dataesr/scientific-paragraphs-categorization huggingface dataset:
Argo dataset and classification of Argo profiles To investigate the vertical structure of mesoscale eddies in the NEB region; we used the available Conductivity Temperature Depth (CTD) profiles acquired from autonomous floats of the Argo international program. These temperature and salinity vertical profiles are available at https://www. coriolis. eu. org/Observing-the-Ocean/ARGO. Vertical profiles from the Argo program in the Tropical Atlantic are available after 2000 (e. g. ,  Aguedjou et al. , 2021) , reason why we limit our analysis to 2000-2018. The mean thermohaline structure was first investigated by estimating the mixed layer depth (MLD), the isothermal layer depth (ILD) and the upper and lower limits of isothermal, halocline and pycnocline layers. The MLD was estimated using the criterion recently applied in the region (e.
Wind data Wind data were used to investigate the contribution of wind stress and wind stress curl in the formation of mesoscale eddies in the study region. The wind dataset used in the present study is the quality-assured monthly update of the ECMWF Reanalysis v5 (ERA5) available from 1979 to 2019 on the grid 0. 25°x 0. 25°. The dataset includes zonal and meridional wind component estimated at 10 m above sea level. The data is produced by Copernicus Climate Change Service (C3S) and is available at https://www. ecmwf. int/en/ forecasts/datasets/reanalysis-datasets/era5. We used zonal and meridional component of the wind to estimate the wind stress and the wind stress curl within the northeast Brazil. The wind stress and its curl were estimated based on  Gill and Adrian (1982)  and considering a non-linear drag coefficient from  Large and Pond (1981)  modified for low wind speed  (Trenberth et al.
Surface current data To provide a more comprehensive picture of upper layer's dynamic, we used the near-surface velocities climatological data derived from Global Drifter Program (GDP). Velocities are obtained from satellite-tracked drifting buoys drogued at 15 m depth. The climatology was built from dataset spanning from 1979 to 2015. The data were low-pass filtered to remove high-frequency currents and oscillations associated with local inertial periods. The data is monthly available on the grid 0. 25°x 0. 25°. This climatology is an update version of the older version described in  Laurindo et al. (2017)  (https://www. aoml. noaa. gov/phod/gdp/mean_velocity. php).
Funding This work was supported by the  CAPES/PRINT  ( n°8 8887. 470036/2019-00 )through a  PhD internship scholarship grant for AND. This work was a contribution to the  LMI TAPIOCA , the  SMAC  project ( CAPES/COFECUB  no. 88881. 142689/2017-01 ), the  PADDLE  project (funding by the  European Union 's  Horizon 2020 Research and Innovation Programme-Grant Agreement 73427 ) and  EU H2020   TRIATLAS  project under Grant Agreement  817578.
Data availability statement All the data used in this study are publicly available. The altimetry datasets can be obtained from the Copernicus Marine Environment Monitoring service (CMEMS: http://marine. copernicus. eu). The sea surface velocities current from DRIFTER are available at https://www. aoml. noaa. gov/phod/gdp/mean_ velocity. php. The wind datasets are publicly available at https:// www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/era5. Argo profiles are available at https://www. coriolis. eu. org/ Observing-the-Ocean/ARGO.
Amplification and analysis was carried out with MeltDoctor™ HRM Master Mix (Thermofisher) reagent on a CFX thermocycler with CFX Maestro software version 2. 2 (Biorad) according to MeltDoctor product specifications. Phenotyping of tnfa crispants was performed by RT-qPCR using oligos: tnfa qFw GCTTATGAGCCATGCAGTGA, qRv AAGTGCTGTGGTCGTGTCTG; 18 s qFw TCGCTAGTTGGCATCGTTTATG, qRv CGGAGGTTCGAAGACGATCA. Homogenate was processed for RNA extraction following product specification for Trizol LS (Thermofisher). Reverse transcription was carried out from 2 μg total RNA with the Applied Biosystems™ High Capacity cDNA Reverse Transcription Kit (Thermofisher). Quantitative PCR was carried out with PowerUp™ SYBR™ Green Master Mix (Thermofisher) on a CFX thermocycler with CFX Maestro software version 2. 2 (Biorad).
Acknowledgements We thank the  Centenary imaging facility core and Sydney Cytometry  staff  Drs Kristina Jahn ,  Angela Kurz , and  David Liu , for their assistance. We thank  Dr Renjing Liu  for providing material supplies for this study. Australian National Health and Medical Research Council   CJ Martin Early Career Fellowship   APP1053407  and Project Grant  APP1099912 ;  The University of Sydney  Fellowship  G197581 ;  NSW Ministry of Health  under the  NSW Health Early-Mid Career Fellowships Scheme   H18/31086 ; the  Kenyon Family Foundation Inflammation Award; Australian-French Association for Research and Innovation (AFRAN) Initiative ;  The University of Sydney Marie Bashir Institute 2019 Seed Funding to S. H. O. Sydney Medical School Summer Scholarship  to  J. Y. K. Postdoctoral  fellowship granted by  Labex EpiGenMed , an " Investissements d'avenir" program   ANR-10-LABX-12-01  to M.
CLSM image analysis Projections of the biofilm structural dynamic were constructed from xyzt images series using IMARIS 9. 3 (Bitplane, Switzerland). Individual cell length and numbers were extracted from 4D-CLSM with the ImageJ (v1. 53) particle analysis function. Space-time kymographs were constructed with the BiofilmQ visualisation toolbox  [44]. Local density color code was calculated with BiofilmQ after Otsu segmentation and visualized with Paraview 5. 9  [45].
Synthesis and Cy3-labelling of cDNA, and hybridization of the sample to the B. subtilis T3 2 × 400K tiling array (Agilent-044473) were performed following Agilent Technologies protocols, as previously described  [47]. The microarray was scanned with Agilent Technologies Scanner, model G2505C. Grid: 044473_D_F_20121025. Protocol: GE1_107_Sep09. The tiling array data set is available from NCBI's Gene Expression Omnibus (GEO) database (accession number GSE190460).
Acknowledgments This work was supported by  INRAE. P. Sanchez-Vizuete  was the recipient of a PhD grant from the  Région Ile-de-France (DIM ASTREA). Y. Dergham  is the recipient of fundings from the  Union of Southern Suburbs Municipalities of Beirut, INRAE ,  Campus France PHC CEDRE 42280 PF and Fondation AgroParisTech. L. Tournier  and  V. Fromion  ( INRAE ) are acknowledged for fruitful discussions,  R. Losick  for the gift of the strain  TMN547 , and  M. Jules  for the gift of strains  BBA0184  and  BBA9006.
Type 2 diabetes mellitus (T2DM) is associated with the development of chronic comorbidities, which can lead to high drug utilization and adverse events. We aimed to identify common comorbidity clusters and explore the progression over time in newly treated T2DM patients. The IQVIA Medical Research Data incorporating data from THIN, a Cegedim database of anonymized electronic health records, was used to identify all patients with a first-ever prescription for a non-insulin antidiabetic drug (NIAD) between January 2006 and December 2019. We selected 58 chronic comorbidities of interest and used Bayesian nonparametric models to identify disease clusters and model their progression over time. Among the 175,383 eligible T2DM patients, we identified the 20 most frequent comorbidity clusters, which were comprised of 14 latent features (LFs).
Discussion This study confirmed the potential of using a large electronic healthcare database to identify clusters of chronic disease comorbidities among patients with newly treated T2DM. This is the first analysis that applied a Bayesian nonparametric model to real-world electronic medical records to identify distinct comorbidity clusters and disease progression patterns based on hidden latent features. In our case example of patients with T2DM, we could identify 14 different latent features that were strongly associated with a primary disease. Importantly, we identified comorbidity patterns consistent with the literature, pointing to the applicability of this approach in medical data. Thus, we found that Bayesian nonparametric models are a powerful tool to use in electronic health records to identify unique comorbidity clusters and health trajectories.
Methods Data source. The IQVIA Medical Research Database UK (IMRD-UK) incorporates data supplied from The Health Improvement Network (THIN), which is a Cegedim database of anonymized electronic health records generated from the daily record of General Practitioners (GPs). It includes data from more than 18 million patients from over 800 GP practices in the UK and about 6% of the UK population. The database contains detailed information about patient characteristics (i. e. , year of birth, sex, practice registration date, practice de-registration date, ethnicity), medical conditions (i. e. , diagnoses with dates, referrals to hospitals, symptoms), medications (i. e. , drug name, formulation, date, strengths, quantity, dosing instructions), in practice immunizations, laboratory tests, and results, and other patient-level data (i.
e. , smoking status, height, weight, alcohol use, pregnancy, birth, death dates). For medical conditions, all diagnoses are coded according to the Read clinical code system, a comprehensive coding language with over 100,000 codes and are comparable to the international classification of diseases (ICD) system. The IMRD contains routinely collected patient data from participating GP practices. Informed consent from all patients to have their data included in the IMRD is obtained by the GP and patients have the option to opt out of the data collection at any time. Ethical approval for the use of the IMRD for medical and public health research was approved by the London-South East Research Ethics Committee (Ref 18/LO/0441). Ethical approval for the protocol of this project was obtained by the IMRD Scientific Research Council (SRC reference number: 20SR062).
Study population. To identify patients with T2DM, we included all adult patients (age 18 +) with a firstever prescription of a non-insulin antidiabetic drug (NIAD) between January 1st 2006 and December 31st 2019. The date of the first NIAD prescription defined the index date (start of follow-up). In order to identify new users, patients were required to have a minimum of one year of valid data collection prior to the first-ever prescription of a NIAD. Patients with a history of polycystic ovarian syndrome (PCOS), gestational diabetes, or insulin prescription prior to the index date were excluded since these conditions are treated with NIAD, although not necessarily T2DM patients. Chronic disease conditions. Chronic diseases were identified as conditions that last longer than one year and require medical attention  24.
Acknowledgements The authors would like to acknowledge the following individuals for their assistance in the project. Dr. Guillaume Obozinski ,  Dr. Victor Cohen ,  Dr. Ekaterina Krymova ,  Dr. Esra Suel ,  Dr. Izabela Moise , and  Dr. Anna Susmelj  for the input and methodological discussions. Dr. Sofiane Sarni  and  Fotis Georgatos  for software assistance. Dr. Melanie Fernandez Pradier  for her invaluable help in adapting and implementing the models.
Data availability The data that support the findings of this study are available from IQVIA Medical Research Data (IMRD), a Cegedim Database, but restrictions apply to the availability of these data. The datasets generated and/or analyzed during the current study are not publicly available due to the fact that they were licensed for the sole use of the current study so they are not publicly available but are available from the corresponding author on reasonable request and with permission of IQVIA. For further information on how to access the data contact IQVIA at IMRDEnquiries@iqvia. com.
otherwise, and other factors relating to living conditions. To understand these factors, the first data used come from the 2007, 2012 and 2017 3 French censuses. They provide us with the necessary information to understand the demographic and social structures of the territories. For territorial wealth indications, the Localized Social and Fiscal File (Fichier Localisé Social et Fiscal) of the same years  (2007, 2012 and 2017)  will also be used, as well as the Zoning in Urban Areas (ZUA, in French, Zonage en Aires Urbaines), to distinguish metropolises from peripheries and rural fringes. 4 Finally, in order to measure healthcare supply as well as the presence of public and private services, this work also relies on data from the Permanent Database of Facilities (PDF, in French, the Base Permanente des Équipements), listing all facilities available at the town level.
The degree of integration in metropolises will be derived from the ZUA by the percentage of the population living in peripheral France. As the data are structured, this choice implies including within the same canton towns that do not necessarily belong to the same urban area. But in this way, the question of the heterogeneity of peri-urban areas is addressed. Indeed, this approach includes towns with weak or marginal links to major metropolises. Moreover, territorial deprivation will be measured using the following ad hoc index: D = 1 ICU × (100 -%Unemp) (1) with ICU the median income per consumption unit and %Unemp the percentage of unemployed people. In this way, the higher the index, the more deprived the canton is. In order to make the index between 0 and 1, its maximum (D max ) and minimum (D min ) values are used.
All the variables presented above are summarized in Table  1. 3 The analyses require a period of at least 10 years and the Permanent Equipment Database is only available from 2007. As the movements studied are very slow and the models used are very complex to compute, the panel is limited to five-year variations. 4 The ZUA hubs correspond to a towns group where at least 40% of the active working population works within the same hub. A distinction is then made between large urban areas (more than 10,000 jobs), medium-sized areas (5000 to 10,000 jobs), small areas (1500 to 5000 jobs), multipolarized towns or those outside influence areas (insee. fr, consulted on 07/05/2021). Subsequently, we will distinguish large areas and multipolarized towns associated with them as being the metropolitan France, while the towns of the other categories will be apprehended as the peripheral France.
This analysis therefore excludes almost 40% of GPs who are mostly employed. Their location is not totally free, since they have to work where there is employment, particularly in clinics and hospitals. This probably implies a slightly different location mechanism for them than for self-employed people. While the income issue may be less pronounced, the QoL issue may come into play in a similar way. However, this cannot be verified here. 8 In the PDF, there is no information about hospital GPs. 9 Because of missing data, the number of cantons in the database was reduced earlier from 3665 to 3490. 10 A table of comparisons of the characteristic's means between retained and deleted cantons is available in Supplement A. 11 The explained variable's density function is presented in Supplement B.
National Sarcoma Networks Databases All patients with a review in a NETSARC+ center or discussed in the NETSA sarcoma-specialized MTB were registered since 2010 in the NETSARC + database, ga ing biological and clinical data (https://netsarc. sarcomabcb. org/, accessed on 19 A 2022). The database contains 60 items divided into four themes describing, for exam the characteristics of the patient (age, sex) and tumor (size, depth, grade, location, h logic type, and subtype), the diagnosis and review of the tumor, key information a the management (types and sites of first and potential secondary surgery, final quali resection) and follow-up (relapse, survival), successive presentations of the file, and sion-making at MTB. The patients' address, diagnosis and clinical data, place of sur and patient follow-up are prospectively collected and implemented in the database As the NETSARC+ network is expected to improve the access to sarcoma expertise, the IGéAS research program was designed to assess the ability of this reference networks' organization to address geographical inequalities in cancer management  [22].
National Sarcoma Networks Databases All patients with a review in a NETSARC+ center or discussed in the NETSARC+ sarcoma-specialized MTB were registered since 2010 in the NETSARC + database, gathering biological and clinical data (https://netsarc. sarcomabcb. org/, accessed on 19 April 2022). The database contains 60 items divided into four themes describing, for example, the characteristics of the patient (age, sex) and tumor (size, depth, grade, location, histo-logic type, and subtype), the diagnosis and review of the tumor, key information about the management (types and sites of first and potential secondary surgery, final quality of resection) and follow-up (relapse, survival), successive presentations of the file, and decision-making at MTB. The patients' address, diagnosis and clinical data, place of surgery, and patient follow-up are prospectively collected and implemented in the database.
fr/inegalites-sociales/plateforme-2/, accessed on 24 May 2022) and based on ten social variables selected and weighted according to their association with individual data from the European Union Statistics on Income and Living Conditions survey  [36] ; -the average travel time to the closest reference clinical center calculated with Odomatrix software  [22]. All variables were included in a backward selection procedure to keep factors significant at a 5% level in the final multivariable model. All analyses were performed using SAS software, version 9. 4 (SAS Institute, Cary, NC, USA).
Results Our study included 16. 8% bone, 69% soft-tissue, and 14. 2% visceral sarcomas, with a median age of 61. 8 years. The follow-up information needed for the survival study could be collected for 2281/2837 (80. 4%) eligible patients. Patients without follow-up data and not included in the survival cohort were a little older (median of 65. 6 years vs. 61. 8 in the survival cohort), with smaller (33. 4% with tumor less than 50 mm vs. 25. 3% in the survival cohort), more superficial (25. 2% vs. 16. 6% in the survival cohort), and fewer grade three tumors (39% vs. 56. 1% in the survival cohort). Patients without follow-up data and those included in the survival cohort were homogeneous in terms of social deprivation (European Deprivation Index), geographical context (GeoClasH), and spatial accessibility to reference centers.
Discussion Our study reports no geographical inequalities in the survival of patients with sarcoma, in the context of national organization driven by a reference network. This is an original result since inequalities in sarcoma management and survival have been found in the USA  [37] [38] [39] [40] , in England  [41] , and in Denmark  [42]  based on national cancer databases or registries. While the clinical specificities of rare cancers and the reference centers' remoteness could have contributed to strengthen the spatial inequalities in cancer management and survival  [21] , which have already been proven in France for many cancer locations  [19, 20, 43] , these results suggest the ability of the reference network organization to address the social and spatial inequalities in cancer management.
We recognize that our study also has some limitations or methodological biases. Even if the databases of the French sarcoma reference networks support an upwards reconsideration of the incidence of sarcomas  [26] , we cannot claim that our results are based on a nationwide exhaustive cohort of sarcoma patients. As an example, sarcoma-diagnosed patients without a pathological review or MTB discussion in reference centers cannot figure into our analyses. Given the estimated incidence of sarcoma in the international literature  [53] , we estimate the IGéAS cohort covers at least 90% of the national population. Moreover, 20% of the follow-ups were not collected for the 3-year survival analysis due to long-lost patients or non-response from the centers/practitioners contacted. The patients without follow-up were slightly older than those included in this study but otherwise have clinical characteristics (size, grade, depth of tumor) more conducive to survival, which allows us to rule out a selection bias in terms of initial prognosis.
The median follow-up in the IGéAS survival cohort was 53 months but this varied greatly by region. The "region" variable was therefore excluded from the survival analysis because of this methodological bias. Considering the results' interpretation, the more or less active traceability of reviews and MTB depending on the center could partly explain the regional variations observed. It may have been interesting to use social information at the individual level to complete the assessment of potential inequalities, but national sarcoma databases are not allowed to collect social information about patients. Finally, the use of the IRIS (infra-municipality) scale would have supported a more accurate measure of the social deprivation, but national sarcoma databases can only collect the patient's municipality at diagnosis.
98, 95% CI 2. 50 to 3. 56]. Clinical and geographical determinants of the 3-year death of sarcoma patients in France (source: IGéAS survival cohort, RRePS-ResOs-NETSARC databases). Univariable Multivariable Factor Label (N = 2281) (N = 2152) Deaths/N Hazard Ratio (95% CI) p-Value Deaths/N Hazard Ratio (95% CI) p-Value Sex 0. 0363 0. 0477 Male 429/1188 1 407/1120 1 Female 352/1093 0. 86 (0. 75-0. 99) 330/1032 0. 86 (0. 74-1. 00) Age <0. 0001 <0. 0001 [0-15[ 21/97 1 19/89 1 [15-18[ 13/43 1. 19 (0. 60-2. 37) 13/42 1. 18 (0. 58-2. 40) [18-25[ 36/97 1. 88 (1. 10-3. 23) 35/95 2. 00 (1. 14-3. 50) [25-50[ 124/464 1. 27 (0. 80-2. 02) 121/443 1. 67 (1. 02-2. 73) [50-70[ 298/838 1. 98 (1. 27-3. 09) 282/795 2. 64 (1. 63-4. 27) ≥70 289/742 2. 83 (1. 82-4. 41) 267/688 4. 29 (2. 64-6. 97) Type of tumor 0.
Human ILC2s (hILC2s) express c-Maf Within the ILCs, c-Maf was shown to be expressed at early stage during ILC differentiation  (Harly et al, 2018)  and in mature intestinal murine ILC3s  (Parker et al, 2020). However, in humans, c-Maf transcripts were preferentially found in ILC2s (hILC2s;  Bjorklund et al, 2016). To explore the role of c-Maf in hILC2s, we evaluated its protein expression within circulating ILCs. In peripheral blood, hILC2s expressed higher levels of c-Maf compared with hILC1s or hILCPs  (Fig 1A and B and Appendix Fig S1) , suggesting that c-Maf could play a role in this lineage. Moreover, c-Maf expression varied within the hILC2 compartment, being higher in the cKit low than in the cKit high hILC2s  (Bernink et al, 2019; Hochdorfer et al, 2019; Fig 1C and D). These data indicate that c-Maf is upregulated in fully mature and lineage committed hILC2s, but it is also highly expressed in the hILC2s that can have hILC3 features  (Bernink et al, 2019; Hochdorfer et al, 2019).
Mouse ILC2s (mILC2s) lack c-Maf at steady-state, but express it upon stimulation To study the role of c-Maf in ILC2s in vivo, we monitored c-Maf expression in murine ILC subsets. c-Maf expression was analyzed in different organs of wild-type C57BL/6 mice including blood, lymph node (LN), spleen, lungs, and gut. In contrast to hILC2s, at steady-state, c-Maf was not expressed by mILC2s in any of the organ analyzed, but rather in mILC3s, as previously described  (Parker et al, 2020; Fig 2A and B). However, because our human data suggested preferential expression of c-Maf in fully mature ILC2s, we stimulated mILC2 using cytokines and/or known ILC2 triggers. Mice were treated with IL-33, IL-25, TSLP, or PGD2 and then sacrificed to analyze c-Maf expression as well as mILC2 subset distribution according to their expression of ST2 and KLRG1.