dataesr/scientific-paragraphs-categorization huggingface dataset:
FUNDING We acknowledge the MRI platform, member of the national infrastructure France-BioImaging supported by the French National Research Agency (ANR-10-INBS-04, «Investments for the future»), the labex CEMEB (ANR-10-LABX-0004), and NUMEV (ANR-10-LABX-0020). We also wish to acknowledge the "Réseau d'Histologie Expérimentale de Montpellier"-RHEM facility supported by the SIRIC Montpellier Cancer (Grant INCa_Inserm_DGOS_12553), the european regional development foundation and the occitanian region (FEDER-FSE 2014-2020 Languedoc Roussillon) for processing our animal tissues, histology techniques, and expertise. This study was partially funded by the IRP (LIA) MAST (CNRS). Fondecyt 1190926 to SM. AR and DM were ANID fellows 21170393 and 21160069 respectively.
Acknowlegment This work benefited from the financial support of the  LabeX LaSIPS  ( ANR-10-LABX-0040-LaSIPS ) managed by the  French National Research Agency  under the '' Investissements d'avenir" program  ( ANR-11-IDEX-0003-02 ). The authors would like to thank  Francois Brisset  (ICMMO) and  Thomas Reiss  for technical assistance for SEM imaging.
Raman data acquisition Raman spectra analysis was performed a Witec Confocal Raman Microscope System alpha 300R (Witec Inc. , Ulm, Germany). Excitation in the confocal Raman microscopy is achieved through Nd: YAG laser (Newport, Evry, France) with a wavelength of 532 nm. The incident laser beam was focused onto the surface of the specimens using a ×20 NIKON objective (Nikon, Tokyo, Japan). Then Raman backscattered radiation mixed with Rayleigh scattered were passed through an edge filter to block the Rayleigh's. Acquisition time of a single spectrum was set to 0. 5 s 150 × 150 points per image and the analysis was executed using a piezoelectric table, leading to a total of 22,500 spectra for each image; each spectrum corresponded to a spatial unit (defined as: voxel). Data acquisition was performed using Image Plus software from Witec.
Data analysis Data analysis was performed using two methods. The first method provided integrated Raman intensities in specific regions at particular peaks. Data processing was performed using Image Plus software 5. 2 from Witec. Each image regarding such integrated intensities provided a map of the region. Using a "look-up" table, each color showed different Raman peak intensity, which represented different intensity of phosphate, carbonate or organic components. For instance, dark hues were characteristic for low intensities of a specific element, while the yellow color indicated great presence of selected region. The second method was based on a graph calculator and an Image Cross Section. It was realized using the Witec Project Plus 5. 2 (Ulm, Germany) software. An image representing a ratio of two different reconstructed images/elements was reconstructed using Witec Project Plus software.
Visit phase During the visit phase, the dental surgeon obtained the patient consent then created a digital medical record. The dental surgeon then recorded the video using a Soprocare camera (Acteon group, La Ciotat, France) in "D-light" mode, which was connected to the e-DENT software (e-DENTECH, Montpellier, France). The e-DENT software stores the data on a secure server and manages each patient's data. It also provides the necessary information system for data transfer, secure identification of the different parties, and production of a report for each assessment that uses asynchronous telemedicine. The patient was sitting on a classical examination chair facing a laptop. The practitioner stood behind the patient and used his right hand to hold the camera, and his left hand to move the head of the camera.
Acknowledgements: The authors want to acknowledge  Jennifer Dobson  for her work in the translation of the document and her participation in writing process. They also want to acknowledge the work of all the staff of the eating disorders department of the  University Hospital of Montpellier  who helped the investigators with the data collection. Contributorship:  Nicolas Giraudeau : Conception, Design, analysis and interpretation. Paul Camman: Acquisition, analysis and interpretation. Laurence Pourreyron: Conception and Design. Camille Inquimbert: Analysis and interpretation. Patrick Lefebvre: Conception, Design and interpretation.
Human Growth Factor/Cytokine Array Human growth factor/cytokine array membranes were purchased from Abcam (Cambridge UK) for the detection and semi-quantification of 43 growth factors/cytokines in the secretome of DPSCs following 2D or 3D culture. Conditioned medium was collected on day 14, and the assay procedure was performed as described in the manufacturer's instructions. The arrays were imaged using a Syngene G: BOX, with GeneSys software and semiquantified using ImageJ, an image analysis programme, with a microarray plugin. For each array membrane, the integrated density of the spots was measured and then the mean integrated density of negative controls was subtracted for background correction. To ensure that array results could be compared across the various experimental conditions (2D versus 0% RGD or 10% RGD), the 2D array was designated as the reference array membrane.
Statistical Analysis All results were expressed as the mean of three independent experiments with three to six replicates per experiment. Statistical analysis was undertaken using GraphPad Prism Software (Version 8; GraphPad Software Inc. , San Diego, CA, USA). Data that passed the Shapiro-Wilk or D'agostino and Pearson test were analysed by means of one-way ANOVA followed by Dunnett's post hoc correction for multiple comparisons. Otherwise, data were analysed non-parametrically by means of a Kruskal-Wallis test with Dunn's correction for multiple comparison testing, as indicated in the figure legends. The level of statistical significance was set at p < 0. 05.
Once assembled, the performance of the multiplexed device was measured by amperometry against Ag/AgCl reference electrode fabricated by electrodeposition. Incubation time as short as 10 min was enough to obtain a linear response within a physiologically relevant range. Furthermore, the sensor was integrated in a device powered by a small lithium-ion polymer battery. The device was able to measure and send the data simultaneously via Bluetooth technology. The applicability of the device in real serum and saliva samples was proven both for positive and negative cases. The potential use of noninvasive sampling (i. e. , saliva), together with the fast response and good performance without sophisticated laboratory equipment or trained personnel at a reasonable price make this pioneer device a strong candidate for fast patient triage to block the spread of the virus.
The best Youden index indicated the optimal diagnostic threshold. Sensitivity and specificity of both assays were compared using McNemar's chi-square test. To determine the correlation between the BDG titres, we performed linear regression on samples ranging from 31 to 500 pg/ml for FA and from 2. 359 to 600 pg/ml for WA. All statistical analyses were performed using Prism v9. 0 (GraphPad software). P<0. 05 was considered to be significant).
Ethics Statement The study was a non-interventional evaluation of a new test with no change in the usual procedures, and the clinicians were blinded to the evaluated test results. The clinical data were collected and registered with the approval of our hospital review committee (reference number: 2018000000077). French Public Health Law (CSP Art L1121-1. 1) does not require specific approval from an ethics committee for this kind of study, which is exempt from the requirement for informed consent. The 2015 STARD guidelines for diagnostic accuracy studies were followed. 22.
Objective: Probiotics are increasingly used in oral prevention and treatment conditions, but little is known about their abilities. The aim of this review is to clarify, summarize and disseminate current knowledge about the mode of action of in vitro probiotics on factors involved in the pathogenesis of periodontitis. Method: 2495 articles were identified in three databases (Medline, Web of Science, SpringerLink) and 36 studies included in this scoping review. Results: Twenty-four probiotic species were identified, the majority of which were Lactobacilli or Bifidobacteria. Lactobacillus rhamnosus (38. 8%) and Lactobacillus reuteri (38. 8%) were found to be the two predominantly studied probiotic species and three main mechanisms of action of probiotics could be classified as: (i) modulation of the immuno-inflammatory response, (ii) direct actions of probiotics on periodontopathogens by adhesion or nutritive competitions and/or the secretion of antimicrobial molecules and (iii) indirect actions through environmental modifications.
Search strategy Medline (Pubmed), SpringerLink and Web of Science databases were screened for entries between January 1 2009 and November 30 2020. Several search term combinations were used in the electronic databases: "probiotics + oral biofilms"; "probiotics + periodontal diseases"; "probiotics + periodontitis"; "probiotics + inflammatory response" and "probiotics + in vitro + cytokines". A complementary search in the grey literature and in the bibliography of the articles selected by the search strategy was also carried out.
Screening methods and data extraction Articles were analyzed in this review according to the PRISMA-ScR guidelines for writing and reading scoping reviews  (Peters et al. , 2015; Moher et al. , 2009). A total of 2495 articles were identified using the search strategy. After article titles and summaries were screened by two independent reviewers, 1732 duplicates and 592 off-topic papers were removed. A total of 171 fulltext articles were further analyzed according to the inclusion/exclusion criteria, assessed by two independent reviewers. 26 of these were retained and included in this scoping review. 133 articles were excluded for irrelevant probiotics' species, clinical or animal studies. Two studies were not available in English language and 10 studies did not use an in vitro periodontal model.
The tests were performed at constant crosshead speed of 0. 5 mm. min -1. The engineer compressive strength was calculated in megapascals (MPa) according to the formula: σ c = 4P/πD 2 ; where P is the recorded load during the test and D is the initial sample diameter. A One Way Analysis of Variance test was applied using Sigma plot (release 11. 2, Systat Software, Inc. , San Jose, CA, USA) to determine whether significant differences existed in the compressive strength values between the different composites. In all tests, a statistical significance level of α = 0. 05 was adopted.
X-ray diffraction. The XRD measurements were carried out on a D8 Discover Bruker powder diffractometer in the Bragg-Brentano geometry. The diffractometer was equipped with a front monochromator (CuKα1 wavelength λ = 0. 154056 nm). The samples were ground to powder to reduce preferential orientation of the crystallites observed in pressed samples. The diffraction patterns were refined by the Rietveld method using the Fullprof software  [29]. The background, modelled as a polynomial function based on ten experimental points, was refined, as well as the zero shift. Preferential orientation was taken into account using a March model. The atomic positions of the crystal structures were kept fixed. The lattice parameters, crystallite sizes, isotropic Debye-Waller factors were refined for each phase, as well as their scale factor.
X-ray diffraction. The X-ray diffraction pattern of pristine plaster can be indexed with two calcium sulfate hydrate phases from the ICDD database: Bassanite The diffractograms of the plaster@TA composites are very similar to those of the pristine plaster, and the same conclusions concerning the absence of any other phase than Bassanite or Gypsum, or amorphous phase can be withdrawn (Fig. S5  in the supplementary data file). Each of these XRD patterns has been refined and the ratio of Gypsum and Bassanite is constant within the error bar at a ratio around 1 to 3 (Fig. S5  in supplementary data). One cannot therefore explain the weaker compressive strength observed for the plaster-10%TA composite (Fig. 2A ) by a peculiar modification of its structural properties with respect to the pristine plaster or plaster with other compositions in TA.
In the present investigation, we therefore collected multiple samples-subgingival plaque, tongue scrapings and salivawhich are characterized by substantially different ecological conditions and microbial communities  [11] [12] [13]. Metagenomics and metatranscriptomics were employed to perform concomitant characterization of taxonomic composition as well as specific bacterial gene expression profiles. Furthermore, pairing of metagenomic and metatranscriptomic data enabled us to determine species-specific bacterial activity as expressed by the ratio of messenger RNA to the corresponding genomic DNA. In other words, the resolution of our paired deep sequencing approaches enable us to discriminate between members of the microbiota, with overexpression of important pathways (log10 (RNA/DNA) > 0), as compared to members with less expression (log10(RNA/DNA) < 0).
1A ). In general, the highest abundances of periodontal pathogens were observed in subgingival plaque samples, followed by saliva, and with the lowest abundance in tongue biofilm (Supplementary Fig. 1B ). Global bacterial pathway expression is shaped mostly by site, but also by periodontitis Figure  2a  shows overall pathway species contribution richness for both metagenomic (DNA) and metatranscriptomic (RNA) datasets in all samples. As can be seen, all pathways were not expressed (RNA) by all taxa with a potential of expressing the corresponding pathways (DNA). Some pathways, such as adenosine ribonucleotides de novo biosynthesis and guanosine ribonucleotides de novo biosynthesis, which are critical to cell survival, could be expressed by almost all members of the oral microbiota (>190 taxa).
As seen, high species-specific expression of certain pathways was observed, as demonstrated by LACTOSECAT-PWY and ANAGLYCOLYSIS-PWY by Streptococcus salivarius and Streptococcus infantis, respectively. Figure  2d  shows the pathway expression by bacterial transcriptional activities, of the 25 bacterial species identified with significantly different relative abundance in samples from patients with periodontitis versus healthy controls (Supplementary Fig. 1A ). Bacterial expression profiles were heavily influenced not only by site but also by health status. For example, higher bacterial activity of Leptotrichia hofstadii was observed in both subgingival plaque and saliva collected from healthy controls, as compared to samples from patients with periodontitis. Significantly different bacterial pathway expression in subgingival plaque and saliva in patients with periodontitis While α-diversity metrics of potential and expressed pathways (DNA and RNA datasets, respectively) were not significantly different between site or health status (Supplementary Fig.
As seen, the bacterial signatures, in terms of DNA and RNA expression, were completely different based on site. Furthermore, it was evident that while some species despite high abundances of specific pathways within the metagenomes, were actually not contributing extensively to the total expression profiles, as evident from their low levels of concomitant pathway's proportion in the RNA dataset. In addition, RNA expression of the 22 significant pathways was clearly different in subgingival plaque samples in health versus diseases, with much higher RNA expression by L. hofstadii in health. When comparing samples from each site based on health status, a total of 14 pathways were identified with significantly different RNA/DNA ratios in subgingival plaque, whereas a significantly higher RNA/DNA ratio of sucrose degradation III was observed in saliva of the healthy samples.
Thus, our metagenomic data not only confirm previous findings from the Human Microbiome Project but the alignment of data also underline the robustness of the methods and databases used in the present study. While predominant bacterial species identified in subgingival plaque were almost completely absent in tongue, small amounts were identified in concomitant saliva samples. PCR and microarray techniques have been used to demonstrate concordance of specific bacterial species in subgingival plaque and saliva in patients with periodontitis  14, 15. Importantly, saliva is sterile, when entering the oral cavity  16. Therefore, our species-resolution data analysis confirms the assumption of the salivary microbiota as a conglomerate of bacteria shed from various oral surfaces. In health, substantial site-specific variations in taxonomic composition were observed within some genera, as exampled by major differences in relative abundance of specific Rothia and Streptococcus species identified in subgingival plaque versus tongue.
We have previously showed an impact of periodontitis on salivary bacterial activity  10. However, this is the first study to perform simultaneous characterization of bacterial pathways and their gene expression profiles in subgingival plaque, tongue, and saliva. It is therefore interesting that pathways and genes identified with significantly different RNA/ DNA ratios in saliva and tongue were not the same as those identified in subgingival plaque. Therefore, data suggest that periodontitis acts differently on bacterial gene expression in different oral compartments as visualized in the present study by subgingival plaque, tongue, and saliva. Another interesting finding, which was evident from analysis of our high-resolution dataset, was that expression of Gingipain_K was solely identified in samples from patients with periodontitis (Fig.
Furthermore, we pooled subgingival samples collected from the deepest periodontal pockets. Obviously, pooled subgingival plaque samples are not representative of specific subgingival sites  42. In addition, samples were collected from both smokers and nonsmokers. Smoking has been demonstrated to profoundly impact the taxonomic composition of the subgingival microbiota  8, 43, 44  , whereas the effect on the salivary microbiota is probably less pronounced  45. Therefore, we directly tested the association of smoking on DNA, RNA, and RNA/DNA expression (Supplementary Fig. 6A, B ). Indeed, data showed limited association of smoking with these parameters, as compared to the main endpoints tested, oral site, and periodontitis. Nevertheless, future studies on bacterial activity should ideally be performed in nonsmokers.
Four patients with periodontitis were current daily smokers, as compared to three current smokers in the control group. All 22 participants signed informed consent. The study was approved by the regional ethical committee (H-16016368) and reported to the local data authorization of University of Copenhagen (SUND-2018-8). A total of three microbial samples were collected from each participants, including subgingival plaque samples (n = 22), tongue scrapings (n = 22), and stimulated saliva samples (n = 22), which were collected between 8:00 a. m. and 11:00 a. m. Microbial samples were consequently collected in the same order: stimulated saliva, tongue coating, and subgingival plaque according to standardized protocols  13, 47, 48. Stimulated 1 mL of chewingstimulated saliva was collected using paraffin gum.
Read preprocessing lllumina TruSeq adapters, 5′ or 3′ bases with quality scores lower than 20, as well as read pairs having a mate with any ambiguous base (i. e. , N) or shorter than 150 or 50 bp for DNA and RNA sequences, respectively, were trimmed using Atropos 49 in paired-end mode (version 1. 1,25, -max-n 0 -n 1 -q 20,20 -quality-base 33 -minimum-length 150/50 -O 6). RNA reads were then subjected to sortmeRNA 50 version 2. 1, default parameters, 5S, 16S, 23S, 18S, and 28S databases), using provided rRNA databases for in silico depletion of ribosomal RNA. Ribosomal RNA sequences represented an average of 83. 9% of the quality-trimmed RNA reads (71. 1-88. 4%). Human reads were then removed from both metagenomic and metatranscriptomic datasets by aligning DNA and mRNA read pairs to the human genome (GRCh38, ftp://ftp.
ebi. ac. uk/ pub/databases/gencode/Gencode_human/release_28/GRCh38. primary_ assembly. genome. fa. gz, downloaded on August 8, 2018). Alignments were performed using Bowtie2 51 (version 2. 3. 4. 1, -dovetail) for DNA reads and hisat2 ( 52 version 2. 1. 0, -dta) for RNA reads, respectively. Samtools 53 (version 1. 3, view -b -f 12 -F 256) and bedtools 54 (version 2. 24. 0, bamToFastq) were then used to identify and extract read pairs which consistently did not map the human reference genome, those were considered for microbial and taxonomical and functional profiling.
Taxonomic and functional community profiling Taxonomic composition of metagenomes was assessed using MetaPhlAn (version 3. 0. 1, June 25, 2020, -min_ab 0. 000001 using the latest available database, i. e. , mpa_v30_CHOCOPhlAn_201901). Briefly, MetaPhlan relies on read mapping against a built-in collection of clade-specific marker genes database which allows an unambiguous estimation of species relative abundance across samples with a low species-resolution false positives rate as compared to read classifiers. This is especially true when working with human microbiome data (https://www. researchgate. net/publication/ 343635031_Tutorial_Assessing_metagenomics_software_with_the_CAMI_-benchmarking_toolkit). As the tool is based on marker genes and is not designed to assign taxonomic information to all reads nor rRNA sequence as taxonomic marker, a direct taxonomic classification of the metatranscriptomes is not possible.
Sample-specific taxonomic profiles generated using MetaPhlAn on metagenomes were used to filter the HUMAnN2 55 built-in pangenomes database to the organism present in the sample. Metagenomic and metatranscriptomic reads were then mapped using Bowtie2 to samplespecific pangenomes. Metagenomic and metatranscriptomic reads failing to align to the pangenome databases were then blasted against UniRef90 using DIAMOND 56. Hits were counted per gene family and normalized for length and alignment quality. Gene family abundances were then combined into pathways level using MetaCyc  57  and normalized to relative abundances. Functional gene family tables were regrouped to KEGG's orthologs and Enzyme (enzyme commission number, i. e. , EC numbers) with the provided humann2 UniRef90_to_KO and UniRef90_to_EC numbers mapping files, respectively  55.
Materials and Methods This cross-sectional study included consecutive patients older than 70 years who were admitted to a single geriatric day hospital for CGA as part of routine care during a 2-year period. Based on the usual activity of the unit the duration of the inclusion period was selected to include at least 10 subjects per descriptive variables. Patients received written information about the use of their anonymized health data for clinical research purposes and were given the option of refusal. The study was approved by the Ethical Committee of the CHU (university hospital center) of Bordeaux (GP-CE2020-43). Patients who had undergone an intra-oral health examination performed by a dentist during the CGA were included in the study. Patients unable to perform the TUG or SPPB were excluded.