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.

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.

Metastatic leishmaniasis. A chronic inflammatory response to Leishmania's viral endosymbiont.

Leishmania parasites have been plaguing humankind for centuries as a range of skin diseases named the cutaneous leishmaniases (CL). Carried in a hematophagous sand fly, Leishmania usually infests the skin surrounding the bite site, causing a destructive immune response that may persist for months or even years. The various symptomatic outcomes of CL range from a benevolent self- healing reddened bump to extensive open ulcerations, resistant to treatment and resulting in life- changing disfiguration. Many of these more aggressive outcomes are geographically isolated within the habitats of certain Neotropical Leishmania species; where about 15% of cases experience metastatic complications. However, despite this correlation, genetic analysis has revealed no major differences between species causing the various disease forms. We have recently identified a cytoplasmic dsRNA virus within metastatic L. guyanensis parasites that acts as a potent innate immunogen capable of worsening lesionai inflammation and prolonging parasite survival. The dsRNA genome of Leishmania RNA virus (LRV) binds and stimulates Toll-Like-Receptor-3 (TLR3), inducing this destructive inflammation, which we speculate as a factor contributing to the development of metastatic disease. This thesis establishes the first experimental model of LRV-mediated leishmanial metastasis and investigates the role of non-TLR3 viral recognition pathways in LRV-mediated pathology. Viral dsRNA can be detected by various non-TLR3 pattern recognition receptors (PRR); two such PRR groups are the RLRs (Retinoic acid-inducible gene 1 like receptors) and the NLRs (nucleotide- binding domain, leucine-rich repeat containing receptors). The RLRs are designed to detect viral dsRNA in the cytoplasm, while the NLRs react to molecular "danger" signals of cell damage, often oligomerizing into molecular scaffolds called "inflammasomes" that activate a potent inflammatory cascade. Interestingly, we found that neither RLR signalling nor the inflammasome pathway had an effect on LRV-mediated pathology. In contrast, we found a dramatic inflammasome independent effect for the NLR family member, NLRP10, where a knockout mouse model showed little evidence of disease. This phenotype was mimicked in an NLR knockout with which NLRP10 is known to interact: NLRC2. As this pathway induces the chronic inflammatory cell lineage TH17, we investigated the role of its key chronic inflammatory cytokine, IL-17A, in human patients infected by L. guyanensis. Indeed, patients infected with LRV+ parasites had a significantly increased level of IL-17A in lesionai biopsies. Interestingly, LRV presence was also associated with a significant decrease in the correlate of protection, IFN-y. This association was repeated in our murine model, where after we were able to establish the first experimental model of LRV-dependent leishmanial metastasis, which was mediated by IL-17A in the absence of IFN-y. Finally, we tested a new inhibitor of IL-17A secretion, SR1001, and reveal its potential as a Prophylactic immunomodulator and potent parasitotoxic drug. Taken together, these findings provide a basis for anti-IL-17A as a feasible therapeutic intervention to prevent and treat the metastatic complications of cutaneous leishmaniasis. -- Les parasites Leishmania infectent l'homme depuis des siècles causant des affections cutanées, appelées leishmanioses cutanées (LC). Le parasite est transmis par la mouche des sables et réside dans le derme à l'endroit de la piqûre. Au niveau de la peau, le parasite provoque une réponse immunitaire destructrice qui peut persister pendant des mois voire des années. Les symptômes de LC vont d'une simple enflure qui guérit spontanément jusqu' à de vastes ulcérations ouvertes, résistantes aux traitements. Des manifestations plus agressives sont déterminées par les habitats géographiques de certaines espèces de Leishmania. Dans ces cas, environ 15% des patients développent des lésions métastatiques. Aucun «facteur métastatique» n'a encore été trouvé à ce jour dans ces espèces. Récemment, nous avons pu identifier un virus résidant dans certains parasites métastatiques présents en Guyane française (appelé Leishmania-virus, ou LV) et qui confère un avantage de survie à son hôte parasitaire. Ce virus active fortement la réponse inflammatoire, aggravant l'inflammation et prolongeant l'infection parasitaire. Afin de diagnostiquer, prévenir et traiter ces lésions, nous nous sommes intéressés à identifier les composants de la voie de signalisation anti-virale, responsables de la persistance de cette inflammation. Cette étude décrit le premier modèle expérimental de métastases de la leishmaniose induites par LV, et identifie plusieurs composants de la voie inflammatoire anti-virale qui facilite la pathologie métastatique. Contrairement à l'homme, les souris de laboratoire infectées par des Leishmania métastatiques (contenant LV, LV+) ne développent pas de lésions métastatiques et guérissent après quelques semaines d'infection. Après avoir analysé un groupe de patients atteints de leishmaniose en Guyane française, nous avons constaté que les personnes infectées avec les parasites métastatiques LV+ avaient des niveaux significativement plus faibles d'un composant immunitaire protecteur important, appelé l'interféron (IFN)-y. En utilisant des souris génétiquement modifiées, incapables de produire de l'IFN-y, nous avons observé de telles métastases. Après inoculation dans le coussinet plantaire de souris IFN-y7" avec des parasites LV+ ou LV-, nous avons démontré que seules les souris infectées avec des leishmanies ayant LV développent de multiples lésions secondaires sur la queue. Comme nous l'avons observé chez l'homme, ces souris sécrètent une quantité significativement élevée d'un composant inflammatoire destructeur, l'interleukine (IL)-17. IL-17 a été incriminée pour son rôle dans de nombreuses maladies inflammatoires chroniques. On a ainsi trouvé un rôle destructif similaire pour l'IL-17 dans la leishmaniose métastatique. Nous avons confirmé ce rôle en abrogeant IL-17 dans des souris IFN-y7- ce qui ralentit l'apparition des métastases. Nous pouvons donc conclure que les métastases de la leishmaniose sont induites par l'IL-17 en absence d'IFN-v. En analysant plus en détails les voies de signalisation anti-virale induites par LV, nous avons pu exclure d'autres voies d'activation de la réponse inflammatoire. Nous avons ainsi démontré que la signalisation par LV est indépendante de la signalisation inflammatoire de type « inflammasome ». En revanche, nous avons pu y lier plusieurs autres molécules, telles que NLRP10 et NLRC2, connues pour leur synergie avec les réponses inflammatoires. Cette nouvelle voie pourrait être la cible pour des médicaments inhibant l'inflammation. En effet, un nouveau médicament qui bloque la production d'IL-17 chez la souris s'est montré prometteur dans notre modèle : il a réduit le gonflement des lésions ainsi que la charge parasitaire, indiquant que la voie anti-virale /inflammatoire est une approche thérapeutique possible pour prévenir et traiter cette infection négligée.

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.

Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity.

Multiple sclerosis (MS), a variable and diffuse disease affecting white and gray matter, is known to cause functional connectivity anomalies in patients. However, related studies published to-date are post hoc; our hypothesis was that such alterations could discriminate between patients and healthy controls in a predictive setting, laying the groundwork for imaging-based prognosis. Using functional magnetic resonance imaging resting state data of 22 minimally disabled MS patients and 14 controls, we developed a predictive model of connectivity alterations in MS: a whole-brain connectivity matrix was built for each subject from the slow oscillations (<0.11Hz) of region-averaged time series, and a pattern recognition technique was used to learn a discriminant function indicating which particular functional connections are most affected by disease. Classification performance using strict cross-validation yielded a sensitivity of 82% (above chance at p<0.005) and specificity of 86% (p<0.01) to distinguish between MS patients and controls. The most discriminative connectivity changes were found in subcortical and temporal regions, and contralateral connections were more discriminative than ipsilateral connections. The pattern of decreased discriminative connections can be summarized post hoc in an index that correlates positively (ρ=0.61) with white matter lesion load, possibly indicating functional reorganisation to cope with increasing lesion load. These results are consistent with a subtle but widespread impact of lesions in white matter and in gray matter structures serving as high-level integrative hubs. These findings suggest that predictive models of resting state fMRI can reveal specific anomalies due to MS with high sensitivity and specificity, potentially leading to new non-invasive markers.

Comparaison objective de systèmes d'imagerie conventionnelle et numérique en mammographie

L'objectif de ce travail est le développement d'une méthode de caractérisation objective de la qualité d'image s'appliquant à des systèmes de mammographie analogique, utilisant un couple écran-film comme détecteur, et numérique, basé sur une technologie semi-conductrice, ceci en vue de la comparaison de leurs performances. La méthode développée tient compte de la gamme dynamique du détecteur, de la détectabilité de structures de haut contraste, simulant des microcalcifications, et de structures de bas contraste, simulant des opacités (nodules tumoraux). La méthode prend également en considération le processus de visualisation de l'image, ainsi que la réponse de l'observateur. Pour réaliser ceci, un objet-test ayant des propriétés proches de celles d'un sein comprimé, composé de différents matériaux équivalents aux tissus, allant du glandulaire à l'adipeux, et comprenant des zones permettant la simulation de structures de haut et bas contraste, ainsi que la mesure de la résolution et celle du bruit, a été développé et testé. L'intégration du processus de visualisation a été réalisée en utilisant une caméra CCD mesurant directement les paramètres de qualité d'image, à partir de l'image de l'objet-test, dans une grandeur physique commune au système numérique et analogique, à savoir la luminance arrivant sur l'oeil de l'observateur. L'utilisation d'une grandeur synthétique intégrant dans un même temps, le contraste, le bruit et la résolution rend possible une comparaison objective entre les deux systèmes de mammographie. Un modèle mathématique, simulant la réponse d'un observateur et intégrant les paramètres de base de qualité d'image, a été utilisé pour calculer la détectabilité de structures de haut et bas contraste en fonction du type de tissu sur lequel celles-ci se trouvent. Les résultats obtenus montrent qu'à dose égale la détectabilité des structures est significativement plus élevée avec le système de mammographie numérique qu'avec le système analogique. Ceci est principalement lié au fait que le bruit du système numérique est plus faible que celui du système analogique. Les résultats montrent également que la méthodologie, visant à comparer des systèmes d'imagerie numérique et analogique en utilisant un objet-test à large gamme dynamique ainsi qu'une caméra, peut être appliquée à d'autres modalités radiologiques, ainsi qu'à une démarche d'optimisation des conditions de lecture des images.<br/><br/>The goal of this work was to develop a method to objectively compare the performance of a digital and a screen-film mammography system in terms of image quality and patient dose. We propose a method that takes into account the dynamic range of the image detector and the detection of high contrast (for microcalcifications) and low contrast (for masses or tumoral nodules) structures. The method also addresses the problems of image visualization and the observer response. A test object, designed to represent a compressed breast, was constructed from various tissue equivalent materials ranging from purely adipose to purely glandular composition. Different areas within the test object permitted the evaluation of low and high contrast detection, spatial resolution, and image noise. All the images (digital and conventional) were captured using a CCD camera to include the visualization process in the image quality assessment. In this way the luminance reaching the viewer?s eyes can be controlled for both kinds of images. A global quantity describing image contrast, spatial resolution and noise, and expressed in terms of luminance at the camera, can then be used to compare the two technologies objectively. The quantity used was a mathematical model observer that calculates the detectability of high and low contrast structures as a function of the background tissue. Our results show that for a given patient dose, the detection of high and low contrast structures is significantly better for the digital system than for the conventional screen-film system studied. This is mainly because the image noise is lower for the digital system than for the screen-film detector. The method of using a test object with a large dynamic range combined with a camera to compare conventional and digital imaging modalities can be applied to other radiological imaging techniques. In particular it could be used to optimize the process of radiographic film reading.

Ecdysone triggered PGRP-LC expression controls Drosophila innate immunity.

Throughout the animal kingdom, steroid hormones have been implicated in the defense against microbial infection, but how these systemic signals control immunity is unclear. Here, we show that the steroid hormone ecdysone controls the expression of the pattern recognition receptor PGRP-LC in Drosophila, thereby tightly regulating innate immune recognition and defense against bacterial infection. We identify a group of steroid-regulated transcription factors as well as two GATA transcription factors that act as repressors and activators of the immune response and are required for the proper hormonal control of PGRP-LC expression. Together, our results demonstrate that Drosophila use complex mechanisms to modulate innate immune responses, and identify a transcriptional hierarchy that integrates steroid signalling and immunity in animals.

Copeptin Levels Remain Unchanged during the Menstrual Cycle.

BACKGROUND: Copeptin, a surrogate marker for arginin vasopressin production, is evaluated as an osmo-dependent stress and inflammatory biomarker in different diseases. We investigated copeptin during the menstrual cycle and its relationship to sex hormones, markers of subclinical inflammation and estimates of body fluid. METHODS: In 15 healthy women with regular menstrual cycles, blood was drawn on fifteen defined days of their menstrual cycle and was assayed for copeptin, progesterone, estradiol, luteinizing hormone, high-sensitive C-reactive protein, tumor necrosis factor-alpha and procalcitonin. Symptoms of fluid retention were assessed on each visit, and bio impedance analysis was measured thrice to estimate body fluid changes. Mixed linear model analysis was performed to assess the changes of copeptin across the menstrual cycle and the relationship of sex hormones, markers of subclinical inflammation and estimates of body fluid with copeptin. RESULTS: Copeptin levels did not significantly change during the menstrual cycle (p = 0.16). Throughout the menstrual cycle, changes in estradiol (p = 0.002) and in the physical premenstrual symptom score (p = 0.01) were positively related to copeptin, but changes in other sex hormones, in markers of subclinical inflammation or in bio impedance analysis-estimated body fluid were not (all p = ns). CONCLUSION: Although changes in estradiol and the physical premenstrual symptom score appear to be related to copeptin changes, copeptin does not significantly change during the menstrual cycle.

Early functional differentiation in the chick embryonic disc: interaction between mechanical activity and extracellular matrix

The mechanical behaviour of ectodermal cells in the area opaca and the supracellular organization of fibronectin in the adjacent extracellular matrix were studied in whole chick blastoderms developing in vitro. The pattern of spontaneous mechanical activity and its modification by immunoglobulins against fibronectin were determined using a real-time image-analysis system. The pattern of fibronectin was studied using immunocytochemical techniques. It was found that the ectodermal cells in the area opaca actively develop a radially oriented contraction, which leads to a distension of the area pellucida from which the embryo develops. Abnormally increased tension resulted in perturbations of gastrulation and neurulation. An optimized mechanical equilibrium within the blastoderm seems to be necessary for normal development. Anti-fibronectin antibodies applied to the basal side of the blastoderm led rapidly and reversibly to an increase of tension in the contracted cells. This observation indicates that modifications of the extracellular matrix can be transmitted to cytoskeletal elements within adjacent cells. The extracellular matrix of the area opaca contains fibronectin arranged in radially oriented fibrils. This orientation corresponds to the direction of migration of the mesodermal cells. Interestingly, the radial pattern of fibronectin is found in the regions where the ectodermal cells are contracted and develop radially oriented forces. This observation suggests that the supracellular assembly of the extracellular materials could be influenced by the mechanical activity of adjacent cells. Possible modulations of the supracellular organization of extracellular matrix by other factors, e.g. diffusible metabolites, is also discussed. The presence of characteristically organized extracellular matrix components, of spatially differentiated cell activities and of reciprocal interactions between them makes the young chick blastoderm an excellent system for physiological studies of the coordinated cellular activities that lead to changes in form, complexity and function.

Multiple refugia and barriers explain the phylogeography of the Valais shrew, Sorex antinorii (Mammalia: Soricomorpha), Sorex antinorii (Mammalia: Soricomorpha)

The aim of the present study was to investigate the genetic structure of the Valais shrew (Sorex antinorii) by a combined phylogeographical and landscape genetic approach, and thereby to infer the locations of glacial refugia and establish the influence of geographical barriers. We sequenced part of the mitochondrial cytochrome b (cyt b) gene of 179 individuals of S. antinorii sampled across the entire species' range. Six specimens attributed to S. arunchi were included in the analysis. The phylogeographical pattern was assessed by Bayesian molecular phylogenetic reconstruction, population genetic analyses, and a species distribution modelling (SDM)-based hindcasting approach. We also used landscape genetics (including isolation-by-resistance) to infer the determinants of current intra-specific genetic structure. The phylogeographical analysis revealed shallow divergence among haplotypes and no clear substructure within S. antinorii. The starlike structure of the median-joining network is consistent with population expansion from a single refugium, probably located in the Apennines. Long branches observed on the same network also suggest that another refugium may have existed in the north-eastern part of Italy. This result is consistent with SDM, which also suggests several habitable areas for S. antinorii in the Italian peninsula during the LGM. Therefore S. antinorii appears to have occupied disconnected glacial refugia in the Italian peninsula, supporting previous data for other species showing multiple refugia within southern refugial areas. By coupling genetic analyses and SDM, we were able to infer how past climatic suitability contributed to genetic divergence of populations. The genetic differentiation shown in the present study does not support the specific status of S. arunchi.

Positional scanning-synthetic peptide library-based analysis of self- and pathogen-derived peptide cross-reactivity with tumor-reactive Melan-A-specific CTL.

Synthetic combinatorial peptide libraries in positional scanning format (PS-SCL) have recently emerged as a useful tool for the analysis of T cell recognition. This includes identification of potentially cross-reactive sequences of self or pathogen origin that could be relevant for the understanding of TCR repertoire selection and maintenance, as well as of the cross-reactive potential of Ag-specific immune responses. In this study, we have analyzed the recognition of sequences retrieved by using a biometric analysis of the data generated by screening a PS-SCL with a tumor-reactive CTL clone specific for an immunodominant peptide from the melanocyte differentiation and tumor-associated Ag Melan-A. We found that 39% of the retrieved peptides were recognized by the CTL clone used for PS-SCL screening. The proportion of peptides recognized was higher among those with both high predicted affinity for the HLA-A2 molecule and high predicted stimulatory score. Interestingly, up to 94% of the retrieved peptides were cross-recognized by other Melan-A-specific CTL. Cross-recognition was at least partially focused, as some peptides were cross-recognized by the majority of CTL. Importantly, stimulation of PBMC from melanoma patients with the most frequently recognized peptides elicited the expansion of heterogeneous CD8(+) T cell populations, one fraction of which cross-recognized Melan-A. Together, these results underline the high predictive value of PS-SCL for the identification of sequences cross-recognized by Ag-specific T cells.

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

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

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

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

Homemade thermometry instruments in the field.

OBJECTIVE: Esophageal temperature is the gold standard for in-the-field temperature monitoring in hypothermic victims with cardiac arrest. For practical reasons, some mountain rescue teams use homemade esophageal thermometers to measure esophageal temperature; these consist of nonmedical inside/outside temperature monitoring instruments that have been modified to allow for esophageal insertion. We planned a study to determine the accuracy of such thermometers. METHODS: Two of the same model of digital cabled indoor/outdoor thermometer were modified and tested in comparison with a reference thermometer. The thermometers were tested in a water bath at different temperatures between 10°C and 35.2°C. Three hundred measurements were taken with each thermometer. RESULTS: Our experimental study showed that both homemade thermometers provided a good correlation and a clinically acceptable agreement in comparison with the reference thermometer. Measurements were within 0.5°C in comparison with the reference thermometer 97.5% of the time. CONCLUSIONS: The homemade thermometers performed well in vitro, in comparison with a reference thermometer. However, because these devices in their original form are not designed for clinical use, their use should be restricted to situations when the use of a conventional esophageal thermometer is impossible.