dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequence Library Analysis Data quality control and analyses were performed using the QIIME pipeline  (Caporaso et al. , 2010)  hosted on the INRA MIGALE bioinformatics platform. Reads were first assigned to samples using the split_library. py script. This step also included a quality filtering step based on read length (200 < read length < 1000 bp) and quality: Number of ambiguous bases < 6; Mean quality score > 25; Maximum homopolymer < 6; No mismatches in primers). Clustering and taxonomy assignment were then done using the pick_de_novo_otus. py script. Briefly, reads were clustered using UCLUST with a degree of similarity of 97% to generate OTUs. Taxonomy was then assigned using the Ribosomal Database Project (RDP) Classifier and the Greengenes 16S reference database (available at: http://blog.
Determination of Total Bacterial Population of Samples The total bacterial population of samples was estimated by quantitative PCR on 16S rRNA using primers S-D-Bact-0341-b-S-17 and S-D-Bact-0785-a-A-21. Quantitative PCR was performed using a CFX96 Touch real-time system (Bio-Rad, Hercules, CA, USA). The reaction mixture contained SsoAdvanced Universal SYBR Green Supermix (Biorad), 0. 5 µM of each primer and 5 µL of total genomic DNA samples. Thermal cycling consisted of 30 s at 95 • C, followed by 40 cycles of 10 s at 95 • C, and 30 s at 60 • C. Genomic DNA of Staphylococcus aureus was used to generate standard curves. Data were normalized with regard to the volume of foremilk and are thus expressed as equivalent copy numbers of S. aureus genomes per ml of foremilk.
Discriminant Analysis Data were subjected to discriminant analysis in order to identify differentially abundant taxonomic units between Clusters C1 and C2 (Figure  4 , Table  1 ). Complete results of discriminant analysis, corresponding to the LDA score and p-value, are included in Table  S5. An overview of the cladogram indicated that most of the discriminant taxa were more abundant in Cluster C2 (Figure  4A ). Among the most discriminant taxa with an LDA score higher than 5, the Bacilli class was found to be more abundant in Cluster C1 than in Cluster C2, with a mean relative abundance of 52. 6 and 22. 7%, respectively. This was notably related to a higher abundance of Staphylococcus, which belongs to Bacilli, and which was present at 34 and 6. 7% in Clusters C1 and C2, respectively (Figure  S2 ).
ACKNOWLEDGMENTS We are grateful to the  Biogenouest Genomics and the Environmental and Functional Genomics Platform core facility  for technical supports. We are grateful to the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing computational resources. We thank  Antoine Eveno ,  Michel Fargetton ,  Arnaud Mottin ,  Joseph Orinel  and  Jean Parois  for their technical assistance during the sampling campaign. We thank  Gail Wagman  for revising the English.
1 ). GFB plots encompass forests of various origins (from naturally regenerated to planted) and successional stages (from stand initiation to old-growth). A total of over thirty million trees across 8,737 species were tallied and measured on two or more consecutive inventories from the GFB plots. Sampling intensity was greater in developed countries, where nationwide forest inventories have been fully or partially funded by governments. In most other countries, national forest inventories were lacking and most ground-sourced data were collected by individuals and organizations (Data Table  S1 ).
Data collection and standardization Our current study used ground-sourced forest measurement data from 45 forest inventories collected from 44 countries and territories (Fig. 1 , Data Table  S1 ). The measurements were collected in the field from predesignated sample area units, i. e. Global Forest Biodiversity permanent sample plots (hereafter, GFB plots). For the calculation of primary site productivity, GFB plots can be categorized into two tiers. Plots designated as 'Tier 1' have been measured at two or more points in time with a minimum time interval between measurements of two years or more (global mean time interval is 9 years, see For each GFB plot, we derived three key attributes from measurements of individual trees-tree species richness (S), stand basal area (G), and primary site productivity (P).
To investigate the potential effects of plot size on our results, we plotted the estimated elasticity of substitution ( ) against plot size, and found that the scatter plot was normally distributed with no discernible pattern (Fig. S2 ). In addition, the fact that the plot size indicator I2 had the second lowest (0. 8%) importance score  (49)  among all the covariates (Fig. 6 ) further supports that the influence of plot size variation in this study was negligible. Across all the GFB plots there were 8,737 species in 1,862 genera and 231 families, and S values ranged from 1 to 405 per plot. We verified all the species names against 60 taxonomic databases, including NCBI, GRIN Taxonomy for Plants, Tropicos -Missouri Botanical Garden, and the International Plant Names Index, using the 'taxize' package in R  (50).
Out of 8,737 species recorded in the GFB database, 7,425 had verified taxonomic information with a matching score (50) of 0. 988 or higher, whereas 1,312 species names partially matched existing taxonomic databases with a matching score between 0. 50 and 0. 75, indicating that these species may have not been documented in the 60 taxonomic databases. To facilitate inter-biome comparison, we further developed relative species richness (Š), a continuous percentage score converted from species richness (S) and the local maximal species richness (S * ) using * S S S  . ( 2 ) Stand basal area (G, in m 2 ha -1 ) represents the total cross-sectional area of live trees per unit sample area. G was calculated from individual tree diameter-at-breast-height (dbh, in cm): i i i dbh G      2 000079.
M denotes total removal of trees (including mortality, harvest, and thinning) in stem volume (in m 3 ha -1 ). Y represents the time interval (in years) between two consecutive inventories. P accounted for mortality, ingrowth (i. e. recruitment between two inventories), and volume growth. Stem volume values were predominantly calculated using region-and species-specific allometric equations based on dbh and other treeand plot-level attributes (Table  1 ). For the regions lacking an allometric equation, we approximated stem volume at the stand level from basal area, total tree height, and stand form factors  (52). In case of missing tree height values from the ground measurement, we acquired alternative measures from a global 1-km forest canopy height database  (53). For Tier 2 plots that lacked remeasurement, P was measured in mean annual increment (MAI) based on total stand volume and stand age  (51) , or tree radial growth measured from increment cores.
Geospatial random forest We developed geospatial random forest-a data-driven ensemble learning approach-to characterize biodiversity-productivity relationship (BPR), and to map BPR in terms of elasticity of substitution  (31)  on all sample sites across the world. This approach was developed to overcome two major challenges that arose from the size and complexity of GFB data without assuming any underlying BPR patterns or data distribution. Firstly, we need to account for broad-scale differences in vegetation types, but global classification and mapping of homogeneous vegetation types is lacking  (58) ; and secondly, correlations and trends that naturally occur through space (  59 ) can be significant and influential in forest ecosystems  (60). Geostatistical models (cf. 61) have been developed to address the spatial autocorrelation, but the size of the GFB data set far exceeds the computational constraints of most geostatistical software.
Global analysis For the global-scale analysis, we calibrated the nonlinear mixed-effects model parameters ( and 's) using training sets of 500 plots randomly selected (with replacement) from the GFB global dataset according to the bootstrap aggregating (bagging) algorithm. We calibrated a total of 10,000 models based on the bagging samples, using our own bootstrapping program and the nonlinear package nlme (62) of R, to calculate the means and standard errors of final model estimates (Table  2 ). This approach overcame computational limits by partitioning the GFB sample into smaller subsamples to enable the nonlinear estimation. The size of training sets was selected based on the convergence and effect size of the geospatial random forest models. In pilot simulations with increasing sizes of training sets (Fig.
Mapping BPR across global forest ecosystems For mapping purposes, we first estimated the current extent of global forests in several steps. We aggregated the 'treecover2000' and 'loss' data  (66)   We mapped productivity P and elasticity of substitution  (31)  across the estimated current extent of global forests, here defined as areas with 50 percent or more forest cover. Because GFB ground plots represent approximately 40 percent of the forested areas, we used universal kriging (cf. 61) to estimate P and for the areas with no GFB sample coverage. The universal kriging models consisted of covariates specified in Fig. 6 (B) and a spherical variogram model with parameters (i. e. nugget, range, and sill) specified in Fig. 7. We obtained the best linear unbiased estimators of P and and their standard error across the current global forest extent with the gstat package of R  (67).
3B ) and estimates of global forest productivity (Fig. S1 ). The first 10 percent reduction in tree species richness would lead to 0. 001-0. 597 m 3 ha -1 yr -1 decline in periodic annual increment, which accounts for 2-3 percent of current forest productivity. The raster data are displayed in 50-km resolution with a three-standard deviation stretch. 1  for a detailed description). Correlation coefficients between these variables were represented by sizes and colors of circles, and × marks coefficients not significant at =0. 05 level (A). Variable importance (%) values were determined by the geospatial random forest (see Materials and Methods). Variables with importance values exceeding the 9% threshold line (blue) were selected as control variables in the final geospatial random forest models (B).
ACKNOWLEDGMENTS We are grateful to all the people and agencies that helped in collection, compilation, and coordination of the field data, including but not limited to  T. Malone ,  J. Crowe ,  M. Sutton ,  J. Lovett ,  P. Munishi ,  M. Rautiainen , staff members from the  Seoul National University Forest , and all persons who made the two Spanish Forest Inventories possible especially the main coordinators,  R. Villaescusa  ( IFN2 ) and  J. A. Villanueva  ( IFN3 ). This work was supported in part by  West Virginia University  under the  USDA McIntire-Stennis  Funds  WVA00104  and  WVA00105 ;  U. S. National Science Foundation  (NSF) Long- Term Ecological Research Program at Cedar Creek  ( DEB-1234162 ); the  University of Minnesota Department of Forest Resources and Institute  on the Environment; the  Architecture and Environment Department of Italcementi Group, Bergamo (Italy) ; a  Marie Skłodowska Curie fellowship ;  Polish National Science Center Grant   2011/02/A/NZ9/00108 , the  French ANR  ( CEBA :  ANR-10-LABX-0025 ) and the  General Directory of State Forest National Holding DB; General Directorate of State Forests , Warsaw, Poland (Research Projects No.
1/07  and  OR/2717/3/11 ); the 12th  Five-Year Science and Technology Support Project  (Grant No. 2012BAD22B02 ) of China; the  U. S. Geological Survey  and the  Bonanza Creek Long Term Ecological Research Program  funded by the  National Science Foundation  and the  U. S. Forest Service  (any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government) ;  National Research Foundation of Korea  ( NRF-2015R1C1A1A02037721 ),  Korea Forest Service  ( S111215L020110 ,  S211315L020120  and  S111415L080120 ) and  Promising-Pioneering Researcher Program  through  Seoul National University (SNU ) in 2015; Core funding for  Crown Research Institutes  from the  New Zealand Ministry of Business, Innovation and Employment's Science and Innovation Group ; the  DFG Priority Program 1374 Biodiversity Exploratories ; Chilean research grants  FONDECYT  No.
1151495  and  11110270 ;  Natural Sciences and Engineering Research Council of Canada  ( RGPIN-2014-04181 ); Brazilian Research grants  CNPq   312075/2013  and  FAPESC 2013/TR441  supporting  Santa Catarina State Forest Inventory (IFFSC) ;  General Directorate of State Forests, Warsaw, Poland; Bavarian State Ministry for Nutrition, Agriculture , and  Forestry  project  W07 ;  Bavarian State Forest Enterprise (Bayerische Staatsforsten AöR) ;  German Science Foundation  for project  PR 292/12-1 ;  European Union  for funding the  COST Action FP1206 EuMIXFOR ;  FEDER/COMPETE/POCI  under Project  POCI-01-0145-FEDER-006958  and  FCT -Portuguese Foundation for Science and Technology  under the project  UID/AGR/04033/2013 ;  Swiss National Science Foundation  Grant  310030B_147092 ; and the  European Union 's  Horizon 2020 research and innovation programme  within the framework of the  MultiFUNGtionality Marie Skłodowska-Curie Individual Fellowship (IF-EF)  under grant agreement No  655815.
Here, we combined our data from Tn-seq with RNA sequencing to confirm the expression of sRNAs in IRs having a regulatory function over gene expression. A total of 390 sRNAs located in IRs were identified by RNA sequencing, of which 30 were located in IRs already described by Fagnocchi and coworkers to contain sRNAs. We did not find all the sRNAs characterized earlier by Fagnocchi et al. Two reasons can explain this discrepancy: (i) we used a different meningococcal strain, and (ii) our experimental conditions were different. While we only verified gene under mid-log-phase growth conditions, the other authors performed transcriptional expression profiling of N. meningitidis after exposure of the bacterium to stress signals (e. g. , heat shock, oxidative stress, iron and carbon source limitation), thus identifying putative sRNAs differentially expressed in vitro.
S1  in the supplemental material). The main differences observed concern the need for synthesis of cofactors and vitamins on GC broth (GCB) agar compared to requirements for growth on other media. These discrepancies are likely due to differences in metabolite profiles between each medium. The list of N. meningitidis essential genes was further compared to essential genes of other organisms listed in the Database of Essential Genes (DEG)  (43) , which lists bacterial genes essential for viability in different species. From the 383 essential genes determined in our study, 29% had essential orthologs with Escherichia coli  (43, 44) , 26% with Haemophilus influenzae (45), 48% with Pseudomonas aeruginosa  (46) , and 55% with Salmonella enterica serovar Typhi  (47)  (see Table  S6  in the supplemental material).
meningitidis survival. Moreover, our genetic screen is the result of a competition between different mutants harboring a transposon insertion for a single gene within a mixed population, thus explaining some possible discrepancies between the two studies. For example, the fur gene was classified by us as essential, whereas a deletion mutant has been proven to be viable despite having an important growth defect  (49). We further compared our list of genes that caused growth defects and of the essential genes to the minimal gene set created by Gil and coworkers  (50) , which can be found in the publicly accessible thematic database NeisseriaScope within MicroScope  (51). The minimal gene set within NeisseriaScope includes wellconserved housekeeping genes for basic metabolism and macromolecular synthesis, many of which are essential.
MATERIALS AND METHODS Bacterial strains and growth conditions. N. meningitidis Z5463, a naturally transformable capsulated serogroup A strain, was used to generate saturating Tn insertion mutant libraries. Z5463 belongs to the same sequence type (ST) as strain Z2491, i. e. , , subgroup IV-1, expressing OpaA and OpaC and in the same clonal group as Z2491, thus allowing the use of the genomic sequence of the latter strain. Because N. meningitidis Z2491, whose genome has been completed by the Sanger Center  (20) , is not transformable, we routinely work with strain Z5463  (59). A comparison between both strains has been made in the public databases for molecular typing and microbial genome diversity (PubMLST), and it has been found that both strains have 168 single nucleotide polymorphism (SNP) differences, and only two genes are present in one strain and not in the other: loci NEIS2357 and NEIS2494.
(13). Disposable flow chambers composed of six independent flow channels (-Slide VI 0. 4; ibidi) were used. hCMEC/D3 or FaDu cells were seeded at a density of 0. 3 ϫ 10 5 /cm 2 and incubated for 7 days at 37°C in 5% CO 2 until confluent. Examination of the cell layers was performed before each flow assay, and only channels with a uniformly confluent layer were used. Transposon mutant library construction. Construction of mutant libraries was performed using the Template Generation System II kit (Thermo Scientific), which includes Entranceposon KanR-3, a derivative of the bacteriophage mu containing a kanamycin resistance marker. Briefly, genomic DNA from N. meningitidis Z5463 was extracted by using chloroform and then ethanol precipitated. In vitro transposition reactions were carried out with 0.
88 g of MuA transposase, 80 ng of Entranceposon KanR-3, and 5 g of N. meningitidis Z5463 genomic DNA. Reaction mixtures were incubated for 3 h at 30°C following the manufacturer's instructions and ethanol precipitated. Purified transposition reaction products were transformed into naturally competent N. meningitidis Z5463 as described previously  (63, 64)  and selected on GCB agar plates containing 200 g of kanamycin per ml. On the following day, transformants were counted with an eCount colony counter (Heathrow Scientific), scraped off of agar plates, and stored at Ϫ80°C in GCB supplemented with 20% glycerol. When a large amount of transformants was reached, they were pooled into libraries of approximately 70,000 CFU per library. To separate transformants from any residual agar, cells were grown in GCB for a maximum of 2 h, after which libraries were collected and stored at Ϫ80°C in GCB supplemented with 20% glycerol (Fig.
Likewise, the 6 endothelial libraries (InEndo. 1 to -3 and OutEndo. 1 to -3) were sequenced together in a single flow cell lane and yielded between ~7 and ~10 million raw reads per sample (Table  1 ). Libraries enriched in transposon-containing bacterial fragments were sequenced on an Illumina MiSeq (paired-end sequencing of 300 plus 300 bases, from 2 to 6 samples per run). Bioinformatic analysis of Tn libraries. HITS data analysis was performed as described previously  (14) , with minor modifications. The detailed procedure is described in Text S1 in the supplemental material. Gene essentiality was determined based on the log 2 of the measured number of transposon-containing reads per gene divided by the expected number of transposon-containing reads per gene (based on the number of possible transposon insertion sites per gene, the mutant library size, and the sequencing depth) as determined via TMM normalization (see Table S1A in the supplemental material).
Determination of the log 2 FC for identification of conditional essential genes was performed with the binary logarithm of the number of reads of the target sample (output libraries harvested after selection onto epithelial or endothelial cells, respectively) divided by the number of reads of the gene within the control sample (input libraries grown before selection in epithelial or endothelial cells, respectively) (see Table  S1D ). Web tools used for analysis. Putative orthologs of N. meningitidis genes were identified by using the DEG database (http://tubic. tju. edu. cn/ deg/)  (43). The protein families in DEG corresponded to homologous ORFs with identical assigned functions. Metabolic pathways and subsystems for N. meningitidis strain Z2491 were obtained based on Kyoto Encyclopedia of Genes and Genomes orthology  (66).
The list of transporter proteins was obtained from data available from the Transporter Protein Analysis Database (TransportDB) at http://www. membranetransport. org. RNA sequencing. (i) Isolation of bacterial RNA. Bacteria grown in Ham F-12 medium (PAA Laboratories) supplemented with 10% fetal calf serum (FCS; PAA Laboratories), 20 mM HEPES (PAA Laboratories) at 37°C in a humidified incubator under 5% CO 2 and under shaking conditions to the mid-logarithmic phase were harvested by centrifugation. Bacterial pellets were resuspended in 1 ml of TRIzol reagent (Life Technologies) and frozen at Ϫ 80°C. RNA isolation was performed according to TRIzol RNA isolation procedure. Quality of the bacterial RNA was measured using a Bioanalyzer 2100 (Agilent). To remove contaminating genomic DNA, samples were treated with 0.
25 U of DNase I (Fermentas) per g of RNA for 45 min at 37°C. Preparation of 3 whole transcriptome libraries and 3 sRNA enriched libraries as well as the RNA-Seq experiments are detailed in supplemental Text S1 in the supplemental material. (ii) Bioinformatic analysis of expression data. Totals of 75. 2 million reads and 2. 1 million reads were obtained from the whole-transcriptome libraries and the sRNA-enriched libraries, respectively. Reads matching ribosomal genes based on SortMeRNA  (67)  and low-quality reads were removed using Trimmomatic (parameters: leading, 8; trailing, 10; sliding window, 4:5) (68). The remaining sequences were independently mapped with Bowtie version 0. 7. 12 (69) to the reference sequence of Neisseria meningitidis Z2491 (accession number AL157959. 1). These data are presented in Table  S2  in the supplemental material.
The expression of mRNA was measured by extracting the read counts with HTSeq (70) and transformed into RPKM values for each replicate. We arbitrarily defined the cutoff for classification of a genomic region of interest as the median of RPKM values of intergenic regions (see Table  S2D  to F in the supplemental material). Thus, a genomic region was considered transcribed if it had an RPKM value greater than 2. 3. Out of a total of 1,994 CDS (not including rRNAs or tRNAs), 1,831 were above this transcriptional threshold (92%), whereas 163 CDS were below (8%) and thus not expressed under these experimental conditions. Alignment files were then used to detect putative sRNAs with the help of sRNA-Detect  (71). That tool allowed us to detect 6,088 putative sRNAs in the wholetranscriptome data set and 4,530 putative sRNAs in the sRNA enriched data set.
The set of sRNA candidates provided by sRNADetect from the two data sets were combined. Next, genes flanking each sRNA candidate were extracted from the annotation for N. meningitidis, and only sRNA candidates with a clear assignment to a particular strand, requiring that at least 99% of the reads originated from the plus or minus strand, were kept. When an intergenic region contained more than one sRNA, the candidate with the highest coverage was selected. The average coverage of the sRNA and the flanking genes was next calculated by using bedtools  (72)  for each strand and by using customized Python scripts. In order to analyze whether the sRNAs overlapped a promoter region, the tool PromBase  (73)  was used, and putative promoter regions were annotated in the genome of N. meningitidis (see Table  S2B ).
ACKNOWLEDGMENTS We are grateful to  Alain Charbit  for critical reading of the manuscript, helpful advice, and support. This work was supported by  ANR  grant  ANR-14-IFEC-0006-01 call ERANET INFECT-ERA 2014 , the grant program  EMERGENCE  from  La Mairie de Paris , and a postdoctoral grant supported by the DIM Malinf from the  Conseil Régional de l'Ile-De-France. The laboratory of X. N. is supported by  INSERM ,  CNRS ,  Université Paris Descartes , and the  Fondation pour la Recherche Médicale.