Technology-enhanced education for Millennials: how the Information Society is changing the way of learning

The Information Society has provided the context for the development of a new generation, known as the Millennials, who are characterized by their intensive use of technologies in everyday life. These features are changing the way of learning, prompting educational institutions to attempt to better adapt to youngneeds by incorporating technologies into education. Based on this premise, wehave reviewed the prominent reports of the integration of ICT into education atdifferent levels with the aim of evidencing how education is changing, and willchange, to meet the needs of Millennials with ICT support. The results show thatmost of the investments have simply resulted in an increase of computers andaccess to the Internet, with teachers reproducing traditional approaches to education and e-learning being seen as complementary to face-to-face education.While it would seem that the use of ICT is not revolutionizing learning, it isfacilitating the personalization, collaboration and ubiquity of learning.

The handaxe and the microscope: individual and social learning in a multidimensional model of adaptation

When individuals learn by trial-and-error, they perform randomly chosen actions and then reinforce those actions that led to a high payoff. However, individuals do not always have to physically perform an action in order to evaluate its consequences. Rather, they may be able to mentally simulate actions and their consequences without actually performing them. Such fictitious learners can select actions with high payoffs without making long chains of trial-and-error learning. Here, we analyze the evolution of an n-dimensional cultural trait (or artifact) by learning, in a payoff landscape with a single optimum. We derive the stochastic learning dynamics of the distance to the optimum in trait space when choice between alternative artifacts follows the standard logit choice rule. We show that for both trial-and-error and fictitious learners, the learning dynamics stabilize at an approximate distance of root n/(2 lambda(e)) away from the optimum, where lambda(e) is an effective learning performance parameter depending on the learning rule under scrutiny. Individual learners are thus unlikely to reach the optimum when traits are complex (n large), and so face a barrier to further improvement of the artifact. We show, however, that this barrier can be significantly reduced in a large population of learners performing payoff-biased social learning, in which case lambda(e) becomes proportional to population size. Overall, our results illustrate the effects of errors in learning, levels of cognition, and population size for the evolution of complex cultural traits. (C) 2013 Elsevier Inc. All rights reserved.

Dietary resveratrol prevents alzheimer's markers and increases life span in SAMP8

Resveratrol is a polyphenol that is mainly found in grapes and red wine and has been reported to be a caloric restriction (CR) mimetic driven by Sirtuin 1 (SIRT1) activation. Resveratrol increases metabolic rate, insulin sensitivity, mitochondrial biogenesis and physical endurance, and reduces fat accumulation in mice. In addition, resveratrol may be a powerful agent to prevent age-associated neurodegeneration and to improve cognitive deficits in Alzheimer's disease (AD). Moreover, different findings support the view that longevity in mice could be promoted by CR. In this study, we examined the role of dietary resveratrol in SAMP8 mice, a model of age-related AD. We found that resveratrol supplements increased mean life expectancy and maximal life span in SAMP8 and in their control, the related strain SAMR1. In addition, we examined the resveratrol-mediated neuroprotective effects on several specific hallmarks of AD. We found that long-term dietary resveratrol activates AMPK pathways and pro-survival routes such as SIRT1 in vivo. It also reduces cognitive impairment and has a neuroprotective role, decreasing the amyloid burden and reducing tau hyperphosphorylation.

Dietary resveratrol prevents alzheimer's markers and increases life span in SAMP8

Resveratrol is a polyphenol that is mainly found in grapes and red wine and has been reported to be a caloric restriction (CR) mimetic driven by Sirtuin 1 (SIRT1) activation. Resveratrol increases metabolic rate, insulin sensitivity, mitochondrial biogenesis and physical endurance, and reduces fat accumulation in mice. In addition, resveratrol may be a powerful agent to prevent age-associated neurodegeneration and to improve cognitive deficits in Alzheimer's disease (AD). Moreover, different findings support the view that longevity in mice could be promoted by CR. In this study, we examined the role of dietary resveratrol in SAMP8 mice, a model of age-related AD. We found that resveratrol supplements increased mean life expectancy and maximal life span in SAMP8 and in their control, the related strain SAMR1. In addition, we examined the resveratrol-mediated neuroprotective effects on several specific hallmarks of AD. We found that long-term dietary resveratrol activates AMPK pathways and pro-survival routes such as SIRT1 in vivo. It also reduces cognitive impairment and has a neuroprotective role, decreasing the amyloid burden and reducing tau hyperphosphorylation.

The definition of quality of life in solid organ transplantation: A psychological qualitative model

Quality of life has been extensively discussed in acute and chronic illnesses. However a dynamic model grounded in the experience of patients in the course of transplantation has not been to our knowledge developed. In a qualitative longitudinal study, patients awaiting solid organ transplantation participated in semi-structured interviews: Exploring topics pre-selected on previous research literature review. Creative interview was privileged, open to themes patients would like to discuss at the different steps of the transplantation process. A qualitative thematic and reflexive analysis was performed, and a model of the dimensions constitutive of quality of life from the perspective of the patients was elaborated. Quality of life is not a stable construct in a long lasting illness-course, but evolves with illness constraints, treatments and outcomes. Dimensions constitutive of quality of life are defined, each of them containing different sub-categories depending on the organ related illness co-morbidities and the stage of illness-course.

Dietary resveratrol prevents alzheimer's markers and increases life span in SAMP8

Resveratrol is a polyphenol that is mainly found in grapes and red wine and has been reported to be a caloric restriction (CR) mimetic driven by Sirtuin 1 (SIRT1) activation. Resveratrol increases metabolic rate, insulin sensitivity, mitochondrial biogenesis and physical endurance, and reduces fat accumulation in mice. In addition, resveratrol may be a powerful agent to prevent age-associated neurodegeneration and to improve cognitive deficits in Alzheimer's disease (AD). Moreover, different findings support the view that longevity in mice could be promoted by CR. In this study, we examined the role of dietary resveratrol in SAMP8 mice, a model of age-related AD. We found that resveratrol supplements increased mean life expectancy and maximal life span in SAMP8 and in their control, the related strain SAMR1. In addition, we examined the resveratrol-mediated neuroprotective effects on several specific hallmarks of AD. We found that long-term dietary resveratrol activates AMPK pathways and pro-survival routes such as SIRT1 in vivo. It also reduces cognitive impairment and has a neuroprotective role, decreasing the amyloid burden and reducing tau hyperphosphorylation.

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.

Long-term follow-up of high-dose chemotherapy with autologous stem-cell transplantation and response-adapted whole-brain radiotherapy for newly diagnosed primary CNS lymphoma: results of the multicenter Ostdeutsche Studiengruppe Hamatologie und Onkologie OSHO-53 phase II study.

Background We previously reported the results of a phase II study for patients with newly diagnosed primary central nervous system lymphoma treated with autologous peripheral blood stem-cell transplantation (aPBSCT) and response-adapted whole-brain radiotherapy (WBRT). Now, we update the initial results. Patients and methods From 1999 to 2004, 23 patients received high-dose methotrexate. In case of at least partial remission, high-dose busulfan/thiotepa (HD-BuTT) followed by aPBSCT was carried out. Patients refractory to induction or without complete remission after HD-BuTT received WBRT. Eight patients still alive in 2011 were contacted and Mini-Mental State Examination (MMSE) and the European Organisation for Research and Treatment of Cancer quality-of-life questionnaire (QLQ)-C30 were carried out. Results Of eight patients still alive, median follow-up is 116.9 months. Only one of nine irradiated patients is still alive with a severe neurologic deficit. In seven of eight patients treated with HD-BuTT, health condition and quality of life are excellent. MMSE and QLQ-C30 showed remarkably good results in patients who did not receive WBRT. All of them have a Karnofsky score of 90%-100%. Conclusions Follow-up shows an overall survival of 35%. In six of seven patients where WBRT could be avoided, no long-term neurotoxicity has been observed and all patients have an excellent quality of life.

Male mutation bias and possible long-term effects of human activities.

The ability of a population to adapt to changing environments depends critically on the amount and kind of genetic variability it possesses. Mutations are an important source of new genetic variability and may lead to new adaptations, especially if the population size is large. Mutation rates are extremely variable between and within species, and males usually have higher mutation rates as a result of elevated rates of male germ cell division. This male bias affects the overall mutation rate. We examined the factors that influence male mutation bias, and focused on the effects of classical life-history parameters, such as the average age at reproduction and elevated rates of sperm production in response to sexual selection and sperm competition. We argue that human-induced changes in age at reproduction or in sexual selection will affect male mutation biases and hence overall mutation rates. Depending on the effective population size, these changes are likely to influence the long-term persistence of a population.

Long-term changes in synaptic transmission of the lateral amygdala induced by strong "in vitro" activation

Résumé : L'amygdale latérale (AL) joue un .rôle essentiel dans la plasticité synaptique à la base du conditionnement de la peur. Malgré le faite que la majorité des cellules de l'AL reçoivent les afférentes nécessaires, une potentialisation dans seulement une partie d'entre elles est obligatoire afin que l'apprentissage de la peur ait lieu. Il a été montré que ces cellules expriment la forme active de CREB, et celui-ci a été associé aux cellules dites de type 'nonaccomrnodating' (nAC). Très récemment, une étude a impliqué les circuits récurrents de l'AL dans le conditionnement de la peur. Un lien entre ces deux observations n'a toutefois jamais été établi. t Nous avons utilisé un protocole in vitro de forte activation de l'AL, résultant dans l'induction de 'bursts' provenant de l'hippocampe et se propageant jusqu'à l'AL. Dans l'AL ces 'bursts' atteignent toutes les cellules et se propagent à travers plusieurs chemins. Utilisant ce protocole, nous avons, pour la première fois pu associer dans l'AL, des cellules connectées de manière récurrente avec des cellules de type nAC. Aussi bien dans ces dernières que dans les cellules de type 'accommodating' (AC), une diminution dans la transmission inhibitrice, à la fois exprimée de manière pré synaptique mais également indépendant de la synthèse de protéine a pu être observé. Au contraire, une potentialisation induite et exprimée au niveau pré synaptique ainsi que dépendante de la synthèse de protéine a pu être trouvé uniquement dans les cellules de type nAC. De plus, une hyperexcitabilité, dépendante des récepteurs NMDA a pu être observé, avec une sélection préférentielle des cellules du type nAC dans la génération de bursts. Nous avons également pu démontrer que la transformation d'un certain nombre de cellules de type AC en cellules dites nAC accompagnait cette augmentation générale de l'excitabilité de l'AL. Du faite da la grande quantité d'indices suggérant une connexion entre le système noradrénergique et les états de peur/d'anxiété, les effets d'une forte activation de l'AL sur ce dernier ont été investigués et ont révélés une perte de sa capacité de modulation du 'spiking pattern'. Finalement, des changements au niveau de l'expression d'un certain nombre de gènes, incluant celui codant pour le BDNF, a pu être trouvé à la suite d'une forte activation de l'AL. En raison du lien récemment décrit entre l'expression de la forme active de CREB et des cellules de type nAC ainsi que celui de l'implication des cellules de l'AL connectés de manière récurrente dans l'apprentissage de la peur, nos résultats nous permettent de suggérer un modèle expliquant comment la potentialisation des connections récurrentes entre cellules de type nAC pourrait être à la base de leur recrutement sélectif pendant le conditionnement de la peur. De plus, ils peuvent offrir des indices par rapport aux mécanismes à travers lesquels une sous population de neurones peut être réactivée par une stimulation externe précédemment inefficace, et induire ainsi un signal suffisamment fort pour qu'il soit transmit aux structures efférentes de l'AL. Abstract : The lateral nucleus of the amygdala (LA) is critically involved in the plasticity underlying fear-conditioned learning (Sah et al., 2008). Even though the majority of cells in the LA receive the necessary sensory inputs, potentiation in only a subset is required for fear learning to occur (Repa et al., 2001; Rumpel et al., 2005). These cells express active CREB (CAMP-responsive element-binding protein) (Han et al., 200, and this was related to the non-accommodating (nAC) spiking phenotype (Viosca et al., 2009; Zhou et al., 2009). In addition, a very recent study implicated recurrently connected cells of the LA in fear conditioned learning (Johnson et al., 2008). A link between the two observations has however never been made. In rats, we used an in vitro protocol of strong activation of the LA, resulting in bursting activity, which spread from the hippocampus to the LA. Within the LA, this activity reached all cells and spread via a multitude of pathways. Using this model, we were able to link, for the first time, recurrently connected cells in the LA with cells of the nAC phenotype. While we found a presynaptically expressed, protein synthesis independent decrease in inhibitory synaptic transmission in both nAC and accommodating (AC) cells, only nAC cells underwent a presynaptically induced and expressed, protein synthesis dependent potentiation. Moreover we observed an NMDA dependent hyperexcitability of the LA, with a preferential selection of nAC cells into burst generation. The transformation of a subset of AC cells into nAC cells accompanied this general increase in LA excitability. Given the considerable evidence suggesting a relationship between the central noradrenergic (NA) system and fear/anxiety states (Itoi, 2008), the effects of strong activation of the LA on the noradrenergic system were investigated, which revealed a loss of its modulatory actions on cell spiking patterns. Finally, we found changes in the expression levels of a number of genes; among which the one coding for $DNF, to be induced by strong activation of the LA. In view of the recently described link between nAC cells and expression of pCREB (phosphorylated cAMP-responsive element-binding protein) as well as the involvement of recurrently connected cells of the LA in fear-conditioned learning, our findings may provide a model of how potentiation of recurrent connections between nAC neurons underlies their recruitment into the fear memory trace. Additionally, they may offer clues as to the mechanisms through which a selected subset of neurons can be reactivated by smaller, previously ineffective external stimulations to respond with a sufficiently strong signal, which can be transmitted to downstream targets of the LA.

Melanin-specific life-history strategies.

Abstract The maintenance of genetic variation is a long-standing issue because the adaptive value of life-history strategies associated with each genetic variant is usually unknown. However, evidence for the coexistence of alternative evolutionary fixed strategies at the population level remains scarce. Because in the tawny owl (Strix aluco) heritable melanin-based coloration shows different physiological and behavioral norms of reaction, we investigated whether coloration is associated with investment in maintenance and reproduction. Light melanic owls had lower adult survival compared to dark melanic conspecifics, and color variation was related to the trade-off between offspring number and quality. When we experimentally enlarged brood size, light melanic males produced more fledglings but in poorer condition, and they were less often recruited in the local breeding population than those of darker melanic conspecifics. Our results also suggest that dark melanic males allocate a constant effort to raise their brood independently of environmental conditions, whereas lighter melanic males finely adjust reproductive effort in relation to changes in environmental conditions. Color traits can therefore be associated with life-history strategies, and stochastic environmental perturbation can temporarily favor one phenotype over others. The existence of fixed strategies implies that some phenotypes can sometimes display a "maladapted" strategy. Long-term population monitoring is therefore vital for a full understanding of how different genotypes deal with trade-offs.

Comparison of quality of life after stereotactic body radiotherapy and surgery for early-stage prostate cancer

Background: As the long-term efficacy of stereotactic body radiation therapy (SBRT) becomes established and other prostate cancer treatment approaches are refined and improved, examination of quality of life (QOL) following prostate cancer treatment is critical in driving both patient and clinical treatment decisions. We present the first study to compare QOL after SBRT and radical prostatectomy, with QOL assessed at approximately the same times pre- and post-treatment and using the same validated QOL instrument. Methods: Patients with clinically localized prostate cancer were treated with either radical prostatectomy (n = 123 Spanish patients) or SBRT (n = 216 American patients). QOL was assessed using the Expanded Prostate Cancer Index Composite (EPIC) grouped into urinary, sexual, and bowel domains. For comparison purposes, SBRT EPIC data at baseline, 3 weeks, 5, 11, 24, and 36 months were compared to surgery data at baseline, 1, 6, 12, 24,and 36 months. Differences in patient characteristics between the two groups were assessed using Chi-squared tests for categorical variables and t-tests for continuous variables. Generalized estimating equation (GEE) models were constructed for each EPIC scale to account for correlation among repeated measures and used to assess the effect of treatment on QOL. Results: The largest differences in QOL occurred in the first 16 months after treatment, with larger declines following surgery in urinary and sexual QOL as compared to SBRT, and a larger decline in bowel QOL following SBRT as compared to surgery. Long-term urinary and sexual QOL declines remained clinically significantly lower for surgery patients but not for SBRT patients. Conclusions: Overall, these results may have implications for patient and physician clinical decision making which are often influenced by QOL. These differences in sexual, urinary and bowel QOL should be closely considered in selecting the right treatment, especially in evaluating the value of non-invasive treatments, such as SBRT.

Neurocognitive deficits and personality traits among euthymic patients with mood disorders in late life.

BACKGROUND: Previous studies revealed that acute depressive episodes are associated with both cognitive deficits and modified personality patterns in late life. Whether or not these psychological changes are present after remission remains a matter of debate. To date, no study provided concomitant assessment of cognition and psychological functions in this particular clinical setting. METHOD: Using a cross-sectional design, 58 remitted outpatients (36 with unipolar early-onset depression (EOD) and 22 with bipolar disorder (BD)) were compared to 62 healthy controls. Assessment included detailed neurocognitive measures and evaluation of the five factor personality dimensions (NEO-Personality Inventory). RESULTS: Group comparisons revealed significant slower processing speed, working and episodic memory performances in BD patients. EOD patients showed cognitive abilities comparable to those of elderly controls. In NEO PI assessment, both BD and EOD patients displayed higher Depressiveness facet scores. In addition, the EOD but not BD group had lower Extraversion factor, and Warmth and Positive Emotion facet scores than controls. CONCLUSIONS: After remission from acute affective symptoms, older BD patients show significant impairment in several cognitive functions while neuropsychological performances remained intact in elderly patients with EOD. Supporting a long-lasting psychological vulnerability, EOD patients are more prone to develop emotion-related personality trait changes than BD patients.

Engaging students in an e-learning experience : how to manage information resources through wikis and web blogs

The purpose of this paper is to describe the collaboration between librarians and scholars, from a virtual university, in order to facilitate collaborative learning on how to manage information resources. The personal information behaviour of e-learning students when managing information resources for academic, professional and daily life purposes was studied from 24 semi-structured face-to-face interviews. The results of the content analysis of the interview' transcriptions, highlighted that in the workplace and daily life contexts, competent information behaviour is always linked to a proactive attitude, that is to say, that participants seek for information without some extrinsic reward or avoiding punishment. In the academic context, it was observed a low level of information literacy and it seems to be related with a prevalent uninvolved attitude.