PDA

View Full Version : Updated page and tree on Y-haplogroup T



Maciamo
12-01-17, 13:13
I have created two phylogenetic trees for haplogroup T and T-CTS2214. I have also updated the history section (http://www.eupedia.com/europe/Haplogroup_T_Y-DNA.shtml).

http://cache.eupedia.com/images/content/T-tree.png

http://cache.eupedia.com/images/content/T-CTS2214-tree.png

The P77 and CTS6507 branch underwent a major expansion during the Early Bronze Age, from approximately 2500 BCE. The phylogeny suggests that this expansion took place from the South Caucasus region, including the Armenian Highlands, and spread in various directions around the Middle East and Europe. The European branch appears to have propagated through a Mediterranean route to Greece, Italy (including Sicily and Sardinia) and Iberia. Historically the Kura-Araxes culture (https://en.wikipedia.org/wiki/Kura%E2%80%93Araxes_culture) is the best match for this expansion. While the Proto-Indo-Europeans (haplogroups R1a and R1b) were expanding from the Pontic-Caspian Steppe to central and northern Europe and Central Asia, the Kura-Araxes people, on the other side of the Caucasus, also developed a contemporary Bronze Age culture that expanded across West Asia, and possibly as far east as Pakistan and India. The Minoans, Europe's oldest proper civilisation (as opposed to archeological culture), could be an offshoot from that Kura-Araxes expansion. Kura-Araxian men would have belonged primarily to Y-haplogroup J2a1 (http://www.eupedia.com/europe/Haplogroup_J2_Y-DNA.shtml#J2a_Mediterranean), but also to a lower extent to T1a-P77 and J1-L858.

Sile
12-01-17, 18:02
@Maciano

thanks

Tree from CTS54 below



T-CTS54 (https://www.yfull.com/tree/T-CTS54/) Z19926 * Z19902 * CTS6769+26 SNPsformed 6800 ybp, TMRCA 3700 ybpinfo (https://www.yfull.com/tree-info/T-CTS54/)

T-CTS54* (https://www.yfull.com/tree/T-CTS54*/)

id:YF06979USA [US-VA]
id:NA20758TSI


T-CTS8489 (https://www.yfull.com/tree/T-CTS8489/)CTS10538 * CTS8489 * Z19953+5 SNPsformed 3700 ybp, TMRCA 3100 ybpinfo (https://www.yfull.com/tree-info/T-CTS8489/)

id:ERS256892ITA [IT-CA]
T-CTS8489* (https://www.yfull.com/tree/T-CTS8489*/)
T-Y17493 (https://www.yfull.com/tree/T-Y17493/)Y17497 * Y17499 * Y17500+11 SNPsformed 3100 ybp, TMRCA 375 ybpinfo (https://www.yfull.com/tree-info/T-Y17493/)

id:YF04232
id:YF04203USA [US-NC]


T-Z19945 (https://www.yfull.com/tree/T-Z19945/) Z19945formed 3100 ybp, TMRCA 3100 ybpinfo (https://www.yfull.com/tree-info/T-Z19945/)

T-Z19945* (https://www.yfull.com/tree/T-Z19945*/)

id:YF07608ITA [IT-TV]


T-CTS1848 (https://www.yfull.com/tree/T-CTS1848/) CTS1848formed 3100 ybp, TMRCA 2500 ybpinfo (https://www.yfull.com/tree-info/T-CTS1848/)

id:YF07168BEL [BE-WHT]
id:HG01051PUR
id:HG01530IBS









All of these have a STR of DYS390=22

There are still many in the T ftdna project with DYS390=22 who have not been fully tested................but are all European, usually central or eastern european

The above tree with USA people are all from the British isles and only from Ireland, Scotland, Wales and Cornwall ..............seems like the Gaelic areas of britain

Sile
13-01-17, 07:00
IMO, one needs to treat the T haplogroup like the R1 group, they split ibto R1a and R1b.
T needs a similar split .............like it is shown in the ftdna T project

Maciamo
13-01-17, 10:08
IMO, one needs to treat the T haplogroup like the R1 group, they split ibto R1a and R1b.
T needs a similar split .............like it is shown in the ftdna T project

Where would you place that split? Between T1 and T2, T1a and T1b, or further down? T2 and T1b are extremely rare so it's useless to have separate pages for them. If it's further down, it would be a three-way split. But since T1a1, T1a2 and T1a3 all descend from Early Neolithic farmers from the Fertile Crescent and are all found in similar regions today, why have separate pages? Also the whole T population worldwide is far smaller than even some deep clades of R1b like DF27 or L21.

Sile
13-01-17, 11:17
Where would you place that split? Between T1 and T2, T1a and T1b, or further down? T2 and T1b are extremely rare so it's useless to have separate pages for them. If it's further down, it would be a three-way split. But since T1a1, T1a2 and T1a3 all descend from Early Neolithic farmers from the Fertile Crescent and are all found in similar regions today, why have separate pages? Also the whole T population worldwide is far smaller than even some deep clades of R1b like DF27 or L21.

If you look at this link

https://upload.wikimedia.org/wikipedia/commons/a/a7/Haplogroup_T-M184_tree.png

The bottom left is T1a2 (L131 ) .................the bottom right is T1a3

and all the rest is T1a1

T1a1 went every where that there is T .................while T1a2 and T1a3 had less areas ............I would even say that T1a2 and T1a3 combined did not overpass T1a1 in numbers ................the numbers are even reflected in ftdna T project

Sile
13-01-17, 22:17
I amended the tree below for T , to show from early bronze age and older the path of T

http://i103.photobucket.com/albums/m153/vicpret/Haplogroup_T-M184_tree_1000changes_zpsbkd1icld.png (http://s103.photobucket.com/user/vicpret/media/Haplogroup_T-M184_tree_1000changes_zpsbkd1icld.png.html)

we can clearly see that T1a2 went only into Europe and is a small branch to the bottom left.

T1a3 is barely existing and has not broken out ( pink circle bottom right )

T1a1 is the main branch it is either Persian, West-asian, Armenian/Caucasus or European

Alpenjager
15-01-17, 22:17
Where would you place that split? Between T1 and T2, T1a and T1b, or further down? T2 and T1b are extremely rare so it's useless to have separate pages for them. If it's further down, it would be a three-way split. But since T1a1, T1a2 and T1a3 all descend from Early Neolithic farmers from the Fertile Crescent and are all found in similar regions today, why have separate pages? Also the whole T population worldwide is far smaller than even some deep clades of R1b like DF27 or L21.

http://www.eupedia.com/forum/newreply.php?do=newreply&p=498747

My first aproach on the LT-L298 origin 49600-41400 ybp, their descendant haplogroup T-M184 45500-39700 ybp their main branches T1-L206 (T1a-M70, T1b(xM70)) and T2-PH110 29300-24500 ybp as well as the L2-M595 25700-20800 ybp subclade of L-M20. I haven't added L1 because should be geographically overlapped by T-M184.
Used samples:
All known samples belonging to T2-PH110, L2-M595, T1-L206 (xM70) as well as all known samples belonging to basal subclades of T1a-M70. T1a-M70 basal subclades of: Y8614, Y11675, L162, PH141, L446. Samples found here: YFULL, FTDNA haplogroup T project, Hallast14 et al, Frigi05, Mendez11, Jakovski11 among other published papers.

8364


http://www.eupedia.com/forum/threads/29941-Haplogroup-in-T-in-Germany

I have made a bar chart showing the mtDNA H frequency among all tested Early Neolithic populations. I have excluded H5 results because this is found in Anatolia and is not found in Karsdorf.

Interesting findings is that Karsdorf match pretty well with Eastern Balkans while starcevo-koros and LBK from southwestern Germany match with Anatolia.

I have not included any unreliable sample and I decided count as H5 an ambiguous H vs H5 sample from Barcin. I have not included two samples from Portugal.

83698370


Haplogroup T1a1 doesnt seems to be descended directly from EN framers from the Fertile Crescent. Rather than that seems to be descended from farmers coming from west Black sea (Eastern Balkans) or North Black sea. There is virtually 0% T among Anatolian farmers and there is no evidence that travelled together with the available data. There is as evidence as there is with I1 found among EN farmers in LBKT. Found inhabiting close lands is not the same as arrive together. Even having autosomal admixture is not evidence of any common homeland.
LBK population was composed of "different groups without close social or biological kinship" Meyer et al.
So, there is a good chance that T1a1 and I1 among any other "non found among Anatolian haplogroups" arrived from any other place.

T1a2 Is not clear If arose to the south of Black Sea or to the west. Even T1a3 could be originated in anyplace around the Black sea.
.
I agree T-M184 doesn't seems to need a separate page but indeed, there should be, at least, a page for L-M20. This haplogroup seems to have deep roots around Eastern Europe and Black sea, despite to be widely widespread outside Europe through their L1 branch and specially their L1a subclade.

Sile
15-01-17, 23:42
http://www.eupedia.com/forum/newreply.php?do=newreply&p=498747



http://www.eupedia.com/forum/threads/29941-Haplogroup-in-T-in-Germany



Haplogroup T1a1 doesnt seems to be descended directly from EN framers from the Fertile Crescent. Rather than that seems to be descended from farmers coming from west Black sea (Eastern Balkans) or North Black sea. There is virtually 0% T among Anatolian farmers and there is no evidence that travelled together with the available data. There is as evidence as there is with I1 found among EN farmers in LBKT. Found inhabiting close lands is not the same as arrive together. Even having autosomal admixuture is not evidence of any common homeland.
LBK population was composed of "different groups without close social or biological kinship" Meyer et al.
So, there is a good chance that T1a1 and I1 among any other "non found among Anatolian haplogroups" arrived from any other place.

T1a2 Is not clear If arose to the south of Black Sea or to the west. Even T1a3 could be originated in anyplace around the Black sea.
.
I agree T-M184 doesn't seems to need a separate page but indeed, there should be, at least, a page for L-M20. This haplogroup seems to have deep roots around Eastern Europe and Black sea, despite to be widely widespread outside Europe through their L1 branch and specially their L1a subclade.

Maybe it is best to confirm with this site

http://www.semargl.me/haplogroups/

Run by one of the Yfull team

They only use, 37 marker or above tested or Bigy from ftdna

Sile
10-02-17, 19:35
T1a2 branch only from yfull .................there is nothing south or east of map presented......but there is in USA and Cuba which I excluded because they come from northern Spain or British isles

http://i103.photobucket.com/albums/m153/vicpret/yasim%20t_zpskka4kxcb.jpg (http://s103.photobucket.com/user/vicpret/media/yasim%20t_zpskka4kxcb.jpg.html)

Sile
17-03-17, 19:50
http://www.eupedia.com/forum/newreply.php?do=newreply&p=498747



http://www.eupedia.com/forum/threads/29941-Haplogroup-in-T-in-Germany



Haplogroup T1a1 doesnt seems to be descended directly from EN framers from the Fertile Crescent. Rather than that seems to be descended from farmers coming from west Black sea (Eastern Balkans) or North Black sea. There is virtually 0% T among Anatolian farmers and there is no evidence that travelled together with the available data. There is as evidence as there is with I1 found among EN farmers in LBKT. Found inhabiting close lands is not the same as arrive together. Even having autosomal admixture is not evidence of any common homeland.
LBK population was composed of "different groups without close social or biological kinship" Meyer et al.
So, there is a good chance that T1a1 and I1 among any other "non found among Anatolian haplogroups" arrived from any other place.

T1a2 Is not clear If arose to the south of Black Sea or to the west. Even T1a3 could be originated in anyplace around the Black sea.
.
I agree T-M184 doesn't seems to need a separate page but indeed, there should be, at least, a page for L-M20. This haplogroup seems to have deep roots around Eastern Europe and Black sea, despite to be widely widespread outside Europe through their L1 branch and specially their L1a subclade.

http://www.eupedia.com/forum/image/png;base64,iVBORw0KGgoAAAANSUhEUgAABS8AAAI/CAIAAAAZW7sLAAAgAElEQVR4nOyd25nrKgxGU5cLoh5XQzMuxv OQxOYiLslEgjhrfefh7DGxuMgSP Dktt9u/Md//Md//Md//Md//Md//Md//Md//Gf5322/3XYAAAAAAAAAMAM1DgAAAAAAAGANahwAAAAAAADAGtQ4AAAAAA AAgDVD1Lh3t5vzfWW3dblVWNZNt64An8W7mkOnT8a9dO/TAhciDH2VOBcUu5eSQ2Z0h9MH8awiHY/eti5aXViw3jG4HzFcdI/M3RTo7XmdSkw57mOt63vdvNazmuiHzOARNJrgVtpuUJl6z2tPg Wa1PjbSDrXeyEEfMly9/4iJ981ejT G aUh9k76xLYuqHH4MrwLPDlx7G1dzgAQxEM008/yTBz12ULiQznBx707i7 yMPpD9D16j1If77 K9ebg/hPvsvgTptgg496vWrY9LaLgt9OO 1jr2l43s/W8lG68jAT/ti7qgrzWdv3K1Hteewo0r/WxkXao9UYO jfN w beN/M1fhbi9qyGt93v6LG4buIfDZzbO SAOCdeVCAifBuca6YELd1WZbo6rYutfQWafHdbLfnK2k8eq11E gXrrcH9J9u6CsEnmI67MN1qb9FWdup0HXa cR9rXdnrpraeXlZ2vvyZSuK1FoWeN6pMvee1p0DzWR8baYdab Sgf9N9/wETb3M1fg kJXVd RSn0uF6tB0bNf7beLesfl1kN9nWxfnIQzbv01I1MY4cr1B99Ly 7JV2vb701uCaVOGuzLlo5uWB2Kz0HJtaPi bjPta6oddNZz0xqp2Mpe1nmwmAqIvMKjOfHp7F r7vIyLtPNaHDcD11fhxAuLFBQ9JtMT74seBiuA4Uf0NXed3Yac vc9956lc/hizp7Hk3U3muVDhfTWu/WoNzIF3y7oV4mWwtFnd0onFeFKW5Z4SlZ55dOowVZbUQ4vaUo2 wZ/kx5LZbrclPO 6X97pprRv0/L7LAtjitHq3GleqzMx6eLwat4 081jX3y4o3f/yajx4HeE1OS7k4fss9Xnbpypxblm35/5Pfib4YS/u6CPClO7TLO/cI0Hc6 lcuObARBeKvKnGoz9ycOTKPOKcsC34jKbNycR5bdxs7xspduzR p6N1kbIY370reIby dniudmb8/pfLzTpuP A101q3aTnzyqkE12L9C61zK4yM vhwWp8QKSdxvqzjKr3l 5/dTWe79L0tlbc5RbiRHS/8A0BQU/n6rpYr0r55EsO4mjFRBfqvKfGY79S p4NmIJj1TENTMeadfuA5R58BjXeTfkIW7QQNk6ZGOyMF07wDdD D8eGz578N32IdO 4/4XVTWjfq cBCaOO1bav/2RUX/S0qM7MeHmd9VKSdwfoTOQd9jvL9L67GkzgqiYhEdRdE7uNPgho vdF56ranGJfVTUePJFxoX7gyQ8ube EFwnAM1fknOOBcHzDOaNiJfFnOHvJf4jYg9Ex3cG6uLdMV44xd LnqlapQZ952ZNtcHYcf8Rr5vQulnPhxZvCfpzyFLLbCozqx4eb 323j7QTWdf 1aza/S txuXfqSj Dm5Tjaffp17sPCGNo8ZhDt5X4 fWEHvjVyZ7I d5cL0asY4PS8udzT/Bvu8lTRj YaguUhXjPW8L6p2h7VPjhudmx477z3jddNYNe17ELLf3tEyvMj Pr4eFqfDeOtNNY13xjvX3/K6txKYz2f0Vqhz8UOk8UxKhxmANOqkOd6AzQczewc8MmD7p5RO I71YvkHVtaUrZ/l1JVmPRuSXittwqLJ9UFhW6xNz523H/E6ya0btnzxRqZmOp7R0CrMjPr4RnUuG2kncL6yG3xfd8vrcblM Notx8uBIH6tUjhmFB9Rv/9cBmoc5uAtNS75HILqosRv5Ei/1VBMG2LQTTIrYrxM17nZUbuUasIkm6aUtxC81hfsiG3P/ idSu9PPe5jrevK4bmtd5X4dHWsrHWsgyhWZmY9PIsaH7EOMsr6 CznoLXruf1k1Xs6aj6PpPYszgkOE9 05g3n8rH1wJTgc77x8n1r5RBnFn0aNQ5331XjybUY359N3N AKpDPQ3GNKPlSYu4b6Gy1eY2ZdpCRM5M3Au6Vk7XzT xnaUtsjT9dLrjOP 1jrynJ4aut9JT7D4zE0nDrWdym1KzOzHh5gfWykHR3naznoE3T e/5JqPFCvklhudXf5rFDwqahQJM9T4m9Euhc/07p4n0r50MSy vjT5YYD5I5dmHyKBcIPn56Gl12K2EPCJb7j/xMPSXfR6zOM9BNw0pWb1DJ207rON82Wcu3TUnxd82t2y22PI5 19ajYgHEfa131 40ntx4U00yzh3tbC3Gp7SaVqfd857hczfrYSDvSeisH/Zeu 2uPe4mb6e NAwAAAAAAAABqHAAAAAAAAMAe1DgAAAAAAACANahxAAAAAAAAA GtQ4wAAAAAAAADWoMYBAAAAAAAArEGNAwAAAAAAAFiDGgcAAAA AAACwBjUOAAAAAAAAYA1qHAAAAAAAAMAa1DgAAAAAAACANahxA AAAAAAAAGtQ4wAAAAAAAADWDFHj3t1uzveV3dblVmFZN926Anw W72oOnT4Z99K9TwtciDD0VeJcUOxeSg6Z0R1OH8SzinQ8etu6a HVhwXrH4H7EcNE9MndToNrzcfxU6PyJrd x97rzkurID/P5mvXwolHUDIwZTXD7xl2rMnWf154C1e6vnygnjjaDY53iuNdz XCMD6nGzV OP2PrSk 2d9IltXVDj8GV4F3hy4tjbupyPfzAJQTP9LM/UUM9ZiQ/lBB/37iz ysLoD9H36D1Kfbz/Ktabg/tPvMviT5hig4x7v2rZ9vS6Qt/PbT0qZdrz27rckqz14ZnXQJ vW5eqoBkxo6WWpN/1DNbGXbUy9Z7XngLV76 dKOeONiOtq457Pcc1MqAqN3M1fnT0K22U1fixU1Dt9F5LOZY3u XhB/vlNM/TI13i3NF4bOty7JEV7d1SUuGCSadSSvus309jUevtU6iYL01uP 9kW1ch ATTcRemW 0tWunWn2ztt1kPLtt63b6tSzz7yv6gaF3Z5xvWNe0J5M UwsqHRKHnjSpT93ntKZB8f6tEOWm0mSDWqYx7Pcc1MqAy5mr8P sgldV35FKfS4Xq0HRs1/tt4t6x XWQ32dbF chDNu/TUjUxjhyvUH30vLslXa9vvTW4JpU4a6Mkycpmj5V8XX d0vpx0dzrhF3R6AyXrnVDnx877oep5JmymQBIPW9XmQnVuFmin DLazBDrrKa U1Ri33dzNX6cdHtxyUESLfGHFwLjjIVX9D1/l9z3fq0/vcd57K5Y/BehpL3t1kngsV3lfjw15uAVO8W9atEC Dpc3qZlosxpOyLPeUqPTMo1PNdVGhHlrUZqDCnuXHKO9WWQS9G a0/L43xuvjIppYWH 3zY8d932UBbHFavVuNK1VmPjVulyhnjDYTxLrm1U9RX2Ux3ay4 marx4LWz1 S4IFrus9TnbZ9u49yybs9lrfxM8MNePMxHhCndp1neuUeCuNfT uXDNgYkuFHlTjUd/5ODIlXnEuS3fHn9G02ZKO6 Nm 19I 1TfKPVuPZxQu8KnqF8flbeod224OpNL hNaP1xYaDXWXx732CfHzvup5F0omuR3ouK1KQy06nx0SsRvx7r Oq5 sA6V1tWvfhhTNZ7v0vT2tbjLLcSJ6H7hOwCCns7VdbFelfLJl9 nE0YqJLtR5T43HfqX0fUowBceqYxqYjr3J9tHWPfgMarybQscG XTpYjRvsjIvNL Xgj9Fuu9Z3mU1rfQKv09ejY31 7LgHdbi9O0/ p11x0d iMqjxyaLNFNYt1Hj9nI/eKSARSzWexFFJRJROSNT3xs8itTSSTkpralxSPxU1nnyhceHOA Clv7o0fBMc5UOOX5IxzccA8o2kj8mUxd8h7id IvG8QHtAeq8Z1xXjjF0ueqXrQHLG30EWsD/e6Y146eNw1fX7suB8I3yGvP4esjbt Zb5CjStVY8JoM4l1k0et/tuxtuLNTo3Lv1NR/B3cphpPv0 9fsQq/ixqHObgfTV f1TOFybQU9ckeyPneXC9GrGOD0vLnc0/wb7vpVla IehalxVjPe8Fa53hravX70bOEO1tD7c67zLJjkqNRjr82PHXcQ st/e0Xa8y06lxw0Q5XbSZxrr2uNdznOb3ohQwU NSGBXehSx/ulWwMHSiIEaNwxxwUh3qRGeAnicFO7fK8qCbRyS U71I3rGlJeURJxgVhUnvpkAs0j5I9xzxJ6wP97r8j0ojP9DnO6 wfpcymdHrrXaKletsVKzOfGrdLlLNFm3msq477ZNvi 77bqXE5jHbL8XIgiF rFA54xUfU7z XgRqHOXhLjUs h6C6KPEbOdJvNRSTlhh0k3k0YrxM17nZUXvjasIkm4iUNwnUhE lXv6ptXsxu3dzr5AUCezWuK8YHj7tcHavY3LEOoliZ dS4XaKcPdqMs6437vUc90IG/Cw2arx80OFxNL3V1oJoCe/bc7RkW10m3YPD8c7L96mVT5RR/GnUONR5X43Hv7F3uzmfvrsBVyCdgeYeU/Khwtw1nFagxWtMqIvCqxp25W3Yu6Vk7VxJkO37Xl4Sj5YgTb9L bKz1V0t82row9GNOrqrGqrHjnlq12hW/U l5g8rMqMatEuV80WaWWKc07rUc17qqi74aD9SrJJZbDS6f0go FRWK5HlK/I1I9 KnZhbvUykfmlhWH3 63HCA3LHlbUy5QPjh09PwsksRe0i4xHf8f Ih6S56Pc ln4CTrtyk yphzbrOd72Wcq08UdH8OnWx7dGVH7OeF7P2umjwtbbF7X2 ad1i3E OXrYW4lLbTSpTH/fOJ0LJunainDXajI91auNez3GtDKjMzfT3xgEAAAAAAAAANQ4A AAAAAABgD2ocAAAAAAAAwBrUOAAAAAAAAIA1qHEAAAAAAAAAa1 DjAAAAAAAAANagxgEAAAAAAACsQY0DAAAAAAAAWIMaBwAAAAAA ALAGNQ4AAAAAAABgDWocAAAAAAAAwBrUOAAAAAAAAIA1qHEAAA AAAAAAa4aoce9uN f7ym7rcquwrJtuXQE i3c1h06fjHvp3qcFLkQY ipxLih2LyWHzOgOpw/iWUXKj17W5XbWOwb3I4aL7jGw7UL9NPy3I QenfD5Lui1rvPgDvO6ea1nNdEPmYGLG01wK203qEy957WnQLX7 6yfKKbPMaOuNHPQhw9X7j5h43 zV GOYXxpi76RPbOuCGocvw7vAkxPH3tblDABBPEQz/SzPxFGfqyQ lBN83Luz CsLoz9E7dELss692Mf7r2K9Obj/xLss/oQpdmjb0 sKgbEj5D6LKDw1fQH/UepKXjez9byUbryMBP 2LuqCvBHrlCtT73ntKVD9/tqJctosM9Z6Iwf9m b9h028b Zq/K1lZVmN77tfUePwXUQ mzm2d0kA8M48KMBEeLc4V0yI27osS3R1W5daeoumGLvZbs9XIj 162 rClKO9TZlZrw/uP9nWVQg wXR8ZNufF4y2Jiv7hLo1aAT81uqcgnVlr5vaenpZefjzZyqJ11 oUet6oMvWe154Cyfe3SpQzZpmB1hs56N9033/AxNtcjd8DaUldVz7FqXS4Hm3HRo3/Nt4tq18X2U22dXE 8pDN 7RUTYwjxyt0PHrbumjlJWmm0hhck0qctTFt 9OiwZZF1br6LKTqdd7dkgde37qh101nPTGqnYyl7WebCYCoi8w qM6EaN0uU02WZuayPWoz5ATV nIB4ccFDEi3xvvhxoCI4TlR/Q9f5Pcjv2cGM5xmGZamWP8bsaSx5d5N5LlR4X41rv1oDc Ddsm6FeBksbVa3dGIxnpRluadEu2eEfTtD67u6GK/NQO3bnuTzEdYH7VKel46phuHeeKkeP2TdoOf3XRbAFqfVu9W4U mXmU N2iXL2LDM6x2lvF5Tuf3k1HryO8JocFzLhfZb6vO0zQTu3rNtz WSs/E/ywF3f0EWFK92mWd 6RIO71dC5cc2CiC0XeVOPRHzk4cmUecU7YmHtG0 ZU5rw2brb3jXScXFXst57koX1w27tCC0e0fdu24OpNL gVT 3enNf/cqP2Kf3RalzP6ya1btLzZxXSia5Fei8qUpPKTKfGp1mJGJxl5s hxqt5fuv/V1Xi S9PbWnGXW4gT0f3CNwQEPZ2r62K9KuWTLzmIoxUTXajznhqP/UrpezZgCo5VxzQwHWvW7QOWe/AZ1Hg35Y6N8tG4U3wGO OFE3zD2675jVpFZRI0 P5vw3dog7EerMYVvW5K60Y9H1i4vTtP/qddcdHfojKo8fmyzDxx3j7H7fvl1XgSRyURUTqLVt8bP4sUOi 91lTjkvqpqPHkC40LdwZIeXNv/CA4zoEavyRnnIsD5hlNG5Evi7lD3kv8Rpo980xXZpowK6F7gq8 WmAa3vbfQh6wLD46pMomOi45V45peN6F1s54PLd4S9OeQpZbZV OYr1LhSNSbPMkOtN3LQv6nd/9JqXP6diuLv4DbVePp96sXOEx4s1DjMwftq/NycYW/8ymRv5DwPrlcj1vFhabmz SfY9737HJ1SiO ZJ kNW8/bggPbfpQap8YNT 0mgzFUjat63XTWDXtexCy397RMrzLTqXHDRDl5lhloXfON9fb9 r6zGpTDa/yWlHf5Q6DxREKPGYQ44qQ51ojNAz/24zg2bPOjmEYnvVC/SvUM7ZJ6kKEx6tySGtf0oZWa9pNAtZueljYwBb7Eqn8eYzbplz xdrZGKq7x0BrcrMp8btEuXcWWaY9ZHb4vu X1qNy2G0W46XA0H8WqVwzCg on7/uQzUOMzBW2pc8jkE1UWJ38iRfquhmDbEoJtkVsR4md6ZyogZqp 4wyaYp5S2EUW1v1UvDev5Hpa35rtPao/bGdeXw3Na7Sny6OlbWOtZBFCsznxq3S5RTZ5lB1l/IQW/Rc//LqvFy3nocTe9ZnBEcIrxvz9GS42ftgyvB4Xjn5fvUyifKKP40a hzqvK/Gk 8TujmfvrsBVyCdgeYeU/Khwtw1nFagxWs0H7190/sp1vYMVWPc5M3Au6XBbY/taW6dFHo es70UvvMalxZDk9tva/EZ3g8hoZTx/oupXZlZlTjVolyuiwzOsfVctAn6Lz/JdV4oF4lsdzq7vJZoeBTUaFInqfE34h0L34mVvE lfKhiWX18afLDQfIHbsw/RMLhB8 PQ0vuxSxh4RLfMf/Jx6S7qLX5zfpJ Ck OjFg6L5VbO1yKDzTbOlXPu0NLjt0ZUxPR/HXWvrUTGlI/L2Xvcd1oNimmn2cDBrIS613aQy9Z7vHBcl69qJctIsM9J6Kwf9 l677a3tdiZvp740DAAAAAAAAAGocAAAAAAAAwB7UOAAAAAAAAI A1qHEAAAAAAAAAa1DjAAAAAAAAANagxgEAAAAAAACsQY0DAAAA AAAAWIMaBwAAAAAAALAGNQ4AAAAAAABgDWocAAAAAAAAwBrUOA AAAAAAAIA1qHEAAAAAAAAAa1DjAAAAAAAAANYMUePe3W7O95Xd 1uVWYVk33boCfBbvag6dPhn30r1PC1yIMPRV4lxQ7F5KDpnRHU 4fxLOKlB 9rMvtrHcM7kcMt91jWxct9 kIekc/fH4Aeq2PbfsP9by z9esZzXRD5nBI2g0wa203aAy9Z7XngLV7q fKKfMMsOtt69 wHB1ZEdMvG/2avwxzC8NsXfSJ7Z1QY3Dl Fd4MmJY2/rcgaAIB6imX6WZ Ko56zEh3KCj3t3Fn9lYfSHqD16Qda5F/t4/1WsNwf3n3iXxR85xT4uWrY9LaLgt30hd2Dbx1of0/PaPl 3npfSjZeR4N/WRV2QN2KdcmXqPa89BarfXztRTptlBltXHvdmjhs28b6Zq/G3llZlNb7vfkWNw3cR Wzm2N4lAcA786AAE Hd4lwxIW7rsizR1W1d0pJh ommGLvZbs9XIj162 rClKO9TZlZrw/uP9nWVQg pYmq4mylHPQstiwaIXdY28daH9Xzyj7fsJ5eVu6CPJ4k8VqLQs 8bVabe89pTIPn Volyxiwz0nrv1TfpznEDJt7mavweSEvquvIpTqXD9Wg7Nmr8t/FuWf26yG6yrYvzkYds3qelamIcOV6h49Hb1kUrLwnWW4NrUomn Uc3AVLj3VnoOTKwfF4e0faz1cT1v6PNje37f5e1nmwmA1PN2lZ lQjZslyumyzDTWjaa BTPXV PHCYgXF1ol0RLvix8HKoLjRPU3dJ3fhZ369D73nady WPMnsaSdzeZ50KF99V47 ud8N14t6xbIV4GS5vVLZ1YjCdlWe4p0e4ZYd/O0PquLsbFGejDpr0mtFqTr7RsWNvHWp g56NC5jt1Bj2/77IAtjit3q3GlSoznxq3S5SzZ5lx1m1mJqVVlsur8eB1hNfkuJ AN7rPU522fqsS5Zd2ey1r5meCHvbijjwhTuk zvHOPBHGvp3PhmgMTXSjyphqP/sjBkSvziHPC5tQzmjZT2nlt3GzvG k4uarYbz3JQ1mM795lLTxMWmvC5zq5/tcLFVs2rO1jrU/Q83GZS/b8WYV0omuR3ouK1KQy06nxaVYiBmeZodbNToWILby6Gs93aXpb K 5yC3Eiul/4hoCgp3N1XaxXpXzyJQdxtGKiC3XeU OxXyl9zwZMwbHqmAamY826fcByDz6DGu m3LFRPhqnTAx2xvMJerQIaKiL4sNnz38bvsU6sO1jrQ/v aSIls P7fnAwu3defI/7YqL/haVQY3Pl2XG5zgLPSzkOEPrCZZqPImjkohIVHdB5D7 JKjxQuel15pqXFI/FTWefKFx4c4AKW/ujR8ExzlQ45fkjHNxwDyjaSPyZTF3yHuJ30izZ57pSqX32uOiK 8aFXyyJDi0aa0LBdU21wci2/3bPpyVsz82a9Xxo8ZagP4cstcymMl hxpWqMXmWGWhd/2kTcpyh9Qw7NS7/TkXxd3Cbajz9PvVi5wkPFmoc5uB9NX5uULA3fmWyN3KeB9erEe v4sLTc2fwT7PvefW5WKcT3zJP0hk14WzD503hNaHhudnjbf7bn swJ6Pj 250XMcntPy/QqM50aN0yUk2eZgda1x73 RvyV1bgURvu/qLPDHwqdJwpi1DjMwXtqnJPqv0N0Bui5J9W5YZMH3Twi8Z3qRb rysdd6s65lXVGYiFsGpeV0K1VW0okW8 Oxbf/lns um6oyy54v1sjEVN87AlqVmU N2yXKubPMSOuq417bFte3LmOlxuUw2i3Hy4Egfq1SWM6Pj6jff y4DNQ5z8JYal3wOQXVR4jdypN9qKKYNMegmmRUxXqZ3pjJihqo nTLJpSmELwfy0dv5H7wbNj83bPtb6LD2vK8YH97xszMpaxzqIY mXmU N2iXLqLDPUut649 S4y6rxcux HE3vWZwRHCK8b8/RkuNn7YMrweF45 X71Monyij NGoc6ryvxpPv1Lk5n767AVcgnYHmHlPyocLcNZxWoMVrNB 9fdP7Kdb2TEVj3OTNwNLBC2tFGnm6XnKdUA Ptj5FzyuL8YnU OMxNJw6VlpmUJkZ1bhVopwuy8yU4zQ6vTPHXVKNB pVEsuNlF85KxR8KioUyfOU BuR7sXP5CLep1I NLGsPv50ueEAuWMXpkBigfDDp6fhZZci9pBwie/4/8RD0l30ep5LPwEnxUcvHhTNr5qtRYbq4L5NKddWFtKVDiuXbce R79PGYko9o 1vrontfx V7rQTHNNHt0srUQl9puUpl6z3eOi5J17UQ5aZaZIMepjXtXjtP 2uhI3098bBwAAAAAAAADUOAAAAAAAAIA9qHEAAAAAAAAAa1DjA AAAAAAAANagxgEAAAAAAACsQY0DAAAAAAAAWIMaBwAAAAAAALA GNQ4AAAAAAABgDWocAAAAAAAAwBrUOAAAAAAAAIA1qHEAAAAAA AAAa1DjAAAAAAAAANagxgEAAAAAAACsGaLGvbvdnO8ru63LrcK ybrp1Bfgs3tUcOn0y7qV7nxa4EGHoq8S5oNi9lBwyozucPohnF Sk etETrNWBHQ/ MdAfzYKN1mXupsCwtr9kXWfksZ7duyOgKVrPaqIfMoNH0GiCW2 m7QWXqPa89BardXz9Rlq0Pj7TxTFWhA4Zl2I77j5h43 zV MPJXnIw76RPbOuCGocvw7vAkxPH3tblDABBNEYz/SzPxFGfqyQ lBN83Luz CsLoz9E5dHzLntCP52EOh78Z5FPj12jdUHGvV/9uO8MbHuf9bPUiLb/ovVmQFO1npfSjZeR4N/WRV2Q19quX5l6z2tPger3106UjZ4fHGmTx 7D9gdm2Ob9h028b Zq/K1FbVmN77tfUePwXUQ mzm2d0kA8M48KMBEeLc4V0zH27osS3R1W5daeoumGLvZbs9XIj x627oKj6dKB5YffDWbjdZtqwvTrfYmqW3bu6wHVVCrBNbje7cC mq719LKy8 XPVBKvtSj0vFFl6j2vPQWS72 VKOWeHx5plZ6xpnXtDNt9/wETb3M1fh/kkrqufIpT6XA92o6NGv9tvFtWvy6ym2zr4nzkIZv3aamaGEeOV h69NSez8KNt5IvqFBr3bYuWvUY2/bqkHp3Sx45rGtabwU0XeuJUe1kLG0/20wARF1kVpkJ1bhZouxo3ahIO3rd84VCn6/E9dX4cf7ixQUPSbTELHcY7gOFH9DV3nd2GnPr3PfeepXP4Ys6e x5N1N5rlQ4X01bvLyKgzHu2XdCvEyWNqsbunEYjwpy3JPiZ6e0 VvMKO/Y2K1L11on7Fl jLFtr4z742EapId/2XpWD1PrBm3fd1kAW5xW71bjSpWZT43bJcr2bQdFWoPp5dgMW7//5dV48DLEa3JcyMP3Werztk 3cW5Zt eyVn4m GEv7ugjwpTu0yE6XN8AACAASURBVCzv3CNB3OvpXLjmwEQXiry pxqM/cnDkyjzinLAt IymzanMeW3cbO8b6Qnf3lnuWjzXii2 4OdRB/n yudnx7a9fXpziCL9ZetSReysm7T9rEI60bVI70VFalKZ6dT4NC sRIyLtvm1bcHVEpI3LDMlxV1fj S5Nb2vFXW4hTkT3C98QEPR0rq6L9aqUT75iIY5WTHShzntqPPY rpW/5gCk4Vh3TwHSsmLcPWO7BZ1Dj3XTtWqg9eMX5cRAx7v/WGjy5dVEuNpyl2bW9fHQxWgC11YS/bL1QEyPrRm0PLNzenSf/06646G9RGdS41LpRkTZG87sLx2bY v0vrsaTOCqJiNIJifre Fmk0HnptaYal9RPRY0nX2hcuDNAypt74wfBcQ7U CU541wcMM9o2oh8Wcwd8l7iN9LqGd3f9OibI pN1hute6Zqq1maYdvl/aLwuKi5Iv1l62kJ23OzZm0PLd4S9OeQpZbZVOYr1LhSNZq3tY6 0bxbSsK6bYev3v7Qal3 novg7uE01nn6ferHzhAcLNQ5z8L4aP7eG2Bu/MtkbOc D69WIdXxYWu5s/gn2fe9YCNPfJeucI34 x/S0Tu8M7di2y9bDPxgr0l 2nhXQe jGtl3ELLf3tEyvMtOpccNE2Xla2y7SFkrZj7t2hq3f/8pqXAqj/V R2uGNhc4TBTFqHOaAk pQJzoD9NwN7NywyYNuHpH4TvUilXysvWhfsF5SqR8ev97Wea13 Gge2XbRU2kqwWQv4ZevZdVNVZtn2Yo1MTPW9I6BVmfnUuF2i7N 6dHqvGra2P3Bbf9/3SalwOo91yvBwI4tcqhWNG8RH1 89loMZhDt5S45LPIaguSvxGzvnFkT4oURh MegmeR0xXqZyejM7a2W0Z5L/8ePbFi 0TmuSNqztFesvlsC6gnVdMT647bIxK2sd6yCKlZlPjdslyl41P mId5IHaJvWoDNtz/8uq8XLWfBxN71kaEtwxvG/P0ZJtdZl0Dw7HOy/fp1Y UUbxp1HjUOd9NZ58m9HN fTdDbgC6Qw095iSDxXmruG0Ai1eo7zAm2GnCaPR/niCqbUuWTvf9H4Ed0zbW9ZfKoH1z1tXFuMTqfHHY2g4dazvUmp XZkY1bpUomxM880gb29PcpB6SYTvvf0k1HqhXSSy3urt8Vij4V FQokucp8Tci3YufaV28T6V8aGJZffzpcsMBcscuTD7FAuGHT0/Dyy5F7CHhEt/x/4mHpLvo9flN gk4KTx6pWz04SevkRnjp/9zZluti69rfsmvfdv7rEfFlI5DYF22Xg1o6taDYppp9nBvayEu td2kMvWe7xwXJevaibLd85rdX257dMXWunaG7bq/tteVuJn 3jgAAAAAAAAAoMYBAAAAAAAA7EGNAwAAAAAAAFiDGgcAAAAAAA CwBjUOAAAAAAAAYA1qHAAAAAAAAMAa1DgAAAAAAACANahxAAAA AAAAAGtQ4wAAAAAAAADWoMYBAAAAAAAArEGNAwAAAAAAAFiDGg cAAAAAAACwBjUOAAAAAAAAYM0QNe7d7eZ8X9ltXW4VlnXTrSvA Z/Gu5tDpk3Ev3fu0wIUIQ18lzgXF7qXkkBnd4fRBPKtI dHLutzOesfgfsRw0T0Gtl2on4b/Tmz9zrYuWg/uMK b13p40ShqBsaMJriVthtUpu7z2lOg2v31E Ws0WZ4nNcc90aOU7Ze5Gavxh/D/NIQeyd9YlsX1Dh8Gd4Fnpw49rYuZwAI4iGa6Wd5Jo56zkp8KCf 4uHdn8VcWRn I2qMXZJ17sY/3X8V6c3D/iXdZ/AlT7NC2p9cVAuPc1qNSV/K6ma1LVdCMmNFSy7Yu6oK8EeuUK1Pvee0pUP3 2oly3mgzOs6rjnsjxw2ceN/M1fjR1Feea1mN77tfUePwXUQ mzm2d0kA8M48KMBEeLc4V0yI27osS3R1W5e0ZJh oinGrrrP9vVIj962ujDlaG9TZtbrg/tPtnUVgk8wHR/Z9ucFfWed1HpwWS8lDPC6qa1r2hPIn6kkXmtR6HmjytR9XnsKJ N/fKlHOF20miPMdV9 kkeOUrdcxV N3Fyup68qnOJUO16Pt2Kjx38a7ZfXrIrvJti7ORx6yeZ Wqolx5HiFjkdvWxetvCRYbw2uSSXO2pi2/WnRYMtiSuvHxeSB17du6HXTWd93s3E/TCXPlM0EQOp5u8pMqMbNEuXE0easiW2c77qqXYnrq/HjBMSLp/Il0RLvix8HKoKDXPU3dJ3fA5/PDmY8zzAsS7X8MWZPY8m7m8xzocL7arzj1Re4AN4t61aIl8HSZ nUzLRbjSVmWe0q0e0bYtzO0vquL8doM1L7tST7/KevPS8dUw3BvvFSP37BuNe77Lgtgi9Pq3WpcqTLzqXG7RDlttH kwLsfZzExKOe7yajx4HeE1OS6Ilvss9Xnbp9M6t6zbc1krPxP8 sBd39BFhSvdplnfukSDu9XQuXHNgogtF3lTj0R85OHJlHnFuy7 fHn9G0mdLOa Nme99Ix5lhxX7rSR7ahxm9K7RwRNu3bQuu3vSC3oTWHxfufx6t xvW8bkLrZuN GkknuhbpvahITSoznRofvRIxPtqcl4flOLNTIWILr67G812a3t aKu9xCnIjuF74hIOjpXF0X61Upn3zJQRytmOhCnffUeOxXSt zAVNwrDqmgelYs24fbd2Dz6DGuyl3bJSPxsyTHkW0d8YLJ/iGt13ru8ymtR6M9WA1ruh1s1vXHPegDrd358n/tCsu ltUBjU WbTZZ4jzFnpYznFW1hMs1XgSRyURUTqfUd8bP4sUOi 91lTjkvqpqPHkC40LdwZIeXNv/CA4zoEavyRnnIsD5hlNG5Evi7lD3kv8Rpo980xXgzShrhhv/GLJ4Lb3FrqI9ei46Fg1rul1s1vvLfQvhO Q159DlpplU5mvUONK1Zgw2mQFxsR5k0et6M2XVuPy71QUfwe3q cbT71Mvdp7wYKHGYQ7eV P3R V8YQI9dU2yN3KeB9erEev4sLTc2fwT7PvefW5WKcT3zJP0hq3n bcGBbT9KDVPjltaTwRiqxlW9bnLrRymzcGmW23varleZ6dS4Ya KcLtrIZQbEee1xr e4K6txKYwK70KWP90qWOg8URCjxmEOOKkOdaIzQM Tgp1bZXnQzSMS36lepHvXYogiVRQmjW3xsAqD1fhPWC9tZIw4N 6t7HmNu60cpsymdng4SLdXbrliZ dS4XaKcLdoUCl1NjTdz3IXVuBxGu V4ORDEr1UKB7ziI r3n8tAjcMcvKXGJZ9DUF2U I0c6bcaimlDDLpJZkWMl mdqYyYoeoJk2yaUt5CGNX2Vr0ubn3g3riuHJ7b h3Nr5gWqmMVmzvWQRQrM58at0uUs0ebfVic1xv3nhx3WTVePmb xOJre9dpE7hDhfXuOlhw/ax9cCQ7HOy/fp1Y UUbxp1HjUOd9NR7/xt7t5nz67gZcgXQGmntMyYcKc9dwWoEWr9F89PZN76dY2zMVjX GTt2Hvlga3PbbXu31/DeuvltCyriyHJ7RuN 6pVatd8TuVnjeozIxq3CpRThdtZspxGp1ey3H61mvoq/FAvUpiudId 77XTmkFn4oKRfI8Jf5GpHvxUzOL96mUD00sq48/XW44QO7Y8jamXCD88OlpeNmliD0kXOI7/j/xkHQXvZ7n0k/ASfHRiwdF86tma5GhOrhvU8q1T0uD2x5d THrebHPD/8gr5vcusW4nxyPmLUQl9puUpn6uHc EUrWtRPlpNFmghynNu6tHKdrvcHN9PfGAQAAAAAAAAA1DgAAAA AAAGAPahwAAAAAAADAGtQ4AAAAAAAAgDWocQAAAAAAAABrUOMA AAAAAAAA1qDGAQAAAAAAAKxBjQMAAAAAAABYgxoHAAAAAAAAsA Y1DgAAAAAAAGANahwAAAAAAADAGtQ4AAAAAAAAgDWocQAAAAAA AABrhqhx72435/vKbutyq7Csm25dAT6LdzWHTp Me nepwUuRBj6KnEuKHYvJYfM6A6nD JZRcqPXtblptbzany0ElF8qtjf1kXLfYa1/SXrA9oe5w6FCkzZ8x0BTdF6VhP9kBkMs9EEt9J2g8rUe157ClS 7v36inDXLjM9xiuPeyHEWbZe42avxR1NfaqZ30ie2dUGNw5fhX eDJiWNv63IGhyAmoJl lmfiqOesxIdygo97dxZ/ZWH0h6g9ekHWuRf7eP91PPjPIp 27V0Wf QU 7h4pbb3Wd/HtT15sj9sf9qebwY0Vet5Kd14GQn bV3UBUEj1ilXpt7z2lOg v21E W8WWZ0jlMd90aO0297kZu5Gn9raVVW4/vuV9Q4fBeRz2aO7V3y8HtnGhBgMrxbnCsmhW1dliW6uq1LWjJM P9EUYzfb7flKpEdvW12YcrQ3SSu7VSqSaBWCT2miqqhMBrS9y3 pQBfu2R4 xFvP1fCug6VpPLyt3QR5PknitRaHnjSpT73ntKZB8f6tEOV WmSHHtaSSPH2bVdwFyN3wNpSV1XPsWpdLgebcdGjf823i2rXxf ZTbZ1cT7ykM37tFRNjCPHK3Q8etu6aOWlgvWt5AsqFCeqiduZm LVqe7VlY9s aiViXM 3Apqu9cSodjKWtp9tJgBSz9tVZkI1bpYop8wyU1g3mvrWzCi2X cBYjR nAF5caJVES7wvfhwqCI4T1d/QdX4Pclx2OOF5hmFZquWPoXwaS97dZJ4LFd5X472vd8J3492yb oV4GSxtVrd0YjGelGW5p0S7Z4R9O2XrtuvS4gz04VD2itSq7ZW WjW27QcCftuejQuY7dQbjvu yALY4rd6txpUqM58at0uUM2aZOazbzExqqyyabRewVePBkfzX5 LiQDe6z1Odtn0nKuWXdnsta Zngh714mI8IU7pPs7xzjwRxr6dz4ZoDE10o8qYaj/7IwZEr84hzwubUM5o2U9p5bdxs7xvpOLmq2G/Fs6M3562 Zsa77P6HP1krUru2t8 Km6vxbduCq8Ztn6Dn4zLD3hFQn9CJE12L9F5UpCaVmU6NT7MSM SLLzGLd7FSI3ELltguYqvF8l6a3r8VdbiFORPcL3xAQ9HSurov 1qpRPXvSPoxUTXajznhqP/cr6uybAkmPVMQ1Mx7pt 4DlHnwGNd5NuWOjfGSoTOIDWM9/aw1e9J2SRwWiRUBDXWTX9kLLBrY9RvPbxKbs aSIVqobO 6Bhdu78 R/2hUX/S0qgxqfKcvMYL199TMIOW63abuApRpP4qgkIkrnsep742eRwtC l15pqXFI/FTWefKFx4c4AKW/ujR8ExzlQ45fkjHNxwDyjaSPyZTF3yHuJ30izZ57pykoXCcOnN 1nf8l8siQ7uGStSw7bLu9MD2/5moQ9ZH93zaQnbc7Nm4x5avCXozyFLLbOpzFeocaVqTJdlprGu/7QJOS6rgOHsyE6Ny79TUfwd3KYaT79PvTh0woOFGoc5eF NnxsU7I1fmeyNnOfB9WrEOj4sLXc2/wT7vnefm1UK8a/MET9fAeGNueRP49W44bnZsW0vlBqnxg17PiugF6rGjruIWW7va ZleZaZT44aJcrosM4117XHveStcr 0CZmpcCqP9X9TZ0SeFoRMFMWoc5oCT6lAnOgP03JPq3LDJg24e kfhO9SLdu5RmM5WSVvrw IlbBqXldCtVZtR20dLotoulruZ1BevJdVNVZjnuxRqZmOp7R0C rMvOpcbtEOV WmcW66rg3tsWjSlxNjcthtFuOlwNB/FqlsJwfH1G//1wGahzm4C01LvkcguqixG/kSL/VUExaYtBNsgtivEzvTMVwhpr/8eObpNk0pbCFYL4/bND2ivUXS2ha3xW/7HfqntcV44PHXTZmZa1jHUSxMvOpcbtEOWGWmcS63rj35rhdse 0CNmq8HLsfR9N7FmeETgnv23O05Php9 BKcDjeefk tfKJMoo/jRqHOu r8eQ7dW7Op 9uwBVIZ6C5x5R8qDB3DacVaPEazUdv3xR/jrQwF4lGMJRt4MLB28sFakum1vWX pxGetx37Wu63zKev7JD2vLMYnUuOPx9Bw6lhpmUFlZlTjVolyu iwzPsd1XX2bWo4zbLuAvhoP1Ksklhspv3JWKPhUVCiS5ynxNyL di5/JRbxPpXxoYll9/OlywwFyxy5MgcQC4YdPT8PLLkXsIeES3/H/iYeku j1PJd Ak6Kj148KJpfNVuODPHT/zmzpVxbWUhXOqxs3/Y 61Exy7ZHV67ldX3WGwFN3XpQTDPNHp1sLcSltptUpt7zneOiZF 07UU6aZSbIcWrj3spxJm0vcTP9vXEAAAAAAAAAQI0DAAAAAAAA 2IMaBwAAAAAAALAGNQ4AAAAAAABgDWocAAAAAAAAwBrUOAAAAA AAAIA1qHEAAAAAAAAAa1DjAAAAAAAAANagxgEAAAAAAACsQY0D AAAAAAAAWIMaBwAAAAAAALAGNQ4AAAAAAABgDWocAAAAAAAAwJ ohaty72835vrLbutwqLOumW1eAz JdzaHTJ NeuvdpgQsRhr5KnAuK3UvJITO6w mDeFaR8qOXdbmd9Y7B/YjhonsMbLtQPw3/ndj6nW1dtB7cYV43r/XwolHUDIwZTXArbTeoTN3ntadAtfvrJ8pZo83wOK857o0cp2y9 yM1ejT G aUh9k76xLYuqHH4MrwLPDlx7G1dzgAQxEM008/yTBz1nJX4UE7wce/O4q8sjP4QtUcvyDr3Yh/vv4r15uD Ey BOm2KFtT68rBMa5rUelruR1M1uXqqAZMaOllm1d1AV5I9YpV6b e89pToPr9tRPlvNFmdJxXHfdGjhs48b6Zq/Gjqa8817Ia33e/osbhu4h8NnNs75IA4J15UICJ8G5xrpgQt3VZlujqti5pyTD9RF OMXXWf7euRHr1tdWHK0d6mzKzXB/efbOsqBJ9gOj6y7c8L s46qfXgsl5KGOB1U1vXtCeQP1NJvNai0PNGlan7vPYUSL6/VaKcL9pMEOc7rr5JI8cpW69jrsbvLlZS15VPcSodrkfbsVHjv4 13y rXRXaTbV2cjzxk8z4tVRPjyPEKHY/eti5aeUmw3hpck0qctTFt 9OiwZbFlNaPi8kDr2/d0Oums77vZuN mEqeKZsJgNTzdpWZUI2bJcqJo81ZE9s433VVuxLXV PHCYgXT VLoiXeFz8OVAQHuepv6Dq/Bz6fHcx4nmFYlmr5Y8yexpJ3N5nnQoX31XjHqy9wAbxb1q0QL4 OlzepmWizGk7Is95Ro94ywb2dofVcX47UZqH3bk3z U9afl46phuHeeKkev2Hdatz3XRbAFqfVu9W4UmXmU N2iXLaaPNgXI6zmZmUctzl1XjwOsJrclwQLfdZ6vO2T6d1blm3 57JWfib4YS/u6CPClO7TLO/cI0Hc6 lcuObARBeKvKnGoz9ycOTKPOLclm PP6NpM6Wd18bN9r6RjjPDiv3Wkzy0DzN6V2jhiLZv2xZcvekFv QmtPy7c/zxajet53YTWzcb9NJJOdC3Se1GRmlRmOjU eiVifLQ5Lw/LcWanQsQWXl2N57s0va0Vd7mFOBHdL3xDQNDTubou1qtSPvmSg zhaMdGFOu p8divlL5nA6bgWHVMA9OxZt0 2roHn0GNd1Pu2CgfjZknPYpo74wXTvANb7vWd5lNaz0Y68FqXN HrZreuOe5BHW7vzpP/aVdc9LeoDGp8smizzxDnLfSwnOOsrCdYqvEkjkoionQ o743fhYpdF56ranGJfVTUePJFxoX7gyQ8ube EFwnAM1fknOOBcHzDOaNiJfFnOHvJf4jTR75pmuBmlCXTHe MWSwW3vLXQR69Fx0bFqXNPrZrfeW hfCN8hrz HLDXLpjJfocaVqjFhtMkKjInzJo9a0Zsvrcbl36ko/g5uU42n36de7DzhwUKNwxy8r8bvj8r5wgR66ppkb Q8D65XI9bxYWm5s/kn2Pe9 9ysUojvmSfpDVvP24ID236UGqbGLa0ngzFUjat63eTWj1Jm4dI st/e0Xa8y06lxw0Q5XbSRywyI89rjXs9xV1bjUhgV3oUsf7pVsNB5 oiBGjcMccFId6kRngJ4nBTu3yvKgm0ckvlO9SPeuxRBFqihMGt viYRUGq/GfsF7ayBhxblb3PMbc1o9SZlM6PR0kWqq3XbEy86lxu0Q5W7Qp FLqaGm/muAurcTmMdsvxciCIX6sUDnjFR9TvP5eBGoc5eEuNSz6HoLoo8 Rs50m81FNOGGHSTzIoYL9M7UxkxQ9UTJtk0pbyFMKrtrXpd3Pr AvXFdOTy39TuaXzEtVMcqNnesgyhWZj41bpcoZ482 7A4rzfuPTnusmq8fMzicTS967WJ3CHC /YcLTl 1j64EhyOd16 T618ooziT6PGoc77ajz jb3bzfn03Q24AukMNPeYkg8V5q7htAItXqP56O2b3k xtmcqGuMmb8PeLQ1ue2yvd/v GtZfLaFlXVkOT2jdbtxTq1a74ncqPW9QmRnVuFWinC7azJTjND q9luP0rdfQV OBepXEcqU79n2vndIKPhUViuR5SvyNSPfip2YW71MpH5pYVh9/utxwgNyx5W1MuUD44dPT8LJLEXtIuMR3/H/iIekuej3PpZ Ak KjFw K5lfN1iJDdXDfppRrn5YGtz268mPW82KfH/5BXje5dYtxPzkeMWshLrXdpDL1ce98IpSsayfKSaPNBDlObdxb OU7XeoOb6e NAwAAAAAAAABqHAAAAAAAAMAe1DgAAAAAAACANahxAAAAAAAAA GtQ4wAAAAAAAADWoMYBAAAAAAAArEGNAwAAAAAAAFiDGgcAAAA AAACwBjUOAAAAAAAAYA1qHAAAAAAAAMAa1DgAAAAAAACANahxA AAAAAAAAGtQ4wAAAAAAAADWDFHj3t1uzveV3dblVmFZN926Anw W72oOnT4Z99K9TwtciDD0VeJcUOxeSg6Z0R1OH8SzipQfvazL7 ax3DO5HDBfdY2Dbhfpp G9HyD064fNd0Gtd58Ed5nU162JNrtbzeU10nzHJotgyg8rUe15 7ClS7v36inDLLjLbeyEEfMly9/4iJ981ejT G aUh9k76xLYuqHH4MrwLPDlx7G1dzgAQxEM008/yTBz1uUriQznBx707i7 yMPpD1B69IOvci328/yrWm4P7T7zL4k YYoe2Pb2uEBg7Qu6ziMJT0xfwH6Wu5HV162mRy/V8buRpYlsXdUHeiHXKlan3vPYUqH5/7UQ5bZYZa72Rg/5N8/7DJt43czX 1uKmrMb33a ocfguIp/NHNu7JAB4Zx4UYCK8W5wrJsRtXZYlurqtSy29RVOM3Wy35yuRH r1tdWHK0d6mzKzXB/efbOsqBJ9gOj6y7c8LRluTlX1C3Ro0An5rdU7BurLXNawHVy7d 8/u S89UEq 1KIy7UWXqPa89BZLvb5UoZ8wyA603ctC/6b7/gIm3uRq/h/GSuq58ilPpcD3ajo0a/228W1a/LrKbbOvifOQhm/dpqZoYR45X6Hj0tnXRykvSTKUxuCaVOGtj2vanRYMti6p19VlI 1eu8uyUPvL51Q6/75Z7fd3n72WYCIOois8pMqMbNEuV0WWYu66MWY35AjR8nIF5c8 JBES7wvfhyoCI4T1d/QdX4P8nt2MON5hmFZquWPMXsaS97dZJ4LFd5X49qv1sAceLesW yFeBkub1S2dWIwnZVnuKdHuGWHX0ND6ri7GazNQ 7Yn XyE9UG7lOelY6phuDdeqoeV9d/o X2XBbDFafVuNa5UmfnUuF2inD3LjM5x2tsFpftfXo0HryO8Jse FeHyfpT5v 0zQzi3r9lzWys8EP zFHX1EmNJ9muWde6Snez2dC9ccmOhCkTfVePRHDo5cmUecE7aH ntG0OZU5r42b7X0jHSdXFfutJ3loH9z2rtDCEW3fti24etMLes VTuzfn9b/cqH1Kf7Qa1/O6X 75swrpRNcivRcVqUllplPj06xEDM4yc Q4Ve8v3f/qajzfpeltrbjLLcSJ6H7hGwKCns7VdbFelfLJlxzE0YqJLtR5T 43HfqX0PRswBceqYxqYjjXr9gHLPfgMarybcsdG WjcKT6DnfHCCb7hbdf8Rq2iMgkafP 34Tu0wVgPVuOKXvfLPR9YuL07T/6nXXHR36IyqPH5ssw8cd4 x 375dV4EsUlEVE6i1bfGz LFDovvdZU45L6qajx5AuNC3cGSHlzb/wgOM6BGr8kZ5yLA YZTRuRL4u5Q95L/EaaPfNMV2aaMCuhe4KvFpgGt7230IesCw OqTKJjouOVeOaXvfLPR9avCXozyFLLbOpzFeocaVqTJ5lhlpv5 KB/U7v/pdW4/CsZxd/Bbarx9PvUi50nPFiocZiD99X4uUXA3viVyd7IeR5cr0as48PSc mfzT7Dve/c5OqUQ3zNP0hu2nrcFB7b9KDVOjRue2k0GY6gaV/W6X 55EbPc3tMyvcpMp8YNE XkWWagdc031tv3v7Ial4J4/1dldvhDofNEQYwahzngpDrUic4APXeFOjds8qCbRyS U71I9w7tkHmSoizq3ZIY1vajlJn1kk60mJ2XNjIGvMWqfB7jl3 u WCMTU31vKGhVZj41bpco584yw6yP3Bbf9/3SalwO4t1yvBwI4tcqhWNG8RH1 491oMZhDt5S45LPIaguSvxGjvRbDcW0IQbdJLMixsv0zlRGzFD 1ZFE2TSlvIYxqe6teGtbzPyptzXedFR 1N64rxn 652VjVtY6VmEUKzOfGrdLlFNnmUHWX8hBb9Fz/8uq8XL0fBxN71mcERwivG/P0ZLjZ 2DK8HheOfl 9TKJ8oo/jRqHOq8r8aTb7W5OZuwFXIJ3/5h5T8qHCzDmcVqDFazQfvX3T ynW9gxVY9zkzcC7rXhpXgAAIABJREFUpcFtj 1pbp0Uej56zvRS 8xqXFmM/3TPhzweQ8OpY32XUrsyM6pxq0Q5XZYZneNqOegTdN7/kmo8UK SWG51d/msUPCpqFAkz1Pib0S6Fz/Du3ifSvnQxLL6 NPlhgPkjl2YhIgFwg fnoaXXYrYQ8IlvuP/Ew9Jd9Hr85v0E3BSfPTiQdH8qtlaZND5ptlSrn1aGtz26MqYno/jrrX1qJjSQW17r uzfumePzmaaS3EpbabVKbe853jomRdO1FOmmVGWm/loP/SdX9trytxM/29cQAAAAAAAABAjQMAAAAAAADYgxoHAAAAAAAAsAY1DgAAAAAA AGANahwAAAAAAADAGtQ4AAAAAAAAgDWocQAAAAAAAABrUOMAAA AAAAAA1qDGAQAAAAAAAKxBjQMAAAAAAABYgxoHAAAAAAAAsAY1 DgAAAAAAAGANaryEd7fbbVm3D95yW5fb7eb8B28JAAAAAAAA34 iBGveuX4He9erJoYa9k/ uw2ENNQ6fJnHljNg/7qXxmR8kDIaVQBQUu5dKg6hwh9MH8awiHY/eti5aXViw3jG4HzFcdI/M3RTo7XmdSkw57mOt63vdvNazmuiHzOARVJ7p7s3n3SJZ1Hp rHX9CVj5/mMj7fg4r9jzE/i8xE1bjT8G9aUBLW1LK2xXv14JgH/hXeBWiZdt63I /kE8RDP9LM 8UM9ZiQ/lBB/37iz ylLpD9H36D1Kfbz/Ktabg/tPvMviT5gDt3V5/lNpabmj559FFPx22nEfa13b62a2npfSjZeR4N/WRXcS2nje9ZNFvefHWteegNXuPzbSjo7zqj0/3OeL3JTV FtL2N5VxshKIJsag9/Br4FTZWs 3iWOX3oY4DfwbnGumBC3dVmW6Oq2LmnJMP1EqWY32 35ShqPXmudRMF6a3D/ybauQvAJtIEL86H2Fq14a4uzQvON 1jryl43tfX0srLz5c9UEq8/bK36vNslC7nnx1rvu6pjfWyknSHOt6yTQ L6Csxu9h89VtZtQ4/AjtRwM1/tt4t6x XWQ32dbF chDNu/TUjUxjhyvUH30vLslXa9vvTW4JpU4a7MuWgmyYHYrPQcm1o L5uM 1rqh101nPTGqnYylSafpBCA2ZpYsxDaOtd55Vdv6vu8jIu0U1o 08f5DPC6iq8eO8w4sL2q o8cLLlcctnguaySuV4d3DdwTOlZPDmLwkWrO7rNtxPTnnh8CHk PfVePPVF7gE3p1BSN4jam/pxGI8KctyT4lKzzw6dZgqS qhRW0mIuxZfozyXplF/px23C/vddNaN j5fa/McI1mjfHzbpcspNuOta5u9IX720faOazbzExG byAphoPXj54TY53q/HwUP 5bn5KbveI2/c07ly4OPD4XFBJ4X7LsgR3iIYst3voo2V5LCYFn1L81hn4Xt5U 49EfrSapMIK7Gpe2BZ hq5nSzmujZ3vfRbFjjz4drYuUxfjuXcEzNM/P7sUTy48Fc 2vF5p03H/A6ya1btLzZxXyt1it4nP0vBsmC lMxFDr3Ve1rY ItLNYt5HBo3xeQFGN53syvT0bbfulRD2Xf1Hw4y/Jdw/EES3s3sTdkr3xRJsf9y7ajduZjCPzXkh4T43HjsRX9V ZhxrPQ ixZt0 YLkHn0GNd1Po2KBLB sig51xsflSNv4oxb2ywOT93xoVmHLcf8LrprRu1POBhfQ1VqPs njzvqPGOq5rWR0XaGay3r36GcT4voKfGk6gpSYZEdddOC5z3SE RIRrAVnejg447RxTTJV4zVfkBItJuOo/JKE3wdb 6NH5zOiBq/JocaT0LoGV9rHiJF4ZHvJX4VYs9EB/fG6iJdMZ4uOufG9eJO3 xcS6hMOO4/4nUTWjfr dBiOrm0mDRmz7thsuhU43bW9Y3239860k5jXf9pG nzAlpqvCBZS796G493vxovRql NR5XtrSRnajxTruocajzvho/t4bYG78ypxoPT6uHaaSSLbK5q1AWNV5CniOGfxiqi1TFeM/bgnpnaF ZnVvt1A0c95/xuumsG/a8iFlul553u2RRPAszzLq60Zfubxppp7Gu3fNjfV5ASY1LQbP/C1Ff2BsvddJLajysX3zWvbg33mcXNQ51OKkOdQI1fu4Gdm7Y5G E4D32GXxn6beQdW1pktj8/qSpMGtviYRXsZmklhW4xOx877j/idRNat z5Yo30TRWed7tk0Z7kWFvvvaptPSg0TI2Psq7a88N9XkBHjctB s1uO96lxMVQ9f665W43Hv 8cWC7r6n67qHGo85Yal5wbQXVRIjV Tg D4S4mLTEMJzEIMV6m69zsqF1KNWEind4rWPKlL3n7L2Lb8z96N 2B23lfiotZ15fDc1rtKfLo6 tZqz7tVspD7daz1vqva1s9ChpF2Eut6PT DzwtoqPFyjnwcTe9ZipGGP/v7437Bbnb0jmUi3AtqPBU2j2I1Xd1rV1LjTH3h5H01nn7xgfO7 X1npuRzpDDT3mEq4lGJNmF7Q4jVm1kVKwkTeDAxWmjvfFPsnhb ZHnq735Tozj/tY68pyeGrrfSU w MxNNkVLz7vx3X9ZFHo17HWu66qWB8bacfH a6rbzOJzwt8Wo0/pIEgusMrxU5OOyrVv9nfo0 EqxtnSR/e0/mkhn5dt BPj1vEhaRGtezmhqJKoZp nSwmFCafYoHY 57lkFWXIvaQcC3x P9iWIwWFnOOzxGJZLqyldp8oWm9OrhvUzqXK U83a/ZLbddyrt21qNiA8Z9rHUdr/sO60ExzTR7uLfR97ZVn/cHusmi1fMjrXd6xeetj420E8R5tZ6fwudL3BR/bxwAAAAAAAAAJFDjAAAAAAAAANagxgEAAAAAAACsQY0DAAAAAA AAWIMaBwAAAAAAALAGNQ4AAAAAAABgDWocAAAAAAAAwBrUOAAA AAAAAIA1qHEAAAAAAAAAa1DjAAAAAAAAANagxgEAAAAAAACsQY 0DAAAAAAAAWIMa/wDbutxuy7qNrgcAAAAAAAB8CXpq3LvbzfkXCofcpe22LsHfum9 mylFH1Dh0kfp6Suzn99Jz j6oEoa/SnQJiklhU7zD6YN4VpGOR29bF60uLFjvGNyPGC66R ZuClR7Po6fCp0/sfU79l53XlId WE X7MeXjSKmoExo2nllOPedVXXun6iJNoMGPdGjlO2XuSmpMYfAf Slp1dq/0uafhTsjUM/3gW 4l30lGzrcrp7MAmZ/hEALZ6Jo56zEh/KCT7u3Vn8K KrPX2P3qPUx/uvYr05uP/Euyz hIltW5fnP 9XLdueXlfo 7mtR6VMez6Z4YQRRN 6ts/XrUtV0IyYkfixmFlOO 7Nq9rWtRMl0WbIuDdy3MCJ901Hjb 1Y5yFnkibzIxClIKr4tfAUxI1vu/eJR7vnXlQgInwbnGuKHy2dVmW6Oq2LmnJMP2ksUpx5fvraTx6r XUSBeutwf0n27oKwSfQBi5MctqbJtKtP9nab7MeXLb1un1bl3h k/1B0bqyzzesa9oTyJ8po7nlfOPee1XLulWiJNqYjnsjxylbr6Oj xu9 lCmNBrEa/xopvqPG4V3azwhq/Lfxbln9ushusq2L85GHbN6npWpiHDleofroeXdLul7femtwTSp x1kZpal42eyzy6/rrlNaPi ZeZ7hPMtbnx477YSp5pmwmANONe/dVJetmiZJoM2rc62YuosaP42wvHr0Pnv3qU3 ckUtOUZRfBgjf/jjXRg4l9HR8KQzeSnv8wVXnXLrBKdekVHP4Vd5X4x2vvsAF8G5 Zt0IoDVY9q8vbsRhPyrLcU6LSM49OHTdTieuhRW0GKuxZfozyb pVF0JvR vPSGK LD3TqbZSM9fmx477vshq3eQ9y5nEfocbtEiXRZqwaL W4a6jx4N2y1 T4M zUXtE4j/Q75x5ll3VL i7SOEF9ztc/DmdflkW4gfD2TlinKECmrx4UalKsOfwwb6rxsrfDxbir8Uf4SB frhciVf74UuCp/g33vOUM4Wo1rH2b0ruAZytlL3qnbwgx70wt6E1p/XBjodRbf3jfY58eO 2kkNmCT3mce9wFq3DBREm3GqvFSjruEGs 3YnqbFH1pQc35hLvmp9yPAsnEIdkbjyV3sAgo7a7LdxTEuVyTl 08LwNV5T41XfQwuxUON57Hj2JtsHzbbg8 gxrspH2GLFsLGqXGDnfHCCT7lCXq77Zqny6a0PoHX6evRsT4/dtyDOtzenUL/0 6k4/6DajziN6ONjR4un/e4ghpPgqWkFEqHMJ7eXjg4ntygY/c8Ut35/QRNE/xEkHRs/dylypcv89rmNRkxwjAzb 6NHwg BpfiUONJLD0DbWMilYXjIe8lfiPyrkV4QHusGtcV49GZMtG4Xt zp6le1zp/Q nCvO2atg8dd0 fHjvuB8B3y oulM4/7JGpcqRpEm3FqvJbjvl Nyz9GUfyxW1mNB/cpH5MrLtqfL4Kne0JpVYpqXJJI5y0Lh4ny98aFmjDzhYj31XjF 2 FCnGo8PK0eppFKUMnmrkJZYlIJeZYW/mGoGlcV4z1vheudoe3rV 8GqnFL68O9Lp7g6O3VjfX5seMuYpbbZx73AWrcMFESbUaNez3H fb0al2Kl8MJjAen8rfjJ5lpWIYYlt6zvjRcNCEeHwqDVPqnOzB eevKfGOan OwRq/Iw9nYvXeTzO1/z5TvUieceWVptHnGBUFCaNbfGwCoPV E9YH 51 R VRn6gz3dYP0qZvdejt94lWpp03EeocbtESbQZMu7NHPftalyOl d1yXPw9BckLCh6ULuIdBzkLKz1FNS5VOQhDuRwv3hQ1DlXeUuN 1H4NLEanxM1UGw10MKmI8TuZTiPEyXedmR 2NqwmTbJpS3kJQEyZd/ar2pe6zWzf3OnnCZa/GdcX44HGXq2MVm2ce9xFq3C5REm3sx70nx323Gi fpXgcTW 1TIo oiAvrec8C532/Jp eXnw1n5ZjYfngI9/Zid24vc5HyZrNUGNQ8L7arzoY3Ap0hlo7jElHyrMXcNpBVq8xu QzFQ278sZIsNAcJ0ytbbvSblWUg02/U2qs9VdLfNq6MPQDlImyGB887qlVlc3IIpOP wA1bpUoiTbG417LcfrWa3xIjT8UgdCs8Eq5dUmpeI86 vwa/im4W1j08Yv1jwJPRR5/Jq6xVP tYCm7uKyrK3wurEn1fvBrZL4tb2PKBcreDpch9pBjcMNl3MRD0 l30ep5LPwEnfWlLK2M3rVcH921KZxTFjKj6depi26MrP2Y9L2b tddHga22P2vt807rFuJ8cvWwtxCcd984nQsm6dqIk2piPeyvH6 VpvcPv8740DAAAAAAAAQBXUOAAAAAAAAIA1qHEAAAAAAAAAa1D jAAAAAAAAANagxgEAAAAAAACsQY0DAAAAAAAAWIMaBwAAAAAAA LAGNQ4AAAAAAABgDWocAAAAAAAAwBrUOAAAAAAAAIA1qHEAAAA AAAAAa1DjAAAAAAAAANagxj/Ati6327Juo sBAAAAAAAAX4KeGvfudnP hcIhd2m7rUvwt 6bmXLUETUOXaS nhL7 b30nL4PqoThrxJdgmJS2BTvcPognlWk/OhlXW5nvWNwP2K46B4D2y7UT8N/J7Z Z1sXrQd3mNfNaz28aBQ1A2NG08pK2w0qU/d57SlQ7f76iZJoI9y7kYM ZLh6/xET75uSGn8E0JceX6n9L2n6UbA3Dv14F/iKd9FTsq3L6e7BJGT6RwC0eCaO lwl8aGc4OPencW/Ir7aU3v0tnV59t692Mf7r2K9Obj/xLss/oSJbWjb0 sKgXFu61GpK3ndzNalKmhGzEj8WMwsG7FOuTL1nteeAtXvr50o iTai9UYO jfN w beN901PhbO8bZ0x5pk5kJH1uAKn4NPCVR4/vuXeLx3pkHBZgI7xbnisJnW5dlia5u61JLb2msUlz5/nqkR29bXRjotTcOMuv1wf0n27oKwSeYjo9s /OCvrNOaj24rJcSBnjd1NY17Qnkz5TR3LLQ80aVqfu89hRIvr9V oiTaRPdu5KB/033/ARNvHTV 96NMaTSI1fjXSPEdNQ7v0n5GUOO/jXfL6tdFdpNtXZyPPGTzPi1VE PI8Qodj962LlqRX5qpNAbXpBJnbUzb/rRosGUxpfXjYvLA61s39LrprO 72bgfppJnymYCIOois8pMqMbNEiXRpnHvMYsxV1Hjx3G2F1c1g qe/KsWPM3LJKYryywDh2x/n2sihhJ6OL0WeW2mPP7jqnEs3OOWalGoOv8r7alz71RqYAWdSu E0mDVs7q8HYvxpCzLPSXaPSPs2xla39XFeG0Gat/2 C1KvYbPaP15yXndJ3as181o3Wrc910WwDbvQXaqcaXKzKfG7RI l0aZ b 3tgtL9r6HGg3fLXpPjzye99orGeaTfOfcou6xb0neRxgnqc77 cTj7sizCDYQXZsI6RTEpffWgUJNizeGHeVONl70dLsZdjT/CR7pYL0Su/POlwFX5G x71yk xX7rmQ1oH2b0rtDCEW3ftjDD3vSC3oTWHxfufx49P9bzugmtm4 37aSQ2YJPei4rUpDLTqfHRKxFEm7CMqveX7n8JNZ5vxfQ2KfrS gtoACHfNT7kfBZKJQ7I3HkvuRzn5hZnSHQVxLtfk5dMCcHXeU NVH4NL8VDjeew49ibbh8324DOo8W7KHRvtXFjPk6Ii2jvjhRN8 w9uuebpsSuvBWA eHyt63ezW9c80Pp6uN6fQ/7QrLvpbVAY1TrSR0X5huXz/K6jxJFhKSqF0COPp7YWD48kNOnbPI9Wd30/QNMFPBEnH1s9dqnzFMK9tXpMRIwwz8 be IHgY3ApDjWexNIz0NY8RArHQ95L/EaaPfPMLIM0oa4Yj86UicZHtr230EWsR68FjJ0fa3rd7NZ7C/0L4Tvk9RdLS82yqcxXqHGlahBtyvdu5KB/U7v/96tx ccoij92K6vx4D7lY3LFRfvzRfB0TyitSlGNSxLpvGXh/E7 3rhQE2a EPG Gq94O1yIU42Hp9XDNFIJKtncVShLTCrReY5OabLcsxagN2w9b4 UPbPtRapgat7SeDMZQNa7qdZNbP0qZhUuz3N7Tdr3KTKfGDRMl 0aay1aS97FW5/9ercSlWCi88FpDO34qfbK5lFcJGcsv63njRgHBaJ1Tj7ZPqzHz hCSfVoU6gxs/Y07l4ncfjfM2f71Qv0r1rMUSRKgqT3i2JYW0/Sv2E9dIex4hzs7rnMea2fpQye69Hb71LtFRvu2Jl5lPjdomSaC O2feS2 L7v36/G5VjZLcezB6AkyAselOx6nwc5Cys9RTUuVTmYeORyvHhT1DhUe UuN130MLkWkxs9UGQx3MaiI8ThRUIjxMr1qfMQMVU YZNOU8hbCqLa36nVx6wP3xnXl8NzW72hv2CXVsYrNHesgipWZT 43bJUqijagZe3PQW/Tc/7vVePksxeNoes8ugyg8JGUsrec8C532/Jp eXnw1n5ZjYfngI9/pvIn/VL1u8laTVDjkPC Gi/6GFyKdAaae0zJhwpz13BagRav0Xz09m1V 8nt9gxVY9zkjZFgoXlc22N7mlsn81l/tYSWdWU5PKF1u3FPrapsRhap71JqV2ZGNW6VKIk2yb1rOegTdN 7/e9X4QxEIzQqvlFuXlIr3qKPPr GfgruFRR /WP8o8FTk8WfiGkv13wqWsovLurrC58KaVO8Hv0bm2/I2plyg7O1wGWIPOQY3XMZNPCTdRa/Pb9JPwEnx0YsHRfMrxWuRoTq4b1M6oyhmRPu2R1d zHpe7PPDP8jrJrduMe4nxyNmLcSltptUpj7unU EknXtREm0yay3ctB/6bq/tteVuH3 98YBAAAAAAAAoApqHAAAAAAAAMAa1DgAAAAAAACANahxAAAAAA AAAGtQ4wAAAAAAAADWoMYBAAAAAAAArEGNAwAAAAAAAFiDGgcA AAAAAACwBjUOAAAAAAAAYA1qHAAAAAAAAMAa1DgAAAAAAACANa hxAAAAAAAAAGtQ4x9gW5fbbVm30fUAAAAAAACAL0FPjXt3uzn/QuGQu7Td1iX4W/fNTDnqiBqHLlJfT4n9/F56Tt8HVcLwV4kuQTEpbIp3OH0QzyrS8eht66LVhQXrHYP7EcN F98jcTYHentepRNn6TG039brMunXPR16p1Pdjez6via6fSRZbP a9VmXrPa0 BavfXT5SzRpvxsU5x3Os5rpEB9bgpqfHHWL40jlLvv6TpR8HeO PTjXeAr3kVPybYup7sH8XD6RwC0eKaGes5KfCgn Lh3Z/GviK/29D16j1If77 K9ebg/hPvsvgTJrZtXZ7/vF 1bHtaRMFva9anaPs xOvSIvY9f0ty5odnW2N7PjfyNGExs2z2vGpl6j2vPQWq3187Uc 4bbUbHOtVxr e4RgZU5aajxt9aP82e9kibzIxCfoCr4tfAUxI1vu/eJR7vnfoEACbGu8W5YkLc1mVZoqvbuqQlwwSTxiqj3Z6vpPHot dZJFKy3BvefbOsqBJ9gOu7CJKe9RVvZqdN1WLnnx7f9vGzrdaF h 57ft3WJZ1fZHzStJ5eVuyD3K6O5ZSHaGFWm3vPaUyD5/laJcr5oM0usUxn3eo5rZEBldNT4fYaQKY0GsRr/Gim o8bhXdrPCGr8t/FuWf26yG6yrYvzkYds3qelamIcOV6h uh5d0u6Xt96a3BNKnHWRkkUlc1upefAxHpaE9u2HxfNvW7fx/a82S7N2J7fd3n72WYCIEUbu8pMqMbNEuXE0WawdaOp7xSV2Pdd R40fxxxeXFcInv5qvD3OTiQnh8rH/cO3P87Vj0MJPXfypchzK 3xB1edc kGp1yTUs3hV3lfjQ97uQVM8W5Zt0IoDVY9q1s6sRgXXgbCgSQq PfPo1AG6SKyHFrUZqLBP/zHKu1Wj9glj7Nv vDTG68b2fHxgVG bZmzP77ssgG3eg xU40qVmU N2yXKaaPNcOs2M5P6KovpZoWCGg/eOXhNjj f9NorGucLBc65R9ll3ZKRi5JHUJ/z9Y9DyyzLItxAeGEmrFMUk9KXCwo1KdYcfpg31XjZ2 Fi3NW4tDn1DGzNlFYIXJW/wb5XOvbo09FqXFmM794VPEM5exXPzd6c1/96oY4Ty8Ztf14Y43UT9LzFd0oN7vmzCnELbdJ7UZGaVGY6NT7N SsSwaDOBdbNTIZUW1q9 mM r8XwrprdDo2 oqXWBcNf8lPtRIHGoZG88ltzB8qu0uy7fURDnck1ePi0AV c9NV71MbgUDzWex45jzbp9wHIPPoMa76bQsUGXDlbjBjvjYvP7 0vQ/KGqDwOT937ZvsY5q z7U62bo XADRfUrnof1fGDh9u4U p92xUV/i8qgxieLNsOtt69 hvpJG PXpT uxpMJgqQUomFO96OXdSscHE9u0LF7Hqnu/H6CpjnPyYvH1s9dqnzFMK9tXhOj1R74Gt7cGz8QfAwuxaHGk1h 6BtrGFDYLx0PeS/xGxJ6JDu6NVeO6Yjw6UyYa14s7ffPjMdpgt2/7WK boOePWfGFez60eEvQXyyt9bx Zb5CjStVY8JoM4l1/aetnuMaGfDzfFiNC89u/gC31Hhwn/IxueKi/fkieLonlFalqMYliXTesnB J39vXKgJM1 IeF NV7wdLsSpxsPT6mGiqASVTK8JZYlJJeRZWviHoWpcVYz3vC2od 4b2lfmx dlRTdOy9bFeN7zn4 mV3nfujO15EbPc3tMyvcpMp8YNE V00WYa69rjXs9xmu/LF/isGpfmB/3fximdvxU/2cwYhbCR3LK N140IKwLh mifVKdmS88eU Nc1L9dwjU Bl7Ojds8nicz6xNv6bku8g7trTaPEITKorx3k0B UzYB icHyv5bleWNmz7aK8b3PP5H5X6fmzPF2tkYqrD5xUrM58at0uU s0Wbeayrjvtk2 L7vn9Yjcvzg245nj0AJUFeiJrp8ulxkLOwullU41KVA4fM5Xjx pqhxqPKWGq/7GFyKSI2f08NguItBRYzHSWZFjJfpOis am9cTYxnE5HyJoHX oqbF1TZwPnxCGXSV LD1sf2vDzdMz8v3Vfi09Wxstax9qdYmfnUuF2inD3ajLOuN 71HPdCBvwsH1Tj5QD9OJreswIjCg9JGUtrmPG3jNyc3/2afnl58F5 WY2H54CPf2ZnpeL3OR8mazVBjUPC 2q86GNwKVLVlXtMyYcKei2cVqDFa0yoi8KrGnblzcBgoTlOmFr 7B4W2R56u9 2DzZA7ou0vlfi49XE9v4udP0gXWc3fkhmmAZWWGVRmRjVulSin izbTxDqlca/luNZVXT6kxh KQKh4eKXcqqRUvEcdfX4N/xTcLSzq/PN zu HIo8/E9dYqv9WsJRdXNbVFT4X1qR6P/g1Mt8uTIFe9Ha4DLGHHIMbLtQmHpLuotfzXPoJOOlLW1rzhab1 6uC TelcrjxR0fya3XLb48hnZ32GtkfFzL1uVM9nxtW2xYf1/MnRTmshXvV5dSFe7PnOcVGyrp0oJ402E8Q6tXGv57hWBlTm9vn fGwcAAAAAAACAKqhxAAAAAAAAAGtQ4wAAAAAAAADWoMYBAAAAA AAArEGNAwAAAAAAAFiDGgcAAAAAAACwBjUOAAAAAAAAYA1qHAA AAAAAAMAa1DgAAAAAAACANahxAAAAAAAAAGtQ4wAAAAAAAADWo MYBAAAAAAAArEGNf4BtXW63Zd1G1wMAAAAAAAC BD017t3t5vwLhUPu0nZbl Bv3Tcz5agjahy6SH09Jfbze k5fR9UCcNfJboExaSwKd7h9EE8q0jHo7eti1YXFqx3DO5HDBfd I3M3Babs Rmsx7lDoQLDvK5mPbxo3/YQxXHPa6L7jEkWxZYZVKZJQg8KAAAgAElEQVTe89pToFmtj420 Q603ctCHDFfuP2aCdFNS44 xfGkcpbF5SdOPgr1x6Me7wFe8i56SbV1Odw/i4fSPAGjxzAv12ULiQznBx707i39FfLWn79F7lPp4/1WsNwf3n3iXxZ8wsW3r8vzn/apl2/NSP2Y9GfwP2x/odXXrUhXs2p6X0o2XkeC3mFnW2q5fmXrPa0 B5rU NtIOtd7IQf mef9hE6Sbjhp/a8c4e9ojbTIz4fABVPFr4CmJGt937xKP9059AgAT493iXDEhbu uyLNHVbV1q6S2NVUa7PV9J49FrrZMoWG8N7j/Z1lUIPsF03IVJTnuDeKqen8D6J0f6JevKXtewrmmv03pwWTkZ5 8 U0dyyMO5Glan3vPYUaD7rYyPtUOuNHPRvmvcfOEHSUeP32Jkpj QaxGv8aKb6jxuFd2s8Iavy38W5Z/brIbrKti/ORh2zep6VqYhw5XqH66Hl3S7pe33prcE0qcdZmXbSy3nQ9P976 scGh 6yO9brp2h5d1B33fZe3n20mAKIuMqvMfHp4Fuv7vo LtDNYtx6AkRMkDTV HHN4cVUjePqrUvw4O5GcHCq/DBC BXCujRxK6BnspchzK 3xB1edc kGp1yTUs3hV3lfjWu/WgNz4N2yboVQGqx6Vrd0YjGelGW5p0SlZx6dOkwTJvXQojYTEX ZMP8a0PT/MehTuFZ/XsV43bdsNxn3fZQFs8x5kpxpXqszMeni8Gh8Uaaewrq G4/sPnSApqPHgnYPX5PjzSa8d1T9fN3DOPcou65Z0WaRxgvqcrwEc AX5ZFuEGwgszYZ2imJS elCoSbHm8MO8qcbL3g4X467GH EjXbQVIlfVLgqvwN9r3n3OxoNa59gNe7gmcoZ69Je36c9W0LZx fy/oCe9bzMb7XdZNzPKuRvsQ5R44aVmVkPD1bjoyLtDNafZVS9P7r/2AnS59V4vhXT68rRF3XUWi/cNT/lfhRIHCrZG48l96Oc/MJM6Y6COJdr8vJpAbg676nxqo/BpXio8Tx2HGvW7QOWe/AZ1Hg3hY4NunSwGjfYGReb35em/8GUPT/NuGuerBvrdVO23WjcAwuhDaMpY3nR36IyM vhcdYHRtrh1p9ov7Cc3P9iajwJ1ZJSKB08era6cHA8uUHH7nmk uvP7CZom Ikg6dj6uUtV/hK Wk0MDz3AV/Dm3viB4GNwKQ41nsTSM9DWPEQKx0PeS/xG5J268ODeWFWmK8ajM2Wicb24M2HPTzTumhUY63UTtt1s3EOL twR9LVBqmU1lZtXD463vIyLtNNYbOejfZPcfO0H6sBqXfwqj GO3shoP7lM JldctD9fBE XPNKqFNW4JJHOWxbO7 TvjQs1YeYLEe r8Yq3w4U41Xh4Wj1MI5Wgks2chbLEpBJCzyQv0Q1VZapivOdtQ b0ztNP1/EzjfpQaosZVvW66thuOu4hZbu9pmV5lZtbDw9X4bhxpp7Gu cZ66f5DJ0ifVeNSpBZeeCwgnb8VPylHzfbZ3eSW9b3xogHhtE6 oxtsn1Zn5whNOqkOdQI2fsadzwyaPx/naL9 pXiTv2NJqs/27lKqyqHdLQj4T9gFm6/mZxv0oNWR2rnseY7a2W457sUYmpjp6XrEyM vhGdS4ZaSdxLr5tvjx54t8p7ocqbvleNYRJUFeiJrJrvd5kLOw ullU41KVA4fM5XjxpqhxqPKWGq/7GFyKSI2f08NguItBRYzHSWZFjJfpOrU7ao9UTRZJp/cKlrRk0dw9P9z6rrhtNMrruqzfGdX2rhKfro6VtY5VGMXKzKyH Z1HjoyLtCOsv5KC3qN1/4ATpg2q8fH7ocTS9ZwVGFB6SMpbWMJ FTnt Tb 8PHhrv6zGw3PAxz9T ZN qfrdZK0mqHFIeF NF30MLkU6/809puRDhZlzmF7Q4jVmVmVKskjeDAwWmuOEqbV7MXPPj7Ae97X mttEQr6tan6XtfSU QzLDNKC S6ldmZn18ADrYyPt6Dhfy0GfoHX/cROkD6nxhyIQui28Uu7TpFS8Rx19fg3/FNwtLOr8837O74cijz8T11iq/1awlF1c1tUVPhfWpHo/ DUy35a3MeUCZW HyxB7yDG44TJx4iHpLnp9hpF Ak760pbWTK1pvTq4b1M6lytmRNWv2Z235wdZj66M6nkdr2tan6 LtQTHNNHs8YtZCXGq7SWXqPd85LlezPjbSjrTeykH/pfP YyZIt8//3jgAAAAAAAAAVEGNAwAAAAAAAFiDGgcAAAAAAACwBjUOAAAAAA AAYA1qHAAAAAAAAMAa1DgAAAAAAACANahxAAAAAAAAAGtQ4wAA AAAAAADWoMYBAAAAAAAArEGNAwAAAAAAAFiDGgcAAAAAAACwBj UOAAAAAAAAYA1qvIR3t9ttWbcP3nJbl9vt5vwHbwkAAAAAAADf iIEa965fgd716smhhr2T/67DYQ01Dp8mceWM2D/upfGZHyQMhpVAFBS7l0qDqHCH0wfxrCLlRy/rcjvrHYP7EcNF9xjYdqF Gv7bEXK3ddF6dL7Dus7wV6yHo67kd2N7Pq J7jMmWWz1vFZl6j2vPQWq3V8/UU6ZZYZbb1/9gOHqyI6YeN 01fhjUF8a0NK2tMJ29euVAPgX3gVulXjZti5nAAjiIZrpZ3kmj nrOSnwoJ/i4d2fxV5ZKf4jao7ety7P3lJZXK9abg/tPvMviT5gDh7Y9va4QGPtC7qOUeduHW38WUYgYjSfuluTMD0/MxvZ8buRpImm5nsFaz6tWpt7z2lOg v21E W0WWZ4nFcd90aOGzjxvimr8bcWUr2rjJGVQDY1Br DXwOnytZ8vEscv/QwwG/g3eJcMSFu67Is0dVtXdKSYfpJ57JGuz1fifTobasLc4L2NmVmv T64/2RbVyH4BNPxkW1/XjDamiybaa2PXdS6xWaRaH1bl3gilv1B03pyWbkL8mdKYe1Boh BtjCpT73ntKZB8f6tEOWOWGR7nO66 SSPHKVuvo6zG78nz1W1m1Dj8CO1HAzX 23i3rH5dZDfZ1sX5yEM279NSNTGOHK/Q8ehpCQPZemtwTSpx1sa07U LBlsW1XH37pY8cr9gfStFIAPr2UwsOkGmbT24qNvz y5POm0mAFK0savMhGrcLFFOl2WmsW409S2YuZoaP847vLis ooaL7xcedziuaCZvFIZ3j18N RcOTmMyUuiNbvLuh3X0yyCwIeA99V4x6svcAG8O4OQvC/a3tKJxXhSluWeEu2eEfaqDa3v6mK8NgO1b3v8xrhewys9/ jvQWp8mHWr1/Zk6/FxUi0tPrrn970ywx2xDmJYmfnUuF2inD3LjLNuMzMp5biLqfHg 5YPX5Hi3Gg9f5jhXb0/J7R5zlXsycS5cHHh8LqikcL9lWYI7HFWS7R6ThWV5LCYFn1L87 hP4Xt5U49EfraZKMIK7Gpc2p56hq5nSzmvjZnvfSMfJVcV 65kNaB/c9q7QwhFt37b0K12td2yO7h6ixodZf746qv/FTsWWWXyn1OCeP6sQt9AmvRcVqUllplPj06xEDM4yQ62bnQoRW 3gtNZ7vyfS2LVkDj4m rij/ouBgATWRz cnw8cqcbdkbzzR5se9i3bjdibPL/NeSHhPjceOpPQ9GzAFDzWeh9Bjzbp9wHIPPoMa76bcsVGGGneG 0GBnvHCCb3jbNb9Rq3x0MVoAtVXjA63H5wuf/7Z f1h7DWZ0zwcWbu/OnP9pV1z0t6gMany LDM zlvo4fJJm0up8WSmIEmG0smzvr1x etlw63oRAcfd4wupqmmYiw57d62mz6/yitN8HW8uTd cDojavyaHGo8CaFnfG1MYbMoPOS9xG k2TPP3GGpCeMSuqdma4FpcNt7C33MenRg01yNj7QuBA1rVXbMm e29zqznQ4vp5FJ/0lidc6tX5ivUuFI1Js8yA63rP221HHchNV6QrKVfvY3Hu1 NF7uyX43HlS1tZCdqvNMuahzqvK/Gzw0K9savzKnGw9PqYRqppI1MrwllUeMlenpG7xxpzzxJb9h63 hYc2PajlJEaT7rDXg PtS4rE6t9wnjapHcqYmzPi5jl9p6W6VVmOjVumCgnzzIDrWuPe z3HXUeNSxOF/q/lfGFvvNRbL6nxsH7xWffi3nifXdQ41OGkOtQJ1Pi5J9W5YZOH4 Tz0aX1V7AXo3qEdMk9SFOONbfGwCoPVuJH10uaCjSIda72s0I2 USf5HJb8b2/PFGpmY6njiFCsznxq3S5RzZ5mR1lXHvZnjLqPG5YlCtxzvU NidHj XHO3Go9/3zmwXNbV/XZR41DnLTUuOTeC6qJEajz4hkoflCgMvxiGkxiEGC/TO1MZMUPVE PZNKW8hTCq7a16qVs3P6k 1rqsh62UiSySzc9L95X4dHWsrHWs/SlWZj41bpcop84yQ63rjXtPjruIGi9H6sfR9J6lGGn4s78/7hfsZkfvWCbCvaDGU2HzKFbT1b12JTXO1BdO3lfj6RcfOL/7lZWey5GqrtxjKuFSijXhtAItXqP56GnJgpL1 KrGuMmbgcFK87i2x/Z6t 8/Zf3VEhezHsUYve99lK0LjjdIF1lN4B6PoeHOTaVlBpWZUY1bJc rpssxMOU6j02s5Tt96jU r8Yc0ENoXXmk9 Aep/s3 Hn3iec w8LL68J7OJzX067oFf3rcIi4kNaplNzcUVQrV9OtkMaEwBRILx N73LIesuhSxh4Rricf/F8NitLCYc3yOSCRTfPTiQVHqvmaurA7u25TO5Uo5b0Dboyujej 4opnRQe1br0ozHzHrkelrb4sN6/uRop7UQl9puUpl6z3eOi5J17UQ5aZaZIMepjXsrx lab3BT/L1xAAAAAAAAAJBAjQMAAAAAAABYgxoHAAAAAAAAsAY1DgAAAAA AAGANahwAAAAAAADAGtQ4AAAAAAAAgDWocQAAAAAAAABrUOMAA AAAAAAA1qDGAQAAAAAAAKxBjQMAAAAAAABYgxoHAAAAAAAAsAY 1DgAAAAAAAGANavwDbOtyuy3rNroeAAAAAAAA8CXoqXHvbjfnX ygccpe227oEf u mSlHHVHj0EXq6ymxn99Lz n7oEoY/irRJSgmhU3xDqcP4llFyo9e1uV21jsG9yOGi 4xsO1C/TT8d2Lrd7Z10Xpwh3ndvNazmuiHzMDJjKaVlbYbVKbe89pToNr 99RPllFlmuPX21Q8Yro7siIn3TUmNP8bypXGU2v Sph8Fe PQj3eBr3gXPSXbupzuHsTD6R8B0OKZOOo5K/GhnODj3p3FvyK 2lN79LZ1efbevdjH 69ivTm4/8S7LP6EiW1o29PrCoFxbutRqSt53czW81K68TIS/BYzy0asU65Mvee1p0D1 2snymmzzPA4rzrujRw3cOJ901Hjb 0YZ097pE1mJnxsAar4NfCURI3vu3eJx3tnHhRgIrxbnCsmxG1d liW6uq1LWjJMP2msMtrt UqkR29bXRjotbcpM v1wf0n27oKwSeYjo9s /OC0dbkfNaDy3opYYDXTW09vaycjPNnymhuWeh5o8rUe157CiTf 3ypRzphlhsf5jqtv0shxytbr6Kjxe7TMlEaDWI1/jRTfUePwLu1nBDX 23i3rH5dZDfZ1sX5yEM279NSNTGOHK/Q8eht66IV QXrrcE1qcRZG9O2Py0abFlMaf24mDzw tYNvW4664lR7WQsbT/bTACknrerzIRq3CxRTpdlprFuNPUtmLmIGj OObx49D54 qtS/Dg7kZwcKr8MEL79ca6NHEromWalyHMr7fEHV51z6QanXJNSzeF XeV Nd7z6AhfAu2XdCqE0WPWsbunEYjwpy3JPiXbPCPt2htZ3dTFem 4Hatz1 Z1uv4TNaf15yXveJHet101o36Pl9lwWwzXuQnWpcqTLzqXG7RD l7lhln3WZmUspx11DjwTsHr8nx55Nee0XjPNLvnHuUXdYt6btI 4wT1OV//OFLrsizCDYQXZsI6RTEpffWgUJNizeGHeVONl70dLsZdjT/CR7pYL0Su/POlwFX5G x718lVxX7rmQ1oH532rtDCEW3ftjDDymvkV7X uHD/82g1rud1k1o36fmzCrFj2aT3oiI1qcx0anyalYjBWWaodbNTIW ILL6HG862Y3iZF39ZRcwHhrvkp96NA4lDJ3ngsuR/l5BdmSncUxLlck5dPC8DVeU NV30MLsVDjeex41izbh w3IPPoMa7KXdstE867gyhwc544QTf8LZrni6b0now1oPVuKLXT WndqOcDC7d3p9D/tCsu ltUBjU X5YZH ct9HD5FPYV1HgSLCWlUDry9fT2wsHx5AYdu eR6s7vJ2ia4CeCpGPr5y5VvmKY1zavyYgRhpl5c2/8QPAxuBSHGk9i6Rloax4iheMh7yV I82eeWaWQZpQV4xHZ8pE4yPb3lvoItaj46Jj1bim101o3aznQ4 u3BP3F0lLLbCrzFWpcqRqTZ5mB1vWftlqO 341Lv8YRfHHbmU1HtynfEyuuGh/vgie7gmlVSmqcUkinbcsnN/J3xsXasLMFyLeV MVb4cLcarx8LR6mEYqQSWbuwpl/9o7s2tHdSCKEhcBEQ/RkAzB0B 2QTMyTQ0X77364z0juzQUVTqS4BKTanSemxWaLPfMk SGredpQcO276XM1Lim9WQwTNW4qNe5s67Y80XUcntPy Qq406NKyZK51nG0Lr0uLdz3J9X46VYWXjgsULp/G3xm6drWZWwkfxke2 8aqBwWidU4 cn1Zn5wodrapyT6r9DoMaP2NO5YZPH43zNn3eqV neIzWZJwkKk5Nt8bAKxmr8J6zX9jgMnmIVPo/hzbpmz1drpGKqw cFK NPjeslSt9ZxtK66Lif5ri/rsbLsbJbjmc3QE2QV6Jmsut9HOSsrG5W1XipyoFD5nK8 qOocWhySY23fQweRaTGj lhMNzVoFKMx0lmRYzX6Z2pWMxQ5YRJNk2pbyFYtf2sXg 3brg3LiuHfVvvKnF3dbSsdayDCFbGnxrXS5Sus4ypdblx78lxf 1uN109uvY m96zAFIVHSRmX1jA/hQ57y5y vDx4ar uxsNzwPv/pvInfan6y2SrJqhxSLiuxqs Bo8inYHmHlPzocrcNZxWoMVbnN562yr3p1jPZyoS41beDAwWmu 3aHtvr3b5/hvVvS0hZF5bDrq33lbiH9O/0yNNomUJlPKpxrUTpLst4ynESnd7KcfLWW9ykxt KoNCs8Eq9dUmpeI86 v4cfhT8Wlh0Wj6/Ny3brsjj78Q1LtV/rVjKLo7zPFW F9ak Xvwa2S Xd7GLBeoezs8hthD9sENl3ETD0l30dt5Lv0GHFRvvXhQJF8124 oMzcG9TO1cbjEj6rc9uvJj1vNi9w /kdf9DetBMck0u99i2kK81HaVyrR7vnNchKxLJ0qnWcZBjhMb97 McJ2v9hOH vzcOAAAAAAAAAE1Q4wAAAAAAAADaoMYBAAAAAAAAtEGNAwAAAA AAAGiDGgcAAAAAAADQBjUOAAAAAAAAoA1qHAAAAAAAAEAb1DgA AAAAAACANqhxAAAAAAAAAG1Q4wAAAAAAAADaoMYBAAAAAAAAtE GNAwAAAAAAAGjzQ2p8ncdhGOdV1sAwLWIGAAAAAAAA4CGYq/Fl i8B2/n1l1AeUONgzjINTWIHepXGqX6QPWi141ZQ7FUq/N5Q/oXDB/GsKvVbL tyPesdg3uL4ap7GLa9UD8J/ 0Iues8St06f8O6zPA3rIejLuR3tj2f10T2HitZPOt5qcq0e156 CtT6fflE6TLLmFs/v3qD4ebIWky8B1s1/h7yq8P91dfF98YBOlimwAuXKfLfdR6PABDEQzTTz/JJHO2clfhQTvD1ZTqK/ di6FNp3XrrPH56T2j9tWH9dHD/k2XK4k YMk3bnl4XCIx9IfddSr3t5tY/RQQixskdNyQ58 Z5nG3P50Y JjTmrKc9L1qZds9LT4Havy dKN1mGfM4LzruJznOcOI9mKrx/1xm/fLrAlEc4FuWOfDBRI1v2zIlAWCZ1IMCOGKZxmmqJsR1Hscxurr OY1oyTD9pFFTa7fmTlG69dZ7CFCK9TZlZbw/uf7LOcyH4BNNxy7Z/LihtTdbNnK2PPdS6xmZR0fo6j/G8LftA0npyWbgL8ntKadZaiTZKlWn3vPQUqPz7WonSY5Yxj/MdVy9ykuOErbcxVeOv1JoJEqmvo8bBG feixr/bZZpnJd5LLvJOo/TEnnIuixpqZYYR4436Lj1pIRB2frZ4KpU4qiNats/FhW2LJrjvkxDcsv9gvW1FoEUrGd7stEJMmnrwUXZnt 28vazzgSgFG30KuNQjaslSndZxo11palvxcxvqfH9NMS1Rde rwcPCEzT5/4qPBKRnPc8SuRuWH2ck6Pw8C3X1XjHoy/wAJZpnNdKkAvWI5tbOrEYT8qy3FPjvGcKe9WK1jdxMd6ageq3P XnjhkXPv/vbSI2bWb 8Y3KL9fg4qZQWt 75bStPIXWmlZ1qXKgy/tS4XqL0nmXsrOvMTGo57qfUePBowhU53vH1KHIkDwjk6eWY14T fOyYAh/Q/Du4d68Uqr4mDx3FRjUcfak2VwIKXGi9tTn1C4GlKO67Zzfb Ih0nVwX7rWc2IH1we5kqLbRo 7quwVXBZHt Tt5EjZtZ/zw6Kv9ip2rLNN4pZdzzRxXiFuqk96oiVamMOzXuZiXCOMuYWlc 7FVJs4S p8XzH5quWd3w9daT2u0COFaB4FNL3WOSvJt4/YVoL33JNjceexrv8n8xbjedRbo9Y5wcst A7qPFu6h0b7dLanSFU2BmvnOAzb7vkG7XqRxejBVBdNW5o/TPi8faH9vPD0msw1j0fWBj a2583W5x0V jMqhxf1nGPs5r6OH6SZsfUuPJPKIkKFrn0s6/Xrh1cv0d7i9WRHX4f UX2h52hReS4Hlc3BvfOVwSNf5MjsgUR7kjBJ5MYbNAafJc4l/ktGc KUpTE8YlZE/NtgKTcdt7C91mPTqwqa7GLa1XplKaqmyfM t7nVrPhxbTKab8rLLV8/KV RNqXKgazrOMoXX5u62V435GjVdkbe1v4qbe0PP1yimbymZ5IMa 3aByiGfDZ6wxQ4/Al19X4sUHB3viTSdcJ91df7B830kam1wplUeM1enpG7hxpzzxJ bth6nhY0bPteSkmNJ92hr4dtrZeVidY YTyvkjsVYdvzRdRye0/L5CrjTo0rJkrnWcbQuvS4t3Pcr6jx0jTii5d2dn29sHqbi V3h crJOFKQHQ2tDk qHH4kmtqnJPqv0O0UPiJap0bNnmkzGfWvFO9SvcOrck8SVCMn2 yLh1UwVuNK1mvr/zqK1NZ6XaErKZP8QyG/s 35ao1UTHXccYKV8afG9RKl7yxjaV103E9z3I o8fI0oluOd349l O1pFIIwZVqlOJR8AeiUePwJZfUeJIpUONPJj62U/obT9W0UYyUSZBCjNfpnalYzFDlxHiW/upbCFZtP6uXuHX1k q21st6WEuZlEWy nnpvhJ3V0fLWsfan2Bl/KlxvUTpOsuYWpcb954c9xNqvB7H3xvS5wt0fV9P3qIeLHO2JXp pPTQ FRo9SJ4uADCzhX6uq/H0j/NNy7bMLAU9jlR15R5T86GKXgunFWjxFqe3nuSfYj2fqUiMW3kz 8GXJuO2xvd7t 7usf1viYdajGCP33sey9YLjGekirRleMndVoNEyhcp4VONaidJ dlvGU4yQ6vZXj5K230FXj5QPg6ZVqJ3z99bDfx3muLIGlrlYcr ey5paQWYQ3YH4dTMi rTIGKBWIf/JRDVj2K2EPCVcboNE4xSLVeFhp j1BVpnrrxYMi arZVmRoDu5laudyS nUoO3RFaueD4oJHdT2ar0871GyHrme1La4Wc8f7O3UFuKltqtU pt3zneMiZF06UTrNMg5ynNi4n U4WesnDGZ/b9wJuRjf1nnq AgAAAAAAADgKj uxgvn3YrPSKzzzL4jAAAAAAAA3MWPqvHjKEL5PFZ6Dp4DnQAAA AAAAHAjv63G66 KtXluAAAAAAAAAH6DH1XjAAAAAAAAAIagxgEAAAAAAAC0QY0DA AAAAAAAaIMaBwAAAAAAANAGNQ4AAAAAAACgDWocAAAAAAAAQBv UOAAAAAAAAIA2qHEAAAAAAAAAbVDjAAAAAAAAANr8CTW TMMwLX1l13kcaozzKltRgFOWqeqgwzCknv4q3ev98CDCUNYIXU GxV6lyCIx 4fBBPKtKx623zqNUF1asdwzuLYar7pG5mwC9PS9TCZfjbmtd3u v8Ws9qIh8yg1tQac7aaLtCZdo9Lz0F8mrdNtLax3nBnj/JcVYTpMG/Gn 7xVcusUxpPx7OxfwTLFmmwJeXKXLtdR4P/wziIT77s3zyQjtnJT6UE3x9mY7i3yx0/hB9t9671O3917B Orj/yTJl8SdMvOs8fv73dVWz7WkRAb91O 621qW9zrP1vJRsvIwE/zqP4oK81Xb5yrR7XnoK5Ne6baS1jvOiPX S4wwnSIN7NX5pETxX48fH7JGDJcscuF ixrdtmRK3rbgy/AjLNE5TNSGu8ziO0dV1HtOSYfqJUs2mttvzJzm59c7WSQSsnw3 uf7LOcyH4BNPxKcyc0lu0jZ06WYf1N 621oW9zrX19LKw8 X3VBKvpaj0vFJl2j0vPQXyZ9020nqI8 dXL3KS40wnSO7V CvwZqLl/FvNEwhMP8EF546NGv9tlmmcl3ksu8k6j9MSeci6LGmplhhHjjd o3nrLNCRdL2/9bHBVKnHUZh6ldELF7Fq7D1Ss7xfVx93WuqLXubOeGJVOxqXtZ 50JQKnn9SrjTw97sb5tm0WkdWFdaeobm7GcIDlX4/uJiS XxOvjWDhtFD5EkMDxtIIAACAASURBVD9dOc7rvj/Ppjrcy3U1fvroCzyCZRrntRL/gqXK5pZOLMaTsiz31Gj0zLtTzVRZUg8pWjORwp7lbZTb/u2a/L3WP5cMx/3xXufWukLPb1tZAGucVu9W40KV8ayH7dW4fqT1YV1nZhLnONMJ km81Hjy 8J0cb8fV4JfyB2Pe/7uLnXF8rwwxa4XbuajGow 1JqlgwUuNl7YFP9HxNKUd1 xme3 R1gmr18fWukhYjG/LVPEM4fOz1XOzw7TIv17I6bj/gNc5ta7S80cV8qdYbc6D6FXGsx42VuMGkdaLdbVTIclLd8wmSK 7VeL6r0zs2nWq8/GDM/lFsklkr3M41NR67otB7NsAFbzWeB8B9zfr8gOUWfAc13k2lY4M uNdZFCjvjxeZXzpLdR1UbBCZf/6/4FKvtuP E17m0rtTzgYX0MVabk qKlfGsh 2sW0VaD9bPr95DkuNQ4zWSuFsSHZHHtM8bxF859ptKD8a8PyuI HmatcCcX98Z3 FMBT2dX40kAPKJjy0NKMdTkucS/SLFnooN7trpIVowHB9MqxuXiTt 5WVVtYDvuP J1Dq2r9XxocUiQn3bWWqZTGa962N76ph9p3ViXv9uyHGc7QfKr xst/16L6d3N71Xh2Gr20EPL KmochLmuxo9VJfbGn8yhxsPT6mEaaS89FpYvTz CbdtqmjD8wFQXiYrxnqcF5c7Q9qlxxXOztuP M17nzrpizxdRy 09LZOrjGc9bK7GN VI68a6dM XcpzpBMmtGi F3f5XqlY7MDp7U4ouYdJHjYMwnFSHNoEaP3YDOzds8iCaBzHeq V4l79jaErH s5SiwuRkWzysgt4srabQNfbGbcf9R7zOoXXNnq/WSMVU3zMCUpXxrIc9qHHdSOvFumjPV3Kc6QTJqxovh91uOV4ex 09s3S8Ufi90PNQ4CHNJjSeeiBp/MpEaP6aHwXC3lh7zj5PMihiv03Vu1mqXUkyYlE7v1Z DUJyd5x8uk0jvux53W uycti39a4Sd1dHy1rHOohgZTzrYS9q3GIdxNa6XM 3cpzhBMmnGq9n2Whvu/0DlVXO0qNn0aHP2gv2UONwO9fVePI2o2FatmXGOx9HOgPNPabm Q5W5a5he0OItPOsiIWFS3gwMj5JF VAqHVbaHnm6XD72PO621oXlsGvrfSXu4X0bKs42Gy1TqIxnPWx g3TbS2sf5rquXaeW4/brJBMmfGn Li4LoDq chY4iHd9Izn/un9ZrBXCFzFErk89igfDLh3PimI8i9pDwQM/ 34mHpLvo7TyXfgMOunKN2Hzh1HpzcC9Ty50fS/F1ydfs1tseRz5t61Exg3G3tS7jdX/DelBMMs1WNo1EqbZdpTLtnu8cl6dZt420DuK8WM f5bjUvuoEaXCnxgEAAAAAAACeDmocAAAAAAAAQBvUOAAAAAAAA IA2qHEAAAAAAAAAbVDjAAAAAAAAANqgxgEAAAAAAAC0QY0DAAA AAAAAaIMaBwAAAAAAANAGNQ4AAAAAAACgDWocAAAAAAAAQBvUO AAAAAAAAIA2qHEAAAAAAAAAbVDjAAAAAAAAANr8CTW TMMwLX1l13kcaozzKltRgFOWqeqgwzCknv4q3ev98CDCUNYIXU GxV6lyCIx 4fBBPKtK/dbLulzVel6NWysRxafc/qPbfm49jt4Cd49jrzNs 47VuAfW5UNm0NFKc9ZG2xUq0 556SlQ6/flE6XX 9082kiOezvHnWRAOQb/avztFl 5xDKl/Xg4F/NPsGSZAl9epsi113k8/DOIh/jsz/JJDe2clfhQTvD1ZTqKf7PQ UO0br11Hj 99yp2e/913PifInfbXqYs/oSJ99Ft77Ce3Fs318C319m1PSliMO5RKdl4GQn dR7FBfmJ1wlXpt3z0lOg9u9LJ0q/97t1tBEd93aOO8mAogzu1filxdBcjR8fs0cOlixz4H6JGt 2ZUrctuLK8CMs0zhN1YS4zuM4RlfXeUxLhgkmmmJsars9f5LSr bfOU5g9BLuvfuOLbRqs81wIPsF0/MFt77Ee3Uia1h30vFnbgytW4x5WQbYS cgm8VqKstdpVabd89JToPLvayVKf/e7g2jTcfUi7Rx3kgGFca/GX0kgEy3n36rmFMXeBWhz7tio8d9mmcZ5mceym6zzOC2Rh6zLk pZqiXHkeIOOW2 dR6m5csX6WvMFEVpd8Ni2N61ba8JPTSx63qrttuO X0xCrQCl7WedCUDBimJlHKpxtUTp8n53YV1p6uuiEtu2uVfj 4mJL5co6l1YOG0UPiaQP105zuuei9hUh3u5rsbNHm4BVZZpnNd K/AuWKptbOrEYT8qy3FPjvGcKBxGErX 7Lv1/tGagz217w7pCyHXrdWZttx33z6VpkY VJQGscVq9W40LVcafGtdLlB7vdx/WdWYm7VUW1c0K32o8eHzhOznejqvBL UPxrz/d88/4/heGWLWCrdzUY1HH pO0EGXlxovbQ99ouNpSjuu2c32/iIdJ1cF 616dnSYFo1X7LzrUP79R7e9OO7rugZXta3Hl5V73rbtxuP vvD6WHwOWMjlOum9qkhVKuNOjbtZiTC5351YVzsV0mhhrNuFbj a5O79h0qvHygzH7R7FJZq1wO9fUeOyKQu/ZABe81XgeAPc16/MDllvwHdR4N/WOjfYKFbXBx268Ri01eNE7JbM6PLftXftF32wO3GTdqudjtNtu Pu5BCJXXB/Fm0fblvPc/7RYX/TUqgxr3d7/bRxsNNV7OcX1Xb8ezGo8nkkXRUTtBdarGj/2m0oMx788KoodZK9zJ9ZPqL/hTAU9nV NJADyiY8tDSjHU5LnEv8hpz3zSj5YyKQyf3GQ9OJhWrd4z2951 R4jdNu687mKhm6xbj3t0UFclVhb KIb8tLPWMp3K/Ak1bnVS3fZ N7Quf7e1c9xJBrwfv2q8/Id6qn83t1eNZ6fRSwtg76 ixkGY62r8WFVib/zJHGo8PK0eJor20uPps2 o8Ro9PSN3jvSbOeL9Feh5WvCpbe 7I5bJSI1vyj1fKWWnxhXHPbkN1GOlWm7vaZlcZdypccVE6f9 t7IuPe7tHCf5vHwFt2o8m0du37xaszqO0dmbUnQJgz9qHIThpD q0CdT4sSvUuWGTB9E8iPFO9Srd 4RqM5WaWrl5/Ho3BZ7Y9or1Yik7Paza88VSPzHutS0hrYmgnA4qWmp3qWBl/KlxvUT5B 53I ui4 5sW3zbNr9qvCTGv5Dj5XH8xNb9QuH3QsdDjYMwl9R44omo8ScT qfHiXxpqLT3mHyeZFTFep3emojhDzT 8fZsym4jUNwke1/aG9RSxrROHXpei23ZH4666Ny73EErNWMOWbGX8qXG9RPkH7ncj 63Lj3s5xX2TAe/GpxuvRNtrbbv9AZZWz9AhSdOgzkTmocZDjuhpP3mozTMu2zHjn 40glde4xNR8qr2hG0wq0eIvTW0/yT7HW5iLRaN ek8qbgeFRsue2vWk9bq3k1ok/rzNt /6xzbh/W Ie3reh4myz0TKFynhU41qJ0t39bh/nu65eppXjzq7K4k Nv8VFoQ/CK2eho0jHN5Lzn/un9VoBXCFz1MokpFgg/PLhnDjmo4g9JDzQs/934iHpLno7z6XfgIPqrRcPiuSrZuuRIb777zNby53licqz2n5q Pbqi3Xbjnjdt wuzcc LSabZyqaRKOdeJy7Eqz3fOS5C1qUTpdP73UGcFxv3do47y4DCD O7UOAAAAAAAAMDTQY0DAAAAAAAAaIMaBwAAAAAAANAGNQ4AAAA AAACgDWocAAAAAAAAQBvUOAAAAAAAAIA2qHEAAAAAAAAAbVDjA AAAAAAAANqgxgEAAAAAAAC0QY0DAAAAAAAAaIMaBwAAAAAAANA GNQ4AAAAAAACgDWocAAAAAAAAQJs/ocaXaRimpa/sOo9DjXFeZSsKcMoyVR10GIbU01 le70fHkQYyhqhKyj2KlUOgdEvHD6IZ1XpuPXWeZTqwor1jsG9x XDVPTJ3E6C352Uq4XLcba3Le51f61lN5ENmcAsqzVkbbVeoTLv npadAXq3bRlr7OC/Y8yc5zmqCNPhX42 3 Mollintx8O5mH CJcsU PIyRa69zuPhn0E8xGd/lk9eaOesxIdygq8v01H8m4XOH6Lv1nuXur3/GtZPB/c/WaYs/oSJd53Hz/rmq2PS0i4Ldux93WurTXebael5KNl5HgX dRXJC32i5fmXbPS0 B/Fq3jbTWcV60509ynOEEaXCvxi8tgudq/PiYPXKwZJkD90vU LYtU K2FVeGH2GZxmmqJsR1HscxurrOY1oyTD9RqtnUdnv JCe33tk6iYD1s8H9T9Z5LgSfYDo hZlTeou2sVMn67D xt3WurDXubaeXhZ2vvyeSuK1FJWeV6pMu elp0D rNtGWg9x/vzqRU5ynOkEyb0afwXeTLScf6t5AoHpJ7jg3LFR47/NMo3zMo9lN1nncVoiD1mXJS3VEuPI8QbNW2 ZhqTr5a2fDa5KJY7azKOUTqiYXWv3gYr1/aL6uNtaV/Q6d9YTo9LJuLT9rDMBKPW8XmX86WEv1rdts4i0LqwrTX1jM5YT JOdqfD8x8eWSeH0cC6eNwocI8qcrx3nd9 fZVId7ua7GTx99gUewTOO8VuJfsFTZ3NKJxXhSluWeGo2eeXeq mSpL6iFFayZS2LO8jXLbv12Tv9f655LhuD/e69xaV j5bSsLYI3T6t1qXKgynvWwvRrXj7Q rOvMTOIcZzpB8q3Gg8cXvpPj7bga/FL YMz7f3exM47vlSFmrXA7F9V49KHWJBUseKnx0rbgJzqeprTjmt 1s7y/SOmH1 thaFwmL8W2ZKp4hfH62em52mBb51ws5Hfcf8Dqn1lV6/qhC/hSrzXkQvcp41sPGatwg0nqxrnYqJHnpjtkEybUaz3d1esemU42 XH4zZP4pNMmuF27mmxmNXFHrPBrjgrcbzALivWZ8fsNyC76DGu 6l0bNClxrpIYWe82PzKWbL7qGqDwOTr/xWfYrUd95/wOpfWlXo sJA xmpzUl2xMp71sJ11q0jrwfr51XtIchxqvEYSd0uiI/KY9nmD CvHflPpwZj3ZwXRw6wV7uTi3vgOfyrg6exqPAmAR3RseUgphpo 8l/gXKfZMdHDPVhfJivHgYFrFuFzc6Ts3q6oNbMf9R7zOoXW1ng8t Dgny085ay3Qq41UP21vf9COtG vyd1uW42wnSH7VePnvWlT/bm6vGs9Oo5cWQt5fRY2DMNfV LGqxN74kznUeHhaPUwj7aXHwvLl6UewbVtNE4YfmOoiUTHe87S g3BnaPjWueG7Wdtx/xuvcWVfs SJqub2nZXKV8ayHzdX4phxp3ViX7vlSjjOdILlV46Ww2/9K1WoHRmdvStElTPqocRCGk rQJlDjx25g54ZNHkTzIMY71avkHVtbItZ/llJUmJxsi4dV0Jul1RS6xt647bj/iNc5tK7Z89UaqZjqe0ZAqjKe9bAHNa4bab1YF 35So4znSB5VePlsNstx8vj Imt 4XC74WOhxoHYS6p8cQTUeNPJlLjx/QwGO7W0mP cZJZEeN1us7NWu1SigmT0um9 nMQirPz/MNlEul91 Nua11WDvu23lXi7upoWetYBxGsjGc97EWNW6yD2FqX6/lWjjOcIPlU4/UsG 1tt3 gsspZevQsOvRZe8Eeahxu57oaT95mNEzLtsx45 NIZ6C5x9R8qDJ3DdMLWryFZ10kJEzKm4HhUbIoH0qlw0rbI0 Xy8eex93WurAcdm29r8Q9vG9Dxdlmo2UKlfGshw2s20Za zjfdfUyrRy3XzeZIPlT429xURDd4ZWz0FGk4xvJ c/903qtAK6QOWpl8lksEH75cE4c81HEHhIe6Nn/O/GQdBe9nefSb8BBV64Rmy cWm8O7mVqufNjKb4u ZrdetvjyKdtPSpmMO621mW87m9YD4pJptnKppEo1barVKbd853 j8jTrtpHWQZwX6/mzHJfaV50gDe7UOAAAAAAAAMDTQY0DAAAAAAAAaIMaBwAAAAAA ANAGNQ4AAAAAAACgDWocAAAAAAAAQBvUOAAAAAAAAIA2qHEAAA AAAAAAbVDjAAAAAAAAANqgxgEAAAAAAAC0QY0DAAAAAAAAaIMa BwAAAAAAANAGNQ4AAAAAAACgDWocAAAAAAAAQJs/ocaXaRimpa/sOo9DjXFeZSsKcMoyVR10GIbU01 le70fHkQYyhqhKyj2KlUOgdEvHD6IZ1Wp33pZl6taz6txayWi JTbf3Tbz63H0Vvg7nHsdYZt37Ea98C6fMgMOlppztpou0Jl2j0 vPQVq/b58ovR6v5tHG8lxb e4kwwox Bfjb/d4iuXWKa0Hw/nYv4JlixT4MvLFLn2Oo HfwbxEJ/9WT6poZ2zEh/KCb6 TEfxbxY6f4jWrbfO46f3XsVu77 OG/9T5G7by5TFnzDxPrrtHdaTe vmGvj2Oru2J0UMxj0qJRsvI8G/zqO4ID/xOuHKtHteegrU/n3pROn3freONqLj3s5xJxlQlMG9Gr 0GJqr8eNj9sjBkmUO3C9R49u2TInbVlwZfoRlGqepmhDXeRzH6 Oo6j2nJMMFEU4xNbbfnT1K69dZ5CrOHYPfVb3yxTYN1ngvBJ5i OP7jtPdajG0nTuoOeN2t7cMVq3MMqyFYiH9kkXktR9jqtyrR7X noKVP59rUTp7353EG06rl6kneNOMqAw7tX4KwlkouX8W9Wcoti 7AG3OHRs1/tss0zgv81h2k3UepyXykHVZ0lItMY4cb9Bx663zKDVXrlhfa74 gQqsLHtv2pnVrTfipiUXPW7Xddtz3i0moFaC0/awzAShYUayMQzWulihd3u8urCtNfV1UYts292p8PzHx5RJFvQs Lp43CxwTypyvHed1zEZvqcC/X1bjZwy2gyjKN81qJf8FSZXNLJxbjSVmWe2qc90zhIIKw9W/Xpf P1gz0uW1vWFcIuW69zqzttuP uTQt8rGyJIA1Tqt3q3GhyvhT43qJ0uP97sO6zsykvcqiulnhW4 0Hjy98J8fbcTX4pfzBmPf/7vlnHN8rQ8xa4XYuqvHoQ90JOujyUuOl7aFPdDxNacc1u9neX6 Tj5Kpgv1XPjg7TovGKnXcdyr//6LYXx31d1 CqtvX4snLP27bdeNzfF14fi88BC7lcJ71XFalKZdypcTcrESb3 uxPraqdCGi1sX70Z12o839XpHZtONV5 MGb/KDbJrBVu55oaj11R6D0b4IK3Gs8D4L5mfX7Acgu gxrvpt6x0V6hojb42I3XqKUGL3qnZFaH57a9a7/om82Bm6xb9XyMdtvNxz0IofL6IN4s2r6c9/6n3eKiv0ZlUOP 7nf7aKOhxss5ru/q7XhW4/FEsig6aieoTtX4sd9UejDm/VlB9DBrhTu5flL9BX8q4OnsajwJgEd0bHlIKYaaPJf4FzntmU/60VImheGTm6wHB9Oq1Xtm27vuCLHbxp3XXSx0k3XrcY8O6qrEy sIfxZCfdtZaplOZP6HGrU6q297vhtbl77Z2jjvJgPfjV42X/1BP9e/m9qrx7DR6aQHs/VXUOAhzXY0fq0rsjT ZQ42Hp9XDRNFeejx99g01XqOnZ TOkX4zR7y/Aj1PCz617X13xDIZqfFNuecrpezUuOK4J7eBeqxUy 09LZOrjDs1rpgo/d/vVtalx72d4ySfl6/gVo1n88jtm1drVscxOntTii5h8EeNgzCcVIc2gRo/doU6N2zyIJoHMd6pXqV7n1BtplJTKzePXmwBPbXrFeLGWnh1V7 vljqJ8a9tiWkNRGU00FFS 0uFayMPzWulyj/wP1uZF103J1ti2/b5leNl8T4F3K8PI6f2LpfKPxe6HiocRDmkhpPPBE1/mQiNV78S0Otpcf84ySzIsbr9M5UFGeo Ye3b1NmE5H6JsHj2t6wniK2deLQ61J02 5o3FX3xuUeQqkZa9iSrYw/Na6XKP/A/W5kXW7c2znuiwx4Lz7VeD3aRnvb7R orHKWHkGKDn0mMgc1DnJcV PJW22GadmWGe98HKmkzj2m5kPlFc1oWoEWb3F660n KdbaXCQa7dtzUnkzMDxK9ty2N63HrZXcOvHndaZt3z 2GfdvS9zD zZUnG02WqZQGY9qXCtRurvf7eN819XLtHLc2VVZ/Knxt7go9EF45Sx0FOn4RnL c/ 0XiuAK2SOWpmEFAuEXz6cE8d8FLGHhAd69v9OPCTdRW/nufQbcFC99eJBkXzVbD0yxHf/fWZrubM8UXlW20 tR1e0227c86Ztf2E27nkxyTRb2TQS5dzrxIV4tec7x0XIunSid Hq/O4jzYuPeznFnGVCYwZ0aBwAAAAAAAHg6qHEAAAAAAAAAbVDjAA AAAAAAANqgxgEAAAAAAAC0QY0DAAAAAAAAaIMaBwAAAAAAANAG NQ4AAAAAAACgDWocAAAAAAAAQBvUOAAAAAAAAIA2qHEAAAAAAA AAbVDjAAAAAAAAANqgxgEAAAAAAAC0QY1nrPM4DMO0WNcDAAAA AAAAHouVGn9J3h1P2hc1DpIs09Ak9rxXabzxBwlj5DivHcVepZ LYWvqFwwfxrCodt946j1Jd2Gv9xDuuGq66R ZuAjTbHsdPgc6vWzdve1KN yvh2LrhuO8I3u95TWT9rGSx2DKFyrR7XnoK1Pp9 UTpNdqYWj/JQTcZbvy zQRpsNwbXya9cNNinSf7SsCPsEyBzye3wDqPx 0fxEM008/yyQvtuUriQznB15fpKL5MOFeBvlvvXer2/uuw/ilyt 1lyuJPmKDXefz8r9Ci9VnbZZfxW9bt277Jjbt763bjnpWSjZeR 4F/nUXyGfOLzwpVp97z0FKj9 9KJ0m 0MbV koP m9PfN5sgDajxWB4ByLLMgbNlt8AyJff MolPAMAxyzROUzUhrvM4jtHVdR5b6S1KNZvabs f5OTWO1snEbMutlm0znMh ATT8WjVWvpgQGXPQmlrMjHjoO0q56ScWjcb9/SycBfkfpXEaynKPq9VmXbPS0 Byr vlSj9RRtT6yc56L85/X3DCRJqXGP5EaDM S2AGv9tlmmcl3ksu8k6j9MSeci6LGmplhhHjjdo3nrLNCRdr2N 9rfmCZiX2mgjVo9l2Q0Ua1cSi7eLj7ti67biL3 /bVp6M6kwAirpIrTIO1bhaonQZbfxY1x5 ywmSKzW d8tnFTJ5DrIQLIawYPDxtGTnikrl42eRAnNprcZ53X8g3sr8fH laslUXgBOuq3HpR2vAB8s0zmtlgfils8 3dGIxnpRluadGo2fenaqvxnVXsFszkcIhjNtotF0h6J2PqlHbj fZIXVi3HXeF 33bygJYZ7uoU40LVcafGtdLlB6jjR/r8mo4/n3TCZIbNX5I7uk9e3xdnab3Ewz5CsZxgO6zbnsI7mmaxnk91jk q5dc1ji/pG0r2PDCO7/WhsBrB0xU8gQlXuKjGow9dHDEBIV5qvLQ59Qk/p1OZ45rdbO8vcn5uVluNf5aaNV6x865D feFz88W 3Vd1 CqYNs7Tiwrt11v3P1ZdzDuKvf7UYX8KVabUwl6lXGnxt2sRJhE Wi/WP2X0cpztBMmNGt 27IUB8eWwTyIZnJYsbCO1ymd9Xdwbj6V8oNWj6rM3Dt9xTY3HL sofAXgybzWex7V9zfr8gOUWfAc13k2lY4Mu1Vbj8aGxz/9LDV70TsmsDoZ6eJN9o1bdulXb9cbdn/UYg3FXut8DC ljrDYn1RUrgxr3FG08WP9QzkH3kfw ajxW48k6RXzQPDm6HvMumo9ws3ze15nIzqRPetCduSxc4 Le E5wGgQ1/kh2NZ6suhxzxJaHxGLc8LnEv0h5py48uKesxgvDJzdZT5ewS9X T18PfFhKxrt12xXF3Z/1iodusq93vocV0qio/way1TKcyf0KNC1XDXbRxZP0kB/032e/bTpD rBqvD1JZjVfL/48a36JohSaHb7muxo8NCvbGn8yhxsPT6mEaaWSLJX0VcaEsarx GeZYWfmCvxqXOjvY8LSh3hravXxehh8M6z0/qtV1x3N1Zr5RSGnfF 72IWm7vaZlcZdypccVE6S7auLEu cR67fdNJ0h/Vo1/c 82y/ nGg8 RZDDt3BSHdoEavzYk rcsMnEeCF8Kb4y9K/RfcpKTZnUtNLN49e7JZFkyvvoVmV21lXbrjTuLq0XSyn1vOb9X q2RiqmOnhesjD81rpco/UUbF9bVt8X3j 0mSH9SjRfjwv6HmmurG5Xy/6HG478NzR4TfM0lNV66T/C8hxKp8eBll0tQojL8uRjfytEN3ylyHtGV98ZLH96 UVg6vVd/DsJidn5aLw3rum1XGHe31lMMx111jif3EErNWMOWbGX8qXG9RO kw2phb/yIHXaL1 4YTJM9qPB6q OL7aYbgTWztg5v18sfvft7ClvxAS42noohpLXzFdTWevrxgWrZ l5mjG40glde4xNR8qivE4vaDFWzhU48lo3/6CmfJmYLBuHWdCqd2L2m5VlG3Ftk5OQ6562zfpcXds3XTcvy5x D /bUPGoZXuXUroyHtW4VqJ0F22sY10rB93B2e/bTZCs1HjSI9PyERVv0byE16clvPjpm/AnSp8Vz56ULn1 fJzXpBbx/2fVWObXH1G73V/g WQxoTIFKhaIfflTDgd8FLGHhMuS 38nHpLuorfnN k34KB 6 XFhA7s1q1XM9l/UjuXW8yuoq/ZLbY9uqJt3bjtWR2kNqYdWjcd90IxyTS7d7K2EG/6vLgQr/Z857gIWZdOlE6jjaX1sxz0v3T vs0EabDcGwcAAAAAAAD4SVDjAAAAAAAAANqgxgEAAAAAAAC0QY 0DAAAAAAAAaIMaWX6F0AAAAFpJREFUBwAAAAAAANAGNQ4AAAAA AACgDWocAAAAAAAAQBvUOAAAAAAAAIA2qHEAAAAAAAAAbVDjAA AAAAAAANq81Tj/ Mc//vGPf/zjH//4xz/ 8Y9//FP89w/VRopK6DE77QAAAABJRU5ErkJggg==

Are these new samples from the 600 samples of the west-asian paper then part of only T1a1 ?