dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the municipality of Chamrousse for coring authorization and Chamrousse ski resort for access to Lake Robert. 14 C analyses were acquired thanks to the  CNRS-INSU ARTEMIS national radiocarbon AMS measurement program  at the  Laboratoire de Mesure   14 C (LMC14 ) of the  CEA (French Atomic Energy Commission) Institute at Saclay. The authors thank the Environnement,  Dynamique et Territoires de Montagne  for the X-ray fluorescence analyses. We also greatly acknowledge  G. Sarret  for providing soil and tailing samples from the  Brandes mine  and  F. Giraud  and  E. Janots , of the  Mineralogical Collection of the Grenoble Observatory of Earth Sciences (OSUG)  for providing Chalcopyrite ( N°255 ) samples. Thank also to  R. Buscail ,  CEFREM Perpignan  for providing the N data.
High-quality RNAseq "reads" were aligned to the mouse reference genome mm10 (National Center for Biotechnology Information) with STAR software (version 2. 6. 0). The Python package HTSeq-count was used to count the number of reads overlapping with each gene using ENSEMBL annotation. Differential expression genes between cDC subpopulations were determined with DESeq2 package of Bioconductor software, with an adjusted P-value of <0. 1 (P-value adjusted for multiple testing with the Benjamini-Hochberg procedure) as the cutoff for genes with significantly differential expression in one cell population relative to their expression in another cell population. Ingenuity Pathway Analysis (IPA; QIAGEN Inc. , Hilden, Germany) was then used to translate the differential expressed genes into canonical pathways using the Ingenuity Knowledge Base.
ACKNOWLEDGMENTS We acknowledge the technical assistance provided by the personnel of  Inserm   US006  Anexplo animal facility. Flow cytometry experiments have been done at the CPTP-INSERM U1043 core facility connected to  Toulouse Réseau Imagerie network. We thank  Fatima-Ezzahra L'Faqihi ,  Valérie Duplan , and  Anne-Laure Iscache  for technical assistance and for cell sorting. We thank the  GeT and Bioinformatics platforms  from  Genotoul (Toulouse, France ) for RNAseq analysis. We acknowledge  M. Lebeurrier  for bioinformatic analysis at the  Genomic and Transcriptomic platform  from the  Centre de Physiopathologie de Toulouse Purpan (Toulouse, France).
FUNDING This work was supported in part by the  Institut National de la Santé et de la Recherche Médicale  and the  Centre National de la Recherche Scientifique  and by grants from the  Agence Nationale de la Recherche  ( ANR-10-BLAN-1332  and  ANR-13-BSV1-0017 ), the  Medical Research Foundation (FRM) , the  Idex Toulouse Attractivity Chair Program , and the  Midi-Pyrénées Région  to SG.
Visualization and reporting of genotyping results were done automatically on the Clinical Array Reader (CAR®) as part of the automated autoclart®plus workflow. All samples with an invalid result (no human CFTR amplification detected or no spiked CFTR plasmid amplification detected) were retested once, and the second result was considered definitive. The CLART4S assay run-protocol was independent of sample media. As part of the validation, a posteriori optimization of genotype specific cut off values was conducted against detection of ≥CIN2 and < CIN1, resulting in two LBC specific, optimized assay reading software versions. The final dataset was analyzed using the ThinPrep and SurePath specific assay reading software versions.
Danish procedures In Denmark, women ≥30 years with ASCUS and a concurrent HPV-positive test result are referred to colposcopy with biopsies, as are women with high-grade squamous intraepithelial lesions (HSIL), atypical squamous cellscannot exclude HSIL (ASC-H), atypical glandular cells (AGS) or cytological sign of carcinoma and women with continued ASCUS and LSIL cytological diagnosis. Danish screening guidelines requires biopsies from all aceto-white lesions or 4-counter clockwise random biopsies from all four quadrants in cases where no lesions are visible upon colposcopy. All histological data included in the study were retrieved from the Danish Pathology Data Register.
ThinPrep collected LBC medium on the other hand does not contain formaldehyde and consequently is considered less challenging with respect to HPV testing. Finally, we do take note that SurePath and ThinPrep population were collected from two different cervical cancer screening programs, which could also contribute to the assay performance variance observed. From an operational perspective, an outcome of this study has been that the manufacturer of CLART4s equipped two software versions on the HPV analysis platform, optimized for either SurePath and ThinPrep collected samples, respectively, which constitute an adept solution to the issue.
Acknowledgements We thank  Ali Hamdi Said Al-Fattal ,  expert laboratory technician, Copenhagen , for his diligent work on this project. This study is an investigator driven, company sponsored study. The manufacturer of the CLART4S assay,  GENOMICA SAU, Madrid, Spain , had the right to read and comment on the manuscript, but had neither editorial rights, any role in the final interpretation of the data or in the decision to submit the manuscrip.
Results A time-resolved meta-omics dataset. To characterize the niche space of lipid-accumulating populations as well as resistance and resilience of the microbial community, we sampled a municipal BWWTP weekly over a 14-months period (from 2011-03-21 to 2012-05-03). Additionally, two preliminary time-points outside of the time-series were included  13, 26. Samples were split into intracellular and extracellular fractions, followed by concomitant biomolecular extractions  27  and high-throughput measurements (Fig. 1 ). MG, MT, and MP data were obtained on the intracellular fractions and MM data was generated on both the intracellular and extracellular fractions. After quality filtering, the per-sample averages of MG and MT reads were 5. 3 × 10 7 (±7. 7 × 10 6 s. d. ) and 3. 3 × 10 7 reads (±1.
For the MP measurements, we obtained a per-sample average of 1. 5 × 10 5 ± 8. 2 × 10 3 (s. d. ) MS2 spectra and a total of 7. 6 × 10 6 MS2 spectra. Of 7. 8 × 10 5 identified peptides, 3. 3 × 10 5 (43%) could be matched to 2. 1 × 10 5 predicted coding sequences of the 78 rMAGS. Per time-point, on average 1. 5 × 10 4 ± 4. 5 × 10 3 (s. d. ) spectral matches, i. e. , on average 94% of all rMAG-associated matches could be assigned to genes with predicted functions, i. e. , assigned KEGG ortholog groups (KOs). To study the community-wide resource space and metabolite turnover, we measured metabolite levels by an untargeted approach using gas chromatography (GC) coupled with mass spectrometry (MS) (Supplementary Data 3). In total, 89% (58 of 65) of the identified metabolites could be linked to enzymes encoded by the rMAGs.
We estimated resource uptake by calculating intracellular vs. extracellular metabolite ratios for 42 metabolites detected in both fractions (Supplementary Data 3). Additionally, six abiotic parameters were measured during sampling, as well as 34 parameters recorded continuously as part of the BWWTP online monitoring (Supplementary Data 4). We also generated MG and MT data for the ex situ experiments. These simulated the fluctuating conditions within the BWWTP, namely the short-term response to pulse disturbances of oleic acid influx under shifting dissolved oxygen conditions. We sequenced DNA and RNA fractions obtained at 0, 5, and 8 h after addition of oleic acid, yielding on average 1. 02 × 10 8 MG and 9. 33 × 10 7 MT reads per sample. The increased sequencing depth compared to the in situ time-series was important to obtain a fine-grained view on short-term responses to oleic acid.
Mapping of the sequencing reads to the selected set of rMAGs revealed mapping percentages comparable to the in situ time-series (mean: 21% ± s. d. : 1% for both MG reads and MT reads). Overall, our meta-omics dataset comprehensively describes mixed microbial communities underlying lipid-accumulation processes in BWWTPs, and in particular their functional potential, composition, activity, as well as substrate availability and assimilation. Distinct niche types. To resolve the fundamental niches of the pertinent bacterial populations through their functional genomic potential, we assigned KOs to the rMAGs' predicted coding sequences. We hypothesized that individual populations would form clusters based on the similarity/dissimilarity of their functional potential. We found four distinct clusters of rMAGs by projecting pairwise Jaccard distances of KO presence (Fig.
Additionally, acetyl-CoA acetyltransferases involved in the degradation and biosynthesis of Fig. 1  Overview of the study design. Samples are derived from in situ sampling of an anoxic tank of a municipal biological wastewater treatment plant. Metagenomic (MG), metatranscriptomic (MT), metaproteomic (MP), and meta-metabolomic (MM) data is generated. Physicochemical data is also collected. Additionally, MG and MT data is generated for ex situ experiments using biomass from the same system and fed with oleic acid under different oxic conditions to evaluate short-term responses to pulse disturbance. The time-series meta-omics data is integrated to define metagenome-assembled genomes (MAGs) over all time points. Representative MAGs (rMAGs) across time are selected for further analysis. The rMAGs' functional potential is used to infer the fundamental niches.
Abundance and activity data are derived from the functional omics and substrate usage is inferred per population. The variability of gene expression is used to assess the phenotypic plasticity of the individual populations. fatty acids were prevalent throughout all FunCs. The enrichment in FunC-1 and FunC-3 for genes involved in lipid accumulation are consistent with previous metabolic characterizations, with FunC-1 consisting mainly of Actinobacteria for which TAG accumulation has been described  33. FunC-3 contains Betaproteobacteria and Gammaproteobacteria that have been characterized as TAG, WE, and/or PHA accumulators, e. g. , Thauera spp. , Albidiferax spp. , or Acinetobacter spp. 33, 34. Importantly, we observed a difference between these FunCs in the utilization of acetyl-CoA. Specifically, FunC-1 members showed an enrichment in functions related to the ethylmalonyl-CoA pathway (crotonyl-CoA reductase and enoyl ACP We further determined specific functional enrichment for the four FunCs in relation to the breakdown of other macromolecules (including CAZymes and proteases), nitrogen cycling, stress response, and motility (Supplementary Data 5).
We computed distances between the rMAGs' abundance profiles (based on their pairwise correlations) and found that the dynamics of rMAGs can be partially explained by the FunC assignment (PERMANOVA R 2 = 0. 12, Pr > F = 0. 002; no significant difference in dispersion; Supplementary Fig. 3 ), thereby linking FunC membership to temporal abundance shifts. The most abundant taxa (Supplementary Data 2) included Candidatus Microthrix (26. 0% relative abundance across the time-series; referred to as Microthrix in the remaining text), Acinetobacter (8. 1%), Haliscomenobacter (8. 0%), Intrasporangium (7. 2%), Leptospira (6. 3%), Albidiferax (5. 7%), and Dechloromonas (2. 4%) (Fig. 3a ). Several of the recovered rMAGs belonged to filamentous taxa according to the MiDAS field guide database for organisms in activated sludge  37  , such as the highly abundant Microthrix, and Haliscomenobacter, as well as the less abundant Anaerolinea (1.
Discussion The ability to reconstruct population-level genomes and infer their functional potential from metagenomes allows identification of the fundamental niches of distinct community members. Unprecedented views of realized niches are achieved by tracking functional gene expression via MT and MP analyses, as well as actual resource usage resolved via comparative metabolomics analyses of intracellular and extracellular metabolites. The joint resolution of fundamental and realized niche breadths of individual populations is key to understanding the ecological processes within microbial communities, including, but not limited to, how such consortia respond to disturbance. Here, through the application of our novel framework for the integration of multi-meta-omics datasets, we were able to track community-wide and population-resolved traits longitudinally in situ as well as ex situ.
Polar and nonpolar metabolites, DNA, RNA, and proteins were extracted in a sequential co-isolation procedure  13, 51. Around 200 mg of frozen samples were weighed out. Extracellular metabolites were extracted from the supernatant with cold chloroform and methanol-water, and separated into polar and nonpolar fractions. Intracellular metabolites were isolated in the same way after a lysis step by cryomilling, followed by sequential spin column-based (Qiagen Allprep) purification of RNA, DNA, and proteins. Abiotic factor measurements and data processing. At the time of sample collection, the following physico-chemical parameters were measured inside the tank with a portable field kit (Hach) on-site: pH, conductivity, oxygen-levels, and temperature. Additionally, online monitoring measurements were recorded by the BWWTP operators including nitrate, phosphate, ammonium, dry-matter and dissolved oxygen levels at the outflow as well as conductivity and pH at the inlet, and pH and temperature inside the sampled tank (referred to as operational measurements).
MS/MS scans were carried out with an isolate width of 2 m/z and a normalized collision energy of 35. Additional details of the metaproteome preparations and measurements are described in a previous study  13. We converted raw mass spectrometry files to MGF format using MSconvert 53 using default parameters. The Graph2Pro pipeline  54  was used to process the resulting files together with the corresponding MG and MT co-assembly graphs from MEGAHIT  55. The Graph2Pro pipeline uses FragGeneScan  56  to predict proteins from the long edges in the assembly graphs (i. e. , from the contigs). In addition, it predicts tryptic peptides that span multiple edges in the graphs. Search databases were constructed using the putative proteins and tryptic peptides, respectively. These were used for initial peptide identification with the MS/GF+ search engine  57.
Identified tryptic peptides were then combined and used as the constraints for Graph2Pro to predict protein sequences from the co-assembly graphs. The generated sample-specific databases produced by Graph2Pro were used for the final metaproteomic searches using MS/GF+ (second search) to produce the final identification results. MS-GF+ was used for the final peptide identification with custom parameters: the instrument type was set to highresolution linear trap quadropole (LTQ) with a precursor mass tolerance of 15 ppm an isotope error range of -1 and 2 and the minimum and the maximum precursor charges were set to 1 and 7, respectively. We estimated the false discovery rate (FDR) with a target-decoy search approach using reverse sequences of the protein entries while preserving the C-terminal residues (KR).
2 mL/min. The GC oven temperature was held at 80 °C for 1 min and increased to 320 °C at 15 °C/min. Then, the temperature was held for 8 min. The total run time was 25 min. The transfer line temperature was at a constant 280 °C. The mass selective detector (MSD) was operating under electron ionization at 70 eV. The MS source was held at 230 °C and the quadrupole at 150 °C. Full scan mass spectra were acquired from m/z 70 to 700. All GC-MS chromatograms were processed using the MetaboliteDetector software  60 (v. 2. 5). The software package supports automatic deconvolution of all mass spectra. The following deconvolution settings were applied: peak threshold: 6, minimum peak height: 6, bins per scan: 10, deconvolution width: 2 scans, no baseline adjustment, Minimum 15 peaks per spectrum, No minimum required base peak intensity.
Compounds were automatically annotated by retention time and mass spectrum using an in-house mass spectral library. Detected metabolite derivatives (_xMeOX_xTMS/_xTMS) were used for further statistical data analysis. Metabolites detected in blanks at a mean intensity level of more than 75% of the mean level in samples were removed as contaminants. Metabolites that were not detected in all pool samples were also removed from subsequent analysis as well as metabolites not detected in at least 25% (90% for correlation analyses) of samples. Metabolite intensities were normalized with respect to pool samples to account for instrument drift as described previously  51  by dividing the intensity values by the mean of up to two preceding and subsequent pool samples according to the measurement sequence.
50 in the difference of completeness and contamination estimates. Furthermore, rMAGs without taxonomic assignment on kingdom level were removed as they could represent misassembled contigs, resulting in a set of 78 coherently taxonomically annotated rMAGs that were used for the time-series analysis. For downstream analyses, MG and MT reads from all time-points were mapped using bwa mem  64  per time-point using the rMAGs as references. MG and MT depth-of-coverage per time-point were computed on the gene and contig level by dividing the summed depth per base by the length of the respective sequence. Assembled contigs from IMP were annotated with Prokka v1. 11  58  including prediction of full-length coding sequences (CDS) with prodigal v2. 60  65. Predicted CDS were also searched with an in-house Hidden Markov Model (HMM) database  61  of KEGG ortholog groups (KO) using HMMer v.
1. 12b  66. We inferred compounds linked to CDS through CDS-to-reactions links from predicted enzymes with their respective KO annotation and EC assignment. Links to FOAM ontology categories  67  were assigned to each CDS by matching KO annotations. To assign MIMAG classifications  68  for all MAGs, assembly statistics, e. g. , N50, were computed with the R-package seqinr v3. 6-1  69. tRNAs were predicted with Aragorn v1. 2. 38  70  and MAGs were screened for rRNA genes with barrnap 0. 9  71. Taxonomic assignment of rMAGs was performed using AMPHORA2  72  in combination with sourmash-lca v. 2. 0. 0a1  73 , kmer-length:21 and threshold:4 and an existing database including approximately 87,000 microbial genomes (downloaded on 2017-11-09 from https://osf. io/s3jx8/download). If no taxonomic assignment was possible by whole genome-comparison (sourmash-lca), predictions for unassigned levels were augmented with consensus predictions using AMPHORA2: Assignments based on individual marker genes were combined by summation of the associated assignment probabilities.
Statistical analyses. Statistical analyses were performed using R 3. 4. 4 and R 3. 6. 1  76  with prevalent use of the tidyverse R-package  77. Determination of niche types. Annotated KOs for the individual rMAGs were summarized in a binary presence/absence matrix, in which 0 s indicated absence and 1 s indicated presence of at least one gene annotated with the respective KO. Subsequently, pairwise binary Jaccard-distances between the rMAGs based on these KO profiles were calculated and projected into two-dimensional space by multidimensional scaling (MDS). To determine the clustering of rMAGs in the resulting embedding, the appropriate number of clusters was determined by utilizing the k-means function for a range of centroids (ranging from 1 to 9 centroids) and determining the total within-sum-of-squares error as a measure of variability of resulting clusters.
Functional clusters (FunCs) were then determined by k-means clustering. Enrichment of individual KOs within the assigned FunCs was determined with Fisher′s exact test based on the number of rMAGs with the assigned KO. Resulting p-values were adjusted by FDR correction (function p. adjust method = "fdr") and KOs with a p-value 0. 05 were considered as enriched within a FunC. To test the relationship of rMAGs abundance and FunC assignment, pairwise Pearson correlation (cor. test in R) was computed between the relative abundance values of the rMAGs across time. Resulting correlation coefficients (ρ) were transformed to distances with the following formula: 1 À ρþ1 2. Dispersion of these distances was assessed with the betadisper function of the vegan package  78. Association of the FunC assignment to the distances was tested using the adonis function.
Whole genome-based pairwise distances between all rMAGs were calculated with mash v. 2. 2. 2  30  (-k 21 -s 10,000) and embedded in two dimensions using MDS. PROCUSTES from the vegan package  78  was used to map the whole genomebased embedding onto the KO profile-based embedding. PROTEST from the vegan package was used with 9999 permutations. Linking abiotic factors to population abundances. Measurements of abiotic factors (metabolites and physico-chemical parameters), as well as ratios of intracellular and extracellular metabolite intensity ratios were transformed to z-scores. Relative abundances of rMAGs were associated to abiotic factor levels by testing for correlation (cor. test function, method = "spearman"). Additionally, abiotic factor levels were placed onto a 2D ordination of MG or MT-based abundance profiles (Bray-Curtis dissimilarity) applying the vegan function scores.
If the MG depth of a gene was below one, the MT depth was considered instead of the MT/MG ratio to avoid inflating active KOs for lowly abundant populations. Binary Jaccard distances of the resulting KO profiles were determined for each rMAG separately for each timepoint. Clusters were determined in each of the resulting 51 ordinations with the hdbscan function of the dbscan package with a minimum of five members per cluster. The resulting clusters were used to assess which functions were expressed over time by different subsets of rMAGs with a focus on rMAGs assigned to the same clusters as Microthrix D51_G1. 1. 2 or Acinetobacter A01_O1. 2. 4.
Acknowledgements We thank  Mr Bissen  and  Mr Di Pentima  from the  Syndicat Intercommunal a Vocation Ecologique (SIVEC) , for their permission to collect samples and access to the monitoring platform of the Schifflange wastewater treatment plant. We also thank  Dr. Olivia Judson  for her feedback on the title and abstract. Bioinformatic analyses presented in this paper were carried out using the high-performance computing facilities of the  University  of the Luxembourg. This work was supported by an  ATTRACT programme grant  ( ATTRACT/ A09/03 ) and  INTER  programme grant ( INTER/SYSAPP/14/05 ), two  CORE  programme grants ( C15/SR/10404839 ,  C17/SR/11689322 ), and a  PRIDE doctoral training unit grant  ( PRIDE15/10907093 ) to P. W. , and a grant to A. R. S ( PDR-2013-1/5748561 ) all funded by the  Luxembourg National Research Fund (FNR).
Data availability Meta-omics data from five individual time-points has previously been published  13, 26, 51. The MG and MT FASTQ files and the sample-wise MT-assembly and co-assembly contigs are available on NCBI BioProject PRJNA230567. MP data has been deposited in the PRIDE database under the accession number PXD013655. Raw metabolomics data is available at MetaboLights under the accession MTBLS2021, while processed intensities after identification are provided with this manuscript (Supplementary Data 3). Similarly, physico-chemical parameters are provided with this manuscript (Supplementary Data 4). Processed and intermediary data files from the combined multi-omic analyses, e. g. , annotated and normalized MT, MG read counts, are available at Zenodo (https://doi. org/ 10. 5281/zenodo.
Information sources We searched the following electronic databases to identify reports of relevant clinical trials: Cochrane Wounds Specialised Register, Cochrane Central Register of Controlled Trials (CENTRAL), Ovid MEDLINE Ovid Embase, EBSCO CINAHL Plus. There were no date, language or publication status restrictions. We also searched the following clinical trials registries:ClinicalTrials. gov, World Health Organization International Clinical Trials Registry Platform, EU Clinical Trials Register (April 2018).