dataesr/scientific-paragraphs-categorization huggingface dataset:
A summarized representation of the network was generated by displaying each subgroup (level 2) as a node with a size proportional to the number of species in the subgroup, and drawing an edge to a PC if >50% of the subgroup sequences encode this PC, except for the larger group ('Protoinoviridae':Subfamily_1) where connections are drawn for PCs found in >25% of the sequences. The network was then visualized using Cytoscape  58  , with nodes from the same group (level 1) first gathered manually, and nodes allotment within group automatically generated using Prefuse-directed layout (default spring length of 200). To evaluate the taxonomic rank to which these groups and subgroups would correspond, we calculated pairwise amino acid identity percentage of pI proteins for genomes (1) between groups and (2) within groups but between subgroups, using Sequence Demarcation Tool  59.
These were then compared with the pairwise amino acid identity calculated with the same approach for established viral groups, namely, Caudovirales order using the terminase large subunit (TerL) as a marker protein, Microviridae using the major capsid protein (VP1) as a marker protein and Circoviridae using the replication initiation protein (Rep) as a marker protein (see Supplementary Notes). Distribution of inovirus sequences by host and biome. The distribution of hosts for inovirus sequences was based on detections in IMG draft and complete genomes, that is, excluding all metagenome-derived detections but including detections in metagenome-assembled genomes (published draft genomes assembled from metagenomes). Host taxonomic classification was extracted from the IMG database. For visualization purposes, a set of 56 universal single-copy marker proteins  60, 61  was used to build phylogenetic trees for bacteria and archaea based on all available microbial genomes in IMG  23  (genomes downloaded on 27 October 2017) and about 8,000 metagenome-assembled genomes from the Genome Taxonomy Database 62 (downloaded on 18 October 2017).
Marker proteins were identified with hmmsearch (version 3. 1b2, hmmer. org) using a specific HMM for each of the markers. Genomes lacking a substantial proportion of marker proteins (>28) or which had additional copies of >3 single-copy markers were removed from the data set. To reduce redundancy and to enable a representative taxon sampling, DNAdirected RNA polymerase β-subunit 160 kDa (COG0086) was identified using hmmsearch (hmmer 3. 1b2) and the HMM of COG0086 (ref. 63 ). Protein hits were then extracted and clustered with cd-hit  45  at 65% sequence similarity, resulting in 99 archaeal and 837 bacterial clusters. Genomes with the greatest number of different marker proteins were selected as cluster representatives. For every marker protein, alignments were built with MAFFT 64 (v7. 294b) and subsequently trimmed with BMGE (v1.
12) using BLOSUM30 (ref. 65 ). Single-protein alignments were then concatenated, resulting in an alignment of 11,220 sites for the archaea and 16,562 sites for the bacteria. Maximum-likelihood phylogenies were inferred with FastTree2 (v2. 1. 9 SSE3, OpenMP) 66 using the options: -spr 4 -mlacc 2 -slownni -lg. A distribution of inovirus sequences across biomes was obtained by compiling ecosystems and sampling location of all metagenomes where at least one inovirus sequence was detected. This information was extracted from the GOLD database  67  , and the map was generated using the BaseMap functions from the matplotlib python library  68.
Estimation of inovirus prevalence and co-infection patterns. Prevalence and coinfection patterns were evaluated from the set of sequences identified in complete and draft microbial genomes from the IMG database, that is, excluding detections from metagenome assemblies. To control for the presence of near-identical genomes in the database, prevalence and co-infection frequencies were calculated after clustering host genomes based on pairwise ANI (cut-offs: 95% nucleotide identity on 95% alignment fraction). Prevalence was calculated at the host genus rank as the number of genomes with one or more inovirus sequence detected. Cooccurrence of inoviruses was evaluated based on the detections of distinct species in single-host genomes. Finally, we evaluated the rate of bacteria and archaea co-infected by an inovirus and a member of the Caudovirales order, the group of dsDNA viruses including most of the characterized bacteriophages (both lytic and temperate) as well as several archaeoviruses.
To identify Caudovirales infections, we used the gene encoding the terminase large subunit as a marker gene, and searched the same genomes from the IMG database for hits to the PFAM domains terminase_1, terminase_3, terminase_6 and terminase_GpA (hmmsearch, score ≥ 30). Phylogenetic trees of inovirus sequences. Phylogenies of inovirus sequences were based on multiple alignment of pI protein sequences. To obtain informative multiple alignments, an all-versus-all blastp 49 of all pI proteins was computed and used to identify the nearest neighbours of sequences of interests. For sequences detected in archaeal genomes, an additional 10 most closely related sequences with e ≤ 0. 001, bit score ≥ 50 and a blast hit covering ≥50% of the query sequence were recruited for each archaea-associated sequence to help populate the tree.
A similar approach was used for the tree based on the integrase genes from archaea-associated inoviruses: the protein sequences for the three integrase genes were compared with the NCBI nr database with blastp  49  (bit score ≥ 50, e ≤ 0. 001) to gather their closest neighbours across archaeal and bacterial genomes. Resulting data sets were first filtered for partial sequences as follows: the average sequence length was calculated excluding the top and bottom 10%, and all sequences shorter than half of this average were excluded. These protein sequences were next aligned with MUSCLE (v3. 8. 1551)  43  , automatically trimmed with trimAL (v1. 4. rev15)  69  (option gappyout), and trees were constructed using IQ-TREE (v1. 5. 5) with an automatic detection of optimal model 70 and displayed using iToL  71.
The optimal substitution model, selected based on the Bayesian information criterion, was VT + F + R5 for the the pI phylogeny of archaeal inoviruses, and LG + R4 for the integrase phylogeny of archaeal inoviruses. Annotated trees are available at http://itol. embl. de/shared/Siroux (project 'Inovirus'). Functional affiliation of iPFs. An automatic functional affiliation of all iPFs was generated by compiling the annotation of all members based on a comparison to PFAM (data extracted from the IMG). To refine these annotations for functions of interest, namely, replication initiation proteins, integration proteins, DNA methylases and toxin-antitoxin systems, individual iPF alignments were submitted to the HHPred website  57  , and the alignments were visually inspected for conserved residues and/or motifs (Supplementary Table  5 , motifs extracted from refs.
72, 73 and the PFAM database v30 (ref. 74 )). To identify toxin-antitoxin protein partners, all inovirus sequences were screened for co-occurring genes including an iPF annotated as toxin and/or antitoxin, and the list of putative pairs was next manually curated (Supplementary Table  5 ). This enabled the identification of putative antitoxin proteins detected as conserved uncharacterized iPF frequently observed next to a predicted toxin iPF. Finally, putative structural proteins and DNA-interacting proteins were specifically searched for. Putative structural proteins were predicted as described above for the isolate reference genomes, that is, as sequences of 30-90 amino acids, after in silico removal of signal peptide, if detected, and displaying 1 or 2 TMD. For the most abundant iPFs predicted as major coat proteins, the secondary structure was predicted with Phyre2 (ref.
75 ). For DNA-interacting proteins, PFAM annotations were screened for HTH, RHH, Zn-binding and Zn-ribbon domains. In addition, HHsearch was used to compare the iPFs to 3 conserved HTH domains from the SMART database  76  : Bac_DnaA_C, HTH_DTXR and HTH_XRE (probability ≥ 90). CRISPR spacer matches and CRISPR-Cas systems identification. All inovirus sequences were compared with the IMG CRISPR spacer database with blastn, using options adapted for short sequences (-task blastn-short -evalue 1 -word_size 7 -gapopen 10 -gapextend 2 -penalty -1 -dust no). Only cases with zero or one mismatch were further considered. Next, the genome context of these spacers was.
Software and code Policy information about availability of computer code Data collection Data were collected using a custom set of scripts specifically designed to identify inovirus genomes. These are available at https:// github. com/simroux/Inovirus/tree/master/Inovirus_detector Data analysis For specific reference inoviruses, genes were predicted de novo using Glimmer v3. Sequence similarity searches were conducted using blast+ v2. 7. 1, hmmer 3. 1b2, hhpred (online at https://toolkit. tuebingen. mpg. de/#/tools/hhpred) and hhsearch v2. 0. 15. Sequences were clustered using InfoMap 0. 18. 25, mummer 3. 0, SDT 1. 0, and cd-hit 4. 7. Viral sequences (non-inoviruses) were automatically detected using VirSorter v1. 0. 5. Signal peptide and transmembrane domains were predicted using SignalP 4.
1 and TMHMM 2. 0c. Trees were built using FastTree2 and IQ-Tree 1. 5. 5, based on alignments computed with muscle 3. 8 or MAFFT v7. 294b and automatically trimmed with trimAL v1. 4 or BMGE v1. 12. Alignments were manually inspected using Jalview v10. 0. 2. Statistical analyses, sanger sequenced reads interpretation, and automatic classifier design were conducted in R 3. 4. 1, using the following packages: randomForest, party, glmnet, sangerseqR, sangeranalyseR, and readR. Secondary structure of putative inovirus major capsid proteins were predicted using Phyre v2. 0. Figures were generated with R 3. 4. 1 using the ggplot2 package, Cytoscape v3. 6. 1, iTOL v4. 4. 1, and python v3. 6. 2 using matplotlib v2. 0. 2. Constraints in sequences to be synthesized were automatically identified and adjusted using BOOST v1.
Acknowledgements The MobM strain was provided by  D. J. Ferguson ,  Miami University, Oxford, OH, USA. Its genome was sequenced and assembled by the  US Department of Energy Joint Genome Institute  through a  Community Science Program initiative  to K. C. W. (CSP no. 1777 ). T. Meulia at the  Molecular and Cellular Imaging Center, Ohio State University , Wooster, OH, USA, performed the transmission electron microscopy of MobM samples. We gratefully acknowledge the contributions of many principal investigators who sent extracted DNA for isolate genome and metagenome sequencing as part of the  Department of Energy Joint Genome Institute Community Science Program , and allowed us to include in our study the inovirus sequences detected in these publicly available data sets regardless of publication status (the complete list of data sets in which inovirus sequences were detected including principal investigators is available in Supplementary Table 2 ).
This work was conducted by the  US Department of Energy Joint Genome Institute , a  Department of Energy Office of Science User Facility , under contract no. DE-AC02-05CH11231  and used resources of the  National Energy Research Scientific Computing Center , which is supported by the  Office of Science of the US Department of Energy  under contract no. DE-AC02-05CH11231. R. A. D. and K. C. W. were partially supported by funding from the  National Sciences Foundation Dimensions of Biodiversity  ( Award 1342701 ). M. K. was supported by l'  Agence Nationale de la Recherche (France)  project  ENVIRA  ( ANR-17-CE15-0005-01 ). The  Bondy-Denomy lab  (A. L. B. and J. B. -D. ) is supported by the  UCSF Program for Breakthrough in Biomedical Research , funded in part by the  Sandler Foundation , the  NIH Office of the Director  ( DP5-OD021344 ) and  NIGMS  ( R01GM127489 ).
Data availability The following files are available at https://genome. jgi. doe. gov/portal/Inovirus/ Inovirus. home. html: Gb_files_inoviruses. zip: GenBank files of all representative genomes for each inovirus species; Ref_PCs_inoviruses. zip: PCs from the references (raw fasta, alignment fasta and hmm profile); iPFs_inoviruses. zip: protein families from the extended inovirus data set (raw fasta, alignment fasta and hmm profile); MobM_C_primer_amplicon. fasta: multiple sequence alignment of the C primer products with the Methanolobus MobM genome (NZ_FOUJ01000007), confirming that C primer products span the junction of the excised genome. Accession numbers of all inovirus sequences used as reference are listed in Supplementary Table  1. Accession numbers of all genomes and metagenomes mined, including detailed information for each (meta)genome in which some inovirus sequences were detected are available in Supplementary Table  2.
Methods NMR spectra were recorded at 20 o C on Bruker Avance III 400 MHz and Bruker Avance III 500 MHz spectrometers. Mass spectra were obtained using an Agilent Q-TOF 6520 mass spectrometer. General procedure for synthesis of R18/counterion pairs. 10 mg (0. 012 mmol) of Rhodamine B octadecyl ester perchlorate (R18/ClO4) was mixed with 3-10 equivalents of the appropriate counterion salt in DCM and the mixture was sonicated for 5 minutes. Products of ion exchange were purified on preparative TLC plates using DCM/MeOH mixtures as eluent. Results are summarized in the Table  S1. Other details are given in Supporting Information. Estimation of counterion sizes was done with Chem3D software using MM2 method for structure energy minimization. Preparation of fluorescent NPs. PLGA was dissolved at 2 mg/ml in acetonitrile containing different amounts of rhodamine-counterion pairs (5 and 50 mM with respect to the polymer).
ACKNOWLEDGEMENTS This work is supported by the  European Research Council   ERC Consolidator grant  BrightSens  648528. BA is supported by  LabEx Chimie des Systèmes Complexes. AR is supported by the  ANR JC/JC  grant supertrack  ANR-16-CE09-0007. Prof. Yves Mely  and  Prof. Pascal Didier  are acknowledged for providing access to wide-field microscopy.
Differential impact of infection on cytokine/chemokine secretion patterns. To obtain a large overview of the inflammatory and non-inflammatory immune response following HAE infections, we quantified a panel of 27 different cytokines and chemokines using a bead-based multiplex immunoassay (Bio-Plex, Bio-Rad, USA). To facilitate the comparison between experimental conditions, results for each cytokine/chemokine were expressed as the log-transformed ratio compared to mock condition and presented in a radar chart (Fig. 2 ). All raw data are presented in Supplementary Table  1. First, the general pattern of cytokine/chemokine secretion compared to mock was relatively similar between the different viruses tested. Indeed, this pattern was notably characterized by the high induction of interleukin 2 (IL-2) and interferon gamma-induced protein 10 (IP-10) in the context of all viral infections studied.
Transcriptional profiling of infections in HAE. For a better understanding of the underlying host response to infection, HAE were lysed at 72 hpi, 5 dpi or 6 dpi and total RNA was extracted. cDNA libraries were then produced, amplified, and subjected to high-throughput sequencing. A Principal Component Analysis (PCA) was performed after data normalization, to assess if the generated data could allow differential clustering between experimental infections and the mock condition. The PCA shows a clear first level of differential clustering between the members of the Orthomyxoviridae and Pneumoviridae families, but also enables the specific distinction of mock-, Influenza-, hMPV-and hRSV-infected HAE (Fig. 3 ). Interestingly, differences were tenuous between influenza virus types and subtypes.
Most of the genes associated with these GO terms were present either in the list of 98 down-regulated genes constituting the global virogenomic signatures of infection or in the list of 195 down-regulated genes common to influenza viruses and hRSV (Fig. 4B ). Interestingly, only the cilium-assembly BP was enriched in the context of hMPV infection, despite a similar number of downregulated genes compared to hRSV, suggesting major differences between these two Pneumoviruses. To better illustrate the impact of infection on cilium-related BP, we used the list of genes associated to these BP and explored the functional association networks of their protein products using the STRING database 19 Table  1. Number of differentially over-or under-expressed genes compared to mock. Differential expression was calculated by comparing each condition to the mock using a linear model.
8. 4 (Illumina). The FastQC software (http://www. bioinformatics. babraham. ac. uk/projects/fastqc) was used for quality controls of the raw data. Reads were trimmed using the Trimmomatic software  42  , with a minimum quality threshold of Q30. Reads were pseudo-aligned to the Homo sapiens genome (GRCh38. p11) using the Kallisto software  43. The results of the main quantification (abundance estimate in Transcripts Per Million) were used for principal component analysis (PCA) with the R packages factoextra and FactoMineR  44. Statistical analysis was performed in R3. 3. 1 with the package EdgeR 3. 14. 0 45. Differential expression was calculated by comparing each condition to the mock using a linear model. The Holm procedure was used to control the false discovery rate. Genes with an absolute fold change >2 and a corrected p-value < 0.
45. Robinson, M. D. , McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinforma. Oxf. Engl. 26, 139-140 (2010). 46. Dennis, G. Jr. et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 4, (P3 (2003).
Acknowledgements The authors want to thank  Jacques Corbeil  and  Frederic Raymond  (Research Center in Infectious Diseases of the  CHU de Quebec and Laval University, Quebec ) for their help and useful advice. This work was funded by grants from  FINOVI foundation , the  Région Auvergne Rhône-Alpes  (CMIRA No. 14007029  and  AccueilPro COOPERA  No. 15458 grants), and  Canadian Institutes of Health Research  (No. 229733  and  230187 ). Claire Nicolas de Lamballerie  was funded by  National Association for Research in Technology (ANRT). Guy Boivin  is the holder of the Canada Research Chair on influenza and other respiratory viruses. Funding institutions had no participation in the design of the study, collection, analysis and interpretation of data, or in the writing of the manuscript.
Study Population This observational prospective cohort study was conducted at the Stroke Unit of the University Hospital of Dijon, France (Study Registration Number 2017-A01906-47; Clinical Trial Number NCT03288090). This study received approval from the national ethics committee (CPP SUD-EST 4). All patients gave their oral consent to participate according to the French legislation. Patients ≥18 years old who suffered hemispheric acute ischemic stroke defined according to the WHO criteria and confirmed by imaging (CT-scan or MRI) who received a revascularization therapy (intravenous thrombolysis and/or mechanical thrombectomy) according to current recommendations were included between August 2017 and May 2018. Exclusions criteria were major co-morbidities prior to ischemic stroke that may be responsible for significant fatigue, previous dementia or aphasia, persistence of severe aphasia, or severe disability (modified Rankin scale score > 3) at 6 months preventing a reliable cognitive assessment.
Data Collected, Patients' Follow-Up, and Outcomes Measured At inclusion following data were collected: demographics, vascular risk factors including hypertension (high blood pressure recorded in a patient's medical history or patients under antihypertensive treatment), diabetes mellitus (reported in the medical record or patients taking insulin or oral hypoglycemic agents), hypercholesterolemia (reported in the medical history or patients treated with lipid-lowering therapy), current smoking, history of coronary heart disease, atrial fibrillation, excessive alcohol consumption (defined as alcohol intake ≥3 units a day in men and ≥2 in women). Stroke severity at onset was evaluated using the National Institutes of Health Stroke Scale (NIHSS) score. Patients were evaluated face-to-face by a senior neurologist at 6 months.
According to the assumption, the station was designed basing on a pneumatic actor with a piston of the size Ø320 mm, which at the pressure of 10 bars can generate the force of 80 kN. Figure  7  provides the described station. In order to collect data during the process, the station was equipped with a force sensor with an amplifier and a displacement sensor, as follows: • the force sensor HBM RSCC3/5T with an RM4220 transducer (HBM, Darmstadt, Germany), the nominal range of 5 tons, accuracy of ±0. 25% and braking load over 10 tons; • a displacement sensor-Keyence GT2-H32 Sn32 (Keyence, Osaka, Japan) with accuracy of 5μm; and, • a FESTO pressure sensor supervising its magnitude during the tests. The data from sensors was transferred to PC with the use of myPCLab transducer connected.
According to the assumption, the station was designed basing on a pneumatic actor with a piston of the size Ø320 mm, which at the pressure of 10 bars can generate the force of 80 kN. Figure  7  provides the described station. In order to collect data during the process, the station was equipped with a force sensor with an amplifier and a displacement sensor, as follows: • the force sensor HBM RSCC3/5T with an RM4220 transducer (HBM, Darmstadt, Germany), the nominal range of 5 tons, accuracy of ±0. 25% and braking load over 10 tons; • a displacement sensor-Keyence GT2-H32 Sn32 (Keyence, Osaka, Japan) with accuracy of 5μm; and, • a FESTO pressure sensor supervising its magnitude during the tests. The data from sensors was transferred to PC with the use of myPCLab transducer connected. The data from sensors was transferred to PC with the use of myPCLab transducer connected.
Results The first test was based on a micrograph photo of the formed joint cross-section, which enabled us to analyze the process of forming the joint and interference between the materials of the connection block flange and the pipe flange. Thanks to the test, it was possible to determine that key parameters of the proper forming process are: the alpha angle between the connecting block flange and pipe flange, and the point of bending, which can be found in Figure  9. The basis of the study was the data obtained by sensors mounted on the prototype station while performing the forming process. During the process reaction forces appearing on joints were observed by an HBM force sensor and a Keyence displacement sensor observed the distance that was traveled by the forming tool. The values observed with both sensors for samples sets no.
1, 3, 5 can be found in Figures  11 12 13     The basis of the study was the data obtained by sensors mounted on the prototype station while performing the forming process. During the process reaction forces appearing on joints were observed by an HBM force sensor and a Keyence displacement sensor observed the distance that was traveled by the forming tool. The values observed with both sensors for samples sets no. 1, 3, 5 can be found in Figures  11 12 13. The basis of the study was the data obtained by sensors mounted on the prototype station while performing the forming process. During the process reaction forces appearing on joints were observed by an HBM force sensor and a Keyence displacement sensor observed the distance that was traveled by the forming tool. The values observed with both sensors for samples sets no.