Learning-induced plasticity in auditory spatial representations revealed by electrical neuroimaging.

Auditory spatial representations are likely encoded at a population level within human auditory cortices. We investigated learning-induced plasticity of spatial discrimination in healthy subjects using auditory-evoked potentials (AEPs) and electrical neuroimaging analyses. Stimuli were 100 ms white-noise bursts lateralized with varying interaural time differences. In three experiments, plasticity was induced with 40 min of discrimination training. During training, accuracy significantly improved from near-chance levels to approximately 75%. Before and after training, AEPs were recorded to stimuli presented passively with a more medial sound lateralization outnumbering a more lateral one (7:1). In experiment 1, the same lateralizations were used for training and AEP sessions. Significant AEP modulations to the different lateralizations were evident only after training, indicative of a learning-induced mismatch negativity (MMN). More precisely, this MMN at 195-250 ms after stimulus onset followed from differences in the AEP topography to each stimulus position, indicative of changes in the underlying brain network. In experiment 2, mirror-symmetric locations were used for training and AEP sessions; no training-related AEP modulations or MMN were observed. In experiment 3, the discrimination of trained plus equidistant untrained separations was tested psychophysically before and 0, 6, 24, and 48 h after training. Learning-induced plasticity lasted <6 h, did not generalize to untrained lateralizations, and was not the simple result of strengthening the representation of the trained lateralizations. Thus, learning-induced plasticity of auditory spatial discrimination relies on spatial comparisons, rather than a spatial anchor or a general comparator. Furthermore, cortical auditory representations of space are dynamic and subject to rapid reorganization.

Semantic domains and supersens tagging for domain-specific ontology learning

In this paper we propose a novel unsupervised approach to learning domain-specific ontologies from large open-domain text collections. The method is based on the joint exploitation of Semantic Domains and Super Sense Tagging for Information Retrieval tasks. Our approach is able to retrieve domain specific terms and concepts while associating them with a set of high level ontological types, named supersenses, providing flat ontologies characterized by very high accuracy and pertinence to the domain.

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.

E-learning initiatives in forensic interpretation: report on experiences from current projects and outlook

This paper reports on the purpose, design, methodology and target audience of E-learning courses in forensic interpretation offered by the authors since 2010, including practical experiences made throughout the implementation period of this project. This initiative was motivated by the fact that reporting results of forensic examinations in a logically correct and scientifically rigorous way is a daily challenge for any forensic practitioner. Indeed, interpretation of raw data and communication of findings in both written and oral statements are topics where knowledge and applied skills are needed. Although most forensic scientists hold educational records in traditional sciences, only few actually followed full courses that focussed on interpretation issues. Such courses should include foundational principles and methodology - including elements of forensic statistics - for the evaluation of forensic data in a way that is tailored to meet the needs of the criminal justice system. In order to help bridge this gap, the authors' initiative seeks to offer educational opportunities that allow practitioners to acquire knowledge and competence in the current approaches to the evaluation and interpretation of forensic findings. These cover, among other aspects, probabilistic reasoning (including Bayesian networks and other methods of forensic statistics, tools and software), case pre-assessment, skills in the oral and written communication of uncertainty, and the development of independence and self-confidence to solve practical inference problems. E-learning was chosen as a general format because it helps to form a trans-institutional online-community of practitioners from varying forensic disciplines and workfield experience such as reporting officers, (chief) scientists, forensic coordinators, but also lawyers who all can interact directly from their personal workplaces without consideration of distances, travel expenses or time schedules. In the authors' experience, the proposed learning initiative supports participants in developing their expertise and skills in forensic interpretation, but also offers an opportunity for the associated institutions and the forensic community to reinforce the development of a harmonized view with regard to interpretation across forensic disciplines, laboratories and judicial systems.

How can we teach EBM in clinical practice? : an analysis of barriers to implementation of on-the-job EBM teaching and learning.

Introduction: Evidence-based medicine (EBM) improves the quality of health care. Courses on how to teach EBM in practice are available, but knowledge does not automatically imply its application in teaching. We aimed to identify and compare barriers and facilitators for teaching EBM in clinical practice in various European countries. Methods: A questionnaire was constructed listing potential barriers and facilitators for EBM teaching in clinical practice. Answers were reported on a 7-point Likert scale ranging from not at all being a barrier to being an insurmountable barrier. Results: The questionnaire was completed by 120 clinical EBM teachers from 11 countries. Lack of time was the strongest barrier for teaching EBM in practice (median 5). Moderate barriers were the lack of requirements for EBM skills and a pyramid hierarchy in health care management structure (median 4). In Germany, Hungary and Poland, reading and understanding articles in English was a higher barrier than in the other countries. Conclusion: Incorporation of teaching EBM in practice faces several barriers to implementation. Teaching EBM in clinical settings is most successful where EBM principles are culturally embedded and form part and parcel of everyday clinical decisions and medical practice.

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.

Reversal of activity-mediated spine dynamics and learning impairment in a mouse model of Fragile X syndrome.

Fragile X syndrome (FXS) is characterized by intellectual disability and autistic traits, and results from the silencing of the FMR1 gene coding for a protein implicated in the regulation of protein synthesis at synapses. The lack of functional Fragile X mental retardation protein has been proposed to result in an excessive signaling of synaptic metabotropic glutamate receptors, leading to alterations of synapse maturation and plasticity. It remains, however, unclear how mechanisms of activity-dependent spine dynamics are affected in Fmr knockout (Fmr1-KO) mice and whether they can be reversed. Here we used a repetitive imaging approach in hippocampal slice cultures to investigate properties of structural plasticity and their modulation by signaling pathways. We found that basal spine turnover was significantly reduced in Fmr1-KO mice, but markedly enhanced by activity. Additionally, activity-mediated spine stabilization was lost in Fmr1-KO mice. Application of the metabotropic glutamate receptor antagonist α-Methyl-4-carboxyphenylglycine (MCPG) enhanced basal turnover, improved spine stability, but failed to reinstate activity-mediated spine stabilization. In contrast, enhancing phosphoinositide-3 kinase (PI3K) signaling, a pathway implicated in various aspects of synaptic plasticity, reversed both basal turnover and activity-mediated spine stabilization. It also restored defective long-term potentiation mechanisms in slices and improved reversal learning in Fmr1-KO mice. These results suggest that modulation of PI3K signaling could contribute to improve the cognitive deficits associated with FXS.

Audit report on the Muscatine Agricultural Learning Center for the year ended December 31, 2011 and the six months ended December 31, 2010

Audit report on the Muscatine Agricultural Learning Center for the year ended December 31, 2011 and the six months ended December 31, 2010

Penser la médecine communautaire s'apprend avec les pairs. [The spirit of community medicine: students learning from students].

At the University of Lausanne third-year medical students are given the task of spending a month investigating a question of community medicine. In 2009, four students evaluated the legitimacy of health insurers intervening in the management of depression. They found that health insurers put pressure on public authorities during the development of legislation governing the health system and reimbursement for treatment. This fact emerged during the scientific investigation led jointly by the team in the course of the "module of immersion in community medicine." This paper presents each step of their study. The example chosen illustrates the learning objectives covered by the module.

Beyond the 'dyad': a qualitative re-evaluation of the changing clinical consultation.

OBJECTIVE: To identify characteristics of consultations that do not conform to the traditionally understood communication 'dyad', in order to highlight implications for medical education and develop a reflective 'toolkit' for use by medical practitioners and educators in the analysis of consultations. DESIGN: A series of interdisciplinary research workshops spanning 12 months explored the social impact of globalisation and computerisation on the clinical consultation, focusing specifically on contemporary challenges to the clinician-patient dyad. Researchers presented detailed case studies of consultations, taken from their recent research projects. Drawing on concepts from applied sociolinguistics, further analysis of selected case studies prompted the identification of key emergent themes. SETTING: University departments in the UK and Switzerland. PARTICIPANTS: Six researchers with backgrounds in medicine, applied linguistics, sociolinguistics and medical education. One workshop was also attended by PhD students conducting research on healthcare interactions. RESULTS: The contemporary consultation is characterised by a multiplicity of voices. Incorporation of additional voices in the consultation creates new forms of order (and disorder) in the interaction. The roles 'clinician' and 'patient' are blurred as they become increasingly distributed between different participants. These new consultation arrangements make new demands on clinicians, which lie beyond the scope of most educational programmes for clinical communication. CONCLUSIONS: The consultation is changing. Traditional consultation models that assume a 'dyadic' consultation do not adequately incorporate the realities of many contemporary consultations. A paradox emerges between the need to manage consultations in a 'super-diverse' multilingual society, while also attending to increasing requirements for standardised protocol-driven approaches to care prompted by computer use. The tension between standardisation and flexibility requires addressing in educational contexts. Drawing on concepts from applied sociolinguistics and the findings of these research observations, the authors offer a reflective 'toolkit' of questions to ask of the consultation in the context of enquiry-based learning.