A Small World Perspective on Urban Systems

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

Increased titers of anti-Saccharomyces cerevisiae antibodies in Crohn's disease patients with reduced H-ficolin levels but normal MASP-2 activity.

BACKGROUND AND AIMS: Mannan-binding lectin (MBL) and ficolins are microbial pattern recognition molecules that activate the lectin pathway of complement. We previously reported the association of MBL deficiency with anti-Saccharomyces cerevisiae antibodies (ASCA) in patients with Crohn's disease (CD). However, ASCA are also frequently found in MBL-proficient CD patients. Here we addressed expression/function of ficolins and MBL-associated serine protease-2 (MASP-2) regarding potential association with ASCA. METHODS: ASCA titers and MBL, ficolin and MASP-2 concentrations were determined by ELISA in the serum of patients with CD, ulcerative colitis (UC), and in healthy controls. MASP-2 activity was determined by measuring complement C4b-fixation. Anti-MBL autoantibodies were detected by ELISA. RESULTS: In CD and UC patients, L-ficolin concentrations were significantly higher compared to healthy controls (p<0.001 and p=0.029). In contrast, H-ficolin concentrations were slightly reduced in CD and UC compared to healthy controls (p=0.037 for UC vs. hc). CD patients with high ASCA titers had significantly lower H-ficolin concentrations compared to ASCA-low/negative CD patients (p=0.009). However, MASP-2 activity was not different in ASCA-negative and ASCA-positive CD patients upon both, ficolin- or MBL-mediated MASP-2 activation. Finally, anti-MBL autoantibodies were not over-represented in MBL-proficient ASCA-positive CD patients. CONCLUSIONS: Our results suggest that low expression of H-ficolin may promote elevated ASCA titers in the ASCA-positive subgroup of CD patients. However, unlike MBL deficiency, we found no evidence for low expression of serum ficolins or reduced MASP-2 activity that may predispose to ASCA development.

An active contour-based atlas registration model applied to automatic subthalamic nucleus targeting on MRI: method and validation.

This paper presents a new non parametric atlas registration framework, derived from the optical flow model and the active contour theory, applied to automatic subthalamic nucleus (STN) targeting in deep brain stimulation (DBS) surgery. In a previous work, we demonstrated that the STN position can be predicted based on the position of surrounding visible structures, namely the lateral and third ventricles. A STN targeting process can thus be obtained by registering these structures of interest between a brain atlas and the patient image. Here we aim to improve the results of the state of the art targeting methods and at the same time to reduce the computational time. Our simultaneous segmentation and registration model shows mean STN localization errors statistically similar to the most performing registration algorithms tested so far and to the targeting expert's variability. Moreover, the computational time of our registration method is much lower, which is a worthwhile improvement from a clinical point of view.

Nonlinear analysis of human physical activity patterns in health and disease.

The reliable and objective assessment of chronic disease state has been and still is a very significant challenge in clinical medicine. An essential feature of human behavior related to the health status, the functional capacity, and the quality of life is the physical activity during daily life. A common way to assess physical activity is to measure the quantity of body movement. Since human activity is controlled by various factors both extrinsic and intrinsic to the body, quantitative parameters only provide a partial assessment and do not allow for a clear distinction between normal and abnormal activity. In this paper, we propose a methodology for the analysis of human activity pattern based on the definition of different physical activity time series with the appropriate analysis methods. The temporal pattern of postures, movements, and transitions between postures was quantified using fractal analysis and symbolic dynamics statistics. The derived nonlinear metrics were able to discriminate patterns of daily activity generated from healthy and chronic pain states.

Evolution: social selection for eccentricity.

The dress code of paper wasps, like that of humans, is related to their social habits: species with a flexible nest-founding strategy have highly variable black-and-yellow markings. This color polymorphism facilitates individual recognition and might have been selected to permit complex social interactions.

Endoplasmic reticulum stress promotes inflammation through activation of the NLRP3 inflammasome

SummaryMulticellular organisms have evolved an immune system in order to cope with the constant threats they are facing. Foreign pathogens or endogenous danger signals released by injured or dying host cells can be readily detected through a set of germline- encoded pattern-recognition receptors. The NOD-like receptors are a cytoplasmic family of pattern-recognition receptors that have recently attracted considerable attention due to their ability to form inflammasomes, which are molecular complexes responsible for the activation of caspase-1 and the subsequent processing of the pro¬inflammatory cytokines IL-IB and 11-18 into their mature, bioactive form.In this study, we describe a novel pro-inflammatory signaling pathway, whereby the endoplasmic reticulum promotes inflammation through activation of the NLRP3 inflammasome. This was shown to be independent of the classical endoplasmic reticulum stress response pathway constituted by the effectors IREla, PERK and ATF6a. In keeping with other known NLRP3 activators, generation of reactive oxygen species and potassium efflux were required. We also provide evidence that calcium signaling is critical to this pathway, and possibly integrates signaling triggered by various NLRP3 inflammasome activators. Moreover, the mitochondrial channel VDAC1 was instrumental in mediating this response. We thus propose that the endoplasmic reticulum acts as an integrator of stress and is able to activate the mitochondria in a calcium-dependent manner in order to promote NLRP3 inflammasome activation in response to a wide range of activators.Given the role played by inflammation in the pathogenesis of atherosclerosis, we decided to investigate a possible role for the NLRP3 inflammasome in the progression of the disease. Using an ApoE mouse model, we find that deficiency in the NLRP3 inflammasome components NLRP3, ASC or Caspase-1 does not impair atherosclerosis progression, nor does it impact plaque stability. While previous studies have clearly shown a role for the interleukin-1 family of ligands in atherosclerosis, our results suggest that its contribution might be more complex than previously appreciated, and further research is thus warranted in this field.RésuméLes organismes multicellulaires ont développé un système immunitaire pour faire face aux menaces qui les entourent. Des pathogènes étrangers ou des signaux de danger relâchés par des cellules de l'hôte en détresse peuvent être rapidement détectés via un assemblage de récepteurs spécifiques qui sont présents dès la naissance. Certains membres de la famille de récepteurs NOD ont récemment attiré beaucoup d'attention au vu de leur capacité à former des inflammasomes, complexes moléculaires responsables de l'activation de la easpase-1 et de la maturation des cytokines pro-inflammatoires IL- 1β et IL-18 en leur forme bioactive.Dans cette étude, nous décrivons une nouvelle voie de signalisation pro-inflammatoire, par laquelle le réticulum endoplasmique induit l'inflammation via l'activation de l'inflammasome NLRP3. Cette voie est indépendante de la voie classique de réponse au stress du réticulum endoplasmique, qui comprend les effecteurs IRE1, PERK et ATF6. Comme pour d'autres activateurs de NLRP3, la génération de radicaux libres d'oxygène ainsi que Γ efflux de potassium sont requis. Nous montrons également que le calcium joue un rôle critique dans cette voie, et intègre possiblement la signalisation provoquée par divers activateurs de l'inflammasome NLRP3. De plus, le canal mitochondrial VDAC1 est essentiel dans cette réponse. Nous proposons donc que le réticulum endoplasmique agit comme un intégrateur de stress, activant la mitochondrie d'une façon calcium-dépendante pour promouvoir l'activation de l'inflammasome NLRP3 en réponse à divers activateurs.Au vu du rôle joué par l'inflammation dans la pathogenèse de l'athérosclérose, nous avons étudié un possible rôle pour l'inflammasome NLRP3 dans la progression de la maladie. Dans un modèle de souris ApoE, l'absence des composants de l'inflammasome NLRP3 que sont NLRP3, ASC et Caspase-1 n'influence pas la progression des plaques ni leur stabilité. Alors que d'autres études ont démontré un rôle pour les membres de la famille de l'interleukine-1 dans l'athérosclérose, nos résultats suggèrent que leur contribution pourrait être plus complexe que précédemment apprécié, et d'autres recherches dans ce domaine sont donc nécessaires.

Efficient total variation algorithm for fetal brain MRI reconstruction.

Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy, Total Variation (TV)- based energies and more recently non-local means. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm or fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n2) and O(1/√ε), while existing techniques are in O(1/n2) and O(1/√ε). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.

Mental imagery for full and upper human bodies: common right hemisphere activations and distinct extrastriate activations.

The processing of human bodies is important in social life and for the recognition of another person's actions, moods, and intentions. Recent neuroimaging studies on mental imagery of human body parts suggest that the left hemisphere is dominant in body processing. However, studies on mental imagery of full human bodies reported stronger right hemisphere or bilateral activations. Here, we measured functional magnetic resonance imaging during mental imagery of bilateral partial (upper) and full bodies. Results show that, independently of whether a full or upper body is processed, the right hemisphere (temporo-parietal cortex, anterior parietal cortex, premotor cortex, bilateral superior parietal cortex) is mainly involved in mental imagery of full or partial human bodies. However, distinct activations were found in extrastriate cortex for partial bodies (right fusiform face area) and full bodies (left extrastriate body area). We propose that a common brain network, mainly on the right side, is involved in the mental imagery of human bodies, while two distinct brain areas in extrastriate cortex code for mental imagery of full and upper bodies.

Histones trigger sterile inflammation by activating the NLRP3 inflammasome.

Sterile cell death mediated inflammation is linked to several pathological disorders and involves danger recognition of intracellular molecules released by necrotic cells that activate different groups of innate pattern recognition receptors. Toll-like receptors directly interact with their extrinsic or intrinsic agonists and induce multiple proinflammatory mediators. In contrast, the NLRP3 inflammasome is rather thought to represent a downstream element integrating various indirect stimuli into proteolytic cleavage of interleukin (IL)-1β and IL-18. Here, we report that histones released from necrotic cells induce IL-1β secretion in an NLRP3-ASC-caspase-1-dependent manner. Genetic deletion of NLRP3 in mice significantly attenuated histone-induced IL-1β production and neutrophil recruitment. Furthermore, necrotic cells induced neutrophil recruitment, which was significantly reduced by histone-neutralizing antibodies or depleting extracellular histones via enzymatic degradation. These results identify cytosolic uptake of necrotic cell-derived histones as a triggering mechanism of sterile inflammation, which involves NLRP3 inflammasome activation and IL-1β secretion via oxidative stress.

Surface assembly of poly(I:C) on PEGylated microspheres to shield from adverse interactions with fibroblasts.

By expressing an array of pattern recognition receptors (PRRs), fibroblasts play an important role in stimulating and modulating the response of the innate immune system. The TLR3 ligand polyriboinosinic acid-polyribocytidylic acid, poly(I:C), a mimic of viral dsRNA, is a vaccine adjuvant candidate to activate professional antigen presenting cells (APCs). However, owing to its ligation with extracellular TLR3 on fibroblasts, subcutaneously administered poly(I:C) bears danger towards autoimmunity. It is thus in the interest of its clinical safety to deliver poly(I:C) in such a way that its activation of professional APCs is as efficacious as possible, whereas its interference with non-immune cells such as fibroblasts is controlled or even avoided. Complementary to our previous work with monocyte-derived dendritic cells (MoDCs), here we sought to control the delivery of poly(I:C) surface-assembled on microspheres to human foreskin fibroblasts (HFFs). Negatively charged polystyrene (PS) microspheres were equipped with a poly(ethylene glycol) (PEG) corona through electrostatically driven coatings with a series of polycationic poly(L-lysine)-graft-poly(ethylene glycol) copolymers, PLL-g-PEG, of varying grafting ratios g from 2.2 up to 22.7. Stable surface assembly of poly(I:C) was achieved by incubation of polymer-coated microspheres with aqueous poly(I:C) solutions. Notably, recognition of both surface-assembled and free poly(I:C) by extracellular TLR3 on HFFs halted their phagocytic activity. Ligation of surface-assembled poly(I:C) with extracellular TLR3 on HFFs could be controlled by tuning the grafting ratio g and thus the chain density of the PEG corona. When assembled on PLL-5.7-PEG-coated microspheres, poly(I:C) was blocked from triggering class I MHC molecule expression on HFFs. Secretion of interleukin (IL)-6 by HFFs after exposure to surface-assembled poly(I:C) was distinctly lower as compared to free poly(I:C). Overall, surface assembly of poly(I:C) may have potential to contribute to the clinical safety of this vaccine adjuvant candidate.

Automated identification of minimal myocardial motion for improved image quality on MR angiography at 3 T

OBJECTIVE: Imaging during a period of minimal myocardial motion is of paramount importance for coronary MR angiography (MRA). The objective of our study was to evaluate the utility of FREEZE, a custom-built automated tool for the identification of the period of minimal myocardial motion, in both a moving phantom at 1.5 T and 10 healthy adults (nine men, one woman; mean age, 24.9 years; age range, 21-32 years) at 3 T. CONCLUSION: Quantitative analysis of the moving phantom showed that dimension measurements approached those obtained in the static phantom when using FREEZE. In vitro, vessel sharpness, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were significantly improved when coronary MRA was performed during the software-prescribed period of minimal myocardial motion (p < 0.05). Consistent with these objective findings, image quality assessments by consensus review also improved significantly when using the automated prescription of the period of minimal myocardial motion. The use of FREEZE improves image quality of coronary MRA. Simultaneously, operator dependence can be minimized while the ease of use is improved.

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.

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

Classificació i reconeixement de vídeo

El principal objectiu d’aquest projecte és aconseguir classificar diferents vídeos d’esports segons la seva categoria. Els cercadors de text creen un vocabulari segons el significat de les diferents paraules per tal de poder identificar un document. En aquest projecte es va fer el mateix però mitjançant paraules visuals. Per exemple, es van intentar englobar com a una única paraula les diferents rodes que apareixien en els cotxes de rally. A partir de la freqüència amb què apareixien les paraules dels diferents grups dins d’una imatge vàrem crear histogrames de vocabulari que ens permetien tenir una descripció de la imatge. Per classificar un vídeo es van utilitzar els histogrames que descrivien els seus fotogrames. Com que cada histograma es podia considerar un vector de valors enters vàrem optar per utilitzar una màquina classificadora de vectors: una Support vector machine o SVM

Targeting Toll-Like Receptors: Promising Therapeutic Strategies for the Management of Sepsis-Associated Pathology and Infectious Diseases.

Toll-like receptors (TLRs) are pattern recognition receptors playing a fundamental role in sensing microbial invasion and initiating innate and adaptive immune responses. TLRs are also triggered by danger signals released by injured or stressed cells during sepsis. Here we focus on studies developing TLR agonists and antagonists for the treatment of infectious diseases and sepsis. Positioned at the cell surface, TLR4 is essential for sensing lipopolysaccharide of Gram-negative bacteria, TLR2 is involved in the recognition of a large panel of microbial ligands, while TLR5 recognizes flagellin. Endosomal TLR3, TLR7, TLR8, TLR9 are specialized in the sensing of nucleic acids produced notably during viral infections. TLR4 and TLR2 are favorite targets for developing anti-sepsis drugs, and antagonistic compounds have shown efficient protection from septic shock in pre-clinical models. Results from clinical trials evaluating anti-TLR4 and anti-TLR2 approaches are presented, discussing the challenges of study design in sepsis and future exploitation of these agents in infectious diseases. We also report results from studies suggesting that the TLR5 agonist flagellin may protect from infections of the gastrointestinal tract and that agonists of endosomal TLRs are very promising for treating chronic viral infections. Altogether, TLR-targeted therapies have a strong potential for prevention and intervention in infectious diseases, notably sepsis.