MyHits: a new interactive resource for protein annotation and domain identification.

The MyHits web server (http://myhits.isb-sib.ch) is a new integrated service dedicated to the annotation of protein sequences and to the analysis of their domains and signatures. Guest users can use the system anonymously, with full access to (i) standard bioinformatics programs (e.g. PSI-BLAST, ClustalW, T-Coffee, Jalview); (ii) a large number of protein sequence databases, including standard (Swiss-Prot, TrEMBL) and locally developed databases (splice variants); (iii) databases of protein motifs (Prosite, Interpro); (iv) a precomputed list of matches ('hits') between the sequence and motif databases. All databases are updated on a weekly basis and the hit list is kept up to date incrementally. The MyHits server also includes a new collection of tools to generate graphical representations of pairwise and multiple sequence alignments including their annotated features. Free registration enables users to upload their own sequences and motifs to private databases. These are then made available through the same web interface and the same set of analytical tools. Registered users can manage their own sequences and annotations using only web tools and freeze their data in their private database for publication purposes.

Can Families Resist Managerial and Financial Revolutions? Swiss Family Firms in the Twentieth Century

The aim of this contribution is to highlight the long-term evolution of family capitalism in Switzerland during the twentieth century. We focus on 22 large companies of the machine, electrotechnical and metallurgy (MEM) sector whose boards of directors and general managers have been identified in five benchmark years across the twentieth century, which allows us to distinguish between family-owned and family-controlled firms. Our results show that family firms prevailed until the 1980s and thus contradict the dominance of 'managerial capitalism'. Although we observe a decline of family capitalism during the last decade of the century, the significant remaining presence of family firms in 2000 allows us to relativise the advent of investor capitalism.

Respiratory surveillance of patients with Duchenne and Becker muscular dystrophy.

Duchenne muscular dystrophy is is the most common form of the childhood muscular dystrophies. It follows a predictable clinical course marked by progressive skeletal muscle weakness, lost of ambulation before teen-age and death in early adulthood secondary to respiratory or cardiac failure. Becker muscular dystrophy is less common and has a milder clinical course but also results in respiratory and cardiac failure.Altough recent advances in respiratory care and new technologies have improved the outlook many patients already received only a traditional non-interventional approach. The aims of this work are: to analyse the pathophysiology and natural history of respiratory function in these diseases, to descript their clinical manifestations, to present the diagnostics tools and to provide recommendations for an adequated respiratory care in this particular population based on the updated literature referenced.

GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.

Convergences and divergences of diabetic patients' and healthcare professionals' opinions of care : a qualitative study

OBJECTIVES: To investigate opinions' convergences and divergences of diabetic patients and health-care professionals on diabetes care and the development of a regional diabetes programme. BACKGROUND: Development and implementation of a regional diabetes programme. RESEARCH DESIGN: Qualitative study using focus groups to elicit diabetic patients' and health-care professionals' opinions, followed by content analysis. SETTING AND PARTICIPANTS: Eight focus groups: four focus groups with diabetic patients (n = 39) and four focus groups with various health-care professionals (n = 34) residing or practicing in the canton of Vaud, Switzerland, respectively. RESULTS: Perceived quality of diabetes care varied between individuals and types of participants. To improve quality, patients favoured a comprehensive follow-up while professionals suggested considering existing structures and trained professionals. All participants mentioned communication difficulties between professionals and were favouring teamwork. In addition, they described the role that patients should have in care and self-management. Financial difficulties were also mentioned by both groups of participants. Finally, they were in favour of the development of a regional diabetes programme adapted to actors' needs. For patients indeed, such a programme would represent an opportunity to improve information and to have access to comprehensive care. For professionals, it would help the development of local networks and the reinforcement of existing tools and structures. DISCUSSION AND CONCLUSIONS: Acknowledging convergences and divergences of opinions of both diabetic patients and health-care professionals should help the further development of a programme adapted to users' needs, taking all stakeholders interests and priorities into consideration.

Evolution in subdivided plant populations: concepts, recent advances and future directions.

SUMMARY: Research into the evolution of subdivided plant populations has long involved the study of phenotypic variation across plant geographic ranges and the genetic details underlying that variation. Genetic polymorphism at different marker loci has also allowed us to infer the long- and short-term histories of gene flow within and among populations, including range expansions and colonization-extinction dynamics. However, the advent of affordable genome-wide sequences for large numbers of individuals is opening up new possibilities for the study of subdivided populations. In this review, we consider what the new tools and technologies may allow us to do. In particular, we encourage researchers to look beyond the description of variation and to use genomic tools to address new hypotheses, or old ones afresh. Because subdivided plant populations are complex structures, we caution researchers away from adopting simplistic interpretations of their data, and to consider the patterns they observe in terms of the population genetic processes that have given rise to them; here, the genealogical framework of the coalescent will continue to be conceptually and analytically useful.

Preliminary slope mass movement susceptibility mapping using DEM and LiDAR DEM

Hazard mapping in mountainous areas at the regional scale has greatly changed since the 1990s thanks to improved digital elevation models (DEM). It is now possible to model slope mass movement and floods with a high level of detail in order to improve geomorphologic mapping. We present examples of regional multi-hazard susceptibility mapping through two Swiss case studies, including landslides, rockfall, debris flows, snow avalanches and floods, in addition to several original methods and software tools. The aim of these recent developments is to take advantage of the availability of high resolution DEM (HRDEM) for better mass movement modeling. Our results indicate a good correspondence between inventories of hazardous zones based on historical events and model predictions. This paper demonstrates that by adapting tools and methods issued from modern technologies, it is possible to obtain reliable documents for land planning purposes over large areas.

Genomics and the origin of species.

Speciation is a fundamental evolutionary process, the knowledge of which is crucial for understanding the origins of biodiversity. Genomic approaches are an increasingly important aspect of this research field. We review current understanding of genome-wide effects of accumulating reproductive isolation and of genomic properties that influence the process of speciation. Building on this work, we identify emergent trends and gaps in our understanding, propose new approaches to more fully integrate genomics into speciation research, translate speciation theory into hypotheses that are testable using genomic tools and provide an integrative definition of the field of speciation genomics.

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.

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.

E-tools for civic education : experiences from the election campaign in Switzerland 2007

Political participation is often very low in Switzerland especially among students and young citizens. In the run-up to the Swiss parliamentary election in October 2007 several online tools and campaigns were developed with the aim to increase not only the level of information about the political programs of parties and candidates, but also the electoral participation of younger citizens. From a practical point of view this paper will describe the development, marketing efforts and the distribution as well as the use of two of these tools : the so-called "Parteienkompass" (party compass) and the "myVote"-tool - an online voting assistance tool based on an issue-matching system comparing policy preferences between voters and candidates on an individual level. We also havea look at similar tools stemming from Voting Advice Applications (VAA) in other countries in Western Europe. The paper closes with the results of an evaluation and an outlook to further developments and on-going projects in the near future in Switzerland.

Interét et limites de l'evaluation du risque de récidive d'actes illégaux dans les expertises psychiatriques [Benefits and limitation of the assessment of second offense risk in psychiatric expertise].

The legislatives evolutions imply an important recourse to the psychiatric expertise in order to evaluate the potential dangerousness of a subject. However, in spite of the development of techniques and tools for this evaluation, the dangerousness assessment of a subject is in practice extremely complex and discussed in the scientific literature. The evolution of the concept of dangerousness to the risk assessment involved a technicisation of this evaluation which should not make forget the limits of these tools and the need for restoring the subject, the meaning and the clinic in this evaluation.

Medical residents' feedback on needs and acquired skills following a short course on cross-cultural competence

Objectives The purpose of this study is to assess short and long term changes in knowledge, attitudes, and skills among medical residents following a short course on cultural competency and to explore their perspectives on the experience. Methods Eighteen medical residents went through a short training programme comprised of two seminars lasting 30' and 60' respectively over two days. Three months later, we conducted three focus groups, with 17 residents to explore their thoughts, perspectives and feedback about the course. To measure changes over time, we carried out a quantitative sequential survey before the seminars, three days after, and three months later using the Multicultural Assessment Questionnaire. Results Residents expressed a wide variety of perspectives on the main themes related to the content of the training - culture, trialogue, stereotypes, status, epidemiology, history and geopolitics - and related to its organization - relevance, volume, timing, target audience, training tools, and working material. Using the MAQ, we observed a higher global performance score (n=16) at three days (median=38) compared to results before the training (median=33) revealing a median difference of 5.5 points (z=2.4, p=0.015). This difference was still present at three months (∆=4.5, z=2.4, p=0.018), mainly due to knowledge acquisition (∆=3) rather than attitudes (∆=0) or skills (∆=1). Conclusions Cross-cultural competence training not only brings awareness of multicultural issues but also helps participants understand their own cultures, perception of others and preconceived ideas. Physicians' education should however also focus on improving implementation of acquired knowledge in cross-cultural competence.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.