dataesr/scientific-paragraphs-categorization huggingface dataset:
Nonmetric multidimensional scaling (NMDS) with stable solution from random starts was performed for the ordination of samples based on functional similarity using the KO abundance tables (Fig. 2 ) and the Pfam, COG, and EC number abundance tables (Supplementary Fig. 4 ). The Bray-Curtis distance measure was used for the abundance tables. All NMDS analyses were performed with the subsampled version of each abundance table. The partitioning of the variance in the Bray-Curtis distance matrix among oceans and oceanic basins was performed using permutational multivariate analysis of variance (PERMANOVA)  107  through the adonis function in the vegan R package (v2. 5. 6)  95  with 10,000 permutations. The two samples corresponding to the shallowest sampling station (station 62; 2400 m depth) were excluded from Fig.
2  as appeared as clear outliers (i. e. , showed the high distance to any other sample), but they were included in Supplementary Fig. 4. Metagenomic Assembled Genomes from Deep Malaspina bathypelagic samples. To build the Malaspina Deep Metagenome-Assembled Genomes (MAGs) catalog (MDeep-MAGs), all 58 metagenomes from the Malaspina expedition were pooled and co-assembled (megahit v1. 2. 8; options:-presets metalarge-min-contig-len 2000)  108. Resultant contigs were de-replicated with cd-hit-est (v4. 8. 1 compiled for long sequence support; MAX_SEQ = 10000000, with options -c 0. 95 -n 10 -G 0 -aS 0. 95 -d 0)  97. With this procedure, we increased the sequence space and we obtained a total of 421,891 contigs larger than 2000 bp. Metagenomic reads were back-mapped to the contigs dataset (bowtie 109 v2.
0. 18, lineage_wf)  64  , and the placement in the prokaryotic tree of life of each MAG with completeness larger than 50% and contamination lower than 10% was used to plot a tree depicting the phylogenetic relationships between them (Fig. 5 ; iTOL 113 v4). Finer taxonomic assignations of the resulting 317 MAGs were estimated against the Genome Taxonomy Database (release r89) using GTDB-Tk 114 (v1. 0. 2; classify_wf) (Supplementary Data 9). Briefly, the annotation relies on both taxonomic placement in a backbone tree (using marker genes) and average nucleotide identity (ANI) comparison based on ~150,000 genomes, spanning isolates, MAGs as well as SAGs. Deep Malaspina MAGs annotation and metabolic prediction. All 619 bins were annotated, including gene prediction, tRNA, rRNA, and CRISPR detection with prokka  115  (v1.
13) with default options, using the estimated Domain classification from CheckM output as an argument of the-kingdom option. Additionally, MAGs' predicted coding sequences were annotated against the KEGG orthology database  44  with kofamscan  116  (v1. 1. 0; database timestamp 2019-10-15), and against the PFAM database (release 31. 0) with hmmer 117 (v3. 1b2) with options-domtblout -E 0. 1. Primarily KEGG orthology was used to determine the energetic metabolism of the Malaspina Deep-Sea MAGs. A total of 83 marker genes from carbon fixation, methane, nitrogen, hydrogen, and sulfur metabolisms were selected (Supplementary Data 7) and their presence was explored along the low-quality (LQ) bins and medium quality (MQ) and high-quality (HQ) MAGs (https:// malaspina-public. gitlab. io/malaspina-deep-ocean-microbiome/).
A selected pool of 25 MAGs highlighting the potential for chemolithoautotrophy, mixotrophy, and non-cyanobacterial diazotrophs (NCDs) (Supplementary Data 9and Supplementary Data 10) was further explored within the MDeep-MAGs. Estimation of module pathway completeness within the Deep Malaspina MAGs dataset. Module completeness per MAG was estimated by calculating the percentage of KOs belonging to each module over its total KO number (KEGG release 2019-02-11). Deep Malaspina MAGs abundance in the global bathypelagic Ocean. The abundance of each MAG was assessed by mapping competitively the reads from the 58 metagenomes against the MAGs contig database using blastn  104  (v2. 7. 1; options -perc_identity 70 -e-value 0. 0001). Metagenomic reads were randomly subsampled to the smallest sequencing depth value (4,175,346 read pairs) with bbtools (v38.
1. 3. 1) to a total of 1651 sequences. We translated the sequences to amino acids (bacterial genetic code) and we removed redundancy by clustering all amino acid sequences to 98% of identity with cd-hit  97 (v4. 8. 1)  to a final dataset of 586 sequences. We then added 19 sequences from 17 MAGs annotated with either K10944 or PF12942 and aligned all of them in mafft  125 (v7. 402)  with the G-INS-i option. We built a maximumlikelihood tree with FastTree 123 (v2. 1. 10) with default options and the Jones-Taylor-Thorton maximum-likelihood model and plotted it in iTol 113 v4. To analyze the phylogeny of the nifH gene of 3 potentially diazotrophic MAGs, we retrieved their blastp best hits against NCBI's nr database (July 2020). We then added all nifH sequences between 240 and 320 residues long belonging to phylum Proteobacteria at NCBI Identical Protein Groups (IPG) database (query ((nifH AND ("240"[SLEN]:"320"[SLEN]))) AND proteobacteria[Organism]) and, additionally, we included 9 nifH sequences from MAGs from the Tara Oceans expedition  62  , making a total of 192 sequences.
Acknowledgements We thank the  R/V Hespérides captain  and crew, the chief scientists in Malaspina expedition legs, and all project participants for their help in making this project possible. This work was funded by the  Spanish Ministry of Economy and Competitiveness (MINECO)  through the  Consolider-Ingenio program  (Malaspina 2010 Expedition, ref. CSD2008-00077 ). The sequencing of 58 bathypelagic metagenomes was done by the  U. S. Department of Energy Joint Genome Institute , supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02 05CH11231  to  SGA  ( CSP 612  " Microbial metagenomics and transcriptomics from a global deep-ocean expedition "). Additional funding was provided by the project  MAGGY  ( CTM2017-87736-R)  to S. G. A. from the  Spanish Ministry of Economy and Competitiveness, Grup de Recerca   2017SGR/ 1568  from  Generalitat de Catalunya , and  King Abdullah University of Science and Technology (KAUST)  under contract OSR # 3362  and by funding of the  EMFF Program  of the  European Union  ( MERCLUB  project, Grant Agreement 863584).
Data availability All data generated or analyzed during this study are included in this published article (and its supplementary information files). All raw sequences are publicly available at both DOE's JGI Integrated Microbial Genomes and Microbiomes (IMG/MER) and the European Nucleotide Archive (ENA). Individual metagenome assemblies, annotation files, and alignment files can be accessed at IMG/MER. All accession numbers are listed in Supplementary Data 1. The co-assembly for the MAG dataset construction can be found through ENA at https://www. ebi. ac. uk/ena with accession number PRJEB40454, the nucleotide sequence for each MAG and their annotation files can be found through BioStudies at https://www. ebi. ac. uk/biostudies with accession S-BSST457 and also in the companion website to this manuscript at https://malaspina-public.
GenOMICC/ISARIC4C Whole-genome sequencing at mean coverage of 20x was performed on the Illumina NovaSeq6000 platform and then analyzed using the DRAGEN pipeline  (software v01. 011. 269. 3. 2. 22 , hardware v01. 011. 269). Variants were genotyped with the GATK GenotypeGVCFs tool v4. 1. 8. 1.
Model training The dataset was divided into a training set and a testing set (90/10), and the entire procedure described in this section was performed using only samples in the training set. A bootstrap approach with 100 iterations was adopted to train the model. At each bootstrap iteration, 90% of the samples were selected (without replication), and the following two steps were performed: (step 1) selection of the most relevant features for each Boolean representations; and (step 2) definition of the weights of the Integrated Polygenic Score (IPGS). After the 100 bootstrap iterations, the information extracted on relevant features and weighting factors are merged to define the final IPGS (step 3). The IPGS is then used, together with age and sex, for training a model that predicts the COVID-19 severity (step 4).
Step 3: IPGS definition In this step, the data extracted at each bootstrap iteration in steps 1 and 2 are combined to define the IPGS. First, for each of the Boolean features, of all the 12 representations, the number of times this feature was selected in the 100 bootstrap iterations is computed. Then, the entire bootstrap procedure is repeated using random input phenotypes, and the 5th percentile of the number of times that a feature is associated with a random phenotype is estimated. This threshold, computed separately for each Boolean representation, was used to select which Boolean features are included in the final model. As no significant association is expected among the Boolean features and the random phenotype, the threshold of the 5th percentile is expected to exclude with a 95% level of confidence the possible false positive associations.
As a further evaluation of the importance of the IPGS score on the severity prediction, univariate logistic regression models using as independent variables age (continuous represented in decades), sex, and IPGS were fitted to the dataset that combines both the training set and the testing sets for a total of 2240 patients. These models were used to estimate the odds ratios and the p values of the association with the severe phenotype. Furthermore, a multivariable logistic regression was fitted using IPGS, age, and sex together. Finally, a multivariable logistic regression was performed using as predictor variables: IPGS, age, sex and comorbidities (congestive/ischemic heart failure; asthma/ COPD/OSAS; diabetes; hypertension; cancer). This latter model has been fitted in the training set, where the information on comorbidities was available.
Finally, we summed all the weights of each Boolean feature by gene. Briefly, Pathway enrichment analysis was made using the GSEApreranked module (v. 7. 2. 4) of the Genepattern platform  (Wang et al. 2010) , on several pathway categories (BIO-CARTA, KEGG, REACTOME, GOBP, HALLMARKS, C7 and C8), limiting the size of gene sets to the 10-300 range and performing 10,000 permutations. The networks W geneA = ∑ Features Gene A ABS(mean ) × count × F. showing similarity of significant pathways were built using the EnrichmentMap algorithm  (Subramanian et al. 2005)  in the Cytoscape suite (v. 3. 8. 2)  (Merico et al. 2011; Shannon et al. 2003). Parameters used for network creations are: Jaccard Overlap Combined Index (k constant = 0. 5), edge cutoff 0. 05.
Feature selection and gene discovery The definition of the single terms of the IPGS formula requires 4 separate steps (Fig. 1):  (1) the definition of a severity phenotype adjusted by age and sex; (2) the conversion of genetic variants into Boolean features that represent the presence of variants in different frequency ranges in each gene; (3) the selection of those features that are associated with disease severity; and (4) the optimization of the weighting factors appearing in Equation  1. These 4 steps were executed using data from a training set extracted from the GEN-COVID dataset (90% of the patients, n=1780, see Methods). The phenotype adjusted by age and sex was computed using an ordered logistic regression model, with the purpose of facilitating the extraction of features associated with the genetic basis of COVID-19 severity (Fig.
For genes with more than one significant Boolean feature, we summed up the scores of each feature. Gene Set Enrichment Analysis (GSEA) was then performed using two separate ranked gene lists (Supplementary Table  7 ) for females and males, followed by the generation of similarity networks using Enrich-mentMap (Fig. 4A ). The usage of rank-based search method allows to identify statistically significant pathways starting from extensive list genes, as each gene is associated with its specific importance. Although no pathways satisfied the 0. 25% FDR threshold normally required for standard GSEA analyses, the set of pathways considered significant using more relaxed thresholds on p values were shown to group in meaningful modules, providing useful information on pathogenetic mechanisms and on the genes that could explain how they can be affected.
Furthermore, as opposed to PRS, IPGS relies on both polymorphisms and rare variants is capable of differentially weighting features in an indirectly proportional way in respect to frequency, and therefore, to protein impact. Each patient indeed is assigned both a number and the list of her/his common and low-frequency polymorphisms relevant to COVID-19 supported by medically actionable information and of rare and ultrarare variants conferring either risk of severity or protection from severe disease. Drawing on the entire picture presented through IPGS analysis, personalized adjuvant therapy could be envisaged. At the time of writing, a platform trial based on genetic markers is being discussed with the Italian Medicines Agency  (EudraCT Number: 2021-002817-32). Within 25 reported genomic SNPs demonstrably related to COVID-19 susceptibility/severity, 5 were reported to be in LD with coding variants  (COVID-19 Host Genetics Initiative et al.
Acknowledgements This study is part of the  GEN-COVID Multicenter 1 3 and Compute Canada  is acknowledged. TwinsUK is funded by the  Welcome Trust, Medical Research Council, European Union , the  National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust  in partnership with  King's College London. These funding agencies had no role in the design, implementation or interpretation of this study. JBR has served as an advisor to GlaxoSmithKline and  Deerfield Capital. His institution has received investigator-initiated grant funding from  Eli Lilly ,  GlaxoS-mithKline and Biogen  for projects unrelated to this research. He is the founder of 5 Prime Sciences. SweCovid received funding from the  European Union 's  Horizon 2020 research and innovation program  under Grant agreement No  824110 , the SciLifeLab/KAW national Covid-19 research program project grants to MH ( KAW 2020.
This study owes a great deal to the  National Institute of Healthcare Research Clinical Research Network (NIHR CRN ) and the  Chief Scientist Office (Scotland) , who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. We also acknowledge funding provided by the  German Research Foundation (DFG)  through the  NGS competence centres  ( INST 37/1049-1, INST 216/981-1, INST 257/605-1, INST 269/768-1  and  INST 217/988-1 ) and scientific support through the  German COVID multiomics initiative (DeCOI). Recruitment of the  COMRI Study/of part of the DeCOI samples  was funded by the  Klinikum rechts der Isar ,  Technical University Munich, Munich, Germany. We received additional support by the  DFG  ( LU 1944/3-1 , to  K. U. L. and  SCHU 2419/2-1 , to E.
Data availability and data sharing statement The data and samples referenced here are housed in the GEN-COVID Patient Registry and the GEN-COVID Biobank and are available for consultation. You may contact the corresponding author, Prof. Alessandra Renieri (e-mail: alessandra. renieri@unisi. it). Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.
, 2020 ). The aim was to compare methods and assess the performance over clinical data from an in-house test set. Evaluation was performed by ranking the methods against each other, for both the detection and segmentation of UIAs, by determining detection and segmentation metrics. This paper provides an overview of the challenge including the organisation, the results, a detailed evaluation of methods submitted and their performance on the test data. This paper follows the structure outlined in the Biomedical Image Analysis challengeS (BIAS) guidelines for transparent reporting of biomedical image analysis challenges ( Maier-Hein et al. , 2020 ).
Challenge Organisation The results of the ADAM Challenge 2020 were presented at the 23 rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) on October 8 th , 2020. From 3 rd April 2020, participants could register on the website ( http://adam. isi. uu. nl/ ) to participate in the challenge. They could download a training dataset (for full details on the data, see Section 2. 3 ) to train and develop fully automatic methods for the challenge. Participants were also allowed to use their own training data, as long as they referenced this in their method descriptions. Once trained, methods were containerised by participants with Docker  ( Merkel, 2014 )  and submitted to the organiser. Examples and instructions are provided on the website ( http://adam.
isi. uu. nl/methods/ ). The containerisation allowed easy evaluation of the methods, guaranteeing it could be run on our platform. Submitted containers were run on an individual training case from the training dataset, containing UIAs, and the results were sent back to the participant for verification. If technical issues or bugs occurred, teams were allowed to resubmit a new version with the bugs fixed. The final verified, submitted methods were evaluated on a test set of images (see Section 2. 3 ) using evaluation code that was made publically available ( https://github. com/hjkuijf/ADAMchallenge ). If the method required, NVIDIA Titan Xp GPUs were used for evaluation. The deadline for submission for consideration for the challenge leaderboard at MIC-CAI was 17 th August 2020 and the results and awards were announced at the MICCAI conference (8 th October 2020).
Mission of the challenge The ADAM Challenge consists of two tasks. Task 1 had the aim of automatic detection of UIAs on TOF-MRAs. Task 2 was for a method that could perform automatic segmentation of UIAs on TOF-MRAs. Participants could submit to one or both tasks, and methods submitted to task 2 were also assessed for task 1. The target cohort is the term used to describe the patient group of which data would be acquired for the final application of the submitted methods  ( Maier-Hein et al. , 2020 ). For the ADAM Challenge the target cohort was any patient undergoing a clinical brain TOF-MRA to screen for the presence of an UIA. To reflect the clinical setting, some MRA scans were negative (i. e. a patient without any diagnosed UIAs) and some scans had more than one UIA. A patient in the target cohort may be scanned for the following reasons: (1) follow-up scans of patients with diagnosed UIA(s), with or without additional treated aneurysms; and (2) patients screened for positive family history of UIAs or aSAH.
The challenge cohort is the term used to describe is the patient group of which the challenge data was acquired, for both the training and the test datasets  ( Maier-Hein et al. , 2020 ). The challenge cohort consists of a subset of patients, who had an available TOF-MRA, from a cohort of patients at the University Medical Center (UMC), Utrecht with at least one diagnosed UIA and cohorts of persons screened for UIAs because of a positive family history for aSAH. The assessment aim of the challenge is to find a method that performs optimally for the automatic detection and segmentation of UIAs from the TOF-MRAs in the challenge cohort test dataset.
Challenge data sets A total of 254 brain TOF-MRA scans were included with 282 untreated UIAs. The training dataset provided to participants consisted of 113 training cases, while the test dataset consisted of 141 cases, where each case contained a TOF-MRA and a structural image (either T1 -, T2weighted or FLAIR). All MRIs were performed at the UMC Utrecht, the Netherlands, on a variety of Philips scanners with field strength of either 1, 1. 5 or 3T. The MRAs had an in-plane voxel spacing range of (0. 195-1. 04) mm and slice thickness range of (0. 4-0. 7) mm, without a set acquisition protocol. This was due to the clinical nature of the data and that it was taken from several studies across a long period of time  (between 2001 and 2019). The subjects with UIAs ( N = 53) had a median age of 55 years (range 24-75 years), with 75% of subjects being female.
A subset ( N = 156) of the dataset includes two scans from the same subject, both a baseline and a > 6 month follow-up scan, to reflect the real clinical data. The UIAs ranged in size, with a median maximum diameter of 3. 6 mm and a range from 1. 0-15. 9 mm. 25% ( N = 52) of the scans contain multiple UIAs and 28% of the scans contained treated (either coiled or clipped) UIAs ( N = 59). The median age of the population without UIAs was 41 years (range 19-61 years) and 65% were female. This reflects the clinical setting, as UIAs are more common in females and the older generation  ( Vlak et al. , 2011 ). The dataset was realistic and diverse, reflecting different standard clinical protocols used between MR-scanners and over time.
Training and test data Subjects were randomly split into training and test sets and it was ensured that both sets contained an adequate number of scans without any UIAs. Every case in the dataset contained one TOF-MRA and one structural ( T1 / T2 /FLAIR) MR image of the same subject. The training dataset consisted of 113 cases: 93 cases containing at least one untreated, UIA (35 baseline and 35 follow-up cases of the same subject and 23 cases of unique subjects) and 20 cases of subjects without UIAs. The test dataset consists of 141 cases: 115 cases containing at least one untreated UIAs (43 baseline and 43 follow-up cases of the same subject and 29 cases of unique subjects) and 26 cases of subjects without UIAs. The training data is available on the challenge website and requires a registration and acceptance of our terms of distribution.
Annotation procedure All UIAs were diagnosed on the scans as part of clinical routine. The UIAs were manually segmented from the original TOF-MRAs using inhouse developed software implemented in MeVisLab (MeVis Medical Solutions AG, Bremen, Germany). A contour was drawn around the outline of the UIA, on all axial slices of the MRA. The parent vessel and any branching vessels were excluded from the annotation and annotations were always drawn starting from the UIA neck to the UIA dome. An experienced interventional neuro-radiologist ( > 10 years of experience) trained a second rater with considerable experience in medical image analysis and annotation software, but not specifically UIAs. The trained second rater annotated all images in the dataset. Finally, the first and   ( Klein et al. , 2010 )  d) binary aneurysm image derived from annotations as described in Section 2.
3. 3 Bottom Row: e) pre-processed TOF-MRA using n4 bias field correction  ( Tustison et al. , 2011 ) , f) structural MR image preprocessed using n4, g) pre-processed structural MR image aligned to TOF-MRA using pre-determined registration parameters. second rater assessed the full dataset together and made required modifications to the annotations in consensus to form the official ground truth data set. During annotation, the raters had access to the structural image and a radiologist report made at the time of the scan, indicating the location and size of the UIA. The same annotation procedure was performed for all treated UIAs and dilated to create a slightly larger mask for exclusion of treated aneurysm. The resulting annotations were converted to binary masks and voxels were considered part of the UIA if they were > 50% inside the contour.