Sound objects in time, space and action

SOUND OBJECTS IN TIME, SPACE AND ACTIONThe term "sound object" describes an auditory experience that is associated with an acoustic event produced by a sound source. At cortical level, sound objects are represented by temporo-spatial activity patterns within distributed neural networks. This investigation concerns temporal, spatial and action aspects as assessed in normal subjects using electrical imaging or measurement of motor activity induced by transcranial magnetic stimulation (TMS).Hearing the same sound again has been shown to facilitate behavioral responses (repetition priming) and to modulate neural activity (repetition suppression). In natural settings the same source is often heard again and again, with variations in spectro-temporal and spatial characteristics. I have investigated how such repeats influence response times in a living vs. non-living categorization task and the associated spatio-temporal patterns of brain activity in humans. Dynamic analysis of distributed source estimations revealed differential sound object representations within the auditory cortex as a function of the temporal history of exposure to these objects. Often heard sounds are coded by a modulation in a bilateral network. Recently heard sounds, independently of the number of previous exposures, are coded by a modulation of a left-sided network.With sound objects which carry spatial information, I have investigated how spatial aspects of the repeats influence neural representations. Dynamics analyses of distributed source estimations revealed an ultra rapid discrimination of sound objects which are characterized by spatial cues. This discrimination involved two temporo-spatially distinct cortical representations, one associated with position-independent and the other with position-linked representations within the auditory ventral/what stream.Action-related sounds were shown to increase the excitability of motoneurons within the primary motor cortex, possibly via an input from the mirror neuron system. The role of motor representations remains unclear. I have investigated repetition priming-induced plasticity of the motor representations of action sounds with the measurement of motor activity induced by TMS pulses applied on the hand motor cortex. TMS delivered to the hand area within the primary motor cortex yielded larger magnetic evoked potentials (MEPs) while the subject was listening to sounds associated with manual than non- manual actions. Repetition suppression was observed at motoneuron level, since during a repeated exposure to the same manual action sound the MEPs were smaller. I discuss these results in terms of specialized neural network involved in sound processing, which is characterized by repetition-induced plasticity.Thus, neural networks which underlie sound object representations are characterized by modulations which keep track of the temporal and spatial history of the sound and, in case of action related sounds, also of the way in which the sound is produced.LES OBJETS SONORES AU TRAVERS DU TEMPS, DE L'ESPACE ET DES ACTIONSLe terme "objet sonore" décrit une expérience auditive associée avec un événement acoustique produit par une source sonore. Au niveau cortical, les objets sonores sont représentés par des patterns d'activités dans des réseaux neuronaux distribués. Ce travail traite les aspects temporels, spatiaux et liés aux actions, évalués à l'aide de l'imagerie électrique ou par des mesures de l'activité motrice induite par stimulation magnétique trans-crânienne (SMT) chez des sujets sains. Entendre le même son de façon répétitive facilite la réponse comportementale (amorçage de répétition) et module l'activité neuronale (suppression liée à la répétition). Dans un cadre naturel, la même source est souvent entendue plusieurs fois, avec des variations spectro-temporelles et de ses caractéristiques spatiales. J'ai étudié la façon dont ces répétitions influencent le temps de réponse lors d'une tâche de catégorisation vivant vs. non-vivant, et les patterns d'activité cérébrale qui lui sont associés. Des analyses dynamiques d'estimations de sources ont révélé des représentations différenciées des objets sonores au niveau du cortex auditif en fonction de l'historique d'exposition à ces objets. Les sons souvent entendus sont codés par des modulations d'un réseau bilatéral. Les sons récemment entendus sont codé par des modulations d'un réseau du côté gauche, indépendamment du nombre d'expositions. Avec des objets sonores véhiculant de l'information spatiale, j'ai étudié la façon dont les aspects spatiaux des sons répétés influencent les représentations neuronales. Des analyses dynamiques d'estimations de sources ont révélé une discrimination ultra rapide des objets sonores caractérisés par des indices spatiaux. Cette discrimination implique deux représentations corticales temporellement et spatialement distinctes, l'une associée à des représentations indépendantes de la position et l'autre à des représentations liées à la position. Ces représentations sont localisées dans la voie auditive ventrale du "quoi".Des sons d'actions augmentent l'excitabilité des motoneurones dans le cortex moteur primaire, possiblement par une afférence du system des neurones miroir. Le rôle des représentations motrices des sons d'actions reste peu clair. J'ai étudié la plasticité des représentations motrices induites par l'amorçage de répétition à l'aide de mesures de potentiels moteurs évoqués (PMEs) induits par des pulsations de SMT sur le cortex moteur de la main. La SMT appliquée sur le cortex moteur primaire de la main produit de plus grands PMEs alors que les sujets écoutent des sons associée à des actions manuelles en comparaison avec des sons d'actions non manuelles. Une suppression liée à la répétition a été observée au niveau des motoneurones, étant donné que lors de l'exposition répétée au son de la même action manuelle les PMEs étaient plus petits. Ces résultats sont discuté en termes de réseaux neuronaux spécialisés impliqués dans le traitement des sons et caractérisés par de la plasticité induite par la répétition. Ainsi, les réseaux neuronaux qui sous-tendent les représentations des objets sonores sont caractérisés par des modulations qui gardent une trace de l'histoire temporelle et spatiale du son ainsi que de la manière dont le son a été produit, en cas de sons d'actions.

Role of genetic and social factors in modulating the efficiency of division of labour in social insects

Summary : Division of labour is one of the most fascinating aspects of social insects. The efficient allocation of individuals to a multitude of different tasks requires a dynamic adjustment in response to the demands of a changing environment. A considerable number of theoretical models have focussed on identifying the mechanisms allowing colonies to perform efficient task allocation. The large majority of these models are built on the observation that individuals in a colony vary in their propensity (response threshold) to perform different tasks. Since individuals with a low threshold for a given task stimulus are more likely to perform that task than individuals with a high threshold, infra-colony variation in individual thresholds results in colony division of labour. These theoretical models suggest that variation in individual thresholds is affected by the within-colony genetic diversity. However, the models have not considered the genetic architecture underlying the individual response thresholds. This is important because a better understanding of division of labour requires determining how genotypic variation relates to differences in infra-colony response threshold distributions. In this thesis, we investigated the combined influence on task allocation efficiency of both, the within-colony genetic variability (stemming from variation in the number of matings by queens) and the number of genes underlying the response thresholds. We used an agent-based simulator to model a situation where workers in a colony had to perform either a regulatory task (where the amount of a given food item in the colony had to be maintained within predefined bounds) or a foraging task (where the quantity of a second type of food item collected had to be the highest possible). The performance of colonies was a function of workers being able to perform both tasks efficiently. To study the effect of within-colony genetic diversity, we compared the performance of colonies with queens mated with varying number of males. On the other hand, the influence of genetic architecture was investigated by varying the number of loci underlying the response threshold of the foraging and regulatory tasks. Artificial evolution was used to evolve the allelic values underlying the tasks thresholds. The results revealed that multiple matings always translated into higher colony performance, whatever the number of loci encoding the thresholds of the regulatory and foraging tasks. However, the beneficial effect of additional matings was particularly important when the genetic architecture of queens comprised one or few genes for the foraging task's threshold. By contrast, higher number of genes encoding the foraging task reduced colony performance with the detrimental effect being stronger when queens had mated with several males. Finally, the number of genes determining the threshold for the regulatory task only had a minor but incremental effect on colony performance. Overall, our numerical experiments indicate the importance of considering the effects of queen mating frequency, genetic architecture underlying task thresholds and the type of task performed when investigating the factors regulating the efficiency of division of labour in social insects. In this thesis we also investigate the task allocation efficiency of response threshold models and compare them with neural networks. While response threshold models are widely used amongst theoretical biologists interested in division of labour in social insects, our simulation reveals that they perform poorly compared to a neural network model. A major shortcoming of response thresholds is that they fail at one of the most crucial requirement of division of labour, the ability of individuals in a colony to efficiently switch between tasks under varying environmental conditions. Moreover, the intrinsic properties of the threshold models are that they lead to a large proportion of idle workers. Our results highlight these limitations of the response threshold models and provide an adequate substitute. Altogether, the experiments presented in this thesis provide novel contributions to the understanding of how division of labour in social insects is influenced by queen mating frequency and genetic architecture underlying worker task thresholds. Moreover, the thesis also provides a novel model of the mechanisms underlying worker task allocation that maybe more generally applicable than the widely used response threshold models. Resumé : La répartition du travail est l'un des aspects les plus fascinants des insectes vivant en société. Une allocation efficace de la multitude de différentes tâches entre individus demande un ajustement dynamique afin de répondre aux exigences d'un environnement en constant changement. Un nombre considérable de modèles théoriques se sont attachés à identifier les mécanismes permettant aux colonies d'effectuer une allocation efficace des tâches. La grande majorité des ces modèles sont basés sur le constat que les individus d'une même colonie diffèrent dans leur propension (inclination à répondre) à effectuer différentes tâches. Etant donné que les individus possédant un faible seuil de réponse à un stimulus associé à une tâche donnée sont plus disposés à effectuer cette dernière que les individus possédant un seuil élevé, les différences de seuils parmi les individus vivant au sein d'une même colonie mènent à une certaine répartition du travail. Ces modèles théoriques suggèrent que la variation des seuils des individus est affectée par la diversité génétique propre à la colonie. Cependant, ces modèles ne considèrent pas la structure génétique qui est à la base des seuils de réponse individuels. Ceci est très important car une meilleure compréhension de la répartition du travail requière de déterminer de quelle manière les variations génotypiques sont associées aux différentes distributions de seuils de réponse à l'intérieur d'une même colonie. Dans le cadre de cette thèse, nous étudions l'influence combinée de la variabilité génétique d'une colonie (qui prend son origine dans la variation du nombre d'accouplements des reines) avec le nombre de gènes supportant les seuils de réponse, vis-à-vis de la performance de l'allocation des tâches. Nous avons utilisé un simulateur basé sur des agents pour modéliser une situation où les travailleurs d'une colonie devaient accomplir une tâche de régulation (1a quantité d'une nourriture donnée doit être maintenue à l'intérieur d'un certain intervalle) ou une tâche de recherche de nourriture (la quantité d'une certaine nourriture doit être accumulée autant que possible). Dans ce contexte, 'efficacité des colonies tient en partie des travailleurs qui sont capable d'effectuer les deux tâches de manière efficace. Pour étudier l'effet de la diversité génétique d'une colonie, nous comparons l'efficacité des colonies possédant des reines qui s'accouplent avec un nombre variant de mâles. D'autre part, l'influence de la structure génétique a été étudiée en variant le nombre de loci à la base du seuil de réponse des deux tâches de régulation et de recherche de nourriture. Une évolution artificielle a été réalisée pour évoluer les valeurs alléliques qui sont à l'origine de ces seuils de réponse. Les résultats ont révélé que de nombreux accouplements se traduisaient toujours en une plus grande performance de la colonie, quelque soit le nombre de loci encodant les seuils des tâches de régulation et de recherche de nourriture. Cependant, les effets bénéfiques d'accouplements additionnels ont été particulièrement important lorsque la structure génétique des reines comprenait un ou quelques gènes pour le seuil de réponse pour la tâche de recherche de nourriture. D'autre part, un nombre plus élevé de gènes encodant la tâche de recherche de nourriture a diminué la performance de la colonie avec un effet nuisible d'autant plus fort lorsque les reines s'accouplent avec plusieurs mâles. Finalement, le nombre de gènes déterminant le seuil pour la tâche de régulation eu seulement un effet mineur mais incrémental sur la performance de la colonie. Pour conclure, nos expériences numériques révèlent l'importance de considérer les effets associés à la fréquence d'accouplement des reines, à la structure génétique qui est à l'origine des seuils de réponse pour les tâches ainsi qu'au type de tâche effectué au moment d'étudier les facteurs qui régulent l'efficacité de la répartition du travail chez les insectes vivant en communauté. Dans cette thèse, nous étudions l'efficacité de l'allocation des tâches des modèles prenant en compte des seuils de réponses, et les comparons à des réseaux de neurones. Alors que les modèles basés sur des seuils de réponse sont couramment utilisés parmi les biologistes intéressés par la répartition des tâches chez les insectes vivant en société, notre simulation montre qu'ils se révèlent peu efficace comparé à un modèle faisant usage de réseaux de neurones. Un point faible majeur des seuils de réponse est qu'ils échouent sur un point crucial nécessaire à la répartition des tâches, la capacité des individus d'une colonie à commuter efficacement entre des tâches soumises à des conditions environnementales changeantes. De plus, les propriétés intrinsèques des modèles basés sur l'utilisation de seuils conduisent à de larges populations de travailleurs inactifs. Nos résultats mettent en évidence les limites de ces modèles basés sur l'utilisation de seuils et fournissent un substitut adéquat. Ensemble, les expériences présentées dans cette thèse fournissent de nouvelles contributions pour comprendre comment la répartition du travail chez les insectes vivant en société est influencée par la fréquence d'accouplements des reines ainsi que par la structure génétique qui est à l'origine, pour un travailleur, du seuil de réponse pour une tâche. De plus, cette thèse fournit également un nouveau modèle décrivant les mécanismes qui sont à l'origine de l'allocation des tâches entre travailleurs, mécanismes qui peuvent être appliqué de manière plus générale que ceux couramment utilisés et basés sur des seuils de réponse.

An empirical hierarchical Bayesian unification of occupational exposure assessment methods.

In occupational exposure assessment of airborne contaminants, exposure levels can either be estimated through repeated measurements of the pollutant concentration in air, expert judgment or through exposure models that use information on the conditions of exposure as input. In this report, we propose an empirical hierarchical Bayesian model to unify these approaches. Prior to any measurement, the hygienist conducts an assessment to generate prior distributions of exposure determinants. Monte-Carlo samples from these distributions feed two level-2 models: a physical, two-compartment model, and a non-parametric, neural network model trained with existing exposure data. The outputs of these two models are weighted according to the expert's assessment of their relevance to yield predictive distributions of the long-term geometric mean and geometric standard deviation of the worker's exposure profile (level-1 model). Bayesian inferences are then drawn iteratively from subsequent measurements of worker exposure. Any traditional decision strategy based on a comparison with occupational exposure limits (e.g. mean exposure, exceedance strategies) can then be applied. Data on 82 workers exposed to 18 contaminants in 14 companies were used to validate the model with cross-validation techniques. A user-friendly program running the model is available upon request.

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.

Using self organizing maps on compositional data

Self-organizing maps (Kohonen 1997) is a type of artificial neural network developedto explore patterns in high-dimensional multivariate data. The conventional versionof the algorithm involves the use of Euclidean metric in the process of adaptation ofthe model vectors, thus rendering in theory a whole methodology incompatible withnon-Euclidean geometries.In this contribution we explore the two main aspects of the problem:1. Whether the conventional approach using Euclidean metric can shed valid resultswith compositional data.2. If a modification of the conventional approach replacing vectorial sum and scalarmultiplication by the canonical operators in the simplex (i.e. perturbation andpowering) can converge to an adequate solution.Preliminary tests showed that both methodologies can be used on compositional data.However, the modified version of the algorithm performs poorer than the conventionalversion, in particular, when the data is pathological. Moreover, the conventional ap-proach converges faster to a solution, when data is \well-behaved".Key words: Self Organizing Map; Artificial Neural networks; Compositional data

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Prediction of postpartum depression using multilayer perceptrons and pruning.

OBJECTIVE. The main goal of this paper is to obtain a classification model based on feed-forward multilayer perceptrons in order to improve postpartum depression prediction during the 32 weeks after childbirth with a high sensitivity and specificity and to develop a tool to be integrated in a decision support system for clinicians. MATERIALS AND METHODS. Multilayer perceptrons were trained on data from 1397 women who had just given birth, from seven Spanish general hospitals, including clinical, environmental and genetic variables. A prospective cohort study was made just after delivery, at 8 weeks and at 32 weeks after delivery. The models were evaluated with the geometric mean of accuracies using a hold-out strategy. RESULTS. Multilayer perceptrons showed good performance (high sensitivity and specificity) as predictive models for postpartum depression. CONCLUSIONS. The use of these models in a decision support system can be clinically evaluated in future work. The analysis of the models by pruning leads to a qualitative interpretation of the influence of each variable in the interest of clinical protocols.

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.

Identificación de puntos de pesca en los viajes pesqueros de anchoveta monitoreados por satélites mediante redes neuronales artificiales

La sostenibilidad de los recursos marinos y de su ecosistema hace necesario un manejo responsable de las pesquerías. Conocer la distribución espacial del esfuerzo pesquero y en particular de las operaciones de pesca es indispensable para mejorar el monitoreo pesquero y el análisis de la vulnerabilidad de las especies frente a la pesca. Actualmente en la pesquería de anchoveta peruana, se recoge información del esfuerzo y capturas mediante un programa de observadores a bordo, pero esta solo representa una muestra de 2% del total de viajes pesqueros. Por otro lado, se dispone de información por cada hora (en promedio) de la posición de cada barco de la flota gracias al sistema de seguimiento satelital de las embarcaciones (VMS), aunque en estos no se señala cuándo ni dónde ocurrieron las calas. Las redes neuronales artificiales (ANN) podrían ser un método estadístico capaz de inferir esa información, entrenándose en una muestra para la cual sí conocemos las posiciones de calas (el 2% anteriormente referido), estableciendo relaciones analíticas entre las calas y ciertas características geométricas de las trayectorias observadas por el VMS y así, a partir de las últimas, identificar la posición de las operaciones de pesca. La aplicación de la red neuronal requiere un análisis previo que examine la sensibilidad de la red a variaciones en sus parámetros y bases de datos de entrenamiento, y que nos permita desarrollar criterios para definir la estructura de la red e interpretar sus resultados de manera adecuada. La problemática descrita en el párrafo anterior, aplicada específicamente a la anchoveta (Engraulis ringens) es detalllada en el primer capítulo, mientras que en el segundo se hace una revisión teórica de las redes neuronales. Luego se describe el proceso de construcción y pre-tratamiento de la base de datos, y definición de la estructura de la red previa al análisis de sensibilidad. A continuación se presentan los resultados para el análisis en los que obtenemos una estimación del 100% de calas, de las cuales aproximadamente 80% están correctamente ubicadas y 20% poseen un error de ubicación. Finalmente se discuten las fortalezas y debilidades de la técnica empleada, de métodos alternativos potenciales y de las perspectivas abiertas por este trabajo.

Profiling of counterfeit medicines by vibrational spectroscopy

Counterfeit pharmaceutical products have become a widespread problem in the last decade. Various analytical techniques have been applied to discriminate between genuine and counterfeit products. Among these, Near-infrared (NIR) and Raman spectroscopy provided promising results.The present study offers a methodology allowing to provide more valuable information fororganisations engaged in the fight against counterfeiting of medicines.A database was established by analyzing counterfeits of a particular pharmaceutical product using Near-infrared (NIR) and Raman spectroscopy. Unsupervised chemometric techniques (i.e. principal component analysis - PCA and hierarchical cluster analysis - HCA) were implemented to identify the classes within the datasets. Gas Chromatography coupled to Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FT-IR) were used to determine the number of different chemical profiles within the counterfeits. A comparison with the classes established by NIR and Raman spectroscopy allowed to evaluate the discriminating power provided by these techniques. Supervised classifiers (i.e. k-Nearest Neighbors, Partial Least Squares Discriminant Analysis, Probabilistic Neural Networks and Counterpropagation Artificial Neural Networks) were applied on the acquired NIR and Raman spectra and the results were compared to the ones provided by the unsupervised classifiers.The retained strategy for routine applications, founded on the classes identified by NIR and Raman spectroscopy, uses a classification algorithm based on distance measures and Receiver Operating Characteristics (ROC) curves. The model is able to compare the spectrum of a new counterfeit with that of previously analyzed products and to determine if a new specimen belongs to one of the existing classes, consequently allowing to establish a link with other counterfeits of the database.

Bayesian image reconstruction with space-variant noise suppression

In this paper we present a Bayesian image reconstruction algorithm with entropy prior (FMAPE) that uses a space-variant hyperparameter. The spatial variation of the hyperparameter allows different degrees of resolution in areas of different statistical characteristics, thus avoiding the large residuals resulting from algorithms that use a constant hyperparameter. In the first implementation of the algorithm, we begin by segmenting a Maximum Likelihood Estimator (MLE) reconstruction. The segmentation method is based on using a wavelet decomposition and a self-organizing neural network. The result is a predetermined number of extended regions plus a small region for each star or bright object. To assign a different value of the hyperparameter to each extended region and star, we use either feasibility tests or cross-validation methods. Once the set of hyperparameters is obtained, we carried out the final Bayesian reconstruction, leading to a reconstruction with decreased bias and excellent visual characteristics. The method has been applied to data from the non-refurbished Hubble Space Telescope. The method can be also applied to ground-based images.

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.