Research plans in Europe for radiation health hazard assessment in exploratory space missions.

The European Space Agency (ESA) is currently expanding its efforts in identifying requirements and promoting research towards optimizing radiation protection of astronauts. Space agencies use common limits for tissue (deterministic) effects on the International Space Station. However, the agencies have in place different career radiation exposure limits (for stochastic effects) for astronauts in low-Earth orbit missions. Moreover, no specific limits for interplanetary missions are issued. Harmonization of risk models and dose limits for exploratory-class missions are now operational priorities, in view of the short-term plans for international exploratory-class human missions. The purpose of this paper is to report on the activity of the ESA Topical Team on space radiation research, whose task was to identify the most pertinent research requirements for improved space radiation protection and to develop a European space radiation risk model, to contribute to the efforts to reach international consensus on dose limits for deep space. The Topical Team recommended ESA to promote the development of a space radiation risk model based on European-specific expertise in: transport codes, radiobiological modelling, risk assessment, and uncertainty analysis. The model should provide cancer and non-cancer radiation risks for crews implementing exploratory missions. ESA should then support the International Commission on Radiological Protection to harmonize international models and dose limits in deep space, and guarantee continuous support in Europe for accelerator-based research configured to improve the models and develop risk mitigation strategies.

Interactive molecular networks obtained by computer-aided conversion of microarray data from brains of alcohol-drinking rats.

Lists of differentially expressed genes in a disease have become increasingly more comprehensive with improvements on all technical levels. Despite statistical cutoffs of 99% or 95% confidence intervals, the number of genes can rise to several hundreds or even thousands, which is barely amenable to a researcher's understanding. This report describes some ways of processing those data by mathematical algorithms. Gene lists obtained from 53 microarrays (two brain regions (amygdala and caudate putamen), three rat strains drinking alcohol or being abstinent) have been used. They resulted from analyses on Affymetrix chips and encompassed approximately 6 000 genes that passed our quality filters. They have been subjected to four mathematical ways of processing: (a) basic statistics, (b) principal component analysis, (c) hierarchical clustering, and (d) introduction into Bayesian networks. It turns out, by using the p-values or the log-ratios, that they best subdivide into brain areas, followed by a fairly good discrimination into the rat strains and the least good discrimination into alcohol-drinking vs. abstinent. Nevertheless, despite the fact that the relation to alcohol-drinking was the weakest signal, attempts have been made to integrate the genes related to alcohol-drinking into Bayesian networks to learn more about their inter-relationships. The study shows, that the tools employed here are extremely useful for (a) quality control of datasets, (b) for constructing interactive (molecular) networks, but (c) have limitations in integration of larger numbers into the networks. The study also shows that it is often pivotal to balance out the number of experimental conditions with the number of animals.