dataesr/scientific-paragraphs-categorization huggingface dataset:
S2C † ). The DNA sequences preceding the KLIKK-peptidases encoded proteins of 118-152 residues (Fig. 1 ), including signal peptides of 20 residues that were predicted with high condence (Fig. S1B † ). To assess the distribution of these ORFs and the cognate KLIKK-peptidases, we analysed the genomes of 10 contemporary and four ancient Tannerella strains. 31 The genes were absent from all non-pathogenic strains, suggesting they represent a disparate group of T. forsythia virulence factors. We therefore cloned all the novel ORFs preceding the KLIKKpeptidases and expressed the recombinant proteins for further analysis (Fig. S3  and Table  S1  †). We named them potempin (Pot) A, B1, B2, C, D, and E (see Fig. S4B  † for the UniProt database codes (UP)). Of note, we identied several other "orphan" ORFs in T.
0. The PotC:mirolase complex (∼10 mg mL -1 in 2 mM calcium chloride, 50 mM sodium chloride, 5 mM Tris$HCl, pH 8. 0) was crystallized using 19% PEG monomethyl ether 2000 in 100 mM mixed succinic acid, sodium dihydrogen phosphate and glycine at a molar ratio of 2 : 7 : 7 (pH 8. 0). Isolated selenomethionine-derivatised PotD mutant I 53 M (∼14 mg L -1 in 5 mM Tris$HCl, 50 mM sodium chloride, 0. 02% sodium azide, pH 8. 0) was crystallized using 20% PEG 3350, 0. 2 M diammonium hydrogen citrate. Finally, native and selenomethionine-derivatised PotE (∼9 mg mL -1 and ∼10 mg -1 , respectively, in 50 mM sodium chloride, 5 mM Tris$HCl, pH 8. 0) were crystallized using 20% PEG 8000, 100 mM HEPES, pH 7. 5 and 20% PEG 1000, 100 mM Tris$HCl, pH 8. 5, respectively. Diffraction datasets were collected at 100 K on beam line ID23-1 of the ESRF synchrotron (Grenoble, France) or beam line XALOC of the ALBA synchrotron (Cerdanyola, Catalonia, Spain) on PILATUS 6M detectors (Dectris).
Structure solution, model building and renement The structure of the PotA:karilysin complex was solved rst by likelihood-scored molecular replacement with Phaser. 82 The coordinates of the protein part of the karilysin catalytic domain (Protein Data Bank access code (PDB) 4IN9  38, 83  ) were used as a searching model to nd the only molecule present in the asymmetric unit (a. u. ) of the crystal. The phases derived from this solution were used for density modication and automatic model building with the Arp/wArp suite. 84 The structure of the PotA:MMP-12 complex was solved by molecular replacement with the coordinates of PotA from the PotA:karilysin complex and the protein part of the human MMP-12 catalytic domain (PDB 1JK3 62 ) to nd the two complexes contained in the a. u. Finally, the structure of unbound PotA, with one protomer per a.
The partial model solution and the structure factors (with separate Friedel mates) were then fed into Autosol, which found 10 heavy-atom positions and derived phases with an estimated mean gure-ofmerit of 0. 88. The Fourier maps aer the structure-solution procedures were used for manual model building and completion with the Coot program,  93  alternating with crystallographic renement using Rene in Phenix 94 and Buster/Tnt,  95  until the nal models were obtained. Both procedures included non-crystallographic symmetry restraints where required and translation/libration/ screw-motion renement. The nal structures were validated with the wwPDB Validation Service. Table  S3  † provides statistics for the renement and validation parameters, as well as the respective PDB access codes.
Miscellaneous Structural similarity was assessed using Dali 99 and PDBeFold at pdbe. org/fold based on program Ssm. 100  Structures were superimposed using Ssm in Coot, with Fatcat 101 or with Lsqkab. 102 igures and coulombic surfaces were prepared using Chimera,  103  and protein interfaces were calculated using Pisa 104 at https://www. ebi. ac. uk/pdbe/pisa/. The interacting surface of a complex was dened as half the sum of the buried surface areas of either molecule. Phylogeny was assessed using Multalin 88 based on a sequence alignment using standard parameters. This alignment was also used to compute pairwise sequence identities, with and without signal peptides, with the Sequence Manipulation Suite. 105 Lipoprotein signal peptides were predicted using LipoP v1. 0. 106.
Acknowledgements The authors thank  Laura Company ,  Xandra Kreplin  and  Joan Pous  from the joint IBMB/IRB Automated Crystallography Platform and the  Protein Purication Service  for assistance during purication, crystallization and SEC-MALLS experiments. The authors also thank the  ESRF  and  ALBA  synchrotrons for beamtime and the beamline staff for assistance during diffraction data collection and  Dr Ashu Sharma  ( University of Buffalo, Buffalo, NY, USA ) for providing rabbit antiserum against the BspA protein of T. forsythia. This study was supported in part by grants from  Polish (National Science Centre and Ministry of Science and Higher Education), US (NIH/NIDR) ,  Spanish ,  Danish and Catalan public and private bodies  (grant/ fellowship references  PID2019-107725RG-I00  and  PDC2022-133344-I00  by  MICIN/AEI/10.
Study Design and Sample Size Calculation The study was a prospective longitudinal single-arm clinical investigation of the predictability of partial pulpotomy with MTA in permanent molars exhibiting symptoms and signs of irreversible pulpitis. The sample size was determined using a sample-size calculation program, based on the teeth numbers included in previous studies which investigated both the outcome of full and partial pulpotomy in teeth which have reversible and irreversible pulpitis  [15, 26, 27]. Sample size was calculated using G*Power 3. 1. 9. 2 software (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany) based on a previous study  [15]  in order to have 95% power, an alpha error probability of 0. 05 and degree of freedom at 1 which has been concluded from a total sample size of 51 teeth.
Statistical Analyses The SPSS 24. 0 software (SPSS Science, Chicago, IL, USA) was used to perform the statistical analyses. The level of significant difference was at α = 0. 05. Fisher's exact test and the Chi-square test were used to assess the influence of the depth and activity of a carious lesion and the bleeding time on the outcome of treatment. The preoperative caries depth assessment inter-observer agreement and the postoperative intra-observer reproducibility and inter-observer agreement in terms of any pathological changes in the periapical or furcal area were assessed using Cohen's Kappa coefficient.
While holding the abutment in place with this controlled torque value of 5 Ncm, the standard abutment's screw was tightened to the torque indicated by each implant manufacturer with the specific implant system torque wrench (Table  1 ). All torque applications were performed with torque wrenches calibrated on a digital torque tester (Series TT01; Mark-10 Corp). Rotational freedom angle values were measured by using a contactless measurement microscope (Inexiv VMA-2520; Nikon Metrology Inc) with a computer-controlled, imageanalysis software program (Inexiv Auto Measure; Nikon Metrology Inc). This equipment allowed precise, three-dimensional (3D) measurements of 1 µm for distances and 0. 001 degrees for angles. All measurements were made by the same operator (Y. B. ) in a closed microscope room maintained at a constant temperature and humidity.
The rotational freedom (arc of motion) value (∠ E1AE2) was calculated by subtracting the 2 measurement values: RAE1 -RAE2 = E1AE2. Rotational freedom angle values (∠ E1AE2) were obtained for all 30 paired implant-abutment assemblies in each implant system (Fig. 2 ). A power analysis was carried out to determine the sample size after a pilot study, indicating that a specimen size of n=30 was adequate for a power of 80% at a significance threshold of α=. 05 (epiR package, v0. 9-96; The University of Melbourne). For each implant system, rotational freedom angles were summarized as descriptive statistics (means, standard deviations). All statistical analyses were performed with a statistical software program (IBM SPSS Statistics, v26; IBM Corp). The Kolmogorov-Smirnov normality test was applied to all implant systems, and the Kruskal-Wallis test was performed.
RESULTS Mean rotational freedom angle values ranged from 0. 05 degrees (Tapered Screw-Vent) to 2. 49 degrees (Naturall+). The mean rotational angle values were 0. 17 degrees for Bone Level, 0. 50 degrees for ID CAM M, and 1. 98 degrees for the Seven implant system (Fig. 3 ). The Kolmogorov-Smirnov normality test indicated that the data distribution was not normal (P<. 001) and that there were significant differences in mean rank rotational freedom angle values by implant systems (P<. 001), which made it possible to classify them (Table  2 ). Hence, the least rotational freedom angle values were obtained by using the Tapered Screw-Vent implant system followed in increasing order by Bone Level, ID CAM M, Seven, and Naturall+. The Wilcoxon signed-rank test showed that rotational freedom angle values for all implant systems were significantly different from each other (P<.
Aim: To evaluate (1) whether periodontitis has an influence on the prevalence/ incidence of respiratory diseases (chronic obstructive pulmonary disease [COPD], asthma, community-acquired pneumonia [CAP], obstructive sleep apnoea [OSA] and COVID-19), and (  2 ) what is the impact of periodontal therapy on the onset or progression of respiratory diseases. Materials and Methods: An electronic search was performed on Pubmed, Cochrane Library and Scopus databases up to October 2021, to identify studies answering the PECOS and PICOS questions. Results: Seventy-five articles were selected. Meta-analyses identified statistically significant associations of periodontitis with COPD (n studies = 12, odds ratio [OR] = 1. 28, 95% confidence interval [CI] [1. 16; 1. 42], p <. 001), and OSA (n s = 6, OR = 1.
| Data analyses The inter-reviewer reliability (percentage of agreement and kappa correlation coefficient) was calculated using Stata14 software (StataCorp LP, Lakeway Drive, College Station, TX, USA). The statistical heterogeneity among studies was assessed using the Q test based on chi-square statistics  (Cochran, 1954)  as well as the I 2 index  (Higgins et al. , 2003) , in order to know the percentage of variation in the global estimate that was attributable to heterogeneity. To be included in the meta-analyses (MAs), studies had to report ORs, RRs or HRs for the assessed respiratory disease (i. e. , COPD, asthma, OSA, CAP or COVID-19) among people with or without periodontitis (different definitions of periodontitis and indicators of the periodontal status were considered). Mean values were directly pooled and weighted mean ORs, RRs or HRs were provided with their respective 95% CIs.
Study-specific estimates were pooled with both the fixedand random-effects models  (DerSimonian & Laird, 1986). If a significant and high heterogeneity was found, then the random-effects model results were presented. Subgroup analyses were performed for each respiratory disease and for adjusted/unadjusted estimates. Forest plots were created to illustrate the association observed for the different studies and the global estimation. STATA ® (StataCorp LP) and OpenMeta (Analyst) inter-cooled software packages were used to perform all analyses. Statistical significance was defined as a p value <. 05.
| COPD and periodontitis A total of 34 manuscripts (Table  1 ) reported on the association between COPD and periodontitis: 16 cross-sectional studies, 15 case-control studies and three cohort studies (two of them prospective with a follow-up of 5 years and one retrospective with a follow-up ≥18 years). Thirteen investigations analysed information/ databases from previous epidemiological studies. Regarding the quality assessment, among the case-control studies, 11 publications of 16 scored below 6/9; all 3 cohort studies included presented low risk of bias; 1 of 15 cross-sectional studies scored below 6/9; and the 2 case series included were considered to have a high risk of bias. Significant small-study effects were observed when adjusted models were considered (Egger's linear regression test: p =.
| Asthma and periodontitis Nineteen publications were included (Table  3 ): seven cross-sectional studies, nine case-control studies and three cohort studies. Three studies analysed information/databases from previous epidemiological studies. Regarding the quality assessment of the publications, among the case-control studies, two of eight publications scored below 6/9; the six cohort studies included presented low risk of bias; and two of six cross-sectional investigations scored below 6/9. Significant smallstudy effects were observed for cross-sectional and case-control studies together when adjusted models were considered (Egger's linear regression test: p =. 010). Asthma was diagnosed with several tools: questions/questionnaires, WHO definition, Global Initiative for Asthma (GINA) classification, spirometry and/or having a receipt of medical treatment.
Search strategy A literature search was performed in the electronic Pub-Med database (National Library of Medicine, NCBI) to identify relevant studies published up to June 2022. The following search terms were used: education, school, learning, teaching, learn, teach, educational, threedimensional, 3D, 3-dimensional, printing, printed, print, anatomy, anatomical, anatomically, and anatomic. OR anatomic[Title/ Abstract]). Additional articles were identified through a manual search in the PubMed database and by looking through the references of other scientific articles. No date restriction was applied but the "human" filter was used.
Study selection Four hundred eighteen studies were identified in the MEDLINE database; 139 articles were excluded by the "human" filter. After the title and abstract were analysed, 103 studies were selected for reading of the full text. Thirty-four articles were excluded because they were either pathological models (9 articles), medical/surgical training models (4 articles), animal models (4 articles), 3D radiology models (1 article) or were not original scientific papers (16 articles). A total of 68 articles were included in this review. Figure  1  summarizes the selection process with a flowchart.
Model design data Thirty-three articles (48%) studied the cephalic region, 19 (28%) the thoracic region, 17 (25%) the abdominopelvic region and 15 (22%) the limbs. Fifty-one articles (75%) mentioned 3D printing of bone as an anatomical model or within a multi-slice anatomical model. Regarding the original model or file used for designing the 3DPAM, 23 articles (34%) mentioned the use of patient data, 20 articles (29%) the use of cadaver data, 17 articles (25%) the use of a database, and 7 studies (10%) did not disclose the origin of the file used. In 47 studies (69%), the 3DPAMs were designed from CT scans, while 3 studies (4%) specified using micro-CT scans. In 7 articles (10%), the 3D objects were designed from optical scanners, in 4 articles (6%) from MRI and in 1 article (1%) from a camera and microscope.
In 14 articles (21%), the origin of the source files for the design of the 3D model was not mentioned. The average spatial  resolution was less than 0. 5 mm for creating the 3D files. The best resolution was 30 µm  [80]  and the highest was 1. 5 mm  [32]. Sixty different software applications (segmentation, modelling, design, or printing) were used. Mimics (Materialise, Leuven, Belgium) was the most used (14 studies, 21%), followed by MeshMixer (Autodesk, San Rafael, CA) (13 studies, 19%), Geomagic (3D System, Morrisville, NC) (10 studies, 15%), 3D Slicer (Slicer Developer Orientation, Boston, MA) (9 studies, 13%), Blender (Blender Foundation, Amsterdam, The Netherlands) (8 studies, 12%) and CURA (Geldermalsen, The Netherlands) (7 studies, 10%). Sixty-seven different printer models were mentioned with five printing processes.
This tool is suitable for evaluating satisfaction but nevertheless has important biases that one must be aware of  [86]. The educational relevance of 3DPAMs was evaluated mostly in medical students, including first-year students in 14 of the 33 studies identified. In their pilot study, Wilk and colleagues reported that medical students felt 3D printing should be incorporated into their learning of anatomy  [87]. Eighty-seven percent of students surveyed in the Cercenelli study felt that their second year was the best time to use 3DPAMs  [84]. Results from Tanner and colleagues also showed that students were better if they had never studied the area  [46]. These data suggest that the first years of medical school are the best time to incorporate 3DPAMs into the teaching of anatomy. Ye's meta-analysis corroborates this idea  [18].
0 (Slic3r), Blender Meshlab, iNtel- lect Cranial Navigation system, blender, 3D printer Modality of acquisition NS Contrast enhanced CT CT CT CT and MRI 3D scan and micro-CT Precise Initial model anatomical part Knee joint Previous study. stl file Hepatobil-Database iary system with stomach and duodenum Orbit Patient Airway 21-month- with bone old patient pieces and adult woman archived Female pelvis Patient with bone pieces Temporal bone Cadaver Anatomical human region Lower-limb Abdomen Head Thorax Pelvis Head Author (Year) Ruiz and Dhaher (2021) [33] Smillie et al. (2021) [34] Vatankhah et al. (2021) [35] Weatherall et al. (2021) [36] Abdulcadir et al. (2020) [37] Chae et al. (2020) [38] Chedid et al. (2020) [39].
12 Initial model Modality of acquisition Database NS Cadaveric bone CT Cadavers CT and micro- CT Patient NS NS CT Patient CT Cadaver 3D scan Cadaver CT Anatomical Precise human region anatomical part Thorax Ribs, lung, liver and abdomen Head Skull Head Bone pieces Head Cranial nerve, brainstem, skull Neck and thorax Larynx and tra- chea Head Skull, mandible and maxilla Head Skull Head, neck, Isthmus thorax, abdomen of the thyroid, and pelvis diaphragm and aorta Author (Year) Bücking et al. (2017) [68] Chen S. et al. (2017) [69] Favier et al. (2017) [70] Javan et al. (2017) [71] Kavanagh et al. (2017) Legocki et al. (2017) [72] Lozano et al. (2017) [73] Fasel et al. (2016) [74].
Acknowledgments: Unfortunately,  Christel Neut , our co-author did not witness the completion of this publication, into which she had put so much work and dedication. All of the authors wish to pay tribute in this final contribution to the tireless researcher, expert microbiologist and attentive supervisor she was. We would also like to thank  Jéremy Verin , ( Controlled Drug Delivery Systems and Biomaterials ),  Jacques José , ( Institut des Sciences Analytique de Lyon, UMR 5280 ),  Nina Attik , ( Laboratoire des Multimatériaux et Interfaces, Université Lyon  1) for their valuable contribution to this work.
Material and methods Four IOSs and a laboratory tabletop scanner were included in this study (Table  1 ): the Primescan and Omnicam IOSs (Dentsply Sirona), the Medit i700 IOS (Medit Corp), the TRIOS 4 IOS, and the E3 tabletop scanner (3Shape A/S). A nominal post-and-core root canal preparation for a mandibular premolar tooth was designed with CAD software (Fig. 1A ), (Catia V5, Dassault Systems). The design consisted of an oval sectioned volume with a completely flat upper face, this face features a nominal 10 mm deep conical hole, 3 mm wide at the entrance, with a conicity of 4 •. Using CAD software Meshmixer (Autodesk Inc. ), gingival and dental elements were added to the nominal element to mimic the preparation environment and help with stitching during digitizing. In order to evaluate the influence of the presence/absence of adjacent teeth during the scan, two different models presenting the same nominal element were designed.
The STL files were exported from each IOS software for metrological analysis. The GOM inspect software (Zeiss) was used for metrological analysis, the methodology used on GOM inspect software is presented in Fig. 3. The reference CAD STLs of the PAT and WAT models were imported into the software as a base for different alignments, this allowed positioning every scan with the axis of the conical pit parallel to the Z-axis as they are designed in the CAD models. The reference STL file from the micro-CT was aligned to the CAD model using a best-fit algorithm. A nominal cone was constructed as the smallest circumscribed cone of this reference STL (the smallest possible cone containing all the points of the STL). The scanners' STLs were then imported, the root canal preparation of the digitized nominal element was isolated and aligned on the CAD model with the same best-fit algorithm.
L. Dupagne et al. important for the scanners using triangulation technology, and although angulations that were impossible to achieve in the mouth were avoided, it is obvious that the digitization conditions were easier than in a clinical setting. Despite all these concerns, it seems that for newer IOSs, the measurement error results appear to be largely satisfactory for the fabrication of post-and-copings or post-and-cores Fig. 3. Graphical summary of the methodology used in the metrological software (Gom inspect). The model was cropped using planes perpendicular to the Z axis at 0 mm and 9 mm depths.
Acknowledgments The authors thank the entire staff of the  URB2i research lab  for their help in the construction of the methodology of this study. The authors thank the following people for making their dental scanner available for inclusion in this study: 3DDentalStore and  Dr Benjamin Attuil  for the Medit i700,  Dr Marwan Daas  for the TRIOS 4, and the dental surgery department of hospital  Louis Mourier  for the Omnicam and Primescan.
Data collection The questionnaire (Supplementary file) was developed as part of a collaborative multidisciplinary project and was based on the one conducted in Australia  10. It was distributed online using Google Forms. The first part of our questionnaire was relevant to all healthcare professionals (including midwives, nurses, speech therapists, physiotherapists, pharmacists, pediatricians, and medical general practitioners), whereas the second part was specifically given to midwives. The questionnaire integrated both quantitative and qualitative elements. It was divided into four sections. There were three question types: five open-ended questions, eight yes or no questions, and five multiple choice questions. The first section collected information about the survey respondent's background and training.
Therefore, some singular anatomical structures are seldom to be covered due to the inherently limited datasets leading to a poor generalization potential of the model. For example, mandibular retrognathia and maxillary prognathia as exhibited in Fig. 1(b ) are categorized into skeletal class II malocclusions. While in Fig. 1(c ), the subject shows an apparent maxillary retrognathia that refers to skeletal class III malocclusion. Irregularities from metal alloy artifacts or underlying diseases are marked with red rectangle boxes. It is thus extremely hard to detect the landmarks of incisors accurately and robustly from maxilla The problems related to the imaging devices such as beam hardening, inhomogeneity, truncation effects, noise, and low tissue contrast increase the challenge of detecting anatomical landmarks in a fully automatic way.
Therefore, the prior knowledge is embedded into the geometric constraint that allows the model to effectively learn the implicit spatial relationships between the landmarks from a limited number of CBCT scans. 2) AWing loss is used to penalize the difference between the ground truth and the prediction, which enables the detection framework to pay much attention on pixel values near the mode of the regressed volumetric heatmap and results in lower localization error. 3) Experimental results conducted on two test datasets have shown that our model can locate skull landmarks with high accuracy and significant robustness. It reveals that the approach could assist clinicians in analyzing 3D cephalometric CBCT data and this way in planning surgical correction, especially for patients who suffer from severe CMF malformations.