dataesr/scientific-paragraphs-categorization huggingface dataset:
Subclass results To get further indications of the classification performances, we detail the confusion matrix for each sex-stage subclass in the dataset (Table  1 ). We remind, however, that these values are still computed based on the population class (Atlantic and Mediterranean) only and that the performances for subclasses (e. g. , Atlantic female hatchling) are detailed after the classification process. With LDA, the lesser precision values are achieved for hatchlings and mature males with shape data and for hatchlings only with form data (Table  1 ). The less complex dental morphologies of the dataset are also visually identified in these groups that display one or three tooth cusps. Recall values are low for Mediterranean hatchlings and mature males for which the recall reaches 10. 5% with shape data (Table  1 ).
We aimed to compare the results from this traditional workflow with the classification performances obtained with Random Forests on raw shape data. Doyle, Gammell & Nash (2018) already compared the classification performances of LDA and Random Forests on the shells of populations of common periwinkles (Littorina littorea) distributed in different niches. They recommended the use of Random Forests over LDA because the former is more straightforward and robust, does not make assumptions about the data nor necessitate the user to check the violation of LDA assumptions  (Doyle, Gammell & Nash, 2018). However, the models of  Doyle, Gammell & Nash (2018)  do overfit and the results obtained might not be optimal. Overall, LDA models achieve lower performances than Random Forests with our dataset, which can be due to information reduction.
ACKNOWLEDGEMENTS We acknowledge the  MRI platform member of the national infrastructure France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 , "Investments for the future"), the  labex CEMEB  ( ANR-10-LABX-0004 ) and  NUMEV  ( ANR-10-LABX-0020 ) and thank  Renaud Lebrun  for his help with microCT imaging. We acknowledge the contribution of  SFR Biosciences  ( UMS3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL) AniRa-ImmOs facility  and thank  Mathilde Bouchet-Combe  for her help with microCT imaging. We thank  Sylvie Agret  for her help generating 3D surfaces.
Data Availability The following information was supplied regarding data availability: The 3D surfaces are available at MorphoMuseum: Berio F. , Bayle Y. , Agret S. , Baum D. , Goudemand N. , Debiais-Thibaud M. , 2022. 3D models related to the publication: Hide and seek shark teeth in Random Forests: Machine learning applied to Scyliorhinus canicula. MorphoMuseuM. DOI 10. 18563/journal. m3. 164. The code, landmark and semilandmark coordinates are available at GitHub: https://github. com/fberio/scanicula-teeth.
Cohort 2 ScW and VsW (omental) biopsy samples were obtained from severely obese (BMI >35 kg/m2) and lean subjects (BMI ≤25 kg/m2) who were undergoing elective surgery. Obese patients were candidate for bariatric surgery and the studies were conducted in Paris France and Spain in accordance with the Helsinki Declaration and approved by the Ethics Committee of Clinical Research (CPP Ile-de-France 1, Fibrota study N° clinical trial NCT01655017) and University Hospital of Girona Dr. Josep Tructa). All patients were characterized for detailed corpulence and metabolic phenotyping as described in  16. Blood sampling were performed one month before the surgery at the fasting state. Paired AT samples (visceral and subcutaneous fat) were obtained at the time of the surgery by the same surgeon. Signed informed consents were obtained in all lean and obese individuals in agreement with ethic regulation.
Gene expression and pathway enrichment analyses Raw counts were subjected to differential gene expression analysis via DESeq2  29  and different R packages (Supplementary software). Genes, which showed raw counts lower of equal to 2 in 50 % of all samples, were removed prior to the differentially expressed analysis. Wald test was applied to extract differentially expressed genes (DEGs, Supplementary Information, supplementary Table  2 , 3, 6-8). Obtained DEGs were annotated via customized python scripts. Pathway enrichment analyses were performed with PIANO 30 , using the gene set collection C2 retrieved from the Molecular Signatures Database (MSigDB)  31, 32  (Supplementary Information, supplementary Table  4, 5 ). Bonferroni and Holm method was applied to correct for multiple testing.
Acknowledgments. work was funded by  Wellcome-Trust  strategic award [ 100574/Z/12/Z ],  MRC MDU  ( MC_UU_12012/2 ),  H2020 EPoS (Elucidating Pathways of Steatohepatitis -Grant Agreement 634413and the  British Heart Foundation  ( RG/18/7/33636 ). The  Disease Model Core, Biochemistry Assay Lab the Histology Core and the Genomics and Transcriptomics Core  are funded by  MRC_MC_UU_12012/5  and a  Wellcome- Trust Strategic Award  [ 208363/Z/17/Z ]. We thank the  Wellcome-Trust Sanger Institute Mouse Genetics Project (Sanger MGP ) and its funders for providing the mutant mouse line (Pepd<tm1a(KOMP)Wtsi. Funding and associated primary phenotypic information may be found at www. sanger. ac. uk/mouseportal. "We thank the  Disease Model Core  from the  Wellcome-MRC Institute of Metabolic Science and Agnes Lukasik  for their technical assistance in the animal work.
All animal work was carried out in the  Disease Model Core (MRC Metabolic Diseases Unit  [ MRC_MC_UU_12012/5 ];  Wellcome-Trust Strategic Award  [ 100574/Z/12/Z ]). We also thank  Genomics and Transcriptomics core , the Histology core and  Gregory Strachan  from the Imaging core for their technical assistance. All serum biochemistry was conducted by the  Biochemistry Assay Lab (MRC Metabolic Diseases Unit  [ MRC_MC_UU_12012/5 ]). Clinical studies in France were supported by  Contrat de Recherche Clinic (CRC APHP, Fibrota  to JAW and KC), by  National Agency of Research (ANR-Captorto KC ) and by  EFSD  (to KC). KC was funded by  H2020 EPoS (Elucidating Pathways of Steatohepatitis -Grant Agreement 634413. MD and Märta Beijer Foundation researcher. He is supported by project grants from the  Swedish Heart-Lung Foundation  (20140543, 20170678, 20180706) and the  Swedish Research Council  ( 2015-03657 ,  2019-01417 ).
For a few cases, the IIR can improve the CALIOP-based CAD by confirming part of the remaining ambiguous cloud layers and correct some aerosol layer misclassifications often embedded in larger cloud structures. IIR can also help with the cloud-aerosol classification where lidar signal is very noisy or when low laser energy shots occur. The preliminary version of a CAD score based on the IIR observations proposed in this paper could serve as a first step for the design of a future CAD making use of both the lidar and the IIR observations. To help further in the discrimination between cloud and aerosol layers, the collocated MODIS level-2 MOD04 aerosol data products based on multispectral analysis for identification of smoke or other non-dust aerosol layers may be complementary to the IIR analysis.
Electrode structures generation We have reported in our previous articles computational methods for stochastic and manufacturing simulation-based generation of LIB electrodes  [ 26 , 27 ]. In this work, the stochastic method of electrodes generation was chosen to have electrodes with controlled porosity and keeping all the other parameters ( e. g. electrode formulation) constant. The details of the technique can be found in Ref. [28]  The obtained electrode mesostructures are meshed by using our voxelization algorithm INNOV in the Matlab® environment  [29].
Pore size distribution The pore phase of the generated electrodes was further segmented into individual pores using the watershed method to obtain pore size distribution of the studied mesostructures  [ 9 , 10 ]. Specifically, the Poro-Dict library was used as part of the GeoDict® software package. Due to the discrete voxelated nature of the studied mesostructures, over-segmentation of the pore space is a common artifact induced by watershed-based methods. Many post-processing methods exist to alleviate the effect of such artifacts. In GeoDict® this is solved by reconnecting the initially segmented pores only if the percentage of surface area that is shared between them is bigger than a chosen value. A low threshold value results in many pores being reconnected, while the contrary happens when a high threshold value is selected.
Acknowledgments A. A. F. , A. S. , O. A. , J. X. , H. O. , M. C. , and M. M. acknowledge the  European Union 's  Horizon 2020 research and innovation program  for the funding support through the  European Research Council  (grant agreement  772873  , " ARTISTIC  " project). A. A. F. and O. A. acknowledge partial funding from the  Région Hauts de France  and from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no  957189  ( BIG-MAP ). A. A. F. acknowledges the  Institut Universitaire de France  for the support. J. Y. and A. A. F acknowledge also the funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no  875489  (SONAR).
Next, we applied gene sets defined by the immunological C7 collection from the Molecular Signature Database  (31)  to confirm the biologically relevant phenotypes of the defined clusters. Clusters 4, 5, and 6 were associated with gene sets previously shown to be upregulated in effector T cells. Gene sets associated with a memory T cell phenotype were preferentially associated with clusters 0, 1, and 8 (Figure  6F ). Applying RNA velocity  (32) , a directional flow from proliferative effector-associated CD8 + T cells (clusters 4, 5, and 6) through the intermediate state (clusters 1, 2, 3, 7, and 8) toward memory CD8 + T cells (cluster 0) (Figure  6G ) was detected. Major dynamic-driving genes for the inferred T cell dynamics were the upregulated genes Satb1, Itga4, and Kifl1 with a positive velocity, as well as the downregulated Lgals1 gene, for which a negative velocity was detected (Figure  6H ).
Acknowledgments We thank  Hans-Peter Rahn  and  Kirstin Rautenberg  from the  Flow Cytometry Platform  ( Max Delbrück Center [MDC ]) for expert technical help; we acknowledge  Il-Kang Na  ( Charité -Universitätsmedizin Berlin ) for providing MataHari mice and  Carlo Croce  ( OSUCC ) for the Eμ-Tcl1 mouse strain. We are grateful to  Michael Moles  ( MDC ) for critical reading of the manuscript. This work was funded by the  DFG  (grant  SFB/TR36 [ project  B12] ,  RE 1103/6-1 ) awarded to AR and UEH. AR and UEH received research funding from  Fate Therapeutics. Address correspondence to:  Armin Rehm ,  Max Delbrück  Center for Molecular Medicine,  Department of Translational Tumorimmunology ,  Robert-Rössle -Str. 10, 13125 Berlin, Germany. Phone: 49. 30. 9406. 3817; Email: arehm@mdc-berlin. de.
Data availability. scRNA-Seq data have been deposited in NCBI's Gene Expression Omnibus (GEO)  (51)  and are accessible through GEO Series accession number GSE156611 (https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc= GSE156611). All data are available in the main text or the supplemental materials. Statistics. Results are expressed as the arithmetic means ± SEM. P < 0. 05 was considered statistically significant, as determined by the 2-tailed Mann-Whitney test for non-normally distributed data, or the 2-tailed paired and unpaired Student's t test for normally distributed data and large sample sizes, where appropriate. Study approval. Animal studies were performed according to institutional and Berlin State guidelines (approved by the Landesamt für Gesundheit und Soziales, Berlin; approval TVV G 0089/10, G 0091/15, and G 0373/13).
One locus was detected in both the male-only and female-only analyses, but not the full (combined sexes) analysis (Figure  1 ). This was due to heterogeneity between the maleonly and female-only signals (Table  S4 ). Of the 410 genetically independent signals identified across all analyses, 44 (representing 36 loci) had significant heterogeneity (P < 1×10 -6 ) between the male-only and female-only results, with effect sizes greater in men for all 36 loci (Table  S3 ). Thirty-two (89%) had previously been associated with urate  9  and include the 10 loci with the strongest effects on urate  31  (SLC2A9, ABCG2, SLC17A1, SLC16A9, GCKR, SLC22A11, INHBC, RREB1, PDZK1, SLC22A12). ABCG2 was heterogenous in all datasets excepting African, and SLC2A9 was heterogenous in the trans-ancestry meta-analysis and European GWAS.
Genetic relationships with other phenotypes To investigate the genetic correlation between gout and other complex phenotypes, LD score regression was undertaken using 934 traits reported in the UK Biobank, the European gout GWAS data and, for comparison, data generated from a GWAS of serum urate in European UK Biobank participants without gout (Methods). There was significant genetic correlation between gout and 348 phenotypes, which spanned 25 out of 27 broad phenotype categories (Figure  3 , Figure  S7 , Table  S6 ). As expected, the strongest correlations were with gout and urate (r g ≥ 0. 89). Outside of cardiovascular disease and its body composition-related risk factors, established as associated with gout, negative correlations were seen with two sexrelated blood biomarkers, testosterone (r g = -0.
Cis-and trans-eQTL co-localization analyses To identify candidate causal genes that are possibly controlled through regulation of gene expression, we focused on gout association signals that co-localized with cis-and trans-eQTL across 49 tissues in the Genotype Tissue Expression (GTEx) database. Of the 342 trans-ancestry and European lead SNPs present in GTEx (or its proxy if the lead variant was not present), 290 (84. 8%) had at least one co-localized eQTL (posterior probability of colocalization (PPC) ≥ 0. 5) representing 890 genes. There were 2,717 cis-eQTL over multiple tissues for 626 genes and 332 trans-eQTL (31 were both cis-and trans-eQTLs) over multiple tissues for 326 genes with evidence of co-localization (Table  S10 ; Figure  S10 ). Of the 921 eQTL implicated by co-localization analysis, ten had control of expression by both a cis-and trans-eQTL (ADK, HSP90AA1, RABEP2, SEPT9, SPN1, VPS9D1, RNF157, SLC16A9, TANC1 and TUBB8P7).
For example, rs1171614 is the lead SNP for a colocalized cis-eQTL for SLC16A9 (encodes kidney monocarboxylate transporter SLC16A9 involved in carnitine transport) and SLC16A9 is a trans-eQTL of rs73592376 at the CUBN (proximal tubule uptake receptor) locus. We also noted that 47 of the 343 eQTL that had an eQTL in multiple tissues had opposing effects on gene expression between tissues (Table  S11 ). Focusing on genes that may have a role in gout inflammation there were 11 genes with eQTLs in whole blood, of these genes five (AQP10, BAG4, FADS1, SCAP, RP11-936I5. 1) had a direction of effect opposite to all other tissues in which an eQTL was observed. Only five co-localized kidney cortex eQTL were observed in GTEx (FGF5, HLF, ITIH4, RP11-307C18. 1, NUDT2), therefore we also leveraged comprehensive kidney eQTL data from the Susztaklab Kidney Biobank 34 to identify additional kidney eQTL.
8) with a gout genetic association using the Liu et al. 44  dataset (Table  S18 ). The 50 kidney eQTL that also had at least one kidney meQTL were enriched for transcription factor binding of HNF4G using the enrichr gene set search engine  45  (P = 0. 03). For each of the kidney eQTL with colocalized meQTL we looked for SNPs in linkage disequilibrium (r 2 ≥ 0. 8) with the lead gout SNPs that overlap a transcription factor binding site for HNF4G using HaploReg. We found three variants that overlap and disrupt a matching HNF4A/G motif: rs2453583 at SLC47A1, rs1165183 at H2BC1 (at the SLC17A1-4 locus) and rs260512 at SKI. Regulatory control of SKI and H2BC1 has not previously been associated with gout or serum urate.
Fine-mapping of loci We used three approaches (Bayes factor, FINEMAP  46  , and PAINTOR  47  ) for fine-mapping the 376 unique loci in the European and trans-ancestry datasets from all analyses (full, maleonly, and female-only. We considered 99% credible sets from any one of the separate approaches with at least one variant with posterior inclusion probability (PIP) ≥ 0. 5. Between the three fine-mapping approaches and cohorts, we found a credible set with at least one variant with PIP ≥ 0. 5 in 285 loci (76. 6%) (Table  S19 ). The fine-mapping of complex phenotype loci is compromised when summary statistics derived from meta-analysis of cohorts using different genotyping arrays and/or imputation panels are used. Therefore, we first identified and excluded compromised loci using SLALOM  48.
We found 117 genes with prioritization scores between four and seven, with transcription start sites closest to the lead gout-associated SNP and/or evidence from activity-by-contact for regulation of expression by an enhancer. This prioritization evidence strongly warrants follow-up analyses in these 117 genes. Among these genes are FADS1, DGAT2, and the highest scoring gene FADS2, that are all involved in lipid metabolism, and IRF5, IL1R1, TRAF4, IL6R, IK, and MAST3 all implicated in the inflammatory response. To support our prioritization approach, we identified category enrichments using DAVID  52  for the gene-disease association dataset class category 'Immune' (P = 2. 9×10  -11  ) and the transcription factor NFκB (P = 3. 8×10 -17 ) for 585 genes scored ≥3 in the prioritization scoring (Table  S31 ).
The IL1RN locus and IL-1β response to monosodium urate crystal stimulation The Human Functional Genomics Project (500FG) is a repository of cytokine QTL (cQTL) data, including data on association between IL-1β and IL-6 response to monosodium urate crystal/C16 stimulation in peripheral blood mononuclear cells  53. We investigated the associations between the 500FG IL-1β or IL-6 response to stimulation and 41 lead SNPs from the European or trans-ancestry GWAS that were not associated with urate in the UK Biobank (Methods) and had genotype data available in the 500FG dataset (either the variant itself or a proxy; r 2 ≥ 0. (Figure  S14 ). The G-allele associated with increased expression of IL1RN in the testis and skin (β = 0. 29, P = 4. 0×10  -11  ; β = 0. 13, P = 1. 7×10  -8  ) and reduced expression in subcutaneous adipose (β = -0.
Xanthine oxidoreductase Xanthine oxidoreductase (XOR), the sole enzymatic source of urate that oxidizes hypoxanthine to xanthine and then xanthine to urate, is a key gout intervention point with urate-lowering therapy. XOR is transcribed and translated as xanthine dehydrogenase (XDH), with the circulating form exhibiting dehydrogenase and oxidase activity. Given the key role of XOR in the pathogenesis of gout and evidence for association of a burden of rare variants in XDH with reduced urate level  54  , we explored the significant association of the XDH locus with gout in more depth. There were clear signals of association with urate in the independent UK Biobank (Methods) and Tin et al. 9  studies that overlapped the gout signal (Figure  6 ), with the gout risk allele in strong LD with the urate-increasing allele in each of the urate datasets.
The gout risk allele associates with increased CpG methylation at all sites except cg02337499, which is found at the promoter of UVRAG. A GeneHancer physical connection was observed between a maximally associated variant rs11236533, the lncRNA and DGAT2. Functional data for rs11236533 indicates that it is bound by CTCF (a CHIP gene which also maps to a male-only gout-associated locus) in numerous cell types including monocytes and neutrophils and is predicted to alter a CTCF motif. Rs11236533 physically connects to DGAT2 via two GeneHancer regions containing additional maximally associated variants (region 1: rs7947512, rs10899119, rs11823869 and rs10219339; and region 2: rs11236530). In the ImmuNeXUT dataset  62  , rs11236533 is the lead variant for an eQTL for DGAT2 and CTD-2530H12. 2 in neutrophils and the gout risk allele decreases expression of both genes in blood.
Implication of the PIK3-AKT-mediated insulin signalling pathway, that may stimulate glycolysis and provide substrate for the Krebs cycle, suggests that the pathogenesis of gout involves insulinstimulated production of substrate for epigenomic remodelling. Indeed, IGF1R and IRS1 are central to insulin signalling, are co-localized eQTLs, and have been implicated in the training mechanism  96. CHIP also associates with heart failure  [97] [98] [99]  and deteriorating kidney function  100  and may link the etiologies of gout and co-morbid cardiorenal conditions via an inflammatory state. Supporting a role for the NLRP3-inflammasome, TET2-CHIP has been implicated in heart failure through the NLRP3-inflammasome and IL-1β Other limitations represent inherent current limitations of the field. For example, we performed co-localization analysis for eQTLs using tissues largely from GTEx and meQTL datasets generated from whole blood and kidney.
With respect to meQTL in particular, additional datasets are needed across the gout disease state, for example the gut (important in urate excretion), the liver (urate production), the synovial lining (urate transport into the synovium), the kidney in different developmental stages, white blood cells and their subsets during the gout flare (e. g. monocytes) and, ex vivo, monocyte response to urate crystals. While we did not include replication in our study design, high correlation between gout and urate effect sizes reduces our concern about false positives. Finally, some methods of gout diagnosis (self-report and use of administrative medication data) are not as direct as clinical diagnosis. However, we have previously reported that these survey definitions of gout contributing to the Global Genetics of Gout Consortium (one of the contributing datasets (Table  S1 )) have 82% sensitivity and 72% specificity 112 , similar to a definition from the Study for Updated Gout Classification Criteria (SUGAR) that used five weighted items that included a clinical measure (hyperuricemia) 112.
9b4 137 with an ancestrymatched 1000 Genomes Project reference panel (Supplementary Notes). Significant loci were designated based on their chromosome and start/end position (in Mb) of the significant region (e. g. chr 5:129. 52MB-131. 88MB). A regional association plot (Locus Zoom) was created for each locus (Figure  S2 ) using an R script "LocusZoom-like Plots" 138 with GENCODE human genome build 37 (release 38) gene annotation file to define gene start/end positions and gene names (HGNC gene symbol)  139. Gene track information was coloured based on MAGMA  28.
LD score regression with UK Biobank traits and diseases LD score regression  29  was used to run genetic correlation analysis of UK Biobank phenotypes with European gout GWAS summary statistics. Summary statistics for 934 primary phenotypes (Supplementary Notes) and baseline v1. 1 reference LD scores for European ancestry was used. Since we included the FinnGen cohort in our European GWAS meta-analysis, we excluded all FinnGen phenotypes to avoid complete sample overlap (Supplementary Notes). We considered any genetic correlation with our European gout GWAS to be significant if the p-value was less than a Bonferroni-corrected significance threshold of P < 5. 4×10 -5 (0. 05/934). The resulting significantly correlated GWAS were then tested for horizontal pleiotropy using two-sample Mendelian randomization package TwoSampleMR 144.
Enrichment analysis of transcription factor (TF) binding at meQTL CpG sites 1,544 TF ChIP-seq experiment data sets (344 TFs and 221 cell lines)  40  were downloaded (Data Availability). Eight TF ChIP-seq experiment datasets were excluded to ensure all of the ChIP-seq experiments were from human cell lines (Supplementary Notes), leaving 338 unique TFs for enrichment analysis. Positions for 232,477 independent CpG sites used in GoDMC were extracted. For each TF, the total number of CpG sites the TF overlapped (±50bp) within the CpG sites was determined and 520 co-localized meQTL CpG sites identified. 338 two-by-two contingency tables were constructed and Fisher's exact test was used to determine if there was significant enrichment of TF binding to the meQTL CpG sites.