dataesr/scientific-paragraphs-categorization huggingface dataset:
01 mg/kg bw for beta-cyfluthrin was used in a conservative approach for setting the HBM-GVs. The database for cyfluthrin/beta-cyfluthrin concerning toxicity is extensive and the level of confidence with this regard medium to high. Evaluations were performed by several competent authorities within the process of approval procedure for use as biocide or pesticide. However, there is great uncertainty regarding the amount of the cyfluthrin metabolite 4-FPBA excreted in urine by humans, because available information is based on a single study with a single individual only.
05 µg/L in Belgium. The 95th percentiles exceeded the 3-PBA screening value for all children databases (RCRs between 1. 14 and 2. 17). The highest value was calculated for Belgium, followed by Cyprus (RCRs 1. 95). Measured levels for 4-FPBA, in contrast, were well below the proposed screening value for this biomarker. When the screening is conducted for the sum of 3-PBA and 4-FPBA, the RCR are slightly higher, ranging from 1. 33 for France and 2. 19 for Belgium, with 3-PBA as the most relevant component in all cases. The refinement based on variability in human toxicokinetics presented in Figure  2  suggested a real probability of exceedance below 5%. An additional probabilistic refinement was conducted accounting for the differences in the ADIs between the different pyrethroids in addition to the individual variabilities in the urinary excretion of the marker.
As exposure data were aggregated, the first step is to transfer the percentiles into a distribution. The best fit for the Belgian dataset is for a loglogistic distribution (shape 2. 025207; scale 1. 687245). Individual variability in urinary 3-PBA excretion was modelled taking into account the estimations by Remer et al. [52]  and van Haarst et al. [53]  as reported by Aylward et al. [15]. The best fit was for a Weibull distribution (shape 2. 4009431, scale 0. 3465461). Figure  3  summarises the results. The refinement based on variability in human toxicokinetics presented in Figure  2  suggested a real probability of exceedance below 5%. An additional probabilistic refinement was conducted accounting for the differences in the ADIs between the different pyrethroids in addition to the individual variabilities in the urinary excretion of the marker.
As exposure data were aggregated, the first step is to transfer the percentiles into a distribution. The best fit for the Belgian dataset is for a loglogistic distribution (shape 2. 025207; scale 1. 687245). Individual variability in urinary 3-PBA excretion was modelled taking into account the estimations by Remer et al. [52]  and van Haarst et al. [53]  as reported by Aylward et al. [15]. The best fit was for a Weibull distribution (shape 2. 4009431, scale 0. 3465461). Figure  3  summarises the results. The probabilistic refinement based on variability in human toxicokinetics suggests a potential risk for about 2% of Belgian children if exposure is linked to the more hazardous pyrethroid, lambda-cyhalothrin; the risk of exceedance is reduced to 1-0. 1% for assumptions considering a mixed exposure to several pyrethroids with different ADIs.
Tau-Fluvalinate In addition to its use in plant protection products, tau-fluvalinate is also applied as a miticide in apiculture. It is frequently detected in beeswax  [63] , and honey with frequent exceedances of maximum residue limits  [64]. Consumer exposure is mostly associated to residues in fruits (e. g. , pears, oranges) with low detection frequencies and no risk concerns in the EU  [64]. There is no specific metabolite for tau-fluvalinate that could be used as biomarker. Therefore, risk assessment of tau-fluvalinate is based on determination of 3-PBA, but using the ADI for this pyrethroid in the calculation. The 95th percentiles for all monitored groups provided by HBM4EU are well below the proposed realistic HBM-GV GenPop ; however, in the specific case of the Belgium children database, the 95th percentile, 7.
05 µg 3-PBA/l, is slightly higher than the proposed conservative GV for children, 6. 4 µg 3-PBA/l. When the upper limit of the 95th percentile is considered, accounting for the exposure uncertainty, the realistic GV is exceeded for the Israel database and the conservative GV is exceeded for all children databases except Slovenia. The biomarker for tau-fluvalinate is based on a common metabolite for several pyrethroids. In the case of the maximum 95th percentile (Belgium children), the exceedance of the tau-fluvalinate conservative GV would require that over 90% of the measured biomarker results from tau-fluvalinate exposure; and a contribution higher than 26% would be required for exceeding the maximum upper confident limit of the 95th percentile (Israel children). This is unlikely considering that the recent EU reports on pesticides residues in food  [62, 64]  indicate consumer exposure levels below 0.
Data Availability Statement: Summary of EFSA toxicity assessments and monitored levels in food are available through the documents published in the EFSA Journal https://efsa. onlinelibrary. wiley. com/journal/18314732 (accessed on 25 May 2022) at Open-EFSA web https://open. efsa. europa. eu/ (accessed on 25 May 2022), or Zenodo https://zenodo. org/record/6322020#. YkSAbChBzD4 (accessed on 25 May 2022).
0. 7. 15  (Li and Durbin 2010)  and BAMs were generated with samtools v1. 9  (Li et al. 2009). Samples prepared and sequenced by more than one method had high Pearson correlations (0. 85-0. 94) in the number of unique insertions identified per gene (STable X3-pre12), and therefore were combined into a single BAM file before performing downstream analysis. For the majority of the analyses, BAMs were combined by sample, for ease of processing and to prevent redundant insertion site identification. BAMs were parsed with a custom python script which identifies the first base of the read as the position of the insertion. The script output all unique insertion positions and the number of reads per insertion position. Positions were annotated using bedtools v2. 26. 0  (Quinlan and Hall 2010)  and a custom GFF containing amended annotations for the custom genome (Supplemental File X1 -GAP1.
gff). All analyses use unique insertion positions, and do not take into account the number of reads per unique insertion position. Uniquely identified insertion sites are supported by an average of 18. 6 sequencing reads. The libraries have between 85,327 and 329,624 unique insertion sites identified, with an average of 176,664 insertion sites, corresponding to approximately one insertion per 69 bases in the yeast genome (NCBI R64 assembly; STable 2). We normalize for differences in sequencing depth by calculating insertions per million: number of unique insertion sites per feature/(total unique insertion sites/1,000,000)  (Levitan et al. 2020 ). We do not normalize for gene length, as we are comparing genes between strains, not within strains. All code used for analysis can be found on GitHub https://github.
Data Access All raw and processed sequencing data generated in this study have been submitted to NCBI Sequence Read Archive (SRA; https://www. ncbi. nlm. nih. gov/sra/) under accession number: PRJNA910831. All code used in computational analysis is publicly available on github: CNV_essentiality 18 Supplemental_Fig_S2 -There is no relationship between CNV size and relative fitness. A-B) The fitness of evolved strains containing GAP1 CNVs was determined by pairwise competition experiments with a nonfluorescent, unevolved reference strain in glutamine-limited chemostats. C-D) Average growth rate of GAP1 CNVs relative to the ancestral, euploid strain in YPGal batch culture.
32 Supplemental Tables: All Supplementary tables are publicly available here: https://github. com/pspealman/CNV_essentiality/tree/main/Supplementary_Tables/ Supplemental_Table_S1. Strain characteristics. CNV type based on long read sequencing and genome assembly from  (Spealman et al. 2022). More information about SNPs/indels including reference sequence and mutant sequence can be found in  (Lauer et al. 2018 ) S10 Supplemental_Table_S19 -STable 8 Mann-Whitney U test for Log2FoldChange of gene expression. This table shows the result of a Mann-Whitney U test comparing the log2 transformed TPM normalized RNA-seq abundances between the evolved CNV containing strains and the ancestral euploid strains. This test is conducted on the CNV associated genes and copy number normal genes. We find that the mean of CNV associated genes is significantly higher in the CNV strains than the euploid ancestor, with the CNV strains on average being 1.
Acknowledgements We thank members of the  Gresham, Schacherer and Vogel Lab. This work was made possible by grants from the  NIH  to P. S. ( F32-GM131573 ) and D. G. ( R01-GM134066 ,  R01-GM107466 ) and from the  NSF  to G. A. ( DGE1342536 ) and DG (MCB1818234). J. S. is supported by the  European Research Council (ERC)   Consolidator grant  ( 772505 ). J. S. is a fellow of the  University of Strasbourg Institute for Advanced Study (USIAS)  and a member of the  Institut Universitaire de France.
, 2018; da Silva et al. , 2022; Wong et al. , 2015). In the absence of a nomenclature system such as that provided by cgMLST, an alternative strategy was needed for identifying and naming lineages. To address this challenge, a genotyping framework ('GenoTyphi') was developed that uses marker SNVs to assign Typhi genomes to phylogenetic clades and subclades  (Wong et al. , 2016) , similar to the strategy that has been widely adopted for Mycobacterium tuberculosis  (Coll et al. , 2014). The GenoTyphi scheme was initially developed based on an analysis of almost 2,000 Typhi isolates from 63 countries  (Wong et al. , 2016). This dataset was used to define a global population framework based on 68 marker SNVs, which were used to define four primary clades, 15 clades, and 49 subclades organised into a pseudo-hierarchical framework.
Given the wealth of existing and emerging WGS data for Typhi, we aimed to create a system to enhance visibility and accessibility of genomic data to inform current and future disease control strategies, including identifying where empiric therapy may need review, and monitoring the impact of TCVs on AMR and vaccine escape. In forming the Global Typhoid Genomics Consortium (GTGC), we aim to engage with the wider typhoid research community to aggregate Typhi genomic data and standardised metadata to facilitate the extraction of relevant insights to inform public health policy through inclusive, reproducible analysis using freely available and accessible pipelines and intuitive data visualisation. Here, we present a large, geographically representative dataset of thirteen thousand Typhi genomes, and provide a contemporary snapshot of the global genetic diversity in Typhi and its spectrum of AMR determinants.
Ethical approvals Each contributing study or surveillance programme obtained local ethical and governance approvals, as reported in the primary publication for each dataset. For this study, inclusion of data that were not yet in the public domain by August 2021 was approved by the Observational / Interventions Research Ethics Committee of the London School of  Hygiene and Tropical Medicine (ref #26408) , on the basis of details provided on the local ethical approvals for sample and data collection (Table  S1 ).
Sequence data aggregation Attempts were made to include all Typhi sequence data generated in the 20 years since the first genome was sequenced, through August 2021. Genome data and the corresponding data owners were identified from literature searches and sequence database searches (European Nucleotide Archive (ENA); NCBI Short Read Archive (SRA) and GenBank; Enterobase). Unpublished data, including those from ongoing surveillance studies and routine public health laboratory sequencing, were identified through professional networks, published study protocols  (Carey et al. , 2020) , and an open call for participation in the GTGC. All data generators thus identified were invited to join the GTGC and to provide or verify corresponding source information, with year and location isolated being required fields ('metadata', see below).
Sequence analysis Primary sequence analysis was conducted on the Wellcome Sanger Institute compute cluster. Genotypes, as defined under the GenoTyphi scheme  (Dyson and Holt, 2021; Wong et al. , 2016)  were called directly from Illumina reads using Mykrobe v0. 12. 1 with Typhi typing panel v20221207, and collated using the Python code available at https://github. com/katholt/genotyphi (v2. 0)  (Ingle et al. , 2022). Illumina reads were assembled using the Centre for Genomic Pathogen Surveillance (CGPS) assembly pipeline v2. 1. 0 (https://gitlab. com/cgps/ghru/pipelines/dsl2/pipelines/assembly/)  (Underwood, 2020) , which utilises the SPAdes assembler (v3. 12. 0)  (Bankevich et al. , 2012; Prjibelski et al. , 2020). One readset failed assembly and was excluded. Assemblies were uploaded to Pathogenwatch to confirm species and serovar, and to identify AMR determinants and plasmid replicons  (Argimon et al.
Metadata curation and variable definitions Owners of the contributing studies were asked to provide or update source information relating to their genome data, using a standardised template (http://bit. ly/typhiMeta). Repeat isolates were defined as those that represent the same occurrence of typhoid infection (acute disease or asymptomatic carriage) as one that is already included in the data set. In such instances, data owners were asked to indicate the 'primary' isolate (either the first, or the best quality, genome for each unique case) to use in the analysis. Repeat isolates were then excluded from the data set entirely (excluded from Table  S2 ). Data provided on the source of isolates (specimen type and patient health status) are shown in Table  S3. This information was used to identify isolates that were associated with acute typhoid fever.
, 2021 Ingle et al. , , 2019;; Matono et al. , 2017). Countries were assigned to geographical regions using the United Nations Statistics Division standard M49 (see https://unstats. un. org/unsd/methodology/m49/overview/); we used the intermediate region label where assigned, and subregion otherwise. To identify isolate collections that were suitably representative of local pathogen populations, for the purpose of calculating genotype and AMR prevalences for a given setting, data owners were asked to indicate the purpose of sampling for each study or dataset. Options available were either 'Non Targeted' (surveillance study, routine diagnostics, reference lab, other; n=11,086), 'Targeted' (cluster investigation, AMR focused, other; n=1,862) or 'Not Provided' (n=17). Antimicrobial resistance (AMR) determinants and definitions AMR determinants identified in the genome assemblies using Pathogenwatch were used to define AMR genotype as follows.
Genotype and AMR prevalence estimates and statistical analysis All statistical analyses were conducted in R v4. 1. 2, code is available in R markdown format at https://github. com/katholt/TyphoidGenomicsConsortiumWG1 (v1. 0, doi: 10. 5281/zenodo. 7487862). Genotype and AMR frequencies were calculated at the level of country and UN world region (based on 'country of origin') as defined above. Inclusion criteria for these estimates were: known 'country of origin', known year of isolation, non-targeted sampling, assumed acute illness (see definitions of these variables above). A total of 10,726 genomes met these criteria; the subset of 9,478 isolated from 2010 onwards were the focus of the majority of analyses and visualisations. The prevalence estimates reported in text and figures are simple proportions; 95% confidence intervals for proportions are given in text and supplementary tables where relevant.
Data visualisations All analyses and plots were generated using R v4. 1. 2, code is available in R markdown format at https://github. com/katholt/TyphoidGenomicsConsortiumWG1 (v1. 0, doi: 10. 5281/zenodo. 7487862). Data processing was done using the R packages tidyverse v1. 3. 1, dplyr v1. 0. 7, reshape2 v1. 4. 4 and janitor v2. 1. 0; figures were generated using packages ggplot2 v3. 3. 5, ggExtra v0. 9, patchwork v1. 1. 1, RColorBrewer v1. 1-2 and pals v1. 7; maps were generated using packages sf v1. 0-5, rvest v1. 0. 2, maps v3. 4. 0, scatterpie v0. 1. 7, ggnewscale v0. 4. 5; trees were plotted using ggtreeio v1. 18. 1 and ggtree v3. 2. 1.
Data availability statement All data analysed during this study are publicly accessible. Raw Illumina sequence reads have been submitted to the European Nucleotide Archive (ENA), and individual sequence accession numbers are listed in Table  S2. The full set of n=13,000 genome assemblies generated for this study are available for download from FigShare: doi 10. 26180/21431883. All assemblies of suitable quality (n=12,849) are included in the online platform Pathogenwatch (https://pathogen. watch/organisms/styphi), where they can be interactively explored and included in user-driven comparative analyses. All underlying code developed for data analysis is freely available at https://github. com/katholt/TyphoidGenomicsConsortiumWG1 (v1. 0, doi: 10. 5281/zenodo. 7487862).
Overview of available data A total of 13,000 confirmed Typhi genomes were collated from 65 studies and five unpublished public health laboratory datasets (see Tables  1, S2 ). N=35 genomes had assembly sizes outside of the plausible range (4. 5-5. 5 Mbp, see Figure  S1 ), leaving n=12,965 high quality genomes originating from 111 countries. The distribution of samples by world region (as defined by WHO statistics division M49) is shown in Table  2 , with country breakdown in Table  S4. The majority originated from Southern Asia (n=8,231), specifically India (n=2,705), Bangladesh (n=2,268), Pakistan (n=1,810) and  Nepal (n=1, 436). A total of n=1,140 originated from South-eastern Asia, with >100 each from Cambodia (n=279), Vietnam (n=224), the Philippines (n=209), Indonesia (n=145), and Laos (n=139).
The most common countries of origin for travel-associated isolates were India (n=1,241), Pakistan (n=783), Bangladesh (n=264), Fiji (n=102), Samoa (n=87), Mexico (n=60), Chile (n=49), Papua New Guinea (n=45), Nigeria (n=42), and Nepal (n=39). For some typhoid-endemic countries, the majority of genome data originated from travel-associated infections captured in other countries; those in this category with total n≥10 genomes are Guatemala (n=22/22), El Salvador (n=19/19), Mexico (n=60/61), Peru (n=14/14), Haiti (n=12/12), Morocco (n=12/13), Iraq (n=19/19), Malaysia (n=35/35), Fiji (n=102/144) and Papua New Guinea (n=45/86) (full data in Table  S4 ). In total, n=10,726 genomes were assumed to represent acute typhoid fever and recorded as derived from 'non-targeted' sampling frames, i. e. local population-based surveillance studies or reference laboratory-based national surveillance programmes that could be considered representative of a given time (year of isolation) and geography (country and region of origin) (see Methods for definitions).
Geographic distribution of genotypes The breakdown of genotype prevalence by world region, for genomes isolated from 2010 onwards, is shown in Figure  1a  (denominators in Table  2 , full data in Table  S5 ). Annual breakdown of regional genotype prevalences is given in Figure  S2  (raw data, proportions, and 95% confidence intervals in Table  S5 ). Notably, while our data confirm that H58 genotypes (4. 3. 1 and derived) dominate in Asia, Eastern Africa, and Southern Africa, they were virtually absent from other parts of Africa, from South and Central America, as well as from Polynesia and Melanesia (Figure  1 ). Instead, each of these regions was dominated by their own local genotypes. Typhoid fever is no longer endemic in Northern America, Europe, or Australia/New Zealand. The genotype distributions shown for these regions were estimated from Typhi that were isolated locally but not recorded as being travel-associated; nevertheless, these genomes can be assumed to result from limited local transmission of travel-associated infections, and thus to reflect the diversity of travel destinations for individuals living in those regions.
Global distribution of AMR We estimated the regional and national prevalence of clinically relevant AMR profiles in Typhi for the period 2010-2020, inferred from WGS data from non-targeted sampling frames for which country of origin could be determined (as per genotype prevalences, see Methods). In order to understand the potential implications of these AMR prevalences for local empirical therapy, we categorised them according to a traffic light-style system (see Methods), whereby amber colours signal emerging resistance of potential concern (<10%), and red colours signal levels of antimicrobial resistance that may warrant reconsideration of empirical antimicrobial use (>10%; see Figures  2  and S5 ). The regional view (Figure  S5 , Table  S7 ) highlights that ciprofloxacin non-susceptibility (CipNS) is widespread, whereas ciprofloxacin resistance (CipR), azithromycin resistance (AziR), and XDR have been mostly restricted to Southern Asia.
Robustness of national estimates across studies The estimates of genotype and AMR prevalence represented here reflect post hoc analyses of data that were generated for a variety of different primary purposes in different settings, by different groups using varied criteria for sample collection, including in-country surveillance and travel-associated cases recorded in other countries. To explore the robustness of these national-level estimates, we compared prevalence estimates for the same country from different studies/sources, where sufficient data existed to do so. Southern Asian countries were each represented by multiple in-country data sources plus travel-associated data collected in three or four other countries. Figure  S12  shows genotype prevalence estimates derived from these different sources (for laboratories contributing ≥20 isolates each) and Figure  6a  shows the annual genotype frequency distributions (for years with ≥20 isolates).
Strengths and limitations This study presents the most comprehensive genomic snapshot of Typhi to date, with 12,965 high quality genomes originating from 111 countries in 21 world regions. The consortium model provides improved consistency and completeness of source data aggregated from 77 laboratories and 66 unique studies. Our dataset also includes 1,290 novel genomes sequenced by public health laboratories that would not otherwise have been published, including travel data from countries not previously represented in published Typhi genomics studies (e. g. El Salvador, Guatemala, Haiti, Mexico and Peru). However, it is a post hoc analysis of isolates that were cultured in different contexts (including routine diagnostics, as well as study settings where culture would not normally be undertaken) and sequenced for different reasons (including retrospective studies, outbreak investigations, and routine surveillance).
It is therefore important to maximise information recovery from available data sources, especially WGS, which provides data on the emergence and spread of AMR variants. While the inference of AMR phenotype from WGS is currently highly reproducible and accurate for Typhi  (Argimon et al. , 2021; Chattaway et al. , 2021) , continued phenotypic antimicrobial susceptibility testing remains crucial to monitor for emerging mechanisms and to guide changes in empiric therapy. For now, routine sequencing of travel-associated Typhi infections diagnosed in high-income countries helps to fill some molecular surveillance gaps for some regions, assuming that accurate travel history is available and the sequence and metadata (including country of origin) are shared  (Ingle et al 2019). For example, our study included >3,000 genomes shared by public health reference laboratories in England, Australia, New Zealand, France, Japan, and the USA.