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VP P >uBQY3X>XQY&X?>X?u>XQY@Xt%hjjx >t >u Ysjhh" >uK-23Ã>u aA6V 3ãù 45!@E4%!@!@!ù 4%!@3҃ܛ676/6GtB+ àã^À>tÀCPUvF FS^(sކĉFPUvS.3F2>^uu ttu tu t"ètPF=uX F%8=Xu4n []uXPS6G6G[XVSQRW^Nӊ̀8tRVu u@uu4tt 'u ?6;6u 6<tJVt u5 t+6;6u,6;6u^ 6 uP t>Jy.J-6;6t# ,6;6u 6Vu uuFʀPy uʋu$˛tÀFf_ZY[^g$_ZY[^QNtmN8t t00uTt?6;6uG 6 Y$-~Nt!Fun~~t >@Nn3PF%=Xs@u\@WVQS׃>t>VNـ؀t 8v ~~~FNF^[Y^_> Su8u1u+u$uuuu u@u. uL![Xϣ%<Л. á3:t >$? %% >  .. % >  .. PUVv NNv$ &İS P=P2\UVvNNv)S3TX;u(@%0=0u@ QY. tq.."c MS Run-Time Library - Copyright (c) 1992, Microsoft CorpError allocating %d bytes of memory; total allocated so far was %ld Input error: %s Alleles:,%s %d and %d This specifies whether alleles are given as nucleotide sizes or repeat numbers.This specifies whether alleles are given as nucleotide sizes or repeat numbers.Bootstrapping can be used either to generate many distance matrices or to estimate the standard errors of distances. If you request bootstraps, you'll be asked how many, and whether all of the matrices should be output or only their mean and standard error.Bootstrapping can be used either to generate many distance matrices or to estimate the standard errors of distances. If you request bootstraps, you'll be asked how many, and whether all of the matrices should be output or only their mean and standard error.Use this submenu to specify which distance measure you want.Use this submenu to specify which distance measure you want.This is the name of the file from which data should be read.This is the name of the file from which data should be read.Distances can be calculated over all loci or separately for each locus.Distances can be calculated over all loci or separately for each locus.This is the name of the file where results should be written.This is the name of the file where results should be written.This specifies the repeat length (e.g., dinucleotide=2, trinucleotide=3, etc.)This specifies the repeat length (e.g., dinucleotide=2, trinucleotide=3, etc.)Use this submenu to specify which statistics you want.Use this submenu to specify which statistics you want.The anomalous frequency option checks for odd frequency counts.The anomalous frequency option checks for odd frequency counts.The primer (flanking region) error is used in calculating the linearity estimate.The primer (flanking region) error is used in calculating the linearity estimate.The frequency distribution option prints the mean, median, mode, minimum, and maximum allele size, and the frequency of each allele, by locus or by taxon.The frequency distribution option prints the mean, median, mode, minimum, and maximum allele size, and the frequency of each allele, by locus or by taxon.The incommensurable taxa option checks for pairs of taxa with no loci in common.The incommensurable taxa option checks for pairs of taxa with no loci in common.The linearity option prints the duration of linearity estimated according to each locus.The linearity option prints the duration of linearity estimated according to each locus.The missing data option checks for taxon-locus combinations with no data.The missing data option checks for taxon-locus combinations with no data.The outlier option checks for alleles which are either much shorter or much longer than expected; the criterion is user-specifiable by multiples of interquartile range above/below low or high quartiles, or by standard deviations above/below the mean.The outlier option checks for alleles which are either much shorter or much longer than expected; the criterion is user-specifiable by multiples of interquartile range above/below low or high quartiles, or by standard deviations above/below the mean.The pairwise-specific allele option checks each pair of taxa for alleles present in one taxon but not in the other.The pairwise-specific allele option checks each pair of taxa for alleles present in one taxon but not in the other.The mutation rate is used in calculating the linearity estimate.The mutation rate is used in calculating the linearity estimate.The taxon-specific allele option checks for alleles present in only one taxon.The taxon-specific allele option checks for alleles present in only one taxon.The variability option calculates fifteen diversity indices: Fst (by variance and by heterozygosity methods), standardized Rst, and average and total values for heterozygosity, variance, number of alleles, allele size range, maximum allele size, and entropy of allele size distribution.The variability option calculates fifteen diversity indices: Fst (by variance and by heterozygosity methods), standardized Rst, and average and total values for heterozygosity, variance, number of alleles, allele size range, maximum allele size, and entropy of allele size distribution.The crosstabulation option shows how many data values are found in each taxon-locus combination.The crosstabulation option shows how many data values are found in each taxon-locus combination.D1: The average square distance was derived employing the analytical theory developed by Moran for the distribution of alleles mutating under a strict stepwise mutation process in a population of finite constant size with non-overlapping generations. It and its family of related distances is superior to other distances for microsatellites in they have a linear expectation with time making them good for evolutionary studies. D1 = sum over alleles i and i' of ((i-i')^2 ni ni'), where ni is the number of alleles with i repeats (the prime indicating another population).D1: The average square distance was derived employing the analytical theory developed by Moran for the distribution of alleles mutating under a strict stepwise mutation process in a population of finite constant size with non-overlapping generations. It and its family of related distances is superior to other distances for microsatellites in they have a linear expectation with time making them good for evolutionary studies. D1 = sum over alleles i and i' of ((i-i')^2 ni ni'), where ni is the number of alleles with i repeats (the prime indicating another population).L.L. Cavalli-Sforza (1995) personal communication, and D. Goldstein (1996) personal communication.L.L. Cavalli-Sforza (1995) personal communication, and D. Goldstein (1996) personal communication.Fst: The coancestry coefficient Theta has been estimated in Reynolds et al. as Theta = a / (a+b), where a is the variance between taxa and b is the variance within taxa. The distance Fst = -ln(1-Theta).Fst: The coancestry coefficient Theta has been estimated in Reynolds et al. as Theta = a / (a+b), where a is the variance between taxa and b is the variance within taxa. The distance Fst = -ln(1-Theta).Dkf: The kinship coefficient kf is defined as 'the probability that a gene taken at random from I, at a given locus, be identical by descent to a gene taken at random from J at the same locus.' In general, for two individuals (or populations) A and B, it is calculated as kf = sum over all alleles of P[A(i)] P[B(i)], where P[A(i)] is the proportion (relative frequency) of allele i in individual (or population) A. The distance Dkf = -ln(kf), or Dkf' = 1-kf.Dkf: The kinship coefficient kf is defined as 'the probability that a gene taken at random from I, at a given locus, be identical by descent to a gene taken at random from J at the same locus.' In general, for two individuals (or populations) A and B, it is calculated as kf = sum over all alleles of P[A(i)] P[B(i)], where P[A(i)] is the proportion (relative frequency) of allele i in individual (or population) A. The distance Dkf = -ln(kf), or Dkf' = 1-kf.Ddm: The (delta mu)^2 measure of distance (Ddm) for microsatellites was derived in order to improve on D1. Ddm has a smaller variance than D1 and, in populations at mutation-drift equilibrium, Ddm is independent of population size. It is based not on the mean squared difference, as is D1, but on the squared mean difference between alleles of two populations. Thus Ddm = (mu(A)-mu(B))^2, where mu(A) is the mean allele size for population A.Ddm: The (delta mu)^2 measure of distance (Ddm) for microsatellites was derived in order to improve on D1. Ddm has a smaller variance than D1 and, in populations at mutation-drift equilibrium, Ddm is independent of population size. It is based not on the mean squared difference, as is D1, but on the squared mean difference between alleles of two populations. Thus Ddm = (mu(A)-mu(B))^2, where mu(A) is the mean allele size for population A.Gst: Nei's identity for two taxa is defined as id = (sum over all alleles of P[A(i)] P[B(i)])/sqrt(sum over alleles of P[A(i)]^2 sum over alleles of P[B(i)]^2). Nei's standard distance Gst = -ln(id), or Gst' = 1-id.Gst: Nei's identity for two taxa is defined as id = (sum over all alleles of P[A(i)] P[B(i)])/sqrt(sum over alleles of P[A(i)]^2 sum over alleles of P[B(i)]^2). Nei's standard distance Gst = -ln(id), or Gst' = 1-id.Dps: The proportion of shared alleles is defined as the mean of the minima of the relative frequencies of all alleles in the taxonomic units being compared (individuals or populations), i.e.: ps = (sum over all alleles of MIN{P[A(i)], P[B(i)]})/n, where n is the total number of alleles for all loci. The distance Dps = -ln(ps), or Dps' = 1-ps.Dps: The proportion of shared alleles is defined as the mean of the minima of the relative frequencies of all alleles in the taxonomic units being compared (individuals or populations), i.e.: ps = (sum over all alleles of MIN{P[A(i)], P[B(i)]})/n, where n is the total number of alleles for all loci. The distance Dps = -ln(ps), or Dps' = 1-ps.Rst: Slatkin's Rst is an analogue of Wright's Fst, adapted to microsatellite loci by assuming a high-rate stepwise mutation model instead of a low-rate K- or infinite-allele mutation model. It's defined as Rst = (Sbar - Sw)/Sbar, where Sw is the sum over all loci of twice the weighted mean of the within-population variances V(A) and V(B), and Sbar is the sum over all loci of twice the variance V(A+B) of the combined population. Goodman's method, used here, calculates pairwise Rst as Vb/(Vb+Vw) using standardized repeat numbers, where Vw, within-group variance, is (V(A)+V(B))/2; Vb, the between-group variance is (MSb-MSwErr)/Nbar; Nbar, the average sample size, is nA+nB-(nA^2+nB^2)/(nA+nB); MSb, the mean square between groups, is nA*Xbar[A]+nB*Xbar[B]; and MSwErr, the within-group error, is Nbar*(V(A)/nA+V(B)/nB)/2.Rst: Slatkin's Rst is an analogue of Wright's Fst, adapted to microsatellite loci by assuming a high-rate stepwise mutation model instead of a low-rate K- or infinite-allele mutation model. It's defined as Rst = (Sbar - Sw)/Sbar, where Sw is the sum over all loci of twice the weighted mean of the within-population variances V(A) and V(B), and Sbar is the sum over all loci of twice the variance V(A+B) of the combined population. Goodman's method, used here, calculates pairwise Rst as Vb/(Vb+Vw) using standardized repeat numbers, where Vw, within-group variance, is (V(A)+V(B))/2; Vb, the between-group variance is (MSb-MSwErr)/Nbar; Nbar, the average sample size, is nA+nB-(nA^2+nB^2)/(nA+nB); MSb, the mean square between groups, is nA*Xbar[A]+nB*Xbar[B]; and MSwErr, the within-group error, is Nbar*(V(A)/nA+V(B)/nB)/2.Dfs: The fuzzy set similarity measure is calculated by finding the set of alleles in population A (call it set A), the set in population B (call it set B), and dividing the cardinality of their intersection by the cardinality of their union, i.e., fs = |A^B| |AvB|. The distance Dfs = -ln(fs), or Dfs' = 1-fs.Dfs: The fuzzy set similarity measure is calculated by finding the set of alleles in population A (call it set A), the set in population B (call it set B), and dividing the cardinality of their intersection by the cardinality of their union, i.e., fs = |A^B| |AvB|. The distance Dfs = -ln(fs), or Dfs' = 1-fs.Dsw: The stepwise weighted genetic distance measure is 'an extension of Nei's minimum genetic distance', and is based on frequency-weighted means of the absolute value of the difference in number of repeats over pairs of alleles i and j, both within and between populations. In general, for two individuals (or populations) X and Y, it is calculated as Dsw = dxyw - (dxw + dyw)/2, where dxw is the weighted mean within X, dyw is the weighted mean within Y, and dxyw is the weighted mean between X and Y, i.e., where i is in X and j is in Y.Dsw: The stepwise weighted genetic distance measure is 'an extension of Nei's minimum genetic distance', and is based on frequency-weighted means of the absolute value of the difference in number of repeats over pairs of alleles i and j, both within and between populations. In general, for two individuals (or populations) X and Y, it is calculated as Dsw = dxyw - (dxw + dyw)/2, where dxw is the weighted mean within X, dyw is the weighted mean within Y, and dxyw is the weighted mean between X and Y, i.e., where i is in X and j is in Y.For help on an option, type ? followed by the option. The help option prints a short explanation of a menu item. To return to the menu, hit . This begins the analysis. This returns to the main menu. This returns to the main menu with no distance selected. %s Select the type of distance measure you want: (A) Average square (D1) (D) Absolute difference (Dad) (F) Fst (K) Kinship coefficient (Dkf) (M) Delta mu squared (Ddm) (N) Nei's standard (Gst) (P) Proportion shared alleles (Dps) (R) Standardized Rst (S) Fuzzy set similarity (Dfs) (W) Absolute product (Dsw) (0) None (?X) Help on option X pskffsniTransform by (L) -ln(%s) or by (M) 1-(%s)? Adjust distance by subtracting pairwise average within? Select the statistics you want: YesNo(M)issing data %-30s [%s] YesNo(A)nomalous frequencies %-30s [%s] YesNo(I)ncommensurable taxa %-30s [%s] YesNo(T)axon-specific alleles %-30s [%s] YesNo(P)airwise-specific alleles %-30s [%s] YesNo(O)utliers %-30s [%s] hingessigmas(C)riterion for outliers %-30s [%d %s] YesNo(X)tabulation %-30s [%s] YesNo(L)inearity %-30s [%s] (R)ate of mutation %-30s [%f] (E)rror from primer %-30s [%d] YesNo(F)requency distribution %-30s [%s] YesNo(V)ariability %-30s [%s] (?X) Help on option X ! Back to main menu %-30s Enter number of interhinge ranges (+/-): Enter number of standard deviations (+/-): Enter length of primer error: Frequency and variabilityFrequency%s by (L)ocus or by (T)axon? (H)inge checking, (S)igma checking, or (N)one? Enter mutation rate: Frequency and variabilityVariability%s by (L)ocus or by (T)axon? Select the option you wish to change: # Repeats# Nucleotides(A)llele sizes %-30s [%s] (R)epeat length %-30s [%d] (D)istances %-30s [Dad: Absolute difference] [D1: Adjusted average square] [D1: Average square] [Ddm: Delta mu squared] [Dfs: -ln(Fuzzy similarity)] [Dfs': 1-(Fuzzy similarity)] [Dkf: Adjusted -ln(Kinship coefficient)] [Dkf': -ln(Kinship coefficient)] [Dkf: Adjusted 1-(Kinship coefficient)] [Dkf: 1-(Kinship coefficient)] [Gst: -ln(Nei's identity)] [Gst': 1-(Nei's identity)] [Dps: -ln(Proportion shared alleles)] [Dps': 1-(Proportion shared alleles)] [Fst] [Dsw: Absolute product] [Standardized Rst] [No] YesNo(L)ocus-specific distances %-30s [%s] YesNo(S)tatistics %-30s [%s] Missing data %-30s Anomalous frequencies %-30s Incommensurable taxa %-30s Taxon-specific alleles %-30s Pairwise-specific alleles %-30s Crosstabulation %-30s hingessigmas Outliers %-30s [%d %s] Linearity %-30s taxonlocus Frequency distribution %-30s [by %s] taxonlocus Variability %-30s [by %s] All ofMean of(B)ootstraps %-30s [%s %d] (B)ootstraps %-30s [%d] (B)ootstraps %-30s [0] (I)nput data from file name %-30s [%s] (O)utput results to file name %-30s [%s] (?X) Help on option X ! Ready %-30s Enter number of bootstraps: Show (A)ll or (M)ean of bootstrapped distances? Enter name of input file: Enter name of output file: Enter length of repeat: stdinstdoutstdinrCan't find data file named %s stdoutwCan't create output file named %s Warning: taxon %s locus %s allele %d has been assigned a frequency of zero. This will be treated as missing data. There are only %d possible bootstrap samplings for %d loci. %d bootstraps will probably include only ~%d of them. Do you want them generated exhaustively instead? Warning: locus %s is not polymorphic. Warning: locus %s appears to have a repeat length of %d Treat it as repeat length %d? Error: locus %s has alleles shorter than the primer error of %d. Primer error will be decreased to %d. Locus %s has outlier alleles of size %d Missing data: TaxonLocus%-15s%-15s %-15s%-15s None Odd frequencies: TaxonLocusNumber%s %-15s%-15s %6d %-15s%-15s None Pairs with no loci in common: %-15s%-15s None Taxon-specific alleles: TaxonAlleleLocus%10s%6s%14s %10.10s%6d%14s None Frequency of pairwise taxon-specific alleles: PercentFrequencyLocus%10s%10s%10s %10.10s%10d%10.3f %d locus=%s %d %10.10s %.3f %d %10.10s %.3f Standard errors of distances: %10.10s %.3f Linearity estimates: tau(maximum)tau(range)Locus%15s%13s%15s %15s%13.0f%15.0f By average%-15s%13.0f%15.0f Average%-15s%13.0f%15.0f Crosstabulation: %10s%3c %10.10s%3d%6d %10s%3d Frequency distribution: FrequenciesMaximumMinimumModeMedianMeanTaxonLocus%10s%9s%9s%9s%9s%9s %s %10.10s%9.3f%9d%9d%9d%9d%3d Average%-10s%9.3f%9.3f%9.3f%9.3f%9.3f Diversity indices: Tot EntAvg EntTot MaxAvg MaxTot RanAvg RanTot AllAvg AllTot VarAvg VarTot HetAvg HetRst StdFst HetFst VarTaxonLocus%10s%9s%9s%9s%9s%9s%9s%9s%9s%9s%9s%9s%9s%9s%9s%9s %10.10s%9.3f%9.3f%9.3f%9.3f%9.3f%9.3f%9.3f%9.3f%9.0f%9.3f%9.0f%9.3f%9.0f%9.3f%9.3f Average%-10s%9.3f%9.3f%9.3f%9.3f%9.3f%9.3f%9.3f%9.3f%9.3f%9.3f%9.3f%9.3f%9.3f%9.3f%9.3f _C_FILE_INFO=H `Q  \\WI WI &I EEE50P 0PX000WP ``````ppxxxx(null)         ((((( H 5h!?sqrtmܧ׹fq @ @6C ?pow [ Z [ ee~e [ log10 d  log d  expw 1#SNAN1#QNAN1#INF1#IND2f$7yACCe+0004y444xyz: 010203040506070809101112M61xx2 B. - ,- . :}#&6 6 6  @??@ A@@:I@@<>R6000 - stack overflow R6003 - integer divide by 0 R6009 - not enough space for environment run-time error R6002 - floating-point support not loaded R6001 - null pointer assignment x_DOMAIN error y_SING error z_TLOSS error : MATH - floating-point error: einvalid fdenormal gdivide by 0 hoverflow iunderflow jinexact kunemulated lsquare root m nstack overflow ostack underflow pexplicitly generated NB0NB09@CV:dos\unlink.asmCV9dos\wrt2err.asmCV9D newseg.asmCV29| searchsg.asmCV86 linkseg.asmCV84 initseg.asmCV fdata.asmCV8 fmalloc.asmCV8 stackava.asmCV6_ write.asmCV  .\emoem.asmCV .\emu8087.asmCV6 .\matherr.asmCV .\huge.asmCV6.\fltusedc.asmCV\6' .\fabs.asmCV6D .\cfpsig.asmCV5K .\cfout.asmCVr5Z .\cfinn.cCV>53 .\87ftol.asmCV4G .\87floor.asmCV`3 .\87cdisp.asmCV43, .\87ctran.asmCV(3 .\87csqrt.asmCV- .\cvt.cCV*h.\strgtold.asmCVN*h .\strgtod.asmCV) .\mantold.asmCV$ .\tenpow.asmCV" .\x8tmul.asmCV6! .\x8fout.asmCV .\fixups.asmCVx .\87tran.asmCV .\87sqrt.asmCV .\87disp.asmCVv2lmul.asmCVh dos\d_commit.asmCV.:rand.cCVcxtoa.asmCVH hmemset.asmCV hmemcpy.asmCV strtok.asmCVZcommit.cCVLj sprintf.cCVDrewind.cCVcgets.asmCV*y fgets.asmCV& closeall.cCVdos\diffhlp.asmCV+ dos\hdiff.asmCVjndos\apisim.asmCV ctype.asmCV cmiscdat.asmCVH" toupper.cCV _fptostr.cCVitoa.asmCV.Vatox.asmCVXatof.cCVatoi.asmCV strlen.asmCV) strcmp.asmCVR< strcpy.asmCVT strcat.asmCV growseg.asmCVfree.asmCV malloc.asmCV seekfast.asmCV ncommode.asmCV txtmode.asmCV dos\read.asmCV4 dos\open.asmCV dos\lseek.asmCV dos\close.asmCVDNstream.cCVJ  output.asmCV Lfflush.cCV6 _sftbuf.asmCV <_open.cCV K _getbuf.asmCVv 7 _freebuf.asmCV _flsbuf.asmCV _filbuf.asmCV_file.cCV _cflush.asmCVDprintf.cCVT@ fprintf.cCVPfopen.cCV6fclose.cCV4 farstub.asmCVUdos\dosret.asmCV%dos\stdalloc.asmCVVados\nmsghdr.asmCVdos\stdenvp.asmCVdos\stdargv.asmCV$ chksum.asmCV$ chkstk.asmCV crt0fp.asmCV"dos\crt0msg.asmCV"|dos\crt0dat.asmCV dos\crt0.asmCV MICRPROG.OBJ ( micrprog.obj&| | myalloc rnmemorytotal&  fieldCheckpstringrfieldsptokenbuffer"3 /uran"PTO KOiran rn rm& binomial rn rm ri rkAanswer&S OyinitBag bag&,02 .freeBag bag&ptP LemptyBag bag ri" Nbump bag rindex r increment rircountrnewdata"8<B >mempty bag rindex&L H frequency bag rindex*\ X bagFrequency bag rirresult&PT WbagMean bag rir frequency@result@count*   bagVariance bag ri@ frequency @sum@count@squares*04a ](bagCardinality bag rirresult&  bagMedian bag ririndex"l h#!addBagaBag bBag ri rn&X\ "bagUnionaBag bBag ri rnresult. j "bagIntersectionaBag bBag ri rnresult&_ [!  bagMinimum bag ri&\`_ [  bagMaximum bag ri&  bagLoHinge bag ririndex& }  bagHiHinge bag ririndex&dh  bagMode bag ririndex rmax&  bagEntropy bag ri rm@result @p:, 0 continuousRelativeBagEntropy bag@result2 ;#relativeFrequencies bag ri@totalFrequency@result* H D% initLexicon$lexicon*H L /' findLexEntryentry ptokenrposition* - )) findLexWord$lexicon ptoken* + initLexEntryptoken  rnentry*h l  - getLexEntry$lexicon entryptokenrposition& 3 /") getLexWord$lexicon ptoken*  U/ lexItemNodeentry  rnpanswer&X \ * &1lexWord$lexicon  rn&< @  &6 DMDistancertaxarloci  3s 4drbootr featureFlag ri rj rkrlocus @num@diff @den&4 08 SRDistancertaxarloci  3s 4d@variancerbootr featureFlag ri rj rk rni rnjrlocus @num @den @n0 @mi @mj @mij @si @sj@sigmaB@sigmaW @msB@emsW& %6 ASDistancertaxarloci  3s 4drbootr featureFlag rmaxrlocus @num @den @ni @nj @Dab ri rj rk rl1 rl2 rmin&% ! 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