dataesr/scientific-paragraphs-categorization huggingface dataset:
For construct validity, EFA was used to assess the factor structure of the GOHAI. Due to the small number of participants, we did not perform a confirmatory factor analysis (CFA). Indeed, for CFA, recommendations range from 150 to 1000 subjects  [36, 37]. The factor extraction criterion was eigen value more than 1. An item is considered to have loaded onto a factor when the factor loading exceeded 0. 30. Prior to the extraction of the factors, The Bartlett's test sphericity, scree plot, and the Kaiser-Mayer-Olkin (KMO) test were used. The KMO index ranges from 0 to 1, with 0. 50 considered suitable for analysis. The Bartlett's Test of Sphericity should be significant (p < 0. 05) for factor analysis to be suitable  [38, 39]. R Commander® and SAS® Software were used for the statistical analyses.
Acknowledgements The authors thank the staff and patients of all the hospitals ( La Chartreuse Psychiatric Centre, University Hospital of Dijon and Semur and Auxois Hospitals ) that participated in the survey. We thank  Nicolas Abello  for assistance in writing the manuscript and  Philip Bastable  for his help with English language correction. We are grateful to the  Regional Health Agency of Burgundy (Agence Régionale de Santé de Bourgogne)  and the  University Hospital of Dijon  for the financial support given to the study.
Colonies obtained were then identified with our systematic MALDI-TOF MS screening on a Microflex spectrometer (Bruker Daltonics, Bremen, Germany) as previously described  [15]. When a colony was unidentifiable, a 16S rRNA gene amplification was performed by standard PCR using fD1-rP2 universal primers (Eurogentec, Angers, France) as previously described  [16]. The complete 16S rRNA gene was then sequenced using internal primers (536F, 536R, 800F, 800R, 1050F, 1050R) as previously described  [17]. Sequences were finally corrected using the Codon Code Aligner software (http://www. codoncode. com) and then BLASTed (Basic Local Alignment Search Tool) against the GenBank nucleotide collection (nr/nt) or against GenBank 16S ribosomal RNA sequences (http://blast. ncbi. nlm. nih. gov/Blast. cgi). After the 16S rRNA gene sequence comparison, the strain is considered as a new species if the percentage of similarity is 98.
Phylogenetic classification A custom Python script was used to automatically retrieve all species from the same family of strain Marseille-P2749 T and download 16S sequences from National Center for Biotechnology Information (NCBI) by parsing NCBI results and the NCBI taxonomy page. It only keeps sequences from type strains found on the List of Prokaryotic Names With Standing in Nomenclature (LPSN) website (http://www. bacterio. net/). The phylogenetic tree was constructed using MEGA6 (Molecular Evolutionary Genetics Analysis) software  [19].
Spectral database search was performed using MS Search 2. 0 operated with the Standard Reference Database 1A (National Institute of Standards and Technology, Gaithersburg, MD, USA) and the FAMEs mass spectral database (Wiley, Chichester, UK). Antibiotic susceptibility was tested using a disk diffusion method  [22]  on Mueller-Hinton E agar (bioMérieux). Seventeen antibiotics were tested (i2a, Montpellier, France): vancomycin.
Genome annotation, assembly and comparison Open reading frame (ORFs) were predicted using Prodigal (http://prodigal. ornl. gov/) with default parameters. However, the predicted ORFs were excluded if they spanned a sequencing gap region. The predicted bacterial protein sequences were searched against the GenBank  [24]  and Clusters of Orthologous Groups database (COGs) databases using BLASTP. The tRNAs and rRNAs were predicted using the tRNAScan-SE  [25]  and RNAmmer  [26]  tools respectively. Signal peptides and numbers of transmembrane helices were predicted using SignalP  [27]  and TMHMM  [28]  respectively. Mobile genetic elements were predicted using PHAST  [29]  and RAST  [30]. ORFans were identified if their BLASTP E value was lower than 1e-03 for an alignment length greater than 80 amino acids.
If alignment lengths were smaller than 80 amino acids, an E value of 1e-05 was used. Such parameter thresholds have already been used in previous works to define ORFans. Artemis  [31]  and DNA Plotter  [32]  were used for data management and visualization of genomic features respectively. The Mauve 2. 3. 1 alignment tool was used for multiple genomic sequence alignment  [33]. The mean level of nucleotide sequence similarity at the genome level between strain Marseille-P2749 T and other bacteria (Fusobacterium perfoetens ATCC 29250 T JHXW00000000, Fusobacterium nucleatum ATCC 49256 T CP003700, Fusobacterium varium ATCC 8501 T ACIE00000000, Fusobacterium mortiferum ATCC 25557 T ACDB00000000, Fusobacterium russii ATCC 25533 T ARMK00000000 and Ilyobacter polytropus DSM 2926 T CP002281) was estimated using average genomic identity of orthologous gene sequences (AGIOS) software  [13].
Phylogenetic analysis After a 1-day preincubation period at 37°C in a blood culture bottle with sheep's blood, filtered rumen and antioxidants, and a 1-day anaerobic incubation period of cultivation of this enriched liquid medium seeded on Colombia agar with 5% sheep's blood, the strain Marseille-P2749 T was first isolated. MALDI-TOF MS was unable to provide reliable identification for colonies from this strain because its spectrum was not part of the database (Fig. 1(a) ). The phylogenetic analysis, based on 16S rRNA, showed that strain Marseille-P2749 T exhibited a 95. 9% sequence similarity  [35]  with Fusobacterium periodonticum strain ATCC 33693 T (GenBank accession no. X55405), the phylogenetically closest species with standing in nomenclature. Because the 16S rRNA sequence divergence was >1.
3% from its phylogenetically closest species with standing in nomenclature  [36] , we propose the creation of a new species which putatively classifies it as a member of the Fusobacterium genus within the Fusobacteriaceae family in the Fusobacteria phylum. The phylogenetic tree shows the position of strain Marseille-P2749 T relative to other phylogenetically close neighbours, based on the 16S rRNA gene sequence (Fig. 2 ). The 16S rRNA sequence of strain Marseille-P2749 T was deposited in European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) under accession number LT576389 (Table  1 ). To compare the spectrum of strain Marseille-P2749 T and the spectra of other related bacteria, a gel view was performed (Fig. 1(b) ).
Cells are susceptible to colistin, amoxicillin, amoxicillin/clavulanic acid, penicillin, clindamycin, metronidazole, oxacillin, imipenem, tobramycin, ceftriaxone, rifampicin and doxycycline. The length of the genome is of 1 809 169 bp, and the G+C content is 27. 33 mol%. The accession number of the 16S rRNA gene sequence deposited in EMBL-EBI is LT576389, and the accession number of the genome sequence is FMJA01000000. The type strain Marseille-P2749 T (= CSUR P2749 = DSM 103085) was first isolated from the duodenum liquid sample of a 60-year-old man. The habitat of this microorganism is the human gut. NMNI.
75 Amino acid transport and metabolism F 45 2. 73 Nucleotide transport and metabolism H 85 5. 16 Coenzyme transport and metabolism I 48 2. 92 Lipid transport and metabolism P 61 3. 71 Inorganic ion transport and metabolism Q 11 0. 67 Secondary metabolites biosynthesis, transport and catabolism R 90 5. 47 General function prediction only S 45 2. 73 Unknown function - 523 31. 77 Not in COGs COGs, Clusters of Orthologous Groups database.
For quantitative reverse transcription polymerase chain reaction (RT-qPCR), equal amounts of cDNA (200 ng) were used with the iTaq Tm Universal SyB®Green Supermix (Bio-Rad Laboratories) with primers concentrations and conditions indicated by the furnisher. A Bio-Rad iCycler IQ with iCycler IQ software v3. 1 (Bio-Rad Laboratories) was used with the auto-calculated threshold cycle (C T ) selected. β-Actin was selected as stable reference gene under the experimental conditions of this study. The cycle threshold (C T ) value of each gene was normalized to β-actin using the comparative 2 -ΔΔCT method.
Primary antibodies were incubated overnight at 4°C under gentle agitation. Antigen-antibody binding was detected using horseradish peroxidase conjugated species-specific secondary antibody for 2 h at room temperature followed by several washes with TBS containing 0. 05% Tween 20. Enhanced chemiluminescence substrates for horseradish peroxidase (Perkin-Elmer, Courtaboeuf, France) were applied and signals acquired and quantified using the Image Lab Software v3. 0 (Bio-Rad Laboratories). The intensity determined for a specific band in an assay was normalized to the intensity of the internal control GAPDH band obtained within the same assay. To measure the phosphorylation level of STAT-3, the band intensity obtained with the anti-phospho-STAT-3 (Tyr 705 ) antibody in an assay was expressed relative to the intensity of the band obtained with the antibody detecting the total antigen STAT-3 in the same assay.
P. gingivalis inhibited the mRNA expression of SOCS-3, IRF-1, EGF, and EGFR but not of STAT-3 The study sought to establish whether the infection of HGEC with live P. gingivalis would affect the mRNA expression of various genes potentially implicated in EGF signaling, mainly SOCS-3, IRF-1, EGF, EFGR, and STAT-3. HGEC were infected for 2 h with live and heat-killed P. gingivalis at a MOI of 100. Total RNA was extracted, and the mRNA expression levels of the tested genes were measured by RT-qPCR. The mean data obtained from three different experiments showed that the levels of expression of the mRNAs encoding for SOCS-3, IRF-1, and EFGR were significantly downregulated in infected HGEC (by 8-, 12-, and 5-fold, respectively) compared to corresponding levels measured in uninfected control cells (Figure  1a ).
These decreases were not significantly visualized when the infection was performed with the heatkilled P. gingivalis (Figure  2a ) and with live P. gingivalis at a MOI of 10 (data not shown). No band corresponding to the EGF protein was detected, reflecting the low mRNA expression level measured previously by RT-qPCR and thus explaining the need to supplement the culture medium of HGEC with rEGF to stimulate cell growth.
Methods, level of evidence and grade of recommendation To generate the recommendations summarized herein, a consensus group meeting was organized in Milan in November 2015 by ESMO and the Chordoma Foundation (CF). Representatives from the all the disciplines involved in care and treatment of patients with chordoma participated, including specialists in pathology, radiology, neurosurgery, ENT surgery, orthopedic surgery, general surgery, radiotherapy (RT), medical oncology, and palliative care (PC). A representative from main European, United States and Japanese RT centers with protons/carbon ions facilities and with experience in chordoma joined the meeting. Additional participants included patient representatives, statisticians, and molecular biologists. Prior to the meeting a literature search was conducted (details in the supplementary Appendix 1, available at Annals of Oncology online) to elucidate data upon which to base consensus recommendations.
Acknowledgements We are deeply indebted with the  Chordoma Foundation  and with  F. Longo , from the  European Society for Medical Oncology (ESMO) , for their invaluable support to the consensus building process, without which this effort would not have been possible. We are also deeply grateful to  Chordoma Foundation European Liaison Hans Keulen , who, sadly, lost his battle with chordoma prior to completion of this article, for inspiring and helping to spearhead this international consensus-building effort.
They were corrected by the SD of a uniform region of the phantom. For quantitative spatial resolution analysis, axial and transaxial modulation transfer function (MTF) values were calculated for each Jaszczak area using the average SD and the mean absolute difference CT values, measured for iodine and plexiglass. 6, 7 he reference value (MTF100) was determined from the CT number for pure iodine (CT iodine ) and plexiglass (CT plexi ), according to the Formula (1): MTF100 5 CT iodine 2 CT plexi 2 : ð1Þ A subjective blinded visual analysis was conducted by 10 trained CT users (3 radiologists, 4 dental surgeons and 3 technologists) to qualitatively classify the different acquisitions. This analysis was performed using the free software OsiriX (http://www. osirix-viewer. com). 8 bservers had access to all tools offered by the software (e.
Biofilm characterization by confocal scanning microscopy. After washing with PBS for 15 minutes, biofilms were stained with 5μM of Syto 1 40 nucleic acid dye (Molecular Probes, Lieden, The Netherlands) diluted in PBS for 15 minutes. Flow cells were then observed in situ with a Leica TCS-SP8 confocal laser scanning microscope (Leica Microsystems, Wezlar, Germany). An HC PL Apo 63X, 1. 4 NA, oil immersion objective lens was used for image capture and a numerical zoom of 1. 5 was applied. The 405-nm UV diode and a 420 to 500-nm band-pass emission filter were used to detect all bacteria stained with Syto 1 40. Biofilm stacks (123 × 123μm) acquired at 0. 5μm intervals were scanned with a line average of 2. Leica software (LAS AF V. 2. 2. 1) was used for microscope piloting and image acquisition.
Analysis of images based on Syto 1 40 fluorescence intensity levels were performed in Comstat 2 plugin in ImageJ software V1. 43m (National Institute of Health) to estimate characteristic parameters of biofilms: the biomass, representing the overall volume of the biofilm (expressed in μm 3 /μm 2 ), the average thickness (defined as the average of thicknesses over given locations, ignoring pores and voids inside the biofilm), the roughness coefficient (calculated from the thickness distribution) which is an indicator of biofilm heterogeneity, and the surface to volume ratio (the surface area divided by the biomass) which reflects what fraction of the biofilm is exposed to the nutrient flow. All these parameters are described in  [35]. 1. 2. 6 Quantification of bacteria by RT-PCRq. DNA was extracted from biofilms using the QIAamp DNA Mini kit (Qiagen) according to manufacturer's protocol with minor modifications: after centrifugation at 10 000g for 10 minutes, lysis step at 56˚C using proteinase K was extended to 18 hours.
Diameters were used to calculate maximal and mean bacterial volume for both species, which allow the estimation of the yield coefficient Y xs and the maintenance coefficient m s for each bacterial species with one specific susbtrate. Biomass and average thicknesses of biofilms calculated from fluorescence intensities increased from 3 to 48 hours in all types of biofilms, before a decrease at 72 hours. However, these data reflected mainly living bacteria, as the extent of Syto40 staining is much lower in dead cells than in living cells.
Using the percentage of volume occupied by bacteria in a biofilm, the volume and the dry weight of a bacterium (mean and max), the maximum biomass concentration c ðiÞ xm can be calculated: 38 kg x m -3 for P. gingivalis and 44 kg x m -3 for S. gordonii. In addition, numbers of bacteria produced within 24 hours were evaluated by PCRq. Protein consumption in bacterial culture was measured after 24h. From published data already obtained on S. gordonii  [36] , it was estimated that, in our experimental conditions, glucose was completely consumed after 24h by S. gordonii. These data combined to bacterial size parameters of Table  4  allowed the estimation of the yield coefficients Y xs for P. gingivalis and S. gordonii (Table  5 ). For each species, the maximum specific growth rates m ðiÞ m , the half saturation coefficient K ðiÞ s and the maintenance coefficient m ðiÞ s were calculated from growth curves established by measures of OD at 600nm in mono species bacterial cultures at different dilutions of BHIe.
Estimation of damage parameters using mono-species biofilms. As damage parameters for each species (α (i) , β (i) , γ (i) and δ (i) ) could not be obtained experimentally, these values were estimated on the basis of experimental mono-species biofilms data obtained at substrate concentrations of 16 g. L -1 for proteins and 2 g. L -1 for glucose. For a 2D model and flat biofilms, the simulated value of the biomass is fully correlated to the mean thickness. Mean thickness and roughness coefficient were therefore used to fit damage parameters. Mean thickness, measured at 3h, 24h and 48h, was used but only the value of roughness at 48h is useful because the previous values were very dependent on the unknown initialization. The rate of damage removal β (i) and the first coefficient of damage effect γ (i) were arbitrarily fixed, while α (i) and δ (i) parameters were fitted for each species thanks to the data of the monospecies biofilms.
Data obtained from these published studies allowed to understand adherence mechanism and to highlight molecules involved in co-adherence of both species  [10, 21, 23]. The purpose of the present mathematical simulation was to understand the following steps of biofilm development, especially growth step. To this aim, biological and simulated experiments were performed in nutriments enriched-medium and times of analysis were chosen from 3 to 72h after inoculation of bacteria. In a first approach, biofilm formation was studied under static conditions (without flow) to reduce the number of variable parameters of the mathematical modeling. Diffusion parameters were set up from values obtained in experimental biofilms. Experiments performed with AlexaFluor555-conjugated IgG anti-rabbit antibody and TMR-star in PBS confirmed that small molecules such as TMR-Star move faster than antibodies, as diffusion is dependent on molecule size  [37].
Conclusions The new mathematical biofilm model developed in this work is especially suited to study interactions between different bacteria species in two species biofilms: either independence between species, competition for substrate and production of toxic substance by one species. The last model, with production of a toxic substance, was validated by experimental data on P. gingivalis and S. gordonii species and is now available to explore different experimental conditions. For example, damage localization associated to biofilm growth was analyzed according to substrate concentrations. New species and new nutrients can be added in this model, the only limitation being the availability of the growth parameters for each species and nutrient. In this study, experimental data were obtained in rich medium (BHI).
Each sample was amplified using primers incorporating a unique barcode and Roche 454 adapters. PCR amplicons were purified, sized, quantified, and pooled in equimolar proportions. Emulsion PCR and unidirectional sequencing of the libraries were performed using the Lib-L kit and Roche 454 GS-FLX Titanium sequencer at the Department of Biochemistry, University of Cambridge. Processing and analysis of sequences was carried out using the mothur analysis suite version 1. 36. 1  [6]  based on the Schloss SOP (January 2016)  [7]. Sequences were clustered into operational taxonomic units (OTUs) at a sequence dissimilarity distance of 0. 015 using an average neighbour algorithm and then classified using a naïve Bayesian classifier implemented in mothur with the Human Oral Microbiome Database (HOMD) reference dataset (version 13.
2). The α-diversity of bacterial communities based on OTUs were analysed using approaches implemented by mothur: diversity of the communities were estimated using Simpson's inverse diversity index  [8]  and the total richness of the communities was estimated using Chao1  [9]. For β-diversity analyses, the datasets were subsampled so that the number of sequences was equal in all samples (equal to that of the sample with the lowest number of sequences in the dataset). To compare the β-diversity of samples based on OTUs, the Jaccard Index (ratio of shared OTUs to distinct OTUs) and the θ-YC metric (compares community structure by accounting for the relative abundance of taxa)  [10]  were used to generate distance matrices in mothur, and visualized as ordination plots by nonmetric multidimensional scaling (NMDS) plots.
Ordination plots were generated in R (r-project. org) using the rgl and ggplot2 packages. Analysis of molecular variance (AMOVA)  [11]  as implemented by mothur was performed to determine whether clustering patterns seen in the NMDS plots were statistically supported by differences in the distance matrix. Homogeneity of molecular variance (HOMOVA) was performed to compare the genetic diversity between groups. Parsimony  [12]  was used to compare community structure between groups. Samples were compared between treatment groups and over time within groups. An oligotyping analysis was also performed. Sequences identified as belonging to the genus Actinomyces were extracted from the quality-filtered mothur dataset and formatted using the 'mothur2oligo' tool (available at https://github. com/michberr/ MicrobeMiseq/tree/master/mothur2oligo).
Minimum entropy decomposition (MED) analysis  [13]  was performed on 33,777 reads from 117 samples with MED pipeline version 2. 1 (available at https://meren. github. io/projects/med/). The parameters used were minimum substantive abundance of a MED node (-M) = 7 and maximum variation allowed in each node (-V) = 4 nt. The MED algorithm is similar to the previously described oligotyping algorithm  [14] , differentiating taxa on the basis of single-nucleotide differences in the positions of highest entropy. This method results in a very fine discrimination of taxa and deals effectively with sequencing errors. Compared with oligotyping, the MED pipeline allows an automated analysis, which, needing less supervision from the user, can be applied to taxonomic groups of higher diversity with fast and reproducible outputs.
To identify the MED nodes at species level, a representative sequence from each was compared to the HOMD database through the web tool (available at http://www. homd. org). The matrix describing the number of sequences of each MED node present in each sample was first converted to a proportion matrix dividing each count by the total number of sequences for that sample. Counts of oligotypes from the same species were combined to give species-level relative abundances. These were then compared before and after treatment using the paired Wilcoxon test.
Statistical Analysis Antibiofilm tests were performed in quadruple, and the results were expressed as mean values ± standard errors of the means. Differences in the biofilm eradication potentials were evaluated by an unpaired non-parametric analysis of variance (Kruskal-Wallis test) followed by Dunn's multiple comparisons test of each mean versus the mean of the untreated (positive) control. Approximate eradication efficiency (percentage) was calculated according to the following equation: (Average [control wells]-Average (treated wells)/Average (control wells)]×100. GraphPad Prism® Software (Version 6. 05; GraphPad Software, Inc. ) was used for statistical analysis, considering a P < 0. 05 to be statistically significant.
2a ). The 58 kDa protein spot was localized by running a second gel in parallel, for Western ligand blotting with biotinylated hFg (Fig. 2b ). The 58 kDa protein corresponded to two major spots (Fig. 2b , lane 3) matching the spots detected on the silver-stained gel (Fig. 2a , lane 3). The protein spot of interest was excised and characterized by MALDI-ToF on peptide mixtures after in-gel digestion. Figure  2c  shows the MALDI mass spectrum of a tryptic peptide mixture produced from the protein spot arrowed in Fig. 2a. Twenty-two experimentally obtained tryptic peptide masses were found to match predicted peptide masses to within 0. 1 Da, covering 64% of the amino-acid sequence. Protein sequence database searches identified the protein as a P. acnes host cell-surface attachment protein (Table  1 ) (GenBank accession number DQ469873.