Pseudomonas aeruginosa is an opportunistic bacterial pathogen able to thrive in highly diverse ecological niches and to infect compromised patients. Its genome exhibits a mosaic structure composed of a core genome into which accessory genes are inserted en bloc at specific sites. The size and the content of the core genome are open for debate as their estimation depends on the set of genomes considered and the pipeline of gene detection and clustering. Here, we redefined the size and the content of the core genome of P. aeruginosa from fully re-analyzed genomes of 17 reference strains. After the optimization of gene detection and clustering parameters, the core genome was defined at 5,233 orthologs, which represented ~ 88% of the average genome. Extrapolation indicated that our panel was suitable to estimate the core genome that will remain constant even if new genomes are added. The core genome contained resistance determinants to the major antibiotic families as well as most metabolic, respiratory, and virulence genes. Although some virulence genes were accessory, they often related to conserved biological functions. Long-standing prophage elements were subjected to a genetic drift to eventually display a G+C content as higher as that of the core genome. This contrasts with the low G+C content of highly conserved ribosomal genes. The conservation of metabolic and respiratory genes could guarantee the ability of the species to thrive on a variety of carbon sources for energy in aerobiosis and anaerobiosis. Virtually all the strains, of environmental or clinical origin, have the complete toolkit to become resistant to the major antipseudomonal compounds and possess basic pathogenic mechanisms to infect humans. The knowledge of the genes shared by the majority of the P. aeruginosa isolates is a prerequisite for designing effective therapeutics to combat the wide variety of human infections.

International audienceThe Monte Carlo method is partially reviewed with the objective of illustrating how some of the most recent methodological advances can benefit to concentrated solar research. This review puts forward the practical consequences of writing down and handling the integral formulation associated to each Monte Carlo algorithm. Starting with simple examples and up to the most complex multiple reflection, multiple scattering configurations, we try to argue that these formulations are very much accessible to the non specialist and that they allow a straightforward entry to sensitivity computations (for assistance in design optimization processes) and to convergence enhancement techniques involving subtle concepts such as control variate and zero variance. All illustration examples makePROMES - UPR CNRS 8521 - 7, rue du Four Solaire, 66120 Font Romeu Odeillo, France use of the public domain development environment EDStar (including advanced parallelized computer graphics libraries) and are meant to serve as start basis either for the upgrading of existing Monte Carlo codes, or for fast implementation of ad hoc codes when specific needs cannot be answered with standard concentrated solar codes (in particular as far as the new generation of solar receivers is concerned). (C) 2013 Elsevier Ltd. All rights reserved