https://github.com/ClockConnectome/clock-connectome
Tip revision: 0c820068458bd9249785c704716acfd1a9301f1c authored by gabrielle9 on 01 August 2022, 23:54:06 UTC
Update README.md
Update README.md
Tip revision: 0c82006
clockConnectomeOverview.ipynb
{
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{
"cell_type": "markdown",
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"source": [
"# Hemibrain-identified clock neurons "
]
},
{
"cell_type": "markdown",
"id": "9c2ea850-39a7-4dab-b5d3-03d5972a0f56",
"metadata": {},
"source": [
"We queried the hemibrain for the known clock neurons and found 28 individual neurons that matched our criteria. They are listed below with their corresponding hemibrain IDs."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "32f6f244-3cef-4373-a3c5-7cdde14401a0",
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"outputs": [
{
"data": {
"text/plain": [
"'0.1.0'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"from neuprint import Client\n",
"c = Client('neuprint.janelia.org', dataset='hemibrain:v1.2.1', token='')\n",
"# insert personal token above. see https://connectome-neuprint.github.io/neuprint-python/docs/quickstart.html#client-and-authorization-token for instructions\n",
"c.fetch_version()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f47b13e6-fb08-42e4-8fa1-019e0d1a0e3e",
"metadata": {},
"outputs": [
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" <th></th>\n",
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" <th>phase</th>\n",
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" <th>0</th>\n",
" <td>2068801704</td>\n",
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" <td>s-LNv_R_1</td>\n",
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" <td>5813064789</td>\n",
" <td>LNd</td>\n",
" <td>LNd_R_3</td>\n",
" <td>LNd3</td>\n",
" <td>evening</td>\n",
" <td>E3</td>\n",
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" <tr>\n",
" <th>11</th>\n",
" <td>356818551</td>\n",
" <td>LPN</td>\n",
" <td>LPN_R_1</td>\n",
" <td>LPN1</td>\n",
" <td></td>\n",
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" <th>12</th>\n",
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" <td>LPN</td>\n",
" <td>LPN_R_2</td>\n",
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" <td>450034902</td>\n",
" <td>LPN</td>\n",
" <td>LPN_R_3</td>\n",
" <td>LPN3</td>\n",
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" <td>LPN_R_4</td>\n",
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" <th>15</th>\n",
" <td>264083994</td>\n",
" <td>DN1a</td>\n",
" <td>DN1a_R_1</td>\n",
" <td>DN1a1</td>\n",
" <td></td>\n",
" <td></td>\n",
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" <th>16</th>\n",
" <td>5813022274</td>\n",
" <td>DN1a</td>\n",
" <td>DN1a_R_2</td>\n",
" <td>DN1a2</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>5813010153</td>\n",
" <td>DN1pA</td>\n",
" <td>DN1pA_R_1</td>\n",
" <td>DN1pA1</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>324846570</td>\n",
" <td>DN1pA</td>\n",
" <td>DN1pA_R_2</td>\n",
" <td>DN1pA2</td>\n",
" <td></td>\n",
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" <th>19</th>\n",
" <td>325529237</td>\n",
" <td>DN1pA</td>\n",
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" <td>DN1pA3</td>\n",
" <td></td>\n",
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" <th>20</th>\n",
" <td>387944118</td>\n",
" <td>DN1pA</td>\n",
" <td>DN1pA_R_4</td>\n",
" <td>DN1pA4</td>\n",
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" <th>21</th>\n",
" <td>387166379</td>\n",
" <td>DN1pA</td>\n",
" <td>DN1pA_R_5</td>\n",
" <td>DN1pA5</td>\n",
" <td></td>\n",
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" </tr>\n",
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" <th>22</th>\n",
" <td>386834269</td>\n",
" <td>DN1pB</td>\n",
" <td>DN1pB_R_1</td>\n",
" <td>DN1pB1</td>\n",
" <td></td>\n",
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" <th>23</th>\n",
" <td>5813071319</td>\n",
" <td>DN1pB</td>\n",
" <td>DN1pB_R_2</td>\n",
" <td>DN1pB2</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
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" <th>24</th>\n",
" <td>1884625521</td>\n",
" <td>l-LNv</td>\n",
" <td>l-LNv_R_1</td>\n",
" <td>lLNv1</td>\n",
" <td></td>\n",
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" <th>25</th>\n",
" <td>2065745704</td>\n",
" <td>l-LNv</td>\n",
" <td>l-LNv_R_2</td>\n",
" <td>lLNv2</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>5813001741</td>\n",
" <td>l-LNv</td>\n",
" <td>l-LNv_R_3</td>\n",
" <td>lLNv3</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>5813026773</td>\n",
" <td>l-LNv</td>\n",
" <td>l-LNv_R_4</td>\n",
" <td>lLNv4</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bodyId type seqInstance labels phase subphase\n",
"0 2068801704 s-LNv s-LNv_R_1 sLNv1 morning M\n",
"1 1664980698 s-LNv s-LNv_R_2 sLNv2 morning M\n",
"2 2007068523 s-LNv s-LNv_R_3 sLNv3 morning M\n",
"3 1975347348 s-LNv s-LNv_R_4 sLNv4 morning M\n",
"4 5813056917 LNd LNd_R_4 LNd4 evening E1\n",
"5 5813021192 LNd LNd_R_5 LNd5 evening E1\n",
"6 5813069648 LNd LNd_R_6 LNd6 evening E2\n",
"7 511051477 5th s-LNv 5th s-LNv_R_1 5th sLNv evening E2\n",
"8 296544364 LNd LNd_R_1 LNd1 evening E3\n",
"9 448260940 LNd LNd_R_2 LNd2 evening E3\n",
"10 5813064789 LNd LNd_R_3 LNd3 evening E3\n",
"11 356818551 LPN LPN_R_1 LPN1 \n",
"12 480029788 LPN LPN_R_2 LPN2 \n",
"13 450034902 LPN LPN_R_3 LPN3 \n",
"14 546977514 LPN LPN_R_4 LPN4 \n",
"15 264083994 DN1a DN1a_R_1 DN1a1 \n",
"16 5813022274 DN1a DN1a_R_2 DN1a2 \n",
"17 5813010153 DN1pA DN1pA_R_1 DN1pA1 \n",
"18 324846570 DN1pA DN1pA_R_2 DN1pA2 \n",
"19 325529237 DN1pA DN1pA_R_3 DN1pA3 \n",
"20 387944118 DN1pA DN1pA_R_4 DN1pA4 \n",
"21 387166379 DN1pA DN1pA_R_5 DN1pA5 \n",
"22 386834269 DN1pB DN1pB_R_1 DN1pB1 \n",
"23 5813071319 DN1pB DN1pB_R_2 DN1pB2 \n",
"24 1884625521 l-LNv l-LNv_R_1 lLNv1 \n",
"25 2065745704 l-LNv l-LNv_R_2 lLNv2 \n",
"26 5813001741 l-LNv l-LNv_R_3 lLNv3 \n",
"27 5813026773 l-LNv l-LNv_R_4 lLNv4 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from neuron_criteria import getClock\n",
"clock_df = getClock(l_lnv=True)\n",
"clock_df"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c613d6d9-26d9-4a23-8482-5b4a5c7b096e",
"metadata": {},
"outputs": [],
"source": [
"# save the table of clock neuron identifiers\n",
"clock_df.to_csv('clock.csv')"
]
},
{
"cell_type": "markdown",
"id": "214d01e5-46d6-4b55-b34d-b78c55f4e0c1",
"metadata": {},
"source": [
"# Connections between clock neurons"
]
},
{
"cell_type": "markdown",
"id": "07de759f-d0b1-4b08-a689-7342045bed51",
"metadata": {},
"source": [
"We first explored the connections that are made among the clock neurons by retrieving data about their connections from the hemibrain. The heatmap below shows their connections to other clock neurons and their strength."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "55d754be-6804-41f5-ba68-665050456d7d",
"metadata": {},
"outputs": [
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" <td>DN1pB2</td>\n",
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" <th>186</th>\n",
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" <td>5813021192</td>\n",
" <td>5</td>\n",
" <td>DN1pB2</td>\n",
" <td>LNd5</td>\n",
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" <th>187</th>\n",
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" <td>5813056917</td>\n",
" <td>4</td>\n",
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" <td>LNd4</td>\n",
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" <th>188</th>\n",
" <td>5813071319</td>\n",
" <td>5813069648</td>\n",
" <td>11</td>\n",
" <td>DN1pB2</td>\n",
" <td>LNd6</td>\n",
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"<p>189 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" bodyId_pre bodyId_post weight instance_pre instance_post\n",
"0 264083994 296544364 1 DN1a1 LNd1\n",
"1 264083994 356818551 3 DN1a1 LPN1\n",
"2 264083994 480029788 1 DN1a1 LPN2\n",
"3 264083994 511051477 55 DN1a1 5th sLNv\n",
"4 264083994 5813022274 10 DN1a1 DN1a2\n",
".. ... ... ... ... ...\n",
"184 5813071319 2007068523 1 DN1pB2 sLNv3\n",
"185 5813071319 5813010153 3 DN1pB2 DN1pA1\n",
"186 5813071319 5813021192 5 DN1pB2 LNd5\n",
"187 5813071319 5813056917 4 DN1pB2 LNd4\n",
"188 5813071319 5813069648 11 DN1pB2 LNd6\n",
"\n",
"[189 rows x 5 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from connection_utils import clock_neuron_connections\n",
"\n",
"# getting the connections between clock neurons\n",
"clock_conns_df = clock_neuron_connections(clock_df, 'intra_clock', min_weight=1)\n",
"clock_conns_df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ce4fe2c5-cf50-4f40-81eb-46e3bb9f63da",
"metadata": {},
"outputs": [],
"source": [
"# save table\n",
"clock_conns_df.to_csv('intra_clock_connections.csv')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3dca7dee",
"metadata": {},
"outputs": [],
"source": [
"from neuprint import merge_neuron_properties\n",
"\n",
"clock_df_ind = clock_df\n",
"\n",
"#allows the matrix to be later sorted in the same neuron order as clock_df\n",
"clock_df_ind['clockIndex'] = clock_df.index\n",
"\n",
"conns = merge_neuron_properties(clock_df_ind, clock_conns_df, ['type','labels', 'clockIndex'])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ca1fc991-0a3b-4ed2-adc0-3e1b85c4ac21",
"metadata": {},
"outputs": [
{
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" <thead>\n",
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" <th>labels_post</th>\n",
" <th>sLNv1</th>\n",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>sLNv1</th>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sLNv2</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sLNv3</th>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sLNv4</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LNd4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LNd5</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LNd6</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>37</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5th sLNv</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>25</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>12</td>\n",
" <td>9</td>\n",
" <td>14</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LNd1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LNd2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LNd3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LPN1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LPN2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LPN3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DN1a1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>81</td>\n",
" <td>55</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DN1a2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>75</td>\n",
" <td>63</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DN1pA1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>15</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DN1pA2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>13</td>\n",
" <td>37</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DN1pA3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>30</td>\n",
" <td>33</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DN1pA4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>34</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DN1pA5</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>30</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DN1pB1</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DN1pB2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lLNv1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lLNv2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lLNv3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lLNv4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>27 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
"labels_post sLNv1 sLNv2 sLNv3 sLNv4 LNd4 LNd5 LNd6 5th sLNv LNd1 \\\n",
"labels_pre \n",
"sLNv1 0 4 5 9 0 0 0 0 0 \n",
"sLNv2 6 0 3 1 0 0 0 0 0 \n",
"sLNv3 3 5 0 5 0 0 0 0 0 \n",
"sLNv4 3 0 5 0 0 0 0 0 0 \n",
"LNd4 0 0 0 0 0 1 0 0 0 \n",
"LNd5 0 0 0 0 5 0 1 0 0 \n",
"LNd6 0 0 0 0 31 21 0 37 0 \n",
"5th sLNv 1 1 3 1 18 25 40 0 0 \n",
"LNd1 0 0 0 0 0 0 0 0 0 \n",
"LNd2 0 0 0 0 0 0 0 0 3 \n",
"LNd3 0 0 0 0 0 0 0 0 6 \n",
"LPN1 0 0 0 0 0 1 0 0 0 \n",
"LPN2 0 0 0 0 0 2 0 0 0 \n",
"LPN3 0 0 0 0 2 2 0 1 0 \n",
"DN1a1 0 0 0 0 0 0 81 55 1 \n",
"DN1a2 0 0 0 0 1 0 75 63 0 \n",
"DN1pA1 0 0 0 0 9 6 15 25 0 \n",
"DN1pA2 0 0 0 0 6 13 37 25 0 \n",
"DN1pA3 0 0 0 0 5 8 30 33 0 \n",
"DN1pA4 0 0 0 0 2 2 34 22 0 \n",
"DN1pA5 0 0 0 0 8 5 30 25 0 \n",
"DN1pB1 3 0 0 0 3 4 4 2 0 \n",
"DN1pB2 0 0 1 0 4 5 11 4 0 \n",
"lLNv1 0 0 0 0 0 0 0 1 0 \n",
"lLNv2 0 0 0 0 0 0 0 0 0 \n",
"lLNv3 0 0 0 0 0 0 0 2 0 \n",
"lLNv4 0 0 0 0 0 0 0 0 0 \n",
"\n",
"labels_post LNd2 ... DN1pA2 DN1pA3 DN1pA4 DN1pA5 DN1pB1 DN1pB2 lLNv1 \\\n",
"labels_pre ... \n",
"sLNv1 1 ... 0 0 0 0 1 1 0 \n",
"sLNv2 0 ... 0 0 0 0 0 0 0 \n",
"sLNv3 0 ... 0 0 0 0 0 1 0 \n",
"sLNv4 0 ... 0 0 0 0 0 0 0 \n",
"LNd4 0 ... 0 0 0 0 2 0 0 \n",
"LNd5 0 ... 0 0 0 0 0 0 0 \n",
"LNd6 0 ... 9 14 10 13 1 0 1 \n",
"5th sLNv 0 ... 8 12 9 14 3 1 0 \n",
"LNd1 6 ... 0 0 0 0 0 0 0 \n",
"LNd2 0 ... 0 0 1 0 0 0 0 \n",
"LNd3 3 ... 0 0 0 0 0 0 0 \n",
"LPN1 0 ... 0 0 0 0 1 0 0 \n",
"LPN2 0 ... 0 0 1 0 1 0 0 \n",
"LPN3 0 ... 0 1 1 1 1 1 0 \n",
"DN1a1 0 ... 0 0 0 0 0 1 0 \n",
"DN1a2 0 ... 0 0 0 1 0 0 0 \n",
"DN1pA1 0 ... 1 0 1 2 0 2 0 \n",
"DN1pA2 0 ... 0 0 1 2 3 1 0 \n",
"DN1pA3 1 ... 2 0 0 1 1 0 0 \n",
"DN1pA4 0 ... 1 0 0 0 0 0 0 \n",
"DN1pA5 0 ... 1 2 1 0 0 1 0 \n",
"DN1pB1 1 ... 0 1 3 1 0 5 0 \n",
"DN1pB2 1 ... 1 1 3 2 5 0 0 \n",
"lLNv1 0 ... 0 0 0 0 0 0 0 \n",
"lLNv2 0 ... 0 0 0 0 0 0 3 \n",
"lLNv3 0 ... 0 0 0 0 0 0 3 \n",
"lLNv4 0 ... 0 0 0 0 0 0 0 \n",
"\n",
"labels_post lLNv2 lLNv3 lLNv4 \n",
"labels_pre \n",
"sLNv1 0 0 0 \n",
"sLNv2 0 0 0 \n",
"sLNv3 0 0 0 \n",
"sLNv4 0 0 0 \n",
"LNd4 0 0 0 \n",
"LNd5 0 0 0 \n",
"LNd6 1 2 1 \n",
"5th sLNv 0 0 0 \n",
"LNd1 0 0 0 \n",
"LNd2 0 0 0 \n",
"LNd3 0 0 0 \n",
"LPN1 0 0 0 \n",
"LPN2 0 0 0 \n",
"LPN3 0 0 0 \n",
"DN1a1 0 0 0 \n",
"DN1a2 0 0 0 \n",
"DN1pA1 0 0 0 \n",
"DN1pA2 0 0 0 \n",
"DN1pA3 0 0 0 \n",
"DN1pA4 0 0 0 \n",
"DN1pA5 0 0 0 \n",
"DN1pB1 0 0 0 \n",
"DN1pB2 0 0 0 \n",
"lLNv1 1 2 1 \n",
"lLNv2 0 1 1 \n",
"lLNv3 1 0 0 \n",
"lLNv4 1 2 0 \n",
"\n",
"[27 rows x 27 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from neuprint import connection_table_to_matrix\n",
"\n",
"# generate a matrix of clock-to-clock connections\n",
"conns_matrix = connection_table_to_matrix(conns, group_cols='labels', sort_by='clockIndex')\n",
"conns_matrix"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "678f2a26-d190-40fa-a2b1-de74282e122b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(123.0, 0.5, 'presynaptic')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1152x864 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn\n",
"\n",
"fig = plt.figure(figsize=(16, 12))\n",
"\n",
"# a mask to block out zeros\n",
"mask = np.zeros_like(conns_matrix)\n",
"mask[conns_matrix==0] = True\n",
"\n",
"# plot connectivity heatmap\n",
"seaborn.heatmap(conns_matrix, vmin=1, mask=mask, annot=True, cmap=seaborn.light_palette(\"purple\", as_cmap=True), cbar_kws={'label': 'connection strength'})\n",
"plt.title('Connectivity among clock neurons')\n",
"plt.xlabel('postsynaptic')\n",
"plt.ylabel('presynaptic')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "9197c790-dbe0-43e1-b2ab-731bcaaaf0fc",
"metadata": {},
"outputs": [],
"source": [
"fig.savefig('BigOneHeatmap_purple.svg')"
]
},
{
"cell_type": "markdown",
"id": "58e3feca-819d-4957-b4c6-fecb628ed2a1",
"metadata": {},
"source": [
"These data were also used to show how the clock neurons are interconnected in a network graph. l-LNvs have no medium or strong connections to other neurons on top of most connections likely occuring outside the hemibrain volume, so are not included"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c050217c-8779-4d89-be9b-e5d8c1e0bec2",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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qmsrxet0EmUKZnLGQiycvJ9gU1m4cRVbY+24+9noXKo2EAiheheBYE+lz4jGF9Z4/js9lw9Xgt/44aw7haS4HSYU+LB1D1FiM0TlogxNF4VKBYAgjBI9AIOgUdqeHN1cd4t9fHCDYpOPea6ewaFoqkiRxtKSe/3x+gE17SnC6T1ZBz06L5MWfLOn2mLYqO7mrSkibGYc5ykDBxgoaS5qRVBJJk6OInxCJSt37IsTrqMNRfQBnzQGctYdRfC7U+lAM0WMxRo3FEDlaWH8EgiGGEDwCgeCseH0yKzfn8dLHe7G1uLhu4WhuXjYOs0Hbts+tv/mUI4V17Y6955pJLL90bK/NRVEUao81cXxLJV6XD1OEnpFzEgiKNvbaGO3G9HlwNeThqD6Ao2Y/3uYKkNTow9L9S19ROWiDE4T1RyAY5AjBIxAIOkRRFLbsLeX593ZRWNHEJReO4I4rJxAX0T6y6TuPvU95bfsK6X956GImZ8X2+tzcDi/HN1dQl28FCeLHRZA0JRq1tu8TD3rttThqDuCsPoCz7jCKz43aEIohKscvgCJGo9L2nQATCATdQwgegUDQjkPHa/nbOzvZfbSKKaNj+cG1k8lMPnME047DFfzoj2uQ5cDLyad/vI6wYEOfzbO+0ErBxgrcdi+GEB3pc+K7HMXVE/zWn2M4qvfjqD6At6Wy1fozEmN0DsbYiWjN0f02H4FAcGaE4BEIBG2U1dh44f3dfLW9kPSEUH5w7WQuGBN/1uUanyxz/7Nr2HWkMqA9KszER898p6+njNflo2hbFVWH/KHmMaPDSJkeg0bf+ZIWvTYXe03r0tcBXHVHUHxutCHJmOOnYIqbgsYU1e9zEggEfoTgEQgENDU7eXXlft5bl0tYiIE7rpjApTNGoFade4nonx/u4ZWV+wBIibXwP4uzWbujiKvmjuKiicl9PfU2mspbyF9fjtPqRmvSkH5RPOGpwf02/unIPhfO6gPYK3bgqN6H4nOjs6RgipuKKW4yGlPkgM1NIDgfEYJHIDiPcbm9vP3VEf712X5kBW5aMpbrFozGoO9ciq5tB8t54Lk1KArodWpe/MkS0hPah5j3Fz6vTMmOan+GZgWis0JJmxGLWtf/1p5Tkb0uHNX7sFfswFm9H0X2oAtNwxQ3xS9+RMLDXkVRFFpKv8bdVIQhIhNj7ERRWFYgBI9AcD4iywpfbi3gHx/uprbJwVVzMrnlsnFd8repabRz8y9X0mjz18t6YsUMls0a2VdT7hK2agd5a0txNLrRh2jJmJ9ISOzgCCOXvU6/+CnfgaNmP8hedKHpmOKnYIqdhMYYPtBTHPJYC1bTePjttm1NUByWjGWY4qYI4XMeIwSPQHCe8e3Bcv727k6OlTQwb3IKd109kaSYkC6dw+uTue/3q9h7zF/6YcmMdH5668y+mG638XlkirZWUXmwHiRInBhJ4uToPsnb011krxNH1V7/slfNAZC96MNGYoqbgjFuMhpD6EBPcUhS9c0zuOqPtmuXtCZC0i/Fkn4JiuyluWQz7sbjSCoNksaAzpKCIXwUavF7H5YIwSMQnCccLa7n7+/tYtvBcsaNjOIH104hJ717TrQvvL+L1z87APjrZ734kyUY9dpzHDUwNBTbyFtfjsfuxRxlIGN+Yq9mae4tZI8dR9U+Wiq246w5CIqMPtwvfkyxk1EbLAM9xSFD+Yaf+fMlnYGwnO/hba7AdnxNh/0acyyGiCyCUi5CF5LUV9MU9DNC8AgEw5wjhXW8snIfm/aUkBwTwt3XTOKiiUndTpT3zf4yHvzTVwAYdBpe+ukS0uJDe3HGvY/H4SV/Yzn1x22o1BIpF8YQOyZ80CYLlD127FV7sJfvwFl7CFAwRudgTpqFMSoHSTWwPkmDFcXnoTH3Q2zHV511P0NkNj5XEx5b2TnPaYq/gNDMK4WT+TBACB6BYJhysKCGV1bu4+t9ZSTFBLNi6TgWXZCGRt19H4aq+hZW/GolTc0uAP73tplcemF6b025T1EUhZqjTRzfXIHPIxOaaCZ9bgL6oMFpmTqBz92CvWI7zcWb8FiLUestmBNnYE6aidYcM9DTGzR4miup3fVCp0RMWPaNeJrLaS7e0LmTqzSEpC3CMuoKITaHMELwCATDjH3Hqnl55V6+PVhBapyFFctyWDA1tVMh5mfD65X5wTNfsj+/BoDLZo3k8RUzemPK/YrT6iZvXRnWCjsavZoRF8URmT40lovcTcU0l2ympWwbiteOPnwU5qSZmOImo1IPvmW6/sLnbKRyy+/wOevPup+k0hGctpDQrKuQvU6aizbgbM2X5LXX4HM2nPV4Y8wEIibegUqt683pC/oJIXgEgmHC7txKXv5kHzuPVJKeEMotl41j7qQUVKreWbb52zs7+c+XBwFITwjlxZ8sQa/rXPj6YEORFcr31lG8vRpFVojKsJA2K25AkhV2B8XnwV65i+aSzbjqjiBpjJjjp2FOmoXOkjJol+r6AtnronrrM7ibis64j9oQRsjIpQQlzURSdfw3qygKPkctzrpc7JW7cVbv63A/fdhIoqbei0rbfxm9Bb2DEDwCwRBGURR2HK7g5U/2sfdYNaOSw7ll2ThmT0jqNaEDsG5nET/5u9/8b9JreOl/l5ISOzSsImejpdbJ0a9KcTS40AVpyZifgCV+aN3IvPYamku20FK6BZ+zEW1wIkFJszAlTEetG1rvpTvU7v4n9vJvAxvVBkyx4wEJQ/gozIkXnlHonAlXfR6NR97D1ZDXrk9nSSHmwkeR1IN7OVQQiBA8AsEQRFEUth0s5+VP9nEgv4bRqRHcetl4Zozr/ardeSUN3Pnk5zhcXgB+ccdsLr4grVfHGEhkr0zRt9VU7PNXe48fH0HytGhUPfB1GggURcZZc5Dmks04qvaCJGGKnUhQ0iz0EVnDMv+Msy6X6q2/D2iTVFqipz+EPmxEj8+vKAq246tpPPxOu76g1AWEj7mhx2MI+g8heASCIcSJCuYvr9zHkcI6xqZHcetl485Z76q7NDU7ue03n7VVQr/x4mzuu25Kr48zGGgqa+bY2jLcLV5M4XoyFiRijui7wqd9ic9lpaVsK80lm/E2V6A2RhCUNBNz4sxhk9hQURSqtvwWd1NhQHvkpLswxU3u1bFaSrdSt+9VUHwB7VFTf4gxOqdXxxL0HULwCARDAFlW2LSnhFdW7uNocT0TRsVw62XjmJwV22f+Gl6fzI//uIadrUVBp42J4/c/XNCjKK/Bjtflo2BTBbV5TUgqieQLookfFzFkfWIURcHdkE9zyWbsFTtQfG6MsZMISVuILix9yL4vgJby7dTt/kdAmzlxJhHjV/TNeGXbqNvzYkCbShdM3JxfodYF9cmYgt5FCB6BYBDj9vhY820hb646SH5ZI1NGx3LLsnFMzIzt87Gf++923l5zGICEqGBe+ukSQsznRyRQbV4T+RvL8bllQuJNZMxLQB88tCNzZK+TlrKt2I6vwdtShS40jeDUBZjiJnfZv2UwUPX1UwH+NZJKR9zcX/epBatuz8u0lH0T0BYycimhmVf22ZiC3kMIHoFgEFLf5OD99bl8sP4oDTYnF+YkcPOSHMZlRPfL+J9uyeP/vfI14HdS/scTSxiRENovYw8WXM0e8taV0VTWglqnYsSsOCIzLEPaKgInfX1sx1fjrD2M2hBKUMp8gpIvGjJOzl5HHeVrHwtoC0m/hNCsa/p0XNnrpHLTr/Daa9raJI2JhAVPodIMzeXP8wkheASCQURucR3vrDnC6m+Po1GrWDIjne8syOrXiKiDBTXc8/SXeLwyAE/+YC4XTUzut/EHE4qiULG/nqJtVSg+hYgRIYy4KA6tYehZRDrCbS3FVvgVLWVbkSQV5oQZBKctQBvU9xbEnmDN/5LGI+8GtMXN/U2/JGLsaGkrNOs7hKQv7vOxBT1DCB6BYIDxyTKb95Ty1prD7DlaRWyEme/Mz+Ky2RkEm/p3GaWm0c5tv/6U2iYHALdfMZ5bLxvfr3MYjNjrnRz7qoyWOic6k4aR8xIITRo+fhs+l5Xm4g3YitYju6wYonIITluIIXL0oLRoVW7+fwHOyjpLCrGzftovYyuyj4oN/xtg5VEbQomf/9SwjIQbTgjBIxAMEM12N59szuPdtUeoqG1mfEY01y0czewJSQPiGOzy+Lj3mS85WFALwJyJyfy/u+f0aj6foYzskynZUUPZbv/vJ3ZsOCnTY1Brhs9NTvF5aKnYju34ajzWUrTBCX4/n4QLBk12YdnnpvTL+0CR29pCs64hJP2SfpuDrWg9DQf+E9AWM/Mn6ENT+20Ogq4jBI9A0M+UVFl556sjfLYlD7dXZtG0VK5bOJrMlIgBm5OiKPz21a/5dEs+ACMSQvm/xy/FbBCJ1U7HWtHCsbVluGwejKE6MhYkEhRlHOhp9SqKouCqP4rt+GocVftQ6cwEJc8lOGXugFdtdzUep2rLbwPaYmY83it5dzqL7HVSuupHAWHqllFXYMlY1m9zEHSd4bEQLRAMchRFYeeRSt5afZiv95diCTJww8XZXDU3kwjLwN8s3117pE3shJh1PHXvPCF2zkBInJnx16ZTuKWS6txG9n9QQNKUaBImRCINE2uYJEkYIjIxRGTiaanGVvgVtuOrseZ/jjl+GsEjFqELSRqQubmbik+fLdqQhH6dg0pjQB8+Elddblubo3q/EDyDHCF4BII+xOX28uXW47z91WEKyhrJSArjiRUzWDgtDb12cNRt2nG4gj+/tQMAlSTx6zsvIiEqeIBnNbjR6NSMnJdAWGow+RvKKf62moYiGxkLEjGEDI6ln95Ca44mfMyNhI66guaSza1Ozt9gjB5PSMayfl/G8VhLA+cXFDsghVONUTkBgsfdeBzZY0elNfX7XASdQwgegaAPyC9tYOXmPL7YWoC1xcXsCUk8+D/TmDAqZlA5gZbX2PjpCxvwyf6V7fuum8zU7PgBntXQISIthOAYI3nry2ksbmbPO/mkzYglOit0UH3OvYFKayJkxMUEpy7AXr6dprxPqdry/zBE5WAZdRn60P4pN+JzNQZsa4Li+mXc0zFEjj6tRcHTUtVvvwdB1xGCRyDoJWx2N6u3HWflljyOFNYRHmJg2cyRXDl31KC0mNidHh7723qsLW4AlsxI57qFp1/EBedCZ9Iy+tJkqg41UPhNJfkbymkqayF9ThzqQWLF600klRpz4nRMCdP8wufYSqq2/BZD1BgsGZehD0vv0/Fljz1ge6CqlmvM7XNieVuqheAZxAjBIxD0AFlW2JVbycrNeazfVYzPJzNjXCK33DuOC8cmoBmkETyKovD/XvmavNIGAEanRvDw96YPO6tEfyFJErFjwrEkmMldXUptXhMtdU4yL07CFDY8s1P78/ZcgCl+KvaKnViPraTq6ycxRI4mJOMyDOEZfTJue8EzMEtIKo0BlT4E2WVtazs1VF0w+BCCRyDoBhV1zXy2JZ9Pt+RRWddCSqyF7185gcXTRwwKJ+Rz8dqn+1m3swiACIuRJ38wb9D4FA1ljKF6cq5K4/imCqpzG9n3XgEj58YTOXJgI5v6EklSYY6fiiluMo7K3TQd+4Tqb55GH5GFJWMZhojMXh1P9p4ueAbu+6YxReEOEDy1AzYXwbkRgkcg6CQuj4+Nu4tZuTmPHYcrMOo0LJiWyrJZGYwdETlkrCOb9pTwjw/3AKDVqPjtPXOJChOOlr2FWqNi5LwEguNMHN9UwdE1pVgr7aReGINqGBdelSQVprjJGGMn4qjaQ9OxlVRv/T368FH+pa6IzD76jgzc906tC1yqVnzOAZqJoDMIwSMQnAVFUThaXM/KzXms2nYcm93NhFEx/GTFTOZNScaoH1qh24Xljfzyxc1t2w8vv4Cc9KgBnNHwJSYrDHOkgdxVJVQeqKe5xkHmoiT0QUPrb6arSJIKU+wkjDETcVTvpenoJ1Rv+wP6sJF+5+aInmVvVmmM+E7ZVryOnk+62wyNhxyBHyF4BIIOaGp28uXW43y6JY9jJQ1Ehhq5au4ols4cSVJMyEBPr1tYW1w8+rd12J0eAL4zP5Nls/rGz0LgJyjSyPhr0jm2royGQht7381n1ILEYVWW4kxIkoQpZgLG6PE4q/fTdOwT6va+RsyMR1HrLSCpuiV8VJrAJSzZM5CCRzCUEIJHIGil2e5m895S1u4sZNuBchQFZk1I5M6rJjJtTPyAlHvoLXyyzC/+uYmSKhsAkzJj+OF1Uwd4VucHGr2arMVJlO+po+jbKg59WkTSlCgSJ0cNmWXQniBJEsaYcRiic/C5rKh1QUiq7vuLSadVJT/dp6d/EYUKhhJC8AjOa5rtbjbtLWHdjiK2HSzH45XJSY/inu9M5uIL0ggLNpz7JEOAF97fzdYD5QDERpj5zV1zzhhBpigKyF5knwvF60LxuU6+lj2gKBBwo5ZaLfsSILV2nd4vnXzd9p+E1LZ99v2ldsdLgWNIEiq1HkljQKUxIKkG16VNkiQSJkYSFG3k6JpSSnbUYKtykLEgYdhUXj8XkiSh6YWyFGp94Dm8LdU9Pmd38blsAduSenhcL4Yr58c3TTAocDqdGAwGFEUZ0Cdbm93N5j0lrN1RxLeH/CJn3Ei/yJk3KZno8IHJ69FVFEVG8baKkVZBcvK1G9nnRPG5Wbu3kf984Y8k0WvgsYWNeI6+QrXX3x8obNwoXidD/slVpUGlNiBp9H4BpDag0pwiiDSG1v5T2k/fPrGv2tAji8SpWBLMjP/OCI6uKaWxpJm97xaQuSiR4Jjz22m8K9cEbXBgGQmPrRxF9vXaZ9QVvPZAsaUxC3+4wYwQPII+Jy8vj+XLl7N48WJ++ctfDsgcThU52w6W4/UNvMhRFAXF68TnasLnsiK7rfic/tc+tw3F6wwQIyctLW7/tuw55xgFDUE8u3Ec4L8Z3DXxMLHuWhyVffzmBhrZiyw3g6c5wMG126g0pwinU0STRt/WFiiuDCdFky4Ytd6CSmdGklTozFqyl6VS/G0V5XvrOPBRIakzYokdE3ZeLHF1hCRJVFZWEhsbe859dSGJAduK7MFrr0bbzxmXZa8T2R1o4dGY2icjFAwehOAR9Dk2mw2NRsPOnTuxWq2EhPSP06/N7mbT7hLW7izk24MVeH0y4zOiuffaycybnNJnodiy13VSvLitfgHjakJ2nXztc1mRXdZOiZbu0ujU8vtt2Xhkv9i5clQxFyaKPCHdQvYiu5uBHggoSY1aH9L6E0pIkAX9RAO1BTIVO/JoroghZfpItGbLoFuS6wtOWHWqq6v58ssvuf/++/nJT37Cgw8+2G6fU9EGJ55+KtyNhf0ueLwtVe3aNCZh4RnMDP9vlWDA+fzzz7n88supqqrivffe45ZbbkGWZVSq3ncCtra42NRqydl+qAKfLDNuZM9FjuLznCJerMitoqUjMaP4XL38rrqOV5b447ejqXf4s/xOiq3juuyiAZ7VeY7iw+dswOdsAE5+FpYT+t8NlRv9L1W6INR6i//HYEGtD217rWoVTGq9BZVm6GZxliSJuro6HnvsMWJiYhg3bhzJycnASaHjbizA52zCGDuxTfio9cGojeH4HPVt57JX7cWceGG/zt9Rc+i0FgmtOaZf5yDoGkLwCPqME6JGo9GQlpZGREQEX3/9Nbfcckuvip3SKivfHCjjm/1l7Dhc2SZy7rtuMnMnpxAV2rHIURQF2W3z34TOIF5OtCsDGgnSdV7dl05und+5Mz7Izr1TclGdvlqi0rQ6+uqR1HpUap3fX0Wj87e39gW+DqwErigKbf4+yql+P0rAthKwzyn7te3jbz95SOt+CoH7t55X4ZR2BVDk1uU+J4rPiez1v5Zbf/xtTpC9nfr9DTSyuxnZ3YzHVnbW/SSN4TRhdEIchbSKI/+2SmsaVMtliqLwzTff8Kc//YmlS5cycuRIDh48yKJFiwC/GFIUGWftEZqOfoguLJ2w0de21ekyRo+nuWhd2/mcNfuRfe52f599ibPmQMC2Pix9QLM+C86NEDyCPkOlUuH1etm6dSuPPPIIBQUFvPjii9xyyy088sgjjB7dvUKVTpeXXbmVbD1Qxjf7yymrsaHVqBifEc0Pr5/CnEnJbSJHUWS8zga8LdX+H3sNnpZqvHb/9mCwxpwd6TTRofM716p1gYKkTbTo+XSvmzXH/TWyzAY1v71rBqlxS04TNvoBcfIcaBTZe1IM+Zytouj07VP+DxBPrjbhdKIfpVc8hLr/frxOvF5nh8srAag0J4XRiR9jGFpzNBpTNBpzNCpN/0UY7dy5k6effpq7776bxYsX8+STT5KRkUFoaGibdUeSVFgylqIPS6Ph8LtUff0kxthJhGZdgyl2UoDgUXxunDUHMcVO7Jf5yx47roa8gDZD1Nh+GVvQfYTgEfQpTU1NhIaGcuutt7Jz506qqqqIjY1l9OjR+Hw+1Opz33QVRaGo0sq2A2V8c6CMPblVuL0ycZFBTB8bz/Sxkxmfqkfnrcdrr8ZTcYCavBOipgZFdvfDO+08Km0Qan1w69P3CZ+OE0sV/tdqXYi/KKJK06Un8z1Hq3j+y1WAP3L7l9+fw6is9j4P5yuSSoNapwFd7zipKz7PGYSTq9W65Gi1GDa2OqT7f/z+QP2I7MXnqMPnqDvjLiq95RQBFON/3brd20tnGo0GRVG45557mDVrFqWlpTz77LMd7muIzCZ21k9pKdtKU+5HVGz4GUHJFyFpzCjelrb9bIXr+k3wtJR+A4oc0GaMzumXsQXdRwgeQZ+i0+lwOByEh4fzz3/+E5vNxlNPPQVwVrFjd3rYecRvxdl6oJyK2mZ0GhXj04K4ZV4IkxJaiNGV43XswVtRQ2PZwC5VSBrjSbFyqpjRhZz0vdAFo9aH9JlDakVdMz/5+wZ8Pv9Sz11XT2LGOCF2+hJJrUWt1sJpNZXOhSJ7T1k2bWoTQ3Lra6+zEZe1AUlpRpLkc5+wF5BdTbhcTbjqj7XrU+tD/eLHHB0gijTmKFTqrouhCRMm8NFHH3Hs2DEefvhhdu3axeuvv84f/vCHDgW+JKkISpyBKW4KzYVf0ZT3OYov8EHGVXcYV30e+vCRXZ5PV1BkL9bjqwLa1MYItCFJfTquoOdIiqIM8YQbgqHE8ePHWbduHddeey3BwYE3CZ8s88an37LtQDn7C5vx+CAuxMeEOCvjIirIjmhAr+mfiz+ApNadtLy0CZcQVCdet1pkVPqQfvUd6Ahrs4u7nvqCwoomABZOS+WXd8weVH4bgq6hKAqlu6sp21mMWtVCzGgt4UmcEuUXaDU6XQD0F2pDaIdWIY05+ozfixPW3ebmZh599FHGjh3LjTfeSGhoaKfG9LltNBx6B3vZNwHthshsoi/4cU/f0llpKf2Gur0vB7SFZl9PSNrCPh1X0HOEhWcYoygKOw5X8NnX+dQ02PH6ZCxBBmbkJHDx9LR+LXzp8/lQqVSkpaWRlpZ2hvnCO+sKSDJW890x9UyIaSA2qJPVh1UaJFXnRYdKa0ZjivT/GMP9FhldMCp9cKu4Ce5Xn4ae4HJ7eeSva9vETlZqBE/cPEOInSGOJEkkTYohJCaIo2tKKN3no7khiIwFF7TLzhyY06kJn9uGz2XzL6e5rficVmR3k7/N01UHfOWsBTp9zkZ8zkZc9Ufb9UVPfxhDxKh27Sesuz6fj4SEBJYtWxbgv3Mu1LpgIifcSrXXgbNqT1u7s/YQ9oqdmOImn/ndKAruhnyaSzbjddShyB5UWjOGyNEEJc7wLyWfAdljp/HohwFtKq2ZoKTZ55yzYOARFp5hiNvj4/31ubzx5UFaHB4crsDlHqPev35+yYXp3Lw0h5hOJt2TPXa8jnq05hgk9dnFkiLLIElduulaGyqp3vQzdOrO/0lqgxOImvpDf7r509bUTy134N8++Xq4iAGfLPPTv29kw+5iABKigvm/xy8hPEREiwwn3C0ecleXYqu0ow/SknlxEkHR3fuMlYDIOAIj5zq6HUgSKApNeZ9hzVvZpbHMSbMIy76+Sw8PXcm67GmupGLDzwiI5JM0xM56At1pS0yKItNcvBlrwefILlurRezkcZJah6IomOImYxm5FG1Q7GnHK9Tt/if2iu0B7ZaMy7CMurzT708wcAjBM8yw2d386NnVFJQ34nKfPYJErZIw6jU898AistMiz7ifoijYCr+iKfdDFJ8LjTmW6At+hMYYgaIo+Bz1eGyluG2leGxleKxloA0iaup9aLSdu9DJXhd1e1/GUbmr0+9VExRH7IzHkTSGYSNguoKiKPzhjW95f10uAKHBBv7x2CUkDtFq7oKzI/uUtuzMkkoibWYsMdn9l51Z9rqw5n+JNe+Tzh+k0pCw4JnWKMNzW5QVWcZRtZvG3PfRBieiC05AG5yINiQRjSkSSWqfzqL+wBsBEVsASGrCx9+KOX4qkiQh+1zU7ngeV0PeuZf+JBWSSkvUlB9giDwZSdpcspn6fa8F7KrWW4ib80tU2qFRjuZ8RwieYYTL7eWO335OUWUTHm/nfV2Meg3/eOJS0hPC2vXJHjt1+15rJ0S0QQmotEbctjIUrwO3T0V+QxBH6iwcqQ3haH0Ib/7ueqLDznwhUGSvP2y8pYqGQ+/gqjvcwV4SamMYGtOpzpJR/u2g2PMiI+2ZeP2z/bzw/m4ADDoNf3344rMKV8HwoK7ASt66MnwemagMCyMuiket7f0knh2hKAquusP+1A4tVa3/+9M9nClEX6UPITTzKkzx00CRz2rtkb1Oarb/pcPlMUmtQ9sqgHTBCWhDEtEGJyCpdVRt+W27nEWWUVcQMnIJKArV3/4Rd0NBlzKbSyod0Rf8GH34SBRFpuTze9q9x+gLfowhMrvT5xQMLELwDGJSU1OpqqpCo9GgVqvJzs7mpptu4vvf/z4qlYoVK1bw2muvsW3bNqZNm8bvXvuaD7/8hm/ffIgLb/M/idQWbKPi4CrsdcUERY1gzNLHOxwrPMTAh09/J6CCtrP2CLW7XuCtz3fz0HMb+duj81k2ewQAzW4NuXUhHKkLIbcuhILGYLyyCqPGS2aElayIJuZPG0X29KvbhbTKHgeg0FyyhebijXhbqlr9aaICHB61pmj/U10nngxPUFJSwk033URlZSUqlYrvf//73H///Vx//fXk5votIY2NjYSGhrJnzx4A9u3bx5133onVakWlUrF9+3YMhsHtv/PZlnx+88oWwG+pe/q++VyYk3COowTDBUeji9xVJdjrXRjD9GQtTsIY2j5a6tZbb2XlypVER0dz4IA/UV59fT3XX389hYWFpKam8vbbbxMWFobH4+H2229n165deL1ebrrpJh5/vOPrxekoiozPUXeKAKrG01LV+roWFB+SWo8pbgrBaQvRmKORJFXAA4uiyHispVRu/nWXfhdqfShqUyTuxoKAZe2IiXdijp9CY+5H2ApWMWPF69Q0OlCfkoHzOwtGMXdKIs+/s5ejRQ3otWoWTEvmf2+/gCCTDkljJGHB04BExYb/bc2S7Sd4xGLCRn+nS3MVDCzn7+PxEOGTTz5h4cKFNDU1sWHDBu6//362bdvGK6+8AkB4eDg//elPef/DlXy59Tju0yw7Gn0QcWMuxtFUgbW8IwuKH6fby+a9JcydnAKAq6mI6m1/AOCdNccICTLwwifFlJoXk98QTInVb7kJM7jIirAyI7GA0ZFWkkJaTmb0bSrDmm8iZMQivz+PSovHVoGr6Tj4vGiDYomaei8aY0SvWWo0Gg1/+MMfmDRpEjabjcmTJ7No0SLeeuuttn0efPBBLBZ/FmKv18vy5cv517/+xfjx46mrq0Or7T9n7u6w9UAZv3v967btR2+6UIid8wxjqJ6cq0ZQsKmCmqON7PvwONNuzmy3vLVixQruvfdebrrppra2J598kgULFvDYY4/x5JNP8uSTT/LUU0/xzjvv4HK52L9/P3a7nezsbG688UZSU1PPOR9JUvkfWExREDUmoE+Rffic9W0CqKVkM7LPgz5sBKbYSX4fIUmFqy6XhoNvdvl34XM14nM1djgnRfZiK/yqLRfXyz+7mFkTAr8rH67P477rJ3DBmDjcXh/3PbOO3778Lb+9dxYoPlpKt2JOnE6bLyCgC00nNPPKLs9VMLAIwTNEsFgsXH755cTGxjJ9+vS2Ans333wzb7zxBr//+39RSe3z2oQm+C8+Vbnr2/U5bTXsfvshRsxcQcnuD1nymsQvfvY4V1x3K9s3fMmR4pHsL3Kz7UAlo+b/gP3rnmdkuY8xCVYuyyglM8JKtMnJ6S4EKq3Jv+4enIDGGIanuQJtcCKSSoPOkoTO0nf5KuLi4oiL8xcRDA4OZvTo0ZSVlZGd7Tc7K4rC22+/zdq1awFYtWoV48aNY/z48QBERET02dx6gyOFdQG5du64cgLLZvVt3hHB4EStVTFyXjwhsUYKt1b5/W9P+y5edNFFFBYWBrR99NFHrF+/HvBfP+bOnctTTz2FJEm0tLTg9XpxOBzodLpeKfQrqdSniKHAPkX24XU24rPX4m2pxhCdg7e5AretDNll7fHY9srd7YMZTuPKuSe/P0Y03Lg4kz/+x7+Er/jcWAu+wJRwQds+hsjRRE6+57xeTh+qiE9siDFt2jQSExPZtGkTACaTiSeeeIJfPf07Rl3SOfPz6TRVHGbid57G3VzDT372a15dW0t44hgSg4NpKPiQ1OQ4XrhFw135FsZ4PuKOiScyikqt6+inOhcmoNaHDgon4sLCQnbv3s0FF5y8WG3atImYmBgyMjIAOHr0KJIksXjxYmpqarjhhht45JFHBmrKZ6W02saDf/qqLeruqjmjWLFUZHc9n5EkiZjscEzhnV+CraqqansoiIuLo7q6GoDvfOc7fPTRR8TFxWG32/njH/9IeHh4n8z7BJJKjdYUgdYUgSEyM6DP57LisZXhtrYGQ9hK8djKu+SHYzu+usvlY749UMmo5JP+jLK7GU+Tv9irKX4qEeNvFWJniCI+tSFIfHw89fUnKwXfeeedPPKTX9FQsheDJfYsR3ZM0sQrUWv1mMMTGTlxASHOwzz5g7sIr3iZpf/dxk3LskkPa+bKOem899Ux7rjKf5ONmHA75oRpvfa+epPm5mauueYannvuuYCn1DfffJMbb7yxbdvr9bJ582a2b9+OyWRiwYIFTJ48mQULFgzEtM9Ig83JA8+tocHmz0s0e0ISD3x32qAQloKBJzjWRE/dMb/99lvUajXl5eU0NDQwe/ZsFi5cyIgRI3ppll3jRGLPUyOlSqub+PcnO2lobCIu2M34mAbSzcVIrpoOz+G11wZs3/Gb1WjUJ/0Un7hlGjdektW2vWl3Ke9+dYyPnr3i5EGShNdRR/CIRQSnzu8wUkwwNBCCZwhSVlYW8OSl0+lIGH85JbveJ2Pu3V0+n87sX8aRFXARTG7eHh756yasVQpFlc0Uh93Em/lq4kYZOfKvHew5Vs+0KZMwxU3qtffUm3g8Hq655hq++93vcvXVV7e1e71e3n//fXbu3NnWlpiYyJw5c4iM9Ec3LVmyhF27dg0qweNweXjoT19RWm0DICc9il99fzbqXqw4Lxj6dFb8xsTEUFFRQVxcHBUVFURHRwPwxhtvcMkll6DVaomOjmbmzJns2LFjwATP6TQ1O7nzd1+2iX6AdzCh02QTHxXEBaOCuGW2Bm9zuT9iS1KjyIE5yP7500XtfHhOsOtINT98Zj1/f3wBIxIsJzsUGUWRRSblYYC4Yg4xtm/fTllZGbNmzWprkySJxDFz8bkd1BftPMvRHeNuOVlQ0NVch9bkN+fWHNuMgsJ//vI4//vYIzzy/15HUSTufqmFB74cy8/++TWvrtzH5j0lVNQ29/gJszdQFIXbbruN0aNH88ADDwT0rVmzhqysLBITT9aXWrx4Mfv27cNut+P1etmwYUObv89gwOuT+ekLGzlc6P+MUmItPH3fPPQ68awi6B6XX345r73mj+J87bXXuOIKvzUjOTmZtWvXoigKLS0tbN26laysrLOdql/wemUKyhr5y9s7A8TOCdxemcIKK29tKOeWF+oJHX0NoVnXoA2JR+pkyZcD+bXc/utVPHP/7A4EkQq1ViTyHA6Iq+YQwWq1snHjRu6//36WL19OTk6g70ZiTCjlE6+kcOu/A9oVWfbnu5H9Tymy1w0qFapT1qBL93zMiFm34LLVUH1sExlz7kT2uqk7vp0RM28hLGl82771hTso3f0Rx8uup7Ciia9OSTpqNmoZER/KyMQwRiSGMiIhjOSYEMJD+i8x4JYtW/jXv/5FTk4OEyZMAOC3v/0tS5Ys4b///W/AchZAWFgYDzzwAFOn+hOULVmyhKVLl/bLXM+Foig886+tfLPfn18k0mLk2R8vwBI0uEPmBYOHG2+8kfXr11NbW0tiYiK//OUveeyxx7juuut46aWXSE5O5p133gHgBz/4Abfccgtjx45FURRuueUWxo0b129zVRSF2kYH+WUN5Jc2kFfaSEFZA4UVnc8rVlHbzFv//CWzR7qInHQ32qA4XK6msx6TW1jPzT//gl/eeSELL0jpYA8ZTVBcN96RYLAh8vAMYk7Nw6NSqcjOzmb58uXcddddqNVqVqxYQWJiIr/5zW/4bEs+v//PN2x98zEcjWVteXiqj24if9OLAeeNypjFyIvuaBelhaIQN3YxCeOWUpu/lcJtbzDphmcDxJHsdbPzvz9m5EV3EJY8oVPvw6TXkBAdTFJMCInRISTFBJMUHUJiTDBhwednluTO8OJHe3j5k30AmAxann9kMaOS+9aJVCDoaxRFoanZRUm1jYKyBvJLG8kvbSC/rAFrS88LoN5zZTbLl03B57bjbsyndtf/ofhczLz1v+3y8MyekEBIkI53vzqGUX/yOpcQHcSa5/05drTBCcRd9Isez0sw8AjBM0xwub0s+fHb7epmnY0Tgmf6LS+j1WrR69S0ODofAdEbmI1aEqOCSYw5VQiFkBgdTGiQ/rwVQx9uOMrT/9oKgEat4tkfLWDKaPGUKRgaKIpCvdVJabWV0mpb209ZtY3SaivNfXSdkST49y8uIbRpPfqwkRhjJ1C25iFkt61751PrCc/5HuZTwtIFQxexpDVM0Os0XLdwNG+tPoTzHDW0TketkkiICuLfv7ycxmZXqzm5ofWpq5Hj5Y1dKlXRFVocHnKL68ktrm/XF2TU+oXQKdahxOhgkmKCh/WyzqY9Jfz+39vatn9660whdgSDDllWqG20nyJorJTWnBA1ti49fPUGEjA9M4R//+c/HK7S88PvTWB6nApLxmU0Hnn33DW0OjijpDH4kyMKhgVC8Awjbr9iPLlFdew+WnXOwqGnYjbp+NMDi1CrVURYjERYjEwbE9/W7/XJlFZZyTtlXT2vtIHKupa+eBttNDs8HCms40hhXbu+YJOOh747jflTU4dVtNL+/Bp+9o+NyK2G13uvnczFF6QN8KwEAj8er4+fvLCBsmobZTXNuD1de7jqSxTgmyNWwB91uvd4C1NyfASlzMXVeBxH5c4uiR5Joydm+kNdKm0jGNyIJa1hhsfr484nP+dYcQM++ewfrV6nJjTIwPOPLCYuMqjLYzXb3RSUNfqtQWUnLUL9tSwWaTHy4k+XEB5sQKNpn2V6qFFU2cRdT35BU7M/Udr1C0fzw+unnLfLeoLBhdPt5el/beWLbwr6bcxIi5H0xDDSE0NJTwjD6fby+/9s49S7VrRFQ3VTe2uSJUjPGz9fRKglBCQVDQffpKX063OLHpUWlUZP9PSH0AWLki3DCSF4hhkVtc0s//nHfG/JWD7ZlEdFbXO7fYx6DcEmHTdenM2y2RmYDb33BKMoCpX1LRSUNlJcZfWbuatslFRbqapvobf/2iJDjfz6zotIiw9Fpz236JHwh/Gf0BCSJPmz8bf+f2p7f1LbaOfOJ79o+7wWTEnhl9+/CJVKiB1Bz1EUBeXE/woostJmRTzXV/KEP84/PtzN6m2FfTI/g07DiITQNmGTnhhGekIoocHtl653Hanko41HcbsczIjOZUuui43FMe32iwuy8393RBIx+iqQJBTZi6v+GLaC1Tjrc/1vXDkplCS1HkmtJTh1IUEpc1Druv4QKBjcCMEzjFAUhfufXU1STAgPL5/O439bz4bdxW39o1MjmD42gYmZMUzOiu33m7rL46O8xkZJ1UlHxpJWUVRVb+/XuZwNjVpFhMVAhMVEhMVIpMVIRKix9bW/LSLUSFiwISBra3dpcbi55+kvOVbir8Q8KTOGZ3+0sFMCTnD+4vXJNNqc1FkdNFid1DU5qLc6qW9yUG91UGd10mB1UNfk6JXop54iSRAdZiYxOvjkT0wI6QlhxEcGdVrcK7IXa/4XbP56Ey/vTqOi2dTBWAov/HACOTnjURSZ6q2/RxuSRPgYf1oKr6Mee8UOvI56FJ8bR81BZGc9wSMuIWz0NQHn8nll1Jrhs2x+PiN8eIYRH208Rmm1jd/9YB4A+/OrA/pXLBvH7Al9V7jzXOi1atLiQ0mLD23X53J7Ka2xUVrld34sqbZRWuX/v6ahf8WQ1ydTVW8/pwhTSRKhwfqTQqhNFBlbRZGp7fWZxIvH6+OJ5ze0iZ30hFB+94N5Quycp/hkmSabi3qrX8jUWx2tAsYvaBra2p00NTt73WLaU1SSRGzkCVET4o/AbBU3cVHB6Hv4d+1qKCB/2795ZauJLaWBVdnVkoxP8QuTa+ZlkpPjzx9mK1iNz2UlKutk1nWNMZyQERe3bTcdW0nT0Y9w1ee2tbU0ODn4USFepw9JgsiRFkbMiRfiZwgjBM8woaKumb++s5MnfzAXs0HL0eI66q2BWUnTE0IHZnKdQK/T+E3ZCWHt+pwub1sUyKlCqLTKSm2TYwBm60duNfXXW51tguVMBJt0RLYKogiLXwiFhxjYsLuYfXn+OkBRYSb+cP8Cgk2dyw4rGBrIsoK1xXUWS8zJ9kabq22pabCiUUvERwaR0Bo12SZuooOJjTCj7QN/Otnnpv7Ih3yw9iBvHUrB7jl567Lo3Xwvp4Cg0Fj+tDGC9KRwvn+lP7LKbSuj6djHRE9/CJVaf8bzGyKyaOIj3I2FyB47Kq2Jgx8ex+vyR6cqCtQca6KxtJnUGbFEjrQI37ohiFjSGgYoisKP/riGhKhgHvnedBptTm765SfUNp4UA0a9htV/uXHY+YTYnR7KamyUnLAMtS6XlVRZ2wm+oYBaLWHSazEZTvxoMBm0mE/bbvvRd9B2ynZvLLmdryiKgscr43R5sbs8OF1eHG5v67b/f4fLi9Pt/9/h8uBwebG1uE+xxDhosDnx+YbWZVarlokLVZEYaSYpIZqkuGiSYvzCJjrc1K+Rka76PL5Z+19e3BpGQWNwW7uEwqK0Cm4cX03C+GswJ84IECGK7KVyy28xRo8nNPOKjk4dsG/pqh+h+FxETbkXXdhYtr185Iz7B0UZGbUoEUOIeDgZSggLzzBg5eY8iiub+O3dc3B7fDz+/PoAsQMQ14U18qGEyaAlIymcjKT2GYhbnJ6TTtNtfkNWKupaqG9ynDOKbSDw+RRsdjc2e+/4XOh16lMEVMfCyHx6W4Dg8rdp1CokCSQkkE46e0ut/5x4LSF12OY/5mR/bz4de70yDrcXh9PTJkgcp/z4hYonoP2EeHE4A/ucbW3+fQbj30hvYdAoxJgdxAY5iY8wkBQXQZgmDLk0mBBdEBnzEogeFTpg85O9Lo7vep8XvyhlQ3FyQF+qpZnbJxxj3NhswrLvQa0PaXd807FPAAlLxrlLxUgqDfrwDJw1B3DWHUEXPvas+zfXODjw0XEm3pCBWiseKoYKQvAMcarqW/jz2zv4zV1zMBt1/PvzA+w9Vt1uP5P+/MslYTZoyUyOIDM5ol2fLCs0NvuXEeqaHNQ2Olpf26ltclDXul3b5BhUuUa6isvtw+X2dVh0cTAQKJJOEUStndIZ2/w43T68vr5JijmUOeFfFh6sx2L0YtHaCdM2EqWtJj5MQ0piPDEJ6RjCR6INTkBSnVyGqj9u5ehXpeStLcPZ6CJpanS/L980Vx3ijfdW8ta+SBzekxFYRo2Xa0cXsSTbQdT4WzFG53R4vKshH9vxr4id+QSSqnO3OUNEVpvgCctWo9aq8HnO/LflbvHSVNZMeGp7sSUYnAjBM4RRFIU//Gcb8yencEFrosCgM/h/eHxD96bdF6hUEuEhRsJDjGScxY9bURSaHR7qGluFUIBAsreJoromR7+X5RgOKAooKKfERg9fi0pPkSSwBBkIDzEQEWIkLMRARKsvWLjF/7ccalQI8pWhc+bhbTiEx1aONiQRQ/go9OEXogtLR2No7yd3KuFpIYy9Mo0jnxdTuqsWR5ObkfMS+sVZV/Y42LD6Xf6+uplSW2xA35zkSm4cU0jiqAsJzboGlbZ9dBb4LUN1e1/GMuoKtMHxHe7TEfqITAA81lJ8bhumcD22qrP7CArrztBCCJ4hzMbdJRwoqOW/vzm5Pn3lnFEkRQdz3x9WB+w7qgMrh+DcSJJEsElHsElHagfRZafidHlbxY/9NKuRg9omO+U1zZRWd6+mj2D4EmLWEWExEhZiJCLE4BfirULG3+4XOKEdpEGQPXac9Udx1e3AWXcEj7UMVXA86ogszKOuQB8+CrXO3OU5BUUayblqBEe+KKYu34rL5iHrkiR0pr6zFBce28Vf3tjCNyUW4OScR4TauGV8PlkJOiLG3YchcvRZz9N45D3U+lCC0xZ0aXydJRlJY0TxOnDVHcVgiTmr4NEa1QTHdCy6BIMTIXiGKC1OD39881t+eN0UQsyB0QcVHZR8uOeaif01tfMWg17TFrVyOqVVVu555su27bmTk/nuxWOos54URfVWJy0ON3anF7vT4/9xedq2+7s2kaD7BJt0bULlVAtMhMXQKmz87WEhhi5FNckeB66GPJx1R3DV5eK2lqA1x6CPyMIycplf4Ojb//11B32QlrFXpHHsq1LqC23se/84oy9NxhzRu3XsHHYrr7zxPm9vV3D7LG3twToPN2QXMi+1kpC0+YRmXoVKc/axnbWHaSnbStzsnyFJXbO+SJIKQ0Qmjqo9OGsPYwxLPuv+HoeP5honIXFC9AwVhOAZorz00V4SY0JYPL19naVNe4oDtqPDTMO62OZgp6zGxr2/X9XmSD5tTBy/uuMiNF1cIvDJ/oihFkerGHL6o4daTogjpydQLHWw3XJK+/nu+yJJYNRpMOj9Pya9FoNe09ZmbP058dqgO9lmPMO+J/p6K4+S7HXhajiGqy4XZ10u7qYiNKYoDBGZBI9YjCEis0OH3d5CrVWRuTiJom1VlO+p48CHxxm1KJGw5J6LKkVRWLthE3/9IJeqlpMPbRIKi0ZUcN3oIkJDwwkf/zCG8FHnPJ/sdVK37zXCRn8HjSmyW3MyRI7GUbUHR80BIqdeR8n2apSzfE3y1pcx4dp0VCI3z5BACJ4hyNHiej7ccJRXfra0Q2fCgwW1Adsd5bYR9A8Vdc3c+8wqqluTJ47PiOa3d8/tstgBUKtUmI06zMbeCYV1e3zthZHrVHF0UizJshLgb3NqmQI40edvO7Xf39u+/UQyjJMlD07Z/5Q2Tu0/MfaJ1/iTWRpaRYbxNGFyJhHjb9eg06oHXS4V2efG3ZCPs+6IX+A0FqIxhqOPyCI4dQH6iEw0htB+nZMkSaROj8Vo0VOwqZzDnxeTNiOWuJzuL5MXllbyh5c/Z2exGjgpdrIimrhlfD4pFjvBaYuwZF5+1vw5p9J4+D205mjMSbO7PS9jdA4NB9/E56hDTR1jLkul8JsqnE1uvK72fpDOJjdle2pJmhLd7TEF/YcQPEMMWVZ45t9bufHibFJiLe36axrs7RMOJob20+wEp1JV38J9z6yiqt6/xJiTHsXv71+AqRdrl/UEnVaNTqvusF6RoH9QZC+uVoHjqsvF1Xgctd6CISKLoOSLMEz8Phpj+5QLA0HM6DAMITpyV5VwfEsljiY3aTO7VqLG7vTw4lureXdLNV75pBUszODiu2OPMzOxBm1wHBHj7kMflt7p8zprj9BSvpW42T/vkYjVmKLQmGPxtlTiqN5HSPpixl09AoCibVWU7a5td0zp7lqiMkIxWEROnsGOEDxDjI83HaPR5uKmJR3nifh6f2m7tvREYeHpb2oa7Nz3+1WUtxYDzU6L5NkfLejVQq2CoYmnpRpnzQEcNQdx1eWi0hrRR2RhTpxJxPhbu70c0x9YEszkXJXG4c+KqTxQj9fpY+S8BFTqs4sMRVFY9fVh/vr2t9S1qAC/hVMtySwdWcZVmSUYdQohI5ZgyViGpO789+TEUlZo1jW98rszRo/DdrwSR/V+QtIXt7UnTY6i/rgVR2NgjizFp1CwuYLRS5IHncVQEIgQPEMIu8PDf1cd4qHlF6DXdfzRbd7bXvCMFIKnX6lrcnDf71e1RWRlpUTwxx8v7LWlKMHQQvY6cdYewVlzEGftQXzORvQRozBEjiEs6xo0QXFD6kZpDNUz9so0Dn1aRG1eEz6Pj1GLks4Ytn6spJ4/vPYV+wodnBA6AONj6rk5p4D4YAfa4EQixq9AZ0np8nwacz9AY4ogKPmi7r6lAIzRY7EdX4WrIa+tzASASqNixOw4Dn5S5N9RgvDUYKIyLCBJWCvsWOK7HhEn6D+E4BlCaLUq/vXLy84Y1eHy+NhxqCKgTa2WSI4VibH6i3qrX+wUV1kByEgK47kfLxT1sc4jFEXGYy3FUXMAZ81BXA35aMzRGKPGEDb2f9CHj0KlHtp/DzqThrGXp3L482Iaipo5/GkRWZcmo9GdvDZZW1z88/1v+WBjAbJyUtBFmxzcNK6AybH1SCo1lpGXETJySacTBJ6Ks+4oLSVbiLvo512OyjoT+vAMJI0BxevEWXsYU9zktj5LQhDRmaGo9WoSJ0YiqSU0OjWKoiB7ZWSfjEqUcxm0CMEzhDhX+Oru3Epcp2UFTom19EkxP0F7Gm1OfviH1RRWNAH+Yq1/emARIUGdc7oUDF18LivO2kM4ag7irDmIInsxRGZjTphOxITbBo0fTm+i0avJXppC7qoSGkuaOfhxIdlLU1Dr1Xyy+RgvvPstTXaZE3mxtSofV2aWcFlGGTq1jM6SQvi4FehCErs1vuxzUb/vVUKzrkZjiuq19yWpNBgis3FU7sJRvS9A8ACkz41H9sqoT4nEkyQJtVaNfJ5HPg52hOAZRnx7sKxdm1jO6h+szS7uf3Y1BWWNAKTFW/jTg4uEQ/Awxe9sXICz5iCOmgN4bKXoLCkYosYQNfkedKFpAeUa+nw+ijIgy2JqrYqsS5I4traMunwr77+yh8+qSjlWZg3Yb1p8Ld/LKSDK5AKVBkvGlYSMuLhHv6Om3A9RG8IISpnbw3fRHmP0OL/gqTmAosgB1qMT4qYjJJWEz+M7Y79gYBGCZ5igKAq3XzGRELOBN748SHNrmYMRCaEDO7HzAGuLX+wcK2kAIDk2hD8/eDHhIcYBnpmgN/Haa9osOM66I6jUegxRYwhJvwRD5GjUuqB+nU99fT0vvfQS27ZtY/HixaxYsQKttv+d4lVqFYaxIbyx5QC7SmsC+hKC7awYl09OdCMAutARRIxbgTY4rkdjuurzaC7eRGw3Egx2BmPUWIwx47GMurJLx0mShCRJAyZABWdHCJ4hhNvtRqfreO1fkiRMBi03XpzN1fMyufeZVeSVNpB2jnIIgp7RbHfz4z+uIbe4HoCkmGD+8tDFRFiE2BnqKD4Pzrojrb44B/A66tGHZWCMGtNapylhwG5q+fn5LFu2jNmzZ3P33XfzxBNPYLFYuO666/p1Hk3NTl76eB8frM8NqCxv0nq5JrOYxenlaFQKkkqHJfNKgtMW9FigyD43dftewZJ5JVpz3+S/URssREz8/jl9rZxOJ4cPH6ampoa0tDQyMjJA8ufnMYaKpezBhhA8Q4D33nuPTz/9FJfLxcMPP8yECRPOuK9ep0GrUXHLZeP4yd83kNRBmQNB79DicPPj59ZwuLAOgPioIP7y4MVEhYpU80MVn8uGo3ofjup9OGsOotaHYIjOIWz09egjMlFpBsdNLD09na1bt2Kx+HNxTZs2jcbGxn4b3+3x8e7aI7z26X5s9pNh2mpJZlFaBVdnFROi95dC0YePInzczb0mTppyP0KtCyE4dX6vnO9MnEvsKIrCU089RWFhIQUFBZhMJp5++mlycnLQB2nxOLxojeIWO5gQn8YQ4Oc//znPPPMM69at46WXXiIjIwOVSkV2djbz5s1r95SpUqmYOS4Ro15DfJQQPH2B3enhwT991ZbVOi4yiL8+dDHR4SIsdSihKIo/yVzVXhxVe3E1HkdnScUYM57QUVcM6pBxi8VCVVUVt912G3v37iUqKgqXy4Ve33eiTFEU1u0s4vn3dlFe0xzQNzm2ju+OPU58sKN1XzVW6ywSxl6G1tw+SWp3cDXk01y8gdhZP+2TpayusHXrVjZu3MhTTz3FlClT+Pvf/84zzzzD66+/jgJYK+1EpIkI2cGEEDyDnOeff56srCwuvfRScnJymDhxIg888AAqlYpnnnmGsLAwJk5sXxhUlhXCQgy9VtNHcBKHy8PDf17Lvjy/v0JMuIm/PnQxsRH968Mh6B6K7MPVkO8XOdV78DkaMURlY06aSeTku/u0NlVvExkZydKlS3n55Zd54oknePrpp3nooYcwGnt/SfVgQQ1/fmsH+/MD/XRSLc18L6eAMVFNbW3akGTU4ddRvsVL7pdlZMyHyJE9Ez2Kz0Pd3lexjLocbVBsj87VXd555x3Ky8u5//77KSwsJCIigilTpgCg1WqJj4/H5/Oh1qgJiTNhrbCL4qKDCCF4Bjk+n4977rkHgLfeeovly5fz+OOPt/V98MEH7QSPzyezZV8pCZHCutPbOF1eHvnLOnYfrQIgKszEXx5aTFykEDuDGdnrxFlzEHvVHpzV+0GlwRg9jrDR16GPHD1k8+Ko1WruvvtuAO644w6effZZamtrSUpK6rUxKmqb+fv7u1jzbWFAe7jBxQ1jCpmVVI3qhBFMUhGSvgRLxlIklQZtkI3cVSUcXVOKz+0jJrv74flNxz5GpTURnLaw+2+mh8THx/P0009z//33c+ONN/L666/zi1/8AkmSKCws5IEHHkCt9j9kqjQqynbXEBSdfM5M1IL+QQieQcipHv733XdfW/uDDz6Ix+Np2y4qKmLkyJHtjvd4ZV7/bD9jR/RebgqBP7Hjo39bx84jlQBEWoz85cGLSRR+UoMSr6O+1YqzF2ddLlpzDMaY8URNux+dJWXAl0R6G61Wi8PhIDKyd0pTNNvdvP7Zft5ecxi392R+Gb3ax+WjSlg2sgy95mS7xhxNxPjb0IeNaGsLSwkme1kKhz8vJn9jBV63TMKErs/P1XgcW+HaAV/KmjlzJiaTiV/+8pdYLBZmzJjBli1biIqK4rvf/S45OTknd5YVGktbqDhQR8L4wVsu5HxCCJ5BiNcnnzFZ4Imw07y8PNatW8dzzz3Xbp/8sgaOlTRwyYWdL74nODtuj48n/rae7a2ZrMNDDPz5oYtFFutBhKIoeKzF2Fv9cTy2MvThozDFjCd87Hd7NTndYKGhoYHPP/+cN954g8LCQh5++GEMhp7lfvL6ZD7aeJSXPtpLY7OrrV1CYV5KJddmFxFm8AQcE5Qyl9Cs73To1B0SZ2bM5akcWllE0dYqvC4fydOiO+0bpfg81O99BUvGZWiDehbO3hu88cYbrF69msOHDxMZGckNN9zAxRdfTHx8fNs+slem+lgTiqxQsr2GqJEWdGZRR2+gEYJnkLHnaBXvr8/lF7fPRqXq+ILgdDopKirixz/+cbu1ervTwyuf7AMQEVq9hMfr46cvbOCbA/7EjqHBBv784MWkxvWOI6ag+7SFjrdacmSvG2P0WELSF2OMGttWB2m4Yjabqa+vZ/ny5dxwww09OpeiKHy9v4y/vbOzLVv4CXKiG1g+toAUiz2gXa23ED5uBcbojosZnyAo0kjOlWkcXFlE2e5avC4fI2bFIZ3hGncqTcdWIqn1BKct6vqb6gMSEhJYsWIFAF6vF43m5G1UbrWEVeU2UvRNVVtb8fZqRs5N6Pe5CgKRFEVRzr2boD+QZYU7fvsZcyYlc9OSnLPue+JjO/0pqbq+hasefQ9FgTd+dTmpIg9Pj/B6Zf73/zayYXcxACFmHX99aDEjk0QG64FC9rlxVh/AXrkTR9VeVLpgTLETMEaPRx8+sls1mc53jpXU85e3d7DjcGVAe2KIg++OyWdCTAOnG2RMcVMIG/vdLiVcdNncHFxZhLPJTeTIEEbOSzyrf4u7qYiqb54mZuYT6IIHl2DoKLmgo8nFwY8Lcbd4A3eWYMK16ZjCReb1gURcGQYRa7Yfp97q5PpF2efctyNzsN3p4ZWV+1AUkCRESHoP8fpkfvHipjaxE2zS8acHFwmxMwDIPpdf5FTswFG9H7UhDFPcZGJmPDagCQCHOjWNdv754R4+3ZLHqY++FqPCtZl5zEup5PRamJLGRPjY/8EUP63Lv3d9sI6xV6Zx+NMiavOseN3FZC5KQq1t75ejyF7q9r5CyMglg07sQMfXYKNFjznCgLslMGQfBYq2VjF6SderwQt6DyF4Bgkut5e/v7ebu6+ZiL6boeQOl5fPvykAICbcLELSe4BPlvn1S5tZu6MIgCCjluceWEhmcsQAz+z8Qfa6cFTvw16xE2fNfjTGCIxxU4jJWIY2KF6InB7QbHfz5qpDvLnqEE73SWuEViOxLKOKy9LzMGl97Y4zRI4mfNyKHhVD1Rk1jLkslSNfFNNY3Mzhz4rIuiQZjT7wetWU9ymSSkPIiEu6PdZAkDI9hoaSZjht7aShuJmmshYsCSJX10AhBM8g4a01hwkPMbBwalqnj3G5vXhlBbVKosXh4dG/rsPdWi09KVo403YXnyzz/175mtWtYbgmg5Y//ngho1NFpEVfI3udJ0VO9QE05ihMcZMJHXU52uD4c59AcFZcbi/vrcvlX58foOkUh2SAuVkS30nZRqTJ1e44SaUlNOsaglLn9UqUlEavZvSSFHJXl9BY3MzBTwoZvTQFXWtmYo+tHFv+KmJmPtavRVjPhCL7UHxuJLUWJPVZxbYp3EBMVhhVhxva9RV+U8m4a0YIsT5ACMEzCKi3OvjX5wf4/Q/nn9FRuSPuevILkPzh0UkxFg4dr23rS4wRy1ndQZYVnnztG75otZSZ9Br++KMFjBEh/n2G7HG0ipwdOGsOojHH+EVO5pWDIipnOOD1yXy6JY+XP9lHTUOg43FOWgjfzdxPqrG4w2N1lhQiJtzW65+FWqsia3ESeevKqM2zcuDD44xZloIuSEP9/n8RnDYfXUjv5RPqCV5HHfX7/4W7qZCYCx9FF5J41v2TpkRRc6wR2Rto5mmpdVJ7rImoUaF9OFvBmRCCZxDw0sd7mTo6jvEZMZ0+pqiyqa1g5ZwrJwSIHYBEYeHpMrKs8PS/t/LplnwADDoNv79/ATkj+6ZA4fmM7LHjqNqHvXIHjpqDaIPi/CIn65oBy6I7HJFlfymIf364h+Iqa0DfyMRQvjvNTZbyORJy+4MlFSEjl2IZuaTPHMFVahUZ8xNR6yqoOtTA/o8KGTGlBK+zkaiMZX0yZnfQmqORPXYUrxN7+fZzCh6dWUv8+EhKd9a06yv+tpqIESGoNMMrD9RQQAieAaawvJHPvs7nXz+/rEvHfbW9sO31wqmpfLn1eEC/CEnvGoqi8Ic3tvHxxmMA6HVqnvnhfCaM6rwIFZwd2WPHXrXHv1xVewhtUDymuCmEjr6uz6pen68oisK2g+W88P5ujrY+GJ0gPiqI2y5NZ7zqc7xN+R0erzHHEDHhNvShnV9i7y6SSmLE7Dg0ejUVe4uwHvsAy+hbUakHR6HWE5jjp9BoLcZesR1L5pXnXJZKGB9B1aEGPI7AiC1Xs4eKA/XdSsAo6BlC8Awwf313J1deNIrEmM5bZBRFYfW2QgCyUiOIiwqivNYWsI/I/tt5FEXhuf9u54P1RwHQaVQ8fe88JmcJS0NPkT0t2CtbRU7dYXTBCZjiphA+5oZhmQhwMLA/v4YX3tvVVv7kBBEWI7csG8f89CZsh1/C63V0eHxQyjxCR1/Tr4JDkiRSLohB1fI2LbWpVG00MtrcQkjc4HHwNcVNofHI+3jtNXisxegsZ4+4UuvUJE2JomBTRbu+0l01RGeFojWIW3B/In7bA8j2Q+Xsz6vhZ7fO6tJxeaUNFFX6E4MtmpZGdb0dzymp31WSJELSO4ksK/z57R2889URALQaFU/eO4+p2cJBtrv48+Tso6VsG46aA+hCkjDFTiZ87P+gMYmn2r4iv6yB//tgN5v3lAa0B5t0fPeSMVwzZwTO/Pew7t/U4fFqfSjh41dgjBrTH9Nth6N6P5I3j9DRD1K/xcahlUVkXpxEWMrguJZpTFHoQtNwNx6npXz7OQUPQMzoMCr21+FodAe0+9wypbtqSZshHqr6EyF4BgifLPOXt3dyy7JxhAR17Unq1CJ+C6aktImfE8REiJD0zuD1yfzu1a/bQvk1ahW/vWcu08cOvpwfgx1FkXHV5dJStg175S7UegvmhOmEZV8nLDl9TFmNjRc/2suqbQUBuXT0OjXXLRjNdy8Zg8FXTe32J/G2VHZ4DmPsZMJzvodaNzAWFdnrov7AfwjN+g5BycloTE0c+6qMI18WkzE/sceV1nsLU9wU3I3HsVfsIDTrmnMua0kqiZTpMRz5oqRdX+WBeuLGhmMIGZqFa4ciQvAMEJ9/nY/D5eWa+ZldOk5RFNa0+u+Mz4gmOtzM5r2BT3SJwrpzTlweH//7fxvanoZ1GhW/uWsOM8ed3RlRcBJ/7aoSWsq3YS/7FgUFc/xUYqY/iDYkWYTe9jF1TQ5eXbmPjzYew+s7aeFVqyWuuGgUK5bmEGEx0ly4lsoj74LsbXcOSaUjbMwNmJNmDejn1XT0YzTGCMxJfmt3ZLoFtVbVVmnd6/YR24NK672FKW4KjYffweeow914PKBQ6pkISwkmJM6EtSIwOk6RFYq/rWLUwsERiXY+IATPAGB3evjHB3v48f9MO2OR0DNx6HgtFbX+LJ4Lp6UCUFp9mv+OCEk/Ky0ON4/+dR27cv0+DiaDlmfum8fETGFe7gxee12ryNmG11GHKW4SERNuQR+RNewqkA9GbHY3//niAG+vORKQNFCS/Evct18xgcToYHxuGzU7/oqzel+H59EGJxI58ftogwc29N/dVExz8frWSugnRVdYcjDZS1M5/HkRBRv9fjADLXo0xnB0Yem4G/KxV2zvlOCRJL+VZ/8Hx9v11eZZiRvnIDja2MGRgt5GCJ4B4K3Vh4mPCmLupOQuH3siGZ5aJTFvsn8NubQ6MNxUhKSfmQabkwef+4ojRXWAvxDoH3+0gMwUkUH5bMieFuwVO2kp24arsQBDZDYhGcswxowbdNE0wxWny8s7a4/w788PYLMH+oTMGp/I96+c2Fb2xFl7mLo9L+FzNXV0KoJS5xOW9R1/Ir0BRFFk6ve/TsiIxR3m+QmJMzFmWSoHVxZSsLEClUoiOmtgS7uY46a2Cp4dhI6+tlMiPzjGRER6CHX51nZ9Rd9UMubyVGER7QeE4OlnbHY3/119iCfvndflP3CfLLO2dTlr8uhYwkP8TwUlVYEWHhGS3jGVdc386I9rKK70X3Riws0898BCUmIHh3/AYEPxeXBU76OlbKvf+diSgjn+AiIn34VaJ/7G+guvV+aTzcd45ZN91DYFRlZNGBXD3VdPbMsVpchemo5+jDX/C9rVNgBU2iDCx6/AFDO+P6Z+TmyFa5G9LkLSLz3jPkHRRrKXpnBwZRF568uRVNKAJu4zxk2m4dBb+JyNuBryMYRndOq4lAtiqD9uQ5EDPxdrhZ2GombCU8V3qq8RgqefeXvNYUYlhzOxG/ld9h6tbrvgnShB4ZPl9iHpXQhxP18orGjiR8+upro1y2xKrIXnHlhITPjgCXsdDPidj4/6l6wqdrY6H18gnI8HAK9X5vNv8nnt0/2U1wYWo8xICuPuayZxwZiTNcW89hpqd7+Iu7Ggw/PpIzKJmHAbGsPgKH7rddTTdPQjoqbcd05LU3CMiewlyRz6tIhj68qQ1BKR6QPzoKIxhKIPz8BVfxR7+fZOCx5DiI7YMWFU7K9v11e8vZqwlCBh5eljhODpR2x2N2+tPsTT983v1vEnnJU1ahVzWpfDOgxJjwzq8VyHE0cK63jguTU0ttYOykqN4Nn7FxAabBjgmQ0e3NZSWsq2Yi//FkWRhfPxAOL1ynz2dT6vfba/zV/vBEkxwdxxxQTmT0kNKEPTUv4t9fv/jdJRbh1JhWXU5YSkXzpofKwURaHhwBuY4qZgiBjVqWNC4syMvjSFw58VcXRNKZJKIiJtYB7uTPFT/YKnchdhY27o9O81cXIU1bmN+NyBma3tdU4aimyEp4qH1b5ECJ5+5K3Vh8hKjehW9l6vV2bdTn/l7uk58QSb/KGMp/vv3HBxtghJP4VdRyp55K/rsDs9AEzKiuWpe+dhNgys78JgwOdsoqVsKy1l3+C112KKnUT4uBUYIjIHRcHG8w2P18fnXxd0KHSiw0ysWDaOZTNHojmlJIHsddFw8E1aSrd0eE61MYLIiXegD0vv07l3FUflLlyNBcTN+XWXjrMkmMm6JJnDXxRzdHUpmYuTCB+APD2m2Ek0HHgD2dWEq+4ohsisTh2nNWhImBhJ8bbqdn0lO2sISwkWDxh9iBA8/YTN7ubtNYd55ofds+5sP1zRVt341Irqp/rvfHfxGG6/ckKP5jmc2LSnhP99YQPuVgvYRROT+OX3L0J/HgtCRfbiqDlAS8kWHDUHMERkEpJ+KcbYCcL5eIDweH1+i86n+6msawnoiwk3cdOSHJbOHNnuQcbdVEzt7n/gbQnMqHwCU9xUwnOWo9Ka+mzu3UH22Gk4+CZh2dd3K+9PaFIQWRcnceTLEnK/LCHr0mTCkvrXqq3Wh6CPyMJVdxh7xfZOCx6AuLHhlO+tw+v0BbS31DhpLG4eNIkWhyNC8PQTb60+xOjUyC4VCD2VNd/6Qxr1OjWzxp/MFXMiJD05JoTbrhh/Xt/MT+Xzr/P57atf42t1EFwyI53Hbr4QjXpwmPT7G09zJc0lm2kp24qk0hCUNIuwMTeiMQ58bpPzFY/Xx6db/EKnqr690Ll56TiWzEhvJ3QURcFW+BWNR97rOLeOWkfYmBsxJ84clNaCxtwP0IYkYoqf1u1zhKUEk7kokdzVJeR+UczoJclYEvpX9Jjjp7QKnp2EZd/Q6Yg3tVZN/LgIir/t2MoTmix8efoKIXj6AWuLi7fXHOb3P1zQreNdbi8bdvszdc4an4TplOWYE0taqXEWvF4ZRNJO3lp9iD+9taNt+4ZF2dx77eQAn4fzAdnrxF6xg5aSLbibijDGTiJywm3oIzIHjS/H+cjZhY6Zm5fmsHRmeoc5unwuG3V7X8FZs7/Dc2tDWnPrdBDiPRhwNeTTUvoNcRf9vMc39fC0EDIWJHJ0TSmHPy8me2lKv9beMsVNoeHgf/0pG6r2YI6f2uljY8eGU7antp0vT3O1g6bSFkL72WJ1viAETz/w9prDjE6LZFxG9ypCr91Z1OaDsqg12eAJTixp6bRqzveHAkVR+OdHe3h15cmbwZ1XTeSmJWPPmycmRVFwN+TTXLIZe8UOtEGxmJNmETX1vkG3tHG+4fb4+HRLHq9/dqCd0ImNMLcuXXUsdACctYeo3fMy8hly6wSnLvCXO+ikpeHjjz/moosuIjQ0tEvvo7sospf6/f/CkrGs1yL+ItMtKLLCsbVlHPqsmDFLUwiO7Z+/c5XWhDFuMvayrbSUbO6S4NHo/Faekh017fpKdtZgSTSfN9es/kQInj7G2uLirTWHefb+7ll3AD7eeAyASIuRGTknl7M6Ckk/X5FlhWff/Jb31+UC/qyzD/7PBVw9r2ulO4YqJxyQm0s2I7ttmBKmEzPjUXQhIm39QHNS6Oynqj6wvEBshN+is2TGmYWOIvtoOvrRmXPr6IKIGLcCYydz6yiKwtVXX01sbCyXX355l99Pd7EWrAYkgtMW9up5ozJCUWTIW1fGoc+KyF6W2m+Zi4OSZmEv24qz9jBee22XiuPG5URQvq+unZXHVmnHWm7HkiBSZvQ2QvD0MW+tPsyYEZFticG6SmF5I3uP+dd6l5wWoXF6SPqpNDU18bvf/Y4PP/yQmhr/U0R0dDRXXHEFjz32WL891fUHXq/Mr1/efDILtVriZ7fNYtG0tLMfOMRRZB+Omv0BDsiWUZdjipkw4Bl0BX6hs3KzX+icyP90grjIIG5eksOlM0actbyMz9lI7e5/4qo/2mG/PiKrNbdOaOfm5HazbNkyoqOj+fvf/w74BdAJa8Kpr3sTT0s11rxPiZ7+IJKq92870ZmhKLJC/oZyDq0sZMzlqQRF9r3o0YePQmOKxmuvprl0C6Gjruj0sRq9mrix4ZTuqm3XV7KzRgiePkAInj7E2uzi7a8O8+yPuv9E89GmY22vL589MqDv9JD0Uy9U1113HfPnz2f9+vXExvprRFVWVvLaa69x7bXXsnr16m7PaTDhdHn5yQsb+GZ/GeB36v7dMK943rED8g1ojKI8xmDA7fHxyeZj/OuzA+2ETnxkEDcvzeHSC9MDHl46wll7mNrd/0R2d2DFlVRYRl1JSPriLvlj5eXlYTabWbjQf0167rnnKCjwJyq86qqrmDdvXq+LHkVRaDj4JubEGehD++4hJGZ0GLJP4fjmCg59UsSYy1MxR/Rtri1JkjAnzaIp931aSrZgybisS59H3LgIyvfXI3sCH1yt5S1YK1r61SfpfEAInj7kv2sOMXZEFDnp3Vuvdnl8fPGN/2I0NTuO+NOqoJ9eUkJ1ykWqsLCQRx99NKA/NjaWRx99lJdffrlb8xls2OxuHvnL2jYLWLBJxzP3ze+2r9Rgxu+AvJOWks2tDsgThQPyIMPl8bFy0zFe//wANR0InRXLcrhk+rmFjqLIWPM+p+noR3S0hKU2RhI58fYu5daprKxk//79TJo0ieXLl7Nv3z6mTp1KWFgY9957L4WFhdx///28/fbbZGV1PsS6Mziq9uBuKiJy4h29et6OiBsbjiIrFH5dycFPChl7eSqm8L4VPUGJF9J09EN8zgacNYcwRo/t9LFag4a4MX4H5tMp2VnDmGVC8PQmQvD0EdZmF2+vOcJzP+6+dWfDruK23DtXXNQ+ffnpVdJPfShLSUnh6aef5uabbyYmxh8KX1VVxauvvkpS0tD366hrcvDAc2s4VtIAQHiIged+vKiteOJwwdV4nObijdjLt/sdkBNnEjX1XlRacSEcLNidHj7eeIw3Vx9qL3SiglixdByXTB9xTqED4HM3U7fnJZw1BzrsN8ZOImLczV1yQN+3bx9XX301c+fO5aWXXmLatGloNBquuOIKfvKTn7RZcw4dOoTV2r64ZU+QfS4aDr1FaNY1/eY0Hz8uAkVWKNpaxcGVRYy9PBVjaN/lmFIbQjFGj8NRtYfmkk1dEjwAceMjqDhQh+wNFLdNpS3YquwEx4hgg95CCJ4+4s3Vhxg3Moqx3bTuAHzcupwVGmxg9oT2IuX0Ja1Tw67feustnnzySebMmUNVVRWSJBETE8Pll1/O22+/3e05DQbKa2zc/+waymr8gi8+MojnHlhE4jApmir7XNjLt9NctB5PSzVm4YA8KGm0OXl37RHe+epIu+rlCVHBrFiWw+ILOid0AFwNBdTu+j98zva1lpDUhI7+DsGpC7q03OTxeHj11Vf5xS9+wfLly/nqq6946qmnyMjIYMWKFW3ncjqd7N+/n5aWlnOcsWtY879ArQ/FnHhhr573XCRMiET2KZRsr2619KRhsPRdzo6gpFk4qvbgqNqLz2VFre98iQidUUNMdjgV++ra9ZXsrCF7SUpvTvW8RgiePqCp2ck7Xx3hTw9037pTUmVl15FKAJaeIYLj9IKCp14Ijx49yhNPPMFTTz2F3W7nySefZNeuXQCo1UM3OWFBWSM/+uNqahv9NYNGJITyxx8vJCp06D8FeZoraS7eQHPJ12hMEQSnzMUUfwEqjciAPJiorGvmzVWH+HjTMVzuwGy5CVHB3LJsHBdPT+t0kktFUWguXEvD4XdA8bXrVxvCiJx0Z7fKQ2i1WhwOBwcO+C1GCxYs4M0330SlUrFhwwamTJnCoUOHuPvuu1m0aBHz5s3r8hhnwtNSja1gFTEXPjogy65Jk6NQZIXSnTUc+KSQsVekYgjuG9FjiBqLWm/B5/JHS4aMuLhLxyeMj6DqYD2yL9DK01jcTHO1g6B+ijob7gjB0we8ueow40dGM2ZEz607AJfN7rgab11TYKHAUx/8br31Vvbu3QvAj370I8xmM4899hhfffUVt9xyC++//3635zZQHCyo4cE/fYW1xf80PWZEJH/44QJCgoauIFBkH46qvdiK1uNuyMMYN5noaT9EFzpC5OEYZBSUNfKfLw6w6tvj+E67MWUkhfG9S8cyd3JKl7J5yx4H9ftfw16xs8N+Q9QYIibchlrXfevlzTffzD/+8Q8eeOABUlJSKCgo4I477uD9999HkiTeeecdrr32Wu67775uj9ERDYfewpw4A50luVfP2xWSpkSh+BTK9tRy8ONCxl6Rhj6o9yMYJZUac+JMrPmf0Vy8ieC0RV36/urMWmJGh1FxoL11r3RXDVmXDNzvcDghBE8v0+Jw8966I/yxB5FZHq+Pz7bkAzApM4bk2PbmUY/X1+bfcwKJk18wWZbRaPwf744dO9qsO7NmzWLChAndnttA8e3Bch5/fj0Olz+V/rQxcfzunrkY9UMz/NrrbKSleCPNJZuQVFqCkucQOemOHt3YBH3D/vwa/vX5fjbvKW3XNykzhuWXjuWCMfFdFqhuaym1u/6Ot6V9iQGQ/BXORy7psXVk6tSp6PV6/v3vf1NZWckbb7xBbGwsr732GlVVVTzxxBPodL1r+XBU7cXdWEDE+Ft79bxdRZIkki+IRvbJVOyvb3Nk1pl7/7phTvILHm9LJe6GfPThI8990CnET4ik8lADihwopusLbbTUOjD3Q5j9cEcInl7mk015pCeE9sh3Z9OeUhpsTgAuv2hUh/u0t+5IARaesWPH8sorr3DLLbcwfvx4duzYwZQpUzh69Cha7dASCWt3FPGLf27C6/OHbs6fksLPbps15KrCK4qCq+4ItqL1OKr3YYwaQ3jOzRiiskWk1SBDURS2HijnX58fYM/R9sU5L5qYxPJLxnb7e95csoWGA/9BkT3t+lS6YCIn3o4hMrtb5z4dtVrNxIkTmThxYlvI+fr166moqMDtdve62FF8Hr+jcubV3SoO2ttIkkTqjFgUWaHyYAMHPylkzOVp6Ey9e/vTmqPRR2TiqsuluWRzlwWPPkhLdFYoVYca2vWV760jY0FiB0cJuoIQPL2I1yfz9leHuf/6zqcY74iPN/mTjIWYdcyZ1LEp83TBYzZqA54wX3zxRe6//35+85vfEBkZyYUXXkhSUhJJSUm8+OKLPZpff/LxxmM8/a+tyIr/qeeKizJ4aPkFqFVDRyDIHjvNpV/TXLQB2dNCUNJswuZch8Yk8uYMNrw+mbU7ivj35wfIKw288ajVEosvGMHyS8aQGh/arfPLPjcNB96gpXRLh/36sJFETPo+GkPfRBtKksQHH3zAI488wnPPPdcnEZvWgi9Q6YIwJ83s9XN3F0mSSJsVhyJD1eEGf3LCy1LRGnv3FhiUNBtXXS72iu2EZV+PSts1q0zixEiqjzS2s/LU5ltJme7pE8vU+YQQPL3Ixt3FqFUqZk3ovhIvr7Gx/VAFAJdemH7G6uenCx6jPvCjtFgsvPrqq9hsNgoKCvB6vSQmJraFqA8F/v35AZ5/b1fb9k1LxnLnVROHjG+Lu6kIW9F67OXforOkYhl1GabYSX2SaVbQM1xuL59uyeeNLw+2CwYw6DRccVEGN1ycTUx49y0WnpYqane+gMfWfmkMIHjEYkIzr+zzv4+ZM2fy3nvvMW7cuF4/t9degzX/C6KnPzzorJaSJDHiojhkn0LN0UYOrSwi+7IUtIbe+32bYifRoDUhe+zYK7YTlHxRl47XB+uIzLBQk9sY0K7ICpWHGkieOvxyjPUn4srbSyiKwpurDnH9otE9sj58sjmPVmMGl3eQe+cEpwse7xlKTAQHBzN+fOdq7AwWfLLM8+/u4s1Vh9ra7r12Mv+zeMwAzqpzyD439ood/pDy5gp/SPnMJ9AFD9/Mz0MZm93NB+tzeWv14bZl5BOEmHVcu2A035mfiSWoZ8nr7BU7qdv3KorX2a5P0hiJGH8rptgJPRqjs0RHRxMd3Tc3zoZDb2NOmI4+NLVPzt9TJEli5Nx4FFmhNq+JQ58WMWZZKhp97yyPS2otpvgLaC5aR3PJ5i4LHoD4nIh2ggeg8mA9iRMjUXUyzYGgPULw9BIH8msorrSyZEbXQ0dP4PXJfLo5D4BxI6NIO4vZ/HTB43B7uz3uYMLu9PCLFze1OYiqJInHbp7OsllnFn+DAU9LNc1FG2gp3YLaEEpQylzMCdNRafo2y6uge9Q22nlrzWE+WH8UuzPQjyY6zMSNi8dw+eyRPXaKV2QvjYffxVb4VYf92pBkoibf1WvVwwcSR/UBXPXHiJv7m4GeylmRVBIZ8xNQZIW6AiuHPitizNIU1LreET1BybNpLlqHu/E4bmspupCuWfzNkQZC4k1YywOTWHqdPmrymojJGl7JVfsTIXh6iTdXHeKKizIwGbp/gfx6Xym1rULmijM4K5/gdMETYRn6HvxV9S088pe1bdmT9To1v7h99hn9mAYaRVFw1h7CdnwNzrojmGInETXlXnRh6UNm2e18o6TKyhtfHuSzr/PbFd5NjbOw/JKxLLog9awFPTuL11FP7a7/w91Y0GF/UPIcwrKvHxaFXhWfh4aDb2LJvAq1Luic+/eFs3RXkFQSGQsSUeQS6gttHPqsmOylKai1Pbee6EKS0FlScDcV0Vy8kfCx/9Plc8TnRLQTPAAV++uIzgwV15duIgRPL1BabePrfaW887ure3Sejzb4c+8EGbXMm3z27JrDTfAcOl7Lo39d1/a+IkONPH3vfLJSB59jr+xzYy/bhu34GnyeZoJT5hIx/pYuZVcV9B+KorArt4q31hxiy97StiXjE4wZEcn3Lh3LrPFJAdnKe4Kj+gB1e15C9jS365PUOsJzvoc5YXqvjDUYsB5fhUprJCh59ln3e++99/j0009xuVw8/PDDA5oiQ6WWGLUokSNfltBY3Mzhz4sZfWlyr4ieoOQ51O9/nZbSrwnNvKLLpWDCUoLRB2tx2QKtj/Y6F9Zyu6ik3k2E4OkF3l5zmHlTUokK636238LyRr454K/4fcmF6Rj0Z/9oaoeR4PlqeyG/fnkLbo8/y2xmcjhP3ze/R7/PvsDnbMJWtJ7m4vWo9aEEj7gYc/y0YfGEPhxxe3ys+baQt9YcarMansr0sfF879KxTBgV02tPzIoi03T0E6x5n9JR4U9NUBxRk+5CGxzfK+MNBryOOqx5nxN9wY/P6aj885//nGeeeYZ169bx0ksvkZGRgUqlIjs7m3nz5vW75UKlVpF1cRKHvyimqbSF3FUlZF2SjErds3mYE6bTmPsBsttGc/EmQtIv6dLxkkoiLieCwq8r2/VV7K8TgqebCMHTQ6wtLj7dksfzjy7u0XneXO130JUkuG7h6HPuf7qFJ3IICh5FUXj10/3888M9bW1zJibzs9tnDqqEgu6mYmyFa7CX78AQmU3kxDtbq5QLs/JgpN7q4MMNR3l/XS711kAnYbVaYv7kFP7nkjFkJveu9VD2tFC7+8UzFv40xV9AeM7yYefX1XDobUxxU85Z+uL5558nKyuLSy+9lJycHCZOnMgDDzyASqXimWeeISwsjIkTJ/bTrE+i0qjIWpzM4c+KaCxpJn9DOSPndT2R5KlIai1BKXOwHluJrXAtwWkLuxx9F50ZSvH2amRP4NJrfaENZ5O7T2uDDVeE4OkhH288xui0yB5dPOubHHz5jX+df87E5HMWwZRlhXrr0LbwuDw+fvfq16zadryt7XuX+sPOe2tZoScoioyjeh+2gjW4mwoxJ84g9qKfozUPnbD+84380gbeWnOYVVsLcJ/mnxNs0nHlnFFcMy+T6B6Elp8Jt62M2h1/w2uvad+p0hCWfT1ByXOGnUh21BzCWXuE+E44Kvt8Pu655x7AX9x4+fLlPP744219H3zwwYAIHgC1VkXWJckc+Og4NUcb0QdpSJ7Ws+96cMo8rPlf4HM2YK/chTl+WpeO1+jVxGSGdlhuouJAHWkz43o0v/MRIXh6gMfr4521R3hkec/W4t9fn9t2gb7x4nNnV21qcbWr5TOUBE+91cFjf1vPgXz/zUGjVvHoTdNZOrNrmUn7AtnrpKX0a2zHv0KRvQSnzidqyt1dXoMX9A+yrPDNgTLeWn2IHYfbm/+TY0O4fuFoLu3EMnF3sVfspG7vKyg+V7s+tTGSqMl3obMMv4rXiuyl4eCbhGZeiVp/7pIop9bqevDBB/F4TvqnFBUVMXLkwH7/NXo1o5eksP+DAkp31aIL0hKbHd7t86n1IZjjp7VeT9ZgipvaZcEbmxPeoeCpPtJI0tRoNL0UWXa+IARPD1i7owijXsOFOd3PseJye3l/XS7gd57MGXnu/Bh1jYHWnSCjFr1uaHyU+WUNPPKXdVS0JnezBOn57T1zmThqYC0nXkcdtsJ1NBdvQhsUgyXzSkyxE0WSwEGKw+Xhs6/zeWfNEYqrrO36p42J4/qF2VwwJr7PLIaKItOU+xHW/M867DdE5RA58bZhK5Ztx9e0Ld10hxMlbvLy8li3bh3PPfdcL86ue+iDtIxeksKBj45TsKkCnUlDeGr3gxGC0xbSUvq1P0S9G/W1jBY9YSnBNBTZAtp9Hpma3EbicgZfUMdgRlzNu8mJRIM3LMru0QX1828KaGwtAnrjxZ1LrFfbFBiuGGEZXM69Z+Kb/WX87/9tbMt7khJr4Zkfzj/nEl5f4mrIx3Z8DfaqPZhixhM97Yfn9EUQDBxV9S28u/YIH288hs3uDujTaVQsvnAE1y/MZkRCaJ/OQ/bYqd39zzP664SMXIpl1OWDLttwb+F11NN0bCXR037Uo/fodDopKirixz/+MUbj4LBSmyMMZC1O5tCnRRxdU8qYy1IJjuneNVYXkoQ+IgtX3RGsx9cQ1UXBAxA/Lryd4AGo2F9P7JhwpEHgAjBUEIKnm+zOraKqvoVLpo/o9jlkWeG/rdmE4yODmDOpc3Vt6psCHTEjQgfHheJMKIrCO18d4c9v7WiriTU1O47f3DWHYFP/O94psg975S5sx9fgaa4gKHk28XN+I2pbDWIOFtTw39WHWb+zCJ/cfjn36nmZXDlnFGHBfe8Q7PfXeR6vvX2Vc0mt92dNjpvU5/MYSBqPvIspblKXLRanYzAYmD9/PvPnz++lmfUOlgQzGfMTOLqmlMOfF5NzZRrGUH23zhWStpCauiM4KnfhtdeiMUV27fh4M6ZwPfb6wCVTp9VNQ7GtRxao8w0heLrJm6sOcfXczB75BXy9r7TNHH/dws6XpGhn4QkZvFEfXq/MH9/8lg82HG1ru2ruKH58wzQ0/ZwiXfbYaS7ehK1oLZKkJjh1AeakmcMuama44PXJbNhVzFtrDrf5e53KqORwrl80moVTeydRYGewV+yibu/LHfrraEzRRE65Z9iXEXHWH8NRvZ/4Ob2TUXmwOnJHjrTgbvFQ+E0Vhz4tIueqNHSmrkePGqJz0Jhj8LZUYStcS1j2dV06XpL8Ier5G8rb9VXsrxeCpwsIwdMNiiqb2H64gsduvrBH5zlRKyrYpGPZrM4/KbVPOjg4l7Rsdjc/fWFDWzFUlSRx/w1T+M78rH69yHkdddgKVtNcshmdJYWw7BsxxowbtssNQx2b3c0nm47xzldHqKpvCeiTJJg9IYnrF2UzISO63/6O/Pl1Pm7Nr9MeQ9RYIifegUo7OL+LvYWiyDQeeouQ9CWoDZaBnk6fEz8+Elezh4r99Rz+vJixl6eiPkNB5zMhSSqCUxfQcPANmks2Y8m4rMtV1CMzLBRtq8Lr9AW0N5W10FLnxBwhHto6gxA83eDD9UeZPyWlR5FRRwrr2H20CqDLJSna5eAZhEtapdU2Hv7zWooqmwAwGbT8+s6LeuTg3VU8tnKsBV9gL9+BMXYiMRc+PCyjZYYLuUV1vL8ul9XfFuI8rTacSa9h2ewMrl2QRUJU//p8+f11XsRZs7/D/pD0JVgyrzgvBLS9bBs+dzMhaQsHeir9RuqMWNx2L3X5VnJXlXYrMaE5cQZNRz/0W5lLt3T596fWqIjNDqN0V227vsoD9aTPGT6JLPsSIXi6iMvj4/Nv8nnq3nk9Os+bqw4C/kRo1y44d6LBUzld8ISHDC7Bs/toFU88v56mVmfsuMggnr5vHukJ/VP0ztWQjzX/C5w1hzAnXkjsRb9Aa+6b6tCCnuFye/lqexHvr8/l0PH2F/O4yCCunZ/FslkjCRoAfy+PrZyanX/D23Imf51bMMVN7vd5DQSyz0Vj7vuEjr7uvMouLkkSGfMS8Ni9/sSEG8sZObdriQlVGj1ByRdhzf8C2/GvCE6d32WBHDsmnLI9tSiBKaaoyWsidUZsr5TEGO4IwdNFNuwqJjzEyLhOhI+ficq6ZtbuKAJg0bS0LpdQGMwWnk+35PHU61vx+vzfypz0KH73g7l9Lsr8hTwPYs37Are1mOCUuYSPXX5emN2HIiVVVj5Yf5RPt+S1i7YCmDAqhusWZDF7YlKnfdt6G3vlbur2vHQGf50oIqf8YNj765yKrWAVamMEprgpAz2Vfkel8Scm3P/hcWpyG9EHaUme2rV7QFDKPKwFq/E5anFU7umyY7vOrCVihIXavKaAdtkjU1dgJToztEvnOx8RgqeLfLzpGJfNzuiR78A7Xx1pizTpTKLB0xmMhUNlWeGF93fx7y8OtrVdfEEaj6+Ygb6La95dQVFk7BU7seZ/js/ZSHDaIqKm3DPsfSmGIl6fzJa9pby/PrfNr+tUTAYtl144gqvmZvZ5WPnZOFkPa2WH/X5/nduHbX6djvA6G7Dmf0n09AcHrZNxX6PRq8le2pqYcGcNOrOmS4kJNcZwTHGTsZd/i+346m5F8sWMDm0neACqjzQIwdMJhODpAqVVVg7kVfObOy/q9jlaHG4+3uSvij5ldCwZSV3L5Nni9OBwBfo3DLTgcbg8/PLFzWzcXdLWdseVE1ixNKfPLo6Kz0NL2TdY879AURRC0hdjTpyBSi3qyww2ahrtfLLpGB9tPEZNg71df0ZSGFfNzeTiC9K65MvWF8geO7V7XsJZva/D/pD0S7FkXnle+OucSlPuBxhjJqAPTRvoqQwo7RMTaglP7bxPWXDaIuzl3+JqyMPVWIg+NLVL44fEmzusom6tsONodHU7dP58QQieLvDJ5jxmT0wmtAe5Pj7elEeLw//H2tlEg6dSf5p1R6dRDUgumxPUNNh5+C9rOVrsT3+u06r52W0zmT8ltU/Gkz0Omos3YD2+BrUuGMuoKzDFTUFSiRTrgwlFUdh5pJL31+eyaXdJu9w5Wo2KBVNTuWpuJmNHRA4Kq4HHVtHqr1PVrk9S61r9dc6/5Rx3UxH2il3EzfnVQE9lUBCYmLCkS4kJ9aGp6MNG4mrIw3Z8NfqJd3RpbEmSiM4KpWR7+zQN1bmNpFwgav2dDSF4OonXK/Ppljx+ccfs7p/DJ/P2msMApMVbmD626571tac7LFuMA3azOFxYy6N/Wdc2pwiLkafunUd2WtcSa3UGn8uK7fhX2IrWoQtJJGLczRiixg6KG6XgJNYWF59/nc8HG45SXNm+5EN8VBBXzRnF0pkje/Tg0NvYK/dQt/clFK+zXZ/GFEXk5HvQhSQOwMwGFkVRaDj0NsFpi9AYu19XarjRk8SEwWmLcDXkYa/YgTfzSjSmqC6NHT3qzIIneWq0yLx8FoTg6SRb9pVi0GuYlBnb7XOs3na8La/IDYuyu3Wzrms8vazEwCxnrdtZxK9e2ozL7c8LkZEUxtP3zSemlytRe+01WAtW0VLyNfrILKKn/rDH2V0Fvc/hwlo+WH+U1d8eb/ubOIFKkpgxPoGr52YyLbvvalt1B0WRaTr2CdZjZ/DXicwmYuL3UevOH3+dU3FU7sbbUkXU1PvOvfN5RuRIC65mD0Vbqzj0WRE5V45AZzr3LdUYOwGNORZvSyVNeZ8RMe7mLo2rD9YRmhREY0lzQLvH7qWhpJnwlIEr1TPYEYKnk3y88RiXzcro9sXa65N5ZaXfLyAqzMTF3SxJ0S5Cq5+TDiqKwuufHeD/Ptjd1jZrQiK/uH12r/pfuK2lWPO/wF65E1PcFGJmPn5ePmEPZpwuL2u2F/L++lyOFNa16w8PMXDZ7AyuuCiD2IigAZjh2ZG9Tur2vIijam+H/SHpl2DJvOq889c5geLz0HjkXSyZV4ls5GcgfnwE7pYTiQmLOpWYUJJUWDKWUbfnRVpKv8EycmmXy01EZ4a2Ezzgd14WgufMCMHTCSrrmtlxpILHV3Q/s/KXWwsorfYXgLtpydhuRy61y8Fj6b8Lkdvj48nXv+GLbwra2r67eAx3XTOx10KHXfV5NOV/hqs2F3PSTOLn/KrLJl9B35Jf1sDKTXl89nV+hyHlE0fFcNXcUcyZlNxvJR+6itdeR82Ov+CxlbXrk9Q6wsetwBw/dQBmNniwFa5F0hgxJ/Yso/xwRpIkf2LCFi91BZ1PTGiKn0rTsZWnWHlu6tK44WnBaPRqvK5Aa2pDkQ23w4vOKG7tHSF+K51g5eY8po9NIDK0e9YUr/ekdScm3MRlszK6PZeBsvA02Jw88fx69h7zJ2BTqyUeWT6dy2Z3/72cirMul6ajH+O2lhCcMo+IcStQ60WNmMGCze5mzbfHWbk5j8MdWHPMRn9I+ZVzBjakvDO4GvKp2fE3ZHf7CtRqYyRRU35w3lsTfS4bTXmfEjXlB+ethauzSJJExvwEPA5/YsKCTeWkzzl7YkJJUmEZuZS6vS/RUvp1q5Wn88WLVWoVURkWKg7UB7QrMtQebSR+fO/7UQ4HhOA5Bz7Z76z80PLp3T7HF1vzKa/xmx9vWpKDrgd5aU53Wu4PH54jhXX85IUNVNT630OIWcdv7/7/7N13fFP1+gfwz8neTfcetKVltJS9p+wl7r0VL4p7X716VRxXf+6FeyBex3WggICo7E1ZZRRoKd0j6Uqzxzm/PwJp07SlI23S9Hm/Xr5e5JyTnKdSkiff8TxTMXxA59czAc7pMUv1SdSfXg1bQwmU/WYhfOQ9He4zQ7oHy3I4eKoSa7bnYVN2Iaw2h8c1/eODcdm0dMwc7fst5e1hKN2D6iNfAqzd41xfX6/TVP2p3yAJHQBJaLqvQ+kVmhYmrMqtg0h+4cKEsphRqM9bDbuhCrr83xGSeWOH7hkxINgj4QGAytw6RA8JpQ0dLaCE5wL2HisHx6FTO6oA5+jOl2ucPXgiQ+QdahLakubb0rsz4eE4Dqu2nMJb3+2Dze6snJwQqcL/3XcR4iM7P/pCiY5/q6jWY93OM1i7Iw9lWs91AjKJEDNGJ2HBhFQM9pMt5RdyocXJiqTpCB54JZU3AGBtKIWhZCeiJv/b16H0KgIxH4PmJSDnlwKUZGsglgsQ2UZhQobHhyp1PmoOfwF98Q6oUudBIG3/KI88TAJ5uAQGjfvOQlOtBfoqU7u3yvcllPBcwG9bT2PBxNROr1H5fWe+60Pj5vmZXV7T0FNVlk0WG15ZsRt/7ClwHRuXGYt/3zERKnnnilu5JzqlUCbPpETHT1htDmw7VIw12/Ow93gZOM7zmmFpkVgwMRVTRyRAKvb/0ZzzWIcVNYe/gLF8v+dJhofgwddCmTi1x+PyV3UnfoQicSr1n+sEsVKEgfOdhQnzt5VDKBe2uYhYHjMGutNrYTdWQZe3DiGZN3TofhHpwSjQeFYtr8qto4SnBZTwtKG63oSdOSW475rOFRuz2R346nfn6E5UqBzzJ6R0KR6b3YE6vXtfn9Bu6KN1trweTy3fjIIyZwlzHsNg8SVDcePcjE7tUqNEx3+dKqrBmu15+GPPGegMnguQw4NlmDc+BfPHpyCuC6N6vuIw10Gz/31Y6896nGMEUoSPWAJJWMfbuwQqU9VRWOvPIqyDBfFII2dhwngcX1uEUxvbLkzI8PhQ9T8/yrP93ChP++sdhfcPwtldFeAc7t9QtNRQtEWU8LRh7Y48jBgQhehObqn9fWe+a93LLfOHdHl0p0bnPnTJMECwl4u3bdxbgP98tcvVviJYKcFzd07CyIHRHX4tZ6KTey7RKaNEx0/oDBZs3FOA1dvzXBWymxLweZg0NB4LJqZi9OBonzXv7CprfRE0+9+Dw1zrcU4gC0f4qHshVHT89zpQcawDdSd+QFD/i6kXXRcFxSqQOi0Gp/8qdRYmvLQfpEEtj4w7R3nWOGuO5a9DSMb17b6PQMxHaD+VR38tBzUUbRElPK1gWQ6rt+Xh7is63uANcI7GnF+7ExOmwLzxXRvdATyns9QKCQR873wYWW0OvPe//fjx75OuY1n9I/D8PyYjvIO70zwTnVkIH3Uf1fLwIZblsP9EOdbsyMPWA0WwnluT1VRKrBoLJvXH7DH9/KoKcmcYKw6h+tCnLXY6F4ekIWzEXeCL/K82kC/pi7aCA6BI6HyvQNIovL8aVoMdhbsrcWJtETIu7dfidnHXWp4jXzpHeVLmdmiUJ2IANRRtL0p4WnE0XwODyYqJWZ3bnrp2R76rqvItCzIhEHQ9MWle80St9E6juPJqPZ7+cCuOF2hdx66fPRj/uHRYh+KmRMf/lGv1WLsjz+33sSmFVIiZY/phwcRUDEjs/Ts7OI5Dw5kNqMv9GYDnQiR5/ESEZFwPhkdvfU2xNiPqT/+G0KzbaOG2F8VkhcKit6HiaA1yfy/C4IuTWpxmkseOQf3pNXCYtNDlr0dIxnXtvkdQbOsNRc06KyQqaqh8Hv2rb8Wf+wowbWRip6ahrDYHvlrrrLsTG67EnLFdH90BAKPZ/RfaG9uAd+WU4rlPt7nWbyikQjx12wRMGZbQ7tdwT3TKKdHxMYPZhq0HirBuVz72n6ho8ZqRA6OwYGIqpgxLgFgUGG8DHGtHTc5KGEp2tHCWgXrgFVD2m9nrk7ruUJ+3FqKgREgjMn0dSkBhGAb9xkfBarSj5owOJzcWY+CcBI9+VwxPgKDUeajJWQF98TbnWh6Jut33iEhXo3i/Z38tbX494oZR4dbzAuOdzsvsDhZ/7SvEC3dN6dTz12zPQ2WNs+fVrQuGeGV0BwD4zf6RyLqwU8bBsvjst8OuaTfAWVPlxbumIi6ifaXJXYnOqd9g01dQouNDdjuLPcfKsH73GWw/XOzRzwpwFr2cPyEV88anICY8sMrPO6wN0GZ/CEvNKY9zDF+M0GF3QBY5tOcD6wXspmroz25G5MQnfR1KQGJ4DNIuisUxox11RXqc2V6OlMmeZU7kceNQn7cWDlO1c5Rn8DXtvkd4WssJT3W+jhKeJijhacGhU5Xg8xlkpXZ8W6bV5sCKczuz4iOVmDW2n9fiGpsRi9gIJUrPtajo7AhPjc6EZz/Z5vbt/+LJ/fHgNaPa9W2/xURn9P2U6PQwjuOQk6fBhj1n8Pf+QtTrPderiAQ8TB6egAUTUjFiYFSvXYDcFltDOTT734Xd6PmGz5eEIHzUPRCp4n0QWe9Qf+o3yGJGQKSM9XUoActZmDAeOb8UoPJ4LRRhEo8aPc5RnvnOUZ6iLVClzGn3KI9EJYIiQgp9lfs6T4PWDFO9pdUF030NJTwt2Li3ANNHJnVqC/bqbadRVesc3bllwRCvLSoGnLtnPnx8Dm5dtgbaOhNkko7/9R0+XYmnP9oKbZ3zH4ZYxMej14/FvHZumTdXn0L9yV9g01dAlUKJji+cLavDhj0F2LinoMXCgIBzwfmsMf0wfWQSVIrAfbMzaY5Be+AjcHaTxzmRuh/CRywFXxLkg8h6B2tDKYxl+xE95XlfhxLwhBIBBsxNQM5PZ3BmewWkwRKoot03hDQd5WnIX4/gDozyhKaoPBIe4Nwoz3Aa5QEo4fFgszuwObsIbzwwvcPPtdga6+4kRKowc3TnRnfsdjsEAs+/Gh6PQZBCjI+emItbl63t0AgPx3H49o/jWP7TAThY52LO+EglXrprKlLigi/4fGt9EepO/gJL7RmoUmZTotPDNHVGbNxTgD/2FLS4lRwA+sUEYfbYZMwc3Q/RYYG/A6nh7CbUHv/O2UCoGVnMKIQMuQU8Pi3YbEv9yV+gSJzcoT5OpOM4jgPDMJCpxUifHYfja4pw8o9iDLk8GWJF4/t441qer6Ev2gpl8qx279gKTVahcFelx3FKeBpRwtPMvuPlUMhEGNSv483XVm055Ro5uXVhx0d3OI7Dk08+ieuvvx4ZGRktXiPg8xAaJMUHj83C3/sL2/W6eqMVL36xE1sOFrmOTRuRiCdvGQe5tO0PBJuhEvUnf4Wp6jCUSRchdOgd1Guoh+iNVmw+UIQ/9pxBdm5Fi9WPw9RSzBrTD7PGJKN/fHCfWJDLsQ7UHv8e+sJNLZ4P6r8Qqv4L+8T/i66w1OTBXH0KMUNu9nUoAc9ms0Ekcr7XquOUSJ4UhTPbKnByQzEyFiWB12SdpzxuPOrz1sFh0qL+1K8Izbq1XfeQKEVQREqhr2w2rVVthqnOAqk6cEd624sSnmb+3HcWM0YldfjNUmew4IvVhwEAiVFBmDE6qUPPNxqNuOqqqxAfH++R7Jz/dnCeSMhHbLgSF0++cKfyU0U1eGr5FpRqnOt++HwG91w5EldNH9Dmz2g310J3eg0MJbshjxuH6Kkvtns+mXSeze7ArpxS/LGnANsPl7TYsFMuFWLaiETMGtMPw9IjA3JdTmtYmxHagx/DrDnmcY7hCRGSdQvkMaN9EFnvwnEc6nJ/gip5FviiwFrA7k/efvttFBYWoqysDDNnzsTtt98OAIgcFAJDtQWVx2uRv7UMqdNiXe/HDE8A9YDLUH3wYxhKdkGZNB2ioPbtmg1LVnkkPMC5UZ4R4bCZ7TDVWqEIl7glWX1Fr054OI7DPz/YDG2dCVfPHIiLRiZ26c3fYrVj68FiLH98doef+9XaHNfW7qVXDO9QHBaLBbfddhtsNhuWL18OADh16hRCQ0OhVqvB5/PBsix4TV5TLBIg5AIjSGu2n8Zr3+x1fWhGBMuwbMkUZKa0PrzpsBqgy18HfeEmSCOHImryv6mnTjdjWQ5H8qrwx54C/LXvrEe9JQAQCngYlxmL2WOTMX5IHMTCvlcrxW7UQLPvXdj0nr2DeGIVwkcshTg42QeR9T7mqiOwGasQ3u9+X4cSkBoaGvDRRx/h66+/xtdffw2tVoulS5dCp9PhwQcfBMMwSJ4YjYYKIzSn6iEPkyJmSOO0oix6JBoKNsJaV4C63B8RPvrBdn0JD00JwtkWprW0Z3SIzgrFoe/zYTPZIZIJEDs8DJEDgvtU4tOrE57qehO2HiwGAPz74234YvUR3LpwSKcTn105pQhTS5HajjUtTZVUNeB/f+UCAEYMiMKEDhYrtNvtGDJkCCwWC3bu3ImffvoJx48fh0KhAJ/Px3fffeeW7JzX2pSZ2WLH6//dg7U78l3HRg+Oxr/vmNRqKwrWbkFDwZ/QndkAcXAqIsc93u5vFaRz8ktr8cdu57qclooCAs6GnbPG9sO0EYmdbtoaCKz1haja9w5Yi87jnFAVh/CR93So03RfxnEs6k7+gqDUBbQOr5u89NJL+OKLLzBnzhyEhYVhyJAh2LBhA7Zv3w6r1QqRSASGx2DA7AQc+fkMzu6qgCxEDHWcc+0dwzBQD7wSVbtehVl7AmbNMUgjWl7m0JRYIYQyUgq7xYGQJBVYBwvN6XoYq82oPauDzeRsGWQ12lGwvQLF+zQI6x+ExDGR4PEZNFSaYLfYwRPwIFYKIVGJAmpqmOG4llYG9A5VNQZc8thPHsflUiFevWca/rNiF4xmO8QiPvpFqzEsPRLD06PQPyG4xYToXx9uQVJ0EO5YNLRDcTy1fAs2ZReCYYAvnl6AtIT2LTI7ffo0LBYLMjIyUFVVhS+++AI///wzIiMjsXLlStTV1eGxxx7D8OHD8dhjj7XrNYsrdXhq+RbklTj7BzEMcNvCLNyyILPFn5lj7dAXbUV93loIZREIGnApJCFp7f/hSbtxHIf8kjpsyi7EpuxCnC33LAcPOFs8zBqbjJmjkxDVyT5ugcRUlePcidVCmwhpZBZCh95BH9wdoC/ZCd3pNYie8jxVnO4Ga9aswQMPPIDXX38dKpUKDz30EFasWIHMzEyYTCZIpe69BOtK9Di+thACER9DLk92q4ysyV4OU8UBCBUxiJr0TLuqYFsNNvBFfIABwDk/A87uroTdYof2tOcXBgCQhogRlqzyqOUjkgmgipFDFSNDSJISIlnXi936Uq/8bec4DtsOFeOdH/a3eN5gsmHPsTIUVza4jpVp9NhxpASAs5rwoslpuHFehutbs8Fsw44jJbjj4qwOxXL4dCU2ZTsXD88bn9LuZGfnzp2YN28eQkJC8MsvvyArKws33HADYmNjcdVVV0EkEkGlUmHs2LEe/0Basym7EC9+sdNVkTlIIcaziydhzGDPIlccx8JYuhd1p34FTyBGaObNkERkBlQ27w84jsPp4lr8vb8Qm7MLUVTZ8htORLAMM8f0w+wxyUiN79gIYyDTF21DzdGVLe7EUibPhnrAZWCYvjMk31Wcw4b6U79BnX4pJTvdZPfu3XjnnXcwb948AEBmZiYMBucIbkvv5eo4BZLGRuLsrkrkrnc2GuWfm7JWD7gMpsrDsOnLYCjZCUXCpAveXyT3TEoSx0Ti4Pd5rT7HqrfBWOv5hcJqtEObVw9tXj3O7qhA9JBQxA4Ng0DcO6fUe91vfLlWjzf+u9eVvDQXE6bALfMzMXdCCjJTIqCpM6LBaMWJAi0OnqpEvd4CvcmGbzYcw2/bTuPm+Zm4/KIBKNc24N+3T0RSjLrdsbAsh3d/yAYASEQC3HnpsHY9j+M4bNiwAS+88AJkMhluv/12fP/990hJScHll1/uWs2v1+vxv//9z7XQzfP+znU9djuL93/KxvcbT7jODU4OwwtLpiAyxH1HFcdxMFUdQf3JX8A5rFCnL4IsZjR9aHgRx3E4WViDv/efxabsIteC8eaCFGJMGZ6A2WP6Iat/ZKfqPgUqjuNQf/o36E6vaeEsg+CM66FM7Fwl9L6soWgLeEI5ZDGjfB1KwDKZTNi3bx/mzZuHo0ePwmq1wm63t/mc6CGhMGjN0JyuR96mMqTNjAPDMBDKI6FInAr92b9Qd+pXyGJGdWo0k+EBsVmhKNjh2WqGJ2CQMjkG8jAJwAHmBitYGwtTvdWtHR3r4FB6UIvK47WIGxGO6MyQXvcFuVdNaR0+XYmH3/oLRovzl2dgUijyS2phtbMYnByG2y/OwpjBMa3+JbAsh4LyOqzbmY8f/8qF1c7iksn98cC1o8EwAJ/PA68Df4F/7DmDZz/ZDgC4/eIs3N6B0SGHw4Hq6mpERETg+eefx/r16/HLL78gMjISALBv3z7cf//9uPTSS/Hoo496PN9ssYMDhwaDFU9/tBU5+Y1DkVfPGIi7rxju0QfMXH0Sdbm/wG7SIih1PhQJk+hbnpdwHIcTBdX4O9uZ5JS3UhBQrZRg6vAETBuRgGHpUV4tTBko2uqJxfBFCBt2J6SRHRuJJQBrM6Fs85MIHXo7pOEXXg9COqe6uhrXXHMNJBIJgoODERwcjLfffvuCz3PYWRz9tQAGjRkJoyIQN8K5ucRhbUDZpqfA2U1Q9V8IddrFnYrLYWOxf8VJOGzO0VJJkAjxI8IRlhrk0dsLAOwWB3QVRujKDNDm62DVu/dyDEsNQuq0GPB60XtYr0l4jp3R4P43/oTRbINSJsLdVwzHwon9UVRRD73JhsHJYR3KNiuq9fjvhmO46/LhkHaiJ5XFasc1//oVlTUGhKml+P7FSzr1OoBzpOaJJ57Avn37sGnTJjQ0NOCbb75BdHQ0Fi1a5HG9yWJDdm4F9EYr3vl+P+rOtRSQSYR48pbxuGhkotv1jUUD86FKmQNl0nTwBH13Aay3sCyHYwVa/L3/LDZnF7W68DhEJcHUEYmYNiIRWf0jKMlpA2s3Q3vgwxa3nfNESoSPuhditffatfQldad+haX6NCLGPtzrvpn3NlarFTt37kRMTAxSU1Nb3HTSEovehiM/5cNmcmDA3ASEJDpLBujyN6Au90cEZ9wARcLkdv39WSwWHDp0CDt27MBDDz0Eh82B0kNamOqsCElUtprotIS1syg/VoPSA1rYLY2lMtRxcqTPjndNwfm7XpHwnC6uwdJXN0BvskGtEOO9R2cjOVbtldd2sGyndnSt+D0HH/58EADw5C3jsWBiapfisFgseO6557Bq1So0NDRg1apVGDFihMd1JosNm7OL8OIXO8GBcxWjS4lV48W7piIhSuW6tnnRQGXyHCoa2EUsyyEnv8q5JudAETTn2og0FxYkxdQRCZg2MglDUsP7VK2cznKY61C1713YdEUe5wTyCISPup9KJHSSw6JD2aYnETHmIdq67+d05QYcW10IHp9B5mXJkAWLwTls0B7+HKFDbm5zSqt5+ZLTp0/jsssuw8qVK5GVlQWbyQ6byQ5ZSOcW+dstDpz+uxS1hY3T9IpIKTIuTuoVIz1+n/DY7SxuWbYGZ0rroJSJ8N6js9A/vn0Lg7tLg8GCSx//GUazDf3jg/H50/O98oG2cuVK3H333fj2228xf/58j/Mmiw2/bjmFd86tGzpv7rhkPHrDWEjEzukph0WH+lOrYSjZAXnceKj6L6CigV3gYFkcOV2FTdmF2JxdBG29Z2EvAAgPlmHaiERcNCIRGSnhtCanA2wN5aja9zYcpmqPcyJ1MsJH3UMF8rqg5uh/4bDUI3zEXb4OhbRDxfEanNlaDkmQCEMuS4ZAzIfdqAVfEgKmhc8ag8EAgUAAsbhx5N5isUAsFuOdd97BgQMH8OWXX8JudaCuWI+wlM73l+NYDvlby1CVW+c6Fj0kFP3GR3X6NXuK3y/g+HbjcZwprQMAvHjXFJ8nO4Cz6N9r912EB9/6E/deNdIryY7BYMA333yD33//HRMnTvQ4z3IcVqzNwVe/H3UdEwl4eOi6MVg4KRUMw4Bz2KAr+BO6/N8hCRt0rmhgZJdj64vsdhaHzu3A23KgCDU6c4vXRYbInUnOyEQM6hdGSU4nmGtOQbv/fbA2z9EyaeRQhA67Azw+TcF2lt2ogaF4B6Im/cvXoZB2ihoUAoPWjMrjtTj1VwkGzkkAXxra6lTWyy+/jI8++gj33XcfkpOTsWDBAgQFOZMai8WC8PBwsCwLgYjvXJzcBQyPQcoU59qdimPOvn7lR6qhjpMjOMG/v5T4dcJTpmnA5+faNcwbn4KRA6O77V7n2zc0HxJsiUjIR3piCJZcOsxrMcnlcqxatcotQ28a2+qtp9ySHYYBPnpyLtITQsFxHAyle1B38mfwRSqEj7q3y7V07HY71q5di1GjRiEmxnNbeyBqMFqxK6cU2w8XY3dOKfQmW4vXRYcpcNG5NTkD+7X+JkQuzFi+H9pDnwGs5y4WReI0BA++hnYQdlHdyVWQxY6BUNF975/E+/pNiIKxxoK6Ij2K9lUhcUwkOJZtcYRn3rx5rnWfW7Zswf/93/9h0KBBqKurg9lsxvvvv+/6bBPJhLA0WCFWdr6xLsMwSBofiYYqIwwa55fBvE1lGHp1CoQS/00r/DcyACvXH4PF6oBKLsI9V3quZ/EmlmVhtVrbXfNGKhbikqnpXo2hpWSHZTm8/f0+VyXn88LUMqQnhMJSk4faEz/AYa6HesClXtli/u233+K5555DcnIydu3ahcGDB+PGG29sVzLY25RqGrD9UDG2HS7B4dOVcDhanuGNDVfiopHOJCc9sfdtx/RHujMbUXfif3Db+3qOesAVUCbPov/PXWStL4Kp8hCip77g61BIB/H4PKTPiseRn/JRelALeZik1amo8ePHIyMjAwMGDMAdd9yBsrIybN++HWFhYYiLi0NsbCyuvvpq/Pjjj+DxmRYbEXcmvrTpcTj84xmwdhY2kx3lOTVIGOW/6+z8NuExmG34Y/cZAMDVMwZB3UpLBG949913UVRUhOLiYlx22WW46qqr3Bp2Nm/eeV539zOy2Vk88d4m7Dpa6nFOIeZBk70cZu1xqFLmQtlvBnj8zmXsNTU1KCoqwtChQ1FXV4dt27bhq6++wpgxY3D06FFce+21WLRoEVQq1YVfzM85WBbHC6qx/VAxth8uQUFZXavXJseqMWloPKaNSOwznch7AsexqDvxPzQU/Ol5kuEjNOtWyGPH9HxgAaju5C9QJE6DQELFLHsjkUyAAbMTcPTXAuRtKoUqSgahjN/il9rrrrsOb7/9NiZOnIiYmBhcddVVbuclEgk+/fRTLF68GCK5dz76pWox4keFo/Bc/66KozWIHRrqt7u2/Dbh2binAEaLHXwegwWTurYDqi05OTn46quv8Oijj2L48OFYvXo1xo4di4SExj5SLX3QtZYEeQPHcTBb7XjgzT+Rk+esr5OeGIKThTWuawTWcvBFKsRMfRF8cecSEYfDgcceewyffvopIiIicPr0aajVaqxatQr33nsvACAqKgo1NTX44osvcP/99/fKUR6TxYa9x8ux43AJdhwuQW1Dy+tx+HwGw9OcvdAmZsUhJty/56N7I9ZhRfWhz2CqOOBxjhFIET7ibkjCBvggssBjrj4JS+0ZhA69w9ehkC5QREiRMiUGp/8uxYnfizDkipZ32V177bWIjnZOW7b0hf2BBx7AI488gsWLFwNwFhLk8bv+GRY1KNi1Xd1ucaDyeC1issK6/LrdwW8TntXbTgMAJmbFI1wt67b7vPXWW7jxxhtx9dVXw2q14sCBA3jppZfw4YcfguM4vPLKK3jwwQc9ppu6I9nhOA5lWj2OndHi/R+zoak1QiIS4L6rR4LP4/DyV3tc1waFJSIkc0GX7sfn8zFs2DDs2rULjzzyCFatWoVLLrkECxYswDPPPIMnn3wSO3bswKRJk7BixQrcf//9vSbZ0dQZseNwCbYfLsb+4+Ww2j1bEwCAUibCuMxYTMyKw9iMWChknZ/XJm1zWPXQ7n8fllrPEvd8STDCR98PkTLWB5EFHo7jUJf7E1QpVIoiEISnqaHXmlF+pBp1JQao4+QtfgZNnToVR48eRWJiIqqqqpCTk4OoqCgcPnwYer0eubm5WL9+PebMmQOO9c4Gbb6Qj6iMEJRkO7+cl+fUIHqIf65t9MuEp8FoRW6hc3vq7LHdV2TMarVCpVK56t2IRCLce++9uPLKK6HVavHrr7/i+PHjHslOd4zuaGqNePHLHdh7rNx1bHByGJ6+bQLCcAb/XfUngMYdV3K5d5pKXn311RAKhbjooouwcuVKXHLJJVi2bBnWrVuHxx9/HGlpafjPf/6DO+64A0VFRW4jX/6E4zjkldRi+6ESbDtcjNyzntubz4sNV2LS0HhMzIrDkNQICAS9I4nrzexGDar2vgO7wbO0vVAZh/DR99G0ixeZqo7AbqqBst9Fvg6FeEnS2EgYa8w4u7MCWVekgGlh1shut+Pll19GfX09Dhw4gFmzZqGmpgb19fW4+OKLMXfuXDgczsKBHMeBgXc+x6IzQlByQANwzuKJxhoL5KH+19DXLxOenLwq16KqrLTu2VbNcRxEIhGWLVuG2tpa1/GEhARcfvnl+OKLL/D7779j2bJlHs/1drLz176zePXr3WgwWgE4p1ZuW5CFqyeo0HDyY9QYKsAppwCocz1H5qWV8EKhszr05Zdfjq+++gpVVVWIjIzELbfcgptvvhkMw+Dw4cNISUlBSIjvSwI0ZbU5cPBU5bn1OMWorGm5CCDDAJkp4ZiQFY9JWXFIjA7yy28fgcpaX4iqfe+AtXg2ThWHDkT4iCXgCbtvFLev4TgO9ad+Q1DKXNrOH0AYHoO0GXE48vMZ6KuMUEV7jtwJBAI899xzqK6uxsiRI2G1WmEwGBAW5jnFxOPzvPblXSgVQBUlg67c+R5cW9RACU97HT5dBQBIjApCcDctVmYYBgUFBejXrx8UCudoidVqhUgkwqJFizBp0iSMGTOmxZo43tJgtOL1b/bgjz0FrmMJUSr868YhiDJtQvXeA1D2m4nwUffC8dtxuCc8nWtj0Zp+/fohMzPTtZ6J4ziYTCbs3bsX77zzDsaNG+f6/+RLJVUN2HOsFHuOleHAiQpXX7XmpGIBxgyOwYSsOIwfEtdtv0ekbaaqHGgPfATO4dmJWRY7FqFDbqZ+bl5mqjwM1qKDImGyr0MhXiaUCDBgTgKK9lRBHioBX+Q5zJOamorUVOe6V6lUCqlU6pHY2O12CATe/XenTlC4Ep66Ij3ihoV79fW9wS/faY6dcc4FDkntvv9h77//Ph5//HHMmTMHS5YswYwZM1xdytPT03HRRRfhsssu67b77z9Rjhc+34GqJq0JrpiWiuuyKmEreAOIGoboKcsgkDpHVYxm95ow3k54AOCuu+7CsmXLsHjxYuTm5uLgwYP48ccfsWDBAixdutTr92sPg9mGA7kV2HOsDHuOlrXaeRxwVjqemBWHSUPjMSw9qtt30ZG26Ut2oubIVwDnuX5KlToPQWmX0Eibl3Eci/rTv0KVOhcM3/vvEcT35CESRKSrYTM7Wkx4zuM4DvX19Xj55ZcxYMAAsCyLkSNHQqFQICUlBYB3ZyuC45Uo2uMcrGioNIK1s+D52XIBv0x4Ks41YUyM7nz56wv5/vvv8dprr4FhGDzzzDN44YUXcPPNN+PWW2+FwWDAtGnTPLb1eYPF5sCHPx/A9xtPuI6FqaV4aL4KqY6fwTREInLcoxAFuTcANZrdRzK6I+E5efIk/vzzTyQlJeHtt9/Gtddei7vu6tlS9CzL4XRJDfYcLcPeY2U4kqeB3dHygmMASE8IwcSh8Zg4NA5p8VQfx1/oCv5E3fHvWzjDIDjjeigTp/R4TH2BqeIQWKseivhJvg6FdKPQZBWqC3QQSvitJj0Mw0AgEOC///0vnn/+eVRVVWHTpk1YtWoVzGYzpk+fjhdffNFrMclCxeDxGbAODhwLmBtskAX715Sq3yU8HMeh+lyvotCg9hUB7CiWZfH8888jLCwMGRkZuO6667B69WqsWLECn3/+OXJzc7tlRONkUTWe/3Q7CsrqXcemZAbj1kEHoWAsUGdcD2nk0BY/tJuP8EjF3v2r27dvH1atWoVvv/0WV199tVdf+0JqdCbsPVaOPcecSU5r28YBQK2UYPSgaIzJiMHoQTHd9jtCOuf8+hFd3hqPcwxfhLBhd0IameWDyAKfc3TnN6hS59PoTh8QkqQEa299p5XNZoNCocDChQtx5swZLFu2DCtXrsS0adNw8uRJ14yGt9bxMAwDsUoEU61z+tqss1LCcyEGkw0Wq3MVeXd9mPF4PEydOtX1WKlU4rrrrsN1112HH374AXfeeSeeeOIJr93PwbL4Zv0xfPrrYddohUIqwOLxeowJ/hPq1IVQJk5tcy2DoZuntEaNGoU1azw/pLqDze5ATp7GOU11rAynimpavZbPZ5CZEoExg2MwZnAM0hJCqF+Vn+I4FrXHvoO+cJPHOZ5IgfBR90Gs7r5dl32dqeIgWJsJirgJvg6F9ACGYQCGa3Xq6PyGlHnz5mHx4sUwGo1gWRZxcXG48847MXLkyMbX8RJJk4THorN67XW9xe8Snqbf7kNU3k94OI6DxeaASMgHr4W/6DNnzuCGG26ARCLxeF5nfjFKNQ1Y9tl2HDlXQBAAshIEuDNjHxJSh0Gd/gL44gsXuDNZun9Kqzu5FhsfLcOB3NYXGwNATJgCYzKcCc6IAVGQS6k2jr/jWDuqD38BY9lej3N8STAixjxIvZy6kXN0ZzVUqfNodKcP4Qt4YFtphwMAH374IfLz8yEQCKDRaPDMM88gPj6+xTZG3iBRNf7umRta7kXoS36X8DRNKbpjOUZ+SR1e/+8evP/orBZvsGTJEq8U1+M4Dmt35OGtb/e5PtyFAgbXDynHgiF2hGZ07Nuu56Jlv/urc2MwWXHgZGW7FhtLxQIMT49yJTlxEUpai9OLsA4LtNkfwazJ8TgnkEchYswDEEhDfRBZ32EszwZrN0MRT6M7fY2x1gxZsBg8vufnVmZmJoKCgjBlyhRotVrX7q3mX+C9Na3VdKTJW4UNvcnvPjWFTXbWWG0Or7/+zpwSBKskrSY1arXa45jV5oCAz2t3AlajM+GVFbux7VCx61i/UDvuGZWHzDHzIY8b1+EGn80THrmfjfAYzTYcyatCdm4FDuRW4GRhDdg2OtT1jw92TlNlxCIzJRwi2lHVK7E2IzT73m2xerJQlYCI0Q+0awSTdN750Z2g1Hm0xb8PMmjMkKpbHrGZMGECJkxwJsEs27j5o2ly47CxYB2sX3c59xa/+wmbfvC11g6gK3YeKcGCif1bPGezs7DaHR7JhN3Bgscw7Vo7sv1QMV7+apdrao4BcHF6GW6dk4SwAf/qdIG1ntiW3hEmiw05eRocOFmB7NwK5J6thqONjF6tEGP0uXU4owfTYuNA4DDXo2rfW7DpSjzOiUPSED7yHvCE9Pfc3Yxl+8A5rJDHjfd1KMQHWAcLfZUJQTGttxDhOM7jS77D5gDAoOSABnwBD3Ej/K9ujrf5XcLTtHaKyezdOcB6vRnHzmjx4l1TPc7ZHSzufPl3RAbLcdvFWegfHwyW43CmtA7v/S8br983vc3XNppteOeH/fht62nXsXC5FQ9MM2DC9Bu73COo+bZ0b+/SuhCL1Y6cfGeCcyC3AscLqtvcLi4S8DA4OdyV5NBi48BiN2pQtedN2I0aj3PSyCyEDfsHrSXpAc7RnTUI6r+ARnf6KJ6Ah5JsDRRhLRciBNxHdOxWB1gbi5IDWlSdrANrZ6GMlHol4eHaGNX3B373L0QqFkApE6HBaG1z3Udn7DlWjv7xIS2OLvy06SROFtbgZGEN0hNDkZYQgj/3FOC5T7cjSCFus99STl4VnvtsO8o0etexi5JrcP+1YxGWNKbLc6MOlvVILiTdnPBYbA4cO5/gnKzEsTMa2NoYcRPweRicHIYRA6IwLD0KGclhEIv87teLeIG1oRSaPW/BYanzOCePG4eQzJvB8GiKsicYy/YCnAPy2LG+DoX4iEQpRH2p4YKFCM8rydag7Eg10CQ3aagywWa2d3lay6pv/GIukvnf+7/fRcQwDJKig5CTr8HZ8voLP6EDsnPLMWqQ504RTa0Rn/xyEICztcP1cwYDAE4XO7dLx0W0vAbBZnfg89VH8PXvR13rVVRiG+6fq8TsOXeBJ/DOSvgWCtW2uMOsK2x2B46d0eLgyUpkn6zA0XxNm2uo+HwGg5LCMHxAFIanRyIzJaLbkzDie5bafGj2vQPW5tm3TNlvBtQDr+zw+jTSORzrQP3pNc66OzS602dJg507is9sK0f6rHjwhU0WDjucsySswwq+yDnlxRfy3JId54VAfakBYSldK/ZrbrIVXaLyv921fvmvpPsSngo8doPnN6E3v9vr2kn16A1jXeuISqucIzax4Z4JT0FZHZ7/dDtONqkhMzLBhqcWz0JkdLxX4+Y8fju7voPNbmdx4qwW2bkVOHiyEkfyq1z1j1rC5zEYkBSK4elRGD4gCpkp4T5fR0R6lklzHNrsD1rsixWUdolzSzTtrusxztEdjkZ3+jihlA+BhI+6Yj3ObCtD8uQYZ8VjOwvd2X0wFq0CTyRH1ISnwPAEUMcrULzfcyqaEh4fOd9SIq+4FizLeWXtR7lWj6oaI4akRrgd33G4BJuziwAAc8YlY8SAKNe5gvI6AO4Jj93B4rs/juPT3w67RkAkAhZ3L0jE5fOn+u0bvs5gwdF8DXLO/Xf8jBZma+u1cHgMg7TEEIw4l+AM6R/hdzvDSM8xlmdDe/ATgGueFDMIzrgOysSpvgirz3KN7vSfT9OHfRzDMJAFi6ErN0Jzqh6KCCmiM0LBsUDhPg7Bqnow5lro8tchqP9CKMKl4It4cFjdpw50ZYYuxWE12mA3N74/SIIo4WmXYemRAJxFCHMLqzGon2dr+/Y6W1aH8moDyjQNyEgJd5t2aTBa8crXuwAASpkI91w5wnWuvFqPogodACDzXBPTU0U1ePnLnW6jOgNjBXh2yRzER4d0OsYLaWkdGIPWEyuO41Bc2YCc/CocyavC0XyNWzuLljAMkBYfgmHpURgxIApZ/SOgkPnfLyzpefqirajJWQmPcXCGj9CsWyGPHeOTuPoyQ+lugAHkMfT/ngCqaJmrU3nN2QZEZ4RCIOYjZfowFP59CirVTtSfXgNJeAasdWehjmJQXeS+SNlUZ4XVYINI3rkvtnXFjQmTJEgEgdj/EnG/THjSE0IRppZCW2fC9sPFXUp4Vm05hf/9nQuOA4IUYrzz/T7cPD8TQQoJ3vl+H7R1zr5dD1w7yq2y8+6cUgCAWMTH4H5h+PDnA/hm/THX1mshn8Ot8wbihgUjIWih4JM3tbjuvUm+Y7HakVtYjSN5GuTkV+FongZ1es9pB7enM0BKbLBrDc7QtEio5P7V94T4ni5/Hepyf/Y4zvBECBuxBNKITB9E1bdxrB26vLUISruYRncIACAkSYWSA1oAzpEau8UBgZgPZaQMwYPmw3TmNEQiDSp3vQqwdkgZMYAlQLMvzvWlBoSnqTsVQ21R4yaj4ARFJ3+S7uWXCQ+Px2BiVjxWbTmF7YdKcOclwzr9WoNTwrF6ex5MFjvq9Rb87+9cXDIlHccLqrF2Rz4AYMKQOMwZm+z2vF1HnQlPalwwFr+0DkWVOte5zCQFnrxjOhKjuq+bu5sWhni2Hy5BXnEtcvKrcLKwps0t4oBz99vg5DAMSY1ARko4BieHQ0kjOKQVHMehLvcnNJzZ4HGOEcgQMepeiENSfRAZcY7u8CGLGe3rUIifkIdLIJILYDXYwbHO5CO8vxoAEJ0RgoLSeHCsBgx7bhkDZwGPbwDrcE9M6ss6l/BwLIe64sZdysEJ/lls1C8THgCYNNSZ8OSV1OLYGQ0GJ3euRkBmSrhrrYqAz8OziyciRCXBva//AcA5lfXYTWPd1t5o64yuEZ5jZ7Su41IRg7uvGIlLpw7osZoyDpZFXkmtx/EXPt/R5vOiQuUYkhqBzJRwZKSGIyU2uNtHokhg4DgWNTlfw1C83eMcT6xCxOgHIVLF+SAywrF21J9eC3X6JbQbjrgwDIOQJCUqjjk/K8qP1iAsNQgMw8BQsh1C7kDzwRyIZQ0wNTRLeEo7t45Hm69zrQniCRioojtXYLe7+W3CM3pwNOIjlSiubMBXa3Pw6r0Xdep1IkPkEAn5sFgduG3hEFw0Mgkvf7kTmlrnfOf9V49CuNr9L+eN/+6FvVlDtnGDI/HYzRMRGdJ6NUtvqNebceJstWuB8bEzWo8qy83x+QzSE0KRmRKOzHNJTniwf/7CEf/GOWzQHvoUpooDHuf40jBnE1B5RAvPJD3BULILDF8IWcwoX4dC/EzkwBBXwqOvNKG+1AB1nAKioEQwAik4u8ntemWIEaZmpe4sDTaYddYO7bDiOA6lhxp3fYX3V7fYvd0f+G3Cw+fxcOPcTLz05U5sP1yC08U16B/f/oXBHMfBZmchFPAQFSqHXCLEzfMzsftoKVZvd/b9GZcRi7njk8HajDBrT8CiGIz/W7kbmw8UuV4nSC7Ag9eNxczR/by+A0tvtOJkUQ1yz2px4mw1cs9Wo0yrv/ATAYzNiMHQtEhkpkZgYGIo1cAhXcY6LNDu/wBm7XGPc0JlLMJHPwCBRN3zgREA50Z38tZCPeByGt0hHuRhEgQnKVF71pnFlGRroI5TQKzuh5hpL6L+1GroCzfj/KpQkSgPQIrH69SV6BE1qP2ftXXFehirz60ZZYCYof7bKJjh/LgWtN3O4sonf0FljQGjB0fjjftntDmVZLbY8ee+AqxcfwwlVQ2uMtdqhQSXTk3DvHEp+HFTLhqMVtjsLO65cgTC1DJoD32JP3afworjg6EzNm6rmzw0Do/fPB7BSkmXfxaTxYZTRTXIPVvtSm6argtqS0KUyrVj7LyN711L28SJ17B2MzT73oOl5qTHOZE6GeGj7nMVLiO+oS/ZCV3e74ie8jwlPKRF+ioTjvx8xvU4bUYcwlIb15paG8pQc2wtBBI+WF4a6rVJsOhtMNZY3LaU80U8hPcPQvSQUEiDWt/M4rCzOPLTGZhqnQlPaIoK6TO9W4fOm/w64QGANdvz8NKXOwEA9101EtfMGuRxDcty+OTXQ/h+4wkwDGCyeNaXEQn54DgOYwbH4NEbxkCtlEAo4KOy7Cyef/9XHKwMdrt+4tA4vHpP56bRLDYH8oprXInNibPVKCyvb7N7+HlKmQgDEkMxsF8oMlMjkJEcBqGAjxn3fOt23Z/vXUuF/4hXsDYTNPveabHjuSRsEMJG3O21quGkcziORfnWZ6FKng1F/ARfh0P82Il1hagtdM4U8EU8ZF2R4jZFxXEcWDuLqtw6FO2rAsdyYO0tfDbxnGuDFOES9J8eB4nSc5rrzPZyVBw9V6aFAbIuT4Y8zH8bBvv9PMj8CSnYeaQEmw8U4YOfDmBYeiTSExuHzOx2Fk98sAkHcitgbqNS8PkigXuOleGuVzbgwyfmIEwtgyo0FnX8JACNdWoSIlV4fvHkdsVnszuQX1KH3MJqnDirRe7Zapwpq4PDceHkRiYWID0xFAOSnP8NTAxFbITSY+rM4OUmqoScx9oMqNr7Nqx1BR7npFHDETZsMbUt8AOmykPg7BaqeUQuKHlSDA5X5sNudsBhZXHqzxJkLEoCr8mmleJ9GlQcr2k50TmPdVb5b6g04ciP+Rh8cT/IQxtnO2rONjQmOwDihoX5dbID9IKEh2EYPHHTOJw4q0VljRGPv7cJ7z06C3ERKnAchxe+2IHs3Io22yI0ZbOzqKw1YOn//YHPn54PPo/BsLQolFbUwMryIeCxeP4fk1tcE2O3sygor8PJwsZpqbyS2jabap4nFvGRFh/iltwkRAW1b7dXCyNDflrQmfQiDmsDqva8BZuuyOOcLGY0QrNuozovfoDjOOjyfocqeRYln+SCxAohUqfGIne989+1vsqE3PVFGDgvEQzDoPSQ9sLJTlMcYLewOPbbWWRdmQKxQghzgxWnNha7LlFESBE3wv83M/jdv56kpCRUVlZCIBCAz+dj0KBBuOmmm/DM7Yvw4Jt/YeevbyP+tSuxc+cuCNWJ2HqwGHXaMhz632MYd/tXAADtmT0oP/YHjNVFUIQnY/D8f7rdw+HgUFmtx9e/H8U/Lh2KyFAprKzzjT3Ksg+DUxZDLBaD5TgEh0Zh8oLbIIocgrPl9e1KboQCHlLjgp2JTVIoBiaFITE6qNPbwjtaaZmQC3FYdKja8wZsDaUe5+Rx4xEy5GZaJ+InLNUnYDdVQ54w0dehkF7g/Gcon+GDAQ9J0cmYP+lSvDDzKdgtLP6xdDF+3/UrPnn8WwxKchYOLakqwtX/nocdy48CAP7KXo8f/v4ap4tPYlBSBt576EvYrQ7kby3DoHmJMNZYwJ6bxeALeUibHgce3/8/k/wu4QGA1atXY8aMGaivr8eWLVtw//33Y+qePXjzn//B/C2fQCxT4ql//QtDFjzeYj8ogViB6MGzYKovh67sRIv3sNpZ/Lz5JO64OAsWKwsGwNjMWOzeoEd02jjEjbsDAFBXkoPvP34Ow69+HSKpZ6FBPo9Bcqz6XHIThoFJoUiOVUMo6N5vxjTCQzrLbq5D1e7XYTdUeJxTJExGcMb1lOz4kfq836FMmgEen9ZRkfZZvXo1pl80HYfXncSGNX/i7R//g4rFBXh2yX8AACp5ED7+7R28dd8nLT5fJQvCVRfdiMKKAhw4ucd58FxHdauxcYmFQMzHwHkJftk3qyV+mfCcFxQUhIsvvhhRUVEYO3YsHn74YYzLiEVJTARycraiRr4Zsoh0j+epYwcDACpPbvY4Z27Q4OAPjyB5wi3IPrgKkf+9D9MX3gCOPxS7ckpRXKGDucmiZ3VcJvhCCSy6KkgVavSLVqN/fDAG9nMmN6lxwRCLuvd/o1+vKie9it1U40x2jFUe5xRJ0xE86Gq/bYDbF1lq82GtL0T4iLt9HQrpZRgeg6x56QiJCEFkRCRuW3Y1ZqVcBo4D5o5dhI371uLgqX0YluZZ02nUwHEAgN+2/+hxruJYLe599h/YvmM77KwVWV9kYfny5Rg8eHC3/0xd5dcJz3mjR49GXFwctm3bBqVchElJ8RiQnopVv/yIwfOf6tRr1pefwNArXkV6FPDLh48hfvydrkQJcO6WSolVw1Z9AkcEwOcv3YqsAYkQCXt+TUOLG+noQ4l0kN2oQeXu1+EwVXucUybPPlffhX6v/Ikufx2UidPAE1IhUdJxDMMgYVQE5qtnIubTWBw6lQ0AkAgluGn2Ynz82ztY/sjX7X49zsFBc6oOs2bMwmeffgZliByPP/44rr/+ehw6dKibfgrv6TXj1jExMaipca4Il4iFGHvRpTA3aFFbfLhTrxc/7BLwhWLw5NGYNudSCOtzcMeiLMwYnYT6ov3Ys2IJvnzuMnz++iN45ul/YVRmsk+SHQAtDvHQxxLpCJuhCpW7Xmsx2VGlLqBkxw9ZG0ph1pyAst8MX4dCernw/mpEhUdBZ6hzHVs06SpU1pRj19FtHXotu8WBux9YgrDoEIjFYjz77LM4fPgw6uvrL/xkH+s1CU9paSlCQppUf2QEiBu6CMUHfm55Ve8FiOTOre0OlsXsySMQE8ThtoVZSIwKwlVXXYW6ujoYjUbk5+djxYoV+Oijj7z1o3QY10LGQ59NpL1s+nJU7XoVDnONx7mgtEVQpy+iZMcP6fLWQR4/AXyxytehkABQXlkGlVzteiwSinDLvCX4ZPW7LX7GtMbucOCf//wnUlJSoFKpkJSUBADQarVtP9EP9IqEZ9++fSgtLcXEiY27FIIUYsQMnAKH1YSawuwOv6bV4Pymq5KLUVRUhJiYmBavS0pKwty5c7F69erOBd9NaJcWaQ9rQykqd/0fHBbPb1/qAVcgqP8CH0RFLsRu1MBYkQ1V8ixfh0ICwL59+1BeWY6s/sPdjs8bdwkMJj22Hvqr3a/15/7f8euvv+LPP/9EfX09zp49C6CVpRd+xq8THp1OhzVr1uCaa67BDTfcgMzMTNe5EQOjIBAKEDfsEpQdWev2PI5lwdqt4FgWHOf8M8u67+YqOfQbhIwdSWoLvvjiC1x99dUtxlBSUoL169f7dEFWL/g9In7IWl+Eql2vgbU2eJwLHnQNVCmzfRAVaQ9d/gbIo0dBIAvzdSikF2v6GXrdtdchOaa/23kBX4DbFtyNlX985nbcwTpgsVngYB1gOQ4WmwV2hw0MA3BSG8RiMUJDQ2E0GvHkk0/25I/UJX65aHnhwoUQCATg8XgYNGgQHnroISxZssTtmvSEUMSEKWBOGYvSw2tgtzS2tdfk7UD+tk9dj/d8tRjh/ScidfJi1zFVVDp2//dh5P4mxiOPPIJZsxq/SX3//fdYtWoVAECpVGLRokX497//3U0/7YW1mO/QAA9pg6WuAFV73gJnN3qcC864AcrEKT6IirSHw1wHQ8lORE38l69DIb1Ua5+heX+XeVw7c+Q8fL3+U+gMjaPA6/esxksrGn//LrpvBOaOXYRnbnsJdz34Dxws2IvY2FiEhIRg2bJlWL58eY/8XF3l97202rJuVz7+7+s9Ldbiac35benjb/scs8al4t93TOrGCL2jRmfCgof+53Zs28c3gM/z6wE64iOWmjxU7XsbnN3c7AyDkCE3Uy8mP1d74kfYDVUIH0lb0Yl3NVSZcOy3gvZXWW5GFirG0CtTvRxVz+nVn5jTRyYhJlwBQScqPEokQixeNNT7QfUQWsNDWmKuPomqvW95JjsMD6FDb6dkx885rAboCzdDlTrX16GQAKSMkCIoVtGpqsg8PoPkiS2vde0tenXCIxLy8c7DMxEaJINA0LEf5bX7piMmXNlNkXlZrx2DIz3JrD0Ozd53wDks7icYPsKG3UmNJ3sBfeHfEAcnQ6zu5+tQSIBKnxkHWagETAeSHh6fQcrUGKiie3c9qF6d8ABAiEqKr55ZgPSEEEhEgja3a8vEAsTExuN0UQ2GpUf3XJBd1FK+Q7uISVOmqhxU7XsXHGt1P8ETIGzEEsiiR/gmMNJurN2MhoK/oEqZ5+tQSADjCXjIWJSEkESlc6Snjc8SnoAHnpCH9DkJCO+v7rEYu4tfLlruKJVCjE+enIfjBVr8d8MxbD9UDKGQDwaAg+Vgs7OICVPgH5cOw9jMGBw8WQmWY5GWEOrr0NulpWVWVDeFnGesOATtgQ8BzuF+gidA+IilkEZk+CYw0iH6om0QyCMgDvVsl0OIN/H4PKTPioepzoKyI9XQnKpzfqac+1jhHBzESiFih4YhNDUI/A7OoPirgEh4zhvULwwvLJkCncGCkqoGGExW/Lz5FLYcKEJRpQ5SiQBSsRBqpQR3vbIBbz80Exkp4b4Om5BOM1UebjHZYXgihI+6B5KwgT6KjHQE57ChoeCPc41b6csM6RlStRgpk2OQNC4KpjoL7BYHeHwGQpkA0qDAa1YbGGlbMyq5GIP6hWHUoBjMGtM4F559wtkdelC/MCy5bBie/mgrdHpLay/jN2gJD2mJqeooNC0lO3wxwkffT8lOL2Io3Q2eUA5pxBBfh0L6IL6QB0W4FOo4BVTR8oBMdoAATXiaGp4e6fpz9skK15+vuGgABvULw7LPd4Bl/TylaBYefQEkJs1xaLLfB5oV1GQEUkSMeRCS0DQfRUY6iuNY6PLXQ5UyBwwT8G/JhPhMwP/rClJI0D8+GABwqrAGOoNzRIdhGPzz5nE4W16Pb/845ssQL6j5Gh7akt63mbW50O5/r4VkR4KI0fdDHJzio8hIZ5gqDoLjHJBFj/J1KIQEtIBPeABg+IAoAADLcTh0qtJ1XCET4YUlk/H56iM4crrKV+F1GI3w9F3m6lPQ7H8XHGtzO87wxYgYRclOb6Qr2AhlvxlgeHxfh0JIQOsTCc/IAY1b0LNzK9zOpSeG4u4rhuPpj7eirqF5ZVr/4DHhRglPn2SpyYNm3zvgHO5bz50LlO+FOKT3VkDtqyy1+bA1lEERP/HCFxNCuqRPJDxD0yLA5zmzhOYJDwBcNjUdQ1Ij8Pxn2/1yPU8v7v5BvMRSe8bZLqJZUUGGJ3TuxqKtzL1SQ8FGKBImgSeQ+DoUQgJen0h45FIRBiQ5a+6cKa1Djc7kdp5hGDxx01iUahqwcv1RX4TYMZT/9CmWurMtt4vgCRA2cintxuql7EYtjJWHoUy6yNehENIn9ImEBwBGnFvHAwD7T3iO8silIiz7xxR8tTYHB5us8/EHYpF7uSQHy8FuZ30UDelJ1voiaPa+Cc7unqQ7iwreDWn4YN8ERrqs4exfkEUNg0DaOwqgEtLb9ZmEZ9SgxqZnu3JKW7wmLSEE9141Ev/+aKvHKJAvSUWe9SFNHegQT3onq64EVXveBGszup9g+AgbvgTSiEzfBEa6jLUZoS/eDmW/mb4OhZA+I6AqLbclKzUCcqkQBpMNe46WwsGy4PM8871Fk/sjLEgKHsOA4zi/qHoqFvHBMEDTpTxmix1Kmch3QZFuZWsoQ9WeN8Da9O4nGB7Cht0JWWSWbwIjXqEv3g6RKp6ahBLSg/rMCI9AwMPoc6M8dXoLThRUt3gdwzCYkBUHtVLiF8kO4Iyp+SiP0WJr5WrS29n05ajc8zpYa0OzMwxCh94BWfRwn8RFvINjHWg4+xeN7hDSw/pMwgMA4zJjXX/emVPS6nX+kug0JRG7JzxmC01pBSKboRJVu18Ha9E1O8MgdOjtkMdQcbrezlhxAAzDh5RG6QjpUX0y4RGL+BALe1eRL6lY6PbYRAlPwLEbNaja/ToclvpmZxiEZN0CeewYn8RFvIfjODSc+cNZaJDaSBDSo/rMGh4ACA2S4ombxmHG6KR2P6eurg56vR6xsbE+HfmRNhvhoYQnsNiNWlTufg0Oc63HuZAhN0ERN94HURFvs9bmw2aogpz+PgnpcX3uK8bc8SmQSYSQSYQXvLahoQEfffQR7rzzTjDnFjH7CiU8gctuqkHl7tfhMNV4nAvOuIGq8AYQXcFGKBOnUKFBQnygzyU8QkHbP3LTpEYqlWLIkCHYsGEDTp8+DbvDd7Vvmq/hMdGi5YBgN9eiavdrcJi0HueCB18HZeIUH0RFuoPNUAVT1REokqb5OhRC+qQ+NaXVGo7jsHHjRsyaNQsMw6C+vh5r1qzBihUrUFtbi2XLliEhIQF8HoPaBjOClT3/7az5CA8tWu79HOY6VO1+HXajxuOcetDVUNIHY0BpOPs3ZNEjIZAE+zoUQvokSnjg3JX1ySefYMeOHUhISMAnn3wCsViMm2++GbfddpvbtTKJAOXaBkSHKXs0Rlq0HFgclgZU7nkDdoNnVW/1gCug6jfDB1GR7sLaDDAUb0fkuEd9HQohfRYlPOc88MADmDRpEq6++mo8//zzmDVrlutcSUmJa9GygMfD0TNaqBQSyNuxDshbaA1P4GBtRlTtfQt2fbnHuaD0S6BKme2DqEh30hdtgygoCaKgRF+HQkifRQnPORMmTEBmZibuu+8+jBs3Dna7HStXrsR3332H+Ph4WCwWjBs3DnfddRemDEvAi1/sxL/vmAger2d2bklElPAEAtZhgWb/e7DpijzOBfVfiKDU+T6IinQnjrWj4exfCMm4wdehENKn9blFy21Zvnw5DAYDNm/ejIULF+K5555DQkICli5dirvuugvr1q3DW2+9BaGAh/4Jwfh89eEei00mabaGh3pp9Toca4c2+yNYak57nFOlzIOq/0IfREW6m7E8GwxfDAn1PiPEpyjhaWL8+PGYMWMGtm3bhhtvvBGffvopEhMTsWLFCowbNw6PPfYY1q1bB4ZhcO2sQfjx71xszi7skdiaj/AYzbRLqzfhOBbVhz+HWZPjcU6RdBGC0i/xywrfpGs4joOOCg0S4hdoSquZP/74Azt37sTTTz8NAMjMzMRVV10Fq9WKsWPH4oorroBOp4NKpcJbD87APa9tRHyUCimx3bvzwmOXFo3w9Bocx6H26Dcwlu3zOCeLHYvgQVdTshOgLDWn4TBVQx43ztehENLn0VeOZiZPnoz8/HxotVro9Xo89dRTmDx5MkQiEQQCARYvXgyVSgUAGJAUhutnD8bj722CTm/p1rg8dmmZKeHpLepP/gJ90VaP49LIoQgdcgt98w9gDQUboUicAh5f7OtQCOnz6J22GYlEgiVLluDee+9FUlISdDodbr/9drdrmhYnvHl+JtLiQ/D0x1u7tTChR+FBGuHpFXT566DLX+dxXBw6AGHD7gTD61093Uj72Y0amDRHoUikekqE+AOa0mrBQw89hMrKSojFYqjVao/zTacfeDwG/7ptAu58eR2W/3QA9141sltiCg+Wuj2mwoP+r6FwC+pyf/Y4LlL3Q/jIpWD4PVfWgPS8hsItkEUOhUCi9nUohBD08RGetnpjRUZGQq1Wg+O4C/bQkkmE+M/SaVi7Iw8bdp/xdpgAgEH9wjA8PdL12EitJfyaoWwvao9+43FcqIhB+Kj7qJdSgOMcNhiKd9DoDiF+pE8nPO1ZKMowTLuui4tQYtk/JuPVr3cj92y1N8Jzw+fx8Np90zFpaDwAGuHxZ6aqHFQf+hyAe6LMl4YhfMyD4IsUvgmM9BhD+T7wJUEQh/T3dSiEkHP6dMIDACzrvXU3owbF4I5FQ/HE+5tQU2/yymu+//77+OYb50iBRCzAc4snYd74ZEp4/JS55hS02csBzuF2nCcOQsSYB2l6o4/Qn90EReJU2n1HiB/p8wmPt9+Qrpk5EMPSo/DUh1tgszsu/IQLyMrKwhtvvIEPPvgAgDPpeeT6sRgxMOqCU22kZ1nrC6HZ9x441n26kSeUIWLMgxDKI3wUGelJlroC2AyVkMeO9XUohJAmKOFhGFhtjYkJy3YtiWAYBk/cNBYWmwNvfetZd6U9zicyFosFEydOxIYNG/Dtt9/iyJEjAJxJz0PXjYHF2vWEiniHTV+Oqr1vgbO7j+wxfDHCR90PkTLWR5GRnqYv3Ax53Dhap0WIn+nzCQ8AFJTV4Wi+BmWaBuw6Wtrl1xOLBPjP3VOx5WARVm051eHnMwwDi8WC66+/Hrt27YJerwcAyGQy1zUyiYAWLvsJu7EaVXveBGvVu5/gCRA+cinEwcm+CYz0OIdVD2PZPigTpvo6FEJIM7QtHUByrBoLHvofGoxWyCRCrH3zKoiFXauPEhEix0t3TcWDb/2JfjFByOofecHnWCwWMAwDkUgEsViMyy+/HFdddRUyMzMxffp0JCQkuK612zlax+MHHBYdqva+AYe51v0Ew0PYsDshCRvom8CITxiKd0AUnAKhMtrXoRBCmqERHgBCAR+Thzl3PxnNNuz2wigPAAzpH4F7rxqJJz/YgsoawwWv//333/HOO++4Hs+bNw9DhgzBe++9h2eeeQYikQiAs1P66//dQ8UHfYy1GVG1903YDVUe50KG3AxZ1DAfREV8heNYNBRuhpK2ohPilyjhOWf6qCTXn//ad9Zrr3vJlDRMHZ6Af76/GZYLJCijRo3C999/jzfeeAMGgwGffvopMjMzkZycDB7P+Vdlstjw3cbj+Ht/IbWX8CHWYYFm37uw6Uo8zgUPugaKuPE+iIr4krnqKMA5II3M8nUohJAWUMJzzsgB0QhSOPvd7Dhc4tXpogeuHQWxiI//rNjd5s6quLg4rF+/Hhs2bMAVV1yB48ePY+7cua7zZqsd63edwSerDrkek57HsXZosz+EpTbP41xQ/4VQ9pvug6iIrzUUboIiYTK1CyHET1HCc45AwMOU4c41MiaLHTtzvDOtBTinzF5cMgUHT1bgx79z27w2NDQUa9euxccff4yPPvoIU6ZMcZ07kleF177Z43psojU8PY7jWFQf+hRmzVGPc8qk6VD1X+iDqIiv2QxVMFfnQpEw2dehEEJaQQlPEzO6aVoLAEKCpPjP0mn48OeDOHiyos1rBQIB4uPjIRC4ryn/eu1RNB0gMtEurR7FcRxqj30LY3m2xzl53HioB11Fheb6KH3hFsiihoMvVvk6FEJIKyjhaWJoWiSClc7aGTtzSmA0ezehGJAUisduHIsn3t+MMk1Dh58vErkPldMIT8/S5a+HvnCzx3Fp5DCEZN4EhqF/Tn0R67DAULKdFisT4ufoHboJAZ+HaSMTAQAWqwM7jnguSO2q2WOTsWBiKh57b1OHEyqZ2H3Eh7al9xx9yU7Un/TsfC4OHYiwYYtp3UYfZizbD74kBKLgFF+HQghpAyU8zUw/l/AA3p/WOu+uy4cjNEiKZZ/t6FBlZ0mzhMdICU+PMGmOoebICo/jQlUCwkfeDYYv9EFUxB9wHAd94SYok6bRdCYhfo4SnmaG9I9AWJAUALA7pxQGk9Xr9xDweVh252TkldTi41UH2/08qdj9g9XbU27Ek7W+qMVmoHxpGCJG3UftA/o4a10BbIYqyGLG+DoUQsgFUMLTDJ/XOK1ltbPYerC4W+6jUojxxgPT8dvW0/hpU9s7t847v23+vLoGc3eERs6xGzWo2vcOOIfF7ThPqEDE6PvBlwT5KDLiLxoKN0ERNx48gfjCFxNCfIoSnhbMGN3P9effd+Z3233iI1X4v/suwvIfD2DLwaILXh96buTpvJp6Sni6i8PagKq9b4O11LsdZ3gihI+6B0JFlI8iI/7CYWmAqTwbisSpvg6FENIOlPC0ICM5DAlRzu2l2bkVKKnq+I6q9hqcHI7n7pyMZZ9uR06eZ4uCpoJV7tMnNTpTK1eSrnBWUX4fdkNlszMMQocvhpgWpxIA+uLtEIf0p+SXkF6CEp4WMAyDhRNTXY/X7vCsqOtNE7LicM9VI/Hou3+jsKK+1etCVe4jPNWU8HgdxzpQffATWOs8R/aCM66HLHJozwdF/A7HsdAXbYGCtqIT0mtQwtOKueNSwOc7d12s3ZEHu4Pt1vtdMiUNl08bgIfe+gvaOmOL14Q0G+GpbTB3aJcXadv5woKmysMe51SpC6BMnNLCs0hfZNYcO9c3a4ivQyGEtBMlPK0ICZJiwpA4AIC2zoQ9x8q6/Z53LMrC8PRIPPLO3zC0sAMrpNkIj8PBQWeweFxHOkeX/zv0RVs8jsvjJiAo7WIfRET8lb54O+RxE6nYJCG9CP1rbcPCSf1df16zrXuntQDnVNrjN45DiEqCp5Zvhs3uvhVaIhZAJnHfmk7TWt6hL96B+pOrPI5LwjMQknkD1VghLg6LDqaqI1DET/B1KISQDqCEpw1jBscgTO0cVdl+pBg19d2fXAgEPLywZAp0eiv+89Uuj+7qoUHNprV0tFOrq0xVR1GT41lYUBSUiLDh/wDDE7TwLNJXGUp2QRKSBoEszNehEEI6gBKeNgj4PMyf4Fy87HBwWLer+7aoNyWTCPHafRfhcF4VPvrlkNu55tNa1T2QhAUya30htAc+BDj3NVoCWTjCqbAgaYbjOOd0VvxEX4dCCOkgSnguYEGT3Vqrt+V5jLh0l5AgKd58YAZ+23oKP2862Xi8WcJTQyM8nWY3alC1923PwoIiBcJH30+dr4kHS20eWKuedusR0gtRwnMBseFKjBjgrLNRVKnDkQvUyvGm84UJP/gx21WYsPmUVk9MswUih6UBVXvfAmt1r7HE8EUIH3UfhPJIH0VG/JmheBvkcWOpfxohvRAlPO2wcJL7KE9PGpwcjmcXT3IVJgz2GOGhhKejWIcFmv3vwm5olrwyPIQN+wfE6n4tP5H0aazNCGN5Nk1nEdJLUcLTDlOGJ0IpEwEA/t5f2C0NRdsycWi8qzAhms2o0S6tjuFYB6oPfAJrXYHHuZCMG6iuCmmVoWwfhMo4iJSxvg6FENIJlPC0g1jIx+yxzm/9ZqsdG/ee7fEYzhcm/PFv90aj1E+r/TiOQ83Rb2CqaqGwYP+FUCRM8kFUpLcwFG+DgkZ3COm1KOFppwUTG2vyrN522icx3LEoC4P6uW+FpSmt9tPlrYWheJvHcXn8RAT1X+iDiEhvYdUVw6avgCxmlK9DIYR0EiU87ZSWEIIBiaEAgBNnq5FXXNvjMTAMgweudX/DrWuwwMF2b9uLQGAo3YP6U796HJeEZyIkgwoLkrbpi7dDFjOKyhQQ0otRwtMBblvUt/tmlCdcLXN7zHIc6huovURbLDV5qD7ypcdxUVDSucKC/J4PivQarMMKQ8lums4ipJejhKcDZo7pB7HI+eG4YfcZWGyOCzzD+0RCvmsB9Xm0cLl1dqMGmuwPANbudtxZWPBe8ARiH0VGegtTxUEIJGqI1Mm+DoUQ0gWU8HSAUibCtBGJAACdwYqNezx3+vSE5sUHf9vqm9Emf8fajNDse9ez1o5AhvBR91FhQdIu+uJtkCdMomlPQno5Sng66IppA1x//n7j8R6rvNxU8+KDq7edxtZzhQmJE8c6oD3wEWz6cvcTDB/hI5ZAqIjyTWCkV7EZqmCpzYc8doyvQyGEdBElPB00KDkMmSnhAID80jpk51b0eAzNiw/OGJWE5z/djpx8TY/H4o84jkPt8e9g1h73OBeScT0kYQN9EBXpjQzFOyCLHAq+SOnrUAghXUQJTydcPXOQ68/fbfT8UO1uoUHuCY9KIcE9V43EY+/+jcKK+h6Px9/oz/4NfeFmj+PK5FlUa4e0G8c6YCjZAXk8/c4QEggo4emEycPiERUqBwDsPFLa40lGiKpZPy2dCZdMScNlU9Px0Ft/9ekO6qbKI6g9/r3HcWnkUKgHXO6DiEhvZdIcBXgCSMIGXPhiQojfo4SnEwR8Hq6c3jgt8sOfJ3r0/p4d050Jzh2LsjA8PRIPv/0XDGZbj8bkD6y6EmgPfozm/TeEqgSEDr0DDEO/7qT9DMXboYifQL83hAQI+pfcSQsnpkImFgAA1u08A52+52rhNJ/Sqj7XXoJhGDx+4ziEqCR4avlm2O19pyChw1wPzb53wTnc/x74YjXCR91D289JhzjMdTBpjkIeN8HXoRBCvIQSnk5SyESYf64Qodlqx6892G6ipSmt8wQCHl5YMgU6vRUvf7XTJ7vIehrrsEKT/T4c5hq34wxfhPBR90AgCfZRZKS30pfsgiR0AATSEF+HQgjxEkp4uuDK6QNxvjTHj3/n9tiISkizEZ56vcXt3jKJEK/ddxEOn67CR78c6pGYfIXjWNQc/ryF7ucMQofeAVFQok/iIr0Xx3EwlOygysqEBBhKeLogLkKJSUPjAQCaWiP+zi7skfsGKzz7+dQ2uHdNDwmS4s0HZ+C3bafx3w3HeiQuX6g/9SuM5dkex9UDLocsapgPIiK9nbWuAKy1AdKIIb4OhRDiRZTwdFHTLeo9VYhQIOBBrXBfk9JS1/T4SBXefmgGVvye0+MLq3uCvmQndHm/exyXx0+EMnmWDyIigcBQuhuy6JFg+EJfh0II8SJKeLpoaP8IpCc45/lPnK1GTl7PFP9rPq3V2lb0/vEhePuhmfjst8P4edPJngitR5hrTqHmyAqP4+LQdIRkXE9tAEincKwdxvJ9kMWO9XUohBAvo4SnixiGwVUzG7eof/dnzxQibGvhcnPpiaF484EZ+PDnA1jdg4uru4vNUAXt/uUA5968VSCPRPiIu8DwBD6KjPR2Js0xMHwJxMGpvg6FEOJllPB4wYxRSQg7N+Ky9UAxyrX6br+nZy0ecytXOg1KDsPr90/HOz/sx7qd+d0ZWrdibQZo9r0D1ub+/5gnlCN81H3gCeU+iowEAmPpLshjx9IIISEBiBIeLxAK+LhsWjoAgOU4/O+v3G6/Z/OEpz3VlTNTI/DqvRfh9f/uxR8+6vTeFRxrhyb7Q9gNle4nGD7CRtwNoTzCN4GRgMDajDBVHqFGoYQEKEp4vOSSKWkQCfkAgNXbT3d7pePmHdMvNMJz3rC0SPxn6VS8umIX/t7fM7vKvIHjONQc/S8s1Z7JZMiQmyAJTfNBVCSQGMuzIVTFQaiI8nUohJBuQAmPl6iVEswdlwwAMJhs+G1r966Vab5oWVtnbPdzRw6Mxgt3TcGLX+zA1oNF3g6tWzQUbISheJvHcVXKPCjixvsgIhJoDKW7IafFyoQELEp4vOjqGY2Ll/+74RgsVnu33SsqVOH2uFTT0KHnj82IxfN3TsZzn27HziMl3gzN60xVOag78aPHcWnUCASlL/JBRCTQ2I3VsNSdgSxmlK9DIYR0E0p4vCgpRo1pI5yVfavrTfi1G0d54iKUbo+1dSaYLR1LsCZkxeGZ2yfimY+2Ys+xMm+G5zU2fQW0Bz9B84agoqAkhA69lRo7Eq8wlO2GNGww+CLlhS8mhPRK9GnhZbctbKzOunL9UVhsjjau7rywICnEIr7bsVJtx0Z5AGDK8AT885bxeOqDzdh/otxb4XkFazNBk/0BOLv7gmy+JAThI+8Bj08NQUnXOVtJ7IaMFisTEtAo4fGylLhgTB2eAMA56tJddW8YhkFsuPu30ZKqjic8ADB9VBIevXEsnnh/Mw6eqrzwE3oAx7GoPvwZ7Hr3JIzhCRE+8m7wJUE+iowEGpuuCA5LPaSRWb4OhRDSjSjh6Qa3Nhnl+XrdUVi7aZSn+bRWaScTHgCYPTYZD147Co++8zdy8qq6GlqX1Z9eDVPlYY/jIUNupoagxKsMJbsgix4BHl/k61AIId2IEp5u0D8+BJOHNTYVXbM9r1vu0zzh6ewIz3nzJ6TinitH4OG3/8LxM9ouvVZXGCsOQHd6jcdxZfIsqpFCvIpjHTCU7aXdWYT0AZTwdBP3UZ6cbhnliYtQuT0uqdJ1+TUvmZKGf1w6DA++9SdOFlZ3+fU6ytpQiupDn3scl4QNgnrA5T0eDwlsZu1xMHwhxCH9fR0KIaSbUcLTTdITQjFxaBwAoLLGiN+7oZ2Dt0d4zrv8ogG4dcEQ3P/GRuQV13rlNdvDYTVAu/99cA6L23GBLByhw+6kHVnE6wyluyGPGUO/W4T0AfSvvBvdtqBxEeRXa3Ngs3t3lCe2WcJTWWPw2kjSNbMG4YY5GbjvjY04U1rnlddsC8exqD74MexG927zDF+MsJFLwRdRjyziXazdDFPFIZrOIqSPoISnGw1ICsX4IbEAnMmIt0d5IoJlEAoa/wo5DijzYuPSG+Zm4MrpA3Df63+gsKLea6/bkrrcn2HWenaaD826DSJlbLfem/RNxvJsCBRRECpjfB0KIaQHUMLTzW5b2DjKs+L3o7DbWa+9Np/HQ0yY93ZqteTWBUOwaHIa7n3tD5RUdn2NUEsMpXvQcGaDx3FV6gLIood3yz0JMZTuhjxunK/DIIT0EEp4utmgfmEYl+EcoSjX6rFul3dHeTzX8Xg/KbljURbmjE3Gva//gbIOtrC4EGt9IWqOfOVxXBqZhaC0hV69FyHn2U01sNSchjxmtK9DIYT0EEp4esBtFzfu2PpqbY5XR3m6a+FyUwzD4K7Lh2PqiETc+/pGVFR7Z9rMYdE5Kymz7p3lBfIohGbdTgtJSbcxlu2FJGwg+GLVhS8mhAQE+kTpAYOTwzFmsHOdQJlWjw17znjttT2KD3p5BOY8hmFw31UjMT4zFve+thGa2vZ3Z28Jx9qhPfARHKYa9/sIpAgfuRQ8obSVZxLSddQZnZC+hxKeHnLbxY1reb5ckwO7wzujPM13anXHCM95DMPgwWtHY+TAKNz72h+orjdd+EmtqD3+Ayw1p5rfAWHDFkOoiOpaoIS0wdpQCrtRA2nkUF+HQgjpQZTw9JDMlHCMGhQNwDkK88fuAq+8blyzflrl1XqvTpk1x+MxePSGschICcd9r/+BGl3Hkx590TboCzd5HA9KvxTSiExvhElIq4zl2ZBGDAFPQM1nCelLKOHpQbc32bH1ya8HYbHau/yaUaEK8HmM67HDwaGixtDl120Lj8fgn7eMQ//4ENz/+kbU683tfq6lNh81x/7rcVwWPRKqlDneDJMQDxzHwVi+H7Lokb4OhRDSwyjh6UFD+kdgwpDG6ss//JXb5dcUCHiIClW4HeuOnVrN8Xk8/Ou2CUiKDsL9b/wJncFywefYzXXQZi8HWPdET6iKQ8iQW8AwTCvPJMQ7bPoyOEw1kERk+DoUQkgPo4Snhy29YrhrRGbF7zmobWj/6EhrvNk1vSMEfB7+fcckRIcq8OCbbSc9nMMGbfYHcFjcCxjyhAqEj1hK0wukRxjL9juns/j0+9YX2PQVqMn5BrozG8Hau/5eS3o3Snh6WFKMGgsnORsVGkw2fLH6cJdfs2nC0z9EhxT9CjQU/AXOYWvjWd4hEPDw/D8mISJYjqWvboC2znP3FsdxqDn6Dax1zdYtMTyEDb8TAllYt8dJCE1n9T01OSugL9qMuhM/oOzvf6I+bx0lPn0YJTw+cPvFWZCJBQCAX7acQlFF16agmiY8aSE6hPA1qD3+HUr/fqJHEh+hgI9lSyYjPTEUd72ywaM4ob5wMwwlOzyepx54JSRhA7s1NkLOszWUwmGupemsPsRuqHL9mbXpUX/yZ5T88SCqc74GAOiLtkJ74ENoD36KmqPfQF+yE3ZTta/CJd1M4OsA+qLQICmun5uBT1YdgsPBYflPB/Dy0qmdfr24iMbiaWJ+Y/NQ1qpD7fHvUHvifwgedA10Z9aB4QnAEyohDkmFJDQd4uBUr9S8EfB5ePKW8Xj3f/ux5JX1eOvBmUiOVcNSm4/a4997XC+PGwdl0vQu35eQ9jKW74c0Mgs8vsjXoZAeYK0vBGtroV4YZ4ehaCuE8ijUnfjB/VzhZgAAXxoGSdhAqPrNhFAZ3f3Bkh5BCY+PXDtzEH7ZfBLaOhO2HCzCoVOVGJoW2anXio1QIkGlx42ZBRgcXudxni9WgS8NgVARDc5ugd1cg4YzG871r2IgCRuAoPRLIVb369LPxOM5ixOq5GLc/ep6/N/ScQgp/hjg3Du4i4KSEJJxIy1SJj3m/HSWesDlvg6FdDPWYUH9yd/QULARANfqdXyJGmB4AOdZxsNh0sJQvA2G4u2Qx09EUNrFEEjU3RYz6RkMx3Gt/0aQbrVmex5e+nInAGBgUig+eXIeeLyOJQEOqwG1ub9AX7QFDpZBYb0cqSHO1g/i0AEISlsISUiax/PsRi3MNadg1p6AsXw/wNohix6BoPTLIJRHdPln+99fJ/Dhj3vx8JgcZIQ3LlTmiZSImvgvCKQhXb4HIe1l1RWjcucriJv5Jhi+0NfhkG5iayiDZv97sBs1rV7DlwQjKG0RFPETYKnNh0lzDJzDAoepGubqU2Ctnps+GJ4I6gGXQdmPRqV7M0p4fMjBsrj1+bXIK6kFADx35yTMHN3+URaboQpVu1+Hw1yDo9Xh+CQ7EQCwoH8JRo6fh6zh7esEbTdWo+7UKhhL9wA8PsKGLu5yl/L6U79h7d+78cnB/rhn5EmMiqkGwCBizIO0bof0uLrcX2A3VSNs2B2+DoV0E7tRg8qdr7jvBOUJXGUwROpkBKVdDEnYoFZHlzmOg11fAZP2GBryN8BhqXM7r0yeDfWAy2l0upeihMfH9hwrw4Nv/gkAiA5T4NtliyAS8i/4PJuhElW7X4PDqkfY0Dvw+DfVyM6tcJ1/7MaxuGSK58hOW6z1RdBkfwCHqQbBGddDmTilYz/MOSbNUWj2vgOAQ3Z5CN7dn47bsvKxcMZ4BPWf36nXJKSzOI5D+eZ/QT3oSsionURAclh0qNz5CuzGxkXKioTJUKUugFmTA4E8EuKQtA4lKqzDgoaCP6HL3wDO3lhRXh47DiFDbgLDoxUhvQ3t0vKxMYNjXI1Fy7V6/Pj3hYsR2o1aVO1yJjvhI5dCFj3CK7V4REEJiBr/BISqWNQeXQld/roOv4bdVIPqg5/h/Nz5iOgaPDbuOL48kob1Z7u2RoiQzrDpiuGwNkAaNtjXoZBuwHEctAc+ckt21AMuR0jmjRBIg6FImAxJaHqHR2V4fDGCUucjetLTEMgap/kNpbta3IhB/B8lPH7gnitHgHfuH+NXa3PabNXAcRyqj3wJh1WH8JH3QBru3GLbPOHpbBNRvkSNyLGPQhScgrrcX2DWnmj3c50d0D8Ea9O7Hc+MF+Dth6bjq7U5+PTXQ6BBRdKTzu/OorU7gclYtsetEbEyebZX29QIZOGIHP84REGJrmP6ws0wVh7y2j1Iz6CExw+kxAVj/oQUAECD0Yov1+S0eq2heBss1SehSp4NaXjjN9amW9OBrrWX4AllCBu+BDyhHNWHPofDqr/wkwDUnvifZ3FBngBhw5dgcP84fPDYbKzZnoc3v90HlqWkh3S/87uz5FRsMCCxdjPqTvzoeiwOSeuWnXh8sQoRYx6GoMmGjpojX8FhrvP6vUj3oYTHT9xxyVBIRM454Z82nWxxhMZurkXtif9BII9CUP+Fbuc8Rng0DV0aSRFI1AjJuhkOSx1qzhXpaouhbC/0Z//2OB486GqI1UkAnFWmlz8xB3uOleLFL3bA7ui+ru6EAIBNVwSHVQ9J2CBfh0K6gS5/Q+MiZYaH4MHXdtuCYp5QitChiwHGucaStepRc9SzETLxX5Tw+IlwtQzXzXa+KdsdLD78+YDHNQ0Ff4GzmxGSeYPH8HxMuHsDUYvVAW29CV0hixwKefxEmCoOwFJ7ptXrbA3lqDmywvP5MWOgSHBf+BwdqsDyx+Ygr6QWTy7fDIvN4fE8QrzFULYPsqihNJ0VgDjWAUPxNtdjReJUiFRx3XpPsToJ6vRFrsemyoOwNpR26z2J91DC40eumz0YISoJAODv/YU4fLrSdY51WGAo3gZRUBIkoekez5WKhQhTu1dM7uw6nqaCUhcADB+6/PUtnmftZmgPLAfncG8cKlTEICSz5eKCIUFSvPfobOj0Fjz89l8wmLu/5xfpexp7Z43ydSikG5i1x5tsQWeg6jerR+6r7DcLfGlj/7+G/A09cl/SdZTw+BGZRIjFlwx1Pf6/lXtgtzunfYxl+8DajFAkTWv1+c3X8ZR2YR3PeQJZKOQxo2GqPAhbQ7nbOY7jUJPzNWx69+MMX4ywEXe12QFdKRPhrQdnQiTg4b7X/mhzoTYhnWGtPwvWZqK6TwGqaX8+cegACGShPXJfhseHKmV2Yxxle2A3Uv+t3oASHj+zYGIqBiQ5/+GeKa3DD385d0npi7eDJ1JA3sa3VW/t1GpOee4fd0PhJrfj+qItMJbt9bg+ZMjNECqiLvi6ErEAr9wzDTHhStz96gZoalvoe0NIJxnLs53TWVQvJeBwrB2myiOux4r48T16f3ncePBEyvPBwFC6s0fvTzqHEh4/w+fx8NgNY13b1D/77TDKq7Sw1hVAGp7Z5lqE5glPYXnXR3gAQKSMhVCVAFPVEddCaEtdQYu1KBRJ0yGPaf8UglDAx7OLJyKrfyT+8Z91KKn0Tsykb2uczqLdWYHIqisGx56fCmcgjcjq0fvz+CLI4xqTLFPV0R69P+kcSnj80ICkUFw2zVkl2WSx45c1GwCOhbiFtTtNJUUHuT3OL631WkzSiAw4TNWw6yvgsOqhPfChq2T7eSJ1CoIHXtHh1+bzeHj0hjGYOboflryyHnnF3oub9E3W+rPg7DSdFaialr8QKqLBE0rbuLp7SCMy3eJxtNCDi/gXSnj81J2XDENokPMfMVufBwAtLlZuKiUu2O1xqaYBJot3FgRLw53/uE1VR1B96DM4TDVu53kiBcKG39np6QOGYXDX5cNx9cxBWPp/G5CTV3XhJxHSCmPZfkgjh9F0VoBqumtUFJzskxjEwSlgBOcTLQ5mzXGfxEHajxIeP6WQiXDf1c7h+JTgBtRZxLDz1W0+JzpUAam48Q2e44CCsvo2ntF+InU/MAIJ9MXbYdY0H75lEDp0sVc6oN84NwN3XzEcD771F3Yfpe2epOM4joOp8iBkUcN8HQrpJjZ943uDKCjJJzEwPAEkoQNcjy11rZfuIP6BEh4/NmNUEkYNjEao1ILyBjG+XHukzet5PAbJsWq3Y2e8NK3F8Pjgi1SwGyo8zgWlLYQ03HuF3RZNTsM/bx6Hp5Zvwd/7C732uqRvsOnL4LDoIKbprIDFWhqnjwSS4Dau7F5CVazrz3aDxmdxkPahhMePMQyDh68bjRCpBTUmMb7ZcAwFZXVtPqf5tFZ+SdvXt5fdVAO7ucbjuCQ8A6pU73dAnz4qCS/eNQUvfbkTq7ed9vrrk8BlqjwESfhg8PgiX4dCugHHcW7tbnhiZRtXdy+BLNz1Z7uREh5/RwmPn4sN5UHE51BtEsPh4PDayj1ttoxIaTbCk1fS9REeZ1PQjz0WKfMlIQgdejsYpnt+jcZmxOKN+6fjvf9l478bjnXLPUjgMVUehjSyZ3ftkJ7D2U0A11ihnS9StHF19xI26aJuN2nBcdQux59RwuPn2HPfZBih8x/1wVOVWL+r9bnilNhmIzyldV2OoS73J1jr8t0PMnyEjVjS7W82Q/pH4L1HZ+Gb9cfw4c8HqNM6aZPdXAdrfRGkEUN8HQrpJqzdvWUOI5D5KBKA33TdImsHazP4LBZyYZTw+LtzoydThjX2iHn3h/3Q6S0tXp4Sp3Z7XNdgRk0XemoZKw6hoeBPj+POpqD9Ov26HdE/PgQfPjEHf+wpwGsr91CnddIqU+UhiINTffqtn3Qvhuc+VdlYj6fneYxu01uTX6OEx88x5zrzxkfIMX1kIgCgTm/B8haaiwJAkELi0VOrs/V47KZq1Bz5wuO4OGwQFIlTO/WanRUfqcKHj8/BwVOVeO7TbbDZqeko8WSqPAxp1FBfh0G6EdNsbVbzPn6EtIYSHn93ro4Ix9px3zWjIJM4Ky3/uvU0cvJbXiTnMa3ViYXLHGuH9uAnYG2e7R6CUua22BS0u0WEyPHBY7NRUtWAB9/8s9VRLtI3sXYzzNW5kNH6nYDWvNo857D6KBLS21DC4+fO7zRh7WaEq2W4s0lz0ddW7obd4blIrvm01obdZ/Dn3oIOTQXVn/oN1trm63acvy48oe/mzNVKCd5/dDaCFBIsfvl3FFVQKwriZNYchVAe6bZzhgQehuE1KfgHOCy+q3DMNku2GB7fR5GQ9qCEx8/xhDLwhArYDZUAgMumpSMtwblQ7nRxLX78K9ft+rNlddh/wr1WzsmiGjzz8bZWp8GaM2mOQZe/zuO4OKQ/AEAgC+vwz+FNErEAy/4xGdNGJGHxS7/jQK5nbSDS9xgrDkIaScUG+wKhvHF3lE1f5rM47Eat68+MQOqWiBH/QwlPLyBQRLoSHgHf2Vz0/IzSx6sOoVTj/IbDcRye+nALThV51ssBgL3HWn9jMJTuQenfT6D0r8egzV7ucV4WPQIMTwSeUO7TEZ7zeDwGSy4bhvuvHoVH3/0ba7ZTrZ6+jGPtMFUdhYzW7/QJQmWM68+2Bl8mPI0tcASycJ9M9ZP2o4SnFxDKo2A3asE5nLsRBiWH4fJpzr5aZqsdL3+5EyzLwWZnUarRt/o6aqWkxeMOcx2qD38Bh6kaDnOtxyJAvjQMIZk3wW7U+N10wbwJKXjt/ul4/8cDeP/HbNrB1UdZak6DJxBDqErwdSikBwgVjRWOfTvC07iO0t/eG4knSnh6Aee3GQ7W+sY2C0suG47oMOfW2wMnK7FiXQ4+/PmgR2uJpsZlxLZ43Fyd61bIyx0PYcPvBBieM+FRRHXyp+g+w9Ii8ck/52LbwWI8tXyL1xqmkt7DWHkI0sgs+obdRwiVje9l1vpCsHazT+Kw1p11/bnpNBvxT5Tw9ALS8AwAgKkqx3VMJhHinzePcz3++JdD+G7jceSerUa4uuUpp7GZLSc8bQ4JMwx4AinM2hMA54A0zHs9s7wpLlKFj5+cC73JiqWv/gFNnefuMhKYnM1CD9H6nT5EHNIfDO/cbi3WDrOm5yuxszYjLLV5jTE1aSRK/BMlPL2AQBENgSwcpqrDbsfT4kNcozxNLZiY6nFMKhYgMUrV4utb64tavznngFlzzJVsScIHdyDynqWSi/HmAzOQlhiCO178HSeLqn0dEukBNl0xWJsJktA0X4dCeghPIIakScNiY+WhHo/B+SXQuUuW4YsgObepg/gvSnh6AYZhII0YAltDqduugDe/24dyreeaHbuDxS0LMt2OpSWEtDrcb2m+/bwZgSIGZs1RiIISwRe3nDT5C4GAh8dvHIurZwzE0lf/wLZDxb4OiXQzY+UhSCMywJyrWUX6hqYjeqbKI2B7uABh0yRLEjrQoz4Q8T+U8PQSsuiRAICGws2NxyQtv8Hnl9Zi8aKhmDO2H2RiAZJjg/DKPdNavJbjOHCOlue/GZ4Q6oFXgnNY4TDXQho1vGs/RA9hGAbXzR6MZ26fgOc+3Y7/bjhGPbgCmHM6a6ivwyA9TBo5BDhXiZ6zG6Ev2tZj97aba2Es2+ceC/F79JWolxAFp0AcnAp94WaoUuaCL5LjvqtHISxIhpXrj8JkaexkfjRfA4Zh8Mwdky74ugzDgOGLmlUrZaBInIqg1HngS9So3PkKGL4YysQp3fCTdZ/JwxLw/mNyPPbuJhRV6vDIdWMgEFCOH0jsxmrY9OWudW6k7+CLlJDHjoWhZAcAoCF/PZQJU3pkpKXhzJ+ujR48oRyymDHdfk/SdfTu30swDANV6jxwDgv0Z/8CAIiFfNy6cAi+f/ESzBmX7HbteRzHwlKTB2PFQRjL98OsPe7RLiJ0+BJnwSyGB5E6GdHTXkRIxnXgS9Qw15yGpTYPisQp4AnlPfPDelF6Qig+fXIeTp6txoNv/QmdgdpRBBJT5SFIQtL8ojYU6Xmq1HkAnO93Dks99EVb2/U8jmNhqSuAseIQDGX7YNIca3fFZodVD33RFtdjZdJF4AnEHY6d9DyGo7H+XoPjOFRsfwF2owbRk572qPuQnVuBXzafxM3zM5EcIYC+aAsaCv4Gx9rPvycAYMCxdsiihkOVPAuioNbrlnCsHZW7XoVVV4yYaS9BIAlu9Vp/Z7LY8PynO1BQXofX7r0IcZH+vRaJtE/l7tchixoOZVLLU7Yk8GkPfQZj6W4AAMMXI2rSM61uEWdtBuiLd0B35g9wdjPAMM4O5wwDjrVBGp7pfF8MTmlxzSPHcdAe+BCmigPn7idCzEWvgC/y3DxC/A8lPL2MpTYflbtehSioHyLHPdpi75aGwi2oPf698wHbSk0ahgeGJ4AkbBDCht3Z4jBw3clV0OWtRVD6JQhKne/NH8MnWJbDhz8fwOrteXjp7qkYlhbp65BIF7A2A0o2PuxMxqUhvg6H+IjNUImKrc+DY53T8qKgRESOf8JjEbuhbD9qDn8BAK5rPTmn+EVBCQgfeS94QvdWEfqibajJWeF6rEqdD3X6JV77WUj3oimtXkYcnIKgtIthrctH/enVHufrT69F3fEfnIlOa8kOAHAsOIcVZs0xVO76P1cV5/PM1aegy/sd4pA0qFLmevvH8Akej8HdV4zA0iuG45G3/8K6nW3vTiP+zVSVA5EqjpKdPk4oj0Tw4Gtcj631hag99h04rrGxckPRNtQc/hwca20j2QEADpzDAktdASp2vAjWZnJ/3ePfuR6LgpIQ1H+BV38W0r1o0XIvpEqZC7PmBHR5v4MvUkLZbzoAwFC2D7q83zH+lhXQ1JnA5zUOyV4xPQ33XD0UT763HUfytKiqMWL7Z1cjPlIJW0MJtIc+RfiIuwAAlroCaA8sB08oRejQ28EwgZUXL5jYHzFhSvzzg80orNDhzkuGgsejCr29janyMO3OIgAAefxEmLXHYSzfDwDQF22BLGY0JKFpMGtPoO7Ytxh/y9ctvi/OnZCEZz/ahXKtATwegzGDo/D8kvGIigCq9r2DyHGPAeCgyV7u2tzB8MUIHbaYSiH0MvS31QsxDA9hw/8Bzb63UXv8Ozgs9VClLULt8e9d314+f2YWJg51r6ysqTViyog43H1lFi57tHF0iGNtMFflwNpQCoe5FtrsDwGGh/BR9wTst+fhA6LwyZPz8Mg7f6G4Uoenb5sAiZj+OfQWHOuASXMcqpQ5vg6F+AGGYRCSeSOsuhLYDRUAANbqrFHmfF90jmC39r749fNzEBkqh8XmwOtf78dTH+zAZ8/Mgk1XDEvNKYiCkoAmqz9CMm+gVhK9UGB9de9D+GIlIsY+AknYIOjy16Fq9+vg7KY2nxMeLMNN8wchK82zyR3HOlB9+Ato9r0HnkCCyHGPQRIS2JVrE6JU+OTJuahrMGPp/22AltpR9BrWugIwfAGEqnhfh0L8BE8oQ+S4x5zJyTnW+iLYDZrWnwTn+2JkaOMOVD6Ph7PlOgAA57BAl78ezpXNABg+QofeAXnsWG+HT3oAJTy9GE8gQfioeyGPnwhr7elmtXQ6ioWtvhACRRQixz8BkSrOa3H6syCFBG89NAPJsWosful3nC6u8XVIpB1MmhxIwwYH3HQr6RrnF8GHITlXl0lXsBEcZ7/As4DSKj0yr16B9Mu+wMe/HMGSyxsLCZqrc+Ew14Phi5zvt7FUc6e3ojH8Xo7hCRA65GYYS/e4hm0BYPELGyHgN34YPHnraFw75wLN7XhChAy+DgJZWHeF65eEAj6evGU8Vq4/hrte2YDnFk/ChKy+kfD1Vqaqo1ClzPZ1GMQP8QQShI9cCptBA8vx71z9roDW3xdjIxTI+f4m1DWY8e2Gk0iJU7uuYXgCWHWFCBtxF0TKmJ78UYiXUcITADiOc9baaeKTf830mKu+EIYnuOC0WKBiGAY3zs1AfIQS//54KxZfMhRXzRjYav8x4jsOiw62hhJIwgZd+GLSJzE8AUTKaLB297Y5F3pfVCsluHx6f8y992fs+eo6Z3LEcWDtFkp2AgAlPAGAYZhzBbS8UFKpj+86mDoiEZGhcjz+7iYUVujw0LWjqR2FnzFpjkIUlETF3sgFMQwfHX1XdDg4aOvM0ButUCslAAAejxqDBgJ6Jw8Q7S2tb7baYbU5e8BYbQ6YrU1GhjjW77uh94SBSWH49Kl5OHZGg4ff+QsNxq6sjSLeZtYchTSCemeRC+O1Iylet7MA+SV1YFkO1fUmLPt0NwanhLqSHTAMePS+GBCo0nKAqD3+AxrO/g1wDky47TuPehOThsbi43/NROKCTz2eW7jmDgAAXxKMmIteoWmcc4xmG579dBtKKhvw6r0XIS5C6euQ+jyOY1G68SGEj74fYnU/X4dD/JzuzB+oP/krONba6vviuKwYfLoqB9V1ZiikQozNjMYTt45y/XtnBFLEzXyDau4EAEp4AoTdqEXZlqcB9sI7ElrC8EUISr8MqnNFDImTg2Xx4U8H8du203j6tgmYOJS2QfuSpTYf3QHCSAAAEmlJREFUmv3vIXbG67RDi1wQazOg5M9HOv2+CJ4Ayn4zETzgMu8GRnyC3jEChEAWBnFwCtDZDwEOUMSN825QAYDP42HplSPw+E3j8Nxn27H8pwOwO9gLP5F0C5PmKCS0HZ20E08ohyxqOMB49hxsDwYMlIlTvRsU8Rl61wggoVm3gieQdPh5DE+EkKxb270OqC+6aGQiPntqPnYcKcEDb2xEdX3f3M3ma2bNMUjDaf0Oab/gwdeAL1IC6NhUPcMXQT3wyoCtNt8XUcITQATSUESMfRQ8obzdIz0MTwj1oCshjxnZzdH1fucrM0eEyHHr82tw6FSlr0PqUxyWBljrCyEJH+zrUEgvwhcpETn+MeeGjHaO9DA8EZQpc6FMmtbN0ZGeRGt4ApDdVIOaIytgrjnpPOAxf82A4QvBF6sRnHEtfWPuII7j8OvW03j3h/24/eIsXDtrEC307gGG0t1oKPgLUROf8nUopBdyWBtQk7MSpqojABigSaHW8xi+GDyhDOqBV0IeM6rngyTdihKeAGY31UBfuAWGkp1g7UZwHAseXwxxaDpUybMgUifTB3UX5J6txlMfbkFafAieunU8FDKRr0MKaNpDn0EgDYM6fZGvQyG9mMOig75oG/TF28Ba9eA4B3h8EUTqZKiSZ0Mcmk7viwGKEh5CukCnt+D5z7ejqEKHF++agv7xNN/fHTiORemfjyB85FLn4nxCCOkgWsNDSBeoFGK8es9FWDAxFXf9Zz3W7sjzdUgByVpfCHAOiKj2DiGkk6iSEiFdxOMxuGleJgb1C8O/P96GI3lVeOja0RCL6J+Xt5g1x2g7OiGkS+jdgxAvGTkwGl8+swCF5fX4x3/Wo1TT4OuQAoap6igk1E6CENIFlPAQ4kXhwTK898hsDE+Pwm3L1mLboWJfh9TrOawGWOsLIA2j7eiEkM6jhIcQLxMIeLjv6pF44uZxeP6z7fjgx2yqztwFZu1xCJVx4EuCfB0KIaQXo4SHkG4ybYSzOvOunFLc/3rfqc5sqq6G3Wz22uuZNTlUK4oQ0mWU8BDSjZzVmechKlSOW/pAdebDX3yB7+bNw9dTp2Lb889DV1LSpdfjOBYmzTFIImg6ixDSNVSHh5AewHEcftt2Gu98H7jVmR02G76ZMQN2o9F1jOHxED9pEkbcdReCU5z1cxxWK+oLC8EXCiFSKiEJCWn1/4W1vgiVu19D3Mw3wfA61wCSEEIASngI6VHnqzP3jw/Gv26dEFDVmS0NDfjmootaPT9i6VLEjBqFdXff7ZYUySMjETViBKKHD0f0qFFQxsS4zunyN8BSm4fwkUu7NXZCSOCjhIeQHqYzWLDssx04W16Pl+4OjOrMDpsN2e+/j6PffNPqNRK1GrHjxiF/3bo2Xyth6lSMXLoU6qQkVO19G9LwDCj7Tfd2yISQPoYSHkJ8gGU5rFx/FCvW5uCBa0djwcRUX4fUadUnT2Lrc8+h9vTpNq8LSUtD0kUX4cCHH17wNRk+H/0XLkBUejESZ/8bQmXMBZ9DCCFtoVKwhPjA+erMg5PD8cxHW5GTV4WHrut91ZnLs7Pxx333wWG1tn4RwyAiMxMzXn8dQrkcQqkU5QcPwmE2Q1dcjIbSUo+ncA4HTq36FWcVQqgzdAgbQAkPIaRraISHEB/T1Brx9EdbYLLY8eJdUxEXofR1SO1Sm5eHtYsXw6rXt3ieJxQibdEiDLn5Ziiiolp9HX1FBSoOHEDRtm04++efHueFcjlmvPYaokeO9FrshJC+hxIeQvyA3c7ig58OYO2OPDx163hMHpbg65DaZNRqsfrmm2GoqnId4wkEGHzddWDtdohVKqTOn99motOS6pMnse/dd1G2Z4/bcZ5QiGkvv4zEKVO8Ej8hpO+hhIcQP7I5uxAvfbkTl0xJw52XDoOA75+lsv7+5z/dRmMYHg/TX30VCV5KSE6u+hk7X34ZXJMC1QKZDJesXAlVfLxX7kEI6Vv8892UkD5q6ohEfPYvZ3Xm+17fiMoag69D8lBx6JDH1NPYRx/1WrIDAIkTB2DoTVngi8WuY3ajEZuffhqs3e61+xBC+g5KeAjxM/GRzurMSdFBuOnZ1fhjzxn4y0Asx7LY++abbsf6zZyJgVdc4dX7mLW5SJg4ETNefx1oUpRQe+wYDn7yiVfvRQjpGyjhIcQPScQCPHbjWDxzx0S8/d1+PPPxNuj0Fl+HhZJdu6A9ftz1mC8SYdS993r9PubqXEjCBiB2zBhk3nij27kjX33V4s4uQghpCyU8hPixCUPisPL5i2G1OXDjc6ux73iZT+NpXjRw8LXXQhEd7dV7sDYjrPWFEIcOAAAMX7IEYQMHus5zDkebBQ4JIaQllPAQ4ueClRL8Z+lULF6UhX9+sAVvfbcPFmvPr2OxGQwo3Ly58QDDYICXp7IAwFJzGgJZBAQSNQCALxRizMMPu11z6rffYKqp8fq9CSGBixIeQnoBhmGwYGJ/fPXvBcg9q8Wty9biZGF1j8ZwdvNmOCyN02pRw4d3eNt5ezinswa6HYvMykLksGGuxw6LBce//97r9yaEBC5KeAjpRWLDlXj/sdmYMy4Zd72yASt+z4GDZS/8RC8o27vX7XHKnDndch+zNheSc9NZTQ25+Wa3xwUbN3bL/QkhgYkSHkJ6GT6Ph5vmZWL547OxftcZLH11A0o1Dd1+3+oTJ9wex44d6/V7OCwNsDWUQhya5nEubvx4yCMiXI91xcWoLyryegyEkMBECQ8hvVR6Yii+eHo+BiSF4ebn1mDN9tPdtn3dZjCg7uxZ12NJSAjkkZFev4+l5iSEqnjwRQqPcwzDIHb8eLdjJTt3ej0GQkhgooSHkF5MLBLggWtG4aW7puDjVYfwzw82o7bB7PX7VJ88CTRJpsIGDgTTpD6Ot7Q2nXVeHCU8hJBOooSHkAAwenAMVj57MYQCPm545jfsOFLi1ddvXvcmJDXVq69/3vn6O62JHT0aDK/xbavm9OluiYMQEngo4SEkQKgUYiz7x2Tcf81IPP/pdrz69W6YLDavvLZFp3N7LA0J8crrNmU31cBu1EIc0r/Va4RyudtUmkmrhd3s/REtQkjgoYSHkAAza0wyVjy7EEWVOtz83BocO6Pp8mua6+vdHovV6i6/psc9qnMhUieBJ5C0eZ0yNtbtcUOJd0ezCCGBiRIeQgJQZIgc7zw0E5dNTcd9r2/Ep78egt3e+e3r1mYjPGKVqqsherBUt71+5zxlXJzbYx21mSCEtAMlPIQEKB6PwTWzBuHjJ+di26Fi/OM/61BYUX/hJ7bAYbW6PeaLRN4I0YXjOJi1J9uV8DTdmg4AlvrO/UyEkL6FEh5CAlxKbDA+eXIehg+Iwu3L1uLnTSc7vH1dKJO5PbaZTN4MEXajBqy1AeLglAte2x27wwghgY8SHkL6AJGQj6VXjMD/3T8dK9cfxSNv/w1tnbHdzxc0T3gMBq/GZ6nOhSg4GQxf6NXXJYSQ8yjhIaQPGZYWia+fXYhglQQ3Prsam7ML2/U8jxEeY/uTpfYw15yGOMSzujIhhHgLJTyE9DFyqQj/um0CHrtxLF75ejde+HwHDCZrm8+RBAW5PTZUVno1JkvNaUja2I7udm2DexsNvpBGhQghF0YJDyF91LQRifj62YWo0Zlw47OrcehU60lMUL9+bo9r8/K8FofdVAOHuQ6i4OR2Xd+8CGLzbeqEENISSngI6cPC1DK8fv90XD9nMB55+y988GM2rDaHx3XBKe6LiWvz870Wg6XmNERBieDxxe26vnndnebb1AkhpCWU8BDSxzEMg8unDcBnT89Hdm4FFr/0O/JLat2uESuVbhWO9eXlHlNLnWWpOdVmdeWmOJZ1G+ERyGSQBAd7JQ5CSGCjhIcQAgBIjArCR0/MxaSh8Vj80jp8+usht9Ge0PR0t+tLd+3yyn0tNafbnfBoT5xwayURlJBA29QJIe1CCQ8hxEUg4OGORUPx0T/nYOeRUty6bA2O5jtbU8RNmOB2beGmTZ2+j8OqR9nmf6Fq79uw6cvBE0rBcReuBN28O3rM6NGdjoEQ0rdQwkMI8dA/PgQfPzkX88an4P43NuKt7/YhfMx4oMloSvHOnbBbLJ16fZ5AArtRC7PmKACgatdr0O7/4ILPa57wxI0f36n7E0L6Hkp4CCEtEvB5uH5OBr56ZgHyimtw57s7IUtpnNayG42dHuVheAIIVXFND0CZMrvN5zSUlkJz7JjrsVAuR2RWVqfuTwjpeyjhIYS0KS5ShXcfmYWb5mVguy3S7dzhzz8Hx3auKak0fLDzDwwPwRk3XLAOT87KlUCTlhhx48aBJxB06t6EkL6HEh5CyAUxDINFk9Pw5FuPwSGSuo7XFRTgbCdHeURBzto+stjxUCZMbPNaU3U1Tq9e7XZs8LXXduq+hJC+ib4eEULaLSYuAiNuvgGHPvnEdWzfB8uB1CF44uPteOvBmYgOU7T4XHNdHYq2bIGxuhqszQahXAapPBnxQ2684H0Pf/EFHE3WC0UNG4aIIUO6/gMRQvoMhuto22RCSJ9m0enww8UXuzUQLYwdjj/DJ2D0oBi8+eAMt+s1x44hZ+VKFG/dCobPdy50ZlnwhELw+HzIIyMx5Oab0W/mTAgkEo/7le7Zgw333ON2bOZbbyG+2a4xQghpC01pEUI6RKxSIevWW92OJZYeQGJtHk4V1cBotgEAOI7D3rffxu9LluDsX3/BYbXCbjIB59b8sDYb7GYz6gsLsev//g+/XHstDFVVbq9rqq3F1n//2+1YRFYW7c4ihHQYJTyEkA7LuOEGRI0Y4XZscvGfuH1yDIQCHjiOw46XXkLujz/CYTa7LTZuid1kgr68HL/ecAOMWi0AZ1XlPx9+GKbqatd1QrkcU557jooNEkI6jBIeQkiH8fh8TF22DGK12nVMxNowMhwQCvjI/fFHnFm/3lUV+bEjR3BndjYabDa313n22DHcvn8/tBYLOIcDFp0O6+6+GxzLgmNZj55d4594gpqFEkI6hRIeQkinyMLDnaMtfL7rGF8oBOtw4MDHH7u1gACAMJEIe2tqXI9LjEZYm21p5xwOGCorUbpnD1iHA3yh0HWu/8KFSJkzp5t+GkJIoKOEhxDSaXHjx2PGa6+BL27sdF6ycyccVqvHteNCQ7GzyfTUjupqjA8N9bjObjQiZ8UKt2Ppl16KCU895cXICSF9DSU8hJAuiZ84EXOXL4c4KAgAkPP117AbjR7XpSgUMDkcKDOZwHIc9tXUYGwLCQ8AVB05AkNlJQBg6B13YPw//wlek5EkQgjpKKrDQwjpsojMTCz4/HNY9XrU5uW1et240FDsqq5GmlKJaIkEwSJRi9fxRCLUFRRg7KOPImV22y0nCCGkPSjhIYR4RVBCAjiO81i709S40FC8cvIkNBYLxoWFtf5iLAu72UzJDiHEa2hKixDiNQzDgN/KqA0AhInFCBeJkFNfjxFNdnh54PEgbKEIISGEdBaN8BBCvEoaFuZWhbm5W5KSYHQ4IObz4WilPg/ncEAeFdVdIRJC+iAa4SGEeNWgq65qsUXEeRESCZLk8jZfQxoaipC0NG+HRgjpw6iXFiHEq6x6Pb6dM8et2WdHCKRSjH7wQQy49FIvR0YI6ctohIcQ4lUihQIpc+e2uZanLQyPRwUGCSFeRwkPIcTrxj78MJRxceAJOrZMkC8WY+Ybb0AolXZTZISQvooSHkKI1wkkEsz76COo+/Vzq8LcKoaBQCLBRa+8gqjhw7s/QEJIn0NreAgh3cZuNuPIV1/h+A8/gLPbYWtWgZkvFgMch5gxYzDi7rsRkprqo0gJIYGOEh5CSLdj7XYUbduGU6tWwaDRgLXZIFIqETt2LAZcdhlkbRUhJIQQL6CEhxBCCCEBj9bwEEIIISTgUcJDCCGEkIBHCQ8hhBBCAh4lPIQQQggJeJTwEEIIISTgUcJDCCGEkIBHCQ8hhBBCAh4lPIQQQggJeJTwEEIIISTgUcJDCCGEkIBHCQ8hhBBCAh4lPIQQQggJeJTwEEIIISTgUcJDCCGEkIBHCQ8hhBBCAh4lPIQQQggJeJTwEEIIISTgUcJDCCGEkIBHCQ8hhBBCAh4lPIQQQggJeJTwEEIIISTgUcJDCCGEkIBHCQ8hhBBCAh4lPIQQQggJeJTwEEL+v906kAEAAAAY5G99j68oAtgTHgBgT3gAgD3hAQD2hAcA2BMeAGBPeACAPeEBAPaEBwDYEx4AYE94AIA94QEA9oQHANgTHgBgT3gAgD3hAQD2hAcA2BMeAGBPeACAvQC8fIfaYMRVXQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import graph_network\n",
"from neuprint import fetch_simple_connections\n",
"\n",
"clock_no_llnv = getClock()\n",
"clock_IDs_no_llnv = clock_no_llnv['bodyId']\n",
"conns_df = fetch_simple_connections(clock_IDs_no_llnv, clock_IDs_no_llnv, min_weight = 3)\n",
"\n",
"# Evening cells are separated into subgroups\n",
"G, conns_df_me = graph_network.clock_type_network(conns_df, clock_no_llnv, 'clock_connectivity_graph')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "1a8082bc",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Evening cells are all together\n",
"G, conns_df = graph_network.clock_type_network(conns_df, clock_no_llnv, 'clock_connectivity_graph', separate_E_cells = False)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ee073927",
"metadata": {},
"outputs": [],
"source": [
"conns_df_me.to_csv('intra_clock_mediumstrong_separate_e.csv')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "340c79ac",
"metadata": {},
"outputs": [],
"source": [
"conns_df.to_csv('intra_clock_mediumstrong_connections.csv')"
]
},
{
"cell_type": "markdown",
"id": "f798cfc3-9424-4c44-a0bf-2432cbaeb8d7",
"metadata": {},
"source": [
"# Connections made by clock neurons with all neurons "
]
},
{
"cell_type": "markdown",
"id": "8dcbd162-c7c6-4c82-8a27-fb5f35b21f19",
"metadata": {},
"source": [
"Clock neurons form connections with many other neurons besides clock neurons. The code below generates two large tables with details of the partner neurons to the clock neurons and the synaptic weights for those connections."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "6f125c13",
"metadata": {},
"outputs": [],
"source": [
"# The main thing here is to sort this information from highest to lowest number of synaptic connections for each neuron\n",
"from neuprint import fetch_simple_connections\n",
"\n",
"pre_conns = fetch_simple_connections(None,clock_df['bodyId'])\n",
"post_conns = fetch_simple_connections(clock_df['bodyId'],None)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "b7ce67ea-f44b-4665-b693-ce7e2d7e03c6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">sLNv1</th>\n",
" <th colspan=\"3\" halign=\"left\">sLNv2</th>\n",
" <th colspan=\"3\" halign=\"left\">sLNv3</th>\n",
" <th>sLNv4</th>\n",
" <th>...</th>\n",
" <th>lLNv1</th>\n",
" <th colspan=\"3\" halign=\"left\">lLNv2</th>\n",
" <th colspan=\"3\" halign=\"left\">lLNv3</th>\n",
" <th colspan=\"3\" halign=\"left\">lLNv4</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>bodyId_pre</th>\n",
" <th>instance_pre</th>\n",
" <th>weight</th>\n",
" <th>bodyId_pre</th>\n",
" <th>instance_pre</th>\n",
" <th>weight</th>\n",
" <th>bodyId_pre</th>\n",
" <th>instance_pre</th>\n",
" <th>weight</th>\n",
" <th>bodyId_pre</th>\n",
" <th>...</th>\n",
" <th>weight</th>\n",
" <th>bodyId_pre</th>\n",
" <th>instance_pre</th>\n",
" <th>weight</th>\n",
" <th>bodyId_pre</th>\n",
" <th>instance_pre</th>\n",
" <th>weight</th>\n",
" <th>bodyId_pre</th>\n",
" <th>instance_pre</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5813022753</td>\n",
" <td>None</td>\n",
" <td>18</td>\n",
" <td>5813022753</td>\n",
" <td>None</td>\n",
" <td>26</td>\n",
" <td>5813022753</td>\n",
" <td>None</td>\n",
" <td>23</td>\n",
" <td>5813054835</td>\n",
" <td>...</td>\n",
" <td>42</td>\n",
" <td>1283406432</td>\n",
" <td>aMe2</td>\n",
" <td>19</td>\n",
" <td>5813011683</td>\n",
" <td>aMe2</td>\n",
" <td>26</td>\n",
" <td>5813011683</td>\n",
" <td>aMe2</td>\n",
" <td>46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5813054835</td>\n",
" <td>None</td>\n",
" <td>13</td>\n",
" <td>5813054835</td>\n",
" <td>None</td>\n",
" <td>13</td>\n",
" <td>5813054835</td>\n",
" <td>None</td>\n",
" <td>19</td>\n",
" <td>5813022753</td>\n",
" <td>...</td>\n",
" <td>29</td>\n",
" <td>5813021202</td>\n",
" <td>None</td>\n",
" <td>16</td>\n",
" <td>5813022753</td>\n",
" <td>None</td>\n",
" <td>25</td>\n",
" <td>1594117455</td>\n",
" <td>aMe2</td>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5813040744</td>\n",
" <td>None</td>\n",
" <td>9</td>\n",
" <td>5813040744</td>\n",
" <td>None</td>\n",
" <td>12</td>\n",
" <td>5813040744</td>\n",
" <td>None</td>\n",
" <td>8</td>\n",
" <td>5813040744</td>\n",
" <td>...</td>\n",
" <td>25</td>\n",
" <td>5813022753</td>\n",
" <td>None</td>\n",
" <td>16</td>\n",
" <td>1283406432</td>\n",
" <td>aMe2</td>\n",
" <td>24</td>\n",
" <td>1283406432</td>\n",
" <td>aMe2</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1664980698</td>\n",
" <td>sLNv2</td>\n",
" <td>6</td>\n",
" <td>5813069844</td>\n",
" <td>None</td>\n",
" <td>5</td>\n",
" <td>5812980250</td>\n",
" <td>SLP309</td>\n",
" <td>5</td>\n",
" <td>2068801704</td>\n",
" <td>...</td>\n",
" <td>23</td>\n",
" <td>5813040744</td>\n",
" <td>None</td>\n",
" <td>16</td>\n",
" <td>1594117455</td>\n",
" <td>aMe2</td>\n",
" <td>22</td>\n",
" <td>5813021217</td>\n",
" <td>None</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2008061833</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>2007068523</td>\n",
" <td>sLNv3</td>\n",
" <td>5</td>\n",
" <td>2068801704</td>\n",
" <td>sLNv1</td>\n",
" <td>5</td>\n",
" <td>2007068523</td>\n",
" <td>...</td>\n",
" <td>23</td>\n",
" <td>5813011683</td>\n",
" <td>aMe2</td>\n",
" <td>14</td>\n",
" <td>5813040744</td>\n",
" <td>None</td>\n",
" <td>20</td>\n",
" <td>1501332510</td>\n",
" <td>None</td>\n",
" <td>21</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>374</th>\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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>375</th>\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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>376</th>\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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>377</th>\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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>378</th>\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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>379 rows × 84 columns</p>\n",
"</div>"
],
"text/plain": [
" sLNv1 sLNv2 sLNv3 \\\n",
" bodyId_pre instance_pre weight bodyId_pre instance_pre weight bodyId_pre \n",
"0 5813022753 None 18 5813022753 None 26 5813022753 \n",
"1 5813054835 None 13 5813054835 None 13 5813054835 \n",
"2 5813040744 None 9 5813040744 None 12 5813040744 \n",
"3 1664980698 sLNv2 6 5813069844 None 5 5812980250 \n",
"4 2008061833 None 4 2007068523 sLNv3 5 2068801704 \n",
".. ... ... ... ... ... ... ... \n",
"374 NaN NaN NaN NaN NaN NaN NaN \n",
"375 NaN NaN NaN NaN NaN NaN NaN \n",
"376 NaN NaN NaN NaN NaN NaN NaN \n",
"377 NaN NaN NaN NaN NaN NaN NaN \n",
"378 NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" sLNv4 ... lLNv1 lLNv2 \\\n",
" instance_pre weight bodyId_pre ... weight bodyId_pre instance_pre weight \n",
"0 None 23 5813054835 ... 42 1283406432 aMe2 19 \n",
"1 None 19 5813022753 ... 29 5813021202 None 16 \n",
"2 None 8 5813040744 ... 25 5813022753 None 16 \n",
"3 SLP309 5 2068801704 ... 23 5813040744 None 16 \n",
"4 sLNv1 5 2007068523 ... 23 5813011683 aMe2 14 \n",
".. ... ... ... ... ... ... ... ... \n",
"374 NaN NaN NaN ... NaN NaN NaN NaN \n",
"375 NaN NaN NaN ... NaN NaN NaN NaN \n",
"376 NaN NaN NaN ... NaN NaN NaN NaN \n",
"377 NaN NaN NaN ... NaN NaN NaN NaN \n",
"378 NaN NaN NaN ... NaN NaN NaN NaN \n",
"\n",
" lLNv3 lLNv4 \n",
" bodyId_pre instance_pre weight bodyId_pre instance_pre weight \n",
"0 5813011683 aMe2 26 5813011683 aMe2 46 \n",
"1 5813022753 None 25 1594117455 aMe2 31 \n",
"2 1283406432 aMe2 24 1283406432 aMe2 29 \n",
"3 1594117455 aMe2 22 5813021217 None 26 \n",
"4 5813040744 None 20 1501332510 None 21 \n",
".. ... ... ... ... ... ... \n",
"374 NaN NaN NaN NaN NaN NaN \n",
"375 NaN NaN NaN NaN NaN NaN \n",
"376 NaN NaN NaN NaN NaN NaN \n",
"377 NaN NaN NaN NaN NaN NaN \n",
"378 NaN NaN NaN NaN NaN NaN \n",
"\n",
"[379 rows x 84 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from connection_utils import ranked_lists\n",
"\n",
"# generate table of presynaptic connections to all clock neurons\n",
"pre_sort_conns = clock_neuron_connections(clock_df, 'in', min_weight=1).sort_values('weight', ascending = False)\n",
"\n",
"all_pre = ranked_lists(pre_sort_conns, clock_df, 'in')\n",
"all_pre"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e66bf810-6c2a-4ea9-81a7-683369de0de0",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">sLNv1</th>\n",
" <th colspan=\"3\" halign=\"left\">sLNv2</th>\n",
" <th colspan=\"3\" halign=\"left\">sLNv3</th>\n",
" <th>sLNv4</th>\n",
" <th>...</th>\n",
" <th>lLNv1</th>\n",
" <th colspan=\"3\" halign=\"left\">lLNv2</th>\n",
" <th colspan=\"3\" halign=\"left\">lLNv3</th>\n",
" <th colspan=\"3\" halign=\"left\">lLNv4</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>bodyId_post</th>\n",
" <th>instance_post</th>\n",
" <th>weight</th>\n",
" <th>bodyId_post</th>\n",
" <th>instance_post</th>\n",
" <th>weight</th>\n",
" <th>bodyId_post</th>\n",
" <th>instance_post</th>\n",
" <th>weight</th>\n",
" <th>bodyId_post</th>\n",
" <th>...</th>\n",
" <th>weight</th>\n",
" <th>bodyId_post</th>\n",
" <th>instance_post</th>\n",
" <th>weight</th>\n",
" <th>bodyId_post</th>\n",
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" <th>bodyId_post</th>\n",
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" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>355453590</td>\n",
" <td>SLP316</td>\n",
" <td>75</td>\n",
" <td>5813047586</td>\n",
" <td>SLP316</td>\n",
" <td>84</td>\n",
" <td>355453590</td>\n",
" <td>SLP316</td>\n",
" <td>97</td>\n",
" <td>355453590</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>5813040494</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>1884625521</td>\n",
" <td>lLNv1</td>\n",
" <td>3</td>\n",
" <td>5813075715</td>\n",
" <td>aMe6c</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>325122525</td>\n",
" <td>SLP316</td>\n",
" <td>64</td>\n",
" <td>355453590</td>\n",
" <td>SLP316</td>\n",
" <td>61</td>\n",
" <td>325122525</td>\n",
" <td>SLP316</td>\n",
" <td>71</td>\n",
" <td>5813047586</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>5813075715</td>\n",
" <td>aMe6c</td>\n",
" <td>3</td>\n",
" <td>511051477</td>\n",
" <td>5th sLNv</td>\n",
" <td>2</td>\n",
" <td>5901222742</td>\n",
" <td>aMe6b</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5813047586</td>\n",
" <td>SLP316</td>\n",
" <td>57</td>\n",
" <td>325122525</td>\n",
" <td>SLP316</td>\n",
" <td>60</td>\n",
" <td>5813047586</td>\n",
" <td>SLP316</td>\n",
" <td>69</td>\n",
" <td>325122525</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>1884625521</td>\n",
" <td>lLNv1</td>\n",
" <td>3</td>\n",
" <td>1563929167</td>\n",
" <td>aMe14a</td>\n",
" <td>2</td>\n",
" <td>695124651</td>\n",
" <td>aMe3</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>325455002</td>\n",
" <td>SLP403</td>\n",
" <td>20</td>\n",
" <td>325455002</td>\n",
" <td>SLP403</td>\n",
" <td>31</td>\n",
" <td>325455002</td>\n",
" <td>SLP403</td>\n",
" <td>23</td>\n",
" <td>325455002</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2152426385</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>5813021217</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>5812979048</td>\n",
" <td>aMe6a</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>294783216</td>\n",
" <td>SLP403</td>\n",
" <td>19</td>\n",
" <td>294783216</td>\n",
" <td>SLP403</td>\n",
" <td>24</td>\n",
" <td>540998882</td>\n",
" <td>SMP232</td>\n",
" <td>13</td>\n",
" <td>294783216</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2152426324</td>\n",
" <td>None</td>\n",
" <td>1</td>\n",
" <td>5813025330</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>829141094</td>\n",
" <td>aMe15</td>\n",
" <td>5</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>533</th>\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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>534</th>\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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>535</th>\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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>536</th>\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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>537</th>\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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>538 rows × 84 columns</p>\n",
"</div>"
],
"text/plain": [
" sLNv1 sLNv2 \\\n",
" bodyId_post instance_post weight bodyId_post instance_post weight \n",
"0 355453590 SLP316 75 5813047586 SLP316 84 \n",
"1 325122525 SLP316 64 355453590 SLP316 61 \n",
"2 5813047586 SLP316 57 325122525 SLP316 60 \n",
"3 325455002 SLP403 20 325455002 SLP403 31 \n",
"4 294783216 SLP403 19 294783216 SLP403 24 \n",
".. ... ... ... ... ... ... \n",
"533 NaN NaN NaN NaN NaN NaN \n",
"534 NaN NaN NaN NaN NaN NaN \n",
"535 NaN NaN NaN NaN NaN NaN \n",
"536 NaN NaN NaN NaN NaN NaN \n",
"537 NaN NaN NaN NaN NaN NaN \n",
"\n",
" sLNv3 sLNv4 ... lLNv1 lLNv2 \\\n",
" bodyId_post instance_post weight bodyId_post ... weight bodyId_post \n",
"0 355453590 SLP316 97 355453590 ... 3 5813040494 \n",
"1 325122525 SLP316 71 5813047586 ... 3 5813075715 \n",
"2 5813047586 SLP316 69 325122525 ... 2 1884625521 \n",
"3 325455002 SLP403 23 325455002 ... 2 2152426385 \n",
"4 540998882 SMP232 13 294783216 ... 2 2152426324 \n",
".. ... ... ... ... ... ... ... \n",
"533 NaN NaN NaN NaN ... NaN NaN \n",
"534 NaN NaN NaN NaN ... NaN NaN \n",
"535 NaN NaN NaN NaN ... NaN NaN \n",
"536 NaN NaN NaN NaN ... NaN NaN \n",
"537 NaN NaN NaN NaN ... NaN NaN \n",
"\n",
" lLNv3 lLNv4 \\\n",
" instance_post weight bodyId_post instance_post weight bodyId_post \n",
"0 None 4 1884625521 lLNv1 3 5813075715 \n",
"1 aMe6c 3 511051477 5th sLNv 2 5901222742 \n",
"2 lLNv1 3 1563929167 aMe14a 2 695124651 \n",
"3 None 2 5813021217 None 2 5812979048 \n",
"4 None 1 5813025330 None 2 829141094 \n",
".. ... ... ... ... ... ... \n",
"533 NaN NaN NaN NaN NaN NaN \n",
"534 NaN NaN NaN NaN NaN NaN \n",
"535 NaN NaN NaN NaN NaN NaN \n",
"536 NaN NaN NaN NaN NaN NaN \n",
"537 NaN NaN NaN NaN NaN NaN \n",
"\n",
" \n",
" instance_post weight \n",
"0 aMe6c 13 \n",
"1 aMe6b 8 \n",
"2 aMe3 8 \n",
"3 aMe6a 6 \n",
"4 aMe15 5 \n",
".. ... ... \n",
"533 NaN NaN \n",
"534 NaN NaN \n",
"535 NaN NaN \n",
"536 NaN NaN \n",
"537 NaN NaN \n",
"\n",
"[538 rows x 84 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# generate table of postsynaptic connections from all clock neurons\n",
"post_sort_conns = clock_neuron_connections(clock_df, 'out', min_weight=1).sort_values('weight', ascending = False)\n",
"\n",
"all_post = ranked_lists(post_sort_conns, clock_df, 'out')\n",
"all_post"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "522f01b6-caed-43a4-a795-5cb214ec0617",
"metadata": {},
"outputs": [],
"source": [
"# saves the tables as csvs\n",
"all_post.to_csv('ranked_lists_post.csv')\n",
"all_pre.to_csv('ranked_lists_pre.csv')"
]
},
{
"cell_type": "markdown",
"id": "e6a80362-a196-438f-82cb-0c9ec2e73d84",
"metadata": {},
"source": [
"The code below generates a summary table of all of the connections the clock neurons make. In general, most of the synapses that clock neurons make are between non-clock neurons."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "87b3e70b-88e5-45dd-93ba-562d3a395abb",
"metadata": {},
"outputs": [],
"source": [
"from connection_utils import clock_neuron_connections, synapse_count, synaptic_partner_numbers\n",
"\n",
"# get connections from clock neurons to anything else\n",
"outconns_df = clock_neuron_connections(clock_df, 'out')\n",
"# get connections to clock neurons from anything else\n",
"inconns_df = clock_neuron_connections(clock_df, 'in')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "96550b1a-f16d-4e46-a31e-fdea6fc7cd55",
"metadata": {},
"outputs": [],
"source": [
"# get total output synapse counts for clock neurons\n",
"outsyns_df = synapse_count(outconns_df, 'out')\n",
"\n",
"# get total input synapse counts for clock neurons\n",
"insyns_df = synapse_count(inconns_df, 'in')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "fa6ffbbf-c319-4875-a806-29f7328bdac5",
"metadata": {},
"outputs": [],
"source": [
"# merge synapse counts onto summary table\n",
"conn_summary_df = outsyns_df.merge(clock_df, 'right', on='bodyId')\n",
"conn_summary_df = conn_summary_df.merge(insyns_df, 'left', on='bodyId')\n",
"conn_summary_df = conn_summary_df[['seqInstance','bodyId','num_out_syns','num_in_syns']]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "c24d971d-d7d9-4695-a397-ed419518f6d1",
"metadata": {},
"outputs": [],
"source": [
"# get number of postsynaptic partners from outconns_df and merge onto conn_summary_df table\n",
"outpartners_df = synaptic_partner_numbers(outconns_df, 'out')\n",
"conn_summary_df = conn_summary_df.merge(outpartners_df, 'left', on='bodyId')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "3643aafc-08d1-4bff-9b4d-36610fd10809",
"metadata": {},
"outputs": [],
"source": [
"# get number of presynaptic partners from inconns_df and merge onto conn_summary_df table\n",
"inpartners_df = synaptic_partner_numbers(inconns_df, 'in')\n",
"conn_summary_df = conn_summary_df.merge(inpartners_df, 'left', on='bodyId')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "33a21997-fbe3-4be6-a34c-d7cbb248f51d",
"metadata": {},
"outputs": [],
"source": [
"# show columns in the order we want them displayed\n",
"conn_summary_df = conn_summary_df[['seqInstance','bodyId','num_in_syns','num_presyn_partners','num_out_syns','num_postsyn_partners']]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "ad10580c-19ca-4ad4-83d9-cd70755b1b47",
"metadata": {},
"outputs": [],
"source": [
"# get clock output synapse count for clock neurons \n",
"#(clock_conns_df established in 'Connections between clock neurons' section)\n",
"clock_syns_out_df = synapse_count(clock_conns_df, 'out', intra_clock=True)\n",
"\n",
"# get clock input synapse count for clock neurons\n",
"clock_syns_in_df = synapse_count(clock_conns_df, 'in', intra_clock=True)\n",
"\n",
"# merge to the summary table: the number of input synapses made to clock neurons from other clock neurons and the number of unique partners for each\n",
"conn_summary_df = conn_summary_df.merge(clock_syns_in_df, 'left', on='bodyId')\n",
"conn_summary_df = conn_summary_df.merge(clock_syns_out_df, 'left', on='bodyId')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "b406c3ef-39b1-48f0-8835-623ee2c668b2",
"metadata": {},
"outputs": [],
"source": [
"# get number of postsynaptic partners of the clock neurons using value_counts of the pre-synaptic bodyIds\n",
"out_clock_partners_df = synaptic_partner_numbers(clock_conns_df, 'out', intra_clock=True)\n",
"\n",
"# get number of presynaptic partners of the clock neurons using value_counts of the post-synaptic bodyIds\n",
"in_clock_partners_df = synaptic_partner_numbers(clock_conns_df, 'in', intra_clock=True)\n",
"\n",
"# merge to the summary table: the number of output synapses made from clock neurons on other clock neurons and the number of unique partners for each\n",
"conn_summary_df = conn_summary_df.merge(in_clock_partners_df, 'left', on='bodyId')\n",
"conn_summary_df = conn_summary_df.merge(out_clock_partners_df, 'left', on='bodyId')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "3f76ea3f",
"metadata": {},
"outputs": [],
"source": [
"from connection_utils import intra_conns\n",
"\n",
"type_conns = intra_conns(clock_df, 'type')\n",
"conn_summary_df = conn_summary_df.merge(type_conns, how = 'left', on='bodyId')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "df5914f4-9b7f-4848-a52e-abb2bc34badd",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>seqInstance</th>\n",
" <th>bodyId</th>\n",
" <th>num_in_syns</th>\n",
" <th>num_presyn_partners</th>\n",
" <th>num_out_syns</th>\n",
" <th>num_postsyn_partners</th>\n",
" <th>num_clock_in_syns</th>\n",
" <th>num_clock_out_syns</th>\n",
" <th>num_clock_presyn_partners</th>\n",
" <th>num_clock_postsyn_partners</th>\n",
" <th>class_syn_in</th>\n",
" <th>class_syn_out</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>s-LNv_R_1</td>\n",
" <td>2068801704</td>\n",
" <td>113</td>\n",
" <td>44</td>\n",
" <td>411</td>\n",
" <td>73</td>\n",
" <td>16</td>\n",
" <td>21</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>s-LNv_R_2</td>\n",
" <td>1664980698</td>\n",
" <td>112</td>\n",
" <td>34</td>\n",
" <td>464</td>\n",
" <td>74</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>s-LNv_R_3</td>\n",
" <td>2007068523</td>\n",
" <td>159</td>\n",
" <td>63</td>\n",
" <td>472</td>\n",
" <td>66</td>\n",
" <td>17</td>\n",
" <td>14</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>13</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>s-LNv_R_4</td>\n",
" <td>1975347348</td>\n",
" <td>121</td>\n",
" <td>42</td>\n",
" <td>386</td>\n",
" <td>66</td>\n",
" <td>16</td>\n",
" <td>8</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>LNd_R_4</td>\n",
" <td>5813056917</td>\n",
" <td>1278</td>\n",
" <td>363</td>\n",
" <td>2272</td>\n",
" <td>520</td>\n",
" <td>94</td>\n",
" <td>5</td>\n",
" <td>12</td>\n",
" <td>3</td>\n",
" <td>36</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>LNd_R_5</td>\n",
" <td>5813021192</td>\n",
" <td>1169</td>\n",
" <td>319</td>\n",
" <td>2343</td>\n",
" <td>525</td>\n",
" <td>95</td>\n",
" <td>9</td>\n",
" <td>13</td>\n",
" <td>4</td>\n",
" <td>22</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>LNd_R_6</td>\n",
" <td>5813069648</td>\n",
" <td>1621</td>\n",
" <td>226</td>\n",
" <td>2040</td>\n",
" <td>538</td>\n",
" <td>358</td>\n",
" <td>156</td>\n",
" <td>11</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>5th s-LNv_R_1</td>\n",
" <td>511051477</td>\n",
" <td>1413</td>\n",
" <td>220</td>\n",
" <td>1992</td>\n",
" <td>492</td>\n",
" <td>295</td>\n",
" <td>159</td>\n",
" <td>13</td>\n",
" <td>20</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>LNd_R_1</td>\n",
" <td>296544364</td>\n",
" <td>648</td>\n",
" <td>241</td>\n",
" <td>988</td>\n",
" <td>305</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>LNd_R_2</td>\n",
" <td>448260940</td>\n",
" <td>750</td>\n",
" <td>326</td>\n",
" <td>949</td>\n",
" <td>348</td>\n",
" <td>13</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>LNd_R_3</td>\n",
" <td>5813064789</td>\n",
" <td>683</td>\n",
" <td>237</td>\n",
" <td>998</td>\n",
" <td>265</td>\n",
" <td>11</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>LPN_R_1</td>\n",
" <td>356818551</td>\n",
" <td>1309</td>\n",
" <td>379</td>\n",
" <td>2110</td>\n",
" <td>462</td>\n",
" <td>28</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>LPN_R_2</td>\n",
" <td>480029788</td>\n",
" <td>936</td>\n",
" <td>241</td>\n",
" <td>1268</td>\n",
" <td>280</td>\n",
" <td>46</td>\n",
" <td>53</td>\n",
" <td>11</td>\n",
" <td>6</td>\n",
" <td>32</td>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>LPN_R_3</td>\n",
" <td>450034902</td>\n",
" <td>980</td>\n",
" <td>237</td>\n",
" <td>1296</td>\n",
" <td>285</td>\n",
" <td>59</td>\n",
" <td>43</td>\n",
" <td>7</td>\n",
" <td>11</td>\n",
" <td>49</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>LPN_R_4</td>\n",
" <td>546977514</td>\n",
" <td>330</td>\n",
" <td>97</td>\n",
" <td>947</td>\n",
" <td>239</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",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>DN1a_R_1</td>\n",
" <td>264083994</td>\n",
" <td>1196</td>\n",
" <td>189</td>\n",
" <td>1442</td>\n",
" <td>238</td>\n",
" <td>17</td>\n",
" <td>152</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>DN1a_R_2</td>\n",
" <td>5813022274</td>\n",
" <td>1167</td>\n",
" <td>210</td>\n",
" <td>1412</td>\n",
" <td>239</td>\n",
" <td>16</td>\n",
" <td>156</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>10</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>DN1pA_R_1</td>\n",
" <td>5813010153</td>\n",
" <td>429</td>\n",
" <td>172</td>\n",
" <td>525</td>\n",
" <td>188</td>\n",
" <td>32</td>\n",
" <td>67</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>DN1pA_R_2</td>\n",
" <td>324846570</td>\n",
" <td>390</td>\n",
" <td>142</td>\n",
" <td>523</td>\n",
" <td>175</td>\n",
" <td>23</td>\n",
" <td>89</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>DN1pA_R_3</td>\n",
" <td>325529237</td>\n",
" <td>382</td>\n",
" <td>131</td>\n",
" <td>556</td>\n",
" <td>203</td>\n",
" <td>31</td>\n",
" <td>84</td>\n",
" <td>6</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>DN1pA_R_4</td>\n",
" <td>387944118</td>\n",
" <td>408</td>\n",
" <td>157</td>\n",
" <td>414</td>\n",
" <td>146</td>\n",
" <td>31</td>\n",
" <td>64</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>DN1pA_R_5</td>\n",
" <td>387166379</td>\n",
" <td>445</td>\n",
" <td>149</td>\n",
" <td>495</td>\n",
" <td>174</td>\n",
" <td>37</td>\n",
" <td>75</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>DN1pB_R_1</td>\n",
" <td>386834269</td>\n",
" <td>966</td>\n",
" <td>300</td>\n",
" <td>1679</td>\n",
" <td>431</td>\n",
" <td>19</td>\n",
" <td>41</td>\n",
" <td>10</td>\n",
" <td>14</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>DN1pB_R_2</td>\n",
" <td>5813071319</td>\n",
" <td>877</td>\n",
" <td>267</td>\n",
" <td>1799</td>\n",
" <td>419</td>\n",
" <td>14</td>\n",
" <td>50</td>\n",
" <td>9</td>\n",
" <td>15</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>l-LNv_R_1</td>\n",
" <td>1884625521</td>\n",
" <td>444</td>\n",
" <td>77</td>\n",
" <td>35</td>\n",
" <td>25</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>l-LNv_R_2</td>\n",
" <td>2065745704</td>\n",
" <td>234</td>\n",
" <td>56</td>\n",
" <td>38</td>\n",
" <td>30</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>l-LNv_R_3</td>\n",
" <td>5813001741</td>\n",
" <td>378</td>\n",
" <td>69</td>\n",
" <td>19</td>\n",
" <td>13</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>l-LNv_R_4</td>\n",
" <td>5813026773</td>\n",
" <td>457</td>\n",
" <td>72</td>\n",
" <td>152</td>\n",
" <td>69</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" seqInstance bodyId num_in_syns num_presyn_partners num_out_syns \\\n",
"0 s-LNv_R_1 2068801704 113 44 411 \n",
"1 s-LNv_R_2 1664980698 112 34 464 \n",
"2 s-LNv_R_3 2007068523 159 63 472 \n",
"3 s-LNv_R_4 1975347348 121 42 386 \n",
"4 LNd_R_4 5813056917 1278 363 2272 \n",
"5 LNd_R_5 5813021192 1169 319 2343 \n",
"6 LNd_R_6 5813069648 1621 226 2040 \n",
"7 5th s-LNv_R_1 511051477 1413 220 1992 \n",
"8 LNd_R_1 296544364 648 241 988 \n",
"9 LNd_R_2 448260940 750 326 949 \n",
"10 LNd_R_3 5813064789 683 237 998 \n",
"11 LPN_R_1 356818551 1309 379 2110 \n",
"12 LPN_R_2 480029788 936 241 1268 \n",
"13 LPN_R_3 450034902 980 237 1296 \n",
"14 LPN_R_4 546977514 330 97 947 \n",
"15 DN1a_R_1 264083994 1196 189 1442 \n",
"16 DN1a_R_2 5813022274 1167 210 1412 \n",
"17 DN1pA_R_1 5813010153 429 172 525 \n",
"18 DN1pA_R_2 324846570 390 142 523 \n",
"19 DN1pA_R_3 325529237 382 131 556 \n",
"20 DN1pA_R_4 387944118 408 157 414 \n",
"21 DN1pA_R_5 387166379 445 149 495 \n",
"22 DN1pB_R_1 386834269 966 300 1679 \n",
"23 DN1pB_R_2 5813071319 877 267 1799 \n",
"24 l-LNv_R_1 1884625521 444 77 35 \n",
"25 l-LNv_R_2 2065745704 234 56 38 \n",
"26 l-LNv_R_3 5813001741 378 69 19 \n",
"27 l-LNv_R_4 5813026773 457 72 152 \n",
"\n",
" num_postsyn_partners num_clock_in_syns num_clock_out_syns \\\n",
"0 73 16 21 \n",
"1 74 10 10 \n",
"2 66 17 14 \n",
"3 66 16 8 \n",
"4 520 94 5 \n",
"5 525 95 9 \n",
"6 538 358 156 \n",
"7 492 295 159 \n",
"8 305 10 9 \n",
"9 348 13 7 \n",
"10 265 11 9 \n",
"11 462 28 9 \n",
"12 280 46 53 \n",
"13 285 59 43 \n",
"14 239 NaN NaN \n",
"15 238 17 152 \n",
"16 239 16 156 \n",
"17 188 32 67 \n",
"18 175 23 89 \n",
"19 203 31 84 \n",
"20 146 31 64 \n",
"21 174 37 75 \n",
"22 431 19 41 \n",
"23 419 14 50 \n",
"24 25 7 5 \n",
"25 30 4 5 \n",
"26 13 7 6 \n",
"27 69 3 3 \n",
"\n",
" num_clock_presyn_partners num_clock_postsyn_partners class_syn_in \\\n",
"0 5 6 12 \n",
"1 3 3 9 \n",
"2 5 4 13 \n",
"3 4 2 15 \n",
"4 12 3 36 \n",
"5 13 4 22 \n",
"6 11 16 1 \n",
"7 13 20 NaN \n",
"8 3 2 9 \n",
"9 6 3 9 \n",
"10 5 2 8 \n",
"11 9 5 5 \n",
"12 11 6 32 \n",
"13 7 11 49 \n",
"14 NaN NaN NaN \n",
"15 4 7 12 \n",
"16 3 7 10 \n",
"17 10 11 5 \n",
"18 7 9 5 \n",
"19 6 10 2 \n",
"20 10 7 3 \n",
"21 9 10 5 \n",
"22 10 14 5 \n",
"23 9 15 5 \n",
"24 3 4 6 \n",
"25 4 3 3 \n",
"26 4 3 5 \n",
"27 3 2 2 \n",
"\n",
" class_syn_out \n",
"0 18 \n",
"1 10 \n",
"2 13 \n",
"3 8 \n",
"4 3 \n",
"5 6 \n",
"6 52 \n",
"7 NaN \n",
"8 9 \n",
"9 6 \n",
"10 9 \n",
"11 6 \n",
"12 48 \n",
"13 32 \n",
"14 NaN \n",
"15 10 \n",
"16 12 \n",
"17 4 \n",
"18 4 \n",
"19 5 \n",
"20 2 \n",
"21 5 \n",
"22 5 \n",
"23 5 \n",
"24 4 \n",
"25 5 \n",
"26 4 \n",
"27 3 "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conn_summary_df"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "9de3743a-1de6-4d19-8357-5a1df856d95b",
"metadata": {},
"outputs": [],
"source": [
"# export table to csv\n",
"conn_summary_df.to_csv('connection_summary_for_clock_neurons.csv')"
]
},
{
"cell_type": "markdown",
"id": "8ef55376-2c35-41b6-9761-9a51a330d022",
"metadata": {},
"source": [
"The number of synapses made by the clock neurons is highly variable between cell classes. "
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "7c1eb232-437e-42ef-a528-3ee7404486c3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'synapse counts')"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<Figure size 800x800 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"labels_clock_df = clock_df[['bodyId', 'labels']]\n",
"conn_summary_df = conn_summary_df.merge(labels_clock_df, on='bodyId')\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.pyplot import figure\n",
"\n",
"# make the bar plot\n",
"fig = figure(figsize=(10,10), dpi=80)\n",
"conn_summary_df.plot.bar(x='labels',y=['num_out_syns','num_in_syns'],ylabel='synapse counts')\n",
"plt.title(\"synapse counts\")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "4610f389-3571-4b46-a645-20284569fb49",
"metadata": {},
"outputs": [],
"source": [
"fig.figure.savefig('in_out_conns_bar_plot.svg')"
]
},
{
"cell_type": "markdown",
"id": "ba99f67b-1718-463f-9684-a494d192c923",
"metadata": {
"tags": []
},
"source": [
"# Overview of clock network by neuron classes"
]
},
{
"cell_type": "markdown",
"id": "b5a57289-af34-4871-8327-8098ca4b3b6b",
"metadata": {},
"source": [
"A summary table is generated with the same columns as above but for classes of clock neurons."
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "d5dda7cf-e77f-41bc-9524-1d0c3a21ef25",
"metadata": {},
"outputs": [],
"source": [
"from connection_utils import group_summary\n",
"\n",
"conn_type_df = group_summary(conn_summary_df, clock_df, 'type')"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "527fc7cc",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>num_in_syns</th>\n",
" <th>num_presyn_partners</th>\n",
" <th>num_out_syns</th>\n",
" <th>num_postsyn_partners</th>\n",
" <th>class_syn_in</th>\n",
" <th>num_clock_in_syns</th>\n",
" <th>num_clock_out_syns</th>\n",
" </tr>\n",
" <tr>\n",
" <th>type</th>\n",
" <th></th>\n",
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" <th>LNd</th>\n",
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" <th>LPN</th>\n",
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],
"text/plain": [
" num_in_syns num_presyn_partners num_out_syns \\\n",
"type \n",
"l-LNv 1497 258 228 \n",
"s-LNv 456 134 1684 \n",
"5th s-LNv 1413 220 1992 \n",
"LNd 6064 1627 9505 \n",
"LPN 3469 868 5535 \n",
"DN1a 2341 377 2832 \n",
"DN1pA 2034 731 2493 \n",
"DN1pB 1833 557 3468 \n",
"\n",
" num_postsyn_partners class_syn_in num_clock_in_syns \\\n",
"type \n",
"l-LNv 121 16 5 \n",
"s-LNv 230 49 10 \n",
"5th s-LNv 492 0 295 \n",
"LNd 2416 85 496 \n",
"LPN 1180 86 47 \n",
"DN1a 455 22 11 \n",
"DN1pA 866 20 134 \n",
"DN1pB 840 10 23 \n",
"\n",
" num_clock_out_syns \n",
"type \n",
"l-LNv 3 \n",
"s-LNv 4 \n",
"5th s-LNv 159 \n",
"LNd 110 \n",
"LPN 19 \n",
"DN1a 286 \n",
"DN1pA 359 \n",
"DN1pB 81 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conn_type_df"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "f1c9a129-e087-474d-89cb-bfa311689f6f",
"metadata": {},
"outputs": [],
"source": [
"# export to csv file\n",
"conn_type_df.to_csv('class_conn_summ.csv')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
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