Genomics of wine yeast: functional analysis of new and variable genes

Projecte de recerca elaborat a partir d’una estada a l’Institut National de la Recherche Agronomique, França, entre 2007 i 2009. Saccharomyces cerevisiae ha estat el llevat utilitzat durant mil.lenis en l'elaboració de vins. Tot i així, es té poc coneixement sobre les pressions de selecció que han actuat en la modelització del genoma dels llevats vínics. S’ha seqüenciat el genoma d'una soca vínica comercial, EC1118, obtenint 31 supercontigs que cobreixen el 97% del genoma de la soca de referència, S288c. S’ha trobat que el genoma de la soca vínica es diferencia bàsicament en la possessió de 3 regions úniques que contenen 34 gens implicats en funcions claus per al procés fermentatiu. A banda, s’han dut a terme estudis de filogènia i synteny (ordre dels gens) que mostren que una d'aquestes tres regions és pròxima a una espècie relacionada amb el gènere Saccharomyces, mentre que les altres dos regions tenen un origen no-Saccharomyces. S’ha identificat mitjançant PCR i seqüenciació a Zygosaccharomyces bailii, una espècie contaminant de les fermentacions víniques, com a espècie donadora d'una de les dues regions. Les hibridacions naturals entre soques de diferents espècies dins del grup Saccharomyces sensu stricto ja han estat descrites. El treball és el primer que presenta hibridacions entre espècies Saccharomyces i no-Saccharomyces (Z. bailii, en aquest cas). També s’assenyala que les noves regions es troben freqüent i diferencialment presents entre els clades de S. cerevisiae, trobant-se de manera gairebé exclusiva en el grup de les soques víniques, suggerint que es tracta d'una adquisició recent de transferència gènica. En general, les dades demostren que el genoma de les soques víniques pateix una constant remodelació mitjançant l'adquisició de gens exògens. Els resultats suggereixen que aquests processos estan afavorits per la proximitat ecològica i estan implicats en l'adaptació molecular de les soques víniques a les condicions d'elevada concentració en sucres, poc nitrogen i elevades concentracions en etanol.

Public sector rationing and private sector selection [paper accepted and forthcoming in Journal of Public Economic Theory]

We study the interaction between nonprice public rationing and prices in the private market. Under a limited budget, the public supplier uses a rationing policy. A private firm may supply the good to those consumers who are rationed by the public system. Consumers have different amounts of wealth, and costs of providing the good to them vary. We consider two regimes. First, the public supplier observes consumers' wealth information; second, the public supplier observes both wealth and cost information. The public supplier chooses a rationing policy, and, simultaneously, the private firm, observing only cost but not wealth information, chooses a pricing policy. In the first regime, there is a continuum of equilibria. The Pareto dominant equilibrium is a means-test equilibrium: poor consumers are supplied while rich consumers are rationed. Prices in the private market increase with the budget. In the second regime, there is a unique equilibrium. This exhibits a cost-effectiveness rationing rule; consumers are supplied if and only if their costbenefit ratios are low. Prices in the private market do not change with the budget. Equilibrium consumer utility is higher in the cost-effectiveness equilibrium than the means-test equilibrium [Authors]

Host specialist clownfishes are environmental niche generalists.

Why generalist and specialist species coexist in nature is a question that has interested evolutionary biologists for a long time. While the coexistence of specialists and generalists exploiting resources on a single ecological dimension has been theoretically and empirically explored, biological systems with multiple resource dimensions (e.g. trophic, ecological) are less well understood. Yet, such systems may provide an alternative to the classical theory of stable evolutionary coexistence of generalist and specialist species on a single resource dimension. We explore such systems and the potential trade-offs between different resource dimensions in clownfishes. All species of this iconic clade are obligate mutualists with sea anemones yet show interspecific variation in anemone host specificity. Moreover, clownfishes developed variable environmental specialization across their distribution. In this study, we test for the existence of a relationship between host-specificity (number of anemones associated with a clownfish species) and environmental-specificity (expressed as the size of the ecological niche breadth across climatic gradients). We find a negative correlation between host range and environmental specificities in temperature, salinity and pH, probably indicating a trade-off between both types of specialization forcing species to specialize only in a single direction. Trade-offs in a multi-dimensional resource space could be a novel way of explaining the coexistence of generalist and specialists.

Immunodéficience commune variable: ce qu'il faut savoir [Common variable immune deficiency: what you need to know].

Common variable immune deficiency is the most frequent primary immune deficiency, characterized mainly by a disorder of B lymphocytes differentiation and a deficit in immunoglobulins. The clinical manifestations include recurrent infections, non-infectious lung and digestive involvements, autoimmune diseases, and an increased susceptibility to cancers. Recent breakthroughs have been made in the understanding of some genetic mechanisms of the disease. Replacement therapy with intravenous immunoglobulins remains the treatment of choice, which allows significant improvement in the survival and quality of life. However progress should be made in the understanding of the pathophysiology and in the early detection of this disease, since a delay in the diagnosis may have harmful consequences in terms of morbidity and mortality.

MAMOT: hidden Markov modeling tool.

Hidden Markov models (HMMs) are probabilistic models that are well adapted to many tasks in bioinformatics, for example, for predicting the occurrence of specific motifs in biological sequences. MAMOT is a command-line program for Unix-like operating systems, including MacOS X, that we developed to allow scientists to apply HMMs more easily in their research. One can define the architecture and initial parameters of the model in a text file and then use MAMOT for parameter optimization on example data, decoding (like predicting motif occurrence in sequences) and the production of stochastic sequences generated according to the probabilistic model. Two examples for which models are provided are coiled-coil domains in protein sequences and protein binding sites in DNA. A wealth of useful features include the use of pseudocounts, state tying and fixing of selected parameters in learning, and the inclusion of prior probabilities in decoding. AVAILABILITY: MAMOT is implemented in C++, and is distributed under the GNU General Public Licence (GPL). The software, documentation, and example model files can be found at http://bcf.isb-sib.ch/mamot

Hemispheric asymmetry and theory of mind: Is there an association?

Introduction. In autism and schizophrenia attenuated/atypical functional hemispheric asymmetry and theory of mind impairments have been reported, suggesting common underlying neuroscientific correlates. We here investigated whether impaired theory of mind performance is associated with attenuated/atypical hemispheric asymmetry. An association may explain the co-occurrence of both dysfunctions in psychiatric populations. Methods. Healthy participants (n 129) performed a left hemisphere (lateralised lexical decision task) and right hemisphere (lateralised face decision task) dominant task as well as a visual cartoon task to assess theory of mind performance. Results. Linear regression analyses revealed inconsistent associations between theory of mind performance and functional hemisphere asymmetry: enhanced theory of mind performance was only associated with (1) faster right hemisphere language processing, and (2) reduced right hemisphere dominance for face processing (men only). Conclusions. The majority of non-significant findings suggest that theory of mind and functional hemispheric asymmetry are unrelated. Instead of ''overinterpreting'' the two significant results, discrepancies in the previous literature relating to the problem of the theory of mind concept, the variety of tasks, and the lack of normative data are discussed. We also suggest how future studies could explore a possible link between hemispheric asymmetry and theory of mind.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

PHA/HSCB Hidden Harm Action Plan

This joint PHA/HSCB Hidden Harm Action Plan - Responding to the needs of children born to and living with parental alcohol and drug misuse in Northern Ireland, has been prepared for the DHSSPS in response to the PfA target on Hidden Harm. The plan was approved by DHSSPS in October 2009.

Paleopharmacology and pollen: theory, method, and application

Parasitism was a universal human condition. Because of this, people developed herbal medicines to treat parasites as part of their pharmacopoeias. We propose that it is possible to recover evidence of medicinal plants from archaeological sites and link their use to specific health conditions. This is a multidisciplinary approach that must involve at least paleoethnobotanists, archaeoparasitologists, paleopathologists, and pharmacologists.