<?xml version="1.0" encoding="UTF-8"?><GraphPadPrismFile PrismXMLVersion="5.00">
<Created>
<OriginalVersion CreatedByProgram="GraphPad Prism" CreatedByVersion="7.0b.154" Login="mnoviski" DateTime="2016-11-14T15:16:41-08:00"></OriginalVersion>
<MostRecentVersion CreatedByProgram="GraphPad Prism" CreatedByVersion="7.0b.154" Login="mnoviski" DateTime="2017-09-18T17:47:50-07:00" MinVersion="7.0.0.0"></MostRecentVersion>
</Created>
<InfoSequence>
<Ref ID="Info0" Selected="1"></Ref>
</InfoSequence>
<Info ID="Info0">
<Title>Project info 1</Title>
<Notes>
</Notes>
<Constant><Name>Experiment Date</Name><Value>2016-11-14</Value></Constant>
<Constant><Name>Experiment ID</Name><Value></Value></Constant>
<Constant><Name>Notebook ID</Name><Value></Value></Constant>
<Constant><Name>Project</Name><Value></Value></Constant>
<Constant><Name>Experimenter</Name><Value></Value></Constant>
<Constant><Name>Protocol</Name><Value></Value></Constant>
</Info>

<TableSequence>

<Ref ID="Table0" Selected="1"></Ref>
<Ref ID="Table11"></Ref>
</TableSequence>
<Table ID="Table0" XFormat="none" TableType="OneWay" EVFormat="AsteriskAfterNumber">
<Title>GFP MD Baff MZ</Title>
<FloatingNote ID="Sticky0" Auto="0" Color="Yellow" Left="202" Top="275" Width="530" Height="325" ScrWidth="0" ScrHeight="0" ScrDPI="96">
<WebLink Flags="0" ToolTip="@help:stat_howto_ttestunpaired" URL="@help:stat_howto_ttestunpaired">
Step-by-step instructions for performing an unpaired t test
</WebLink>

<B><Font Size="11" Color="#000000" Face="Verdana">
How the data are organized
</Font></B>
<Font Size="11" Color="#000000" Face="Verdana">
<BR></BR>The two columns define two groups. Note that, unlike many statistics programs, Prism does not define groups using a grouping variable. Instead, the groups are defined by columns. 
</Font>
<B><Font Size="11" Color="#000000" Face="Verdana">
<BR></BR><BR></BR>The goals
</Font></B>
<Font Size="11" Color="#000000" Face="Verdana">
<BR></BR>- To determine if the differences between the two group means
</Font>
<Font Size="11" Color="#000000" Face="Verdana">
<BR></BR>   is greater than you&apos;d expect to see by chance.
</Font>
<B><Font Size="11" Color="#000000" Face="Verdana">
<BR></BR>- 
</Font></B>
<Font Size="11" Color="#000000" Face="Verdana">
To determine the 95% confidence interval for the difference
</Font>
<Font Size="11" Color="#000000" Face="Verdana">
<BR></BR>  between the two means.
</Font>
<B><Font Size="11" Color="#000000" Face="Verdana">
<BR></BR><BR></BR>How to perform an unpaired t test
</Font></B>
<Font Size="11" Color="#000000" Face="Verdana">
<BR></BR>Click  Analyze, choose t test from the list of column analyses, then choose an unpaired t test on the dialog. Click the link below for detailed instructions.
</Font>
</FloatingNote>

<YColumn Width="81" Decimals="0" Subcolumns="1">
<Title>IgD-/-</Title>
<Subcolumn>
<d>1758</d>
<d>1358</d>
<d>991</d>
</Subcolumn>
</YColumn>
<YColumn Width="81" Decimals="0" Subcolumns="1">
<Title>IgM-/-</Title>
<Subcolumn>
<d>1000</d>
<d>997</d>
<d>888</d>
</Subcolumn>
</YColumn>
<YColumn Width="113" Decimals="0" Subcolumns="1">
<Title>IgD-/-_BaffTg+</Title>
<Subcolumn>
<d>1671</d>
<d>1585</d>
<d>2287</d>
</Subcolumn>
</YColumn>
<YColumn Width="115" Decimals="0" Subcolumns="1">
<Title>IgM-/-_BaffTg+</Title>
<Subcolumn>
<d>765</d>
<d>659</d>
<d>571</d>
</Subcolumn>
</YColumn>
</Table>
<Table ID="Table11" XFormat="none" TableType="OneWay" EVFormat="AsteriskAfterNumber">
<Title>Kappa MD Baff MZ</Title>
<FloatingNote ID="Sticky1" Auto="0" Color="Yellow" Left="202" Top="275" Width="530" Height="325" ScrWidth="0" ScrHeight="0" ScrDPI="96">
<WebLink Flags="0" ToolTip="@help:stat_howto_ttestunpaired" URL="@help:stat_howto_ttestunpaired">
Step-by-step instructions for performing an unpaired t test
</WebLink>

<B><Font Size="11" Color="#000000" Face="Verdana">
How the data are organized
</Font></B>
<Font Size="11" Color="#000000" Face="Verdana">
<BR></BR>The two columns define two groups. Note that, unlike many statistics programs, Prism does not define groups using a grouping variable. Instead, the groups are defined by columns. 
</Font>
<B><Font Size="11" Color="#000000" Face="Verdana">
<BR></BR><BR></BR>The goals
</Font></B>
<Font Size="11" Color="#000000" Face="Verdana">
<BR></BR>- To determine if the differences between the two group means
</Font>
<Font Size="11" Color="#000000" Face="Verdana">
<BR></BR>   is greater than you&apos;d expect to see by chance.
</Font>
<B><Font Size="11" Color="#000000" Face="Verdana">
<BR></BR>- 
</Font></B>
<Font Size="11" Color="#000000" Face="Verdana">
To determine the 95% confidence interval for the difference
</Font>
<Font Size="11" Color="#000000" Face="Verdana">
<BR></BR>  between the two means.
</Font>
<B><Font Size="11" Color="#000000" Face="Verdana">
<BR></BR><BR></BR>How to perform an unpaired t test
</Font></B>
<Font Size="11" Color="#000000" Face="Verdana">
<BR></BR>Click  Analyze, choose t test from the list of column analyses, then choose an unpaired t test on the dialog. Click the link below for detailed instructions.
</Font>
</FloatingNote>

<YColumn Width="81" Decimals="0" Subcolumns="1">
<Title>IgD-/-</Title>
<Subcolumn>
<d>4700</d>
<d>5477</d>
<d>5381</d>
</Subcolumn>
</YColumn>
<YColumn Width="81" Decimals="0" Subcolumns="1">
<Title>IgM-/-</Title>
<Subcolumn>
<d>7870</d>
<d>7814</d>
<d>7636</d>
</Subcolumn>
</YColumn>
<YColumn Width="113" Decimals="0" Subcolumns="1">
<Title>IgD-/-_BaffTg+</Title>
<Subcolumn>
<d>3652</d>
<d>3652</d>
<d>4392</d>
</Subcolumn>
</YColumn>
<YColumn Width="115" Decimals="0" Subcolumns="1">
<Title>IgM-/-_BaffTg+</Title>
<Subcolumn>
<d>5009</d>
<d>3585</d>
<d>5175</d>
</Subcolumn>
</YColumn>
</Table>

<!--Analyses, graphs and layouts as compressed binary. Don&apos;t edit this part of the file.-->

<Template dt:dt="bin.base64" xmlns:dt="urn:schemas-microsoft-com:datatypes">eNrsXQlgU0X6n5ekzdnScsnt4+gFNM19IEdKy1EtWLAgxwqENsVKm9Y0hdaLoqIo
isDqLqBIuRQWEXcVvBAri7vqiuCNxwrKrqJL/4LrgYvS/8w78iZpXpO2CW3qfDB9
782bN/PN8Wbm/X4zXwpyJk6cND3bRNVpAABrqDWUBLpiMEuyBQyDPgckwE/4awq6
jdBJpNDVFaQBMIHzLYEuDrrvKTakHDoYCDRBQdcognjuyMeERA2IECFChAgRIrEs
Em7Ml3HjvwI6JSkWIkSIECFCJOZESoqACBEiRIgQIUKECBEiRIgQIUKECBEiRIgQ
abdIwXj4d7oUgHyZcOwL/UZwzgqaXwuCltslURSQd1Mw1+XS+jgTiPddpclMQNFN
xoWOY9fhycD9A8JT7xFwrv/AvezyvW9UFYpieCYHk5nryUBY1tdeSYT//KX/udHj
asrL6CUuT1VphXvMEL1WN4R2uYsqikvdi8YMmVE4MdM2ZNxY1ejBuVfnFM4umEBX
lpVWeemCGePz83LoIZlZWdmVlWWurKzcwly6ID/vmkIaxpGVNWHqEHrI9V5v5ais
rKVLl2qdKJS2qKIcBazKKvBUVLo83tp8GFkmfEBb7C0eApNhY/dTB/oWlxZ5x6qU
oxe7asfCKLjIKj2lbq+2wLnINbHCU+70atmDF2pegG65PKOz0BPwQS6C4DF4S4sW
u7zaIo/L6a3wPaMcXeWFtxdhwW+oWMiGHZ3F3QsRZanXVZ7t8ThrhUid6BKdCUop
254xpaDmfL3BNt+gt823WOebrLiGLetY5XV6XRPLnIvwOFEii1yesbrRWfwpo3KW
ryCz+Iz4/EJlomDK5ApP6U0Vbq+zbLqrqqvUjUi20JNQ6bKxVsPoLOakM9XE1Z5S
F1TYC1+yrlMPQTKFl6DevwQ7S1VcU+Qsg8XUdaohIEO+F0HfCd+DmXAMKi3qYv1R
kEx17t6IV7jLvQoiGetsr0RV9cJCNp1KJ5yVzS91l1TM58ssWF0UTCkoyC1AYXMq
il1TneUuoah85dOaWgu32lpZb3jF4TUXMheYRvkuLzPhwudTra4r0crCagurLrwO
fZoWepzuqjJmeLuG0YVRPaYLP4wsdbqaKCytWOSM8aIXyQOmj9uZWdYJirvF3jW7
+IbqKq+rGHlOdxV5Y7Eu2pbBgFhbKOWWb7FTAotRazGbdGaD1WgyWs34mMSHMdta
CINVX6dtIbCpd/EmEpjD5m2EqbJMvUmrtxrsVqPFaLDpDUGrmw1k1Ol0RqPZYod/
7MGCWa1Wrc1g1RksRotVbxNpOvaWAnWytgMLMQ9OfmDBxnQP37qMxUK3L+g/w+3s
kv1+WDkkHX+r20iX6vnDyyLp+tvReiori7tu9y+WuU72lSWSUnZB3kyWGWoG9+h0
WoMuXIDHW1vpagEwCii0wsDKahlMEdVVTNVwNRVXlJ8PBmrKKzo6i6HVxqri69jd
xHKGD2UFalnsdDuBOB/qL7gVEzYelp7M9pQ6y0B74pEwcbU2njUYOxrvuJXZJ43s
o9zGnGkGAPAgUAg8bhN7RfldSfyupH5XfHyrmLPTTQA8BN6Ts3uxkX/btSVChAgR
IkSIdLSggflhwK/h+htYIiH7tIl0hEgcdmaeLgFKMBger3WVFV2fVkV7aa+ryktX
lNCTJhbQU3Lp8c6SEnrKHKACdMC0Ek1M+YkrIBNSpkhGgSR4SAZNzHT/Alc6z4EB
IPiLnsy5K3wfDEhGg5HwrwqwhhmbwpAxoB9TmZTfglShnuqZNZ4S3wdQ85rERRpQ
e4H3v/Hpx3dquAEppHM88DckNY4pAb/0ZT39WpGDKblC58LqMqeH9riqqsu8Vdhz
PlEIH1YS5jkmtuABJeEGlIYbUBZuwLhwA8aHG1AebkBFuAGVzQNOBLlQcwm8+VUC
6SWJEOlKc09pmMNJODIf9oXzma4+nNDfLuusR75TnM8NX/O5a65fXtZW9x4Tywbf
bP8x0BMbehWcQ3cfB9VY54yOF2EE3eGxgF7iLKt20enstCyj3Y2grkfFi4cnrRmH
sn6xCXQXkqYCkvbSHrRmCkQs6SH9pv0na2+yg02awpKWBCQ9na66sdrpcRWDCCU9
e+CSbJf+P1yuJVjS0oCkc0tLSlwel7vIRS90eZe6XG663OV0V7U56T8hudfTwCYt
xZKWBSSdX7HU5aGLKtwlpcWMAmWl5aVe0Pakp173v4dc393GJS3Dko4LSHpGZWVk
k+5zcvGvB1fN4pKOw5KOD0h6ItfMIpb0F2ObchbfI+GaWTyWtFzs5ZrINbP2Jm07
/HBJ9lWnuGYm776LmSbjn0hI+KTxfpmX8J6QtPoJaaufIFoRrYhWRCuiFdGKaEW0
IloRrX4rWtk5bkQaOW4EgAgRfYHfGDHJjUgjwo3Et5Ib8aO/RLkREIIbkYS4T7gR
wo0QIUKEcCOEG4kwNyKNJjdS4yjsk7fvdRFuRBpNbuTtXO/64trz44JzI9JociPV
hx798tCjH4pwI9JociNPIG7km1sdwbkRaTS5Ecfedds2LqwR4Uak0eRGUqlRFy8k
POQIzo1Io8mNfH0DfWZpt7PjgnMj0mhyI9Pf9tz7wNlvCTdCtCJaEa2IVkQrohXR
imhFtCJaxSA3IuuE3EjgN0ZMciOyiHAj8nZwI1SbuZFQ+0oIN0K4ESJEiBBuhHAj
EeZGZNHkRs6/2+svOsMbItyILJrcSKqnduy5+F9FuBFZNLkRa/q/JbnLTolwI7Jo
ciMzZ0CprhXZNyKLJjey8mKN5vjzD4pwI7JociPeq+137/tlmQg3IosmN5K2beKv
a5PHi+wbkUWTG3E820f1+rrdhBshWhGtiFZEK6IV0YpoRbQiWhGtYpAbkXdCbiTw
GyMmuRF5RLgRTSu5EbxRiNvUCrUvREq4EcKNEG6ECBHCjRBu5NJyI/JociOgyP6M
9O2HRbgReTS5EcmR/FrlwZ4iNrXk0eRGpP2ovLdXNIpwI/JociO70b6Rm9aJcCPy
aHIjsi+mX3eBrhfhRuTR5EZGTzh4w01HPCI2teTR5EYmfdg4JqlHlgg3Io8mN0J/
98u0Ez8cJNwI0YpoRbQiWhGtiFZEK6IV0YpoFYPciAIowdCg3MhVzspKZwexI4Ff
GTHJjijayI6o/diRhFayI6lY3Gmi7EhqCPYjjbAjhB0h7AgRIoQdIezIpWVHFFFl
RyY/vWns60tE2BFFNNmR3HH3n7qicJgIO6KIJjvi/Ob8iSuv+laEHVGAaP/iiGW7
iFUtRTTZkezl8if637jBEZwdUUSTHXnmwWnL/v7MbpGdI4posiNgV31ytipdhB1R
RJMdWT/zk29qd/2VsCNEK6IV0YpoRbQiWhGtiFZEK6JVDLIjyk7JjgR+ZcQkO6KM
CDuS2Ep2JB2LO0OUHUkPwX5kEHaEsCOEHSFChLAjhB25tOyIMprsiGPbyvJ7Dr8r
wo4oo8mOpK7b/OFle/8rYldLGU12ZN1dnl7mxvdF2BFlNNkRpvbXrxBhR5TRZEdK
nrzt+Nxda0X2jiijyY6Yrx13Zk/9NhF2RBlNduSWKbPLq19KEmFHlNFkR+g3P0p4
ee9xwo4QrYhWRCuiFdGKaEW0IloRrYhWMcWOKOok3Ld4N/g3qBWt01LWBeLkEWVC
Ar8oYoUGgcWgBjoJW3Uazqm4UlnPuWmcuxG6KuyauM7harhv5mlBaljBs0iwgukt
ct8rKmfqUw4OwRDB/PBXmgp4Y9Aruhrkc740FkqFvdYS7gs+PkjnEPgudmNaXh/M
l88buuuoRLuP+gRApMiXf569S2Gu+V2c0+LvImHv8r9pG3i3W93ogHT5knBx5/xq
ZVWQkmK7JlSueYtyM7MyQRKI9934L0OkIZWSOIfkAWACAimF/3hOPJa9QEGJr+Gy
183BKk1FQOkpl1ppSbuU7uYr6floEChcNKJdyie2UnlpBJSfcumVv4crdQECV3AD
a1zrDFa2NNSKqSHWHXS1Thrlq/lQq+FajZti3e1kOOvUrqAVQy0uacz8lmrRj39Z
Awc6dqhFvoOwCDTY+8LOOiMy1C7Dh9q+/IC4DHue6en6YCnxREUJdMP4LpxiNcTz
05dzSla/JGCg8ERKmEAL8gKpUJTcSdgzUlzYujjHZN5/DQWahe8F/9wJBlyJznNQ
eFkS8+zN0K2UrGTO5fDPSqonE+YodDbZacZ/B5rKxz3XLM4XoDsed4wJ8wR0O+Mb
fOfXy9lzFCZdwYZ5Grrzitd85w3K15rF+QrSR3WcCYOozIbeBXm8zmsuE3RO0gg6
58f3vPK/oJ7yHwf6hqh2IHI/1HV7n/8tp5cMDsDL5yhmpVSZi852O8tqb3IV51SU
VZe76dwlVVrudPwMd6Wz1OMq5kfXpaXe62l+xC2q8HhcRd7SCjdN8zyY76Squrzc
6aml6WtKF7lLS0qLnG5vWS1dzHGjXjq9gB5N67Q6c8Y4mr7a7cqkKzy0d2lFptdZ
WgZT5CIaxyaXySUGb9DekXRxyeSKpfTC0kV0KRz5r3f54i2CD9BTXE43/Wf6mglT
0HSgiMuLiH8uTYsRtjRtN6fglGap2+vyQLVo2kcv0+kur5O/yGBZQNpbAZ8qr4Re
MBOeUid8FkY2cSSdO9EN/5QUt7PA3ic1GOM1KMyN+2CT8Oco4Rs1WI+gwebG68D1
vv41kfurAL3hlK0v9408GA7jk+GQNg2+8QtBCrgbDATbwWXMvRdBP/A+TLsRujiq
H7ic6gtsFBvbNKo/KKMSQC2lBLdTyeD3VArYD2RQoV6YLomcnonc9AJNFXsDYQVn
X84/ntGEnVKgicdkjkFXcEN7Mne9ELoUbuJ9N3QDAbuOdTt0lwHWxseL0PXj0nyf
Kzs0aWjkztGIE0exYVC8l1OsHgjzsnHnPZkcAtAfsPkpo9i4ke61FKsLSu92io0D
xft7itUNtTj+g6qvyDRjKBC26kZomlHCTDMW+KYZQM5OM7TIX9owuaVBA4WplzVM
5pcF1/Wqm+SLR83GcyV0ZyU0E78XuteouryW4kRfApMla3zhj0vqfFOFp+ILfP4N
UtZfy4w6LeuJmsJJhc4Xnu7B6oyqrgG2Y/7ckQ4md9w0A1ziYT+W00sGj8O/25ph
3/44AhyqWA/YL+rN9uGzXVWFviHEO8akNRn0cLAYY9SazGa7Ra+1wv7faNHaTCNp
9xijxWKG12aLVmdjrjMNdgsTxGLV6jNNdrNWj/ryTLtNq7fp4FN2m0Fr1FtH0gb4
X6e16Ax2d9XUivdjSdngg8c20L7BIxEMgJ335RiskwLPR8DQI6F/FuyY7T5oB37z
wLdxLrxeDDvPGnhciT23GvY0G+D1ZpjaFuj2o48k0F1k8EjkBo0B2OBxORCW/6cA
YT/8CCAsMhzJhUcDRhZ3RD2cnesTEhkt2XTRIDEX6y8WcwNKMgdRJ3HhVnJp9WJy
wcbXm4GfWH80MGzmdEdlvwWDBVsCq+IjB1Z1haJqT1cTHlh1MwGEuixYtTUIWLU1
CFjVLyhY1a8DwKr+omBVPywlKffmoOleahCwqh8BqwhYRcAqP6gjR4A6srsQ1JEt
4p/T9cAqUoMxBFb1I2BVTIJV/UWmGSlBwKp2TjMiBVYxwI8KA37UoBlYhapkjVTX
IliFhn6am66g853cOZoqLODAKqSzTr5AAJ+SQoNVlRhY5egu6Aynbr7zulQCVsUK
WFUP/z4SBP/JDcR/chn8x6DT2RAWg+M/eq3ZiOAfk95oscOe32CAPgzSo7eZbJyH
nvEwWW0sOKQ3aO2ZRjuMEHXjenhDp4VP6/Vanc7EAz92u03PolSxoGXw4eKRKMFT
EpAJHQtPqX33cmGNzoCuBHaXVfDZ5dhzd8Nw6+D1evjcxojDU/xG1EzuPBjmoubi
zeW6fjQszODOk7j+iMdcqriwSL/lGOZyN4a5rMMwl/UY5rIxTHhK3jHwVGctqujD
U2QtFVlLNSAoPDWgA+CpgaLw1AAsJf53Cku4/AfCUwMIPEXgKQJP+YEb439b4Mb4
rgdPkRqMIXhqAIGnYhKeGigyzUgLAk+1c5qB4JoGioVrmCqKw+ApSRjwlBSDpxQY
PKUU4KlKbC3VyhBrqdDQr+CmK+irYA23rgrp74hf4DunMXgKhAFPLcDXUmHwVEMK
pjOBp2IGnkJLKR4WWZ7kB/iYrM0BH5vehBAfg1YPJRD1ab5OKdOks2qNTCCT1pyp
NxosqBM362FMaBWS3QjD6n24j85s5NZQxYCSwQeLh9sJTsXDrlAKuzoBZBoIz4fC
J1KgS4MdsR5bO5UN63M6fAOL4ZMe6Oqw5+6CHfVaeP1H+NyGkOBUPDdI9MQGi8sw
xGUghrgMxRCXFAxxScMQFz22ICgbQ1ymczqge8XcfXTPgyEudRjicheGuKzFEJc/
YojLhjDBKTXUGKUlYk04FBgV60XTTjCK7KH/Te2h51sNCn8I/h1CKYL64UBU6D30
EuydACC6e+hTQUt76NNAS3vo00FLe+gzgNge+lQsDyyAGwN76NMioPQl30Of3i6l
O3gPfUYElO9Ue+g1ZA/9pd1DT5Ylk2XJw4PyPsM7gPcZIcr7DMdSknPalHCAUyDv
M5zwPoT3IbyP2A7snC7EGuT8JvfQkxrs7LzPcML7xCTvM0JkmjEpCO/TzmmG2LJk
5H+WWpAXkveJw/bQ98X20CcIe+grpQLvo5CE5n346QoKnyQV9tDTcnaqUI50k1f6
eJyGMPbQO5QC73MS20PvyBB4n4YMwvvECu+zh1k9Fsa2dBwVgf2jwWoI3J5u1upt
FpZiMZgtzRb7NtudrtfbWH7FptVnGnQWE7M53ajTIYLFbjBY9Gat2Uew6A06jgWK
OZWDDyS7IrRgOdH3RCbsyI0wtAn6W2EnnY1xQuwAkwQ8sCNdBjvk1Rg09CDsdTbD
6x0wtcfbvGA5ketkMzHiwwiETeImIGwSt2LERzZGfEzDiI+FWN/hwVbhLuPudwfs
xnCe+HgQIz42Y8THDoz4eDxMTiihtb852doly7FWWNFfskygKwJdjQwKXY3sAOgq
UxS6GomlpODeNDTJmx0EuhpJoCsCXRHoiix4JUuWSQ12DHQ1kkBXMQldZYpMM2YF
ga7CnGZ4mUB1QZcsA4mwZLkOW7JMh7Gj/iS2ZJlfptyXGfoF6KqeW7KMICeHtDIk
dNUH21Ffz52jrBfEO3znSXKHD4qq7xYautJhS5YR1OXbUY8tWa5LIdBVrEBXj3Dr
CwM/yZutB9bpzJbhgdCPXqfVc9CP0WhGMNCfaYPJpLUwSI/VakVAj1XHwDnQw2C2
ofXBBrNJa9ZbDDbUhRvNJotOa7da7XqDVmfwwT5mo4VFqjq7hsGHifVtBKaSuL/x
3DAhAExoVz27UFkKhsMu2MR0q6zkwJoshM4F470Rulux5+6AXTQCqh6Ez/4hKDCF
r2mKFxkm+BW5l2NYSwq2IjcNw1qGY1iLiev+E7lPEx5rKQTC9nAXVx7J3PpWDaff
rRjWcgeGtazGsJYHMazlD2ECU4mRBqZivbAIMEWAqegDU9qgwJS2A4CpLFFgSoul
pOTeRDRjnBsEmNISYIoAUwSYIityyJoqUoMdA0xpCTAVk8BUlsg0Y04QYCrMaYYY
MMVMPyhhTVUdtqYqSRJ6TdXJOAyYSsDWJyUIe+nXSAVgSicLDUytwYCpPtKVPjOR
DrnON204y62vQjrUhbGmisbWVNXjv0uSDvzWVxFgKjaAqd3c4ongsE8LS5SMOpu9
+cZ1g5EFgew6k9FmZ/atmyxaK2tB0WzXoSVKOh4Wstj1zJIlvdaaqdcbdahHN5j1
Bhi5yWAxafU2M48CGW1mK4tTxZjCwQeRx9sJWyWCQbATH4ytp8qCnbgZPmGFzg47
6BwMtpoO67kIOi/srJfBjvQ+DLZaB/02wett8LkdIWErfj3VIGwQGYwtEcrCkBgz
hsRYgbBp1o4hMTkYEjMdQ2KKMCTGy4XllwglcPrdhyEx6zAkZhOGxGzDkJgdIWGr
7qF/pX46zHW+TPicTQyAqGK9YNoJUYEeQAfPHosrUT6p6Z3Ilo0U9KQMYBUMJ4NO
I2MbfjEsJyn37KNgHjYSo2dWcD9tLg1MzXGjgy9r/B4PY/D+MsyvGBjgv55M2fNP
9IY+wFf1zfOFlweOSNYzv/UiafasvxaB+1PF7ktC3JeGuL+WmSnJgP/+dQbF8N3h
cybhcnIZGC1a02fHca+8Q7hWyNkYKC4mCR7D8eVsyM+2OPwiWrkNu36Kq3HW4LYM
dobBdJjtEL9mY2DrOx6+TwrYZseL1EFL5RUooNPFs6dOE9ASdzF8hww030YtJjsB
HeKNDj9eVaviRaH3OPzzQIWdFtWqtMKPV92qeNVB8iAJOy1Jq9IKP15Nq+LVBOSB
6gJtieoCbYnqAm1J0gXakqQLtCVJzLallLqbgs45ThzYWDPTtHAcmoWjWQflQ82Q
jAd3NntGOEqwdP1G4SBzqKVMiEwG70ItYwLYTLH2e9jrf1GbKQq7vkO6mZJg10Pi
NlNS5nqF3++q+T6YfDm5i9Pqbu64kjk6wB4GOUPnWrGpQApXTJToNIktpjmA34nH
hjQwhrdYGeZoXrGS4BhDi8UUPJuCJhHIpuS3kc2A5OE0efefkAAHP80VbfSOZY7m
XY0IViSaTSCWTUyT4NkEeDbF3m8+m7IW5/ZPyVpXm8K3R7uzGbI2QWtqM+63kc34
ZpHPYGTfOLzRRu/dbCmbvCYRyKY8orUZ2WxGsDYVXT2bfeo0AROsIT5Tj0gPdBzK
AGWqZpPEwcIuVAE+BXTdIOZ+nuyg4h3NT0nlsi8UT2reTWJhrv6wuz4L+2lWxU8Y
gCgOmzbxQFCgOwuiL4M59kUAGBC5VuO3fie8CTjVAjNBO9gSUoDb475LQCX0WFzv
RLaE0BGVkK4NJaS7BCUU2BIowK8XlGBtB5folR5qX0fjRiWi9rUblhxqX+goidHS
k17C0pOAO+Xr5G+r1ih2yvfL96jKFA3yt+QPqQphf6ejuNKjW1F6FPM1E1XR+zrE
5uWEv7m8X0+QxeTjzEUd82QlvHcj06GbuBAj6rr75hIJrK1dRkYy5MAUp2dRqdtZ
Rs+pcLsyGYSY8s282YwnMtO7ntx3VDEc+xczhPNp4EbRO9pSfe6wqq9M8VTS18pN
yWsU7yW9qqxN3qk4k7RH6UwGwChlq0+WHn71GaWy9M5afcckbPUdlqClxc2rj2L+
wVlXg1B9aA42q7DUWwbrrR8TRupXbxofwh6s9kA7a08iUn9C7dVKDyiXynYkbJKe
VF4jW56wT3peeYWsNKHtL18tzOAO+Dw6HlACsFQmHJH/Jui/nDuehP7XyIQj8t8H
/Uu543nof4VMOCJ/tvbulLa29tZzL9+Bi8FfPglTe938Xj40l5ha7bFaMxGpmD5l
Yl5GZ3v9ziV8pfoscaOmV6Ja/ULiEk1m4lD11sR5sGQMbXj9DPD1+wxm5is4GKDj
RhjPuQThiPxfgP5qNXtcAv17YUfkvxUeh3JHpEcmdkT+bAXep2ptBW4FbAWiBbfB
KlDKVKDc7/VD3waz9dz7RzOZlIm8f/GMSh3xFiqBJv5x1fXyUYm6+H+ocuW9E/vE
P636nVzbiadf6XVrsFKUMI5fAL5EDHU8v5r9Uqh52KGBOeFrIjB4E+OUcEIiAT2p
VbAmKPAosyiJ8n0EKeE/abMEMnxxPgJ+Bux6NLw28Ok0y77GtcT68SQy5QdRigGl
waKSco5PWMnNs5ScCxUeVxj5YqXFnTc18f58q0Au3aHzFYUw3LWlAftrINaAd8t7
aOTKUYlH5VbNKUXvxGfll2suKDp/A6a4f/4N+MNfRR5qWMU2YMcDWANGWamv3+5z
fCWhBizt2AaM1GnegKVRasB8amINuInVqIkrKOacb8HMOTzj/S91A75SuUv5mGpU
olt5RHm7qnfiTOU+5UZV52/AEl/jxRuw7ReRhw6vZxvwZ1tgA5ZgPXBdXZ3P4T2w
LOoNuCXeqQmp07wBy8Igy9rSgPnUWuqBYZgmrqCYc74HRucoGO9/qRvw16o7NBM0
oxIT1I9rBmt6J/6gekBj13T+BixlPokCG/CG/4k8xC9cOn031oBRVt577z2fw3vg
uI5twEid5g04LkoNmE+tpR4YhmniCoo551swcw7PeP9L1YD7OOqY5ZsrqeXMisjh
48ucRYtpp7uYvvb6Uq8L1oOMy+rtgI5vObpwljSgIHw4NBShtxkVyIIFC5hrdDx9
+jRzHkthuqKQ+iFChEhr5Ewd/5XeCBxSYeg1BYEaiBAh0rI0giSZP5fX1AVHZxKG
hCFhwg8jjK0SbGwlJUXCkDBteYuk5C0iYUiYdr5FMvKdR4RIG7/z+LcojrxFRIi0
8y2KJ28RESLtfIvk5C0iQqRNcsZR57jDt3RDsJi1jVlAIiD625lFHbxpK1yUfncV
wH+JCrqersiGXnOWOMv08PhkPwBqPm78w9vuj3U979r0duPC8leHXbl7/yfqwhc3
92xMLZ1o3rVz8u+e+CRBteDjJ2bfd585e2/NuntXvfT6vOUfHjcWpw3rm6Y05Gk/
LjR295R9Ne7Hn2+5zd64ef+FdUnfVY5fWT1p0YWzvzxf5czflHf05KePzj47vv/F
vQu2zht6eL3tp2NJ0vubBn90jj4t7x5ffjj7xe8nfhi36V6qqu8DOYt1L+XMHPfy
lJe2mD0H6vdcv7NwctzJl+OS9mXvzKnZVfXZDT9onxtz8P0fLv7zfuunfY78dGv3
3f0WP2Q84B298YdP/77yjDq3d/mZhLcyjastNUkj/20492ryswO/rFl4x8s/PVO5
Z9ODL5+YdPOx6adufrvmqfrBu1Yv2jXpzDe6E78WX/fDy+8uf23hhQ80fz3z2L1N
+ep/nLnuljdST+W88eLCL6fvXDNj0rxKeuzqVzP/Xj30a8WgZz/44JOEQ3/OlP37
s5mNX8c3Fv+47einv1xIeMO+7oWzs45vf+Dqbuqrjv2315gJ32Y8suqyGd/3HDPh
6rdHTNo77ZpnJ069ib4lYdN/9r4yqXpv+QOZh88tLVzRZFDNe6f63S9TH974s25Q
6j7P+Y+yni0wLxg0bMiwr59Itvx++hv2U3MnyPOmPVk19sobvus3LM44+p9JX9Zm
r3/njbM9fiyw7Hp0Su9V81PKJH/6i+XctFHPl98+obb8duOIBT1GjrihhzI/z3V+
19zcj7bfZNlw9CsU7I0TqbanJZZX/rLVsrah8NnXn97/uuf5kc4eBTMuWl7Z0Osp
5R967btHVrLhk2HvN6nrHY1T57jcxd05I25S2OTQKiCRXxpoyYxbaInh1VhYtOGb
cZOKmHFLCGnG7a4omnFjzdqxZtxSsajTsHN8D1gGdu5vxi01sH4DJC3E/fQQ9zNC
3A/HjBtvGBCVLtrL1zYzbolMrP3BADAQj0HyBBvy4EGHX0Qr/4pds0bYujExDAKX
A7qNZtySmBgGgyFgKDHj1oIQM27+Qsy4ETNuxIxbpNoSMeNGzLi1ry2x5mT6N5tz
BDPjJpiT0XE/1IM/E44Zt+ZzqGBm3FIDzLilBZhxSw8w45Zxqcy4DRCdJkXWvllL
xXQJ7JsN/G1kcxAInCazxtNoBz/NFW30js2O5l2NmBk3sWwCsWximkTAjNvlLc7t
I2ffrNXZjKxFLPq3kc3BzSKPjhm3tmQzgmbchkS0NiObzQjW5tCunk3WjJsURN6M
21TZp4p3NLckeWRA+aRmFjHjxpcPZkfEqWbtiKxQs3ZEytWxYEdEBdNLDd+OyO/+
wjZV1z4/OyLBJZgdEdDZ7YhI2wE7BwsfsGc2iB0RwXWsHZHxKtaOSImKtSMyVRUL
dkTUML208O2IvPeSD7f1b8C4HRG8AUs7tgG33o5IexpwKDsiIIgdEb7BdrwdkVNq
1o6IXMPaEflWHQt2RDQwvfTw7Yis5IiH9D8HNGB/OyJCA5Z1bANunR2R9jbg0HZE
QBA7ImwD7ng7IivUrB2RXWrWjshadSzYEUmA6WWEb0eEZ86szwQ0YNyOCN6A4zq2
AbeOuW5vAw7FXIMgzDXfgDvKjoi/KeNbOBPGs2LelHH/S2rKGH1jIVPG6BurK5gy
HnRJTRmfj09X/E6dqhwgz1fY1BKlSV6soNXf4KaM01tlTbXT2sK9vYl9q1eLmDJO
YPC0JD9jnCi2wHU9wa3isr9/3HFWccsUhqSvlZcnr1HMTHpVeSFpp8KdtEd5Kkmo
R1mr6rHz2jSexdk0zhexaZwYgzaNd0op9UOy2zRvSlPUN8rmaP4lHaOeLRuvaXvt
7YQZvA0+j46UGoCHZMIR+b8J/edwxxTof6NMOCL/f0H/8dxxDPSfLROOyL+tNo33
czaND4nYNO4mUnuzO3HtEYPGfO0lxZ5BY2LMjRgLExVSP0SIEGmNEGNuRIhETogx
NxKGhCFhiDE3EoaEIcbcSBgShhhzI0Kkq33nEWNuRIhE6i0ixtyIEGnvW0SMuREh
0ja5NMbc9Jwxt8GCMbf1n0w9pOvz91WPq7u9IjtWcxN16qXXvziyYGH+sHWVn39+
l2bnqE0jMu6n9tZ0P7Wi4osRZ/uN/esvX1oyPzHMzK9clTK48eOLL/944acL2fU/
bn94lWLLH8vvfEY+etYdF0t/zW3s/9JjV+4ae2xBdsJnV+nWbp40ubHv0WP0Q/2b
HJ/fmHZ6sOeO3r/UJ9yyO13z0+yH19/5vy1VupfqDs5bkjB3YOPfnnrEsmOLZPAO
qukDsLd6fHnD1pt2zCl/tOClmx9P33P33N09+h7ZX/6v3apxw7dVlcyyvfnp2q/O
DPhq5NKnqsc+e8y2tGHr0w/lHV/xyv99c/ipWbITu+1L964AJ+Sf9fUsPzej4tOP
Cu7ftTr/G/u33+lO2D54a+5Pzx05tP+6P36qG7H8ixNHn3ft/+qEZ8OxxMcSLDp9
4Wsf5tzyyvKaf1x2bcK732+3Hts0a+6LyYeefKvPYx/kNk69c7+1pizZ+HPtJs+P
21O9KwZnvnn04zHzbCtu/nHz8/JDrprnDnhAxQffP/m3n8//34zPl058a8iEjP88
k/+/fxdb3PctPnWk27Vrd9/QQ93dec87Gfdfpd9R/F3GqlNbc386YH/ynhe/eO27
Stf/Nb5QMlN2JH9634uL1h4eVr3+wMGDU5Yrd05/QLl5A0g9kN/z932MKakb/lZs
GzA1qL+pX7Pwy+55/cyyj1y9e3w978t/XjjysuqXx94Z8/Wj3qtfSDs57+QV53sc
rf18Dmu8zVYn4ZoY2rdZ4Km4wVXkpUvdJRW0njPdhgu+aitYW4/maq1LvgAei1bM
dBt6jF3zxUcvvJkjHOW+zQNy3rPhU5Vq7DuUm1ldNqGm0uUpLXe5vXSu0+sCFcwi
sKkVSzL1pkyDTm8BbmZFLRYuLxeGYlJ3c4G9roUVFYv9biiEqhQ8E/wicnmEO0o2
uLeiqKJM8GUO3NW9YJiQ6XG3fPvr2CNvvjk2WAMQujebQ1rniED3STn+HwAA//8D
AHi4JJs=</Template></GraphPadPrismFile>