dataesr/scientific-paragraphs-categorization huggingface dataset:
16S Sequencing Data Analysis The 16S rRNA gene sequencing data from the Illumina runs were then analyzed using the Quantitative Insights Into Microbial Ecology version 2 (QIIME2) software suite  [13]. QIIME 2 computes error-corrected amplicon sequence variants (ASV) for Illumina read sequences. Primer-free sequences were imported into QIIME2 q2cli v2019. 10, visually inspected with demux2, and processed via Deblur  [14]  to obtain the representative ASV sequences. Representative sequences and their abundances were extracted using the feature table  [15]. A naive Bayes classifier  [16]  was fitted with 16S rRNA gene sequences extracted from Greengenes version 13_8  [17]. QIIME2 plugins were executed with standard parameters, and Deblur parameter "-p-trim-length" 240. Diversity analyses were also performed using the q2-diversity plugin, which computes alpha and beta diversity metrics, with a sampling depth of 4960.
Cytokine Profile Is Altered in Diseased Sites Compared to Healthy Sites A total of 24 patients were recruited, consisting of 13 men (54%) and 11 women (46%). Twenty of them presented a periodontitis stage 3 (grade B or C) and 4 presented a periodontitis stage 4 (grade B or C). The stages of periodontitis were diagnosed on clinical charts and radiographic data according to the criteria of new classification. Demographic data, the mean of cigarettes smoked daily, and clinical parameters of the included patients are presented in Table  1. Six major cytokines, involved in periodontal inflammation, were measured and analyzed in GCF, i. e. , IL-1β, IL-6, TNFα, IL-4, IL-10, IL-17a. IL-2 concentrations in GCF were likely under the detection level of the kit to be analyzed. As a conventional internal validation, the GCF amount collected in healthy sites were significantly lower than in diseased sites (Wilcoxon test, p < 0.
Methods A search strategy of the literature was performed on Medline (PubMed database), and Cochrane Library, covering all articles published from January 2008 to September 2020, to answer the following research question: what is the contribution of virtual reality headsets in reducing patients anxiety, as well as intra-and post-operative pain related to oral surgery procedures? The following research equation was applied by one author (AF), according to the key words answering the research question, with Boolean operators, in Mesh terms: ("virtual reality" AND "oral surgery" AND ("anxiety OR pain")). The inclusion criteria were original papers such as study protocols, reported clinical cases and clinical studies, including participants of both sexes, of all ages in good health and having no contraindication to the use of virtual reality (no sight/hearing problems or psychotic disorders).
We excluded animal studies, in vitro studies, studies not published in English or French or full texts not available, and studies in which the use of virtual reality was not appropriate for our research (VR intended for practitioners for clinical situations, interventions not related to oral surgery). After the duplicates removal, the titles and abstracts of the remaining potentially relevant articles were screened by two independent reviewers (AF and SB) according to the study inclusion and exclusion criteria. Possibly eligible studies were subject to a full text evaluation. Data from all of the included articles were extracted and summarized independently by the two reviewers (AF and SB) using the same data extraction form. The following variables were recorded: -Authors, year and title -Studies Methods -Primary and secondary outcomes -Biases estimation.
To this, we added previously published microbiome data from chimpanzees (n = 1) (13), Neanderthals (n = 4)  (13) , and presentday modern humans (n = 10)  (23) , for a total dataset of 124 individuals (Fig. 1A , SI Appendix, Table  S1 , and Dataset S1). We also generated eight new radiocarbon dates for archaeological individuals, for a total of 44 directly or indirectly dated ancient individuals in this study (Dataset S1). Here, we investigate the structure, function, and core microbial members of the human oral microbiome within an evolutionary framework, seeking to determine whether a core microbiome can be defined for each African hominid group, whether the core is phylogenetically coherent, and whether some members of the core are specific to certain host groups. We test whether the oral microbiome of hominids reflects host phylogeny, finding that African hominid oral microbiota are distinguished by major taxonomic and functional differences that only weakly reflect host relationships and are likely influenced by other physiological, dietary, or behavioral factors.
Results Preservation of Oral Microbiota in Dental Calculus. Authenticating ancient DNA (aDNA) preservation is a necessary and essential step for all paleogenomic studies. However, these methods have been underdeveloped for ancient microbiomes. Here, we apply a multistep procedure of both conventional and new methods to evaluate and validate oral microbiome preservation in our dataset (SI Appendix, Fig. S2 ). First, we applied a reference-based metagenomic binning of reads to the National Center for Biotechnology Information (NCBI) nucleotide (nt) database (  27 ) (Dataset S2) and then developed and applied a method to assess the decay of the cumulative percentage of known oral taxa in samples compared to a panel of oral and nonoral reference metagenomes (SI Appendix, Fig. S3A  and section S3.
4. 1). This allowed us to remove samples that did not exhibit a taxonomic composition consistent with an oral origin. We then cross validated these results using SourceTracker (28) (SI Appendix, Fig. S3B ) and inspection by principal coordinate analysis (PCoA, SI Appendix, Fig. S5 A-D ). To samples exhibiting good oral microbiome preservation (Fig. 1B ), we then applied the R package decontam  (29)  to detect and remove putative laboratory and environmental contaminants prior to downstream analysis (SI Appendix, section S3. 6). Next, we examined each dataset and confirmed the presence of DNA damage characteristics of ancient samples, including short fragment lengths and elevated levels of The evolution and changing ecology of the African hominid oral microbiome cytosine to thymine deamination (Fig.
1C  and SI Appendix, Fig. S4 )  (30). Finally, to reduce potentially spurious assignments for compositional analysis, we removed low-abundance taxa using thresholds optimized at different taxonomic levels (SI Appendix, Figs. S7 and S8 and sections S3. 6 and S5. 2). The resulting 89 wellpreserved dental calculus datasets consist of samples ranging from the present day up to 100 ka. The Core African Hominid Oral Microbiome. We performed PCoA on our dataset of well-preserved samples and found considerable overlap in the microbial composition of African hominid dental calculus, as well as howler monkeys (Fig. 1B ), suggesting the existence of a core microbiome that has been maintained for more than 8 My, based on fossil and molecular evidence of host divergence among African hominids  (31, 32) , and possibly since before the catarrhine-platyrrhine split ca.
Because of this, we sought to begin to define the African hominid core oral microbiome as a group and for each genus separately. For a microbial taxon to be considered "core"  (44, 45) , we required it to be present in at least two-thirds of the populations making up a given host genus, counting as present only those populations in which it is found in at least half of individuals to account for variation in preservation (SI Appendix, Fig. S9A  and section S5. 2). We then calculated the intersection of each core microbial genus (Fig. 2A ) and species (Fig. 2B ) across all host taxa (Dataset S3). Most "core" taxa are shared across all three African hominid genera (Gorilla, Pan, and Homo) and howler monkeys, whereas fewer are "core" only to African hominids (Gorilla, Pan, and Homo), Pan and Homo, or Homo (Fig.
This is in contrast to studies demonstrating strong associations between diet and taxonomic/ functional composition in modern gut microbiomes  (57, 58). Evolutionary Histories of Oral Microbial Species Reflect Homo Interactions. We next examined the phylogenies of individual microbial taxa to determine if host evolutionary relationships are reflected at the microbial genome level. To improve genome coverage and reduce potential noise from DNA damage, we selected a representative subset of well-preserved calculus samples across all host genera (n = 19; SI Appendix, Table  S1  and Dataset S1) and constructed uracil-DNA glycosylase-treated (UDG) libraries to remove deaminated cytosines  (59) , which we then deeply sequenced and analyzed together with a subset of four of the present-day modern humans.
Recently published human genomic data including this individual has revealed that its associated genetic ancestry component was largely displaced across Europe after 14 ka (24, 60) during postglacial warming. Turning to our low-coverage metagenomic datasets, we assessed additional European Upper Paleolithic and Mesolithic groups (SI Appendix, section S6. 6) and found that they show a similar pattern (albeit at lower resolution), with the oral taxa of individuals dated to before 14 ka mostly falling with Neanderthals and those after 14 ka mostly clustering with present-day modern humans  (24, 60). This pattern suggests that the reconstructed oral bacterial genomes from El Mirón reflect a standing microbial diversity in Homo that was present in Europe during the Middle and Upper Paleolithic, but which was later replaced following subsequent migrations of modern human populations from elsewhere.
Because oral microbiota are primarily inherited through caregivers  (61, 62) , additional sampling and ultradeep sequencing of Paleolithic European and Asian dental calculus may prove informative about the poorly understood interaction dynamics between archaic and modern humans. Homo-Specific Shifts in Oral Biofilm Are Linked to Dietary Starch Availability. The metabolic potential of a microbial community, which is inferred from its total gene content, can offer insights into biofilm ecology and function that cannot be understood from taxonomy alone. To better characterize the metabolic and functional differences among hominid oral biofilms, we compared the gene content of dental calculus metagenomes from well-preserved samples of the larger sequencing dataset using two different methods of functional classification, HUMAnN2 (63) and AAD-DER  (64) , and found moderate concordance in overall results.
Reference Dataset S3 for additional information. The evolution and changing ecology of the African hominid oral microbiome relate to carbohydrate processing (SI Appendix, Fig. S14 ), are much more abundant in Homo, and largely derive from Streptococcus (SI Appendix, Fig. S13 ), something also observed in primate gut microbiomes  (66). We therefore investigated the distribution of Streptococcus across our samples (Fig. 5B ) using a classification system based on biochemical characteristics and genetic relatedness   67). We find that Streptococcus species belonging to the Mitis, Sanguinis, and Salivarius groups are dominant in Homo, while these same groups are effectively absent in chimpanzees, and nonhuman primates in general are characterized by much higher proportions of Streptococcus species in the Anginosus, Mutans, and Pyogenic groups (Fig.
This copy number expansion is estimated to have occurred along the modern human lineage after the divergence from Neanderthals in the Middle Pleistocene  (75, 76). It has been argued this increase relates to dietary shifts during the evolutionary history of modern humans and specifically to an increased reliance on starch-rich foods  (20, 21). We next calculated the ratio of reads aligning to abpA and abpB sequences compared to all Streptococcus reads in the deepsequenced dataset. We find that abpA and abpB reads are nearly absent in the nonhuman groups but are prevalent and significantly more abundant in Homo (Mann-Whitney U test Homo versus non-Homo: abpB, α = 0. 05, U = 128, P = < 0. 001, 95% CI = 0. 686 to 0. 851; abpA, α = 0. 05, U = 112, P = < 0. 001, 95% CI = 0. 398 to 0. 861). In particular, abpB is present in all deeply sequenced Homo individuals, and abpA is especially prevalent in modern humans (Fig.
Materials and Methods Materials. Our sampling strategy aimed to collect dental calculus from a minimum of two independent populations, each consisting of at least five individuals, for each host genus and modern human lifestyle group (excepting Alouatta) (SI Appendix, Table  S1   Laboratory Methods. For all museum and field station samples, we performed DNA extraction in dedicated cleanroom facilities using a protocol optimized for the recovery of degraded and fragmentary DNA  (86). Present-day calculus was extracted as previously described  (23). For all samples, DNA was built into dualindexed Illumina libraries (87) and shotgun sequenced. In addition, a subset of samples were separately subjected to UDG treatment  (88)  A comparison of the two databases is provided in SI Appendix, section S3.
3. Given the challenges of low preservation and contamination in ancient microbiome studies, we performed a multistep procedure to screen for and remove poorly preserved samples and contaminant OTUs from the non-UDG-treated dataset (SI Appendix, Fig. S2 ). We developed a visualization for the identification of calculus samples with weak oral microbiome signatures (SI Appendix, Fig. S3 ). This procedure involves comparing identified taxa to their previously reported isolation source(s), ranking these taxa from most to least abundant, and tracking the cumulative percentage of oral taxa along this rank (termed here as "decay"). Samples with a low percentage of oral taxa after an initial "burn-in" based on stabilization of curve fluctuation were removed from downstream analysis. Reference SI Appendix, section S3.
4 for details. We compared this method to results obtained using SourceTracker (28)-which was performed on 16S-mapped reads filtered from shotgun data using EAGER (with comparative present-day modern human and environmental metagenomes as sources), followed by closedreference clustering using QIIME (92)-and found concordance between the two methods (SI Appendix, Fig. S3 ). We next used the R package decontam  (29)  to statistically detect putative laboratory and environmental contaminants (as present in negative controls and a set of archaeological bone samples-SI Appendix, section S3. 6), which were then removed prior to downstream analysis. To authenticate the remaining OTUs, we utilized the output of MaltExtract (  93 ) in the MaltExtract-Interactive Plotting App (MEx-IPA) tool (DOI: 10.
5281/zenodo. 3380011), which we developed for rapid visualization of characteristic aDNA patterns, such as cytosine to thymine deamination, short fragment lengths, and edit distance from reference (SI Appendix, Fig. S4  and section S3. 5). After mapping with EAGER to well-known oral taxa, we also validated DNA damage patterns using DamageProfiler (Fig. 1C )  (94). Microbial Compositional Analysis. To remove low-abundance environmental contaminants or spurious hits, we selected a minimum abundance cutoff of 0. 07% of alignments for genus-level and 0. 04% of alignments for species-level identifications (SI Appendix, Figs. S7 and S8 and section 5. 2). We normalized profiles through phylogenetic isometric-log-ratio transformation (95) of the abundance-filtered OTU tables and then performed PCoA on the resulting euclidean distances (SI Appendix Fig.
S5  C and D and SI Appendix, section S4. 1). To statistically verify host genus clusters, we used the adonis function from the R package vegan to perform PERMANOVA  (35)  analysis after controlling from unequal sample sizes (SI Appendix, section S4. 2). After removal of poorly preserved samples, oral communities show distinct centroids for each host genus (bootstrapped PERMANOVA, ɑ = 0. 05, P = 0. 001, pseudo-F = 5. 23, R 2 = 0. 28); Alouatta was excluded due to small sample size. We performed bootstrapped hierarchical clustering  (96)  on the euclidean distances of centered log ratiotransformed OTU tables and visualized the results in the form of a heatmap (Fig. 3  and SI Appendix section S4. 3). Sample and taxon clustering was performed with the McQuitty hierarchical clustering algorithm, and taxon blocks within the heatmap were selected by visual inspection.
Bootstrap values of sample clusters were estimated through the R package pvclust  (96). Species oxygen-tolerance metadata was obtained from the BacDive database (97) via the BacDiveR R package (DOI: 10. 5281/zenodo. 1308060). For validation of the observations made on the heatmaps, we also performed grouped indicator analysis (98) (SI Appendix, section S4. 4). Clustering of human oral microbiomes by variables such as time, geography, and dietary subsistence was assessed using PCoA, PERMANOVA, and hierarchical clustering (SI Appendix, section S4). Core Microbiome Analysis. Using the contaminant-filtered OTU tables of wellpreserved samples, we converted all taxa above the minimum support threshold to a presence/absence profile. Taxa were required to be present in at least half (50%) of the members of a population for it to be considered core to the population and to be present in at least two-thirds (66%) of populations to be considered core to a host group (SI Appendix, Fig.
S9 ; reference SI Appendix, section S5. 2 for parameter experimentation details). We then generated UpSet plots  (99)  to visualize the microbial intersections of each host group at both the species and genus levels (Fig. 2  A and B), and we also compared the results between both databases. Further discussion on the exclusion of the common soil genus Mycobacterium from core genera is provided in SI Appendix, section S5. 2. Validation of results through smaller sample sizes was carried out by bootstrapping analysis, which was performed by randomly subsampling (with replacement) individuals from each host genus and rerunning the core calculation procedure to 1,000 replicates (SI Appendix, section S5. 3). We created a diagram of the core human oral microbiome (Fig. 2C ) based on published fluorescence in situ hybridization (FISH) images of human dental plaque  (8, 100).
(23)Dataset S1). Dental calculus was sampled from twentieth-century skeletal remains of wild Alouatta (A. palliata), Gorilla (G. berengei beringei; G. berengei graueri; G. gorilla gorilla), and Pan (P. troglodytes schweinfurthii; P. troglodytes ellioti; P. troglodytes verus) and from archaeological Neanderthals and modern humans using established protocols (DOIs: 10. 17504/protocols. io. 7vrhn56 and 10. 17504/protocols. io. 7hphj5n). Although many present-day human dental plaque datasets are publicly available, they have been shown to not be directly comparable to dental calculus(23), and consequently we generated dental calculus data for present-day humans. The study of deidentified present-day dental calculus was approved by the Institutional Review Board for Human Research Participant Protection at the University of Oklahoma (IRB no.
The TEM observations of the enamel crystals indicated that the CDL was in fact a planar defect. Several hypotheses have been proposed in its possible origin. One of them is that the CDL is a transformation-mismatched layer from octacalcium phosphate (OCP) to HAP. However, the HAP-OCP interface model generated does not fully reproduce the TEM data  [8]. Another possibility is that the CDL is a Ca-rich region within enamel crystals and therefore cannot be the residual of the OCP-HAP mismatch  [8, 9]. The contrast of the CDL, when observed with a scanning transmission electron microscope (STEM) using an annular detector, gave additional clues on the CDL's structure. Modern STEM microscopes were equipped with aberration correction systems, and the lowand high-angle scattered electron annular detectors provided bright-field (BF), low-angle annular dark-field (ADF), and high-angle annular dark-field (HAADF) images with sub-angstrom resolution  [10].
The main observation parameters were as following: voltage, 200 kV; spot size, 9; probe current, 0. 04 nA; and working length, 56-91 mm for HAADF observations with internal detection angles of 41 to 92 mrad and 220-230 mm for HAADF observations with internal detection angles of 10 to 35 mrad for ADF observations. Because low-dose STEM images are very noisy, the image contrast was increased by digital processing using the Fast Fourier Transform (FFT) and dot filters with the GATAN's Digital-Micrograph software (Gatan Inc. , Boston, MA, USA). JEMS software  [18]  was used for the simulation.
Acknowledgments: The authors thank  Gustaaf Van Tendeloo  for ESTEEM support at  EMAT, University of Antwerpen (Belgium) , and  Johan Verbeeck  for comments and suggestions. The authors also thank  Stuart Torner  for technical support and practical suggestions. This research received funding from the  European Union Seventh Framework Programme  under Grant Agreement  312483-ESTEEM2  ( Integrated Infrastructure Initiative-I3 ). The authors also thank  S. Tehuacanero-Nuñez , and  P. López-Arriaga  for their technical support in the elaboration of this work.
Numerical simulation The computational model suggested in this study was developed using COMSOL Multiphysics v5. 4 software (Comsol inc, USA). It is based on a finite element formulation. An implicit method was used to solve non-linear differential equations, with a mesh closely adapted to the geometry studied. The numerical simulation is conducted in 12 iterative steps to cover twelves weeks of peri-implant bone healing (Fig. 1 ). At each iteration, the spatio-temporal distribution of each variable is stored as well as their proportion in the region of bone healing. It is to notice that biological activity was only computed in areas concerned by bone healing. Before simulating the osseointegration of different thread design, this model was calibrated by comparison with data from scientific literature  (Berglundh et al.
Results and Discussion Gelation Kinetics and Mechanical Properties of the Alg-Cat Based Gels Gelation Kinetics Under the experimental conditions used, the gelation kinetics of Alg-Cat is fast: the storage modulus being higher than the loss modulus after about 2 min of equilibration between the two steel plates of the rheometer (Figure  1a ). The G' value lies between 25 and 35 Pa and it remains constant under deformations up to 50% (Figure  1b ), whereas it increases for frequencies above 10 Hz when deformation is constant (Figure  1c ), which is characteristic of a viscoelastic material with such a framework  [53, 54]. We also evaluated the gelling kinetics of D-CTL/Alg-Cat gel in a preliminary assay with increased values of G' up to 40 Pa (data not shown). cacodylate 0. 1 M-osmium tetroxide at 1% m/vol) for 30 min at 4 °C, washed two times for 10 min and then conserved in the wash buffer.
Gelation Kinetics Under the experimental conditions used, the gelation kinetics of Alg-Cat is fast: the storage modulus being higher than the loss modulus after about 2 min of equilibration between the two steel plates of the rheometer (Figure  1a ). The G' value lies between 25 and 35 Pa and it remains constant under deformations up to 50% (Figure  1b ), whereas it increases for frequencies above 10 Hz when deformation is constant (Figure  1c ), which is characteristic of a viscoelastic material with such a framework  [53, 54]. We also evaluated the gelling kinetics of D-CTL/Alg-Cat gel in a preliminary assay with increased values of G' up to 40 Pa (data not shown). The maximal adhesion strength of the Alg-Cat hydrogels to stainless steel was obtained after 18 h of aging (Figure  2b ) and was equal to 5.
Acknowledgments: We are indebted to  Éric Mathieu  and  Naji Kharouf  for their contribution in the SEM experimentations, to  Abdurraouf Zaet  and  Sophie Hellé  for their help in microbiology and to  Cosette Betscha  for HPLC analysis. Acknowledgments: We are indebted to  Éric Mathieu  and  Naji Kharouf  for their contribution in the SEM experimentations, to  Abdurraouf Zaet  and  Sophie Hellé  for their help in microbiology and to  Cosette Betscha  for HPLC analysis.