Preferences of patients with advanced lung cancer regarding the involvement of family and others in medical decision-making

Objective: To explore the preferences of competent patients with advanced lung cancer regarding involvement of family and/or others in their medical decision-making, and their future preferences in case of loss of competence. Methods: Over 1 year, physicians in 13 hospitals in Flanders, Belgium, recruited patients with initial non-small-cell lung cancer, stage IIIb or IV. The patients were interviewed with a structured questionnaire every 2 months until the fourth interview and every 4 months until the sixth interview. Results: At inclusion, 128 patients were interviewed at least once; 13 were interviewed 6 consecutive times. Sixty-nine percent of patients wanted family members to be involved in medical decision-making and this percentage did not change significantly over time. One third of these patients did not achieve this preference. Ninety-four percent of patients wanted family involvement if they lost competence, 23% of these preferring primary physician control over decision-making, 41% shared physician and family control, and 36% primary family control. This degree of preferred family involvement expressed when competent did not change significantly over time at population level, but did at individual level; almost half the patients changed their minds either way at some point during the observation period. Conclusions: The majority of patients with lung cancer wanted family involvement in decision-making, and almost all did so in case of future loss of competence. However, as half of the patients changed their minds over time about the degree of family involvement they wanted if they lost competence, physicians should regularly rediscuss a patient's preferences

Herschel -ATLAS: Extragalactic number counts from 250 to 500 microns

Aims. The Herschel-ATLAS survey (H-ATLAS) will be the largest area survey to be undertaken by the Herschel Space Observatory. It will cover 550 sq. deg. of extragalactic sky at wavelengths of 100, 160, 250, 350 and 500 μm when completed, reaching flux limits (5σ) from 32 to 145 mJy. We here present galaxy number counts obtained for SPIRE observations of the first ~14 sq. deg. observed at 250, 350 and 500 μm. Methods. Number counts are a fundamental tool in constraining models of galaxy evolution. We use source catalogs extracted from the H-ATLAS maps as the basis for such an analysis. Correction factors for completeness and flux boosting are derived by applying our extraction method to model catalogs and then applied to the raw observational counts. Results. We find a steep rise in the number counts at flux levels of 100–200 mJy in all three SPIRE bands, consistent with results from BLAST. The counts are compared to a range of galaxy evolution models. None of the current models is an ideal fit to the data but all ascribe the steep rise to a population of luminous, rapidly evolving dusty galaxies at moderate to high redshift

Capital-risque et capital-développement : des propositions de réforme

Mortier Denis. Capital-risque et capital-développement : des propositions de réforme. In: Revue d'économie financière. Hors-série, 1995. Partenariat public-privé et développement territorial . pp. 299-304

Le pour et le contre: ou Lettres sur la postérité

info:eu-repo/semantics/publishe

Bayesian varying coefficient model with selection: An application to functional mapping

International audienceHow does the genetic architecture of quantitative traits evolve over time? Answering this question is crucial for many applied fields such as human genetics and plant or animal breeding. In the last decades, high-throughput genome techniques have been used to better understand links between genetic information and quantitative traits. Recently, high-throughput phenotyping methods are also being used to provide huge information at a phenotypic scale. In particular, these methods allow traits to be measured over time, and this, for a large number of individuals. Combining both information might provide evidence on how genetic architecture evolves over time. However, such data raise new statistical challenges related to, among others, high dimensionality, time dependencies, time varying effects. In this work, we propose a Bayesian varying coefficient model allowing, in a single step, the identification of genetic markers involved in the variability of phenotypic traits and the estimation of their dynamic effects. We evaluate the use of spike-and-slab priors for the variable selection with either P-spline interpolation or non-functional techniques to model the dynamic effects. Numerical results are shown on simulations and on a functional mapping study performed on an Arabidopsis thaliana (L. Heynh) data which motivated these developments

Bayesian varying coefficient model with selection: An application to functional mapping

International audienceHow does the genetic architecture of quantitative traits evolve over time? Answering this question is crucial for many applied fields such as human genetics and plant or animal breeding. In the last decades, high-throughput genome techniques have been used to better understand links between genetic information and quantitative traits. Recently, high-throughput phenotyping methods are also being used to provide huge information at a phenotypic scale. In particular, these methods allow traits to be measured over time, and this, for a large number of individuals. Combining both information might provide evidence on how genetic architecture evolves over time. However, such data raise new statistical challenges related to, among others, high dimensionality, time dependencies, time varying effects. In this work, we propose a Bayesian varying coefficient model allowing, in a single step, the identification of genetic markers involved in the variability of phenotypic traits and the estimation of their dynamic effects. We evaluate the use of spike-and-slab priors for the variable selection with either P-spline interpolation or non-functional techniques to model the dynamic effects. Numerical results are shown on simulations and on a functional mapping study performed on an Arabidopsis thaliana (L. Heynh) data which motivated these developments