dataesr/scientific-paragraphs-categorization huggingface dataset:
Due to the isolated character of this last storm, it was not investigated. The TEC responses were investigated with the analysis of data from permanent GNSS stations located at similar geomagnetic longitudes and at slightly shifted latitudes in the equatorial region of Africa. Figure  2  shows the effects of the multiphase storms on the low latitude ionosphere of the Africa sector and the GNSS TEC and rTEC change for the MAL2, NKLG, BKFP, RABT, and CLBR stations. The locations of these stations are shown in Table  1. The TEC diurnal variation shows the characteristic pre-dawn minimum followed by an early morning increase that reaches a maximum after noon, with TEC values between 40 and 70 TECU with respect to the night side, depending on the station. Then, clear rTEC enhancements above 60% appeared during the HSSW on 19 February at NKLG.
In general, the IRI-2012 model shows narrower crests, whereas the IGS crests are wider and have a more abrupt distribution of values than that of the IRI-2012, which shows a smooth shape. Figures  6  and 7  show the keograms (time-longitudinal dependence along red lines shown in Figure  5 ) of the TEC near the EIA crests. Here, we remove the quiet-time TEC by setting Am = 6 in the PCA-based model of  [62]. For all three datasets (IGS, IRI-2012 and PCA-based model), the profiles shown in Figure  5  (the southern and northern EIA crests and the magnetic equator) were first extracted, and the TEC differences to quietconditions (Am = 6) TEC from 10 to 28 February 2014 were computed. Finally, the median values of the full keogram were averaged to detect the correlations with space weather and geomagnetic indices.
Here, we remove the quiet-time TEC by setting Am = 6 in the PCA-based model of  [62]. For all three datasets (IGS, IRI-2012 and PCA-based model), the profiles shown in Figure  5  (the southern and northern EIA crests and the magnetic equator) were first extracted, and the TEC differences to quiet-conditions (Am = 6) TEC from 10 to 28 February 2014 were computed. Finally, the median values of the full keogram were averaged to detect the correlations with space weather and From Figure  6 , the EIA crests at both hemispheres are clearly variable during the storms, and the enhancements are very prominent in both hemispheres, immediately after the SSCs and the HSSW events, and followed by a significant variability in the main phases of the storms. The TEC enhancements in the low-latitude ionosphere on February 19 resulted from an increased merging electric field, E m.
• The IRI-2012 model did not represent the actual TEC variability during the storms period, showing no distinction between events, and a clear bias of ~20 TECU to the actual quiet-time TEC. There was also a detection of a clear discrepancy in the distribution of TEC along the EIA, showing that IRI-2012 underestimates the northern crest at the African and Asian sectors and at both crests in the American sector. The median February average from the IGS GIM TEC shows enhanced values in the southern hemisphere, whereas the IRI-2012 does not show this asymmetry. [62]  is available in  [63]  at the Zenodo repository (https://zenodo. org/record/3563463, accessed on 15 February 2022). Special thanks are given to Catherine M Jones and to the anonymous reviewers for helping to improve the manuscript.
Figures6 and 7show the keograms (time-longitudinal dependence along red lines shown in Figure5) of the TEC near the EIA crests. Here, we remove the quiet-time TEC by setting Am = 6 in the PCA-based model of [62]. For all three datasets (IGS, IRI-2012 and PCA-based model), the profiles shown in Figure5(the southern and northern EIA crests and the magnetic equator) were first extracted, and the TEC differences to quiet-conditions (Am = 6) TEC from 10 to 28 February 2014 were computed. Finally, the median values of the full keogram were averaged to detect the correlations with space weather and.
Figures Figures6 and 7show the keograms (time-longitudinal dependence along red lines shown in Figure5) of the TEC near the EIA crests. Here, we remove the quiet-time TEC by setting Am = 6 in the PCA-based model of [62]. For all three datasets (IGS, IRI-2012 and PCA-based model), the profiles shown in Figure5(the southern and northern EIA crests and the magnetic equator) were first extracted, and the TEC differences to quiet-conditions (Am = 6) TEC from 10 to 28 February 2014 were computed. Finally, the median values of the full keogram were averaged to detect the correlations with space weather and.
, 2018). The current study illustrates an approach for harnessing gut microbes to identify chemical entities naturally contained within fiber preparations emanating from such manufacturing streams, and then defines the effects of one such entity on host physiology, characterizing mechanisms underlying its "mining" by gut bacteria, and determining whether results obtained from preclinical models translate to humans. We previously used gnotobiotic mice colonized with defined consortia of cultured human gut bacterial strains to characterize the effects of adding different dietary fiber preparations to a high saturated fat, low fruit, and vegetable diet (HiSF-LoFV) formulated based on the National Health and Nutrition Examination Survey (NHANES) database of diet consumption patterns by people living in the United States  (Ridaura et al.
ovatus strains tested (Tables  S6A  and S7 ). Functional assignments for these proteins were made by identifying their best scoring alignments with the sequences of experimentally characterized CAZymes in the CAZy database (www. cazy. org) (Table  S6B ). The results disclosed members of CAZyme families with reported activities against the backbones of homogalacturonan (GH family 105 [unsaturated rhamnogalacturonyl hydrolase/unsaturated glucuronyl hydrolase]) or rhamnogalacturonan (PL9 and PL11 [rhamnogalacturonan lyase] and GH28 [RGI-specific a-galacturonidase]  [Ndeh et al. , 2017; Luis et al. , 2018] ). These structures are prominently represented in pectin and in our OF preparation (>50% of glycosyl linkages, Table  S1B ). The PULs also included CAZymes with predicted activities directed at oligosaccharides linked to these backbone structures (arabinofuranosidase [GH43_18], galactosidase [GH36], and apiosidase [GH140]).
30) (A statistically significant log 2 -fold change in the relative abundance of a taxon at week 5 compared with the pre-intervention period was defined by a q value < 0. 1 [linear mixed effect model] [see Table  S8C and Delannoy-Bruno et al. , 2022] ). Using shotgun sequencing datasets generated from fecal DNA samples collected at the end of weeks 1 and 5 of the OF study  (Delannoy-Bruno et al. , 2022) , we performed a Spearman correlation between (1) the abundances of 213 annotated CAZyme genes (GHs and PLs) that were present in at least one study participant at these time points and (2) the fecal levels of N-methylserotonin prior to fiber supplementation and at the end of week 5 (Table  S8D ). CAZyme genes whose log 2 -fold changes in abundance were significantly correlated with levels of N-methylserotonin (q value < 0.
Materials availability This study did not generate new unique reagents. All bacterial strains can be obtained from ATCC or as described in the key resources table. Data and code availability Annotated B. ovatus genomes, microbial RNA-seq, and COPRO-seq, liver and colonic RNA-seq datasets from gnotobiotic mice have been deposited at the European Nucleotide Archive (ENA: PRJEB40461). Metabolomics data are available in the EMBL-EBI Metab-oLights database (MetaboLights: MTBLS2331). Shotgun and 16S rDNA amplicon sequencing datasets generated from human fecal DNAs are available in ENA (ENA: PRJEB44020).
Gnotobiotic mice Experiments involving gnotobiotic mice were performed using protocols approved by Washington University Animal Studies Committee. Ten-week-old male germ-free C57BL/6J animals were housed in plastic flexible film gnotobiotic isolators (Class Biologically Clean) at 23 o C under a strict 12-hour light cycle (lights on a 0600h, off at 1800h). Germ-free animals were weaned onto an autoclaved, low-fat, plant polysaccharide-rich chow (catalog number 2018S, Envigo) administered ad libitum. Four days prior to colonization, mice were switched to a diet formulation containing ingredients that in aggregate represented the upper tertile of saturated fat consumption and the lower tertile of fruits and vegetable consumption in the USA population as reported in the National Health and Nutrition Examination Survey (NHANES) database (see  Ridaura et al.
Human studies with pea and OF snack prototypes Two separate open-label, single group assignment studies were performed involving members of the Missouri Adolescent Female Twin Study (MOAFTS) cohort  (Bucholz et al. , 2000)  who were aged 31-45 years at the time of enrollment. The first study with the pea fiber snack was performed between April and August 2017, while the second study with the OF snack was conducted between August and December 2017. The design of the two studies were identical except for the fiber snack supplement used and the number of participants in each study. All participants provided written informed consent and the studies were approved by the Washington University Institutional Review Board (IRB ID#201611122). (ClinicalTrials. gov NCT03078283). Details of the human studies are described elsewhere  (Delannoy-Bruno et al.
43 x 10 7 ± 3. 74 x 10 6 reads/liver sample, and 3. 27 x 10 7 ± 1. 23 x 10 6 reads/colon sample). Reads were aligned to the Mus musculus GRCm39 genome assembly with STAR version 2. 7. 0d. Gene count data were generated from the number of uniquely aligned reads (featureCounts Subread version 1. 6. 2a). The R package DESEQ2  (Love et al. , 2014)  was used to perform differential gene expression analysis; results were filtered based on an adjusted Benjamini and Hochberg FDR p-value <0. 05. Gene set enrichment analysis was carried out using ClusterProfiler  (Wu et al. , 2021)  with an adjusted p-value cutoff of <0. 05 and minimum gene-set size of 3; over-representation was carried out using a log 2 fold-change cut-off of R1. In vitro screening of bacterial strains for N-methylserotonin releasing activity A given bacterial strain was grown in monoculture at 37 C in TYG medium in an anaerobic chamber (atmosphere; 75% N 2 , 20% CO 2 and 5% H 2 ) to stationary phase.
A 400 mL volume was transferred to an AllPrep 96 DNA plate and was centrifuged at 3220 x g for 1 min at room temperature. The RNA flow-through was purified as described in the AllPrep 96 protocol; DNA was then eluted from the column and retained. Libraries were prepared from extracted RNA using the Illumina Stranded Total RNA Prep Ligation with Ribo-Zero Plus and were sequenced on an Illumina Next-Seq instrument using single end 75-nucleotide reads (1. 33 x 10 7 ± 1. 06 x 10 6 reads/microbial sample). Reads were aligned to assembled genomes using Bowtie. The resulting counts table was passed onto the R package DESEQ2 for differential gene expression analysis, where results were filtered as described in Figure  4C. Sequence-based comparisons between genes expressed and/or present in B. ovatus strain TSDC 17.
1 x 150 mm, 1. 7 mm, Waters Corp. , Milford, MA) that was heated to 35 C. For analyses carried out in the positive ESI mode, the mobile phase consisted of 0. 1% formic in water (A) and 0. 1% formic acid in acetonitrile (B). For analyses in the negative ESI mode, the mobile phase consisted of 5mM ammonium bicarbonate formic in water (A) and 5mM ammonium bicarbonate in acetonitrile/water (95:5 v/v) (B). A flow rate of 0. 3 mL/minute was applied (gradient program: from 0 to 14 minutes, mobile phase B eluted from 5% to 100%, followed by 3 minutes at 100% of B). An equilibration time of 3 minutes was used. Data were collected in the range from m/z 50 to 1000, and m/z 150 to 650 for MS full-scan analysis and MS/MS analysis, respectively. The key parameters of Qtof were set as follows nozzle voltage, 1000 V for positive and 1500V for negative; capillary voltage, 3000 V for positive and 3500v for negative; drying gas, N 2 ; drying gas flow rate, 10 L/minute; collision gas, high purity N2; drying gas (N 2 ) temperature, 325 C; vaporizer/sheath gas temperature, 350 C; sheath gas flow rate, 12 L/min.
To ensure accurate mass measurements, reference masses m/z 121. 0509 and 922. 0098 were automatically delivered using a dual ESI source during analyses. The mass accuracy of the LC-MS system was generally better than 4 ppm. The resulting raw data sets were deconvoluted using MassHunter Profinder B. 08. 00 software (Agilent Technologies) which generated a list of molecular features. These features were subsequently filtered using in-house scripts to identify those that were only present in all samples obtained from mice that were colonized and fed the OF supplemented HiSF-LoFV diet. Initial characterization of the resulting subset of features was performed by monoisotopic mass search in METLIN (www. metlin. scripps. edu) and HMDB (www. hmdb. ca). These features were fragmented by targeted MS/MS with collision energy from 0 to 40 V.
5 minutes); 20-45% solvent B (7. 5-13 minutes); 45-99% solvent B (13-20 minutes); 99-0% solvent B (20-22 minutes) at a flow rate of 0. 25 mL/minute. Mass spectra were acquired in negative mode using the following conditions: capillary voltage set at 2000V; nitrogen as the nebulizer gas (45 psi); drying gas flow rate and temperature of 13 L/minute and 250 C, respectively; sheath gas flow rate and temperature of 12 L/minute and 325 C. Transitions were taken from the Agilent Metabolomics dMRM Database. S7  Predicted HTCS_Rgu-2 binding sites (PUL number and the ID of their component genes are based on B. ovatus TSDC 17. 2 and described in Table  S7 ). Predicted members of the HTCS_Rgu-2 regulon are indicated by the solid line on top. The sequence logo shows the consensus for identified HTCS_Rgu-2 binding sites.
ACKNOWLEDGMENTS We thank  Dave O'Donnell  and  Maria Karlsson  for their assistance with the gnotobiotic mouse husbandry;  Janaki Guruge  for her contributions to culturing bacterial strains;  Marty Meier ,  Hao-Wei Chang , and  Matt Hibberd  for their help with the analysis of the absolute abundances of bacterial strains in gnotobiotic mice;  Sid Venkatesh  for his assistance with RNA-seq;  Michael Patnode  for his many insightful comments and suggestions; and  Sophie Le Gall ,  Luc Saulnier , and  Brigitte Laillet  ( Institut National de Recherche  pour L'agriculture, L'alimentation et L'environnement at  Nantes, France ) for the carbohydrate analysis of the orange and pea fiber preparations. We are grateful to members of  Andrew Heath 's group.
Sampling, extraction, and sample storage In total, 56 effluent samples were taken from 52 European WWTPs located in 15 different countries: Germany (17), Switzerland (6), Czech Republic (5), Spain (5), Croatia (2), Greece (3), Netherlands (3), Romania (2), Slovenia (2), Sweden (2), Austria (1), France (1), Hungary (1), Serbia (1) and Slovakia (1). Different capacities and conventional treatment technologies, as well as two advanced treatment technologies are included. One sample was taken at a WWTP with AC treatment (EU019). Three WWTPs applying ozonation were sampled before and after this treatment step, providing two samples each (incl. ozonation: EU032, EU128, and EU130). One of these WWTPs had already been sampled before the upgrade, resulting in a total of 56 (52 + 4) samples, which are treated as independent samples in the data set.
1 to 5000 ng/L. These calibration standards were extracted based on a laboratory-scale SPE method using 200 mg of HR-X sorbents and the same solvents for elution as for the LVSPE method. The calibration aliquots for analysis were prepared the same way as the sample aliquots. Instrumental blanks were prepared with the same composition (methanol:water 70:30) and run for 4-6 replicates in each sequence. For the measurement, a reversed-phase LC separation on a Thermo Ultimate 3000 LC and an injection volume of 5 µL were used. HRMS data was acquired in full scan combined with data-independent mode, using a quadrupole-Orbitrap MS (QExactive Plus, Thermo Scientific) with electrospray ionization (ESI), and separate runs in positive and negative mode. To quantify compounds which have been measured at concentrations > 5000 ng/L in the first run, extracts were diluted 50-fold using LC-MS grade methanol.
Data processing and evaluation The obtained Thermo. raw files were converted to the mzML format using ProteoWizard (v. 3. 0. 18265) (Chambers et al. , 2012)  and processed using the software MZmine 2. 38  (Pluskal et al. , 2010)  for peak picking, deconvolution, alignment, gap filling and peak annotation as detailed in  Beckers et al. (2020). The in-house R package MZquant  (Schulze et al. , 2021a)  was used for the semi-automated quantification of compound concentrations. A generalized additive model was used for calibration, which was trimmed to the detection range with at least four data points (calibration levels) included. Some compounds were checked manually, using the vendor software TraceFinder (version 4. 1, Thermo Scientific). These were either quality controls (QCs) or compounds, which could not be reliably annotated automatically based on previous experience.
In total, 44 out of 56 samples were analysed using the complete mix of 499 standard compounds (Mix-2), while the first samples were analysed with a previous mix (Mix-1), containing compounds (Table  S2 ). The new compounds from Mix-2 were subsequently added by retrospective analysis of the LC-HRMS files, together with a newly measured Mix-2 calibration line, considering the retention time (RT) and the mass-to-charge ratio (m/z)  (Wagner, 2020). Method detection limits (MDLs) were determined on the basis of US-EPA guidelines (US-EPA, 2011). To account for the impact resulting from different handling of non-detects, concentrations below the MDL were set to zero (dataset A) or to MDL/2 (dataset B). The latter was used as the default dataset in this paper, if not stated differently. An overview of the final concentrations (dataset A & B) of the 56 WWTP effluent samples is presented in the supporting information (Tables  S5, S6 ), and is also available on PANGAEA  (Finckh et al.
Mode of action According to the conceptual understanding of the modes-of-action (MOA) of chemicals presented in  Busch et al. (2016) , a database query was performed. Then, all detected compounds were annotated by their specific MOAs, which were retrieved from PubChem (National Library of Medicine, 2021), Drugbank  (Drugbank, 2021; Wishart et al. , 2018) , PPDB (University of Hertfordshire, 2021), and Wikipedia  (Wikimedia Foundation, 2021). In case of ambiguous information, individual expert assignment of a compound to the best-known or the environmentally most relevant MOA was done. Following a plausibility check on these preliminary results, a literature review on uncertain and missing compounds was added to fill as many gaps as possible. Finally, the specific MOAs were further categorized into higher-level MOA groups (an updated MOA database will be published separately) (Table  2 ).
Environmental risk assessment Environmental risk of a chemical is considered present if the risk metric exceeds an effect threshold for a species or species assemblage. The risk metrics were consistently calculated as the quotient of exposure (measured environmental concentration, MEC) and hazard (toxic potency indicator). Risks are ranked higher with increasing degree of exceedance. As part of this study, three risk metrics were applied (Table  3 ), with different final interpretation: (1) the RQ metric applying regulatory EQS and PNEC values, often considering single species endpoints from one of three trophic levels, (2) the SSD-based HU metric, using chronic toxicity data to statistically derive a measure of species assemblage impacts, and (3) the TU metric, based on acute toxicity data for three different BQEs (i.
Risk quotient Risk quotients (RQs) were calculated by dividing the MEC of a compound i by the corresponding PNEC i or EQS i (including compoundspecific AFs between 2 and 1000) downloaded from the NORMAN Ecotoxicology Database (NORMAN Network, 2021): RQ i = MEC i EQS i or PNEC i (1) According to the database description, the lowest PNECs are preferably based on experimental eco-toxicity data. In case of no or Table  1  Scoring system for prioritization. Categorization according to  Gros et al. (2017). The respective data can be found in the supporting information (Table  S7 ). RQsum = ∑ n i=1 RQ i (2) If RQsum is below 1, the risk metric indicates a sufficient safety of the sample.
Toxic units Toxic units (TUs) were calculated according to  Malaj et al. (2014) ,  Sprague (1970) , by dividing the MEC of a compound i by the respective acute effect concentrations (i. e. EC 50 or LC 50 ) for algae, crustacean, and fish, respectively, resulting in three TU values (TU algae , TU crust , TU fish ) per compound. TU BQE,i = MEC i EC 50,BQE,i (5) EC 50 or LC 50 data were selected in the following order: (1) Experimental data retrieved from the US-EPA ECOTOX database (US-EPA, 2021) or, if no experimental data were available, (2) predicted LC 50 / EC 50 values using the ECOSAR type baseline toxicity model for the BQEs fish, daphnia and green algae in Chemprop 6. 7. 1 (UFZ Department of Ecological Chemistry, 2021). For (1), a statistical value was calculated based on real measurements.
In brief, this effect concentration is the 5th percentile of all effect concentrations available per BQE. The different effect categories included mortality, growth inhibition, population and movement inhibition. In addition, only the data sets with "short-term effects" (≤ 120 hours or ≤ 5 days) were considered. If the measured or predicted ecotoxicological data exceeded the predicted water solubility by more than half a log unit, the measured or predicted ecotoxicological value was replaced by the predicted water solubility. Further details on the effect data acquisition procedure are published in  Schulze et al. (2021b). The dataset is available on zenodo  (Schulze, 2022). Mixture risks were calculated by summation of all TU i per BQE of all detected target compounds, yielding the TUsum based on the CA model.
Detected compounds and use groups All analysed and detected compounds are summarized in Table  S5  (dataset A) and Table  S6  (dataset B), respectively. In addition, all results are available on PANGAEA  (Finckh et al. , 2022). In the following section on the results, dataset B (Table  S6 ) was used as the default of all figures, where concentrations below the MDL were set to MDL/2. Within the set of 56 European WWTP effluent samples, 366 out of 499 analysed target compounds were detected in at least one sample (Fig. S1, left ). In most effluents, between 200 and 250 compounds were detected (Fig. S1 , right); 107 compounds occurring almost ubiquitously with a detection frequency of at least 90%, while 53 compounds were detected in less than 10% of the samples and 18 compounds were detected only once.
Compounds were assigned to their specific use groups based on the information available from databases (Drugbank, Pubchem, PPDB, and Wikipedia) and from literature. The list of detected target compounds includes a variety of pharmaceuticals and pesticides, but also surfactants, food, plastic and rubber additives, per-and polyfluoroalkyl substances (PFAS), UV filters and corrosion inhibitors, summarized in a group called "others". In total, 111 pharmaceuticals, 96 pesticides and 98 other parent compounds were detected, complemented by 12, 39 and 10 transformation products (TPs), respectively (Fig. 1 , right). Within the three use group categories (pharmaceuticals, pesticides and others), the fractions of detected versus analysed compounds were 79%, 69% and 73%, respectively, and thus quite similar (Fig.