p-adic L functions and trivial zeroes

The following is adapted from the notes for the lecture. It announces results and conjectures about values of the p-adic L function of the symmetric square of an elliptic curve.

Occurrence of two somatostatin variants in the frog brain: characterization of the cDNAs, distribution of the mRNAs, and receptor-binding affinities of the peptides.

In tetrapods, only one gene encoding a somatostatin precursor has been identified so far. The present study reports the characterization of the cDNA clones that encode two distinct somatostatin precursors in the brain of the frog Rana ridibunda. The cDNAs were isolated by using degenerate oligonucleotides based on the sequence of the central region of somatostatin to screen a frog brain cDNA library. One of the cDNAs encodes a 115-amino acid protein (prepro-somatostatin-14; PSS1) that exhibits a high degree of structural similarity with the mammalian somatostatin precursor. The other cDNA encodes a 103-amino acid protein (prepro-[Pro2, Met13]somatostatin-14; PSS2) that contains the sequence of the somatostatin analog (peptide SS2) at its C terminus, but does not exhibit appreciable sequence similarity with PSS1 in the remaining region. In situ hybridization studies indicate differential expression of the PSS1 and PSS2 genes in the septum, the lateral part of the pallium, the amygdaloid complex, the posterior nuclei of the thalamus, the ventral hypothalamic nucleus, the torus semicircularis and the optic tectum. The somatostatin variant SS2 was significantly more potent (4-6 fold) than somatostatin itself in displacing [125I-Tyr0, D-Trp8] somatostatin-14 from its specific binding sites. The present study indicates that the two somatostatin variants could exert different functions in the frog brain and pituitary. These data also suggest that distinct genes encoding somatostatin variants may be expressed in the brain of other tetrapods.

cDNA structure, tissue distribution, and chromosomal localization of rat PC7, a novel mammalian proprotein convertase closest to yeast kexin-like proteinases.

By using reverse transcription-coupled PCR on rat anterior pituitary RNA, we isolated a 285-bp cDNA coding for a novel subtilisin/kexin-like protein convertase (PC), called rat (r) PC7. By screening rat spleen and PC12 cell lambda gt11 cDNA libraries, we obtained a composite 3.5-kb full-length cDNA sequence of rPC7. The open reading frame codes for a prepro-PC with a 36-amino acid signal peptide, a 104-amino acid prosegment ending with a cleavable RAKR sequence, and a 747-amino acid type I membrane-bound glycoprotein, representing the mature form of this serine proteinase. Phylogenetic analysis suggests that PC7 represents the most divergent enzyme of the mammalian convertase family and that it is the closest member to the yeast convertases krp and kexin. Northern blot analyses demonstrated a widespread expression with the richest source of rPC7 mRNA being the colon and lymphoid-associated tissues. In situ hybridization revealed a distinctive tissue distribution that sometimes overlaps with that of furin, suggesting that PC7 has widespread proteolytic functions. The gene for PC7 (Pcsk7) was mapped to mouse chromosome 9 by linkage analysis of an interspecific backcross DNA panel.

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.

Generation of representation models for complex systems using Lagrangian functions

In this article, a new methodology is presented to obtain representation models for a priori relation z = u(x1, x2, . . . ,xn) (1), with a known an experimental dataset zi; x1i ; x2i ; x3i ; . . . ; xni i=1;2;...;p· In this methodology, a potential energy is initially defined over each possible model for the relationship (1), what allows the application of the Lagrangian mechanics to the derived system. The solution of the Euler–Lagrange in this system allows obtaining the optimal solution according to the minimal action principle. The defined Lagrangian, corresponds to a continuous medium, where a n-dimensional finite elements model has been applied, so it is possible to get a solution for the problem solving a compatible and determined linear symmetric equation system. The computational implementation of the methodology has resulted in an improvement in the process of get representation models obtained and published previously by the authors.

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().

Determination of symmetric VL1 formulas : algorithm and program SYM4 /

Mode of access: Internet.

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.

Some new symmetric function tables.

"Reprinted from the Transactions of the Royal Society of Canada, 3d ser., 1908-1909, v.2, sect.3."

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

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

Rank test of location optimal for hyperbolic secant distribution

There are at least two reasons for a symmetric, unimodal, diffuse tailed hyperbolic secant distribution to be interesting in real-life applications. It displays one of the common types of non normality in natural data and is closely related to the logistic and Cauchy distributions that often arise in practice. To test the difference in location between two hyperbolic secant distributions, we develop a simple linear rank test with trigonometric scores. We investigate the small-sample and asymptotic properties of the test statistic and provide tables of the exact null distribution for small sample sizes. We compare the test to the Wilcoxon two-sample test and show that, although the asymptotic powers of the tests are comparable, the present test has certain practical advantages over the Wilcoxon test.

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.

R-estimator of location of the generalized secant hyperbolic distribution

The generalized secant hyperbolic distribution (GSHD) proposed in Vaughan (2002) includes a wide range of unimodal symmetric distributions, with the Cauchy and uniform distributions being the limiting cases, and the logistic and hyperbolic secant distributions being special cases. The current article derives an asymptotically efficient rank estimator of the location parameter of the GSHD and suggests the corresponding one- and two-sample optimal rank tests. The rank estimator derived is compared to the modified MLE of location proposed in Vaughan (2002). By combining these two estimators, a computationally attractive method for constructing an exact confidence interval of the location parameter is developed. The statistical procedures introduced in the current article are illustrated by examples.

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.