Try now in the
Open Cloud »
(no sign-in required)

Get Started

An introduction to Wolfram Programming Lab Explorations.

Run the code to calculate 2+2:

Hint: click anywhere in the code, hold and press .

2 + 2

Get a list of numbers from 1 to 10. Try numbers other than 10:

SHOW/HIDE DETAILS

Wolfram Language commands like Range are called functions.

Built-in functions always begin with a capital letter and are followed by square brackets ([]).

This gets a list of 10 numbers:

Range[10]

The curly braces in the output indicate a list.

HIDE DETAILS
Range[10]

Draw a size-100 disk. Try sizes other than 100:

SHOW/HIDE DETAILS

Lots of Wolfram Language functions take options.

This ImageSize option says to make the overall size of the graphics be 100. The arrow is typed as a dash (-) followed by greater than (>):

Graphics[Disk[], ImageSize -> 100]

HIDE DETAILS
Graphics[Disk[], ImageSize -> 100]

Get a picture of a horse. Get pictures of some other common animals:

SHOW/HIDE DETAILS

You can use plain English in Wolfram Language computations.

To get a picture of a horse, press and type =. In the input box (), type picture of a horse, then run the code:

\!\(\* NamespaceBox["LinguisticAssistant", DynamicModuleBox[{Typeset`query$$ = "picture of a horse", Typeset`boxes$$ = RowBox[{ TemplateBox[{"\"horse (domestic)\"", RowBox[{"Entity", "[", RowBox[{"\"Species\"", ",", "\"Species:EquusCaballus\""}], "]"}], "\"Entity[\\\"Species\\\", \\\"Species:EquusCaballus\\\"]\"", "\"species specification\""}, "Entity"], "[", TemplateBox[{"\"image\"", RowBox[{"EntityProperty", "[", RowBox[{"\"Species\"", ",", "\"Image\""}], "]"}], "\"EntityProperty[\\\"Species\\\", \\\"Image\\\"]\""}, "EntityProperty"], "]"}], Typeset`allassumptions$$ = {{ "type" -> "MultiClash", "word" -> "", "template" -> "Assuming ${word1} is referring to ${desc1}. Use \ \"${word2}\" as ${desc2}. Use \"${word3}\" as ${desc3}. Use \ \"${word4}\" as ${desc4}.", "count" -> "4", "Values" -> {{ "name" -> "Species", "word" -> "horse", "desc" -> "a species specification", "input" -> "*MC.%7E-_*Species-"}, { "name" -> "Movie", "word" -> "horse", "desc" -> "a movie", "input" -> "*MC.%7E-_*Movie-"}, { "name" -> "PopularCurve", "word" -> "horse", "desc" -> "a popular curve", "input" -> "*MC.%7E-_*PopularCurve-"}, { "name" -> "WordData", "word" -> "", "desc" -> " referring to English words", "input" -> "*MC.%7E-_*WordData-"}}}}, Typeset`assumptions$$ = {}, Typeset`open$$ = {1}, Typeset`querystate$$ = { "Online" -> True, "Allowed" -> True, "mparse.jsp" -> 1.244342`6.54648475351301, "Messages" -> {}}}, DynamicBox[ToBoxes[ AlphaIntegration`LinguisticAssistantBoxes["", 4, Automatic, Dynamic[Typeset`query$$], Dynamic[Typeset`boxes$$], Dynamic[Typeset`allassumptions$$], Dynamic[Typeset`assumptions$$], Dynamic[Typeset`open$$], Dynamic[Typeset`querystate$$]], StandardForm], ImageSizeCache->{113., {7., 16.}}, TrackedSymbols:>{ Typeset`query$$, Typeset`boxes$$, Typeset`allassumptions$$, Typeset`assumptions$$, Typeset`open$$, Typeset`querystate$$}], DynamicModuleValues:>{}, UndoTrackedVariables:>{Typeset`open$$}], BaseStyle->{"Deploy"}, DeleteWithContents->True, Editable->False, SelectWithContents->True]\)

When you run the code, the input box may change into the exact Wolfram Language code for a picture of a horse:

\!\(\* NamespaceBox["LinguisticAssistant", DynamicModuleBox[{Typeset`query$$ = "picture of a horse", Typeset`boxes$$ = RowBox[{ TemplateBox[{"\"horse (domestic)\"", RowBox[{"Entity", "[", RowBox[{"\"Species\"", ",", "\"Species:EquusCaballus\""}], "]"}], "\"Entity[\\\"Species\\\", \\\"Species:EquusCaballus\\\"]\"", "\"species specification\""}, "Entity"], "[", TemplateBox[{"\"image\"", RowBox[{"EntityProperty", "[", RowBox[{"\"Species\"", ",", "\"Image\""}], "]"}], "\"EntityProperty[\\\"Species\\\", \\\"Image\\\"]\""}, "EntityProperty"], "]"}], Typeset`allassumptions$$ = {{ "type" -> "MultiClash", "word" -> "", "template" -> "Assuming ${word1} is referring to ${desc1}. Use \ \"${word2}\" as ${desc2}. Use \"${word3}\" as ${desc3}. Use \ \"${word4}\" as ${desc4}.", "count" -> "4", "Values" -> {{ "name" -> "Species", "word" -> "horse", "desc" -> "a species specification", "input" -> "*MC.%7E-_*Species-"}, { "name" -> "Movie", "word" -> "horse", "desc" -> "a movie", "input" -> "*MC.%7E-_*Movie-"}, { "name" -> "PopularCurve", "word" -> "horse", "desc" -> "a popular curve", "input" -> "*MC.%7E-_*PopularCurve-"}, { "name" -> "WordData", "word" -> "", "desc" -> " referring to English words", "input" -> "*MC.%7E-_*WordData-"}}}}, Typeset`assumptions$$ = {}, Typeset`open$$ = {1, 2}, Typeset`querystate$$ = { "Online" -> True, "Allowed" -> True, "mparse.jsp" -> 2.203392`6.794636761773483, "Messages" -> {}}}, DynamicBox[ToBoxes[ AlphaIntegration`LinguisticAssistantBoxes["", 4, Automatic, Dynamic[Typeset`query$$], Dynamic[Typeset`boxes$$], Dynamic[Typeset`allassumptions$$], Dynamic[Typeset`assumptions$$], Dynamic[Typeset`open$$], Dynamic[Typeset`querystate$$]], StandardForm], ImageSizeCache->{233., {10., 17.}}, TrackedSymbols:>{ Typeset`query$$, Typeset`boxes$$, Typeset`allassumptions$$, Typeset`assumptions$$, Typeset`open$$, Typeset`querystate$$}], DynamicModuleValues:>{}, UndoTrackedVariables:>{Typeset`open$$}], BaseStyle->{"Deploy"}, DeleteWithContents->True, Editable->False, SelectWithContents->True]\)

To get a picture of something else, click the box and it will turn back into an input box. Select horse by double-clicking it, and type something else, like bird:

\!\(\* NamespaceBox["LinguisticAssistant", DynamicModuleBox[{Typeset`query$$ = "picture of a bird", Typeset`boxes$$ = RowBox[{"EntityValue", "[", RowBox[{ TemplateBox[{"\"birds\"", RowBox[{"Entity", "[", RowBox[{"\"Species\"", ",", "\"Class:Aves\""}], "]"}], "\"Entity[\\\"Species\\\", \\\"Class:Aves\\\"]\"", "\"species specification\""}, "Entity"], ",", TemplateBox[{"\"image\"", RowBox[{"EntityProperty", "[", RowBox[{"\"Species\"", ",", "\"Image\""}], "]"}], "\"EntityProperty[\\\"Species\\\", \\\"Image\\\"]\""}, "EntityProperty"]}], "]"}], Typeset`allassumptions$$ = {{ "type" -> "MultiClash", "word" -> "", "template" -> "Assuming ${word1} is referring to ${desc1}. Use \ \"${word2}\" as ${desc2}. Use \"${word3}\" as ${desc3}. Use \ \"${word4}\" as ${desc4}. Use \"${word5}\" as ${desc5}.", "count" -> "5", "Values" -> {{ "name" -> "Species", "word" -> "bird", "desc" -> "a species specification", "input" -> "*MC.%7E-_*Species-"}, { "name" -> "Movie", "word" -> "bird", "desc" -> "a movie", "input" -> "*MC.%7E-_*Movie-"}, { "name" -> "PopularCurve", "word" -> "bird", "desc" -> "a popular curve", "input" -> "*MC.%7E-_*PopularCurve-"}, { "name" -> "Person", "word" -> "a bird", "desc" -> "a person", "input" -> "*MC.%7E-_*Person-"}, { "name" -> "WordData", "word" -> "", "desc" -> " referring to English words", "input" -> "*MC.%7E-_*WordData-"}}}}, Typeset`assumptions$$ = {}, Typeset`open$$ = {1}, Typeset`querystate$$ = { "Online" -> True, "Allowed" -> True, "mparse.jsp" -> 1.238328`6.54438068642158, "Messages" -> {}}}, DynamicBox[ToBoxes[ AlphaIntegration`LinguisticAssistantBoxes["", 4, Automatic, Dynamic[Typeset`query$$], Dynamic[Typeset`boxes$$], Dynamic[Typeset`allassumptions$$], Dynamic[Typeset`assumptions$$], Dynamic[Typeset`open$$], Dynamic[Typeset`querystate$$]], StandardForm], ImageSizeCache->{103., {7., 16.}}, TrackedSymbols:>{ Typeset`query$$, Typeset`boxes$$, Typeset`allassumptions$$, Typeset`assumptions$$, Typeset`open$$, Typeset`querystate$$}], DynamicModuleValues:>{}, UndoTrackedVariables:>{Typeset`open$$}], BaseStyle->{"Deploy"}, DeleteWithContents->True, Editable->False, SelectWithContents->True]\)

HIDE DETAILS
\!\(\* NamespaceBox["LinguisticAssistant", DynamicModuleBox[{WolframAlphaClient`Private`query$$ = "picture of a horse", WolframAlphaClient`Private`boxes$$ = RowBox[{ TemplateBox[{"\"horse (domestic)\"", RowBox[{"Entity", "[", RowBox[{"\"Species\"", ",", "\"Species:EquusCaballus\""}], "]"}], "\"Entity[\\\"Species\\\", \\\"Species:EquusCaballus\\\"]\"", "\"species specification\""}, "Entity"], "[", TemplateBox[{"\"image\"", RowBox[{"EntityProperty", "[", RowBox[{"\"Species\"", ",", "\"Image\""}], "]"}], "\"EntityProperty[\\\"Species\\\", \\\"Image\\\"]\""}, "EntityProperty"], "]"}], WolframAlphaClient`Private`allassumptions$$ = {{ "type" -> "MultiClash", "word" -> "", "template" -> "Assuming ${word1} is referring to ${desc1}. Use \ \"${word2}\" as ${desc2}. Use \"${word3}\" as ${desc3}. Use \ \"${word4}\" as ${desc4}.", "count" -> "4", "Values" -> {{ "name" -> "Species", "word" -> "horse", "desc" -> "a species specification", "input" -> "*MC.%7E-_*Species-"}, { "name" -> "Movie", "word" -> "horse", "desc" -> "a movie", "input" -> "*MC.%7E-_*Movie-"}, { "name" -> "PopularCurve", "word" -> "horse", "desc" -> "a popular curve", "input" -> "*MC.%7E-_*PopularCurve-"}, { "name" -> "WordData", "word" -> "", "desc" -> " referring to English words", "input" -> "*MC.%7E-_*WordData-"}}}}, WolframAlphaClient`Private`assumptions$$ = {}, WolframAlphaClient`Private`open$$ = {1}}, DynamicBox[ToBoxes[ AlphaIntegration`LinguisticAssistantBoxes["", 1, Dynamic[WolframAlphaClient`Private`query$$], Dynamic[WolframAlphaClient`Private`boxes$$], Dynamic[WolframAlphaClient`Private`allassumptions$$], Dynamic[WolframAlphaClient`Private`assumptions$$], Dynamic[WolframAlphaClient`Private`open$$]], StandardForm], ImageSizeCache->{123., {7., 16.}}, TrackedSymbols:>{ WolframAlphaClient`Private`query$$, WolframAlphaClient`Private`boxes$$, WolframAlphaClient`Private`allassumptions$$, WolframAlphaClient`Private`assumptions$$, WolframAlphaClient`Private`open$$}], DynamicModuleValues:>{}, UndoTrackedVariables:>{WolframAlphaClient`Private`open$$}], BaseStyle->{"Deploy"}, DeleteWithContents->True, Editable->False, SelectWithContents->True]\)

Apply an oil painting effect to a picture. Try some other pictures:

SHOW/HIDE DETAILS

To try ImageEffect on a different picture, delete the picture from the code and press and type = to create an input box where the picture used to be. Then type picture of a cow or whatever picture you want into the input box:

ImageEffect[\!\(\* NamespaceBox["LinguisticAssistant", DynamicModuleBox[{Typeset`query$$ = "picture of a bird", Typeset`boxes$$ = RowBox[{ TemplateBox[{"\"birds\"", RowBox[{"Entity", "[", RowBox[{"\"Species\"", ",", "\"Class:Aves\""}], "]"}], "\"Entity[\\\"Species\\\", \\\"Class:Aves\\\"]\"", "\"species specification\""}, "Entity"], "[", TemplateBox[{"\"image\"", RowBox[{"EntityProperty", "[", RowBox[{"\"Species\"", ",", "\"Image\""}], "]"}], "\"EntityProperty[\\\"Species\\\", \\\"Image\\\"]\""}, "EntityProperty"], "]"}], Typeset`allassumptions$$ = {{ "type" -> "MultiClash", "word" -> "", "template" -> "Assuming ${word1} is referring to ${desc1}. Use \ \"${word2}\" as ${desc2}. Use \"${word3}\" as ${desc3}. Use \ \"${word4}\" as ${desc4}. Use \"${word5}\" as ${desc5}.", "count" -> "5", "Values" -> {{ "name" -> "Species", "word" -> "bird", "desc" -> "a species specification", "input" -> "*MC.%7E-_*Species-"}, { "name" -> "Movie", "word" -> "bird", "desc" -> "a movie", "input" -> "*MC.%7E-_*Movie-"}, { "name" -> "PopularCurve", "word" -> "bird", "desc" -> "a popular curve", "input" -> "*MC.%7E-_*PopularCurve-"}, { "name" -> "Person", "word" -> "a bird", "desc" -> "a person", "input" -> "*MC.%7E-_*Person-"}, { "name" -> "WordData", "word" -> "", "desc" -> " referring to English words", "input" -> "*MC.%7E-_*WordData-"}}}}, Typeset`assumptions$$ = {}, Typeset`open$$ = {1}, Typeset`querystate$$ = { "Online" -> True, "Allowed" -> True, "mparse.jsp" -> 3.2521708`7.9637183396276034, "Messages" -> {}}}, DynamicBox[ToBoxes[ AlphaIntegration`LinguisticAssistantBoxes["", 4, Automatic, Dynamic[Typeset`query$$], Dynamic[Typeset`boxes$$], Dynamic[Typeset`allassumptions$$], Dynamic[Typeset`assumptions$$], Dynamic[Typeset`open$$], Dynamic[Typeset`querystate$$]], StandardForm], ImageSizeCache->{103., {7., 16.}}, TrackedSymbols:>{ Typeset`query$$, Typeset`boxes$$, Typeset`allassumptions$$, Typeset`assumptions$$, Typeset`open$$, Typeset`querystate$$}], DynamicModuleValues:>{}, UndoTrackedVariables:>{Typeset`open$$}], BaseStyle->{"Deploy"}, DeleteWithContents->True, Editable->False, SelectWithContents->True]\), "OilPainting"]

You can also drag images from most websites and drop them right into the code.

HIDE DETAILS
ImageEffect[\!\(\* GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJx8vGVwXNmWtvnFzJ/5NdMXistVZrbMbIssZmZmZmZmKVNKJTOKmZmZmY2y zHZR3+6vJ2Ji3pMpq3TrVnfEqlM7T548efaz3/WutVN17zG3YDOv/+N//a// Ff5/4WDmGq0WFuYaa/5veGEZFO7rHeTpoRsU4entGXbH7f/ESRr+8cA/xDgg Ijs0Lj8isSgqmZyYRU3PY+WQuSSqkMwQlzAkFLq4lCFmcisSMoof6jjcUre7 eM/i2FW9Cw+sjlzV+/s5jS8vaH1xXvOrC1pfntc8HF+c01DEV2e1vjqr8Yf4 8swjRShe/v3so8Pxt9MaCOLdM2pfnlb94pTKF6dV/3pa7YvT6l+eUv/ipNrf T6n89bTyX86o/OW02l9Pq/zllPLh+NtJ5b+eePiv8ZfjD/5y4j6OB+O/nnzw 1+P3/3LsHo4HgZd/OXYH8W9H7xLxwx3E/3Pk9v995EZwPAEqJpWckFmamsfM LGTllfALKXyAorLL6OwyKlOKQSFV5O6fZGIfilDRd71w3+KHy7pA9NV5na8v 6H59TpsYy4kdDBTEwOrrc5qIw6zk5//ppYLSwQChQApKfzuDUMcRrBB/P6Vg hQAoNZyXj1UOoClY4aiIP+W2H2Alx4X424kHiH+BRgSI/eXHu//2462QpPyI lKLYzOKknNK0AgZY5VN4xQwRWDF4lUx+FUtQzRHVMoW1CemUmOTiqESSV2im hrnf2bvmoPTNRb2vzut9e17/64v7uA5CgQsYEYfF9vm85gGofw51eey/BLS/ nlH/yznNv59Vk6NTl3MjQoHuMCtFgKQiDnD9d9wUZwilnXzwr6z+wA3EwCoq g5SQU5qaT88oYmUUMLNJrDwyh0wTsYU1HHENV1ovrGhlC+vSc+lFpYL8EpF7 QOJDA9czd8y+uqj71UV9QlefAzI7HF+CEiSneHmQp/KTfxpfnNVSgDrI5b+f 0/rbWc2/ntc6yNa/yQMDRZIeoJPLT1UuP9XPwvsjw8PQ/lx1CqUdDsVJObHA +JywlMIDXaUXMtPy6Gk5NGABK7akhiOtFZQ38SSNFKaUJawqKBG4+Mbd0rT7 Vkn760t6X1zSxfGri9q/4yIA/gm3fwV4gOjw+X3tHaZ3VgvEFPpUjBEKyX15 Ru2fjO6f9LYf8jxVPwxNkap/Krz/Vodybn5RWYGx2VHJRXHpJbCslFxWUjYj LZdJooqRgzRuGVtUzRXXMfk1yERxZTONJYtPK7V2jz1+3ZigpGSI47dKSEYD RXx9yUAutv348oLO4fhTbv+MTlcexPjrz0GY3lm9r88S51FKvjivrcB12OgO ex0kd5jeAbTP3NQ+n/lj5v4proOxm1+iT2haSHRuWGx+SGxeSEy+T0h6RAIp u4hHootINAFTUAlKpSwZhS0TVjRXVrfnFwu9w3IuPrQFmW+VjL+9ZAjXOgB1 EMTLC/rg9jl0Dwfx1nm9f40vzx+c1zlg9c1ZZK7ePsP9LNb94jxiX3i/hzx/ CRuEAv9QXg9xQxw+oyiy8sKh8odQKFARTl6xnoEpPsGpIAZKLv7Jpk6hnmGZ gVHZkFl6PqOghIc6SGGXoz7S+dViWR2NU+kTkXfhgc3XFw2/u2yiwPXtZaNv lAwVcTAmiH0+qdDbQRwITxGH31K8u4/unO4353UQYPjlBT1CnJ9ZKXD90e7O ah0e/0n8Wc09zBN2pwgk+EGvIg9VK9dIZ78EUPIMSXMPSrPxjNGxDtC3C7Zx j3T0iUPrlZLLINElZIYUJTKXLChlS4voMjvfpBO3zb67Zvb1ZeNvrph8c8no gJIiDhAdnFTg+tMg4HweHBKh/heH0P0d9JQMISSkPCgBI06ClYLnQdpCjQpc B3AOEvy/Q3cATSE2AqMcFCHOfUr7YeQYBj523nEugSkW7jF6dqF3dJxVTbz1 bIM0TLy0zHyi0mn59LJCmrSAIs7IZ+cUC0LiC1WNvcDq+G0rOS6j76+aHAb1 3RVjnMHxTwHuZ+j/hI6oF19eMvjykuEXF3Hch/n3czr41IEmiSS9pIOX0Bsh MPlRgUVOTOczQ90Dpf0PJRhl+nDr8oeeWRHKJl4ILatAHZvgGxqOl9Vsb+m4 6NqGmbvHqZn63dR01rMLD4gnp5P4EFUuiZ9VJAyOK7J2j7+h4XRJ1enkbesj 10y/u2IIMgQceXx71eTbz6yIDJUHtIec/R3d55N/iK/l735NgCJYIQhpndc5 clH31C2LI1dMkIxfXyDMEEi/VNI5nLnApTgSg8Me+Ds03T+h9LnQHG7/FB2g vAnUOogTd01PP7A4c9/i5B3T47dMv7usd+qW2QNDX2UTn9s6Hje0PS8oO2Ds FpoVGluQlsvOKOB7h2U5+qaau8bf1fE8c8fq3AOHH66bKfggvvlMDIGJI0kR B8SIUGjvssn3V0y/vWpKpPAVE8VligAfcCZI4vwlg+PXTdVM/XUtAx8aeuHr Lqm6HL1uTghMyfDvl/QP8vePfvj55R+KiCJ/D708xEqRs59ZHS7TihLz7TVk EB7eANpQJA7W7vRd6/P3rc/cszl51+bEHWvguqvrZuUWE5NCTchm+kfluAWm WrjEPDIPfGDof/KO7TdXCO/CxA9CQeDw4GAsD4LDkRsW3183xwcVrPaJXSHu A4GBFXhiakaOcVkl1dFppU6BGXd03PTsY/Uc45FiXykZ4WJ8BIOvLvy5DR6G ts/tn4vI79AOtTR/aPkOQmEv+35yiVjuozctTt+1Pf/Q/uRtSwjmh5uWx25Z Yqxi7GvhHheSWBSWSHILTDZ2CNa3Db+p5Xbkqtl31y0PWGGgiMPo/jR+vGWF myuuPFDXt9csDpDiPJbPwiPJJSDDIzTDO6rw4gMrVRM/XdvwW9rux25YnHrg BOC4A4FLyYhIWCUjRbIr8vfL/6aI/KsODyUyAU3eV+sfYkjwhJi/U9oHpZgC WB27Za6k5gxdwYt+uEms/tEblqfuWKmYByEBscSBkTmegWlGdiEwtItqLt9e NVcoBIGBIg4TOzj/7XWzb66ZInAGrBQzPbiMwAVtfz4D2Ry9baXrGPPQ0EPV xPeBofdlZXsskKNvso13koZl6MnbNqCN2yqSl1CaQmZ/jN9rylfy5P2fi/I/ 12XdA4wARbCSW4RiNQk+103OPbBDJmJw5Ib5Dzetj9ywOnnf/qaet0dAenI2 JzaFkphBD4khmbnEwdMOI0KAgCIwxlwUA1A6HDh59La1AukBWDlGeV24Zkpc ds0Cxwuq9nf13R9ZRahbhus7xDj4pyXlsoNi8zXNA689clVAPkhhBS5icMX4 kAfu4/pWPjiIf2pslIwPCvThnvBLJb3fSRL2ux+HxX/suu2xm/IcuWH+/U0L tAdoPuHnwcmM2Ex2UiY9r5CHBj4kvljZ2P/762byACWrIzdtv79li2nig1DO eWXnG9reZ+7ZIZG/u2H29XXTb2+aI368aYeLT92zPX3fhsB1zfrba1bEWzds v72+Tw/QTt2xeWjga+kcY+6Z6Bmab+6RouMU6R2Vn5DGyC0RBydRUFnOPnCE dSi6l2+VgM7866tgZYRQTApaBboDet8pKWDul+ODMwrgCil+fYjql3I1HqKk qFyffRjrfvV3MUAAaA/MXOIdfZJsPOICIrKDIzPzyDymoD40kXJb3+v4XXuQ OXLL8ggUeNP6+xs2cDAQxgcvPnJ7aBF+1yjwzAN7nPnuptU31y3w7tc3LX+8 bXvqrt19k8CLGi74OPAq7qBghWVCqJqHRmVy0gqFdr4pelZB1zTdz2s4u4Tk pmSzaPx614A0NbMAPcdEhe/Jw0JeaEzBB8TkDYypIuu/vrqvBIAlmFzZB/WV vOb+QZkHdZyQ3+fxN4eaosOsiO+9YY6vwATP3HfQsY5KyOKb2wfZuoTHJhXn FHHQyacUcCw84pUeuf1wA1qyPgL3uGXzgxyXIvDy6F3rS9puylZhV7S9cEOC 4Q0bxNe3LY7ds72o6gqMNwx8jj+0wxcduW7z/U3imhMP7M+pu1zR9HALyy9g VkJCSfk8O7foa5qeZ9RdghJoRdQycU1Hfok0Lptl4ZkOAcvT3/Kry8THD/go rPLAJIkpy6cpJ7k/34PBQTmWQyDM5zv5Rz6DMjnUPe676+cvMkHKfEN4u/XZ +w5almEp+cKYZFJcckkJvRLde1oRzyk49aau2xllpx9u2X13x+7Huw6QikIP P9y2Aii8PHbH5vhdmwsabpj+7wxvWiLdQOOukf91He/rer5H79rsCxKJf8ce F0NvRk4xEcmUYlYFjVclqu3No0gemgUqaXt6h+cKytqq24ebOidBzMAxFulM CBuavGEtZ0WoVGF3B4N9XHKAhMyuK7RngoHi5H45VlxDZBNKj/k/YfldV5+v 37/YFKwIs7puiSJ4Xd0ZT5iYS8+nyJARriGZjsHZapZB59QcIY+jd+y+u+2A Ocp1tc8KJ4/ddTx20+bUA4dTyo5Q3ZHbdrgAyjlyG0XQRtUm4qah9x3DgNP3 7K9r+8B2jgL1HcdTD51P37ezdE/KKhJTWTUsYVNV81BN6whX1qLjEKlqE4a9 Q23zMLusRVjVIqtrdwzKw2JBlkBNsLppRaBDG/M59qHJ45sb+8dvbuzTO2Cl KC4KqjhiCvLpE66LIFzi+u+N0H6xlt/zu8/8T9xzUFJ3v2vg5xNX4hWdq28f oWzse0vPE2I4q+ZyWsUJrH68Yy0XFcHq2B07qALzPXHf8egdB1jZyQdOP9yx /fGuHaEZiO2O9ZFbFmceut0xDbmk5aHjnPzIKkrNJNDSI/W+gd9ldXd181A7 n/Tw+BIyTSar7gaZLIqEI26iChtsArJNPBLYkpYianlMRilTVkflluNT392U rwKyG2UFa3HdRpHp3x+GdsNcXjs+D26aH0D7vUBfszhgC/MEf6KOywcH1xw0 kISlyAEq3BXHM6rOFzU9Tj2wVzYPuqnvjoSCBSFlMP2j9+yhFhwxhqgUrJB3 Jx46nVZ1PfHQBWfw7o8PHL67Q1x27K49SAIgLr6k6XtGzQ3Cu6rjZ+WbbeYW G5ZKi0hhBUQVe4flFNEqaNwanqSxtWcmq1gckU5jChpzaRVqZiH2/hl0QUN4 LCmbJKpu6Y1MKsSe9Og9ZywEvg62+e1tSxD7PW5YKQIXIA6/VJAk6B0SoeLk 90RBJ+wCKwuBKT7yh7ZHwUqResTFt+TZdJcwbYwxOHrPERI6/sD5OFLmnr08 HH+QS+jEfZx0xstj951OqbgB12lllx8eOH1/zwFH6Aovz6q4nlPzPHrX9dwj j7OqbvIzzmpW4VCLQ0B6SDItr0QKIbGlDTRBhbiqvayhp1RQzRC3VDUOA+Yl ZQcURBq/lsqprajr6x2cKubUatpEXdYNOKnsjEzE2n13xwqIkO+KkBOzVsS3 t233K7X8pYLeYYZ4a//lrX1rJZbgrh3EQFz/GY6ij1LEd7csFAL+HtscdKSE bOTKQaLddVCgkKcY/NwOL8EKlE4qu55R84CcoKtzau6XNDwQF7U9T6q5H7lL XIOPIG0xrx/vu5xRccW7SpqelzTclTQ9UCVvG3jrOkZFpJZSePU0US2VXw5Q wqo2MkvCFNbyylrjcjn2vumxGTRJTWtbz8T49Mrk5DJT0m7qlnDfLOzcIy+o lCgud2xRaHA8cs9BPrBXzBQDvPxMz1ZxUhFyqjYKeop3CWu9Q4BVXKbA/ies CA1bK5aDaAPuWsvB4huhKCcYEQIWdPyeE54NiKAfrCmIYQxE13R975oEo8Cp mIXoO8RpWoVcUHYCVWC8rOunpOuN1DtKuBlxMSjdNPQFsRt6wbeMQswDc1xD spPyOTRRfWP3OKKiqY/CreBK65Pz2ZklIl5FV1PHyPjM0tTi5sTM6tjYEk/W gU7+krrzaWU3haTxtKB05J7iSCwuJkJYwV0HYsnu2H9/F3OxJ6Dd3adH6I1g 9bsa5azsFRfjGhyJwa19LR2IUIHod1afRSXXlRPMHLkDhYAV5gsVgdUpFRco DVJBr6VqEewWXmjrm2brnRqVwYtPpwXFldzR9UauKVtHXdH1Adjjyu4nld3B VknbS9UyTMM6Wt0i5rZ+gEVgPjpPEruqor6/vK6PLWnKL5Wi8rLF9RlkAVPU 0No9OTm3ND232jU0X9s+2NQ6wpe1g62BS+zxe3AGVxiXQucKVhggjt13wLsE gbtOxJTv2iswKjgogBBOIk+czzMlIOPkwU0QYEUU2c+dz2dN2uw3lnKvVggb 64UAH6wgpqkQElhh7lDaWXXPK9re2LXZ+2eGJ9OxDwpNKEktEBXSyii8xtBE qrFzrL5z3APzsIsaXiceuh1TccdnL2t5adpG2AfkaFiGPzD0N3KLh5MzhPV0 Xi2ZXkHh1GQU8OHkDR2jgDMxszExvzkyOdvUNsgQt5E4NcLKDga/oYRfH1Mg xW1/uOd69IHrcXlAuogf7sn98y5Rly9p+51Q8fjxgdORz3MnVPfASeG3ckE6 yN0YA6eDlwegCFZyH1NY2WFQCkSoLEQQkO0UTn7sLvgQrFDdjj50BCjCplSg E2eYhmtEiU9McWBscVwWJyWfH5NOD02hphaK82nVKQUiv5gSY7cUpN4pFY8L uv63jINMPJJt/NLAED2Jrl1EfB4f+yaupBXHvGJhDlmMVqFzYGpydm1+aXth eWd0Zq2us7+YW+UbXRKayihkVRbQqyLSGbaBObeNQ8HquLIncB27R6Q5BidV PfF4lzS9bxkG4gjxY8o/yjHKp+BEzAJ8CGLOck26wEsVtDEGMQVzwjQeuhz4 2+9ykotQkXoHUtwHdd8BJezzqjkeU3Y6gVqm6n5GzeuUusd5dQ9dp0Sn4Hwb nzSPiML4PGFUJgsdkXc0OTiJCmlhoGUfc9Mo8KZx2Hlt34dmwZivX0KpmXeG iWcGmtsCegW/vI3CrkW68cqby2p7hyZWllZ35hY3ZhfW+0ZnZXW9KWR+SCoF zG0Ds+ILeEEpjIcmQTf0/ZS0fE4pex1V9iAQ3fc4hgVVdscYpcTYK0vVOvq8 GrGmwHVcxZXICNSah0gNR1iHfOBKqP2h24EyFWdwMSZ7TBnp46rgcxAHhvb9 XUXSKUAREj0cRA4qu+A+YIUyhyNeohIBF7wdxEw801wiCn1iKaZemV5xVIfA bG37KGz9MKMLml5X9PxPK3to2EYBlKlHmp5rurFXZny+iC5uKmJWFTIqsylC WX03VLS4+mRhdXtmcb21Z6xUWAk3g25tAtM07aKNXJNNvVKxMb+g6XPDIOiy ti+m9qOK5zEV19MPvQg/VPYELsDRsI/XtIu9quMDp73wyPuMhpe8rHifVXeH GjUdkpC/BBw5WwVk3OqkwlTlrKCHS9o+/wzK9oCV3BvtPnvjPh/ihg/tsRZy p9pfhRNq7kfRvau4n1b3PvPIB8aFSnfbJFjXNVHLPuqBaYihW7K5T/ptk6CL uv5nNP2Jy9RQCDy1HBN1HONu6Psraflhm+wQmJqUzQhNpKSR+Pl0KU/WMDA6 PTW/CmL9E4vS2p6kIq5vDNUriu4cWWjskaplG3fXNPyilvdZzcCrhmFIMcj7 mLrnUTW3Uw88T6t4n1DxOqXmAzgq5hHGHulqNtG6rulqtokox6i51kFkZauY uyZhek6p1/QCjspZYUan1LzOaviee+SDwA3BCmxvm0aoOyT+eNtR0Rgo2pLv 7zgreiG5nPZTGKCIkidvkOTtE8KNYHUgLVVPouNS8QCHC5p+wAWA57R87plE 69nH2vml6jlF3zEOVrOMgh7gLa6BmfctwlXsiHw8o+pz+pH/6Ufuui4pNoE5 Jm4xqeSyAm4Dvay1pXu0b2i6tW+0prk3n1UXkFTqF08NSmJ4RVMsfLNUbKJu GgRc1PI/rxWoZBB2TjsQj4EHAJ8Tqj7whJNqvhf1Qsz881CXPaPyXMJynCOK LbyzNW1iH1pF6rvHq1iEQnK6TvFXdUJPqHlDRZe1fPRcU5Cw5v6Ft02jQPuY uisEcEUvGL6BXCN86TaRcUTqyUvkobwDKBcwPypPXlAi4OzjctlnpeJBWCgy Uc0Ti0Ish4bXqUeeeNqLGj5WvrnBKSyH4EI95yQ0je4RZK9oalKhyCYw74qO H2rT2UdBp9Ux8IFI8OQWPulh6Vzf+OLUYuyMB2pa+jnlzdkUsU8MyS0sPzSV FZBEM/dO0XZKfGgZfcMo9KyG/znNABzPPPI79cgXfE6r+8oV7ofxeZ2gm4bB Vt7p4elc94gi64BcY/cMQ5cUVduYB5bhRu5pwWmCkFT2Ve0A4hk0vUMy+AlF Mk2rsBs6vreMInCTc9r+Cpld1vb//r4N0XsT2zS7Iw/sv3tg/8N9wsMP8k4B 6seH7rDNYyqehDF+jmPyIyEnFe+T6uDjjefEKhCL+4gYwFfVbRJ1HZM0bZN0 HBOwsg7+OZaeWcEppTb+uUrq3krawec0gi/rhNwwCj6vhWQMQDF1DCN5RRcH JZJLeLWlgnpsBp1Csuz8M6290h0CshxCco280uAzV3RDlfRCz2gEYEYQM776 lLof8hGD8xreJ1V9zmoF4DFuGYWZe2V5x9CM3dIs/POMPLPhkNpOKSpWMU7h KKbs8DQm1H7hka+lXzZd1oHuroBRa+WdifZYSSfwzCOw8sei3DePlpuS/Wch od9wUDQDcndyPmB1VNmNAKXqdUCJAKXqdpxIQG+wIkBp+BELgQfG86sT34Lv UtIOeWgZq2yVoOuS5hlb4h5e6BZeHJTGMvXLvawdeEkrCB+5aRxxzzr6gnbA HaPwB+bhtkH50dmCmHRqAVWSRy2LzuK6R5fYh5O07ROUzcIhiTuWuDjokl7E eW0QDkIOYjpYfQRhmMSYENsZbM81Aq4aRuCr9VxTtR3TVKxj1WxiTNzTcSs9 x3ivKDJARaXTvGNJzmElSUVS7KSqG/tlNX1pReXu4aWmPnlX9cLxXWr2SVpO KYruQhHE5vceMTii7EhUBGWXg1D4EiREHBFqhCMhCDmp+ZxR9ZUrigCFuKAR eF49SI7L75xm4HWTMBijvnsmamJAAjU2h+8cUnDfJuaSbggUDkQ3TCLv28Sp 2iQ4hRZ7xjH1XeJDUujoH4q5NUXs6kxqrUcCQ8M19Z5F9GX9EHzqsn4Ynp/A pRtyXsMXrFAyAAfKPKfph7w7rRUMt8TJszqhQHrNKPyuecRd86hbJuHoYTwi KQmFZTn06qg0VmKBmCKoL2BW51DLueWdtc3DXb0zOJIZNdh0+CcwlC0SrxmE 4vEeWsft25G8N9v/oQC9E/jIy5wCFFoLBNxbEYSTq3mB0kHeHYA6L19c4rEf BRArrhMAFNcMAo180vXdk618sxNyeGROdVgaU8Um+rSGz1mdoDPagVeMwjQd Uy288uBmVoGFBl6pUGBSoSSJJA5NZzqHkVEFrppGXzAIhpAgQqtAkqlnlq5L soptwh2zyIs6gRd1gi/qhJzTCjqrE3warHSD8dXEWDvknH7oJcOIK4bBKMRm vjnJReKEbG4evRLdfg6tHqyowjpp/WBb3+TA2NLw5HrfyFJH//zA5FbP+GYJ t8HAJRWsrhhF4IuAYh8U4dVO8lCA8jhOtChEEHJSGLiq91lVL1RSonip++6H hh8C5qBIBEiFeE5iEHhKOwBOcsc4yCOqKCKDTeI11bWOYk/Hq+oKTmao2sWf 1Qs+bxB2ST9YwybeKYiMlumaMRYxxtAnC96CrbSuW8pN4xBQwpTPy1mZeGcn FpbFZvIz6XWWAUXKDknQLXL5kk74Bb2oc3qR5wwiiNvqhZzXC8fNLxiGKRlF 6rilB6QwSIJmlqyVJ2ksYZXFFwii8yUhaazcUhlYDY1hD7U6tbg9PL02sfRk dn13aefVwsZucnGFmm38DZNoDYcUtEafWTkRbZKqMzolgEK5PEBE1FN1H5QV xDk177NqvmfV/BAKORE2BbWDlRZRNcDnNKqblt8FXWJZrxtEuEWXMqVdvIou tqSlq29qZGJF3NBN4tT5JNAhicvGkXetYvSckm0DizSdU6Cxa+ZRNyxjrplE XDUOv2wYetkw/IJhuJxV6Hm9UBPf7AJ2PV3UmlYsM3BNuWEabhZAsougq9ol IyXxpaAk/3jURZi/QZiSQch1wxA8A1XUVNHQXd06WNXYLarqDE1nm/tnO0eQ SoTtTFnb4Oji7MrOyvbu7NrjUew65zYmFrdmV7Zahxes/LO0ndMdw4oVDbzC kWDXR9VQ7Nyhn4OAHYEGcKHKIKAomDYqLLJMbuMEpVNyVpDTGUJLgZjjHbPw SxCVTsQt42jXSDJT2Cqp6skpFtW3Dk7MrNd2jqPAZVPrNB2ToRl151TLoEKT wMIHjilKRhHXTaOvmsZADMgdxAX9sIsG4UQHBZEYROq5p6WQyyj8loQCMZIa XTe2MB7xLP8UdmAqy9gjWcUqElUSNo64ZxalYZ+Itg0qYokbselu6BoT1nQk 5fMsfbK0nJO0XZL8kuiF3Jr2/pn51eeza0/7xhf6Jpa6Rua6hmcGpxb7J+YT i0S+CayYXDGx31fxQDkjBtCPmi+KnSLdFJRwJAbqvgQZdC9E30iEotwQjQ18 lXDpQOCCYyBNIA+P2FIT91TUNfRRcTkicWV3RX1/Y9d0Z/907+BMz/ByefNg SEIpdjeXTaNvmEXB/B/Yx12xiIHMFKwuGUdeMYy8bBBBgNInBHbBKAK4jLzy 0L27hWfHF0qTKbWxOVKfmJKoLG5MDqOpb6Z3crOqbSYynZdFq2eUDxSyW6My +HnMWmnjUFPndHv3OLZOuYyqwFSORQDZIogCe0SWSRoGe8dW+0ZXm7ummjvH GzpG2/qmGzvHWnomh8cXOkfWchgNIck08EE/QOyb1HzA5+gDd6LJxz5F0x/c CCHJGxiC1SOf88hEDf9TKMfaQQQZTT8UI8Ah6o52IJpkyP6uWZxbJKWIV5tO FqIDJwsbaLyWvsGlmeXHc+uvJua2JqaXRsbXmtqGsorLYAI3LJFHsTouGerO yUomUUomkdeMoy8bRylYQVcXjSJADJmoZBp9xSxSxSrZJaQ4JpcdnErHZpxf 2Ts293xhY29qYWd68fHK1qu5lRf9o6vdw2vdI8uDExv1bRNcWRtb1t7UNdvY NQkfgJ+7x9D1vYtsQpmxeTKmrINb0dHaP13XPtbQOtHeM9/UMVbfPlLTMtjY MdE3MNvcPVfEbYzP46Pf3q9lqp4X1D1uGQYi0FGflW/uQACs0OARstHwx4YC lE4j6bQDzukFIukIRLqwkXCAuqQXhtkpW8SS+O2wBZhDeUM/XVpfVts7M7e9 uvliZvUJHGBxZXtu4fHU1Frv2HpgCs/Ip1DVNtXcl/zIPhkaI4CYRAMaWCEN EReNI4GOoIdyZhqmbJvhnShKp4gL6FWVLWMTC4/nVp8urz1d3Xi+sfN68+n7 5fUXM/Mbc2vPZ1eeTC+szS1tD02siat7eJWdTFkrTdRewGoOSOYaeOWiLML3 qLw6hrilqWesZ2RxbGYLF/eNLPQOz/cMLbT1zLR1TtW2jcLrsiiy02qEtyMN r+v5BSbS0HVA28be6Vr2cWih0QADIyEhVHaDcFQ3dDhwSxQaxDnd4Ev6oaAE S4GjYi7XTWLUrWILGPUt3dOtvVMccRNd2tg1MLOy/mxlY3dp8+ny9uPlrWfL m3tLK8+ml7Y6hxbFdSPY9Zh55aNfvWIWc9kshjiaRiH2oZlgDEXF3DKPv2YW c8M6Vdk+MZUsHhhbm1x8srLx/MmL94+fv1tcfbL99N2zlz+tbO+B0trj3fUn rx6/eLPx+Pn82rPx+a3K5v60Qn5MljCpqIws6IjPl6Jt6B9dFlf1iusHekbm YeYL60/XHu+t7rwE/9Hp9e7B2Y6+mZa+idrO0bKmgYdWEUY+uTquKdgW8cra JdUdKOghaYwUkowi7g5K5T+yi0MLp6TjD7vGPl3fLQ390lndAKI26YJbGLKD SBAjIllumsa4hBdzpZ0dfXMdvQv1HZNVbWP9Y3NLa093nr3feLy78Wx388Xr 9cdv13bezK1tzs4tj00uCaoHPRN4hr5F18xi5axABpYVLYcWjTOXzYnxDYtE Zafsh66Z1mGknsmtta1Xa1u7T3bfPn/z86uP/7mz+/75qw+Ipc3dpa29nZev 9z78+8v3v20/e7Wy+RTTX9h8gaxkSLpJgkampFVWO9A9MgsJVTSPS+r7kcLz a0+wmlsv9h6/fLfx9PXC+vOx2fXe8YW2gcn2wena9mG0WJYBBdi8k+iVqOaD owuiyjY0zIKyNgBnSDqxYzL3TnMMLfCIoXjHlHjHlALXddPIi4ZRSsZEtUKf BqXBkyGDR44pFHFn18Bcv1zDQzMbWB2s+OaTl1vP327s7BHH528gg83nH1Yf P1ta3RoYnipvGrEJKdHyznvkkgMmV8xjr8rjslncFfN4DHAGuG7bpYBVNKmC JmvtHlpZf/JmZ/ft05fvwOrpq09ghfHum08vXv20+ezNi713bz/9+6uP/8B5 aAwPsPH07caTd/DMvqk1VL3q9nGUY7h3bedk39gitLe48QLwt568frL3/vHL D7gJTg7OLHUOziBHKpsGsXd7ZB1T0TDeN7gwt/QYeCG86fntgaG5yvpeKC2P Vh2RzioVd0rqR7OKpSgrFr45N02jwOqiIdrpGG3PXE33TPjzDYt49ACiuv6h 8dnxKXz7k8WdZ6ubO+vrz7H6m893CVaPP2w9efv02d7ms3dQ+8Lai/r2oUJW jYlf/nXLGFWHjOvYU1gmEkfzJCXzxCuWyXh5xSrxhk3CLYdUdacs2M7g0PjQ 6Mryzi5Y7ey9e/b64967nwlR7X16/9P/fvfTb6/ff3r//rc3739+9f6XJ3sf t56/3n76ZnXzJbhBZsNTC70jc1RZO0XaVdk63DU0C09Dt7C08XJ9G1e+337x Dndef/Yavej40kbv6FJL92xZ3eBJrQDsLzoH1yA5VBDk9drms8ml7caOqc7e ydqWscziMhq/vrahExaUz+s2CClVd0g1cIx1C0xPzRUERhYUUKTltb2FzLKA GHJkYklLz/jwzOrY7PL8ysba49fPX7x+9nxv79WbF69ebz3dhbaRDltP9whb 2HoGfxicXIktlKl75N6yjrtpnXDHLvGGVTz43LBMum6VomSRqmSSdM0i45J1 9lWLpKgcHlQKJ19Zf7VKONXe05dvdl+9f/fx3999/BWI3nz46fW7n968/3X3 wy+77z/tviGycmf3IwI6gbpWHr9EkkIVvimC4FwJmlJp3fggasHqYyzuyvbz NblRbDzdw1osbO0urj4bHF+F/CS1nfDte4ZBjS2jC6sb8yhSGy+2Hr+cWd6G qFq7x8vr+rCj7B5eaO8eEVZ2uIbmPbKL9Y2nLGz/NDS5vbP7j+XtN4vru5uP 3yxuvBmdfdE3uj4+t4GPzy/vrG8933n2dnv30+7rn/fefUKmbO99WnvyFgCX Np/Pb+7BP6eXdvrGll2jKLds02/bJt+xS7ntmHHfPu2mZcIVq/TLFilo1cz9 8tRtEk188kx8Mobmn758/enJ83dwP2Tck93XEBWS7s2HfyDjXn8gvghp+PLN L09ffXj6+t3zV++e7RE62Xr+nmD17BUCi4VywK4aLuS31LYOMARN3cNzs4s7 RBpu7S7vgNIeGniwwgBPO7/+cmhqvaFn4opBqHcMTV5TlicWlsEKC7e4ttM/ PFdW0wGRTC09RmXB/VFHsOsHDczxw8//BT7Q/JNXbz/88o+Xrz+sbr+GXePr puZX5xa3Nnf2Hj97/fTF68evf8ODYXEfv/q4/vTd2tYemgcs4rS8SZ5c2EJf ClZXzeJvWyfdtIy/bZfx0DFD2SlTyTrznn1Sy8DC2Nx2Cb+xvW+huX/2xftf X739tPfml91XH8EB+YUlQOy9/fdX73578fYnoIN3Pd39AMORuxnhaWAFs4KS 15+83H6C9H8FPeABmvvnhyZWAKqzZxo7QRjC/PoL5B36DcwRiYY1BS6cnFl5 2je9Als298lOIUmqOkYHp1fx/FjryYW1ydk1DIAICY4p46l29j7BkH/79f/9 7df//PQTofk3Hz5C9h8w/vTL7rtfkQtY6IXV7bW1Z1s7b1Cvd168gJg3N59v bT/bQGw+WVvdnp9fn1reGl9YR5UZmlpt75tLp9ZYBRcFpIvc41imgSVGfiR1 NyIlObWDuPnLvdfP3//73ssPbz7+BuXgez/89B8ff/7H+0+/vfz4H3Cql29/ grSIhdt7L4+PgAMVId/hVIRZEaxeg9Xa01ePYZXPX27u7K7vvIAwwGpganl4 chOtMnITvRkCLQdeoqXBDnFyYWNocnFkerl7dA478Yu6vur2cZ5xdAqvfnhq ZXplG5kLRNADBL/78s2L3dd7b36CNl68/vhq7937D798+uV/f/j5t0+//vbp Fzz2f+L45udfX3/4CRax/eLN9pPXEM/i1tPFzZ2ZRchsA80h9DY1uzQ9vTw2 tUw83sRM7+hCU99cdcuopGmUUdEjrB2oaJmKLSy3j6BouGVkMWqRVtsoA6/e 7H367fWrD1Av7Bo6BqWffv4HMO59+A+4E0oeNLzz/DUEsw4UL14ToIj0IYSE wHmMl7ZfwKyePH+1vfuKYLX1FL0ZalnfxOLIzCamDP+EVy+tP4aHAAJiYfMZ Pri88WRiZrW1d+KyWYKOV466S5q2fVJ2iWxyZpX41A4q7Gtk+t6bj7g5BIOy AkQv3xBnUGve//wfWGI88y+//NdPv/7Xp1//v1effgFJeQX5iPREvk+tbncM zVR0jNZ1z1R3TAob+iXNw7yaQVHDcMvQfF37AFnYFF9cEVcgyaRV5jMqKhp6 qxoHU8gVcaTqgBQmLOLjh182d55jjVDOXr37CCwfPv7y/qdf3376BWME2qdn e2+xrGvbz9HiLmw+QSxto/qj/r7AMxC2s/UMjo3GaWH98eLW87Xtp1AUyiJ0 jpsvET4AY98GIjBZXnuMmjixsD218mQOG4Hnb2B9KOIwYWxmUffVnFKMffPc wotbemcggMnZDVyP/cjm41fPXyKt3u6++un1azznr6/evYdn4gn3PvwKMi9f /0w884efX737D8wInrC+/QqlGZ3P5ou3K49f8Su7o8gVqezWHEF3SJ7UIZZu HckMzJEViDpyWI32UaWPPLKNAwpcY6mRmTyWrLVUUJtaUh2XLxVUdC6sPNt5 +gaCQY/xYvcNKpqCFbL+xbuPRB+19wFaQlpBMHPrz/DASG3E9MomPGRueRMZ BGPErgq2sLhG/MBCMNl8ipYYTRSScevxC3BDoza7uk60DUvEX2xhoWMLW8RP WGtPwXl9+yVKHgoQ9hfo99Qdkm1DCwvoNT1jC2NTa8OT66MzayNzGzA3CAwi hE7w2Hi2Z3tv4Eg7z/dw3HxCfBHagK2nL4hmb4dolvCN8ockFhdToAlbNF2S DDzTrYLyHMJLzALzNVzTdbzyTIJKLINomj4lD1zzNb0LrSJYztHMbEZjqawz g9FAFrQ1tI+jxwN2WDE0gCV7/uYTGoO3H36DMT57+zN8CaCQaEs7ewpQ0yuP sUmBDY7PEWaLlIdgCFecX5uYXwUHHMfmV3DEQ8KpsPdBpSZ+sFrZGltcx3xH Z1awr5+ZX4FTTcwT9xmbXkT2zSyu90+sZ5RWYROh7pzqEFpQSC1v6p9o7Z7E ZrxrZI7okeY3MGusDmJh5SnxJ+DNJ8sbO1gjyBXt0/Qy/H9janF9ZoX47w0I S1xcx0PKP7sysbhR1jQUlsbOKa3klHXUd810DK/KWiado6h3HbIeuZGV3cn3 XfJUXfPU3YsMAqieyYLwwvJUam0uu4Eu60KjggQBDaQY0ZO/+QU2jgr47PUv KKlYL6wU3Hh+4zkooQyNL2yOzKyPzK7Bckem8QzLw7NYcbDaGJ1bH55awpnB qcXBiSUiplfG5hYhCfTw8KuO0TVh01h1+yhIzi+vzS7iDov4yMD4fM/wLPZo jX1zETnCWxYxyvbx/ilscW2/tKmXI2mgcKqqW4Y7B6b6RucHp5ZH51ZBeGhy GU1j/8wqHgZ1AYFuc3R2gXikqZWx6TnFwwxNL6BkDEys9k8s9ozOtGLvObiE xo9YQZjs3ru9V+9bBleswqkPnXIfuBapuBWouOQ+dC5Qds3X8inS8S3wSeaF 5pUHZIqLRc3do4vT67swmbn1J/Obu/BewmQeE+60tL41v76FaqUQACSheMKe saWekUV02t0Tcz14zpk1FPfh6TWC4exa/+QSCkrP0Fzb8HTnwFhz95igtotf 3ZLHavJME+QLO5qHl7pH53vHF7vHl9uGl4GosX+mqmuS1zQeTa7FdtjUv8A1 vhTKz6KXZZAFsWnF/PKO5s5xYpc9OIVa2Tk8g51Ifce4tHmMJW2T1XfjPFYH zzm5tIkBtgw9o3Ndw1PdYzPw846B2ba+aWzPW/snu4eWoLTFVZTClc2tJ8+e 7yKvy5om9P2L77sAVL6qa8ED10Iko7J7IdBpuOVoeZN1fUkB2dKE4mpB43hV 1xRaoK6xJaSD3E+g5E3odnhuZWh6BVLBwmHp+8YX4CHtg7NtAzMtfVPtIzN4 cvQkADgwQWCEfrpGZtuGZ9r6Jht6Rxs6h6qbhpjSFoa0JZlSaxzGckmVBBdU +mcJI0mV8NhsflceryOd3ZRAq/PJlBoGlpr65JgFF1uHkY180h2jS3I4LcL6 EQlKVc98R99MdfOApLGPKm0mcypzqOW+ibQ8Wq2gqgc9Khpd4iep5W3ICevY MTDdPjDZ2D3S1DPa2D5MROcIOn/sUrHoS+vbQLS89YIwt80nXT2T7iki8FGB tFyL7rsWARdSEqHuUfLArVjTq1jbr1TbpxjP5p3Co4jacnlNwtq+ztHVrvH1 mt7Zyp7ZhsHFjqE5SJdQwth859A0AlSxjkiKtsHx9oHx/uGZgREij/AWHq+1 b7y+Z7ShY7iyrV/WhKLcT5O2F3BasS56wRzdIJZeMFszgK4VSDMIphsH08yD S03CqHqBFA3/Uk1/hqFPlppzJlZT1SULDbOeT0FkYRmKOFXYJq7uwb6mRNyS WCTIZ5Snl0gD0/gkdkMxs7Z/fAXWCj/BEg9NL7UNzTV0jQAgQDUPjDV19nf3 T/SPLQwOz88sbKIcE63L1i4C0NBu4bx3qgh5p+JBfkCAKpJDIxHhUazqRVX2 KFbzKnnkWaLuTdHxLUEtMAoqtY9khOVVRxXXuSXzHWLZUaRaUcOopGVM1jpa 1jJY0zbUPjgNUD3D072DU8QYqu4b7+obb+vFg43Vd45WNvYJajukdV3sqq4s dnsGryOV0WYfzTOOZhuF83WC2FpgFcJFYKATyNQLpOsE0jWDWBqBzEcBDD3f LGXntIf2qQ8c0rTc8lSd0kx900MzqFGZrGJWFY1fS+LU5TKquJWdJE41VdJV 0zpCZlTVtoxAJHB7uCgytLZ7pLZ9EEmKTVPH6CyqD4o10bFg8764vrS8gb5l aXVncUnu/NMbNe3TdhF0GLuaF/mhWxH4qLuTVd1Ij7zIau5kUHoIUF5FWr4U FR+amh/toQ9VzZ8NhqqeJWo+FE0/2iNfmrYPxSWW4ZPKD8zkFwjaKZJ2Qd1g RetYTedkQ89UY988ihTk3dI1hqeStY7gXUZZJ72yi1bWEZ5fZh7ONAylmoYz oSj1UK5eMFM/hKMbwtYJZilCK5ChHcREaAZxcIS6fDNEHskcu1CyUzg5k1LP qOzl1nTLGvv5NZ1saUN5fQ/2IF3DxN810LgSv9iMzkvqeznl7XVdE+39hJM0 9UyUtfRzq1oZZd3c6gFZ05C0YaBraHZ6AYV7eXpudXpueWR8bmJ6ZXQSlWVR 2joenCMzC6KoeBU/8ilR9ZRryR05SFL2yFfzALoiAAFGQFPzLgUrVX+aii9T xQdjBtYXafIogKXqzwBMNB56fsWhpCaPVKFrAiu0oDIkvzyB1lAg6JC1zrQP rdR2TXPrhorKejK5bfGUutiSBlxgHkkw0Q5lA4JOGEc7XKAfxtYP40JaOoFE JiLkGmPjjHYIT9O/xNAvxzmCEpjOKuA2cCu7OtpGhsemMcG5hceotpBxZ/90 Z/fE8PhS/8T85NzS4sJq7/hCXe9IqaQ5NodbKqgXV7VzZS3ssrYCTlVIKjso iRGTzYvNFrLFrUPjs72jE+2DUx09o529Y01do9h9s8pbIvJEhsFETqkHQB4A QgY0Tb9SvVAOYRRBnEdg4kVW9qaq+tI1fKl4qeFXrAEs/mT1gBLNAIqOfzE+ qxbCeRTE1gwWagbyHvlQtfzp2gEMLX8qbqIXyrAOIYVmCdMpldG5fK8Uuksy 2z6OaR1BMw9hQE5awVz1QBaSyyhCaBhOx2fx7TqhXJ1Qvl6IUC+ErxvMIxDJ k1Hdn2kTL0xjNUNaDlFMCrtWVtlaUz8wMDCDcjO5tD42tdo2vNIyTPyE2D80 jZYARWdkdqV/am5geAqz9k0ToHA4pQqto7l2MRzXZKFBIFnTh0gc/SBGArWJ W9ufRG/xzpVaxJU5J5e7ZJTbJshMwpkGwVSoXTOYCfdW9StR9StV9y41CqL7 J/Gy6BV1Tb2ZpRWwdDy/hh8duUYcAxg4wl2JQSDzIDBZ7UAiQbRCWNAA5kUk UShXI0wMtWiGsB6FMHFeP0SiFSIyRDaFcdTDRHoBJQExOQmp2QWFxUxmcR65 KCIhNz+/MCsn2zcy3TCoWCtCphUmNgkucgrJsQ7NSc6lVEqFdeUClD8D31yP WFoyScaUtdU0E78Tdg9NVTR1w9LTSiv5VR2VDV1IfPT53UMzvVPLKChoIVIp ZVbhxdo+6IsKUeVBCTKAFcNMtHxJfgml2RSxfyrDLoKk643mvICQkE+xHGax lg9V3bcUR6hFw7tYx59qFS3IF/Q1DyzMr7+s7l7yTJNAIQSEQIZGEONRIF0r gIkAnIPQkqcJFEKkEiKEgyPkQUQIVzeMpxshRFrphfP1QllGkQJoRjNCZBNV El/AodC4NDq7pJRSQinKyctNy8xjMih52UmZWTlRmQyrUJJjDC2ukJdayEws ZLEFYgGbSi3KKubVGgflm4ezvNMEsQXcUmGjtLavb2i2rLknIovlGUfKpkuE Vc2CsrbKhqGmtiHYJmy8uX2ourU/llJtGEwFGXmmlCj7MbWw9D4lZhF0mrCp tXOUV92Rz6qLLZTpB5JxDSovQIEYxkRp9i7VhkWjN/ArNQxn5UuG+mcew5OF bXO+eTUoQHJ3pWuHErJRFKmDIMgoIpQtzx0u+IASEIGPkfykXrjQJIyvGybQ i2Dh/lpREoBNzyMz6BQhhyng8WkMCofDKikqDAvyDwlwjQxyohZlZGdlZOUX yyqbaBwOjcPKKcin04qryniNdeVTi9sR5HJlpwyPuBKKuJFd3o5WHw0JfKai Y0LUNFbXM97aP1HV1I/a19QxIqhsbeufGJ1a6BqcDsrgQC2P/IrV3HPVPbJ1 vHJMg6lmESz7GBo6tNHxFdwHOztBTa9zihAuTUjOn66OKhzMRE7p+BFqgUsD hUYA2TKsBDcMyOK7JHOQqqCE88ip32VzKBR8iAhj6YQLdENFhJDCeOCDMASx SFDioRPQixTpRIuNInkg5hzL4rNKhQwSs5RcWlzApBXKxLy6cpFMzGHR8kk5 MXlpkSlxAQ01osKcNFpJbk2NtK5KVFcpqaqU8gXshvbBkExWcDp74fGr7Rfv 4E71PfJ+smOorn0MbQDKX9/obPfILAbIwcGJhdnFLdQ4bBYKeK1m4aW6/mSb 8CKPhNJcWlk2syE4rxybFJKwQ1rTL6jpFzYMkHhNAbkVmgFUPT+CjBpsOYih HciCFasHMtSD0M+wjSKYMHzjcKpRWKlOSKleBEcnnEc0OXIg/wQHEc5D6Ebw iQhnI9d0w6VExkUI9CNEehES3UiRfiTXKFaCMwbRIp2octNwhn0U8f/xJRHQ SwuzC/KzeSyShFPMIGUxizO4XEpDtQzikQgZxSUZtNJsPoNcI+NUVwjqy3g1 lVKBgMdgMbEnquuZ3Nn79O//9Y/d1+9W1p8Nz84PTC2jD69tHRieXEV/Pjy+ ACFhKwGHn19aRxvQOzIFpWE7nM1uyhF0No+sDMw+QUchaxyMJ5W7RNPCcgRh BVLbOK5rCtcvmeWVAutgohyjckEw0BXyC7LRCmXrhkMbMBmGfijDIFqoG8HT RhFH+YZaACFMQCRRuFAhmwNEepGCzyHSjxLqR4kNIsVEUYsSAY5ujMQgSqAX JTaMkeKetrH8mDxGOolLKirg0UlCVimNlM2lZEtouVxSGiUvNj87Pic9Wcih VciETCaJxShoqa8sE7PKJFwmtYBGLSaXkFhsGvFHol//8fbjp713r3B8++6n l2/ebzx/h1a8GXu81cc7z94TP3/NLqJnmFlcn5tfwW4c5j+5sDW7/BQ6HJre wm69oWemsmkYjXEusz6JUl8g6rCOpWoEs/RDac7RTKOAIs1gKgoTKOkFUuUN DFGhCNcNZRL1OoxjGEF4i3YY3yASecSXJxQfAW3sHyMFcixC0Pg9YioMYkWG sQIjJFq01ChWbBgnNIwTQ1RGcRXGcWVmMfz0QnplpYjJFbBohczC1HIOuYKZ yyelsHLi2PnxbFJqaqR3UnRwTnJUSX56bnp0pZgpE7JplDwyOae0OI9KI/MF LImQBS29fv/u3fuP737++c3Pv757/9PL3bdA1DYwNbO6s/F4F5v69Z0XqxuP 17ef7Dx9ubr6eGJ6YXJuZX5tB4mJ7cPkzHL/+BK/qgsNKohxagYKeY2Mii7/ TJ5pGMUnqzyG0uCQJEbvpxnG1CUqOAdhCC8K48FYjEK4kARBIJxnFCE1DBfA ZIzCucZRfGhDPwpJJDSI4RvGiBVBQDgUhrHl4GMSJzKJk4CMQbzUOEFskiA2 RX8SLzWJl2HjyWcUSVhFfB6TXpxZlBLOKkpi5Ubnx/lx8uOZWZG0nPjUcK/M 2MCEEJfYQOfC1FABI59ckE6jFfD5dImEw2JT6XRSUV7y7us3r99/eP/h096b jzvPXy6vb62sbhF/m1jYeP7qw7Nd4r8HQGzuPH/ydPfF7uudp6+w+cWeBRt8 iqCirL5jcWVzdfPl8Px2Y9cw9ozxBQKqsK6E35hRWsWvHRA3TeaJur1yaoiW OBylirBihDHyLhCdWCkaA/1IKZgQcCLFxlFCk0i+SRTByihGAA5GcUKjOL4x gUJ6EIAgD5CRmcRKzRJkgGOUWIYwTpCaJ8tMk3CUmCSWxRVIypgF1JLCYlI6 rSCFRc5ikDOLM6Oz4wO5hUkSSjKHnFKaFU9OD08KdsiM8ZZxC+iUbCq1EFKE pXPYtMKCbBazlEkjre68WNneA43lNeJ/yjE2h1xbmVjeXN548uT5m+0nuxuP n60+fgp1gdLjJ7ubz17hJPZ6QzNrPWMLozNLkBy2w/2TSw2dQ4XCDpK4S1jb m8dpLOC2SRoGsQ0Pzea5J3FQymG8hhEwIi5UZJcgSuZ2FJb1h5c0mEawjCM5 wAIgxDFWuB/gkwAsUtN4QifyKFOEWWK5WWIZIZ5EiUVihXlCuWlSmWlqmWVy mV1qtXlSuUVquVlqlXlSRUYRXUArpDIZdGqukF5USsolF2ZRSFmk3ERydgyP kkrJjo4LcsmK9ZUysttq+ZUSBotO5vFZUplAJhUKBWyBkCORiERCLlJpcGKj b3xuYFT+o0rPUPvAeOfY3PDUwsT86tTiOmJkehHlb3xubXZhc2R2aWxqmci+ gZmhsfm5ZeKnv86BMUF5U2KRCO16KqcjiiR1jac5JvEd4mh20fSwPEm+sMM8 TqgfKjCL5DqlyeyTxT7pwuq+2fmdlxNru4zqYY/Mcl3ASRQCzqEACgKLOXEs A4E/iZQKy+QKy5RKy7Qqy9RymxSJe269fUa9ZVq1WVqjW0YZnUkTcelMDhU5 JWYzSkn5Yf6uceH+mcnhedlRjJI0sBJQUqScwgoRWcQuZtPIMHMWhykU8/h8 Lo/L5Am4HPyLzRBJeT0Dk22Ivsn6zuHajr669oHaLoIYomNwoq2X6B+q23oq m7orG3sqmjsrGrvq2gabeycbOoZru4aljYON/TOihmGfTKlVDBuNKLop/6Jq m4wK43gRssMvvzKiqMIoigV/dkyW+qSJIwrFwsbhqeWNxeWlxdU11NwoUo1R vMQ0UWSSKCMUQkS5IqAN0LBIKbNQMDkcqdBPFUEprcI6tcIuo9YmowrhkF4L XPZJkugcQiSwHT6nsFzKK5cIUxLC44NdcxOD2SWpLGqakFtcLS5tLKMK2YX0 0kw2o5jLokoEXDQJPAGbx+NgwOayWBw2jkaGV3ML0gfGZ9sx9/bBhs4B2HVN e29FXVdVcw+wlNV0iCtaJHUt4vr28sbuxu7Rpp4xUGrun2bVj3hl8IJzRYnU uvDiGvfMcuMornk83zmt3C2vxjmvxjJBiqTAGeNIplmswCpR4pdTG5wtZlc0 IpdRVmCD00tbLX1TKdR6iyQJkTtgklIGDiCjCAUWK9BIryQirRqhYEJEeq1F RrVNZrV9ZhXOW2Y22WXXQ05Yr+QCKuoXvaQgNzeZz8yvErHhz5TCRH5Jaq2o QEiNZ5ES0VxViYrF3GIht0QiovF4NCAViVhcHpODgK+z2ThyuWxkpbWlkoOT VnxSpFAobG7rbugaqW7sFdc2iyrbBNXtvMp2cVW7pLqtrLm7sqMfG5/m7gni 5+jhuRJRg3um0DSGb5cksI5BM8kwi2KgYBnF8CyiucaxfLMYDs5bxnJRmAwT ZOZxUrNEgWuymFs3NrWytr7zbHbtaV3ndCarLr60MkvQ5ZJVCRVZp5ZBIQdh k1YOPlbplVCpbWb14bDLqrHLqsLRJrvBNqvOMafKMbfWIafOPUsUU8QhC6sg lSpxEZ2ck5UWzSzJEVPzyLlRUl5+FSdXREkQlMaJ6dnlfHKFqEQs4tZVoVCx pVKuRESVSBgSmZAv5AEUpMXnsSRiHpfHiEmwdfXQtLdX83E3LiCT6GVteP6q lj52eUeRoDlf0FQiamRXtPJqu2TNfWifatqGiN8Yu8fzeXWuyWyrBCEEQ0Qc zyyGa5YgMY0XWsTxccQZmwSBVRwPJQnmY51UhlLuX1A1u/l2fuNpx8gCSdJR IOwNLqp0zy5zTpXZpkqgE+v0chxt0xEVdpnl9lkVAGKTVYmjfXa1PGoRDjk1 inDMa3DMrXfMawIl15wy3wx6THo2m00RC6kSRpqAmi7hkAozImlFqRxyXBk7 vVZYVMnOqGCn1pZRa8u4dWWc2kp+Ta2spoJXU8VvrJXW12DAE0tFHB5bKORL 0Wmxi5OSwkwttYOiHYIjrIKCdMICdLyDvL0SKBnUikJmRWgW2yWh1CuNFZrF TKUIs2iy3FIZhVlRzK0o5VViQx1fKE6l1vpnSawTCT5WsXxTuXjg4dCPeaIY IjFJkVnI08o6WWSRLEJB986TCcoaKuu70xk1vjlS3yyZW4aAoJFeY5tZbpsh AxYbQjNVDlnVkIpTbrVDTpV9bg00g3DKqzsI5/x6hH1+g3NunXNes1N+czy3 vYjGKEwLqWGnV3PSKngFQFElKWblRlJyIqScZFDiFsQJyRF1/Ey05RKpAJ05 Nn01tYLmegmkJeMDIL1cwiCTi/hCAbw9JNRXW+fe1WvHj5/7ytLhmoODqquL vrefcZCPTWSwvnewnWNUAUwmIEXMEjbEkSTe2QKnNJFpHMstge6fJfbP5ubR qmPzhTYJPOskqU0yzy6lzD4d3isCItsEkXWi0DJVYpkmBkYMrNKkSCLIBmGV Ue6aUxFcUu+QV+uWX+WeVe2QXmmfJXPJkDllVFnlNdhlE+GXKaLzZNLKmkyK wDenzCa/3bmg1bOgyr6oxrGo1YnU6VjU5lRY5ZNfFl/AySYzUwtKCkl55QKK iEeF+ZSzc7i0LCGvEL7NLUkSUFNLc6OZxUnMwuii9EABMwsqqqsWS8W0MjGj vaGirUHcUM1oqBE2NtSxBRI331ALawcXN0dljTsnLvxw/PyRU5d+uHjzuJnV dWOjKw+Vf9DQueTsrO3mqBIabh8YnxCaS80orSmVNOewagPyJO4Z4lhKZZGk O41Wx6zqQNswMrtWKGpzT+U6p/Hsk4W2yWK7JAKdVYqMUFGKFB2OfWo5+Nih SUirAEwEZAP9OGdJ7XMqXHIrwA2yscmptctrRC1wzxYG5cL/RYlkIY0tpDM4 eRRWfBE/qLDCI7/arajFjVTrUSTzzasIKpCl0yRkXkV+CSMvv7CUyREI2SJO EZeaV84pEjKy6yQlAkZmSV60iJ7JLkmnklLZpWnMojgeLb2rSdbZVFVdzkd0 t9d0tlaKRUWlyFYpNys718zKVs/ERFVL44zSmeNnfzh16fhppWNnrxy7/uCC hcUdG+s7Do43tQ2UXD01QkMsggMMI8KdohKCONLaDJoomy4LyuF7p/HbhhaG Fh53jCxNr2zPLq4MDE8MTi2XSls8soR26ULwcUzi2qfKoBybdKkD4cZVgOOM cUa5S3qFU0aFU1alQ3alXQ6SqxygnHPKHfMqnApqHAoa7fKbPHIl6RRBel5J dgmDxGRnZObmZOUXkYrJNBqUE14k9CG1BpPKE6gc+ABfVIENmkzMkQg5aJ9E fJaEVyLmFkq4hUJahoCd01hGaZKQyjg53JIUVkkm7D0jIZhHz+xslPa21cGX muulPZ0NUik7Kzs6OMzVL9jH0NxUWUPDycXZ2MLwtNKJ4xePAdSJC0cxPn/t zLmrJw1NrmtqnTE1v2hufcPa/o6b+6OwYIPwINOwENuEpPiWvoHWoSmSoLGA 11rR2FHf0CStrK1r7xM39KIKiGtaqWUtAaQ6h2z0NpUO6SLHDCk8xz5D4pRR 7pQpIyKjnHDd7CrnzAr4j2teJVIPR8/cCpeCOofCWpeCMq9caVhJVSyJm4N9 WEERusdSWnFxMSkvM5dUVEApLcrPy4nPKw7N5GYzRJKayqpKWUu5oFrCLBMw mmtFDWVs7GIEpHheSYKIT+bQsqpkJXXltK5KxmATr0pQJKDn8Bk5Ek5xf2f1 UF9Ta3N5Q52Izsjx8LJX13xwT/XWrfs3bzxQNrWzdvZy1tLXPHXh2MmLR04o /XDu+tnLty8q3bpw6cbFG/eva+j9/yS5Z3Sc52EuqMSWLIpiB4je6wyAGUzv DdN777333vtg0Hsn2HsTOyWKlEg1ypIsyWq2SmzHkh3HchJnc3Pj3bM/9uw9 2Zd3z3kPzuADMcD3fE8FhwnUTp4IYneSTWaq3UkNh/kWAy0SM/g8+kzKd/PB 3fc/+fLdj757+MEv3n7r8YlLV+a2LoTnL3lmLkdnjwdmTpprVw31KyCwTDNX zLNXrfPgAJVds81dBpgAZZkW7lkXbwGIrEvPPMq1dMO5dMe7dNOx+pp9+W58 43px9fTO2SvHTpzNl6aiqcLp8+fOXji5sjy/u3Nsa2tjfWMJFOnN7Y1jx47t Ht+6cv7k2d2V3ZXJy+d3Tm8tnlzKXVjKnJ+N3lqJ3dnNnd+dXVksXjg5Byz6 5tnVt28fu391+9Lp5cf3L3760ZP33nrt/IWt2lRCY+QiiQMQeF/vUA90HCZW qIOxpNakhsB7eiCdQ/B+YFB9I+39zxg1AMeMYIgoJG5cq0drTHibh+XyMDQa olA6anfTrTaGUkvFYNrGhg8zOaillfqjR2++9vY7b77zwcOPv7n55JehuYvu 2Wvl02+E1+94F+66p8/bZi66Fq7a5q4/w2rxhgV00WVQR288QwlcX7ppW71r X79jX73rXL3nWnvVvXrDt/pqbOVmqLxWnV3d3txaW1m3BZIKky9eqF69fml7 axUAVZ+e3NpeO7azfun0sdsXti6eXLl4fPX8ibUL57YuH5+/e6x+fc7z6lLg eFS5GxJ9dKn85OrClRMLr946fXFn5tq5xffunwRb+Mmjm++9df/yheOlfEKh 4WGpkGFEe+9YRz+8h8Ii+2N+T9BNZeAhY10o3DAUMwToNDja2w/thiIg47gh NHF0HDsKQ0MdVnIoIbL7WV4fz+Xk8kVjE7x+m4vmCvAojHaeaESrwdks7Hot fub8ic3zt0EpWjt3J798prB5o3L6oW/plfDi9ejimcDipfTGK/7FmwArx/IV +8ItwBn38iuB5WvptfOBtVec67dd6zedm/fcm6+511/1bd5Mrd3wpOdNnlgw UXB6okKFQWB0CpQOmd6xC6busc3V9SWwW0HBuXR68+bFE0BK4Fw5s3793PHN 1fqdrfQvTiY/OR6+W9Gc9guupJS367o3tyI3NguLswVQrq5dnDm2Xrh8dn1n dzmdiRmNUiodNY4ehGP7EUQIgYmX6YQmp1KhYVMmRphsOJdPQuNGgY2D7BvH wEh0ApmGReAHx9D9WDKMSB3X6dB6Mz4YF9rtNLeL7XBz5FqUSNFvcxCtHnIg wQ8G2Q4HNZ5QBvzaZH2utn7m0v3HJ248rqxd0sXnQvPny8du1M7cDS9dqu7c yKxeja1dTm1diy5diC1fBngun7szf+xCdvVsZP2V6NbN4Nq10NqV5NZ1cCY3 L8fydbMnaHCEmTLbhMLKUZs0tpDJHVpaWrl4FsyKE1cunrp54fi1MxvAuu9e PXf9/M7ta7sXdle2p6JPdiI3c8JH86ZPTid/vhF6fdr06ZnkRydi1+d850/M TU+5CmldrRr2+fUms1il4RGpQ+P4PjR2hMXD6axSo10p07LpnFGJGmt2MXkC JAIzOATtgcK6KQwkR0SlMFBEMgyQkM5C0ZhoLHFEbcSpDFidAWM0oT0eejgu s3uZKgPM46O7AxN6OzKe4ufzSreX7g3KUiVvZTJTWlySuvI4vh0jsNLVAXNi Lr59I75yaerEtR0wGj/46uLdN4rz69NrO7PLJ+c3jkeytdm14/H6Zmbj6uKZ m6tnLs8dOxMsztsCKSA3qcHCV5oYUhNZrOUqdDpHSKa3lIuVN+/ffe3eK69c PH77/C5w79dunwNYndmZL2c8c1nv03PTX99deXqu9JdfXP9f3z/6ze36u6vO h4vguN/YSR9fCAViknMnp0+eWjJZRSoFQSyBy1U4mY6uM4hMNqlMTeOJsXwp RmUk6qxUpgiOJQ2NovoQmCEWGyeXUXlCLJOHYLBhdDaCQBnFEIZxxCFfUhiM S52uCa1u3GzDOP1cm4tZruoKRX0oJrY6UAYbMhwVeHwMT4QfCbGTMXE4HSBL TCiWDsHUo9hmFNcmDs8WNy/vXLt/67U3H7/5zslT59a2j69v7+Rz5XylXp5e 3tzY9UZL+flT67vnTp48WZ2sGU02MK6keocnlvfHU45A2O6LuCPJSCY/t7Jy 9+b1p2+89uTBjVdvnHv7/pU37l9468G1dx5cv3dp89a51bsnZzZC8t+9e/n/ /es3/89fPv/zh1c/v1B4dyv0xnr41rT948vTH70yNz1pO3d6YW42FQpq4mFZ IiGzOPh6E1djZNNYMAKtj8rsU2gJSgOBIRjB0aBwfB+BChUICVoVw2HjezwC hRrP4I6Q6KN40jCRMkybGAsmRf4o3+mm+0JcX5QdiEq1OoJRj3I4GUYT2Rug AbjMVmIsIQ7Eeakke2PVXql48tW02e0l8/UYjg3Pd6P4NqExkK7OT84uL84v ra1tlGfXQpnq2vp2LFsulOuTtVlvuBjJzm5sn6rWpiORWD6ftwdDSrM3mp5c WFs7efoEwHDrxInrr1x67+0H7715991Ht968d+HhjdOvXzt568LajdNrpzYn 759ffu/Oydu7tRvr2R+/fPw///D5f/7u3X98tHu3bj+ZNry6lnyymXiwEnnv TPXmsWw555iu+6cqAa9fLlHgKIxx2gScxBrC0vrx1F6ZhqjWU0EDx5J7AW3o /FGlhhT2S2br7krRFA7wdXoiVwKns0bI9CE2HyZT48JJqc1NAYrzhwWxjCyd 19nsDLuFpDeiAiGu289yuPHRlDAYZnu8tFhCEI5yYyF+Pq2oVfxOr4cq0KCZ OqrYRhXYRCq73ZtcWN0ulKdZcgdVak8WZwFom5vbpepsojTvCKT1jqDBGciU Z2oLq1pXgCFUUTl8h9c+vzC9vLRw7/4rv/v66edP73//1Xt/+Oa97375+i/f uf2rp/c/fXzptfNrV0/NPX313EcPzj6+tHHl2NS/fv3O//1Pn/3ls/u/fXDi WFQRk1OmvIrtsAyAdrHmfbCTv7hbjAZlcimexBwlcpBEBrjxMRpniC2EAw3q jDwuH8XnI7RamtnMttqo2ZRyez08N2XJp2U+94TbwzI7GXoz0e3nhBKSYFxs ttOVWqTRjAHVPRwSJ5Nii42QzemcHpLXzyhUjN4QS2kc9QZI6YTU6aPH07JA kOlxYkFlrZTcvpCZyhciqFKy0CfRB5WmYChT88WKcnNYagpzpY5sZR7QbG5p O16Y5EoVJpd3cmE5P70os/iJLDGVw1PoJJlccLJWTEQD7zy8+ek7V8+sZC6u lz5/98oPX73+7Yd3vn7/3g+fPfjVu9feurn7zS/fevfVixdWy+/ePf+Xb5/+ 8MnDT+6f+ObNyxtJvYo0YOHiwnJy3SObjxpXM5Zzi9GQSywSIXUWllTHFCnR WhNZpSXzhShAMLGE6rSJ8ylbJWPPJzQLdffORmh6UlfJK4tZFSiZXv9EIiUG PAnF+e4QU2/DeQJ8i4vq8jPMFqTNTNVoR1T6cY2erLei7E5SLq/PFjVqM0xl gIYjomiaHwxzUnk5eJEvKqNBTiTIy9aiQrVljKoQG32OSImvdoNj8SQDsZIz WMkWpzd3TqbyVbXBXJ2sXLxwemWhplTKKXR2Np3ZWKmvL6W3lvMbi7VTmwt3 z61v1IMuBVE3MZL1iHbmfXfPzVw/Pnnv/NwvH5//wyevfvXxE49FKaaPv3Fl +/dfvvPz1y7fPj3/8/uXLizEHSI0DzuM72+RUGE5tyrukGZN3N35dDqlMxkp VivT6qQaLBQuB8FmwX1eZa0cqJc8KzOxtenI9kJ4ezEOIEonRZWCtpjT+oOc dFYKPvUHmQAcg40sUIzROb0s0YDVS/WGmAoVXKoY0pnRXMEIg9kGmnw0Jtdb sIBODi/V7CLZnNhkhhtJC3NlVSwjKOYVyaggkdZEYk6ZQSHRaRWWEE3mIHC0 PLktlCxXZjeCsQyommC1e/2+EzvLN08txa3CqEOxVvCfn02fm4msZkxTcWPC Lo9apSfmc6eW0xEzU00etgmxCTe7lpRXwsrVmufmydqN4/UrJ5b8Dh2ojbsz 8V88vHzv4s7ZlfzVrekTtUDUyJSQYYSRbmh3g5FP8mi4VjY2Z5cWQ+rrF2Ye v378xuXFO9c3Vxaz1ZJ/fjpYL9smC5bN+eiZrdyxlVg+oS5l9emE0uvhGoyE YJhXLoNP5f6AQKMncgRwIn0wXVQbPWStC++LSgJxrjfMd3on+IJhsXgIxF+x ZLY78P4Arzbr8sdYDh/R5kV5QtSZWUM6yyuVdNmEOpeXB1yscESSyvgEOjdO ZKeIrBJjRG4NeePJXG2KIVIQJ9j+YGAyGz5e852dDu4UHSciwhkdLs4aMKFb WcNHePAWAbrDrWCUQmqbFKWjjWho8Om8+dxm9NiUe3PaPxXX3zy3cH6lslzP JQLm6ZT50YXlP3713qNL85tpd8Uu9SgnjAKikDSCG+tjwIbMHLyCNp41c2Ia 9lt3T//wD2//yz9++T/++O2f/+mLb796e3UhlI4KV2c8x9cSqzO+mF/kcVHd VrZKQZGrMBLFmMs1EfAI1So8gzFEJPaOo0CH74qkJAYLIRrnl8rGdEGRrahS BZFWj1AoEfW6Z2e7pjUSJap+ANfqaqZYkadSinCSW64olpes1ZomFuFVirrJ KWOubC1lVPm4iM4BLLegxCapNaqyWCk0qpaNW3Kxr+dk10vqsyX9marzWMGx 6BMHpEQxeZQM62Egeg1crIYBM/MwOirCKCSnfdpsUH1yJXhtt/z6zc1zW+XZ rG0qa7XLGWo+iUdFL5ccs2n7qWOb771z/86ZlZpXGdWzUha+RUiWgHfgkxQM OBXZGzcI6gnHqZ25j9+8/Mkb1z97cu2XDy7+4o1L7z66eG63NlP2lXJmn5tl NzLcbrFKRdbraTI5UqnFKrVk2gQCg4XBUB0jsAHQ9vHkHp0Va3ES3V5aJqfU mXC+KAeYudfHAfYeTyh2TuSW16M8Sa/FRpqdjczNe2YWXEB9kSizVlXOLljq 0+bJirFUUC2v+i0WInAzs02skItFfJZGytXziX4d51jRdnfO+WTV+/Zm9N3T lTvrqcsLoRNZU1xLF+KH2PhhGX3cJqUY+Ziwjm3i4AV4qIqPq8b05ZBiNmO+ fqJ+fqea9Sj0EiJs8MhIT6NTw7m8ntucDPd2NHV0NO4sl4/P52xSUtIuzrrU XjXfoWDquYQJ5EBAxzKK6XG/6umrG5+8cfb++dmdenyp5L1+av7RrePbK8lU TOx20BIRucXB0OkJegNJb6JJlXgyHYLEdI+jOlG4ThJ1hMkdl2nQ7tCEQjcK eJVIiSwuuis4YXMxMlltpqBVGxBGGy5XMiay0mhcMD8fWV/PlKeNwLWCIXos zk6kBcAAl5bcqTivXJIVyxpfhBdwkC5MOy5W7Zfq9lNJ2aOt6CcXyl+/Mvn1 9ZnPr859dGnuyenq5RnfekSZMjCVdLiUNq6YQOp5WJec7laxbBK6koHikaAO GbEWNNRjhpWyp5ww6fkEErq/qeEnTMLY1mT41EzswaW1TMwFhfSPQTokXAyb Ak06ZNuTidVSrBQyh3RiLRNvEOLR0M6Zkvmztzffvr2wO+dxyGl+Hev1qxu/ eHJzZz2ZTHDAiLM7JoDu5OpxqXKcJ0LgiANEGoTKGmZwB4B1A1Wa7WRQFcJJ sdlB8AdZ2ZKyNmsD0FkcFLCj1XpsKMFKF6XBGC+SAHsKHk9Klpdj7iAXdK1M HjRYhtONTcTZyRQvmWYVsgKA2OKM9aN71R9enfz8dOrRsv3bq6Uf39n94dH6 b19b/e2j7c9uLj+9OHtvI308b552CSNKKpCecgIJlKjlYB1yhl8vcCk4Oi5O y8I4ZGSHiBbRMcs+Jbg7MnJwoOdoU+OLfpv8wlJqMWU+MZ/ZmM/O1vPZuBMD azdIKeWI8c6pZXB2p9Mpi1zLwIoZCDi0qRATf/r2xkdv7GzVXes1/xdvXf3w wcXH9y+mkiqLHQ0g4opG+WKoUDoqksFZ/BGBBCdVksUKNFh/ZgvZ5iA5XXi3 h1ibdmqN+FxeCwABpFIaYBLlSCDIr05ZYhleMMYFCRhOCBIpqctLzGTVa+ux UkkTSnC8YYbPS9BrIAr1oME+Us1IP7i/8G+fX//Pz8//65ONHx9t/fp67fvX N354+/RvHh377vVjX9xd/+zu2jsXpm6vpE7kbStRbcrIdspoACUZDQ7w8Wv5 TiXHyKcY+Di7gGzkEawCAsj9ildd9qiwSEhj477B3paZnPvMXGy95FopBXJB 3epM5s7FzeunFndm0rWY7eLG5L0zqxeWyjMxq0cyQRzvp5L74k7OjdP5M2uJ 4/PRKzvFV8/PP7m1u7Va0Jsoch2MJ4Ey2FBgU1odXqfHqdQYnYmk1OA0BoLD w3S7GaCEe7wkj5dicdAAi4olQ66kl2qgdh95ctYMgjJX0nqCE6CGgXoQTcoq dfPsgqlU0edz+lhMtHEscvJcdn3Vu7TsSmaEIT/5Nx9c+p9f3vrTo93vbs5/ dX36s0tTX92Y/cN7l7594/ivHhz79Wu7X9zd/PLe1gfXFh4er1xbiM+FNf+/ FQNeGbjPMguoT8slKulIORWunkAbueSgmlq2i2oedcGl6eps2bf/Rdz44ImF xJn55FrZfXwuXU9arp6YvX1m5d1bZ964uL09nb64MwPg2iiGl7MeIOEJ7LCE Pxq2c1fKzrObudMrhd15/xZwgNmgQU8SiGEixTibPyiWoXQ6LPBbgwGtVI2C Y7MRPR46OLGYwO2muT1U1/9eN6AbhCJ8UNGFikGrhwy0mUxqK3Wry8cEcMUz ktlF7+ySszqpX1zy7+wkd7bCy6velY3Q6fPF3VO59aXgdz+/9H98fuc3Dza/ vrb41bW5z65Pf3lz7ncPN3/3xunPby189+j49+9c/O3js1/c2ni4W7y5njw3 E5gKaIIatoFPUNCRKsazA0ADialhIrUsrElE8Sp5OSu/7lWV3GriWP/+/S+/ vO9FIRN7Zat8ciY2m7KdWkif36hc3KxfO7l48/jCw/Obl7fnQOO6vju3mPWt Zr0nF7IhI1/CHcmElCAp6kXLbMFVSKkcZopMBhOLIXIZgseHiqXDgE5WG9Zm f/bRYkJ53eRIiBnw0cALkHpOJ8VsefZVtw9MP3Q0LsoWdSYnzhNmAkYVChZf iF+sWmJpcSovm110A3blCkogVdCpZqbMCwvO+qw9U1BPlkxfv3/1P7549bsH Gx9dnvz0yvwvr0z++tbcbx6s//n90z/+/PwPj499/9ap3z05++3Dkx9fXbo+ G7w47VvNGEBLNAsIchpCSBiTUuBAcYBaDindIQUdgwCaZMwsKTmUQEouLbej 9UhDw+GXXv6ZVkYFWJ1ZSq8VvIsZy8Wd6Wu785d2F47NpLdKoVsnVwGv7p1b OTGb3ci5zq9V14u+qFNcSOsqWTNwp0hQZLUxdBa8xoy22olGHU6lQoJx5/aQ /UGix0dwuQmBICPop4eDE34v3eumArgcNpzNggHH5SUDrEBTBVvGHZjwR9gA gVRaCerW9Ix7ds5dnzYBoMp1QyYnr9YsG5uxUIhTKKkzRWUhJ//6vXP/+dX9 bx9sfHy59t7Z/EeXpr58Zfrr2/M/fnDpXz658uOH5/709vnfPzn9zaMTX9zb /sX15cvTwdWYZjogT5mFeg6OixkCyS4hj5sFZKBEcMXEJ1okNLeCFdLzfBpB 2KoahXa2tDYcbTq8Z+8LQg5qux45vZhayDoLHgnoTpe3Z66cWKjHzVNh3U49 eW61cvfMwsWV0k7BfWlj6vxCPuVWREPCeEQciYpdHqrHy3b46SojQqdD2yx4 twsfDbMiYUbATwInGpnw+8heF8nvoThtOHC8NqTbMu6xIqI+ssNJ8vgYoGoa TBiwcWwOIpg24Sg3EhHkCwawB2uT1nLVMD1rr06a5uZ96bxuZsk9NWMJe5kf Plz7r6/v/v6N7a/uzn58ofbxxcovr818em3y2/sr//LxrT99fOWP75/9/sm5 7x4cB071yzvrb5+bujAbXE2Y5v3qiJatosGEhBEBAQ5syiFjWUVUs4Ck4xBd SmbSKI2ZRbCe5q62w0ebDnW0Hmo8evDFvT/FjvdPJ+0n5pKgd0VMnJxXOxm3 n92oVSLagkscMQqKPu3ZufTpevxMPXzz+Nyrx5cmo8ZkWJhPCaJBdjhI9LuI JuOY2QRz2YhBHymdpKdizJifkg4xUhFqJkZNR2n5ODMfY1ez/LmqZKEsnCvz 5ivCpZoCtAu/nwn8KpmWADpFYsL/HXnkyUnjsZ0MqPpLS9HVtdjmZqhStmxu JCdnXKW6KZOQPr4y+5+/vvfD2yeB3D6/Uf/0XPXnJwu/urHw5e3579868eMH V/7wzrnfPjnx28envry1+snV+Q8vzd1cT++WPZsZ+3LYGJBTbEIiWHAKBs4o oJtENAOPbBXT3CqBX8MtefSTQQsa2n348Iu9Xa3wodbOzqMt7UcG+9rDZvFm LbRQtDkV1KiBn3DKpjLOnZlIyirQTiBSVsli3Lyd92xmPeeXMg/OrFxaLc5k 9DtLhp0F/caMfL7AX6lKZguCuap8aUa5NC1bm9ZszOrnC2LwpaWaYLHMW61J 12vS5apgbYq/PCkEZ67InSsKNVo4EGw8Ichknv3xsFa1e73sQIAFAIxERImE ZHEhOFmzJ5LCyYq1XrWB6p5KSUAN/tvXj/709Pw3r21+dWv508u1X5wufHSu 9g/3V0CP+tPTs9+/ufPN7dVf3V385tW1Dy/Vnp6tPNzOXV9ObWYdNbei7lZH 1AyPlKagIbg4qJSBldOxKhYOsMtvlKVs6oJXXw/Zk26dREBzG+VBi3gM0tnU eghwTDSBBVSZShs9BpaGNa7hIjMB1fZMbDnvmwqZ6l5NzSWdDanrQfNq3nXt 2PTDy+tnlpKnV1zbc8q1imytKtmalG9UpWtT8sW6cqYg3pjWnlyxLZeElRit nmHVkrTpFGM6RV8sshZK7EqKlouQoi5cNsoAKanTIcFAttnINjPZaeUYtPhA gBMOcx0OeijEA7np89CA3YV8zGxSEvSxjy/5/u2r+z9+9Mq3j3Y+vjYL2vgn l6q/vF7//NbSZ1frv7m//g8Pt4AS31iLPjoW/vjKzIeXpx7u5m6vJa8sxJej 5oJVUrTKk3qeS0xRMVBCEoyNhtLhA0IiWDpsj4Yf0MryHl3Za5pPe6sRV8yp 8Ov5cj61rb3hYON+CnbUp+fkA2qvkQ2AktEhNjm5EDbMJOznp+JFI8fFHl9J m9++cfp3n7z5bz989pfffvxff/ry9oWFkJt0Zjt8fjtwbM60PaWtJOirdcP6 lL2S5FRTHIDJdF6UDrGBGJN+dDqEKScnZsuaZHTCY8dNVXRzdYPZTLDbKaBO mPRYrQpl1lMdFobPx5HL4Q77hMfNcTlxdhvS4yD7nIRogJ70c3795MS/f3Hn D+9f/OzO4qdX68Cpvro99/X9paeXqu+ezoBm9fnN2cfHE9th0bGM9P5m5v52 5ty091TNfarq38l5qm5V0iQCx6dgGbgEJqKPPt7HGB/UsElm0YRZTLfKOdWQ ZTrunAyagXEFTVyw72wqDp/PADMWMtBOxw26dTy3gaUWIlPApqyCmZw3ahGV bILTJe/xqv/Hb9/577/9+IdvP/vui6dv3r/80eM7f/n+m9u3d3JZ3WxFP5VX r9ZN6TCtlpWVEtKohwRex0OYxRljraQN+alBH8LnHAn7qJU8cBvR1oavWtaA IAA1g83uU0hhUhFEp0QJORAAF6AWXwAFbU0kHDPqkSYdym4k2XQIOb//+k4a kOp37537x3fOfnVnAcTfV7fmf/twHTTPh8eiT04kP39l5v2zhSt1w1qQm1ah cmb6Vs64m7dupc07Gft62lHza9MOedwoDumFIP4mxnuBw2s4BKBBkxDMHFHc Ks+5tbWILelQBHVcr4YD3Myu5poNcjh8AD7WAxloJWMG9BI8FdulEWLcGmbC oVwqBr1yws3d6l9/+OK///v/PHNsGQaFxPxWvYxvlAnmq7l6PReLyhM+Vi4m qmQU6SA3GaDF/cREgBz3k2IBbDbJzKbZsQgp4IMHPXCfCxOL8FIxQcCHy2X5 K0tuFqubyx1g0fs4zG4mtVsqGFVLcUxav1KF4fIgE4xeg4YQ8ckcRpqY1ec1 Ub95fO7rt07/4weXgWl/d3f1GVBv7Pz2/sqvr0y/vRt7cir9/oXKw63w8SS/ ZsYEeGNqamdcQ1gMyGcckrWIEWBV8WtARQStAASfXcpQMzE87DAwdrAEbZKJ uE0zGTKWA6a8V+/UsEHi+9UiGQWr5pMCHqOAQyHhRmBwSGvTiyxCH53YOzp4 kAbvYiD6lRxMKaL66sO7f/vbX/7jb/8jFHI999zfNx/agxwexI1CcdAeyGCP 1UAuhVnZsCAWECT8vKB9POIbTQUIxQirECNlYthoEJZNosPesbAHHnCNp+LU YxveYACRy9Iuns3JRTCxZFAsgIg5EBa1XcwaEAghVhNFIhnVyBE2HUrHH9Vw RsF1u5pycSkEiuXXjza/e3Lm2wc7n997htU39ze+urv80eXqO6fSb51Mv3Es dm/JvhXjJeQwF7PXwxv1yUY9cmxERVmK6oFfTboUQICgiOqYSDMP5xCTTRy8 hol2K9lRqyxgkuc8uqxbm7Qp/EoOYKBBTBaRxyjYQSp5DIWCkOEQIqz38JG9 DYdepBO6JkhdsOHDNNQQpP9nW6u5//rrH//859//65//+cqdmxQSfGiwp72n 7XDjT1uaj7QebTRKcTEv3WGCBhzokAUXs9HiLqLfhon5iJkAPuAgucyoaJCa y4jrBVHaA8/EkJOTQrcb4fGgnQ4kn9vJ4bZZwA4SQDiMJqNiRCgcMFvQNjPS YkSadTCDbNShRpnVMJ+RdGc3+8mdjU9urX56b+3zOxufvjL74dnyp9dnfnFp 8vHJ1FsnkgCrV9cDr9T1q0FmRocOCaFxBapowNl5UK8YC/wkpeeUnNK4gRdU c51imlfOdMomXHKmRUS1iOilgMWvFQbAiJYxwJRO2BQOxYSQCmNhIUNdhyF9 R/BYKGq4FzfW09p6sK35EBbeIeQgydhu/EgzCdX56N7Z//5ff/v3f/nHP//p 2w9//sjv1OrUkoGBjkMH/n7fgZ929RxxmikxFznoIASsyISbEHeSY258LsrK Bhm50EQuLJ7MySN+xkzVWEqKUn5yPIgrZwS5ONtqhJl0EKmmV6UZMhkgCnGf RtHrMIwaDaNWF8xlh1mNIyEfPuIlgXe2GyE2JeTmZvLptdUPriy8f2XqvXO1 t0/l3j2ef3ou/+ZW9LWt8OtbkTeOJW8vui6VlMugs0lGvfyhiAgyYyV5xaMm 1mhAQojqJ5IWIcAqphcmTJKURe6SsU0Catgo96h4KYcmohOAbQhopppA+4xC LhGKH+1EDre3N+yhInvRsF7UaD8J2TcCae/oaOxoO9DVdYRBHCLCW9DQQ4v1 8Bcfvfb41VOXz07PlTxM7MDBPc/te+m5w3uf2/vSc5DRfV4XIeEmpb2suAsX dWFjPkIpxZnKiMpR3lxBu1SwFSPCpUnLbF4fdzGASL02bC7In8mpg1acxzAu UrfLVQNyeZtE1OKwjbnMcJ2uV2PsMRkHfM7RsAfps4yFnTCQC1bFwMU5/8NT lXfO1Z6eL7+5k7y7GgT4vAaINGW5Nme/UDXcXQlcrpnO5xXLnomkAmWl9/gE g7MOet3NDClIITnVpyJH9Jy4QRBQcTwyplfOAXZkF7OCeilooTYZ08jGiUgj fOwQFz0opo8TYd1U5GBv+/7e1gM+HY+IHECM9aLHOhCwrgP79xw+9HJja0Nb y0sTFKhWATUoYC4jNeqg+SxEIbOLT26dILTTiO0iTr9ejUxE+MUkO+UllSPM Ypic8BJrOX45xikFWEt59WxGPRVRLmU1cylV2cfP+TgpDz/tZ2b8/LhrImgl xN0UhqBRquxRG3ol4iaJqN1pQbtdMJNp2OVAJCM4j3U46oEXE6SgH+bUD6+k 1DeXw6/vph9tp17fDN1c8d9cclys6k+mxMcz8uMp2aVJy8mccjvCL+mwOQ3R y4NoqV0zLvZKVFw0s9MmXkhDCykYcYMIVG4gQ7ecY5eyHTKORcw0iulKNp6J 6mfhhmjwXjqsl4YaIIx1sYiw/s79Aup42qUaG2iGj3QDxIDDgz5/tPFgc2tT d28TAjVYKbPTYfJMXr4+pVmsykMRWi5GKyRYQT9hblq6vmhYmTYulqT5CGMq N1FJUKtZ3lROUAyxl7KKpayqGhRMBgQLcelUkDsV5pUCnEKAV45wlybNc2X9 6oxlqW6UKfrUuh6Jukut6tRpBpXSXqN2yKqDehyYgBfpsA2VspRsHFfJM/IJ cslP2y0YbqxEbyyFXplzXJ1xnC1oNqPcZQ9tLcQBJWE1LFjwM2ddtJwaExDC HMwhHqq5bGEcS6sKBkbeyis6+Ukd1y2hAl6FjWKzdALYMg0zwibB+RQEnwQj jPXgYf1YSDd6uH2k9wiQG9AgfLAxG9A7lazu5r1IWO8EET5BxWARI1gU2NVH urqbmzoOhUPYmRwrG6AsVkTVHMPhHUtG0LUiMxJCpRK4Yp5ey3FrUUbMS67l meU0q5yhzZdEiznFWlk9nRDUYzzwcTElno6wVwqyyYQQhGM9KdiYN28u2BYn daCMWS3jdsewXNNpt0AMmkGjbsCkhAJ5unUwp3nM7RmNx3BO02A6iJ3MMSpx csHDPF7x3t5KnJs0n8qqt0LiaSdx0ogr6zEFHapgwvuEEBd3wMMfMYDiQehm wFtKZvpyUFC18vJmdtLICMgYbhnNJqap2XjieH9n6/7W5r3d7QehvU0AKMRw 13BX81D30f7Og4NdB9Aj7aPdB9n4AdDVgckDgmHgPWIOWSPl82h4BhXOpaBw uLFDDXv08rFTc+rdGe3alHJuRlCp0FNRXDlHT8fwMT+mXGAm41gAY9RHLGQ4 +TS7kifsLKjn85KlqnQyxQV6LEQnKkl2LclZrCqmSrKpkmQmJ6pnBWAHPev2 Sb7TidBqOrWGHqAyrxPlc8M04kGHBpa0E5zaMY8Tlo6SFssysDd3FhWzJWrY hq0FJbc2kjtZ9WZUuhIQ5fWopGzczeoLCiAB8ZiS2C5AHhWhm2mQQ9TRZuJw Q93GmveLomqqnQuziVB6Hl7BHAczkDreBzTV33m4p+NQR8u+oZ7Gkb7mgZ6j 3S2H+rqOdrTuHR1oJIz3AKyMIoJRgCWNd8KHG/GILoOS7zHrLHI+crRdLZ4o ZCMw2CAL33t6TnF2Xl+OsCcrwlwIH3Mhaxl2OcEKWTHVNKuYogDpTWY5mRi9 mOLU8/R6lgEW8UJVUk0yA34UgDESxObS9MmyOBolZVJMgFUpzsqFafkIHQjW ZIK6AbVsw15nn1k/6jaPglSVcTuzHlLWyyyE6QknvuClLBb49SytnECvTokr YQao4lsZ3bRfOBMU5A2UtIYQEMP9olE7DyLEtgmwnaShA/CuFwdaX+QiOzai qrKVaeaOq6hDEvKglD7Gwg1OYAYwQ62IvqPAlDhEGKgB49B24OHtLS/1NB8A AILXgFQYSBsQoIACFZCGaejucWiDVcuaKsaSAWfArEbCu/kcCpmMHu7tNSqp 2zOirZJgLiXKBXnzeVkpQK5FWcUgtxxkz6UEaRe+ECDMZTn5ILUQohRDjGKU lvQRp3LCySQrk6T6XGi/b3x6SljIM/MFBvg4neMV08ywFzNVFmZiVJse7jNg PM/CbjhgwSdctLiDBDw/asN41YiYFVX0UbN2fCmEny+zN2dFa1XmVIZW8PLT Vt5cQp23U/JGRsFEj2rwfhlCS++hjx4hQp8deN++jubnnRLUsawxY6RHNGQr H81DdQP06OPdE8he6lgHcaiZjezjoPopY53IwaMDnfv72l8a7Ng/3HkYNtBA R/djIC3gOnGsFciQR+5nYLvfun/m3YdX7DqpjE3raNt/4OBL+17+WVtj4+DA wXiYulUXzWf5YTMpZMIfn9UB88l5JqaTvJkEL2snr+Yls4BRUdZ8hpVx0etp YcpHrSaF9Qwn6Mb4vAjAq0ycmMlS4gl8LIjKR4npBCWXnogEiCEfQcUcMPMH bfLhtAvr12PU9L6IARM3433a0ZABHbHBoxZkJUhfKDBW6oLdRfVGhTVfYQoZ HVzycMkvTOiJOQM9Z6AGlGivbNwuGGGjW1H9B2jjLaihAz2de4NKXNnBTump eQszrmHU3Ir1jHUp552OWiZD+piRJ8YP0KBN1JFWAB0B0oIZbkRDmlBDTfix NgqmFzV8FNFziIHs4BD76ejWyYztn795+uX79yVcyt4XftLQ8GJr2xHQvZob 9x9s+Akc0TZXE8Q8qHKMtTZpyPqJpSBluSirxtiVGLMe51T8VCCKyTBvMkqr hCdmMmJQroAR5SIkkI+JOCHkwmTD5GqNW63wUmFsNkHyOkYAo8IevM2IMHJG QjpMwkYM6XAhMzpuoWSd1JgOGbdhin5GMUJMe4lRMybjQU5nJ5aKgt0Z5VRm wqAc6u/6+wn4ITmxDdApoSU4+TArd0Q7McBANFPhrWLyMAvTDek9qGUOR9S4 qJocV1NmvNK7W5V3L288urjx+tnlpzeOv3/75GsXViajRikFQoY0M8d7xGQo DdmFGW7CQpqR0EbkwGHySJOQOMinDNhU5F999OBf/+EXn79zE4ceefHFF5pb D0CHOvv7m48efamhaf/BQy+rFBC/Gx/x4IHZhu24YpwaNI+GbehKCgiK5VFD Ux4CkORqUQbEuJiTLeVkOT+1nKSX09xoCJMOMQoRVrnIyyQmQFYWc2yrdjDq JeRj7ICdmLDSEg6634yxyZFyflvMjUt6yG7psNcACxuQCdt4xkXOOicC+pGo FVb2kRJ2TNg6XkvRncYxOuIgYeRFFaNFx+rhI5q4iBYZuYcAOYCBHGEiOvi4 HgKsVUIfdIsRTjFeNzEG2HV2Jn7r2MKjq8cenFt9eGnz00dXf/OL13/85v3f ffLo9ulln5ozgegijLYwkF10RCcJ1kaANrLR3WoOPBdSffL2tT//5uO//ubT G2dWGhr37dn3cmPTy53tB1vbDwDQGhoPNDcdoZL72BN9OgVSyR/QSkd8dpTT CLUohhNewtqUPOWlBMyIeoo7FRfGrOiUjTSfECYduGIMIMPJhvFg12TCnGqW n4nQigluIkT2m1GVBC8TmChEOD4jXiOC6BXDZuW4Stbjt487jGNBE0or6QMa DOrGPIqxkJaYdlIqYVrahgHaBD9rNkFfrvLWZs2RACnjI4CH5dEgJZQuPX9Y we4lwg+x0c0iQhsDe5RL7bKJ4QLCgBA/VPGIYhZBKWg/sZA/v1YFiL1z+8yH r1359I0bv37v1d99/PhXT+/PpN208Q7cMICon4cf5hMHuPg+q5Lywevnf/+r d//4zQf/9OXTkEv/dz95bs/eF/Yd/NmRI3uONO1paDrY0PBy69G9/d0NsJFm Gn5YyB5hkHomKF1K6ajdiIs56fkQ36rB2LXouJ/k1OEjNmbASI7ZyBkfMxtk g/mcC5GBfTkNqGc8jLOSPhI4PhM66iSB66BRyLiDYBoYFCMq0YBBN2zXjTlN 44BaITcBWH3EgQ+AHS0fj1iIAJCclzQZ4eS9jKwDl/ah0kG83wkHQVyI4+MB NFhYIdt4LcMoJBggZSJmuELUbNWMGIVQg2A8bGTWI+qppCnlUJWCxtm0G8B1 cr5wZWvujasnn7xy6sbxxTevH//qnXtv3zqZcivwI61c3BBIQAKsuZyw/OnX P//+Vz//6/effffJm1jU6HM/ee6lvc+/+NLzhw6/1NHVcOToAQDay3ueO3To Zy2NewBoPV0He7pfbm/d2997BAo9DB85jIW3QwcOoOEtKGQDYryBzx4l4dpp pE6HhZQIibxmImBd1Elxm1BpH7kaoRciVDCIYi6iRTnoNcECNjiH1gu4qhUM KfjdUmGnRQpzGbAAH7thVCcf8rhxet2wWQ2zKEbN8p6QFVnwMII6nE02CjRY CKIjbljMCwu7BwP+wUQYFfOOztdoc5PMUhRfjKLAxbki16lEmCVjIT1JQR00 iVAZu6Ti19Tj9lrUWgqYFnOhUwvlE7O5pVxgsxpbKYQ2KuHHN4+X43YBFabk obVS4kdvXv/373/9p+8+/Y8/fn5ua2bPS8//dM9PADI/feEnBw/taW09uHf/ TwBoBw/ub+082N/beLRpT2v7ocaWlxoaDx492tDQvOdI60vNLYeaml9sankZ XG9s/Qk4R5r/7kjbT/ohe5CwQxjIAYscxiW2qoS9fhM8YUcVg4CNWL8V5jGN BO3jcS9awB7mTfRS6I04+iEWp0csHBDx22XiVr1+yGSEu1xUkx4vFfRKhYMa OdqqHPM5CRE/LZtmJWKUQBQTiBKiQWp9kjeXEYTDhFyJMVuh+JOjYlMLmBi+ AGSpyl4uayTUVjq1WcnDkhFd+NFuBRuTC+qOLaYubdYub0ztzCbXq9HdWm4q oncq6AoyzC6f8Bl5a6XI23dOvP/4yl9/+OL/+uff/+2P37398B5keOy5F59r aP67zt49XF5na8fLDY3P9/S3jA63tx7ZA4V2jww3Hmn8WVdv02BPW9ORn/3v rNzb2ravue3F9o4DrZ37QO9taXu+rfPIQN/+kaH9mPFWjaLPougOWscqBUox jg07oDH3WMSP8PmgPsd4LECs5gQeK0LI7RVxu9icVha3XcjtkUv6BNxWlXTA pB6x6kaVwn4Ze8AgHuGS24X0XocC4TCgXEY0GKRBP95hgSmVPSrlgD9IKOf5 Hv84aCaxCCqcGA/GUbEoNhRHeB3QYpIVcuIZzEMsajsR0wSD7MWMHeUTIKCN 12L2Kzuzd88sXNue2qzGUw6JV0HRcJGkkTZE/yHQUV+/vvvnX3/wu8/e/fLp g6tnF+hUyM9eeg6Na+cIBniSXoG8s3vo+a7e5wf6XiJgWoXCoeHRvXhC0yj8 YFvHz1pbnm9u2dPatL+lbf+Rphcbj77Y0PzS0baftbXv7+w+3NZxGAE/yqK3 4VGNOulwJS1YLUnyMYLPPuLQDQEWJWKYTBgZdcLjbgRwGzABNMphnWpAKGhX SoY1kiGNqt+oGzPrkFY1QiFo9xiQfj3OJBzTC8Z8emLczApaqB4tMeCmFJJ8 uxJm1kBl0h62sE2k7HK6oPEIzmiBGK39Pj/cE0REoiiHe1Sn7zLbYWpLl0rV zRd06nVQGrmFPHZ0At0qZ6GqCfsru1NXtqoZt9wmpXlkJBZhYKTrALTn8N49 z5Vjth+/eO/jN65vTPlJuEN8QTeVddSgGVdqevjig1xJM5r0IpPTwqQe9Xtx ZveoQttldYxodINMegeBdPRI03MHD75wqGHfoeYXQVY2dx1obH2hpW0fsLj2 9oNIRKNOM+Cz4xxKZNhNTZiRPsOIy4HQygatWkgwOFZJ4LMBIAFsNogrRkk8 VheffZTJbpDw+2TCAbawQyrrV0gHJ+gtaiXUpkP4DKiIlRqxsCcT6q26L+sW F3wqv5GqF0CAuTlNSL0GojJA+ZJmlbwVzIFUaqJYYXsiGI1lIB5HGRxDSlOL UNNu8UDzBXo0QXmmbgucRTkk43exiZ0OJXkmY9+eikatPAOPyMUOwgebhjqf /R1v//6fMtB9WbvIqyOkHCBu4BJ5q9rQ7TSP5LPEWGzc78ak4uRnUy5I0uqO Ujn7LK5BnbZBq22zmkdVmn6VHtE9cOBAwz6QlY1th4527jnaurez52Bj056m 1j3dvS85bOOlFLMU40W8pCKIKvMY+N3UigG1uNNk6g7YB3MRQsSFSPqxUR+a QekWC7p4kk65FILBHEJSDk8I2sTSTiqnic7t1KhgFjVUL4NYVfhKRLVSsVZj yqWSdzKpD+hJoGZ4LDgJt8NoHkkmafEgLg7eOYxLZala25DWOuj1DmlsfSJt I5G3Tyxr02l7AkEkeJR2+0g6izeZ+zjUg2xsE5/Sp+MjNSwYC9UD6T7U23oA BNn+/X/f3PgCcnA/n9hmUfTGrYiZDI3D3afRdM9OCgIuqN8DDxtRUSdCI28x WwYCoR6BqI0rOux0dBl0XRp1u0bbqdb1IMgNDa3PZLiv+aUGoMq2g4cbf3q0 6fmO3pfb2l8y6TDFOB10UVDdfU5IwAk32+EO97hVP+DzwrWaDrNhSK/pU8g6 rGYomtDCF/bReS1UejOJ1EjmtZLYR3iKTq60exy3l8fvsOpgbPpRIE+PHhd2 UbxOrM9BDboZTiMWfEmnhCglgwbzcCxGzucmJuu8WArn98KslkGzF+r2j8pN 3RJtJ0/UEnKhVJJOjbIz6EOpFW16a0skBtxsJGTDW2VwAalVROrh4TowY839 XXt7u/f0tz8/ge9QgMImGIj5MYkAaSrLtloGAGGA32YiFKN2SMfvdOuHPB5o OIIMR4ZdTqjG2GeyD8UilEBg1OcfC4agPFlHU/vzDU0vvHz4pw2NP2lq2wes vqf/QO/ggf6+QxhYg1ePBDU77cfYHcMO07BKN6Ay9ZhN/Ub9sEzWrlR2GI0D fP4RgDxN0MvkdYkU/TJFn0ozJNX0SdQ9JG6jVNyv0fe7nCNm/aiQ1ybgNSql vQ4LXGvtl0ha+cJWkwGaipD16gG3C+v2obwhtDeKTuSImRI5FcCk/CiDa0ig bCVzDtE5R0HLnUxRgFKcVrhG2W5S99jtfTZjv0U36LWgQHOuBBlGIcQshiSc LIloUK8arUR5oAeCZ2FUwkGISGQ9NgssHESCdzBpBsNunNeOALD7feM232g8 g7W7h/XGw2Z7P0fVJZV0elwQTxASS45m0jQmu3N47EBX557B/gM9A/thyLa2 rheONj833PcCi9ItoLdHjONpLz4UJAFeAWQA4BbLMLhZiwFqNQPQulWKFru1 HzPRRCAe5rKamOwjTG6D3tQHAOSLOoS8Dper3+nqTsRJAAqFuh2ISKbsZAoP ysE32oajAVQqgs6lcDbroE7frTT28pRtSlNfMkMB6nbqB03OMaMDJlYOuT3E dIBSSRG8XpTbjnbZxsGASoUJoODppINWJSTjw4GR7tGOO0DQcLqN2mGffSxs grkNo3pFv1E+JuQ2c/itfHGb1dCnkXTkYrRKhg22XjiO1duHeeoOvroJT9vr 83XOznNVTiiwNa8bms3iKjO07TXt9KTAH6VpFKNMRhsSvW8MsR9LPsrnD2ll /TL5gE4zBrRslw5ZDciQAwFuDfyGNivEoOq2qgajbiS4F7u2N+KADYy9QKMe FrEPMTkHuLIWgeSI0zlks4xx+c1aQ6PD3enyQ2yuoUSO7PIjnv0b7hGTfjDm Gi9E0MkILBaGxoIjBnWbyzmmMY1O8DsshjHw6HWGEY18CDinkNcFmOlQDwfs w1bHWCbKBhsh66N4jSNJD8FnwESdBGCbJlWfQdxjkvZZZUNhJ9Ko6tRIukB2 WzRDYG35bAigOJtlxKUfna+IZ6tCrawVUDccQdv9WINrnKNrJzAPyaUNVnO3 3tETDI3HQvDleX4ogUzEUCvznEAQYdT1uOxQjwtlNkPUhn4Q3x47VG3oZQla 7XqoWzNm0466jVC/AwYqKCBYKogMmOAuBdSrHvOpQZeGi+TDbjcilyTozH08 ZYvFMQBKkcHYwxQedvoGXP5Bi6vTbGkHRqFWdQp5R5Si5pALU46RK0lMrUia qTKiXgio7h7LmMWIRKEPk7GHJcJuGqMFbAG3YcyigFqUA9kAxWt+Vk6SXkbQ iov5cDZFTzZI0gv7raohoM1okOyyjAZcSLt+JOHDOJwQibw96BqPB0HXJU0m J8A/TvsJGQ/LrRudKYvqRdZMhet3QXXWEbmuX6htNDnhoDQCqmczqMkyfWmW M1mkBfzj8RQin0HEgwi3vTefwycjOK+j16BvUcnbQSjodd0TrMPAyUN2NCCz WzcSdWAKfpJL1592I8C+XkooV9Oa5aRqPiyV63rV1m6rc1Cs6ZDpu7OAP+FR nbNLqm93ugdBxwhHoCHvQMQ96rYMxTyIkGMg4UdWM7R4CNzaYDqESYWxcS+4 x2GwpAwahJg/KOUP6GQj8yXVQkkWMKNyAcpMnu83DevFA4A2pRjZYu6vxidW qrKwA21W9LtMsGSYEouhgTkHfYhEDGfxDto8gwZNi90ykowzpjPcrJ8YMWC0 vKG4i+g2wwAVo2F02DXi8Y4ls+RUGuKyQINO5EyZlgzDN2ZlGwuCmSpzdUYS TYzEE+PHl1SJEAyYZDyErhdxSf+oxzRk0/SBaADsBSsm6sXn4kSPbiRmwYYs 8JgDlbeTltPKKQ9vNa7czuo3UhqVrZtraMZQn2cIGtmSRp2pxxEaE2gPKfUd Xs9YwDcSCkLzcVS9QAaPqZjA1YuYTByeSmDCoVHAqEKEmIkTkyFMLT/h0A3Y DMNiEXDLDpNkKBOjFnNsp24k4SQ6tQNgOHh0iGqSmQ0jk3H8TE4UtmBqKRZg S9yBCzlHfb7hYBgWjyNKJbrJ3uP09ESCw0ppF52632ODgYUed1LTz/5gwsuE cBEPJuRHp4LoZBQOvmV1ljRbZCbDCLuxy2EcAk1pJk9bnuaAGml3dEbisGwY 7rf3glgBnh9y9c0UJtIBdMAymnIhEjZQQcdqCXYhRnGohoAYPcaxgB5WMNJC cnROT1yJyE8WLSEpnCFrxImOsMSdZFYjgfUyR9IoN3bprG1AyKAO2a29Nluv w9Gv1Rz1+yH+0FAkDI1EBnMJbDKMqmXJ4DeslSjRwPBMjboyJwTYOm0jYHSL +a2grhtBxGtGIjZSPkkIg0dpGy8XJ5J+MMz7n/0vuYuSDxJn0qzVsjTqhga9 w/EoOhwZDThHPXZIKAypV8lA7w4rNFtg5KJovXYo7CeEfYjJPLGUoCTchJQP W4yj42EYeJohxxDwKJtlwKGFBoz9uSBuZ1GU9CHj4bFyATU/TUxFR4opitc9 DH5Q0AFxm/rCrrFcEB0yjsUcmJKbHLFjQECL2Q1KYYdNNDTp5KfV5JKdCdR3 omI7P+OdkHUhmUe5kgEmv40hbJoQNIlVnRZbj1w3oNYO2l0jRvuARNtscvSr jR0C1T6gSrenP2KHlsOEhB+Wj6LLaXQtj3p2qrRIZDyfoljMY0JlayKLMTsH 3NYxo2IoHUfrDG1iUYPHN+QwjCY8SJAvSR8xZILELLDJLCcdwaWjGLcTCuor WBP5KNlo7HDZely64VQMBSw0Exm32aB6TZfT2htwdgFPCBvgUTuyGiVnIxil vDvsHQH69XvRVtkAQD7hRlRjGKehrwC8fYq6OEsuZVEzJUapSPS5hmymLquh G5ibzdDuNUPzYZJPMmJTQkDOggjQSHus4gGLAFJxcnI2Vs0jrLoFsyE5eaId z2wbRbzI4TUJJO08aTude4QvaxXIh2jspgn2UYm66//r0MyeE0evKJ6HpFLJ JL3YGIPYQYABA8bYrDb7vkogAZLQwiKExGbAYGO3u90z3T0zVf2Qyl+c67xR UAXS/c4953dUtPomnNBJkwsgbUk8V0TPij//45CBxelj+qlg3Uzd9yv/UHKS PRNwlzAMTJcX4sy+3l6t19fF4kdQpjC0gf/cby+B7b8c4iNGT1Q/wDeMaTuQ UrOqh4oEswKRcKTxZZ+B6ioN7HPBdzdzgRJ+PSRmE//jKvS8veRonSzYd2Pf jDmbcq6tEiBxy2RoB/oC636QI4/LwKdZcDsJiH37bw+3X9fB/dz/43Nqt/Dv Nn6mpWXIU3kSGNC2cd8Iwfe6TY6abgazcZR1xDkBGrm2I3d7TCTNVNrOFdxr KrmhE6HQUTSNpMtIva4tF9WVGlIjLLGK1h8+iqZUiawunv5YbWuARQedM6Hn AjwYMnZIMdAw7DtZ0guUfQI5Il8yIydMO3J7UqroxLFttnYuFa8wMvOiHWr1 bHEOpgFe9+179ufPzHIKF6mHjRAnXp7xVYsWmoYo8T/trp+fYj9+VJ53cYmz fXnMvB6uN6JvLvm3EvhbbCtbd5v44S78MHdP+9Z69QhGCoYJDQWAYTp0rMf+ T5vw89S7lcLfnwv/+VL8fp/4bVOE7P71U+LPH9nXbWI99SqT8HLsh82VGJdC O8adQLOmJWq62cBH9+xdEgXI7JZcTMFVDpy0oLeWvNDUoil1o2VmaVupqCvl 0Xze5L74q9v9LhlTxSNavOkg27Zez9Fpm2GJ8IZu2LMBAAD5QB7BsQq0kWVN AmNjSMuQ8mElLU+ZRMG8VDxQ3Pab66+vqdnShXWPq9VTgTdBJ/28yfx8aq17 /v34cq/EFDq6Fa8Oy5BEhcaMVRT9D/vQz5fM79uUIrtY1gBKEynDoxJfjp2w OLOxF0zvcRMReQ+JaYecCcRGtS1ky9RnkLflHbrWiwuQKFgfvL5fhZ/uLuWR /bCLfN4l7hTfZnYFm3s3Pnu5i8ocoK9nIaIC6WQavln3SmauAHiwghUgcNDz JkP/zoY/srizSaG5qgWQmO6i+SySiCPptCGaOPGef6jmLfmCI5fXtZvmHoFy jIOiLZ2WrYcbIHD5roNpW+dD70z0DAdnLGPFG1q8bh5zvsN9DO5xItpfXwq7 XXSueAnyOJr7S7Gm6vf1Dw/x5136j5fmyyL2dZ/az6N7KbGbBhfD8xl3+bq7 fbpPAY28rK//+7U+n0BHMzIY8rC4WPC+AanlSP2Ic8xFaDr6LqYH41rJ/s3U Kw287Zae46xgFxArkHd92tTvWLsdkIEdJCqLZ+OhE2Kl19ayPYs89Ex5FICW J80MYV7P/HBf2ehJI4E0Mkg5fRwJ/A2axWoaUwYhjjwfkJ5kUXOT1Wbymmz6 Y/DqXzdRTSikit+q49e6VEwTDKtzeS1eMfYw61u/oKySEOI6drZjnvCe/SoO J7K7i1A9K8uhJGlKJt4xlAPHTwYjVJLPmpi6L9jB4jDsA8Zqyu3Tqew8HOLL xTV0bXGASkP3fHJ5WET38uWwgz6uIt8eUg+z6G52ATj0fBd7Xoe+bKNy36kM HArrGnb0ZO24hZ1Uyx9a9ZP7RfjzJjLjPVspSJNGqHJkz1ZsILnKO7JjgJOt VbQ9qNgdRw9KKGNrNNX14gneOG1hBhigQFkZ/G3gzapOHvkV0VdMqepJBCvr mI6J6dmB51tNMBlPvw0VXncd/uAL/jOR05QKBpaP5DOW1K0+kzMUM2g6rr25 QTghgFUtPRwSzdNuWxjC3e+gePV0xHrmk9CAcUpjL3TG0cTbY1AICKytx0kE 4FYQ7X3B1sRO+b5zOQ9ImwtqhG73QaCdtz+vzq5pxsTQ6EIJ/nrIvGxunjZp yLKF4PrzufLtkF4s/KBPmOHLPLQa+9iuAQhtOTwbsw6qqycIDdza/SpyUAJT 2gWBuJKvOx1rOncSiP8jWzvOl9WFymmLsMM71ZpZYN04Yao29VhNX6+fNnET QRi7pB5KH2wuxGi1rMHqGqHvZTtnfdopS+4+YyVwI0nou4SF6521q8hVUHWb 0d7mELApGFE+Zc4kzZHIUaVkbRSs9TIq8P5cCinlEZZ2YmUTXoHAMsLCCpT3 /xylhfO6W4WV2WWXthVK6g5lBpAeSz4okqLihovJJ99DQk0kD0kjzEC32vjB NObSBdgXz3g2q4hAO2Tet1DeHuYovFNmXQ/rMMWbeN7GtHTAFcuRRxm77yYe mUdhAQcD+2IRgDzl+rbP6xtotaOucwSgUtHnippURZ0sn0DLC4T/nisiiZyq VNU3m4YaZizj5kZN1yKN5TpSrBy32toGzK2OwKelkrpcOCZxFO6OILWCYOYF B8fa8Zam2dBQHVsHM6bjllRWE0ufhiLHwfDRbUQTuVbf3Kgj0fflpC4f10DH z6SQ24SqlFPVUwaiYQRxEi1rs2it580C52u1EZLQwawYxgmzAmyQZhfQ9EFU 44kdihXVQmcT32Lqm0wcXQ5hBcNmCHHvrlcMgAqATND315MoxzqBfoeUUeTO ACm7rGk0PlvKgc04uJ76lYEb8h1Sg2fRLomQbXWjqoHfnQvn85GbJ62g/GYd TWVVNdJ8GfsFgCdZVEeT7zNldR03wSgyJQ3wdp92gf6zpaNyQ1Uo/tJuITSN wubC/oIt14oGyK9664jsaoi3p4U6nNRgGILVEbJp+B/ES0P3 "], {{0, 150}, {100, 0}}, {0, 255}, ColorFunction->RGBColor], BoxForm`ImageTag[ "Byte", ColorSpace -> "RGB", Interleaving -> True, MetaInformation -> { "URL" -> "http://www.wolframcdn.com/waimage/hset050/38f/\ 38f9223561eca408b7e8b89bcd8fd365_v002s.jpg", "Source" -> "http://en.wikipedia.org/wiki/File%3ACH_cow_2.\ jpg"}], Selectable->False], BaseStyle->"ImageGraphics", ImageSizeRaw->{100, 150}, PlotRange->{{0, 100}, {0, 150}}]\), "OilPainting"]