Adapter and enzymatic functions of proteases in T-cell activation.

Proteases control many vital aspects of humoral and cellular immune responses, including the maturation of cytokines and the killing of target cells. Recently, it has become evident that triggering of the T-cell receptor controls T-cell proliferation through proteases such as mucosa-associated lymphoid tissue 1 (MALT1) and Caspase-8 that act both as adapters and enzymes. Here, we discuss the role of these and other proteases that are relevant to the control of the T-cell response and represent interesting targets of therapeutic immunomodulation.

Measurement of the specific heat and determination of the thermodynamic functions of relaxed amorphous silicon

The specific heat, cp, of two amorphous silicon (a-Si) samples has been measured by differential scanning calorimetry in the 100–900K temperature range. When the hydrogen content is reduced by thermal annealing, cp approaches the value of crystalline Si (c-Si). Within experimental accuracy, we conclude that cp of relaxed pure a-Si coincides with that of c-Si. This result is used to determine the enthalpy, entropy, and Gibbs free energy of defect-free relaxed a-Si. Finally, the contribution of structural defects on these quantities is calculated and the melting point of several states of a-Si is predicted

Emerging functions of myelin associated proteins during development neuronal plasticity and and neurpdegeneration.

Adult mammalian central nervous system (CNS) axons have a limited regrowth capacity following injury. Myelin-associated inhibitors (MAIs) limit axonal outgrowth and their blockage improves the regeneration of damaged fiber tracts. Three of these proteins, Nogo-A, MAG and OMgp, share two common neuronal receptors: NgR1, together with its co-receptors (p75(NTR), TROY and Lingo-1), and the recently described paired immunoglobulin-like receptor B (PirB). These proteins impair neuronal regeneration by limiting axonal sprouting. Some of the elements involved in the myelin inhibitory pathways may still be unknown, but the discovery that blocking both PirB and NgR1 activities leads to near-complete release from myelin inhibition, sheds light on one of the most competitive and intense fields of neuroregeneration study during in recent decades. In parallel with the identification and characterization of the roles and functions of these inhibitory molecules in axonal regeneration, data gathered in the field strongly suggest that most of these proteins have roles other than axonal growth inhibition. The discovery of a new group of interacting partners for myelin-associated receptors and ligands, as well as functional studies within or outside the CNS environment, highlights the potential new physiological roles for these proteins in processes such as development, neuronal homeostasis, plasticity and neurodegeneration.

Traces of functions in Fock spaces on lattices of critical density

Following a scheme of Levin we describe the values that functions in Fock spaces take on lattices of critical density in terms of both the size of the values and a cancelation condition that involves discrete versions of the Cauchy and Beurling-Ahlfors transforms.

Uniform estimates in the Poincare-Aronszajn theorem on the separation of singularities of analytic functions

We study the possibility of splitting any bounded analytic function $f$ with singularities in a closed set $E\cup F$ as a sum of two bounded analytic functions with singularities in $E$ and $F$ respectively. We obtain some results under geometric restrictions on the sets $E$ and $F$ and we provide some examples showing the sharpness of the positive results.

Circadian clock-dependent and -independent rhythmic proteomes implement distinct diurnal functions in mouse liver.

Diurnal oscillations of gene expression controlled by the circadian clock underlie rhythmic physiology across most living organisms. Although such rhythms have been extensively studied at the level of transcription and mRNA accumulation, little is known about the accumulation patterns of proteins. Here, we quantified temporal profiles in the murine hepatic proteome under physiological light-dark conditions using stable isotope labeling by amino acids quantitative MS. Our analysis identified over 5,000 proteins, of which several hundred showed robust diurnal oscillations with peak phases enriched in the morning and during the night and related to core hepatic physiological functions. Combined mathematical modeling of temporal protein and mRNA profiles indicated that proteins accumulate with reduced amplitudes and significant delays, consistent with protein half-life data. Moreover, a group comprising about one-half of the rhythmic proteins showed no corresponding rhythmic mRNAs, indicating significant translational or posttranslational diurnal control. Such rhythms were highly enriched in secreted proteins accumulating tightly during the night. Also, these rhythms persisted in clock-deficient animals subjected to rhythmic feeding, suggesting that food-related entrainment signals influence rhythms in circulating plasma factors.

Hardy’s inequalities for monotone functions on partly ordered measure spaces

We characterize the weighted Hardy inequalities for monotone functions in Rn +. In dimension n = 1, this recovers the standard theory of Bp weights. For n > 1, the result was previously only known for the case p = 1. In fact, our main theorem is proved in the more general setting of partly ordered measure spaces.

Distinct profiles of cytotoxic granules in memory CD8 T cells correlate with functions, differentiation stage, and antigen exposure

RAPPORT DE SYNTHÈSE : Les profils des granules cytotoxiques des cellules T CD8 mémoires sont corrélés à la fonction, à leur état de différentiation et à l'exposition à l'antigène. Les lymphocytes T-CD8 cytotoxiques exercent leur fonction antivirale et antitumorale surtout par la sécrétion des granules cytotoxiques. En général, ce sont l'activité de dégranulation et les granules cytotoxiques (contenant perforine et différentes granzymes) qui définissent les lymphocytes T-CD8 cytotoxiques. Dans cette étude, nous avons investigué l'expression de granzyme K par cytométrie en flux, en comparaison avec l'expression de granzyme A, granzyme B et de perforine. L'expression des granules cytotoxiques a été déterminée dans lymphocytes T-CD8 qui étaient spécifiques pour des différents virus, en particulier spécifique pour le virus d'influenza (flu), le virus Ebstein Barr (EBV), le virus de cytomégalie (CMV) et le virus de l'immunodéficience humaine (HIV). Nous avons observé une dichotomie entre l'expression du granzyme K et de la perforine dans les lymphocytes T-CD8 qui étaient spécifiques aux virus mentionnés. Les profils des lymphocytes T-CD8 spécifiques à flu étaient positifs soit pour granzyme A et granzyme K soit pour le granzyme K seul, mais dans l'ensemble négatifs pour perforine et granzyme B. Les cellules spécifiques à CMV étaient dans la plupart positives pour perforine, granzyme B et A, mais négatives pour le granzyme K. Les cellules spécifiques à EBV et HIV étaient dans la majorité positives pour granzyme A, B et K, et dans la moitié des cas négatives pour la perforine. Nous avons également analysé, selon les marqueurs de mémoire de CD45 et CD127, les profils de différentiation cellulaire: Les cellules avec les granules cytotoxiques contenant exclusivement le granzyme K, étaient associées à un état de différentiation précoce. Au contraire, les protéines cytolytiques perforine, granzyme A et B, correspondent à une différentiation avancée. En outre, les protéines perforine et granzyme B, mais pas les granzymes A et K, sont corrélées à une activité cytotoxique. Finalement, des changements dans l'exposition d'antigène in vitro et in vivo suivant une infection primaire d' HIV ou une vaccination modulent le profil de granules cytotoxiques. Ces résultats nous permettent d'étendre la compréhension de la relation entre les différents profils de granules cytotoxiques des lymphocytes T-CD8 et leur fonction, leur état de différentiation et l'exposition à l'antigène.

Post-Nonlinear Mixtures and Beyond

Although sources in general nonlinear mixturm arc not separable iising only statistical independence, a special and realistic case of nonlinear mixtnres, the post nonlinear (PNL) mixture is separable choosing a suited separating system. Then, a natural approach is based on the estimation of tho separating Bystem parameters by minimizing an indcpendence criterion, like estimated mwce mutual information. This class of methods requires higher (than 2) order statistics, and cannot separate Gaarsian sources. However, use of [weak) prior, like source temporal correlation or nonstationarity, leads to other source separation Jgw rithms, which are able to separate Gaussian sourra, and can even, for a few of them, works with second-order statistics. Recently, modeling time correlated s011rces by Markov models, we propose vcry efficient algorithms hmed on minimization of the conditional mutual information. Currently, using the prior of temporally correlated sources, we investigate the fesihility of inverting PNL mixtures with non-bijectiw non-liacarities, like quadratic functions. In this paper, we review the main ICA and BSS results for riunlinear mixtures, present PNL models and algorithms, and finish with advanced resutts using temporally correlated snu~sm

Peroxisome proliferator-activated receptors: three isotypes for a multitude of functions.

The peroxisome proliferator-activated receptors (PPARs) are fatty acid and eicosanoid inducible nuclear receptors, which occur in three different isotypes. Upon activator binding, they modulate the expression of various target genes implicated in several important physiological pathways. During the past few years, the identification of both PPAR ligands, natural and synthetic, and PPAR targets and their associated functions has been one of the most important achievements in the field. It underscores the potential therapeutic application of PPAR-specific compounds on the one side, and the crucial biological roles of endogenous PPAR ligands on the other.

Ex vivo characterization of human CD8+ T subsets with distinct replicative history and partial effector functions.

After antigenic challenge, naive T lymphocytes enter a program of proliferation and differentiation during the course of which they acquire effector functions and may ultimately become memory cells. In humans, the pathways of effector and memory T-cell differentiation remain poorly defined. Here we describe the properties of 2 CD8+ T-lymphocyte subsets, RA+CCR7-27+28+ and RA+CCR7-27+28-, in human peripheral blood. These cells display phenotypic and functional features that are intermediate between naive and effector T cells. Like naive T lymphocytes, both subsets show relatively long telomeres. However, unlike the naive population, these T cells exhibit reduced levels of T-cell receptor excision circles (TRECs), indicating they have undergone additional rounds of in vivo cell division. Furthermore, we show that they also share effector-type properties. At equivalent in vivo replicative history, the 2 subsets express high levels of Fas/CD95 and CD11a, as well as increasing levels of effector mediators such as granzyme B, perforin, interferon gamma, and tumor necrosis factor alpha. Both display partial ex vivo cytolytic activity and can be found among cytomegalovirus-specific cytolytic T cells. Taken together, our data point to the presence of T cells with intermediate effector-like functions and suggest that these subsets consist of T lymphocytes that are evolving toward a more differentiated effector or effector-memory stage.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Functions for estimating stem diameter and tree age using tree height, crown width and existing stand database information

Abstract