dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We are grateful to all patients and their parents who participated in the trial as well as to the treating clinical teams of the 18 hospitals and their referring centers ( Gustave Roussy, Villejuif, France; Institut Curie, Paris, France ;  Centre Léon Bérard, Lyon, France; Hôpital La Timone, Marseille, France; CHU Nantes, France; Centre Oscar Lambret, Lille, France; CHU Bordeaux, France; Hôpital Trousseau, Paris, France; Strasbourg, France ;  CHU Nancy, France ;  CHU Toulouse, France; Children's Health Ireland, Crumlin, Ireland; CHU Angers, France; Hôpital Robert Debré, Paris, France ;  Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Italy ;  Vall d'Hebron Barcelona Hospital Campus, Spain; Hôpital Jeanne de Flandre, Lille, France ;  Hospices Civils de Lyon, France).
Data Sharing Agreement Sequencing data and basic clinical annotations have been deposited in European Genome-phenome Archive (EGA; hosted by the EBI and CRG) with the data set accession code EGAS00001005935. Further information about EGA can be found on https://ega-archive. org ("The European Genome-phenome Archive of human data consented for biomedical research"; http://www. nature. com/ng/journal/ v47/n7/full/ng. 3312. html). Additional, more detailed clinical data from the clinical trial can be requested by completing the data request form for Gustave Roussy clinical trials (https://redcap. gustaveroussy. fr/ redcap/surveys/?s=DYDTLPE4AM) for validation by the trial steering committee and the sponsor prior to transfer of detailed clinical data.
Data and Methods Here we use classical observations and reanalyzes datasets, indices and statistical methods, detailed in Text S1 in Supporting Information S1 (statistics robust thanks to a sufficiently-large number of effective degrees of freedom, ∼85 to ∼140). The ENSO index T E is Niño3. 4 relative Sea Surface Temperature (RSST, i. e. , Figure  1. Schematics of the optimal orthonormal phase space for the Recharge Oscillator (RO). Panels a and b show respectively the fast adjustment zonal tilt mode dependent of T E (h regressed onto T E , i. e. , K) and the pure recharge mode (h ind regressed onto our default best recharge index h ind_ew+sw ), independent of (uncorrelated to) T E (h unit: satellite SLA in cm; c. f. Sections 1 and 3 for details). Black arrows illustrate other used indices based on usual h.
Geophysical Research Letters IZUMO AND COLIN 10. 1029/2022GL101003 6 of 10 e. g. ,  Fedorov, 2010). r Te,h_ind is larger than r Te,h (Figure  2i ), further confirming that (T E ,h ind ) is a better basis of RO phase space. Here we explain the spatial patterns of Figure  2 's various panels (full explanation in Text S2 in Supporting Information S1), robust among datasets and periods (cf. Figure  S2  in Supporting Information S1), and why there are similarities among some of them. F 2 map shows us how h ind (x, y, t) would look like if ENSO windstress anomalies would blow for a long time, for example, because of long-lasting La Niña conditions. The slow recharge is as expected in the western and central equatorial Pacific, through: (a) downwelling equatorial Rossby waves to the west  (Jin, 1997a (Jin, , 1997b;; Wyrtki, 1985) , and off-equatorial ones in the southwest; (b) upwelling equatorial Kelvin waves to the east forcing coastal Kelvin waves propagating poleward along the eastern boundary and thus leading to a leakage of negative OHC anomalies toward the poles along the eastern boundary  (Izumo, Lengaigne, et al.
The skill increase from h ind_eq to h ind_eq + sw is statistically significant, more thanks to r h_ind (p = 0. 01|0. 001|0. 001) than to r Te (p = 0. 09|0. 01|0. 23) for satSLA|O-RAS5_SLA|ORAS5_Z20 respectively). And trajectories are even smoother (Figure  3g ). Including the southwest agrees with Izumo et al. (2018a; see also  Santoso et al. , 2017; Ramesh and Murtugudde, 2013). h ind_eq + sw is now our default best recharge index. ⎛ ⎜ ⎜ ⎝ ℎind ⎞ ⎟ ⎟ ⎠ : ⎛ ⎜ ⎜ ⎝ ℎind ⎞ ⎟ ⎟ ⎠ = ⎛ ⎜ ⎜ ⎝ 1 -2 - ⎞ ⎟ ⎟ ⎠ ⎛ ⎜ ⎜ ⎝ ℎind ⎞ ⎟ ⎟ ⎠ (7) Using normalized h ind_eq + sw , we obtain: F 1 ≈ F 2 ≈ 0. 17 ± 0. 03, R ≈ ε ≈ 0. 00 ± 0. 03 months -1. These coefficients are robust among datasets and periods (Table  S1  in Supporting Information S1). R and ε are negligible. This reduces the parameter space. And the RO equations system has the form of a harmonic oscillator excited by noise  (Burgers et al.
Geophysical Research Letters IZUMO AND COLIN 10. 1029/2022GL101003 8 of 10 improves the skill for all datasets/periods, more clearly for SLA than for Z20 (Table  S3  in Supporting Information S1; since we have statistically-significant contributions from h ind_eq + sw and IOD, but not from T E itself, this new recharge index and updated datasets confirm earlier studies of  Izumo et al. , 2010 Izumo et al. , , 2014 Izumo et al. , , 2016;; Dayan et al. , 2014; Jourdain et al. , 2016). Results are robust among datasets. Only some subtle second order differences remain between SLA and Z20 (OHC being inbetween) for h ind , likely related to different weighting of the first baroclinic modes (Figures S2, S6, S8, and S10 in Supporting Information S1). Note that we are not trying to address "how to best estimate the thermocline depth"  (Vijayeta et al.
, 2020) , but rather "how to best isolate the recharge mode. " SLA and Z20 each have their own advantages with presently-available datasets: Z20 seems better in a perfectly-observed and non-warming ocean (Tables  S1  and S3  in Supporting Information S1), but SLA is observed globally by satellite in near real-time and is more available in climate models outputs (e. g. , Coupled Model Intercomparison Project, CMIP). Here we have neglected possible seasonal cycles of parameters and asymmetries/non-linearities for the sake of simplicity. Knowing ENSO seasonal phase-locking, taking into account such seasonal cycles could be one next step. We could include asymmetrical/non-linear terms (Table  S2  in Supporting Information S1). To have a simple h ind definition, we have also neglected a weak non-linearity in the fast response of h to T E (Figure  3c ; Figures  S7c  and S8c  in Supporting Information S1) likely due to τ x anomalies longitudinal position, further to the east during El Niño than during La Niña, favoring a larger discharge.
Matthieu Lengaigne, Dr. Jérôme Vialard, and Pr. Fei-Fei Jin for discussions. T. I. and M. C. would like to thank UPF for hosting M. C. because of COVID-related closed Australian borders; this latter fact was paradoxically essential for allowing us to make this work mature. We also thank UNSW Sydney for allowing a flexible work agreement for MC during the pandemic. M. Colin was first supported by the Australian ARC Grant FL150100035 and kindly hosted by UPF, and then directly funded by the ZMT's core budget. T. Izumo was funded mainly by IRD core budget, with some funding from ANR ARISE project. presence of external forcing (see also  Johnson and Kosaka 2016; Khodri et al. 2017; Izumo, Khodri et al. 2018; Williams and Patricola 2018, and Okumura 2019). The deep convection threshold SST>~27-28°C (e.
Supplementary Text S2. Discussion on the mechanisms behind TE and hind tendency equations Here we explain physically and mathematically the spatial patterns of Fig. 2 's various panels, very robust among datasets and periods (cf. Suppl. Fig. S2 ). And why there are similarities among some of them. Along these explanations we discuss the mechanisms behind the various tendency equations terms (descriptions done here by convention in the recharge case related to previous La Niña conditions; vice versa in the discharge case related to previous El Niño conditions). F2 map (Fig. 2h ; and its equivalents in Suppl. Fig. S2 ) shows us how hind(x,y,t) would look like if ENSO windstress anomalies would blow for a long time, e. g. after longlasting La Niña conditions. This physical meaning of F2 is mathematically because the temporal integral of dhind/dt equation (  6 ) corresponds to the regression of hind(x,y,t) onto the temporal integral of TE, as ε is negligible.
Main messages of Table 1) adding the SouthWest box (5ºS-15ºS, 120ºE-170ºW) improves the skill for all datasets/periods, more clearly for SLA than for Z20. 2) ORAS5 Z20 skill is better than ORAS5 SLA one, suggesting that thermocline depth metrics could perform better in a perfectly-observed ocean. Yet SLA has the advantage of being observed by satellite in near real-time, and the additional advantage of being more available in climate models outputs (e. g. CMIP) than Z20. 3) we have statistically-significant contributions from hind_eq+sw and IOD, but not from TE itself (the IOD contribution a is always significantly negative, while the coefficient c for ENSO itself is never significant at 90% level). Hence this improved recharge index with updated datasets confirms earlier studies emphasizing the IOD influence on following year's ENSO phase  (Izumo et al.
Jin et al. 2007;e and ORAS5 on the same period, from 1993 (also verifying the unsensitivity to the change of the final year from 2021 to 2018). Central rows: skill stable over the long period 1959-2018 using ORAS5, with interdecadal variations filtered out ('filt') or not. Bottom rows: verifying equivalence between ENSO indices. We have also verified the non-sensitivity to SST datasets: e. g. ORAS5 SST leads to almost no change (here we use OISST for recent periods and HadiSST for long periods from 1959). Note that the RO, even if theoretically undamped as R and ε are negligible, is actually still damped because residualT (equation 6) is not a pure red noise but includes non-linear terms neglected in our 1 st order linear approximation. Indeed, if we add to the TE tendency equation a simple nonlinear term cTE 2 (quadratic term notably related to the non-linear response of convection to TE; e.
Data Availability Statement All data used is open data: OISSTv2 (https://psl. noaa. gov/data/gridded/data. noaa. oisst. v2. html), CMAP1 (https:// psl. noaa. gov/data/gridded/data. cmap. html), ERA5-ORAS5 (https://www. ecmwf. int), Copernicus SLA (https:// doi. org/10. 48670/moi-00148), and HadiSST (https://www. metoffice. gov. uk/hadobs/hadisst/). We acknowledge use of NOAA pyferret open source software for analyses and figures.
Observational analyses Study participants and data collection in EPIC and UK Biobank EPIC is a multi-center cohort study including $520,000 adults recruited between 1992 and 2000 from ten European countries (n ¼ 367,898 women, aged 35-75 years old at enrollment)  (18). All participants provided written informed consent to participate in the EPIC study and the EPIC study was approved by the ethics committees of the International Agency for Research on Cancer (IARC) as well as every center. The UK Biobank is a prospective cohort of $502,000 adults from the general population across the UK (n ¼ 273,383 women, aged between 40 and 69 years at recruitment) enrolled between 2006 and 2010  (19). This research has been conducted using the UK Biobank Resource under application number 41115. In both cohorts, detailed information was collected on sociodemographics (including age, education, socioeconomic status), lifestyle factors (including smoking habits, physical activity, dietary intake, and alcohol consumption), reproductive, medical and anthropometric data at baseline.
Participants provided information on current/former/never regular smoking status. Additional data on smoking intensity (number of cigarettes per day), smoking duration, and type of smoking (cigarettes, cigars, pipes) were also collected. For the current analyses, we excluded the following individuals: men (n ¼ 153,426 in EPIC, 229,122 in UK Biobank); women with prevalent cancer (excluding non-malignant skin cancer) at recruitment (identified by linkage to cancer registry data or active follow-up; n ¼ 21,212 in EPIC, 21,217 in UK Biobank); women who had a history of hysterectomy (n ¼ 35,970 in EPIC, 44,496 in UK Biobank); and women with no follow-up data (n ¼ 2,431 in EPIC). In EPIC, we also excluded 7,541 participants with missing information on smoking status. In UK Biobank, 28,380 women who smoked occasionally in the past (as regular cigarette smoking was the focus of our study; n ¼ 27,260 occasional smokers were excluded) or who had an unknown smoking status, were also excluded.
We used a competing risk approach to assess whether the risks associated with ever smoking and lifetime smoking differed by tumor subtype  (23). This approach uses a data augmentation method to create a separate observation for each subject for each subtype and then stratifies on event type  (23). Heterogeneity by subtype was assessed through a likelihood ratio test of a model estimating separate associations for each subtype and a model evaluating the same association for both subtypes. Statistical tests were all two-sided and a P value <0. 05 was considered statistically significant. Analyses were conducted using SAS software version 9. 4.
Multivariable MR analyses were also performed for educational attainment to account for possible pleiotropy between socioeconomic status and smoking-related phenotypes including approximately 1. 1 million European ancestry individuals from 71 cohorts  (37). Variants that were associated with BMI, alcohol consumption and educational attainment at a genome-wide significance level and were independent of smoking-related variants (R 2 LD < 0. 001) were also included in the multivariable MR models (Supplementary Tables  S1  and S2 ). Missing regression coefficients of the association of any variant on BMI, alcohol consumption, educational attainment and on endometrial cancer were replaced by those of a suitable proxy genetic variant (minimum LD R 2 ¼ 0. 8) where available. The MR statistical analyses were implemented in the Mendelian randomization R package  (38).
Data availability Data used in the MR analyses can be found in supplementary material. Researchers can apply to use the UK Biobank dataset by registering and applying at http://ukbiobank. ac. uk/register-apply/. For information on how to submit an application for gaining access to EPIC data and/or biospecimens, please follow the instructions at https://login. research4life. org/tacsgr0epic_iarc_fr/access/index. php.
We were also able to evaluate associations of smoking by histological subtypes of endometrial cancer with higher precision and no differential associations were found. There was little evidence of heterogeneity of the association of smoking and endometrial cancer risk by subgroups of menopausal status, MHT use, and BMI. Smoking-related characteristics such as smoking intensity and smoking duration do not seem to significantly alter endometrial cancer risk. A limitation of our MR study was that our use of summary-level data meant we were unable to assess the associations by menopausal status, and subgroups of other risk factors (e. g. , BMI, exogenous hormone use). In addition, in our MR analyses, UK Biobank participants were included in both smoking-related phenotypes and endometrial cancer datasets, which might have introduced some bias in the MR estimates.
Acknowledgments This work has been conducted using the UK Biobank Resource under Application Number  41115 , and we express our gratitude to the participants and those involved in building the resource. UK Biobank  is an open access resource. Bona fide researchers can apply to use the UK Biobank dataset by registering and applying at http:// ukbiobank. ac. uk/register-apply/. We thank the  National Institute for Public Health  and the  Environment (RIVM) ,  Bilthoven , the  Netherlands , for their contribution and ongoing support to the  EPIC Study. We are grateful to all the participants in the  EPIC-Norfolk  study who have been part of the project and to the many members of the study teams at the  University of Cambridge  who have enabled this research. The coordination of EPIC is financially supported by  International Agency for Research on Cancer (IARC)  and also by the  Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London , which has additional infrastructure support provided by the  NIHR Imperial Biomedical Research Centre (BRC).
1998 ); results we could not confirm here, the liver and colon being the only additional digestive organs besides salivary glands represented in our dataset for this species. However, the comparison with the two myrmecophagous species seems to fit well with its less specialized insectivorous diet and actually further underlines the contrasted specific use of distinct CHIA paralogs for chitin digestion in anteaters and pangolins. Our results demonstrate that in the case of the southern tamandua (T. tetradactyla) and the Malayan pangolin (M. javanica), two myrmecophagous species that diverged about 100 Ma ago  (Meredith et al. 2011) , convergent adaptation to myrmecophagy has been achieved by using paralogs of different chitinase genes to digest chitin, probably due to phylogenetic constraints leading to the loss of CHIA1, CHIA2, CHIA3, and CHIA4 in the ancestor of Ferae (Carnivora and Pholidota) as suggested by  Emerling et al.
Chitinase gene family tree reconstruction Reconstruction of chitinase gene family evolution -The chitinase family in placental mammals appears to be composed of nine major paralogs (CHIA1-5, CHIT1, CHI3L1, CHI3L2, OVGP1). Mammalian sequences similar to the protein sequence of the human chitinase gene (NP_970615. 2) were searched in the NCBI non-redundant protein database using BLASTP (E-value < 10). The protein sequences identified by BLASTP were then imported into Geneious Prime  (Kearse et al. 2012 ) and aligned using MAFFT v7. 450  (Katoh and Standley 2013)  with the default parameters. Preliminary gene trees were then reconstructed with maximum likelihood using RAxML v8. 2. 11  (Stamatakis 2014 ) under the LG+G4 model  (Le and Gascuel 2008)  as implemented in Geneious Prime. From the reconstructed tree, the sequences were filtered according to the following criteria: (1) fastevolving sequences with an E-value greater than zero and not belonging to the chitinase family were excluded; (2) in cases of multiple isoforms, only the longest was retained; (3) sequences whose length represented less than at least 50% of the total alignment length were removed; (4) in case of identical sequences from the same species the longest was kept; and (5) sequences labeled as "Hypothetical protein" and "Predicted: low quality protein" were discarded.
This procedure resulted in a dataset containing 528 mammalian sequences that were realigned using MAFFT. This alignment was then cleaned up by removing sites not present in at least 50% of the sequences resulting in a total length of 460 amino acid sites. A maximum likelihood tree was then reconstructed with RAxML-NG v0. 9. 0  (Kozlov et al. 2019 ) using 10 tree searches starting from maximum parsimony trees under the LG+G8+F model. The species tree of the 143 mammal species represented in our dataset was reconstructed based on COI sequences extracted from the BOLD system database v4  (Ratnasingham and Hebert 2007 ) by searching for "Chordata" sequences in the "Taxonomy" section. Sequences were aligned using MAFFT, the phylogeny was inferred with RAxML and the topology was then adjusted manually based on the literature to correct ancient relationships.
To determine the optimal rooting scheme, a rapid reconciliation between the resulting gene tree and species tree was performed using the TreeRecs reconciliation algorithm based on maximum parsimony  (Comte et al. 2020)  as implemented in SeaView v5. 0. 2 (Gouy et al. 2010). The final chitinase gene family tree was produced using the maximum likelihood gene family tree reconciliation approach implemented in GeneRax v. 1. 1. 0 (Morel et al. 2020 ) using the TreeRecs reconciled tree as input (source and result available from Zenodo). GeneRax can reconstruct duplications, losses and horizontal gene transfer events but since the latter are negligible in mammals, only gene duplications and losses have been modeled here (--rec-model UndatedDL) and the LG+G model was used. Ancestral sequence reconstructions -Ancestral sequences of the different paralogs were reconstructed from the reconciled tree using RAxML-NG (--ancestral function, --model LG+G8+F).
The sequences were then aligned in Geneious Prime with MAFFT (source and result files available from Zenodo). Given that active chitinases are characterized by a catalytic site with a conserved amino acid motif (DXXDXDXE;  Olland et al. 2009; Hamid et al. 2013) , this motif was compared among all available species. Additionally, the six conserved cysteine residues responsible for chitin binding  (Tjoelker et al. 2000; Olland et al. 2009 ) were also investigated. Chitinase gene synteny comparisons -The synteny of the nine chitinase paralogs was compared between the two focal ant-eating species in our global transcriptomic analysis (T. tetradactyla and M. javanica), an insectivorous xenarthran species (D. novemcinctus), an insectivorous primate species with five functional CHIA genes (Carlito syrichta) and human (Homo sapiens).
For H. sapiens, synteny information was added from  Emerling et al. (2018)  and completed by using Genomicus v100. 01  (Nguyen et al. 2022 ). For C. syrichta and D. novemcinctus, genome assemblies have been downloaded from the National Center for Biotechnology Information (NCBI) and from the DNA Zoo  (Choo et al. 2016; Dudchenko et al. 2017)  for M. javanica and T. tetradactyla. Synteny information was retrieved by blasting (megablast) the different CDS sequences against these assemblies. Scaffold/contig names, positions and direction of BLAST hits were retrieved to compare their synteny (source and result files available from Zenodo). Genes with negative BLAST results were considered probably not functional or absent.
Comparative transcriptomics Transcriptome assemblies and quality control -Adapters and low quality reads were removed from raw sequencing data using fastp v0. 19. 6  (Chen et al. 2018 ) using default parameters except for the PHRED score which was defined as "--qualified_quality_phred ≥ 15", as suggested by (MacManes 2014). Then, de novo assembly was performed on each individual transcriptome sample using Trinity v2. 8. 4  (Grabherr et al. 2011 ) using default parameters (result files available from Zenodo). For one individual vampire bat (D. rotundus), three salivary gland transcriptomes (SRR606902, SRR606908, and SRR606911) were combined to obtain a better assembly. For each of the 104 transcriptome assemblies, completeness was assessed by the presence of Benchmark Universal Single Copy Orthologs (BUSCO v5) based on a dataset of 9,226 single-copy orthologs conserved in over 90% of mammalian species  (Manni et al.
2021). This pipeline was run through the gVolante web server  (Nishimura et al. 2017)  to evaluate the percentage of complete, duplicated, fragmented and missing single copy orthologs within each transcriptome (Table  S2 ). Transcriptome annotation and orthogroup inference -The 104 transcriptome assemblies were annotated following the pipeline implemented in assembly2ORF (https://github. com/ellefeg/assembly2orf). This pipeline combines evidence-based and genemodel-based predictions. First, potential transcripts of protein-coding genes are extracted based on similarity searches (BLAST) against the peptides of Metazoa found in Ensembl  (Yates et al. 2020 ). Then, using both protein similarity and exonerate functions  (Slater and Birney 2005) , a frameshift correction is applied to candidate transcripts.
Candidate open reading frames (ORFs) are predicted using TransDecoder (https://github. com/TransDecoder/TransDecoder) and annotated based on homology information inferred from both BLAST and Hmmscan searches. Finally, to be able to compare the transcriptomes obtained from all species, we relied on the inference of gene orthogroups. The orthogroup inference for the translated candidate ORFs was performed using OrthoFinder v2 (Emms and Kelly 2019) using FastTree  (Price et al. 2010)  for gene tree reconstructions. For expression analyses, orthogroups containing more than 20 copies for at least one species were discarded.