dataesr/scientific-paragraphs-categorization huggingface dataset:
PVL impacts mortality and other factors impact severity in a dose-dependent manner We examined the possible association of previously identified severity parameters (death, hemoptysis, and leukopenia)  (Gillet et al. , 2021)  with the proteomic data, assuming that such an FIGURE 2 Variations in the expression of virulence factors between clinical strains. The relative amount of virulence factors expressed in log2 and categorized into functional groups are represented using violin plots. A total of 136 clinical strains were analyzed. The red vertical lines represent the medians and the blue lines represent the 1 st and 3 rd quartiles. analysis based on quantitative data could reveal novel associations, particularly regarding the virulence factors of core genome, thus present in all strains.
Acknowledgments We thank  Binh Diep ,  Simon Foster ,  Julie A. Morrisey ,  Ambrose Cheung  and the  Network on Antimicrobial Resistance in Staphylococcus aureus (NARSA)  for distribution by  BEI Resources, NIAID, NIH , for providing strains. We thank the scientists and technicians of the  French National Reference Center for Staphylococci  for strain characterization, and  Veŕeńa Landel  from the  Hospices Civils de Lyon (France)  for help in manuscript preparation.
Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The proteomic data have been deposited on Peptide Atlas with accession n°PASS03811. The genomic data of the detectability and validation sets presented in the study are deposited in the ENA repository, accession number n°P RJEB60963. The genomic data of the CAP strains set presented in the study are deposited in the ENA repository, accession number n°PRJEB54685.
The SEBP analysis is based on the technique developed by  Meng et al. 11  (see Methods for more details). We collect the broadband seismograms from two large-aperture arrays at teleseismic distance, located in China (CH) and Alaska (AK, the lower inset in Figure  1a ). Slowness correction terms for the mainshock are derived using nearby aftershocks (Supplement Table  S1  and Extended Data Figures  4 5 ) to further mitigate the spatial bias caused by path effects (Methods). The coseismic slip distribution of the Mw 7. 8 event is shown in Figure  2   According to the FFI, the rupture duration is ~100 s, with the southwest rupture tip reaching the southern Amanos fault, close to the Karasu Fault (Figure  1 ). The total rupture length is estimated to be ~370 km, based on our SEBP results. This value is very close to that estimated by field and satellite data (~368 km, ref.
SAR Data Processing The Sentinel-1 SAR images are processed with the InSAR Scientific Computing Environment (ISCE) version 2 software 1 using the "topsApp. py" application  2. The two-level 1 Single Look Complex (SLC) data are coregistered with each other using the geometrical co-registration with refinement from the enhanced spectral diversity technique  3. Then we applied the speckle tracking (also known as pixel tracking, amplitude cross-correlation) analysis to pairs of Sentinel-1 SLC images (detailed pairs information are listed in Supplement Table  S5 ) to estimate the coseismic surface deformation in slant range (radar line-of-sight) and ground azimuth (alongtrack) direction. For the speckle tracking, we oversampled the coregistered SLC by a factor of 32, used a cross-correlation window or chip with a relatively large size of 128 pixels in range direction and 64 pixels in azimuth direction (about 585 x 900 m on the ground) to reduce the estimation noise 4.
The geodetic data include 24 high-rate GNSS recordings (UD, EW, NS for 8 stations, displacement waveforms, Extended Data Figure  2 ) and static ground displacements derived from Sentinel-1 SAR data (Figures  1a  and 1b , Extended Data Figure  3 ; see SAR Data Processing for more details). The combination of multiple datasets could provide better constraints on the slip amplitude, slip distribution, rupture speed, and rise time than any single dataset  13, 14. The seismic and high-rate GNSS data are transformed into wavelet domains. Compared with conventional inversion only in frequency or time domain, the wavelet transform allows us to simultaneously capture the characteristics in the high-frequency and low-frequency and preserve the information in the timedomain  8, 12. The misfits for static ground deformation measured by SAR are calculated separately since they could not be included in the wavelet transform.
5 event, respectively. The mainshock and aftershocks' hypocenter locations and magnitudes based on the AFAD catalog are shown in Supplementary Tables S1(a)-(d), along with comparisons of our aftershock locations before and after slowness calibration (Extended Data Figures  4  and 5 ). The root-mean-square distance error relative to the AFAD locations decreases by at least 61% after calibration for all our applications. The dense network of accelerometer stations along the Pazarcık and Amanos segments (white triangles in Figure  1a ) provides us with a good benchmark to validate the SEBP results. We collect recordings from 33 near-field accelerometer stations. After detrending and demeaning accelerometer recordings, we bandpass-filter them using a frequency band of 1-4 Hz to match the BP band of the CH array application.
Acknowledgements We thank  Gilles Peltzer  for valuable comments and discussions. We thank the  Disaster and Emergency Management Authority of Turkey (AFAD)  for providing strong motion data and local earthquake catalog. We thank  TUSAGA-Aktif  for providing high-rate GNSS data. This work contains modified Copernicus data from the Sentinel-1 satellite provided by the  European Space Agency  (ESA). L. X. , S. M. , and L. M. were supported by  NSF  grant no. EAR-1848486  and Leon and Joanne V. C. Knopoff Fund. C. J. was supported by grants from the  Southern California Earthquake Center (SCEC) -funded by  NSF  cooperative agreement  EAR-0109624  and  USGS  cooperative agreement  02HQAG0008. J. -P. A. was supported by the  French government  through the  UCAJEDI Investments in the Future project  ( ANR-15-IDEX-01 ) managed by the  French National Research Agency (ANR).
Data availability The Copernicus Sentinel-1 data are provided by ESA and obtained from the Alaska Satellite Facility (https://search. asf. alaska. edu/) via Seamless SAR Archive (SSARA), a service provided by EarthScope Consortium. The fault rupture traces are obtained from USGS  (Reitman et al. , 2023). The high-rate GNSS data are provided by TUSAGA-Aktif (https://www. tusagaaktif. gov. tr/). The strong motion data are available at the Disaster and Emergency Management.
Code availability The SAR processing is performed using the ISCE-2 software (Rosen et  al. , 2012), available at https://github. com/isce-framework/isce2 with postprocessing recipe available at https://github. com/yunjunz/2023-Turkey-EQ. The MATLAB code of SEBP is available at https://github. com/lsmeng/MUSICBP/tree/SEBP. The Python code of Mach wave analysis is available at https://github. com/lsmeng/MUSICBP/tree/MachWave. All other codes used in this study are available upon request.
Materials Ungulate teeth were selected from the stratigraphic subcomplexes CIII, CII inf. , and CII sup. from Lazaret Cave. We selected those that were identified at the species level and whose occlusal surfaces presented facets of wear (i. e. belonging to adult individuals). All lower and upper right teeth were selected, with some left teeth added to increase the sample size when they did not belong to the same individuals as the right ones (according to tooth size and stages of wear). For subcomplex CIII, the sample was later subdivided into four sub-units (GUA A to D) to compare the microwear patterns in C. elaphus. The list of specimens analysed and the raw data from tooth mesowear and microwear analyses are available at http:// dx. doi. org/ 10. 5281/ zenodo. 73085 55  (Rivals 2022).
1999; El-Zaatari 2010; Uzunidis et al. 2021). Microwear features were identified and quantified on the paracone of the upper teeth and the protoconid of the lower teeth. Two types of features were identified according to the classification proposed by  Solounias and Semprebon (2002). Pits are circular or sub-circular features that appear either bright (small pits) or dark (large pits). Scratches are long microfeatures with straight and parallel sides. These features were quantified in a standard squared area of 0. 16 mm 2 using an ocular reticule. The results obtained on the fossil samples were compared with a dataset established from extant ungulates with known diets  (Solounias and Semprebon 2002; Rivals et al. 2010). Finally, to estimate the duration of the faunal assemblages' accumulation at the Lazaret Cave, the standard deviation (SD) and the coefficient of variation (CV) computed on the numbers of scratches were employed to quantify the variability of the microwear signal  (Rivals et al.
2010). When comparing the three subcomplexes from the Lazaret Cave, dietary diversity appeared to be higher in subcomplex CII sup. , where grazers, mixed feeders, and browsers Fig. 3  Bivariate plot of the number of pits and scratches for the ungulates from Lazaret Cave. Error bars correspond to standard error of the mean (±1 SEM) for each sample. Plain ellipses correspond to the Gaussian confidence ellipses (p = 0. 95) on the centroid for the extant leaf browsers (LB) and grazers (G) based on the reference database from  Solounias and Semprebon (2002)  were present. This suggests that a greater diversity of vegetal resources was available to the ungulates in subcomplex CII sup. than in the other subcomplexes. By contrast,  Valensi and Abbassi (1998)  detected no environmental change throughout the sequence.
South Col Glacier elevation change We used aerial photographs and tri-stereo Pléiades data to generate DEMs and investigate surface elevation changes. The Pléiades DEM was generated from imagery acquired on 23 March 2017 using the Ames Stereo Pipeline software  (Berthier et al. , 2014; Beyer et al. , 2018). The 1984 DEM was generated from a subset of images acquired by Bradford Washburn supported by Werner Altherr and Swissair Photo and Surveys over the wider Everest region on 20 December 1984 using a Wild RC-10 camera  (Washburn, 1989)  supported by the National Geographic Society and the Boston Museum of Science. We focus on three images in which the South Col Glacier is located centrally within the frame to avoid peripheral image distortion, and we processed images from a single flight line in the survey to avoid problems associated with DEM merging.
To ensure the precise coregistration of both DEMs prior to differencing, we removed shifts (all below 2 m) following  Nuth and Kääb (2011). We modified the GAMDAM glacier inventory (GG18;  Sakai, 2019)  over the surroundings of the South Col using the Pléiades orthoimage (0. 5 m) to delineate glacier boundaries, allowing for DEM co-registration over stable, off-glacier surfaces. We also defined the outlines of South Col Glacier, which is not identified as a single glacier in the original GG18 inventory (Fig. 1 ). Following DEM differencing, surface elevation change data (dH) were filtered to remove outliers, with values outside the range of 5 times the standard deviation of dH estimates within 50 m elevation bands removed below 6800 m a. s. l. Above 6800 m, or from the base of the much steeper Lhotse face, we applied a threshold of 3 times the standard deviation of dH estimates, as the range of elevation change values here include high-magnitude elevation changes (± ∼ 35 m) associated with crevasse field evolution captured by both DEMs.
A total of 14 images with poor co-registration were horizontally shifted by manually selecting four GCPs. All other images are well co-registered and orthorectified in the original SRE product. We produce natural color composites using the band combination 7-4-3, corresponding to red, green and blue bands, respectively. The equalize_adapthist function from the Python scikit-image package was used to obtain a rendering that highlights the blue-ice areas (Fig. 2 ). We rely on visual inspection of the images only and did not attempt to automatically classify the snow-covered areas. The South Col Glacier exhibits strong seasonal contrasts in terms of snow cover. Here we show only one image per month for the year 2019 (Fig. 2 ), but the whole image series is available  (Brun, 2022). From January to June, the glacier surface is partially covered by snow, while ice is exposed in multiple places.
Acknowledgements. CNRM/CEN is part of  Labex  OSUG@2020. We thank  Evan Miles ,  Fabien Maussion  and  Vincent Favier  for insightful discussions and comments on the initial version of this work. We thank the editors ( Francesca Pellicciotti  and  Thomas Mölg ), the five reviewers (including  Ann Rowan ,  Horst Machguth  and  Martin Lüthi ),  Enrico Mattea ,  Marcus Gastaldello ,  Nicolas Steiner  and the authors of Potocki et al. (2022) for insightful and constructive comments and discussions which improved the quality of this article. Financial support. This research has been supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (IVORI; grant no. 949516 ), the  French Space Agency (CNES) , and the  ANR  for the  iFROG  project (grant no.
F. Brun et al. : Everest South Col  Glacier did not thin during the period  Code and data availability. The dH map is on a public repository, together with the aerial photograph (https://doi. org/10. 5281/ zenodo. 7529761, King, 2023). The Pléiades DEM is available at https://doi. org/10. 5281/zenodo. 6979691 (Berthier, 2022). License restrictions apply to the Pléiades orthoimages, which are available upon request to Etienne Berthier, pending the signature of a license agreement with CNES. The uncertainty analysis code is available at https: //github. com/rhugonnet/uncertainty_analysis_SCG/blob/main/ uncert_SCG_from_Hugonnet2022. ipynb (last access: 9 August 2023), and, based on the Python package, xDEM is available at https://github. com/GlacioHack/xdem (last access: 9 August 2023; DOI: https://doi.
org/10. 5281/zenodo. 8220229, Hugonnet, 2023). VENµS data are available at https://doi. org/10. 5281/zenodo. 6685515  (Brun, 2022). The wind erosion code is available at https://github. com/ antonplanchot/wind-snow-scg  (Planchot and Amory, 2023). Mass balance simulations presented in this study are available at https://doi. org/10. 5281/zenodo. 7006744 (Réveillet et al. , 2022).
| Reference genome The reference genotype for this study was "V. berlandieri 10585" (NCBI, BioProject ID: PRJNA886625) from the collection of  INRAE-Bordeaux, (Villenave d'Ornon, France). This genotype was selected on the basis of its rooting capacity and use in other experiments. "V. berlandieri 10585" leaves were harvested from the INRAE Bordeaux grapevine collection (Villenave d'Ornon, France). Two young leaves with a width of about 5 cm were collected, frozen, and stored in a -80°C freezer for DNA extraction. DNA was extracted with the Tip 100 Qiagen Genomic kit, according to a slightly modified version of the manufacturer's protocol. We incubated 0. 5 g of ground plant material with 9. 5 mL of G2 buffer supplemented with 1% PVP-40, 19 μL RNase A, and 500 μL proteinase K for 3 h at 50°C for lysis.
11. 7. Paternal and maternal kmers were identified with yak-0. 1 software, using the parental reads. The outputs were then used in hifiasm-v0. 15. 5 to bin long reads and to assemble the two haplotypes. For each haplotype, contigs were aligned with PN40024. v4 with minimap2 version 2. 17. The best contig alignments were used to build an AGP file, and each pseudomolecule was then reconstructed. We refined the pseudomolecules by repeating the process, beginning with an alignment of each haplotype against the other, previously reconstructed haplotype. The embryophyta_odb10 lineage package from BUSCO 5. 3. 1 software was used in genome mode to estimate the completeness of all assemblies.
| Genotyping by sequencing Leaves were sampled from all 286 genotypes at Geisenheim University, Germany. Two leaf discs of 1. 5 cm diameter were sampled from each genotype and placed on ice. The leaf discs were then frozen in liquid nitrogen and freeze-dried in a Martin Christ, Beta 2-8LD freeze-dryer. DNA was extracted from all lyophilized samples as described by  Cormier et al. (2019)  in Corning/ Costar deep 96-well 1. 1 mL plates. Libraries were prepared at UMR AGAP, CIRAD (Montpellier, France) as described by  Elshire et al. (2011). Based on our results, the amount of DNA was normalized to 50 ng/mL. We prepared 96-plex GBS banks with the restriction enzyme ApeKI. Illumina Hiseq 4000 short-read 2 × 150 bp sequencing was performed by Genewiz. Three 96-plex plates were used. The row data are available on NCBI, BioProject ID: PRJNA886619.
| SNP calling GBS data were processed with the Genotoul cluster, Toulouse, France. Reads were demultiplexed with a script available from https://github. com/timfl utre (demultipley. py) and cleaned with Cutadapt  (Martin, 2011)   VCFtools  (Danecek et al. , 2011)  was used for filtering based on the following criteria: minimum depth of 3, maximum of 50% missing data, minor allele frequency of 0. 05, and a minimum mean depth of 5. In total, 104,378 SNPs were conserved. We considered 281 genotypes with less than 60% missing data.
The results were visualized by plotting PCA results constructed with the first four principal components according to marker information. The number of clusters in the PCA was chosen based on the optimal K value from STRUCTURE analysis considering admixed individuals as a subpopulation (K = 3) and from the visualization of within-cluster sums of squares (K = 5). We tested for isolation by distance by calculating Nei's distance matrix between genotypes with the adegenet package in R. As there were several genotypes originating from each mother plant, the mean genetic distance was calculated between mother plants so as to obtain a single value for the genetic distance between two mother plants. A distance matrix was constructed from the GPS coordinates of the mother plants with the sp package in R.
Impacts were classified into four categories: "high" (e. g. , loss of function of the protein), "moderate" (e. g. , modification of protein efficacy), "low" (probably no impact on protein), and "modifier" (mostly noncoding variants or variants for which there was no evidence of impact). Kimura's neutral theory of evolution  (Kimura, 1968)  suggests that most mutations are deleterious or neutral. We therefore calculated the genetic load as follows: This Equation (1) considers "P del " as the proportion of deleterious alleles for one individual. SNPs with missing data were not considered for each individual. The accumulation of deleterious alleles in each population was evaluated as the mean P del of the individuals assigned to each population according to STRUCTURE analysis divided by the number of deleterious alleles for all individuals from each population.
Elevation was analyzed separately from the other environmental parameters because it was measured during the sampling campaign, whereas the other environmental variables were obtained by climatic interpolation. We performed GEA with the BLINK model in GAPIT with default settings. Before running GWAS, the SNPs dataset has been imputed with the snp. impute function available in GAPIT using the "Major" argument in order to impute missing data by the major allele of each SNP. Because the imputation procedure led to changes in the allele frequency at each position, mainly for SNPs with a large number of missing data, a new MAF > 0. 05 filtration was applied excluding 15,574 SNPs. The BLINK model was used because of the ease with which false discovery rate can be controlled in this model  (Huang et al.
, 2018). The information about population structure obtained from STRUCTURE (K = 2) was used as a covariate. In this model, kinship was derived from pseudo-QTN information. Bonferroni correction was applied to the calculated p-values. The significance thresholds were, thus, set at 0. 05/n and 0. 01/n where "n" is the number of markers used. The GEA analysis was performed on the 281 genotypes with 88,804 SNPs after the recalculation of minor allele frequency in GAPIT. The genes attributed to each marker by snpEff were used to identify the related protein families with the UniProtKB (The UniProt Consortium, 2021) database. If several results were available, only one was retained. If the corresponding protein was unknown, we used the molecular function attributed by IEA:InterPro  (Camon et al.
| DISCUSS ION De novo whole-genome sequencing of V. berlandieri made it possible to explore the genetic structure of a wild V. berlandieri population with genome-wide molecular markers. We identified two distinct subgroups that could be explained by isolation by distance. However, no significant differences in genetic diversity or genetic load were found between subpopulations. Using geographic coordinates and climatic data from meteorological stations, we characterized the environments from which the subpopulations were collected and identified several QTL associated with environmental conditions and agronomic indices of climatic conditions. The complexity of the plant genome renders short-read sequencing highly challenging, due to the large numbers of repetitive sequences and heterozygosity  (Schatz et al.
ACK N OWLED G M ENTS We thank Benjamin Bois for providing the environmental data for each sampling site, Vera Maria Pohl for sharing her knowledge and helping with V. berlandieri collection and sample sequencing, Ana Clara Fanton and Julie Sappa (from Alex Edelman & Associates) for correcting the English, and Joachim Schmid, Frank Marty, and Peter Cousins for prospecting and collecting V. berlandieri seeds in Texas. This research was supported by the State of Hesse (Germany), Région Nouvelle Aquitaine (France), and the BAG (Bordeaux, Adelaide, and Geisenheim) alliance.
Whole-genome binding maps were generated in EBs, EEs, and ECs (Figure  1B ), and we used our previously generated ISC-profiling datasets of these factors. 32  Cell-type specificity of the GAL4 drivers was verified by combining GFP expression and immunostaining of specific markers of ISCs (Delta), and EEs (Pros), and the nuclear size of polyploid ECs (nucleus diameter > 7 mm). EBs are diploid (nucleus diameter < 7 mm) and negative for Delta and Pros (Figures 1C-1F 000 ). The EEPs could not be profiled as an efficient, specific GAL4 driver is not available. Dam construct expression specificity was also validated (Figures S1A-S1D 0 ). The binding profiles of the five proteins in all cell types were predictably associated with known genomic loci and features, such as HP1 at pericentromeric heterochromatin and Pc along Hox clusters (Figures  S2A-S2C ).
First, there was another Pc-enriched state also defined by RNA Pol II and Brm binding, hence named ''PcG-mixed'' (PcG-M). A similar signature was described in the developing brain. 11  Second, a RNA Pol II weak (RNA Pol II-W) state was found, similar to the Pol II state but with a lower RNA Pol II occupancy (Figures  1H  and S3A ). We then assessed the relationship between chromatin states, gene expression, and chromatin accessibility. To this aim, we combined chromatin accessibility profiling using targeted DamID (CATaDa) for each cell type (Figure  S3E ),  47  an assay for transposase-accessible chromatin followed by sequencing (ATAC-seq) on fluorescence-activated cell sorting (FACS)-sorted ISCs/EBs (Figure  1K ), and available cell-type-specific RNA sequencing (RNA-seq) (Figure  S3F ). 48  Pol II and Swi/Snf states displayed the highest values of transcript levels and chromatin accessibility, whereas HP1, PcG, and Black states had the lowest (Figures  1K, S3E,  and S3F ).