Aix-Marseille University

About: Aix-Marseille University is a education organization based out in Marseille, France. It is known for research contribution in the topics: Population & Galaxy. The organization has 24326 authors who have published 54240 publications receiving 1455416 citations. The organization is also known as: University Aix-Marseille & université d'Aix-Marseille.

Adaptive Genetic Variation on the Landscape: Methods and Cases

Abstract: There is a growing interest in identifying ecological factors that influence adaptive genetic diversity patterns in both model and nonmodel species. The emergence of large genomic and environmental data sets, as well as the increasing sophistication of population genetics methods, provides an opportunity to characterize these patterns in relation to the environment. Landscape genetics has emerged as a flexible analytical framework that connects patterns of adaptive genetic variation to environmental heterogeneity in a spatially explicit context. Recent growth in this field has led to the development of numerous spatial statistical methods, prompting a discussion of the current benefits and limitations of these approaches. Here we provide a review of the design of landscape genetics studies, the different statistical tools, some important case studies, and perspectives on how future advances in this field are likely to shed light on important processes in evolution and ecology.

Radial distribution of stars, gas, and dust in sings galaxies. ii. derived dust properties

Abstract: We present a detailed analysis of the radial distribution of dust properties in the SINGS sample, performed on a set of ultraviolet (UV), infrared (IR), and Hi surface brightness profiles, combined with published molecular gas profiles and metallicity gradients. The internal extinction, derived from the total-IR (TIR)-to-far-UV (FUV) luminosity ratio, decreases with radius, and is larger in Sb-Sbc galaxies. The TIR-to-FUV ratio correlates with the UV spectral slope β, following a sequence shifted to redder UV colors with respect to that of starbursts. The star formation history (SFH) is identified as the main driver of this departure. Both L_TIR)/L_FUV and β correlate well with metallicity, especially in moderately face-on galaxies. The relation shifts to redder colors with increased scatter in more edge-on objects. By applying physical dust models to our radial spectral energy distributions, we have derived radial profiles of the total dust mass surface density, the fraction of the total dust mass contributed by polycyclic aromatic hydrocarbons (PAHs), and the intensity of the radiation field heating the grains. The dust profiles are exponential, their radial scale length being constant from Sb to Sd galaxies (only ~ 10% larger than the stellar scale length). Many S0/a-Sab galaxies have central depressions in their dust radial distributions. The PAH abundance increases with metallicity for 12 + log(O/H) < 9, and at larger metallicities the trend flattens and even reverses, with the SFH being a plausible underlying driver for this behavior. The dust-to-gas ratio is also well correlated with metallicity and therefore decreases with galactocentric radius. Although most of the total emitted IR power (especially in the outer regions of disks) is contributed by dust grains heated by diffuse starlight with a similar intensity as the local Milky Way radiation field, a small amount of the dust mass (~ 1%) is required to be exposed to very intense starlight in order to reproduce the observed fluxes at 24 μ m, accounting for ~ 10% of the total integrated IR power.

A systematic framework for functional connectivity measures

Abstract: Various methods have been proposed to characterize the functional connectivity between nodes in a network measured with different modalities (electrophysiology, functional magnetic resonance imaging etc.). Since different measures of functional connectivity yield different results for the same dataset, it is important to assess when and how they can be used. In this work, we provide a systematic framework for evaluating the performance of a large range of functional connectivity measures—based upon a comprehensive portfolio of models generating measurable responses. Specifically, we benchmarked 42 methods using 10,000 simulated datasets from 5 different types of generative models with different connectivity structures. Since all functional connectivity methods require the setting of some parameters (window size and number, model order etc.), we first optimized these parameters using performance criteria based upon (threshold free) ROC analysis. We then evaluated the performance of the methods on data simulated with different types of models. Finally, we assessed the performance of the methods against different levels of signal-to-noise ratios and network configurations. A MATLAB toolbox is provided to perform such analyses using other methods and simulated datasets.