dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion(s): These data highlighted delayed cartilage resorption and impairment of chondrocyte differentiation and osteoblast differentiation in Fgfr3N534K/+ mice during fracture healing of the mandible. All these data will improve our understanding of the hypochondroplastic bone features during bone repair. Purpose: To clarify their roles in this process, 2 months old male wild type, OPN knockout (OPN -/-), BSP -/-and OPN/BSP double knockout (DKO) mice (housed in CEEAL-UJM agreement Nr98) were distributed in control (CT) and vibrated (VB) groups (n=10/group). Methods: Mice stayed on a vibrating plate (2g/45Hz), 15 min/day for 6 weeks (plate kept still for CT). Bones were collected for microtomography, histomorphometry, RT-qPCR. Results: Vibration reduced trabecular volume in OPN -/-mice (vertebra BV/TV: -18%, pb0.
An a priori sample size calculation was not performed, but the sample size of 12 was chosen as appropriate to assess certain biological reactions in a preclinical study. A post hoc sample size analysis demonstrated that the sample size of 12 animals resulted in a study power of 63%. This assumes that the mean BIC coronal for the submerged would be 62. 80 and 69. 60% for the transgingival implant group with a standard deviation of 18. 90 and that a difference of 9% or less is unimportant. Alpha (1 tailed) was set to 0. 05. All statistical analyses were performed using SAS software (version 9. 4. 2016, SAS Institute Inc. , Cary, NC, USA). Linear mixed models were fit using the algorithm PROC MIXED which uses the method of restricted maximum likelihood (also known as residual maximum likelihood).
2. 6) Surface observations After the bending tests, the roughness of the fractured surface areas was measured with an optical confocal profilometer STIL CCS Prima, (Chromatic Confocal Sensor, Aix-en-Provence, France) with the MG140 optical detector. Samples were beforehand metallised. Then a few micrometres spot of white light sweeps across the surface and the intensity of the reflected light is analysed. As each wavelength has a different focal length, the most intense reflected wavelength gives information on the distance between the spot and the surface. The coupling with the global reflected intensity enables to draw the profile of the surface. The lateral resolution is 1 µm and the vertical resolution is 30 nm. The roughness analysis was made with the Gwyddion software  [15]. Samples were also observed with a Philips XL30 (Semtech Solutions, Billerica, Massachusets, USA) environmental Scanning Electron Microscope (SEM).
Concerning the objects manufactured with CAD-CAM, a systematic review from 2020 concludes that removable partial denture framework fabrication has better accuracy when compared to traditional impressions, with gap clinically acceptable  [23]. Another review highlights interest in microCT imaging in CAD-CAM dentistry, in particular as high resolution tool to evaluate the marginal gap or shape and volume to improve the quality of prostheses  [24] , which indicates that there is no clear scientific study on the resolution of IOSs and that the only data available is that given by the manufacturers. While many studies have been published to evaluate the accuracy of IOSs, it is a combination of trueness and precision as they are defined below: Accuracy: closeness of agreement between a quantity value obtained by measurement and the true value of the measurand; Trueness: the closeness of agreement between the average value obtained from a large series of test results and an accepted reference value; Precision: the closeness of agreement between independent test results obtained under stipulated conditions results and an accepted reference value  [25] ; Precision was studied by repeated measurements of the same sample and by evaluation of the distribution of such values.
Mesh extraction The IOS 3D mesh was directly imported from a computer connected to the scanner via the operating software CS Restore (Version number 6. 14. 7. 3, Carestream, Rochester). The 16bit tiff microtomography slice files (1,315 files) were processed with Fiji software (v1. 51, the National Institutes of Health) and the threshold was determined using the gray shade mean value of the internal material of the tip. The images were then converted into the 8-bit format. The resulting image files were visualized with a plug-in 3DViewer in Fiji software  [26]  that allows the computation of a 3D mesh corresponding to the surface of the scanned object in an STL binary file. To determine the threshold between the air and the object, a mean value of the gray shade of object was extracted from a 2D tiff image with the Fiji software.
Data analysis: mesh comparisons The alignment of the meshes (IOS mesh and microCT mesh) and the calculation of the distance were performed with CloudCompare software (version 2. 10-alpha, EDF R&D). First, the best fit record was automatically calculated with an algorithm by minimizing the distance between each mesh vertex. Then, each vertex of the 3D mesh was projected onto the nearest face (triangle) of the reference mesh. The distance to the vertex was measured and a color look-up-table was used to visualize the distances. Such a method allowed us to visualize the distances between the IOS and the microCT meshes.
Method for resolution approximation Resolution is the average the minimum distance between the two faces of the tip or the minimal curvature of the tip determined by the IOS device. To achieve this measurement, we have developed a set of two calculations. • Distance between two tip faces No simple method exists to automatically select the vertex immediately before the curvature on the edges of the tip. The distances were calculated with the Meshlab software tool by the selection of the vertex of interest. To minimize error by the manipulator, 30 measurements were collected for each 3D image and means and standard deviations were calculated. • Curvature calculation The curvature of the tip top was calculated with a specific function in the Meshlab software using the discrete mean curvature of a normal operator given by the following formula shown in  [27] : 𝐾 (𝑥 𝑖 ) = 1 2𝐴 Mixed ∑︁ 𝑗 ∈𝑁 1 (𝑖) cot 𝛼 𝑖 𝑗 + cot 𝛽 𝑖 𝑗 (𝑥 𝑖 -𝑥 𝑗 ), (1) where  Mixed is a normalization term that represents the area defined for each vertex of a nonobtuse triangle connected to the circumcenter point and for each vertex of an obtuse triangle connected to the midpoint of the edge opposite to the obtuse angle; a simple algorithm (pseudocode) allows full surface tiling;   is the position of the vertex where the curvature is evaluated,   is the difference in the first neighbors of vertex  1 () and    and    are the angles opposite to the segment formed by the vertices  and  (Fig.
Acknowledgements 3D data acquisitions were performed using the µCT (microCT) facilities of 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 "), and by the  Labex CEMEB  (grant  ANR-10-LABX-0004 ) and the  NUMEV  (grant  ANR-10-LABX-0020 ).
Corpus Materials and method of the systematic review A systematic review of the literature was performed according to the PRISMA methodology. An electronic search was conducted up until April 15, 2019 in the Scopus and PubMed databases. The keywords MeSH ("fibroma" and "odontogenic tumors") were used. The search equation was on Scopus: "KEY ("fibroma") OR KEY ("Odontogenic tumors") AND ALL ("central odontogenic fibroma") AND (LIMIT-TO (DOCTYPE, "ar") OR LIMIT-TO (DOCTYPE,"re"))", and on PubMed: "("Fibroma" [Mesh]) AND ("Odontogenic Tumors" [Mesh]) AND ((Review[ptyp] OR Case Reports[ptyp])". No restrictions on the date of publication have been imposed. This main search was supplemented by a manual search in the bibliographic references of the selected articles. After removing the duplicates, the identified articles were selected based on the following inclusion and exclusion criteria: Criteria for inclusion: -Cases involving humans; -Studies respecting the current WHO definition (2017)  [1] ; -Studies reporting COF case(s) mentioning information on at least age, gender, location and/or radiological features, histological diagnosis and/or surgical technique; -Articles in English or French.
Criteria for exclusion: -Studies with uncertain diagnosis; -Studies without mention of location (central or peripheral); -Purely immunohistochemical studies on benign odontogenic tumours; -Studies on histological variants of COF: giant cell COF, amyloid-like protein deposition COF, granular cell odontogenic tumours. The article selection process is described in the PRISMA flow chart (Fig. 1 ). 81 articles (7 retrospective studies and 74 case reports) were included bringing the total number of COF cases listed to 135. Data from these cases were extracted using a dedicated grid. This data concerned (1) age and sex, (2) clinical features, (3) radiological features, (4) histological features, (5) treatments performed, (6) follow-up duration,  (7)  possible recurrence and malignant transformation.
Where the histological or radiological characteristics of COF were not fully described, two authors independently evaluated the figures to remedy it. The included publications had a low level of scientific evidence (level 4) according to the evaluation criteria of the National Agency for Accreditation and Evaluation in Health (ANAES). Based on the data from the included articles, the COF recurrence rate was calculated as follows: Total number of recurrences Total number of lesions included in the study Â 100 Responses to the educational objectives Epidemiology COF is considered by the WHO to be a rare tumour but no epidemiological data are shown  [1]. It represented 1. 5% of central odontogenic tumours (16 cases out of 1088 biopsied tumours)  [3]. This was probably an overestimate since histological variants of COF (such as ossifying odontogenic fibroma) that are no longer recognized by the WHO have been accounted for.
The maximum penetration depth was calculated using the ImageJ Software (ImageJ, NIH). Data were analyzed by Mann-Whitney U and Kruskal-Wallis tests (p < 0. 05). A significant difference was shown between the four groups at 1 mm (p = 0. 0116), whereas similar results were observed at 5 mm (p = 0. 20). WVC allowed better diffusion for both sealers at 1 mm (p = 0. 01) and 5 mm (p = 0. 034). The maximum penetration of the Bio-C and HiFlow sealers was more important at 5 mm with the two obturation techniques. Within the limitations of this study, WVC enhanced the penetration of calcium silicate-based sealers into the dentinal tubules in comparison with the SC technique at both levels.
Confocal Laser Analysis Each section was examined under CLSM (10× magnification) (Zeiss LSM 710, Wetzlar, Germany). The emission wavelength was set at 561 nm. Digital images were analyzed with the software Image J (ImageJ software, NIH) to measure the maximum sealer penetration depths (µm) in the dentinal tubules at 4 circumferential points (12, 3, 6, and 9 o'clock). The tool "distance" was applied from the root canal surface to the deepest extent of the visible sealer. Measurements were performed by 1 observer and repeated 2 times to ensure reliability.
Statistical Analysis The normality of the distribution was analyzed using the Kolmogorov-Smirnov test. The significance level was set at p ≤ 0. 05 and the confidence interval at 95%. The Kruskal-Wallis test was used within groups to compare differences between middle and apical portions. The nonparametric Mann-Whitney U test was used for pairwise comparison between the type of sealers and the filling techniques (p < 0. 05). Data statistical analysis was conducted by using SPSS 16. 0 software (Chicago, IL, USA).
Strains and Cloning Procedure Lactococcus lactis NCDO 2118 wild-type (L. lactis WT) strain was grown at 30 °C in M17 medium (Difco) containing 0. 5% glucose (GM17), without agitation, or in the same medium solidified with 1. 5% agar for 18 h. The nucleotide sequence encoding the SlpB surface protein from Propionibacterium freudenreichii CIRM-BIA 129 (Pf 129) was obtained from the database of the National Center for Biotechnology Information (NCBI), deposited under accession number CDP48273. 1 (CDS 5503. 7173). The sequence was optimized for expression in Lactococcus lactis NCDO2118 bacteria in the OptimumGeneTM program (GenScript Corporation) and synthesized by GenScript Corporation (Piscataway, NJ, USA) and cloned into pUC57 vector. The optimized SlpB protein sequence was synthesized with the restriction sites NsiI-3′ and EcoRI-5 to cloning into the plasmid pXYSEC (chloramphenicol resistance).
Other cuts of the paraffinized colon samples were produced and stained by the periodic acid-Schiff (PAS)  (Prisciandaro et al. , 2011)  in order to count the mucusproducing goblet cells. Ten random field images from each sample were made using the ×40 objective, and then, using ImageJ software (version 1. 8. 0) the intact goblet cells were counted. The total number of goblet cells was expressed as the number of cells per high-power field (HPF) (×40, 108. 2 µm 2 ).
Statistical Analyses Data were analyzed using one-way ANOVA followed by Tukey's post-test and performed in GraphPad Prism version 9. 1 for Windows (GraphPad Software, San Diego, CA, USA). The experimental assays were performed in triplicate, and the results were expressed as mean ± standard deviation. Asterisks demonstrated in all figures represent the significant differences between the experimental groups and were indicated as follows: *p< 0. 05; **p < 0. 01; ***p < 0. 001; ****p < 0. 0001.
Conception of the three experimental setups The design was done with SolidWorks TM software (SolidWorks® 2016 for Windows, Dassault Systems SolidWorks Corp. , Waltham, Massachusetts, USA), a 3D computer aided design (CAD) program and a rapid prototyping by 3D printing (accuracy 1/ 10 mm) was made. All experimental setups represent a high maxillary right canine. We chose the "VariSimplex'' shape, the most common dental arch form found in the general population  [24].
, 2017; Li et al. , 2018; Zook et al. , 2014). Hence, when planning the creation of a new large whole-genome sequencing dataset using a methodology that is in some sense alternative, it is vital to establish that it does not introduce unexpected and problematic patterns into the data that would render the new dataset incomparable to existing WGS datasets. Saliva has many advantages over blood in terms of the logistics of data collection and individual participation levels  (Hansen, Simonsen, Nielsen, & Hundrup, 2007; Sun & Reichenberger, 2014). The POPGEN project in France envisages the creation of a French genomic reference panel by gathering DNA samples using Oragene OG-600 saliva kits. These are to be sent out, completed by participants and returned through the post. A similar approach was used in a recent study in the USA which took advantage of both social media and the use of saliva kits to increase the engagement with vast numbers of individuals  (Brieger et al.
Such reported statistics, we felt, are worrisome considering they could predict very high error rates in saliva-based sequencing data and could predict unwanted artefacts that could significantly inhibit the utility of a large population-wide WGS dataset. Furthermore, many of the current publications that compare blood against saliva have employed very small sample sizes (<10) and few have examined WGS data. In this study, we compare WGS data derived from blood and saliva DNA from 39 individuals. Without gold-standard genomes to evaluate the quality of the sequencing in GAZEL-ADN, we have leveraged information on intrinsic difficulties in sequencing certain regions of the genome set out in a collection of studies from the Genome in a Bottle (GiaB) project  (Zook et al. , 2016 (Zook et al. , , 2014 (Zook et al.
Comparison of blood and saliva derived WGS data at the individual level Details of the recruitment of individuals, the DNA extraction, sequencing, alignment, and genotype calling for blood and saliva are given in the Methods. We examined similarities between single sample WGS variant calls from blood and saliva for each of the 39 individuals. In Supplementary Figure  1 , the number of called variants, the mean individual read depths, and the mean genotype quality scores are presented. We also calculated per-individual statistics from BAM files; Supplementary Figure  1  includes results for mean insert length of reads, estimated base error rates, and mean read quality. We saw similar numbers of variants being called (mean of 4,619,812 for saliva against a mean of 4,615,935 for blood) but higher statistics regarding the depth and quality in saliva.
Comparison of blood and saliva derived WGS data at the whole study level Joint sample calling was performed for blood and saliva for all 39 samples, giving a variant call file (VCF) with 78 columns of WGS data that contains 12,085,848 bi-allelic variants across the 22 autosomal chromosomes. The differences in observable quality in the single sample calls translated here to small differences in levels of genotype missingness (per individual) between saliva and blood (mean of 0. 006 for saliva against 0. 011 for blood). For each polymorphic genetic variant present in the joint-called VCF, we calculated the F-measure  (Dice, 1945; Sørensen, 1948)  (also known as the Sørensen-Dice coefficient, see Methods) between the 39 saliva-and blood-based genotypes. The Fmeasure ranges between 0 and 1, with a value of 1 indicating complete agreement between calls from blood and saliva and lower values indicating diminished agreement of variant calls across the 39 individuals.
Discordances between blood-and saliva-derived genotypes can be assumed to indicate genotyping errors in at least one of the two datasets as we should not anticipate any significant contribution from somatic mosaicism between saliva and blood at this depth of sequencing  (Hall et al. , 2020). In Figure  1 (a), the distribution of the F-measures is displayed for all 12,085,848 variants. 93. 61% of all variants show perfect agreement between blood and saliva. Subsequently in Figure  1 (a), we split these variants into two genomic region-sets: high-confidence regions indicated by the GiaB project and the complementing set of low-confidence regions. Subsequently, we focused only on the variants that pass our quality control (QC) thresholds (see Methods). The GiaB high-confidence region list was accessed from ftp://ftp-trace.
Salivary Microbiome We constructed a pipeline to investigate the possibility of sequencing salivary microbiomes from the raw sequencing data derived from saliva. This was necessitated as our raw data had been generated using technology designed for sequencing human DNA while studies of microbiome data typically involve different molecular sequencing approaches  (Fricker, Podlesny, & Fricke, 2019). Our goal was to identify reads that were not of human origin and to map such reads to known bacterial reference genomes. The pipeline we used is described fully in the Methods. Applying this to the 39 individuals of the pilot study, we successfully captured an average of 40 million reads that passed quality control measures and could be identified as non-human (Supplementary Figure  4 ). These groups of non-human reads were then aligned to known bacterial 16S rRNA gene reference libraries.
This enabled us to confidently establish that the differences between blood and saliva were entirely similar to the differences that one should expect if either an individual's blood or saliva had been sequenced twice. Thus, while high concordance between sequencing data in blood and saliva has previously been reported  (Kidd et al. , 2014; Trost et al. , 2019; Wall et al. , 2014; Yao et al. , 2020; Zhu et al. , 2015) , we have been able to better contextualise such a result. This has afforded the conclusions that the WGS data generated from the saliva collection kits is of high quality; and that it will be possible to analyse the prospective POPGEN dataset alongside existing datasets (for analyses such as common or rare variant association studies) without fear of harmful batch effects. Technical bias due to differences is technology being used within a study is a non-negligible problem.
The large scale of many recent genetic studies and the time required to assemble genomic datasets may result in different sequencing techniques being used at the start and at the end due to evolving guidelines and technology. The herein proposed novel method of pseudo-replication represents a valuable tool for assessing the potential impact of technological bias within a study as it can provide good approximations for benchmarks of whole-genome sequencing reproducibility. In our study, we have concentrated on appraising the suitability of saliva for WGS in humans for the calling of SNPs and INDELs. In both  Yao et al. (Yao et al. , 2020)  and  Trost et al. (2019)  the question of the accuracy of the calling of copy-number variations (CNVs) from saliva is also discussed where greater discordance (than for SNPs or INDELs) between blood and saliva has been presented.
Prospectively, the 39 paired datasets of GAZEL-ADN will provide valuable opportunities for evaluating the calling of CNVs from saliva and also for similar investigations for structural variants, transposable elements, and mitochondrial variation. Here, we have also demonstrated that the sets of non-human reads found in saliva carry sufficient information for taxonomical descriptions of the salivary microbiome even when samples were treated for human genome sequencing. We were able to show that the principal groups of bacterial taxa found in our study matched those found in a large public microbial reference dataset. We could also highlight differences between individuals in terms of presence or absence and relative abundance of certain taxa in their salivary microbiome profiles. These findings merit further investigation and, as a prospective of this study, we plan to continue to determine a best strategy for extracting salivary microbiome information from WGS data from saliva and to further develop a bioinformatics pipeline for the generation of microbiome data from read data initially generated for human whole-genome sequencing.
Data Collection Sixty individuals from the GAZEL cohort (2007) from whom WGS data was already available were selected at random and asked to donate saliva for sequencing. WGS data from saliva of 39 of these individuals were investigated in this study. We refer to this pilot study of 39 individuals as GAZEL-ADN. This study was approved by the Ile de France XI Ethics Comittee (approval number 18021) on 8th March 2018 and all the 39 individuals signed an informed consent. The remaining 21 individuals of the original 60 were not analysed for the following reasons: 13 individuals did not return their kits, 4 individuals returned their kits too late (outside of a pre-specified time limit of the pilot study) and 4 were removed after failing to meet quality control thresholds for their collected DNA samples.
These two sequencers have a common underlying technology, and have recently been shown to perform similarly  (Zhou et al. , 2019) ; the main difference being the increased speed and throughput of NovaSeq. Generation of sequencing data was performed using the same bioinformatics pipeline for blood and saliva. Alignment was performed using the software bwa (v. 0. 7. 15)  (Li & Durbin, 2009)  to the human reference genome hs37d5; a human reference genome from genomic build 37 that includes dummy contigs for mapping well-known non-human contaminates. This alignment was followed by genotype calling using the  GATK Haplotype Caller (v. 3. 8) (DePristo et al. , 2011).