Systems Genetics of Metabolism: The Use of the BXD Murine Reference Panel for Multiscalar Integration of Traits.

Metabolic homeostasis is achieved by complex molecular and cellular networks that differ significantly among individuals and are difficult to model with genetically engineered lines of mice optimized to study single gene function. Here, we systematically acquired metabolic phenotypes by using the EUMODIC EMPReSS protocols across a large panel of isogenic but diverse strains of mice (BXD type) to study the genetic control of metabolism. We generated and analyzed 140 classical phenotypes and deposited these in an open-access web service for systems genetics (www.genenetwork.org). Heritability, influence of sex, and genetic modifiers of traits were examined singly and jointly by using quantitative-trait locus (QTL) and expression QTL-mapping methods. Traits and networks were linked to loci encompassing both known variants and novel candidate genes, including alkaline phosphatase (ALPL), here linked to hypophosphatasia. The assembled and curated phenotypes provide key resources and exemplars that can be used to dissect complex metabolic traits and disorders.

Uncovering the genetic basis of adaptive change: on the intersection of landscape genomics and theoretical population genetics

A workshop recently held at the Ecole Polytechnique Federale de Lausanne (EPFL, Switzerland) was dedicated to understanding the genetic basis of adaptive change, taking stock of the different approaches developed in theoretical population genetics and landscape genomics and bringing together knowledge accumulated in both research fields. Indeed, an important challenge in theoretical population genetics is to incorporate effects of demographic history and population structure. But important design problems (e.g. focus on populations as units, focus on hard selective sweeps, no hypothesis-based framework in the design of the statistical tests) reduce their capability of detecting adaptive genetic variation. In parallel, landscape genomics offers a solution to several of these problems and provides a number of advantages (e.g. fast computation, landscape heterogeneity integration). But the approach makes several implicit assumptions that should be carefully considered (e.g. selection has had enough time to create a functional relationship between the allele distribution and the environmental variable, or this functional relationship is assumed to be constant). To address the respective strengths and weaknesses mentioned above, the workshop brought together a panel of experts from both disciplines to present their work and discuss the relevance of combining these approaches, possibly resulting in a joint software solution in the future.

Multisensory Integration: Flexible Use of General Operations.

Research into the anatomical substrates and "principles" for integrating inputs from separate sensory surfaces has yielded divergent findings. This suggests that multisensory integration is flexible and context dependent and underlines the need for dynamically adaptive neuronal integration mechanisms. We propose that flexible multisensory integration can be explained by a combination of canonical, population-level integrative operations, such as oscillatory phase resetting and divisive normalization. These canonical operations subsume multisensory integration into a fundamental set of principles as to how the brain integrates all sorts of information, and they are being used proactively and adaptively. We illustrate this proposition by unifying recent findings from different research themes such as timing, behavioral goal, and experience-related differences in integration.

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.

Rpe65-Gene Transfer Using an Integration-Deficient Lentiviral Vector

Purpose: We previously demonstrated efficient retinal rescue of RPE65 mouse models (Rpe65-/- (Bemelmans et al, 2006) and Rpe65R91W/R91W mice) using a HIV1-derived lentiviral vector encoding for the mouse RPE65 cDNA. In order to optimize a lentiviral vector as an alternative tool for RPE65-derived Leber Congenital Amaurosis clinical trials, we evaluated the efficiency of an integration-deficient lentiviral vector (IDLV) encoding the human RPE65 cDNA to restore retinal function in the Rpe65R91W/R91W mice. Methods: An HIV-1-derived lentiviral vector expressing either the hrGFPII or the human Rpe65 cDNA under the control of a 0.8 kb fragment of the human Rpe65 promoter (R0.8) was produced by transient transfection of 293T cells. A LQ-integrase mutant was used to generate the IDLV vectors. IDLV-R0.8-hRPE65 or hrGFPII were injected subretinally into 1 month-old Rpe65R91W/R91W mice. Functional rescue was assessed by ERG (1 and 3 months post-injection) and cone survival by immunohistology. Results: An increased light sensitivity was detected by scotopic ERG in animals injected with IDLV-R0.8-hRPE65 compared to hrGFPII-treated animals or untreated mice. However the improvement was delayed compared to integration-proficient LV and observed at 3 months but not 1 month post-injection. Immunolabelling of cone markers showed an increased number of cones in the transduced area compared to control groups. Conclusions: The IDLV-R0.8-hRPE65 vectors allow retinal improvement in the Rpe65R91W/R91W mice. Both rod function and cone survival were demonstrated even if there is a delay in the rescue as assessed by scotopic ERG. Integration-deficient vectors minimize insertional mutagenesis and thus are safer candidates for human application. Further experiments using large animals are now needed to validate correct gene transfer and expression of the RPE65 gene as well as tolerance of the vector after subretinal injection before envisaging a clinical trial application.

Quantification of Myocardial Perfusion Using 13N-ammonia PET: a Cross-Comparison Study on Analysis Software Packages - Carimas, PMOD and FlowQuant

Aim. Several software packages (SWP) and models have been released for quantification of myocardial perfusion (MP). Although they all are validated against something, the question remains how well their values agree. The present analysis focused on cross-comparison of three SWP for MP quantification of 13N-ammonia PET studies. Materials & Methods. 48 rest and stress MP 13N-ammonia PET studies of hypertrophic cardiomyopathy (HCM) patients (Sciagrà et al., 2009) were analysed with three SW packages - Carimas, PMOD, and FlowQuant - by three observers blinded to the results of each other. All SWP implement the one-tissue-compartment model (1TCM, DeGrado et al. 1996), and first two - the two-tissue-compartment model (2TCM, Hutchins et al. 1990) as well. Linear mixed model for the repeated measures was fitted to the data. Where appropriate we used Bland-Altman plots as well. The reproducibility was assessed on global, regional and segmental levels. Intraclass correlation coefficients (ICC), differences between the SWPs and between models were obtained. ICC≥0.75 indicated excellent reproducibility, 0.4≤ICC<0.75 indicated fair to good reproducibility, ICC<0.4 - poor reproducibility (Rosner, 2010). Results. When 1TCM MP values were compared, the SW agreement on global and regional levels was excellent, except for Carimas vs. PMOD at RCA: ICC=0.715 and for PMOD vs. FlowQuant at LCX:ICC=0.745 which were good. In segmental analysis in five segments: 7,12,13, 16, and 17 the agreement between all SWP was excellent; in the remaining 12 segments the agreement varied between the compared SWP. Carimas showed excellent agreement with FlowQuant in 13 segments and good in four - 1, 5, 6, 11: 0.687≤ICCs≤0.73; Carimas had excellent agreement with PMOD in 11 segments, good in five_4, 9, 10, 14, 15: 0.682≤ICCs≤0.737, and poor in segment 3: ICC=0.341. PMOD had excellent agreement with FlowQuant in eight segments and substantial-to-good in nine_1, 2, 3, 5, 6,8-11: 0.585≤ICCs≤0.738. Agreement between Carimas and PMOD for 2TCM was good at a global level: ICC=0.745, excellent at LCX (0.780) and RCA (0.774), good at LAD (0.662); agreement was excellent for ten segments, fair-to-substantial for segments 2, 3, 8, 14, 15 (0.431≤ICCs≤0.681), poor for segments 4 (0.384) and 17 (0.278). Conclusions. The three SWP used by different operators to analyse 13N-ammonia PET MP studies provide results that agree well at a global level, regional levels, and mostly well even at a segmental level. Agreement is better for 1TCM. Poor agreement at segments 4 and 17 for 2TCM needs further clarification.

Stage-specific integration of maternal and embryonic peroxisome proliferator-activated receptor delta signaling is critical to pregnancy success.

Successful pregnancy depends on well coordinated developmental events involving both maternal and embryonic components. Although a host of signaling pathways participate in implantation, decidualization, and placentation, whether there is a common molecular link that coordinates these processes remains unknown. By exploiting genetic, molecular, pharmacological, and physiological approaches, we show here that the nuclear transcription factor peroxisome proliferator-activated receptor (PPAR) delta plays a central role at various stages of pregnancy, whereas maternal PPARdelta is critical to implantation and decidualization, and embryonic PPARdelta is vital for placentation. Using trophoblast stem cells, we further elucidate that a reciprocal relationship between PPARdelta-AKT and leukemia inhibitory factor-STAT3 signaling pathways serves as a cell lineage sensor to direct trophoblast cell fates during placentation. This novel finding of stage-specific integration of maternal and embryonic PPARdelta signaling provides evidence that PPARdelta is a molecular link that coordinates implantation, decidualization, and placentation crucial to pregnancy success. This study is clinically relevant because deferral of on time implantation leads to spontaneous pregnancy loss, and defective trophoblast invasion is one cause of preeclampsia in humans.

European Integration Without EU Membership : Models, Experiences, Perspectives

At the beginning of the 1990s, the concept of "European integration" could still be said to be fairly unambiguous. Nowadays, it has become plural and complex almost to the point of unintelligibility. This is due, of course, to the internal differentiation of EU membership, with several Member States pulling out of key integrative projects such as establishing an area without frontiers, the "Schengen" area, and a common currency. But this is also due to the differentiated extension of key integrative projects to European non-EU countries - Schengen is again a case in point. Such processes of "integration without membership", the focus of the present publication, are acquiring an ever-growing topicality both in the political arena and in academia. International relations between the EU and its neighbouring countries are crucial for both, and their development through new agreements features prominently on the continent's political agenda. Over and above this aspect, the dissemination of EU values and standards beyond the Union's borders raises a whole host of theoretical and methodological questions, unsettling in some cases traditional conceptions of the autonomy and separation of national legal orders. This publication brings together the papers presented at the Integration without EU Membership workshop held in May 2008 at the EUI (Max Weber Programme and Department of Law). It aims to compare different models and experiences of integration between the EU, on the one hand, and those European countries that do not currently have an accession perspective on the other hand. In delimiting the geographical scope of the inquiry, so as to scale it down to manageable proportions, the guiding principles have been to include both the "Eastern" and "Western" neighbours of the EU, and to examine both structured frameworks of cooperation, such as the European Neighbourhood Policy and the European Economic Area, and bilateral relations developing on a more ad hoc basis. These principles are reflected in the arrangement of the papers, which consider in turn the positions of Ukraine, Russia, Norway, and Switzerland in European integration - current standing, perspectives for evolution, consequences in terms of the EU-ization of their respective legal orders1. These subjects are examined from several perspectives. We had the privilege of receiving contributions from leading practitioners and scholars from the countries concerned, from EU highranking officials, from prominent specialists in EU external relations law, and from young and talented researchers. We wish to thank them all here for their invaluable insights. We are moreover deeply indebted to Marise Cremona (EUI, Law Department, EUI) for her inspiring advice and encouragement, as well as to Ramon Marimon, Karin Tilmans, Lotte Holm, Alyson Price and Susan Garvin (Max Weber Programme, EUI) for their unflinching support throughout this project. A word is perhaps needed on the propriety and usefulness of the research concept embodied in this publication. Does it make sense to compare the integration models and experiences of countries as different as Norway, Russia, Switzerland, and Ukraine? Needless to say, this list of four evokes a staggering diversity of political, social, cultural, and economic conditions, and at least as great a diversity of approaches to European integration. Still, we would argue that such diversity only makes comparisons more meaningful. Indeed, while the particularities and idiosyncratic elements of each "model" of integration are fully displayed in the present volume, common themes and preoccupations run through the pages of every contribution: the difficulty in conceptualizing the finalité and essence of integration, which is evident in the EU today but which is greatly amplified for non-EU countries; the asymmetries and tradeoffs between integration and autonomy that are inherent in any attempt to participate in European integration from outside; the alteration of deeply seated legal concepts, and concepts about the law, that are already observable in the most integrated of the non-EU countries concerned. These issues are not transient or coincidental: they are inextricably bound up with the integration of non-EU countries in the EU project. By publishing this collection, we make no claim to have dealt with them in an exhaustive, still less in a definitive manner. Our ambition is more modest: to highlight the relevance of these themes, to place them more firmly on the scientific agenda, and to provide a stimulating basis for future research and reflection.

Integration of Notch 1 and calcineurin/NFAT signaling pathways in keratinocyte growth and differentiation control.

The Notch and Calcineurin/NFAT pathways have both been implicated in control of keratinocyte differentiation. Induction of the p21(WAF1/Cip1) gene by Notch 1 activation in differentiating keratinocytes is associated with direct targeting of the RBP-Jkappa protein to the p21 promoter. We show here that Notch 1 activation functions also through a second Calcineurin-dependent mechanism acting on the p21 TATA box-proximal region. Increased Calcineurin/NFAT activity by Notch signaling involves downregulation of Calcipressin, an endogenous Calcineurin inhibitor, through a HES-1-dependent mechanism. Besides control of the p21 gene, Calcineurin contributes significantly to the transcriptional response of keratinocytes to Notch 1 activation, both in vitro and in vivo. In fact, deletion of the Calcineurin B1 gene in the skin results in a cyclic alopecia phenotype, associated with altered expression of Notch-responsive genes involved in hair follicle structure and/or adhesion to the surrounding mesenchyme. Thus, an important interconnection exists between Notch 1 and Calcineurin-NFAT pathways in keratinocyte growth/differentiation control.

Monitoring of noninvasive ventilation by built-in software of home bilevel ventilators: a bench study.

BACKGROUND: Current bilevel positive-pressure ventilators for home noninvasive ventilation (NIV) provide physicians with software that records items important for patient monitoring, such as compliance, tidal volume (Vt), and leaks. However, to our knowledge, the validity of this information has not yet been independently assessed. METHODS: Testing was done for seven home ventilators on a bench model adapted to simulate NIV and generate unintentional leaks (ie, other than of the mask exhalation valve). Five levels of leaks were simulated using a computer-driven solenoid valve (0-60 L/min) at different levels of inspiratory pressure (15 and 25 cm H(2)O) and at a fixed expiratory pressure (5 cm H(2)O), for a total of 10 conditions. Bench data were compared with results retrieved from ventilator software for leaks and Vt. RESULTS: For assessing leaks, three of the devices tested were highly reliable, with a small bias (0.3-0.9 L/min), narrow limits of agreement (LA), and high correlations (R(2), 0.993-0.997) when comparing ventilator software and bench results; conversely, for four ventilators, bias ranged from -6.0 L/min to -25.9 L/min, exceeding -10 L/min for two devices, with wide LA and lower correlations (R(2), 0.70-0.98). Bias for leaks increased markedly with the importance of leaks in three devices. Vt was underestimated by all devices, and bias (range, 66-236 mL) increased with higher insufflation pressures. Only two devices had a bias < 100 mL, with all testing conditions considered. CONCLUSIONS: Physicians monitoring patients who use home ventilation must be aware of differences in the estimation of leaks and Vt by ventilator software. Also, leaks are reported in different ways according to the device used.