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.     

Mitotic drug targets

Mitosis is the key event of the cell cycle during which the sister chromatids are segregated onto two daughter cells. It is well established that abrogation of the normal mitotic progression is a highly efficient concept for anti‐cancer treatment. In fact, various drugs that target microtubules and thus interfere with the function of the mitotic spindle are in clinical use for the treatment of various human malignancies for many years. However, since microtubule inhibitors not only target proliferating cells severe side effects limit their use. Therefore, the identification of novel mitotic drug targets other than microtubules have gained recently much attention. This review will summarize the latest developments on the identification and clinical evaluation of novel mitotic drug targets and will introduce novel concepts for chemotherapy that are based on recent progress in our understanding how mitotic progression is regulated and how anti‐mitotic drugs induce tumor cell death. J. Cell. Biochem. 111: 258–265, 2010. © 2010 Wiley‐Liss, Inc.     

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)     

Reaction mechanism of surfactant‐sensitized chemiluminescence of bis(2,4,6‐trichlorophyenyl) oxalate and hydrogen peroxide induced by gold nanoparticles

The chemiluminescence (CL) of bis(2,4,6‐trichlorophyenyl) oxalate with hydrogen peroxide in the present of cationic surfactant and gold nanoparticles was studied. The CL emission was obviously enhanced in the presence of surfactant at a suitable concentration, with a synergetic catalysis effect exhibited. Different sizes of gold nanoparticles (15 and 50 nm) showed different effects on CL intensity. Mechanisms of the CL reaction and sensitization effect are discussed. Copyright © 2008 John Wiley & Sons, Ltd.     

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.