dataesr/scientific-paragraphs-categorization huggingface dataset:
3e-08 per site per year  68. DArTseq genotyping. A mapping population encompassing 186 individuals from a self-progeny of cultivar R570 and a panel of 34 accessions belonging to the Saccharum genus were genotyped through DArTseq at Diversity Arrays Technology Pty Ltd. , Australia. Two adapters corresponding to two different restriction enzyme overhangs were used; a PstI-compatible adapter, including a barcode region for multiplexing and a reverse NspI-compatible adapter. Only fragments that contained both adapters (PstI-NspI) were used to build sequencing libraries (Illumina, USA). The resulting libraries (two copies per sample) were sequenced in batches of 94 per lane on Illumina Hiseq2500. The raw sequencing data obtained were then demultiplexed using GBSX (https://github. com/GenomicsCoreLeuven/GBSX), filtered for quality using FASTQC (https://www.
bioinformatics. babraham. ac. uk/projects/fastqc/), and barcode adapters were removed using Cutadapt (http://opensource. scilifelab. se/ projects/cutadapt/). Only sequence reads ≥ 30 bp were kept for further analysis, representing 885 million reads for the mapping population and 161 million reads for the diversity data, corresponding to an average of around 5 million reads per sample. Construction of the R570 cultivar SNP-based genetic map. Identification of high-confidence single-dose SNP markers: DArTseq data from the 186 individuals from the self-progeny of R570 were analyzed to identify high-confidence singledose SNP markers useful for genetic mapping. The analysis was based on a de novo approach involving the assembly of a pseudomolecule that was further used as a template for read mapping.
This pseudomolecule was assembled by clustering all DArTseq reads using CD-Hit-EST  69  with the following parameters: -c 0. 9, -al 0. 9, -p 1, and -M 0. Clusters were then randomly assembled to form the pseudomolecule, with each cluster being separated by a stretch of 1000 "N". A pipeline (available at https://github. com/SouthGreenPlatform/VcfHunter/) was developed to perform the following complete process: (1) DArTseq reads were aligned on the pseudomolecule using BWA-MEM (http://bio-bwa. sourceforge. net); (2) a local realignment of reads around indels was performed with the GATK IndelRealigner (https://software. broadinstitute. org/gatk/documentation/tooldocs/3. 8-0/ org_broadinstitute_gatk_tools_walkers_indels_IndelRealigner. php); (3) metrics were generated at each single-nucleotide position within realignments (count number of A, C, G, and T at each position) with Bam-readcount (https://github.
com/genome/bam-readcount); (4) SNP positions on the pseudomolecule and allele count were converted in a standard variant-calling format file (VCF); and (5) the VCF was then screened to determine the genotype (homozygous vs. heterozygous) of each individual and at each position. Several criteria were taken into account to improve the genotype determination accuracy. First, a 30× minimum and 1000× maximum read depth was required and only diallelic polymorphic markers were considered. For heterozygous genotypes, the minor allele then had to be represented by at least two reads with a frequency of at least 4%. Ambiguous genotypes with a minor allele frequency ranging from 1 to 4% were converted as missing data (uncalled genotype). Homozygous genotypes were determined when no variant was observed or with a frequency of <1% (considered as sequencing error).
1 software 74 , a BLAST-based algorithm to compute protein homology analysis with use_bootstrap set to 1 and the default parameters for other options to identify orthologous genes between each genome pair. S. bicolor genes with orthologs in all of the aforementioned grass species were considered as core grass genes in this study. Primary protein sequences (with the longest transcript) for core grass genes were obtained from Phytozome for further analysis. Microcolinearity of sugarcane and sorghum genes: BAC sequences from the sugarcane STP were aligned with BLASTN on the sorghum genome to identify orthologous genomic regions between the two genomes. Then the gene content was compared for each orthologous region. Sugarcane/sorghum orthologous gene pairs located in the expected orthologous regions were considered as colinear genes, while orthologous gene pairs located in regions that differed from those expected were considered as non-collinear genes.
Divergence timing: Protein sequences from sugarcane gene models were aligned with the sorghum proteome using BLASTP and best bidirectional mutual hits were retained. The 17,704 resulting orthologous gene pairs were realigned using CLUSTALW (https://www. ebi. ac. uk/Tools/msa/clustalw2/). These alignments were used to guide nucleic coding sequence alignments with PAL2NAL (http://www. bork. embl. de/pal2nal/). Synonymous substitution rates (Ks) were calculated using the Nei-Gojobori method implemented in the PAML package. All of these steps were automated with an available Python script (synonymous_calc. py, https:// github. com/tanghaibao/bio-pipeline/tree/master/synonymous_calculation). Divergence time (T) was then estimated using T = MKs/2k, where MKs represents the median Ks (0. 1268) calculated from 17,704 orthologous gene pairs and k is the substitution rate of 6.
Acknowledgements We thank the  International Consortium for Sugarcane Biotechnology  for financial support of this work. This work was supported by the  South Green Bioinformatics platform  (http://southgreen. cirad. fr). The work conducted by the  Joint BioEnergy Institute  and the  Joint Genome Institute  was supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231  with the  Lawrence Berkeley National Laboratory.
Code availability. The pipeline developed to perform the complete process of SNP mapping, calling, and filtering is available at https://github. com/ SouthGreenPlatform/VcfHunter/. Data availability. BAC and STP sequences, as well as gene annotations are available on the sugarcane genome hub (http://sugarcane-genome. cirad. fr). BAC sequences have also been deposited in the European Nucleotide Archive (ENA) at the EMBL-European Bioinformatics Institute under accession number ERZ654945.
6. Recording of kinematics and spatiotemporal data by the Vicon® optoelectronic system with a sampling frequency of 120 Hz. Three 30-seconds tests were performed during which the subject was asked to walk at 4 km/h while looking straight (Fig. 5-a ). In between each trial, the subject continued to walk. 7. Placement of Lombax® Dorso corset. 8. Recording of gait kinematics and spatiotemporal data with wearing of corset (Fig. 5-b ). 9. Data processing was filtered to eliminate noise. A biomechanical model OLGA (Optimized Lower-Limb Gait Analysis) was applied to calculate the joints centers by the positions of the markers combined with the anthropometrics parameters of the subject.
2011). It also has the additional advantage of offering optical spectra datasets of the targets, facilitating follow-up studies. We limited ourselves to the Galaxy, because studying individual stars in the Magellanic Clouds remains a challenge even with dedicated XMM-Newton or Chandra observations (e. g. ,  Oskinova et al. 2013; Nazé et al. 2014b). The low spatial resolution as well as the limited sensitivity of current X-ray facilities renders the identification of individual stellar sources with L X < 10 32-33 erg s -1 difficult if not impossible. In addition, only "classical" Be stars were kept: very young objects (e. g. , HAeBe) and Be stars belonging to high-mass X-ray binaries (HMXBs) were excluded, as their high-energy emission is of a different nature than the emission we wished to study.
Stellar properties In total, 84 Be stars are associated with X-ray sources in current X-ray archives (Table  1 ). About two-thirds of them have usable spectra and are thus studied in more detail in Sect. 4. Table  2  lists them along with their main physical parameters, which are used throughout this paper: spectral types, magnitudes and color excesses, distances, bolometric luminosities, effective temperatures, and projected rotational velocities. Most spectral types were extracted from the BeSS database. In the absence of a given luminosity class, we assumed a class V. Simbad provided us with V-band magnitudes, and distances were derived from Hipparcos and Gaia (DR1 and DR2) parallaxes or from photometric estimates given in the literature. In all cases, we retained the most accurate measurement.
Following  Luri et al. (2018) , we quadratically added a 100 µarcsec systematic uncertainty to all Gaia DR2 parallaxes. Some of the brightest stars have no parallaxes in Gaia DR2  (Gaia Collaboration et al. 2018)  or have parallaxes that appear to be less accurate than those provided by DR1 or by the new Hipparcos reduction of van  Leeuwen (2007). The vast majority of the E(B-V) excesses were extracted from the Stilism database 3 (Capitanio et al. 2017) using the distance estimates. For the most distant targets that are located beyond the limit of the Stilism database, we used the infrared 3D map of  Marshall et al. (2006)  or took values from the literature. We assumed Av = 3. 1×E(B-V). To derive bolometric luminosities, we used the bolometric corrections from  Nieva (2013) , log(T e f f ) given by de  Jager & Nieuwenhuijzen (1987)  and absolute V magnitudes from  Wegner (2007).
XMM-Newton We downloaded from the archives all XMM-Newton datasets associated with the Be stars detected in the 3XMM. In addition, we downloaded the recent datasets that are not included in the 3XMM DR7, with the Be stars  Menkhib, HD 55135, CD-44 4392, HD 90599 and HD 90563, HD 305627, HD 306111, HD 117172, V807 Cen, and HD 225985  in their fields of view. These XMM-Newton data were locally processed with the Science Analysis Software (SAS) v16. 0. 0 using calibration files available in Oct. 2017 and following the recommendations of the XMM-Newton team 4. These observations were taken in various mode and filter combinations. We checked in each case that the filter was adapted to the target optical/UV luminosity. This was the case for all but three stars: (1) all observations of 48 Lib were taken with a thin filter, while the target magnitude requires a thick filter and the X-ray data therefore suffer from heavy optical loading and cannot be used (this target was thus discarded from the list of matches reported in Table  1 ); (2) One observation of V374 Car (ObsID=0113891201, Rev.
164) was taken with a medium filter rather than a thick one like the other exposures and this dataset was therefore discarded from further analyses; (3) The observations of V807 Cen were taken with the thin filter rather than the medium one but since the star is not detected (see below), this does not change the analyses. We also checked modes, as they allow for different levels of pile-up. Considering the 3XMM count rates, three potential problems exist: (1) from its count rates, π Aqr is at the limit of pile-up, but a previous, dedicated study showed that there is no pile-up problem for the XMM-Newton data  (Nazé et al. 2017 ); (2) BZ Cru is much too bright in X-rays for the full-frame mode used in ObsIDs 0109480101/201/401, hence those datasets were discarded; (3) γ Cas was observed in small-window mode, but its EPIC count rates still are above the pile-up limits, although a run of epatplot does not reveal the typical signatures of pile-up and we therefore kept the circular extraction as in  Smith et al.
(2012b). After the initial pipeline processing, the European Photon Imaging Camera (EPIC) observations were filtered to keep only the best-quality data (pattern 0-12 for MOS and 0-4 for pn). To assess the crowding near the targets in order to choose the best extraction region, a source detection was performed on each EPIC dataset using the task edetect_chain, which uses first sliding box algorithms and then performs a PSF fitting, on the 0. 3-10. 0 keV energy band and for a log-likelihood of 10. In this context, we note that, regarding the recent datasets, only Menkhib and HD 90563 are detected, and they were thus added to the detection list (Table  1 ). Furthermore, the results on the older datasets indicated no formal detection for NGC 869 1164, HD 93190, BQ Cru, or HD 228860: these sources thus are faint, at the limit of detection (indeed, the DR7 processing considers a log-likelihood detection level of 6, which is lower than our local processing).
e. , a minimum of ten counts) was reached in each spectral bin of the background-corrected spectra; unreliable bins below 0. 25 keV were discarded. Reflection grating spectrometer (RGS) data were also processed using the initial pipeline, but only for sources that are bright and centered in the field of view: γ Cas, V1075 Sco, δ Sco, Alfirk, and Menkhib. As for EPIC data, a flare filtering was also applied (using a threshold of 0. 12 cts s -1 ) when needed. Dedicated response files were calculated for both orders and both RGS instruments, and were subsequently attached to the source spectra for analysis. These high-resolution data are not reported here in detail, but they were used to check the results of the EPIC spectral fits. Finally, it is also important to examine the distance between the detected DR7 counterpart and the Be star.
For µ Cen, only the high proper motion star UCAC3 96-143035 seems to be detected in addition to µ Cen, but its 3XMM counterpart, 3XMM J134901. 2-422841, lies at 0. 7" of the optical source. For BD+61 2355, the field of view is centered on 2MASS J22535512+6243368, which is at 0. 2" from its counterpart 3XMM J225355. 1+624336, and other sources seem well centered (e. g. , 2MASS J22531578+6235262, which is at a similar off-axis angle as BD+61 2355, is at 0. 4" of its counterpart 3XMM J225315. 8+623526). In the field of Cl* NGC 884 LAV 1703, the late-A star NGC 884 2079 lies at 0. 5" of 3XMM J022142. 9+571830. This seems to indicate the absence of astrometric problems in these datasets, hence the association of these three Be stars with the 3XMM sources are doubtful. Finally, Chandra data also exist for the remaining sources, and help settle the association question.
Chandra We downloaded from the archives all Chandra datasets associated with the Be stars detected in the CXOGSG. In addition, we downloaded the grating data associated with HD 42054 as well as recent datasets, not included in the CXOGSG, with the Be stars V402 Car, CW Cep, Cl* NGC 3293 FEAS 32, and HD 215227 in their field of view. These Chandra data were locally reprocessed with CIAO 4. 9 and CALDB 4. 7. 3. A source detection using wavelets (wavdetect) was run on these new datasets, considering the full energy band, PSF maps, and two scale sizes. Only Cl* NGC 3293 FEAS 32 and HD 215227 are detected, providing for the latter a confirmation of the XMM-Newton detection. Regarding CXOGSG counterparts, we may note that most matches occur within 1", including for the new detections. Only Cl* NGC 869 LAV 1039 and ALS 4570 show larger distances (1.
1 ′′ ) radius. The surrounding annuli (with radii 20-60 px, or 47. 1-141. 4 ′′ ) were used for background for all but a few cases in which the source lies too close to  Histograms of the X-ray luminosities, X-ray to bolometric luminosity ratios, and hardness ratios. When several observations of the same object existed, the values used to derive the histograms correspond to averages. The dubious cases (colons in Table  1 ) are not considered in these figures and that the high-luminosity parameters of PZ Gem were used. the field-of-view edge; in these cases, a nearby circle devoid of sources was considered instead. The RMF matrix from the calibration database was used, but specific ARF response matrices were calculated for each dataset using xrtmkarf, considering the associated exposure map.
For Chandra, the CXOGSG catalog does not provide count rates; however, fluxes are available for most of the detections in the CSC (v1. 1 or v2, although the latter is incomplete at the time of writing). These fluxes do not rely on any model, they simply correspond to the sum of the energies of each incident source photon, scaled by the local value of the ARF at the location of the incident photon. A&A proofs: manuscript no. 33842corr_final We converted these count rates or observed photon fluxes using WebPIMMS 8 for absorbed apec models, as suitable for massive stars. In these models, the absorbing column was fixed to the interstellar one (estimated using the color excess of Table 2 and the formula of  Gudennavar et al. 2012). Furthermore, since the actual spectral properties are unknown and a range of temperatures was found for the thermal components in the spectral fits (see previous section), we decided to convert count rates for two temperatures in order to obtain upper and lower limits: values of 0.
2008 ), but we detect in our dataset a much larger variation in X-ray luminosity than reported before. In a Chandra exposure (ObsID 4474), 43 Ori brightens by one order of magnitude, reaching γ Cas characteristics (see below). However, some pile-up may artificially harden the spectrum in this case, therefore we do not count it as a secure γ Cas candidate. A more thorough investigation with more observations clearly needs to be performed to understand the nature of the X-ray emission of this system. Up to now, BD-13 • 4928 was unknown to display a particularly bright X-ray emission. However, with only a slightly harder character than common O-stars, it certainly does not show a rather flat spectrum at high energies, typical of the very hot plasma detected in γ Cas objects. The most likely candidates for explaining its X-ray properties are thus colliding winds or magnetic confinement, hence this target should be monitored, both optically and in X-rays.
010 -0. 098±0. 013 cts s -1. Optically, a dense decretion disk seems also required for a star to display a γ Cas behavior. Unfortunately, except for γ Cas and π Aqr, no optical spectra at the exact dates of the X-ray observations are available in the BeSS database for γ Cas analogs. However, available data at other times seem to confirm this tendency (Table  5 ). Known γ Cas analogs also display a limited range in spectral type, from late-O to early-B. This is also the case of our new cases, although V782 Cas and V771 Sgr may be of slightly later type than the bulk of γ Cas analogs. We note, however, that the spectral types are quite uncertain for these two stars. Finally, we examined the rotational velocities. In our sample, the harder emissions are usually reached only for the fastest rotators (left panel of Fig.
Summary and conclusion We have performed a survey of Oe and Be stars in the X-ray range using XMM-Newton, Chandra, and Swift data. To this aim, we have cross-correlated the BeSS catalog of Be stars with the 3XMM-DR7, Chandra-CXOGSG, and XMM-SL2 catalogs. We also searched for more recent public observations of such stars (not yet included in these catalogs). Some shortcomings are inherent to this approach: the BeSS database may be incomplete, Be phases may be missed for some objects (which would thus not appear in the list of Be stars), some X-ray datasets may not yet be public, and upper limits were not investigated (because of the large variation in quality of such limits between objects and the absence of reliable exposure maps in some cases, such as the XMM slew survey). However, focusing on homogeneous catalogs and secure detections ensures reliable results for the targets under investigation, which is why we preferred this approach.
For PZ Gem, only the "high" state (i. e. , the observation with the highest X-ray luminosity) is reported here. The short-term variability flag comes from Table  1 , and the long-term variability flag was set when significant variations of the observed fluxes (see Table  3 ) or Swift count rates are detected. Equivalent widths of the Hα line are estimated from the spectra in the BeSS database taken at the closest time with respect to the X-ray observation(s) (see also  Smith et al. 2016  for typical values for the previously known γ Cas analogs). These BeSS observations are rarely simultaneous. In this context, we note that  Rauw et al. (2013)  reported.
Acknowledgements. YN acknowledges support from the  Fonds National de la Recherche Scientifique (Belgium) , the  Communauté Française de Belgique , and the  PRODEX XMM-Newton  contract. CM acknowledges financial support from the  French Centre National d'Etudes Spatiales (CNES). We thank  R. Lallement  for providing us with automated distance-reddening curves from the STILISM database. This research has made use of the ADS as well as the SIMBAD database, operated at  CDS (Strasbourg, France). It also used the VizieR catalogue access tool (CDS, Strasbourg, France -initially described in A&AS 143, 23) and the facilities of the XCat-DB developped by the  Survey Science Center of the XMM-Newton satellite at Strasbourg Observatory. In addition to XMM-Newton, this work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www.
cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This work has also made use of the BeSS database, operated at  LESIA  ( Observatoire de Meudon, France ) and available on http://basebe. obspm. fr,as well as data and/or software provided by the  High Energy Astrophysics Science Archive Research Center (HEASARC) , which is a service of the  Astrophysics Science Division at NASA/GSFC  and the  High Energy Astrophysics Division of the Smithsonian Astrophysical Observatory. Article number, page 18 of 21 Nazé et al. : Hot stars observed by XMM-Newton II.
2017)e from the BeSS database except for those marked with s , which are from Simbad. In this context, we note that Simbad mentions O7V, F5III, and B0. 5III as spectral types for the companions of 15 Mon, CX Dra, and Alfirk, respectively. b)all E(B-V) extracted from the Stilism interface at http://stilism. obspm. fr(Capitanioet al. 2017)unless specified otherwise. c) DR1; Gaia DR1 (Gaia Collaboration et al. 2016), DR2: (Gaia Collaboration et al. 2018), H07: (van Leeuwen 2007) d) after de Jager & Nieuwenhuijzen (1987). e) Mean of values from all quoted references.
Gene Prediction, Functional Annotation, and Analysis of Taxonomical Distribution Gene prediction for the draft genome sequences of A. polyphaga and other Acanthamoeba species was performed using the Prodigal program, as we searched for sequences best matching those of giant viruses. This tool identifies ribosome binding sites to localize translation initiation positions and localizes precisely the 3 end of each gene  (Hyatt et al. , 2010). The hits identified as those best matching with giant virus genes were then checked and compared with the 47,246 genes predicted for A. polyphaga using GeneMarkES, a program developed specifically for eukaryotes  (Lomsadze et al. , 2005). For the functional annotation, sequences homologous with ORFs predicted from non-redundant scaffolds were searched for in the NCBI GenBank protein sequence database (nr) using the BLASTp program  (Altschul et al.
Comparative Genomic Analyses Predicted protein sequences of A. polyphaga were compared with those from giant viruses and those predicted from the draft genome sequences of the 13 other species of Acanthamoeba (Supplementary Table  S2 ). The orthologous genes (≥100 amino acids) of A. polyphaga and the other Acanthamoeba were identified using the Proteinortho program  (Lechner et al. , 2011). Phylogenetic reconstructions were then performed using two sets of aligned sequences. First, 16 draft genome sequences of Acanthamoeba strains classified in 14 species including two A. polyphaga strains and two A. castellanii strains were aligned by using the progressive Mauve program  (Darling et al. , 2010). Second, the 18S ribosomal DNA sequences from 33 different Acanthamoeba strains were retrieved, including those of the 16 Acanthamoeba strains classified in 14 Acanthamoeba species.