dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sources A data search (1990-2021) was performed by the three authors using PubMed's electronic databases of all types of clinical and laboratory trials, systematic reviews, case reports as well as classic textbooks. The search terms used, in simple or multiple conjunctions, were "maxillary implant overdentures, " "implant overdenture design, " "overdenture attachment systems, " "implant overdentures complications ", "prosthodontic maintenance ". Review articles and references from different studies published in English language were included to identify relevant studies. An additional manual search was conducted through the bibliographies of all relevant studies and review articles.
Surface Modification by Femtosecond-Laser Machining The surface of the PCL scaffolds was processed with a Ti:sapphire fs-laser (Quantronix-Integra-C, Hamden, CT, USA) emitting at a central wavelength of 800 nm with a pulse duration (τ) of 130 fs. The repetition rate for (ν) was set at 1 kHz. The number of applied laser pulses (N) was adjusted by the velocity of a vertical translation stage controlled by computer software (LabView). All scaffolds were adjusted on the translation stage (moving in XY directions), which was positioned perpendicular to the direction of the laser beam. The parameters chosen for material processing for all in vitro experiments were as follows: N = 2 or 10; laser fluence (F) 0. 08 J/cm 2 and distance between two adjacent laser spots (d x ) 45 or 35 µm. This decision was based on the findings of a previous investigation into this subject  [24].
Two-and three-dimensional topographical images in true colors were obtained by vertical scanning of selected modified or control areas. All images were obtained at 20× magnification with the field of view being 636. 61 × 477. 25 µm 2 or at 10× (field of view 1. 27 × 0. 95 mm 2 ). Based on the acquired images in true colors, the roughness of the modified areas on the material was evaluated in accordance with ISO 4287. For this purpose, the arithmetical mean height (Ra) was taken into account. All data obtained by the 3D optical system was processed via ProfilmOnline software (www. profilmonline. com, accessed on 6 April 2022). All samples used for in vitro analyses with osteoblast-like and bacterial cells were fixed in 1. 5% glutaraldehyde solution (prepared in 25% cacodylate solution, TAAB laboratories equipment Ltd.
Contact angles and surface energy were calculated by ADVANCE software (KRÜSS GmbH, Hamburg, Germany) fitting the drop profiles to the Young-Laplace equation. Surface energy (SFE) was calculated by the software following the Owens-Wendt-Rabel-Kaelble (OWRK) equation (Equation (  1 ))  [25]. γ 12 = γ 1 + γ 2 -2 (γ d 1 γ d 2 ) -2 (γ p 1 γ p 2 ), (1).
Resazurin Assay Metabolic activity of MG63 cells on PCL samples was assessed through the Resazurin assay on day 1, day 3, and day 7 for both non-induced and induced cell cultures. Briefly, all samples were transferred to a fresh well plate before incubation for 3 h in 10% Resazurin solution (Resazurin sodium salt, Sigma-Aldrich R7017) prepared in a complete medium (alpha-MEM with 10% FBS, 100 IU/mL penicillin, 100 µg/mL streptomycin, and 2. 5 µg/mL amphotericin B) at 37 • C. Fluorescence (530 nm excitation/590 nm emission) was measured in a microplate reader (Synergy HT, Biotek, Winooski, VT, USA) with Gen5 1. 09 Data Analysis Software.
Funding: This work was supported by:  EUROPEAN UNION'S H2020 research and innovation program  under the  Marie Sklodowska-Curie Grant  Agreement AIMed No. 861138 ; BULGARIAN NATIONAL  SCIENCE FUND (NSF)  under grant number No. KP-06-H48/6  ( 2020-2023 ), and  H2020 FET Open METAFAST  Grant Agreement No. 899673. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
2 , Supplementary Table  S2 , Supplementary Fig. S4 ). Other factors influencing individual variability in immune response to pathogens are age, lifestyle (diet, smoking, exercise etc), previous immune history (viral, bacterial), and the specific HLA-alleles that affect the presentation of major antigenic epitopes  39. As some of these periodontal bacteria belong to orange or red complex groups of pathogenic oral species, the observed potential cross-reactivity of the antibody immune response (Fig. 2 , Supplementary Fig. S4 ) could contribute to the dysbiosis of oral microbiota and thereby periodontal health. We identified immunoprofiles stratifying the individuals with ACS from individuals with no CAD or stable CAD (Fig. 3 ). Our dataset has the advantage that we could identify and compare the presence of potential immunological markers in different CAD types, even in a limited study cohort.
Reproducibility of Mimotope Variation analysis was confirmed by establishing the correlation coefficient of two replicates as R = 0. 87 (p < 0. 0001). Statistical analysis of clinical characteristics of samples. Differences in proportions of genders, diabetes condition and smoking status were assessed in clinical sub-groups using Chi 2 test (MedCalc, 19. 7. 2, MedCalc Software Ltd, Ostend, Belgium; https://www. medcalc. org). For statistical analysis, two-sided Mann-Whitney U test was used for comparing two groups or two-sided Kruskal-Wallis test for >2 groups using R package "ggpubr"  53  and "ggplot2"  54. Mimotope variation analysis. Peptide antigens were selected from random peptide phage modified library (PhD12, NEB) with 10 9 different 12-mer peptide sequences  30, 31. Two μl of serum/plasma samples, previously precleared to plastic and E.
coli/wt M13 phage lysates were incubated with 2. 5 μl library (~5 × 10 11 phage particles) and immunoglobulin G (IgG) fraction was recovered using protein G-coated magnetic beads (S1430S, NEB). Captured phage DNA was analyzed by Illumina HiSeq sequencing of 50-bp single end reads using barcoded primers for sample multiplexing. Peptide abundance correlation coefficient (R) in two replicates by Pearson analysis was 0. 87 (p < 0. 0001) (Supplementary Fig. S1 ) (R package "ggpubr"  53  ). For further data analysis, sequencing errors and known artefacts were eliminated. Selecting peptides. Group-enriched peptides (TopPeptide sets) were selected for clinical sub-groups (Supplementary Table  S1 ). Peptides were selected for no-CAD (n = 292,667), stable-CAD (n = 279,020), and ACS (n = 308,445), and periodontally healthy (n = 450,531 peptides), gingivitis (n = 450,590), and periodontitis (n = 342,261) groups, using the criteria that these were to be identified in ≥10% individual samples of the group with abundance threshold ≥10 sequence counts in at least one sample.
Sequence-based unsupervised clustering of peptide antigens. Exhaustive sequence pattern search tool SPEXS2 was used (http://egonelbre. github. io/spexs2/) for sequence-based unsupervised clustering of peptide antigens. Starting from the 292,667 group-enriched peptides (TopPeptide set) identified for no-CAD group, all were used as input to SPEXS2 in random order with the search criteria: peptide coverage threshold: ≥4; motif coverage threshold: ≥4 fixed amino acid positions; hyper-geometric P value <10 -5 ). Sequence pattern searches were performed in 2 iterative runs, where peptides from which a consensus was identified in the first run were excluded from the subsequent run. As a result, 4366 distinct motif consensus sequences were identified, which were contained in 29. 0% or 84,873 of the original 292,667 peptides.
12. 2019). Only exact alignments where all fixed amino acid positions of a motif (minimum of 4 positions) to match with the target were allowed. The pathogen database comprised of 15,928 proteins, of which 15,116 matched at least with one epitope motif. For each pathogen, the individual alignment load was calculated per 20-amino acid fragment of the protein, in two frameshifts (0 and -10 aa), yielding in 10 aa overlaps between considered fragments. Altogether we analyzed ~480,000 distinct 20 amino acid fragments. The top 40 fragments for each bacterial species, from any frameshifts, with the highest total alignment loads were selected based on the formula: Alignment load ðper 20aa fragmentÞ ¼ sum of abundance of motifs aligned count of motifs aligned If higher alignment loads were encompassing two side-by-side regions (one with a frameshift), a longer 30-amino acid fragment was considered.
Individual alignment loads (Supplementary Data 1) were compared across pathogen species using Kruskal-Wallis test for comparing all groups and two-sided Mann-Whitney U test for pair-wise comparisons. R packages "ggpubr" and "ggplot2" were used for calculation and visualization. Identifying target types of potential epitopes of periodontal bacteria. Using the top 40 antigen fragments of periodontal pathogens, 12mer substrings were extracted for each aligned epitope motif (of the TopMotif set), whereas substrings shorter than 12mer were discarded (33 were found). Altogether 1691 substrings were selected, position weight matrices were built and the resulting enriched amino acid positions were visualized as sequence logos, all using a custom in-house tool with WebLogo  55, 56  integration (parameters: no counts, distance cut-off 9, 10 minimum unique substrings in a cluster, similarity index 15).
The analysis yielded 26 target types with consensus sequences, of which top 5 most prevalent are shown on Fig. 2b. Amino acid-based clustering of group-differential epitopes. Sequence homology clustering analysis reduced 705 out of 995 epitope motifs into 62 clusters of similar epitopes (≥3 identical amino acid positions) whereas 290 out of 995 epitope motifs either formed too small clusters (<3 motifs in a cluster) or were not similar enough with any other epitope (<3 identical amino acid positions). Additional R packages used in data analysis and visualization were: "readr", "dplyr"  57. Based on the selected peptides, position weight matrices were built and the resulting enriched amino acid positions were visualized as sequence logos, all using a custom in-house tool with WebLogo 55,56 integration.
Group-specific analysis of 62 clusters. The average abundance for each of the 62 clusters with the defined sequence logo was calculated across clinical sub-groups. Abundance values in clinical sub-groups were normalized with average abundance across all groups (Supplementary Data 2). Clusters with average abundance values of <150 were left out of further analysis. Next, hierarchical clustering was performed based on the normalized abundance values using Pearson correlation coefficients for clustering distance and ward. D2 clustering method (R package "pheatmap")  58. R packages used in data analysis and visualization included "readr", "dplyr", "forcats", "reshape2"  54, 57  , and "viridis"  59. Individual-based clustering of 62 clusters. Abundance of peptides in individual immunoprofiles was calculated for each cluster.
Using log10 values of the abundances, Pearson correlation coefficients R were calculated pair-wise for all 62 clusters (Supplementary Data 3). The clusters were subsequently grouped using R package "pheatmap" with ward. D2 as clustering method and Pearson correlation coefficients as clustering distance. Five distinct larger groups were identified and defined as epitopes A-E. For each of the epitopes A-E, the containing clusters were compared to determine consensus sequences describing the core epitopes (Supplementary Data 7). Alignment on EBV VP26. Epitope A has been previously mapped to Epstein-Barr virus (EBV) protein VP26  30. To validate the mapping in the current clinical cohort, epitopes from individual immunoprofiles (n = 96) were aligned to the primary sequence of EBV protein VP26, a component of the viral capsid antigen (VCA) (UniProt accession code Q3KSU9, EBV strain GD-1, date accessed: 25.
11. 2020). Signal-to-random ratio for each sample was calculated across primary sequence of EBV VP26, where signal represented the count of aligned (≥6 matching amino acid positions) unique peptides defined by MVA and random represented the count of aligned random peptides (≥6 matching), generated by sequence scrambling of peptides (Supplementary Data 4). As controls (Ctrl), peptides from EBV CA seronegative subjects (n = 9) were analyzed similarly. ELISA. CMV and EBV serostatus was measured from serum samples with ISO/IEC 17025 accredited methods. In brief, serological analyses were performed with anti-CMV IgG ELISA method (EUROIMMUN EI 2570-9601G) and with anti-EBV CA (capsid antigen) IgG ELISA method (EUROIMMUN EI 2791-9601G) according to manufacturer's specifications. Absorbance was measured at 450 nm with Spec-traMax Paradigm (Molecular Devices).
Acknowledgements We thank  A. -H. Pool  ( Department of Neuroscience, University of Texas Southwestern Medical Center ) for his expertise that supported this work. The members of Protobios team are thanked for their excellent technical assistance. This study was supported by research funding grants of  Protobios  ( 5. 1-4/20/170 , and  PRG573 ) from the  Estonian Ministry of Education and Estonian Research Council , respectively, and  H2020-MSCA-RISE-2016  ( EU734791 ) and  H2020   PANBioRA  ( EU760921 ) projects from the  European Union. K. P. was partially supported by the  European Union  through the  European Regional Development Fund  (Project No. 2014-2020. 4. 01. 15-0012 ). N. E. V. was supported by  H2020 PANBioRA (EU760921 ) project from the  European Union. P. P. was supported by the  Sigrid Jusélius Foundation.
Tertiary bone grafting was performed in fully grown patients starting at the age of 17 if needed according to the previous technique. The restoration of missing teeth in cleft patients with orthodontic gap closure, conventional prosthetics (fixed, removable, or overlay), or dental implants was decided and performed in this stage of fully growth patient. A single specialized examiner collected all information from patient medical records, pictures, and radiographs. Regardless of the type of rehabilitation, except for one patient who was treated with Disk-implant ® (Victory, France) and was excluded, all patients with clefts and dental rehabilitation of the edentulous space treated from 2016 to 2019 were included in this study. Preoperative data were registered for all patients who underwent CLP management.
After three years of follow-up, three data records were complete: implant survival, marginal bone loss (MBL), and patient satisfaction (Fig. 3 ). Implant survival was recorded and used to calculate the implant survival rate. Marginal bone loss was evaluated using calibrated CBCT and was defined by the distance, in millimeters, from the implant shoulder to the alveolar crest  [26]. The criteria proposed by Albrektsson et al. [27]  served as a baseline for evaluating implant success, stipulating that vertical bone loss for osseointegrated implants should be 1. 5 mm for the first year, and < 0. 2 mm annually thereafter. Consequently, in this study, we considered an MBL of 1. 9 mm as the threshold value (1. 5 mm for the first year and 0. 4 mm for the second and third years). The patients with implants (group 1) in this study were divided into two groups: "Relative Success" (RS) group (no implants were lost and the MBL was ≤ 1.
Purpose To estimate the prevalence of the sphenoidal emissary foramina (SEF), and the effect of possible moderators on it. Methods A systematic online literature search was conducted. The pooled prevalence with 95% confidence intervals was estimated. Outlier and influential analyses were performed. The presence of small-study effect and publication bias were evaluated. Moderator analyses were executed to investigate the effect of the specimens' continent of origin, type of study (dried skull or imaging), probing for the evaluation of SEF patency (conduction and instruments used), side dominance (bilateral or unilateral), morphometric data [SEF diameter, distances SEF-Foramen ovale (FO) and SEF-Foramen spinosum (FS)], and the methodology used for the morphometric measurements (caliper, DICOM Viewer, and image analysis software) on the estimated prevalence.
Search strategy A systematic literature search was conducted by two independent assessors in August 2022 using Publish or Perish software  [15]. Through this application, all available databases except for the Web of Science (Crossref, GoogleScholar, OpenAlex, PubMed, Scopus, and Semantic Scholar) were scanned using combinations of the following keywords: ["foramen Vesalius", "sphenoidal emissary foramen", "presence", "occurrence", "prevalence", "incidence")]. Notably, in Semantic Scholar and OpenAlex, only single keywords were used since both databases' application programming interfaces did not support the use of Boolean operators. After duplicates' removal, each publication's reference list was manually scanned for potentially non-identified studies. The systematic literature search flowchart (Fig.
Criteria for study selection and data inclusion and extraction All original studies reporting data regarding SEF prevalence were included with no restriction on language or publication date. Case reports, review articles, letters to the editor, conference abstracts, doctoral thesis, studies with no fulltext or detailed abstracts available, and articles that could not be cross-verified by multiple secondary sources were excluded. Out of each publication, the extracted data included the authors, year of publication, continent of origin (Europe, Asia, and America), type of data (dried skulls and imaging), probing for evaluating SEF patency (yes or no), instrument used for probing (bristle, wire, and other), total sample, reported SEF frequency (total, bilateral, and unilateral), type of dominance (bilateral: when the bilateral to unilateral foramina ratio was greater than 1, otherwise, unilateral), morphometric data (SEF diameter, SEF-FO, and SEF-FS distances), and the  methodology used for the morphometric measurements (caliper, DICOM Viewer, image analysis software).
In publications not mentioning the continent of origin, the country where the study originated from, was eventually recorded and in case of an article written in a non-Latin language (e. g. , Russian), the full paper was downloaded and translated using the Google Translate website (https:// trans late. google. com). Additionally, in manuscripts where only the bilateral or unilateral percentages were reported, the respective frequencies (bilateral and unilateral frequency) were calculated by converting each percentage to integers with no decimal approximation.
Statistical analysis Statistical analysis was carried out using RStudio (version: 2022. 7. 1. 554) software (RStudio Team (2022)). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA for MacOS. The DerSimonian and Laird random-effects model was used to estimate the pooled prevalence and its respective 95% confidence intervals (CI). No logit or double arcsine transformation were made since the observed proportions identified across studies were between 0. 2 and 0. 8  [21, 37]. Heterogeneity presence across studies was estimated by constructing a forest plot and tested using the Cochran's Q statistic and its respective p value. The Higgins I 2 statistic and its respective 95% CI were used for quantifying the magnitude of true heterogeneity in effect sizes. An I 2 value of 25%, 50%, and 75% indicated low, moderate, and high heterogeneity.
In the performed univariable regression analyses, except of the aforementioned covariates, the SEF diameter, as well as the SEF-FO and SEF-FS distances were assessed as per their relationship with the respective effect sizes. Moreover, the presence of interrelated moderators was checked to avoid potential multicollinearity issues prior the conduction of the multivariate regression analysis. Due to the limited availability of data about the SEF diameter, and the SEF-FO, and SEF-FS distances in the given dataset, they were not used in this analysis. To detect the presence of publication bias, a Doi plot and a funnel plot were created. The asymmetry of each plot was estimated by calculating the LFK index  [9]  and Egger's tests' p value, respectively. Additionally, to detect the presence of the small-study effect, the phenomenon that smaller studies may show different, often larger effects than large ones  [33] , a funnel plot of the prevalence against the sample size was constructed and regression-based Egger's test was estimated.
Moderator analysis The results of the subgroup analyses are summarized in Table  2. The SEF prevalence varied significantly only by the type of dominance (p = 0. 016). The results of the performed regression analyses display the existence of a statistically significant, and a marginally non-significant association of the reported SEF prevalence with dominance (p = 0. 005), and type of data (p = 0. 060), respectively (Table  3 ). Specifically, according to the multivariable regression results, the unilateral dominance was associated with a 13. 0 percentage points decrease in the reported SEF prevalence. In other words, when a sample of dried skulls is examined, the reported SEF prevalence will be 13. 0% smaller if the frequency of the identified unilateral foramina exceeds the one of the bilateral.
Introduction: The Erasmus+O-Health-EDU project aims to gain a comprehensive view of oral health professional (OHP) education in Europe, through the development of web-based surveys and online toolkits. A glossary to facilitate a common language through which academic teams could cooperate and communicate more accurately was identified as a key need within the project. The aim of ARTICULATE was thus to create a shared language, with a European focus, for terms and concepts used in the field of OHP education.
For instance, the question "what is the length of your programme?" may be interpreted as including or excluding preparatory courses and/or obligatory or voluntary postgraduate vocational training. This has lead institutions, even within the same country, to give different answers. It is clear that in order to collect robust data in the future, it is essential that the terms used in data collection exercises are understood in the correct context by both the respondents and the users. As well as being important in supporting the creation of comparable datasets from OHP educators throughout Europe, the use of common terms about OHP education by stakeholders and policymakers also has potential regulatory and legal concerns, since the correct use of guidelines and other decision support tools to enhance the quality of oral healthcare depends on the use of common terms and concepts.
The pan-European O-Health-Edu project, funded through the European Union Erasmus+programme as part of the "Strategic partnerships for higher education" initiatives, is based on a vision of OHP education as a key determinant of the health of European populations. One of the specific objectives is thus to collect data about how OHP education is undertaken in institutions across Europe by means of an online survey. The findings of this survey will be incorporated into a comprehensive data hub, which can be updated in real-time to ensure long-term accuracy and relevance. In the light of the difficulties outlined above with regard to terminology, a needs analysis within the O-Health-Edu project recognised the importance of including an updated and relevant resource, where terms specifically related to OHPs education are clearly defined and explained with a European focus, in the data hub.
KEYWORDS dictionary, Europe, lexicon, oral health, professional education, terminology none of the published glossaries was deemed broad enough to meet the needs of the O-Health-Edu project. In this paper, therefore, we describe the creation of ARTICULATE, a unique freely available online glossary, developed as part of a toolkit within the O-Health-Edu project. The aim of ARTICULATE is to create a shared language, with a European focus, for terms and concepts used in the field of OHP education as a resource to meet the needs of the OHP education community as well as our partners and collaborators.