Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms.

The state of the art to describe image quality in medical imaging is to assess the performance of an observer conducting a task of clinical interest. This can be done by using a model observer leading to a figure of merit such as the signal-to-noise ratio (SNR). Using the non-prewhitening (NPW) model observer, we objectively characterised the evolution of its figure of merit in various acquisition conditions. The NPW model observer usually requires the use of the modulation transfer function (MTF) as well as noise power spectra. However, although the computation of the MTF poses no problem when dealing with the traditional filtered back-projection (FBP) algorithm, this is not the case when using iterative reconstruction (IR) algorithms, such as adaptive statistical iterative reconstruction (ASIR) or model-based iterative reconstruction (MBIR). Given that the target transfer function (TTF) had already shown it could accurately express the system resolution even with non-linear algorithms, we decided to tune the NPW model observer, replacing the standard MTF by the TTF. It was estimated using a custom-made phantom containing cylindrical inserts surrounded by water. The contrast differences between the inserts and water were plotted for each acquisition condition. Then, mathematical transformations were performed leading to the TTF. As expected, the first results showed a dependency of the image contrast and noise levels on the TTF for both ASIR and MBIR. Moreover, FBP also proved to be dependent of the contrast and noise when using the lung kernel. Those results were then introduced in the NPW model observer. We observed an enhancement of SNR every time we switched from FBP to ASIR to MBIR. IR algorithms greatly improve image quality, especially in low-dose conditions. Based on our results, the use of MBIR could lead to further dose reduction in several clinical applications.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

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.

3D noise power spectrum applied on clinical mdct scanners: effects of reconstruction algorithms and reconstruction filters

The noise power spectrum (NPS) is the reference metric for understanding the noise content in computed tomography (CT) images. To evaluate the noise properties of clinical multidetector (MDCT) scanners, local 2D and 3D NPSs were computed for different acquisition reconstruction parameters.A 64- and a 128-MDCT scanners were employed. Measurements were performed on a water phantom in axial and helical acquisition modes. CT dose index was identical for both installations. Influence of parameters such as the pitch, the reconstruction filter (soft, standard and bone) and the reconstruction algorithm (filtered-back projection (FBP), adaptive statistical iterative reconstruction (ASIR)) were investigated. Images were also reconstructed in the coronal plane using a reformat process. Then 2D and 3D NPS methods were computed.In axial acquisition mode, the 2D axial NPS showed an important magnitude variation as a function of the z-direction when measured at the phantom center. In helical mode, a directional dependency with lobular shape was observed while the magnitude of the NPS was kept constant. Important effects of the reconstruction filter, pitch and reconstruction algorithm were observed on 3D NPS results for both MDCTs. With ASIR, a reduction of the NPS magnitude and a shift of the NPS peak to the low frequency range were visible. 2D coronal NPS obtained from the reformat images was impacted by the interpolation when compared to 2D coronal NPS obtained from 3D measurements.The noise properties of volume measured in last generation MDCTs was studied using local 3D NPS metric. However, impact of the non-stationarity noise effect may need further investigations.

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.

HARNESSING iNKT CELLS WITH RECOMBINANT CD1d FUSION PROTEINS TO REDIRECT INNATE AND ADAPTIVE IMMUNITY TO THE TUMOR

Les thérapies du cancer, comme la radiothérapie et la chimiothérapie, sont couramment utilisées mais ont de nombreux effets secondaires. Ces thérapies invasives pour le patient nécessitent d'être améliorées et de nombreuses avancées ont été faites afin d'adapter et de personnaliser le traitement du cancer. L'immunothérapie a pour but de renforcer le système immunitaire du patient et de le rediriger de manière spécifique contre la tumeur. Dans notre projet, nous activons les lymphocytes Invariant Natural Killer T (iNKT) afin de mettre en place une immunothérapie innovatrice contre le cancer. Les cellules iNKT sont une unique sous-population de lymphocytes T qui ont la particularité de réunir les propriétés de l'immunité innée ainsi qu'adaptative. En effet, les cellules iNKT expriment à leur surface des molécules présentes aussi sur les cellules tueuses NK, caractéristique de l'immunité innée, ainsi qu'un récepteur de cellules T (TCR) qui représente l'immunité adaptative. Les cellules iNKT reconnaissent avec leur TCR des antigènes présentés par la molécule CD1d. Les antigènes sont des protéines, des polysaccharides ou des lipides reconnus par les cellules du système immunitaire ou les anticorps pour engendrer une réponse immunitaire. Dans le cas des cellules iNKT, l'alpha-galactosylceramide (αGC) est un antigène lipidique fréquemment utilisé dans les études cliniques comme puissant activateur. Après l'activation des cellules iNKT avec l'αGC, celles-ci produisent abondamment et rapidement des cytokines. Ces cytokines sont des molécules agissant comme des signaux activateurs d'autres cellules du système immunitaire telles que les cellules NK et les lymphocytes T. Cependant, les cellules iNKT deviennent anergiques après un seul traitement avec l'αGC c'est à dire qu'elles ne peuvent plus être réactivées, ce qui limite leur utilisation dans l'immunothérapie du cancer. Dans notre groupe, Stirnemann et al ont publié une molécule recombinante innovante, composée de la molécule CD1d soluble et chargée avec le ligand αGC (αGC/sCD1d). Cette protéine est capable d'activer les cellules iNKT tout en évitant l'anergie. Dans le système immunitaire, les anticorps sont indispensables pour combattre une infection bactérienne ou virale. En effet, les anticorps ont la capacité de reconnaître et lier spécifiquement un antigène et permettent l'élimination de la cellule qui exprime cet antigène. Dans le domaine de l'immunothérapie, les anticorps sont utilisés afin de cibler des antigènes présentés seulement par la tumeur. Ce procédé permet de réduire efficacement les effets secondaires lors du traitement du cancer. Nous avons donc fusionné la protéine recombinante αGC/CD1d à un fragment d'anticorps qui reconnaît un antigène spécifique des cellules tumorales. Dans une étude préclinique, nous avons démontré que la protéine αGC/sCD1d avec un fragment d'anticorps dirigé contre la tumeur engendre une meilleure activation des cellules iNKT et entraîne un effet anti-tumeur prolongé. Cet effet anti-tumeur est augmenté comparé à une protéine αGC/CD1d qui ne cible pas la tumeur. Nous avons aussi montré que l'activation des cellules iNKT avec la protéine αGC/sCD1d-anti-tumeur améliore l'effet anti- tumoral d'un vaccin pour le cancer. Lors d'expériences in vitro, la protéine αGC/sCD1d-anti- tumeur permet aussi d'activer les cellules humaines iNKT et ainsi tuer spécifiquement les cellules tumorales humaines. La protéine αGC/sCD1d-anti-tumeur représente une alternative thérapeutique prometteuse dans l'immunothérapie du cancer. - Les cellules Invariant Natural Killer T (iNKT), dont les effets anti-tumoraux ont été démontrés, sont de puissants activateurs des cellules Natural Killer (NK), des cellules dendritiques (DC) et des lymphocytes T. Cependant, une seule injection du ligand de haute affinité alpha-galactosylceramide (αGC) n'induit une forte activation des cellules iNKT que durant une courte période. Celle-ci est alors suivie d'une longue phase d'anergie, limitant ainsi leur utilisation pour la thérapie. Comme alternative prometteuse, nous avons montré que des injections répétées d'αGC chargé sur une protéine recombinante de CD1d soluble (αGC/sCD1d) chez la souris entraînent une activation prolongée des cellules iNKT, associée à une production continue de cytokine. De plus, le maintien de la réactivité des cellules iNKT permet de prolonger l'activité anti-tumorale lorsque la protéine αGC/sCD1d est fusionnée à un fragment d'anticorps (scFv) dirigé contre la tumeur. L'inhibition de la croissance tumorale n'est optimale que lorsque les souris sont traitées avec la protéine αGC/sCD1d-scFv ciblant la tumeur, la protéine αGC/sCD1d-scFv non-appropriée étant moins efficace. Dans le système humain, les protéines recombinantes αGC/sCD1d-anti-HER2 et anti-CEA sont capables d'activer et de faire proliférer des cellules iNKT à partir de PBMCs issues de donneurs sains. De plus, la protéine αGC/sCD1d-scFv a la capacité d'activer directement des clones iNKT humains en l'absence de cellules présentatrices d'antigènes (CPA), contrairement au ligand αGC libre. Mais surtout, la lyse des cellules tumorales par les iNKT humaines n'est obtenue que lorsqu'elles sont incubées avec la protéine αGC/sCD1d-scFv anti- tumeur. En outre, la redirection de la cytotoxicité des cellules iNKT vers la tumeur est supérieure à celle obtenue avec une stimulation par des CPA chargées avec l'αGC. Afin d'augmenter les effets anti-tumoraux, nous avons exploité la capacité des cellules iNKT à activer l'immunité adaptive. Pour ce faire, nous avons combiné l'immunothérapie NKT/CD1d avec un vaccin anti-tumoral composé d'un peptide OVA. Des effets synergiques ont été obtenus lorsque les traitements avec la protéine αGC/sCD1d-anti-HER2 étaient associés avec le CpG ODN comme adjuvant pour la vaccination avec le peptide OVA. Ces effets ont été observés à travers l'activation de nombreux lymphocytes T CD8+ spécifique de la tumeur, ainsi que par la forte expansion des cellules NK. Les réponses, innée et adaptive, élevées après le traitement avec la protéine αGC/sCD1d-anti-HER2 combinée au vaccin OVA/CpG ODN étaient associées à un fort ralentissement de la croissance des tumeurs B16- OVA-HER2. Cet effet anti-tumoral corrèle avec l'enrichissement des lymphocytes T CD8+ spécifiques observé à la tumeur. Afin d'étendre l'application des protéines αGC/sCD1d et d'améliorer leur efficacité, nous avons développé des fusions CD1d alternatives. Premièrement, une protéine αGC/sCD1d dimérique, qui permet d'augmenter l'avidité de la molécule CD1d pour les cellules iNKT. Dans un deuxième temps, nous avons fusionné la protéine αGC/sCD1d avec un scFv dirigé contre le récepteur 3 du facteur de croissance pour l'endothélium vasculaire (VEGFR-3), afin de cibler l'environnement de la tumeur. Dans l'ensemble, ces résultats démontrent que la thérapie médiée par la protéine recombinante αGC/sCD1d-scFv est une approche prometteuse pour rediriger l'immunité innée et adaptive vers le site tumoral. - Invariant Natural Killer T cells (iNKT) are potent activators of Natural Killer (NK), dendritic cells (DC) and T lymphocytes, and their anti-tumor activities have been well demonstrated. However, a single injection of the high affinity CD1d ligand alpha-galactosylceramide (αGC) leads to a strong but short-lived iNKT cell activation followed by a phase of long-term anergy, limiting the therapeutic use of this ligand. As a promising alternative, we have demonstrated that when αGC is loaded on recombinant soluble CD1d molecules (αGC/sCD1d), repeated injections in mice led to the sustained iNKT cell activation associated with continued cytokine secretion. Importantly, the retained reactivity of iNKT cell led to prolonged antitumor activity when the αGC/sCD1d was fused to an anti-tumor scFv fragments. Optimal inhibition of tumor growth was obtained only when mice were treated with the tumor-targeted αGC/CD1d-scFv fusion, whereas the irrelevant αGC/CD1d-scFv fusion was less efficient. When tested in a human system, the recombinant αGC/sCD1d-anti-HER2 and -anti-CEA fusion proteins were able to expand iNKT cells from PBMCs of healthy donors. Furthermore, the αGC/sCD1d-scFv fusion had the capacity to directly activate human iNKT cells clones without the presence of antigen-presenting cells (APCs), in contrast to the free αGC ligand. Most importantly, tumor cell killing by human iNKT cells was obtained only when co- incubated with the tumor targeted sCD1d-antitumor scFv, and their direct tumor cytotoxicity was superior to the bystander killing obtained with αGC-loaded APCs stimulation. To further enhance the anti-tumor effects, we exploited the ability of iNKT cells to transactivate the adaptive immunity, by combining the NKT/CD1d immunotherapy with a peptide cancer vaccine. Interestingly, synergistic effects were obtained when the αGC/sCD1d- anti-HER2 fusion treatment was combined with CpG ODN as adjuvant for the OVA peptide vaccine, as seen by higher numbers of activated antigen-specific CD8 T cells and NK cells, as compared to each regimen alone. The increased innate and adaptive immune responses upon combined tumor targeted sCD1d-scFv treatment and OVA/CpG vaccine were associated with a strong delay in B16-OVA-HER2 melanoma tumor growth, which correlated with an enrichment of antigen-specific CD8 cells at the tumor site. In order to extend the application of the CD1d fusion, we designed alternative CD1d fusion proteins. First, a dimeric αGC/sCD1d-Fc fusion, which permits to augment the avidity of the CD1d for iNKT cells and second, an αGC/sCD1d fused to an anti vascular endothelial growth factor receptor-3 (VEGFR-3) scFv, in order to target tumor stroma environment. Altogether, these results demonstrate that the iNKT-mediated immunotherapy via recombinant αGC/sCD1d-scFv fusion is a promising approach to redirect the innate and adaptive antitumor immune response to the tumor site.

How the gut links innate and adaptive immunity.

Mucosal surfaces represent the main sites in which environmental microorganisms and antigens interact with the host. Sentinel cells, including epithelial cells, lumenal macrophages, and intraepithelial dendritic cells, continuously sense the environment and coordinate defenses for the protection of mucosal tissues. The mucosal epithelial cells are crucial actors in coordinating defenses. They sense the outside world and respond to environmental signals by releasing chemokines and cytokines that recruit inflammatory and immune cells to control potential infectious agents and to attract cells able to trigger immune responses. Among immune cells, dendritic cells (DC) play a key role in controlling adaptive immune responses, due to their capacity to internalize foreign materials and to present antigens to naive T and B lymphocytes, locally or in draining organized lymphoid tissues. Immune cells recruited in epithelial tissues can, in turn, act upon the epithelial cells and change their phenotype in a process referred to as epithelial metaplasia.

Automatic front-crawl temporal phase detection using adaptive filtering of inertial signals

This study introduces a novel approach for automatic temporal phase detection and inter-arm coordination estimation in front-crawl swimming using inertial measurement units (IMUs). We examined the validity of our method by comparison against a video-based system. Three waterproofed IMUs (composed of 3D accelerometer, 3D gyroscope) were placed on both forearms and the sacrum of the swimmer. We used two underwater video cameras in side and frontal views as our reference system. Two independent operators performed the video analysis. To test our methodology, seven well-trained swimmers performed three 300 m trials in a 50 m indoor pool. Each trial was in a different coordination mode quantified by the index of coordination. We detected different phases of the arm stroke by employing orientation estimation techniques and a new adaptive change detection algorithm on inertial signals. The difference of 0.2 +/- 3.9% between our estimation and video-based system in assessment of the index of coordination was comparable to experienced operators' difference (1.1 +/- 3.6%). The 95% limits of agreement of the difference between the two systems in estimation of the temporal phases were always less than 7.9% of the cycle duration. The inertial system offers an automatic easy-to-use system with timely feedback for the study of swimming.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Control of the adaptive response of the heart to stress via the Notch1 receptor pathway.

In the damaged heart, cardiac adaptation relies primarily on cardiomyocyte hypertrophy. The recent discovery of cardiac stem cells in the postnatal heart, however, suggests that these cells could participate in the response to stress via their capacity to regenerate cardiac tissues. Using models of cardiac hypertrophy and failure, we demonstrate that components of the Notch pathway are up-regulated in the hypertrophic heart. The Notch pathway is an evolutionarily conserved cell-to-cell communication system, which is crucial in many developmental processes. Notch also plays key roles in the regenerative capacity of self-renewing organs. In the heart, Notch1 signaling takes place in cardiomyocytes and in mesenchymal cardiac precursors and is activated secondary to stimulated Jagged1 expression on the surface of cardiomyocytes. Using mice lacking Notch1 expression specifically in the heart, we show that the Notch1 pathway controls pathophysiological cardiac remodeling. In the absence of Notch1, cardiac hypertrophy is exacerbated, fibrosis develops, function is altered, and the mortality rate increases. Therefore, in cardiomyocytes, Notch controls maturation, limits the extent of the hypertrophic response, and may thereby contribute to cell survival. In cardiac precursors, Notch prevents cardiogenic differentiation, favors proliferation, and may facilitate the expansion of a transient amplifying cell compartment.

Innate receptors for adaptive immunity.

Pattern recognition receptors (PRRs) are commonly known as sensor proteins crucial for the early detection of microbial or host-derived stress signals by innate immune cells. Interestingly, some PRRs are also expressed and functional in cells of the adaptive immune system. These receptors provide lymphocytes with innate sensing abilities; for example, B cells express Toll-like receptors, which are important for the humoral response. Strikingly, certain other NOD-like receptors are not only highly expressed in adaptive immune cells, but also exert functions related specifically to adaptive immune system pathways, such as regulating antigen presentation. In this review, we will focus particularly on the current understanding of PRR functions intrinsic to B and T lymphocytes; a developing aspect of PRR biology.