Calibrating and testing tissue equivalent proportional counters with 37Ar

A method for testing and calibrating tissue equivalent proportional counters with37Ar is described.37Ar is produced by exposure of argon in its normal isotope composition to thermal neutrons. It is shown that - up to volume ratios of 0.01 of argon to the tissue equivalent gas - there is no appreciable effect of the argon admixture on the function of the proportional counter. Conventional calibration methods with characteristic x-rays or with -particles require modifications of the detectors, and they test only small sub-volumes in the counters. In contrast, argon permits calibrations and tests of the resolution that are representative for the entire counter volume and that do not require changes in detector construction. The method is equally applicable to multi-element proportional counters; it is here exemplified by its application to a long cylindrical counter of simplified design that is part of such a multi-element configuration

Radially restricted linear energy transfer for high-energy protons: A new analytical approach

Radially restricted linear energy transfer (LET) is a basic physical parameter relevant to radiation biology and radiation protection. In this report a convenient method is presented for the analytical computation of this quantity without the need for complicated simulation. The method uses the energy-re-stricted LETL, as recently redefined in a 1993 ICRU draft document and supplements it by a relatively simple term that represents the energy of fast rays lost within distancer from the track core. The method provides a better fit than other models and is valid over the entire range of radial distance from track center to the maximum radial distance traveled by the most energetic secondary electrons.L r computed by this approach differs only a few percent from the values Contribution to the international symposium on heavy ions research: space, radiation protection and therapy, 21–24 March 1994, Sophia-Antipolis, Franc

Comparison of scores for bimodality of gene expression distributions and genome-wide evaluation of the prognostic relevance of high-scoring genes

<p>Abstract</p> <p>Background</p> <p>A major goal of the analysis of high-dimensional RNA expression data from tumor tissue is to identify prognostic signatures for discriminating patient subgroups. For this purpose genome-wide identification of bimodally expressed genes from gene array data is relevant because distinguishability of high and low expression groups is easier compared to genes with unimodal expression distributions.</p> <p>Recently, several methods for the identification of genes with bimodal distributions have been introduced. A straightforward approach is to cluster the expression values and score the distance between the two distributions. Other scores directly measure properties of the distribution. The kurtosis, e.g., measures divergence from a normal distribution. An alternative is the outlier-sum statistic that identifies genes with extremely high or low expression values in a subset of the samples.</p> <p>Results</p> <p>We compare and discuss scores for bimodality for expression data. For the genome-wide identification of bimodal genes we apply all scores to expression data from 194 patients with node-negative breast cancer. Further, we present the first comprehensive genome-wide evaluation of the prognostic relevance of bimodal genes. We first rank genes according to bimodality scores and define two patient subgroups based on expression values. Then we assess the prognostic significance of the top ranking bimodal genes by comparing the survival functions of the two patient subgroups. We also evaluate the global association between the bimodal shape of expression distributions and survival times with an enrichment type analysis.</p> <p>Various cluster-based methods lead to a significant overrepresentation of prognostic genes. A striking result is obtained with the outlier-sum statistic (<it>p </it>< 10^-12). Many genes with heavy tails generate subgroups of patients with different prognosis.</p> <p>Conclusions</p> <p>Genes with high bimodality scores are promising candidates for defining prognostic patient subgroups from expression data. We discuss advantages and disadvantages of the different scores for prognostic purposes. The outlier-sum statistic may be particularly valuable for the identification of genes to be included in prognostic signatures. Among the genes identified as bimodal in the breast cancer data set several have not yet previously been recognized to be prognostic and bimodally expressed in breast cancer.</p