dataesr/scientific-paragraphs-categorization huggingface dataset:
This approach allowed Garcia et al. [15]  to identify the ARH gene (which spans 29 kb and has nine exons). This gene, now designated LDLRAP1 (low-density lipoprotein receptor adaptor protein 1), encodes an adaptor protein. It contains a phosphotyrosine binding domain that binds the consensus sequence NPXY present in the cytoplasmic tails of several cell-surface receptors, including the LDLr  [149]. This adaptor also interacts with clathrin, the major structural component of the vesicles involved in the endocytosis of the LDLr  [150, 151]. Thus, this protein is an active member of LDL-LDLr-specific endocytic pathway. The LDLRAP1 gene database (http://www. LOVD. nl/LDLRAP1)  [152]  lists 100 variants but only 15 are pathogenic and have been reported from around the world, notably in Lebanon (three mutations), Syria, Turkey (two), Iran, Pakistan, Italy (Sardinia [two] and Sicily), Spain, England (two), the United States, Mexico, Japan (two), and African-Zulu, most of them being insertions or deletions causing a frameshift and truncated protein  [153].
It is noteworthy that genetic testing has the potential to provide a definitive diagnosis; guide optimal management, genetic counseling, and prevention; initiate and adapt the most effective treatment quickly and eventually a more aggressive and earlier strategy; help in the management of the disease; and prevent its complications  [199]. Besides the clinical impact of FH, the economic burden of hypercholesterolemia also highlights the importance of FH diagnosis to prevent and treat the disease and avoid its complications. This should be generalized to all countries, including those with low income, that need to be helped with the implementation of these procedures. Furthermore, with the spread of machine-learning technologies and digital technology, the identification of probands could be facilitated through the exploration of electronic health records and the creation of clinical alerts and would allow an improvement of diagnostic strategies and classify FH patients with high specificity in pediatric datasets  [200]  and adults  [201].
Suspect database generation Previously curated suspect lists of pesticides and their metabolites were aggregated among the five laboratories. Phase I metabolites were mainly retrieved from the registration dossiers of the respective compounds provided by the European Food Safety Authority (https://www. efsa. europa. eu/en/calls/consultations), which are typically derived from exposure studies using animals as a proxy for humans. The final aggregated list contained >4,600 possible pesticide metabolites. Additionally, for all generated suspect list entries, the sulfate conjugate [+SO 3 ] for negative mode (if O was present in the molecular formula) and glucuronide [+C 6 H 8 O 6 ] conjugates for both ionization modes (if N or O was present in the molecular formula) were added. The aggregated suspect list is available for download from zenodo (https://doi.
Fullscan MS 1 data analysis workflow The MetAlign software suite  (Lommen and Kools, 2012; Lommen et al. , 2011; Lommen et al. , 2019; Lommen, 2014; Lommen, 2009)  was used for processing and comparison of single-stage full-scan accurate mass data on a HP Z820 workstation with two Intel® Xeon® E5-2690 CPU 2. 90 GHz processors (2x8 cores, 2x16 virtual) and 64 GB RAM with 64-bit Windows 10 operating system. The data pre-processing workflow (software and workflow description on zenodo, https://doi. org/10. 5281/zenodo. 6530623), for retention time (RT) alignment and mass calibration corrections is summarized in Fig. 1  and contained the following steps: 1 A lock mass correction during format conversion using multiple lock masses corresponding to persistent mobile phase ions (see Table  S3  for m/z values)  (Lommen et al.
, 2011). 2 Data reduction (ca. 100-500 fold) using MetAlign  (Lommen and Kools, 2012; Lommen, 2009). 3 Interlaboratory alignment of the RT using quality control external standard mixes and transferring all RTs to that of the reference laboratory (see Figures  S1-2 ). 4 Corrections for mass calibration errors in the reduced-size data files per sequence using multiple m/z from quality control samples, quality control external standard mixes, internal standard mixes and some known, ubiquitous system contaminants (see Figures  S3-4 )  (Lommen et al. , 2011). 5 Sequence retention drift corrections using m/z values of internal standards and some ubiquitous known abundant signals. Further details on the processing steps are given in the Supplementary Materials, Table  S4. After this processing stage, all datasets from different laboratories are assumed to be comparable on the RT scale and to possess a mass precision ≤ 1.
5 ppm. This corrected data was subsequently used for automated isotope pattern recognition, adduct and elemental composition analysis followed by annotation using the suspect database  (Lommen, 2014). No normalization was performed on the Fig. 1. Schematic strategy of the annotation and confirmation workflow applied in this study. The grey area describes the MS 1 data analysis performed within MetAlign. The resulting tentative annotations were prioritized by manual review and the sample with highest signal intensity was chosen for MS 2 spectra acquisition for the confirmation workflow. signal intensity, as all different types of normalization have some drawbacks and can significantly influence the results  (Cook et al. , 2020). Elemental composition analysis based on the elements C, H, N, O, P, S, Cl, F, Br was performed on the isotope patterns in the dataset between RT of 2.
0 and 18. 5 min, a m/z range of 100-800 m. u. , using a mass error ≤ 1. 5 ppm and a threshold of signal intensity (centroid mode) in a range between 10 3 -10 4 a. u. For elemental compositions containing Cl and Br, the detection of the characteristic 37 Cl and 81 Br isotope pattern was mandatory. The calculation of elemental compositions results typically in about 800,000 elemental compositions per LC-HRMS data file. Because this is an unmanageable number of tentative detects, a further data reduction step was required focusing on subsets based on the presence of F, Cl, Br and PO 3. However, a few metabolite annotations (i. e. , thiabendazole, pyrimethanil, cyprodinil, propamocarb and ametoctradin) without these elemental composition traits were kept, as their RT information for the LC system used was available from other unpublished studies (relating confirmed high pesticide intake from food vs potential metabolites in human urine).
Examples of probable ambiguous chemical formulas found are for instance that of dihydroxy-diclofenac identical to the herbicide chlorazifop and that of 3,5-dibromo anthranilic acid (metabolite of bromhexine/ambroxol) identical to 3,5-dibromo-4hydroxybenzamide (metabolite of the herbicide bromoxynil). In practice, pesticide metabolites can be distinguished from minor pharmaceutical metabolites based upon the simultaneous presence of high intensity signals of the parent pharmaceutical and/or its main metabolites. A pre-screening using a list of elemental compositions of commonly used halogen-containing pharmaceuticals (subset of a list of 200 most commonly used medicines in 2019 (see dataset on https://doi. org/10. 5281/zenodo. 6530623) was performed as a first step in the analysis. If these particular pharmaceuticals were found at high signal intensities (cut-off > 5x10 6 ) the corresponding samples were flagged.
Flagged samples were typically not used as a primary source for discovery if elemental compositions of pesticide metabolites had some similarity with the pharmaceutical present. b) Small molecules such as halogenated phenols, cresols, benzoates, anilines (most of which are conjugated) were put in a separate list and not used as potential pesticide metabolites for human biomonitoring because they were regarded as not specific enough. Metabolites related to bromotryptophan, chlorotryptophan and sucralose were listed separately and omitted from confirmation. These annotated but excluded compound categories are made available (see folder Work_-folder_search_templates_HBM4EU/ Miscellaneous_small_chlorinated_or_brominated_compounds, https://doi. org/10. 5281/zenodo. 6530623). c) The remaining tentative pesticide metabolite annotations were sorted by parent name per sequence.
The isotope patterns and the RT consistency were then manually checked and anomalies filtered out. The resulting RT and isotope pattern combinations were searched over all processed sequences. If blank samples contained the RT-isotope pattern combination, the occurrence was noted as a background contamination. d) Parent pesticides with more than one marker (parent and/or multiple metabolites) detected in the overall dataset and showing (partial) co-occurrence with each other were prioritized (examples are given in Figure  S5-S10 ). If multiple metabolites occurred for the same pesticide, their relative RTs were checked with regard to each other. In general, metabolites that are more polar than the parent compound are expected to elute earlier than the parent. Therefore, conjugated metabolites should elute earlier than the corresponding unconjugated metabolite; hydroxylated or demethylated earlier than the parent.
Human liver S9 incubation Human liver S9 incubation experiments were performed for 69 pesticides. Pesticides were selected based on what was found in the suspect screening and what was commercially available (See Table  S6 ). The human liver incubation procedure was performed as previously described  (Huber et al. , 2021). The chromatography as well as the instrumental settings for the LC-Orbitrap-MS measurements are described in Section 2. 2. 4. The inclusion list was based on the precursor ion masses of the metabolites originating from the suspect screening. The reference information extraction from the acquired raw files (RT, MS 2 spectra at two different collision energies) was performed with the same workflow as described for the spectral database generation  (Huber et al. , 2021).
MS 2 data processing All sample files were converted into. mgf and. mzML files using the ProteoWizard version v3. 0. 18265 function msconvert  (Kessner et al. , 2008). The data analysis workflow was performed in R version 4. 0. 3. Spectra extraction was performed using the mzR package  (Chambers et al. , 2012)  and the msPurity package  (Lawson et al. , 2017)  was used for evaluating the purity of all spectra related to the tentative annotations within a RT window of ±30 s. The Spectra package  (Rainer et al. , 2022)  was used to generate head-to-tail plots and to calculate forward and reverse dot product scores between the spectra measured from the urine samples and spectra acquired from reference standards or human liver S9 incubations of parent pesticides. A mass tolerance of 10 ppm was used for matching the fragment ions.
Spectra were cleaned using the command line tool Genform  (Meringer et al. , 2011) , applying the molecular formula of the suspect and a threshold of 10 ppm. A confirmation was automatically set if at least three fragment ions were matched and a reverse dot product score >0. 4 for dd-MS 2 observed. Furthermore, all spectral comparisons were manually reviewed to check if isobaric contamination was causing low dot product scores. Sulfate and glucuronide conjugate metabolites were confirmed based on an in-silico deconjugation procedure  (Huber et al. , 2022). To this end, the spectra of the conjugate metabolite were compared with the reference spectra of either the parent pesticide or the phase I metabolite, also stemming from Fig. 2. Data processing workflow applied to prioritize and annotate pesticide exposure markers detected by the applied suspect screening approach based on the first-stage full-scan MS 1 dataset.
different collision energies, however without applying a strict dotproduct score threshold. For further verification of the method, we evaluated a selection of deconjugated samples. In cases when experimental MS 2 spectra could be acquired for a tentative annotation but without a corresponding reference metabolite generated by the human liver S9 experiment, a MS 2 data modeling approach was used to identify the corresponding marker based on the fragmentation tree computation, chemical fingerprint prediction and compound class prediction of the java-based software framework SIRIUS  (Dührkop et al. , 2019)  version 4. 9. 9, further details see Table  S7.
27x10 7 (ESI+) and 1. 4x10 7 (ESI-) possible annotations for 2,088 samples. The majority of these are assumed to be false-positive annotations not related to the molecular structures of the suspect list entries. After a selection of Cl, Br, F and PO 3containing features, the remaining dataset consisted of 9. 28x10 4 (ESI+) and 1. 43x10 5 (ESI-) tentative annotations in 2,088 samples. Many of these annotations originate from the same compounds in different urine samples. These remaining annotations were used in the next prioritization step, referred to as manual review by an expert. Figures  S5-S10  (Spearman rank correlation) are given to illustrate the co-occurrence of different known metabolites of the same pesticide in the same sample. A direct correlation between metabolites of the same pesticide is oftenbut not always -present.
In practice, MS 2 information could only be acquired for about one third of selected tentative annotations (N = 377; see Fig. 2 ) in urine samples. For annotations with low signal intensities there were clear difficulties in MS 2 data-dependent acquisition even when applying an inclusion list to force triggering of the precursor ions. In some cases, data was acquired by targeted MRM acquisition. However, these spectra contained impurities, which decreased the ability to draw conclusions from the spectral matching to the reference spectra. Therefore, lower scores for spectral comparisons were accepted in three cases acquired by MRM following manual review. The commonly accepted Schymanski confidence scale  (Schymanski et al. , 2014)  was used to attribute a level of confidence to each annotated exposure marker.
Conclusions This study illustrates the application of a multicentric suspect screening approach in the context of a large-scale human biomonitoring study. The application of harmonized and quality-controlled sample preparation and LC-HRMS analysis methods, together with harmonized data processing has enabled the reporting of comparable and transferable confirmation results for datasets analyzed in five different laboratories. Overall, joint efforts allowed us to confirm 46 pesticides and their metabolites from a prioritized sub-set of 498 tentative assignments (representing about 80 pesticides) despite the analytes having low signal intensities against high matrix background. We were unable to further substantiate many of the tentative annotations because the low-level signals meant no data-dependent MS 2 spectra could be acquired, or these were severely affected by impurities from coeluting isobaric compounds.
Additionally, since the identification efforts were prioritized for halogenated and PO 3 -containing compounds (together with a few other tentative annotations from prior studies), a wide range of further suspect pesticide metabolites remain to be evaluated. Thus, the reported results are reflecting only a part of the pesticide exposure of humans. However, to our knowledge our study represents the first suspect screening approach for pesticides handling a high number of tentative annotations from thousands of datasets and leading to a high number of confirmations. Further automation of data handling would facilitate dealing with these large numbers of datasets in a routine fashion. The number of confirmed annotations may be enhanced in the future with better availability of reference standards and additional in vitro incubation experiments to generate reference data for metabolites.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  733032 HBM4EU. C. M. V. , E. J. P, L. Š. , P. Č. & J. K. acknowledge the RECETOX research infrastructure supported by the  Ministry of Education, Youth and Sports of the Czech Republic  ( LM2018121 ) and the  Ministry of Education, Youth and Sports of the Czech Republic  ( CZ. 02. 1. 01/0. 0/0. 0/17_043/0009632 ). This work was supported from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  857560  and No 857340. E. J. P. acknowledges support from  Czech Operational Programme Research, Development and Education -Project MSCAfellow4@MUNI  (No. CZ. 02. 2. 69/0. 0/0. 0/20_079/0017045 ). L. D. and K.
W. would like to thank the  MetaboHUB French National Infrastructure (MetaboHUB- ANR-11-INBS-0010 , 2011). The QExactive Plus LC-HRMS used at  UFZ  is part of the  major infrastructure initiative CITEPro (Chemicals in the Terrestrial Environment Profiler ) funded by the  Helmholtz Association. The  Dutch Ministry of Agriculture, Nature and Food Quality  are acknowledged for co-funding through projects  1277354201  and  1267333701. The  European Union  is acknowledged for co-funding through the  European Regional Development Fund Operational Programme (ERDF)  of the Valencia Region (2014)(2015)(2016)(2017)(2018)(2019)(2020). This publication reflects only the author's view and the  European Commission  is not responsible for any use that may be made of the information it contains.
Perspectives Identification of serine and threonine targeted by O-GlcNAcylation and prediction of O-GlcNAcylation sites are the main future challenges. In 2011, dbOGAP, the first database of O-GlcNAcylated proteins reported approximately 800 proteins, mainly human, with approximately 400 identified O-GlcNAcylated sites  (Wang et al. , 2011). A decade later, with the development of new approaches, including the feasibility of mass spectrometry, more than 16,000 proteins have been reported as O-GlcNAcylated (half of them in humans) and nearly 14,000 O-GlcNAc site locations have been identified across 43 species on "The O-GlcNAc DataBase v1. 2"  (Wulff-Fuentes et al. , 2021; Malard et al. , 2021). To our knowledge, YinOYang, O-GlcNAcpred II and OGTsite are the three best known prediction tools available in August 2022  (Gupta and Brunak, 2002; Jia et al.
Currently used pesticides are rapidly metabolised and excreted, primarily in urine, and urinary concentrations of pesticides/metabolites are therefore useful biomarkers for the integrated exposure from all sources. Pyrethroid insecticides, the organophosphate insecticide chlorpyrifos, and the herbicide glyphosate, were among the prioritised substances in the HBM4EU project and comparable human biomonitoring (HBM)-data were obtained from the HBM4EU Aligned Studies. The aim of this review was to supplement these data by presenting additional HBM studies of the priority pesticides across the HBM4EU partner countries published since 2000. We identified relevant studies (44 for pyrethroids, 23 for chlorpyrifos, 24 for glyphosate) by literature search using PubMed and Web of Science. Most studies were from the Western and Southern part of the EU and data were lacking from more than half of the HBM4EU-partner countries.
Materials and Methods Literature searches were performed in PubMed and Web of Science, for each of the search terms 'pyrethroid*', 'chlorpyrifos OR chlorpyrifos-methyl', and 'glyphosate OR AMPA' combined with 'urine AND human' or 'human biomonitoring' restricted to publications published between 01. 01. 2000 and 30. 06. 2022. We used no language restriction if an English abstract was provided. After exclusion of duplicates, all abstracts (and if necessary, method sections) were screened and only studies that presented HBM data based on urine samples collected in HBM4EU participating countries (i. e. , EU Member States, as well as Norway, Iceland, Israel, Switzerland, and UK) were included. Publications with a focus on method development and/or validation were excluded if they presented HBM-data from less than 20 individuals and/or the participants were directly exposed to the pesticides as a part of the study.
Pyrethroids We identified 44 studies presenting urinary pyrethroid metabolite concentrations in European populations from 13 different countries (Table  2 ) mainly from the Western and Southern part of the EU. The datasets represent a range of different population groups including pregnant women (5 studies) and children  (12 studies). Most of the studies were regional and only three studies claimed to be nationally representative for the respective population group, i. e. , for pregnant women  [19]  and adults  [20]  in France and for children in Germany  [21]. The majority of studies were based on single spot urine samples (25 studies) or first morning voids (FMV) (12 studies). The generic pyrethroid metabolite, 3-PBA, representing the combined exposure to most pyrethroids, was included in most studies, while inclusion of more specific metabolites was more variable and only three studies included CFCA which is a metabolite of bifenthrin and λ-cyhalothrin  [22] [23] [24].
Of the included studies, 18 reported enzymatic deconjugation using β-glucuronidase, 18 reported acidic hydrolysis, and 8 studies did not mention any deconjugation step. For these studies, it was not possible to determine if the description was missing or deconjugation was not performed. Two of these datasets, based on the French PELAGIE cohort  [28, 29] , and one from the Spanish INMA-Granada cohort  [30]  reported very low 3-PBA concentrations compared to the other studies. Thus, we suspect that these concentrations might be underestimated. The potential impact on the concentrations of the more specific pyrethroid metabolites is difficult to assess since these concentrations were more like those in other datasets. Regarding the analytical platform, LC-MS/MS was used for all the metabolites in 19 studies.
Two datasets from children and mothers from the PELAGIE cohort, respectively, used LC only for 3-PBA and 4-F-3-PBA while other metabolites were analysed by GC-MS/MS  [28, 29]. GC-MS/MS was used for all metabolites in 21 of the studies. One study used an immunoassay  [31]  and one study did not describe the analytical method but referred to an ISO9001 accredited lab  [22]. Reported LOD/LOQs for, e. g. , 3-PBA varied from a LOD of 0. 004 to a LOQ of 0. 8 µg/L between the included studies leading to large variation in detection frequencies. The wide variation in urine sampling and analytical methods hamper the comparability of the results across studies and the possibility to assess time trends and geographical differences in pyrethroid exposure. Two studies from Sweden reported increasing 3-PBA concentrations between 2000 and 2017 among young adults  [23]  and between 2009 and 2014 among women after delivery  [32].
Discussion In this review, we identified HBM-studies that measured the internal exposure to pyrethroids, chlorpyrifos, and glyphosate in European population groups by analysing urinary concentrations of suitable biomarkers. We included studies that have been published from 2000 until June 2022. For all three substance groups the number of studies increased during the years and the majority were published during the last ten years. Variation in analytical methods displaying different sensitivities impacted the reported frequencies of detection and the urinary concentrations. Besides, the urine sampling methods varied, and the quantitative data was reported differently. Thus, direct comparison of the urinary concentrations across the studies was not always possible although many of the studies participated in external quality control programs such as the German External Quality Assessment Scheme (G-EQUAS).