dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Dr Jean-Yves Gagner  and  Stéphane Guerraud  ( Agence Régionale de Santé des Pays de la Loire ),  NSK, SANOFI , and  Delphos Implants SA  for their material and human support for this national training program. We thank  Mr. Pierre-Emmanuel Menuet  ( 3D-Medlab company ). We also thank  Mrs. Sophie Domingues  for her careful reading and correction of the English version of this manuscript. This training programme received funding from the  Agence Régionale de Santé (ARS) des Pays de la Loire (French Ministry of Health ). The sponsor had no role in the study design, collection, analysis, or interpretation of data, nor in the writing of the manuscript or in its submission for publication.
t001 Search strategy Three independent authors (JRL, CMCE and MRB) conducted a PubMed, Cochrane Library and Scopus search to identify articles published between January 1990 and December 2019 about the role of reflux in the development of dental disorders. There was a high degree of agreement between authors (p<0. 05). Clinical and experimental studies were screened if they had database abstracts, available full-texts or titles referring to the condition. The following keywords were used: 'reflux'; 'laryngitis'; 'laryngopharyngeal'; 'gastroesophageal'; 'dental'; 'teeth'; 'decay'; 'caries'; 'erosion'; and 'mucosa'. Authors analyzed the number of patients, study design, inclusion and exclusion criteria, quality of trial and evidence level (EL). All clinical score/index are characterized by a high score in pathological cases.
GLI1 expression is broadly accepted as a marker of Hedgehog pathway activation in tumors. Efficacy of Hedgehog inhibitors is essentially limited to tumors bearing activating mutations of the pathway. GLI2, a critical Hedgehog effector, is necessary for GLI1 expression and is a direct transcriptional target of TGF-β/SMAD signaling. We examined the expression correlations of GLI1/2 with TGFB and HH genes in 152 distinct transcriptome datasets totaling over 23,500 patients and representing 37 types of neoplasms. Their prognostic value was measured in over 15,000 clinically annotated tumor samples from 26 tumor types. In most tumor types, GLI1 and GLI2 follow a similar pattern of expression and are equally correlated with HH and TGFB genes. However, GLI1/2 broadly share prognostic value with TGFB genes and a mesenchymal/EMT signature, not with HH genes.
We compiled data from publicly available gene expression datasets from over 23,500 cancer patients, of which over 15,000 with survival annotations, well above those available from The Cancer Genome Atlas (TCGA). While GLI1/2 expression is correlated with both HH and TGFB expression, their prognostic value is tightly correlated with that of TGFB, not HH. High GLI1/2 and TGFB expression, associated with a mesenchymal/EMT signature, often represent parallel markers of poor clinical outcome. Inversely, high expression of HH is mostly associated with increased survival.
To understand this discrepancy, a large cohort of breast cancer patients' data  22  was further analyzed. Intra-dataset z-score GLI(1/2), TGFB(1/2/3) and HH(S/I/D) and metagene expression values were sorted according to increasing GLI2 expression, then aligned to the molecular subtypes. High GLI1/2/TGFB and mesenchymal/EMT metagene expression was associated with the normal-like subgroup of tumors with better prognosis, and inversely correlated with luminal-type tumors of poor prognosis (Supplementary Figure  S2 ). A hypothesis may be that the mesenchymal/   S4B. (B) Heatmap representation of the coefficients from the linear models for each of the 3 metagenes in each tumor type, based on the combined expression of either GLI1 and GLI2, or the three HH or TGFB genes. Corresponding numerical values are provided in Supplementary Table  S4B.
Discussion HH inhibitors have failed to fulfill expectations placed on them as powerful anti-cancer drugs  2. Despite widespread expression of GLI1 in tumors, considered to be a marker of HH activation, HH inhibitors have solely been granted FDA approval for the treatment of advanced basal cell carcinoma of the skin, a type of tumor that bears activating mutations of the HH pathway  25. Clinical trials on other solid (or non-solid) tumors have overall failed  2. Based on our earlier work that identified TGF-β as a potent transcriptional inducer of GLI2, consequently leading to SMO-independent induction of GLI1  12, 13  , we hypothesized that GLI1 expression in tumors may be driven by TGF-β, not HH, which would explain the lack of efficacy of anti-HH approaches. Large-scale datamining from gene expression datasets representing 23,500 patients and 37 types of neoplasms addressed the issue of whether GLI1/2 expression in tumors warrants therapeutic approaches targeting upstream HH signaling.
Material and methods transcriptome series. A set of 135 transcriptome series related to 37 cancer types was collected from public repositories (ArrayExpress, GEO, TCGA). Series that included multiple cancer types were split accordingly, yielding to a total of 152 distinct transcriptome datasets representing over 23,500 patients. Details and accession numbers are provided in Supplementary Table  S1. pre-treatment, normalization. Datasets based on Affymetrix microarrays were normalized independently using the justRMA function from the Affy R package (with default parameter). For TCGA RNA-Seq datasets, raw counts were normalized using the upper quartile method  26. Datasets from other sources were used as furnished, after log2 transformation of expression values. All probe sets data were aggregated by HUGO Gene Symbol.
correlation analyses. Transcripts with available measures in at least 30 cancer types were selected for further analyses (n = 19,540). The correlation between their expression and that of GLI1 and GLI2 was calculated independently in each of the 152 datasets, yielding 152 correlation matrices (dimension: 19,540 × 2). Correlations were then averaged across all 152 dataset matrices, yielding a unique (19,540 × 2) matrix used to plot Fig. 1A. The 152 matrices were also averaged per cancer type, yielding 37 sub-matrices that were reduced to the 8 genes of interest (GLI1, GLI2, TGFB(1/2/3) and HH(S/I/D), Supplementary Table  S2 ) and used to plot Fig. 1B.
Metagene calculation. Three published gene signatures corresponding to critically important oncogenic activities were selected: (i) mesenchymal-EMT 27 , (ii) Stemness 28 ; (iii): Cell Cycle: https ://www. genom e. jp/dbget -bin/www_bget?pathw ay+hsa04 110. Gene content for each signature is listed in Supplementary Table  S3. For each of these signatures and each dataset, the average zero-centered expression of the genes, both measured and included in the signature, was calculated for each sample. Linear models. Within each dataset, based upon the metagene values for each of the three oncogenic signatures, the lm function from the stats R package was used to perform a linear regression of the metagene variable, using GLI1, GLI2, SHH, IHH, DHH, TGFB1, TGFB2 and TGFB3 expression as predictive variables.
To allow for inter-datasets and inter-variables comparisons, all variables were z-scored within each dataset before linear modeling (common unit = standard deviation). For each model in each dataset, correlations between predicted values and observed metagene values (z-scores) was recorded and averaged across datasets by cancer type, then represented as heatmaps. For each linear model in each dataset, coefficients of the predictive variables were recorded and averaged across datasets by cancer type and metagene. Numerical values are provided per series and per cancer type (Supplementary Tables  S4A  and S4B , respectively).
Survival analyses. Univariate Cox models of overall survival in 26 tumor types with cohorts of more than 50 patients and at least 10 death events were calculated using the survival R package. Aggregation of Hazard Ratios (H. R. ) and related confidence intervals across datasets of a given cancer type for a given variable were calculated using the meta R package. All genes and metagenes were z-scored intra-dataset prior to modeling, to allow for inter-datasets comparisons (common unit = standard deviation). Numerical values are provided per series and per cancer type (Supplementary Tables  S5A  and S5B , respectively).
Acknowledgements This work was supported by the  French national program Cartes d'Identité des Tumeurs (CIT)  funded and developed by the  Ligue Nationale Contre le Cancer , grants from  Ligue Nationale Contre le Cancer (Equipe Labellisée   LIGUE-2016 ),  Institut National du Cancer (INCa   MELA13-002 ) and  Donation Henriette et Emile Goutière  (to A. M),  Fondation ARC  (to D. J. ), and institutional funding from  Institut National de la Santé et de la Recherche Médicale ,  Centre National de la Recherche Scientifique, Institut Curie , and  Université Paris-Sud.
Experimental procedures and data collection The descriptive variables and study criteria were collected by the two investigators (dental practitioners). At the first appointment, general health-related data (age, height and weight) and oral health criteria, detailed in the following section, were gathered during children's clinical examination and an interview with the caregivers. A second session, within the following month, was organized according to the participants' schedule for the mastication evaluation tests. It was planned between meals, in the mid-morning or mid-afternoon. Children were invited to masticate samples of three solid foods with different textures that are commonly used in the research laboratory: cheese (CHS), breakfast cereals (CER), and raw carrot (CAR). For each child, 12 calibrated food samples were prepared: four samples of the breakfast cereal Fitness® Nature (Nestlé) (1 g ± 10%), four of the raw carrot and four of the cheese Emmental Meule d'or® (Entremont).
This threshold allows the collection of a ready-to-swallow food bolus for further granulometric analysis. Consequently, four mastication sequences were video recorded for each tested food. The first two masticatory sequences allowed live recording of the natural number of cycles needed before swallowing (Nc)  (Linas et al. , 2019). This number was later verified by individual viewing of the video sequences (Imovie® software) in random order by two dental practitioners also involved in a previous study  (Linas et al. , 2019). Both investigators were trained in recording masticatory kinematic parameters according to the routinely used procedures in mastication studies in the research laboratory  (Nicolas, Veyrune, Lassauzay, Peyron, & Hennequin, 2007). Agreement between the values measured by the two investigators was ensured for each mastication sequence (interindividual variation of less than 10%).
Statistical analysis The required sample size was estimated from a preliminary pilot study that measured the mean D50 value for raw carrot (D50 CAR) between children with healthy teeth and children with ECC (eight first included children of each group). The mean D50 CAR value was higher in the ECC group than in the control group (4. 55 µm versus 3. 08 µm respectively) (SD = 0. 98). Calculations were based on this difference for a continuous criterion with independent values and indicated the need for at least 10 subjects for each group (α = 5%, β = 10%, epiR package 0. 9-30). As children could refuse certain food samples, leading to missing data, thirteen children were finally included in each study group. A statistical analysis was conducted using SPSS® (IBM, v20). The significance threshold value was set at p<0.
05. The number of FUs and the NOT-S mean scores were compared between both groups with a series of Mann Whitney U tests. For each food, the individual repeatability of masticatory parameters (Ti, Nc, Fq and D50) was verified (Wilcoxon-Sign-Rank test). Food sample refusal rates (Fisher's exact test) and masticatory parameters (Mann Whitney U test) were compared between groups for each food. In addition, effect size was measured by (η 2 ) (partial eta squared). For the masticatory criteria, all recorded data of all subjects included in the study were analysed. Missing data corresponded to food sample refusals. Potential correlations were sought for all children between dmft scores, number of FUs, NOT-S scores and masticatory parameters (Spearman's test).
Descriptive data Thirteen children suffering from ECC (9 girls and 4 boys), and thirteen children with healthy oral state (5 girls and 8 boys) were included in the study. Children with ECC all exhibited the severe form of the disease. Data regarding general and oral health are gathered in Table  1. Both groups were similar in age, weight and height (U tests of Mann Whitney, NS), and sex (Fisher's exact test, NS). Both groups were comparable concerning the presence of dysmorphology (Fisher's exact test, NS) (Table  1 ).
Methods This phase 2, double-blind, randomized study (NCT0 1350115) adhered to the Declaration of Helsinki and International Council for Harmonisation Consolidated Guideline E6 for Good Clinical Practice. Approval to the study protocol and all amendments was provided by the Ethics Committees at Erasmus Hospital, UZ Leuven, the Christian-Albrechts-University of Kiel, and the University of Vienna; and by the Institutional Review Board at Aurora, Ontario. All patients provided written informed consent before enrollment. Adults with at least two BCCs were eligible to enroll if they met at least one of the following diagnostic criteria for NBCCS: keratocystic odontogenic tumors, either from medical history or optional radiography at screening; at least three palmar and/or plantar pits; history of bilamellar calcification of the falx cerebri or bifid, fused, or markedly splayed ribs; first-degree relatives with NBCCS; and PTCH1 mutation identified in genome from nontumor tissue.
gingivalis, PorE is unique. For instance, a search with the BLAST program in the genome of Duncaniella muris, a species highly abundant in the murine intestinal tract  38  and that potentially possesses a functional T9SS as PorK-L-M-N homologs are present in its genome, results in one PorE homolog. Indeed, this protein shares the same architecture as PorE (46% sequence identity with 98% sequence coverage), it shares the three main residues involved in PG binding, suggesting that it actually interacts with PG, and it is predicted to be a lipoprotein. On the other hand, multiple PorE homologs are present in some species. For instance, fourteen PorE homologs are present in the genome of Tannerella forsythia, another periodontal pathogen  39  that potentially possesses a functional T9SS (around 50% sequence identity with more than 95% sequence coverage, conservation of the PG binding residues, and prediction as lipoproteins).
1 M sodium acetate, 0. 1 M MES pH6. 5, and 30% (w/v) PEG 2000. Crystals were mounted in cryo-loops (Hampton CrystalCap Magnetic) and were briefly soaked in crystallization solution supplemented with 20%(v/v) ethylene glycol. The crystals were flash-cooled in a nitrogen-gas stream at 100 K using a home cryocooling device (Oxford Cryosystems). Data collection and processing, and Accession Code. X-ray diffraction data were collected at Soleil synchrotron Proxima1 beamline and were processed using XDS package  43. The structure of the OmpA_C-like domain was determined by single wavelength anomalous diffraction (SAD) using data collected at the selenium X-ray absorption peak (0. 97911 Å) from a selenomethione-derived crystal. The substructure determination for four methionine sites and the phase calculation were performed in Phaser  44  as embedded in Phenix  45.
Model building was performed by the multivariate algorithm in Crank2 that simultaneously combines the structure determination steps, including the scaling and extracting the anomalous signal, finding the heavy atom sites, and solvent flattening  46. The initial structure model was improved through iterative refinement with AutoBuster 47 and manual refitting with COOT  48. The validity of the refined structure was assessed by MolProbity  49. Data collection and refinement statistics of the PorE OmpA_C-like domain are reported in Table  1. The atomic coordinates and structure factors have been deposited in the Protein Data Bank (PDB) under accession code 6TOP.
Acknowledgements We thank the  Soleil synchrotron (Saint Aubin, France)  for beam time allocation. This work was supported by the  Centre National de la Recherche Scientifique  and the  Aix-Marseille Universite , and grants from the  Agence Nationale de la Recherche  ( ANR-15-CE11-0019-01 ) and the  French Infrastructure for Integrated Structural Biology (FRISBI ,  ANR-10-INSB-05-01 ). T. T. N. T. doctoral fellowship was supported by  Schlumberger Foundation Award  ( CFn°-907440F ) and the  Eiffel program  from the  Ministère Français des Affaires Etrangères  ( CFn°-861733C ).
Statistical Analysis All statistical analyses were performed using GraphPad Prism 6 software. DPSCs experiments were performed with sixindependent healthy donors. HGFs experiments were performed with three-independent healthy donors. Microbiological assays were performed at least three times in triplicates. Histograms represent mean ± SEM and statistical analyses were performed using Mann & Whitney test. For each test, a value of p < 0. 05 was accepted as statistically significant.
Search strategy Four databases were screened: PubMed, Embase, Cochrane Library, and Scopus. The references cited in the articles included were also checked. The published scientific articles from May 1999 to January 2020 were systematically assessed for this review. Search strategies were developed and performed in the four electronic databases. The search terms were divided in three parts: -Teeth, for example, dental, enamel, dentin, dental, dentistry, teeth, and tooth. -Bioactive glasses, for example, bioactive glass, bioglass, 45S5, and nanobioactive glass. -Bioactivity, for example, remineralization, bioactivity, bioactive, and biomineralization.
Data extraction One author (CM) extracted the data using a prepiloted data collection form, and a second author (BG) verified data extraction independently for completeness and accuracy. Data obtained were as follows: general study details (first author, date of publication); population: BAG investigated (e. g. glass label, size of particles if mentioned, technique used to elaborate BAG, BAG's composition, comparison with); and the objectives, criteria, methods (solutions, time of storage, and analysis), and outcomes of studies. Any potential conflict was resolved by a joint discussion between the two authors.
Main characteristics of selected articles A wide range of analysis of bioactivity was presented from the included studies. The most common analysis was the formation of apatite by in vitro evaluation in solutions. Other analysis corresponded to enamel and/or dentin remineralization in vitro, mineralization ability of cells in vitro, and pulp capping or pulpotomy in vivo. Data obtained were as follows: BAG investigated, criteria, methods, and main results.
Data collection from RA patients History taking, physical examination, laboratory tests, and review of medical files were systematically performed to collect data from RA patients. Current / past medication use were obtained from information provided by patients, and based on the review of medical records. All patients had at least one chest HRCT and one measurement of forced vital capacity (FVC) and diffusing capacity of lung for carbon monoxide (DLCO) performed during the inclusion period. In the subset of French patients with ILD, HRCT lung images and pulmonary function tests (PFTs) were obtained both at baseline (time of blood sample collection) and at a followup visit. The ILD status of patients with RA was established by chest HRCT. The chest HRCT pattern was classified as usual interstitial pneumonia, UIP or non-specific interstitial pneumonia, NSIP  [9].
The obtained suspensions were sonicated for 10 min to break down aggregates (Elmasonic S 30, Elma Electronic, Wetzikon, Switzerland, 37 Hz). This procedure has no effect on both C. albicans and C. acnes viability according to literature data  [18, 51]. In these microbial suspensions, cells concentrations were determined using flow cytometry (FCM). For dual-species conditions, FCM allowed us to distinguish the yeast population from that of bacteria according to their respective sizes and morphologies  [22]. Measurements were performed on a CytoFLEX (Beckman Coulter, Indianapolis, IN USA) managed by CytExpert 2. 0. 0. 153 software (Beckman Coulter, Indianapolis, IN, USA) and equipped with a blue laser (λ ex = 488 nm) and a 488/8 bandpass filter. The flow rate used was 30 µL•min -1.
Funding: This work was supported by a grant ( 2018-2019 ) of the  AAP Collaborative Research Action  of the universities of Tours and Poitiers; This work was also supported by the 2015-2020 State-Region Planning Contracts (CPER),  European Regional Development Fund (FEDER) , and intramural funds from the  Centre National de la Recherche Scientifique (CNRS)  and the  University of Poitiers. The authors thanks the  ARD Cosmétosciences  and the  Centre Val de Loire Region  for financial support ( ARD 2017-00118114 ). Acknowledgments: The authors also wish to thank  Didier Debail  and  Garry Holding  for revising the English text.
MEDLINE and conference abstracts of American Society of Clinical Oncology (ASCO) and European Society of Medical Oncology (ESMO) meetings were searched from inception until November 2019 for studies evaluating molecularly tailored therapies for CUP. All prospective studies comparing molecularly tailored to empiric therapies for CUP were included. All data were abstracted by two authors independently (ER and ZB). The meta-analysis was performed using a random-effects model and the inverse variance method in order to account for possible heterogeneity in treatment effect due to differences in the molecular tests used, study design, and the included patient populations in the four studies. A subgroup analysis by study design (randomized vs. non-randomized) was performed in order to better account for this factor.
The primary comparison was that of tailored versus empiric therapies for CUP. The primary endpoint was OS and the secondary endpoint was PFS. All analyses were done using the Review Manager software, version 5. 3 (The Nordic Cochrane Center, The Cochrane Collaboration, Copenhagen, Denmark). The threshold for statistical significance for the tests of overall effect and subgroup comparison was 0. 05 and that for the tests of heterogeneity was 0. 10. The I 2 coefficient was used to quantify the degree of heterogeneity within and between subgroups. All hypothesis tests were tests were formulated prior to data collection. The pooled analysis included 628 patients treated empirically and 398 treated by tailored therapies. For the primary outcome, a non-statistically significant trend towards improved OS was found with tailored versus empiric treatment for CUP (Figure  1 ; HR = 0.
ACKNOWLEDGMENTS We acknowledge the contribution of the  Normandy Network of Microbial Metagenomics (RNAmb) , supported by  Région Normandie  and the  European Union (FEDER). This work was supported by grants from  Evreux Portes de Normandie  and the  FUI SKIN O FLOR research program. D. S. is the recipient of a doctoral grant financed by the  ANRT. The LMSM is a member of the industrial cluster  Cosmetic Valley  and of  CNRS GDR 3711 Cosm'Actifs.