242 research outputs found

    The many ways of coping with pressure

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    En libre-accès sur Archimer : http://archimer.ifremer.fr/doc/00027/13797/11043.pdfInternational audienceThe current paper reviews strategies employed by microorganisms from the deep biosphere, especially piezophiles (from the greek piezo = to press and philo = love), to cope with high hydrostatic pressure (HHP) prevailing in these biotopes. The aim of this review is not to constitute an exhaustive report of our current knowledge on the physiology of piezophiles, as recent reviews have covered part of this subject in detail (Abe, 2007; Lauro and Bartlett, 2008; Michiels et al., 2008; Simonato et al., 2006). Rather, we illustrate here, via a few chosen examples, where we stand in our understanding of the mechanisms employed by microorganisms from the depths of our planet to cope with HHP

    A Random Field Model and its Application in Industrial Production

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    International audienceWe propose a new tool of decision support in front of a globally unknown phenomenon which is modeled by a random field representing simultaneously our knowledge and our lack of information.This tool is the distribution of a random variable called failure risk probability. Before giving the precise definition of this object, we describe an industrial context in which the decision problem occurs and we discuss Bayesian random field model constructions

    Complete Genome Sequence of Bradyrhizobium sp. Strain BDV5040, Representative of Widespread Genospecies B in Australia

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    International audienceWe report the complete genome sequence of Bradyrhizobium sp. strain BDV5040, representative of Bradyrhizobium genospecies B, which symbiotically associates with legume hosts belonging to all three Fabaceae subfamilies across the Australian continent. The complete genome sequence provides a genetic reference for this Australian genospecies.Bradyrhizobium sp. strain BDV5040 was isolated in 1995 from a root nodule of Bossiaea ensata (Fabaceae, Faboideae, Bossiaeeae) collected in Ben Boyd National Park, New South Wales, Australia (37°12′S, 149°57′E; altitude, 140 m), in the course of a survey of rhizobia associated with native shrubby legumes in southeastern Australia (1). It is a representative of Bradyrhizobium genospecies B, which occurs under different climatic and edaphic conditions across the whole Australian continent and exhibits a broad host range encompassing all three Fabaceae subfamilies (1–4).Strain BDV5040 was grown from a lyophilized stock in 30 ml of yeast extract mannitol broth (5) at 25°C and 200 rpm for 5 days. Genomic DNA was prepared by successive phenol-chloroform extractions as described (6). DNA quantification and quality control were performed using a NanoDrop spectrophotometer, a Qubit 4 fluorometer, and agarose gel electrophoresis. The same DNA was used for Nanopore and Illumina sequencing. Illumina libraries were obtained using the Nextera XT kit following the manufacturer’s instructions, starting with 1 ng of genomic DNA, and were analyzed by paired-end 2 × 300-bp sequencing on a MiSeq instrument. Poor-quality regions (Q 1,500 bp) and quality (score of >8) using Nanofilt v2.5.0 (11), and adapters were removed using Porechop v0.2.4 (12). Long reads were further reduced to 800 Mbp as a target quantity using Filtlong v0.2.0 (13) (parameters: --min_length 2000 --keep_percent 90 --target_bases 800000000). Illumina and Nanopore reads were coassembled using Unicycler v0.4.8 (14) with default parameters, resulting in a single component with eight segments and incomplete status (length, 7,622,333 bp; N50, 7,339,313 bp). Completion was obtained by exporting the sequence path from Bandage v0.8.1 (15) and filling a last gap using Pilon v1.23 (16) and by manually comparing the sequence with Unicycler 003_long_read_assembly.fasta. The assembly and complete chromosome sequence were carefully inspected by visualizing the alignment of long and short reads using minimap2 v2.17 (17) and IGV v2.7.2 (18). Finally, the chromosome was rotated to start at dnaA.The circular chromosome is 7,622,528 bp long, with an average G+C content of 63.92%. The sequence was automatically annotated by the NCBI Prokaryote Genome Annotation Pipeline (PGAP) v4.13 (19). The genome consists of 7,092 protein-coding genes, 48 tRNAs, 1 copy each of the 5S, 16S, and 23S rRNA genes, and 88 pseudogenes.Data availability.The genome sequence of Bradyrhizobium genospecies B strain BDV5040 is available in NCBI GenBank under accession number CP061379. The raw sequence reads are available under SRA accession numbers SRX9514896 and SRX9514898 under BioProject number PRJNA662585 and BioSample number SAMN16089659

    Automated region of interest retrieval and classification using spectral analysis

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    Efficient use of whole slide imaging in pathology needs automated region of interest (ROI) retrieval and classification, through the use of image analysis and data sorting tools. One possible method for data sorting uses Spectral Analysis for Dimensionality Reduction. We present some interesting results in the field of histopathology and cytohematology

    Towards a computer aided diagnosis system dedicated to virtual microscopy based on stereology sampling and diffusion maps

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    An original strategy is presented, combining stereological sampling methods based on test grids and data reduction methods based on diffusion maps, in order to build a knowledge image database with no bias introduced by a subjective choice of exploration areas. The practical application of the exposed methodology concerns virtual slides of breast tumors

    Coordinated Regulation of PPAR Expression and Activity through Control of Chromatin Structure in Adipogenesis and Obesity

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    The nuclear receptor peroxisome proliferator-activated receptor gamma (PPARγ) is required for differentiation and function of mature adipocytes. Its expression is induced during adipogenesis where it plays a key role in establishing the transcriptome of terminally differentiated white fat cells. Here, we review findings indicating that PPARγ expression and activity are intricately regulated through control of chromatin structure. Hierarchical and combinatorial activation of transcription factors, noncoding RNAs, and chromatin remodelers allows for temporally controlled expression of PPARγ and its target genes through sequential chromatin remodelling. In obesity, these regulatory pathways may be altered and lead to modified PPARγ activity

    How to better estimate bunch number at vineyard level?

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    Despite the extensive use of sampling to estimate the average number of grape bunches per vine, there is no clearly established sampling protocol that can be used as a reference when performing these estimations. Each practitioner therefore has their own sampling protocol. This study characterised the effect of differences between sampling protocols in terms of estimation errors. The goal was to identify the most efficient practices that will improve the early estimation of an important yield component: average bunch number. First, the appropriateness of including non-productive vines (i.e., dead and missing vines) in the sampling protocol was tested; the objective was to determine whether it is relevant to estimate two yield components simultaneously. Second, sampling protocols with sampling sites of varying size were compared to determine how the spatial distribution of observations and potential spatial autocorrelation affect estimation error. Third, a new confidence interval for estimation error was determined to express expected error as a percentage. It aimed at designing a new tool for finding the best sample size in an operational context. Tests were performed on two vineyards in the South of France, in which the number of bunches per vine had been exhaustively determined on all the plants before flowering. The results show that the simultaneous estimation of number of bunches and proportion of dead and missing vines increased the estimation errors by a factor of 2. Despite the low spatial autocorrelation of bunch number, the results show that the observation must be spread across at least 2 or 3 sampling sites to reduce estimation errors. Finally, the confidence intervals expressed as a percentage were validated and used to define an adequate sample size based on a compromise between the expected precision and the variability observed in the first measurements
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