dataesr/scientific-paragraphs-categorization huggingface dataset:
We utilized three components of this reference dataset for our analyses: (i) simulated sequencing data (1. 67×10  7  Illumina paired-end 150 nt reads) from each of the 64 mock fecal samples, (ii) anonymized reference contigs from the 791 reference genomes, and (iii) reference abundances of contigs/genomes in each fecal sample. First, we investigated the effect of kallisto quantification of contigs on the fidelity of MAG assembly. To do so, we quantified the reference contigs using either kallisto or bowtie2 and the short-read simulated Illumina data. Next, we assembled MAGs using MaxBin2 49 , MetaBAT2 50 and CONCOCT  89  with data from either kallisto or bowtie2 contig quantitation as input. The output of each MAG assembly method for each sample was combined using DAS Tool  51. Finally, each MAG set was compared against 791 intact reference genomes using AMBER  90  (Extended Data Fig.
Boxes with a black outline and the thick black arrows emanating from them indicate that binned contigs were used as input to AMBER for MAG assembly comparisons across methods. (c) Boxplot describing summary statistics (completeness and purity) for MAG assembly approaches. Boxplots indicate the median, first and third quartiles; whiskers extend to the largest value no further than 1. 5 × the interquartile range. (d) Number of MAGs, obtained from each assembly and quantitation strategy, that are distributed across two quality metrics (completeness, contamination). The number of 'gold standard' MAGs (theoretical maximum) in the simulated metagenomic dataset is indicated by a horizontal dashed line. *, P<0. 05; **, P<0. 01; ***, P < 0. 001 (Wilcoxon test for panel c; Fisher's exact test for panel d).
performed metabolic pathway reconstruction and annotation of MAGs, while S. H. , V. L. , N. T. and B. H. annotated PULs and CAZymes in MAGs. M. C. H. identified WLZ-correlated MAGs and conducted analyses of the enrichment of their encoded functions. D. M. W. generated and together with M. C. H, C. Z. , D. R. , A. O. and A. A. analyzed microbial RNA-Seq datasets. J. J. C. , C. B. L. , G. C. , N. P. B. , Y. C. performed LC-MS analyses of food and fecal glycans. J. L-G. isolated P. copri strains with assistance from D. M. W. and M. C. H. M. C. H. , D. M. W. and S. H. designed the in vitro growth experiments, which were performed by M. C. H. , D. M. W. , and S. H. with assistance from J. L-G. Y. W. , H. W. C. , and E. L. integrated the results of gnotobiotic mouse experiments described in the accompanying paper with the MAG-based analysis described in this paper.
I. M. , S. D. , and M. M. , under the supervision of T. A. , conducted the clinical study of MDCF-2 and RUSF and supplied all biospecimens used for the analyses conducted in this report. M. J. B. oversaw databases of clinical metadata and the biospecimen archive. T. A. and J. I. G. oversaw this research. M. C. H. , D. M. W. and J. I. G. wrote the paper with invaluable input from co-authors. Competing Interests: A. O. and D. R. are co-founders of Phenobiome Inc. , a company pursuing development of computational tools for predictive phenotype profiling of microbial communities. C. B. L. is a co-founder of Infinant Health, interVenn Bio, and BCD Bioscience -companies involved in the characterization of glycans and developing carbohydrate applications for human health.
This indentation method is close to the colloidal probe AFM but with a cantilever deflection detection method based on interferometry  [12, 13]. Among other examples, ferrule-top micro-indentation was used to map the viscoelastic properties of brain tissue  [14]  and the heterogeneous elastic moduli of chicken embryos  [15]. The same technique was also used to characterize elasticity gradients produced by the enzymatically controlled dephosphorylation of Fmoc-FF-phosphorylatedY peptide leading to its self-assembly and formation of stiff structures along the surface of hydroxypropyl(methylcellulose) hydrogels. Those elasticity changes were correlated with changes in chemical composition  [16]. In the present investigation, the data obtained from ferrule-top micro-indentation was be compared with macroscopic measurements using a shear rheometer as a function of the genipin concentration used to crosslink the gelatin hydrogels.
Immunophenotype Extended immunophenotyping of Dg, R2, and R3 cells was performed usi man Cell Surface Marker Screening Kit from Biolegend ® ((Biolegend Europe B. V dam, The Netherlands) (LEGENDscreen). The Human Cell Surface Marker Scr contains 4 96-wells plates. Each well contains a PE-coupled antibody targeting o surface antigen. Cells were first incubated with an anti-CD19-APC coupled an olegend ® , clone HIB19) in order to select the leukaemic blasts by gating CD1 analysis. A minimum of 10 000 CD19 + cells was acquired on a CANTO II cyto Biosciences, Le Pont de Claix, France) (Plateforme CytoCell-Nantes Universit results were analysed using FlowJo v10. 8. 1 software (BD LifeSciences). Results w in Relative Fluorescence Intensity (RFI in log) which is calculated as median flu intensity of the targeted-antigen coupled antibody/median fluorescence inten unspecific control isotype.
Immunophenotype Extended immunophenotyping of Dg, R2, and R3 cells was performed using the Human Cell Surface Marker Screening Kit from Biolegend ® ((Biolegend Europe B. V, Amsterdam, The Netherlands) (LEGENDscreen). The Human Cell Surface Marker Screening Kit contains 4 96-wells plates. Each well contains a PE-coupled antibody targeting one human surface antigen. Cells were first incubated with an anti-CD19-APC coupled antibody (Biolegend ® , clone HIB19) in order to select the leukaemic blasts by gating CD19 + cells for analysis. A minimum of 10 000 CD19 + cells was acquired on a CANTO II cytometer (BD Biosciences, Le Pont de Claix, France) (Plateforme CytoCell-Nantes University), and the results were analysed using FlowJo v10. 8. 1 software (BD LifeSciences). Results were given in Relative Fluorescence Intensity (RFI in log) which is calculated as median fluorescence intensity of the targeted-antigen coupled antibody/median fluorescence intensity of the unspecific control isotype.
In addition, lack of co-stimulatory molecules (CD80, CD86, ICAM-1) and expression of molecules leading to immune cell anergy (CD47, HLA-II or PD-L1) through leukaemic cells or the bone marrow microenvironment have also been described  [16] [17] [18] [19] [20]. Therefore, pilot studies combining a second injection of CAR-T and a PD1 blockade were conducted, with encouraging results  [7] [8] [9] , and a Phase I/II study is ongoing (CAPTiRALL EUDRACTN: 2021-003035-28). Other strategies targeting pathways known to contribute to T-cell exhaustion were also explored with encouraging results (CTLA-4, TIM-3, TGF receptor)  [21]. In our case, we did not find a lack of co-stimulatory molecules or expression of checkpoint inhibitors. Nevertheless, we identified the appearance of CD14 + /HLA-DR lo/neg monocytic cells in the R3 sample.
4 × 10 -3 per mg OT/kg bw/d  (Huuskonen et al. , 2022). Urinary mass-balance approach  (Apel et al. , 2020)  with a F UE of 0. 75 and PBPK modelling was used to convert biomonitoring data into external intake. Cancer: Linear extrapolation was used to estimate cancer risks. Mean and P95 levels of the urinary o-toluidine levels, reverse calculated as external intake levels, were used as reasonable exposure estimates for risk characterisation. DMF: HBM-GV workers of 10 mg/g creatinine  (Lamkarkach et al. , 2022)  was divided by an assessment factor of 10 to consider more sensitive population subgroups, yielding a provisional HBM-GV GenPop of 1 mg/g creatinine for DMF metabolite AMCC  (Mahiout et al. , 2022b).
DMF (N,N-dimethylformamide) Arsenic: inorganic arsenic (iAs), monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA)   , 2013b, 2017a). This resulted in a cancer slope of 1. 7/1000 for 1 μg iAs/kg bw per day. The sum of iAS, MMA and DMA levels was converted into external intakes using the urinary mass-balance approach  (Apel et al. , 2020)  with an F UE used earlier by  ( Hays et al. , 2010b). Cancer risk was estimated on the basis of the urinary excretion of iAs, MMA and DMA, converted into external intake levels. Cancer risk was calculated using the cancer slope of 1. 7/1000 for 1 μg iAs/kg bw per day. U-Cr data were converted into corresponding Cr(VI) air levels using published regression formulae  (Chen et al. , 2002; Lindberg and Vesterberg, 1983; Viegas et al. , 2022)    BHR risk) (ECHA, 2020) for working life long excess BHR risk was used for the risk assessment.
External exposures to MDI and 2,4/2,6-TDI were reconstructed by a developed PBPK method  (Scholten et al. , submitted) , and HDI by a published correlation equation  (Maitre et al. , 1996). BHR risk was assessed on the basis of the estimated distributions of external NCO air concentration, reconstructed from urinary diamine levels using the RAC dose-response, or the published correlation equation. Due to the highly skewed distribution with few very high exposures driving the higher percentages, estimated excess risks beyond 7. 5% were capped at this level to avoid possible unrealistic extrapolation beyond the highest excess risk reported by RAC. (Ali et al. , 2012). HBM data was used to estimate the daily intake of TCEP, TCIPP using means of a urinary mass-balance approach  (Apel et al. , 2020)  with a F UE of 0.
63. Arithmetic mean and P95 levels of HBM data were used to estimate average and high dietary exposure. The occurrence data of selected food categories originated from a Belgium food basket study  (Poma et al. , 2018)  and dietary intake was estimated on the basis of national food consumption data and occurrence data. Both the estimated dietary intake and the estimated daily intake, which was based on HBM data, were compared to the HBGVs and the RCR was calculated. Probable Daily Intake, (PDI) of pyrene was reversecalculated from HBM aligned studies data on 1-OHPyr, using PBPK INTEGRA modelling  (Sarigiannis et al. , 2020). Following approach was used: Mahiout et al. (2022b) 1. Pyrene intake (PDI) was estimated from HBM data on 1-OHPyr, 2. Pyrene intake was then associated with PAH4 intake, based on the assumption that pyrene was a surrogate of PAH4.
3. PAH4 intake was alternatively derived from the country specific food residue and the food consumption data, available in the EFSA reports  (EFSA, 2008 (EFSA, , 2015a  Provisional HBM-GVs were derived from existing ADIs using the urinary massbalance approach for forward calculation. (Apel et al. , 2020). F UE for individual pyrethroids varied between 0. 09 and 0. 85 The tiered approach was used. Aggregated HBM data (median and P95 and their confidence intervals) obtained from HBM4EUaligned studies were used as conservative estimates of population exposure levels in the first step. In the refined assessment, the probabilistic refinement using the full distributions of the metabolite levels were used for Monte Carlo analysis. HBM-GVs were used for BBzP, DEHP, DiBP and DnBP  (Lange et al. , 2021).
These approaches were subsequently used to calculate the biomarker levels corresponding to the health-based values for external intake -In some cases, for example, lead and cadmium, we used published effect estimates from epidemiological studies on the associations between biomarker levels and health effects for RA/EBoD estimations -Identifying relevant HBM data to be used for the RA/EBoD calculations -Performing RA or EBoD calculations based on HBM data. -Analysing the benefits and challenges, including uncertainties, of using HBM in RA in comparison to using external exposure data The HBM data for the RAs and EBoD calculations were acquired from either the published literature or from HBM4EU-aligned studies. In some cases, data from the participating institutions' own databases were also included.
Aprotic solvents In the RA  (David et al. , 2021) , HBM-GVs GenPop were used for NMP and NEP  (David et al. , 2021) , whereas a provisional HBM-GV GenPop , derived from the HBM-GV worker value  (Lamkarkach et al. , 2022) , was used for DMF. NMP and NEP exposure data were obtained from two studies conducted in Germany, which used data on adults from the German Environmental Specimen Bank (ESB), taken between 1991 and 2014  (Ulrich et al. , 2018) , and data from the German Environmental Survey of Children and Adolescents V (GerES V)  (Schmied-Tobies et al. , 2021). In addition, DMF samples from the ESB were analysed for the metabolite AMCC for the period 2000 to 2021 (data unpublished). The exposure of adults  (Ulrich et al. , 2018) , children and adolescents  (Schmied-Tobies et al. , 2021)  was well below the guidance values for both NMP and NEP.
The maximum values of the studies were 4. 7-10 factors lower than the corresponding HBM-GV GenPop values. The maximum value found in the data from ESB on the DMF metabolite AMCC was a factor of 2. 5 lower than the provisional HBM-GV GenPop of 1 mg/g creatinine. Even when the combined exposure to NMP and NEP was considered, the values were not exceeded. The calculated hazard index (HI) was well below 1 in all the cases considered (i. e. , children, adolescents, and adults) with maximum HI values of 0. 3. The HI for young adults was calculated using the combined exposure to NMP, NEP and DMF, resulting in a maximum HI value of 0. 6. However, in 'real-life situations', possible combined exposure with other reprotoxic substances present in the environment should be considered, as these might increase the risk of common effects.
Bisphenols An RA was performed for bisphenol A and S, for which sufficient HBM data were available and for which HBM-GVs were derived  (Mahiout et al. , 2022b; Meslin et al. , 2022). Bisphenol F was also addressed, but no HBM-GV could be derived, and thus, no RA was conducted for this substance. The HBM data were gathered from the HBM4EU data repository, and a literature search was performed. In comparison to the HBM-GVs, the calculated RCRs were very low for Bisphenol A (BPA), ranging from 0. 01 to 0. 14 (all populations combined), contrary to those obtained for Bisphenol S (BPS) ranging from 0. 4 to 28. 9 (all populations combined). This was due to the fact that the HBM-GVs derived for BPS are based on endocrine-disrupting health effects occurring in animals at very low doses, contrary to the values calculated for BPA on the basis of the temporary tolerable daily intake (t-TDI) from EFSA (EFSA, 2015c).
Chromium Lifelong occupational lung cancer risks due to Cr(VI) exposure in welding and chromium-plating activities  (Mahiout et al. , 2022a)  were estimated on the basis of total U-Cr data (P95, representing realistic worst case) spanning almost 40 years. Published regression formulae, based on concurrent measured U-Cr and air Cr(VI) concentration data in workplaces, were used to relate the measured internal 1 https://www. foodpackagingforum. org/news/efsa-proposes-to-lower-dai ly-tolerable-intake-of-bisphenol-a and https://connect. efsa. europa. eu/RM/s /publicconsultation2/a0l1v00000E8BRD/pc0109. and estimated external exposures  (Chen et al. , 2002; Lindberg and Vesterberg, 1983; Viegas et al. , 2022). This enabled subsequent RA, based on a published lung cancer dose-response formula for external Cr (VI) exposure  (Seidler et al.
Diisocyanates Occupational exposure to methylene diphenyl diisocyanate (MDI), toluene diisocyanate (2,4/2,6-TDI), and hexamethylene -diisocyanate (HDI) was assessed on the basis of urinary diamine (U-MDA, U-TDA and U-HDA) data retrieved from FIOH's HBM database  (Huuskonen et al. , 2023). The database, which is not publicly available, consists of approximately 1000 HBM samples from 2008 to 2021, sent to FIOH for exposure monitoring by occupational health service units. Urinary diamine levels were converted into external isocyanate (NCO) air levels using a PBPK model developed for 2,4/2,6-TDI and MDI  (Scholten et al. , submitted)  and the published correlation between air-HDI levels and U-HDA levels  (Maitre et al. , 1996). The European Chemicals Agency's (ECHA) RAC exposure-excess risk relation (ECHA, 2020) was used to assess the working life long bronchial hyperresponsiveness (BHR) risk.
Flame retardants HBM data were used to estimate the extent to which the dietary intake of organophosphorus flame retardants (OPFRs) may contribute to the total OPFR exposure among children  (Plichta et al. , 2022). The urinary mass-balance approach was employed to calculate the estimated daily intake (EDI) of TCEP, TCIPP and TDCIPP. The occurrence data of selected food categories were taken from a published food basket study, and dietary exposure was estimated on the basis of national food consumption data and occurrence data. The results of the HBM4EU-aligned studies showed that the TDCIPP metabolite BDCIPP had the highest detection frequency (37-97. 7%). The detection frequency for the TCIPP metabolite BCIPP was below 55% and for the TCEP metabolite BCEP, it ranged between 19 and 63. 3% (summarised for Belgium, Denmark, France, Germany, Slovenia, and Slovakia).
Their maximum contributions to the RfDs were below 1. 2% (TDCIPP) and 3% (TCEP). The EDI and dietary intake for TCIPP contributed only 0. 01-0. 26% to the RfD of 80 μg/kg bw/d. These results suggest that exposure to TDCIPP, TCIPP and TCEP is not likely to cause adverse health effects on the basis of currently available toxicological data. The estimate includes several uncertainties and limitations in the exposure and hazard assessment. The main uncertainty is related to the hazard assessment; the HBGVs used were published before 2012 and thus do not consider toxicological data published later. Similarly, the information on toxicokinetics, including excreted urinary fractions (F UE ), was limited. For TCIPP and TCEP, F UE was derived from TDCIPP data. In addition, the limit of detection (LOD) and limit of quantification (LOQ) values for the analytical methods were quite high in both biomonitoring and the food analyses.
Mycotoxins HBM data on deoxynivalenol (DON) was obtained by a bibliographical search (children, adolescents, and adults) and from the HBM4EU-aligned studies (adults) that provided data for France, Germany, Iceland, Luxembourg, Poland, and Portugal. The RA was conducted using two different approaches: comparing the HBM data with the TDI-based HBM-GV for DON  (Mahiout et al. , 2022b) , and reverse dosimetry to back-calculate external exposure from the measured urinary DON levels and comparing it to the TDI  (Alvito et al. , 2022). As both these approaches used the urinary mass-balance method for conversion between external and internal levels, the results were very similar. The HBM data obtained from the literature review showed that exposure to DON was generalised and affected different ages and specific groups of the population.
PAHs Based on the HBM4EU-aligned studies, the geometric mean of the urinary 1-hydroxypyrene (1-OHPyr) among European adults ranged from 32. 7 ng/g creatinine to 268. 6 ng/g creatinine. This metabolite was considered a surrogate for PAH4: benzo(a)anthracene (BaA), benzo(b) fluoranthene (BbF), benzo(a)pyrene (BaP) and chrysene (CHR). The new data from the HBM4EU-aligned studies were in line with the urinary 1-OHPyr values previously reported in the literature  (Sarigiannis and Karakitsios, 2019) , with no significant difference; i. e. , a pooled mean of 97 ± 44 ng/g creatinine compared to 113 ± 78 ng/g creatinine in the aligned studies (p = 0. 693, Student's t-test)  (Mahiout et al. , 2022b). 1-hydroxypyrene levels were converted into pyrene (and PAH4) intake using PBPK modelling and ELCR was calculated on the basis of previously published dose-responses (ECHA, 2018).
Data on children's pyrethroid exposure were available in Belgium, Cyprus, France, Israel, the Netherlands, and Israel. Adults were sampled in France, Germany, Israel, and Switzerland. Whereas no risk was observed among adults, among children, P95 levels exceeded the screening value in all studies, with the highest value (sum of 3-PBA and 4-FPBA) obtained in Belgium (7. 1 μg/L). This suggests pyrethroids are a potential health concern and that further refinement of the RA is warranted. A probabilistic refinement was performed, taking account the differences in ADI (up to 20 times) between the various pyrethroid parent compounds and the variability in urinary excretion of 3-PBA with regard to individual substances. However, this approach still resulted in a potential risk among about 2% of Belgian children when using the worstcase assumption, that their exposure was due to the most hazardous pyrethroid with the lowest ADI (lambda-cyhalothrin).
PFAS mixtures The recent internal exposure data on European adolescents generated in the HBM4EU-aligned studies approach (dataset with N = 1957, sampling years 2014-2021) revealed that this population is exposed to Per-and polyfluoroalkyl substance (PFAS) mixtures. Mixture risk assessments of these PFAS mixture exposures were conducted for both median (P50) and highly exposed (P95) percentiles of the HBM4EU teenager study population, based on three hazard-based approaches: the HI approach, the sum value approach as used by the European Food Safety Authority (EFSA), and the Relative Potency Factor (RPF) approach  (Bil et al. , 2022 (Bil et al. , , 2023)). The HI approach resulted in the highest risk estimates based on P95 exposure, up to an HI of 6. 2 for immune effects, as seen in the French study population.
Phthalates mixture The internal exposure data on European children and adolescents generated in the HBM4EU-aligned studies approach revealed that almost all children and adolescents are exposed to phthalate mixtures  (Vogel et al. , in press ). As phthalates act in a dose-additive manner, a mixture risk assessment was performed  (Lange et al. , 2022). The risk of combined exposure to five anti-androgenic phthalates (i. e. , BBzP, DEHP, DiBP, DnBP and DiNP) was determined using the HI approach. For this, HBM-GV GenPop were utilised  (Lange et al. , 2021). For DiNP, a provisional value (HBM-GV GenPop-MRA ) was derived based on foetal testis testosterone suppression and multinucleated gonocytes, using the urinary mass-balance approach to convert external exposure into internal urinary metabolite levels  (Apel et al.
Discussion As part of the HBM4EU, we performed risk assessments of the HBM4EU priority compounds, the primary aim of which was to exemplify how HBM data can be used in RA, to describe the challenges and how they can be overcome, and to identify whether RA based on biomonitoring data raises potential concerns about health risks of specific substances. Using these examples, we discuss the different opportunities, benefits, and pitfalls of using HBM in chemical risk assessments, to guide regulatory risk assessors, and to facilitate its use.
, in preparation). In the HBM4EU, analyses assessed the exposure determinants of phthalates among children and adolescents in the HBM4EU-aligned studies, as well as in existing data sets on the adult population. Furthermore, an analysis of the time trends of phthalate exposure in Danish and German young adults offers insights into the effectiveness of recent phthalate regulations  (Vogel et al. , 2023). Similarly, it is sometimes challenging to identify the contribution of occupational exposure when exposure from non-occupational sources is extensive. BPA is a good example of a case in which high background levels of total BPA in urine from food sources made the identification of occupational dermal exposure from cashier receipts difficult  (Meslin et al. , 2022; Ougier et al. , 2021c). Although this worker group did not have higher exposure levels than the general population, some uncertainties remained about the potentially higher free BPA exposure via the skin than via oral exposure, which cannot be determined by measuring total urinary BPA levels  (Meslin et al.