dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods The overall procedure for collecting and pre-treating literature data is briefly illustrated in Fig. 1. Firstly, a literature review to identify relevant publications presenting kinetics data was carried out. This research was based on a query of scientific bibliographical databases in accordance with the PRISMA guidelines  15  (Step 1). The second step consisted in converting raw data from scientific publications (texts, tables or figures) into a ready-to-use numerical dataset with a manual collection for tables or a semi-automated collection after digitalization of figures (Step 2). Afterwards, an inactivation primary model was fitted on each kinetics to estimate the viral infectivity reduction parameter and its uncertainty (Step 3). Finally, a quality-ranking step (Step 4) was performed to evaluate the quality of the data collected from kinetics.
A date restriction was defined: only publications from the 1 st of January 2020 were collected. The search was limited to publications with abstracts written in English. The last query process using those above-mentioned criteria performed on the 25 th of August 2021 identified overall 14,267 references exported to EndNote software, after duplicate removal. From this corpus, a thematic filter about "Persistence" was built with a "from group" tools in EndNote software. This filter consisted in selecting articles with "persisten*" "survival" or "stability" in the field "Title-Abstract-Keywords", joined by the logical connector AND with "environment*". This search resulted in 418 references. These records were afterwards filtered in accordance with the PRISMA Statement guidelines  15  (Fig. 2 ), using different inclusion/ exclusion criteria based on title, abstract and sometimes full-text when needed.
Query theme Keywords (1) "SARS-CoV-2 and coronavirus" "sars-cov-2" OR "covid-19" OR "coronavirus" OR "corona virus" OR "2019-ncov" OR "novel coronavirus" (2a) "Human and food" "food" OR "bread" OR "dairy products" OR "eggs" OR "fast foods" OR "flour" OR "fruit" OR "meal" OR "meat" OR "raw foods" OR "salads" OR "vegetables" OR "foodborne" OR "gastrointestinal" OR "intestine" OR "digestive" OR "feces" OR "stool" OR "fecal" OR "clams" OR "oysters" OR "cockles" OR "mussels" OR "scallops" OR "molluscs" OR "bivalvia" OR "shellfish fish" OR "shellfish farming" (2b) "Water" "seawater" OR "sea water" OR "marine" OR "wastewater" OR "water treatment plant" OR "water" (2c) "Environmental persistence" "environment*" AND ("survival" OR "persistence") Table  1. Keywords used in the four themes for weekly bibliographic database queries.
studies on persistence on materials, surfaces or aerosols or (2) studies on working environment. The exclusion criteria were (1) studies on therapeutic or vaccine development, (2) studies on untreated wastewater, (3) studies on diet or nutrition (4) language other than English and French, and (5) full text not available. A first screening step identified 82 references for which full papers were read to determine if they were included as fully documented kinetics of persistence data (either in tables or in figures) or used to complete the identification of relevant publications (e. g. in the case of reviews). All the above screening and completion stages identified a final total number of 65 studies in which available raw kinetics data could be extracted from tables and/or figures. Information associated with kinetics data.
The second step consisted in converting raw data from scientific publications into ready-to-use numerical dataset by extracting data from texts, tables and/or using figure digitalization techniques. This step provided several "kinetics", meaning the tracking of viral titer at different time points under different conditions. Kinetics corresponding to viral genome quantification (e. g. by RT-qPCR techniques) were excluded since such quantification did not represent the viral infectivity. In total, 464 kinetics were available from the 65 identified studies  11, 13, 14,  (Tables  2  and 3 ). It is worth noting that kinetics for coronaviruses other than SARS-CoV-2 collected in these studies were also retained for analyses. Several information associated with the above kinetics were gathered in an Excel spreadsheet (more details in the Data records section).
Data extraction. The persistence kinetics data were taken from texts, tables or figures in the research papers identified above. Data from tables or texts were manually filled into the database. Raw data from figures were extracted using the R package metadigitize  78. This tool provided the possibility to process simultaneously many figures, as well as reproducibility (e. g. , correcting the digitalized data, sharing digitalization). For reproducibility purposes, all files associated with raw digitalizations of our study are provided and detailed in the Data records section. The monitored time points extracted from all studies (extractions from tables or figures digitalization) were converted and expressed in hours in order to homogenize for comparative purposes between studies. Viral infectivity reduction parameter.
= - with N 0 and N corresponding to the number of infectious viruses at the initial time point and the time point t (expressed in hour), respectively. The model was fitted independently on each kinetics using the function nls() from the R package nlstools  80  running with the 'nl2sol' algorithm from the Port library. The starting parameter values necessary for the fitting algorithm was set depending on the kinetics curves or optimized using the R package nls2  82  in some rare cases of non-convergence. For each kinetics, a value of D was estimated as well as its uncertainty expressed by standard error value SE. The value of log 10 D was computed accordingly for each kinetics. Finally, the coefficient of variation was calculated as the proportion: CV=SE/D.
Evaluation of kinetics quality. In this work, kinetics data were collected from different publications in which the laboratory experiments were not conducted with the same design. These data represented then important variabilities, e. g. in terms of number of time points, replicates, etc. Therefore, criteria can be useful to evaluate and classify the quality of the collected kinetics. Indeed, the quality of raw data was susceptible to influence the statistical estimation of D. Criteria-based ranking approaches have been proposed in some predictive microbiology studies aiming to deal with difficulties in terms of data selection (inclusion or exclusion for modelling)  83, 84. Herein, for the establishment of a quality score by kinetics, we considered three criteria: (i) the number of the time points of the kinetics; (ii) the importance of the extracted point considering if it represented a single value or multiple measure (i.
e. at least two technical replicates); and (iii) the value of the coefficient of variation (CV) of the estimated value of D, characterizing the fit quality of the inactivation model. For each kinetics, we attributed three scores corresponding to these three criteria and classified them into different categories. It is worth noting that ones can arbitrarily define the threshold values separating these categories as well as the given corresponding score values depending on the studies and the extracted dataset. In our work, for each kinetics, the score associated with the number of time points, denoted s 1 , was firstly defined as follows: s n n n n 1( 3) , 2(  4 6 ) , 3(  6 8 ) , 4 (  8) , t t t t 1 =              ≤ ≤ < ≤ < ≥ where n t corresponds to the number of time points collected from the kinetics.
The score s 2 , based on the importance of points ('unique' or 'multiple'), was defined as follows: =       . s unique multiple 1( ) , 3( ) 2 The score s 3 , based on the coefficient of variation CV i of the kinetics i, was given as follows: =              >. ≤ <. ≤ <. <. s CV CV CV CV 1( 0 3) , 2(0 2 0 3) , 3(0 1 0 2) , 4( 0 1) ; 3 or s 3 = 1 for some kinetics for which standard errors and coefficients of variation could not be computed (kinetics with only two points). Finally, a global score S taking into account all criteria was calculated for each kinetics: = × +. S s s s 1 2 3 The calculated scores for all kinetics are gathered in Spreadsheets and R Data objects provided in the Data Records section.
Data Records Intermediate data: Figure digitalization raw files. The digitalized source figures (jpg or png image files) were used in the second step of the collection procedure (see Fig. 1 ). The digitalization were carried using the R package metaDigitise  78  that automatically created the directory denoted 'caldat' containing raw digitalization files to assure traceability and also to avoid re-doing manual digitalization at every run of the procedure. Raw digitalization files generated by metaDigitise were automatically renamed like their corresponding image files. All sources figures and digitalization raw files used herein are provided in a data repository  85. Input and output quantitative data spreadsheet. Output spreadsheets were obtained at the end of the overall collection procedure under the Excel and CSV file formats (see Fig.
; and • (iii) the medium grouping the above matrices as four classes, which are "liquid media", "porous surface", "non-porous surface" or "aerosol" (column Medium); • Information related to the kinetics monitoring methods including: • Input and output as RData object. All input used and output obtained at the end of the collection workflow is also provided as a ready-to-use RData object (DATASET. RData)  85. From this RData object, when opened in R/RStudio softwares, one can extract: • the input and output data spreadsheet described above (object DATASET); • raw data (from tables or figures) associated with the monitoring of each kinetics (measured values at each sampling time points), only the data above LOQ are recorded (object kinetics_rawdata); • regression plots (inactivation linear model, see Method section) generated for each kinetics (object regplot).
Technical Validation The technical validation focused on the figures' digitalization step, since the latter remained a manual work that could probably vary from one user to another. In order to check the quality of the data collected by digitalization, this step was re-conducted repeatedly by three independent users for evaluating its repeatability and its reproducibility. This checking procedure was performed on a random sample of eleven kinetics among those collected, and each kinetics was digitalized three times per user. The values of the parameter D were afterwards estimated as described in previous sections. The comparison between the values of D estimated by different users was firstly done by fitting the major axis regression model on bootstrap data generated for each pair of users  86, 87.
Afterwards, the Gage R&R tool from the R package SixSigma  88, 89  was used in order to identify and quantify the error parts in the estimated values of log_10 D due to the user repeatability as well as the between user reproducibility, respectively. The R scripts and data associated with the technical validation procedure are provided (see the 'Code Availability' section below). As illustration, the comparison between users (denoted user 1, 2 and 3, respectively) by major axis regression is plotted in Fig. 3  (user 1, plotted in the X-axis, was arbitrarily chosen herein as the reference one for comparison). The results of the Gage R&R analysis showed a good repeatability: the latter estimated a very low error part due to intra-user variation, estimated at only 0. 01% of the overall variation.
Usage Notes Re-use of figure digitalization files. The digitalization step were done using the R package metaDigitise  78  providing reproducible and flexible tools for tracing every digitalization. In practice, the digitalizations were done using R commands (check the R scripts provided in the Code Availability section) allowing users to process the different image files ready-to-digitalize. This process consisted, for each image (plot), to click manually on the different chosen points of the image to calibrate the plotted axes and convert afterwards the different clicked points (from curves, barplots, etc. ) to numerical values saved in R object. The different groups of points can be assigned with user-defined group names in order to separate different kinetics from the same image if necessary.
Acknowledgements This research was funded by the  French Research National Agency (ANR)  in the framework of the  SACADA  Project ( ANR-21-CO13-0001 ). The authors are grateful to collaborators of the SACADA consortium:  Géraldine Boué ,  Steven Duret ,  Michel Federighi ,  Maxence Feher ,  Lisa Fourniol ,  Yvonnick Guillois ,  Pauline Kooh ,  Sophie Le Poder ,  Anne-Laure Maillard ,  Jean-Claude Manuguerra ,  Mathilde Pivette ,  Jessica Vanhomwegen ,  Prunelle Waldman.
Study Group The study was approved by the Bioethics Committee of the Medical University of Lodz (RNN/04/18/KE). All patient's data remain confidential and have been used for research purposes only. Information on the performed treatment was introduced to a clinical patient card entered an electronic database with a restricted access code. Before the analysis, the data were anonymized. Patients were admitted from October 2017 to February 2019. RCT was carried out with the patients' informed consent to participate in the study. Sample size estimation revealed 377 patients needed for the survey. Calculations were completed with a margin of error of 5% and confidence level of 95%. The inclusion criteria consisted of single-or multi-rooted teeth demanding primary root treatment. Teeth with complex anatomy, roots with severe apical resorption, external or internal resorption, open apex and calcifications were excluded from the study.
X-rays of low quality or with additional artifacts were not included in the study. According to inclusion and exclusion criteria, the final sample group included data from medical records of 798 patients, including 900 teeth and 1773 obturated canals. All treated patients were admitted by dentistry students (Medical University of Lodz), GPDs at the Endodontics Department, and by endodontists at the Endodontics Clinic of Clinical Hospital in Lodz. Both institutions have the same location and equipment in terms of the dental materials used during endodontic treatment.
Assessment of the Root Canal Filling A retrospective randomized double-blind comparison study was conducted to assess the quality of the RCT. An X-ray with an X-ray positioning holder was performed before the final restoration of the tooth. X-ray images were taken with GENDEX Expert DC (KaVo, Biberach/Riss, Germany). The images were evaluated using the VixWin Platinum software (KaVo, Biberach/Riss, Germany). The distance between the end of the canal filling and the radiographic apex of the tooth root was measured. All data were coded and blindly assessed by two endodontists (K. P. , M. R. ). The two examining operators were calibrated before the examination. The calibration was performed on 30 cases. The quality of the filling on the X-ray was assessed according to ESE standards  [9]. The assessment method of radiographs was a modified version of the technique introduced by Balto et al.
of 30 Phenotypical differences observed among the twenty-seven isolates could reflect genetic diversity. Indeed, sequencing of the full 16S rDNA of these isolates revealed two distinct phylogenetic groups: A and B (Figure  1A ). Finegoldia magna (F. magna), the most closely related bacterial species to P. micra, was used to set the root of the phylogenetic tree. Interestingly, all P. micra isolates from phylotype A were haemolytic whereas isolates belonging to phylotype B were not. Likewise, most phylotype B isolates were "compact" whereas phylotype A isolates were not. To deepen our analysis, the ten P. micra genomes available on the NCBI database were clustered based on the presence/absence of genes. Grouping of isolates confirmed the existence of phylotypes A and B (Figure  S1C ). Recent clinical studies using 16S rDNA sequencing from tissue had shown the presence of Parvimonas sequences in tumoral and normal homologous colonic mucosa of CRC patients, suggesting that this microorganism may be able to adhere to colonocytes or the colonic cellular matrix  (Flemer et al.
P. micra phylotype A induces DNA methylation changes Global DNA methylation was measured using a 5-methyl-cytosine dosage assay. In vitro co-cultures of both P. micra phylotypes showed a significant increase in global DNA methylation of primary cells as compared to NS condition (p<0. 05) or to F. magna (Figure  4A ). To identify affected genes, a genomewide DNA methylation analysis was performed on the human colonic primary cells exposed or not to P. micra phylotypes or F. magna, using the Infinium MethylationEPIC BeadChip from Illumina. The methylation status of >850,000 CpG sites was assessed at all colonocyte genome targeting sites already known to be methylation-sensitive (Data set ready on Mendeley Data with the following DOI: 10. 17632/nwj3bgbg5m. 1). These were then classified by context (in or outside of CpG islands) and regions: body genes (3' or 5' UTR, first exon, exon bond, and body), promoters (200 or 1500 bases upstream of the transcriptional start site [TSS200 and TSS1500]) or intergenic.
Clinical parameters of the patients, such as body mass index (BMI), age, sex, and disease history were referenced. The cumulative methylation index (CMI) score was previously determined  (Sobhani et al. , 2011). This score was calculated from the methylation status of three genes (wif1, penk and npy) involved in colorectal carcinogenesis and was considered negative for CMI<2 or positive for CMI≥2. Clinical data are summarized in Table  S2.
Whole genome sequencing (WGS) of fecal samples Metagenome sequencing was performed by the high-throughput platforms BIOMICS (Institut Pasteur, Paris, France) and EMBL (Heidelberg, Germany). Sequencing was performed in pairs, using the HiSeq 2000/platform 2500 equipment, over a length of 100 bp of DNA and at a sequencing depth of 5 Gbp  (Zeller et al. , 2014). The raw data have already been reported in several papers  (Schmidt et al. , 2019; Sobhani et al. , 2011; Zeller et al. , 2014). For this study, one hundred and sixty-six fecal samples from the CCR1 and DETECT cohorts were considered. The Diamond/MEGAN6 bioinformatics pipeline  (Bağcı et al. , 2019)  was used for metagenomic assignment. Sequences were filtered for an average quality (Phred score) greater than 20 over a window of two consecutive bases and a length greater than 100 bp using Trimmomatic software (version 0.
35). Good quality sequences were translated in the six possible reading frames and aligned to the NR reference library (RefSEQ non-redundant protein database) in which proteins/peptides that are more than 99% similar are combined into a single organism-associated group with a specific identifier (O'  Leary et al. , 2016). A sequence can be assigned to several taxa. The MEGAN6 LCA (Lowest Common Ancestor) algorithm  (Huson et al. , 2016)  was used to resolve multi-mapping reads: when a sequence was assigned to two (or more) taxa of different phylogeny, it was assigned to the top taxonomic level in common.
16S rDNA sequencing of tissue samples For this study, 71 pairs of homologous tissue samples (matched tumor and healthy tissue) were analyzed. After amplification of the 16S rRNA gene V3-V4 region, pairwise sequencing was performed to a length of 250 bp on the Illumina MiSeq platform. The resulting raw database was cleaned of sequences corresponding to human or phage sequences. Adapters and primers, as well as 5' and 3' ends with a Phred quality score over a 2 bp sliding window of less than 30 were removed using Trimmomatic (version 0. 35) software. Paired sequences were merged using the FLASH2 tool (version 2. 2. 00). Taxonomic assignments were performed using MALT/MEGAN6 bioinformatic pipeline  (Huson et al. , 2016)  with default parameters and the SILVA rRNA database version 123  (Quast et al.
Bacterial strains, culture and characterization Escherichia coli pks+ strain IHE3034 was used as a positive control for double-strand DNA breaks. Shigella flexneri 5a BS176, lacking the virulence plasmid, was used as a negative control for bacterial adhesion and autoaggregation negative control. The reference strains ATCC 33270 of P. micra  (PmA)  and ATCC 29328 of Finegoldia magna were obtained from the Pasteur Institute Collection (CIP). In this study, twenty-seven clinical isolates of P. micra, including HHM BlNa17 (PmB), were collected from the bacteriology departments of three Parisian hospitals: Henri Mondor, Cochin, and Pitié Salpetrière (Table  S1 ). The identification of the different isolates was confirmed by mass spectrometry and 16S rDNA sequencing analysis (Eurofins Genomics) and sequences were deposited on GenBank (Table  S1 ).
16S rDNA whole sequence from positions 112 to 1302 were aligned with the ClustalW program (EMBL) and the phylogenetic tree was calculated using the PhyML program. Pan-genomic comparison: available complete P. micra genomes were downloaded from the NCBI database. Analysis of pangenomes and comparison of common or strain-specific gene carriage was performed using the Roary program  (Page et al. , 2015)  with a 95% identity threshold and the use of default parameters.
Flow cytometry Cell monolayers were dissociated with TrypLE™ Express Enzyme (Gibco) at 37°C for ten to twenty minutes. The cells were recovered in DMEM/F12 at RT and then centrifuged for five minutes at 300g. After washing the pellet in PBS, cells were stained with the Live/Dead fixable cell stain kit (ThermoFisher) for twenty minutes on ice, following the supplier's instructions. Cells were analyzed using a FACS Attune (ThermoFisher) and data were analyzed using FlowJo software (v10. 1).
1% Triton-BSA overnight at 4°C. After washing in 0. 1% PBS-Triton, the cells were incubated with 1:400 secondary antibodies coupled to Alexa Fluor 488 (ThermoFisher) in 0. 1% PBS-Triton for 45 minutes at RT. Cells were washed again in 0. 1% PBS-Triton and the nucleic acids were labeled with 1 µg/mL DAPI for 5 minutes before cells were mounted on a slide with ProlongGold (ThermoFisher). Fluorescence images were taken using a confocal fluorescence microscope (Zeiss) equipped with an Opterra system (Bruker), and the number of cells was analyzed as the number of positive cells/total cells using Fiji  (Schindelin et al. , 2012)  or Imaris 9. 8 software (URL: http://www. bitplane. com/imaris/imaris) software.
The quality was supported by multiple quality control (QC) measures, including tests for proper bisulfite conversion, staining, and specificity of the internal controls, as determined by Illumina GenomeStudio software. Average-beta values (proxy for methylated DNA level between 0, unmethylated, and 1, fully methylated) were normalized to internal controls and corrected by background subtraction. Nonautosomal CpGs (n=14,522) and CpG probes with suboptimal detection (p<0. 05 in at 80% of samples) (n= 29,316), as well as single nucleotide polymorphisms (SNPs) associated CpGs (n=59), were removed from our analyses. Beta values were transformed into M-values as described by  (Du et al. , 2010) , to be able to use the Limma statistical model  (Wettenhall and Smyth, 2004 ) package R Limma, version 4.
0. 2. The four conditions (Non-stimulated NS; ATCC; BlNa and FM) and individuals were kept as variables. Pairwise comparisons were obtained from a contrast matrix. No adjustment method was used. For each gene (n=25,560), a t-test on log-fold-change was performed on four categories of probes depending on their CpG context and gene localization: probes in CpG island context and 1/ localized in genes promoters (TSS1500+TSS200) or 2/ in body gene; and probes out of CpG island context 3/ in promoters or 4/ in body gene. Genes with >2probes, p-value <0. 05, and with a log-FC >0,1 (absolute value) were considered significant. Using three different databases, the genes were classified as oncogenes, tumor suppressor genes, or involved in Epithelial-Mesenchymal Transition (ONGene  (Liu et al. , 2017) , TSGene 2.