Structural Analysis of Networks

Network analysis naturally relies on graph theory and, more particularly, on the use of node and edge metrics to identify the salient properties in graphs. When building visual maps of networks, these metrics are turned into useful visual cues or are used interactively to filter out parts of a graph while querying it, for instance. Over the years, analysts from different application domains have designed metrics to serve specific needs. Network science is an inherently cross-disciplinary field, which leads to the publication of metrics with similar goals; different names and descriptions of their analytics often mask the similarity between two metrics that originated in different fields. Here, we study a set of graph metrics and compare their relative values and behaviors in an effort to survey their potential contributions to the spatial analysis of networks.

The future key role of LC-high-resolution-MS analyses in clinical laboratories: a focus on quantification.

For the last decade, high-resolution (HR)-MS has been associated with qualitative analyses while triple quadrupole MS has been associated with routine quantitative analyses. However, a shift of this paradigm is taking place: quantitative and qualitative analyses will be increasingly performed by HR-MS, and it will become the common 'language' for most mass spectrometrists. Most analyses will be performed by full-scan acquisitions recording 'all' ions entering the HR-MS with subsequent construction of narrow-width extracted-ion chromatograms. Ions will be available for absolute quantification, profiling and data mining. In parallel to quantification, metabotyping will be the next step in clinical LC-MS analyses because it should help in personalized medicine. This article is aimed to help analytical chemists who perform targeted quantitative acquisitions with triple quadrupole MS make the transition to quantitative and qualitative analyses using HR-MS. Guidelines for the acceptance criteria of mass accuracy and for the determination of mass extraction windows in quantitative analyses are proposed.

Introduction

Although increasing our knowledge of the properties of networks of cities is essential, these properties can be measured at the city level, and must be assessed by analyzing actor networks. The present volume focuses less on individual characteristics and more on the interactions of actors and institutions that create functional territories in which the structure of existing links constrains emerging links. Rather than basing explanations on external factors, the goal is to determine the extent to which network properties reflect spatial distributions and create local synergies at the meso level that are incorporated into global networks at the macro level where different geographical scales occur. The paper introduces the way to use the graphs structure to identify empirically relevant groups and levels that explain dynamics. It defines what could be called âeurooemulti-levelâeuro, âeurooemulti-scaleâeuro, or âeurooemultidimensionalâeuro networks in the context of urban geography. It explains how the convergence of the network multi-territoriality paradigm collaboratively formulated, and manipulated by geographers and computer scientists produced the SPANGEO project, which is exposed in this volume.

A Small World Perspective on Urban Systems

The theory of small-world networks as initiated by Watts and Strogatz (1998) has drawn new insights in spatial analysis as well as systems theory. The theoryâeuro?s concepts and methods are particularly relevant to geography, where spatial interaction is mainstream and where interactions can be described and studied using large numbers of exchanges or similarity matrices. Networks are organized through direct links or by indirect paths, inducing topological proximities that simultaneously involve spatial, social, cultural or organizational dimensions. Network synergies build over similarities and are fed by complementarities between or inside cities, with the two effects potentially amplifying each other according to the âeurooepreferential attachmentâeuro hypothesis that has been explored in a number of different scientific fields (Barabási, Albert 1999; Barabási A-L 2002; Newman M, Watts D, Barabàsi A-L). In fact, according to Barabási and Albert (1999), the high level of hierarchy observed in âeurooescale-free networksâeuro results from âeurooepreferential attachmentâeuro, which characterizes the development of networks: new connections appear preferentially close to nodes that already have the largest number of connections because in this way, the improvement in the network accessibility of the new connection will likely be greater. However, at the same time, network regions gathering dense and numerous weak links (Granovetter, 1985) or network entities acting as bridges between several components (Burt 2005) offer a higher capacity for urban communities to benefit from opportunities and create future synergies. Several methodologies have been suggested to identify such denser and more coherent regions (also called communities or clusters) in terms of links (Watts, Strogatz 1998; Watts 1999; Barabási, Albert 1999; Barabási 2002; Auber 2003; Newman 2006). These communities not only possess a high level of dependency among their member entities but also show a low level of âeurooevulnerabilityâeuro, allowing for numerous redundancies (Burt 2000; Burt 2005). The SPANGEO project 2005âeuro"2008 (SPAtial Networks in GEOgraphy), gathering a team of geographers and computer scientists, has included empirical studies to survey concepts and measures developed in other related fields, such as physics, sociology and communication science. The relevancy and potential interpretation of weighted or non-weighted measures on edges and nodes were examined and analyzed at different scales (intra-urban, inter-urban or both). New classification and clustering schemes based on the relative local density of subgraphs were developed. The present article describes how these notions and methods contribute on a conceptual level, in terms of measures, delineations, explanatory analyses and visualization of geographical phenomena.

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.

The evolution of unicoloniality in ant societies : recognition, dispersal and population genetic structure

Introduction Societies of ants, bees, wasps and termites dominate many terrestrial ecosystems (Wilson 1971). Their evolutionary and ecological success is based upon the regulation of internal conflicts (e.g. Ratnieks et al. 2006), control of diseases (e.g. Schmid-Hempel 1998) and individual skills and collective intelligence in resource acquisition, nest building and defence (e.g. Camazine 2001). Individuals in social species can pass on their genes not only directly trough their own offspring, but also indirectly by favouring the reproduction of relatives. The inclusive fitness theory of Hamilton (1963; 1964) provides a powerful explanation for the evolution of reproductive altruism and cooperation in groups with related individuals. The same theory also led to the realization that insect societies are subject to internal conflicts over reproduction. Relatedness of less-than-one is not sufficient to eliminate all incentive for individual selfishness. This would indeed require a relatedness of one, as found among cells of an organism (Hardin 1968; Keller 1999). The challenge for evolutionary biology is to understand how groups can prevent or reduce the selfish exploitation of resources by group members, and how societies with low relatedness are maintained. In social insects the evolutionary shift from single- to multiple queens colonies modified the relatedness structure, the dispersal, and the mode of colony founding (e.g. (Crozier & Pamilo 1996). In ants, the most common, and presumably ancestral mode of reproduction is the emission of winged males and females, which found a new colony independently after mating and dispersal flights (Hölldobler & Wilson 1990). The alternative reproductive tactic for ant queens in multiple-queen colonies (polygyne) is to seek to be re-accepted in their natal colonies, where they may remain as additional reproductives or subsequently disperse on foot with part of the colony (budding) (Bourke & Franks 1995; Crozier & Pamilo 1996; Hölldobler & Wilson 1990). Such ant colonies can contain up to several hundred reproductive queens with an even more numerous workforce (Cherix 1980; Cherix 1983). As a consequence in polygynous ants the relatedness among nestmates is very low, and workers raise brood of queens to which they are only distantly related (Crozier & Pamilo 1996; Queller & Strassmann 1998). Therefore workers could increase their inclusive fitness by preferentially caring for their closest relatives and discriminate against less related or foreign individuals (Keller 1997; Queller & Strassmann 2002; Tarpy et al. 2004). However, the bulk of the evidence suggests that social insects do not behave nepotistically, probably because of the costs entailed by decreased colony efficiency or discrimination errors (Keller 1997). Recently, the consensus that nepotistic behaviour does not occur in insect colonies was challenged by a study in the ant Formica fusca (Hannonen & Sundström 2003b) showing that the reproductive share of queens more closely related to workers increases during brood development. However, this pattern can be explained either by nepotism with workers preferentially rearing the brood of more closely related queens or intrinsic differences in the viability of eggs laid by queens. In the first chapter, we designed an experiment to disentangle nepotism and differences in brood viability. We tested if workers prefer to rear their kin when given the choice between highly related and unrelated brood in the ant F. exsecta. We also looked for differences in egg viability among queens and simulated if such differences in egg viability may mistakenly lead to the conclusion that workers behave nepotistically. The acceptance of queens in polygnous ants raises the question whether the varying degree of relatedness affects their share in reproduction. In such colonies workers should favour nestmate queens over foreign queens. Numerous studies have investigated reproductive skew and partitioning of reproduction among queens (Bourke et al. 1997; Fournier et al. 2004; Fournier & Keller 2001; Hammond et al. 2006; Hannonen & Sundström 2003a; Heinze et al. 2001; Kümmerli & Keller 2007; Langer et al. 2004; Pamilo & Seppä 1994; Ross 1988; Ross 1993; Rüppell et al. 2002), yet almost no information is available on whether differences among queens in their relatedness to other colony members affects their share in reproduction. Such data are necessary to compare the relative reproductive success of dispersing and non-dispersing individuals. Moreover, information on whether there is a difference in reproductive success between resident and dispersing queens is also important for our understanding of the genetic structure of ant colonies and the dynamics of within group conflicts. In chapter two, we created single-queen colonies and then introduced a foreign queens originating from another colony kept under similar conditions in order to estimate the rate of queen acceptance into foreign established colonies, and to quantify the reproductive share of resident and introduced queens. An increasing number of studies have investigated the discrimination ability between ant workers (e.g. Holzer et al. 2006; Pedersen et al. 2006), but few have addressed the recognition and discrimination behaviour of workers towards reproductive individuals entering colonies (Bennett 1988; Brown et al. 2003; Evans 1996; Fortelius et al. 1993; Kikuchi et al. 2007; Rosengren & Pamilo 1986; Stuart et al. 1993; Sundström 1997; Vásquez & Silverman in press). These studies are important, because accepting new queens will generally have a large impact on colony kin structure and inclusive fitness of workers (Heinze & Keller 2000). In chapter three, we examined whether resident workers reject young foreign queens that enter into their nest. We introduced mated queens into their natal nest, a foreign-female producing nest, or a foreign male-producing nest and measured their survival. In addition, we also introduced young virgin and mated queens into their natal nest to examine whether the mating status of the queens influences their survival and acceptance by workers. On top of polgyny, some ant species have evolved an extraordinary social organization called 'unicoloniality' (Hölldobler & Wilson 1977; Pedersen et al. 2006). In unicolonial ants, intercolony borders are absent and workers and queens mix among the physically separated nests, such that nests form one large supercolony. Super-colonies can become very large, so that direct cooperative interactions are impossible between individuals of distant nests. Unicoloniality is an evolutionary paradox and a potential problem for kin selection theory because the mixing of queens and workers between nests leads to extremely low relatedness among nestmates (Bourke & Franks 1995; Crozier & Pamilo 1996; Keller 1995). A better understanding of the evolution and maintenance of unicoloniality requests detailed information on the discrimination behavior, dispersal, population structure, and the scale of competition. Cryptic genetic population structure may provide important information on the relevant scale to be considered when measuring relatedness and the role of kin selection. Theoretical studies have shown that relatedness should be measured at the level of the `economic neighborhood', which is the scale at which intraspecific competition generally takes place (Griffin & West 2002; Kelly 1994; Queller 1994; Taylor 1992). In chapter four, we conducted alarge-scale study to determine whether the unicolonial ant Formica paralugubris forms populations that are organised in discrete supercolonies or whether there is a continuous gradation in the level of aggression that may correlate with genetic isolation by distance and/or spatial distance between nests. In chapter five, we investigated the fine-scale population structure in three populations of F. paralugubris. We have developed mitochondria) markers, which together with the nuclear markers allowed us to detect cryptic genetic clusters of nests, to obtain more precise information on the genetic differentiation within populations, and to separate male and female gene flow. These new data provide important information on the scale to be considered when measuring relatedness in native unicolonial populations.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Bacterial flagellin elicits innate immune defenses and cardiac dysfunction in vivo

Résumé Les agents pathogènes responsables d'infection entraînent chez l'hôte deux types de réponses immunes, la première, non spécifique, dite immunité innée, la seconde, spécifique à l'agent concerné, dite immunité adaptative. L'immunité innée, qui représente la première ligne de défense contre les pathogènes, est liée à la reconnaissance par les cellules de l'hôte de structures moléculaires propres aux micro-organismes (« Pathogen-Associated Molecular Patterns », PAMPs), grâce à des récepteurs membranaires et cytoplasmiques (« Pattern Recognition Receptors », PRRs) identifiant de manière spécifique ces motifs moléculaires. Les récepteurs membranaires impliqués dans ce processus sont dénommés toll-like récepteurs, ou TLRS. Lorsqu'ils sont activés par leur ligand spécifique, ces récepteurs activent des voies de signalisation intracellulaires initiant la réponse inflammatoire non spécifique et visant à éradiquer l'agent pathogène. Les deux voies de signalisation impliquées dans ce processus sont la voie des « Mitogen-Activated Protein Kinases » (MAPKs) et celle du « Nuclear Factor kappaB » (NF-κB), dont l'activation entraîne in fine l'expression de protéines de l'inflammation dénommées cytokines, ainsi que certaines enzymes produisant divers autres médiateurs inflammatoires. Dans certaines situations, cette réponse immune peut être amplifiée de manière inadéquate, entraînant chez l'hôte une réaction inflammatoire systémique exagérée, appelée sepsis. Le sepsis peut se compliquer de dysfonctions d'organes multiples (sepsis sévère), et dans sa forme la plus grave, d'un collapsus cardiovasculaire, définissant le choc septique. La défaillance circulatoire du choc septique touche les vaisseaux sanguins d'une part, le coeur d'autre part, réalisant un tableau de «dysfonction cardiaque septique », dont on connaît mal les mécanismes pathogéniques. Les bactéries à Gram négatif peuvent déclencher de tels phénomènes, notamment en libérant de l'endotoxine, qui active les voies de l'immunité innée par son interaction avec un toll récepteur, le TLR4. Outre l'endotoxine, la plupart des bactéries à Gram négatif relâchent également dans leur environnement une protéine, la flagelline, qui est le constituant majeur du flagelle bactérien, organelle assurant la mobilité de ces micro-organismes. Des données récentes ont indiqué que la flagelline active, dans certaines cellules, les voies de l'immunité innée en se liant au récepteur TLRS. On ne connaît toutefois pas les conséquences de l'interaction flagelline-TLRS sur le développement de l'inflammation et des dysfonctions d'organes au cours du sepsis. Nous avons par conséquent élaboré le présent travail en formulant l'hypothèse que la flagelline pourrait déclencher une telle inflammation et représenter ainsi un médiateur potentiel de la dysfonction d'organes au cours du sepsis à Gram négatif, en nous intéressant plus particulièrement àl'inflammation et à la dysfonction cardiaque. Dans la première partie de ce travail, nous avons étudié les effets de la flagelline sur l'activation du NF-κB et des MAPKs, et sur l'expression de cytokines inflammatoires au niveau du myocarde in vitro (cardiomyocytes en culture) et in vivo (injection de flagelline recombinante à des souris). Nous avons observé tout d'abord que le récepteur TLRS est fortement exprimé au niveau du myocarde. Nous avons ensuite démontré que la flagelline active la voie du NF-κB et des MAP kinases (p38 et JNK), stimule la production de cytokines et de chemokines inflammatoires in vitro et in vivo, et entraîne l'activation de polynucléaires neutrophiles dans le tissu cardiaque in vivo. Finalement, au plan fonctionnel, nous avons pu montrer que la flagelline entraîne une dilatation et une réduction aiguë de la contractilité du ventricule gauche chez la souris, reproduisant les caractéristiques de la dysfonction cardiaque septique. Dans la deuxième partie, nous avons déterminé la distribution du récepteur TLRS dans les autres organes majeurs de la souris (poumon, foie, intestin et rein}, et avons caractérisé dans ces organes l'effet de la flagelline sur l'activation du NF-κB et des MAPKs, l'expression de cytokines, et l'induction de l'apoptose. Nous avons démontré que le TLRS est exprimé de façon constitutive dans ces organes, et que l'injection de flagelline y déclenche les cascades de l'immunité innée et de processus apoptotiques. Finalement, nous avons également déterminé que la flagelline entraîne une augmentation significative de multiples cytokines dans le plasma une à six heures après son injection. En résumé, nos données démontrent que la flagelline bactérienne (a) entraîne une inflammation et une dysfonction importantes du myocarde et (b) active de manière très significative les mécanismes d'immunité innée dans les principaux organes et entraîne une réponse inflammatoire systémique. Par conséquent, la flagelline peut représenter un médiateur puissant de l'inflammation et de la dysfonction d'organes, notamment du coeur, au cours du choc septique déclenché par les bactéries à Gram négatif. Summary Pathogenic microorganisms trigger two kinds of immune responses in the host. The first one is immediate and non-specific and is termed innate immunity, whereas the second one, specifically targeted at the invading agent, is termed adaptative immunity. Innate immunity, which represents the first line of defense against invading pathogens, confers the host the ability to recognize molecular structures common to many microbial pathogens, ("Pathogen-Associated Molecular Patterns", PAMPs), through cytosolic or membrane-associated receptors ("Pattern Recognition Receptors", PRRs), the latter being represented by a family of receptors termed "toll-like receptors or TLRs". Once activated by the binding of their specific ligand, these receptors activate intracellular signaling pathways, which initiate the non-specific inflammatory response aimed at eradicating the pathogens. The two pathways implicated in this process are the mitogen-activated protein kinases (MAPK) and the nuclear factor kappa B (NF-κB) signaling pathways, whose activation elicit in fine the expression of inflammatory proteins termed cytokines, as well as various enzymes producing a wealth of additional inflammatory mediators. In some circumstances, the innate immune response can become amplified and dysregulated, triggering an overwhelming systemic inflammatory response in the host, identified as sepsis. Sepsis can be associated with multiple organ dysfunction (severe sepsis), and in its most severe form, with cardiovascular collapse, defming septic shock. The cardiovascular failure associated with septic shock affects blood vessels as well as the heart, resulting in a particular form of acute heart failure termed "septic cardiac dysfunction ", whose pathogenic mechanisms remain partly undefined. Gram-negative bacteria can initiate such phenomena, notably by releasing lipopolysaccharide (LPS), which activates innate immune signaling by interacting with its specific toll receptor, the TLR4. Besides LPS, most Gram-negative bacteria also release flagellin into their environment, which is the main structural protein of the bacterial flagellum, an appendage extending from the outer bacterial membrane, responsible for the motility of the microorganism. Recent data indicated that flagellin activate immune responses upon binding to its receptor, TLRS, in various cell types. However, the role of flagellin/TLRS interaction in the development of inflammation and organ dysfunction during sepsis is not known. Therefore, we designed the present work to address the hypothesis that flagellin might trigger such inflammatory responses and thus represent a potential mediator of organ dysfunction during Gram-negative sepsis, with a particular emphasis on cardiac inflammation and contractile dysfunction. In the first part of this work, we investigated the effects of flagellin on NF-κB and MAPK activation and the generation of pro-inflammatory mediators within the heart in vitro (cultured cardiomyocytes) and in vivo (injection of recombinant flagellin into mice). We first observed that TLRS protein is strongly expressed by the myocardium. We then demonstrated that flagellin activates NF-κB and MAP kinases (p38 and JNK), upregulates the transcription of pro-inflammatory cytokines and chemokines in vitro and in vivo, and stimulates the activation of polymorphonuclear neutrophils within the heart in vivo. Finally, we demonstrated that flagellin triggers acute cardiac dilation, and a significant reduction of left ventricular contractility, mimicking characteristics of clinical septic cardiac dysfunction. In the second part, we determined the TLRS distribution in other mice major organs (lung, liver, gut and kidney) and we characterized in these organs the effects of flagellin on NF-κB and MAPK activation, on the expression of pro-inflammatory çytokines, and on the induction of apoptosis. We demonstrated that TLRS protein is constitutively expressed and that flagellin activates prototypical innate immune responses and pro-apoptotic pathways in all these organs. Finally, we also observed that flagellin induces a significant increase of multiple cytokines in the plasma from 1 to 6 hours after its intravenous administration. Altogether, these data provide evidence that bacterial flagellin (a) triggers an important inflammatory response and an acute dysfunction of the myocardium, and (b) significantly activates the mechanisms of innate immunity in most major organs and elicits a systemic inflammatory response. In consequence, flagellin may represent a potent mediator of inflammation and multiple organ failure, notably cardiac dysfunction, during Gram-negative septic shock.

A multidimensional segmentation evaluation for medical image data.

Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based on a measure that depends on the number of segmented voxels inside and outside of some reference regions that are called gold standards. Although some other measures have been also used, in this work we propose a set of new similarity measures, based on different features, such as the location and intensity values of the misclassified voxels, and the connectivity and the boundaries of the segmented data. Using the multidimensional information provided by these measures, we propose a new evaluation method whose results are visualized applying a Principal Component Analysis of the data, obtaining a simplified graphical method to compare different segmentation results. We have carried out an intensive study using several classic segmentation methods applied to a set of MRI simulated data of the brain with several noise and RF inhomogeneity levels, and also to real data, showing that the new measures proposed here and the results that we have obtained from the multidimensional evaluation, improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.

Reverse immunology approach for the identification of CD8 T-cell-defined antigens: advantages and hurdles.

One of the challenges of tumour immunology remains the identification of strongly immunogenic tumour antigens for vaccination. Reverse immunology, that is, the procedure to predict and identify immunogenic peptides from the sequence of a gene product of interest, has been postulated to be a particularly efficient, high-throughput approach for tumour antigen discovery. Over one decade after this concept was born, we discuss the reverse immunology approach in terms of costs and efficacy: data mining with bioinformatic algorithms, molecular methods to identify tumour-specific transcripts, prediction and determination of proteasomal cleavage sites, peptide-binding prediction to HLA molecules and experimental validation, assessment of the in vitro and in vivo immunogenic potential of selected peptide antigens, isolation of specific cytolytic T lymphocyte clones and final validation in functional assays of tumour cell recognition. We conclude that the overall low sensitivity and yield of every prediction step often requires a compensatory up-scaling of the initial number of candidate sequences to be screened, rendering reverse immunology an unexpectedly complex approach.

Probabilistic base calling of Solexa sequencing data.

BACKGROUND: Solexa/Illumina short-read ultra-high throughput DNA sequencing technology produces millions of short tags (up to 36 bases) by parallel sequencing-by-synthesis of DNA colonies. The processing and statistical analysis of such high-throughput data poses new challenges; currently a fair proportion of the tags are routinely discarded due to an inability to match them to a reference sequence, thereby reducing the effective throughput of the technology. RESULTS: We propose a novel base calling algorithm using model-based clustering and probability theory to identify ambiguous bases and code them with IUPAC symbols. We also select optimal sub-tags using a score based on information content to remove uncertain bases towards the ends of the reads. CONCLUSION: We show that the method improves genome coverage and number of usable tags as compared with Solexa's data processing pipeline by an average of 15%. An R package is provided which allows fast and accurate base calling of Solexa's fluorescence intensity files and the production of informative diagnostic plots.

Bgee: integrating and comparing heterogeneous transcriptome data among species

Gene expression patterns are a key feature in understanding gene function, notably in development. Comparing gene expression patterns between animals is a major step in the study of gene function as well as of animal evolution. It also provides a link between genes and phenotypes. Thus we have developed Bgee, a database designed to compare expression patterns between animals, by implementing ontologies describing anatomies and developmental stages of species, and then designing homology relationships between anatomies and comparison criteria between developmental stages. To define homology relationships between anatomical features we have developed the software Homolonto, which uses a modified ontology alignment approach to propose homology relationships between ontologies. Bgee then uses these aligned ontologies, onto which heterogeneous expression data types are mapped. These already include microarrays and ESTs.

Histone deacetylase inhibitors impair innate immune responses to Toll-like receptor agonists and to infection.

Regulated by histone acetyltransferases and deacetylases (HDACs), histone acetylation is a key epigenetic mechanism controlling chromatin structure, DNA accessibility, and gene expression. HDAC inhibitors induce growth arrest, differentiation, and apoptosis of tumor cells and are used as anticancer agents. Here we describe the effects of HDAC inhibitors on microbial sensing by macrophages and dendritic cells in vitro and host defenses against infection in vivo. HDAC inhibitors down-regulated the expression of numerous host defense genes, including pattern recognition receptors, kinases, transcription regulators, cytokines, chemokines, growth factors, and costimulatory molecules as assessed by genome-wide microarray analyses or innate immune responses of macrophages and dendritic cells stimulated with Toll-like receptor agonists. HDAC inhibitors induced the expression of Mi-2β and enhanced the DNA-binding activity of the Mi-2/NuRD complex that acts as a transcriptional repressor of macrophage cytokine production. In vivo, HDAC inhibitors increased the susceptibility to bacterial and fungal infections but conferred protection against toxic and septic shock. Thus, these data identify an essential role for HDAC inhibitors in the regulation of the expression of innate immune genes and host defenses against microbial pathogens.

Early co-occurrence of a neurologic-psychiatric disease pattern in Niemann-Pick type C disease: a retrospective Swiss cohort study.

BackgroundNiemann-Pick disease type C (NP-C) is a rare autosomal recessive disorder of lysosomal cholesterol transport. The objective of this retrospective cohort study was to critically analyze the onset and time course of symptoms, and the clinical diagnostic work-up in the Swiss NP-C cohort.MethodsClinical, biochemical and genetic data were assessed for 14 patients derived from 9 families diagnosed with NP-C between 1994 and 2013. We retrospectively evaluated diagnostic delays and period prevalence rates for neurological, psychiatric and visceral symptoms associated with NP-C disease. The NP-C suspicion index was calculated for the time of neurological disease onset and the time of diagnosis.ResultsThe shortest median diagnostic delay was noted for vertical supranuclear gaze palsy (2y). Ataxia, dysarthria, dysphagia, spasticity, cataplexy, seizures and cognitive decline displayed similar median diagnostic delays (4¿5y). The longest median diagnostic delay was associated with hepatosplenomegaly (15y). Highest period prevalence rates were noted for ataxia, dysarthria, vertical supranuclear gaze palsy and cognitive decline. The NP-C suspicion index revealed a median score of 81 points in nine patients at the time of neurological disease onset which is highly suspicious for NP-C disease. At the time of diagnosis, the score increased to 206 points.ConclusionA neurologic-psychiatric disease pattern represents the most characteristic clinical manifestation of NP-C and occurs early in the disease course. Visceral manifestation such as isolated hepatosplenomegaly often fails recognition and thus highlights the importance of a work-up for lysosomal storage disorders. The NP-C suspicion index emphasizes the importance of a multisystem evaluation, but seems to be weak in monosymptomatic and infantile NP-C patients.

Application of text-mining for updating protein post-translational modification annotation in UniProtKB.

BACKGROUND: The annotation of protein post-translational modifications (PTMs) is an important task of UniProtKB curators and, with continuing improvements in experimental methodology, an ever greater number of articles are being published on this topic. To help curators cope with this growing body of information we have developed a system which extracts information from the scientific literature for the most frequently annotated PTMs in UniProtKB. RESULTS: The procedure uses a pattern-matching and rule-based approach to extract sentences with information on the type and site of modification. A ranked list of protein candidates for the modification is also provided. For PTM extraction, precision varies from 57% to 94%, and recall from 75% to 95%, according to the type of modification. The procedure was used to track new publications on PTMs and to recover potential supporting evidence for phosphorylation sites annotated based on the results of large scale proteomics experiments. CONCLUSIONS: The information retrieval and extraction method we have developed in this study forms the basis of a simple tool for the manual curation of protein post-translational modifications in UniProtKB/Swiss-Prot. Our work demonstrates that even simple text-mining tools can be effectively adapted for database curation tasks, providing that a thorough understanding of the working process and requirements are first obtained. This system can be accessed at http://eagl.unige.ch/PTM/.