Remote collaborative multi-user informal learning experiences: design and evaluation

This paper presents a customizable system used to develop a collaborative multi-user problem solving game. It addresses the increasing demand for appealing informal learning experiences in museum-like settings. The system facilitates remote collaboration by allowing groups of learners tocommunicate through a videoconferencing system and by allowing them to simultaneously interact through a shared multi-touch interactive surface. A user study with 20 user groups indicates that the game facilitates collaboration between local and remote groups of learners. The videoconference and multitouch surface acted as communication channels, attracted students’ interest, facilitated engagement, and promoted inter- and intra-group collaboration—favoring intra-group collaboration. Our findings suggest that augmentingvideoconferencing systems with a shared multitouch space offers newpossibilities and scenarios for remote collaborative environments and collaborative learning.

The underestimation of compliance to direct help requests explained by adaptive sampling

Helping behavior is any intentional behavior that benefits another living being or group (Hogg & Vaughan, 2010). People tend to underestimate the probability that others will comply with their direct requests for help (Flynn & Lake, 2008). This implies that when they need help, they will assess the probability of getting it (De Paulo, 1982, cited in Flynn & Lake, 2008) and then they will tend to estimate one that is actually lower than the real chance, so they may not even consider worth asking for it. Existing explanations for this phenomenon attribute it to a mistaken cost computation by the help seeker, who will emphasize the instrumental cost of “saying yes”, ignoring that the potential helper also needs to take into account the social cost of saying “no”. And the truth is that, especially in face-to-face interactions, the discomfort caused by refusing to help can be very high. In short, help seekers tend to fail to realize that it might be more costly to refuse to comply with a help request rather than accepting. A similar effect has been observed when estimating trustworthiness of people. Fetchenhauer and Dunning (2010) showed that people also tend to underestimate it. This bias is reduced when, instead of asymmetric feedback (getting feedback only when deciding to trust the other person), symmetric feedback (always given) was provided. This cause could as well be applicable to help seeking as people only receive feedback when they actually make their request but not otherwise. Fazio, Shook, and Eiser (2004) studied something that could be reinforcing these outcomes: Learning asymmetries. By means of a computer game called BeanFest, they showed that people learn better about negatively valenced objects (beans in this case) than about positively valenced ones. This learning asymmetry esteemed from “information gain being contingent on approach behavior” (p. 293), which could be identified with what Fetchenhauer and Dunning mention as ‘asymmetric feedback’, and hence also with help requests. Fazio et al. also found a generalization asymmetry in favor of negative attitudes versus positive ones. They attributed it to a negativity bias that “weights resemblance to a known negative more heavily than resemblance to a positive” (p. 300). Applied to help seeking scenarios, this would mean that when facing an unknown situation, people would tend to generalize and infer that is more likely that they get a negative rather than a positive outcome from it, so, along with what it was said before, people will be more inclined to think that they will get a “no” when requesting help. Denrell and Le Mens (2011) present a different perspective when trying to explain judgment biases in general. They deviate from the classical inappropriate information processing (depicted among other by Fiske & Taylor, 2007, and Tversky & Kahneman, 1974) and explain this in terms of ‘adaptive sampling’. Adaptive sampling is a sampling mechanism in which the selection of sample items is conditioned by the values of the variable of interest previously observed (Thompson, 2011). Sampling adaptively allows individuals to safeguard themselves from experiences they went through once and turned out to lay negative outcomes. However, it also prevents them from giving a second chance to those experiences to get an updated outcome that could maybe turn into a positive one, a more positive one, or just one that regresses to the mean, whatever direction that implies. That, as Denrell and Le Mens (2011) explained, makes sense: If you go to a restaurant, and you did not like the food, you do not choose that restaurant again. This is what we think could be happening when asking for help: When we get a “no”, we stop asking. And here, we want to provide a complementary explanation for the underestimation of the probability that others comply with our direct help requests based on adaptive sampling. First, we will develop and explain a model that represents the theory. Later on, we will test it empirically by means of experiments, and will elaborate on the analysis of its results.

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.

Cation Transport Coupled to ATP Hydrolysis By the (Na, K)-ATPase : an integrated, animated model

An Adobe (R) animation is presented for use in undergraduate Biochemistry courses, illustrating the mechanism of Na+ and K+ translocation coupled to ATP hydrolysis by the (Na, K)-ATPase, a P-2c-type ATPase, or ATP-powered ion pump that actively translocates cations across plasma membranes. The enzyme is also known as an E-1/E-2-ATPase as it undergoes conformational changes between the E-1 and E-2 forms during the pumping cycle, altering the affinity and accessibility of the transmembrane ion-binding sites. The animation is based on Horisberger's scheme that incorporates the most recent significant findings to have improved our understanding of the (Na, K)-ATPase structure function relationship. The movements of the various domains within the (Na, K)-ATPase alpha-subunit illustrate the conformational changes that occur during Na+ and K+ translocation across the membrane and emphasize involvement of the actuator, nucleotide, and phosphorylation domains, that is, the "core engine" of the pump, with respect to ATP binding, cation transport, and ADP and P-i release.

Quantitative genetics of learning ability and resistance to stress in Drosophila melanogaster.

Even though laboratory evolution experiments have demonstrated genetic variation for learning ability, we know little about the underlying genetic architecture and genetic relationships with other ecologically relevant traits. With a full diallel cross among twelve inbred lines of Drosophila melanogaster originating from a natural population (0.75 < F < 0.93), we investigated the genetic architecture of olfactory learning ability and compared it to that for another behavioral trait (unconditional preference for odors), as well as three traits quantifying the ability to deal with environmental challenges: egg-to-adult survival and developmental rate on a low-quality food, and resistance to a bacterial pathogen. Substantial additive genetic variation was detected for each trait, highlighting their potential to evolve. Genetic effects contributed more than nongenetic parental effects to variation in traits measured at the adult stage: learning, odorant perception, and resistance to infection. In contrast, the two traits quantifying larval tolerance to low-quality food were more strongly affected by parental effects. We found no evidence for genetic correlations between traits, suggesting that these traits could evolve at least to some degree independently of one another. Finally, inbreeding adversely affected all traits.

Iowa School Library Program Guidelines: Libraries, Literacy and Learning for the 21st Century,February 2007

These guidelines were created by a Task Force appointed by the State Library of Iowa and the Iowa Department of Education to provide assistance to local school districts in developing school library programs. These include a summary of the data collected annually by the State Library of Iowa in its Survey of School Libraries. This data will allow local schools to compare themselves in terms of collections, budgets and staffing to schools of similar size throughout the state.

Social and individual learning of helping in humans and other species.

Helping behaviors can be innate, learned by copying others (cultural transmission) or individually learned de novo. These three possibilities are often entangled in debates on the evolution of helping in humans. Here we discuss their similarities and differences, and argue that evolutionary biologists underestimate the role of individual learning in the expression of helping behaviors in humans.

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.

Statewide Work-Based Learning Intermediary Network, FY2014, November 26, 2014

The Iowa Department of Education (DE) was appropriated $1.45 million for the development and implementation of a statewide work-based learning intermediary network. This funding was awarded on a competitive basis to 15 regional intermediary networks. Funds received by the regional intermediary networks from the state through this grant are to be used to develop and expand work-based learning opportunities within each region. A match of resources equal to 25 percent was a requirement of the funding. This match could include private donations, in-kind contributions, or public moneys. Funds may be used to support personnel responsible for the implementation of the intermediary network program components.

A Classification of Tools for Learning Logic

A table showing a comparison and classification of tools (intelligent tutoring systems) for e-learning of Logic at a college level.

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.

On learning dynamics underlying the evolution of learning rules.

In order to understand the development of non-genetically encoded actions during an animal's lifespan, it is necessary to analyze the dynamics and evolution of learning rules producing behavior. Owing to the intrinsic stochastic and frequency-dependent nature of learning dynamics, these rules are often studied in evolutionary biology via agent-based computer simulations. In this paper, we show that stochastic approximation theory can help to qualitatively understand learning dynamics and formulate analytical models for the evolution of learning rules. We consider a population of individuals repeatedly interacting during their lifespan, and where the stage game faced by the individuals fluctuates according to an environmental stochastic process. Individuals adjust their behavioral actions according to learning rules belonging to the class of experience-weighted attraction learning mechanisms, which includes standard reinforcement and Bayesian learning as special cases. We use stochastic approximation theory in order to derive differential equations governing action play probabilities, which turn out to have qualitative features of mutator-selection equations. We then perform agent-based simulations to find the conditions where the deterministic approximation is closest to the original stochastic learning process for standard 2-action 2-player fluctuating games, where interaction between learning rules and preference reversal may occur. Finally, we analyze a simplified model for the evolution of learning in a producer-scrounger game, which shows that the exploration rate can interact in a non-intuitive way with other features of co-evolving learning rules. Overall, our analyses illustrate the usefulness of applying stochastic approximation theory in the study of animal learning.

Virulent bacterial infection improves aversive learning performance in Drosophila melanogaster.

Virulent infections are expected to impair learning ability, either as a direct consequence of stressed physiological state or as an adaptive response that minimizes diversion of energy from immune defense. This prediction has been well supported for mammals and bees. Here, we report an opposite result in Drosophila melanogaster. Using an odor-mechanical shock conditioning paradigm, we found that intestinal infection with bacterial pathogens Pseudomonas entomophila or Erwinia c. carotovora improved flies' learning performance after a 1h retention interval. Infection with P. entomophila (but not E. c. carotovora) also improved learning performance after 5 min retention. No effect on learning performance was detected for intestinal infections with an avirulent GacA mutant of P. entomophila or for virulent systemic (hemocoel) infection with E. c. carotovora. Assays of unconditioned responses to odorants and shock do not support a major role for changes in general responsiveness to stimuli in explaining the changes in learning performance, although differences in their specific salience for learning cannot be excluded. Our results demonstrate that the effects of pathogens on learning performance in insects are less predictable than suggested by previous studies, and support the notion that immune stress can sometimes boost cognitive abilities.

ACID-SENSING ION CHANNELS: functional and molecular characterization in putative nociceptors, and modulation by nerve injury and serine protease

Résumé Les canaux ioniques ASICs (acid-sensing ion channels) appartiennent à la famille des canaux ENaC/Degenerin. Pour l'instant, quatre gènes (1 à 4) ont été clonés dont certains présentent des variants d'épissage. Leur activation par une acidification rapide du milieu extracellulaire génère un courant entrant transitoire essentiellement sodique accompagné pour certains types d'ASICs d'une phase soutenue. Les ASICs sont exprimés dans le système nerveux, central (SNC) et périphérique (SNP). On leur attribue un rôle dans l'apprentissage, la mémoire et l'ischémie cérébrale au niveau central ainsi que dans la nociception (douleur aiguë et inflammatoire) et la méchanotransduction au niveau périphérique. Toutefois, les données sont parfois contradictoires. Certaines études suggèrent qu'ils sont des senseurs primordiaux impliqués dans la détection de l'acidification et la douleur. D'autres études suggèrent plutôt qu'ils ont un rôle modulateur inhibiteur dans la douleur. De plus, le fait que leur activation génère majoritairement un courant transitoire alors que les fibres nerveuses impliquées dans la douleur répondent à un stimulus nocif avec une adaptation lente suggère que leurs propriétés doivent être modulés par des molécules endogènes. Dans une première partie de ma thèse, nous avons abordé la question de l'expression fonctionnelle des ASICs dans les neurones sensoriels primaires afférents du rat adulte pour clarifier le rôle des ASICs dans les neurones sensoriels. Nous avons caractérisé leurs propriétés biophysiques et pharmacologiques par la technique du patch-clamp en configuration « whole-cell ». Nous avons pu démontrer que près de 60% des neurones sensoriels de petit diamètre expriment des courants ASICs. Nous avons mis en évidence trois types de courant ASIC dans ces neurones. Les types 1 et 3 ont des propriétés compatibles avec un rôle de senseur du pH alors que le type 2 est majoritairement activé par des pH inférieurs à pH6. Le type 1 est médié par des homomers de la sous-unité ASIC1 a qui sont perméables aux Ca2+. Nous avons étudié leur co-expression avec des marqueurs des nocicepteurs ainsi que la possibilité d'induire une activité neuronale suite à une acidification qui soit dépendante des ASICs. Le but était d'associer un type de courant ASIC avec une fonction potentielle dans les neurones sensoriels. Une majorité des neurones exprimant les courants ASIC co-expriment des marqueurs des nocicepteurs. Toutefois, une plus grande proportion des neurones exprimant le type 1 n'est pas associée à la nociception par rapport aux types 2 et 3. Nous avons montré qu'il est possible d'induire des potentiels d'actions suite à une acidification. La probabilité d'induction est proportionnelle à la densité des courants ASIC et à l'acidité de la stimulation. Puis, nous avons utilisé cette classification comme un outil pour appréhender les potentielles modulations fonctionnelles des ASICs dans un model de neuropathie (spared nerve injury). Cette approche fut complétée par des expériences de «quantitative RT-PCR ». En situation de neuropathie, les courants ASIC sont dramatiquement changés au niveau de leur expression fonctionnelle et transcriptionnelle dans les neurones lésés ainsi que non-lésés. Dans une deuxième partie de ma thèse, suite au test de différentes substances sécrétées lors de l'inflammation et l'ischémie sur les propriétés des ASICs, nous avons caractérisé en détail la modulation des propriétés des courants ASICs notamment ASIC1 par les sérines protéases dans des systèmes d'expression recombinants ainsi que dans des neurones d'hippocampe. Nous avons montré que l'exposition aux sérine-protéases décale la dépendance au pH de l'activation ainsi que la « steady-state inactivation »des ASICs -1a et -1b vers des valeurs plus acidiques. Ainsi, l'exposition aux serine protéases conduit à une diminution du courant quand l'acidification a lieu à partir d'un pH7.4 et conduit à une augmentation du courant quand l'acidification alleu à partir d'un pH7. Nous avons aussi montré que cette régulation a lieu des les neurones d'hippocampe. Nos résultats dans les neurones sensoriels suggèrent que certains courants ASICs sont impliqués dans la transduction de l'acidification et de la douleur ainsi que dans une des phases du processus conduisant à la neuropathie. Une partie des courants de type 1 perméables au Ca 2+ peuvent être impliqués dans la neurosécrétion. La modulation par les sérines protéases pourrait expliquer qu'en situation d'acidose les canaux ASICs soient toujours activables. Résumé grand publique Les neurones sont les principales cellules du système nerveux. Le système nerveux est formé par le système nerveux central - principalement le cerveau, le cervelet et la moelle épinière - et le système nerveux périphérique -principalement les nerfs et les neurones sensoriels. Grâce à leur nombreux "bras" (les neurites), les neurones sont connectés entre eux, formant un véritable réseau de communication qui s'étend dans tout le corps. L'information se propage sous forme d'un phénomène électrique, l'influx nerveux (ou potentiels d'actions). A la base des phénomènes électriques dans les neurones il y a ce que l'on appelle les canaux ioniques. Un canal ionique est une sorte de tunnel qui traverse l'enveloppe qui entoure les cellules (la membrane) et par lequel passent les ions. La plupart de ces canaux sont normalement fermés et nécessitent d'être activés pour s'ouvrire et générer un influx nerveux. Les canaux ASICs sont activés par l'acidification et sont exprimés dans tout le système nerveux. Cette acidification a lieu notamment lors d'une attaque cérébrale (ischémie cérébrale) ou lors de l'inflammation. Les expériences sur les animaux ont montré que les canaux ASICs avaient entre autre un rôle dans la mort des neurones lors d'une attaque cérébrale et dans la douleur inflammatoire. Lors de ma thèse je me suis intéressé au rôle des ASICs dans la douleur et à l'influence des substances produites pendant l'inflammation sur leur activation par l'acidification. J'ai ainsi pu montrer chez le rat que la majorité des neurones sensoriels impliqués dans la douleur ont des canaux ASICs et que l'activation de ces canaux induit des potentiels d'action. Nous avons opéré des rats pour qu'ils présentent les symptômes d'une maladie chronique appelée neuropathie. La neuropathie se caractérise par une plus grande sensibilité à la douleur. Les rats neuropathiques présentent des changements de leurs canaux ASICs suggérant que ces canaux ont une peut-être un rôle dans la genèse ou les symptômes de cette maladie. J'ai aussi montré in vitro qu'un type d'enryme produit lors de l'inflammation et l'ischémie change les propriétés des ASICs. Ces résultats confirment un rôle des ASICs dans la douleur suggérant notamment un rôle jusque là encore non étudié dans la douleur neuropathique. De plus, ces résultats mettent en évidence une régulation des ASICs qui pourrait être importante si elle se confirmait in vivo de part les différents rôles des ASICs. Abstract Acid-sensing ion channels (ASICs) are members of the ENaC/Degenerin superfamily of ion channels. Their activation by a rapid extracellular acidification generates a transient and for some ASIC types also a sustained current mainly mediated by Na+. ASICs are expressed in the central (CNS) and in the peripheral (PNS) nervous system. In the CNS, ASICs have a putative role in learning, memory and in neuronal death after cerebral ischemia. In the PNS, ASICs have a putative role in nociception (acute and inflammatory pain) and in mechanotransduction. However, studies on ASIC function are somewhat controversial. Some studies suggest a crucial role of ASICs in transduction of acidification and in pain whereas other studies suggest rather a modulatory inhibitory role of ASICs in pain. Moreover, the basic property of ASICs, that they are activated only transiently is irreconcilable with the well-known property of nociception that the firing of nociceptive fibers demonstrated very little adaptation. Endogenous molecules may exist that can modulate ASIC properties. In a first part of my thesis, we addressed the question of the functional expression of ASICs in adult rat dorsal root ganglion (DRG) neurons. Our goal was to elucidate ASIC roles in DRG neurons. We characterized biophysical and pharmacological properties of ASIC currents using the patch-clamp technique in the whole-cell configuration. We observed that around 60% of small-diameter sensory neurons express ASICs currents. We described in these neurons three ASIC current types. Types 1 and 3 have properties compatible with a role of pH-sensor whereas type 2 is mainly activated by pH lower than pH6. Type 1 is mediated by ASIC1a homomultimers which are permeable to Ca 2+. We studied ASIC co-expression with nociceptor markers. The goal was to associate an ASIC current type with a potential function in sensory neurons. Most neurons expressing ASIC currents co-expressed nociceptor markers. However, a higher proportion of the neurons expressing type 1 was not associated with nociception compared to type 2 and -3. We completed this approach with current-clamp measurements of acidification-induced action potentials (APs). We showed that activation of ASICs in small-diameter neurons can induce APs. The probability of AP induction is positively correlated with the ASIC current density and the acidity of stimulation. Then, we used this classification as a tool to characterize the potential functional modulation of ASICs in the spared nerve injury model of neuropathy. This approach was completed by quantitative RT-PCR experiments. ASICs current expression was dramatically changed at the functional and transcriptional level in injured and non-injured small-diameter DRG neurons. In a second part of my thesis, following an initial screening of the effect of various substances secreted during inflammation and ischemia on ASIC current properties, we characterized in detail the modulation of ASICs, in particular of ASIC1 by serine proteases in a recombinant expression system as well as in hippocampal neurons. We showed that protease exposure shifts the pH dependence of ASIC1 activation and steady-state inactivation to more acidic pH. As a consequence, protease exposure leads to a decrease in the current response if ASIC1 is activated by a pH drop from pH 7.4. If, however, acidification occurs from a basal pH of 7, protease-exposed ASIC1a shows higher activity than untreated ASIC1a. We provided evidence that this bi-directional regulation of ASIC1a function also occurs in hippocampal neurons. Our results in DRG neurons suggest that some ASIC currents are involved in the transduction of peripheral acidification and pain. Furthermore, ASICs may participate to the processes leading to neuropathy. Some Ca 2+-permeable type 1 currents may be involved in neurosecretion. ASIC modulation by serine proteases may be physiologically relevant, allowing ASIC activation under sustained slightly acidic conditions.

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.