dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and Discussion A total of 3717 papers were retrieved in all databases. A flowchart designating the study selection process according to the PRISMA Statement is presented in Figure  1. The literature review retrieved 2790 papers for the initial examination after eliminating the duplicates. Afterward, 2760 studies were excluded following reviewing the titles and abstracts, leaving a total of 30 articles to be assessed by full-text evaluation. From these, 8 studies were not considered for the qualitative analysis for various reasons: 4 were clinical studies  [18] [19] [20] [21] , 2 studies did not evaluate bond strength  [22, 23] , and in 2 studies, there was not a control group using DDS  [24, 25]. Thus, a total of 22 studies were considered in the qualitative analysis. One study was excluded from the quantitative analysis because data on bond strength were not available  [26] , giving 21 studies considered for the metaanalysis  [12, [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46].
Literature Search The literature search was independently conducted by two reviewers (R. B. and L. H. ) until 20 January 2022. Six electronic databases were selected to recognize manuscripts that might be included: PubMed (MedLine), The Cochrane Library, ISI Web of Science, Scielo, Scopus, and Embase. The search strategy and keywords used in PubMed are listed in Table  3. The full search strategy for The Cochrane Library, ISI Web of Science and Scielo, Scopus, and EMBASE databases is presented as supplementary material (Tables  S1-S4 ). The reviewers also hand-searched the reference lists of involved manuscripts for identification of supplementary papers. After the initial screening, all articles were introduced into Mendeley Desktop 1. 17. 11 software (Glyph & Cog, LLC, London, UK) to exclude duplicates.
Randomisation Patients will be randomly assigned to one of the two arms: "ICA" and "IANB" at a ratio of 1:1. The randomisation list will be computer-generated by the study statistician and the randomisation process will be centralised through a secured clinical data processing website managed by Ennov© Clinical system software (Ennov, Paris, France). The investigators will enter the patient's information (initials, age, and date of birth mentioning the month and year only), and the software will provide the patient's unique allocation number and randomisation group.
Statistical analysis The analysis will be based on intention-to-treat (ITT) using the "missing=failure" strategy. A robustness analysis, supported by the per-protocol approach, will be carried out if the patients present with protocol deviations (error of randomisation). Qualitative variables will be described by the percentages and numbers of each modality. Quantitative variables will be described by means and standard deviations. Confidence intervals at 95% will be presented. A paper case report form (CRF) will be created for each patient inclusion for data collection and will be based on the establishment of a clinical database. All information required by the protocol must be provided in the CRF, necessary for statistical analyses. To assess the primary endpoint (time to return to baseline heart rate and blood pressure), the mean values will be estimated by the Kaplan-Meier method (survival curves) and compared using Log-Rank tests.
To assess the secondary endpoint (time to baseline O 2 and change in measurements over time), the 2 arms will be compared in the same way as the heart rate and blood pressure parameters. Univariate and multivariate Cox models will be used to investigate the factors associated with the time to the baseline of cardiopulmonary constants. The difference in pain between D0 and D+3 will be compared between the 2 groups a Student's t-test or a Wilcoxon test. Data for the analysis are collected from the first questionnaire (French version of Corah's dental anxiety scale), the three phases of cardiopulmonary monitoring, the VAS assessment, and the postoperative questionnaire given to the patient at the end of the procedure. The information recorded on the postoperative questionnaire will be collected by a telephone call 3 days Table  1  Schedule of enrolment, interventions, and assessments during COQ study protocol after the procedure.
1) Background and objective: Tooth movements described as unexplained, aberrant, unexpected, unwanted, or undesirable can occur in the presence of an intact orthodontic retention wire, without detachment or fracture. This iatrogenic phenomenon, known little or not by many practitioners, responsible for significant dental and periodontal complications, both functional and aesthetic, is called "Wire Syndrome" (WS). It is therefore considered an undesirable event of bonded orthodontic retainers, which must be differentiated from an orthodontic relapse. The objective was to perform, for the first time, a systematic review of the literature in order to define the prevalence of WS and to study its associated clinical characteristics. (2) Methods: A systematic review of the literature was performed following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and recommendations using an electronic search strategy on four databases complemented by a manual search.
Manual search To complement the electronic searches, a manual search was conducted: -From the bibliography of articles selected by the electronic search; -From the search engine of a selection of orthodontic and dental journals: This research was conducted using the reference management software Zotero version 5. 0. 96. 2 (https://www. zotero. org accessed on 1 September 2021). Two authors carried out the entire procedure independently. In the case of disagreement, a third author was interviewed, and a mutual discussion was conducted to reach a consensus. After avoiding duplicates, the inclusion of articles was carried out in three steps: -Reading of titles; -Reading of abstracts; -Reading of the full text. For articles with no available abstracts, full-text articles were read for eligibility assessment.
A more thorough quantitative and statistical analysis is thus expected to support (or not) the visual impression, but also to give more precise information regarding the chemical species identified in the XRF spectra, and to quantify as well the degree of correlation (i. e. any statistical pattern or relationships or even dependence between the datasets). Correlations are useful quantities to be examined, since they can potentially indicate a predictive relationship to be exploited. Nevertheless, one has to be particularly cautious in drawing conclusions, and several issues have to be carefully considered: • the smaller the size of the available dataset, the more likely is it to observe a correlation that might not be real and not observable if the same set can be extended. Thus, with small datasets (or small parts of them), correlations can be unreliable.
• presence of single unusual points in the datasets (outlier) can make the computed correlation coefficients highly misleading. • a correlation between two variables is not a sufficient condition to establish such a causal relationship relating these variables, in either direction. As previously shown  [95] [96] [97] [98] , the XRF contrasted maps can be analyzed using Intensity Correlation Analysis methods in order to highlight possible correlations related to the presence of the different chemical elements in a sample. Other correlation coefficients can be used as well, but they will not be discussed here. The reader can also refer to the detailed work of Dunn et al. [98]  and references therein. Let us briefly describe some correlation approaches  [99] [100] [101] [102] [103] [104] , considering the case of two XRF contrasted images (labeled in the following as image 1 and image 2) of the same lateral size and resolution (same number of points), obtained on the very same sample region, but showing the distribution of two different chemical elements.
X i , j and Y i , j are the intensity of the pixel of coordinates (i , j ) for the two images, respectively. (A) In a first step, scatter plots can be used. If one expects that the dataset of image 2 (Y i , j ) is somehow dependent of the one in image 1 (X i , j ), plotting them as a scatter plot (using Y and X axis respectively) allows drawing some conclusions: (i) a positive correlation between X and Y variables is found if the obtained pattern exhibits a positive slope (from lower left to upper right of the graph, i. e. the Y variable tends to increase when the X variable is increasing); (ii) a negative correlation is found if the above mentioned trend is opposite; (iii) no correlation generally translates into no trends noticed in the scattered plots. Possibly, the result can be fitted (but no "universal" model exists) and the degree of correlation can thus be quantified by the parameters of the fit and their comparison between similar datasets.
(B) The Pearson colocalization coefficient for two variables is defined as the ratio of the covariance of the two variables by the product of their standard deviation: r p = (X i , j -X ave )(Y i , j -Y ave ) (X i , j -X ave ) 2 (Y i , j -Y ave ) 2 with the summations being performed over the entire considered dataset/image, i. e. , all the pixels of coordinates (i , j ), and X ave and Y ave denoting the average intensity for the two images, respectively. The obtained parameter representing the correlation of the intensity distribution in the two images is thus independent of their brightness. With this definition, r p parameter takes values from -1 to +1. A value of r p close to 1 means that the images are strongly correlated or there is a good colocalization of the signal in the two images (brighter areas in image 1 correspond to brighter areas in image 2).
r p ≈ 0 is a signature of no correlation of the two images. As also pointed out above, we should note in this case that finding a value close to 0 for the r p parameter does not necessarily imply that the respective variables (X and Y ) are independent. In the particular case of r p close to -1, the images are negatively correlated (or excluded), having reversed contrast: image 2 is the negative of image 1, or brighter areas in image 1 correspond to darker areas in image 2 and vice versa. In practice, for the datasets, and in order to reduce errors for real sample images, the Pearson colocalization coefficient should be calculated on the investigated object only. The background is excluded from the calculations (masked out by setting it to a non-physical negative value, for example) the above summations are performed only inside the sample (for the pixels of coordinates (i , j ) for which X i , j > 0 and Y i , j > 0).
An example is shown in Section 6. 6. All the correlation/colocalization approaches might have advantages and drawbacks-none of them should a priori be considered better than the other. A careful examination is always needed and the choice of one approach over the other largely depends on the particular sample, datasets available and the question(s) to be addressed. A tentative workflow to guide the researcher in identifying and applying the proper (or better) technique can be found in  [98].
4). The analysis can be, of course, performed on the full dataset (XY map) or regions of it. Figure  12  (b to e) shows XY raster maps with XRF contrast corresponding to various elements. The correlation analysis was performed for all the possible resulting combinations of the situations mentioned above (more than four elements, for full image and three ROIs), but we will give in the following as a detailed example, two situations: correlations of XRF maps with Ti-Fe and Ti-Zn contrasts only for Raster maps with XRF contrast corresponding to (b) Ca (intensity with a maximum equal to 20), (c) Ti (intensity with a maximum equal to 1000), (d) Fe (intensity with a maximum equal to 60), (e) Cu (intensity with a maximum equal to 200) and (f) Zn (intensity with a maximum equal to 140) respectively.
Acknowledgments This work was supported by the  Physics and Chemistry Institutes of CNRS  and by contracts  ANR-09-BLAN-0120-02 ,  ANR-12-BS08-0022 ,  ANR13JSV10010-01 , convergence UPMC  CVG1205 ,  Labex Matisse, Labex Michem  and  CORDDIM-2013-COD130042. The authors are grateful to the  SOLEIL Synchrotron Facility  and  ESRF  for beam time allocation and would like to very much thank the support groups and beamline teams of SOLEIL and  ESRF  for their help during experiments.
Aims: During the COVID-19 pandemic the search for complementary methods to enhance manual disinfection in dental and medical practices raised relevance. We sought evidence for the addition of ultraviolet-C (UV-C) disinfection to manual cleaning protocols -and whether it improves the logarithmic (log) reduction of surface pathogen colonies. Methods: This review was registered at the International Prospective Register of Systematic Reviews (PROSPERO) under the number CRD420200193961. Six electronic sources were consulted looking for clinical trials performed in healthcare environments in which pathogens were quantified by colony-forming unit (CFU)-enumeration before and after interventions, all databases were last consulted on May 2021. We assessed the risk of bias using an adapted Revised Cochrane Risk of Bias Tool (RoB 2).
Search strategy and study selection We based the search strategy on the PICO question: Can the addition of UV-C technology enhance the disinfection of contaminated surfaces in healthcare facilities? It was executed on five electronic databases (PubMed, Embase, Scopus, Web of Science, and the Cochrane Library) and one gray literature source (Grey Matters). Sources were last consulted on March 2021. The MeSH terms used were: "Health facilities", "Dental Clinic*", "Hospital*", "Ultraviolet Radiation C", "Ultraviolet rays" and "Disinfection". Both MeSH and the other input terms were adapted correctly according to each source's syntax rules, and the Boolean operators (OR, AND) were used to combine the terms as shown in Table  S3. We inserted all citations found into a reference management software (EndNote®, version X7, Clarivate Analytics), and duplicates were manually and automatically deleted.
Extraction data We extracted the following data from the included studies: authors, country, study design, health facility, description of the surfaces, sample characteristics (number of samples and pathogens identified in samples), UV-C technology intervention (device, wavelength (nm) applied, UV-C dose (J/m 2 ) or irradiance (W/m 2 ), exposure time), manual cleaning disinfectants, results (CFU counts, reduction (%), log reduction), statistical analysis and outcome. When missing, we calculated log reduction in those studies in which CFU counts were specified before and after interventions. In cases where the data were incomplete, doubts about the methodology, or cases in which the articles were unavailable for reading in full-text, the authors were contacted by email weekly for five consecutive weeks.
Risk of bias assessment We assessed the risk of bias using an adapted  [21]  Revised Cochrane Risk of Bias Tool (RoB 2)  [22, 23]. Each study was analyzed for selection bias, performance bias, detection bias, attrition bias, and reporting bias through the following domains: description of sample calculation or large sample size; standardization of sample procedures (reproducible and comparable between groups); standardization of the manual disinfection method and blinding of operators; standardization of the UV-C performance and blinding of operators; blinding of outcome assessment; incomplete outcome data before intervention (description of pathogens identified in samples and colonization counts before the intervention); incomplete outcome data after the intervention (description of pathogens identified in samples, colonization counts after the intervention, and calculation of colonization reduction% and log reduction-or data available to calculate); and selective outcome reporting.
Study selection We identified 1722 records in database searching. After duplicates removal, automatically and manually, we screened 1012 records by title and abstract and reviewed 65 full-text documents for potential eligibility; 54 articles did not meet inclusion criteria. The reasons for exclusions are detailed in Table  S4. Finally, 12 studies fulfilled the eligibility and we included them for qualitative analyses  [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] , no extra articles were found in references or other sources. Each phase of the selection study is summarized in Fig. 1.
Discussion Our systematic review aimed to summarize and analyze the evidence from clinical trials assessing UV-C disinfection's ability to enhance the log reduction of infectious agents remaining on surfaces after manual cleaning protocols. We included experimental and quasi-experimental trials after eligibility criteria. Out of the 12 included studies, only 1 study was judged to be a high risk of bias and all of them reported an enhanced log reduction of pathogen colonization after the UV-C intervention. However, the significance of this improvement varied according to the UV-C device and the manual cleaning protocol of choice. No merging of data was possible due to heterogeneity across the included studies. Thus, our review is focused on a qualitative analysis of the literature. Some studies revealed that coronaviruses can persist viable on surfaces, such as stainless steel or plastic, for up to 72 h  [9, 10].
mentagrophytes complex isolates and 8 control isolates from patients with terbinafi ne-susceptible clinical presentations of tinea (6 cases of tinea pedis or tinea unguium and 1 each of tinea faciei and tinea corporis) collected September 1-October 15, 2018. We sequenced the internal transcribed spacer (ITS) using ITS1 and ITS4 primers, which allowed us to ascribe the isolates to 1 of the 7 different genotypes of T. mentagrophytes or the 2 genotypes of T. interdigitale  (2, 3). We aligned sequences using Geneious Prime version 2020. 0. 4 software (https://www. geneious. com) and compared them using the Mycobank DNA database (https://www. mycobank. org). We amplifi ed the SQLE gene of all studied isolates using previously reported primer pairs (4). To ease the SQLE gene sequence analysis, we shortened the PCR products by designing 2 internal primers, SQLE-F2 (5′-[658]-TGGGGCCTGGAGCT-TATAGA-[677]-3′) and SQLE-R2 (5′-[885]-CCTTCTC-CAACGCAGCTTCA-[904]-3′).
We compared our sequences to wild-type T. mentagrophytes complex reference sequence EZF33561 from GenBank  (5)  and submitted the new sequences to GenBank (accession nos. MW898018-32). We determined MICs as recommended  (6). We obtained approval from the Commission Nationale de l'Informatique et des Libertés, the national data-protection agency in France (approval no. 903395), to ensure that patient data would be kept anonymous according to national regulations. We generated a phylogenetic tree based on alignment of the ITS sequences (Figure  2 ). The 7 resistant isolates (Table  2 ) belonged to T. mentagrophytes ITS type VIII, whereas 6/8 control isolates belonged to a different subgroup close to genotypes I and II associated with the anthropophilic T. interdigitale and 2/8 to genotype VI (3).
Statistical analysis DC was expressed in percent by means and standard deviations. Normal distribution was con rmed by a Shapiro-Wilk test and the equality of variances assessed with a Levene test. A 3-way-ANOVA followed by Tukey's post hoc test was performed for the factors "resin", "time" and "temperature". For all the tests, the signi cance level was set at p < 0. 05. Tests were carried out with XLSTAT (Addinsoft, Paris, France) and Graphpad Prism 9 software (Graphpad Software Inc, California, USA).
Data extraction and management The following data were extracted from each study and reported in a pre-designed data extraction form: general characteristics: the name of the rst author, year and country in which the study was conducted, clinical trial design, sample size, allocation method, blinded study, and characteristics of the groups compared. The data on the study population comprised of: source, type of diabetes, duration of diabetes, blood glucose control at inclusion, and presence of other conditions. The nature and severity of periodontitis were also recorded (chronic/adult, aggressive/juvenile, stage and grade, mild, moderate, or severe periodontitis), as well as alcohol, tobacco, and medication use. Intervention: type of periodontal treatment (scaling + root planning/debridement), oral hygiene instruction, use of antimicrobials or other treatments, supragingival scaling, periodontal follow-up.
Publication bias was assessed by visual inspection of funnel plot analysis and Egger's test  (35). When substantial heterogeneity was found in our results, a subgroup analysis was performed to assess the source. Subgroup analysis was conducted according to:i) methodological quality of the studies, ii) severity of periodontitis, iii) glycaemic control (HbA1c between 7 and 7. 5; between 7. 5 and 8 and > 8), iv) age, v) sex, vi) smoking status, and vii) other chronic diseases (arterial hypertension, chronic kidney disease, joint disease, respiratory diseases, etc). Intra-examiner variability for study inclusion was assessed using Cohen's κ coe cient (36). Data analysis was performed by R software Version 4. 0. 2.
Study characteristics and risk of bias The electronic search of databases and gray literature yielded 1848 articles. After the elimination of duplicates and examination of titles and abstracts, 46 articles were eligible for this review. Full texts of these 46 articles were retrieved and 36 of them were excluded for the reasons mentioned in Fig. 1. Finally, 10 articles were included in this systematic review and meta-analysis. The characteristics of the included studies and their main results are reported in Table  1. In two studies, the maximum follow-up period for HbA1c assessment was 9 months  [Botero et   (24). A total of 1128 participants, 615 in the intervention group (non-surgical periodontal therapy with or without adjuvant and oral hygiene instruction) and 513 in the control group (no treatment, supragingival scaling, placebo or oral hygiene instructions) were included in the meta-analysis for the primary outcome.
Statistical analysis Categorical variables were expressed as numbers (percentage). Continuous variables were expressed as means (standard deviation, SD) in the case of normal distribution or medians [range] otherwise. Normality of distribution was assessed using histograms and the Shapiro-Wilk test. Comparison of the risks factors and the comparisons of the complication rates between the two groups of approaches (patients with impacted LTM versus with erupted/absent LTM) were studied using Chi-square tests (or Fisher's exact tests when expected cell frequency was <5) for categorical variables and Mann-Whitney U or Student t tests (regarding the normality of distributions) for continuous variables. Statistical testing were done at the two-tailed a level of 0. 05. Data were analyzed using SAS software package, release 9.
g. palpation results and clinical history). Radiographs with the indication of no rib fracture, postoperative internal xation of the rib, poor quality breathing, and surface foreign bodies that affected the diagnosis were excluded. Inconsistencies were resolved through discussion. In total, 1080 radiographs with rib fractures were obtained. The work diagram is illustrated in Fig. 1. To collect data for constructing the CNN-based rib fracture detection model, the radiologists marked the fractures on the graphs. One radiologist marked the fracture sites on 1,080 radiographs with the following signs: (1) complete rip disruption with a lucent line, (2) disruption of the inner or outer cortex, (3) fracture rib end displacement, and (4) rib deformity with callus formation. As shown in Fig. 2 , all rib fractures are marked by blue boxes.