Showing results 421 to 440 on 7490 in total
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In the first part of this talk we present a recent analysis of Yersinia genomes. The Yersinia family accumulated many rearrangement mutations in the last few tenthousands years, which offers an unprecedented opportunity to study evolution of genome structure and arrangement. During the analysis, we realized that the current MCMC methods that provide samples from the posterior distribution of genome rearrangement scenarios converge slowly. The second part of the talk is about the theoretical convergence of MCMC methods for inferring genome rearrangements.
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The Istituto Nazionale per la Fauna Selvatica performs consulting and research activities to promote the knowledge and the conservation of wildlife with special reference to mammals and birds. I'm working in the mammal section of the Institute and I carry on researches on the use of space, habitat and population dynamics of wild ungulates (red, roe and fallow deer, wild boars). In the last years I was interested in the problem of population estimate of wildlife population using distance sampling. At Castelporziano I developed a long-term research on ungulates of Mediterranean using capture-mark-recapture to investigate population dynamics of fallow deer and wild boar. Recently we started a project to use GPS-GSM collar to investigate habitat selection of red deer. Together with field work I'm very much interested in developing statistical and mathematical models. A recent approach was to use distance sampling data to investigate local density to be correlated with short term variation in habitat quality.
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La caractérisation de l'influence des facteurs environnementaux sur la fertilité des couples est un enjeu important de santé publique. Dans une approche épidémiologique, cette caractérisation peut reposer sur différents modes d'échantillonnage des sujets. L'approche la plus couramment utilisée est rétrospective et repose sur le recrutement de femmes ayant déjà eu une grossesse. Cette approche a l'avantage d'être simple du point de vue logistique mais exclut les couples involontairement inféconds. Nous illustrerons les conséquences de cette exclusion en termes de biais et de perte de puissance statistique à partir de simulations et de données réelles, et discuterons les intérêts et limites de trois approches alternatives : 1) l'approche de cohorte incidente (dans laquelle les couples sont recrutés avant le début d'une tentative de grossesse et suivis), 2) l'approche de cohorte prévalente (dans laquelle on recrute les couples au cours de la tentative de grossesse, avec un suivi) et 3) l'approche des durées en cours, qui repose elle aussi sur un échantillon de couples recrutés en cours de tentative de grossesse, mais sans suivi. Dans chaque cas, les modèles de survie adaptés au mode d'échantillonnage seront brièvement présentés et illustrés, notamment à partir de l'Observatoire épidémiologique de la fertilité en France. Références : Keiding, N. (1992) Independent delayed entry (with discussion). In Klein, J. P. and Goel, P. K. (eds) Survival analysis: State of the Art. Kluwer, Dordrecht, pp. 309-326. Keiding, N., Kvist, K., Hartvig, H., Tvede, M. and Juul, S. (2002) Estimating time to pregnancy from current durations in a cross-sectional sample. Biostatistics 3, 565-578. Scheike, T. H. and Keiding, N. (2006) Design and analysis of time-to-pregnancy. Stat Methods Med Res 15, 127-140. Slama, R., Kold-Jensen, T., Scheike, T., Ducot, B., Spira, A. and Keiding, N. (2004) How would a decline in sperm concentration over time influence the probability of pregnancy? Epidemiology 15, 458-465. Slama, R., Ducot, B., Carstensen, L., Lorente, C., de La Rochebrochard, E., Leridon, H., Keiding, N. and Bouyer, J. (2006) Feasibility of the Current-Duration Approach to Studying Human Fecundity. Epidemiology 17, 440-449.
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A central question in molecular evolution concerns the nature of phenotypic transitions, in particular if neutral mutations hamper or somehow facilitate adaptability of proteins or RNAs to new requirements.Proteins and RNA have been found to accomplish different task by fluctuate between different phenotypes (structures), with frequencies and thus intensity of the associated trait being proportional to their stability. Therefore, functional promiscuity may correspond to different structures with energies close to the ground state which then represent multiple selectable traits. We here postulate that these near-ground state structures facilitate smooth transitions between phenotypes. Using biophysical model systems with exhaustive mappings of genotypes (sequences) onto phenotypes (structures), we demonstrate that this is indeed possible because of a smooth gradient of stability along which any phenotype can be optimised and also because of mutational proximity of similar phenotypes in genotype space.Our model provides a rationalisation of the intriguing, and otherwise puzzling experimental observation that adaptation to new requirements, e.g. latent function of a promiscuous enzyme, can proceed while the "old", phenotypically dominant function is maintained along a series of seemingly neutral mutations.Thus pleiotropy may facilitate adaptation of latent traits BEFORE gene duplications and increase the effective adaptability of proteins
When humans reason about functional structures of RNA, they speak of long hairpins with miRNA precursors, of clover leaf structures with tRNA, of neighouring hairpins with attenuators, and so on. Most of the time, we do not care about individual base pairs or helix sizes, while the overall arrangment of helices and loops really matters.RNA folding programs, however, used to be ignorant of abstraction in RNA, deceiving us with a single, minimum free energy structure, or overwhelming us with a plethora of near-optimal structures, most of which are quite similar and therefore redundant.RNA shape abstraction alleviates this situation. RNA shapes are abstract structure images, retaining adjacency and nesting of structural features, but disregarding size information. Shape abstraction integrates well with dynamic programming algorithms, and hence it can be applied during structure prediction rather than afterwards. This avoids exponential explosion in the near-optimal folding space, and provides a non-heuristic and complete account of an RNA molecule's structural inclinations. Quite magically, some long-studied problems become easy.In the presentation, I will shortly review the notion of abstract shapes. I will then discuss several applications of this concept, including a highly effective filtering method when working with structural classes of RNA described by covariance models, such as provided by the Rfam data base.
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