The endocrine disruptor monoethyl-hexyl-phthalate is a selective peroxisome proliferator-activated receptor gamma modulator that promotes adipogenesis.

The ability of pollutants to affect human health is a major concern, justified by the wide demonstration that reproductive functions are altered by endocrine disrupting chemicals. The definition of endocrine disruption is today extended to broader endocrine regulations, and includes activation of metabolic sensors, such as the peroxisome proliferator-activated receptors (PPARs). Toxicology approaches have demonstrated that phthalate plasticizers can directly influence PPAR activity. What is now missing is a detailed molecular understanding of the fundamental basis of endocrine disrupting chemical interference with PPAR signaling. We thus performed structural and functional analyses that demonstrate how monoethyl-hexyl-phthalate (MEHP) directly activates PPARgamma and promotes adipogenesis, albeit to a lower extent than the full agonist rosiglitazone. Importantly, we demonstrate that MEHP induces a selective activation of different PPARgamma target genes. Chromatin immunoprecipitation and fluorescence microscopy in living cells reveal that this selective activity correlates with the recruitment of a specific subset of PPARgamma coregulators that includes Med1 and PGC-1alpha, but not p300 and SRC-1. These results highlight some key mechanisms in metabolic disruption but are also instrumental in the context of selective PPAR modulation, a promising field for new therapeutic development based on PPAR modulation.

Insight in the factors associated with the presence of protective antiviral T-cell response.

T cells play a primordial role in antiviral immunity. Virus-specific T-cell responses can be characterized by a number of independent variables. These include the magnitude of the response; the functional quality of the response, i.e. the types of cytokines secreted after stimulation and the proliferative or lytic potential; the tissue distribution of the T cells; the breadth of the response; and the avidity of the response. All of these together constitute the T-cell response to antigen (Ag) and comprise potential variables that may correlate with antiviral protective immunity. Substantial advances have recently been obtained in the characterization of virus-specific T-cell responses. These studies have shown that the quality (in term of functional profile) rather than the quantity of Ag-specific T cells was associated with protection. Recently, the term polyfunctional has been used to define T-cell responses that, in addition to typical effector functions such as secretion of IFN-g, TNF-a and MIP-1b and cytotoxic activity, comprise distinct T-cell populations, also able to secrete IL-2 and retaining Ag-specific proliferation capacity. The term \only effector" defines T-cell responses/ populations able to secrete cytokines such as IFN-g, TNF-a and MIP-1b and endowed with cytotoxic activity but lacking IL-2 and proliferation capacity. Several models of virus infections (HIV-1, cytomegalovirus [CMV], Epstein Barr virus [EBV], influenza [Flu] and Herpes Simplex virus) exclusively differentiated on the basis of Ag exposure and persistence, were investigated: 1) antigen clearance, 2) protracted Ag exposure and persistence and low Ag levels, 3) Ag persistence and high Ag levels, and 4) acute Ag exposure/re-exposure. These analyses have demonstrated that polyfunctional and not \only effector" T-cell responses were associated with protective antiviral immunity. However, the factors and mechanisms governing the generation of functionally distinct T-cell populations remain to be elucidated. Recently, several studies have shown a major influence of HLA genotype in the evolution of HIV and the progression of HIV-associated disease. In particular, certain HLA-B alleles were most closely associated with non-progressive disease and low viral load or disease and had a dominant involvement on the clinical course of HIV-associated diseases. In this study, we have investigated the relationship between HLA restriction and the functional profile of Tcell responses in order to determine whether HLA-B influenced the generation of polyfunctional CD8 T-cell responses. To be able to address this issue, we studied CD8 T-cell responses against HIV-1, CMV, EBV and Flu in 128 subjects. These analyses enabled us to demonstrate that HLA-Arestricted epitopes were mostly associated with \only effector" T-cell responses while, in contrast, polyfunctional CD8 T-cell responses were predominantly driven by virus epitopes restricted by HLA-B alleles. We then characterized eventual differences in the responsiveness of CD8 T-cell populations restricted by different HLA-A and HLA-B alleles. For this purpose, we investigated the T-cell receptor (TCR) avidity for the cognate epitope of polyfunctional and \only effector" CD8 T-cell populations. Our results indicated that overall virus-specific CD8 T-cell populations recognizing virus epitopes restricted by HLA-B alleles were equipped with lower avidity TCR for the cognate epitopes when compared to those recognizing epitopes restricted by HLA-A alleles. In conclusion, these results provide the rationale for the observed protective role of HLA-B genotypes in HIV-1- infection and new insights into the relationship between TCR avidity and functional profile of virus-specific CD8 Tcells.

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.

PPAR disruption: Cellular mechanisms and physiological consequences

Endocrine disruption is defined as the perturbation of the endocrine system, which includes disruption of nuclear hormone receptor signalling. Peroxisome proliferator-activated receptors (PPARs) represent a family of nuclear receptors that has not yet been carefully studied with regards to endocrine disruption, despite the fact that PPARs are known to be important targets for xenobiotics. Here we report a first comprehensive approach aimed at defining the mechanistic basis of PPAR disruption focusing on one chemical, the plasticizer monethylhexyl phthalate (MEHP), but using a variety of methodologies and models. We used mammalian cells and a combination of biochemical and live cell imaging techniques to show that MEHP binds to PPAR gamma and selectively regulates interactions with coregulators. Micro-array experiments further showed that this selectivity is translated at the physiological level during adipocyte differentiation. In that context, MEHP functions as a selective PPAR modulator regulating only a subset of PPAR gamma target genes compared to the action of a full agonist. We also explored the action of MEHP on PPARs in an aquatic species, Xenopus laevis, as many xenobiotics are found in aquatic ecosystems. In adult males, micro-array data indicated that MEHP influences liver physiology, possibly through a cross-talk between PPARs and estrogen receptors (ER). In early Xenopus laevis embryos, we showed that PPAR beta/delta exogenous activation by an agonist or by MEHP affects development. Taken together our results widen the concept of endocrine disruption by pinpointing PPARs as key factors in that process.

Degeneracy of antigen recognition as the molecular basis for the high frequency of naive A2/Melan-a peptide multimer(+) CD8(+) T cells in humans.

In contrast with the low frequency of most single epitope reactive T cells in the preimmune repertoire, up to 1 of 1,000 naive CD8(+) T cells from A2(+) individuals specifically bind fluorescent A2/peptide multimers incorporating the A27L analogue of the immunodominant 26-35 peptide from the melanocyte differentiation and melanoma associated antigen Melan-A. This represents the only naive antigen-specific T cell repertoire accessible to direct analysis in humans up to date. To get insight into the molecular basis for the selection and maintenance of such an abundant repertoire, we analyzed the functional diversity of T cells composing this repertoire ex vivo at the clonal level. Surprisingly, we found a significant proportion of multimer(+) clonotypes that failed to recognize both Melan-A analogue and parental peptides in a functional assay but efficiently recognized peptides from proteins of self- or pathogen origin selected for their potential functional cross-reactivity with Melan-A. Consistent with these data, multimers incorporating some of the most frequently recognized peptides specifically stained a proportion of naive CD8(+) T cells similar to that observed with Melan-A multimers. Altogether these results indicate that the high frequency of Melan-A multimer(+) T cells can be explained by the existence of largely cross-reactive subsets of naive CD8(+) T cells displaying multiple specificities.

Uncovering the genetic basis of adaptive change: on the intersection of landscape genomics and theoretical population genetics

A workshop recently held at the Ecole Polytechnique Federale de Lausanne (EPFL, Switzerland) was dedicated to understanding the genetic basis of adaptive change, taking stock of the different approaches developed in theoretical population genetics and landscape genomics and bringing together knowledge accumulated in both research fields. Indeed, an important challenge in theoretical population genetics is to incorporate effects of demographic history and population structure. But important design problems (e.g. focus on populations as units, focus on hard selective sweeps, no hypothesis-based framework in the design of the statistical tests) reduce their capability of detecting adaptive genetic variation. In parallel, landscape genomics offers a solution to several of these problems and provides a number of advantages (e.g. fast computation, landscape heterogeneity integration). But the approach makes several implicit assumptions that should be carefully considered (e.g. selection has had enough time to create a functional relationship between the allele distribution and the environmental variable, or this functional relationship is assumed to be constant). To address the respective strengths and weaknesses mentioned above, the workshop brought together a panel of experts from both disciplines to present their work and discuss the relevance of combining these approaches, possibly resulting in a joint software solution in the future.

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

Endogenous growth with capital in R&D production functions

[eng] In this paper we claim that capital is as important in the production of ideas as in the production of final goods. Hence, we introduce capital in the production of knowledge and discuss the associated problems arising from the public good nature of knowledge. We show that although population growth can affect economic growth, it is not necessary for growth to arise. We derive both the social planner and the decentralized economy growth rates and show the optimal subsidy that decentralizes it. We also show numerically that the effects of population growth on the market growth rate, the optimal growth rate and the optimal subsidy are small. Besides, we find that physical capital is more important for the production of knowledge than for the production of goods.

Endogenous growth with capital in R&D production functions

[eng] In this paper we claim that capital is as important in the production of ideas as in the production of final goods. Hence, we introduce capital in the production of knowledge and discuss the associated problems arising from the public good nature of knowledge. We show that although population growth can affect economic growth, it is not necessary for growth to arise. We derive both the social planner and the decentralized economy growth rates and show the optimal subsidy that decentralizes it. We also show numerically that the effects of population growth on the market growth rate, the optimal growth rate and the optimal subsidy are small. Besides, we find that physical capital is more important for the production of knowledge than for the production of goods.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Bayes linear spaces

Linear spaces consisting of σ-finite probability measures and infinite measures (improper priors and likelihood functions) are defined. The commutative group operation, called perturbation, is the updating given by Bayes theorem; the inverse operation is the Radon-Nikodym derivative. Bayes spaces of measures are sets of classes of proportional measures. In this framework, basic notions of mathematical statistics get a simple algebraic interpretation. For example, exponential families appear as affine subspaces with their sufficient statistics as a basis. Bayesian statistics, in particular some well-known properties of conjugated priors and likelihood functions, are revisited and slightly extended

Functions of the peroxisome proliferator-activated receptor (PPAR) alpha and beta in skin homeostasis, epithelial repair, and morphogenesis.

The three peroxisome proliferator-activated receptors (PPAR alpha, PPAR beta, and PPAR gamma) are ligand-activated transcription factors belonging to the nuclear hormone receptor superfamily. They are regarded as being sensors of physiological levels of fatty acids and fatty acid derivatives. In the adult mouse skin, they are found in hair follicle keratinocytes but not in interfollicular epidermis keratinocytes. Skin injury stimulates the expression of PPAR alpha and PPAR beta at the site of the wound. Here, we review the spatiotemporal program that triggers PPAR beta expression immediately after an injury, and then gradually represses it during epithelial repair. The opposing effects of the tumor necrosis factor-alpha and transforming growth factor-beta-1 signalling pathways on the activity of the PPAR beta promoter are the key elements of this regulation. We then compare the involvement of PPAR beta in the skin in response to an injury and during hair morphogenesis, and underscore the similarity of its action on cell survival in both situations.