dataesr/scientific-paragraphs-categorization huggingface dataset:
Sources and representativeness of human biomonitoring data HBM data is available from different sources for use in chemical RA. These sources include research studies published in the scientific literature, summary reports from population surveys (e. g. , reports from the German Environmental Survey 2  or the USA National Reports on Human Exposure to Environmental Chemicals 3  ) or summary reports on occupational biomonitoring data from national laboratories performing HBM analyses or from companies' health or exposure surveillance programmes. The HBM4EU dashboard 4  is a platform that contains summary statistics on HBM4EU priority chemicals from existing European general population HBM data collections, obtained through the HBM4EU project. These data collections typically include summary statistics of biomarker levels including mean, median, P90, and P95 levels, confidence intervals (CIs), or ranges.
For example, the aprotic solvents (NMP, NEP, DMF) RA was based on only two German studiesthe ESB program and the GerES. Some risk or EBoD assessments, such as o-toluidine, lead and methylmercury assessments, were based solely on published data (Table  1 ). In the RAs focusing on occupational exposure (Cr(VI), o-toluidine, diisocyanates), we used either published data or database data from a national biomonitoring laboratory (FIOH's HBM database, not publicly available). The main challenges related to the use of these occupational biomonitoring database data are related to the limited availability of contextual data on the tasks covered and possible bias towards companies with good occupational safety and health practices, as small companies with less knowledge on monitoring practices and more poorly functioning occupational health services may not be included in monitoring programmes.
As lead was not included in the HBM4EU-aligned studies, its health impact assessment used earlierpublished blood-Pb data, collected in different countries over different time periods in the last 20 years. On the other hand, the global consumption of lead is increasing today, because of an increasing demand for energy-efficient vehicles  (Rudnai, 2019). The same might also apply to the RA of phthalates, which used data gathered between 2014 and 2021. We observed that participants of studies with earlier sampling periods  (2014, 2015, 2016)  had significantly higher HI levels than participants of studies that collected samples in later years. However, geographical differences and differences within single data sets seem to be more pronounced than this time effect. In the case of occupational exposure, the type of industry and processes might generally be more important than, for example, the country of location, especially if we consider risks in Europe, with harmonised OSH legislation.
Overall, although some uncertainties might be related to the representativeness of HBM data, in many cases of RA, HBM data are often not the only source of exposure data, and even a dataset with limited representativeness can provide valuable support for RAs performed on the basis of external exposure/intake data. In many cases, the RAs performed under the HBM4EU were able to confirm the results of the earlier RAs that used external exposure estimates, reducing the related uncertainty. This was the case with, for example, the RAs of acrylamide, arsenic, and BP-3, all of which raised concerns about the representativeness of or some confounders in the HBM data.
Assessment of dataset quality and comparability The quality of the available data is another aspect the risk assessor needs to consider when using HBM data in regulatory RA. In most RAs for the HBM4EU, the HBM4EU-aligned study data were mainly analysed by laboratories that succeeded in the HBM4EU ICI/EQUAS quality process  (Esteban Lopez et al. , 2021)  or used equivalent quality assurance schemes. With some biomarker data, it was not possible to obtain comparable quality with the HBM4EU QA/QC efforts. Although this may bring some uncertainty to the assessment, it was, however, not usually considered a major source of uncertainty in any of the RAs performed. However, some differences between the analytical performance of the laboratories also related to their ability to quantify biomarkers at low concentrations.
It is also useful when supporting data are needed to complement external measurement and/or modelled data. PBPK modelling for reverse or forward dosimetry was used for the RAs of PAHs, bisphenols, diisocyanates and o-toluidine. In the case of PAHs, the probable daily intake (PDI) of pyrene was estimated using the reverse-dosimetry of urinary 1-OHPyr. For the RA of diisocyanates, a specific PBPK model was created for MDI and 2,4/2,6-TDI, based on existing industrial hygienic and biomonitoring data  (Scholten et al. , submitted). This PBPK model was used to convert urinary MDA and TDA (metabolites of MDI and 2,4/2,6-TDI) levels into external exposure levels for assessing the risk of asthma related to the exposure to MDI and 2,4/2,6-TDI  (Huuskonen et al. , 2023). In the case of BPA and BPS, the RA was based on the HBM-GVs derived using existing PBPK models for these compounds  (Meslin et al.
Use of exposure biomonitoring data to assess combined effects in specific chemical groups We also applied HBM data in the assessment of combined exposure to specific mixtures. These included pyrethroids, PFASs and phthalates. In addition, we assessed the risk of PAHs, which are complex mixtures of hundreds of compounds. The RA of phthalates employed the HI approach to assess the combined risk of five phthalates with a similar anti-androgenic mode of action. The RA used individual data on biomarker levels to avoid the possible over-conservativeness associated with the use of aggregated data  (Lange et al. , 2022). Pyrethroids are an interesting example, as many of their members share the common metabolite 3-PBA (3-phenoxybenzoic acid), which is a much used biomarker of pyrethroid exposure. Even the more specific pyrethroid biomarkers cover more than one substance.
It also expresses the toxicity of the other compounds relative to this as relative potency factors  (Bil et al. , 2022 (Bil et al. , , 2023)). The results of the RAs and the advantages and disadvantages of the different approaches for mixture PFAS assessment are further discussed in the paper by  (Bil et al. , 2023). In the case of PAHs, pyrene was used as an indicator substance for mixtures of PAHs. Pyrene Probable Daily Intake (PDI), estimated from urinary 1-hydroxypyrene levels, was used to estimate PAH4 intake, based on the relative proportion of pyrene and PAH4 derived from the EFSA data of food residue and food consumption country-specific data (EFSA, 2015a). ELCR was calculated on the basis of the ECHA-RAC dose-response relationship for PAH4  (ECHA, 2018). These examples clearly demonstrate different options for conducting RAs considering the combined effects of chemically structurally and toxicologically related substances.
a These are uncertainties specific to the HBM-based approach. Fig. 1. Aspects to consider when using human biomonitoring data in chemical risk assessment. Detailed graphical presentations for the steps related to the derivation of biomonitoring guidance values can be found in, for example, Fig. 1  in  (Apel et al. , 2020)  and Fig. 2  in  (Gurusankar et al. , 2017). successfully used in RA, and its inclusion generally benefits RA, providing it with more confidence especially when used together with other exposure data. It is, however, important to consider the uncertainties involved. Although there are some specific uncertainties related to the use of HBM data, the main uncertainties are often not related to the use of the HBM data per se, but to the dose-response and toxicological data used, or the limitations in the size of the dataset, making them universal to RA in general.
Acknowledgements This project received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  733032 HBM4EU , and co-funding from the authors' organisations. The results presented here are based on a huge body of work performed as part of Task 5. 3 of the HBM4EU in 2017-2022. First and foremost, we would like to acknowledge all our Task 5. 3 colleagues for their valuable contribution to the project work, including the individual RAs and EBoD calculations. In addition, we would like to thank all our other  HBM4EU  colleagues, the HBM4EU-aligned study data owners, and the other stakeholders who provided support for and feedback on our work during its course.
Chromium: hexavalent Workers Finnish Institute of Lung cancer Dose-response for lung cancer chromium [Cr(VI)] (adults) Occupational Health's relative risk (RR) by (Seidler et al. , (FIOH) HBM database, 2013), based on a meta-analysis of (aggregated data) on epidemiological studies. In urinary total chromium addition, excess life-time cancer (U-Cr) during cases estimated using the cancer 1980-2016 risk evaluations of ECHA (2013a), DECOS (2016) and SCOEL ( European Commission et al. , 2017a)(4 × 10 -3 per μg Cr (VI)/m 3.
We show that CTPS1 and CTPS2 are partially redundant and not equivalent to promote cell proliferation in correlation with differences in their enzymatic activity. Analysis of public databases of more than 1,000 inactivated cancer cell lines for CTPS1 or CTPS2 confirmed that cell growth is highly dependent on CTPS1 but not or less on CTPS2. In conclusion, our study documents that CTPS1 and CTPS2 are critical factors of cell proliferation with partially redundant roles.
The combined absence of CTPS1 and CTPS2 (in CTPS1+2 null cells) as it could be expected almost completely abolished the CTPS activity. These data suggest that the contribution of CTPS2 to the total CTPS activity of HEK cells is low (10-20%) and negligible when CTPS1 is expressed. We next assessed the effect of CTPS1 and/or CTPS2 inactivation on the proliferation of HEK cells. Growth of control, CTPS1-KO, CTPS1-low, CTPS2-KO, and CTPS1+2-KO cells in the presence or absence of cytidine supplementation were evaluated using a livecell analysis system that measures cell confluency over time. Addition of cytidine had no effect on the proliferation of control, CTPS2-deficient cells, or cells with reduced CTPS1 levels, supporting the notion that CTP production by these cell lines was sufficient to sustain cell growth (Fig  1E ).
CTPS1 is essential for cancer cell growth in most cancer cell lines To extend our observations on tumor cell lines other than HEK and Jurkat cell lines, we further examined the requirement for CTPS1 and CTPS2 for cell growth and survival of a large number of tumor cell lines from different tissues using data from the Project Achilles CRISPR-based genome-scale loss-of-function screening  (34, 35  Preprint, 36 Preprint, 37). Dependency scores for CTPS1 and CTPS2 were extracted through the DepMap website. Interestingly, although the CTPS2 Chronos guide depletion scores were close to 0 (mean = -0. 038), highlighting its non-essentiality in cancer cell survival and proliferation, we observed a CTPS1 guide depletion (high negative scores towards -1) in most of the 1,070 cell lines (Chronos score mean = -0.
Cell culture Jurkat cells (RRID:CVCL_0065; ATCC) were cultured in RPMI, 10% FBS, 1% penicillin-streptomycin (PS) (complete RPMI). HEK 293T cells (RRID:CVCL_0063; ATCC) were cultured in DMEM, 10% FBS, 1% PS (complete DMEM). Calcium and magnesium-free PBS and 1x trypsin-EDTA were, respectively, used to wash the cells and to detach adherent cells. All of the abovementioned reagents were from Life Technologies. CTPS1-or/and CTPS2-deficient cell lines were maintained with 200 µM of cytidine (Sigma-Aldrich). 3-Deaza-uridine (3-DU) (Sigma-Aldrich) was used as a non-selective inhibitor of CTPS1 and CTPS2.
Cell proliferation For the resazurin proliferation assay, cells were seeded at a density of 150,000 cells/ml in 96-well plates. At 24 and 48 h, 10 µl of CellTiter-Blue (Promega) was added, and cells were further incubated at 37°C for up to 4 h. Absorbance at 560/595 nm was measured and analysed according to the manufacturer's instructions using a Tecan Infinite 200 Pro plate reader (Tecan Life Sciences). For CSFE incorporation-based proliferation assay, cells were incubated overnight with 3 mM of hydroxyurea (Sigma-Aldrich), washed, and labelled with CFSE (Invitrogen) according to the manufacturer's instructions, resuspended in complete medium and cultured for 4 d. Cells were then analysed using an LSRFortessa X-20 cytometer and FlowJo software (BD Biosciences). Proliferation of HEK cells was analysed as a percentage of confluency.
Cell cycle Cells were washed, resuspended in complete medium, and cultured for 24 h with the indicated concentrations of cytidine or 3-DU. The cells were then incubated for a further 1 h with 10 µM EdU, fixed, and stained according to the manufacturer's instructions (Click-iT EdU Cell Proliferation Kit for Imaging; Life Technologies). All data were collected on LSRFortessa X-20 cytometer, and data were analysed using FlowJo software (RRID:SCR_008520; BD Biosciences).
Annexin V/7-AAD apoptosis Cells were washed and seeded at a density of 150,000 cells/ml in the presence or absence of 40 µM of 3-deaza-uridine or 200 µM of cytidine in 24 well plates (one well per time point). At each time point, the cells were stained with FITC-conjugated annexin V and 7-AAD according to the manufacturer's instructions (BD Biosciences) and analysed using a LSRFortessa X-20 cytometer and FlowJo software (RRID:SCR_008520; BD Biosciences). HEK cells, maintained with 200 μM of cytidine, were washed, detached using trypsin-EDTA (Life Technologies), and seeded in six-well plates at a density of 26,000 cells/cm 2 in the presence or absence of 200 μM of cytidine in DMEM, 10% FBS, 1% penicillinstreptomycin. At each time point, the cells were imaged using an Axio Vert A1 microscope (Zeiss) at the indicated magnification.
Intracellular FACS The cells were fixed and permeabilized using the IntraPrep Permeabilization Reagent (A07803; Beckman Coulter) according to the manufacturer's instructions. Cells were stained first with an anti-CTPS1 antibody (ab133743; Abcam), an anti-CTPS2 antibody (ab190462; Abcam), or an isotype-matched antibody (rabbit IgG, ab172730; Abcam) and then labeled with an AF647-goat anti-rabbit secondary antibody. All data were collected on an LSRFortessa cytometer and analysed using FlowJo version 9. 3. 2 software (RRID: SCR_008520; BD Biosciences).
CTPS activity measurement CTPS activity was measured in cell extracts as previously described  (28, 29, 42). Briefly, cell pellets were sonicated on ice. Thirty micrograms of proteins were used for measuring CTPS activity in reaction mixture containing 1. 7 mM EDTA, 13 mM MgCl2, 1. 3 mM ATP, 0. 2 mM GTP, 13 mM glutamine, 1. 3 mM phosphoenolpyruvate, 10 mM NaF, 1. 3 mM UTP, and 10 μM stable CTP isotope as an internal standard in 10 μl Hepes buffer, 70 mM, at pH 8. 0. The reaction mixtures were incubated at 37°C for 90 min, and then the enzymatic reaction was stopped by the addition of 2 vol of HClO4. 5 μl of sample were injected onto an Acquity HSS T3 column, 1. 8 μm particle size, 2. 1 Å~100 mm (Waters), connected to an Acquity H-Class ultraperformance liquid chromatography interfaced with a Xevo TQ-S triple-quadrupole mass spectrometer (Waters), both controlled by MassLynx software (MassLynx, RRID:SCR_014271; Waters).
The CTP identification and detection were performed in the electrospray positive ion mode with multiple reaction monitoring mode (MRM). Quantification was performed using TargetLynx software. The threshold of CTPS activity detection corresponds to the mean of activity detected in Jurkat cells deficient for CTPS1 (that did not express CTPS2) from four independent experiments (51 ± 5 nmol of CTP/g protein/min)  (27).
Acknowledgements We acknowledge  Meriem Rahmani , the cytometry platform ( SFR Necker, Paris France ) directed by  Corinne Cordier , the cellular imaging platform ( Imagine Institute, Paris, France ) directed by  Meriem Garfa-Traor  é for their technical assistance. We thank  Andrew Parker  (present Chief Executive Officer of  Step-Pharma ) for discussions and the critical reading of the manuscript. We also thank the members of the  Latour laboratory  for discussions. This work was supported by grants from  Ligue Contre le Cancer-Equipe Labellis ée (France)  (to S Latour),  Inserm (France) ,  ANR-18-CE15-0025-01  (to S Latour), and  ANR-10-IAHU-01  ( Imagine Institute ) and Proof of Concept ERC-2015-PoC_Master/ ERC-2015-PoC_680465_SAFEIMMUNOSUPPRESS (to  A Fischer ,  S Latour ). N Minet  is a fellowship recipient of the  Association Nationale de la Recherche Technologique (ANRT)  under agreement with the industrial partner  Step Pharma.
The final concentrations of G 3 , melittin, LL37 were at 3 lM, 1 lM, and 1 lM, respectively. The lipid monolayers before and after exposure to the AMPs were characterised by NR. The SURF reflectometer at the ISIS Pulsed Neutron Source (Didcot, UK) and the FIGARO reflectometer at the ILL Continuous Neutron Source (Grenoble, France) were used to perform NR measurements, with the same setup as described previously  [19]. Deuterated or hydrogenous lipids spread on D 2 O or on null reflecting water (NRW, D 2 O/H 2 O = 8/92, v/v, SLD = 0) were the four isotopic contrasts of a typical dataset. Dynamic measurements involving AMP binding to the spread lipid monolayer were performed under the NRW contrasts by the high flux FIGARO reflectometer  [30]  at the incident angle of 0. 62. Each dynamic run was scanned by NR for 4 min.
After reaching equilibrium, each monolayer was scanned by either the SURF reflectometer at 3 incident angles (0. 35°, 0. 65°, and 1. 5°), or the FIGARO reflectometer at 2 incident angles (0. 62°, 3. 8°) and fixed wavelength resolution of 7%. The measured neutron reflectivity R(Q) was plotted as a function of momentum transfer (Q). Both instruments cover the Qrange of 0. 01-0. 20 Å À1 , the upper value being determined by the sample background, as analysed in model fittings. The environmental temperature was 20-21 °C. A dataset with four isotopic contrasts were simultaneously fitted by a Motofit package based on the optical matrix formula  [31]. A 2-layer model (lipid tail and head layers) or a 3-layer model (lipid tail, head, and AMP adsorption layers) were used to analyse the monolayer system in the absence and presence of AMPs, respectively.
These differences are however linked to the low molecular weight of G 3. The differences are much smaller in the micromolar unit (Figure  S5 ). All reflectivity profiles shown in Figure  S6  were measured from lipid monolayers kept at 28 mN/m on either D 2 O or NRW, and the continuous lines represent the best 2-layer fits, with the top 1st layer representing the lipid tails in air and the bottom 2nd layer representing the head groups immersed in water. The R(Q) profiles in D 2 O are close but much higher than those on NRW. Furthermore, the reflectivity measured from the deuterated LPS is higher than the one from the hydrogenous LPS under each solvent contrast. Simultaneous model fitting of the 4-contrast datasets led to thicknesses of 13 Å and 23 Å for the tail and head layers and volume fractions of 0.
Structures of the LPS and DPPG monolayers before and after AMP binding After AMP injection and equilibrium as described previously (Fig. 2 ), NR reflectivity profiles were measured following equilibrated AMP binding to the LPS monolayer under the same 4 isotopic contrasts (Figure  S6 ). AMP binding caused dramatic changes in reflectivity profiles under the same water and lipid contrasts. Model fitting of the 4-contrast datasets (Table  2 ) from the same 2-layer model revealed that the thicknesses of the LPS tail and head layers remained largely unchanged upon G 3 binding. However, both the acyl chain and head layers contained G 3 , with its volume fractions of 0. 29 and 0. 10 and surface concentrations of 0. 46 and 0. 27 mg/m 2 , respectively. In contrast, binding of the two natural AMPs significantly thickened the LPS monolayer.
Acknowledgements We thank the studentship support from the  University of Manchester , the  China Scholarship Council ,  Syngenta ,  Lonza , and  STFC. We acknowledge funding support from the  National Natural Science Foundation of China  (Project No. 22102187 ). We also thank  Dr. Luke Clifton  for his assistance to NR experiments. The authors acknowledge the neutron beam time awarded by the  ISIS  and  ILL Neutron Sources  (DOI:  10. 5286/ISIS. E. RB1820088 ,  DOI: 10. 5291/ ILL-DATA. 9-13-802 ). We thank a European grant from a  Marie Curie Fellowship ITN  (grant number  608184 ) under SNAL (small nano-objects for alteration of lipid bilayers), a  BBSRC LINK grant  with  AstraZeneca  ( BB/S018492/1 ) and an  Innovate UK Knowledge Transfer Partnership  project with Lonza under KTP 10809.
Bacteroides and Prevotella genomes from the Hadza microbiota vary in prevalence across populations To compare Prevotella and Bacteroides from non-industrialized lifestyle populations, we isolated and sequenced 6 Bacteroides strains (from 4 species) and 7 Prevotella copri strains from stool samples collected from 13 Hadza individuals. Importantly, P. copri is a species known to encompass extensive genomic diversity and its division into multiple species has been discussed  (Tett et al. , 2019). Single isolate genomes were assembled using both MiSeq generated short reads (146bp) and nanopore generated long reads (10-100kb) (Table  1 ). To determine relatedness of the Hadza genomes to previously sequenced genomes, we calculated the average nucleotide identity (ANI) distance to the closest genome present in the National Center for Biotechnology Information (NCBI) GenBank.
Clustering Genomes into Subspecies All public Bacteroides and Prevotella genomes were downloaded from NCBI GenBank on 1/26/2021 using the program ncbi-genome-download (https://github. com/kblin/ncbi-genome-download). For Bacteroides, all genomes marked as "representative genome" in RefSeq (n=53) and genomes marked as assembly level "Complete Genome" or "Chromosome" in Genbank (n=71) were retained for further analysis (n=113 genomes retained of 1,229 total genomes). For Prevotella, all available public genomes were retained (n=368). Public genomes were clustered along with the isolate genomes recovered in the study using dRep v3. 2. 1  (Olm et al. , 2017)  using the command "dRep dereplicate -S_algorithm fastANIsa 0. 98 -SkipMash -nc 0. 65" to ensure that genomes with ≥ 98% ANI and ≥ 65% alignment coverage according to FastANI  (Jain et al.
CAZyme Annotation CAZyme annotations were performed for each isolate. An additional 20 strains of Prevotella copri available at NCBI, with variable assembly levels, were annotated as well for comparative purpose, with the isolates and two model strains. All amino acid sequences were first compared to the full-length sequences stored in the CAZy database (Sept. 2021)  (Drula et al. , 2022)  using BlastP (version 2. 3. 0+)  (Camacho et al. , 2009). Queries obtaining 100% coverage, >50% sequence identity and E-value ≤10 -6 were automatically annotated with the same domain composition as the closest reference homolog. All remaining sequences were subject to human curation to verify the presence of each putative modules. During this process, the curator could rely on (i) bioinformatics tools, including BLAST against libraries on either full-length protein, modules only or characterized modules only, and HMMER version 3.
Acknowledgements We thank  Bryan Merrill ,  Madeline Topf , and  Michelle St. Onge  for technical support;  Gabriela Gall Rosa  for help with analysis;  Samuel Lancaster ,  Brittany Sison , and  Audrey Zhang  for assistance processing RNAseq data;  Michael Fischbach  and  Niokhor Dione  for providing P. copri N-01;  Brian Yu  and  Rose Yan  for sequencing support;  David Schneider  for advice and tissue culture hood use;  Bernard Henrissat  for advice on CAZyme annotation. R. H. G. was supported by an  NIH/NHGRI   T32 training grant  ( 5T32HG000044-23 ) and a  Blavatnik Family Fellowship. This work was supported by grants from the  NIH  to J. L. S. ( R01-DK085025 ,  DP1-AT009892 ). J. L. S. is a  Chan-Zuckerberg Biohub  investigator.