First attempt at death data
authorNeil Smith <neil.git@njae.me.uk>
Fri, 8 May 2020 18:17:38 +0000 (19:17 +0100)
committerNeil Smith <neil.git@njae.me.uk>
Fri, 8 May 2020 18:17:38 +0000 (19:17 +0100)
deaths-radar.png [new file with mode: 0644]
uk-deaths-data/publishedweek172020.csv [new file with mode: 0644]
uk-deaths-data/publishedweek2015.csv [new file with mode: 0644]
uk-deaths-data/publishedweek522016.csv [new file with mode: 0644]
uk-deaths-data/publishedweek522017.csv [new file with mode: 0644]
uk-deaths-data/publishedweek522018.csv [new file with mode: 0644]
uk-deaths-data/publishedweek522019.csv [new file with mode: 0644]
uk_deaths.ipynb [new file with mode: 0644]

diff --git a/deaths-radar.png b/deaths-radar.png
new file mode 100644 (file)
index 0000000..814154f
Binary files /dev/null and b/deaths-radar.png differ
diff --git a/uk-deaths-data/publishedweek172020.csv b/uk-deaths-data/publishedweek172020.csv
new file mode 100644 (file)
index 0000000..1a04d01
--- /dev/null
@@ -0,0 +1,19 @@
+Week number,Week ended,"Total deaths, all ages",Total deaths: average of corresponding week over the previous 5 years 1 (England and Wales),Total deaths: average of corresponding week over the previous 5 years 1 (England),Total deaths: average of corresponding week over the previous 5 years (Wales),,,,Persons,Deaths by age group,,,,,,,,,,,,,,,,,,,,,Males,,,,,,,,,,,,,,,,,,,,,,Females,,,,,,,,,,,,,,,,,,,,,Deaths by region of usual residence,E12000001,E12000002,E12000003,E12000004,E12000005,E12000006,E12000007,E12000008,E12000009,W92000004
+,,,,,,Deaths by cause,Deaths where the underlying cause was respiratory disease (ICD-10 J00-J99),Deaths where COVID-19 was mentioned on the death certificate (ICD-10 U07.1 and U07.2),,<1,1-4,5-9,10-14,15-19,20-24,25-29,30-34,35-39,40-44,45-49,50-54,55-59,60-64,65-69,70-74,75-79,80-84,85-89,90+,,Deaths by age group,<1,1-4,5-9,10-14,15-19,20-24,25-29,30-34,35-39,40-44,45-49,50-54,55-59,60-64,65-69,70-74,75-79,80-84,85-89,90+,,Deaths by age group,<1,1-4,5-9,10-14,15-19,20-24,25-29,30-34,35-39,40-44,45-49,50-54,55-59,60-64,65-69,70-74,75-79,80-84,85-89,90+,,North East,North West,Yorkshire and The Humber,East Midlands,West Midlands,East,London,South East,South West,Wales
+1,03/01/20,12254,12175,11412,756,,2141,0,,48,8,4,4,6,11,17,32,54,69,115,239,361,486,696,1164,1535,2049,2457,2898,,,30,5,2,1,5,7,7,21,32,43,62,137,191,275,415,697,826,1053,1098,982,,,18,3,2,3,1,4,10,11,22,26,53,102,170,211,281,467,709,996,1359,1916,,673,1806,1240,1060,1349,1162,1113,1814,1225,787
+2,10/01/20,14058,13822,12933,856,,2477,0,,50,9,8,9,16,23,37,46,68,85,191,279,426,604,857,1341,1724,2290,2697,3297,,,29,4,4,7,8,14,28,28,46,52,114,156,259,355,509,761,924,1172,1231,1127,,,21,5,4,2,8,9,9,18,22,33,77,123,167,249,348,580,800,1118,1466,2170,,707,1932,1339,1195,1450,1573,1272,2132,1487,939
+3,17/01/20,12990,13216,12370,812,,2189,0,,69,7,5,4,10,25,37,47,77,118,189,306,461,562,803,1210,1612,2103,2421,2924,,,38,4,4,3,5,21,31,29,49,72,106,200,295,338,471,690,886,1080,1117,991,,,31,3,1,1,5,4,6,18,28,46,83,106,166,224,332,520,726,1023,1304,1933,,647,1696,1278,1106,1407,1457,1073,2064,1466,767
+4,24/01/20,11856,12760,11933,802,,1893,0,,53,9,4,8,15,30,36,38,79,116,160,280,381,535,791,1167,1474,1863,2188,2626,,,28,6,4,4,14,23,31,22,45,79,90,181,215,334,482,663,819,951,1014,910,,,25,3,0,4,1,7,5,16,34,37,70,99,166,201,309,504,655,912,1174,1716,,612,1529,1187,1024,1231,1410,1028,1833,1253,723
+5,31/01/20,11612,12206,11419,760,,1746,0,,50,6,5,4,23,23,28,58,76,100,163,278,382,525,732,1196,1445,1811,2124,2583,,,22,4,4,3,17,16,19,40,45,58,107,180,247,324,437,683,827,919,970,872,,,28,2,1,1,6,7,9,18,31,42,56,98,135,201,295,513,618,892,1154,1711,,561,1461,1136,1015,1262,1286,1092,1820,1233,727
+6,07/02/20,10986,11925,11154,729,,1572,0,,30,8,4,4,10,34,23,38,71,95,157,289,371,512,689,1120,1358,1698,2040,2433,,,14,5,2,4,8,23,11,24,53,55,95,175,208,306,423,626,713,875,943,843,,,16,3,2,0,2,11,12,14,18,40,62,114,163,206,266,494,645,823,1097,1590,,564,1529,1072,922,1052,1259,987,1729,1157,690
+7,14/02/20,10944,11627,10876,722,,1602,0,,43,6,2,4,16,26,27,40,85,92,165,288,345,490,641,1113,1305,1704,2039,2517,,,29,4,1,3,9,19,19,27,52,58,99,172,209,287,400,660,726,870,995,852,,,14,2,1,1,7,7,8,13,33,34,66,116,136,203,241,453,579,834,1044,1665,,573,1427,1059,976,1159,1172,967,1688,1169,728
+8,21/02/20,10841,11548,10790,724,,1619,0,,51,5,6,7,20,18,29,60,77,117,182,232,346,511,695,1048,1338,1696,1927,2475,,,32,5,4,2,14,14,17,39,46,72,105,140,195,285,400,598,778,894,906,893,,,19,0,2,5,6,4,12,21,31,45,77,92,151,226,295,450,560,802,1021,1582,,539,1477,1087,924,1116,1167,1032,1675,1118,679
+9,28/02/20,10816,11183,10448,698,,1547,0,,49,7,6,7,24,25,28,50,85,103,155,261,347,494,682,1111,1255,1713,2015,2398,,,30,6,4,5,16,19,21,32,51,64,95,147,189,286,417,647,709,871,956,870,,,19,1,2,2,8,6,7,18,34,39,60,114,158,208,265,464,546,842,1059,1528,,572,1476,1078,919,1174,1115,1085,1587,1133,651
+10,06/03/20,10895,11498,10745,720,,1583,0,,56,11,2,7,21,23,39,53,72,104,155,258,358,481,679,1090,1325,1798,1969,2391,,,31,7,1,2,13,16,27,33,45,69,93,153,220,278,388,646,725,938,932,841,,,25,4,1,5,8,7,12,20,27,35,62,105,138,203,291,444,600,860,1037,1550,,568,1490,1112,930,1098,1149,982,1726,1170,652
+11,13/03/20,11019,11205,10447,727,,1508,5,,53,13,3,6,18,39,29,55,80,90,179,260,401,500,685,1068,1366,1738,1951,2483,,,24,5,1,2,13,26,20,41,48,50,109,168,246,296,424,625,748,904,935,885,,,29,8,2,4,5,13,9,14,32,40,70,92,155,204,261,443,618,834,1016,1598,,590,1472,1053,915,1187,1211,964,1751,1174,675
+12,20/03/20,10645,10573,9841,677,,1541,103,,44,2,6,4,15,22,31,41,66,100,160,245,390,469,686,1094,1373,1694,1902,2302,,,25,2,3,3,11,17,22,25,47,63,99,158,229,281,404,629,763,892,905,820,,,19,0,3,1,4,5,9,16,19,37,61,87,161,188,282,465,610,802,997,1482,,522,1443,1012,947,1115,1043,1008,1657,1156,719
+13,27/03/20,11141,10130,9414,665,,1532,539,,49,8,1,4,12,17,33,55,71,95,163,235,381,522,699,1106,1397,1850,2016,2428,,,27,3,1,1,8,11,22,40,40,65,97,146,235,286,423,666,788,1005,984,883,,,22,5,0,3,4,6,11,15,31,30,66,89,146,236,276,440,609,845,1032,1545,,542,1538,982,922,1035,1182,1297,1822,1092,719
+14,03/04/20,16387,10305,9601,667,,1952,3475,,51,8,5,8,9,20,32,54,67,106,220,376,531,733,1044,1690,2179,2826,3015,3413,,,26,2,2,3,7,12,22,39,36,67,124,244,336,453,662,1039,1303,1570,1520,1327,,,25,6,3,5,2,8,10,15,31,39,96,132,195,280,382,651,876,1256,1495,2086,,770,2137,1436,1246,1812,1717,2511,2294,1520,920
+15,10/04/20,18516,10520,9807,671,,1759,6213,,38,6,4,4,8,16,41,45,108,114,249,412,598,852,1149,1797,2418,3195,3564,3898,,,28,3,3,1,5,12,29,30,60,65,148,247,372,548,706,1098,1451,1795,1826,1521,,,10,3,1,3,3,4,12,15,48,49,101,165,226,304,443,699,967,1400,1738,2377,,849,2597,1503,1452,2182,1984,2832,2604,1560,928
+16,17/04/20,22351,10497,9787,661,,1777,8758,,51,6,5,4,20,17,40,52,92,132,242,428,679,945,1272,2108,2817,3840,4444,5157,,,24,4,2,1,12,12,28,32,56,77,147,229,434,591,791,1280,1635,2100,2111,1879,,,27,2,3,3,8,5,12,20,36,55,95,199,245,354,481,828,1182,1740,2333,3278,,1155,3195,1960,1632,2536,2466,3275,3084,1854,1169
+17,24/04/20,21997,10458,9768,662,,1574,8237,,54,6,3,3,11,25,34,66,98,170,279,438,639,927,1248,1990,2741,3772,4349,5144,,,33,4,3,2,5,11,23,39,62,111,175,274,407,578,795,1220,1614,2007,2085,1780,,,21,2,0,1,6,14,11,27,36,59,104,164,232,349,453,770,1127,1765,2264,3364,,1103,3109,2095,1711,2481,2299,2785,3334,1924,1124
diff --git a/uk-deaths-data/publishedweek2015.csv b/uk-deaths-data/publishedweek2015.csv
new file mode 100644 (file)
index 0000000..83dbf2e
--- /dev/null
@@ -0,0 +1,56 @@
+Week number,Week ended,"Total deaths, all ages",Total deaths: average of corresponding,,,Persons,,,,,,,,Males,,,,,,,,Females,,,,,,,,Deaths by region of usual residence,E12000001,E12000002,E12000003,E12000004,E12000005,E12000006,E12000007,E12000008,E12000009,W92000004
+,,,,Deaths by underlying cause,"All respiratory diseases (ICD-10 J00-J99)
+ICD-10 v 2013 (IRIS)",Deaths by age group,Under 1 year,01-14,15-44,45-64,65-74,75-84,85+,Deaths by age group,Under 1 year,01-14,15-44,45-64,65-74,75-84,85+,Deaths by age group,Under 1 year,01-14,15-44,45-64,65-74,75-84,85+,,North East,North West,Yorkshire and The Humber,East Midlands,West Midlands,East,London,South East,South West,Wales
+1,02/01/15,12286,11837.6,,2418,,56,16,198,1229,1845,3548,5392,,29,9,108,689,1042,1865,1915,,27,7,90,540,803,1683,3477,,699,1718,1261,1012,1296,1221,1233,1811,1272,725
+2,09/01/15,16237,12277,,3521,,80,28,298,1552,2387,4505,7387,,45,15,192,912,1347,2306,2596,,35,13,106,640,1040,2199,4791,,753,2282,1607,1328,1713,1712,1549,2525,1697,1031
+3,16/01/15,14866,11145,,3251,,46,28,316,1393,2107,4191,6785,,25,13,212,813,1205,2104,2443,,21,15,104,580,902,2087,4342,,799,1968,1442,1286,1542,1657,1275,2427,1498,936
+4,23/01/15,13934,10714,,2734,,61,20,318,1387,2019,3964,6165,,34,15,211,837,1105,2030,2215,,27,5,107,550,914,1934,3950,,798,1806,1386,1206,1400,1565,1220,2196,1510,828
+5,30/01/15,12900,10492,,2278,,66,20,329,1368,1869,3685,5563,,42,11,215,799,1100,1908,1986,,24,9,114,569,769,1777,3577,,717,1669,1220,1119,1395,1450,1119,1991,1398,801
+6,06/02/15,12039,10320.4,,2115,,66,30,286,1280,1774,3425,5178,,37,18,173,753,1052,1821,1912,,29,12,113,527,722,1604,3266,,669,1586,1151,1071,1229,1360,1097,1843,1297,720
+7,13/02/15,11822,10325,,1985,,66,18,312,1281,1798,3398,4949,,31,13,204,749,1059,1826,1850,,35,5,108,532,739,1572,3099,,661,1608,1128,1021,1161,1284,1108,1836,1283,710
+8,20/02/15,11434,10429.8,,1994,,47,18,303,1225,1714,3309,4817,,25,7,187,733,973,1713,1809,,22,11,116,492,741,1596,3008,,669,1466,1068,965,1152,1276,1102,1713,1257,739
+9,27/02/15,11472,10313.8,,1931,,48,25,296,1267,1749,3355,4731,,28,14,184,760,997,1749,1749,,20,11,112,507,752,1606,2982,,606,1466,1113,973,1149,1286,1062,1767,1290,736
+10,06/03/15,11469,10203.6,,1806,,44,19,291,1225,1850,3279,4759,,29,11,181,763,1087,1689,1696,,15,8,110,462,763,1590,3063,,622,1559,1106,966,1188,1232,1063,1709,1290,712
+11,13/03/15,10951,10160.2,,1744,,38,17,272,1228,1725,3122,4547,,27,7,158,707,981,1623,1590,,11,10,114,521,744,1499,2957,,573,1462,1040,904,1101,1172,1070,1731,1219,661
+12,20/03/15,10568,10020.6,,1642,,44,20,302,1254,1621,3029,4298,,17,15,194,755,929,1570,1600,,27,5,108,499,692,1459,2698,,566,1401,982,872,1083,1158,1092,1531,1192,680
+13,27/03/15,10493,9169.4,,1550,,45,21,286,1181,1650,3020,4289,,29,11,177,709,926,1576,1595,,16,10,109,472,724,1444,2694,,512,1392,1032,891,1071,1098,1080,1566,1164,666
+14,03/04/15,9062,9734,,1393,,47,17,214,976,1405,2642,3760,,27,12,137,589,819,1364,1410,,20,5,77,387,586,1278,2350,,490,1251,841,784,980,971,877,1352,920,580
+15,10/04/15,10089,10172.6,,1525,,57,18,271,1133,1601,2913,4096,,39,10,160,667,921,1477,1560,,18,8,111,466,680,1436,2536,,533,1384,1023,917,994,1062,1002,1488,1005,660
+16,17/04/15,11639,9666.2,,1730,,47,22,321,1342,1849,3323,4735,,24,17,209,803,1095,1721,1757,,23,5,112,539,754,1602,2978,,626,1521,1103,999,1294,1289,1089,1810,1212,671
+17,24/04/15,10599,9557.6,,1550,,56,20,298,1274,1682,3061,4206,,36,12,197,759,978,1620,1523,,20,8,101,515,704,1441,2683,,601,1405,1009,889,1115,1050,1000,1708,1129,662
+18,01/05/15,10134,9771,,1469,,55,21,277,1162,1628,2928,4040,,36,11,176,696,962,1574,1511,,19,10,101,466,666,1354,2529,,516,1385,950,856,1053,1112,961,1530,1110,628
+19,08/05/15,8862,9182.8,,1250,,35,19,224,954,1499,2625,3505,,18,11,156,554,890,1417,1316,,17,8,68,400,609,1208,2189,,503,1166,895,748,949,910,827,1343,901,589
+20,15/05/15,10290,9479.4,,1404,,50,20,309,1227,1703,2998,3983,,32,11,207,714,1024,1565,1502,,18,9,102,513,679,1433,2481,,544,1404,1038,890,1057,1099,949,1552,1100,622
+21,22/05/15,10005,9414.4,,1325,,57,14,304,1176,1680,2926,3839,,35,7,198,705,987,1605,1419,,22,7,106,471,693,1321,2420,,514,1367,989,870,1018,989,906,1587,1122,614
+22,29/05/15,8213,8410.4,,1074,,53,22,213,1014,1365,2464,3082,,34,11,125,595,791,1303,1184,,19,11,88,419,574,1161,1898,,457,1116,866,663,844,838,793,1216,816,588
+23,05/06/15,10157,8867.4,,1256,,48,23,295,1224,1692,2895,3968,,24,17,175,732,986,1485,1450,,24,6,120,492,706,1410,2518,,472,1337,1024,865,1042,1150,961,1538,1094,648
+24,12/06/15,9548,9181.8,,1242,,52,10,270,1188,1586,2781,3660,,35,5,165,722,934,1475,1391,,17,5,105,466,652,1306,2269,,528,1262,953,817,975,997,880,1483,1022,606
+25,19/06/15,9312,8815.4,,1234,,54,22,279,1163,1536,2652,3602,,38,13,181,695,872,1332,1372,,16,9,98,468,664,1320,2230,,459,1248,896,757,943,1020,892,1496,983,595
+26,26/06/15,9190,8811.8,,1168,,51,19,298,1135,1573,2682,3425,,31,10,188,700,945,1439,1297,,20,9,110,435,628,1243,2128,,514,1254,916,756,927,998,887,1396,916,597
+27,03/07/15,9205,8758,,1121,,49,19,288,1065,1615,2560,3603,,30,10,207,619,909,1301,1372,,19,9,81,446,706,1259,2231,,489,1293,868,750,964,977,901,1382,1017,535
+28,10/07/15,9015,8610.6,,1022,,65,18,272,1151,1548,2568,3393,,29,9,190,682,909,1403,1313,,36,9,82,469,639,1165,2080,,491,1238,851,817,941,939,842,1370,949,554
+29,17/07/15,8802,8667.6,,977,,58,14,268,1128,1438,2486,3405,,34,6,155,688,828,1323,1293,,24,8,113,440,610,1163,2112,,466,1127,855,781,882,952,881,1320,933,574
+30,24/07/15,8791,8606,,960,,57,14,288,1096,1583,2520,3233,,38,10,177,651,912,1346,1249,,19,4,111,445,671,1174,1984,,459,1196,846,734,938,934,833,1382,905,529
+31,31/07/15,8617,8652.2,,940,,51,20,299,1084,1446,2493,3223,,30,12,183,627,839,1350,1195,,21,8,116,457,607,1143,2028,,437,1195,846,715,917,941,780,1217,1000,546
+32,07/08/15,8862,8543,,1006,,54,21,264,1095,1471,2576,3379,,25,13,186,680,886,1349,1340,,29,8,78,415,585,1227,2039,,462,1227,953,664,913,908,858,1388,895,564
+33,14/08/15,9148,8560.2,,1055,,51,15,286,1169,1523,2577,3525,,35,10,185,715,893,1365,1339,,16,5,101,454,630,1212,2186,,458,1250,888,792,950,1014,879,1370,974,548
+34,21/08/15,9121,8689,,978,,66,21,298,1159,1543,2655,3379,,33,14,194,667,896,1434,1289,,33,7,104,492,647,1221,2090,,475,1223,915,747,969,943,901,1364,1000,557
+35,28/08/15,9026,7755.4,,1074,,54,11,314,1100,1530,2621,3393,,37,8,204,668,879,1372,1271,,17,3,110,432,651,1249,2122,,445,1262,896,797,834,981,855,1358,967,603
+36,04/09/15,7878,9031.4,,900,,48,12,224,981,1318,2310,2985,,27,7,148,612,778,1219,1165,,21,5,76,369,540,1091,1820,,438,1044,772,663,774,856,803,1188,825,489
+37,11/09/15,9258,8719.8,,1062,,54,14,278,1177,1537,2571,3623,,25,5,175,678,917,1396,1396,,29,9,103,499,620,1175,2227,,471,1262,931,814,932,922,867,1423,1065,554
+38,18/09/15,9097,8807.6,,1032,,40,18,287,1098,1549,2627,3478,,25,10,182,680,889,1394,1273,,15,8,105,418,660,1233,2205,,462,1175,852,748,973,942,901,1448,1020,555
+39,25/09/15,9529,8954.6,,1117,,56,15,295,1156,1531,2791,3682,,29,11,184,704,922,1475,1399,,27,4,111,452,609,1316,2283,,476,1267,922,795,969,981,917,1540,975,664
+40,02/10/15,9410,9091.4,,1149,,65,18,299,1133,1569,2636,3690,,35,8,184,680,896,1411,1400,,30,10,115,453,673,1225,2290,,495,1260,908,814,957,984,918,1419,1086,552
+41,09/10/15,9776,9098.2,,1198,,43,13,297,1196,1599,2820,3807,,22,7,183,711,931,1471,1416,,21,6,114,485,668,1349,2391,,505,1283,915,821,951,1105,976,1457,1076,652
+42,16/10/15,9511,9181,,1165,,59,26,256,1133,1585,2781,3668,,38,12,177,678,937,1464,1410,,21,14,79,455,648,1317,2258,,494,1291,895,809,997,1030,912,1402,1039,612
+43,23/10/15,9711,9357.6,,1183,,60,22,292,1158,1603,2786,3789,,37,12,188,706,943,1496,1479,,23,10,104,452,660,1290,2310,,480,1276,951,835,1053,1022,921,1461,1079,607
+44,30/10/15,9618,9468.4,,1237,,50,14,277,1137,1565,2671,3879,,23,8,185,695,914,1414,1476,,27,6,92,442,651,1257,2403,,523,1299,886,814,967,1030,969,1506,1003,593
+45,06/11/15,9994,9466.8,,1247,,48,24,304,1275,1654,2853,3826,,22,9,200,760,956,1454,1405,,26,15,104,515,698,1399,2421,,524,1409,1014,883,1084,1011,955,1482,1002,606
+46,13/11/15,9938,9650.8,,1235,,53,26,261,1258,1607,2804,3926,,34,15,179,795,980,1499,1479,,19,11,82,463,627,1305,2447,,530,1376,960,767,1027,1018,993,1544,1081,613
+47,20/11/15,9830,9498,,1317,,56,21,295,1172,1648,2835,3802,,35,15,202,720,950,1554,1445,,21,6,93,452,698,1281,2357,,527,1317,937,844,1071,1043,966,1470,1019,611
+48,27/11/15,9822,9496.8,,1156,,33,21,317,1219,1621,2759,3850,,19,11,206,719,922,1510,1445,,14,10,111,500,699,1249,2405,,512,1322,994,844,990,1066,915,1536,1037,589
+49,04/12/15,10365,10208.2,,1345,,59,25,295,1216,1707,3003,4056,,40,14,194,711,1021,1558,1572,,19,11,101,505,686,1445,2484,,534,1425,1012,919,1087,1065,998,1522,1127,659
+50,11/12/15,10269,10440.4,,1413,,37,25,286,1196,1632,2928,4153,,14,10,192,722,916,1562,1552,,23,15,94,474,716,1366,2601,,540,1400,1050,915,1006,1018,985,1536,1102,688
+51,18/12/15,10689,11221,,1437,,66,28,365,1255,1782,3023,4169,,37,10,234,726,1036,1602,1502,,29,18,131,529,746,1421,2667,,618,1526,1060,897,1101,1086,990,1656,1089,646
+52,25/12/15,8630,8139.2,,1233,,49,26,246,1010,1387,2451,3461,,33,7,151,625,797,1260,1315,,16,19,95,385,590,1191,2146,,480,1167,891,707,826,916,870,1347,877,535
+53,01/01/16,7524,8139,,1128,,39,12,146,882,1259,2197,2988,,25,6,85,510,737,1139,1185,,14,6,61,372,522,1058,1803,,407,1074,755,646,799,653,766,1123,757,516
diff --git a/uk-deaths-data/publishedweek522016.csv b/uk-deaths-data/publishedweek522016.csv
new file mode 100644 (file)
index 0000000..751d09f
--- /dev/null
@@ -0,0 +1,55 @@
+Week number,Week ended,"Total deaths, all ages",Total deaths: average of corresponding,,,Persons,,,,,,,,Males,,,,,,,,Females,,,,,,,,Deaths by region of usual residence,E12000001,E12000002,E12000003,E12000004,E12000005,E12000006,E12000007,E12000008,E12000009,W92000004
+,,,,Deaths by underlying cause,"All respiratory diseases (ICD-10 J00-J99)
+ICD-10 v 2013 (IRIS)",Deaths by age group,Under 1 year,01-14,15-44,45-64,65-74,75-84,85+,Deaths by age group,Under 1 year,01-14,15-44,45-64,65-74,75-84,85+,Deaths by age group,Under 1 year,01-14,15-44,45-64,65-74,75-84,85+,,North East,North West,Yorkshire and The Humber,East Midlands,West Midlands,East,London,South East,South West,Wales
+1,08/01/16,13045,11701.4,,2046,,49,17,299,1482,2179,3727,5292,,31,10,178,867,1246,1897,1982,,18,7,121,615,933,1830,3310,,705,1748,1284,1067,1396,1401,1226,1951,1424,809
+2,15/01/16,11501,13016.4,,1835,,64,24,326,1363,1925,3283,4512,,36,14,221,812,1159,1709,1656,,28,10,105,551,766,1574,2856,,600,1532,1148,956,1208,1237,1048,1771,1263,711
+3,22/01/16,11473,11765.4,,1775,,50,18,345,1293,1831,3327,4606,,32,8,238,791,1079,1734,1750,,18,10,107,502,752,1593,2856,,612,1602,1119,929,1209,1253,1068,1710,1219,720
+4,29/01/16,11317,11289,,1810,,47,14,324,1322,1859,3147,4599,,25,8,217,773,1069,1643,1754,,22,6,107,549,790,1504,2845,,631,1516,1131,858,1195,1236,1065,1730,1207,717
+5,05/02/16,11052,10965.6,,1748,,60,22,314,1281,1779,3181,4405,,35,14,203,767,1040,1683,1655,,25,8,111,514,739,1498,2750,,620,1459,1109,954,1197,1160,1059,1604,1169,690
+6,12/02/16,11170,10704,,1730,,60,21,331,1356,1826,3058,4502,,36,12,203,782,1062,1639,1666,,24,9,128,574,764,1419,2836,,599,1466,1066,917,1262,1155,1040,1718,1222,700
+7,19/02/16,10590,10669.4,,1700,,52,22,305,1333,1720,2982,4175,,27,14,196,803,1015,1616,1577,,25,8,109,530,705,1366,2598,,588,1443,1083,881,1088,1057,999,1596,1171,657
+8,26/02/16,11056,10657.6,,1734,,58,20,267,1294,1774,3127,4516,,34,13,176,733,995,1650,1707,,24,7,91,561,779,1477,2809,,582,1497,1115,930,1087,1180,1021,1707,1215,696
+9,04/03/16,11285,10613,,1774,,39,21,318,1328,1864,3180,4533,,22,13,212,799,1024,1709,1745,,17,8,106,529,840,1471,2788,,582,1505,1107,913,1144,1240,1093,1742,1218,721
+10,11/03/16,11010,10544,,1768,,54,22,273,1340,1755,3171,4390,,27,10,172,795,1030,1713,1711,,27,12,101,545,725,1458,2679,,569,1525,1097,911,1104,1190,1008,1694,1148,734
+11,18/03/16,11022,10405,,1709,,55,22,295,1358,1781,3070,4434,,31,10,194,835,1048,1596,1705,,24,12,101,523,733,1474,2729,,552,1460,1102,917,1136,1143,1058,1595,1292,738
+12,25/03/16,9635,10209.4,,1485,,55,18,247,1148,1567,2713,3878,,32,13,157,683,898,1437,1482,,23,5,90,465,669,1276,2396,,474,1331,937,809,891,923,900,1542,1122,668
+13,01/04/16,10286,9668,,1688,,49,15,264,1184,1698,2992,4079,,26,10,171,662,941,1550,1583,,23,5,93,522,757,1442,2496,,578,1362,968,859,942,1079,1039,1611,1104,712
+14,08/04/16,11599,9592.4,,1971,,53,15,306,1327,1941,3238,4719,,34,7,201,768,1109,1738,1743,,19,8,105,559,832,1500,2976,,606,1514,1128,911,1232,1190,1111,1828,1319,742
+15,15/04/16,11417,10206.2,,1809,,63,18,311,1312,1927,3166,4617,,32,10,189,774,1137,1688,1789,,31,8,122,538,790,1478,2828,,594,1572,1105,900,1183,1217,1007,1785,1282,738
+16,22/04/16,10925,10104.8,,1597,,65,24,319,1292,1816,3085,4324,,40,15,208,766,1039,1613,1647,,25,9,111,526,777,1472,2677,,539,1470,1008,908,1134,1189,999,1683,1250,714
+17,29/04/16,10413,9776.6,,1396,,43,22,327,1301,1694,2927,4098,,22,14,212,792,1003,1534,1548,,21,8,115,509,691,1393,2550,,571,1371,1014,857,1065,1147,969,1634,1128,629
+18,06/05/16,9137,10127.8,,1261,,55,23,348,1075,1490,2583,3559,,34,15,249,629,870,1410,1367,,21,8,99,446,620,1173,2192,,495,1280,941,703,929,954,876,1365,994,569
+19,13/05/16,10637,9058.4,,1447,,49,19,308,1277,1768,2994,4218,,36,5,195,743,1057,1563,1673,,13,14,113,534,711,1431,2545,,547,1420,1055,914,1069,1190,971,1646,1143,652
+20,20/05/16,9953,9699.2,,1328,,61,21,328,1149,1686,2855,3853,,34,12,209,687,960,1532,1464,,27,9,119,462,726,1323,2389,,533,1327,927,862,1013,1147,884,1534,1092,611
+21,27/05/16,9739,9524,,1248,,63,19,326,1185,1681,2697,3767,,36,10,205,716,957,1441,1417,,27,9,121,469,724,1256,2350,,484,1300,936,826,995,1103,884,1489,1082,624
+22,03/06/16,7909,8417.2,,982,,35,19,214,963,1445,2253,2980,,16,9,128,546,827,1188,1180,,19,10,86,417,618,1065,1800,,431,1138,814,680,776,790,710,1242,817,500
+23,10/06/16,9873,9047.2,,1292,,58,21,268,1181,1613,2875,3857,,28,10,167,694,923,1535,1466,,30,11,101,487,690,1340,2391,,542,1306,1038,839,1011,1119,939,1468,1004,584
+24,17/06/16,9386,9388.4,,1225,,49,22,255,1195,1604,2703,3556,,28,13,174,725,931,1463,1367,,21,9,81,470,673,1240,2189,,473,1252,923,858,981,1037,858,1410,993,578
+25,24/06/16,9365,8986,,1206,,57,15,295,1161,1549,2659,3625,,29,9,192,675,909,1443,1454,,28,6,103,486,640,1216,2171,,528,1272,925,804,1015,966,831,1408,1039,558
+26,01/07/16,9228,8852,,1106,,46,13,303,1192,1570,2573,3530,,26,9,204,701,897,1369,1280,,20,4,99,491,673,1204,2250,,488,1157,936,801,954,971,880,1497,964,549
+27,08/07/16,9138,8872.2,,1062,,49,18,287,1176,1552,2567,3489,,30,9,175,698,917,1359,1334,,19,9,112,478,635,1208,2155,,507,1269,910,770,956,950,796,1407,1031,524
+28,15/07/16,9388,8706.8,,1101,,54,15,279,1153,1634,2649,3603,,33,8,186,683,964,1395,1336,,21,7,93,470,670,1254,2267,,485,1242,948,784,943,1054,798,1463,1052,599
+29,22/07/16,9350,8776.2,,1168,,55,18,282,1115,1586,2638,3650,,37,9,192,667,936,1459,1414,,18,9,90,448,650,1179,2236,,538,1295,877,830,914,988,847,1443,1023,560
+30,29/07/16,9335,8712.2,,1190,,39,15,312,1074,1567,2641,3683,,20,8,203,630,924,1433,1389,,19,7,109,444,643,1208,2294,,476,1223,910,762,923,1013,940,1414,1027,610
+31,05/08/16,9182,8682.6,,1112,,56,19,276,1171,1542,2588,3524,,29,11,187,690,923,1416,1302,,27,8,89,481,619,1172,2222,,467,1316,879,739,983,1004,822,1311,1052,580
+32,12/08/16,9172,8651.4,,1065,,57,15,277,1183,1552,2557,3520,,37,9,172,738,902,1364,1288,,20,6,105,445,650,1193,2232,,492,1244,886,780,941,1031,801,1346,974,641
+33,19/08/16,9070,8671.8,,1032,,42,17,276,1078,1610,2624,3423,,26,10,191,653,946,1383,1324,,16,7,85,425,664,1241,2099,,476,1279,887,742,951,1005,831,1317,983,574
+34,26/08/16,9319,8783.4,,1093,,48,16,241,1104,1577,2680,3652,,23,4,157,699,899,1480,1395,,25,12,84,405,678,1200,2257,,499,1250,860,793,1039,991,822,1419,1000,619
+35,02/09/16,7923,8020,,917,,43,14,228,968,1415,2233,3020,,21,9,135,564,825,1204,1196,,22,5,93,404,590,1029,1824,,398,1114,800,626,764,877,795,1251,797,470
+36,09/09/16,9399,8813.2,,1036,,48,23,283,1161,1562,2659,3661,,30,17,185,710,944,1398,1345,,18,6,98,451,618,1261,2316,,478,1312,942,844,974,981,873,1384,1033,552
+37,16/09/16,9124,8825.8,,1027,,56,15,265,1170,1583,2628,3406,,27,6,153,690,900,1397,1338,,29,9,112,480,683,1231,2068,,491,1222,914,736,898,998,839,1433,988,576
+38,23/09/16,8945,8839.4,,1039,,58,15,264,1132,1548,2564,3364,,30,11,173,670,910,1404,1251,,28,4,91,462,638,1160,2113,,435,1230,890,730,892,979,854,1399,941,563
+39,30/09/16,8994,9105.6,,979,,53,15,297,1151,1549,2506,3422,,22,7,194,697,925,1327,1350,,31,8,103,454,624,1179,2072,,481,1219,856,787,971,879,852,1345,987,592
+40,07/10/16,9291,9133.2,,1060,,57,21,275,1115,1647,2641,3531,,30,8,176,637,992,1385,1381,,27,13,99,478,655,1256,2150,,465,1331,907,777,938,945,863,1456,1024,562
+41,14/10/16,9719,9210,,1153,,72,14,272,1145,1633,2746,3835,,41,6,183,674,961,1444,1508,,31,8,89,471,672,1302,2327,,520,1328,940,791,966,1069,919,1524,1056,587
+42,21/10/16,9768,9225.6,,1238,,50,21,276,1158,1652,2724,3887,,25,9,178,687,965,1485,1488,,25,12,98,471,687,1239,2399,,511,1322,919,831,977,1020,931,1542,1078,624
+43,28/10/16,9724,9445.4,,1270,,58,16,271,1239,1567,2800,3773,,25,11,170,741,908,1494,1472,,33,5,101,498,659,1306,2301,,530,1401,914,850,966,1034,860,1472,1043,624
+44,04/11/16,10152,9459,,1276,,52,21,281,1186,1723,2833,4056,,31,11,188,670,1011,1451,1598,,21,10,93,516,712,1382,2458,,535,1351,1041,850,1013,1048,904,1630,1114,644
+45,11/11/16,10470,9584.8,,1342,,54,15,278,1279,1709,2954,4178,,28,6,187,730,1021,1576,1565,,26,9,91,549,688,1378,2613,,546,1390,1017,889,1045,1127,976,1691,1139,626
+46,18/11/16,10694,9751.4,,1374,,57,26,355,1225,1703,2992,4332,,31,18,250,709,1001,1566,1730,,26,8,105,516,702,1426,2602,,547,1513,1025,892,1117,1184,941,1605,1154,681
+47,25/11/16,10603,9572,,1413,,49,26,284,1226,1716,3062,4239,,27,12,174,734,1013,1621,1616,,22,14,110,492,703,1441,2623,,536,1432,1022,887,1090,1131,987,1679,1154,661
+48,02/12/16,10439,9615.4,,1371,,58,15,345,1191,1752,2961,4116,,35,8,225,692,984,1557,1574,,23,7,120,499,768,1404,2542,,550,1491,973,888,1076,1176,936,1578,1112,643
+49,09/12/16,11223,10042,,1591,,57,28,310,1266,1808,3176,4578,,29,13,202,749,1019,1676,1738,,28,15,108,517,789,1500,2840,,589,1529,1193,932,1164,1212,1029,1658,1201,693
+50,16/12/16,10533,10317,,1522,,72,22,281,1219,1675,2943,4316,,36,16,180,748,979,1500,1595,,36,6,101,471,696,1443,2721,,561,1444,1036,893,1098,1107,933,1634,1145,663
+51,23/12/16,11493,11061.8,,1640,,56,16,339,1253,1927,3170,4725,,30,6,218,754,1057,1682,1796,,26,10,121,499,870,1488,2929,,679,1594,1097,981,1149,1207,1052,1784,1223,691
+52,30/12/16,8003,7925,,1312,,41,7,178,849,1269,2294,3364,,24,5,99,451,738,1229,1288,,17,2,79,398,531,1065,2076,,456,1087,794,759,715,774,761,1236,846,558
diff --git a/uk-deaths-data/publishedweek522017.csv b/uk-deaths-data/publishedweek522017.csv
new file mode 100644 (file)
index 0000000..9ff8ae3
--- /dev/null
@@ -0,0 +1,55 @@
+Week number,Week ended,"Total deaths, all ages",Total deaths: average of corresponding,,,Persons,,,,,,,,Males,,,,,,,,Females,,,,,,,,Deaths by region of usual residence,E12000001,E12000002,E12000003,E12000004,E12000005,E12000006,E12000007,E12000008,E12000009,W92000004
+,,,,Deaths by underlying cause,"All respiratory diseases (ICD-10 J00-J99)
+ICD-10 v 2013 (IRIS)",Deaths by age group,Under 1 year,01-14,15-44,45-64,65-74,75-84,85+,Deaths by age group,Under 1 year,01-14,15-44,45-64,65-74,75-84,85+,Deaths by age group,Under 1 year,01-14,15-44,45-64,65-74,75-84,85+,,North East,North West,Yorkshire and The Humber,East Midlands,West Midlands,East,London,South East,South West,Wales
+1,06/01/17,11991,11782,,2321,,58,18,215,1232,1880,3450,5136,,32,9,143,718,1070,1803,1907,,26,9,72,514,810,1647,3229,,602,1665,1208,1016,1243,1187,1173,1899,1228,744
+2,13/01/17,13715,12692,,2690,,56,18,287,1372,2149,3886,5947,,33,10,199,819,1237,2015,2230,,23,8,88,553,912,1871,3717,,738,1840,1351,1170,1439,1450,1296,2161,1419,825
+3,20/01/17,13610,11773,,2625,,54,19,301,1374,2132,3701,6029,,28,10,183,837,1202,1902,2282,,26,9,118,537,930,1799,3747,,676,1786,1383,1190,1414,1484,1276,2195,1346,835
+4,27/01/17,12877,11441,,2373,,52,16,286,1273,1946,3652,5652,,28,7,189,763,1164,1893,2074,,24,9,97,510,782,1759,3578,,627,1666,1242,1116,1362,1488,1198,1999,1278,881
+5,03/02/17,12485,11128,,2147,,55,14,308,1239,1930,3509,5430,,27,8,209,751,1149,1854,2059,,28,6,99,488,781,1655,3371,,631,1625,1243,1045,1293,1384,1210,1920,1359,749
+6,10/02/17,12269,10935,,2093,,57,17,293,1342,1940,3483,5129,,29,9,196,825,1136,1754,1887,,28,8,97,517,804,1729,3242,,635,1566,1224,1036,1234,1411,1161,1916,1339,723
+7,17/02/17,11644,10835,,1932,,49,18,295,1316,1811,3242,4913,,33,10,176,794,1071,1719,1884,,16,8,119,522,740,1523,3029,,610,1528,1186,964,1243,1253,1095,1847,1217,690
+8,24/02/17,11794,10983,,1872,,47,20,286,1298,1816,3214,5112,,26,8,173,754,1031,1680,1923,,21,12,113,544,785,1534,3189,,636,1519,1159,954,1216,1327,1114,1834,1312,701
+9,03/03/17,11248,10980,,1766,,53,22,254,1252,1823,3142,4699,,32,14,153,733,1039,1646,1768,,21,8,101,519,784,1496,2931,,606,1488,1123,963,1184,1328,930,1673,1221,715
+10,10/03/17,11077,10816,,1660,,54,17,316,1240,1756,3086,4608,,29,5,211,748,1045,1636,1760,,25,12,105,492,711,1450,2848,,570,1464,1110,1038,1163,1230,969,1738,1144,634
+11,17/03/17,10697,10641,,1561,,59,7,274,1260,1722,3015,4360,,38,4,171,744,996,1573,1672,,21,3,103,516,726,1442,2688,,581,1450,1121,963,1140,1150,946,1593,1083,653
+12,24/03/17,10325,10193,,1436,,55,13,268,1256,1739,2940,4054,,27,7,174,762,1000,1612,1512,,28,6,94,494,739,1328,2542,,535,1364,1065,947,1034,1134,930,1557,1107,635
+13,31/03/17,10027,9863,,1324,,65,20,307,1202,1656,2838,3939,,32,12,196,705,969,1519,1455,,33,8,111,497,687,1319,2484,,509,1313,1045,870,1024,1109,888,1520,1064,658
+14,07/04/17,9939,10001,,1378,,59,21,277,1181,1705,2748,3947,,35,8,186,701,1005,1491,1565,,24,13,91,480,700,1257,2382,,523,1373,929,835,1048,1056,905,1522,1075,642
+15,14/04/17,8493,10624,,1143,,38,20,235,1003,1468,2397,3330,,20,8,145,634,849,1255,1250,,18,12,90,369,619,1142,2080,,410,1217,795,708,868,918,801,1316,863,577
+16,21/04/17,9644,10625,,1311,,68,19,280,1130,1585,2834,3721,,39,13,172,658,943,1529,1423,,29,6,108,472,642,1305,2298,,517,1362,1010,812,978,996,896,1409,984,654
+17,28/04/17,10908,10246,,1435,,70,20,273,1315,1822,3132,4276,,41,10,167,784,1104,1687,1655,,29,10,106,531,718,1445,2621,,583,1495,1053,882,1125,1137,1053,1678,1159,727
+18,05/05/17,9064,9984,,1154,,44,18,268,1035,1529,2557,3613,,25,11,167,648,892,1345,1441,,19,7,101,387,637,1212,2172,,479,1279,872,803,902,956,804,1356,997,600
+19,12/05/17,10693,9158,,1423,,41,23,323,1275,1813,3019,4196,,22,11,192,752,1088,1631,1657,,19,12,131,523,725,1388,2539,,551,1368,1053,988,1059,1153,970,1685,1155,687
+20,19/05/17,10288,9902,,1325,,38,18,291,1320,1794,2854,3973,,20,10,192,787,1061,1506,1503,,18,8,99,533,733,1348,2470,,553,1329,1030,817,1079,1124,914,1639,1161,615
+21,26/05/17,10040,9640,,1286,,71,21,307,1187,1722,2744,3983,,40,10,192,737,1022,1448,1553,,31,11,115,450,700,1296,2430,,519,1349,936,802,1088,1118,953,1526,1118,602
+22,02/06/17,8332,8417,,1076,,49,12,223,995,1475,2361,3217,,29,9,141,604,864,1260,1204,,20,3,82,391,611,1101,2013,,408,1208,818,726,849,813,787,1217,897,586
+23,09/06/17,9766,9170,,1229,,57,27,308,1227,1632,2785,3727,,37,20,203,746,950,1486,1477,,20,7,105,481,682,1299,2250,,514,1311,950,841,1060,1016,876,1527,1066,584
+24,16/06/17,9367,9473,,1062,,60,20,259,1188,1663,2554,3619,,30,12,172,726,949,1372,1400,,30,8,87,462,714,1182,2219,,499,1272,918,819,1016,990,829,1380,1015,599
+25,23/06/17,9627,9117,,1215,,65,17,300,1175,1570,2715,3785,,36,12,194,698,905,1490,1465,,29,5,106,477,665,1225,2320,,509,1328,901,841,991,991,902,1473,1058,608
+26,30/06/17,9334,8956,,1170,,54,13,275,1121,1656,2676,3536,,36,7,166,672,982,1405,1314,,18,6,109,449,674,1271,2222,,451,1174,914,804,996,1028,903,1456,1038,546
+27,07/07/17,9263,8959,,1012,,60,23,293,1202,1622,2584,3477,,33,15,182,731,984,1397,1391,,27,8,111,471,638,1187,2086,,495,1278,904,802,985,1010,820,1395,996,556
+28,14/07/17,9376,8893,,1141,,56,23,270,1144,1660,2694,3528,,30,11,158,690,982,1446,1398,,26,12,112,454,678,1248,2130,,502,1225,953,862,973,1005,843,1393,1031,564
+29,21/07/17,9113,8946,,959,,64,19,331,1210,1541,2531,3415,,31,12,224,727,938,1331,1358,,33,7,107,483,603,1200,2057,,514,1196,856,809,947,970,867,1389,979,551
+30,28/07/17,8882,8889,,1004,,37,15,280,1184,1539,2439,3388,,20,10,170,704,931,1289,1304,,17,5,110,480,608,1150,2084,,456,1174,881,738,941,949,783,1381,963,586
+31,04/08/17,8941,8762,,1009,,47,12,269,1131,1510,2542,3427,,27,6,170,700,887,1356,1304,,20,6,99,431,623,1186,2123,,507,1192,891,784,911,955,792,1345,946,590
+32,11/08/17,9038,8772,,937,,55,17,257,1141,1580,2620,3368,,26,8,179,685,946,1393,1326,,29,9,78,456,634,1227,2042,,479,1171,882,772,943,1005,858,1346,982,572
+33,18/08/17,9299,8802,,1081,,50,20,293,1146,1568,2601,3617,,27,11,196,678,927,1381,1414,,23,9,97,468,641,1220,2203,,464,1260,849,747,936,1018,929,1481,998,592
+34,25/08/17,9382,8953,,1121,,51,11,286,1095,1549,2667,3723,,29,9,182,665,894,1439,1452,,22,2,104,430,655,1228,2271,,482,1299,848,783,966,1002,907,1502,1001,570
+35,01/09/17,8149,8062,,940,,45,18,211,997,1448,2279,3148,,26,6,126,611,845,1222,1221,,19,12,85,386,603,1057,1927,,434,1117,836,667,780,853,766,1205,908,555
+36,08/09/17,9497,8940,,1058,,53,17,273,1190,1613,2644,3703,,34,11,197,690,968,1432,1451,,19,6,76,500,645,1212,2252,,512,1307,921,808,1017,1066,866,1445,993,542
+37,15/09/17,9454,8929,,1022,,48,18,277,1207,1641,2628,3634,,23,12,189,707,949,1432,1452,,25,6,88,500,692,1196,2182,,539,1285,877,758,1001,990,871,1493,1006,609
+38,22/09/17,9534,8923,,1088,,49,21,294,1157,1614,2623,3776,,29,13,196,694,944,1426,1488,,20,8,98,463,670,1197,2288,,504,1221,919,814,978,1096,900,1422,1074,585
+39,29/09/17,9689,9120,,1153,,45,27,243,1160,1686,2698,3830,,21,16,161,669,952,1438,1532,,24,11,82,491,734,1260,2298,,507,1305,957,782,990,1071,882,1468,1109,593
+40,06/10/17,9778,9248,,1208,,66,20,284,1171,1677,2677,3881,,38,10,192,672,964,1403,1568,,28,10,92,499,713,1274,2313,,501,1289,946,818,1003,1057,923,1521,1063,631
+41,13/10/17,9940,9411,,1219,,48,11,245,1185,1678,2846,3926,,27,5,149,706,965,1534,1519,,21,6,96,479,713,1312,2407,,513,1340,1011,827,1001,1085,928,1497,1068,640
+42,20/10/17,10031,9452,,1199,,62,13,293,1165,1731,2836,3929,,30,7,191,717,1022,1504,1545,,32,6,102,448,709,1332,2384,,550,1359,1019,806,997,1056,934,1541,1098,650
+43,27/10/17,9739,9563,,1144,,54,25,269,1166,1658,2765,3802,,34,14,168,712,975,1452,1494,,20,11,101,454,683,1313,2308,,509,1375,903,743,1058,1037,931,1539,1020,608
+44,03/11/17,9984,9562,,1244,,40,18,288,1243,1659,2875,3860,,30,14,181,737,977,1563,1454,,10,4,107,506,682,1312,2406,,536,1356,1006,850,1014,1129,897,1473,1105,595
+45,10/11/17,10346,9871,,1314,,47,20,322,1251,1734,2889,4081,,26,14,212,736,1057,1583,1608,,21,6,110,515,677,1306,2473,,510,1365,1042,931,1117,1132,931,1572,1117,598
+46,17/11/17,10275,10030,,1362,,48,9,308,1242,1682,2901,4084,,26,8,208,723,963,1557,1616,,22,1,100,519,719,1344,2468,,502,1381,1027,898,1096,1091,949,1539,1103,666
+47,24/11/17,10621,9861,,1319,,50,20,310,1308,1728,2962,4234,,24,11,220,763,1016,1596,1624,,26,9,90,545,712,1366,2610,,550,1419,1089,862,1080,1148,1038,1623,1151,639
+48,01/12/17,10538,9858,,1436,,56,21,295,1216,1737,2945,4266,,33,10,200,735,991,1585,1633,,23,11,95,481,746,1360,2633,,562,1453,1045,878,1065,1168,972,1608,1123,636
+49,08/12/17,10781,10322,,1497,,58,20,268,1224,1761,3027,4423,,38,13,168,760,1029,1619,1736,,20,7,100,464,732,1408,2687,,612,1538,1013,970,1066,1139,1008,1649,1140,630
+50,15/12/17,11217,10353,,1603,,39,19,267,1282,1776,3176,4658,,23,10,173,753,1048,1690,1791,,16,9,94,529,728,1486,2867,,595,1509,1048,971,1128,1226,1042,1765,1204,708
+51,22/12/17,12517,11130,,1905,,52,20,307,1397,1995,3523,5220,,25,10,188,849,1114,1796,2004,,27,10,119,548,881,1727,3216,,719,1709,1181,1039,1318,1322,1153,1961,1332,762
+52,29/12/17,8487,7831,,1431,,23,18,172,895,1275,2444,3654,,15,12,97,529,665,1289,1410,,8,6,75,366,610,1155,2244,,500,1194,840,685,844,878,814,1305,863,541
diff --git a/uk-deaths-data/publishedweek522018.csv b/uk-deaths-data/publishedweek522018.csv
new file mode 100644 (file)
index 0000000..747d4f2
--- /dev/null
@@ -0,0 +1,55 @@
+Week number,Week ended,"Total deaths, all ages",Total deaths: average of corresponding,,,Persons,,,,,,,,Males,,,,,,,,Females,,,,,,,,Deaths by region of usual residence,E12000001,E12000002,E12000003,E12000004,E12000005,E12000006,E12000007,E12000008,E12000009,W92000004
+,,,,Deaths by underlying cause,"All respiratory diseases (ICD-10 J00-J99)
+ICD-10 v 2013 (IRIS)",Deaths by age group,Under 1 year,01-14,15-44,45-64,65-74,75-84,85+,Deaths by age group,Under 1 year,01-14,15-44,45-64,65-74,75-84,85+,Deaths by age group,Under 1 year,01-14,15-44,45-64,65-74,75-84,85+,,North East,North West,Yorkshire and The Humber,East Midlands,West Midlands,East,London,South East,South West,Wales
+1,05/01/18,12723,12079,,2361,,52,18,208,1290,1976,3612,5565,,32,11,118,748,1120,1865,2080,,20,7,90,542,856,1747,3485,,640,1747,1300,1085,1313,1404,1130,1902,1393,783
+2,12/01/18,15050,13167,,3075,,73,17,302,1561,2321,4155,6621,,42,5,186,918,1347,2120,2499,,31,12,116,643,974,2035,4122,,837,2113,1481,1266,1468,1677,1342,2371,1552,904
+3,19/01/18,14256,12418,,2829,,59,22,286,1507,2191,3866,6325,,33,12,185,888,1268,2009,2390,,26,10,101,619,923,1857,3935,,783,1828,1387,1175,1544,1562,1239,2287,1535,885
+4,26/01/18,13935,11954,,2672,,50,25,298,1459,2157,3824,6122,,26,18,219,889,1273,1978,2264,,24,7,79,570,884,1846,3858,,779,1855,1363,1165,1433,1553,1264,2184,1460,850
+5,02/02/18,13285,11605,,2479,,41,14,339,1404,1988,3661,5838,,24,8,216,809,1186,1960,2204,,17,6,123,595,802,1701,3634,,740,1728,1285,1154,1441,1468,1208,2046,1382,815
+6,09/02/18,12495,11333,,2197,,45,23,293,1347,2032,3376,5374,,26,13,190,816,1183,1786,2043,,19,10,103,531,849,1590,3331,,663,1615,1206,1109,1335,1359,1041,1867,1378,801
+7,16/02/18,12246,11059,,2081,,48,17,318,1377,1953,3492,5041,,24,11,196,836,1168,1851,1999,,24,6,122,541,785,1641,3042,,612,1665,1230,1024,1285,1307,1171,1810,1283,803
+8,23/02/18,12142,11125,,2128,,26,13,294,1378,1896,3398,5137,,18,7,206,840,1067,1731,1915,,8,6,88,538,829,1667,3222,,671,1677,1196,1034,1251,1300,1105,1757,1314,789
+9,02/03/18,10854,11037,,1900,,45,11,254,1229,1728,3028,4559,,25,6,168,745,998,1579,1751,,20,5,86,484,730,1449,2808,,551,1485,1125,907,1308,1088,1060,1611,997,634
+10,09/03/18,12997,10935,,2302,,47,27,287,1362,2019,3691,5564,,26,16,195,806,1158,1945,2142,,21,11,92,556,861,1746,3422,,677,1690,1192,1072,1390,1390,1177,1919,1560,896
+11,16/03/18,12788,10774,,2271,,47,17,329,1316,1989,3594,5496,,25,7,219,798,1157,1879,2075,,22,10,110,518,832,1715,3421,,614,1690,1203,1116,1343,1358,1121,1954,1414,918
+12,23/03/18,11913,10289,,1946,,46,15,278,1349,1917,3342,4966,,35,5,182,810,1133,1732,1885,,11,10,96,539,784,1610,3081,,576,1516,1124,1038,1263,1251,1095,1853,1283,774
+13,30/03/18,9941,9944,,1657,,43,20,261,1065,1586,2884,4082,,21,7,179,674,921,1580,1505,,22,13,82,391,665,1304,2577,,498,1335,973,819,1019,1074,910,1518,1047,633
+14,06/04/18,10794,10286,,1761,,46,22,260,1229,1764,3013,4460,,28,13,167,718,1029,1605,1699,,18,9,93,511,735,1408,2761,,534,1470,1105,886,1054,1196,1014,1578,1142,730
+15,13/04/18,12301,10328,,1971,,36,25,337,1382,2053,3442,5026,,19,14,220,812,1197,1870,1974,,17,11,117,570,856,1572,3052,,631,1613,1199,1068,1228,1425,1133,1883,1293,743
+16,20/04/18,11223,10395,,1641,,54,21,301,1386,1880,3109,4472,,29,10,191,830,1106,1678,1722,,25,11,110,556,774,1431,2750,,559,1456,1037,986,1170,1248,1039,1743,1212,688
+17,27/04/18,10306,10381,,1426,,57,12,340,1213,1707,2906,4071,,32,6,227,731,980,1522,1566,,25,6,113,482,727,1384,2505,,516,1391,987,904,969,1209,947,1613,1117,614
+18,04/05/18,10153,9807,,1369,,51,21,308,1363,1725,2907,3778,,29,14,201,815,1008,1539,1511,,22,7,107,548,717,1368,2267,,534,1347,922,892,1113,1117,917,1570,1082,633
+19,11/05/18,8624,9526,,1055,,48,21,247,1115,1437,2384,3372,,26,12,163,648,841,1280,1343,,22,9,84,467,596,1104,2029,,510,1116,886,740,823,937,841,1294,929,530
+20,18/05/18,10141,9978,,1248,,52,24,300,1330,1760,2791,3884,,29,16,205,791,1017,1509,1543,,23,8,95,539,743,1282,2341,,504,1390,888,943,1105,1125,913,1511,1068,667
+21,25/05/18,9636,9727,,1179,,60,13,294,1258,1659,2687,3665,,33,8,170,731,994,1431,1509,,27,5,124,527,665,1256,2156,,501,1278,913,828,983,1053,932,1512,997,603
+22,01/06/18,8147,8163,,1065,,46,18,250,998,1431,2330,3074,,27,14,171,581,845,1259,1210,,19,4,79,417,586,1071,1864,,458,1142,769,716,859,839,784,1187,830,538
+23,08/06/18,9950,9767,,1270,,46,19,298,1195,1700,2881,3811,,32,9,193,723,968,1510,1524,,14,10,105,472,732,1371,2287,,548,1309,985,884,1065,1157,890,1435,1057,607
+24,15/06/18,9343,9313,,1150,,60,17,286,1199,1607,2670,3504,,33,7,182,698,949,1479,1368,,27,10,104,501,658,1191,2136,,454,1167,947,777,967,991,921,1442,1085,561
+25,22/06/18,9256,9232,,1103,,55,21,308,1161,1613,2550,3548,,27,13,203,696,994,1382,1447,,28,8,105,465,619,1168,2101,,475,1217,944,764,971,985,855,1448,1019,562
+26,29/06/18,9212,9052,,1055,,43,22,306,1184,1652,2508,3497,,23,15,193,726,936,1346,1393,,20,7,113,458,716,1162,2104,,484,1214,882,822,954,1011,824,1408,983,599
+27,06/07/18,9258,9030,,1086,,50,23,286,1150,1548,2611,3590,,26,15,181,697,892,1418,1443,,24,8,105,453,656,1193,2147,,477,1280,895,788,998,940,814,1426,991,625
+28,13/07/18,9293,9018,,1123,,48,21,304,1140,1600,2633,3547,,25,15,204,677,957,1421,1388,,23,6,100,463,643,1212,2159,,470,1239,901,758,961,993,941,1403,1025,583
+29,20/07/18,9127,9032,,1107,,45,15,304,1166,1577,2484,3536,,28,8,187,703,925,1333,1455,,17,7,117,463,652,1151,2081,,471,1229,942,730,949,953,905,1373,971,548
+30,27/07/18,9141,8890,,1019,,59,13,291,1193,1566,2628,3391,,36,10,197,727,922,1417,1277,,23,3,94,466,644,1211,2114,,477,1219,907,762,897,1008,955,1374,941,560
+31,03/08/18,9161,8763,,1005,,62,18,286,1155,1536,2620,3484,,33,12,190,685,863,1357,1388,,29,6,96,470,673,1263,2096,,486,1228,817,830,961,1010,883,1396,957,574
+32,10/08/18,9319,8818,,1019,,59,18,328,1175,1608,2563,3568,,30,11,237,725,960,1439,1378,,29,7,91,450,648,1124,2190,,448,1231,913,793,946,1028,947,1453,997,537
+33,17/08/18,8830,8905,,941,,64,11,253,1130,1558,2489,3325,,38,7,161,669,918,1311,1339,,26,4,92,461,640,1178,1986,,446,1171,880,742,910,991,870,1361,902,518
+34,24/08/18,8978,9033,,921,,44,22,250,1083,1601,2560,3418,,30,8,165,659,901,1381,1365,,14,14,85,424,700,1179,2053,,477,1134,913,780,983,993,808,1392,900,565
+35,31/08/18,7865,8162,,827,,51,11,233,1017,1442,2150,2961,,30,8,151,575,815,1123,1177,,21,3,82,442,627,1027,1784,,437,1058,779,630,793,877,741,1170,845,511
+36,07/09/18,9445,9069,,1098,,45,20,323,1196,1621,2638,3602,,27,9,194,709,985,1361,1475,,18,11,129,487,636,1277,2127,,534,1152,956,804,1005,996,904,1464,1000,594
+37,14/09/18,9191,9091,,999,,55,18,275,1180,1600,2576,3487,,29,9,179,722,922,1353,1387,,26,9,96,458,678,1223,2100,,465,1231,865,814,956,999,834,1440,986,579
+38,21/09/18,9305,9063,,1034,,69,18,292,1171,1623,2601,3531,,42,11,179,709,945,1373,1431,,27,7,113,462,678,1228,2100,,475,1226,912,776,947,990,865,1503,986,598
+39,28/09/18,9150,9285,,987,,50,10,270,1083,1607,2629,3501,,29,6,184,652,929,1443,1397,,21,4,86,431,678,1186,2104,,483,1262,904,766,965,957,868,1375,965,560
+40,05/10/18,9503,9388,,1050,,40,17,287,1200,1627,2696,3636,,16,7,173,709,965,1461,1443,,24,10,114,491,662,1235,2193,,514,1302,948,781,1020,990,898,1387,1020,595
+41,12/10/18,9649,9511,,1124,,47,20,328,1212,1607,2741,3694,,23,14,229,699,987,1451,1437,,24,6,99,513,620,1290,2257,,486,1291,963,832,980,1071,860,1491,1043,606
+42,19/10/18,9864,9602,,1141,,43,18,301,1209,1654,2769,3870,,26,14,195,760,965,1493,1549,,17,4,106,449,689,1276,2321,,527,1274,968,815,979,1045,941,1570,1088,640
+43,26/10/18,9603,9601,,1154,,65,24,309,1200,1657,2642,3706,,34,15,202,702,953,1474,1466,,31,9,107,498,704,1168,2240,,526,1215,956,880,1079,993,905,1376,1004,633
+44,02/11/18,9529,9692,,1090,,46,24,289,1151,1569,2700,3750,,27,15,192,677,937,1448,1530,,19,9,97,474,632,1252,2220,,511,1330,964,810,890,1042,896,1465,988,604
+45,09/11/18,10151,10003,,1295,,44,12,308,1157,1666,2949,4015,,24,7,195,687,971,1589,1549,,20,5,113,470,695,1360,2466,,520,1403,981,813,1081,1094,925,1543,1106,642
+46,16/11/18,10193,10104,,1268,,47,29,292,1238,1716,2819,4052,,24,15,196,734,986,1519,1684,,23,14,96,504,730,1300,2368,,490,1408,969,899,1047,1128,940,1538,1077,656
+47,23/11/18,9957,10050,,1240,,62,22,312,1223,1700,2766,3872,,35,8,197,700,982,1455,1568,,27,14,115,523,718,1311,2304,,511,1357,1013,848,1001,1071,903,1503,1062,657
+48,30/11/18,10033,10087,,1224,,58,20,317,1246,1658,2829,3905,,32,5,205,733,953,1504,1531,,26,15,112,513,705,1325,2374,,505,1330,998,838,1076,1077,948,1523,1040,673
+49,07/12/18,10287,10509,,1341,,45,15,326,1218,1696,2965,4022,,24,9,220,701,991,1573,1532,,21,6,106,517,705,1392,2490,,539,1377,1006,830,1098,1106,958,1594,1076,674
+50,14/12/18,10550,10519,,1386,,51,13,295,1265,1814,2962,4150,,26,9,190,747,1057,1576,1598,,25,4,105,518,757,1386,2552,,561,1422,1108,873,1034,1130,1007,1573,1106,708
+51,21/12/18,11116,11343,,1509,,41,23,333,1306,1867,3136,4410,,24,15,222,810,1112,1680,1708,,17,8,111,496,755,1456,2702,,625,1439,1148,930,1145,1124,1035,1688,1225,724
+52,28/12/18,7131,7909,,1053,,22,11,166,792,1205,2013,2922,,10,7,103,450,694,1051,1146,,12,4,63,342,511,962,1776,,459,1020,688,651,660,695,655,1062,756,461
diff --git a/uk-deaths-data/publishedweek522019.csv b/uk-deaths-data/publishedweek522019.csv
new file mode 100644 (file)
index 0000000..584bc15
--- /dev/null
@@ -0,0 +1,55 @@
+Week number,Week ended,"Total deaths, all ages",Total deaths: average of corresponding,,,Persons,,,,,,,,Males,,,,,,,,Females,,,,,,,,Deaths by region of usual residence,E12000001,E12000002,E12000003,E12000004,E12000005,E12000006,E12000007,E12000008,E12000009,W92000004
+,,,,Deaths by underlying cause,"All respiratory diseases (ICD-10 J00-J99)
+ICD-10 v 2013 (IRIS)",Deaths by age group,Under 1 year,01-14,15-44,45-64,65-74,75-84,85+,Deaths by age group,Under 1 year,01-14,15-44,45-64,65-74,75-84,85+,Deaths by age group,Under 1 year,01-14,15-44,45-64,65-74,75-84,85+,,North East,North West,Yorkshire and The Humber,East Midlands,West Midlands,East,London,South East,South West,Wales
+1,04/01/19,10955,12273,,1736,,43,15,215,1199,1766,3078,4639,,28,5,128,693,992,1590,1758,,15,10,87,506,774,1488,2881,,574,1456,1148,964,1180,1136,1021,1586,1136,718
+2,11/01/19,12609,13670,,2214,,50,20,280,1419,2179,3590,5071,,20,13,168,852,1265,1857,1997,,30,7,112,567,914,1733,3074,,643,1612,1279,1089,1299,1386,1137,1927,1394,809
+3,18/01/19,11860,13056,,1972,,59,29,319,1373,2004,3414,4662,,39,14,211,831,1131,1837,1828,,20,15,108,542,873,1577,2834,,627,1566,1197,979,1187,1344,1073,1936,1236,683
+4,25/01/19,11740,12486,,1942,,42,22,339,1438,1936,3266,4697,,26,10,225,860,1154,1766,1862,,16,12,114,578,782,1500,2835,,640,1593,1137,1037,1131,1291,1045,1875,1229,734
+5,01/02/19,11297,11998,,1931,,57,15,307,1367,1852,3126,4573,,38,10,208,805,1045,1664,1834,,19,5,99,562,807,1462,2739,,618,1485,1131,963,1150,1202,1066,1718,1191,745
+6,08/02/19,11660,11623,,1918,,54,25,267,1387,1955,3251,4721,,36,17,152,869,1100,1718,1895,,18,8,115,518,855,1533,2826,,608,1595,1133,988,1241,1283,1088,1738,1251,701
+7,15/02/19,11824,11301,,1931,,49,17,305,1372,1911,3392,4778,,27,8,201,799,1117,1776,1932,,22,9,104,573,794,1616,2846,,569,1618,1189,1033,1231,1342,1061,1802,1207,748
+8,22/02/19,11295,11383,,1890,,59,30,276,1395,1824,3169,4542,,30,16,159,850,1088,1696,1804,,29,14,117,545,736,1473,2738,,610,1495,1124,928,1143,1218,1045,1777,1234,695
+9,01/03/19,11044,11051,,1786,,52,20,288,1264,1826,3117,4477,,24,10,182,745,1059,1656,1787,,28,10,106,519,767,1461,2690,,572,1425,1109,927,1188,1214,1009,1712,1178,684
+10,08/03/19,10898,11286,,1657,,45,16,303,1342,1857,3042,4293,,32,10,197,839,1112,1664,1689,,13,6,106,503,745,1378,2604,,582,1463,1083,946,1150,1116,1004,1708,1198,622
+11,15/03/19,10567,11095,,1559,,57,24,299,1311,1718,2933,4225,,30,11,194,788,1043,1531,1633,,27,13,105,523,675,1402,2592,,527,1393,1054,879,1092,1198,975,1601,1149,666
+12,22/03/19,10402,10456,,1486,,49,24,293,1249,1713,2948,4126,,29,9,200,774,965,1596,1630,,20,15,93,475,748,1352,2496,,532,1437,1087,890,1062,1066,925,1640,1089,628
+13,29/03/19,9867,10076,,1314,,45,17,289,1222,1643,2794,3857,,26,8,205,728,978,1470,1501,,19,9,84,494,665,1324,2356,,529,1278,943,875,930,1136,910,1540,1047,654
+14,05/04/19,10126,10268,,1412,,41,13,296,1232,1614,2937,3993,,21,8,184,725,930,1589,1592,,20,5,112,507,684,1348,2401,,518,1277,959,876,1050,1224,928,1582,1039,642
+15,12/04/19,10291,10362,,1396,,47,23,288,1265,1712,2907,4049,,31,14,194,783,995,1543,1629,,16,9,94,482,717,1364,2420,,510,1341,1034,858,1084,1188,937,1623,1037,637
+16,19/04/19,9025,10291,,1262,,48,21,251,1100,1446,2547,3612,,24,11,146,644,852,1420,1430,,24,10,105,456,594,1127,2182,,459,1225,910,759,928,1014,834,1350,919,580
+17,26/04/19,10059,10317,,1406,,34,18,273,1207,1730,2811,3986,,16,9,171,724,970,1543,1588,,18,9,102,483,760,1268,2398,,551,1365,996,856,1008,1084,951,1495,1053,678
+18,03/05/19,11207,9804,,1581,,46,18,297,1334,1869,3207,4436,,26,14,207,821,1089,1709,1725,,20,4,90,513,780,1498,2711,,541,1493,1125,986,1136,1139,1040,1782,1251,688
+19,10/05/19,9055,9488,,1239,,56,17,262,1094,1513,2579,3534,,34,9,159,624,868,1362,1411,,22,8,103,470,645,1217,2123,,484,1214,924,776,906,943,807,1456,922,600
+20,17/05/19,10272,10036,,1355,,44,14,304,1274,1650,2864,4122,,24,7,192,795,977,1528,1685,,20,7,112,479,673,1336,2437,,548,1402,980,887,1082,1112,927,1570,1078,658
+21,24/05/19,10284,9747,,1324,,51,21,309,1262,1765,2946,3930,,29,12,193,751,1044,1564,1532,,22,9,116,511,721,1382,2398,,530,1397,1024,888,1070,1097,876,1620,1126,627
+22,31/05/19,8260,8125,,1105,,45,16,239,991,1382,2403,3184,,22,11,147,565,784,1247,1287,,23,5,92,426,598,1156,1897,,436,1176,782,699,840,842,809,1252,886,518
+23,07/06/19,10140,9860,,1290,,48,18,306,1223,1741,2846,3958,,26,9,199,757,1015,1599,1614,,22,9,107,466,726,1247,2344,,554,1349,1025,880,1082,982,919,1516,1182,626
+24,14/06/19,9445,9408,,1134,,46,18,298,1149,1658,2672,3604,,22,11,198,673,963,1464,1442,,24,7,100,476,695,1208,2162,,507,1313,889,817,950,1012,900,1452,986,598
+25,21/06/19,9458,9302,,1134,,46,20,279,1150,1625,2711,3627,,25,11,196,714,963,1480,1427,,21,9,83,436,662,1231,2200,,478,1344,931,751,938,1011,900,1510,1032,542
+26,28/06/19,9511,9177,,1143,,39,21,273,1214,1605,2692,3667,,22,9,186,738,969,1509,1503,,17,12,87,476,636,1183,2164,,475,1250,984,790,1013,1028,892,1481,1005,564
+27,05/07/19,9062,9123,,1076,,33,26,255,1112,1561,2650,3425,,24,17,170,637,910,1420,1318,,9,9,85,475,651,1230,2107,,437,1269,896,750,940,981,833,1431,962,534
+28,12/07/19,9179,9170,,1081,,44,16,259,1140,1564,2616,3540,,23,7,174,698,900,1394,1471,,21,9,85,442,664,1222,2069,,458,1272,932,747,949,995,875,1357,972,588
+29,19/07/19,9080,9100,,1024,,45,14,279,1136,1500,2610,3496,,31,10,180,683,863,1380,1426,,14,4,99,453,637,1230,2070,,478,1287,841,809,962,994,829,1328,981,553
+30,26/07/19,9112,9023,,996,,57,14,267,1117,1598,2580,3479,,36,7,165,660,951,1374,1351,,21,7,102,457,647,1206,2128,,534,1237,895,774,882,1024,833,1376,982,543
+31,02/08/19,9271,8963,,1116,,57,11,265,1123,1597,2664,3554,,30,6,164,702,921,1445,1455,,27,5,101,421,676,1219,2099,,536,1234,936,782,929,988,911,1424,926,570
+32,09/08/19,9122,9026,,1048,,57,12,245,1095,1578,2575,3560,,26,9,156,662,938,1410,1387,,31,3,89,433,640,1165,2173,,530,1149,867,795,953,1026,891,1334,1010,542
+33,16/08/19,9093,9025,,1033,,54,24,277,1244,1573,2530,3391,,28,13,197,743,927,1371,1436,,26,11,80,501,646,1159,1955,,497,1237,857,749,969,933,892,1355,978,589
+34,23/08/19,8994,9113,,981,,47,8,264,1127,1582,2479,3487,,31,4,169,681,942,1357,1439,,16,4,95,446,640,1122,2048,,505,1203,927,726,918,964,826,1389,963,553
+35,30/08/19,8242,8197,,854,,45,16,224,1026,1419,2319,3193,,23,10,138,629,846,1240,1232,,22,6,86,397,573,1079,1961,,466,1153,832,697,826,838,784,1245,814,558
+36,06/09/19,9695,9177,,1125,,54,19,268,1199,1643,2775,3737,,29,9,183,715,955,1487,1480,,25,10,85,484,688,1288,2257,,473,1248,937,867,939,1115,976,1518,1014,582
+37,13/09/19,9513,9260,,1028,,60,12,297,1169,1617,2654,3704,,31,3,178,709,946,1421,1547,,29,9,119,460,671,1233,2157,,482,1328,916,774,1001,1088,916,1399,1020,567
+38,20/09/19,9440,9197,,1056,,45,18,264,1174,1592,2695,3652,,23,11,165,716,918,1412,1463,,22,7,99,458,674,1283,2189,,494,1283,954,827,974,1049,889,1385,982,572
+39,27/09/19,9517,9283,,1045,,55,14,269,1197,1547,2760,3675,,28,8,174,704,922,1514,1527,,27,6,95,493,625,1246,2148,,538,1299,906,842,1027,1014,840,1404,1017,611
+40,04/10/19,9799,9450,,1114,,68,15,325,1189,1665,2780,3757,,37,7,218,705,950,1489,1508,,31,8,107,484,715,1291,2249,,491,1322,948,875,1008,1059,846,1573,1059,597
+41,11/10/19,9973,9651,,1209,,46,16,302,1137,1595,2869,4008,,26,10,208,686,966,1525,1609,,20,6,94,451,629,1344,2399,,527,1400,1005,847,1027,1073,887,1557,1026,590
+42,18/10/19,10156,9726,,1236,,54,14,303,1154,1628,2920,4083,,32,10,193,676,937,1551,1641,,22,4,110,478,691,1369,2442,,567,1400,1019,863,1093,1083,886,1556,1040,622
+43,25/10/19,10021,9674,,1252,,49,14,281,1198,1663,2799,4017,,23,6,186,699,963,1550,1617,,26,8,95,499,700,1249,2400,,540,1361,1001,846,1019,1063,895,1491,1107,670
+44,01/11/19,10164,9777,,1333,,45,19,289,1196,1663,2938,4014,,27,9,176,682,980,1501,1573,,18,10,113,514,683,1437,2441,,537,1338,1033,877,967,1100,976,1547,1118,642
+45,08/11/19,10697,10142,,1363,,52,7,314,1236,1676,2998,4414,,29,3,199,738,991,1616,1737,,23,4,115,498,685,1382,2677,,553,1455,1053,880,1110,1181,974,1701,1102,653
+46,15/11/19,10650,10226,,1451,,46,19,271,1254,1673,3070,4317,,19,9,172,739,980,1635,1692,,27,10,99,515,693,1435,2625,,607,1402,1070,889,1080,1090,1041,1690,1083,674
+47,22/11/19,10882,10124,,1423,,57,19,283,1225,1743,3163,4392,,32,9,168,724,1042,1724,1776,,25,10,115,501,701,1439,2616,,582,1464,1039,931,1138,1166,1023,1618,1203,699
+48,29/11/19,10958,10164,,1566,,56,14,312,1237,1751,3142,4446,,35,10,209,739,1008,1746,1808,,21,4,103,498,743,1396,2638,,563,1469,1139,935,1184,1116,1025,1648,1166,689
+49,06/12/19,10816,10585,,1505,,50,17,315,1275,1689,3078,4392,,32,11,204,756,985,1639,1748,,18,6,111,519,704,1439,2644,,558,1477,1011,958,1083,1184,986,1647,1157,737
+50,13/12/19,11188,10622,,1637,,52,32,315,1313,1793,3215,4468,,29,16,187,746,1028,1711,1772,,23,16,128,567,765,1504,2696,,585,1637,1102,973,1130,1176,1044,1690,1130,699
+51,20/12/19,11926,11499,,1839,,53,19,368,1316,1903,3299,4968,,25,9,229,768,1095,1733,1995,,28,10,139,548,808,1566,2973,,670,1712,1173,1050,1269,1238,1028,1709,1288,767
+52,27/12/19,7533,8014,,1166,,34,13,148,773,1185,2231,3149,,20,6,85,448,666,1123,1218,,14,7,63,325,519,1108,1931,,411,1200,737,729,720,716,653,1094,766,496
diff --git a/uk_deaths.ipynb b/uk_deaths.ipynb
new file mode 100644 (file)
index 0000000..eed19c1
--- /dev/null
@@ -0,0 +1,3067 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import itertools\n",
+    "import collections\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from scipy.stats import gmean\n",
+    "\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " publishedweek1620201.xlsx   publishedweek522018withupdatedrespiratoryrow.xls\n",
+      " publishedweek172020.csv     publishedweek522019.xls\n",
+      " publishedweek172020.ods    'Weekly_Deaths - Historical_NI.xls'\n",
+      " publishedweek172020.xlsx    Weekly_Deaths_NI_2020.xls\n",
+      " publishedweek522016.xls     weekly-march-20-scotland.zip\n",
+      " publishedweek522017.xls\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls uk-deaths-data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_data_2020 = pd.read_csv('uk-deaths-data/publishedweek172020.csv', \n",
+    "                       parse_dates=[1], dayfirst=True,\n",
+    "                      index_col=0,\n",
+    "                      header=[0, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "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>Week number</th>\n",
+       "      <th>Week ended</th>\n",
+       "      <th>Total deaths, all ages</th>\n",
+       "      <th>Total deaths: average of corresponding week over the previous 5 years 1 (England and Wales)</th>\n",
+       "      <th>Total deaths: average of corresponding week over the previous 5 years 1 (England)</th>\n",
+       "      <th>Total deaths: average of corresponding week over the previous 5 years (Wales)</th>\n",
+       "      <th>Unnamed: 6_level_0</th>\n",
+       "      <th>Unnamed: 7_level_0</th>\n",
+       "      <th>Unnamed: 8_level_0</th>\n",
+       "      <th>Persons</th>\n",
+       "      <th>Deaths by age group</th>\n",
+       "      <th>...</th>\n",
+       "      <th>E12000001</th>\n",
+       "      <th>E12000002</th>\n",
+       "      <th>E12000003</th>\n",
+       "      <th>E12000004</th>\n",
+       "      <th>E12000005</th>\n",
+       "      <th>E12000006</th>\n",
+       "      <th>E12000007</th>\n",
+       "      <th>E12000008</th>\n",
+       "      <th>E12000009</th>\n",
+       "      <th>W92000004</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 1_level_1</th>\n",
+       "      <th>Unnamed: 2_level_1</th>\n",
+       "      <th>Unnamed: 3_level_1</th>\n",
+       "      <th>Unnamed: 4_level_1</th>\n",
+       "      <th>Unnamed: 5_level_1</th>\n",
+       "      <th>Deaths by cause</th>\n",
+       "      <th>Deaths where the underlying cause was respiratory disease (ICD-10 J00-J99)</th>\n",
+       "      <th>Deaths where COVID-19 was mentioned on the death certificate (ICD-10 U07.1 and U07.2)</th>\n",
+       "      <th>Unnamed: 9_level_1</th>\n",
+       "      <th>&lt;1</th>\n",
+       "      <th>...</th>\n",
+       "      <th>North East</th>\n",
+       "      <th>North West</th>\n",
+       "      <th>Yorkshire and The Humber</th>\n",
+       "      <th>East Midlands</th>\n",
+       "      <th>West Midlands</th>\n",
+       "      <th>East</th>\n",
+       "      <th>London</th>\n",
+       "      <th>South East</th>\n",
+       "      <th>South West</th>\n",
+       "      <th>Wales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-01-03</td>\n",
+       "      <td>12254</td>\n",
+       "      <td>12175</td>\n",
+       "      <td>11412</td>\n",
+       "      <td>756</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2141</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>48</td>\n",
+       "      <td>...</td>\n",
+       "      <td>673</td>\n",
+       "      <td>1806</td>\n",
+       "      <td>1240</td>\n",
+       "      <td>1060</td>\n",
+       "      <td>1349</td>\n",
+       "      <td>1162</td>\n",
+       "      <td>1113</td>\n",
+       "      <td>1814</td>\n",
+       "      <td>1225</td>\n",
+       "      <td>787</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2020-01-10</td>\n",
+       "      <td>14058</td>\n",
+       "      <td>13822</td>\n",
+       "      <td>12933</td>\n",
+       "      <td>856</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2477</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>50</td>\n",
+       "      <td>...</td>\n",
+       "      <td>707</td>\n",
+       "      <td>1932</td>\n",
+       "      <td>1339</td>\n",
+       "      <td>1195</td>\n",
+       "      <td>1450</td>\n",
+       "      <td>1573</td>\n",
+       "      <td>1272</td>\n",
+       "      <td>2132</td>\n",
+       "      <td>1487</td>\n",
+       "      <td>939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>2020-01-17</td>\n",
+       "      <td>12990</td>\n",
+       "      <td>13216</td>\n",
+       "      <td>12370</td>\n",
+       "      <td>812</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2189</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>69</td>\n",
+       "      <td>...</td>\n",
+       "      <td>647</td>\n",
+       "      <td>1696</td>\n",
+       "      <td>1278</td>\n",
+       "      <td>1106</td>\n",
+       "      <td>1407</td>\n",
+       "      <td>1457</td>\n",
+       "      <td>1073</td>\n",
+       "      <td>2064</td>\n",
+       "      <td>1466</td>\n",
+       "      <td>767</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>2020-01-24</td>\n",
+       "      <td>11856</td>\n",
+       "      <td>12760</td>\n",
+       "      <td>11933</td>\n",
+       "      <td>802</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1893</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>53</td>\n",
+       "      <td>...</td>\n",
+       "      <td>612</td>\n",
+       "      <td>1529</td>\n",
+       "      <td>1187</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>1231</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1028</td>\n",
+       "      <td>1833</td>\n",
+       "      <td>1253</td>\n",
+       "      <td>723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>2020-01-31</td>\n",
+       "      <td>11612</td>\n",
+       "      <td>12206</td>\n",
+       "      <td>11419</td>\n",
+       "      <td>760</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1746</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>50</td>\n",
+       "      <td>...</td>\n",
+       "      <td>561</td>\n",
+       "      <td>1461</td>\n",
+       "      <td>1136</td>\n",
+       "      <td>1015</td>\n",
+       "      <td>1262</td>\n",
+       "      <td>1286</td>\n",
+       "      <td>1092</td>\n",
+       "      <td>1820</td>\n",
+       "      <td>1233</td>\n",
+       "      <td>727</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows Ã— 84 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Week number         Week ended Total deaths, all ages  \\\n",
+       "            Unnamed: 1_level_1     Unnamed: 2_level_1   \n",
+       "1                   2020-01-03                  12254   \n",
+       "2                   2020-01-10                  14058   \n",
+       "3                   2020-01-17                  12990   \n",
+       "4                   2020-01-24                  11856   \n",
+       "5                   2020-01-31                  11612   \n",
+       "\n",
+       "Week number Total deaths: average of corresponding week over the previous 5 years 1 (England and Wales)  \\\n",
+       "                                                                                     Unnamed: 3_level_1   \n",
+       "1                                                        12175                                            \n",
+       "2                                                        13822                                            \n",
+       "3                                                        13216                                            \n",
+       "4                                                        12760                                            \n",
+       "5                                                        12206                                            \n",
+       "\n",
+       "Week number Total deaths: average of corresponding week over the previous 5 years 1 (England)  \\\n",
+       "                                                                           Unnamed: 4_level_1   \n",
+       "1                                                        11412                                  \n",
+       "2                                                        12933                                  \n",
+       "3                                                        12370                                  \n",
+       "4                                                        11933                                  \n",
+       "5                                                        11419                                  \n",
+       "\n",
+       "Week number Total deaths: average of corresponding week over the previous 5 years (Wales)  \\\n",
+       "                                                                       Unnamed: 5_level_1   \n",
+       "1                                                          756                              \n",
+       "2                                                          856                              \n",
+       "3                                                          812                              \n",
+       "4                                                          802                              \n",
+       "5                                                          760                              \n",
+       "\n",
+       "Week number Unnamed: 6_level_0  \\\n",
+       "               Deaths by cause   \n",
+       "1                          NaN   \n",
+       "2                          NaN   \n",
+       "3                          NaN   \n",
+       "4                          NaN   \n",
+       "5                          NaN   \n",
+       "\n",
+       "Week number                                                         Unnamed: 7_level_0  \\\n",
+       "            Deaths where the underlying cause was respiratory disease (ICD-10 J00-J99)   \n",
+       "1                                                         2141                           \n",
+       "2                                                         2477                           \n",
+       "3                                                         2189                           \n",
+       "4                                                         1893                           \n",
+       "5                                                         1746                           \n",
+       "\n",
+       "Week number                                                                    Unnamed: 8_level_0  \\\n",
+       "            Deaths where COVID-19 was mentioned on the death certificate (ICD-10 U07.1 and U07.2)   \n",
+       "1                                                            0                                      \n",
+       "2                                                            0                                      \n",
+       "3                                                            0                                      \n",
+       "4                                                            0                                      \n",
+       "5                                                            0                                      \n",
+       "\n",
+       "Week number            Persons Deaths by age group  ...  E12000001  E12000002  \\\n",
+       "            Unnamed: 9_level_1                  <1  ... North East North West   \n",
+       "1                          NaN                  48  ...        673       1806   \n",
+       "2                          NaN                  50  ...        707       1932   \n",
+       "3                          NaN                  69  ...        647       1696   \n",
+       "4                          NaN                  53  ...        612       1529   \n",
+       "5                          NaN                  50  ...        561       1461   \n",
+       "\n",
+       "Week number                E12000003     E12000004     E12000005 E12000006  \\\n",
+       "            Yorkshire and The Humber East Midlands West Midlands      East   \n",
+       "1                               1240          1060          1349      1162   \n",
+       "2                               1339          1195          1450      1573   \n",
+       "3                               1278          1106          1407      1457   \n",
+       "4                               1187          1024          1231      1410   \n",
+       "5                               1136          1015          1262      1286   \n",
+       "\n",
+       "Week number E12000007  E12000008  E12000009 W92000004  \n",
+       "               London South East South West     Wales  \n",
+       "1                1113       1814       1225       787  \n",
+       "2                1272       2132       1487       939  \n",
+       "3                1073       2064       1466       767  \n",
+       "4                1028       1833       1253       723  \n",
+       "5                1092       1820       1233       727  \n",
+       "\n",
+       "[5 rows x 84 columns]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data_2020.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "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>Week number</th>\n",
+       "      <th>Week ended</th>\n",
+       "      <th>Total deaths, all ages</th>\n",
+       "      <th>Total deaths: average of corresponding</th>\n",
+       "      <th>Unnamed: 4_level_0</th>\n",
+       "      <th>Unnamed: 5_level_0</th>\n",
+       "      <th>Persons</th>\n",
+       "      <th>Unnamed: 7_level_0</th>\n",
+       "      <th>Unnamed: 8_level_0</th>\n",
+       "      <th>Unnamed: 9_level_0</th>\n",
+       "      <th>Unnamed: 10_level_0</th>\n",
+       "      <th>...</th>\n",
+       "      <th>E12000001</th>\n",
+       "      <th>E12000002</th>\n",
+       "      <th>E12000003</th>\n",
+       "      <th>E12000004</th>\n",
+       "      <th>E12000005</th>\n",
+       "      <th>E12000006</th>\n",
+       "      <th>E12000007</th>\n",
+       "      <th>E12000008</th>\n",
+       "      <th>E12000009</th>\n",
+       "      <th>W92000004</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 1_level_1</th>\n",
+       "      <th>Unnamed: 2_level_1</th>\n",
+       "      <th>Unnamed: 3_level_1</th>\n",
+       "      <th>Deaths by underlying cause</th>\n",
+       "      <th>All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS)</th>\n",
+       "      <th>Deaths by age group</th>\n",
+       "      <th>Under 1 year</th>\n",
+       "      <th>01-14</th>\n",
+       "      <th>15-44</th>\n",
+       "      <th>45-64</th>\n",
+       "      <th>...</th>\n",
+       "      <th>North East</th>\n",
+       "      <th>North West</th>\n",
+       "      <th>Yorkshire and The Humber</th>\n",
+       "      <th>East Midlands</th>\n",
+       "      <th>West Midlands</th>\n",
+       "      <th>East</th>\n",
+       "      <th>London</th>\n",
+       "      <th>South East</th>\n",
+       "      <th>South West</th>\n",
+       "      <th>Wales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-01-04</td>\n",
+       "      <td>10955</td>\n",
+       "      <td>12273</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1736</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>43</td>\n",
+       "      <td>15</td>\n",
+       "      <td>215</td>\n",
+       "      <td>1199</td>\n",
+       "      <td>...</td>\n",
+       "      <td>574</td>\n",
+       "      <td>1456</td>\n",
+       "      <td>1148</td>\n",
+       "      <td>964</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>1136</td>\n",
+       "      <td>1021</td>\n",
+       "      <td>1586</td>\n",
+       "      <td>1136</td>\n",
+       "      <td>718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2019-01-11</td>\n",
+       "      <td>12609</td>\n",
+       "      <td>13670</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2214</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>50</td>\n",
+       "      <td>20</td>\n",
+       "      <td>280</td>\n",
+       "      <td>1419</td>\n",
+       "      <td>...</td>\n",
+       "      <td>643</td>\n",
+       "      <td>1612</td>\n",
+       "      <td>1279</td>\n",
+       "      <td>1089</td>\n",
+       "      <td>1299</td>\n",
+       "      <td>1386</td>\n",
+       "      <td>1137</td>\n",
+       "      <td>1927</td>\n",
+       "      <td>1394</td>\n",
+       "      <td>809</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>2019-01-18</td>\n",
+       "      <td>11860</td>\n",
+       "      <td>13056</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>59</td>\n",
+       "      <td>29</td>\n",
+       "      <td>319</td>\n",
+       "      <td>1373</td>\n",
+       "      <td>...</td>\n",
+       "      <td>627</td>\n",
+       "      <td>1566</td>\n",
+       "      <td>1197</td>\n",
+       "      <td>979</td>\n",
+       "      <td>1187</td>\n",
+       "      <td>1344</td>\n",
+       "      <td>1073</td>\n",
+       "      <td>1936</td>\n",
+       "      <td>1236</td>\n",
+       "      <td>683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>2019-01-25</td>\n",
+       "      <td>11740</td>\n",
+       "      <td>12486</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1942</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>42</td>\n",
+       "      <td>22</td>\n",
+       "      <td>339</td>\n",
+       "      <td>1438</td>\n",
+       "      <td>...</td>\n",
+       "      <td>640</td>\n",
+       "      <td>1593</td>\n",
+       "      <td>1137</td>\n",
+       "      <td>1037</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>1291</td>\n",
+       "      <td>1045</td>\n",
+       "      <td>1875</td>\n",
+       "      <td>1229</td>\n",
+       "      <td>734</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>2019-02-01</td>\n",
+       "      <td>11297</td>\n",
+       "      <td>11998</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1931</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>57</td>\n",
+       "      <td>15</td>\n",
+       "      <td>307</td>\n",
+       "      <td>1367</td>\n",
+       "      <td>...</td>\n",
+       "      <td>618</td>\n",
+       "      <td>1485</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>963</td>\n",
+       "      <td>1150</td>\n",
+       "      <td>1202</td>\n",
+       "      <td>1066</td>\n",
+       "      <td>1718</td>\n",
+       "      <td>1191</td>\n",
+       "      <td>745</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows Ã— 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Week number         Week ended Total deaths, all ages  \\\n",
+       "            Unnamed: 1_level_1     Unnamed: 2_level_1   \n",
+       "1                   2019-01-04                  10955   \n",
+       "2                   2019-01-11                  12609   \n",
+       "3                   2019-01-18                  11860   \n",
+       "4                   2019-01-25                  11740   \n",
+       "5                   2019-02-01                  11297   \n",
+       "\n",
+       "Week number Total deaths: average of corresponding         Unnamed: 4_level_0  \\\n",
+       "                                Unnamed: 3_level_1 Deaths by underlying cause   \n",
+       "1                                            12273                        NaN   \n",
+       "2                                            13670                        NaN   \n",
+       "3                                            13056                        NaN   \n",
+       "4                                            12486                        NaN   \n",
+       "5                                            11998                        NaN   \n",
+       "\n",
+       "Week number                                              Unnamed: 5_level_0  \\\n",
+       "            All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS)   \n",
+       "1                                                         1736                \n",
+       "2                                                         2214                \n",
+       "3                                                         1972                \n",
+       "4                                                         1942                \n",
+       "5                                                         1931                \n",
+       "\n",
+       "Week number             Persons Unnamed: 7_level_0 Unnamed: 8_level_0  \\\n",
+       "            Deaths by age group       Under 1 year              01-14   \n",
+       "1                           NaN                 43                 15   \n",
+       "2                           NaN                 50                 20   \n",
+       "3                           NaN                 59                 29   \n",
+       "4                           NaN                 42                 22   \n",
+       "5                           NaN                 57                 15   \n",
+       "\n",
+       "Week number Unnamed: 9_level_0 Unnamed: 10_level_0  ...  E12000001  E12000002  \\\n",
+       "                         15-44               45-64  ... North East North West   \n",
+       "1                          215                1199  ...        574       1456   \n",
+       "2                          280                1419  ...        643       1612   \n",
+       "3                          319                1373  ...        627       1566   \n",
+       "4                          339                1438  ...        640       1593   \n",
+       "5                          307                1367  ...        618       1485   \n",
+       "\n",
+       "Week number                E12000003     E12000004     E12000005 E12000006  \\\n",
+       "            Yorkshire and The Humber East Midlands West Midlands      East   \n",
+       "1                               1148           964          1180      1136   \n",
+       "2                               1279          1089          1299      1386   \n",
+       "3                               1197           979          1187      1344   \n",
+       "4                               1137          1037          1131      1291   \n",
+       "5                               1131           963          1150      1202   \n",
+       "\n",
+       "Week number E12000007  E12000008  E12000009 W92000004  \n",
+       "               London South East South West     Wales  \n",
+       "1                1021       1586       1136       718  \n",
+       "2                1137       1927       1394       809  \n",
+       "3                1073       1936       1236       683  \n",
+       "4                1045       1875       1229       734  \n",
+       "5                1066       1718       1191       745  \n",
+       "\n",
+       "[5 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data_2019 = pd.read_csv('uk-deaths-data/publishedweek522019.csv', \n",
+    "                       parse_dates=[1], dayfirst=True,\n",
+    "                      index_col=0,\n",
+    "                      header=[0, 1])\n",
+    "raw_data_2019.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "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>Week number</th>\n",
+       "      <th>Week ended</th>\n",
+       "      <th>Total deaths, all ages</th>\n",
+       "      <th>Total deaths: average of corresponding</th>\n",
+       "      <th>Unnamed: 4_level_0</th>\n",
+       "      <th>Unnamed: 5_level_0</th>\n",
+       "      <th>Persons</th>\n",
+       "      <th>Unnamed: 7_level_0</th>\n",
+       "      <th>Unnamed: 8_level_0</th>\n",
+       "      <th>Unnamed: 9_level_0</th>\n",
+       "      <th>Unnamed: 10_level_0</th>\n",
+       "      <th>...</th>\n",
+       "      <th>E12000001</th>\n",
+       "      <th>E12000002</th>\n",
+       "      <th>E12000003</th>\n",
+       "      <th>E12000004</th>\n",
+       "      <th>E12000005</th>\n",
+       "      <th>E12000006</th>\n",
+       "      <th>E12000007</th>\n",
+       "      <th>E12000008</th>\n",
+       "      <th>E12000009</th>\n",
+       "      <th>W92000004</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 1_level_1</th>\n",
+       "      <th>Unnamed: 2_level_1</th>\n",
+       "      <th>Unnamed: 3_level_1</th>\n",
+       "      <th>Deaths by underlying cause</th>\n",
+       "      <th>All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS)</th>\n",
+       "      <th>Deaths by age group</th>\n",
+       "      <th>Under 1 year</th>\n",
+       "      <th>01-14</th>\n",
+       "      <th>15-44</th>\n",
+       "      <th>45-64</th>\n",
+       "      <th>...</th>\n",
+       "      <th>North East</th>\n",
+       "      <th>North West</th>\n",
+       "      <th>Yorkshire and The Humber</th>\n",
+       "      <th>East Midlands</th>\n",
+       "      <th>West Midlands</th>\n",
+       "      <th>East</th>\n",
+       "      <th>London</th>\n",
+       "      <th>South East</th>\n",
+       "      <th>South West</th>\n",
+       "      <th>Wales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2018-01-05</td>\n",
+       "      <td>12723</td>\n",
+       "      <td>12079</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2361</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>52</td>\n",
+       "      <td>18</td>\n",
+       "      <td>208</td>\n",
+       "      <td>1290</td>\n",
+       "      <td>...</td>\n",
+       "      <td>640</td>\n",
+       "      <td>1747</td>\n",
+       "      <td>1300</td>\n",
+       "      <td>1085</td>\n",
+       "      <td>1313</td>\n",
+       "      <td>1404</td>\n",
+       "      <td>1130</td>\n",
+       "      <td>1902</td>\n",
+       "      <td>1393</td>\n",
+       "      <td>783</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2018-01-12</td>\n",
+       "      <td>15050</td>\n",
+       "      <td>13167</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3075</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>73</td>\n",
+       "      <td>17</td>\n",
+       "      <td>302</td>\n",
+       "      <td>1561</td>\n",
+       "      <td>...</td>\n",
+       "      <td>837</td>\n",
+       "      <td>2113</td>\n",
+       "      <td>1481</td>\n",
+       "      <td>1266</td>\n",
+       "      <td>1468</td>\n",
+       "      <td>1677</td>\n",
+       "      <td>1342</td>\n",
+       "      <td>2371</td>\n",
+       "      <td>1552</td>\n",
+       "      <td>904</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>2018-01-19</td>\n",
+       "      <td>14256</td>\n",
+       "      <td>12418</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2829</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>59</td>\n",
+       "      <td>22</td>\n",
+       "      <td>286</td>\n",
+       "      <td>1507</td>\n",
+       "      <td>...</td>\n",
+       "      <td>783</td>\n",
+       "      <td>1828</td>\n",
+       "      <td>1387</td>\n",
+       "      <td>1175</td>\n",
+       "      <td>1544</td>\n",
+       "      <td>1562</td>\n",
+       "      <td>1239</td>\n",
+       "      <td>2287</td>\n",
+       "      <td>1535</td>\n",
+       "      <td>885</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>2018-01-26</td>\n",
+       "      <td>13935</td>\n",
+       "      <td>11954</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2672</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>50</td>\n",
+       "      <td>25</td>\n",
+       "      <td>298</td>\n",
+       "      <td>1459</td>\n",
+       "      <td>...</td>\n",
+       "      <td>779</td>\n",
+       "      <td>1855</td>\n",
+       "      <td>1363</td>\n",
+       "      <td>1165</td>\n",
+       "      <td>1433</td>\n",
+       "      <td>1553</td>\n",
+       "      <td>1264</td>\n",
+       "      <td>2184</td>\n",
+       "      <td>1460</td>\n",
+       "      <td>850</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>2018-02-02</td>\n",
+       "      <td>13285</td>\n",
+       "      <td>11605</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2479</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>41</td>\n",
+       "      <td>14</td>\n",
+       "      <td>339</td>\n",
+       "      <td>1404</td>\n",
+       "      <td>...</td>\n",
+       "      <td>740</td>\n",
+       "      <td>1728</td>\n",
+       "      <td>1285</td>\n",
+       "      <td>1154</td>\n",
+       "      <td>1441</td>\n",
+       "      <td>1468</td>\n",
+       "      <td>1208</td>\n",
+       "      <td>2046</td>\n",
+       "      <td>1382</td>\n",
+       "      <td>815</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows Ã— 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Week number         Week ended Total deaths, all ages  \\\n",
+       "            Unnamed: 1_level_1     Unnamed: 2_level_1   \n",
+       "1                   2018-01-05                  12723   \n",
+       "2                   2018-01-12                  15050   \n",
+       "3                   2018-01-19                  14256   \n",
+       "4                   2018-01-26                  13935   \n",
+       "5                   2018-02-02                  13285   \n",
+       "\n",
+       "Week number Total deaths: average of corresponding         Unnamed: 4_level_0  \\\n",
+       "                                Unnamed: 3_level_1 Deaths by underlying cause   \n",
+       "1                                            12079                        NaN   \n",
+       "2                                            13167                        NaN   \n",
+       "3                                            12418                        NaN   \n",
+       "4                                            11954                        NaN   \n",
+       "5                                            11605                        NaN   \n",
+       "\n",
+       "Week number                                              Unnamed: 5_level_0  \\\n",
+       "            All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS)   \n",
+       "1                                                         2361                \n",
+       "2                                                         3075                \n",
+       "3                                                         2829                \n",
+       "4                                                         2672                \n",
+       "5                                                         2479                \n",
+       "\n",
+       "Week number             Persons Unnamed: 7_level_0 Unnamed: 8_level_0  \\\n",
+       "            Deaths by age group       Under 1 year              01-14   \n",
+       "1                           NaN                 52                 18   \n",
+       "2                           NaN                 73                 17   \n",
+       "3                           NaN                 59                 22   \n",
+       "4                           NaN                 50                 25   \n",
+       "5                           NaN                 41                 14   \n",
+       "\n",
+       "Week number Unnamed: 9_level_0 Unnamed: 10_level_0  ...  E12000001  E12000002  \\\n",
+       "                         15-44               45-64  ... North East North West   \n",
+       "1                          208                1290  ...        640       1747   \n",
+       "2                          302                1561  ...        837       2113   \n",
+       "3                          286                1507  ...        783       1828   \n",
+       "4                          298                1459  ...        779       1855   \n",
+       "5                          339                1404  ...        740       1728   \n",
+       "\n",
+       "Week number                E12000003     E12000004     E12000005 E12000006  \\\n",
+       "            Yorkshire and The Humber East Midlands West Midlands      East   \n",
+       "1                               1300          1085          1313      1404   \n",
+       "2                               1481          1266          1468      1677   \n",
+       "3                               1387          1175          1544      1562   \n",
+       "4                               1363          1165          1433      1553   \n",
+       "5                               1285          1154          1441      1468   \n",
+       "\n",
+       "Week number E12000007  E12000008  E12000009 W92000004  \n",
+       "               London South East South West     Wales  \n",
+       "1                1130       1902       1393       783  \n",
+       "2                1342       2371       1552       904  \n",
+       "3                1239       2287       1535       885  \n",
+       "4                1264       2184       1460       850  \n",
+       "5                1208       2046       1382       815  \n",
+       "\n",
+       "[5 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data_2018 = pd.read_csv('uk-deaths-data/publishedweek522018.csv', \n",
+    "                       parse_dates=[1], dayfirst=True,\n",
+    "                      index_col=0,\n",
+    "                      header=[0, 1])\n",
+    "raw_data_2018.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "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>Week number</th>\n",
+       "      <th>Week ended</th>\n",
+       "      <th>Total deaths, all ages</th>\n",
+       "      <th>Total deaths: average of corresponding</th>\n",
+       "      <th>Unnamed: 4_level_0</th>\n",
+       "      <th>Unnamed: 5_level_0</th>\n",
+       "      <th>Persons</th>\n",
+       "      <th>Unnamed: 7_level_0</th>\n",
+       "      <th>Unnamed: 8_level_0</th>\n",
+       "      <th>Unnamed: 9_level_0</th>\n",
+       "      <th>Unnamed: 10_level_0</th>\n",
+       "      <th>...</th>\n",
+       "      <th>E12000001</th>\n",
+       "      <th>E12000002</th>\n",
+       "      <th>E12000003</th>\n",
+       "      <th>E12000004</th>\n",
+       "      <th>E12000005</th>\n",
+       "      <th>E12000006</th>\n",
+       "      <th>E12000007</th>\n",
+       "      <th>E12000008</th>\n",
+       "      <th>E12000009</th>\n",
+       "      <th>W92000004</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 1_level_1</th>\n",
+       "      <th>Unnamed: 2_level_1</th>\n",
+       "      <th>Unnamed: 3_level_1</th>\n",
+       "      <th>Deaths by underlying cause</th>\n",
+       "      <th>All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS)</th>\n",
+       "      <th>Deaths by age group</th>\n",
+       "      <th>Under 1 year</th>\n",
+       "      <th>01-14</th>\n",
+       "      <th>15-44</th>\n",
+       "      <th>45-64</th>\n",
+       "      <th>...</th>\n",
+       "      <th>North East</th>\n",
+       "      <th>North West</th>\n",
+       "      <th>Yorkshire and The Humber</th>\n",
+       "      <th>East Midlands</th>\n",
+       "      <th>West Midlands</th>\n",
+       "      <th>East</th>\n",
+       "      <th>London</th>\n",
+       "      <th>South East</th>\n",
+       "      <th>South West</th>\n",
+       "      <th>Wales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2017-01-06</td>\n",
+       "      <td>11991</td>\n",
+       "      <td>11782</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2321</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>58</td>\n",
+       "      <td>18</td>\n",
+       "      <td>215</td>\n",
+       "      <td>1232</td>\n",
+       "      <td>...</td>\n",
+       "      <td>602</td>\n",
+       "      <td>1665</td>\n",
+       "      <td>1208</td>\n",
+       "      <td>1016</td>\n",
+       "      <td>1243</td>\n",
+       "      <td>1187</td>\n",
+       "      <td>1173</td>\n",
+       "      <td>1899</td>\n",
+       "      <td>1228</td>\n",
+       "      <td>744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2017-01-13</td>\n",
+       "      <td>13715</td>\n",
+       "      <td>12692</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2690</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>56</td>\n",
+       "      <td>18</td>\n",
+       "      <td>287</td>\n",
+       "      <td>1372</td>\n",
+       "      <td>...</td>\n",
+       "      <td>738</td>\n",
+       "      <td>1840</td>\n",
+       "      <td>1351</td>\n",
+       "      <td>1170</td>\n",
+       "      <td>1439</td>\n",
+       "      <td>1450</td>\n",
+       "      <td>1296</td>\n",
+       "      <td>2161</td>\n",
+       "      <td>1419</td>\n",
+       "      <td>825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>2017-01-20</td>\n",
+       "      <td>13610</td>\n",
+       "      <td>11773</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2625</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>54</td>\n",
+       "      <td>19</td>\n",
+       "      <td>301</td>\n",
+       "      <td>1374</td>\n",
+       "      <td>...</td>\n",
+       "      <td>676</td>\n",
+       "      <td>1786</td>\n",
+       "      <td>1383</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>1414</td>\n",
+       "      <td>1484</td>\n",
+       "      <td>1276</td>\n",
+       "      <td>2195</td>\n",
+       "      <td>1346</td>\n",
+       "      <td>835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>2017-01-27</td>\n",
+       "      <td>12877</td>\n",
+       "      <td>11441</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2373</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>52</td>\n",
+       "      <td>16</td>\n",
+       "      <td>286</td>\n",
+       "      <td>1273</td>\n",
+       "      <td>...</td>\n",
+       "      <td>627</td>\n",
+       "      <td>1666</td>\n",
+       "      <td>1242</td>\n",
+       "      <td>1116</td>\n",
+       "      <td>1362</td>\n",
+       "      <td>1488</td>\n",
+       "      <td>1198</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>1278</td>\n",
+       "      <td>881</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>2017-02-03</td>\n",
+       "      <td>12485</td>\n",
+       "      <td>11128</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2147</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>55</td>\n",
+       "      <td>14</td>\n",
+       "      <td>308</td>\n",
+       "      <td>1239</td>\n",
+       "      <td>...</td>\n",
+       "      <td>631</td>\n",
+       "      <td>1625</td>\n",
+       "      <td>1243</td>\n",
+       "      <td>1045</td>\n",
+       "      <td>1293</td>\n",
+       "      <td>1384</td>\n",
+       "      <td>1210</td>\n",
+       "      <td>1920</td>\n",
+       "      <td>1359</td>\n",
+       "      <td>749</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows Ã— 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Week number         Week ended Total deaths, all ages  \\\n",
+       "            Unnamed: 1_level_1     Unnamed: 2_level_1   \n",
+       "1                   2017-01-06                  11991   \n",
+       "2                   2017-01-13                  13715   \n",
+       "3                   2017-01-20                  13610   \n",
+       "4                   2017-01-27                  12877   \n",
+       "5                   2017-02-03                  12485   \n",
+       "\n",
+       "Week number Total deaths: average of corresponding         Unnamed: 4_level_0  \\\n",
+       "                                Unnamed: 3_level_1 Deaths by underlying cause   \n",
+       "1                                            11782                        NaN   \n",
+       "2                                            12692                        NaN   \n",
+       "3                                            11773                        NaN   \n",
+       "4                                            11441                        NaN   \n",
+       "5                                            11128                        NaN   \n",
+       "\n",
+       "Week number                                              Unnamed: 5_level_0  \\\n",
+       "            All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS)   \n",
+       "1                                                         2321                \n",
+       "2                                                         2690                \n",
+       "3                                                         2625                \n",
+       "4                                                         2373                \n",
+       "5                                                         2147                \n",
+       "\n",
+       "Week number             Persons Unnamed: 7_level_0 Unnamed: 8_level_0  \\\n",
+       "            Deaths by age group       Under 1 year              01-14   \n",
+       "1                           NaN                 58                 18   \n",
+       "2                           NaN                 56                 18   \n",
+       "3                           NaN                 54                 19   \n",
+       "4                           NaN                 52                 16   \n",
+       "5                           NaN                 55                 14   \n",
+       "\n",
+       "Week number Unnamed: 9_level_0 Unnamed: 10_level_0  ...  E12000001  E12000002  \\\n",
+       "                         15-44               45-64  ... North East North West   \n",
+       "1                          215                1232  ...        602       1665   \n",
+       "2                          287                1372  ...        738       1840   \n",
+       "3                          301                1374  ...        676       1786   \n",
+       "4                          286                1273  ...        627       1666   \n",
+       "5                          308                1239  ...        631       1625   \n",
+       "\n",
+       "Week number                E12000003     E12000004     E12000005 E12000006  \\\n",
+       "            Yorkshire and The Humber East Midlands West Midlands      East   \n",
+       "1                               1208          1016          1243      1187   \n",
+       "2                               1351          1170          1439      1450   \n",
+       "3                               1383          1190          1414      1484   \n",
+       "4                               1242          1116          1362      1488   \n",
+       "5                               1243          1045          1293      1384   \n",
+       "\n",
+       "Week number E12000007  E12000008  E12000009 W92000004  \n",
+       "               London South East South West     Wales  \n",
+       "1                1173       1899       1228       744  \n",
+       "2                1296       2161       1419       825  \n",
+       "3                1276       2195       1346       835  \n",
+       "4                1198       1999       1278       881  \n",
+       "5                1210       1920       1359       749  \n",
+       "\n",
+       "[5 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data_2017 = pd.read_csv('uk-deaths-data/publishedweek522017.csv', \n",
+    "                       parse_dates=[1], dayfirst=True,\n",
+    "                      index_col=0,\n",
+    "                      header=[0, 1])\n",
+    "raw_data_2017.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "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>Week number</th>\n",
+       "      <th>Week ended</th>\n",
+       "      <th>Total deaths, all ages</th>\n",
+       "      <th>Total deaths: average of corresponding</th>\n",
+       "      <th>Unnamed: 4_level_0</th>\n",
+       "      <th>Unnamed: 5_level_0</th>\n",
+       "      <th>Persons</th>\n",
+       "      <th>Unnamed: 7_level_0</th>\n",
+       "      <th>Unnamed: 8_level_0</th>\n",
+       "      <th>Unnamed: 9_level_0</th>\n",
+       "      <th>Unnamed: 10_level_0</th>\n",
+       "      <th>...</th>\n",
+       "      <th>E12000001</th>\n",
+       "      <th>E12000002</th>\n",
+       "      <th>E12000003</th>\n",
+       "      <th>E12000004</th>\n",
+       "      <th>E12000005</th>\n",
+       "      <th>E12000006</th>\n",
+       "      <th>E12000007</th>\n",
+       "      <th>E12000008</th>\n",
+       "      <th>E12000009</th>\n",
+       "      <th>W92000004</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 1_level_1</th>\n",
+       "      <th>Unnamed: 2_level_1</th>\n",
+       "      <th>Unnamed: 3_level_1</th>\n",
+       "      <th>Deaths by underlying cause</th>\n",
+       "      <th>All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS)</th>\n",
+       "      <th>Deaths by age group</th>\n",
+       "      <th>Under 1 year</th>\n",
+       "      <th>01-14</th>\n",
+       "      <th>15-44</th>\n",
+       "      <th>45-64</th>\n",
+       "      <th>...</th>\n",
+       "      <th>North East</th>\n",
+       "      <th>North West</th>\n",
+       "      <th>Yorkshire and The Humber</th>\n",
+       "      <th>East Midlands</th>\n",
+       "      <th>West Midlands</th>\n",
+       "      <th>East</th>\n",
+       "      <th>London</th>\n",
+       "      <th>South East</th>\n",
+       "      <th>South West</th>\n",
+       "      <th>Wales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2016-01-08</td>\n",
+       "      <td>13045</td>\n",
+       "      <td>11701.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2046</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>49</td>\n",
+       "      <td>17</td>\n",
+       "      <td>299</td>\n",
+       "      <td>1482</td>\n",
+       "      <td>...</td>\n",
+       "      <td>705</td>\n",
+       "      <td>1748</td>\n",
+       "      <td>1284</td>\n",
+       "      <td>1067</td>\n",
+       "      <td>1396</td>\n",
+       "      <td>1401</td>\n",
+       "      <td>1226</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1424</td>\n",
+       "      <td>809</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2016-01-15</td>\n",
+       "      <td>11501</td>\n",
+       "      <td>13016.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1835</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>64</td>\n",
+       "      <td>24</td>\n",
+       "      <td>326</td>\n",
+       "      <td>1363</td>\n",
+       "      <td>...</td>\n",
+       "      <td>600</td>\n",
+       "      <td>1532</td>\n",
+       "      <td>1148</td>\n",
+       "      <td>956</td>\n",
+       "      <td>1208</td>\n",
+       "      <td>1237</td>\n",
+       "      <td>1048</td>\n",
+       "      <td>1771</td>\n",
+       "      <td>1263</td>\n",
+       "      <td>711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>2016-01-22</td>\n",
+       "      <td>11473</td>\n",
+       "      <td>11765.4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1775</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>50</td>\n",
+       "      <td>18</td>\n",
+       "      <td>345</td>\n",
+       "      <td>1293</td>\n",
+       "      <td>...</td>\n",
+       "      <td>612</td>\n",
+       "      <td>1602</td>\n",
+       "      <td>1119</td>\n",
+       "      <td>929</td>\n",
+       "      <td>1209</td>\n",
+       "      <td>1253</td>\n",
+       "      <td>1068</td>\n",
+       "      <td>1710</td>\n",
+       "      <td>1219</td>\n",
+       "      <td>720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>2016-01-29</td>\n",
+       "      <td>11317</td>\n",
+       "      <td>11289.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1810</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>47</td>\n",
+       "      <td>14</td>\n",
+       "      <td>324</td>\n",
+       "      <td>1322</td>\n",
+       "      <td>...</td>\n",
+       "      <td>631</td>\n",
+       "      <td>1516</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>858</td>\n",
+       "      <td>1195</td>\n",
+       "      <td>1236</td>\n",
+       "      <td>1065</td>\n",
+       "      <td>1730</td>\n",
+       "      <td>1207</td>\n",
+       "      <td>717</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>2016-02-05</td>\n",
+       "      <td>11052</td>\n",
+       "      <td>10965.6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1748</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>60</td>\n",
+       "      <td>22</td>\n",
+       "      <td>314</td>\n",
+       "      <td>1281</td>\n",
+       "      <td>...</td>\n",
+       "      <td>620</td>\n",
+       "      <td>1459</td>\n",
+       "      <td>1109</td>\n",
+       "      <td>954</td>\n",
+       "      <td>1197</td>\n",
+       "      <td>1160</td>\n",
+       "      <td>1059</td>\n",
+       "      <td>1604</td>\n",
+       "      <td>1169</td>\n",
+       "      <td>690</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows Ã— 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Week number         Week ended Total deaths, all ages  \\\n",
+       "            Unnamed: 1_level_1     Unnamed: 2_level_1   \n",
+       "1                   2016-01-08                  13045   \n",
+       "2                   2016-01-15                  11501   \n",
+       "3                   2016-01-22                  11473   \n",
+       "4                   2016-01-29                  11317   \n",
+       "5                   2016-02-05                  11052   \n",
+       "\n",
+       "Week number Total deaths: average of corresponding         Unnamed: 4_level_0  \\\n",
+       "                                Unnamed: 3_level_1 Deaths by underlying cause   \n",
+       "1                                          11701.4                        NaN   \n",
+       "2                                          13016.4                        NaN   \n",
+       "3                                          11765.4                        NaN   \n",
+       "4                                          11289.0                        NaN   \n",
+       "5                                          10965.6                        NaN   \n",
+       "\n",
+       "Week number                                              Unnamed: 5_level_0  \\\n",
+       "            All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS)   \n",
+       "1                                                         2046                \n",
+       "2                                                         1835                \n",
+       "3                                                         1775                \n",
+       "4                                                         1810                \n",
+       "5                                                         1748                \n",
+       "\n",
+       "Week number             Persons Unnamed: 7_level_0 Unnamed: 8_level_0  \\\n",
+       "            Deaths by age group       Under 1 year              01-14   \n",
+       "1                           NaN                 49                 17   \n",
+       "2                           NaN                 64                 24   \n",
+       "3                           NaN                 50                 18   \n",
+       "4                           NaN                 47                 14   \n",
+       "5                           NaN                 60                 22   \n",
+       "\n",
+       "Week number Unnamed: 9_level_0 Unnamed: 10_level_0  ...  E12000001  E12000002  \\\n",
+       "                         15-44               45-64  ... North East North West   \n",
+       "1                          299                1482  ...        705       1748   \n",
+       "2                          326                1363  ...        600       1532   \n",
+       "3                          345                1293  ...        612       1602   \n",
+       "4                          324                1322  ...        631       1516   \n",
+       "5                          314                1281  ...        620       1459   \n",
+       "\n",
+       "Week number                E12000003     E12000004     E12000005 E12000006  \\\n",
+       "            Yorkshire and The Humber East Midlands West Midlands      East   \n",
+       "1                               1284          1067          1396      1401   \n",
+       "2                               1148           956          1208      1237   \n",
+       "3                               1119           929          1209      1253   \n",
+       "4                               1131           858          1195      1236   \n",
+       "5                               1109           954          1197      1160   \n",
+       "\n",
+       "Week number E12000007  E12000008  E12000009 W92000004  \n",
+       "               London South East South West     Wales  \n",
+       "1                1226       1951       1424       809  \n",
+       "2                1048       1771       1263       711  \n",
+       "3                1068       1710       1219       720  \n",
+       "4                1065       1730       1207       717  \n",
+       "5                1059       1604       1169       690  \n",
+       "\n",
+       "[5 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data_2016 = pd.read_csv('uk-deaths-data/publishedweek522016.csv', \n",
+    "                       parse_dates=[1], dayfirst=True,\n",
+    "                      index_col=0,\n",
+    "                      header=[0, 1])\n",
+    "raw_data_2016.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "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>Week number</th>\n",
+       "      <th>Week ended</th>\n",
+       "      <th>Total deaths, all ages</th>\n",
+       "      <th>Total deaths: average of corresponding</th>\n",
+       "      <th>Unnamed: 4_level_0</th>\n",
+       "      <th>Unnamed: 5_level_0</th>\n",
+       "      <th>Persons</th>\n",
+       "      <th>Unnamed: 7_level_0</th>\n",
+       "      <th>Unnamed: 8_level_0</th>\n",
+       "      <th>Unnamed: 9_level_0</th>\n",
+       "      <th>Unnamed: 10_level_0</th>\n",
+       "      <th>...</th>\n",
+       "      <th>E12000001</th>\n",
+       "      <th>E12000002</th>\n",
+       "      <th>E12000003</th>\n",
+       "      <th>E12000004</th>\n",
+       "      <th>E12000005</th>\n",
+       "      <th>E12000006</th>\n",
+       "      <th>E12000007</th>\n",
+       "      <th>E12000008</th>\n",
+       "      <th>E12000009</th>\n",
+       "      <th>W92000004</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 1_level_1</th>\n",
+       "      <th>Unnamed: 2_level_1</th>\n",
+       "      <th>Unnamed: 3_level_1</th>\n",
+       "      <th>Deaths by underlying cause</th>\n",
+       "      <th>All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS)</th>\n",
+       "      <th>Deaths by age group</th>\n",
+       "      <th>Under 1 year</th>\n",
+       "      <th>01-14</th>\n",
+       "      <th>15-44</th>\n",
+       "      <th>45-64</th>\n",
+       "      <th>...</th>\n",
+       "      <th>North East</th>\n",
+       "      <th>North West</th>\n",
+       "      <th>Yorkshire and The Humber</th>\n",
+       "      <th>East Midlands</th>\n",
+       "      <th>West Midlands</th>\n",
+       "      <th>East</th>\n",
+       "      <th>London</th>\n",
+       "      <th>South East</th>\n",
+       "      <th>South West</th>\n",
+       "      <th>Wales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2015-01-02</td>\n",
+       "      <td>12286</td>\n",
+       "      <td>11837.6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2418</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>56</td>\n",
+       "      <td>16</td>\n",
+       "      <td>198</td>\n",
+       "      <td>1229</td>\n",
+       "      <td>...</td>\n",
+       "      <td>699</td>\n",
+       "      <td>1718</td>\n",
+       "      <td>1261</td>\n",
+       "      <td>1012</td>\n",
+       "      <td>1296</td>\n",
+       "      <td>1221</td>\n",
+       "      <td>1233</td>\n",
+       "      <td>1811</td>\n",
+       "      <td>1272</td>\n",
+       "      <td>725</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>2015-01-09</td>\n",
+       "      <td>16237</td>\n",
+       "      <td>12277.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3521</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80</td>\n",
+       "      <td>28</td>\n",
+       "      <td>298</td>\n",
+       "      <td>1552</td>\n",
+       "      <td>...</td>\n",
+       "      <td>753</td>\n",
+       "      <td>2282</td>\n",
+       "      <td>1607</td>\n",
+       "      <td>1328</td>\n",
+       "      <td>1713</td>\n",
+       "      <td>1712</td>\n",
+       "      <td>1549</td>\n",
+       "      <td>2525</td>\n",
+       "      <td>1697</td>\n",
+       "      <td>1031</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>2015-01-16</td>\n",
+       "      <td>14866</td>\n",
+       "      <td>11145.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3251</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>46</td>\n",
+       "      <td>28</td>\n",
+       "      <td>316</td>\n",
+       "      <td>1393</td>\n",
+       "      <td>...</td>\n",
+       "      <td>799</td>\n",
+       "      <td>1968</td>\n",
+       "      <td>1442</td>\n",
+       "      <td>1286</td>\n",
+       "      <td>1542</td>\n",
+       "      <td>1657</td>\n",
+       "      <td>1275</td>\n",
+       "      <td>2427</td>\n",
+       "      <td>1498</td>\n",
+       "      <td>936</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>2015-01-23</td>\n",
+       "      <td>13934</td>\n",
+       "      <td>10714.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2734</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>61</td>\n",
+       "      <td>20</td>\n",
+       "      <td>318</td>\n",
+       "      <td>1387</td>\n",
+       "      <td>...</td>\n",
+       "      <td>798</td>\n",
+       "      <td>1806</td>\n",
+       "      <td>1386</td>\n",
+       "      <td>1206</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>1565</td>\n",
+       "      <td>1220</td>\n",
+       "      <td>2196</td>\n",
+       "      <td>1510</td>\n",
+       "      <td>828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>2015-01-30</td>\n",
+       "      <td>12900</td>\n",
+       "      <td>10492.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2278</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>66</td>\n",
+       "      <td>20</td>\n",
+       "      <td>329</td>\n",
+       "      <td>1368</td>\n",
+       "      <td>...</td>\n",
+       "      <td>717</td>\n",
+       "      <td>1669</td>\n",
+       "      <td>1220</td>\n",
+       "      <td>1119</td>\n",
+       "      <td>1395</td>\n",
+       "      <td>1450</td>\n",
+       "      <td>1119</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>1398</td>\n",
+       "      <td>801</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows Ã— 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Week number         Week ended Total deaths, all ages  \\\n",
+       "            Unnamed: 1_level_1     Unnamed: 2_level_1   \n",
+       "1                   2015-01-02                  12286   \n",
+       "2                   2015-01-09                  16237   \n",
+       "3                   2015-01-16                  14866   \n",
+       "4                   2015-01-23                  13934   \n",
+       "5                   2015-01-30                  12900   \n",
+       "\n",
+       "Week number Total deaths: average of corresponding         Unnamed: 4_level_0  \\\n",
+       "                                Unnamed: 3_level_1 Deaths by underlying cause   \n",
+       "1                                          11837.6                        NaN   \n",
+       "2                                          12277.0                        NaN   \n",
+       "3                                          11145.0                        NaN   \n",
+       "4                                          10714.0                        NaN   \n",
+       "5                                          10492.0                        NaN   \n",
+       "\n",
+       "Week number                                              Unnamed: 5_level_0  \\\n",
+       "            All respiratory diseases (ICD-10 J00-J99)\\nICD-10 v 2013 (IRIS)   \n",
+       "1                                                         2418                \n",
+       "2                                                         3521                \n",
+       "3                                                         3251                \n",
+       "4                                                         2734                \n",
+       "5                                                         2278                \n",
+       "\n",
+       "Week number             Persons Unnamed: 7_level_0 Unnamed: 8_level_0  \\\n",
+       "            Deaths by age group       Under 1 year              01-14   \n",
+       "1                           NaN                 56                 16   \n",
+       "2                           NaN                 80                 28   \n",
+       "3                           NaN                 46                 28   \n",
+       "4                           NaN                 61                 20   \n",
+       "5                           NaN                 66                 20   \n",
+       "\n",
+       "Week number Unnamed: 9_level_0 Unnamed: 10_level_0  ...  E12000001  E12000002  \\\n",
+       "                         15-44               45-64  ... North East North West   \n",
+       "1                          198                1229  ...        699       1718   \n",
+       "2                          298                1552  ...        753       2282   \n",
+       "3                          316                1393  ...        799       1968   \n",
+       "4                          318                1387  ...        798       1806   \n",
+       "5                          329                1368  ...        717       1669   \n",
+       "\n",
+       "Week number                E12000003     E12000004     E12000005 E12000006  \\\n",
+       "            Yorkshire and The Humber East Midlands West Midlands      East   \n",
+       "1                               1261          1012          1296      1221   \n",
+       "2                               1607          1328          1713      1712   \n",
+       "3                               1442          1286          1542      1657   \n",
+       "4                               1386          1206          1400      1565   \n",
+       "5                               1220          1119          1395      1450   \n",
+       "\n",
+       "Week number E12000007  E12000008  E12000009 W92000004  \n",
+       "               London South East South West     Wales  \n",
+       "1                1233       1811       1272       725  \n",
+       "2                1549       2525       1697      1031  \n",
+       "3                1275       2427       1498       936  \n",
+       "4                1220       2196       1510       828  \n",
+       "5                1119       1991       1398       801  \n",
+       "\n",
+       "[5 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data_2015 = pd.read_csv('uk-deaths-data/publishedweek2015.csv', \n",
+    "                       parse_dates=[1], dayfirst=True,\n",
+    "                      index_col=0,\n",
+    "                      header=[0, 1])\n",
+    "raw_data_2015.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "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>total_2020</th>\n",
+       "      <th>total_previous</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>12254</td>\n",
+       "      <td>12175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>14058</td>\n",
+       "      <td>13822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>12990</td>\n",
+       "      <td>13216</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>11856</td>\n",
+       "      <td>12760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>11612</td>\n",
+       "      <td>12206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>10986</td>\n",
+       "      <td>11925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>10944</td>\n",
+       "      <td>11627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>10841</td>\n",
+       "      <td>11548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>10816</td>\n",
+       "      <td>11183</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>10895</td>\n",
+       "      <td>11498</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>11019</td>\n",
+       "      <td>11205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>10645</td>\n",
+       "      <td>10573</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13</td>\n",
+       "      <td>11141</td>\n",
+       "      <td>10130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14</td>\n",
+       "      <td>16387</td>\n",
+       "      <td>10305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>15</td>\n",
+       "      <td>18516</td>\n",
+       "      <td>10520</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>16</td>\n",
+       "      <td>22351</td>\n",
+       "      <td>10497</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>17</td>\n",
+       "      <td>21997</td>\n",
+       "      <td>10458</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    total_2020  total_previous\n",
+       "1        12254           12175\n",
+       "2        14058           13822\n",
+       "3        12990           13216\n",
+       "4        11856           12760\n",
+       "5        11612           12206\n",
+       "6        10986           11925\n",
+       "7        10944           11627\n",
+       "8        10841           11548\n",
+       "9        10816           11183\n",
+       "10       10895           11498\n",
+       "11       11019           11205\n",
+       "12       10645           10573\n",
+       "13       11141           10130\n",
+       "14       16387           10305\n",
+       "15       18516           10520\n",
+       "16       22351           10497\n",
+       "17       21997           10458"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "deaths_headlines = raw_data_2020.iloc[:, [1, 2]]\n",
+    "deaths_headlines.columns = ['total_2020', 'total_previous']\n",
+    "deaths_headlines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "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>total_2019</th>\n",
+       "      <th>total_previous</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>10955</td>\n",
+       "      <td>12273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>12609</td>\n",
+       "      <td>13670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>11860</td>\n",
+       "      <td>13056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>11740</td>\n",
+       "      <td>12486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>11297</td>\n",
+       "      <td>11998</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   total_2019  total_previous\n",
+       "1       10955           12273\n",
+       "2       12609           13670\n",
+       "3       11860           13056\n",
+       "4       11740           12486\n",
+       "5       11297           11998"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dh19 = raw_data_2019.iloc[:, [1, 2]]\n",
+    "dh19.columns = ['total_2019', 'total_previous']\n",
+    "dh19.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "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>total_2018</th>\n",
+       "      <th>total_previous</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>12723</td>\n",
+       "      <td>12079</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>15050</td>\n",
+       "      <td>13167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>14256</td>\n",
+       "      <td>12418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>13935</td>\n",
+       "      <td>11954</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>13285</td>\n",
+       "      <td>11605</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   total_2018  total_previous\n",
+       "1       12723           12079\n",
+       "2       15050           13167\n",
+       "3       14256           12418\n",
+       "4       13935           11954\n",
+       "5       13285           11605"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dh18 = raw_data_2018.iloc[:, [1, 2]]\n",
+    "dh18.columns = ['total_2018', 'total_previous']\n",
+    "dh18.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "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>total_2017</th>\n",
+       "      <th>total_previous</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>11991</td>\n",
+       "      <td>11782</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>13715</td>\n",
+       "      <td>12692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>13610</td>\n",
+       "      <td>11773</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>12877</td>\n",
+       "      <td>11441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>12485</td>\n",
+       "      <td>11128</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   total_2017  total_previous\n",
+       "1       11991           11782\n",
+       "2       13715           12692\n",
+       "3       13610           11773\n",
+       "4       12877           11441\n",
+       "5       12485           11128"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dh17 = raw_data_2017.iloc[:, [1, 2]]\n",
+    "dh17.columns = ['total_2017', 'total_previous']\n",
+    "dh17.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "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>total_2016</th>\n",
+       "      <th>total_previous</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>13045</td>\n",
+       "      <td>11701.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>11501</td>\n",
+       "      <td>13016.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>11473</td>\n",
+       "      <td>11765.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>11317</td>\n",
+       "      <td>11289.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>11052</td>\n",
+       "      <td>10965.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   total_2016  total_previous\n",
+       "1       13045         11701.4\n",
+       "2       11501         13016.4\n",
+       "3       11473         11765.4\n",
+       "4       11317         11289.0\n",
+       "5       11052         10965.6"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dh16 = raw_data_2016.iloc[:, [1, 2]]\n",
+    "dh16.columns = ['total_2016', 'total_previous']\n",
+    "dh16.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "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>total_2015</th>\n",
+       "      <th>total_previous</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>12286</td>\n",
+       "      <td>11837.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>16237</td>\n",
+       "      <td>12277.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>14866</td>\n",
+       "      <td>11145.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>13934</td>\n",
+       "      <td>10714.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>12900</td>\n",
+       "      <td>10492.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   total_2015  total_previous\n",
+       "1       12286         11837.6\n",
+       "2       16237         12277.0\n",
+       "3       14866         11145.0\n",
+       "4       13934         10714.0\n",
+       "5       12900         10492.0"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dh15 = raw_data_2015.iloc[:, [1, 2]]\n",
+    "dh15.columns = ['total_2015', 'total_previous']\n",
+    "dh15.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "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>total_2020</th>\n",
+       "      <th>total_previous</th>\n",
+       "      <th>total_2019</th>\n",
+       "      <th>total_2018</th>\n",
+       "      <th>total_2017</th>\n",
+       "      <th>total_2016</th>\n",
+       "      <th>total_2015</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>12254.0</td>\n",
+       "      <td>12175.0</td>\n",
+       "      <td>10955.0</td>\n",
+       "      <td>12723.0</td>\n",
+       "      <td>11991.0</td>\n",
+       "      <td>13045.0</td>\n",
+       "      <td>12286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>14058.0</td>\n",
+       "      <td>13822.0</td>\n",
+       "      <td>12609.0</td>\n",
+       "      <td>15050.0</td>\n",
+       "      <td>13715.0</td>\n",
+       "      <td>11501.0</td>\n",
+       "      <td>16237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>12990.0</td>\n",
+       "      <td>13216.0</td>\n",
+       "      <td>11860.0</td>\n",
+       "      <td>14256.0</td>\n",
+       "      <td>13610.0</td>\n",
+       "      <td>11473.0</td>\n",
+       "      <td>14866</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>11856.0</td>\n",
+       "      <td>12760.0</td>\n",
+       "      <td>11740.0</td>\n",
+       "      <td>13935.0</td>\n",
+       "      <td>12877.0</td>\n",
+       "      <td>11317.0</td>\n",
+       "      <td>13934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>11612.0</td>\n",
+       "      <td>12206.0</td>\n",
+       "      <td>11297.0</td>\n",
+       "      <td>13285.0</td>\n",
+       "      <td>12485.0</td>\n",
+       "      <td>11052.0</td>\n",
+       "      <td>12900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>10986.0</td>\n",
+       "      <td>11925.0</td>\n",
+       "      <td>11660.0</td>\n",
+       "      <td>12495.0</td>\n",
+       "      <td>12269.0</td>\n",
+       "      <td>11170.0</td>\n",
+       "      <td>12039</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>10944.0</td>\n",
+       "      <td>11627.0</td>\n",
+       "      <td>11824.0</td>\n",
+       "      <td>12246.0</td>\n",
+       "      <td>11644.0</td>\n",
+       "      <td>10590.0</td>\n",
+       "      <td>11822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>10841.0</td>\n",
+       "      <td>11548.0</td>\n",
+       "      <td>11295.0</td>\n",
+       "      <td>12142.0</td>\n",
+       "      <td>11794.0</td>\n",
+       "      <td>11056.0</td>\n",
+       "      <td>11434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>10816.0</td>\n",
+       "      <td>11183.0</td>\n",
+       "      <td>11044.0</td>\n",
+       "      <td>10854.0</td>\n",
+       "      <td>11248.0</td>\n",
+       "      <td>11285.0</td>\n",
+       "      <td>11472</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>10895.0</td>\n",
+       "      <td>11498.0</td>\n",
+       "      <td>10898.0</td>\n",
+       "      <td>12997.0</td>\n",
+       "      <td>11077.0</td>\n",
+       "      <td>11010.0</td>\n",
+       "      <td>11469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>11019.0</td>\n",
+       "      <td>11205.0</td>\n",
+       "      <td>10567.0</td>\n",
+       "      <td>12788.0</td>\n",
+       "      <td>10697.0</td>\n",
+       "      <td>11022.0</td>\n",
+       "      <td>10951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>10645.0</td>\n",
+       "      <td>10573.0</td>\n",
+       "      <td>10402.0</td>\n",
+       "      <td>11913.0</td>\n",
+       "      <td>10325.0</td>\n",
+       "      <td>9635.0</td>\n",
+       "      <td>10568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13</td>\n",
+       "      <td>11141.0</td>\n",
+       "      <td>10130.0</td>\n",
+       "      <td>9867.0</td>\n",
+       "      <td>9941.0</td>\n",
+       "      <td>10027.0</td>\n",
+       "      <td>10286.0</td>\n",
+       "      <td>10493</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14</td>\n",
+       "      <td>16387.0</td>\n",
+       "      <td>10305.0</td>\n",
+       "      <td>10126.0</td>\n",
+       "      <td>10794.0</td>\n",
+       "      <td>9939.0</td>\n",
+       "      <td>11599.0</td>\n",
+       "      <td>9062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>15</td>\n",
+       "      <td>18516.0</td>\n",
+       "      <td>10520.0</td>\n",
+       "      <td>10291.0</td>\n",
+       "      <td>12301.0</td>\n",
+       "      <td>8493.0</td>\n",
+       "      <td>11417.0</td>\n",
+       "      <td>10089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>16</td>\n",
+       "      <td>22351.0</td>\n",
+       "      <td>10497.0</td>\n",
+       "      <td>9025.0</td>\n",
+       "      <td>11223.0</td>\n",
+       "      <td>9644.0</td>\n",
+       "      <td>10925.0</td>\n",
+       "      <td>11639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>17</td>\n",
+       "      <td>21997.0</td>\n",
+       "      <td>10458.0</td>\n",
+       "      <td>10059.0</td>\n",
+       "      <td>10306.0</td>\n",
+       "      <td>10908.0</td>\n",
+       "      <td>10413.0</td>\n",
+       "      <td>10599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>18</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11207.0</td>\n",
+       "      <td>10153.0</td>\n",
+       "      <td>9064.0</td>\n",
+       "      <td>9137.0</td>\n",
+       "      <td>10134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>19</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9055.0</td>\n",
+       "      <td>8624.0</td>\n",
+       "      <td>10693.0</td>\n",
+       "      <td>10637.0</td>\n",
+       "      <td>8862</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>20</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10272.0</td>\n",
+       "      <td>10141.0</td>\n",
+       "      <td>10288.0</td>\n",
+       "      <td>9953.0</td>\n",
+       "      <td>10290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>21</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10284.0</td>\n",
+       "      <td>9636.0</td>\n",
+       "      <td>10040.0</td>\n",
+       "      <td>9739.0</td>\n",
+       "      <td>10005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>22</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8260.0</td>\n",
+       "      <td>8147.0</td>\n",
+       "      <td>8332.0</td>\n",
+       "      <td>7909.0</td>\n",
+       "      <td>8213</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>23</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10140.0</td>\n",
+       "      <td>9950.0</td>\n",
+       "      <td>9766.0</td>\n",
+       "      <td>9873.0</td>\n",
+       "      <td>10157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>24</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9445.0</td>\n",
+       "      <td>9343.0</td>\n",
+       "      <td>9367.0</td>\n",
+       "      <td>9386.0</td>\n",
+       "      <td>9548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>25</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9458.0</td>\n",
+       "      <td>9256.0</td>\n",
+       "      <td>9627.0</td>\n",
+       "      <td>9365.0</td>\n",
+       "      <td>9312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>26</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9511.0</td>\n",
+       "      <td>9212.0</td>\n",
+       "      <td>9334.0</td>\n",
+       "      <td>9228.0</td>\n",
+       "      <td>9190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>27</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9062.0</td>\n",
+       "      <td>9258.0</td>\n",
+       "      <td>9263.0</td>\n",
+       "      <td>9138.0</td>\n",
+       "      <td>9205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>28</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9179.0</td>\n",
+       "      <td>9293.0</td>\n",
+       "      <td>9376.0</td>\n",
+       "      <td>9388.0</td>\n",
+       "      <td>9015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>29</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9080.0</td>\n",
+       "      <td>9127.0</td>\n",
+       "      <td>9113.0</td>\n",
+       "      <td>9350.0</td>\n",
+       "      <td>8802</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>30</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9112.0</td>\n",
+       "      <td>9141.0</td>\n",
+       "      <td>8882.0</td>\n",
+       "      <td>9335.0</td>\n",
+       "      <td>8791</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9271.0</td>\n",
+       "      <td>9161.0</td>\n",
+       "      <td>8941.0</td>\n",
+       "      <td>9182.0</td>\n",
+       "      <td>8617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>32</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9122.0</td>\n",
+       "      <td>9319.0</td>\n",
+       "      <td>9038.0</td>\n",
+       "      <td>9172.0</td>\n",
+       "      <td>8862</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>33</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9093.0</td>\n",
+       "      <td>8830.0</td>\n",
+       "      <td>9299.0</td>\n",
+       "      <td>9070.0</td>\n",
+       "      <td>9148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>34</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8994.0</td>\n",
+       "      <td>8978.0</td>\n",
+       "      <td>9382.0</td>\n",
+       "      <td>9319.0</td>\n",
+       "      <td>9121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>35</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8242.0</td>\n",
+       "      <td>7865.0</td>\n",
+       "      <td>8149.0</td>\n",
+       "      <td>7923.0</td>\n",
+       "      <td>9026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>36</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9695.0</td>\n",
+       "      <td>9445.0</td>\n",
+       "      <td>9497.0</td>\n",
+       "      <td>9399.0</td>\n",
+       "      <td>7878</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>37</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9513.0</td>\n",
+       "      <td>9191.0</td>\n",
+       "      <td>9454.0</td>\n",
+       "      <td>9124.0</td>\n",
+       "      <td>9258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>38</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9440.0</td>\n",
+       "      <td>9305.0</td>\n",
+       "      <td>9534.0</td>\n",
+       "      <td>8945.0</td>\n",
+       "      <td>9097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>39</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9517.0</td>\n",
+       "      <td>9150.0</td>\n",
+       "      <td>9689.0</td>\n",
+       "      <td>8994.0</td>\n",
+       "      <td>9529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>40</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9799.0</td>\n",
+       "      <td>9503.0</td>\n",
+       "      <td>9778.0</td>\n",
+       "      <td>9291.0</td>\n",
+       "      <td>9410</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>41</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9973.0</td>\n",
+       "      <td>9649.0</td>\n",
+       "      <td>9940.0</td>\n",
+       "      <td>9719.0</td>\n",
+       "      <td>9776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>42</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10156.0</td>\n",
+       "      <td>9864.0</td>\n",
+       "      <td>10031.0</td>\n",
+       "      <td>9768.0</td>\n",
+       "      <td>9511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>43</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10021.0</td>\n",
+       "      <td>9603.0</td>\n",
+       "      <td>9739.0</td>\n",
+       "      <td>9724.0</td>\n",
+       "      <td>9711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>44</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10164.0</td>\n",
+       "      <td>9529.0</td>\n",
+       "      <td>9984.0</td>\n",
+       "      <td>10152.0</td>\n",
+       "      <td>9618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>45</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10697.0</td>\n",
+       "      <td>10151.0</td>\n",
+       "      <td>10346.0</td>\n",
+       "      <td>10470.0</td>\n",
+       "      <td>9994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>46</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10650.0</td>\n",
+       "      <td>10193.0</td>\n",
+       "      <td>10275.0</td>\n",
+       "      <td>10694.0</td>\n",
+       "      <td>9938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>47</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10882.0</td>\n",
+       "      <td>9957.0</td>\n",
+       "      <td>10621.0</td>\n",
+       "      <td>10603.0</td>\n",
+       "      <td>9830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>48</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10958.0</td>\n",
+       "      <td>10033.0</td>\n",
+       "      <td>10538.0</td>\n",
+       "      <td>10439.0</td>\n",
+       "      <td>9822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>49</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10816.0</td>\n",
+       "      <td>10287.0</td>\n",
+       "      <td>10781.0</td>\n",
+       "      <td>11223.0</td>\n",
+       "      <td>10365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>50</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11188.0</td>\n",
+       "      <td>10550.0</td>\n",
+       "      <td>11217.0</td>\n",
+       "      <td>10533.0</td>\n",
+       "      <td>10269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>51</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11926.0</td>\n",
+       "      <td>11116.0</td>\n",
+       "      <td>12517.0</td>\n",
+       "      <td>11493.0</td>\n",
+       "      <td>10689</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>52</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7533.0</td>\n",
+       "      <td>7131.0</td>\n",
+       "      <td>8487.0</td>\n",
+       "      <td>8003.0</td>\n",
+       "      <td>8630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>53</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>7524</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    total_2020  total_previous  total_2019  total_2018  total_2017  \\\n",
+       "1      12254.0         12175.0     10955.0     12723.0     11991.0   \n",
+       "2      14058.0         13822.0     12609.0     15050.0     13715.0   \n",
+       "3      12990.0         13216.0     11860.0     14256.0     13610.0   \n",
+       "4      11856.0         12760.0     11740.0     13935.0     12877.0   \n",
+       "5      11612.0         12206.0     11297.0     13285.0     12485.0   \n",
+       "6      10986.0         11925.0     11660.0     12495.0     12269.0   \n",
+       "7      10944.0         11627.0     11824.0     12246.0     11644.0   \n",
+       "8      10841.0         11548.0     11295.0     12142.0     11794.0   \n",
+       "9      10816.0         11183.0     11044.0     10854.0     11248.0   \n",
+       "10     10895.0         11498.0     10898.0     12997.0     11077.0   \n",
+       "11     11019.0         11205.0     10567.0     12788.0     10697.0   \n",
+       "12     10645.0         10573.0     10402.0     11913.0     10325.0   \n",
+       "13     11141.0         10130.0      9867.0      9941.0     10027.0   \n",
+       "14     16387.0         10305.0     10126.0     10794.0      9939.0   \n",
+       "15     18516.0         10520.0     10291.0     12301.0      8493.0   \n",
+       "16     22351.0         10497.0      9025.0     11223.0      9644.0   \n",
+       "17     21997.0         10458.0     10059.0     10306.0     10908.0   \n",
+       "18         NaN             NaN     11207.0     10153.0      9064.0   \n",
+       "19         NaN             NaN      9055.0      8624.0     10693.0   \n",
+       "20         NaN             NaN     10272.0     10141.0     10288.0   \n",
+       "21         NaN             NaN     10284.0      9636.0     10040.0   \n",
+       "22         NaN             NaN      8260.0      8147.0      8332.0   \n",
+       "23         NaN             NaN     10140.0      9950.0      9766.0   \n",
+       "24         NaN             NaN      9445.0      9343.0      9367.0   \n",
+       "25         NaN             NaN      9458.0      9256.0      9627.0   \n",
+       "26         NaN             NaN      9511.0      9212.0      9334.0   \n",
+       "27         NaN             NaN      9062.0      9258.0      9263.0   \n",
+       "28         NaN             NaN      9179.0      9293.0      9376.0   \n",
+       "29         NaN             NaN      9080.0      9127.0      9113.0   \n",
+       "30         NaN             NaN      9112.0      9141.0      8882.0   \n",
+       "31         NaN             NaN      9271.0      9161.0      8941.0   \n",
+       "32         NaN             NaN      9122.0      9319.0      9038.0   \n",
+       "33         NaN             NaN      9093.0      8830.0      9299.0   \n",
+       "34         NaN             NaN      8994.0      8978.0      9382.0   \n",
+       "35         NaN             NaN      8242.0      7865.0      8149.0   \n",
+       "36         NaN             NaN      9695.0      9445.0      9497.0   \n",
+       "37         NaN             NaN      9513.0      9191.0      9454.0   \n",
+       "38         NaN             NaN      9440.0      9305.0      9534.0   \n",
+       "39         NaN             NaN      9517.0      9150.0      9689.0   \n",
+       "40         NaN             NaN      9799.0      9503.0      9778.0   \n",
+       "41         NaN             NaN      9973.0      9649.0      9940.0   \n",
+       "42         NaN             NaN     10156.0      9864.0     10031.0   \n",
+       "43         NaN             NaN     10021.0      9603.0      9739.0   \n",
+       "44         NaN             NaN     10164.0      9529.0      9984.0   \n",
+       "45         NaN             NaN     10697.0     10151.0     10346.0   \n",
+       "46         NaN             NaN     10650.0     10193.0     10275.0   \n",
+       "47         NaN             NaN     10882.0      9957.0     10621.0   \n",
+       "48         NaN             NaN     10958.0     10033.0     10538.0   \n",
+       "49         NaN             NaN     10816.0     10287.0     10781.0   \n",
+       "50         NaN             NaN     11188.0     10550.0     11217.0   \n",
+       "51         NaN             NaN     11926.0     11116.0     12517.0   \n",
+       "52         NaN             NaN      7533.0      7131.0      8487.0   \n",
+       "53         NaN             NaN         NaN         NaN         NaN   \n",
+       "\n",
+       "    total_2016  total_2015  \n",
+       "1      13045.0       12286  \n",
+       "2      11501.0       16237  \n",
+       "3      11473.0       14866  \n",
+       "4      11317.0       13934  \n",
+       "5      11052.0       12900  \n",
+       "6      11170.0       12039  \n",
+       "7      10590.0       11822  \n",
+       "8      11056.0       11434  \n",
+       "9      11285.0       11472  \n",
+       "10     11010.0       11469  \n",
+       "11     11022.0       10951  \n",
+       "12      9635.0       10568  \n",
+       "13     10286.0       10493  \n",
+       "14     11599.0        9062  \n",
+       "15     11417.0       10089  \n",
+       "16     10925.0       11639  \n",
+       "17     10413.0       10599  \n",
+       "18      9137.0       10134  \n",
+       "19     10637.0        8862  \n",
+       "20      9953.0       10290  \n",
+       "21      9739.0       10005  \n",
+       "22      7909.0        8213  \n",
+       "23      9873.0       10157  \n",
+       "24      9386.0        9548  \n",
+       "25      9365.0        9312  \n",
+       "26      9228.0        9190  \n",
+       "27      9138.0        9205  \n",
+       "28      9388.0        9015  \n",
+       "29      9350.0        8802  \n",
+       "30      9335.0        8791  \n",
+       "31      9182.0        8617  \n",
+       "32      9172.0        8862  \n",
+       "33      9070.0        9148  \n",
+       "34      9319.0        9121  \n",
+       "35      7923.0        9026  \n",
+       "36      9399.0        7878  \n",
+       "37      9124.0        9258  \n",
+       "38      8945.0        9097  \n",
+       "39      8994.0        9529  \n",
+       "40      9291.0        9410  \n",
+       "41      9719.0        9776  \n",
+       "42      9768.0        9511  \n",
+       "43      9724.0        9711  \n",
+       "44     10152.0        9618  \n",
+       "45     10470.0        9994  \n",
+       "46     10694.0        9938  \n",
+       "47     10603.0        9830  \n",
+       "48     10439.0        9822  \n",
+       "49     11223.0       10365  \n",
+       "50     10533.0       10269  \n",
+       "51     11493.0       10689  \n",
+       "52      8003.0        8630  \n",
+       "53         NaN        7524  "
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "deaths_headlines = deaths_headlines.merge(dh19['total_2019'], how='outer', left_index=True, right_index=True)\n",
+    "deaths_headlines = deaths_headlines.merge(dh18['total_2018'], how='outer', left_index=True, right_index=True)\n",
+    "deaths_headlines = deaths_headlines.merge(dh17['total_2017'], how='outer', left_index=True, right_index=True)\n",
+    "deaths_headlines = deaths_headlines.merge(dh16['total_2016'], how='outer', left_index=True, right_index=True)\n",
+    "deaths_headlines = deaths_headlines.merge(dh15['total_2015'], how='outer', left_index=True, right_index=True)\n",
+    "deaths_headlines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc74803e650>"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "deaths_headlines['total_2020 total_2019 total_2018 total_2017 total_2016 total_2015'.split()].plot(figsize=(10, 8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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2FsBpxljzFXfu6vIYgGIA4vxCcJyIeB0rjrsGeYOoJABTTCbTzUQ0XSKR6BITE5GVlaWcMGFCeGpqKqWkpECn013xqsaSkhIoFArExMT4pf8jdfToUaSlpSEsLEyU9vPy8pCcnIyQkBCUlZXh7NmzKC4udhQWFrYVFBS4bTabQy6Xl7S3t+9paWk5ACCPMdYoSmcDjIii4dmM+iUAT/ARK+56w0esOO4q5w2iUohoitFonAtgWnR0dFRqairl5OSET5s2TTl58uTLTYtdMYVCgd7e3oAdfyj+GrEaLbvdjtDQUEilUiQlJSEpKQm33nqrHECkr38lJSXmvLy82fv37289cOBAj8lkcslksvMdHR2fNjc3+4KtBtGehP+sB/AULplm5YSXl5enl8lkbwLIwPVRt9IN4KTT6fzOlClT6sXqBA+sOO4qQ0RRAHJMJtN8ADfFxMREJiUlBU+bNk0xd+7coMmTJ0Or1QraJ4VCgba2NkHb7O/SHCshMcbAGLtsYCeRSJCSkoKUlBQsWbIkAvD0+fz586a8vLxsb7DVazQaXXK5/HxHR8cnzc3N/w/A54wx8SLWESKi2wDUM8byiGiO2P253slksjeNRmOaTqdrlkgk1/z0lNvtJpvNll5bW/smgDvE6gcPrDhujPMGUrMtFssCl8s1a8KECSHz588PmTt3buiUKVOg0WhQW1uL5uZmpKWlidJHhUKBnp4eUdoGxA2senp6oFAoRvw4iUSC5ORkJCcnY/HixX3B1oULF4xHjhyZ8e9///uH+/fvd1sslqqurq4Pm5ubd2HsB1ozAdxBRLcCUAJQEdEWxtj9IvfrepVxvQRVACCRSJhOp2utra3NELMfPLDiuDHGF0iZzeYFbrd71oQJE0JuvfXW4Hnz5oXddNNNCA4O/tJj1Go1Lly4IHxnvcQOrFwul2iBVUdHh99yuyQSiW8qkZYuXRoJAFVVVbo9e/ZM3LFjxw/279/vjo6Orurs7NzeL9AaM+UfGGM/BfBTAPCOWD3JgypRSa6XoMrH+3xFnfbkgRV31RhkE2QtgPcBxAEoA3DP1bYCy1snaq53RGp2RkbGFwKpkJCQIY+hVCrR09MDxpgo2+sEBQWJnmMlVmDly68KFIvFgqVLlw4UaP3wwIED7ujo6Kre3t5/22y27QCO8pIP3FhSUlIiX7p0abzNZpNLJBIsW7bM9vzzz9fX1dVJFyxYkFBVVaWwWCw927Ztu6DT6Vz5+fnK5cuXxxUVFYWsXLmy6mc/+1md71hr1qzRv/POOzrGGB544AHbqlWrRMujuhweWHFXm69ckuC7EsAnjLGXiWil9/LT4nRt+IjIGhYWtlClUj2QlJRkueuuu4K/8Y1vDDuQGkhoaKhfR09GQiKRQOwVxmLt12i326HT6QRr79JAq6SkRPfnP/856/jx4ysOHjzoio6OPlhVVfV/AD5mjHUI1rFLMMb2ANgjVvvc2CCXy/HKK69czM7O7mxubpZMmjQp/dZbb2174403oubMmdO+du3ac88884xx1apVxtdff71Kr9c7N2zYUPHBBx9o+h/n6NGjynfeeUd37NixYqVS6c7JyUlZsGBB64QJE8QbKh8ED6y4q92dAOZ4z78Nzz/yMRdYeVfu3aDT6e6TSqV3Tps2LXTp0qXqO++8Ux4bG+uXNjQaDZqbm0UrOQBAtBEzMdntdsTHx4vWvlwux/3334/ExMRIl8uFI0eO3PXXv/715u3bt/dER0dfaG5ufqezs3Orb+cAjhOS1Wp1WK1WBwBoNBp3YmJiV0VFRdCuXbvUubm5ZwBgxYoVjTk5OakAqiwWi9NisTi3bdum7n+cEydOBE+ePNkeHh7uBoCZM2e2v//+++oJEybUfalRkfHAiruaDLQJsoExVgMAjLEa7z5+YwIRhcAzxfdto9E4c8aMGbIlS5Zo582bR8MtvDkSGo0GNTU1otWSksvlcDgcCAoKEqV9sXR1dQ2Y9yaU+vp6pKamAvAUOJ0xYwZmzJihWr9+PUpKSnRbt27Nevfdd180m82tvb29HzQ2Nr4H4DgTe4iRu+6cOXMmqKioKCQnJ8fe2Ngo8wVcVqvV0dTUdNl4JCsrq+tnP/uZpba2VhoaGsp2794dMXHiRNFGZC+HB1bc1eRLmyCL3aFLEZEmODh4kVarXR4XF5d4xx13KBYtWqSaPn16wOssRUREoLi4OKBtXI4vgf16Cqzcbk86k1ijdIwx2O32QSvkJyUl4cknn1Q8+eSTuubmZt3OnTuf3rJly3eOHTvmsFgsn1ZXV28GkMsYcwnbc05o27Zti6mvrx9dnsEg9Hp955133lk5nPu2trZKFi5cmPjyyy9XarXaEecBTp48ufuxxx6rvfnmm1NCQkLc6enpnTLZ2AxhxmavOG4Ag2yCXEdEJu9olQmA4MmMRKSUSCTzY2Jink5MTEx48MEHQxYtWhScnJwsaD/kcjlcLpdoidy+wCoQo3FjVVdX16hz4vyhpaUFERERwwrsNBoN7rvvPrrvvvuiHA4H9u7de9///d//fWP37t09RqNxe11d3esACvlIFudvPT09NH/+/MRFixY1LVu2rAUAIiMjneXl5XKr1eooLy+Xa7Va51DHefzxxxsef/zxBgB45JFHLNHR0WOy9AgPrLirwmU2Qd4OYBmAl70/twnUHwk8JREetVgss+6++27lrbfeGh4XF4eUlBQhujCgiIgItLW1Qa1WD31nPxO75IIY7Ha7qDlt9fX10OtHPvstl8sxd+5czJ07V9PT04OdO3d+97XXXlt44sSJDq1W+3Zzc/NmxlhFALrMiWS4I0v+5na7sXjxYmtKSkr3iy++2JcPNW/evJaNGzdGrl27tnbjxo2Rt9xyS8tQx6qqqpJZLBbnuXPngnbs2KH+7LPPxtysBcADK+7qMdgmyEcB/JWIHgJQAWBRIDtBRBP0ev33jUbjXV/5ylcUK1as0M6aNQsSiQQOhwMHDx4UNbBSq9Vobm6+rgIrt9st6opAMQMrm82GuLi4KzqGQqHAXXfdRXfddVdUa2tr1N/+9rdnXn/99e+bzeb61tbW1zo7O/9ytZUw4caO3bt3h23dujUyOTm5a9y4cekAsHr16qrVq1fXLFiwINFqtUaZzeberVu3ngeAiooK2dSpU9M7OjqkRMQ2btxoKC4uPqnVat133HFHYktLi0wmk7H169dX6HS6MTmFzTdh5rghEFG0RqN5UKFQLB8/fnzYD37wg8j58+fTQNW2Dx8+jIyMDNE+bFtaWnDhwgVMnjxZ8LbFqv7udDpx+PBhZGdnC9ouABQWFiImJkbwLYQAwOFw4NChQ5g9e3ZAjn/x4kVs2bKle/Pmze1dXV2n6+rqfud0Ov/FGOsOSINXaKA6d+L2SHyFhYVlEydOvBb2nxyRwsLCqIkTJ8aJ1T4fseK4ARCRXCaT3aHX65/JysqK+f73v69ZtGiRTKPRXPZxFosFVVVVfau0hKZSqUTbs0/MESsxi4OKFUQ3NDQEdGPt6OhorFy5Urly5UrlyZMndZs3b87629/+1m02m3fW1NT8ijF2KmCNj96lde44TnDXw27XHDdsRGTV6/XrjEZj5fe///0/7tmzZ3J+fr7u4YcfHjKoAgCj0Yja2loBejowiUQCqVQKh0P4XU581d+FJuZ2NmKWl7DZbIIVJs3IyMC6devCy8vLdZs2bfrW7Nmz95jN5lPBwcHLiEgpSCc47irBAyvuukdEUqlUOt9isRyeOHHi0XXr1j1aVlZm+N3vfqca6co+uVwOpVIp2qgR4Mmzam1tFbzdoKCg62rEyul0BryExuU0NTUJPgUplUpx2223UW5ubtSRI0fSv/Wtb20ym81VRqPxDSJKErQzX+arc5dHRA+L3BfuOsYDK+66RUQarVb7rMFgKP/2t7+9Zffu3dMKCgp0S5culQ6UPzVcFosF1dXiFbn2JbALTSqV9tV1EpLL5RIlwOno6AjoHoFDta1UKkUN7EwmE5YsWRJUVlamfe211x6cOnXqQbPZfEQqlX7Du2pWaDMZY5MBfAPAD4koMMlnHDcEHlhx1x0iyjSbzX+Li4s7+8ILLzx/9uxZy+bNm9Xp6el+Ob5vOlCshSG+rW2uF2KNWImZXyXkNOBgqqqqYLFYIJfLsXDhQslnn32m+/jjj2984IEH3jUajeUajeZpIhJseWr/OncAfHXuOE5wPLDirgvkme+7xWQyFc6ePfuTt99+++4LFy5EPfbYYwqVSuXXtmQyGUJDQ9He3u7X4w6XbzNmMchkMjidQ9b586vrMbAabf0qfyovL8el+1ymp6fjrbfeUp8+fTr6+eefX221Ws+aTKZ3iMgayL4QUSgRhfvOw1Pn7mQg2+S4wfDAirumEZFUqVQuNhgM5+6999539+7dm5mbmxv1ta99LaC1j8xmM6qqqgJ2/MshIigUCnR3C78qXoyVgW63+7qaCnS73ejo6BC1flZzczOCg4OhVA6ctx4REYEnnnhCceHCBd1rr722NC0t7XOz2bydiPwzLPxlBgD7iagQwGcAdjDGdgWoLW4ESkpK5NOmTUtJSEgYn5SUNH7NmjV6AKirq5POmDEj2Wq1ZsyYMSPZZrNJASA/P1+ZlZU1LigoaPKqVasM/Y+1evVqfVJS0vjk5OTxt99+e3xnZ+eY3PGdB1bcNYmIFCqV6vsGg6H0oYce2piXl5f47rvvaoTaZsZgMKCuru66mw4UI4FdrFWBdrtdlMCqpaUFarVatKKogGe0ajiFSSUSCRYsWCA5depU1J/+9Kfbp06dmms2m/cSkV+n6RhjFxhjE72n8Yyxl/x5fG705HI5XnnllYsXLlw4dfTo0eLNmzfr8/LylC+88IJpzpw57eXl5SfnzJnTvmrVKiMA6PV654YNGypWrFhR1/84paWl8k2bNhkKCgqKzp07d8rlctGbb74pfAG5YeCBFTcqRCQlonwi+pf38s1EdIyIThLR20QkSo00IgqLiop6zmQyVTzyyCO/PnXqVMwf/vAHlcViEbQfMpkMYWFhoqzOAzyBVUvLkDtE+J1SqRR8pEysESu32w0xNoG12WyiTgM6HA60tLSMqIYWEWHu3Ln47LPPorZt2zZr7ty5/zabzceIaC6JGSFyAWe1Wh3Z2dmdAKDRaNyJiYldFRUVQbt27VKvWLGiEQBWrFjRuHPnTg0AWCwWZ05OTqdcLv/St1KXy0UdHR0Sh8OBrq4uSXR0tPB1ZYaBB1bcaD0GoBjo2zfvbQCLGWMZAMrh2bdPMESk1el0v7JYLOcfe+yx5/70pz/p165dGxoZGSlkN77AbDaLtjpQrJWBYk0FCj1i1dPTI2r9qkAWBh3KxYsXER0dPeoRs6lTp+Ljjz+O/OSTTyZ985vf/JvRaCxWKBTfFGklISegM2fOBBUVFYXk5OTYGxsbZVar1QF4gq+mpqbLfkuJj493/PCHP6yNj4/P1Ov1E8PDw10LFy4Ur67NZfDK69yIEVE0gPkAXgLwBIBIAD2MsbPeu+wG8FMAmwXoi8VgMLxotVrveuaZZyKWLVsmVygUOHLkCJqbmzGcop6BYjAYcObMGaSlpQk+beMr1skYE7RthUIheA0vMaYCxcqvcjgccLvduJJyIFeCMYaKigrcdNNNV3ystLQ0fPDBB5ry8nLNmjVrNv/rX/96JSwsbE1HR8c7jLExORJxNdu2bVtMfX19iD+PqdfrO4e7uXNra6tk4cKFiS+//HKlVqsdcV0Wm80m3bFjh7qkpOREZGSka/78+Qmvvfaa9gc/+EHTyHseWPwbAjca6wE8BcD3x9EAQE5Evr257gYQE8gOEFGUwWDYlJSUVLB+/frl58+fj3r44Yflvg+c5ORknDt3LpBdGJJUKoVKpRJlSg4QZ3WgGEnzYkwFirUiMNDb2AylubkZYWFhfg3srFYr3nzzzYjjx49bv/e97603GAwXQkND7+cjWNeOnp4emj9/fuKiRYuali1b1gIAkZGRzvLycjkAlJeXy7Va7WWXE3/44Yeq2NjYHrPZ7FQoFOyuu+5qOXjwoHgrOC6Dj1hxI0JEtwGoZ4zlEdEcAGCMMSJaDGAdESkAfAQgIGvuiSg0MjLy2ZiYmO/+/Oc/Vy9dulQ20PLKlBYAACAASURBVIeqVquF0+lEW1sb/F1OYSR804FijJz5EtiFDADEmAp0uVyQy+WCtmm32yHGNHN9fT2Ezhfsr7S0dFhJ66Oh1+vxm9/8Juypp54Ke+655/6wffv2F6RS6SNut/sjJtYqkGvIcEeW/M3tdmPx4sXWlJSU7hdffLEvIX3evHktGzdujFy7dm3txo0bI2+55ZbLfgONi4vrPXbsWFh7e7skNDTU/Z///Cd8ypQpnYF/BiPHvxFwIzUTwB3eneTfA3AzEW1hjB1ijM1ijN0IYC8Avw4XEZFcrVY/bjQazz/zzDNPnDt3LuqBBx4YMKjySU5ORklJiT+7MWJ6vR719fWirA4UI4H9esmxEmvEqrm5WfBtbHx6e3vR3t4e8Pb1ej02bdqkOnz4cNKdd975F6PReMzfqwg54ezevTts69atkfv37w8fN25c+rhx49Lff//9iNWrV9d8+umnKqvVmvHpp5+qVq9eXQMAFRUVMoPBkLlp0ybDunXrTAaDIbOpqUly8803d9x+++3NmZmZaampqePdbjc98cQTNrGf30D4iBU3Ioyxn8KTPwXviNWTjLH7iUjPGKv3jlg9DU/+1RUjIklwcPASg8Hw8kMPPaRZuXJlaHh4+LAeGxUVhdOnT6OzsxMhIX5NLRg2qVSKiIgIUT4QIyIiUFxcLGibMpkMLpdL0DbFmAoU43fKt42NWBtOV1ZWIjY2VrCcvbi4OPzjH//QHD9+XPPoo4/uNJlMhbW1tT9gjJ0WpAOcX8ybN8/OGMsb6LZDhw6dvfS62NhYZ11d3fGB7r9u3brqdevWibdf2DDxESvOX/6HiIoBHAfwIWPsP1dyMG+l9K8bDIYz999//x+OHz8e/dJLLw07qPIeA0lJSaKPWlksFlGKhcrlcrhcLlH27xOS0MnrvtFHoRckiLmNDWMMlZWViIkJaOrkgDIzM5Gbm6v961//+pXMzMz9ZrP5AyISbz6U44bAAytu1Bhjexhjt3nP/w9jLI0xlsoYW38lxyWiGw0GQ96dd9753qFDh5I2bdoUMdq6PUajEc3NzaJUIffR6XRoaGgQZTowIiJC8FV6EolE0FEroacCu7q6EBwcLFh7PmJuY9PQ0ICIiAjBc9n6mzVrFgoKCiKffPLJhYmJiScMBsNrRCTesl+OGwQPrLgxg4iiTSbT/5s1a9a/P/roo0n/+Mc/NPHx8Vd6TCQkJOD8+fN+6uXISSQSqNVqNDUJvypYjHpWQudZCT0VKEZ+ldjb2JSVlQUsaX0kWlpaMH36dDpz5ozmF7/4xXeio6PPREZGPi1iQWI1EX1ARKeJqJiIpovRD25s4TlWnOiIKCgyMvKZ+Pj4H77++uvaefPm+TXgt1gsOH/+PHp7e0Ur6uibDhR6JZlGo0FpaamgbfoCq4FykNxuN5xO54Ant9sNt9sNxhgYY33nfT8BT5BKRH0/iQhtbW1oaGhAT08PZDLZgCd/TtuJEViJuY1Nd3c3urq6oFarBW+7P8YYioqKkJGRAalUigcffFC+ZMkS3Zo1a57fvHnzw0S0nDG2V+BubQCwizF2NxEFARAnmZMbU3hgxYkqKCjo63q9/o1HHnkk6qmnngoJROFDiUSCuLg4lJaWIjU11e/HH46oqCicOnVK8IKdKpUqoNvqMMb6Pnh7enrQ29sLu92OM2fOQCaToaenBw7Hf2s9EhFkMhnkcvmXgp9LgyapVPqFAMrXXv/gy+Vywel0ore3F21tbX1BmsPh+ELQ1r99hUKBoKAgKBSKL51CQkKGnO6y2+2ClzwQcxqwoqJC0KT1wdTV1SEkJAQRERF91wUHB2Pt2rWhDz30UMKDDz74T6PReLCuru5hxlhNoPtDRCoAswF8GwAYY70AegPdLjf28cCKEwURxRgMhrduvvnmyZs2bdLExsYGtL2YmBjs3bsXiYmJouzvJpFIoNFo0NjYKGiBR4lEAqlUCofDMar8GF/g1NHRgc7OTnR1daGzsxOdnZ19AZNSqURwcHBfcBIaGoqgoCBYrVYoFAq/jxhdqrGxEfHx8cOqhO52u9Hb24uenp4vnNra2voCRF8gFhwcjJCQkL6fISEhCAsLE6Xqus1mQ0JCgqBtAp73v6qqCrNmzRK87f7cbjdOnz6NadOmDXh7YmIicnNztdu3b7/10UcfLdRqtb9tbm7+DWMsIPX0vBIA2AC8RUQTAeQBeIwxJmxVXm7M4YEVJyjvtN9zcXFx3//f//1fv0/7DUYqlSImJgbl5eVITEwUoskv8U0HCl05W61Wo7W19bLt+nJ47HY72tvbYbfbYbfb4Xa7oVQqERoaipCQEISHh8NgMPSN7AwUMFVVVQmaDzSS5HWJRAKlUgmlUjnkMbu7u/uCyfb2dtTV1cFut6OlpQXHjh1DWFgYwsPD+34qFIqABJAOhwOMMVGmsevr66HVakX5MtJfRUUFDAbDkIsG7rjjDsnXvvY13Zo1a57bvHnzd2Uy2XKn0xmo6UEZgMkAHmWMHSGiDQBWAng+QO1dlUpKSuRLly6Nt9lscolEgmXLltmef/75+rq6OumCBQsSqqqqFBaLpWfbtm0XdDqdKz8/X7l8+fK4oqKikJUrV1b97Gc/qwOAwsJCxb333tv3z/vixYuKp556qmrVqlX14j27gfHAihNMUFDQPL1evymQ036XExcXh3379iEuLk7wukcAEBkZiZMnTwq+is2XwO4LrBwOB9ra2tDS0oLW1ta+VYOhoaEIDw/vC57CwsJG9TopFApBE/UDUW5BIpH0jVJd2tb+/fsxZcqUvgC0trYWJSUl6O7uhlwuh1qtRkREBCIiIhAeHn7FwZaYZRbKysowbtw4Udr2cTgcKC0tRXZ29rDuP8D04KG6urrvBmB68CKAi4yxI97LH8ATWHH9yOVyvPLKKxezs7M7m5ubJZMmTUq/9dZb2954442oOXPmtK9du/bcM888Y1y1apXx9ddfr9Lr9c4NGzZUfPDBB19Y8Tlx4sSe06dPFwGA0+mE0WicuHjxYnH2CxsCD6y4gCOiWO+03yQhpv0GI5PJYDKZcPHiRVitVsHbl0gk0Gq1aGxsFOyD0ul09tUgam1thd1u7ytaGhERgcTERISHh/s1MLmWVwX6RuJ8056XjgL29vaitbUVLS0tfSNcvj0j1Wo1NBrNiIMtm80myjY2XV1d6O3t/UJOkxhKSkoQFxc34qnsftOD3/BOD67zTg/6ZYNnxlgtEVUSUSpj7AyAuQCK/HHsa4nVanVYrVYHAGg0GndiYmJXRUVF0K5du9S5ublnAGDFihWNOTk5qQCqLBaL02KxOLdt2zboaont27erYmNje1JSUsZkThsPrLiAISKJRqN5Ii4ubtXatWtDlyxZInp5j4SEBBw8eBAxMTGiVLC2WCy4ePFiwAKrrq4uNDU1oampqa/MglqthtPpRGpqKsLCwgKehCx0YCVkgdChVgQGBQVBp9N94f3tP0J49uxZtLe3Izg4GFqtFlqtFmq1+rJTbU1NTZgwYYJfn8dwlJeXi/IFpL+uri7U1dVh9uzZoz6Gb3rw5z//+TNvvvnmg0S0iDFW4KcuPgrgz94VgRcALPfTca9JZ86cCSoqKgrJycmxNzY2ynwBl9VqdTQ1NQ07HvnLX/6ivfvuuxsD19MrwwMrLiCIKEGv1//9nnvuSVyzZk14fn7+qBOo/cn3wVddXY3o6GjB24+MjMSJEyf8Nh3Y0dEBm82GxsZGtLW1QaFQIDIyEkajEWlpaX0f2Ha7fdCcKH+Ty+VfWAkYaEKutBxN4rpcLkdkZOQXSm10dnaiqakJ1dXVOHXqFIgIWq0WUVFRiIyM7Ps76ejoQEhIiOBfAtxuN2pqaq4ooPGH06dPIzU19Yqff3BwMF566aWwpUuXJt17772f6PX6P9pstme9K/lGzRug3XBFnRPItm3bYurr6/1aDkKv13cOd3Pn1tZWycKFCxNffvnlSq1WO+rtILq7u+njjz+O+O1vf3txtMcINB5YcX5FRBK1Wv3j+Pj4n/75z3+Omj7dUy8vMTERRUVFmDhxosg99PTlyJEjsFgsgi8hJyJERkaioaFhVMvne3t70dDQgPr6ejQ3NyM4OBg6nQ5JSUlQqVSDPh+NRoPm5maYTKYrfQpDEmNZvlBt2u12v4w2+vK3fMG9w+FAc3MzbDYbzp49C4lEAp1OB4fDIfhiB8BT2kCn04mSi+jT1taGzs5OGI1Gvx0zPT0d+fn52pdffvkHr7766l1EdDdjrNBvDXAD6unpofnz5ycuWrSoadmyZS0AEBkZ6SwvL5dbrVZHeXm5XKvVDmsF5wcffBCRnp7eGRMTE8gVn1eEB1bXECKSAvgcQBVj7DYimgvg1/BU2LcD+DZjLGAb5xFRvF6v//uiRYsSf/3rX6v6r+CJiYnBxYsX0dTUJPhmxJdSKpVQq9Woq6vz6z/t4bJYLKioqBhWYOV2u9Hc3Iy6ujo0NDSAiKDT6RATE4PMzMxhf5NXq9VoaWkRJLACPIGO0En6QghUcVC5XA69Xt/3O9HT04OGhgacOnUKUqkUDQ0N0Ol0MBqNgmz+XFZWhoyMjIC3czlFRUVIT0/3e9Ask8nw3HPPhSxcuDDpnnvu+USn0/2xoaHhWX/lXo1Vwx1Z8je3243FixdbU1JSul988cU63/Xz5s1r2bhxY+TatWtrN27cGHnLLbcMKxH9vffe095zzz3Cb2MxAjywurY8BqAYgMp7+XUAdzLGionoBwCeg7eYnT95c6kei4+Pf2bLli1RM2bMGOg+yMzMRF5eHmbNmiX6B25SUhLy8/NhMBgEH2HRarU4fvz4oIGHw+GAzWZDbW0tWltbodFoYDAYkJycPOqpVKErsCsUCvT29g5Z1uBq43K5BCk9oFAoYDKZcPbsWeTk5KCzsxM2mw2FhYXo7e2FXq+H0WgMSDX2jo4OuN1ujGTDc3+rr6+HTCaDRhO4rQDT09NRUFAQ+Ytf/OKHv//9732jV8cD1uB1avfu3WFbt26NTE5O7ho3blw6AKxevbpq9erVNQsWLEi0Wq1RZrO5d+vWrecBoKKiQjZ16tT0jo4OKRGxjRs3GoqLi09qtVp3e3u7ZP/+/aq33367XNxndXk8sLpGEFE0gPkAXgLwhPdqhv8GWREAqgPQbpxer//73XffnfSb3/xGdbk6M2FhYTCZTCgpKUFKSoq/uzIioaGhCA4OFrxgJ4C+USebzQaDwQDAk6RbW1uL2travg/O+Ph4v31wKpVK9PT0CJaP5Etgv5YCq97eXkFzBFtaWqDRaCCRSBAWFoawsDDEx8f3Bd4XLlxAW1sbtFotjEYjoqKi/DJ1V15eLuq+gIwxnD59GlOmTAl4WzKZDM8//3zIwoULk++9997/6PX6N2022/PX+uiVkObNm2dnjOUNdNuhQ4fOXnpdbGyss66ubsAANzw83N3S0uKvhQcBwwOra8d6AE8B6P818zsA/k1EXQDaANzkr8a8uVQ/iouLe3bLli1RM2fOHNbjkpKSsG/fPpjNZtE2lPVJTk7GyZMnRclhMZvNOH/+POx2O6qrqyGRSGA0GpGZmRmwqt6hoaGCFe4MCgoSdGWgEISuuD7YNjZyuRxmsxlmsxlutxtNTU2ora1FUVERVCoVLBYL9Hr9qEaF3W436urqRK1dVVlZCa1WK+hrPX78eBQUFESuXbv2kT/84Q8LvKNXJwTrAHdNubYSIK5TRHQbgPoBvhU8DuBWxlg0gLcA/NZP7UXr9fojixcvXn3q1KlhB1WAp5bThAkTcPz48b6NdcWiUqkglUr7yhIIobe3F2VlZSguLkZdnSfdYOrUqZg5cyYSExMD+mHiS2AXglKpRHd3d8DbEfJ3SOjNl20225BBv0QiQVRUFDIyMjBnzhwkJCSgoaEBubm5yM/PR319Pdzu4S/AqqmpgcFgEG2q3uVy4fz586KMaMtkMqxatSr0k08+SUlPT/9Ur9f/3Ju3ynEjwgOra8NMAHcQURmA9wDcTEQ7AEzsVxX4fQBfTn4aoZCQkNstFkve+++/P+X1119XjSaRVqvVIiwsDJWVouRSfkFKSgrOnTsX0DacTicqKytx6NAhHD58GE6nE5MnT0Z8fDxCQkIEmy7TaDRoaRGmULEvxyrQGGNjpoaVP/nKVYxkGxsigkaj6QuyYmNjUVNTgz179uD48eNoamoaMhAtKysTtXbV+fPnERMTI8r2PT4ZGRkoLCyMXL58+WN6vf4wEQmz4oO7ZvDA6hrAGPspYyyaMRYHYDGA/wC4E0AEEfm++n0NnsT2USGiIIPBsOnGG298u6CgQD9nzpwrStRJS0vD+fPnRZ8u0mg0cDqdfdu6+AtjDE1NTcjPz8e+ffvQ2dmJCRMmYPbs2UhKSkJwcDDMZjOqq/2e9jaoiIgIQQMrId7bsVQc1J+GM1p1Ob6yHhMnTsScOXNgNBpRWlqK3NxcnDt3bsDRxPb2dkgkEsE3mPbp6elBVVUV4uPjRWm/P5lMhl/+8pdh69atmxIdHX1KqVTeInafuKsHz7G6RjHGnET0XQB/JyI3gGYAD47mWEQUr9Ppdjz++OPWp556KsQfH2RyuRypqak4efKkIEmql5OcnIySkhJMnjz5io/V3d2NyspKVFVVITw8HFarFVlZWQMmjKvVarS1tcHlcglSL0gul8PlcglSBkGhUAgyFShkSYfOzk5BSh0AnsDKXwVsJRJJXykHh8OBqqoqHD16FEFBQYiNje2b+hM7af3MmTNITk4WtXZWf3a7HWazmQ4fPqxZtGjRu3q9fovNZnuCMTZm6ydxYwMfsbrGMMb2MMZu857/J2NsAmNsImNsDmPswkiPFxYWtjgmJubIhx9+mLZy5Uq/BFU+ZrMZTqcT9fXibk4eFRWFjo4OdHZ2jurxjDHU1tbiyJEjOHr0KORyOWbOnIkpU6YgKipq0FV4RASDwdCXayUElUrl99G5gQg1YiXUPoGMMcGmHX2jnYEoNSCXyxEXF4dZs2YhLS0NjY2N2LNnD06cOIHa2tq+VapCs9vtaG1tFWVPxIG43W4cO3YMWVlZsFgs2L9/v+aRRx55UK/XHyOiGLH7x41tPLDiBkRESoPB8Kfs7OzXjh8/rps2bVpA2snMzERRURGcTvG+BBIRkpKSRpxr1dvbi5KSEuzZswf19fVIS0vDrFmzRrRhrMViEXQ6UKgE9qCgIEFyrISaCuzu7hYsF06obWxUKlVfPpYvcDxy5Ahqa2sFX1hSVFSEtLQ0Uar2D6S4uBhmsxlqtWcfYIlEglWrVoX+/e9/z4iOjv48ODj4TpG7eNUoKSmRT5s2LSUhIWF8UlLS+DVr1ugBoK6uTjpjxoxkq9WaMWPGjGSbzSYFgPz8fGVWVta4oKCgyatWrfpCpN/Q0CC95ZZbEuLj48cnJCSM//jjj8WZtx4CD6y4LyGiFJ1Od/zZZ5+9e+fOnRrfP5dACA4ORmxsLM6e/VI5E0EZjUa0tLQMa/qqtbUVBQUFOHjwICQSCbKzs5GZmQmVSjXkYy+lUqlgt9sFCyyFSmAX6gNSqKlAofOrArVJ90AkEgna2tqQnZ2NjIwM1NXVITc3F+fPnxdkz8fGxkYwxkQpezIQm82G1tZWJCYmfum27OxsKigo0E+fPv0tg8HwhnfzZe4y5HI5XnnllYsXLlw4dfTo0eLNmzfr8/LylC+88IJpzpw57eXl5SfnzJnTvmrVKiMA6PV654YNGypWrFjxpaH8hx9+OObrX/96W2lp6amioqKirKyswOcbjAIPrLgvUKlUy+Pj4w989NFHyT/60Y+UQnxAxsfHo7GxEa2trQFvazBEhMTERJw/f37A291uN6qqqnDgwAGcPn0aZrMZOTk5SEhIuKKikUJPB6pUKkFf50CPfAg1FShkYDVY/apAaW1thVwuR3BwMMLDwzFx4kT4SqgcOHAAhYWFAZs+ZoyhuLgY6enpATn+SPX29uLkyZOYNGnSoF8OIiMj8cknn2iefPLJ+3Q6XSERfTkC8yMiSiWign6nNiL6cSDb9Cer1erIzs7uBACNRuNOTEzsqqioCNq1a5d6xYoVjQCwYsWKxp07d2oAwGKxOHNycjrlcvkX/nk0NTVJjhw5Ev7jH/+4AQCUSiWLiopyCf18hoMHVhyAvqm/v82dO/e3hYWFUVlZWUK2jczMTNFrW5nNZthsti9MYTmdTly4cAG5ubloaWnBpEmTMG3aNOj1er+NylgsFlRVVfnlWEORSCSQSqWCjEQIMR0o1FSgUMVB3W43Ojs7BV2ZV1ZW9qWkdblcjsTEROTk5MBkMqGoqAiHDh1CQ0ODX/9Ga2pqEB4eLur2OT6MMeTn52PcuHG43A4SgOd/1v/8z/+E7NixY1xsbOzh8PDwRQHs1xnGWBZjLAvAFACdAP4ZqPYC6cyZM0FFRUUhOTk59sbGRpnVanUAnuCrqanpsovpTp8+rdBqtc5FixbFpaWlpd97773Wtra2MRnD8FWBHIjIpNPpdj/77LOJjz76qCCjVJeKiIhAZGQkSktLkZCQIHj7gCfoiIuLQ2lpKeLj41FaWorq6mpER0cjOzs7YNuZhIeHo7OzE06nU5B96NRqNVpbWwM+9eJLYFcoFAFrQ8gRq4GmhvytubkZGo1GsKlUp9OJpqYmZGZmDng7EfWtKGxtbUVJSQmKi4uRlJQEo9F4Rf10u904e/YsbrrJbxtCXJHy8nIolcoRbVQ+depUFBYWRt1xxx0bdTrdDQ0NDT9ljA2/IuvIzQVwnjE24r3ytm3bFlNfX+/XZa16vb5zuJs7t7a2ShYuXJj48ssvV2q12hG/Rk6nk4qLi0M2bNhQcfPNN3csX7485vnnnzdu2LBBuCTVYRqT0R4nHCK6wWg05r333ntpQk39DSY1NRUVFRXo6uoSrQ9RUVG4cOECDhw4AIVCgdmzZ1/R5sfDQUQwGo2ora0NWBv9qdVqQRLYhVgZKFSOlVDJ60JPA168eBEWi2VYAVJERASmTJmCKVOmwGazITc3F+Xl5SOq7N5faWkpTCbTmNhPsr29HeXl5Rg/fvyIH6tWq/Hpp59qFi9e/H29Xv//iCiQw42LAfwlgMcPiJ6eHpo/f37iokWLmpYtW9YCAJGRkc7y8nI5AJSXl8u1Wu1lE03j4uJ6DQZD780339wBAPfee29zYWGhMPVPRoiPWF3H1Gr1suTk5Fd27doVKdYoUX9SqRTp6ek4fvw4ArUKcTAdHR04e/Ys2tvbodfrERERIWhNH7PZjOLiYr/VLrocjUaDoqKigLcjRGAlxFSg2+0GEQkyitTQ0CDIyJhPRUUFbrzxxhE9JiQkBJmZmejp6embJo+NjUVcXNywRw8dDgcqKiowa9as0XTbr1wuF/Lz85GVlTXqEWOpVIpXX301fPLkybOffvrpfCL62mhGlS7Hmyh/B4Cfjubxwx1Z8je3243FixdbU1JSul988cW+ZNJ58+a1bNy4MXLt2rW1GzdujLzlllsuu6omNjbWaTQaewsLCxUTJ07s+eijj1SpqaljMnmdB1bXISKS6HS69ZmZmd/9/e9/rxwLQZWPXq9HZWUlqqurYTabA95eZ2dnX0CVkpICvV4Pl8uFffv2IT4+XrBihSqVCt3d3XA4HAEdHQP+uxlzoAk1YhXo90io/CpfPppQ27k0NzcjODh41CNGCoUCaWlpSE5OxoULF7B3717ExcXBarUOGeyePXsWCQkJgkx9D6W4uBgWiwURERFXfKzly5cHpaWlJX/zm988IpPJ7nY6nfv90EWfbwA4xhgTrvCdH+zevTts69atkcnJyV3jxo1LB4DVq1dXrV69umbBggWJVqs1ymw2927duvU8AFRUVMimTp2a3tHRISUitnHjRkNxcfFJrVbrfvXVVyuWLl2a0NvbS7GxsT1/+ctfysR8boMR/7eaExQRqfR6/YfLli2b/NJLLymPHDmChoaGMbPUGfDs1XXw4EHodLqABRldXV04e/YsWltbkZKSgokTJ/aNSMhkMpjNZlRWVgo6auWbDoyJCWz9QSLqq4weyGkYhUIBu90esOMDwoxYCRVYNTQ0CFpmYaCk9dGQyWRISUlBfHx83whWQkICYmJiBnxvOjs70dDQMCZWAtbV1cFut49qCnAwN910Ez777DPDvHnztmm12lVNTU1/8NOhl+AqnAacN2+enTGWN9Bthw4d+lKdndjYWGddXd3xge4/Y8aMrpMnT456azah8Byr6wgRJer1+oL169dP/9WvfhUml8txww034MSJE6OuOh4ICoUCiYmJKC72/99Pd3c3Tpw4gc8++ww6nQ6zZs0aMAk3Pj4eZWVlo84fGQ0hVwcKUc/qWsmxEqrUQn19vWCBlcPh8PsCBt82VTNnzkRnZyf27t2LioqKL/0NFRcXY9y4caIXA+3p6UFRUdGgW05dCYvFgqNHj2pnz579ksFg+D8iuqJBDCIKgWe/13/4p4dcIPHA6joRFBT01ejo6EM7d+6MX7JkSd8wkFKpRFZWFj7//HNRq59fKiYmBna7HU1NTX45nsPhQHFxMQ4fPgytVovZs2fDbDYP+g81KCgIOp1O0KroYWFh6O3tFaRiuRAJ7DywGj7GWN+KQCFUVlYiOjo6IMFNUFAQ0tLSMH36dLS3t2Pv3r191dxbWlrQ09MjaIL+QHylFdLT0wM2ahscHIx//vOfEY8++uginU53iIi0oz0WY6yTMRbJGBOv2B83bDywusYREUVGRj45fvz49z///HPdQBsNazQaxMXFIT8/X9Q6Uv35aludOHHiikaN3G43SktLsX//fgQHB2P27NnDXgXlKxgq5Gsi1OpAIba2ESp5PdA5VkIEVkJtYwN4gorKykrExsYGtB2FQoHx48dj2rRpqKmpwcGDB1FYWIjx48eLPlpVWlqK0NDQgO+NSER4fIsZlgAAIABJREFU7rnnQjZv3jzJYDAUEJH/5hy5MYsHVtcwb5L6m3Pnzn3u8OHD2sv9E4mNjYVSqRzxfnmBFBYWBpPJhJKSkhE/1rcx8t69e9Hd3Y3s7GzExcWN6INLqVRCrVYLukmyUNOBSqUSPT09AQ0aJRKJIJXXAx2MOJ3OgC8oELLMQlNTE8LCwgRLkg8ODsakSZNgMpnQ3d2NkpISUVMP2traUFlZKWiO1+233y79z3/+ExMbG/tpUFDQHMEa5kTBA6trFBEF6fX6D7/1rW/d8/7770cMp0jj+PHj0dDQIFg9peFISkpCTU3NiJKgW1pacPDgQVRXV2PatGlIS0sb9Qejb3NmoUatQkND4XA4BJkOFGp1YCBfu0CvCnQ4HIKsXLPZbIIFVmVlZYiPjxekLR+3243KykrMnj0bMTExOHr0KE6dOiXIDgD9+UorTJo0SbAVvz7p6ek4fPiwLiEh4e/h4eH3CNo4JygeWAmEiKRElE9E//Je3tdv76dqItrqx7bCdTrdvqeeeuorr7zySthwh90lEgluuOEGnD59Gu3t7f7qzhWRSCSYMGHCsLa76enpQX5+PoqKipCRkYHJkycPuTXFUEJDQxESEoKGhoYrOs5ImEwm1NTUBLwdIaYD5XJ5QHP3Aj1iJcSKQCG3senp6YHdbhcsl8unsrISOp0OwcHB0Ov1mD17NsLCwrB//36Ul5cL9sXl1KlTiI2NHdWG6f5gMplw5MgR7bhx4/5Xq9X+SJROcAHHAyvhPAagb5kbY2xWv/2fDsFPqz2ISK/T6T5bv379pJ/85CcjjiqCgoIwefJk5OXlCf5tcjBarRahoaGorBy4vh1jDKWlpTh48CAMBgOmT5/ul5o0PsnJyYJOkQo1HXgtrAwMdLkFIfKrmpubodWOOq95RCorKxETEyNojpNvv83k5OS+64gIVqsV2dnZaG9vx4EDBwL+u1hbW4uuri5BS6gMJCIiAvv27dPceOONq/V6/S9J7ISzACspKZFPmzYtJSEhYXxSUtL4NWvW6AGgrq5OOmPGjGSr1ZoxY8aMZJvNJgWA/Px8ZVZW1rigoKDJq1at+kL+isVimZCSkpI+bty49IyMjDQxns9w8MBKAEQUDWA+gDcHuC0cwM0ArnjEioji9Xr90S1btqTcd999o04KUalUSElJQV5e3phJZk9PT8f58+e/9CHd1NSEffv2oaurC7NmzbrsSr/RUqlUkMlkgmwDA3gqW7tcroAnfkdERAT8wywoKCigzyPQU4FCBFZClVlgjOHixYsBr5N2qZKSElit1gGn4+VyOTIyMpCZmYlTp07h+PHjAZkG7+7uRnFxcUBKK4yGUqnEjh071Lfeeuv39Hr9n4hI2HlJAcnlcrzyyisXL1y4cOro0aPFmzdv1ufl5SlfeOEF05w5c9rLy8tPzpkzp33VqlVGANDr9c4NGzZUrFixYsDk1tzc3LOnT58uGsv1rHhgJYz1AJ4CMNDytgUAPmGMtV1JA0Q00WQyHdyxY0fs17/+9St+X81mM9RqtSBbnwyHr0bOqVOnAHimNAoKCnD69GlMmjQJ6enpAc2FEXrUymw2B3w6UC6Xw+VyBbRWV6BHrK6FqUChCoM2NDRArVYHPBG/v+7ubtTW1g45SqRSqTBjxgxoNBocOHAAFRUVfvtS5yutMH78+IBuCD5SUqkUb731lur+++9faDabPyUi8TdNDACr1erIzs7uBACNRuNOTEzsqqioCNq1a5d6xYoVjQCwYsWKxp07d2oAwGKxOHNycjrlcvnY+FY/CjywCjAiug1A/WCVZ+GHarpBQUFzYmNjP87NzTXecMMNV3KoL0hNTUVHRwcuXrzot2NeCZPJhN7eXpw8eRIHDx6EXq/H9OnTER4eHvC2NRoNnP+fvfeObus6072fjUaAAEECBEAQ7A0kwd6rRNJlybYSF9lylESJrNw4ziSe+Gac+MYzWY41mfH4ziT55iY3mdE3cctIsmTLjuQiy7EsyZJIigXsvZMAewXY0M/9gwKiwgbyHJBK+FtLy0XA2QfAwcG73/I8NhuMxk3Fv+tGpVJ5pBwoFosZfU18Ph8mE3N2XkyXApkOrJzZGU8EO3QprbtDW1sb1Gr1uj4jQghCQkJQWFgIo9GI0tJSWno9u7u7IRaLt1w7azmMRiO+/OUvC370ox9ly+XyUkIIfT0M25D29nZeS0uLd1FR0dzk5CQnLCzMCiwFX1NTU+vaGd97770xCQkJ8b/4xS+2j13IbexY2jBPAYCHCSEPAeADEBNCjlEUdZAQ4g8gG0tZqw0hFoufjIyM/I9Lly5JAwMDaTrlJQghSEtLQ1lZGUQiEfz8/Gg9vrssLCzAZrNBp9Phnnvu8fju05m1ysjIYHwtgUAAiqIYt51xNrAz9dl6eXkxWm5kshRIURTjpcbx8XGPZKtMJhMWFxc9+h02Go2Ym5tDSkqKW89zlgdnZmZQW1uLgIAAxMTEbCiAnpmZwdDQEAoLC91+LtPMz8+jpqYGWVlZKC4u9goODk7+/ve/X0UIKaIoivZ09dmzZ0PGxsa86TymQqFYWK+5s8FgYO3bty/q1Vdf1Uml0g2lyUtLS9vCw8Otg4ODnHvuuUedkJBgevDBB5n1zdoAOxkrhqEo6kWKooIpigoHcADARYqiDt746/0APqIoakNbeplM9lxcXNx/VlRU0B5UOXHa3tTV1THe87MSFEWhu7sbVVVViI+PR2xsLLq7uz1+HjKZDAsLCx6RKACWMnRMK78z3cB+Nzevm81mxoN3T8ksDAwMICwsjPF1bqa1tRUajWbDPU1+fn4oLCwEIQRXr151+zq12Wyoq6tDWlqaR4RX3cFkMqGqqgppaWmuHr79+/dzTp06FRUQEFBJCIlZ4xB3FWazmezduzdq//79U4cOHZoBAH9/f1t/fz8XAPr7+7lSqXTN8eHw8HArsFQu3Lt370x5eTnzo7QbYCdjtbUcAPDqRp4ol8t/mp6e/vwHH3zgx2RGA1iSHNBoNKiurkZeXp5Hb1JGoxENDQ2QSqXYtWsX2Gw2pFIprl27BqPR6NGxaUIIYmJi0NXV5fYufCOoVCrU1NQgMjKSsTXEYjEMBuZcMpxmz0zBZI8V043rThub5ORkxtZwrjM4OIhdu3Yxus7NTExMgMVibXrakcViQa1WIzAwEPX19ZBIJIiLi1tXFrGpqQkREREeaRVwB6vVisrKSiQkJNyRQSwpKWF9+umnwQ899NAVQsi9FEXR1uS63swS3TgcDhw4cCBMrVabXn75ZVdD+p49e2aOHj3q/8orr4wcPXrU/4EHHlg1cjYajSy73Q6JROIwGo2sS5cuif/hH/7Bc55jbrATWHkQiqIuA7h8038Xb+Q4CoXiSHZ29g/OnDnj56lGVIVCgdnZWTQ2NnokqHA4HOjs7MTo6CiSk5NvuQE57W7q6+tdO1pPERAQgPb2dsZLdABcGlyLi4ub1uNaCRaLBTabDavVykifjyea15kq1THdXzU3NwehUMj4RmVsbAz+/v4eEToFlgK5lpYWLGeftVF8fHxQUFCA3t5eXL16FYmJiasaSA8NDcFqtTJu2+MudrsdlZWViI6OXrEEnJKSggsXLijvu+++i4SQ+yiKavLwadLKZ599Jjpz5ox/TEzMYlxcnAYAjhw5MnjkyJHhxx57LCosLEymUqksZ86c6QaAgYEBTlZWlmZ+fp5NCKGOHj0a0Nra2jQyMsJ57LHHogHAbreTxx9/fPKJJ57wTNOrm+wEVncZCoXilby8vO+99957vp66UTqJjIxETU0N402ws7OzqKurg0KhQGFh4bI/PL6+vpBKpejt7WU0o3M7hBCXh2BCAvO2XyqVCkNDQ4iKimJsDT8/PxgMhlV/qDYKm81mdOqQoijGAuu5uTlG+5881V/V19eH+HjPSf7o9XpIJBLas32EEERGRkKpVKKurg6jo6PLZq8WFxfR3t6OgoKCbSGt4ISiKGi1WgQFBUGlUq362Pj4eFy8eDHg3nvv/ZwQcj9FUQ0eOk3a2bNnz9xKw1vl5eUdt/+/0NBQ2+jo6B2vVyqVWtrb27fHmPoabK/C8w4rQgghAQEB/7Z79+7vvf/++x4Pqm6cA1JTU6HT6TA5OUn78Z1Cn1qtFklJSYiNjV11Nx8bG4uBgQEsLi7Sfi6roVKpMD4+7hHbGU+osPv5+XlMo+tugulSoCf8ARcXF2G1Wj1WMrfb7ejq6oJarWZsDW9vb+Tl5YHP57taApxQFIWamhokJSV5zAtxPVAUhfr6evj6+q57UxobG4vLly8rQkJCPiOEpDF7hjvQyU5gdRdACCFyufxXxcXFT586dcrX0x5XN8Nms5GZmYmGhgZaAxqTyYSKigrMzs5i165d65pe4nA40Gg0aGxspO081gOLxUJ4eDh6e3sZX4vP54MQwmjwyLS1DZvNZtTWhimYLME6HA4sLi4yrpHV39/v0ab1np4eBAcHM97078wcp6Wloba2Ft3d3aAoCp2dnZBKpYxkXzdDa2urq1/MHaKjo53B1aeEEPq0dHZglJ3A6i5ALpf/2z333HP47bff3tKgyolAIEBKSgqqqqpo+cEcHh5GeXk5IiMjkZyc7FbPjEKhAJvN9oi33s2EhIRgaGjIIwED0xY3TJsxM91nxQTO8iVTpaSpqSnGbWwcDgeGh4fXLDvRhcVigV6v92hpXiwWo7CwECaTCVevXsXw8DBiY2M9tv566O7uxsLCApKSkjZ0PUVGRuLKlSvykJCQc4QQ+hrXdmCMncBqmyOXy/93UVHRt48fP+67nUaGpVIpwsLCUF9fv2GFZOc4tE6nQ0FBwYbLIomJiWhra/OotyGbzUZoaCj6+voYX4vpciAhhNHpvbsxsFpcXIS3N62SP7fgiTLgyMgI5HI5ozpcN9Pe3o7o6GiPreeEzWZDrVbDYrHAZrNhZGTEo+uvhk6nw9jYGNLT0zcVpIeHh+PSpUvy4ODg84QQ2qeHhoaGFI2NjQmNjY0JnZ2dEXa7ffs0p92FbJ9f6h3uQKVSvZqXl/fdkydPbotM1e04/b+6urrcfq7TeNXPzw9ZWVmb6ofw8vJCVFQUWls9ax0VFhYGnU4Hu93O6DpeXl5gs9lYWFhgbA0m9azuxsCqp6eH0XOemJhgvFzlyTLg/Pw8pqenERwc7JH1bqexsRGxsbHYvXs39Ho9GhoaGP9ersXo6Cj6+vqQlZVFy+RnVFQULl68KA8KCvoTISSRhlMEAJjNZu74+HiARqNpSUpKagZAJiYmPOMK/hfKTmC1TZHL5T9LTU39mx/84AdiTwlSboTExESMjY1hdHRZv8xl0el00Gq1SE1NRXh4OC3llpCQEMzOzmJqamrTx1ovHA4HKpUKOh3z8jBMlwOZbGBnKrBiyiDcZDLh448/xuXLl9Hf30/78c1mM1gsFqM2NvPz83A4HB7TcGppaUF8fPyWTOHp9XpQFIXg4GBwuVxkZWVBKBSirKzMY2K+tzM1NYW2tjbk5OTQKnMRExODzz//XKFSqS4QQjR0HZeiKOJwOFgOhwMOh4PF4/E8l/7/C2QnsNqGyGSyFzIzM//nBx98IM7NzYVWq2VUHXszsFgsZGZmorW1FXNzqzsL2O1215h0QUEBfH3ps8UihCAlJQWNjY2MjvffTkREBPr6+hhfk+lyIJMN7EwFVkyJg5aVlcFsNkMoFOLkyZMYHx+n9fieMF32pC/g1NQU7Ha7R6QjbmdhYQFdXV1ITk52BXXOxvbExERUVVUx7l5wO0ajEfX19cjOzmZkMjE2NhYXLlwICAwM/JwQEr3Z43l5eVkVCsVIY2Njcn19fQqbzbZLJBLa9KG6urq4OTk56sjIyITo6OiEn//85woAGB0dZefn58eEhYUl5ufnx4yPj7MB4D/+4z+karVao1arNWlpaXHl5eWuCZLTp0+Lw8PDE0NDQxP//u//XknXOdLNTmC1zfD19f1GXFzcT86ePevH4XAgEomQnZ2Nurq6bTsS7+XlhbS0NFRXV6/Y5zQ3N+cq/WVkZDCyWxeJRFAqlRsqTW4UHo8HuVzO+M2bx+OBx+OtGbxuFD6fD7PZzEgWiMnAiu4S+ezsLK5fv46AgAAcPHgQHA4Hx48fp8UM2MnY2BijQYjdbsfo6CiYsrm6GacYqEZDW/Jk3TgcDtTU1CA5OXnZ+4lEIkF+fj50Op3HNlwLCwvQarXIzMxkbKIUWNK5OnfunDIgIOAiIWRTAYbVamUbDAa/xMTExpSUlAa73c4aGxujrRTI5XLxy1/+Ut/T09NcVVXV+tprrym0Wi3/Zz/7WWBxcfFsf39/U3Fx8exLL72kBIDo6GhzaWlpe0dHR8uLL7449Mwzz4QBSz25P/zhD0PPnTvX0dHR0fzee+9JtVotsyrNG2QnsNpG8Pn8PaGhof9+/vx5yc07HaFQiOzsbNTX1zOiH0UHvr6+iImJQU1NzR0/zkNDQ6iurkZycjJtpb+ViImJwfDwsEdLAE7BUKZKU06cYqFMwdR0IFOBFRM+gV988QXsdjvCwsKgUCjwta99DYuLizh+/Dgtr4GiKMzMzDBqhjw8PAylUukR66nh4WGIRCKPWks56ezshFwuX3W6ksfjITs7GwKBAKWlpYzKlpjNZlRWViI1NdUjJdjU1FS8/fbbQXK5/AtCyIY/AIPBIObxeGYej2djsViURCKZmZubo03ALSwszFpYWLgAABKJxBEVFbU4MDDAO3/+vN8zzzwzCQDPPPPM5CeffCIBgPvvv39eLpfbAaCkpGR+ZGSEBwCXL18WhoWFmTUajYXP51P79u2bOn36tOdcxd1gJ7DaJhBCMlUq1bGLFy9KlxMl9Pb2Rk5ODhobG2kvTdBFUFAQxGIx2traAPx5N+uc+mPyx8QJi8VCUlLSpqYV3YXP58PPz4/xaaS7tRx4t5QCJycnUVNTc4sxbmBgIJ588kmMj4/jnXfe2XRDtCdsbDzVtO5wONDR0YG4uDjG17qdyclJjI+PIyZmba9iQgiio6Oh0Whw/fp1RjanTv8/jUYDiURC+/FXoqSkhPXb3/42Qi6XXySEbEg8jMfjWRYWFkR2u51FURSMRqOPQCBgZES4vb2d19LS4l1UVDQ3OTnJCQsLswJLwdfU1NQdzWi/+c1vZCUlJQYA0Ol0vKCgIJcqc3BwsGVwcHD7qMDexI6lzTaAEBITFBT00cWLF2WrlQgEAgFyc3NRUVGB+Ph4xse1N0JcXBwqKyvR39+PkZERiMViZGdne7SpVSqVQigUQq/XIyQkxCNrxsTEQKvVQqlUMvZauVwu+Hw+ZmdnGdkRO4NDut8zDofDiN4X3aXAixcvgsPhID09/ZYgOSoqCl/+8pdx9uxZfPDBB3j00Uc3/BkzLbMwOzsLFovFuPAosNTHpVQqGffMvB2r1YrGxkZkZ2e7FaD6+/sjNzcX1dXVCAkJoa0HzW63o7q6GhEREVtyT96/fz93dHQ04ec///mHhJAHKYpaPvr/1rdC0NR0h4aIGIDa4WAtUlQKIQRBgIPPYnkBWDtCTExcwOuvr2t6x2AwsPbt2xf16quv6qRS6Zp12Q8//NDn2LFjsrKysjZg+WEVQohnds9uspOx2mIIIYEBAQEXz507F7CeLzqfz0dubi7a2tq2lV6LE0II1Go1GhsbIZVKt2xSSKPRoKury2Nj/t7e3hAKhZiYmGB0HSbLgX5+fowMSTD1+dNZChwcHERLSwvy8/NBUdQdVjapqakoKSlBQ0MDLl68uOF1mPYH9FTTutVqRX9/P6KjN9077RZOa5jo6OgN6YwJBALk5+djcnIS9fX1m+67oigKtbW1CAgI2DKpCQB49tln+d/61rfyFArFG2QDXzgvFssiYrMXhCzWgoDFMtH9jTWbzWTv3r1R+/fvnzp06NAMAPj7+9v6+/u5ANDf38+VSqWu3VdFRYXge9/7XtiZM2e6lEqlHQBCQ0NvyVDp9XqeSqXaltOLOxmrLYQQ4iuXy784efKkKjk5ed3P8/LycmWuHA6Hx5SV18Po6ChaW1uRkZGB9vZ2hIaGMm5vsRxcLhexsbFobm5GerpnxIqjo6PR1NTE6A+nUqlEWVkZI+rSXC4XdrudkWk7FotF+3HpylhRFIULFy64POj6+vqWzQju2rULBoMB165dg6+vLzIz3XMYsdvtMJlMjGWT7HY7JiYmPGIO3tnZiYiICFqlBNaDXq8Hi8XaVBDDZrORnp6O7u5ulJeXIzMzc0P3KIqi0NDQAKFQ6FG1+ZV45ZVXRIODg4+cP3/+FQAv3vGAdWaW6MbhcODAgQNharXa9PLLL7t0efbs2TNz9OhR/1deeWXk6NGj/g888MAMAHR2dvL2798f9frrr/cmJye7dsZFRUXzfX19/La2Nl54eLj1/ffflx4/frxnK17TWuxkrLYIQghfoVBc/N3vfhdRXFzs9ufA4/GQm5uLnp4e6PV6Jk7RLZw+Xd3d3cjLy0NgYCDi4+NRXV3tUfmDmwkMDITVavVYT5pYLAaHw2F0epPL5UIgENxiPEsnYrGYkWMz0WdFV6DW3d2Nvr4+7N69G15eXiuaLxNCsHfvXqjVapw7dw7t7e1urTM9Pc2ojc3g4CACAwMZb1pfXFzE2NgYQkNDGV3ndubn59Hd3Y2kpKRNH8vZdxUTE4OysrINZWqdn/9W9JgtByEEb7zxhjg1NfW7/v7+z271+Tj57LPPRGfOnPG/du2aT1xcnCYuLk5z6tQp3yNHjgxfunRJHBYWlnjp0iXxkSNHhgHgpz/9aeDMzAznb//2b8Pi4uI0iYmJ8YBrunDggQceUMfExCQ8+uijU5mZmczYRWwS4qkG3x3+DCGELZfLP33ppZcKnn322U01KDibJkNCQjx+o3PicDjQ0NAAQgiSkpJuubF3dnZicXER7mTk6GRxcREVFRXYtWuXR6w2pqen0dHRgZycHMbW0Ov1mJubY+SG7jSWjoiIoPW4dXV1CA8Pp3WAYWxsDOPj45vK0FAUhaNHj8JsNuPZZ58Fm83GlStXUFBQsOL1YrFY8NZbb2FsbAyHDh1ad/akpaUFEomEMRmEq1evMj7mDwA1NTVQqVRQKj0nI+RwOFBaWorExETam8Pn5uZQXV2N+Ph4BAQErOs5vb29mJiYQGZm5pa0OqyGyWRCYWHhdHt7+3euXbv2i5SUFGb7E7Yh9fX1spSUlPCtWn8nY+VhCCFEoVC89fTTT+dsNqgClqL4nJwcDA4OesS37nasVisqKiogFAqRnJx8x245OjoaFouFEQXr9SAQCBASEoKOjg6PrCeRSGC32xnLKAFL5cCRkRFGph6Zsrbh8XjbMmPV2NiI0dFR3HPPPWCz2aAoCna7fdUgnMfj4Wtf+xp8fHzw9ttvr3vKjEkbG4PBAB6Px3hQZTAYsLi4uO4AhC7a29sREBDAyMSdSCRCfn4+urq6XBuL1RgcHMTw8PCm/f+Ygs/n48KFCxKVSnXUYrFsS52nv3R2AqvbIISwCSG1hJCPbvw3IYT8MyGkgxDSSgj5wWaOL5fLf/7AAw98+Z/+6Z9o0wnhcDjIzs7GyMgIeno8V3JeXFxEeXk5QkNDERMTs+xNhhCC1NRU9Pf3e9Ru5mYiIyMxMTHBaLBzM2q1Gp2dnYwdn8PhQCgUMvJ6xGIxDAYD7cdlwuR5sz1WNpsNly5dglKpRGLikvWaxWJZV7+NUCjEwYMHAQDHjx9fU/+LaRubvr4+2rOMy9HS0oKEhASPBhQTExOYmppal7TCRnG2VkxOTqK5uXnFTcvY2Bi6u7uRlZXlcbNpd/Dz88OFCxekRqNRPj8/vxNceZid5vU7eQ5AK5amUAHgKQAhAOIoinIQQjY8TysUCvelpqZ+77XXXhPTfWNis9nIyspy9TQxPa1jMBhcqsf+/v6rPpbD4SAzMxMVFRXIzc1lfFd9O4QQJCcno6GhAQUFBYz/KPj7+6OtrQ3z8/OMNSo7vQPptAUClprM2Ww2rFYrrUEAn8/H/Pw8LBYLTCYTLBYLrFYrbDbbHX8cDgcoinL90/nvhBDXHxaLhYWFBdjtdlgsFnC5XHA4nFv+cLlceHl5wcvLa9kma6dV1MGDB13XhDufmVQqxVe/+lW89dZbOHHiBA4dOrSihQmT04BWqxVTU1OMl9vHxsbA4/E8okfnxGKxoLGxEbm5uYx/b9lsNjIyMtDa2orq6mqkp6ffEjxNT0+jpaUFeXl5jPo80oHZbEZ/fz8kEgmrr68vRq1Wt3C53K11pf4rYiewuglCSDCAvQD+GcDf3fjffwPgaxRFOQCAoqixDR47KTw8/P//6KOPJExN0jiDK61Wi46ODqjVakbWGRsbQ0tLC7KyspZt8l0Ob29vJCcno7q6Gvn5+R7f7fn6+kIikXhkZ+9sjO3q6kJKSgojaygUCnR0dDAiZ+Hr6wuDweBW2crhcMBkMmFhYQGLi4tYWFhw/bvFYnEFTRMTE/Dy8gKPx7slGBIIBOByuWCz2WCxWGCxWLcEUc7XeHOwNTQ0BJvNhoCAgFsCs/n5edhsNlitVpjNZpjNZpewJ4vFgpeXF7hcLi5dugSVSgWpVOoKzioqKgDgFu+51QgODsYTTzyBU6dO4fTp0zhw4MCy5cnx8XHGRDsHBwcRFBTEaOBBURRaW1uRlZXF2BrLrVlfX4/Y2FiPbcYIIdBoNOjr60N5eTmysrLg5eWF2dlZ1NXVIScnZ0umnN1hdnYWWq0WGo0Gk5OTUCqV3O7u7hi1Wt3mCTX+rcbhcBAAWzMxdYOdwOpW/h3ACwBunrWOAvAVQshjAMYB/ICiKLfqPIQQWUBAwLlz5875M63Ky2KxkJGRgdraWrS2tiIuLo7WG65Op0N/fz/y8vLcvsH4+/sjJCQEdXV1W9KfEBsbi2vXrkGpVDJ+ow4ICEB7eztMJhOKJ0MmAAAgAElEQVQjAopOH0mDwUB7BsGpwL5cYGW1WjE7O4u5uTnXP502IQKBAN7e3hAIBPDx8UFAQAAEAoHL47CjowMZGRm0nefU1BRYLJZbmSC73Q6z2YxLly7BbDYjMTERfX19WFhYQF9fn8tnUq/X47777oNMJltTKT02NhYPPfQQPv74Y3z88cf40pe+dMu17bSxSU1N3fiLXQGKojAwMIDs7Gzaj30zAwMDkMlkG9KO2syaXC53S+RkwsPDIRAIUF5ejpSUFNTV1SEjI8Ojr38jTExMoKmpCenp6RCLxZifn4fdbic+Pj6CgYGBsPDw8K1pdvUQDoeDjI+P+wJo2srz2AmsbkAI+RKAMYqitISQ4pv+yguAiaKoTELIPgCvA9jlxnG5crn809deey0wPj6e3pNeARaLhfT0dNTV1bkMUukIYnp6ejA6Oorc3NwN69eEh4fDYDCgp6cHUVFRmz4nd+BwONBoNC7VZiYhhLg8BJnSFXKWA5kIrFpaWmAymTAzMwODwQCDwYD5+XlwuVyIRCL4+PhAJpMhIiICAoFgzeuLiR4ru93u9nXIZrPhcDhQX1+PhIQE5OXlAQC6urrw+eefQy6XQ61Wo7y8HO+++y5SUlLg5eUFQgh8fX1df5zSGk4yMzNv0bjavXu36+9mZ2chEokY2UjMzMzA29ubUfVzm82Gnp4eFBYWMrbG7czOzqK3t9eja96Os0G/tLQUqampW+KH6A4DAwPo7+9Hbm6u63oIDg6GXq+HyWRijY+PS4eHh1ne3t70OYpvPxwAmmw227e38iR2Aqs/UwDgYULIQwD4AMSEkGMA9ADeu/GYPwJ4w52DKhSK15977rm4vXv3erT25Wwab2hoQFNTExITEzd8Y6coCh0dHTAajcjOzt50GS8pKQnl5eXw8fHxuAWEQqGATqfD8PAwY2PvToKCgtDV1QWLxbJi781mUCgUaGtroyVwttvtMBgMmJycxNTUFMbGxlBbWwtfX1/4+fkhODgY3t7eG16Hy+XCaqVXJHmjzetOo+WSkhIAS6Xt06dPQ6FQIDo6Gvfeey8SEhJw+vRpVFRUoKioCPn5+Zibm4PBYMDAwACMRiMoioKvry+kUimkUilKSkpgNBpx6dIliMViV4aKyf4qTyitd3d3IzQ01GN9RQ6HA7W1tUhNTfW4AOnN2Gw2dHZ2QqPRoLOzEyKRyKP9ZeuFoii0t7fDYDAgLy/vlveMy+W6Wh/CwsJYmZmZ6Ojo+LrNZivdqvP9a+Avv+C6TiiKepGiqGCKosIBHABwkaKogwDOALjnxsOKAKx7bl8qlT5XUFDw5RdffHFL8sfOpm1CCBoaGjY0nk9RFJqbm7GwsIDMzExaeqOc5crm5uY1p6mYIDExEW1tbbT/0N8OIQQRERGMTWqy2WyIxeINySPYbDaMjo6iubkZ165dw9WrVzEwMAA+n4/ExERIpVKkpaVBo9FApVJBKBRuKnhjIluzEbkFp9Fyeno6/P39MTc3hxMnToDL5eLAgQPgcDgghCAwMBDf+c53kJSUhMuXL+PEiRPgcDgIDw9Hamoqdu/ejcLCQoSGhsJisaC5uRlffPEFAgMDoVQq8eGHH7rKikz5A1qtVhgMhjWHRzaDyWTC8PCwRyYOnbS2tkKlUm1pEONwOFBdXY3Q0FBERkYiOzsbdXV1jBg4bwa73Y6amhpYrVZkZ2evGoh6e3vj008/lSoUivcIIVsjevhXwk5gtTavAnicENII4F8ArCvFyOPxioODg186ceKE71ZqnRBCkJCQAC6Xi7q6OreCK4fDgbq6Olf2i87XwefzkZaWhurqasYDnNvx8vJCZGQk2traGF8rODgYw8PDjL1GZzlwLSiKwvT0NNrb21FaWorS0lKMj4/D398f2dnZKC4uRmpqKkJCQiAUCiGVSmnXsyKE0Kq9tRGvwEuXLoHNZqOoqAhWqxWnTp3CwsICvvrVr96hA+Xl5YXHHnsMjzzyCAYHB/Gf//mft8hosNlsSKVSREdHIycnB8XFxUhMTMQ999wDkUiEkydP4sKFC5idnWUksNTpdAgJCWG0V7G9vR0xMTGMq7k7GRsbg9Fo9HibwM04/f9kMplLdFkoFCInJwdNTU0YHR1d4wiewWKx4Pr165BIJEhKSlrXdRASEoJ3331XIZfLPyOEbO+GsbuYncBqGSiKukxR1Jdu/PsMRVF7KYpKoigqj6Ko+rWeTwiJUCgUp86fPy/1tPP7CueD+Ph4CAQC1NTUrMtixuFwQKvVQigU0tajdTt+fn6IiopCbW0tI2KXqxEaGgqj0cio/Qyw9OMbGhrKmECqXC7H+Pj4su+f1WqFXq9HdXU1Ll++jL6+PgiFQmRkZKCoqAiJiYlQKpXLlin9/Pxof2/oFgl1txQ4NDSE5uZm5OXlQSgU4uzZs9Dr9di3bx9UKtWKVjapqan4zne+Ax8fH5w4cQJ/+tOfXFOGN0MIgUgkQkxMDL797W9DJBKhpqYGwJLy/OXLl9Hc3IzJyclNX+/OpvWQkJBNHWc1ZmdnYTQaPdY8bjab0dzcjLS0tC0T3qQoCk1NTRAIBHdI1ggEAuTl5aGjo2NdmxkmmZ+fR1lZGaKiotz2KSwoKCCvvPJKmEKheH8jhs07rA375Zdf3upz+IuCECKSy+WlH3zwQch28ZAClm76MpkMc3Nz6O3tRWBg4Io3L4fDgaqqKvj7+68o/EkXzlLW1NQUY6rUy0EIgVQqRV1dHUJDQxl/jY2NjQgNDWXE3Hh6ehp8Ph8CgQALCwsYGBhAa2srBgYGwOPxEBYWhtjYWAQGBt7RdL0SXC4Xvb29tP5wj4+PQywW09ZoPTIyAj8/v3VNeFIUhTNnzsBiseCJJ57AlStXUF1djfvuu89l0j06Ogo+n79sCcrb2xspKSlYXFxEZWUluru7XY37y+Hl5YWoqChUV1djZmYGDz30EKKiolwyEW1tbZiengZFURAIBG5fF1NTUzCZTJsyI16Luro6xMbGemQSjqIoaLVaREdHb2kJsLOzE2azecUMEJvNhkqlQnNzs6sU72mmpqZQU1ODtLS0Dd8z09PTOb29vYq+vj7RCy+8cJHmU/yrZydjRSM37GrO/vKXvwz1pN6LO6jVakilUlRVVS2763YGVTKZzGPpeI1Gg5mZGQwNDXlkPScikQhKpRLd3d2MrsPhcKBSqaDTMWMuL5PJ0NzcjCtXrqC+vt41Fbp7927ExsbC19fX7cCRz+fDbDbTmkmk24jZnVJgT08Pent7sXv3brS1teHq1atIS0tDfn6+6zErZayccLlc7N27F/v378fExASOHj2KlpaWFR8vl8uRmJiI+fl5HD9+HDabDUqlEqmpqSguLkZ4eDhmZmZw7do1VFVVYWhoaNnv5HIw3bQ+OTkJQgij/Vs309fXB4FA4FH/wdvp7+/H9PQ0UlJSVv2+OG3E+vr6PJ65GhwcdAmmbjYA/fWvfy2OjY39G6FQ+BhNp7fDDXYCKxqRSqU/eeyxx7K+8Y1v0D8CRiNRUVFQKBR3BFdbEVQBS9mjjIwM1+ShJ4mJicHQ0BDjTfQRERHo7e1dVxl2PZhMJvT09ODatWvQ6XRYWFhAdnY28vLyVs2kuINQKKT1faE7sFpvKZCiKFy4cAF+fn6Qy+X44IMPEBERgb17997yA7pWYOVEo9HgmWeegUwmw7vvvouPPvpo2R46s9kMqVSKJ598EqOjozh58iRsNhuAP2dMNRoNioqKoFarMTMzgytXrqCmpgajo6MrXitmsxlzc3OM+OYBS++XU6bFExiNRgwMDDAmS7IehoaGoNfrkZmZua5gncPheDS4ck5mDwwMID8/n5bvN4fDwYcffijx9/c/SggJ3/QBd3CxE1jRBCEkU6lUPv/rX//aZ+1Hbz0REREIDAxERUWFy0ZkK4IqJ1wuFxkZGaipqYHFYvHYuiwWC4mJiRuemlwvPB4PCoViUzdhh8OB4eFhVFRUoKqqChRFITMzEwUFBVAoFLQHh06hULrYqoxVU1MTRkZGkJWVhdOnT0MqlWL//v13BGXuyGJIJBIcPnwY+fn50Gq1+P3vf4+JiYlbHuOUWYiJicGjjz6Kvr4+vPfee3cETE6NLI1G48pkjY2N4fLly2hqarpjs6HT6RgtXw8NDcHX13fdrgqbwW63o7a2FmlpaVvmvTcxMYGuri63pWQ8FVw5ddcWFxeRk5NDq+yFRCLBO++8I1coFB8RQra3T89dxE6PFQ0QQnwDAwPLX331Vblard5S7RV3cKaSm5ubMTw8DLlcvqXTOF5eXuDz+WhubmbcouNmvL29MTU1BavVSrv33s2IxWI0NTUhLCzMrdc2OzuLrq4utLS0uOxynCVd57XGZrMxPDzsEjWkA4fDgYmJCdqOaTabYTQaaZMe0Ol0UKlUq37f7HY7Tp06BbFYDL1eD7vdjm9+85t39MbYbTYMdXYizA2PTRaLhaioKAQFBaGhoQFVVVUuxXlCCLq6uhAcHAyBQICAgAB4e3vj+vXrMBqNiI2NXdG03Pn4sLAwOBwOdHd3o7e3FxRFwdvbG83NzUhMTGQkEHGO76elpXnkPtbc3AyZTLZlJcCZmRk0NDRs2KqGxWIx2nNltVpRVVUFiUSC+Ph4RqYzg4ODYbFYRE1NTYE//vGPP6Z9gb9CdjJWm+RGX9U7v/rVr6TFxcUoLy+/Y+e6nQkKCoLdbsfs7KxrtHgrUSqVkMlkq/auMIFGo3GJeTIFn8+HRCLByMjImo91OBzQ6/W4du0ampubIZFIXJN8y928ZTIZLdNmN+Pn50er5MJWlAKdzePAUsnpK1/5CqRS6a0PWlyEY+9e5D3+OHDDK9AdYmJi8N3vfhdBQUE4e/Yszpw5A5PJdIfdUHZ2NoqKilBXV4fPPvtszc+KxWIhMDAQOTk5yMrKgtVqxZUrV2C327GwsOD2ea6H3t5eBAUFecQPb3R0FPPz8x7VyLqZubk51NbWIisra1MDFUxlrhYWFlBWVoawsDBER0czutF88cUXvWNjYw94e3vvpeN4hJDnCCFNhJBmQsj/pOOYdxM7gdUmkUgkf7tnz56cAwcOcBUKBXJzc9HW1obOzk6PSwi4i1OvRaVSITExEeXl5R4tw61ETEwMFhcXMTAw4LE1uVwu1Go1mpubGV3Hac680rVhMpnQ1taGL774AkajEenp6cjNzYVKpVp1t8pisSCVSmkVMORyubDb7bT1hXm6FGg2m3HlyhWIRCKMjY3hkUceuXPzMD8PfOlL4Hz2GShvb2DPHqC62u1z8fHxwTe+8Q0UFxejsbERR48ehc1mu+PHsKioCFlZWSgvL0dp6frFr/l8PmJiYuDj4+PSYLt27RoGBwdp+3wsFgt0Op3b4/sbwWQyoaWlZcukFUwmE6qrq5Geng6hULjp43E4HGRnZ7tsvzbLzMwMKioqkJSU5BG5CxaLhffee08ilUpfJ4QEbeZYhJBEAE8DyAaQAuBLhJAYOs7zbmEnsNoEhJBkuVz+0tGjR131I4FAgPz8fJjNZlRWVm6LQGU5KIpCY2MjBAIBYmJiEBgYiNjYWFy/fp3WH7+NQAhBWloa+vr6GNeZuhmVSgWLxYLx8XHG1vD29oZQKLwjqzk9PY3q6mpUVlbC29sbu3fvhkajcWvUXaVS0d7rIRaLaRso4PF4tH4f1lJeLysrw8LCAubm5lBUVISkpKRbH2A0Ag88AFy+jJFXX8XERx8BEglw//1Aba3b58NisVBUVIRDhw7BYrGgrKwMZ8+exezsn63ZCCF48MEHkZSUhM8//xxarXbdxzeZTLBYLIiIiEBOTg7S09MxMzODL774Au3t7Zt+bzs6OhAVFcV4CZCiKNTV1UGj0XgkM3Y7VqsVFRUVSExMpLX075wWbGtr29QGZ3h4GHV1dcjOzr4zu8ogcrkcx44dc/ZbbeYiiAdwnaKoBYqibAC+APBXNXm4E1htEEKIKCAg4IOzZ8/63z6h4WyIDgkJQVlZGe0K1nTQ3t4OiqJwszF0QEAA4uLicP36ddoNc92Fw+EgMzMTdXV1HjsXQgiSkpLQ3Ny87rH3jRATE+PKaI6NjaG0tBSdnZ2IjIzErl27EBoauqH+GZlMhqmpKdoyGAC9DewsFov2LO5K2Y65uTlXRigpKQlFRUW3PmB6eimAun4dePtt6EtKwIuOBi5dAsRi4L77gPo1tYCXJSwsDDk5OcjMzERDQwN+85vf4OrVq67JQUIIHnnkEURHR+Pjjz9ed9m7v7//loybt7c3EhISsHv3bvD5fJSVlaGxsXFDZcL5+XlMTU0xKjjqpKenByKRiNZ+wPVit9tRWVkJtVrNiG4ej8dDdnY2GhsbYTAY3H5+d3c3enp6kJ+fT0smzV2Ki4vJ008/HSOXy1/dxGGaAOwmhPjfUHd/CADzF9Y2Yiew2iAKheLYv/zLvwSuJgKqUqlcN1dn8+l2oLu7G3Nzcy4fwZtRKBRISEjA9evXsbi4uEVnuIS3tzeSkpJQXV3NaKBz+5ohISHo6Fi3JaTbCIVCWK1WXLp0CYODg0hOTnbtTjfrxyeVSmnt8ZNIJLRvDDzxPfjkk09gt9uhVCrx8MMP3/q+TkwA99wD1NUBp0+jTq3GJ598gt///vc4XV2N5v/7f+EQCJaCq6Ymt9d2lk/37t2L73//+4iKisLFixfx29/+Fk1NTaAoCmw2G08++SSCg4Px/vvvr+kn6RQWDQq6s0rDZrMRFhaGoqIi+Pv7Q6vVoqamxq1MY2trK+Li4hgvyxkMBgwODt6yofMUTv+/4OBgRg3YBQIBMjMzUVNTg7m5uXU9h6IoNDQ0uIyUmTBtXy9HjhwRRkREHObxePdu5PkURbUC+N8APgNwHkA9ABuNp7jt2QmsNoCfn9/Tu3btKj58+PCaV79IJEJBQQEMBgNqampcOjZbRX9/P8bHx5Genr7iTVQmkyE5ORkVFRWMNcmuF5lMBpVKxbgcws1ERkZiYmKCdk0th8OBgYEBV9+P0y/Rx4c+hY6goCBahVbFYvGGdt4rweVyGfeG7OvrQ0tLC3g8Hg4ePHhraWtkBCguBtragLNnUaFU4uzZsxCLxVCr1ejr68Ppmhr8dt8+zFutMO/aBd2f/uTW93ZqasolrCmVSvGVr3wFhw4dgkAgwHvvvYfXX38der0eXC4XX/3qV+Hv74+TJ0+uWsYdHR2Fv7//qmU6QghUKhUKCwsREhKC5uZmVFRUrPn5TU9Pw2q1MmIUfTN2ux11dXVbIq3gLD9KpVKEhYUxvp5IJEJ6ejqqq6vX3KDabDZUVlbCy8sLaWlpHvNlXAk2m40zZ85IZTLZMULIhtKKFEW9RlFUOkVRuwFMAehc6zl/SewEVm5CCInz9/d/5c0331x3cZ7NZiM1NRVyuRylpaUeF8F0Mjw8vG4RPKlUipSUFFRWVq5718UUzqmh3t5ej6znLAnSFcxRFAWdTocrV65gbm4O+fn5yMjIACGE9mvB398f09PTtJUDWSwWWCwWbcGQU9GdKcxmM06ePAkA+NrXvnZrOUWvB4qKgN5eUB99hKtCIc6fPw+1Wo2MjAw8+uijeP755/HMM88g7ckn8aef/ARWmw1+jz+O3//4xzhx4gQqKyvXnL4cGxuDXC6/5f+Fh4fj6aefxsMPP4yZmRm89tpreP/992GxWHDw4EGIRCIcP358xf4+d5TWCSGQy+XIy8tDTEwMWlpaUFlZuey15kkxUKfUCJ0bifXiDLRv9/9jEl9fX9cGdaX+t8XFRZSVlUGlUq0owbEVBAYG4vXXX1coFIqzhBC34wRCiOLGP0MB7APwNt3nuJ3Z0bFyA0KIl0KhuPbxxx8Hb6QXwdfXF1KpFDU1NeBwOB71mZqenkZLSwtyc3PXLTAnEAggkUig1Wohk8m2pNEUWPqhUCgUaG1thUAg8EjvAZ/Px+zsLObn5zescO0s3zg/75SUFAQGBrp26wKBAD09PbRO/RBCMDc35zIEpgODwQAej0eLZ9zU1BQEAgEtx7o92HA4HPjDH/6A6elpJCUlIS8v7+YHL2WqxsZAnT+PCzekC5KSklBSUgKLxQKlUul630JDQxFfWAjs3QvesWNIbmhAQ3g4qnt6UFlZiYaGBkxMTICiKPj4+NySSXKW1W7fvBBCEBgYiMzMTBBCUFtbi8rKSrBYLJSUlKCpqQkNDQ2Ij4+/Zfx/YWEBg4ODiIlxf7BKIBAgJCQEfD4fra2tGB0dhY+Pj+u7PDIyArPZzKg9DrC0qZuammLM0H01urq6MD8/v6ZVDRMIBAIIBAI0NTUhKCjolmvCYDCgqqoKSUlJW2rlsxLR0dFkeHjYt6uri/fCCy9cdue5R44cOX/kyJEfA/gKgOcoivKsfs4WQ7ZL38/dgEKh+Pcf/vCH33nxxRc35SdgtVpRV1cHHo/HmNDfzczPz6OyshI5OTkb+kEzGo3QarXIyMjYEtNRJyaTCeXl5cjOzvZIcGWz2XDt2jXk5OS4bSExPj6O1tZWSCQSxMTELKuTQ1EUSktLkZaWRuvrmZycxMDAANLS0mg5nk6ng8lk2tAP++10dHRAKBQu2yvkLpcvX0ZxcTGApffy3LlzqK6uBpfLxfPPP//njUBnJ3DvvcDsLKjz53FuYgLV1dXIyMjA3r17odfrYTabV85mNDUBJSUAn4+Zs2fRYbO5RDutVitYLBZCQ0MRFRWFkJAQ6HQ6FBYWrnn+BoMBn3/+ORobGyESiZCRkYGKigoIhUIcPnzYdU20trZCJBLR0lg+MTGBtrY2iEQiqNVq132BDouUlVhcXMT169dRUFDg8d6hgYEBDA0NITs7e0tLbL29vZicnHRlqkdHR9Ha2orMzEyPKNxvFIvFgqSkpMmOjo7df23B0WbYCazWCSEkMzk5+dOamhopHYEQRVHo7e2FXq9HRkYGY4GCc+w7NTV1U6ads7OzqK6uRlpa2pa6z09PT6OxsRH5+fkeUYYeHR3FwMAA1muqbTQa0dLSAg6Hg/j4+DU/15GREYyOjiIlJYWO0wWwdG1dvnwZRUVFtPyYzM3NobW1dd3vwWr09/fDZrNtWuHf6f93//33AwAqKipw/vx5AMCePXuQm5u79MDW1qWgymqF49NPcba/Hw0NDcjPz8d9990HQogrAF41a1Bfv9TwLhIBV64AYWGw2WzQ6XTo7u5GV1eXS79IJpPhW9/61rqDFb1ej08//RR6vR5SqRQGgwEKhQKHDh0Cl8vFF198gd27d9O2AaMoCiMjI2hsbIS3tzdyc3MZ+y5RFIXy8nLExMTcUR5lmpGREXR1dTH6+tyhubkZhBB4e3tDr9cjKytry6oA7lBXV4c9e/a0jo2NJd+QT9hhDXZKgevgRgnwi3PnzqnoujkQQiCRSCAWi6HVasHn82nvPaBztNjLywsKhQI1NTXw9fVldIe7GgKBwGUXolKpGE/ti0QiDA8Pg8VirbqzNJlMaG5uhk6nQ1xcHKKiota1OxcKhWhvb0dAQABtHmCEEMzPz4OiKFp2w1wuFx0dHbQoZDsNhDf7PdJqtbhy5QoSEhKg1+tx5swZeHl5wcfHB48++uhSQNnQsBQMAbB99hlOd3SgubkZJSUlKCkpcV07fX19CAoKWv3zUiqX5Bn+67+AU6eAffvAkkggkUgQGRmJzMxMZGRkYHZ2FgMDA+jq6kJ8fPy6rgGxWIy0tDT4+/ujq6sLJpMJc3Nz6O3thVwuB5fLpVWagBACPp8PvV4PlUqFpqYmsFgs+Pr60v596u7uBofDYbzUeDuTk5NobW11q/WBaWQyGRobGzE7O4uCgoJtc15roVQqMT097d3a2sp74YUXLm31+dwN7DSvrwO5XP6vzz//vDI2Npb2Y0skEhQUFKC/vx/Nzc20NR07p2ACAwNpq98LhUJkZ2ejvr4eU1NTtBxzI4SGhkIgEDAqiXAziYmJaG1tXbaB2+FwoKurC9evX4dcLkdBQYFbon5O77/u7m46T5lWsVBCCLy8vGjRE6NLfd3pt3j8+HG8++678PPzg9lsRklJyVJ2QqtdKt/xeLBcuIC3GxrQ1taGBx54ALt3774lgFhYWFhfiTw9HfjTn4DJyaVj3/b+ikQiSCQSHDhwABMTE3jzzTfXPZzgHJh49tlnUVJSAjabjcHBQZw8eZIR5e2uri5EREQgJiYGhYWFmJubw9WrV2n9Xs/MzGB4eNjj0goGgwGNjY3Izs7eUtmCm7Hb7aiurkZAQADYbPZdZXsGAC+//LJQJpN9jxCSsNXncjewE1itASEkMygo6OvPP/88YykaHo/nci0vLy+nRT+qra0NXl5etNtTeHt7Iycnx9W8u1VoNBpMTU2ty3dvs/D5fJeNyM1MTEzg6tWrsNvt2LVr14YzaCqVChMTE7SqkkskEhiNRtr0v+jSs6IrsPLx8YFGo8H09DQoigJFUQgICFhSVy8vX8pUicUwf/YZjlVWore3Fw8//DBycnJuOY7zuesumWZlAZ9+CoyNLa0xPOz6q9nZWYjFYsTExODgwYMwGo1488033XrfuFwudu/ejeeeew6BgYFYXFzE6dOnaZUaWVxcxOjoqEt2gMvlIiEhAenp6Whvb0dtbe2mPyObzeaSVvBkb9P8/DxqamqQmZm5ZVn12zGZTCgrK0NAQAASExORlZWF1tZWWmVMmMbhcOCnP/2pf2Bg4IebVGX/q2CnFLgKN0qAl8+dOxfEdH8AIQT+/v4QCASoqamBSCTacN/V4OAgxsfHGfPh4nK5UCqVqK2tdVm0eBpCCAICAlBbWwt/f3/GexV8fX3R1dXlKtc2NDRgcnISqampCAwM3NSPByEEhBCMj4/TpgZNCMHCwgIcDgctJWabzYaZmZlNl/BYLBZ6e3s3rSU0OzuLy5cvw2q1wuFwwGw249FHH4V/U9OSTY1SiYVz5/DfV74iiF8AACAASURBVK5geHgYjz/+OJKTk+84jslkwuTkJIKDg9e/eHAwsHs38LvfAWfPAk88AYhE0Ol08PHxgZ+fH/z8/BAZGYmamho0NjZCrVa7NTji5eUFLy8v6PV6TE5Owmg0Qq1W0/J9bmpqQnh4+B2DKDweD8HBwa5sN7BkxL2RNRsaGhAYGMi4NtbNmM1mVFRUIC0tbUuHbG5mdnYWVVVViI+Pdw1scDgcyGQy1NTUIDAwcFv0f60ERVEYHBxEfX098vLyYLFYeK2trV47JcHV+YvIWBFC2ISQWkLIRzf++01CSC8hpO7Gn9SNHFcul//bj370o0C1Wk3vCa++JvLy8tDR0eGynXEHg8GArq6uVQVA6YDP57sMp+kwHd0IPB4P6enp0Gq1jHsyOks1VVVVuH79OoKCgpCTk0NbUBkSEoLh4WFaxTPpFAuly9qGzWbTUu6+evUqjEYjHnvsMVdTt7C0FHjwQSA0FLMff4w3LlzA+Pg4Dhw4gISE5SsYc3NzG+tDKygAzp0D+vuXmuPHxzE2NnZLIBEUFIRDhw7BZrPhjTfecOt7YrfbMT4+joMHD7qkGa5du+b+ed6G0WjEwsLCiu0BTpHRXbt2YXFxEVevXnU7szI0NASr1Xqn4TWDOP3/EhIStnS45mbGx8eh1WqRnp5+R4ApEokQHx+P6upqWi2o6MRsNqOqqgrj4+MoLCyEQqHAz372M6FMJvubG0bLO6zAX0RgBeA5AK23/b8fUxSVeuNPnbsHJIRkBQUFff3v/u7vPJ5P5vP5yMvLg81mW1Vc7nbMZjNqamqQkZHhkcZILy8v5ObmoqOjA8M3lUQ8iVgsRmxsLLRaLaPK7HNzcy7TapVKRbvujHNkv7+/n7Zj+vr6YnZ2lpZyoFPYk673eLPHcXrxTUxMwG63I1Wvh/x//A/YIyMxc+YMXv/kExiNRnz9619fVSZiw4EVsJS1+vBDoLsb1H33wTE+fkf5SalU4vDhw2CxWHjrrbfWHegODw9DqVRCoVC4pBsuXryI6urqjZ3rDZqbm9elJcXhcKDRaJCamoqGhga0trau6zpaXFxEe3u7RzWjnEM60dHRHp88XImBgQG0tbUhNzd3xexZQEAAFAoFmjZgm8Q0g4ODKCsrQ1hYGNLS0ly/J1wuF6dOnfJXKBSnd0qCK3PXB1aEkGAAewH8nsZj8hUKxal33nmHFmmFjcBisZCQkIDw8HCUlpau2VTq9MHSaDQe1UXh8XjIzc1Fd3c3bc3S7hIYGAipVLpuM1t3cDgc6OzshFarhUajQX5+PoaHhzE/P0/7WmFhYdDpdLT1RTmFVenKKAqFQlpeN4fD2bS1E5fLhUwmQ3l5Oe43GvHwm29iXKnEW089hdc+/BAmkwnf/OY315xGGxoaWlZjbN3ccw/wwQdAezvSX3oJWOZ1yWQyHD58GF5eXnjrrbcwMDCw5mH7+/td5dJdu3bBz88PXC7XLdPm2xkbGwOXy3VL8FYsFqOwsBA8Hm/N5naKolBTU4Pk5GSPNY1TFAWtVguVSsVIk/9Gzqe1tRUjIyPIy8tb89qKjo6GxWKhdUO1GcxmM6qrqzEyMoKCgoJlp1GTk5Pxne98J1gmk73s+TO8O7jrAysA/w7gBQC351P/mRDSQAj5/wghbjXgKBSKf/vxj3+spEMQcbMolUpkZ2ejubkZ3d3dK+70m5qaoFAotsQxnsvlIicnB319fdDpdB5fHwDUajXm5+eh1+tpO6bRaMS1a9fgcDiwa9cuSCQSsNlsWu1ubobD4UClUq3rh3e9BAUF0Rbw0lUOpKOB3eFwoKOjA7FaLfL+z/8Byc6G7o03oJufh8lkwlNPPbWmCGl1dTUuXLiAP/7xj5trzL//fuh//nN4NzQAv/jFsg+RSCQ4fPgwfHx8cOzYsVVNl41GI9hstqsni8vlYu/evbBarRCLxesybb4diqLQ1ta2oQk9QgiioqKQlZWFtrY2NDY2LhsYd3Z2QiqVunwSmYaiKNTX18PX15cWKZDNYrfbUVNTA7vdjqysrHX1ThFCkJqaiv7+/i2dtAaWsqRlZWUICgpCRkbGqsHxSy+9JJTL5d/dKQkuz10dWBFCvgRgjKIo7W1/9SKAOABZAKQA/pcbx8wICgr66laUAFdCKBQiPz8fc3NzqK6uvqMPp7+/HxaLxaM+WLfjDK50Ot2W7L4IIUhLS0N3d/emp9coikJ3dzfq6uqQmpqK2NjYW5rTnUMGTGToIiMj0dfXR1vfhVgsxvz8PC3m335+fttmMnBkZATi997DY++/D7JrF3S//z0uarXw8vKCzWZbM8CurKzExx9/DF9fXxgMBvzXf/3Xpq7bnuxsOB5/HPjZz4Dm5mUfIxaL8dRTT0EikeDEiRNob29f9nH9/f13ZNqio6Oh0WiwsLAAPz+/NU2bb0en00EqlW6qJ1AoFCIvLw8ikQjXrl275VqYmprC6OgomJCkWYm2tjawWCx4sgd2JSwWC65fvw6pVIrExES3yqAcDgeZmZmor6+nZSLcXSwWC7RaLQYHB5Gfn4/AwMA1n3NbSXBryjrbmLs6sAJQAOBhQkgfgJMA7iGEHKMoaphawgzgDQDZ6zkYIYSlUCiOHTt2zH+rHcZvh81mu7zmbjZynpmZQX9/P1JTU7fcwJPD4SAnJwfDw8MeM0y+GS6Xi8zMTNTW1m5Yc2lxcRHl5eUwmUwoKChYsT9Co9Ggs7OT9qZ5pxgknRpUAQEBtJQDt1Ng1f+Tn+CRs2dhLylBz29+g//+4x8hFArxzDPPICoqCp988smK72FFRQU++eQTqNVqpKSk4Omnn4ZAIMAf/vAHVFVVuX0uJpMJHA4HrN/9DhCLgaeeWrYkCCw1LT/11FMICAjAO++8g+bbgjCbzYaJiYllM8979uwBi8WCj48PvL29cfz48XVJntjtdnR3d9MSgBBCEBERgczMTDQ2NqKjowMWiwUNDQ1IT0/3mLRCd3c35ufnkZSUtOX3vbm5OZSVlSEqKmrDmTNvb28kJSVBq9V6tJl9ZGQEpaWlUCqVyMzMdGu6OikpCQcPHlT5+fn9LYOneFeyvaIHN6Eo6kWKooIpigoHcADARYqiDhJCAgGALH3jHgWwru5AsVj83SeffFLlCaf3jRIcHIyMjAzU1taip6cHdXV1SE9P3zYju2w2G1lZWRgbG6Nd9HI9CIVCJCQkbOgGpdfrcf36dajVaiQkJKxqIcLj8aBWq+/4YaSDyMhI9PT00FZqpKscyOVyYbfbN33jpyOwkk5Ool2tRvevfoUTZ85AKpXi8OHDkEgk2LdvH3x8fPDOO+/c0RN2/fp1nD9/HnFxcXjwwQfh4+MDf39/fPvb30ZUVBTOnTuHjz76yK0+t/Hx8aWmaYViSYKhuhr4139d8fECgQDf/OY3ERwcjPfee88lbQAs9XytpIcmFotRXFyMvr4+5Ofng8Vi4b//+7/XnNrr7u5GSEgIrX1PIpEIBQUFcDgcuHjxIoKDgz0mu6LT6TA2Nsb45PN6mJycRFVVFdLS0jY90CKTyRAQEIDW1tvnsOjHarWitrYWAwMDyM/P37B35z/+4z/6iESivyeEbI+pgW3CXR1YrcJxQkgjgEYAMgD/tNYTCCFSkUj0s1deeWV7CKCsgo+PD/Lz89HV1QUul7tthPCcOIOrqakpj6mj34xCoYBSqURjY+O6Hm+z2VBbW4vR0VEUFhauW0tKpVLBbDbTLpTK5/MhkUhoEz8Vi8VYWFigRcpBLBavW018JTYbWC0uLuJPe/ag/Ec/wqmzZ6FUKnHo0CHX0Ia3tzeefPJJLCws4PTp065AsKysDJ9++ini4+PxxBNPYHFx0fUcPp+PAwcOoLCwEFqtFn/4wx/W3ah/i8zC/v1Lf15+ecm8eZX34Otf/zoiIv4fe28e3lZ9ZgGfnzZbsmRJXiRZ8r7v+74kIZCErARCCIQmBMJWhpbyPfQrpfP0mWmHKZ3OlAKl0w4QoJSmZGkoWQkJiR3vtrzv+77Km7zIsizd74+g2zjetNpu+c7z5EliX11dSVf3nt/7nvccP/ztb39DcXExgNttwJVsClJSUiCVSpGbm4tHHnkEWq0Wf/rTnzAzM7Pk9lqtFr29vXbRIBljnpydndHT07MmwyuDg4Po6OhAUlLSuoYqA7cn52pra5GamgqhUGiTfQYGBmJyctKuxsdDQ0PIzc2Fu7u71XmFTk5O+PWvfy2WSqX/a8ND/IfHPw2xoijqJkVRe77591aKoqIoioqkKOo7FEVNrfZ4qVT621/96lcuts7rsxe6urogk8mgUCiQl5eHqalVX+KagsFg0NlpDQ0NdrVCWAr+/v7Q6/WrtiQnJyeRl5cHFxcXxMfHm2VTQQhBdHQ0ampqbDbJZ0RgYCBaWlps9r7JZDKbtANtIWC3llj19vYChKCzrw8+Pj44cuTIosWFh4cHdu/ejY6ODly7dg15eXn46quvEB4ejgMHDoDJZGJ6enpBlYXBYODee+/FgQMH0NfXh/fee29VGxGKoqBWqxfeWN99FxCJbrcEVyCzHA4Hjz32GEJCQnD58mVcu3YNDg4OKy6UGAwGdu/ejcnJSdTX1+Oxxx7D+Pg4Pv300yXf08bGRgQFBdkswPlOzMzMoKWlBUlJScjIyEBvby+qqqps/l0wYnR0FPX19UhOTl7XCj1FUWhqakJ3dzfS0tJsurAlhCA+Ph719fXLkmVLodPpUFFRgfb2dqSlpcHT09MmFb+HH36Y5e/vfw8hJGX1rb8d+KchVtaAEJLg7e29/bHHHtsY/bRVMDY2hr6+PtqOITo6GqWlpTYzg7QVGAwG4uPjodFoUF9fv6bkihCCmJgY9PT0LFtR6u7uhlKpRFxcHJ09Zy54PB68vLxsXpkzOtrbqhqmUChsMjFpC52VtcTKOHkql8tx+PDhZVfcsbGxSEpKQkFBAa5du4aIiAiaVAHLe1hFRkbiqaeeAkVROHHixIo+Q2q1GgKBYOG54+4O/O//3s4rXKElCNzWJR48eBARERHIy8tDX1/fqq1WLy8vxMfHo6ioCI6Ojnj44YfR39+PU6dOLRhSmJqawsTEhMVtnpVgMBhoawU2mw02m42kpCTw+Xzk5+fb3I5ErVajsrISycnJdk9ZWAkGgwEVFRXQaDRITk62i18gh8NBTEyMTfVWw8PDyM3NhYuLC5KTk62zGLkLhBB8+OGHLlKp9I//v5D9Nr71xIoQwpBKpZ989NFHruvdrzcFc3NzqKysRHx8PH2DEIlESE9PR3d3N6qrqzeUk69xnFin06GmpmZNyRWTyaRFtneu/vR6PSoqKjA0NLSiQN1U+Pn5YXh42OoW2d0ICgpCc3OzTfZFURRu3LiBwsJCq/ZjnKKzBtYSq76+PgiFQuzYsWPVG5vRsoDBYCAzM3NB+2glc1APDw8888wz8PDwwNmzZ3H9+vUlz93h4eGlY1sOHAAOHQL+/d+BVVrSTCYTe/fuhVwuR2VlJU6dOrXqUMR9990HLpeLCxcuIDg4GA888ADa2tpw7tw5+vtfV1eHsLAwu+iQmpqa4O7uviBwnBACf39/REZGoqSkxGYLvZmZGSiVSiQmJpoVC2Rr6HQ6FBYWQiAQIDo62q6tSBcXF3h4eFit4Zyfn0dVVRVaWlqQmpoKb29vu5wPISEhOHz4sIdIJHrR5jv/B8S3nliJxeLn9+/f77WRBetGUBSF8vJyBAcHLxKKcjgcJCcng8vlIj8/f13GdpeDsWUGwC7+TyuBy+UiJiYGpaWlmJ+fh0ajQX5+PoRCodmtv+XAYDAQHR1t89cmEAjAYrFs4m9TUFCAubk5XL9+3apWHoPBAIPBsEqvxWKxLG4XGbPLXF1dV20HZWdnIzs7G2FhYeDxeDh9+vSCadH5+fkVP38+n48nnngC8fHxyM3NxV/+8pdFhHBoaGh5t+/f/hYQi1dtCQK3PYQ2b96M+++/H01NTThx4sSKBJbL5WLbtm3o6elBeXk5YmJisH37dtTV1eHSpUtQqVSgKMpm2ZN3YmRkBCMjI8tOGYrFYmRkZKC7uxt1dXVWfSe0Wi2Ki4sRExNjk8xLSzEzM4P8/Hz4+voiMDBwTUTzAQEBmJmZsTjVYmRkBLm5uRAKhUhNTbW7FvfnP/+5Uchu+5PuHwzf6hBmQoiLq6vr2R/96EcirVZLG0BuVHR2dkKv1y8b0UEIob1qlEolnJyc1iUgeSkYXcBHRkbQ29sLmUy2ZhM9xgtKfX09Ojs7ERkZuezklaVwdHSEWq2GRqOxaVaZk5MTmpqazAsJvgszMzM4d+4cvLy8oFarMTw8bNWY+sTEBDgcjlXVg46OjlVd0ZfC6Ogo8vPz4e/vD09Pz2UrTjdv3kR2djZiYmLw4IMPwtPTE0VFRRgeHkZERATm5+fR39+/ap6d0SeJx+OhuLgYDQ0NCAgIAI/HozV8yxoJ83hAQADw1lsAh3M7AmcJUBSFqqoqREZGwsfHB3K5HGVlZaisrISPj8+KkSgdHR2orq5GbGwsrSs0vs5NmzbZvG2m0+lQWlqKxMTEFacMmUwmFAoFRkZG0NbWBqlUava11RjpFRYWZheCaCrGxsagVCoRExOzpqHSxmtmeXm5WWHNer0edXV16OvrQ0JCAtzd3dfkWvtNiLdDbm5u4CuvvHLa7k+4gfGtrlhJpdJ3fvnLX4q2b99Om951d3evudDaFExNTaGjo2PZMNk74erqSk8NrodwfDkQQhAeHg4ul4uysrI1bVlSFIXJyUlIpVK7OUOHhoaivb3dYg+tpSASiWAwGKxqMxYXF0On02HXrl3w9/dHa2uryROTS8EWAnYGg2FR1cqoExOLxUu2Yowtz+zsbMTGxmLfvn1gMBjw8fHB9u3b0djYiFu3bi0Srq8EQgiSk5Nx9OhRTE9P4/3330draytGRkZWv+E/9BDw6KPAz34GVFUtucnY2BicnJxoEhQUFITjx4+DxWLho48+WrYdRAjBrl27oNVqce3aNQDA1q1bERwcjI6ODpu45N8Jo9N5UFCQSaSaEIKwsDB4enoiLy8Pk5OTJj+XwWBASUkJ/Pz81pTM3I2+vj5UVVUhJSXFrCggW4HD4SAiIgLl5eUmXcdHR0dx69Yt2sx1rVunBw8eZPn6+m79tgvZv7XEihCS4OXltePw4cMsQgh8fHyQkZGBsbEx5OXl2cQI0VYwGAwoLy9HbGysyasWBwcHpKWlgaIoFBQUWO0bZCsQQhAaGgqBQLAmZngGgwGVlZUYGRnBvffei/HxcZtl590NY3CtNaRlKVijtZqbm0NxcTGCg4MhkUgQHx8PNzc3fPnllxZPHYnF4nUTsPf29oLNZoPP5y8iVhRF4euvv0ZOTg7i4uJoUmVEcnIyoqKicOPGDdTX15udqenr64tnn30WQqEQn376KXJzc02rpLzzzootwaWqdxKJBE8//TQ8PDxw5swZZGdnL3ljlUgkSEtLQ0VFBTo7O0FRFCQSCfh8Pq5cuWLT71d3dzddiTIHCoUCcXFxUCqVJn33jJmDEonEqkqtNaAoCi0tLbRn2Hpqu4yf50oTznq9HrW1taivr0dSUhL8/PzWxePLKGSXSCTfaiH7t5JYLSdY53A4iI6ORnR0NGpra1FZWWlzZ21L0NTUBIlEYnaLybhiDAgIQH5+PkZGRux0hOYjODgYYrEYJSUldhvP1ul0KCoqAo/Ho/VUCQkJqK+vN2v1bA6kUikIITb1oXF1dYVGo7Fo0qqsrAwajQaZmZkAbk+UBQUFYXZ2FlevXrXoeJycnKy293B0dLSYWCkUClAUtYA0URSF69evIzc3F/Hx8di7d++iGwshBHv37oVUKsWNGzcwPj5utlZMJBLhqaeeQmhoKKqrq/HXv/4VSqVy5f24uQG//z1QXg784hcLfjU3Nwe1Wr1kFdXJyQlHjx5FdHQ0bt68iXPnzi0ZTbRp0yYIhUJcvHgRra2t8PT0xI4dO9Df34/y8nKzXt9ymJqaQltbG6Kioix6vFAoRFpaGlpaWlYkCBRFobq6Gk5OTggICLD0cK2CwWBAVVUVJicnkZqaapfJP3MRHh6O7u7uJa9bY2NjyM3NhaOjI9LT09dd/hEaGmqxkJ0QcoIQMkQIqbnjZy6EkK8IIc3f/L32pUMz8a0kVjwe7/F9+/YplmurOTs7Iz09HW5ubsjLy0N7e/u6tdNGR0ehUqmW13GYAKlUitTUVNTX19vUG8laBAYGwt3d3S7kyihS9/b2RlBQEH2TdXBwoFfPtjDMXAqRkZGor6+3ST4fcJsQWFK10uv1KCgogI+PD7y8vADcvlnPzMwgOjoalZWVZof5Go/HwcHBqpYnh8Mxm1jNz89jYGAACoUCer2e1uxQFEV7VSUkJGDPnj3LrtbZbDYOHToEQgguXbqEN954A++99x6+/PJL1NXVmUQYORwO9u7dS7vzX7hwAW+++SauX7++fMv2wQeBw4eBn/8cqKykf9zd3Q0vL69lj5fFYmH//v3YunUrqqur8fHHHy86Rg6Hg507d2J4eBgFBQUICAhAREQEvL29cf36dasHWYwV85iYGKv8oxwcHJCamoqRkRHU1tYueR1qamoCRVEIDQ215pAthk6nQ3FxMbhcLmJjY9fdhNQIJpOJ2NhYlJeX01VIvV6P+vp61NbWIiEhAQEBAevuRG/Ez3/+cwGXy32NEGLuyPVHAO6/62evArhOUVQQgOvf/H9DY2OcNWsIQghHIBD85+uvv77iB04IgUKhQFZWFjQaDW7durXmFR/jqGxcXJzVX3Aul4v09HRoNBpac7MR4O/vD5lMhqKiIpsRkYmJCRQWFiIyMnLJtoVQKERwcDCUSqVdSKajoyP8/f1tGk0hkUgwMTFh1k2yqqoKarWarlYBQHNzMyorK9Hc3AwXFxdcuHDBonPBWj8rS1qB/f39MBgMUCgUMBgMYDAYoCgKV69eRX5+PhITE7F79+5Vby5isRjJyck4dOgQ0tPTwWKxUFpaitOnT+N//ud/8Pbbb+Pzzz+HUqnE8PDwsjYLMTExePbZZ3Hs2DH4+voiLy8Pb731Fs6ePbu0Z9jbbwOurnRLkKIomlitBEIIsrKycPDgQQwMDOD9999f1FILCQmBh4cHenp6MDc3B0IIdu7cidnZWdy8eXO1t3ZFNDQ0QCaT2URjxGQykZCQAEIISktLFyyo2tvboVarER0dvS4EwZgT6unpieDg4A1DUowQCoXw8PBAQ0MDxsfHkZubCzabjYyMDLPb2vYGn8/Ha6+9JnRzc/upOY+jKCoHwN1j0A8A+Pibf3+M2zF1GxrfOmIlFApffPrpp8XLjkjfBaNuJj4+Hs3NzVAqlTYVJ6+E2tpa+Pn52ay0y2AwEBUVBS8vrw2lI/P19aWntqwlfENDQygvL0dSUtKKInW5XA6hUGi3XC5vb2+o1WqbCYgJIQgMDDQ5f5GiKDpc1dhSGR4expkzZyAUCjE9PQ0fHx+MjY1ZdOO1VsDu6Oho9vfISFY8PT1pYvXll1+isLAQycnJ2LVrl0k3Q4qiwGQyERoainvvvRdPPvkkXn31VRw/fhzbtm2DVCpFc3MzLly4gN/97nf41a9+hZMnTyI3NxddXV2Yn5+n8wGN+sxHHnkE3/ve95CcnIzm5mZ88MEH+OCDDxa68ru6An/4A1BRAfznf2JkZATOzs4mZ/iFh4fjySefhMFgwIkTJxaY0s7MzMDT0xM6nQ55eXkAbrvtJyQkoKSkxGJdoUqlwvj4OAIDAy16/FIwDrG4u7vTNiC9vb3o7+9ft/w/42IsIiJi3XRdpsDf3x+9vb0oKytDfHz8mlk/WILnnnvOgcfjPUEIWZwobh6kFEX1A8A3f6/fNIOJ+IdwGrcVCCF8mUz2/7766qtmMxU+n4/U1FQMDAygsLAQCoUCAQEBdisVq1QqaDQa2v/JlpDL5XB2doZSqYSPj4/FruO2hLe3N5hMJoqKipCSkmKRrqG3txdtbW1IS0szacw8NDQUxcXFtG7HljB6d5WXly8yprQUcrkczc3NmJubW/Vm3NDQgJGRERw4cACEEMzMzODkyZPgcDh46qmn8Mknn6CmpgYREREoKChAZGQkPDw8TD4WsVi8alzQSnBwcDC7Atzb2wtnZ2cIBALo9Xp8/fXXUCqVSElJwY4dO0w+h7Va7aLzg8lkwtPTk76pUhSF0dFRdHV1oaurC93d3TSRYTKZcHJygqOjI5ydnenPViwWY8eOHdiyZQsqKytRVFSEs2fPQiAQIDk5GfHx8eA98ADw+OPAf/wHBgMD4btzp1nvgVwux9NPP42//OUvOHnyJLZv3063+RMSEmjfp9TUVAgEAtxzzz2ora3FlStXcPToUbO+53Nzc6iurkZqaqpdrg++vr7gcrnIyckBi8VCRkbGutjdDA4O0qLvjVb5uRNqtRoVFRXw8PCASqVaV0G9KWCz2XjjjTfEL7/88n8BeGK9j2ct8a2qWLm5uf3kRz/6kdCaL49MJkNWVhYoikJOTg6GhoZseIS3MT8/j5qaGruWxPl8PjIzMzE2NoaysjKbteGsgUKhgL+/PwoLC80eGujo6EBnZydSU1NN9u4x5nK1tLRY7Sa+FAQCASQSiUU6pqVACIGfn9+q+6MoCrm5uRCLxQgPD4der8epU6egVqtx6NAhODs7IyMjg66k8Hg8nD9/3qwJMqP43NJWqiWtwN7eXnh6eoKiKNTV1UGpVCItLc0sUgWs7LhuBCEErq6uiIuLwwMPPIAXX3wRr7zyCg4dOoTY2FjMz8/jzJkz+N3vfoeysrIFLS0HBwckJyfjxRdfxGOPPQZ3d3dcv34db775Js6fPw/VT38Kys0Nvj/5CcQWmL86Ozvj2LFjCAsLw9WrV/HXv/4VGo0GEokEW7ZsgcFgQE5ODoDbn+0999yDjo4O1NXVmfwc+PLwRAAAIABJREFUFEWhoqICoaGhdjWWNC4QDAbDugwKtbe3o6WlBenp6RuWVBkMBjQ1NaGiogKxsbGIjIyEp6fnugTcm4tDhw4xxWLxLkKIvxW7GSSEeADAN3/b/qZrY3xriBUhxJ3H4x1/4YUXrA5JYjKZCA4ORkpKCrq6ulBcXGzTbKzGxkb4+PjYfUXCZDIRFxdHi/TtNSlnDuRyOYKCglBYWGjyjbe5uRmDg4MWVbqMk4Ll5eV2saQICgpCT0+Pzc4PLy8v9Pf3r9gybW9vR19fHzIyMmiRdmdnJ/bt20dXZLy9vcHn81FbW4vk5GT09/ejqKjIrGNxcnKy+HWZS6ymp6cxPj4OhUKBwcFB9PX1IS0tDdu2bTN78WEKsVoKTk5OCA0NRUhICA4ePIiHH34YbDYb58+fx1tvvUW3tYwghCA4OBhHjhzBd7/7Xdqd/92TJ3Hh0UfhMDoKEhcHfPQRYCZB5XA4OHjwIDIzM1FTU4OmpiYYTY7j4uJQVlZGt2oTEhIglUpx9epVk1vtXV1dcHBwMKuKaS4mJydRUVGB9PR0JCQkoLi42C4LnKVAURRqamowMjKC1NRUk9uxaw1jSDxFUcjMzKTNYgMCAqBSqdbs/bIUDAYDb731lqtMJnvHit18gb9XvJ4A8Dfrj8y++NYQK6lU+sbrr78utuUXiMvlIjExEf7+/igtLUVDQ4PVlZ+xsTGMj49b5EptKXx8fBAbGwulUmmToF5rIZPJEBoaisLCwhV1OBRFoba2FpOTk0hKSrK4jcDn8xEeHo7S0lKb+2oxmUxERkbaLO6GwWDA29sbHR0dy26Tm5sLPp+PmJgYlJSUoKysDJmZmQvayrdu3YJarQaTyURDQwOCgoJw48YNs3RT1vhZsVgss/R0xvNSoVDQ2qH77rvPooquOeagS2F4eBhSqRQRERF49tln8fjjj8PFxQVXr17Fb37zG9y8eXORR5hEIsHevXvx8ssv45577kG1uzvee/FFaKOigCefvJ0raGb1ihCCiIgIxMbGore3Fx988AHGxsawadMmMBgMZGdnA7h9zuzcuRNqtRq5ubmr7ndyctJkM2JLodFooFQqkZCQAB6PB6FQiKSkJJSXl9skwmklzM/Po6SkhBbSb8S0DaOPVllZGaKiohASErJATmAMma+srNxQ2bBLYfv27cTb2zuFEBKz2raEkJMACgCEEEJ6CCHHAbwBYBshpBnAtm/+v6HxrSBWhBAfoVC47/Dhw3bRlLm5uSErKwtsNhu5ubno6+uz6CZq9E+JiYlZc82TUChERkYG+vv7UVVVZTdvKVMhkUgQERGBwsLCJSfhjCtOvV5vk6lJiUQCqVSKmpqa1Tc2E25ubuByuejt7bXJ/nx8fNDT07PkZ9Tb24v29nakpqais7MTV65cQUhICLZu3Upv09nZiYqKCohEIuj1evT398Pf35+ubpl67opEIosF7Oae3z09PSCEQC6XA7hdPbL0M7e0YgXcvinrdDq6PWYcKjh27BieeuopeHt7Izs7G7/5zW/w5ZdfLrJf4PF4CA0NxZYtWzAnleLN3bsx9qMfAefOATExwI0bJh+LwWBAQ0MDtm/fjiNHjmBqagrvv/8+1Go1kpKSUFVVheHhYQC3z5nIyEjk5eWt+Jnp9XqzzYjNhdG0NioqakFcD5/PR0pKCqqqquw2gT07O4uCggJIpVK7BVRbi6mpKeTl5UGn0yErK2tZ/0JnZ2dIJBKTB1rWE++++66rTCb7w2rbURT1GEVRHhRFsSmK8qQo6gOKokYoirqXoqigb/62L/O2Ab4VxEomk739m9/8xsWeniQMBgMBAQFIS0vD4OAgCgoKzG6tNTc3Qy6Xr1uvn81mIzExEU5OTsjPz7fYmdtWcHNzQ1RUFIqKihYci5FUURRlVebd3QgICIBOp0NnZ6dN9ncnwsPDaeG5tWCxWFAoFOjq6lr0u7y8PNru4cyZM3B3d8eDDz5Iv0d6vR4XL16EUCjEsWPHIBQKQQhBbm4uNm3ahJaWFpPJpbWWC4QQk1fbvb29kEqlNjFr1Gg0FuuGRkdHl5029fLywqOPPorvfve7CAsLQ1FREd566y188cUXC4hCR0cHPeEnEInwO4EA3adP384WvPde4NVXARPOk66uLkgkEnC5XPj6+uL48eNwcHDAxx9/DHd3d7DZ7AUTn9u2bQODwVjRGLahoQEKhQJCodD0N8UMzM/Po7i4GCEhIUu+j1wuFykpKaiurrY5uVKr1SgsLERoaCh8fHxsum9bgKIotLa2QqlUIiIiAmFhYasuHoKDg9HX12e1Ya+9kZiYiNjY2CBCyOb1Ppa1wD89sSKERCoUivT7779/TV6r0YAyLCwMFRUVqKmpManlMTk5icHBwXVzGzaCEEIbDBYVFdnUQdwSuLq6IiYmhtax2YtUAbdfe2xsLDo7O23ejuBwOAgODl42981c+Pn5oaOjYwExUalUqK+vR1xcHM6ePQsGg4HHHntsgZi/oKAAw8PD2LlzJ4RCIW1OOT09DY1GA4VCgStXrphEqtlsNvR6vcWtCFN1VhRFoa+vzyaTm8ZjtfS8GRoaWjW7TiKR4MEHH8T3vvc9JCQkoLq6Gr/97W9x+vRptLe3Q6vVQiQS0SJ0Nzc3fFRdjfpPPwWeeQb45S+BtDSgsXHZ55ifn18UAO3m5objx4/Dw8MDX3zxBRQKBerq6tDf3w/gdoUjKysLDQ0NS1Y5hoaGoFar4e9vjc54eRgMBpSWlsLb2xsymWzZ7exBroaHh2mLAlOtdtYS09PTyM/Px+zsLDIzM032DGMwGLTZ70Yxfl4Ob775pouHh8d7ZCOWCW2Mf3piJZPJ/vfdd991W+vPUiwWIzMzEwKBgPa/We7EN8Y4REVFbRinXxcXF6Snp6O9vR11dXXr2scXi8WIjY1FUVERysrK7EKqjGAymUhKSkJlZaXVjtV3Qy6XQ6vVQqVSWb0vNpsNqVS6oL2Yl5cHFouFvr4+jI2N4ZFHHlnQRhgfH0d2djYtwK6trUVtbS19zuXn52Pz5s1mxd04OztbHBBtKrFSqVTQarU28ReamZmxSl81MjICFxcXk7YVi8XYtWsXXnrpJWRmZqK1tRV//OMf0dHRQb9uJycnPPHEE5DL5Th96RIqX3jhdluwsxOIiwP+7/+WFLa3tLTAx8dnUQXPGIMTERGB9vZ2MJlMXL9+nf59WloaxGIxrly5sqCVrNVqUVtbi7i4OLt8ryiKQnl5Odzc3ODt7b3q9rYkV52dnWhoaEBqauqC1uNGAEVRaG9vR0lJCcLCwmg3f3MgFovB5/NtJjWwNSiKQk9PDwYGBpCWlubJZrMfXO9jsjc2xl3cTiCEZERGRoalpKxP0LbRPDAzMxMTExPLmnL29fWBx+OtS3r6SjBGUDCZTBQUFKyZMepSEAqFEIlEGBoasrvvFpfLRXR09CJnaGth9LZaYBppBfz9/dHa2gqKojAxMYGqqiq4uLigs7MTu3fvXtDuoCgKly9fBiEE999/PxoaGvDXv/4Vrq6umJ+fB5/PB0VRKC4uRnp6uslxN9YYhZpKrO4UrlsLa4TrGo0GbDbbbO0Rn8/Hvffei5deegne3t5ob2/He++9R1eDHR0dceTIEfj6+uLzzz9HiUIBVFUBmZnAc8/djsO5g4zPzs5iYGBg2QEXFouFAwcOICsrC3q9Hq2trWhpaaF/d//990OlUqG4uBjA360VwsLC4Oho9dD0IhirzI6OjmYZjd5JriypIFMUhfr6egwODiItLc0ur80azMzMoKCgANPT08jKyjKZsC+FsLAwNDc3b5hEDeDvleacnByMj48jLS0N77zzDtfFxeXX/+wBzf/UxEomk73561//enn77TUCm81GVFQUHe5cUVFB31Dm5+fR1NSEsLCwdT7KpUEIQUhICIKDg1FQUGCTaoslaGpqAiEEGRkZKCsrs/uYsaurK7y8vFBRUWHTEjuPx4Onp6fZuX9LwdHRES4uLhgYGEBBQQEoisLQ0BBSU1MRHx+/YNvGxkY0NTVhy5YtGBoawunTp+Hh4YGnn34aHh4emJ6eBiGEroSYGndjjc7KHGLl4OAANzc3i57nTlgjXDe6rVuKiYkJJCYm4ujRo5ibm8P777+P0tJSUBQFDoeDw4cPIyQkBJcuXUJuWxtw5Qrw618Dly8DUVHAN1XEhoYGBAcHr1jdJoRg69at2LVrFwDg1KlTNAEOCgpCYGAgsrOzMTU1hY6ODnC53BXbc9agubkZ8/PzCA8PN/uxXC4XycnJqKysNKsyqtfroVQqYTAYkJSUZDchviWgKAqdnZ201iwyMtLqyUQOhwN/f380rtA+XitQFIWBgQHcunULKpUKycnJiIyMhIODA+RyOR588EGxg4PDwfU+Tnvin5ZYEULig4OD/SxNY7cHjOHO7u7uyM/PR1tbGxobG+Hn52eyqeV6wd3dHampqfQNei37+W1tbVCr1YiNjYWzszM9lm2ruJjl4OvrCxaLZfOpG39/fwwNDdnENywwMJA2y6QoCoGBgdi2bduCbebm5nD58mVIJBJIJBJ89tlnkEgkePzxx+Hg4ICsrCwwmUx6xXz+/Hns3r3bpLgboVBoMck1lVgZnfGNVUqDwWBxxdJaYrWavmoldHR0wMfHB76+vnjuuefg6+uLixcv4uzZs9BqtWCxWDh48CCioqJw/fp1XL9xA9QPfgAUFwMuLsCOHdC++CKmR0dN9pdKSkpCcnIydDod/vCHP6C3t5euWup0Oly5cgVdXV12s1bo7OzE2NiYVZPOPB4PSUlJUCqVJvmmabVaFBYWwtXVFRERERtq8k+j0aCwsBATExPIzMxcMXbLXHh7e2NsbMzi1ry1MC7scnNzMTAwgMTERERHRy8aFPnXf/1XZ5FI9B//zFqrDUGsCCFMQkg5IeTCXT9/hxBi0biDTCb7rzfeeMP6Ja6NcWe4s1qtRnt7+4Z1/L0bXC4XaWlp0Ol0KCoqWhOn5O7ubgwODi7IEOPz+fQq1t6eN1FRURgcHLSpw75RcGoLbysej0fn14lEIhw4cGBRJePmzZtQq9VITEzEZ599BldXVxw5coS+4Lm4uIDH42FkZAQCgQBqtRp9fX2IjY1FQUEBLX5e7rUwGAyLWhCmEKu5uTkMDQ0taAMaDAaLV/iWEiuKoqBWqy3W6MzMzECv19OPd3JywuOPP46tW7eirq4O//d//4f+/n4wmUzs378f8fHxyM3NxZUrV0BFRwOlpcCLL8Lh3XeR8NFHZpGF7du3QyAQYH5+Hh9++CHq6urg6uqKlJQU1NbWQiQS2cXLqb+/Hz09PUhMTLRaO8rn8xEXF4eSkpIVtY9TU1MoKChAYGAg/Pz8rHpOW4KiKHR1daGwsBCBgYGIjo62eRWNEIKoqChUV1evuZBdpVIhPz8fPT09iI+PR2xs7LIG1wqFAlu3bnVhMBg71vQg1xAbglgBeAnAgjRcQkgigKUNPFYBISRYLpfHpKWl2eLY7AImk4mZmRnEx8fTI7a2FkvbAwwGAxEREfDx8VnVE8daDAwMoLOzc0nzTx6PR3ve2LM9yWAwkJiYiLq6OpuONItEIgiFQqutHYyDEYQQHDlyZJGOZHBwEIWFhQgODsa1a9cgEolw9OhR+qLX3d2NP/7xj1Cr1SCE0BfkGzduIDMz06S4G5FIZFHVyhRi1d/fD4qiFgjXjQHMlkCn01nksj0xMQFnZ2eLqx+dnZ2LRvwJIcjKysKxY8eg0+nwwQcfoKSkBIQQ7NmzB2lpaSguLsYXX3wBg4MDhn/6U/QfOQLHTz4xy++KyWRi69at0Ov1EIlEOH36NPLy8uDm5gYXFxdcvHhxRfJsCVQqFZqbm5GcnGwz0iYSiRAVFYXi4uIlF3UjIyMoKSlBXFwcpFJrc39th9nZWRQXF2N0dBSZmZl2nUoUiURrKmQfHR1Ffn4+Ojo6EB0djfj4eJM0jP/+7/8ulkgk/7UGh7guIOs9okkI8QTwMYDXAfw/FEXt+UbYdg3AYQDNFEWZtcT08PA48+GHHz50//33b9hSY19fH4aGhhAbGwvg70Gg9g53tiWmp6ehVCrh6ekJPz8/m5bcx8bGUFVVhbS0tBVvhLOzsygqKkJYWJhVbZrVMD4+jsrKSqSnp9vESwm4ra+7deuWxcLa0tJSXLx4EQwGAykpKUhMTFwggKUoCh9++CGGh4dhMBjA5/Nx7NgxCAQCAEBtbS3OnTsHZ2dnxMXF4datW9DpdPDw8EB/fz8CAgIQGxuLs2fPYvv27VhuodLd3Y3Z2dkFo//LgaIozM7OYmZmBlNTU2hpaYFCoYBWq8X8/PyCPxRFobu7mw7WvjNXTqPRQCAQgMVi0X84HA4cHBzA4XDg6OgIR0dHcLlcsNlsEEIwPz+PgoICZGVlmf1eNzc3w9HREV5eXmY/1mAwIDs7G5s2bVqWZMzMzODzzz9Hc3MzwsPDsXfvXjg4OCAnJwc3b95EeHg43N3dkRgRAb7xc6iuBkz04zIYDPjd734HQggkEgnq6uoQEBCAffv24cSJE9Dr9Th+/PiyZpTmYHx8HBUVFUhNTbWLYHxwcBAtLS30YA1wW4fX1taGpKQku2YbmgOKotDb24vm5mZERETY9fp0J+bm5pCXl4esrCy7acvGx8fR0NAABoOB0NBQiyq5u3btGrl8+fI+iqLy7XCI64qNQKzOAPgFAAGAV74hVi8BYFAU9SYhZMocYkUI8QwJCSmvr69fc4sFU2EMSU1LS1ugrTJO8PT19SEsLGxDrbqWg16vp726YmJibEI6pqenUVxcjJSUFJPyEo2aitDQULu+Zz09Pejt7UVycrLNSOTg4CC6u7uRmJho1uOqqqpw7tw5EEJw7NgxODs7o7GxEXdOwJaVleH8+fNgs9lwcnKiDUEpikJeXh6uX79Om1qyWCxcu3aNDhQ2VpOeeOIJekX6wgsvLHnjnZqaQn19PZKSkgD8nTxNTk5icnISU1NTmJqaotuFXC4XXC4XDg4O6OzsRFRUFBwcHMBiseipOxaLBUIITp06hf7+frz00kv0801PT6Ourg7x8fELiNjc3Bzm5uYwOzuLubk5aDQaaDQa+nlZLBbm5uYQGBgIgUAAPp9v8vmal5eHhIQEi4iC0f5iNR0TRVHIz8/H9evXIRKJ8PDDD0Mul6OgoABXr14Fn89HbGwsokdG4H7oEPCjHwFvmJ7uUVtbizNnzmD37t10BeX73/8+tFotTpw4AT6fj6eeesoqYjI1NYWSkhIkJydbZWuxGjo6OqBSqRAfH4/m5maMjY0hISHBZosea6HValFZWQk2m43IyMg1Py6jX1poaKhN96tWq9HQ0ACDwYDQ0FCriHh5eTl2796d39fXl2HDQ9wQWNdRCULIHgBDFEUpCSFbvvmZHMBBAFss2adUKv23n/3sZ+KNSqqA2ye9XC5fJFg3hjt7eXmhtrYWHR0diIyMtOsFylowmUzExMSgu7sbeXl5iI+Pt8orZm5uji7nmxpC7eDggLS0NBQWFsJgMNgtONbT0xNqtRqNjY02u2BJpVJ0dXVhYGDA5KmshoYGmlQdPXqU9gUyGAyYmJiAUCjE9PQ0rl69CgaDAUdHRxw9ehRCoRB6vR6XLl1CWVkZIiIisH//fnpV6+Lign379uHcuXO0HcSZM2dw/Phx/P73v8fFixdx+PDhBaSSoigQQjA2Noba2lpMTExAq9WCy+XS5MXLy4smMXd/L/v7+1f8vHp6eha10PR6PRgMBphMJphMpkmDH0a/oPHxcdpdf3JyEnq9Hnw+n7bzEIvFi1b5RuJmafXF2CZZDcapV29vb5w5cwYnTpzAtm3bkJSUhO7ubszMzCAvLw+5FIWHUlIQ8atfoTctDfI9e0xqt4WHh0Mmk+Hrr7/Gvn37cPr0aeTk5GDPnj04dOgQ/vSnP+Gzzz7Dd77zHYsqHbOzsygtLUVcXJzdr1m+vr6Ynp7GzZs34ebmhuTk5A1T5e/r60NjYyPCwsLsNmm5Gnx8fJCTkwNfX1+bVA0nJyfR2NiIubk5hIaGWmUNYURcXBz8/PyCCSHRFEVVWb3DDYT1nkHNALCPELILgCMAZwC1ALQAWr65CPMIIS0URa1qgEIIcfPx8XngwIEDG9YjY25uDl1dXSu2I4zhziqVCqWlpZBIJAgKCtpQI8N3w8vLC0KhEGVlZQgICLCoZaLX61FSUmLRSojD4SAtLQ1FRUUwGAw28TxaCsaokr6+Pjq3zlpERUWhoKAAbm5uq37GLS0tOHXqFAghePzxxxd4GQUHB6O5uRmJiYm4cOECtFoteDwejh07BrFYjNnZWZw5cwatra3IzMzE1q1bFxAduVwOjUaDuLg4lJeXg8lkYnp6GqWlpdi6dSuuXLmC6upq+Pr6Ynh4GKOjo5iamgKXywUhBEKhEIGBgWZPuBrJ2d1Qq9WYnJxc9FlaorEihECn00Emky343CiKwtTUFCYmJjAwMID6+npQFAWxWAxXV1e4u7tjbGzMYqsHoy7PHMG8l5cXnnvuOfztb3/DlStXUFtbS09YaTQaNDU1ocXLC361tWA9/zz++8UXERgaitDQ0BXff0IIIiMjce3aNSiVSsTGxqK8vBwZGRnw9fXF/v37cfbsWXz++ec4cOCAWVVZnU6H4uJiREZG2qSdaMrzjY+Pg8FgQCAQbAhSNTc3h6qqKpogW6LlsxUYDAZCQkLQ0NBAy00swfT0NBobGzEzM4PQ0FCbWJ7ciV/84hduhw4d+iWAnTbd8TpjXe/UFEX9GMCPAeCbitUrFEXtuXObb1qBJrnKubm5vfqTn/xEuBHTyo1obm5GQECASSTJGO7c0dGBW7duISQkBB4eHhtqfPhOODs7IzMzE5WVlRgZGUFUVJTJwlWjSaFcLrd4lcdms5GSkoLi4mIYDAaLyN1qIIQgISEBeXl54PP5NnFydnR0hJ+fHxoaGhAZGbnsdp2dnfjzn/8MiqLw6KOPLoo/cnV1RX19PUpKStDQ0AA2m40nn3wSLi4umJiYwJ///GeoVCrs27cPcXFxi/bv4eGB/Px87Ny5E01NTXSsTX5+Ph5++GGIRCJcuHABmzdvhkKhQEhICAQCAQghqKurA4vFMptUcTgczM3NLfk4owD3bsd1S6cCp6amFlXHCCEQCAQQCAT088zPz2NsbAwjIyNob2/H1NQUXF1dMTw8DFdXV7Nu4p2dncsaea4EHo+HRx99FLm5ubhx4wb6+/sxMDCAjIwMxMTEICYmBvN8PviHDmFvczMucjioqakBk8mEn58f7a5/J6GbmJiAXq/Hrl27cOnSJbrqmpOTgwceeACRkZGYmJjAtWvXIBQKF9l2LAe9Xo/i4mIEBQXZ/Ma7FIxkPzg4GBKJBIWFhXByclozDdNS6O/vR0NDA0JDQ+1WMTcXMpmMtqox9zql0WjQ2NiIyclJhISEwN3d3S73naysLLi7uycSQgIoitr4adImYt01VkasQqxWXe4RQgRyuby1vb3dfT1XCivBKPbOysoy+yTVarWor6/HzMwMIiMjN1w0w52gKAodHR3o7u5GQkKCSW2B5uZmaDQak1omq8EY9KpQKOwWtjo5OQmlUon09HSbrEyNuidXV1dwuVwYDAZQFAWDwQCDwYDh4WF8/fXXoCgKycnJkMvli7ahKAqDg4OoqKgAABw/fhwKhQJ9fX04efIkdDodHnnkkRWz4IqKiujA6PPnz9OTggKBAHv37sVf/vIXREVFYf/+/Qse19/fj/HxcbONbpVKJYKCgpY8n7/66isUFhbixz/+8YKFyPDwMAYHB1ckoUshJycHGRkZZpOyGzduICQkhK7SOTs7QyaTQSKRrKid0ev1yMnJwebNmy2uqFRXV4PBYKC5uRlVVVVgsVhISUlBenr6bS3UQw8Bly/DUFmJHkdHNDQ0oKGhgZ7W9fT0pCtZDQ0NiI+Ph0AgQE5ODm7cuAEPDw8MDAzgX/7lX+Dq6gqKonDp0iWUlpZi586dSE5OXvH4DAYDSkpKIJPJ1iTYeHR0FJWVlYiNjaWTKrRaLQoKCpCYmLjm1jVzc3O0vYFRK7iRMDY2hoaGhmUHT+7G7OwsmpqaMD4+juDgYEilUrsv5M+fP2949tlnT/X39z9m1ydaQ2yY3hJFUTcB3Fzi5yZ9U8Ri8fd/8IMfCDYqqQKA+vp6hIWFWXSiOjg4IDY2FmNjY6isrIRIJEJoaOiGEWveCUII/Pz8IBKJUFJSQlfaloPRJ8pW9hjGm4+xcmUPPxuBQIDQ0FCUlpYiNTXV6laEcWrt1KlTq25rjCJZCffddx8UCgUdXcPj8XDkyJEVV/UzMzNgMpnIz8+HUCiETCbD6Ogo5ubmMDk5CZVKhfT0dOTm5iI6OnoBQROLxWhvbzftxd4BR0fHZS0Xent7IZPJFlV3LWkFUhQFvV5vNqnSaDS0Y7RcLqfjgwYGBtDS0gIulwuFQgGpVLroOPv7+yGTySw+N6anpzE2NoasrCxEREQgMzMTN2/eRG5uLkpKSpCamorU//5vOH79NRjPPw/v69fh7e2Nbdu2YXh4mCZZ165dw7Vr1+Dp6Um3hbKysmihOYPBQHZ2Nh566CEQQrBz505MTk7iypUrcHZ2XlZPSFEUKisrIRaL14RU9fX1obm5edFQizH43rjQWatr4uDgIOrq6hAcHGw36YG1EIvF4HA4q4aHa7VaNDc3Y2RkBEFBQXbLYl0Ke/bsYfD5/PsIIR4URdnW92OdwPy3f/u39T4Gq0EIcRAKhX/+5JNPRBuVWI2NjWF4eBghISFW7YfL5cLb25ueOmEymVb569gTXC4XcrkcjY2NGB8fh5ub26LjnJqaQlVVFVJSUmx6QWQwGJDL5WhuboZWq7WJ2PJu8Pl8aDQa9Pf3Wz2NaDAY8MUXX4DH4yE9PR0Xtjr5AAAgAElEQVSbN29GXFwcfH196ZiKjIwM7N27F8nJyUhNTUVaWhrS0tKQnp4OV1dXtLS0QCgUYteuXXB0dERbWxv+9re/QSaT4Yknnlgyi1Kn06G7uxs1NTUYHByEu7s7pqamkJWVBW9vbxQXF8PHxwcTExNobW3Frl270NbWhqamJsTHx9NEhcVioaWlBb6+vmadixMTEyCELKpYGQwGXL58GSEhIYtsHCYnJ6HT6cxqOxmNRs1tD/f19YHL5dLnDyEEjo6OcHNzg6+vLwQCAVQqFerr6zEyMgIOh0Nrzqqrq61a/FRWViIoKIiu+PJ4PISHhyMsLAwTExNQKpVQNjVBGh4O108/Bby8gG+MdJ2cnODj44OEhATazqKnpwclJSVgs9lQKBQIDAzEyMgIhoaGMDQ0hPDwcDg5OdExVm1tbSgpKYG/v/+SFcW6ujp63N6e1x+KotDa2ore3t5lLRwcHR3BZrPR2Ni4wKXfHtDpdKiqqsLo6Ogii5ONCJFIhMrKyiUzVufm5tDY2IjGxkZ4eHggKipqze8nhBC4uLg45OXlCX74wx9eXLMntiPWX/FnA3C53Ed37tzpptFo1txx1lQ0NDTYLA+QEAJvb29kZmZCrVbb3ajTGnA4HCQnJ8PBwQH5+fkLTFB1Oh09RWSPEjqTyURSUhJGR0dtks23FIKCgqDVatHV1WXVfpqbmzE+Po4tW7Zgfn4e7u7u4HA4OH/+PCiKwtatW3HffffB3d2dNnYUiURwdnZGVVUVLly4AIVCgWeeeQaenp7Iy8vDl19+idDQUBw7dmxBi4SiKKhUKpSVlSEvLw86nQ6JiYlIT0+Hv78/nJycoFarIZFIkJycjM7OTlrwfeLECWzfvn3JuBsnJyeTIkfuxHImoUNDQ9DpdEtWAoxTgebAUsf11fIBnZ2dERYWhs2bN8PPzw89PT3Izs5GVdXtISdTJ1vvxujoKPR6/ZLPLZVKcejQITzzzDPw8vLCn7lcdPn7Y/4HP4DurvNQo9Ggp6cHBw4cwAsvvAAfHx98+eWX+OCDDzA0NIT9+/fTU6UXLvw9+ILNZuOxxx6DQCDAyZMnFyUctLS0YHZ21u6RMQaDAVVVVZicnERqauqKJFWhUIDP59s1L88Y2eLu7o6kpKQNF+y8FHg8HlxcXNDX10f/TKfTobGxEfn5+XBycsKmTZvg5eW1bgv0Rx99lMlmsw8QQjbuCLwZ+KeoWP32t7/97A9/+IPEeAM1ClI3wqQIcNsReHx8fJHY2FowmUxIJBKIRCLU1dVhZGRkyXHx9QYhBK6uruDxeCgrK4OTkxN4PB6USiV8fHzsKjolhMDDw4Mer3d1dbXpxcNouFhdXQ2BQGCxB9Dly5cxPz+PvXv3wtnZGUVFRbhw4QL0ej3S09Nxzz33LHqMwWDApUuXkJeXh4iICGzatAnZ2dm4cOECpqenERoaioMHD9Lnw/z8PLq6ulBVVYXZ2Vn4+voiNDQUrq6ui84ZlUoFd3d3eHp6ory8HFwuF9PT05ifn0dfXx8CAwNRVlaG4OBg2nD07tgWU6DVajE5ObmIQBgzKbdt27boPR0fHwdFUWZVCoaHh8Fiscx6DEVRtLXGaucMIYQOMvb09ERbWxtmZ2cxOjoKBwcHuopl6vOWlZXRwbXLQSAQICoqCgGBgVA6OCDkyy/R/tVXaE1IoLUxJSUlCAsLg7OzMxwdHREVFQU3NzfU1taisLAQer0eO3bsQHV1NVQqFUQiET08wuFw6M+5vr4ekZGR4HA46OrqwuDgoE2ialaCceHl7OyMiIgIk57L3d0dra2tYDKZ9Hlpq2OpqamBSqWiq1QbsUuwHIRCIaqqqqBQKNDW1oaamhq4uroiJiYGYrF43V8Lg8HA1NQUKS0tHX7ttddK1vVgbIB/eGJFCEnKyMh49uWXX+Z5eHhAJpNBpVKhpqYGWq2WdmdeT1RUVCA8PNxuwkYHBwd4enrSk3UURUEoFK77l+Vu8Hg8eHh4oLa2Fj09PeDxeCa5dVsLI7nq6upatiVpDRgMBtzd3aFUKiGTycxu/ahUKly9ehUZGRlQKBRQKpXIy8uDwWBAfHw8du5cPIms1Wpx6tQp1NbWIiwsDDMzM7hx4wbGx8eRnJyMBx98EJOTk/Dy8qJXp/X19eDxeIiMjISXl9eyN3sej4f6+nr4+PiAzWaDy+WitLQU0dHRGBwchEajAYPBgMFgQGdnJ+Li4kAIgcFggEqlMqstOj8/D5VKtUiDV1JSgomJCdx7772LjnFsbAwMBsOssf7e3l467sNUTExMYGZmxmxbDYPBgPb2dmzZsgVcLhcdHR1oaWkBk8kEn89f9dzr7++HTqczWbckFAoRlpGBielpeJ47h9zJSXzd10dXHe+cSjQuBOLi4jAzM4Pi4mI0NDRgy5YtaGpqQlNTE90SBG6fC8aWcEdHB9zd3dHV1WXTqJqloNFoUFRUBF9fX7NSHYyvr7y8HBKJxCaDJcPDw1AqlZDL5QgPD9+QutbVQAihNWpGQrXRyGF4eDj7/fffD//hD3/49j86L9kYJR0r4OHh8dPXXnuNjgh3dHREaGgoNm/eDC6Xi8LCQpSXl69b4rdKpQKHw7H7FB8hBHK5HFlZWdBqtbh165ZdM/QshfHzMTpyr5YVZysQQhAXFwedTofa2lqbt4x5PB6ioqJQWlpKm2uaiuLiYtro8p133kF2djYoioK7uzu2b9++aHu1Wo0TJ06gpaUFfD4f9fX1GB0dxbZt2/Dyyy9j+/btEIvFkMlkyM/PR1FREZydnbF582aEhISsSvBZLBbdDgRuG/nJ5XK0trbSOq2BgQHweDz09/ejqKgIwG0tx/j4uFmvfblWYG9vLzw9PZe88FvSCpyenja7Fbia4Hc59Pb2Qi6Xg8FgwMXFBQkJCUhOTsbExASys7PR1taG+fn5JR9rMBjQ1NRkkQGt+D/+A1RsLA7cuAFngwGFhYXLkgAul4t9+/bhiSeeAIPBwBdffAEXFxcYDAZ8/PHHC7Ifvby88NBDD6GnpwdffPEFkpKS7LpYHR8fR2FhISIiIiwShXM4HMTExKCsrGzFjMvVMD8/j6qqKrS0tCAlJQXe3t4bioiYAiPJz8nJgaurK9hsNvz8/OxKii2Fu7s7MjMznQFsWu9jsRb/0MSKECJxcnJKWcpsk8lkwsfHB5s2bYJCoUBdXR3y8/MxMDCwZjosYyvBWsG6OWCxWAgLC0NiYiJaW1tRWlq6ocKd5+bmUFlZiczMTAQEBCA/P3+RfsNeIIQgOjoaFEXZJQHezc0Nnp6eqKysNHnfWq0W5eXlYLPZuHLlCn3Bi4yMxJ49e1BfvyCbHB0dHXj33XcxNDQE4LaA/sEHH8T3v/99pKenw9HREVNTU1AqlVCpVNBqtcjIyICXl5dZZEShUNA+UsZJsampqQU3OpVKBS6Xi6+//hrj4+Ngs9nQ6/Vm3cw4HM4iYqXVajE8PLzsTdUSH6uZmRmz9U6r6auWQ1dX16Jqk6OjIz3Zp9frcevWLbS0tCwi4R0dHZDJZJZpd9hskPffB1Olws4rV+AsENAkfTn4+vri+eefR1ZWFv09nJmZwaeffrrgumHMMFWpVLh165b5x2YiBgYGUFFRgaSkJLi6uq7+gGXg4uJCV8ctwcjICHJzc+Hs7IzU1NQNkz9oKozV5OzsbMzNzSEzM5OOSevp6Vnvw1sWP/7xj13kcvm/rfdxWIt/aGLl6ur6vVdeeUW00irCWBpOTU1FVFQUBgcHkZ2djfb29mVXjbbC8PAwHB0dbdrrNxVOTk5ISUmBl5cXioqK0NTUZHYlxdagKArl5eUIDQ2Fk5MTpFIpUlJSUFtbi9bW1jUhvEb3aSaTaRYBMhW+vr5gMBhoa2tbdduRkRF8+OGHmJ+fB5PJREJCAiYmJhAcHIz9+/fDy8sLGo0GIyMjGBsbw8mTJ/Hxxx9jbm4OXl5eOHLkCJ599llER0fTLullZWWoqKighxt8fX3R3d1t9uuQSCQYGhqi3x/jqH5dXR1ts8Bms6HRaKDX62mRvbOzs1nVYQaDsegzMBK6lYiVOSTRuH9zqg2WxtiMj4/DwcFh2cex2WwEBQUhKysLFEUhJycHbW1t0Ov1dNROYKBJfshLIyEBA8ePw+Orr/DEV19hsKtr1cENFouFrVu34vnnn6ezJIeHh/HJJ59Ap9NhZmYGSqUS+/bto81xjV5ptkRbWxtaW1uRnp5uEz+qgIAAzMzMLBBtrwZj9mljYyOSk5PNnnJdbxiDy3NycjAzM4OMjAyEhITQlcvAwEC0trZaVcmzJxISEiAWiyMJIbZ3d15D/MMSK0IIk8ViPf2d73zH5Ia3QCBATEwM0tPTodPpcOvWLdTW1tqlokNRFJqamta0WrUUpFIpNm3aBEIIbt26hcHBwXU7lra2NlpnZQSPx0NGRgamp6dRUlJCB+baE4QQWvNmbbtgqX1HR0ejv78fw8PDS26j0Whw5coVvPvuuxgcHIRAIMCOHTtQVlYGPz8/HDx4EEwmkyaBZ86cwdtvv42mpiZwuVwcOXIETz31FPz9/UEIoU0KlUolFAoFMjIyaKdkPz8/dHR0mP0aWSwW+Hz+gpbQvffeS1eljL5bRm+ntrY22tPIkgnVO8nVasTK3FagRqMxu+IwMjJikYt4R0eHSU7rLBYLQUFByMzMxPz8PHJyclBWVgZfX1+r2my9vb3ofeYZUK+/DpcrV/Dkn/+MwosXTVpASCQSvPDCC3SruL+/H7///e9RUFCA2NhYODs7Y+fOnfDz88P58+fR2dlp8XHeCYqiUFNTg7GxMaSmptosCsbY/jdGsqyG0dFR3Lp1C05OTkhLS7N4onM9QFEUent7kZ2dDbVajbS0NISFhS16LzkcDmQymdVTzPbEq6++KnZzc3vZ1O0JIScIIUOEkJo7fnaQEFJLCDEQQsxLuLcB/mGJFYPB2Llnzx6uJWGfHA4HwcHB2Lx5M4RCIUpLS1FaWmpTywJjm2StnYCXAoPBQFBQEFJSUtDT04OioiKzx+KthVqtRk9PD8LDw5c8vujoaCgUCuTl5S24mdsLhBCEhYWBz+fbnFwxGAwkJiaipqZmwfus1+tRWFiIt99+G8XFxfSUaGRkJD7//HMoFAo8+uijC26szc3NGBgYAAD4+fnhpZdeoitGer0eLS0tyMvLg0gkQlZW1iKnZDabDalUSpMVc3BnOxC43XbcvHkzOjs7ERYWBoqi0NfXh/j4eAC3x/VZLJbZOis2m72getzT00M70C8FcytWllgtDA0Nmd0GNObXmfM4NpuN4OBgxMfHY3R0FN3d3RZrI2dmZtDc3IyY2FiQ114DTp6ER3c3dr3+OtqvXTNpHxwOh55AFYlEGB0dRWdnJ62tYzKZOHjwIMRiMT777DOrr5nGlAQWi7XAF81W4HA4iI6ORnl5+bLkUq/Xo7a2FvX/H3fvHR7HeZ79/mYXi94WWLRF7x1YAERvlCWKlCiJsYolUrJIS1YsOY792Y6Pk8+xr/h8+a74OF9ynBx3yUmkqEuWJcWFlEyKaERvRO+9914Wu3P+oHbMAmBn0Qjmvi5eJGdnZ9+ZnX3neZ/nfu67pYXU1FSLyPK3G6IoMjIyQmFhIdPT02RkZBAbG7stjzI0NHRHi62DwqOPPqq0srI6IwiC3KTJfwAnbtrWCDwMFO7l2OTijg2svLy8/vbrX/+6y26OoVAo8PPzIycnh+DgYDo6OiguLmZ4eHjXN11nZ+eBdLxZAjs7O1JSUggLC6O6upqWlpZ9L4fCtYmrtraWpKSkbSdOX19fUlJSqKuro6+v70BKg5GRkbi6ulJVVbWnE42trS06nY6qqir0ej2tra389Kc/5cKFC2i1Wr70pS+hVCqxtbWloqICT09PnnzyyRtWmGtra1y4cAG4lk04deqUNGGOjY1RVFSE0Wg0q0ETGhq6o1LrzeVAgLS0NDQaDcPDwzg4OGBlZUVrayvp6ekYDAbeffddix+2NjY2rK6uAn9aed/sD3g9LOVY7SSwmpqaspjjMzg4uGNxyq6uLpKSkkhOTt4RN9JUZo+Pj/8TYf2JJxA//hiHlRV8Hn4Y8coVWcdKSUnB2dkZo9GIjY0NAwMDLCwsSK/b2dlx+vRpRFHk9ddfl747S7G6ukppaSk+Pj77KjLq7u6OWq2ms7PzltdmZmYoLi7G1taWrKwsWfZbhwEm+6ri4mLGx8dJS0sjPj5eVuna2toajUbDyMjhFDm3tbXlkUcesbOysnpIzv6iKBYC0zdtaxFFcf8EzczgjgysBEEI9PDwCI2Njd2r4+Hu7k5aWhpJSUlMT09TUFBAZ2fnjkpTJtf1w+rn5+7uTk5ODra2thQVFTE0NLSvQUxrayu+vr6yroeTkxPZ2dlMTU1RW1t7IIFfWFgYGo2GioqKPeWhqdVqtFotv/jFL3jrrbdQKBScOXOGp556Cmtra9ra2lhfX0etVvPUU0/dMim+9957rK+vExAQwGOPPSZlwCoqKhgYGCA9PZ2IiAizQYaNjQ1ubm4WT6QmVf/rM1BKpZL77ruP2dlZAgMD2djYYGVlhaWlJUJCQpifn6ehocGibs/rOwPn5uZYWlrathvM0lKgpYGVycbGkuBNFEX6+/slsU1LMDc3x+rqKl5eXjg6Ot7Ajezs7JQV8Le3t+Pu7n5LMKjMz6f39ddZUakQP/MZePdds8dSKpXSd2kK5D/88MMb9nF3d+fxxx9nenqad9991+JFyfz8PKWlpURFRe3omlmKqKgoRkZGpGy40WikpaWFpqYmUlJSCA0NvWOyVBMTE5SUlDA0NERycjKJiYkWl7p3utg6KHz1q1919vDw+JvbPY6d4o4MrDw8PP7Ht771rVv9OfYADg4OxMXFkZOTgyAIFBcX09DQYFHprLOzc3cE1AOAQqEgODiYrKwsJicnuXLlyr5IUkxOTjI3N2eROKqVlRVJSUm4u7tTUlLC4uLino/rZoSEhODt7U1FRcWeBXMrKytcunSJmZkZUlJSeP755wkPD0cQBAoLr2WoHR0d+fznP3/LSrmuro729nYcHBx46qmncHd3x2g0UlJSQnBwMEeOHLFoMg0LC6Ozs9PiifTmciBcu1bR0dF0dHRIXn6NjY0kJSVhZ2fH7Owsb731luxFyfWBlaljabvAar9LgTspA87MzODo6Lgjrbrm5mZiYmJueLB7eXmRm5srdRBulwWcnp5mYmKCiIiITV+PfPBBfv1Xf8W4ry889hj8n/8DW9wHoihy9epVoqKiOH36tJSN6uzsvGUMQUFBnDx5kq6uLimzKgfj4+PU1NRw5MiRHXVd7gQKhQKdTkddXR3T09MUFxejUqnIzs4+FHQNOZiamqKkpIS+vj4SExNJTk7ecYbNzs4OJyenTXmgAwMD3HXXXURHRxMbG8u//Mu/AEiSLuHh4Rw7dky6H0RR5Ktf/SphYWEkJCRQU1MjHevll18mPDyc8PBwXn75ZWl7dXU18fHxhIWF8dWvfvWWeSkiIgKtVhsoCMLeqmofEO64wEoQBJVSqTz96KOP7qsQh0qlIjQ0lKNHj6LRaKirq6O8vJzJycltH05LS0usrq7uqlX4IGFjY0NiYiKxsbFcvXqVhoaGPSOQ6/V6GhoaJAFJSyAIAoGBgVI5bSccIUsRFBSEr68v5eXluw6u1tbWeO2115iYmOCJJ57A3d1dkkgwtZRbWVlx7ty5WzJ5w8PDfPjhhwiCwNmzZ1lZWaG4uBg3NzesrKx21GVqb2+/5US6HTw9PZmYmLjlnr/33nsRRRF7e3v0ej0ODg6cP3+eBx98EICenh5eeeUVWQuS6wOroaEhrKysthUZtbQUuL6+bhEhemJiwmL9Krmk9ZsxPj6OtbX1pmKnSqWSyMhIUlJSaG5uprGx8Zb70uRbl5SUtGWwqVAoSDt5kl+dPs388ePwrW/BX/wFbHKPt7a2IggCERERRERE8Oyzz0qZ1LfeeuuW/ZOTk8nIyKCiooLKSvOC2X19fbS1tZGZmXng3dKOjo5YW1tTWVmJTqcjLCzsjshSzczMUFpaSldXF/Hx8Rw5cmRPrp1psXUzrKys+Kd/+idaWlooKyvjJz/5Cc3NzfzgBz/g7rvvpqOjg7vvvpsf/OAHwDXXiI6ODjo6OvjlL3/JCy+8AFwLxL7//e9TXl5ORUUF3//+96Vg7IUXXuCXv/yl9L7z58/fMo5vfetbbp6ent/Y9YneBtxxgZVCobjv1KlTtgfl0WRS7Ta1rfb19VFUVMTAwMCm6e87IVu1GVxdXcnOzsbFxYXi4uI94Ti1tLQQEhKyKw0YFxcXsrOzGRoa4urVq/tOuAwICCAwMJCysrIdB5jr6+u89tprjIyM8NhjjxEeHk5KSgptbW0MDg7yyiuvAHDy5MlbjJGnpqZ45ZVXEEWRrKwspqenqaurIyEhgdjYWGJiYmhsbNzsY80iLCzMYs9EhUKBi4vLLdkK0/3S3d1NaGgoKysrrKys0NzcLPnHjY6O8tJLL5kN5m7OWPn4+GwbOFlSCjTta4mdzMLCgkUPrvX1debn5y024xVFkZaWFrMeoo6OjhL/p7i4+Abdt6tXrxIWFmY2cxEfH4+zlxevP/QQ4re+BT/7GZw6Bddlg7u7u1laWiI+Pl66Xp6enjz77LPANV7fBx98cMu8cOzYMSIiIvjDH/5AV1fXlufa3NzM+Pg4WVlZ++ZCsRXm5+elxYmDg8Ntl56Rg7m5OUkqJzo6mrS0tD2ll5hcSW7+bfv4+EgNKU5OTkRHRzM0NMQHH3zA2bNnATh79izvv/8+AB988AFPP/00giCQkZHB7OwsIyMjXLhwgWPHjuHm5oZarebYsWOcP3+ekZERqXNREASefvpp6VjX47Of/axCoVA8KgjC4fJok4E7LrDy8fH5H1/60pd2RVrfKVxdXUlJSSE1NZXFxUUKCgpoa2uTHgqrq6vMzs5aZOlxmHC9ufPCwgLFxcU77vqZmppicXFxT/gTKpWK1NRUHBwcKCkpkdU6vRv4+fkREhJCWVkZ6+vrFr1Xr9fzxhtvSMa3JrkNa2trgoKCeO2111hdXcXNzY3ExMQb3js/P88rr7zC+vo6Tk5OWFtbI4oiOTk5uLhcu+W9vb0l4qqlMB3TUkHWzcqBgBSIz83NoVQqUavVNDY2EhYWhk6n49y5c+j1en71q19tq+tlCqwMBgMjIyNm1bYtyVgtLS1ZVC6ZnZ212A5qYGBgRwa2/f39aDQaWW39JumMtLQ0WlpaaGlpkVrmtyP6m6BQKMjPz2dsYoKWc+fg5z+HCxcgNxeGhhgcHGRsbIzk5ORbzkOj0RAXFwdcK1G//fbbN/wuFAoFDz/8MB4eHrzzzju3dDUaDAaqq6sRRZEjR44cqOq3Scm+rq4OnU5HZGQkOp3uQBZpO8XCwgKVlZU0NzdL3dyW2DdZAnOLrd7eXmpra0lPT2dsbEySyvHx8ZEy8ENDQ/j7/0l2ys/P75rsxzbbr79nTdtvhrW1Nffff781cM925yAIwhtAKRApCMKgIAjPCoLwWUEQBoFM4HeCIMivVe8B7qjAShAEF5VKFa/T6W7rOOzs7IiOjiY3NxcbGxtKS0upq6ujtbX1jmrV3QoqlYq4uDh0Oh0tLS3U1tZaREY2GAw0NDSQmJi4Z9dCEARCQ0OJjY2lvLx83/W4tFot4eHhlJWVyT73jY0N3nzzTXp7e/nsZz97g7REW1sbb7zxBhsbG4iiKK3WTFhZWeHVV19laWkJURQJCAggLi6OqKioW7Iz8fHxO+7oDA8Pp7293aL3eHh4bFoCV6lUHD9+nMnJSYKDgyUT8D/+8Y84Ozvj7u7OF7/4RVxcXHjttdeora3d9PimwGpsbAyDwWA2ULCEY2Upv8rSMqBJkPH6B4gcbGxs0N3dvSUvaivY29uTlZWF0Wjk6tWrkvSGHMTFxeHu7n5Njf3P/xz+67+gsxNDWhqjly+Tmpq65XXNzs6W/t3a2sq//du/3dDUYGNjw+nTp7GysuL111+XFj9ra2uUlpai0WikTOZBYWFhQfLczMnJkbI9jo6OaLVai7O3+w2TY4Lpe83MzLQ4C2op3Nzc0Ov1m5bsFxcXeeSRR/jRj360baZss8qGIAgWb98Mzz33nKuvr+9XtzsHURRPi6LoI4qiShRFP1EUfyWK4m8+/beNKIpeoige3+4Ye407KrCysbF57Ny5cw6HJXCxsrIiKCiI/Px8vL29GRwclFZ+h7XbwhI4OTmRmZmJl5eXVOOXs8pra2vD399/X1qX3dzcyMrKoru7m5aWln29zt7e3kRFRVFWVma2pXxjY4O3336b7u5uTp06RXx8PHBt0ikoKODNN9+U9vPz87uB82PKck1NTSEIAhqNhlOnTm05qdra2hIUFERra6vF52Ra+VqiFaZQKFCr1UxNTd3yWlRUFMHBwfT390tZtZWVFXp6epidncXV1ZUvfOELBAcH8+GHH3Lx4sVbvjNTYCWHuA6WlQJ3ElhZQqiemprCxcXFYlHLrq4uAgICdmToK4oiMzMzJCQkUF9fL5t/aMpajY+P09zcDPfdx9xvf4thZYWU730Pq23uCW9vb8kWySQC+9JLL92g6u/q6srjjz/O/Pw8b7/9NrOzs5SWlhIREbEj/tlOIYoinZ2d1NTUEB8fv+niJDQ0lLGxsdvmIXs9lpeXqa2tpba2Fn9/f7Kysg6UoxscHExPT88N2/R6PY888ghPPvkkDz/8MHCtocLUWTwyMiItQPz8/G64DwYHB9Fqtdtuv95Wx7T9ZszPz+Po6IhSqcwTBOHO0MH4FHdUYOXu7v6Vs2fPHjrTJkEQ0Ov1hIaGEhcXx8jICAUFBfT29t4RtfztYDJ3zsnJkdTqtxMwnJubY3p62qKVtKWwsbEhIyMDQRAoLS3dsY6OHMZEs8gAACAASURBVHh6ehITE0NZWdmWukIm/aaOjg4eeOABTBnVtbU13nrrLS5fvixN7A899BBf+MIXWF1dZWBgAIPBwDvvvMPAwACurq4YDAYeffRRsw/cwMBAZmdnLRbjhGtZK0tX61qtdlNrEJOP4Pr6Ou7u7szMzBASEkJ/f7/EBbO1teX06dOkpKRQXFzMu+++ewN/TalUYjQaGRoawsHBQQrQtoIoihYFVnIDfL1ej8FgsIj/sxPS+urqKiMjIwQHB1v0PhPa29vx8PAgICCA7OxshoeHqa+vlzXXxMbGotFoKCgoYG5ujhqjEeN77yEMD8Ojj8I2vML09HSMRiMzMzPk5eVhY2PDyy+/fIO9jb+/P6dOnaKvr4/33nuPpKSkHRlZ7xSLi4uUlJSg1+vJycnZsoSmUChITEzk6tWrt20RvLKyQn19PVVVVfj4+JCTk4Onp+eBVzy8vb2ZmJiQMuCiKPLss88SHR3NN77xJ+74Qw89JHX2vfzyy5w6dUrabuKFlpWV4eLigo+PD8ePH+ejjz5iZmaGmZkZPvroI44fP46Pjw9OTk6UlZUhiiKvvPKKdKy1tTW6u7spLCykpaUFNzc3zpw5g42NzWcP9KLsEndMYCUIgq9Go9Ee5MrHEpgmWGdnZ3Q6HZmZmaytrVFYWEhzc/OhMkLeCaysrIiKiuLIkSN0d3dTWVl5yzmZzI2vJ7/uFwRBICoqirCwMEpLS3esVi0HHh4exMfHU15efgu/y2g08t5779HW1saJEydISUkBrslM/PKXv6St7ZpGna+vL88//7zUvWUSgjQFZAEBAZIsgxyOnsk6ZydcEXd3d9bW1iySsdBoNFt2xHp4eJCWlkZ3dzdarZaBgQE0Gs0N94hSqeTkyZMcO3aM5ubmTTsGBwcH8fPz29N7Z2lpSXbGylJR0LW1NZaXly3mv7S1tREeHm6RZIQJU1NTTE1NSSVElUoldYlduXLF7DxjylpNTEzw29/+luTkZGyPHoUXX4TLl+FrX9vyvVFRUVJnXVVVFefOnSMgIIAPPviAjz/+WLoPXV1dsbOzw97e3myQvFcQRVESVo2NjSU6Otosl8vFxQVXV9cDt3dZW1ujsbGRiooKPDw8yM3Nxdvb+7ZRSBQKBb6+vlIWqaSkhP/8z//k0qVL6HQ6dDodv//97/nrv/5rPv74Y8LDw/n444/567/+awDuv/9+QkJCCAsL47nnnuOnP/0pcK268N3vfpfU1FRSU1P53ve+J2Xhf/azn/HFL36RsLAwQkJC0Ol0lJeXU15ejiiKpKenk56ejlar5ZlnnnFwd3ffthx42HDHsO1dXV3PPv/88/vD4NslZmZmsLW1vaH7zcbGhsjISMLDwxkaGqKyshIHBwdCQ0P3jYh4EHBwcCAtLY3x8XHKy8vRarWEhoaiVCoZGBjAxcXlwCZTuJZRcnJyorq6Gi8vr31roXZ3dychIYGKigqJSG80Gnn//fdpbm7m2LFjpKenA9cyCu+++y4bGxsIgsBdd91Fdnb2DQ9SpVLJ3Nwcra2thISEIIoiNjY2kp2IHDg7O+Ph4UFPT49FOmHwp1ZruXxFhUKBu7s7k5OTm5bK8vPzaWhowGAwsLa2RlhYGMvLyywsLEi/C0EQyMrKQq1W89577/HSSy9x5swZPDw8MBqNTE9Pyx6PXBgMBtnee+Pj4zf4WJqDSRDUkvttYWGB+fl5EhISZL/HBJN8SXp6+g2fKQgCISEhuLi4UFZWRkJCwrYBYmhoKE5OTnR2dvLmm2+Sm5tL9FNPITQ2wg9/CHFx8OUv3/I+pVJJamoqn3zyCevr6zQ2NvLkk09y/vx5rly5wsTEBAkJCSwtLWEwGA5snltaWqKurk6ydbKEHB8ZGUlxcTE+Pj575lG4FdbX1+ns7GR8fJywsLAD55xtB1MndGBgIDk5OVtm8S5evHjLNkEQ+MlPfrLp/s888wzPPPPMLdtN2euBgQEmJiaYnp4mOjp6Uy5XeHg4Li4uQYIgeIqiOG7hqd0W3DEZK1tb2y8+/vjjlhMSDgDd3d1blr4UCgX+/v7k5uYSGBhIW1sbJSUlDA8P39E8LE9PT/Ly8lAoFBQVFTE4OEhXVxdRUVEHPhY7OzuysrJYW1ujoqLC4k4+uXBzc0On01FRUcHCwgL/9V//RUNDA3fddRdZWVmIosilS5d444030Ov1uLq68sUvfpHc3NwbgipRFPnoo4+oq6sjJCQEBwcHenp6OHr0qMXGrxEREQwMDFjcKenp6cn8/LxFmdStyoFwrdx39913MzY2RnBwMM3NzSQlJW16PtHR0bd0DJrGIafDTS7W19ct4jBNT0/LJguLoihZ2FiCzcRA5X5efX09ERERW8qXuLu7k5GRQXNzM729vZvus7GxQVVVFU888QSnTp1Cr9fzzjvv8LOf/YyG06cRT56Er34VNnmAwrUHokmRv6ioCL1ez8mTJ7nvvvvo7OykqqqKxMREqbN1PyGKIj09PVRWVkpilpZ2HKpUKsLDw2lpadmnUSJZWl25cgVHR0fy8vL2PDO7W9jY2ODk5LSvmX+4Vgbv7OyksLCQjo4OPD09OXr0KHFxcdsS5J977jkXJyenz+/r4PYQd0RgJQhCbFRUlNN+d0jsBKurqywuLpqdkE2k5PT0dHQ6HVNTU1y+fJmurq49E+Q8aCgUCsLCwkhPT5dKXpZ0D+71WOLi4vD39+fKlSs74h7JgUly4+2336auro68vDzy8vJYW1vjlVdeoaioCLj2AHrhhRc2JWVevHiRsrIyqbO0ra0NJycnqYxoCZRKJXFxcRZzRQRB2FIgcCtoNBqmp6e3LD3qdDp8fX0l0cvN1LpN8PX1vaFj0CTHsNn12iksIa4vLy9bZGMzMTGBm5ubRYHb5OSkZJ9lKQYGBlAqlWavj2mRMTk5SVNT0w33hNFopLKykuDgYLRaLTqdji9/+cs88sgjALz3wQf8Ii+PlaAgxMceg014eCZniuXlZVZWVigpKWF9fR29Xk9gYCCTk5OSr+B+BlbLy8uUlpayuLhITk7OrrrnfH19WVxc3PM5Y2Njg/b2dsmLMC8vj4CAgB2VgA8CISEht5DY9wIGg4GhoSHKysqorKxEqVSSmZlJamoqXl5esq7HmTNnrB0dHf98zwe3Tzic3/BN8PT0/NILL7xw+KIqrikJBwUFWbT6cHBwID4+Xkq5FhcX09jYuO/6TPuF9fV1bG1tiY+Pp6amhubm5gPx+NsMWq2W1NRUrl69Sk9Pz75kBZeWlhgcHMTb25ukpCQmJib413/9V3p7e7G2tubMmTM88MADmz50L168SElJCdHR0cTHx/Pqq6/i5OREenr6DR00lkCj0WBjY7NlNmkr+Pj4MDU1JTsYNgUFW61qTUT2paUltFotY2Nj17rPtsD1HYPT09Oo1eo9FY60hLhuqcyCpaR1kxjo9RIccrG4uEh3d7fUaWoOSqWSlJQUBEGgqqoKg8GAKIrU1NTg6el5Q1bQtCB54YUX+NznPofC1ZVfnjzJ6vo6K/fey8YmnaBpaWlsbGzg4+NDWVkZn3zyicSTWVpaoq+vD2BfvFJFUaSvr4+KigoiIiKIj4+XXerdCoIgEB8fT0NDw57MFwaDgc7OToqKirCysiIvL4+goKBDG1CZ4Orqyvr6+p48h0RRlMSNCwsLmZubIzY2ltzcXIKDgy0uu3p5eREUFKQWBCF814M7AOzbNy0IglIQhFpBEH776f9/JQhCvSAIVwVBeFcQBFlLSUEQFIIgPPrggw8eurtSFEWGh4ctLgeYoFKpCAsLIz8/Hzc3N2pqaqioqGBqauqOKROKokhTUxNxcXFoNBpyc3Oxs7OTyoO34zwcHBzIzs6+1vVUU7OnQZ4oivzhD3/AwcGBRx99lI8//pif/exnLC8vExAQwNe+9jXCwzf/7V+8eJHi4mIiIyMJCQnhnXfewdvbm2eeeYaMjAyGhoZ2nIqPjY2lvb3dojKoiZuznXjnzfD19d02gPP19UWn09HX14erqyv19fXbXn9Tx2BCQoIkQrkdLLmfLCGuW+IPuLKywtramkVcwqGhIVxcXCz2pTMajdTW1qLT6SwKIARBICYmBg8PD0lnz8Tx3Gr/6OhonnvuOe7/yle49MILWPf3M5CTQ9mnXXYmmFrmFxcXMRqNDA4O4unpKR3blPXY64zVysoK5eXlzM3NkZOTg0aj2bNjOzs74+rqeoMMgKUwGAxSRxtAbm4uISEhByqIulsEBgbuisy/vLxMW1sbBQUF9PT04Ovry9GjR4mJidn1/fDlL3/ZXaPRPLergxwQ9jNY+RpwfeH666IoJoqimAD0A1+ReZzsvLw8m93YouwXTOWA3a6YFAqFJGkQHh5OT08PxcXFDA4OHlp1YBPGxsawtbWVHjImhejs7Gympqb2zdzZHJRKJTqdDg8PD0pKSvZsDFevXmVgYIC7774blUpFW1ubZD9z7ty5LTlSly5dori4mLCwMLy8vPjd735HWFgYTz/9NPb29lKWoaGhYUcrRmtra8LDw7fNEG0GPz8/RkdHZZej3dzcmJmZ2fa+vOeee1CpVNjZ2bGyskJDQ8O2x1QqlaSnp8taoFgqtSAnkBFFkcXFRdkTf39/P4GBgbL2hWsP3I6ODkmF3xK0trbi7e29YyJ4UFAQdnZ2jI6OysqwCYJAeHg49//wh8z8r/9FcGsr4re+xY9+9COKi4ul7GZ4eDgLCwvodDoGBwf56KOPcHR0xNvbWwq89ypjJYoi/f39lJeXExoaSkJCwq7n3M0QGRlJZ2enxRI5RqOR3t5eCgsL2djYICcnh7CwsH0Z437Dx8eHkZERixYwGxsb9Pf3c+XKFWpra7GzsyM7O5uUlBQ8PDz2jEv2Z3/2ZwqlUnlGOEzktC2wL4GVIAh+wEngJdM2URTnP31NAOwAWd+cVqv9yvPPP38oy4CmrqC9hFqt5siRIxw5coT5+XkKCgoszkQcFIxGI62trZt6nVlbW99g7nz16tXbcg4BAQEkJSVRU1Oz41KbCWtra/zxj3/E19eX0NBQXnrpJQwGAxkZGahUqi2tYi5dukRRURHBwcE4OTlRWFhIYmIijz/++A0pcTs7OxITE6mqqtpRls3X15eVlZVNhTy3gkKhIDAwcEuy880wcQW38/9zcHDg6NGjjIyMEBkZKeuBbmtrK6skaYk46PLysqxmAEtsbIxGo8VZatPK3dIy58TEBLOzs7vyHu3p6cFgMHDkyBHKy8tlmWLDp9/z//yf8Jd/SWZpKTnt7Vy8eJEf/ehHvP/++ygUCtzc3BgaGiI9PZ2Kigpqa2sJDQ1lbm4OGxubHYmf3ozV1VUqKiqYnp4mOzvbIvFWS2FtbY2/v7/sDK7RaKS/v5/CwkJWV1fJyckhIiJiT877dsHKygo3Nzez/p6iKDIxMUFNTQ1FRUWsrKyg0+nIzs7esfCtOTg6OpKWlmYLpO35wfcY+5Wx+hHwfwE3LGsFQfh3YBSIAv4/cwcRBEFhNBqP5ufn78sgd4P19XWWlpb2raXYzs6OmJgYcnNzUalUXLlyhfr6eokYehjQ39+Pl5fXtibLJrNetVpNSUnJnpg7WwpnZ2eys7MZGxuTLaS4GQoLC1lcXCQ/P59XXnmFxcVF7O3tueeee0hPT6exsfGWCemTTz6huLgYf39/VCoVtbW1ZGdnc+rUqU1LBG5ubgQFBVFXV2fxdTJpW5lkD+QiICCAwcFB2e/ZyjvweqSmpuLh4SG7vHm9EfN2kOsTKIqi7OzW+Pi4bH7V2NgYGo1GdnlnfX2dgYEBi+UwTHIGSUlJO17xDw8PMzIyImUOTB2tluiX8c//DPfcQ+bLL/NCfLxU3h0ZGSEnJ4exsTH8/f0JCQnhd7/7HS4uLpJ0yG5g6rosLS0lODgYnU53IAFLcHAwg4OD296LprEVFhaysLBAVlYWUVFRd3RAdT0CAgK2LAcuLi7S0tLC5cuXGRoaIjAwkKNHjxIZGWlxR/NO8Oyzz7p7eXk9ve8ftEvseWAlCMIDwLgoitU3vyaK4hcALddKhI/LOFxadna28jDWqPdDzHAzWFlZERwcLNnmNDQ0UFZWxvj4+G3lYen1enp6emStpgVBwN/fn5ycHBYXFykuLrbYCHi3UKlUpKSk4OTkRElJieyVuwmTk5OUlZURGxvLRx99xNzcHKIoSro5dnZ2Uqu7ycewsLCQoqIiSRupvb2d48ePc88992x73wQEBGBjY7MjLzMHBwd8fX0t6vazsrLC19dXNrdCrVYzNze3bSCmVCq57777WFxcpKamxuwxLQms5ARLq6ur2Nramt0PLLOxMTWryEV7e7uk8yYXoihSV1dHVFTUtouW7TAxMUFnZyepqanSZ7u6upKcnExlZaX84MrKCt5+GzEwEPWzz3JvTAz33HMPzc3NNDU1oVarKSoq4uGHH5aysbvF2toalZWVjI+PS2rkBwWlUkl4eLjU5Xw9TJzawsJCZmZmyMjIIDY2dt/1rw4arq6uLC0tSRUGvV5Pb28vxcXFNDQ04OTkRF5eHjqdDnd39wOVjbjnnnsAHjywD9wh9iNjlQ08JAhCL/Am8BlBEF41vSiKogF4C3jE3IE8PT1Pnz59+lCWAU2B1UFBEAS8vLzIysoiJiaGoaEhCgsL6evruy22Od3d3QQFBVm0SlOpVMTGxqLT6Whra6O2tnZf7WhuhomsHR8fT0VFheR7ZQ6iKHLhwgWsrKwYGRlhbm4Ob29v7OzsSE5OlvazsbEhMzOTtrY2PvroIwoKCvDw8GB9fZ2hoSEeeeQRMjIyZH1mbGwsk5OTjI6OWnyeJh80S7KbwcHB9Pb2yuL0CYKAh4eH2XJBcHAw0dHRtLe3m/UmVCqVssqfckuBcvlVer0eo9EoK8OyvLyMwWCQzcVaWlpiamrKYoPmvr4+bGxsLBIrvR6zs7M0NTWRnp5+y+/TxcXF4uBqxdaWyu9+F+X6OsHf/S7ZGRk8+OCDdHd3IwgCY2Nj9Pf388QTT0jB8U6dJoaHh7ly5QoBAQEkJyffliyQr68vc3Nz0uJLFEXGxsYoKipiYmKCtLQ04uPjZQfudxoEQcDX15eWlhaqqqoki6CUlBQyMzPx8/O7bYR8BwcHwsLCbAVB2Hl9/ACw54GVKIp/86mrdBDwBHAJ+LzpQnzKsXoQMOsgq1AoTh0/fvzQEdXm5uawtbXd09ZwS+Ds7ExSUhIZGRmsrq5SWFhIa2vrgQUp6+vrDA8PW0TgvR5OTk5kZGTg7e1NWVmZbHPnvYJarSY7O5u+vj6amprMfnZ7ezudnZ2oVCoWFha47777GBwcJD09/ZbVqrW1NSsrK5SVleHo6MjKygrz8/M8+eSTsrreTFAoFBw5coTW1laLy78KhYL4+HiLtK1UKhVeXl6yu6LklAMB7r33Xqytrc0GYXJXvXJLgXIDq6mpKdndZb29vRbd8y0tLURHR1uszN7X10dsbKzs91yPxcVFamtrSU1N3XJ+uj64MtcoMTs7S1lZGWEPPIDipz+FggL43/+b5ORkHnvsMebm5lAqlVy8eBFPT0/J802v11vE9VtfX6eqqoqRkRGys7Px9vaWf9J7DEEQiIiIoK2tjfHxcUnQ+ciRIyQmJu44i3gnYH5+nqamJvr6+hgZGSE0NJT8/HzCw8MPzXk/9dRTamdnZ7OJmduJg5IwEICXBUFoABoAH+D/3vYNghAcFBRkb2l78kFgP0jrO4HJNicvLw97e3vKy8upqakxmx3YLbq6uggJCdmVLosgCPj4+JCbm8vGxoa0GjwoWFtbSyv60tLSLVfYGxsb/OEPf0CpVLK+vs6TTz5Jb28vKpWKtLS0W/Z98803KSsrw8vLi+XlZfR6PefOnduRKbW1tTXJyclUV1dbLCKrVqtxdna2qHU6NDSU7u5uWcGYq6sr8/PzZrOlrq6unDhxQhYXUaFQmD3eXmes5MosGI1GRkdHZWeRZmZm0Ov1FpWxDAbDjqQVTFhZWaGqqoqUlBSz+l0uLi4S52qrBdno6Ch1dXWkpaVdE+B8+ml46in4/vehsJDo6GjOnDkDXAtQKysrb5gXL126JGvcIyMjlJSUoNVqSUlJORSlNaVSycjICN3d3eh0ui1dBP474GbjY7VazdGjR3Fzc0OhUBwqhXiAhx56yMrR0fHJ2z2O7bCvgZUoipdFUXxAFEWjKIrZoijGi6IYJ4rik6Yuwa3g7Oz86L333us+Pz9/qDSdRFFkcnLyQOv+5qBUKgkICCAvLw9/f39aW1spKSmxuG1WDtbX1yXC6l5AqVQSGRlJamqqZE9xUEKpppVpZGQkZWVlmwZ2ly9flgLVM2fO4OzsTENDAykpKTes4Obm5iTT5bi4OGZmZrC3tyc5OXlXyvrOzs5ERERQXV1t8XcZFRVFd3e37EymjY0N7u7uskqkgiDg6enJ+Lh56y6TRIOczzfXOSqXYyVXHFSujc3IyAheXl6yifPNzc0WZ51aWlrw9fXdkdfm+vo6FRUVxMfHy5Y5UKvVxMXFUV5efst17+7upquri6ysrD9dR0GAn/4UQkLgySdhaoqQkBDOnj2LIAhcuHBB0rBSKpXbWuuYxlxdXc3g4CBZWVl7qrq/U8zMzFBaWkpPTw+xsbGoVCqLtcfuBBiNRkZGRqioqNjU+FipVOLv778rXa/9glarxcnJyVsQBPXtHstWOHSimyY4Ojo+dfLkSUVbWxuXL1+moaGBiYmJ267rNDk5KUXyhw0m7kt6ejqJiYlMTExQUFBAd3f3nolkdnR07DpbtRns7e1JS0sjMDCQiooK2traDow7ptFoyMzMpL29XdKlgj+tpAVB4KmnniIoKIjS0lIEQSAzM1N6f3d3Nz//+c+Znp4mLy+P9vZ27O3t+cIXvsBdd93F0NCQbDmDzaDVanF1dbXYz0ylUhEdHU1jY6Ps95hsbuQEcXLLga6urrLsQmxsbMwGgXJLgWtra2Y5MMvLy9ja2so6niWk9dHRUezt7S3ScRofH2dxcXFHmc2NjQ0qKiqIjIy02C5Ho9EQERFBZWWlpNDe0NAgkbNvyR45OcGbb8LYGDzzDIgi/v7+3H333RiNRj788EMAqanlnXfe2fS7Hxsbo6SkBG9v723LlgeFubk5ysvLaW9vJyYmhtTUVAIDA1ldXT1Undi7gSiKzM7O0tDQwOXLl5mamiIqKoq8vDxCQ0Nv+Q5MC6fDlNgw4XOf+5yDUqm8/3aPYyscvugAEATBxcHBwSctLY3U1FTy8vLw8vJiZGSEgoICqqqqGBoaui0ee0NDQztWWj9IODo6kpCQQFZWFgaDgaKiIpqamnaVDVpbW2NiYmJfSfsmc2elUklRUdG+ZN02g62tLZmZmRgMBsrLy5mZmeHll18G4OGHHyYoKIilpSVqa2tJSEjA2dlZsiN69dVXUSgUHDt2jNLSUpydnTl37hyurq5YWVmRlpbG2NgYXV1dOx5fZGQkCwsLFq8gvb29JfKtHNjZ2eHk5CSrLOvi4sLi4qLZoN3R0VEWUVpOZ6CcUqDRaEQQBLMlDLllwMXFRQRBkJUBMxqNtLW1WWRGvra2RlNTEzqdzuKyi9FopKqqioCAgB3zknx8fPD19aW6upry8nJUKhXJyclbB5wpKfDDH8KHH8KPfwxAZmYmrq6u0sLXFODp9Xreeustaa7W6/XU1tbS29tLZmbmbZ9L5+fnqaiooLm5mYiICNLT028QO46MjNy0Q/BOws3Gxx4eHrKMj5VKJS4uLrKyzQeNRx55xN7Hx+fc7R7HVjiUgZVSqbzvscces7/u/3h6epKQkMDRo0cJCwtjYWGB0tJSrly5Qnd394GUj4xGIzMzMzsyUb1dMCly5+fn4+rqSnV1NZWVlTuSO+ju7t6XbNXNMJk7Z2RkMDIyQnl5uWXaO7v43JiYGAIDA3nxxRdZW1u7wWqlrKyMjY0NsrOzWVtb4+233+bixYtoNBqOHj3Kxx9/jFqt5uzZszdMWEqlktTUVKampnYkoQDXJvnk5GS6urosNouNi4uzyL8xPDxc1jjllgMFQZCVjZITWMkpBS4tLckKguT6A1pCWu/r6zOr7XY9TNIKMTExFneZiaJIbW0tGo1m15xPb29vaU6IiooyH+B97Wtw8iT81V9BXR0KhYLPfOYz0iKorKxMMvoeHR3lww8/ZGxsjOLiYjQaDWlpabe1q25xcZHq6moaGhoIDQ0lMzMTtfrWypJGo2FtbW3T+eeZZ57B09PzhqaUv/u7v5NsnXQ6Hb///e+l1/7hH/6BsLAwIiMjuXDhgrT9/PnzREZGEhYWxg9+8ANpe09PD+np6YSHh/P4449bJLC8nfGxt7e37Dlcblb6oJGQkIAoiomCIBxK8bBDGVj5+Pice+SRRzadGQVBwNXVVUphmgT06uvrKSgooKWlhZmZmX3JcphWuIeNzCcHCoUCX19fcnNzCQ0Npauri6KiIoaGhmSVV/V6PaOjowcqMWFra0tycjIRERHU1NTQ1NR0IObO09PTrKysoFKpiImJQRRFSVsnOjoaURR58cUXaWtrIzw8HJ1Ox/nz5/H09OTs2bObcjJMXX5zc3M7XgGbtLhqa2tlGyfDtSxUUFCQ7M91dHTE2tpaVlfXXpYD5QZW5kp3cojrJhsbc/sZDAYmJiZkZYNMej+WKKX39PRgb2+Pl5eX7PfAnzw6bW1td6XMDteyNqWlpaSkpGBlZSWZKG8LQYB//3fQaODxx2FxkdjYWGxsbKR2fZOQ5l133UVjYyMFBQVkZGTg7+9/2+bQ5eVlamtrqaurIyAggOzsbLML5bCwsE2zzefOneP8+fO3bP/6179OXV0ddXV13H//tWpVc3Mzb775Jk1NTZw/f54vf/nLGAwGDAYDf/EXf8Ef/vAHmpubeeONNyRbqm9/+9t8aZiY8gAAIABJREFU/etfp6OjA7Vaza9+9attx2kyPq6vr98T42NAklU5bOVAQRA4duyYFZB7u8eyGQ5dYCUIgpXRaExKSkqStb+dnR3BwcFkZmaSlZWFi4sL3d3dXL58mfr6esbGxvaMq3OnlAHNwc3NjdTUVFJSUpidnaWgoICOjo5tS6t9fX0EBATcFm6Zm5sbubm5ODg47Lu5s8lkGa6R1VdXV6mqqqKiooK1tTV8fHx48cUXWV1dJTs7G09PT/74xz+i1Wol37+toFAoSElJYWlpiZaWlh2dg6OjI7GxsVRVVVnENwwKCmJmZkZ2tisiIkJW1srZ2ZmlpSWzAa9arTZbUtirUqAc4vrMzAyurq5mH/DDw8OyV/idnZ0WabvNz88zMDBATEyMrP1v/iy9Xr+j916P8fFxampqOHLkCB4eHiQlJdHX1ycvo+3hAa+9Bh0d8Jd/iUKhkJTXU1JScHd3Z2Fhgd7eXrRaLT09PbeNI7uyskJ9fT1VVVVotVqL7HE8PT2ZnZ29JeOal5cnq/EB4IMPPuCJJ57AxsaG4OBgwsLCqKiooKKigrCwMEJCQrC2tuaJJ57ggw8+QBRFLl26xKOPPgrA2bNnef/99zc99s3Gx1qtds+MjxUKBe7u7js2h99PnD59Wu3j4/P52z2OzXDoAisg+5577rHayYpGpVJJLbv5+fn4+voyMTFBUVERFRUV9Pf3W7TSvx4Gg4H5+fl9s7C5HbC3t5dWNEqlkpKSEq5evXpL2ttoNDIwMLBj3aq9gCAIBAUF3WDuvB+yEpcvX2ZpaYnQ0FCCgoJITEyU1KWdnJy4dOkSXl5e3H///SwsLFBSUkJAQABPPfWUrNKGIAgkJSWxtrZGc3PzjoIrT09PvLy8zJob3/y5JrsbOZ9p4pmYu8Ym4VpzHC61Wr1nGSs5pUBzmSi5ZcC+vj5Z9/3KygpjY2OyfyMmaYWkpCSLxRZNgU9iYuKuMj+9vb20t7eTmZkpPYCVSiVHjhyhvr5ensjn0aPwt38L//Ef8NprGI1GrK2tKS4uJj4+HrhG5j916hTr6+uyJRj2CqurqzQ0NFBRUYGnpye5ubl4eXlZdN1MwsJyPQR//OMfk5CQwDPPPCMtJoaGhm7opPbz82NoaGjL7VNTUxJH8/rtJpiMj0tKSqitrcXW1nZfjI/h8JYD8/PzEUXx2GE0ZT50gZWXl9eTjz/++K7V1hUKBRqNhri4OPLz84mOjpbKOcXFxXR0dFjE2xkbG7P4B3mnwMrKipCQEPLz8/H09KS+vp7y8nIpBTw4OIi3t/ehcGs3mTvHxcXR2Ni4p+bO4+PjFBUVIQgCn/3sZ4FrGazy8nI2NjZYWFggKiqKhx56iPr6eurr6wkJCeHJJ5+0qKtJEAQSExMxGAyyA52bERoaysbGhkXdhs7Ozmg0GtkPCLlcKzkTr62tLaurq9ue60GWAicmJswKg87Pz6NSqWTpF7W2thIZGSk7o9vU1ERAQIBFnYNwrVN1cHCQI0eO7Dh7bCojTkxMkJmZecu9a29vT0JCAlVVVfKy/d/7HuTkwPPPo+zpwdvbW8pwuLm5sbKygl6vJy0tjZqaGoaHh3c0bktgaggoKyvDzc2NvLw8fHx8djx/+/r6MjY2ZrZh6oUXXqCrq4u6ujp8fHz45je/CbDpfS8IgsXbJycnNzU+DgwM3DeVend3d2ZmZm57R/7NsLGxIS4uzhqIvt1juRmHLrASBOGevTZdFgQBJycnwsPDycnJITU1FWtra5qamrh8+TJNTU1MTU1tO+mPjo7eVjXgg4AgCHh7e5OdnU1UVBQDAwMUFBTQ1tZ2W7NVm8HFxYWsrCzc3NwoKSmht7d3V+VBvV7P22+/jSiKxMfHS6Wk4uJiOjs7USgUhISEEBQUxG9/+1va29uJiIjg9OnTO5rQBEEgPj4ehUJBfX39jgyXdTodAwMDFilcR0REMDAwIKvZw93dfUvi7vVwdnaWxFC3g4ODw7YejSqVyuwx5JQCNzY2tv1O5NrY9Pb2ypJYmJ+fZ3l5Wfb8MDo6ysrKikWeg3BN6qWjo4O0tLQdW4oYDAaqqqoQBIEjR45seRx3d3e0Wi1NTU3mD2plBa+/jqhS8dDrrzPS14dCoZCkSgDefvttVldXUalUvPXWW1y5coWamhoaGxtpb2+XlL6np6dZXFxEr9fv6Pes1+tpaWmRunNNlYvdLogVCgWBgYFm+WcmrTOFQsFzzz1HRUUFcC3jNDAwIO03ODiIVqvdcrtGo2F2dpaNjQ3JX9XW1pbBwcEbjI/lNGnsFoIgoFarD9zfVQ4efvhhtYODw3E5+wqC8G+CIIwLgtB43bZ/FAShVRCEq4Ig/EYQhD0pSd3+FMR1EARBHRMT47jfN4uNjQ2BgYEEBgZK5NSBgQGuXr2Ki4sL3t7eeHp6ShkaURSZm5vbtGvkvytMthdDQ0N0dHRQXl6OVqslKCjotmvOmCAIAn5+fnh5edHe3k5xcTGxsbGyeQ/X48KFC1KAkpt7jQ/Z09PDJ598IvGITp48SWlpKf39/Wg0Gu6///5dZfEEQSA2NpbW1lapLGTJA8BUtikrKyMjI0NWJ5pSqSQ2NpaGhgbS0tLMfp4pa2WO8+jj48Po6Oi2wrEmAvtW2SQ5526uFKjX681+J5OTk2azVRsbG0xNTUnlrO3Q1NRETEyMrPGvrq7S0tJCVlaWRd/13NwcjY2NZGRk7DgzYcrY+/n5yQrqQkJCqKysZHh42Kx457SDA8Nf/zpx3/sej7S2cvmuuxgdHZUCt/n5edrb2yVKxccff2z28wVBwNraGmtra2xsbG74++btVlZWODg4MDMzQ0hICHl5eXvOB/X396ekpITQ0NAtv7uRkRFJnf83v/mN1DH40EMPcebMGb7xjW8wPDwsBciiKNLR0UFPTw++vr68+eabvP7662xsbJCWlsbf//3fc/z4cX7/+99z9uxZdDrdnp6TXJh+33Ltnw4Kn/nMZ6zUavWfAf+vjN3/A/gx8Mp12z4G/kYUxQ1BEP4f4G+Ab+92XIcqsAJyT5w4caA9uEqlEm9vb0nvZ2ZmhtHRUTo6OrCxscHb2xtra2vUavV/yzKgOQwNDaHT6XBycmJwcJCysjKcnZ0JDQ21uIyxXzCZOy8sLNDY2IiNjY1F7estLS1UV1ejUqkIDAxEo9GwsLDAr3/9axwdHZmfn+czn/kMg4ODNDY2otFoeOyxx6itrSUiImJXitGCIBAdHU1bWxvV1dUkJydb9ECws7MjMTGRqqoqsrKyZGUyPDw8GBwcZGRkxOzYPTw8aG1tZWVlZdvATavV0tzcvG1gpVarzXaWCoKwbfBkrhQoh7g+Pj5utgnF1KgiRwtLpVLJWnSZ5BFM3XNysbS0RE1NDampqTuWKFhYWKC6upqYmBjZrhEmPmBJSQkuLi6bXleDwUBbWxszMzPovvEN6Ogg8vXXCfna1/jn2Vnc3d1xd3enpaWFEydOEBcXx0svvcTc3BxPP/00giCwtrbG+vo66+vr0r9v/vv615eXl2943VSuDA0N5fTp0/tmEKxSqXBzc2NsbAxvb29Onz7N5cuXmZycxM/Pj+9///tcvnyZuro6iRP6i1/8Arhmqv65z32OmJgYrKys+MlPfiKN88c//jHHjx/HYDDwuc99jpWVFUpKSvjmN7/Jt7/9bV599VWSkpJ47rnn9uW85ECj0UgNN4fpORgREYHBYIgSBEEhiuK2tUpRFAsFQQi6adtH1/23DHh0L8Z1qAIrHx+fPztx4sTu2hh2AUEQcHNzkzIeS0tLjI6O0tjYiEKhoK2tDW9vb5ydnQ/VzbVfWF5eZn19XSLsBwYGEhAQwMTEBM3NzRiNRkJDQ/H09DwU18Nk7jw6OkpZWRl+fn5mdbfm5ub48MMPpWxKRkYGRqORX//615J6t4eHB9HR0dJK8tFHH8XT0xO1Wk1NTQ1TU1PExsbuaoUcGRlJR0cHVVVVFvNn3Nzc8Pf3p66ujuTkZFnfRWxsLCUlJXh4eGybATFpEXV2dm6bvXFycmJ1dRW9Xr/l8VxdXWlt3d573dramvX19S0DCHOlQDnE9ZmZGbOZqL6+vlu8IG+GKIq0tLRw5MiRbfczobu7G2dnZ4vssFZXV6msrCQpKWnH1iqTk5OSDZOliyGVSkViYiK1tbVkZWXdcO1nZ2epr6/Hz8/vTxm4f/kXuHgR1Re/SOo//iPFVVV8/vOfp6WlRSK033///bz00kvU19dz7NixHZ2TwWCgt7eX/v5+/Pz8qK2tZWBgYN+7loODg2lsbMTb25s33njjltefffbZLd/7ne98h+985zu3bM/JyeE3v/kN4+PjuLu74+/vL3WsVlVV7en4dwqlUom9vT2Li4u77jTcS3xKiRBGRkZiAPkWE5vjGeCtPRjW4eJYiaJ4NCsr63YPQ4KDgwOhoaFYW1uTnZ2Ng4MDHR0dXL58matXrzI+Pn7oCH17ic04JiZRyIyMDOLj4xkdHZXafA9CY8ocrjd3NinOb6UibgqgDAaDJEIbEhLCJ598InWDLS4ucv/99/P73/+emZkZTpw4IWkOmYyY7ezsuHLlirwuqm0QHh6Ou7u7ZC9iCUxt/nLV3a2trQkLC5M0c7aDj48PU1NTZonlPj4+2/oMqlQqDAbDtr8ZcwR2c6VAc8T1paUlbG1ttz3G7OwsdnZ2ZrNDAwMDuLu7y+K5zM7OMjQ0RHS0fJ6tXq+noqKCuLi4HXcjDwwM0NzcTGZm5o4zzGq1Gg8PD6mRwWg00tLSQmNjI8nJyTeWxtRq+NWvoLmZ7I8/RhAEWltbcXFxYXx8nOXlZUlAs6yszOI2fqPRSE9PD4WFhRgMBnJzcwkPDyciIoLFxUVGR0d3dI5y4eTkhCiKu7a5WV9fv8X4OC8vj4SEhENbHfH29t7367sTPPjgg2oHB4edReifQhCE7wAbwGt7MaZDE1gJgqB2c3Pbd36VpVhYWMDOzg47Ozv8/Pw4cuQI+fn50k1mstgZHBy8LRY7+wWDwcDY2Ni2pSInJycSExPJyspCr9dTVFREc3PzrgOMvcD15s69vb1UVFTcQtguKChgYGCA9PR0pqamyMjIoKOjg+LiYsnAODk5me7ubnp7e4mLiyM5OfmGY5gyOtHR0ZSVlckyJd4OoaGheHl5UVFRYXFwFRcXx9jYmOwx+Pn5sby8bJb8LrfdXKvVmu34cnZ2Zn5+a/91OYGVuVLgdoHVxMSEWf0iOaR1g8FAV1cXERER2+4H1/hadXV1JCUlyc6oGAwGSeNoJ7wWURRpbW1leHiYrKysXauch4eHS1zU4uJirKysyM7O3jx7ceIEPPccNv/6r+SpVNTW1pKYmAjAJ598AsDdd9+NSqXi/PnzskjqRqORvr4+CgsLWVtbIycnh4iICIlPZxJJ7ezs3NV5ykFISIhkNm0Jrjc+Lisr29T4+DBDjqzK7cB1PKsdQRCEs8ADwJPiHgkkHprACsi97777bp/HwRbYrBtQoVDcYLETHh7O0tISpaWllJSU0NXVtW33052AkZER2cKI1tbWREREkJ+fj7OzM5WVlVRXVx8Kjyl7e3tSU1MJDg6msrJSMnfu7e2lqKiIxMRExsbGcHBwwN/fn9/85jd4eXkxPz+Pvb09UVFRVFRUoFareeCBB7ZcSbq7u5OVlUVnZ+eOxT9NCAoKQqvVSjIPcmFSd29ubpYlJWLStmpsbDSbefXz82N0dHTbxYOjo6PEhdkK5vSszAVWckqB28kjjI+Pb1uK0+v1zM7Omg1murq68Pf3l6Vm3dTURHBwsOwSisn/z8/Pb0f8PYPBQE1NjSRxsFcyKS4uLtTX1xMfH094ePj2WZV/+ifw9yf7xRcRl5Yk78arV69iNBpxdHQkPz+frq4u2tvbtzyMSe6lsLCQpaUlsrKyiIqKuqXc7OjoiI+Pz44toyyBl5cXU1NTshY+1xsfFxQUmDU+PswwjXWnWpD7hYiICIxGY5QgCBbHM4IgnOAaWf0hURT3zBfv0ARWWq32tvKrtsL4+Pi2dhOCIODi4kJkZCR5eXkS+fjq1av7brGzn+jv77fYf0yhUODn50dubi5BQUFS9md4ePi2n7+Hhwe5ublYWVnxySef8O6776JWq0lPT6ejo4Pk5GTef/99jEYjkZGRDA8Pc/fdd3P+/HmJV2VuErSxsSEzMxNBECgtLd3VBGTis5WXl1uUCbWxsSEpKYnq6mpZ73NwcECr1Zp9IJnazc3pZpkrB7q6um4bcO8mYyWK4ravG41GsxyswcFB/Pz8tg0a1tbWGBoaIjg4eMt9TBgeHmZtbU32b0kURerr61Gr1TuSOFlfX6esrAy1Wk18fPyelJTm5+cpLi7G2tqaqKiobb9fCU5O8O//jlV3N5+tqqKmpgatVsv6+rrEs0tLS0Oj0XDhwoVbFhCiKDI8PExhYSGzs7NkZmYSExOzbSAbFhbG4ODgvmfMTXSD7a7DZsbH+fn5Zo2PDzsOY9bqU11AAdjWhkAQhDeAUiBSEIRBQRCe5VqXoBPwsSAIdYIg/HwvxnRoAiuj0Xg0MzPzdg/jBmxsbLCxsWFRGv16i53s7GxcXFzo6emRukVGR0f3zGJnv7C0tIQoijvWSBEEAXd3d9LS0khKSmJ6eprLly9LNhy3CwqFgtDQULRaLUtLSwQFBVFeXo5SqWRxcZGhoSHuvfdeysrKpNLX9PQ09957r9RCbQ6CIBAVFUVYWBhXrlyxSGPqZvj5+REcHExZWZlFIqguLi6EhYVRU1MjK6ANDQ1lbGzMbJYrMDCQwcHBbe9fc+VAFxeXbdXcd8OxMjUbbIXZ2dltbWxEUaS/v3/bzkZA8og0V7pZWVmhra0NnU4nO8BpaWnBysqK8PBwWftfj6WlJa5cuUJoaCghISEWv/9mGI1GOjo6qKurIzExkcjISEJDQ5menpZnjXTXXfCVrxDz0Ue4Xb0q/YYKCwuBa+X6EydOMDMzw5UrV4Br38Ho6ChFRUVMTk6SlpZGXFycrMxOeHg4oijK5hnuBv7+/vT399+wzWR8XFpauivj48MMk3fg7YZer2dsbIzm5maKi4uJiYnRODo6bqtnJYriaVEUfURRVImi6CeK4q9EUQwTRdFfFEXdp3+e34vxHYquQEEQ3GJjYx0OG79qampqV7odVlZWaLVatFqtZJA5OjpKa2srdnZ2kszDYUsHDwwMWJyt2goODg7ExcWh1+vp7++X3O1DQkIORNxuM5gCg6ioKN566y3UajW1tbWkpaXR3d0tZa3Onz9PVFQUqampFn+Gp6cnTk5OVFdX4+3tva32zXbQarUIgiBpVck1UvX19WVubo7W1tb/n703D24kP68EX+Ig7psgCBC8AJDgfbN4V1dbarcldXRvr1fytmPckuUZaxXhkGM8ETvSjCRbXikkhWcd4xnHSmFbI7XCsmSFw7JWUkvdcndXFYtk8SySxRu8ARIkcZC4SVy5f7DzNwBxELzZ3fsiGMUik0ACyMzfy+9733unCqZZLBbq6+sxNTWV1V+JzWajqKgIGxsbGRdukUiEaDSKo6OjtMc1i8UCi8XKOD3I5/PP3QrMRV+VrQ24v78PsVic9Xz0+/3weDynThUy1gr19fU5f2bLy8sIhUI5T3YmggnfbW5uvpTYLZ/Ph8nJSajVavT29pL3nGkfT05OJv08I77xDdC/+hX+15//HD9ubASbzcbu7i5JsjAajaiqqsKjR49IbItEIkFbW1tOjveJKCoqgkAgwPLyMvGPuiow1y6/349wOAyr1Qq32w2NRoO6urpbNTl3mWBujK7bduHo6AgulwsulwtutxssFgtKpRIqlQomkwkFBQXUP/7jP74E4P++tp3KgttCofs+8pGPnO5ueM3IReiaK5gqTm1tLQnIDIfDSRE7Pp/vxltmNE3n5G90VnC5XBiNRty7dw8qlQqTk5MYHh6G0+m89tfMtApYLBZisRjC4TCEQiEkEgnm5ubQ2dmJt99+GxKJBC+99NK5LyACgQDd3d0IhUIYGRk5d7VOq9XCbDafub1YXV0Nj8eTU4SIQqGAVCpNuQs/ifLycmxsbGTVZOXSDsxUtbpIxeqiwvVcROtzc3Oorq4+9ZhYXl6GXC7P+cbMarXC6XSe2SQWOPbcevr0KTo7Oy9MqmiaxvLyMiYmJlBfX4+qqqqU91sqlUKtVucWpyQSgfre9yBxu9H0gx8Q/7Dh4WGySXt7O6LRKIaGhtDS0oKmpqYzkyrgf1akl5eXr/yaEgwGweFw8OjRI6ytrUGr1V5a8PFtBpNictGpyNMQCoVgs9kwNTWF+/fvY2xsDB6PBxqNBj09Pejr60NtbS3xmayoqEAsFjOfR2d1FbgVFSudTve/PP/887fuaGSEhlcBiURCYnaOjo6wu7uL+fl5BINB5Ofno7CwEEql8trLxw6HA0ql8somVCiKIlW8g4MDrKysYG5uDuXl5SgqKrqW18sQK6akHQqFUF9fj9nZWfB4PKyuriIcDuP3fu/3LjxNxVSDtre3MTAwgKampnMtfhqNBiwWC48fPyYWD6eBoii0trZiYGAAIpGIBCtnAlM5yFZF5XK5KCwshM1my1jV1Ol0mJyczEhSFAoF9vf305KOvLy8UytWmY5Nv9+fsSLFxNhkqh6Fw2H4fL6srv1M7NVpZIkxGe7p6cm6HYOdnR1sbGygs7PzTMc/49rtcrnQ3d194aw4v99P9F29vb1ZrwEVFRXo7++HTqc7/Rzp6QH97/892v7yL7E7NYVNhQJTU1Noa2vD+vo68vLyIBAIIJVKL1zFNplMmJmZuZKbw2g0CrvdTm4+dDod/H7/uSqM72UUFBTA4XBcmlaMpmkEAgFSkfJ4PODz+VCpVNDr9airqzt1PXrXzJay2+21AHJPp78i3Ap2F4/H7942fdXh4SE4HM61BA/zeDyUlJTgzp076OvrI87YDx48IKGl16VNstlsp2pMLgtyuRytra1ob2+Hz+cjuYRXPXVyeHiIvLw8bG1tQSQSIRKJQKfTQSwWQyQSYXt7G2az+dKqlcDxRbitrQ3T09PnzjVUq9Woq6vD8PBwzgJdLpeL1tZWPHny5NT3lcvloqqqCjMz2X32GP1ZptcgFAoRi8VweHiY9veMGWs6nFyg4vE40TpGo9Gs71s2YbrT6cz6eVqtVhQXF2fVX83NzaGmJqs+FtFoFFNTUzk76LtcLiwuLp55ei8ej2NqagrBYBAdHR0XIlU0TWN1dRVjY2OoqalBTU3NqQsZh8NBVVVVTj5oAMD62tcQKC1F73e/C9G7JHdgYAB1dXVoa2sDj8e7lGvcZdsu0DRNgo8fPXqEYDBIgo/Ly8uhVCpvZYbeVeKiOiuapuH1erG6uorR0VHcv38fc3NziEajMBgMeOaZZ9DV1YXKykqoVKqcb/JfeOEFhVAo/NC5d+wSceMVK4qi+EajUXLb9FWX2QY8C9hsNjQaDTQaDRnVZSJ28vLyiC4rl4rFWRGLxW4kE1EgEKCmpgaVlZWw2WwYGhqCXC6H0Wi8krJ6KBQCn8+H1WqFQqFAIBBAYWEhlpaWCGGpqqrCwMAAysvLUVpaeil3pGKxGD09PZienobL5UJjY+OZibtKpUJDQwOGh4fR3t6e0x2+RCJBdXU1xsfHT62KaLVaWK3WrLYEPB4PKpUqa1VAp9PBbrennZwTi8Xw+/3EaDEQCCR9+Xw+3L9/H8BxxS/xvQ8EAnjw4AH5P0VR4PP5EIvFODg4gN/vB4vFSqmiZIuxoWkaVqs1a4Vpe3sbUqn01OPx6dOnOesHvV4vnj59io6Ojpx1WMBx9W1sbAz5+fkwmUwXOjYDgQAmJychl8vR19d3pkp1YWEh1tfX4Xa7T8/n5PNx+O1vQ/Gxj+HDb7+Nnz7/PHg8Hql65BLAnQsSp1zv3r177sfx+/2wWq3Y2dkhE5pKpTLlvS4qKoLNZoNKpbrorr9nIBQKcXh4eKpZL4N4PA6Px0MqUsFgEBKJBCqVCmazGRKJ5FKur52dnRyVSvVhAP/1wg92Qdw4sQJQ39LScuvqqA6HI6dx6qsEkyquUChQXV2NYDAIu92OJ0+eIBqNQqPRXGrEDrOY3lRZm8PhoKysDKWlpdjb2yOVE6PRCLVafWn7xVSsnE4nVCoVcZOPxWJk7JsJ1V1aWkJ/fz/q6urOFe58Emw2G83Nzdjc3MTAwABaWlrOTB6VSiWampowMjKC9vb2nKJOGG+u2dnZU4XX9fX1GB4ehlKpzEj8TCYTRkdHodVq034uOp0OExMTSedQJBKB0+mE2+1GKBQiAddMpVCpVEIkEmFsbAzt7e1pycb9+/dx79498v94PI5QKAS/3w+bzYa9vT0sLy/j6OgIYrEYCoWCVBUyvW6n0wmZTJax6sNMx3V2dmZ727C1tYVYLJZTxTcYDGJ8fBxtbW1nukkKBoMYHR1FRUXFhVpdNE1jfX0dGxsbaGhoONexzYSIT09Po6enJ+P56fP5sLi4iLBUCvHdu2h48ACDzc3Y2Ngg2zBRRpcBk8mE/v7+U/MtTyISiWBraws2mw1sNhvFxcWoqKjIevOTn59PfODeD1N/uUKhUMDtdqdti8diMezv7xMiFQ6HIZPJoFKpUFdXB6FQeCVrTG1tLSKRyOmp6deAGydWfD7/zt27dy8+xnLJ8Hg8lzJdc5kQCoUwGo0wGo1k3JQRvatUKhQWFiI/P//cJ/jW1hYppd8kKIoiVTumZDw3N4eysjIUFxdfWP8VCoXIiR2NRqFSqcDlcuH3+wmZZRbZmpoa+P1+zMzMkLDni+quAKCkpARyuRzj4+MwmUxZg4nTQS6Xo6WlBaOjo2hra8uJnJlMJoyPj5O4nkwQCAQoLS3F4uIiamtrM24jkUgyTtoJBALSRtnf38fe3h5isRjy8/ORn5+PeDwOtVq7PrKxAAAgAElEQVSdYr4LHE8GMuT3NLBYLIhEItA0DaVSSabBaJqG3+/H/v4+lpeXEQwG8eTJExQUFECj0SQ99vr6etbjnhEnZ/vcg8EgLBZLVoLB4OjoCCMjIyTcPFccHBwQF/OLkPxgMIjJyUlIJBL09vZeSO7AEON01ctAIIDFxUUEg0FUVVUhPz8fr//+76Py8WP81ttv4+81GjIdyuVyL41YVVRU4OHDh1hZWTl1OpCmaezt7cFqtcLv96OoqAitra05EzJmKMnpdJ4pA/K9DrVaDafTifz8fEQikSQiFYvFyOBGSUnJlXRX0iEvLw8SiURAUZTwMs0+z4MbJ1b5+fkfam9vv/H9SATTKrrNgkQulwu9Xg+9Xo94PA6Xy4WdnR3Mzs5CLBajsLAwZQHJhmg0Cr/ff6rA+bohlUrR1NSEo6MjrK+v4+HDhygsLER5efm5CU4oFCIi6IODAxQXF8PhcJApwZMtYLFYjI6ODuzu7uYc7pzra+vp6cHk5CRcLldOIs1EyGQytLa2YmxsLKeA3XcDSzE4OAiJRJJ1cS4rK8PAwAA8Hk/GY6KiogJTU1MpC8rh4SFsNhuCwSCePn0Ko9GI1tbWpM8rHo9jf38/LbE6bTIwHfx+f1L7jZlekkgkiMViUKlUUKlU2N3dxdDQEAQCAYqLiyGTyRAKhTLeRDE2IX19fRmfO9Fa4TStE5P/V1NTc6aWu91uJ1qs88omGJ+utbU11NXVXchKJhFVVVV4/Pgx8WoKBoNYWlqCz+cjWkXmWio1GPCwrw+/+etfo3x5mYjY8/LyUiKnzgudTgeBQACLxZKRWHm9XtLyVqlUMBqNWT3OsqGoqAibm5sfGGIVDofJecHEZzHWB0y27k2hvb2dZbFYGnFsBHpjOPfKQFEUm6KoJxRF/fzd//+AoqhFiqJmKIr6HxRF5aSmjEajTQ0NDefdjSuBy+W6lLbPdYHFYkGtVqO+vh737t2D2WxGMBjE48ePc47YYXxlbiuZ5PF4MJvNeOaZZyAWizEyMoLx8fHcjApP4PDwEEdHR6QiptFosLi4CJlMlpZYAccLdWFhIQl3fvjw4YVzAYFjgtzW1gaxWIzBwcEzLy5SqRTt7e05vxccDgdtbW2YmprKKoBn/Iqmp6czCsYZzydmWs7hcODx48cYGxsDm81GZ2cnuFwuSkpKUkhwtmib8xCrbMJ1h8MBjUYDuVxOjiGz2Qy3242HDx+Cw+FkFNovLS3BYDBkreosLS0R4pYNsVgMo6OjMBgMZ1qEV1ZWsLq6iu7u7nOTqlAohOHhYRwcHKC3t/fSSBVwXGHUaDRYWVnB9PQ0xsbGUFhYiN7eXhQUFOAP/uAPUFBQgLq6OqjVaox0dCCg0eA333gD//gP/4DnnnsONE0jHA6Dpml87nOfg8lkQkNDAyYmJsjzvPbaa6ioqEBFRQVee+018vPx8XHU19fDZDLhc5/7HMnvPGm7cFXBx0qlEgcHB6fGQr1XkWh98ODBA5K7SlEUOjo6cPfuXdTV1UGr1d4oqQKAu3fvygUCQceN7gQuVrH6YwDzAJjb5B8A+Dfvfv8PAP4tgG9lewCKongGg0F8XaXCXOF2uy99VPe6QFEUpFIppFIpzGYzQqEQdnd38fTpUxweHqKgoACFhYUpFxK73X4ut+frBovFQnFxMfR6PZmoYqZJCgsLc7o4BoNBxONxlJWVYXt7G3w+P2nhzDa0wIQ7l5SUYGZmhoQzn8d3hwFFUTAajVAoFBgeHkZ1dXXaSk4miMVi3LlzB6OjoznpZYRCIerr6zE2Nobu7u6MVTKpVIr8/Hysrq7CaDSm3cZoNGJychIsFgtSqRQ1NTVJlTOKotJqXZh2XzqjwUzEKttEoN/vT6ttYmJsThISmUwGqVSK3d1d6PV6jI2NQSAQwGQykepVMBiE0+nMOgnodrvhcDjQ3d2dcRtm3ycmJqDVajOK6NP9zdOnTxGNRtHV1XWuCimTtbe8vIza2torqaocHR0hHA5jeXkZzc3NKVE6n/rUp/BHf/RHePXVV6FWqxHjcPD/9vTglX/+Z/StrEB+7x7m5uYgl8vxy1/+EhaLBRaLBcPDw/jsZz+L4eFhuN1ufOUrX8HY2BixEXnxxRehUCjw2c9+Fn/zN3+Dzs5OfPSjH8WvfvUrmEwmPH36FFtbW2Cz2bBarTg8PERRURE6Ojou1ZT5/dQOpGmaBLO7XC4cHByQYZWT1gfBYBChUOhWGVy3tbWxVSrVh3DDAvZzESuKovQAPgbgawD+BABomn494fcjAHIRjdQ3NzffuhLJ/v7+qWPV7xUIBAKUlZWhrKwM0WgUDocDGxsbxK+G8cvy+XzvqQwriqKIXicQCGB1dRWLi4soLi5GSUlJxpZMNBolzuvMNgcHB+ju7sbTp8f2J7nczQsEArS3t8PhcGB0dBQajSanmJNsUCqV6OnpwcTEBNxud1pzxkwQiUS4c+cORkZGUF9ff2r1JD8/H3q9nrh1ZyKklZWVxK8okRwlLtixWIxUI06CibhJR8xEIlHaShOPx0sbr5MtBzCTOejBwUHGagTTBiopKUFxcTHcbjcWFxcBHJurWiwWVFVVZXxvIpEIpqencefOnayfE03TmJ6ehkQiyXkgJhqNYnx8HHK5/NyZf4eHh5iamgKPx0Nvb++Ffa5OgiFTe3t7MJlMxDLh5L7evXuXmInK5XKw2Wz8UihET3ExfuOdd+D4jd/Av87MoKGhAT/96U/x6quvgqIodHZ24uDgAHa7Hffv38dzzz1Hbhqee+45/OpXv8K9e/fg9XrB2PW8+uqr+MlPfoI/+7M/AwC89dZbaG1thdlsvlKZQ2FhIXZ2dt5zxIqZzGWIlM/ng1AohEqlQllZGWQyWcZjW6VSwe123yotcl1dHaLR6I0L2M/bCvyvAP5PACm1z3dbgL8H4FenPQiPx2t/5plnbs+nApALw3X4V103OBwOtFotmpubce/ePRQXF8PlcuHhw4eIRqPY3NzM2BK5zRCJRKivr0dPTw9omsajR48wMzOTtq2W2P6KRCLg8/nIz8+HSCQiVZKztEmYcGcul4v+/v4LB07n5eWho6MDbDYbQ0NDZ/o8hEIhOjo68PTp05x8ZsrLy8Fms7G6uppxGzabTSa/aJomWW4PHz6E1+tFT08PWlpaMrpwZ8sOzORnlalilW3yKhwOp21DZLONSHRaZ6oOHR0dqKiowJMnT+BwOLJOXE5PT8NkMp1arVxcXARFUTCbzVm3YxAKhTA4OAidTgez2XxmUsWQ3qGhIZSVlaGpqelSSVUkEsHCwgIxnr179y70ej2MRiPW19ezZkmyWKzjARs2G2/+5m9C7PPB9M//jGg0SqbyEiuPTMxNtp8zgx/MufL06VMS9FxfX4+6uror147m5+eTlvhtRjweJ8bMw8PDuH//PpaWlgAc30Tdu3cPHR0dMJlMUCgUWW8YbqOHF4/Hg1gsFlIUdaNtsDOzB4qiXgCwR9P0OEVR99Js8v8AeEjTdP9pj6VWqz9824Tr+/v71+7jdBNgFhKVSoV4PA6pVEr8cWiaJlYOl+Uxch3gcrkwmUwwGAzY2dnBxMQEeDweabNRFEUuvkKhEG63GwKBAJWVlQCOL8wURZ25tM1Eaej1eszNzWFjY+NCeWHMIqxUKjE0NIT6+vqcyZ5AIEBnZydpKZ52B11fX4+hoSFIJJKM26rValitVqyvr2N3dxd5eXlJ7u9KpRLz8/Npq0Y8Hg9sNhvBYDCFgCgUCuzs7KRMRGYiVplyAqPRaMYbIYfDkTbXMBQKkTHwk1AoFKTlOzo6iqKiIhiNxqTntlqtoCjq1GnO1dVV+P1+tLa25nQeeTweEidzHh3U0dERpqenwWazL71KFY1Gsba2BpvNhrKyMjzzzDNJ7wmXy4VWq4XNZss6dapWq491STodbD096B4awq9/93czkhKKotL+jqIokkv5+PFjRCIRkiHHHIfXZZnDYrEgkUjg9Xpv1QBQLBbDwcEBqUgdHR1BKpWSeDWRSHTu6/t1RNucB21tbazl5eVGAI9vah/OU7HqAfAiRVHrAH4E4Dcoivp7AKAo6k8BqPFue/A0RKPR5sbGxnPswtXB7XZ/oMzemJH44uJimEwm9Pb24s6dO+Dz+VhYWMCDBw8wMzMDp9P5nhFnslgs6HQ69Pb2wmQyYXV1FY8ePYLNZiMifrVajb29PcjlcqK/uWi1jsfjobm5GWazGZOTk5iZmbmQ6aFarUZnZycWFxextLSU890wn89HZ2cnFhYWsLOzk3VbFouF1tZWzM7OZhxwiMfjEAqFmJmZQVlZGVpaWlI0UxUVFbBYLGn/PlPV6jwVq3StwHQaKgDkvU9XycpmObG7uwuhUIjy8nLcvXsX8Xgc/f395O48EAhgZWXlVD8wm82G3d3dnCNP9vb28OTJE7S3t5+LVG1vb2NwcBB6vR4tLS2XRqpisRhWVlbQ398PFouFu3fvory8PC3JLS8vx9raWtZjNT8/H1KpFEKhEMMvvwwWgN+enAQAFBcXw2q1km1tNht0Oh30ej35OU3TWFlZQSQSgc1mw9bWFmpra9HX14d4PA69Xo+NjQ1IpdJrbVMx7cCbRDQaxd7eHubn5zEwMID+/n7YbDYIhUI0NTXh3r17aGlpQWlpKcRi8YVumhlz3sua5rws3AYB+5mJFU3TX6BpWk/TdBmA/x3A2zRN/xuKov4tgOcBvELT9KkrMEVRPIFAII/FYreqfPpBqVgx8Pl8EIvFSRfJdBE7W1tbePDgAcbHx7G1tXVtETsXhUKhQFtbG9ra2uDxeDA0dDyFy4zhM4J9xmjyMqpzSqUSvb29kEgkePToEaxW67mPcYFAgK6uLkQiEQwPD+fs9cPj8dDZ2QmLxXJqCDOfz0dzczPGxsaIQSqDQCCAgYEBAEBDQwN2d3fTPoZarYbP50s7aZiJWHG5XMRisRTCzmKx0r5fmSpWmfRVDocjLUGJx+Ow2+1pReTxeByLi4uorq4m+2I2m9Ha2oqFhQXMzs5ifHwcjY2NWYnL3t4e1tbW0NbWlpNObn19HUtLS+jq6srJ8DUR4XAYY2NjsNvt6OnpgVarPdPfZ0IsFsPa2hoePnyIeDyOvr4+GI3GrDpCHo8HpVKZNYSbsV9wOp2wcTgY6epC89QUCre38dxzz+H73/8+aJrG48ePIZPJoNVq8fzzz+ONN97A6Ogofv7zn+PXv/41Xn75Zfz2b/82VCoVZmdnQdM0vv/97+PFF1/ExsYGysrKrrXartFoLmVS+CwIh8PEZufhw4cYHBwkN4xtbW24d+8eGhsbodfrr8RPSqlUYn9//9If9yJob29nq1Sq37jJfbjMNty3AWwAGHr3YP5nmqb/PMv2ddXV1dz5+XkEAgEyVZT4dd2jmzRN4/Dw8FIMIN8ryGTwyOBkxI7H48HOzg6Wl5evPGLnMiEQCFBbW4uVlRUAx5YawPHFEDhuwVwmwacoCqWlpdDpdFhYWCDtwfPcQbNYLNTW1sJut2NwcBCNjY05kf+8vDzSFmTu5DOBiRCamJhAe3s7KIqC1WrFysoKeT6aprG1tZU2wiRxxP1kJScvLw9cLjdtZUkqlcLr9aZ9X05ODGbSWPn9/rRtV4fDkfY17+7uQq1WpyUIm5ubUKvVKcezWCxGV1cXBgcHTzUvdbvdmJubQ1dX16lVIyaDMBQKoaur68zDD3a7HQsLCzCbzZc2yRyPx2G1WrG6ukoqv2epfplMJkxMTJD9eeWVV3D//n04nU7o9Xp88YtfBHD8+cRiMXwdwD+KxfjNN98E+z/9J7z11ltEu/a3f/u3sFqtsFqteOWVV/Dxj38cHA4HX/3qV4lm7Vvf+hY+9alPIRQK4SMf+Qju3LmDsbGxrO3IqwCXywVFURn1fpeBw8ND0tbb398Hm82GUqlEfn4+KisrL31A4TTI5XK4XK6cJ12vA+8K2G+0FXYhYkXT9H0A99/9/kyPxeFwWp577jlee3s7gOMSps/ng9frJWZ44XCYpJ4zXyerK5cJhlS9VzRFl4G9vT3k2o6lKApyuRxyuRxVVVUIBoPY2dkhETuMlYNMJru17yFTcWEy6La3t7G3t0cWtMuunnK5XNTX15NcOLFYjOrq6nNdeLVaLaRSKcbHx6HX61FeXn7q+8zlcpPIVUlJScZt9Xo9PB4PFhYWEAqFQNM0enp6yMWaoig0NjZibGwMfX19KeehVqvF0tISjo6OUnRqOp0OW1tbRM/GgPGzOkmsOBwOotFo0kKRqRXo9/vTVmn29/fTtusYi4yTYDREvb29Kb8D/icZb2trw+joKCorK1PIjM/nw9TUVE4j/bFYDOPj4xCLxTlrsBiEw2HMzMwgFouhu7v7UkbeGdH7ysoKNBoNenp6znWcCoVCcLlc8rn+8Ic/TPp9LBbD1772NXR1dcFiseD7P/0pdr/2NZT/l/+Cse98B3/9138Nl8uFzc1NeL1eBINBNDY2oru7G1//+tdTnq+trS0pOHxsbAwArp1YAcdtTqfTeSkkl6ZphEIhOJ1OuFwueDwe5OXlQaVSQafToba29sIpFBeFTCYjN6u3BXw+HyKRSEhRFJ+m6RuZxrox4XhBQUFHXV0duWpyOBySi8eAqSB5vV54vV7s7u7C5/MRV+VEwnUZVabbGGNzlYjH4zg6Ojq3B5NQKITBYIDBYEAkEsHe3h5WVlbg9XqhVCqh1WrPlE5+1YjFYoRY+f1+yOVy9Pb2wuv14mc/+xnZLpsY+ryQSqXo7u7G9vY2BgYGiAXGWQmoSCRCT08PZmZmcmpJAcfnVkdHB0ZHR4l/VyYYDAa88847KCoqSku4E0NuT065URQFg8GA1dVV0kpjoNVq8fjx4xRiJZfLk6bzGDA6q5PEKt1NVbpKWCAQgEAgSNk+EAggHo+nrXAtLy+jtLQ07fsZDofx9OlTdHZ2QiAQoKenB+Pj4/D5fKisrCR+XePj42htbT31nDo8PMTo6ChKSkrOTAB2d3cxNzeHysrKS6kU0DSN7e1tWCwWqNVqdHV1XZioGQwGrK2tobm5OeV3TJWF0TTu7+8D/+7fwfnd76L8W9/CO888A6VKlTH4+DRsbGxALBbfiMmzWq0murCzgolhYipSXq8XQqEQSqUSpaWlkMvlty6PkM/n4+joKK0f3U3CbDZjZWXFAGDuJp7/xogVi8WqO3mRPQmKoiAQCCAQCEjLBjheIP1+P7xeLxwOBwldZdLSmS+JRHKmRT1bfMf7EW63+9L0ZFwuF0VFRSgqKrqUiJ2rwM7ODhkFT3RY5/F4sFqt4PF44PP56O/vh1arRVlZ2aW2hSmKQlFRETQaDQl3rq2tPfOwBJvNRmNjI2w2GwlyPs2DjMPhEBPReDyedlKOmUhramrC0tISvF5v2sc1Go3o7+9HUVFRih5Ir9fjwYMHMJlMSQQlLy8PeXl5KXooqVQKj8eT8hwMsUrcNp3GiqbptJWsvb29tL5ajPbmJA4PD7Gzs4O7d++m/I6maUxNTcFsNpMWIZfLxZ07dzA7O4uJiQnU1NRgZGQEDQ0Np34WPp8P4+PjqKmpOZPvUSQSwczMDMLhMLq6ui58bDLWGUtLS1Aqlejs7Ly04z0/Px+zs7MZ22JqtZoIvZeXlxEKhSBqasKH3noLd9vawDnndZgJl75ufRUDhUJB/PBOAyOtYIgU4+mmUqlgMpkglUpvFVnJBIFAgMPDw1slB2lsbBS+/vrrlfigEatIJKI/a/AsAzabDZlMlkKCjo6OSHVrdXUVPp8PNE1DLBYTsiWTyTK2+w4ODs4chvtehsPhyOoyfl4wETtqtZoY0O3s7GB4eBgsFotYOZxVpHtRbGxskO9DoRAhNOPj42QK7fnnn0dDQwO2trYwPDwMiUQCo9F4qYSbw+EkhTuvr6+fK9xZr9dDKpViYmICBoMha5sPOD5vGP1JPB5PCh52Op2YmZkhgc4SiQTj4+Po7u5OWRhZLBbq6+sxPT2Nrq6upHOJxWKhtLQUa2trKdWpoqIibG1tJVW62Gw2WCwWCeNlkG4yMB2ByuZfdTJAOh6PY3d3F1VVVSnbLywsoLKyMm1FYHNzE1wuN6UKwbwPKysrePvtt9Hc3HxqlcThcGB2djYnMnzy9czOzpLA7ossuEzw8NLSEqRSaZJ1xmWBoiiUlJTAarWmmMPSNA2hUEgmQhkz2Oi7ny3nAq/N7XbD7/ffSBsQOD4m+Hx+2ioq4yHFtPYODw8hk8mgUqlQU1NzIeuDm4RMJsPBwcGtIlZ1dXUimUxWD+BfbuL5b4RYURTFKSsry7vssiaPxyMLOoN4PE6qW263G+vr6wiFQuByuSli+XReO+9nOJ3OpMX1KpAYsVNZWYnDw0Ps7u5idnYWoVAIarUaWq32QllduWJzcxNisRh+v58E80ajUTx+/Ji0jerr68Fms4kbt9PpxMLCAmKxGAwGw6XmKYrFYnR2dmJnZwePHz9O65d0GqRSKXp7ezE1NQW32032PxNYLBba2towMTGBxcVFmM1mMp6dWLGQSCSoqqrC+Pg4Ojo6UvaJ8QqyWq0phK60tBQPHz5MydgrLCzE4OBgSgtRLpfD4/EkTfBlIlYn9yPdRGA8HkcwGExZ2Ox2OzQaTcpjeL1e+P3+tK1Pv9+fVXcVj8ext7eH4uJirK6uQq1WZ2zNbm5uYmNj40yVoWg0Ss4Vpg15ETgcDiwuLkIkEqGlpeXc2YO5QK/XY2hoCAaDARRFJQUfJ34GfD4fY2NjaGd+lsVg9DQwN083RayA42qc0+kEj8fD/v4+qUhFIhHI5XKoVCo0Nja+b9YamUwGj8dzadOol4HKykpIpdLWm3r+m6pYlRmNxmvxWEicNkxEOBwm1a3NzU0cHBwgEAhgdHQ0qbolFArfk3cRp4EZc7/uKRI+n4/S0lKUlpamROzI5XIUFhZCrVZfusaJpmlsbGxAJpORO2SVSoXp6Wlicnfv3r2k56UoihB1v9+P1dVVLCwsoKSkBCUlJZe2j8xrXllZwcOHD8/cIuJwOMT9fGBgAK2trVkXTMa7amJiAqOjo2TRPqmrKSwshMfjwdzcXFqxd3V1NR49egSNRpP0t2w2G3q9Hpubm0ktRy6XCz6fD5/Pl6RxUigU2N/fTyFWJ1uE6VqBfr8/5bUyliknz9v19XU0NTWlvI65uTnU1NSkbB+Px/HkyRM0NTWl/ayZ/L+CggIYjUbY7XYMDQ2lCNdpmsbCwgKJXsn1uGGqiOXl5WhoaLjQdcjlcmFhYQF8Ph9NTU3XUi3Oy8uDQCDA7Ows3G43eDweiouLUVVVBYfDQUTmFosFLBYLceb1nbD8OAs2NjYgEokuNWQ6V0QiEZKvt7e3h/X1dSiVSqhUKpSXl9+qTL3LhFwuT/Ieuw2oqKhANBrNLergCnBTxKqyoaHhRj0N8vLySNYccHwnt7Ozg/LyckK4GENJNpudRLakUum1E5LLRrpJrOsGE7Gj1WpB0zT29/exs7ODxcVFCAQCosu6DN3H3t4eDg8Pky64SqUSr7/+OgQCAcLhMNra2jL+vVgsRkNDA8LhMDY2NtDf34+CggIYDIZLKYGz2WxUVlaiuLgYs7OzWFtbQ11dXc4VBYqiUF5eDoVCkXFi7eT2JSUlGBsbQ1FRUUbtW2VlJcbGxmC1WlNCjrlcLqqqqjAzM4PW1uSbw7KyMjx69AhlZWVJZIhpBya24+RyOYkgYZBrK9Dv96csouksRHw+H1gsVsr76XA4iJj6JObn56HVajNaQTx9+hQikYi0urRaLVgsFoaHh4nVQiwWw+TkJHg8Hu7cuZMTOYpGo8TJ/s6dOxeqbOzv72NhYQFsNhv19fXXkgfKtFytVit8Ph8ODw9TyKZKpSKO6vv7++jq6kL4XY+581asGH1VaWnptdwMHx4ewu12w+Vywe12E9f3oqIieL1ePPPMM1e+D7cBAoEgrX/dTeLdyfQbM6S8EWIlEolq6+vrz5f3cUVgQojFYjHEYnHSohSNRgnZ2trawvz8PCKRCLGCYMiWSCS6dVMbmeByuW5kaiYTKIqCUqmEUqkk+qOdnR2Mj48jHo9fOGKHaRHodDrYbDaw2Wxsb2/D5XKBoqic2yJ5eXmoqKggFYqxsTEIBAIYDIZLeT8FAgHa2trgdDoxNjYGjUYDk8mUc5VDLpeju7sbT548gdvtRk1NTdpj0uPxYHZ2Fvfu3YPFYsHMzAzq6upS3luKotDc3IzBwUGIxeKUYQetVkvaO4lkhsvlorCwEDabLalVqNFosLy8nJSBx7RnE8FMGyUiFoulEKtAIJAiRk8XY5NOtE7TNObn51NIIfMYHo+HhPuexNLSEuLxeIpeS6PRkGio5uZmjI+PQ6vVph0WSAeXy4WnT5+irKws7eeRKxjbDOC4snjVN1GMENtqtcLpdEKtVsNsNkMqleL+/fspN6JcLhdyuRz7+/sQiUSora3FBHOcnrNidXBwAK/Xe2VtwGAwSNp6BwcH4HK5UKlUKCwsRHV1ddI5urKy8oHxRKQoCmw2+0qmqS8ChULBoihKRtN06nTMFeNG3gWFQtFmNptvVX/N7/dnHF3mcDhk0WfAeIwwhMtut8Pv9xMrCIZsSaXSW1kCdrvdaGhouOndyAixWAyTyQSTyYRwOIzd3V0sLi6SCkVhYSFUKlXORHZzcxN8Pp98xlKpFIODg+Dz+Tg8PERnZ+eZ9o/FYpEpSLfbjZWVFczNzaG8vJxULi6C/Px89PX1YX19Hf39/TCbzdBqtTkttEyW3/LyMgYHB9Ha2ppUVQsEAsQIVCAQoL6+HrOzs5ienk7bcuJwOGhtbcXIyEjaabT6+noMDw9DqVQmXVgNBgOGhoZQXFxMHpPL5UIoFJIbGQAknzFxIcrLy0tbsTq5QJ/URTLO9IkVuFgsBofDgZqamqS/tSOyDioAACAASURBVNlsUCgUKYSa8Yg6KcxnsL6+Do/HQ4xUT0Kv18Pr9RJBey7ak1gshvn5efK459U++Xw+LCwsIBqNkrzJq8Th4SGJlREKhSguLkZtbW3S8a9Wq+FwOJImuxNRUVEBNpuN+AU1VkwQeDY7kVyRzvpAIBBApVKhpKQEDQ0NOQUUX5Zp620Hc3N0012QRNTW1lKzs7MVAMaybUdR1P8AwGQg1737s/8LwEsA4gD2AHyKpunsERYJuBFiRdN09WlWC9eNk7qP00BRFIRCIYRCIQoLC8nPY7EYMTrd3d3F0tISwuEweDxeEtkSi8U35u/EkMLbNMWRDXl5eSguLkZxcTFisRhcLhe2t7cxMzMDqVSKwsJCFBQUZGzPMvoqJp8MOG41bW1tgcvlorKy8kKaDIZ0B4NBrK2tYWlpCcXFxRk9kXIFi8WCwWBAUVER5ufnibFlLu0ciqJQUVEBhUKBx48fo7a2FgUFBYhGoxgbG0vS2VAUhdraWszPz2NychJNTU0phEEkEqGurg5jY2MpDuECgQAlJSVYXFxMmsTj8XhQqVSw2+1JCwzTDkx8HUxuIHMucTgcYo3B4GQrkDFzTdxXplqSiO3t7RSyG4vFsLy8jJ6enqRtaZrGkydPUFVVlbbasL29ja2tLXR2dmYkuS6XC7u7u9BoNDmJet1uN6anp1FSUoLa2tpzVan8fj8WFxdxeHiIqqqqK807jcVi2NnZgdVqRSQSgV6vR1dXV8Z2clFREVZXV5OIld1uJ1EozDlJNFbnJFYbGxsQCATnmnSmaRper5cQKUa7d17rA5VKhb29vf+fWN0gGhsbxT/+8Y8rcQqxAvA9AH8N4PsJP/sLmqa/BAAURX0OwJcB/B+5PveNEKt4PJ5/24KOI5HIpXgssdls4k7OgKbpJCuI5eVl+P1+0DSd1uj0qvUBzCTVe1GUz2azUVBQgIKCgqSInZWVFXA4HBKxk1jF2N/fh9/vR1VVFcnCCwQCyMvLQzgcPnO1KhOEQiFqa2tRWVkJq9WKgYEBKJVKGAyGC4mFeTwempqasL+/j6mpKSgUCpjN5pxIW35+Prq6ujAxMUEWDIPBkNLSoygK1dXVWFpawsTEBJqbm1PuyJk8wOnp6RTyVV5ejoGBgRQvOJPJhNHR0aRqm0ajgcViQVVVFfkZI2BPvEk5iZPi9WAwmHJzwEzoJWJjYyOl3be6ugq9Xp9yzm9sbIDP56clQ4xnXrboma2tLSwvL5PJv6GhIUKy0r2excVFuN1utLW1nesYCQaDWFxcRCAQQGVlJcnhu2wwWiir1Qq32w2NRoPa2tqcbkblcjl8Ph9p5dI0jV/84hdER8dkdMYv2ApkgrVzef2M9QFDpEKhEKRSKVQqFaqqqi58fVQoFFhcXDz337/XIJFIbl1mYE1NDV+tVrcA+Ids29E0/ZCiqLITP/Mm/FcE4EzDdtdOrCiKElZWVnJu06IeiUSutDfMpIDz+fwkHUqiFYTT6cTq6irJITtpdHqZ+/d+cZg/GbETCoWws7ODqakphMNhFBQUQKvVkhZBbW0tHA4HgOMKJeNqfBmtg0RwuVwYDAaUl5eT/eFwODAajUS0ex4oFAr09vZic3MTjx49gtFoTGqzZQKfz0dXVxeGhobg9/sztoApioLZbIbFYiEO4ifJVXl5OSYnJ7G2tpakG6IoCg0NDZiamkJvby/ZJ0aHuLe3R8gFh8OBSCSC1+slJIxxYE/ESd3GSbsFxlCRAbP4J74+j8cDLpebRMDC4TBsNluKGajP58PGxkZaa4WDgwPMzs6is7MzLaGlaRoWiwVutxvd3d1km9bWVgwNDUEsFie19w4ODjA1NYWioiL09PSc+ZgIhUJYWlqCx+OB2WxGQUHBlRCqUCgEq9WK7e1tSCQSFBcXn3lCkZmudTgcKCwsJEHuzz77LN555x0EAoFjIfsFWoEejwcHBwfo6OhI+/tYLEasD5xOZ5L1QUNDw6VbHzCDC7fNkfyqIBaLsbm5edO7kYTKykrw+fxzZwZSFPU1AK8C8AB49ix/exMVK1M6g76bRDovnOtAohVEojEpYwXh8Xiwvr4On8+HeDwOkUiURLjOawVxcHBwpnH+9woEAgHKy8tRXl6eFLEzMjICDoeTNMLPZrMRDAbx/PPPX9mFj6IoMvXo8XiIDqusrAxFRUXnagWfDHfe3NzMKdx5f38f8Xgc9fX1JMg5k/6moqICKysrGB0dRVtbW9J+MgRqaGgIUqk0qYXK3PGfJF0mkwlTU1NJVRumHcgQK8a9OXEh4vF4CIfDScQqcV9OnreBQABCoTCJfKWLy1lcXITJZEp6rFgsRqwV0k0ePnnyBHfu3EnbHozH45iamgKLxcKdO3eSnp/H46GxsRETExOk7bi0tASHw4GWlpYzyQ+AYxNki8UCl8uFioqKC9swpEM0GoXdbofVagVN09Dr9UmZkedBYWEhrFYrJBIJ3nrrLZSVlaGzsxPvvPMOgsEgAFzIbuGkvioSiZCJPZfLhXg8DoVCgfz8fJSWll6LqFwsFidpCd/PEAqF5HO8LTAajYhEIsbTt0wPmqb/M4D/TFHUFwD8EYA/zfVvb4JYVTQ2Nl6dK905cFZ91VXjpBUEcHxHHAgE4PV6cXBwgM3NTQSDQXA4nBSj09MugB6PBxUVFVf9Mm4UTMROQUEB3njjDRQWFsLlcmF+fp78nmndXQdkMhlaWlpweHiItbU1PHz4EDqdDmVlZecabkgMd56ZmYFQKER1dXXax4rFYpieniaiaJlMhvHxceh0OmLeeBKMUeno6Cja29uTyAabzUZbWxvxbEq82zebzSQSiKkSicVi8Hg8uFwuov1hYn2qq6vJ84tEoqQqFCNoZx7/ZCvw5MDJycnEaDSaMqQRCASwv7+f4ss1Pz+PoqKiFIf9UCiEsbGxjFOjkUgEo6OjxMsq3XupUCig0Wjw9OlTorlKrOrlgnA4DIvFAofDAZPJdG4tVibQNJ0UfKzVatHY2HhpBqIKhQLT09N48803EQ6H8bGPfYy0YYPBYHIr8BwVq5WVFeTl5RFHeWbKmNFI3USUFmOc+UEgVsx5mSnP8ybA4/HA4XBEFEVRNCPIPB/+AcAvcJuJlVAoNJpMpltlOev3+2+V9UA6UBSV1goiEonA5/PB4/HAZrPB6/UiGo1CKBSmWEEwvjGMmP6DgIWFBUQiEbS1tcFoNOInP/kJgONppqKiIgwNDRFd1nVULfl8Pqqrq1FZWQmbzYbHjx9DKpXCaDSe6wIslUrR1dWF7e1tDA4OorS0NMU7an5+HqWlpWSRFAqF6OnpwezsLEZHR9Hc3JyWjJeXlxNfpjt37iS1oxmjybGxMXR3d5Pfsdls1NbWYnp6OqktU1FRgfn5eUKs2Gw2JBIJDg4OiN6LEbAnEqvEycDTKlYOhyOJMG1tbaGoqCiJgMzNzSWROeA41Njv96eQ7HA4jJGREdTX16eNNAoGgxgdHUVFRUVWkXI8Hkc8HofNZkNTU9OZgpMjkQhWVlaws7MDg8GA6urqS124AoEArFYr7HY75HL5uYOPTwMTWzQ9PY3e3l5y08hisYjGij6DxioUCpFq1OzsLCwWC8rKyqDValFTU3Mrxv5lMllazd/7FUzV6ia6P4mIRqMIBoMIhUKQy+V5NptNiuN2Xs6gKKqCpmnLu/99EcBCtu1P4tqPPrlcbrxN1vfA8Un6Xo0X4HK5Ga0gPB4P8d4KBAJgsVgQCASgaRpOpxNSqfRGQ5GvA1NTU+DxeKirq8M777yDeDwO4Lgq+IlPfIKYGSZG7BQWFl7J4pIINpuN0tJSlJSUwOFwYG5uDvF4HEaj8cx6mcRwZ4vFgv7+ftTV1UGlUpFR8ZOkgYnv2drawsDAAJqbm9OSh9LS0iRylUjAFAoFysrK8OTJE7S1tZF9VqvVRJfDEA7msRONaXU6Hba3twmxUigU2NnZIW3xdMQqkVQcHR2R4zddjM3Gxgbu3LlD/u92u5PCt5nHmJubQ3d3d9J7Ho1GMTIyArPZnHbCbn9/n0xQZgsy93q9mJqaQkFBAXp7ezE5OZnRjqOsrIwEx7PZbPzwhz/E/Pw8/uIv/gK7u7soKyvDj3/8YygUCtA0jT/+4z/G66+/DqFQiO9973toaWkBALz22mv46le/CgD44he/iE9+8pNJzxOJRLC9vQ2r1Qo2m43i4mL09fVdKRlh7CREIlGSto2iKIRCoWPymaFixVTrGSLl8XjA5/PJ5J3FYkFlZSU+/vGP3wpCxUAul8NisZy+4fsEQqEQoVDoSokVMwgWCoUIeWL+ZaQEbDYbQqGQMZmmZmZmtMhCrCiK+iGAewDyKYqy4bgy9VGKosw4tlvYwBkmAoEbIFYcDqf0to2gvt8yAhOtIBJJbDQaxdraGmKxGOx2OxYXFxEOh8Hn85OqW2Kx+NaUcy8Cn8+H1dVVlJeXIxQK4fHjx+R3zc3NZFFOjNhxOp2wWq2Ynp6GTCYjVg5XdcGmKIpMOTL7y1SYiouLz/S8HA4H1dXVKCkpwczMDNbW1uDz+bI6fjPtr/HxcZSVlaGkpCRl2+LiYkKuOjo6kshVSUkJvF4vWdwY1NbWYnBwMCk7r7KyEhaLBe3t7QCAgoICLCwskDiZkw7sPB6PxA0Bya1AZsKM2df9/f2km4v9/X0IBAKipaFpGnNzc0ltQcZaoaamJqmCG4/HMTY2hpKSkrRTina7HUtLSylt0ETQNI3l5WXY7XY0NjYSYllYWIjV1dWMGZ2//vWv4ff7YbVaweFw0N/fjxdeeAGf//zn8Y1vfAPf+MY38M1vfhO//OUvYbFYYLFYMDw8jM9+9rMYHh6G2+3GV77yFYyNjYGiKLS2tuLFF1+EXC6Hw+HA5uYmaaGe9De7SgwODuLg4ABNTU1Jxw+LxUIsFoPH4yEaKzoahe/dgZ6T1gcGg4FYH9y/fx+Dg4Oora3Fyy+/fGP2NZnATB1/UATsl6GzisViCIVCSYSJ+T4SiQA4vi4wxEkgEEChUJBz/eS6ZTKZ4v/6r/+qRZaKE03Tr6T58Xcu8jqunVhFo9Gi21axisVit+pO56rA4XBA0zSKi4tJO4KmaRweHhIriN3dXfh8PtJ6PGl0+l66QMzMzICmadTW1qK/vz/JFyldfE2iXUNixM7S0hL4fD6J2LmqxUgikaCxsRHhcJgYg2o0GpSXl5/pOUUiETo6OjA9PY2joyNsb2/DYDBkXHjEYjF6enowPT0Nl8uFhoaGlPOhqKgILBYLjx8/RkdHR1Kls6amBsPDw9jZ2SFEhMfjwWg0Ym5ujoQbK5VKEtXC+LhJpVJCiphJKqYyxePx4HQ6yfMkVqwCgUBSdWpvby+pEnVStG632yEWi5ParWtraxCJREmiepqmMTk5ifz8/JRwaZqmsbq6it3d3aTJv5Pw+XyYnJyEWq1Gb29v0sXeZDLh4cOHKC4uTiJzsViMBILX1dWRCtLPfvYz3L9/HwDwyU9+Evfu3cM3v/lN/PSnP8Wrr74KiqLQ2dmJg4MD2O123L9/H8899xwhmc888wz+7u/+Dm1tbVCpVDAajZDL5dd6Hu/v7+Phw4fEAT5x0pN5b2w2G2LvHp8zb72FMJ8PpVIJs9mckrZA0zTeeOMNDA8Po7m5GS+88MKtvRFkEgQ+CA7sQqEwq+UCTdOIRCJpSRNTtWS6KswXc3MrFArB5XLPfNyWlZUJAVx7Jefa2UQ8HpdnK51fN+Lx+HuKLFwUPp8v6S6coihyECcuMPF4nBidOhwOEtGQl5eXRLaY1sVtBFN1KigowC9+8Yuk6JR0ba9EnIzYCQQC2NnZwcTEBGKxGInYOatxYC7Iy8tDZWUlTCYTtre3MTY2BqFQmNZ/KhPC4TBcLheeffZZbG5uor+/H9XV1Rndr5kg542NDQwMDKSdWGNaWAy5YogBE+o8ODgIkUhE/q64uBg2mw1ut5ss9BUVFbBYLGhubgZwTNi2t7fJ76VSKbxeL+RyeVaN1Ul9ldPpJFWgSCQCj8dDApfj8TiWlpaS/MqYLNBEg1CapjE7Owsej5dSUYrH43j69ClisRg6OzvTLuQM8bLZbGhsbEw7qclms2E2mzE/P4+mpibE43Fsbm5ibW0NAPCVr3wFLBYLn/nMZ/CHf/iH2N3dJVVnrVaLvb09AMf6sUTtjl6vx9bWFra2tqDT6ch+5OXlwefz4e7duzdyntI0jV/+8pegKArPP/88rFYrHA4HuFwuXC4XudlxOBzYKirCoUaDuh//GNQXvgCkIa7xeBw///nP8eTJE3R0dFzpVO9lgJkM/CAQKyZgnfEFS2zVMecxY33CVJwKCgrI+nMVx6der+dJJJKryTjKgptoBXJv04nwXnIgvwycvNPPBBaLBZlMlkJAEo1O19bW4PV6QdM0qQYwXwKB4EYveLu7u9jZ2YHRaMT4+DgAEL8cFot15golE7ZrNBpJxM7S0tK5I3ZyAYvFgl6vJ7E5FosF4XAYBoPh1Hgbi8UCo9EIHo+HiooK6PV6zM7OEvf2TMdAaWkp5HI5xsfHUVFRkSK01mg0oCiKkKvECJqWlhaMj4+T0XzGmmF8fBx9fX1gsVhQq9VYWFgg7feCggLMz8+TdgkjYE9HrBJbgYnEKhwOg6IoUkGyWq3Q6/Xk/VlfX0dhYSHZV8Zaobm5Oelivry8jHA4TEgfg0gkgvHxcSgUClRWVqZ93/1+PzFv7e3tzbpIaLVarK2tYXFxEXa7HYWFhejt7cXIyAh0Oh329vbw3HPPpeQQJiLdkJPL5cLGxgZCoRBomkZHRwcePHgAoVB4Yzc/CwsLsFgs6OrqwtbWFvb29mCz2aDT6aBUKgmx6urqwvLyMuY/8xk0//mfA//9vwN/8idJjxWLxfAv//IvmJmZQV9fH5599tlbTaqA4yq03+8/lxv8bUM0Gk2rbQqFQsR4ORgMgs/nQygUQiQSIT8/H0Kh8Ma6HVqtFhKJ5NyWC+fFtRIriqIEZrP5VtVs32/6qmygaTplsuqs4PF4UKvVSReKeDxOrCD29/exsbGBYDAILpebYgVxXS3X6elpsFgs5OfnY2RkBGVlZVhbW4NCoSAL8XmRGLETj8fhdDpht9sxMzMDiURCWoYX8f1JBEVRUKlUUKlUCAQCZFEuKSlBSUlJyvMcHR2lZOOdDHcuKChARUVF2s9DJpOhp6cHT548gcvlQl1dXRJhLCgoSKpcMTcmUqkUlZWVGB8fR0dHB2kna7VaLC8vE1JiMpmwsrKC+vp6sFgsyOVyuN1uqFQqKBQK0sbjcDjkgg0ktwL9fj+xVnA6nWTKjKZpWK1WEp4ciUSwsbGBvr4+8jizs7MoKSlJagtubGzA7Xan5P+FQiGMjo7CYDAkec0xoGkaa2tr2NzcRENDw6nTxTRNY2trC6FQCDabDX19faStymhPCwoK8PLLL2NkZAQajQZ2ux1arRZ2u528Zr1ej83NTRwcHMBqtWJpaQl5eXloamrCyMgIjMbjtcRms+HevXtZ9+mycXR0RDyk3n77bXL8KhQKlJSUYHJyEg0NDYhEIqBpGiwWCzqdDjKZDJb8fDR/9KPAn/4p8Du/A7xL7KPRKP7pn/4Ji4uL+NCHPpTWxPU2QiwWY3s754i5G0OiKPwkeUonCmdyE5nvmWvQ/fv3b1UGrU6nA4fDed9XrLQ6ne4ifhKXjg8SsbqqtHUWiwWJRAKJRJJU4YhEIqS6ZbVaiRXESaNTxgrissC0bYqKimC328HhcHBwcADgOOLF7XZf2nOxWKykiB2v14udnR0MDQ2Bw+FAo9FAq9Ve2jHGZPZFIhHiwJ6fnw+DwUCqUCsrKxn9qU6GO1dWVkKn06Vsy+Vy0d7ejtXVVQwMDKC1tTXpNeTn56OhoYFMCzK/0+l08Hq9mJubI5OIJpMJ/f390Ol0hGgtLS3h6OgIPB6PtANVKhWkUikxcU23/8zPEv2u9vb2iB7K7XZDLBYTsmKxWFBeXk4I5M7ODkKhEOrr68lj2u122Gy2lBafx+PBxMQEGhoa0k4GBoNBPHnyBDKZDH19fVlvWGiaht1uh8VigUqlQm9vL8bHx8lkYyAQQDweh0QiQSAQwJtvvokvf/nLePHFF/Haa6/h85//PF577TW89NJLODw8RFtbG/7qr/4KJpMJDocDWq0Wzz77LBobG/HVr36VaF3efPNNfP3rX8+4X5eBROuD/f19cDgcqFQqFBQU4IUXXsAPfvADLCwskJikaDSKeDyOUCgE4Fh7x2azj32fvF7gv/03oLYW+A//AfjRjxAOh/GjH/0Ia2tr+OhHP0qGH94LYCpWNw3m/U6nbWKCy3k8HiFKQqEQcrkcQqEwrSg8EyiKulVeVlqtFtFo9H2vsdKWlJTcKpV4KBQ6VW/zfoHP57tWjxEul0sqLQxomkYwGCSEy2azIRgMJrnQM1/ntYJgpuGKi4sxNzeHlpYWTExMQCaTIR6PX5negaIo0j41m81pI3YKCwsvRTzM5XJhNBphMBhgt9sxOTkJLpeLkpIS7O7uZm0jnQx33tjYSBvuTFEUjEYjFAoFhoeHUVNTk6TRUiqVaGxsxMjISFLWndlsxujoKGw2G/R6PbF2mJ6eRldXFyiKgsFgwMrKCmpqapCfn4/Z2VlyV8x4HjHtxHQXakYAfTLGJlG0HgqF4HA4SLXq8PAQ8/PzSdYKTqcTS0tL6O7uTiJGu7u7mJ+fR3t7e8o5w4R6r6+vo76+PmvgMU3T2Nvbw+LiImQyGe7cuUMqfIzerKWlBbu7u3j55ZfJa/vd3/1d/NZv/Rba29vxiU98At/5zneg0WjwpS99CaOjo/jQhz6E8fFxvPLKKxAKhfjud79LPpMvfelLhHx8+ctfvlSPPub8dTqdcLvd8Hg84PF4UCqV0Ov1qKurS3ofCwoK8JGPfAS/+MUv8Pbbb+PDH/4wMc5kJj6ZY0omk2FnZwcwGoEvfAH4sz9D+JOfxN+/S3xfeuklopt7ryAvLy+pnX0VoGk6ybvp5L/M+cO06JiYKY1Gc25ReCbw+fwkU9+bhlQqRTwev3aH1msnVuXl5bdKxfdBmdgAkOLzcxOgKAoikQgikSjFCoIRy29vbxNjT+YiwHzlYgUxPT0NHo+HnZ0d8Pl8Uq1qamrC8vLytX3eJyN2HA4H1tbW4PF4oFAoUFhYCLVafaHWLEVR0Ol00Ol0ODg4wOTkJCKRCDHHzPZeJYY7M0L/qqqqlNaiUqlEd3c3CXKuqqoij6tQKNDc3Ezib5gJrubmZgwODkIsFkMul0OpVEIsFsNqtaKkpAR6vR4PHjxARUUFuFwuFAoFXC4X8vPzIZfL4fF4kJ+fT3RWJ7P+GNLNaAZZLBaOjo6SzH7n5+dhNpvBYrGItUJdXR0R3Xs8HszMzKTk/62trWFrawtdXV0pRrqhUAiTk5MQi8Xo7e3N2NqmaRoOhwNLS0sQiURoa2tLWWwYvdnh4SEMBgOmpqZSHoOiKPzlX/4lCT4uLi4mwwHf/va30z73pz/9aXz6059O+7uzgqZpIkh2uVwkY1OlUqGsrAwymezU87GtrQ12ux0DAwPkxuLg4AA2mw0ASJVbJpMhEAgck+r/+B8R//73Efj938fOZz6D/+13fieptf1eAaPpvEgVh5ncTqdtOjw8BJAqCler1eT769TXMVOQt4VYvau9vH4t+XU+GY/H05eUlNyOd/xdMO2IDwJCoVDOU2XXDQ6HA4VCkbR/iVYQHo8HOzs78Pv9oCgKEomEkC2ZTEY+w3A4jPn5eZSVlcFisaCjowPDw8MA8P+x9+bRbd33mffnYgcIgAQJ7iu4iItISdQuUotTx23iJI6bOI4dL1mcSc+ZzunM6cx0pmem550z5+1p086cTns67ZsmqeM4suNx0qlTO46zypKshaIoSlxEUtxXcAMJgMQO3PcP+v4EcBMpURQk+TkHRxAB3IsLXNz73O/3+T4P1dXVtLW13ZMAaq1WKwhQLBZjbm6OiYkJurq6MJlMQpd1J/uiUnk9cuQIIyMjvP/+++Tn51NSUrJu9c9ms9HY2MjIyAhnz56ltLR0hZ+VXq/n8OHD9PT0cOHCBfbu3SsIampqKvv27aO5uZl9+/aJWKX9+/fT1NREQ0MDer2e6upqzp49K7ZT0b0p7cixsTHsdjs2m425ubk1iVW8cD3eZmFkZESEUrvdbvx+v6iG9PX1YbVaxXMXFxdpaWnhwIEDCV5XHR0dBAIBjhw5knBCkmWZ4eFh+vv7qaurS4ibWo7Z2Vm6urqEO/1aVWIl93FoaIjKykrx960IPr5dxGIx3G63IFI+nw+LxUJGRgY7duxYYX2wUTz++ONMT0/z1ltv8dRTTwmNGSQSK1ia2NTpdLz/iU/w6b/7O76xsID9PiRVCoxGI36/f82LWkUUvlq1KRKJIEkSBoNBtOmSQRS+FrajQrdZWCwWlSRJZlmWt60nu2FiJUmSGmgGxmRZ/rQkSf8G+HdAGZApy/LMugsAbDZbRbJ5WMW7Nz/o8Pl8m4rTuNdYywoiGo2K6tbU1BS9vb2CIM/PzxMOh5mdnUWv1+PxeFCpVGIC7W7pzDYDlUolWqSyLLOwsIDT6eTSpUsAwsphs/mV09PTpKWlYbFYqKmpEbE5586dw2azUVpauuYyJUmiqKiI3Nxcuru7OXv2LLW1tQlEV5IkKisrmZqa4vz58wkEw2q1sn//fpqbm6mvryctLY2UlBR27txJc3MzR44cQavVUllZSXt7O/v27aOoqIjTp09TWlqK3W6nvb2dWCyWYBS6fDIQEomVEmMjy7KwT1AIkpKnp3g8KdYKgUBARPkoy4lEIrS0tGCxWNi3b98KAfvVq1cxGo3rOpS7XC66u7vRaDTU1dVtKKIoPz+fM2fOUFpaitPp3NLg440gGo0yPz8viFQgECA1NZWMjAxqa2tvVxSqawAAIABJREFUO+h9OdRqNU8//TTf/va3+elPf0pdXR2Tk5MAgnAoxGpsbIxTp06xWFDAx37nd7D//d/DH/wBFG+7BvmOIcsyer2eyclJDAbDmqLw+GpTeno6+fn5CaLw+wWr/V7vNfLz8+XOzs5cYNts8DdTsfq3wHVAOVp8ALwNnNroArRabUmyEauHxRwUHhyhvlqtJi0tbUXlKRAI8Jvf/AZYOsnl5uZy/fp1MeY/OTmZFMQqHkr1zWKxUFFRQTAYxOl00tnZmRCxY7PZbtlKGBwcTHA/12g0lJSUUFxczNTUFO3t7cBSwHJmZuaqJ0ytVkttbe264c5ZWVlYLBYuX75MdnY25eXlYjsOHjzIpUuX2L17NzabTTjKt7W1sXv3bvLy8hgZGRGBycp0W2lpqYjgsdvtQvC7FrFKT08XgtyUlBRBKrVaLZOTk+j1emFG2drayv79+4V2q6mpidraWrH/KESrqKiI4riTt0LWent72blzZ0LAczzm5+fp7u4GlsxSN6rZlGWZ+fl5YrEYv/nNbygqKtrS4OPVoARTK0QqEolgs9nIyMigsLDwrlrPmM1mvvjFL/Lyyy/T1taGx+MBSKh8Arz77rsAvPDCC6Q89xxUV8O/+3fwYc5nMmEjonDlQlBpz92OKPx+gV6vZ3Fx8V6/jQR8qOtOPmIlSVIB8CngT4E/BJBl+cqHj214ZbIs2x8EP4/7FQ86iTQYDAlky+FwMDExQSwWIzMzk+HhYWKxGE6nk7NnzybE+FgslqT4bPR6vYjYiUajTE9PbyhiR3E0Xq3NKUkS2dnZZGdn4/F4RGxOSUkJBQUFq2owlHDniYmJVcOdjUYjDQ0NdHZ20tTUJCKCUlJSOHjwIE1NTcJ+oLS0lCtXrjAwMIDD4RDThIpO5+zZs5SUlJCfn8/o6CiZmZnodDoCgQB6vV74MsVPBBYVFeFyuURFbXBwkIqKCmRZpqurS4i329vbcTgcmM1motEoTU1NlJeXi0qbx+OhpaWFnTt3JliIBAIBkTN59OjRVSsHHo+Hrq4uotEoVVVVG26zLw8+LisrY2ZmZt2Bg9tFKBRKIFKwpJlT4mG2WwaRl5fHZz7zGRGGDjeJlTIlGI1Geemll25Wqf/kT5bE7D/9KTz++La+33A4vKq2yefzCXPp+GqTxWIR6Qw6nQ5JkpiYmMDtdie0ex9U6PX6LZ263grk5eXpgbV793cBGz2T/C/gj4DN9SaWIRaLWTbb3ribiDccfNDxsDjMxxOO+MpBbm6uELF/5jOfwWg0isnEoaEhPB4PsVgMk8mU4Cy/Va2Q24FarU6I2Jmfn8fpdHLjxg10Op14zGg0Mjk5uWqu3XJYrVb27NlDMBhkcHCQ06dPk5OTg8PhWFHJU4TxWVlZItx5586dgpSoVCpqa2sF+VKqVCaTSWjbamtrsdvt7N69m3PnzmG1WsnIyKCoqIju7m5qamrIyckRovZr164Ri8Ww2WzMz8+L9m78b3VxcRGTycTIyIho7yqkcmhoSOhPxsfHCYfDFBUVEYvFuHz5Mvn5+cIvanp6mo6ODvbu3ZvQthsbG6Onp2fFFKSChYUFuru7CQQCVFVVrTsVqGC94GPFC0vJP7wTBAKBBOsDtVpNeno6mZmZ7NixIylaS7t27eL999/H5XKJwOnR0VFOnjwpJlETPvc//EN45ZWlduBv/RZsUcVZ0XCuFegLN0XhCnm6HVG4yWRiYmJiS95zsiMZW4E2m00HbN84PBsgVpIkfRqYkmX5siRJj9zJyqLRqOleT6XF42ESrj8s2xpPrMxmc4L4+cyZM2KSCZa8mOJFyLIsC6NTt9vN8PAwPp8PjUazwgpiu09QkiQJcX91dbWI2Lly5QqRSIRwOEx1dfWGA1/1ej2VlZUiNqepqQmz2UxZWdmKVtbycOfBwUF27twp2ka5ublYrVYuX75MYWEhJSUlGI1GDh8+zMWLF6muriYrK4v9+/cLU1GHw8HZs2dxu92UlpZy/vx5ioqKsNvtzMzMCAF7fn4+wWBQTFUpjuMqlYrp6WnKy8sZGBgQIdr9/f0cPXoUv99Pd3e30FVdvXqVtLQ0YcUwNDTE8PAwhw8fFoQyGAxy7do11Go1jY2NK7SXi4uL9PT0sLi4SGVl5S3dtJXJwFsFH0uSRGZmJtPT0xsix/HL9/l8gki53W50Oh0ZGRnk5eWxc+fOpI2bUqlUIoC5qamJX/3qV6SkpGCz2VaemHU6+N//Gx59FL75zSXz0A3gVqJwIMGCQJl2VAJ9t9KCINnIxt1CMhIrq9Wq1Wq12+qptJGKVSPwhCRJjwMGwCpJ0g9kWX5+sytTq9V6p9OJVqtFq9Wi0WjE/XtROXpYyAYstQQehm2NJzw6nQ6tVkswGGRxcRGXy7WuY7PiFG42m0VVA5aqDYpYfnR0VBidmkymFVYQ21Xdio/Y8fv9nD17lrGxMbq7u8nIyCAnJwe73X7L35VSPSkoKBDTbJFIhNLSUnJychK2Rwl3npyc5OLFi+Tl5VFWVoZarSYlJYXGxkba2tq4fPkye/bswWAwCHIlyzLZ2dns3r2b5uZmGhoa2LVrF9euXePo0aPY7XbGx8fJz89neHiYqqoqBgcHKSsrIxgMimpOIBDAaDQSCoVENNHY2BjHjh2jr6+PoqIiNBoNTU1N1NXVodPp6OzsRKPRJLQKvV4vR44cEUR8fHyc7u5uqqqqWK4D9fv99PT0iHZOVlbWut+zYog7NTVFenr6hoKP8/LyGBoaWpdYKYMOCpHyeDybtj5IBgSDQWZnZzEajQQCAd59913S09P5yle+ws9//nPGxsZWvui3fgueeQb+7M/g+eeRS0sJhUKrapuU1rEiCldIk81muyei8GSclLtb0Gq1hMPhe/02EmCxWDCbzVtn5rYB3JJYybL8x8AfA3xYsfoPt0OqANRqtcbr9Yor7HA4LO7HYjHxPCX3azkBiydiy/+v0Wg2fVBRTAgfBjws04/xFSudTieu2AcHB0XlZbPQarUikFmBLMv4/X5R3RofHxdZhMurW3eb0Hq9XnJzc6mtrSUWizE7O8vExAQdHR0iYicrK2vd71+SJFHBW1hYWBGbE/+5Zmdnk5mZSV9fX0K4s1qtZs+ePYyMjPDBBx9QX1+P1WoV5CoWi5Gbm0txcTGtra3s27ePjIwMBgYGKCsr49KlSxw9elT4kAUCATQaDaFQSFSsvF6vEKtnZmYyNTVFRkYGkUiEiYkJjh8/zo0bN0hPT8dut9PX14ff72fv3r3EYjGuXLmCwWAQ0TWhUIi2tjZkWV5RpQoEAty4cQOXy8WOHTvWtTwIhUKMjo4yNjYmIo+qqqo2XDGy2WxcvXo1oeIoy3KC9YHiNp+RkUF5efldCQDfDiiTj7FYTEgUdDodBoMBq9XK9evXiUajK9p04a9+leq33kKqrsZTU4N3716Chw8TO3QIY3o6qampgkglE8G8H7+j20UybuuHnYtb9+u3ELet1pUk6Q9Y0l3lANckSfqpLMtfv8VrohsR8MVisQTStfy+3+8X9+Mfiw8mValUtyRmbrdbONYqjyXjjrEVeFiMUOMJgFarFSe23t5eKisrt+wzkCQJk8kkPKgUKBNAiu9WT08PoVBInDSUm8Vi2bKD/8zMjND5KBOQmZmZCRE7Fy5cSNBsrdeSN5vN1NXViZy9M2fOkJmZSWlpqZgqValUIty5s7NTtAfNZjOFhYWkpqbS0tJCWVkZhYWFCeSquLgYt9stvpMzZ86IduL09DR2u53p6WlSUlLw+XwAIuNSIRdTU1MUFxdz48YNqqur6e7upqKiArfbzdTUFA0NDYyMjDA9Pc3BgwcJhUJcunSJ/Px8HA4HsBRvo5iIxlcog8Egvb29TE9PU1FRQW1t7arHhVgsxuTkJCMjIwQCAfLz8zl48OBtEWlFBK0Q9OXWBzU1NVse/XSvMDQ0BCwRV51Ox8GDBzl79izf+c53BIl+//33MZvNCaJw08GDRH/9a7T/5/+Qdvo0aa+8Ai+/vNQqPHgQTpyARx6BI0cgiSQnCjbapv8IWwuLxYJard5W88JNEStZlk/xob2CLMt/A/zNRl8rSZLK4XBs6EyiUqnQ6/V3dKUfjUbXJGahUEi0hsLhMO3t7eI58eRMrVZvuGKm/F+j0STlj+dhaXvGVyDjW8x+v39bwkFXs4JQAk6V6tbU1JSI81jN6HSz+8/s7Czl5eUr/r5axM7k5CRtbW0Eg8EEK4e1rBfKy8uFx1JLSwt6vZ6ysjJRvTMajezbt4/Z2VlaWlqw2+3s2LEDq9VKY2MjV69exeVyUVtby6FDh2hqaiIWi1FbW8v58+exWq3U1NTQ1tZGTU0Nra2t1NTUMDg4KBy64eagycLCAjk5OcJYNBwOI0kSHo+HmpoaPvjgAw4cOMD09DRDQ0McPnwYn89Hc3OzqKyFw2Ha2tqIRCLCvBSWKti9vb1MTk5SVlZGTU3Nis9FqSKNjIwwMzNDZmYmlZWVtxWLFYlEmJubExWphYUFIpEIJSUld9364G7iVqLw7u5uJElClmVRfYtEIly4cAGAxx57jIaGhtUXfvjw0g3A7YazZ+H99+HUKfjzP4c//VPQaG4SrRMnoLERtjHKazUoLbKHoWsAyUUiP7yITTqN1VbBZDKZti2AWZk2WY9M9Pf3o1arE7xrFCil6tUqY5FIhEAgsOrfl/eXFbK1kVamcl+tVm/5ThkMBjdkWHi/Y3krUIHRaKSsrOxevCXhnGwwGBK8kGKxGAsLC7jdbmZmZujv7xdX8cvbiWu1lJRA240csI1GIyUlJZSUlBCJRJiammJwcJCrV6+uG7GjUqmEa/zc3Bx9fX10dHTgcDjIy8sThqerhTvv27ePwcFBEeQcT67279/P+fPn2b9/P2q1Go/Hg8FgEO7f+fn5TE1NibagQqyUWKSRkRGKi4vp7OwU5EzRZHV1dXHkyBHcbjfXrl1j7969pKamMjk5SWdnJxUVFeTn5yNJEuFwmP7+fsbHxyktLeX48eMrqomBQEC0+kwmE4WFhezcuXNTVUfFuHZ2dhaXy0UsFhPWBw6HA7/fT19fHwUFBRte5r1AvCh8OXmKF4XH2xDEi8INBgPvvfcesOQBlpqaSn9/v/gsW1paqK6uvrV9RWoqfOpTSzcArxc++GCJZL3/PvzlXy5pstRq2L//ZkWrsRG2+VioiLofBmKlVquTytrnQxPgB5ZYWSwWy7YRq40gEomsSbwkSRLk7HbbR7IsE41G1yRgikZn+d+Vg5OCzRIzjUazgpw9LHqy5a3AaDQKsCIcNhkQHzwdD6W65fF4GBwcxOv1EovFSElJSfDeUiwjbqdaotFoBFmSZRmXy4XT6aSrqwuj0Sgidpbv+zabjf379+P3++nv7+fGjRsUFBRQXFyMTqcTZKurq0uEFDscDtLS0rh06RKVlZUcOnSIS5cuEYvFqK+vp7m5mf3793Pp0iV27dpFT08PmZmZhMNh5ufnSUlJIRgMolarCQaDuN1uMjMzRftRkiQhWE5NTeXy5cscPnyYqakp+vv7OXz4MBqNhtbWVoLBIEeOHMFgMBCJRBgYGGB0dJSSkhKOHz+esI9Eo1Hhhh4OhykoKODIkSMbPjkGAgFcLhczMzPMzc0JAqpU9Zb/HrVarahk3ivIskwoFFq12rRVovDy8nLee+891Go1fr+fv//7vwfgueeeQ6PR8Prrr/Pd736XZ599dnNJERYLfOITSzeAhQU4d26JZL3/PvzVX8Ff/AWoVLB37xLJOnECjh6FuxxzlYyi7rsFjUYjQtKTARaLhVgstq0+T9u55War1ZoctcEPEYlE7irZkCRJVKxut6yvJJev1dZcXFxcQczC4bAgFMr78Pl8LC4uioPfrYhZvD7pfsLyipXiR3M/Bbjq9Xqhk1KgTIR5PB7m5uYYGhrC5/MRjUbR6/UMDg4K7dZm92lJkkTEDiyJ4ScnJ2lubkaWZaHLip96NBqN7Ny5k8rKSkZGRkRsTllZGWazmd27dzM/P58Q7qwEObtcLvbt20dLSwuxWIyKigo6OjooLS1lbGxMEM7JyUmi0WjCVJUkSUxPT5OdnY3dbheTfIof1eXLl9m3bx/Dw8PMzc1x5MgR5ufnaW9vp7y8nIKCAmKxGH19fQwPD1NUVJRAqGRZZm5ujpGRERF8vHPnzg3FC8VbH8zPz6PVasnIyCA3N5eamppbnmhUKhWSJG2Jn9VaUJzCVzO8VJzCdTqdqDQZjUZyc3O3VBSekZFBamoqHo9HDBA88sgjlJaWAvDSSy9x8uRJXnnlFZ566qmENIFNwWyG3/7tpRuAzwfnz99sHf7N38D/+B8gSVBff7N1eOwYpG/tEJlCNh4GJNu2ms1mYrHYtorutrVilZqamnTEKllY9VqIn5C8E3J2/vx5qqurUalUCQQsHA4TDAZXELPVJjU3UzG7VzYaCqlQUuWVk0X6Fh8otxvx0TfxV/Gtra1YrVZkWWZkZCTBCiLe6HQzwmdlPeXl5QSDQSYnJ7l+/To+nw+73U5OTg7p6enC7sDhcFBSUsLk5CTXrl1DpVJRVlaG3W6nsbGR0dFREe588OBBent7aWpqYs+ePXR2dor3ubi4yMLCAnl5eTidThYXF8W2KV5WJpNJeHhlZmYSi8Xo6emhqqqKlpYWdu3aRW9vLxqNhr1794pooMOHDwsCOjg4SEFBQULu32aDj1ezPlBaXkVFRezateu29n2LxYLX673toHClEr5atSkajSbkb5pMJiwWC1lZWZhMJuEUfrchSRJZWVm43W40Gg3Z2dmcPXuWyspKYRPy0ksv8dprr/HDH/6Qxx9/nP3799/5ik2mJS+sRx9d+r/fDxcv3mwd/t3fLVW1JAl27bpZ0Tp+HDZgArseko1s3E0k27aaTCZisdi2Cha3lVilpaUlVQnkfiBWWwFJkojFYncU2xKLxVatjCn/VyY1l1fW4smZciLeDDHbrI2Gsn1qtRqv1ysqdw+qj8zi4iI1NTUJ7SnFOFJpJ46NjbG4uJjQelRut2pr6fV6YbkQjUaZmZlhbGyMtrY2rFYrubm5ZGZmotVqRWXL7XbT399PZ2eniM3Jycmhu7ubDz74gNraWtLT02lqamLnzp2MjIxgMpnweDxkZ2czNDSERqMR5EKZ+FXa8xaLRRChrKwsbDYbN27cYMeOHXR3d5OdnY3VauXcuXM4HA5qa2sZHR2lv7+f3NxcEVETiUQYGRnZUPCxMmE5MzODy+USQdDp6elban1gtVrxeDyrEqt4Ufhy8qRUZpXquFJxyszMFEQqWY51nZ2d9PX1AZCZmcmzzz7Lt771Ld544w2+8Y1vYDQaMZvNfOUrX+FHP/oR77zzDvPz8zz66KNbS/yMxiXy9MgjS/8PBKCp6WZF6x/+Af76r5ceq6u7WdE6fhzWyI1cC8r+9jAg2YjVhxfZ28o9trUVmJqamhy/7A/xMEXaRCKRO2ovqFQqdDrdHYkvV7PRiP+/In7drI1G/P34512+fFn8X8khe9Cw2qSRIu5OSUlJMLuMRCLC6HRiYoLu7m5CoRBGo3GF0elqvwu1Wi0yB9eL2ElNTaW+vp5AICBic3JzcykvLxdTuAaDgX379tHW1kZGRoYIUx4dHcVmsxGJRAiFQoIYS5KUcFNOvop2KT8/nxs3blBWVsbc3ByTk5McOHCAubk5zpw5Q1ZWliBNs7OzjIyMMD8/T25u7qrBx7FYjPn5eVGR8vv9Io6nqqrqrpjBKi3A6elpQY4VEqXoc9YThSfLFNZ6aG5u5p133qGgoIDR0VECgQApKSk8/fTTvPzyy/zTP/0TX/rSl4S31TPPPMNPf/pTPvjgAzweD0888cTdI4gGwxJpOn58KZ8wFIJLl25WtP7xH+Fv/3bpuTU1N8XwJ07AKtFH8Ug2snE3oVarEy6okwEPMrFKsVqtSaWelmX5oSFWcO/N27bKRmMtj7NgMChaf5FIhN7eXvG6pqYm+vv7gZU2GhsZDEhGG414wrkRaDQaEYsTv4xAICCqW5OTk3i9XtF6jCdc8UL25RE7Pp8vIWInKyuLnJwcKisrqaioYHR0lIsXLwp7hcXFRRGBEwwG8fv9GAwGzGYzMzMz4rNWDtAKaVYsUZTHlXbW0NAQDoeD3t5eiouLSU9Pp7m5mYyMDI4cOSLibpTg46KiIvbs2SOWs9z6IBwOk5aWRkZGBrt27RL+XbeL1UTh8feVY5FGoyEYDIrPNi8vD5PJdN8PnsiyzOnTpzl16hQ7duzgqaee4s/+7M9YXFwEoKCggE9+8pO88847nDp1io997GPA0jHjU5/6FKmpqfz617/G6/XyxS9+cXs8+XS6pQnCxkb4L/8FwmFobr5Z0Xr1VfhQeE9V1c2K1okTEOeLBku/vQf14m45lA5JMkG1zSf67SRWaq1Wm1QsJpm8Nj7CxqBMaq4FZSAhHA5TX1/P+Pg4sBQZ0tDQICY11xoGCAQCeL3eVclbPJZPaq5FzO6mjcZWeJPFa27ig2+j0agQy09PT9Pb2yvWt9zoVK1WYzKZKC0tpbS0lHA4zNTUFH19fXg8HtLT08nNzaWxsZG5uTm6urqIRqPs2LFDtNdyc3MZHR3FYrFgNBoJBoNiAk0hy4pLt0Jylc90enoam80mpvuGh4dJS0tj7969uFwuLl26tCL4WHmPCpFSgp/tdjslJSWbPnHHYjECgcAKbdNyUXh8tWk1p/BAIMCVK1eEkPtBgCzLvPvuu1y6dIndu3fzmc98RvyGQ6EQXq8Xi8XCvn37GBsb4/Tp0+Tn5wvRuiRJHDt2jNTUVN566y3+8R//keeee+62pmHvCFrtkvnokSPwn/8zRCLQ0nKzovXaa/Ctby09t6IioaKl1mqTjmzcLahUqqTb1u0uoGwnsVKp1eqkYjHKgfojPDiIRCIYjUbC4TB2u10YEQ4MDNDQ0JAwqbnVNhrKfZ/Pt6rFxnJytllipgwDKPtsIBC4a1fuarVamIvGI94Kor+/H6/XiyzLmM3mBKPTvLw88vPzRcSO0+mko6ND5DCazWYxeZebm4vL5cJoNLK4uCgSEBQCDEsnYIVoRaNR8RxlyEL5nufm5nA4HExOTnL58mXhpaVSqZidneX69eu4XC5UKpXwkKqoqLhlRWi7ROHJGGJ7J4hGo/zzP/8z7e3tHDlyhMcee2zFZ9HX1yeqh48//jhOp5N/+qd/4hvf+EbC0MmuXbuwWCy88cYbfOc73+G5557bVGj1lkMxIj14EP7oj5aIVmvrTXuHN9+E73wHAHtxMbq9e+HJJ5cI1yreiQ8KlN9ikuGWP0BJkv4R+DQwJcty7bLH/gPwl0CmLMszt1rWthIrlUqVVCzmYWsFPgyIRqOYzWY8Hg8LCwuUlpbS19cnKldbgbtloxFPxBYXF1f1Pou30VCGA5qamjZMzBSPs9vFalYQsViMxcVFPB4PLpeLwcFB/H4/Wq1WkK38/Hyqqqrw+/04nU4GBweF8ahCWsxmc8IQhEKs4g/SSpVZuQEinNlisSRoriKRCC6Xi4sXLwrrg5ycHKqrqxN0Okru42o2BEr7ZrtE4Q/ShV4oFOKNN96gv7+fj3/84zQ2NorHZFkWRFkhVrB0sfH000/zD//wD7zxxht8/etfTyC9DoeDr33ta5w8eZKXX36ZL3zhC6umDtwTaDRLRqT798O///cQjcK1a/D++0Teew/Lr34F//f/Lj23pORm2/CRR5b+/4B89yqVKumI1QZ/V98D/hb4/rLXFgKPAcMbXd9HFasHZGf+CEuQZZm0tDTGx8cZHx+nurqavr4+MSWXLO7zW2GjMTExgcvlwuFwrNnWXG2Sc7XA880Qs+U2GiqValUrCKXN43a7GR4eFlOaJpOJzMxMDAaDeA7c1FMppDMajRKNRlccpJWqlfLelYlVJY/R5XLh8/nIyMigsLCQ6upqQqEQPp9PeE2JYN9lonCFPKWnp2Myme4bUXiywefzcfLkSSYmJnjiiSeor69PeFz5ro1GI319fSJkG5ZMaD//+c9z8uRJ/uVf/oXf/d3fTfgOsrKy+PrXv87Jkyd57bXX+MxnPrNi+UkBtXrJH6u+noUXXmB8dJQ6uFnRevtteOWVpecWFt4Uwp84AWVl9y3RSkaN1UYgy/JpSZJKVnnor1jKRX5ro8v6qGL1UcXqgUIsFhPtKyWM9+233wbg6tWrHDt27F6+vS2FEmVzJ8LqeBuN1SY2V7PRWG1Scz3xvzJ1qGii4lMHPB6PiKtRYjB8Pp9oJ6x25atcEMViMXFT/q7X67Hb7fj9fiYmJhgfH0elUiVUm9LS0sjNzcVkMiXlUML9Drfbzauvvorb7eaLX/wilZWVK56jfGcmk4mpqSnGx8cTonzKy8v52Mc+xm9+8xvy8/M5dOhQwustFgtf/epXefPNN/nJT37C/Pw8jzzySNJ+l5IkEQPYvXvp9gd/ALEYdHbeFMP/7GdLgniA/PzEilZFxX1DtO7XVuCqL5KkJ4AxWZavbmbf+qhidZ/srB9hY5BlWbhkz83NYTabRZjvlStXOHr06APznW+FXchW2misRsCUwPPVWp7LD77K37RaLaFQaM2rXuWCSDmAK6Qp3i1cqTbdj+kBbrf7vh2s8Xg8fPe73yUUCvH888+vmsMKN+1PlP23t7d3RUbisWPHGBsb4+c//znZ2dmUlJQkPK7X63n22Wd5++23OX36NIFAgE9+8pNbv1FbgGg0itvtTvyjSgW1tUu33/99kGXo6rophv/Vr5YE8QA5OUvGpn/5lxBnoZKMUAZPkgmDg4N2SZJUsixvuJQmSZIJ+C/Ab292fdtJrO6/o8RHuC+h1+tRqVSixeRwOLhy5Qpzc3OMjo5SWFh4j9/h1iBZTr7LbTRuVQVbrT2peFYpr73VgTkUCokpS0WTplJdoAOeAAAgAElEQVSpBOFKhs/lTnDq1Kn7chu8Xi9er5eMjAz6+/sZGBhY8ZyZmRl6enqQJAmz2YzJZKKjo2PV5WVmZjI6OsoPfvADdu3atWor32q1kpaWRmtr62231e82lCrtqVOnbv3k6uql2+/9HsaREdKuXiWttRX7j3+M//x5Wv/6r4ksBQsnJRT7kKmpqXv9VpZDBWymR1kGOAClWlUAtEiSdFCWZed6L9xOYhVLNhabpCXLj3AHUL7TlJQUYZtQXV3NlStXkCSJK1euPDDEKt6KYKug6JtWI0LrEaPN6LaMRiOSJBEMBoXuKRwOi9cpj633+1QqbbIsYzAYRHUqEokkWDXEV7Di/01mX6hTp07xiOIGfh8iNTWV3/zmNxgMBo4cOSL+7vP5ePfdd+no6CAnJ4cnnniCnp4eQqEQkiStuc0HDx7ke9/7Hp2dnbz44ovkLfOIAuju7sZutyft5+ZyuRgZGWH37t23v5Bf/ALzpz7F0W9+E37+8yXn+CTEwMAAKpVqzWrlvYDD4ZgZGBiI3vqZNyHLchsgLPYlSRoE9ifbVGAsEonEWGKNSYFknF74CHcG5WSclpaG1+tlfn6eoqIiYGmyq6Ojg0984hN31PpKFix3OL7VpOFaxGh5YPd6wnWDwbCpScNYLCb8sNxuN1NTUwQCAXQ6HVarVVS6lNaQWq0mGAyK3+ZqOiuVSiW23WAwEIlEhObK7/cLR/K0tDQR4Ozz+Zibm2NsbAy/3y9sHAwGwwrSdT85mScjjh07htPp5Be/+AXZ2dmUlpZy/fp13nnnHfx+P4888ghHjx4FllqAJpMJl8u15vKsVitf/vKX+d73vserr77Kl7/85QSbhUgkwtTUFBUVFXd9224XW1Jdfuwx+MEP4Jlnlm4//vHSJGKSIRaLJV0LXv4Q6z1HkqTXgUcAuyRJo8D/I8vyd29nfdv5rUSj0WhSsZj7dXrhI6wN5YRst9sZGRlhenqazMxMYQoJcP369Tu7crwLuJU31loRQKFQiImJCbGc9Sb6UlJSbumNdSfvP97jyu12s7CwIDRvVqsVu91OaWkpi4uLTE5OMj09jdFoxGKxoNPpEvLu4j2/ZFlOmBpTWn4KoVOiYBYXFzEajWRlZaFWqxkbG2N+fl5YLdjtdmw2m7BHUFznlQnBxcVFZmZm8Pv9BAIBZFkWpp6rVb2S7eSRLJAkic9+9rPMzMzwox/9iMLCQnp6esjJyeGFF14QRrRKldJkMjE6OrruMlNTU3nxxRf53ve+x/e//32+8pWvkPVhXt/U1BSyLCfENyUbtmxQ6umnYXoa/s2/gW98A7773aQTtSeLRGGzkGX52Vs8XrLRZW1rxSoWiyUVsfqoYvXgQXH9zcvL48qVK4yNjVFTU4PNZhMn8tbW1i0lVrdyc1+vYhSP9dzclQm2+L8vLCwwPj6+7SQxGo2KzEGFRIVCIWF3YLVaKS8vx2KxoFKpREVhbGyMzs5ObDYbWVlZWCwWhoeHWVhYwGQy4fP50Ov1QmulaKUUYgWIIOZ4YqUQw1gsJnIHp6amSE9PZ9euXRiNRubm5piYmKCzsxOVSkVGRgYZGRnCJHQ1yLK8whh0enpa3Ffe4/Jql3J/M8ag8et8EKDX69m/fz/vvvsuPT09HDt2jBMnTiSQUYUsG41GfD7fLU/INptNVK6+//3v8+Uvf5nMzExxYZHMxGpLB6V+//dhagr++39fCoP+8z/fmuVuEZJx2n67f1fbSqw+qlh9hLsNpdqhTBgpAsrMzExcLhd6vZ7BwUHm5uZEZt56+YNr3V8usF4vf9BgMGCxWLY8f1CpuNwtKMaZCoFSTFcV3yqr1Up2djYVFRUronX8fj/Dw8M4nU6CwSCZmZmUlJRgNpsZHh6mu7ubzMxMiouL6e/vF0abOp0Ov98vCJVGoxHtwFgsJj6zeINJxbHdbrcTCARwuVzs2LEDjUbDwMAAXq+XvLw8KioqhCv/7OysEFHHYjFBsDIyMsS2KEHAOp1uzfiUaDSaUPXyer1MTk7i9/uF/k2v169a9TIYDCtOQOFwOKn1XxuBz+fjZz/7GW1tbdhsNubm5oRxazwikQgajQaTyUQsFiMUCt0yoik9PX1F5WpiYgKDwUBaWtrd3Kw7wpaTjf/235bI1Te/uUSu/vAPt27Zd4gknbbfVu6x3cQqqVjMR+L1+x/LR/0VI0hFs+N0OmlvbxdTZ/Pz8wD88z//Mw6HA1iqcq0VvqzX6zGbzas+dq8PHgaDYcuIVSQSSSBQHo+HcDiMyWQSVajc3FxSUlJWPUHIsozH48HpdDI5OYlarSYnJ4e6ujpSUlJYWFigv78fl8tFUVER+/bto7u7G5/PR0lJCf39/eTm5jI9PY0kSej1+gTNmKKJC4VCohWoEL+MjAw8Hg8ajYapqSmqq6txOp3Mz8+zY8cO0tPTmZiYoKWlBUmSKCwsJDc3V+h04gOYBwYGCIfD2Gw2QbTWmzRTq9WkpKSQkpKy6uNKizTe1X1+fh6fzyfajQo5jPcj83g8SS+yXw1dXV28/fbbCVqqc+fO8etf/5rc3FwaGhrEcxVipXy3SsXyVrDb7aJy9corr2A0GsnJybnnv8f1EN/K3hJIEvzt38LMzJLLe2YmvPDC1i3/DvBRxWp7iZV/YWFhU6r8u42HrRWYbL3v+LH89QTXy+/HVxmXm1NqNBrh76TT6fD5fOKg29vbC0BJSQkzMzO8+OKL97VO5nb2X1mWRfyMcltcXESj0STEz1RXV9/ypB6fAzgzM4PZbCYnJ4fDhw+Lib3Z2Vna29uJRCKUlpZSU1NDX18fLS0tVFdX43K5GB8fp6ioCKfTiUajIS0tTejHlEqg0u5T3pNarRbC9ZmZGYqLixkeHqa6upquri6ys7PZu3cvvb293Lhxg8rKShoaGvD5fIyOjnL27FmsVitFRUVkZGQkxPQonkMzMzOMjIwQCARITU0VRCslJWXDvyNJksTUolIhXY5IJCJI1+TkJNFolBs3biSI7JWKV7KK7OOrVDk5OTz//POCuB49ehSn08kvf/lLsrOzKSsrA24GpitkyufzrfkZLUdmZqaoXHm9Xvbu3Xt3NmyLoJDILYVavSRmd7ngq1+F9HT41Ke2dh23gWSsWMW2uTW1ncRqwe12h7dxfbeE4vb8MEDRHm0VkVg+lr+RkfyNjOXH3zcYDKv+/VZXQzMzM5SWluJwOOju7mZhYYGamhphFFpbW8vbb79NT08P1dXVW/J53Cso+/Bq32soFFpRhYrFYphMJhGwXFhYiMlk2vCBMBwOMzk5idPpFH5FOTk57Ny5U3wv0WiU4eFhBgYGsFgsVFZWkpqayuTkJGfPnqWgoIBDhw5x5coV0tPTycnJwel0YrPZiEajgqRZLBbRAlQIpKK7UnIATSYTxcXFDA0NUV1dTWdnJ3V1dczOznLlyhV2796NWq2mu7tbEKzKykp27NiBy+VieHiYtrY2cnNzKSwsJCUlBbVaTXp6uggAlmUZt9vN7OwsnZ2dLC4uYjabBdGyWq13dCLRaDQiEsjv92OxWEQ1FZZOVMpkY7zI3ufzCWsJRWS/mtbrbl88xFepTpw4wbFjxxLWGS9m//GPf8y/+lf/CpvNRjgcTsjbVKrMG0V2djaf/vSn+dGPfkRXVxcnTpxImsiq5bgrxApAr1/KH/zYx+ALX4Bf/hLiqoL3AltendsCPMjEyjs/P59ULGb59NGDDK1WSyQSQa1WrzqWvxFitNpY/lpeRcvH8pX721EhUlq8e/bsobu7m6amJkpKSqioqODSpUtEo1GsVivNzc33PbGyWCy43W40Gk0CgYoPQU5NTaW4uFjovDYLn8/HxMQEk5OTRCIRoataTiiCwSCDg4OMj4+Tk5PDoUOHMBgMLCwscPHiRXQ6HYcPH2ZxcZELFy6wc+dOvF4vU1NTFBYWMjo6isFgoLCwkImJCUEytFqtIFaRSERMG2ZlZaHRaASp6urqor6+nqtXr1JeXk5OTg6tra3k5uayZ88eFhcX6e7upqenh6qqKkGMIpEITqeTa9euEY1GKSwsJC8vT1THJEkiLS2NtLQ0ysrKkGWZhYUFZmdn6e3txePxYDKZxPJSU1Nv+8Ti8XgS8hYBIfBeqyV5OyL7+H8V77DNYr0q1XLodDq++MUv8u1vf5s33niDr33tawSDQfR6vWiB+ny+Tb8HZdI3HA7zyiuv8JWvfEUkLyQTIpHIHUVPrQurFd59Fxob4dOfhjNnYOfOu7OuDeCukcjbxIcXZ9vKPbaVWLnd7qTSWN2PxGqtsfxbESO3283Zs2cFsVnPq2i1sXxlAivZSryrQafTEQqFxFW/EvKqGAu2tLSwd+9eTp06hcvlEpWJ+wHBYBC32y0I1PT0NNPT06Snp2O1WsnIyMDhcNxRi0iWZebn53E6nUxNTaHT6cjJyaG+vn7Vk7vH46Gvrw+3243D4eD48ePCrLOzs5OZmRlqa2ux2Wz09vYyOTnJ4cOHGR0dZW5ujsrKSlpbW6mpqaG3txdJkigoKGBiYgKv10t6err4rXo8HiRJEifwsbExqqur6e/vp7q6mmvXrnHw4EFaW1vJyMigsbGRGzdu8MEHH7Bnzx7279+Px+Ohq6uLWCxGVVUVaWlpFBQUUFBQgN/vZ3R0lA8++ACz2UxRURGZmZkJn6UkSaLCVFJSgizLItx5cHAQt9uNTqcTRMtms234gsLj8Wya7N+OyN7tduN0OoU5K2xOZH+rKtVqSE9PF+HKP/nJT6irqxPO63B7xMrpdKLVannuued47bXXxLSgOcmcyZXq3F1DVtaSaWhDA/zO78AHH8A9MuhMNmL1oQFtcDvXua2tQI/Hk1SCpu0mVkpbYyMts+V/X/6+1yJG8YG38Y9du3aN0tLSpJ6c2Sro9XqCwSBWqxWLxYLX66Wvr08c+CcnJzlx4gSSJHH58mUee+yxe/yOVyIajbKwsJBAopQrfEULVVZWRl5eniAud7q+6elpnE4nc3NzpKamkpOTQ3l5+apaKyWyoq+vD0mSKCsrY8+ePaJaODo6yo0bN3A4HBw7doxIJEJTUxMpKSk0NDRw48YNvF4vu3bt4sKFC9TX19Pa2kptbS3t7e0YjUZyc3MZGhoiEomQkpIiqqy5ubkYDAb6+/txOBzMzs6SlpaGx+OhrKyMK1eucODAATo7O2lra2PXrl3k5OTQ0tJCfn4+ZWVlHDx4kPn5ebq6ulCpVFRVVWG1WjEajVRUVFBeXs78/DwjIyN0dHSQlZVFYWHhqq0mSZKEgF0xow0EAszOzjI+Pk5HR4doLypeWmt9pndrKnCrRPYmk4mBgQE6OjrIyMjgySefpLi4eMPEsby8nEcffZRf/epXSJLEiRMnxEXA7RCriYkJsrOzKS4u5ktf+hInT54U5Gqtbb0X2Bay4XDAe+/B8eNL5OrsWbDb7+46V0GyESuv14tKpVrcznVueOslSVIDzSwlPX9akiQH8EMgHWgBXpBleb18Da/H40mqcsdmidXtehWtN5a/XFOkTKBt5Vg+3GwFPgzQ6/UEAgEhTu7s7OTq1auiEqDX62ltbaWqqorW1lY+9rGP3bMDgWKZoPhBKZYGSlXEarWSlZVFeXk5BoNhxesNBgN9fX23te5gMCj0Uj6fD7vdTmFhIbt27VqzlRWJRBgZGWFoaAibzUZdXV1C68XtdtPe3o7ZbKaxsRGdTsf8/Dytra3s2LGD3Nxcurq68Pv91NfXc/HiRaqqqpicnCQ3N5fJyUlKS0sZGBigqqqK1NRUgsEgBoMBr9eLVqvFZrMxMTFBWloaJpOJoaEh9u3bx5UrV6itrSU3N5fW1lb27dtHf38/Fy9eZP/+/Rw9epTu7m5RvUpLS+Pw4cO4XC7a29vR6XRUVlZisViQJAmbzSZ0X5OTk1y/fp1gMCiqW+u59xsMBvLz80VbLxQK4XK5mJ6epqurC0BYPKSnp6PX6/H5fHevXXQLbFRk7/P5cDqXYtLKyspwu91cvHhxhch+udZLr9eLY1hjYyMTExO0t7dTWVmJ3W7HaDRuWmMlyzJOp1P4uBUXF/Pss8/y2muvCYf2ZMkO3DYbjV274F/+BX77t+Hxx+HXv4Ztrt4lKbHybuc6N7P1/xa4DiiXbN8E/kqW5R9KkvT/AS8Bf7/O6xcWFhbumaJNqRTFE6DFxcVbEqN4UrTaWL7yr06nIyUlZVVdUTK0z5QqzsOA+IN0fn4+HR0dXL9+XURe7Ny5k5aWFp544gmuX7/O9evXqauru+vvKxKJCGNNhUSFw2FRZbRareTk5GA2mzes0dHr9UL/tpGqgdfrFZYIsCQArq6uxmw2r7uf+v1+BgYGmJycJD8/n4aGhgRiEQqF6OrqwuPxUFdXR2pqKrIsMzg4yPDwMPv37yclJYXOzk7C4TD19fW0t7djt9sxm810d3dz+PBhzp07R0FBgTBAtdlsDA8Po9fricVimM1mNBoNc3Nz7Nmzh97eXqqrq+nu7qa+vp7m5maOHj1KMBgU1SqTycS5c+c4cOAANTU1uFwumpubKSoqwuFwkJ6eTkNDAzMzM1y7dg2j0UhlZaWoeKjVavLy8sjLyyMYDDI6OsqFCxeEHiw7O/uW35fSTo23eHC5XMzOztLX1ydORgaDQcTyJBuUydFPfOITTE5O0trayje+8Q1hsBqLxQgEAqLitVxkD4iqenV1NYODg7z99tukpaUJk9DNwOVyEQqFEoxBHQ4HzzzzDK+//jqvvvoqL7744qoXJNuNUCi0fTFax47BG2/A5z63dHv7bdjGCK9kI1YfXqh6tnOdG9p6SZIKgE8Bfwr8obR0BP4t4EsfPuUV4L+xDrGSZTlaXFx8WxorpUS+URPHjY7lB4NBMfKriDiXPyfZphtuFw8TsTKZTMzNzQE33ZhjsRgjIyPAErG6du0aw8PD2Gw2Ll++vKXEStHcxDuT+3w+VCqVIFB5eXlUVVVtycFWMWG0r1L2l2UZl8uF0+kUzvM5OTkcOHBgQ55Bc3Nz9Pf3s7i4iMPhoKqqKuE3IcsyQ0NDDAwMUF5eTl1dnZjau3r1Kmq1msbGRlQqFW1tbQDs3r2bkZERgsEgO3fu5MKFC9TV1TE4OEhJSQlOp5P8/Hwx3Tk4OChsNFJTU/H5fOIixu/3Y7VaCYVCwtKhra2NPXv2cO3aNbq7u6mqqsJgMHDx4kXq6+tJT0/n2LFjdHV1ce7cOfbs2UNKSgp2u52MjAymp6dpaWnBYrGwY8eOhCqSXq+nrKxMVGtGRkbo6uoS1b7U1NQNXUhpNBqysrJELEs0GqWpqQmtVktra2uCxYPdbt/U5Obdhlqt5vOf/zzf+ta3+NGPfsRLL70kjpUmk0kI+Zdjuch+x44ddHR08P3vf1/E25w+fXpVa4nVRPaK4/pywXxZWRlPP/00b7zxBj/4wQ944YUXNrSv301sK7ECeOIJ+Pa34Wtfgy9/GU6ehG08lyXLvgpLF5OxWMy9nevcKK38X8AfAUrNPwOYl2VZ6S2NAvmrvTAesVhMnp2d3RBJWisYdjXyc7tj+VNTU8zMzAhdxIMMvV4vyMaDDpPJxNjYGHDzoJuSksLAwACw9Fns3r2b1tZWGhsbOX36tMgU3CzC4fAKSwNlAig1NVX4Qm3G+2izsNvtzM7OCmKlRMg4nU7cbjc2m42cnByqqqo2VNWSZZmJiQn6+/vR6XSUlZWRnp6+4v27XC46OjpIT0/n6NGjotXh9XppaWnB4XBQVFSELMtcvXoVrVZLTU0Nc3NzDA4O0tDQwOjoKGazGavVytWrVzl+/Dhnz56loaGB/v5+0WZUQpfNZjNzc3NkZWUxPT0t/Ktqampob2/nyJEjTE1NMT4+zq5du2hubqa/v5/S0lIOHjzIpUuXqKysJDc3l507dzI7O8ulS5coKSmhuLgYSZLIysoiMzOTyclJmpubSUtLY8eOHSsqH4plRSwWY2pqihs3buDz+cjPz6egoGBTlRK1Wk0gEODQoUPCGkWxeGhvb8fn82GxWIQgXmlX3iukpqby5JNP8vrrr/Pee+/xqQ34J8WL7M1mM7FYDL1ez8LCAmazWbTu4nVea4nsTSYT169fF4HcPp8vQWS/Y8cOvvCFL/Dmm29y8uRJnn/++XsavH5PTDO/+tWlXMH/9J+WtFZ/8zdJlyu4HVhYWCAajc5v5zpvSawkSfo0MCXL8mVJkh5R/rzKUzciTFePj48nkB+ltH8vxvLjg3kfdOj1ehGx8aAjvq1gMBjEVJkSbyPLMkeOHOHy5csEg0HUajXNzc188pOfXHOZyoh9PIHy+XxotVosFovwhFLsAbYTdrudvr4+dDodTqeTUChEVlYWDoeDtLS0TXlUDQ8PMzw8jN1up76+flUBcCAQoLOzk2AwyJ49exI0VqOjo/T29rJ3716sViuyLHPlyhWMRiNVVVUEAgGuXr3KoUOHiEaj9PX1cfToUYaGhigsLBQnSLhJppRqSCwWIyUlhZGREYqKimhvb2ffvn2cOXNGxNVMTU2xe/duPvjgA2w2G3v37hVWDwUFBTQ0NNDc3IzP56OsrIyMjAyOHj3K9evXuXDhAnv27MFoNIrJw+zsbCYmJrh48SIZGRmrxveoVCrR5guFQoyNjXHp0iW0Wi2FhYXk5OTc8nim5CXGB00rGq/y8nJkWcbr9TI7O0tPTw9er3fLLB5uFzt27ODIkSOcP38eh8NBTU3Nhl63sLDAO++8Q1dXFxkZGdTV1XH+/HlOnz7N448/jtlsXnOqTxHZz8zMCLd+p9OZEJytiOyNRiPHjh3j9OnTvPrqq7zwwgv3hFzdUxPq//gfl6Jv/uf/hOxs+K//9a6uLhkNt71eL6FQaHY717mRilUj8IQkSY8DBpY0Vv8LSJMkSfNh1aoAGL/VgsLhsK+2tjZpyoTKWP7DAEXQ/TBguVA/Ly+PoaGhhEDfjIwMqqqquHr1KpWVlVy9epWPf/zjaLVagsFgAoH6sJRMSkoKVqsVm81GcXGxOAHfCyyPkPF6vQSDQXbt2rXpaajFxUX6+/tF9Ta++hSPWCzGwMAAIyMjVFZWJsSIRKNR2tvbCYfDNDY2otVqicVioqVWWVlJNBqlubmZuro6TCYTly9fprKyErVazfDwMEePHqW/v5+8vDzm5+dJS0sTk5CKEWpKSopoBfr9fhGorETZXLx4kePHj1NXV8eVK1doaGjgwIEDXLhwAZ1OR1ZWFocPH6a1tZVr165RV1eHRqOhrq6OmZkZLl68KCptihlpXl4eubm5jI2Ncf78eTFMsNpJWqfT4XA4cDgceL1eRkZG6OnpIT09ncLCQmw226r7zPj4+LohwpIkiTayw+FY1+LBbreTlpa2LRenjz76KMPDw/zkJz8hJydnXesSWZa5fPkyv/zlLwmHw+zcuZMnn3xS6HHOnz9PZmYmBw4cWHMZisi+ubmZaDTK7/7u765oOyrtRsXDy2AwMD4+zpkzZ8RnshGR/VbhnuY/ShL8xV8skas/+ZOl6Jvf+727trpoNJpU+ipYIlaLi4uu7VznLT8BWZb/GPhjgA8rVv9BluXnJEl6E3iKpcnALwNv3WpZKpXK7/f779nky3I8TLqjh2lbYekEp5yUc3JyaG9vJy8vj/HxcdFmPnz4sJjQCgaDvPXWW2RkZKDT6RJOYhaLJSmib2KxGDMzMzidTmZnZ7FYLCJCZmRkBJVKtWFSpWiv+vr6CIVClJaWst5Fz/T0NJ2dneTk5KzwLPL5fFy+fJn8/HwcDocIN29ubsZms1FRUYEsy1y7do2CggLsdjtTU1NEIhFBenNzc9FqtTidThoaGhgYGMBms4nvUKleKTE+sixjs9lwuVyUlJRw/fp1Dh06RFZWFsPDw5SUlJCRkUFPTw+VlZUcPHiQ8+fPi6nC+vp6enp6aGpqYt++fWg0Gux2O0ePHqWjo4OJiQl2794tROSKt1ZeXh4jIyOcO3eOnJwcysrK1jxpWiwWampqqK6uZnp6moGBAa5du0ZeXh6FhYUJAnWn08nhw4c3uiusavHg9/uZnZ1ldHSUtrY2NBqNqGgpVduthlqt5qmnnhJ6q6997WurrmdycpK3336b0dFRSkpKqK2tFQavAB//+MeZnZ3l3XffJT09XcTerIaRkRGuXr1KY2PjqloupfsRCAT4+c9/TiwW48UXX6T4Q1+njYrsV3Oxvx0ne2UfvmdQqeC734XZWfjX/3qpLfj5z9+VVd3zbV0FHo8nGggEkqsVuA7+E/BDSZL+X+AK8N1bvUCtVi8oJexkwP1oEHq7UE5MDwssFgsLCwvo9XpRCVBaVufPn2d4eBhY0l653W7MZjOhUIhHHnkkaSqqsCR6VfRSSoRMbm4utbW1Ca2fvLw8oWtaD7FYjLGxMQYGBkhJSaGiomLdfDafz0dHRweyLHPgwIEVv12n00lXVxe7d+8Wy1EqU3a7XZwgBwYGkCSJkpISYRx66NAhZFlmYGCAhoaGhODh+fl58vLy8Hg8CRUruBk+reisqqurRZWioqJCxObs2LGDc+fOkZmZSXp6OgcPHhTWC0oVbXR0VEwMGo1GNBoNu3fvZmpqigsXLlBeXk5BQYHYJ1QqFcXFxRQWFjI0NMTZs2fJz8+ntLR0TeKi6LaysrIIh8OMj48nBEKnpqYmhBHfLoxGo7CCgKV9Z3Z2lqmpqRUWD8oFxFYgLS2Nz372s7zxxhv84he/SGiph8Nh3n//fc6fP49er+fJJ59k165dtLa2JrSQVSoVn/vc53j55Zd58803eemll1bVPMZiMd59910sFgvHjx9f8z0NDQ3xwx/+EI1Gw1e/+lWys7MT1rURkb1ipqpkOMZXweLd8Je72S8X2SfFlKdWC2++CY89Bl/6EvzsZ0sxOFuMYDB4T7Vsq2F+fj4IJK3dArIsnwJOfXi/H9sfrZQAACAASURBVDi4mddLkjTncrkSdvJ7iWQ6gW4HlErCgzLpuBqi0ajSUxcnE5/PhyRJou07MjLCF77wBbRaLcPDwwSDQSorK2lra0uKUvbi4iJOpxOn00k0Gl0zQiYeRqMRWZbXPIiHQiEGBwcZGxsjOztbEIm1EI1G6e3txel0UlNTs+IkF4vFuH79Ol6vN8F6IRKJcOnSJXJzcykpKQGWql3j4+McOXIESZLo6emhqKgIo9HI2NgYdrsdnU4n2oDKZ5CSksL09LRozyj6DbPZzMLCAna7nRs3blBdXS3yAquqqiguLqa3t5eqqir27t1LU1MTjY2NmEwm9u3bR3NzM4cPHxZExGg0cuHCBfbu3Sucy7OysrDZbHR0dDA+Ps7u3bsTxOgqlUq0DAcHBzlz5gxFRUWUlJSsW9HQarUUFxdTXFzM4uKiMCA1mUzMzMyQkZGxZcclnU5Hbm6uuLAIh8PMzc0xMzNDX18f0WiUtLQ0MQ15J7YEVVVVHDp0iIsXL1JSUkJ1dTW9vb288847zM/Ps2fPHh577DFBzL1e7wodlV6v55lnnuE73/kOr7/+Ol//+tdXEPkrV64wMTHB5z73uTVP4F1dXfz4xz8mNTWV559/ftOmyPEi+7VeG41GV4jslRim5SL7QCCAVqtldnZWBGffk2OwybTkcXX8OHz2s3DqFGxxeLXiOZdMmJ6eDgH3TcVq04hGo8MTExNJlc/2MJANBcrETTI5Et8uFBIR7wm1uLiISqXCYrGg0+mIRCLU19ej1+uZn59ncHCQwsJCRkZGOH36NI8++igWi4WZmRmOHTvG5cuXGR4eprS0dNu3ZW5uTlgiKO3LvXv3bupKt6ioSJALBV6vl/7+fubm5igpKeHYsWPrEkfFdLG7u5vCwkKOHTu24rcRCAS4fPkymZmZHDp0SBABxV29oKBAtKcWFxfFtJ5arRaTbkePHkWWZXp7ezl48KBY79GjR/H7/cKNOxgMrrAwSElJYWFhgczMTP5/8t47urXzvhLdB40ACIAkCJAg2MEC9t4vb7FsFa84smVHSZSxrWQ0Gae8JGtWVlYmk3jy1nv/TZxxZjJvJhnFsefFTiwlz5JGsnQty7pXLJe9d5Ag2EGiF6KX8/6AzidUEuRlu/Je664r4aIcAAfn29/vt397s1gs+P1+KJVKDAwMoLq6GmVlZRgYGEBZWRmEQiEqKysxPz+P9vZ2SCQSNDc3Y3x8HL29vUSX1NnZSbIjmY0fl8tFS0sLjo6OMDIygurq6oQcPzabjYqKCpSWlkKn02FgYIAQp9NaRpmZmVCr1dDr9aipqcHu7m5CIPRFgsvlJlg8WK1WmM1mbG9vw+/3E4uH3NzcM1s8PP3009jd3cVbb72FmZkZrK+vQyaT4eWXXyYkG/gkgSLZeZidnY1f/dVfxfe+9z28/vrr+NrXvkY+R4/Hgw8//BAlJSUpkwampqbw4x//GEqlEr/2a792ad0RNpudlsje7XZjfX0dFEVhf3+fkDEAJIA6mdbr0jZ3UmnEnb2vD/j85yPRN5WVF/b0N7FitbW1FQCgv8rXvFJi5XQ6tYz3yE0Boz269lLtFUAoFBLh75OEQCAQY6zpdDoRCAQgFApjfKGiTS6DwSAePXpE+v2tra1YXl5GU1MTdnd3MTw8jOrqalL9YCoN6+vrV0KsUkXIVFdXn/uiWlhYiIGBAVRVVRHjSQBQqVRoamo6dZF0Op1YXFxERkYGenp6ku48jUYjFhcX0dDQEFPFCgQCGB8fR2lpKWlFBYNBTE5OoqWlBXw+n+ismGM5PDwk5pB2u514UxmNRlIpSKbZEIlExOBULpfDaDSisLAQcrkch4eHUCqVqK6uxurqKlpaWlBUVASDwYDd3V0UFxdDKpWipqYGY2Nj6O3tBYfDgUgkQm9vLyYmJuDxeGKIQH5+PnJycrC4uEhsHOKPicPhoKqqCmVlZdjc3MTAwABUKhWKi4tP3LQZDAZIpVJCeE4LhL5IsNlsyGQyYtPxuBYPLBYL1dXVePjwIdbX13Hnzp2kRD5ZtSoaRUVF+OIXv4gf/ehHeOedd/D888+Doig8ePAAHo8Hn//85xOOg6ZpDAwM4OHDh6isrMSLL754rQt8tJM9h8NBRUVFQhxStMieGURg/jsUCpHniG81CgSCxxPZFxZGcgX7+yMO7cPDwAmDE2eBz+e7cSHYe3t7ND7lxGprb2/PB+DGqNt+HonVTQVN03C5XAnGmozjs0QiQVFREclCPAkcDgfhcJhUI1UqFbKysnB0dAQ2mw02m4033ngDbW1tCIfDCIVCKCsrw/r6Op599tlLeX8+n4+0+DweD+RyOYqLi9Hc3Hyh7Z8HDx5AJpOhvr4+abZdPAKBADQaDcxmMxoaGpJOdtE0jfX1dRiNRvT29saQLr/fj7GxMZJdyNx/ZmYGKpWKaK90Oh2xBmCqVa2trQAiYcpMNchqtZJjSEWsGNIol8uxvb2NwsJClJaWkiGFgoICbG5uwuFwQCKRoLGxEcPDw5BKpcjMzER+fj78fj8mJiaId1RGRgZ6e3sxMzMDl8uFuro68r3weDy0tbVBr9fj0aNHUKvV5L1Gg8vlQq1Wo7y8HFqtFgMDA6ioqIjRaUVja2srpsLI4XDOFAh9kUhl8WAymbC2tobj42NkZmYSoiWRSAhpjBan5+XlwWAwwO/3J90k2O32lGHRDJgpzYGBAchkMlRWVmJychLt7e0JhqCM7mpychJNTU14/vnnb8SwCYNUUUWMyD7VbzReZH98fAyDwQCPx5NUZB9NvE4V2avVwLvvRnRWzz0HfPQRcAE5sj6fL6lR8XXCYrFQAK7UxPGqxST6ra0tN24gsfp5QLQj+XXD7/cnGGtGWxowvlCP4zjNBDAzHj8tLS346KOPoFKpcHR0BKvVShbo4+NjVFVV4f79+7BYLCeOjacLxvsqOkJGoVCgrq7uQnd1Xq8XOp0Oh4eHkMlkCIVCaZE1Jix5Y2OD+BAle4zf78f09DQkEgl6e3tjKjB+vx+jo6Oorq6OWfA0Gg0EAgGKi4sBRNo4Ozs7uH37NgAQvUlmZiYJdK6urgYA2Gw2UjVMRqwY8ToQaR3Nzc2BpmmIxWKEw2Giz6qrq8Py8jJ6enrA5XLR1NRELBhYLBaKi4vh9/sxMzODtrY2UBQFNpuN9vZ2rKysYGJiAu3t7TELVEFBAXJzczE/P0+qV6lsF2pra6FSqbCxsYGPPvoIVVVVUCqV5DN2Op0IBoMpSUZ8IPTOzs6pgdAXiWiLB5VKRTY+ZrMZm5ubsNvt4PP5MJvNmJ6ehkAgwAsvvIDGxka89957GB0dRVlZGdRqdczz2my2BHKUDPfu3YPZbMYHH3yAubk58Pl8PPXUUzH3CQaDeOONN7C8vIy+vj587nOfu3Ha2fNGvJxHZO9wOJKK7JNNOHI7OkC98QbwC78QcWr/yU+Axyww3MSpwEAgEKCv2GDrOojVjXLkPE/455MKxlzxKhEOhxOMNT0eT4ylQWlpKcRi8YXrCpioF2bham1txUcffQQOhwOXy4Xm5mbMzc0BiCxyDLFaX19Hd3f3uV4zHA6TCBmTyQShUHimCJmzwG63Q6vVklbmnTt3wGazMTU1FePEnuqxi4uLEIvFJCw5GaxWK+bm5lBTU5OwGPp8PoyOjqKmpiZmIEWv18NiscR8hgsLC6irqyMkZX19HfX19eRYGKNgRpvCVMSSDRMw/lLMwsForsRiMcrKyrC1tYX6+npIpVKw2Wziqs+03NbW1ojOs6KiAsvLy1hYWCBxPBRFoa6uDtvb22RiMLpCx+Px0NHRgYODAwwPD6O2tjYlUcjIyEB9fT28Xi/W19exsbFBSKhGoyFk8iSkCoT2+/3E5f0q2l4URRFdEWNd4PF48MYbb4CmaTQ0NMDpdJLcxt3dXbz55pv4rd/6rRjyaLVaY6p0J73eF7/4RRwcHMBoNOL27dsxnQWv14vXXnsNW1tbeOaZZ9Db23vxb/oxEQgELk0vdRaRPUO+EkT2XC6U3/wmqv78z+H6/Odhe/VVCKRS4ul1Vu3xjZiAjILH4wFN01feprlyYsVEjdwUCIVCWCxX6h12bcjMzITL5bq05/d6vQnGmkwlQSKRIDc3F+Xl5USYfNmQSqXQarVEL5OVlYXKykqSMSYSiYhGx2QyESfusxKrQCAAo9EIvV4Ph8MBqVQKhUKB2traC29JMCLvzc1Not2InySrqqoiAcfx8Pv9WFlZwfHxMRoaGlJWSxgbhP39fXR2dibo8jweD8bHxxMmBh0OB9bW1khVCIiYX7LZbCKatlqtJDcRiG0DMvEmJ71/iqIgFArhcrkgFotJ60ksFqOgoIDkA7LZbNTV1WFqagoymQwURaGqqgqPHj2CyWQin09tbS3m5uaI5xUDxgR2dHQU7e3tCVVGpVIZU71qbGxM2aLm8/lobGyEx+OBRqPB2toaMao9Cy4iEPoiIRAI8NnPfhbr6+sQCoXo6OiA2WyGyWRCaWkpjEYjvv/97+PLX/4ycnNziQ9Zupoxmqbh9/vBYrEwNzeHzs5OUon+wQ9+AKPRiBdeeAFNTU2X/E7Ph9PO58tGWiL7nh64RSJk/uEfgv3SS9D+1V/BxeWeKLJn/o4njeFw+Ea1YfV6Pbhc7uFVv+5VEyuHzWa7UXXa86SqP6lgLrjM4nReMJYG0SSKKQEz+XgVFRUQiUTX+iOTSCRwOmPtS1pbW/HP//zPyMvLg0ajwZe+9CW8+uqrmJycRFdXF6qqqjAxMXFqaKrH4yF6KSZCRqVSnSlC5ixg4mZ2d3chlUrR3Nyc8mLJaNDMZjNZuKPDkquqqk4UswcCAZLt19fXl/AdejwejI2NobGxMYYYMC3DtrY28tkFAgFCtBisr6+jqqqKHJfBYCAVDMZxHYicZ9EkgVmUmcoJQ6zkcjkWFhZQUVFB4mX0ej2KioqQmZmJnJwc7O3tobi4GBRFoa2tDaOjo6RSR1EUyRVkgqAZ5OXlgc/nY2pqCvX19Qm2ExkZGaR6NTQ0hLq6uhPtZAQCAZlKZAYs1Gr1uXIqLyoQ+nGRn5+P6upqMgzAEL/Gxkbk5OTg/v37WFxchEAggM/nIxNy6Vg8DA4OwuVy4Ytf/CLeffddvPbaa/jFX/xF/PCHP4TL5cJLL72EygucartoMJXUmwpGII9/9++AwkIIvvpVNPzu70Z8rqJsOuJF9nt7ewkiez6fD7/fj4ODg4sR2V8A9Ho9aJq+2jYNrphY0TRNFxQU3KhW4E0XdF80zjIZSNM0vF4vsTNwOBw4Pj4GRVEkH4/xWLppfXUgshDzeLyY8rRarSa6LYPBQFy9TSYTpqamUFVVhdHRUWxtbcW0aWiaJoGwBoMBHA4HCoUCzc3Nl2p463a7sbm5CaPRiOLiYhIXcxpqamowOzuL/v5+WK1WLC4uIjc391S7BYfDgenpaWKMGQ+Xy4WJiQk0NTXF6NDC4TCJqInW/qysrKCiooKcH0xINfNYm80GiURCyJvVaiWi8Hi9BmMSymKxyDQnEKnEer1ehEIhsNlslJaWYnZ2lhy/Wq3G8PAwlEol2Gw2BAIBiTHq6OgARVFgsVhob28n0TfRwnSJRIKenh4yMRgf2k5RFAoLC5Gbm4u5uTno9XrU19en/J5sNhsCgQD6+vrgcrmwtrYGjUaDmpqaM1ewGFxkIPR5cPv2bXznO9/B1NRUTEuus7MTU1NTWF1dxe/8zu9gfX2daA+3trYQCASQnZ0dY/HAwGKxYGRkBM3NzWSy9LXXXsOrr74KHo+Hl19+OcH+4qbB6XReiF7zSvDLvwzk5AAvvADcuhWZHKysTFtkbzabYbPZ4HQ6E0T2PB4vpdbrMiusBwcHcLvd2kt7gRS4cidEiqKc110ejQaHwyGOzj8PEIlEcDqdCcQqGAzGWBo4HA4EAgEIBAIiJi8oKEBmZuYT5fklk8lgMpmIiJrNZqO5uRljY2MAImaCEokEfr8f77//Pl555RXweDxoNBpUVFTAbDaTCBmJRHJqjMlFgPG10mq18Pl8RFh+ls9dLBZDLBZjaGgIXC4XbW1tp/7mdnZ2oNPpkra9gMjum7FPiNd0rKysQCqVxuTdWSwWHB8fo7Gxkdy2sbFBqlVAbBsQiJAOJsw33myQCWIGIuex2fxJrqpUKoXVaoVMJiPnqNPpJJ5mRUVF2NzcJK+tVCphMBiws7ND9EJsNjsm+ia6isTn89Hb24vp6Wm4XC7U1NQk7MT5fD66urqwt7eHoaGhBEsKIPLdLi0tkfggkUiE9vZ2OBwOrK6uEoJ1khv+SbiIQOjzoKioCGVlZRgZGUFnZych7ywWC08//TT+8R//EZOTkwgGg2hvbwefz4darUY4HIbNZoPZbMb8/Dw8Hg8kEgmkUinJ9vvc5z4HIDIBx5DrhoaGG0+qgMhvhjm/ngg8/TTw4EHE46q/P1K5amk58SGMyJ4x7Y0fVkglsne73fB6vUlF9tF/czicc1e99vf3gxaLZfOk+1AU9fcAvgDAQNN0w8e3/Z8AfhOA8eO7/Qeapt9N93WvnFhxOJxDvV6vjr64Xjd+nkxCmQWJCfFlLA3YbDbRQhUWFqKmpubGGb2dB3l5edjc3CTECoi0A0dGRiAWi7G6ugqxWIxQKASz2Yy33noL+fn5WF5eRmZmJmQyWdIImctAOBzGwcEBdDodBAIBKioqzrXbDYfDxBQ0GAyiu7v7xO8yFAoR1/lbt24lrWg5nU5MTk7GuJMz2N3dhcvlignPDYfDWFhYQHt7O7koMrlsjLaJpmkSScMcR7RxZHzFKjqWiRGsM5DL5TAYDOS5GRE7Q+pUKhUGBgZQUlJCnrOhoQFDQ0PIzc0lpJPL5aKrqwujo6MJBJLD4aCzsxNLS0uYnp5GS0tLAklhYmpkMhnm5uZwcHCA+vp68p4ODg4gEokSPkOJRIKuri7YbDasra0BiFQdT7MlOAnnDYQ+L/r7+/H9738fc3NzaG9vJ7dXVlaivLwcH330Edra2hLIslQqhVQqJZmSDocDQ0ND0Ol0UKlUWF5ehs1mw9jYGGQyGeRyOSYmJlBcXBxD2m8iUlkt3Gh0dgJDQxGPq7t3gf/9vyN/n4JUwvXziOxtNhsODg5inOz5fH5SrddJmt3t7W0XTvew+h6A/wbg/427/ds0TX/rlMcmxZUTq3A4vKPX63GTiNWnyZE8GoFAIMHSwO/3IxgMgs1mE1+ox7E0uOnIysqC3W6P0ZUx/lEmkwl7e3uoqKiA1WpFRUUFlpaWUFFRAY/HQzLlLht+vx/b29vEA6ijo+PckzUGgwHLy8soKCjAnTt3sLe3h9XV1ZTi3uPjY0xPT6OkpASlpaVJzwOHw4GpqSniXB4Nq9WKzc1N3Lp1K+axGxsbKCgoiKmSbWxsoLKyktyPmdhkyInD4YghEvEuztF5gTwej1xwAZB4Gwb5+flYXV0l7UE2m43KykpoNBqyGHM4HLS0tGBmZga3bt0ixJnP56OzsxPj4+Po7OyMeQ8URaGhoQE6nQ6jo6Po7OxMSloFAgG6u7uxs7NDqldZWVnQaDQxerN4ZGdno7u7GxaLBcvLy+BwOAnt1fPgLIHQ54VKpYJSqcTw8DBaW1vJ50lRFJ555hn87d/+LYxG44nPQVEUzGYzpqamUFNTgxdeeAE//vGPMT8/j9zcXFRWVpKoo7feegs5OTlJW9Y3AcFgECwW68m8ttbURIxDn3028ueHPwS+9KUTH+J2u89daU1HZM94ejEEzGKxJHWyjyZdm5ubIZxCrGiaHqAoquxcB54CV06sXC6X9uDg4Kpf9kQ8qY7kDBjvnmgC5Xa7SV9cIpEQ3xsWi4WBgYG0xp0/DWDaLU6nkyxONE1DrVYT6wmn0wmn04nm5mYsLS2RH/fR0dGlEqvj42NotVpYrVaUlpaeqn86CW63G4uLi6AoCl1dXWSXXFJSgtHRUWI5EI2DgwNoNJqkrT0GNpsNMzMzCQQDiEyBzs7OoqurK+a4j4+PodfriWcVc1+r1RpTYYhvA1qt1pgLs9frTQjqjQ4S53A4CAQCRAPCYrFIlYvFYqGgoAD7+/tEE1VUVASdThczqZWdnY2CggKsrKwQ+wcgUhFra2vD5OQkuru7E4hHeXk5BAIBHj16hI6OjqQLAkVRKC0thVwux+zsLAmJTkePKJVK0dvbC7PZjIWFBdI6e1wJxWmB0AUFBec+BymKQn9/P15//XUsLy/HxM4oFAqS4Rg9oBAPq9WKf/mXf4FMJsPdu3fx3e9+F4eHh7h16xaeeuopsFgsGI1GLC8vIxQK4cGDB1CpVESjlZOTc+1ZnwwYc9onFsXFwOBgxOfqK18BXn0V+Nf/OuXdGU3fZYCiKFKtSgVGZM9UvUwmE3Z3d3kAzks4/g+Kor4OYBLAH9I0nbYJ5JWfgTabTbu9ve0HcGP6TIwNwXkmc64aPp8vwdIgHA5DJBJBIpEgJyeHjImn2imxWKxzm9Y9iVAoFDg4OCDhxjabDSKRCBkZGQiFQhCJRKQqI5fLySShXq+/8IkjmqZJCG44HE47biYVQqEQ1tfXcXR0lDQsmaIotLS0YGxsjAjfw+EwlpeX4XK5ThTDWywWzM/Po6urK2HTEQ6HMTk5iYaGhph/Y2JrGhsbY1qnWq0WKpWKvE/mc4gmM9HGoECii3N0KxAAmQxkFmnGOoOpYJSWlmJycpIQK4qiUFtbi5WVlZi2ZUVFBUZGRhLIZ1ZWFhobG2NyBaOhUCggEAiImD+V8FwoFKK8vBxra2vY3NyESCRKu8Wbm5uLvr4+GI1GzMzMQCQSkQGMx0WyQOjBwUFkZ2ejuLj4XIHQNTU1kMlkGBoaQn19fcz3zRDdn/3sZ/jKV76S8Fi/34/XXnsNNE2js7MT3/ve90BRFF566SUySLK6uoq3334bPp8Pzz77LLq7u+Hz+Yh33PLyMmkvymQySKXSS9VDnoR0HOZvPHJzgQ8+iBCrV14BTCbgj/4ISHJeMMa814VkInuLxeIBYE79qJT4HwD+bwD0x3//JYDUrDIO17Gyri8sLDgBnG/85RIgFotx0zIMw+FwgqWB1+sFj8cjlgbl5eUQi8VnFqNmZWURv6VPM7xeL46OjnBwcACz2Yzy8nKUlpaipaUFFEXh+PgYAwMD2N7eRl9fHwYHB6FWq7Gzs4Ps7GwcHl6c/UkoFMLe3h62trYgkUhQW1v7WBddmqah1+uh0WhShiUzEAgEUKlUWFxcRE1NDaampqBQKGIWvngwlZLo6lf0a8/Pz6OgoCCByO3t7SEzMzPm3PL7/TFaKiBC2rKzs2OOOX6H7/f7k04FMmAmAxlilZeXh62tLUKsBAIBeDxezAInl8uh1Wpj3PUpikJra2uMBQOD3NxcVFdXY3x8HD09PQmbkaysLDIxWFFRkXTH7vV6sbq6ir6+PgSDQczOziI7O5t4bZ0GpsrE6MgmJyeRlZWF6urqCzNjzMzMRE1NDdRqNSwWC3Z2ds4VCM1Urd58802sr68TQnR8fIycnBz09vZicHAQPT09MZ8VTdN46623iPXGu+++C6VSiRdffBHZ2dnw+Xy4f/8+ZmdnoVAo8OUvf5mce3w+n1g8AJHKBeOlpdFoEA6HIZVKSVXrqiaY7Xb7kyVcTwWRCHj7beDll4E//mPAYAD+038Con67TLD2TfKwCoVC8Pl8Xpqmw6ffOxY0TR8x/01R1KsA3jnL46+FWC0uLt6oMbzo0e2rBtM7jq9CMS2srKwsyOVyMrJ+Ef36rKws2Gy2Tx2xYrLNmAgZFouF/Px8NDY2Ym5uDhUVFTHC2c7OTgwNDSEUCkEulyMjIwN2ux0ejweFhYUXQra9Xi+2trag1+tRUFCA7u7uxx59Z8KS+Xx+yrDkeBQXF2N3dxcDAwPo6Og4cazfaDRiaWkpaQsMiOTbMdW2aPh8PmxsbKC/vz/mdp1Oh7KyshgSFd8GDAQCYLPZMfc5SbwORMiAw+Eg/5+dnQ2bzRajpysvL8fW1haam5vJ/erq6jA/Px+jCxMIBKipqcHMzAy6urpifmcFBQXw+/2YmppCZ2dnAoEVCATo7e3F1NQUXC4XqqqqyOMZG4qGhgZkZGQgIyMDfX190Ol0GBwcRHNzc9q6FIqikJ+fj7y8POj1eoyPjxPR90XZKVAURQjIeQOhGxoa8ODBAwwNDZHPQq/XQ6FQID8/H9PT03j//ffx67/+6+RzGhwcxPLyMrKysrCysoKOjg48++yz4HA42N7exptvvgm73Y7bt2/j7t27Jy7gXC6XTEYCEa2T1WqF2WyGTqc70eLhImG325/sVmA0eDzgBz8AZDLgL/8yUrl69VXg43PB6/XeOMud3d1dcLncc3lYURRVQNM0swC8AGDxLI+/cmJF07SlqKjoSnN7TkNGRgbJHrtMRFsaRIvJ+Xw+sTTIz8+HSCS61Am0rKwsbG1tXdrzXyWiI2SMRiMyMzMJgYmuPCgUChweHsaYP4pEIjQ3N2NmZoa4rQ8MDACInBNWqxVer/dci5bdbichwNFxM48DJizZYrGQyJZ0QNM0mTRjfLtSwWAwYGVlJSVhYwT/fX19CSR/aWkJarU6ZuENBoM4ODjAnTt3Yo6HCXxmkEx3Ex8HEq+xEolEMeQ3mZ5OLpdjaWmJaLGAyAQe89hov6qCggIYDAZsb2/HnCdApK3o9/sxOzuL1tbWhPfOTBMuLCxgdnYWzc3NYLFYWF5ehlwuj6nsURQFlUqFvLw8zM7OQiqVQq1Wp31+UBRFgqb39/cxOjoKuVyOqqqqC53kjQ+E3t3dTSsQms1mo6+vD++99x6xszg6OkJXVxcyMjJw7949/PjHPyYO+RqNBg8ePACbzYbL5cJXvvIVNDQ0IBgM4oMPPsDw8DBycnLwG7/xGzHTvWd5H9HfQSqLB5lMhtzcXGRm/r22TAAAIABJREFUZj72BjYcDoOm6RtVwXlssFjAf/2vQF4e8B//I2A2A6+9BnxstXDTjFA1Gg1CodDCafejKOqfANwDIKMoag/AnwO4R1FUCyKtwC0A3zjLa1+LyCYcDjuOj4/zb4qXFUVR4HA4F6Y7omma5DIxBMrlcoHFYhFLg4KCAqjV6muxNJBIJLDb7Vf+uheFQCAAg8GAw8PDtCNklEolpqenExbMvr4+zMzMYG1tDX/wB3+AkZERBINB0nKKJ2MngaZpHB0dYXNzEywWCxUVFSRK5XEQHZasUqlShiUng8/nw/T0NLKzs9HX1we3242JiQn09vYm7DAPDw+h0WjQ09OTdPfpdruxsLCAnp6ehM/ZaDQiEAjE+FgBwPb2NoqLi2PubzabIZVKYzYP8cJ1BtHvM74VGG+5AETagUajkRArxrxzf38/5nusqanB6OgoFApFzHHU19djeHgYubm5CQtFVVUVlpaWsLy8HKMNiz6+pqYmaLVajI6OorCwEC6XK+l9gQgxvHXrFrRaLYaGhtDc3JxS1J0MFEWhqKgISqUSe3t7ePTo0aX5rAkEAlRXV6OqqiqtQOjW1lYMDAxgcHAQMpkMHA6HnFNtbW0YHx/HT3/6U2RlZeH1118HEMn2/JVf+RXIZDIYDAb86Ec/wtHREdra2vDss89e2LUymcWD3W6H2Wwm2kORSEQqWhKJ5My/4ZME+k80KAr45jcjlavf/d3IxODbb8PpdN4Yb0oGq6urwaOjo8nT7kfT9EtJbv7O47z2tRArLper2djYqGo5xXjsKhGv10gXgUAATqczhkSFQqEYY83CwsIL2QVdFNhsNjgczqmxLTcJbrebRMgEAgHk5+ejoqIi7dgOgUBA2q7RlRiZTEaqWSaTCT09PRgcHCTmk3q9/lRiFQwGsbu7i+3tbeTk5KCpqenCLjI2mw2Li4uQSCQnhiUnA7Mjj45ZyczMRF1dHRFkR/srabVa9PT0JH2NYDCIyclJNDc3J7QHQ6EQaR1GfxehUAg7Ozsx04FApA0YXSli3mf0beFwOOF7ja9Ysdls0DSdYKUxPz+PiooKcr+SkhKMj4/H2Enw+XwoFApsbW3FtDTjLRjiCWRdXR2pcCazjKEoCpWVlcQb7Pbt2yeen8z98/PzMTs7C7lcjurq6jNVrFksFkpKSlBUVETsHQoLC6FSqS58QCXdQGgul4uenh787Gc/w8LCQkzbN9o09O/+7u8QDoeJtQKXy8WjR4/w4Ycfgs/nxwjXLwsURSE7OxvZ2dmoqKggzvBmsxkbGxtwOBwQCASEaMVrA5MhWsP3qcRv/3ZE2P7VrwJ37sD77W8j/wat5wAwNzfnCIVCa9fx2tdCrJxO55RGo/mFJ4lY0TRNLA0YEuV2u8HhcGIsDcRi8bVNoZwFUqkUFouF6BBuGqIjZI6OjohuoqWl5dyaCKZyEb3oAsC9e/fwwx/+EIODg3jhhRcwPDwMk8mEzMzMEwXsHo8Hm5ubMBgMKCoqQl9f34UR1eiw5KampjNpNWiahlarxeHhIXp6ehKIUF5eHnw+H8lHPDg4wNbWFnp6epKeuzRNY3Z2FqWlpUkXi7W1NZSUlCS8zt7eXsL4PtO6jffVip8oitdXAREiFZ+SkJGREePQLhQKY+JtgAiJEgqFsNlsMVWxyspKDA4Oori4OOZ9M5uhlZWVmHYl8MmU5cTEBLa3t5OKkx0OB/R6PTo6OoiR6Gk6KrFYnFC9OutwA4vFQllZGYqLi7G9vU3eW1lZ2aVMAJ8WCN3W1oahoSFMT0/HGIYCIJ93OBxGV1cXnnvuOdjtdrz55pvY3t5GTU0NvvCFL1zLlBkT2SUWi1FWVkY6ECaTCTs7O5ifnweXyyWtw5ycnAQCbrFYSILApxa//MuAVAp86UtQfe1rYH3wQYRs3RAsLi4GAWiu47WvhVhZrdb5paUlN4AbY0krFothtUZsKvx+fwyBYiwNMjMzIZFIkJ2dfaqlwU3HTSRWoVAIJpMJh4eHsFgsFx4ho1QqMT4+nkCsqqurwePxoNPpwOfziZGkWCxOSqwsFgs2Nzfh8XhQXl6O2traC9PE0TSNra0tklV4ViuGQCCAmZkZCAQC9PX1pTyu4uJi+Hw+DA8Pg6KopBNvDNbX15GRkZGSRJjN5gTBejgchk6nSzDDNJlMkEqlMe/J4/EkuCcnI1bxFSvgk3ZgdBWSObejdU2ME3s0weFwOCgvL8f6+nrCIqhSqTA2Noajo6OEUGUmV3BsbAw8Hi+m/el2uzE1NUXMZSUSCSYmJlBVVZVQpYsHi8VCVVUVqV4xOZxnPbfYbDZUKhVKSkqwtbWFwcFBYqtwWZqfVIHQxcXF2NjYgN1uh0wmA03TGB0dxfvvvx9zvPPz83jvvfdA0zSef/55Mrl7E0BRFIRCIUpKSoh1B5ONd3BwgKWlJbDZbDJ5mJOTc+3WA1eGz30OePAArKefBu8zn4lE4LS2XvdRAYhU4QEYruO1r8vIaH12dtaFayZW4XAYx8fHcDgcMJlM0Ov1ODw8BI/HI1WosrIyiMXiT53nk1QqxcbGxnUfBvx+P46OjnB4eEjiTgoLCxN8kC4CGRkZ4PF4CWP9FEWhuroai4uLmJ2dxd27d6HRaHB8fAyXy0Um1vR6PXQ6HVlELjoSxGw2Y2lpCTKZ7FxmoXa7HTMzM6iurj51EQcixMLr9UIqlaZccI+OjkiLNB6M7UIy8ndwcAC5XJ5QwTs4OEhwyk6mR0lFrKLd1oFPvKyi/a4YnVU0scrNzcXi4mKMiB2ItAkHBgaI4ScDpjI1MjKC7OzshGNh4m2YXEGZTBaTpcjos4RCIfr6+jAxMQG3242KiopTzxmJRIL+/n6sr69jeHgYzc3N55ou43A4qKysRGlpKXQ6HXmfJSUllz4cwwRCDw8PQ6vV4q233sKtW7cwMzMDjSZSRGhpaUEgEMDIyAiAyHfxpS996dzu3VcJPp+PwsJC0uL0+/2wWCwwmUxYWVmBx+MhwedXafFwHfA3N2Pxv/93tP3Jn3wSgXPv3rUek8/nQzAYdNE0fS2DctfFFjZWV1ev7MVomo4x1rTb7UT0yhhrKhQKWCwW3Lt378bslC4TPB6PiLSvenLl+PiYtPjC4TDy8/OhVqshFosv/bMvLS3Fzs5OQovn1q1bWFxcxODgIH7v934PHA6HnCNTU1PweDyQy+Voa2u78PFsr9dLJtfSCUuOB03T2N7exs7OTkoX8HgwLcynnnoKGxsbmJqaQltbW8yC63Q6sbKykrLyxVSA4ltWTCuyu7s75vZwOAyr1RpjfQBEhOvxLcZUrUCfzxdzm0gkSohJyc3NJYs3A8ZZfHd3N0ZTxWKxiGloW1tbzGP4fD7RVMXrx4DIb6irqwtjY2Oorq7G2tpa0ixFRm80NzeX1Dw1GVgsFtRqNRQKBWZnZ1FQUBATB3QWcLlcVFdXo7y8HFqtFgMDA1CpVCgqKrr0/MtgMIiqqiocHBxgaGgI+/v7xJeruroa77wTsQdSKBR4+eWXn9i8Vh6PRywednZ24PF4kJOTE2PxkJOTQ4jWRfmP3QTYbDbwmpo+icB57rm0InAuE1qtFlwuV3tdr38txIqmaXdBQUEoWnR6UQiFQjGWBna7PcbSQCKRoLKyEmKxOOFHrNVqE0wJP83IycmB1WqN2e1fBmiahtVqhV6vh9FohEAggEKhICn3V4m8vDysrKwkhG7n5+dDIBDAZrNhb28PhYWF2N7eBhAZ27179y4KCgouVOwfDoeh1Wqxv7+Pmpqac7Vlg8Eg5ufnQVFUUrF1MmxsbMBisaCrq4ss4BsbG5iYmEB7ezuJimHIVrL37PF4sL29ndACBCJVruzs7ITv1mg0Jp2SjHdcByLEKn7xiZ8KBCLESqfTxdwWH2/DoLi4GI8ePUJ5eXnMMeTl5UGr1SZ1ys7Pz4fBYCCBwPFgpuWmpqbQ1dWVUhfFYrHQ0tKC9fV1jI+Po729Pa32dlZWFvr7+7G2tkaqV+cda+dyuaipqYFKpcLGxgYGBgZQWVmJwsLCS9nQ7O3tIRAIwO124/j4GMfHx+BwOKirqwOLxcLrr78OiUSCuro6LC8vQ6/XX1okylXCZDJBpVIhOzsbeXl5ACLrEmPxsLu7C6/Xi6ysLEK0btJw01lht9sjFeeiImBgAPjCFyJO7f/zf0bc2q8BGo0GXq937lpeHNdXsQKbzTaazWbFeRd1ZsIrehrv+Pg4xtKA0SikS5Sys7Nht9vJj+HTDqZlchnEKhgMwmg0kgiZ7OxsKBQKqNXqa22rMqah8S0pJu5kenoa77zzDpRKJQ4ODhAIBKDT6cjinZWVBZlMBrlcTv6Wy+Vn3oEyYclKpRK3b98+V9XQ6XQSC4l0HZ7X1tbgdDrR0dERQywrKyvB4/EwMjKCjo4OzM3Nobq6OmULamFhAXV1dQnfJU3T2NjYQGsSncXBwQHRqETfP1p8zsDn8yW0B+MNQoFPAtTjER9vA4C0+C0WS4xBKkVRqKurw9LSEnp7exMWuLq6OgwNDUEmkyV8HgcHB1hfX0dHRwdWVlaQlZWVcrPAtJz39vYwMjKCzs7OtM4bpqpms9kwPT2NoqKimHigs4LH46Guro4QLK1Wi6qqKhQUFDz24h4IBLCxsYHl5WWsrKyQCWnmffz+7/8+lpeXcf/+fchkMuTn5xMvsp/85Cd46aWXnuhqDjN0E0+w2Ww2IVFAZFPF6BOXlpbgdrshFotJFM9VVO8vCna7/ZNNYXQEzr/5N4DRGHFrv+L3sry87DUajdNX+qJRuLYVjqKoFY1G05jOoh4MBmNMNR0OB4LBILE0YHyhMjMzH6uUzDiS/7wQK5lMdqE6K6/XSywRvF4v8vLyYiJkbgrKysrIAgVEdpP7+/ukGmI0GvHiiy+ioKAA9+/fh1AoxBe+8AWYTCYYjUaYTCZMTk4iGAyS58zMzEwgWzKZDCKRKOa9u1wuLC0tgcVipXQ2Twf7+/tYX19Ha2trWtNjNE1jdXUVbrcb7e3tSb+PkpISCIVCPHz4EAqFIqVOS6/Xg81mJ/2dmM1mCASCpNmCNpsN8ZPA0YHI0UhXvB7tcB7928/Ly4NOp0vQc5WVlUGn0yU4zzM6KoPBkCBWZ7PZaG1txczMDPr7+4nNw/r6OsxmM/r6+sDlcsHhcIiNxUnVqKKiIggEAoyOjiZtHaZCdnY2qV49evQILS0tjyWQ5vP5aGhogMfjgUajwcbGBqqrq5Gfn3+m32swGCRkam1tjXQIZDIZamtrySaCpmn89V//NdHJmUwmImqvr69HQUHBhQVCXxcYP6fTPj8Wi5Vg8eB0OmE2m6HRaOB0OiEUCgkZy8rKurFt0oTfcGZmRGf1678O/MmfRMjVX/xFTATOZWNubu4Y1zQRCJyDWFEUxQcwACDj48f/C03Tf05R1FMAvoVIuPIUgFdomg6meh6r1Tq5srLyYl9fHzkDaZqG2+2OqUK53W6w2WxCoAoLC1FbW3splgZZWVk3LjPwMsF8huf1s0oWIaNQKNDQ0HDjzOKiIRQKwePxYDQaYbFYcHBwAIVCgaeffhqrq6vw+/149OgRent7AUQmvZRKZUzWHU3TsNlshGwxhGthYSFGB8QsMMy0EBAxoTxvlZDxjPL5fCcGKEeDpmksLy8TDddJF32fzweJRAKHw4Ht7W2UlJTE3D8QCJDcu2TQaDQJ+jUgUqFL5tSdykgxFbGKbwUCke/T4/HEkAym+hwvN8jJySHfUfzz19bWYmJiAnl5eQnHKZFIUFJSgqWlJdTW1mJ2dhYZGRno7u4mC55cLkcgECC5gidVIXNzc9HZ2YnJyUnU1tYmkLlUYLPZqKurg8ViwcTEBEpKShJam2eFQCBAc3Mz3G431tbWCMFK5awOfEKmFhcXodFoiEu+SCSCQCCA3W4nWZ1AhADTNI1AIACpVIpnnnkGCoUiqfnmRQVCXwfihybSBUVRZI0rLy8na6HZbMbW1hbsdjt4PB4hWsksHq4DzGBPwnfD4wHf/37ESPQ//+dIBM7f/R2JwLlsLC4u0gDWr+TFkuA82wEfgKdomj6mKIoLYIiiqJ8A+F8APkvTtIaiqP8LwMs4wb3U5XJNPHz40PmZz3xGwlgahEIhCIVCMlVSXFwMoVB4ZT8oZsLo5wkymQwmkymtKTIgUhkwm83EUFMkEkGhUCREyNxkOBwOhMNhTExMoL6+PiZupra2FvPz85ifn8e9e/fA5/Ph9Xqxs7ODxsZG8hzRRonRRpGMuWA02drf38fc3ByUSiVeeeWVc+88mVF+pVKJxsbGtH4XNE1jcXERNE2jubn5xMfYbDZsbGyQuJrFxUUYDAa0tLQQAreyskJyK+NhtVrJJige+/v7KC8vT/qYZOdeMrKfrBUIfOJBF02sksXbMLeXlJRgd3cXlZWVMc8jFAohk8lIDEs8ysrKMDQ0hIcPH6K+vj7pcSuVSpIrGN9uTXbc0RODyT6fVJBKpbh9+zZWVlYwMjLyWP5uDIRCIVpbW3F8fIy1tTWsr69DrVYjNzcXx8fHODo6wurqKra2tmCxWBA/cBUMBontgEQiQWtrK7RaLZaWlnD37l08fPgQYrEY3/jGN068VlxUIPR1wGg0Jni0nQcURSEzMxOZmZmkfe7xeGCxWLC/v08sHqKJ1nX4JyZrexKwWMB/+S+RCJxvfjMSgfP668AlZTMy+HiNCtI0fW3xImcmVh+PLzI5EtyP/4QA+GiaZkpvPwXwJzjZFn5mamoKbDYbpaWlN8LSgKIosNnshJHsTzPy8vKwt7d3IrGKj5DJzc2FQqFAXV3djdg1pQOapmEwGKDVaklWm8/nQ35+fsx7YKoRFEVhbGwM5eXlWFlZwe7ubgyxSoVoc0G5XI6FhQXk5uZCr9ejpqbm3KTq8PAQq6uraGpqOlNG4Pz8PNhsNhoaGk6tVM3OzqKjo4Oc+y0tLdjf38fw8DAaGhrAYrHgdDpTfg7r6+tJXbJDoRAcDkfSMXqbzZbSSPE053UGDLGKr/rEx9swKCoqwvDwcFLrg+rqauJcHm9sur6+DpqmSSRKKpSVlcHn82F+fv5UMsvj8dDT04OZmRkSf5PuRpL5Xs1mM8bHx4nW7nE3oiKRCO3t7XA6nVheXsZ3vvOdhCxVoVCI/Px8lJaWIi8vDzKZjNh2bGxsEGL79ttvQ6VSYWBggKQfpHttjQ+E1uv1Zw6EvkqEw2F4vd5LC3UWCAQJFg9msxkGgwHMlD3jpZWbm3slG90TiRUQ0Vb92Z9FKle/8zvAM88Ab78NXKKlhkajAZfLvbY2IHBOjRVFUWxE2n2VAP4fAOMAuBRFddA0PQnglwCcmJZJ07RdoVA4CwsLJddNqKKRnZ0Nq9X6c6OzysnJwfz8fELLJDpCJhgMIi8v70wRMjcFwWAQe3t7xBqgsbGRTFUFAgFotdqYLDeVSoWMjAwIhUJMTU3h7t27WFlZSZg8OwmBQABra2uwWq1oaGjA/v4+ee6zIhwOY3V1FXa7PWm+XyowbukZGRmora098TsLh8OYnJxEXV1dQgu3sLCQnCNWqxU9PT1Jn4vRPSYjTwaDIWl7LRwOIxwOJxXAJ0OqVqBIJMLubmKIvVwux9zcXIIhLJfLRXZ2NoxGY8LvnMvloqSkBFqtFmq1GkBEN7a4uAilUon+/n6YTCbMzMyk/CwAEF+0lZWVUx242Ww22tvbsbq6iomJCbS1tZ1pk5mbm4v+/n6srKxgdHQULS0tFyIAF4vF6OrqwuDgIIBPKmydnZ0pjy8UCmF3dxf9/f34wQ9+AC6Xi62tLRQUFKCtrQ1vv/02dnd3E4YYTgOHw0FxcTGKi4vPFAh9lTCZTAnavcsEY07LGNQGAgFYrVaYzWZotVrye7xMiwer1Zpe5NBv/VaEXP2rfwXcuQP85CdAml2Ss2JycpJ2Op0PLuXJ08S5ts80TYdomm4BUASgC0A9gF8F8G2KosYBOAGk1Fcx4HK5q1fpZ5UOcnNzYbFYrvswrgwsFgsSiQQ2mw1WqxUrKyv46KOPMDc3B4qi0Nraijt37qCmpgbZ2dnXfvFKFx6PB8vLyxgcHITf70dfX1/CqLpSqYTBYIgxneRwOFCr1XC73cSBH4hcNON37fGgaZpktYnFYvT39yMnJwebm5vIyMhICCg+DV6vFyMjI2Cz2SmDkZMhHA5jenoaQqHwVFIFRCb88vPzU24mhEIhmVaanZ3Fzs5OAvlJlZ0HRNqAycboU+12U2n+UrUCk4UxM8ft8/mSkjHGiT0ZysvLodfr4XA4MDs7S6b+GBf0vLw8SCQSaLWpbXIoiiLC8JPuF31/Rms1MjJy6rkWDw6Hg8bGRlRVVWFsbAzb29spCepZwNh4ACCke3p6mvwu4sHEGK2srGB7exvBYBD5+fn46le/ivr6erDZbCwtLT3WMTEWF3fv3kVFRQX0ej0ePnyIpaUlOJ3Ox3rux8Hh4eG1JllwuVzk5eWhtrYW/f39uH37NoqKiuByuTAzM4MHDx5gZmYGOzs7cLlcF3J+OJ3O9O0/fumXgHffBba2gFu3gPXLkUANDQ3Z7Xb7o0t58jTxWDJ9mqZtAB4CeI6m6RGapm/TNN2FiLj91E/Nbrd/ODU1dS3OqKnAxGH8PIAJUfX5fBgdHcXm5iaysrLQ19eH3t7eBDfqJwE2mw1TU1OYmJiAWCzG3bt3SWRNPFgsFnGljkZtbS18Ph+USiUWFhbI+Pze3t6Jrzs8PAy73Y7+/n7SkqFpGpubmygvLz9TG9BkMmFkZATV1dVQq9VpE9pwOIypqSlIJJK0Hre1tYVgMJhQ1YmGy+WCXq9He3s7+vv74XA4MDAwgMPDQyKydbvdSQX5jK9cMgIVn93HIJmwHEhdscrIyEhwZGeQaqOUnZ0Nn8+XlMCEQiFkZmZieHgYcrkc3d3dCbqe2tpaHBwcwGazJX1dAGRjYjAYklbUkqG0tBQ1NTUYHR1NSV5OgkwmQ39/P2w2G8bGxpJaUZwVbW1tYLPZODw8RG1tLSoqKrCwsIDJyckYQkvTNHQ6HZRKJd577z0Akarh1772NfD5fGRkZKCqqopMCD4uGJ1jc3Mz7ty5g5ycHLKZ2tzchN/vf+zXSBc0TcNsNl9pxeo0MBostVqNvr4+3L17F2VlZfD7/VhcXMTDhw8xOTkJnU4Hh8Nx5u8kWRTVqfjsZ4EHD4DjY6C/H5i+eEeER48eBQBcm9UCcA5iRVGUnKKo7I//WwDgcwBWKYrK+/i2DAB/DOBvTnsup9M5Mjg4mPrKdA3g8/nw+/1Jd8afBvh8Puzs7GB8fByDg4MwGo1QqVQQCoVob2+/cbqFdEDTNHF21mg0KCsrw+3bt1FcXHwqmSktLcX+/n7MwsxkE2ZmZsLtdhNSsLOzk/B4Rpu0tLSEpqYmNDY2xnx+VqsVdrs97TYgTdPQaDRYW1tDT0/PmSaMQqEQJiYmIJVKU1aPosGYFZ5kh8HotBi3cC6Xi4aGBnR1dUGv12N4eBjz8/Mpo1qYrL1k/2a1WlNOBCbzgkqlsWL+Ldr+goFcLofBkDwurKSkhJjAApG2MROCzFhlJJtaY16vtbUVs7OzSV83+n6dnZ3Y3t4mE3KngXH4n56eTnCVTwccDgfNzc1QqVQYHR3F7u7uYxGZjIwMIsgeHR1Fbm4ubt26hdLSUszOzmJ6ehoulwt7e3uQy+W4f/8+vF4vcnJy8PWvfz1mc1ZfX4/j4+Okv6XHARMI3d3djc7OTtA0jZGREYyPj0Ov11/69dzhcCQ1nb5JYLFYyMnJQWVlJbq7u3Hv3j3SxtNoNHj48CHGxsag1Wphs9lO/czMZnPaes8YdHQAQ0MAnx+JvnlwcV27cDgMo9EYoGnafGFPeg6cR9xUAOB/fayzYgF4nabpdyiK+guKor7w8W3/g6bpD9N4rumRkZFTW4ZXDbFYDIfDkfSi/ySCiZBhKgwKhQK1tbUxfitMqPCTVKEKBALY2dnBzs4OZDIZWltbzzwxxGazUVZWhs3NTaKpYSJANjc3UVhYSMK5Nzc38dRTTwFIDEtOJVLe3NwEkJ6+yu/3Y2ZmBiKRCL29vWe6SDOkKj8/P63pMo/Hg/n5+VNtAfb29kgrMBoCgQCtra0wmUyYmJhgsrkSYlL29/dTajDiMxsZpKpYpWoFAp9M9MZXxmQyGdbW1pI+prCwEIODgyguLoZOp4PBYEBxcTHJaczKysLy8nJCNA8DsViM8vJyLC4uJvhzRYPD4aCrq4vkCqazGEkkEvT09JCJwXQNYKORl5eHnJwcLC4u4uDgAM3NzedOOrh9+zZmZmYwMzODZ555BiwWi3i1GQwGUr0SiURYWVkBn8/HK6+8kiDkrq6uBofDwfLy8rneUzrg8/kJgdArKyuQy+UoKSlJ2zfsLLjuNuB5kMziweVywWw2Y3NzEw6HAxkZGUSjlZ2dHXOtsFgsaU+TJ0CtBh49iojZn3sO+Kd/Ar785cd+TxsbG+BwONcegnueqcB5AAm2yjRN/xGAPzrjc9kLCgr815FXdxKY9sGTSqxomobFYsHh4WFMhExHR0fKC6tCoYBerz+XwPqq4XK5sLm5CZPJhJKSEvT39z9WlY0J4lWpVOR5amtrsbS0hOrqajz4eEel1+tJNMXS0hLkcvmpYck6nQ4SieTUxdRqtWJubg5qtfrMWqxgMIjx8XEUFhamtViFQiFMTk6iqanpRCLt8/mwsbGRNLaGwdHRERobGyGTyaDT6fDRRx8hPz8fxcXFEAgEcLlcSckT43+TjDz6fL6krVumtZoMjM4qftHkcDgkbDr63A+HwzCZTAiFQhgZGUFNTQ1qa2tjjkcqlYKiKJhMppS+YyUlJTAYDDg4ODhxkWFyBUdHR9He3p5WqDKfz0dvby+mp6fhdrtRU1NzZo0jl8tFa2srjo6OSGulGD5UAAAgAElEQVT5rLEx4XCY6HcMBgPee+89ktFosVjgdDrhdrtjSO9Xv/rVpJscHo9H2oHPPvvspVd4ogOhDQYDNBoN3G43CgsLUVRUdGGRWkdHR8T37kkFM8kpEonIdcTj8cBsNmNvbw8LCwtkY5Cbmwur1XrqYMaJKCwEBgcjETgvvgj8zd8Av/mbj/UepqamcHx8/PCxnuQCcO3jeBwOZ21tba3wsb6gC4ZUKoVGo3kiSAaDYDBILBHsdjtycnKgUChQU1OTFmlVKpWYnJy8se+ZIYtMnqNKpTrVQiBdsNlsqFQqrK+vkwtFVVUVOBwOnE4nZDIZzGYzwuEw/uEf/iFmkUnVZgIiC5JGo0Fubi4ePnxIbmPeD0MSKIoCj8dDZ2fnmStujCFlSUkJiotPHMQlrzs7O0tMF0/C0tIS1Gp1StLq9/thNBoJIamtrUV1dTUODw+xvLxM3KM9Hk9C5SKVMSgQEe2fNQ9PLBanFC4z8TZM9XF/fx8mkwlyuRy1tbXY2dlJSTbq6uowPT2N27dvJz3XKIpCc3MzhoeHkZOTcyJRFQgE6OjowOTkJLq7u9May+dwOOjs7MTy8jKmpqbQ2tp6rk1ofn5+TPWqqakJbDYbTqeT5PhF/3f8/0djcnIy4TNgs9mQy+XIzc1FRUUF1tfXYbPZUFlZmVB9rKurIxYml1W1igdjXqxQKOD3+7G/v4+JiQlwuVwUFxdDoVCce3N/fHwMHo/3xEko0oFAIEBRURFJMPD5fGTT7nQ68ejRI1LRkkqlZ7d4kEqBn/40Qqz+7b+NGIn++39/7gicoaEhm81mGz7Xgy8Q106sHA7Hg8nJyc/U1dXdmHGzky7SNwkejwdHR0c4PDyEz+eDXC5HWVkZcnJyzkw4GBHiTWsHhsNh7O/vQ6fTITMzE1VVVUkFz4+L4uJiDA4OkvfP4/FQWVmJ1dVV3Lt3D++88w4AEE3OWaKAjo6OTtXX/Omf/umZfdwCgQDx2kq3CqHVasHhcFBWVnbi/YxGIwKBwInVM51Oh7KyspiqA5vNJl47IyMjyMrKwtzcHHw+H2QyGfLy8iCVSmG1WlN+j+cJQs/MzMTh4WHMbTRNw+PxIBwOY2VlBRsbG8jJyUFBQQHx5WLeh9vtTkp0RCIRsrOzsb+/nxCPw4DH46GxsRHT09PEXDUVxGIxWlpaSPRNOu+ToijU19dDp9ORjMHzBMXzeDy0tbVBr9fju9/9bspzUiAQkHMxlX6sra0N5eXlKCgogNlsBkVRCUa5TCZifn4+KioqyKLLtAOXlpaujFhFg8fjoby8HOXl5XA6ndjd3YVGo4FUKkVxcfGZr5+ppl4/jWCmmxm39ZqaGlgsFpjNZmxsbCAUCsVYPKRVEczMBN56KxKB8x/+QyQC51vfOlcEzvDwcAARK6hrxU0gVo+GhoZsX//61y/PMeyMoCgKQqEQLpfrRrn80jQNh8NBImTYbDYUCgUaGxsv5DgLCwuxv7+f4Eh9HfD7/dja2sL+/j7y8/PTDqw9L1gsFmpqarCysoK2tjYAkZ316uoqRkdHIZFI4Pf7QdM0wuEwQqHQqeJOpnWVmZmJjIwMcLlc8ofFYsHtdkOv18csZOnC7/djbGwMlZWVabcODQZDWi0LJjanu7v7xEiTg4MD3LlzJ+m/BwIB+Hw+YvcQCoVgMplwdHREgqBlMhkCgQDEYjEyMzMJuU+lsToJfD4fdrudVGxtNhvcbjcEAgGkUilYLBbu3r2btPVUWlqK7e3tmMiiaKjVajx69IgsKMkgk8lgNBpTmqRGIycnB3V1dST6Jt1KR3l5OYRCIQnKPm9sVEFBAXJzc2EwGFBbWws2mw2XywWLxQKbzUYmCUUiEcnsUygUKCgowOLiIn72s58hHA6joaGBGKHGnwdM3l9hYSHxnFIqlVCpVKQduLKygueee+5aBd9isRh1dXWora2F0WiETqfD/Pw8lEolaWefBJqmodfriSXFzwuY9jiXy0V+fj4x5w2FQsRLa3t7G36/H1lZWcjNzYVMJoNAIEh+TeFygX/4h4jX1be/HSFXf//3Z4rACYfDODo6Cl63cB24AcQKwPSjR49unICdaR9cN7GKj5ARi8VQKBTo6em5cGddpVKJ0dHRayVWTqcTm5ubsFqtKC0tPVXDdJHIy8sjEzFcLpcMMHi9XgSDQQQCgaTj/qnAtPpcLhfcbjchWszttbW10Ov1Z56s8fl8GBsbg1qtTjtj7vj4GMvLy2mJ4jUazamLyvb2NoqLi1MSjaOjIygUCnIRZbPZMRfgDz/8EJWVlbDb7dDr9XC5XMT6wOVyYX5+nrRXoo/X6/VicXERfr8fgUAAfr+fRKm43W5YrVZIJBJy/MzrOxwOHB8fJ9U2KZVKDAwMQK1WJ/1sMjIyUFhYCJ1Od+JvQ61WE4uG06qqeXl5CAQCmJiYQHd3d9ptqPz8fPD5fExMTKCpqenc4/1qtRrLy8tgsVhYWFiASCRCfn4+6urqCInKyspK0MH19vbiwYMHWFpawvPPP4+1tTVUVVWlPH7G0qSoqIh4vBUVFZFNzM7OzqnV06sARVHIy8sj38vBwUFagdBMu/vT2AY8CSaTKenkMZvNhkwmI5pEJnzdbDZjYWEBbrcbEomEVLRiAqtZLOCv/ioSgfNnfwZYrWeKwPm4Gn+6adwV4NqJFU3TtoKCAn8wGLz2SJtoyOVyrKysXMuPPhAIkBaf0+kkETL19fWXurvj8Xjg8XiJaeWXDJqmYTQaodVqQdM0Kioq0NTUdOVmpExpe3x8HDweDw0NDQk70XA4TEgW84dZ5KP/eL1evP/++0RQztzPaDRif3+ftKkoijpTILPX68XY2Bhqa2vTTgcIBAJEn3NaJcjhcMBkMp0oWA+FQtjZ2cHt27dT3md/fz+lsJVpt0ql0gRSSdM0yeJjPtdowfre3h4UCgUhXVwul7QlhoeHiXN+PBjbhWTEis1mIy8vD4eHhykF6BUVFRgYGEBJSUnKDQ2LxUJbWxsmJibSCsguLCyEz+fD9PQ0Ojo60j7fs7KyyMSgSqVK2aI8CZWVlUQbBUSqU1qtNqmZKXM/5g9FUQgEAvjWt75FDIaj//2kPwyRY/y/FhcXbwSxigaXy0VpaSlKS0tPDYTe29s71+f/JMPv94OiqLTIJBP/xPzOma6L2WzG6uoqyfhkiFZWVhaoP/1TQC4Hfvu3gaefBt55J60InImJCdrtdj983Pd3EbgRTIbD4czMzs4WdnR0XPehEDDj2/FRL5cFl8tFWnyMW3FVVVVKH53LAtMOZKwHLhOhUIjEzWRlZaG+vj6taanLAFPSX1tbg1AoRHFxcVLCw2KxCAE9CZubmwiHw7h9+zaqqqrgcrkwNTWFxsZGvP766/B6vTAYDKBpOu3pU4/Hg7GxMTQ0NKRNxmiaxvT0NCorK08dM2c8q04jtbu7u1AqlSk3QgyxTCVAP0m4DsQG0MaDy+XGLGzRYH6zyYhVXl4eZmdnU1acSktLSQsoGdhsNioqKqDRaNDQ0JDy2DMzM4mBJtNSPgkqlQp+vz+tzz0aAoEAfX19mJychMvlQnV19ZmuE0KhEEVFRTg6OgKHwyEES6VSQalUQigUIhQKJfwJBoMk1cDn8xGNHfNvTFX3tD8MdnZ2oNPpUFpaeiM9oKIDoRnfNyYQmvn8ruJaeZNw0pTsaaAoikxqqlSqBIsHu90OPp+P3Hv3UPCd70D0jW+AunMHuH8/MkV4Au7fv2+1WCzvn+vALhg3glgdHh7+fx988MHnOjo6Lmb29QLAnACp3KEfFzRNw2az4fD/J+/NoyM7y3Pf367SUKV5lkrzPM9TqyX1YAzGgL0gDlwShgTugRCSlZXkACfJSsghZ2U4IcBJyAUcfM1g4hiH4BAbAw7G7m7NUmmex9IsleYqqVSSatj3D3lvepCqdqmr1OrcZ61emNKuXV/t2vv73u99n/d5VldZW1sjICCAhIQEKioqHih5XKfT0dTU5PFE7QkODg4wGAxyhsATuxZfwGw2MzQ0RFBQEPX19ahUKpqbm9HpdGcut87MzMhlEClgKysrk4mxUukKUBRY7e/v09nZ6ZEJM8DY2BihoaGKyLWSn6KrAMzpdDI7O0t9ff2px6ysrLjU9Nne3j71O7gzQJdEQk8qPUlmzCedW6vVyvY2p70XcJmtlRoc3HEvpQVXaTYjLy+PgYEBxsfHyc/Pd3u8BEkfa2hoiN7eXsrLyz0KTnJycnjjjTdITU3FZDIBx3pVRqORwMBAioqKTv0t/v7v/x6TycS73/1uj0vZt/MU4fhZuXnzJpmZmYpEfR8EpMxyTEyMbAit1+s5PDyUTez//1IOXF9f91qW7iSJh/39/WO/w/Jy+Ju/oeTP/gxHfT2mjg4iY2JO3dDdunXLwbFv8QPHhbiD7Xb7jVdeecVz/wYfQ+JZeQsOh4PV1VX6+vq4ceMGBoPhwlnI+Pn5ERkZycbGhtfPbTKZ6OnpoaOjg+DgYK5evUpeXt4DC6psNhuDg4P09/dTUFBAeXm5TDLPzs5mdHT0zOeemZkhOTmZyclJ5ufnqa+vlwN0p9N5R9DqLrDa29ujs7OTsrIyjxax5eVlTCbTqaTs22G1Wpmbm3O7+15eXiY2NtZlwLm8vOwykHOVsXJHXHcnEnqSZ6CE6OhoNjdP57Wmp6ffocR+N6RSsbv7QpJgmJqakoNnd8eXlpbK/EJPoFKpKCkpITw8nPb29lOtfU6CRLLXarVyYGUymaitrSU6Oprm5uYT57+joyM5s/ezn/3Mo/HCL0uLUuY3Pz+fhoYGLBYLt27dum+leF9DMoQODQ2lrKyMw8NDWlpa6O7ulrPQ/5XhqqPXG5AqBuXl5ZT/4R8i5OQgBAayurZGS0sLTU1NDA8Ps7q6Kt/vRqMRm822Kori4WnnFQThW4IgrAmCMHTX678nCMK4IAjDgiB80Rvf4UJkrERRnNXpdPaLyLPq7u5W5t59Cg4PD2XVc6vVSmxsLCkpKZSWll7InRkcix4aDAaP7FROgyiKrK6uMjMzg5+fH1lZWaeWcs4LoiiysLDA9PQ0WVlZJ+phJScns7i4eKa0t9TtJ3VA1dbWyucXRfEOjoLNZnM5Se3u7qLX66msrPRIMdpkMjExMUFDQ4Oiaz04OEhhYaHL508URaanp09VIofjRffo6OjUrI8oiqda1oD7wEoqO52UHQgJCXEZGMXFxbG+vn4qNy0hIUHOGp1GxpYaHNwtLv7+/pSWlsoSDO6edUEQqKyspKOjQybLK4UgCGRlZREUFERrayvV1dWKmm4kM2mpC1AQBNbX12XCdkxMDP39/SwvL1NUVCTfG0NDQzQ0NDAzM8P09DTemLcDAgIoLCyUNbBmZmbIyclBp9NdSON3m83G7u6uTPTPyclhZ2eH+fl5hoeHiYuLIzU11WM9tosOi8WCRqM5v7Xr9ddRDQygevZZSt6yVbLZbGxtbbGxscHk5CROp5Ouri6Ojo5+4uZs3wH+H+A56QVBEB4B3guUiqJ4KFnz3S8uTBTj5+fX09fXl3iReFYajQa73e7xxLG7uyvzpQC52+ZhecgiIyMZGBhwW5ZxBbvdLtvNREVFUVZWdq6E+NOwvb3N8PAwERERLhXbpaxDZ2cnV65c8Ug8cPIt1/aUlJR7OmdMJpPcGRgeHo7JZDr1vjCbzXR3dytW6pZweHhIb28vVVVVin6/lZUVmcDtCkajkYiICJfaNO7KgO4aI5QEVqdlrLRarcsMUXR0NGNjYy7PLTkQuCp1FBUVMTg46FazKioqipiYGCYnJxXxcNRqNTU1NbS3t8sitJ5Ap9Oh0WgUZzcl7amBgQH8/f3x8/O7I0Ol1Wq5dOmS3M1XXFwsN28kJiZSWlpKV1cXLS0tXLt2zaOxnobAwECKi4s5ODhgYmKCqakpcnNzT/WbfFCQyrzSmCRD6MjISNncfnh4GJvNJqu8e7uL+0FgfX3dKxtuxfi7vwOdDj78YfmluyUe7HY7Tz/9tHljY+NVV6cSRfGWIAjpd738aeB/S5kuURRPV3z2ABcmZWI0Gv/t9ddfv9dq/gFD0qdxBUkSYXh4mBs3bjA6OkpgYCA1NTU0NjaSk5Pz0ARVcDxJSBkbT7G/v8/Q0BBNTU04nU4aGhooLS194EGVZJY8OjpKaWkpxcXFboOOoKAg0tLSXC7Gt0MURVmjSZKukLqfJNxeYpX86E7a/e3s7NDd3U11dbVHQZXT6aS7u5uCggJF95zNZmNsbMwlIRuOv9vk5KRbKY77KQPC/ZUCpet4Winmdnub0yBpWrlCWFgYQUFB9wiSnoTc3FzW19ddliBvh7+/P7W1tYyMjMgelZ4gMjKSS5cuMTg4yPLystvjc3JysNlsxMTESAa2d/xdEATS0tKora1lbGyM7u5uCgsLEQRB9s3s7Oz0evlLo9FQWlpKdXU1q6urNDc3X6gy2+Li4qlOB5IhdF1d3T2G0Kurqz43hPYljEajxwH/mdHbe6zK/vu/Dy7mBD8/P9rb2w84G78qF7giCEKHIAg3BUGoOetwb8eFCaxsNtuNl19++cLJnet0uhMn0Nu1Tm7evMnCwgLR0dFcuXKF2tpaUlNTHygh+36RkpLC/Py84olsa2uLrq4uenp6iIyM5Nq1a2RnZz9wQqfT6WRmZobW1lbi4uK4fPmyR4FKeno6JpPJLefs8PCQ9vZ2OZh8//vfjyiKvPTSS3dMpLcvXHa7/cRy0vb2Nn19fdTW1nockEsehkr1rcbGxk6VKLgdGxsbBAUFuSwxSfIIro5xV0JTWgo8DVqtVi5tnQR3vMmgoCDUajVms2vKZ35+PuPj424XSUmCYXBwUDH/KTAwkNraWvr6+s7kACE1YczOzjI1NeXyGc7MzJS7Ag8PD9ne3j5xnNJvHx8fj16vZ2trC41GQ3JyMvv7+xgMBo/HqfS7lJeXU1lZyeLiIi0tLT7hf3qCnZ0dAgMDFc3vkiH0tWvXyMvLY2Njg5s3bzI4OCjz2h4WOBwOrFbr+SUJvvQlCAmBT33K5WGrq6scHR0ZXfGrXMAPiATqOPY6/lfBC6nRCxNYiaI4ZzAYbKdZKDwoREZGsrOzI+sOSbYSra2tmEwmMjIyuH79OuXl5fflN3XREBgYSEhICFtbW6ce43Q6WVxcpKmpiZmZGbKysmhsbCQpKelC8Mc2NjZoamri8PCQK1eukJiY6HE5QRAEKioqGBoaOnVh3NraorW1lczMTFlpPDIykieeeELWwLl9TNK12d/fvyd7s7W1RX9/P7W1tR6L087NzXF4eKhY4HV7exuz2UxqaqrbYycnJ08UBLwd7oyIwbcZK3BPYJc8Hl3BHYkdjgO4+Ph4t8fBcXCQnZ1Nf3+/4o1KUFAQVVVV6PV6l4HiafD396euro7d3V0GBgZOvWb+/v7y5kHCSdm1lZUV7HY7FRUV1NTUMDo6yvDwsFwCfP311z0eoycIDg6msrKSsrIyZmdnaW1tdTk3+RIGg4GMjAyP3xceHk5xcTHXrl0jNjaWiYkJbt68ydTUlMss6kXBuZYB5+bgxRePgyo3zT03btwQDw4OXjnjJy0CL4nH6AScwNm0JG7Dg1/9boOfn5++t7f3QQ9DhiRmJggCb775Jr29vTidTkpLS7l27RoFBQVn8uV7WJCZmXlil9LR0RGTk5PcvHkTk8lEVVUV1dXVHrdd+wpWqxW9Xs/09DTV1dUUFBTcF7lWq9WSnZ3N4ODgHa9LZG7J/uXuLFFJSQmlpaXcvHmT+fl54DiwkoLvw8PDO4KMjY0NBgYGFBv03o6trS3m5uYoLy9XdD86nU4GBwcpKytze/z29jZ+fn5uM33uAiun04nT6XT5W9wPxwrcB1bh4eGYzWaXAU58fDwbGxun+uRJyM7OZnZ2VlEmKjk5GZVK5VF5PSwsTOb5HR0dKX6fBJVKRXl5OVqtls7OzlPHmZOTw+7urvy73J3ROzg4YGxsTL5XgoODqa+vR6PRsLCwgEajYWVl5Z6yty8QGhpKdXU1xcXFTE1NnVhu9yUODw8xm81n1nGCX3L5ampquHz5Mmq1mq6uLtrb21laWvLI3eE8sbq66pI/6VX8n/9zbMT8+7/v9tBXX311e2tr66dn/KQfAW8DEAQhFwgA7jsleqECK6PR+NKD5lk5nU7W1tYYGBjgxo0bTE5OEhMTQ2xsLPX19WRlZT1wm5vzQmRkJAcHB/KOeW9vj/7+flpbW1Gr1Vy5coWioiKPgwBfweFwMDExQUdHBykpKVy6dMlrv1VycrLcTQjHpWBJnLGhoeHUa/Dud7+biIgIXnrpJVkU9PYFWyqLra2tMTw8TF1dnceSG1arlf7+fqqrqxUHkNPT08THxyvivk1MTLjNVkkaUa7uBZPJ5Laz8X5Lge4CK0EQCA0NdVnqEwSBxMREtxwlKduj1JC7pKSE6elpLBaLouPhmAAvuQGcJZsvCAK5ubmkpKTQ1tZ2YvZL6nqWfpvbAytJYLaoqOgO8rXUiVhZWSmXhm7cuOHx+M6KsLAwamtryc/PZ2xsjI6ODrflW29gbm6OtLQ0r22mJUNoaS41mUzcunWL/v5+tra2LgynTBRFl/pzXsXWFjzzDHzoQ3AKj+12NDU1OYAud8cJgvAC0AbkCYKwKAjCfwO+BWS+JcHwfeA3RS9c9AvTFQjHPKtXXnnF/Cd/8ifnKhR6dHTE2toaKysr7O3tERMTg06no7i4GJVKhd1up7m5+TyHdGGQlpbGyMiIrKj8oOxm3MFoNDI6OkpSUhJXr171SSmytLSUlpYW1Gq1HGy4a4sPDAzkqaee4lvf+hb/8R//cU/KPyIigtXVVSYmJs4klOpwONDr9ZSUlCgOcC0WC8vLyy4taSSYzWbZsd4VvFEGBE4V/5TgLmMVHBzsNnCRZBdcBXmpqano9Xq3ZdK0tDRu3bpFenq624DY39+fsrIyenp6aGhoUHyPxsfHc3R0hF6vp7a29kz3dlJSEhqNhvb2dioqKu74HSIiIoiNjZUDt5WVFflvExMTREZG3kNYtlgs6PV69Hq9HMjOzMwo+o29iYiICOrq6tja2mJoaIiAgADy8vJ8wgNyOp0sLS0pem7Ogvs1hPYltre3j+1mzmPe/8Y3YH8fPvtZt4e+pWW1ooRfJYrir5/yp494OkR3uFCBlSiKc+elZyVZyKyuruJwOIiPjyc3N/dECxk/Pz80Gs25e+g9SEh2MwaDgf39fS5fvuxTUbizwmKxMDQ0hJ+fH5cuXfLp5OPv709CQgI9PT00NjYqXkCSk5O5fv06b7755j1/Ozw8xGAwnMlUWxRF+vv7SU5O9sjiZmBgQN40uIMSbhUcB1buLFy2t7fJzMxUNM7T4I5jFRgYyOGh6zk2NjaW3t5el1w0rVZLYGCg20BBpVKRl5fH2NgYFRUVbscfGRlJfHw84+PjioRbJaSkpHB0dERvby+VlZVnWuCio6OpqalBr9eTn59/R1knJyeHtrY2ALlZZ319nY2NjTtU9ldWVujo6GBoaAiHwyE/b9nZ2bztbW9jcHCQ2NhYcnNzz5VnGRUVRX19vVxO12q15OXlebW6sLq6SmxsrM/XprMaQvsS51YGPDiAr34V3vUuKClxe/hb/Kof+35gnuFClQIB1Gp1l16v9/p5RVFka2uLkZERuStDrVZTWVkpK4C7isgTEhIUtVc/7JD4FLdu3cJqtVJXV0dmZua5pNk9gd1uZ3R0lO7ubrKzs6mqqvJpUGW32+np6WF/f5+ioiLZMFopGhsb5TR6SEiIbGa7vLx8pqAKfmmb44mJ7eLiIkFBQURHR7s91mKxsL+/7zZoOzg4QBRFt9ffbDa75GnZ7Xa3zR/uSoGCICAIgtvOwaOjI7dcFiUkdjieG/b39xV3eeXk5MgCh54gKysLrVbL0NDQmUtEISEh1NfXMz09fQd/Mjc3F1EUUalU7O3tsbu7y9DQEFVVVYiiyMjICN/+9rf55je/ycjICKmpqfj7+2O323niiSf40Ic+hE6nkzNxzc3ND2TOiImJob6+nuTkZHp6eujr61Okfu8OoigyMzNzJtL6/UAyhG5oaKCsrAyLxUJTUxO9vb1sbGycW6lwbW3tfGQWnnsO1tbgc59TdPgrr7yydR/8Kp/hwgVWKysr33vppZdOJ0l4gLstZGZnZ4mIiKChoYG6ujpF6XsJOp3ujhT5fzWYzWZ6e3vp6OhAq9Vy9epV8vPz0Wg0ZGZmYjAYLkS9XxRFlpaWaGpqQqPR0NjYqChIuB/s7e3R0tJCdHQ0FRUVZGRk4Ofn55H9iEqlknVvpAVdFEXi4+PPJEmxvr7OysqKR2XZo6MjpqamKCwsVHT81NQUOTk5bs+vpAxos9lQq9Uusxju+FXgvhQIvzRjdoWYmBi32lKxsbFsbW25JacLgkBhYSEjIyMuj7v9eEmCwVNSekFBAXa7XRahPQsCAgKoq6tje3tbDtJSUlLQaDRygN/c3Exubi7d3d189atf5Qc/+AFms5nr16/L80FCQgK//du/TVVVlXyPqFQqcnNzKS8vp6+vj4mJiXPXbZIyPo2NjSQkJKDX6xkYGDhTd6WEra0ttFrtA+XXSobQ169fJyUlhYWFBW7cuMHY2JhHvD1PYTab0Wq1vs+SORzw5S9DVRVcv+72cFEUuXHjhgNo9+3APMeFKgUCOJ3O/3zppZesX/ziF89Uczs4OMBoNN5jIaOk+8kVAgMDUavVbg1YHyaIoojRaJQzH1lZWcTExNxznQICAoiOjmZlZcXtAupLmM1mBgcH5V33eeiELS0tMTk5SUVFxR2cnJKSEtra2ggNDVW8k5Paw61Wq0yyfuGFF7h06RKPPvqo4gBLKn9evnzZo3LL8PAwubm5ij7n4OCAnZ0dSt+ykXCF5eVl3DkmKOHeKAms3JUC4ZcEdlfZsQKYqwYAACAASURBVNjYWLe7cEkod2lpyW1WMDIyEn9/f8U7e6lU1d/fT1VVleLfUXIE0Ov1zM7OepStvB1Stn58fJzOzk6qqqrIyspifHwcODbk7unpwW63k56ezuOPP44gCPz4xz9mf3+fRx991KVVT1hYGI2NjUxOTtLS0kJZWZlH+nHegCAIJCQkEB8fz8rKCp2dnURHR5OTk+Px3DE5OelR6daXOMkQur+/H6fTSUpKitcNoZeWlrxmuuwSL78MExPHMgsK1uqenh5UKlWPKIoXS6OJCxhYiaK4m5ycPD83NxcruV27OZ69vT1WVlYwGo3yw+QLC5mkpCSWlpbuyzvwIsBut7OwsMDc3ByRkZGUlJS4vVZZWVl0d3c/EO8uSR3cZDJRXFx8LuRYp9PJ8PAwVquVhoaGeyYqlUpFVVUV7e3tBAUFKeLeSaUfqeMsJiaGjY0NOjo6mJ6e5n3ve59bMrzdbkev11NeXu7SWuZurK+v32Ge6w6Sj6K739pqtSIIgtuxKDFudeUhKEGlUrnNICkhsLuzt5GQmppKe3u7ok6wwsJCurq6iI2NVfSMJCYm8otf/II33niDiooKKisrFXHlbr/3AgICzrzZkUyl5+fnaW1tJSgoSCawr62tUV5ezqVLl4iKiuK1116jp6eHuLg4PvzhDyvi20j8s4SEBHp7e0lMTCQ7O/vc5w+py1On07G0tERbWxtxcXFkZ2crKsFLJV5PvDrPC5IhdEpKClarlYWFBVpaWggNDSUlJUXxvXgapM23Ep7lfUEU4YtfhIwMeOopRW/54Q9/aFlaWvq2bwd2Nly4UiCAyWT63o9+9KNTc+ROp5ONjQ2Ghoa4efMmo6OjaDQaamtraWxsJDs72yddIYmJiQ91OdBqtTI8PExTUxN2u536+nrKysoUXStJefk8VY9FUWRubo7m5mbCw8NpaGg4l6Bqf39fXmhqampO3f1pNBoqKirQ6/VuCdM2m00uRYSHh1NbW8vGxob8ffb29nj22We5cePGqdwfqfU9MzPTo0YCh8PB8PAwJSUliibZo6Mj1tfXFS3YSsqAoDxj5W6h8yRj5QpK7G3gOFMdHBysyF5G4q5JkhxKIJHtOzo6+NrXvsa3vvUtent73ZYI1Wo1tbW1TE1NubXccofo6GgMBgNdXb/sWA8PD+e9730vNpuNp59+WjaT/uQnP+kxiTk8PJwrV65gt9tpaWlx+9v4ClIG8urVqwQHB9PS0sLY2JjbQH1qakqx6O6DhFarJTc3l2vXrpGRkcHKygo3btxgeHj4TAr+cBxUhoSE+L4M2NIC7e3wmc+Aws/6t3/7t31RFF/z7cDOBvUXvvCFBz2Ge/Cnf/qny5ubm7/5yU9+Uq652Ww2jEYjExMTTExMYLfbiY+Pp6CggJSUFMLDw33+46vVaoxGI+Hh4Q+VXY1kPDw/P09CQgIlJSXExMR4rBIfHBzM2NjYqR5Z3sT29jZ6vV62BImKijqXna7RaKSvr4+SkhKSkpLcfqZGoyEoKIjBwUGXivNTU1MsLi5ycHDA1tYWq6urpKSkYDQaCQ4Oxmq1kpaWRn9/P5OTk6Smpt5Tcp6YmMDPz8/jSX58fJyoqCjFNjdTU1PExMQoCt6Gh4fJy8tz++xNTEyQm5vr8noajUaCgoJcBvoWiwWr1eoys6NWq5mZmcFdxvvw8JDDw0O3mYiAgADm5ubQ6XQuj4Pj9v/+/n5SUlIUlfcMBgPb29uEhYURGRkpK6V3dnayvb1NUFDQiZ3KcPw94+Pj6e3tdWuOfRqmp6f553/+ZzY2NkhKSsJqtSKKIjabDZvNxssvv0xgYCC/9mu/RmVl5Zk7/QRBIDY2ltDQUPr6+mQJjwch2yIIAhEREaSlpbG3tycbzp/k22mxWFhYWCAvL+/CScycBkEQ0Gq1JCQkkJqaisPhYGpqCoPBgNPpJDg4WPHcL+nd+dzG5vd+DzY2jsnrCsqYCwsLfOMb35g0m83/4NuBnQ0XrhQIIIrigk6n2zUajbH7+/uSFxBxcXFkZmYSERHxwG5yqRyYn5//QD5fKZxOJysrKxgMBgIDA8nMzLzv4CQsLIyAgAA2NjbuS3nYFQ4PDxkZGcFqtVJeXn5uvlSiKDI2NsbOzo7H/K24uDj29/fp7u6mpqbmjmssGTNbLBY+/elPyyWVoKAg5ubmCAgIwGKxEBgYyMLCAo888ggdHR380z/9E48++ih1dXUIgsDKygpbW1vU1dV59L3MZjMbGxs0NjYqOt5ut7O8vMzVq1fdHru/v49arXa7oFutVjQajdt77+DgwK1lhruuQDjupFKiXh0bG8v09LRbraro6GiGhoY4Ojpym1ELCAggJSWFmZkZRZQBrVbL4eHhPcRqf39/BgYG6O3tJTY2lvLycsrKyu4JtjUaDTU1NXR2dlJTU6NYDsZut/P666/T0dFBaGgowcHBLC4uolKpUKvVsnZfRUUF73znO722kYyMjKSxsZGxsTFaW1spLy9/YJxVlUpFRkYGqampzM7O0tTUREpKChkZGXLgMT4+7nZDcJEhGUInJiZycHAgl0K1Wi2pqanExcWdGiyLosja2prv17qxsWN+1f/8n6BQi+/ll1+2mc3m7/l2YGeH10uBgiBoBEHoFAShXxCEYUEQ/uKt1x8VBKFHEIQ+QRCaBUFwue0WBOHHzzzzDKIoUlZWdmEsZCTZhYvQIXcSbDYbU1NT3Lx5k+3tbSorK6mpqSE6Otor1y0vL4/x8XGvf3+n08n09DRtbW3Ex8dz+fLlcwuqDg4OaGtrQxCEM4l0wnFrflhY2B1+cKIoMjo6itVqpbKykoCAAJ588kmefPJJrFYrwcHBcnns8PAQQRBoaWnh/e9/P9nZ2fznf/4nzz33HAsLC4yPj9/RfaUEkmaV0hIgHJOWU1NTFe1ovVkGhOMSpDfI63AcmLgrp4WHh2Mymdzey5J+kNISX0ZGBktLS27Lw3AcWJ30fSwWixwcbmxs8POf/5wvf/nLvPDCC0xOTt7xHslHT6mv4NraGt/85jfp6OjA399fLhNdvXpVJkQDVFdX8+STT3o9O69WqykqKqKgoICurq4H3nGsVqvJysriypUriKLIrVu3mJmZwWQysb+/f37+eD6Gp4bQ29vbRERE+N7/9ktfAo0Gfvd3Fb/le9/73s7e3t5LPhzVfcEXHKtD4G2iKJYB5cDjgiDUAd8APiyKYjnwL8CfuTrJysrK97q6ujYyMzMvjGUKHHMzQkJCLpwzucViYXBwkJaWFgRBoLGxkeLiYq9fu9DQUDQajVe5VpJZss1mo7Gx8UxmyWfF5uYmbW1tZGdnk5+ff1+fm5eXh1qtZmRkBFEUZePmu/37Kisr+fjHP45arWZjY4Oqqio0Gg0Oh4OjoyN+8IMf8Nhjj/He976X5eVlvvvd75KVleWx1tXs7CyRkZGKeWkOh4OFhQW3JTQJKysrispjSojr4D25BVBGYBcEQfYOdAcpsFISAKjVanJzc+UOO1e4W+7lpPtPEARUKhWiKDIxMcG//Mu/8Ld/+7f86Ec/kiUjJINfV56ATqeTn/3sZzz99NMyLyskJIT3vOc9PProo7S0tLC2toZKpeLjH/846enpdHd3+8y7LioqiitXrmCxWGhra/OK3tT9wM/Pj5ycHBobG7HZbDQ3NxMREXFhN9H3g7sNocfHx+8xhF5cXHTbTHPfWFmB730PPv5xUBjA7u7uMjc3ty+K4qxvB3d2eL0U+JbPjsRO9H/rn/jWP6nfNhxwbcQFPZ2dnY7zUGH3FCkpKczPz5+rdcNJEEWRzc1NpqensdvtZGZmUlxc7POgJC8vj76+vhOlGTyBRKZ3Op1UV1efa0lAFEWmpqYwGo1n8uc7CYIgUFxcTE9PD7du3SIqKupUnamkpCR+67d+ix/+8Id0d3dTWVnJwcGBXAb95je/ySc+8Qlqa2tpb2+nra3NI26b1Wplbm5OcQkQjnkLiYmJip43i8WCn5+fomzGzs6OIsV1Jc+6klIg/JLA7i6gk2QXlPCswsLC2NzcVFQGT0xMZGZmxq1bw933XVpaGpmZmQiCgF6vx2Qy3RFISiVVyR+yv7+foKAg8vLyaGxsJDc3l87OTurq6u4w++7q6qKpqYmjoyNUKhVlZWUEBgZSWFjI66+/LmfjAgMD+Z3f+R1ZGmF2dpa2tjZqamp8witVq9UUFxezublJZ2cn6enpXvXiOwv8/f3R6XSsr6+jVqu5desWWVlZJCcnP7QlwdMgGUInJCRwdHTE0tISXV1d+Pn5sbu7q1jz7sz46lfBbof//t8Vv+W1114TnU7nj3w4qvuGTyIWQRDUQDeQDXxNFMUOQRA+AfxEEAQrYAZckkVEUXQmJSU1tba2vl8J3+M8ERcXx8jICA6Hw/dp0hPgdDpZXFxkdnaW4OBg8vLyzjXICwkJISgoiLW1NcWE6NvhcDiYnp5meXmZwsLC81H0vQ2SNUhwcLBLLZ6zQhAE7HY7gYGBLifi4OBgPvKRj/CLX/yC1tZWkpOTeeyxx/j5z3/O4eEhX/va17h8+TJXrlzhzTff9Ei3aGhoiIKCAsWbEqfTyezs7B32Ja6gtAwoiqIiGQWlUJqxCgkJUdTJJ9nbKGknT09PZ2ZmRlFgdbtoaG1t7anH3R1YabVawsPDCQ0NpbGxkd3dXbmz6/Dw8I4uRikYNJlM9Pb20tvbi1arRafTsbOzQ0VFBX19ffT29srlvaKiIt71rnfR09PD0dERzz33nByoBgcH84lPfOIOvan09HSCgoJoa2ujqqrKZ+X56OhoGhsbGRkZob29nfLy8gfqjSdZDkVHR5OZmSnTK3Jycs41o36ekAyhMzIymJqawm6309TURFRUFCkpKd6n4ezuHvsCPvUUeNCQ88ILL2ytra294L2BeB8+kVsQRdHxVskvGagVBKEY+EPg3aIoJgPfBr7i7jzLy8vfefHFFy+Wlwq/FJ47b+mFw8NDOWVrsVioqamhqqrqgWTOJFd5T9Pkq6urNDU1oVKpuHr16rkHVTs7O7S2tpKSkqLYL08pnE6nTEy/fv06JpPJrUK2SqXiHe94Bx/4wAdYW1ujpaWFRx99VB5XW1sb6+vrhIWF8bOf/UxRULG6uoogCB4FvcvLy8TGxiouNyotAyr113Q4HIp+C7Va7VHGyh20Wi02m00OPFwhMjISi8WiiDsFx8GClFV29fnwyxKgIAgUFRUxPz+P2WwmNDSUJ598kj/+4z/mYx/72B1lWpPJhMlkQqVSERMTQ0REBFarlZmZGfR6Pc888wx6vR673U5kZCSf+tSneO9730trayv9/f00NzfjcDjw9/cnKCiIj3/84yfOJXFxcbKsiC/lVvz8/CgtLSU7O5uOjg7m5+cfSBlue3sbh8MhOzoEBgZSVFQkmz3funWLlZWV/5IlQglra2tUVlZy/fp1dDodBoOBmzdvMjExcV8K9nfgmWfAZIL/8T8Uv8XhcNDa2uoAutwe/ADhUx0rURR3gBvAu4AyURQ73vrTi4CSrfEbP/7xj5XNYueM1NRU5ufnz+WzzGYzfX19tLW1ERgYyNWrVykoKHigO7qgoCBiYmIUE3r39vZob29naWmJS5cukZ2dfa4mraIoym7xNTU1XleQdzqddHd3ExoaSn5+Pmq1mqqqKnZ2dpiYmHD7/sLCQj7xiU+g0Wj4xS9+QWxsrKx2PDQ0hMViwWg00t7u2r3BZrMxOjpKcXGx4rGLoigLgirB3t4eAQEBioIwpcR1JfwqUJ6xCgoKUszZiY6OllXxXUEQBI+feylrddoifHdgBcfBY3l5Ob29vXcEkWlpaXzsYx/jj/7oj3j7298ul84lXb+dnR38/PyIj4+Xg1lRFKmtreXTn/40kZGRfP/736etrY3NzU3CwsJk38rf+I3fcGkNFR4eTl1dHSMjIx7pdJ0FsbGxNDQ0sL29TWdnp1utMW9C8kU8qQSm0WgoKSmhtraWtbU1mpqaMBqN/+UCrP39fURRJDg4WLYHqqqqoqGhgcDAQHp6emhtbWVhYUHRhuRE2Gzw938P165BTY3it7W1taFWq5tFUTxfnyQP4YuuwFhBECLe+m8t8HZgFAgXBEHqP37HW6+5hCiKVofDMTY0NOTtYd43goODEUXRZ4RLSfG2ra2N4eFhEhMTuXbtGunp6Q+k/HgScnNzZX7XabDb7YyMjNDT00NOTo7PzZJPG0NPTw8mk4mGhgavc7kcDgddXV1ERkbe0WIvKWTv7e0xPDzsdgKOjY3lox/9qMz7WVxcJDU1lYCAAHmB/fnPf87zzz/P6OjoiZmbsbExMjMzPSq9ra6ueqSDtLS0pDgw9SZxHZR3BUpkbyWLXlxcHGtra26PA2SLG6WLaWhoKGFhYSwvn0wpPe2ah4eHk5KScqL/oEajoaGhgc985jP85m/+5h3t8Ha7HaPRyN7eHoGBgbKli9Fo5B/+4R9kf8u6ujq5c/IjH/mIouymVqulvr6e5eVln3QG3w5/f3/KysrIyMigra2NxcXFcwlgVldXCQ4Odmm/o9VqKSsro6qqiuXlZVpaWu5bpPUiYX5+/kQJEq8aQr/4IiwsKDZblvD888+bL6ra+u0QvH2zCoJQCnwXUHMcuP2rKIr/SxCEXwH+F+AEtoH/WxRFty62/v7+H/i93/u9Z7/yla+cT++9B1hYWMBisXhV58Nut8v8qYiICDIzM8/dY8sTSIFVXl7eHa9LZsmTk5Okp6eTnp7+QHgJkrm0pFfjbUhBlaSxdhKkDkGHw+HSs9LhcNDW1kZeXh4BAQH09PTQ09PD3t4efn5+cgAr/XdQUBAlJSWUl5eTkJDA9vY2IyMj1NfXK77WoijS3NxMVVWV4g7SmzdvUl9fr8iPrKmpicuXL7vleq2urrK9ve3Wj+2tUgBXrlxx+9nt7e2UlZW5DeQlRfBr1665PSdAf38/Op1OcRn74OCA9vZ2rly5cuKm6K/+6q8QRRGHw0F+fj4f/OAHgePfpqOjg4yMDLeBz97eHj09Pej1enZ3dxEE4cSFLiIigqeeeopXX32Vzc1NPvKRjyjuApUgiiKDg4PY7XbKy8t9nnm22Wxyh61EuvcFnE4nt27doq6uzqONyd7eHuPj4xwcHJCfn+9zU3hf4i1jY65cuaKInymVuhcWFtjZ2UGn05GSkuJ68yqKUFYGTicMDIDC+8fhcJCSkrK+srKSLIqiZ+7l5wxfdAUOABUnvP7vwL97ej673f7Kv/7rvx586UtfCj3P0pES6HQ6mpqavKLKa7VaMRgMGI1GkpKSuHz58kOh7p6RkcGtW7dIS0uTJ6PbzZIbGho8lgnwFhYXF5mamqKystInwandbqezs5OkpCSXi5PULTgxMUFXVxdVVVX3LLCS5lRiYqKsm/PII49w9epVJiYm0Ov1crbBbreTlJREWFgYer2ejo4OufzjSRcgHEtdBAcHKw6qdnd30Wg0ioIqp9OJw+FQNEF7UgpU2v4v8azcBVZ+fn74+/tjtVoVZVPT0tKYmJhQHFhpNBp0Oh2zs7Mnllu1Wq0sDXF7Nk4QBCoqKmhtbSU8PNzlYh8SEsLVq1dpbGyU75fp6Wngl1m+srIyHnvsMZ5//nnW19f59V//dY+DKmlcpaWlsu5cTU2NT59xf39/KioqMBqNtLa2kpub6xMZgNnZWXQ6nceNFiEhIVRVVWE2mxkbG2NiYoL8/HyPbKcuCjY2NoiKilLc9HKaIbQoiiQnJ59sCP2f/wmDg/DtbysOqgDefPNNgNcvelAFF1R5/XaIoniQlJTU0tzc/L6L1h3o5+dHVFTUmbvj4JiDMj09jcViISMjg/z8/HPlHt0vVCoVubm5jI6OUlRUxNjYGGaz+dzMkk+Cw+GQd7gnGSh7Azabjc7OTlJTUxXJIAiCQF5eHnNzcye2rxsMBuA4UL0darWagoICCgoK2Nra4qc//SlTU1MsLS1hNBrlALynp4fp6Wmmp6cJCwsjOzubrKwst2XByclJSkpKFH9vpd2AcEyuVmpce3h4qKhE68kGRgqslAg8xsbGsr6+riirGRERIaulKy1rZ2Vl0dTURGpq6j334+2B1d1BoySJ0NfXx6VLl9x+f5VKRX5+Pvn5+YyPj9Pc3MzGxgYHBwdMTEywvLzM+vo6H/jAB+7b/y4rK4ugoCBaW1upqanxuVxKfHw8kZGRDA4Osry8TGlpqdc2n0dHR8zNzSnKhJ6GsLAwamtr2dnZkTXM8vPzL6R582mYnZ09832h2BD6i1+ExET40Ic8Ov/TTz+9tbKy8rUzDe6ccSG9Au/GZz/72bWjo6Mnf/VXf/XBsbVPQVBQEJOTkzLRWAlEUZQj+52dHTmgCg8PfyjbeCUPQYPBIHfbPShivcViobOzk5iYGAoLC33CR7PZbHKJxpPfHZC5TL29vURHRxMYGMjGxgYGg4Hq6mqXQbVWq6W0tJT9/X2Wl5dxOp3Mz88zNzdHWFgYTzzxBBkZGdjtdiYmJhgYGKC1tZXp6WnMZjP+/v6EhobK99jW1pZijSkJQ0NDiq/r6uoqGo1G0c59eXmZiIgIRZkzpbITNpuN7e1tRZklPz8/FhcXPWpqUKppBccBj0qlYmVl5Z7xDA8Py4LDISEhVFTcmfAPCQlhZ2eHvb09oqKiFH2elP1+17veRX19PVFRUWxubmI0GnnnO995z2ecFaGhoURERNDd3U14eLjPn3vJokWlUtHb24tGo/GKBMTQ0BApKSle2QxqNBqSk5MJCgpibGyM1dVVQkNDL3wFwmq1Mj8/fw+t4yzw9/cnOjparmQsLS0xOjqKs6uLqL/7O/j858GDRMn+/j6f+cxnti0Wyx8+DDHLhc9YvYWbv/jFL46UlgvOE2FhYTgcDkVt5Tabjfn5eebn54mJiaGiouKB+WR5C1tbWwwPDxMZGYnZbH6gInorKyuMj49TVlbmszT80dERHR0dZGVlnbmzMC4ujsDAQLq7u8nMzGR6eprLly8rDgIfe+wxJiYm2Nvbw+Fw4HQ6WV9f58UXXyQtLY26ujp+5Vd+hZWVFaamppienubGjRvcuHEDrVZLVlYWWVlZmM1mj7JVZrOZoKAgxRnA7e1txUGbL57tkJAQuRzmDmFhYZjNZkRRVHT/JiUl0dTU5JGPXGpqKrdu3WJ/f/+OAFKr1cp8qNNU0wsKCmhpaSEmJsZlBsTpdMrZ2ttFQsvLyykvL2dra4v+/n4sFovX5p6IiAguXbpEV1eXrPPka+h0OqKiouTsVUlJyZnLkdvb21gsFkpLS706xqioKC5fvszm5iaDg4NoNBry8vIU+zmeNzzRyVMKQRCIiooiKirqeJ38m7/BFhZGV3ExOoOBpKQkRb/byy+/LDqdzh+ID0kL5kNRc3qrtfJHP/nJTy7kRc3IyGB2dvbUv1ssFoaGhmhubkYURRobGykpKXmog6qDgwN6e3sZHx+nvLyciooKYmNjXV4HX8HpdDI8PMzc3Bz19fU+C6oODw9pb2/3yuIRHh5OTU0Ng4ODcuZKKfz8/Hj88cdxOBzyDtvpdBIZGcn29jYvvvgiX//611leXqahoYFPfvKTfO5zn+Opp54iNzcXg8HAf/zHf3Dz5k0GBgYUt0wvLS15xG0xm82KuW2+CKw0Go3iVn1BEAgLC1NsVeXn50dMTAxGo1HxeARBoKCggNHROxuiby/VnvZbSBIMfX19p3LMDg8PZYPdysrKEwP1qKgoKioq6Orq8qqMQVBQEPX19czNzTE5OXkuHXyBgYFUV1eTkJBAS0sLq6urHp9DaizxxE/TU0RHR1NfX09KSoos5Pqg7XvuhsPhYHV11adBsVqtJvzP/gz/xUUqH3kEp9NJW1sbnZ2drK6uuuz2/frXv76xvr7+Tz4bnJfxUJQCAT73uc8tbm5ufuA3fuM3Lo5x4FsIDg5mZGSElJQUuZQjiiJbW1sMDQ3J7enFxcVER0dfGLmEs8DpdDIzM8PIyAjp6enk5+fLC2JkZKRMwD4vGyKr1UpXVxehoaGUlpb67HMPDg7o6OigoKDgzHy62yGKIv39/WRkZHB4eMji4qJLp/m7ER0dzdzcHEajkYyMDPb397FYLDidTurq6jg8PKSnp4fOzk4sFgvx8fHy73X58mWcTif+/v709PQwMjJCbGysy4BUFEWGh4cpLCxUNEabzcby8rLiHfDMzIxs5eIOSnfWgiAwNzdHamqqovM6HA7MZrPiri6tVusxDSA4OJi5uTlCQkLkstn8/DyLi4vAcQmlru5kUwrpOVtYWCAhIeGOv+3s7KDX68nLy3P7fTUaDcHBwfT395OYmOi1+UitVpOUlMTi4iJGo5G4uLhzyV6HhYWRmJjIxMQEa2trxMTEKP5Oc3Nz+Pn5eVzS9xSCIBAcHExqaioqlYrBwUF2dnYIDw/3CQfUUywtLREQEHA+gs2BgTI/OT09nZCQEFZWVhgZGWFvbw+NRnPHZmNjY4O//Mu/XN3d3f1z3w/OO3goMlZvYWBoaMiys7PzoMdxD1QqlTyhSHYzzc3NzM7OyqaeEi/gYcb6+jpNTU3Y7XauXLmCTqe7Y+L08/MjLy+P4eHhcxtPe3s7eXl5HpVkPIXVaqW9vZ2ioiKvTTyTk5NoNBrS0tIoLS0lLi6O5uZmRYbAcDxRZ2dnk5aWxtzcHEdHRwQFBWGz2WhtbUWlUvHRj36U/Px8urq6+Md//Ee+//3vMzs7i8ViISAggA996EN8+MMfxul08txzz/Hv//7vp6qVm81mgoODFQeuSoVB7/5O3oYSM2YJkn6YUkg0AE+zD3eLht7OS3LX8ZiWlsbBwYHs+iCKIjMzMwwMDFBdXa046I+NjSU7O5vOzk6vmixLPoTBwcF0dHScWtr0NgIDA6mpqSE2Npbm5mZFv+Ph4SEGg8ErnCKlkBwRrly5Qmxs4Wsv+gAAIABJREFULJ2dnQwODp6rCOpJ8EUZUCnCw8MpKSnh+vXrJxpCf//737dZrdb/94EM7ox4aDJWX/jCF/jrv/7ryMTExIaqqqoLF6EEBgbS29vLwsIC/v7+FBUVkZqa+kDV0b2F/f19+vr62N7epqKiAp1Od2qQGBISwsLCAgEBAT4rdYqiyMTEBAsLC9TW1vpU52t/f5/Ozk5KSkoUE5XdYXV1laWlJSorK+VgIjw8nKioKHp6ehAEwW0jg6S0/Z73vIeamhrCwsLY3t6WA6OdnR2ZaP7kk08SEBDA6Ogoer2ekZERIiMjiYuLQ6fTyePQ6/X09PTI8gC3f/7MzAxxcXGKicLLy8uyOKY7SCR8pRP77OysYqNek8mEWq1WxGvx8/PDYDCQnJyseBOkUqlYW1tT1HkoQaPRsLGxgUqlIiQkhLW1Ndn6SK1W09DQcOp7BUGQ/Q1jYmIYGBjA4XBQXV3tsUxAaGgoTqeTqakpr/rfCYJAdHQ0KpWK/v5+4uPjzyUrI5VzdTodY2NjbGxsuKwQ9PX1kZWV9UB0AgVBIDQ0lLS0NGw2GwMDA1gsFiIiIs69orG9vY3ZbD6T9IY3IQgCISEhJCUlkZiYyN7eHmNjY/zFX/zFwcLCwn/7whe+4N6j6oLgwgUorrC9vf3sN77xDffOqueI3d1d+vv70ev1aLVaCgsLKSws/C8RUDkcDsbHx+ns7CQtLY3a2lq3XVuSxs3w8PDZ7Q5cQCKPOxwOLl++7DVz35MgdRiWlZV5TfRvd3eXsbGxEzsAw8LCaGxsZHNzk+7ubo6OTpZrkeQkSktLEQSBoKAgLl26xKc+9Sk+9alPcenSJfz8/HA6nbzxxht84xvfICQkhD/4gz+QuVmtra185Stf4Tvf+Q79/f2y7UlCQgKvvvoqzz77rMxZEUWRtbU1j7J129vbijNWR0dHHhGPlfoFgnLPQAkxMTEuvf3uhk6nc8sPOQkFBQWMjY3hdDrvyAIq+V6BgYEyEV6n01FWVnbmxTg9PZ3w8HBZe8ibSEpKoqSkhPb2ds6z0qDRaKitrSUqKorm5uYTVdGljN/dJdXzhiAIJCcnc/XqVUJDQ2ltbWV0dPTcMn3wyzL8RYJkCJ2UlMTOzs6UKIqnGvMKgvAtQRDWBEEYuu21FwVB6Hvr36wgCH3nM/JjPFSBlSiK86urq0Zfe1UpGAdra2u0t7czODhIQkIC165do7y8XNYjepghiqJslqxWqz02S9ZqtWRmZp5ox3E/2NraorW1lfT0dMVcn7Nid3eXzs5OysvLvUaGt9lsdHd3U1lZeWog4efnR2VlJYmJiadaZUxMTJCSknJikJuQkMDjjz/OH//xH/O2t70NlUrF0dERP/3pT/nyl7+MwWDgfe97H7/7u7/LtWvXsFgsvPrqq3z5y1/mZz/7GSUlJTzxxBPs7OzwzW9+k9dee4319XVCQkI84q950nXmKXFdqV8geB5YeVoOVKvVxMXFeUyc1mq1xMXF3eM76C6wcjqdjIyMsLq6SlJSkldKSLm5uahUKsbGxu77XHcjKiqK2tpa+vr6zkQuPyskX8e6ujqmpqbuaNKw2WyMjY151BHra6hUKlJTU7l69SparZbm5mbGx8d9HmDt7++zv7+vWMbjvPHd7353f3Nz8x/cHPYd4PHbXxBF8YOiKJaLolgO/BB4yUdDPBEPVWAFYDKZvv7cc895yV7bMzgcDubm5rh16xZLS0sUFhZSX19PfHy8nIYWBEFxZ9FFxN7eHh0dHSwvL1NXV3dms+TU1FT29vY82v2fBolHMjw8TG1trc93mWazGb1eT1VVlddETkVRpLu7m9zcXEWlh8TERHlRkOxDpLGtr6/fIyR6N9RqNVeuXOGzn/2svBs9OjpifHyc559/njfeeIO0tDQ+/elP89u//ds0NDSwtbXFK6+8wk9+8hMSEhJIT0+nvb2d73znO3L5UklWw2q1EhgYqLi05GlgpdQvEDwPrKKjoz2+Z9PS0s7UDZuTk4PBYLgjmHJ1fU0mE83Nzfj7+1NfX09paSnLy8v3nQ0SBIGSkhIsFotieQpPEBwcTH19PdPT07J7wHlBq9VSV1dHeHg4zc3NbG5uMjw8THZ29oWT7oHjACs9PZ2rV6/i7+9Pc3MzU1NTPsn+A7L5+kXUTxRFke9+97v7BwcH/+bmuFvAiS7qwvEX+7+AF3wwxFPhda9AX0MQhPC0tLQpg8EQc143w8HBAQaDQW5HTU9PP/Wh3NzclMUeHybY7XbGx8fZ3NykuLjYKzsYi8VCV1fXqR5pSmCz2ejr6yMgIIDi4mKf8w9MJhM9PT1UV1d7RXhQwvDwMGq12mNfSVEUmZ+fx2AwUFBQwOTkpMeq9pLn3GuvvXbP38LCwqisrKS8vFw2Cx4aGmJkZASz2YxarUYQBHliDwoKklXds7OzT8yarayssLOz49b3T8LCwgJHR0cn2r2chM7OToqLixXb8Ny4cYPr168rOhagtbWViooKj8r5ra2tlJaWeqxRJHXYdnd3A8dBzp//+Z3NTw6Hg4mJCTY2NigrK7sjMN/b20Ov19PY2HjfHbFOp5OOjg6Sk5MVuQmc5fy9vb3ys3zei7nEl7TZbDzyyCPn1rl8P7Db7czOzrKwsEBaWhppaWlemwOPjo5obW3l2rVrFzKwam5u5oMf/OCrS0tLT7g7VhCEdODHoigW3/X6VeAroiie64J88e+suyCKoikpKenWjRs3nnrkkUd8+lkmk4np6Wl2d3fJyMjg6tWrbm/qqKgoRkdHvSrA50vcbpackZFBYWGh1x6y4OBg0tLSGBkZOVPa3Ww209PTQ3Z2ts/boeGYF9Tf309NTY1XRfwWFxexWCzU1NR4/F5BEEhLSyMuLo729nZEUfSYvycIAlVVVWxubhIaGsr09DQLCwuIoojZbJbFQ+Pi4qiurubRRx/lscceY2Fhge7ubrmkm5SURHBwsFxakV7Lzs4mOztb7nzd3t72KDA/SynQk042tVqNzWZTTKCOi4tjbW3NIzJveno6c3NzFBUVKX6P9L7W1lb5/9+90d3c3GRoaIjk5GQaGxvveTZDQkLIyMhgaGiI8vJyjz77bqhUKmpqamhrayMgIMArsiJ3n7+yslLmbVZVVZ1rcOPn54coiqSlpdHc3ExpaemFLYFJ8PPzk7t/DQYDt27dkg3l75cKMTMzQ0ZGxoUMqgC+9KUvbS0vL//1fZ7m1znnbBU8hBkrAEEQah5//PGf/vSnP/W6jbjEL5qZmcHPz4/MzExiYmI8uvlWVlZYW1ujrKzM28PzKkwmE0NDQ4SEhFBQUOATI1UpW5KZmekRT0vK0lRWVno1c3Qatra2GBgYUETQ9wQ7Ozv09/dTX19/X51RBwcHtLa2kpOTw9TUFBkZGYo74wCmpqYQBEHOCjmdTjo7O2lqarpHLkDq6ioqKkKj0ZCQkMD09DQdHR3Y7XZKS0vJz8/HaDQyNTUlazBJqu4Ab3/72xV7pA0PDxMbG6v4/ujt7SUzM1Px+Xt7e8nIyFCc5TOZTExOTnqUdXY6ndy8efOezZfkymA2mzGbzezu7t7xv2azGZPJdEdA9fnPf56joyOGh4ex2WxuxYRFUUSv18vdVPeLo6Mj2traKCkp8VngsbCwgMFgoLa21qcNKLdDr9ej0+lISkrCYrHQ19dHREQE+fn5D422oM1mY3p6mpWVFbKysjzqYL0ddrudpqYmRcmCB4HNzU2KiopmjUZjphK19ZMyVoIg+AFLQJUoios+G+wJeOgyVm9B39fXt2M0GqO9tauy2+2y3UxUVBRlZWVnzlokJCQwPj7uEzVpb+Do6IixsTF2d3cpLi72qUmoIAiUl5fT1tZGRESE2+DN4XAwMDCA0+mkoaHhXHa0GxsbDA0NcenSJa92cx4eHtLX10dNTc19t5sPDg5SVFREfHw8Op2OiYkJmpubKSoqcrv4ORwOFhYW7jCYValU1NXVUVNTQ1dXFzdu3ODo6IjY2Fj29/fZ2Njg5s2bwHGHTmJiotyxODQ0xODgIFVVVXzwgx9EpVIxPT3N1NQUU1NT7O/vMzY2Rl5eHqWlpWRlZbmcvA8PDz1aXD3NWEk8K6WBVVhYGLu7u4rsbURRZH19HYPBwODgIG1tbcBxoCWK4qlcMMk7EI4zardzaEZHR1lfXyc/P18Rn1AQBMrKymhpaSEyMvK+7+GAgABqa2tpb2+nqqrKJ3IEKSkpaLVa2trafPYZt2NxcVHWG4Rf8r5mZmbk7JWvHBu8CX9/f/Lz88nMzGRycpJbt26RnZ1NUlKSR5v/ubk5kpOTL2RQBfDss88eWK3Wr96nhc3bgbHzDqrgIc1YAYSEhPzOH/3RH33p85///H3NIvv7+xgMBtbW1khOTiYtLc0rmZv5+XksFotinsl5QBRF5ubmMBgM5OTkePww3g9WVlZYXFykurr61M/c29ujp6eH1NRUj7Ix94O1tTVGR0e5dOmSV3fOkl1DTk7OfYuKrq6uytfuduzu7srcrcLCwlOzGrOzsxweHroUQrRYLLz55puyjpVEZh8bG7sno+Xn54dGo8FisaBSqaitraWxsZGgoCB2d3dpa2vDZrMxPDyM1WolKCiI4uJiSktLT9RLamtro7KyUvEmZHBwEJ1Op1hXbGVlBZPJ5BG/rbu7m6ysrHuCMVEU2djYYHZ2FoPBgMFg8Lq44+OPP051dbXHi97Gxgbj4+PU19d75dnZ3d1Fr9dz6dIlr2Zx7/6M7u5uCgsLfab6LXGrGhoaTtzg7O3t0dfXR3R0NLm5uRc22DgJBwcHTE1Nsbm5SU5Ozj36cydByq42NjZeCNX3uyGKIhkZGRtzc3PZoii67QQTBOEF4DoQAxiB/ymK4rOCIHwHaBdF8WmfDvikMT2sgZUgCKHJyckzc3NzMWdJhW5tbTE9Pc3BwQGZmZkuRS/PAunmbWho8EmJzVNIZslRUVHk5uY+kAeqt7eX6OhoUlNT7/nb8vIyExMTlJeXe60Tzx2MRiPj4+NcunTJ65nF/v5+QkJCFBOyT4OUsnel2bW+vs7o6ChRUVHk5OTc8V08vQ9XV1d57bXXmJ2dlRWRS0pK2NnZYWxsjJmZmRO7XiUuWG5uLpGRkeTn5+NwOGQ+1vj4OA6Hg+joaEpLSykpKZEzBFIJTWkwMDIyQkxMjOKF2Gw2Mzk5SVVVlaLj4bhUZbVaycnJYXNzk9nZWTmYkgJNQRDu4UQJguAyo6ZWq2XLDo1GI/9W+/v7mM1m9vf3+djHPnZmscbR0VHUajW5ublnev/dkHiHly9f9ln2/fDwkK6uLpKTk72u/i2KIq2treTn57vUohNFkenpaZaWligrKzu3OchbsFqtTExMYDKZyM3NlTvVT8Lc3BwWi4XCwsJzHqUyvPHGG3z0ox99aWlp6Vcf9FjOioc2sAJITEx8/plnnvn197znPYpmZKfTycrKCjMzM2g0GrKysnxKXpydncVqtT7QrNXBwQEjIyMcHh5SXFx8Lnyl02C322lubqayslJO/UsGyvv7+1RWVp5bwLeyssLk5CR1dXVeD3xnZ2fZ3Ny8Q1n9rBgaGpIVml1BFEUWFxeZnp4mLi6O7OxsAgICWFhYwGw2e0SqFkWR0dFRfvzjH2O1HiubxMTEkJ2dTU5ODjExMSwuLjIxMcHMzAy7u7v3nEOn05Gbm0taWhrJyck4HA5GRkYYGBhgbm4OOJbkKCkpYXt7m3e84x2Kxzc2NkZ4eDg6nU7R8Q6Hg+bmZq5du+b2e+/s7LC+vs7y8jKjo6Ps7e3JgZRKpUIURZeSCIIgyHIQnoqGSvjwhz9Mdnb2md7rdDppaWlRVCJWirW1NcbHx6mrq/PZ8+lwOOjp6SEoKMirDTRjY2OIoqh4Dt7d3aWvr4/Y2FhZ3+thwv7+PuPj4+zt7ZGXl0dsbOwd1/KibfhPwmOPPbb585///HFRFPUPeixnxUMdWAmCUHj58uWbra2tLmsCR0dHzM3NyUa3GRkZPktt344HeRNLZsmLi4vk5eWRkJBwIbo/zGYzvb29NDQ0yIKZCQkJ56qlsrS0hMFg4NKlS/8fe+8d3Fh6XwueD4EgmAnmTDDnnMMEl1cr11PJO2urvNq1rSnLliWrRk/zNGNLstaSpee32udZuezaqVJZweNnW7K9siXtPo0sS/I0uxnBnMCcSRAAQSITIML99g/2hdndDADudwH0TJ8q1vSQwHc/gsC95/5+53cO8wvF6ekptFot+vr6BLcUzGZzYK1gXxuO43BwcIDt7W3k5uZCr9eH5VBvMplwcHCA4uJibGxsYHNzE7u7u/D7/QFX5MrKSlRUVIAQgt3dXczPz2NnZ+da4lFUVITi4mKUlpYiJSUFa2trWFhYgMlkAoCAdowXYOfk5Fz7+vFh0CkpKddWPm/CO++8gxdeeAGEEPj9fuj1ehwcHMBgMOD09BQWiwUOhyMk93FCCIqKitDW1ob09HSkpqYiKSkJ9+/fx+DgYMCmwuv1PvLf09NT7O3tgeO4wO/JEziz2Yz+/n5B8SK8zclNra9wcHh4GIiQEqtVxhN6p9OJtrY2wccxGo3Y3NxEb29vSOcWPuZHr9cHbEieNjgcDqytrcHtdqO6ujrQNt/d3YXb7Q7Z9iVSePgeWz0+Po4dDU0YeKqJFQDk5+fPDg0NtVRWVj7xM4fDge3tbZydnaG4uBjFxcUR9y7Z39+Hw+GIaNn15OQEWq0Wubm5qKioiDnNwN7eHo6OjnBxcYGmpiZmcTHB4ODgAPv7++jq6mJOqviw5p6eHsECYo7jMDw8jNbW1rCqjBzHYWlpCUdHRwFLhFBuJhYWFpCbm/tIu83j8WB3dxcbGxvY2NgItAT5Cll5eTl2dnZQXFyM4eHhQCg58KRben5+fsA9ns/JOzk5wcXFBYBL0qJUKhEXFxeoFF1cXASqR1KpNEDWCgsLERcXB7/fD5fLFXCTttvtsFgscDqdMJvN4DgOPp8vZPJ0VVwuk8ng8/nQ3NyM973vfde+pmtra4GAbR58WsPm5ibi4uJQUVFxrVja4/FgZGRE8LTWwcEBTk5O0NbWFvYaj4M/l7a3t4t6E7S7u4vDw0N0dnaG3X50u90YGxsTFHtls9kwNzcXOI8+bdUr4PJ3WFtbg8/nQ2VlJRYXF2NWWwUAr732muPNN9/8pMvl+m/R3osQPPXESi6Xv/TRj370r77+9a+nAv8uLt3a2oLf70d5efmt/WaxwXEc7t+/L6pGgcf5+TmWlpZACEF9fX1EqnKhglKK1dVV7O3tobKyUrAGKRTwhK6rq4s5wfb5fBgdHWVmrsq7LYd7Z0kpDbRdbTYbNjc3oVQqoVaroVKpbv08UEoDFZ6bLib854yvZvEVGIlEgpycnAAps1qtWFtbg9n87xGfvM7I5XLd2i4LJbomVMhkMigUCiQlJSE1NRUpKSmQSqVwOp2wWCwwGo2BrMbi4mJ4PB7o9XpkZWXhAx/4wK3VMrfbHTDG9fl8ODo6CmjWKioq7pw25onmdTeLoWB6eho5OTlMPeD4HLvGxkZRz6lGoxFarRbt7e0h31jwgyNVVVUhhWPftNbGxgaMRiNaWlqiKqUQAqvViunpaXAch46OjpjUkLndbqjVaoNery+mlF4flPqU4KknVoQQWU5Ozv7a2lqexWLB7u4ukpOTUV5eLqqNQCgIR+cSCniRsF6vR11dneCTiVi4uLjA9PQ0VCoVysvLMTo6ipaWloj8nXjnfDFaGZRSzMzMIDMzk0lCPAvH+pOTExwcHAQqFpRSmM1m7OzswOl0oqSkBAUFBdcSTF5jFIoP28XFBcbHx2E0GuFyuWAwGB6ZJkxOToZUKoXdbn9E2C2RSKBUKhEfHw+/3w+/3x9om/n9fubBwMDlyHp8fHwgqNrn88Htdgf2FRcXh/T0dKhUKthsNhiNRlBK8cILL6Cnpyeov8no6CgUCgVsNtudaQ2Pw+/34/79++jr6xN0M+b1ejEyMsLUm41SioWFBcTHx986ZcoCvEFwQ0ND0BOgwOVwg1QqZbo/3o+uoKAgZiNgbgP/nmpoaMDm5mYgBSKW2pxvvfWW7/XXX/+Lk5OTz0R7L0Lx1BMrAMjMzPzj3/zN3/zfP/axj5HS0tKIGc4FC0ophoaG0NPTw3RvvJnp2toaioqKoFarY7ZcfXp6ioWFBdTX1wfaS/yotdjVvK2tLZhMprBG2IPBxsYGXC4XmpqaBK9FKcX4+HhAJB4uRkdH0djYeO0dttvtxt7eHnQ6HdLT01FcXIz09PTAxWJ+fh75+fkhE/SZmRmUlZUhLS0NlFI4HA7o9Xro9XoYDIaAnincc85V7yeJRIKEhAQoFAooFArEx8dDIpHA4XDAarUGBPVyuRxZWVmPXEDOzs5gtVoDbUeJRIK4uLjAe8Pn88Hj8QT2WV5ejg984AN33uX7fD4cHx9jf38/4PTe29sb1mfy4OAAFotFcFDw2dlZQKfH6tzAG5JmZmbemVkpFHz1r7S0NKiYnaOjIxweHqKrq4s5+eE4LhD71dLSwjSdQWxsbW2B47hAFfT09BSrq6tQKBSoqamJid+lrq7OtLKy0kwp1UV7L0IRdWJFCIkHcB+AApeGpd+jlH6REPIAAH9VyAagoZT+TzeskVlaWrqytbUVlvVCJHB0dITT01MmF1/gkpQsLS1BoVCgrq4u5sgkD36MWa/Xo729/QntkcFgwNbWFnp6ekQhhRsbG7BYLGhvbxdlfd59PNwL6OM4PDyEyWQSFE9ydnaGzc1NdHV13fo4vp23v78Pu92O/Px85OXlYXJyEi+++GLIF6Z79+7hueeeu/V18Hq9MBqN0Ov12Nvbw+bmJjiOQ2pqKlJTU5GZmYmMjAwkJiYiISEBSqUy8MUTH51OB7vdfmtFwuVyYWNjA2tra9jc3Ay09YDLilRpaSnUajXUajWys7Of+F0ppYGMPj6a6TpwHAeTyYSjoyNYLBbk5eWhuLgYSqUyMLgSjp6FUooHDx6gra1N8EVvbW0NlFKmgmW/34+JiYlA5VNM+Hw+TE9PIzU1FdXV1Te+L61Wa2AwRkwNkdlsxsLCAgoLC1FWVhbz1SvesmVwcPCJCvXJyQnW1taQkJCA6urqqMWwjYyM4EMf+tC/6nS6/zEqG2CMWCBWBEAipdRBCJEDGAbwHyml41ce808AfkgpvVHQlpub+1dvvvnmb/7Kr/xKTDIrXvPS2toq6ETp9Xqxvr7ONCxZLHi93sAIdX19/Y0X3PX1dVxcXAi+O78KSinW19fhcDjQ2toqCqniA3BZVdx44bLQKdKJiQlUV1eHpKPwer04Pj7Gzs4OXC4XKioqkJeXF/SJ1uv1Ynx8/BF392Cg1WqRkZERUi6dwWDA2dlZ0CP0Pp8POzs7mJiYwAsvvBDINAwGNpsN6+vrj5izchyHs7Mz6HQ6nJ6eQqVSobCw8Ant2sbGBmQyWdhVHZPJhJ2dnbAyJq8iWC+nUOH1ejE2NoaamhrRzD15UEqxtLQEj8eDlpaWJyrPFxcXARf3SOig/H4/VldXYbFY0NLSEtO5sHxl6qb3IT9Ysba2htTUVFRVVTFNoAgGg4ODpuHh4f+BUjoX0QOLhKiTEHoJx8P/lT/8CrA9QkgygF8A8IPb1jEYDF/8whe+cBZtongTCCGora0NBNqGCkopDg4OMDw8jMTERAwODsY0qbJYLBgZGUFRUREaGxtvvZBVVlbC7XZjf3+fybEfH9sWg1R5vV5MTU2htbWVWRtTq9WisrJSEKmy2WzgOC5kcapcLkdxcTFSU1PR0tICmUyGxcVF3Lt3D1qtFmdnZ7cKyS0WS1iC2HBin0IVtctkMlRWVqKwsDAkUgVcasPsdjsuLi5wdHSE6elpDA0N4ejoCLm5uXj++efR3NyMjIyMJyoXxcXF2N/fD7v1mZmZGSBxQkAIQWtrKxYWFuD1egWtdRVyuRzd3d3QarWPDCeIAUJIwFR2fHz8kQokx3GYnp5GbW1txMTlUqkU9fX1qK2txeTk5I02I9GGy+WCXq+/VftJCEFOTg4GBweRnZ0NjUaDhYUF5okCN2F+fh6bm5tb7xZSBcQAsQIAQoiUEDIHwAjgp5TSiSs/fgnAzymlttvWoJTuWyyWkZ/+9Kex9+5+iMzMTFBKcXp6GtLzrFYrRkdHAx43paWlMVt+ppRid3cXCwsL6OjoCCoUlj/x88aaQo/Ph9e2traK8jrxYvXKykpmwnuTyQS32y24rbKxsRH2NBnHcTCbzcjJyUFpaSl6enowMDCA9PR07O3tYWhoCBMTE9jZ2Qlk6fGwWCxhZa2FS6xCyQrkkZCQEDA8vQt+vx8mkwmrq6uBAGy73Y7y8nK88MILaG5uRnZ29q0kjZ86FEI6+JsxoRfthIQEVFVVYX5+nikBUCgU6Orqwtzc3LVGsaxRVlYWGHxxOp2BSlZmZmZIVU9WUKlUGBwchNPpxNjY2BPxT9HG6uoqampqgrqZIIQgLy8Pzz33HDIyMjA+Po7l5eWAFlEs/OEf/uGZXq9/XdSDRBgxEcJMKfUDaCGEpAH4PiGkgVK69PDHHwbwzWDW0ev1n/385z/f/773vS981a/IqK+vx+zsLAYGBu686Hs8noD7s9hhySzg8/kwPz8PiUSC/v7+kITiMpkMnZ2dGB8fR2dnZ1jtUkopFhcXQQhBU1OTaORzdXUVKSkpzLQlfr8fS0tLggW3TqcTLpcr7HbPyckJMjMzH9mDTCZDXl4e8vLyQCmF0+kM5Cs6nU4kJCRApVLBYDCE1cqNRMWKBx/G/HjbhlIKt9sNi8WCs7OzQHUuLS0N2dnZqK2thcfjCSsmprS0FLu7u2FXl1NSUpCUlITj4+OgblJuQ0FBAYxGY8D4lRUSEhLQ3t6O6elp5kHm1yE3Nxfx8fHQaDTIyMiA3+8XbE0hBFK0trCUAAAgAElEQVSpFA0NDTg9PYVGo4FarUZxcXHUb36tVivOz89DJpyEkIBR7+HhIcbGxpCTk4Py8nLmRtebm5uYmpo6ppQ+YLpwlBETxIoHpdRCCLkH4P0AlgghGQC6cFm1Cub5q/n5+UsTExMvdHd3i7jT8MH75uh0uhsvzI+HJYtJEljBbrdjZmYGpaWlYVsOKJVKtLW1haVbopRifn4ecrmcaSTG4zg6OoLNZrtTGB4KNjY2UFhYKHgkfnNzM+CEHg6Ojo5uzWojhCApKQlJSUkoKysDpRTn5+c4PT2FzWbD/Pw8OI5DcnIyUlNTkZycjKSkJCQkJNy4J0ppyK1aPjImVCQlJcFms0GhUMDhcMBms8FqtcLtdiM+Ph5paWnIzMx8Ikvz4uICU1NTYRErlUqFxcVFeDyesC9KNTU1GB8fR25uruC2dmNjI4aHh6FSqZhOgqWkpKCxsREajQa9vb2iJ02kpaVBrVZjeXk5Zs6PGRkZGBgYgFarxfj4OFpaWiKuVboKrVaL+vr6sF8bPlmgoKAABwcHGBkZQX5+PsrKypgNB3zpS1+ynJyc/AGTxWIIUSdWhJAsAN6HpEoJ4BcB/J8Pf/whAP+dUhp0s/f4+Pj1z33uc//yb//2b5Gz8w4R1dXVGBsbQ25u7hNVnbOzs0Bp+7opjljE4eEhNjc30draKriqlpqairq6OkxOTqK3tzeoqhfHcZidnUVCQgJqampEO8larVZsbGygv7+f2THsdjuMRiMGBgYEreNyuWCxWMKeOvX7/bBarSG18wghSExMhEQiQVpaGvr6+sBxXMDygI9u4dsj/HQfP+2nUCjg9/vh9Xohk8mCfk1vagVyHAePx4OLiwtcXFzA7Xbj/Pw84Mbucrng8/ngcDiQlJQElUoFtVp958Xv6j5DvaDwF6eDg4OwzXDj4+ORm5uL3d1dlJWVhbUGD5lMhubm5sD0HEv9YUZGBqqrq6HRaNDT0yPquevs7Ax7e3t48cUXMT8/HwjMjjbBkslkaGpqwsnJCcbHx1FeXo6ioqKI78toNCIuLo6JEahEIglkfu7v72N4eBiFhYVQq9WC/sY6nQ4/+9nPzBzHvS14kzGGWJgKbALw1wCkuNR8/SOl9MsPf3YPwFcppf8Sypp5eXlTP/vZz9rFMuRkgcfdld1ud0Ab1NDQEBO+InfB7/cHevAtLS1MR5z39vZgNBrR0dFx60mJ4zjMzMwgOTlZVMNCfuqoo6OD2d+Gn9aqr68XfAJcWlpCenp62O1JvV4Pk8mEhoaGkJ97fHwMi8Vy65Qe327j42Z4onN8fIyUlJQnRNVSqTTgW0UIeSQA2efzwW63IzEx8ZHKFSEEcXFxj3hbJSQkBIgc77/U398f8u+4vLyMjIwM5Obmhvxcj8eD0dFRPP/882FfYPmReVZxJBsbG/B6vaJEbR0cHAQSDsSaxp2cnAxER3Ech8XFRVBK0dTUFDNefl6vN3B+bG5ujpglDsdxePDgATo7O0VJ3/D7/djd3cX+/n4g/zMcf8BXXnnF9q1vfeuT5+fnf8t8k1FG1ImVGCCEvPjSSy9975//+Z9jdmzO7/fjwYMH6Orqgk6nw+HhIWpqaqIavxMKzs/PMT09HSgNi7HnlZUVeDyeG0v9HMdhamoKKpUKFRUVzI9/9ThjY2OoqKhgKpDd3d0N6OeEgMWFe3p6GmVlZWEJ0LVaLVQqVcikw+FwYGVl5Qk7Ad5DiuM4UEoD/yWEgBACr9eLhYWFoF3Qr+Kdd97Biy++GNJzgEv92fHxcdgVwdnZWRQVFQkyfd3b24PT6WRChiilgdgXIXu6CVtbW7BYLGhra2N6bnC5XJiYmHiiOs775Z2cnKCjoyOmsvCMRiOWl5dRWVmJgoIC0c/vW1tb8Hq9ogct+3w+bG9vByQEJSUlQZNas9mM2traQ4PBoKaU+kTdaBQQG9SePe6NjY0Zd3d3o72PGyGVSpGXl4d79+7B7/djcHAQubm5TwWp0uv10Gg0qK+vFzXegT8xrK6uPvEzv98PjUaDzMxMUUkVcFkNys7OZkqq3G43dnZ2mJz8tre3oVarw/47+P1+2Gy2sKtmrK0WCCGQyWSB6tPV9mF8fDzi4+NBKQ3rLvlqoHIoUKlUgmwPeBG7EBQXF+Pk5CToycbbwE/i8vov1igvL4dSqcTS0hKzKUSPxwONRoPGxsYnJAeEEFRUVKCkpASjo6MxNZ2XnZ2NgYEBnJycYHJyUtQpO7fbjYODg4iI+WUyGaqqqjAwMICLiwsMDQ0FMkPvwte+9jWn0+n86ruRVAHvUmJFKaUmk+lzX/7yly3R3st1OD8/h0ajgc1mC2SSiRG1whocx0Gr1WJ3dxd9fX2i+2jx033n5+fY3NwMfN/n80Gj0SA3N1ew5uQu7O3twePxMCdvS0tLqK2tFaxD4Y09g4n7uAlGo/Fa9/FgQCnFxcVFWG2OcCYCgfDF68ClgN3pdIZ1TIVCEfYFOy0tDefn54IuqoQQ1NTUYGVlJew1rkKpVKK6uhpzc3OieDDV1tbC5/MFZA9C4PP5MDExcafJaX5+PhobGzExMQGLJXZO/3K5HK2trSguLsbo6Ch0OnFSW5aXl1FTUxPR64lcLkdNTQ36+/vhdDpx//59HBwc3Piecjqd+OY3v2l3OBxBTfs/jXhXEisA8Pl8/+/bb79tMRgM0d5KALxb7+TkJNRqNTo7O9Hc3AytVhv2hSJScLvdGBsbg1QqRXd3t+hTPzz4O2teCM2fYAsKCm6dYGMBXiDb0tLCtCqn1+tBKQ1Lr/M49vb2UFxcLEhXcnR0FLY2y+l0hq05C5eQEUIEESuHw3H3A69BdnY2Tk5OwnouISRgGCoE2dnZAWsIFsjPz0dcXBwzc96rIISgubkZZrMZe3t7Ya/DV6fVanVQVWOVShXw1jo+Pg77uGIgNzcX/f39OD4+xtTUFNNqoclkgtfrZXJeCQdxcXGoq6tDb28vrFYrhoaGoNPpniBYX//6190XFxdvUkrFNciKIt61xIpSytnt9i9+5StfudVYNEJ7gU6nw4MHDyCXyzE4OBgIuE1ISEBubi62t7ejvMubYTKZAnqM27K6xIJEIkFHRwcODg4wNDSEkpISpj4818HlcmF+fh4dHR1Mp5t8Ph9WVlaYxPf4/X4cHByEbW/B78fhcIQ9zWk2m8NuIV5cXIRF0IW8/4QQq6ysLBiNxrCPXVBQgMPDQ0HVIUII6urqmJiG8mhoaAiYvrIG/9k9PDwMq0rD6yjz8vJQWFgY9PMSExPR19eH7e1tbG1thXxcMREXF4f29nYUFBRgZGSECfnjOA7Ly8tMY8HChUKhQENDA7q7u2EymXD//n0cHx+DUgqXy4Wvfe1rNrPZ/OfR3qeYeNcSKwA4Pz//u+9973umg4ODqO3BbrdjfHwcBoMBvb29KC8vf6K6UFFRgcPDQybaCZbg8/bW1tbQ09MTIIPRAC9m5gXMYh9ramoKTU1NzKdqVldXoVarmUwI7e/vIz8/XxDxE9IGBC6JVTiCdyD8VqAQJCYmhk2skpOT4XA4wiY0crkcKpUq7KoXj7S0NCgUCkEk7ypkMhlaWlowOzsblqP9XZBKpejq6sLm5iZMJlPQz+NJVWZmZlh5i3FxcYHqycLCQsxFzuTl5aGvry8QlSSkerWzs4OcnJyYyixUKpVoampCZ2cnDAYDhoeH8Z//8392u1yuP6eUim/TH0W8q4kVpdR/enr6H19//XVxg6yuAT9qOzc3h+rq6lsz5aRSKWpra7G0tHTtz6MBj8eDiYkJeL1e9Pb2RtXo7uLiAuPj46iqqsLg4CB2dnZwdHQkyrEopZibm0NRURHTwFrgUuRtsVgEVZh4cBzHxNdISBsQuPydwq12hdsKFIJwNVbAZbUoNTVVUBuOhYgduNQvra6uMiMLaWlpyM/Pv3ZQhAXkcjm6urqwtLQU1OvHk6qMjIyw/b+Ay4oZf+7VaDRhDS6ICYVCgY6ODuTm5mJkZAThSFdcLhf29/ej6j5/GxISEtDS0gK1Wo233nrLaTabvxbtPYmNdzWxAgCfz/eje/fuHYYbfhwqroYlJyUlYWBgICiRN2+zEAuaALPZjNHRUZSUlKC+vj6qvjButxvj4+OoqalBbm5uIPhVLHK1vb0NmUzGXL/FcRwWFhbQ3NzMpOJ2dHSEnJwcQWPlPp8PTqcTKSkpYT2f4zhwHBf2HqJRsZJKpfD7/WETkuzsbEGVotTUVFxcXAiuTickJCAzM5OpNqq8vBw2m41ZJexxxMfHo7OzE7Ozs7dWDVmRKh6EEFRXVyM/Px+jo6Mx1xkALtvEvb292Nvbw+zsbNBh2ZRSLCwsoL6+PuYHoN544w27zWb7XCiG308r3vXEilJKDQbDx1955RVhEfFBwGKxYGRkBBaLBQMDAygpKQnpItrY2IjV1VWmCfShgFKK7e1tLC4uorOzE3l5eVHZBw+Xy4Xx8XHU1dUhOzs78H2xyJXRaIRerxdFp7C9vY3s7GwkJycLXov37BFarTIYDIJ806xWqyCnfb/fH5WLgUKhCLvtkpWVJbiVV1JSwoQQVVVVYXt7m1kVhh8UETN4NzExMRBb5XY/eX1lTaquoqioCPX19ZiYmIDVamW6NgvwxDMrKwvDw8NBEVydTge5XP7I+TEWcXR0hL//+78/dTgc377tcYSQbxNCjISQpSvfayGEjBNC5gghU4QQdnliIuFdT6wAgFI6qtVql8fGxkRZ3+PxYH5+PpBb1djYGNZdvEKhQEVFBZaXl0XY5e3wer2Ynp6G3W5Hf39/1Hv15+fnmJiYQGNj47XaLp5c7e7uCpo44uF0OrG8vIz29nbmFbrz83McHh4yK9Xr9XqoVCrBbTQWbUAWkRmRhhCdlUKhAMdxgm5+CgoKoNPpBE8Cy+VyFBcXMxVnx8fHo7a2VjQLBuCyatfQ0BCQGvDgJ34zMzOZkyoeGRkZ6OjowOzsrGiVOSEghKCwsBC9vb3Y2dnB/Pz8je81j8eD9fV1wQbDkcDv//7vW8xm83+ilN4l4nsLl1nBV/FfAfwxpbQFwB89/P+YxnuCWAGAXq//xCc/+clTlicLSil2dnYwMjKCzMxM9PX1hd1W4VFYWAi32y34rjgU2Gw2jIyMICcnB83NzVEvKTudTmg0GjQ1Nd2qc5LL5ejp6YFer3/E5ypUeL1eTE1NobW1lbnmhy/VNzQ0MHldKaWBsGUh8Hq9OD8/F/R+FSJc9/l8gl6PaFkuAEBmZiZOT0/Dfr5UKkVmZmZYeprHoVarcXx8fG31J1zk5uZCqVQy0YLdBD7sWqPRBHIYx8fHUVhYKLo3XVJSEnp7e7G+vi7q7ygE8fHx6OrqQnp6OkZGRq69HiwuLqK6ujpi1jfhYm1tDT//+c+PPB7PD+56LKX0PoDHu0sUAH+iSgUgjgkYQ7xniBWldPn4+PjB22+/zcQw6vT0FA8ePIDL5cLg4CCzqALe+2V5eTkiQsuDgwPMzs6ira1NkMkkKzgcDmg0GrS0tASlTZNKpejs7ITVag1rBJ1SitnZWZSXl4tSfdHpdFAoFMxiQ05OTpCYmCh4WlGv1wtu9dpstrCJmVB9VTRMQnkItV0ALkXsLCqtEokEVVVVWFtbE7zWVdTX12N/fx82m3huNbyFwsTEBEZHRwOBxZGAQqFAb28vTCYTlpeXY25iEPh377Pu7m5sbm5iYWEhcE0wGAzw+/3Iz8+P8i7vxqc+9akzg8HwCRr+i/xpAH9KCDkA8AaAz7HbnTh4zxArANDr9Z9+9dVXz4SU4N1uN6anp7GxsYG2tjbU1dUxT3FXKpUoKSkRbUIHuNS3zM3NwWg0or+/X3CljQVsNhsmJyfR3t4eEsmRSCRoa2uDz+cLeax6bW0NiYmJIXnkBAu+VM8yDHxzc5NJS1Gn0wk6KXu93kBQcjgQSqwkEknUKlZC422AS+sGv9/PJHolLy8PdrudqQ+VVCpFa2uraBYMPLKysmC32xEXFxdxY0upVIr29nYQQjA5OSnq7ykESqUSPT09SElJwfDwMAwGA7Rabdi5lZGERqPBwsLCGqX0gYBlPgHgVUppEYBXAXyLze7Ew3uKWFFK96xW6w/+9m//NmSBBMdx2NjYwPj4OAoKCtDT0xO243QwKC0thdVqFXwCvw5OpxMjIyNITU1FW1sbc2IYDqxWK6anp9HR0REWySOEoLGxEQqFIuiTpE6ng8ViYRJqex20Wi0qKyuZlerPzs4gk8kEC+C9Xi/cbregdYTqq1gQq3AvhAkJCYIIjVQqRXx8vGBSVFJSwqQVddU0lCVSUlJQVFTEfF0eFosFExMT6OzsRHJysmjHuQ38a5eTk4PR0VGmLVWWIISgtLQ04CivUChiKmj6OlBK8clPfvJUr9d/XOBSHwHwzw///f8AeCZejzUYjcbPfeELX7CGMvViNBpx//59UEoDYcligxCClpaWR8q/LHB8fIzJyUk0NTUJCu5lCYvFgpmZmcAJNlzwWWo5OTkYGxu7dbLJZrNhfX09cMfKGqenp3C5XILE4Y9jY2MDVVVVgtc5Pj4W/B62WCxh66uA6LYC+b+3kPYPi3ZgXl4eDAYDkzgrlUoFQkhIBpzBQK1Ww+l0Qq/XM11Xr9djbm4OXV1dUKlUqK+vh9vtFqSVFIKSkhJUV1djfHxcFAd6VrDb7UhLS0Nubi4ePHggyo03K/zkJz+hh4eH45TSBYFL6QA8//DfvwBAePikyHjPEStKqcnlcn3jzTffvJNZOZ1OTExMYH9/H93d3aiqqoqosDsxMRFlZWVMjEM5jsPS0hL29/fR19cXM9NcZ2dngRMsqwpgSUkJqqqqMDY2du1J0uPxYGZmBu3t7aLc9fn9fiwuLqKpqYkZabPZbOA4jsnfTafTCSZ8QqJsgOhWrIDL9ooQPyMhuYE8pFIpsrOzmXnX1dXVYWVlhaleiLdgWFlZYVbN2dnZwdbWFvr6+gLTx/xxTCaTKLmFwSA7OxttbW2Ynp6O6PBQsLi4uIBWq0VLSwvKysrQ2dmJlZUVLC8vx1wbk+M4fPrTnz7V6/WvhPI8Qsh3AYwBqCaEHBJCPgrgdwD8X4SQeQD/BcDH2O+YLd5zxAoATCbTn/zpn/7p2U2TPT6fD6urq5iamkJ5eTk6Ojqi5jxeVFQEj8cj6OTrcrkwOjoKhUKBrq6umJkiOT09xcLCArq7u5nbO/AnyampqUcmuHifnJqaGiaeUtdhY2MDhYWFTH+njY0NJtoqj8cDj8cjmMQ6nU5Bv180NVaAcJ0V/3yhJIaVEzu/p9TUVObGuQqFAvX19ZidnRX0+1JKsby8jNPTU/T09DxxHuJzBff395lXyIJFSkoKenp6sLq6GjWCdx34RIja2trA54bPQ4yPj8fw8DDM5ogHjNyIb33rWx6z2fwDSulOKM+jlH6YUppHKZVTSgsppd+ilA5TStsppc2U0m5K6bRY+2aF9ySxopQ6rVbra5/+9Kctj30/EJYcFxeHwcFBZtNc4YKfElxdXQ3rjtFoNAacyysrK2Oi9QdcTrctLS2hu7tbNNLKnySXl5cDE1harRaZmZmitXPtdjuMRiPTkXGn0wmXy8UkYuf4+FjwNKDL5YJCoRD0XopmKxAQTqwIIUhLSxN8MUtMTIREIhG0l6uorq7GxsYG8woGb24bbli81+uFRqMBIQTt7e03Vv5lMhm6urqwtrYmyNJCCOLj49Hb2wu9Xs+8Ahgu9vf3ER8f/8R5ixCC8vJytLe3Y3l5GSsrK0xay0JgNpvxxS9+8cxoNH4mqhuJIt6TxAoAXC7Xd3/6059uT05OArhstYyNjcFoNKKvrw9lZWVRjXK5CoVCgbq6upBM+yilWF1dxebmJnp7e6NOEK+Cn2rp6ekRvRKoVCrR19cHk8mEkZERnJ+fi5apxXtWNTY2Mn3v8NUqFqRY6DQgIFxfBUS/FSiUWAFs2oEAOxE7cHmuKCgowM5OSIWCoFBbW4ujo6OQXcsdDgdGR0dRWFiIurq6O9/HcXFx6OrqwuLiYtQc0mUyGTo7O+H3+zEzMxPVVpvT6cTOzs6t08VJSUno7++HXC7H8PBwVJ3lP/OZz1jtdvtnKaXieXXEOGKDOUQBD6Nufv3ll18+W1hYwPz8PGpra9HS0hLx/LJgkJOTg4SEhKC8b/jQYkopent7Ix50exv0ej3W1tbQ29sbsddZJpMFRLh8K0wM7O/vIyUlRTDpuAqXywWr1coksuLi4gI+n09wi5IFsfL5fIKmUYW2AoW4r/PIzMxkQqxyc3NxcnLC7OJdXl6Og4MD5u/zqxYMwQ7UGAwGTE1NoaWlJSRdn1KpREdHB2ZmZgR5jgkBIQQNDQ1QqVQYHx8X7bxxGziOw+zsLJqbm+/8vBBCUFFRERh6Wltbi3j1anZ2Fm+//faew+H4bxE9cIzhPUusAIBSumK1Wr//ox/9yDcwMMD0gigG6urqsLe3d6tp39nZGUZHR1FWVoba2tqYaf0Bl9WSjY2Na/UVYsLtdmN+fh4DAwMBUbvFYrn7iSEeY3t7G7W1tUzX3draQkVFBZO/I4s2ICBcuM5DyO8ktBUoJC/w6hpC422AS5KYl5cHnY6NobRUKkV5eTnW19eZrHcVycnJKC0tvTN2i1KKjY2NQMU8nEzJpKQktLa2YnJyUrTswmCgVqtRXl6O0dFRZi3bYLG+vo6srKyQrk0pKSno7+8HIQTDw8OimrxeBcdx+MhHPnJqMBh+XYAZ6LsC72liBQBHR0ev/sVf/IUplsdWechkshvvGPlg3uXlZXR3dyMnJydKu7weh4eH2N7ejjip8vv9mJqaQmNjIxISEpCdnY2Ojg7Mz89jd3eXmX5iaWkJtbW1TD3BPB4PTCYTM3dlFm1ASikuLi4EVUE5jhNMFIW2AgkhgtcALqtWLCwOSkpKmDix8ygqKsLZ2Zko1Z6SkhJcXFzcOFDj9XoxOTkJt9stuDKdlpYWCE6OVjg9cFlV5ElepLRfRqMRZ2dnYVms8I78LS0tmJubw/r6uujVq29/+9seg8HwQ0rpoqgHegrwnidWlFK73W7/zKuvvhp7cefXICUlBWq1+hGHcf5E5nQ60d/fLzjuhDX29vawt7eHnp6eiJra8Zqn/Pz8RzRmvB7BbDZjZmZGsE+YwWAApZS5IH57e5uZ15jb7Ybf7xf83nA6nYInCoXqqwDhrUDgsh0olHhkZ2czCfNVKpWQy+XMqguEENTW1mJlZYXJeo+v3dLSgtXV1ScsK8xmM0ZGRlBYWMhMa5iVlYWKioqou6OnpqYGhmFYT14+DpfLheXlZbS1tQn6/KekpGBgYAAcx2FkZEQ0jy6z2Yw/+qM/MhuNxv8kygGeMjzVxIoQEk8I0RBC5gkhy4SQP374fUII+RNCyDohZIUQ8qnb1nE6nd/913/9162pqanIbFwgiouLAVxqeqxWK0ZGRlBQUICmpqaYEdzz2N3dhU6nQ3d3d8Qd3nkBr1qtfuJnfPUvOztbULnc5/NBq9WisbFR0F4fh9frxfHxMbPstOPjYyaVLxZtQBbESmgrEGAjYE9PT2c25s7SegG4JCQ+n0+UMfy4uDg0NDQELBgopdje3sbi4iI6OzuZZ9jl5+cjLy8P09PTUZ3SUyqV6O3txcHBAdbX10XZC8dxmJmZQWNjIxN9rEQiQU1NDRobGzEzM4PNzU3m+37ttdesNpvts5TSp6JAITZi6yocOi4A/AKltBlAC4D3E0J6ALwMoAhADaW0FsDf37bIQyH7b7z88sun0R5VDRaNjY1YX1/H9PQ02tvbmTp8s8LW1hYMBgO6uroiTqpOTk6g0+nuNOksKipCe3s7Zmdnsbe3F/IJZ3V1FWq1mvmAwO7uLoqLi5kRZRZtQOCSWEV7IhAQ3goE2AjY+XgbFi237OxsnJ6eMk1a4KNuxCAAWVlZSEtLw9raGqampuBwONDf38/ck46HWq1Gamoq5ufno0qu5HI5urq64HK5MDc3x7zFtrq6iszMTOaT3GlpaRgYGIDX68XIyAgzvdjc3Bx+9KMf7Tmdzr9msuC7AE81saKX4N8d8odfFJehjV+mlHIPH3dnrZ5Sqj05OfneN77xjciPfoQIn8+HxcVFpKSkgBASU1N/PDY2NnB6eorOzs6IutUDl+2qpaUldHR0BHXs5ORk9Pf3w2KxQKPRBC2UtVgssFgsKCkpEbrlR+D3+3F4eMhsXbfbDUopE2sLi8USlhD5KmKlFZiUlMSEEGVlZTGZDiSEoKCggGmbKSUlBQkJCaIZbmZkZGBzcxNpaWloamoS/bNeVVUFiUQiakB9MJBIJGhqakJycjLGx8eZ6b8MBgOsViuT6KrrIJVKUVtbi/r6ekxNTWFra0sQSeU4Di+//PIzwfpjeKqJFQAQQqSEkDkARgA/pZROACgH8GuEkClCyI8JIUEZFxmNxte/9KUvncWykN3hcGBkZAQZGRno7u5GRUVF1O/gHsfa2hqsVis6Ojoi3pr0+XyYnp5GS0tLSIRTJpOhubkZJSUlGB0dhcFguPXxHMdhYWGBaWwNj/39fRQUFDCr8rGqVnEcB47jBOvk3k2tQICdzgq4bPOzFLEDQE1NDfPRe7/fj6WlJWxvb6O3txdHR0dMK203gQ9bdzqd2NraEv14d+2loqICpaWlGB0dFRzK7XK5oNVqBeuqgkF6ejoGBwfhdrsxOjoa9g3GW2+95dXr9c8E64/hqSdWlFI/pbQFQCGALkJIAwAFADeltAPANwB8O8i17Ha7/TOvvPIK21l8Rjg6Ogp4wvDVjKKiIsjl8qifZIBLsfjKygqcTifa29sjTqoopZidnUVpaWnY7arc3Fz09fVhd3cX8/PzN14sdnZ2kJWVhZSUFCFbfgIcx2F3d/daXVi4YEWsrFar4GoVEDutQJlMxkQMzVe+WBCX+GR1zjQAACAASURBVPh4KJVKproopVKJnJwcZoTNarVieHgYCQkJ6OnpQUZGBsrKyrC4GJlrKyEEbW1tMBqNODw8jMgxb0N+fj6ampowMTER9t+Nn15uamqKmL+fVCpFfX09amtrMTk5iZ2dnZBu0E0mE77whS+cGgyGZ4L1x/DUEyselFILgHsA3g/gEMA/PfzR9wE0BbuO0+n87s9+9rOln/zkJzEjtuKrI0dHR+jv73/i4tbY2AiDwcDsrjkcUEqh1WpxcXGB1tbWqPhnra+vQ6lUBsT94YLPVExLS8Pw8PAT4/Tn5+c4ODgQpVx/dHSEnJwcZtOTLpeLWbvYYrEw8a+KlVYgcKmXEepnxcfbsPJGKy0tZV61qqiowO7urqCWFcdxWFtbw8LCAtra2lBWVhb4nBcVFcHv94s+LcdDIpGgs7MTOzs7d1aXI4H09HR0d3djYWEhZD8yPgewqKiISWxVqFCpVBgYGIDD4cDY2FjQlbff+q3fslit1leeCdafxFNNrAghWYSQtIf/VgL4RQCrAH4A4BcePux5AEE75VFKqdFo/LXf/u3fPo1mLACP8/NzjIyMIDExEZ2dnddecPnw0uXl5ai4FFNKsbi4CL/fj+bm5qiQKr1ej7OzM9TV1TFZjxCCkpISdHV1YXNzE/Pz8/B6vQELh4aGBuZ6Et6LrLy8nNmarKpVABvhOgDBPlgAm1YgwLYdyEJnBVx6Y1ksFqa+TXK5HKWlpdjc3Azr+WazGQ8ePIBEIkF/f/8TAeZ8pun6+rrglliw4HMFV1ZWEAvyjYSEhEC1O5TJu62tLchkMpSWloq7wVsgk8nQ2NiIqqoqaDSaOwd5vve97/k0Gs2Y0+n8XgS3+dTgqSZWAPIAvEMIWQAwiUuN1X8H8FUAv0IIWQTwfwD47VAWpZTqrFbrH/ze7/1eVFuCBoMBExMTqK+vR3l5+a2ERaFQoLW1FVNTUxE10qOUYn5+HhKJBI2NjVEhVXa7Haurq6K0HxMSEtDd3Q2VSoXh4WFotVooFApRshePj4+hUqmYtgJYEiubzcak9enxeARX5Fi0AgE2XlYAOwE7cElSCgsLmbe5SkpKYDAYnvCeug0+nw/Ly8vQarVob29HZWXljZ8xuVyOpqamgAVDJMBXl+fn5yPmMH4b5HI5enp6YLfbsbCwcCf5NxqNMBgMzO1awkVmZiYGBgZgtVoxMTFx7XvFZDLhU5/61KnBYPiNKGzxqcBTTawopQuU0lZKaROltIFS+uWH37dQSv8DpbSRUtpLKZ0PdW273f7Wz3/+84V/+Zd/iXhLkNcqbW9vo6+vDyqVKqjnpaWloby8PGInNl7TpFAoUF9fHxVS5fF4MD09jba2NtEc3QkhKCoqQkdHR6Cd4na7mR6Dr1ZVVFQwW/P8/BxSqZQJUfN6vZBKpcyIKwvn9ViqWMXFxTGJt+FRVFSE/f19pp9j3s8o2Ik6o9EY0FL19fUFZQybkZGBjIwMUeJ0bkJCQkIgVzBS1bLbIJFI0NLSAqVSCY1Gc+N7wul0QqvVRmXI5zbIZDI0NTWhvLwcExMTT7wPX375ZbPZbP4EpTQyFvRPIWLnrxljeOht9b/8zu/8jimSLUG3242xsTFIJBL09PSEfFEsLCxEYmKi6Cc2juMwPT2NxMTEqGUSUkoxPT2N6upq5iLy67C1tYXm5mYUFxdjfHwcW1tbzCatTk5OkJSUxNQ1n2W1ipW+ihVRiLVWIHBZtWIRbwNcVmKSkpKYm3vm5OTg/Pwct53Tzs/PA+2g7u7ukN3/q6qqcHJyEtH2XHJyMpqamkKySxEThBBUVVWhsLAQY2NjT1R+vF5vYBApUmL1UJGVlYX+/n6cnZ1Bo9HA7XbjH//xH31TU1OjLpfr+9HeXyzjGbG6BZTSY6vV+gcf//jHI8KsTCYTxsbGUFFRgerq6rDJSl1dHcxms2hCUo7jMDU1hdTUVFRXV4tyjGCg1WqhUqmYBAvfhdPTU7hcLhQUFCA3NxeDg4Pw+Xx48OABk4vpxsYG02oVEJvEioVwHWDXCkxISGBW5cjKymI6QFJaWhpID2AFQkjANPRx+P1+rK+vQ6PRoLS0FJ2dnWF5n0kkErS2tmJhYSGisgSVSoW6ujpoNJqIWD8Eg8LCQtTX12N8fDxAZnln9fLyciafKTEhl8vR0tICtVqNH//4x3j11VctBoPhN6O9r1jHM2J1B+x2+1+/8847c2+//bZoLUE+CX51dRU9PT3Izs4WtB4hBO3t7dja2mIeGOr3+zE5OYmMjAxUVgZlDyYKDg8P4XQ6RTPSuwq/34/FxcVHPKukUimqq6vR0dGBra0tTE9Ph6RduYqzszPExcU9IQgWAqfTCblczqw9ykq47vF4mBArVhUriUQSiGQRCpVKxbTCpFKpYLfbBU8tPo709HTI5fIACaSUwmAw4MGDByCE4LnnnhN8DkpMTERFRQUWFhZYbDloZGdnQ61WY3JyUvTQ4WCRkZGBzs5OzM7OQq/XY3FxEWlpaSgsLIz21oJGdnY2/vIv/9Jydnb2O5TS6E8KxDieEas7wLcEP/axj5lYjVNfhcfjCZSv+/r6mLhjA5d3Gp2dnVhYWGDW6vD5fNBoNMjJyWE6uRYqLBYLtra2ImbrsLm5iYKCgmujOhITE9Hd3Y3CwkJoNBqsrKyEfJe+sbHBnKQeHR0xzWtzOp1MokrcbjezihWrC2d8fDwTzZxEIoFSqWQ2mUsIQXFxMQ4ODpisdxV1dXVYWVmBxWLB2NgYDg8P0d3dfas4PVTwxEGM/d913JycHMzMzMSMcXJSUhL6+vqwuLgoqrO6WPiHf/gH3+zs7LDL5fpBtPfyNOAZsQoClFK91Wp9/Xd/93eZMiuLxYLR0VEUFxejoaGBuYBRqVSira0NU1NTgnUHPKnKz8+P6liw2+3G7OwsOjo6mHk93Qa73Q6DwXAnkczJycHg4CCUSiWGh4exs7MT1IXfarWC4zjmLQG9Xs+sRcqTIRYkNtZagQB7nRXLdmBRUREODg6YEwRCCPx+P6anp1FXV4f29nZmN3VX0dTUhK2trYjbwJSVlSExMRGLi4sxQ65MJhMSExOhVCqxvLwcM/u6C0ajEa+++qrJYDB8JNp7eVrwjFgFCYfD8Tf37t2b/dGPfiT4NplSip2dHSwsLKCzs1NUjVBqairq6uowOTkZ9oXI6/ViYmICxcXFzHPxQgEvmK+vrxct6PUqeM+qxsbGoEivRCJBaWkpBgYG4Ha7cf/+feh0ultPoBsbG8zvXh0OB+Li4mKuDQiwJVasLkwsiRVLPyvgsvKcmprKTBTv9XqxsrICjUaDqqoqEEKCmvYLF1ctGCLdmqupqQGlNKITijfh7OwMGxsb6OzsDGSYTk5OxowW7DZ85CMfMZvN5o/f1QIkhHybEGIkhCxd+V4zIWSMELJICPn/CCHiTxnFAJ4RqyDx0Dj0wx/72MdMQnRLPp8PMzMzsFqtoibBX0V2djaKiorCKo17PB6Mj49DrVZHXROwuLiI3NxcwfqPYLG/v4+UlJSQSYVcLkdtbS26u7txcnKCBw8eQK/XP/HaOxwOuN1u5m7LR0dHKCgoYLaexWKJOWLFEiyJFe+LxZJElJaWYnd3V9AaXq8Xa2trAfuEwcFBFBYWoqSkBNvb22w2egNUKhWysrIiTnAIIWhqaoLNZmM+BBAKHA4H5ufn0dXVBblcDkIIamtrkZOTg7GxMebWLSzxd3/3d96HLcAfBvHwt3CZfHIV3wTwWUppIy5TUF5nvMWYxDNiFQIopQar1fq7v/qrv2oO527ZZrNhZGQE2dnZaGlpET0J/ipKSkqQlJSEpaWloMkVT6oqKiqY6nXCwc7ODvx+P8rKyiJyPLfbje3tbdTU1IS9hlKpRHNzMzo6OqDX6zE8PAyj0Rh4/Tc3N0UZANDr9cjNzWW2ntlsZtaqjEVixcokFLi8mKenpzOLtwEu/elcLldYF2Cfz4f19XUMDw8jLi4Ozz//PEpKSgIV2NLSUuh0OtEtCqqqqnB6esp8mOYu8LmCx8fHEYvbuQq3242pqSm0tbU90WotKSlBTU0NxsfHY8Lc9HFsbm7iM5/5jN5gMPx6MI+nlN4H8HhVqxrA/Yf//imAX2G4xZjFM2IVIhwOxw+0Wu0/vfHGGyGNgB0cHGBmZgatra0oKioSa3u3oqamJjBSfRcuLi4wNjaG6urqiNgZ3AaTyYTDw8OIxuUsLS2hpqaGiY4rISEBLS0taGtrw+HhIUZGRrC/vw+r1cq8+ma32xEfH89Mf0YpZRJBwyMWiRUr8ToP1jorXsS+v78f9HO8Xi/W19fx4MEDSKVSPPfcc1Cr1U+0tKVSKSorK7G2tsZsv9eBEBKwYGA95XgXpFIpOjs7sbW1FdE8Va/XC41Gg/r6+hvDy7OystDW1oaZmRmmLWShuLi4wAc/+MFTg8HwEqVUCOtbAvDBh//+EIDoXPwijGfEKgwYjcZPvvHGG3sajebOx/r9fszPz8NgMKC/vz8iRpY3gc/zstls2NrauvFxLpcL4+PjqKurQ05OTgR3+CTOz8+xuLgY0CVEAgaDARzHMSeUiYmJaGtrQ0tLC7a2tuDxeHB4eMi0bcS6Deh0OplqcDwej2gO+eGCEAJCCLO/A8t4Gx6FhYU4Ojq6s9rscrmwvLyMkZERyOVyPPfccygvL7/1s5Ofnw+r1Qq73c50z48jISEBVVVVmJ+fj7hwWy6Xo7u7G1qtlrnp6nXw+XyYmJhAZWUlsrKybn1sSkoKenp6sLq6yjx8O1y88sorNoPB8F8ppdMCl/otAJ8khEwDSAYQWVYdJTwjVmGAUuoxGo3/4UMf+tCtFgxOpxMjIyNITk5Ge3t7RKbY7gLvcXVycnLtGLTL5cLExAQaGhruPCGIDZ/Ph6mpKTQ3N4sysXTTMbVarajZXbzOYmBgAHa7HUNDQ9ja2hIsZKWUQq/XMyXDLNuAwOUeYym+gwfLdmBcXBwopUzNMWUyGVQq1Y0VF4fDgbm5OWg0GqSmpgYqVMHcjNxmGsoaBQUFkMlkEbdgAP49V3Bubo6Zpu46cByHyclJlJSUBH1zFh8fj97eXhgMBqysrER1YvD73/++74c//OHs2dnZnwpdi1K6Sil9H6W0HcB3Adx8R/8uQuyd4Z4SUEq3zWbzpz784Q9brvsQHB8fY3JyEo2NjSgrK4tK5MtNkEgk6OjowN7eHo6PjwPfdzqdmJiYQFNTE3NBdaiglGJubg4lJSVBZyWywNraGkpLS0Ulctvb21Cr1VAqlairq8PAwAA4jsODBw+wtLQU9gXebrcjISGBKYFnOREoxsUiFicDAXGqVo+L2Hljz7GxMSwsLCA/Px/PPfccCgsLQyav/Oc9EhqoxsZGbG9vi0pubkJCQgLa29sxNTUVtqHvbeBjtnJyckKWfMhkMnR2dgamn1nZiYSCvb09fOITnzAajcb/mTL4cBFCsh/+VwLgCwC+LnTNpwHPiJUA2Gy2787MzLz95ptvBgQaHMdheXkZe3t76OvrY3ZRYg2ZTIbu7m5sbGzg5OQEDocDGo0GLS0tESUyN2FzcxMKhSKi9g4WiwVms1lUny6v1wu9Xv/ISVcul6OyshLPP/88VCoV5ubmMDEx8YjQPRiwbgMClz5bN+lDQoXX62VK+lhaLrCsWAHsbReAS+sUr9cbaOUPDQ1Br9ejvr4efX19yM7OFnQDx1etxK6WyGQyNDc3R8WCAbhsvTU2NkKj0TDVe1FKMT8/j5SUlLCHbAghqK+vR0ZGBsbHxyOae+j1evHBD37wzGQyfSgcd3VCyHcBjAGoJoQcEkI+CuDDhJB1AKsAdAD+iu2uYxPPiJVAGI3G3/7KV75yND8/D5fLhbGxsUA/P9a0JI9DLpejq6sLi4uLGBsbQ1tbW0xkVxkMBpycnKC+vj5ix+Q9q67G1oiB3d3dR6ayrkIikSA/Px/9/f2ora2FTqfD0NAQNjc37xRX89ULlm1AjuPg9/uZkSHWwvVYNQkFLmNjzs7OmJEUSmkg1Hh0dBQA0N/fj+bmZma6zeTkZKSkpECn0zFZ7zakp6cjNzcXq6uroh/rOmRkZKC6uppZriClFMvLy5BKpUx86dRqNSoqKjA2Nhaxyt7rr79u1+l0/7fP5xsN5/mU0g9TSvMopXJKaSGl9FuU0j+nlFY9/PosiyrY04BnxEogKKUuo9H4gZdeesnyzjvvoLq6OmC89zTA4/EEdC/RKD0/DrvdjpWVFbS3t0dUi7O9vY2srCxRhwv8fj8ODw9RXFx852NTUlLQ0tKC/v5+SKVSaDQaTExMQK/XX3uXb7PZkJiYCJlMxmy/NpuNWbUKYE+sWOUFAuyJlUQiQUJCguAq2MXFBTY3NzE0NISdnR1UVlYiLi4OarVaFM1mTU0NNjY2InIuqKiogMViido0XG5uLoqLizE1NSXofcSTKo7j0NDQwOzcn5OTg9bWVkxOToreov3xj3/Mffe73102mUx/LOqB3iN4RqwYgFK6ajabX//GN75hi7Y2KRRYLBZMT0+ju7sbfX19WFhYCNwVRwNerxfT09NobW2N6Ej++fk5Dg4ORM/v2t/fD4h3g4VcLodarcZzzz2HmpoanJyc4N69e1heXobVag1URMRoA7IWrotRsWJFrORyOXMX7HB1Vn6/HzqdLkCmpVIp+vr60N7ejpycHGRnZ8NgMDDdKw+FQoG8vDzBhqTBgLdgWFpairgFA4/i4mJkZmZidnY2rOriVVLV2NjI/IY6NTUVPT09WF5exuHhIdO1eeh0Onz0ox81Go3GX6aUxkZy9VOOZ8SKEcxm8zfHxsZ+/u1vf/upGCc9OzvD3Nwcurq6kJSUBKVSie7u7qiRK170WVVVxbRKEsxxFxcXUV9fL6qdA8dx2N3dhVqtDnuN1NRUNDY24vnnn0d6ejrW19cxNDSE1dVVHB8fM7fGYClcB2K7FQhcan9YTvJlZ2cH7ZvEcRwMBgNmZmZw//59mM1mVFdXB6b7rsoKSkpKRCU+5eXl2N/fjwjZUSqVqKmpwdzcXNQm4SoqKhAfHx9yfp/YpIqHUqlEX18fDg8Psba2xvR18vv9+OVf/uUzk8n0v1JKI2fy9S7HM2LFEAaD4Tc+//nPH83OzkZ7K7fi9PQUCwsL6OrqeiRSJ5rkamVlBampqRF3eD8+PoZcLhfdWuLw8BA5OTlM2jdSqRT5+fno7OxEf38/CCHw+XwYHh7G+vo67HY7k5OvzWZj2hqN5VYgwF7AnpiYiPPz8xv36Pf7YTQaMT8/j6GhIRiNRpSWluKFF1641VQyOTkZHMeJFmwsk8lQVlaGjY0NUdZ/HHl5eVAoFFH1cKqrq4PX6w36d44UqeIhk8nQ1dWFi4sLpqL/1157zb63t/dNj8fzDpMFnwHAM2LFFJRSp9FofN8HP/jBE7FK9UJxcnKCxcVFdHd3IyEh4YmfR4NcHR0dwW63C4qPCQd8flpDQ4Oox6GUYnt7G+Xl5czX5ltYTU1N6O3tRXx8PFZWVjA0NISlpSWYTKawTsJerxdSqZSpzi3WK1asdVZ8vM1VQ0qPx4ODgwNMTk7i/v37MBgMKCgowAsvvIDGxkaoVKqgLtKlpaWiEpHi4mKYTCacn5+LdoyrqK+vx+7urugmpTeBN082m813vq6RJlU8JBIJGhsbkZqaivHxccHV1b/5m7/xfOc735k6OTn5HKMtPsNDPCNWD0EIiSeEaAgh84SQZULIHz/8/luEkB1CyNzDr5bb1qGUbppMpv/tl37pl84iOSobDIxGI7RaLXp6em71abpKrsQWllqtVmxubqKtrS3ign+tVouKigrRpzePj4+RkZEhim6MUgqj0Yjs7GzExcWhuLgYXV1dGBwcRFZWFo6OjjA0NITp6Wns7+8H7d1jsViYT4iyjMYB2GqsAPbECrjUWe3v72N9fR0jIyOYmJiAy+VCdXV1gExlZmaG/N7Py8u7cZCBBfig4JWVFVHWfxwymQytra2YnZ2N2hAN7+93eHj4iL/fVfDTw5EmVTwIISgvL4darcbo6GjYVcvJyUm89tprB0aj8YPPdFXswW6E6OnHBYBfoJQ6CCFyAMOEkB8//NnrlNLvBbuQy+X6aUZGxldffvnlP/zOd76TGgsTgnq9Huvr6+jp6QnqAq9UKtHb24uJiQn4fD5R8gL5snZHR0fEXelPT09xfn6OpqYmUY9DKcXW1hY6OjpEWd9sNiMlJeUJfZhUKkVOTg5ycnJAKYXVasXJyQlmZ2fh8XiQkZGBrKwsZGRkXPvai0WsWJJY1q3ApKQkwUG9lFKcn5/j5OQERqMRDocDFxcXaGhoQHt7OzNiKZFIkJubi+PjY+ZDCzyys7OxtbXFXGt3E1JTU1FQUICVlRXRq8g3QSqVoqurK2Cbk5mZGfgZx3GYnZ1FQkICampqojr5nZeXh/j4eGg0GjQ3N4fkPajT6fDSSy8ZjUbjL1JKI+/S+h7As4rVQ9BL8G8y+cOvsIUqZ2dnb7zzzjv/+md/9mfs7X1DhE6nw8bGRtCkiodCoUBvby+2t7dDCoANBhzHYWpqCnV1dUyz6II99uLiouieVcBl65UfDhADOp3uzgsrIQRpaWmorKxEX18fBgcHkZubi9PTU4yPj2NoaAjz8/M4PDwMtH7EuJhyHMd0QIB1xSocewSO42A2m7G1tYXJyUncu3cPS0tL8Pv9qK2txYsvvoikpCTk5OQwrdYB4ovYgciZhvIoKyuD3W6PaFjy4+D9/ZaWlmC1WgFc6uH4uKDa2tqYsNNJT09Hd3c3FhcXg/Yec7vdeP/7339mNBp/jVK6K+4O37t4VrG6AkKIFMA0gAoAb1JKJwghnwDwJ4SQPwLwcwCfpZTe2eOjlFJCyK9/9atfnWpoaKh/3/veFxUSe3h4iN3/n70zD2/rLNP+/UreJMu7Ja/xEsdrYseJHWdt0qYrYehCYRg6ZWs7pZRSlnbK2qHwfRQopaEFQmnpMKQNUGjLV9oAQxuyOE5iO/Fux/tua7N2WbLW5/tDknESJ/Gic06cnN916Yqi5byvbFnn1rPcz/AwtmzZsqSoUMjs9PTp0/B4PGGrE+ro6JhtHeebvr4+ZGVlnVO4z+VaXEXFiAh6vR5lZWWLep5UKoVSqZwt2Pf5fDCZTDAajRgfH4fT6YTD4cDk5CQSExORkJCw7DQmFyfmcNdYSaVSEBGIaN4TJxHBbrfDYrHAbDbDYrHA4/EgISEBycnJKCkpgUKhuOC5SqUSU1NTYW/MiI2NhUQigc1mQ1xcXFiPHSIhIQEymQxarRbp6emcrDGXkAXDyZMnw/K+WyoxMTHYtGkTGhoasHHjRnR0dCA7O5vXSRALQS6XY9u2bWhsbITD4UBBQcFFRR8R4d5777WMj49/1+12H+F3p9cWorCaAxH5AFQyxhIB/Ikxtg7A1wFoAEQBeAnAVwF8d4HHczPGbv7kJz/ZVFtbm1lYWMjV1udldHQUY2Nj2LJly7KMI0MdKU1NTeju7kZxcfGyvrGNjIyEVaQtBpvNBo1Gg+uuu47ztYxGI6Kiojg76RmNRiQmJi67wFwqlSI1NXU27eF0OtHY2Ai5XA69Xo++vj643W7I5XLEx8cjLi4OCoUCCoViwe8rr9cbVvPS0L7DXWMUHR2NmZkZMMZgs9lgt9ths9lgsVjg8/kQGxuLxMREqFQqFBYWLujEr1QqMTY2xknHa6iIncvUWWlpKerr66FSqXgx7Y2JiUFZWRmam5uxefNmwaJDsbGxWLt2LWpra7Fu3borTlSFiIyMxJYtW9Da2oq2tjaUl5fP+3v6wQ9+4KitrX3XaDQ+L8A2rylEYTUPRGRmjB0BcBsRPRu82cUY+zWAxxd5LC1j7F8+8IEPvHfmzJkUvjyahoeHoVarsXnz5rCc0CQSCaqqqtDW1ob29vYlF24ajUaMjIzM2gTwydyxNXycIHp7ezntdJycnOTkZG02m6FUKpGdnY3s7GwAgZ+d0+mExWKB3W6frR/y+XyIiYmBQqGAXC6HTCaDXC6fHQYd+h2HuyMQWF7Eyu/3Y2ZmBg6HAw6HA06nE9PT07BYLKirq0NsbCwUCgXi4uKQmZmJ0tLSJdcBJiUloa2t7aKRsOWQlpaG7u5u+Hw+znzYZDIZVCoVRkdHOZ2jOZe0tDTodDoMDQ0tee7ecnE4HDh79ixKSkpmzX35rgVdKBKJBJWVlejr60NDQwOqqqrO2evBgwd9e/fu7dHr9Z8RcJvXDKKwCsIYUwLwBEWVDMBNAH7IGMsgIjULfCLeCaBjsccmoub4+Pgv3HHHHT8/dOhQEpdGlEBgPItOp0NNTU1YP2wZY6ioqEB3dzcaGxuxcePGRYk2p9OJ1tZWbNmyhVMzzosxOjqKuLg4XgpxQ7UZXM1eJCJMTU1xMk/RbDZf8DNijM0KpvP34XK5YLfb4XQ6YbPZoNVq4XA4ZtvBQx/wbrcbPT09iI6ORnR0NCIiIhAREYHIyMjZ61KpdMHiI1Rj5ff74fV6L7h4PB64XK4LLn6/H4wxxMTEzL6muLg4pKWlISEhAVKpNKwCYu54m3DXE0okEmRkZGBycvKcwd7hprCwEMePH+dVXJSVleH48eNITU3ldNTUfJjNZjQ3N88WhsfGxqKhoUGwz66FwBhDUVERJiYmcPLkSWzatAkymQzd3d24//771Xq9/hYiCp8DrshFEYXVP8kA8JtgnZUEwB+I6F3G2D+CoosBaAHw0FIObrVaf6dSqaoff/zx/9i7dy83uSEA/f39MBqNqKmp4SQqE2rDHhkZwcmTJ1FTU7OgSITP58Pp06dRUVHBWSH3pXC5XBgcHMSOHTt4Wa+vrw9cpn4NBgOSkpI4+R2bTKYFO8SHBMrFCrOJCF6vF2NjY7BYLIiLi4PL5YLFYplXDC1mrExozuXo6Oisq2ah1QAAIABJREFUMJsr1iIjIxEdHY3Y2NhZMRcVFXXJE6Pb7ebEYiTkws5Fo0Zubi7OnDnDqbCKjIxEbm4uBgYGePObk0qlsxYMO3bs4E3QaDQadHd3n2OgnJGRAbfbjdOnT2PTpk28zjFdLFlZWYiJicGpU6eQn5+PPXv2TGm12j1ENCX03q4VRGEVhIjaAGyY5/bd4VpDr9f/529/+9sN5eXl2++7776wmyf19PTAarWiurqa8z/83NxcyGQynDx5ElVVVZesIyIitLS0YNWqVRBqlmJHRwdKSkp4+bYdarHn8rVOTExwkgYMRaDC1cHGGJtNCSYlJYV1zxMTE5ieng7rjEeFQoGhoaGwHS+EUqlEZ2cnJ2ktmUyGyMhIWCwWTsdB5eXl4dixY7N/+3wQHx+PnJwcdHZ2cm6NAgBDQ0OYnJzEtm3bLrAGyc3NhcvlQmtrKyorK6+IzsCLkZKSgvXr1+PWW291aTSah4ioXeg9XUtcubL7KoSI/Dqd7l++/vWv9x48eDBs7UxEhLNnz8Jut6Oqqoq3b1MqlQobN27E6dOnLzl9fWBgABEREbzVZ5yPTqeDz+fjxItrPvr7+7FmzRrOjk9EMBqN53jshIvp6WlOuiW5qrEKd/G6TCZbsInqYrjceJvlkpeXx7n1gkQiQXFxMXp6ejhd53zy8vLgdDqh0Wg4WyPkpm4wGLBly5aL+q0VFhYiMjISXV1dnO0lHPj9ftx3332W4eHhbzscjjeF3s+1hiiseIaIHDqd7vr77rtv9NSpU+E4Hrq6ujAzM4ONGzfyHqKOj4+/5PR1nU4HrVaL8vJyXvcVwuv1orOzk7f1nU4nrFYrpzYSU1NTSE5O5iwNyEUNGhfCiouuQMYYGGOcHDc5Ofmc8TbhRKVSwWg0LiqVuhTS09MxPT0Nq9XK6TpzYYyhsrISZ8+exczMTNiPHypTYIyhqqrqkilHxhjWrl2LmZkZ9Pf3h30v4YCI8PnPf9528uTJ/Xq9/odC7+daRBRWAkBEBp1Ot+uuu+5SL2dkBBGho6MDXq9X0NB0yKV9fHwcZ8+enfUsstvt6Orq4iU1eTF6enqQl5fHW+oiFK3i8ncxMTHBmdv2ShJW4faxCiGXyzmZkadUKjkzvmSMISsra94vN+Fep7S0lPeITXR0NNauXYvm5uaweqI5nU6cOHECSqUSZWVlC/q7DXltTU1Nhd04ORw8/fTTjrfeeuvvOp3ui0Lv5VpFFFYCQURjGo3m5ltvvVW/lA/DkHVAqFNP6Hx/yEiUiNDQ0ACHw4EzZ86gsrJSMJM/i8UCk8nEWwrS5XLBYDBwmnL0+/0wGo2c1W9xVaezUlKBQCBtt9QZbJdCqVRyOnszJyeHlxN9cnIypFIp53NEz0elUiEuLg4DAwNhOZ7RaMSpU6dQWlq66M+I0FzB0dFRTlOUi+WVV15xP//882d0Ot2/EV92+SIXIAorASGizsnJyTt3795tWEyKIFQMHhkZibVr1wouqkIwxlBWVoasrCwcPnwYWVlZnNkNXI65nlV8/XwGBwexevVqTtebmppa0tDeheD3++Hz+Tgp8Pf5fCvCIBTgZhgzgNkifrfbHfZjAwFjTZlMxlm6cS5lZWXnRKf5orS0FJOTkzCbzcs6zsjICDo6OrB58+Yl1yqGjJN7enouWWPKF++++67vG9/4Ro9er7+NiC6ZE2aMrWKMHWaMnWWMdTLGvhi8PZkx9h5jrC/4L/feNFchorASGK/Xe2JycvKBG2+80bSQolm/34+mpibI5fIrZmbV+djtdmRmZmJ8fFywmV+Dg4NISUnhzf/G4/FAo9HMGmpyBZdpQKvVymlXWbjhKhXIlbACuI9a8VHEDgSieklJSZynHs8nZMHQ0tKypHoyv9+PtrY2TE1NYdu2bRf4si2WqKgo1NTUoL29nde6s/M5ceIE3X///SM6ne56IlpIHtsL4DEiKgWwBcDnGWNlAL4G4BARFSI4wo27XV+9iMLqCsBut/+/kZGRb3zoQx8yX+rDwu/348yZM4iLi1v2WBmuCH2brKysxNatW9HX14e+vj5ev9k6nU6MjY2huLiYtzWHh4eRm5vLaS2Z3++H2Wxe1CT7xWAymTiJMPp8Pk5+ListYgUE0llcCqvU1NTZGYZcU1xcjP7+fk7E7aWIi4tDXl4eOjoW59Xscrlw8uRJyOXyRZsbXwqZTIbq6mqcOXOGk9q8y9HV1YW7775brdPpdhGRcSHPISI1ETUFr9sAnAWQBeAOAL8JPuw3CJhiiywSUVhdIRgMhhdbW1t/+slPftIynwjx+XxobGxEcnJyWH17wonVakVvby+qqqrAGEN0dDS2bt0Kp9M5O8SZa0IpwLVr1/JmKOjz+TA+Ps75LDG9Xs9ZGhBYWYXrAHc1VlFRUZy9VxMTE2EymTj7osEYQ3Z2NsbGxjg5/lyioqKQnZ2NwcFBztc6n9zcXLjdbkxOTi7o8UajESdPnkRhYSEnzSUKhQKVlZVoaGiAy+UK67EvxdjYGG677TadRqO5iYiWFD5kjOUh4OFYDyCNiNRAQHwB4K69+SpGFFZXEFNTU99+//3333ziiSdsc28PiSqVSiXI4OKF4Ha70dTUdMGMKolEgoqKCmRkZKCurm521AtXqNVqREZGQqlUcrrOXEZGRpCVlcW5kOMyDQgEhDEXqVMuhRVX0RKpVMqJdYFEIkFsbCxnETEgUMQ+NjbGS5R49erVGB8f51VMAP+0YOjp6bmk7xgRYWBgAJ2dnaipqeHUBiUpKQlr165FfX09L18ijUYjdu/ebZicnLyTiJbUXs4YUwB4E8CXiEi4XOZVhiisriCIiPR6/X/s37+/9oc//KETCPgw1dfXIyMjY8FjRvjG7/fj9OnTKCkpuagDe3Z2Nqqrq9Ha2orh4WFOPvQ9Hg96eno4mZ93Mfx+P0ZGRjj/3fh8PlgsFs7mHHo8HkilUk5SdistYgVw1xkIcF9nFRUVBYVCAaNxQVmhZSGVSlFYWIje3l7O1zqfqKgolJeXX9SCwe12z3Yob9++fdn1VAtBqVSioKAAjY2NnKZIbTYbbrjhBuPExMRnvF7vyaUcgzEWiYCoOkBEbwVv1jLGMoL3ZwAQpkh2hSMKqyuMoDv7nT/+8Y9PPfvss876+nqsWrWK8zTTcujs7ERqairS09Mv+TiFQoHt27fDbDajqakp7N/qurq6sGbNGl7tHcbHx5Gens75qBy9Xg+lUslZGtBsNnPWwcmVsOKqxgpY2XVWAJCfn89LETsQmE1nMpk4jcJdjNTUVCQmJl5g1mkymXDixAmsWrUK5eXlvProZWVlISMjA01NTZx8gbTb7bj++uuNQ0NDjzgcjneWcgwW+CB5BcBZInpuzl1/BvCp4PVPAXh7ebu9NhGF1RUIEXn0ev1te/fubTt8+LCby+Gqy2V0dBQul2vBA4elUikqKyuhUqlQV1cXtk4ag8GA6elpzrvy5kJEsxYLXMN1GnAlCivGGGfpLi6FVWxsLJxOJ2eiEAikpUIzK7kmZLOyHLPj5VBSUgKNRjNbuzYwMICOjg5s2rSJk3maCyE/Px/x8fFoa2sL63t0enoaN9xwg2lgYODLVqv1d8s41HYAnwCwmzHWErzsAfADADczxvoA3Bz8v8giEYXVFQoRuScnJ3e98MILjfv27Qv/HIcwYDQaMTw8vCTX91WrVqGqqgrNzc0YHBxc1oeP3+9HR0cH70aparUaKSkpnEfIfD4frFYrp55gXBWuA9wJKy7hUlgBAeHDZaqOMYZVq1bxUsQOBCJHIfNavpFIJNiwYQOam5tRX1+P6elpbNu2jZOZl4uhqKgIjDF0d3eH5XgOhwM33nijqb+//z/NZvP+5RyLiI4TESOiCiKqDF7+QkQGIrqRiAqD//L/C70KEIXVFQwRufR6/U1PPfVUy8svv3xFiSun04nW1lZUV1cvuW05Li4OO3bsgMPhwKlTp5Y8/Lavrw8ZGRlQKBRLev5SICLOhy2H0Gq1UKlUnIpGroYvAytTWHFZYwXwkw5ctWoVxsfHebM6CY26EcLw2263w+v1wufzoaKigreO4EvBGEN5eTmmp6eX3TnpdDpx0003mXp7e79mMpleCdMWRThCFFZXOEQ0o9frd3/rW99qe+mll/htvbkIoaGl5eXlyy4IlUqlWLduHdasWYNTp04tuH06hN1uh0aj4UXgzEWv1yMuLo6XGYSTk5OcpgFnZmYQHR3NmXBzuVyIiYnh5NhcEarf4kokpKamci6sIiMjkZiYiKmpKU7XCREfHw+FQgG1Ws3LekCguae1tRUjIyPYuXMnoqOjMTExwdv6l4Mxho0bN0Kr1S7ZTNXpdOLmm282dXd3P2k0Gl8K8xZFOEAUVisAInLqdLrrv/WtbzULnRYkIrS2tiI7O3vJoyDmQ6lUYvv27ZicnFxwYXvIs4rv4lQgECVbaF3ZcvB6vbDb7Zw6onOZBgQCHYfhHmfDB1FRUZyNn4mMjIREIuHs+CH4cmIPUVJSgt7eXk7rx0KYTCYcP34ciYmJqKmpQUxMDNavX4/e3l5BjDovhkQiwaZNmzA0NLToSRQOhwO7d+82dXZ2ft1oNP6coy2KhBlRWHEMYyyGMdbAGGsNzmT6znn3/5QxdtliDiJy6vX6G5566qnTL7zwgmDianBwEBKJhJPBxlFRUaiqqoJSqURdXd1l52+NjY1BoVBw5kR+MQwGw2xLO9fodDrO04Bms5lTYQXgipwScDm4rrPi2nYBCBiSOp1OzMzw85ERExODjIwMTsWc3+9HT08POjs7sWnTJuTm5s6+vyIjI1FRUYGmpiZexN1CCc0V7OrqWnAdWrD7z9Td3f2fJpPplxxvUSSMiMKKe1wAdhPRegCVAG5jjG0BAMZYNYAFVyQH04I3fe9736t/7rnnllaQtAz0ej3UajWnReKhotvQcNO2trZ5o1culwsDAwMoLS3lZB+Xoq+vjzf3e667AQHuRtkAgZMg16KKq3Qd10aeSqWSl1maubm5GB0d5XydEAUFBRgZGeHEJNNsNuP48eMAcNEC9ZSUFKSmpqKvry/s6y+H6Oho1NTUoLW1FTab7ZKPtdlsuP766429vb1fEmuqVh6isOIYChD6dI4MXogxJgXwIwBPLPJ4Lp1Od8sPf/jDkz/4wQ94E1fT09Po6OhAdXU1L2k3uVyOrVu3IiEhAcePH7/gBNTR0YGSkhLO/aPOJ+Qcz8ewYq/Xi+npaU4HSRMRpzVQXBeuc+1lxWUBe2JiIsxmM+fF3llZWZiYmOCtqDwiIgKrV68Oq7Dx+Xzo6upCR0cHKisrUVxcfMnPoaKiIuj1ekG6FC+FXC5HVVUVzpw5c9FmHYvFgl27dhn7+voeXW73n4gwiMKKBxhjUsZYCwIutu8RUT2ARwD8OTSXaTEQkVun0922d+/efzz22GN2rj8wvV4vTp8+jcrKSl6LkBljyM3NxZYtWzA8PIympia43W7odDp4vV5kZGTwtpcQfNVWAYBGo0F6evqK7QYEuBdWXLqvc50K5GO8DRAQOikpKbxEx0Lk5ORAr9eHpdbJYDCgtrYW0dHR2L59+4K+aEgkEmzcuBGtra28jJdZDPHx8aioqEBDQ8MFNXZqtRqbN2829PX1fdZisRwQaIsiy0QUVjxARD4iqgSQDaCGMbYTwEcB/HQZx/TodLrbX3vttdfvueceCxdzzYLroKmpCatXr+a8DudiyGQybNq0CWlpaTh+/DhaW1tRUVHB+z5ChospKSm8rDc5Ocm5wSHXhetut5tzYcXV6BC5XM55EbRKpeJF8PBdxM4YQ0lJybJMQz0eD9ra2tDT04NNmzahoKBgUV8y5HI5CgsL0dbWtuQ9cEVycjJKSkpQX18/O5Oyr68PW7du1Q8NDd1ts9neEHiLIstAFFY8QkRmAEcA3ABgDYB+xtgwADljrP8ST73Y8fw6ne4/Dh069JNbbrnFzMVJoLe3F3K5HEK7vzPGkJWVhdTUVERFRaGtrY33zh8+o1UejwdOp5PTNCDAfeF6yMqBK7iMWIVO4lxGhPkoYAcCUZLQe4ovVCoVXC4XzGbzop5HRFCr1Th+/DgSEhKwdevWJUdVs7OzwRjjzSh1MaSlpSE/Px+NjY1oaGjArl27NCMjIze5XK6jQu9NZHmIwopjGGNKxlhi8LoMwE0AzhBROhHlEVEeAAcRLcmIiYhIp9M91dLS8tUdO3YYw1lToFarYTQaUVZWFrZjLgeLxQKLxYKdO3di9erVaGhoQE9PD6fDTkM4nU7YbDYolUrO1wL+mQbkGrPZzGm9GB81Vlz+/mUyGadiJDTeho/3cG5uLkZGRjhfJ0Ro1M1iTEPtdjvq6+uhVquxdevWczr+lkp5eTkGBgY4rZdbKtnZ2ejt7fXfeeedGrVavZWIrrzwmsiiEYUV92QAOMwYawPQiECN1bvhXsRoNL7U29v7mc2bNxuWakQ3F6vVip6eHlRVVfHuETUfIc+qUEeiUqnEzp07IZFIUFtbC61Wy+n6IZd1vmwD+EgD+v1++Hw+ThsAVnKNFcB9nRUQSAuZTCZO1wCAzMxMqNVqXm0IEhMTER0dfdl0p9frxdmzZ3HmzBkUFhZi48aNYavnvFItGADgwIED7ieeeKJHrVZXENGw0PsRCQ/CnzGvcoiojYg2BGcyrSOi787zmLAYItnt9j8PDQ19aNu2bbrl1Da43W40NTVh48aNiIqKCsfWls3Q0BBSUlLOia5IJBIUFhZi8+bNGBsbQ0NDAyfpQZfLBYPBwFuxvMfjwczMDOLi4jhdx2q1ct7duJK7AgF+hBVfdVZSqRRKpRIajYbzteZSWlqK7u7ueaNWobRfqDj9uuuu46SGMTk5GWlpaejp6Qn7sZfKs88+6/jyl7/crNPpaoiI+3ywCG+Iwuoqw+v1nhwbG7th9+7dkydOnFh0cYjf78eZM2dQXFzMeX3PQnE6nRgdHUVxcfG898tkMlRXVyM/P382PRjOYv7BwUGsXr2at2iVWq3mRcRx6V8VQoxYXZ7U1FTexs7wnQ4EAkXkqampF3hp2Wy2c9J+q1ev5jQ6XlhYCKPRyNvP+mIQEb7yla/YfvSjHx3W6/U759jxiFwliMLqKoSIujQazZa77rpr+N13311U8cbZs2eRnJwsiJXBfIRSgGVlZZcdrBpKD0ZERKC2thajo6PLLjz2eDzQaDTIzs5e1nEWw8TEBOdpQID7jkAgEP3kMurJZVcgwI+wCo23cbm4HwUaFxcHv9/Pe71RUVERBgcH4fV64XK50NraipaWFhQVFYU17XcpQnP72tvbOR8ldDG8Xi8+/vGPW1577bXXdTrd7UQkzEZEOEUUVlcpRDSm0+mq77vvvq5f/epXC/rEHhsbw/T0NG+u4gtBrVYjIiICKpVqQY+XSCQoKCjA9u3bYbPZUFtbu6w0y9DQEHJzc3mrM3O73fB4PLyMy7FarbxEJbmM9HGdCuRyXuBc+OoOBPi3XgAC4jE7Oxv19fU4efIkUlNTsWPHDt7HUclkMhQXF6O1tZU3w9QQdrsdt9xyi+nQoUM/0ev1DxLRlVXwJRI2RGF1FUNERr1ev+Wb3/xm7Re+8AXbpb7Zm0wmDA4OYuPGjVfMXDePx4Oenh6sW7du0c+NiorC2rVrUVVVhdHRUZw6dQpWq3VRx/B6vZiYmEBubu6i118qfKUBPR4PpFIpp4KRjxMX16lAxhjnUTGAX2GVkZEBnU7HSyciEHgfjI6OYnx8HHa7HTU1NcjKyhLscyYzMxORkZG8jvkZHh5GdXW1oamp6St6vf4puswfB2NsFWPsMGPsbHDG7BeDt380+H9/cCSayBWIKKyucojIodPpbnn99df33XDDDab5PGVmZmbQ0tKC6upqRERECLDL+enq6kJBQcGyanRiY2NRXV2N4uJitLe3o6WlZcHt86Ojo8jOzr5sCjKc8JUGtFgsnNdXcZ0GBLhPBQKB9xDXqbOkpCRextsAgZ9ZWloa1OpFD31YFEQErVaL2tpaWK1WbN++HeXl5VfEDL9169ZhaGiI8zQvABw7doy2bdum6enp2WM2m/9ngU/zAniMiEoBbAHwecZYGYAOAB8GcIyb3YqEA1FYXQMEva6+1tzc/Nmqqqqp3t7e2ft8Ph9Onz6NdevWcTraZLEYjUZMT0+HzZg0KSkJ27ZtQ3p6OhobG9HW1nZJgeX3+zEyMoK8vLywrL8QXC4XfD4fL7+Hq6FwHeA+FQjwU2fFGONlnRBcFrETEfR6Perq6jA5OYmqqiqsW7cOUVFRyMjIgM1mu+wQYq6JiIhAZWUl5xYMv/jFL2Y+8pGPdKvV6ioialjo84hITURNwes2AGcBZBHRWSK6clobReZFFFbXEDab7Y+Dg4M37ty5c+Jvf/ubn4jQ3t6OjIwM3owvF4Lf70d7e/usZ1W4YIwhPT0d1113HVJTU9HQ0ID29nbMzMxc8Njx8XGkp6fzOuSZrzQgwE/hOh/Cio+IFV+CR6lU8jbPLzY2FhKJJOwCZ2pqCidOnMDo6CgqKyuxYcOGc74ozDUNFZrExERkZmYua+zOxfB4PHjggQesTz311Ht6vb6KiCaXeizGWB6ADQDqw7U/EW4RhdU1BhG1abXaDZ/61KfavvnNb7r9fj9Wr14t9LbOob+/HxkZGZwVcDPGkJmZiZ07dyI5ORmnTp1CZ2fnbFcWEc1aLPDJ5OQksrKyeFmL6+HLAH/CiuuIFR+pQCDgZ8VXnRUQ3iJ2o9GIEydOYHh4GBUVFaiqqrro329ycjIYY4LbHgBAQUEBLBZLWH/uBoMBO3bsML399tvP6XS6O4hoydb9jDEFgDcBfImIFlckKiIYorC6BiEivU6n27x///53fvCDH1iEaj2eD7vdDrVajTVrljThZ1GE5g/u2rUL8fHxOHnyJDo6OjAyMoKUlBTORcFcZmZm4Pf7IZPJeFkrOjqa8+JhMRW4OORyOWZmZngrKk9LS8PU1NSS1wul/E6cOIH+/n6sW7cO1dXVCzK2XeyoG65gjGHDhg3o6OgIi91FZ2cnqqqqptrb2z+p1+u/c7ki9cvsLRIBUXWAiN5a9uZEeEMUVtcoROQeHx//yKFDh76zefNmI99uzBfZE9ra2lBeXs7rGB3GGFatWoVdu3YhMTFx9kOWzzqQqy0NCFw9qcCIiAjexE5ycjLCOe/zUkgkEmRkZGBiYmJRzyMiTE5O4vjx4xgbG8O6detQU1OzKOsOhUKBxMTERa/NBTKZDCUlJWhpaVmW0Hv77be9N9544+jIyMhOh8OxrLFlLPCt5xUAZ4noueUcS4R/RGF1jWMwGPZ2dXV9dNOmTbrTp08LupexsTHExsby7m0TgjGGqKgoZGZmIicnB+3t7aivr+flRMfHbMAQZrP5qhJWfMx/i4yM5MXPiu904GKK2H0+H4aHh3H06FEYDAZUV1dj48aNS/ZCKy4uRl9fH2+i9VJkZGQgJiZmSalRIsJ3vvMdx4MPPtik1WoriSgcRVvbAXwCwG7GWEvwsocxdhdjbBzAVgAHGWP/G4a1RMKMKKxE4Ha7/zE+Pr71gx/84MCLL77oEiI873K5MDAwgLKyMt7XnktfXx+KioqgUqmwbds2FBUVYWBgAHV1ddBoNJykLpxOJ4iIlzQgwE9HIHD1pAIB/tKBKSkpvNYeyWQyREVFwWKxXPQxbrcbfX19OHbsGFwuF7Zt24by8vJlv1+jo6ORlZWFoaGhZR0nXKxduxajo6OL8rszm8247bbbzPv27fu9TqfbTkRhmaZNRMeJiAVnzFYGL38hoj8RUTYRRRNRGhHdGo71RMKLKKxEAABENKjT6SqeeuqpP99xxx3mxZppLpeOjg4UFxfz2oV3PgaDAdHR0ecU3SYlJWHTpk1Yv349dDodjh49ioGBAXg8nrCty2e0iojgcrl4GSFyNUWs+Cpg53O8TYj8/Px5IzUWiwUtLS04ceIEpFIprrvuOhQXF4fVm6ygoABjY2OCjZiZS8iCoaWlZUFRtPr6elRUVOjr6uoe0mq19xNR+AaUiqxoRGElMgsROTQazb8eOXLkSxUVFVNNTU28rKvT6eD1egWfT9jX14fCwsJ571MoFKioqMD27dsBAHV1dWhtbV20m/t8qNVq3oQVH92AIYiI81o5PmqsAP4iVgC/Luyh9YxGIzweD/x+PyYmJlBXV4fu7m5kZmZi165dWL16NSfmwVKpFGvWrMFcbz0hSUhIQFZW1iXtIPx+P77//e87b7/99rNjY2Ob7Xb76zxuUWQFIAqrqwDGWAxjrIEx1hocd/Cd4O2vBG9rY4y9EWzdvSxWq/U3IyMj2z7wgQ/0Pvfcc04uU4NerxddXV1h96xaLKFUSEJCwiUfFxkZiYKCAuzatQsZGRno6uqaNUJcSuTE6XSCMcZLBAngr3Cdr3Ty1ZYKBAJ1Vnz5WQH/9HdramrC0aNHYTabUVlZic2bN0OlUnH+d5mdnT1rCHwlsHr1akxPT0Or1V5w39TUFG644QbTT37yk1d1Ol0lEV0ZeUyRKwpRWF0duADsJqL1ACoB3MYY2wLgy0S0nogqAIwCeGShBySiPp1OV/HMM8/84ZZbbjFzVcDd29uLnJwc3uqLLrWPxQyfZoxBpVJhy5YtWL9+PYxGI44cOYLOzs5FdRPymQYE+Ctc93q9vKR1+YpYyeVyOBwOztcBAsaVfIy38fv90Gg0qK+vh06ng9VqxY4dO7B27VpepzAwxlBaWsqJUedSYIyhsrISXV1d55gHHz16lCorK3WNjY2f0mq1nyUi4fOXIlckorC6CqAAoa/TkcELhQzlgq27MgCL+qQmIpdGo/n0qVOnHly/fr3+5MmTYd23xWKBwWBAfn5+WI+7WOx2O9xu95K7ERUKBdatW4frr78eSUlJ6OzsxPHjxzEyMnLZWiy6n3qCAAAgAElEQVQhhNXlonLhIOSVxTV81VhJJBIQES+ROMYY4uLiOLP7sNls6OzsxNGjR6HX61FaWopdu3YhKSmJt6jc+SiVSni9Xt6sJi5HTEwMysrK0NLSAq/Xi//6r/+a/uhHP9o+MTFR5XA43hF6fyJXNqKwukpgjEkZYy0AdADeI6L64O2/BqABUALgp0s5ts1m++P4+HjNHXfc0fW9733PEY4TWcizSugUIHDp2qrFIJFIkJmZiS1btqCqqgoulwt1dXVobm6GwWC44KTscDgglUp5MyL1+/3w+Xy8RJLcbjcvr4uvVCAQONnON/6IC8Jtu+DxeDAyMoLjx4+js7MTSUlJ2LVrF8rLy2ftEsLpxL4UrhTT0BBpaWlwOp3YunWr/cUXX3w5OJpmXOh9iVz5iMLqKoGIfERUCSAbQA1jbF3w9s8AyERgiOfHlnH8Yb1eX/nCCy/sv/76603L/dAfGhpCcnIyL9GTS+FwOGCz2cI+K1Emk6GoqAi7du1CTk4ORkdHceTIEXR0dMymeSYmJngbYQMAVquVt583Hx2BAH+pQID/Avbl1ll5vV5MTEygoaEBJ06cgMvlQlVVFbZs2YLMzMwLGgtSUlJgsVjC2vG6GOLj4xEbG4srwawYAN577z3/vffeq21pafmwTqf7stj1J7JQwt/mISIoRGRmjB0BcBuAjuBtPsbY6wD+E8Cvl3FsD4DPyeXyd9evX//Kvn37Uu+8807pYo/jdDoxOjqKHTt2LHUrYWNgYABr1qzhLGrGGENKSgpSUlLg8/mg1+sxMDAAm80Gl8uF6upqTtadD778q4CrLxUI/FNY8TGwXC6Xw+VywefzQSpd+J+Y3++HTqfDxMQEbDYb0tLSUFJSsiATz9AEgrGxMcHmh5aUlKC+vh5paWm8Tl+Yi8PhwFe+8hXrn/70p+7grL8rQ+mJrBjEiNVVAGNMyRhLDF6XAbgJQA9jbE3wNgbgQwC6w7Gew+E4qFar1z300EOH7rrrrkUXtre1taGsrIyT9u3F4HK5YDAYeLN5kEqlSE9PR1VVFTZu3IioqCgMDAzg6NGj6OnpgcVi4TQNwldHIMBfxIrPNDKfEStg4eNtvF4vNBoNmpubZ13RQ52rpaWli3JGX7VqFUZHRwVLx8lkMqSnpy/YDT7c1NbWUllZ2dTrr7/+pE6n2yKKKpGlIEasrg4yAPyGMSZFQCz/AcBBALWMsXgADEArgM+Fa0EimgJwq0Kh+Lfy8vLn9+3bl3LHHXdc9qv15OQkIiIioFKpwrWVJTMwMIDVq1cLUuOl0WhQUFCAnJwceDweaLVa9PX1wWazISUlBenp6UhNTQ3rt3ar1brk8SOLhS9hxSd8mYSGCNkuzBchm5mZgVarhUajgdPphFKpRE5ODiorK5f1fo6KikJ8fDyMRiNSUlKWs/0ls2bNGtTW1iI7O5s3w2CHw4HHHnvM+tZbb/XpdLqPENEwLwuLXJWIwuoqgIjaAGyY567tXK9tt9t/zxg79NnPfva3v/nNb6p/9atfJV6su87j8aCnpwfbtm3jeluXJSRmSkpKBFlfrVZj69atAALeWNnZ2cjOzobf78fU1BQ0Gg06OzsRFxeH9PR0qFSqZTleezweSKVS3tIrV6Ow4rN4HQBSU1PR09MDINDsYbPZZsVUyHtq7dq150wKCAd5eXkYGhoSTFhFREQgPz8f/f39KC0t5Xy948eP0yc+8QmD2Wz+v2az+QW6UqrnRVYsorASWTZEpAdws0Kh+Hh5eflPfvGLX6TcfvvtF0Svzp49i4KCgivihDs0NIS8vDxB6jjsdjuio6PnFUoSiQQqlQoqlQpEBKvVCo1Gg4aGBvj9fqSmpkKpVCI5OXlRtTcWi4W3+irg6hRWjDEwxuD3+3l533i9Xni9XjQ2NsJut0OhUCAtLQ01NTWc/myTkpLQ3t4u6O8wJycHx44dQ15eHmced06nE48//rj1jTfeEKNUImFFFFYiYcNut/+OMfb+gw8++Nv9+/dXv/zyy4mhmh6j0Qi73Y7y8nKBd/nPbqmdO3cKsv5CuwEZY0hISEBCQgKKi4vh8XgwNTUFtVqNzs5OREVFQaVSQalUIj4+/pIpID4L14FAEfVihN9KIZQOjIuLC/uxvV4vDAYD9Ho9DAYDIiIiIJPJEBcXh+rqat5S1owx5OTkYGxsDGvWrOFlzfORSCQoKSlBd3c3NmyYLxi/PE6cOIF7771XbzabnzaZTC8Q0YI6IBhjqwDsB5AOwA/gJSJ6njH2IwTqWN0ABgB8hojMYd+4yIpAFFYiYeW86NXeF198Ublnzx5Je3s7qqqqBPesAoDR0VFkZ2cLduLXaDRLSodGRkYiIyNjttje6XRCp9PN1mbJZDIkJycjOTkZSUlJ57w+k8mEsrKysL2Ga5VQAXs4hJXT6YTRaITRaITJZAIQsDxQqVQoKSlBREQETCYThoaGeP+7yc7ORl1dHQoKCgT7m01LS8PAwAAsFkvYbEKCUSrbnCjVYkfSeAE8RkRNjLE4AGcYY+8BeA/A14nIyxj7IYCvA/hqWDYtsuIQhZUIJwSjV4ceeOCB31VXV2/5xje+IQ93LchS8Pv9GBkZEczqwWq1IiYmJixFuTKZDLm5ucjNzQURzZ6oJyYm0NnZCYlEgqSkJCQnJ8Nms0Eul4fhFVwer9fLq2hljIGIeBEASy1g9/v9sNlss0LKarUiOjp6tlGhtLR03i7ZxMTE2W5RPgVOZGQkEhMTodfrBWs0YYzNmoaG6hGXw+HDh+n+++83BKNUzy80SjUXIlIDUAev2xhjZwFkEdHf5zzsFICPLHvDIisWUViJcAYR6RhjN/3jH/+4+/Tp089//etfT37kkUdihEwRjY+PIz09nbduo/OZnJzkxBSUMQa5XA65XI7s7GwAgYJ1k8kEnU4Ht9uNo0ePIiYmZja9mJiYCLlcHvYTNt+1OSGTUD7sOxQKBQwGwyUf4/P5YLVaYTabYbFYYLVa4ff7ERcXh+TkZKxZs+ayqdsQc8fb8NXRGSIvLw+9vb2CdvAmJSUhMjISOp1uyfuYnJzEQw89ZGpoaGjXarWfClctFWMsD4Gmofrz7roPwOvhWENkZSIKKxFOCXbYvMEY+8v3vve9p/ft23fvr3/96xQhOgOJCIODg2H59rvU9TUaDW/RssjISKhUqlmTydLSUszMzMye8CcmJjA9PY2oqCjExcUhLi4OCoUCcXFxiI6OXrLgEkJY8W0SCgSiUNPT07Db7bDZbLOXubVxOTk5iI+PX5boC7mw8y2sEhMTMTMzg5mZGcTExPC69lzKysrQ2NgIpVK5qPekx+PBc88959y7d++UwWB4yOPx/CVce2KMKQC8CeBLoZmswdu/iUC68EC41hJZeYjCSoQXiMgB4EuMsZ9/+MMffnXnzp3FP/vZzxL5/DasVquRkpIiWKdTKB3HtzGq2WyeLVyPiYlBeno60tPTZ+93u92wWq2w2+3QaDTo7++Hy+WCVCqFQqGYvchkMsjlckRGRl7yBMe3sOJyXiARYWZmBg6HY3b8kcViwZEjR8AYQ2xs7KwYTUtLQ1xcXNg7BlUqFVpaWgQpJM/NzcXIyAiKi4t5XzuEXC5HamoqxsbGkJOTs6Dn/OMf/6AHH3zQYLFYXp6amvouEYXNJ4MxFomAqDpARG/Nuf1TAP4FwI2iZcO1jSisRHiFiPoYY1vfeeedu44dO/bTr33ta8mPPPJIDNdig4jQ39+PTZs2cbrOpeB7NmAIk8mE/Pz8i94fFRWF1NRUpKamnnO71+uF3W6H3W6ftX1wOByzs+RiYmIgl8shk8kgk8kQHR2N6Ojo2SgYXyx1XqDP54Pb7YbL5YLL5cLMzAycTiccDgecTic8Hg8YY+e8zpSUFOj1emzdupW31yiTyZY03iYcZGVloba2FkVFRYI2nhQWFqKurg6ZmZmX/GIyMTGBhx56yNTY2Niq1Wo/E24LheAUi1cAnCWi5+bcfhsCxeq7gl8iRa5hmCisRYSCMSZXKpXfS0pKuve///u/U7dv587PVKvVQq1Wo7KykrM1LgUR4ejRo9ixYwevESsiwpEjR3DDDTeE/bihSI7T6YTT6ZwVKKExLKE6tqioKERFRSEyMhIRERHnXObexhiDRCKZ99/QSd3v94OIQESz1zs7O5GTk4Po6OhZ76fQxePxnHM9JFCAgCALicGoqCjExMTMRuVCkbn5aGlpQV5eHq/2Fe3t7UhLSxOk3qmtrQ0qleqcKKcQDAwMwOfzoaio6IL7PB4Pnn32Wefzzz8f9rTfXBhjOwDUAmhHwG4BAL4B4AUA0QBCBXiniOghLvYgcuUjRqxEBCP4ze7LjLF9d9999/7rrruu5Gc/+1liWlpa2Nfq7+/H+vXrw37chWKxWKBQKHhPA05PTyM2Njbsx2WMzUaqzqetrQ1ZWVlISUkBEc0KmvnEztzbQ0JpPvHk9/svEFqh69PT05iYmIBMJjtHrMXExMz+zEO3R0dHL/t3EBsbC7vdzquwUqlUgnXo5eXloaurS3BhlZeXh2PHjiE3N/ecVHMo7Wc2m18yGAz/J5xpv/MhouMIjAg7H06EnMjKRBRWIoITTA9ue/fdd++sra396aOPPpr85S9/WRYux2WDwYDo6Oiwj/5YDJOTk8jMzOR9XT4HL4eYW2PFGJuNWHFFa2srVq1ahYuNUgo3CoUCFouFl7VCpKSkoLs7LDPUF018fDy8Xi8cDgdvlh3zIZVKUVRUhO7ubqxfvx69vb344he/aGxubm7TarWfJiJhJjeLiJwH//M8RFY8jLEYxlgDY6yVMdbJGPtO8PYDjLEexlgHY+y/g0WeC4KIyOl0/kmr1RY+++yzTxcWFup++ctfur1e77L329fXh8LCwmUfZ6kQEbRaLbiIxF0Os9ksqLDiAy6L1+djbmcgX4SibnzOKpxLqIhdaDIzMzE4OIh///d/t+7cufPs3/72tw9rNJobRFElciUhCiuRpeACsJuI1gOoBHAbY2wLAi3GJQDKAcgAPLDYAxOR02g0/t+JiYniJ5988pdFRUVTb775pm+ptYBmc2CqRLicm5e6h/j4eEGc3s1mM++v3ev18pry5NNuAQh0qS3FJHS5KJVK6PV63tcFAoJGo9Hw+nM+H7PZjMcee8z+8MMPj//xj3+8T6vVriWio4JtSETkIojCSmTRUIDQV/bI4IWI6C/B+whAA4DsZaxh1ul0jw4NDVU88sgjfygvLzccPnx40cfp6+ubt9iVT4RKA/r9fvh8PkHMUPnsIFtqV+BSkUqlszVgfKJSqaDT6XhdM4RUKoVKpYJGo+F9bafTie9///vO0tJS9a9+9av/1Gg0+W63+03R0kDkSkUUViJLgjEmZYy1ANABeI+I6ufcFwngEwD+ttx1iEitVqvv6ezs3HrPPfe8v337dkNzc/OCnmuz2eB2u3mrvZkPIlqWa/RysFqtvEerfD5f2H2cLgffqUAAiI6Ohsvl4nXNhISE2fE2QpCbm4vh4WHe1vN6vXjppZfchYWF+mefffb7Go2mwGq1vkhEy68PEBHhEFFYiSwJIvIRUSUCUakaxti6OXfvA3CMiGrDuF6fWq2++cSJE7fu2bPnzO23324aGBi45HP6+/sFra0CAsXjCQkJgqQBTSYTr51rQMBslG8DVr4jVoAwdVaMMcTHx8NqtV7+wRwQav7g+nUTEd58801/UVHR1JNPPvnLiYmJomC3n5PThUVEwoQorESWBRGZARwBcBsAMMa+DUAJ4CscrXdGrVZXHzx48F+3b9/ee99991nGxsYueJzD4YDdbodSqeRiGwuGq9mAC0GIwvWZmRlBhBXfESshhBUgbJ0VELA84KqInYjw97//HRUVFYZHHnnkD0NDQ+u1Wu2jwc8YEZEVgyisRBYNY0zJGEsMXpcBuAlAN2PsAQC3Avj4UibHLwafz/e+VqstPXDgwAM1NTV9H/vYx0w9PT2z9/f396OgoEBQt2gigl6vF0zcWSwW3ufLCRWx4ltYxcbGClLALmSdFQCkp6dDp9OFNULo9/vxxhtv+MvKyqY+/elP/7+Ojo5tarX640Q0GbZFRER4RBRWIkshA8BhxlgbgEYEaqzeBfAigDQAJxljLYyx/+JyE0Tkd7lcb2g0muI33njjY9dff33bLbfcYjh58iSMRiMyMjK4XP6yGI1GJCYm8l5zBAScqKVSKe9rCxGxkkqlvKcC4+LiBIlYyWQyuN1u3l9vCIlEgvT0dKjV6mUfy+1245VXXvEWFBRMfeELX9jf3d29cXJy8i4i6g3DVkVEBEM0CBVZNETUBmDDPLcL8n4Kdge9B2A9Y2zL2bNnX8jIyCh65plnEnbt2iVY1Eqo2YBAIFrFd30VEPCw4sLp/VIIEbGKiYmB0ylMyU9KSgoMBoMgDRFAoIi9ubkZ2dlLa/qdnp7Giy++6Nq7d6/V7XYf0Ov1TxPRovObjLFVAPYDSEdgvMxLRPQ8Y+z/ALgjeJsOwKfF6JcIn4gRK5GrCiI6NTY2VtPY2Ljjnnvu+Ut5efnU22+/7ef7xEtEMBgMFww25gshCtcB/s1BAWG6AufOLuQbodOBcrkcUql00UX0JpMJ3/72tx1r1qzRPv30009PTEys0el0X16KqAriBfAYEZUC2ALg84yxMgA/IqKKYHPNuwA4jZyLiJyPKKxErkqIqGNycvKDnZ2dNQ899NDvi4qKpl599VVfOJzcF4LBYEBSUpIgaUBAmFE2gDDCSoiIFRCos3I4HLyvG4pYCcliitjVajUeffRRW2lp6cTevXu/ptFo8gwGw3eJaFntjUSkJqKm4HUbgLMAss47biwA0e9KhFdEYSVyVUNEQ2q1+t8HBgbKH3/88V/m5+frn3nmmRmTycTpukKmAQHuhi9fDqGElRA1RwqFQpACdqHH2wBAWloapqamcKkvKq2trbjnnntMGzZsGHr55Zc/r9Vq86xW60+5GJLMGMtDoDyhPvj/7zHGxgD8O8SIlQjPiMJK5JqAiDRarfbz4+Pja55++ulvlZaWjt17772m9vb2sK/l9/thMBiQkpIS9mMvhFABuRC1ZR6Ph3endyFSgUAgYiVEATsgfDqQMYbMzExMTp5buuTxePD666/7Kysr9Xv27Kn73e9+91GtVlvgdDpf5crYkzGmAPAmgC+FolVE9E0iWoXAmK1HuFhXRORiiMJK5JqCiKxms/nHWq0278CBA/926623ntqwYcPUG2+84fd4PGFZIySqhEoDCuFfNRe+BZ2QESuhhJXQflYAkJOTM5sO1Gq1ePLJJ6fz8vJ0jz766C9bW1trJiYmdhDRIS5HzwSnPLwJ4AARvTXPQ34L4G6u1hcRmQ9RWIlckxCRn4j+Pjk5ubWlpWXz5z//+Zdzc3N1X/3qV+2jo6PLOrbQaUChCtf9fr8gUTKhaqyEFFYJCQmwWq2CjbcBAp2R7e3tuPnmm02VlZX9zz333FcmJyfztFrtw0Q0zPX6LPBmewXAWSJ6bs7tc8ct3A6gm+u9iIjMRbRbELnmIaJBAA8xxr70wgsv/Ourr776REFBQdoTTzyRsmfPHraYcTR+vx8mkwnr16/nbsOXwWQyIT8/n/d13W43oqKieF9XqFRgVFQUwhXlXCxzx9vwPQ9yamoKr7zyyswvfvELm9frPT4xMfE0EZ3mdRMBtiMwk7Q9OLcUAL4B4H7GWDECdgsjAB4SYG8i1zCisBJZETDGYgAcAxCNwPv2DSL6NmPsEQBfAlAAQElEU0tdI1hUux/AfsbY2v7+/iekUukHPvGJT8g/85nPxBYVFV32GFNTU0hJSRHMO4uI4HK5EBMTw/vaQhSuA8KlAoGAqPN6vYiI4P+jVKlUQqfT8SKsvF4v3n//ffz85z83NDY2Wh0Ox/M2m+1/iMjC+eIXgYiOA5jvD+0vfO9FRGQuorASWSm4AOwmInuwruI4Y+yvAOoQ8Ko5Es7FiKgTwKcYY/If//jHd+7fv/9L8fHxeQ8++GDCPffcE5WWljbv8yYmJpCTkxPOrSwKoboBAWGFlRARK+Cfo234jhoBAWHV3NzM2aBxIsLp06fx0ksvmQ8ePOgG8De1Wr0PQAOXdVMiIisdUViJrAiCH+ShgpbI4IWIqBngrmCaiBwIFMD+ljGmeuqpp+595plnPpufn5/08MMPJ991113SkJDx+/0wm82orKzkZC8LQSj/KkA4YSVUKhD4Z52VEMJKJpPB4/GEPWI2MDCAX//619Ovvfaaw+v1Nk1MTDwP4H0iEibvKSKywhCFlciKgTEmBXAGwBoAPyeiej7XJyIdgOcAPMcYW9PX1/fg448//vGamhrZ5z73uZSKigqkpqYKOvjZbDYLNiNRKGHFGBNUWNlsNkHWBgJmoUajcdnjbfR6PX7729+6X3rpJYvZbB6dmpp6we12v0VEwlTni4isYERhJbJiICIfgErGWCKAPzHG1hFRh0B76QfwBGPsq++8886mxsbGh6VS6R033nij9OGHH46rqakRRGCZzWaUlJTwvi4QEFbx8fG8ryukkFUoFGEZSLxUQnVWSxFW09PTePvtt/379u0zDAwMWBwOxy+tVuurRKTlYKsiItcMorASWXEQkZkxdgTAbQAEEVZz9kIAGgA0MMYi9+/ff9OhQ4ceIaLqG264IeKee+5J3r17Ny/F5H6/Hz6fj3eDzhBCRayEJFRjJRQpKSk4e/bsgh8/Pj6OP//5z55XX33VPDw87PB6vW9MTU29RES9HG5TROSaQhRWIisCxpgSgCcoqmQAbgLwQ4G3dQ7BGpS/AvgrYyziwIED2w4dOnQvEe0pLS2Nuvfee5M/9KEPSZebtrkYVqtVkIhRiGtRWIXqu4hIkMhZREQEIiMj4XQ6IZPJLrifiNDc3Iw333xz+o9//KNjenp60mazvWaz2d4koiHeNywicg0gCiuRlUIGgN8E66wkAP5ARO8yxh4F8ASAdABtjLG/ENEDQm4UAILjO44FL2CMFbe0tPzrk08++fGkpKTUj33sY4q7775bVlZWFrYTspCF60DAx+paE1ZAwM9KyNeuUqmg1+tnu1FnZmZw+PBh/P73vze9//77XolE0qxWq3/t8/n+RkTmcK/PGFuFgE1JOgLeUS8R0fNz7n8cwI+wTDsUEZGVAhO7ZkVE+IUxlhoVFfUvKpXq0wDKPvCBD0R+9KMfTdy+fTvkcvmSj9vc3Iz8/HxBXNcB4MiRI7j++uuvubXb2tqQlZUl2GxIi8WCuro6aLVa32uvvWbs7Ox0SySS/1Wr1a8CqOO6m48xlgEgg4iaGGNxCDSY3ElEXUHR9SsAJQCqRGElci0gRqxERHgmeHL5HwD/wxiLfvnll68/ePDgx/1+/67U1NTYPXv2yG699VbF1q1b503vXAyLxSJYKvBK+IImVDouZLnAp7CamJjA4cOH6d133zWeOHHCL5FIpqxW6+9NJtMfAXTz6TNFRGoA6uB1G2PsLIAsAF0A9iIQUX6br/2IiAiNKKxERASEiFwA/jd4AWMspaOjY+eBAwfu8vl81y1UaHk8HkilUsEGP3s8HsGK5oFAZ6CQworrgcgTExM4cuQIHTx40FhXV+fz+XwTDofjHZPJ9FcAZ64UjynGWB6ADQDqGWO3A5ggolYhOzdFRPhGFFYiIlcQRGQA8Kfg5QKhpVQqYz/4wQ/Kb7nlltgtW7bMCi2LxSJYChAI1PUIMUYnRKiIXAhhGRsbi+Hh4bAec3JyEocPH6aDBw8ajx8/7vf7/RNOp/Mdo9H4VwCnrxQhNRfGmALAmwiMmPIC+CaAWwTdlIiIAIjCSkTkCmYeoZXa3t6+89VXX73L5/Ntl8vlio0bN7LS0tLkqqoqyZo1axaVPgwXQheuh+YFCjGzTy6Xw+FwLPn5Go0GZ86cwalTpxxHjx61DwwMgDE24XQ6372ShdRcgmOm3gRwgIjeYoyVA8gHEIpWZQNoYozVEJFGwK2KiHCOKKxERBbAJYZA5wP4PYBkAE0APkFEbq72EazPeit4AWMsenBwsDw6OrpGpVLd7PF41svlckVlZSXbtWtX4qZNmyLWr1+/rKL4hSC01YKQY21Caa6FpCInJydDImr62LFj04ODg2CMTXq93uNarfYIAu+h4ZU0i48FXvQrAM4S0XMAQETtAFRzHjMMoFosXhe5FhCFlYjIwrjYEOivANhLRL9njL0I4H4Av+BrU8EardPByz5gVmytfeeddzalpaXd5PP5NkRHR8dXVlayHTt2JJSVlUUWFRUhNzc3bBGemZkZwSNWQgkr4J9Rq9DcSLvdjr6+PvT29qK5udleW1vrGB4epvNE1BkAoytJRF2E7QA+AaCdMdYSvO0bRPQXAfckIiIYot2CiMgiYYzJARwH8DkABwGkE5GXMbYVwFNEdKugG5wHxlgUgLWMsfXp6enVERERFR6PJzcqKkqWm5tLFRUV0RUVFXHFxcWSoqIipKenL6oQvKurCykpKUhLS+PuRVyCM2fOoLCwkNeuSLfbjaGhIfT29uLYsWO+4eFhe29vr8tgMJDf77dERET02u32JpPJ1IyAiBq/CkSUiIjIZRAjViIiC+T8IdAABgCYg2agADCOQJv5FUcwPdkcvPxP6HbGmGR0dDS7tra2KCYmpjQ1NbUawFqv15sRGxsbWVRUhPLy8pj8/HxFZmYmy8zMREZGBtLS0s6Jdl2NqUC73Q61Wj17GRsb8/T19dnb29s94+Pj8Hg8jsjIyCG3292q1WrPAOgF0EdEprBuREREZEUhCisRkQVy/hBoAKXzPYzfXS0PIvIDGA1e3p97H2MsemBgIP+vf/1rYURERHZycvKa6OjofCLK9nq9SqlUGh0VFRWhUqkoJSUlrri42FtQUBCbmZkpycjIQFJSEhQKBeLi4qBQKDjt2FtIKtDtdsNms0N0enkAAAVYSURBVMFut8Nms0Gv12NychLj4+Ou4eFhx8jIiGd8fBxms5l5PB4PY8wWERGh8fv9o9PT04Nms3kQwBiAHgBqMfokIiIyH2IqUERkCTDGvg3AAeCrWAGpQK4IRvFUCIwcymCMZaSkpBTGxMTkSySSZL/fH+f3+xU+n08ukUgiJBKJVCqVSiQSiVQul1NcXBzFx8cjISFBkpCQIImNjZVKpVImlUohlUolQW8uFhERwYgIPp+PvF4v+f1+8vl85Pf74fF4/BqNJsrpdLrsdrvHZrPBZrMxt9sNv9/v8/l8fr/f7wPglkql0xKJxAbA5vP5Jm0224DNZhsBMImAyaUagHUliaaLjZRhjD0F4D8AhEy2xLonEREeEIWViMgCmGcI9N8RGAL9KQBvzilebyOifULudSUQ7CSTA/j/7d1LaFxlFMDx/6lFECoqhYpIoCrWBwUjpVLEUsxCREFQUTcuxF0tPopufIKgIMEuFEE3vhbiAww+ilgi2qhgCyrF+KQKulGqIiStoKIcF/cbHZLMkJS5c2c6/x9cJvfeIffscvKd756zBjixHGuAE4AAWjMhW8dxVEnDUsc/wJFyHG591vl25iDpNFIGuB44kpmPNhqgNGIsBUrL02kI9JfASxHxENX+paebDHJYlBWh38txqOFwhlqXkTKSGuCKlSQdI8pImfeBjVStQG4C5qnacdzpxnqpfs0MFpO0IhExFhHvRcRXEfFFRNxerl8QER9FxGxEvBkRzUxhVuPaR8pk5jxVP7WzgHGqFa1dDYYnjQxXrKQh0GUfzfPAXZk5ExE3A2dk5v1Nxqr+K01rdwN7Wt3PF9xfD+zOzI19Dk0aOa5YSUMgM3/KzE/Lz4eB1j6ac6hKPwDTwLXNRKimLDVSplw/re1rVwOf9zs2aRSZWElDpqw+XAjsp/pjeVW5dR0w1kxUo6dTebbcuzUivinXJ2sOpTVSZiIiDpTjCmCylIg/Ay4FdtYchyQsBUpDpeyjmQEezsypiDgXeBxYC7wB3JaZa5uMcVR0Kc+eCtwLXJmZf0bEusz8uclYJfWPK1bSkCj7aF4FXsjMKYDM/DozL8vMTcCLVGN26oyh0yb68YjYV1ZLPo6Ii+qMYxB0Kc9uBx4pA7IxqZJGi4mVNAS67KNZVz5XAfcBT9Ucyt9Ur+2fB2wBdkTE+cAk8GBmjgMPlPPadEnwXm4rh30fEQfqjKMtnvX8X57dAGyNiP0RMRMRm/sRg6TBYINQaTi09tHMtiUL9wBnR8SOcj4FPFtnEF2aUSbQavVwEtWImDq1Erz/ynARMZ2ZN7S+EBG7gLma41jU5iAiVgOnUCWem4FXIuLMYRqTI+noucdK0lFZ0IzydGAP1TiaVcDFmflDH2N5HXgiM6fLeVANlp7IzIM1PndRm4OIeJuqFLi3nH8HbMnMXzr+IknHDEuBklZsiWaU24GdmTlG9fZZ30b7LCjDtWwFDtWcVC1ZngVeAybKdzYAxwO/1hWHpMHiipWkFemwSjMHnJyZWRKOucysvQv8wrck264/CXybmbV1G4+IS4APgFmqYdBQlWffAZ6h6nj+F1UD13frikPSYHGPlaRl67JK8yOwDdhLtVpT20pRWyyL3pIs11cD1wCb6nx+Zn5IVfpcyo11PlvS4HLFStKydVmlmQceo/pn7Q/glsz8pMY4gmqcz2+ZeceCe5cDd2fmtrqeL0mdmFhJGjqdErzMfCsingP2ZWbdrSckaRETK0mSpB7xrUBJkqQeMbGSJEnqERMrSZKkHjGxkiRJ6hETK0mSpB4xsZIkSeoREytJkqQeMbGSJEnqkX8BtpoRtD21EvsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(10, 10))\n",
+    "ax = fig.add_subplot(111, projection=\"polar\")\n",
+    "\n",
+    "theta = np.roll(\n",
+    "    np.flip(\n",
+    "        np.arange(len(deaths_headlines))/float(len(deaths_headlines))*2.*np.pi),\n",
+    "    14)\n",
+    "l19, = ax.plot(theta, deaths_headlines['total_2019'], color=\"grey\", label=\"2019\")\n",
+    "l18, = ax.plot(theta, deaths_headlines['total_2018'], color=\"grey\", label=\"2018\")\n",
+    "l17, = ax.plot(theta, deaths_headlines['total_2017'], color=\"grey\", label=\"2017\")\n",
+    "l16, = ax.plot(theta, deaths_headlines['total_2016'], color=\"grey\", label=\"2016\")\n",
+    "l15, = ax.plot(theta, deaths_headlines['total_2015'], color=\"grey\", label=\"2015\")\n",
+    "l20, = ax.plot(theta, deaths_headlines['total_2020'], color=\"red\", label=\"2020\")\n",
+    "\n",
+    "# deaths_headlines.total_2019.plot(ax=ax)\n",
+    "\n",
+    "def _closeline(line):\n",
+    "    x, y = line.get_data()\n",
+    "    x = np.concatenate((x, [x[0]]))\n",
+    "    y = np.concatenate((y, [y[0]]))\n",
+    "    line.set_data(x, y)\n",
+    "\n",
+    "[_closeline(l) for l in [l19, l18, l17, l16, l15]]\n",
+    "\n",
+    "\n",
+    "ax.set_xticks(theta)\n",
+    "ax.set_xticklabels(deaths_headlines.index)\n",
+    "plt.legend()\n",
+    "plt.title(\"Deaths by week over years\")\n",
+    "plt.savefig('deaths-radar.png')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}