Enteric neural crest cells regulate vertebrate stomach patterning and differentiation

International audienceIn vertebrates, the digestive tract develops from a uniform structure where reciprocal epithelial-mesenchymal interactions pattern this complex organ into regions with specific morphologies and functions. Concomitant with these early patterning events, the primitive GI tract is colonized by the vagal enteric neural crest cells (vENCCs), a population of cells that will give rise to the enteric nervous system (ENS), the intrinsic innervation of the GI tract. The influence of vENCCs on early patterning and differentiation of the GI tract has never been evaluated. In this study, we report that a crucial number of vENCCs is required for proper chick stomach development, patterning and differentiation. We show that reducing the number of vENCCs by performing vENCC ablations induces sustained activation of the BMP and Notch pathways in the stomach mesenchyme and impairs smooth muscle development

Shear test on viscoelastic granular material using Contact Dynamics simulations

By means of 3D contact dynamic simulations, the behavior of a viscoelastic granular material under shear loading is investigated. A viscoelastic fluid phase surrounding the solid particles is simulated by a contact model acting between them. This contact law was implemented in the LMGC90 software, based on the Burgers model. This model is able to simulate also the effect of creep relaxation. To validate the proposed contact model, several direct shear tests were performed, experimentally and numerically using the Leutner device. The numerical samples were created using spheres with two particle size distribution, each one identified for two layers from a road structure. Our results show a reasonable agreement between experimental and numerical data regarding the strain-stress evolution curves and the stress levels measured at failure. The proposed model can be used to simulate the mechanical behavior of multi-layer road structure and to study the influence of traffic on road deformation, cracking and particles pull-out induced by traffic loading

An unexpected connection between Bayes A−A-optimal designs and the Group Lasso

International audienceWe show that the $A$-optimal design optimization problem over $m$ design points in $\mathbb{R}^n$ is equivalent to minimizing a quadratic function plus a group lasso sparsity inducing term over $n\times m$ real matrices. This observation allows to describe several new algorithms for $A$-optimal design based on splitting and block coordinate decomposition. These techniques are well known and proved powerful to treat large scale problems in machine learning and signal processing communities. The proposed algorithms come with rigorous convergence guaranties and convergence rate estimate stemming from the optimization literature. Performances are illustrated on synthetic benchmarks and compared to existing methods for solving the optimal design problem