Strong reproductive barriers in a narrow hybrid zone of West-Mediterranean green toads (Bufo viridis subgroup) with Plio-Pleistocene divergence

Background One key question in evolutionary biology deals with the mode and rate at which reproductive isolation accumulates during allopatric speciation. Little is known about secondary contacts of recently diverged anuran species. Here we conduct a multi-locus field study to investigate a contact zone between two lineages of green toads with an estimated divergence time of 2.7 My, and report results from preliminary experimental crosses. Results The Sicilian endemic Bufo siculus and the Italian mainland-origin B. balearicus form a narrow hybrid zone east of Mt. Etna. Despite bidirectional mtDNA introgression over a ca. 40 km North-South cline, no F1 hybrids could be found, and nuclear genomes display almost no admixture. Populations from each side of the contact zone showed depressed genetic diversity and very strong differentiation (FST = 0.52). Preliminary experimental crosses point to a slightly reduced fitness in F1 hybrids, a strong hybrid breakdown in backcrossed offspring (F1 x parental, with very few reaching metamorphosis) and a complete and early mortality in F2 (F1 x F1). Conclusion Genetic patterns at the contact zone are molded by drift and selection. Local effective sizes are reduced by the geography and history of the contact zone, B. balearicus populations being at the front wave of a recent expansion (late Pleistocene). Selection against hybrids likely results from intrinsic genomic causes (disruption of coadapted sets of genes in backcrosses and F2-hybrids), possibly reinforced by local adaptation (the ranges of the two taxa roughly coincide with the borders of semiarid and arid climates). The absence of F1 in the field might be due to premating isolation mechanisms. Our results, show that these lineages have evolved almost complete reproductive isolation after some 2.7 My of divergence, contrasting sharply with evidence from laboratory experiments that some anuran species may still produce viable F1 offspring after > 20 My of divergence.

Train-the-trainer: E-learning zur Förderung einer praxisorientiertem Vermittlung von evidenzbasierter Medizin

Hintergrund: Trotz ihrer Etablierung als essentieller Bestandteil der medizinischen Weiter-/Fortbildung werden europa- wie schweizweit kaum Kurse in evidenzbasierter Medizin (ebm) angeboten, die - integriert im klinischen Alltag - gezielt Fertigkeiten in ebm vermitteln. Noch grössere Defizite finden sich bei ebm- Weiterbildungsmöglichkeiten für klinische Ausbilder (z.B. Oberärzte). Als Weiterführung eines EU-finanzierten, klinisch integrierten E-learning- Programms für Weiterbildungsassistenten (www.ebm-unity.org) entwickelte eine europäische Gruppe von medical educators gezielt für Ausbilder ein e-learning-Curriculum zur Vermittlung von ebm im Rahmen der klinischen Weiterbildung. Methode: Die Entwicklung des Curriculums umfasst folgende Schritte: Beschreibung von Lernzielen, Identifikation von klinisch relevanten Lernumgebungen, Entwicklung von Lerninhalten und exemplarischen didaktischen Strategien, zugeschnitten auf die jeweilige Lernumgebungen, Design von web-basierten Selbst-Lernsequenzen mit Möglichkeiten zur Selbstevaluation, Erstellung eines Handbuchs. Ergebnisse: Lernziele des Tutoren-Lehrgangs sind der Erwerb von Fertigkeiten zur Vermittlung der 5 klassischen ebm-Schritte: PICO- (Patient-Intervention-Comparison-Outcome)-Fragen, Literatursuche, kritische Literaturbewertung, Übertragung der Ergebnisse im eigenen Setting und Implementierung). Die Lehrbeispiele zeigen angehenden ebm-Tutoren, wie sich typische klinische Situationen wie z.B. Stationsvisite, Ambulanzsprechstunde, Journalclub, offizielle Konferenzen, Audit oder das klinische Assessment von Weiterbildungsassistenten gezielt für die Vermittlung von ebm nutzen lassen. Kurze E-Learning-Module mit exemplarischen «real-life»-Video-Clips erlauben flexibles Lernen zugeschnitten auf das knappe Zeitkontingent von Ärzten. Eine Selbst-Evaluation ermöglicht die Überprüfung der gelernten Inhalte. Die Pilotierung des Tutoren-Lehrgangs mit klinisch tätigen Tutoren sowie die Übersetzung des Moduls in weitere Sprachen sind derzeit in Vorbereitung. chlussfolgerung: Der modulare Train-the-Trainer-Kurs zur Vermittlung von ebm im klinischen Alltag schliesst eine wichtige Lücke in der Dissemination von klinischer ebm. Webbasierte Beispiele mit kurzen Sequenzen demonstrieren typische Situationen zur Vermittlung der ebm-Kernfertigkeiten und bieten medical educators wie Oberärzten einen niedrigschwelligen Einstieg in «ebm» am Krankenbett. Langfristiges Ziel ist eine europäische Qualifikation für ebm- Learning und -Teaching in der Fort- und Weiterbildung. Nach Abschluss der Evaluation steht das Curriculum interessierten Personen und Gruppen unter «not-for-profit»-Bedingungen zur Verfügung. Auskünfte erhältlich von rkunz@uhbs.ch. Finanziert durch die Europäische Kommission - Leonardo da Vinci Programme - Transfer of Innovation - Pilot Project for Lifelong Learn- ing 2007 und das Schweizerische Staatssekretariat für Bildung und Forschung.

Implementing statistical learning methods through Bayesian networks (Part 2): bayesian evaluations for results of black toner analyses in forensic document examination

This paper presents and discusses the use of Bayesian procedures - introduced through the use of Bayesian networks in Part I of this series of papers - for 'learning' probabilities from data. The discussion will relate to a set of real data on characteristics of black toners commonly used in printing and copying devices. Particular attention is drawn to the incorporation of the proposed procedures as an integral part in probabilistic inference schemes (notably in the form of Bayesian networks) that are intended to address uncertainties related to particular propositions of interest (e.g., whether or not a sample originates from a particular source). The conceptual tenets of the proposed methodologies are presented along with aspects of their practical implementation using currently available Bayesian network software.

Learning spatial filters for multispectral image segmentation

We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters.

Disaster Risk, Livelihoods and Natural Barriers, Strengthening Decision-Making Tools for Disaster Risk Reduction: A case study from northern Pakistan

The goal of this interdisciplinary study is to better understand the land use factors that increase vulnerability of mountain areas in northern Pakistan. The study will identify and analyse the damages and losses caused by the October 2005 earthquake in two areas of the same valley: one "low-risk" watershed with sound natural resources management, the other, "high-risk" in an ecologically degraded watershed. Secondly, the study will examine natural and man-made causes of secondary hazards in the study area, especially landslides; and third it will evaluate the cost of the earthquake damage in the study areas on the livelihoods of local communities and the sub-regional economy. There are few interdisciplinary studies to have correlated community land use practices, resources management, and disaster risk reduction in high-risk mountain areas. By better understanding these linkages, development- humanitarian- and donor agencies focused on disaster reduction can improve their risk reduction programs for mountainous regions.

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.

Stages of change, barriers, benefits, and preferences for exercise in RA patients: a cross-sectional study

OBJECTIVES: To determine the distribution of exercise stages of change in a rheumatoid arthritis (RA) cohort, and to examine patients' perceptions of exercise benefits, barriers, and their preferences for exercise. METHODS: One hundred and twenty RA patients who attended the Rheumatology Unit of a University Hospital were asked to participate in the study. Those who agreed were administered a questionnaire to determine their exercise stage of change, their perceived benefits and barriers to exercise, and their preferences for various features of exercise. RESULTS: Eighty-nine (74%) patients were finally included in the analyses. Their mean age was 58.4 years, mean RA duration 10.1 years, and mean disease activity score 2.8. The distribution of exercise stages of change was as follows: precontemplation (n = 30, 34%), contemplation (n = 11, 13%), preparation (n = 5, 6%), action (n = 2, 2%), and maintenance (n = 39, 45%). Compared to patients in the maintenance stage of change, precontemplators exhibited different demographic and functional characteristics and reported less exercise benefits and more barriers to exercise. Most participants preferred exercising alone (40%), at home (29%), at a moderate intensity (64%), with advice provided by a rheumatologist (34%) or a specialist in exercise and RA (34%). Walking was by far the preferred type of exercise, in both the summer (86%) and the winter (51%). CONCLUSIONS: Our cohort of patients with RA was essentially distributed across the precontemplation and maintenance exercise stages of change. These subgroups of patients exhibit psychological and functional differences that make their needs different in terms of exercise counselling.

The influence of learning on evolution: a mathematical framework.

The Baldwin effect can be observed if phenotypic learning influences the evolutionary fitness of individuals, which can in turn accelerate or decelerate evolutionary change. Evidence for both learning-induced acceleration and deceleration can be found in the literature. Although the results for both outcomes were supported by specific mathematical or simulation models, no general predictions have been achieved so far. Here we propose a general framework to predict whether evolution benefits from learning or not. It is formulated in terms of the gain function, which quantifies the proportional change of fitness due to learning depending on the genotype value. With an inductive proof we show that a positive gain-function derivative implies that learning accelerates evolution, and a negative one implies deceleration under the condition that the population is distributed on a monotonic part of the fitness landscape. We show that the gain-function framework explains the results of several specific simulation models. We also use the gain-function framework to shed some light on the results of a recent biological experiment with fruit flies.

Time Series Input Selection using Multiple Kernel Learning

In this paper we study the relevance of multiple kernel learning (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear combination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for optimizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simulated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).