dataesr/scientific-paragraphs-categorization huggingface dataset:
LDH Measurement Assay To determine the bacterial impact on the osteoblast-like cells, the lactate dehydrogenase (LDH) activity was evaluated with a cytotoxicity detection kit (Roche, Bâle, Switzerland), following the manufacturer's instruction. Briefly, after 3 h of cells/bacteria interaction and after 48 h post-infection, supernatants were collected and filtered to remove cells debris and bacteria. Then, they were incubated with a substrate during 15 min in the dark and absorbance was measured at 490 nm with a correction at 700 nm. Each experiment was done three (independent) times and with at least two technical repeats (at least six raw data points).
Static Biofilm Model (Crystal Violet Staining Model) As previously described, the biofilm biomass was evaluated by crystal violet staining  [19, 20]. An isolated colony of C. acnes was diluted in 1 mL of BHI medium in a 48-well plastic microtiter plate or in presence of a titanium piece. After 5 days of anaerobic incubation at 37 • C, the planktonic growth was evaluated by measuring the absorbance at 600 nm. The plates were gently washed 3 times with water and 1 mL of 0. 18% crystal violet was applied for 20 min. After washing with water, 1 mL of 95% ethanol was added to each well and thus the stained biofilm was evaluated by measuring the absorbance at 595 nm. All results are expressed with the subtraction of the blank: medium without bacteria. Each experiment was done three (independent) times and with at least three technical repeats (at least nine raw data points).
Fluorescent Microscopy Biofilms were stained using the LIVE/DEAD BacLight Bacterial Viability kit (Molecular Probes, Eugene, OR) for the microscopy experiments. A ratio of SYTO-9 (green fluorescence, all live and dead cells) to propidium iodide (PI) (red fluorescence, damaged bacterial membrane cells or "dead" bacteria) of 1:5 was used. Image acquisitions were performed on an Axiovert 200M inverted microscope using a ×40 objective and the dedicated Axiovision v 3. 2. 6 software (Carl Zeiss, Oberkochen, Germany). Equal acquisition times were set for each SYTO-9 and PI channel in any condition of all the experiments. Surface quantification of live and dead bacteria was determined using ImageJ software (v1. 50i, National Institutes of Health, Bethesda, MD, USA). Each experiment was done three (independent) times and with at least two technical repeats (at least six raw data points).
Persistence of Biofilm After internalization, bacteria were seeded at different passages on blood agar plates or on a BHI solution to observe the persistence of the biofilm. From Culture 1 (P1 = bacteria seeded after 3 h and Gentamicin treatment after internalization) to Culture 5, PO represents the bacteria culture before internalization. Biofilm formation was measured by crystal violet staining as described above. Each experiment was done three (independent) times and with at least three technical repeats (at least nine raw data points).
Phylotype and MLST Data Analysis Phylotype and MLST were performed according to the protocol available at https://pubmlst. org/pacnes/ and the clonality was carried out by MLST on all isolates as previously described  [20]. Briefly, bacterial DNA was extracted using the QIAmp DNA Mini kit (Qiagen, Hilden, Germany) according to the manufacturer's instruction. Bacterial genes aroE, atpD, guaA, lepA, sodA, tly and camp2 were amplified by end-point PCR. DNA amplification samples and sequencing primers were sent to the Genewiz society for sequencing bacteria. For each gene, the DNA sequences were entered into the "Cutibacterium acnes MLST database" to identify the corresponding allele. All gene alleles were recorded in the database and, for each C. acnes strain, a phylotype and MLST profile were automatically attributed by the pubmlst database.
The exponential growth in medical knowledge À 16 millions citations in the MEDLINE ® database in 2007 and more than 30 millions in 2020-along with technological advances in both diagnostics and therapeutics have been essential drivers of medical specialization throughout the past decades  [1]. Specialization is a logical consequence of the evolution of Medicine (and Surgery), but it is probable that overspecialization may carry significant drawbacks  [2, 3]. In surgery for instance, early (and too important) specialization may lead to poor surgical training and the inability to change to a new subspecialty or to address more "general" surgical problems. Furthermore, overspecialization can become an invitation to medical overconsumption, considering that there is always room for another (more specialized) medical opinion  [2].
Optical Numeric Microscope Observations After the shear bond tests, the enamel surfaces were investigated under a digital microscope (KEYENCE; Osaka, Japan) and then analyzed using the VHX-5000 software to calculate the percentage of each area at 50× magnification to define the type of fracture. Three types of failure modes were categorized:  17  Type I: Adhesive failure: <20% of the adhesive stayed on the enamel; Type II: Cohesive failure: >80% of the adhesive stayed on the enamel; and Type III: Mixed failure: certain area presented type II while other areas presented type I.
Micro-computed tomography Micro-CT was performed on the hemi-mandible in the GEROM research unit (Angers, France) with a Skyscan 1272 (Bruker). The samples were placed in microtubes, filled with water to prevent desiccation. The tubes were fixed on brass stubs with plasticine and scanned with the following parameters: 9 µm resolution, X-ray energy of 70 kV and 142 µA for 1. 9 s exposure, 0. 2° rotation step, 5. 000097 μm image pixel size. The data were reconstructed with NRecon (v. 1. 7. 0. 4, Bruker-MicroCT).
Quantification of alveolar bone loss The DataViewer software (v. 1. 5. 6, Bruker-MicroCT) was used to visualise bone and produce the sagittal slices. All images were reoriented such that the cemento-enamel junction (CEJ) and the root apex (RA) appeared  [11]. Among the sagittal images, the image which showed the most recession of alveolar bone was selected for measurement. The distance from the alveolar bone crest (ABC) to the CEJ was measured in µm with the CTAn software (v. 1. 18. 8, Bruker-MicroCT) at two interdental sites: between the first and the second mandibular molars (M1M2) or between the second and the third mandibular molar (M2M3). As described elsewhere, the CEJ-ABC distance was the shortest distance from ABC to the line connecting the adjacent CEJs  [11]. Measurements of root lengths from the CEJ to the RA were also taken to assess the percentage of remaining alveolar bone using an equation previously described: Percent remaining bone (%) = ([root length -CEJ-ABC]/root length) × 100  [12].
Statistics Data were analysed using the R (v. 3. 5. 0) and RStudio softwares (v. 1. 1. 383, https://www. rstudio. com/). Nonparametric tests were used and considered significant for p < 0. 05. The results were expressed as mean ± standard error of the mean. The Kruskal-Wallis with Dunn's multiple comparisons tests were used to compare means for quantitative data related to weight, alveolar bone parameters, alpha diversity indices and taxa relative abundances. The PERMANOVA and ANOSIM tests were performed on beta diversity metric with the QIIME2 diversity plugin  [15]. A linear discriminant analysis of the taxa relative abundance was computed with the LEfSe algorithm (parameters set to default) in Galaxy (https://huttenhower. sph. harvard. edu/galaxy/). All plots were made with 'ggplot2ʹ for RStudio  [16] , except for the LEfSe results and the 3D PCoA plot (EMPeror)  [17].
They were first observed using an optical numeric microscope (KEYENCE, Osaka, Japon) and then analyzed using the VHX-5000 communication software (KEYENCE, Osaka, Japon) to measure the total area of the voids at 2, 5, and 8 mm from the apex. The void percentages were calculated by dividing the area of the voids measured during the analysis with the total area of the root canal of each specimen. Finally, the polished surfaces were treated with 37% phosphoric acid (5 s) and 2. 5% NaOCl (3 min) and thoroughly rinsed with distilled water for 30 s in order to eliminate the smear layer resulting from the polishing process and to visualize the sealer infiltrations in dentinal tubules (tags)  [27]. The specimens were dehydrated in a graded series of ethanol solutions and sputter-coated with a gold-palladium alloy (20/80 weight %) using a Hummer JR sputtering device (Technics, CA, USA).
Statistical Analysis The collected data were analyzed with SigmaPlot release 11. 2 (Systat Software, Inc. , San Jose, CA, USA). Shapiro-Wilk test was used to verify the normality of data. However, when the normality was not verified thus, Kruskal-Wallis test (one-way analysis of variance on ranks) was used. The proportions of void areas corresponding to each filling technique, the values for roughness, compressive strength analysis, antibacterial activity, and contact angle measurements were compared by one-way ANOVA. A two-way ANOVA test was applied to determine whether significant differences existed in the pH values of the different solutions in contact with the two sealers and the solubility measurements at 7 and 14 days. A statistical significance level was set at α = 0. 05.
Objectives: Salivary gland ultrasonography (SGUS) is increasingly applied for the management of primary Sjögren's syndrome (pSS). This study aims to: (i) compare the reliability between two SGUS scores; (ii) test the reliability among sonographers with different levels of experience. Methods: In the reliability exercise, two four-grade semi-quantitative SGUS scoring systems, namely De Vita et al. and OMERACT, were tested. The sonographers involved in work-package 7 of the HarmonicSS project from nine countries in Europe were invited to participate. Different levels of sonographers were identified on the basis of their SGUS experience and of the knowledge of the tested scores. A dedicated atlas was used as support for SGUS scoring. Results: Twenty sonographers participated in the two rounds of the reliability exercise.
Accordingly, the leading pSS experts (35 partners from 13 countries) have recently started the HarmonicSS (http:// harmonicss. eu) initiative to envelop independently reported cohorts and metacentric data, including SGUS in a dedicated Workpackage (WP), namely WP7. This study was created within the HarmonicSS initiative, and it is preliminary to further studies on the application of artificial intelligence in SGUS. At the current stage of the initiative, this study aims to: (i) evaluate the reliability among sonographers with different levels of expertise in SGUS; (ii) compare the reliability performance between two different semi-quantitative SGUS scores, widely used and easy to perform, being the "extremes" in the year of publication (i. e. , the scores by De Vita et al. , 1992, and by OMERACT, 2019); (iii) provide a data set that will be publicly available, to serve as a standardized benchmark for further studies.
Salivary Gland Ultrasound Scores A simple, semi-quantitative 0-3 SGUS scoring system was recently selected by a systematic review and meta-analysis as more appropriate for diagnostic purposes in terms of specificity and heterogeneity in pSS, with respect to the other scoring systems available (e. g. , 0-16 and 0-48)  (19). One of the aims of the work-package 7 (WP7) of the HarmonicSS project is to develop and improve the role of SGUS for pSS management. Participants and coordinators of WP7 agreed to use two four-grade semi-quantitative scoring systems, namely De Vita et al. score  (7)  and OMERACT score  (16) , for the assessment of major SGs morphology in pSS patients enrolled in the HarmonicSS project. The score by De Vita et al. is the long-standing available in the literature in pSS, is easy-to-perform, and includes both hypo/anechoic areas and hyperechoic bands as the main sonographic features to define parenchymal SG inhomogeneity, while in the OMERACT score parenchymal inhomogeneity is supported only by the presence of hypo/anechoic areas.
Reliability Exercise and HarmonicSS Data Set A pool of 225 sonographic static images (83 normal-appearing images, 42 images with mild inhomogeneity, 47 images with moderate inhomogeneity, and 53 images with severe inhomogeneity) of major SGs [114 parotid glands (PGs) and 111 sub-mandibular glands (SMGs)], from 150 patients with suspected pSS or definite pSS, was independently scored in two rounds. The sonographic images were previously collected and de-identified from the database of four rheumatologists involved in the exercise (AZ, AH, VM, ODL) and were different from those presented in the atlas. Four different ultrasound machines were used to store images, i. e. , Samsung RS85, Philips Epiq, GE Logiq E9, and ESAOTE MyLab70. For both scoring rounds, each observer was provided with an anonymized and uniquely randomized data set in order to ensure that scorings performed in this study could be not influenced with others' scorings.
The reliability exercise was carried out remotely by using the HarmonicSS web-based platform (https://private. harmonicss. eu). For each round, the participants had to apply the De Vita et al. score and the OMERACT score for each of the 225 images. The described data set, together with accompanying script files and instructions for their usage, are publicly available on the GitHub repository (https://github. com/ ArsoVukicevic/Assessment-of-pSS-fromSGUSimages/tree/ master/3%20HarmonicSS%20benchmark%20dataset) that will be further managed by the HarmonicSS group and authors of this study.
We then computed Light's kappa (mean of intra-rater kappa values), median, 1st and 3rd quartile, minimum (min), and maximum (max) kappa values. The bootstrap percentile method was used to compute the 95% CI of every Light's, Fleiss and Cohen kappa. Furthermore, we converted every 0-3 score database in 0-1 score database considering scores 0 and 1 as normal-appearing scores and converted to 0; whereas scores 2 and 3 were held as pathological scores and were converted to score 1. Every analysis was repeated in the 0-1 score database. We stratified squared weighted kappa coefficients by levels of SGUS experience and the knowledge of the tested scoring systems. We obtained kappa for four levels combining experience and use information: U-E, U-NE, NU-E, and NU-NE. The results were then compared to evaluate the overlap between 95% confidence intervals.
Results The urbanization gradient is characterized by differences in diet, habitat and socio-cultural conditions, but not in medical practices or gut protozoa prevalence. In order to get a comprehensive overview of the biological and cultural factors associated with the sampled urbanization gradient (represented by a rural, a semi-urban and an urban population), we performed a multivariate ordination analysis for non-dietary information, dietary 24h-recall (quantifying the nutrients and energy intake in the last 24 hours) and dietary FFQ data (Food Frequency Questionnaire, assessing the average consumption of selected food categories over time) separately. Non-dietary data included anthropometric traits, information on stool consistency and a number of parasitological, medical, sanitary and ethnographic variables (Supplementary Table  1 ).
Saliva microbiome diversity is shaped by habitat-related factors. For the saliva microbiome, only the models predicting 2 D ph (complete dataset) and 1 D ph (rural-urban subsets) had R-squared values >= 0. 1 and were thus considered for interpretation (Fig. 3 , Supplementary Table  3 , Fig. 4 ). The strongest predictors of 2 D ph in all three data subsets were urbanization level and access to electricity. In the rural-urban datasets, 1 D ph and 2 D ph were additionally predicted by water source and the type of floor in the house (Supplementary Table  3 , Supplementary Fig. 1 ). As both of these variables and access to electricity were strongly correlated with urbanization in pairwise comparisons (Supplementary Fig. 1 , Supplementary Table  4 ), the observed decrease in saliva microbiome phylogenetic diversity ( 1 D ph and 2 D ph ) with urbanization (Fig.
5C ). The results based on rural-urban dataset were different: if FFQ variables were included, ordistep kept the average consumption of soya and cassava sticks, type of floor, presence of animals in the house and type of toilets; without the FFQ variables, only the sanitary conditions were retained (Supplementary Fig. 6 ). Saliva microbial communities in the studied populations could be grouped into two types (stomatotypes): S1, dominated by Prevotella and Streptococcus and S2, enriched in Enterobacteriaceae. The urban population, with 84% of individuals hosting the S1 stomatotype, significantly differed from the semi-urban and rural population, where both stomatotypes were equally represented (Fisher's exact test, rural -urban: p = 0. 005, semi-urbanurban: p = 0. 054, Supplementary Fig. 7 ).
14, p = 0. 052). Subsequently, we checked if the similarity between the gut and saliva microbiome varied across the urbanization levels. We found that the gut and saliva microbiomes of rural The order ( 0 , 1 , 2 and Inf ) represents the weight given to taxa abundances. Detailed description of diversity indices can be found in Table  1  and Methods. Corresponding plots showing the alpha diversity predictors for the ruralurban data subsets can be found in Supplementary Fig. 4. Complete results are in Supplementary Table  3. individuals shared slightly less ASVs than the microbiomes of semi-urban and urban individuals (Fig. 6 , F2, 92 = 19. 95, p < 10 -8 , 0. 98% decrease). Conversely, we found no significant influence of the urbanization level on Bray-Curtis dissimilarities between the saliva and gut microbiome, despite a decreasing trend (F 2,92 = 2.
It is noteworthy that we observed different proportions of shared species depending on the enterotype, suggesting that enterotypes might differ in their resistance to colonization by external bacteria. In our analysis, we were faced with challenges due to complexity of the dataset, comprising many correlated and interacting variables. In our decisions regarding e. g. appropriate thresholds for the variable selection in the random forest regression or definition of strong correlations, we chose to be strict in order to minimize false positives and avoid over-interpretation. Potential issues with correlations and interactions are well illustrated by the factors associated with the saliva microbiome composition: whereas it is conceivable that habitat and sanitary conditions  46  , diet  33  or age  47  shape the saliva microbiome, the fact that purging, gut eukaryotes and number of children are among the top predictors is puzzling.
24. In conclusion, urbanization was associated with minor shifts in diversity of the gut and saliva microbiome, but also with changes in the gut microbiome composition that were reminiscent of those associated with industrialization. Although some of the important factors shaping the gut microbiome, such as stool consistency and diet, are the same as in industrialized populations, gut eukaryotes (Entamoeba sp. ) represent the major determinant of diversity and composition here. This may be one of the main confounders when comparing industrialized (virtually gut-eukaryote free) and non-industrialized populations. In order to advance our understanding of how industrialization affects the human microbiome, we need to account for these confounders, for example by routinely collecting more cultural data as well as by including parasitological information.
For saliva, the participants spit 2 mL of saliva into a 15 mL falcon tube, to which we added the same volume of a homemade buffer (5 mM TRIS, 5 mM EDTA, 5 mM sucrose, 10 mM NaCl, 1% SDS, pH = 8). In addition, we recorded contextual information (metadata) about the participants (Supplementary Table  1 ) in collaboration with local investigators and trained research assistants. Specifically, we collected dietary information using a quantitative 24-hour recall questionnaire (recording the amount of all the items the individual has eaten during the last 24 hours, which is then transformed into the amounts of nutrients and energy ingested) and a qualitative food frequency questionnaire (FFQ, reporting how often the selected types of food were consumed during the last month  50  ). The FFQ data were collected only for the rural and urban population.
01 steps and applied 80% confidence cutoff for the consensus OTU taxonomy. OTU representative sequences were defined as the sequences with the smallest average distance to all the other sequences within a given OTU. In order to explore the effect of OTU clustering method, we used also another, phylogeny-based approach, bdtt  66  , which creates the OTUs by slicing of a phylogenetic tree (whose construction is described below) at a specified evolutionary distance. We sliced the tree in the range 0. 01-0. 15 branch length at 0. 01 steps. We modified the original bdtt script in order to add the new, sliced tree and the OTU table to the output. The consensus taxonomy was found as the lowest common ancestor of all sequences within an OTU. The number of reads and ASVs per sample before and after quality control are reported in Supplementary Table  9.
The advantage of these indices is that they are intuitive and easily interpretable: they have common units and possess doubling property (i. e. diversity of a union of two communities with completely distinct species is the sum of diversities of the two component communities). Jost  70  refers to these diversity indices as "diversity of order 0, 1, 2". We use here their mathematical denotations -0 D, 1 D, 2 D and Inf D -for brevity. Chao et al. 71  developed a corresponding Hill-number framework for phylogenetic diversity. The main idea is the same, but here the diversity is defined as effective number of lineages, corresponding to the phylogenetic diversity of a perfectly even community composed of perfectly distinct species. We used the hillR R package  72  to calculate average phylogenetic diversity indices of order 0, 1 and 2 of 20 dataset replicates subsampled to 6500 reads/sample.
We refer to these indices as 0 D ph , 1 D ph , 2 D ph. Filtering for beta diversity analysis and community type determination. In order to get rid of the noise and to reduce the complexity of the dataset, we performed a two-step unsupervised filtering implemented in the PERFect package  73. Briefly, the method compares the total covariance of the community data ("OTU table") before and after removal of a taxon. This "difference in filtering loss" is then compared with a null distribution and the taxon is retained or discarded for a given significance level alpha (here alpha = 0. 1). Based on the empirical evidence and authors' suggestions, we assumed that at least 50% of taxa were not informative and therefore fitted a skew-normal distribution to the observed filtering loss values. In the first step, called simultaneous filtering, the taxa are ordered according to the number of occurrences and filtered sequentially as long as the filtering loss values come from the null distribution; in the second step, called permutation filtering, the taxa are ordered according to the p-values from the first step and filtered based on an additional permutation step (1000 replicates) that ensures that only the taxa that are uninformative for any combination of retained taxa are removed.