Cell surface expression of the epithelial Na channel and a mutant causing Liddle syndrome: a quantitative approach.

The epithelial amiloride-sensitive sodium channel (ENaC) controls transepithelial Na+ movement in Na(+)-transporting epithelia and is associated with Liddle syndrome, an autosomal dominant form of salt-sensitive hypertension. Detailed analysis of ENaC channel properties and the functional consequences of mutations causing Liddle syndrome has been, so far, limited by lack of a method allowing specific and quantitative detection of cell-surface-expressed ENaC. We have developed a quantitative assay based on the binding of 125I-labeled M2 anti-FLAG monoclonal antibody (M2Ab*) directed against a FLAG reporter epitope introduced in the extracellular loop of each of the alpha, beta, and gamma ENaC subunits. Insertion of the FLAG epitope into ENaC sequences did not change its functional and pharmacological properties. The binding specificity and affinity (Kd = 3 nM) allowed us to correlate in individual Xenopus oocytes the macroscopic amiloride-sensitive sodium current (INa) with the number of ENaC wild-type and mutant subunits expressed at the cell surface. These experiments demonstrate that: (i) only heteromultimeric channels made of alpha, beta, and gamma ENaC subunits are maximally and efficiently expressed at the cell surface; (ii) the overall ENaC open probability is one order of magnitude lower than previously observed in single-channel recordings; (iii) the mutation causing Liddle syndrome (beta R564stop) enhances channel activity by two mechanisms, i.e., by increasing ENaC cell surface expression and by changing channel open probability. This quantitative approach provides new insights on the molecular mechanisms underlying one form of salt-sensitive hypertension.

Textural classification of land cover using support vector machines: an empirical comparison with parametric, non parametric and hybrid classifiers in the Bolivian Amazon

Land cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims to establish an efficient classification approach to accurately map all broad land cover classes in a large, heterogeneous tropical area of Bolivia, as a basis for further studies (e.g., land cover-land use change). Specifically, we compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbour and four different support vector machines - SVM), and hybrid classifiers, using both hard and soft (fuzzy) accuracy assessments. In addition, we test whether the inclusion of a textural index (homogeneity) in the classifications improves their performance. We classified Landsat imagery for two dates corresponding to dry and wet seasons and found that non-parametric, and particularly SVM classifiers, outperformed both parametric and hybrid classifiers. We also found that the use of the homogeneity index along with reflectance bands significantly increased the overall accuracy of all the classifications, but particularly of SVM algorithms. We observed that improvements in producer’s and user’s accuracies through the inclusion of the homogeneity index were different depending on land cover classes. Earlygrowth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land cover classes were mapped with producer’s and user’s accuracies of around 90%. Our approach seems very well suited to accurately map land cover in tropical regions, thus having the potential to contribute to conservation initiatives, climate change mitigation schemes such as REDD+, and rural development policies.

Syndrome métabolique, diabète sucré et vulnérabilité: une approche "syndémique" de la maladie chronique [Metabolic syndrome, diabetes mellitus and vulnerability: a syndemic approach of chronic diseases].

The pandemic metabolic syndrome is generally attributed to our lifestyle. The current therapeutic strategies are centered on the behavioral changes and pharmacotherapy. A deeply analysis reveals the importance of the socio-cultural determinants with a "dose-responses effect according to the socio-economic level. The "syndemic" theory, which puts at the same level the socio-cultural environment, the behaviors and biomedecine, suggests a more holistic approach. This theory suggests introducing other partners of care, such cultural-mediators and welfare workers trained in the care, to have finally an approach centered on the roots of the causes. The healthcare networks centered on the management of the costs of health should not forget the socio-cultural dimension, unless wanting to select the good cases.

The metaphysics of quantum gravity : a causal approach

The dissertation investigates some relevant metaphysical issues arising in the context of spacetime theories. In particular, the inquiry focuses on general relativity and canonical quantum gravity. A formal definition of spacetime theory is proposed and, against this framework, an analysis of the notions of general covariance, symmetry and background independence is performed. It is argued that many conceptual issues in general relativity and canonical quantum gravity derive from putting excessive emphasis on general covariance as an ontological prin-ciple. An original metaphysical position grounded in scientific essential- ism and causal realism (weak essentialism) is developed and defended. It is argued that, in the context of general relativity, weak essentialism supports spacetime substantivalism. It is also shown that weak essentialism escapes arguments from metaphysical underdetermination by positing a particular kind of causation, dubbed geometric. The proposed interpretive framework is then applied to Bohmian mechanics, pointing out that weak essentialism nicely fits into this theory. In the end, a possible Bohmian implementation of loop quantum gravity is considered, and such a Bohmian approach is interpreted in a geometric causal fashion. Under this interpretation, Bohmian loop quantum gravity straightforwardly commits us to an ontology of elementary extensions of space whose evolution is described by a non-local law. The causal mechanism underlying this evolution clarifies many conceptual issues related to the emergence of classical spacetime from the quantum regime. Although there is as yet no fully worked out physical theory of quantum gravity, it is argued that the proposed approach sets up a standard that proposals for a serious ontology in this field should meet.

Identifying genetic signatures of selection in a non-model species, alpine gentian (Gentiana nivalis L.), using a landscape genetic approach

It is generally accepted that most plant populations are locally adapted. Yet, understanding how environmental forces give rise to adaptive genetic variation is a challenge in conservation genetics and crucial to the preservation of species under rapidly changing climatic conditions. Environmental variation, phylogeographic history, and population demographic processes all contribute to spatially structured genetic variation, however few current models attempt to separate these confounding effects. To illustrate the benefits of using a spatially-explicit model for identifying potentially adaptive loci, we compared outlier locus detection methods with a recently-developed landscape genetic approach. We analyzed 157 loci from samples of the alpine herb Gentiana nivalis collected across the European Alps. Principle coordinates of neighbor matrices (PCNM), eigenvectors that quantify multi-scale spatial variation present in a data set, were incorporated into a landscape genetic approach relating AFLP frequencies with 23 environmental variables. Four major findings emerged. 1) Fifteen loci were significantly correlated with at least one predictor variable (R (adj) (2) > 0.5). 2) Models including PCNM variables identified eight more potentially adaptive loci than models run without spatial variables. 3) When compared to outlier detection methods, the landscape genetic approach detected four of the same loci plus 11 additional loci. 4) Temperature, precipitation, and solar radiation were the three major environmental factors driving potentially adaptive genetic variation in G. nivalis. Techniques presented in this paper offer an efficient method for identifying potentially adaptive genetic variation and associated environmental forces of selection, providing an important step forward for the conservation of non-model species under global change.

Approche diagnostique et prise en charge des hémoptysies massives [Management of massive hemoptysis : a multidisciplinary approach]

Plus de cent maladies peuvent se manifester par une hémoptysie, qui peut être le reflet d'une pathologie sous-jacente potentiellement sérieuse. Elle n'est massive que dans 5% des cas et devient alors une entité clinique souvent dramatique, mortelle dans 30% des cas, qui nécessite une approche multidisciplinaire en milieu de soins intensifs. Quelques cas cliniques introduisent la discussion des aspects diagnostiques et thérapeutiques de leur prise en charge. Après avoir assuré la survie immédiate, l'origine du saignement sera localisée par une endoscopie qui permettra de réaliser un éventuel tamponnement endobronchique. Une artériographie doit ensuite être effectuée, afin de tenter d'obtenir l'hémostase par embolisation du réseau artériel bronchique responsable de l'épisode d'hémoptysie dans la majorité des cas.

Tackling Food Poverty: Only a more comprehensive approach will work

Lessons from the Decent Food for All(DFfA) Intervention

Developing a population approach to gambling health issues

The Institute of Public Health in Ireland (IPH) has developed this briefing paper to highlight the health impacts connected to gambling and in particular problem gambling.  This paper was developed to give information to Government Departments who are currently reviewing gambling legislation across the island of Ireland.  It draws attention to the impact problem gambling can have on the individual, family and community health and well-being.

A modular approach for integrative analysis of large-scale gene-expression and drug-response data

High-throughput technologies are now used to generate more than one type of data from the same biological samples. To properly integrate such data, we propose using co-modules, which describe coherent patterns across paired data sets, and conceive several modular methods for their identification. We first test these methods using in silico data, demonstrating that the integrative scheme of our Ping-Pong Algorithm uncovers drug-gene associations more accurately when considering noisy or complex data. Second, we provide an extensive comparative study using the gene-expression and drug-response data from the NCI-60 cell lines. Using information from the DrugBank and the Connectivity Map databases we show that the Ping-Pong Algorithm predicts drug-gene associations significantly better than other methods. Co-modules provide insights into possible mechanisms of action for a wide range of drugs and suggest new targets for therapy

Multi-stage oligopoly models with nested logit demand structures: A simplifying approach

Solving multi-stage oligopoly models by backward induction can easily become a com- plex task when rms are multi-product and demands are derived from a nested logit frame- work. This paper shows that under the assumption that within-segment rm shares are equal across segments, the analytical expression for equilibrium pro ts can be substantially simpli ed. The size of the error arising when this condition does not hold perfectly is also computed. Through numerical examples, it is shown that the error is rather small in general. Therefore, using this assumption allows to gain analytical tractability in a class of models that has been used to approach relevant policy questions, such as for example rm entry in an industry or the relation between competition and location. The simplifying approach proposed in this paper is aimed at helping improving these type of models for reaching more accurate recommendations.

High throughput sequencing and proteomics to identify immunogenic proteins of a new pathogen: the dirty genome approach.

With the availability of new generation sequencing technologies, bacterial genome projects have undergone a major boost. Still, chromosome completion needs a costly and time-consuming gap closure, especially when containing highly repetitive elements. However, incomplete genome data may be sufficiently informative to derive the pursued information. For emerging pathogens, i.e. newly identified pathogens, lack of release of genome data during gap closure stage is clearly medically counterproductive. We thus investigated the feasibility of a dirty genome approach, i.e. the release of unfinished genome sequences to develop serological diagnostic tools. We showed that almost the whole genome sequence of the emerging pathogen Parachlamydia acanthamoebae was retrieved even with relatively short reads from Genome Sequencer 20 and Solexa. The bacterial proteome was analyzed to select immunogenic proteins, which were then expressed and used to elaborate the first steps of an ELISA. This work constitutes the proof of principle for a dirty genome approach, i.e. the use of unfinished genome sequences of pathogenic bacteria, coupled with proteomics to rapidly identify new immunogenic proteins useful to develop in the future specific diagnostic tests such as ELISA, immunohistochemistry and direct antigen detection. Although applied here to an emerging pathogen, this combined dirty genome sequencing/proteomic approach may be used for any pathogen for which better diagnostics are needed. These genome sequences may also be very useful to develop DNA based diagnostic tests. All these diagnostic tools will allow further evaluations of the pathogenic potential of this obligate intracellular bacterium.

Coevolutionary networks: a novel approach to understanding the relationships of humans with the infectious agents

Human organism is interpenetrated by the world of microorganisms, from the conception until the death. This interpenetration involves different levels of interactions between the partners including trophic exchanges, bi-directional cell signaling and gene activation, besides genetic and epigenetic phenomena, and tends towards mutual adaptation and coevolution. Since these processes are critical for the survival of individuals and species, they rely on the existence of a complex organization of adaptive systems aiming at two apparently conflicting purposes: the maintenance of the internal coherence of each partner, and a mutually advantageous coexistence and progressive adaptation between them. Humans possess three adaptive systems: the nervous, the endocrine and the immune system, each internally organized into subsystems functionally connected by intraconnections, to maintain the internal coherence of the system. The three adaptive systems aim at the maintenance of the internal coherence of the organism and are functionally linked by interconnections, in such way that what happens to one is immediately sensed by the others. The different communities of infectious agents that live within the organism are also organized into functional networks. The members of each community are linked by intraconnections, represented by the mutual trophic, metabolic and other influences, while the different infectious communities affect each other through interconnections. Furthermore, by means of its adaptive systems, the organism influences and is influenced by the microbial communities through the existence of transconnections. It is proposed that these highly complex and dynamic networks, involving gene exchange and epigenetic phenomena, represent major coevolutionary forces for humans and microorganisms.