Classic and Inverse Compositional Gauss-Newton in Global DIC

International audienceToday, effective implementations of Digital Image Correlation (DIC) are based on iterative algorithms with constant linear operators. A relevant idea of the classic Finite Element (or more generally global) DIC (FE-DIC) solver consists in replacing the gradient of the deformed state image with that of the reference image, so as to obtain a constant operator. Different arguments (small strains, small deformations, equality of the two gradients close to the solution...) have been given in the literature to justify this approximation, but none of them are fully accurate. Indeed, the convergence of the optimization algorithm has to be investigated from its ability to produce descent directions. Through such a study, this paper attempts to explain why this approximation works and what is its domain of validity. Then an Inverse Compositional Gauss-Newton (ICGN) implementation of FE-DIC is proposed as a cost effective and mathematically sound alternative to this approximation

A role for miR-132 in learned safety

Abstract Learned safety is a fear inhibitory mechanism, which regulates fear responses, promotes episodes of safety and generates positive affective states. Despite its potential as experimental model for several psychiatric illnesses, including post-traumatic stress disorder and depression, the molecular mechanisms of learned safety remain poorly understood, We here investigated the molecular mediators of learned safety, focusing on the characterization of miRNA expression in the basolateral amygdala (BLA). Comparing levels of 22 miRNAs in learned safety and learned fear trained mice, six safety-related miRNAs, including three members of the miR-132/-212 family, were identified. A gain-of-function approach based upon in-vivo transfection of a specific miRNA mimic, and miR-132/212 knock-out mice as loss-of-function tool were used in order to determine the relevance of miR-132 for learned safety at the behavioral and the neuronal functional levels. Using a designated bioinformatic approach, PTEN and GAT1 were identified as potential novel miR-132 target genes and further experimentally validated. We here firstly provide evidence for a regulation of amygdala miRNA expression in learned safety and propose miR-132 as signature molecule to be considered in future preclinical and translational approaches testing the transdiagnostic relevance of learned safety as intermediate phenotype in fear and stress-related disorders