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.

Microcontroller-driven fluid-injection system for atomic force microscopy.

We present a programmable microcontroller-driven injection system for the exchange of imaging medium during atomic force microscopy. Using this low-noise system, high-resolution imaging can be performed during this process of injection without disturbance. This latter circumstance was exemplified by the online imaging of conformational changes in DNA molecules during the injection of anticancer drug into the fluid chamber.

Neutral and selection-driven decay of sexual traits in asexual stick insects.

Environmental shifts and lifestyle changes may result in formerly adaptive traits becoming non-functional or maladaptive. The subsequent decay of such traits highlights the importance of natural selection for adaptations, yet its causes have rarely been investigated. To study the fate of formerly adaptive traits after lifestyle changes, we evaluated sexual traits in five independently derived asexual lineages, including traits that are specific to males and therefore not exposed to selection. At least four of the asexual lineages retained the capacity to produce males that display normal courtship behaviours and are able to fertilize eggs of females from related sexual species. The maintenance of male traits may stem from pleiotropy, or from these traits only regressing via drift, which may require millions of years to generate phenotypic effects. By contrast, we found parallel decay of sexual traits in females. Asexual females produced altered airborne and contact signals, had modified sperm storage organs, and lost the ability to fertilize their eggs, impeding reversals to sexual reproduction. Female sexual traits were decayed even in recently derived asexuals, suggesting that trait changes following the evolution of asexuality, when they occur, proceed rapidly and are driven by selective processes rather than drift.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Gravity-driven slug motion in capillary tubes

The velocity of a liquid slug falling in a capillary tube is lower than predicted for Poiseuille flow due to presence of menisci, whose shapes are determined by the complex interplay of capillary, viscous, and gravitational forces. Due to the presence of menisci, a capillary pressure proportional to surface curvature acts on the slug and streamlines are bent close to the interface, resulting in enhanced viscous dissipation at the wedges. To determine the origin of drag-force increase relative to Poiseuille flow, we compute the force resultant acting on the slug by integrating Navier-Stokes equations over the liquid volume. Invoking relationships from differential geometry we demonstrate that the additional drag is due to viscous forces only and that no capillary drag of hydrodynamic origin exists (i.e., due to hydrodynamic deformation of the interface). Requiring that the force resultant is zero, we derive scaling laws for the steady velocity in the limit of small capillary numbers by estimating the leading order viscous dissipation in the different regions of the slug (i.e., the unperturbed Poiseuille-like bulk, the static menisci close to the tube axis and the dynamic regions close to the contact lines). Considering both partial and complete wetting, we find that the relationship between dimensionless velocity and weight is, in general, nonlinear. Whereas the relationship obtained for complete-wetting conditions is found in agreement with the experimental data of Bico and Quere [J. Bico and D. Quere, J. Colloid Interface Sci. 243, 262 (2001)], the scaling law under partial-wetting conditions is validated by numerical simulations performed with the Volume of Fluid method. The simulated steady velocities agree with the behavior predicted by the theoretical scaling laws in presence and in absence of static contact angle hysteresis. The numerical simulations suggest that wedge-flow dissipation alone cannot account for the entire additional drag and that the non-Poiseuille dissipation in the static menisci (not considered in previous studies) has to be considered for large contact angles.

Targeting IL-1β and IL-17A driven inflammation during influenza-induced exacerbations of chronic lung inflammation.

For patients with chronic lung diseases, such as chronic obstructive pulmonary disease (COPD), exacerbations are life-threatening events causing acute respiratory distress that can even lead to hospitalization and death. Although a great deal of effort has been put into research of exacerbations and potential treatment options, the exact underlying mechanisms are yet to be deciphered and no therapy that effectively targets the excessive inflammation is available. In this study, we report that interleukin-1β (IL-1β) and interleukin-17A (IL-17A) are key mediators of neutrophilic inflammation in influenza-induced exacerbations of chronic lung inflammation. Using a mouse model of disease, our data shows a role for IL-1β in mediating lung dysfunction, and in driving neutrophilic inflammation during the whole phase of viral infection. We further report a role for IL-17A as a mediator of IL-1β induced neutrophilia at early time points during influenza-induced exacerbations. Blocking of IL-17A or IL-1 resulted in a significant abrogation of neutrophil recruitment to the airways in the initial phase of infection or at the peak of viral replication, respectively. Therefore, IL-17A and IL-1β are potential targets for therapeutic treatment of viral exacerbations of chronic lung inflammation.

Genetic bottlenecks driven by population disconnection.

Connectivity among populations plays a crucial role in maintaining genetic variation at a local scale, especially in small populations affected strongly by genetic drift. The negative consequences of population disconnection on allelic richness and gene diversity (heterozygosity) are well recognized and empirically established. It is not well recognized, however, that a sudden drop in local effective population size induced by such disconnection produces a temporary disequilibrium in allelic frequency distributions that is akin to the genetic signature of a demographic bottleneck. To document this effect, we used individual-based simulations and empirical data on allelic richness and gene diversity in six pairs of isolated versus well-connected (core) populations of European tree frogs. In our simulations, population disconnection depressed allelic richness more than heterozygosity and thus resulted in a temporary excess in gene diversity relative to mutation drift equilibrium (i.e., signature of a genetic bottleneck). We observed a similar excess in gene diversity in isolated populations of tree frogs. Our results show that population disconnection can create a genetic bottleneck in the absence of demographic collapse.

On the transcriptional role of NLRC5 and the function of NLRC5-driven MHC class I expression in the immune system

Major histocompatibility complex (MHC) molecules are of crucial importance for the immune system to recognize and defend the body against external attacks. Foreign antigens are presented by specialized cells, called antigen presenting cells, to T lymphocytes in the context of MHC molecules, thereby inducing T cell activation. In addition, MHC molecules are essential for Natural Killer (NK) cell biology, playing a role in NK cell education and activation. Recently, the NOD-like receptor (NLR) family member NLRC5 (NLR caspase recruitment domain containing protein 5) was found to act as transcriptional regulator of MHC class I, in particular in T and NK cells. Its role in MHC class I expression is however minor in dendritic cells (DCs). This raised the question of whether inflammatory conditions, which augment the levels of NLRC5 in DCs, could increase its contribution to MHC class I expression. Our work shows that MHC class I transcript and intracellular levels depend on NLRC5, while its role in MHC class I surface expression is instead negligible. We describe however a general salvage mechanism that enables cells with low intracellular MHC class I levels to nevertheless maintain relatively high MHC class I on the cell surface. In addition, we lack a thorough understanding of NLRC5 target gene specificity and mechanism of action. Our work delineates the unique consensus sequence in MHC class I promoters required for NLRC5 recruitment and pinpoints conserved features conferring its specificity. Furthermore, through genome-wide analyses, we confirm that NLRC5 regulates classical MHC class I genes and identify novel target genes all encoding non-classical MHC class I molecules exerting an array of functions in immunity and tolerance. We finally asked why a dedicated factor co-regulates MHC class I expression specifically in T and NK lymphocytes. We show that deregulated NLRC5 expression affects the education of NK cells and alters the crosstalk between T and NK cells, leading to NK cell-mediated killing of T lymphocytes. Altogether this thesis work brings insights into molecular and physiological aspects of NLRC5 function, which might help understand certain aspects of immune responses and disorders. -- Les molécules du complexe majeur d'histocompatibilité (CMH) sont essentielles au système immunitaire pour l'initiation de la réponse immunitaire. En effet, l'activation des lymphocytes T nécessite la reconnaissance d'un antigène étranger présenté par les cellules présentatrices d'antigènes sur une molécule du CMH. Les molécules du CMH ont également un rôle fondamental pour la fonction des cellules Natural Killer (NK) puisqu'elles sont nécessaires à leur processus d'éducation et d'activation. Récemment, NLRC5 (NLR caspase recruitment domain containing protein 5), un membre de la famille des récepteurs de type NOD (NLRs), a été décrit comme un facteur de transactivation de l'expression des gènes du CMH de classe I. A l'état basai, cette fonction transcriptionnelle est essentielle dans les lymphocytes T et NK, alors que ce rôle reste mineur pour l'expression des molécules du CMH de classe I dans les cellules dendritiques (DCs). Dans des conditions inflammatoires, l'expression de NLRC5 augmente dans les DCs. Notre travail démontre que, dans ces conditions, les transcrits et les niveaux intracellulaires des molécules du CMH de classe I augmentent aussi d'une façon dépendante de NLRC5. A contrario, le rôle de NLRC5 sur les niveaux de molécules de surface reste minoritaire. Cette observation nous a conduits à l'identification d'un mécanisme général de compensation qui permet aux cellules de maintenir des niveaux relativement élevés de molécules de CMH de class I à leur surface malgré de faibles niveaux intracellulaires. De plus, il semblait nécessaire de s'orienter vers une approche plus globale afin de déterminer l'étendue de la fonction transcriptionnelle de NLRC5. Par une approche du génome entier, nous avons pu décrire une séquence consensus conservée présente dans les promoteurs des gènes du CMH de classe I, sur laquelle NLRC5 est spécifiquement recruté. Nous avons pu également identifier de nouveaux gènes cibles codant pour des molécules de CMH de classe I non classiques impliqués dans l'immunité et la tolérance. Finalement, nous nous sommes demandé quel est l'intérêt d'avoir un facteur transcriptionnel, en l'occurrence NLRC5, qui orchestre l'expression du CMH de classe I dans les lymphocytes T et NK. Nous montrons que la dérégulation de l'expression de NLRC5 affecte l'éducation des cellules NK et conduit à la mort cellulaire des lymphocytes T médiée par les cellules NK. Dans l'ensemble ce travail de thèse contribue à la caractérisation du rôle de NLRC5, tant au niveau moléculaire que physiologique, ce qui présente un intérêt dans le cadre de la compréhension de certains aspects physiopathologique de la réponse immunitaire.

Emergence of oriented circuits driven by synaptic pruning associated with spike-timing-dependent plasticity (STDP)

ABSTRACT: Massive synaptic pruning following over-growth is a general feature of mammalian brain maturation. Pruning starts near time of birth and is completed by time of sexual maturation. Trigger signals able to induce synaptic pruning could be related to dynamic functions that depend on the timing of action potentials. Spike-timing-dependent synaptic plasticity (STDP) is a change in the synaptic strength based on the ordering of pre- and postsynaptic spikes. The relation between synaptic efficacy and synaptic pruning suggests that the weak synapses may be modified and removed through competitive "learning" rules. This plasticity rule might produce the strengthening of the connections among neurons that belong to cell assemblies characterized by recurrent patterns of firing. Conversely, the connections that are not recurrently activated might decrease in efficiency and eventually be eliminated. The main goal of our study is to determine whether or not, and under which conditions, such cell assemblies may emerge out of a locally connected random network of integrate-and-fire units distributed on a 2D lattice receiving background noise and content-related input organized in both temporal and spatial dimensions. The originality of our study stands on the relatively large size of the network, 10,000 units, the duration of the experiment, 10E6 time units (one time unit corresponding to the duration of a spike), and the application of an original bio-inspired STDP modification rule compatible with hardware implementation. A first batch of experiments was performed to test that the randomly generated connectivity and the STDP-driven pruning did not show any spurious bias in absence of stimulation. Among other things, a scale factor was approximated to compensate for the network size on the ac¬tivity. Networks were then stimulated with the spatiotemporal patterns. The analysis of the connections remaining at the end of the simulations, as well as the analysis of the time series resulting from the interconnected units activity, suggest that feed-forward circuits emerge from the initially randomly connected networks by pruning. RESUME: L'élagage massif des synapses après une croissance excessive est une phase normale de la ma¬turation du cerveau des mammifères. L'élagage commence peu avant la naissance et est complété avant l'âge de la maturité sexuelle. Les facteurs déclenchants capables d'induire l'élagage des synapses pourraient être liés à des processus dynamiques qui dépendent de la temporalité rela¬tive des potentiels d'actions. La plasticité synaptique à modulation temporelle relative (STDP) correspond à un changement de la force synaptique basé sur l'ordre des décharges pré- et post- synaptiques. La relation entre l'efficacité synaptique et l'élagage des synapses suggère que les synapses les plus faibles pourraient être modifiées et retirées au moyen d'une règle "d'appren¬tissage" faisant intervenir une compétition. Cette règle de plasticité pourrait produire le ren¬forcement des connexions parmi les neurones qui appartiennent à une assemblée de cellules caractérisée par des motifs de décharge récurrents. A l'inverse, les connexions qui ne sont pas activées de façon récurrente pourraient voir leur efficacité diminuée et être finalement éliminées. Le but principal de notre travail est de déterminer s'il serait possible, et dans quelles conditions, que de telles assemblées de cellules émergent d'un réseau d'unités integrate-and¬-fire connectées aléatoirement et distribuées à la surface d'une grille bidimensionnelle recevant à la fois du bruit et des entrées organisées dans les dimensions temporelle et spatiale. L'originalité de notre étude tient dans la taille relativement grande du réseau, 10'000 unités, dans la durée des simulations, 1 million d'unités de temps (une unité de temps correspondant à une milliseconde), et dans l'utilisation d'une règle STDP originale compatible avec une implémentation matérielle. Une première série d'expériences a été effectuée pour tester que la connectivité produite aléatoirement et que l'élagage dirigé par STDP ne produisaient pas de biais en absence de stimu¬lation extérieure. Entre autres choses, un facteur d'échelle a pu être approximé pour compenser l'effet de la variation de la taille du réseau sur son activité. Les réseaux ont ensuite été stimulés avec des motifs spatiotemporels. L'analyse des connexions se maintenant à la fin des simulations, ainsi que l'analyse des séries temporelles résultantes de l'activité des neurones, suggèrent que des circuits feed-forward émergent par l'élagage des réseaux initialement connectés au hasard.

Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling.

How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model's prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information.

HIF-driven SF3B1 induces KHK-C to enforce fructolysis and heart disease.

Fructose is a major component of dietary sugar and its overconsumption exacerbates key pathological features of metabolic syndrome. The central fructose-metabolising enzyme is ketohexokinase (KHK), which exists in two isoforms: KHK-A and KHK-C, generated through mutually exclusive alternative splicing of KHK pre-mRNAs. KHK-C displays superior affinity for fructose compared with KHK-A and is produced primarily in the liver, thus restricting fructose metabolism almost exclusively to this organ. Here we show that myocardial hypoxia actuates fructose metabolism in human and mouse models of pathological cardiac hypertrophy through hypoxia-inducible factor 1α (HIF1α) activation of SF3B1 and SF3B1-mediated splice switching of KHK-A to KHK-C. Heart-specific depletion of SF3B1 or genetic ablation of Khk, but not Khk-A alone, in mice, suppresses pathological stress-induced fructose metabolism, growth and contractile dysfunction, thus defining signalling components and molecular underpinnings of a fructose metabolism regulatory system crucial for pathological growth.

Long-Lasting Metabolic Imbalance Related to Obesity Alters Olfactory Tissue Homeostasis and Impairs Olfactory-Driven Behaviors.

Obesity is associated with chronic food intake disorders and binge eating. Food intake relies on the interaction between homeostatic regulation and hedonic signals among which, olfaction is a major sensory determinant. However, its potential modulation at the peripheral level by a chronic energy imbalance associated to obese status remains a matter of debate. We further investigated the olfactory function in a rodent model relevant to the situation encountered in obese humans, where genetic susceptibility is juxtaposed on chronic eating disorders. Using several olfactory-driven tests, we compared the behaviors of obesity-prone Sprague-Dawley rats (OP) fed with a high-fat/high-sugar diet with those of obese-resistant ones fed with normal chow. In OP rats, we reported 1) decreased odor threshold, but 2) poor olfactory performances, associated with learning/memory deficits, 3) decreased influence of fasting, and 4) impaired insulin control on food seeking behavior. Associated with these behavioral modifications, we found a modulation of metabolism-related factors implicated in 1) electrical olfactory signal regulation (insulin receptor), 2) cellular dynamics (glucorticoids receptors, pro- and antiapoptotic factors), and 3) homeostasis of the olfactory mucosa and bulb (monocarboxylate and glucose transporters). Such impairments might participate to the perturbed daily food intake pattern that we observed in obese animals.

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.