dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design and selection criteria The ESME-OC database (NCT03275298) centralizes data of patients selected from 01/01/2011 until 12/31/2017 in one of the 18 cancer centers from the Unicancer network. Data extraction date was 01/06/2020. At the date of data cut-off, 10,263 patients with EOC, aged 18 or more, had been included. In the present work, we selected patients diagnosed from 01/01/2011 with pathological subtype documented and limited our analysis to endometrioid and serous histologies. In compliance with French regulations, the ESME Ovarian Cancer database (NCT03275298) was authorized by the French data protection authority (Registration ID 1976564 and authorization N°DE-2017-311). Moreover, in compliance with the applicable European regulations, a complementary authorization was obtained on 14-Oct-2019 regarding the ESME research Data Warehouse.
Data collection and statistical analysis The level of statistical significance was set at α = 0. 05. All tests will be two-sided. Statistical analyses were performed with the SAS® 9. 4 software. Categorical variables were described using counts and frequencies (computed based on available data for the considered criterion), and quantitative variables were described using medians and ranges. An arbitrary 50-years cutoff was also used to dichotomize the age variable, as it can be considered as a surrogate for menopausal status. Patients' characteristics between endometrioid and serous tumors arm were compared using Chi-square or exact Fisher's tests for qualitative variables, and rank-Wilcoxons tests for quantitative variables. Follow-up was measured from the date of diagnosis to the date of last news for live patients.
Population demographics Out of the 10,263 patients included in the ESME-OC database, we identified 2854 serous EOC and 326 endometrioid EOC diagnosed from 01/01/2011 until 12/31/2016, for a whole population of 3180 patients (Figure Supplementary Fig. S1 ). Demographics are detailed in Table  1. The median age at diagnosis was 63. 3 years. Most of the patients had a good performance status (86. 4% PS 0-1). FIGO stage III was the more frequent (65. 7%) with 24. 5% of stage I-II tumors. Eighty percent were high-grade tumors. A minority of the patients (13. 7%) had a personal history of cancer prior to EOC diagnosis, including 302 breast cancers and 44 Lynch syndromeassociated cancers. Data concerning cancer family history was available for 2423 patients, including a quarter (26. 7%) with a family history of breast and/or ovarian cancer.
Discussion The present analysis of French national ESME-OC Unicancer database confirmed that women with endometrioid ovarian cancer were younger and had earlier stage tumors compared to women with serous ovarian cancer. They were less likely to display BRCA1/2 mutations but their tumors were more frequently DNA mismatch repair deficient. Endometrioid subtype was correlated to better overall and progression-free survivals, irrespectively of age, stage, grade, and ECOG PS. Within the endometrioid subtype, we identified older age at diagnosis, advanced FIGO stage, high pathological grade, and BRCA mutations, as significant poor prognostic features. Previously published data on histotype-specific survival have been contradictory and presented some limitations. Most of the studies were published prior to the 2014 WHO guidelines and did not systematically reflect current knowledge of ovarian cancer pathogenesis  [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27].
In the first report from the prospective Lynch syndrome database  [42] , Møller et al. described very good survival outcomes after first ovarian cancer in patients receiving colonoscopy and gynecological surveillance. Thus, family planning and cancer surveillance might also play a role in the more favorable outcomes of endometrioid ovarian cancer compared to serous subtype. Our work had some limitations. First, its retrospective design may have induced some recruitment, information, and treatment biases. However, the fact that patients were included in several reference cancer centers with homogeneous treatment guidelines limits these biases. Moreover, our short period of inclusion (2011-2016 vs. 2004-2014 for the SEER study  [9] ) reduces potential biases associated with treatment guidelines modifications over time.
Second, very few of our patients received PARP inhibitors, a gold standard for ovarian cancer treatment for homologous recombination deficient tumors treated in the first-line setting and for platinum-sensitive recurrent tumors  [43] [44] [45] [46] [47] [48]. However, only a very small part (0 to 5%) of the patients included in pivotal randomized trials assessing PARP inhibitors efficacy had endometrioid cancer. PARP inhibitors benefits in this population are thus still unknown. Moreover, the rates of BRCA1/2 mutations and homologous recombination deficiency remain unclear in this subset. We observed in the present work that more than 14% of endometrioid tumors harbor BRCA1/2 mutations, which is less than for serous tumors but remains clinically significant. As the ESME research program is dedicated to clinical data hosted in the ESME Data Warehouse, no formalin-fixed paraffinembedded sample is available via the ESME-OC database.
Ethics approval In compliance with French regulations, the ESME Ovarian Cancer database (NCT03275298) was authorized by the French data protection authority (Registration ID 1976564 and authorization N°DE-2017-311). Moreover, in compliance with the applicable European regulations, a complementary authorization was obtained on 14-Oct-2019 regarding the ESME research Data Warehouse. No formal dedicated informed consent was required but participating centers handle process to ensure that patients are informed about the re-use of their electronically recorded data.
Acknowledgements We thank the 18 participating  French Comprehensive Cancer Centres (FCCC)  for providing the data and each ESME local coordinator for managing the project at the local level. Moreover, we thank the all members of  ESME Strategic /Scientific Committees  for their ongoing support. 18  Participating French Comprehensive Cancer Centers (FCCC) :  I. Curie ,  Paris/ Saint-Cloud, G. Roussy, Villejuif, I. Cancérologie de l'Ouest, Angers/Nantes, C. F. Baclesse, Caen, ICM Montpellier, C. L. Bérard, Lyon ,  C. G-F Leclerc , Dijon,  C. H. Becquerel ,  Rouen; I. C. Regaud , Toulouse;  C. A. Lacassagne ,  Nice ;  Institut de Cancérologie de Lorraine, Nancy ;  C. E. Marquis , Rennes;  I. Paoli-Calmettes ,  Marseille; C. J. Perrin ,  Clermont Ferrand ;  I. Bergonié , Bordeaux; C. P. Strauss, Strasbourg;  I.
The model bathymetry of the primary and secondary channels was provided by SIWRP from 2008, but the river and estuarine bathymetries were updated with the 2018 collected survey data  68  , which guarantees an accurate starting point for future projections. The gauge data within the VMD (water level, discharge, and salinity) was collected by Southern Regional Hydro-Meteorological Centre (SRHMC) and received from Southern Institute for Water Resources Planning (SIWRP). The existing and planned sluice gates were incorporated in the model (see Fig. 5 ). The 3D surface water model of the VMD  48, 58  was forced by daily averaged discharge from upstream (Kratie, Cambodia) and water level from downstream offshore boundary with assumed constant salinity of 35 PSU. The water level boundary condition consisted of 13 leading tidal constituents (M 2 , S 2 , N 2 , K 2 , K 1 , O 1 , P 1 , Q 1 , MF, MM, M 4 , MS 4 , MN 4 ), the subtidal ocean surge and the average sea level (0 m at present and increasing in time to replicate SLR).
Acknowledgements Special thanks to the personnel of the  Southern Institute for Water Resources Planning (Ho Chi Minh City, Vietnam)  to openly support the project during its development. We sincerely appreciate how DHI supported our research by providing us with wind, water level and velocity data over the continental shelf. We are grateful to  Ms. Anke Verheij  of  VEI B. V. (Dutch Water Operators)  for providing insights and supporting the project through a successful collaboration in its later stage. We extend our gratitude to  Mr. Etienne Espagne  and  Dr. Alexis Drogoul  of  AFD  for the fruitful cooperation. The PCR-GLOBWB calculations were computed on the Dutch national supercomputer, Cartesius, with the support of  SURFsara. This research is part of the " Rise and Fall " project, funded by  NWO-WOTRO  ( W 07.
69. 105 ), Urbanizing Deltas  of the World-1 ( UDW1 ) and  Sepehr Eslami 's contribution to the work was partly funded by  Agence Française de Développement (AFD) , through Facility  2050 , within the framework of the research project  GEMMES-Vietnam. Hal Voepel 's contribution to this project was supported by the  United Kingdom's Natural Environment Research Council (NERC)  ( NE/S002847/1 ).
Data availability Delft3D-FM is an open source numerical model https://oss. deltares. nl/web/delft3dfm. The underlying gauge data (observed water level, discharge, and salinity) provided by the SIWRP, following the organizational policy, can be provided upon request for noncommercial use from a limited access repository  91  (https://doi. org/10. 5281/ zenodo. 4771261). The 2015 wind data originally developed by NCEP/NOAA, can be downloaded from the DHI repository under https://www. metocean-on-demand. com/ #/main. Projections of upstream discharge from the five GCM's and downstream sea level rise can be openly accessed in an online repository  92  (doi: 10. 5281/zenodo. 4771240). For the spatio-temporal variations of land subsidence  57  we refer to the original publications and its lead author philip.
minderhoud@wur. nl. The geometry data of the network of primary and secondary channels, following the internal policy of the institute, can only be provided in direct communication with the SIWRP (siwrp@siwrp. org. vn). The 2018 contemporary bathymetry data can be downloaded from https://hydra. hull. ac. uk/resources/hull:17952. The salinity projection maps in the next three decades and the associated sensitivity analyses are available to be downloaded from an open-access repository  87  (https://doi. org/10. 5281/zenodo. 4772967).
Birds were weighed to the nearest 2 g using a Pesola spring balance, and their skull length (head and bill), wing length, and tarsus length were measured to the nearest 0. 5 mm with a sliding caliper (data summarized in Supplemental Data, Table  S1 ). The birds were monitored after release to ensure that they returned to the nest, which they all did. The study was approved by the governor of Svalbard and the Norwegian Animal Research Authority. All handling and sampling of the birds were in accordance with the regulations of the Norwegian Animal Welfare Act.
Data treatment and statistical analyses From a total of 60 kittiwakes caught, 4 individuals were sampled in both years. All these individuals were females, and in the data treatment only one random (determined by a coin toss) observation for each of these individuals was included. Furthermore, one male and one female kittiwake were excluded from the TT4 data sets because of too high variance between their duplicated plasma samples (>15%). Thus, the final sample size for male kittiwakes was 30 individuals for biometric variables and PFAS concentrations and 29 individuals for TT4 concentrations. For female kittiwakes the final sample size was 26 individuals for biometric variables and for TT3 and PFAS concentrations and 25 individuals for TT4 concentrations. Statistical analyses were performed using R, Ver 3.
6. 1 (R Development Core Team 2019). The data (concentrations of PFAS and thyroid hormones) were resampled by bootstrapping (using the infer package, 5000 replicates). Some of the PFAS variables contained observations which were below the limit of detection (LOD; <0. 05 ng/mL for all compounds) and were thus left-censored. Specifically, branched perfluoroctane sulfonate anion (brPFOS), perfluorotetradecanoate (PFTeDA), perfluorohexanoate (PFHxA), perfluoroheptanoate (PFHpA), perfluorooctanoate (PFOA), perfluorohexane sulfonate (PFHxS), and perfluorodecane sulfonate (PFDS) contained observations below the LOD; PFHxA, PFHpA, PFOA, PFHxS, and PFDS contained >70% censored observations and were omitted from analyses. Because it is inappropriate to substitute nondetects with a value  (Helsel 2006) , the nonparametric Kaplan-Meier technique was used on brPFOS and PFTeDA using the NADA package to obtain the descriptive statistics.
Based on the bootstrapped data, mean values were calculated for the mean, the 95% confidence interval (CI) of the mean, and median. The body condition of each individual bird was determined using the biometric variable (length of skull, wing, or tarsus) that was most correlated to body mass. Skull length was most strongly correlated to body mass (r = 0. 50, p < 0. 001). The standardized residual of linear regression plots of skull length as the independent variable against body mass as the dependent variable was determined to be the body condition. The kittiwake data set was examined for differences between the years (2013 and 2014) by comparing the 95% CIs of the means, as described by Erickson and Rattner (2020; see specifically the section "Confidence intervals"). Differences in thyroid hormone and PFAS mean concentrations between male and female kittiwakes were also examined by comparing their 95% CIs.
Afterward, linear regression of single PFAS was employed to further investigate which PFAS were correlated with thyroid hormones because effects of PFAS may depend on their carbon chain length  (Costantini et al. 2019). To account for the censored PFAS-brPFOS and PFTeDA-linear regression with maximum likelihood estimation was employed, as described in  Jaspers et al. (2013). Again, the correlations were considered meaningful if the 95% CI of the regression slope did not contain zero. The plotted linear regressions were bias-corrected by subtracting the mean difference in "original" coefficients and bootstrapped coefficients from the "original" coefficients. Likewise, associations between PFAS and the body condition of the kittiwakes were also examined with linear regression models. R scripts can be found in the Supplemental Data.
Acknowledgment-O. Chastel,  F. Angelier , and  S. Tartu  were supported by  Institut Polaire Français  ( IPEV  project  330 , to O. Chastel),  Agence National de la Recherche  ( ANR  project  PolarTop , to O. Chastel), and  INSU-EC2CO 2014  (to O. Chastel). A. V. Ask received the  Arctic Field Grant  from the  Research Council of Norway  for the field season of 2014. At the  Norwegian Institute for Air Research , we thank  D. Herzke ,  A. Götsch , and  L. Hanssen  for their facilitation and help with the  PFAS analyses. At Centre d'Etudes Biologiques de Chizé , we thank  C. Parenteau  for her help in the T3 and T4 assays. We thank  S. Nilsen  for her help with the kittiwake sexing. We also thank  T. Cielsiski  and  P. Massicotte  for helpful discussions on the manuscript. S. Dielschneider  is gratefully acknowledged for his help with the language.
Material and methods (a) Study area and temperature data The Scandola MPA was established in 1975 and acknowledged by UNESCO as a world heritage site in 1980. Located off the NW coast of Corsica, it is remote from industries or dense human populations. Most human activities are prohibited within its waters with the exception of scientific diving and boating. Thus, in the absence also of any other apparent disturbance during the study period, MHWs were detected as the most significant source of disturbance for the monitored populations. Detailed information on the extreme warm conditions experienced in Scandola during the summer of 2003 and their relation to mass mortality can be found in the previous studies  [25, 26]. Here, we focused on exploring (i) if MHWs have increased in frequency over the last decades in Scandola and (ii) if monitored populations have been recurrently exposed to heat stress after being firstly impacted in 2003.
To answer the first question, we analysed sea surface temperature (SST) trends over the satellite era  by extracting SST data from the nearest available pixel from Scandola in the CMEMS Mediterranean dataset  [30]. From this dataset, the total number of MHW days per year during the warm hydrological period (June to the end of November; JJASON) were calculated with respect to the 30-year climatological period  following the MHW-detection method proposed by  [1] , which considers MHWs as discrete events with temperatures warmer than the 90th percentile (based on local climatology) for 5 days or more. For the second question, we used in situ, hourly, multi-year temperature data obtained for Scandola at 20 m depth based on a standard protocol implemented by the T-MEDNet initiative  [31]. From this dataset, we calculated the number of extreme heat days per year (i.
rubrum, 24 permanent quadrats of 20 × 20 cm (0. 96 m 2 in total) were set up at 18 to 21 m depth and photographically sampled. The sample size was selected in accordance to previous studies on these species  [10, 18, 29, 32]. Within each quadrat, we (i) counted the number of colonies, (ii) measured their maximum height (using a ruler for P. clavata, and the ARPENTEUR photogrammetric software  [33]  for C. rubrum) and (iii) estimated the extent and type of injury in every colony as the proportion of each colony's total surface with no tissue (i. e. denuded axis) or overgrowth by other organisms  [29]. A total of 801 P. clavata colonies (more than 130 per population) and 64 C. rubrum colonies were surveyed in May 2004. From these data, we calculated five population parameters: (i) the percentage (%) of affected colonies, measured as the proportion of colonies exhibiting more than 10% of injured surface with either recent or old injuries  [10, 29] ; (ii) the mean percentage of extent of injured tissue, estimated as the average injured tissue per colony in each population; (iii) the density, calculated by averaging the number of living colonies found in each quadrat; (iv) the size structure, obtained by grouping the size of the measured colonies into different size classes (from 0 to 10, greater than 10 to 20, greater than 20 to 30, greater than 30 to 40, and greater than 40 cm height in P.
The family functions of the models were selected based on the nature of the dependent variables and after visually checking the most likely distribution of the data  [35]. The models were fitted using the functions 'glmer' and 'clmm' from the lme4 and ordinal packages  [36, 37] , and their overall performance was visually inspected by observing the residual distributions and quantile-quantile plots. Finally, post hoc Fisher's exact tests were used to explore if the size structure of each P. clavata population changed over time  [38]. All analyses were performed using the software R v. 3. 5. 0 (R Core Developer Team, 2018).
ACKNOWLEDGEMENTS We wish to thank  O. Maillet ,  A. Heidmann ,  P. Verlot  and  F. Marquardt  for very useful discussions. We acknowledge support from the  ERC CoG  grant  ULT-NEMS No. 647917  (E. C. ),  StG  grant  UNIGLASS No. 714692  (A. F. ), the  STaRS-MOC  project from  Région Hautsde-France  and  ISITE-MOST  project (X. Z. ). A. D. A. was supported through a  Leverhulme Trust Research Project Grant  ( RPG-2018-213 ), and M. S. was supported by the  Academy of Finland  (contracts  308290 ,  307757 ,  312057 ), by the  European Research Council  ( 615755-CAVITYQPD ), and by the  Aalto Centre for Quantum Engineering. The work was performed as part of the  Academy of Finland Centre of Excellence program  (project  312057 ). We acknowledge funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No.
Implications of all the available evidence Our results show that the physiological colonisation of newborns in the NICU with Enterobacter cloacae complex isolates must be carefully monitored, particularly for the presence of E bugandensis isolates that might induce fatal outcome in this fragile population. This study highlights the need for a better discrimination of Enterobacter species inside the E cloacae complex. The current identification techniques (matrix assisted laser desorption ionisation-time of flight) are not able to discriminate E bugandensis from other E cloacae complex species, restricting the discovery of E bugandensis virulence. The pathogenic mechanisms and factors contributing to diseases associated with E cloacae complex are not understood. Of note, this scarcity of available information might be because many articles, clinicians, and GenBank submissions misname E cloacae complex species with the generic E cloacae name.
Antimicrobial susceptibility testing was done for all strain as described in the appendix (p 2). Whole genome sequencing data (Genbank accession number, sequence typing, and acquired resistance genes) and antimicrobial susceptibility patterns of E cloacae complex isolates included in the genomic comparison are reported in the appendix (p 7). The seven E cloacae complex isolates responsible for bacteraemia and the clinical context are listed in table 2. Three isolates of E bugandensis, responsible for fatal septic shocks, and other Enterobacter cloacae complex species and subspecies (eg, Enterobacter xiangfangensis and Enterobacter hoffmanii) were used in in-vivo infection models (appendix p 3). Pathogenicity was evaluated according to published methods,  22  with some modifications. The protocol is detailed in the appendix (p 3).
Metabolic Labeling Confluent cells were incubated in M199 medium and exposed either to SIN-1 or IL-1β alone or in combination. After 6 or 12 h incubation, 0. 3 mCi of Tran [ 35 S]-label (1077 Ci/mmol) (MP Biomedicals, DBA Italia, Milan, Italy) was added (0. 1 mCi/mL) and cells were incubated for additional 6 or 12 h. After two washes, cells were lysed in ice-cold lysis buffer, and after centrifugation at 10,000× g for 10 min at 4 • C, COX-2 was immunoprecipitated as described above. Immunoprecipitated proteins were lysed in Laemmli buffer and separated on 7% SDS-PAGE gels. After exposure to Kodak films, bands were acquired by densitometer (GS800; Bio-Rad) using the image analysis software QuantityOne (version 4. 5. 2; Bio-Rad, Segrate, Milan, Italy).
They all have the advantage of removing a large part of the externally forced variability from greenhouse gases and aerosols (see Methods) as opposed to detrending methods  9, 29  , which only remove the effect of anthropogenic greenhouse gases emissions  28. A regression analysis between the mean of the three AMV indices with surface temperatures and precipitation observations from the instrumental dataset CRUTS4  30  , shows significant and widespread links of the AMV with both variables, in particular over the continents (Fig. 1b -k), consistently with early studies  [4] [5] [6] [7]. The last millennium is well-covered in space by high-resolution proxy records describing local or regional precipitation or temperature variations, making it very suitable for producing annually-resolved paleoclimate reconstructions.
Results: An AMV reconstruction based on optimal regression model approach The availability of large numbers of annually-resolved proxy records sensitive to temperature and precipitation provides a key opportunity to test and develop new objective reconstruction methodologies using advanced techniques. The different reconstructions produced in this study use proxy records significantly correlated at the 95% confidence level with the target AMV indices (cf. Fig. 1 ). The selection of these proxies is made from a large set of Northern Hemisphere and annually-resolved proxy records that includes the PAGES 2k database  35  and 41 other records published elsewhere (Extended Data Table  1 ). This database (hereafter P2k+) has been constructed using different quality criteria (see Methods) and comprises a total of 457 records.