GWAS of human bitter taste perception identifies new loci and reveals additional complexity of bitter taste genetics.

Human perception of bitterness displays pronounced interindividual variation. This phenotypic variation is mirrored by equally pronounced genetic variation in the family of bitter taste receptor genes. To better understand the effects of common genetic variations on human bitter taste perception, we conducted a genome-wide association study on a discovery panel of 504 subjects and a validation panel of 104 subjects from the general population of São Paulo in Brazil. Correction for general taste-sensitivity allowed us to identify a SNP in the cluster of bitter taste receptors on chr12 (10.88- 11.24 Mb, build 36.1) significantly associated (best SNP: rs2708377, P = 5.31 × 10(-13), r(2) = 8.9%, β = -0.12, s.e. = 0.016) with the perceived bitterness of caffeine. This association overlaps with-but is statistically distinct from-the previously identified SNP rs10772420 influencing the perception of quinine bitterness that falls in the same bitter taste cluster. We replicated this association to quinine perception (P = 4.97 × 10(-37), r(2) = 23.2%, β = 0.25, s.e. = 0.020) and additionally found the effect of this genetic locus to be concentration specific with a strong impact on the perception of low, but no impact on the perception of high concentrations of quinine. Our study, thus, furthers our understanding of the complex genetic architecture of bitter taste perception.

The learning process as a cooperative task among teachers and students

This research project gathers several ideas and guidelines on professional improvement as a teacher. This study includes two empirical studies. The first one focuses mainly on the teacher's figure. It is meant to be a study of the several resources that the teacher uses in order to construct the student's knowledge in an English classroom context. The second empirical study focuses on the students. It is a study on how students learn cooperatively by analyzing their oral productions when working in small groups

Teachers' reactions on students' responses: essential for an adequate task monitoring

This research project analyzes the reactions the teacher has on students' responses. Different techniques as discourse markers, types of questions and repair sequences are taken into account, but the author puts a special emphasis on non-verbal communication. To be aware of all these ways of reacting in a class interaction is essential for an adequate task monitoring

Analyzing complexity in foreign language monologic oral production in a CLIL context

The study tested three analytic tools applied in SLA research (T-unit, AS-unit and Idea-unit) against FL learner monologic oral data. The objective was to analyse their effectiveness for the assessment of complexity of learners' academic production in English. The data were learners' individual productions gathered during the implementation of a CLIL teaching sequence on Natural Sciences in a Catalan state secondary school. The analysis showed that only AS-unit was easily applicable and highly effective in segmenting the data and taking complexity measures

What do we gain from simplicity versus complexity in species distribution models?

Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence-environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence-environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building 'under fit' models, having insufficient flexibility to describe observed occurrence-environment relationships, we risk misunderstanding the factors shaping species distributions. By building 'over fit' models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.

Strong isomorphism reductions in complexity theory

We give the first systematic study of strong isomorphism reductions, a notion of reduction more appropriate than polynomial time reduction when, for example, comparing the computational complexity of the isomorphim problem for different classes of structures. We show that the partial ordering of its degrees is quite rich. We analyze its relationship to a further type of reduction between classes of structures based on purely comparing for every n the number of nonisomorphic structures of cardinality at most n in both classes. Furthermore, in a more general setting we address the question of the existence of a maximal element in the partial ordering of the degrees.

Evolution of decision-making and learning under fluctuating social environments

The capacity to learn to associate sensory perceptions with appropriate motor actions underlies the success of many animal species, from insects to humans. The evolutionary significance of learning has long been a subject of interest for evolutionary biologists who emphasize the bene¬fit yielded by learning under changing environmental conditions, where it is required to flexibly switch from one behavior to another. However, two unsolved questions are particularly impor¬tant for improving our knowledge of the evolutionary advantages provided by learning, and are addressed in the present work. First, because it is possible to learn the wrong behavior when a task is too complex, the learning rules and their underlying psychological characteristics that generate truly adaptive behavior must be identified with greater precision, and must be linked to the specific ecological problems faced by each species. A framework for predicting behavior from the definition of a learning rule is developed here. Learning rules capture cognitive features such as the tendency to explore, or the ability to infer rewards associated to unchosen actions. It is shown that these features interact in a non-intuitive way to generate adaptive behavior in social interactions where individuals affect each other's fitness. Such behavioral predictions are used in an evolutionary model to demonstrate that, surprisingly, simple trial-and-error learn¬ing is not always outcompeted by more computationally demanding inference-based learning, when population members interact in pairwise social interactions. A second question in the evolution of learning is its link with and relative advantage compared to other simpler forms of phenotypic plasticity. After providing a conceptual clarification on the distinction between genetically determined vs. learned responses to environmental stimuli, a new factor in the evo¬lution of learning is proposed: environmental complexity. A simple mathematical model shows that a measure of environmental complexity, the number of possible stimuli in one's environ¬ment, is critical for the evolution of learning. In conclusion, this work opens roads for modeling interactions between evolving species and their environment in order to predict how natural se¬lection shapes animals' cognitive abilities. - La capacité d'apprendre à associer des sensations perceptives à des actions motrices appropriées est sous-jacente au succès évolutif de nombreuses espèces, depuis les insectes jusqu'aux êtres hu¬mains. L'importance évolutive de l'apprentissage est depuis longtemps un sujet d'intérêt pour les biologistes de l'évolution, et ces derniers mettent l'accent sur le bénéfice de l'apprentissage lorsque les conditions environnementales sont changeantes, car dans ce cas il est nécessaire de passer de manière flexible d'un comportement à l'autre. Cependant, deux questions non résolues sont importantes afin d'améliorer notre savoir quant aux avantages évolutifs procurés par l'apprentissage. Premièrement, puisqu'il est possible d'apprendre un comportement incorrect quand une tâche est trop complexe, les règles d'apprentissage qui permettent d'atteindre un com¬portement réellement adaptatif doivent être identifiées avec une plus grande précision, et doivent être mises en relation avec les problèmes écologiques spécifiques rencontrés par chaque espèce. Un cadre théorique ayant pour but de prédire le comportement à partir de la définition d'une règle d'apprentissage est développé ici. Il est démontré que les caractéristiques cognitives, telles que la tendance à explorer ou la capacité d'inférer les récompenses liées à des actions non ex¬périmentées, interagissent de manière non-intuitive dans les interactions sociales pour produire des comportements adaptatifs. Ces prédictions comportementales sont utilisées dans un modèle évolutif afin de démontrer que, de manière surprenante, l'apprentissage simple par essai-et-erreur n'est pas toujours battu par l'apprentissage basé sur l'inférence qui est pourtant plus exigeant en puissance de calcul, lorsque les membres d'une population interagissent socialement par pair. Une deuxième question quant à l'évolution de l'apprentissage concerne son lien et son avantage relatif vis-à-vis d'autres formes plus simples de plasticité phénotypique. Après avoir clarifié la distinction entre réponses aux stimuli génétiquement déterminées ou apprises, un nouveau fac¬teur favorisant l'évolution de l'apprentissage est proposé : la complexité environnementale. Un modèle mathématique permet de montrer qu'une mesure de la complexité environnementale - le nombre de stimuli rencontrés dans l'environnement - a un rôle fondamental pour l'évolution de l'apprentissage. En conclusion, ce travail ouvre de nombreuses perspectives quant à la mo¬délisation des interactions entre les espèces en évolution et leur environnement, dans le but de comprendre comment la sélection naturelle façonne les capacités cognitives des animaux.

Northern Ireland Speech and Language Therapy Task Force report (PDF 1,016 KB)

Speech and Language task force report

Performance on a Virtual Reality Angled Laparoscope Task Correlates with Spatial Ability of Trainees

The aim of the present study was to investigate whether trainees' performance on a virtual reality angled laparoscope navigation task correlates with scores obtained on a validated conventional test of spatial ability. 56 participants of a surgery workshop performed an angled laparoscope navigation task on the Xitact LS 500 virtual reality Simulator. Performance parameters were correlated with the score of a validated paper-and-pencil test of spatial ability. Performance at the conventional spatial ability test significantly correlated with performance at the virtual reality task for overall task score (p < 0.001), task completion time (p < 0.001) and economy of movement (p = 0.035), not for endoscope travel speed (p = 0.947). In conclusion, trainees' performance in a standardized virtual reality camera navigation task correlates with their innate spatial ability. This VR session holds potential to serve as an assessment tool for trainees.

Activity in inferior parietal and medial prefrontal cortex signals the accumulation of evidence in a probability learning task.

In an uncertain environment, probabilities are key to predicting future events and making adaptive choices. However, little is known about how humans learn such probabilities and where and how they are encoded in the brain, especially when they concern more than two outcomes. During functional magnetic resonance imaging (fMRI), young adults learned the probabilities of uncertain stimuli through repetitive sampling. Stimuli represented payoffs and participants had to predict their occurrence to maximize their earnings. Choices indicated loss and risk aversion but unbiased estimation of probabilities. BOLD response in medial prefrontal cortex and angular gyri increased linearly with the probability of the currently observed stimulus, untainted by its value. Connectivity analyses during rest and task revealed that these regions belonged to the default mode network. The activation of past outcomes in memory is evoked as a possible mechanism to explain the engagement of the default mode network in probability learning. A BOLD response relating to value was detected only at decision time, mainly in striatum. It is concluded that activity in inferior parietal and medial prefrontal cortex reflects the amount of evidence accumulated in favor of competing and uncertain outcomes.