AgShare : Building Community and Content with Multiple Partners

Michigan State University and OER Africa are creating a win-win collaboration of existing organizations for African publishing, localizing, and sharing of teaching and learning materials that fill critical resource gaps in African MSc agriculture curriculum. By the end of the 18-month planning and pilot initiative, African agriculture universities, faculty, students, researchers, NGO leaders, extension staff, and farmers will participate in building AgShare by demonstrating its benefits and outcomes and by building momentum and support for growth.

Cluster analysis of behavioural and event-related potentials during a contingent negative variation paradigm in remitting-relapsing and benign forms of multiple sclerosis

Background: Event-related potentials (ERPs) may be used as a highly sensitive way of detecting subtle degrees of cognitive dysfunction. On the other hand, impairment of cognitive skills is increasingly recognised as a hallmark of patients suffering from multiple sclerosis (MS). We sought to determine the psychophysiological pattern of information processing among MS patients with the relapsing-remitting form of the disease and low physical disability considered as two subtypes: 'typical relapsing-remitting' (RRMS) and 'benign MS' (BMS). Furthermore, we subjected our data to a cluster analysis to determine whether MS patients and healthy controls could be differentiated in terms of their psychophysiological profile.Methods: We investigated MS patients with RRMS and BMS subtypes using event-related potentials (ERPs) acquired in the context of a Posner visual-spatial cueing paradigm. Specifically, our study aimed to assess ERP brain activity in response preparation (contingent negative variation -CNV) and stimuli processing in MS patients. Latency and amplitude of different ERP components (P1, eN1, N1, P2, N2, P3 and late negativity -LN) as well as behavioural responses (reaction time -RT; correct responses -CRs; and number of errors) were analyzed and then subjected to cluster analysis. Results: Both MS groups showed delayed behavioural responses and enhanced latency for long-latency ERP components (P2, N2, P3) as well as relatively preserved ERP amplitude, but BMS patients obtained more important performance deficits (lower CRs and higher RTs) and abnormalities related to the latency (N1, P3) and amplitude of ERPs (eCNV, eN1, LN). However, RRMS patients also demonstrated abnormally high amplitudes related to the preparation performance period of CNV (cCNV) and post-processing phase (LN). Cluster analyses revealed that RRMS patients appear to make up a relatively homogeneous group with moderate deficits mainly related to ERP latencies, whereas BMS patients appear to make up a rather more heterogeneous group with more severe information processing and attentional deficits. Conclusions: Our findings are suggestive of a slowing of information processing for MS patients that may be a consequence of demyelination and axonal degeneration, which also seems to occur in MS patients that show little or no progression in the physical severity of the disease over time.

Differential cognitive impairment for diverse forms of multiple sclerosis.

BACKGROUND Cognitive impairment is a common feature in multiple sclerosis (MS) patients and occurs in 60% of all cases. Unfortunately, neurological examination does not always agree with the neuropsychological evaluation in determining the cognitive profile of the patient. On the other hand, psychophysiological techniques such as event-related potentials (ERPs) can help in evaluating cognitive impairment in different pathologies. Behavioural responses and EEG signals were recorded during the experiment in three experimental groups: 1) a relapsing-remitting group (RRMS), 2) a benign multiple sclerosis group (BMS) and 3) a Control group. The paradigm employed was a spatial attention task with central cues (Posner experiment). The main aim was to observe the differences in the performance (behavioural variables) and in the latency and amplitude of the ERP components among these groups. RESULTS Our data indicate that both MS groups showed poorer task performance (longer reaction times and lower percentage of correct responses), a latency delay for the N1 and P300 component, and a different amplitude for the frontal N1. Moreover, the deficit in the BMS group, indexed by behavioural and pyschophysiological variables, was more pronounced compared to the RRMS group. CONCLUSION The present results suggest a cognitive impairment in the information processing in all of these patients. Comparing both pathological groups, cognitive impairment was more accentuated in the BMS group compared to the RMSS group. This suggests a silent deterioration of cognitive skills for the BMS that is not usually treated with pharmacological or neuropsychological therapy.

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

Decoding sequence learning from single-trial intracranial EEG in humans.

We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep) in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT) using the fingers of their non-dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep) or a later consolidated phase (day 2, after sleep), whereas it failed to do so for trials belonging to a control condition (pseudo-random sequence). Accurate single-trial classification was achieved by taking advantage of the distributed pattern of neural activity. However, across all the contacts the hippocampus contributed most significantly to the classification accuracy for both patients, and one fronto-striatal contact for one patient. Together, these human intracranial findings demonstrate that a multivariate decoding approach can detect learning-related changes at the level of single-trial iEEG. Because it allows an unbiased identification of brain sites contributing to a behavioral effect (or experimental condition) at the level of single subject, this approach could be usefully applied to assess the neural correlates of other complex cognitive functions in patients implanted with multiple electrodes.

Dispersal strategies, few dominating or many coexisting: the effect of environmental spatial structure and multiple sources of mortality.

Interspecific competition, life history traits, environmental heterogeneity and spatial structure as well as disturbance are known to impact the successful dispersal strategies in metacommunities. However, studies on the direction of impact of those factors on dispersal have yielded contradictory results and often considered only few competing dispersal strategies at the same time. We used a unifying modeling approach to contrast the combined effects of species traits (adult survival, specialization), environmental heterogeneity and structure (spatial autocorrelation, habitat availability) and disturbance on the selected, maintained and coexisting dispersal strategies in heterogeneous metacommunities. Using a negative exponential dispersal kernel, we allowed for variation of both species dispersal distance and dispersal rate. We showed that strong disturbance promotes species with high dispersal abilities, while low local adult survival and habitat availability select against them. Spatial autocorrelation favors species with higher dispersal ability when adult survival and disturbance rate are low, and selects against them in the opposite situation. Interestingly, several dispersal strategies coexist when disturbance and adult survival act in opposition, as for example when strong disturbance regime favors species with high dispersal abilities while low adult survival selects species with low dispersal. Our results unify apparently contradictory previous results and demonstrate that spatial structure, disturbance and adult survival determine the success and diversity of coexisting dispersal strategies in competing metacommunities.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

Statistical learning theory for geospatial data. Case study: Aral sea

In recent years there has been an explosive growth in the development of adaptive and data driven methods. One of the efficient and data-driven approaches is based on statistical learning theory (Vapnik 1998). The theory is based on Structural Risk Minimisation (SRM) principle and has a solid statistical background. When applying SRM we are trying not only to reduce training error ? to fit the available data with a model, but also to reduce the complexity of the model and to reduce generalisation error. Many nonlinear learning procedures recently developed in neural networks and statistics can be understood and interpreted in terms of the structural risk minimisation inductive principle. A recent methodology based on SRM is called Support Vector Machines (SVM). At present SLT is still under intensive development and SVM find new areas of application (www.kernel-machines.org). SVM develop robust and non linear data models with excellent generalisation abilities that is very important both for monitoring and forecasting. SVM are extremely good when input space is high dimensional and training data set i not big enough to develop corresponding nonlinear model. Moreover, SVM use only support vectors to derive decision boundaries. It opens a way to sampling optimization, estimation of noise in data, quantification of data redundancy etc. Presentation of SVM for spatially distributed data is given in (Kanevski and Maignan 2004).

Effects of inbreeding on aversive learning in Drosophila.

Inbreeding adversely affects life history traits as well as various other fitness-related traits, but its effect on cognitive traits remains largely unexplored, despite their importance to fitness of many animals under natural conditions. We studied the effects of inbreeding on aversive learning (avoidance of an odour previously associated with mechanical shock) in multiple inbred lines of Drosophila melanogaster derived from a natural population through up to 12 generations of sib mating. Whereas the strongly inbred lines after 12 generations of inbreeding (0.75<F<0.93) consistently showed reduced egg-to-adult viability (on average by 28%), the reduction in learning performance varied among assays (average=18% reduction), being most pronounced for intermediate conditioning intensity. Furthermore, moderately inbred lines (F=0.38) showed no detectable decline in learning performance, but still had reduced egg-to-adult viability, which indicates that overall inbreeding effects on learning are mild. Learning performance varied among strongly inbred lines, indicating the presence of segregating variance for learning in the base population. However, the learning performance of some inbred lines matched that of outbred flies, supporting the dominance rather than the overdominance model of inbreeding depression for this trait. Across the inbred lines, learning performance was positively correlated with the egg-to-adult viability. This positive genetic correlation contradicts a trade-off observed in previous selection experiments and suggests that much of the genetic variation for learning is owing to pleiotropic effects of genes affecting functions related to survival. These results suggest that genetic variation that affects learning specifically (rather than pleiotropically through general physiological condition) is either low or mostly due to alleles with additive (semi-dominant) effects.

Cognition, mood and fatigue in patients in the early stage of multiple sclerosis.

QUESTION UNDER STUDY: Cognitive impairment occurs during multiple sclerosis (MS) and contributes to the burden of the disease, but its effect in the initial phase of MS still needs to be better understood. METHODS: We prospectively studied 127 early MS patients presenting with a clinically isolated syndrome (CIS) or definite MS, a mean disease duration of 2.6 years, and with minor disability (mean Expanded Disability Status Scale score 1.8). Patients were tested for long-term memory, executive functions, attention, fatigue, mood disorders, functional handicap and quality of life (QoL). Twenty-one CIS patients were excluded from study as the diagnosis of MS could not be confirmed. RESULTS: Over the 106 MS patients analysed, 31 (29.3%) were cognitively impaired (23.6% for memory, 10.4% for attention and 5.7% for executive functions). Cognitive deficits were already present in CIS patients in whom the diagnosis was not yet confirmed (20%). Impaired cognition was associated with anxiety (p = 0.05), depression(p = 0.004), fatigue (p = 0.03), handicap (p <0.001) and a lower QoL (p <0.001). After adjustment for QoL, handicap, depression, anxiety and fatigue were no longer associated with the presence of cognitive deficits. CONCLUSIONS: In this well-defined early MS group one third of the patients already exhibited cognitive deficits, which were usually apparent in an effortful learning situation and were generally mild. Mood disorders, fatigue, handicap and decreased QoL were all associated with the occurrence of cognitive deficits. QoL itself appeared to take all the other factors into account. Our results confirm the existence of an interplay between cognitive, affective and functional changes and fatigue in early MS.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Kernel-based mapping of orographic rainfall enhancement in the Swiss Alps as detected by weatherradar

In this paper, we develop a data-driven methodology to characterize the likelihood of orographic precipitation enhancement using sequences of weather radar images and a digital elevation model (DEM). Geographical locations with topographic characteristics favorable to enforce repeatable and persistent orographic precipitation such as stationary cells, upslope rainfall enhancement, and repeated convective initiation are detected by analyzing the spatial distribution of a set of precipitation cells extracted from radar imagery. Topographic features such as terrain convexity and gradients computed from the DEM at multiple spatial scales as well as velocity fields estimated from sequences of weather radar images are used as explanatory factors to describe the occurrence of localized precipitation enhancement. The latter is represented as a binary process by defining a threshold on the number of cell occurrences at particular locations. Both two-class and one-class support vector machine classifiers are tested to separate the presumed orographic cells from the nonorographic ones in the space of contributing topographic and flow features. Site-based validation is carried out to estimate realistic generalization skills of the obtained spatial prediction models. Due to the high class separability, the decision function of the classifiers can be interpreted as a likelihood or susceptibility of orographic precipitation enhancement. The developed approach can serve as a basis for refining radar-based quantitative precipitation estimates and short-term forecasts or for generating stochastic precipitation ensembles conditioned on the local topography.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

Natural variation in learning ability in "Drosophila melanogaster"

Learning is the ability of an organism to adapt to the changes of its environment in response to its past experience. It is a widespread ability in the animal kingdom, but its evolutionary aspects are poorly known. Learning ability is supposedly advantageous under some conditions, when environmental conditions are not too stable - because in this case there is no need to learn to predict any event in the environment - and not changing too fast - otherwise environmental cues cannot be used because they are not reliable. Nevertheless, learning ability is also known to be costly in terms of energy needed for neuronal synthesis, memory formation, initial mistakes. During my PhD, I focused on the study of genetic variability of learning ability in natural populations. Genetic variability is the basis on which natural selection and genetic drift can act. How does learning ability vary in nature? What are the roles of additive genetic variation or maternal effects in this variation? Is it involved in evolutionary trade-offs with other fitness-related traits?¦I investigated a natural population of fruit fly, Drosophila melanogaster, as a model organism. Its learning ability is easy to measure with associative memory tests. I used two research tools: multiple inbred and isofemale lines derived from a natural population as a representative sample. My work was divided into three parts.¦First, I investigated the effects of inbreeding on aversive learning (avoidance of an odor previously associated with mechanical shock). While the inbred lines consistently showed reduced egg-to-adult viability by 28 %, the effects of inbreeding on learning performance was 18 % and varied among assays, with a trend to be most pronounced for intermediate conditioning intensity. Variation among inbred lines indicates that ample genetic variance for learning was segregating in the base population, and suggests that the inbreeding depression observed in learning performance was mostly due to dominance rather than overdominance. Across the inbred lines, learning performance was positively correlated with the egg-to-adult viability. This positive genetic correlation contradicts previous studies which observed a trade-off between learning ability and lifespan or larval competitive ability. It suggests that much of the genetic variation for learning is due to pleiotropic effects of genes affecting other functions related to survival. Together with the overall mild effects of inbreeding on learning performance, this suggests that genetic variation specifically affecting learning is either very low, or is due to alleles with mostly additive (semi-dominant) effects. It also suggests that alleles reducing learning performance are on average partially recessive, because their effect does not appear in the outbred base population. Moreover, overdominance seems unlikely as major cause of the inbreeding depression, because even if the overall mean of the inbred line is smaller than the outbred base population, some of the inbred lines show the same learning score as the outbred base population. If overdominance played an important part in inbreeding depression, then all the homozygous lines should show lower learning ability than¦outbred base population.¦In the second part of my project, I sampled the same natural population again and derived isofemale lines (F=0.25) which are less adapted to laboratory conditions and therefore are more representative of the variance of the natural population. They also showed some genetic variability for learning, and for three other fitness-related traits possibly related with learning: resistance to bacterial infection, egg-to-adult viability and developmental time. Nevertheless, the genetic variance of learning ability did not appear to be smaller than the variance of the other traits. The positive correlation previously observed between learning ability and egg- to-adult viability did not appear in isofemale lines (nor a negative correlation). It suggests that there was still genetic variability within isofemale lines and that they did not fix the highly deleterious pleiotropic alleles possibly responsible for the previous correlation.¦In order to investigate the relative amount of nuclear (additive and non-additive effects) and extra-nuclear (maternal and paternal effect) components of variance in learning ability and other fitness-related traits among the inbred lines tested in part one, I performed a diallel cross between them. The nuclear additive genetic variance was higher than other components for learning ability and survival to learning ability, but in contrast, maternal effects were more variable than other effects for developmental traits. This suggests that maternal effects, which reflects effects from mitochondrial DNA, epigenetic effects, or the amount of nutrients that are invested by the mother in the egg, are more important in the early stage of life, and less at the adult stage. There was no additive genetic correlation between learning ability and other traits, indicating that the correlation between learning ability and egg-to-adult viability observed in the first pat of my project was mostly due to recessive genes.¦Finally, my results showed that learning ability is genetically variable. The diallel experiment showed additive genetic variance was the most important component of the total variance. Moreover, every inbred or isofemale line showed some learning ability. This suggested that alleles impairing learning ability are eliminated by selection, and therefore that learning ability is under strong selection in natural populations of Drosophila. My results cannot alone explain the maintenance of the observed genetic variation. Even if I cannot eliminate the hypothesis of pleiotropy between learning ability and the other fitness-related traits I measured, there is no evidence for any trade-off between these traits and learning ability. This contradicts what has been observed between learning ability and other traits like lifespan and larval competitivity.¦L'apprentissage représente la capacité d'un organisme à s'adapter aux changement de son environnement au cours de sa vie, en réponse à son expérience passée. C'est une capacité très répandue dans le règne animal, y compris pour les animaux les plus petits et les plus simples, mais les aspects évolutifs de l'apprentissage sont encore mal connus. L'apprentissage est supposé avantageux dans certaines conditions, quand l'environnement n'est ni trop stable - dans ce cas, il n'y a rien à apprendre - ni trop variable - dans ce cas, les indices sur lesquels se reposer changent trop vite pour apprendre. D'un autre côté, l'apprentissage a aussi des coûts, en terme de synthèse neuronale, pour la formation de la mémoire, ou de coûts d'erreur initiale d'apprentissage. Pendant ma thèse, j'ai étudié la variabilité génétique naturelle des capacités d'apprentissage. Comment varient les capacités d'apprentissage dans la nature ? Quelle est la part de variation additive, l'impact des effets maternel ? Est-ce que l'apprentissage est impliqué dans des interactions, de type compromis évolutifs, avec d'autres traits liés à la fitness ?¦Afin de répondre à ces questions, je me suis intéressée à la mouche du vinaigre, ou drosophile, un organisme modèle. Ses capacités d'apprentissage sont facile à étudier avec un test de mémoire reposant sur l'association entre un choc mécanique et une odeur. Pour étudier ses capacités naturelles, j'ai dérivé de types de lignées d'une population naturelle: des lignées consanguines et des lignées isofemelles.¦Dans une première partie, je me suis intéressée aux effets de la consanguinité sur les capacités d'apprentissage, qui sont peu connues. Alors que les lignées consanguines ont montré une réduction de 28% de leur viabilité (proportion d'adultes émergeants d'un nombre d'oeufs donnés), leurs capacités d'apprentissage n'ont été réduites que de 18%, la plus forte diminution étant obtenue pour un conditionnement modéré. En outre, j'ai également observé que les capacités d'apprentissage était positivement corrélée à la viabilité entre les lignées. Cette corrélation est surprenante car elle est en contradiction avec les résultats obtenus par d'autres études, qui montrent l'existence de compromis évolutifs entre les capacités d'apprentissage et d'autres traits comme le vieillissement ou la compétitivité larvaire. Elle suggère que la variation génétique des capacités d'apprentissage est due aux effets pleiotropes de gènes récessifs affectant d'autres fonctions liées à la survie. Ces résultats indiquent que la variation pour les capacités d'apprentissage est réduite comparée à celle d'autres traits ou est due à des allèles principalement récessifs. L'hypothèse de superdominance semble peu vraisemblable, car certaines des lignées consanguines ont obtenu des scores d'apprentissage égaux à ceux de la population non consanguine, alors qu'en cas de superdominance, elles auraient toutes dû obtenir des scores inférieurs.¦Dans la deuxième partie de mon projet, j'ai mesuré les capacités d'apprentissage de lignées isofemelles issues de la même population initiale que les lignées consanguines. Ces lignées sont issues chacune d'un seul couple, ce qui leur donne un taux d'hétérozygosité supérieur et évite l'élimination de lignées par fixation d'allèles délétères rares. Elles sont ainsi plus représentatives de la variabilité naturelle. Leur variabilité génétique est significative pour les capacités d'apprentissage, et trois traits liés à la fois à la fitness et à l'apprentissage: la viabilité, la résistance à l'infection bactérienne et la vitesse de développement. Cependant, la variabilité des capacités d'apprentissage n'apparaît cette fois pas inférieure à celle des autres traits et aucune corrélation n'est constatée entre les capacité d'apprentissage et les autres traits. Ceci suggère que la corrélation observée auparavant était surtout due à la fixation d'allèles récessifs délétères également responsables de la dépression de consanguinité.¦Durant la troisième partie de mon projet, je me suis penchée sur la décomposition de la variance observée entre les lignées consanguines observée en partie 1. Quatre composants ont été examinés: la variance due à des effets nucléaires (additifs et non additifs), et due à des effets parentaux (maternels et paternels). J'ai réalisé un croisement diallèle de toutes les lignées. La variance additive nucléaire s'est révélée supérieure aux autres composants pour les capacités d'apprentissage et la résistance à l'infection bactérienne. Par contre, les effets maternels étaient plus importants que les autres composants pour les traits développementaux (viabilité et vitesse de développement). Ceci suggère que les effets maternels, dus à G ADN mitochondrial, à l'épistasie ou à la quantité de nutriments investis dans l'oeuf par la mère, sont plus importants dans les premiers stades de développement et que leur effet s'estompe à l'âge adulte. Il n'y a en revanche pas de corrélation statistiquement significative entre les effets additifs des capacités d'apprentissage et des autres traits, ce qui indique encore une fois que la corrélation observée entre les capacités d'apprentissage et la viabilité dans la première partie du projet était due à des effets d'allèles partiellement récessifs.¦Au, final, mes résultats montrent bien l'existence d'une variabilité génétique pour les capacités d'apprentissage, et l'expérience du diallèle montre que la variance additive de cette capacité est importante, ce qui permet une réponse à la sélection naturelle. Toutes les lignées, consanguines ou isofemelles, ont obtenu des scores d'apprentissage supérieurs à zéro. Ceci suggère que les allèles supprimant les capacités d'apprentissage sont fortement contre-sélectionnés dans la nature Néanmoins, mes résultats ne peuvent pas expliquer le maintien de cette variabilité génétique par eux-même. Même si l'hypothèse de pléiotropie entre les capacités d'apprentissage et l'un des traits liés à la fitness que j'ai mesuré ne peut être éliminée, il n'y a aucune preuve d'un compromis évolutif pouvant contribuer au maintien de la variabilité.

Cambios en el uso y la concepción de las TIC, implementando el Mobile Learning.

This research analyses the actual use and conception of the ICT mobility that a life long learning group of students have. The students have participated in a Mobile Learning experience along an online postgraduate course, which was designed under a traditional e-learning perspective. The students received a tablet PC (iPad) in order to work at the course and also to use it in their personal and professional life. A complete and original pre-test / post-test questionnaire was applied before and after the course. This instrument was scientifically validated. Thru the questionnaire, uses tendency and students perceptions were studied. Frequencies, purposes, habits of use and valuation, as well as the device"s integration into their personal, social and professional life were studied. The analysis intents to apply the 'Social Technographics Profile" by Bernoff (2010) to classify, by profile groups, the users of the actual Internet. Finally a reflexion of the reasons and limits of the theory, in this study, and also the relation to reality is presented. The Inter-coding reliability and validity shows the possibility of applying the instrument on wider samples in order to get a closer look to the uses and actual conceptions of the ubiquitous ICTs.