École normale supérieure de Cachan

About: École normale supérieure de Cachan is a education organization based out in Cachan, Île-de-France, France. It is known for research contribution in the topics: Decidability & Finite element method. The organization has 2717 authors who have published 5585 publications receiving 175925 citations.

KIT mutation analysis in mast cell neoplasms : recommendations of the European Competence Network on Mastocytosis

Abstract: Although acquired mutations in KIT are commonly detected in various categories of mastocytosis, the methodologies applied to detect and quantify the mutant type and allele burden in various cells and tissues are poorly defined. We here propose a consensus on methodologies used to detect KIT mutations in patients with mastocytosis at diagnosis and during follow-up with sufficient precision and sensitivity in daily practice. In addition, we provide recommendations for sampling and storage of diagnostic material as well as a robust diagnostic algorithm. Using highly sensitive assays, KIT D816V can be detected in peripheral blood leukocytes from most patients with systemic mastocytosis (SM) that is a major step forward in screening and SM diagnosis. In addition, the KIT D816V allele burden can be followed quantitatively during the natural course or during therapy. Our recommendations should greatly facilitate diagnostic and follow-up investigations in SM in daily practice as well as in clinical trials. In addition, the new tools and algorithms proposed should lead to a more effective screen, early diagnosis of SM and help to avoid unnecessary referrals.

Skin Model Shapes: A new paradigm shift for geometric variations modelling in mechanical engineering

Abstract: Geometric deviations are inevitably observable on manufactured workpieces and have huge influences on the quality and function of mechanical products. Therefore, many activities in geometric variations management have to be performed to ensure the product function despite the presence of these deviations. Dimensional and Geometrical Product Specification and Verification (GPS) are standards for the description of workpieces. Their lately revision grounds on GeoSpelling, which is a univocal language for geometric product specification and verification and aims at providing a common understanding of geometric specifications in design, manufacturing, and inspection. The Skin Model concept is a basic concept within GeoSpelling and is an abstract model of the physical interface between a workpiece and its environment. In contrast to this understanding, established models for computer-aided modelling and engineering simulations make severe assumptions about the workpiece surface. Therefore, this paper deals with operationalizing the Skin Model concept in discrete geometry for the use in geometric variations management. For this purpose, Skin Model Shapes, which are particular Skin Model representatives from a simulation perspective, are generated. In this regard, a Skin Model Shape is a specific outcome of the conceptual Skin Model and comprises deviations from manufacturing and assembly. The process for generating Skin Model Shapes is split into a prediction and an observation stage with respect to the available information and knowledge about expected geometric deviations. Moreover, applications for these Skin Model Shapes in the context of mechanical engineering are given.

Nanodiamond as a Vector for siRNA Delivery to Ewing Sarcoma Cells

Abstract: The ability of diamond nanoparticles (nanodiamonds, NDs) to deliver small interfering RNA (siRNA) into Ewing sarcoma cells is investigated with a view to the possibility of in-vivo anticancer nucleic-acid drug delivery. siRNA is adsorbed onto NDs that are coated with cationic polymer. Cell uptake of NDs is demonstrated by taking advantage of the NDs' intrinsic fluorescence from embedded color-center defects. Cell toxicity of these coated NDs is shown to be low. Consistent with the internalization efficacy, a specific inhibition of EWS/Fli-1 gene expression is shown at the mRNA and protein level by the ND-vectorized siRNA in a serum-containing medium.

Topographic Maps and Local Contrast Changes in Natural Images

Abstract: We call “natural” image any photograph of an outdoor or indoor scene taken by a standard camera. We discuss the physical generation process of natural images as a combination of occlusions, transparencies and contrast changes. This description fits to the phenomenological description of Gaetano Kanizsa according to which visual perception tends to remain stable with respect to these basic operations. We define a contrast invariant presentation of the digital image, the topographic map, where the subjacent occlusion-transparency structure is put into evidence by the interplay of level lines. We prove that each topographic map represents a class of images invariant with respect to local contrast changes. Several visualization strategies of the topographic map are proposed and implemented and mathematical arguments are developed to establish stability properties of the topographic map under digitization.

A Probabilistic Criterion to Detect Rigid Point Matches Between Two Images and Estimate the Fundamental Matrix

Abstract: The perspective projections of n physical points on two views (stereovision) are constrained as soon as n ≥ 8 However, to prove in practice the existence of a rigid motion between two images, more than 8 point matches are desirable in order to compensate for the limited accuracy of the matches In this paper, we propose a computational definition of rigidity and a probabilistic criterion to rate the meaningfulness of a rigid set as a function of both the number of pairs of points (n) and the accuracy of the matches This criterion yields an objective way to compare, say, precise matches of a few points and approximate matches of a lot of points It gives a yes/no answer to the question: “could this rigid points correspondence have occurred by chance?”, since it guarantees that the expected number of meaningful rigid sets found by chance in a random distribution of points is as small as desired It also yields absolute accuracy requirements for rigidity detection in the case of non-matched points, and optimal values of n, depending on the expected accuracy of the matches and on the proportion of outliers We use it to build an optimized random sampling algorithm that is able to detect a rigid motion and estimate the fundamental matrix when the set of point matches contains up to 90% of outliers, which outperforms the best currently known methods like M-estimators, LMedS, classical RANSAC and Tensor Voting