Poly-N-acetyl glucosamine fibers are synergistic with vacuum-assisted closure in augmenting the healing response of diabetic mice.

BACKGROUND: Vacuum-assisted closure (VAC) has become the preferred modality to treat many complex wounds but could be further improved by methods that minimize bleeding and facilitate wound epithelialization. Short fiber poly-N-acetyl glucosamine nanofibers (sNAG) are effective hemostatic agents that activate platelets and facilitate wound epithelialization. We hypothesized that sNAG used in combination with the VAC device could be synergistic in promoting wound healing while minimizing the risk of bleeding. METHODS: Membranes consisting entirely of sNAG nanofibers were applied immediately to dorsal excisional wounds of db/db mice followed by application of the VAC device. Wound healing kinetics, angiogenesis, and wound-related growth factor expression were measured. RESULTS: The application of sNAG membranes to wounds 24 hours before application of the VAC device was associated with a significant activation of wounds (expression of PDGF, TGFβ, EGF), superior granulation tissue formation rich in Collagen I as well as superior wound epithelialization (8.6% ± 0.3% vs. 1.8% ± 1.1% of initial wound size) and wound contraction. CONCLUSIONS: The application of sNAG fiber-containing membranes before the application of the polyurethane foam interface of VAC devices leads to superior healing in db/db mice and represents a promising wound healing adjunct that can also reduce the risk of bleeding complications.

Broad-scale adaptive genetic variation in alpine plants is driven by temperature and precipitation.

Identifying adaptive genetic variation is a challenging task, in particular in non-model species for which genomic information is still limited or absent. Here, we studied distribution patterns of amplified fragment length polymorphisms (AFLPs) in response to environmental variation, in 13 alpine plant species consistently sampled across the entire European Alps. Multiple linear regressions were performed between AFLP allele frequencies per site as dependent variables and two categories of independent variables, namely Moran's eigenvector map MEM variables (to account for spatial and unaccounted environmental variation, and historical demographic processes) and environmental variables. These associations allowed the identification of 153 loci of ecological relevance. Univariate regressions between allele frequency and each environmental factor further showed that loci of ecological relevance were mainly correlated with MEM variables. We found that precipitation and temperature were the best environmental predictors, whereas topographic factors were rarely involved in environmental associations. Climatic factors, subject to rapid variation as a result of the current global warming, are known to strongly influence the fate of alpine plants. Our study shows, for the first time for a large number of species, that the same environmental variables are drivers of plant adaptation at the scale of a whole biome, here the European Alps.

SOCS3 Transactivation by PPARγ Prevents IL-17-Driven Cancer Growth.

Activation of the transcription factor PPARγ by the n-3 fatty acid docosahexaenoic acid (DHA) is implicated in controlling proinflammatory cytokine secretion, but the intracellular signaling pathways engaged by PPARγ are incompletely characterized. Here, we identify the adapter-encoding gene SOCS3 as a critical transcriptional target of PPARγ. SOCS3 promoter binding and gene transactivation by PPARγ was associated with a repression in differentiation of proinflammatory T-helper (TH)17 cells. Accordingly, TH17 cells induced in vitro displayed increased SOCS3 expression and diminished capacity to produce interleukin (IL)-17 following activation of PPARγ by DHA. Furthermore, naïve CD4 T cells derived from mice fed a DHA-enriched diet displayed less capability to differentiate into TH17 cells. In two different mouse models of cancer, DHA prevented tumor outgrowth and angiogenesis in an IL-17-dependent manner. Altogether, our results uncover a novel molecular pathway by which PPARγ-induced SOCS3 expression prevents IL-17-mediated cancer growth. Cancer Res; 73(12); 3578-90. ©2013 AACR.

The International LAM Registry: a component of an innovative web-based clinician, researcher, and patient-driven rare disease research platform.

BACKGROUND: A relative inability to capture a sufficiently large patient population in any one geographic location has traditionally limited research into rare diseases. METHODS AND RESULTS: Clinicians interested in the rare disease lymphangioleiomyomatosis (LAM) have worked with the LAM Treatment Alliance, the MIT Media Lab, and Clozure Associates to cooperate in the design of a state-of-the-art data coordination platform that can be used for clinical trials and other research focused on the global LAM patient population. This platform is a component of a set of web-based resources, including a patient self-report data portal, aimed at accelerating research in rare diseases in a rigorous fashion. CONCLUSIONS: Collaboration between clinicians, researchers, advocacy groups, and patients can create essential community resource infrastructure to accelerate rare disease research. The International LAM Registry is an example of such an effort. 82.

Numerical analysis of wave-induced fluid flow effects on seismic data: Application to monitoring of CO2 storage at the Sleipner Field

In this work we analyze how patchy distributions of CO2 and brine within sand reservoirs may lead to significant attenuation and velocity dispersion effects, which in turn may have a profound impact on surface seismic data. The ultimate goal of this paper is to contribute to the understanding of these processes within the framework of the seismic monitoring of CO2 sequestration, a key strategy to mitigate global warming. We first carry out a Monte Carlo analysis to study the statistical behavior of attenuation and velocity dispersion of compressional waves traveling through rocks with properties similar to those at the Utsira Sand, Sleipner field, containing quasi-fractal patchy distributions of CO2 and brine. These results show that the mean patch size and CO2 saturation play key roles in the observed wave-induced fluid flow effects. The latter can be remarkably important when CO2 concentrations are low and mean patch sizes are relatively large. To analyze these effects on the corresponding surface seismic data, we perform numerical simulations of wave propagation considering reservoir models and CO2 accumulation patterns similar to the CO2 injection site in the Sleipner field. These numerical experiments suggest that wave-induced fluid flow effects may produce changes in the reservoir's seismic response, modifying significantly the main seismic attributes usually employed in the characterization of these environments. Consequently, the determination of the nature of the fluid distributions as well as the proper modeling of the seismic data constitute important aspects that should not be ignored in the seismic monitoring of CO2 sequestration problems.

Inflammation And Autoimmune Responses Are Independent Of Peripheral Mhc Class Ii Expression Driven By Ciita Piv In Collagen Induced Arthritis

Background In rheumatoid arthritis (RA), non-professional antigen presenting cells (APCs) such as fi broblast-like synoviocytes (FLS) can express MHC class II (MHCII) molecules and function as non-professional APCs in vitro.Objective To examine the regulation of MHCII expression in FLS and to investigate the role of FLS as non-professional APCs in collagen-induced arthritis (CIA). Methods Expression of MHCII, CIITA and Ciita isoforms pI, pIII and pIV was examined by RT-qPCR, immunohistochemistry and fl ow cytometry in human synovial tissues, arthritic mouse joints and human as well as mouse FLS. CIA was induced in mice knockout for the isoform IV of Ciita (pIV-/-), in pIV-/- mice transgenic for CIITA in the thymus (pIV-/- K14 CIITA) and in control littermates in the DBA/1 background by immunising with bovine collagen type II (CII) in complete Freund's adjuvant.Results HLA-DRA, total CIITA and CIITA pIII mRNA levels were signifi cantly increased in the synovial tissues from RA compared to osteoarthritis patients. Human FLS expressed surface MHCII via CIITA pIII and pIV, while MHCII expression in murine FLS was entirely mediated by pIV. pIV-/- mice lacked both inducible MHCII expression on non-professional APCs including FLS, and in the thymic cortex. The thymic defect in pIV-/- mice impaired CD4+ positive selection, thus protecting pIV-/- mice from CIA by preventing CD4+ T cells immune responses against CII and blocking the release of IFN-γ and IL-17 in ex vivo stimulated lymph node cells. The production of T dependent, arthritogenic anti-CII antibodies was also impaired in pIV-/- mice. A normal thymic expression of MHCII and CD4+ T cell repertoire was obtained in pIV-/- K14 CIITA Tg mice. Immune responses against CII were restored in pIV-/- K14 CIITA Tg mice, as well as the arthritis incidence and clinical severity despite the lack of MHCII expression by mouse FLS. At histology, infl ammation andneutrophils infi ltration scores were not reduced in pIV-/- K14 CIITA Tg mice, while the bone erosion score was signifi cantly lower than in controls.Conclusion Over expression of MHCII is tightly correlated with CIITA pIII in the arthritic human synovium. MHCII is induced via CIITA pIII and pIV in human FLS. In the mouse, MHCII expression in the thymic cortex and in FLS is strictly dependent upon Ciita pIV. The lack of Ciita pIV in the periphery of pIV-/- K14 CIITA Tg mice lowered the bone erosion score but did not signifi cantly protect from infl ammation and autoimmune responses in CIA.

Context- and Prosody-Driven ERP Markers for Dialog Focus Perception in Children.

The development of language proficiency extends late into childhood and includes not only producing or comprehending sounds, words and sentences, but likewise larger utterances spanning beyond sentence borders like dialogs. Dialogs consist of information units whose value constantly varies within a verbal exchange. While information is focused when introduced for the first time or corrected in order to alter the knowledge state of communication partners, the same information turns into shared knowledge during the further course of a verbal exchange. In many languages, prosodic means are used by speakers to highlight the informational value of information foci. Our study investigated the developmental pattern of event-related potentials (ERPs) in three age groups (12, 8 and 5 years) when perceiving two information focus types (news and corrections) embedded in short question-answer dialogs. The information foci contained in the answer sentences were either adequately marked by prosodic means or not. In so doing, we questioned to what extent children depend on prosodic means to recognize information foci or whether contextual means as provided by dialog questions are sufficient to guide focus processing.Only 12-year-olds yield prosody-independent ERPs when encountering new and corrective information foci, resembling previous findings in adults. Focus processing in the 8-year-olds relied upon prosodic highlighting, and differing ERP responses as a function of focus type were observed. In the 5-year-olds, merely prosody-driven ERP responses were apparent, but no distinctive ERP indicating information focus recognition. Our findings reveal substantial alterations in information focus perception throughout childhood that are likely related to long-lasting maturational changes during brain development.

Systemic antibodies can inhibit mouse mammary tumor virus-driven superantigen response in mucosa-associated lymphoid tissues.

Many mucosal pathogens invade the host by initially infecting the organized mucosa-associated lymphoid tissue (o-MALT) such as Peyer's patches or nasal cavity-associated lymphoid tissue (NALT) before spreading systemically. There is no clear demonstration that serum antibodies can prevent infections in o-MALT. We have tested this possibility by using the mouse mammary tumor virus (MMTV) as a model system. In peripheral lymph nodes or in Peyer's patches or NALT, MMTV initially infects B lymphocytes, which as a consequence express a superantigen (SAg) activity. The SAg molecule induces the local activation of a subset of T cells within 6 days after MMTV infection. We report that similar levels of anti-SAg antibody (immunoglobulin G) in serum were potent inhibitors of the SAg-induced T-cell response both in peripheral lymph nodes and in Peyer's patches or NALT. This result clearly demonstrates that systemic antibodies can gain access to Peyer's patches or NALT.

Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease.

ObjectiveCandidate genes for non-alcoholic fatty liver disease (NAFLD) identified by a bioinformatics approach were examined for variant associations to quantitative traits of NAFLD-related phenotypes.Research Design and MethodsBy integrating public database text mining, trans-organism protein-protein interaction transferal, and information on liver protein expression a protein-protein interaction network was constructed and from this a smaller isolated interactome was identified. Five genes from this interactome were selected for genetic analysis. Twenty-one tag single-nucleotide polymorphisms (SNPs) which captured all common variation in these genes were genotyped in 10,196 Danes, and analyzed for association with NAFLD-related quantitative traits, type 2 diabetes (T2D), central obesity, and WHO-defined metabolic syndrome (MetS).Results273 genes were included in the protein-protein interaction analysis and EHHADH, ECHS1, HADHA, HADHB, and ACADL were selected for further examination. A total of 10 nominal statistical significant associations (P<0.05) to quantitative metabolic traits were identified. Also, the case-control study showed associations between variation in the five genes and T2D, central obesity, and MetS, respectively. Bonferroni adjustments for multiple testing negated all associations.ConclusionsUsing a bioinformatics approach we identified five candidate genes for NAFLD. However, we failed to provide evidence of associations with major effects between SNPs in these five genes and NAFLD-related quantitative traits, T2D, central obesity, and MetS.

Optimization of preservation conditions of As (III) bioreporter bacteria.

Long-term preservation of bioreporter bacteria is essential for the functioning of cell-based detection devices, particularly when field application, e.g., in developing countries, is intended. We varied the culture conditions (i.e., the NaCl content of the medium), storage protection media, and preservation methods (vacuum drying vs. encapsulation gels remaining hydrated) in order to achieve optimal preservation of the activity of As (III) bioreporter bacteria during up to 12 weeks of storage at 4 degrees C. The presence of 2% sodium chloride during the cultivation improved the response intensity of some bioreporters upon reconstitution, particularly of those that had been dried and stored in the presence of sucrose or trehalose and 10% gelatin. The most satisfying, stable response to arsenite after 12 weeks storage was obtained with cells that had been dried in the presence of 34% trehalose and 1.5% polyvinylpyrrolidone. Amendments of peptone, meat extract, sodium ascorbate, and sodium glutamate preserved the bioreporter activity only for the first 2 weeks, but not during long-term storage. Only short-term stability was also achieved when bioreporter bacteria were encapsulated in gels remaining hydrated during storage.

Multiscale descriptions of density-driven flow instabilities in porous media

Les instabilités engendrées par des gradients de densité interviennent dans une variété d'écoulements. Un exemple est celui de la séquestration géologique du dioxyde de carbone en milieux poreux. Ce gaz est injecté à haute pression dans des aquifères salines et profondes. La différence de densité entre la saumure saturée en CO2 dissous et la saumure environnante induit des courants favorables qui le transportent vers les couches géologiques profondes. Les gradients de densité peuvent aussi être la cause du transport indésirable de matières toxiques, ce qui peut éventuellement conduire à la pollution des sols et des eaux. La gamme d'échelles intervenant dans ce type de phénomènes est très large. Elle s'étend de l'échelle poreuse où les phénomènes de croissance des instabilités s'opèrent, jusqu'à l'échelle des aquifères à laquelle interviennent les phénomènes à temps long. Une reproduction fiable de la physique par la simulation numérique demeure donc un défi en raison du caractère multi-échelles aussi bien au niveau spatial et temporel de ces phénomènes. Il requiert donc le développement d'algorithmes performants et l'utilisation d'outils de calculs modernes. En conjugaison avec les méthodes de résolution itératives, les méthodes multi-échelles permettent de résoudre les grands systèmes d'équations algébriques de manière efficace. Ces méthodes ont été introduites comme méthodes d'upscaling et de downscaling pour la simulation d'écoulements en milieux poreux afin de traiter de fortes hétérogénéités du champ de perméabilité. Le principe repose sur l'utilisation parallèle de deux maillages, le premier est choisi en fonction de la résolution du champ de perméabilité (grille fine), alors que le second (grille grossière) est utilisé pour approximer le problème fin à moindre coût. La qualité de la solution multi-échelles peut être améliorée de manière itérative pour empêcher des erreurs trop importantes si le champ de perméabilité est complexe. Les méthodes adaptatives qui restreignent les procédures de mise à jour aux régions à forts gradients permettent de limiter les coûts de calculs additionnels. Dans le cas d'instabilités induites par des gradients de densité, l'échelle des phénomènes varie au cours du temps. En conséquence, des méthodes multi-échelles adaptatives sont requises pour tenir compte de cette dynamique. L'objectif de cette thèse est de développer des algorithmes multi-échelles adaptatifs et efficaces pour la simulation des instabilités induites par des gradients de densité. Pour cela, nous nous basons sur la méthode des volumes finis multi-échelles (MsFV) qui offre l'avantage de résoudre les phénomènes de transport tout en conservant la masse de manière exacte. Dans la première partie, nous pouvons démontrer que les approximations de la méthode MsFV engendrent des phénomènes de digitation non-physiques dont la suppression requiert des opérations de correction itératives. Les coûts de calculs additionnels de ces opérations peuvent toutefois être compensés par des méthodes adaptatives. Nous proposons aussi l'utilisation de la méthode MsFV comme méthode de downscaling: la grille grossière étant utilisée dans les zones où l'écoulement est relativement homogène alors que la grille plus fine est utilisée pour résoudre les forts gradients. Dans la seconde partie, la méthode multi-échelle est étendue à un nombre arbitraire de niveaux. Nous prouvons que la méthode généralisée est performante pour la résolution de grands systèmes d'équations algébriques. Dans la dernière partie, nous focalisons notre étude sur les échelles qui déterminent l'évolution des instabilités engendrées par des gradients de densité. L'identification de la structure locale ainsi que globale de l'écoulement permet de procéder à un upscaling des instabilités à temps long alors que les structures à petite échelle sont conservées lors du déclenchement de l'instabilité. Les résultats présentés dans ce travail permettent d'étendre les connaissances des méthodes MsFV et offrent des formulations multi-échelles efficaces pour la simulation des instabilités engendrées par des gradients de densité. - Density-driven instabilities in porous media are of interest for a wide range of applications, for instance, for geological sequestration of CO2, during which CO2 is injected at high pressure into deep saline aquifers. Due to the density difference between the C02-saturated brine and the surrounding brine, a downward migration of CO2 into deeper regions, where the risk of leakage is reduced, takes place. Similarly, undesired spontaneous mobilization of potentially hazardous substances that might endanger groundwater quality can be triggered by density differences. Over the last years, these effects have been investigated with the help of numerical groundwater models. Major challenges in simulating density-driven instabilities arise from the different scales of interest involved, i.e., the scale at which instabilities are triggered and the aquifer scale over which long-term processes take place. An accurate numerical reproduction is possible, only if the finest scale is captured. For large aquifers, this leads to problems with a large number of unknowns. Advanced numerical methods are required to efficiently solve these problems with today's available computational resources. Beside efficient iterative solvers, multiscale methods are available to solve large numerical systems. Originally, multiscale methods have been developed as upscaling-downscaling techniques to resolve strong permeability contrasts. In this case, two static grids are used: one is chosen with respect to the resolution of the permeability field (fine grid); the other (coarse grid) is used to approximate the fine-scale problem at low computational costs. The quality of the multiscale solution can be iteratively improved to avoid large errors in case of complex permeability structures. Adaptive formulations, which restrict the iterative update to domains with large gradients, enable limiting the additional computational costs of the iterations. In case of density-driven instabilities, additional spatial scales appear which change with time. Flexible adaptive methods are required to account for these emerging dynamic scales. The objective of this work is to develop an adaptive multiscale formulation for the efficient and accurate simulation of density-driven instabilities. We consider the Multiscale Finite-Volume (MsFV) method, which is well suited for simulations including the solution of transport problems as it guarantees a conservative velocity field. In the first part of this thesis, we investigate the applicability of the standard MsFV method to density- driven flow problems. We demonstrate that approximations in MsFV may trigger unphysical fingers and iterative corrections are necessary. Adaptive formulations (e.g., limiting a refined solution to domains with large concentration gradients where fingers form) can be used to balance the extra costs. We also propose to use the MsFV method as downscaling technique: the coarse discretization is used in areas without significant change in the flow field whereas the problem is refined in the zones of interest. This enables accounting for the dynamic change in scales of density-driven instabilities. In the second part of the thesis the MsFV algorithm, which originally employs one coarse level, is extended to an arbitrary number of coarse levels. We prove that this keeps the MsFV method efficient for problems with a large number of unknowns. In the last part of this thesis, we focus on the scales that control the evolution of density fingers. The identification of local and global flow patterns allows a coarse description at late times while conserving fine-scale details during onset stage. Results presented in this work advance the understanding of the Multiscale Finite-Volume method and offer efficient dynamic multiscale formulations to simulate density-driven instabilities. - Les nappes phréatiques caractérisées par des structures poreuses et des fractures très perméables représentent un intérêt particulier pour les hydrogéologues et ingénieurs environnementaux. Dans ces milieux, une large variété d'écoulements peut être observée. Les plus communs sont le transport de contaminants par les eaux souterraines, le transport réactif ou l'écoulement simultané de plusieurs phases non miscibles, comme le pétrole et l'eau. L'échelle qui caractérise ces écoulements est définie par l'interaction de l'hétérogénéité géologique et des processus physiques. Un fluide au repos dans l'espace interstitiel d'un milieu poreux peut être déstabilisé par des gradients de densité. Ils peuvent être induits par des changements locaux de température ou par dissolution d'un composé chimique. Les instabilités engendrées par des gradients de densité revêtent un intérêt particulier puisque qu'elles peuvent éventuellement compromettre la qualité des eaux. Un exemple frappant est la salinisation de l'eau douce dans les nappes phréatiques par pénétration d'eau salée plus dense dans les régions profondes. Dans le cas des écoulements gouvernés par les gradients de densité, les échelles caractéristiques de l'écoulement s'étendent de l'échelle poreuse où les phénomènes de croissance des instabilités s'opèrent, jusqu'à l'échelle des aquifères sur laquelle interviennent les phénomènes à temps long. Etant donné que les investigations in-situ sont pratiquement impossibles, les modèles numériques sont utilisés pour prédire et évaluer les risques liés aux instabilités engendrées par les gradients de densité. Une description correcte de ces phénomènes repose sur la description de toutes les échelles de l'écoulement dont la gamme peut s'étendre sur huit à dix ordres de grandeur dans le cas de grands aquifères. Il en résulte des problèmes numériques de grande taille qui sont très couteux à résoudre. Des schémas numériques sophistiqués sont donc nécessaires pour effectuer des simulations précises d'instabilités hydro-dynamiques à grande échelle. Dans ce travail, nous présentons différentes méthodes numériques qui permettent de simuler efficacement et avec précision les instabilités dues aux gradients de densité. Ces nouvelles méthodes sont basées sur les volumes finis multi-échelles. L'idée est de projeter le problème original à une échelle plus grande où il est moins coûteux à résoudre puis de relever la solution grossière vers l'échelle de départ. Cette technique est particulièrement adaptée pour résoudre des problèmes où une large gamme d'échelle intervient et évolue de manière spatio-temporelle. Ceci permet de réduire les coûts de calculs en limitant la description détaillée du problème aux régions qui contiennent un front de concentration mobile. Les aboutissements sont illustrés par la simulation de phénomènes tels que l'intrusion d'eau salée ou la séquestration de dioxyde de carbone.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.