Organisation of the equine immunoglobulin constant heavy chain genes. II. Equine cgamma genes.

The number of immunoglobulin G constant heavy chain genes (cgamma genes) varies broadly among mammalian species, reflecting structural and functional differences between expressed immunoglobulin G (IgG) isotypes and allotypes. Up to now equine IgG isotypes have been defined only at the biochemical and serological level. It is still not clear how many IgG isotypes exist in horses and whether there are any allotypes. Here, we describe the isolation and characterisation of equine cgamma genes. An equine genomic lambda phage library was screened with a human cgamma4 probe. Cross-hybridising equine cgamma sequences were cloned twice and characterised by restriction mapping with the human cgamma4 and a murine sgamma1 probe. Genomic equine DNA probes for both, cgamma genes and corresponding switch regions (sgamma), were isolated and used for a more detailed BamHI restriction analysis, comparing genomic DNA of various horses. This analysis reveals the existence of at least five, or probably six cgamma genes in the equine haploid genome. Beside the porcine system, this is the highest number of cgamma genes described for any mammalian species. Moreover, for two of these cgamma genes, BamHI restriction fragment length polymorphism became evident.

Organization of the equine immunoglobulin heavy chain constant region genes; III. Alignment of c mu, c gamma, c epsilon and c alpha genes.

Previous restriction analysis of cloned equine DNA and genomic DNA of equine peripheral blood mononuclear cells had indicated the existence of one c epsilon, one c alpha and up to six c gamma genes in the haploid equine genome. The c epsilon and c alpha genes have been aligned on a 30 kb DNA fragment in the order 5' c epsilon-c alpha 3'. Here we describe the alignment of the equine c mu and c gamma genes by deletion analysis of one IgM, four IgG and two equine light chain expressing heterohybridomas. This analysis establishes the existence of six c gamma genes per haploid genome. The genomic alignment of the cH-genes is 5' c mu/(/) c gamma 1/(/) c gamma 2/(/) c gamma 3/(/) c gamma 4/(/) c gamma 5/(/) c gamma 6/(/) c epsilon-c alpha 3', naming the c gamma genes according to their position relative to c mu. For three of the c gamma genes the corresponding IgG isotypes could be identified as IgGa for c gamma 1, IgG(T) for c gamma 3 and IgGb for c gamma 4.

Dimensions of projections of sets on Riemannian surfaces of constant curvature

We apply the theory of Peres and Schlag to obtain generic lower bounds for Hausdorff dimension of images of sets by orthogonal projections on simply connected two-dimensional Riemannian manifolds of constant curvature. As a conclusion we obtain appropriate versions of Marstrand's theorem, Kaufman's theorem, and Falconer's theorem in the above geometrical settings.

MOREMATA: Stata module (Mata) to provide various functions

This package includes various Mata functions. kern(): various kernel functions; kint(): kernel integral functions; kdel0(): canonical bandwidth of kernel; quantile(): quantile function; median(): median; iqrange(): inter-quartile range; ecdf(): cumulative distribution function; relrank(): grade transformation; ranks(): ranks/cumulative frequencies; freq(): compute frequency counts; histogram(): produce histogram data; mgof(): multinomial goodness-of-fit tests; collapse(): summary statistics by subgroups; _collapse(): summary statistics by subgroups; gini(): Gini coefficient; sample(): draw random sample; srswr(): SRS with replacement; srswor(): SRS without replacement; upswr(): UPS with replacement; upswor(): UPS without replacement; bs(): bootstrap estimation; bs2(): bootstrap estimation; bs_report(): report bootstrap results; jk(): jackknife estimation; jk_report(): report jackknife results; subset(): obtain subsets, one at a time; composition(): obtain compositions, one by one; ncompositions(): determine number of compositions; partition(): obtain partitions, one at a time; npartitionss(): determine number of partitions; rsubset(): draw random subset; rcomposition(): draw random composition; colvar(): variance, by column; meancolvar(): mean and variance, by column; variance0(): population variance; meanvariance0(): mean and population variance; mse(): mean squared error; colmse(): mean squared error, by column; sse(): sum of squared errors; colsse(): sum of squared errors, by column; benford(): Benford distribution; cauchy(): cumulative Cauchy-Lorentz dist.; cauchyden(): Cauchy-Lorentz density; cauchytail(): reverse cumulative Cauchy-Lorentz; invcauchy(): inverse cumulative Cauchy-Lorentz; rbinomial(): generate binomial random numbers; cebinomial(): cond. expect. of binomial r.v.; root(): Brent's univariate zero finder; nrroot(): Newton-Raphson zero finder; finvert(): univariate function inverter; integrate_sr(): univariate function integration (Simpson's rule); integrate_38(): univariate function integration (Simpson's 3/8 rule); ipolate(): linear interpolation; polint(): polynomial inter-/extrapolation; plot(): Draw twoway plot; _plot(): Draw twoway plot; panels(): identify nested panel structure; _panels(): identify panel sizes; npanels(): identify number of panels; nunique(): count number of distinct values; nuniqrows(): count number of unique rows; isconstant(): whether matrix is constant; nobs(): number of observations; colrunsum(): running sum of each column; linbin(): linear binning; fastlinbin(): fast linear binning; exactbin(): exact binning; makegrid(): equally spaced grid points; cut(): categorize data vector; posof(): find element in vector; which(): positions of nonzero elements; locate(): search an ordered vector; hunt(): consecutive search; cond(): matrix conditional operator; expand(): duplicate single rows/columns; _expand(): duplicate rows/columns in place; repeat(): duplicate contents as a whole; _repeat(): duplicate contents in place; unorder2(): stable version of unorder(); jumble2(): stable version of jumble(); _jumble2(): stable version of _jumble(); pieces(): break string into pieces; npieces(): count number of pieces; _npieces(): count number of pieces; invtokens(): reverse of tokens(); realofstr(): convert string into real; strexpand(): expand string argument; matlist(): display a (real) matrix; insheet(): read spreadsheet file; infile(): read free-format file; outsheet(): write spreadsheet file; callf(): pass optional args to function; callf_setup(): setup for mm_callf().