No abstract

IL-1 receptor-activated kinase 1 (IRAK1) is involved in signal transduction downstream of many TLRs and the IL-1R. Its potential as a drug target for chronic inflammatory diseases is underappreciated. To study its functional role in joint inflammation, we generated a mouse model expressing a functionally inactive IRAK1 (IRAK1 kinase deficient, IRAK1KD), which also displayed reduced IRAK1 protein expression and cell type–specific deficiencies of TLR signaling. The serum transfer model of arthritis revealed a potentially novel role of IRAK1 for disease development and neutrophil chemoattraction exclusively via its activity in nonhematopoietic cells. Consistently, IRAK1KD synovial fibroblasts showed reduced secretion of neutrophil chemoattractant chemokines following stimulation with IL-1β or human synovial fluids from patients with rheumatoid arthritis (RA) and gout. Together with patients with RA showing prominent IRAK1 expression in fibroblasts of the synovial lining, these data suggest that targeting IRAK1 may be therapeutically beneficial. As pharmacological inhibition of IRAK1 kinase activity had only mild effects on synovial fibroblasts from mice and patients with RA, targeted degradation of IRAK1 may be the preferred pharmacologic modality. Collectively, these data position IRAK1 as a central regulator of the IL-1β–dependent local inflammatory milieu of the joints and a potential therapeutic target for inflammatory arthritis.

Dans cet article, nous nous sommes intéressés aux problèmes de reconnaissance noncoopérative de cibles (NCTR) en tant que problème de classification supervisée. Après une présentation du système d'acquisition des profils distance radar et du problème de reconnaissance, suivie d'une étude statistique des données, nous proposons d'utiliser un algorithme des K plus proches voisins (KPPV) dont les performances sont détaillées en fonction du nombre de voisins K, du type de distance utilisée et de la nature des données utilisées (débruitées ou non). Dans un second temps, cet algorithme a été parallélisé sur un processeur many-coeurs (GPU : Graphics Processing Unit). Les opérations arithmétiques et le modèle d'accès mémoire ont été étudiés pour obtenir la meilleure parallélisation des calculs. Enfin, nous terminons par une discussion autour des perspectives envisageables pour la méthode proposée, notamment en s'intéressant à d'autres espaces de représentation ou à d'autres méthodes de classification. ABSTRACT. In this paper, first, we present the problem of Non Cooperative Target Recognition (NCTR) as a supervised classification problem. After a presentation on the radar acquisition system of range profiles and the problem of recognition, followed by a statistical study of data, we use a classical classification method of K Nearest Neighbors (KNN) to do this classification. We explore and compare the performances of this algorithm based on the choice of the distances, the choice of K and the nature of used data (denoised or not). KNN algorithm has been executed initially on CPU with Matlab and then on GPU. Arithmetic operations and memory access pattern has been studied to get the best parallelization. Finally, we conclude with a discussion about possible perspectives for the proposed method especially by focusing on other representation spaces or other classification methods.

Abstract-In this paper, we present the problem of Non Cooperative Target Recognition (NCTR) as a supervised classification problem. After a brief presentation on the radar acquisition system of range profiles and the problem of recognition, we use a Fuzzy-Logic based algorithm to do this classification maximizing the recognition rate while controling the error rate. Unlike classical NCTR algorithms, this new algorithm allows to control error rate under a fixed value and maximize the recognition rate.

International audienceIn this paper, a method of data visualization and classification performance estimation applied to target classification is proposed. The objective of this paper is to propose a mathematical tool for data characterization. The principle is to use a non linear dimensionality reduction technique to describe our data in a low-dimensional space and to model embedding data by Gaussian mixture model (GMM) to estimate classification performance graphically and analytically