dataesr/scientific-paragraphs-categorization huggingface dataset:
Regular updates of the of the mOTU database will be made available at: http://motu-tool. org. Collection of MGs from reference genomes and metagenomes. The 25,038 reference genomes used for the mOTU database were downloaded from the pro-Genomes database  13. Metagenomic data were downloaded from the Genbank Sequence Read Archive (https://www. ncbi. nlm. nih. gov/sra) and the European Nucleotide Archive (https://www. ebi. ac. uk/ena) (accession numbers are listed in Supplementary Data 1). Most samples were obtained from human microbiome studies, including 1210 samples from different major human body sites (oral, skin, gut and vaginal  14, 15  and 1693 further samples from various human gut microbiome studies  [16] [17] [18] [19] [20] [21]. In addition, we used 243 metagenomic samples from an ocean microbiome study  22.
Across all metagenomic samples used to construct the mOTUs, 1223 ref-mOTUs were detected and could be used for 100-fold resampled 5-fold crossvalidation. We also assessed the agreement of the MGCs for each mOTU in terms of relative abundance and prevalence across metagenomic samples (Supplementary Figures  2b, c ). Relative abundance and prevalence showed higher agreement for meta-mOTUs than for ref-mOTUs. This was expected since the binning algorithm is directly influenced by these two parameters. We additionally evaluated the homogeneity of GC content among the MG sequences within each mOTU (Supplementary Figure  2d ). meta-MGCs showed very homogeneous GC content, as expected for genes that originate from the same genome, but not for erroneously binned MG sequences. Construction of the mOTUs2 mapping database.
We compiled a sequence database against which short metagenomic reads can be aligned to quantify the abundance of MGCs and mOTUs. To construct a non-redundant mOTUs mapping database, we removed identical MG sequences. MG sequences in the database were extended at the start and end of the gene by up to 100 nt, based on their genome or metagenomic assembly of origin, to reduce known mapping artifacts at gene boundaries. The resulting non-redundant database consists of the sequence files in FASTA format along with MGC and mOTU annotations, as well as the coordinates of the coding segments of the MG sequences. The sequence files were further indexed for searches with BWA  42. For SNV calling, we constructed an additional database that only consists of the centroid (medoid) sequence of every MGC so that SNVs can be identified with respect to one reference sequence per MGC.
Taxonomic annotation of meta-mOTUs. To assign taxonomic affiliations to meta-mOTUs, we first annotated each MG using Uniprot's UniRef90 (https:// www. uniprot. org/uniref, release 2017_08) as a reference protein sequence database  43  , which was supplemented with a set of additional marine protein sequences as described in  44. Similarities between translated MG sequences and reference database entries were computed using MMSEQS2  45  with the following parameters: search -a true -e 1E-5 --max-seqs 1000. Taxonomic affiliation was assigned using a weighted Lowest Common Ancestor (LCA) approach as follows: for each MG, all protein matches in the reference database with a value ≥90% of the highest bitscore were kept. Then, outlier taxa were excluded by using a bitscore-weighted LCA annotation that covered at least 75% of the sum of all bitscores of each MG.
Next, we transferred the annotation of the best-scoring MG member to each MGC and used the MGC annotations to assign a taxonomy to meta-mOTUs as follows: for each meta-mOTU and for each taxonomy rank, we required at least three MGCs to be annotated to consider the meta-mOTUs as annotated at this rank. Annotated meta-mOTUs were considered consistent if at least half of the MGC taxonomy annotations were in agreement. Phylogenetic analysis of mOTUs. To explore the phylogeny of mOTUs (ref-mOTUs and meta-mOTUs), a reference tree was reconstructed by combining the phylogenetic signal of the ten sets of marker genes selected (Supplementary Figure  4 ). For this, all marker genes were translated into amino acid sequences and analyzed using ETE Toolkit v3. 1. 1  46. In particular, the program ete-build was used to run the following phylogenetic workflow: First, each set of marker proteins was independently aligned using ClustalOmega  47.
Next, alignment columns with less than three aligned residues were removed. Finally, the ten individual MG alignments were concatenated and used to infer a maximum likelihood phylogenetic tree using IQTree  48  and the LG model. The mOTUs2 profiling workflow. The mOTUs2 workflow for taxonomic profiling consists of three steps: alignment of metagenomic sequencing reads to MGs, estimation of read abundances for every marker gene cluster (MGC), and calculation of mOTU abundances. As input, mOTUs2 expects the user to provide quality controlled sequencing reads. These are aligned to the MGs of the mOTU database using BWA (mem algorithm, default parameters)  42. The resulting alignments are filtered and only those with at least 97% nucleotide identity are retained. Further, alignments are filtered according to their lengths (default: 75 bp minimum alignment length; can be adjusted using the -l option).
dist files) and major allele (allele. dist files). Benchmarking mOTUs2 against other tools. To evaluate its accuracy and robustness, we benchmarked mOTUs2 against two established tools for taxonomic profiling of metagenomic samples: MetaPhlAn2 7 , which is based on clade-specific marker genes, and Kraken 50 , which is based on exact alignments of genomic k-mers. MetaPhlAn2 (version 2. 6. 0) was executed with default parameters. For Kraken-labelled analyses, we executed Kraken for read classification and calculated relative abundances with Bracken 6. Kraken and Bracken were installed as version 1. 0. 0 using conda. The Minikraken database (version mini-kraken_20171101_8GB_dustmasked) was downloaded from https://ccb. jhu. edu/ software/kraken/. The Minibracken database was downloaded from https://ccb.
jhu. edu/software/bracken/ on 1 February 2018. We executed kraken using paired-end and single-end data using default parameters. Abundance estimation with Bracken was performed with the following parameters: -k minikraken_8GB_75mers_distrib. txt -l S -o result. abundance. bracken. Comparison of mOTUs with metagenome-assembled genomes. We further validated the mOTUs using metagenome-assembled genomes (MAGs) reconstructed from different environments. For this purpose, we first extracted 4880 metagenomic sequencing runs from human gut samples available from the European Nucleotide Archive (accession numbers are listed in Supplementary Data 2). Raw reads from each run were assembled using metaSPAdes v3. 10. 0 51 and subsequently binned with MetaBAT2 (v2. 12. 1)  52  with a minimum contig length threshold of 2000 bp.
From these samples, we selected 1845 MAGs (from Supplementary Data 2) that were reconstructed from these metagenomes. Correspondence between MAGs and mOTUs was established using the following procedure: first, we extracted the ten MGs from the MAGs using fetchMGs (see above), obtaining a set of MG-MAGs. Second, we aligned the MG-MAGs to the MG database of the mOTUs using vsearch -usearch_global (parameters: --id 0. 96 --minqt 0. 7). Finally, we evaluated the congruency of the MG-MAGs to mOTU matches. For this, we first checked if at least three MG-MAGs could be assigned to a mOTU (by mapping to a MGC that is part of a mOTU). If this was not the case the MAG was annotated as "unassigned/-1". Next, we removed all alignments to MGCs not assigned to mOTUs and assigned a MAG to a mOTU if >50% of the MG-MAGs were consistently matched to the same mOTU.
Otherwise (if no majority mOTU was found) the MAG is annotated as "inconsistent". Benchmarking mOTUs2 using simulated metagenomes. To be able to assess taxonomic quantification accuracy, ten human gut metagenomic samples were simulated using 15,102 Human gut MAGs: a subset of the 19,302 MAGs described before, excluding the MAGs created from samples used to construct the mOTU database (Supplementary Figure  8 ). MAGs with an ANI > 96. 5% were de-replicated to have one representative MAG per species (cut-off according to ref. 4  ). The ANI was calculated with the fastANI tool [https://github. com/ParBLiSS/FastANI]. The corresponding fastq files (as well as the simulated abundance data) are available at: http://motu-tool. org/download. html. Metagenomic read data were simulated using BEAR  54  : first, we generated 100 M inserts (2 × 100 M paired-end reads of 150 nt length) with 350 nt insert distance (standard deviation: 30) using gen-erate_reads.
py. Second, trim_reads. pl with default parameters was used to add the quality scores, introduce errors and shorten the reads. Every sample was simulated based on mOTUs2 profiled relative abundances from ten real samples. For each simulated sample, we randomly selected 50 MAGs with a representative reference genome sequence in the superset of the Kraken, MetaPhlAn2, or ref-mOTU databases and 50 additional MAGs sampled from those that lacked any reference database representation (which does not preclude these MAGs to map to meta-mOTUs). The benchmark was performed by evaluating precision-recall plots of the simulated metagenomes based on the number of true positives (TP) false positives (FP) representing species that are predicted but not present in the real sample, and false negatives (FN) representing species that are missed by the profiler.
Based on the resulting profiles (https://motu-tool. org/data/All_2481_at_least_500. motu. nr. out. 20180307. tsv), we classified a mOTU to be present in a specific environment if it was detected in more than three samples from that environment. Analysis of community structure. We assessed correlations of the Shannon index calculated based on 16S rRNA gene-based analyses and three metagenomic profiling tools (mOTUs2, MetaPhlAn2 and Kraken). For this we used data from two different biomes: metagenomes generated from stool samples of a colorectal cancer (CRC) study  21  and metagenomes from seawater samples of the Tara Oceans expedition  22. For the CRC study, amplicon sequencing data of the V4 region of the 16S rRNA were downloaded from the European Nucleotide Archive (ENA) database (http://www.
ebi. ac. uk/ena): accession number ERP005534. For the ocean water samples, 16S rRNA gene containing fragments were extracted from metagenomic sequencing reads (miTAGs  55  ). To ensure comparability between the data sets, we extracted the first 100 bp from each miTAG sequence starting from the V4 primer sequence. Ribosomal RNA data were initially processed using USEARCH 56 (version 9. 2. 64) as follows: paired-end reads were merged and quality-filtered using the fastq_mergepairs command with default settings. Merged reads were filtered using the fastq_filter command (-fastq_maxee 0. 1). Sequences were de-replicated using the fastx_uniques command, singletons were excluded and the remaining unique sequences were clustered into operational taxonomic units (OTUs) at 97% with chimera removal using the cluster_otus command.
More specifically, for each body site, we compared community compositional distances between samples from the same individual (intra-individual) to distances between this and other individuals (inter-individual). Canberra and Bray-Curtis distances were computed with the vegdist R function of the vegan package and the log-Euclidean distance was computed as the Euclidean distance of the log-transformed relative abundances after the addition of a pseudocount smaller than the smallest non-zero value. For each of the three distances and each sample, we identified the most similar sample (i. e. the one with the minimum distance value) and determined the proportion of cases in which both samples belonged to the same individual. Analysis of metatranscriptomes. To demonstrate the use of mOTUs2 to assess basal transcriptional activity of microbial community members, we used a dataset from 36 samples for which metagenomic and metatranscriptomic sequencing data are available  31.
Comparison of SNV profiles from MGs and whole genomes. To assess the comparability of SNV profiles generated with mOTUs2 and whole genomes, we used samples from 2807 human microbiome samples  14, 15  and 139 prokaryoteenriched metagenomes from the Tara Oceans project  22. Metagenomic reads were mapped to the mOTUs centroid database using the mOTUs2 command map_snv and in addition to a set of 5306 reference genomes  13. Genomic distances of strain populations between samples were estimated based on SNV profiles computed both on mOTUs and the whole genomes using the motus snv_call command. The filtering parameters used within the snv_call command were adapted to the specificity of datasets and references. The allele frequency tables were filtered using a horizontal coverage (-fb) equal to 40% for whole genome-mapped reads and 80% for mOTU-mapped reads, a vertical coverage (-fd) of 10, a per position coverage (-fc) of 5 and a position prevalence (-fp) of 0.
To determine whether intra-individual distances were smaller than inter-individual distances (see Supplementary Figure  17b )-indicating individuality of strain populations-we used ROC analysis. ROC curves (see Supplementary Figure  17a ) ascertain how accurately small distances predict whether a pair of samples originated from the same individual (with similarly small inter-individual distances being considered false positives) when systematically varying the distance cutoff. ROC curves can be summarized by the area under the curve (AU-ROC) with higher values corresponding to clearer separation between intra-and interindividual distances (Fig. 5b  and Supplementary Figure  17a ). Confidence intervals on the AU-ROC (Fig. 5b ) were obtained by bootstrapping using the pROC package  58.
Acknowledgements We thank members of the  IT core facility  and  Y. Yuan  for managing computing resources at  EMBL , and the IT support team of the  Institute of Microbiology  and the  HPC team at ETH Zürich  for managing computing resources and the  Euler/Leonhard clusters of ETH Zürich. This work was supported by  EMBL ,  ETH Zürich  [ PHRT-521 ], the  Helmut Horten Foundation , and the  Novartis Foundation for Medical-Biological Research  [# 17B077 ], the  BMBF  through the  German Network for Bioinformatics Infrastructure (de. NBI)  [# 031A537B ], and the  European Molecular Biology Organization  [ ALTF 721-2015], LTFCOFUND2013 [PCOFUND-GA-2013-609409 ].
Data availability To generate the mOTU database, we used reference genome sequence data from the proGenomes database (http://progenomes. embl. de) as well as metagenomic sequence data from the Genbank Sequence Read Archive (https://www. ncbi. nlm. nih. gov/sra) and the EMBL European Nucleotide Archive (ENA; https://www. ebi. ac. uk/ena) with accession numbers listed in Supplementary Data 1. Human gut metagenomic data and metagenome assembled genomes are available at the ENA (accession numbers are listed in Supplementary Data 2; MAGs can be downloaded from: http://ftp. ebi. ac. uk/pub/ databases/metagenomics/mags-gut_19k. tar. gz). The 10 human gut metagenomic samples simulated from metagenome-assembled genomes are available on Zenodo (https://doi. org/10. 5281/zenodo. 1473645). All other relevant data is available upon request.
Introduction Terminology systems provide standardized meaning of terms within a given domain. In medicine, many efforts have been undertaken into their development  [1] [2] [3]. There are different ways to distinguish types of terminology systems, among which is the categorization of interface, reference, and aggregation terminologies  [4, 5]. Interface terminologies provide close-to-user descriptions of concepts, including colloquialisms, various languages, and custom abbreviations. Interface terminologies can be linked to reference terminologies (e. g. , SNOMED CT), which may provide logic-based definitions of concepts, enabling search and aggregation based on hierarchical ordering and properties. Aggregation terminologies are classifications (e. g. ICD-10), which provide classification rules to represent information as non-overlapping classes, used for statistics-oriented data processing.
Analysis of results We developed a Java application to calculate the concept and term coverage, and to generate the input files that were required for the calculation of IAAs in R. The R scripts use agreestat functions  [13]  to calculate the IAA with Krippendorff's alpha measure  [14]. A complete description with examples of the calculation of these indicators is in Appendix E. All material and software produced are available in a Gi-tHub repository  [15]. The concept coverage is calculated as the percentage of codes within an annotation group. Two types of concept coverage measurements are calculated, viz. Strict coverage which only considers codes annotated with Full coverage and Inferred coverage scores, opposed to Loose coverage which considers, in addition, Partial coverage scores. Term coverage is calculated as the percentage of tokens covered with the interface terms from the corresponding terminology setting.
Acknowledgments This paper is produced as part of ASSESS CT which is funded from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  643818. We thank our project colleagues:  Anne Randorff Højen ,  Reza Fathollah Nejad ,  Vesna Kronstein Kufrin ,  Kornel Marko ,  Riikka Vuokko ,  Virpi Kalliokuusi ,  Benny Van Bruwaene. We thank the annotators  Hester Albers ,  Erica Wilthagen ,  Chris Wroe ,  David Robinson ,  Xavier Aimé ,  Jean Charlet ,  Paul Maurice ,  Claudia Riede ,  Laura ,  Robert Liljenström ,  Sebastian Jarl ,  Hämäläinen Päivi  and  Mäkelä-Bengs Päivi.
The validation of these five selected atmospheric correction algorithms provides an assessment of their accuracy and information about the most accurate algorithm over these coastal waters. The validation work exposed here is the first done over French coastal waters for the OLCI sensor. Actually, a similar work was published for OLCI AC development and validation, but over inland waters and not coastal waters  [52]. The background about atmospheric correction, the description of the five considered atmospheric correction algorithms, a full description of the study areas, in situ and satellite datasets, and the match-up procedure are provided in Section 2. In the next Section 3, the results of the match-up exercises are presented and discussed in Section 4 with the sensitivity of the results to different parameters where field Ocean Color Radiometry (OCR), match-up number impacts, AC performance impacts, and AC concomitant differences are highlighted.
The switch between both algorithms is carried out according to the turbid water flag  [33]. The BlAC includes the correction of blue absorbing aerosols  [75]. C2R-CC is based on artificial neural networks (ANN)  [40, 76] ). The ANN are trained with large datasets of simulated ρ A (λ) and ρ w (λ). Then, the trained neural nets perform the inversion of remotely-sensed ρ w (λ) from the remotely-sensed ρ toa (λ). C2R-CCAltNets is similar to C2R-CC  [40, 76]. Both use ANN. Here, the bio-optical models were revised for the simulation of ρ w (λ), and the ANN training procedure was improved with a new exponent for the detritus absorption coefficient (a d ) for an extended range of neural net training with more coverage of especially high backscatter waters and a larger number of training samples  [76].
When possible, the wind speed was measured. To generate the radiances, the radiometric calibration was carried out using the dedicated auxiliary data and software delivered with the ASD spectro-photometer. After performing the radiometric calibration, the first step of the post-processing protocol consisted of double-checking the quality of the measured radiances L p , L s , and L t. For each station, the coefficient of variation (CV Med ), the standard deviation over the median ratio, were computed at each λ. If CV Med (L x (λ)) was below 5%, 10%, and 10% for x = p, s, and t, respectively, the scans were conserved and used to compute the median value. If not, the relative median absolute difference (RMedD) was computed for each scan (L i , Equation (  3 )). The scans with values of RMedD(L i (λ)) equal to or below 10% for L p , L s , and L t were conserved and used to recompute the median Med(L x (λ)).
Remotely-Sensed Ocean Color Radiometry In the frame of the Sentinel-3 Validation Team, the last released version v2. 23 Level-2 (L2) Full Resolution (FR, spatial resolution of 300 m) reprocessed ocean color data and Level-1B (L1B) FR reprocessed top of atmosphere remotely-sensed radiance data were available and used for the validation of the OLCI-A ρ w product. Data from 26 April 2016-November 29, 2017 were downloaded via Copernicus Online Data Access repro (CODArep (https://codarep. eumetsat. int/)). After 30 November 2017, data were collected via the Earth Observation Portal (EO-Portal (https://eoportal. eumetsat. int/)) and Copernicus Online Data Access (CODA (https://coda. eumetsat. int)). The OLCI-A L2 was used for the validation of BlAC. L1B OCLI was processed using different software: the C2R-CC Processor Version 1.
0 plug-in, under the Sentinels Application Platform (SNAP) v6. 0 software  [76]  for C2R-CC and C2R-CCAltNets; the Polymer v4. 10 archived package for Polymer  [51] ; the SeaWIFS Data Analysis System (SeaDAS) v7. 5 for NASA  [97]. BlAC and Polymer v4. 10 were system vicariously calibrated (SVC), while this was not the case for the other ACs. The SVCs of these former AC were performed using Case I water measurements from the MOBY (Marine Optical BuoY)  [98]  and BOUSSOLE (BOUée pour l'acquiSition d'une Série Optique à Long termE)  [99, 100].
QAS gives quantification of the full spectrum quality of ρ w retrievals with reference to an internal dataset  [92]. QAS values have to be closer to one for the highest quality of retrieved spectra. χ 2 provides information about the full spectrum relative errors of AC retrievals and has to be null. SAM indicates the mean of the full spectrum difference between the AC retrieved and the field measured ρ w spectra and has to be equal to 0 •.
The black solid lines represent the 1:1 line. The colored solid lines show the linear regression lines between the field-measured and AC retrievals of ρ w. The color lines code and the number of match-ups are indicated in the text box of the top left scatterplot (λ = 400 nm), where magenta, blue, orange, green, and red colors stand for the baseline atmospheric correction algorithm (BlAC), the Case 2 regional coast color atmospheric correction algorithm (C2R-CC), the alternative neural nets of C2R-CC atmospheric correction algorithm (C2R-CCAltNets), Polymer, and NASA, respectively.
Here, Polymer became the most efficient AC when taking only the AC concomitant match-ups. Table  3  is similar to Table  2. Taking into account only the common match-ups and the ±2-h time window gives quite a similar analysis in comparison to the other ∆t. C2R-CCAltNets had the highest QAS value (0. 99), while BlAC had the lowest value (0. 82). For χ 2 , NASA had the lowest value (2. 18), while BlAC had the highest value  (4. 19). In terms of SAM, the minimum value was obtained for Polymer (6. 80) and the highest for NASA (15. 01). For S tot , Polymer had the maximum value (65. 33) followed closely by C2R-CCAltNets (58. 05), while BlAC presented the lowest score  (36. 11). On the common match-ups, the results were slightly different, with Polymer being the most accurate AC on our dataset, closely followed by C2R-CCAltNets.