Differing trends in the association between obesity and self-reported health in Portugal and Switzerland: data from national health surveys 1992-2007.

BACKGROUND: The escalating prevalence of obesity might prompt obese subjects to consider themselves as normal, as this condition is gradually becoming as frequent as normal weight. In this study, we aimed to assess the trends in the associations between obesity and self-rated health in two countries. METHODS: Data from the Portuguese (years 1995-6, 1998-6 and 2005-6) and Swiss (1992-3, 1997, 2002 and 2007) National Health Surveys were used, corresponding to more than 130,000 adults (64,793 for Portugal and 65,829 for Switzerland). Body mass index and self-rated health were derived from self-reported data. RESULTS: Obesity levels were higher in Portugal (17.5% in 2005-6 vs. 8.9% in 2007 in Switzerland, p < 0.001) and increased in both countries. The prevalence of participants rating their health as "bad" or "very bad" was higher in Portugal than in Switzerland (21.8% in 2005-6 vs 3.9% in 2007, p < 0.001). In both countries, obese participants rated more frequently their health as "bad" or "very bad" than participants with regular weight. In Switzerland, the prevalence of "bad" or "very bad" rates among obese participants, increased from 6.5% in 1992-3 to 9.8% in 2007, while in Portugal it decreased from 41.3% to 32.3%. After multivariate adjustment, the odds ratio (OR) of stating one self's health as "bad" or "very bad" among obese relative to normal weight participants, almost doubled in Switzerland: from 1.38 (95% confidence interval, CI: 1.01-1.87) in 1992-3 to 2.64 (95% CI: 2.14-3.26) in 2007, and similar findings were obtained after sample weighting. Conversely, no such trend was found in Portugal: 1.35 (95% CI: 1.23-1.48) in 1995-6 and 1.52 (95% CI: 1.37-1.70) in 2005-6. CONCLUSION: Obesity is increasing in Switzerland and Portugal. Obesity is increasingly associated with poorer self-health ratings in Switzerland but not in Portugal.

Self-potentials in partially saturated media: the importance of explicit modeling of electrode effects

Self-potential (SP) data are of interest to vadose zone hydrology because of their direct sensitivity to water flow and ionic transport. There is unfortunately little consensus in the literature about how to best model SP data under partially saturated conditions, and different approaches (often supported by one laboratory data set alone) have been proposed. We argue that this lack of agreement can largely be traced to electrode effects that have not been properly taken into account. A series of drainage and imbibition experiments were considered in which we found that previously proposed approaches to remove electrode effects were unlikely to provide adequate corrections. Instead, we explicitly modeled the electrode effects together with classical SP contributions using a flow and transport model. The simulated data agreed overall with the observed SP signals and allowed decomposing the different signal contributions to analyze them separately. After reviewing other published experimental data, we suggest that most of them include electrode effects that have not been properly taken into account. Our results suggest that previously presented SP theory works well when considering the modeling uncertainties presently associated with electrode effects. Additional work is warranted to not only develop suitable electrodes for laboratory experiments but also to assure that associated electrode effects that appear inevitable in longer term experiments are predictable, so that they can be incorporated into the modeling framework.

A new self-expanding aortic stent valve with annular fixation: in vitro haemodynamic assessment.

OBJECTIVE: Balloon-expandable stent valves require flow reduction during implantation (rapid pacing). The present study was designed to compare a self-expanding stent valve with annular fixation versus a balloon-expandable stent valve. METHODS: Implantation of a new self-expanding stent valve with annular fixation (Symetis, Lausanne, Switzerland) was assessed versus balloon-expandable stent valve, in a modified Dynatek Dalta pulse duplicator (sealed port access to the ventricle for transapical route simulation), interfaced with a computer for digital readout, carrying a 25 mm porcine aortic valve. The cardiovascular simulator was programmed to mimic an elderly woman with aortic stenosis: 120/85 mmHg aortic pressure, 60 strokes/min (66.5 ml), 35% systole (2.8 l/min). RESULTS: A total of 450 cardiac cycles was analysed. Stepwise expansion of the self-expanding stent valve with annular fixation (balloon-expandable stent valve) resulted in systolic ventricular increase from 120 to 121 mmHg (126 to 830+/-76 mmHg)*, and left ventricular outflow obstruction with mean transvalvular gradient of 11+/-1.5 mmHg (366+/-202 mmHg)*, systolic aortic pressure dropped distal to the valve from 121 to 64.5+/-2 mmHg (123 to 55+/-30 mmHg) N.S., and output collapsed to 1.9+/-0.06 l/min (0.71+/-0.37 l/min* (before complete obstruction)). No valve migration occurred in either group. (*=p<0.05). CONCLUSIONS: Implantation of this new self-expanding stent valve with annular fixation has little impact on haemodynamics and has the potential for working heart implantation in vivo. Flow reduction (rapid pacing) is not necessary.

Diabetes care: opinions, needs and proposed solutions of Swiss patients and healthcare professionals: a qualitative study.

AIMS: To explore, both among patients with diabetes and healthcare professionals, opinions on current diabetes care and the development of the "Regional Diabetes Program". METHODS: We employed qualitative methods (focus groups - FG) and used purposive sampling strategy to recruit patients with diabetes and healthcare professionals. We conducted one diabetic and one professional FG in each of the four health regions of the canton of Vaud/Switzerland. The eight FGs were audio-taped and transcribed verbatim. Thematic analysis was then undertaken. RESULTS: Results showed variability in the perception of the quality of diabetes care, pointed to insufficient information regarding diabetes, and lack of collaboration. Participants also evoked patients' difficulties for self-management, as well as professionals' and patients' financial concerns. Proposed solutions included reinforcing existing structures, developing self-management education, and focusing on comprehensive and coordinated care, communication and teamwork. Patients and professionals were in favour of a "Regional Diabetes Program" tailored to the actors' needs, and viewed it as a means to reinforce existing care delivery. CONCLUSIONS: Patients and professionals pointed out similar problems and solutions but explored them differently. Combined with coming quantitative data, these results should help to further develop, adapt and implement the "Regional Diabetes Program".

Cell viability and noninvasive in vivo MRI tracking of 3D cell encapsulating self-assembled microcontainers.

Several molecular therapies require the implantation of cells that secrete biotherapeutic molecules and imaging the location and microenvironment of the cellular implant to ascertain its function. We demonstrate noninvasive in vivo magnetic resonance imaging (MRI) of self-assembled microcontainers that are capable of cell encapsulation. Negative contrast was obtained to discern the microcontainer with MRI; positive contrast was obtained in the complete absence of background signal. MRI on a clinical scanner highlights the translational nature of this research. The microcontainers were loaded with cells that were dispersed in an extracellular matrix, and implanted both subcutaneously and in human tumor xenografts in SCID mice. MRI was performed on the implants, and microcontainers retrieved postimplantation showed cell viability both within and proximal to the implant. The microcontainers are characterized by their small size, three dimensionality, controlled porosity, ease of parallel fabrication, chemical and mechanical stability, and noninvasive traceability in vivo.

Qualitative modeling identifies IL-11 as a novel regulator in maintaining self-renewal in human pluripotent stem cells.

Pluripotency in human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) is regulated by three transcription factors-OCT3/4, SOX2, and NANOG. To fully exploit the therapeutic potential of these cells it is essential to have a good mechanistic understanding of the maintenance of self-renewal and pluripotency. In this study, we demonstrate a powerful systems biology approach in which we first expand literature-based network encompassing the core regulators of pluripotency by assessing the behavior of genes targeted by perturbation experiments. We focused our attention on highly regulated genes encoding cell surface and secreted proteins as these can be more easily manipulated by the use of inhibitors or recombinant proteins. Qualitative modeling based on combining boolean networks and in silico perturbation experiments were employed to identify novel pluripotency-regulating genes. We validated Interleukin-11 (IL-11) and demonstrate that this cytokine is a novel pluripotency-associated factor capable of supporting self-renewal in the absence of exogenously added bFGF in culture. To date, the various protocols for hESCs maintenance require supplementation with bFGF to activate the Activin/Nodal branch of the TGFβ signaling pathway. Additional evidence supporting our findings is that IL-11 belongs to the same protein family as LIF, which is known to be necessary for maintaining pluripotency in mouse but not in human ESCs. These cytokines operate through the same gp130 receptor which interacts with Janus kinases. Our finding might explain why mESCs are in a more naïve cell state compared to hESCs and how to convert primed hESCs back to the naïve state. Taken together, our integrative modeling approach has identified novel genes as putative candidates to be incorporated into the expansion of the current gene regulatory network responsible for inducing and maintaining pluripotency.

A Behavioral Account of the Labor Market: The Role of Fairness Concerns

In this paper, we argue that important labor market phenomena can be better understood if one takes (a) the inherent incompleteness and relational nature of most employment contracts and (b) the existence of reference-dependent fairness concerns among a substantial share of the population into account. Theory shows and experiments confirm that, even if fairness concerns were to exert only weak effects in one-shot interactions, repeated interactions greatly magnify the relevance of such concerns on economic outcomes. We also review evidence from laboratory and field experiments examining the role of wages and fairness on effort, derive predictions from our approach for entry-level wages and incumbent workers' wages, confront these predictions with the evidence, and show that reference-dependent fairness concerns may have important consequences for the effects of economic policies such as minimum wage laws.

Besoins, prescriptions et sécurité des produits sanguins labiles ; autosuffisance en produits sanguins labiles [Roundtables of SFTS Congress 2013: Needs, indications and safety of blood products; self-sufficiency in blood products].

The current issues debate brings together experts around the themes of self-sufficiency (in its national and European aspects) and of needs in cellular blood products. The point of view of the manufacturer and prescribers of blood products are confronted.

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.

Subjective mental time: the functional architecture of projecting the self to past and future.

Abstract Human experience takes place in the line of mental time (MT) created through 'self-projection' of oneself to different time-points in the past or future. Here we manipulated self-projection in MT not only with respect to one's life events but also with respect to one's faces from different past and future time-points. Behavioural and event-related functional magnetic resonance imaging activity showed three independent effects characterized by (i) similarity between past recollection and future imagination, (ii) facilitation of judgements related to the future as compared with the past, and (iii) facilitation of judgements related to time-points distant from the present. These effects were found with respect to faces and events, and also suggest that brain mechanisms of MT are independent of whether actual life episodes have to be re-experienced or pre-experienced, recruiting a common cerebral network including the anteromedial temporal, posterior parietal, inferior frontal, temporo-parietal and insular cortices. These behavioural and neural data suggest that self-projection in time is a fundamental aspect of MT, relying on neural structures encoding memory, mental imagery and self.