pathmatrix: a geographical information system tool to compute effective distances among samples

pathmatrix is a tool used to compute matrices of effective geographical distances among samples using a least‐cost path algorithm. This program is dedicated to the study of the role of the environment on the spatial genetic structure of populations. Punctual locations (e.g. individuals) or zones encompassing sample data points (e.g. demes) are used in conjunction with a species‐specific friction map representing the cost of movement through the landscape. Matrices of effective distances can then be exported to population genetic software to test, for example, for isolation by distance. pathmatrix is an extension to the geographical information system (GIS) software arcview 3.x.     

Assessment of Optimal Selected Prognostic Factors

The identification and assessment of prognostic factors is one of the major tasks in clinical research. The assessment of one single prognostic factor can be done by recently established methods for using optimal cutpoints. Here, we suggest a method to consider an optimal selected prognostic factor from a set of prognostic factors of interest. This can be viewed as a variable selection method and is the underlying decision problem at each node of various tree building algorithms. We propose to use maximally selected statistics where the selection is defined over the set of prognostic factors and over all cutpoints in each prognostic factor. We demonstrate that it is feasible to compute the approximate null distribution. We illustrate the new variable selection test with data of the German Breast Cancer Study Group and of a small study on patients with diffuse large B‐cell lymphoma. Using the null distribution for a p‐value adjusted regression trees algorithm, we adjust for the number of variables analysed at each node as well. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Conditional and Unconditional Simulation of Healthy Patients' Visual Fields

This paper describes a simulation problem, motivated by the study of glaucoma, a very serious and widespread ocular illness. To ascertain whether a patient suffers from glaucoma, a perimetric test is done, but the evolution of the disease is very slow, and large longitudinal sets of tests taken on the same patient are needed to study its evolution, to analyze the efficiency of existing methods to detect the progression of glaucoma and to develop new ones. Simulation can be a very useful procedure to get appropriate data sets to work with. Our aim in this work is to simulate several VFs in a healthy patient to reflect his evolution in time. We use a spatio‐temporal model to simulate from, taking into account the correlation existing between the observed (or simulated) values in space and time. Two different simulation procedures (unconditional and conditional) are studied, and applied to obtain the simulations we are interested in. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Describing Ordinal Odds Ratios for Stratified r × c Tables

For an r × ctable with ordinal responses, odds ratios are commonly used to describe the relationship between the row and column variables. This article shows two types of ordinal odds ratios where local‐global odds ratios are used to compare several groups on a c‐category ordinal response and a global odds ratio is used to measure the global association between a pair of ordinal responses. When there is a stratification factor, we consider Mantel‐Haenszel (MH) type estimators of these odds ratios to summarize the association from several strata. Like the ordinary MH estimator of the common odds ratio for several 2 × 2 contingency tables, the estimators are used when the association is not expected to vary drastically among the strata. Also, the estimators are consistent under the ordinary asymptotic framework in which the number of strata is fixed and also under sparse asymptotics in which the number of strata grows with the sample size. Compared to the maximum likelihood estimators, simulations find that the MH type estimators perform better especially when each stratum has few observations. This article provides variances and covariances formulae for the local‐global odds ratios estimators and applies the bootstrap method to obtain a standard error for the global odds ratio estimator. At the end, we discuss possible ways of testing the homogeneity assumption.     

RELATIONSHIP BETWEEN CELL CYCLE AND LIGHT‐LIMITED GROWTH RATE IN OCEANIC PROCHLOROCOCCUS (MIT9312) AND SYNECHOCOCCUS (WH8103) (CYANOBACTERIA)1

The relationships between growth rate, cell‐cycle parameters, and cell size were examined in two unicellular cyanobacteria representative of open‐ocean environments: Prochlorococcus (strain MIT9312) and Synechococcus (strain WH8103). Chromosome replication time, C, was constrained to a fairly narrow range of values (～4–6 h) in both species and did not appear to vary with growth rate. In contrast, the pre‐ and post‐DNA replication periods, B and D, respectively, decreased with increasing growth rate from maxima of ～30 and 10–20 h to minima of ～4–6 and 2–3 h, respectively. The combined duration of the chromosome replication and postreplication periods (C+D), a quantity often used in the estimation of Prochlorococcus in situ growth rates, varied ～2.4‐fold over the range of growth rates examined. This finding suggests that assumptions of invariant C+D may adversely influence Prochlorococcus growth rate estimates. In both strains, cell mass was the greatest in slowly growing cells and decreased 2‐ to 3‐fold over the range of growth rates examined here. Estimated cell mass at the start of replication appeared to decrease with increasing growth rate, indicating that the initiation of chromosome replication in Prochlorococcus and Synechococcus is not a simple function of cell biomass, as suggested previously. Taken together, our results reflect a notable degree of similarity between oceanic Synechococcus and Prochlorococcus strains with respect to their growth‐rate‐specific cell‐cycle characteristics.     

Ubiquitin‐specific protease‐like 1 (USPL1) is a SUMO isopeptidase with essential,non‐catalytic functions

Isopeptidases are essential regulators of protein ubiquitination and sumoylation. However, only two families of SUMO isopeptidases are at present known. Here, we report an activity‐based search with the suicide inhibitor haemagglutinin (HA)‐SUMO‐vinylmethylester that led to the identification of a surprising new SUMO protease, ubiquitin‐specific protease‐like 1 (USPL1). Indeed, USPL1 neither binds nor cleaves ubiquitin, but is a potent SUMO isopeptidase both in vitro and in cells. C13orf22l—an essential but distant zebrafish homologue of USPL1—also acts on SUMO, indicating functional conservation. We have identified invariant USPL1 residues required for SUMO binding and cleavage. USPL1 is a low‐abundance protein that colocalizes with coilin in Cajal bodies. Its depletion does not affect global sumoylation, but causes striking coilin mislocalization and impairs cell proliferation, functions that are not dependent on USPL1 catalytic activity. Thus, USPL1 represents a third type of SUMO protease, with essential functions in Cajal body biology.     

A sequential expression system for high-throughput functional genomic analysis

A method employing sequential rounds of cell-free protein synthesis (CFPS) was developed to identify gene products influencing the complex metabolic systems that result in protein accumulation and folding in vitro. The first round of CFPS creates an array of cell extracts individually enriched with a single gene product expressed in-parallel from linear DNA expression templates (ETs). The cell extract is engineered to enhance template stability and to provide reaction conditions conducive for general protein activation. Following first-round expression, linear templates are selectively degraded and a plasmid template for a reporter enzyme is added to initiate a subsequent round of protein expression. Reporter concentration and activity identify first-round gene products that affect amino acid and nucleic acid stability, energy supply, protein expression, stability, and activation. This sequential CFPS system provides a unique format for the functional genomic identification of broadly diverse metabolic activities.