Deciduous Trees and the Application of Universal DNA Barcodes: A Case Study on the Circumpolar Fraxinus

The utility of DNA barcoding for identifying representative specimens of the circumpolar tree genus Fraxinus (56 species) was investigated. We examined the genetic variability of several loci suggested in chloroplast DNA barcode protocols such as matK, rpoB, rpoC1 and trnH-psbA in a large worldwide sample of Fraxinus species. The chloroplast intergenic spacer rpl32-trnL was further assessed in search for a potentially variable and useful locus. The results of the study suggest that the proposed cpDNA loci, alone or in combination, cannot fully discriminate among species because of the generally low rates of substitution in the chloroplast genome of Fraxinus. The intergenic spacer trnH-psbA was the best performing locus, but genetic distance-based discrimination was moderately successful and only resulted in the separation of the samples at the subgenus level. Use of the BLAST approach was better than the neighbor-joining tree reconstruction method with pairwise Kimura's two-parameter rates of substitution, but allowed for the correct identification of only less than half of the species sampled. Such rates are substantially lower than the success rate required for a standardised barcoding approach. Consequently, the current cpDNA barcodes are inadequate to fully discriminate Fraxinus species. Given that a low rate of substitution is common among the plastid genomes of trees, the use of the plant cpDNA “universal” barcode may not be suitable for the safe identification of tree species below a generic or sectional level. Supplementary barcoding loci of the nuclear genome and alternative solutions are proposed and discussed

The 2MASS Redshift Survey - Description and Data Release

We present the results of the 2MASS Redshift Survey (2MRS), a ten-year project to map the full three-dimensional distribution of galaxies in the nearby Universe. The 2 Micron All-Sky Survey (2MASS) was completed in 2003 and its final data products, including an extended source catalog (XSC), are available on-line. The 2MASS XSC contains nearly a million galaxies with Ks <= 13.5 mag and is essentially complete and mostly unaffected by interstellar extinction and stellar confusion down to a galactic latitude of |b|=5 deg for bright galaxies. Near-infrared wavelengths are sensitive to the old stellar populations that dominate galaxy masses, making 2MASS an excellent starting point to study the distribution of matter in the nearby Universe. We selected a sample of 44,599 2MASS galaxies with Ks =5 deg (>= 8 deg towards the Galactic bulge) as the input catalog for our survey. We obtained spectroscopic observations for 11,000 galaxies and used previously-obtained velocities for the remainder of the sample to generate a redshift catalog that is 97.6% complete to well-defined limits and covers 91% of the sky. This provides an unprecedented census of galaxy (baryonic mass) concentrations within 300 Mpc. Earlier versions of our survey have been used in a number of publications that have studied the bulk motion of the Local Group, mapped the density and peculiar velocity fields out to 50 Mpc, detected galaxy groups, and estimated the values of several cosmological parameters. Additionally, we present morphological types for a nearly-complete sub-sample of 20,860 galaxies with Ks = 10 deg.Comment: Accepted for publication in The Astrophysical Journal Supplement Series. The 2MRS catalogs and a version of the paper with higher-resolution figures can be found at http://tdc-www.harvard.edu/2mrs

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing

Purpose Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the “ClinVar low-hanging fruit” reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. Results We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). Conclusion The “ClinVar low-hanging fruit” analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock

IMproved Assessment of the Greenhouse gas balance of bioeNErgy pathways (IMAGINE)

Rapport de projetControversy is brewing about the potential greenhouse gas (GHG) savings resulting from the displacement of fossil energy sources by bioenergy, which mostly hinges on the uncertainty on the magnitude of nitrous oxide (N2O) emissions from arable soils occuring during feedstock production. The life-cycle GHG budget of bioenergy pathways are indeed strongly conditioned by these emissions, which are related to fertilizer nitrogen input rates but largely controlled by soil and climate factors. The IMAGINE project, funded by by the ENERBIO/Tuck Foundation from January 2010 to December 2011 aimed at improving the estimation of N2O emissions from local to regional scales using ecosystem models and measurements and modeling of atmospheric N2O in the greater Paris (France) basin. Ground fluxes of N2O were measured in two locations to assess the effect of soil type and management (in particular drainage), crop type (including lignocellulosics such as triticale, switchgrass and miscanthus), and climate on emission rates and dynamics. Atmospheric concentrations of N2O were monitored with high precision in 3 sites relevant to the Paris basin, using Radon tracing to produce source estimates. High-resolution maps of N2O emissions over France were simulated with a generic ecosystem model, O-CN, and an agro-ecosystem model, CERES-EGC, using geographical databases on soils, weather data, land-use and crop management. The models were tested against the ground flux measurements, and the emission maps were fed into the atmospheric chemistry-transport model CHIMERE. The maps were tested by comparing the CHIMERE simulations with time series of N2O concentrations measured at various heights in two locations in 2007. The emissions of N2O, as integrated at the regional scale, were used in a life-cycle assessment of representative biofuel pathways (bioethanol from wehat, sugar-beet and miscanthus; biodiesel from oilseed rape). Effects related to direct and indirect land-use changes (and their impact on soil carbon stocks) were also included in the assessment. The spatial distribution of the N2O emission generated with the O-CN and CERES-EGC ecosystem models differed markedly: O-CN simulated higher emissions in the west of France due to livestock farming whereas CERES-EGC emphasized the greater Paris basin with intensive cereal farming. This was partly due to variations in forcing such as N application rates, cropland area, and soil properties, but also to modelling concepts. In particular, the simulation of soil water balance and the response of N2O emissions to surface moisture follow different approaches in both models. On an annual basis, N2O emissions from agricultural soils over France totalled 17, 56 and 69 Gg N2O-N with the CERES-EGC, O-CN, and EDGAR32 maps, respectively. In both atmospheric measurement sites, simulations with the EDGAR32 map were closest to the observed concentrations, especially in spring when fertilizers are applied. This points to an underestimation by the ecosystem models by 20 to 80%, although both models compared well with measured ground fluxes in a few cropland test sites. Various causes for this pattern may be explored. First, indirect emissions via nitrate leaching were about half of the direct emissions according to the EDGAR database but were ignored by the ecosystem models. These may be easily introduced based on their simulations of nitrate leaching fluxes. Secondly, the regional inputs of mineral N-fertilizer were generally lower than fertilizer sales, by up to 50%, and this should be corrected. Lastly, models were mostly parameterized in sites with low N2O emissions rates, and should be tested in sites with higher emission potentials. The main results of the project may be summarized as follows: - an ambient air monitoring network was established for high accuracy N2O measurements, compatible with existing networks elsewhere in the world, in 3 sites in France; - high time resolved N2O surface concentrations were made available for two stations, along with estimates of N2O sources based on Rn-tracing over the 2 years of the project; ground fluxes were measured on arable crops in two locations (Orléans and Grignon), emphasizing the effect of rainfall patterns, drainage, fertilizer input rates and timing on N2O emissions. Measurements carried out over perennial lignocellulosic crops evidenced much smaller (up to an order of magnitude) N2O emission rates from these plants. two ecosystem models were improved and tested for the prediction of daily N2O emissions from agricultural and forest soils, resulting in prediction errors similar to the uncertainties in the observation erros. - simulated regional and global N2O emission maps are derived using a validated, process based N-cycle model implemented in the dynamic vegetation model; - The impact of higher model resolution is documented on simulated N2O mixing ratios with partitioning between the different emission sources; - A feasibility study for N2O inversions has been achieved; - A database contains the results open to the scientific community for further analysis; This report synthesizes the findings of the project, for each work-package, and closes with a tentative budget for N2O emissions in France in 2007 combining bottom-up and top-down estimates for the various sources (biogenic and non-biogenic)

Etude des mutations sur les sites de clivage du gène gag du VIH-1 chez des patients inféctés par le VIH-1 et traités par inhibiteurs de protéase au CHU de Reims

REIMS-BU Santé (514542104) / SudocSudocFranceF

Preservation and Evolution of Organic Matter During Experimental Fossilisation of the Hyperthermophilic Archaea Methanocaldococcus jannaschii

International audienceIdentification of the earliest traces of life is made difficult by the scarcity of the preserved microbial remains and by the alteration and potential contamination of the organic matter (OM) content of rocks. These factors can confuse interpretations of the biogenicity and syngenicity of fossilised structures and organic molecules found in ancient rocks. In order to improve our knowledge of the fossilisation processes and their effects at the molecular level, we made a preliminary study of the fate of OM during experimental fossilisation. Changes in the composition and quantity of amino acids, monosaccharides and fatty acids were followed with HPLC, GC and GC-MS analyses during 1 year of silicification of the hyperthermophilic Archaea Methanocaldococcus jannaschii. Although the cells themselves did not fossilise and the accompanying extracellular polymeric substances (EPS) did, our analyses showed that the OM initially present in both cells and EPS was uniformly preserved in the precipitated silica, with amino acids and fatty acids being the best preserved compounds. This study thus completes previous data obtained by electron microscopy investigations of simulated microbial fossilisation and can help better identification and interpretation of microbial biosignatures in both ancient rocks and in recent hydrothermal formations and sediments

Environmental assessment of biofuel chains based on ecosystem modelling, including land-use change effects

The potential greenhouse gas (GHG) savings resulting from the displacement of fossil energy sources by bioenergy mostly hinges on the uncertainty on the magnitude of nitrous oxide (N2O) emissions from arable soils occuring during feedstock production. These emissions are broadly related to fertilizer nitrogen input rates, but largely controlled by soil and climate factors which makes their estimation highly uncertain. Here, we set out to improve estimates of N2O emissions from bioenergy feedstocks by using ecosystem models and measurements and modeling of atmospheric N2O in the greater Paris (France) area. Ground fluxes were measured in two locations to assess the effect of soil type and management, crop type (including ligno- cellulosics such as triticale, switchgrass and miscanthus), and climate on N2O emission rates and dynamics. High-resolution maps of N2O emissions were generated over the Ile-de-France region (around Paris) with two ecosystem models using geographical databases on soils, weather data, land-use and crop management. The models were tested against ground flux measurements and the emission maps were fed into the atmospheric chemistry-transport model CHIMERE. The maps were tested by comparing the CHIMERE simulations with time series of N2O concentrations measured at various heights above the ground in two locations in 2007. The emissions of N2O, as integrated over the region, were used in a life-cycle assessment of representative biofuel pathways: bioethanol from wheat and sugar-beet (1st generation), and miscanthus (2nd generation chain); bio-diesel from oilseed rape. Effects related to direct and indirect land-use changes (in particular on soil carbon stocks) were also included in the assessment based on various land-use scenarios and literature references. The potential deployment of miscanthus was simulated by assuming it would be grown on the current sugar-beet growing area in Ile-de-France, or by converting land currently under permanent fallow. Compared to the standard methodology currently used in LCA, based on fixed emissions for N2O, the use of model-derived estimates leads to a 10 to 40% reduction in the overall life-cycle GHG emissions of biofuels. This emphasizes the importance of regional factors in the relationship between agricultural inputs and emissions (altogether with biomass yields) in the outcome of LCAs. When excluding indirect land-use change effects (iLUC), 1st generation pathways enabled GHG savings ranging from 50 to 73% compared to fossile-derived equivalents, while this figure reached 88% for 2nd generation bioethanol from miscanthus. Including iLUC reduced the savings to less than 5% for bio-diesel from rapeseed, 10 to 45% for 1st generation bioethanol and to 60% for miscanthus. These figures apply to the year 2007 and should be extended to a larger number of years, but the magnitude of N2O emissions was similar between 2007, 2008 and 2009 over the Ile de France region

Sensitivity analysis and uncertainty quantification for environmental models

International audienceEnvironmental models often involve complex dynamic and spatial inputs and outputs. This raises specific issues when performing uncertainty and sensitivity analyses (SA). Based on appli- cations in flood risk assessment and agro-ecology, we present current research to adapt the methods of variance-based SA to such models. After recalling the basic principles, we propose a metamodelling approach of dynamic models based on a reduced-basis approximation of PDEs and we show how the error on the subsequent sensitivity indices can be quantified. We then present a mix of pragmatic and methodological solutions to perform the SA of a dynamic agro-climatic model with non standard input factors. SA is then applied to a flood risk model with spatially distributed inputs and outputs. Block sensitivity indices are defined and a precise relationship between these indices and their support size is established. Finally, we show how the whole support landscape and its key features can be incorporated in the SA of a spatial model.Les modèles environnementaux contiennent souvent des entrées-sorties complexes de par leur nature dynamique et spatiale, ce qui soulève des problèmes spécifiques pour leurs analyses d'incertitude et de sensibilité (AS). A partir d'applications en évaluation des risques d'inondation et en agro-écologie, nous présentons des recherches en cours pour adapter les méthodes d'AS à de tels modèles. Après un rappel des principes de base, nous proposons une approche de métamodélisation de modèles dynamiques basée sur une approximation par base réduite d'EDPs et nous montrons comment l'erreur sur les indices de sensibilité qui en découle peut être quantifiée. Nous présentons ensuite un cocktail de solutions pragmatiques et méthodologiques pour l'AS d'un modèle agro-climatique dynamique avec des facteurs d'entrée non standards. Puis l'AS est appliquée à un modèle de risque d'inondation avec des entrées-sorties spatialisées. On définit des indices de sensibilité par bloc et une relation précise est établie entre ces indices et leur taille de support. Enfin, nous montrons comment l'ensemble du paysage et de ses caractéristiques clés peut être incorpore à l'AS d'un modèle spatial

Sensitivity analysis and uncertainty quantification for environmental models

National audienceEnvironmental models often involve complex dynamic and spatial inputs and outputs. This raises specific issues when performing uncertainty and sensitivity analyses (SA). Based on applications in flood risk assessment and agro-ecology, we present current research to adapt the methods of variance-based SA to such models. After recalling the basic principles, we propose a metamodelling approach of dynamic models based on a reduced-basis approximation of PDEs and we show how the error on the subsequent sensitivity indices can be quantified. We then present a mix of pragmatic and methodological solutions to perform the SA of a dynamic agro-climatic model with non standard input factors. SA is then applied to a flood risk model with spatially distributed inputs and outputs. Block sensitivity indices are defined and a precise relationship between these indices and their support size is established. Finally, we show how the whole support landscape and its key features can be incorporated in the SA of a spatial model.Les modèles environnementaux contiennent souvent des entrées-sorties complexes de par leur nature dynamique et spatiale, ce qui soulève des problèmes spécifiques pour leurs analyses d'incertitude et de sensibilité (AS). A partir d'applications en évaluation des risques d'inondation et en agro-écologie, nous présentons des recherches en cours pour adapter les méthodes d'AS à de tels modèles. Après un rappel des principes de base, nous proposons une approche de métamodélisation de modèles dynamiques basée sur une approximation par base réduite d'EDPs et nous montrons comment l'erreur sur les indices de sensibilité qui en découle peut être quantifiée. Nous présentons ensuite un cocktail de solutions pragmatiques et méthodologiques pour l'AS d'un modèle agro-climatique dynamique avec des facteurs d'entrée non standards. Puis l'AS est appliquée à un modèle de risque d'inondation avec des entrées-sorties spatialisées. On définit des indices de sensibilité par bloc et une relation précise est établie entre ces indices et leur taille de support. Enfin, nous montrons comment l'ensemble du paysage et de ses caractéristiques clés peut être incorporé à l'AS d'un modèle spatial