dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Manually collected data were entered into a database and the results are presented as median values ± standard error to the median (sem) or as scatter dot plots. The red line denotes the median of raw data. Statistical analyses were performed using unpaired ttests (GraphPad Prism (RRID:SCR_002798)). Statistically significant differences required a p value < 0. 05 (*p < 0. 05, **p < 0. 01, ***p < 0. 001). Group comparisons were made using one-way analysis of variance (ANOVA), a parametric test, and Tukey's post-hoc test. The two-tailed t-test was used to compare two groups for parametric data (GraphPad Prism (RRID:SCR_002798)). No allowance for multiplicity has been applied. For some readout analyses, we performed simple random sampling where a select number of individuals from an entire population is randomly selected.
Caveats and limitations The possible confounding effect could be caused by the heterogeneous distribution of CCPs and FFP, the time period and absence of donor characteristics (sex, age, body mass index (BMI), ABO group, rhesus, time from symptom onset, time from end of symptoms, duration of symptoms, fever, breathing difficulties and loss of taste and smell). One study limitation may be linked to the lack of sample size calculation, depending solely on one time period. Finally, clinical aspects were less detailed in our study but the efficacy of Convalescent Plasma to Treat COVID-19 Patients, a Nested Trial in the CORIMUNO-19 Cohort (COR-IPLASM) (NCT04345991), is under analysis.
Acknowledgments We would like to thank the medical staff and personnel of the  Etablissement Français du Sng, France , for medical and technical support throughout our investigations. We are extremely grateful to the blood donors for taking part in this study. This work was supported by grants from the  French National Blood Service-EFS , the  Association "Les Amis de Rémi" Savigneux, France and the "Fondation pour la Recherche Médicale (Medical Research Foundation ) - REACTing 2020 ". We wish to warmly thank teams from both branches of the  Laboratory of Human Genetics of Infectious Diseases. The latter is supported by the  Howard Hughes Medical Institute , the  Rockefeller University , the  St. Giles Foundation , the  National Institutes of Health (NIH)  ( R01AI088364 ), the  National Centre for Advancing Translational Sciences (NCATS) , the  NIH Clinical and Translational Science Award (CTSA)  programme ( UL1 TR001866 ), a  Fast Grant  from  Emergent Ventures , the  Mercatus Center at George Mason University , the  Yale Centre for Mendelian Genomics  and the  GSP Coordinating Centre  funded by the  National Human Genome Research Institute (NHGRI)  ( UM1HG006504  and  U24HG008956 ), the  Yale High Performance Computing Centre  ( S10OD018521 ), the  Fisher Centre for Alzheimer's Research Foundation , the  Meyer Foundation , the  French National Research Agency (ANR)  under the " Investments for the Future" programme  ( ANR-10-IAHU-01 ), the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID ), the  French Foundation for Medical Research (FRM)  ( EQU201903007798 ), the  FRM  and  ANR   GENCOVID  project, the  ANRS-COV05, ANR GENVIR  ( ANR-20-CE93-003 ) and  ANR   AABIFN-COV  ( ANR-20-CO11-0001 ) projects, the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  824110  ( EASI-genomics ), the  Square Foundation, Grandir -Fonds de solidarité pour l'enfance , the  Fondation du Souffle (Children's Aid Foundation) , the  SCOR Corporate Foundation for Science , the  Institut National de la Santé et de la Recherche Médicale (INSERM)  ( National Institute for Health and Medical Research)  and the  University of Paris.
Patients This retrospective study was conducted in compliance with the ethical standards of the Declaration of Helsinki and was approved by the local ethical committee (CER-VD, reference number: 142/09). Informed consents to use the patients' data for research purposes were obtained from patients' legal representatives. We screened the hospital's database for adult patients (minimum age of 16 years) who were admitted to the acute neurorehabilitation unit between 1. 11. 2011 and 31. 12. 2019, and have suffered from a severe brain injury initially resulting in coma. They were diagnosed with disorders of consciousness based on the Coma Recovery Scale -Revised (CRS-R)  (Giacino et al. , 2004)  criteria at the admission to the unit and had undergone DWI imaging. Patients with low quality imaging data (eddy currents, large susceptibility and motion artefacts) were excluded from the analyses.
Atlas-based connectivity matrix Structural connectomes were calculated on the basis of the multiscale probabilistic atlas of human connectome. (Alemán-Gómez et al. , 2022)  This atlas was derived from the diffusion data of 66 healthy adult subjects included in the Human Connectome Project. It models white matter connectivity between cortical and subcortical grey matter regions, parcellated at 4 different scales (Lausanne parcellation). (Alemán-Gómez et al. , 2022)  Each normalized FA image was overlaid with the probabilistic tractography atlas and the mean FA values were calculated for each bundle connecting each pair of regions of the scale 1 (95×95 regions). The connectivity strength in the structural connectome thus presented the mean FA values along the voxels belonging to the bundle connecting each pair of regions within the selected parcellation scheme.
Demographic and clinical scores We initially identified 148 patients in the hospital's database who were diagnosed with disorders of consciousness based on the CRS-R criteria. The DWI images acquired after the brain insult resulting in the disorders of consciousness diagnosis were available for 52 patients. Their age ranged between 16 and 83 years (mean = 51. 7, SD = 18. 3). The data of 7 patients were first removed due to the presence of larger magnetic susceptibility artefacts, and 2 patients were excluded due to the DWI interslice inconsistencies. 4 more patients were additionally excluded due to larger motion artefacts as assessed by the automatic quality control. The final sample thus consisted of 40 patients (16 females, 16-84 years, mean = 53. 5, SD = 16. 4; Kolmogorov-Smirnoff test of normality: D = 0.
Liver Histological Sections and Scoring Paraformaldehyde-fixed, paraffin-embedded liver tissue was sliced into 3-µm sections, deparaffinized, rehydrated, and stained with hematoxylin-eosin or Sirius red for histopathological analysis. Slides with two sections of a single hepatic lobe were digitized using a Mirax scanner from 3DHistech. Sections were visualized using CaseViewer software version 2. 4. 0. 119028 and were individually scored using a NAFLD scoring system (NAS) adapted from Kleiner et al. [7]. ImageJ public domain software (ImageJ Website, https://imagej. net/ij/, accessed on 5 January 2023) was used to assess the area covered by lipid droplets or Sirius red staining. A total of four variables were qualitatively assessed and ranked with a score: hepatocellular steatosis, liver inflammation, lobular fibrosis.
A model was fitted using the limma lmFit function  [12]. Pair-wise comparisons between biological conditions were applied using specific contrasts. A correction for multiple testing was applied using the Benjamini-Hochberg procedure  [13]  to control the False Discovery Rate (FDR). Probes with FDR ≤ 0. 05 were considered to be differentially expressed between conditions. Hierarchical clustering was applied to the samples and the differentially expressed probes using the 1-Pearson correlation coefficient as distance and Ward's criterion for agglomeration. The clustering results are illustrated as a heatmap of expression signals. The enrichment of KEGG pathways was evaluated using Enrichr (Biotools). The differentially expressed gene list of CAR-/-females was in silico compared in BaseSpace Correlation Engine (Illumina, NextBio) to liver signatures of female ERα-/-knock-out mice fed an HFD (D12492, Research Diets) for 10 weeks (GSE95283).
Before analysis, all Z-stacks images were combined (Z-project, sum) using Fiji. For GFAP quantification: images were converted to RGB stack format. The signal threshold was adjusted to 200 units for each image. The area of GFAP signal was calculated, setting threshold sensitivity equal for each image. GFAP data are expressed as a percentage of ROI total pixels. For Iba1 quantification, a cell counter tool was used to calculate the total number of DAPI cells and the number of Iba1+ cells in each ROI. Data are expressed as a percentage (Iba1+/tot DAPI+) × 100.
Data Representation and Statistical Analysis All data are presented as the mean +/-standard error of the mean (SEM). Differential effects were analyzed on GraphPad Prism (version 9. 00; GraphPad Software) to evaluate the effect of CAR deletion combined with high-fat diet in male or female mice. For each sex, the WT group was compared to the CAR-/-group using two-tailed Student's t-test. To compare histological score ranks, the Mann-Whitney test was used. A p-value < 0. 05 was considered significant.
Acknowledgments: We thank  Colette Bétoulieres  and the  EZOP facility in ToxAlim  for breeding of CAR-/-mice,  Virgile Richard  of  SciempathBio  for the work on analysis of histological slices for NAFLD/NASH/fibrosis scoring, Anexplo facility of Genotoul for plasmatic analysis, Metatoul facility of I2MC for hepatic neutral lipid analysis. This work was supported by grants from the  " Agence Nationale de la Recherche" ANR-Hepatobrain. Fabiana Oliviero  is the recipient of a PhD fellowship from the  ANR-Hepatobrain.
Background: Physical activity (PA) surveillance, policy, and research efforts need to be periodically appraised to gain insight into national and global capacities for PA promotion. The aim of this paper was to assess the status and trends in PA surveillance, policy, and research in 164 countries. Methods: We used data from the Global Observatory for Physical Activity (GoPA!) 2015 and 2020 surveys. Comprehensive searches were performed for each country to determine the level of development of their PA surveillance, policy, and research, and the findings were verified by the GoPA! Country Contacts. Trends were analyzed based on the data available for both survey years. Results: The global 5-year progress in all 3 indicators was modest, with most countries either improving or staying at the same level.
11, 12  The report identified a wide range of gaps and differences in PA surveillance, policy, and research across countries, world regions, and income groups. It was estimated that one-third of the countries had periodic surveillance, one-quarter had standalone PA policies, and two-thirds had PA research outputs, thus consolidating the urgent case for periodic monitoring of these indicators. 11  The second GoPA! data collection was conducted from 2019 to 2020 (referred to as "GoPA! 2020 survey"), to enable evaluation of national and global changes in the capacity for PA promotion. 9  Such evaluation was needed to support global PA leadership and advocacy and to improve national capacities for PA promotion. The aim of this paper was to assess the trends in PA surveillance, policy, and research globally, based on data from the GoPA! 2015 and 2020 surveys.
A full list of countries can be found elsewhere. 9  The same protocol was used in the GoPA! 2020 survey to ensure comparability of results between countries and over time. 11  Only countries that had their data approved by Country Contacts were included in the analysis of this paper. For some of the analyses, countries were grouped into 6 WHO regions, including Africa (AFRO), Eastern Mediterranean (EMRO), Europe (EURO), The Americas (PAHO), South-East Asia (SEARO), and Western Pacific (WPRO). 13  Countries were also grouped by their gross national income per capita into High Income (HIC), Upper Middle Income (UMIC), Lower Middle Income (LMIC), and Low Income (LIC), according to the 2020 World Bank's classification. 14  Information on total population and Gini inequality index was obtained from the World Bank 14 and Our World in Data  15  websites.
The development and data collection methods of the GoPA! Policy Inventory are described elsewhere. 19  PA Research. The GoPA! working group conducted a systematic review of peer-reviewed articles to assess the quantity of PA research that was conducted using country-specific data and published between 1950 and 2019. The review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and registered in the PROSPERO database (ref: CRD42017070153). The searches were conducted from August 2017 to May 2020 in PubMed, Scopus, and Web of Science databases. Details about the literature search can be found elsewhere. 9, 10, 12, 20  The population-adjusted contribution to worldwide PA research was estimated for each country using the following formula: ðcountry's articlesÞ=ðcountry's populationÞ ðworldwide total articlesÞ=ðworwide populationÞ.
Scoring System The GoPA! conceptual model for quantifying country-level capacity for PA promotion (ie, an aggregate of data on surveillance, policy, and research for PA) was used to assign a rating for each country. 21  The scoring protocol and variable definitions are described in Table  1. Country Contacts revised and approved the country data, and the core research team scored and analyzed it based on the standardized scoring system presented in Table  1. More details on development of the country capacity categorization for PA promotion can be found elsewhere. 21.
Data Analysis Descriptive analyses of surveillance, policy, and research indicators were conducted for all countries in the sample and stratified by world region and income group. PA surveillance, policy, and research progress were determined based on comparisons between the first and second surveys (Table  1 ). The statistical analyses were conducted in STATA (version 17. 0, StataCorp) and the graphs were conducted in R (version 4. 1. 3, R Foundation for Statistical Computing).
EURO was the region with the highest percentage of countries (71. 8%) that improved or stayed at the highest level for this indicator. AFRO was the region with the highest percentage of countries (30. 8%) that stayed or decreased to the lowest level for the indicator (ie, did not report the existence of any policy or reported the existence of an NCD plan including PA in only one of the two GoPA! surveys). A decreased capacity was reported in 11. 8%, 10. 0%, 5. 1%, and 3. 4% of PAHO, EMRO, EURO, and WPRO countries, respectively (data not shown in tables). More than half of HICs (60. 0%) improved or stayed at the highest level for this indicator, while this was achieved by 38. 9% of UMICs, 7. 4% LMICs, and none of the LICs. Also, 20. 0% of LICs decreased or stayed at the lowest level for this indicator (Figure  6 ).
Discussion The key findings on the status and progress in PA surveillance, policy, and research based on data from the GoPA! 2015 and 2020 surveys are as follows: First, the overall capacity for PA promotion varied greatly across countries, world regions, and income groups. The highest capacity was found for EURO, followed by WPRO and PAHO regions, and the lowest was found for the AFRO region and LICs and LMICs. This translated to an estimated 145 million people or 2. 0% of the world's population living in countries with a low capacity for or no data on PA promotion. Second, although most countries benefit from some kind of PA surveillance, policy, and research, having periodic national PA surveillance, standalone policies, and high research productivity (ie, all of the 3 elements underpinning PA promotion) is very uncommon.
Most of these 53 countries are in the AFRO and EMRO regions, and this lack of data may have affected the evaluation and comparisons between regions. Second, only the availability of reported PA policies was analyzed. It is possible that in some countries PA policies and research production exist within the gray literature or informal documents but were not reported by the Country Contact or were not picked up by the comprehensive searches. Third, other monitoring efforts use different indicators to quantify various elements of PA policy limiting comparability. For example, the HEPA monitoring framework for the European Union  42  and the Active Healthy Kids Global Alliance 53 are limited to the European Union countries and children, respectively. Fourth, GoPA! has yet to conduct case studies to shed light on the country-specific circumstances that contributed to the observed progress on indicators but might not have been captured by the scoring method employed.
Acknowledgments The authors would like to thank all  GoPA! Country  Contacts and their teams for reviewing, providing, and approving data for their countries. We appreciate their contributions over the past decade. In particular we would like to thank:  Aaron Sim  ( Singapore ),  Abchir Houdon , ( Djibouti ),  Angela Koh  ( Singapore ),  Audrey Tong  ( Singapore ),  Bharathi Viswanathan  ( Seychelles ),  Franklyn Edwin Prieto Alvarado  ( Colombia ),  Enrique Medina Sandino  ( Nicaragua ),  Galina Obreja  ( Republic of Moldova ),  Geoffrey P. Whitfield  ( United States ),  Gladys Bequer  ( Cuba ),  Isabel Cardenas  ( Bolivia ),  Juan Rivera  ( Mexico ),  Kyaw Zin Thant  ( Myanmar ),  Lisa Indar  ( Caribbean Islands ),  Louay Labban  ( Syrian Arab Republic ),  Lyna E. Fredericks  ( Virgin Islands ),  Migle Baceviciene  ( Lithuania ),  Mya Lay Sein  ( Myanmar ),  Nazan Yardim  ( Turkey ),  Olavur Jokladal  ( Faeroe Islands ),  Omar Badjie  ( Gambia ),  Saad Hassan Aden  ( Djibouti ),  Sawadogo Amidou  ( Burkina Faso ),  Seyed Ali Hosseini  ( Iran ),  Sigridur Lara Gudmundsdottir  ( Iceland ),  Takese Foga  ( Jamaica ),  Tatiana I Andreeva  ( Ukraine ),  Than Naing Soe  ( Myanmar ),  Thelma Sanchez  ( Costa   Rica ),  Tigri Tertulie Lamatou Nawal  ( Benin ),  Vera Amanda Solís  ( Nicaragua ), and  Wilbroad Mutale  (Zambia).
] [10] [11] [12]  However, all 5 studies evaluated CVH at only 1 point in time. Therefore, it is unknown whether a change in CVH is related to the risk of cancer. Finally, the extent to which an association between CVH and cancer exists beyond the smoking metric is poorly known because this question was evaluated in 1 study addressing baseline CVH only. 8 ing longitudinal data from a large communitybased study, the main objective of this study was to examine the association between baseline and change in CVH and incident cancer. The secondary objectives were to explore these associations for the most common site-specific cancer, including lung, breast, prostate, and colon cancer, and to evaluate whether associations with cancer existed beyond the smoking metric. Given that primordial prevention has been primarily introduced to combat CVD, to contextualize our findings, the association of CVH with incident cardiac events and mortality was also examined.
METHODS PARTICIPANTS. The GAZ et ELECTRICITE de France (GAZEL) cohort is a prospective cohort study that was set up in 1989 aiming to address the determinants of several chronic diseases in adults, with an emphasis on occupational factors. 13 It recruited employees of France's national electricity and gas company (Electricité de France-Gaz de France). At study recruitment, a total of 20,625 employees (15, CVH METRICS. The 7 CVH metrics were defined according to the American Heart Association criteria using self-reported data as detailed in Table  1. 6 Each metric was assigned a score of 0, 1, or 2 to define poor, intermediate, or ideal level, respectively. [17]  Briefly, before retirement, incident cancer events were ascertained from the employer's medical registry, which has been validated for accuracy and completeness.
Incidence rates per 1,000 person-years and their 95% CI were calculated using Poisson regression when examining the change in CVH, inverse probability of attrition weighting analysis was used. 19 To account for the change in the ascertainment of cancer and cardiac events before and after retirement, the analyses were conducted separately in the period before and the period after retirement. Furthermore, the associations between baseline and change in each individual metric with incident cancer were also examined. Lastly, the association between the average of the CVH score in 1990 and 1997 and the risk of cancer (all site and site specific) was calculated. Statistical analyses were performed using SAS version 9. 4 (SAS Institute, Inc), and a P value # 0. 05 indicates statistical significance in the main analyses.
and Institut National de la Santé et de la Recherche Médicale and received grants from the Cohortes Santé TGIR Program, Agence Nationale de la Recherché (grant ANR-08-BLAN-0028), Agence Française de Sécurité Sanitaire de l'Environnement et du Travail (grant EST-2008/1/35), Caisse d'Assurance Maladie des Industries Électrique et Gazière), and Caisse Centrale d'Activités Sociales du Personnel des Industries Électriques et Gazières. Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/World Health Organization. The authors have reported that they have no relationships relevant to the contents of this paper to disclose.
France and Institut National de la Santé et de la Recherche Médicale and received grants from the Cohortes Santé TGIR Program, Agence Nationale de la Recherché (grant ANR-08-BLAN-0028), Agence Française de Sécurité Sanitaire de l'Environnement et du Travail (grant EST-2008/1/35), Caisse d'Assurance Maladie des Industries Électrique et Gazière), and Caisse Centrale d'Activités Sociales du Personnel des Industries Électriques et Gazières. Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/World Health Organization. The authors have reported that they have no relationships relevant to the contents of this paper to disclose.
(2011)  based on a circularity parameter (circularity larger than 1. 25 and image area larger than 16 pixels). Only these non-spherical particles were used to calculate ice-phase properties. The ice particle size distributions were cal-https://doi. org/10. 5194/acp-2022-855 Preprint. Discussion started: 16 January 2023 c Author(s) 2023. CC BY 4. 0 License. culated for non-spherical particles using area-equivalent diameter. Possible contamination by shattering artifacts were removed using inter-arrival time analysis and image processing according to  Field et al. (2006). The remaining combined uncertainty in the number concentration is 50%  (Baumgardner et al. , 2017). The resolution of the CIP data products is given in 1-Hz  (Dupuy et al. , 2019)  and here we averaged the data over 10-s periods to increase the counting statistics corresponding a lower detection limit of 0.
01 L -1. On Polar P6 high-frequency measurements of wind vector and air temperature were performed in a nose boom using an Aventech five-hole probe and an open-wire Pt100 installed sidewards in a Rosemount housing  (Hartmann et al. , 2018). Humidity was measured with 1-Hz resolution with a Vaisala HMT-333, which includes a temperature and HUMICAP humidity sensor. Based on the temperature measurements (uncertainty of 0. 1 K), the humidity data were corrected for adiabatic heating  (Hartmann et al. , 2018). In this paper we use merged thermodynamic measurements providing aircraft position, air pressure, temperature, relative humidity, and the horizontal wind vector at a resolution of 1-Hz  (Hartmann and Chechin, 2019). Aerosol particle concentration in the nominal size range from 60 to 1000 nm were measured with the ultra-high sensitivity aerosol spectrometer (UHSAS,  Cai et al.