dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to members of  Sun laboratory  for reagents, comments and other contributions to this project. The work was supported by grants from  National Key Research and Development Program of China  ( 2020YFC2002800 ,  2016YFC1302400 ),  National Natural Science Foundation of China (NSFC)  ( 81472709 ,  31671425 ,  31871380 ) to Y. S. , the  Strategic Priority Research Program of Chinese Academy of Sciences  ( XDB39010500 ) to Y. S. , the  fund of Key Laboratory of Tissue Microenvironment and Tumor of Chinese Academy of Sciences  ( 201506 ,  201706 ,  202008 ) to Y. S. ;  Anti-Aging Collaborative Program of SIBS  and  BY-HEALTH (C01201911260006 ) to Y. S. , the  University and Locality Collaborative Development Program of Yantai  ( 2019XDRHXMRC08 ) Y. S. , and the  U.
Data sources We used data from the AP-HP Health Data Warehouse ('Entrepôt de Données de Santé (EDS)'). This warehouse contains all available clinical data on all inpatient visits for COVID-19 to any Greater Paris University hospital. The data included patient demographic characteristics, vital signs, laboratory test and RT-PCR test results, medication administration data, medication lists during current and past hospitalizations in AP-HP hospitals, current diagnoses, discharge disposition, and death certificates.
4% (N=183) received at least two BZRA medications. The median delay from hospital admission to first prescription of BZRA was 0. 94 days (SD=0. 55). PT-PCR test results were obtained after a median delay of 1. 2 days (SD=12. 7) from hospital admission date. This median delay was of 1. 0 day (SD=11. 6) in the exposed group and of 1. 2 day (SD=12. 8) in the non-exposed group. Over a mean follow-up of 14. 5 days (SD=18. 1; median=7 days), 1,320 patients (9. 2%) had an end-point event at the time of data cutoff on May 1 st. Among patients who received a BZRA, the mean follow-up was 12. 8 days (SD=13. 1, median=8 days), while it was of 14. 5 days (SD=18. 3, median=7 days) in those who did not. All baseline characteristics were independently and significantly associated with the endpoint. A multivariable Cox regression model showed that age, sex, hospital, any medical condition, any current psychiatric disorder, respiratory depression and any other clinical marker of severity were significantly and independently associated with the endpoint (eTable.
Acknowledgements The authors acknowledge financial support from the  European Commission  through  H2020   Future and Emerging Technologies Graphene Flagship  (Grants Nos. 881603  and  785219 ), from the  French National Research Agency (ANR)  through " EPOS BP " ( ANR-17-CE24-0023-03 ) " STEM2D " ( ANR-19-CE24-0015 ), and as part of the  "Investissements d'Avenir" program  ( Labex NanoSaclay , reference:  ANR-10-LABX-0035 ).
The images were then treated with ImageJ software. HeLa Targeting and Imaging: HeLa cells (1 Â 10 4 cells/well) were seeded in glass-bottomed culture dishes and incubated with MCNDs (100 μg mL À1 ) for 0, 8, and 24 h at 37 C. Cell Mask green from Sigma-Aldrich was then added to each well and the cells incubated for additional 10 min at 37 C. To confirm the receptor-mediated uptake, competition experiments were performed: HeLa cell cultures were pretreated with 50 μL of a saturated FA solution for 1. 5 h, prior to adding MCNDs. All confocal images were acquired using a Leica laser scanning confocal fluorescence microscope. The saturated FA solution was prepared according to the following procedure: 10 mg of FA was added to 1 mL PBS and the suspension was sonicated for 10 min. After centrifugation at 10 000 rpm for 20 min, the supernatant was collected.
5% thereafter. In vivo fluorescence imaging was performed using an IVIS Kinetic (PerkinElmer, USA) with 640 AE 15 nm excitation and 680 AE 15 nm emission filters. Whole-body fluorescence imaging was performed before and at several time points after the intravenous administration of MCNDs. Mice were sacrificed at 5 and 24 h postinjection, and some organs were collected for ex vivo fluorescence imaging. Organs from control mice were also imaged to determine the autofluorescence of each organ. Statistical Analysis: All statistical analyses were performed using GraphPad Prism (GraphPad software). All datasets were analyzed for significance using two-way ANOVA with Bonferroni posthoc test. All data were addressed as means AE SEM. All data were performed at least in three independent replicates of experiments.
Acknowledgements The authors gratefully acknowledge the  Centre National de la Recherche Scientifique (CNRS) , the  International Center for Frontier Research in Chemistry (icFRC) , and financial support from the  Agence Nationale de la Recherche (ANR)  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026_CSC ) within the  Investissement d'Avenir program  ( ANR-10-120 IDEX-0002-02 ). The authors gratefully acknowledge the financial support from the sponsored by  Shanghai Sailing Program  ( 20YF1424500 ). The authors wish to thank  C. Royer  and  V. Demais  for help with transmission electron microscopy (TEM) analyses at the  Plateforme Imagerie In Vitro de l'ITI Neurostra (Strasbourg, France) ,  D. Ihiawakrim  for high-resolution TEM, and  G. Reina  for his help with confocal images.
2B  right; see also Fig. S2C ). To explore whether capsicumicine features are compatible with carbohydrate-binding modules (CBMs), we performed a BLAST and amino acid alignments between capsicumicine and chitin-, chitosan-, and polysaccharide intercellular adhesin (PIA)-binding proteins (see Table  S2  in the supplemental material). UniProt tools  (26)  and CAZy information crossing  (27)  showed that capsicumicine does in fact present CBM homology with all tested proteins (see Fig. S3  in the supplemental material). Capsicumicine shifts staphylococcal synthetic matrix. To confirm these interactions in the absence of bacteria metabolic or regulatory influences, we adapted a model of artificial staphylococcal biofilm assembly to test it. For this purpose, we used chitosan in order to mimic natural poly-N-acetylglucosamine (PNAG) biofilm.
Primers (see Table  S1  in the supplemental material) were designed using the Primer3 program and then produced by Eurofins Genomics. Applied Biosystems StepOnePlus equipment and software were used. Relative transcript levels were determined by the 2 2DDct method (  60 ). (iii) Cytotoxicity assays. Cytotoxicity assays were performed on the ImPACcell robotic platform (BIOSIT, Université de Rennes 1). Multiparameter high-content screening (HCS) and high-content analysis (HCA) of chemical markers related to cell viability were done on 7 different mammalian lines as follows: HuH7 (hepatocellular carcinoma), Caco-2 (colorectal adenocarcinoma), MDA-MB231 (breast adenocarcinoma), HCT116 (colorectal carcinoma), PC3 (prostatic adenocarcinoma), NCL-H727 (lung carcinoma), and MCF7 (breast cancer). The number of viable cells is presented as residual cell percentage compared to the dimethyl sulfoxide (DMSO) control average.
(iii) Confocal fluorescence microscopy. Capsicumicine-fluorescein isothiocyanate (capsicumicine-ITC) (10 mM) was used to detect the capsicumicine peptide, whose antibiofilm activity was previously verified. After incubation for 1, 4, or 24 h, the well contents were carefully detached, centrifuged at 11,000 Â g for 2 min at 4°C, and then washed with sterile 0. 9% NaCl. The suspension was visualized directly or after adding 0. 1 mg/ml concanavalin A conjugates (Alexa Fluor 633; Invitrogen) or 2 mg/ml Calcofluor white dye (Fluorescent Brightener 28; Sigma-Aldrich). The pseudonajide FITC-labeled peptide is a known antimicrobial peptide that permeates the bacterial cell and was used as a permeability reference  (61). Images were acquired via resonant scanner with a Leica SP8 DMI 6000 CS confocal microscope with hybrid detector, and ImageJ software was used for image analysis.
The samples were conserved at 5°C between each recording. Spectra were averaged over 5 records. Water CD contributions were subtracted from CD spectra before processing. Spectra were processed using KaleidaGraph (Synergy Software). Molar circular dichroism (D« ) per residue and molar ellipticity per residue (u ) (MER) were computed from the difference of the delta absorbance recorded by the spectrometer. Raw delta epsilon per residue spectra were analyzed using the CDSSTR program (64) and different reference protein data sets provided by the DICHROWEB facility  (65). MER data curves were smoothed for presentation, using the interpolate and weighted data (5%) routines provide by KaleidaGraph. TANGO and SALSA predictions. Tango (66) and Salsa (67) predictions were run using the AMYpdb online web server (http://amypdb.
ACKNOWLEDGMENTS We thank  Daniel Thomas  for his support and  Juliana Berland  for insightful comments on the manuscript. We thank the  Microscopy Rennes Imaging Center (MRic)  and CMEBA facilities. This study was funded by the  CAPES-COFECUB program , whose institutional partners are the  Brazilian Ministry of Education's CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior ) agency and the  French Ministère de l'Europe et des Affaires Etrangères (MEAE)  and  Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation (MESRI). Support was also received from the  Transfer Acceleration Company SATT Ouest-Valorisation. R. G. V. B. , S. C. B. G. , A. J. M. , S. N-L. , and R. G. designed the research; R. G. V. B. , S. C. , R. S. , S. N-L. , and S. B. performed research; R.
Sequencing adaptors were removed using UrQt  (Modolo and Lerat 2015)  and trim Galore (https://www. bioinformatics. babraham. ac. uk/projects/trim_galore/). Trimmed reads were aligned on reference genes retrieved from FlyBase (ftp://ftp. flybase. net/releases/ FB2019_01/dsim_r2. 02/fasta/dsim-all-gene-r2. 02. fasta. gz) using TopHat2  (Kim et al. 2013). Gene count tables were generated using eXpress  (Roberts et al. 2011). TE count tables were generated using the TEcount module of TEtools  (Lerat et al. 2017) , and the list of sequences used in  Roy et al. (2020)  and available at ftp://pbil. univ-lyon1. fr/pub/datasets/Roy2019/. Gene and TE count tables were concatenated to make the complete count table, which was further analyzed using the DESeq2 R package (version 1. 18)  (Love et al. 2014).
Acknowledgments This work is dedicated to the memory of  Christophe Terzian , who initiated this project but left us too early. We acknowledge the contribution of Structure F ed erative de  Recherche Biosciences  ( UMS3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL)  facility: Arthro-tools platform. This work was performed using the computing facilities of the  CC LBBE/ PRABI , and the  Qubit Invitrogen of Institut de G enomique Fonctionnelle de Lyon  (managed by  Benjamin Gillet , who we thank for his availability). Sequencing was performed by the GenomEast platform, a member of the " France G enomique "  Consortium  ( ANR-10-INBS-0009 ). We thank  Francis Jiggins , who provided us with the D. melanogaster strain naturally, persistently infected with the Hap23 DMelSV virus. We thank  Cristina Vieira ,  Fr ed erick Arnaud ,  Rita Rebollo ,  S everine Chambeyron ,  Matthieu Boulesteix ,  H elene Henri , and  William Palmer  for helpful discussions.
We thank  Justine Picarle , Vincent M erel, Ang elique Champavere,  Elisa Dell'Aglio , and  Nelly Burlet  for technical help. We also thank  Cl ement Gilbert ,  Maria del Pilar Garc ıa Guerriero ,  Emilie Brasset ,  Fabrice Vavre, Dupressoir ,  Franc¸ois Leulier , and  Philippe Marianneau  for helpful discussions. This work was supported by the  ANR LABEX   ECOFECT  ( ANR-11-LABX-0048  of the  Universit e de Lyon , within the  program Investissements d'Avenir  [ ANR-11-IDEX-0007 ] operated by the  French National Research Agency , grant  ERMIT ) and  ANR   TEMIT grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Study Cohort The study was conducted within the Quebec Pregnancy Cohort (QPC), which is a population-based cohort with prospective data collection on all pregnancies covered by the Quebec Prescription Drug Insurance, from January 01, 1998 to December 31, 2015 (presently, the QPC includes data from 1998 to 2015, and will be updated soon)  (Bérard and Sheehy, 2014 ). Individual-level information for all pregnant women and children are obtained from province-wide databases and linked using unique personal identifiers (Supplementary Figure  S1 ). The first day of the last menstrual period (LMP) is defined using data on gestational age, which is validated with ultrasound measures in patients' charts  (Vilain et al. , 2008). Prospective follow-up is available from 1 year before LMP, during pregnancy, and until December 31, 2015; children are followed from birth until December 31, 2015 (Supplementary Figure  S2 ).
The QPC data sources include the medical claims database (RAMQ: diagnoses, procedures, indicators of socio-economic status), Quebec Prescription Drug Insurance database (drug name, start date, dosage, duration), hospitalization archive database (MedEcho: diagnoses/procedures), and Quebec Statistics database (ISQ: patient socio-demographics, gestational age, and birth weight). Birth weight in ISQ has been found to be valid  (Vilain et al. , 2008). The RAMQ medication database in the QPC represents 36% of women between 15-45 years of age (Régie de l'Assurance  Mala, 2015). Validation studies have shown that publicly insured pregnant women have similar characteristics and co-morbidities with those who have private medication insurance  (Bérard and Lacasse, 2009). Pregnant women in the QPC were eligible for this study if they were continuously covered by the Quebec Prescription Drug Insurance for ≥12 months before and during pregnancy, and had given birth to liveborn singletons.
This was done because twin pregnancies are at increased risk of adverse pregnancy outcomes regardless of gestational medication exposures. We also excluded pregnancies exposed to known teratogens as described by  Kulaga et al. (2009) , and those resulting in minor malformations alone or chromosomal abnormalities in the newborns (Figure  1 ). Minor malformations are selectively identified and do not reflect the true prevalence; chromosomal abnormalities are not related to medication use. The study was approved by the Sainte-Justine's Hospital Ethics Committee. The Quebec "Commission d'accès à l'information" authorized database linkages.
Chloroquine or Hydroxychloroquine Exposure We identified CQ and HCQ prescriptions (Supplementary Table  S1 ) filled from the Quebec Prescription Drug Insurance database, using timing of exposure determined by the dispensed date and duration of treatment. The relevant exposure time-window for the analyses of malformations was the first trimester (0-14 completed weeks of gestation since LMP; organogenesis)  (Bérard et al. , 2016; Howley et al. , 2016) ; the relevant exposure time-window was the second/third trimester of pregnancy (>14 weeks of gestation until delivery) for the analyses of prematurity and LBW (intra-uterine growth). Pregnancies were considered as exposed if women had filled at least one CQ or HCQ prescription during the relevant time-windows or if they had filled a prescription with a duration that overlapped the relevant time-window (to consider CQ/HCQ half-life).
The comparator group was defined as pregnancies with no exposure to CQ/HCQ during the time-windows of interest. Exposure to other anti-malarials (Supplementary Table  S2 ) were used as an active comparator to take into account potential indication bias. Indication bias was also considered by adjusting for diagnoses of malaria, and diagnoses of SLE, and RA or use of other anti-rheumatic drugs in the analyses. Duration of use was defined as the number of days with active prescription fillings, and the defined daily dose was defined as a CQ equivalent dosage divided by the duration of use  (Liu et al. , 2020). Data on prescription fillings have been validated and compared to maternal reports in the QPC; the positive predictive value (PPV) of prescription drug data was ≥87% (95%CI: 70-100%) and the negative predictive value (NPV) was ≥92% (95%CI: 86-98%)  (Zhao et al.
Outcomes Cases of prematurity were identified from the RAMQ and MedEcho databases, and defined as delivery at less than 37 weeks gestation. Cases of LBW were identified from the ISQ database, and defined as a newborn of less than 2,500 g. Cases of MCM diagnosed in the first 12 months of life were identified from the RAMQ and MedEcho databases and defined according to ICD-9 and ICD-10 codes (Supplementary Table  S3 ), which have been validated against patient charts with high PPV (78. 1%) and NPV (94. 2%)  (Blais et al. , 2013). All organ systems were considered and high PPV (over 80%) have also been reported for specific MCM  (Blais et al. , 2013). Twelve months after birth was needed to allow for late detection, and validation of early diagnoses.
We also considered whether pregnant women were followed by an obstetrician/gynaecologist (yes/no), and if other medications were used during pregnancy (besides CQ/HCQ and medication used to identify comorbidities), including HIV drugs (Supplementary Table  S4 ). The unit of analysis was the pregnancy. Means and proportions for continuous and dichotomous variables were calculated, respectively. Crude and adjusted odds ratios (aOR) with 95% confidence intervals (95%CI) were calculated for each outcome separately. Multivariable generalized estimating equations were used to estimate the association between CQ/HCQ and prematurity, LBW, and MCM risks, independently, accounting for clustering by family (mother). All statistical analyses were performed using SAS (SAS Institute Inc. , Version 9. 2, Cary, NC, United States).
18, 95%CI 0. 93-1. 50; 165 exposed cases), compared to propensity-score-matched unexposed pregnancies. No significant associations between exposure to CQ or HCQ individually and risk of MCM, preterm birth or small for gestational age were identified. This study did not investigate the risk of specific defects. While, using the Medicaid Analytic eXtract  (MAX, 2000 (MAX, -2014) )  and IBM MarketScan Research Database  (MarketScan, 2003 (MarketScan, -2015)) ,  Huybrechts et al. (2021)  compared HCQ-exposed pregnancies with matched unexposed pregnancies, and found a small increase in the risk of MCM associated with first trimester HCQ use (adjusted RR 1. 26, 95%CI 1. 04-1. 54; 112 exposed cases); it was 1. 33 (95%CI 1. 08-1. 65) for a daily dose of >400 mg and 0. 95 (95%CI 0. 60-1. 50) for a daily dose of <400 mg.
, 2021). One potential limitation is missing information on potential confounders such as smoking, alcohol, and over the counter folic acid use. Nevertheless, tobacco and alcohol dependence, and prescribed folic acid were used as proxies for these variables. The majority of folic acid users were on high dose (>5 mg/d), and thus this was likely a proxy for high-risk pregnancy as well in our study. The identification of MCM was based on diagnosis codes that had been recorded in claims and hospital databases. If misclassification of exposure would be present, it would bias the results of our study toward the null excepting that a higher proportion of malformation diagnoses were identified in pregnancies of mothers with SLE or RA. In spite of the fact that we have shown a high positive predictive value for malformations  (Zhao et al.
, 2017) , the potential for some misclassification still exists. We used filled prescriptions and not actual intake, but  Zhao et al. (2017)  have shown that prescription fillings data in the QPC were valid when compared to maternal report. Indeed, given that we have prescription fillings data, we wanted to ensure that women took their medications (intake). Patient charts are not good for this because they are providing medication prescribing and not intake. Hence, we have compared pregnant women's selfreport of medications use (any medications in real-time to minimize recall bias) to the data on medication fillings (claims database used to put in place the QPC) on a sample of women included in the QPC  (Zhao et al. , 2017). Given that we consider self-report the "gold standard," we have calculated the PPV and NPV for the use of the QPC data on medications.
Ozonesondes Ozonesondes measure in situ vertical profiles of temperature, pressure, humidity, and ozone up to 30-35 km, with a vertical resolution of ∼ 150 m for ozone. The ozonesondes data come mainly from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) database (http://www. woudc. org/, last access: 19 October 2021), and the Southern Hemisphere Additional Ozonesondes (SHADOZ) database (http://croc. gsfc. nasa. gov/shadoz/, last access: 19 October 2021). The sonde measurements use electrochemical concentration cell (ECC) technique, relying on the oxidation of ozone with a potassium iodine (KI) solution  (Komhyr et al. , 1995)  Their accuracy for the ozone concentration measurement is about ± 5 %  (Deshler et al. , 2008; Smit et al. , 2007; Thompson et al. , 2003). We use a database of ozonesonde measurements from 2007 to 2012, including 24 stations in the midlatitudinal bands (30-60   Li et al.
A comparison done in Europe, where more in-dependent IAGOS measurements are available, shows that trend calculation can be strongly affected by the sampling of the considered data sets and the time period considered when analysing short-term trends. Particular caution should be taken to not overinterpret short-term trends and when comparing trends derived from different data sets with different sampling. In addition, comparisons between the trends calculated from the OMI/MLS O 3 tropospheric columns and the IASI ones, calculated using the same tropopause height and sampling, show large discrepancies, as already stated by the TOAR  (Gaudel et al. , 2018) , and point toward a need to better understand how the differences in vertical sensitivity of the satellite observations impact the observed tropospheric columns and the derived trends.
4  for details on the sensitivity tests). The residual trend and its contribution is also given. Code and data availability. The IAGOS data set is available at https://doi. org/10. 25326/20, and, more precisely, the time series data are available at https://doi. org/10. 25326/06 (Boulanger et al. , 2021). The distribution of the IAGOS data onto the model grid is based on an updated version of the Interpol-IAGOS software, which can be found at https://doi. org/10. 25326/81 (Cohen et al. , 2021b). The LMDZ, INCA, and ORCHIDEE models are released under the terms of the CeCILL license. The mode codes, input data, and outputs are archived in the CEA (Commissariat à l'énergie atomique et aux énergies alternatives) high-performance computing centre (TGCC) and are available upon request. The IASI observations (level 1C) are available from the AERIS data infrastructure (https://www.
aeris-data. fr, Boonne, 2021). The full archive of the IASI ozone product retrieved from the level 1C data is available, upon request to Gaëlle Dufour (gaelle. dufour@lisa. ipsl. fr), for the Asian domain considered here between 2008 and 2017. In addition, the monthly gridded partial columns derived from IASI and INCA, used to calculate the trends in the project, are available through https://doi. org/10. 14768/283b78e9-a4c1-4481-926f-adabdfee8496 (Dufour, 2021).