Adaptive tracking of EEG oscillations.

Neuronal oscillations are an important aspect of EEG recordings. These oscillations are supposed to be involved in several cognitive mechanisms. For instance, oscillatory activity is considered a key component for the top-down control of perception. However, measuring this activity and its influence requires precise extraction of frequency components. This processing is not straightforward. Particularly, difficulties with extracting oscillations arise due to their time-varying characteristics. Moreover, when phase information is needed, it is of the utmost importance to extract narrow-band signals. This paper presents a novel method using adaptive filters for tracking and extracting these time-varying oscillations. This scheme is designed to maximize the oscillatory behavior at the output of the adaptive filter. It is then capable of tracking an oscillation and describing its temporal evolution even during low amplitude time segments. Moreover, this method can be extended in order to track several oscillations simultaneously and to use multiple signals. These two extensions are particularly relevant in the framework of EEG data processing, where oscillations are active at the same time in different frequency bands and signals are recorded with multiple sensors. The presented tracking scheme is first tested with synthetic signals in order to highlight its capabilities. Then it is applied to data recorded during a visual shape discrimination experiment for assessing its usefulness during EEG processing and in detecting functionally relevant changes. This method is an interesting additional processing step for providing alternative information compared to classical time-frequency analyses and for improving the detection and analysis of cross-frequency couplings.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Modeling sequencing errors by combining Hidden Markov models.

Among the largest resources for biological sequence data is the large amount of expressed sequence tags (ESTs) available in public and proprietary databases. ESTs provide information on transcripts but for technical reasons they often contain sequencing errors. Therefore, when analyzing EST sequences computationally, such errors must be taken into account. Earlier attempts to model error prone coding regions have shown good performance in detecting and predicting these while correcting sequencing errors using codon usage frequencies. In the research presented here, we improve the detection of translation start and stop sites by integrating a more complex mRNA model with codon usage bias based error correction into one hidden Markov model (HMM), thus generalizing this error correction approach to more complex HMMs. We show that our method maintains the performance in detecting coding sequences.

Generating controlled image sets in cognitive neuroscience research.

The investigation of perceptual and cognitive functions with non-invasive brain imaging methods critically depends on the careful selection of stimuli for use in experiments. For example, it must be verified that any observed effects follow from the parameter of interest (e.g. semantic category) rather than other low-level physical features (e.g. luminance, or spectral properties). Otherwise, interpretation of results is confounded. Often, researchers circumvent this issue by including additional control conditions or tasks, both of which are flawed and also prolong experiments. Here, we present some new approaches for controlling classes of stimuli intended for use in cognitive neuroscience, however these methods can be readily extrapolated to other applications and stimulus modalities. Our approach is comprised of two levels. The first level aims at equalizing individual stimuli in terms of their mean luminance. Each data point in the stimulus is adjusted to a standardized value based on a standard value across the stimulus battery. The second level analyzes two populations of stimuli along their spectral properties (i.e. spatial frequency) using a dissimilarity metric that equals the root mean square of the distance between two populations of objects as a function of spatial frequency along x- and y-dimensions of the image. Randomized permutations are used to obtain a minimal value between the populations to minimize, in a completely data-driven manner, the spectral differences between image sets. While another paper in this issue applies these methods in the case of acoustic stimuli (Aeschlimann et al., Brain Topogr 2008), we illustrate this approach here in detail for complex visual stimuli.

Enhancing the vertical resolution of surface georadar data

There are far-reaching conceptual similarities between bi-static surface georadar and post-stack, "zero-offset" seismic reflection data, which is expressed in largely identical processing flows. One important difference is, however, that standard deconvolution algorithms routinely used to enhance the vertical resolution of seismic data are notoriously problematic or even detrimental to the overall signal quality when applied to surface georadar data. We have explored various options for alleviating this problem and have tested them on a geologically well-constrained surface georadar dataset. Standard stochastic and direct deterministic deconvolution approaches proved to be largely unsatisfactory. While least-squares-type deterministic deconvolution showed some promise, the inherent uncertainties involved in estimating the source wavelet introduced some artificial "ringiness". In contrast, we found spectral balancing approaches to be effective, practical and robust means for enhancing the vertical resolution of surface georadar data, particularly, but not exclusively, in the uppermost part of the georadar section, which is notoriously plagued by the interference of the direct air- and groundwaves. For the data considered in this study, it can be argued that band-limited spectral blueing may provide somewhat better results than standard band-limited spectral whitening, particularly in the uppermost part of the section affected by the interference of the air- and groundwaves. Interestingly, this finding is consistent with the fact that the amplitude spectrum resulting from least-squares-type deterministic deconvolution is characterized by a systematic enhancement of higher frequencies at the expense of lower frequencies and hence is blue rather than white. It is also consistent with increasing evidence that spectral "blueness" is a seemingly universal, albeit enigmatic, property of the distribution of reflection coefficients in the Earth. Our results therefore indicate that spectral balancing techniques in general and spectral blueing in particular represent simple, yet effective means of enhancing the vertical resolution of surface georadar data and, in many cases, could turn out to be a preferable alternative to standard deconvolution approaches.

Histone deacetylase inhibitors impair innate immune responses

SUMMARYThe innate immune system plays a central role in host defenses against invading pathogens. Innate immune cells sense the presence of pathogens through pattern recognition receptors that trigger intracellular signaling, leading to the production of pro-inflammatory mediators like cytokines, which shape innate and adaptive immune responses. Both by excess and by default inflammation may be detrimental to the host. Indeed, severe sepsis and septic shock are lethal complications of infections characterized by a dysregulated inflammatory response.In recent years, members of the superfamily of histone deacetylases have been the focus of great interest. In mammals, histone deacetylases are broadly classified into two main subfamilies comprising histone deacetylases 1-11 (HDAC1-11) and sirtuins 1-7 (SIRT1-7). These enzymes influence gene expression by deacetylating histones and numerous non-histone proteins. Histone deacetylases have been involved in the development of oncologic, metabolic, cardiovascular, neurodegenerative and autoimmune diseases. Pharmacological modulators of histone deacetylase activity, principally inhibitors, have been developed for the treatment of cancer and metabolic diseases. When we initiated this project, several studies suggested that inhibitors of HDAC 1-11 have anti-inflammatory activity. Yet, their influence on innate immune responses was largely uncharacterized. The present study was initiated to fill in this gap.In the first part of this work, we report the first comprehensive study of the effects of HDAC 1- 11 inhibitors on innate immune responses in vitro and in vivo. Strikingly, expression studies revealed that HDAC1-11 inhibitors act essentially as negative regulators of basal and microbial product- induced expression of critical immune receptors and antimicrobial products by mouse and human innate immune cells like macrophages and dendritic cells. Furthermore, we describe a new molecular mechanism whereby HDAC1-11 inhibitors repress pro-inflammatory cytokine expression through the induction of the expression and the activity of the transcriptional repressor Μί-2β. HDAC1-11 inhibitors also impair the potential of macrophages to engulf and kill bacteria. Finally, mice treated with an HDAC inhibitor are more susceptible to non-severe bacterial and fungal infection, but are protected against toxic and septic shock. Altogether these data support the concept that HDAC 1-11 inhibitors have potent anti-inflammatory and immunomodulatory activities in vitro and in vivo.Macrophage migration inhibitory factor (MIF) is a pro-inflammatory cytokine that plays a central role in innate immune responses, cell proliferation and oncogenesis. In the second part of this manuscript, we demonstrate that HDAC1-11 inhibitors inhibit MIF expression in vitro and in vivo and describe a novel molecular mechanism accounting for these effects. We propose that inhibition of MIF expression by HDAC 1-11 inhibitors may contribute to the antitumorigenic and anti-inflammatory effects of these drugs.NAD+ is an essential cofactor of sirtuins activity and one of the major sources of energy within the cells. Therefore, sirtuins link deacetylation to NAD+ metabolism and energy status. In the last part of this thesis, we report preliminary results indicating that a pharmacological inhibitor of SIRT1-2 drastically decreases pro-inflammatory cytokine production (RNA and protein) and interferes with MAP kinase intracellular signal transduction pathway in macrophages. Moreover, administration of the SIRT1-2 inhibitor protects mice from lethal endotoxic shock and septic shock.Overall, our studies demonstrate that inhibitors of HDAC1-11 and sirtuins are powerful anti-inflammatory molecules. Given their profound negative impact on the host antimicrobial defence response, these inhibitors might increase the susceptibility to opportunistic infections, especially in immunocompromised cancer patients. Yet, these inhibitors might be useful to control the inflammatory response in severely ill septic patients or in patients suffering from chronic inflammatory diseases.

MAMOT: hidden Markov modeling tool.

Hidden Markov models (HMMs) are probabilistic models that are well adapted to many tasks in bioinformatics, for example, for predicting the occurrence of specific motifs in biological sequences. MAMOT is a command-line program for Unix-like operating systems, including MacOS X, that we developed to allow scientists to apply HMMs more easily in their research. One can define the architecture and initial parameters of the model in a text file and then use MAMOT for parameter optimization on example data, decoding (like predicting motif occurrence in sequences) and the production of stochastic sequences generated according to the probabilistic model. Two examples for which models are provided are coiled-coil domains in protein sequences and protein binding sites in DNA. A wealth of useful features include the use of pseudocounts, state tying and fixing of selected parameters in learning, and the inclusion of prior probabilities in decoding. AVAILABILITY: MAMOT is implemented in C++, and is distributed under the GNU General Public Licence (GPL). The software, documentation, and example model files can be found at http://bcf.isb-sib.ch/mamot

Bacterial flagellin elicits innate immune defenses and cardiac dysfunction in vivo

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

Cancer cell-autonomous contribution of type I interferon signaling to the efficacy of chemotherapy.

Some of the anti-neoplastic effects of anthracyclines in mice originate from the induction of innate and T cell-mediated anticancer immune responses. Here we demonstrate that anthracyclines stimulate the rapid production of type I interferons (IFNs) by malignant cells after activation of the endosomal pattern recognition receptor Toll-like receptor 3 (TLR3). By binding to IFN-α and IFN-β receptors (IFNARs) on neoplastic cells, type I IFNs trigger autocrine and paracrine circuitries that result in the release of chemokine (C-X-C motif) ligand 10 (CXCL10). Tumors lacking Tlr3 or Ifnar failed to respond to chemotherapy unless type I IFN or Cxcl10, respectively, was artificially supplied. Moreover, a type I IFN-related signature predicted clinical responses to anthracycline-based chemotherapy in several independent cohorts of patients with breast carcinoma characterized by poor prognosis. Our data suggest that anthracycline-mediated immune responses mimic those induced by viral pathogens. We surmise that such 'viral mimicry' constitutes a hallmark of successful chemotherapy.

Musical training intensity yields opposite effects on grey matter density in cognitive versus sensorimotor networks.

Using optimized voxel-based morphometry, we performed grey matter density analyses on 59 age-, sex- and intelligence-matched young adults with three distinct, progressive levels of musical training intensity or expertise. Structural brain adaptations in musicians have been repeatedly demonstrated in areas involved in auditory perception and motor skills. However, musical activities are not confined to auditory perception and motor performance, but are entangled with higher-order cognitive processes. In consequence, neuronal systems involved in such higher-order processing may also be shaped by experience-driven plasticity. We modelled expertise as a three-level regressor to study possible linear relationships of expertise with grey matter density. The key finding of this study resides in a functional dissimilarity between areas exhibiting increase versus decrease of grey matter as a function of musical expertise. Grey matter density increased with expertise in areas known for their involvement in higher-order cognitive processing: right fusiform gyrus (visual pattern recognition), right mid orbital gyrus (tonal sensitivity), left inferior frontal gyrus (syntactic processing, executive function, working memory), left intraparietal sulcus (visuo-motor coordination) and bilateral posterior cerebellar Crus II (executive function, working memory) and in auditory processing: left Heschl's gyrus. Conversely, grey matter density decreased with expertise in bilateral perirolandic and striatal areas that are related to sensorimotor function, possibly reflecting high automation of motor skills. Moreover, a multiple regression analysis evidenced that grey matter density in the right mid orbital area and the inferior frontal gyrus predicted accuracy in detecting fine-grained incongruities in tonal music.

Microtubule-associated protein-2 immunoreactivity: a useful tool in the differential diagnosis of low-grade neuroepithelial tumors.

Complex and variable morphological phenotypes pose a major challenge to the histopathological classification of neuroepithelial tumors. This applies in particular for low-grade gliomas and glio-neuronal tumors. Recently, we and others have identified microtubule-associated protein-2 (MAP2) as an immunohistochemical marker expressed in the majority of glial tumors. Characteristic cell morphologies can be recognized by MAP2 immunoreactivity in different glioma entities, i.e., process sparse oligodendroglial versus densely ramified astrocytic elements. Here, we describe MAP2-immunoreactivity patterns in a large series of various neuroepithelial tumors and related neoplasms (n = 960). Immunohistochemical analysis led to the following conclusions: (1) specific pattern of MAP2-positive tumor cells can be identified in 95% of glial neoplasms; (2) ependymal tumors do not express MAP2 in their rosette-forming cell component; (3) tumors of the pineal gland as well as malignant embryonic tumors are also characterized by abundant MAP2 immunoreactivity; (4) virtually no MAP2 expression can be observed in the neoplastic glial component of glio-neuronal tumors, i.e. gangliogliomas; (5) malignant glial tumor variants (WHO grade III or IV) exhibit different and less specific MAP2 staining patterns compared to their benign counterparts (WHO grade I or II); (6) with the exception of melanomas and small cell lung cancers, MAP2 expression is very rare in metastatic and non-neuroepithelial tumors; (7) glial MAP2 expression was not detected in 56 non-neoplastic lesions. These data point towards MAP2 as valuable diagnostic tool for pattern recognition and differential diagnosis of low-grade neuroepithelial tumors.

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

Quantitative proteomics reveals subset-specific viral recognition in dendritic cells.

Dendritic cell (DC) populations consist of multiple subsets that are essential orchestrators of the immune system. Technological limitations have so far prevented systems-wide accurate proteome comparison of rare cell populations in vivo. Here, we used high-resolution mass spectrometry-based proteomics, combined with label-free quantitation algorithms, to determine the proteome of mouse splenic conventional and plasmacytoid DC subsets to a depth of 5,780 and 6,664 proteins, respectively. We found mutually exclusive expression of pattern recognition pathways not previously known to be different among conventional DC subsets. Our experiments assigned key viral recognition functions to be exclusively expressed in CD4(+) and double-negative DCs. The CD8alpha(+) DCs largely lack the receptors required to sense certain viruses in the cytoplasm. By avoiding activation via cytoplasmic receptors, including retinoic acid-inducible gene I, CD8alpha(+) DCs likely gain a window of opportunity to process and present viral antigens before activation-induced shutdown of antigen presentation pathways occurs.

Automatic recognition of postural allocations.

A significant part of daily energy expenditure may be attributed to non-exercise activity thermogenesis and exercise activity thermogenesis. Automatic recognition of postural allocations such as standing or sitting can be used in behavioral modification programs aimed at minimizing static postures. In this paper we propose a shoe-based device and related pattern recognition methodology for recognition of postural allocations. Inexpensive technology allows implementation of this methodology as a part of footwear. The experimental results suggest high efficiency and reliability of the proposed approach.

Predicting clinical scores from magnetic resonance scans in Alzheimer's disease.

Machine learning and pattern recognition methods have been used to diagnose Alzheimer's disease (AD) and mild cognitive impairment (MCI) from individual MRI scans. Another application of such methods is to predict clinical scores from individual scans. Using relevance vector regression (RVR), we predicted individuals' performances on established tests from their MRI T1 weighted image in two independent data sets. From Mayo Clinic, 73 probable AD patients and 91 cognitively normal (CN) controls completed the Mini-Mental State Examination (MMSE), Dementia Rating Scale (DRS), and Auditory Verbal Learning Test (AVLT) within 3months of their scan. Baseline MRI's from the Alzheimer's disease Neuroimaging Initiative (ADNI) comprised the other data set; 113 AD, 351 MCI, and 122 CN subjects completed the MMSE and Alzheimer's Disease Assessment Scale-Cognitive subtest (ADAS-cog) and 39 AD, 92 MCI, and 32 CN ADNI subjects completed MMSE, ADAS-cog, and AVLT. Predicted and actual clinical scores were highly correlated for the MMSE, DRS, and ADAS-cog tests (P<0.0001). Training with one data set and testing with another demonstrated stability between data sets. DRS, MMSE, and ADAS-Cog correlated better than AVLT with whole brain grey matter changes associated with AD. This result underscores their utility for screening and tracking disease. RVR offers a novel way to measure interactions between structural changes and neuropsychological tests beyond that of univariate methods. In clinical practice, we envision using RVR to aid in diagnosis and predict clinical outcome.