Recherche

Introduction

Building rich web environments aimed at helping scientists analyze their data is a common trend in bioinformatics. These software packages offer the flexibility and power of a high-level programming language, but they do not provide a user interface, enable component and workflow definition. JFlow combines a user friendly interface with an intuitive python API.

Methods

Jflow user interface gathers five jQuery plug-ins providing user oriented views.

• availablewf lists all runnable workflows accessible to users,
• activewf monitors all started, completed, failed, aborted and reseted workflows,
• wfform presents workflow editable parameters in a form,
• wfoutputs displays all outputs produced by the workflow organized per component,
• wfstatus shows the workflow execution state as a list or an execution graph.

Jflow integration: ( a ) a piece of the NG6 HTML code source in which is positioned an empty div to build the activewf plug-in and a modal box for the wfstatus plug-in. ( b ) The jQuery code in charge to build Jflow plug-ins and manage user action. When the select.activewf event is thrown from activewf-div , a function is called with two parameters: event and workflow . The last parameter stores all the workflow’s information, such as its name and its id, used in this example to update the modal box title and to build the wfstatus plug-in. ( c ) The status of the illumina_qc workflow with the id 26 displayed as a graph in the NG6 application

MCSMUS can extract minimal unsatisfiable or maximally satisfiable sets of clauses from CNF formulas. It has facilities for extracting either a single, several, or all such subsets. It has can use several SAT solvers as backend and comes with minisat, glucose, and lingeling backends.

This package generalises sensitivity analysis to simulation models with multivariate output. It makes it easy to run a series of independent sensitivity analyses on a set of output variables and to plot the results. Alternatively, it allows to apply sensitivity analyses to the variables resulting from the application of a multivariate method (such as PCA or splines or polynomial regression) to the output data (Lamboni et al., 2009).

The regression function can be defined explicitly as a function of independent variables and of unknown parameters or it can be defined as the solution of a system of differential equations. Heteroscedasticity of errors can be taken into account by modelling the variance function.

Several additional tools are included: plotting functions, functions to process series of estimations, calculate confidence intervals and confidence regions for parameters and functions of parameters, and process calibration study. The description of the models can be provided by using a symbolic syntax.

L’objectif de cette bibliothèque est de faciliter la modélisation et la simulation de modèles individus centrés, en dimension 1 ou 2.

On cherche a représenter la colonisation et l’expansion de populations. On distingue la phase de reproduction de la phase de compétition. La reproduction dépend des paramètres suivants :

• Le nombre maximal/minimal de descendants
• Une période juvénile
• Un effet Allee
• Un domaine d’expansion (finie ou pas)
• Une loi statistique de dissémination (loi gamma, exponentielle, exponentielle puissance

Ces paramètres seront ajustables par l’utilisateur via l’API (Application Programming Interface). La compétition dépend des paramètres suivants :

• de l’age
• du voisinage
• d’une loi statistique de compétition

Ces paramètres seront ajustables par l’utilisateur via l’API en dimension 1 ou 2. On cherche a représenter la colonisation et l’expansion de populations. On distingue la phase de reproduction de la phase de compétition.

bibliothèque C, développée à partir des bibliothèques stl, boost.
 

• Système d’exploitation : Debian squeeze 6.0.3
• Version du noyau Linux : 2.6.32-5-amd64
• Compilateurs : g++, gcc, gfortran, mpicc
•  R 2.15.1, R 3.1.0, Rmpi
• Diyabc  1.0.4.37
• openmpi-1.4.5 compilé avec OGE: mpicc,
• openmp: libgomp1
• Freefem++ 3.19.1

Date de mise en service :  2009-08-10

To analyze feedback in a single time series create a KDD object (Key Day Dataset) with the construction function kdd.from.raw.data and test fragmented time directionality with the function feedback.test.
To analyze the spatial pattern of feedback from a set of time series collected across a spatial domain,
create indices of feedback with the function feedback.stats, map the index with map.statistic, krige
the index with krige and test spatial variation in feedback with krige.test.

This website provides maps to explore where rainfall feedback could be occurring. We have produced these maps to encourage research on mechanisms involved in feedback and that could lead to the discovery of means to favor positive rainfall feedback, in other words to favor situations where a rainfall event will increase the likelihood of subsequent rainfalls. You will find details about how the maps were made and how to use them by clicking on DESCRIPTION, HOW TO USE, APPLICATIONS, and MAKE MORE MAPS in the navigation bar on the top of the page. You will find the maps by clicking on JAN-DEC, APR-SEPT, and OCT-MAR. Data used to make these maps can be downloaded by clicking on DATA.

Inferring reproductive and demographic parameters of populations is crucial to our understanding of species ecology and evolutionary potential but can be challenging, especially in partially clonal organisms. Here, we describe a new and accurate method, cloncase, for estimating both the rate of sexual vs. asexual reproduction and the effective population size, based on the frequency of clonemate resampling across generations. Simulations showed that our method provides reliable estimates of sex frequency and effective population size for a wide range of parameters. The cloncase method was applied to Puccinia striiformis f.sp. tritici, a fungal pathogen causing stripe/yellow rust, an important wheat disease. This fungus is highly clonal in Europe but has been suggested to recombine in Asia. Using two temporally spaced samples of P. striiformis f.sp. tritici in China, the estimated sex frequency was 75% (i.e. three-quarter of individuals being sexually derived during the yearly sexual cycle), indicating strong contribution of sexual reproduction to the life cycle of the pathogen in this area. The inferred effective population size of this partially clonal organism (Nc = 998) was in good agreement with estimates obtained using methods based on temporal variations in allelic frequencies. The cloncase estimator presented herein is the first method allowing accurate inference of both sex frequency and effective population size from population data without knowledge of recombination or mutation rates. cloncase can be applied to population genetic data from any organism with cyclical parthenogenesis and should in particular be very useful for improving our understanding of pest and microbial population biology.

Genetic variation in pathogen populations may be an important factor driving heterogeneity in disease dynamics within their host populations. However, to date, we understand poorly how genetic diversity in diseases impact on epidemiological dynamics because data and tools required to answer this questions are lacking. Here, we combine pathogen genetic data with epidemiological monitoring of disease progression, and introduce a statistical exploratory method to investigate differences among pathogen strains in their performance in the field. The method exploits epidemiological data providing a measure of disease progress in time and space, and genetic data indicating the relative spatial patterns of the sampled pathogen strains. Applying this method allows to assign ranks to the pathogen strains with respect to their contributions to natural epidemics and to assess the significance of the ranking. This method was first tested on simulated data, including data obtained from an original, stochastic, multi-strain epidemic model. It was then applied to epidemiological and genetic data collected during one natural epidemic of powdery mildew occurring in its wild host population. Based on the simulation study, we conclude that the method can achieve its aim of ranking pathogen strains if the sampling effort is sufficient. For powdery mildew data, the method indicated that one of the sampled strains tends to have a higher fitness than the four other sampled strains, highlighting the importance of strain diversity for disease dynamics. Our approach allowing the comparison of pathogen strains in natural epidemic is complementary to the classical practice of using experimental infections in controlled conditions to estimate fitness of different pathogen strains. Our statistical tool, implemented in the R package StrainRanking, is mainly based on regression and does not rely on mechanistic assumptions on the pathogen dynamics. Thus, the method can be applied to a wide range of pathogens.

An R package coupling polygon and point processes for assessing risk due to contaminant and their impact on exposed individuals

Implements basic tools for storing, handling and visualizing disease outbreak data, as well as simple analysis tools. OutbreakTools defines the new formal class obkData which can be used to store any case-base outbreak data, and provides summaries for these objects, alongside a range of functions for subsetting and data manipulation. It implements a range of graphics for visualising timelines, maps, contact networks and genetic analyses. It also includes a simple case-base outbreak simulation tool.

Le package R MTK résulte de la collaboration entre des statisticiens spécialistes des méthodes d’exploration numérique et des informaticiens spécialistes de la programmation orientée-objet. Il en résulte une conception et une architecture d’ensemble originales pour un package R qui a pour objet
de concilier l’interactivité d’un langage de script comme R et l’efficacité de programmation orientée-objet comme Java.

Exploitant la technologie offerte par R, cette architecture repose sur une représentation homogène des facteurs d’entrée, caractérisés par des lois de distribution, et sur une
décomposition de l’expérimentation numérique en étapes : i) choix des facteurs d’entrée et de leurs distributions d’incertitude ; ii) plan d’expérience ou d’échantillonnage (nous ne distinguerons pas les deux concepts) pour déterminer les combinaisons de niveaux des facteurs à simuler ; iii) simulation proprement dite pour obtenir les sorties du modèle ; iv) analyse des résultats de simulation ; v) restitution des résultats.