dataesr/scientific-paragraphs-categorization huggingface dataset:
Erythromycin-sensitive clones were then confirmed by PCR for the absence of plasmid (see Table  2  for primer sequences). RAPD. Random amplification of polymorphic DNA (RAPD) (56) was carried out with DreamTaq enzyme (for primer sequences, see Table  2 ). Cycling conditions were 40 cycles consisting of 94°C for 1 min, 31°C for 1 min, and 72°C for 2 min; the final extension was continued for 7 min at 72°C. One microliter of liquid culture was used as the DNA template. The PCR products were separated by electrophoresis on a 1. 5% agarose gel. Accession number(s). The sequences of ICEs have been deposited in the EMBL Nucleotide Sequence Database under accession numbers LT622825 to LT622837.
ACKNOWLEDGMENTS We thank  Stéphane Bertin  and  Emilie Robert  for their technical help. N. D. is recipient of a scholarship funded by  INRA and Région Grand Est (formerly Région Lorraine). This work received financial support from the  Région Lorraine and Université de Lorraine  ( 2011-2013 ) and from  ANR  ( MATICE  project  ANR-15-CE21-0007 ).
Background: The aim was to compare the efficacy of the passive ultrasonic irrigation PUI and the Xp-endo Finisher (FKG-Dentaire, La-Chaux-de-Fonds, Switzerland) in removing the calcium hydroxide paste from root canals and from the apical third. Material and Methods: Sixty-eight root canals of single-rooted teeth were shaped using the BT-Race files (FKG-Dentaire, La-Chaux-de-Fonds, Switzerland). Ca(OH)2 was placed in all samples except for the negative control group (n=4). Remaining teeth were randomly divided into three groups: G1-Xp (n=30), G2-PUI (n=30) and the positive control group (n=4). Removal procedure consisted of three repeated one-minute-cycles. Samples were split longitudinally, photos of halves were taken at X6. 4 magnification and were analyzed using the ImageJ-Software (The National Institutes of Health NIH, Bethesda, Maryland, USA) to calculate the percentage of surfaces with residual Ca(OH) 2 ; the results were compared using the Wilcoxon-Mann Whitney test.
4 photographs using the ImageJ program (The National Institutes of Health NIH, Bethesda, Maryland, USA). Two observers (The 1St and the 3rd authors) carried out several pixel-calculation tests with the Ima-geJ software to calibrate the calculation method. The calculation method was as following: Firstly, the canal borders were manually selected on the program which allowed the calculation of the root canal surface area in pixels. Then the total area covered in residual Ca(OH)2 was calculated by selecting the borders of all the individual patches of Ca(OH)2. In this way, it was possible to obtain the percentage of the canal surface covered by residual Ca(OH)2 was obtained (Fig. 2 ). Photographs of the apical third at X16 magnifications were also coded to prevent the identification and were evaluated by two examiners (The 1st and the 3rd examiners) to give a score to each sample using the following scoring-system: 0: No Ca(OH)2 was found in the apical third, 1: the Ca(OH)2 is covering less than 10% of the apical third, 2: the Ca(OH)2 is covering less than 50% of the surface of the apical third, 3: the Ca(OH)2 is covering more than 50% of the surface of the apical third, 4: the Ca(OH)2 is covering all the surface of the apical third (Fig.
). Strain Marseille-P3388 T is the type strain of the new species 'Gabonibacter massiliensis'. MALDI-TOF MS spectrum accession number. The MALDI-TOF MS spectrum of 'Gabonibacter massiliensis' is available online (http://www. mediterranee-infection. com/ article. php?laref=256&titre=urms-database). Nucleotide sequence accession number. The 16S rRNA gene sequence was deposited in GenBank under Accession number LT631520. Deposit in a culture collection. Strain Marseille-P3388 T was deposited in the Collection de Souches de l'Unité des Rickettsies (CSUR, WDCM 875) under number P3388.
The digital imaging and communication in medicine (DICOM) data from the CBCT consist of voxels demonstrating their own gray level based on specific calculations of absorbed X-ray. A voxel is the equivalent of a 2D image pixel with an added third dimension for depth. The computer uses an algorithm for voxel data to reconstruct 3D volume. Following, a 3D surface model of the bone can be generated using a specific algorithm, which is a process qualified of mesh generation  [19]. To identify the surface of interest, a threshold value for the grey scale corresponding to the bone has to be specified by the operator in order to make voxels visible or not  [20]. Special 3D reconstruction software (Mimics, Leuven, Materialise, Belgium) was used to perform these procedures. The surface of the bony structure to be grafted was saved as a standard tessellation (.
Soft tissue closure was achieved using 4. 0 sutures. Sutures were removed 10-15 days following surgery during the first follow-up visit. The optimal solution would be to not use a provisional prosthesis during the 6 month healing period. In the esthetic area, fixed temporary bridges were delivered to the patient, or they were given a removal prosthesis, which was designed so as to not impinge the graft. During the healing period, patients were advised to pay attention to any potential soft tissue dehiscence, whereby a follow-up visit would be necessary. Control CBCT scans were taken 6 months following surgery to determine optimal implant width and length (Fig. 4a, b ). DICOM files obtained from CBCT were imported into a 3D reconstruction software program (Mimics, Materialise, Leuven, Belgium) where after accurate segmentation, a 3D model of the postoperative condition was obtained and saved as an.
Enterocins are safe bacteriocins (7) with possible applications as food additives and antibiotic-complementary and leishmanicidal agents (8-11). The genome of E. faecalis DD14 was sequenced. The sequence data were obtained using Illumina MiSeq and HiSeq 2500 technology platforms, with 2-ϫ 250-bp pairedend reads (MicrobesNG, Birmingham, United Kingdom). The closest available reference genome was identified using Kraken, and the reads were mapped to this genome using BWA mem (http://bio-bwa. sourceforge. net) to assess the quality of the data. De novo assembly of the reads was performed using SPAdes (http://bioinf. spbau. ru/spades). The functional annotation of predicted genes was achieved using the RAST server (http:// rast. nmpdr. org) (  12 ) to predict open reading frames (ORFs), followed by annotation using the SEED database (13).
DD14A and peptide DD14B, respectively, which are highly similar to enterocin MR10A and MR10B  (14). Another structural gene coding for enterolysin A was found in a cluster containing a putative associated immunity protein  (15, 16). The DNA coding for polyribonucleotide nucleotidyltransferase was identified with the MultAlin tool (http:// multalin. toulouse. inra. fr/multalin/) using the sequenced genome of E. faecalis V583 (GenBank gene identification no. 1201910). The Uniprot database (http://www. uniprot. org/uniprot/) entry is UniProtKB-Q82ZJ2 (PNP_ENTFA). RAST analysis revealed vancomycin B-type resistance protein (VanW)  (17) , putative coding genes for resistance to ␤-lactamases and fluoroquinolones, genes coding for virulence factors such as aggregation substance (Asal), and siderophore componentcoding DNA.
Furthermore, the presence of a putative clustered regularly interspaced short palindromic repeat (CRISPR), which stands as a barrier to foreign DNA uptake, was identified in the first contig of E. faecalis DD14 by use of the CRISPR finder tool  (18). Accession number(s). The whole-genome sequence has been deposited GenBank under the accession no. CP021161. The version described in this paper is the first version, CP021161. 1.
ACKNOWLEDGMENTS Genome sequencing was provided by  MicrobesNG  (http://www. microbesng. uk),which is supported by the  BBSRC  (grant number  BB/L024209/1 ). We are grateful to  Phileo (Société Lesaffre)  for financial support and  Agence Nationale de la Rechecrhe for ANR-Sincolistin  grant  CE-0015  (awarded to D. D. ).
Clinical data assessment. The following parameters were appraised: Number of Residual Teeth (NRT) and Decay Missing Filled (DMF) for dental history; Plaque Control Record (PCR)  [44]  and Gingival Bleeding Index (GBI)  [45]  for presence of bacterial plaque and gingival inflammation. For patients with periodontitis, in a second clinical session after dental prophylaxis, indices assessing the severity and extent of the periodontitis i. e. , PD, and CAL were recorded. Simultaneously, BOP was scored  [46, 47]. Clinical parameters were assessed at four sites on each tooth (mesiobuccal, distobuccal, mesiolingual, and distolingual) using a manual periodontal probe (Hu-Friedy, Chicago, Il, USA), at 20 g of pressure. Tooth sites excluded from the examination were impacted teeth, retained roots, and teeth with indeterminable cemento-enamel junction.
H NMR spectroscopy For NMR analysis, samples were thawed at room temperature and centrifuged for 2 min at 2000g before analysis. A volume of 0. 6 mL of saliva was placed into a 5-mm-diameter specific tube together with 0. 1 mL of D 2 O. The proton spectra were acquired at 500 MHz on a Varian Unity Inova 1 spectrometer at 25˚C. A signal was acquired after a 90˚pulse of 32K data points on a spectral window of 5000 Hz. The relaxation delay was 4s. The water signal was suppressed by a pre-saturation sequence using low-power irradiation (0. 03 W for 2s) on the water-signal frequency during the relaxation delay. The resulting free induction decays obtained with 128 transients were processed by NMR Pipe software. A Fourier transformation was applied with an exponential window function to produce a 1 Hz broadening line.
Spectra were phased and a multipoint linear baseline correction was applied. Each spectrum was referenced using a propionic acid signal (1. 04 ppm). The spectral region between 0-9 ppm was divided into 9000 spectral regions of 0. 001 ppm width, called buckets, using in-house C code. The water region was excluded  [5. 1; 4. 3 ppm]. The icoshift tool for Matlab (Matlab 1 2014, The Mathworks Inc. , Natick, MA, USA) was used for realignment  [49]. Each bucket was integrated and scaled using probabilistic quotient normalization  [50].
Statistical analysis Multivariate analysis. Principal component analysis (PCA) was performed on clinical and NMR data. PCA was used to assess the homogeneity of the cohort and to detect and exclude any outliers defined as observations located outside the 95% confidence region of the model. For clinical data analysis, PCA was performed on the patient group to assess the homogeneity of the patient cohort regarding the clinical data and to detect any relationships between clinical variables. This analysis was performed with the SIMCA-P statistical package (Umetrics, Umeå Sweden). For NMR data analysis, PCA was performed with an in-house Matlab 1 code using the same algorithm as SIMCA-P to detect any group separation based on signal variability. An orthogonal projection to latent-structure (OPLS) analysis was run to discriminate patients and controls.
Multivariate analysis of clinical data PCA was performed on periodontal variables from the 26 patients. The score plot is shown in Fig  1. No outlier was detected in the plan defined by the first and second principal components, PC1 and PC2, which correspond to 63% of the variability in the clinical data. Considering the positions of variables on the loadings plot, the loss of periodontal tissues (including CAL-MEAN, percentage of sites with severe or moderate CAL, BL), the criteria of active disease (MPD), and the percentage of bleeding sites (BOP) were strongly correlated and contribute strongly to PC1 variability.
Representation of the periodontitis population by this cohort of patients Clinical data indicated that our sample of 26 patients, aged from 18 to 64, was representative of patients with active periodontitis according to the usual criteria (i. e. inflammation, PD, CAL and BL). Cases were recruited according to the periodontal criteria (see Materials and methods) eliminating stable disease or morphologic abnormalities without inflammatory process (i. e. recession).
Acknowledgments We want to thank  Prs Catherine Chaussain  ( Conseil Scientifique UFR Odontologie Universite Ṕaris Descartes ) and  Jean-Jacques Lasfargues (Departement d'Ontologie, Hopital Bretonneau Paris)  for their constant support. We wish to acknowledge the contribution of  Sandy Field, PhD , for editing this manuscript. The study has been partly supported by a grant from  Institut Francais de Recherche Odontologique  (IFRO, 2013). There was no additional external funding received for this study.
Genome annotation and analysis. The assembled genome sequences of the nine A. thereius strains were annotated using a local installation of the Prokka platform v1. 11  [22] , using the feature prediction tools Prodigal v2. 60  [23] , ARAGORN v1. 2  [24] , Barrnap v. 0. 5 (http://www. vicbioinformatics. com/software. barrnap. shtml), Infernal v1. 1. 1, and SignalP v4. 1  [25, 26]. Further, protein-encoding sequences (CDS) were annotated by BLAST analysis (evalue cut-off of 10 -6 and 50% of identity) using all available bacterial proteins in the UniProt database 2015_04  [27]  and HMMER analysis using the Pfam  [28]  and TIGRFAM  [29]  databases. The genome sequences of the A. thereius strains were also annotated by the Rapid Annotation using Subsystem Technology (RAST)  [30, 31]  platform. Annotations from Prokka and RAST were merged in one final annotation file in order to reduce the number of hypothetical proteins.
CDS of interest were additionally analysed by PSI-BLAST (default setting) and metabolic pathways were constructed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database  [32]. Plasmid sequences were identified using PlasmidFinder version 1. 2  [33]  and the PATRIC  [34]  plasmid database (plasmid_seq, version 30/6/2014) and prophage regions were detected using PHAST  [35]. Antibiotic resistance genes were searched for by BLASTn analysis using the antibiotic resistance gene-annotation database (ARG-ANNOT;  [36] ) using default parameters and the comprehensive antibiotic resistance database (CARD,  [37] ) by BLASTp analysis with an default e-value cut-off of 10 -30 ; virulence factors were identified by BLASTn analysis using the human pathogenic bacteria virulence factor database (VFDB;  [38] [39] [40] ) with default parameters.
Comparative genome analysis. A maximum likelihood phylogenetic tree of a concatenated alignment of 239 single-copy orthologs was reconstructed. One-to-one ortholog genes were obtained by comparing a genome database comprising the A. thereius strains that have been sequenced in this study, all other Arcobacter genomes available from NCBI and Campylobacter jejuni NCTC11168 as outgroup. Orthologues were predicted using the Ortho-MCL software v1. 4  [46]  using the following settings: BLASTp e-value cut-off: 10 -6 ; identity cut-off 50%; reciprocal hit length cut-off: 50%. Further, in order to include only orthologs without evidence of recombination or gene conversion, a stringent filtering step was added. Briefly, the probability of recombination for each orthologous group was calculated using the PhiPack software  [47] , and discarding alignments without sufficient phylogenetic signal or with a p-value < 0.
05 (with 1000 permutations). Nucleotide corresponding to 239 single copy ortholog sequence sets were aligned with MUSCLE  [48] , trimmed with TrimAL  [49]  to remove columns with >80% gaps and concatenated. The maximum likelihood tree was built with RaxML  [50]  using the GTRGAMMA substitution model and 100 bootstrap replicates. Comparative analysis between A. thereius LMG24486 T and A. butzleri RM4018 and within the A. thereius strains were performed using the OrthoMCL software v1. 4  [46]  using the following settings: BLASTp e-value cut-off: 10 -6 ; identity cut-off 50%; reciprocal hit length cutoff: 50%. EDGAR  [51]  comparative genome analysis was also used to obtain the core and the pan genome plot of A. thereius species and to construct a synteny plot between A. thereius LMG24486 T and A.
butzleri RM4018 following default parameters. A phylogenetic analysis using MEGA 6  [52]  was performed based on the amino acid sequence of the virulence genes that were in common in all nine A. thereius strains. Further, amino acid sequences of Campylobacter and/or Helicobacter species were included in the analysis in the cases where an appropriate amino acid sequence could be retrieved from the NCBI data repository. Each dendrogram was made using the Neighbor-Joining method. Bootstrap values of >50% generated from 100 replicates are shown next to the branches. The evolutionary distance were computed using the p-distance method and are in the units of the number of amino acid differences per site. The Average Nucleotide Identity based on BLAST+ calculation (ANIb) was performed for the nine A.
thereius strains using JSpecies Web Server (JSpace WS) with default parameters  [53]. The genome of A. butzleri RM4018 and A. cibarius LMG21996 were used as an outgroup. In addition, in silico DNA-DNA hybridisation was calculated for all A. thereius strains using the genome-to-genome distance calculator following as alignment method the recommended GGDC 2 BLAST+  [54]. Genomes of A. butzleri RM4018 and A. cibarius LMG21996 were included as comparison. MLST profiles were obtained for the nine A. thereius strains using the Arcobacter pubMLST database (http://pubmlst. org/Arcobacter/) and according to the protocol of Miller et al. [55]. The nucleotides sequences were aligned and concatenated in the order aspA, atpA, glnA, gltA, pmg and tkt. The gene glyA was however not included as it was absent in A.
thereius strains 213, 216, 440, 452 and 11743-4. A dendrogram was constructed with MEGA 6 using the Neighbor-joining method. Bootstrap values of >50% generated from 100 replicates are shown next to the branches. The evolutionary distance were computed using the p-distance method and are in units of the number of bases differences per site. Alignment of the nine genomes of A. thereius has been generate with ProgressiveMauve [56] using default parameters. Clusters of orthologous group families (COG) were assigned, for all the nine A. thereius strains, using the rps-BLAST program against the NCBI Conserved Domain Database (CDD) and with an e-value cut-off of 1. 0 × 10 -3. Only the top hits were retained and the genes belonging to each COG categories were counted, and the distribution were compared using a Pearson chi-squared test using the IBM SPSS statistic program v23.
Background: The "General Oral Health Assessment Index" (GOHAI) was widely used in clinical or epidemiological studies worldwide, as it was available for use in different languages. Therefore, the aim of this study was to evaluate the psychometric characteristics of the GOHAI in a representative sample of patients with schizophrenia. Methods: A total of 90 schizophrenic patients (in-patients and out-patients) were recruited from the participants of the "buccodor study" (NCT02167724) between March and September 2015. They were selected using a random stratified sampling method according to their age, sex, or residential area (urban/rural area). GOHAI validity (construct, predictive, concurrent and known group validity) and internal consistency (reliability) were tested. Test-retest reliability was evaluated in 32 subjects.
Study population The sample size recommendations range from 1. 2 to 20 subjects per item for exploratory factor analysis (EFA)  [21] [22] [23] [24]  while for Tabachnick and Fidell  [25]  300 subjects are needed. There is a lack of consensus regarding how to compute the sample size to assess patient-reported outcome measures  [22]. Given the low prevalence of schizophrenia and to ensure the feasibility of the study, we included 90 subjects (7. 5 participants per item). For the present study, 90 patients were recruited from the BUCCODOR survey (NCT02167724). BUCCODOR is a Multicentre cross-sectional study, which evaluated the oral health of patients with schizophrenia in the department of "Côte d'Or" in France (530 000 inhabitants)  [26]. The patients were selected and examined between March and September 2015.
They were selected using a stratified randomization procedure, according to their age, sex or residential area (urban/rural area). A list of out-or in-patients schizophrenic persons was drawing up between from the administrative database of the three psychiatric hospitals of the department. They have been selected according to the population characteristics in Côte d'Or. In this French department 30% live in an urban area, 10% in a semi-urban area and 60% live in a rural area according to the National Institute of Statistics and Economic Studies (INSEE) data  [26]. A person who refused to participate in this study was replaced by a person with the same characteristics. In 1960, France opted for mental health care based on the definition of local catchment areas. The country was divided into sectors of approximately 70000 inhabitants  [27].
Data analysis Reliability The internal consistency of the GOHAI scale and the test-retest reliability were assessed. Internal consistency was assessed by Cronbach's α coefficient and inter-item correlation  [32]. All 90 subjects were included for the internal consistency test. Cronbach's α value > 0. 75 indicates excellent reliability whereas values between 0. 40 and 0. 75 show fair to good reliability, and values of <0. 40 indicate poor reliability  [32]. Test-retest reliability was assessed by repeating the GOHAI questionnaire in a subset of 32 participants. There is no sample size or power calculation for testretest, but a general rule of thumb is that 25 to 50 patients make an adequate sample for validation in the data analysis  [22]. For the second administration, done three weeks after the first, patients completed the questionnaire on paper and the responses to the items of the GOHAI scale were collected during a telephone call.
Validity Predictive validity was evaluated by examining the association between the GOHAI score and the global question on oral health satisfaction, socio-economic status (SES) (age, sex, level of education, residential area), data relating to oral health (tooth brushing frequency, last dental visit) duration of mental illness and smoking habit. It was hypothesized that SES, behavioural variables and perception in OHrQOL were predictive of GOHAI scores  [22, 35]. Concurrent validity was evaluated by comparing the GOHAI scores with the objectively assessed dental status. It was hypothesized that there would be a correlation between the GOHAI scores and the number of decayed, missing and filled teeth (DMFT) or the OHI-S index. It was hypothesized that participants reporting functional problems, psychosocial impacts, pain or discomfort and thus having a low GOHAI score would be more likely to report dissatisfaction with their oral health  [22, 35].
Known-groups validity was evaluated by comparing the GOHAI scores between in-and out-patients. It was hypothesized that in-patients would be most affected by the disease and would have a poorer perception in OHrQOl, than out-patients, and that GOHAI would be sensitive to differences between the two groups of patients  [22, 35]. As the data was skewed, non-parametric statistics tests were used. The Mann-Whitney U test was used to compare the score across the carious, missing and filled teeth, age, sex, level of education, duration of mental illness, dental attendance, oral behaviour variables and the mean GOHAI score. While the Kruskal-Wallis H test was used to evaluate statistical differences between the DMFT, OHI-S and the differential residential area (outor in-patients). The Jackknife method was used to calculate the confidence intervals.