dataesr/scientific-paragraphs-categorization huggingface dataset:
9. 3. 0. The half-maximal inhibitory concentration (IC 50 ) was calculated using the equation, ,  (1)  where f(x) is the reduction of GFP signal, x is the concentration of the antibody in pg/mL, "Min" and "Max" are the plateau values of the Y axis, "Hill" is the Hill coefficient, and IC 50 is the concentration of antibody where the reduction of GFP signal is halfway between the "Min" and "Max" values. Data and code availability. All raw and normalized array data are publicly available on the Gene Expression Omnibus (GEO) database with the accession no. GSE222765. Code used for data analysis and figure generation are available from the corresponding authors upon request. Statistics. R, RStudio, and various R packages were used to perform analyses  (43, 44). For normalization, MFI values for "bare bead" IDs were subtracted from MFI values for antigen-conjugated bead IDs, and replicate MFI values were averaged.
The average MFI for each antigen was calculated using samples from healthy individuals (all obtained before December 2019). Serum samples were considered "positive" for antibodies recognizing a specific antigen if the normalized MFI was > 5 SD above the average MFI for HC for that antigen and if the normalized MFI was > 3,000 units, a more stringent threshold than those commonly published in related literature  (3). Based on the SAM algorithm  (45) , statistically significant antigens were identified using FDR-adjusted P values (q < 0. 001), 2-fold change cutoffs, and 10,000 permutations. Statistical differences in MFI between groups for each antigen were determined using 2-sided Wilcoxon rank-sum tests with Bonferroni correction. P values less than 0. 05 were considered significant. Data were visualized in GraphPad Prism v.
9. 3. 0. Complexheatmap v. 2. 8. 0 was used for all heatmaps  (46). Upon publication of this study in a peer-reviewed journal, deidentified array data will be uploaded to the Gene Expression Omnibus (GEO) database (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE222765). Within the Stanford ICU cohort, analyses evaluated predictors for the development of autoantibodies as well as pertinent ICU outcomes related to the presence of autoantibodies in patient serum. Fisher's exact test was used to evaluate the association between age (< 60 versus ≥ 60), sex, immunocompromised status, WBC count (< 12,000 versus ≥ 12,000/μL), and infection status with the development of ACA and CTD-AAb. To control for interactions between variables, 2 adjusted models were performed using.
Acknowledgments AF was funded by  Stanford University  and the  Vice Provost for Undergraduate Education 's ( VPUE )  2019-2020  Major Grant. AJR was supported by  NHLBI   R01 152083  and the  Stanford ICU Biobank  by  K23 HL125663. CS is supported by the  Universities Giessen and Marburg Lung Center (UGMLC) ; the  German Center for Lung Research (DZL) ;  University Hospital Giessen and Marburg (UKGM)  research funding according to article 2, section 3 cooperation agreement; and the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation  Project -ID 197785619 -SFB 1021 ,  KFO 309 (P10 ), and  SK 317/1-1  (project no. 428518790 ) as well as by the  Foundation for Pathobiochemistry and Molecular Diagnostics. ETLP was funded by  NIH   UC4 DK112217  and  NIH   UM1-AI144288  ( Autoimmunity Centers of Excellence, ACE).
CS received consultancy fees and research funding from  Hycor Biomedical and Thermo Fisher Scientific , research funding from  Mead Johnson Nutrition  (MJN), and consultancy fees from  Bencard Allergie. EKMM received consultancy fees from  Roche  and  Boehringer Ingelheim. ETLP receives research funding from  Janssen Research and Development , consultancy fees and research funding from  Roche Diagnostics , is a paid consultant for Enpicom, and serves on the scientific advisory boards of the Antibody Society, the  Immune Epitope Database , and the  American Autoimmune Related Diseases Association. AJR serves on a scientific advisory board for  Merck. PJU serves on the scientific advisory boards of SeraNova, 4DMT, and the  Arthritis National Research Foundation. He is cofounder and member of the Board of Directors of the  Physician Scientist Support Foundation.
Plant material One to six individuals were collected for each of the 22 populations of glauca, magnolli, and transsilvanica from NW, NE, and SE France and South Poland (Fig. 1 ; Table  S1 ). In addition, Melica uniflora Retz. , Melica minuta L. , Melica nutans L. , and Melica amesthystina Pourr. were sampled as putative outgroups. To collect material in the wild, we used species distribution data from the SILENE database and from the database of the Société Botanique d′Alsace. Voucher specimens were deposited at the Herbarium of the University of Strasbourg (STR). For morphometric analyses, we included an additional specimen identified as klokovii from Herbarium collections and excluded one population from RAD-seq analyses (Saint-Varent, "Stv," Fig. 1 ; Table  S1 ) because of damaged vegetative and floral structures.
DNA extraction, library preparation, and sequencing Approximately 40 mg of silica dried leaf material was selected for DNA extraction using the Qiagen Dneasy Plant Mini Kit (Qiagen, Hilden, Germany) as per the manufacturer′s instructions. The DNA concentration was measured using the Qubit fluorometer with the dsDNA HS Assay Kit (Thermo Fisher Scientific, Darmstadt, Germany), and then adjusted to 30 ng/µL. Microsynth Ecogenics (Balgach, Switzerland) generated RAD libraries using the EcoRI-MseI enzyme combination using a method following  Peterson et al. (2011)  including the use of sample-specific barcodes. Samples were sequenced on an Ilumina NextSeq platform to produce 75-bp single-end reads. We used the SimRAD R-package version 4. 0. 2 (Lepais & Weir, 2014)  to estimate the total number of reads necessary to achieve a mean sequencing depth of 12 reads per locus per sample.
RAD locus assembly and SNP calling The program ipyrad v. 0. 7. 21  (Eaton, 2014)  was used to demultiplex raw reads, remove adapter sequences, trim bases with Phred scores < 31, and remove reads shorter than 50 bp after trimming. Some samples were removed based on their limited number of reads. Trimmed reads were assembled de novo using a cluster threshold of 90, a minimum of four samples per locus, and a maximum number of two alleles per locus for consensus reads. These analyses were conducted at the high-performance computing center of the University of Strasbourg. After several preliminary tests, four assemblies were generated with the already described parameters but differing in the number of samples and taxa included (Table  1 ). The first assembly (a1) contained a reduced number of samples per population to focus on M.
One-way analysis of variance (one-way ANOVA) was used to assess the significance of differences among the three taxa. If Shapiro-Wilk tests did not confirm residuals of a normal distribution, the nonparametric Kruskal-Wallis test was used. Bonferroni correction and Wilcoxon tests were used to identify significant differences among the three taxa. Morphometric affinities were represented in a principal component analysis (PCA) to group population samples without a priori taxonomic assignment. Then, a discriminant analysis (DA) was carried out to identify the most explicative variables of our a priori taxonomic assumption by correlating them with coordinates  (Hardion et al. , 2012). Model accuracy of DA and the κ coefficient were established with a confusion matrix using the function confusionMatrix() of caret R-package  (Kuhn, 2008).
Those nine variables were subjected to PCA and DA. Kruskal-Wallis and Wilcoxon tests were used to assess the significance between groups of surveys for each variable. R-package TR8 version 4. 0. 2 (Kattge et al. , 2011)  was used to obtain species biological and ecological data and vegan  (Oksanen et al. , 2016) , ade4  (Dray & Dufour, 2007) , and caret  (Kuhn, 2008)  were used to perform statistical analyses.
4 ), and ecological differences (Fig. 5 ) (  de Queiroz, 2013). Consequently, the species status of Melica transsilvanica is supported in our study and, contrary to Hempel′s taxonomic treatment, magnolii also seems appropriate for recognition as a species. The origin of Melica amethystina was suggested by  Hempel (2011)  to be the result of the hybridization of Melica minuta (section Melicella) with glauca, morphologically distinct in having the ciliate outer vein of the lemma, as used to differentiate members in subsection Ciliatae. Our results support this hybridization hypothesis between two different sections of subgenus Melica (Fig. 3C ). In addition, M. amethystina is the only European tetraploid with a chromosomal number of 2n = 36 similar to South American Melica violacea Cav. (CCDB database,  Rice et al.
RNAseq analysis Raw sequencing data quality controls were performed with FastQC (v 0. 11. 5). Salmon (0. 10. 0) was used for the quantification of gene expression from the raw sequencing reads, and the reference mouse genome GRCm38 and the annotations of protein-coding genes from gencode vM20 were utilized as an index. Unless otherwise specified, the analyses were performed using R (v3. 5. 1). Starting from the Salmon transcript quantification, we used the R packages tximport  (Soneson et al, 2015; v1. 10. 1) and DESeq2  (Love et al, 2014; v1. 22. 2)  to perform the differential expression analyses (Wald test and P-value correction with the Benjamini-Hochberg method). We used the R packages clusterProfiler  (Yu et al, 2012; v 3. 10. 1) and msgidbr (v 6. 2. 1) to test the pathway enrichment of a list of genes.
We tested the list of genes against pathways from the msigdb hallmark, C2 and C5 gene sets. Overrepresentation P-values were corrected with the Benjamini-Hochberg method. Single cell RNA sequencing (scRNAseq) CAFs and ductal tumor cells were obtained from a pool of 5 KC mice, as described above. After tissue dissection and dissociation, FACS purified suspended cells were partitioned into nanoliter-scale Gel Bead-In-Emulsions (GEMs) with the Chromium Single Cell Controller (10XGenomics) at the Single Cell Platform (CLB/CRCL). After cell encapsulation and barcoding, library preparation followed the standard 10XGenomics 3 0 scRNAseq protocol comprising reverse transcription, amplification and indexing. Sequencing was performed using a NovaSeq Illumina device (Illumina). Illumina bcl files were basecalled, demultiplexed and aligned to the mouse mm10 genome using the cellranger software (10XGenomics).
All downstream analyses were performed using R/Bioconductor/CRAN packages, R version 4. 0. 3 (2020-10-10) -"Bunny-Wunnies Freak Out" [https://cran. r-project. org/; http:// www. bioconductor. org/; https://cran. r-project. org/] on a linux platform  (x86_64-pc-linux-gnu [64-bit] ). Filtered barcoded matrices were used to create a Seurat object  (Stuart et al, 2019)  for CAFs and Ducts cells that were subsequently merged. A total of 4,949 cells (4,072 CAFs and 877 Ducts) remained after filtering for quality parameters (number of features per cell < 6,000, fraction of mitochondrial genes < 10%). SCTransform was used to simultaneously normalize, identify variable features, and scale the data. Following dimension reduction with PCA, the first 20 dimensions were used to construct a shared nearest neighbor (SNN) graph using the FindNeighbours function.
Clusters were identified with a resolution of 0. 5 and projected in two-dimensional plots using Uniform Manifold Approximation and Projection (UMAP; arXiv:1802. 03426v3). Differentially expressed genes for each cluster were identified using the FindMarkers function. In addition, a list of PeSC markers obtained from bulk RNAseq data was used to calculate a PeSC score. This permitted the identification of a common cluster between CAFs and Ducts with a high PeSC score. This putative PeSC cluster was isolated and pathway analysis was performed in its full list of unique markers using EnrichR R  (Kuleshov et al, 2016)  and pathfindR  (Ulgen et al, 2019)  CRAN packages. Significant markers and pathways were selected with a threshold of adjusted P-value (FDR) below 0. 05.
001, and ****P < 0. 0001. The P-values were calculated using Student's t-test. H Definition of the PeSC score (bulk RNAseq) applied to the single-cell RNAseq analysis and identification of cluster 7. I Application of PeSC score to the human PDAC public single-cell RNAseq database and identification of a cluster with the highest PeSC (red square) among the mesenchymal population (Fig EV2D) in either Normal Pancreas (left) or Human PDAC (right). Source data are available online for this figure.
Acknowledgements The authors thank the staff at  ANICAN (Cancer Research Center of Lyon)  staff for the maintenance of the mouse strains and  Christophe Vanbelle  for the helpful assistance with the confocal microscopy experiments. This study was supported by grants from  La Ligue Contre le Cancer  (AH and PB),  Bristol Meyers Squibb Foundation (AH) ,  INSERM Transfert (AH) ,  Sanofi  iAward (AH),  Institut National du cancer (INCA)   AAP 2019 , and  Fondation de France  (AH and PG), ZCW and YJZ were both supported by a  Chinese Scholarship Council (CSC)  Fellowship. ZCW was supported by  Science and Technology Commission of Shanghai Municipality (Shanghai Administration of Foreign Experts Affairs) , no. 21430711900.
01, ***P < 0. 001, and ****P < 0. 0001. The P-values were calculated using Student's t-test. H Definition of the PeSC score (bulk RNAseq) applied to the single-cell RNAseq analysis and identification of cluster 7. I Application of PeSC score to the human PDAC public single-cell RNAseq database and identification of a cluster with the highest PeSC (red square) among the mesenchymal population (Fig EV2D) in either Normal Pancreas (left) or Human PDAC (right). Source data are available online for this figure.
6 are identified as kaons with an efficiency of 89. 4%, while 4. 2% of pions are misidentified as kaons. The PID (misidentified) efficiency is evaluated using the signal MC data sample, where the PID of the tracks are known. It is defined as the number of tracks selected under a PID hypothesis divided by the number of tracks before any PID requirements with the same (opposite) PID hypothesis. The Λ candidates are reconstructed via pπ -pairs. The distance of the Λ decay vertex with respect to the IP is greater than 0. 35 cm with an efficiency of 99. 5%. For Ξ -→ Λπ -, the vertex formed from the Λ and π -is required to be at least 0. 35 cm from the IP with an efficiency of 94. 1%, and to be a shorter distance from the IP than the Λ decay vertex  [29, 30]. These efficiencies are obtained by comparing the numbers of MC signal events with and without applying these selection requirements.
Acknowledgments This work, based on data collected using the Belle detector, which was operated until June 2010, was supported by the  Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan , the  Japan Society for the Promotion of Science (JSPS) , and the  Tau-Lepton Physics Research Center of Nagoya University ; the  Australian Research Council  including grants  DP180102629 ,  DP170102389 ,  DP170102204 ,  DE220100462 ,  DP150103061 ,  FT130100303 ;  Austrian Federal Ministry of Education, Science and Research (FWF)  and  FWF Austrian Science Fund  No. P 31361-N36 ; the  National Natural Science Foundation of China  under Contracts No. 11675166 , No. 11705209 ; No. 11975076 ; No. 12135005 ; No. 12175041 ; No. 12161141008 ;  Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS) , Grant No.
• Inclusion in the MiBACT catalogue (Ministry of Cultural Heritage and Activities and Tourism)  [53] ; • Inclusion in the UNESCO World Heritage List [54]; • Inclusion in the FAI catalogue (Fondo Ambiente Italiano)  [55] ; • Recognition in the ''Bandiera Arancione TCI" (Italian Touring Club)  [56] ; • Inclusion in state or regional catalogues as protected assets  [57] ; • Inclusion in protected areas catalogue (e. g. , regional or national park) and therefore recognised and protected by park authorities. Subsequently, we collected data on the touristic interests of the same 10 geosites and cultural sites by submitting questionnaires to tourists within the geopark area. In fact, geosite assessment methods are often too scientific and hardly meet touristic interest  [24]. Hence, we compared the results of the touristic interest questionnaires with the results of the scientific assessment.
Results Geosite recognition and assessment permit us to affirm the role of geoheritage within geoparks, helping the comparison of different geosites and their suitability either to conservation or geotourism development  [21]. Although there is not a perfect model to assess geosites, because of the subjectivity included in each method, several valuable methods were developed  [7, [10] [11] [12] [13] [14] [15] [16] [17] [18] [19]. Moreover, research has indicated that methods applicable to any geosite utilise similar criteria and generate similar results when analysing geosites  [36, 37]. In our study, we selected 10 geosites and 10 cultural sites within the SVUGGp. The geosites were selected from the SVUGGp geosite database  [58]. Out of a total of 68 geosites, 10 were selected in order to have a small but representative sample of the entire area; thus, their geographical location and primary interest were taken into account.
For assessing the 10 selected geosites within the SVUGGp, we used the method proposed by Brilha  [7] , i. e. , the method that is most previously adopted in areas comparable to our study area such as Italian UGGps  [64, 65]. Then, the same method was adapted to be suitable for the assessment of the 10 selected cultural sites in the same area. Finally, through questionnaire submission, we collected data regarding the interests of tourists of the SVUGGp on visiting the same geosites and cultural sites.
Tourist Data and Comparison with Assessment Data As a means to know the expectations of visitors to integrate them with the assessment data with the aim of helping future geosite and cultural site development and management, we collected the data from the participants through an online survey and an on-site questionnaire. Figure  7  shows the results of the participants' interest in visiting geosites, while Figure  8  shows the results of the participants' interest in visiting cultural sites. In green are the values from 1 to 5, where 1 means "I do know it, but I am slightly interested in visit the site" and 5 means "I am very interested in visiting the site". In red is the 0 value that corresponds to "I do not know the site and I am not interested in visiting it". Overall, there is a close similarity between participants' interest in the geosites and interest in the cultural sites, and in both cases, a significant number of participants declared to not know the geosites or cultural sites and to not be interested in visiting them.
Earthquake detection The internal clocks of the OBSs were synchronized on deployment and recovery, and a linear drift correction was applied. Earthquake events were network detected by the CONDET program in the SEISAN software 36 using the STA/LTA trigger algorithm, and an automatic picking procedure  37  was used to pick P and S wave arrival onsets. These events were registered in the SEISAN database, and P-and S-wave arrival onsets were manually refined (Supplementary Fig. 1 ).
1-D velocity model The 1-D P-wave velocity model was calculated using the VELEST program  38. The initial velocity model was extracted from a seismic refraction experiment across DF1 from the SISMOSMOOTH cruise  23. This model generally agrees with a broader velocity model at the same area 39 (Supplementary Fig. 2 ). Only events with OBS ≥6 and GAP≤180° were used in the VELEST program. The best-fitting P-wave velocity model was iteratively searched (Supplementary Fig. 2 ). The final root-mean-square (RMS) is 107 ms. The S-wave velocity (Vs) model is calculated using a best-fitting Vp/Vs ratio of 1. 7, based on the Wadati diagram that plots the travel time of P-wave versus travel time differences of P-and S-waves (S-P time; Supplementary Fig. 3 ).
Earthquake location and relocation The initial earthquake locations were searched by the NonLinLoc software with the Oct-tree algorithm  40  and the SWIR 64°30'E velocity model (Supplementary Fig. 2 ). The maximum likelihood hypocenter is used to locate each earthquake event, and a three-dimension error ellipsoid (68% confidence) is generated from the posterior density function (PDF) scatter samples  40. 507 events of SMSMO (122) and RVSMO (385) catalogs were located with four or more stations, and 388 events of SMSMO (88) and RVSMO (300) catalogs are well located with horizontal and depth errors of <5 km and RMS residual of <100 ms (Supplementary Figs. 8a-1 to 8d-1). Station corrections, given by the average travel time residuals at each OBS calculated by the NonLinLoc software in an iterative way, were applied for the P and S phases (Supplementary Fig.
4 ): the mean absolute station correction is 60±40 ms in both catalogs. P-and S-waves travel time residuals follow the Gaussian distribution with an average RMS misfit of 34 ms (Supplementary Fig. 9 ). Bootstrap analysis of location errors was applied for four chosen groups of NonLinLoc located events to show the stability of the hypocenter estimates: 1) two swarm events in the RVSMO catalog, 2) two deep events in the RVSMO catalog (beneath smooth and volcanic seafloor), 3) one hangingwall event in the SMSMO catalog, and 4) two detachment-fault events in both SMSMO and RVSMO catalogs (Supplementary Fig. 10 ). We also applied the SWIR 65-66°E velocity model 32 (Supplementary Fig. 2 ), constrained from a more magmatically-robust area than our study area  23, 33  , to locate earthquakes recorded in our study area using the NonLinLoc software (Supplementary Figs.
8a-2 to 8d-2). Earthquakes with more distant epicenters to the OBS network tend to have deeper hypocenters to form an inverted V shape of along-axis hypocenter depth distribution (Supplementary Figs. 8b-2 ), which is similar to what was proposed at the SWIR 65-66°E  32. NonLinLoc hypocenters with six or more stations, horizontal and depth errors of <5 km, and RMS residual of <100 ms, were relocated using the Double-Difference Hypocenter (HypoDD) algorithm  41. The relocation uses both catalog and cross-correlation (Supplementary Figs. 11a-3  to 11d-3) and runs using the python module HypoDDpy 42. A time window of 300 ms was applied based on pickings of P and S arrival onsets, and crosscorrelated waveforms with a correlation coefficient lower than 0. 6 were rejected. Supplementary Fig. 12  shows the differences between the NonLinLoc locations and the HypoDD relocations for 30 randomly selected events.
Acknowledgments We thank  Sylvie Leroy , chief scientist of the SISMOSMOOTH cruise, the science parties, and the crews of the  RVs Marion Dufresne and Pourquoi Pas ? for collecting the data. We also thank  Zhiteng Yu ,  Soumya Bohidar , and  Aude Lavayssière  for their help in the data processing. All figures were created using the GMT software 52. We thank the funding of the  ANR  project " Ridge-Factory-Slow " ( ANR-18-CE010002-01 ). Jie Chen  was supported by the  China Scholarship Council  ( 201808330437 ). This is IPGP contribution # 4239.
Code availability The software and codes used in this study included: SEISAN software  36  (http://www. seisan. info/), VELEST program 38 (https://seg. ethz. ch/software/velest. html), NonLinLoc software 40 (http://alomax. free. fr/nlloc/), Double-Difference Hypocenter (HypoDD) algorithm 41 (Version 2. 0; https://www. ldeo. columbia. edu/~felixw/hypoDD. html) and its python module HypoDDpy 42 (https://doi. org/10. 5281/zenodo. 18907), HASH  48  and FOCMEC  49  programs internally in the SEISAN software, and GMT 52 (Version 6; https://www. generic-mapping-tools. org/).