dataesr/scientific-paragraphs-categorization huggingface dataset:
Electron microscopy For negative stain EM grid preparation, 4 ml of sample was applied between mica and carbon layer and stained using sodium silicotungstate (SST) 2%. After removing the mica part, the grid was deposited on the carbon layer and dried at room temperature. Micrographs were collected at 2. 5 mm defocus using a FEI Tecnai F20 operated at 200 kV on 4 k*4 k CETA FEI CCD camera. For cryo-EM grid preparation, Quantifoil grids 400 mesh 2/1 were glow-discharged at 30 mA for 1 min. 3 ml of sample were applied on the resulting glow-discharged grids and excess solution was blotted during 2. 5 s force seven with a Vitrobot Mark IV (FEI) and the grid frozen in liquid ethane. Two biologically independent datasets, arising from different expression and purification batches, were collected on two high-end cryo-electron microscopes and gave consistent structures.
The first cryo-EM dataset was collected on a FEI Polara F30 microscope operated at 300 kV equipped with a K2 summit GATAN direct electron detector and resulted in a 3. 8 A ˚resolution structure. The second dataset, that gave rise to the 3. 3 A ˚resolution structure presented in the present article, was collected on a FEI Titan Krios operated at 300 kV equipped with a Gatan Bioquantum LS/967 energy filter coupled to a Gatan K2 direct electron detector camera. For this second dataset, automated data collection was performed using EPU FEI software. Zero-loss micrographs were recorded at a 46,860x magnification giving pixel size of 1. 067 A ˚with defocus ranging from 0. 8 to 3. 5 mm. In total, 4328 movies with 28 frames per movie were collected with a total dose of 40 e -/A ˚2.
Image processing Micrographs were initially selected based on visual quality inspection. Movie drift correction was performed in Motioncor2  (Li et al. , 2013)  excluding the two first frames. CTF parameters were determined in Gctf  (Zhang, 2016)  (RRID:SCR_016500). All subsequent processing steps were performed in Relion2. 1 and Relion3 software  (Scheres, 2012; Zivanov et al. , 2018)  (RRID:SCR_016274). Straight HTNV-NC were manually picked and computationally cut with an inter-box distance of 38 A ålong the helical axis into overlapping boxes of 400*400 pixels, resulting in 168,709 extracted segments. 2D classification was used to eliminate bad quality filaments. The best 2D classes were aligned and padded in a square of 1200*1200 pixels 2. The individual 2D Power Spectra (PS) of each best class were averaged and used for a first estimation of the helical parameters using Fourier Bessel indexing  (Cochran et al.
Model building and refinement The monomeric crystal structure of HTNV-NP comprising residues 113 to 429 (PDB code: 5FSG) was initially fitted into the EM density as two separate rigid bodies containing residues 113-398 and 399-429. Loops comprising residues 146-155 and 349-360, b-hairpin 160-172 and the N-terminal arm residues 80 to 112 -previously not present in the crystal structure -were manually built in Coot  (Emsley et al. , 2010). The three RNA nucleotides visible in the density map were manually built and adjusted using Coot  (Emsley et al. , 2010)  (RRID:SCR_014222) and RCrane  (Keating and Pyle, 2012). NP monomeric model was symmetrised according to the helical parameters to form a filament model of 7 subunits. Fiber model was then iteratively rebuilt and all-atom refined using stereochemical and NCS restraints within PHENIX  (Adams et al.
Acknowledgements We thank  Stephen Cusack  for providing the synthetic gene and enabling JR and HM to initiate this project while being post-doctoral researchers in his lab. We thank  Karine Huard ,  Angelique Fraudeau ,  Delphine Guilligay ,  Pascal Fender  and  Alice Aubert  for technical advices on expression and purification;  Jean-Pierre Andrieu  for N-terminal sequencing;  Maria Bacia  and  Michael Hons  for help with cryo-EM data collection;  Leandro Estrozi  for advices on helical symmetry determination and discussion on image processing. This work used the platforms of the Grenoble Instruct-ERIC Center (ISBG:  UMS 3518 CNRS-CEA-UGA-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB).
Lan et al. reported "Influences of Transformational Leadership, Transactional Leadership, and Patriarchal Leadership on Job Satisfaction of Cram School Faculty Members"  [3]. In this study, 400 New Taipei City cram school faculties were invited to be the research objects to study the influences of transformational leadership, transactional leadership, and patriarchal leadership on job satisfaction. This study adopted the method of intentional sampling to conduct a questionnaire survey. After collecting data, SPSS 12. 0 software was used to analyze the descriptive statistics, reliability analysis, description of statistics, t-test method of single-factor analysis of variance, and regression. This study aimed to provide suggestions and references for the cram schools' leaders to change their leadership styles and improve their employees' job satisfaction.
Tu et al. reported " A Study on the Preference of Healing Products for Single Office Workers of Various Lifestyles"  [6]. The purpose of this study is to determine the preference of single office workers with different lifestyles regarding healing products. The lifestyle characteristics and preferences of single office workers were investigated via a questionnaire survey. Through SPSS statistical software, the preference factors of single office workers of different groups regarding different healing products were analyzed, and the lifestyle characteristics and preferences of each group were subsequently obtained. The results show that the lifestyle of single office workers could be divided into three groups. On the whole, single office workers considered healing products to be interesting and impressive with attractive and charming appearances, as well as offer the function of transforming negative emotions.
This study hopes that the design principles obtained can provide an important reference for product designers to design relevant healing products for single office workers. Lee et al. reported "Effects of Sports Activity on Sustainable Social Environment and Juvenile Aggression"  [7]. This research used the Korean Children and Youth Panel Survey (KCYPS) which surveyed 2378 adolescents by multi-stage stratified cluster sampling from 98 schools across South Korea. The data was analyzed by reliability analysis, correlation analysis, exploratory factor analysis, multiple regression, and path analysis with SPSS ver. 23. 0 program for Windows. The results were as follows: Firstly, sports activity had a statistically significant effect on the sustainable social environmental factors, and teacher and friend relationships.
A total of 111 students from a university digital media department were invited to participate in a three-dimensional virtual reality (3D VR) painting experiment in which students created paintings using Google Tilt Brush. A 5-point scale based on the attention, relevance, confidence, satisfaction (ARCS) learning motivation model was adopted to collect student data. Perform a factor analysis of the data twice to select the appropriate factor (p = 0. 000 < 0. 05). Specifically, exploratory factor analysis was used to classify factors based on four constructs. The Cronbach alpha values of ARCS were 0. 920, 0. 929, 0. 693 and 0. 664, respectively, both > 0. 6, which still indicate favorable reliability. The results show that immersive VR can promote students' motivation and interest in learning 3D animation.
Inspired by this teaching method, students can independently seek product-testing objects to review their design concepts and develop a stronger motivation for self-learning. Tu et al. reported "A Study on Needs for Automotive Interiors Based on Lifestyle Characteristics of Taiwanese Female Office Workers"  [37]. This study adopted case interviews to determine female car-purchasing trends in best-selling automobile manufacturers, as well as their opinions on needs for automotive interiors, which were used as the basis for the questionnaire design. In the second phase, the questionnaire method was used to discuss the life characteristics and needs of female office workers for automotive interiors, and the statistical software SPSS was employed to analyze the differences in needs for automotive interiors among female office workers of different types; and finally, a design principle suitable for this consumer group was concluded.
Each amplicon library was subsequently diluted with low TE buffer (Qiagen, Oslo, Norway) to obtain an equimolar concentration (3 nM) before sequencing. The concentration of the normalized amplicon libraries was validated on the TapeStation (Agilent Biosystems, Santa Clara, United States). The normalized library pool was further diluted to 12 pM, spiked with equimolar 10% Phix control and then paired-end sequencing was performed on an Illumina Miseq sequencing machine (Illumina, San Diego, CA, United States) in 2 runs with inter-run calibrators (i. e. , few samples of known sequencing depth) to minimize eventual differences between sequencing runs. FASTQ files from each sample generated from the sequencing machine were used for data analysis.
Sequence Data The quality of the raw reads obtained after high-throughput sequencing was checked using FastQC  (Andrews, 2010). Only the forward reads containing the V3 region of the 16S rRNA gene were used for the downstream analysis, since their quality was better than the reverse reads. Construction of Operational Taxonomic Unit (OTU) and Taxonomy Tables, Using the UPARSE Pipeline The forward reads were processed and analyzed by UPARSE (USEARCH version 9. 2. 64) software  (Edgar, 2013). The reads were truncated to 240 bp, to remove the low-quality base pairs at the 3 -end and then quality-filtered. Furthermore, chimeric sequences were removed using the UCHIME algorithm  (Edgar et al. , 2011)  and then, quality filtered sequences were clustered into operational taxonomic units (OTUs) at 97% sequence similarity threshold.
This threshold was chosen because higher cut-off scores may lead to overmerging of up to 15-32%  (Mysara et al. , 2017). It has also been suggested that 100% is the optimal identity threshold for identifying species using V4 regiontargeted sequences  (Edgar, 2018c). Taxonomy annotation of short 16S rRNA tags using large databases like SILVA, Greengenes, or the full RDP database may give unreliable predictions  (Edgar, 2018a,b). Hence in the present study, we employed the 16S rRNA RDP training set with species names v16. The OTU sequences were assigned to different taxa using the SINTAX algorithm  (Edgar, 2016)  with a bootstrap cutoff value of 0. 5. Afterward, OTUs with a confidence score < 1 at the domain level and the OTUs belonging to the phyla Cyanobacteria and Chlorophyta were removed.
Diversity and Composition Analyses Due to differences in sequencing depth, the OTU table was rarefied to the lowest number (10,604) of sequences per sample to get an even sampling depth to facilitate comparisons between the treatment groups. Furthermore, to employ content and mucus samples from the same fish, only 21 fish from each group were considered for the downstream analyses. Adding on the tank water and biofilm samples, in total 157 samples were used for the downstream analyses. The R package "iNEXT" v2. 0. 12 was used to plot the rarefaction and extrapolation curves for the species richness of the intestinal bacterial assemblage  (Hsieh et al. , 2016). Codes were executed to calculate and generate diversity indices, core and rare microbiota (relative abundance of core taxa and least abundant taxa) and the corresponding plots, using the R packages "phyloseq" v1.
Statistical Analysis Statistical analysis was performed using R studio v3. 4. 3. To detect significant differences in alpha diversity, Kruskal-Wallis test followed by Dunn's test was employed. As for the beta diversity analysis, the dispersions of the communities were checked using betadisper; thereafter Adonis (PERMANOVA) followed by pairwise comparisons was employed (999 permutations) to understand the significant dissimilarities of the communities. To detect the differentially abundant OTUs in the treatment groups, a tool for microbiome analysis-"ANCOM" v1. 1-3  (Mandal et al. , 2015)  was used, and "Boruta" v5. 3. 0 R package  (Kursa and Rudnicki, 2010)  was employed to find the relevant OTUs that caused the differences in the three study groups. Furthermore, Pearson's Chi-squared test was performed to clarify differences in proportions of the dominant bacterial taxa in the three groups.
Prediction of Carbohydrate Degradation Capability and Butyrate-Biosynthesis by Significantly Abundant or Relevant Bacteria Genome mining was performed to detect the occurrence of butyrate-producing genes in the genome of certain bacteria associated with AlgOS-L group. The genomes of these bacteria (selected butyrate producers) in the DI of Atlantic salmon fed AlgOS-L were retrieved from GenBank database (Table  1 ) and annotated using PROKKA version 1. 13  (Seemann, 2014). Butyrate production abilities of the bacteria were assessed by evaluating the distribution of the pathways in each genome, i. e. , by understanding the genomic arrangement of butyrate gene clusters suggested by  Anand et al. (2016). Genomes were scanned for genes known to be involved in butyrate production and these sequences were then scanned in protein databases using phmmer from HMMER v.
3. 1  (Finn et al. , 2015)  with the default E-value parameter cutoff. Phmmer uses a hidden Markov model to predict protein domains by aligning amino acids to databases such as Pfam  (Finn et al. , 2016). Metabolic pathways associated with SCFA production were constructed using KASS  (Moriya et al. , 2007). The corresponding pathway IDs were analyzed as described by  Vital et al. (2014). dbCAN2  (Zhang et al. , 2018)  was used to annotate Carbohydrate-Active Enzymes (CAZymes) present in the genomes of the bacteria listed in Tables  1, 2. These CAZymes give an indication of the carbohydrate metabolic capacity of the bacteria  (Lombard et al. , 2013). Glycoside hydrolases (GH), glycosyl transferase (GT), and polysaccharide lyases (PL) were among the carbohydrate-active enzymes that were scanned in the genomes of the mentioned bacteria.
154) were different. DPCoA showed significant differences between biofilm and the fish-associated intestinal bacterial communities (Supplementary Figures  S3B-G ). Since GH1 GH3 GH3 GH3 GH3 GH5 GH13 GH5 GH13 GH13 GH28 GH13 PL7 GH31 PL9 PL17 GH28 PL22 PL1 # of genes 127 133 114 147 * The GenBank IDs are given in Table  1. the DNA extraction from water was performed using a different kit, we have not presented the comparison between the water bacterial communities and the intestinal bacterial communities.
ACKNOWLEDGMENTS Cermaq Norway AS ,  Hopen, Bodø  is acknowledged for providing the fish. We are thankful to  Ghana Vasanth  for her assistance in sample collection, and  Martina Kopp  for her technical help in sequencing the libraries. The support of  Nord University  research station staff during the fish sampling is acknowledged. We express our gratitude to  Bisa Saraswathy  for her assistance in data analysis, scientific input and preparation of the manuscript.
Isolation and growth conditions In 2017, we isolated from the stool sample of a healthy 39-yearold male Pygmy an unidentified bacterial strain. Screening was performed by MALDI-TOF MS on a Microflex LT spectrometer (Bruker Daltonics, Bremen, Germany) as previously described  [7]. The obtained spectra (Fig. 1 ) were imported into MALDI BIOTYPER 3. 0 software (Bruker Daltonics) and analysed against the main spectra of the bacteria included in two databases (the Bruker and the constantly updated MEPHI databases https:// www. mediterranee-infection. com/urms-data-base/). The study was validated by the ethics committee of the Institut Fédératif de Recherche IFR48 under number 2016-011. Initial growth was obtained 24 h after culture in a Colombia agar enriched with 5% sheep's blood (bioMérieux, Marcy l'Etoile, France) under anaerobic conditions at 37°C.
Strain identification The 16S rRNA gene was sequenced in order to classify this bacterium. Amplification and sequencing were performed using the primer pair fD1 and rP2 (Eurogentec, Angers, France) and the Big Dye® Terminator v1. 1 Cycle Sequencing Kit and ABI Prism 3130xl Genetic Analyzer capillary 3500xL Genetic Analyzer capillary sequencer (Thermofisher, Saint-Aubin, France) respectively, as previously described  [8]. The 16S  rRNA nucleotide sequences were assembled and corrected using CODONCODE ALIGNER software (http://www. codoncode. com). Strain Marseille-P4358 T exhibited a 98. 26% sequence identity with Enterococcus gallinarum strain LMG 13129 (Gen-Bank accession number NR104559), the phylogenetically closest species with standing in nomenclature (Fig. 4 ). Consequently, Enterococcus mediterraneensis was classified as a new member of the genus Enterococcus, family Enterococcaceae, phylum Firmicutes, with the strain Marseille P4358 T as the type strain of the new species Enterococcus mediterraneensis.
Genome sequencing Genomic DNA was extracted using the EZ1 biorobot (Qiagen, Courtaboeuf, France) with the EZ1 DNA tissue kit, then   sequenced on the MiSeq technology (Illumina, San Diego, CA, USA) with the Nextera Mate Pair sample prep kit (Illumina), as previously described  [9]. Genome assembly was performed with a pipeline incorporating different softwares (SPADES  [10] ), on trimmed (TRIMMOMATIC  [11] ) or raw data. GAPCLOSER was used to reduce assembly gaps. Scaffolds <800 bp and scaffolds with a depth value <25% of the mean depth were removed. The best assembly was selected by using different criteria (number of scaffolds: 3, number of contigs: 3). The genome of strain Marseille-P4358 T is 2 699 190 bp long with a 40. 90 mol% G+C content. The degree of genomic similarity of Marseille-P4358 T with closely related species was estimated using the ORTHOANI software  [12].
Acknowledgements We sincerely thank  Taku Sakazume ,  Takashi Irie ,  Yusuke Ominami ,  Kyoko Imai ,  Shigeki Matsubara ,  Akiko Hisada , and all the  Hitachi Team in Japan  for the collaborative study we are conducting together between  Hitachi High-Technologies Corporation  and the  Institut Hospitalo-Universitaire Méditerranée-Infection , and for the installation and services on the Tabletop Microscope TM4000Plus from  Hitachi  in our facility.
The representation of information about cancer and rare diseases in Wikipedia clearly illustrates the difference between cancers and rare diseases. Most of the cancer types listed on the National Cancer Institute site  [24]  are represented in Wikipedia. At the same time, we observe that only 8. 5% and 15% of the phenotypes described in Orphanet  [25]  and OMIM  [26] , respectively, are addressed by topics in Wikipedia. For our calculations, we use only the 47 rare renal diseases listed in  [27]  and represented in Wikipedia. We hope that, through the prism of Wikipedia, our results will make it possible to gain a better understanding of the influence of pharmaceutical companies on these diseases and to obtain specialization profiles for these pharmaceutical companies. The paper is constructed as follows: the mathematical methods are described in the Section Methods and data are overviewed in the Section Datasets.
Datasets We consider the English Wikipedia edition as at May 2017 with N = 5416537 articles (nodes) and N l = 122232932 hyperlinks between articles. This network has also been considered in  [16, 17, 19, 20]. For the REGOMAX analysis, we select N c = 195 world countries (see the list and PageRank order in  [19, 20] ), the N ph = 34 largest pharmaceutical companies (see Table  1 ), N rd = 47 rare renal diseases (see Table  2 ), and N cr = 37 types of cancer listed in  [20]. Thus we consider a total of N r = N c + N ph + N rd + N cr = 313 Wikipedia articles as nodes of interest. All data sets are available at  [30]. The present study complies with the Wikimedia terms of use. Table  1. List of the 34 largest pharmaceutical companies ranked by the relative PageRank index K r of their corresponding articles in Wikipedia.
Acknowledgments This work was partially supported by the  Programme Investissements d'Avenir   ANR-11-IDEX-0002-02 , reference  ANR-10-LABX-0037-NEXT  ( THETRACOM  project). It was also partially supported the  Programme Investissements d'Avenir   ANR-15-IDEX-0003 ,  ISITE-BFC  ( GNETWORKS  project) and by the  Bourgogne Franche-Comteregion  ( APEX  project).
J. L. was supported by  ANR-15-IDEX-0003 ,  Programme Investissements d'Avenir, ISITE-BFC  (project  GNETWORKS ), http://i-site. ubfc. fr; and  Bourgogne Franche-Comte ŕegion  (project  APEX ), https://www. bourgognefranchecomte. fr. D. S. was supported by  ANR-11-IDEX-0002-02 , reference  ANR-10-LABX-0037-NEXT ,  Programme Investissements d'Avenir  (project  THETRACOM ), http://www. next-toulouse. fr. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Multi-omics profiling of a yeast panel The BYxRM Saccharomyces cerevisiae yeast segregant panel used in this study results from a cross of a laboratory strain (BY4716) isogenic to the reference strain S288C, and a derivative of a wild isolate from a vineyard (RM11-1a). This cross was previously used to study the genetic contribution to molecular traits -including RNA and protein levelsand to test novel systems genetics approaches  [15] [16] [17]. We grew the two parental haploid yeast strains and 110 of their recombinant offspring under tightly controlled conditions, with multiple replicates for some of the strains (Fig. 1 , Supplementary Data S1). The transcriptomes of 150 cultures were sequenced at high coverage (38-186x) allowing for the quantification of 5,429 transcripts in all samples (Supplementary Data S2).
4% missing values across all samples (Supplementary Data S5). belonging to the 402 genes for which measurements at each molecular level were available. Broad-sense heritability was estimated by comparing the variation between replicates from the same strain (which is non-genetic) to the total variation between strains. When the intra-strain variation is small compared to the inter-strain variation, it can be concluded that the genetic contribution to trait variation is large under the experimental conditions tested  20. c, Proportion of molecular traits affected by at least one QTL are shown separately for each molecular layer. Shaded areas indicate the proportion of traits for which a local QTL was found. As expected, we observed that in most cases, protein abundances were positively correlated with their transcript levels (average r=0.
Discussion In this study, we present the first dataset that concurrently quantifies the effects of natural genomic variation on the transcript (eQTL), protein (pQTL), and protein phosphorylation (phQTL) layers. Whereas previous 'omics' QTL studies have primarily focused on the abundance of biomolecules, our study integrates abundance with the states of biomolecules. Hence, the conceptual advance of our study is the possibility to discover the consequences of genomic variation, for example the phosphorylation-mediated activation of a signaling pathway component, through the different layers of a molecular network. The three hotspots containing IRA2, HAP1, and GPA1/STE20 exemplify this notion. Quantifying transcript and protein levels along with the state of the proteins indicated by phosphorylation from the same yeast culture maximized the comparability of the data, thus facilitating data integration.
RNA extraction Total RNA was isolated from deep frozen aliquots of yeast pellets using the RiboPure™ RNA Purification Kit, yeast (Ambion), which includes a DNase treatment to eliminate contamination. RNA quality was assessed using RNA ScreenTape assay (Agilent). All RNAs were of very high quality (Supplementary Data S1, median RIN 9. 8, minimal RIN 9. 1).