Evidence of jaagsiekte sheep retrovirus-induced pulmonary adenocarcinoma in Ouled Djellal breed sheep in Algeria

We report the occurrence of lung cancer in a six months old lamb of Ouled Djellal breed from Algeria. The main clinical sign was a considerable amount of whitish foamy fluid discharge from the nostrils when the animal head was lowered and the rear end was lifted. The postmortem examination revealed the presence of enlarged, heavy and edematous lungs with diffuse or foci areas, reddish or white-gray in color. The gross and histological lesions of the lungs were compatible with pulmonary adenocarcinoma. Lung adenocarcinoma in sheep is caused by jaagsiekte sheep retrovirus (JSRV) and originated from differentiated alveolar type II cells and non-ciliated bronchiolar epithelial Clara cells. We evidenced the expression of the oncogenic JSRV by immunostaining of lung slides with specific antibodies against the JSRV envelope. The viral proteins were expressed only in the tumor cells from the affected areas. As already described in other countries, JSRV-induced lung adenocarcinoma is present in the sheep population in Algeria. (C) 2020 Urmia University. All rights reserved

An asymptotic preserving scheme for the Kac model of the Boltzmann equation in the diffusion limit

International audienceIn this paper we propose a numerical scheme to solve the Kac model of the Boltzmann equation for multiscale rarefied gas dynamics. This scheme is uniformly stable with respect to the Knudsen number, consistent with the fluid-diffusion limit for small Knudsen numbers, and with the Kac equation in the kinetic regime. Our approach is based on the micro-macro decomposition which leads to an equivalent formulation of the Kac model that couples a kinetic equation with macroscopic ones. This method is validated with various test cases and compared to other standard methods

Uncertainty quantification for kinetic models in socio-economic and life sciences

Kinetic equations play a major rule in modeling large systems of interacting particles. Recently the legacy of classical kinetic theory found novel applications in socio-economic and life sciences, where processes characterized by large groups of agents exhibit spontaneous emergence of social structures. Well-known examples are the formation of clusters in opinion dynamics, the appearance of inequalities in wealth distributions, flocking and milling behaviors in swarming models, synchronization phenomena in biological systems and lane formation in pedestrian traffic. The construction of kinetic models describing the above processes, however, has to face the difficulty of the lack of fundamental principles since physical forces are replaced by empirical social forces. These empirical forces are typically constructed with the aim to reproduce qualitatively the observed system behaviors, like the emergence of social structures, and are at best known in terms of statistical information of the modeling parameters. For this reason the presence of random inputs characterizing the parameters uncertainty should be considered as an essential feature in the modeling process. In this survey we introduce several examples of such kinetic models, that are mathematically described by nonlinear Vlasov and Fokker--Planck equations, and present different numerical approaches for uncertainty quantification which preserve the main features of the kinetic solution.Comment: To appear in "Uncertainty Quantification for Hyperbolic and Kinetic Equations

A Multilevel Monte Carlo Asymptotic-Preserving Particle Method for Kinetic Equations in the Diffusion Limit

We propose a multilevel Monte Carlo method for a particle-based asymptotic-preserving scheme for kinetic equations. Kinetic equations model transport and collision of particles in a position-velocity phase-space. With a diffusive scaling, the kinetic equation converges to an advection-diffusion equation in the limit of zero mean free path. Classical particle-based techniques suffer from a strict time-step restriction to maintain stability in this limit. Asymptotic-preserving schemes provide a solution to this time step restriction, but introduce a first-order error in the time step size. We demonstrate how the multilevel Monte Carlo method can be used as a bias reduction technique to perform accurate simulations in the diffusive regime, while leveraging the reduced simulation cost given by the asymptotic-preserving scheme. We describe how to achieve the necessary correlation between simulation paths at different levels and demonstrate the potential of the approach via numerical experiments.Comment: 20 pages, 7 figures, published in Monte Carlo and Quasi-Monte Carlo Methods 2018, correction of minor typographical error