dataesr/scientific-paragraphs-categorization huggingface dataset:
In total 662 micrographs were collected automatically using the EPU software (FEI). Micrographs were acquired in nano-probe mode using 49 movie frames with a dose of ~2 electrons/Å 2 /frame resulting in a total dose of 98 electrons per Å 2 at a pixel size of 1. 002 Å. Helical reconstruction. In order to obtain both a higher resolution map of the core of the BCL10-MALT1 filaments, and a lower resolution map of the entire assembly, we performed two 3D refinements, varying the diameter limitation used during refinement, and the total dose used prior to particle extraction. For the higher resolution map, the frames 2 to 7 (total dose ~14 electrons/Å 2 ) out of a total of 49 frames were motion-corrected and dose-weighted using MotionCor2  46  , while for the lower-resolution map, the frames 2 to 16 (total dose ~32 electrons/Å 2 ) were processed.
The low dose dataset was used to determine the helical symmetry and to obtain the map of the BCL10-MALT1DD filament core, while the higher dose dataset enabled to get information about the periphery of the filaments. For both datasets, the defocus estimation was performed with CTFFIND/ CTFTILT  47. In total, 2618 straight sections of filaments were boxed manually using the e2helixboxer submodule of EMAN2 48 from a selected subset of 370 micrographs. The average length of picked BCL10-MALT1 filament sections was ~756 Å and the total length ~0. 2 mm (Supplementary Table  1 ). All subsequent processing steps were performed in the helical reconstruction software package SPRING  49. In total 25,576 segments were extracted using a segment size of 500 Å and segment step size of 30 Å. For 2D classification and initial symmetry estimation, phase-flipped, verticalized segments were extracted from the low dose dataset, whereas for symmetry refinement and 3D reconstruction, convolved, nonrotated segments were used.
The sum of the power-spectra of all verticalized segments was calculated using Segmentexam module, confirming the helical nature of the specimen. Helical parameters were determined as follows. First, 40 class averages were obtained using k-means clustering algorithm from SPARX  50  as implemented in the Segmentclass module. Class averages showed a repetition of the projection pattern along the helical axis every ~126 Å, suggesting that this distance was a close estimate of the repeat c. The sum of the power spectra of 2D class averages padded in 1080*1080 pixels boxes and showing the biggest number of layer lines (11 class-averages) was then calculated for symmetry estimation. Layer lines are positioned at multiples of ~1/126 Å -1 , confirming the initial estimation of the repeat (Supplementary Fig.
The overall resolution of this map estimated at Fourier shell correlation (FSC) cutoff of 0. 143 was 4. 9 Å (Supplementary Table  1 , Supplementary Fig. 8 ). This map was used as an initial model for the processing of the higher dose dataset using an analogous approach, but this time with the symmetry parameters were kept fixed and a higher refinement diameter of 290 Å was used for visualization of the less ordered filament exterior. Selected 8412 segments corresponding to 50,472 asymmetric units after symmetrization resulted in a map of the whole BLC10-MALT1 assembly with an overall resolution of 5. 9 Å. Both 4. 9 Å and 5. 9 Å resolution raw half maps were symmetrized and used to estimate the local resolution (Supplementary Fig. 8b ) and the B-factor using relion_postprocess. Additionally, in order to assess the resolution of each region of the maps (Bcl10, MALT1 DD, MALT1 Ig1-Ig2-paracaspase domains) FSC curves were calculated between half maps within smooth cylinder masks of various diameters of 0-83 Å, 83-140 Å, and 140-260 Å (Supplementary Fig.
8a ). The final higher resolution map of filament core (Fig. 1 ) used for model building was postprocessed using 3Dinspect module in Spring with a b-factor of -200 Å -2 and a resolution cutoff of 4. 9 Å, whereas the lower resolution map of the entire assembly was post-processed with various resolution cut-off and sharpening, as indicated in Fig. 4. Model building and refinement. The BCL10 CARD domains and MALT1 DD were built using Coot  53. To this end, BCL10 was generated by flexible fitting of a homology model created by the program Phyre  2 54  and under consideration of the BCL10 CARD domain NMR structure (pdb ID: 2MB9). The MALT1 DD was built in the EM density using the related crystal structure (pdb ID: 2G7R) as well as a homology model based on the APAF CARD domain (pdb ID: 2YGS) structure prepared with Modeller  55.
The final model was refined with PHENIX real-space refinement  56. The similarity of the refined model of BCL10 CARD with the unsharpened, unfiltered map of the filament core was assessed by FSC, after helical symmetrization of the model and calculation of a density map with a voxel size of 1. 002 Å using Chimera. Similarly, the refined model and the previously deposited model PDB: 6BZE, were compared to the EMDB entry EMD-7314. Final figures were generated with UCSF Chimera and PyMol  57, 58. Antibodies and DNA constructs. The following antibodies were used for immunoprecipitation (IP), Western Blot (WB), and fluorescence activate cell sorting (FACS). WB was done at a dilution of 1:1000 except when otherwise stated. Anti-HA (3F1 (WB), obtained from E. Kremmer); anti-CARD11 (1D12), anti-IκBα (L35A5, FACS 1:50), anti-p-IκBα (5A5) (all Cell Signaling Technology); anti-FLAG-M2 (F3165, Sigma-Aldrich, WB 1:10000, IP 1 µl); anti-hCD2-APC (RPA-2.
For determination of intracellular IL-2, cells were rested for 12 h and then stimulated with P/I or platebound anti-CD3/CD28 (0. 5 µg/ml CD3 and 1 µg/ml CD28) antibodies for 5 h in the presence of Brefeldin A. After fixation and permeabilization, cells were stained with anti-IL-2 APC antibody (JES6-5H4, eBioscience). FACS analyses were performed using an Attune Flow Cytometer (Applied Biosystems) and analyzed with FlowJo Software (Treestar). Cell lysis and cellular binding studies. For cellular analyses Jurkat T-cells (2-3 × 10 6 ) were lysed in co-immunoprecipitations (co-IP) buffer (25 mM HEPES (pH 7. 5), 150 mM NaCl, 0. 2% NP-40, 10% glycerol, 1 mM DTT, 10 mM sodium fluoride, 8 mM β-glycerophosphate, 300 μM sodium vanadate and protease inhibitor cocktail). For monitoring CBM complex formation after IP or StrepTactin pulldown (ST-PD), Jurkat T-cells (1-3 × 10 7 ) were lysed in co-IP buffer.
Acknowledgements We thank the  MPI of Biochemistry cryo-EM  and biophysics core facilities for generous access and support. We are grateful to the IT teams from  IBS  and  EMBL Grenoble , and specifically  Aymeric Peuch  for help with the usage of the joint IBS/EMBL EM computing cluster which was used as a part of 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). We thank  Simon Widmann  for help with the cell-based assays. K. L. and D. K. are supported by the  CRC1054  project  B02  and  A04 , respectively. A. D. is supported by the  FRM ARF20160936266 grant. K. -P. H is supported by the  Deutsche Forschungsgemeinschaft  ( CRC1064 ,  GRK1721 ), the  European Research Council  ( ERC Advanced Grant  ATMMACHINE), the  Gottfried-Wilhelm-Leibniz Prize  and the  Center for Integrated Protein Sciences Munich (CIPSM).
45 mg/L after a 1 hour infusion  40. Its clearance is ensured in approximately equal proportions by renal and non-renal pathways  40. Delafloxacin received FDA approval after demonstrating its non-inferiority to the combination of vancomycin and aztreonam in two phase 3 studies, in adult patients with acute bacterial skin and skin structure infections (PROCEED Study Group)  41, 42, 43. Its efficacy in community-acquired bacterial pneumonia is currently under investigation in a phase III clinical trial (NCT02679573)  44. The drug is contraindicated in patients with known hypersensitivity to delafloxacin or any of the fluoroquinolone class of antibacterial drugs. Its common side effects include nausea, diarrhea, headache, transaminase elevations and vomiting  43. Of note, FDA issued a black box warning related to the risk of tendinitis, tendon rupture, peripheral neuropathy, CNS effects, exacerbation of myasthenia gravis, hypersensitivity reactions and Clostridium Difficile-associated diarrhoea  40, 43.
Due to its activity against multi-drug resistant bacteria, meropenem-vaborbactam shows promising implications in the treatment of ventilator-associated pneumonia (TANGO II trial)  51, 52. A phase III, multicenter, prospective, randomized, doubleblinded TANGO III trial comparing vabomere and piperacilline/tazobactam in hospital-acquired or ventilator-associated pneumonia is launced and estimated to be completed in 2010 (NCT03006679). The drug is contraindicated in patients with hypersensitivity to any of the two components or to other drugs in the same class, and the most frequently encountered adverse reactions include headache, phlebitis or infusion site reactions, and diarrhea  47. Rare but severe side effects include hypersensitivity reactions, seizures (especially in patients treated with valproic acid), Clostridium difficile-associated diarrhea, thrombocytopenia, neuromotor impairment, development of drug resistant bacteria and overgrowth of nonsusceptible organisms  49.
Plant collection, identification, and taxonomy Samples of Tectona grandis L. f. , 1782 (Verbenaceae) and Vernonia amygdalina Del. (Asteraceae), both plants freely available in Togo, were collected in November 2014. The leaves and barks of Tectona grandis were harvested from the experimental farm of the University of Lomé. The leaves of Vernonia amygdalina were harvested in the village of Sika-Kondji, Yoto district of Togo (6°37'60" N and 1°34'60" E). Preliminary identification of the plants was done in the field. Afterwards, herbarium specimens were prepared and photographs were taken to aid in the confirmation of the identity of the plants. They were identified and authenticated by comparison with available voucher specimens in the herbarium section of the Laboratory of Botany and Plant Ecology of the University of Lomé (Togo), using taxonomic keys of online databases of PROTA (PROTA4U, Plant resources of Tropical Africa) on the website: www.
prota. org. The nomenclature of species was checked using the online database of the International Plant Names Index (IPNI) (http://www. ipni. org/ipni/plantnamesearchpage. do) -Leaves and bark of Tectona grandis voucher specimen Tectona grandis: TOGO15318 -Leaves of Vernonia amygdalina voucher specimen Vernonia amygdalina: TOGO15314.
Major histocompatibility complex (MHC) expression on SRDC and apoptosis of PECs After stimulation, SRDCs and PECs were detached with accutase (Affymetrix eBioscience). SRDCs were centrifuged for 5 min at 300 g and saturated for 30 min on ice in PBS containing 1% bovine serum albumin, 2% mouse serum, and 0. 1% azide (PBS-BSA-azide). After centrifugation, 3 Â 10 5 SRDCs were incubated for 30 min on ice in the dark in PBS-BSA-azide with 0. 5 mg FITC mouse anti-mouse H-2K[k] MHC class I antibody (clone 36-7-5, BD Pharmingen TM ), FITC mouse anti-mouse I-E [k] MHC class II antibody (clone 14-4-4S), or FITC mouse IgG2a, k isotype control (clone G155-178). After centrifugation, SRDCs were suspended in 300 mL PBS and analysed by flow cytometry (BD FACSCalibur TM and CellQuest TM software, BD Bioscience).
16S rRNA Phylogeny of Thermosipho Species Full-length 16S rRNA sequences were manually extracted from the annotated Thermosipho genome assemblies generated in this study as well as publically available genomes. Next we searched the SILVA database version SSU r122 for Thermosipho spp. 16S rRNA sequences with the following setting: Sequence length >1,300 bp, alignment quality >80, pintail quality >80  (Quast et al. 2012). Matching sequences were downloaded and added to extracted 16S rRNA sequences from the de novo sequenced genomes. Multiple identical sequences from the same strain with different accession numbers (e. g. , the four 16S rRNA sequences of T. melanesiensis BI429, five 16S rRNA sequences T. africanus TCF52B) are represented by a single sequence in the data set. The NCBI nr database was then queried using BlastN (blastþ v 2.
2. 26, Camacho et al. 2009)  with the 16S rRNA gene data set described above, to identify Thermosipho sp. like 16S rRNA sequences missed with the above methods. Additional full-length sequences were downloaded from Genbank and combined with the de novo/SILVA 16S rRNA sequences. The final 16S rRNA gene alignment consisted of Thermosipho sequences combined with outgroup sequences from other Thermotogales species. The resulting alignment was used to build phylogenies using Maximum Likelihood as implemented in MEGA 6. 0  (Tamura et al. 2013 ) and the General Time Reversible (GTR) model (G þ I, four categories) as suggested by JModelTest v. 2. 1. 7 (Darriba et al. 2012).
Genome Properties and Selection Analysis The %GC of the three positions in each codon of all genes was using the software codonW version 1. 4. 4 (http://codonw. sourceforge. net/; last accessed October 3, 2018). The N/C-ARSC values were calculated with the python scripts from the publication  (Mende et al. 2017)  after modifying them for usage on our genomes.
Pangenome Analysis Blast Ring genome plots were generated using BRIG version 0. 95  (Alikhan et al. 2011 ) running blastþ version 2. 2. 28  (Camacho et al. 2009)  with the following set up. The nucleotide sequence of one of the fifteen Thermosipho genomes was used to create the blast-database with default settings. Nucleotide sequences of all coding genes were extracted from each genome (NCBI Genbank annotation) using CLC Main workbench Version 6. 8. 3 and were used for BlastN analysis in BRIG with the following settings: max_target_seqs: 1; max e-value cut-off: 1. 0 À4. Alignments with a minimum of 70% similarity were visualized with BRIG. The same procedure was used for all genomes. Complete chromosome sequences or contigs were uploaded for all fifteen genomes to the Panseq 2. 0 server (https://lfz.
Gaps were removed and the edited LCBs were concatenated in Geneious 8 (www. geneious. com). Recombination analysis of the concatenated alignment was done in LikeWind  (Archibald and Roger 2002)  using the maximum likelihood tree calculated in PAUP* version 4. 0b10  (Swofford 2002 ) under a GTRþCþI model as the reference tree. Pairwise Average Nucleotide Identity Blast scores (ANIb) were calculated using the JspeciesWS webtool (http://jspecies. ribohost. com/jspeciesws/; last accessed October 3, 2018)  (Richter and Rossell o 2009; Goris et al. 2007 ). The results were visualized with R (version 3. 2. 1) using the heatmap. 2 function (Package gplots) and the Thermosipho genomes were clustered using Jaccard distances based on pairwise ANIb values (Package Vegan. version 2. 3-0). The IMG/ER Pairwise ANI calculation was used to determine the number of shared genes between each genome separately  (Markowitz et al.
Functional Comparison of Genome The fifteen genomes were compared using the Clusters of Orthologous Genes (COGs) annotations. A COG reference database (version 10) was downloaded from the STRING database (http://string-db. org/; last accessed October 3, 2018). For each genome all protein sequences were aligned using BlastP, with the settings: 1 target sequence; maximum e-value: 1. 0e À20 ; database size 1. 0 7 ; tabular output. Only hits to COG database sequences were retained when the alignment was !70% of the length of the longest protein sequence and were used to build a protein COG classification table. The COG IDs were summarized by classifying them to any of the available COG categories (ftp://ftp. ncbi. nih. gov/ pub/wolf/COGs/COG0303/cogs. csv; last accessed October 3, 2018). For the COG analysis of the species-specific genes, we used the results from the IMG phylogenetic profiler tool to identify cluster specific genes.
The COG classification table was screened for cluster specific genes and summarized into COG categories. Next, we searched for paralogous genes by examining how many orthologous genes, classified as COGs, were assigned per COG. This identified COGs only present once or multiple times in the genomes, and those results were summarized. In order to statistically compare species differences of COG categories we normalized counts by total COG gene annotations, giving relative abundances per genome. R (version 3. 3. 0) was used to identify COG categories that were significantly different between the three species using the nonparametric Kruskal-Wallis test (P-value 0. 01). The R-package ggplot2 (version 2. 1. 0) was used to generate the comparisons graphically.
HGT Detection Genes putatively acquired by HGT were identified using HGTector  (Zhu et al. 2014)  with BLASTp (blastþ v 2. 2. 26). The databaser. py script was used on December 6, 2015 to download per species one representative proteome of all microorganisms from the NCBI refseq database. We compared the predicted Thermosipho spp. protein sequences to the reference database using the following BLAST cut-offs: E-value: 1. 0 À5 , Percentage identity: 70%, percentage coverage: 50%, and a maximum of 100 hits were returned. To determine which genes were putatively acquired by any of the strains we set the HGTector self group to the genus Thermosipho (NCBI taxonomy ID: 2420) and the close group to either the family Fervidobacteriaceae (NCBI taxonomy ID: 1643950), or the order Thermotogales (NCBI taxonomy ID: 2419).
Defense Genes Using the defense genes list created by  Makarova et al. (2011) , we screened the IMG genome COGs annotations for the presence of any COGs involved in any of the mobile DNA defences  (Makarova et al. 2011). We summarized the identified COGs and distinguished between CRISPR-cas associated or RM system genes. CRISPR-cas genes were manually inspected to determine completeness/disruption of the genes. In addition, did we manually inspect IMG gene annotations for the presence of CRISPR-cas genes not identified with the  Makarova et al. (2011)  database. This was used to identify the CRISPR-cas gene clusters and determine the CRISPR type encoded by the genes in a cluster.
Crispr-Spacer Analysis All genomes were uploaded to CRISPR finder (http://crispr. i2bc. paris-saclay. fr/; last accessed October 3, 2018) to detect CRISPR-arrays  (Grissa et al. 2007). CRISPR spacer sequences from each genome were compared using BlastN against all Thermosipho sp. spacer sequences using the following settings: e-value cut-off: 1. 0 À5 , database size: 1. 0 7 , dust: no. The tabular BLAST results were visualized with R-statistics using the Markov Cluster Algorithm (MCL) (v1. 0) and Igraph (v1. 0. 1) packages. Igraph was used for matrix construction. MCL was run using the matrix with the inflation set to 1. 4 and max iterations set to 100  (Enright et al. 2002). In order to search for matching sequences within the genome but outside the CRISPR arrays, for example, target genes, we masked all CRISPR arrays using maskfeat  (EMBOSS v.
6. 5. 7, Rice et al. 2000). Next, we ran BlastN (v2. 2. 26þ) using the spacers of each genome against the own genome using the following settings: e-value cut-off: 1. 0 À5 , database size: 1. 0 7 , dust: no. CRISPR array spacers were also compared against the NCBI nucleotide database to find other species with similar sequences. BlastN was run with the settings: e-value cut-off: 1. 0 À5 , dust: no. Each genome was screened for the presence of prophages (Supplementary Material for details).
Comparative Genomic Analysis of Thermosipho Isolates We screened the NCBI-nonredundant (nr)-and IMG 16S rRNA metagenome databases using BlastN and Thermosipho spp. 16S rRNA genes to assess how our genomes span the available environmental diversity of Thermosipho. This indicated that most cultivated lineages or species, for example, T. melanesiensis, T. affectus, and T. africanus, are well covered by our analysis (fig. 1 , supplementary fig. S1 , Supplementary Material online). Nonetheless, there are several lineages, including T. geolei, T. ferriphilus, and T. activus, for which we do not have genomic data. An IMG database search identified three sequences closely related to T. africanus from oil reservoir and hydrothermal vent metagenomes (data not shown). Two recent studies of subsurface crustal fluids also contained T.
1. -Maximum Likelihood phylogeny of Thermosipho 16S rRNA sequences. The tree was constructed in MEGA6  (Tamura et al. 2013 ) using the General Time Reversible (GTR) model (C þ I, four categories,  Nei and Kumar 2000). The tree with the highest log likelihood is shown. The percentage of trees in 100 bootstrap replicates in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained by applying the Neighbor-Joining method to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site, the bar indicates 0. 05 substitutions per site. The analysis involved 61 nucleotide sequences. Only alignment positions with fewer than 5% gaps, missing data, and ambiguous bases were included, resulting in a total of 1,245 positions in the final data set.
A B C FIG. 2. -Phylogenomic comparison of 15 Thermosipho genomes using core SNPs, ANIb, and the number of shared genes. (A) Neighbor network of the 15 Thermosipho strains. The network is based SNP's in core genome fragments that were present in all genomes with a minimum of 70% similarity. The network was visualized in Splitstree using the NeighborNet algorithm from uncorrected distances  (Huson and Bryant 2006). (B) Heatmap visualization of pairwise ANIb distances. (C) Fraction of genes shared between 15 Thermosipho genomes. Data were generated at the IMG-database using pairwise Bidirectional Best nSimScan Hits, with genes sharing 70% sequence identity and at least 70% coverage. Percentages were calculated by dividing the number shared genes by the total number of genes for the genome on the y-axis.
2012). A comparison of COG annotations (fig. 4A ; supplementary table S2, Supplementary Material online) revealed several categories with significant differences in relative abundance between the three species (P 0. 01 for B, C, H, I, J, O, and R and P 0. 05 for G and L). Two COG categories show large relative abundance differences between species that together may explain a significant fraction of the GS differences. For the first category, COG G (Carbohydrate transport and metabolism), T. africanus genomes have more genes than the other two species (fig. 4A ). Similarly, PFAM based comparisons using the carbohydrate database dbCAN  (Yin et al. 2012 ) (Supplementary Material; supplementary table  S3 , Supplementary Material online), identified several enzyme families involved in breakdown of various beta-linked oligoand polysaccharides and some of these were exclusively found among T.
Acknowledgments The work of T. H. A. H. and C. L. N. , was funded by the  Norwegian Research Council  (award  180444/V40 ). J. L. and C. G. were supported by  Agence Nationale de la Recherche (ANR)  ( ANR-12-BSV3-OO23-01 ). O. A. P. and I. V. K. were supported by the  Russian Science Foundation (RSF)  grant #  16-14-00121. We thank the  Norwegian Sequencing Centre  for sequencing and support with the bioinformatics analysis. Strains are available under request at the culture collection UBOCC (www. univ-brest. fr/ubocc)and  LM2E , Ifremer collection. We acknowledge  Dr A. C. Stu  ¨ken for helpful discussions and feedback on the manuscript.