Insufficient coping behavior under chronic stress and vulnerability to psychiatric disorders

Epidemiological data indicate that 75% of subjects with major psychiatric disorders have their onset in the age range of 17-24 years. An estimated 35-50% of college and university students drop out prematurely due to insufficient coping skills under chronic stress, while 85% of students receiving a psychiatric diagnosis withdraw from college/university prior to the completion of their education. In this study we aimed at developing standardized means for identifying students with insufficient coping skills under chronic stress and at risk for mental health problems. A sample of 1,217 college students from 3 different sites in the U.S. and Switzerland completed 2 self-report questionnaires: the Coping Strategies Inventory "COPE" and the Zurich Health Questionnaire "ZHQ" which assesses "regular exercises", "consumption behavior", "impaired physical health", "psychosomatic disturbances", and "impaired mental health". The data were subjected to structure analyses by means of a Neural Network approach. We found 2 highly stable and reproducible COPE scales that explained the observed inter-individual variation in coping behavior sufficiently well and in a socio-culturally independent way. The scales reflected basic coping behavior in terms of "activity-passivity" and "defeatism-resilience", and in the sense of stable, socio-culturally independent personality traits. Correlation analyses carried out for external validation revealed a close relationship between high scores on the defeatism scale and impaired physical and mental health. This underlined the role of insufficient coping behavior as a risk factor for physical and mental health problems. The combined COPE and ZHQ instruments appear to constitute powerful screening tools for insufficient coping skills under chronic stress and for risks of mental health problems.

New perspectives in the use of ink evidence in forensic science: Part II. Development and testing of mathematical algorithms for the automatic comparison of ink samples analysed by HPTLC

In the first part of this research, three stages were stated for a program to increase the information extracted from ink evidence and maximise its usefulness to the criminal and civil justice system. These stages are (a) develop a standard methodology for analysing ink samples by high-performance thin layer chromatography (HPTLC) in reproducible way, when ink samples are analysed at different time, locations and by different examiners; (b) compare automatically and objectively ink samples; and (c) define and evaluate theoretical framework for the use of ink evidence in forensic context. This report focuses on the second of the three stages. Using the calibration and acquisition process described in the previous report, mathematical algorithms are proposed to automatically and objectively compare ink samples. The performances of these algorithms are systematically studied for various chemical and forensic conditions using standard performance tests commonly used in biometrics studies. The results show that different algorithms are best suited for different tasks. Finally, this report demonstrates how modern analytical and computer technology can be used in the field of ink examination and how tools developed and successfully applied in other fields of forensic science can help maximising its impact within the field of questioned documents.

Time Series Input Selection using Multiple Kernel Learning

In this paper we study the relevance of multiple kernel learning (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear combination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for optimizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simulated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).

Stereological analysis of the rat and monkey amygdala

The amygdala is part of a neural network that contributes to the regulation of emotional behaviors. Rodents, especially rats, are used extensively as model organisms to decipher the functions of specific amygdala nuclei, in particular in relation to fear and emotional learning. Analysis of the role of the nonhuman primate amygdala in these functions has lagged work in the rodent but provides evidence for conservation of basic functions across species. Here we provide quantitative information regarding the morphological characteristics of the main amygdala nuclei in rats and monkeys, including neuron and glial cell numbers, neuronal soma size, and individual nuclei volumes. The volumes of the lateral, basal, and accessory basal nuclei were, respectively, 32, 39, and 39 times larger in monkeys than in rats. In contrast, the central and medial nuclei were only 8 and 4 times larger in monkeys than in rats. The numbers of neurons in the lateral, basal, and accessory basal nuclei were 14, 11, and 16 times greater in monkeys than in rats, whereas the numbers of neurons in the central and medial nuclei were only 2.3 and 1.5 times greater in monkeys than in rats. Neuron density was between 2.4 and 3.7 times lower in monkeys than in rats, whereas glial density was only between 1.1 and 1.7 times lower in monkeys than in rats. We compare our data in rats and monkeys with those previously published in humans and discuss the theoretical and functional implications that derive from our quantitative structural findings.

Molecular modeling of peptide-MHC class I complexes : applications to peptide based immunotherapy

Summary The specific CD8+ T cell immune response against tumors relies on the recognition by the T cell receptor (TCR) on cytotoxic T lymphocytes (CTL) of antigenic peptides bound to the class I major histocompatibility complex (MHC) molecule. Such tumor associated antigenic peptides are the focus of tumor immunotherapy with peptide vaccines. The strategy for obtaining an improved immune response often involves the design of modified tumor associated antigenic peptides. Such modifications aim at creating higher affinity and/or degradation resistant peptides and require precise structures of the peptide-MHC class I complex. In addition, the modified peptide must be cross-recognized by CTLs specific for the parental peptide, i.e. preserve the structure of the epitope. Detailed structural information on the modified peptide in complex with MHC is necessary for such predictions. In this thesis, the main focus is the development of theoretical in silico methods for prediction of both structure and cross-reactivity of peptide-MHC class I complexes. Applications of these methods in the context of immunotherapy are also presented. First, a theoretical method for structure prediction of peptide-MHC class I complexes is developed and validated. The approach is based on a molecular dynamics protocol to sample the conformational space of the peptide in its MHC environment. The sampled conformers are evaluated using conformational free energy calculations. The method, which is evaluated for its ability to reproduce 41 X-ray crystallographic structures of different peptide-MHC class I complexes, shows an overall prediction success of 83%. Importantly, in the clinically highly relevant subset of peptide-HLAA*0201 complexes, the prediction success is 100%. Based on these structure predictions, a theoretical approach for prediction of cross-reactivity is developed and validated. This method involves the generation of quantitative structure-activity relationships using three-dimensional molecular descriptors and a genetic neural network. The generated relationships are highly predictive as proved by high cross-validated correlation coefficients (0.78-0.79). Together, the here developed theoretical methods open the door for efficient rational design of improved peptides to be used in immunotherapy. Résumé La réponse immunitaire spécifique contre des tumeurs dépend de la reconnaissance par les récepteurs des cellules T CD8+ de peptides antigéniques présentés par les complexes majeurs d'histocompatibilité (CMH) de classe I. Ces peptides sont utilisés comme cible dans l'immunothérapie par vaccins peptidiques. Afin d'augmenter la réponse immunitaire, les peptides sont modifiés de façon à améliorer l'affinité et/ou la résistance à la dégradation. Ceci nécessite de connaître la structure tridimensionnelle des complexes peptide-CMH. De plus, les peptides modifiés doivent être reconnus par des cellules T spécifiques du peptide natif. La structure de l'épitope doit donc être préservée et des structures détaillées des complexes peptide-CMH sont nécessaires. Dans cette thèse, le thème central est le développement des méthodes computationnelles de prédiction des structures des complexes peptide-CMH classe I et de la reconnaissance croisée. Des applications de ces méthodes de prédiction à l'immunothérapie sont également présentées. Premièrement, une méthode théorique de prédiction des structures des complexes peptide-CMH classe I est développée et validée. Cette méthode est basée sur un échantillonnage de l'espace conformationnel du peptide dans le contexte du récepteur CMH classe I par dynamique moléculaire. Les conformations sont évaluées par leurs énergies libres conformationnelles. La méthode est validée par sa capacité à reproduire 41 structures des complexes peptide-CMH classe I obtenues par cristallographie aux rayons X. Le succès prédictif général est de 83%. Pour le sous-groupe HLA-A*0201 de complexes de grande importance pour l'immunothérapie, ce succès est de 100%. Deuxièmement, à partir de ces structures prédites in silico, une méthode théorique de prédiction de la reconnaissance croisée est développée et validée. Celle-ci consiste à générer des relations structure-activité quantitatives en utilisant des descripteurs moléculaires tridimensionnels et un réseau de neurones couplé à un algorithme génétique. Les relations générées montrent une capacité de prédiction remarquable avec des valeurs de coefficients de corrélation de validation croisée élevées (0.78-0.79). Les méthodes théoriques développées dans le cadre de cette thèse ouvrent la voie du design de vaccins peptidiques améliorés.

Analysis, modelling and visualisation of spatiotemporal data for urban studies

The proportion of population living in or around cites is more important than ever. Urban sprawl and car dependence have taken over the pedestrian-friendly compact city. Environmental problems like air pollution, land waste or noise, and health problems are the result of this still continuing process. The urban planners have to find solutions to these complex problems, and at the same time insure the economic performance of the city and its surroundings. At the same time, an increasing quantity of socio-economic and environmental data is acquired. In order to get a better understanding of the processes and phenomena taking place in the complex urban environment, these data should be analysed. Numerous methods for modelling and simulating such a system exist and are still under development and can be exploited by the urban geographers for improving our understanding of the urban metabolism. Modern and innovative visualisation techniques help in communicating the results of such models and simulations. This thesis covers several methods for analysis, modelling, simulation and visualisation of problems related to urban geography. The analysis of high dimensional socio-economic data using artificial neural network techniques, especially self-organising maps, is showed using two examples at different scales. The problem of spatiotemporal modelling and data representation is treated and some possible solutions are shown. The simulation of urban dynamics and more specifically the traffic due to commuting to work is illustrated using multi-agent micro-simulation techniques. A section on visualisation methods presents cartograms for transforming the geographic space into a feature space, and the distance circle map, a centre-based map representation particularly useful for urban agglomerations. Some issues on the importance of scale in urban analysis and clustering of urban phenomena are exposed. A new approach on how to define urban areas at different scales is developed, and the link with percolation theory established. Fractal statistics, especially the lacunarity measure, and scale laws are used for characterising urban clusters. In a last section, the population evolution is modelled using a model close to the well-established gravity model. The work covers quite a wide range of methods useful in urban geography. Methods should still be developed further and at the same time find their way into the daily work and decision process of urban planners. La part de personnes vivant dans une région urbaine est plus élevé que jamais et continue à croître. L'étalement urbain et la dépendance automobile ont supplanté la ville compacte adaptée aux piétons. La pollution de l'air, le gaspillage du sol, le bruit, et des problèmes de santé pour les habitants en sont la conséquence. Les urbanistes doivent trouver, ensemble avec toute la société, des solutions à ces problèmes complexes. En même temps, il faut assurer la performance économique de la ville et de sa région. Actuellement, une quantité grandissante de données socio-économiques et environnementales est récoltée. Pour mieux comprendre les processus et phénomènes du système complexe "ville", ces données doivent être traitées et analysées. Des nombreuses méthodes pour modéliser et simuler un tel système existent et sont continuellement en développement. Elles peuvent être exploitées par le géographe urbain pour améliorer sa connaissance du métabolisme urbain. Des techniques modernes et innovatrices de visualisation aident dans la communication des résultats de tels modèles et simulations. Cette thèse décrit plusieurs méthodes permettant d'analyser, de modéliser, de simuler et de visualiser des phénomènes urbains. L'analyse de données socio-économiques à très haute dimension à l'aide de réseaux de neurones artificiels, notamment des cartes auto-organisatrices, est montré à travers deux exemples aux échelles différentes. Le problème de modélisation spatio-temporelle et de représentation des données est discuté et quelques ébauches de solutions esquissées. La simulation de la dynamique urbaine, et plus spécifiquement du trafic automobile engendré par les pendulaires est illustrée à l'aide d'une simulation multi-agents. Une section sur les méthodes de visualisation montre des cartes en anamorphoses permettant de transformer l'espace géographique en espace fonctionnel. Un autre type de carte, les cartes circulaires, est présenté. Ce type de carte est particulièrement utile pour les agglomérations urbaines. Quelques questions liées à l'importance de l'échelle dans l'analyse urbaine sont également discutées. Une nouvelle approche pour définir des clusters urbains à des échelles différentes est développée, et le lien avec la théorie de la percolation est établi. Des statistiques fractales, notamment la lacunarité, sont utilisées pour caractériser ces clusters urbains. L'évolution de la population est modélisée à l'aide d'un modèle proche du modèle gravitaire bien connu. Le travail couvre une large panoplie de méthodes utiles en géographie urbaine. Toutefois, il est toujours nécessaire de développer plus loin ces méthodes et en même temps, elles doivent trouver leur chemin dans la vie quotidienne des urbanistes et planificateurs.

Automatic Mapping and Classification of Spatial Environmental Data

The paper deals with the development and application of the generic methodology for automatic processing (mapping and classification) of environmental data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve the problem of spatial data mapping (regression). The Probabilistic Neural Network (PNN) is considered as an automatic tool for spatial classifications. The automatic tuning of isotropic and anisotropic GRNN/PNN models using cross-validation procedure is presented. Results are compared with the k-Nearest-Neighbours (k-NN) interpolation algorithm using independent validation data set. Real case studies are based on decision-oriented mapping and classification of radioactively contaminated territories.

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations.

Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure-function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure-function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.

Optimization of snowmaking in high mountains ski resort

Ski resorts are deploying more and more systems of artificial snow. These tools are necessary to ensure an important economic activity for the high alpine valleys. However, artificial snow raises important environmental issues that can be reduced by an optimization of its production. This paper presents a software prototype based on artificial intelligence to help ski resorts better manage their snowpack. It combines on one hand a General Neural Network for the analysis of the snow cover and the spatial prediction, with on the other hand a multiagent simulation of skiers for the analysis of the spatial impact of ski practice. The prototype has been tested on the ski resort of Verbier (Switzerland).

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

Vulnerabilität und psychische Erkrankung

Vulnerability and psychic illness Based on a sample of 1701 college and university students from four different sites in Switzerland, the U.S., and Argentina, this study investigated the interrelationships between insufficient coping skills under chronic stress and impaired general health. We sought to develop standardised means for "early" identification of students at risk of mental health problems, as these students may benefit from "early" interventions before psychiatric symptoms develop and reach clinically relevant thresholds. All students completed two self-report questionnaires: the Coping Strategies Inventory "COPE" and the Zurich Health Questionnaire "ZHQ", with the latter assessing "regular exercises", "consumption behavior", "impaired physical health", "psychosomatic disturbances", and "impaired mental health". This data was subjected to structure analyses based on neural network approaches, using the different study sites' data subsets as independent "learning" and "test" samples. We found two highly stable COPE scales that quantified basic coping behaviour in terms of "activity-passivity" and "defeatism-resilience". The excellent reproducibility across study sites suggested that the new scales characterise socioculturally independent personality traits. Correlation analyses for external validation revealed a close relationship between high scores on the defeatism scale and impaired physical and mental health, hence underlining the scales' clinical relevance. Our results suggested in particular: (1.) the proposed method to be a powerful screening tool for early detection and prevention of psychiatric disorders; (2.) physical activity like regular exercises to play a critical role not only in preventing health problems but also in contributing to early intervention programs.

Roles of transient guidepost neurons in corpus callosum formation and guidance

RESUMENeurones transitoires jouant un rôle de cibles intermédiaires dans le guidage des axones du corps calleuxLe guidage axonal est une étape clé permettant aux neurones d'établir des connexions synaptiques et de s'intégrer dans un réseau neural fonctionnel de manière spécifique. Des cellules-cibles intermédiaires appelées « guidepost » aident les axones à parcourir de longues distances dans le cerveau en leur fournissant des informations directionnelles tout au long de leur trajet. Il a été démontré que des sous-populations de cellules gliales au niveau de la ligne médiane guident les axones du corps calleux (CC) d'un hémisphère vers l'autre. Bien qu'il fût observé que le CC en développement contenait aussi des neurones, leur rôle était resté jusqu'alors inconnu.La publication de nos résultats a montré que pendant le développement embryonnaire, le CC contient des glies mais aussi un nombre considérable de neurones glutamatergiques et GABAergiques, nécessaires à la formation du corps calleux (Niquille et al., PLoS Biology, 2009). Dans ce travail, j'ai utilisé des techniques de morphologie et d'imagerie confocale 3D pour définir le cadre neuro-anatomique de notre modèle. De plus, à l'aide de transplantations sur tranches in vitro, de co-explants, d'expression de siRNA dans des cultures de neurones primaires et d'analyse in vivo sur des souris knock-out, nous avons démontré que les neurones du CC guident les axones callosaux en partie grâce à l'action attractive du facteur de guidage Sema3C sur son récepteur Npn- 1.Récemment, nous avons étudié l'origine, les aspects dynamiques de ces processus, ainsi que les mécanismes moléculaires impliqués dans la mise en place de ce faisceau axonal (Niquille et al., soumis). Tout d'abord, nous avons précisé l'origine et l'identité des neurones guidepost GABAergiques du CC par une étude approfondie de traçage génétique in vivo. J'ai identifié, dans le CC, deux populations distinctes de neurones GABAergiques venant des éminences ganglionnaires médiane (MGE) et caudale (CGE). J'ai ensuite étudié plus en détail les interactions dynamiques entre neurones et axones du corps calleux par microscopie confocale en temps réel. Puis nous avons défini le rôle de chaque sous-population neuronale dans le guidage des axones callosaux et de manière intéressante les neurones GABAergic dérivés de la MGE comme ceux de la CGE se sont révélés avoir une action attractive pour les axones callosaux dans des expériences de transplantation. Enfin, nous avons clarifié la base moléculaire de ces mécanismes de guidage par FACS sorting associé à un large criblage génétique de molécules d'intérêt par une technique très sensible de RT-PCR et ensuite ces résultats ont été validés par hybridation in situ.Nous avons également étudié si les neurones guidepost du CC étaient impliqués dans son agénésie (absence de CC), présente dans nombreux syndromes congénitaux chez 1 humain. Le gène homéotique Aristaless (Arx) contrôle la migration des neurones GABAergiques et sa mutation conduit à de nombreuses pathologies humaines, notamment la lissencéphalie liée à IX avec organes génitaux anormaux (XLAG) et agénésie du CC. Fait intéressant, nous avons constaté qu'ARX est exprimé dans toutes les populations GABAergiques guidepost du CC et que les embryons mutant pour Arx présentent une perte drastique de ces neurones accompagnée de défauts de navigation des axones (Niquille et al., en préparation). En outre, nous avons découvert que les souris déficientes pour le facteur de transcription ciliogenic RFX3 souffrent d'une agénésie du CC associé avec des défauts de mise en place de la ligne médiane et une désorganisation secondaire des neurones glutamatergiques guidepost (Benadiba et al., submitted). Ceci suggère fortement l'implication potentielle des deux types de neurones guidepost dans l'agénésie du CC chez l'humain.Ainsi, mon travail de thèse révèle de nouvelles fonctions pour ces neurones transitoires dans le guidage axonal et apporte de nouvelles perspectives sur les rôles respectifs des cellules neuronales et gliales dans ce processus.ABSTRACTRole of transient guidepost neurons in corpus callosum development and guidanceAxonal guidance is a key step that allows neurons to build specific synaptic connections and to specifically integrate in a functional neural network. Intermediate targets or guidepost cells act as critical elements that help to guide axons through long distance in the brain and provide information all along their travel. Subpopulations of midline glial cells have been shown to guide corpus callosum (CC) axons to the contralateral cerebral hemisphere. While neuronal cells are also present in the developing corpus callosum, their role still remains elusive.Our published results unravelled that, during embryonic development, the CC is populated in addition to astroglia by numerous glutamatergic and GABAergic guidepost neurons that are essential for the correct midline crossing of callosal axons (Niquille et al., PLoS Biology, 2009). In this work, I have combined morphological and 3D confocal imaging techniques to define the neuro- anatomical frame of our system. Moreover, with the use of in vitro transplantations in slices, co- explant experiments, siRNA manipulations on primary neuronal culture and in vivo analysis of knock-out mice we have been able to demonstrate that CC neurons direct callosal axon outgrowth, in part through the attractive action of Sema3C on its Npn-1 receptor.Recently, we have studied the origin, the dynamic aspects of these processes as well as the molecular mechanisms involved in the establishment of this axonal tract (Niquille et al., submitted). First, we have clarified the origin and the identity of the CC GABAergic guidepost neurons using extensive in vivo cell fate-mapping experiments. We identified two distinct GABAergic neuronal subpopulations, originating from the medial (MGE) and caudal (CGE) ganglionic eminences. I then studied in more details the dynamic interactions between CC neurons and callosal axons by confocal time-lapse video microscopy and I have also further characterized the role of each guidepost neuronal subpopulation in callosal guidance. Interestingly, MGE- and CGE-derived GABAergic neurons are both attractive for callosal axons in transplantation experiments. Finally, we have dissected the molecular basis of these guidance mechanisms by using FACS sorting combined with an extensive genetic screen for molecules of interest by a sensitive RT-PCR technique, as well as, in situ hybridization.I have also investigated whether CC guidepost neurons are involved in agenesis of the CC which occurs in numerous human congenital syndromes. Aristaless-related homeobox gene (Arx) regulates GABAergic neuron migration and its mutation leads to numerous human pathologies including X-linked lissencephaly with abnormal genitalia (XLAG) and severe CC agenesis. Interestingly, I found that ARX is expressed in all the guidepost GABAergic neuronal populations of the CC and that Arx-/- embryos exhibit a drastic loss of CC GABAergic interneurons accompanied by callosal axon navigation defects (Niquille et al, in preparation). In addition, we discovered that mice deficient for the ciliogenic transcription factor RFX3 suffer from CC agenesis associated with early midline patterning defects and a secondary disorganisation of guidepost glutamatergic neurons (Benadiba et al., submitted). This strongly points out the potential implication of both types of guidepost neurons in human CC agenesis.Taken together, my thesis work reveals novel functions for transient neurons in axonal guidance and brings new perspectives on the respective roles of neuronal and glial cells in these processes.

Prediction of cross-recognition of peptide-HLA A2 by Melan-A-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationships.

The cross-recognition of peptides by cytotoxic T lymphocytes is a key element in immunology and in particular in peptide based immunotherapy. Here we develop three-dimensional (3D) quantitative structure-activity relationships (QSARs) to predict cross-recognition by Melan-A-specific cytotoxic T lymphocytes of peptides bound to HLA A*0201 (hereafter referred to as HLA A2). First, we predict the structure of a set of self- and pathogen-derived peptides bound to HLA A2 using a previously developed ab initio structure prediction approach [Fagerberg et al., J. Mol. Biol., 521-46 (2006)]. Second, shape and electrostatic energy calculations are performed on a 3D grid to produce similarity matrices which are combined with a genetic neural network method [So et al., J. Med. Chem., 4347-59 (1997)] to generate 3D-QSAR models. The models are extensively validated using several different approaches. During the model generation, the leave-one-out cross-validated correlation coefficient (q (2)) is used as the fitness criterion and all obtained models are evaluated based on their q (2) values. Moreover, the best model obtained for a partitioned data set is evaluated by its correlation coefficient (r = 0.92 for the external test set). The physical relevance of all models is tested using a functional dependence analysis and the robustness of the models obtained for the entire data set is confirmed using y-randomization. Finally, the validated models are tested for their utility in the setting of rational peptide design: their ability to discriminate between peptides that only contain side chain substitutions in a single secondary anchor position is evaluated. In addition, the predicted cross-recognition of the mono-substituted peptides is confirmed experimentally in chromium-release assays. These results underline the utility of 3D-QSARs in peptide mimetic design and suggest that the properties of the unbound epitope are sufficient to capture most of the information to determine the cross-recognition.