The Beck Cognitive Insight Scale in outpatients with psychotic disorders: further evidence from a French-speaking sample.

OBJECTIVE: The Beck Cognitive Insight Scale (BCIS) evaluates patients' self-report of their ability to detect and correct misinterpretation. Our study aims to confirm the factor structure and the convergent validity of the original scale in a French-speaking environment. METHOD: Outpatients (n = 158) suffering from schizophrenia or schizoaffective disorders fulfilled the BCIS. The 51 patients in Montpellier were equally assessed with the Positive and Negative Syndrome Scale (PANSS) by a psychiatrist who was blind of the BCIS scores. RESULTS: The fit indices of the confirmatory factor analysis validated the 2-factor solution reported by the developers of the scale with inpatients, and in another study with middle-aged and older outpatients. The BCIS composite index was significantly negatively correlated with the clinical insight item of the PANSS. CONCLUSIONS: The French translation of the BCIS appears to have acceptable psychometric properties and gives additional support to the scale, as well as cross-cultural validity for its use with outpatients suffering from schizophrenia or schizoaffective disorders. The correlation between clinical and composite index of cognitive insight underlines the multidimensional nature of clinical insight. Cognitive insight does not recover clinical insight but is a potential target for developing psychological treatments that will improve clinical insight.

Predictive value of readiness, importance, and confidence in ability to change drinking and smoking.

BACKGROUND: Visual analog scales (VAS) are sometimes used to assess change constructs that are often considered critical for change. Aims of Study: 1.) To determine the association of readiness to change, importance of changing and confidence in ability to change alcohol and tobacco use at baseline with the risk for drinking (more than 21 drinks per week/6 drinks or more on a single occasion more than once per month) and smoking (one or more cigarettes per day) six months later. 2.) To determine the association of readiness, importance and confidence with alcohol (number of drinks/week, number of binge drinking episodes/month) and tobacco (number of cigarettes/day) use at six months. METHODS: This is a secondary analysis of data from a multi-substance brief intervention randomized trial. A sample of 461 Swiss young men was analyzed as a prospective cohort. Participants were assessed at baseline and six months later on alcohol and tobacco use, and at baseline on readiness to change, importance of changing and confidence in ability to change constructs, using visual analog scales ranging from 1-10 for drinking and smoking behaviors. Regression models controlling for receipt of brief intervention were employed for each change construct. The lowest level (1-4) of each scale was the reference group that was compared to the medium (5-7) and high (8-10) levels. RESULTS: Among the 377 subjects reporting unhealthy alcohol use at baseline, mean (SD) readiness, importance and confidence to change drinking scores were 3.9 (3.0), 2.7 (2.2) and 7.2 (3.0), respectively. At follow-up, 108 (29%) reported no unhealthy alcohol use. Readiness was not associated with being risk-free at follow-up, but high importance (OR 2.94; 1.15, 7.50) and high confidence (OR 2.88; 1.46, 5.68) were. Among the 255 smokers at baseline, mean readiness, importance and confidence to change smoking scores were 4.6 (2.6), 5.3 (2.6) and 5.9 (2.7), respectively. At follow-up, 13% (33) reported no longer smoking. Neither readiness nor importance was associated with being a non-smoker, whereas high confidence (OR 3.29; 1.12, 9.62) was. CONCLUSIONS: High confidence in ability to change was associated with favorable outcomes for both drinking and smoking, whereas high importance was associated only with a favorable drinking outcome. This study points to the value of confidence as an important predictor of successful change for both drinking and smoking, and shows the value of importance in predicting successful changes in alcohol use. TRIAL REGISTRATION NUMBER: ISRCTN78822107.

Vulnerabilität und psychische Erkrankung

Vulnerability and psychic illness Based on a sample of 1701 college and university students from four different sites in Switzerland, the U.S., and Argentina, this study investigated the interrelationships between insufficient coping skills under chronic stress and impaired general health. We sought to develop standardised means for "early" identification of students at risk of mental health problems, as these students may benefit from "early" interventions before psychiatric symptoms develop and reach clinically relevant thresholds. All students completed two self-report questionnaires: the Coping Strategies Inventory "COPE" and the Zurich Health Questionnaire "ZHQ", with the latter assessing "regular exercises", "consumption behavior", "impaired physical health", "psychosomatic disturbances", and "impaired mental health". This data was subjected to structure analyses based on neural network approaches, using the different study sites' data subsets as independent "learning" and "test" samples. We found two highly stable COPE scales that quantified basic coping behaviour in terms of "activity-passivity" and "defeatism-resilience". The excellent reproducibility across study sites suggested that the new scales characterise socioculturally independent personality traits. Correlation analyses for external validation revealed a close relationship between high scores on the defeatism scale and impaired physical and mental health, hence underlining the scales' clinical relevance. Our results suggested in particular: (1.) the proposed method to be a powerful screening tool for early detection and prevention of psychiatric disorders; (2.) physical activity like regular exercises to play a critical role not only in preventing health problems but also in contributing to early intervention programs.

The Arts (Music, Arts&Crafts and Drama) as a procedure for learning pre-school content: Grupo Edebé Project

There is nothing as amazing and fascinating as children learning process. Between 0 and 6 years old, a child brain develops in a waythat will never be repeated. At this age, children are eager to discover and they have great potential of active and affective life.Because of this, their learning capacity in this period is incalculable. (Jordan-Decarbo y Nelson, 2002; Wild, 1999).Pre-school Education is a unique and special stage, with self identity, which aims are:attending children as a whole,motivate them to learn,give them an affective and stable environment in which they can grow up and get to be balanced and confident people and inwhich they can relate to others, learn, enjoy and be happy.Arts, Music, Visual Arts and Drama (Gardner, 1994) can provide a framework of special, even unique, personal expression.With the aim of introducing qualitative improvements in the education of children and to ensure their emotional wellbeing, and havingnoticed that teachers had important needs and concerns as regards to diversity in their student groups, we developed a programbased on the detection of needs and concerns explained by professionals in education.This program of Grupo edebé, object of our research, is a multicultural, interdisciplinary and globalizing project the aims of which are:developing children's talent and personality,keeping their imagination and creativity and using these as a learning resource,promoting reasoning, favouring expression and communication,providing children with the tools to manage their emotions,and especially, introducing Arts as a procedure to increase learning.We wanted to start the research by studying the impact (Brice, 2003) that this last point had on the learning of five-year-old childrenschooled in multicultural environments.Therefore, the main goal of the research was the assessment of the implementation of a child education programme attending todiversity in a population of five-year-old children, specifically in the practice of procedures based on the use of Arts (music, arts andcrafts and theatre) as a vehicle or procedure for learning contents in Pre-school stage.Because children emotional welfare was a subject of our concern, and bearing in mind that the affective aspects are of vitalimportance for learning and child development (Parke and Gauvain, 2009), Grupo Edebé has also evaluated the starting, evolving andfinal impact in five-year-old children given that they finish Pre-school education at that age.

Large-scale clustering through functional embedding

We present a new framework for large-scale data clustering. The main idea is to modify functional dimensionality reduction techniques to directly optimize over discrete labels using stochastic gradient descent. Compared to methods like spectral clustering our approach solves a single optimization problem, rather than an ad-hoc two-stage optimization approach, does not require a matrix inversion, can easily encode prior knowledge in the set of implementable functions, and does not have an ?out-of-sample? problem. Experimental results on both artificial and real-world datasets show the usefulness of our approach.

Evaluation of transversal competencies: pilot project in a maths course for first-year business

The aim of this communication is to describe the results of a pilot project for the assessment of the transversal competency "the capacity for learning and responsibility". This competency is centred on the capacity for the analysis, synthesis, overview, and practical application of newly acquired knowledge. It is proposed by the University of Barcelona in its undergraduate degree courses,through multidisciplinary teaching teams. The goal of the pilot project is to evaluate this competency.We worked with a group of students in a first-year Business Degree maths course, during the firstsemester of the 2012/2013 academic year. The development of the project was in two stages: (i)design of a specific task to share with the same students in the following semester when the subjectwould be economic history; and (ii) the elaboration of an evaluation rubric in which we defined thecontent, the aspects to evaluate, the evaluation criteria, and the marking scale. The attainment of theexpectations of quality on the specific task was scored following this rubric, which provided a singlebasis for the precise and fair assessment by the instructor and for the students' own self-evaluation.We conclude by describing the main findings of the experience. There particularly stood out the highscore in the students' self-evaluation given to one aspect of the competency – their capacity forlearning – in stark contrast to their instructor's quite negative evaluation. This means that we have towork both to improve teaching practice and to identify the optimal competency evaluationmethodology.

Characterization of HbpR binding by site-directed mutagenesis of its DNA-binding site and by deletion of the effector domain.

In the presence of 2-hydroxybiphenyl, the enhancer binding protein, HbpR, activates the sigma54-dependent P(hbpC) promoter and controls the initial steps of 2-hydroxybiphenyl degradation in Pseudomonas azelaica. In the activation process, an oligomeric HbpR complex of unknown subunit composition binds to an operator region containing two imperfect palindromic sequences. Here, the HbpR-DNA binding interactions were investigated by site-directed mutagenesis of the operator region and by DNA-binding assays using purified HbpR. Mutations that disrupted the twofold symmetry in the palindromes did not affect the binding affinity of HbpR, but various mutations along a 60 bp region, and also outside the direct palindromic sequences, decreased the binding affinity. Footprints of HbpR on mutant operator fragments showed that a partial loss of binding contacts occurs, suggesting that the binding of one HbpR 'protomer' in the oligomeric complex is impaired whilst leaving the other contacts intact. An HbpR variant, devoid of its N-terminal sensing A-domain, was unable to activate transcription from the hbpC promoter while maintaining protection of the operator DNA in footprints. Wild-type HbpR was unable to activate transcription from the hbpC promoter when delta A-HbpR was expressed in the same cell, suggesting the formation of (repressing) hetero-oligomers. This model implies that HbpR can self-associate on its operator DNA without effector recognition or ATP binding. Furthermore, our findings suggest that the N-terminal sensing domain of HbpR is needed to activate the central ATPase domain rather than to repress a constitutively active C domain, as is the case for the related regulatory protein XylR.

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.

What determines the wavelength of self-organized shoreline sand waves?

Shoreline undulations extending into the bathymetric contours with a length scale larger than that of the rhythmic surf zone bars are referred to as shoreline sand waves. Many observed undulations along sandy coasts display a wavelength in the order 1-7 km. Several models that are based on the hypothesis that sand waves emerge from a morphodynamic instability in case of very oblique wave incidence predict this range of wavelengths. Here we investigate the physical reasons for the wavelength selection and the main parametric trends of the wavelength in case of sand waves arising from such instability. It is shown that the existence of a minimum wavelength depends on an interplay between three factors affecting littoral drift: (A) the angle of wave fronts relative to local shoreline, which tends to cause maximum transport at the downdrift flank of the sand wave, (B) the refractive energy spreading which tends to cause maximum transport at the updrift flank and (C) wave focusing (de-focusing) by the capes (bays), which tends to cause maximum transport at the crest or slightly downdrift of it. Processes A and C cause decay of the sand waves while process B causes their growth. For low incidence angles, B is very weak so that a rectilinear shoreline is stable. For large angles and long sand waves, B is dominant and causes the growth of sand waves. For large angles and short sand waves C is dominant and the sand waves decay. Thus, wavelength selection depends on process C, which essentially depends on shoreline curvature. The growth rate of very long sand waves is weak because the alongshore gradients in sediment transport decrease with the wavelength. This is why there is an optimum or dominant wavelength. It is found that sand wave wavelength scales with λ0/β where λ0 is the water wave wavelength in deep water and β is the mean bed slope from shore to the wave base.

Mesurer la stigmatisation perçue chez les personnes souffrant de troubles psychiques : traduction française, validation et adaptation de la Stigma Scale [French translation, validation and adaptation of the Stigma Scale].

AIM: People suffering from mental illness are exposed to stigma. However, only few tools are available to assess stigmatization as perceived from the patient's perspective. The aim of this study is to adapt and validate a French version of the Stigma Scale (King et al., 2007 [8]). This self-report questionnaire has a three-factor structure: discrimination, disclosure and positive aspects of mental illness. Discrimination subscale refers to perceived negative reactions of others. Disclosure subscale refers mainly to managing disclosure to avoid discrimination and finally positive aspects subscale taps into how patients are becoming more accepting, more understanding toward their illness. METHOD: In the first step, internal consistency, convergent validity and test-retest reliability of the French adaptation of the 28-item scale were assessed in a sample of 183 patients. Results of confirmatory factor analyses (CFA) did not confirm the hypothesized structure. In the light of the failed attempts to validate the original version, an alternative 9-item short-form version of the Stigma Scale, maintaining the integrity of the original model, was developed based on results of exploratory factor analyses in the first sample and cross-validated in a new sample of 234 patients. RESULTS: Results of CFA did not confirm that the data fitted well to the three-factor model of the 28-item Stigma Scale (χ(2)/df=2.02, GFI=0.77, AGFI=0.73, RMSEA=0.07, CFI=0.77 and NNFI=0.75). Cronbach's α was excellent for discrimination (0.84) and disclosure (0.83) subscales but poor for potential positive aspects (0.46). External validity was satisfactory. Overall Stigma Scale total score was negatively correlated with the score on Rosenberg's Self-Esteem Scale (r=-0.49), and each subscale was significantly correlated with a visual analogue scale that referred to the specific aspect of stigma (0.43≤|r|≤0.60). Intraclass correlation coefficients between 0.68 and 0.89 indicated good test-retest reliability. The results of the CFA demonstrated that the items chosen for the short version of the Stigma Scale have the expected fit properties (χ(2)/df=1.02, GFI=0.98, AGFI=0.98, RMSEA=0.01, CFI=1.0 and NNFI=1.0). Considering the small number (three) of items in each subscale of the short version of the Stigma Scale, α coefficients for discrimination (0.57), disclosure (0.80) and potential positive aspects subscales (0.62) are considered as good. CONCLUSION: Our results suggest that the 9-item French short version of the Stigma Scale is a useful, reliable and valid self-report questionnaire to assess perceived stigmatization in people suffering from mental illness. The time of completion is really short and questions are well understood and accepted by the patients.

Development of Self-Cleaning Box Culvert Designs, TR-545, 2009

The main function of a roadway culvert is to effectively convey drainage flow during normal and extreme hydrologic conditions. This function is often impaired due to the sedimentation blockage of the culvert. This research sought to understand the mechanics of sedimentation process at multi-box culverts, and develop self-cleaning systems that flush out sediment deposits using the power of drainage flows. The research entailed field observations, laboratory experiments, and numerical simulations. The specific role of each of these investigative tools is summarized below: a) The field observations were aimed at understanding typical sedimentation patterns and their dependence on culvert geometry and hydrodynamic conditions during normal and extreme hydrologic events. b) The laboratory experiments were used for modeling sedimentation process observed insitu and for testing alternative self-cleaning concepts applied to culverts. The major tasks for the initial laboratory model study were to accurately replicate the culvert performance curves and the dynamics of sedimentation process, and to provide benchmark data for numerical simulation validation. c) The numerical simulations enhanced the understanding of the sedimentation processes and aided in testing flow cases complementary to those conducted in the model reducing the number of (more expensive) tests to be conducted in the laboratory. Using the findings acquired from the laboratory and simulation works, self-cleaning culvert concepts were developed and tested for a range of flow conditions. The screening of the alternative concepts was made through experimental studies in a 1:20 scale model guided by numerical simulations. To ensure the designs are effective, performance studies were finally conducted in a 1:20 hydraulic model using the most promising design alternatives to make sure that the proposed systems operate satisfactory under closer to natural scale conditions.

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.

Self-perception of aging and vulnerability to adverse outcomes at the age of 65-70 years.

OBJECTIVES. This study examines the relationship between self-perception of aging and vulnerability to adverse outcomes in adults aged 65-70 years using data from a cohort of 1,422 participants in Lausanne, Switzerland. METHODS: A positive or negative score of perception of aging was established using the Attitudes Toward Own Aging subscale including 5 items of the Philadelphia Geriatric Center Morale Scale. Falls, hospitalizations, and difficulties in basic and instrumental activities of daily living (ADL) collected in the first 3 years of follow-up were considered adverse outcomes. The relationship between perception and outcomes were evaluated using multiple logistic regression models adjusting for chronic medical conditions, depressive feelings, living arrangement, and socioeconomic characteristics. RESULTS: The strongest associations of self-perception of aging with outcomes were observed for basic and instrumental ADL. Associations with falls and hospitalizations were not constant but could be explained by health characteristics. CONCLUSIONS: A negative self-perception of aging is an indicator of risk for future disability in ADL. Factors such as a low-economic status, living alone, multiple chronic medical conditions, and depressive feelings contribute to a negative self-perception of aging but do not explain the relationship with incident activities of daily living disability.

The establishment of communication systems depends on the scale of competition

How communication systems emerge and remain stable is an important question in both cognitive science and evolutionary biology. For communication to arise, not only must individuals cooperate by signaling reliable information, but they must also coordinate and perpetuate signals. Most studies on the emergence of communication in humans typically consider scenarios where individuals implicitly share the same interests. Likewise, most studies on human cooperation consider scenarios where shared conventions of signals and meanings cannot be developed de novo. Here, we combined both approaches with an economic experiment where participants could develop a common language, but under different conditions fostering or hindering cooperation. Participants endeavored to acquire a resource through a learning task in a computer-based environment. After this task, participants had the option to transmit a signal (a color) to a fellow group member, who would subsequently play the same learning task. We varied the way participants competed with each other (either global scale or local scale) and the cost of transmitting a signal (either costly or noncostly) and tracked the way in which signals were used as communication among players. Under global competition, players signaled more often and more consistently, scored higher individual payoffs, and established shared associations of signals and meanings. In addition, costly signals were also more likely to be used under global competition; whereas under local competition, fewer signals were sent and no effective communication system was developed. Our results demonstrate that communication involves both a coordination and a cooperative dilemma and show the importance of studying language evolution under different conditions influencing human cooperation.