixOLUErZto/t0bEs4PJXzFVzHYWFiCcuwSE6n0H0asiJhlE38QT/NXQlyywUCuiroQpZDueSAA/6wTkt3E8VsGaNiYpRNbMtBVuW6/Wm8OUZTZ4LluTSn95+nUjTqib2VYhXHFnz7atFgvpDErFpsv3czZw8MkzeKq1+MK4p+1xa8f92voQdCqHoZoyaEliSJRLvYlV2aSdWmBhKWJRoVy7BFaN8Vk4wrU7bBQ67x6QNBP4nWOJZhMTe6iFnTnDiue/WUA9FcRRtDGBUTTVdRVQVJEmF0siwjawp4HsVMCX/Yz4bd6646hm05FDMlZFnGKBtEGsLCZWkqJfQZtd1IRZUxKhYL40uCIlObzsiKeG1lVSYYCVAtGzi2gyzJBKNBgtGAcCNqjbPv07fXixyzaqGoMsVMSTSkvc1YhkVmMUc5X8Y2bTG90FT0gM6m2zeQXy4wMzxHNZln/PQkiqbQu6mTe3/hznoBUc5Xhd7HtK+i46xMlCRJwndFqFtLdxO+gM7CRJKp87NUChU27l3Pjns3M3Tb+lXHURSFu3/hdsbPTDIzModeC7KLN0fxPHjhr14HJPY+tuudPm51hKJBPvmbj3PhrYuc2X+ebKpAY2eCrfs2sem2wXcsDN9LVEsGTR2NlIsVsks5ZEWcQ57rEWkI09rbzPSFWYyKgS/g4/Azxxk+fImuDR1oPpVsMsfsxXkKmSKzo/PMjMxz/MXTDN22noe/fO8NF/XdGzt5+Cv38eJfv8b46Sl8QR+u42CbNh3r23n8Hz6E7hchjG/98CiVkqDYXdm8OHUr3UQt18aqW1TfKJamlynnK9ctlmPNUTLJHNlk7gMpqDVdY8u+IV7669epFKuUcxXiLZfF/uV8hVA0yPpbB5gZmWf0+ATb7t70tscUGjjpmgGKIKaMmcUcruvWPwtNHQk+8rUHuPvTt1EpVvGH/Del6XivUClW+P7/8wzDh0YJx0NEG4Q+7fRr55k4PcUTv/ZI3fHsw4ByocIP/vBZpofn6Fp/OXPHrFpMnpvhyT94ji/8T5+q65zWsIY13DxuurEYHh5m48aN78dafq5RLlz+gg5Fg4TiQcyqxaGnjnPpxAQf/41H6+F54XiIT/1/nmD24jQ/+qO/JzU7CxjYplsrPJcxqiaRmA+fH8xKnkDIpVRQqJYMwMMf8hOKBjGrJq7rUq0YqDWqTiaZq4e+SYpMpVAl2hAm1iw0GQvjS6Kwr7kx6T6NRHsDxUyR06+fR/erZJfySIhCybEdvBXnIk84/niuRz5d4PgLB1C1KpoPLJPLBfwVhbznCZ1CS3cTZtUiEPZjVS1s08Yf9qMHNJZnM+BJ6EGdYCQgAuMsB9t2UFwhsKY2tFA1RYTbyUIEHwj7KeXKlHNlklNLaH6t9roI6pfneTiWc5XTSjASoKmrkeTEEq4jRMW+kE7Ppk6K2QpG2UCWJZo6GwnFAijXsP10LLsWoCbWWClWWJpeRvdrWDUnJUmWCIT9OLaLZVg4tkQg7KsJtEXDFm2KogRl9IBOpVDBtoTdbigKgzv7uOtTt7HptsvBiC3dTbyZLWHMpcmn8phVk0AtdE8P6BhVi1hThHhzDMdxas1Hi2gcZ9Ok5jI88asPcctD21aJZ+MtUaKNUdILQsSt+y/z/Z2auYCqKTRcYbsKQi8UjodQNZVP/3dPsO3u61t2nnn9PMOHxwhEAriOg6opBCIBGjsaSM9neeN7b7H+lv6rLGnfDv6gT9ij3r91VSr7B03ncG2HaFOY9bvXsTyXppApIskS8aYYiY4GKoUqtiUE5ZVSlTOvXyAYDZCaTTN7cY758SSu4xJvjpJobxDUQQmOvXAK13H5xD/5yA3rL3bct4X2dS2cPzTK/KVFVF1h3fY+Nt66rh5C2LOpk013bODA9w7jWDaWaaHqKmbNXrqhLU5TZ4LFiRSdG9oJRPzv8KirsTKV9Ly6fGkVViyjr1eUvx/Ydf9WLp2YYP933qwHRNq2QyVfAQm6hzoJhoWz3tSFmXdsLHxBvR4+ea3zzaiYNDUmrvm+hWKhmw6EfC9x8pVzDB8epevHgjGjjWFmL87z8t/up29LF76A722O8sFh5MglZkbm6RnqWmWPrfs1EeR5YZaRI5fY9cC2n+Iq17CGn23cVGNx5MgRPv7xjzM3N/d+refnFsdfPM3woau/oBtaY0ydn+XFv9nPV/71L9T51U51mOzUNxk5fJGlGQtfQCLWqBIMR5EVCVVxgRKeKyPLoPts8su1t9sTF2Qk6kLPlUCySCKMogqHIj3gY+i2DibOTGPVBIqSLOO5DnpAIhwH1/Zo7fYTiIVIL5aZGZnDthw8R2RLlPMVQIStOZZoMFa0AK5jMXVhCZ/fpaHZIbOkYJlX5E5cgXK+gubXqJYNckt5Ib6WJcyqxcyFOZAQ2oSKiWM76H4NV1XAMIUNrqbg2i6qrhCMCoqOUhOTg3ByMg0bx/FIT6bQfJoImnM9UbTIosC/MlxP0RQ0TSUUDwqLSNdFNRWWZtK4tovruFiWzZZ9Q/hDPhbGlkTAoGGjqDKaT2gPHNsR1r2JMNlkHsuwCMWCFLMlKqUqZtmsNXIyoZhYeygaFHayWp5yVjRFml9DUWRiTVFkRaZaqrLv07fxK7/3xaucczw8khNLFHMlbMuhUqhSKVTQMlpdExFriuLYDr6QD702YVBVlWA0RJMksW5H71XHbetrYeOeAY69cOqyK1ftnJNkCUUVr/+Pi0sLmSKXTk5QSBd54S9fY/TEBFvvGmL9LetWFVulfJm/+48/YO7SPKFYCFVTKGXLpGbTxJqixFpjLI4n+f7/8wwPf+VeOgZEunRqNk16PoOiKrQPtF5zR9KxHSbPzzB8aJSpC7Nid36wja13DTGws+8DEUQn2huQFUGhu1bw3uLEEj2bOtEDGqdePcfw0UvklvIYZaEFsi0brUaXqpYMNJ+Koii09bYwfGiU2UfmbyrfoKWn+W2pIYqi8JFfeQBfQOfJP3iW5HSKQDiA7tNo6W1i3fZejLKJ4zjsvG9L3fVrBdWywdJ0CoCmzsRV06FA2I9t2oydnqS1p4lwPLTqfMgsZmkfaL2pJvInRbQxwqd+63EunZxgZnhWGBUoMuFEiM7Bdpq7hU5CkiXhpvcO6N/WQzAaoJAuXuX0ZFsOlWL1Q5f+DUIfdurVswQjgavsbyVJorWvhbnRBcZOTa3a1PhpYvz0pLDg1q52Elu5fezk5FpjsYY1/AS44cbixRdf5LOf/Sz/+T//5/dzPT+XKBfKvPrtAxQyReYuLRCMBmloi6Npal03MX9pgclzMwzs6MMsj/HsH/8Bx1/JsDAJICNJEtmUQzG7jO4DXA/LlLFNCIQVlhfBqHp4rrgoW6ZNIV2sOSKJInjFMahjoA3Np5JLFRg/OUlDe5yhvYNMnZ/FKGUp5wwCIUExauhw6BookZzNkJrxYZsujntFAnVt8nCtC6znuTiWTNkCTYdQ1KWQUXDsFX9Y6u5LgZCfmZF5VFUROQNI6AHRFBXSRdFQOC4SYofPsRwc26lrDTzPqydc25ZzVWHpOB56QKFjsJ3FiSThhlAt1M2oW+6uWrvrUS0Z2LZNOB5ieTErtB+2iy/sQ1UUXE9MlxYnlugYbGNxOsnY6UkUVUZRFBLtcWEfGfYLa1q5RnvxxIXZNm2MsllftySJ/AxReCqENIWm9kYKAV/N214Tu7eqQiDkI9YcZeAadpwLE0ne+tExmrubUH0qy3MZ0Yz5NMqFKmbVIt4UFS5Rnkfv5q56QbiSNjx02yCJtjiFTLFuj9vU1YiiCqrewniS9HwG23bqyd6SJKxpE+1xckt5kfotSaRmlzl7YIRStkTXUAeyLHN2/zDDh0a585N7uecKMer+77zJ7MV5IokIkRr1w/M8Mskcl05O4A+LzJDDz5xgcXKprs+YOD1FKV+p6Yxi7HpwG7d/dDeqJhq7k6+e47VvH+DUa+cxygbRxgjNXY2k5tKcf/Mid3xsN/d+7s6bai48z6tZOEvXzR34cfRt7aZjoE3sqm7qXPV4xWwJz3XZfMcGnv2zlzn8zAnmLs5jVi1UTcU0LBRVqYXWQbVcxTREyn0oFiQ5tcTU+Vm6N3YK7Y4pbKh130+24x0I+Xn8Vx9iYGcff/+ffkRqZplYU4RwIsziRApZkdjz6I56DggIHdnhZ05w4uWzZBezeECsMcL2+zZz+xO7cRyXV771BucOjLA8nyE9l2F2ZI7mrkb6t/fWQysdx2XXg9uuOsffbzS0xHjgC/t46W9ep6WnCVVT6xRIEOYNtmnT3v/ORgJtfS3suG8Lb/7wKJZh09AaQ5KF/XVyKkXP5i423/nhYwlUSwaFbOm6tKGVXKVCunjNn/80YNaMB64HRVHqBiFrWMMa3h1uqLH4zne+w5e+9CX+z//z/+TLX/7y+72mnyssz2f4u//0A84dHEFWZDKLWeGeFA8xeEs/sUYR1ubYLvnlAgCnXv4Rx1/JEIhECYSKWKYQdiqKjGm4uK6NY6nYFuBBpeRgVhGTAKlGHVAkLNMWbkJXVM22ZTM/vlh3kjKrJqZpMbC9jwe+sJ2xI89SzsvoPgVdNwlHYXZcZfaSK3Ym7SumDW8bJ+DhOiJTAg+qZQlN95AVD0UF15FwPRFSoagKO+7fwoU3L9LU3Vj3iHdsYT375g+OsDi5RKVQpXNDO9FikczCIqVCrWnyJJq7m8gu5bENC6Ns1BO1QRRgju0weMs6hvYM8MZ33qRarKKoipiw2N6qJgkgHLNpbl/GKuVIzvmoFsVFSfNr2IZNQ3eM7o2dNHY0kE3mOPb8KahZ9JoVB9cV730wGuDez96O47hMnpuu5XtQcysRIYSOZdcsSqV6AJ1l2ZRyZZQmEVIoyRIDO/uwqqK4jLdEmR1doFI0OPLcSXK1bIYNewa58NYI+eUCm+/YQC5VYPLcFFPnZ+s0M1kS1qa5VIGG1hhNXSLl16yaLE6mCMeDbL59A8/9xSucOzhCpVhBURU617ez5yO72Hb3Jr74Lz/DC3/5KrOjC6tchB744j5UTePNHxxm/MwUjuUwcXYaPBi8pY+WnmYkSaZ9XSvlfJkD3ztE1/p2Bnb0kV8ucO7gRULx0CqufjlfoZguomgKnusRiPhpX9eCL6Dz1J+8QCDkZ9MdwoLTdYQQ/qW/3k+1WOW+L+zjhb98lTd/eJTp87M4tkO8JYZRMZgfX6Rvaw+RhhAHf3CEjoG2q3INzKqJWbXwh3z1aaLrugwfvsSpV88yP7aIJEn0be1m+71brgqc+3FousYjv3w/T/7Bs0ycmSYUE5OpUrYEksTuR3ZQyBQ58swJGloFbc22XQIRP5ZpYZkWRsXEF9CF7bDjChMCAEnCMi0uHn6eUy+/zszIPAC9W7rYfv+99G2/5yeaymy8dZB/8vtf49zBEc6/OUK1VKV3Uxdb94nJ00rx77ouL/zlaxx6+jihaFBMRCTILeV56W/2161+T792nkR7A7c8vJ3R4+MsjiUZPzPN0mya9v5WYk0R7v7s7e9INXq/sPn2DRx/8TRW1aahJV6/3fM85i8t0tjRwIYb0BdIksQDX9yHL6hz8pWzTF2YZYWuumXfEA9+cV89iPTDBM2n1hvaa2GFJqb7PzxOS239rZw/OHJN2pnneVTLBm3rbtxVbg1rWMPVuKHG4vOf/zz/y//yv7yjM9Qabg5m1eSH/+U5ps/NCptGTdiXuq5oIkYOX2Lr3Zvw1RKNNV3FtoqceuUsmu7DH1KRZIloQmNxqopleEiSh+vKeK4LyGi6yDWQZJEwjQfIXk1I69WbipXwOJCo1iYXmk9D0VRs0+bo86dITug8+AsO85M+ZMkCT6KYg8UphWpZQpY9nJrhqqRIeM71OgsPVReP7FqikbBMCduScF0IhoWTVLkgLGVXLGNVn3CFiiYEXUDWRRFkmzaeC/Emi523T9A9kMSxylQrOuePxTj2qp+2/haijWEmz83gVF0qxQrVslG7r0i+bmiNMXtxjkxtB9UXqFl9yi54Ho7joPsc7nosz9a9JSINNp4H+YzGxdNNnD/RTTgWp5gtYxk2/pCv5kdfJLuUY+OeQToH20gvZOvi6Uqxigd88jcf5/DTxzj/1kUcW9AfIk0RbNOmUhCZGitULM2nEYmHLidFN0XRdZ2mzgQrhreGYZJL5nn979+ksFwgnyni2i7+sA9/yF9P0Y43R4nfu5WBHf0sTiZZns+SW8rT0Bbj/i/cRXJqmcWJWpOjyrT1tbD7kR0cee4k46enSLTFaeluxjItJs9MMze6wOO/+iB7Ht3Jxj0DnDswwqWTE5QLFdr6m2npbmLdjj6G9g4wenyC0/vPk13KCUexVIHFyRSeBz6/TnNPE57rcPbABQZ29JGaTVPMFGt2xZO1KYhEIVPEQ+gkKsUqUlS4n+WXC5hVs56jsuIk1tzViO7TOP7SGcKJMEefPyXyACRoaI2jKDL+oI9yocL0hVl23LsF13Y59fr5emOxPJ/hxMtnOHdwGKsqqGtb921ix32bOfLcSd743mE81yOaCON5nuCiH7nEI1+5j533b+Xt0LW+nV/8F5/g7BvDnHtzBLNisuHWAbbevYnujR38+b/6Zt2KVvfrmFVLNG8122GzNiXxPOFYZptOXec0e+ENDv79WSzDJdrox/Pg5CsXGT48zsNfmeeWj3zh5r/IrkCsKcodH7uVOz5263V/Z+r8LCdePkNzV+MqwXFzVyOhWJBDPzqG50Hf5m6CNXvkob2DtPe3kJpZJjm9zPpb+nnkl++7Zhr8B4FyocLyXJqOgVZO779ANinOYc/1KObLNLTGePRr9xNrit7Q8TRd477P3cXuh3cwN7qAYzsk2hto7W3+0Nq2+gI+Nt46wEt/u59MModZNtBqWRsNLTGyyRyRRJi+rd0/7aXWsWnvIEeePcHS9PJVeTdLM8tEEmGG9n44aFs/r3Bdl2wyVzNHCX9o9DlruHHcUGMxMDDAc889x2//9m8TCHxwDin/rWP0xART52fp39aD63ksjidFYJ0sePLZZE54w4cDxJoi9GzuopzPk140iSQ0fEEF3SezPF/FEW6xSDLIgONJSBK4LqiyRzgqUyq4uLaE5yCcjhDaAtfxQIZg1A9IWLWddQB/UKtrAqZHlnnhmxL+kEO5oNDYrjM3YeI4HooCVi0BGsQY3Kxcf6Ss6eA64j6y6uIPiJA71wVV8zCrDorqo7FDcM4lSa7RhVbvqHoIilNzp8FHv7JE30aP7LJMtSQTa7S592OLdPX7uXC6n+buJsqFCktTQtwOwu1J92sEwn5OvXKO4UOjlPMVghE/uaUClmmhKAqSIqPKNo9/aZntd5YxKmGScxKu7dDaq3DHo0kamj2OvRHEF9IpZkrMjS4QvCVQE2Pr2JZDIBygc/DyZ8goG8yNLpBP5fnIrzzInZ/cy7f/4w95/e8OCH51voLu16g6LrIi4wvrYjJVtQjHw+SW8uTTBTbuHaw3FbblcP7AMOlkDq1G9QnHQiiaTCkniuXsYo7WnmYRuAb4gj4iDRF8QYjETHbcu47P/fNPYFRMJs/NYJQNQrEgPZs6OfDkEcZPT9G7uau+S6/7NULRIHOXFnnt795kYFc/6fksx186LayQkZgbXeDES2fpGerkiV97mL2P7SKTzHL8xdPMXlwQWSDUEkwMi8LJotitz1WwTVEYG1WTzsF2lucy5FIF9JrQXlUVoYmxHZo6E4QbQoweH8cf9lPIFDn58tn6LmVjRwMtPU2UcmXe/OFRXNupibVZRZMIhP1kkzmW59KEG8LMXxIFX2o2zff+r6eYH0sSbYzgC/jILxd5/huvcvT5k+RSeeJNsXpyOUC8Jcb8+CIv/e1+ejZ1vmO+RKKtgbs/czv7Pn2bMD6onffjpyfJLuVFzkbVFM2rpgh7YEM0uqZh1ixeo4D4HlgYT+IP2kyeGcYf1OgYiF3xWFGSUxle/tsX6BzaQWvf5rdd20+Ki0fHMCvWNV2MgpEAhUwJ27QJ3r6hfrssyTS0xGloiRMIB9F0te6U90HC8zzO7L/Aq98+QHouC3h4jkelUkHJKnStb2fP4zvZfPuGd2VbGmkIXzPt+8MIx3Yo5cssTaeEjXA0gITE/OgCwWiAhrY493/hrutmgvw00NLTzP2/uI8X/+o1xs9MEU2ISVA+XSQQ8fPQL91zUzk4a3jv4HkeF4+NcfT5k8yOzOM4LpFEhO33bGL3Izuust5fw4cXN9RY7N+/n8cee4xPfepT/OAHP0DTPjyjzZ9lTJ+fxXVdNJ9Gx7pWMotZ8umCKAJVBUVTmBmepX1dG/d+/k6iiQilvIes+HBsA90v0bfZpL17GV/QoVrSGD3rJ7skdAVmFWwbjIpIs1ZU8FzxnwQgS5fZPR6EYmEqxUotBwF0v4OqlrBNj2BEQtMkZi7ZbNjpoukaC5MeZlUmHPPIWiBZHjUHV0HBUISv+1WUKAlMAzQNgmEPRfHYdU8JX1Di9MEAiRaLpTkdLZAgGAni2A5NnXHGT0/iv8aXiz/k47b70rR0VJgcacAyHMyqRzEvo2oKA1tKZJbPc/S1biQkPIRDlT/oo6Wnqc4RLuXK5NMFStkymk+raytWROcbbymzeU+FpdkQqi+M65RxPZmlWQVFUenbuMiRl1VmLoVQNIVLZaPuYCWaI0hOpXAcF39Qr7ls+Wo0N8FDjiYidKxroXdzN0bFZPT4uNBOyKKJ0f0iwVpCIhjxU8wW8QV8GCVD+LI7bt1pStPFDnZDS6zuruPz+zDKBrlUnolzUwztWU96PsPC+Ai966fZcWsaRTHxhy9w7EcX2XzPV1fZRa4kD0cS4auC2gBaehqZGZnnxMtnOPrsSbJLeXqGOlelFI+fnuKH/0V4/JdzFVIzy3iuV093B4S1qeXgWC7VUpXzb16knC8zd2kBs2qzcc8gE2emSE4vY1YtZFW8JvHWuCjMPDERrOQrFLIlJCQijRFcx2VmZI6lmWVCsSCu6xJNRKiWhVvaqtNUkoQIvmwQiARQdR0PEdg2P56kb0t3Pfk4kghjmTaHnj6Gqqv0bhK7tLZlsziVIjm5RLVUpZSr8PcNIT772x97R8GxbdksTi5hWw7x5iixJmGpiyc+Z4Gwn0giQjaZpa2/hXKhQi6Zp5QvEwgH6jvd2WSehtYYja05xk/ZdAwkrnqs5q4Y42eTDL/52nUbC9d1WZxcwiibhONBGjsS72o3PZfKvy09ZkVfdD3ofo1SoXLTj/te4OKxMZ7+0xfxXI/uoQ4UVVBGMwtZSrky2+/ZzG1P7K7/fnYpx8iRMZZml9E0lZ5Nnazb0fczEcRmGhazF+cxKybhhhDt61pXbewcf+kM5w4Ms3HPIMnpFPnlIp7rYls26cUs63ev4+5Pf3AZMDeKWx7cRmN7nNP7LzB+ehI82P3wdrbu20Tv5g9f7sbPC06+cpZn//xlQS1si6NqSt3MY35skY//xqNr04ufEdxQY9HU1MTLL7/Mxz/+cT73uc/x3e9+9/1e188FbNuuFybRxggbbx1k/PSUEGl6Qhwcbghx7+fuYN8n9wIQjETo3zbE2TfeYvd942y9ZYlysQquhKRAz3o/z3+7AaOqouoKpiFjVV38IQVFk0TRbUh4niQaDFnCq+kuSrkijuXi2Cay7CLLHrYpo6oSgaCJhIWquhRzMnc8pnP8NYu5cRvHEU2C6175n5gkSNeKgPAEpccfdDANmViDR2uXhR7wGNwmMTPqw6gG8bCQqNC2rpVitkLHQCue5zF5YQZcD0kWTizxRAHHsXnzhQiOZRNvsvD5HWRXAU8hm1LYsD3Lif1NZJZsrKqw4KyWquRSIsnYF9QJx0Ok5tJYpo2iybQPtLI8m8E0LBzLYWiXyJawHR2lRo3yXA8HB6Ms09TuMbC1xNRIoK6FGDs9WUsnN1kYX2JhPAlQo7BF6N0iClDNd/mj2DHYhqzIJNpitUC8qNAGzGdE2nnVQlYV8stFYk1RvvpvPo+ExML4IqquEYoHeepPnqdaNPCFdFzPReGyuLWhLU4xV2b89DSN7QkWx4e5+4kRugfKZFMyrhsmFA+gKvuZPTnLujv+FbIq1lktVSnlRcJ7pVjFF/StsvoUzYbHhUOjpGbT9G9bnbSs+zS6N3YwfWGW0RMTmFWTcqEicjhqxbDneZTzFWzDxvVEgrdjO+RSecqFCucPDlMuVFh/Sx+J9jjn3/IwqyaaT2PD7nV1obTreOTTRXSfTrwlVr89EPGTXy6QnEwxdNsgZtUi2hhGURUs014VTOe5QuNTzJTYfs9mFieWmDo3Q2tvc/2zu4KV+5XzZUzDQpJg+MglUjNpVE1B8wk61tHnTuI6Lp/8zcevmczseR5n37jAoaePszi5hGO7BKMBNu4ZYMd9W4gkwuRSBRJtcTrWtZJP5akWq4SjIZHHEg0QCPsopst0rG/j9o/tZse9W3j5G/9J0Cev0QxIstBNJSev7fh39sAFDv7gCMmpZVzHwRf0sW57L/s+ddsNpUtfiUgifF1evlgLdX3TtdZaKVUZ6Oy7qcd8L+C6LoefOYFZsejZdNlZS5ZlGjsSOLbLkedOsv3ezQTCAc4eGOaFv3qVzEKuTlN766lj9G/t5olfe+Q9TXN/L7EylRHvd0o4wwV1eoY6ufdzd9I52I5lWhx/8RS6X6d9XStt/S3kUoW6uYRHzSAkU/xQTSxW0Lu5m97N3WLjCz4Qx7c1XB+FTJHX/u4gsiSv+mwFwn5iTSJ9fmBHH7c8tP2nuMo13Chu2BUqHA7z9NNP88UvfvH9XM/PFVq6m3Bspx58lGiLE2uKkE3mMComyakU+z5zG/f+wp31+0iSxM6HH0PhdRqbZ0nN+5ga8aOoEo7tEYy4tPdapJdWdA+CcuTYQoegBzR8oSCarlHMlrBMWxRzritccxBNge4D1xGBb/EWDVXXKBc8NF8VRbZxXWjt9jN6UjxuIOziD8uk5mU8x8VbSe6+jszCdSTyGYVI3OXWB8v0bzLwBRwUSeLCsQbKBRvTKBOKBlA1hcaOBm7/2K08/ScvcO7ACJ7r4noulYJBKFKisBykXBTFsy/gp3vAonu9gVk1yaVlWrsMcDMUM3LdMnZFMGobNpFEmFhLrJb0XRNKux4NrTHSC1lkCWKNLtWSyMGwTbs2yRABfuJCKhEMmUKkbTn4VAWzalNIF5BlGa1drRfPtuWQSxU4+eoZOta1MXp8nPNvXSSaiNC+rhlNVxk5MkYhXRSJ27VcAkVVcByHnqFOqiWD2z66m7s+sbdegM2MzPF3/+mHTJyZoVqqomgKuWSeWItoUiQkfH6dcDwIHpw9MMze+y/R3lNgaiSEqvtp7GjA83SKhTCyMk1h7ttEu/97URj94ChnXr9ApWTgD+o0tMbp2tBRT2d2aqLq5OQSgUjgmoWhKLA9podnKeUrgIQsiddbUiQcyxHnpSIj2Q65ZF4khQd0mrsamR1dYPzUBNPnZwhEhB2pY7u0r4ugaiqZZI5YUwQPD8d28SV8Im1+KY9RMeuZASAsTufHkgRjgrqRml6uT2Ms0xaC56pFS28TW+8eIrOYo1oyaLuO208gHKCUK2NUDNLzWXG8xghqbWJTrWlH5seTvPBXr/GF//FTVxU1x144xbN//goS0NiRQNVFY3PoqeMsTS8zsLOPI8+dJBgV2R0DO/uZPDddzw5p6kzQu7mLLXcNcd/n7yQYERM5za/Xk4av/Zn00PyrdwTnxxZ5+k9f5PXvvIlV+5y0r2tF82mcfu08yakUn/nvnrgp2s/6W9Zx5LmTlPLlqxyFqiWDSCJCrFEiPZ+lsWM1ZayUKyPLEpuvoEl9UEjPZ5gdnb9qTStItDcwe3GOmZF5ApEAz/zZS9iGTd/WnnrzbVZNLh6f4Omvv8Dn/4dPfuBuVjeCU6+e46mvv4CERFtfC5pPpVKocvHYOOmFLJ/97Y8hSRLL85m6aF2W5VWNkut6TJ6bZnFi6UPZWKxgraF4e3ieJwJXFfl9fa1Gj4+TWcxdM6ndF/Sh+3VOv36eXQ9u+9BqjtZwGTeVY+Hz+fj2t7/9fq3l5w4bbh3g4A+PsjCWpH2gtV6YGRWz5tJjkprNcO7gMBtuHahTT/q2NBPVW5i9WCa3XMXzLFxH7BYrmk7XAKSXPIo5CcdyUVQIxXSW5hwUR6WhRfiOZ5M5XNutOc8oYjfcMMERFCrVJxFt0PAHZZLTVbJLDppPRtMdjr1UZGHKwjIUTFPCNGQ8V4irdZ9EtSTyIyQZVop0xF9rIwwJ15EoFeDMWwFmL6kg+ahUO2jqbkALFNh61xBb7x6iva+VaGOYJ//wWWItMToG2yiki0wNz1FMl7AMaGyz8OVcXEeiUpK5dFYFyaazz0ZRXcyqS7Xk4HlSXQcCIh3YAzHG98SupKar9WI11hjBthwyi1myKYXuAYfUfBXbdupN00rvpOkuubQiLD4lUSD7AzqVWoCbbYomUlEUYTmrySQn0ziWoMNpukalWGVpRuwS2raD64iGr5ApoflVGlpirNshHHYaOxLc/sTu+hdtcmqJv/69v+f8wRHsmuMXkhCaVsoGru2QaGvAqJgEIwHhwOTl2XRrhWIuiC8YJBwP1Wkqut9HJhmgseMcVmWCP/jt5zjy7EnKhTJWVUxhCpkSqbk0G/cM0rupi+W5NPGWGKquUs5dn64iy+L1qZYMfEEdn1+jWjbxXK/uVqb5VGzPwzbtejaHWTVRNAXZktF0FV9AJ9wQIjWdZvrCPMuzGfSgju7XyC7mkGSJzHyG9Fy6/j5JgKwo+Pwa46en6VzfxsyFeToH2/Bcj/RCBrNiYRoWscYI7YOtPPrL99Pe3ypS0hWRT3AtL/xoY5jkdArXckhOLqH79XpTYVsOkiyRaI0TiASYujDL3OjCqryKYrbEG987hO7TaOpqxKgYWKZNtClKuCHExJlpeoY62bZvE+ffvIgkCRFtU2eCYthPx7pW7vjYrWy4dUCI+a+4CK/fvZULhyZwLJGbciUsw8LzYGDHZf/+ibPTfO//eooTL5/FdUXonmXaTJyeorEzwYZb1zE7ssChp4/z0V97RHwWPI/FySUK6aKgeA62XUX76d3Sxfa7N3H0hVNEExEaWkXhmVvKk13Ks+uBrbT2NvP637/J9HCFhtYYsiyTS4nGcPcj2xnc1X/dc+v9glXbTFD1a182FVXGrZ2/o6+do5gp0bele/XEzq/TOdDG+JlpJs/NsG577we1/BtCtWxw4MnDyLJwZltBMBqgd3M3E2emOPzMCfZ8ZKfIpnmHIs+73s7SGj7UMA2L8wdHOP36OZbns+h+jc13bmT73Zvel8yYYqYEcNUUeAXBaIDsUg7LtH8maIQ/77jp5O21Dv+9Q6wpyiNfuZenvv4i42em0H06UxdmyCZzqLpK52A7yYkk3/n9p9h2zyYe+wcPig+VPUVDi0Ko4T5CiQVOvXYOs2oTjPgxDchnHIySh1F2cR2QVZnleQkJYbEqqwrVsoE/7K8HxxllE03TSLTKLM9VsC2oFh2MsoNRcXFshDWsJDM/4WPmkg/dZyMrLq6jUDEALDRdo22gg1wqTy5VAEQuA4pIFf7xy4xtyoyd9SNJfmLNMTbd1kepUGZwZx/rb1lHNpmnWqoyenSc84cu4g/6UFSVhtYYhXQB1/EoF2SMikwwLP4eDLsU8zKzYzotnSZdrRaTw0Gyyz40n4xrO2Jn3UM4V9UsbUu5Mo7tEIwEQBI6kexSDkkS9I3J4Qa23VbE57ex8qufRzjqYFZlRk+JSYCiKcRbYnSub6OUL9O/rYdCukgxU6rtmNtY1RKqZqLrOboGqqj+BGMnDUr5MrKisG5bD7pf4+yBYUGDylmYVYtKsUrnYBsP/fNP0DN0eWz8xvcPc+7ASP2xU3NpZElC1mRs0yY1lyYUC1IpVPCFfPRs7GBxfBZNM0kng9hWVRT6GR+JtrgQRhsaklTh+W88w6GnThKKi4yV1MwyZtUESaKcr3Dx2Biu5aL5Ve7+zO1kkzne/OHRa573ruPiOh6tvc2UcmU0nwjjC9kORsmgUqoiyxK+gE6uWsAf0mv0Co9sKo9t2gTCAZq7Gsml8kiGTUt3E8VsSUzkgj6W5zI13YQINnQsBxAJzZpPEztwqkIhU6zZ8A4wfX5WZBFIErbj0t7XzL7P3M62fUP1dOOuDe0k2htIz2eucpUBkQ7d2ismEuVihVBU3M8yLIrZEo0dCeI156nFySWyydyqxuLSyQnSC1kC4QCnXz9HKVvGwyMUDdLW10IwEmDk6Bhf+V8/x+Y7NnD+rVEKy3kGb+lj6LYNDO7qQ9OvfeHdePuDHHvhIFPDKToG4nW+crVkMDeWoX9rGwN77hXrNS1e/OvXWZhIomoKoVgUVVPwBXw4IYfl2TSLTWKKNnJ0jPxygUqxyqvfPsDEmWkqhQqyqtDS08TtH93N9nsuJ6krisKjX7ufSGOYU6+eY+biPJ7nEW2MsO/Tt3HXp/biD/pItDVw/MVTzI8n8VyPREcDO+/fyq4Htv5UdvqjjRERXJkp4gtcrVMp5yv4gz4iiRCjJ8aFDfQ1Cm9/yIdt2MxdWvjQNRZT52dIzabrwZJXQpYlEm1xLh4b485P7KGhNUZ2KUdb6OrpXX65QLghdM3PyBo+3DCrJj/6kxc4/fp5VFUhFAtSypZ5+W/2c/7gCJ/4px95z40T9IBeZwtc6zNjVszaJPnDN+Fbw9W46cZiBclkkmQyWecormD79jUO3M1gaO96Yk1RTr9+nmf+60uU8mX6tvXS3t9MvCWGhES5UOHES2do623mtid2Y1SqTBy3KJcq+MMJ+rcNMHluAqNsceqgzMKkSnu/SSGnY5kqwUgA27JrDkg6kiQSq13XJdIQQ9EEX7+5u5GmtjyWUSW94GFUReCc64DngapKqLpHpVBzdDIunz7CrlZMAFKzaT72649w4HuHmRmdR9NkfAEflmFTLpbxHGpi4poFLtR47EWOPHeyHvR2Zv8wLb3NWFVThONpCs09jTiWw+jxMfKpQu38k5if9LFuS5VA2KVSlAiEXIo5Bd3nYBkyx16LYpkuqi7CBOtuWNSoX6rQDEiSELQP7uyjra+FQrqI63oEI37mxuKcP55n294c/mWZ7HKNKtZkE4q4HH4pwvSor/Z8ZDS/huO6OI5LKBpk3fZecksFjNICnjXJxIUKPp+Eotq4xgiGMU1yWicSD+M4Dkszy8iKTDFbErv0ili7JEtUClWOv3iKgZ19NHUkqJSqHH7mOI7t0NjegOsK0XMhU6rZrAonqYXJJRpaY7T1t1CtWhQyFo4tE4ppGBUN1xH3W55L09iRQNE8ZEXjracuIiuXrX6buhrJJgUtyKwKa1vTMPn4P3mUnQ9sZWZknpOvnGVpdrk2bZBQVRGYNT+2SFNnA+t3i8lLKBZiaXoZ23aQZSH4t0xbiNQliUjNucUyLKpFA0mS8Ad1lFrqdrw5SiQRRg9o2JZDQ1u8bsFqVi10v0aN3YbnCuG+LyhE7I0dDSzPZ3joS/dwz2fvIDWzjCRLtK9rpbmr8aqLXCAc4NZHd/LcX7xCajZNoj2OLMvYlkNyKkUoHuKxX32IEy+dZurCLFZVaDYUVaGpu5H1O/tRFLkWeCjS369EOV8hNZuhWppHgpprl1QPImzsaEDRRV7H5js2svmOGw9OCze08PHf/HWe/qOvMzO8hF0LrdR0mcFdnTz+a79GICSmB5PnZpgfXyTeHGNpennVBV1RRV7L4uQSjZ0NlPNlZi/O8eq332R+fBFVUylkSxQyJS6dmODM/gt84p98hIe+dE/9GLpf577P3cWeR3eyOCmSt5u7G1flNWy5cyObbl9PbimP63pEG8PXbZo+CISiQbbcNcRr3z5ItDGCdsXOqeu4JKeEZqe9v1WcZ/I77Oa7H77d/GrJEJbW15nK+II+Kks5XMdl5wPbePbPXqKYLa1y+DIqJun5DHse3/WO7mdr+PDhxMtnOfXqOdr6WgiEL5ulNHUlmDo3w/PfeJVf+p8/Uw9NfS/Qv62HUCwg7NObV1s0O45LMVfi9o/durax/TOCm24sjh49yle/+lXOnz9fH3OuCC4Flcd5hyOs4cfRvq4VSZY48fIZBnb1EYmvDkMKRgIEwn5OvnKWQCTAge8fITlWwfPGAJlEq0bPhi4q5XmSMx4NLTaqJhOIROhqb6pbbs6OLFBIF4gkIniui2M6JKdTSLJEIBygkCkwdzGHbXmEYgqW4eI4wtJVk0XGhG2IpgLE7uyKUE9VFdEsSIJOtOO+LWi6yvf/4Nl6gagHdCzTxjYskCXwQNZkoo1hbNOhlCvheYIupPs1KqUqk2enBE3Ir+LaLtWS4JBbll0PSZNkiblxH4oK7b0G0UYHzwXHkijlFZ55LcrISb+g2RjCoenKCX2lUMEom4BHor0Bf8hHIBxgeS6D67oEwn4yyTyjx2YYO9nE0gxsu71IW48JHmSWNA48E+PoqxGQJCSEq41rO4yfmsIoG2Inej5D1/oY7d0LpBddbFNHViR0v4Lqi5FbymCWi0SaQ3iun+TUEuW8CJ8LJYSDkVW1CIYDVMsGFw5d4uhzJ3n0l+/HqlpkFrJoPpFtosgK7QNt6LNpcst5wa33xPu09c4hBnb2cezF0zR0bWJpfo6ewRJGJX656C4ZpBcy9G+Wcb0W5sYNApHLFxndp9Hc3YhZFYGD+XSR9bf018V1bf3NdG1o55VvHaBcqIjsjUSIQDhAx0Arj/zy/YIG0xZHVWSR0F0ruF3XFe4yDqi6SiAs7Hkd28UyrVrKcYhqsYrjOCK5HKHdKOcrLE2n8Ad1VE3FqJi1hlGcsJ7nUSlVsS0bf8gvNCuWsJC942O30rW+/R0/r7c+ugPHcnjrqWNMnpsRzYcETR0J7vns7Wzdt4ndD2/HdVxGjozR0tNMJBEi2hipNyqZxSyx5ghdG1Y/XjlfJr2QJt4UrT9vEE5IRsVg7tIiDa1xfIGrk7xd18UyLFRdve5Fv7VvE7/0v/5bJk4eJDkxLt6rgfX0brsdVbt8zPyyyD0JxEPie912Vk0JdL+OVTUpZkrofo3hI5eYH1vEdVwmR6ZBkuo2zsvzGb75f3yPtv4Wtt41tGo9oViIdduvn/wty/L7Qr14t9j72C6Gj1zixMtnUFWVYDxIIOzHNCw6B9u4/xfvQtVUujZ2cGb/BRLtVxfW4jtIoqmr8V2vw3EcJs5MM3LkErlUgUhjmI27B+jf1vMTTXNCsSCyKtcb8h9HpVDBF/QRjAbY/fB2lufSHH/pDKnZZXwBX12Uv+mODdz3uTuvuv8aPtywLZuTr57FH/StaipAfBbb+luZGZ5j+sIcfVveu3yS5q5Gdt6/lTe+f1hsDtXS5yuFKguTSToH29l299A7H2gNHwrcdGPxta99jQ0bNvCnf/qntLa2rglp3iOkZtNUitXrOqxEGyNMXphlcSqFIhfo6DPR1Cy2pZJaUDlzUFzEVc2muVMivdRCx0A7sizj2A75VBFZlfGHRCrxxNkZkVwNuJ5HJBFB92kixduRMSoOmg6xRo1y3kH3SRhVB+MK2rznURfrUrNd1HQN27RZnkuzo/ZFUcqW62La8dNTovCpiZBXdsZKuXJ9guA5Hv6gH1mRyC2JkLNoYwTTs6gUqphVUTytBMat2MJOjfhYnNKIN4uLW7UsM36hCaMiJgvi4LWdwnpTLHZgJUVG1RUaWmPklwsce/GU0LS4HsVcCcd26zuMr/8wzpGXIzR1iETspVkdoyJ2UqRaMJnrShSzJSKJAB3rQrh2mVzKRpGnCGwqg9SE4+RwXWhs10nOmCzPqZQLZTR/DtUXp5SrIBJ4rxDUShL+kK9OiTp7YJi7P3M7gYgfRVNxnMtvkKoqtPY213aVK6Tns+x8YCv/6D98hVe++Qae69G5rp352Y20dBwjGk+TWw5j2xKObRFtsOgYWMdyZi+SfADP8cQkpGzgOS5yLb1cUWRKuTKBiCiELdPi6T99iYvHx2npacIoGeSWC+RTRfwBH3d89FbW37IOz/M49sIpwokIsirXni8g1XIslotIskQxI6x4K4UKeIKWFoj4SU0vo9ZCJYGaEYKHa9qE4qFVWprawOLyOeB5NaMCF8mT6rvLhUyRC4dGGT02hmXYdAy0MnTbejrXt6+i8tz5iT1s3TfE+OkpqmWDcDzEuu099WZA0zUe+er9FLNlPA/CtQLd8zyyS3kK6RL3/uKdVyUqV8uG+Mw6V4usNZ/I7AhGAqusfiulKmf2X+D0a+fIp4v4/Dpb7trItns2X9N5SNMDrN/zAOv3XPWjOnSfcPcKJ8KE4yFKuTLRxkj9567tgCyRW8qz68GtTJ6dAcQ0yh/24/NfblJ8IZ3kRIrv/19PM7Cjd1XD9LME27I5+ORhsgtZkCQySzmSs8v4Azq7H9nBZ/77J2jqEM3Ctn2bGD40SmYxu6oxcl2X2dEFOgbbGNjx7mhQpmHx7J+/zKlXz2GbNj6/jmlYnHjxNFv3DfGRX3ngXdty9mzqpLW3meTU0qpzHsR3fSaZ465P7q2L7h/92v1s3DPIhUMXySzkCMWDbNwzyOCu/jUu/M8gitkSuaV8fUr84/CHfNiWQ3oh+542FpIkce/n70Tzi+DSqQuzIuAzqLNxzyAP/tLdH2oTgDWsxk03FuPj43znO99hcPBnI8TnZwWyLHa6PQ+u1au5jsvybJrmTp3BLUvgaeCFcR2DUMRhac6mmHHxBRVMswnTaquPDSvFKrYpQu7MqkV2MY8/oAtbzFoCdH65QD6Vp1Kq1tfh2AAWqg6SIgMKrvdjE6laxebYjigIazvK/pCPDbvXsfP+Lbz6/x6kkCkRjAfrjjyO7dQaA7emlXDrwu4Va9FgJFBbs4NZtcATBadVtdADOrIq45jOKj9boyqzOK2zcqMkgyy7IMn15HHP84QLUc3KtHdzF7mlPCAxeXYGx3UJx0IY5Rrfv5Zn4Kw8dw8qJYXpi8oVYvTaj2rNh6JK3P0xmdseyVEuGLz+JBRzMn6/SWpewnFtLNMjEJRYnKxilF0c16NSgkopi6obYnLju2wPapuiEdN0DUVRBN2kxm0PRhrYuGeQ/d95C8dxVu1YK6qgzoQbQtz1yb0EQn6MioGqKciyTCixh8lxP509p2juKCLJwspX0VsJt32Zkreepo4LjJ+eIp8p4ViXMwZUVcFxXVzbYWl6mZOvnKVaNjj5yllae5qFXmXltfE8Zi/Oc+zF02y7dzOVQoVLJybo3dSJ5lNZmlkmvZDFdVwRPBf0MT+2gFEywfPoHuoku5jFqJiUcxUkWSYQCYjXXBY0onA8SLVigivsfvGE8E+kUQudhudCIBLAMiyMkkG0MULbulbmxxf5wR8+x9ylBXx+HVlVGD89xbGXTnPvZ+9g7+O3rCq0wg0hEu1xRo6OMXdpgbnRBQZv6adnUyeKojC4q5/H/sGDvPKtN5genmXFxCAUD3LXp/Zy1yeuruwrhSqtvU3kUkUKmSLBSKAuWi/lK0QSYYKxy69ppVjhyT94lguHRvEFxE5yKVfmpb/Zz4VDo3zyNx+jpfvmee49m7qIJCLklwp0D3UycvQSuVSeUFTsaBeyRYLhIO3rWtm6bzPjp6ZqmipWNRUAiqzgD/lIzWUYPT7Btrs33fR6Pgx480dHOfiDozS2N9C5vh3PE0LtQrrI8nyGqfNz9cZicFc/d3/mdt747iHGz0wRigaxLYdqqUprXzOP/YMH6vbHN4tDTx3j2HMnaelpJhS77KpVLlQ4/uIZok1R7v/8Xe/q2Jquce8v3MEP/vA5Js/N0NSZQPeLa8XyfIaujR1CuF2DoigM7OhjYEffu3q8NXy4oKiCcuvY13aPW7l+K+p7T0kS596d3PLQdmZG5oV1fGucjoHWNQrUzxhuurF48MEHOXny5Fpj8R6jY7CNcCJEbilfd0m5EvNjQs/S1lMAbAyjgZlRnaXZMrZh4eFRKYrJgiu1IcuXC0sRPOZhmw5oYFn2Stktfm452GblctEkiw+5ZVhUy+DzVDxPEynXeHis/tKRVyhQjofrOjQ0x2jtbUH36zzwhX3MjIwwcvgS+ZSD63g4DgSCKorq4NhCYC5J4uggmgG3xquUa1a4K2JQx7SZH1+kubsRRZGotzn1At+j3u1IHrJS04g4V+xZe2JKA2JwUciUcBxBv1J9KqonisZqqYrruCg+Fc/2Lm97X9lMXNFUyKqMLElIiszHvqbzyOczeMhUyhHu+qjH2OkyyRmD9IJENl3A55dJJ00icZWGVh1ZllAUi9S8R6VQFQ4ZkoTrurXX1iUUj9aoXB6OZaP6tPrI+uGv3MuZ/edZmkkLO1nELn+lWMX1PPo2d9fD7pq7mrCMs/VUZ4cdzMwNEYstoPtMpkey9G6/F8m3j9Zej+buJs4eHAZPeIsrNU1KLlfGdVwS7Q1kl3J87/9+iuxSgWgisqqpALEr1dbfwszIPGMnJ4kkwhhlg4bWOJqu0jPURc/QZbtBz/OwTYvbn7iVSycnWJ5LY0aDZFMFHNsV6d5zafLpIrIioWgKfdt6mD4/SykvhPiqrgh7YEsRLjYI+h2eyFnJpQrsfGAbrX3N/M3vfYf5sUV6N3evSuBensvwyrcO0NzdRP+2HsyqiWM77P/uIY69cIpqyaw7iR1+9jjb7tnMo1+9D92vs+3uTazb3sPoiQlBGwro9G/tpqnz2jSYQMRPrClGS08zs6PzFLNZPNdF8/lo729B1VUaWi/Taw49c4Jzb16ke0P7qkK1sTPB5LlpXvzr1/nFf/HJm54sRxsj7H5kO6988wCheJDO9W0sjC+RWxbOTL6gj92P7uCj/+hhGtri6AGdQrqAL3B1gKVwhxMarKWZ1E2t48OCcqHCiRdPE46H6pMbSRLWzb6OBFbV4uhzJ9h29xCaLjRid31yL10bOjh7cJiFsUVUXWXjrYMM3TZ4zd3XUr7MyJFLTJyZwjJs2te1snHv4KrGsFKscPKVs4Tj4VVNBQjKbKwpwunXzrH3IzvrhgM3iw27B/jUbz3OW08dY3p4Fnsphz/kZ+/ju7j9o7d+qKhpa3hvEY6H6N3UydmDI8SaIlf9PLeUJ9wQontjxzXu/d4g0hBm023r37fjr+H9x003Fl//+tf56le/ypkzZ9i6detVKdwf//jH37PF/Twh3hxj674hDnz/MLpfq180PM8jPZ/F81ziTTo+PYNhBLlwtEgmaeEP6gRjfhzHo5CpYFsWlfF52vu7VnX5RtnEc11UXaeQLooiS5ZQVFk0HAgtxYoV6oqlom1a2JaHoknYZi3U6srCWhL/E4neXi1wD370Jy8QTQRZGDuO5KQY2i2RScLMRaiUXRTNRELGdWsHk6gnCnvu5QdY8ZFyHCFApiY8XxibR1UdfA3C9rVaqukmPHCcWoMiXdaDUHuGINUpKQCe44LniaA3pUatcj0WJ5IYFaGhsKr26kPIrGooVuA6LqF4iKG9Tex7YoKqEaBYiGPbClpYJtGrQFAiVrFxRmJMDIdYLrkslRS0ZR1FV5FkD1MJYumaEM/bKpRlEeIXClC1Q8wnJaplk2rZIqHv5tvfCeC64DgDBHf9Q84+e5Lc+WIts0BCUhQ0v4/MTJx//huzbNsXwbG3cHqxyqkFj2gijCSJtk6ShqgUypiGyR2JW5j/hkjBPn5pPblgkGqpgpu1cV0Hx7KRJBdNh1C8maKvB8cyOH/xDNHGAHJTDE3zkGUPRXZRZA9FcSlWAkyPl1gfa8RTfFTKYsr147WvUTHRfDqb7tjAPZ+7g4kz0xSzJebHFpg6N0MmKRKcLUNM3bo3dhKJhwnFQixOLqH5NHwBXSRzK5II3FtJhFcVNF2lc7CNx37lASbPTDN7cZ7O9e2rmgqAxo4GJs5O8eJfv06iLcb0yDyLk0ssz6XpGeqif5tIdAdB6Tv63EnC8VB91zgUC7Hj3i1XnzDXwODOfo69cJqG5grNrSWKtabXH7RQ/UHmJkJsvHUdIGhTZ14/TzQRvmr3W1FkWnubmTo3w9ylBToH31k78uO48+N7mBme4/W/f5NcqlATnAvh/if+6Ud48Iv31BuWzXcNceTZE6ja6omm+P7KYFk21bLB8994jWK2xI57t9C7+b2jUrzfmB9bJLOYo3P9tQuqhtY4qdk0S9PLdUclSZLo29J9Q5SR5NQST/7hc8xenEdRFRRF5tzBEQ4/e4IHv3g3O+4T58/STJpsMkdr37VzVGLNMWZH50lOpejf9u4aC4B123vp39ZDajaNUTGJNISINUXf+Y5r+JmGJEnc8tB2xk5PMT+2SEtvM0ptI6uQKZFezHLnx29dE+Wv4W1x043FgQMH2L9/P08//fRVP1sTb/9kuPcX7sAoGZx54wKLk0ui6LcdookI937+Tk69/Aalwiy5tE0maRFrVJEVcWF3HY9Yk04gDLNjNnOjC+hBH5IkmgpJkVBVDc/1UH1iF9d1HcGVRtCYZFlGD2hiilBz+/IFdCzTwapa+II65Xy1XlRLssgDkKA+OpUkCd2vsziRZPjNYZrainSuT2BUojh2kbb+Iskpkf5tmRKOLaYGopkASfHqTlRXQq65IQWDfiTZo1oqYxgeba0KLR02y0kToyxh2xK5ZbXuNmW7Phx82J4PR9LxlAAOASxHw/V0HMvPzHQARQ/ioGKYEo6r4rgqLtq1/3OufTvoeDmNJ59T+N+fuUkBZfUGficH/Fgw8oG/AP5i5V8SsLH23xVwAQsoAKPAj1Z+cA9vh78/uPI3FXjw+r9ow+EzwJmVGz4C8/DtM9e/y/+3vobfqN+myC6K4qIpDqrqInkmfr/Et05H8AckfL71+Hzg94OmOljVMqrPhvUmVqXE9GgW6aJFIDCEf9AjNT6FUcjhUx1cqQRaFX/YIdrko6HRhyIZ3PuFhwg3REhOn8O13WvywsU0sMob3zvEum29hBMhlucylHIVJs/PIMsSnRvakZAIxYLEGsWu8Z5Hd65yy7kR9G/vZf0OmXMHThMMyVi2juvI5JYtLGOcoT1tDOwUVqf5VJ5ipnTdHeRgJEByKkU2mb+hxqKUKzF8+BIXj43VXbUmzk3T3NNM54YO4aRmu5gVk/FTU2QfytUf+9aHt/Pqt95g5MgYiqag+3UcyxbasVKVQNiPpmuEYgGOv3iG4cOXeOSr991ww/XThjAV8K7rsy+rInjTvYY25p1gGhY/+uMXmL04T89QZ1187XkeyakUz3/jFRra4quspa+H91LyKEkSzT+BwHwNP5vo39bLY7/yAC9/8w2mzs+IGz0xTd3zkZ3c9y5pdmv4+cFNNxa/9Vu/xZe//GX+1b/6V7S2vrdexj/v8AV8PPFrD7Pj/q2Mn5qkUqoSa4oyuKuf5q5GjOICbz15nnSyjO6X6k2F50Ep79DYKtG9IYgnNZFoayaXyuM4DsFwgFK+zMXj49iWXLd6lRVlFZfSdYW9rB7QsCoWnuui+Hw4tkswGqR/WzfDh0cxKhau49boOE59giEpEqqmYFYtpkcmaUiUUDSP468UMSpFqmUHRfHQ/SKlWtFcPFdBksW/bS+AJ/txvACGF8AmiIMfmyC248czQ7h2CNvTsD0fthdAmdNRl3VMW8e0fdiuD8PyY3t+HO/HaBkeYHM17OvcfrPwgHfoq2VsZNlBlhw8x0XCRkL8W9NcFFkCz8EfUJBwUFRwLQvHsvFcB88Tu8bBiJ/uDW0Ewn5kGWQZcqkcS1NJbNPGNi00n4KEhyQJNyzdpxJuCGMaNr2bu/GH/KQX8iSnlynlKnge+MMBEu0JYs0xQDiBFbIlZi8u4tgOji30F6Zh43kSHgpICsgavnAQUKlWHYyqg6r7QVZxHAnHlXFc8afrXrs4E78jY1orX0sBKMHC8rV+WwGuHtWvxm1X31QArmDj/OF+8aeu70FhO4EXPHTNRtccfJqNX7dRpSqFZA+6arK82IQ8Xya11EQoCHKxxPyhCpucIIkmDb9uoUebWJqcYmEiyeDOmwty07Qy938qycghhbOHbcxqCSRQFInWbj9Du/L4lFPAwyiaiqzKwkqaq8W6riuc+m6ED52cWuLJP3iOmYvzwlBBhuG3RvE8j6G9g3Rt6KhPJxzHZeLMFEdfOMVDvySa03hzjK/868/xX/7ZX5CcSqH7NRHyaNnEGqMoqkxLXzPdGzuRJInFiSVe/tv99Ax1/tSoNWbVrOWoaO/YADZ2NBCOhygsX22HCZBPFYgkwjS0xW96HWP/f/b+O16y7K7uQL/7pMr55nw75zQ9qSdnaRSRkAAhkAjCfhYGyw/LT882T9hY9rPBxg8DAgwIkBHSSIPSSBrNaGJP6unu6Rxv982xbt3K4cT9/jg39O0w0z1J6a7PZ3dVV9U9derUqaq99u+31jo8xNhCtewCRychBK29zQweHeHY3pP0bOikucv/bBZnSjR3XzrpL8ynzq/kR6zgjWDLrRvp29LDwCuDlHJljKBB7+Yu2vpaVgx7VvCauGZikcvl+NSnPrVCKt4iKIpCz4bOy65O7Xn/fQwffYFzxyYJRwK+HazjayuECtWyzdEXg1RLJZItGe748B7GB6bIjs76rkuOh+u5vt2hIhCKglxY2p+vQtRrJkZARzU0IuEw4ViIcr7Kjrs306j4jjWariI1dTHd2fMUHMIIPYGpJkBtp96wmc5DcThOpRbBUyM4MoQjQthuiLoZxpYhbC+MI0O4zJMA61UOznxxZRlq8+O1jisWKiYKJioWKo352xqoWChYKNjXNDTVJRxR0DSXWFxn+21rkXaDrr6z7LjxDFPDAeKZILomefIrU4yegUybJJGGs8cMpkdV8Pz+c8eWtPW3kG5NUyvXkVLS0tvEB377QTxHcmzvKaZHZogkImy5dQNbbt1AMCwJReWiHegX/sO3mRqaYezMJIqqEAwvTTYdx6VaqLL5lvWUchUe/MS9ZNpTfOcvHyPbPOe30giw6jaJphj3f+zORWvQ4ZNz/Oef/xOshk26LYnnekycn1501KqV6kQSYe78+VsIBA2qxSovfGs/mc40a3b04zn+azTrFuMDk/Rs6mb3A7voWNdFU2cLpw8Ms/fr+5kcmqNWcamWHaoVBz0UxYjGcFwFRQ/R0t/Bxj1bkcLANME0odGAet3j3JEJRgdy1GsertSxHRW0EK4MUK14WK6OKw0sR8OyVWxXx3GXvv4sSwFC1K94/vkE4cTehf8vF14/cu7Sv/j/ftsjHodYbGkkEpBMXnq5cD0eHuXgN6FYy7D5ZgdF+Iw3GFEp522e+YZJa9+zdO+8k1Rrgs41bZw/MnLZiXF+qkCyJf6a/dCO7fDdv3qC8bOT9G70V8xnx3PoAQ09oDN8fIxIPLxonaqqCsnmOCdeOLNoBgCwdtcq/uX//nW+9WffY/zcNLPzOSyBsEGmI8XqHf2Lk5LmniaGjo1wZv85bnzXda+6f282auU6Bx47wtFnT1Kdz4hZvb2X6+7bviys8EKk21Ksv341L3/vEOF4aFmGhVnzXc/u/PDNi25J14Lp4Vlcx72ii1IsHeX80RGklISiIbbfuYkf/N+9hBOhZc9XK9cpzpa548M3v259xQpWsIBoMsKOu7b8sHdjBT+GuGZi8YEPfIAnn3yS1atXvxX7s4JXQbotzQf/9S8yfOJ/MztpYjV0FFUQjkK1bGFbOsFIGtvy2xi++SffQzU0Ole3MTWc9bUFQvquT57Eu6BtTUoFmwi2E8N2o6jhDLFwN2enPBp2kJOPJimXFSx5H5YbwpZhHKLYhHGZ/3FbcDqtvP7XKHDQqKNSv8xlA03UCOo1hNcgFKijCpNopE48YRI0atSLFvWig2fZ4JkI10S4NkJ4y55l2VV50c1XmVsVjUdItSVwHY+2vhZW99vkpyuoXoxNOzPEozlmxhqUCzbZMY9wTBCJG0gcqgUNIRRs20P6VlU0qpYvjXddpoayWHWLvQ/vY9PN63jnr99DpiPFiRfOcOTp4+x/9DBCCLrWd7Djzs209DZRyBZ9m9XLQNNUpJTzeR1+XsK+7xykOFth1dbexQmflJKpoSzf/9unaO7KXGR/7GthFFUhEDKolxu+49Rl3sX2VW2YdZMXv33Ar6DYftp5PBMllory+N89QTgWYuNN67j3l24nZGzhqS8/x9CxcfLZGcIBnTVr++hcG0VRJLVSjunhU6Rq07zvN9+xTD/0/DcO8INTz3D9rTHf/1wIPNcjO5bDcRy237GZ8lyFqaEZFEVh3e7VbLt9E619rdTrUKtBqejw1f/f45w7OkOqqx1PGjQsjYalcergJJWKoLm/F6lGqDVUpsZqWI6BS4iGpeGK0Px1HXe+ItNoKDQaMDNzdeeTjy3A55beN9UmHKwTDjSIhqooTpFHTjTYfqskk1Gwy7dzduIAQ3MmnT1B4lGbSNCkUZijUqhy90dufc1J5tCxUUbPTNCxpm1xxdw2fUITiYcpZEtMj8wuy2QIRoJUi1UaVXORWAD0be7mN/77L3Pu0BAP/eG3qBZr9GzsvCSFWlH86ubs+Ny1HJxrhuu4i8GS4E++v/7H3+HM/vNEkxGiqQiO6XDoieOcPzLCe//FA1d0OLrjw3soZEucOzSEEfAT3hvVBo7jsuWW9dz0nt2vbyflVXzhXPCYG991HYWZEkeePk52dBYjOK8j0hR23rOFPZdxG1vBClawgrcL10ws1q1bx2c+8xn27t3L1q1bLxFv/9Zv/dabtnMruBRtq6/nvo+/m6e//AQd/RaO6XJ8n00oFiUUa6JW9ghGo4hIG7mGilkJc2xEp2buwdai1KwwtoxiySg2sQtGBF+VjD+xruL34y/gKqoCKg00qoSMKjoVFK9KUK8QUKsIp4YqaxhqDeHUwWmgK3V0pQ6OierV0dU6gaCJdFQcW17+91ZAyHBxHEHQ8Ojst4ilXYo5A9cROFGHmlSplhWsuooaBKsurvzbveD0NH//woR1Wa/0RZay4Ld96UGdarFOKBoklorg2C7FXAktkOSVvXGaW88RTyoIJUkwoqAHbOIph+kxnXLR79kW833r0pOUcxUcy8GsW0hP0tSVoVE1eeahFzm29xRt/S2cPXAeEMQzMaSUnHrpLOcODXLrz9y4qHeJJiPkZwrLKhYSuWiPqQd0KoUa2bEcfZu6l034hBC09fntFydfOkNrbzPVYp1UW5JKvkJhpkgoGiIYDVIpVKmWbILhAEZIZ/T0OJMD08yMziKlJBwL0dzTRCQeYvzsJI7lomoKwUiAjtVtVApVDnz/MMMnRinlykgPVE1D01T0gM7g0RHqlQZrd60ikgjT2tvM6f0DTJybXgyyq5XrHHz8MOFYmPQFbSjKgnj51Dhzk3l+4f/9AV+4rSjL2k0iEX80N2v8+mdu5Nuf/z7DJ44AkNRUbMUm3F1AD+psu81c/Lvhk2MMHh0hmghTK9dp7s6wfvcaxs9Nc/aVMcJNzXRsWE3XhrW0rutH6DFKJSiVoFCAYnH55cL1mckq+TyYjk8GHFenVNUpVZfab85Mw5P7F/7XDLzjktNaES7xmMuXjuqk/wAyGUin/XHh9aYmmDhWpVAO0BkILJ7oWsD/afA8j0DImG+r9BaF7Y2qiREylmeszCMQCrDp5vWs2naE8TOTi2ntF8N1vWUr/28WXMfl5EtnOfrsCbKjOfSAzsab1rJlPlvizP7zdK3vWFYhiDfFGDszyRP/8CzdGzovWz2IpaJ88F+9i5MvDXD8+VOU5yq0rfJD/zbcsOZ128e29jWjaiq2aV/2eJTzFXbfuGPxc2oEdB789XvYeNNaTu8/R2m2TDwdZd1uPyBP06/5Z30FK1jBCt40CCmvZrlkCf39V+4ZFkJw/vz5N7xTbyVKpRKJRIJisUg8/uPhcuF5kM/7K58zM3D2ZIXHv3KcyUmLci3I7JyOI1JUzQh1J4rtXWr5eLXQqKFTQqeCLiqkktDcqlOcHMOtzaHJKopTQqOKRhWdKho1NKoowqW932XN5irVsuDk/hB6QNLU5vivIatTnPP7/o2Qh6qBriu4rqRSVBBCzlupKgjBvKvRpVBUj2DEBSm4/u4qvetMzIaK66q0dJZ58ftJDj0bnU8El9jWhT2hl/aHCkWwECCiGRqKomDWTZCgh3SfbHgSI2Rg1i1cy8FDomkq0VSUcDSIqvv+34qqkGpJkGiOsOX6Wbr6zqMqBb7zRY3ZSQ1V0zl7RKValIu7sxDyt0BqoqkIqdYE1z+wk1A06BOIfWeZHp5l620byVyU5psdy4GAtr4WBl4ZJBIPcerlcwRCxiK5aNRMbMumqTPNlls2IFSFfY8cRNM1PM8jlo7R3JVeDGybHJyhrb+JX/531zF+9hTf++vnsKwuxs4VyU8VcGwXs2bi2C5C+BN8x3L9yfu8kFVRfTereCYKQpBuTVIr11EUwc57thIIBZgezXJ87ynW7FxF17p29j96GNd2CcdD2JZNrVRn401rae5qQkrJ4LERHvj4Xdw8vzp89uB5/vG//hNd6zoumzhcLdYo5yv8yu//wlUJURs1k4FXBn3LT8uhtacZ13V58h+eo3Nd+2Kgo+u4nD04yNTQDPVSne7NnVRyFfIzReLpGGuv60d6/vO39jXz/t98J+2rWrFMm9JsCaEoJFvilyRkv/itZ3j0r75Az3qdht1EzQxRN4NU6mFq9SDZrEe50cX62+6hVNaZm4NcDmazHtkZl0JJnW/ren2IhEziEZNYqI5bmSGglAkZVcJ6lc27m0nGLKLBOvmRc9z3c9t44GN3XHFbL33nIN/9Pz+gd/OSfa+ct4M26zYzw1k+9DvvW7RAfjPg2A6PfuFJXvnBMQQQSUZwLIdKoUqmI7WY03O5IFLLtJkanObD/+Z9rLvu7avIWw2LL/6nrzJ6euIS8XZ2NIeUkg9/+n30bux6jS2tYAUrWMFbg2uZO7+ugLwVvHGY5hJRmJ5+9evZLDjLxMVRLitMvQACD0OtYYgyqlskEjQJKFVUrwhWHs0t+uSBMjplDLVMUK2gCBfXhYUsuAhhkl6ccqBKtV71U6XFlZf/VdWhURfYloJuSGxTUJxTUTU5/xr8dhpNA88VoPuVCVUD11FwHVBUFl2dLgfP9XUN0YRDe28DKT3iKZvmTod6RcGsG4uVgNciFeATYok/wdd0lfZVrUyen6ZebuA5LlJIjKCO9DyCYQMlFlysKoRjIQIhA7NhUymUEUKw9rpVtHQ1kc93USpvIhafw2OQwRPTNGp+bohEzmdr+LsVCM07eNVNGlWTaCKymE8hhABFoVaqL6alX4imzjSDR0dYvb2PaCqCWTXpXNvO5Lkp6pUGSN/VKNWWZO2uVWy5bSN//9mHmDw/QzwdRUqfSJx84TSRZIR0WxJFadDUOoCsvERri8Wd7x4lPzvA2m07GTyzFdd20QIaVt1m//cP+YnPIYNMR5piroRre+gBjXqlwcxIjkxHCiEE4ViIQrbE3FSB9v5WrLpFvWKiLrjqeB5i3pRAN3SgzsxojuauJoQQKPN6kgU4tvuqbj2aoS2KiK8GVsOiXq5Tnqtgmw7RRIT+bT30bOpk6Pgo7ataCUX8HI+eDZ2YdZPWniYiyTDF6RJbbllPx+olEa7neYycHOc7f/UDNt64lmPPnqSQLc1nejSz8+6tbL5lw+JqdLqjE0VrwbGmCQemCQcWKokmyCpjWpDuzQYf/Q8qyzOjFBYqjvU6i4Rjbm759Ytvm52FqUmXubwCCKr1ANV6gEniwHId3bMjy4/V/3xUkvk30NICzc3+5cJob4d4ZANWdJJjB0dZsyFAOJwFd4Jauc7UMKy/fh39W95c16Hjz53m4GNHaepML8t5aOrKcP7wEFPDM2y7bdNl/9YI6Hiu9O2430YYQYMHP3Ev3/r89xk5OYZmaKiqSqNmEstEuecXblshFStYwQp+bLBSM30bMTsxx5FnzvLuj++kbl572TyZhNbWC3/AJfmhAbIDp+jpC9DaJkglbGJhk9LMBGf2naU8V6GSrxBPxzFrlq+/EPYl5EA3fItXx2aZnaz0PD9AznGvUC8EXQABAABJREFUWEFYgBCSakkwfl6nUVOxTIFE4DoKuu47QqmqnzNRr2pICZbpEwlN11CU+Um36y21Ll2gexDK/BAKVkMh0m3R1mMSTXjYpsLEYJjnvhtn6EwUhM3ywIlXc7LwKwaaoeK5kkq+Sng+mVlKOW+rqxBJhEm2xKnkazR1pqnkqySbY6iaRlsqQmmuzMS5aRqVpZYZ1zEozLVRrVZwnFlUHT+PA4nVsP3sD/zwMNf1EEJBUQWWafttS/Mr5GaljqopPlG45LgL9PmE7nd94l6e+Ie9ZMdyJJvjFHNlkJK+LWt556/dw6ptvXztf34bz/MIxYIEY0HyUwXMmolre1hWAddpUC0UqJcEXwq10tyZJNVkoGrn6Oh6Hte9kZnJDVgNi7mpIkIIIvEILd0ZPCkxq34lw6oroPg5GNVSDc/zFqsyjuVP9M265RM7b+kY56cLi337mqFRK/niHcd2QAiSLUvhYsmWOKFokGqxdlkBc3muQjR5dR78Fydvq5rK+aPDvPLkUXbevRXN0Bg5OebrD6RED+jsvGcrD3z8Lr79599H1fxcjAuhKAqtvU0c+P5hTr10lubODPGmGNKTjJ6aYOTUBKVchZvfu9vPPdjSQ/varUycVeleU0aRFXy/YINqpQ1PJtl+x3UoikK1WKU4W0bTVTKd6cXqRygEnZ3+uBoMn5jgDz7x54ycq6FEWrBknJoZwlFTWMQpVQNIPUPDjVKzI9TtCJWagecJsll/4ePyCAPv8q9+B8JGiWigQDxcpr21RKG9xNQfHaO9ZxftHWHa2uYJSfz12aZ6nsfhp4+jauol4XGKImhf3crQ8RFyk/nFkLtlfz/vdPd6W5reCNr6WvjIZ36G0/vPc/7IEI7p0L66lQ03rL1sdWUFK1jBCn5Ucc3EQkrJV7/6VZ588klmZmYW8w4W8PDDD79pO/eThIFDg3znLx9nbrKA5+0A/D7oaKhOe4dK3+oQLS3LicNyEgHGJb93grEzEb74+4PEUtHFNhYAoz3BWEynkHXRgzqVgl9tWOynv0g34LnzVYIL5uGqCkbQb8W63Er5xZACKgUNs+6h6X7qdTAiMWuCRl1DVReC8ISfUyEAoaLqGkgIhDWCQlCv1nFM//k0XWIEPX+SKkBRPNbtqNO7vkpzu021rHLuWISB4wkmh5NUSw1c28VbfG2vPUNZaNtp62vBMm3qlQbBSJCmzgztq1po6sygB3RCsSAT56aolRq4tkdTZ4btd25eXC0/8PgRAkGDucn84mp2dmyOmZEsEwNTaLqGami4loPVsNEMDd3QFic0qqagqgqJTAyzZpKfKizaRqq6hue5CGEjcREsb6FxHZdA2GDDDWtRdZUn/2Evg8dGyLSn6N3Uxc3vvZ7Nt6znxPNnmBrKsva61Zx84TTTw1mshk0gZKCEFerVBsVsEXCZGtZ5/pEizZ0NjKBC7/putt4wRSp5iFeeMXC9AKnWBLmJGI7li7NzE3OYDdtvLdPAc3wtSaPSwLGd+SqEXAxgXHh3gpGAb6/Z00x+qojVsDCCBp7rVz4s0+L8kRHSrQm6Nyw597T1tdC3pZsTz58hFAstC7ezGjbF2RJ3fPjmS1LAL4Zl2nz3//zgisnbBx47zAf/1bu55f03MD3kz6Rbe5vo2dRFrVRnbqpAPB297LYrxRr56QIt3U209S8Fm0USYXKTeZ7/5sus2dVPS3cTRkDngY/fyTf/1GToTJZoQqIbCpWiCwTZdf8WejZ18vjfP83xF05TLfqEs2N1K7sf2MmGG9Zckx1ktVTje3/zJIlMhFWqoJSbBqYQEeHrhyIBPvK5D7JudxTHcolnPDIdOo7jVzwWKqoLlwtV1qkpmJz0L6enPRxHoWbFqVlxZsowMA3PHrn8PkUiS8ToSqOtza96XohG1SQ3MUfsCu9DJB4mkvQzSPo2d19ynPLTBRJNMXo3/XCqA5FEhF33bGXXPVuv6vFSSvLTBWplPyck3ZZcsQJdwQpW8EPHNROL3/7t3+Yv/uIvuOuuu2htbV35IrsKlHJlvvfXT1DJV+nf2sN/6nqcSMgmHDDJjs6iqAof+XcfoL3/2i18O9a0se32Tbz83Vd8q9DWKJ45ysiJ8xRmyqjCw/LANv2Ztm8x67sEOfYSWXAv6hRRNb86oOkuZl3DativuS8CP49CSoFlKui6TwQ0XeJYAtNWFh+oBwS25bshuY43H9gnybQnMSoG+ckCquahBySurfoVDE9yy7tL3HRfESMgsSwd6bq0dZXItNk88vcCx9KQ3jXIhgToQcMP/3I9hBDE0zE+/On3oukqz39jP7FUdHEF1HM8bNNBUQRtq1oXSYVE4joutXKN6WGXfd97hdJsCc/zUFU/28N1XAwhUHWNoKogFBXd8N2arLpFIBKgnKsQjodxHRer4Ts4Sa9IJJYHr0QkdBrMUaTaSaWcITtWYnY8R7VQo29LD8987UVe/t4rVOaqtPa2oKoKs+N5vvmnjzI1OA2Knzoejgbp29rD5Hk/n6Je9SshdsPC8xw6VxvzWQSStr4griM5e9ikY/Vadt02TapvPWpwJ6nWBP/zN/6c2Ym8n7BsOYSiQRrVhu/Gowhc28O1XWqlOkbQJRD2CYmUEtt0iCRCGEFftNrUlaY918rkuWlqpTqNuolQBE9/5QX/WEvJlz73T+y8ewub9qzHNm32vHc3xdkyQ8dGiKWiGCGDerlOo9pg/Q1rrsrOdPDIMOMDU6+SvD3KiRdO875PvpP+LT3L7ldUD0W4OM7lPyMzI7NIj8XWtguRbksyeHSEgYODtHT7JLJ7fSc/92/fz7G9Jzn54lls02b1jgxbb9tEz8ZOvvkn3+PswUGSLQlaujM4lsvIyXEmBqYxa3ddk0Xk2QPnmRqcYfW2vvmU7AKFmSKO69K7MUStVEczVHo2LJ9s67o/uW9ru8KG5yG9OdzC/yCXjzGV7WJyKsLUdJip6YVLlampGFOzPUxOqpRKUK3CmTP+uBIUxX/uC8lGa4vOqYH1JEJVelBJxeqEAktfbFJKEk1xNF1l5NQ4qdYk1WINq25SKzfQdJX7P37nZasZP2qYHJzmhW/t5/zh4UUS3r+1h5vefd3rSllfwQpWsII3C9dMLL74xS/y8MMP8+CDD74V+/MTidP7zzE7Pre4Staars7fI2jpaWLw6AgnXjhz1cRCSsns+ByNaoNIIsw9H72dcDzE4aeOMXjwRSbOZWnUJImmAGu2qQwerzE17IuTfXG09F2NhILjeJdUKhQFgiEVhEet7GJb5lVN1qUnEELiOALdgHhGEI7p2KaNqrlUyyquLVA1gRAaiuL3x3uWjaopaJpCpjOFM+QQDOs0ag081xdySwmbrqty031FSnMa9ZqKpgtcJ4SqQe/6Cvd+KMdX/qTFj7q4ygDcaCpCsimORNK1toNKvsqmW9bxjl+523doqtkc23uSmZEsqqZSzlfxXIfuDX209TUvvh9DJ0YpzBSpFmuYNYu5yTyelOiGRjgWQuLrNGLpKKVcGbthIxRBo+L/PYCiqQQjARzbmdedqEhvjnr+EHa9RNeaAIVZQTjaoFw8zsARjVo1jGN5JJrjnN43wFNfeZ7mzgybbl6HEMK3oVQVapUG+77zCr2bu5eeb/58UFQFx3J8/YzrEQxDLOkzy+KsQ73skm4zqFdcjr1Q4/q7Qqzd2Ykw/Mnoml39nDl4DrNm+a9VynmLWb91SSgCVdPJTeYJx0P0beqmkq8xcW6atv4W1u1ezdCxUTzXI94UZ9W2HgJBnfPHRvAcj2qxRktPE72bugnHQkwNzfC3n/0KoViQlp4mQpEgvRu76VrXztCxUcyaSVNnmm13bGLLrRsIRV+9WgEwM5q7YvI2+FkCwyfHcR13SVzrlZDmSwTlPnrXTHD0eZNEqg+hdSPE0nMWsyUCYWNxwiqliXSncBrZ+XBEyE8PcWE2RlNHmjs/fAt3fGgPnustPucL39rPwCtD9GzsWmyTM4IQjncyNTTDs197kbW7+q86x2D83BSKIha339LTtCxcbXJwhqFjo/Dhq9rcpXCzKKJEc3OG5pYcWzdflHYoHXCHEdHfROhrqVZhYgLGx5ePsbGl65OT4Lr+4yYm4OWXFzamAXct23zQsMkkamQSNWLBIjGjhz33dTBy4CUOvXQU1Z5FER56QKV9VZvf8um6l4jqf5QweX6ah//o22TH52jqSJNsSWBWTY49e4rxgSk+8FsPXjGPYwUrWMEK3mpcM7FIJBKsWrXqTXnyZ555hv/+3/87Bw4cYHJykn/6p3/i/e9//xUf//DDD/Nnf/ZnHDp0CNM02bx5M5/97Gd54IEH3pT9easwNTiNpqnLvPcXsCBoHT01flXbGj4xyovfPsDwfK93IGSwansve957PdfdCV/6zy9SLQXZvilOPK1RKTg0ahq1csmfNEZj1MoWmqGRbtVwzBzZCahX/NYlzwUjKAhGFeyGS70Ktvna1Yr5VwNCouu+ZkMRHo7tW73GU5J6BWxPRTN0X0cx74YkhEBRFFbv6MOxHIqzZVzPRfoFDRRFoukeO+8oA1AuqOgBSDV5OF6YWslhasSgd22D7jUWQyevvkc6moygaiprr1tFLBVlbqrAnvfegKIoGEGDd/3GvWy7YxPnDg9Rm0/pPfTkMT9xfB7TI1lGT47D/GvxXA8UQUDXcWyHaqmO57hoiRCxZITiTJHGvOuUEP45IIQgEDbo39pLIVukXqpTmitRL45gGDW239bJ9fen2fvNHGcPVzh/ROI5NSIpnY5VPaze0UduMs/g8VGKs6V5gXSe2dEclmkjFAXXcfHm3afMhsnIqXFq5TpGyCAUC+FYDqVciUBI0qjVCQQj/v7Nn7apZp2JwSoz4zr97b5mQQjBze/ZzZP/uJdyroqqKKiGRigapFapY1Z9DYUe0ojEw2y9fSOBcADd0Lj1Azey7baNxJviPP2V5zn+3CmGT4wCfmvUjru2MD08Q/faTsJxf6JeLVbJjs5SmqtQLdZo62nBdTwO/uAIXeva+eC/ehfxpjhGUL/s5+2KZ654jfgSKf22rfkCrfTyyOrfgXMaIeLsuD3OuSPTTJ07RUvvDGpwBxChWqxhmzaxVIRwPITnFpkdPsj0SJFKwT8BHMtl7dbH8RrtiMAtl9j/Lkz6XcflyDMn/IA249Kv7uauDCOnxjl3eJhtt19eoLz0ciS1ch2rZr7q637D9Wih4qekO/OXF2P+duHfF4nA2rX+uBJc12+3uph8jI/D+bMmZ05UKVQjmHaAhqUznk0wnk0A/kr+9w4C9AIfRgiPRLhKU7JGfKjEi/+fPDc/PcYd7+qlpwd6e/1gwwX4OS8znD88TL3iL+ys2dlPU2f6baneSynZ+/V9ZMfn6Nvcg6IsWdBGUxGGjo/y7MMv8fP/9v0r3QQrWMEKfii4ZmLx2c9+lt/7vd/jr//6rwmFXnsl8NVQrVbZvn07v/Irv8IHP/jB13z8M888w3333cfnPvc5kskkf/M3f8N73vMeXnrpJXbu3PmG9uWthF8luPL9nidRVYVKocqpfQOc2X8Os27S1tfCxpvW0bupCyEEg8dG+Poff5dSrkxzV4ZAOEC9XOfIUyeYOj/Dgx+dpphz6d+cRDcUTuwrU5ixaNQ8zPq8VsKrEE2lqJVq1Cs6iUyAFsWhNCewTQ/b9giGVBpVxxfUahpY9lWHxoGCHvBQFQhGQDckVkNiWQGEYhDPhIilI5TnqpgN37pVMzQ27VlPPB3l2N5T2KY9P3EXqJpPdgIhj5YOm3LRP2UdG99hyW1QKzt4rqClwyXdYl41sfBFwx5N3Rkcy2FuqsB1921j441rsRoWA68McubAeWrlOu2rWrn+HTtp6Wmib3M3j/zl4xx5+gS2bTM7lvMFvUIQS0exLYd6uYHreoDfemSEDOyGw9CJURpVczE/Q3oghSSSCBFPx5gdz9HS3cyG915P/yYdxR6lpa+PekVn/+N5XMfDCEAkrtOzXieeUYm3rkIIHbNmEQgZNCoNjjx9Aul5BMIBIvEwnuuRnyly/vAQ669fy9kDg75IOhr0W8DwBdWKoqAFNKRXo1K0CEUMoomFrwkJsg5KN2hLdpyp1iTNnRmmh7JUS37giU8Kfd1INBFBqApGUGftdau4+xduI56OLrOHfeDjd3HDO3cyPjCF53o0daZ58dsHKM6UFkmFRDJ8YoxKoUpLV4ZCtkS1XGdVXw/JlgTDx0d54dsHeN+/uDTX4bXQ1t+CpquYNZNAeHk2g5SS0lyF3Q9sX1zJlo0fgHMa1NUgdPo2wzs+HuHJr0wzcjKHUF5Bqj0EIwF23rOVmZFZbKvBxOmXGTlVAgyMkIJjeVRLghP7Ghz4zpfZ/Z5W0C8/qzbrFrVS7YoVGFVTEUJQLV45dMbzPE69dJbDTx9n8vwMuYk5ZkZyhGIhWroziAuoxAL56NvSfY1H88Kd6ga1BbwsqJfRLnhZUFsvf9+VNqn6Iu/2dth9SSZdgFP7RvjB//0640NlSvUYxVqUmptGTfahp3p55YU5pmcDFGsxXE+lUI1RqMZYcMF69hT8tz9d2mIyCT090NPtoZiT2PkhwkqOZKRMPFSgvW0fN75rp58lcw1k9vVgdnyOoaMjNHdlFknFAoQQtHQ3MXJyjKmhmdfVWruCFaxgBW8U10wsPvShD/GlL32JlpYW+vr6LgnIO3jw4FVv653vfCfvfOc7r/rxf/RHf7Ts/5/73Of4xje+wbe+9a0faWLRvb6TA48ewbFdNH35qp2Ukka1QaYzzZf+6z8xfnaSQNBA0zVGTo5z+Onj3Pze69nzvt08+7UXqeSry4SHeiZGNBVl8NgQA6+cpFpWSbQonNxXpjhrE46rhGIKlaJNo8S8aLiBEdQpzlYp5vxqQDIDnqf6Ti9jDq7jh8D5M9+l/RWKP/XwLmqNEgKEpiAdD9vUcVWJosXQw0EKuQpmXUHVJXpAIxQNoQd0fzLruJRyFeyGzemXByjnK7742pNoGjj2QoKdgucJFAG64VcFygXwXHtR/CzhygRIYbE9aiGkzQhprNkWJ9GUp7mjzNbb+tl8WxeluTJf+YNvcuTpE771pJQIRaG1r5l3f+Jeujd2Mj2cZfjUGI1KA9ty0DQVzdBoX9XqV3g8l1DMAymplSGc0MhPzdvFSv84arqKO7/vVt2iOFsmGA3Qt6WbX/rdD6F6ByiOqXzrC3XOH8uiqgLdUBg5VadacrEaQaIJF7AAfT5hGGzLpVKoLst2UDWVcDyEa7soqkDVfPtaVddoVKp+OFdQJxQNYZlQK1tAg94NOrrhgjQpzpaJpYM0r3o3Qvif+0K2yNf/93eRQKYzjSIE9UqDarGGEIJMZ5qmjjSFmSItvc0cefoEqqryvk9eOvlPtSZJtSYX/1+cLS0LYKuV6uRnioTjYb/KpSpYdd+BS1UVMh1pBg6eZ24qT7otdfHmXxV9m7vp29zFwKEhutd1LAaVSSmZHs4SSYTZOm9TKr0C2IdBaQKx9P23+cY4vevDDBwKU5ytYiS30btlG8mWOF/7n49w7JmXmR0rEYoaBMI6Zt3FtiS9m8LEMgbPfL1Az9Znab3Ccr0R1DGCxjLXsQvhef65dLnQuoXX8uzDL7H34ZfAk8Sb4mTaU4wPTPHK40fYdPN6ujd0IObJ8OT5aRLNcTbdtO6ajuWFECKANG6F+kPgZueP2Xxvo5cFbETgVoR485yYNtywlu4NnQwcHKSQLaEbGj0bO+lY00Z2NMcXfvcrJJriBKMhSpUgs8UwuWKYXDHCbD7I+ISKiHUznQ0wN7cUYHjkiAJ0zo8l6KpD4qtF1v+3Ktt3x+jvZ3H09UEq9fpcri6HWqmGWTfJdKSX3S6lpDRbJj9TYHp4ln3feYXbP3QzqQsc1FawghWs4O3ANROLj3/84xw4cICPfvSjP3Txtud5lMtl0un0FR9jmiamufRDXCqV3o5dW4a1u/rpWNPG6Olxutd3LCajuq7HxNlJ0m1JRk+NMzWYpXdj17KV3MJMkef+6SWQkvGzk7T0Nl1yzBVF0NzZRDFnIT2N7JhJcdYmntZQVEE574AEIwC2BWbNJNqRRtVUitkSmhEm0RwiGGkwcd7EdedF3u58oNUFDlLSk0jBJa5SQggCQQOrYePYLqFACCPc4tvNug3Meh1FVWjMt1bpAd0XO5fqBMMBFE0hOzbnh1olQtTKDRzXnd82VAoKo2cDrN9Zo5TXkJ5Auiy6kkXiHo2qwuTw5ScoilAIxAwU1W8J6l7fxLs/Ns0N980SjEQwgiA4hlc7y0P/NcZz35xFUZRF29lyvsKpF89y9sB5QrEgdsMh05ZEDWhMnZ/BsVwaNZPcxCz9mwTtXTlCEQchoJRXyU0b5NFRVBUpPYygjm5oCFVZTPkOhg06VrWRaU/5wXWmzuNfrnHqZYtQVGNuxqJRcakUHcy6x/ljVYKREB3r/fMl0RRDURWq5RqBkEooaiI9gW0FkRLshk3vlm7MmknbqhYc28UI6MyOz1EpVFFUBdu0qZbq2I0g7f0KXWtCIGs0qoLiXILbP3w3seYlEn/0mZOMnZ5g441rYV78G46FfF2ArtGoNOY1BgG613ciBJx+eYDJ89N0rH515W88HWP8zNTi/826hWM5ROLzQnrXXWYNGkmEKc6WKM9VrplYaLrGO3/93vnk7TGAxTTkRHOce37xNno2zE8ovTx4FVAv9XONJjV23NEB7llEtAuh++037/vkA4wef5nxAd++uFGzMYIK3WvD9G4Mo2qCwWMqZ14+Tssa33b4cvu45dYNPPml50h3pC4RmeenCsQzUVZt673saxw5Nc6L39xPLBlZZtl73b3bOfL0cU6+eAazbhKOhjDrFsmWOPd//C5aet6Y3akI7EHKKpjPgHuGRZYvkhB4Fxg3v6HtXw6ReJjtd26+5HbHdhbNIhQByViDZKzBmq45YF4vdXyUn/9//Qzrd6+mXIbRUThzyuQf//hlZudC1MkwW4iQK4aZK4WwXY3ZcobZffDcvkv3JR73Scbq1f5Ys8Yfq1dDV5dfgblaBMIB9ICOWTfRdP9zYFsO5w4NkR3LYTUsLNPm+W++zPkjw9zxoZvZec/WlbaoFaxgBW8brplYPPLIIzz66KPceuutb8X+XBP+8A//kGq1yoc/fGVl4X/5L/+F3/u933sb9+pShKIh3v3P7+Pbf/4YY2cmAX8i7nkezd0ZtuxZz7MPv0THmrZL0oOTLQlKuTJHnz2FWbMWk5QvRjAapFbNEEvNce5oDVUTKKqvb6gUHFTNJyCBcBikgvQsEpka4ZCDZUugCY9OGrURFFEiEAniOS4oLOYtLOoKLlMVCEaDRBJhqqUajuUSiASYODeOwCYU8bAMl0bN1zRI2cCqm0ipohkqZtVk9OS437ozL15WFBfpyvkig4eUgkPPRVm7vU5zh0V2XMd1/R/LQMijtcvi8HPRKxKLQNgglorOp0J77LptlrVbSsQy21G0JbeekeOT7P/+KYSIkWprozBT9DUfjr/SXyvVqRSqJJrjaAGdQNBPtzYxcWyFZCZHU0sDx5bUaxqqCvGUg254zE4qmDUW2yUsc7kVlxv2MOsWesD/WE6PJzmxX1DM1pkcBoFJoyqpFgWNGtQqcOQ5m2izRSITJN4UI90WIjc+TmuXpLWjiJSCRi3A1EiISLKJtt5mpoeyBMMBwvEQiUwcq2HhOC5W3SIYCRAIBahX6rhekpnpTsxaDaGobLt7C7d84L6l/XVdju09RSwVRTd01u5axemXBxg8OjpfQXJwTIdwPMzaXauIpSJIKZkZnWX09MRrEosNN67l+AunF9uTVE1ZJIYLgub0BSnktmmjGerrziHItKf4uX/7fs4dGmLo+Ai2adPS3cz6G9ZclHaugdBA2nDZlXbbfwxL1Yxkc4JEU4hUk8BxfEF7S3eAjv4Qmu6fD7ohmB1/dT3Ttjs2c2b/OYaPj9LS00Qk4TuI5SbyNGomd/3CLVfM7Di97yyNmkn7quUtMommGNe/cyfHnztFJBGmb0sP3es6WH/DGpo6rrxoc7UQQkWE3oE0doJzCmQNRAS0jQj17c1oSDbHiSTClPNVMu2XvneVgp9hk2rxj2EsBps2QcCe5GjLPjr3tC+rOjuuIFcMMzFlMDwi6Nx5E7lilMFBGBz07XdLJTh82B8XwzB80rFANC4kHX19l1qMt/Q00b2+g4FXhghvCiGEYOjYCFOD04QTYaTnkelIs/6GNcyN53ns754mmoq8rUniK1jBCn66cc3Eoru7+zXjvN8OfOlLX+Kzn/0s3/jGN2hpabni4z7zmc/wr//1v178f6lUorv7DfQMv0609bXwi//+gwwcHGTs7ASe69He38q63as5tvcUju0SCF1+QhTPxMhPF1A1ZTFz4GKYNZOG28eWPTpHn5uczwnQcCzPd+eRHlIYZNozuFaFZGaOptYSEpge09h6/RzT4xmqRb+SYtZMEk2xRYcfD799Z8Edyl8B89uUhCpIt6Vo6kyjqArTw1lqxSKa1kAIaNQEtilQNUl5rkw5VwEEmqGgGQZ6MOK3dwiB9BzSLTXMuks5r+JJUBW/9WrwZIBnH0lx4z0letebi+F6ngtnDod4+pvJKx7/QMigra+FWqWOa5VYtz1PJLN2GakAOPi0S27KIxKvMzMyS3G2tKSPmXdNAj8hOzcxR1NnhmAkSKVQJdVUxwg45LMC6Sl+loYEs65TykMibVMEahWfEC1MUKQnsS3bt1etNejf6q84z4zWGTmj0r2qgGE4TI1qdPW7KKrkzOEg+axGac7h5Itn2X7nZjTdJRjMkm5x0QNQyEqk52AEK7S0q5h2hEbVREpJ76Zuzh4Y5NyhIRzL8fUgC1kamkYsE2PXvVsJRULEMlE23riW1Tv65nMofNimg1k3F8/HUDTExpvXU5gpYZl+TodVt+jb1EVTZ3rxvLk4PftKWLurn403ruX4c6dJtiSIZ6KE4yHyUwU0Q6djdesya9DZ8Rxd6ztp6W16la2+OoLhAJv3rGfznvVXfpDa7lcrnGFQ+i+935vxdQPaki3tK08c5cRLc2THPIJh3/WrWnTITVqs2xkl2WzgOjaBaOdlqxULSLUkeP+/fJCnvvI8g0eGyY7lUBRBpiPNHR/ew3X3b7vi386M5K68MBEO0Nrbwtpdq/jgv3r3lV/7G4BQW/3j8kNEJBFh8571PPu1F4mno4stb+CL47OjObbetoHm7uXnkOd6eK53ScK7pkpa01VSkQLNWpZf/tQmutYt5WjUajA0BOfP+2NgAM6d8y8HB8Gy4PRpf1wMRfG1HctJh0Jq/c3oZ/OMnpoglo6SHZ3FCBk0qiaaodGzoQNNVWnp8fUWrzxxlLW7Vq1ULVbwlsAybaqFKqquEktFV86zFVw7sfjDP/xDPv3pT/P5z3+evr6+t2CXXhtf/vKX+bVf+zUeeugh7r333ld9bCAQIBC4/I/p241QJMjW2zay9baNy24XF7UVXQwpJdFkmEgiwszILN3rOy65PzuWo3tDJ+GmzSSaH2LyfBXHthGA1ZBohkGytYlIzCE/XcRzG5TyGqqu41gQSTjcfeMUpbkoL/9Ax/Mkrmuj6QZCCBx7uYBbM+adalxfg2GEdG79wA2c2jdAsiUIdoXZCQPH0XEdj/FzZUIRl1jST50WSoBqSZKbbqDVbBx7YSW6QaXgEUuB1fColVVQJZruk5h9j6cYPJmgf2OJZNrEtgWjAwFmJ3WiSZdAyGNuRsM2FS70tHEsl9zkHI2qydabA2zYnSTVsdQyUi05PPHQDF/743HKBUmtYuI65qLtqxHQ/c3N53lYdRurUZzXYPhVnetu97Uk02MG0YSHqklcV+DVNMJRj971DY48r9CoqbiOh1CURQGmoijYlkMgFGDdbn91MTeRp2tVmV23V3nmmyHCMdencgqs21bn/IkQuWmV7Ogsx587RabNorPPZPut/Tz/yDSOU0eg4ThBIjEbGqMcesqhY3Unq7b1UslXKGZLKKria16EHzLmOnU0Q+Oun7/1VVc6jaBOJBlhbrJAojm+eFu6PUUxWyKWilKcLRG+IAXZthyEIpZpKa68fYN3/cZ9JJpiHH/+DGNnJzECBoqmkmyK07upC0UR2JZDdmQWPaBz07t2XdEq1GpYDJ8Yo1auE46F6N3U9bqqG0JoELgD6f5fcMdBafNdjaQ7rxuwwLgNIXzSOnZ2km/92fepFDyshsBzLX8VXxE0ai7Sk2y+SUUoGv07bnzN52/uyvCzn3o308NZitkSqq7SubZ9Ma38SgiEDX+R4QpwHOeKixs/Sbj5PdcxPZz12xqjQUKxEGbNpFKo0rOxkzt+7pZLJkeZjhTRZIRyrrx4rl+I0myZaCpCqi257PZw2K94bLqMSZfr+m1WF5KNCy8XSMnQEDz++IV/2QH8Oslonag2g2ZtoimWpzVdYeN6lUhGAd90m2RLgrHTk5TnKj8W+Rwr+PFBo2Zy8PEjHH3mBKVcBUVT6NvUxa77tl+S9bOCny5cM7H46Ec/Sq1WY/Xq1YTD4UvE23Nzc2/azl0OX/rSl/jVX/1VvvSlL/Gud73rLX2utwttq1oxQvrihOdiFGfLbL19I5tuXs+3/vR7jJ4ep7m7iUDIoF5pMDU4QzlfYfTkGKMnx9FDXaCM4tiCps4oalBSL9s4psPcZBZFuICHomk0qgIjBJqhEAqX2fNAncN723BsgVkrI8IBf0J9QS6EmA9Z81zfLlZKycTAFC9++wBGyGDVZgVsl45VzUjg+ItTuI6/Uu86gmjSIxxzcCxfZ+E5/iq8EQQ9IDEMSWlOIRR1kZ6gUVeQil+dMIIu+dkQk98BzfBQNQ8pwTA8FMUP1GvusJkZN3CspZTxSCJMui3Fqu19/OK/6SER/fri66mVHb72v8d46qtZzIaLooIiwJl/zZ7jYkqJdkGbmjd/p4tYrGJkJwzW7aih6VCcM3BsP9eja7XJjttUKvkaJ/YHQfraEKtuzgcW+q5gkUSI1t7mRaGnbjjsvjPL9KhBaS5AqsXGrEushsBxNOJph2pZ9yeo1Qa3vQduvK+duWyYI3vHmJtWaWoLEAwLbFvHrDfQFN/+9NCTxykXasSbYriON59jAcFIED2gYVsO+777yqsSC0VR2Hb7Jr77Vz/AMm2MgI5A0NrbPN9CViIY8ROBwSfAk+emaO1rYfX2y+sALkY4FuL+j93FTe/eTXbMz0CYHc9x4LGjTA1m8aSHIgTN3U3c+oEbF0nZhZBScuKFM+x9+EWyYzk8V6KoguauDLd+4KbFzI9rgr4DEaojG4+Be37hmUBJQeC9iMCexYceeOww5w4PoRka4UQMu17BCMxXvhqSqSEHTQ9yw4PXsXrXLVf19EII2vpaaOu7crX2YqzdtYoTL5zBsZ1FndcCrIaNQLBqe99Vb+9ycB2X4RNjFLIlNF2le0Pnj5yAOJKI8DO/9SDH9p7i8NPHqRaqxNJRbn7vbrbdvumyE/B0W4r1169m/6OHCCfCy+x+zbpFMVfijg/dvKj/uRqoqt/u1NcHF6+PSemnli8QjQtJx8DAvKC8EqJAL9DLUA2YhkdOzu9vvEZbpkxzfI5UMMuqJwQ37IGOjjdPSL6Cn16YdZNv/dmjHH/+NJF4mGgqgmO5HH/+DIPHRnnw1+9h082vUvVdwU80rplYXOzM9EZQqVQYGBhY/P/g4CCHDh0inU7T09PDZz7zGcbHx/m7v/s7wCcVv/zLv8z/+l//i5tuuompKV/YGQqFSCR+tH68rgXd6ztYta2Xky+epWtd++IqqpSS7GjO9/S/c7PfIvMv3sHz39jH5PkZbNNvi1JUBU3XaOrMEEtHkZ4kEAowfGKM3JRv51rOV1AUl3jKJd4kUVWXWhlqZZ3e9SZ4JUbPqMRTNs3tNlNjAQIhFelZKGIppA781h3nIr1Fo2py8AdH6ehvIxIN09IOnpQce7HE2YMmVh08qdCoQCmvYARcHGvedEoCQqJ6knpFIMMKgaCHYym099oUZlUaNbEoGDdrDfSgS6bVpjSn4roQjPgJ31ZDQVE8IjGXalnBdcAIBth6x0Y++Nvvpm9LN6oyTXXiUWbGZpAiyvCpGoeeLgLQsSrEyMkK1gXGO1KCdDws99LUPUURuI5/EBo1heHTQa67q0YyY+NYCqrmYVsO0tPpXuPQ3G4zMxryLYg9v71CNzSae5pYt3s1RkDHrFuEoiFau0oYts2xFzVc18XzQAiFUl6QmxJIqRGMuCiaQb1S5+CTJs0drUTiZa67Q3L+ZICpYZdizkMo0NQu2XVvhqmRIAceO4znuLSvakXiJ2AjJVpAR1EEU0MznNl/7jXP3a23bfRtefefI9kcJzqfUh6IBChMF0m1JqgWaxRny5TnKmQ6Ujzw8TuvWClYIKoX23bGM7HFCd/q7X1sv3MLQ8dGqFcahONh+jZ3EQhdvjJ5+uUBHvmLx/Acj/ZVbeiGhm3azIzM8tAffpOb37Ob1Tv66FrXccVWoYshhIDAHtC3+roBrwIiBPp6hLJcOH7oyWPYpk1bXwsxK0puSsOs1pDSrx6Ypkcw1sN7f/PXLgnoc10X1/HPkTfaYrBu92p6NnYyfGKM9v5WQtHgoqXs9FCWNbv6WbOj73Vvf+zMBI9/8RnGz07iOr5DVSwdZcedm7ntZ29a1kb3w0Y4FuKGd+5k9wPb57U52muG4t3+oZspzBQZODRMIKj7dt+VBq7jsunm9dz8nku8b183hFiy1L2cnHFuzicaLz4zxzf/7jRlu4XZUpKpXJRqI8BcKcxcKYxvobuRr8/LEKNR2LABNm70Lxeur159qZ5jBSu4Eo4+e4oTL5yhc3XbMnvueCbK5LlpnvzH5+jf2nNV4aQr+MnDNROLj33sY2/ak+/fv5+77lpKSl3QQnzsYx/jC1/4ApOTk4yMjCze/+d//uc4jsMnP/lJPvnJTy7bpy984Qtv2n693VAUhXf86t04tsv5I8NIz+91t02HeFOMu3/uNvrmS4vrd69mzY4+xgemMGsm1VKNR/7iMTIdKWJpv79RqIK1160mlo5x6ImjlPMVYukoeA3iKQuESmFWQ1E8mjuqdK+pIz1JvaYQjkkUVeI60Kh5hMIqgZCHWVvSVEjkEtG4oI3LczyyE7OoByM4mxymRgqcPVRZqhzgl//xwHWY12ssCLAl0bhLpeSH6ukGaLpHJOERb1KoFHzbVs3waNV0pGdjmxCKSMoFn/joAYmUHoVZjXDMJdNuMTORpmd9J7d94CbW7OzHtmye/foIhx+DwswQnowwcc6kWnYwAgqG4RLPKMxOSC7Xn3ahzgRYtI8Fn1gEwwojZ3Ti15skMr4r19QwxJIOkYSGUALEMhHMmof0PISqEEtG2H3/dhpVk3AstFi1au4KUhwVaMZC5gZYtmRuWvOrCyFJKCpwPZVUWwrXmeD7X5pm950qqVadO9YHyc94VEsSVfNYtbHG4EAz+WyAmZGs/3qEQOA7el18TtYrjcuer2bdZPDoCOV8FSOoc/dHbqWtv4Xjz51idjyHoirc8I6dZDrTlHIlsqNzBCIBdty9ha23baSl+1INxOTgNMeePcXpA+fwXI+ude1svXUja3b2X3ZCHQwH2HDDq6SoXfD+vPjtA9ims6yFsFE3mZsuMHFuipGT4/Rt7qKpq4kbH9zJznu2XnUegVBiYFz/qo/JTxUWMyb0gE5rTwtmo4HnmLiOQn66RNuqrmUr5TOjsxx99iQnXzyDa7s0dWXYettGNt287pJqw9UiHAvxvk++g+/9zZMMHx/Fqtsg/BapTXvW8cDH73rdwvfsWI5v/Mn3yE3mae9rIRAO4HmSwkyRZx9+Cdf1uO+X7nhd234roSjKFQnpxYinY3zgX72bky+e4dhzpynnK7T2NbPllg1suHHNVW/nzUA67Y/rrkuSsSY5u/85ejZ2omoqlZrB1FyUodEgg8MGano9U3MJBgagUoH9+/1xIVTV13BcSDYWrv8Yr9mt4C2A53kcefo4gZBxSeaPEILWvmZGT01w7tAQW27deIWtrOAnGVf1C1WtVolEIle90at9/J133ol8leS4i8nCU089ddX78OOGRFOcD/3Oexk8MszgsRHMukVzZ4Z116++xJlF1VQy7Un2fe8Qzzz0PCdfPIumayiqQiwTpb2/laauNM1dGWKpKIGwwe4HdhJL1AgHnmZsoEatZJPIWMRSLooC9aoglnSoVVQqZQ1Vg0BIxfNAD3gIVeA5LJUt5oUhYp5oLMCsWZTyBif328xNW7gOi1KHRTIiQcqFyaJE0SSJlIOUEtfWcF2wGhqBkIcQNu09MGvoSGnS3mMTSTgU5xzOvBLCdaGtx0LivwbHUghFJTfeV6FcCFMth1BUQbIlgeu6PPQH3+TAY0cIxxK0dTtU8lnKBZtqUaLqkkZNwWxoSBwuJhaq5vfFO5azGHB3YZ5HfjZANOFh1RWE4lEt2YSigkjcob3HpTCXxrQ6CMd1oikVq27h2A6WaZOfLuBYLjc+uGtxcmfVNQQK3Wsk4+cFtYqGY4PrCEJR0DSwLQEqhBNRivkwo6emGTqhsnqLy6YbPFq6VNKtEI4WcJ0ohbk2PHeWRHOcQrZ02bYYz/XwHI/WnksJwLHnTvLtP3+M3NgciqYSigVJNMXZ/cB2Pv77P081X0PVVTIdKVRV9d9Tx0VRlStO1k/vP8d3/vJxirO+LkNRFY7tPcXpl89xy/uv57YP3PS6V+unhmaYPD9NU9fSZ6hWrnHqpQGqxRrxTAzHcog3xankK3zvr5/Asf334c1CPB0lO5rztVIJk1UbZ+hbl0U3XColgwNPGqRal/QRwyfH+NafPsrs+ByxdBTN0Bg+Psr5I8MMnxjjnb929+smF+m2FD/36fcxdmaSmZFZANr7W+hY0/aGwt0OP3mM7GiOvi09F+iGBOm2JIoiOPzUcXbevYWmzszrfo4fBYRjIa67bzvX3bf9h70rgE+M7v/lO6iX6gyfGCMUDaIHdIxyjXUp+PAHt/LAx8Pohi8UP3cOTp2CkyeXX1YqSyLyb3xj+XO0t1+ecHR1rbRV/TTCrFuUcmUiicu3/fnOln6o6Ap+OnFVv05r1qzhX/7Lf8nHP/5xOjo6LvsYKSWPP/44/+N//A9uv/12PvOZz7ypO/rTACOgs/76Nay/fs2rPq5aqvFPf/xdzh44TyFbol5poCgCz5OU5yoUposkW+K09bVgmTaKpuI6DuPnbXr6TG66L8vQyQDOvI5TeoCQpFtsXtkbAzSCYd8SU9cEhVkuIYALq/YL1YtgxKWj30LTPDbumuXoSxFmRoPLJoRC+E4n7kX60UDQw3GgNKfhufgZFRIadYXx8wbhqIVZN0hmJJrhMTetYDVUmtps6jUFTZNICZomaWpvsPXmOn3rJU99sw2ET3a+85eP8chfPMbhp47PW5Z6nHrZQlV1GlUHy/RwK2CbGqFYGE2v4iDx5tuchCLQDW3eYcv/fzAS8EPaGja2aeO5KuPnQxghiCZrBAI2uuEhPZXJ8V6mJm8iGK8zcmqAetX0g/3mj+ML39jP2utWse76VYvHZXoiSaORZM3WOnMzLideVilMKnie74RlSwWzrpDuCDB5bhrHlgihYTUszh/zmB6tccO9OltvqiEUj+FzW6lXNTxPcsM7d5GbyPuhd8kIelBHCOFndhSqBCIBbrhgci2l5Pt/+xQP/eG3qBVrBCIGqqYSrUdAwFP/+DyKqnDL+25Y9t4KIV51ElzOV/j+3z5Fo9Kgf0vP4vmSaU+Rnyny3NdfpmttxxWzGV4LPnlbnncxeX7ad/FqSfjBYg17UbMwMzrLS48cYMst64kkrn4x5dWw8eZ1DB4bRVOz3PHuMZraKtSrBpapEI0VuPdnPZr6B5FeFds2eOxvnyY/XaR/69LxSLUkqJZqHHriGN3rO9hx15bXvT+qqtK7sYvejVefdv1qsC2bk/sGiGdil6RBAySa4wwdG2Ho+NjbSiyqxSrF2TKarpLpTL9mq9OPK5o6M3zo37yXky+e5fjzp2lUG3Rv6GDzLRtYd92qxc+fYfjEYONG+JmfWfp7KWFiYolkXEg4JiZgctIfTz65/HkjkUsJx8aNfuVjpa3qJxe6oaEbGo2addn7pZR4Ui5zXFvBTxeuilg89dRT/Pt//+/5vd/7PXbs2MHu3bvp6OggGAySz+c5ceIEL7zwArqu85nPfIbf+I3feKv3+6cah544xtmDgzR1ZxgfmPLD6SJBFEVgNWziyTrNrVVmhseolVw8L8jBHxylXm5wPhklnqzRv7lKfkZQm9chhKIeZ4+EeP57Ccy6X1FwLBekQiAEqXiMSsmlmq8t3xnFI5F2ae22CccktiXo32SSm1GYGjaoVxVf06rOh+1exmVUANWSutjO5DhgmwJN9YXKpw8ZbL2pTltfjE27hxk5HWRqROXWd+XJTeqMnzcwGwJFkbR2m/RvspgcjjOXbWHDDT1kOtLs+85BauUGgZBBKBokNzGHY3t4qsAIxTDrFaR0sEyHYEQihMAIGNg4uK6LovnVoEbFpFqsoqgqqqoSCBsYQZ1SroJju7i2YPBkkof+LEEkWiIQDqIG+ohlVuN5HrNjY7jekm3tQtaIJz2GT4zxt599iN/+008QCBooSogzR1Zz64PD3PkBk9Yelce/IpidACMg0QwVoSZwLBfd0Iil0hRnId5k0t49x+xElcN7VZo7Y1RrGxg518X42XF6N3Vx90duJTs6y8HHjtKomdTKdf+9UP1E8E03r18WMHb46eN8/Y+/S61Uo7mnCU1TcRyXSr6KWbdo62vmwPcPs+POzdc0IT974Dy5iTx9m7ouqUqkWhIUs0WOPXfydROLWDpKMBKgVqwRS0exbYfs2BzBSAAh/M+LrmuLrWCZjjSjp8YZPDbKlls2vK7nvBg3Pngdx549wa5bXiKeKDB8Jrb4ORAiSnNXlM7eSaT5HIPHVzM5OE376kvDRyPxMAW9yOGnT7Dtjk1vqMLwZsKxHBzLueJEQgiBUPzwxbcD5XyFl759gOMvnKZarKNqCh2r27j+HTtYf/2an0g7zHg6xo0P7npdlTYhoLPTHxeLyEulJbJxIeEYGIBqFQ4c8MeF0DRYtw62bIHNm/2xZYuv49BeX6FtBT9C0HSNjTev45mHXiTTkbrke6iYLRFNRujf8vbb+q/gRwNX9TFfv349Dz30EGNjYzz00EM888wzPP/889TrdZqamti5cyd/+Zd/yYMPPvgj82P3kwrbsjnyzAmiiTDFmRKu4xJNRqhXGiSbVO587wyt3QWOvRhDeAqrNnpI4TIxpGDWLCZLgm//fRcbds0Rj1cYGwyQm9LIz2gUcxq2pRBJKahCIIFGxSEYUVGNEKGoS61Yv0BjIInEPLrXmqi6Sj6r09FvIhSDRk0j1WJjj+u4roqq+iJn76LKhxDg2AoSUFWJ6/haAl2XNLXbtHRamHWFTJtCsdhJIj3C6FkDy9KpljXae02iSQezrqCoHsGQ7yR14kATidY+utZ2MHxyDM+TiyndpVwZEETiIRzLb0USgnlBtaReqS+28CD8L1IjpBNNRki3p5g8N0W9bC4KjXVDJxwPUS3U/MqOhJEzCkJJs/mWdWRamxk8NoLreNRK9cXkcVVVcGwXPagRDAWwTJtXHj/Ky999hVt/5kY617ZTyK/i4HMxNmwfZ9ueOXTD48mvqcQzUWbGwxgigOt6xGMhPCnxPJV05xZCaYGcHmDwZJGHPt9Kuk1H1WdZtb2XB3/9HpLNCT74qXcTCAc4e/A81VIdpCQUDdK/tYcHP3HfYtCa1bB45qEXqJZqpFv91hZnPjAw3hSjMFOiUqwBguGT42y6ad1Vn8+zE3OLx/5yiCQiTAxMX9uH5AJkOtKs2tbL0WdPEkmGcW13UTAvpaRWqtHS20wo5rciqaqCRNKomq+x5atH/9Ye3vPPNhLkeeamY+gBn2wLoRFJRlm9YxV60AZ7H/mpONL1LhFxLyCaijI3b5t8OQe5HwaMkEGiKcbMSI5E06WOSgsWt2+H3Wm1WOXrf/xdBl4ZJNWSoKU7g2O5jJwcY2Jginf86t2XTeS+GkhpgawABkKJvubjfxIQj8MNN/jjQtj2UlvVxa1V5TKcOOGPC2EYflVjgXBs3QrbtvlZHT9uXE9K+RNJUK8W2+7YzKl95xg5OU5bXwvBSADP8yjMlCjOlrjlfdf/2Lc9ruD145rWD7q6uvjUpz7Fpz71qbdqf1bwGqhXGtRKdcLxENnRWTRdI5oIIz2Lm+8fYtPuEs99J8rgSZ1Ui0ckoRGO1lAVm/NminLeZXrUY2Y8jVkLoRv+hN+5oKpZK3roAYinBQ2pUK8IbKtCc3cT0vOYmyogXYlmSAIBj9kJg0AIInGPnbdW2LCryvGXUsxOagRCkmrJw8Vv4bkwWwL8KoZu+BN4zxUIBVRNEk853P6eAuu2NxBCUikaiKBDqZghN+3S1OHxwqMJNmyv0L22TqrZfwGlOZUDT0fpXlNiauwortNMdjRHKBLwQwRrJp6z1Bqj6iqybqFqKoqq+PoCT863dAiiKd+m1nNdNu/ZQCwV4WQkSHZ0lkK2RKVQmxfmQqY9Tt+WdlQtwNjZaRo1C9fxyE36LUe5iTmf4LgeiqLgui56QCMc9V2ibMvBbti8+MgBbv2ZG0m1Jtly2yae/8bLzGVvoK3bwrFN0MYZOFolGA1gBHQc2/VbenJloskwTZ1pAkGDddc3ITlHpiPFTe/eTXt/C31buhdbI5o6M3zk332QwSPDTJ6fRkpo7s6wZmf/Mnek0dMTzIzmUDWVcr5Co2YhpYeiqkQTYYJhg/JsGSNg+PqTa4Cma6+qs3Iddz7s8fVBCMFtH7iR7GiOoaMjxOczCCrFGgKIpiL0blyqltiWgyIUosmrtw29mn24/r4M5TGBpjqUC3UQkG4J0dIXJtGaAmmCl0NV674L2RUmLgt6FVV7exdwrIbFwKEhBo8OY9VtmroyrL9+NS3dTaiqytbbN/Gdv3x8MSV9AVJKpoeyNHWlWXWVFsNvBIefOsHAoSF6NnYtWsIaQQjHO5kanOHZr73Iml3912QLK70aWC8irX3gFUGoSG0DIrAHoa167Q38BELXl9qgLoSUMDYGx4/DsWNLlydO+LkcR47440LE4z7BWCAa27b55ONHIId3GaSUDB0b4dhzpxk9NYaiqqy9bhVbbtlAa+/bmyD/w0ZTR5r3/+Y7ePyLzzB2ZgLH8n+D4pkot3/wJm794Gvn8azgJxcrhckfMxhBw080btjzc3SJ63p0rqqwdmuZoVMGw6cDBIIeqioIROKgQDw1SzhaoTArcCp+SBmA1VjSYy9AeuBY4NoGUio4joWiSWLzVqJWw6YyV0FKqJQUinmFTKvDjlsrtHU3eOJrcUbOhZgZ1XHs+RRq1wMumgwJf9JlWQqRmIui+o5QsaTHgx+dY/3OKmZDoV5RSGQcUq2v0KgF8GQQTatTnhMcezlEdkohlnCpVRUGj4eYGgtyzwfK7LrlLJOTzTi2RFEVjKCOZdrYDW/x9Qsh/Amc8HvPI4kwju3Stb4DVVUoZkuLeROluTL5qQLrd6/hk//rV9j79X08/nfPoBku7X0eHT0OemCSw3stakXo3tjLppvWIRBkOlI8+7WXqBaqCEDV/FA6I6gvVvmEECiqYG4yv3iI7vjQzVgNm+N7TzEz0vCdhYIJIklfSFrJV6kWq37lKhVh7a5Vi2096vzEf/31q7ntA5f/or8aXY9Zt7BNm2qxilmzCIQDKIqC57jkpwsYoQCBkG9Te62r0t3rO9AMDbNuXRLO5nmSWqnOze99ddel10JLTzM/+6/fzf5HD3Fq3wDhuH/cejd30b2uk3B8aeV/ZmSWpq70ogvbmwEp6wjrCeKJIhuvb8KTaYSQKKIBjICt+EneqHSvbycUD1LOV9F0hZmRWXITeTzXI5qO4lgOd3x4z9vqQFScLfGtz3+f80eGYT4w0jZt9n3noJ/2fd82tt2+kaHjoxx/7hThWIhYyt/X/HSBSCrC3b9w62sG+L1RuI7L0WdPEI0vz5lYQHN3htHT45w/PHxJSOmVIGUdWfsHsF8BEQERB2yfaDinIPwLCP31VUB+EiEEdHf74x3vWLrd82B4eDnZWCAcpRLs3euPC9HffynhWLPGd7B6uyGl5MVvH+CZh16gUTOJJiNIT7L3ay9y9NmTvOsT97J2108XyexY3cYv/vsPMnpqgsJMcTG3ZqHSvYKfXqwQix8z+Daba3j+G3tJpCymBouUch6b3lUkEFawzACepxFNqEjpr4A3rW4lN1Yk3WIyPRrB9XxiIYSyzC4V5gXWqoqqK5gNv1UHAaoukbJKvSKx6taiXkIRCtKDQlbliYfjHHgqQiDk28p6UuB6Yj4tWllmLwvM7wM4loJZ90i1uEQTLnd/oMi6bXVyWQ3pCgqzOralkpvx6FkzR3N7krNHQ3T11+jf2EBVPU4cjDB0MkRxTgMpeO67UdpO2Nz0jnOEIquZGi6hKAIjZFDK+W4VwYgf/ue6Hrbpu1KZNQsE/up2JkbHmjayo7PEm+K0djex5daNbLl1A/FMjN5N3fSuk7zw9e9RK5kUZoPMTjhkx0w6+gVrt9VAOiB0f1urWzl/eNj3908tb6VwHRehCFzbxbZcju09Sc9G34L0XZ+4l513b2Ho2ChWwyaaitC9voPsaI7nv7mPQ08ep29zN02d6WVZAY7t4Loe/Vve2EpxJBGmMFP0g/w0FVXzHZ5UTUUzNKrFGnZDo3tjJ90bLm/ucCX0b+1h1dYezuw/T8fqNv89md/38YEpmrszbLr56lurroSmzgzv+NV7uO2DNzEzluOxv32a8bOTNGp+G59t2uQm8gSjAe740M1XnWdxVbCOgDsDSgYh/XYrHxGQmp/ejQPG9bT0rWPTTSM8/dALFLMlbNPGmCdcw8dG0QJ+Bsfb1YrheR7f++snGDh4nq51nRhB//ySUjI7PscPvvgMyZY4a3b0855/fj/d6zo48vRxSrkyiqay9Y5N7Lp325smFH81NGom1VJ9sa3tYvhuNYJqsXbZ+y8La59PKtReEBdsV6TBG0LWvwXa6sWU9RVcHoriE4X+fnjPe5Zutyw4c2apkrEwxsdhcNAfF7pUBYN+NWOBaCyQjqZLDezeVIycHOOZr75AIGTQvqp18XYpM4yfneLRLzxJW3/LJd/rP+lQVZW+zd2weUVPsYIlrBCLHzNI2WD7nhEGXhpmcqiOrjdYtaHCrQ/O0txuYRgWp18JYjYE4aiG53qYNQvPExiGJN4UpZyr4rkuzE9OpJTzWgJ1kQC4joequcRTUMy5uKbL2JlhirNLAmzpCQJBF8cR6IakVlaoVVTWbDWxSgrxlIdjKxTn/BUrRZXgCVBUPxzOmzeqFVCvqQTKHpuur7JmS41SXsWsKRRzGp6nEQgp6IZGIKyw7cYS9Zpk0/VVVm9q8PKTUfLTOqkWPz26qd0hmnDJjmvs+36Z8twQuQkIx8IkWmJoukq92sCxfYLlOh6KpuDaDhIwAv5ELjuaY3Z8jlXbeviV//TzbLt900WTOZM99w/QtzrM6UNtzE5YOG4FFI1NN4TR1Cx446D2oaoqbf2tjJ6e8J/bchZbfKSU8y1aHq7rUSvV+NofPUI8E2PnPVt8vcWadjrXtC87F9r6Wujf2oOiKEycm15WebIaFuNnp+jZ2MmaXf1v6JxTVQWkJBgJ4rmSRqWx2I7juh6O7RKKBrnzw3uu2XlH0zXe9Rv3IcTjnD86gm06CHxRe2tfMw/8yt1k2lOvuZ2rRSQRoT8R4ec+/T5eeuQAJ144w+x4DlVXWb2zjxvesZM1O9/Y8boY0j7kh+cpPeAOgFSA4Lz/sg4yCzKGCNyCUBRuef8NPP3QC1RLdQIhHcd0QAgynb6F9KmXzjJwy4a3ZYV07Mwk548M09bfukgqwK+uNXdlGDoxyuGnj7N6ex/BcICb3n0d192/jWqxhqZrRBLht60X3QjqBELGFfUxnueH9i2Q19eClI7f/iQiy0kFzK/AdII7AvZpMH407GfBT6YfPT2B63ikWhP0be6eJ1U/ejAMnyhs2QIf+cjS7bkcHD26RDSOHvVHvX75HI729iWysW0b7Njht2m9WWJx323LpL2/ddntQgg6VrcydGKUM/vP/cjYEK9gBT9MrBCLHyNIKZH1b9HSvI/3/kYLz32rzLabj9HR66dqC8Uj1eyw5YYSB5+NEUlEsEzfJUXTJVKEaVRMv69dV/yALFgsIniuRMyzBkVTCAQckq2SUl5gmZJ6bd6adhEC21ZQVT9UT1EFngtTIxqhCIQiUK9p6Ia/7+lWidXQadR1GlWTSEKhUXVxHYnQVIp5hey4RSmvMT4YwLH8Skcio+HYklBUY/AkjA9Avazx1NdTvPR9l9kpg0TGoandpnddA6H4+5lptTh1MILdqBEMR7BMk1pJo7Wvhdx4jnrVRDc0jKCO53qoiq9z8CsycjEMb2Ykx2N/9zRG0GDjjReEstmnEd4knWv66FzrT7q+/vlxTuwroek6eAa440i1B6Qglo6Qak9iD81SzlfQgwaapuA47nzyNTR1Z9h1z1ZUTSU/XeSZr7yI9CR3/fxl4neBaDLCu//5/Xz3//yA8YEpv/KBQNVV+rZ2865P3PuaLSj56cIyjUVzV2aRcGbHcpx6eYBgNISqqdRKddRkGLNqYjVsPNcj3Zaka127H8L4OpBoivPhf/M+hk+MMT4whed6ZDpSl2g93kwkmuLc/7G72PO+GyjPVdADGun2Sx1O3hR4RX9iqrbgp0OOAXMgF9IlNdC3g+a31IydmSSajHDDO3dSLVTxXI9AOEC6PYVuaAyfGOXYc6feFmIxNTiD1bCvKBRPNMUZPTmOWbcW3yvd0Ek2v/2parqhs/mW9Tz5pedIt6cuqAz5mJssEM9E6d96lW1usg5uAcQVzmth4Ify5C9//9sMs27y5Jf2cuy5035VRoCmqXSubee+X77jkoWJH2VkMnDnnf5YgOvC+fOXVjfOn1+yxH300aXHBwOSbdsFu3bBzp2wa5dPYIKvo7g0fnbyirqchYye7Fju2je8ghX8BGKFWPw4wZsA6wAoLXSuSfGz/2KOc4csxs7FKOU90i1zqLrGxusVpkZdZiZsNF0jGLZwLIVyMUk0oWCEdMpzFTxX4tgO0vWdjBYsUAEQHtUy1E8L4mmNasnDNl3sixYDzYZCMOSTESF84bVZ97Ml6jWVehnfDcqDSlEh2RKiWiqjKC6BoCAQlBRzAiMoMUJhwAQEigKaoWCEFGxLEoyojJzVOf6ShqaqaLrEtQUjZ4OUChqa7tG12iMQ9MhNacxlDRRVMjutg1S44T7BwLEU+akyZeEH5gVrFtVC1bfBFGIxn2JBcyH8f3yb2PE5Hv3CkzR3Z5YCC2Ue/4+WVnLb+oIcfrboC8CFAV6D/FSOsTNZSrkytUKN1r4masU69Uod13GRrkRVFTrWtLHz7i2L7UyZ9hSKInjlB0fZcdcWki0JpoZmFpOcO9e2E01GaO9v5aP/4Wc5f2R4MfSsrd+vZlzYGnUx6tUGT3/5OY6/cIZqvoZEEo6HWLtrFVtu28DhJ45x/sgI2bEc2VE/VC/eFMdqWL4drqrQ1JUm05HGsZ3L9rVfLVRNZdW23tdtK3stkFIyPZxl/OzkPIlJ09LT9dY52ikZv3IlVNDW+XoKLwc4QABkAfQdiyv7uYk5XMfFrJmU5so4tks0ESYUDaJlokSTUcbOTOB53lvuwmebNqW5Cqf3D+BYDsFokGRznEgyQiBo+ASUS3NufljYfsdmzuw/z8iJMZq7M0QSYVzHJTeRp1Ezufsjt159D7jQQWggr2CTu5D2yQ8/tEFKyWN//wz7Hz1Epj21uDhg1kxGTo7zjT95lJ/79Pve1Orf2w1VhbVr/fHBDy7dXi7DN780zLf/cZTzIxGmCs1MFVtomAb79sG+fUuP1TTYtGmJaOzc6Vc3Yq8hDVN1dfnv40WQnvyRrQqtYAVvN17XTODZZ5/lz//8zzl37hxf/epX6ezs5O///u/p7+/n1lsvv7K6gjcBzjmQZVDaQTZQ5BSBUBRFkUSSOpZlkczUiCYtHviI5IXveZw5HGBqSCE7qVOvWYSjIQKRAEJRqFezeM7Sl6WiKr4lqwTXBjTQdEFLd4DJQd9e1TYv+nKV0KipNOrK4v8VBayG7/CkB/xVWWlLzLqgUjCJpxwadYVIQkUgfFcjzwOvxsRQgFJeJdPmkJ8JoAhBIKxSKSm8/DjEUxYtXTazEzrhmAfCwbYE1bJKrSwYPh1iblrHcRSkFJRyGoqiUCk06FwlCIRasS2bLbduJNEc44l/2EslXwVPgiKQDYmqKYuTPKthY9Utoskwhekip146y60/syCEDgCeXx4R/utfvyvKS9/LMTlYp63HYXbc4tzBEyieS0iBREJl9aYMVrGCoSbpWd3CwEunCUcCtHSEKB89SGm2iLQdQmGDnnSERrHK+B8UOZktkh2axqmZKEiGYkF6NnWxdmcfhqaxQUoWTFrcQyeYfHiGibOTVApVAiGDjlUtdKxuIxAy8FyPE08exTw9wY54mGAshEDSGDIp7HuMl/+XQzAaYmdzAiWoc14ZpzY6iBEK0NTTTDgVQzFUtECF6dPDtK3tIH3mCJzX/BnAwjCMVx+quug16Vv1FvzjqSQQ4vJfT/Vqg1MvneXEC2cozZZJNMfYvGcDG25c85qi5lq5zuNffJpT+waolxvzZFila30HD3z8Ltr6Wq7us3gNEMYOpH3EXwEXIX8FfMGu1PMrF0LftPj4aqnO6OkJX8yvCBRVYW6ywOT5Gbo3dBKMGAS1wFveYlSaK/PKk8eYGJjCCGpIT1Ir13Edj0giTFtvM0JRuO7+bW9ZZelakWpN8jO/9SBP/uNeho6Nkh3LoSiCTEfaF5rfv+2qtyVEEKlvA/MJkE2XeqLKHIgEaGsvv4G3EVODMxx/7hTNnZlllcNAOEDPxk7fzWjvSe740J4f4l6+NcgODjP54rfZ3uly/55mVO0sric5NyA4fS6E1raTXL2Lgwf9FquFSsff/u3SNtauZVllY+fO5bqNtbtWM3Ji/ALHwCVYDRtFU+hef236shWs4CcV10wsvva1r/FLv/RL/OIv/iKvvPIKpukvYZfLZT73uc/xne98503fyRXMQ9qA4v/AeTUcx8RxA1TLFcy6hWOCEYBkU52eNRVaftXj2UeaeemJXgqzFmCBFGgBnUa1ges4fhvUwmLjfGYDSDzPRTMEwYhKcdYhFFWIJlRGzzYuaoda2LelL1vP87epSEB683kPIIRKrWSiKIK2XoWWTpXRARfPFWQ6NPo32MxOJTj1ShP3fShLNOGSn9WwTY+RUwqeJ4mlPGplhWrZn8gvpHjblmDgWBjbVIgkXHTdw2ooVKdVQpg0Ttt0d4yQls141ix9Izliszp3yknKtQJBxUOXLppjE3Q9DOmi46K7Nobj0vL0IQKKJHr0i/BHSV91aNUQjUmwPbAkbt0i3nD4pOWC5aFKD/VyK7knLr3pNfHCFW5/4vI3q0DX/LgcFOC6+XE1uPvC/wxf5gHPAH91lRu7EEIgDQMMDXTAEKArYBjIQAwCCYQRWCQijqIyO5bHKJls13QIGDQ8herf6pztaWXdHdswUgk/FjgcXnbpBgI8/bWXOb1/hGRPG21dnaCqNGomQ8dG+caffI+f+/T73vw2Hn0r6DvA3u9PRJUU4PlVC9mA4H2g+u+U67qcPzxEvdKguSu9qPcB32p66PgIyaY47/zEvW8psZBS8vgXnyE7nKWlp4np4Rkc09cgqZpCtVRjfGAKRVXYYW/+kfL1b+7K8KH/53uZHs5SzJbQDI3Ote2vi/yIwE1I+xi45/z3SAR94uvlfBIcvB+h/vCtRodPjNGomJclxoqiEE1GOfH8GW774E0/UVlTUkoOPH6EWrlB7wUhm6oiWLcOMrEJhJjgV//zR4ilY4yNwcGD8MorS5djY3D2rD++/OWlbXd3LxGN1b2bURPnGT09QdfatsXqhNWwGDs76Vdbt/f9EI7AClbwo4drJha///u/z+c//3l++Zd/mX/8x39cvH3Pnj38x//4H9/UnVvBRVBSgABpk8+65EfrTI5YaLpEYJKblBSyIVKtKrWiTThms/+pJOeOCPSAv0rtWI5vDWdoCKGA8FAUsSgkFsLvzxeYuI6cF3L7+ol4RieRdyjMXEVWgfQJRqPmi7wBzLqDEBIhoFa2GTsnqZb8VO5IRCEkPVans+QOp5gJ1lnVNUm6YKI2JNFzOmumBca4SzJsImoeuusSUR3ckkSzXALSJaw4BKWL4TkYrrtkcFsBRi7YvzPXeOynLrg+4F9cPI26mg+Th8BTNTxVw1VUXKEgFRXTdnGlQGgqnlCQQsETCo4nMS0X1dCJZOK+8F3x75dC4NgOnuPRsbYdPagzOz7H7PgcZt1C1TUEvm2rqqmE4iEc2yWWiqIFNPLTRcKxEFJIkBZIC8+TFGdtPNsjGA4QT4QR0kN4Hmaljlmp4VkOuipQVUEwoJHIRAmH9HkrMM+/XBi2PU/CLhgXQkqEaYJ5OcHt7CW3aMAV/UfOAI9/+Ur3ogLvnB8LcDQdVw9g6wHqnor28H+BzuZLSMlrXVqqxuhEhVzNQ8ZitG9bQ9eGThRFQQgDIj+PbLT65MKdnO8bbEUYe8C4eXFCNHpqgmKuTFt/C6VsCSWtLOaOBMIGhWyRUDTE5j3rr/g63wxMDc0wcHCQ1r4W4pk4EwOTOLY7/70hcG0bR3dZvbmbkVPjDJ8Yo/9NtOh9oxBC0NbX8oYrUELthMgvIuvfAHcU5LxzhZKAwP2I4ANvwt6+cfjtnFyR3OkB32rbdVwU4yeHWJRyZUZOjJFuS172tadaE4ycGGf09ASb96xftMJ93/uWHpPNLicaBw/6yeKjo/745jcBosAvEA01aI1N0Zacpj05RU9Lie27+3j3P7v3iqGWK1jBTxuumVicPn2a22+//ZLb4/E4hULhzdinFVwJ2gZQ23CtMb7399Ddp9HW41At+QJoqwGVksbQCUg2C6qTEaZHNBzbxHX8liPw+0H9SoVEEQq6oRJOhOfLvAqarmLVvUVyEYyogMRqSNItBuU5Z7FSAKBISQSLOBZRbCLYhIVNxLMJ4xARNmFsop5NRLGJSIfIqE3QdQhjE8EhNGMvT7k4uvylv1GZqiUUTKEhgyq2qiIDQeqOgpHUCKUa5IoqwyNhLKkQTEO42UOEoeYYzBWjGE2rMF2FrffsYPX1axdX0KWucPbAw9jOceKtKg5QKtXIlxK88EQ7xw42iLYkSXY2Y7mSUrHOpj3rae7MYJs2U8NZutd38N2/eoJ0W4RAWAdUBH5wXGmuTG4iT1Nnmlt/5sZLfjyllAweHeGBX7mLQDjAt/70UaYDM367SjJMvdKgMlehnK9iODrNPU3EkhE23LCG8YFputYlwD4Knq8XqZXhlWcauI5CvKmFnff4rRP1Sp1T+waYmy5QK9Zp7WsmmggTCAfYtGc97/1/3E8oulzgm58pcmb/OXITc+gBnb7N3fRt7gLH4fmHnufYk4cR5jS33PUKqueheGm6+luJhgNgu9AogzWHEHchZCe1XJFn/uFpNCmJRXQU10F1bVTbQrNNnEIJ3bVZt6EFzbagWvVTueYvrbki1KoY7lLPvObYaI5NoF4hClCZgfOnrvn8MoDV8wNAIrCDYbSmNEoygUj4Q8YjEDcgEYeEiUgehsQQJBKQSFA+MkG8kKV7ezcnjglys2U8d6nqFUlGaOpMXTbh+s3E9FCWeqVBa28zuYk5IokIekCjUTXxPI9gJIAe0Fi1rYfJ8zOceunsjxSxeDMhtDUQ/S1wzvqfE2H4FrNK+oe9a4tYyI9xHfeyvf6VQpXeTZ2LJPUnBY7t4rruFV+XX52Ri+nvl0NzM9x/vz8WUCrBoUPLCceJE1CpB6nU+zg307f09y9J/vElwfXXszhaWy95mhWs4KcG1/wt097ezsDAAH19fctu37t3L6tW/XQFxLzdEEoYQu9j5NgXGDo+SSyWprN/jFDEpF7RCUWDuJ6B65QJhjwGjmVwbBXPdZBSQ1GEn0Ogq7i264fszX/pVueKRBSHGA5JzSMqLZJajYhj0jLnkjZc5FyNiGdiSNMnBNInExEu86Utr3DdffXX6CKwjBBOKELJFliajq0r5CuSkqXSQEUGFWJdEj0lcTSVhtA4NxSibOoQUahLDcIK0Q6BiGeYnIapMYljS1q7IRgJEYpF6d8U4T2f6GBy7AzlmXM89ldtdPRMsfG6MnVD4HkqoYhCs6MxPhIjN3sLd//bn4MLQuBKsyUeeWiQcGQTPRmT6aFxzh8rU693UmvY1INTmFWXgAu6YQANZsfnaO7M0KiZBII6LV0QiZnUiyWcBhhBFdcLYdYC6MEgsXQUZ/79ujhETgiBUASNaoNT+84ipW8VHAgZVPJV8tMFPE+iB3Ucy6U8VyE3kccIGb57lnUMIXMgUiBUtKCHquWpVxqEwnNIbw6UFIPHRihmSyQyMXRdY/X2PjLtKcy6xckXztDW17ysf/vosyf5wT88S2G6iKqpeJ7HS48cZPX2XmKZKAcfO0I8E2fzbVUSm8LMTjdRKdQoV6ps3t5FeMGBxT2H1KKI6AeZOjLMvsfzdK/vvMTxB/zU7KnBaT76ux+6bG7Cs//3Wfb+00v0b+5CdWw0y0Szl0Z1IktIlbznl265hJRc6dLOFymOTKM2aoQ8C8Oso3guAonRqMJYFcZGl96vVz/92To/FmBpARp6kLoewgxFMYMRGsejaL87Ba0tkE77Fjrp9NLIZCB0eRen14NauYFuaMTTMeJp/9w3G9Z8yrogFAmSHf3JdsQRQocLdDA/alizs49MR4rp4Sztq1qXLUDUynU8z2PrJXbZP/6IpSLEUlHK+cqyoMsF1CsNtIBOsuXa2hvjcbj9dn8sbqvuh/odPOiP/ft9rUY2K/jud+G73116bHc3y4jG7t3+2sEKVvDTgGsmFv/sn/0zfvu3f5u//uu/RgjBxMQEL7zwAr/zO7/D7/7u774V+7iCCyD0zcxk78Jxv08+12DwVJWuVTMkmwUSDyHKNCqSff8UpT6msqo+B40qac0ljk20USdtWySwicgaMbdBDIsIFlf0tChd3b7VhU5FCVD2VGpSpyZ0qmjYAZ2qNKh4KhWpURcaFU+nho4d0kj2G2TWQnqVhSnClEstaOEdnD2UozBTQuBPGmdHZ3FcD2l5JIsOu3eVfOenSYPgDpeJIQPb8lepAkEPEbIIB0tkOkNUyoDwCIVV4hmbtdstbnhnC6kWA0E30hrlA79RIxRRGT6dJD/ru0O5jkuqWbBjzyzxzuQlydKFbIlqsU6iqZXslMbpwxazYyrxjEIgZBCKhqjkK9img27ofjWoZi0GjG2+KUMm8xLtvRZGMMjMmIvVcBFKiZbOCO1r13H6wDjlXOWyk4IFb35V15ibLBBLR5kcnMa2bArZEkIIgmGfjDTmCQf4acrRhKQ4M+v/6Ar/3TcCCvGURn5GJd3qgTtBtRzw26YSvl1xOB4m2ew76wRCBrF0lKPPnOSGd+4kFA0xfGKUR//mCTxX0relZ1HsaNZMjjxzglKuwtpdq0i3JQmFzyOl3+6TaIpTmCkyNZS9wB0qAp7fh+bbOgrfGvgyxMJz/UT1y90HkGiOI6XEk4AewNUDXNiANVwIsPGmtfCz77ns318Oz3/1BZ78h71Lr1NKarN5xvadxM7mMMwa/d1Jgp6FUi4S1zwi2ARsk7jm0ZLUSYcVlFIJikWcuQIyn0efr6oYjonhmMTrxeWfw1PPvfqOBYNXJh0X/r+pyV9ebWmBZNJ3XgBaepoIRgLUSnU03SeGF8KsmsSbYgRCBrblXHU2xAreGkQSfrr5d/7PDxg+PkqyJYGqq5TnKlgNi+13bXlTgiZ/1GAEDbbdvonH/u5pzJpJ4AIdjed5TA/N0L+tl651b9xqNxRaIgoLaDTg8GF4+eWlcerUUhvVww8vPXbduuVkY+fON5X/r2AFPzK4ZmLx6U9/mmKxyF133UWj0eD2228nEAjwO7/zO/zmb/7mW7GPP1XITeY5+eIZBo+NgISeTV1svHEtLd1NfhzpyZM07X2FGwaH6JgTBB7LE65PE7VNtHKdloLJlobNfVwkGnaucP0iNFCpoFMRBm48TtcNW1Ca4syZLtNFyVRJcH6sRMFScWIxxrImdiSGHQzjCl8Ii4RKseo7uEqPgC5wLEAF3ZAoih+OZ9YVDOHxsQ/PsmpTAyE0PClx3bM0atPUSzuYPFcnnokRy8SoVxrUy3Vsy6Fc8Dj8fIRUs4sekBRmVbLjBqoqyXS4uLbC8KkQekihVtGJJx3ueL9kzfYmPE9SK9UZOzVIKCxo7m5CuBkq+TEmR2IEImEQFq7tEkmEWbW9l/YeBy04gJQmQiz9eGm6n0Tt2H45PhAycOdtCYUQxFIRqqUa1WIN3dCwLQcUwdDxUdJtcW68dxpF1okmE0STOp1rVCzTQ1EkhpFHaDkCYQOrYfihhizv450dnyPZHGf1jl72P3oITVOJpaJMnp/GtR2CF2VY2KZDJB4mlokRT5Qp5V1qVYdks0+kirM2qqHQuyFMac4mEJqibjbTqJr+a9RUejZ2Lmu3iKejzE7mKWRLhKIhjjx9gmqpTt/m7mVkKBAO+MTj+CiK6t/uOAZCLBwv/zGz4zn6NnWhaCpggfCrF619zaRak+SnCrT0XBq1m58uzFvHXj6Gd+2uflItCWaGZ2nrX957XylUUVTB5ls2XPZvr4TTL50lkggvkifHcTl1ZIxyVRDv6Sc7W0Lt7qJSqDKenSIUDrDz7q1EkmHy00UqhSo3vmsXD3z8Lt8FyvP48n/7BmdfPMWq3hRhaWE0ahi1MvbkNGqlxO7d3TQFJMzNLY1cbum64/gznokJf1wtNM3vC2lpob21lZ/PWYwXXTbGUwwW6lgyTS0UI+foVF2Vtr4W3P8/e/8dJud1n3fjn/O06W13tveGsuiFAAEWsIFNhbIK1aVXshUrjpO4vEls5/UVO9dPjt/Yjv3KTiwntqTIsq0SWYUSxQ42AATRy6IssL23mZ1ennJ+fzyLBZYASYBi5354nWuJafvMzLMz5z7n+71vy8YyLVZs7Xj1x3+XUcgVOX+4n/OH+ylki8QbYqzavoLm1Q1vSYN0946VBCJ+jjx5ksFTI5hli+rmOOt3rWH9ru5XtJ5+J7N593rG+yY5vb8Xj88gEPZTXrBJrm2t4q7P3HrdwZ3XitcL27e74yLptLujcbnYGBx0E8Z7e+Ef/sG9naq6uRoXhca2be6/X69Qv2WWeat4TafwV77yFf7jf/yPnD59Gsdx6O7uJhh8b0XZvxFcODbAw3/7JInxJN6AFyGg79ggRx4/wT1fuJ3up/8Zfvd36QKuxeCwIDQyisG84yUlPGQUL/PolHx+ioEA4/MO845BWnooeTykHI2y1NxFS0VS2xTlj77+X6hqjJM5OsDTf/sEA6dGSASTJCeTmJM2juNFSdv4pYXjlFFUFV/Ag1k2F5rFy5SLElUVqLo7ec3nQCARiqR1ZYF4bZ7RvjCaN4R0IJ/O09pto6kTVDdGSSXKaIZGKBbAKpt4/B6KOXf5VgiH6VGdXNrNtnAcQWJSIxixkShMDOoIIdh+d5DNu7Kk5qG4kHydnS9x5sB5whVBIpUQCFeA3kUhW0LVVKJVEaLVYYSiuFahzjTYU9g0IBSBoijUtFRR3VzF9PAsDZ21VNTFGLswSWkh60E6UN1URTDmJzWTppAtUOevZtW2Tm76QC31td9DKs10bUpy7NkUDR1efAH3S1DafmZGRojXraJzUzvjfVMEYwHCFSFsyyYxOY+iCnY9uJP69lpauhvp2XeOurYaRs+7ybsX3Xps017M5KhujhOuCBEIldj+MT9Hn/MyM+YKwliNzm0fq6Kpy8f+nw3Tf6rM7FQCs2RSURulaWUD8YalteW25SyW2ZVLJv0nh4nEw1fdYdF0DceRZJM5olURUslqbEtH14uYphdVVbBtG9t23KR2WUAYmwDwBbxsunMdT/z9s6Rm04QrQ4tBfqmZNMV8idvvWo/hvXquQCQe5vZP3syj39zDYM8I0aowiqaSns1g2zZb797Aii3XXs4ppaRcMtH0S5OWxOQ8mUSWUGVoYXIpSM2mSc9kqG6sJJPMMTueIFIVpqqxEo/fw/E9Pay9eTWNXXUoisK9X7wDq2wycGoEEGi6j1JBJdjawC0f3k7l/ZuvtD69dFCQzV4hNpyZWZJn+kn3DkEyQcAqEhZlPPkMYmoK5uddQbKQNCaAloUBcBvASzRKuc9LRg9gxyqpKD0L36m7tPtx+aipgVhscTfk3UBqNs1P/sejDJwYRtUUNEOj7/ggR/f0sP3+Tex6DSn0rwct3U00r25csAW2CYT97/p8BV/Aywf+5T20rWvhxDM9pOcyeAMeNu9ez/pbu9/07I5w+Mpwv5kZt3TqcrExNeXudhw/Dn/7t+7tgkFXYOzcCTt2wI03uhuLyyzzTuI1a2O/38/WrVtfz2N5T5OaTfPI3z1FNpGjbV3z4qRMSslE/zSPfXMPjesqCa9di6ysZHi2xHReoNXXMG+NYYdzFL1eTp4PMzznpW4jTM+pTI/pZNMGukclECoSClv4wxrFvGTGEpimiseno+kCK2EuJE67DW8T/Sm+9n//bx74tXt5+rv7GDg5TDqRxfDo1LXXkk5kSM2kMcsWhXQBf8RPKBbA4/OQS+cpF8oIReBYEkeVSBMsU7p2tUIQizmsvTHPxIgPx5Lonjy6R0f3elC9K8inp+na5Gd80M/saALbchBCoVwooBuQz2hEKtztl3DMJhi1KeTc7IpSwf0d5RLousTweCkX50lMjGEWbDTdRhEwN57lxLPP07UhQDAadtNpxZUTICndfoq933mM/pOuy9LKbZ2sv2U1N9y7kYf++lFGzo4TqQ5R1RRnon+KbDKHoip0bGzF6/dgeHQ6N7dx3y/fSX1HLZhHkTkTofi56xMG5ZLDhWPZRRcuq2wTrrDZ/ZlNrLn1Jg4/doITz/SQmEqiqArNqxvYcvcGVm/vQgjB5jvX0X98CMu0qG6KM3RmlFK+jOO4bmCBiJ+mlfW0dDcxPTyDL1zBhlvmWHtTHckZ9zlGq3R0Q0FKyT2fhGRyE5n8nfz4vz+C4TGuEBXg7rI1rqynsj6GVbaQ8kqv94t4/AYIFpsp0/NxZqabqK0foJBzyCQd/CE/mlYCewy0NteqdYHt928mn8pz5MmTzI27r4Nj2wQiAW758HY237Xuqr/3Iutv7SYUC3B0zymGekaxTIuGFXVs2NXNultWX9ckTAhBfUctPXvPUbkQmpibzyElqKprjyyAYqaIUN1yL8Ojk5xKLT5GKBZgdnSOvmODNHa55Rqx6ggP/rsHOH9kgP4Tg5TyZaqaKll5Qye1rdWvXCcvhJv2FQpBiysLSoUSj3z9KU7NlbAjtejVBuViGd2jsfH2tez+3C50pDv7mZ52x9QUTE9jjU2QOdtPoX8IOTmFv5ghZObRpI1RLlJZLkJuDkZfxWZNVRd3Q6ipgYYGaGy8clRUvLxoepsgpeSxbz1D37FBmlbWo1/mBpSazbDvxweJN1Sy/ta3pidDCPGyKdHvVrx+D1vv3sDmu9ZRLrpi/+3UqF5VBffd5w5wP2tHRy+JjBdfdH9mMvDUU+64yKpVrsi4KDZWr35XafRl3oVc919esVjkL//yL9mzZw/T09NX1N4eOXLkdTu49xLnDvYxO564onxECEFdezUDJ4c5XnsLt5w8iQCCE0me/tpjXDjSQzEtMISNk3dQmx0CVQJTlbR1S4IRm77Tkvs+NUNVfYlUIoThD1PIpHj8uwq65WBbkJoVSAmKIrAtt4xK0wTTg3N8709/QjFbIp8pYHh0/CG3MDQQ9ROrjTIzPIcjHWrbqsnMZRg9P76YRFouma71bBkUxc2zuNjBWlFdonNtiZnxAP6wByklvpCPeEMFDR01CCWJkElWbF1HXXsNU0OzFHN5Cul+ZkctsikNRTVQNYdQzMYsCVQFglEHs6zgOBpCgGXC49/JcXK/QWW1SXW9TUNHmfrWEpU1NoFAkoe+3sSGm4vEak9R27Z6yWRBSsn4hTOMns9wam8CbyCKWSrx/A8O0LP3LN03rkBRVc4dPL/4pSYUBVVXCVcEkY6D7tG495fvYMcHti6GuEnhZaGDmmDU4MO/1sDg6TwDPTlKBZuKapOuDQrVqzYhVB+3fHg7N9y7kdRMGlVTiNVGl6yKtq1r4Z4v3M6e7zyPN+DF4/cgAEXRibRUsfKGTipqozi2QzFfYtWNu0C1UGU/8bp2ECpSSs4dznD82VHGB4oIPUPDirO0r2/h7IEL6F6dSNzdKXAcydxYAqEItuxej6qqKF6FurZqzh8ZuGrDZCDiwxvwks8UF9KaFfrObsG2NCriQ8TiKXfyTAL0tQjfLyGUS4+jaip3fuZW1ty8ir5jg+TSeULRIB0bW6lujl9Tc2rbuhZa1zZTyBZxbAdfyPuaV5fX3LSKsy+eJz2XWdJ/I6UkO5/FF/IB8qWFiYtctHfOpXJLLvf4PKy9aRVrr7M062rs+/FBjj55itq26sW/XXDLvw4+coxoVYSdD9zgTvYbGpbcVwNiC8Nd5JhioH8KJZumxgc1fokyO7soRJaMi5clk6718OSkO14Jn+/qguPyEY+/pTOrif4p+o8PUtNSteRzAiASD5FJZDj+9CnW3LTyLdm1eC+jKMrbJqzxlRCCRevbD3/Yvcy2oacH9u93x759brbG2bPu+MY33NtFIu5OxkWhsX27u0uyzDJvF65bWHzxi1/k8ccf56Mf/Sjbtm1717lMvFWMXZhA0zUyySzFbGkhNyKEx+cm7HoDXobOjHLLwu0rqm0+9htdHPxZD2f2F8nnKqmslXStL1PKS154TGF8UEVRS2y/M0331jypRISO9XFUTSWT0HBsV1SEYyaFrI6iKm5MhiMxS2CrgnBViMn+abjYyFxzaZInEPj8XreRV4A/7GNqaJpwRYh4YwX5TIGJ/mnKhbIrLmxQNLeG1DShob2EqttYpo0omUjbIVwRom1tM6FKLzVNOqP9ZUpWkpFz42QSWRwrD7JMuawQiavUtkcp9iQo5UvYljtRM0sKmZSOx6ehKBZmCdJJE02VOJZBak5jqNdg1RZBdb1FtFrB67PoP13NSm2IC0no2tq9uOKVHB8lMztBYmYT9R2ti88/3lDBsT2nOLanh7Z1zWy6cx3puQzpuSxmyaRrSzvxhgpGzowhHZPk5BjDp8N0bFrjlslo7aDUgjMFahOartC5IUjnhqC7pGWfB30LXGZr6fV78La8fCDX+lu7aV3TxLmDF3jyH55j7PwEDV111LZVoygKhWyRyYFpmlbU072jG+GrQ+b/EewLSGmw/5Eyz/4ohW1phKtaQavg3ME+FFWhurmSzHyOxMmk20QtJZF4mN2fvXUxV0EIwbpbu7lwbNB1kaq69I3n2A7Tw7MLk2XpNpnWRNENjX0XWlGUABt3hem6aSvC3whqq5u18hKEENS11VDX9to9HYUQSybZr5WuzW1sv38LL/zsMPPTKRxHUi6WSUzN4wt4aV/fwtTQNHPjycXSqZrWS++flBLbtAlGA7/wsVyN7HyOE8+eIRIPX/F8g9EA+XSBY0/3sHn3q6dnX9yhqe+ovb6DKJfhcvExOYkcHSV5pIf86fPoM1OEckn8xaxrvXMxrezlMIyr73g0NV36/+pqd5fkDWB6eJZi7uphdACRqghTQ7Nkkzki8eUZ3zLXhqrC+vXu+NVfdS+bmYEXXrgkNA4ehFQKHn3UHeCKlLVrLwmNHTvcJPHlqdkybxXXLSx+9rOf8fDDD3PTTTe9EcfzniWbzDF0esSdwJu228jqM6htq6ZpVYObOaEqSCeFLD4C5RN4SbHt1gHWbJgmNW+QTVchZQB/GO76uGR8wKaYL7NqY5aK+hYCsz7SczlsWyNSFaWpc5YLJzVKBYnukViWRNrg2BLNAFCxTQvN0EjNZvCHvIiXMcw0vDoTfVNomkZtexXIPDhJonFJOqFRzFtuGrfiJodrumB+zkAAqg522X3O0erw4kr3xlvh/AmdCydOo6gKgWgAVbXJzEIp7+DxqoRjHvwhH/2n3Lm4brg/HUcsWK+6xydtyGcFpSEPugcMw2LovI/aJrjvsxYtK1OcPNhFOuMnGLxAZsYkVlMNskg2OcnAuUaS8+uXPOdizt3FMUsm/pCPWHWUWHUUgKnhGQ789AiV9SHqW8Fy5ji5p8yZ5x9hxwdWcuvHP4ZqtIP3TmT+u274llLjeuTLAjjjoFQgvLctKYvDmQKzx7WBFX6EvhLU9iUT8HBliBvu3cSam1bxxLef4dyLfQydHl18nzo2tXL3Z1YS8D6NLI6BEgZlI+MXEux7eBhfuJGK+jZQoggEsaoIcxNJsvN57vviHeTTGVT6CcVs6jubCcbblywwrNrWyc4HbuCFhw6RmJonEPZjmTbFXJG6jho++Gv3YJYsDj9+nIGTw5QLJcLxIOt3bWfjHWvxvKTh/O2Moijc/smbqOuo4eRzpxnrnSQUCyIErNreRbQqgmPbzIzOkZ7LYHh1qpouNZenZzP4wz66Nr8xVt3Tw7OkZ9M0rqi/6vXR6jDTw7PMjs697G1+YQwD6uvdgXse7/vxQZ45HURuuIFIlduPk5mcI1hIsXNzLRu7oijj4269yMiI+3N01BUn5TIMDLjj5dA09/e90s5HXd0b1Ckrlyd1y7wuVFXBBz7gDnAX5E6cWLqrMTgIJ0+642/+xr1dPL50V+OGG9w8z2WWeTO47k/VhoYGQqE3NpzpvUZyOsXAqWEyc1mqmioxYgZSSgq5IoM9o9iWjRAKrd017uqyeRKUalA60bwOWn6KcGQcTbNJztUBAt0jaFmlYehZaptNIpVZ6lvSlIsSx/GSy/lZsVEwPWojkTi2gm0KFAUMr0DVvZhlN7E1EA24q7G2c8WxSymxbQdd18hnCkSr/G5tvCyiaSXMElhlZTHLwio7INySq4EzAZIzeTrXKkyNB8iniuTTBRzbQWGG7m1hItU+hs4m0L2CXCpHKZ+jnHdQNYXsvMXJfWni9R4cWUQ6Esdxa62kIynmbC5W6gnlUjFKPg05qaDr4AsCaKiKJBZP03f2LqxCmHyhyA31LTjE2Pv4AHOTcaLVS/9cEpPzmCUT3aNTyBYXLzfLFiNnx3GcMrWNo1TXK4CXeF2IiYE8P/yrY7zws16iNWtpXreOtdvuorHlKMIZwW1A0UFrR3jvQ2hti6+zLD0FxSdBpnBzpG33Mn0L+D+MEEsn5P6Qjw98+R5ufN8sYxcmcWyHyvoKGlovoJr/CMU04MW1CXM4dzhIPl9HW0fXFTuRFbVRBk4OMzN8its+OLyQQuwAB5CZJ5HGLQjv7QihoigKuz62g5bVjZzef46JgWlX0Gxso7IuSrloEquJ8KFfv49cKo+50P/xTk2uVRSF7htXsHp7F2bZYnpohof/9kkm+qfIJLIg3BKuQqZIa1s1voCHUqFMcmqeUr7Ezg9tu8Kl6vVCypcvw3IRyIXbvVlMDkyz78cH8Yd8VNRGFy8PVQRJzaZ5rD9P5cd30fKZq2Ssl8tug/lFoXG1MT7uNqIPD7vj5VAUV1y8kvior3eF0WXUtlXjC3rJzucJxa6crc1Pp2ld20SoYtnQZJnXF12HLVvccdGEc2JiqdA4fNjdIPzpT90B7m7Ihg2XhMbOnW4L1rIAXuaN4LqFxZ/92Z/xH/7Df+BrX/saLS0tr36HZV6VY0+fYn4qhTfoZXYsSTgewh/y4g/6EELQf3yI9bvWsGpTEczToLa7K9uA6qlF1SPks1l8/jly2Sjlkh8poVxIE6pL4fUtuOsoFRheyeCZFGN9c8RroWVVkLkJST7tUCqpeLwehKIBKr6AisdvYFsOte3VzIzMUSqULvUISLnowGEEPHh9Ksh5kA4SD4kpm3TCQQgwvJJyceFTTILjSAp5jRefbufumilqG1KU4hpef4rC/An8kTizc9spFYfZcncTmWSGiYEZ8uk8Hp+GxydJzUmmhguYpoPHq1DMO9gm2PbCZMlxf5eiukKjXBJQcnc0XLcdsCwJUmLbEIxo5Gc0knOtvPi0yrYP/zLCcUjOfhMhMnh9GUzTg225r325WHZ3cMTSZuXk5Dy5VI7GTgvDSINoA6EyO15i6JxNckrg2Hl0z3kOPWLSs9fP7Z/YzdY7AiCL7g6C2ooQl/15mocx0z/Ctr3oRgeqsXCdk4HyXqTwIfy/dMW5JYSgutl1rgKQ5llk7mHAAHXFpW8WaTE9dAKPxw+iA16SaiKEwOMzme57BmwNlEYQHncryJmF4kNIAcJ7F+BOttvXt9C+vgXHcTi2p4fDjx1jZnQOx5YEIn7W7FzBzge2EbvO8Ko3A9tynal0Q7vmck8hBIZHp3FFPZ/6vQ/Te6iP/pNDWKbNje/fQjaZY/DUCENnRkFAbUsVt3/iJjbftf4NKymtbo4TrgwxP5O+qjvO/EyKaFWYeGPlG/L7r8a5QxfIpfK0rbsyqTsSD5OYmOfMgfO0dF9FWBiGOyN6pe8ey3J3Nl6623H5GBtzbzc25o4DB17+8WpqloiNmrY2bjZSHDldwNywBj3oNkpLKZmfToGAjbevfUssZ5d571FX5/ZpXOzVKJXcpPCLQmP/fvcUvxjs91d/5d6utnZpU/iWLa597jLL/KJct7DYunUrxWKR9vZ2/H4/ur50lTGRSLxuB/deYG4yyY+++nMSE0lAUMyXyPfn0QyNYCyAqikIRbByWyfRinNgaouiAgARwR/tBHkGq5zFMSdJzlSQni1RzObxehTGHR+xeBbN8JHP6oxeAI/XoKa5wI13p+jv8TB0zodVgpxVxhdSiVb50QwNs2ARrgyy44NbeeybTzM1NI034MXw6DiOg2Zo+MNemlc3UsxMMtE/Tygaolx0KOTcVVBVAykFiiZBCncXQbqTt+HzPn70t810rUuz4RaFkb4ivT31JGaqmJsYZuzCJKu3hwnHw4yemyBWHcUb8OGYM0hpk04KilkbobhVDZYlEIqKrmtu4ziXdjHAwfAIFNUVFKWiYHIQUnMlahoVRMZdNS7mitTV1bo7J2KS2x8YJ584SKTKh2UazE41MzHaiaarmKaN4dMJxy/t4hWyBRAOkWgaRxogVIo5m/6TOcyyQ7hSwzIhVp2nyhdgdlzhme++SH3Hh11Xqpcwen6U9PC3wB4kMR1BN2apao5T31GD4Q0BlWAeRjq7EJf1Y1xchb580irLB0GWQHvJpE1oeIMV2KUZcBKgXNnHYZWm8HoLoGy4JEiECmoN2BJKzyGNbQhlaV35vh8f5Onv7kM3NKoa46iaQnouy94fHWRmNMGH/+39+ILX1+8gpXRX/AtlQrHg69ajMNE/xYlnT9N7qA/HdqhqjrP+1m5W39h1XY24wWiAzXetZ/Nd6xeP9/T+XubGkySmUiAlEoFtOdiW/YZZgoZiQdbetIrn//kA/pAPX/DSzCGXzpOdz3Pj+7fgexPLz+bGEhhe42XFlDfoZXb0F/ge0bRLjeiXBwxcjuO4/R6vtPMxOurO0qam3HH4MOB+kuxcGM6TgpQ/RjJUxay/kky8jpq7dtIdF27div7O3IVb5p2Lx+OWQd14I/zmb7qXjYxcEhn79rnCY3ISfvhDd4B7qm7evHRX4yVeDsssc01ct7D45Cc/ydjYGH/0R39ETU3NcvP2L0C5ZPLTv36MxHgCf9hPMBqgsi5Kdj5Hei6Dbdl0bGhdzBDAScNLyl0QAqGvIlDhxSqeoHmFQmJGcHxfgDXbBL6wSiZVgdc3hFaaZOS8H/DgCegoIoXHY3P6UBgjEMG2C1iWTblYZm5inoraKPOzKWzH4fieHirrYwgB+UzBDX+LhlyHIEWQmk5hlXLkszB0No/hFVimRNXc73DbAs2QOLbrPiWl2yQ+P5VCNyrJFXfw/COSU/vOUtuqU9tiYFlFZsfnOPjIEQLRAGbJJBiNIvGi6ArB2ByqWiISl0wNg6LqiIJAItA0FbNkumUgC7sWF5vTFVW6hUSWpFySJGdANSrJpFtIJ7KM909ili2+/Qd/xrbbTxOvLjKeM5ibsIlWmTS3nyISm2Z2bCVW2aSiNkK4InTZWyIQ0kJRLDx+d6I/N1kmlTARAnIpC6EITu0T1LZPE29awdkXL/DQXz/GpjvXuX01K+tRFIWBk0M88a0fsGVnH4gwHp+Hcslk8NQw6dk0q7Z3YXgqwO4Daxiph8A8iSwfAnsChA+pb0J4toAIgnUBxNV3CDrWxzi5dxqrOI/uXyosyqUcjpWmfX386vvnShXY/e7jG5sXL54dT3Dg4SMEIv4lK+YVtVGCsQAXjg7Qs6+XrXdvuOa/m8GeEV78+RGGT49imja+gIfuHSvYdv9molWvfffjwtEBfvo/H2d+OkW4IoSqqwycGKb/+BBj5yfcoK2rCID5mZTrUJXK4w14aVvXTLyhYsln44GfHeGpf3oO6bg7FYqqkJ7N8Og39zA1NM39X7rrDQswu/nD28kksvTsO4vjSDxeg1KhjKopbNm9nm33b371B3kd8Qa9i3bDV8MqW3iDb7Czj6K4S7a1tfBytulSujUllwuNkRHo64Pz55G9vSi5HLFcglguQTtAP/DiD+CPftsVOK2tbidtV5cbvXzx/5ub37Dm8mXeOaTnMpw7eIGBU8M4tqRxRR2rtncRr399wyuamuDjH3cHuB4Jhw4tLaGannY37g4cgD//80v327EDbr4ZbrkF1q1bPm2XeXWuW1js27eP/fv3s2HDtU8Elrk6AyeGGD47RqwuSiFTAtxa7Eg8TLgixPxMCkVVFsLhgqDEwbmKXaNQQWtH8xYRcgsnXkxhBKF51SCKOoVQgmRSbfj842hahuqGMoZXJZOUnD8V49DTdTi2jaKpSNPCsRwcx6SUL+FYDtVNlQSjAcyShW64OxWb7lhLTUsV+396mMREkmh1hPrmAJpuMXjaIpW1se2FDyEBmg6BEGSSuM3bhlsmEI6HXE/v3gmSUykMj87KbZ0Mnx6l/8QQ+VSBzFyOmdEE3qCHQCSA4dWRhCgXbRpWhFixtZbB00mE6qf3YJ+7CmxaKJoCtsTBwfAaSCyktNE0gVWWaLqkttliZszL8OB6zp8ocOaFXjRdpb7TpGPlIRxzjjMHo4QqYiiaZGq4CIqH6tpBmjsU5neuopgvkZiYJ1YTcYPzNJVizsRxdPxBd3dpYqBAJmmi6QqOA6GwQnbe5vSBCcTBBGbJYnYswdTgDIZPp21dM3d9dhd7vrOXfCpHqMJPqeDBsjR0j4Y34CExOc9E/xQtqxsBkLIEhR9A+QXcppaQ20Rf/BHSPAy+TwCQS9tMDGeREqoaDKJV7jF2bQzSskpj8Nw8dZ3FxdXtQrbIRP8Ybat1Oje8zMRdLHzbyPLiRbZls+9HBxk5M0Z9Zy2FbHHJirnh0fF4DU4+d/qahcX5I/089NePkp3PU1kfI+LRyaUL7P3RQUbPT/KR33jfa3LiKWQLPP6tZ8in8rStvZQjE6uOkEm6tqxNK+vp3rFy8T5SSvY/dIjH//fTzM+k0XQVb9BLJB5my+713PrRHaiayux4gn0/fhGv37skB8QX9FLIFjnx7Bk6N7UvOmu93nj9Hj7wL++me+dKeg9dID2XJVoVZuUNnbSta37TA9Ta17dy5ImTlAplPL6l/Qtm2cI27Tesmf26EMLtnq2qgk2brrxaSnfZ96KL1eXjwgV39nbhgjt+/vOldzYMaG+/JDQuH42Ny0EF7wGGz47x0795jOmhWQyvG+R69sB5Dj16jHu+cAert19LDO5rw+dzhcItCzaTUrpeCJfvapw44erokRH43vfc20Ui7k7GLbfArbe6mtzz9nf3XeZN5rqFxapVqygUCm/EsbznGOwZQToO9R219B7qxzKtRYtToQg0XWP47Bhrb15Fx8ZWhFFEmidd1yDho5izOXsow5mDadJzGWJVIA2D88fm8Qa9PPMjk661DpX1DkT8pBKt7P3ZNJW1kmDMz+RAllSyjnhDDTMjs5hFE13XidVGKBfL5FN5CtHAYkqypms0raxnon+K4TNj+EI+EuNJWrobUTUVaUbZfFue+vYIx5+dJzFtohlQUe3B4xfMjBXRPTrCAseyUTQF3dDRNIXhM2MomsL2923m/OH+xVIU70Kja7loUsoXyCXPcecXC5RLKmODtfhj61DUCFCkvq2Wcr6MP+SjmCsxMTBFajZDPl1ASonjKK6VruUKm6oGQTjuZ7jPoGRrJCbGCEa8bNrlobLqFLWN4xTyOoFgivRMlvYNXXiDTRSyRQxPBbtW+7n7X3yG/T/t5fjTPQyfHUVK8Pg9tKxtI5cdxSzlsEwPiUnXctftN1GIxAWqZpAedihk5wlXBKmoi9K2rpl8psDZA+eZHp5lfjpFQ1cTxUI/Xm8OK+t+iquqgsfvYXpolsbOIKrqB3sSintB6G6DtzMJQnN3E6xByukfs/9hLyeeGWR+zgcSglGNlVtD7PpQFYGwyQe+GOLR73cwdDZJuehmkBg+nZXbVnDPx0y8vhxwlYm7LLriYiFzYvT8BE/8/TMcffIkU0MzbrCiV6e2tXrxfAF39TqTyGLb9quWGpllk6e/t49CtkRLd+Pi5N/j9xCJhxjsGeHQY8e581O3vOLjXI0LRweZGZmjcWU9juOQnc8hHYk34CUUC5CcnOfkc2eWCIuf/c+H+ee/+DHlYg7dkCiKhjcUBFnPcz84gMfnYecDN3DhyADpuexVewp8QS9CCHr2nn3DhAW4iecrt3awcmvHG/Y7rpWOja10bmzl3MELVDdXEYi4PQr5TIHJwRk6NrSw4m1wnK+KEG6Be12dO8u6HMdxm8ivJjr6+twSq4sBBS/F64WOjquLjvr65Y7bdwG5VI6H/9cTzI0laV3btNiPczEr5pGvP0VlXXSxN+6NRghX57a3w2c+416WzbrBffv2wfPPuz9TKVcjX9TJHo9bbXhRaOzc6aaHL/Pe5rqFxR//8R/z27/923zlK19h3bp1V/RYhJeTWq4Zq2yhKArVzXGSUylmhmfRvToen4FjS3KZAv6Ql9se3EkwGkDKdaBvAvMg2XSQn/xdib7jOTSjjMcnGe2r4MLxvRRzJSobKsjOKUwNCgKRHDfc6aO+XcWyQhx73qF9jZdSvkghH8Yqm5gl0w1UE65d6fTCsWiGK25itdHFNNeqpjijvWMkf5oiEPFfWvFU6xH2JHWtglAszjM/nAXA41eYmyhSyoNQJY7tOklJKZkbTyAUBdu03F0F26H/+CBICET8CCHQPSrFrImiWCRnBCf2CT7whRKrNk1y/MAhDj0RIzmZwTHnKaTmaO704PX7qGqIc3SPRTFXRDc0zJKFVASGL0AkHmD19g6k1PAEc+z84A08/8MD1DTNY+ijeLwSVdOwrBCa4aDpBWaG+9h4Zx2KFncn0s4Uwihw24M3sfH2tRz42RH6jg9SLpSpbGgml1CZGj3L3PgMxbyD40hUFSpqBYbHIZsOYJVNdEOjmC9RWeuWCvlDPhpXNnDmhV6EELSuaWZ6vI32VUfQ9BKW6YoLw6NjlvLY5VHU0I1gnnfFBXlgwV1KOuDM4cggT/zjUQ49EyccFjR1OAg1QDpp8+KjSdKzJT70pSKxhtU8+DtfZKx3iqkh9/2raa2iaWU9lEJQfAhk5dI+HynBGQO1BbROZsfm+PFf/Zy5iSQVdTEyiSyRqhDlfJnhs2M4jkPHhlaEEBRzJaqbK6+p0XXo9ChTgzNXTZ++uNPXs+8cOx+44bp7BpJT8zjSYXpkhvG+KQrpAo6UGB6deGMF4YogU0Mzi/0QZ144wUP//fuUcgWqGjQUVcNxbPKZBJP9Oapb2jnyxAk23bmWdCKDoiovWzbqD/mYG09e1/G+FtJzGZJT86i6Rk1L/A0rvXo1DI/O+351N4bPoO/YIDOjc4Cbyt69YwV3f/62N7Xn4w1BUS41fN9++9LrbNstq3o50VEsuklpPT1XPq7fD52dVxcdNTXLouMdQu/hfqaGZmhe3bjks88NxK1h4OQwp1/ofdOExdUIBuGOO9wBrtfB8ePw3HOXxswMPPusO77yFbf6b+tW2LXLHTffDMsmou89rltY3HvvvQDceeedSy6XUiKEwLbt1+fI3gPEGyuxTBtFUVmxtYNQLMDU0CylfBmhCPwhLzd9aBvrb+0GQAgDAh9HFmt49u8f4fzRJI2dOoYvStmsY+hCCqHmsR33PfCFqohWFcnPpzn0lM3tsSCtq2BuwiaXzpPLePEGHFZuOMfWW1OU8gojfRFmpw2ssis0/GEf81Mp5sYTi8JC01Vs26FUyBNqucwmU6kCtRXsQQJhSW2zSnLaYry/gFnG7W+QrqBQFAVNV/GF3PKQufEk+VSB48+cppgrEYwFFidimmaiGwAaEsnAmSCJuRCHn8ozMzpDKZslEPKjMsTsuEkhq7Lldg+RyCRdG/zMTQhy6QJIdwLqWDbegB9/JMJ43yRb795ARX0MKOHzzoAIYDtlt+FccXAcBY/PSzFfoJwbwRuJLJT86CA8mGWT53/4Isf2nEI6El/Qy+zwHMW8QnNXF5HKUcqlJFZZwSw7GF4dlCiFnAXCxDZd+1zVUMlnC/iDPgyPjqIoZOdz2JbNxGgngeA81fWDAFimge0poCgmaJvBezuk/sh15VLCS3txpMNob4oTey2qGjoIRmvAOg8kiFZo+P02549mOX+yk/W7P4amGrR0N13hyiM9NyPtfjB73D4NEQTKriuUEkd4348QOsefOc3MyCyta1so5gpM9E9RypXdMighmB6apa6tBsNnUMwXWXfL6mvq1crN53HshdfvKvjDPtJz7g7V9U5MNV1jbjxJIVNA0VRX1CqCUqHMaO8EHp/B6hu7EIrAcRye/qf/QzaVp6LOh7KQhaCoOsGoJJPMk54dxfD5GTs/iS/gxbnoe3wVXCF6pWPT60U6kWHfj17kzIHz5FKFhbDDODfcs5H1u7rfkj65cEWID//b9zE5MM3EwDRISXVznPrO2ne/m5KqXnK2uuuupddZFgwNXV10DA5CPu/WqJw4ceXjhkJXFxxdXVBZ+YaJDmnPgHXadagTPtBWgNr4lpxX7xTG+yZRFAVVvXoAqC/kY/DUCDz4Fhzcy6Bpl6xuf+M33PWk3t5LIuOZZ9xT94UX3PH//r/uqb55sysybrvNFRqRt58J4DKvM9ctLPbs2fNGHMd7khVbO9j/0CGmhmaoba2ieVUjDZ11lApl0nMZHNvhto8vDSIUwsd8egfnTlygotFEal7Gh8sMnR5jZmQOX9iLqqqk59KEK4MkZxuJRCdJzaaZHUvQvSVHIFzLyQOVRCuH2HTTIQyPSakg0DyC9TcVGD6f4qFvVKBoAQRu03MhW1o8Bttyy1Z8lQY+3xiV1WnMskEmVYmjrwAlgiyPEqueRVEgOSNRFOkmfeOgKAq6RyMYC2KVTQQmhsfC8tkUMvNYlrysxNhGyjJSKkQqdUqFEoWsxf6fTjMxpNLQblPbXCAQUfCFoqgem579aU69IFi5JcDM6CwevwqoRCsdVt+gEI7ZXDg1xYs/z7H2ptVs2b3BXTWVWRyniKLGSScNclkf/mCRbNqPI0EIDcEMSAucadDXglLNscePceBnh1E1FSEEhWyRSDxMdUuckbPj+COrqGmdpabZT+/RadJzNlJCPl2kVCi5DbXCoPdgH7pHp6IuRkt3I6GKIGbZZG4iSXVTnPNnbyAx20BV7RBef4bpUfBGb2dF5a8gpQVyduEkeWmDv0JfT4ByoYjhKZOcrUY6K/EHsnh9eYyAiuZVOXO8mw33vnyqslBC4P8csrQPzIMLEwkdPDcjjJsQWjOWaXHmhV5CFSEURWB4DSobKhg7P4Ft2XiDHvKZAmPnJ9AMlY6NbXRfYwmQ4TNAgGXaaPqVZVOlQtntP/EbV7n3K3MxQ8HjMwhd5jDlC3jRPTqTA1P4Qz4URWFyYIDJ/hEMj4aivPQ4BP6QQXY+Sz6VxrZsWtc24fV7yM7nrnCvskyLUqG0pMTq9SSXzvPjv3qEC0cHqKiNUd9Rg2XazI7O8dP/+TiFbJEb37/lDfndr8bF1dm69teeoP6uQ9PcMqiODlhYxFvENN1C+KuJjqEhyGQueYq+lGj05UVH7LWJWjdb5xkoPQ7OPBed9xAhMLaD7/3uYtgyr423uTATAlaudMev/Ip72eCgKzAujv5+Ny384EH40z91N/I2bnRFxq5dbgnVazz9lnkbc93CYteuXW/EcbwniVVHuOvTt/DIN/Yw2DNCuMJ1WMoks2i6ys0f3k5Ld+MV95sbT5KdL1FZH6Nn3wCZZJbcfA7LtCmkC0hHUsgUyc7n3CbwRAOZeS/P/rSMrW7jzs8+yI2/NMbY8f/CWL9gctiHUNx+B0W1ae3Kcs8n4KFvBXEct4RHMy5NoGZG52jqTLF++yh2aZBgVEcz/BTyVYwMdJOYrWd2RqNshUjMTFHd5DYbTw3PIh3pNoU7ErNkYhhFzELGTd2OK8xNOjgW2GYKVQ8hHZtS3kb3KAQjFrbpYHgcCjmob7eoqJZ4/RmE6gXhobFTUsjYzE2UOH+0wMSgoKahTMtKi403p4hUWAhFsm67Ru+JSjp3/BJ17TV4/AbBiE46AdFqgW2rjA/W0LlmiEAoTzqhUFmjYRgW2IMgggjPLizT4vG/f4bRcxOIy7IsVE2hsr6CuvYaEhNJpAyAWsP6W1tITs2TSeboPzFIMV9E01WiNRHC8TDlQpnJ/ilyqTzBqJ91t6xmon+a6eFZKutjzE43MTZUw9TgNKHKIB/9rQ8glCDSngd0eJk4tHTCYW5KYXpimlIxA4DhNahqrKC5uwlPIEU2Wb7qfS9HKCGE7x6k9zZwsiAMV3AsYJk2VtlCqILhs6PuDlyhhG3ZZJJZCtkiZsnELJvseGArt3zkxsWdsFejdU0jlXUx5sYT1LQsLRGQUpKcnGfrvRsJRK7fejafKeDxe7DKFuWiie5x8ysc2yE3n8cf8lHIFZFSUs5PLzh+6ZQKDv7QUnGhqhpmsYjudYjVRIg3VrLm5lUcfuw4ZslcTJrOpfJMD8/SsqaR7hvfmEbNU8+doe/YIM2rG9EXsk80XaO+o5aZ0Tn2P3SIVds7fyE3rWXeJHTddZZaseLK60oldxZ3NdExMgLz85dmeC+lsvLlRccrlTabh6H4E8AHahcIZcHubx5Ke9xsHd99r9OTf3fR0FnHoUePX9VqWkpJIVOgbe1Vclze5rS2uuPzn3f/PTKyVGicP39J+/63/+aKk/XrLwmNW291T8dl3tlct7B49tlnX/H6W1/axLbMK7L25tWE42FOPneavuNDSEfSfWMX627tpmtz+1W3k90JrOT8kQGyySyReBjbtLEt2+2TUMoUskVKhTLzM2mEgFLeIRSv4+aPfpFYbTVW6nESIR3DV0cwlieXymPbNlIqzCcirNxc5shzRaZHZtF0jWg8TKlQZnpkFuGco3vDIKriMDVpMHjWJhDJ09I1QtuKJBMDKykVKjG8XhRNRTM0PAEP0aoIcxOJhV4DhWIuj8dTIpMUhGIq8TqHYgEKWcjM23h9aQAMj01FjQVYlAoqa7Zb2LaPhnbX1hWZAycHioUQGq3dAXxBFV9IobY5x833TxJviJDNtFHMmjiWQzheZsfdKYr2XmAXFbUx1t3Syb4fDqJqJYIxg8nRSqR0qKweo6quSGMHCBFye0l89yP0lZzac4qzL15A1zUi8fCiuDBLJtNDMyiKQPfq1LZUMTU4Q7yxksr6CkKVbrOxUAQen4dwLIgiBF6/B8OrMzU869ai/4vdDPWM8OLPjzJybhykRNVV6rvquP0TNy3mXgihItUa15LYSburhgvnjm2V6e8xmR7VidUqhONhBFDMlxjtnaBYKBOM+K/aXPxyCOEB9Uo7EMOrE4wGOPTYccqFMobXcJ+TxyCfcVPVK+pifOhf38+OD7yMzefL4Av6uPH9W3j0m3uYHJimsqEC3dAoZItMDc0Qb4ixZff663rMi5TyJWpbq7DKNnPjCXLp/MLLJwhG/VQ1Vi5mr4QqQgQjKuGYYGbcxuNTULVLf6eloo1lCTrW1VHVFEcIwT2fvw1fwMPJ584wdHp04fl4WXPzKu769C2vSQy9Go7jcOK5M/hCvkVRcTmV9TGGekbpPz60mLexzDsUjwdWr3bHS8nnFy1yrxgTEzA3544XXrjyvtXVS21yF4bsaEXKve46hnbZjpMQIGJuqWj5ANJz85KFBwApy+DM4dbGxpcGgb5H6NrSTm1bNSO94zSvbHCt0HFFxXjfFNHqMKtvvIqAfIfR1OQ2g19sCB8fvyQynn4azp1z+zaOH4f/7/9zb7Nu3aXSqTvuWN7ReCdy3X/Rt9122xWXXT75Xe6xuH6aVzXQvKoB27JxHOdVmyrr2mtQFIXEeJLK+hiKIvD4DNfJRkqEAI/PoLopTm1rNbZtMzee5P2/upu69hqkk8fM95FO6kSqwkSrI0wNTlNcsH8UqorHb1LfZpOYVQhG/aTnUuSzBUq5IjfeMUW0SiOfr6OmxcEbyJCezXDuqEXH+hRbdk1z4uBqnvvhQfKZIvNTKYKZAIGIn2h1mJmRBJZlI7ARQhJv0Khr9aKo0GiXKBck5ZKCollEKhQCYUExJ5mbVKmsk9x0f4bnfipwLC+qagGa+0VmjwICQ7NYvblMR3eJypoU/pBJYiaKqgr84YtBbAGKBZNo1Rmkk0AoFdz6iQ9Rzh6nZ/8sM+N+FAUGegJUN63iro/aBCuy4H0fwv/RxS3+M/vP4Vg2vorgkh0L3aPjD/uZHUtQWV/B9vdtYbxvirMvnmduPEFqNk0hW6S6ucpdFU/nFxLNJcV8GU1T8Yd9VNbHqG2tZu0tqxk+M0a56AbCNa9uWHQQA9x+B30jOClXaMnE4ubFSK/O/IyXQERF0X0oC3+vF8t8podmkI2VrN7xi3+RKYpCIBpgfjpFZUMFXt8l8WF4daZHZrHKFl1bXpud6Oa71iMUhQM/O8x43yTScdA9rj3v7Z+4ibq211ZW4wt6UTWVjg2tZJO1bimiI/EFvMRqo8yMzC4aFURru+jaXEVyepKKWg/JqTKqJtAMN7slPVeiqcvHB3/9lxY/Gw2vwV2f2cUN925i7MIkju0svrdvVC26VbbIpfL4Alf3g1QUxc1VSS+7/L2r8fvd2dq6dVdel826drhXEx3T05fG888vuZsAqAtCexV01iI7qqAjDqtqoS0OatzNtbGHQFkLLAiK8n5k6QW3LwsBai0YO8HYhhDv8t6aywiE/bzvS3fx0795nMHTI+iGazdbLpaJ1Ua55/+6jeqm+Ft9mK879fXwyU+6A1y35mefdUXGM8/A6dNw8qQ7/uqv3NKpbdvg7rvdsW3bcubkO4HrFhbJ5FL3EtM0OXr0KL//+7/PV77yldftwN6LqJqKytUtN91a7DKGV8cf8lHbXkPPvnNu87emuKuSyQyFbBGhCEIVQYq5IvHGCqYGZ2jorF1Sxy0UgVDAsSS6V6O6pYrk5DzFXIli2cIsWqia5GP/pprNuwwKmTlsS3L20AQ1jQlGz/ux7Fn8YT+RqjCReJjk1DxSeKmuzzPzf05QzJYIRfwkS2VyqTzFXIlIVcitZ59J4wvYNLY7xGo9KAo4jqRU0Ni+28Q0of+URWbeQ3ZeQzfKtHeX+dRvFGntdug7WSAxDVX1Cq77URGkTSBi0Ng2R01TCc1QMYwSiqJQWTOLN2uTmKlHShUpIT1v0NhlgTUARgVef4T7v/zLbNr1LQZ75jDLAUIVHjrWlgnHbDDuXSIqCpkZStleOtYpTI4UF4TBJQyvTno2DQLa1jWz+a71bL9/E6O9E5zefw5d11h1YxfJyRSTQ9NkE1lAUNVUiaopOI7D2QPn6drSQSDsf0VfcyEEeHYgrXOuE5S7JwGoXDhjoehJmlfFGTnvYJdyeIMehBCU8iWK+TL+sJ+uzW2/2AmMu0qeTWapqItRyBSxihaGT8exJYVckUDET7giyPiFydcUAiWEYPOd61izcwUj58YpF01CsQANXXW/UNNvx8ZWQhVBklMpKutihCsvrbKaZYtCrsi6Wy42Ouvs/NA9TA7+A8NnLYxmD+mkRTFrY5kWXRtUPvef7qGy/srY2kg8/JpyNl4LmqHhD/mYn05d9XrHkUgJ/tA73IFpmddOMOgWvW/ceOV1qdTVRUdvLyQSiIksTGRh7wCXS2Pp0WBFNawMwHpg/V3INauQtUfA3Oc2eCuV7ueUNYa0/wmcBHjve081fDeuqOfT/89HOHewj6HTI9iWQ2NXHau2d1JR+95Ypq+thQcfdAe4GvbZZ12R8eSTcObMpWbw//yf3cq8O+64JDQ63gGu1O9FhJTy6kXZ18mzzz7Lb/7mb3L48OHX4+HeMNLpNJFIhFQq9Y6wxs2lchx/+jQnnztNLl3A49VZvWMFhVyRn/3NE1imhVWyFh1s8ukCqqZgeA00Q6N1TROxmgj3fvEOVm1zJ6ZSSmT27xg++XOGet0MAPdyKBfLSCeHzzdPJl3BDbtj6IYPy/Zz/tAZ7PII8boyZ49Vk5nXF12QKusr3JX3VIZ4XZpjB27hhZ+7+QTlokl6NuPa66oKhk8nOTlPMGqzZpvEF/BTzEtSc5KKWoUd92l4vbO0rkxz9lg75XIN9Y2nWLN1FE13AIepEYWeg0HmpuMUsjaCIgidjrUFYtUFKqq9+PwFCrkijuNHNUJ4jAKJuToyqTiFbJFCziZSoZM370T3r6Z5dYPbSGoPI8svuO5H2KBUIoztYNyAEAbSySFLezCzL9B39Dhm0WZsQNJ7ooaR/iaQ7gTXLJlMj8yy62M7+Jd//oUl7+uJZ0/zw68+TPPqRlRVQUqJZdpkUzlGzo4xPTSLEIL2jS1UNcbZdt8mNt+17hUnz1JKKO9DFn/uflGjAQ4/+V9FTh3007DqBqYGk0z0T5HPFEFKPAEPHp/Bqu1dfOmPP4OUkpnROS4cGSA1m8YXdJOkG1fWv2rOBLjlVV/7rW+CZLFEqZgvIYQgVhOhrq2GxNQ8uz+7i50P3PAa/iLeOJ7/4QGe/u5edI9BZV0UVVNJJ7IkJ5N0bGzjw7/xvsV+ECkdEkMPcfjRxzlzME0x56DpsGpriC333UpN58fe1BIPKYtg9S401HtAW4FQwux/6BCPfmPPlTtcuL1aEskX/n+fJFa93GOxzLUjZ0eRJ/4znJ9EDJShb8Yd56YQ+av3aknfguDobkCurnN3N25fCXoKZBER/FcI7drLMZd59zMyAo8/Do895v5MJJZe395+SWTcccey49QbyfXMnV+3b76qqirOnTv3ej3cMkAmmeVHf/lz+o4NEoj48QW9FPNlnvn+C/hDXipqY1TURsilCxRzRTKJLF6/QSFbIpfKU1EbZdX2TnZ/dhfxhksdUUII8G4nWvMiielpsvPKYmaE16/gD+TIJA0614PuawHhZ6x3hJHzDlKGqW+dpW1Vmt4Tde5KdKZIaiZNuDKE4+QoFRR8oWqqm30M9QwTjAXweA2yqRyZRBbpSDw+D7XNgMwzN+VgeASdG3TWbNcJRSGfspifVSkVvCTnytQ1QC4rCIZ1VFWhqsFiNRbJ6WmOPh+k75SfeG2ZWFWecIVKdUMJUJBSoZiXFLIWIijweucY69eZHBYM9XooZGyk2gdM4g16WLW9a8FH/+NIWQBpg/AhFpKlpSwi8/8E5hF0LYplVZPNpmloLxCvG+Tgk3mO7K13Nw2QxGoi7Pr4zive2/b1LcRqIsyNJahuji9ug184MkAmmQMBnZtaqe+sY2pomu/88Q956p+ep6qxgnhDBau3r6BtffOSyb67a3ETaF1g9iCdWcBDqL6I5Qyiqh7qO2qpaakinykgpVsCNN43Sbyhwk2S/skh9v3kINlkzrXmtW32P3SINTtXcs8Xbr9iV+al6IaG7tEpZks0dNVR115DuWQuOoEBzE0m0V/GMvatZOcDN+ALejn02HGmhmdxbIdAxM+2+zdz84e3L2kyF0KhsvUBdv/Kjdz80ePkU/N4g2ECletBqXtTV16leQJZeBjscRbr35QKpGcXa2++gd5DffSfGCJeX0GoIohl2iQmkpRLZW7/xM3XJCoKObfp3h/yXSFQlnnvIeKNyB2/BJt+ilSb3V0IAMdBDk7D6eNwzoe44EX09CDP9CAKJhwfh+PjCEAKgZz8ryAqwOlFmqeWhcUyS2hqgi9+0R22DUePuiLjscdg717Xr+BrX3OHqrphfffc4wqNrVtdk7Vl3nyu+2U/8RL/bCklExMT/PEf/zEbNmx43Q5sGTjw08NcODqwxNFF4lBRF2WoZwSrbJKay1DXXsNQTx/B8CRVdRrJGY1UooKalioGT40w2DO6RFgAoK0lXP9xmsrfIzk5Qj6dQNUchHCYGovT0Bkk3hAG4ccqW0wNzeANeknPlpmd9FFVnycYLpBJ+dE8GtJJo4gC9c0ppifayWcjNK10y7FmRmZBgj/owypbBMJ+Kusr2HRHFWs2Hsa2TBwihKJuiFhmPsf544Kx/jBSOLSumkE35jl72EcwKujaoGMYBWqbI0QqUsRr52lb7aOiNk7bqhwerw9QQfETrpjBG7BIJ3XMskTTyyial77TOk65QENXNVpgJUhBdj7PkSdOIgR84Mv3IIQPXjo/NE+AedzN6xBeqppVElN5ikYV/lCAnfflyRWD5DIhirkyK2/oYMWWK/drg9EAOx/YxmP/ew9jFyaprI8xfmGC5NQ8ukejss6tv7cti7nxJJMD08yMzLFyWycjZ8c58ewZtt69gbs+c+sVriJCrQa1evHQV24b59BjU6TnMoQrQ6iaSijmxqMWskWEgNXbu+jZe5Y9392LP+SjbV3z4uQ4l3JfF2/Qy92fu+0Vz1lVU+neuZKnv7PX7f9RFbz+S2IkOZ0iGA1cV6P4m4WiKGzZvYF1t3YzOTCNbdnEaiKv6Jgk1Br8lXfjf4ucTKTZi8x9xy0FVJvd4EJpgzMDxR8T8Ck88Ov38tz/eYHew/0MnxtDVRXiDRVsvWcjm+68St39ZUwMTHH0yZP0Hu7HNi2C0QDrd61h4+1r8AV9r3jfZd7dCM8upD0O5jFAAxEAStCch/bdiAc/i1DdPwyZ/FNk3znodeD0JOLMBORK4LtoR2ssNHQvs8zVUVVXLGzdCr/3e6678jPPXBIa58656eD79sF/+k+uw/Kdd17a0WhtfaufwXuH6xYWGzduRAjBSyuobrzxRr7+9a+/bgf2XieXytGzv5dodQTNAGmPgj0GsoAidKobKpmdMqisrUQWn2Pnnf1U1dsIxaFc8pDLt5NKrWekt8Dz//wCK7a0L6kbd3ct7iDe1UWg8gCJ8dPks4Ky1cGKNeuJR/5x4YvCnXyWC2WCsQDlQpnhC0FiVUVqG9OomiQazxIM5QiEHWzbQ6mUpME5x/jIClZs7aCqsZKZ0TlK+RIgufEDW9EMjXMHLhCr3sCajSfx+mawbQ0pHc4dLtF7LErLKoNABFZtyhGIaJQKBjPjBQxPka51DkJR8Ads/AGL6pag2whoJdwVsIVJsVD8ePxeqnxlbEci8HLqkE4pm6d1TQxhrEaggIBQLABIzh64wA33brpqI7AsH8b9EnXr0qub4mQSWSb6pyhkBPUtNu2r5zn5QpnWlZJ7PjGP6ryIdNZd4Y6yZfd6DK/OwZ8/z0TfaWZHJohUaFTU19O4shnDb3DmhV6Sk/NUNlSSm89ieHQau+rIJHO8+PARqhorX9XRp6Grji2717PvJ4fIZwrEqiMIRZCayZCZz7LpjrW0rW/hO3/8IwRQWbe0vjcQ8ROriXLq+bNsu2/Tkom2lJYbtmdPuhdoTay/dRW9By8wdGaU2tZqfEEvju2QnEqRSWS56Ze2vab+ijcLw6PTvOrK/oi3G26OwLMgs6B2XPK9F6r7t2CPQ/k5IpU38IF/eQ+JySTz02lUTaGuvQbD+8oZA0OnR/jxf3+ExMQ80eoI3oCP9GyWR7+5h6HTIzzwr+5dFhfvYYTih8BnoLwWaR4Eew6UCoSxBfRNCOWycgk9Bp0BWNkOH9hwFVNsc/H7ZpllroVQCN7/fneAG+VyUWQ88YTrsPyDH7gDXEOziyLjttte2Ul5mV+M6xYWAwMDS/6tKApVVVV4vcsNgK8n6bks+XSeyvowlE+AMwmouI3Kefz+JE7Zy32f9VFMjJOaU8jno2i6QbRK0BSdYj6xH8vawYVjc1w4Nsjml6xOCiFAa8Zf3Yz/sgBt6WSQGRWw3Nspwp20OBCriTA7ZjE54mN+1kfryhn8QZN8RiE1X4cn2IltD9HadQRNLzI+soqqxgjxhgqKuRIj58ddB6lDfUwOTnNqr0NTV4zt91ZT15wim7I4tq+ecF0rgbgA8xz+QBKzBKqu4AuozE4IGru8+AIBEGU3GM4ec3MVZM61khAxkKYb4KavB0qo5klsJ0TPiwrh6laEpxPxki+zYDTAzOgcI2fHr+4w5MyBuKwkRhF0bGglWh1hengGIRJUVA6z+6NVdG8LEK3qReZPg9oI/k8itJbLHqzM2q09rFrdx+iFOYZOzRCqUPEEbAbOhxi5ECI5mSIQ8aPrKkg3JwJcEZSZy3Bszyk23Lbmil2Ll77Pt3/yZiJVYY48eZLZ8QRSSiLxMNvet4nt928mncgyNTRNrCZ61ceIVIUZPj3K+IXJRWEh7Ulk4ftg9gPmwi/zEAut4AP/8nae+qcTjJwdwyy551E4HmLXx3dw04e2veyxLnMdOHNg9bmJ91crvVKq3cwVux+UdQulk9fWFGqZFk98+zlSs5klu1ehWIBysczZAxdoXnXqbdcns8ybixAe8GxHeLYvOBJevQRQGOuR5jHXve+loXlODtARevcbfrzLvHtpaYEvfckdtg2HDl0SGvv3X/Ie+O//3S2R2rHjktDYssXdEVnm9eG6hUVLS8ur32iZXxhVV1E1Fas4jMc/ASLiTpIXsMsWodAEsfCTDIwZZFJ+N/fUtkknvFh2JRWVU9TWj9B3PMD4+QlSs2n6jg3i2A4t3Y2s2bmSxhX1V/5yEXTr9MuHQMTwh3xuQFi2SCgWoGmFn3w6ypnDCv5QhqnhAI40aOxqoqmyCo8/C84AazbtoaG5l3wuwlBvLS8e1Rk+O0fvwT4Mj0akMkQulWfg9DwTQx623L2BDbvWEIidoKqxCgcNKTyY1gw+dRaQeAJhUnMahdIqfKERkCmQquswoujuT2fKLQdBgloHSgU4E6BvwFY+hqMcQjO0K0QFuJNwRQgs07r6G6NEwF7qjCYUQbyhgnidhiydpmNdHIz1lyZ70ga7H5n/LgT/FUIJuKvNhZ9A6Wk0o5rmlTXMjNqYZplYIM2K7heZGFyNWbYIRP04tgNCYFzWmxCpCjM3niQ1m37VCaOqqdxw7yY23LaG2TE3S6SyPoYv4C4ISEciHbnEMnfp6wISiePIhdtnkPlvu45aS2qsc2CeoLbG5JO/+8uMnU8wP51C01UaV9YTrghd9fGXeS2UAevKidpFhAZIdzJ3nQydHmViYOqqdriG1yAQ8XPi2dNsvXcjhuft1y+zzJvPK/YV6WtBXw3mKVBq3e8zJMikaz1rbAOt80071mXe3Vzst9i+HX7/9yGdhj17LgmNCxfguefc8fu/72Zl3HXXJaHR/Par1H1HcU3C4qtf/eo1P+C/+Tf/5jUfzDKXiDdUUN8ZZ/DoMQJdniWiAiAxZbFivcTQZ5gbD5KeE+geHQlk53N4kx58Po2qukEyiSb2PXQIw6MTCPsRiuCFnx7m1HOnuOPTO9h4++YlHuJCCDB2Is2zYA+jqI3Ud9TSe/gCjp3E8Oc4fDRE66okpXwQ0/QSrgiSnssxeu40XeuyWCUL2y5RLs5jm3m61o7jWEEGTwSpaqrFs1CGUVEbwzRNJgdmyKcybL4jRv+RGeZGZhnts5ESrEI1t76/6IoEdIRIo+tT7heSiIAiFkqTCkBgwXp2BtQa93K7H9QqhPd+PPoW4g0DDJ8ZI1p15V6oWbYQivKyK/dC34w0z1195c3qR+CAtmrpCrJQQW0DewCs02Dc4Iqf8mF3VVmJoShQ3Rxn4NQwqUSMiqoZmtvHOIDbn5BL5QmEfUSrLy9DcjNLxHXYrBpeg/qO2isuv5hnkppJ4w9dWd6Snc/jC3mJNyyUMJknFkRFx8IEdgElAKINzF4U4xzNqza+I8qK3pGIsFs+4mRAvUpJkpNzz1EluuTiycFpTu8/x9DpUYQQtK1rpnvHSjcEcIHUbAbHcvD4ri5agtEA2WSWXCqPsewotcyrIIQP/J9GFh5y3facadyAvDB47kB473tPBuUt8+YQDsMDD7gD3Kbvi25TTz4JySR8//vuAFi16pLI2LXLdWVe5tq5pr/kP//zP7+mBxNCLAuL1wlFUbhhdwtjPRaTwx6qGiWqJnAcSWKqTDHv0LpGxzSLhCoiZNMCw2e4/S+OpJgrMTcOgYhNPq0TDzfSuq4dRSgYRoHq+mmCgUOQ3UNmZB2h6tvA2LrYByD0LvA/iCz+FOx+ahslmiKYHCqy92Evzz0c4DO/MUO5rBKJh4hWRxCKJBY9R7loE4m3YJVnQWugXG5E99hkk3vZcpvKyMDSyYqu66zfabN60zOYs4fYtitBLg3Tq+KcPNDAyf0eahsUWlYNkctEUTUdn/+i71wetGa3r8IeBiUPThUI2y2B0rpAX4XQ1y/W/K7f1c1gz4g7WY9cKmuSUjLRP0VNa5z29S+zZKFvBP2Y++WoVIOIAo4rcpwxUOrcXY2XsvClKa1BhHGDW8Iis+7tF6hrq2F+OkVyKoWq6tS3zGJ465gbS+INemhZ04SmX9qvnZ9O0by6YdEu+BfB8OhsvG0Nj3xjD/lMYYm4MMsWMyMzrLu1m5qWKvd5mKcBY6moWHyuxsJzPYcwNv7Cx7bM1RFKEGlshuIjIGNLFx+kA8446CtcUbvAqefP8Nj/fpr0XNYNi5QweGqEY3tOce8X72TlVtdowPBoSClxHOeqFsdm2ULVVQzP8mRwmWtDKBHwf9pdVLEncAPymhYbvJdZ5s2ivR1+9VfdYVlw8CA8+qgrNA4cgLNn3fHVr7qBfDfddElobNrkVlsv8/Jc07fCS/sqlnlz6NrSyj2fDvHE9/P0HplDApqmUVnn4/aPVhGJJZGOJFwZJjldppQroXt1N8fCr6EqM5gFQThi0NrlIMwMnlCclesuEK2YplzykJy2ycwOEgz9H7BOgf+ziIUVTmFscifm1mlwksQ7DeZzCj1HnqK6RcXwZ6moMSmVogjA68sRiFhk5zX8YQvDUInVuA5FU0MzZNOwZlua8WEb216wb8WhuX2AG+/qxypb9J0oUC6qICRd62epaTR5/P+08dD/jrPtzhFaV44Ti0N2roTHH8MXaQVZArvH7alw0kDZnViZPSALIPML2+yusFizcyWjveMcfeIkicl5grEAtmmTnstQWR9j9+due1lbVaEE3JW34iNQOgD2aff3K2EQ1S9f734FJiCW3NbwGazevoLR3nHSc6Mgy4RiHqySRfv6lsUVZSklyakUEth4xytnW1wPm+5az/TwLMef6XGD04I+ysUy5ZJJ2/pW7vz0LZfKHWTxil20pajubZZ5Q3GdeYbAPHtpB0OW3OR1tRbh/cDibuT0yCyPfesZzLK9pG9CSsn4hUke/eYeqpvjxKojNK9uJFwZWgwMvBz3/Jtn051rCUSWG26XuXaEWEjbVq/cNV1mmbeCi/0WO3bAH/yB2/T91FOuyHj0URgcdJPBn37adaOqrITdu12RsXs3NDa+pYf/tuQXWm666Az1XkrLfDNJTOtIabLr/ROcORKiVADDI+jYoLD+Zh+jp4sEAl50A+KNlSSnUhRzRaxymWhlBn+ozJkjNfjDERTVA84MzS2niMQsUslmpFQxyzmmxzzUdbaBeQZZfALh/+jiMQgl6Na/4jqv6oFhIlURGrvqKJYF/sAhLNPCcTQ0rYyigm0JpJNxm5wVd4Vb01UKWYNQrITXb5LLqEgkqjLNum2DKKrD2ICPQESjtkUjPVdk9IJCbfMcNfUlDj8RY7i3ipaVkpVbVKQcpFhwaOxK09K10PPgzAI27mktXFEhfGCdQ+b/EQK/ilACaLrGPf/X7TSvauTEs6eZHZ3D8Onc/EvbWL+rm+rmqld8X4QSBbUVKQ66YWQi6P6UM2CfAbXKLQm6HOk2XQt1YSdEqVg4xqUlVYbPoH1DK1bBoViM82/+x+d44afHOHewj4FTw2i6SrlkEYr6ue3BnazZuZLXC8Ojc9+v3Enn5nZO7zvH3HgCf6SK7htXsGp719ISKbUBrLNXfyApgfKS3Zh3O6nZNMkpt5ekprUK3Xhz+g7cVeDPI0v7wTy4UP6kg3E7wrMToV7qoTpz4DzplzRjg/v5XddRy+CpYXoPXmD7+7YQrgyx+a51PPP9/QggWhNBURTMksnk4Ix7/Z2v7Ea2zDLLLPNOIxqFD3/YHVJCX9+l3oynnoK5OfjOd9wB0N19aTfj1lshsLzW8tqExbe+9S3+5E/+hPPnzwOwYsUK/t2/+3d89rOffV0P7r1MIVvgob9+HLsQ5Nb3ebnxbkE+F8YyHTKJNEOnTmIYZQbOtVNZWyIWT+PxhyjnA+j6NKFwiYGzFSTmWpBIEB58AUlldYZCRsNxFoyepMQsW8yOZzF0D4HIYYT3ToQSc9N8F5J8L5YR+YJeDK9OMVdieqKVisoJKqrHKBX8IBwEFv6QjaL6QOtwJ9xARW2UUMygmMthmZdWvWvqp4lW2Yz1e1A1gdenoGo6sWoVXzBPak6jY02Wiro4paKH+TmV0YE6Bs4LWjoGOX1gitpGC4+niOtipS/8lO6xW5Ogr3P7AS72NwCarrHultWsvXmVW9ahKq/orHQ50uxFFn7o/i5966VdB7sKys9B+QUwbgNl4fGk4/ZXqPWgr3Ev01a6TlHOKChtS3c5ZB5NtwlFbifsaaauvZ7BnhH6jw9RyBaJ1kRYubWDmpaq113Ua7rG6u1drN7e9Yq3E8YGZHm/K+aU+NIrnUkQUYTxyhkJ7wbSiQx7f/giZw6cJ5/Oo6gqVY0V3HDvJtbv6n7ddpNeCaGEEb57kN7bXFc0vK4V6EsYPTeON+C96jmjKG6P1tiFycXLbvrQNqSUHHni5GI/hlAEta1V3P7Jm69u/LDMMsss8y5BCOjsdMev/RqYplsqdVFoHDwIp0+74y/+AgwDbr75ktDYsOG9WTZ13cLiv/23/8bv//7v8+u//uvcdNNNSCnZu3cvX/7yl5mdneU3f/M334jjfM/Re7ifkTPjNK7cxFBfjOb2HqIV0yChssohOWszObqCA0/VccPtkrrG8wSCKYJBh3KhzNCFao7urWP1thgzY0ks08HrL6F7BLksoOQpl7wkJ+fxBDzkUnlUzaGmsUjBeYI1NwZRncOuhavQkNpqhOcmalpaaF7dSO+hPvzhJs713EhD5ixVtcPoqo1tOShqEN2/aaHZ2kUIQfu6II9+W2WsL0u0VkMVBXTDxLYkpYIg3mCAg7tMIBRUVaWQsQhGobLWw9SohqIIBIILJ+uoqZ8iVpVmYkildYUCKLiiwsbdX8mC1QOqW8ohzXNuf8NlCCHQDY2J/inOH+lnftptXu7Y2EpLd+NVxYYsv7hQXvWSybcad3swzJNgHXV7MHCAEqgNCP+D7g4QCzaNvg+5Oyl2r2uPK/RLpVzGDUtEUOfGNjo3tvG2QW0Hz24oPQJ2aqHXRIIzD8KL8L0fob67dyxyqRw/+suf0398kFhtjPqOOmzTYm58np/9z8cpZIvs+MDWN+14hPAsCvmroSgC6Tgve72UEkW99C2o6Rq3PXgTG29fy1DPKOWSSbgyRNvaplfNwFhmmWWWebeh665wuPlm+M//GRIJdxfjYn/G8LD776eegt/5HaiqWlo2Vf8eWYu5bmHxl3/5l/z1X/81n/vc5xYve+CBB1izZg1/8Ad/sCwsXif6TwyhaAqarjMx2kVitp6K+Di6p4ht6fS8CEagGU1Pc/qQwfTEHQTDKRBl8nMnOH8cWteEueWBCiYHc4z05olUgCMVFFVSKhQZ659HOg7hihC+oBdFKWGWcjz/45+RmVS46f01bt02ZSjvd5tx/Z9m5wM3MD08w2DPCNVNcYr5dfSeaqGQGqN7ay3b7yq7k2QpF3xKTXDGqG3rorqrBd17lqmBaaCIpggcC1q7PbSvDXP6QJpySWJ4BY4t0Awby9QoFlQ3+TceRgjB3FSIFx6vo3NNkdpmE7dn4aJFrACMhX8XwTzjPg+tdMXr7DgOz/3gBQ787Aj5dAHdo2GWTJ78x+eoaqxk053raFxRR9u6ZnRDR8qyGwgnold/47Q2kBnXGUoJABpCXwn6OoSytFZd6Csg8CvI8gFXjGCC1uSKH+MGxMvZiL4NcAMWd4NWiyy9CPYQIMDYgjC2uc//Xc6p58/Sf3yIplWN6Ib7UarpKvUdNcyOJXjhp4dYfWPXKyZ3v5m0r2/h3ME+HEeivMRW2LZsbMumpfvKguFoVYTobW+P57DMMsss83ahogI++lF3SAm9vZd2M/bsgZkZ+Md/dAfA2rVwzz2u0LjlFvC9S/NFr1tYTExMsHPnzisu37lzJxMTE6/LQS0DZtFEvWz1sFQMMDF6aYU8n50k3hxm0+3rePYHL9B3YhxfwItpOlg5QWOn4N5P2wT9h3jfZ4r87Fsq5w57qGsU+ANFZsbySAfq2mvwBd0sg0AoSy7txSqVOfhklLU3x4lULtSKixjY/eSmvkd65oN071jJwKkhEhPzzI5bGF6dplUb6dyxEX91D5ReAOc87iRfgFqH8H2Q+3+1m413TbLvxwdJjJ4kFDpL5yaNWLwMikFlnYfJoSKgIRSbWLXNkWejJKccNEMjEL1U4jE24OfkvmqCMYvWVQnXEx0DN0jwIhrgAzl11WbjE8+c5rkfvECoIkRNSxX5TIHzR/pJTMwzcHKIvuOD1HfU0Liinvu/dBdVjSFAAvZCKF8ScFwnKKXa7ekQXoSxEeG961XfZ6E1IrRGpPzAQqCfb4n179sZIcSC89Y6oIj7XnveEz1XjuNw4tnT+EK+RVFxORV1MYZ6Rug7NsiW3RvegiO8kpXbOjn8+AlGzw1Q3y5RRRqEwHYijJx3qGtrYMWW9rf6MJdZZpll3nEIAStXuuNf/2sol91gvotC4/BhOHXKHX/2Z+DxuD0ZF8um1q27Rt+XdwDXLSw6Ozv53ve+x+/93u8tufy73/0uXV2vXJe9zLVT01rF6f29V00zldK1k61trWLb/ZtpXFnP2RcvMDkwjeHRaF/tZ8XqhwgELXB0qhs9fOo3bPpOzVPMCaobBZatUCzGFkSFgz+QQQiH8UEvkaoAQ70OAz05Nt4aBcAsWQz2lLFLL7D34RxTY3F8QS+1bTVs3b2eeGMl1c3xhZrydqRxo7uyT9kVJfpKhHAFTENnHR/77Q8i7W2khv+cQnqWQn4Srz9Jx7oAQkgyySyBYI7Bcz4OPBFAMzQq6mIYngUrUySZpMTrV2jucrhSUCyUfAjDtZ6V6qXLFrAtmyNPnEDVNCJVIaaGZjj53Bk3B8TvweP3YJUtotURhnpGeeh/PMonfudD+EQUyo+yKJoQYI+CGFzoK9HcforrQAjj5YPO3ua456e79CKlg7SGwJkBNNBar9ipeTdglS1y6QK+wNVLjxRFIBTIZ94+zljRqgj3f7GNh//mBUZ6sgv5JwIph6hri3D/L9++7PK0zDLLLPM6YBhuBsauXfCVr8DsrJuZcVFojI66WRqPPw7/7t9Bbe3Ssqmamrf6Gbx2rltY/OEf/iEf//jHefbZZ7npppsQQvD888/z5JNP8r3vfe+NOMb3JKu2dXHo0WPMjiWWBFcBzI0nCcYCrN6+AiEEDZ11NHReqmd3cj+AfBnXztQHwsDjd+i+QYKTpliuZ3baIRpPoRk5EFAq+Bnq7WZ6dApF9SKESTHvOhk5tkPv4T5mRhM0dSi0rYngizaRnc8xeHIIw6Pxkd98/5JGVaFWue5IL0N6LsO+n5zm9F4PhfkyDW0eNtyUp2VVkhXrLQo5h5mpFZjaLUhOgDARisCREtu0SCdymGWF7XcFaV8rgTncHgsbtwRKIKUgM6+RzVh4/BUovgQx/yVf/sTkPDOjc0SqQwycGKLv+BDpuYwrKEybUqFENpElOeXmRQyfHePcwTNs3Dq14DgVg4WeCaR0dy/Kh8B7x5X9F+8BpD2JLPwYrAuXrGaVKNLYhvDe87Yu7bpeNEMjEPaRnExd9XrHkUgH/CHvm3xkL4+0Z2hueZrP/gcf54/XMT5YQghoaPfQuW4Of+RJpLNq0W56mWWWWWaZ14d4HD7+cXdI6eZkXBQZTz8Nk5Pw93/vDnAbvy/uZtx8M3jfPl8lr8o1C4tjx46xceNGPvKRj3DgwAH+/M//nB/96EdIKenu7ubFF19k06ZNb+Sxvqeoaali14M38eQ/PMtgzwjhiiAI4U58fQa3f/JmatuqAXeVGHsIrH7Xyan8LKhdbiOwM7HgFOMAHlAb0Y04x15owOudp6rRh2V6SMzWUSqqIGaxLROAYMQ9PWbHE4z3TQI2mUSGswdHyBf8VDVV0riigQtHBzl/ZIC1N11bXX12PscPv/ow/SeGiNU2UNMeplgc5ef/mKSiusgtH6pk9Y7baGn9IG07vNS0HeK7//XHTI/M4lg2iqoQiATYctc2Pv1/a+jefWCbC3azKkiLUtGkr8dDcjaMZfmoqssydOEs6dyPuOcLt1NRG3MbWSUkJuYZOz+JdCSGR8fw6Auvq0Y2mWO0d5ymlfVoukr/0RfZuDkH2nr3NXfmcJ2oHBDS/f1qK+IVMx7efUgngcz/PViDICrd18LJgzUG9o+QTgn8H3nXlEkpisK6W7p55OtPYZkWmr70ozQ5mSRUGaR9Q+tbc4BXQZaPgTNDILqCjbcJNi65MgL2BTBPgefmt+YAl1lmmWXeAwgBq1e749/+WyiVYO/eS0Lj6FE4ftwdf/InrqjYtcu97X33vdVH/+pcs7DYvHkzmzZt4ld+5Vf41Kc+xbe//e038riWAbbsXk9lXZQTz51hqGcEcFOj192ymvb1LQsp21lk4Z/BPOGuEssyWL2A7tbsy/TCo3lAMUCmUJUsXZu28Ng/aGTzDYs14kKAVKqYGR2kojpG2xofjpnn3IvnSCeyVNfbpOcN+ns0MvP9TI/MsmpbF4oiOH+k/5qFxfGne+g/OURLd+PChCyG7m+kIZZnamiWJ38YpvWG9xFQXIm+4wNb2XDbGl78+RGmBmfw+j1071xJ27pmVM1EFjxQfBJkCvBhS5XekzA36SEYqyAakPiDgnyhnXOH+ijmS3z833+IaE2UaHWYo0+eRAjhOuJcNvG1LQfDZ1AqlJifTqFqKuXCnHsbtdN1gbInXQEnVFBqAAuc6dfnBHgHIUuHXUtfvGCdWBqOJyU430d6tiC0t5Gz1S/I2ptXce7gBQZODlNRFyNUEXRdoSbmMUsm29+3md6DFzh/dACrbNHQWcfqG7to6Kp7awSWfQHwXr2QV6iAjrT6EcvCYpllllnmTcPjgTvucMcf/zFMT8MTT1wSGhMTrvPUpz71Vh/ptXHNwmLv3r18/etf53d+53f47d/+bT7ykY/wxS9+kdtvv/2NPL73NEIIWtY0UVEXwyxbhGKBJYnQUko3T6H8AigNoIbcCZ3ZC3ICd5cisNC0bC1M9nxAknU3ztF/upXzRwaIVkUIxgJYZYu5cQ+aonPL+2YIeMbJJubRlCx1zYJASOfIvhY0I0Yk7pCaSbu7DtURCpnCFcdfyBYwSxa+kHcxMMwyLU4+d4ZgJLBklVeggAhS1ehj5NwYfceHWH9r9+L1/pCP2x686Sqvkga+TyD1bZD7X2BfIJ0IkpzNE46HCIaLeH1ZpibaKJZaaF4lGT4zRu+hPjbevpbVO1by/D8fwBf2Y3h18uk84DbnWmWLYDSAEAq5dAGzaFLb6r/45oCoWAi6uwx7DLe5+72DlBLMI66QtYcB1X1thFh4KQru65L/PoT//Vt8tK8fwWiAD/3r+3juBy9w7lAfo+fGUDSVeEMFrWubOX+kn+mhWbx+D4qmMNgzytE9J9n1sZ1su2/TW7R782q/87117i6zzDLLvN2ornZFxKc+5a7L9fS4AuOee97qI7s2rllY7Nixgx07dvDVr36V733ve3zjG9/grrvuorW1lS9+8Yt8/vOfp/E6s82fffZZ/uRP/oTDhw8zMTHBD3/4Qz70oQ+94n2eeeYZfuu3fouenh7q6+v59//+3/PlL3/5un7v2xUpJTOjcySnUiiqIJfKc3p/LxN9bplOZUMFG25by4Zd3W6+gj3s7lQo9aCE3AdxMiBLuBOEhUmEMEAaQNFNhxYRAoEZHvhXn+bAz2vo2XuO2bFZVE2ldV0XW242WdG9n2IuSy6VJ14L02M6Z48aHN5jI7QSXp+HQNjP/FQKRRFU1l8KQ5von+LIUyc5f7jftYiNBli/aw0bb1+D40jymcKiE9VLUTUVJOTTVwqVl0MIgdA7kOH/B1n8Odn0z6iqTeGPlCkX/YwOrmKofx3SUdF015+/97ArLDbduY54U5zEeBKEWxufS+XRdBVf0Eu0OkwmmSOTzBKvr2DV9g5g0E3SFi/JuJASZBbUHdd87O8ObDfXw5kD5KW+E1jobfeB1ME6jXRSblr0O4xivkRyah5VVaisr1jMN4nEw7z/V+/mpg/NMz+dQtFUYjURvvtff8zsWILWtU1Leo/mxhM8/d29VDfHaVvb/OY+CbXTLXW6aAN9OdIBTIS27Aq1zDLLLPN2QQjXpnbt2rf6SK6d627e9vl8fP7zn+fzn/88fX19fOMb3+Bv/uZv+IM/+AN2797Nww8/fM2Plcvl2LBhA1/4whf4yEc+8qq3HxgY4P777+dLX/oS3/72t9m7dy+/9mu/RlVV1TXd/+1MYjLJnu/upf/4EPlUgeRUkvnZDLHqCO0bWlBVlamBGX527jGmh2fY/bldKFa/20SsXCbonMmFDAmBKy7KIC9atCruBFCpBFkkEExy58d8bL+9SHpmCs3QqawroTjjTI2uYejMBco5nVzW4OkfBdB1m3hNgr4enVhdFYZXZ342TUxGWXlDJwCDPSP85H88QnIyRbQ6jC/oIz2b5eG/fZIDPz3Mqu2dzI0l0A2NYCxwRTKxYztIeFnhcTnSybgN08IApRqhBBH+j3HqiM304HGqm6rIZmKUikudbnRDo5hzMy1C0QC3fvRG9v3oILpXw/DopOYy6AvWtvl0nlK+hNfv4baP30RdVzsyexicwYXE7IXjl9LtZxERhLH5+k+AdzQqCK8bjqdUXnm1dBbEbdntwTDeHvar10IxX+LgI8c48UwP6UQWRRHUtFSx+a71rLtl9eKuQ6wmSqwmCkDPvnNM9E3R0FV3xfldWV/BwKlhTj1/9k0XFsJYjyw/7/YGqS2XxIV03PNZrQX9HfTttcwyyyyzzNuO6xYWl9PR0cHv/M7v0NTUxO/93u/x6KOPXtf977vvPu67jk6Ur33tazQ3N/MXf/EXAKxevZpDhw7xp3/6py8rLEqlEqXSpWC0dDp91du9laQTGX741Z8zcnaMqsZKAmG/2ywtIT3nuhK1djcRjAbIzuc4/PgJOja00tntJkwXCw7ToyWQEI3MI20/Xl8OXS+jKAuryUIAGoigu7shJZSeRFoXCPgVAq0ht5TFfBzHcZgerqCUD5JKSBRVpa5V4cIJBZ/fRDeyJKcM/EEvZsmie8cKmlc3YJZNnvyH50jPZmhd27Q46bJMi8TpBL2H++g7MYh0JLPjSTKpHCu3duL1XyrvmptIEomHaFv/8pMu6aSQxT1gHnV3CNBAawHPLaCtJRhv4eBjIyiehquWmxSyRaqbLk2AN92xjr6jA9i2pGtzO5m5LNPDs8yMzVHIl1i5rYPP/P7HaFvb7D6e/0Fk/p/APo9bs666DfIiivB9EKG9ySvRbzFCCKS+HoqPLuzkvOQGMgf4QYRwXbveGZRLJj/7m8c4+dxZgrEAlXUxHNthom+Khy48RnY+x84P3nDF/WZH55COvGq+BUAoFmTo9MhVraTfSIRaA/6PIvM/cNPe8eIuPpRArUH4HnxXWgMvs8wyy7yTcRwH27LRdO0dYYDymoXFM888w9e//nV+8IMfoKoqDz74IL/8y7/8eh7bFezfv5+77757yWX33HMPf/d3f4dpmuj6lU48/+W//Bf+8A//8A09rl+Uk8+eYeTsGC3djaiayvDZMcySSUVtlGKuxHjfJNXNcfxBH8FogLnxBKf399K6soqDjxQ59nw/iSmL+RmTfBqCER8f+VUP0UqFUMygujnirpwK/0Izd97d1bDOLi2jkibgwyoniUTHsZ0VFHIlSvkSKzeqBMOQmHRIzEjSyQLBqJ/OjS28/8t3oygK/aeHmBiYoratevHkz2fy9B7qo5QvE6kMIYSge+dKevadY/TcOGbRZNOd63Ash7nxJOWyyZ2fuoVwReiqr5V0Msj8txbStCvchmlpgtWLtAYR/o+xcmsHB39+lMREksr6pT0QqZk0Hr/Bqu0rFi9rXdPEPV+4g6f+6TmGTo8ihED36DSvbKBzazvv/9JdS/z9hdYJgV8D8zjSOutOprVW8oUVDB50KOaPE4j4aVvXvEQ0vavx3Az5fwB7HJwwCA8gXVGL6uZ6KAFQXt6C+O3G2QPnOb3/PPUdtXgvy6vwh3zMjSfZ/5NDrNjaQfwl5xhCIF+hV0HKK5Ov3yyEvg6CNcjycbD7AcU9n40NiJf2Cy2zzDLLLPOWkZxOceq5M5zae5Zy0aSiNsK6W7rp3rly0b3y7ch1CYuRkRG++c1v8s1vfpOBgQF27tzJX/7lX/Lggw8SCLzxwUqTk5PUvCQ1pKamBsuymJ2dpa6u7or7/O7v/i6/9Vu/tfjvdDpNU1PTG36s14rjOJzae5ZAxL9Yt13MFVFUBSEE3qCH+ekUyakU/qAbQuYL+pgZneWp7yu88CPwBVKUil4SUyXMskJmXnByf4Cd9yYYPq+Szxu0rQkgsJDSppgtMz+XwCo7WLaXinpJpDLklvUIFcs28PrTBMp5QrEApXwJyzRp7FDZcmsB1RPnxP4AFbVRbvv4TVQslICk57I4loPhvZRXMD0yRz5dIFoTwSxalAsmqqay9uZVXDg6wOTgNKdf6CVSGSbeUMHWezay+a51L32ZFpGlF1xRobZfCpQTXiAE9giy+AjVTb/Bzb+0nT3feZ6hM6NEq8JAgdTMNELAzg9upXl1w5LHXXfLappXN3D+yADJqXkMj05zdyPNqxtQVfWK4xBqJah3ILgDx3F48eGjHHj4WeanU66oElDVWMmuB3fSfeOKK+7/bkNRIji+T0P+m7g9F0VAcYWEUucKWm0FqNfXh/VmI2UZzNPI8lFOPnUSRZp4vAqSasRlH5cVdVEGTg5z4cjAFcKirr0aTVcpF8tL/hbcx5dkkjnW3rzqLVt5Emo1wrf7LfndyyyzzDLLvDqTg9P8+K9+zkT/NMFoAN2jMXJ2goGTI/SfHOL9/2L3Fd8vbxeuWVjs3r2bPXv2UFVVxec+9zm++MUvsnLlyjfy2K7K1VKor3b5RTweDx7P23fV2CpbFLNFPL5LJ4iqazjOwvNa+M8qW4vXmyUTs2hy7KlzVDSuArOXCydSeP0q0biOVS7z4pN+apvBG3AYPJuh/1SJho4C8To48lyAqrpZpKOTTY8x1jdJXXsN7euaEUoNmpZECImiFPGHq4lZNqnZDJqWJZOSDJ1VEUJh2/2bufWjlxqVdUNDSonjXAqhmxtLoHt1BALHthGqa+vqDXhYd8tqdI/G+l2r2X7/Ghq6GvH4Lmv8fQlSmmAecktqrha2ptSBPYCwe9n+vs3EaqMce+oIY2dfRNpztHY5bLjZw+ptz0F+Bun7EEKNL949Eg+z9e5L9f/SyYN1DGnOuzs8WicoV1qFHn78BE/8w7P4/F6aVzeiqgpm2WJ6eJaH/+fjGF6dzo3vHpvVl0P47kA601A+CEi37A6ANOidbpnY23gbV8oCMvddt8ROwPxkFq/fAvM42DVIfe1iyJ8QAqEIsvO5Kx6nbV0zzasbGTgxROPKS3bOjiOZGpwmVBFg7c3XZs28zDLLLLPMewvbtnni288yOThD65om1woft5evkCty8tkzNHTUsv19W97iI7061ywsfD4fP/jBD3j/+99/1RXcN4Pa2lomJyeXXDY9PY2maVRWXqVp9B2AttDEPDuaIFrtuuXEqiOMnZ/ALFtouooEDO8lu9ZS0cQT8FIYnqWuvYX+k3lM0yIYsUBINE8EM1nk+3/twReS2KZJKCrwBUKkU0E6NmygbfWLAOi+IKVCmbHeCbx+Dw2djejGBL5gipkJ15kpXBkkVuWgqiZnjjTjUMX7/sVNPPCv7l3SnNrS3Ui4MkRyKkVlnVur7diuyJBSUsiVqGuvXnwuUMLrTVMV309r8z4opHEKBmg1oK1GGJtA7bg0GZVFt6dCvMzumFg4nZ00QghWbGmhc+Wz5BMZEI34whVuCYrMgnkUKXMQ+GWEcqWYkeYpZOEnbk4FEjcALwTGFvB9ECFcsVrIFTn486N4vAZVl/Vt6IZGfUcNw2fGOPjIMTo2tL6tJ9WvB0L4IPAp0LuR5iGwZ0EJIPQtYGx+27tBuX07B0FtAuEnEBsiPV9wRaUzAZYP9NXubaVEOnJJidRFdEPn/i/dxU+/9hjDZ8YAUFQFq2wRq4mw+3O7aOi8cnd1mWWWWWaZZUbPjTN8Zoza1upFUXERX8CLL+Dl+NM9bN69ftHK/+3ENQuLn/zkJ2/kcVwTO3bs4KGHHlpy2WOPPcbWrVuv2l/xTkBRFNbf2s3D/+sJyiUTw6MTrXbLgqaHZkAReHw6sZoIuXSeqcEZWtY0EooFF3MgilnQjAhoQUDiOJJsdgK7XCZS7aNom0jFx/y8QXq2TP+paaZuqaa5fYBiIYDHZ2CWLCYHp6lrq0bRO9C9OTx+CflBPD4Dy/IxdH4FPUdq2bK7jbs/f9sVjjfhyhCbd6/nme/tQwDRmgihyhATfZOYZROv30Ndm1vKJmUBu3AM7GnqWjxgzoDMABdTxEeQ5YPgvRc8ty+UF3nc4ZS4KtJxf4oFFyzrAsI6QSDa4tb3X0SEQLSDdd5djfYszceQ1gAy/x23P0BtWXDZkiDnofQ0EgXhd80CxnonmBtP0tBVe8XhCCGorI8xem6cuYnklbX470KE8IBnO8KzHSkdhFBe/U5vA6STdXdaRHTx/OneFmbwdA7LUtBUPzgTSNmKED4yyRy+kJf29S1Xfbx4fQWf/N1f4sLRQYZOj2CWLWpbqlhxQyex6re3wFpmmWWWWeatIzmVwiyZL+uOGaoIkprLkE3mFt0I3078Qq5QvyjZbJYLFy4s/ntgYIBjx45RUVFBc3Mzv/u7v8vY2Bjf+ta3APjyl7/MX/3VX/Fbv/VbfOlLX2L//v383d/9Hf/0T//0Vj2F14W1N6/i/JF+zh3sI1oVIVQRpKGrjnQiQ2YuS7giyOTADIZPZ/WNXez+3C6OPd2DZboOO5pHx7EdhBsaQDGbp5wvE4wJNLWAY5loaoHqljLF2ipGL2Q4c6iS2gYfoUiCbDqKN+Ahn8lTyEwTCBXwVHyWQL6eiSNHmDiSYHo8jOXEWH1jA+tuWYWmu7tWUpbdxGVZBCXITR/cDFJy5IkTDJ0exSpblIsmIb+HFVs7CFe6TdnS7GNiYJqa1hAd3bOA7TaSA8gEbrifB/7/7d15fJT1vff/1zV71sm+hxAgbLLILiCIG64c0ap4al3a3vfRVqVVb+vD4932nJ566N27p7WtRz2WWrv4u6tt8dRarNKqCOLCKmERAgkkQEISsm8zmZnv74+JkZCEAkmYLO/n4zEPyXVdM9/P5JuY6zPf5dP2OtizwTkBy3JhnDPCx0xK9zoSoSqwJYBzfEcb+4H2rknFpywn4MK0f4x1amLh/yC8daq94LMtOS0LrETAgH8rxn0xlj0dv6+dUCjUpdjfyZxuJ4FAkHZf+1n8RAwPQyWpAMLV0kN14Z+1DpPmxrHno3pKdreQmuUk1ttAKNBAfVUbDTWNzL12Bplj0nt9SXeUmwsWTOCCBed/yqiIiAxNn663DYV63ugjGAhis9s6rxtsIppYbNmypUvl7k8XWd9111288MILlJeXU1pa2nk+Pz+ftWvX8uCDD/Kf//mfZGVl8ZOf/GTI17CIio3ihvuu5v3XtrL7vX1UllVht9uZe/UMxl6YT3xSLMYYUnKSyR6XgWVZjLswn4/+vI3G2iaSMhIoLz5OVEwTYybWEustx9fcQlODh/LSROqqXXhTXFi2EySlVtFYG8+BwkRyxs8lv2A78QnVhEIhYmJ8WFYSuOZjRd1E1oRoMsdfxuE9ZWxbV8jhPaUc2FZM0daDxCREs+AaO9PmV+C0VwFBsJzY7KNZvPwaLrz08xzaXYa/rZ2DHx/i4PYSGmsaMSFDMNBCY1UZUbEeZl0SwtdSjzFJuKM+/QWKgWAVOMZCsA7Tvg3LGb45s1wXYdp3Q/BgeE2FFQsEwkkFreBadtKWmR07EvXGcndshfoZE2qG9j3hito9TV2yEiFUBIFisKcTnxyHO8pFS0Mr0fFR3S5vqmsmOs5DfFLva0dkMLA6HqHOIzHxDv7hn7J4+3dVHNzZyInyEJbzBN60bC65dT4Ll88d9tPbRETk/MqZkBUelahqIDG9+wh3TUUdk+aPJ26Q3ldENLFYsmRJ5+Lrnrzwwgvdjl1yySVs27ZtAKOKjBhvDFfcvpj518+irqoBh9NBSk5Sr+tZcsZncuGlF/Dh2u1ExXmYPDfEtDk7SUz101QX3v0pIdlHTWUzuz7KIio+gaojzbQ1B4iJbcYfbKam6gIa6lJISjmGr7kChyuK/AVfxIoeh2VZ1FbWs+mPH7HhDx9S9slRLJtFcmYiWQWZpGWV4Qhu5egnseRMnIHDHROeOhQ4gGn5DfEJdzH9kgsAmL10Oge2l7DrvU84su8YNitIbEIQX4uLw3sriY5qob7WQVqOm1ETonE4PEBzuPifFR9+zY49/y17KsTciWl9DQIHwnPfsYE9DVzXYLkXffZNspKBQM+VhiGcVNgmn3Iw0FHQrZcCfdanN6DhxfRZY9MZNTmHoq3F5E3O6TI9rN0foK6ynkU3zeuyXa0MQvZMsKeGq4efNGqRkOJi+b1ZVJUeoeZ4HPa4z5E9fgyxCepPERHpf4lpXqYtnsTGVzZjd9iIS4rFsiyCwRCVpdV4Yt3MvHzqoP1gK6KJhXQX4405o5tQm83G5V9YTGxiLJ98+BFTLjyMZUKUHYjBhHwE/EEaa21kjw1w+c017NmeSktDNPUnGmhrDZA9Ljw1J9Du5tC+DCoO27nyjsV4YgsAqK9uYM2Tf+bgx4eoLKsmxhuNJ9pNc30LpbsPsuTaCmK8URzeZ8PuqcMT3dqx2NxLUupxHPb1EJMfTgYsi4KZYyiYOYbW5jb++JP/x95NkJxhIznLTVScnYZaKN3fgt8XYvyF0dgsG+HRhgCnjjpY9iyI+Z8QLOuovO0E+2gsW3TX61xTML63IHgcbE4INRDerSgGcINlw3Jd2PUba8WAPbFj0XYPc+GNL7wtb8ee/zabjUtXLKS+qoGSXWUkpMaHRzAaW2mqbWbMtDzmXjfSKnEPPZblwbjmQ+sfOn6mEjqTUcs0kpbdStrY67FF9b4VsoiISH9YfPN82n0BCjfspfpYTWcSkZiRwKUrFjJ2+ujIBnga55RY7N+/n3feeYfKykpCoVCXc9/61rf6JTD5+5wuJxffOI85l9bTXr8df2AGBXNtNFZu5sDOJmw2B5Y9mtj4JlLSa2luSKem0o/D0Uh8UhMVJZX4Wn1YdhuzrpjKnGtmdL72tr8VUrr3CDHeaDCG2IQYLMvC63YSn3AMu1VBc/No2n0NFL67t+MT3PDokzfZkF3wASnjr8dydC2IdnDHIfZvO0F2QRJuVwNN9TEYYyPGG95mt+qIj/ScAIlpseHRitBBcMzvlplblgWOUcBpqlzbMsA1H5pXdxQG/PTHPRAekYhaDo5Jp7yuA1zzMC0vd1QsPylZMaFwMuPIA0dB5+HMMenc/NAytv+tkL0fFtHS2Ep0XBTzrp3JhZdNIS5xcA5XSleW+2JMqBb8m8JrLnAC7eGfFfdiLM9lkQ5RRERGAJfHxdVfuowZl0/l0K4y2n3txCXFMm5G/qAfMT/rxOJnP/sZX/nKV0hJSSEjI6PLDZ9lWUosIsDpPIYz3ku0I7y2ICEhEXeUjwOFFvVVAdwucDrrKSvy4omJZdGyEDHJ4ziwO42EtHgmzi1g7PTRnQuBfK0+dr/3CfHJcbQ0tABWl35OSg/XuAi0HCYUCGBZDuKSErE7PIRCBl9rIxUlR6iu3cnkhZd3iXXfRwew2e24Y/KhvZC6KkP9iRgSUxsxJprm+nZqjodIzMyF4BGwJWG5zvUT//aO14gDEx2eWoUJ3yha9vC0F9PYsSj7JK554QXp/s2Aq+P57eFdoezpWFHLO+sZfCo1J5mldy1h8S3z8bf68cR6BnVlzIFkQk3hCtwYsGcM6DazxoQ49efzXFmWI5xsumaE1/GYOrDisJwXgD2/czG6CR7H+AshdBQsd7hytfOC8Ha7IiIi/cCyLDJGp5ExOi3SoZyVs04svvvd7/LEE0/w6KOPDkQ8cs5OWqtizyQtuxJ3dDSVRwKE2tuwbDBlfjxTLjKMnRKPLf4uFtyc1eMrtTX7aGv2ER0Xhb/NDxgMBguLqJgGYuOrMCZAS4MPmw28yUFsVGBMGjZbNN5kO21NTna8eYBxsxd3ucFurGsO17GwpwIBQoGDFBV6GT+1FW9SPU4neKITwkmAIx0r6iYsxzlWaw7sg+A+cE7vWKjdRmdiYQwED3RsN7uky9MsywXRt4FjXHjL21A12NzguBLLPQ/L3nsNAk+0G0/04C3IOJCM8WPa3gL/hxCqAQzYEjDOWVieK7tNVTv3dtqhfSfGvwWC5WB5MM6ZWK6ZXQoenovwSNhoLMfontv2bw7XNwnVAC6wghjfe+GNBqL/Ecve+y5RIiIiw91ZJxa1tbXccsstAxGLnCPr0xtgEwx/Em9Ph2Am3sRjeBNchHAzzpaH3REE2sF9dXhHpV64o924PE58LX4S0724PC7amn3EJVgkphyjttJBeWkU6dlttAdcWLYoIAjBKowjk6joRqoqxlBW1Ep58XHyJn2WGCSmeyndeyS8Na49F2NLo629ml3bW0lIqcffUsWkeeOwYi4JfwpsSzjn74tpL+q6EPvkT5QtgCiMfyfWKYkFdCQX7gXgugjwAQ4sa2SOQJwJY4KY1jXg2xBen2AfBVjh9Qq+NzGhGoi5vdtIz9m34+9o5/2ONRBxYGqh7b8x7Vsh+nYsx2mmx/Wl7UAxpmUNEAT7+M82BDDtECjCtL4MMffq50REREass95o/pZbbuHNN98ciFjkXDmnhneyCZaEkwvs4JoC9nFgtWKzrPDuUvYMrKgVWJ6rTjt1xBPtZvJF46mvrscd7SZzbDq+Fh82qwqHw0/lUQ97tiTR0mwnK6+dWC/Y7Q7cUc0kJh6hsSGJ8iOTCLZ3r98waV4BNruN5oYWIFxQzbJnY2zjOLR/PAf2ziA++24s98I+JRVh7XRkED2zHID/tK9gWTYsK0o3i39P4CD4PgrXIrGnhxfUW47wyJR9FLRvh/a9fW/Hvxl8m8CWDvYxHa+fHa45EjyGaX0FYwJ9b6cHxr8lPHXOlt11lzHLGS6kGDgIgf0D0vbfjS3UjPFvxbS9jfG9H07kREREzrOzHrEYN24c3/zmN/nggw+YOnVqt4rXK1eu7Lfg5MxYNi9Er8C0vBSe3oMDsMAy4FoQrlztGBtOLM7wBnnGFdM4+PEhDu85Qkp2Eg6HnUDLFo6VGNpa2kmYHM2OjTlMW9hC9ug2bPZ2gu1w9FAyx44upOqYjag4D96U+C6vO3b6aKYvuYBt63YSHReFNzUeYwx1x+vxtflZcMMcsgt6H005q++LPR1DsGPUoocc2jSBfUb343LWTPtuwB9ej3KqjpEi0/4xlmv6ubdhghj/R4ATbKcsiLfsYM+BwKHwNsTOiefcTs9thyDwCVjeXuqbeMIjF8Ej4LygX9v+u7H5t2Ha1kKwkvCUyI4paK5FWJ4rsE4tJCkiIjJAzjqxeO6554iNjWX9+vWsX7++yznLspRYRIjlGAux90H7LkygGDBYjnxwTjmpYNyZS85M5MaV17L+d+9TUlhKoD2IZfeSU2Bj+qIMZl6azNt/qOedP9YwcaYdlyeEv60Zf3suIVss1UfKmLZkMinZSV1e1+6wc/UXLyU5M5Gd6/dworwWC0jOSmTG5VMZPSWX44eriE2I6fvOB44pYPsbhI51/5Q5dAIsD5ZTiUW/MPWEd1HqheUJV7buUxtN4cKJvY1kWVFAe3hNzEAwhtOOgGHRZa3TeWDa94R3MCMA9tHhUSITCn8P2v6MsexYnivOa0wiIjJynXViUVJSMhBxSD+wbF5wL8RyL+yX10sblcrNDy2jsrSahhONONhOZtZfcUVng2Vj4fUOKst87N3aQnyiDU80+PwuGmpLyZmQxeKbu28TC+Ftchf8wxxmXTmNmoo6LMuipbGF7X8t5J2XNhHwB/DEuJl00XjmXjuTxLRz21XIsqdA1D+E58UHizpqE9jCN7iWA9xXdtk2VvrASuC008pMW2ftj3Nn7xh5CvbShvnsun5mWTaMYxz4PwB62KHD+AHbadcu9TdjDMa3EWgJT3v8lNVRMDIYBN97GNdFWKeO8IiIiAyAs15jISOLZVmk56VSMHMMo6cvwRWdCaFDYEIkpLr43P3ZXHZLMlHRzbS1RuOOSWfJbQu5+aFlpGSd/kbSHeUmMz+dloZW/vjUG+x6bx+eGA9JmYmAxaY/bmbNk69RW1l/7vG75mDFfhncF3esqbDAOR0r5m4sz9WDtnLlUGO5pnSMSjR0Pxlq7ihGOK2PjcSEE8FQzUlJxElMHVix4MjvWzu9Ne+aHX6PweNd2zdBCB4O1zfp5ylYpxWqhkAxWL1sRWhLC18TLD5/MYmIyIh2RiMWDz30EP/2b/9GTEwMDz300Gmv/eEPf9gvgcngY9mTIWpFePebYBHgJD7esHhZgIuuKcDPDXjiJuDynPnOP4H2AO+8vInmuhZGX5CLZVm0+9uxbOBNjaf448Ns+csOrrzzkm7PNaGm8IJZ2sPTY+z5Pc4ntxzjwjtnmU8/UXcqoehv9jHhHbR874anLNmSCe8KVROeJuWa160Y4dmyLAtc8zHteyF0JLxQ3LKHb/JNI4SOg/uS8MLugeAYD57roO0vENwfTnRMAPCBfRRW1K1Y1vncargdCIYXj/fk098F097zeRERkX52RonF9u3baW9v7/x3b3SzNvxZzglgfwDaCzGBEsDCcozB7ZyKxxb/d59/qrJ9xyg/WEH66FRCoRBH9pdz/FAlrc0+MAZjDG+88DYzLp/auV7DmCDG9274JjZ0gnBtCmf4k2rPsl5rEPR1q1PpnWXZIOoGjC0pPF2os0BeEjgvxvJc1i87a1nO8RB9M6b1zx0bFdiAULhCumsBlmfZgP1/yLKscOLiyMf4d0CwFCwXlnMyOKcNaCHAngPyhhfLhxrA3kNxvlBTuH5Ln6egiYiInBnLmJ7mFAxfDQ0NeL1e6uvriY8/+xth6V+7Nu5lzY//TN7kXA7sKOFoUQUujxNPrJtQ0NBS30JzQytX3LGYO755My6Pi1DrX6HtT+GbSVtax6fWLRA8CvY0rJgvY9l7Lv4nA8+Y1nDhOgzY0gdkfr8J1UP77nCdDMsVniJlH9VZHXukCLX+Gdr+HB4xOjlxNqFw4uWcjBVz74j7voiISP85m3vns168LdKfXFEuLJuNmoo6jh+qIjreQygQouZYLW0tfoKBADabjZ3v7Gbv0ulMW5QN/vXhufQnVzm2osE+FoL7Mb73saI/F7k3NcJZVhQ4xgxsGzZvuIDhCGe5F2OCh8NJlhUT/r0wvvB6E3t2xwiOkgoRETk/zimx2Lx5M7/73e8oLS3F7++6E8yaNWv6JTAZ3nytPg5sP0TR9mLqKus5tKuUgD8AFtRW1BEKGewOO6GAweGxUVlazZ+efZMpcxdgherCn9CeyrKBlQztOzGha7Bs0ef9fYmcT5YtDqLvBP/mcI0P0wA2NzivwnLNw7IP0HoTERGRHpx1YvHb3/6WO++8k6VLl7Ju3TqWLl1KUVERFRUV3HjjjQMRowwztcfr+NOzb3Jod1l4eYRlUVNeR7u/HafbgdPlxOV20O5rxxXlIjUnmZbGVkp2Hqa8OIesLD5bmHoqyw2mGfABSixk+LNsseC5FNyLwLSC5dZ6IhERiYizTiz+/d//nR/96Efcd999xMXF8eMf/5j8/HzuueceMjPP3x7uMjQFA0HWrv4bxR8fJndCFk63E2MMwWCQ/VuKaW1sg1gIhQzuaDcJqV7cUS7amn3YHDYOflxDVpYtXDegp5sn0wS2+PC0EJERxLIcYPVQ+VxEROQ8OevJtwcPHuS6664DwO1209zcjGVZPPjggzz33HP9HqAML6V7j3BodxlZYzNwusO7BFmWxdhpo4lLjMHmsOGJ8ZCel0p6XiqeGDcBfwBjDGm5yRzY5QR7ZnjXoVP3HTD+8FQQ52x9YisiIiJynp11YpGUlERjYyMA2dnZ7Nq1C4C6ujpaWlr6NzoZdspLKjsra58sLimWpMxEbDYbvlY/DqedUDBES2MrjXXNpOYmEx0XhdMdixV1bUehsgPhKtqmJVy0LFgc3gXHPT8yb05ERERkBDvrqVCLFi1i3bp1TJ06lVtvvZWvfe1rvPXWW6xbt47LL798IGKU4aSngskYsGDczHyqj1YTDBia61vAClfnzpucQ9bYdI4eqGDi3AIs5zSIiQrXsggcDBdHs8WB8zos96LwglYREREROa/OOrF46qmnaGtrA+Cxxx7D6XSyceNGbrrpJr75zW/2e4AysFoaWynaVkxFSSVYkD02g7Ez8omK8QxIe6m5ydgddnxtPlyu+vCUplAdYCMxKZWE1BiCQRsFM8cQFechOj6aYCDI0QMVZBdkMumiAgAsZwE4xoGp7VhvEa9doIYZYwLhxDFUHS6AaB+DZU+JdFgiIiLSi7MqkBcIBHjxxRe56qqryMjIGMi4BsxQKJDXcKKRqiMnsCyLjPw0ouN6qKrbD8r2HeX11X+jvKQSAAsLLMgam8F1/3QFmWP6f6vKdn87v/m333N411ZGja3B7jCAGzDUHG+hsc5Jxtgp1FW7wgu5LXC6HYyamM3Suy8lLVc3liOBCRzGtP43BA4BAcLbh3nBNRsr6jqtoRERETlPzube+awrb0dHR7N3717y8vL6FGSkDObEoqWxlY1rPmD3pn001oYXxSekxnPhZVOYd90sXB2LnftDw4lGXnziD1QdOUHu+CzsjvD2rYH2AEf2HSNzbDqff/xzxMT3/yhAxYFNvPrUao4Vg9PjweG0aG0KEhVjY+H1IS66NpPKE3dQXtxIKGRIzUkmd2IWdnsvW8zKsGKCxzHNq8PrZuzZYEWFK0mbGgjVgHsJVtTnsCwr0qGKiIgMewNaeXvevHls3759yCYWg5W/zc+fnn2TPZv2kZSRQO6EbEzIUHu8jrf+v4001jRx9Zcuw2brnyq6n3x0gOOHqxh9QW6X13Q4HeROzKb0k6Ps33KQGZdN7Zf2TpaWeZDbvh7Nvh2pFO1oxO83ZOa5mTgnnpyxLqxQCZk5lWSNvajf25bBz/g3Q/Ao2MeHix5CR/HDFMAO/i3hqtv2rIjGKSIiIl2ddWLx1a9+lYcffpgjR44wa9YsYmK61guYNm1avwU3kuzfcpB9Hx0gZ3wW7qiOaR52SM1JxhPj4eP1e5hy8SRGTczul/aKdx7C7XH1mKjYHXbsDjuHdpf1e2JhjIHgYWITvMy6PJFZlyd2vyhkMMEq9Hn0yGNMANp3hKc9WT0k0VYChKogUKTEYhhp97dz7OBx2n3txCfHkZqTrBEpEZEh6KwTixUrVgCwcuXKzmOWZWGMwbIsgsFg/0U3guz9sAi73fZZUnGSuMQYqo5Uc2BHSb8lFsFACOs0ox+WZREKhvqlrVNf1+AEmk5zlcHqrbK2DHOB3osfAlgWYIWvkSHPGMPOd/fw0dptVJadINgeICo2ijHTRrH4lgWk5iRHOkQRETkLZ51YlJSUDEQcI17DiUbc0e5ezzucDprrmvutvZzxmRRtPdiZEJ4sFDL4fX6yxg7MAv36+gKO7X2dkBVDcqab9FHuz2IwrYATHPkD0rYMdm6wJUOgNPzfU5mOhdy2hPMdmAyA7X8r5C+/eAubzUZabgpOt5Pm+mYKN3zCiWO13PzwMpIyehjVFBGRQemsEwutrRgYCaleyg8e7/GcMYZAe4C4pNh+a2/SvAK2rtvJ8cNVpOeldt7YG2OoKD5OUnoCE+aM7bf2ILyOZMMfPuTjdw7TWBUCsx9PTCxjpsZx2a1pJKYGIVgKzqngKOjXtmVosCwLXHMwgQPhJNM6aUc0YzrWXqSDY1LkgpR+0dzQwqY/foTT5SQ9L7XzeFxiLDHx0RzaXca2vxVyxe2LIxiliIicjbNeCXzixInOf5eVlfGtb32LRx55hA0bNvRrYCPNpIsKMEBbs6/bucaaJqJiPYybMabf2ksblcoVX1iM3WGnZFcpxw9XcfxwFSWFpbhjXCy9+9J+/aQwFArx5q/Ws2HNB9jsMYyaMpO8SV5ivc3s2nSMPz5bSHPtMXBOw4pegWWddc4rw4VrFjhnhZPM4BEINYR3gwoeAMuF5VmGZeu/JFsi49CuMmoq6knJTup2zma34U2JY+/7+2lr6f7/RBERGZzO+O6tsLCQZcuWUVZWRkFBAb/97W+5+uqraW5uxmaz8aMf/Yjf//73LF++fADDHb4KZo1h8oLxFK7fS3xyHN6UOEIhQ11lPa1NbcxfNpvscf07NWna4smkZCexZ9M+Du0uA8ti1tJpXLBgYpdPEPvDsQMV7Nq4l9ScFOISwwv+jT2B2JQqPPGNlO6r55NdS5h97dVKKkY4y3JDzG3gH43xfwTBWrDs4RoWrgXh4ogy5LU2hQutfrrV9anc0W5aGlvxtfjwnGaaqIiIDB5nfAf3jW98g6lTp/Kb3/yG3/zmN1x//fVce+21rF69GoAHHniA733ve0oszpHT5eS6/3klyZmJFL67l4pDlVg2i6SMRBbffBGzr7pwQHZJyRqbMWBrKU5WUlhKW5OPzPzPiu5ZlhPsWTijwBNbwZ7Nzcy5TkmFgGV5wL0EXAvBNAIOsOK0U9AwEh0fnuYWaA/icHZPLlqb2vDEuPHEKKkQERkqzvgubvPmzbz11ltMmzaNCy+8kOeee46vfvWrnduVPvDAA1x0keoO9IUn2s2SWxcy95qZnDhWg2VZpOYm444a+n9Y21p82Oy2Xm8MXVGufl2cLsODZTnB6j5VRoa+/Cm5JGclUlVWTeaY9C7ngoEgDScaWXLlwmHx/z8RkZHijNdY1NTUkJER/mQ7NjaWmJgYkpI++4OfmJhIY2Nj/0c4AkXHRZE7IbujpsXw+KManxRLKBiit0LvrY2tJGfpBlJkpIiKjWLR5y4CC8r2HaO1qY1Ae4C6qgYO7TnCqEnZzLhsSqTDFBGRs3BW805O/bRZ0xLkTI2dkU98ciwnjtV2W6zZ2tRGyBguWDgxQtGJSCRMXTQJp9vJR69vo7z4OMH2IFGxHuZcfSELb5hDQqo30iGKiMhZOKvE4u6778btDn+C3tbWxr333ttZedvn084d0ruUrCQW3DCXt/7fRsr2HSMpIwGb3UbDiUaaG1qZvmRyv29vKyKDm2VZTJpXwPjZY6g8XI2/o/J2YpoSChGRocgyvc1NOcUXv/jFM3rBX/ziF30KaKA1NDTg9Xqpr68nPj4+0uEMKsYYairqaGlowRPjISU7qV9HpYwx7N60j+1/20l5SSUmZPCmxDPtksnMXjodl6eXassiIiIiEhFnc+98xonFcKHEomflJcfZ9OpmSnaW4mvx4fQ4yZuUw0XLZpM3Kadf2woGg9QerycUDJGQGq+EQkRERGSQOpt7Z+3tKVQcquSVH6+lsqyalOxkEtMT8LX4+OSjA5QXH2f5A9cy+oLcfmvPbreTMgALteurG9i/5SCVR07gcNgZNSmHsdPzlLiIiIiInAdKLEY4YwybXt1MZWk1o6eMwmYLT31yuhzEeKMp3XuUDX/4gNyJWdjtPReyGgw++aiIN3+1npryWux2O6FQiI/WbiPvglyuv+fKfq0iLiIiIiLdnfF2szI81R6vo/jjw6RkJ3UmFZ+yLIu0USkcKSqnvLgyQhH+feUlx1m7+m8017Uw+oJc8ibnkD9lFJljMyjeeZg/P/dX2v3tkQ5TREREZFhTYjHCtTS04m/1ExXr6fG8J8aNv9VPS0PLeY7szO3e+AkNJxrJGpveWbARwOV2kj0uk8N7j3BoV1kEIxQREREZ/pRYjHCeGDdOt5O2Fn+P532tfpxuB56YnhOPSDPGULSthLiE2B53sHJHuQj4Axw7eDwC0YmIiIiMHEosRrjkrCTyLsih+uiJHqtiV5VVkzkmnexxGRGI7swEg0EsW+/b4lqWhQmFzmNEIiIiIiOPEosRzrIs5i+bTUJaPIf3HKGlsRVjDG3NPko/OYo72s3C5XOxOwbnwm3LssiblENTXVOP5wPtASzLIiUn+TxHJiIiIjKyaFeoEaSlsZWibcWcOFqD3WEna1wG+VNHkTshm+X3X8OGNR9yZN8xjh+uwul2kl2QwcLlcxk/a3BXxL5g4UT2vL+fmvJakjI/2/0pFDIcKSonIz+VcTPyIxihiIiIyPCnxGKEKCk8zF9+8TaVpdVgwGBwOB2MnpLLdf/zCvIm55I7MZvy4uO0NLTiiXGTNTZj0I5UnCx/6igW3zKfDX/4gJJdpUTHRREKhmhtaiNtVArXfPlyPNHuSIcpIiIiMqyp8vYIUH30BP9v1Ss0VDeSNe6zZMHX6udoUTnjZuaz4hs34HQ5IxzpuTPGUPrJUfa+v4+jRRU4XA4KZo5h0vzxJKZ5Ix2eiIiIyJCkytvSxe5N+zhxrIb8qXlddk5yR7nILsjk0K5SSgpLB/2Up9P5dK1F3qScSIciIiIiMiJp8fYIsH/LQWK8Mb1uxxpsD3Fkf3kEIhMRERGR4UKJxQgQDISw2U/T1RaEgsHzF5CIiIiIDDtKLEaA7IJMmuqaezwXDIbrOyRnJZ3PkERERERkmFFiMQJMWTgBd7SLmoq6LseNMZQfrCAlO4nxs8ZEJjgRERERGRYinlg8/fTT5Ofn4/F4mDVrFhs2bDjt9S+++CLTp08nOjqazMxMvvjFL3LixInzFO3QNHrKKC6+aR5tzW0c2l1K9dEaKkurKSksJSYhhqu+eCkx3phIhykiIiIiQ1hEE4uXXnqJr3/96zz++ONs376dRYsWcc0111BaWtrj9Rs3buTOO+/ky1/+Mrt37+Z3v/sdmzdv5n/8j/9xniMfWizLYuENc7n1kRuYcdlUPDEu4pJjuWTFAv7xsRsZd6GKx4mIiIhI30S0jsW8efOYOXMmzzzzTOexSZMmsXz5clatWtXt+h/84Ac888wzHDx4sPPYT3/6U77//e9TVlbWYxs+nw+fz9f5dUNDA7m5uSOqjsWpjDE97hAlIiIiInKys6ljEbERC7/fz9atW1m6dGmX40uXLmXTpk09PmfBggUcOXKEtWvXYozh+PHj/P73v+e6667rtZ1Vq1bh9Xo7H7m5uf36PoYiJRUiIiIi0t8illhUV1cTDAZJT0/vcjw9PZ2Kiooen7NgwQJefPFFVqxYgcvlIiMjg4SEBH7605/22s5jjz1GfX1956O3kQ0RERERETl3EV+8feqn56ebprNnzx5WrlzJt771LbZu3cpf/vIXSkpKuPfee3t9fbfbTXx8fJeHiIiIiIj0L0ekGk5JScFut3cbnaisrOw2ivGpVatWsXDhQh555BEApk2bRkxMDIsWLeK73/0umZmZAx63iIiIiIh0F7ERC5fLxaxZs1i3bl2X4+vWrWPBggU9PqelpQWbrWvIdrsdCI90iIiIiIhIZER0KtRDDz3E6tWref7559m7dy8PPvggpaWlnVObHnvsMe68887O65ctW8aaNWt45plnKC4u5r333mPlypXMnTuXrKysSL0NEREREZERL2JToQBWrFjBiRMn+M53vkN5eTlTpkxh7dq15OXlAVBeXt6lpsXdd99NY2MjTz31FA8//DAJCQlcdtll/J//838i9RZERERERIQI17GIhLPZi1dEREREZCQbEnUsRERERERk+FBiISIiIiIifabEQkRERERE+kyJhYiIiIiI9JkSCxERERER6TMlFiIiIiIi0mdKLEREREREpM8iWiBPZLBqqGmk8nA1AGmjUohPjotwRCIiIiKDmxILkZO0tfjYuOZDdm3cS8OJJsAQlxTHlIsncvFN84iK8UQ6RBEREZFBSVOhRDoE2gO8vvpvbFzzIWAxamI2oybmYFkW773yEWtX/5V2f3ukwxQREREZlJRYiHQoKSxl9/v7yByTTlJGAja7DZvdRlJGAplj09n7/n6Kd5ZGOkwRERGRQUmJhUiH/duKCQVCRMV2n+4UFeMhFDLs33IgApGJiIiIDH5KLEQ6NNU24XQ7ez3vcrtorG0+jxGJiIiIDB1KLEQ6JKR68bf5ez3va/XhTY0/jxGJiIiIDB1KLEQ6jJ81BqfbSVNd91GJ5voWHC4HE+eMi0BkIiIiIoOfEguRDqMm5zDjsilUHTnB8cNV+Fr9+Fr9VJZWU1lWzfQlFzB6Sm6kwxQREREZlFTHQqSD3W7nijsWk5DmZcfbu6g6cgKAhDQvC5bPYc7VF2K32yMcpYiIiMjgpMRC5CROl5P5y2Yz84qpVB+tASA5KwlPtDvCkYmIiIgMbkosRHrgjnKTPS4z0mGIiIiIDBlaYyEiIiIiIn2mxEJERERERPpMiYWIiIiIiPSZEgsREREREekzLd4eIVqb26gsrQZjSMlJJiY+OtIhiYiIiMgwosRimGv3t/Ph2m18/PZu6irrMQbik2OZumgS8/9hjrZRFREREZF+ocRiGAuFQqz71Xo2/2UHsd4YMkangWXRUN3A+pc3UVfZwPX3XonT5Yx0qCIiIiIyxGmNxTBWuvcoH7+zm9ScZFJzk3G6nThdDpKzksgck8HuTZ9wcMehSIcpIiIiIsOAEothrGhrMf7WdmITYrqdi4r1YAzs23wwApGJiIiIyHCjxGIYa6xtwunufbab2+OivqrhPEYkIiIiIsOVEothLC4xlnZfoNfzvlY/ccmx5zEiERERERmulFgMYwWzxuD0OGmqa+52rrW5DSyYNK8gApGJiIiIyHCjxGIYy52YxfQlk6kqq6bqyAna/QEC7UFOHKul/OBxJi8Yz9gLR0c6TBEREREZBrTd7DBmt9u58s4lxCfH8fHbuykvPo4xBm9KHItvuYgFN8zVVrMiIiIi0i8sY4yJdBDnU0NDA16vl/r6euLj4yMdznnT0thKZWk1xhhSc5J73ClKRERERORkZ3PvrBGLESI6LorRF+RGOgwRERERGaa0xkJERERERPpMiYWIiIiIiPSZEgsREREREekzJRYiIiIiItJnSixERERERKTPlFiIiIiIiEifKbEQEREREZE+U2IhIiIiIiJ9psRCRERERET6TImFiIiIiIj0mRILERERERHpMyUWIiIiIiLSZ0osRERERESkz5RYiIiIiIhInymxEBERERGRPlNiISIiIiIifabEQkRERERE+iziicXTTz9Nfn4+Ho+HWbNmsWHDhtNe7/P5ePzxx8nLy8PtdjN27Fief/758xStiIiIiIj0xBHJxl966SW+/vWv8/TTT7Nw4UL+67/+i2uuuYY9e/YwatSoHp9z6623cvz4cX7+858zbtw4KisrCQQC5zlyERERERE5mWWMMZFqfN68ecycOZNnnnmm89ikSZNYvnw5q1at6nb9X/7yF2677TaKi4tJSko6ozZ8Ph8+n6/z64aGBnJzc6mvryc+Pr7vb0JEREREZJhqaGjA6/We0b1zxKZC+f1+tm7dytKlS7scX7p0KZs2berxOa+++iqzZ8/m+9//PtnZ2YwfP57/9b/+F62trb22s2rVKrxeb+cjNze3X9+HiIiIiIhEcCpUdXU1wWCQ9PT0LsfT09OpqKjo8TnFxcVs3LgRj8fDK6+8QnV1NV/96lepqanpdZ3FY489xkMPPdT59acjFiIiIiIi0n8iusYCwLKsLl8bY7od+1QoFMKyLF588UW8Xi8AP/zhD7n55pv5z//8T6Kioro9x+1243a7+z9wERERERHpFLGpUCkpKdjt9m6jE5WVld1GMT6VmZlJdnZ2Z1IB4TUZxhiOHDkyoPGKiIiIiEjvIpZYuFwuZs2axbp167ocX7duHQsWLOjxOQsXLuTYsWM0NTV1Htu/fz82m42cnJwBjVdERERERHoX0ToWDz30EKtXr+b5559n7969PPjgg5SWlnLvvfcC4fURd955Z+f1n//850lOTuaLX/wie/bs4d133+WRRx7hS1/6Uo/ToERERERE5PyI6BqLFStWcOLECb7zne9QXl7OlClTWLt2LXl5eQCUl5dTWlraeX1sbCzr1q3jgQceYPbs2SQnJ3Prrbfy3e9+N1JvQUREREREiHAdi0g4m714RURERERGsiFRx0JERERERIYPJRYiIiIiItJnSixERERERKTPlFiIiIiIiEifKbEQEREREZE+U2IhIiIiIiJ9psRCRERERET6TImFiIiIiIj0mRILERERERHpMyUWIiIiIiLSZ0osRERERESkz5RYiIiIiIhInymxEBERERGRPnNEOgAZ2vxtfg7sOMTBHSW0NraRnJ3EhDnjyB6XgWVZkQ5PRERERM4TJRZyzhpqGnntv9ZxYHsJAE6Xkz0f7Gfrmx9z0bJZXHzjPGw2DYqJiIiIjARKLOScGIiLoccAAA/ySURBVGNY9+v17N98gJzxWbg8rs7jdZUNbPj9ByRlJDJl4cQIRyoiIiIi54M+TpZzUnGokoPbD5E2KrUzqQCwLIvEdC+WZbHj7V2EQqEIRikiIiIi54sSCzknxw9V0drURow3usfz3tR4jh+qpLGm6TxHJiIiIiKRoMRCBoiFMZGOQURERETOFyUWck7SRqXgiXHTXN/S4/n6qnrS81KIS4o9z5GJiIiISCQosZBzkjkmnbHTR1NZWo3f197lXH1VA6GQ4cJLp2pXKBEREZERQrtCyTmxLIuldy2hrcVH8ceHsdlsOFwOfK0+3NEuFi6fwwULJ0Q6TBERERE5T5RYyDmLT47j5oeWcWBbMUXbimltaiMlO4kJcwsYNTFbBfJERERERhAlFtInnmg3Uy6exJSLJ0U6FBERERGJIE2AFxERERGRPlNiISIiIiIifabEQkRERERE+kyJhYiIiIiI9JkSCxERERER6TMlFiIiIiIi0mdKLEREREREpM+UWIiIiIiISJ8psRARERERkT5TYiEiIiIiIn2mxEJERERERPpMiYWIiIiIiPSZI9IBnG/GGAAaGhoiHImIiIiIyOD26T3zp/fQpzPiEovGxkYAcnNzIxyJiIiIiMjQ0NjYiNfrPe01ljmT9GMYCYVCHDt2jLi4OCzLinQ4g1JDQwO5ubmUlZURHx8f6XDkHKkfhz714fCgfhz61IfDg/rx3BhjaGxsJCsrC5vt9KsoRtyIhc1mIycnJ9JhDAnx8fH6xRsG1I9Dn/pweFA/Dn3qw+FB/Xj2/t5Ixae0eFtERERERPpMiYWIiIiIiPSZEgvpxu128+1vfxu32x3pUKQP1I9Dn/pweFA/Dn3qw+FB/TjwRtzibRERERER6X8asRARERERkT5TYiEiIiIiIn2mxEJERERERPpMiYWIiIiIiPSZEosR7N1332XZsmVkZWVhWRb//d//3eW8MYZ/+Zd/ISsri6ioKJYsWcLu3bsjE6z0aNWqVcyZM4e4uDjS0tJYvnw5+/bt63KN+nFwe+aZZ5g2bVpnwab58+fz+uuvd55X/w1Nq1atwrIsvv71r3ceU18Ofv/yL/+CZVldHhkZGZ3n1YdDw9GjR/nCF75AcnIy0dHRXHjhhWzdurXzvPpx4CixGMGam5uZPn06Tz31VI/nv//97/PDH/6Qp556is2bN5ORkcGVV15JY2PjeY5UerN+/Xruu+8+PvjgA9atW0cgEGDp0qU0Nzd3XqN+HNxycnL43ve+x5YtW9iyZQuXXXYZN9xwQ+cfOfXf0LN582aee+45pk2b1uW4+nJouOCCCygvL+98FBYWdp5THw5+tbW1LFy4EKfTyeuvv86ePXv4j//4DxISEjqvUT8OICNijAHMK6+80vl1KBQyGRkZ5nvf+17nsba2NuP1es2zzz4bgQjlTFRWVhrArF+/3hijfhyqEhMTzerVq9V/Q1BjY6MpKCgw69atM5dccon52te+ZozR7+JQ8e1vf9tMnz69x3Pqw6Hh0UcfNRdffHGv59WPA0sjFtKjkpISKioqWLp0aecxt9vNJZdcwqZNmyIYmZxOfX09AElJSYD6cagJBoP89re/pbm5mfnz56v/hqD77ruP6667jiuuuKLLcfXl0FFUVERWVhb5+fncdtttFBcXA+rDoeLVV19l9uzZ3HLLLaSlpTFjxgx+9rOfdZ5XPw4sJRbSo4qKCgDS09O7HE9PT+88J4OLMYaHHnqIiy++mClTpgDqx6GisLCQ2NhY3G439957L6+88gqTJ09W/w0xv/3tb9m2bRurVq3qdk59OTTMmzePX/3qV7zxxhv87Gc/o6KiggULFnDixAn14RBRXFzMM888Q0FBAW+88Qb33nsvK1eu5Fe/+hWg38WB5oh0ADK4WZbV5WtjTLdjMjjcf//97Ny5k40bN3Y7p34c3CZMmMCOHTuoq6vjD3/4A3fddRfr16/vPK/+G/zKysr42te+xptvvonH4+n1OvXl4HbNNdd0/nvq1KnMnz+fsWPH8stf/pKLLroIUB8OdqFQiNmzZ/Pv//7vAMyYMYPdu3fzzDPPcOedd3Zep34cGBqxkB59ugvGqdl7ZWVltyxfIu+BBx7g1Vdf5e233yYnJ6fzuPpxaHC5XIwbN47Zs2ezatUqpk+fzo9//GP13xCydetWKisrmTVrFg6HA4fDwfr16/nJT36Cw+Ho7C/15dASExPD1KlTKSoq0u/jEJGZmcnkyZO7HJs0aRKlpaWA/i4ONCUW0qP8/HwyMjJYt25d5zG/38/69etZsGBBBCOTkxljuP/++1mzZg1vvfUW+fn5Xc6rH4cmYww+n0/9N4RcfvnlFBYWsmPHjs7H7Nmzuf3229mxYwdjxoxRXw5BPp+PvXv3kpmZqd/HIWLhwoXdtl3fv38/eXl5gP4uDriILRuXiGtsbDTbt28327dvN4D54Q9/aLZv324OHz5sjDHme9/7nvF6vWbNmjWmsLDQ/OM//qPJzMw0DQ0NEY5cPvWVr3zFeL1e884775jy8vLOR0tLS+c16sfB7bHHHjPvvvuuKSkpMTt37jT//M//bGw2m3nzzTeNMeq/oezkXaGMUV8OBQ8//LB55513THFxsfnggw/M9ddfb+Li4syhQ4eMMerDoeCjjz4yDofDPPHEE6aoqMi8+OKLJjo62vzmN7/pvEb9OHCUWIxgb7/9tgG6Pe666y5jTHhLtm9/+9smIyPDuN1us3jxYlNYWBjZoKWLnvoPML/4xS86r1E/Dm5f+tKXTF5ennG5XCY1NdVcfvnlnUmFMeq/oezUxEJ9OfitWLHCZGZmGqfTabKyssxNN91kdu/e3XlefTg0/OlPfzJTpkwxbrfbTJw40Tz33HNdzqsfB45ljDGRGSsREREREZHhQmssRERERESkz5RYiIiIiIhInymxEBERERGRPlNiISIiIiIifabEQkRERERE+kyJhYiIiIiI9JkSCxERERER6TMlFiIiIiIi0mdKLEREREREpM+UWIiIjDCbNm3Cbrdz9dVXRzqULkaPHs2TTz75d6/bvn07119/PWlpaXg8HkaPHs2KFSuorq4e+CBFRKRXSixEREaY559/ngceeICNGzdSWloa6XDOSmVlJVdccQUpKSm88cYb7N27l+eff57MzExaWloiHZ6IyIimxEJEZARpbm7m5Zdf5itf+QrXX389L7zwQrdrXn31VQoKCoiKiuLSSy/ll7/8JZZlUVdX13nNpk2bWLx4MVFRUeTm5rJy5Uqam5t7bffgwYPccMMNpKenExsby5w5c/jrX//aeX7JkiUcPnyYBx98EMuysCyrx9fZtGkTDQ0NrF69mhkzZpCfn89ll13Gk08+yahRozqv2717N9dddx3x8fHExcWxaNEiDh48CMDmzZu58sorSUlJwev1cskll7Bt27Yu7ViWxerVq7nxxhuJjo6moKCAV1999Uy+xSIiI5YSCxGREeSll15iwoQJTJgwgS984Qv84he/wBjTef7QoUPcfPPNLF++nB07dnDPPffw+OOPd3mNwsJCrrrqKm666SZ27tzJSy+9xMaNG7n//vt7bbepqYlrr72Wv/71r2zfvp2rrrqKZcuWdY6YrFmzhpycHL7zne9QXl5OeXl5j6+TkZFBIBDglVde6RL3yY4ePcrixYvxeDy89dZbbN26lS996UsEAgEAGhsbueuuu9iwYQMffPABBQUFXHvttTQ2NnZ5nX/913/l1ltvZefOnVx77bXcfvvt1NTU/P1vsojISGVERGTEWLBggXnyySeNMca0t7eblJQUs27dus7zjz76qJkyZUqX5zz++OMGMLW1tcYYY+644w7zT//0T12u2bBhg7HZbKa1tfWMY5k8ebL56U9/2vl1Xl6e+dGPfvR3n/fP//zPxuFwmKSkJHP11Veb73//+6aioqLz/GOPPWby8/ON3+8/ozgCgYCJi4szf/rTnzqPAeZ//+//3fl1U1OTsSzLvP7662f0miIiI5FGLERERoh9+/bx0UcfcdtttwHgcDhYsWIFzz//fJdr5syZ0+V5c+fO7fL11q1beeGFF4iNje18XHXVVYRCIUpKSnpsu7m5mW984xtMnjyZhIQEYmNj+eSTT85pjccTTzxBRUUFzz77LJMnT+bZZ59l4sSJFBYWArBjxw4WLVqE0+ns8fmVlZXce++9jB8/Hq/Xi9frpampqVss06ZN6/x3TEwMcXFxVFZWnnW8IiIjhSPSAYiIyPnx85//nEAgQHZ2ducxYwxOp5Pa2loSExMxxnRb32BOmXIUCoW45557WLlyZbc2Tl7ncLJHHnmEN954gx/84AeMGzeOqKgobr75Zvx+/zm9l+TkZG655RZuueUWVq1axYwZM/jBD37AL3/5S6Kiok773LvvvpuqqiqefPJJ8vLycLvdzJ8/v1sspyYmlmURCoXOKV4RkZFAiYWIyAgQCAT41a9+xX/8x3+wdOnSLuc+97nP8eKLL3L//fczceJE1q5d2+X8li1bunw9c+ZMdu/ezbhx4864/Q0bNnD33Xdz4403AuE1F4cOHepyjcvlIhgMnsW7+ux5Y8eO7Vw8Pm3aNH75y1/S3t7e46jFhg0bePrpp7n22msBKCsr01a1IiL9QFOhRERGgNdee43a2lq+/OUvM2XKlC6Pm2++mZ///OcA3HPPPXzyySc8+uij7N+/n5dffrlz56hPRzIeffRR3n//fe677z527NhBUVERr776Kg888ECv7Y8bN441a9awY8cOPv74Yz7/+c93+/R/9OjRvPvuuxw9erTXG/3XXnuNL3zhC7z22mvs37+fffv28YMf/IC1a9dyww03AHD//ffT0NDAbbfdxpYtWygqKuLXv/41+/bt64zl17/+NXv37uXDDz/k9ttv/7ujHCIi8vcpsRARGQF+/vOfc8UVV+D1erud+9znPseOHTvYtm0b+fn5/P73v2fNmjVMmzaNZ555pnNXKLfbDYRHBNavX09RURGLFi1ixowZfPOb3yQzM7PX9n/0ox+RmJjIggULWLZsGVdddRUzZ87scs13vvMdDh06xNixY0lNTe3xdSZPnkx0dDQPP/wwF154IRdddBEvv/wyq1ev5o477gDC06TeeustmpqauOSSS5g1axY/+9nPOkcvnn/+eWpra5kxYwZ33HEHK1euJC0t7ey/qSIi0oVlTp08KyIicpInnniCZ599lrKyskiHIiIig5jWWIiISBdPP/00c+bMITk5mffee4//+3//72lrVIiIiIASCxEROUVRURHf/e53qampYdSoUTz88MM89thjkQ5LREQGOU2FEhERERGRPtPibRERERER6TMlFiIiIiIi0mdKLEREREREpM+UWIiIiIiISJ8psRARERERkT5TYiEiIiIiIn2mxEJERERERPpMiYWIiIiIiPTZ/w8jzhiMIb/AvAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "genders = aseg_and_pheno['sex'].unique()\n",
    "\n",
    "# Create a figure with subplots for each site\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 5))\n",
    "\n",
    "colors = ['blue', 'red']\n",
    "for j, gender in enumerate(genders):\n",
    "    data_subset = aseg_and_pheno[(aseg_and_pheno['sex'] == gender)]\n",
    "    X_subset = X[(aseg_and_pheno['sex'] == gender)].sort_values(by='age')\n",
    "    # Plot the fitted curve\n",
    "    ax.plot(X_subset['age'], res.predict(X_subset), color=colors[j])\n",
    "\n",
    "ax.scatter(aseg_and_pheno['age'], aseg_and_pheno['BrainSegVolNotVent'], c=pd.factorize(aseg_and_pheno['sex'])[0], alpha=0.5)\n",
    "\n",
    "# Set the title and axis labels\n",
    "ax.legend(['Male', 'Female'])\n",
    "ax.set_xlabel('Age at Scan')\n",
    "ax.set_ylabel('Brain Volume (mm^3)')\n",
    "\n",
    "# Adjust the spacing between subplots\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
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