Body fat distribution in white and black women: different patterns of intraabdominal and subcutaneous abdominal adipose tissue utilization with weight loss.

BACKGROUND: Intraabdominal adipose tissue (IAAT) is the body fat depot most strongly related to disease risk. Weight reduction is advocated for overweight people to reduce total body fat and IAAT, although little is known about the effect of weight loss on abdominal fat distribution in different races. OBJECTIVE: We compared the effects of diet-induced weight loss on changes in abdominal fat distribution in white and black women. DESIGN: We studied 23 white and 23 black women, similar in age and body composition, in the overweight state [mean body mass index (BMI; in kg/m(2)): 28.8] and the normal-weight state (mean BMI: 24.0) and 38 never-overweight control women (mean BMI: 23.4). We measured total body fat by using a 4-compartment model, trunk fat by using dual-energy X-ray absorptiometry, and cross-sectional areas of IAAT (at the fourth and fifth lumbar vertebrae) and subcutaneous abdominal adipose tissue (SAAT) by using computed tomography. RESULTS: Weight loss was similar in white and black women (13.1 and 12.6 kg, respectively), as were losses of total fat, trunk fat, and waist circumference. However, white women lost more IAAT (P < 0.001) and less SAAT (P < 0.03) than did black women. Fat patterns regressed toward those of their respective control groups. Changes in waist circumference correlated with changes in IAAT in white women (r = 0.54, P < 0.05) but not in black women (r = 0.19, NS). CONCLUSIONS: Despite comparable decreases in total and trunk fat, white women lost more IAAT and less SAAT than did black women. Waist circumference was not a suitable surrogate marker for tracking changes in the visceral fat compartment in black women.

El principio del Universal Design. Concepto y desarrollos en la enseñanza superior

Los principios, las prácticas y la investigación sobre diseño universal han sidoimplantados progresivamente en diferentes ámbitos, respecto al diseño y la preparación de entornos para la atención a las necesidades de las personas con discapacidad. En el contexto internacional, este desarrollo ha estado vinculado al avance en derechos sobre igualdad de oportunidades. En el contexto legislativo español, en la Ley 51/2003 se introducen definiciones sobre «accesibilidad universal» y «diseño para todos», con lo que se genera un marco que posibilita el análisis de fuentes conceptuales y de aplicación en nuestro contexto, de las aportaciones del diseño universal, así como su consideración para la fundamentación de prácticas de innovación e investigación en nuestros ámbitos universitarios. En este trabajo, a partir de una amplia revisión de fuentes y aportaciones de gran trayectoria en este campo, se presentan y analizan distintos enfoques, a través de los cuales se están desarrollando y aplicando prácticas de diseño universal en el ámbito de la enseñanza universitaria, y se plantean sus implicaciones educativas. Este análisis permite concluir que las aplicaciones del diseño universal parecen más prometedoras para el progreso hacia metas de inclusión en el entorno universitario que una perspectiva de «adaptación curricular»; aunque se pone de manifiesto la necesidad de que la investigación que se desarrolle en nuestros contextos aporte pruebas y elementos que favorezcan su implementa- ción. Aplicar prácticas docentes y de planificación en la enseñanza universitaria con bases en el diseño universal podría contribuir a superar, eliminar o evitar en un futuro barreras en el aprendizaje, no solo limitadoras del progreso de las personas con discapacidad, sino también del conjunto del alumnado. Asimismo, las conclusiones de este trabajo plantean aplicaciones y estimaciones de nuevas muestras empíricas como puntos de partida para futuras y posibles determinaciones de enfoques conceptuales.

The Learning Affect Monitor (LAM) - A computer-based system integrating quantitative and qualitative assessment of affective states in daily life.

The Learning Affect Monitor (LAM) is a new computer-based assessment system integrating basic dimensional evaluation and discrete description of affective states in daily life, based on an autonomous adapting system. Subjects evaluate their affective states according to a tridimensional space (valence and activation circumplex as well as global intensity) and then qualify it using up to 30 adjective descriptors chosen from a list. The system gradually adapts to the user, enabling the affect descriptors it presents to be increasingly relevant. An initial study with 51 subjects, using a 1 week time-sampling with 8 to 10 randomized signals per day, produced n = 2,813 records with good reliability measures (e.g., response rate of 88.8%, mean split-half reliability of .86), user acceptance, and usability. Multilevel analyses show circadian and hebdomadal patterns, and significant individual and situational variance components of the basic dimension evaluations. Validity analyses indicate sound assignment of qualitative affect descriptors in the bidimensional semantic space according to the circumplex model of basic affect dimensions. The LAM assessment module can be implemented on different platforms (palm, desk, mobile phone) and provides very rapid and meaningful data collection, preserving complex and interindividually comparable information in the domain of emotion and well-being.

Recent skin injuries in children with motor disabilities.

OBJECTIVE: To determine the frequency of recent skin injuries in children with neuromotor disabilities and its association with disability. DESIGN: Cross-sectional study of 168 children with neuromotor disabilities aged 2-16 years. SETTING: Two outpatient child rehabilitation centres. MAIN OUTCOME MEASURES: Children were classified as unrestricted walkers, restricted walkers or wheelchair dependent. Each participant's body surface was systematically examined for recent skin injuries with the exception of the anal-genital area. RESULTS: The mean age of our sample was 7.8 (SD 3.7) years with a 3:2 male/female ratio. Overall, 64% had cerebral palsy, 17% a neuromuscular disease and 19% other motor disabilities. Participants had on average 5.3 (SD 4.5) recent skin injuries (max 19), of which 2.5 were bruises (SD 3.3, max 16), 2.4 were abrasions, scratches or cuts (SD 3.0, max 16) and 0.4 were pressure lesions (SD 0.8, max 4). There was a significant decrease in the frequency of recent skin injuries and of bruises with increasing severity of motor disability. Most of this variation was accounted for by injuries to the lower limbs. There were no significant effects of gender, learning disabilities or other comorbidities. CONCLUSIONS: Children with neuromotor disabilities present a progressive reduction in the number of skin injuries with decreasing mobility. Therefore, recent skin injuries in this population which are unusual by their number, appearance or distribution, should raise at least the same level of suspicion for physical abuse as in children without disabilities.

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.

Promotion of learning skills in the study of economics. pilot project in the subject area of

This communication is part of a larger teaching innovation project financed by the University ofBarcelona, whose objective is to develop and evaluate transversal competences of the UB, learningability and responsibility. The competence is divided into several sub-competencies being the ability toanalyze and synthesis the most intensely worked in the first year. The work presented here part fromthe results obtained in phase 1 and 2 previously implemented in other subjects (Mathematics andHistory) in the first year of the degree of Business Administration Degree. In these subjects’ previousexperiences there were deficiencies in the acquisition of learning skills by the students. The work inthe subject of Mathematics facilitated that students become aware of the deficit. The work on thesubject of History insisted on developing readings schemes and with the practical exercises wassought to go deeply in the development of this competence.The third phase presented here is developed in the framework of the second year degree, in the WorldEconomy subject. The objective of this phase is the development and evaluation of the same crosscompetence of the previous phases, from a practice that includes both, quantitative analysis andcritical reflection. Specifically the practice focuses on the study of the dynamic relationship betweeneconomic growth and the dynamics in the distribution of wealth. The activity design as well as theselection of materials to make it, has been directed to address gaps in the ability to analyze andsynthesize detected in the subjects of the first year in the previous phases of the project.The realization of the practical case is considered adequate methodology to improve the acquisition ofcompetence of the students, then it is also proposed how to evaluate the acquisition of suchcompetence. The practice is evaluated based on a rubric developed in the framework of the projectobjectives. Thus at the end of phase 3 we can analyze the process that have followed the students,detect where they have had major difficulties and identify those aspects of teaching that can help toimprove the acquisition of skills by the students. The interest of this phase resides in the possibility tovalue whether tracing of learning through competences, organized in a collaborative way, is a goodtool to develop the acquisition of these skills and facilitate their evaluation.

Schemata as a framework for full body and physical design interfaces

Digital art interfaces presents cognitiveparadigms that deals with the recognition of the symbols and representations through interaction.What is presented in this paper is anapproximation of the bodily experience in that particular scenario and a new proposal which has the aim to contribute more ideas and criteria in the analysis of the learning process of aparticipant discovering an interactive space or interface. For that I propose a first new approach where metaphorically I tried to extrapolate the stages of the psychology of development stated byJean Piaget in the interface design domain.

Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme

Recent experiments have established that information can be encoded in the spike times of neurons relative to the phase of a background oscillation in the local field potential—a phenomenon referred to as “phase-of-firing coding” (PoFC). These firing phase preferences could result from combining an oscillation in the input current with a stimulus-dependent static component that would produce the variations in preferred phase, but it remains unclear whether these phases are an epiphenomenon or really affect neuronal interactions—only then could they have a functional role. Here we show that PoFC has a major impact on downstream learning and decoding with the now well established spike timing-dependent plasticity (STDP). To be precise, we demonstrate with simulations how a single neuron equipped with STDP robustly detects a pattern of input currents automatically encoded in the phases of a subset of its afferents, and repeating at random intervals. Remarkably, learning is possible even when only a small fraction of the afferents (~10%) exhibits PoFC. The ability of STDP to detect repeating patterns had been noted before in continuous activity, but it turns out that oscillations greatly facilitate learning. A benchmark with more conventional rate-based codes demonstrates the superiority of oscillations and PoFC for both STDP-based learning and the speed of decoding: the oscillation partially formats the input spike times, so that they mainly depend on the current input currents, and can be efficiently learned by STDP and then recognized in just one oscillation cycle. This suggests a major functional role for oscillatory brain activity that has been widely reported experimentally.

Current cardiovascular risk management patterns with special focus on lipid lowering in daily practice in Switzerland.

Background: There may be a considerable gap between LDL cholesterol (LDL-C) and blood pressure (BP) goal values recommended by the guidelines and results achieved in daily practice. Design Prospective cross-sectional survey of cardiovascular disease risk profiles and management with focus on lipid lowering and BP lowering in clinical practice. Methods: In phase 1, the cardiovascular risk of patients with known lipid profile visiting their general practitioner was anonymously assessed in accordance to the PROCAM-score. In phase 2, high-risk patients who did not achieve LDL-C goal less than 2.6 mmol/l in phase 1 could be further documented. Results: Six hundred thirty-five general practitioners collected the data of 23 892 patients with known lipid profile. Forty percent were high-risk patients (diabetes mellitus or coronary heart disease or PROCAM-score >20%), compared with 27% estimated by the physicians. Goal attainment rate was almost double for BP than for LDL-C in high-risk patients (62 vs. 37%). Both goals were attained by 25%. LDL-C values in phase 1 and 2 were available for 3097 high-risk patients not at LDL-C goal in phase 1; 32% of patients achieved LDL-C goal of less than 2.6 mmol/l after a mean of 17 weeks. The most successful strategies for LDL-C reduction were implemented in only 22% of the high-risk patients. Conclusion: Although patients at high cardiovascular risk were treated more intensively than low or medium risk patients, the majority remained insufficiently controlled, which is an incentive for intensified medical education. Adequate implementation of Swiss and International guidelines would expectedly contribute to improved achievement of LDL-C and BP goal values in daily practice.

Technology educational affordance: Bridging the gap between patterns of interaction and technology usage

Peer-reviewed

Analysing Professional Discourse in Interactive Learning: Integrating Historical and Situational Perspectives

Peer-reviewed

Trabajo colaborativo en entornos e-learning y desarrollo de competencias transversales de trabajo en equipo: Análisis del caso del Máster en gestión de Proyectos en Cooperación Internacional, CSEU La Salle

Peer-reviewed

Experimental Results and Evaluation of SmartBox Stimulation Device in a P2P E-learning System

In this paper, we present the experimental results and evaluation of the SmartBox stimulation device in P2P e-learning system which is based on JXTA-Overlay. We also show the design and implementation of the SmartBox environment that is used for stimulating the learners motivation to increase the learning efficiency. The SmartBox is integrated with our P2P system as a useful tool for monitoring and controlling learners¿ activity. We found by experimental results that the SmartBox is an effective way to increase the learner¿s concentration. We also investigated the relation between learner¿s body movement, concentration, and amount of study. From the experimental results, we conclude that the use of SmartBox is an effective way to stimulate the learners in order to continue studying while maintaining the concentration.

Patterns of practice of regional nodal irradiation in breast cancer: results of the European Organization for Research and Treatment of Cancer (EORTC) NOdal Radiotherapy (NORA) survey.

BACKGROUND: Predicting outcome of breast cancer (BC) patients based on sentinel lymph node (SLN) status without axillary lymph node dissection (ALND) is an area of uncertainty. It influences the decision-making for regional nodal irradiation (RNI). The aim of the NORA (NOdal RAdiotherapy) survey was to examine the patterns of RNI. METHODS: A web-questionnaire, including several clinical scenarios, was distributed to 88 EORTC-affiliated centers. Responses were received between July 2013 and January 2014. RESULTS: A total of 84 responses were analyzed. While three-dimensional (3D) radiotherapy (RT) planning is carried out in 81 (96%) centers, nodal areas are delineated in only 51 (61%) centers. Only 14 (17%) centers routinely link internal mammary chain (IMC) and supraclavicular node (SCN) RT indications. In patients undergoing total mastectomy (TM) with ALND, SCN-RT is recommend by 5 (6%), 53 (63%) and 51 (61%) centers for patients with pN0(i+), pN(mi) and pN1, respectively. Extra-capsular extension (ECE) is the main factor influencing decision-making RNI after breast conserving surgery (BCS) and TM. After primary systemic therapy (PST), 49 (58%) centers take into account nodal fibrotic changes in ypN0 patients for RNI indications. In ypN0 patients with inner/central tumors, 23 (27%) centers indicate SCN-RT and IMC-RT. In ypN1 patients, SCN-RT is delivered by less than half of the centers in patients with ypN(i+) and ypN(mi). Twenty-one (25%) of the centers recommend ALN-RT in patients with ypN(mi) or 1-2N+ after ALND. Seventy-five (90%) centers state that age is not considered a limiting factor for RNI. CONCLUSION: The NORA survey is unique in evaluating the impact of SLNB/ALND status on adjuvant RNI decision-making and volumes after BCS/TM with or without PST. ALN-RT is often indicated in pN1 patients, particularly in the case of ECE. Besides the ongoing NSABP-B51/RTOG and ALLIANCE trials, NORA could help to design future specific RNI trials in the SLNB era without ALND in patients receiving or not PST.

Promoting Metacognitive Skills in a CSCL environment: implications for instructional and technology design

This paper aims to better understand the development of students’ learning processes when participating actively in a specific Computer Supported Collaborative Learning system called KnowCat. To this end, a longitudinal case study was designed, in which eighteen university students took part in a 12-month (two semesters) learning project. During this time period, the students followed an instructional process, using some elements of KnowCat (KnowCat key features) design to support and improve their interaction processes, especially peer learning processes. Our research involved both supervising the students’ collaborative learning processes throughout the learning project and focusing our analysis on the qualitative evolution of the students’ interaction processes and on the development of metacognitive learning processes. The results of the current research reveal that the instructional application of the CSCL-KnowCat system may favour and improve the development of the students’ metacognitive learning processes. Additionally, the implications of the design of computer supported collaborative learning networks and pedagogical issues are discussed in this paper.