dataesr/scientific-paragraphs-categorization huggingface dataset:
Circle size and color indicate the surface area of a landslide, while the magenta line delimits the area with highest spatial density of landslides (see "Methods" section, Supplementary Fig. S2 ), referred to as the landsliding zone. Solid and dashed red lines indicate active thrust and strike-slip or normal faults, respectively  22. Other less well identified faults exist inside the range  27. (c) Probability density distribution of the surface area of landslides triggered by typhoon Morakot for areas greater than 10 m 2. Maps were performed using Matlab R2019b. the 1999 Mw 7. 6 Chi-Chi earthquake  20  , enhanced sediment removal persisted for > 2 years  18, 19  , although export of the coarse fraction of landslide debris may take decades or more  21. The exact volume of sediment export is difficult to estimate, but the landsliding zone must have undergone a progressive surface mass unloading after one of the largest weather-driven erosion events on record.
It is not limited to the vicinity of the landsliding zone, but it also occurred in northeast Taiwan. However, outside the landsliding zone, increases in the b-value appear not to be associated to increases in the number of earthquakes. For instance, North-East Taiwan displays a significant increase in the number of earthquakes associated to a decrease of the b-value. We note that the spatial correlation between earthquake statistics change and the landsliding zone is less well resolved and less robust (see "Methods" section) than the temporal correlation. S3 ). Maps were performed using Matlab R2019b.
Methods Morakot rainfall data. The map of cumulative rainfall during Morakot was obtained by natural-neighbor interpolation of cumulative hourly rain gauge measurements over the period 7 to 9th of August 2009. Data from 377 stations across Taiwan were used from the Data Bank for Atmospheric Research at the Taiwan Typhoon and Floods Research Institute. Morakot landslide catalogue. Mapped landslides were delineated manually by comparing surface reflectivity and morphology on pre-and post-event FORMOSAT-2 satellite images  40  (2 m panchromatic and 8 m multi-spectral). To cover most of the islands we mosaicked multiple cloud-free pre-event (01/14, 05/08, 05/09, 05/10, 06/06) and post-event (08/17, 08/19, 08/21, 08/28, 08/30, 09/06) images taken in 2009. For parts of the inventory, especially east of the main divide, landslides were first mapped automatically and then edited manually.
Acknowledgements P. S. and L. J. acknowledge funding by  Université  Rennes 1 and by  Agence Nationale pour la Recherche (ANR)  through the  EROQUAKE  project ( ANR-14-CE33-0005 ). We are grateful for support by the  France-Taiwan International Associate Laboratory  " From Deep Earth to Extreme Events ", and thank the  Central Weather Bureau of Taiwan  and the  Taiwan Typhoon and Floods Research Institute  for access to their data. We thank  Edwin Baynes ,  Thomas Croissant ,  Dimitri Lague  and  Harsha Bhat  for discussions. We also thank  Roland Burgmann ,  Ya-Ju Hsu  and several anonymous reviewers for their constructive comments on the current version of this manuscript and on previous ones. This study contributes to the  IdEx Université de Paris   ANR-18-IDEX-0001. This is IPGP contribution 4145.
Data availability The datasets used in this study are provided along with the manuscript. The earthquake catalogue is also accessible through the Taiwan Central Weather Bureau (https ://gdms. cwb. gov. tw). The rainfall data is also accessible through Data Bank for Atmospheric Research at the Taiwan Typhoon and Floods Research Institute (https :// www. narla bs. org. tw/en). The landslide dataset is also available by request to O. M. , P. M. and N. H.
code availability The Matlab code used to perform the analysis and the figures of this manuscript, as well as the data needed to process the figures of this paper, are provided here: https ://zenod o. org/recor d/37783 55. The Reasenberg declustering algorithm is accessible through the ZMAP set of tools (https ://www. seism o. ethz. ch/en/resea rch-and-teach ing/produ cts-softw are/softw are/ZMAP/ and https ://githu b. com/Celso Reyes /zmap7 ).
Dead cells were excluded using fixable viability dye eFluor450 (eBioscience), fixable viability dye eFluor455 ultraviolet (eBioscience), propidium iodide (Sigma Aldrich) or DAPI (4',6-diamidino-2phenylindole, Dilactate, Biolegend). Intracellular staining was carried out using human FoxP3 buffer (BD Biosciences) according to the manufacturer's instructions. Data were acquired on an LSRFortessa II (BD Biosciences) and analysed using FLOWJO (Tree Star, Ashland, OR). Identification of NK cells (as opposed to ILC1) was confirmed by the absence of Eomes À CD49a + cells in the gates (see Supplementary material, Figure  S1 ). For most panels, gates were set using appropriate fluorescence minus one or internal negative controls. For in vivo labelling experiments, the sinusoidal gate was set using cells taken from control animals that had received PBS instead of fluorescently labelled antibody (see Supplementary material, Figure  S2 ).
Tbet controls the expression of cell migration mediators in bone marrow NK cells Having confirmed that the accumulation in the bone marrow of Tbx21 À/À mice is caused by a migration defect, we sought to define the mediators involved. We therefore sorted iNK and mNK1 cells from the bone marrow of wild-type and Tbx21 À/À mice, and mNK2 cells (which are not present in Tbx21 À/À ; Fig. 1b ) from wildtype mice and analysed them by RNAseq. Raw RNAseq data and differentially expressed gene lists are available from the National Center for Biotechnology Information Gene Expression Omnibus under accession no. GSE122874, https://www. ncbi. nlm. nih. gov/geo/query/acc. c gi?acc=GSE122874 Consistent with a previous report in the Duane mouse,  12  we found S1pr5 mRNA underexpressed by a factor of approximately sevenfold in both iNK (Fig.
Intracellular persisters also drastically adapt their respiration status, with an expression program resembling that observed in anaerobiosis. As revealed by enrichment analysis, genes related to oxidative phosphorylation (Supplementary Fig. 6 ) were mostly repressed  39  , to the benefit of massive D-lactate fermentation. and Genbank database  23  and their log 2 Fold Change expression levels. c MA-plot of genes related to amino acid metabolism within the stringent response stimulon (Supplementary Fig. 4 ; see reference  55  ). The graph displays the log 2 Fold Change expression as a function of log 2 Base Mean (mean expression signal across all samples). Genes prominently activated after amino acid depletion  24   The transcription signature of the genes related to amino acid metabolism within the SR stimulon (Fig.
1% (w/v) Triton X-100 (Sigma) to release intracellular bacteria. Lysates were centrifuged at 300 g for 5 min to discard cellular debris. Bacteria were collected by centrifugation at 5000 g for 5 min, washed in PBS, and resuspended in filtered PBS. For cfu counting, samples were diluted in PBS before plating on Tryptic Soy agar. Data are expressed as log 10 cfu per mg cell protein after the incubation period compared to the post-phagocytosis inoculum. The same protocol was applied for infection of primary human macrophages and THP-1 monocytes, except that THP-1 cells were growing in suspension, so that washing procedures involved cell pelleting by centrifugation (7 min, 300 g). For other cell types (A549, MCF7, MG63 and primary keratinocytes), the same protocol was applied as for macrophages, except that bacteria were incubated with cells during 2 h at a multiplicity of infection of 50:1.
Prior to microscopy, chambers were removed and slides covered with cover glasses. Infected cells were observed with a Cell Observer SD (Carl Zeiss) and analyzed with Zen v1. 1. 2. 0 software (Carl Zeiss). Flow cytometry. S. aureus isolated from broth cultures or from macrophages were resuspended in filtered PBS, stained with 10 µg mL -1 propidium iodide, and analyzed using a FACSVerse cytometer (BD Biosciences) for GFP signal intensities (FITC channel, medium flow rate). Forward-scatter width (FCS-W) versus forward-scatter area (FSC-A), and side-scatter width (SSC-W) versus side-scatter area (SSC-A) were used to gate out damaged or multiplet cells. Of those, propidium iodide-positive bacteria were gated out. Data were analyzed with FlowJo 10. 5. 2 software (TreeStar Inc. ). The level of replication of the population (F, fold replication) is calculated by the ratio Me 0 /Me t with Me being the median GFP intensity of the bacterial population at a given time.
Both control and persisters conditions were proceeded for the same sorting procedure. Bacteria were immediately proceeded for sorting on the basis of their propidium iodide profile and GFP expression level using the gating methods described above, in a FACSAria III cytometer operated by the BD FACSDiva 8. 0. 1 software (BD Biosciences), under continuous cooling to 4 °C (including the input tube holder and the collection tube) at high flow rate. Samples were then immediately processed for RNA extraction. RNA extraction. S. aureus recovered from infected macrophages or from control samples were centrifuged at 5000 g for 5 min and lysed using the following procedure: pellets were resuspended in Tris-EDTA buffer containing freshly prepared 13 mg mL -1 lysozyme (Sigma) and 130 µg mL -1 lysostaphin (Sigma) for 30  with RNA extraction InviTrap Spin Universal RNA Mini Kit (Stratec) following the manufacturer's instructions.
Sequencing was performed as an SRM run (SR: Single Read, PE: Paired-end Reads, M: multiplexed samples) for 65 bp sequences on HiSeq 2500 Illumina sequencer (65 cycles). The multiplexing level was 6 samples per lane. Bioinformatics analysis were performed using the RNA-seq pipeline from Sequana  70. Reads were cleaned of adapter sequences and low-quality sequences using cutadapt version 1. 11 (ref. 71 ). Only sequences at least 25 nt length were considered for further analysis. Bowtie version 0. 12. 7 (ref. 72 ) with default parameters, was used for alignment on the reference genome (CP000253. 1, NCBI). Genes were counted using featureCounts version 1. 4. 6-p3 (ref. 73 ) from Subreads package (parameters: -t gene -g ID -s 1). Count data were analyzed using R version 3. 4. 1 (ref. 74 ) and the Bioconductor package DESeq2 version 1.
16 (ref. 75 ). The normalization and dispersion estimation were performed with DESeq2 using the default parameters and statistical tests for differential expression were performed applying the independent filtering algorithm. A generalized linear model was set in order to test for the differential expression between the intracellular persisters and control conditions. Raw P values were adjusted for multiple testing according to the Benjamini and Hochberg (BH) procedure  76  and genes with an adjusted p value lower than 0. 05 were considered differentially expressed. For over-representation analysis, S. aureus KEGG gene-sets were downloaded thanks to the EnrichmentBrowser R package version 2. 14. 3 (organism code sao). All the 106 KEGG sets were then tested for the over-representation in differentially expressed genes using the Fisher statistical test.
Statistical analysis and curve fitting. For RNA-seq analyses, differential expressions of transcripts were based on adjusted P-values with a threshold of statistical significance set to 0. 05. Genes descriptions were indicated as described in GenBank database  23  and classified according to KEGG orthology  78  and KEGG pathway  79  databases (organism code CP000253. 1 and sao respectively). Curve fitting and statistical analyses were performed with GraphPad Prism versions 4. 03 or 8.
Acknowledgements The authors thank  N. Dauguet  ( De Duve Institute, Université catholique de Louvain ) for assistance in FACS experiments;  V. Mohymont  for help in real-time PCR experiments;  F. Laurent  and  J. Josse  ( INSERM U1111, International Center for Research in Infectiology, Lyon, France ) for providing MG63 osteoblasts cells;  J. P. Pirnay  and  G. Verween  ( Queen Astrid Military Hospital, Brussels, Belgium ) for providing human keratinocytes;  S. Pyr dit Ruys  ( De Duve Institute, Université catholique de Louvain ) and  M. Putrinš  ( Institute of Technology, University of Tartu, Tartu, Estonia ) for inspiring discussions, and  D. Schryer  for proofreading of the manuscript. This work was supported by the  Belgian Fonds de la Recherche Scientifique (FNRS-FRS)  (grants  T. 0189.
Data availability The source data underlying Figs. 1a, b, e, h, i, l, 3a, b, d, 4a, d, 5a and f are provided as a Source Data file. RNA-seq data reported in this study have been deposited at Gene Expression Omnibus under accession number GSE139659 [https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE139659]. All other relevant data are available from the corresponding author on request.
However, a recent meta-analysis based on more than 30,000 amoA genes deposited in public databases revealed that the cultivated strains and enrichments represent only 7 out of 19 total AOA clades, indicating that the (eco)physiological potential of AOA could be far greater than assumed  (14). Nevertheless, genomes of cultivated species from diverse environments and a large number of fully or partially assembled (meta)genomes of AOA are now available and reveal a very well-conserved core of energy and carbon metabolism  (19, 20) , with ammonia oxidation catalyzed by an ammonia monooxygenase (AMO) and CO 2 fixed via an extremely efficient version of the 3-hydroxypropionate/4-hydroxybutyrate pathway (HP/HB)  (21). This finding confirms the general assumption that AOA play an important role in the global nitrogen cycle, as ammonia oxidation represents the first step of nitrification, which is an essential process to eventually enable the conversion of reactive nitrogen species via denitrification into the inert gaseous compound N 2 , which is then released to the atmosphere.
(113)  were annotated in our genome data set using HMMER (v3. 1b2) (114) as described previously  (12). The best hit was selected in each genome, and a subset of 33 informational protein families that were present in 74 out of 76 genomes of our data set was selected. The sequences from each family were extracted and aligned using MAFFT (linsi algorithm, v7. 313)  (115) , and the alignments were filtered using BMGE (BLOSUM30, v1. 12)  (116). The filtered alignments were then concatenated, a maximum-likelihood tree was inferred with IQ-TREE (v1. 6. 11) using the LGϩC60ϩF model of sequence evolution, and 1,000 ultrafast bootstraps were performed  (117). The 33 families, given with Pfam identifiers, were as follows: PF00177 ribosomal protein S7p/S5e, PF00189 ribosomal protein S3, PF00203 ribosomal protein S19, PF00237 ribosomal protein L22p/L17e, PF00238 ribosomal protein L14p/L23e, PF00252 ribosomal protein L16p/L10e, PF00276 ribosomal protein L23, PF00347 ribosomal protein L6, PF00366 ribosomal protein S17, PF00380 ribosomal protein S9/S16, PF00410 ribosomal protein S8, PF00411 ribosomal protein S11, PF00416 ribosomal protein S13/S18, PF00466 ribosomal protein L10, PF00572 ribosomal protein L13, PF00573 ribosomal protein L4/L1 family, PF00673 ribosomal L5P family, PF00687 ribosomal protein L1p/L10e family, PF00831 ribosomal L29 protein, PF01090 ribosomal protein S19e, PF01157 ribosomal protein L21e, PF01200 ribosomal protein S28e, PF01201 ribosomal protein S8e, PF01280 ribosomal protein L19e, PF01667 ribosomal protein S27, PF01000 RNA polymerase Rpb3/RpoA insert domain, PF01191 RNA polymerase Rpb5, C-terminal domain, PF01192 RNA polymerase Rpb6, PF01912 eIF-6 family, PF09173 initiation factor eIF2 gamma, PF03439 early transcription elongation factor of RNA pol II, PF04406 type IIB DNA topoisomerase, and PF11987 translation-initiation factor 2.
Inference of ancestral optimal growth temperature. A data set of 16S rRNA sequences and corresponding OGTs was extracted from Eme et al. (25)  and from the literature (Table  S1C ). The stem position predictions were obtained from the RNA STRAND database (v2. 0) for all the sequences in our data set that were available (20 rRNA molecules). The 16S rRNA sequences that were available (56 out of 76 genomes; see Table  S1A ) were all aligned together with RNA STRAND sequences with SSU-ALIGN (v0. 1. 1) (118) , and the consensus of the positions found in stems were conservatively selected to be part of stem regions (878 positions). The GC% of the predicted stems was computed for each sequence. A linear regression between the stem GC% and the OGT was computed. A nonhomogeneous model was used in bppml (BppSuite, v2.
Phylogenetic analyses of gained genes. We first gathered a representative data set of 281 genomes from bacteria, archaea, and eukaryotes (see Table  S1B ). This data set, composed of complete genomes (NCBI RefSeq database), was selected to cover a vast range of organismal diversity and was enriched to cover the diversity of organisms from the TACK superphylum by adding 68 highly complete metagenomic bins (Table  S1B ). For protein families of interest, we retrieved all the sequences classed as part of the family in our 76 genomes data set and used them to query the representative data set of genomes using BLAST (v2. 6. 0ϩ) (125). The first 250 sequences corresponding to the best-score hits with an E value lower than 10 Ϫ10 were selected. The corresponding sequences were extracted, dereplicated with UCLUST ("-cluster_fast," 100% identity level, USEARCH v10.
Generation of figures. Drawings of trees and genes were generated with iTOl (v4)  (127)  and GeneSpy (v1. 1) (128), respectively. Data availability. The genomes analyzed in this study are publicly available in NCBI or IMG/JGI databases, and all accession numbers are given in Table  S1A. All data generated in this study are provided in Tables S1 to S4, and generated phylogenetic trees are in Data Set S1.
ACKNOWLEDGMENTS This project was supported by  ERC Advanced  Grant  TACKLE (695192 ) and project  P27017  of the  Austrian Science Fund (FWF). S. S. A. was funded by a  Marie Curie Action  fellowship (grant  THAUMECOPHYL 701981 ). We thank  Thomas Rattei  and  Florian Goldenberg  for providing access to the CUBE servers ( University of Vienna ),  Céline Brochier-Armanet  for early discussions on the project, and  Simon Rittmann  and  Lisa-Maria Mauerhofer  for providing the optimal growth temperatures of methanogens. S. S. A. and C. S. conceived the study. S. S. A. performed the bioinformatic and phylogenetic/phylogenomic analyses. M. K. analyzed the reconstructed genomes. All authors analyzed and discussed the results and wrote the manuscript.
, 2013). Results obtained from single locus genetic markers might not be as reliable as those obtained with multiple genetic loci. In fact, a multi-locus sequence analysis (MLSA) approach of concatenated sequences of seven housekeeping genes was shown to be more promising in identifying wellresolved species clusters  (Bishop et al. , 2009; Jensen et al. , 2013). Bishop et al. (2009)  collected seven housekeeping gene sequences from 420 streptococci to produce a viridans group database and developed a publicly available website and software for electronic taxonomy 1. The seven targeted genes were map encoded for a methionine aminopeptidase, pfl, a pyruvate formate lyase, ppaC, an inorganic pyrophosphatase, pyk, a pyruvate kinase, rpoB, an RNA polymerase subunit, soda, a superoxide dismutase, and tuf, an elongation factor Tu  (Bishop et al.
CLINICAL CASES Case reports of S. intermedius infections were collected from PubMed (last accessed on 08/05/2019) occurring over a 23year period. A total of 101 cases with available patient metadata were retained, 85. 1% (n = 86) of which were reported after the year of 2015, 12. 9% (n = 13) between 2001 and 2014, and 2. 0% (n = 2) prior to 2001. The following demographic and clinical data was obtained from the literature: patient's age, sex, year of infection (if not available the publication date was used), country of isolation, clinical profile (including previous relevant pathologies, surgeries, dental manipulation, etc. ), symptoms, neurological results when available (including sensory losses, quadriplegia, numbness, weakness, movement restriction etc. ), imaging results [mainly computed tomography (CT)], isolation source of the bacterium, coinfection with other bacteria, presence or absence of an abscess and its localization, prescribed antibiotics, and outcome of the disease (recovery or death) (Table  1  and Supplementary Table  S1 ).
SUMMARY This review aimed at summarizing our current knowledge of S. intermedius, its various identification methods, known virulence factors, and regulation of gene expression involved in abscess development. Members of the SAG are part of the normal microbiota being found at various mucosal sites and are frequently encountered in invasive suppurative infections. SAG members share few common characteristics and have a variable Lancefield serogrouping. Different identification approaches were used with the most promising being the MLSA and WGS, with the WGS approach providing a better resolution and discriminatory power. Currently, 42 genome assemblies of S. intermedius are available on NCBI database, and S. intermedius still remains underrepresented compared to other SAG members and other medically relevant streptococci.
Review Article Methodology A literature review of peer-reviewed articles, thesis and books in English (such as defined in  Grant and Booth (2009)   [60] ) has been conducted on the following topics: (i) behavioral and movement ecology of buffalo and cattle in southern Africa; (ii) existing remote sensing tools allowing the discrimination in time and space of determined environmental drivers. The Web of Science database was used to retrieve relevant references via a two steps keyword querying process without time constraint. At each step, a systematic screening based on the title and the abstract was first conducted to select the articles, books or thesis for full-text reading. Selected references bibliographies have also been read to extract additional relevant articles, book or thesis. The first step was to discriminate the environmental drivers impacting buffalo and cattle movements in space and time.
) to discriminate user-determined landcover categories  [93]. For example, the RF algorithm uses a set of decision trees  [94]  and is now widely used  [95, 96] , with the advantages of reliable and rapid execution in processing time of large volume of variables and data  [97, 98]. Such approaches require the definition of a training dataset of the different classes to be distinguished before classification. On the other hand, unsupervised classifications methodologies are more automatic processes, relying on algorithms such as K-means or Agglomerative Hierarchical to discriminate landcover categories  [99]. The two types of classification methods can be applied to classify either image pixels, based on their spectral or textural values, or objects, i. e. , neighboring pixels with similar spectral values aggregated into "objects" prior to the classification process.
As shown by this example, and despite limitations, the data listed in Table  6  presents the advantage to describe fire phenomenon in relation with animal distribution and movement in regions with scarce fire information  [251]. In well-documented areas, these data can potentially be used to complement existing fire databases. Combining better spatial resolution from new sensors such as Sentinel-3 (Table  1 ) and remote sensing-based fire products with designed spectral indexes to detect fire (Table  7 ) is promising. It could potentially reduce errors and uncertainties in satellite-derived fire dates and ignitions, and improve coverage of small fires. The recently launched FireCCI50 product (Table  6 ) offers an increased spatial resolution (250 m) and a better burned area estimation compared to the MODIS fire products  [249].