Human metapneumovirus

National audienceThe human metapneumovirus (hMPV) is a new Pneumovirinae related to the avian metapneumovirus type C. hMPV genome differs from human respiratory syncytial virus (RSV) genome by the gene order and the lack of nonstructural genes. Two genetic sub-groups and four sub-types of hMPV are identified. hMPV infections evolve as regular winter outbreaks which have roughly the same size and overlaping RSV epidemics. Among hospitalized children in Caen. hMPV is detected in 9.7% of the cases after RSV (37%), rhinovirus (18%), influenza virus (14.5%), adenovirus (9%), and parainfluenza virus (5%). Most of hMPV infections are observed in children suffering from bronehiolitis, but the localization to lower respiratory tract and the severity of the disease are less frequent in comparison with RSV infections. hMPV is very difficult to isolate using cell culture. Up to now, the only way for hMPV diagnosis was the TS-CRP assays. But the recent apparition of direct antigenic tests allows us to get a fair, rapid, and economic diagnostic tool. (C) 2008 Elsevier Masson SAS

Autoimmune diabetes onset results from qualitative rather than quantitative age-dependent changes in pathogenic T-cells.

Diabetogenic T-cells can be detected in pre-diabetic nonobese diabetic (NOD) mice after transfer in NOD-SCID recipients. Here we demonstrate that 6-week-old pre-diabetic NOD mice, >2 months before disease onset, already harbor pathogenic T-cells in equal numbers to overtly diabetic animals. The delay in diabetes appearance is explained by the presence of regulatory CD4+ CD25+ T-cells that control diabetogenic effectors and that are, in our hands, transforming growth factor (TGF)-beta-dependent. Our present results suggest, however, that diabetes onset is only partly explained by a decline in this regulatory T-cell activity. Another major factor appears to be the progressive resistance of diabetogenic cells to TGF-beta-dependent mediated inhibition. We propose that progression to overt disease correlates with the pathogenic T-cell's escape from TGF-beta-dependent T-cell-mediated regulation

High Performance Computing for the Reduced Basis Method. Application to Natural Convection

In this paper, we are interested in applying the reduced basis methodology (RBM) to steady-state natural convection problems. The latter has applications in many engineering domains and being able to apply the RBM would allow to gain huge computation savings when querying the model for many parameter evaluations. In this work, we focus on the order reduction of the model — in particular the handling of the non-linear terms, — as well as the design of the RBM computational framework and the requirements on high performance computing to treat 3D models using Feel++, a C++ open source library to solve partial differential equations. Numerical experiments are presented on 2D and 3D models

A Reduced Basis Framework: Application to large scale non-linear multi-physics problems

In this paper we present applications of the reduced basis method (RBM) to large-scale non-linear multi-physics problems. We first describe the mathematical framework in place and in particular the Empirical Interpolation Method (EIM) to recover an affine decomposition and then we propose an implementation using the open-source library Feel++ which provides both the reduced basis and finite element layers. Large scale numerical examples are shown and are connected to real industrial applications arising from the High Field Resistive Magnets development at the Laboratoire National des Champs Magnétiques Intenses

Extended generalized Lagrangian multipliers for magnetohydrodynamics using adaptive multiresolution methods

We present a new adaptive multiresoltion method for the numerical simulation of ideal magnetohydrodynamics. The governing equations, i.e., the compressible Euler equations coupled with the Maxwell equations are discretized using a finite volume scheme on a two-dimensional Cartesian mesh. Adaptivity in space is obtained via Harten’s cell average multiresolution analysis, which allows the reliable introduction of a locally refined mesh while controlling the error. The explicit time discretization uses a compact Runge–Kutta method for local time stepping and an embedded Runge-Kutta scheme for automatic time step control. An extended generalized Lagrangian multiplier approach with the mixed hyperbolic-parabolic correction type is used to control the incompressibility of the magnetic field. Applications to a two-dimensional problem illustrate the properties of the method. Memory savings and numerical divergences of magnetic field are reported and the accuracy of the adaptive computations is assessed by comparing with the available exact solution

firedrakeproject/petsc4py: The Python interface to PETSc

firedrakeproject/petsc4py: The Python interface to PETScfiredrakeproject/petsc4py: The Python interface to PETScFiredrake_20190815.

firedrakeproject/petsc4py: The Python interface to PETSc

This release is specifically created to document the version of petsc4py used in a particular set of experiments using Firedrake. Please do not cite this as a general source for Firedrake or any of its dependencies. Instead, refer to https://www.firedrakeproject.org/citing.htmlThis release is specifically created to document the version of petsc4py used in a particular set of experiments using Firedrake. Please do not cite this as a general source for Firedrake or any of its dependencies. Instead, refer to https://www.firedrakeproject.org/citing.html20181204.