dataesr/scientific-paragraphs-categorization huggingface dataset:
Docking Studies for Non-Covalent Inhibition Each of the 388 compounds from the SDF library (downloaded from https://pubchem. ncbi. nlm. nih. gov/bioassay/1706 (accessed on 5 June 2023)) were docked within the M Pro active site of SARS-CoV-2, centered on the β-keto amide inhibitor, using Arguslab software v4. 0. 1 with the Argusdock engine and default parameters. Compounds were then ranked according to their docking score (Table  S2 ), and the corresponding binding modes of the top 5 compounds were examined using PyMOL. The hydrogen bond networks were generated using LigPlot + v2. 2. 4  [38]. For the five selected compounds, flexible docking experiments with a genetic algorithm engine implemented in Arguslab were performed to corroborate the docking results obtained with the Argusdock engine (Figure  S1B ).
Docking Studies of the 388 Active Compounds from the PubChem BioAssay AID 1706 within the SARS-CoV-2 Protease Active Site Considering the structural homologies and differences between the two proteins, particularly in their active site, the PubChem BioAssay AID 1706 (IBV) was used as a starting collection to look for potential covalent inhibitors and non-covalent inhibitors directed at the SARS-CoV-2 M Pro. The docking method has been validated in our previous study using three covalent inhibitors co-crystallized with SARS-CoV-2 M Pro (PDB codes 5RHF, 5REN, and 5REK)  [16] , with results consistent with the reported crystallographic data  [46].
Docking Studies Looking for Non-Covalent Inhibitors For the non-covalent inhibition of SARS-CoV-2 M Pro , the 388 active compounds on the avian IBV retrieved from the PubChem database were submitted to docking experiments within the active site and ranked according to their calculated docking score (Table  S2 ). The five best binders, with calculated docking scores ranging from -9. 92 to -9. 54 kcal/mol, are structurally related to hydrazine (CID 1632360), thiazole (CID 4586109), benzotriazole (CID 645492 or CID 654498), or diaminophenyl (CID 2193552) derivatives (Figure  5 ).
Docking Studies Looking for Non-Covalent Inhibitors For the non-covalent inhibition of SARS-CoV-2 M Pro , the 388 active compounds on the avian IBV retrieved from the PubChem database were submitted to docking experiments within the active site and ranked according to their calculated docking score (Table  S2 ). The five best binders, with calculated docking scores ranging from -9. 92 to -9. 54 kcal/mol, are structurally related to hydrazine (CID 1632360), thiazole (CID 4586109), benzotriazole (CID 645492 or CID 654498), or diaminophenyl (CID 2193552) derivatives (Figure  5 ). Biomolecules 2023, 13, x 8 of 17.
Docking Studies Looking for Non-Covalent Inhibitors For the non-covalent inhibition of SARS-CoV-2 M Pro , the 388 active compounds on the avian IBV retrieved from the PubChem database were submitted to docking experiments within the active site and ranked according to their calculated docking score (Table  S2 ). The five best binders, with calculated docking scores ranging from -9. 92 to -9. 54 kcal/mol, are structurally related to hydrazine (CID 1632360), thiazole (CID 4586109), benzotriazole (CID 645492 or CID 654498), or diaminophenyl (CID 2193552) derivatives (Figure  5 ). The binding modes of these top five compounds were then studied in more detail. All five interact tightly within the active site, with between one and five H-bonds, and all are located in the close vicinity of the catalytic dyad His41 and Cys145, often interacting with the cysteine residue itself (Table  2 ).
Data source This present research used data from the most recent cross-sectional Haitian Demographic Health Survey (HDHS), a national level survey conducted over 5 months from November 2016 to April 2017, and implemented by the Haitian Institute for Children (HIC) in collaboration with the Haiti National Bureau of Statistics (HNBS), and the Ministry of Public Health and Population (MPHP). The survey gathered information on household population and characteristics, fertility, marriage, sexual activity, nutrition, malaria, HIV-AIDS, maternal and child health, adult and childhood mortality, women's empowerment, domestic violence, and other health-related issues. Four questionnaires were used for the data collection: Household Questionnaire, Women's Questionnaire, Men's Questionnaire and Biomarker Questionnaire.
The other independent variables were current contraceptive use by method type ("no method", "traditional method", and "modern method"), fieldworker visit in last 12 months ("yes" and "no"), and exposure to mass media family planning messages ("yes" and "no"). Exposure to mass media family planning messages was a composite variable created by combining four variables: "have heard about family planning messages on radio in last few months", "have heard about family planning messages on TV in last few months", "have heard about family planning in newspaper/magazine in last few months", and "have heard about family planning by text messages on mobile phone in last few months". In the Women dataset, these four variables were coded as "yes", and "no". After examining the frequency distribution of the responses, we coded it as "yes" if a woman heard family planning messages through at least one of these mass media, and "no" if she did not hear family planning messages through any of the mass media.
Statistical analysis Descriptive statistics were generated to summarize the data and the results were presented as proportions (%). Bivariable analyses (Pearson's chi-square) were conducted to assess the associations between the outcome variable and each covariate. To identify determinants of KOC, a multi-variable analysis (binary logistic regression) was performed and adjusted odds ratios (AOR) at 95% confidence intervals (95% CI) were estimated. All analyses were weighted (HV005/1,000,000) to get unbiased estimates  [29]. Statistical analysis was done in STATA 14 software using svy command to adjust for the complex sampling structure of the data  [30]. A significance level of 0. 05 was considered.
Ethical consideration This study is based on a secondary analysis of publicly available data (https:// dhspr ogram. com/ data/ avail able-datas ets. cfm); therefore, no ethics approval was required from our institutions. However, to obtain permission to access these datasets, we registered and sent a request (on May 3, 2022) including the objective of our study to the managers of the DHS program via their online platform. Two days later, we received approval to access and download the data files. According to the 2016/2017 HDHS report, during DHS data collection, all participants gave their informed consent, and the data were anonymized  [29].
Moreover, two other studies reported better immediate improvements in forehand performance when young beginners (6-9 years and 9-11 years) used a scaled racket (19 inches-0. 483 m or 23 inches-0. 584 m) with low compression balls in comparison with a standard racket (27 inches-0. 685 m) and standard balls  (Buszard et al. , 2014a,b). However, if all these previous studies confirm that scaling racket allows to increase tennis performance in young beginners, the tennis motor skill improvements were only evaluated from a general technical point of view using video replay and technical fundamentals checklists. In these studies, biomechanical data based on full-body kinematics and upper limb joint loadings that underpin and objectify the emergence of more efficient and safer motor patterns as a consequence of the scale equipment are missing.
, Göteborg, Sweden) was used to record the trajectories of the 3-dimensional (3-D) anatomical landmarks. Players were shirtless or wore a bra and a tight short to limit movement of the markers. Post-impact ball speed was measured for each trial with a radar (Stalker Professional Sports Radar, Applied Concepts, Plano, TX, United States) fixed on a tripod and placed 2 m behind the players in the direction of the serve. For each player, the radar's height on the tripod was adjusted according to the impact height. After the capture, 3-D coordinates of the landmarks were reconstructed with QTM software Qualisys AB. , Göteborg, Sweden with a residual error of less than 1 mm. The 3-D motions of each player were expressed in a right-handed inertial reference frame R1 whose origin was at the center of the baseline.
Statistical analyses For each of the three racket conditions, the magnitudes of ball speed, kinematic and kinetic parameters were averaged for each player. One-way analysis of variance with repeated measures was used to analyze the effect of the three rackets on ball speed, serve kinematics and torques. When significant main effects were present, post hoc pairwise comparisons were undertaken using a Holm correction to determine the source of difference. Where data were not normally distributed, significance was determined using a Friedman analysis of variance with repeated measures on ranks and a post hoc Durbin-Conover test. Post-hoc analysis with Durbin-Conover tests was conducted with a Bonferroni correction for multiple comparisons. The level of significance was set at p ≤ 0. 05. Furthermore, we also calculated effect sizes using partial eta squared (η 2 p), defined as small (0.
005 (OSM) and 0. 082 (CEA-CAM5) units in the area under the receiver operating curve (AUC) (Supplementary Data 4). All 36 proteins showed good quality control measures and had less than 20% of values below LOD (Supplementary Data 2, Supplementary Data 3). In a sensitivity analysis, we compared the proteins that would be identified if we used a single split-sample approach for discovery and replication instead of our resampling algorithm (details in Methods section). This showed that there were 29 proteins identified by both methods, 7 markers identified only by the resampling algorithm, and 10 markers identified only by the single split-sample method (Supplementary Fig. 3 ). Markers identified only by the resampling algorithm typically had stronger risk associations in the full dataset and were more consistently associated with risk across the six cohorts compared with the proteins identified only by the single split-sample method (Supplementary Data 6).
Associations with mortality among individuals with lung cancer Using Cox proportional hazards models, we evaluated the extent to which the 36 risk proteins were associated with all-cause mortality following lung cancer diagnosis using both blood concentrations and tumor gene expression in TCGA samples. Whilst 20 proteins were nominally associated (p < 0. 05) with all-cause mortality when measured in blood (Supplementary Fig. 14 ), these associations were weak  in comparison to the association with incident lung cancer risk. Only three proteins (CEACAM5, CDCP1 and VEGFA) were associated with allcause mortality after accounting for multiple comparisons (Supplementary Data 12 and 13). Of the 20 proteins nominally associated with mortality, three were also nominally associated with all-cause mortality when assessed using tumor gene expression (CDCP1, CEACAM5, and U-PAR) in TCGA.
Gene expression in normal and tumor tissue We used data from GTEx to assess mRNA expression for the genes coding for 36 risk proteins in normal tissue. Relative levels of mRNA expression in various normal cell types for 35 markers are shown in Fig. 4a  (data was not available for TNFRSF6B). Three markers (ALPP, SFTPA1, and MUC-16) were expressed primarily by lung cell types, while 4 others (IL2-RA, CXCL13, TNFSF13B, and EN-RAGE) were expressed primarily in immune cells. For mRNA expression in tumor cell types from TCGA, we found that most of the 36 markers were expressed in lung tumor tissue to some degree, but also in a wide variety of other cancer types (Fig. 4b ). The only marker that appeared specifically expressed in lung cancer tissue was SFTPA1.
An interesting observation was that several of the proteins most strongly associated with lung cancer, including CEACAM5, IL6, and SCF, did not have any stable connections with the identified markers. To understand why circulating concentrations of the identified proteins are associated with lung cancer diagnosis, and to assess whether they are likely to be specific to lung cancer-as opposed to cancer at other sites-we used publicly available expression data for a range of normal and tumor tissues. This analysis yielded two notable observations; first, that only three proteins, ALPP, SFTPA1, and MUC-16, were predominantly expressed in normal lung cells compared to cell types of other origins. In contrast, several proteins appeared to be primarily expressed by immune cells, although most were also expressed by other cell types.
The second notable observation was that only one protein-SFTPA1-was predominantly expressed by lung tumor tissue compared to other tumor tissues, whereas most proteins were expressed in a wide range of cancer types. These complementary data suggest that few of the identified markers are likely to have originated in yet-to-be diagnosed lung tumor tissue, but rather are present in the circulation as a somatic response to subclinical cancer. Associations between the identified markers and all-cause mortality after lung cancer diagnosis were weak. Three markers (U-PAR, CEACAM5, and CDCP1) were also weakly associated with all-cause mortality when measured as mRNA in lung tumor tissue in TCGA. Although these associations do not appear important, also considering that stage was not accounted for, they may be consistent with a role for some of the identified markers in tumor progression or an immune or inflammation response in lung tissue.
To summarize, after screening 1162 proteins, we identified 36 markers of imminent lung cancer diagnosis with a wide range of functions and relevance across the hallmarks of cancer. Forthcoming studies will address the extent to which these markers can discriminate future lung cancer cases and their utility for early detection. Our study provides a potential view of the blood proteome in the years leading up to diagnosis of smoking-related lung cancer and can serve as a reference for investigations seeking to identify early protein markers of lung cancer. For the group of markers identified as associated with imminent lung cancer by the resampling algorithm, we carried out additional analyses using the full dataset. For each marker, we calculated odds ratios for lung cancer stratified by histological type, stage, smoking status, cohort, and lead time (time between blood draw and diagnosis) and examined trends by lead time (see Supplementary Methods).
These stratified analyses did not account for multiple comparisons. To describe the association between each marker and smoking intensity, duration, and time since cessation, we used linear regression models fit among controls with adjustment for cohort, age, sex, and smoking status. Similar analysis was run in the full dataset (among cases and controls) while additionally adjusting for case-status. We also estimated stage at the time of blood draw for participants with available information on stage and histology using sojourn times specific to stage, sex, and histological type previously estimated by ten Haaf et al. 10. For the 36 identified proteins we ran pathway enrichment analysis using g:Profiler  16  to examine the biological processes in which they are implicated, and we mapped these outcomes using Cytoscape version 3.
Acknowledgements 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. This study was supported by the  US NCI  ( INTEGRAL program   U19 CA203654  and  R03 CA245979 ),  Fondation ARC pour la recherche sur le cancer and l'Institut National Du Cancer (INCa)  ( INCA201601246/ARC_10450 ,  Fondation ARC et INCa, France) ,  INCa  ( TABAC18-035 ,  France ), the  Cancer Research Foundation of Northern Sweden  ( AMP19-962 ), an early detection of cancer development grant from  Swedish Department of Health ministry , and  Cancer Research UK  [ C18281/A29019 ].
RJH is supported by the  Canada Research Chair of the Canadian Institute of Health Research. The  Trøndelag Health Study  (HUNT) is a collaboration between  HUNT Research Centre (Faculty of Medicine and Health Sciences ,  Norwegian University of Science and Technology NTNU) ,  Trøndelag County Council ,  Central Norway Regional Health Authority , and the  Norwegian Institute of Public Health. The  Singapore Chinese Health  Study was supported by the  US National Institutes of Health  Grant No. R01CA080205 ,  R01CA144034  and  UM182876. Melbourne Collaborative Cohort Study  ( MCCS ) cohort recruitment was funded by  VicHealth  and  Cancer Council Victoria. The MCCS was further augmented by  Australian National Health and Medical Research Council  grants  209057 ,  396414 , and  1074383  and by infrastructure provided by  Cancer Council Victoria.
Data availability The mission of the Lung Cancer Cohort Consortium (LC3) is to facilitate and carry out collaborative research on lung cancer risk and aetiology. The LC3 is committed to facilitating the use of LC3 data by the wider research community for research within its scientific mandate, including: 1-Research on the aetiology of lung cancer incidence and survival. 2-Research on lung cancer risk assessment, early detection, and screening. 3-Research on tobacco exposure and tobaccorelated health outcomes. Access to the LC3 Data are restricted to researchers, who are affiliated with academic, non-profit, or governmental research institutions, and who have no links to the tobacco or arms industries. Access to LC3 Data cannot be granted to commercial entities and/or for commercial purposes, including development of patents.
The LC3 Access Committee processes proposals to access LC3 data on a bi-monthly basis. Access to LC3 data can be obtained via the procedure outlined in the LC3 Access Policy which is available at the following link: https://www. iarc. who. int/wp-content/uploads/2021/12/ LC3_Access_Policy. pdf. Other data sources: We also used publicly available mRNA expression from the Human Protein Atlas  65  and the Pathology Atlas  66. We also used lung tumor RNA-seq gene expression data from The Cancer Genome Atlas (TCGA)  67  which is accessible upon request. Source data are provided with this paper.
Isolation and culture conditions The diversity of Vibrio species associated to three marine microalgal species, Chaetoceros calcitrans, Chaetoceros gracilis and Isochrysis galbana affinis Tahiti, was examined between February and June 2018. Microalgal cultures were directly plated on Thiosulfate-Citrate-Bile salts-Sucrose (TCBS) and incubated at 25°C for 48 h. Vibrio typically grow on TCBS as large green or yellow colonies. Pseudomonas strains appear as small green colonies on TCBS and as small cream colonies on marine agar (MA). A total of 125 colonies were picked randomly and isolated on MA plates. After isolation, strains were identified based on the gyrB gene, suitable for taxonomic identification of Vibrio isolates [data not shown;  (Girard et al. , 2018) ], and revealed the presence of 23 Pseudomonas isolates.
3. 13. 0 (Bankevich et al. , 2012). Strain 536 T was re-sequenced using Nanopore technology (Oxford Nanopore Technology, London, United Kingdom). The quality of the dataset of nanopore reads was assessed with the NanoPack software suite  (De Coster et al. , 2018) , and combined with Illumina using the hybrid assembler Unicycler v0. 4. 8 (Wick et al. , 2017).
BGCs analyses We first used antiSMASH 6. 0 to identify and annotate secondary metabolites BGCs  (Blin et al. , 2021). The MIBiG cluster comparison and ClusterBlast packages, now allow to detect potential unexplored forms of BGCs but also to easily identify identical BGCs in other strains. As not every known BGCs (published) is yet registered in the database, it thus considerably facilitates identifications and comparisons. The synteny of each BGC was manually inspected. NRPS clusters were checked to verify the expected domain organization (i. e. , siderophores, lipopeptides) and the online PKS/ NRPS analysis tool 5  was used to delineate and extract A-domains for amino-acid sequence predictions  (Rokni-Zadeh et al. , 2012; Ye et al. , 2013; Girard et al. , 2020a).
Taxonomic affiliations of marine Pseudomonas Three isolates were selected to confirm rpoD-based taxonomic affiliation and whole genome sequencing was performed on strains 536, 402 and 718. ANIb values confirmed that Pseudomonas strains 402 (96. 60%) and 718 (97. 37%) are belonging to, respectively, P. chengduensis and P. toyotomiensis, while strain 536 represents a new species and was included in the following dataset for further analyses. Most of NCBI Pseudomonas genomes were belonging to strains isolated from soils or plants and only 77 genomes of strains isolated from marine sources were available, 3. 7% of Pseudomonas sp. (65/1775), 3. 7% of P. putida (7/190) and 1. 8% of P. fluorescens (5/272). A large majority of these genomes were obtained in the course of broad metagenomics projects (e.
Stutzerimonas strains carried, as previously reported, either an amonabactin, a ferrioxamine or a vibrioferrin BGC  (Zawadzka et al. , 2006; Essén et al. , 2007). Halopseudomonas sp. gcc21, possess an acinetoferrin-like BGC, with the actA gene upstream the biosynthesis genes (orange Figure  4B ) and an extra biosynthesis gene upstream acbA coding for a pyridoxal-dependent decarboxylase (Pfam: PF0082). Interestingly, there are no hits (ClusterBlast, antismash) with other Halopseudomonas strains but with Alkanindiges illinoisensis DSM 15370 and Acinetobacter strains. Considering the fact that no other Halopseudomonas strain carries such siderophore BGC, we believe that this acinetoferrin-like BGC has been acquired recently in the evolution of this strain.
Unknown clusters We detected numerous putative NRPS clusters, one among Stutzerimonas strains (NRPSa) and 5 among Pseudomonas strains Known and proposed gene clusters involved in the biosynthesis of Enterobactin (A) and Acinetoferrin (B). All genes are scaled to the depicted size. Biosynthesis genes are highlighted in orange (NRPS in brown) and transport genes in blue. (NRPSb to f, Figure  2 ). Accession numbers for these NRPSs genes can be found in Supplementary Table  S10. To date, most Pseudomonas secondary metabolites are NRPS based  (Gross and Loper, 2009)  and NRPSs are a promising source for the discovery of novel bioactive natural products  (Challis, 2008). Beside the putative NRPS clusters, we observed the presence of many RIPPlike genes within the analyzed genomes (1 to 4 RIPP-genes by genome).
Acknowledgments We thank the technical support of  EMBRC-France , whose French state funds are managed by the  ANR  within the  Investments of the Future program  under reference  ANR-10-INBS-02. We acknowledge the  BioPIC an Bio2Mar platforms (CNRS-Sorbonne Université, Oceanological Observatory of Banyuls-sur-Mer ) and particularly  Marie-Line Escande  for technical support and TEM preparation and image generation. 10. 3389/fmicb. 2023. 1071039Frontiers in Microbiology 15 frontiersin. org.
Funding This work was funded by  Sorbonne Universités (JB) , supported by the  EOS  grant  30650620  (LG and RM). CL was supported by the  Research Foundation -Flanders  grant  1S64720N  and the  Research Council of KU Leuven  grant  PDMt2/21/038. TABLE 3 Siderophores peptide amino-acid sequences predictions. Strains for which the amino-acid sequence was previously confirmed by chemical characterization are highlighted in bold. Rectangles are representing gene organization and dotted rectangles indicate strains for which gene orgnization is unkown. D-amino-acids are underlined.