dataesr/scientific-paragraphs-categorization huggingface dataset:
Array-comparative genomic hybridization (aCGH) Tumor DNAs were extracted as previously described  [31]  and controlled on Agilent Bioanalyzer (Agilent Technologies, Massy, France). Genomic profiles of 32 samples were established by using aCGH onto high-resolution 4x180K CGH microarrays (SurePrint G3 Human CGH Microarray Kit, Agilent Technologies, Massy, France). Human female DNA was used as reference (G152A, Promega). Both approaches and analysis methods are described in previous studies  [32] [33] [34]. All probes for aCGH were mapped according to the hg19/NCBI human genome mapping database. The copynumber was estimated for each gene by taking the value of the segment with the highest amplitude, then categorized into "Amp" (Log2ratio > 1), "Gain" (0. 5 < Log2ratio <= 1), "Loss" (-1 <= Log2ratio < -0.
Bioinformatics processing In a first data analysis pipeline, tumor sequence data were aligned to the human reference genome (UCSC hg19) using Burrows-Wheeler Aligner  [36]. Samples were sequenced at an average depth of 380x for the targeted regions. Bam files were processed as described  [35]. Then, the single nucleotide variants (SNVs) calling was done with FreeBayes version 0. 9. 9  [37]  with a minimal alternate variant frequency and coverage set at 0. 02 and 10. Insertions/deletions (indels) calling was done using GATK haplotype caller version 2. 5-2-gf57256b  [38]  with default parameters. The variants, i. e. SNVs and indels, were annotated with the Annotate Variation Software (ANNOVAR, version 2013-11-12). Known variants found in dbsnp129 and dbsnp137 with a variant allele frequency (VAF) superior to 1% (1000G or ESP6500) were removed.
Finally, low frequency SNVs and indels that were suspected to be false positive were systematically inspected with IGV version 2. 3. 32  [39, 40]. In a second data analysis pipeline, both tumor and germinal sequences were analyzed using the same procedure with light modifications. Somatic SNV calling was done with Mutect 1. 7  [41]  and somatic indel calling was done with Scalpel. All variants were then annotated for genes and function using ANNOVAR. Exonic (non-synonymous, stopgain, stoploss, ins/del frameshift, ins/del non-frameshift) and splicing variants found somatic were kept. In order to remove false positives, recurrent variants with none entry in public databases such as COSMIC or dbsnp were removed. In addition, SNV with a t_lod_fstar score under 80 or with an alternate allele frequency < 5% as well as the indels were systematically inspected with IGV.
Acknowledgments We thank  Pr. Edmund Derrington ,  Dr. Servane Tauszigdelamasure  and  Dr. C eline Delloye-Bourgeois  for proofreading the manuscript and helpful comments. Funding VC is supported by funding from the  LABEX CORTEX and Labex DevWeCAN of Universit e de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR).
Routine identification of colonies was carried out using matrix-assisted laser desorption-ionization time-offlight mass spectrometry (MALDI-TOF MS) on a Microflex spectrometer (Bruker Daltonics, Bremen, Germany)  [3, 4] , which failed to identify strain mt23 T. Therefore, the 16S rRNA gene was sequenced with fD1-rP2 primers as previously described  [5]  using a 3130-XL sequencer (Applied Biosciences, Saint Aubin, France). The resulting sequence showed a 95. 1% similarity with the 16S rRNA gene of Lachnoclostridium phytofermentans strain ISDg (GenBank Accession number NR_074652. 1), the phylogenetically closest species (Fig. 1 )  [6]. According to the 16S rRNA gene sequence similarity for species delineation of prokaryotes  [7, 8] , strain mt23 T is representative of a new species within the recently described Lachnoclostridium genus.
We then propose the name of 'Lachnoclostridium massiliosenegalense' (mas. si. li. o. se. ne. gal. en 0 sis. L. gen. masc. n. massiliosenegalense contraction of the Latin name of Marseille where strain mt23 T was isolated and Senegal where the stool sample was collected) for which strain mt23 is the type strain. MALDI-TOF-MS spectrum. The MALDI-TOF-MS spectrum of 'L. massiliosenegalense' is available at http://www. mediterranee-infection. com/article. php?laref=256&titre=urmsdatabase. Nucleotide sequence accession number. The 16S rRNA gene sequence was deposited in GenBank under Accession number LT161890. Deposit in a culture collection. Strain mt23 T was deposited in the Collection de Souches de l'Unité des Rickettsies (CSUR, WDCM 875) under number P299 and in the Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH (DSMZ) under number DSM 102084.
Genotyping and genetic scores calculation Genotyping was performed using published TaqMan (Applied Biosciences/Life Technologies, Grand Island, NY), and KASPar (KBioscience, Hertfordshire, UK) technologies. We selected 19 SNPs that were associated with CHD in whites in genome wide association studies (GWASs) or metaanalysis of candidate gene association studies. 13, 14 A nonweighted GRS was calculated for each individual by summing the number of risk alleles at each locus (0 if risk allele was absent and 1 if heterozygote or 2 if homozygote for the risk allele). A weighted score was calculated by multiplying the number of risk alleles at each locus by the corresponding b coefficient from the CARDIoGRAMplusC4D meta-analysis data (or the CARDIoGRAM GWAS if this was not available). Data on coronary artery disease/myocardial infarction were contributed by CARDIoGRAMplusC4D investigators Results: In Afro-Caribbeans, patients with CHD had higher rates of hypertension (78.
Statistical analysis Only participants with complete data regarding TCRFs and genotype were included for the analysis. In the Afro-Caribbean sample, we randomly selected 2 controls for each case. A c 2 test was used to assess whether each SNP was in Hardy-Weinberg equilibrium. The GRSs were considered as continuous variables. We used logistic regression to assess the association of GRSs and TCRFs with CHD as a binary dependant variable. To evaluate the predictive value of GRS in Afro-Caribbeans, we used the receiver-operating characteristic curve analysis and calculated the area under the curve from logistic models adjusted by age and sex. IBM SPSS Statistics, version 21 (SPSS, Chicago, IL) was used for analysis. All tests were 2 sided and P values of < 0. 05 were considered significant.
Different GRS constructions have been used in many studies, but the GRS usually added only modestly to the disease prediction based on TCRF models. 11, 25 In this study, we used the GRS to assess CHD risk within the Afro-Caribbean population but also through a comparison between 2 populations of different ancestry. Published works using this approach at an interpopulation level are scarce. Our data indicate that based on the observed differences in GRS between the 2 ethnic groups, the expected risk for CHD would be 44%-54% lower in Afro-Caribbeans than in whites, which is comparable to the 50% lower CHD mortality in these individuals reported in France and the United Kingdom. 7, 10 Although we have demonstrated the potential utility of the nonweighted GRS within the Afro-Caribbean population, the data demonstrate that the published weighting from the white population reduces the predictive value of the GRS.
Choice of relevant risk prevention plans (PPR) PPR are prescribed for municipalities. The 11 abovementioned departments gather 4075 municipalities. Therefore, some criteria were defined in order to select only municipalities with relevant approved PPR. Firstly, municipalities were sorted according to the Gaspar database 1 from the DGPR, which gathers information about plans (e. g. existence/absence, phenomena taken into account, approval date). From this, PPR taking into account ³WRUUHQWLDO floods´ and approved after 2005 (it turned out that it was the most optimal date to get a satisfactory number of recent PPR), were selected. Secondly, two other criteria were applied. The first one allows excluding PPR established over SODLQV ¶ areas. In fact, the Gaspar database uses a definition of ³WRUUHQWLDO floods´ which GRHVQ ¶W take into account solid transport, but only the short-time response of streams, which can be found over flat areas and not exclusively in mountains.
Extraction of PPR ¶V data This study aims at highlighting methods/ways of torrential protection integrations within actual hazard and land-use zoning, as well as qualifying the quality of traceability of the process. To do this, several types of information were exhaustively extracted from the 53 selected PPR following strictly the same approach, so the comparison between them would be relevant. Therefore, 304 protective civil engineering structures were counted as long as they were mentioned into documents. Most of them refer to individual structures (e. g. one check-dam), but some relates to protective devices (e. g. a series of check-dams set along a torrent channel). Even if counting individually each single structure would have made sense from a methodological point of view, that ZDVQ ¶W always possible according to PPR preciseness.
To date, several works were initiated to try to bring solid bases to strive towards an unified torrent protection typology. In France, the Rehabilitation of Mountainous Lands department put in place a database where are recorded protection works set over national perimeters within the 11 studied mountain departments. This database gathers many kinds of information on works including their functions. Focusing on sediment traps, work from Piton & Recking (2015)  [25] , makes a relevant review of the literature concerning the function and the design of these particular structures. In Austria, work from  Hübl and Suda (2008)   [26]  gives an overview of debris flow structural mitigation measures (including debris flow breakers, check-dams, debris flow net barriers etc. ) with regard to their functions.
TEER represents a marker of redifferentiation of the epithelial cells across the cell culture. There are some data from adults  [43] [44] [45] [46]  showing lower TEER in asthmatic epithelial cultures, but fewer data are available in children. One study, using cultures derived from bronchial brushings, found no significant difference in TEER between asthmatic and non-asthmatic children hemokine gene expression ans laeoinflammatory transcription factors (NF-kB, AP-1 and STAT1/2) chemokine gene expression ans lae  [36]. Epithelial integrity can be investigated by performing wound repair experiments. Bronchial epithelial cells from asthmatic children lack the ability to successfully repair mechanically induced wounds  [47]. A defect in fibronectin was found to be the potential link to explain this abnormal repair process in asthma.
In radar altimetry the electromagnetic (EM) bias is originated by the smaller reflectivity of wave crests than troughs, thus the average sea surface height is under-estimated. Bias uncertainty is currently the largest factor in altimetry error budgets. The EM bias in a bistatic forward-scattering configuration at L-band, such as in Global Navigation Satellite Systems-Reflectometry (GNSS-R) altimetry, remains one of the major sources of uncertainty in the altimetry error budget. In this work the EM bias is computed using numerical simulations. To do so, a time-dependent synthetic non-Gaussian sea surface is created using the Pierson-Moskowitz and Elfouhaily sea surface height spectra and spreading function. The sea surface is then discretized in facets and "illuminated" using a Right Hand Circular Polarization (RHCP) GNSS signal, previously recorded by an up-looking antenna connected to a data logger.
Acknowledgments The research leading to these results has received funding from the  European Commission  under the  FP7 Research Infrastructures project AIDA , grant agreement no. 262025 , the  French ANR program , the  French IN2P3 Quarks and Leptons program  and the  JSPS KAKENHI  Grant Number  23000002  and the  MEXT KAKENHI  Grant Number  23104007. We would like to thank the  CERN  and DESY test beam facility team for their support. We would like to thank the  CALICE collaboration for productive discussions.
However, the main limitation of airborne sensors is their geographic coverage, restricted to areas accessible with airplanes; some large remote areas such as High Mountain Asia can hardly be monitored. LiDAR data from the Ice Cloud and Elevation Satellite (ICESat) altimeter can also provide information on glacier elevation changes, but these data are too sparse to allow reliable monitoring of the mass budget of individual glaciers (Kropác ěk and others, 2014;  Kääb and others, 2015). DEMs can also be derived from medium-to-high resolution optical satellite images such as Satellite pour l'Observation de la Terre-High Resolution Stereoscopic (SPOT5/HRS) images or radar satellite images as done during the Shuttle Radar Topographic Mission (SRTM). However, the vertical precision of these DEMs (∼5 m) is such that it remains challenging to accurately estimate the MB of individual small to medium-sized glaciers (glaciers < 10 km 2 )  (Paul and others, 2011; Gardelle and others, 2013).
Seasons are in fact, more relevant timescales to better understand the glacier response to climate fluctuations, allowing the partitioning of recent glacier mass loss between changes in accumulation (occurring mainly in winter) and changes in ablation (occurring mainly in summer), at least in the mid-and polar latitudes  (Ohmura, 2011). Hulth and others (2013)  combined melt modelling using meteorological data and snowline tracking measured in the field with GPS to determine winter glacier snow accumulation along snowlines. Huss and others (2013)  calculated the evolution of glacier-wide MB throughout the ablation period also by combining simple accumulation and melt modelling with the fraction of snow-covered surface mapped from repeated oblique photography. Both methods have been applied for only one or two glaciers, and a few years as they are time-consuming and require various datasets (meteorological data, remotely sensed data) that are not widely available.
In this context, the consistent archive of optical SPOT/ VEGETATION (SPOT/VGT) images, spanning 1998-2014 with a daily and nearly global coverage at 1 km resolution  (Sylvander and others, 2000; Maisongrande and others, 2004; Deronde and others, 2014) , remains an unexploited dataset to estimate the year-to-year seasonal MB variations of glaciers. Instead of detecting the exact physical snowline altitude of a glacier as done in earlier studies, our new method focuses on monitoring a 'mean regional' seasonal altitude of snow (Z), in a region surrounding a glacier, during summer (from early May to the end of September) and winter (from early October to the end of April). The interannual dynamics of this altitude Z, estimated from SPOT/ VGT images, is then compared with the interannual variation of observed seasonal MBs (derived from direct glaciological measurements), available continuously every year over 1998-2008, for 55 glaciers in the Alps  (WGMS, 2008 (WGMS, , 2012 (WGMS, , 2013;; Huss and others, 2010a, b, 2015).
The performance of the 55 individual linear regressions between Z and MB are analysed in terms of the linear determination coefficient R 2 and the RMSE. For each of the 55 regressions, we then perform a cross-validation of the temporal robustness and skill of the regression coefficients. For all 55 glaciers, seasonal MBs are then calculated outside of the calibration period, i. e. each year of the 2009-2014 interval. A validation is realized for some glaciers with observed seasonal MB over 2009-2014. Finally, we discuss the possible causes of differences in the results between seasons, between glaciers and present the limits and perspectives of our methodology. 2002). The instruments provide a long-term dataset (16 a) with accurate calibration and positioning, continuity and temporal consistency.
The SPOT/VGT sensors have four spectral bands: the blue B0 (0. 43-0. 47 μm), the red B2 (0. 61-0. 68 μm), the Near Infra-Red B3 (0. 78-089 μm) and the Short Wave Infra-Red SWIR (1. 58-1. 75 μm). Both sensors provide daily images of almost the entire global land surface with a spatial resolution of 1 km, in a 'plate carree' projection and a WGS84 datum  (Deronde and others, 2014). In this study, we used the SPOT/VGT-S10 products (10-daily syntheses) freely available between April 1998 and May 2014 (http://www. vitoeodata. be/PDF/portal/Application. html#Home). The SPOT/ VGT-S10 products, processed by Vlaamse Instelling voor Technologisch Onderzoek (VITO), result from the merging of data strips from ten consecutive days (three 10-d syntheses are made per month) through a classical Maximum Value Composite (MVC) criterion  (Tarpley and others, 1984; Holben, 1986).
Satellite data The terrain elevation over the studied region has been extracted from the SRTM30 DEM, derived from a combination of data from the SRTM DEM (acquired in February 2000) and the U. S. Geological Survey's GTOPO30 dataset  (Farr and Kobrick, 2000; Werner, 2001; Rabus and others, 2003). The SRTM30 spatial resolution is 30 arcsec (∼ 900 m) and the geodetic reference is the WGS84 EGM96 geoid. The DEM has been resampled using the nearest-neighbour method to a 1 km spatial resolution in order to match the resolution of the S10 SPOT/VGT images.
Glacier MB data Glacier-wide winter and summer MB data of 55 different glaciers are used in this study (Table  S1  in the Supplementary Material; Fig. 1 ), covering a total area of ∼400 km 2 , corresponding to 19% of the 2063 km 2 glacier area in the Alps  (Pfeffer and others, 2014). Winter MB (B w ) is measured through to the end of April. Summer MB (B s ) is the difference between annual MB determined at the end of the ablation period (end of September) and winter MB  (Thibert and others, 2013). Winter MB corresponds to the snow/ice mass accumulated during the winter season (from October year-1 to April year of measurement ); thereafter, for more clarity, B w_obs refers to the winter MB measured in April of the year of measurement and B s_obs to the summer MB measured in September of the year of measurement.
The different MB datasets all start before 1998. However, as we later compare these MB data with SPOT/VGT snow maps available since April 1998, we used seasonal MB only after 1998. The first MB dataset is composed of direct glaciological measurements provided by the World Glacier Monitoring Service  (WGMS, 2013). Seasonal MB of 44 Austrian, French, Italian and Swiss glaciers of the European Alps are available between 1950 and 2012 (for the longest series) but with discontinuous data. At the time of access to the database, continuous seasonal MB time series were only available for seven glaciers since 1998 (labelled 'source 1' in Table  S1 ). The second MB dataset provided by  Huss and others (2015)  is derived from recently re-evaluated glaciological measurements. For seven Swiss glaciers (labelled 'source 2' in Table  S1 ), long-term continuous seasonal MB series from 1966 (at least) to 2014 are inferred from point measurements extrapolated to the entire glacier using distributed modelling, with year-to-year variability directly given by the in situ measurements.
The third MB dataset, composed of 41 Swiss glaciers, is a comprehensive set of field data homogenized using distributed modelling, with year-to-year variation constrained by meteorological data  (Huss and others, 2010a, b). The seasonal MB time series of 21 glaciers covering 30% of the total glacier area of Switzerland (labelled 'source 3a' in Table  S1 ), are available for each year of the 1908-2008 period  (Huss and others, 2010a). For 20 other glaciers located in the southeastern Swiss Alps (labelled 'source 3b' in Table  S1 ), seasonal MB time series are provided each year from 1900 to 2008  (Huss and others, 2010b). The continuous seasonal MB series of these 41 glaciers are derived from three different data sources: (1) Geodetic data of volume change for periods of four to ∼ 40 a; three to nine DEMs have been used for each glacier since 1930, mostly originating from aerial photogrammetry or terrestrial topographic surveys for the first DEM.
(2) (Partly isolated) in situ measurements of accumulation and ablation for about half of the glaciers; they were used to constrain MB gradients and winter accumulation. (3) Distributed accumulation and temperature-index melt modelling  (Hock, 1999; Huss and others, 2008)  forced with meteorological data of daily mean air temperature and precipitation, recorded at various weather stations close to each of the glaciers. The model is constrained by ice volume changes obtained from DEM differencing and calibrated with in situ measurements when available. Huss and others (2008)  provide more details on the model and calibration procedure. In order to have the most complete set of continuous interannual MB data to calibrate our method, we chose to    S1 ). Furthermore, seasonal MBs are also available for some of these 55 glaciers outside the calibration period  (2009-2014, i.
e. the end of the period covered by SPOT/VGT data). In winter, 66 additional B w _ obs data are available in total for 19 glaciers over 2009-2014 and in summer, 49 B s_obs measurements are available for 13 glaciers over 2009-2013 (summer 2014 is not covered by SPOT/VGT)  (WGMS, 2013; Huss and others, 2015). Errors associated with B w _ obs and B s_obs are not provided individually per glacier. Zemp and others (2013)  find an average annual random error of 340 mm w. e. a -1 for 12 glaciers with long-term MB measurements. Huss and others (2010a)  have quantified the uncertainty in glacier-wide winter balance (see Supplementary Information in their paper) as ±250 mm w. e. a -1. This number is not directly applicable to all glaciers (depending on the sampling), but to most of them. Systematic errors associated with in situ glaciological MBs are expected to be within 90-280 mm w.
e. a -1 ) (Fig. 6f ; Table  3 ). Therefore, in summer, we can conclude on the robustness and the temporal stability of about 60% of the 55 calibrated relationships (presenting an RMSE s_cal < 400 mm w. e. a -1 and a skill score >0. 65). In order to test the robustness of our approach outside the calibration period 1998-2008, we then compare seasonal VGT MB time series and independent observed MB data (not used for calibration) over the evaluation period 2009-2014.
e. a -1 ) during summer 2009. No obvious explanation was found for this anomalous year. Excluding summer 2009, the MBE s_eval is reduced to 52 mm w. e. a -1. However, validation is made with fewer points in summer than in winter. Furthermore, glaciers used for evaluation in summer are not the best performers over the calibration period: the mean RMSE s_cal of the 13 evaluation glaciers (368 mm w. e. a -1 ) is higher than the mean RMSE s_cal of the entire (55 glaciers) dataset (318 mm w. e. a -1 ). Observed MB measurements for more glaciers and more years will be welcome to improve the robustness of the linear regressions between MB and Z in summer and to provide a more representative error RMSE s_eval on the MB estimated with SPOT/VGT.
The performance of our method during the calibration (in terms of R 2 and RMSE) also varies with the source of the MB data. The best results are obtained with glaciers from source 3 (particularly for winter). The year-to-year variability in this dataset is based on meteorological time series (temperature, precipitation) and distributed modelling. As NDSI and interannual snow cover dynamics are also driven by meteorological parameters, we expected to find a good agreement but we are unable to conclude on the robustness of the method to estimate MB. However, a satisfying mean error for the evaluation period, assessed only with MB data from sources 1 and 2, strengthens the robustness of the method. In fact, these MBs are closer to 'reality' as they are composed of in situ glaciological measurements.