dataesr/scientific-paragraphs-categorization huggingface dataset:
Metabolic profiling. We deployed a comprehensive metabolic phenotyping strategy combining in-house analysis by proton nuclear magnetic resonance ( 1 H-NMR) spectroscopy, gas chromatography coupled to mass spectrometry (GC-MS) and targeted UPLC-MS/MS with untargeted UPLC-MS data generated by Metabolon, as described in detail below: 1 H NMR spectroscopy. 1  H-NMR experiments were carried out using a Bruker Avance spectrometer operating at 600 MHz, as reported  [55] [56] [57]. Structural assignment was performed using data from literature, the Human Metabolome Database (http://www. hmdb. ca/), S-Base (Bruker) and in-house databases  55. 1  H-NMR.
NaturE MEDIcINE spectra were pre-processed and exported to MATLAB for multivariate statistical analyses using O-PLS-DA, as previously reported  58. Absolute metabolite quantifications were also derived using Bruker's In Vitro Diagnostics for research (IVDr) quantification algorithms. GC-MS semi-targeted profiling. Serum samples were prepared, analyzed and processed using standard protocols. In brief, serum samples (100 μl) were cleaned up with methanol protein precipitation, evaporated to dryness, derivatized and injected to an Agilent 7890B-5977B Inert Plus GC-MS system. The chromatographic column was an Agilent ZORBAX DB5-MS (30 m × 250 µm × 0. 25 µm + 10 m Duragard). The temperature gradient was 37. 5 min long, and the mass analyzer was operated in full-scan mode between 50 m/z and 600 m/z.
Peaks were annotated with the use of the Fiehn library (Agilent G1676AA Fiehn GC/MS Metabolomics RTL Library, User Guide, Agilent Technologies, https://www. agilent. com/cs/library/usermanuals/Public/G1676-90001_Fiehn. pdf). Metabolic features with high reproducibility (CV <30%) and linearity (that is, dilution signal rho > 0. 9 and false discovery rate (FDR)-corrected P < 0. 05 (one-tailed Spearman)) were kept in the final dataset, resulting in 102 annotated metabolic features. UPLC-MS/MS isotopic quantification of methylamines and carnitines. UPLC-MS/ MS was employed for the determination of absolute concentrations for TMA, TMAO, choline, betaine, γ-butyrobetaine, betaine-aldehyde, butyryl-carnitine, isovaleryl-carnitine, OH-isovaleryl-carnitine, stearoyl-carnitine, oleoyl-carnitine, linoleoyl-carnitine, myristoyl-carnitine, lauroyl-carnitine and decanoyl-carnitine.
Annotated metabolites and unknown features (denoted X-00000) were identified by comparing sample features with ion features in a reference database of pure chemical standards and previously detected unknowns, followed by detailed visual inspection and quality control, as reported  60. For all metabolomic assays, we randomized the sample preparation order across the whole study so that each sample preparation batch included samples from all study groups. For MS untargeted assays, median batch correction was performed by adjusting batch-wise study sample variable medians according to a scalar derived from adjusting pooled reference sample medians, so that pooled reference sample medians are identical across all batches. The randomized sample preparation batches were also tested for association with study groups using univariate statistics (Fisher's exact test or Kruskal-Wallis test), and P > 0.
05 was observed across all methods (GC-MS, Fisher's exact test, P = 0. 23; UPLC-MS targeted, Fisher's exact test, P = 0. 12; and UPLC-MS untargeted (Metabolon), Fisher's exact test, P = 0. 65). In addition, NMR run order exhibited a Kruskal-Wallis P = 0. 49. To choose a single measurement for the duplicate metabolites observed across platforms, we prioritized measurements based on the analytical quality of the data as follows: (1) Targeted quantification using isotopic standards (for example, UPLC-MS/MS for acylcarnitines and TMA) (2) Relative abundance with structural ID confirmed by native standards (for example, Metabolon UPLC-MS) (3) Relative/absolute or quantification by NMR calibrated against a database/ reference dataset (for example, > NMR quantifications and manually assigned peaks) (4) Relative or quantification with metabolite ID check against a standards database (for example, GC-MS) Drug deconfounding analysis.
The pipeline was used to assess to what extent observed differences among groups of study participants in microbiome, metabolome and bioclinical feature abundance are confounded, in the sense of being consequences of other (treatment or risk factor) variables different among the groups more so than characteristic of the specific phenotype itself. We employed the post hoc filtering approach implemented in the R package metadeconfoundR (version 0. 1. 8; see https://github. com/TillBirkner/ metadeconfoundR or https://doi. org/10. 5281/zenodo. 4721078) that was devised within the MetaCardis consortium  8. The pipeline has two steps. In the first, all associations between -omics features and the set of independent variables (disease status, drug treatment status and risk markers, including age and smoking status) are determined under non-parametric statistics (Mann-Whitney U-test (MWU) or Spearman test, adjusted for multiple testing using the Benjamini-Hochberg method).
The R package was applied to the present dataset considering medication status either as binary variables or as normalized dosages. Our deconfounding pipeline takes into account linear effects related to drug categories. Still, we were not able to control for every possibly lifestyle confounding factor, making a lack of full confounding adjustment a limitation of our study. Statistical analyses. Downsampled microbiome functional profile and taxonomic composition data, metabolite and quantitative clinical phenotype measurements were assessed between and within groups using non-parametric tests (MWU and Spearman test) corrected for multiple testing using the Benjamini-Hochberg approach. All tests undertaken as part of the univariate biomarker analyses involved comparing only two groups. The main exception was the comparison between the three study centers where we applied a Kruskal-Wallis test.
NaturE MEDIcINE Extended Data Fig. 2  | Microbiome findings from the literature. Cuneiform plot shows literature review of gut microbial taxonomic and predicted functional features reported to be associated with IHD, while highlighting their individual replication in the present MetaCardis study group either as a general dysmetabolism biomarker (seen only in case of HC versus MMC), or as an IHD biomarker (seen also in case of MMC versus IHD) (Supplementary Table  15 ). The literature review was performed as a keyword search in PubMed (Medline) using combinations of the words 'microbiota' and 'microbiome' with the word 'atherosclerosis', 'cardiovascular disease', 'coronary artery disease', 'ischemic heart disease', 'myocardial infarction', 'acute coronary syndrome', 'angina pectoris' and 'heart failure'.
NaturE MEDIcINE Extended Data Fig. 6  | Features categorization for ACS subgroup. Microbiome and metabolome features categorized as escalation-, de-escalation-, and ACS-specific biomarkers when features classification scheme (as shown in Fig. 3 , extended Data Fig. 4  and described in supplementary methods) was applied to various group comparisons involving HC, MMC and ACS groups. HC: healthy controls, MMC: metabolically matched controls, ACS: acute coronary syndrome, eSCF: escalation features, DSCF: De-escalation features. Gut microbiome features included taxonomic (prefix: Taxon) and microbiome density indices, whereas metabolome features included serum and urinary metabolites. Only features exhibiting absolute effect size > 0. 1 are displayed whereas the full list is given in Supplementary Table  17 ).
Acknowledgements We are indebted to the MetaCardis study participants and patient associations ( Alliance du Coeur and CNAO ) for their input and interface. Similarly, we are indebted to the  MetaCardis consortium  (http://www. metacardis. net/)collaborators for contributions at multiple levels since the consortium start in 2012. A full list of collaborators is given in the Supplementary Information. We further thank  D. Bonnefont-Rousselot  ( Department of Metabolic Biochemistry, Pitié-Salpêtrière Hospital ) for the analysis of plasma lipid profiles. We greatly appreciate the assistance of nurses, dietitians, laboratory technicians, clinical research assistants and data managers at the  Novo Nordisk Foundation Center for Basic Metabolic Research ; the  Clinical Research Unit at Fredriksberg and Bispebjerg Hospitals, Copenhagen ; the  Clinical Investigation Platform at the Institute of Cardiometabolism and Nutrition for Patient Investigations  and the  Clinical Investigation Center from Pitié-Salpêtrière Hospital, Paris ; and the  University of Leipzig Medical Center, Leipzig.
Quanta Medical  provided regulatory oversight of the clinical study and contributed to the processing and management of electronic data. Our study, Metagenomics in Cardiometabolic Diseases, with the acronym METACARDIS, was sponsored by the  European Union 's  Seventh Framework Program  for research, technological development and demonstration under grant agreement  HEALTH-F4-2012-305312. Assistance Publique-Hôpitaux de Paris  is the promoter of the clinical investigation. Parts of the studies were also supported by  Metagenopolis  grant  ANR-11-DPBS-0001 , the  Leducq Foundation , the  Novo Nordisk Foundation  and the  NIHR Imperial Biomedical Research Centre  and by grants from the  French National Research Agency  ( ANR-10-LABX-46  ( European Genomics Institute for Diabetes) ), the  National Center for Precision Diabetic Medicine , which is jointly supported by the  French National Agency for Research  ( ANR-18-IBHU-0001 ), the  European Union (FEDER) , the  Hauts-de-France Regional Council  ( Agreement 20001891/NP0025517 ), the  European Metropolis of Lille  (agreement 2019_ESR_11) and site  ULNE  ( R-002-20-TALENT-DUMAS ), which is also jointly funded by  ANR  ( ANR-16-IDEX-0004-ULNE ), the  Hauts-de-France Regional Council  ( 20002845 ) and the  European Metropolis of Lille.
The  Novo Nordisk Foundation Center for Basic Metabolic Research  is an independent research institution at the  University of Copenhagen , partially funded by an unrestricted donation from the  Novo Nordisk Foundation. K. Chechi  is supported by the  Medical Research Council Skills Development Fellowship  (grant no. MR/S020039/1 ) and the  Wellcome Trust-funded Institutional Strategic Support Fellowship  (grant no. 204834/Z/16/Z ). L. H. was in receipt of a  Medical Research Council Intermediate Research Fellowship in Data Science  (grant no. MR/L01632X/1 ,  UK Med-Bio ). S. K. F. was supported by the  German Centre for Cardiovascular Research , the  German Research Council  (projects  SFB1365 ,  SFB1470  and  KFO339 ) and the  German Ministry of Education and Research.
Code availability The novel drug-aware univariate biomarker testing pipeline, described in full elsewhere  8  , is available as an R package (metadeconfoundR; Birkner et al. , manuscript in preparation) on GitHub (https://github. com/TillBirkner/ metadeconfoundR) and also at https://doi. org/10. 5281/zenodo. 4721078. The latest version (0. 1. 8) of this package was used to generate the data shown in this publication. In addition, the scripts using this package to perform the analysis presented here are available at https://doi. org/10. 5281/zenodo. 5516219.
Results In total, 6 males and 6 females have been dissected in each model. Mean ages, weight, height and body mass index (BMI) were comparable between the two groups. The SIMLife ® perfusion has been successful in the 12 hemipelvises of Model B. Demographic data are shown in Table  1. Regarding the prevalence of the CMOR, 16 of 24 hemipelvises showed a CMOR (66. 67%), 50. 00% (n=12) of the bodies presented a unilateral CMOR, 16. 70% (n=4) bodies showed a bilateral presentation of the CMOR. In 4 bodies (8 hemipelvises), (2 males and 2 females), no retropubic vascular connection has been found (Table  2 ). The mean distance between the CMOR and the pubic symphysis was 58. 02 ± 6. 27 mm for model A, and 60. 97 ± 9. 94 mm for model B with a p= 0. 022 (Table  3 ). When it is present, 20. 83% of them were arterial, 45.
Acknowledging to all body's donors and their families. "The authors sincerely thank those who donated their bodies to science so that anatomical research could be performed. Results from such research can potentially increase mankind's overall knowledge that can then improve patient care. Therefore, these donors and their families deserve our highest gratitude". Our thanks to our anatomic monitors and to members of the  ABS Laboratory. We wish to thank again the members of the  Radiology Service of the Poitiers University ,  Mme D'Aubigné  and his team of the  "Musée de l'Ecole de Santé Navale de Rochefortsur-Mer ".
Peri-Operative Morbidity and Follow-Up Peri-operative complications including incidences of surgical revision, sepsis, hemorrhage, vascular thrombosis, overall bile duct complication and bile duct stenosis, asymptomatic CMV infection, pathologically confirmed acute cellular rejection and re-transplantation were reported. Blood loss until the first month after LT and length of patient's hospital stay were collected. Occurrences of HCC tumor recurrence after LT and OS were also identified. Post-transplant monitoring was adapted to date of LT and included 6-monthly contrast-enhanced CT-scan or MRI imaging coupled with AFP measurements during the first 5 years of follow-up, then annually during 5 additional years. The database was fixed on March 2021 for the last news.
MTT test Cell viability was evaluated using the 3-(4,5-dimethylthiazol-2yl)-2,5-diphenyl tetrazolium bromide (MTT) assay (Sigma-Aldrich), which measures mitochondrial activity in living cells. A solution at 5 mg/mL was prepared into Neurobasal and then added to cultures at a final concentration 0. 5 mg/mL for 3 h at 37 °C, 5% CO 2. Media were fully removed and 200 μL of DMSO added, then 100 μL of DMSO were transferred into a 96-well plate and absorbance (OD) at 550 nm was read in a spectrophotometer. Data were calculated as the percentage of living cells = (transfected cell OD 550 /control cell OD 550 ) × 100. The mean values ± SEM were obtained from at least five animals by genotype.
Gel zymography We used gelatin zymography on culture supernatants to assess changes in the levels of MMP-2 and MMP-9, also Article No : 2407 ¨ LE ¨ TYPESET MS Code : þ CP þ DISK known as gelatinase A and B, respectively. As previously described  [32] , equal amounts of serum-free supernatants in non-denaturing and non-reducing conditions were subjected to zymography according to the manufacturer's recommendations (ThermoFisher Scientific). Gels were scanned using GeneTools software.
Patch-clamp Whole-cell recordings were performed on neurons with pyramidal shape using an Axopatch200B amplifier (Axon Instruments, Axon Digidata 1550, Molecular Device, San José, California) under visual control, using a Zeiss Examiner A1 infraRed differential interference contrast microscope (Zeiss Meditech, Marly le Roi, France) coupled to a Jenoptik ProgRes MF camera (Carl Zeiss, Jena, Germany). Patch microelectrodes (1. 5 mm OD, borosilicate filament glass, BF150 from WPI) were pulled using a PP-830 electrode puller (Narishige, Fulbourn, Cambridge, UK), filled with 100 mM CsCl, 30 mM CsFl, 10 mM N-2-hydroxyethylpiperazine-N-2-ethanesulphonic acid (HEPES), 5 mM ethylene glycol-bis (b-aminoethylether)-N,N,N' , N-tetraacetic acid (EGTA), and 1 mM MgCl 2. Two mM CaCl 2 and 4 mM Mg-ATP/0. 4 mM Na 2 -GTP was added on the day of the experiment (pH 7.
Funding This work was supported by funding from the  CNRS  and  Aix-Marseille Université  and by public grants overseen by the  French National Research Agency (ANR) ,  MAD5  to SR, as part of the second " Investissements d' Avenir" program. The work was also supported by the DHUNE center of excellence and a CoEN (ADMIRE-MT5) grant to SR, by the  Fondation pour la Recherche Médicale  (FRM) and  Fondation Alzheimer  ( ALZ201912009627 ) to SR, and by  Fondation Vaincre l' Alzheimer  grants to SR. DP received support from the  French government  under the  Programme Investissements d' Avenir, Initiative d'Excellence d' Aix-Marseille Université  via  A*Midex  ( AMX-19-IET-004 ) and  ANR  ( ANR-17-EURE-0029 ) funding. DP was also a recipient of FRM funding-FDT202001011017. JMP was recipient of a doctoral fellowship from  Fondation Vaincre l' Alzheimer.
, 2017b). A 2011 editorial in The BMJ asserted that WikiProject Medicine could become a trusted resource 'if it was assisted, not shunned'  (Trevena, 2011). By 2013, Wikipedia's medical content was being viewed more than 6 billion times a year, more than WebMD, the Mayo Clinic and the World Health Organization websites combined  (James, 2016). WikiProject RNA, founded in 2007, created hundreds of articles describing families of non-coding RNAs based on entries in the Rfam database and worked with the Rfam team to redistribute the Wikipedia content as the primary textual annotation of its RNA families  (Daub et al. , 2008). A follow-up evaluation of this community annotation method noted a dramatic improvement in the content of the Rfam database. By 2011, a significant proportion of web traffic to the Rfam site was found to come from Wikipedia, reflecting its position as an important educational resource  (Gardner et al.
Further, the dissemination of current and high-quality domainspecific articles to the public may have an immediate impact on the field's growth. Recent evidence shows that scientific contributions to Wikipedia can have a causal impact on future developments on the respective field, further emphasizing the unique opportunity Wikipedia affords  (Thompson and Hanley, 2017). The primary goal of this study is to take a first step toward capitalizing on this unique opportunity by linking Wikipedia articles to their relevant ISCB COSIs (and thus to experts with the specialist knowledge to improve these articles) through the creation of a COSI-Article matrix. We also aim to provide analysis of this dataset to provide insight into the variation in computational biology representation on Wikipedia at a domain-specific level.
Defining computational biology articles Wikipedia articles relevant to computational biology were defined as articles tagged as being within the scope of the Computational Biology taskforce of WikiProject Molecular Biology; articles are tagged as such by Wikipedia editors (often WikiProject members) on the articles' talk pages, which provide a place for discussion of the articles. A list of relevant articles (https://wp1. openzim. org/#/pro ject/Computational_Biology/articles) was generated in July 2021 using the WP 1. 0 bot, an automated tool which tracks Wikipedia article assessment data  (Zheng et al. , 2019). In addition to the article titles, metadata including quality and importance ratings for each article were also extracted. Wikipedia articles are rated for both quality and importance by Wikipedia editors, according to defined scales; these ratings are also accessible via each article's talk page.
Defining articles for related fields We extracted data for comparisons with other WikiProjects and Wikipedia overall using the WP 1. 0 bot in October 2021. As in similar studies (e. g. Jemielniak et al. , 2021) , we compared only English Wikipedia articles (i. e. pages with encyclopedic content, removing list pages, templates, project pages and media files). For this assessment, we also removed articles which had not been assigned an importance rating. In the case of WikiProject Biography, where importance is rated as 'Core' (limited to the top 200 biographies) or 'Other', we kept all articles. We also removed a small number of articles with an 'A class' quality rating from our assessment, since this assessment class is often omitted by WikiProjects which lack a dedicated assessment team and is therefore unusually underrepresented (0.
Articles were required to be classified as relevant to at least one existing COSI; otherwise no specific directions were given to hackathon participants. Classifications were entered into a shared Google Sheets spreadsheet and participants were encouraged to discuss the classifications within their groups. Following the hackathon, the classifications of a randomly-sampled subset of articles were reviewed by faculty-level participants. The resulting dataset was a binary matrix, relating ISCB COSIs to Wikipedia articles. The data used in this study are available in the Zenodo repository (doi: 10. 5281/zenodo. 5814765) (Kilpatrick et al. , 2022).
Data mining and analysis Data were imported into R (v. 4. 1. 2) using the googlesheets4 (v. 1. 0. 0) tidyverse package. Article quality ratings were mapped to integer values (Stub ¼ 1, Start ¼ 2, etc. ); article importance ratings were mapped similarly. The trend line and confidence intervals in Figure  2A  and B were computed using a generalized additive model with restricted maximum likelihood, using the mgcv R package  (v. 1. 8-38) (Wood, 2017). Significance values for article enrichment were calculated by applying a two-sided Fisher's exact test to the appropriate 2 Â 2 contingency table. COSI similarity scores were computed for each pair of COSIs using Jaccard index, for each pair The article has attained featured article status by passing an in-depth examination by impartial reviewers.
COSI-Article interactions reveal variability in COSI representation Our analysis of data from the COSI-Article matrix reveals that computational biology domains have a large amount of variability in their representation on Wikipedia. The Education COSI was found to have the highest number of relevant articles (n ¼ 400), while four COSIs [Critical Assessment of Massive Data Analysis (CAMDA), Microbiome, Text Mining and Junior Principal Investigators (JPI)] were associated with fewer than 100 articles. The CAMDA and Microbiome COSIs both derive from critical assessment challenges, therefore the smaller number of relevant articles may arise from their more specialist nature. Articles also show wide variability in the number of COSIs that they are identified as being relevant to; the majority of articles are identified as being relevant to three COSIs or fewer, with more than 30% of articles relevant to only one COSI.
A small number of general computational biology articles (<1%) are identified as being relevant to 10 or more COSIs, with 4 articles identified as relevant to all COSIs. These articles describe three ISCB-affiliated conferences [ISMB, the European Conference on Computational Biology (ECCB) and the Pacific Symposium on Biocomputing (PSB)] and the ISCB Student Council. Surprisingly, the article on Bioinformatics was not identified as relevant to the JPI COSI and the article on the   ISCB was only associated with the BIOINFO-CORE, Education and JPI COSIs. Although many article-level quality and importance ratings were added by members of the Computational biology taskforce of WikiProject Molecular Biology, Wikipedia editors who are not necessarily domain experts may modify these ratings. However, when combined with data from the COSI-Article matrix we find that, as expected, both the current article quality and importance ratings (based on the criteria presented in Tables  1  and 2 ) display increasing trends as the number of relevant COSIs increases, justifying the use of these metrics in quantifying variability between COSIs (Fig.