Wetlands -2363982.849800 2263789.122500 258955.708100 3177424.263300 1 002 Projected GCS_North_American_1983 Linear Unit: Meter (1.000000) NAD_1983_Albers <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.6.0'><WKT>PROJCS[&quot;NAD_1983_Albers&quot;,GEOGCS[&quot;GCS_North_American_1983&quot;,DATUM[&quot;D_North_American_1983&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Albers&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-96.0],PARAMETER[&quot;Standard_Parallel_1&quot;,29.5],PARAMETER[&quot;Standard_Parallel_2&quot;,45.5],PARAMETER[&quot;Latitude_Of_Origin&quot;,23.0],UNIT[&quot;Meter&quot;,1.0]]</WKT><XOrigin>-16901100</XOrigin><YOrigin>-6972200</YOrigin><XYScale>10000</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision></ProjectedCoordinateSystem> 20260311 15082700 20260311 15082700 1.0 150000000 5000 FGDC National Standards and Support Team U.S. Fish and Wildlife Service National Standards and Support Team 608-238-9333 608-238-9334 505 Science Drive Madison WI 53711 US wetlands_team@fws.gov 20260311 ArcGIS Metadata 1.0 U.S. Fish and Wildlife Service National Standards and Support Team 608-268-9333 608-238-9334 505 Science Drive Madison WI 53711 US wetlands_team@fws.gov There is no charge for the online option. Requests for large amounts of data are handled on a cost reimbursable basis. FileGDB https://www.fws.gov/wetlands/data/Data-Download.html https://www.fws.gov/wetlands/Data/KML/Wetlands-Data.kml https://www.fws.gov/wetlands/data/Data-Download.html https://www.fws.gov/wetlands/arcgis/rest/services/Wetlands/MapServer https://www.fws.gov/wetlands/Data/Mapper.html https://www.fws.gov/wetlands/arcgis/services/Wetlands/MapServer/WMSServer?request=GetCapabilities&service=WMS http://www.fws.gov/wetlands/Data/Mapper.html http://www.fws.gov/wetlands/Data/Wetlands-Product-Summary.html http://www.fws.gov/wetlands/data/Data-Download.html File Geodatabase Feature Class Wetlands_PBC 2022-04-26T00:00:00 U.S. Fish and Wildlife Service U.S. Fish and Wildlife Service Washington, D.C. vector digital data Classification of Wetlands and Deepwater Habitats of the United States. U.S. Department of the Interior, Fish and Wildlife Service, Washington, DC. FWS/OBS-79/31. <div style='text-align:Left;'><div><p><span>This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the United States and its Territories. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979). The National Wetlands Inventory - Version 2, Surface Waters and Wetlands Inventory was derived by retaining the wetland and deepwater polygons that compose the NWI digital wetlands spatial data layer and reintroducing any linear wetland or surface water features that were orphaned from the original NWI hard copy maps by converting them to narrow polygonal features. Additionally, the data are supplemented with hydrography data, buffered to become polygonal features, as a secondary source for any single-line stream features not mapped by the NWI and to complete segmented connections. Wetland mapping conducted in WA, OR, CA, NV and ID after 2012 and most other projects mapped after 2015 were mapped to include all surface water features and are not derived data. The linear hydrography dataset used to derive Version 2 was the U.S. Geological Survey's National Hydrography Dataset (NHD). Specific information on the NHD version used to derive Version 2 and where Version 2 was mapped can be found in the 'comments' field of the Wetlands_Project_Metadata feature class. Certain wetland habitats are excluded from the National mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their depth, go undetected by aerial imagery. By policy, the Service also excludes certain types of "farmed wetlands" as may be defined by the Food Security Act or that do not coincide with the Cowardin et al. definition. Contact the Service's Regional Wetland Coordinator for additional information on what types of farmed wetlands are included on wetland maps. This dataset should be used in conjunction with the Wetlands_Project_Metadata layer, which contains project specific wetlands mapping procedures and information on dates, scales and emulsion of imagery used to map the wetlands within specific project boundaries.</span></p></div></div> The present goal of the Service is to provide the citizens of the United States and its Trust Territories with current geospatially referenced information on the status, extent, characteristics and functions of wetlands, riparian, deepwater and related aquatic habitats in priority areas to promote the understanding and conservation of these resources. U.S. Fish and Wildlife Service National Standards and Support Team 608-238-9333 608-238-9334 505 Science Drive Madison WI 53711 US wetlands_team@fws.gov Alaska Idaho New Hampshire Massachusetts Connecticut Maine Oregon Guam Minnesota Hawaii Saipan South Dakota California Wyoming Indiana Arkansas New Mexico Georgia Ohio District of Columbia Mississippi Wisconsin Illinois Pennsylvania New York South Carolina Kentucky Virginia Vermont West Virginia Utah Florida Delaware Texas United States Alabama Colorado Arizona Michigan Tennessee North Carolina Washington Missouri Rhode Island Maryland Nevada Montana Oklahoma New Jersey Iowa Nebraska Kansas Louisiana North Dakota Puerto Rico Virgin Islands ISO 19115 Topic Categories environment oceans geoscientificInformation inlandWaters USFWS National Wetlands Inventory Deepwater habitats NWI Coastal waters Wetlands Hydrography Swamps, marshes, bogs, fens Surface water U.S. Fish and Wildlife Service environment oceans geoscientificInformation inlandWaters USFWS National Wetlands Inventory Deepwater habitats NWI Coastal waters Wetlands Hydrography Swamps marshes bogs fens Surface water U.S. Fish and Wildlife Service Alaska Idaho New Hampshire Massachusetts Connecticut Maine Oregon Guam Minnesota Hawaii Saipan South Dakota California Wyoming Indiana Arkansas New Mexico Georgia Ohio District of Columbia Mississippi Wisconsin Illinois Pennsylvania New York South Carolina Kentucky Virginia Vermont West Virginia Utah Florida Delaware Texas United States Alabama Colorado Arizona Michigan Tennessee North Carolina Washington Missouri Rhode Island Maryland Nevada Montana Oklahoma New Jersey Iowa Nebraska Kansas Louisiana North Dakota Puerto Rico Virgin Islands <div style='text-align:Left;'><div><p><span>None. Precautions - Federal, state, and local regulatory agencies with jurisdiction over wetlands may define and describe wetlands in a different manner than that used in this inventory. There is no attempt, in either the design or products of this inventory, to define the limits of proprietary jurisdiction of any Federal, state, or local government or to establish the geographical scope of the regulatory programs of government agencies. Persons intending to engage in activities involving modifications within or adjacent to wetland areas should seek the advice of appropriate federal, state, or local agencies concerning specified agency regulatory programs and proprietary jurisdictions that may affect such activities. Acknowledgement of the U.S. Fish and Wildlife Service and (or) the National Wetlands Inventory would be appreciated in products derived from these data.</span></p></div></div> Although these data have been processed successfully on a computer system at the U.S. Fish and Wildlife Service, no warranty expressed or implied is made by the U.S. Fish and Wildlife Service regarding the utility of the data on any other system, nor shall the act of distribution constitute any such warranty. No responsibility is assumed by the U.S. Fish and Wildlife Service in the use of these data. Wetlands and Deepwater Habitats of the United States Version 2 - Surface Waters and Wetlands U.S. Fish and Wildlife Serivce, National Wetlands Inventory U.S. Fish and Wildlife Service Washington, D.C. USA Version 6.2 (Build 9200) ; Esri ArcGIS 10.6.1.9273 true -96.762185 -89.968584 43.714913 40.320329 ground condition 1977-01-01 2017-01-01 Digital wetlands data are intended for use us with base maps and digital aerial photography at a scale of 1:12,000 or smaller. Due to the scale, the primary intended use is for regional and watershed data display and analysis, rather than specific project data analysis. The map products were neither designed or intended to represent legal or regulatory products. Questions or comments regarding the interpretation or classification of wetlands or deepwater habitats can be addressed by visiting http://www.fws.gov/wetlands/FAQs.html These data were developed in conjunction with the publication Cowardin, L.M., V. Carter, F.C. Golet, and E.T. LaRoe. 1979. Classification of Wetlands and Deepwater Habitats of the United States. U.S. Department of the Interior, Fish and Wildlife Service, Washington, DC. FWS/OBS-79/31. Alpha-numeric map codes have been developed to correspond to the wetland and deepwater classifications described. For more informtion on the wetland classification codes visit http://www.fws.gov/wetlands/Data/Wetland-Codes.html. The U.S. Fish and Wildlife Service uses data standards to increase the quality and compatibility of its data. The standards used for the wetlands data can be found at: http://www.fws.gov/wetlands/Data/Data-Standards.html and include the FGDC Wetlands Mapping Standard, FGDC-STD-015-2009. Note that coastline delineations were drawn to follow the extent of wetland or deepwater features as described by this project and may not match the coastline shown in other base maps. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant and Content Standard for Digital Geospatial Metadata (CSDGM) file is intended to document the data set in nonproprietary form, as well as in Arc/INFO format, this metadata file may include some Arc/INFO-specific terminology. 1 -128.007240 -65.254818 51.649582 22.762404 None None Polygon and chain-node topology are present. Every polygon has a label. This data set represents the extent of wetlands and deepwater habitats that can be determined with the use of remotely sensed data and within the timeframe for which the maps were produced. Wetlands are shown in all of the 50 states, the District of Columbia, Puerto Rico, the Virgin Islands, Guam and Saipan. The accuracy of image interpretation depends on the quality of the imagery, the experience of the image analysts, the amount and quality of the collateral data, and the amount of ground truth verification work conducted. There is a margin error inherent in the use of imagery, thus detailed on-the-ground inspection of any particular site, may result in revision of the wetland boundaries or classification, established through image analysis. Wetlands or other mapped features may have changed since the date or the imagery and/or field work. There may be occasional differences in polygon boundaries or classifications between the information depicted on the map and the actual conditions on site. The National Wetlands Inventory - Version 2, Surface Waters and Wetlands Inventory was derived using an ESRI Data Model with 481 logical and topological processes. Linear NWI features retained their original NWI Code and were buffered to specific widths based on the following rules: Palustrine = 6m, Riverine Perennial = 8m, Riverine Intermittent = 6m, Marine, Estuarine and Lacustrine = 8m. The NHDFlowline features were buffered to the width based on the FType field using the following general rules: CanalDitch = 4m, Intermittent = 6m and Perennial = 6m. The NHDFlowline features were coded with NWI Codes based on the following general rules: CanalDitch/aqueduct = R5UBFr, CanalDitch/all others = R5UBFx, Intermittent Stream = R4SBC, Perennial Stream = R5UBH. Further refinement of the NWI Codes was implemented based on spatial intersect with known NWI features and specific FCode information from the NHD dataset. Topological modifications were conducted to remove overlapping features. In general buffered NWI linears were clipped into NWI polygons and both the NWI polygons and buffered NWI linears were used to erase buffered NHDFlowlines. Further topological refinements were conducted within the the buffered linear feature layers based on intersections with known NWI features and using a hierarchy based on NWI Code Sub-system and Water Regime. For more specific information on the logical and topological processes involved in deriving this dataset visit: http://www.fws.gov/wetlands/Data/Wetlands-V2-Product-Summary.html This dataset is continually updated in various areas of the Nation with wetlands data from the USFWS, other federal, state and local agencies, tribes, non-governmental organizations and universities. To identify these updated areas review the Data Source layers on the Wetlands Mapper, Projects Mapper on the Mapper homepage, or the Wetlands Project Metadata featureclass that is included in the data downloads. The Wetlands Project Metadata feature class may also include a URL to link to a document that identifies project speciic techniques, source imagery and conventions used for that specific project. All data additions comply with the the FGDC Wetlands Mapping Standard, FGDC-STD-015-2009 and the Wetlands Classification Standard, which can be found at: http://www.fws.gov/wetlands/Data/Data-Standards.html. The National Wetlands Inventory - Version 2, Surface Waters and Wetlands Inventory was derived by retaining the wetland and deepwater polygons that compose the NWI digital wetlands spatial data layer and reintroducing any linear wetland or surface water features that were orphaned from the original NWI hard copy maps by converting them to narrow polygonal features. Additionally, the data are supplemented with hydrography data (high resolution NHD 931v210 or 931v220), buffered to become polygonal features, as a secondary source for any single-line stream features not mapped by the NWI and to complete segmented connections. Specific information on the NHD version used to derive Version 2 and where Version 2 was mapped can be found in the 'comments' field of the Wetlands_Project_Metadata feature class. Spatial information 0 Wetlands and Deepwater Habitats of the United States Wetlands and Deepwater Habitats of the United States U.S. Fish and Wildlife Service U.S. Fish and Wildlife Service Washington, D.C. National Wetlands Inventory Maps ground condition 1977-01-01 Original stable base hard copy maps of wetland and deepwater habitats were created based on USGS state and quadrangle boundaries. These maps were converted to digital files using various software packages (WAMS, ARC and others). The digital files were stored as ESRI Import/Export files corresponding to a single 1:24,000 USGS quadrangle. These digital files were imported and converted to ESRI Coverage format and checked for topological and attribute errors. All coverages were converted from a UTM map projection to an Alber's Equal Area map projection and the horizontal datum was converted from NAD27 to NAD83 were necessary. Polygons attributed as "Uplands" were removed from the dataset and polygons were merged at quadrangle boundaries where the quadrangle line divided polygons with the same attribute. The data was loaded into a seamless SDE geodatabase for the conterminous United States. These steps were conducted using both Arc Macro Language (AML) and ArcMap editing tools. All point data from the original ESRI Coverages were buffered by 11.28 meters (1/10 of an acre) and incorporated into this polygon feature class. Further data improvements included the conversion of all old wetland codes that contained 'OW' to the new code containing 'UB', 'BB' to 'US', 'FL' to 'US', and 'SB' to 'US' except for 'R4SB' codes. Old Water Regimes were also changed, 'W' to 'A', 'Y' to 'C' and 'Z' to 'H'. All polygons labeled as 'OUT', 'No Data' and 'NP' were removed from the database. 2004-01-01 0 IA_Wetlands Reference: Cowardin et al. 1979 U.S. Fish and Wildlife Service Feature Class 0 SHAPE Feature geometry. ESRI Coordinates defining the features. Shape Geometry 0 0 0 OBJECTID Internal feature number. ESRI Polygon 2-dimensional element. ESRI GIS software Sequential unique whole numbers that are automatically generated. OBJECTID OID 4 0 0 SHAPE_Length Length of feature in internal units. ESRI Positive real numbers that are automatically generated. Shape_Length Double 8 0 0 SHAPE_Area Area of feature in internal units squared. ESRI Positive real numbers that are automatically generated. Shape_Area Double 8 0 0 ACRES Area of the polygon in acres. Calculated in an Albers Equal Area projection using ESRI's geometry calculator. ACRES Double 8 0 0 ATTRIBUTE Alphanumeric code identifying the wetland classification of the polygon. http://www.fws.gov/wetlands/Data/Wetland-Codes.html The wetland classification codes are a series of letter and number codes that have been developed to adapt the national wetland classification system to map form. These alpha-numeric codes correspond to the classification nomenclature (Cowardin et al. 1979) that best describes the habitat. (for example, PFO1A). Currently there are over 7,000 code combinations in the dataset with over 14 million possible permutations of the code. For information on the wetland codes visit: http://www.fws.gov/wetlands/Data/Wetland-Codes.html. ATTRIBUTE String 15 0 0 WETLAND_TYPE General description of the wetland based on the Cowardin wetland classification. http://www.fws.gov/wetlands/Data/Mapper-Wetlands-Legend.html Lake Lake or reservoir basin. Lacustrine wetland and deepwater (L). http://www.fws.gov/wetlands/Data/Mapper-Wetlands-Legend.html Freshwater Emergent Wetland Herbaceous marsh, fen, swale and wet meadow. Palustrine emergent (PEM). http://www.fws.gov/wetlands/Data/Mapper-Wetlands-Legend.html Estuarine and Marine Wetland Vegetated and non-vegetated brackish and saltwater marsh, shrubs, beach, bar, shoal or flat. Estuarine intertidal and Marine intertidal wetland (E2, M2). http://www.fws.gov/wetlands/Data/Mapper-Wetlands-Legend.html Other Farmed wetland, saline seep and other miscellaneous wetland. Palustrine wetland (Misc. types, PUS, Pf..) http://www.fws.gov/wetlands/Data/Mapper-Wetlands-Legend.html Freshwater Pond Pond. Palustrine unconsolidated bottom, Palustrine aquatic bed (PUB, PAB). http://www.fws.gov/wetlands/Data/Mapper-Wetlands-Legend.html Estuarine and Marine Deepwater Open water estuary, bay, sound, open ocean. Estuarine and Marine subtidal water (E1, M1). http://www.fws.gov/wetlands/Data/Mapper-Wetlands-Legend.html Riverine River or stream channel. Riverine wetland and deepwater (R). http://www.fws.gov/wetlands/Data/Mapper-Wetlands-Legend.html Freshwater Forested/Shrub Wetland Forested swamp or wetland shrub bog or wetland. Palustrine forested and/or Palustrine shrub (PFO, PSS). http://www.fws.gov/wetlands/Data/Mapper-Wetlands-Legend.html WETLAND_TYPE String 50 0 0 dataset Simple FALSE 0 TRUE FALSE iVBORw0KGgoAAAANSUhEUgAAASwAAADICAYAAABS39xVAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO xAAADsQBlSsOGwAAIABJREFUeJzsvQeYJFd5LvxW6Jwmp42zWburVQ6sVigiIYIBY6KNfQ2Yy/2N sXEO/L444QD3OgAPYBtjDLbAZAwGBAiBJCShLK02593ZmdnJnbu6wn2+qj7dp2uquqt7eldSb716 Rttd8VR1nbe+/MmGYRjw8aLHXy5M4A9nTz7fw+gavC0xiH8b2Wx+1mBAguC6rdP6pZyCVCy4bNsC VEQg25ZpiEDChYKmG1BUDXJQxO/NnMDh43sxUsyb66TSMwgU7odUehayoGA0eQkW8qeQK80CEHDL tt9Gf2I7Dpz9L+w9+0285ZpPYNvIyyFLIdx98PsQjCLOLDyBOze+DofP/BOOlxLYkFqHfrmIg9P3 YGppH0b6XoqbNv8qnpr4FsrKBIZW/T5Onv5bXL/pPRhObMbx7BKemDmN163bjP9z/7vMsQhGCYYQ tt05Hz58mHhXarj62Y2sslARh4wydOShI4FA2+fLoow4t38OKlTo5mciOBECciib31NYToQMRWgo QTOP5TZuXTcgiyICEPGhwXG8DwYOHt+LtcUCtNAuaMFtAAxEF/7aJJ/qfRBlbB99BcpaAXsnvgJd GkZeWcSTp7+Ak7P78EvXfwRPTD6GtCZgRh/A0Mi7sEk28LVHXo0P/uyCSVglNYuNI69FKHop1vWe wULGwHhwEosDb0FvbDNOZJcwlU/j1SMynltcgKBnYUgDKIevhqBN+4Tlw4cdGwJhbAtG8EAhjT2R pOt22XweBUHEYCRuUsMiFPQ0IBMC0YgdlnRVvzzmMDVTTY5NCEMy/5ykQCI9Gqck14iMSO3/DI7j o5qKfYeetJYpBxDIfx+CcqLuGO+97UGs7bsGqlbErTv+Clk1i5KawZXj78ZI3zxOzP0UPz76r5Ai O7Fw9oNYO/Qz+P6xB3H9tj/DV554D07MPoSda34Re9a/Gp8/9CPE5H7MalsgZbOISEkcXprBjr5R PHL833Fsfh3yc/8OXR6GqOrY2r8NOwff5BOWDx88ZEHAPw9vxMeWpjAuh7EnUr+e7CdCRa1KSlHI kmguD0Ey/5zUQ1pGoOUadMgVQuG3zaBsSmiqoUPVteq+Ycmb1KZXCMnLersNiCSt94xsxt3ZNJ5d nMKSvhaCNg89sAbl6G0ILX3aVPW+/Piv4vVXfQyre6/ASze9C09Ofx0P7v0L3LD5N5AvHkZBVTC5 tBeXRkdRKqfxyPQhFHPPohxVTElNFCT0Robw+MR9GAyUcWr+fmzv24hZcR0GIv3oC0VxJD2DwRBw 1fo7MLfqBiTFPO5++oMYTFyCqXLKJywfPnjsCkZxQziJa0JxSII7AdAaURBg34R9DUckx+VEao3w 3Pwk7j5aU8P+4ppXQhIsUjyfINL6+U1X4ztTh/C1QzkU+v8YofTnEEp/FobUD0GdwKn5R/Hx+27H jZt/DXs2/yb0wowpcYXkGLaO3I5PP/bHuGPTG0Bm8dN5HUp5DjsGd+KxE/+CcDAFQwjhbElAsaTi iWMfxy2b34Vz4iUYiyZxrpDFTCGLjFrClWvehgemj2EoEsexwjR2b3wX+qOj+PrJAz5h+fDB4zd6 xxAUBAQFi1jSxSnkVB2j8bE64jFEIBy0tiFbE1mZCEyKyYmq+ZmIwM2W5LRc0TXMFXPV76ZLzJ03 q2gkXdlBW1rKYT3oOu4Y2Wyu+a8jT0NJvMm0Z8nFRwEhCEHPIBLpw57N78G9M2ehhe7AW6//Lfxo 8ihuHduM/3nNn+ETP7oTuXIWSnkR/fEizmVCGB57L5aUNK6J6VB0A4/s/x30pa7G8ewCxiLP4MDh z6MUew0yShq3r9qCU3Mn0FPcj8ez12EkkMWx+Qlct2YYb950lU9YPnww/HJyyPQO8sgUp/HlRz6H 9975oeqyY+k5GJKAjbE+8zsjKzKUM9tTH0ImATRT1eyQBRFhSTalqrKmO5JVGmUkV2DgJw40HFRX 8laSDe6OkS3QDAP/feQZaJHdWLvqGpw99k8oJX8B+uIn8OyZr+JVG96Ozx/6Ie458ABu2fIuGIYG zVBx5bq3Yk3vVXjmzFewWJhAWdeRTdwCfeGnmM/9CNnCOsjhLbhscAeWIGNT73qcLbwSUnkW1/f3 475jX8H42t/EwcN/iHff+GZ8du9n8XMbbsWEJkAyVJ+wfPhgZPUPg+PLlq/quQy/+rJddcs2JPuh VDx4jQzlUoWqaNtghdTcwEIddvaN4uVrtmNTcgCnZ7ImgXUaYhMClSHgrtGtiAkyvnD4cZxcDEGS RxBe+AgMqQ9fffLXkYqM4XUbbsWnH/8Ohia+iqPn7sV87hgW86exeeg203PYH9uA6dwEestxBDI/ gJC6AauHBtAffBm+d/pL+MVdL8MXz8wjuvhN3Hbp3+HYmW9gONaP8UQ/xi7539i7VMBNZCtbPIlb V4/i8amnfMLycXFjYyCMP+1fi7cmBly3kcTlE7wZAREEbtt0xajuRBV8HFZAlCAIApLBMC4bdvdQ uoVYeIEXeY9I69aRTdAF4OtHn4YSuRGiOgVDCEBFFN94+vfQG12NxdxxfGf+W9g4+FIcmv4+BuKb cWrpKeSVeQRju7F1cDX2z94Lw1CRnfoC1q35TYi5u02r2RMT30Z/8TT6R38J+dI0Hpl5DOM9m3Bw +l4cXDiBu9amcHTuGOb1PhxOz0AVe33C8nHx4uWxXnxicAPWBUINt6NwhU5gCYopSYUcVEged63Z ZtqYKL7LaQw9CC4jKDtZMa/jSnH78CaMRpP4uyfvRbHnf0HQ05CUw5jJHDT/CG+8+pPIKwt4xc4/ x22X/D5+cvST2DJwEw7M7sVTUw9Djd6MrcFT2Dz0C9AD43hyahJ3bP91/PTg72JRS+ClG5N46PCf IyCNIhgahSb24IbxN2FzCnj6zIdw1LgFr1q7C2tjPmH5uAjxsmgP3tczirtivdVl04t5DPdEHbdn sVVuBNMKKLCTwe1YRFa5kopYSK7bNgCxGiLRTJqyk1UrdjQ7diSG8IGr7sLH9z+A6byAwsAHYAhB ROb+FLsGtmLX6p/FsdkHcOmq15rbR5N7cP3GHSg88R7IgR6cmLwbheQW7J/+AaYCd2Jd32swkz0M Tc1gMLEL+xemcCCdw2Ub34DeYAjnlh7BpetvwVTmAM7MfhdXrbsM3z7yNQz1+EZ3HxcR+iUZt0d7 8KmhjYiJ9eEFRFZEBkrFUE7/EaKVKPNOgYI6STqif0ntcgNPVgQiK4Ildbl7Ht3QqvHfrrKuiqXw p1e9An/x5D04lbWWC3rBNN8H5Rj6ouvMO7Zv4SQe3PcHOJYYx8n5J7CUP4nr1r8N+6buQSB0De7a 8Ark8/vxnYOfwobBPYgKZZwqj+CSod14YuI7GAmq2DK0B/c+/S6IPW+GEL0Ox07+GS7f/jkE5ZBP WD4uHnxpdBtudolcJxsTkQCTekgSYtHhTMXqFHGRdDSDIgYRbnnfECRTtaSxkN2LiIzZwGiMZC9r FuvVKph9jeLOfmvXrfjLp75nps/s2fG3CBQeNO1V/T07zPirmbkfY1VqEzYO3oitq96MHx/9F2wY fCm2XfJHeGT+HAThDGRZwS3rbsaStB2LxQXsimfx31MTCGW/ill5HOuG7kJZy+NVq7fi6tG/R0FJ 49+e/Ufcuu5On7C6BfsVK3nVRz2i6jx+ZWAH/s7BA8hA5EQTnUkxBMGmspGK5eQZbBdEViQtkfzm bIp3hgbDJBCelBihNLJZ8dfWDui8B2Z+iM291+FXd70Unz/0OCbOfgbp3KO4ddvvQBYCJomtHroL 6wauxdrURnx234+Qli5FNHkTHtn/QYwOvwdPzqsQxU2YLA3hdy+/HRGUcCZzGoKUwtJ8nxlZn4hu xvDARwEphoFgBMVwCi9dtxt9iVGfsHx0J0RDw3oU8Zm+JPbYyIrUI7ITVaPSK9IJn4OnV7bjQSpc TVlcOeTKGEhFbJaDyCBBgNgG+XiMP21wAA07Bm9FOq9gICzjHTt3Y2buCnzt8TsQDiTNaxmNpqAb Oo4tHseSXsDsuY/hfS/9Tzw2cwq3bftN/Mf+H6AkDmNDKoK985M4mZnDpb1D2Nx/qfmnjr8csk1V n8ynMRCMQghsx71nz/qE5aM78RIxj2+t2olUZJXj5FWg1RGUXTohYnCSp2jSt0tYp2ezKBkaNg2m qseSKioiVWtgoEoLbiVnDBjVEAkiVKrMYElppBqqZnI1SWw0RvqegYoEZCgw0GNuRfmMRNjeDfF0 nMn0XgyldkCMCpjILaE/Fkd/Kozbt34JomA5K4issmUFY9FxSLqBHavfiCNLs7hyYA0+8pOfx0n1 GgwkwnjJ8Ho8MHUMx9Nz2JHqBUSLrE9nFzGe7K8/uSTgdGYBmXIJb9pwpU9YProPV4slfG50F1KR Ucf1FhkJZgIz2V1YAnOrcVatYnwggZmlAvL5MpLRmkRlVIz7BBbK4FYfSzQlLMG8BvrjVT2+PA37 TuRE/9Jx+XQcr6RL2xGxb0hdXjsu867KwPpVQ5jLFhEPBRALy0gFyS4XxikljT3rX4dEJWRkMXcQ gfI5aJFBfPbwoxiJJszlZLBnWBtPmKVriuU0EmGrvE9MDkEIAa8fvwxn8xQb78NHF2HQKOIba67G aLBx0CULEbjQ5SvjqcCyAn4qjCrxkG1qxepbB21YJNERhboRRVIOQkiVoRfq5VEirqgRwA+OfgVr E70Q9TRk5TTmSxpQzpjXmFpVkyoJkhg0aTog1UpkJKVgldyj2imfsHx0F96p7sdw4KZly+21qvIU ry3KZhT7Slz+BJJevBKCNSXty8RlKhhJT828fQqX8kOk1yhMgodQ2Z7QLDzCkugag9TTYkSz7mmF Uo7NncMXjj4JsTyB64pn0NtzG15z7R+bDo5DS4ewMbUZmxtkFzihP77BJywf3YX/b/QlZt0lO4is 5lBCPywVhU0swkrDFVqRXpwIwr6MxkYTu1np5CB3Xq9khQpZWfazzslxQhmYK5VwRkpjQ6QXQ+EE 3rPjRizmRvHFR9+JeTWEdalvYnL6bmxf+3bcPHgnDs4dBMJJFJRFRII9Tc8RDfb5hOXj4gEjq/MF q6xxvY5JBm+eEL3Wb3eqGtpuueULYZ8LBSSsCcRwJmc5NCjQdN/CFHpim/AHdz6MAwsT+Lcn3o9f u+aj6I+O4N4DH8Jw724sYQN0OQhbnURX+ITlo6twJroJq21lVIgwGFEQoVDZF54QyNNmRzvBl3zc Fq8e8Wgl5oo/FklDQQ8yEV0LT5l03allFUctKxktY0fk9+LvBxnqeYJj69zGsjoWwyQ1tAgD23tH kFEy+MKh7+Hy/jG8/8YPIRIcxtncOYwP3IDhgWuteyR6z3n0CctHV6FeeqjFTLHlpDqxEsUMnY4M byQhsdpZrYAmNRGtUYkfawS6Fl5FrUapc/uxY/CUw3/m7weRGm8f83KvRsNRnMhkkIoGUYSCq0f6 MZLciD65xyTfVakRlDFkkiTFvxHR0zm8SH4+YfnoKmwLRusmpn16uUk+Fwpkm2pH3SPJJgbZnODN jP0vhL596xMJzGtF9Ms9GO3bjZnMYSDRgyPz5zAWTSFXLmFdwiqASAh6lDx9wvLRVYi6FLxjEkT4 PIUN2OFmyG/XNsXUzQgk5CtqmZuxv5PG9JWgTwpXvZmDic3QDQNFtYyBcMz8rGgqglJrFOQTlo+L Cp2svMDQCgGutGmqDBFJiKZhnexI58OA3gheqqfawban5Olrh9bhwSOfwA2b3o1FpeCZsJg67BOW Dx8uE5MZovmcQrLl2O04pK7RMsveo5ufyWPoBNrWnqPYDrSK/Yc8n3RMUg87VQqHV1tJmuOlQj58 wukaaVu7hLfv7Leweeyu6hjXDe62aN6hkishXVIghayqE+x8qq6b1Vh9wvLRNQjoBRwvlzBeSQch WuA9Ya2gFpBZ6yPoBjahrXQZqWFxvpUWALSDSMNOEnwXn1bAAmt5grIXCuQJ0eu1bB97Zd1LQKJc GyqPIzt7B5OhIBZzJcRiteMTWRF8wvLRNVDFMN42fQgPrL7U/N6J4EhqJPFCRgzyMkcCqZ2JFsd9 IZwRpArTC+R0ZgoZvRepYATDlZxCO3pi1kuHleBhv6JPWD5ekIhrGt569lEopftwKnEFfjx0B9QG jU0Jo4aBE+USJrUSRqVQR9JqSFppFsneSmpOMzh1jvYW9qCbEgx9brWWO1PVzjdUrYB0eRE7Bq9E LJjAuUKm5ev2CcvHCxI7M8cRnfktRIQILs39BEZgIx7o3YhyA9I6Sx5CVcEhpYTRSMiTnaYTZNQp ssIKJEIZovnXqlHcqg12YRCUohiQoqbNjeKu4pLqet3zmSJSJGXZLsUnLB8vSFCX4XLsLqjhqxCZ +3PsmPx77Et8GNNNOtwQ/m76KG5af0XDbbwGiz6fMVvNUHYgUyKraRQx7LH8MqmCsRV213FrzpEt lhEPLz82jfkM8pDzpxEN1hqBULkfCncISCL6EmHourHMjfDC/TV8tIRhpTOtqF4IiOgagrqOcuxO BHLfNdukq7Fb0WMUMe0hH/Br5QKeKOWwKRQ0OyQ7TWyvDU7tsKsuJC3Qd5IY6HOpci76Yx4umnTk G/RKkuQsoGM1i9kSbVIJ81YOIWQW+YtWYs74Tj12EFmxkAE+rYcdyz4G2oJvP0bb8PeDv9dyWKye m1RWFs1OL4EhhCEkdtQdmypn0LFKZc3MTRQrlTRqsfk+YXUNtuaz6NHLWHTxvLwYIBkGdqcXzGsJ 5B5FOfYKGELYrPOthi5HoCySCOEJ0aBWbefuprK1E8NkJwlGQjSRaHLzxmte8mjmaeQheAwwlZYR lkWKNMXpvKyJhtuxiEyselfWdrQvI3H612k/3hPKwG/HE1b9/g77SDAlLfqdEoZsNpClsRNZETRd hSTWU5RPWF2Cs6EQFsvub9IXA25Iz2NLPmd+JrKi97mozaGU+mXzgZ8PeCfjw+lZbEslO+4Zs1Qo b/t0OoShVVJlFUndamURadhjwlYaiNrq/exDyByDTmFZ5hIDJVU3SUvTSz5hdSv6yvXVG1+MGE3/ CJCv5pZIZrdh8+0fCEBr4iUkyIaB69MLeM4o49bUWsdAT/vkYh42J9CkPh/R8a2CSEf0GKnPSIht yyQpJ+8hK7lsB5NOvaIV9ZrfltRWgiYa0HTdJK7Fch5DcgyKVkbQxlA+YXUJorqGzaUcDkdqNbJf bFCVZyAZPdACm6rL+vUeFIUSotEghpRSU6M7kdW2fBZTuooTyGGHWVzFm4ft+UQnWssnK/svV1tF x6qrnaqv1apk5rQtXX+PGDTPZQiLODb7lNnb0A6fsLoImwsvbsI6NvZe9C/lIAnUIMKSeHRBx4De A8wDNybzmFOKOGs2OqjHpbkMNhVy6C9bzodcsYjnFqawqTd+XsrHeCmBTO27WKQ4qZLWess+RGMi yY6kIarxzmpekQrHG7V5RByuo7laa5jbMNWQdbZuJQnby/1jOZLMWO8mdfHr+X3NxiB6pXyOksZQ YovjeXzC6iKcCXqt2/jCxJORPmi6jN25WkChgjIWxDTCRgihfBmDkrCMsMZKRVybXrDkCqMMqXwM gdx/Ye+ZD+LW3vWuE44UJaqf3kx6cIJbHXgnWxFty5MKk+Z44/UwV3OzlbiuaNMpTN2sA+a42LZE YF7Sd7zGqhHo+ETQPBE6SVJOvwURnaaLUDQF//rw6/DaSz+IeHgYiqojKNeOQeP2CauLsDcWx4sZ MU3DkXAMV+Uy1ce6IBTNCZ8WckiEIxgv5PF0rN6YPlhWqjQRyv4nAtn/Nj8vLTyDtHo9BuSQoy1o JZYpL3vHKlexsjOtDIIDiRJxUdhDsEVJqxFYKEe7FSsKJRVz2VlE+y5FIrTJHDGR1VJOQSpW6ZoD 2SesbsFT8RT0/AJezGR158IMwroGSdMgSxL6E0lML1rXNJjqQUgMYFwvY7yYN6Wssihg95JlsyIE 8t+FVNpryjSBQBK3b78MveWQWbK3WInt6YvX28BIyuCrE9Bnpq6RmmI3xjOpwQsF8TFN7PjNUlCc SjifDyQRqMZdsTLSdrDmrnTNnXQ8uJXXoSYTr9/4PiTDtRcvIysGn7C6BDcEgng4HMZThcKy9BWK b/LiYXs+samYQ1/F/kRQNc0kq0QkgrE+agdlYGZpCZlsBrcBmAsEq/YqQU9bMpOWhVg+YS7besl7 sXvgZlPdo0DEtKYgFQ81NQ4zonCOJbJiq7xKEmyS88doRlhOJZy9Qm+xXRmL2WKdoO2SIJH5+SZO htliEet7KRHaUo1PIYc1iC0L8vUJq0vwhqENuKlcwDeXzuAjM5N4phJAGtZ13DV/Dt/vHUCmxeqO FyrJOa6p2JazpCQ7MoUCJhfmIIkiCpVofrn0GPpRC38I5L4DCBKCuW+Y38fWvAov3/JbVdsUSVaD qQhX4b19rKT4HkFoM9XFS3v5LNSWwxHYMUlFTLXhQewUmG2LORHGEKmzs9Vq8vvoCsykD2AkeQne ObAFd6XGsE9ZxJ9Mn0FkYRabQiHk0gv4UaofJbFeOuiTJVN3mdeen6DThKbilXPTDbdJ5/PWtpEI ivRBL5pSlSFatiz6N5T+rCljRKKr8fKrP4ZVUs8ydacTtiSyv3gNCCWSIbWLt+94kYCctnGSgBjY xLaTlVtsFN2XMreOJD4iKwp7oGM4SYbNwLygDMzAz5Kry5WXhZuES6r6KbWAQSlgXixdzwIU08vK 7p1VasZHV2A4eUn186pA3Px7yYZhfDtxBGUZWNz7DHbl0ng0Ud+w8qpIFN/L1LxyXkGPkLPzvTUE DJYt1hyGoZvpG1r0Rhhcj3lLJaR1Il6657PYFlzddJIT6MzGeayBTse1e/G8nIuIxi7J0b0WXbrm uHn73MhBsK1j97+dGK1m3kpGfs2aTOSVM/hqOYhf7xl1TPNhhnqfsLoQ7Icmlei1g1txVsliengW yvQkToSjmAlYD+bV0SiOtZE03auWcXVmCSfCkRXHfZ0KRfDjnn7ctDjXdFtl9m6E+95c/U4qoqQc QjD7VfP7FZf/CW7o3ePZxkS5dytV8VpFK9VAeZvU+SJVvRKbxaQp+kwExpwOXjv9rLRWPeGx0nKz gBU7ppn3jM7hE1aXgA8g5CcsfV4XTOJ/bdmNx0bO4vJzp/DD+VnzoczG1uKxUsnzOch4f/P8cxhX LFVsXTGPvChhIuStlIkbZgNBszgfpdUwCHoWwexXUI7eAl1eA1GdMCUpdfG/UY7ejr54AoVSHqH0 Z8zte/uuwE1b3md2WvZCAFbA5oUlK0K73jZSL4vQTBWJ5ELWOcdJPaXYMgpG9QKS2PjnhUleVF2C wY2s+JrurV7XnKbicLlgfu4RZayWyNiuLCMr/qj0m/mE1eVgbz566PYk1+C6xCqs7TmEH06fwacK pkXIM3q0MsZL4Tr97faFGXxmZM2KxkheTZ28mHWENQ9DjEIu/BRqRIAur4ZYPg6p9BzUyB4oKpGk ARhFRHtvxBtv+WLVq+QGq7xJ5+xZ7YBsOSGP5+bHy+fdCWYwqDV1l0xjeb3tyitZWceql96Y3Yte fkSQjdRmniybuTNyJRWxUG37Z5Uc7pjYZ36+PpzAPasvAZaWlnlMiQjZi9j3El4EYFIEky4CgohX D2zBS3pXITHxXXxxXsKR2LCnYwW1PER9AXLhYSjx15jLAoaBYcrxC3orSeyErCTjZChiphZVIQSh y6OQlKMIz/8NCoN/g1LqnYjM/zXkwk+gBO4AhACUxOtx7c5bsVUebomCnq8gD5bX1yos47WwbHm4 wzmQPDHRS66s6ZgtFDEQDzdUtZveT+5lxFCuLLsqRHLj8iMQQdmX+oR1kYD/6enBG5bi+KO1r8Va 9Xv4c0XFhIeQh4QmQ5eGUY69DIKegyFaEs2OXGZFhEX4UU8/1hfzJgESdGnE/FPDNyAgDZrKgCGN QpdXQdCXzLZPVEdh/aprccPYXUhDaeiWn0sXIcVF6JoEWRQgS9bkO5HJYn2icYYA323ZLUGZCIVU pGbrvUDkztksUFWuBL6SN5J+Y1LkhAo3OIXeUfiAkzGe7J5Ohf6igoyAbtmPaPxs/1ZtajFb5dGb Iyncv2Yn/u/SWaR11ex2xI+F5T7y915Usj5hdQvaCfAjkf7147fg0bP78S95y57QCCXWail/L/Tg NmjipqoRvhMgW9aostympkZ2m3FWMDQUe94DQ0wgEQqhWCxjx+jV6EOw7sF2Qn8ybJbgtScW99gi qZ3gpYoCkYXbdizhl9Z7qRHPJ03b47BgklJNTTOrHCBYjUYnYgnoViE8KjVsBYY2N/Q7pdaQU4Kq gA4kw9UAUxo/GeTz0FdcXSIuivhqet78/Pt9q+rGwrQDpnYu5k+jIId9wuoWtGr0ZNsPCmH82dgl WJg6hEOqiv1Fyt1zhmlnMiEjkP0GEL0NWugyzHqos+4lgLTfpaaXIVZKxAgyDCFhSlj5xacxPHor +sLJhteeL6kIhawyvlRzKVwh3XReQTIaNEuarLT7Davsyap3Ejnwx+JbhXkJBbGTFaq5j4L52frX qAu4dLsHzUI7vKqvQiVVh0iKJDE7WbXjJeTHPKE29lYHpAiOzz7oE5YPYEwI4xOjW/Dt2SP4w0wa ZythD3aIFXVNC11qeu9gNsTsDLKShGfjCVyVqTe82iEYJTP9RgtuN9/+W6P91nKXSRnlDL08iKzs UNokLKYe0RjYJOSPw9tn2rWd8cnaAne9Xkii3ZAIZnUSbMeisVBQJ0l2OZTbbmJxjJOmv/fcnyK+ 7lfrzs2kOSJ8KdiLzWOv8AnLhwVqCnB5bBRxbQpwIawNRSvi3JD6ISn7oYUuN78vyZ15jI6Ho00J yxACptE/kL8XAxs+WlV1Wk1J6WQvwVaw0hLEXo7fiRQkApEFgXWuYeoZkWSg0rSCyIrU0nbKQZN3 8JvPeTPdAAAgAElEQVRjVsBz0BjHywY3VNfR+ej3WSwqGAiTOq+jJPmR7l2Ddhpw8iAxf0ekDx8e XYvfTs/jkLZceZENaxk1hmBkRdiey+BgNI6CLe2nVVADjbQsI6k6G6etWC0RavQGhJY+BSP3HGRc 4ViznN0PWscmMNlyytz6gK2aJrPRPFDIICqIuDacqE5aN9jbUDnBLUWGxVZ5ITHWcUZb4e9sh3Vn lnsg3Qid3WurSqsV/0fqIQV32ptTNENSFvC9hUU8WMzg/WoGx2bux5q+qxCQouZ6MvIPhcOYSO/H UHKr+Uv4hNUlWOlDzIz2rxrYgtnAMbz93FRL5ZnXFQtmI4z0ChOsn4j34ObFWcd1P+gdwJ3zM9CC OxFM3YpXbH29uZwnK0ZA7H7QOmau5g3PS8UiwuH6CUb7ZLNlvOncIfxcvB/XhSkMtTGZeLnrboTk lLrjfh69afIzOyaRilcwZdYOJ7JyImcaPyvvzCLkvUqup8tlfGRx0iSiXZvei/se/UW85dp/ATXN MbhUobFK2lnCj8PyYQc9kG9JrUNOlPF70xPIV+xWVK9qtFSCqJ5FMPMfMMRe04YlJW9EHuuwZ2kO S3IAU8GQWfaFwhweSfa2nG84HXT32q0KJ/CL27chu5hDcngPxoWE6cliKCoaSiUN8US9eug0xanB p0pSJO0eoABMxZyQvfEQ7glsR0Cs7duJtBMGuzTo/TXz/JcHchsBkRXzhBJZPTU7hy0DqaZkvNYI 4EPJtYiFZQTL83jDVR9HOFCrwc/b7Bh8wrrI4VRjmySCdyVWoyAA7z9zEGUphnAoBMkow5B6oIWu QGjpnyFG6M13ZXW/lFpGSlXMci8j0gBGi5fjRHQAz0UTZtpNn1o2jeuk2jmVugkaOq7JLLqO9X9A x6W9A5geSGC0UjeJPFnMMBsOSuafV1AsVrwSj0UxXCxO6vJQfcT8ShRde6kYfnRek77PNwxuHKyp qj01hpfZ+GUsUp7V1ppBEZsGkpXQC+dgVwYpKOI3h61E9WZggc8+YflYBnrEiATeF1+N09qX8En5 JfiNZBxnct9EOHcPBG0RYvRK9MZjmCzOQYqSZBKHqBw1Y6WC6gTKxYcRmvw0Bvp+H9vy42YeYoBa kQtWeMS/OTyol+Sy2FCwDPtOeOzsCQzFU7h6ZJ1pZKcaTrzBvZ1Ozl6wkiYWy43RwrJE6BcCadVQ Gw1/J53GKDh8J+cNk0ibKaa86mg1gRWbJqr7hOVjGZh7nqSWD+54L95SmoOQX0Roz4dQ0v4vhLCA BMUvqTMQhRhSESu0ALjF/H8J7zQfxpO5I3jy+Beg4QAOnSuiLAxDl4bMNA2ndB5qUW8H9SMURREb h0fMCbCmrx+DCGEeiplbR+dhREV/X8zNmOlHr4r2NrQ/NQu05VW3TnoU+UnOH7OTZCuI9XmZrYyJ JBlm/2uXSuk6qLYWqYrMseD0UuGP7kRWtB8fRErwCctHwwlNEsJLQsNAaBgzSwUM9kQwhxL6EQKC fa77k/SQiF0CPXkJZsMxaPkv4NTUN5Ht+QNz/Z6leXxlcLTuLcxKHvMoVYJJnzt5AK+78mZcHRwy vxvcGPmJldF0/GN6CrdGk4i7NAk1j2ums7hb2Hg7E098RF4kQRBoYtslKJL6moVYOG1jdYSpFc9r RJRZW59A+3ZUK8yNr4gEiETcwhAYYc1liuhPtFeFg/Yng/ksiuZzQi1CmNRF4RBeVWKnl4pPWBc5 mkkOfAOFnlTITBvxoiJZFTBFXL/2rSiW5nFiQx9+knoAx2eeQilwOXpH12IjRAzKMh7KWUnPBcn9 uOOpRVwRG3Hsw8dLBW9ODCIiSCYhsA45TqBraHQdRGYsjYeOzd7wzbrDeIkHc9qGpBE7ubqdJ27b 374dSViCx9LLhu3+VSWaxMqpYQBhkxwpcp9+C3p2LEm4Jrm2KlX6hHWRoxWxn01Wt7bubsQVDw1g Z+gmrEnuwKM9D2HRKOBYJIRfyJYxPXUG/YqCEYccQobeUBFv2PUOjIjOfRfZZKNxUfzU68K92Hdq AdvX9tokGEttdAM/oUlFabeyQjvgSaMVLLbQzdlJqrJM48tlHbfQCN7W5CUmjNYz1S5WCYFwS75u 9CI4Wi7ifTMnfMK6WOD0du1UUrXXNutaMInbh1+Ns4vPoFfI42DhAGbP/BPGNcszWOj//x33u3Pn bdgoUkeVesnPHmrAplg4INWRFYFJZU6JxecLrZwr4DKJ+QoPyTZSYJrFRtF6SrOhZ4O9iEgddjsX T+JeCZaeISIt8iBSzbIaydZIsVTQsKiUzGYhbvhmbt4nLB+tgTUqaAdUVYEe0ZGeS9GvFZFMppG4 5NWYW3oOp49/AcbiR1Ds+TUMpXqQkiaQ7L0METGAshjDM6U8LuPCDVigIi+ZeJGIqLkpTUyaxHbV sl1vYCeM8k6kT8dk0pPVYh7LztIs1sleoM9pfS+CpvRpxZd7t8U5hcQ0Iq1ViFZIPIAJ5HHfk7+L tUM3YjS2BZqxBslgtK6ZBf227L5uDITx0NqdPmH5cM4dcwIrZVKf+mLZeuhfUi+bqZgscVeSQrhm 6A5sjl+CA32HsG3N65Erp3H63LOYz+aRGLsZoUAQt6U2YEYD3jR1CPev3omYZEWHs0nIT0amrjil 6tTOL5g2FCe1hN/HXk7ZqsZQKx1MxyFVykzMrcYtUTQ6eVe912G3VK969cupiqjhQCYlj4TBwlR0 1UCQuiR5gBtZ8bFVrZI0u590D4m8Ng7cgL0n/xPzqZfgB4urceeYAEPcCU0CSqX7sHvslUDECn+h ax0J+WENPjh4Sfuwb2uFP1hE5aWMCZMkGCn2RNfg+ugaaLqCWS2DnrE3VaewKAZQyJUxEgni0TW7 kKiUhqG3NAoG4hFrUtkN8fS9FSmQpBfe9U7gr6VW1qV23RZhWcuIKEmCsN8DL9HxzMHPVEc6lnNd +uW1E7xLhFYNLertuPz8zV8y9mMx0IuqHanU6jcNbEhdge89+0Hsw89hTN6Px6bSKEUs1T9fGsPL xoeq94X+pd/JJywfVTR6cO2lVwTbA+/1oXeLDpLEIIZFFs9VQyhmxWoluIlhep0itSPRuVm1TP5f J/tboaQiErK8VkyiYjFcRKasoia7GksVq52LyJnVvKLP9OanY7HzmnW3uO3peEIDNdJ+32jE9mUs lCJk2oIsmYzO56aKUppR0qGxli5ScndrYNfFzkVXQw0wSJKle2BUwkNI8qLxsXtF10BSHbsX9Fuc LqahSpb6eUhNY4OcwK5Lfg/TxSgW5h7B/LkjKCUttd+QhvHdyaN4+aqt1TASkix9wupytNJBpRF4 mw8/gZuRFb9tJ0FqBVUHoH8ZMTGVkFXfPHx2CZvHarlpBCIrgl1ldHMcECE1cihQUwm7Yd2LbMe/ AHg1y66Os/vn5AkMu0g3NK1bzX10i4maRwljFesWCwa13w93x4yE07lF3D95FLv6VuHLZ2bwTaOE aVnH6aCKX+7rxc9Il+OatTK+dvQESslfMAtDKqlfhqAv4Mipj+FY5P24ss9SCykdyyesLsdK0kp4 8CqXnYIaeR87TVe8+ie42NtYy/axMe89E51IiUisGeG263HkCYUnC94uZdkULYmNjU45T+lHTuWY ifSHKzmb7XqdP3XkEUwsLSCqx6AvilgXkbE/bJXjvlMLYL54Dg/94G1YjL0bemADpNAhCNo8tOBW nCxvwaJSRFnXEBClSmiKj65GJzLVZlHCAEW2V9BKvFCnG4ASWVlGfstHSJHToouXMF9QEYvIKKs6 AnLjSb7k0MSClQN2Ck+g5XRWRi6Nmo2ynEFeheN/F/4zSyImsmR1p3gVM8hdo1tT1laM4YwABQfH SKNrYmTFq4DsO6stT6S8KdqPHYlhPDM3i8fDacSNQRiygF+P96KUXcKR2XuRlW80yYqgBbdBKj5l EhbhwaljeOmwtc5Pfu4i0NuHHh23t54TbXiRIGgS82R1PsAi0r2SK01IGjdNNOad5MG+DUas1BJZ spYUFBWRYPNHnnfpswlrd/PTOXnyaBSvxkiFz41zA010krKIINl+bhKV7LK8Wewcr/46HZvdTy8x ePZ7z74zCfIN66/Ahx+/DzOlJdxgrEKJuvAIOtaem8SCMYGlxQMox15e3V8LbDH/GE6l55FVSugJ Rczx+ITVJWgWj+O03lOwZ4crXF4I2K8rX9Ksmksy2bwsCYC/olJZQ4orC03ERB12qGY8v6x6PKht dSni1UC3kAQiYHrpMMnqfNz7SJOxW/eo3lNKRNpO4PG9p4/gdHkOCSOGSWEK+d41eH2kF0UpjKcO fABq+BUwBPdgUcLfHvgx/uSyO83PPmH5cASLsCaVgQILOwW3+Kh2IHhUf4isCFnoiFe25yVLmVXr 48CTlRP5k8RF/9L1kJzKjOhMYuEj1GkZreMlIjtZMTWQiLDWbaf1e7XkEMPFpFg6npvdj42ZSMmy qdWfm9Rtuma78Z+pq/xY2XGOzs/j+2cPmv0RgxQ3JmpYFdMwnzmHucV/whIuhSENNr2ms0sL+NH0 Udw4vMEnrIsZlPXP20d4MHsOySReU2+8wGtJYCdMLuQhCQL6EqH6SngusI/7kWIGd0R7Kt9qU5IZ 8p0Sq91glU7RcRoFrEHUJBmW3EtoVRqhRrDU0MGJ0FupeBp0VR+bkx8bM4VO2BOsifAaNap1Os7X J57GaCKBmD6EiBHC1tQIpqRZLKjP4fTEY1D7/wjB9BegJN/S9Jg/nj2Bm4Y3+oR1McP+UPJgsS+E TpHVSqWu0d4o9ErdFC9BqiRJsKajk6qCeCXw1Nq/BkZSjcjKCmatRb8LFZsUkVW1FVXF2EzguyO7 EQ5TTylcwFIDna+pE+WZFY/exWrpIBtUl/0N3cCXjj2FBaVgrk2FLPVusVTA/oXp6nYBScJcNANJ F3HyxHeqeaNGYMzsgqQFt8CQ3MsVzWUz+Mbkfp+wfFhgE5uBdWhhHX87BbtdphW1J1dSUVY19MRC 5sjYuPgIdLeJvloOmX88+A7K/LicbEfMc8YkUyI3RviyLZ7KTjDhynq7BEdjnkKhWu7ZDXyaUDO4 kZsXsqLrdssQcFMjNUPH3oVJTBcydcv74vHq5/5kEiVFwcTSHDD/cZSENdV1dKd1eRCGmHQ8vmDk EZ7/MPJ9v4cT+UWfsHy4g6YIT1Vkq2GRz26Tolnc10osWLGQjO9qWWxTgRG55kXjx0eqmdcYIZ4E iIS8qjxEVMwGxp+LyMjJFsVSd+wSHG03YiMrZozvVMBvq+hUZdX5bNb8V5YkZPJ5xOQgYmEVMxhE OXJzdTs1stt6KgwNcvFBqOEbIOjpKoEZQhR6cBPkwv04PdvjE5YPC05vcJqMlmrDjMBSW94xHiv1 ekVKBr5emMP/7B9ediw22ewSIX1n4Q9kiyEDsv16ebLyMka2P5EVHZPFRJFax1RovSJx0T2zewVJ 9aJ97Wdi29S3t69JWKopWerV67KToFPit5OERioiywFl0mozsposZZAvWXXLWLXWglaG3uCRoOqn Q/EkdL2AueMfQDnxZkDgpTjWnkg2ycrcR0xCUvaa7dzMawpdbjY9WYzc7BOWD2ewSpGtFOtrFc26 qjhhdzKF3Uh5UlPJNkTSEE1g1uTTSlluDC+lVXhYAZ7LZy0jCQpKJXKa1Qpmb72SJNbZiZzSYngV jpcktSYJx05kS+Owq6P88QUPvQxp+3smD+OBU4cablc9pyCY9kZd1yEbAtIzn4Qh9lQDRKvn1uYg alPQAltN0qpeZ4WsrM/boUZuhKie9gnLhzM6Gf/TyOjcapgDteay9l2umtqTga3gy/rju52PV+0Y WfH1EVptEsqDJK4lNYcfn/gCosFdePnaa+qiyKlrstcUqlCT7dxeAivxzjaCRcqUEF7/YiNVUFFV jKcGoJcfR76wgFLqV2r7qaegy2thSP3QpH5IynMWmRkqDFuxRoISfx1EdcInrIsRXtzkpMJ0rIuL y/JmXZXtoManjLCcwKYMI5Vmx+cJTuCWsdZb9nQVO1mRqsVayNuLCfKgo/XKcfzspneYko7dxtZu vmfeIQyjkXGebU9kSePln4FmRn26tkPpc47LGbVThyPWNITIKhEMQxIymDz3AEqpt8MQY5CU/dCC l5hkxUML7jD/lcr7oAU2AcJyeyJ1XPIJy8cykMGXHulOJU63a/fi8/uoYw9NiyGuhC6pfARSW6kf HtmmaNxWRxzNscIBnwbEExCb+F4kKDYuKz2nFubgRSql85xArq2o8ZVKTWx75240jcfeTGUkJKNR zCwtVb/3RmNIZ+5DWd5k2qUC+e+gHL2jbh9BmwHEOMTycbOTOKl/9FkwlGo+YW1jUu99dDXOFc9i KDxWt8xJuuKlDfJOkRRGRNDJkAYvsM5mqTW8ITyVCi2z6/Q4XAdrMeUmibSigtoTe6tjaTPyn93j 9Yi5BuOybVopP3w+a/oz0L2nPpCNwJPVcCSB4uLXkF74KfTEG0w7lUlWhg7BIC9gJYBXTEBQJ6HL Y9VlemAccvFhx3P4hNXlUMppIDzW9IHlJQu+OkEn6lm1Eilfk1fqYVdPm0kzfNiB/dy0jAWBNpJS eKJqdv/oeLRNo2BcuseMkNzCL9jvYBXrs4oD0tHZcZUmAaBuHZTZ/aLwDQZ+rOy4bjawVsraxAIh 9PYEcfLM96Gk3g1dHq2tFETI+YdQjt0FufSYqRpKZM+il6MQs8ojCiGo4esdj+0TVheDJkQ7b+nz merM2svzkF2Igyc6O+k1m0C0nlQfpxQTkjDdbDYsat2+licWJwKmSd6IrOyERGOjMTZqJuqkuhlN XiBujTjYsZqPceW/Pt3DMwf/DOXo7fVkVUE5epu1XehqBLNfglQ6ACX+M6bK1+z16BNWF4MmWZ77 3Mp+5wuthAvw6igjCNZFr1kTTmZwt2/HVyHg1S7mtfMSUc7G4rUsjltdKbqSk8hiPeINpSM3ybLc gtfSq4TU6ErmiuxpcsdgLAG98DDS8jYzFMH5JJZKHcj/ELq8Cro0YgaHeoFPWBeBAb1V8CrhSgNF W81F5NNlnKoQsDYPzWxRfL12HvZiefYO1/x6p0oEPPilLEiUeQr5lvZuJB2EZJIVC+z00qZM5rZp JcTCKReQl3bPLTyNbz3xXrzymn/HaJK6B8rmPaHrIKnMrBMfDEMRdGQKVsVQhpFkD7b3jWByaQGq dgonl55BOfGmhuMJZr8MJfYaSOWjpvePItoJonoGumyVRJaLj0ANX2e7fh9diVYqD9jJqplxtZPg 02l4uxJNFqdEbHZtjWxY9munyUpSDhFUp1JPmD2IKizkzW7GNCLDDK2gT7ydqhmmUWyaT7hSRB2e BZ5Iy4E+DCW2YSgY5rpfS1VSJ0fD8fSc47G3xPpxeHYKo8kePHfqKSjxn13WQZHFXfEQ9EVopmTF RfXLq81gUorP0qUBiNo5k9AYfMLqUlQ7HWeOAYlLLkhZ5XY8W4xA7Goorw6SzYl5LOmPvJgUbOl1 /CSVsDpYrYCRIlOn+AasRIrMW8hyC+nPLiU5XRf98WWJiayWN/FqDoUL1mTSE0vctteSbaYOpuLj WN/vbOhGE4n2uYUpMwbrEMVbqTJQSTKXS49Dl8ZMO5adrJT4612PR2RF0AMbHcbh46KWqOxgeWov BNjDE2gi1ro8uxOjXY31Yr+xhyqwDjF881jWDozQ7F67nZO/Bh5EL/y5vBwr6NLeXmpDkqQXwEs2 vqutOKx4IIRUuITn5nLQwlfV9gldBbF8oum5pfJhaIHNnsbpE1aXgFSSlYImBj9x2Vv6fHdpcQOR htMEdgOTXuzE0Gj8pCqS9GA16bRqWNEx2Hm9nt/q+FxDq/eMzsvvY0lslmTDwg3A2ci8kHWnO+yM RJOYyqfrlgWNWYSjYzg6QxHqG001j+KpSLoiwtID6xsf1ChBC2yAYJTMfXVp2HVT6qbjE1aXwE1k D8qWMbMddNLe0+nrc1I/eZJyix+bK89DD0RNYmbqHREzLzHVCu9ZJV6sdlv8ua26607xY0w1tMbs fv+c4p1oDMy5YPV7tFAjYZj7FSrdgtjx2fjs52uFrBqRIHsRzCt5M0eQtixrGmAo2BBdwMnZgyho pMYZENVJaMEek6w8QbDUR0OQzOapy1ZXCNDcRurzCavbkYpYHhcvcKsq+XyBr03eaqgFSRdERKTW 8bmTKYkawbMgTK0hMZPUyqqC8mg0FiIhPtSB1Dw6Po2H9mNGeDJ40/2mLspm04tK4GnS4dj2lmBB G9Xx69uNkG+0D+virGoWdbKzycWfQhu6CfqxzwGmV1BAIPcdM1ewU2DeQwafsLoENDmdpIpkYkPV 4+bcpaW+YgK5r+02IKf9mLrBe/MI9u92sAKAdA6n+CQrF9AysLu99dmk4c/Fx1MJtnAKPhVJFgOe 1Wi38zciBb4yKT8Gdp3MM2dVqhCR5BpO8Ot4e5YbOWmVe8QSsPn7YMdK1EN2Rlae2rz/2gx6erfj 9KG/gRqjjjaVYoWpdyzbn3IDKd3GDql8xEp0tp/PKAJ6zjK+25KgfcLqErhJCfSgNmq3TkZ2quWk OuTveZHGWn2b89s77Wt52qSGKgpJIkSyTvsTMdgL1vFSwcTiU1jVc7n52bFGOT9BK1KP/d65jZv+ 3NRAIiACOxafKsNeMySB0biZPauZI0XyYKNjY2MJ4fwYeNjPReEtTG2lmLyCoqA/USv70iMN4Nzs N8yKoXztKqdSx05kZY6/+HiVsMiGRSk55jGEMCBVekqWnoQauqKyB9kbfXQ1eM9WI8yiuKxcrx18 ZPX5qK/EJrW98zKdl30nsKBGe0dmWmZVF62vTc5Hu8cTy13lbiDy6FQDDrfjuKXKWPYspWMR7YkW r8Pe6Zowl8kgHg5DNATkozKy0hUwZOdnhryDzQzu5dirqp8p9opyCu05hDWyIlJTfMLqJvABloLH KHV6v9N7zalTykqM8HxdqWZgUeJOk4quwek6rKDG2v5WpHhNLeTBT2BNzQNSfYE4Xv1sN5POMpIL bXk068bHjSVcIeZWpNhgXSt7S91vN67OCYaqIxEJYGr2gKORnIHqsjc9lhirfjarNdSVTnbYXng+ qtz7OG9opx0UeZsIbqpMqxOGlRf2Qm5M/SL5SWyjXDKDfUIyicyJ6PpCwx2rrkrk4taxpxnonESs vAGf1ENe4mJBqE7J1jmu0gNfN8x+H/Lmi0Ook4qZ5Eb3h35bOhYto+2t0kKWGswkXormVw0B/2P7 LTiVm8TTZ/4LUuFZILTL0QZl3pvQrpbvCWuqSuELhkSeweXPkE9YXYhWDaysASi/DzOIt2qjogfd no7i5lVjJYcbRVE7NVVoBCZteanjtdLONCstI22/L27qodMYY9wyN7sjvcCcXmL27elY9FvXbGLB Zdul8wfx7MRpTJdPI7P4KAxpxJWsVgwhBIGM7g6lkn3C6kI0IyvezU+EpDrk5lmxPd7IinWMIQgt hCC0G+fFxs8kC179pP/T+XmPqaqVUFKtvnmLhTNVo7vX83ghUybV2a+rFfsTAyu3zK6HzjFp619Y dKkA0S7435/Vr2col9P48ZGPI9P7C5g79RCUxFtxPsGrinb4hHURgakR9kk4D8XsYMyD1ACv4FtS NVOd2ulIQ+D386L68mqaIomIiL1VwmokufD1sPKZMiIJbx1qiNycbHC03CthkUrIS4a0H1PjRmB5 zRgYWRFp0zasRLNXtbpRUUWrGoZYfSFMZ4/DEHqRWXgcika5grhg4Ks3EHzCushzCWmC9jg8uK0o O8225ds82MnKq/raSJqwJqo7KN5KFKx6783AH0nTDGi6AUlsXl7G6XuraFRcj8Z1GnmTuHgCZGob /Y7MJuhF+qrVF3NuniFUtqF/V/deARH/CT24BXLpo1aFBdNA3kIkfekpcx/WbMI7bN14Wtzbx4sM fEySk6pGDyp7eFshPVKHmhEFQ6OtvNra7NtZjUDFOi+j21nY9XuxOfH3qy8ZxmK2hP5k2NN+9mBX +k73nC1nhMkKEDo1dHUCI/U1iFYj5xuhGVnVty9jJRGXj8Ppfkml5yAHn4EavgatQDOboX66jrDk 4qNNj8NXeaC29T5hdQnatWfQZGCG9VaO0Wo/wU6DdxIw+5mbtEYSh52IvXg/ZVHwRFYEFvDZCBZB sGh47+AJKoEAplBoGDPXrP48s++16jQQlaPmFVAnZreqC2az1Iq3z45S8ufrty890RLxScVHfcLq FtCEsRe6s4PZfsgeRIGBLKSA9vP6tveKRqk1rcKK1F5e9sZuyyKyOps9bBJRf7zWYdhJanQbG08O K612YD9H/f31fr8FG8mGHKL5ebTaOITuLd3dRteaUxUIYgmGGLe1mq/B9BoaSoMLqfdOlmOvdNxM LB9b1iGaYEijPmF1E5yM0U72DHow2cNOqkkzI3grhnKWzuGFrBTDwLxexogUbHgOGit/bUxKcap4 MBZvXFdJqkw2kfLiHIZIJGXVSrAIwi5JNiMJq8dNTUVl6haTrtgVNLo/RjVlSqy73lDlHvQiuMxr 2Ars56aXVbOms5lyiTaEpBxufPDK/bV6EL6cW2FF2/Hgjel1yx3IiqCRDa3x2X282OGk5vHLvDb/ 9IpW2s9/cmkKfzx/CteGEvjoyDokpYBnbxqpRkRybjFIbtLRSMqyoWi6AlFaPuGtJhUWiCTt0oob YdF1M7KypB+LpjQuKJaFKlg5h83URz7MYDkx9zXITGgltoyux3vArmDWvHKrt05t5kVt1qwwWk9W 1jqnbs6twiesiwgsbohX12hSO8UK8bFVzSYX25/2YYXw7CC1k7WoYsnNC5qKRU3DPflFPF4YwuZ4 zJV4+DHax+qkfmayJ+rUQjsCDmRFYI3XmWvfK5hUZpWXqY2FHBlMcmRjbFX1dto+BNE0wNO9tDSQ hnwAACAASURBVBOzmxHdDa2MRkm8sWoMF/SspSJWDxRwbOtlrQsimLkbSvwNgNA+7fiE1YVwc2tb Kg7VZqg9ovS2L5qVRusf+lYmK53Pilh3Vy3aSRviwZOU/do6ZStzqnhqB6tpRaVf+PWdtP+xmKpm SCBgjoFFppPEycr20LpO3hcqzGeENsCQrGoMrsTUCEIQoj5f11SCRyB/z7JW9nb4hNUl4EMS+MnN Sx9OniPLgFt/nGawl39pVgmAL1vMx/28Ot6H/8jOVsZcOx5NOKaK6RWJwe7Ro2NOpffhdOYkQnIC qciquvUSV/fK6drc1FwqqEdx8iTBuJVHZjl+/HqnBrHWdbVutGeqpRcS7EGwmoPIxtPJCHgGWew3 pV1RnYYur2nrGNRYVVL2uxKWm+2qbhxtndnHCxLT2cOIxetLeqilBUznT5oPdDC+Bvnsybr1NCn6 ei+tKhFe7VWtKB2Ci/RwRSiGA+tq5UMYvEw4Osbq5E7Ekt7SbODx2ojUWXyZqlVKD0vLY8C8mruZ ZGnvt9jo/rVaN0KFUVdqp1EUe7uQBCuvT9Cm2z6GoGdMwuNBVUvV8LXmZy+5iT5hdQnI0Bp18pCF htATGqpOmv7evrrVzH/FEpG9gmQgryVVWlGXeEmlmWfy/HSorlGJIAkmEZDVj0V+g/uXjZXG6VQ/ ygmsPyLtS1KlxhEZux5aQiTEfg+79CZWfjdKqaqNmtqfNS582AnocpOmEg1AFUQVWyiDGrqypWP4 hNUl8PKYuhEHNTVgkdr8tnZjLq9etmLjagVewicobogm6PlQfXifIF0rX1fefh/5sXolZRoznYOa 1dpfEDXJqL4Dj9M9yaJcl//JHB5EWnzpZ7fSM62ipB03OyiuBFTPahlaNMD7hNWFaEVdMzj3PT/p eHc+s5G0+vZuFNxoBx+H1Wz8zIY0gTxW2ZK2X8xg94p5U9lv4BR4INqWWA6P5QTV0eJ9ggQ9sMas uW6WMXYBeQ8pgJS63LjBrZ67dSKlGgJh7xjtE1YXgryBXqUPpaKiGA4GapaM2ygptxGWhwo2Rysx 2kRWpBb1dUCCuNBwin1iKqE9pt/6deq3d7PH2atstGLLshcQ5BEQR1Fin3P/XWlH7wwKdZALD0KN 3OC6TSN7lagvVPsT2vsU+oR1kcJeoI/c4xRBzTr8UmKxk1v/fIE1XTAqhMskA7tamq+SsSW9Pb8Z je2jUayU5U31Lp0qXJCsquiYCpawuiJ5tqISNuruHJRWQzDIS3jWbHzqBql8FIK22JCsmsHgA0wr jSnMj3raJ6yLFXYJjNV0p553nYqdagesa45bsCpJFrwXjKYlxUWRTehC0hcFwhKxkhrbSbWLwakf oht4Ug8HJZOsWgsdbY68+gQQpMh2zay84AYzEt6jUEfdoamdlxp5aV1LMAhhGE6iuRD0CaubwFzb bupgsyz+VssRewUfh9UJ8GoOkQYR64WWtezlh1nJGNZ92oxZqiwjDy5PPiwinrZxkiLZ+mZpRprN UUJg5580a2dF6lRHGgvb2k6ztIa25c/FH//Oy/4KXz/8dcc2Xu1CC25HIPftumUGxWgJzoYEspv5 hNWF4B868qgxI3WzMAClEgnvBa28wb0QFVMHLe+fVeLYi4RB+7Dt2u163AlQfXieHOylb+ywXgzu 1+flxSE1SDIfQ9S8h7wnslHYCuso7Xb8ZMBqF98oxsqpBrtgFJy9gxWvIdVub1Qe2epXWCsW6BNW F4J/G7dSHoXPo+tkHJYXsMluNSS1pD0iAYJTaAEDL02SBPl8EVYrCeKsZnq7Ne29YgGKeY5WSlI3 gh7aAr3o0r6LujUj0dge5QA1Ut+H0A5BWzDTeUgaI/iE1UVwCl5stVCc1+3PVxyWlTzMulC3NtGo goHVwRptq6Dnm0TON0rQzDgskpa9BrO6Jb3zyGsKDDNmavk6QV8ChFp4iahNm8YFiGRLo32olI9c t555/7SAWzkgAzDK0OURs6I9g09YXQh6aGni2dttMfAZ/nb7SCcL77UDu/2mVTA70koK770YoLuo zEzC5CXNaRQxbGti4QZXFVaSIJUOmOQRgIzV2jCOSxPmOkNM1Y+tQkZS6RlowW21YBWDsgbKy0IV GCRlL7TgJZVuTYJDORqL/nx0GdjD2igGhyVL8xObSI7c4Pak32bGeq+xPCuFlwqgzBC+b+FhJIz2 x9HT59wItKwsIWfLx2yEsZ5dZiK2PZfQS/sv1sjUrubqMMx1FK7AtrGDflt2LvKgum1HLwe7JGav 7U/fz+aOVb9rntp5UB33HRDLFPg5VjmZ3JByqBW9oBchqqcrRGdbr2d8wupGeOma4mRz4ata8vBU PbQyJSlOiiaiV1sSi7my6mmRV8pKMaHxsd54bL2bVOiEzb3Xnh/1LjiAvr4BT/c8ryzg+OyD2DR0 8zIvoRebF3vhMG8iAx2LxVY1eikxry9tY+Uweotcs4/N/C1yE9BC24DCovlLn6hIV2RUp7gr53Iz kuVV5GKpGoHlFdbV2DLKFXVSMI/lE5aPOsygiLWo99R4iZpnBDLksq2bMZ8nNppcJBfUwgOkBnaa xmREZHU+qha4gb9H1UYTQmdU63bV27Jpj2q9wUgrMD19xnTDhOdWwZdOtryEdP3W+H3C6kK4PZxe JC8iq2Ydj9sBK2vs1cjbCF5LAJMau9JGEu3Fs1nqXjCQgiQGL2jMGw+n626XxMmI7wY90HoFB1L7 SPJyq43FUCdt+YTVXcgqZSsJVhKqNZzaIQSaSHzr9Ubwqna5TZJmZyFrCd+WirVu9wIWW9RpR8JK y9qweLPOxqIvh9M1k0TbzjPBV4BoB5akxKuGgmXfakJYdcfQcz5hdRPIxmyqWNxzyj+YXtUCkiA6 UZLEC5pJP4yksILpTfFIAw2aNnQarIRyK63jLxSCEE2Vml4DFzKEwx6PRWqfwYVCeDqG2Ep7DR8v eISFzskR52ua2Vuje1GN2NpmkhIFmtIjbTkArP1mF59FMroGc8FavuSFRH98vO2a7QR2LYxshBbO rVULAdaaX7AehHLlM63xojKHwgPInJtD+1g+crfyM6QuOpdhtkro+OgSrPSNyVQFiow+X/FY9sna ih2n2XicKjyM9Vxm/ptGAStBu4XwEuFa0COPdhKbW4VT6g5L0yIsobysNE9ZK2IiM2FT34CsaqCg XJjqHY1qxvuEdZGCNxyz2CmmProZ570Y7e1NQM93mguPRqEUY4h4Gn8nQdKNFQL5wlILGZwyCRZy J/FXj38BmryubnlK+S4Q+hk8H6AKDnrAklR9wrpIwRuONQfCcAoA9SJx8ZHq7UolFJnt1NXYSepz 8wI6qZq0L5FWLYSilVZmOhIeE7IZKa7EC0jX5aXlvFEJX6B7QIQtuByLSVuMtFmiuFPHban4JLT4 OGRlLwT1HCTlWfQm1yKtzbUVprBSMLIi+ITVpXDqFtzKm9bJvOlV5aSJQZOIzk8TgrX3csptcyIc Iiun0Ap+OrL1/L68BGWpt9Ky8QdavDcMFC3eSt32lcCqQOHdMyc1SUWqq5dlS91xSozWQrsQKPwQ oaXPWKqhEIYWuBmh2U+h2Pe7Fd9urdqEXHgAamQPOgqzU/Ty58UnrC5Fhus43L46076EYJFDc4Jz k0Kc4sD4dBbeFuNEFPwktbvxvdwbvm0W4UIFoNI1kjGcEOfGTONhsBP/SiQ5ZhrgyZ6CQamhqS6v M6PLDSmFRHQUSmpdNUVGKj1bJamOk5VZufRwJa+wVm3Uj3S/yNBKUwiaKp00vLMJYfcStuL34lUk p/348S7lT+NURX1Z0FXck180P/9O7yr0ioGmAaV2UqAzs3I3fCkcoULu9MdI1p6L1wmib1R5QVzB b8RMA04SpyH2ILz4Dyj2vAvpUshKk6kkO+vB5j0E7aAmqjwJNQNfS8v8DJ+wugp2qcDrg03eIl4t fHpuAj+dPYVf3HwNJFHumORAhGX3EjYjDrfJ30xlJHw+M4eHSxm8JBzHX85buW9vjA+gN2S1m2ct 3t3AR4WnHexx7H5S2AAvf9nHS2TNbGde0E5pnZWCJNYTyNb1INLlYajhKxGZ/zD01Z+sJTGbFRmc vZ+NEJ7/EAqDf+UpWNRObKzCg09YXQSys7BE2ZWk1mxJDWEgEocqUH31GlpJc2E1yZu9+5sdz01S cdqPxWEx/OWA1R7qlFrC1ZGYeSyhlEchFDYnKIuid1Nd21UDqXkHBY6ye0BEfSG9k15hrxhBVUqP Fc/WbUNVE8rR2zAcjLQV1iCXHoMavAyiNmtaNqmG+0puhU9YXQSnMretqIEMETmAVXLKTITm20aR LagZwZDqRB5Gdk7eG8mqL9jH3I5Kw6QpL2rrWjlk/tnRzr3xAlae5/msK+YFVlBpDfTbqqlLkYgt YUkpQtBJjRYBlxLHXmAIMUAIQJcGTEO6pOyr8/p57lVY6XfoE1aXw4s3LOHyGLQjpdHZWDiE4CH/ rhVvnZPXbCWEw/b1Ep91oYzunYplaxc7QwN4w5ar8M97HzRtWFog3nJ3Zh5VchKaP0ty/kfQUo0I a94nrG6HsKzcyPIp7jbprRpVrU8cdjSeCNzO0Yqa6XQEN7pykuYIZV0zpYe5wjmMJ1fhqXNPY+fo FU3PzcZ/YSqyst7P3qB1eEy7kiPYMTCG52bPNiQr6kFotvWyQS4+DDV8fbXTDUlOujSK3PA/Qiwf dT1eKfWOxgMz/IqjFy2YQblRzYBQJcCwlYeEb7DADO2twilgkgiDH6uT0Z23X9lJ9v7DH8H48GvQ HxtFTzCC7xz8Gqb7rsOXn34//vctn0I0Nr5MRXQqxcITAz8ep0BbUrnythAKImgicrI3ur0ISOpk +X9uyEOtNj6l+9AOYblJ0GQSeOf23fibx+7BZN5qOiFqk5UukBz0PAL576Mcvb1uMSMrPvHZ7IZj qNCDW5p22nED1Xf3CesiBTNmNwuipAnGggxbJZ921SjWvYeHWKnJ5LWSKW1LJMJw4+ZfQ0lTEZKs 675h/C1YKJXw23v+AfsyBtboc4iFDQwEBhoa/ImMmcePjZEnLQIjLoqPssd7kTTZifpc0fM8dcnb fMu6bfiP/T913UYPboRuWI6NRtDltY4lkomsBKNoLfOidgohn7C6HXyKCi89eI2D4u1GXuxFQoeS sNsp1cxfK41bVVRMFzIoqs71zGdK5FdM4sFTJ3G6+ARev3Errl/VW5U8nM5HUhEf0NkIVpzW8tfB +SgqaIeXmvHNcN3gWsiqin879LDjerM8TIOfhLrp2BtULD+Gt+YYDD5hdSlm1QUszj/tut4w9Erp WbcnziCTAeT4GAZiVkVJmmReKhJRjBORTpLzWZJ6xSZwsyhzNymKV6Gc1Bk+4nuumMeHnvsGVsd6 EAsEIVbKFc+VS5jPZbApNQBFV6HqGs6UlkBBD58/tg9F9VHcte7t8Go7YlKV03VZKuZyx0KhSQyY UYn7cpJYRY+qX6BDUtwJJYf+wBLk4kEArUW0LyerlRct9AmrSxHWVIykdriuP5M+jrtPnUXZpbOM bhhQ1DIuW5PCL8VaS2B28gi2oh62WlXBqeOzHB7GpX1jeNmqrRiKJNAbslzzk+WsqbJllCK++OyH oWTuxe1rfxsPzqvYGklhvhBC1igh7qFxAklgjZK73WxUzdQ5mtIXqoBiM1ydGoNY3o2JRRpTFEv5 PNoFhSW0YrNygk9YXYp4aLDh+r4YMFc4jJLemBim5uZRWG0ZuL16o5zsYm61xEVXD593wiJV0E4v Jc2yM62J9yAq1yZ/IhA27TOJQAiHZx9DIPcTaPGDME69H4f6/wCTyWsQOH0Qb1y7y7VM9EqllxdT s9YNiX58+9Q+DMW3oagLdYQlQqyUA/SGRmTl1QDvE1aXgkq7MDB1xS2soRGmS9lqojFJMs2kA8tL WKtyyeDWq9BJCuGDVdsFEZJX6U/QqOuLhsj8XyFn/Bq+V7wWP7N2x/PW9v6FhJAkY20og/H+YRyd tPIxGShPoFMI5L8HJf6zTbfzCatL0SmVIlMs4NnSLC5rIrG5Gd2ZIbwVkhQ62BRisVTAQqmAVTHL nnI2t4RFpYC8qmB9z1ZM5L4DwahMPENFYOk/UB68DF8+/jReNb6zJbvRixmKiyNgsZSFqk1hb6YP u5NpfDsXrKboUDCF2iRHkgzv9Ff1FLpAC5L5QjNTeNw6QxN8wrpIQa3HZVFEycNL8uzsHHasGoCu K0CLydDnu5WVG0g60gzdNLYXNdUMaaDPR9Kz6AtFIAki5GAlmdeMMbIgatMIpT+N+07/CnalxrC5 b3BZz0EWr/V8XJlmC/iwq+jt1PpyS7uic33ywH3I6knMnv4iIksfRWrd36MAK/SjGVlZA9IgaPNA U8Laav67jKyMEgS645X0IJ+wuhTfPfpRK7teCCNuWD9zXlMwU8xZG1C/PLOTSb3L/8bRjabdgkdv mMIcrSRePA8u+nZxIjOPR2ZO4rK+VfjY4Z9gW2IIG2N9GIrETalrNLUT1HTebqsKUIpI+Dp89cgI XnfFFbg0sLxZAqvy+UJLvVFcQkLawffnjkIUo8jkywil/9X0LG/Qfwqx7204O281pEgaMRSEEspc vS4eVEtLFy0yageBwn0wpAGooavM7z5hdRHozT9XmMDRuYcwrfbip9MGSqoVqbwcJcelyUAY14ys Myf0cChuvqspqprsV2FxuV1opWR1vgiPxktS1UAojr5QFDcNb8S25CACggRZEBEUZYjKEXNbwaFU SjD9OUxI/Xj2ZB/WbopXVWxWHqeV1Bk76BjtkpV0geQ6cpL8+OQhaKqG8sIXEdSt5+iZU5/F0Jog JOEOU4JNC7mmI2bSUaMUnkbG+HLk5to+bVyLjxcoHl+cwD8+80DlG71lnQMmm4He0COh+LIa71ZL hRoWjTx6Kr3lOjWNFF0z8/14zx6PRucxbKpRn5jAxtQAwnIAl6ZGMJFbRI8QR088hGggiFByDzZv /WfEY7swUchBDm2BJMZhUKt0w4CkaziemcFENodkPGgjnO61a2VRxj/sv99M0TmZmUG0cH/d+sz0 V7B91+vx7MTSis4jKYeaEpYS/7m67z5h+WgKg+sYzGN27gnEB3abNhN+AvN2FBZxba9V5QZJEKCJ VtL1snVNGpOShMnyEGlvNahi38IUsmVLmlwo5fHKtduhVXIo71p3OUB/AD4QfA9+q2cM706NmOf5 +olnEQuE8K1Te/GZ5+7H6zZehssHVpvXcqHLxlxotftoYQFasQxFU02VTFQnzZbxFEdFtqa8dg6T R/8aidjbkdHad+7ogeZpPXb4hOWjKWh6zqGEEVsnm1Ro0DGmiI9nriUyO09y+0QkY3jEjMBfDqeE Z7c+fPTvUjGPjckBrIv3msum8mkUVBUDDmN+zeIiRmYSeGBVHqvkPjw7M431PT3IlskjpuDo0ixG Ez1YF2qcavJiRwk6fjJ1HGVNxXQ+A0lei0L/+83yxsHMF6HEXwstdDlKpccxHs6hVIpCUevtV2Pa ICal2aY1J3hVzyt8wuoikBpFnj9V71x8DB9HZW/bNZTY6mJQrlU9YAbglVRAbQd94ShkQUJU9hbT lV2awXOaBOVMDnpRRFnTqRJfFT86e8TMSXzH1sYqjBe4pd4w2NN8WpGuQtx95mPxvIypDAUPzZ+G oKPqnKHyMdG5D0AsnzC3CqXvhpIoQA2/BEfyAvriEcxlMnXHonCH8wWfsLoInSjD+8DUMTw4daxu WUCU8O6dezAQrdm1zmfysxtaJT1Kx8m7JD474bQ0hbgaNNVXIjsehmHgdHYR9y2cxE2961akFF6o 1JtUC+cgxwqNK5vNQVPK0NhLTwgiP/BBSMoBs8a7VUE0WL0nSrGMzYMjODwzVT3WpDRTla4EKoks SC0nOTtB0GZ9wuomdMJNvqQsb+lOhCUaVnssPtL9+OxPMD6wG+cDK+k8w5AIhqCzoNDKdUyrebg3 QqdZWDYL06mUGF0OmLFblFdJSOsl/PDoPlx71SqEhPZqUL2QVcG7Dz+OsCHiqZkzy9ZTbXcnZMpF pCpOkpJKwRp6XXyWYZaN6cwLzG/z5aMlXGi1zivcbFuabphBowz94RieWDoLJJeHMURn3mf+G4pe CTF7L3S9iEzoTQiIP2eGRxAyhQIS0aBZjYLU3gtZNvl842h61gz32LtQk5S84szcDPas2YyHTh81 m9fWxWR5SCL3DCHoE1Y3gQzcK4kPaobz0fnFTZJqRbpq1NI9rRQ9HUNUrYkqFx9pGMEtSCL6EDS9 kE5VRl+MUKCbRNUbiJipWI16BLolKT9x7hRGQj3Ypxs4GtZxRWbJVf4UjBKk4qNtNWD1CauLcL4J yx7dLTsEkraKZsTUiIwabWd+E4BUsGY7OVfIok9abksxt43eYH7WojugaaqpSoqhTQholOJTO3ah TLVMDaiGgZygvSAIy1hBHBzdt4eWzqBc+H/tXQeUXGd5va9P39612lUvtixZkq0I2djCTZhmCMaQ HONDEjsk50BiAoYcDuRASICUk1AOIZRDiA8x1RjbARscF1xlWbK6pVXdou07O728mvP9b2b3zU7Z viuGuT5jbXszs7Nv7vu/77/fvWn8ZvhM8cfIiD+LOSok0mm0VPmhCRxEtbAoOQsh9RqU6A9hKFex Mm82qBBWGWImTo9TwRvDRQMudcvEwZFe+GJudDQ1IRw6wcirhpdxMDTpRhkJnZj3c8977CKOAGGx DbzenRmaNWEJucPZjd4APD4vJEWe8NeikRy/O191TTR//97H8r5OfZmUoU30sNjzsUx85fhzeFf7 FqyvbrCb1Wmw/k21dwHLn1lAm4dOi4S6p0YH2Sprvpe785ExVIsSBmWlJIEaylbE3fYFYraoEFYZ YrZkReDVM5DUnyNddV/e9Zp2gx7vscloX9SPFw6+fcrRAkyxBbprJ8B5wBnDMFxboStXQ449BiF9 CIJa/Oqdhe5+EwxpNThLY1dyKf7LzLR/HIa0nn3Mm+NQvfug+j8AzuRh8dngz9zey8bGVqxx1bAm srOUdQ4PZ0WtJMFYmdFqTR1PqYc392taGufDo/jP+It4YOtNCLg9cCsC3Mrv3ltJhYlfXDqJgCDh 6Uu5O8NzAZ0noqZiX2MbngCHK+OFx8JIhDoXUDn6u/cqV1AUsigypfhcVFi6ew/E1MFpC4y42Ay/ qwnRFHlIAfHGr9lhAo6JeidIaMh5b4OY+D+I6aPMKUvzvQ1C+jjk2CM5j5f2v5+tlEjzY0qd0Dw3 wjPytzZhKRtgZVaAlNIixx4Fr3YhVfvxiceikE5D3mz/PmmNdgnA83zJZv1cJRjUG/vGiRfw6e23 LtQm2JLjbHocI5EQiz2bK+giwla4/CThjwz3wXR5EBcEeDNGisWgWDJ0zgD9Nx3k6EMVwio3WcN8 vJvYCmmasuCFni50uOonCEvQuqG7duQdxZnjmf6EPfyqed8O3XMLBPUYIxW6yqr+9zF9D2l0xPRh yLFfIF31Z4ysCLzWB9V/F0yxjUVFgfdM6HlU722A751THnSyJAtDYztfO+tzRQzkNjofuAQR2+pW sMHfhKXjwEgP9jStQgTatF71tqt9aYV+IUyXUC3O8W9+fLQfTVU1OHHxjWl/VtDOwJDWFf5e+hh0 95snPk9rGv6qrR3/4RrFqrFh8JYFyVFWO+GGgrSlIsmVJiyy/aG5wwphlRFso7z5aINmILLkeHhc dOLaJaJVpPEuJl+E5rklJ/HX4hToys6c6CfS99BVWkw8CzVwd859mFIHuxV+HvmP6wz1pDKQ3ug6 t7DbEKTl+vNNtvasKzGGrxx6Gk1uP9YG6pmWKevOWvDYOS7FprsI8bP8m9Pr0pOOYCAWxvGhfM1V IciRHyNZ92nHjiGJRwXWfnCSVRbPXDyOK+ub8GRVLdYkE2hPJyFMIS235YLXdCMiTOf4kPHJEpoq hFVOoP7MfBqnuvuG6X/IstBQfT16Bh+B5r2Z9ZYK3pfnLROK6Gnvkk56700w5jAM6wT1vDTv7Y6n akGfpiQpBedqrJAnfdbp9NHu4/jYlhsRRBotU+Yt5wNakVHZWmrlFslcZOhnSR9WzdrvXMmVZL8R x+MXj8E9jRkjZ4xBVI+xmT+6MGVXWcXsYqaiIZHE+rY2vDYewpVJDrEpVjQuS4bG0bbKzJsYFcIq IyyEB6Y95Prj0o/jt5vuYuoQVO+7mMFawcy6WUD1vgNzhZR4kl3t5fjjrI9lW5LUMcJi6T9z3EVz vp6Firnsd98YH8TDl07gPW1XzDs2niylv3nqxTkdG0on7fgOjmNyDlrxtSg+BNMJZtkTZYPc9vPe XNeC3/Z0lb5D3g1d2co+ZH1KfQBwlIVZbVYxUGr0tiEex2U3ulwGWqeoHcb5CKRZUlCFsMoIumbC MYkyQxjMxja7GjLkTRC0C/a3LDrBJ9+oWYuRUMIeeibrW5JDGAUIaylB5YIprYLuugau0Ncg6PT8 d8AnKRBKNN0LYS5BHTnPZZ6ERcMtR4ZnVqbNBKcKfK3G48UzvaeRnmbO0g5KtS88tBkyVToyk5VW LB7D/XUt+AcOWJeMY3UyAZ9j+sCpipctCSpX+jlVCOv3HWRUp1/MKe2SNfeD1/ug0EqLkRb1nFqg efdBCX8PiXgPNjbvw6nBJ8Br3RM7c0sPk0Wdg3Oz1BXNcytSNZ9gvTKSd3b66jCWjKG9gGShGEqR FZVnVMBkS0Pyy9rW0gHNMOB1u9kYkCDYBDkOlfUUSVhKJEi3UmEZWdC8Zq3fFmcGp7ggLATIfXV3 y2r88vzxWR2ned+WcRmbHWisqffSeezgBVx0edjO7VTSIrQbzag3q3FU6mKkXQgey1UhrN97cGJe H4qEfdQUt6jRaSUhJV9iEUymtAKGvAXnzDCuddm6Kt4MLtMTp5JEZappzojYV3tLnVBODQ7JsgAA FA5JREFU0ynv9bhRI3mmLQcpReeT+x/FvvZNqFfs0NWIloRLkLC9fgX7GRlCZl5xchOhSnLh3jXX so+p7Pq3Y8/g3o1vgtflQo3DKYEef6YlqR/iohBVFuvqmvBK70lYZnrGPUYCXQTmiriaxo1Vdfi6 IMDgZKR5HtdEJiPDOoxWJmvoFYYQMH2sVMyizqxGjRmAyZns6xXCKiPUuF2QRQGqVjgQYDawhBro gr2jR0EWtCtEpGCJVAa0QJXslVd21285QMUXabtMoTnPSjeRTiHBG5CF4qe4s5l9RU0z3t15FX50 8TCua1nFVlkUYjGbwe/uaBC/6j2Ju9ddk3l+Fi1gmeODEyaV4EVgziSJZo4gc8Sz0TEEU2NMSGHv 9C0NLobH0BSoxjFvAFfFIzA5jskdCEE+xHYM01waUS43WZoIi0aeqR85DK1CWBVMD1NsBq/350gM OGEFJMGDlBnNi2JaMnAS0lV/Cl6/lFlT5a5i2qyZP581Abs/E8ymCpEAMhVDpz8/MacQBEnAp7bd kvPzUUPDb7oeRMISIcuTSUSx9EjR+6n25TtJLBTuWL8Nj5w5DIt3OBMuIfYk4tADdTjsDeCi4sb2 WJiVh0RSKahoNGvh5TwY5EfZz/stLwTwCHMxjPLjLJ2nQlhlhgaXD+3eajYjRqELTnuVucIU29nN iW6axicrY3ZTFnXouhSIJA1pbd7XXeAxoMdRrUy/W0lJOq+OdKPZ7WdlYBZux8fTgeN5RlbOHcmA IGNPx534wuHfIK3HHD9dnEhrY3MLDpkOa6sbcKD/wqQxH8FS2ahTqeDShYSqa7gjFgTv8uOYKOEN jx8rUinIlsnkDRzHIcJNvk5ui/xTefYvSaIJFcIqI7hFGZ/Y+hbma0Sn5Q/OvoaxVJwFMRC8koyk rrEoL+rbzAeG1AmLE5gaHcp2LDVKDWtnQZ7km7wNGEpGEWUOAsVp9a7V9u+ws74TZ8Ij7O1Bqvaz 4fzVEPnE05trJmj2BHDHxqvxo+OvYjnRUFWNl7tz5zkFvRuu4D8jWf8PMKfsAC4WhkLj+Gg7cJ/u x7Ak44g/MNHP6uOH0Gm0sp1D+kupnAqXpaBfHK4QVjnCMCzm6U5vJgoR2N3YyYZ6e+Mh9saj1Bda RXzv9H68NJSRLswRKS2Nre3vw8ER58ph8SGmXwevnWdWvaZ3X8mfjVoa/v3Ys0ilg4Ca7yRhCZPl G0k0SLbhFkUkhPUwi3Bbq6cKn91xW550Ibuqon+zhoIkcSAt197aVXi56hx6wnb46FJjd9saHOon T/ZcmEI9m9NkycxLRFiEU298Afc23YdvBdagy+3FVbEIlMzKj+yVm416pDl7kGmAH4FiKYjwQSis m1VB2YA8nDiLh8DZq4N1VfZJODTyc5xOrEFPbBybqpsKkJWVuc1cs0TpM4HaVRD6Hsyf6VtEkAME 6DYDrG1oQp3bxyxiIupmNq4z3W4dkT1TyDOyAWTeJh4t84bySwq6qKyhODIWK2ZCd6T8eDiJjaCo PA/ZNOHlBYQ4A+9YvwM/OvYiRh09soWEzAusDTAVtKqOpZJsvo/9fsYYLMHup9HAsik2wnQQ91Ig 6roDG4Jfxa76r2G/CvxvXRP2jo+iTtexVu+ACg1jfAgpToVqaayX5TM98FrzNc2u4LICJ3AoVKns WnUPnnr+4zjJ72HDulNBIxfUiJ2uxHLCokFk0iwZy7NqmA6k8t7nX0U6Abw63I3/7j4wo+MUUYKs Swghbgdq8AKqFTcz/yPwHI9nL5xiKyeieCIszfGaGxxnExalS1smPLyAY74AakQZ76lvwQt9dtr0 QqLB7WMq90K4ffUWPHzm9YnPs2SVRbLuc3O2e5k7LJw1d2DvhX/Cqaa/RlCUcNBfhe3RCE7jAniL Zz2tDqMFI/w4ApaXvd4aV9klLCvQSEah8RyBE7G58U3oGylc5xSbBywNHsf7X0Vb/W6c03vZ6MZ8 d5/IcsZQrsRCIJZK4YfnDuHm9o1YX92Iv9h83ZzdEP7n7Gvo9NXCr7jYAPPWqhWIp3RUeWUcvTCG U1VJfNtK4ZbgCPyZTQ4xI12gMNLaRAx1moZj1Tre2r6ZSR8WEmlDZ6M3hRTtv+o+kdton4LZOn4u BGj0i2ySU6Gj+Ojqv8SXVImJSlO8gLePDdEykJ3H1WYADWYNuzCMCWGm1aoQVhmh2JuOdvPaancB I/sX9PH65TtxnfQLDPQ8gGTd38GQ50dYC0VWWbw4eAF/0LYana5qVMtzl1zsauzAOzu2FPze9sY2 /Kz/DP41NoyYIE4QlhMNmsrEkt+UFdyquCEJAi7xIsYlCesTsXlNgIo8z0rYQhevtVUNODCQ37ta TpBLLK/1gjfsjSDfmQdxc9MH8aTiwogk46THj02JKHtNaPVqgsd5sQ8ppFmJWCGs3xMcGl24+TQn EhmPb1fo64g3fn1RHoN5a3E+prtaDiiChFdHetDqCaAvHsa1DStzxKD7GjuwefgcklQ+enMdSrMY T6WwVpbx1NhFfOjq6/Gpi13YMNqPKi+VO5OoVrwwxVwKC8UL9728ojzhgjoVN7SvX9CZxFIg8iHx 7kxX0VmyIgwHH8Y9rW/DBakTpzQdL1XVsFJ6bTKO0+JFtJskubDQbNbjkjBcIaxyR1xXWVnSHydn yIWHkiER6mWRGV+x/Lr5wOlmORtQs/0bJ1/AeGz2O5kiROiZwVzygo8ldDacOyoJ2BPuoynG3AO8 AfKbwZk07RFyiIgSm5eLiCJz3VS9VQgbBk7JbgyPD+PdkoIjZDQ4hYx43UIonav2LgaLLhh6frqz JIi4lIgwSUsh2OGm/IIJfWdKVgSaUXXCMGN45ehdeN+mp/D5jN/+y4Ea+AwDzWrK4TRP0mQaTK+g bECDuVQWOkvDLx9+ijWMySFzMZDQ7T4IOYUacn50/e8qsmSVRYJLsZtuCbhtLI3namqR4O1xHVoN nHV7UatruC1IqcdEJnZbfoil9lg47/Yg7PGz0vD7iRh2tXRgs57GyZGsJ/3s0O6zxcGFCGtnSwde H8jfXJkAJTEvVwhsEdshOfh53H3Ft/FgaJS9RoOyzAjrgnCJUZaP8zBNVoWwyhw3t23Ag2eK75CR 24Ec/RnSgT+e0/3H5XWo8zSgj+0wXn5JyF5eQkNd8RUAJeo4gymcIOI3UiqiDs9zyTLRpibx3uEB tnoalhR0phO4Ih6FlpE30OqqKmPdQm86QkiUsNKxk/d4zzl8sm4FZNPebcxCEUSsrK7Lfy4cB0WS mCg4pKZg0UudsFDlkcFbEiQXhy4SvPIcovEEW10Ww0LExs8WZEtEu5Ga583MaHEqgsMHsdN9AI83 rMW4YeCgv5q9ZnW6hs3xKIb5IJM7VAirjGDvEXLQTZPFUdE5XYqssifvXMmKEEx7sanlJoxe+CoS 9V+clQPAYkBMH4LuUN73RcbnfZ9UEmb7RFmbX+qz1GsquxE8mTfZqEyhChy2R8NsrCRrlbIxEWM3 J/YbOu5adTWe7j+DFwft1JqA7GIyjFJwCVIOIZGgstXnx3XNqzFkJnBgqPTxSwVBPT2x6qbEGzn2 EwhqYdNA04qjp/vD+Mf4R3CsqRVj7l143uPFAMdjv9/ezGlVK7OEZQVSXz8x2IUjQz2MtFb586/U Cw0a8fG6MrYrDv/25QJnRmwPr2UgTlJsP1ddh9pMmdZqNKJXKB79fi40ihPjgwg7Vl7TJVXXKG7U KB6WAZhFGiouxMawne/A65cuD7IiSIlfTxAWqeoJlIhUDIYVw+mRL0IZEbBKrEenvAZazRa0tT+A fzGDoOK5QlhlhovjIzgbtqfdnfYoiwUSm2qKBdn8LtLahYKDyLw+YKup5+GpNFMwS98CZMUZuTOB TvdMZgJoRvNi2DlLB2eGEHddDa+isARop4OpaZpIqmpOuXhrOsHEo9WKDzESn6q2cp7glmV2PGnE CC5FxpMDp/GHa7ah1uXBa8G+PCsaAqnUySQwK1x1kpVz6D2aTmIkI3DNgjNGC1pYLw2EXGcN/93g 9WEI6cMlj7JgIKUPAXRLvIShgUfxN9UbEGz/UIWwKpg/Tpz9AlxKHeJUAhQgLNaCNiN5FruLgWI7 ivZjG+CNIMwpQbOspyO48i2A2f9bEM8QzExgJmyF/KQ93SSc5EbI3u8rfeexu6kTzw+eL6ipcmqu xgrs/JHP1a6mTvy6L9cQmciWZi9ZetFygC4ErDTM7B5Tjy/ztdlANQfQFxwAgs9WCKvcQMkpSwnS SFFzmspRyhY0lC0TEV5Z2OXAZdCQtww26MtxlG+YvwrzWm7Eufm5WFBjvK22HueHBmZ8TFd4GLIk YnNtM06MFT6OmvEdvhrWWJ+Knc0dOBoZYPIVJ2jyYNnIKmOrbYNWqtRLFMHrvfO6zwphlRnmE6Q6 1xVNPPAnWIkDCI88DffoZ5Fo+kZuak4BcrDFhiQKXNznSwRCA8vkU1Ul10HgWjGeHISQPoo6bweC sdNodgdoHAAR4SrEC7gTb6ltZWZ+BFKpu2UFg8lJG1/6WrZkIw8tLZ1GwOOBR3Ex59NiIJKvd/nY wDXd9rVuxPloEClNhT+jSXION4/o+WSqQEKd4sH+gfm5b8wVU8toJyyhGXL8UYgJyqi8nnnvU5k9 HejcMeV1LC+AYiqYbiyDCmGVGapiT0KOnoLqv3PJHjPGrYA/EAVGnmYzYkr4O0hVf6QkGZUSG7LG LCexxOf5gvo/WaeCSCIryBTBGa0YDxvwjHwT3VYSHC9i1/aH4RFyy0XCUDLCtGykYh+MThJVIUQy v7WV83jFMZ6YfDMeG7mE+7Zdj28dfn5GxxLubN+BZ0amT25eLFBvsBhhaZ69ELSzkCMPQYn8oOh9 aL7bIcVsqUO66kMs/YjXBzOvogj32OftdKcKYZUf6Eqmu5fWLoROWMFhUSImX4ZCcomqe+e0gqKo ejH5Uh5hUWYiiVSZq8Q8x3SoEU0R6xRkobt3Q0zux8EL/wUj8F6o5uTmAA0923ZyNmRITA9UCn7Z xfRSFEwxW5weGmBjNV2hISQy0gWXKGEokj+psDJQi15hHJcWaYphJjCl1SW/z6unM7ZFRYTLnAJT aIPqu4P12yiujVbtnHmWrXrF5CsTZEWoEFaZYVXVKrw2Vqjlu4jgFAyJe9Fa/Rj6Q0cypLUfmvet rBy0IOWY5c0EutuOg3dCjj3KSoRkzQPzf8pmCLw5imTdp2DIV4L39do20KyCm2yOU3lGcV0Tx82A gKeTJpTCcwNn8dFte3EuOIxwZpWlS4UlGrsCa/CTizOzzVkySQkN0zhdOziZrZjEVIHBe06EKdQw t1OOQls5D0uWhkVlYChDhiZSNR+BEv4uW3BVCKuCGYM3hor6f58NDuOt7fegP/Qx9jlnJVg/y3Bt Yyeh5rl53too3bUdmvuGBdFYsYa0e+/E51M9652okt3wSy7UeH14LT4z94NGtx/DybnFdX33+Iu4 oXUt07jFNZXdpuKG+vWIT4l+X25QFBi1BHK+xgegu3cVJCw6J4TUIQj6eRjiKqi+3fY3OIH9bSjJ m3pkuouOPwrNvadCWBXMHLzWU5SwaPftle6f4tYrPoNfn/h7+2tWypY5kGxgAUhmuXa8DB4YTsWR VgFfphmeVtWJRnshzIasaGMg22fLDqyfC4+ynMSfns/XLHlEGSsba/HgSSqXLiNQGMkUrR2V91R6 UxlPPvxZ0G6yrmxjUwly7GfQam52HJUJNyFPMSbz4JCq/jD7ToWwygiUd2cm9tt/5EUQaTpjvuTo Q1D975/oUVHJF+M24o3Rw6iv24XRMfuKSj0iMfk8NM+Ns7JgvpxAFsOxZJLpoOjfmYLcSgsZ602F LIo5hEU4HR7GFbUtrB/mnGUkbKhtwiNnS4svLxfQBc70NLGcSyn2BDgzCM33Tnt3UaiGKXbATB2A xU+15TEhqEfYDCLtEtKYT8p9TYWwyglxLYyXz38HgnjTojgn5Fgo897MlnYgZwV0ykrhHWs34umx zPLeSrKIeyKu5Ri6XU7MV7Dxy56TuH/HTXih9wxzFR1KRlh5qhtGHold7tCVHTD5WjZPyM4j6lsZ UbZkUgN/lH+ApUNQz2cukhyzpRGtnRXCKieQrkdXB8BxNJqzuFYvunINu/JNtdglUnpucAgbNnwa Z7q+BDH1OlI1by5JVoJ2DobYwWKnyKyY9ZO4BTg16aTXu2FIa+bVSHZVdyAsKzBEHkIBMz2fyzUx bpNnvcxxMEuo10uBBpwfPXOEjfAczAxE39Bah+f6F94XfilAO4Cq/y62qhLUk/CYOpqNlTgn9E64 Xk2Cg8W72XlD+QG0i0ioEFaZwRIa2YlBb9aZvektu1fABpe5OaiY8xFWk+gTdkMUq2BqfXmNWCXy INL+D7DnRzt/VF7m3b/QjHTVPTDkTbMob+1+RxYciQ4pJKMgYWV/NveYyW/rkGM/hYRGPCTeieMe L94dqIHGzAA5BHQVnmlCamkAfb44MT6AVb5LuL7lzazE7I3N331iOUFklUWC53BGyPftqrH8GOdC TN5gG0JOthIqhFVmMKUWuMc+B929B+nAB6c/wNIgqsegyzQ0XPp0oKapmPgtVP97p71bCi+97qrv 41LXx9CffAGGuBK8MQI5+gM2AKtYKjTvbaynUfixBuEOfplpvKj/ZffLJk9cu7cRZk1e3gyyKzGv D7Et9IlfDQLE5LPQXdfa96lnpAtMIvEY65tQqUGhsLSFTokyWYW+Z+wz4IwQhqXbcX0n9Vc8WAce Y0YPI3dFbM0bhSmErXVtODJ2aeJzGoCeOlM4HeLGRlzZ2IZjoUEW8loOoL8D7ToXgtf0YFyIQvdc b+8uO/D/hY/XGpFlcyAAAAAASUVORK5CYII=