dataesr/scientific-paragraphs-categorization huggingface dataset:
Glu635Ala) may have a causal effect. MD simulation for p. Glu635Ala. Because no structure for human ACTN3 has yet been experimentally determined, we constructed a homology model of the p. Glu635 variant monomeric filament using the fully annotated protein (UniProt ID Q08043) using Phyre2 (ref. 93 ), with the p. 635Ala variant mutated in silico. Residue 635 of ACTN3 resides in the 356th residue of the spectrin repeat region and corresponds with residue 628 in ACTN2 (see the Supplementary Methods for more information). For each variant, the spectrin repeats of the ACTN3 monomer were aligned with the crystal structure of the rod domain of alpha-actinin (PDB ID 1HCI), to give the dimeric form of ACTN3. MD system preparation and simulation was conducted with GROMACS 2020. 1 (ref. 94 ) and using mdanalysis v.
T. O. K. was supported by the  Novo Nordisk Foundation  (grant numbers  NNF17OC0026848  and  NNF18CC0034900 ). N. G. M. is funded by a  National Health and Medical Research Council (NHMRC)   Investigator Grant  ( APP1172990 ). S. E. M. is funded by  NHMRC   Investigator Grant  ( APP1172917 ). D. M. is supported by a  Canada Research Chair in Genetics of Obesity. R. C. R. is a  de Pass Vice Chancellor's Research Fellow  at the  University of Bristol. S. R. -d. -P was supported by the  Heart and Stroke Foundation of Ontario  (grant number  NA 7293 ). N. J. S. holds a  National Institute for Health and Care Research (NIHR)   Senior Investigator award. N. J. T. is a  Wellcome Trust  (WT) Investigator ( 202802/Z/16/Z ), is the principal investigator of the  Avon Longitudinal Study of Parents and Children (Medical Research Council (MRC)  &  WT 217065/Z/19/Z ), is supported by the  University of Bristol NIHR Biomedical Research Centre  ( BRC-1215-2001 ), the  MRC Integrative Epidemiology Unit  ( MC_UU_00011 ) and works within the  Cancer Research UK Integrative Cancer Epidemiology Programme  ( C18281/A19169 ).
X. Z. is supported by  China Scholarship Council 201406220101. T. P. is supported by the  Bio-Synergy Research Project  ( 2013M3A9C4078158 ) of the  Ministry of Science, ICT and Future Planning  through the  National Research Foundation of Korea. S. S. is supported by the  Swedish Research Council  (grant numbers  2016-06264 ,  2018-05946  and  2018-05498 ). J. W. C. was partially supported by an  AHA-Bayer Discovery Grant  (grant  17IBDG33700328 ), the  AHA Cardiovascular Genome-Phenome Study  (grant- 15GPSPG23770000 ),  NIH  (grants  R01-NS114045 ,  R01-NS100178 ,  R01-NS105150 ), and the  US Department of Veterans Affairs. H. X. was supported by  AHA  grant  19CDA34760258  and  NIH  grants  R01-NS114045 ,  R01-NS100178  and  R01-NS105150. K. E. N. is funded by  AHA  grants  13GRNT16490017  and  15GRNT25880008 , and by  NIH  grants  R01DK089256 ,  R01DK101855 ,  R01HD057194 ,  R01DK122503 ,  01HG010297 ,  R01HL142302 ,  R01HL143885  and  R01HG009974.
L. F. -R. is supported by an  AHA grant  ( 13PRE16100015 ). C. P. N. is funded by the  British Heart Foundation  ( SP/16/4/32697 ). L. M. H. , C. P. N. , P. S. B. and N. J. S. are supported by the  NIHR Leicester Cardiovascular Biomedical Research Centre  ( BRC-1215-20010 ). T. G. Jr  and D. H. were supported by  US National Science Foundation  grant  IOS-2038528. R. J. F. L. is supported by the  NIH  ( R01DK110113 ,  R01DK075787 ,  R01DK107786 ,  R01HL142302 ,  R01HG010297 ,  R01DK124097 ,  R01HL151152 ). M. d. H. is a fellow of the  Swedish Heart-Lung Foundation  ( 20170872 ,  20200781 ) and a Kjell and  Märta Beijer Foundation researcher. He is further supported by project grants from the  Swedish Heart-Lung Foundation  ( 20140543 ,  20170678 ,  20180706 ,  20200602 ) and the  Swedish Research Council  ( 2015-03657 ,  2019-01417 ).
Data availability European and multi-ancestry meta-analyses summary statistics for the genome-wide association study are available through the NHGRI-EBI GWAS Catalog (https://www. ebi. ac. uk/gwas/downloads/summary-statistics, GCP ID: GCP000358). UK Biobank individual-level data can be obtained through a data access application available at https://www. ukbiobank. ac. uk/. In this study we made use of data made available by: MetaMex https://www. metamex. eu/; Tabula Muris https://www. czbiohub. org/tabula-muris/; Open GWAS https://gwas. mrcieu. ac. uk/; MR Base https://www. mrbase. org/; GTEx Consortium https://gtexportal. org/home/; eQTLGen Consortium https://www. eqtlgen. org/; CommonMind Consortium https://www. synapse. org/#!Synapse:syn2759792/wiki/69613; Brain zQTLServe http://mostafavilab.
code availability We made use of publicly available software and tools such as METAL (https:// genome. sph. umich. edu/wiki/METAL), GCTA (https://yanglab. westlake. edu. cn/ software/gcta/), LD score regression (https://github. com/bulik/ldsc), SMR (https:// cnsgenomics. com/software/smr/) and PLINK (www. cog-genomics. org/plink/).
The magnetohydrodynamic quasi-geostrophic (MQG) model is of use in studies of (quasi-) two-dimensional magnetohydrodynamic turbulence, (e. g. Tobias, Diamond & Hughes 2007)  still in the context of the solar tachocline. In the present paper we construct exact solutions of MQG equations, the magnetic modons, which are steady-moving dipoles of both magnetic field and vorticity. With the help of direct numerical simulations with the Dedalus code  (Burns et al. 2020 ), which we adapted to the MQG simulations, we show that these solutions can be stable or unstable, depending on the intensity of the magnetic field inside and outside the dipole. The construction of the modon solutions follows the lines of that used recently  (Lahaye, Zeitlin & Dubos 2020)  to obtain similar solutions in the quasi-geostrophic limit of the rotating shallow water model with horizontal density and/or temperature gradients, the so-called thermal rotating shallow water (TRSW, which becomes TQG in the quasi-geostrophic limit).
Continuity of the radial derivative of the streamfunction ∂ r ψ + = ∂ r ψ -at r = r 0 leads to the following transcendental equation for eigenvalues λ at given r 0 and κ + , and, hence, α -at a given κ -: C + / 1 -κ 2 + K 1 r 0 / 1 -κ + = C -λJ 1 (λr 0 ) -α -U/(1 + α -), (2. 15) where C ± are given in (2. 14a,b), and prime denotes differentiation of a function with respect to its argument. This transcendental equation is solved numerically using standard routines, e. g. with the fsolve function of the SciPy Python library. The lowest eigenvalue corresponds to a steadily translating vortex dipole, a magnetic modon, the higher eigenvalues correspond to multiple changes of the sign of vorticity inside the separatrix, which are, presumably, less stable and more sensible do dissipation, with more sheared velocity, and will be discarded as is usually done.
Numerical investigation of the stability of magnetic modons As previously mentioned, the MRSW (respectively MQG) model is structurally close to TRSW (respectively TQG). Our experience with TRSW and TQG modons  (Lahaye et al. 2020)  shows that they may be subject to small-scale convective-type instabilities, which are known for vortex solutions in this model  (Gouzien et al. 2017). To check whether this is also the case with MQG modons, and also to see whether sharp gradients of the magnetic fields engender new instabilities, we performed a set of numerical simulations of the MQG equations using a doubly periodic pseudo-spectral code based on the Python library Dedalus  (Burns et al. 2020) , with modon solutions as initial conditions. The typical resolution corresponded to a 512 × 512 grid with a 3/2 dealiasing factor, and we used a split-explicit fourth-order Runge-Kutta method for temporal integration.
After purification, the quartz content was dissolved in HF 48% after addition of 150 µl of a 3025±9 ppm 9 Be solution. Natural Al content was determined by ICP-OES using an ICAP6500 from Thermo. BeO and Al 2 O 3 were respectively mixed with niobium and silver powders prior to measurements. Beryllium data were directly calibrated against the STD11 standard [61] with a 10 Be/ 9 Be ratio of (1. 191±0. 013) x 10 -11. Aluminum measurements were performed against in-house standard SM-Al-11, with a 26 Al/ 27 Al ratio of (7. 401±0. 064) x 10 -12 , previously cross-calibrated against the primary standards certified by a round-robin exercise  [62, 63]. The successive measurement batches are characterized by stable, high level 9 Be and 27 Al currents during AMS measurements (S. I. Table  3 ) that attest for their high reliability (despite low 10 Be and 26 Al counts at the AMS receptors).
Ma In order to reconstruct the landscape at 1. 8 Ma, we first compiled rates of vertical land motions inferred from geomorphological indicators, stratigraphic, and seismic data (S. I. Fig. 1a  adapted from  [6, 7, 32] ). This dataset provides discrete informations that we interpolated in order to obtain a continuous map of uplift rates over the region, which backward integration in time provides a paleo-elevation map. To reconstruct a realistic landscape at 1. 8 Ma, we ran the Landscape Evolution Model (LEM) goSPL  [31]  over a 10,000 years period starting from an adjusted paleo-elevation surface (1 km resolution) derived from the uplift map and past absolute sea level, set to -30 m during Early Pleistocene sea level highstands  [66]. Continuity of mass is the main equation of the model, and is expressed by: ∂z ∂t = U + κ∇ 2 z + ǫP d (P A) m ∇z n ( 1 ) where z is surface elevation (m), t is time (yr), U is vertical land motion (m/yr) and κ the diffusion coefficient for soil creep (set to 5 × 10.
Results A diverse collection of ST58 genomes. We first built a collection of E. coli ST58 genome sequences from isolates that were diverse in origin-temporally, by source and geographically (Fig. 1(a-c )). In total, our genome collection comprised 752 whole-genome sequences of E. coli ST58 isolates collected between 1970 and 2019 from 6 niches sub-divided into 15 sources, Fig. 1b ) across 33 countries on 6 continents (Supplementary Data 1). Of the 752 sequences, 178 were sourced from in-house and collaborator collections and the remainder from Enterobase. Within the collection, 92% of sequences (692/752) were from isolates collected after the year 2000 (See Fig. S1  for full range). The most common niche represented in the collection was livestock, accounting for 68% of the collection (514/752).
3 ). The most striking feature of the BAP2 cluster was the high proportion of sequences that featured ColV F plasmids (308/363, 85%). These plasmids were represented predominantly by three RSTs: F2:B1, F18:A6:B1 and F18:B1 (Figs. 3b, S4, Supplementary Data 2). The initial screening of the cluster sequences for the presence of a ColV plasmid was based on the Liu criteria (see Methods, Fig. S5 ). However, as the Liu criteria are limited by screening for specific genes without consideration of plasmid backbone structure, we sought to corroborate the identification of ColV plasmids via an additional methodology. For this purpose, we aligned de novo assemblies to the backbone of archetypal F2:B1 ColV plasmid pCERC4 and visualised the alignment as a heatmap of binned nucleotide identities (Fig. 4 ).
To test this we performed a pangenome-wide association study (pan-GWAS), using BAP2 cluster membership as the test variable. Within the pangenome, 78 genes coding for non-hypothetical proteins were over-represented in the BAP2 cluster and 55 were underrepresented (these genes are therefore associated with non-BAP2 ST58). We also performed this analysis for the other BAP groups; however, only BAP6 contained genes that met our significance threshold (1E-50) for over-or under-representation, and these p-values were generally far lower than any of the genes associated with BAP2 (Fig. S7 , Supplementary Data 3). Mapping genes identified in the BAP2 analysis back to the core gene phylogeny revealed that a sub-group of approximately 294 genomes within BAP2 contained a highly conserved accessory gene profile (Fig.
This was supported by virulence gene screening which identified both fyuA (306/363; 84%) and cognate HPI marker, irp2 (303/363; 83%), in a high proportion of BAP2 sequences (Fig. S9 ). Twenty-seven genes were present in both BAP2 over-and under-represented groups, indicative of gene families that are likely core to ST58 but have alternative sequences in BAP2 compared to the remainder of the phylogeny (Supplementary Data 3). Most of these paralogous genes were involved in metabolism, membrane transport processes and DNA transcription, notably including genes of the bcs operon involved in cellulose metabolism, an abundant polysaccharide in the bovine rumen. These results suggest that the BAP2 cluster likely displays a number of functional differences to the remainder of ST58 in relation to multiple biological processes.
In addition to these sources, we have observed, in high proportions, ColV plasmids in ST58 BAP2 genomes originating from other food production animals, particularly pigs. Thus, we wanted to investigate how common it was for ColV plasmids to be present in the genomes of E. coli isolated from other sources, as well as the range of E. coli STs hosting ColV plasmids. To do this, we curated and analysed a collection of 34,364 draft E. coli genome assemblies from Enterobase (Supplementary Data 4). We found that within this collection, poultry (2328/4260, 55%) was by far the most dominant source for ColV+ E. coli, followed by porcine (526/2683, 20%) and human ExPEC (735/4465, 16%; Fig. 8a ). Low carriage was observed in human other (523/15088; 3%) and bovine sources (176/6926; 3%). The rate of ColV carriage in all genomes was 13% (4370/34,364; Fig.
Discussion Here, in a genomic epidemiological study, we 752 whole genomes of E. coli ST58 from a variety of sources in order to provide some explanation for its emergence as a human pathogen. We identified the BAP2 cluster; a large, divergent lineage of strains with a broad host range. This cluster had nearubiquitous carriage of ColV-like plasmids, reduced diversity of adaptive colonisation-related traits such as fimH and serotypes, and distinctive accessory gene content, including carriage of yersiniabactin genomic island marker genes and an additional prophage. Among the collection's strains, the BAP2 cluster contained the vast majority of strains isolated from extra-intestinal infections, poultry and swine. Our data indicate that complex interactions between mobile genetic elements, phylogenomic background, and various hosts comprise the mechanisms and networks through which E.
coli ST58 has emerged as a human pathogen  56. Limitations. Epidemiological studies that leverage publicly available genomic data are typically beset by uncertainty as to whether the total dataset represents a reliable proxy for the genomic and source diversity of the entire population. Whilst our source distribution was dominated by cattle, poultry and swine, we believe we have observed most of the genomic diversity present in ST58 because of the large pangenome, variety of ARGs, VAGs and plasmid replicons, and clear population structure. The major question remaining is whether most ST58 from human faeces and ExPEC belong to the BAP2 cluster. Though not included in our study collection, ST58 sepsis isolates from multiple hospitals in Paris predominantly display O8/ O9:H25 serotypes, as well as ColV and HPI marker genes, all of which were typical of BAP2 ST58  3.
In conjunction with our data, this tends to suggest that most ST58 ExPEC will belong to this cluster, however, an expanded dataset with more commensal and pathogenic human sequences would increase our confidence. Some form of virulence characterisation of non-clinical isolates in the BAP2 cluster would have been desirable in order to solidify the linkage between their genomic traits and phenotypes; however, this was outside the scope of the study. Nonetheless, the sharing of genomic elements with well-documented virulence traits between clinical and non-clinical isolates in the cluster strongly supports a degree of innate virulence. Similarly, though antimicrobial resistance phenotypes were not available for all strains, concordance between acquired ARG presence and resistance phenotype is typically very high  57, 58.
The Enterobase analysis supports the role of ColV in pathogenicity in a proportion of human extra-intestinal infections. Of 34,364 draft E. coli genome assemblies from Enterobase, ColV plasmids were present in 16% of ExPEC strains. In ST58, we observed the presence of ColV plasmids in more than 75% of the ExPEC strains. However, ExPEC strains are not the dominant reservoirs of ColV plasmids. Rather, our data supports the widely held opinion that poultry and associated meat are the major reservoirs for ColV plasmids with a carriage rate of 55%  54, 75. Unexpectedly, we found that porcine sources were also a non-trivial secondary reservoir of ColV plasmids, with 20% carriage. ColV plasmids have previously been reported in pigs; however, it is unclear whether ColV plasmids were historically common in production systems or whether they represent a relatively recent, direct or indirect incursion from poultry  76, 77.
Methods Collaborator sequences. A global collection of 190 ST58 E. coli sequences was compiled from in-house datasets and collaborators in Australia, Canada, Czech Republic, Denmark, France, Germany, Netherlands, Poland, Romania, Sweden, United Kingdom and United States. These sequences represented a wide range of sources including human urinary tract and blood infections, wild birds, cattle, companion animals, produce, swine, poultry, wastewater and surface water. Detailed methods regarding culture and DNA isolation for the generation of inhouse sequences are available in their respective publications listed as PMID accessions in Supplementary Data 1. All data from collaborators were sequenced on Illumina platforms (see SRA entries for specific instruments) and was received as raw fastq paired reads.
Individual strain and DNA isolation methods were not provided for collaborator sequences. In-house and collaborator reads were directly uploaded to an Enterobase workspace for quality control (QC) and preliminary analyses in the context of other ST58 sequences. Twelve sequences were excluded as they were determined not to belong to ST58, failed assembly or QC performed by Enterobase. Eight sequences were excluded in preliminary phylogenetic analyses. The final number of 'in-house' sequences in the collection was 178. Of these, 158 were previously unpublished and have been uploaded to SRA under BioProject PRJNA727368. Full metadata and accession numbers for all sequences are available in Supplementary Data 1. Publicly available sequences. Enterobase was queried on 04/12/2019 for released ST58 whole-genome sequences with available metadata for source, collection year, continent and country.
SRA and ENA accession numbers were extracted and parallel-fastq-dump 0. 6. 6 was used to download 614 read sets with the following flags:-skip-technical,-read-filter pass,-dumpbase,-split-files,-clip. Enterobase sequences were named for analysis with their NCBI SRA or EBI ENA accession number beginning with SRR or ERR, respectively. Full metadata and accession numbers for these sequences are available in Supplementary Data 1. Curation and metadata processing. To create consistency and ensure cogency within source information from Enterobase we curated the metadata in R 3. 6. 3 with a range of regular expression substitutions and some unavoidable manual curation utilising the three source information columns from Enterobase to classify sequences by niche and source. Defined niches and their respective sources in parentheses were: livestock (bovine, equine, porcine, poultry, livestock other, ovine); human (ExPEC, human other); wild animal (avian, other mammal); companion animal (canine, feline, avian); food (plant); environment (water, wastewater).
Forty sequences were excluded during this process due to irreconcilable source information. The scripts used to process the raw metadata, as well as the pre-and post-manual curation datasets have been made available as a GitHub repository for reproducibility and transparency (See 'Data availability' and 'Code availability' below). The final collection numbered 752 sequences comprising 178 in-house/collaborator sequences and 574 publicly available sequences. De novo assembly and annotation. Raw sequence reads were de novo assembled with Shovill 1. 0. 4 with default settings and a minimum contig length of 200 bp. The resulting assemblies were annotated by Prokka 1. 14. 5, run with default settings and the-kingdom and-genus flags set to 'Bacteria' and 'Escherichia', respectively. Core and pangenome identification.
fastbaps 1. 0. 6 was utilised to define clusters from the core gene alignment conditioned on the core gene tree and these designations were subsequently used in downstream analyses  88. SNPs were identified by running snp-sites 2. 5. 1 on the core gene alignment, generating a core SNP alignment of 30,771 SNP sites. Pairwise SNPs between strains were counted from the core SNP alignment with snp-dists 0. 6. 3. Pan-GWAS. Scoary calculates associations between gene presence/absence (as determined by Roary) and phenotypic or metadata traits of the strains. We defined the fastbaps clusters as traits and used Scoary 1. 6. 16 with the -no_pairwise flag (to identify over-representation as opposed to causation) and a Benjamini-Hochberg corrected p-value threshold of 1E-50 to identify genes that were over-or underrepresented in those clusters.
Gene and mutation screening. 1] [92] [93] [94]. SNPmediated resistance genotypes were predicted with PointFinder 3. 1. 0 95 , downloaded as a Python script from https://bitbucket. org/genomicepidemiology/ pointfinder/src/master/. fimH alleles were identified by Enterobase. ABRicate 0. 9. 8, a BLAST-based screening tool, was additionally used with default settings to identify IncI1 pMLST and the dfrA5-IS26 deletion signature. Inference of ColV plasmid carriage. We used ABRicate 0. 9. 8 with default settings to implement the ColV screening methodology described by Liu et al to identify de novo assemblies that putatively carried a ColV plasmid  75. Briefly, the Liu criteria considers a strain ColV-positive if it carries at least one or more genes from four or more of the following six gene sets (i) cvaABC and cvi (the ColV operon), (ii) iroBCDEN (the salmochelin operon), (iii) iucABCD and iutA (the aerobactin operon), (iv) etsABC, (v) ompT and hlyF, and (vi) sitABCD.
Thresholds of ≥ 90% nucleotide identity and ≥95% length coverage were applied post-ABRicate in RStudio 1. 4. 1106 with R 4. 0. 5 to determine positive hits for a gene and sum the group counts to infer presence or absence. To strengthen the evidence for ColV plasmids in our collection we aligned assemblies to the backbone of pCERC4, an archetypal F2:B1 ColV plasmid isolated from an ST95 E. coli strain from healthy human faecal flora. Briefly, the 126,270 bp pCERC4 backbone was defined by removing resistance regions from the complete pCERC4 sequence (RefSeq: NZ_KU578032. 1) as described by Moran and Hall  48. Assemblies were aligned against the backbone with ABRicate and outputs concatenated for import into R. We used a custom R script to extract hit ranges for each sequence and represented them as a series of percentage nucleotide identities across 100 bp segments of the plasmid backbone.
These percentages were then visualised against a schematic backbone of pCERC4, generated with SnapGene 5. 2 (GSL Biotech), as a heatmap in ggtree. Enterobase ColV analysis. To compare ColV carriage across E. coli STs we filtered the Enterobase Escherichia/Shigella database for strains deposited prior to January 12, 2020. To be included in the study, strains were required to have metadata relating to their source, geographical and temporal origins. Serotypes, Achtman MLST and fimH alleles were also downloaded from Enterobase. Sources were curated in R as well as manually in Excel in order to reduce the heterogenous Enterobase metadata into six categories: Bovine, Porcine, Poultry, ExPEC, Human Other and Companion Animal. Following removal of Shigella sp. , 35,254 genomic assemblies were then downloaded from Enterobase using a third-party custom script available at https://github.