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.

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

Neither dashboard nor 'mashup' indices: an empirical wealth approach as a pathway to a comprehensive measure of development

The article is composed of two sections. The first one is a critical review of the three main alternative indices to GDP which were proposed in the last decades – the Human Development Index (HDI), the Genuine Progress Indicator (GPI), and the Happy Planet Index (HPI) – which is made on the basis of conceptual foundations, rather than looking at issues of statistical consistency or mathematical refinement as most of the literature does. The pars construens aims to propose an alternative measure, the composite wealth index, consistent with an approach to development based on the notion of composite wealth, which is in turn derived from an empirical common sense criterion. Arguably, this approach is suitable to be conveyed into an easily understandable and coherent indicator, and thus appropriate to track development in its various dimensions: simple in its formulation, the wealth approach can incorporate social and ecological goals without significant alterations in conceptual foundations, while reducing to a minimum arbitrary weighting.

Merging the hypothetical extraction method and the classical multiplier approach: a hybrid possibility for identifying key distributive sectors

The two main alternative methods used to identify key sectors within the input-output approach, the Classical Multiplier method (CMM) and the Hypothetical Extraction method (HEM), are formally and empirically compared in this paper. Our findings indicate that the main distinction between the two approaches stems from the role of the internal effects. These internal effects are quantified under the CMM while under the HEM only external impacts are considered. In our comparison, we find, however that CMM backward measures are more influenced by within-block effects than the proposed forward indices under this approach. The conclusions of this comparison allow us to develop a hybrid proposal that combines these two existing approaches. This hybrid model has the advantage of making it possible to distinguish and disaggregate external effects from those that a purely internal. This proposal has also an additional interest in terms of policy implications. Indeed, the hybrid approach may provide useful information for the design of ''second best'' stimulus policies that aim at a more balanced perspective between overall economy-wide impacts and their sectoral distribution.

A Bayesian network approach to the database serch problem in criminal proceedings

Background The 'database search problem', that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions. The method's graphical environment, along with its computational and probabilistic architectures, represents a rich package that offers analysts and discussants with additional modes of interaction, concise representation, and coherent communication.

The TRANSMED Atlas: geological-geophysical fabric of the Mediterranean region - Final report of the project

Geological research on the Mediterranean region is presently characterized by the transition from disciplinary to multidisciplinary research, as well as from national to international investigations. In order to synthesize and integrate the vast disciplinary and national datasets which are available, it is necessary to implement maximum interaction among geoscientists of different backgrounds. The creation of project-oriented task forces in universities and other research institutions, as well as the development of large international cooperation programs, is instrumental in pursuing such a multidisciplinary and supranational approach. The TRANSMED Atlas, an official publication of the 32nd International Geological Congress (Florence 2004), is the result of an international scientific cooperation program which brought together for over two years sixty-three structural geologists, geophysicists, marine geologists, petrologists, sedimentologists, stratigraphers, paleogeographers, and petroleum geologists coming from eighteen countries, and working for the petroleum industry, academia, and other institutions, both public and private. The TRANSMED Atlas provides an updated, synthetic, and coherent portrayal of the overall geological-geophysical structure of the Mediterranean domain and the surrounding areas. The initial stimulus for the Atlas came from the realization of the extremely heterogeneous nature of the existing geological-geophysical data about such domain. These data have been gathered by universities, oil companies, geological surveys and other institutions in several countries, often using different procedures and standards. In addition, much of these data are written in languages and published in outlets that are not readily accessible to the general international reader. By synthesizing and integrating a wealth of preexisting and new data derived from surficial geology, seismic sections at various scales, and mantle tomographies, the TRANSMED Atlas provides for the first time a coherent geological overview of the Mediterranean region and represents an ideal springboard for future studies.

Markov chain montecarlo method applied to rounding zeros of compositional data: first approach

As stated in Aitchison (1986), a proper study of relative variation in a compositional data set should be based on logratios, and dealing with logratios excludes dealing with zeros. Nevertheless, it is clear that zero observations might be present in real data sets, either because the corresponding part is completelyabsent –essential zeros– or because it is below detection limit –rounded zeros. Because the second kind of zeros is usually understood as “a trace too small to measure”, it seems reasonable to replace them by a suitable small value, and this has been the traditional approach. As stated, e.g. by Tauber (1999) and byMartín-Fernández, Barceló-Vidal, and Pawlowsky-Glahn (2000), the principal problem in compositional data analysis is related to rounded zeros. One should be careful to use a replacement strategy that does not seriously distort the general structure of the data. In particular, the covariance structure of the involvedparts –and thus the metric properties– should be preserved, as otherwise further analysis on subpopulations could be misleading. Following this point of view, a non-parametric imputation method isintroduced in Martín-Fernández, Barceló-Vidal, and Pawlowsky-Glahn (2000). This method is analyzed in depth by Martín-Fernández, Barceló-Vidal, and Pawlowsky-Glahn (2003) where it is shown that thetheoretical drawbacks of the additive zero replacement method proposed in Aitchison (1986) can be overcome using a new multiplicative approach on the non-zero parts of a composition. The new approachhas reasonable properties from a compositional point of view. In particular, it is “natural” in the sense thatit recovers the “true” composition if replacement values are identical to the missing values, and it is coherent with the basic operations on the simplex. This coherence implies that the covariance structure of subcompositions with no zeros is preserved. As a generalization of the multiplicative replacement, in thesame paper a substitution method for missing values on compositional data sets is introduced

The appropriateness of renal angioplasty: the ANPARIA software: a multidisciplinary expert panel approach

Percutaneous transluminal renal angioplasty (PTRA) is an invasive technique that is costly and involves the risk of complications and renal failure. The ability of PTRA to reduce the administration of antihypertensive drugs has been demonstrated. A potentially greater benefit, which nevertheless remains to be proven, is the deferral of the need for chronic dialysis. The aim of the study (ANPARIA) was to assess the appropriateness of PTRA to impact on the evolution of renal function. A standardized expert panel method was used to assess the appropriateness of medical treatment alone or medical treatment with revascularization in various clinical situations. The choice of revascularization by either PTRA or surgery was examined for each clinical situation. Analysis was based on a detailed literature review and on systematically elicited expert opinion, which were obtained during a two-round modified Delphi process. The study provides detailed responses on the appropriateness of PTRA for 1848 distinct clinical scenarios. Depending on the major clinical presentation, appropriateness of revascularization varied from 32% to 75% for individual scenarios (overal 48%). Uncertainty as to revascularization was 41% overall. When revascularization was appropriate, PTRA was favored over surgery in 94% of the scenarios, except in certain cases of aortic atheroma where sugery was the preferred choice. Kidney size [7 cm, absence of coexisting disease, acute renal failure, a high degree of stenosis (C70%), and absence of multiple arteries were identified as predictive variables of favorable appropriateness ratings. Situations such as cardiac failure with pulmonary edema or acute thrombosis of the renal artery were defined as indications for PTRA. This study identified clinical situations in which PTRA or surgery are appropriate for renal artery disease. We built a decision tree which can be used via Internet: the ANPARIA software (http://www.chu-clermontferrand.fr/anparia/). In numerous clinical situations uncertainty remains as to whether PTRA prevents deterioration of renal function.

Reverse immunology approach for the identification of CD8 T-cell-defined antigens: advantages and hurdles.

One of the challenges of tumour immunology remains the identification of strongly immunogenic tumour antigens for vaccination. Reverse immunology, that is, the procedure to predict and identify immunogenic peptides from the sequence of a gene product of interest, has been postulated to be a particularly efficient, high-throughput approach for tumour antigen discovery. Over one decade after this concept was born, we discuss the reverse immunology approach in terms of costs and efficacy: data mining with bioinformatic algorithms, molecular methods to identify tumour-specific transcripts, prediction and determination of proteasomal cleavage sites, peptide-binding prediction to HLA molecules and experimental validation, assessment of the in vitro and in vivo immunogenic potential of selected peptide antigens, isolation of specific cytolytic T lymphocyte clones and final validation in functional assays of tumour cell recognition. We conclude that the overall low sensitivity and yield of every prediction step often requires a compensatory up-scaling of the initial number of candidate sequences to be screened, rendering reverse immunology an unexpectedly complex approach.

An integrated approach for the assessment of HAB dynamics in two NW Mediterranean bays from a GEOHAB perspective

Alfacs and Fangar Bay in the Ebro Delta, NW Mediterranean are the major sites in Catalonia for shellfish cultivation. These bays are subject to occasional closures in shellfish harvesting due to the presence of phycotoxins. Fish kills have also been associated with harmful algal blooms. The comparison of phytoplankton dynamics in both bays offers the opportunity to reveal differences in bloom patterns of species known to be harmful for the ecosystem and aquaculture activities. Field research is underway under the GEOHAB framework within the Core Research Project on HABs in Fjords and Coastal Embayments. The overall objective of this study is to improve our understanding of HAB biogeographical patterns, and key elements driving bloom dynamics in time and space within these semi-constrained embayments. Via the comparative approach we aim to improve the prediction for monitoring purposes, with a focus on Karlodinium spp. associated with massive kills of aquaculture species. This objective is addressed by incorporating long-term time series of phytoplankton identification and enumeration with the first results of recent field work in both bays. The latter includes the application of optical sensors, to yield a complementary view with enhanced spatial and temporal resolution of bloom phenomena.

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest.

Functional connectivity (FC) as measured by correlation between fMRI BOLD time courses of distinct brain regions has revealed meaningful organization of spontaneous fluctuations in the resting brain. However, an increasing amount of evidence points to non-stationarity of FC; i.e., FC dynamically changes over time reflecting additional and rich information about brain organization, but representing new challenges for analysis and interpretation. Here, we propose a data-driven approach based on principal component analysis (PCA) to reveal hidden patterns of coherent FC dynamics across multiple subjects. We demonstrate the feasibility and relevance of this new approach by examining the differences in dynamic FC between 13 healthy control subjects and 15 minimally disabled relapse-remitting multiple sclerosis patients. We estimated whole-brain dynamic FC of regionally-averaged BOLD activity using sliding time windows. We then used PCA to identify FC patterns, termed "eigenconnectivities", that reflect meaningful patterns in FC fluctuations. We then assessed the contributions of these patterns to the dynamic FC at any given time point and identified a network of connections centered on the default-mode network with altered contribution in patients. Our results complement traditional stationary analyses, and reveal novel insights into brain connectivity dynamics and their modulation in a neurodegenerative disease.

A two-stage approach for tonic identification in indian art music

In this paper we propose a new approach for tonic identification in Indian art music and present a proposal for acomplete iterative system for the same. Our method splits the task of tonic pitch identification into two stages. In the first stage, which is applicable to both vocal and instrumental music, we perform a multi-pitch analysis of the audio signal to identify the tonic pitch-class. Multi-pitch analysisallows us to take advantage of the drone sound, which constantlyreinforces the tonic. In the second stage we estimate the octave in which the tonic of the singer lies and is thusneeded only for the vocal performances. We analyse the predominant melody sung by the lead performer in order to establish the tonic octave. Both stages are individually evaluated on a sizable music collection and are shown toobtain a good accuracy. We also discuss the types of errors made by the method.Further, we present a proposal for a system that aims to incrementally utilize all the available data, both audio and metadata in order to identify the tonic pitch. It produces a tonic estimate and a confidence value, and is iterative in nature. At each iteration, more data is fed into the systemuntil the confidence value for the identified tonic is above a defined threshold. Rather than obtain high overall accuracy for our complete database, ultimately our goal is to develop a system which obtains very high accuracy on a subset of the database with maximum confidence.

The achievable region approach to the optimal control of stochastic systems

The achievable region approach seeks solutions to stochastic optimisation problems by: (i) characterising the space of all possible performances(the achievable region) of the system of interest, and (ii) optimisingthe overall system-wide performance objective over this space. This isradically different from conventional formulations based on dynamicprogramming. The approach is explained with reference to a simpletwo-class queueing system. Powerful new methodologies due to the authorsand co-workers are deployed to analyse a general multiclass queueingsystem with parallel servers and then to develop an approach to optimalload distribution across a network of interconnected stations. Finally,the approach is used for the first time to analyse a class of intensitycontrol problems.

Evolution of intraspecific variation : a comparative phylogenetic approach

Understanding the evolution of intraspecific variance is a major research question in evolutionary biology. While its importance to processes operating at individual and population levels is well-documented, much less is known about its role in macroevolutionary patterns. Nevertheless, both experimental and theoretical evidence suggest that the intraspecific variance is susceptible to selection, can transform into interspecific variation and, therefore, is crucial for macroevolutionary processes. The main objectives of this thesis were: (l) to investigate which factors impact evolution of intraspecific variation in Polygonaceae and determine if evolution of intraspecific variation influences species diversification; and (2) to develop a novel comparative phylogenetic method to model evolution of intraspecific variation. Using the buckwheat family, Polygonaceae, as a study system, I demonstrated which life-history and ecological traits are relevant to the evolution of intraspecific variation. I analyzed how differential intraspecific variation drives species diversification patterns. I showed with computer simulations the shortcomings of existing comparative methods with respect to intraspecific variation. I developed a novel comparative model that readily incorporates the intraspecific variance into phylogenetic comparative methods. The obtained results are complimentary, because they affect both empirical and methodological aspects of comparative analysis. Overall, I highlight that intraspecific variation is an important contributor to the macroevolutionary patterns and it should be explicitly considered in the comparative phylogenetic analysis. - En biologie évolutive comprendre l'évolution de la variance intraspécifique est un axe de recherche majeur. Bien que l'importance de cette variation soit bien documentée au niveau individuel et populationnel, on en sait beaucoup moins sur son rôle au niveau macroévolutif. Néanmoins, des preuves expérimentales et théoriques suggèrent que la variance intraspécifique est sensible à la sélection et peut se transformer en variation interspécifique. Par conséquent, elle est cruciale pour mieux comprendre les processus macroévolutifs. Les principaux objectifs de ma thèse étaient : (i) d'enquêter sur les facteurs qui affectent l'évolution de la variation intraspécifique chez les Polygonaceae et de déterminer si l'évolution de cette dernière influence la diversification des espèces, et (2) de développer une nouvelle méthode comparative permettant de modéliser l'évolution de la variation intraspécifique dans un cadre phylogénétique. En utilisant comme système d'étude la famille du sarrasin, les Polygonacées, je démontre que les traits d'histoire de vie sont pertinents pour comprendre l'évolution de la variation intraspécifique. J'ai également analysé l'influence de la variation intraspécifique au niveau de la diversification des espèces. J'ai ensuite démontré avec des données simulées les limites des méthodes comparatives existantes vis à vis de la variation intraspécifique. Finalement, j'ai développé un modèle comparatif qui intègre facilement la variance intraspécifique dans les méthodes comparatives phylogénétiques existantes. Les résultats obtenus lors de ma thèse sont complémentaires car ils abordent aspects empiriques et méthodologiques de l'analyse comparative. En conclusion, je souligne que la variation intraspécifique est un facteur important en macroévolution et qu'elle doit être explicitement considérée lors d'analyses comparatives phylogénétiques.

The importance of individual heterogeneity in the decomposition of measures of socioeconomic inequality in health: An approach based on quantile regression

This paper shows how recently developed regression-based methods for thedecomposition of health inequality can be extended to incorporateindividual heterogeneity in the responses of health to the explanatoryvariables. We illustrate our method with an application to the CanadianNPHS of 1994. Our strategy for the estimation of heterogeneous responsesis based on the quantile regression model. The results suggest that thereis an important degree of heterogeneity in the association of health toexplanatory variables which, in turn, accounts for a substantial percentageof inequality in observed health. A particularly interesting finding isthat the marginal response of health to income is zero for healthyindividuals but positive and significant for unhealthy individuals. Theheterogeneity in the income response reduces both overall health inequalityand income related health inequality.