The stem cell antigen CD34 functions as a regulator of hemopoietic cell adhesion.

Although the CD34 antigen is widely used in the identification and purification of hemopoietic stem and progenitor cells, its function within hemopoiesis is unknown. We have investigated this issue by ectopically expressing human (hu) CD34 on the surface of murine hemopoietic cells. Forced expression of hu-CD34 in the thymocytes of transgenic mice did not appear to affect the development, maturation, or distribution of murine T cells but did significantly increase their ability to adhere to bone marrow stromal layers of human but not mouse origin. Ectopic expression of hu-CD34 on murine 416B cells, a multipotential progenitor that expresses murine CD34, yielded similar results. In both cases hu-CD34-dependent adhesion was enhanced by molecular engagement of the hu-CD34 protein using anti-CD34 antibodies. These results provide evidence that CD34 promotes the adhesive interactions of hemopoietic cells with the stromal microenvironment of the bone marrow thereby implicating CD34 in regulation and compartmentalization of stem cells. We propose that CD34 regulates these processes in part via an indirect mechanism, signaling changes in cellular adhesion in response to molecular recognition of an as yet unidentified stromal CD34 counterreceptor or ligand.

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().

A note on a gamma distribution computer program and computer produced graphs /

"NOAA--S/T 77-2535"

A note on a gamma distribution computer program /

Mode of access: Internet.

Beluga whale distribution, migration, and behavior in a changing Pacific Arctic

Thesis (Ph.D.)--University of Washington, 2016-06

The rat Na+-sulfate cotransporter rNaS2: functional characterization, tissue distribution, and gene (slc13a4) structure

Inorganic sulfate is essential for numerous functions in mammalian physiology. In the present study, we characterized the functional properties of the rat Na+-sulfate cotransporter NaS2 (rNaS2), determined its tissue distribution, and identified its gene (slc13a4) structure. Expression of rNaS2 protein in Xenopus oocytes led to a Na+-dependent transport of sulfate that was inhibited by phosphate, thiosulfate, tungstate, selenate, oxalate, and molybdate, but not by citrate, succinate, or DIDS. Transport kinetics of rNaS2 determined a K-M for sulfate of 1.26 mM. Na+ kinetics determined a Hill coefficient of n=3.0 +/- 0.7, suggesting a Na+:SO42- stoichiometry of 3:1. rNaS2 mRNA was highly expressed in placenta, with lower levels found in the brain and liver. slc13a4 maps to rat chromosome 4 and contains 17 exons, spanning over 46 kb in length. This gene produces two alternatively spliced transcripts, of which the transcript lacking exon 2 is the most abundant form. Its 5' flanking region contains CAAT- and GC-box motifs and a number of putative transcription factor binding sites, including GATA-1, SP1, and AP-2 consensus sequences. This is the first study to characterize rNaS2 transport kinetics, define its tissue distribution, and resolve its gene (slc13a4) structure and 5' flanking region.

Pore size distribution analysis of activated carbons: Application of density functional theory using nongraphitized carbon black as a reference system

The application of nonlocal density functional theory (NLDFT) to determine pore size distribution (PSD) of activated carbons using a nongraphitized carbon black, instead of graphitized thermal carbon black, as a reference system is explored. We show that in this case nitrogen and argon adsorption isotherms in activated carbons are precisely correlated by the theory, and such an excellent correlation would never be possible if the pore wall surface was assumed to be identical to that of graphitized carbon black. It suggests that pore wall surfaces of activated carbon are closer to that of amorphous solids because of defects of crystalline lattice, finite pore length, and the presence of active centers.. etc. Application of the NLDFT adapted to amorphous solids resulted in quantitative description of N-2 and Ar adsorption isotherms on nongraphitized carbon black BP280 at their respective boiling points. In the present paper we determined solid-fluid potentials from experimental adsorption isotherms on nongraphitized carbon black and subsequently used those potentials to model adsorption in slit pores and generate a corresponding set of local isotherms, which we used to determine the PSD functions of different activated carbons. (c) 2005 Elsevier Ltd. All rights reserved.

Approximating differentiable relationships between delay embedded dynamical systems with radical basis functions

This thesis is about the study of relationships between experimental dynamical systems. The basic approach is to fit radial basis function maps between time delay embeddings of manifolds. We have shown that under certain conditions these maps are generically diffeomorphisms, and can be analysed to determine whether or not the manifolds in question are diffeomorphically related to each other. If not, a study of the distribution of errors may provide information about the lack of equivalence between the two. The method has applications wherever two or more sensors are used to measure a single system, or where a single sensor can respond on more than one time scale: their respective time series can be tested to determine whether or not they are coupled, and to what degree. One application which we have explored is the determination of a minimum embedding dimension for dynamical system reconstruction. In this special case the diffeomorphism in question is closely related to the predictor for the time series itself. Linear transformations of delay embedded manifolds can also be shown to have nonlinear inverses under the right conditions, and we have used radial basis functions to approximate these inverse maps in a variety of contexts. This method is particularly useful when the linear transformation corresponds to the delay embedding of a finite impulse response filtered time series. One application of fitting an inverse to this linear map is the detection of periodic orbits in chaotic attractors, using suitably tuned filters. This method has also been used to separate signals with known bandwidths from deterministic noise, by tuning a filter to stop the signal and then recovering the chaos with the nonlinear inverse. The method may have applications to the cancellation of noise generated by mechanical or electrical systems. In the course of this research a sophisticated piece of software has been developed. The program allows the construction of a hierarchy of delay embeddings from scalar and multi-valued time series. The embedded objects can be analysed graphically, and radial basis function maps can be fitted between them asynchronously, in parallel, on a multi-processor machine. In addition to a graphical user interface, the program can be driven by a batch mode command language, incorporating the concept of parallel and sequential instruction groups and enabling complex sequences of experiments to be performed in parallel in a resource-efficient manner.

Research cultures and the pragmatic functions of humor in academic research presentations:A corpus-assisted analysis

Based on a corpus of English, German, and Polish spoken academic discourse, this article analyzes the distribution and function of humor in academic research presentations. The corpus is the result of a European research cooperation project consisting of 300,000 tokens of spoken academic language, focusing on the genres research presentation, student presentation, and oral examination. The article investigates difference between the German and English research cultures as expressed in the genre of specialist research presentations, and the role of humor as a pragmatic device in their respective contexts. The data is analyzed according to the paradigms of corpus-assisted discourse studies (CADS). The findings show that humor is used in research presentations as an expression of discourse reflexivity. They also reveal a considerable difference in the quantitative distribution of humor in research presentations depending on the educational, linguistic, and cultural background of the presenters, thus confirming the notion of different research cultures. Such research cultures nurture distinct attitudes to genres of academic language: whereas in one of the cultures identified researchers conform with the constraints and structures of the genre, those working in another attempt to subvert them, for example by the application of humor. © 2012 Elsevier B.V.

Extreme Situations Prediction by MultidimenSional Heterogeneous Time Series Using Logical Decision Functions

* The work is supported by RFBR, grant 04-01-00858-a

Fuzzy Membership Functions in a Fuzzy Decision Making Problem

Authors analyses questions of the subjective uncertainty and inexactness situations in the moment of using expert information and another questions which are connected with expert information uncertainty by fuzzy sets with rough membership functions in this article. You can find information about integral problems of individual expert marks and about connection among total marks “degree of inexactness” with sensibility of measurement scale. A lot of different situation which are connected with distribution of the function accessory significance and orientation of the concrete take to task decision making are analyses here.

Hermite Polynomials and the Zero-Distribution of Riemann's Zeta Function

AMS Subject Classification 2010: 11M26, 33C45, 42A38.

Some Properties of Mittag-Leffler Functions and Matrix-Variate Analogues: A Statistical Perspective

Mathematical Subject Classification 2010:26A33, 33E99, 15A52, 62E15.

Special Functions and Pathways for Problems in Astrophysics: An Essay in Honor of A.M. Mathai

The paper provides a review of A.M. Mathai's applications of the theory of special functions, particularly generalized hypergeometric functions, to problems in stellar physics and formation of structure in the Universe and to questions related to reaction, diffusion, and reaction-diffusion models. The essay also highlights Mathai's recent work on entropic, distributional, and differential pathways to basic concepts in statistical mechanics, making use of his earlier research results in information and statistical distribution theory. The results presented in the essay cover a period of time in Mathai's research from 1982 to 2008 and are all related to the thematic area of the gravitationally stabilized solar fusion reactor and fractional reaction-diffusion, taking into account concepts of non-extensive statistical mechanics. The time period referred to above coincides also with Mathai's exceptional contributions to the establishment and operation of the Centre for Mathematical Sciences, India, as well as the holding of the United Nations (UN)/European Space Agency (ESA)/National Aeronautics and Space Administration (NASA) of the United States/ Japanese Aerospace Exploration Agency (JAXA) Workshops on basic space science and the International Heliophysical Year 2007, around the world. Professor Mathai's contributions to the latter, since 1991, are a testimony for his social con-science applied to international scientific activity.

Bayesian Prediction of Weibull Distribution Based on Fixed and Random Sample Size

2000 Mathematics Subject Classification: 62E16, 65C05, 65C20.