dataesr/scientific-paragraphs-categorization huggingface dataset:
As depicted in Additional file 2: Figure  S1B , we found a 100% concordance between qRT-PCR and Affymetrix data with respect to the direction of the expression changes, with the magnitude of gene induction higher in qRT-PCR than microarray data. The myocardium subjected to CPB undergoes an obligate period of ischemia  [10, 11]. We were, thus, interested to investigate the contribution of hypoxia to the post-CPB transcriptome. To this aim, we used GSEA to determine the enrichment of hypoxia gene sets in the transcriptional profile of Post-CPB specimens. As shown in Table  6 , eleven hypoxia gene sets were specifically enriched in the ASD transcriptome (FDR q-value ≤ 0. 2 and nominal p ≤ 0. 05), whereas none was enriched in the ToF transcriptome (data non shown). These results indicate that CPB causes profound alterations in the myocardial transcriptome of ToF and ASD patients, modulating both shared and distinct gene sets, and suggest a reprogramming response to CPB-induced acute ischemia specifically in the ASD myocardium.
The blue columns represent DEGs modulated in ToF patients; the red columns represent DEGs modulated in ASD patients. GO terms are listed by decreasing number of DEGs in ToF samples. The p value for each GO term is indicated. b Network analysis. Functional interaction networks among DEGs products were constructed using the STRING-DB software as detailed in the legend of Fig. 2b. Networks are displayed graphically as nodes (DEGs products) and edges (predicted protein-protein associations). Only associations with a high degree of confidence (0. 7) are displayed in the plot ADAMTS1 upregulation was shared by the two groups of patients. The secreted matricellular CYR61 protein was previously found highly expressed in remodeling atrial cardiomyocytes after myocardial infarction and proposed as an early prognostic biomarker of cardiac injury  [89] , while its mutations have been associated with ASD  [90].
Overview Baseline information is collected upon group entry, including gender (derived from nominal information), age at diagnosis, and geographic classification (ie, large, medium, or small population centers or rural). We will manually extract interaction data from the T1DM-VPN Facebook group feed for the period between June 21, 2017 (addition of first member), and March 1, 2020. Enrollment into these groups is controlled by the researchers, and members are aware of their role in overseeing group activity. It is essential to include all members in an SNA. While we will not seek individual-level consent, we will provide a reader-friendly description of the protocol in the Facebook group and invite members to comment, as recommended by our peer leaders during a teleconference. All data will be deidentified prior to analysis by replacing names with identification numbers; the document linking these numbers to individuals will only be available to members of the research team.
After applying these initial codes to the data, any content deemed not adequately coded will be further examined and discussed to determine if new categories need to be added. Two coders will review examples of how the frameworks have been applied in Gaysynsky and colleagues' study. First, a sample of data will be coded, analyzed for concordance between the 2 coders, and discussed to refine the consistency with which codes are applied. Then, all posts and comments will be reviewed to identify which code is most applicable. For a given comment, context (original post, preceding and proceeding comments) will be used to help inform coding decisions. A maximum of 2 codes may be applied to the same content. We will further examine the issues for which informational support is requested.
Network Dynamics and Time in SNA We will evaluate network dynamics over approximately a 3-year period. In epidemiological terms, this is an open cohort, in that members may leave or join at different times. In prior Facebook group studies, researchers have taken a data sample from what they assumed to be a stable time frame. They then take absolute (eg, total number of posts) or per-person (eg, average number of posts per person) measurements  [22, 24]. Instead of making assumptions about the stability of a network, we will actually identify stable time frames by modeling monthly membership growth as a Poisson process, a process in which the average rate of events is known but the exact timing of events is random. We propose that any month for which the probability of observed growth falls below 50% (ie, is statistically unusual based on the known average rate of events) represents the end of a stable time interval and the start of a new stable interval.
Results T1DM-VPN was launched in June 2017, and as of March 1, 2020, it has 196 patient-members. This research protocol received ethics approval from the McGill University Health Centre Research Ethics Board on May 20, 2020. Baseline information about each group member was collected upon their addition into the group. Collection of interaction data is set to be complete by fall 2020, and data analysis is set to be conducted by November 2020.
Atomic displacement is one of the key factors that influence the behaviors of material properties during and after irradiation. Many models, including the international standard metric Norgett-Robinson-Torrens model (NRT), have been developed to calculate the number of Displacement per Atom (DPA) using the energy of Primary Knocked-on Atom (PKA) as a major parameter. However, extensive experiments and simulations indicate that the NRT-DPA model seriously overestimates (about 3 times) the actual DPA. Nordlund recently developed the Athermal Recombination-Corrected DPA (ARC-DPA) model, which shows that the Molecular Dynamics (MD) simulations can be directly used to compute DPA by fitting the simulated data for each isotope. The present work proposes a simpler expression for the efficiency function to calculate the DPA without requiring fitting parameters as needed in the ARC-DPA model.
The efficiency should thus be a constant after the subthreshold energy, which defines the threshold of subcascade formation. Both MD simulations and experimental data show the about 1/3 asymptotic value of the efficiency for many isotopes. Consequently, the asymptotic value of the efficiency (β) should be taken into account: ξ(E a ) = 2E d /0. 8 2E d /0. 8 + βE a + β (11) where β = Z/(1. 5A) [in which Z is the atomic number and A is the atomic mass] is considered as the asymptotic value of ξ(E a ). β ∝ Z/A because stronger Coulomb force or lighter mass leads to longer path that induces "more" sub-cascades. The advantage of this improved efficiency function is that we correlate the atomic displacement efficiency with the atomic number and the atomic mass of the irradiated material without requiring fitting parameters as needed in the ARC-DPA model.
[6] ). The experimental uncertainties are deduced from the RF values compiled in Tables  5  and 16  in Ref. [13]. It can be found that the CB corresponds well with the experimental data. The discrepancies between the CB model and the experimental values are even less than the ARC-DPA model for Fe, Ni, and Cu. Averback deduced experimental DPA numbers of copper and silver according to the measurement of electrical resistivity  [6]. The data of Averback are given as a function of DPA averaged PKA energy, which is larger than the average PKA energy. However, these data can reveal the asymptotic values of displacement efficiency. The total uncertainty of Averback's DPA comes from the uncertainties of the thickness of samples (5% for samples No. 2 & 3 and 10% for other samples) and the correction to convert measured thin film resistivities (10%).
4 and Eq. 11 are used for the ARC-DPA formula and CB-DPA formula, respectively, φ(E) refers to the spectrum of the incident particle. The quantity in bracket in Eq. 12 is defined as the DPA cross section in barn. eV. The ARC model for the Fe isotopes and the CB model for all isotopes are implemented in the processing code NJOY2016  [18]  in this study. The numeric verification of DPA cross sections is presented in Ref. [19]. The DPA cross sections of 56 Fe calculated with the lastest released JEFF-3. 3 library  [20]  is presented in Figure  3  for the NRT, ARC, and CB formulae, respectively. Small difference between the ARC-DPA and CB-DPA cross sections is observed. The Stainless Steel (SS) is used for the material of the reactor pressure vessel in LWR and fuel cladding in fast neutron reactors.
1. 1 nuclear data library  [25]  and the P i j deterministic neutron transport method in APOLLO-2  [26]. Both Figure  1  and Figure  3  show the small difference between CB-DPA and ARC-DPA for 56 Fe. Results in Table 2 confirm that the ARC and the CB models have similar results on 56 Fe DPA calculations in fast neutron reactors, while the CB formula has a simpler form and does.
Conclusions The recently developed ARC-DPA model computes DPA with MD simulations results. The MD-based library of DPA calculations is possible thanks to the ARC-DPA. Inspiring from the annihilation equation, we propose an efficiency function with a simpler form without introducing additional parameters excluded in the current international standard NRT-DPA formula. The simpler form of CB-DPA can simplify the uncertainty propagation, while the calculations of sensitivities to fitted coefficients are required for ARC-DPA. The CB-DPA is validated against with experimental data for Fe, Ni, Cu, and Ag. In addition, the applications in fast breeder nuclear reactors show good agreement with the ARC-DPA metric for 56 Fe. Therefore, the CB-DPA formula can be used to approximatively compute the DPA rates in the case of no MD data is available.
Results Assembling an informative collection of S. Typhimurium isolates. A combination of historical and contemporary S. Typhimurium bloodstream isolates was sampled from two sources (Fig. 1 ). The main dataset consisted of 608 human bloodstream isolates collected between 1996 and 2018 from the Malawi-Liverpool-Wellcome Trust Clinical Research Programme (MLW) in Blantyre, Malawi. To provide context, 72 human bloodstream isolates collected between 1966 and 2012 by the Unité des Bactéries Pathogènes Entériques, Institut Pasteur, Paris, France were also included. This contextual collection was derived from 13 countries in Africa, 1 in Europe and 1 in Asia. A description of the isolates is summarized in Fig. 1 , details of sequencing quality control are available in Supplementary Table  1  and the complete metadata, including genome accession numbers, are in Supplementary Table  2.
Population structure of S. Typhimurium. To define the population structure of S. Typhimurium currently causing BSI in Africa, we explored the relationship of the 680 S. Typhimurium genomes from the MLW and contextual collections. The two predominant sequence types causing BSI in Malawi were ST313 (n = 549) and ST19 (n = 34). A small number of isolates (n = 25) were typed as ST313 single-locus variants (ST3257 (n = 17), ST2080 (n = 3), ST302 (n = 3), ST4200 (n = 1) and ST4274 (n = 1)). The contextual dataset contained two sequence types, ST313 (n = 43) and ST19 (n = 29). In sum, ST313 isolates originated from nine African countries (Fig. 1 ). Previous genome-based analyses of the African ST313 epidemic included isolates from eastern and central Africa  6, 11. Here, by including several Western Africa countries, we have expanded the known geographic range of ST313  6, 8, 11, [20] [21] [22] [23]  to include Cameroon, Central African Republic, Niger, Senegal, Sudan and Togo.
To investigate population structure, a core genome maximum-likelihood phylogeny was constructed, and cluster designation was performed. Three major ST313 clusters, four ST19 clusters and a small number of miscellaneous strains were identified (Extended Data Fig. 1 ). Multi-locus sequence type variants formed discrete sublineages. The three ST313 clusters were clonal, with cluster 1 and 2 corresponding to previously defined lineages (ST313 L1 and L2)  5. The genomes of contemporary isolates revealed that a third lineage has been circulating in Malawi since 2016 (ST313 L3; Fig. 2 ). Characterization based on long-read sequencing showed that ST313 L3 strain BKQZM9 had a circular chromosome (4. 8 Mb) with 99. 98% sequence identity to the ST313 L2 reference strain D23580  5. To fully contextualize ST313 L3, we constructed a core gene maximum-likelihood phylogeny, including previously published ST313 genomes (Supplementary Table  3 ), alongside the current dataset (Extended Data Fig.
ST313 L1 carried a previously unreported 8,274-base pair (bp) plasmid, sharing 99. 27% sequence identity with pAnkS 25 (GenBank NC_010896. 1), which encodes the LsoA-LsoB toxin-antitoxin proteins. This toxin-antitoxin system functions as a bacteriophage exclusion system in Salmonella  26  (Supplementary Discussion 1). ST313 L3 strain BKQZM9 contained the 95 kb plasmid (pSLT), a 1,975-bp plasmid (pBT3) and a 2,556-bp plasmid (pBT2, identified using short-read data only). Thus, the plasmid profile of ST313 L3 shared more similarity with ST313 L2 than other lineages, but lacked pBT1 (Fig. 2.
Total number of isolates with each resistance profile is indicated below the matrix. The combination matrix was created using the UpSetR package  87. The bubble plot (top) depicts the relative number of isolates (bubble size) with each resistance profile (combination matrix) per year (y axis). Lineage assignments are shown using bubble colour to identify lineage-specific aMR trends. Local Malawi antimicrobial usage policy 9 is highlighted in background colours overlaid on the timeline in the bubble plot. The bubble plot was created using R ggplot2  88. 4 ). For example, the KatE catalase protects bacteria from environmental oxidative stress, and shows a maximal activity in stationary phase cultures of S. Typhimurium ST19  15. The E117G KatE mutation caused low level catalase activity in ST313 L1, L2, L2.
Methods Dataset. Salmonella samples were derived from two archived blood culture collections. The main dataset was sourced from the MLW in Blantyre, Malawi and consists of over 14,000 Salmonella bloodstream isolates from patients with iNTS disease at the Queen Elizabeth Central Hospital between 1996 and 2018. Isolates listed as S. Typhimurium were extracted from the metadata file for the entire collection and stratified by AMR profile into the following four categories: susceptible, resistant to one first-line agent, resistant to two first-line agents, or MDR. First-line agents were considered to be ampicillin, cotrimoxazole and chloramphenicol. Random subsampling was then performed using a random number generator (Excel, Microsoft) and selected samples were collected and resuscitated from the freezer archives at MLW.
We successfully recovered 647 isolates for whole-genome sequencing. To complete the MLW data, a contextual dataset was sourced from the Institut Pasteur, Paris. The dataset consisted of 96 S. Typhimurium isolates collected from human extraintestinal sites from patients contaminated in Algeria, Burkina Faso, Cameroon, Central African Republic, Republic of the Congo, France, Côte d'Ivoire, Madagascar, Mali, Niger, Senegal, Sudan, Togo and Vietnam between 1966 and 2012. All 96 S. Typhimurium samples were selected and sent for whole-genome sequencing. Whole-genome sequencing of short reads. Isolates were prepared by inoculating a single bead of frozen Salmonella stock into a FluidX 2D Sequencing Tube (FluidX) containing 100 μl buffered peptone water (Oxoid, CM0509) and incubating overnight at 37 °C.
Phylogenetic reconstruction of dataset. High-quality pseudogenomes (MLW n = 608, contextual n = 72) were concatenated, plasmids were excluded and Gubbins 66 v2. 2 was used to remove recombinant regions and invariable sites. The resultant multiple sequence alignment of reference-based pseudogenomes (12,013 variant sites) was used to infer a maximum-likelihood phylogeny using RAxML-ng 67 v0. 6. 0 with 100 bootstrap replicates to assess support. To assign clusters, rhierBAPs  68  was used, specifying two cluster levels, 20 initial clusters and infinite extra rounds. Visualizations were performed using interactive Tree Of Life (iTOL) 69 v4. 2.
Phylogenetic reconstruction of dataset and publicly available dataset. For contextual interpretation, previously published S. Typhimurium ST313 sequences were retrieved from public repositories (Supplementary Table  2 ) and constructed in a core gene phylogeny with our data. Roary 70 v3. 11. 0 was used to generate a core gene alignment and SNP-sites 71 v2. 3. 3 was used to extract SNPs. The resultant multiple sequence alignment (15,240 variant sites) was used to construct a maximum-likelihood phylogeny using RAxML-ng 67 v0. 6. 0 with 100 bootstrap replicates to assess branch support. Visualizations were made using iTOL 69 v4. 2. Temporal phylogenetic reconstruction. To determine the evolutionary history of ST313, a chronogram was produced using Bayesian phylogenetic inference. Because the MLW collection is substantially larger than the contextual collection and to reduce computation time, 150/680 samples were chosen for inclusion in the analysis as described below.
Sampling reflected original sample selection for this study and included all contextual isolates which passed quality control (n = 72) and a subset of MLW isolates randomly selected using a random number generator (n = 78, Microsoft Excel). A reference-mapped multiple sequence alignment (7,231 variant sites) was created as described above. BEAUTI 72 v2. 6. 1 and BEAST2 72 v2. 6. 1 were used to create and execute three independent chains of length 250,000,000 with 10% burn in, logging every 1,000 and accounting for invariant sites. We included the prior assumptions of a coalescent Bayesian skyline model for population growth, and a relaxed log normal clock rate to account for rate heterogeneity amongst branches (the full model is described in Supplementary Data 1). Tracer 73 v1. 7. 1 was used to assess convergence, with all parameter effective sampling sizes being >200.
The invasiveness index has been validated using multiple Salmonella serovars, and the approach clearly discriminated between the S. Enteritidis and S. Typhimurium lineages associated with extraintestinal infections in sub-Saharan Africa  11, 47. The distribution of invasiveness index values for each lineage were compared using the Wilcoxon-Mann-Whitney test  46  implemented through R 75 v3. 4. 0. A custom-made database of the top 196 invasiveness predictor genes was then created from the multi-fasta file provided  47. SRST2 76 v0. 2. 0 was used to flag any genes with mutations in ST313 L3 compared with ST313 L2. Exact mutations were investigated manually using MegaX 77 v0. 1. AMR testing and statistical analysis. Genetic determinants for AMR were identified using staramr v0. 5. 1 (https://github.
com/phac-nml/staramr) against the ResFinder  78  and PointFinder 79 databases. Phenotypic antimicrobial susceptibility testing was performed using the EUCAST disk diffusion method  80  (Antibiotic discs: ampicillin 10 μg, chloramphenicol 30 μg and trimethoprim/sulfamethoxazole 25 μg from Mast Group). To compare the results of genotypic against phenotypic testing, sensitivity and specificity were calculated for first-line agents used over the study period using MedCalc's test evaluation calculator with Clopper-Pearson confidence intervals (https://www. medcalc. org/calc/diagnostic_test. php).
A custom-made database of the identified regions was created, along with known variants. Sequences were downloaded using the online tool SalComD23580 17. SRST2 76 v0. 2. 0 was then used to identify alleles present and the non-exact matches were investigated manually using MegaX 77 v0. 1. Pr phage and plasmid presence or absence were manually mapped onto the phylogeny using iTOL 69 v4. 2. Th  conservation of pseudogene-associated mutations was identified at the population level and integrated into the temporal phylogenetic reconstruction using iTOL 69 v4. 2. These an lyses allowed inference of branches in which functional gene loss and plasmid and prophage acquisition occurred. Pheno ypic testing was conducted to determine the relevance of plasmid and functional gene loss as detailed in Supplementary Methods 1.
Approximately 120× coverage (600 Mb, 60,164 reads, read N50 33 kb) was generated. Hybrid genome assembly with the Illumina reads was done using Unicycler 56 v0. 4. 4 which resulted in a 4. 8-Mb circular polished chromosome (GenBank accession: CP060169), a 94-kb circular plasmid (pSLT) (accession CP060170) and a 1,975-bp circular plasmid (pBT3) (accession: CP060171). Prokka 59 v1. 12 was used for annotation as above.
Genomic comparison of ST313 lineages. To determine the basic architecture of the ST313 L3 BKQZM9 genome, the identity of the chromosomal and plasmid regions were compared with existing sequences from ST313 L1 (A130), ST313 L2 (D23580) and ST313 UK (U2) using the Basic Local Alignment Search Tool (BLAST)  85  and visualized using the Artemis Comparison Tool 86 v13. 0. 0. Putative prophage and plasmid sequences were extracted and identified using a BLAST  85  search against known ST313 prophages and plasmids and the non-redundant NCBI nucleotide database. Prophage and plasmid presence or absence were also determined experimentally (Supplementary Discussion 2). Reporting Summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
58immomatic 55 v0. 36 was used to trim adapters and Seqtk v1. 2-r94 (https://github. com/lh3/seqtk)wasused to trim low-quality regions using the trimfq flag. Fastqc v0. 11. 5 (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/) and multiqc v1. 0 (http://multiqc. info)were used to pass sequence reads according to the following criteria: passed basic quality statistics, per base sequence quality, per base N content, adapter content and an average GC content of between 47% and 57%. Only high-quality reads were used in downstream analysis. Unicycler 56 v0. 3. 0 was used to assemble genomes and QUAST 57 v4. 6. 3 was used to evaluate assembly quality to standards consistent with Enterobase58. Specifically, N50 > 20 kb, 600 or fewer contiguous sequences, total number of bases between 4 Mbp and 5.
8 Mbp. Prokka 59 v1. 12 was used to annotate the genomes. Serotyping was confirmed using the Salmonella In Silico Typing Resource 60 v1. 0. 2 and compared with the original metadata table. The strains were assigned a multi-locus sequence type (MLST) using MLST 61 v2. 10 on the basis of the conservation of seven housekeeping genes. Mapped reads were then cleaned and sorted using the SAMtools suite 63 v1. 7. Reads were realigned against the reference using GATK 64 v3. 7 by creating targets for realignment (RealignerTargetCreator) and performing realignment (IndelRealigner). Removal of optical duplicates was completed using Picard v2. 10. 1-SNAPSHOT (https://broadinstitute. github. io/picard/). Sequence variants were called using Bcftools v1. 9-80 (http://samtools. github. io/bcftools) to generate a reference-based pseudogenome for each sample with greater than 10× depth.
Acknowledgements We thank present and former members of Lab H at the  Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool , including members of the  Hinton and Baker groups  for invaluable discussions, in particular  C. Chong ,  M. Horsburgh ,  S. Owen  and  K. Costigan  for their support, contributions and advice; the MLW clinical research programme and the  Institute Pasteur  for access to their Salmonella archives;  P. Ashton  for helpful comments on the manuscript; and  R. Low  for his assistance in uploading metadata to repositories. C. V. P. is supported by the  John Lennon Memorial Scholarship  from the  University of Liverpool  and a  Fee Bursary Award  from the  Institute of Integrative Biology at the University of Liverpool. B. K. was funded by an  AESA-RISE  fellowship from the  African Academy of Sciences.