dataesr/scientific-paragraphs-categorization huggingface dataset:
The colonized specimens were submerged in the staining solution and incubated in dark, for five minutes, at room temperature. After incubation, the excess of the staining solution was removed using a filter paper and the specimens were examined immediately. The samples were visualized in reflection and transmission mode by using a Leica microscope (TCS-SP CSLM model), equipped with PL FLUOTAR (40x NA 0. 7, electronic zoom 1), and a He-Ne laser tuned on 633 nm wavelength. A lateral resolution of about 600 nm was achieved. The Leica software was used for examining the surface topography  [39]. In order to examine the adherence of viable cells on the substrates surface, the samples were immersed in free culture medium inoculated with standard fungal suspension, incubated in static conditions for 2 hours, and then examined using a Nikon C2 confocal laser scanning microscope.
Viable Counts Assay. The sterile substrata were added in a sterile saline inoculated with ∼105-106 CFU/mL of microbial suspensions. The samples were incubated under stirring at 37 ∘ C for 72 h. The number of fungal viable cells developed in the presence of the samples was assessed using 10 L of fungal suspension at 24 h, 48 h, and 72 h. For this purpose the fungal suspensions were further diluted and 10 L of each serial dilution was plated in duplicate on LB agar. After 24 h of incubation at 37 ∘ C, viable cell counts were performed and the number of CFU/mL for each sample was evaluated. 3. 9. Image Analysis. The CLSM images acquired during the experimental session were processed using ImageJ software. The Candida albicans biofilms developed on the surface of the layers were investigated using a three-dimensional (3D) model.
Statistical Analysis. All the experiments regarding the antifungal properties of the layers were done in triplicate and repeated three times. The biofilm development was estimated by plotting the mean absorbance values of C. albicans ATCC 26790 biofilm cells developed on the samples. The statistical analysis was conducted in agreement with other previous studies on surface and biological evaluation of hydroxyapatite-based coatings on titanium deposited by different techniques presented by Massaro et al. [40]. Furthermore, the data image analysis was carried out using the Student -test and the values of  < 0. 05 were accepted as significant.
Acknowledgments The studies reported in this paper have been funded by the  National   PN II 259/2014  project. The SEM analysis on samples was possible due to EU-Funding Grant  POSCCE-A2-O2. 2. 2-2013-1/Priority Direction 2 , Project no. 638/12. 03. 2014, cod  SMIS-CSNR 48652.
In those cases, the flap was closed using absorbable sutures (Vicryl 3/0, Johnson & Johnson International, Brussels, Belgium). A flapless approach was used when the buccal plate was preserved and the gingival contour was maintained. Healing caps (Nobel Biocare) were snapped over the abutments. The implant length, bone quality (data not presented), insertion torque, flap design, occurrence of complications, and feasibility of immediate provisionalization were documented at the end of surgery. One week after surgery, all the surgical sites were examined and the sutures were removed in patients who had undergone flap surgery.
The change in mesial and distal marginal bone levels over time was assessed by comparing the intraoral radiographs taken at implant insertion with those taken at the follow-up visits. The marginal bone level was assessed at the mesial and distal sides of each implant. A reference level was marked at the implant platform, and the bone level was subsequently measured from the reference level to the first point of bone-to-implant contact using Adobe Photoshop software (Adobe System Incorporated, San Jose, CA, USA). The measurements were initially made in a pixel format. Linear measurements (mm) were performed after the images were calibrated according to the respective implant lengths. The bone level was also measured at the distal aspect of the neighboring tooth, if the neighboring tooth was visualized on the same radiograph.
The first bone-toimplant contact evaluated by the radiograph taken at implant insertion was defined as the baseline. Marginal bone remodeling was calculated as the difference between the baseline and the readings taken at the follow-up examinations. The measurements of mesial and distal bone height were averaged for each implant. All calculations of the bone levels and of the changes in bone levels were performed using the SAS system version 9 (SAS Institute, Cary, NC, USA).
Acknowledgements ICCC would like to thank  Lisbet Brike  and  Amy Caldwell-Nichols  for organising travel and accommodation for the conference in Leuven and to  Amy Caldwell-Nichols  for additional document and manuscript preparation support. Thank you also to GC for the use of their premises in Leuven. Grateful thanks also go to  Professor Edwina Kidd , for her presentation, invaluable discussions at the meeting in Leuven and input into improving this manuscript and finally, for so generously sharing their wisdom and experience. We thank  Professor Wolfgang Buchalla  for his contribution to the meeting and discussions on the composition of the manuscripts and respect his decision not to support their content through authorship.
Frontal images of the setup were captured every two seconds using a fixed digital camera for Digital Image Correlation (DIC). A fixed 100-point correlation grid was positioned along the main axis of the mandible on the lateral view at t¼ 0 as shown in Fig. 4. The DIC grid was further tracked in the image series by means of 2D-image registration using MATLAB and Statistics Toolbox (Release 2012b, The MathWorks, Inc. , Natick, Massachusetts, United States). The deflection curve as obtained from the grid deformation at the end of the loading (i. e. 6 N m) was used to assess the rigidity of the anterior and posterior boneimplant interfaces. In order to calculate the deflection, the raw displacement field obtained from the DIC was smoothed using a three point moving average filter. The mean gradients of the displacement curves proximally and distally to each interface were calculated using numerical differentiation on a five-point stencil.
One empty sample of each implant was positioned in the mCT specimen holder at varying angulations to the X-ray beam from 201 to 701 to assess the amount of titanium- related artifact as a function of implant orientation in the scanner. The DICOM data were post-treated using the abovedescribed procedure to calculate the BV, TV and TBV specifically generated by the artifacts. Statistical analyses were performed using XLSTAT (Addinsoft, Paris, France). The median, interquartile, and range of BV/TV for each sub-volume were calculated. The BV/TV differences between the porous implant and control implant were compared between group A and group B. A 'worst-case' study was further conducted using the same comparisons of porous vs control implants after subtraction of the artifactrelated BV/TV as obtained from the empty implant assessment in the porous-implant only.
5 kV lens, 7 kV). The generated spectra were then compared to the Bruker database, which was supplemented with the new species found through the culturomics project. The resulting score enabled the identification or not of tested species: a score of 2 with a validly published species enables identification at the species level, a score of 1. 7 but <2 enables identification at the genus level and a score of <1. 7 does not enable any identification. After three assays, unidentified colonies were sequenced using 16S rRNA for formal identification. Isolated colonies were suspended in 200 μL distilled water for DNA extraction using an EZ1 DNA Tissue Kit (Qiagen, Venlo, Netherlands). The amplification of the 16S rRNA was performed by a standard PCR using the universal primer pair FD1 5 0 -AGAGTTTGATCCTGGCTCAG-3 0 and RP2 5 0 -ACGGC-TACCTTGTTACGACTT-3 0  [11].
The PCR product was purified and sequenced using the Big Dye Terminator Sequencing kit v. 1. 1 (Perkin-Elmer, Courtaboeuf, France) with the following internal primers: 536F, 536R, 800F, 800R, 1050F and 1050R; 16S rRNA amplification and sequencing were carried out as previously described by Steven et al. [12]. The 16S rRNA nucleotide sequence was corrected using Chromas Pro 1. 34 software (Technelysium, Tewantin, Australia), and the BLASTn searches were performed by National Center for Biotechnology Information (http://blast. ncbi. nlm. nih. gov. gate1. inist. fr/ Blast. cgi). MEGA6 (Molecular Evolutionary Genetics Analysis) software  [13]  allowed us to construct a phylogenetic tree. Sequences alignment of the different species was performed by CLUSTALW, and calculation of the evolutionary distance was done with the Kimura two-parameter model  [14, 15].
Genome annotation and comparison Open reading frames (ORFs) were predicted using Prodigal  [16]  with default parameters, but the predicted ORFs were excluded if they spanned a sequencing gap region. The predicted bacterial protein sequences were searched against the GenBank database  [17]  and the Clusters of Orthologous Groups (COGs) database using BLASTP. The tRNAScanSE tool  [18]  was used to find tRNA genes, whereas ribosomal RNAs were found using RNAmmer  [19]  and BLASTn against the GenBank database. Lipoprotein signal peptides and the number of transmembrane helices were predicted using SignalP  [20]  and TMHMM  [21]  respectively. ORFans were identified if their BLASTP E value was lower than 1e-03 for alignment length greater than 80 amino acids. If alignment lengths were smaller than 80 amino acids, we used an E value of 1e-05.
Such parameter thresholds have already been used in previous works to define ORFans. Artemis  [22]  was used for data management and DNA Plotter  [23]  for visualization of genomic features. The Mauve 2. 3. 1 alignment tool was used for multiple genomic sequence alignment  [24]. To estimate the mean level of nucleotide sequence similarity at the genome level, we used the MAGI homemade software to calculate the average genomic identity of gene sequences (AGIOS) among compared genomes. Briefly, this software combines the Proteinortho software  [25]  for detecting orthologous proteins in pairwise genomic comparisons, then retrieves the corresponding genes and determines the mean percentage of nucleotide sequence identity among orthologous ORFs using the Needleman-Wunsch global alignment algorithm.
Genomes from the genus Planococcus and closely related genera were used for the calculation of AGIOS values. The genome of Planococcus massiliensis strain ES2 T (GenBank accession no. CCXS00000000) was compared to the one of Planomicrobium glaciei strain CHR43 (NTS) (GenBank accession no. AUYR00000000), Planococcus halocryophilus strain Or1 (GenBank accession no. ANBV01000000), Planococcus donghaensis strain MPA1U2 (NTS) (GenBank accession no. AEPB01000000) and Bacillus subtilis subsp. spizizenii strain TU-B-10 (GenBank accession no. CP002905). To estimate the overall similarity between the genomes, the Genome-to-Genome Distance Calculator (GGDC) was used  [26, 27]. The system calculates the distances by comparing the genomes to obtain high-scoring segment pairs (HSP) and interfering distances from a set of formulas: 1, HSP length/total length; 2, identities/HSP length; and 3, identities/total length.
Strain identification We did not obtain a significant MALDI-TOF score for strain ES2 T against the Bruker database, suggesting that our isolate was not a known species. Its spectrum was added to our database (Figure  1 ). The gel view highlighted the spectral differences with other members of the genus Planococcus (Figure  2 ). PCR-based identification of the 16S rRNA of our new isolate (GenBank accession no. LK021122) revealed 1516 bp long sequences. This indicated a 97. 95% 16S rRNA sequence similarity with Planococcus halocryophilus (GenBank accession no. AJ314745), the phylogenetically closest validated Planococcus species (Figure  3 ). The other closest species were P. donghaensis (97. 72%), Planococcus glaciei (97. 06%) and B. subtilis (91. 92%). The species P. massiliensis, P. halocryophilus and P.
Genome properties The GenBank Bioproject number is PRJEB6479 and consists of 192 large contigs. The draft genome of P. massiliensis ES2 T consists of six scaffolds with 32 contigs and generated a genome size of 3 357 017 bp with a 46. 0% G+C content (Table  2 , Figure  6 ). Of the 3405 predicted genes, 3357 are protein-coding genes and 48 are RNAs (eight genes are 5S rRNA, two are 16S rRNA, three are 23S rRNA and 35 are tRNA). A total of 2601 genes (66. 90%) were assigned a putative function. A total of 75 genes (1. 93%) were identified as ORFans. The remaining genes were annotated as hypothetical proteins. The properties and statistics of the genome are summarized in Table  2. The distribution of genes into COGs functional categories is presented in Table  3.
P. massiliensis ES2 T (= CSUR P1103, = DSM 28915) was isolated from a stool sample of a healthy Senegalese man. It exhibited a genome size of 3 357 017 bp with a 46. 0% G+C content. The 16S rRNA sequence was deposited in GenBank under accession number LK021122, and the whole genome shotgun sequence has been deposited in GenBank under accession number CCXS00000000.
84 Carbohydrate transport and metabolism E 317 9. 44 Amino acid transport and metabolism F 80 2. 38 Nucleotide transport and metabolism H 95 2. 82 Coenzyme transport and metabolism I 131 3. 90 Lipid transport and metabolism P 169 5. 03 Inorganic ion transport and metabolism Q 85 2. 53 Secondary metabolites biosynthesis, transport and catabolism R 487 14. 50 General function prediction only S 269 8. 01 Function unknown - 1183 35. 23 Not in COGs COGs, Clusters of Orthologous Groups database.
5B ). The genes were then functionally annotated using the KEGG and eggNOG databases. Functional categories and genes in KEGG/eggNOG were counted (Figs S8 and S9), and KEGG ontology (KO) enrichment was analysed (Table  S7 ). Interestingly, the groups "infectious diseases: bacterial" and "metabolism of terpenoids and polyketides" were enriched in the halitosis samples (both tongue coating and saliva samples), but not in the healthy samples (Fig. 5C ). We suggest that microbial genes involved in infectious diseases might be relevant to halitosis. Terpenoids and polyketides are secondary metabolites produced by bacteria; they are structurally complex compounds involved in cell defence and communication. The involvement of these metabolites in halitosis was not previously suspected and warrants further investigation.
Our study showed that cbs, aspB, yhdR, sseA and glpE were more abundant in the halitosis groups. Additionally, cysM, which encodes cysteine synthase B and catalyses the H 2 S-to-cysteine conversion in prokaryotes, was less abundant in the halitosis samples (Fig. 6 , Table  S8 ). This might result in higher H 2 S levels in the oral cavity compared with healthy subjects. To further clarify the bacterial contribution to the functional variations, we searched NCBI Bacterial Gene Database by using gene names (e. g. , cysC) and their descriptions (e. g. , adenylylsulfate kinase), and then computed the intersection part with the Kraken taxonomic profile. The candidate species are shown in the Table  S9.
7 , green-coloured shape). Most of the species within the core microbiome of the tongue coating biofilm were Gram-negative anaerobic bacteria. These species are the most adaptable to the tongue coating environment. Similar definitions of core microbiomes in the healthy (TH) and halitosis (TD) tongue coatings were also determined (Fig. 7 , blue and orange shapes). Within the TD core microbiome, L. wadei (OTU398), P. stomatis (OTU135) and P. shahii (OTU283) were candidate halitosis pathogens, as discussed above (Fig. 2A, B ). Taxonomic analysis was also performed with the metagenomic sequencing data using a relatively new method called "Kraken", which is an ultrafast and highly accurate program for assigning taxonomic labels  41. The metagenomic taxa were then compared with the Human Oral Microbiome Database (HOMD)  37, 42  at the genus and species levels, as HOMD is considered a powerful oral microbiome reference and contains more than 600 prokaryote species.
We identified 605 genera and 1,274 species, including more than 196 genera and 708 species in HOMD. The number of species we identified is consistent with Jenkinson's view that the estimated species number might turn out to be higher (~1200) than that in the database  43. Therefore, metagenomic sequencing helps identify a microbiota that is broader than the current database. Of the three major VSCs involved with oral malodour, (CH 3 ) 2 S is the main contributor to extra-oral halitosis  44  , whereas CH 3 SH is more pathogenic than H 2 S in halitosis and is associated with periodontal disease  10, 12. Therefore, we mainly focused on H 2 S in the present study. We attempted to identify candidate halitosis pathogens using 16S rRNA gene pyrosequencing; however, the most differentially abundant OTUs lacked evidence of H 2 S production in vitro (Fig.
The results of the PCR amplification were assessed by electrophoresis of a 1% agarose gel. The 16S rRNA gene PCR amplicons were sequenced with a 454 GS FLX Titanium system (454 Life Sciences, Branford, CT, USA) at the BGI Institute (Shenzhen, China). 454 sequence data processing. Sequence data were processed using Mothur  48. The sequences were demultiplexed using a unique barcode assigned to each sample. The sequences were filtered according to quality scores using the sliding window approach, in which sequences are trimmed when the average quality score over a 50-nt sliding window drops below 30. A maximum of one barcode mismatch and two primer mismatches were allowed. Sequences > 200 nt in length were retained. The sequences were further denoised using a single-linkage algorithm, and chimaeric sequences were removed using the UCHIME algorithm in Mothur.
The quantity and quality of the metagenomic DNA were measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA) and by agarose gel electrophoresis (0. 8%, 120 V), respectively. Next, DNA libraries were constructed according to the standard protocol provided by the manufacturer (Illumina, USA). Quality control of the libraries was performed on a StepOne Plus Real-Time PCR system (ABI, USA) prior to sequencing. The four libraries were sequenced in one lane using an Illumina HiSeq 2500 instrument (Illumina, USA) with 2 × 150 base pair (bp) paired-end (PE) sequencing. Library construction and sequencing were conducted at BerryGenomics (Beijing, China). Quality control of reads and human contamination evaluation. We performed quality control of the sequencing data as follows: (1) reads containing more than 3% N bases were removed, (2) reads containing more than 50% bases with low quality (<3) were removed.
Then, clean reads that mapped to the human genome (hg19) were filtered from each sample using Bowtie 2 with default parameters  53. Taxonomic analysis. All clean reads were aligned to the NCBI bacteria, archaebacteria and virus genome database using Kraken 41 with default parameters. After achieving a taxonomy profiling table, we compared the taxa to the Human Oral Microbiome Database (HOMD)  37, 42  at the genus and species levels. De novo assembly and gene prediction. All microbial PE reads were assembled by SOAPdenovo with default parameters  54. The MetaGeneMark gene prediction tool was used with default parameters to predict genes in the assembled scaffolds  55. The predicted open reading frames (ORFs) were compared against the NCBI non-redundant  56  sequence database using BLAT with default parameters.
Functional analysis. We calculated the relative abundances of genes and then calculated the relative abundance ratios between the halitosis and healthy samples. DEGexp, a method in the DEGseq package  24  , was also used to select genes that differed between the halitosis and healthy samples, although this approach has mainly been used for RNA-seq data. DEGexp was used here because it could correct inexact relative abundance ratios caused by samples with very low abundance. Genes with relative abundance differences of > 5-fold and with P-values < 0. 001 in the DEGexp results were functionally annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) bioinformatics database (8 th KEGG release, December 2014)  57  and the eggNOG 4. 0 database  58. Functional categories and genes in KEGG/eggNOG were counted.
Differential genes were subjected to KEGG Ontology (KO) enrichment analysis, and enriched P-values were calculated according to the hypergeometric test (P < 0. 05 was considered statistically significant). Furthermore, genes that mapped to the sulphur metabolic pathway and to the cysteine and methionine metabolic pathway were analysed. Hydrogen sulphide-related genes were used to construct an H 2 S metabolic map. We defined genes with relative abundance ratios of > 2 as differentially abundant genes in the H 2 S metabolic analysis. To determine taxonomic origins of these genes, we searched the NCBI Bacterial Gene Database by using gene names and gene descriptions, followed by computing the intersection with the Kraken taxonomic profile. The sequencing data from this study have been submitted to Sequence Read Archive (http://www.
Acknowledgements This work was supported by the  National Natural Science Foundation of China  ( 81200762 & 81301475 ), academic collaboration fund to T. X. , the  Peking University School of Stomatology  ( PKUSS20130210 ), the  Scientific Research Foundation for Returned Overseas Chinese Scholars, State Education Ministry , and the  Metagenopolis  grant  ANR-11-DPBS-0001.
Maxillary molar's blocks were demineralized with a 4. 13 % EDTA solution, embedded in paraffin, and the sections were histologically stained. Measurements of the thickness of reactionary dentin and area of inflammation were measured with ImageJ software. Results were compared with Kruskal Wallis and Mann Whitney U tests at p = 0. 05. Results: One week after Dycal capping, a statistically significant large number of aggregates of pulp cells enlightened pulpal inflammation compared to Hydrogel. At 2-3 weeks, reactionary dentin formation was increased at the periphery of the pulp chamber. After 4 weeks, a dentinal bridge sealed partially the pulp exposure, while tunnel defects persisting across reparative osteodentin. In contrast, 1 week after Hydrogel capping, inflammation was barely detectable.
Histological procedures Under intraperitoneal anaesthesia, the 24 rats were perfused through the heart with a buffered 4 % paraformaldehyde fixative solution injected with a Ministaltic pump. Bloc sections of the three maxillary molars were dissected out. The sections included the first molar and extended up to the third molar. They were fixed overnight and demineralized with 4. 13 % EDTA under microwave. The samples were dehydrated, embedded in wax, and sectioned (7-μm-thick sections)  (Decup et al. 2000). After dewaxing, the sections were stained with hematoxylin-eosin and examined with a light microscope. A series of staining methods were used, including Masson's trichrome, Mallory's staining, Alizarin red, and Alcian blue (at pH 2. 3 in order to visualize glycosaminoglycans). Measurements of the thickness of reactionary dentin were performed on figures printed at the same magnification with ImageJ software (National Institutes of Health, MD), 4 weeks after implantation of the capping agent.