dataesr/scientific-paragraphs-categorization huggingface dataset:
Whole-genome sequencing data generation Genomic DNA of M. microti strains was isolated by beadbeating, boiling and ethanol precipitation, as described previously  [44]. Genomic DNA was processed using the SMRTbell template library preparation kit and sequenced at the Biomics platform of the Institut Pasteur (Paris, France) using the PacBio SMRT long-read sequencing technology (Chemistry v2. 1; Sequel ICS v5. 0. 1; SMRT Link v5. 0. 1). In parallel, genomic DNA was also sequenced using the Illumina paired-end short-read sequencing technology. Strains OV254, ATCC 35782, 94-2272 and Maus IV were sequenced at the Wellcome Trust Sanger Institute (Hinxton, UK) using a Genome Analyzer IIx device, while strain Maus III was sequenced at the Biomics platform of the Institut Pasteur (Paris, France) using a HiSeq 2500 device.
Sequencing read datasets of additional M. microti strains for phylogenomic analyses were downloaded from the Sequence Read Archive (SRA) database: strain ATCC 19422 (also known as strain '12'; run SRR3647357)  [45] ; strains isolated from wild boars in Italy G25821 to G25828 (runs from ERR2659163 to ERR2659171)  [46] ; and llama-type strains 416/01 and 8753/00 (runs ERR553376, ERR553377 and from ERR551111 to ERR551113)  [47].
Gene annotation Whole-genome assemblies of M. microti strains were annotated using Prokka v1. 13. 4  [54]  (parameters: --addgenes; --kingdom Bacteria; --gcode 11; --rfam; --rnammer). Genome annotation was performed against the default bacterial core databases, as well as against a custom Genus database (parameter: --usegenus) composed of annotated genes from M. tuberculosis H37Rv (AL123456), M. bovis AF2122/97 (LT708304) and M. bovis BCG Pasteur 1173P2 (LT708304)  [49, 55, 56]. Prediction and synteny analysis of orthologous genes was performed according to the Synima pipeline r20122018  [57]. Briefly, nucleotide sequences of all annotated protein-encoding genes were first compared using blastn from blast+ v2. 2. 26  [53]. Groups of orthologous genes were then determined from nucleotide alignments using OrthoMCL v1.
Variant calling Illumina read pairs were mapped against a given reference genome using bwa-mem v0. 7. 15  [60]  (default parameters). Mapped reads were first locally realigned around indels using Picard Tools v2. 8. 1 (http:// broadinstitute. github. io/ picard/) and GATK RealignerTargetCreator and IndelRealigner tools v. 3. 7. 0  [61]  (default parameters). Read alignments with a mapping quality lower than one and flagged as secondary were then discarded using SAMtools view v1. 3. 1  [62]  (parameters: -q 1; -F 256). Duplicated read pairs were also removed from the alignment map using an in-house Python script to prevent the presence of potential amplification contaminants. Two read pairs were considered as a duplicate if they mapped at the same positions, had an identical sequence and stemmed from the same DNA strand.
Effects of called SNPs and indels on annotated genes were predicted using SnpEff ann v4. 3i  [64]  (parameters: -no-upstream; -no-downstream). Variant intersections were determined using the R package UpSetR v1. 3. 3  [65]. Functional annotation and categorization of genes affected by variants was performed using RAST  [66]. Density of mapped reads and filtered variants was calculated in 5-kb non-overlapping windows and plotted using Circos v0. 69-6  [67].
7 % of the initial polymorphic sites. Maximum-likelihood phylogenetic reconstruction was inferred using RAxML v8. 2. 12  [68]  with the GTR substitution model, GAMMA distribution of rate heterogeneity, Lewis' correction of ascertainment bias and 1000 rapid bootstrap replicates (parameters: -m ASC_GAMMA; --asc-corr=lewis; -f a). Bipartition support of the best-scoring tree rooted using M. tuberculosis H37Rv was computed according to the transfer bootstrap expectation (TBE) from booster v0. 1. 2 [69]. The resulting maximum-likelihood phylogenetic tree that is shown was drawn and annotated using the R package ggtree v1. 14. 6  [70].
Organs were homogenized using an MM300 (Qiagen) apparatus and 2. 5-mm diameter glass beads. Serial fivefold dilutions in PBS (1×) were plated on Middlebrook 7H11 OADC agar and c. f. u. were counted after 3-6 weeks of growth at 37 °C, depending on the strain. The weight of the 10 remaining mice per group was monitored over several weeks and mice were killed when reaching the humane endpoint, defined as a loss of >20 % of bodyweight in accordance with ethics committee guidelines. Sample size choice was determined by taking into account the rule of 3Rs (replacement, reduction, refinement) and statistical requirements. Statistical significance in survival between groups of infected animals was assessed with the log-rank (Mantel-Haenszel) test using the R package survival v3. 2-7  [78]. For the pilot virulence assay, five SCID mice (Charles River) per group were infected intravenously and the delivered dose was determined from the spleen from one mouse per group on the day of infection.
Following incubation, colonies were enumerated (as c. f. u. ), and the concentration of total bacilli in each sample homogenate was calculated. Bacterial load data were expressed as log 10 (total c. f. u. ) and statistical significance was assessed with the one-way ANOVA test followed by Tukey HSD post-hoc test using the R package rstatix v0. 6. 0. For histological analysis, tissue representative of each lung and spleen, sampled consistently between animals, was processed routinely (by formaldehyde fixation) and embedded in paraffin wax. Sections (thickness of approximately 5 µm) were stained with haematoxylin and eosin. The nature and severity of the lesions were assessed by a 'blinded' veterinary pathologist, using a subjective scoring system as described elsewhere  [81]. For the spleen, a score, based on number of lesions, was calculated.
For the lung, each lobe was assigned a score as follows: 0, normal; 1, very few or small lesions, 0-10 % consolidation; 2, few or small lesions, 10-20 % consolidation; 3, medium sized lesions, 20-33 % consolidation; 4, moderate sized lesions, 33-50 % consolidation; 5, 50-80 % consolidation, extensive pneumonia, >80 % consolidation, plus a number of foci of necrosis. Scores from each lobe were combined and a mean score from lung lobes and spleen was calculated for each group. Group mean histopathology scores were then compared between groups and with bacterial loads and statistical significance was assessed with the Kruskal-Wallis test followed by Dunn post-hoc test with Benjamini-Hochberg correction using the R package rstatix v0. 6. 0. All vaccine groups, including saline negative controls, showed similar weight profiles during both the vaccination and challenge phases of the study, and no animals reached their humane endpoint prior to the end of the experiment.
1a , Table  1 ). Notably, M. microti strains depicted differences in their CRISPR-Cas loci that coincided with IS6110 insertion events (Fig. 1b ). ATCC 35782 was the only M. microti strain studied here with a potentially intact CRISPR-Cas locus, albeit with shorter repeat/spacer clusters, as compared to the M. tuberculosis H37Rv control strain (Fig. 1b ). By contrast, the other four M. microti strains displayed truncated CRISPR-Cas loci, with both cas1 and cas2 genes being missing and csm6 being either fully or partly deleted (Fig. 1b ). A section of the repeat/spacer region was identical between all five M. microti strains, for which five out of six spacers matched spacers of M. tuberculosis H37Rv, in agreement with previous spoligotyping studies  [17, 30, 83]. Further database comparisons showed that the remaining sixth spacer of this region was only detected in a few strains of TbD1-intact M.
Gene synteny within M. microti strains and polymorphisms affecting ESX-5 type VII protein secretion To further investigate genomic variability within M. microti strains, reconstructed genomes were annotated using bacterial core databases and a customized mycobacterial database (see Methods). On average, 4120 genes were identified for each genome (Table  1 ), including, on average, 4039 (98. 0 %) protein-encoding genes. Following gene annotation, we sought orthologous and non-orthologous genes among the five M. microti strains (Fig. S3a, b ). Most of the proteinencoding genes (on average 3990, 98. 8 %) belonged to a core gene set that was shared by all five M. microti strains (Table  1 ). However, additional orthologues (on average 37; 0. 9 %) were only identified in some strains but not in others, and a few genes (on average 12; 0.
microti strains into human pathogens. Whether in these special and rare cases humans might serve as a spillover host, similar to situations where other animal-adapted MTBC members, such as M. pinnipedii or M. bovis, may cause sporadic zoonotic human disease  [1, [110] [111] [112] , remains an open question, which is difficult to answer given the small number of available clinical isolates of M. microti. The full genome sequences of these strains provided here, thus, opens new perspectives for comparative analyses with larger datasets, as soon as they become available. Until then, we should consider M. microti strains as a clade of ESX-1-deficient MTBC strains with a substantial variability of virulence that ranges from completely attenuated members, such as the ATCC 35782 vaccine strain, to quite highly virulent strains, such as the Maus IV clinical isolate.
Acknowledgements We thank the pathology team ( Public Health England, Porton, Salisbury, UK ) for the generation of histopathology data and images, the staff of the  Biological Investigations Group (Public Health England, Porton, Salisbury, UK)  for their assistance in conducting the guinea pig study, and the core Sequencing and Informatic groups ( Sanger Institute, Hinxton, UK ) for processing the Illumina whole-genome sequencing. We also thank  Julien Guglielmini ,  Thomas Bigot  and  Varun Khanna  ( C3BI ,  HUB Bioinformatics and Biostatistics, Institut Pasteur, Paris, France)  for their help in data analysis and phylogenetic reconstruction, as well as  Laleh Majlessi ,  Fadel Sayes  ( Institut Pasteur, Paris, France ) and  Anzaan Dippenaar  ( Stellenbosch University, Cape Town, South Africa ) for advice and fruitful discussions.
Funding information This work was supported by grants from the  European Commission  ( TBVAC2020 , grant  260872 ) and the  Agence Nationale de la Recherche  (grants  ANR-16-CE35-0009  and  ANR-10-LABX-62-IBEID ). M. O. was supported by the  Fondation pour la Recherche Médicale  ( SPF20160936136 ) and the  Institut Pasteur  ( Pasteur-Roux-Cantarini postdoctoral fellowship program ). The Biomics platform is supported by  France Génomique  ( ANR-10-INBS-09-09 ) and IBISA.
ELECTROPHYSIOLOGICAL RECORDINGS The motoneurons were impaled with microelectrodes (tip diameter, 1. 0-1. 5 μm) filled with either 3 M KCl or 2 M K-Acetate (resistance 23. 1±5. 9 MΩ [16. 0-33. 0 MΩ], N=13). Recordings were performed using an Axoclamp 2B amplifier (Molecular Devices) connected to a Power1401 interface and using the Spike2 software (CED). The current (Im) and voltage output (10Vm) of the amplifier were low-pass filtered at 10 kHz and sampled at 20 kHz. When recorded, the continuous output I1 and V1, and the DCC monitor output were sampled at 100 kHz. After impalement, identification of motoneurons rested on the observation of antidromic action potentials in response to the electrical stimulation of their axon in the triceps nerve. All care was taken to compensate for the microelectrode resistance and capacitance.
NUMERICAL SIMULATIONS Numerical simulations were conducted using the Brian2 simulator (v. 2. 4. 1) in Python v. 3. 8 and using the SciPy ecosystem (v. 1. 5. 0; Virtanen et al. , 2020). For investigating membrane potential ripples (Figures  2  and 4 ), both the cell and the electrodes are modeled as passive RC circuits with equations: 𝑑𝑉 𝑑𝑡 𝐶 = 𝐺 • 𝑉 ! -𝑉 + 𝐼 !"# 𝐶 = 𝐺 • 𝜏 For the cell, Gin was set to 0. 2 μS and τm to 5 ms. To model the IR drop through the electrode, parameters were chosen so that the electrode was 200× faster than the membrane time constant (τe=τm/200=25 μs). The equation above was solved with Ge=1 μS. Although not quite realistic, this value was chosen so that the response of the electrode would not completely dominate the graphs in Figure  2.
Evolutionary rates and lineage divergence of PPRV Following sequence curation as described below, seventy-six PPRV genomes from Genbank were added to the five novel PPRV genomes generated in this study, yielding a total of eightyone sequences for phylogenetic analysis. These spanned 49 years from 1969 to December 2018 and included isolates from twentyfour countries. Previous phylogenomic analyses have included sequences found to be unreliable by our recombination analysis  (Muniraju et al. , 2014; Bao et al. , 2017; Clarke et al. , 2017; Shabbir, Rahman, and Munir 2020b ), which we excluded (see methods section and Table  S3 ). We therefore first analysed the evolutionary rates and global lineage diversification of PPRV using our Bayesian time-scaled phylogeny of eighty-one genomes. The mean evolutionary rate across the phylogeny, under an uncorrelated relaxed clock model found to be the best fit for the data, was 9.
Phylogenetic analysis of PPRV at the livestock-wildlife interface Including the five novel genomes from this study, twelve wildlifeorigin PPRV genomes are currently available (Table  S4 ), although one of these was excluded from our phylogenetic analysis (KT633939. 1/ibex/China/20 January 2015). Two cases occurred in zoological collections, and the other ten PPRV genomes were from infections of free-ranging wildlife. Eleven of the wildlife PPRV genomes belong to lineage IV and one to lineage III. The only countries having both wildlife and livestock sequences were Mongolia and China. In contrast, three Middle Eastern countries, Israel, Iraq and UAE, had full PPRV genomes available in GenBank from wildlife hosts, but no genomes from infected livestock were available for these countries. Table  S4  summarises the key epidemiological data for the 2016-2017 outbreak in Mongolian wildlife  (Kock 2017; Pruvot et al.
An open-access interactive dynamic visualisation of our global PPRV data set, integrating phylogenetic, spatial, temporal, and host (wildlife/livestock host) data, is available at the permanent link https://microreact. org/ project/5WNeX14MRFvwe8YLhn5a1S/e2d5dafd (last accessed 23 June 2021, to be updated as further genomes become publicly available). The map highlights the proximity of the sampling locations for the wildlife and livestock PPRV genomes in the Western Mongolian provinces of Khovd and Gobi Altai, with <40 km between the livestock sample and one of the saiga antelope sampled in Khovd near the Khar-Us lake 4 months later (Fig. 2 ). The Siberian ibex sample, from Tugrug soum of Gobi-Altai (Table  S1 ), was furthest from the sampling location of the Mongolian livestock (∼355 km) and marginally closer to the Chinese border than other detected cases.
Amino acid polymorphisms associated with host range expansion The five new PPRV sequences were compared with the seventysix other genomes in our data set in order to identify polymorphisms of interest that might be associated with PPRV emergence in Mongolian wildlife (Table  2 ). All point mutations identified by next-generation sequencing (NGS) in the H and F genes of the Mongolian wildlife-origin PPRV were confirmed by direct H and F gene RT-PCR and Sanger sequencing. Among the polymorphisms observed, the Mongolian livestock PPRV (KY888168. 1) encodes asparagine (N) at position 506 of PPRV H, whereas every other sequence encodes aspartate (D) as part of the completely conserved 505 DDD 507 motif. The PPRV H from Siberian ibex has a glycine (G) at amino acid 112, whereas the other eighty sequences in the data set encode serine (S).
Discussion Despite the broad host tropism and impact of PPRV across wild ungulate species, there is a paucity of wildlife-origin PPRV genomes, which, along with the lack of field epidemiological data, has hindered the understanding of viral evolution and dynamics at the wildlife-livestock interface. This understanding is critical in order to design effective disease control and eradication strategies and thereby support the success of the PPR GEP and mitigate the threat of PPRV to both domestic and wild ungulates. In this study, full PPRV genomes were generated from three Mongolian saiga antelope, one goitered gazelle, and one Siberian ibex that were part of a major mortality event in Mongolian wildlife in 2016-2017. These were analysed together with a curated set of all other genomes available in GenBank, to examine PPRV evolution and cross-species transmission.
, 2020)  and therefore one possibility is that T244A influences interactions with MHC I. This region of H is also subject to adaptive evolution: site 244 is detected as under positive selection in the China/Mongolia linage using FEL methods, site 245 is detected by MEME as under episodic positive selection, and site 246 is identified by all four methods in phylogeny-wide testing (Table  3 ) and was also reported previously as a positively selected site  (Liang et al. , 2016). The single genome from Mongolian livestock has two substitutions in H, L263F and D506N, which are not shared by the five wildlife-origin PPRV and should be assessed in other livestock samples. If consistently seen, D506N is particularly noteworthy since it lies at the SLAM binding interface (Fig. 3 ) and all other PPRV strains encode aspartate (D) at this site  (Abdullah et al.
Whole-genome NGS Total RNA from tissues was converted to complementary DNA with SuperScript IV and sequencing libraries prepared with the Nextera XT DNA Library Preparation Kit (Illumina, FC-131-1024). Sequencing was performed using the Illumina NextSeq platform with 150-bp paired-end reads. Sequencing data were mapped to a reference PPRV genome using Burrows-Wheeler Alignment tool (BWA)  (Li and Durbin 2010) , and then consensus calling was performed using SAMtools  (Li et al. , 2009). This was corroborated by removing host reads and then undertaking de novo assembly with SPAdes v3. 8. 0  (Bankevich et al. , 2012) , and then both outputs were aligned to confirm PPRV genome sequences.
PPRV full genome data set curation and recombination analysis In addition to the five novel PPRV genomes, all full PPRV genomes available in Genbank were downloaded (last accessed 22 November 2020). Vaccine sequences were removed from the data set, as well as one sequence noted in GenBank as multiply passaged in cell culture (MN369543. 1). Sequence alignment was performed using multiple alignment using fast Fourier transform (MAFFT)  (Katoh et al. , 2002)  in the Geneious software package, followed by manual editing (e. g. trimming insertions in genomes KM089831 and KM816619). TempEst was run to assess temporal signal in the data and evidenced clock-like evolution (Fig. S4 )  (Rambaut et al. , 2016). The alignment of eighty-five PPRV genomes was then analysed using the Recombination Detection Program (RDP) version 4.
101  (Martin et al. , 2015) , using seven different recombination detection methods (RDP, GENECONV, BootScan, MaxChi, Chimaera, SiScan, and 3Seq) and default settings. Signatures of recombination were detected in four sequences (KR828814. 1/goat/Nigeria/2012-05-09, KJ867541. 1/goat/Ethiopia/2010, KJ867543. 1/goat/Uganda/2012 and KT633939. 1/ibex/China/2015-01-20) that were identified by at least five of the seven different detection algorithms (P < 0. 01) (Table  S3 ). These are most likely the result of laboratory contamination  (Han and Worobey 2011) , and these sequences were considered unreliable and removed from our data set, leaving a total of eighty one PPRV genomes (alignment provided in S1 File).
Phylogenetic analysis The general time reversible (GTR) nucleotide substitution model with gamma-distributed variable rates (G) and some invariable sites (+I) best fitted our data set, according to the Bayesian Information Criterion values calculated using MEGA7  (Kumar, Stecher, and Tamura 2016). ML phylogenetic reconstruction was performed using PhyML with a GTR nucleotide substitution model and 100 bootstrap replicates. Phylogenetic analysis was also performed using a Bayesian Markov Chain Monte Carlo (MCMC) framework using BEAUti and BEAST v1. 10. 4  (Suchard et al. , 2018)  and run via the CIPRES server. Prior to assigning traits to the sequences, marginal likelihood estimation was performed using path sampling/stepping-stone sampling to choose the most appropriate combination of tree model (coalescent constant size or coalescent Gaussian Markov random field (GMRF) Bayesian skyride) and clock models (strict or uncorrelated relaxed) using a GTR nucleotide substitution model (four gamma categories, estimated base frequencies, and no codon partitioning).
At least two independent MCMC chains, of 40 million steps each, were performed for each analysis, and Tracer v1. 7. 1 was used to confirm that the MCMC chains converged at the same level and assess effective sample sizes. LogCombiner was used to combine the output of the independent BEAST runs to generate tree and log files for analysis. MCC trees were generated using TreeAnnotator v1. 10. 4. , and Figtree v1. 4. 4 was used to visualise and interpret MCC trees and derive TMRCA estimates. Evolutionary rates (ucld. mean) were taken from the combined log files analysed in Tracer v1. 7. 1. MCC trees and geocoded metadata were imported into Microreact to visualise temporal and geographic spread  (Argimon et al. , 2016). The MCC tree and log files from the BEAST analysis were uploaded to the SpreaD3 software (Spatial Phylogenetic Reconstruction of EvolutionAry Dynamics using Data-Driven Documents (D3);  Bielejec et al.
Acknowledgements Support for field sample collection and multi-sectoral coordination was provided by the  FAO Crisis Management Centre-Animal Health , the  Wildlife Conservation Society (WCS) , the  Morris Animal Foundation's Betty White Wildlife Rapid Response Fund , and through the  Science for Nature and People Partnership , a partnership of  The Nature Conservancy ,  WCS , and the  National Center for Ecological Analysis and Synthesis at University of California, Santa Barbara. Special thanks to  Khovd and Gobi-Altai Province Veterinary Agencies  for their generous support for wildlife field investigations and response and to  Dr Munkhbaatar Batsukh  and  Dr Burenbayasakh Ravdan  for their support.
Preoperative treatment with melatonin will be limited to 5 days. If surgery has not been performed 5 days after inclusion, treatment will be stopped and data on these patients will be censured. The patients will be followed for 5 days to detect any event related to the medication. If eventually these patients are operated, the postoperative Open access assessment will be performed, and data included in sensitivity analyses. In sum, treatment duration may vary with length of preoperative period up to a maximum of a 10 days period. Perioperative management will follow the 2017 French anaesthesia guidelines for elderly patients. Medical management and data collection will be identical between the two groups in all other aspects. Mandatory biological assessments added by the protocol are plasmatic creatinine, bilirubin, prothrombin time (and factor V if prothrombin time is below 70%) during the baseline visit, and plasma creatinine, sodium, potassium and chloride levels at D1 postoperative.
Adding to this assumption a bilateral type I error of 5% and a power 90%, we need to randomise 718 patients (359/group) in order to have 129 events and to detect a significant difference between arms (including 10% of patients that could not be evaluated). Sample size was computed with a Fine and Gray methods using R package cmprsk. 31 We expect that 10% of included subjects will be secondarily excluded (not randomised) due to presence of delirium at inclusion. Therefore, we need to include 790 patients in order to randomise 718 subjects. Inclusions will continue until 718 patients are randomised.