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REGISTRY Investigators of the European Huntington's Disease Network and COHORT Investigators of the Huntington Study Group., Studying individuals with extreme phenotypes could facilitate the understanding of disease modification by genetic or environmental factors. Our aim was to identify Huntington's disease (HD) patients with extreme symbol digit modality test (SDMT) scores. We first examined in HD the contribution of cognitive measures of the Unified Huntington's Disease Rating Scale (UHDRS) in predicting clinical endpoints. The language-independent SDMT was used to identify patients performing very well or very poorly relative to their CAG and age cohort. We used data from REGISTRY and COHORT observational study participants (5,603 HD participants with CAG repeats above 39 with 13,868 visits) and of 1,006 healthy volunteers (with 2,241 visits), included to identify natural aging and education effects on cognitive measures. Separate Cox proportional hazards models with CAG, age at study entry, education, sex, UHDRS total motor score and cognitive (SDMT, verbal fluency, Stroop tests) scores as covariates were used to predict clinical endpoints. Quantile regression for longitudinal language-independent SDMT data was used for boundary (2.5% and 97.5% quantiles) estimation and extreme score analyses stratified by age, education, and CAG repeat length. Ten percent of HD participants had an extreme SDMT phenotype for at least one visit. In contrast, only about 3% of participants were consistent SDMT extremes at two or more visits. The thresholds for the one-visit and two-visit extremes can be used to classify existing and new individuals. The identification of these phenotype extremes can be useful in the search for disease modifiers., This work was in part funded by a grant from the EuropeanCommission under the 7th framework programme (RD-Connect, grantagreement number 305444).

Observations are the foundation for understanding the climate system. Yet, currently available land meteorological data are highly fractured into various global, regional and national holdings for different variables and timescales, from a variety of sources, and in a mixture of formats. Added to this, many data are still inaccessible for analysis and usage. To meet modern scientific and societal demands as well as emerging needs such as the provision of climate services, it is essential that we improve the management and curation of available land-based meteorological holdings. We need a comprehensive global set of data holdings, of known provenance, that is truly integrated both across Essential Climate Variables (ECVs) and across timescales to meet the broad range of stakeholder needs. These holdings must be easily discoverable, made available in accessible formats, and backed up by multi-tiered user support. The present paper provides a high level overview, based upon broad community input, of the steps that are required to bring about this integration. The significant challenge is to find a sustained means to realize this vision. This requires a long-term international program. The database that results will transform our collective ability to provide societally relevant research, analysis and predictions in many weather and climate related application areas across much of the globe.

SPINE stands for Spacecraft Plasma Network in Europe. Initiated in February 2000, the aims of this network are to share resources and co-ordinate efforts in all spheres related to the interaction of spacecraft with space plasma, including spacecraft charging. So far, one of its main outputs has been the development of complex simulation software, such as SPIS and PicUp3D, and an extensive set of reference data and documents. On a general basis, the aim of SPINE is to become a community of exchange and work regarding the spacecraft-plasma interactions and space plasma process. However, SPINE members are composed of researchers, industrials, users and developers spread around Europe and around the world. The core team of the SPIS project is already split into three groups, ONERA, Artenum Company, located in France, and ESA/ESTEC, in Netherlands, distributed on three separated by hundreds of kilometers. The core team is completed by numerous working groups of active SPINE's members, contributing to the validation of the software in an environment including more than twenty countries in EU and around the world. Members of the SPINE community need a common space, available through the Web, to improve their cooperation and data exchanges. Regarding the software development, the core team needs to share source codes of SPIS and keeps track of the successive versions. The strict respect of a pre-defined development-validation cycle and quality standards (ESA/ECSS E-40) requires the building of a complex production chain. Besides, SPIS has been developed for an open source release and to be progressively transferred to the community in a collaborative approach. Tools used for the development and the maintenance of the project must take into account this specific aspect of the project. In parallel, the validation process of scientific software is getting more and more critical with their increasing complexity. The scientific community has an important role to play. However, it needs to exchange complex data, such as CAD files, corresponding to the spacecraft geometry, environment parameters, simulations and instruments results. The co-writing of documents, scientific papers and technical reports, is also a key point in the whole process. This includes the public diffusion and collection of feedbacks from the international scientific and industrial communities., 資料番号: AA0049206077, レポート番号: JAXA-SP-05-001E

This paper presents a comparison of models for extreme rainfall and flood values. Based on a large set of thousands of rainfall and discharge data, the French ExtraFlo project showed that design estimate of extreme values based on the fitting of a distribution on a limited sample of maximum values is very inaccurate when only a few decades of observation are available. Some interesting alternatives are possible, using a regional approach, or historical data when available, or simulation methods, or sedimentological or geochemical signatures of flood deposits.

National audience; Présentation de la méthodologie de comparaison utilisée dans ExtraFlo

International audience

[Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAU [Departement_IRSTEA]Eaux [TR1_IRSTEA]ARCEAU; An abundance of methods have been developed over the years to perform the frequency analysis (FA) of extreme environmental variables. Although numerous comparisons between these methods have been implemented, no general comparison framework has been agreed upon so far. The objective of this paper is to build the foundation of a data-based comparison framework, which aims at complementing more standard comparison schemes based on Monte Carlo simulations or statistical testing. This framework is based on the following general principles: (i) emphasis is put on the predictive ability of competing FA implementations, rather than their sole descriptive ability measured by some goodness-of-fit criterion; (ii) predictive ability is quantified by means of reliability indices, describing the consistency between validation data (not used for calibration) and FA predictions; (iii) stability is also quantified, i.e., the ability of a FA implementation to yield similar estimates when calibration data change; and (iv) the necessity to subject uncertainty estimates to the same scrutiny as point estimates is recognized, and a practical approach based on the use of the predictive distribution is proposed for this purpose. This framework is then applied to a case study involving 364 gauging stations in France, where 10 FA implementations are compared. These implementations correspond to the local, regional, and local-regional estimation of Gumbel and generalized extreme value distributions. Results show that reliability and stability indices are able to reveal marked differences between FA implementations. Moreover, the case study also confirms that using the predictive distribution to indirectly scrutinize uncertainty estimates is a viable approach, with distinct FA implementations showing marked differences in the reliability of their uncertainty estimates. The proposed comparison framework therefore constitutes a valuable tool to compare the predictive reliability of competing FA implementations, along with the reliability of their uncertainty estimates.

International audience; INTRODUCTION: We had for aim to describe control and investigation of an outbreak caused by a strain of Extended spectrum beta-lactamase producing Klebsiella pneumoniae in intensive care units of the Brest teaching hospital. PATIENTS AND METHOD: The case definition was a patient infected by or carrying the epidemic strain. Control measures and investigations are presented. A case-control study was conducted in the surgical intensive care unit. Each case was matched with two controls based on admission times in the unit. The study focused on diagnostic and therapeutic procedures, and potential contacts with healthcare workers, in this context of cross transmission. RESULTS: Between February and May 2011, nine cases were reported in the surgical ICU and two in the medical ICU. Eighteen controls were matched with the nine surgical ICU cases. Several factors were found to be statistically associated with infection or colonization by the epidemic strain: the surgical block in which patients had been operated and the ward of first hospitalization; the number of trans-esophageal and trans-thoracic echocardiographies, of central venous catheter insertions, and of surgical operations; intubation. The total number of invasive procedures was also found to be statistically higher among cases. CONCLUSION: This study identified factors associated with colonization or infection by the epidemic strain. These factors might have been involved in the transmission tree, and be vulnerable elements for the prevention of nosocomial infections and colonisations, and their epidemic spread.

International audience; OBJECTIVES: The study objectives were to describe the investigation and management of an imipenem-resistant Acinetobacter baumannii outbreak that occurred in the 15-bed ICU of a tertiary care teaching hospital (Brest, France), during the summer 2008. PATIENTS AND METHODS: Patients harboring an imipenem-resistant A. baumannii strain were defined as case patients. We described case occurrence and steps taken to control the outbreak: contact isolation, reinforcement of hygiene procedures, unit shutdown decision, unit disinfection, and reopening. We also made a case control study and a cost analysis of the outbreak management. RESULTS: During a 10-day period, five patients were positive for a single clone of imipenem-resistant oxa-23 A. baumannii. Four patients presented with ventilation-acquired pneumonia and one was asymptomatic. The first two patients died one day after the first swab which led to the identification of A. baumannii. No additional case was noted in the ICU or in other hospital units after deciding to close the ICU. The cost of outbreak management was estimated at 264,553 euros. The case control study identified several factors associated with infection or colonization: length of stay in the ICU, chronic respiratory disease, number of previous antibiotic classes used, duration of ventilation, prone position, echocardiography, and presence of a nasogastric tube. CONCLUSION: This outbreak occurred during the summer period requiring the shutdown of the ICU and inducing a considerable cost. Rapid reactions of the ICU staff during the outbreak enabled to limit the epidemic.

International audience; Extreme value analysis is one of the cornerstones of hazard quantication and risk assessment. Its basic objective is to estimate the distribution of some environmental variable X, e.g. annual maximum of the areal rainfall over some catchment, annual maximum ood, etc. This distribution can be used to estimate the exceedance probability of a given value of X (often expressed in terms of return period), or alternatively, to estimate the p-quantile of X. The estimation of quantiles is of primary importance since they are used to design civil engineering structures (e.g. dams, reservoirs, bridges) or to map hazard-prone areas where restrictions may be enforced (e.g. building restrictions in ood zones). Extreme value analysis has been the subject of extensive research, yielding an abundance of approaches. In Hydrology, several families of methods exist, including (but not limited to): Standard application of extreme value theory (EVT), i.e. estimation of an extreme value distribution based on a sample of block maxima or peaks over a high threshold Climate/Weather-informed application of EVT. This family of methods uses additional meteorological (e.g., weather type, [1]) or climatic (e.g. Interdecadal Pacic Oscillation IPO, [2]) information. Regional approaches, conjointly using data from several sites to perform the inference, which may improve the precision of estimates. Model-based approaches, using a simulation model reproducing the main characteristics of the environmental variable (e.g. [1]). In practice, users and practitioners of extreme value analyses may feel lost facing such an abundance of methods. Consequently, it is necessary to provide them with practical guidelines to choose and implement adequate methods, depending on the conditions of application (e.g. availability of long series, geographical area, type of hydrological regime, etc.). This presentation describes a methodological framework to perform a data-based comparison of competing approaches for predicting extremes. This framework is based on the following principles: The objective is to assess the predictive performance of competing methods (as opposed to standard goodness-of- evaluations). This requires decomposing the available dataset into estimation / validation sub-samples. Reliably quantifying uncertainties is recognized as a primary objective, and the issue of scrutinizing uncertainty estimates is discussed. To this aim, we make use of predictive distributions for extremes, obtained by integrating out parameter uncertainty. Such predictive distributions are standard in a Bayesian context [4] but can also be derived in a frequentist context [5]. Reliability indices are derived in order to compare the performances of competing methods on an objective basis. In a second step, this framework is used to perform a thorough comparison between approaches currently used in France for extreme prediction. The comparison is based on an extensive dataset of long series of rainfall and runo (about 40-50 years of daily data), available for hundreds of sites over France. Results demonstrate the ability of the comparison framework to distinguish between "good" and "bad" approaches, and yield valuable insights into the optimal ambit of each approach.

International audience; Frequency analysis (FA) is one of the cornerstones of hazard quantification and risk assessment. Its basic objective is to estimate the distribution of some environmental variable X, e.g. annual maximum of the areal rainfall over some catchment, annual maximum flood, etc. This distribution can be used to estimate the exceedance probability of a given value of X (often expressed in terms of return period), or alternatively, to estimate the p-quantile of X, i.e. the value having an exceedance probability equal to 1-p. The estimation of quantiles is of primary importance since they are used to design civil engineering structures (e.g. dams, reservoirs, bridges) or to map hazard-prone areas where restrictions may be enforced (e.g. building restrictions in flood zones). FA has been the subject of extensive research, yielding an abundance of approaches. In practice, FA users and practitioners may feel lost facing such an abundance of methods. Consequently, several initiatives aimed at assisting users in realizing their analyses using best-practice methods. In addition to these end-user-oriented guideline documents, a large number of comparisons between competing methods have been reported in the research literature. The French National research project EXTRAFLO aims to perform a thorough comparison between FA approaches currently used in France, based on an extensive dataset of long series of rainfall and runoff. This poster provides a detailed description of the methodology used to perform the comparison, and presents preliminary results of its application to a large runoff dataset. More precisely, the following topics are presented: Difference between descriptive and predictive ability, and the need to decompose the dataset into calibration / validation sub-samples The issue of scrutinizing uncertainty estimates is discussed, and a method based on the concept of predictive distribution is proposed in order to compare the reliability of competing uncertainty estimates. Reliability indices are derived in order to compare the performances of competing methods on an objective basis. This methodological framework is first applied to synthetic data to verify the ability of the comparison framework to distinguish between good and bad FA approaches. Preliminary results based on a large runoff dataset are then discussed.

Frequency analysis (FA) is one of the cornerstones of hazard quantification and risk assessment. Its basic objective is to estimate the distribution of some environmental variable X, e.g. annual maximum of the areal rainfall over some catchment, annual maximum flood, etc. This distribution can be used to estimate the exceedance probability of a given value of X (often expressed in terms of return period), or alternatively, to estimate the p-quantile of X, i.e. the value having an exceedance probability equal to 1-p. The estimation of quantiles is of primary importance since they are used to design civil engineering structures (e.g. dams, reservoirs, bridges) or to map hazard-prone areas where restrictions may be enforced (e.g. building restrictions in flood zones). FA has been the subject of extensive research, yielding an abundance of approaches. In practice, FA users and practitioners may feel lost facing such an abundance of methods. Consequently, several initiatives aimed at assisting users in realizing their analyses using best-practice methods. In addition to these end-user-oriented guideline documents, a large number of comparisons between competing methods have been reported in the research literature. The French National research project EXTRAFLO aims to perform a thorough comparison between FA approaches currently used in France, based on an extensive dataset of long series of rainfall and runoff. This poster provides a detailed description of the methodology used to perform the comparison, and presents preliminary results of its application to large rainfall and runoff datasets. More precisely, the following topics are presented: 1. Presentation of the datasets, including more than 1000 series of daily runoff and more than 2000 series of daily rainfall 2. Decomposition of the datasets into calibration/validation sub-samples 3. The issue of scrutinizing uncertainty estimates is discussed, and a method based on the concept of predictive distribution is proposed in order to compare the reliability of competing uncertainty estimates. 4. Reliability indices are derived in order to compare the performances of competing methods on an objective basis. 5. This methodological framework is applied to the datasets and preliminary results are discussed.