dataesr/scientific-paragraphs-categorization huggingface dataset:
The proportions of each pair of contrast agents were adjusted using a proper MATLAB code that allows calculations based on data obtained from imaging unmixed contrast agents. The concentrations (mg/mL) of the mixtures depended on the corresponding attenuation at 120 kVp. The target attenuation for each solution was chosen within the clinical standard for CT angiography applications, i. e. 280 Hounsfield units (HU) approximately  [25]. Hence, the concentrations of gadolinium, AuNP and iodine were in the range of 0-7. 45 mg/mL, 0-10. 4 mg/mL and 0-8 mg/mL, respectively. The contrast agent concentrations are presented in Table  1.
Image analysis Image analysis was performed using MATLAB (R2015a, MathWorks, Inc. ). The samples were automatically detected on conventional images and circular regions of interest (ROIs) were automatically drawn in the middle of each tube. The same ROIs were used on all images generated on a given dataset and mean and standard deviation were computed for each ROI. Linear regression was performed between the prepared and measured concentrations for all solutions. The cross-contamination of the material decomposition, i. e. the amount of a measured material in other material image (e. g. amount of gadolinium and gold measured in water and iodine images), was measured for each material as the root mean square error (RMSE) of its concentration in the other basis materials' ROIs, where no signal would be expected in the iodine image and where a mean value of 1000 mg/mL would be expected in the water image.
Statistical analysis In order to assess the impact of mixing the contrast agents, a reliability analysis between the non-mixed and the mixed measured concentrations was performed using intraclass correlation coefficients (ICC) and their 95% confident intervals (CI). The statistical analyses were performed using SPSS statistical package version 24 (SPSS Inc. , Chicago, IL, USA) based on a mean rating for each contrast agent (k = 3 e. g. Gd, Gd + AuNP, Gd + I), two-way mixed-effects model.
Acknowledgements We thank employees from the  CERMEP-Imagerie du Vivant  for helping with the experiment. We also thank  Dr. Ira Blevis ,  Dr. Simon Rit ,  Dr. Cyril Mory ,  Dr. Yaron Younes ,  Dr. Luisa Poggi  and  Dr. Joelle Belgamire  for their help in designing and writing the manuscript.
Digital impression, color registration and tooth virtual removal To avoid the risk of extraction of the loose incisor that could occur when taking an impression with alginate, we decided to make a digital impression of the patient's maxillary and mandibular dental arches with an intraoral scanner (TRIOS 2; 3Shape Copenhagen, Danemark) (Fig. 1c  and d ). Vestibular areas were scanned using lip and cheek retractors (Optragate, Ivoclar, France). Arches were then virtually aligned using two vestibular records, as recommended by the manufacturer. Color registration was performed with the Vivodent PE shade guide (Ivoclar, France) (Fig. 1e ). Arch digital impressions were converted into STL files and imported in dental CAD software (DentalCad, Exocad, Germany) (Fig. 2a ). The traumatized incisor was then removed virtually (Fig.
Conception and manufacturing of the removable partial denture A virtual central incisor was generated from the contralateral central incisor (21) by using the mirror tool (Fig. 2e ) and saved in an independent file. The latter was then imported into a second CAD software (Freeform, 3D Systems, US) and two small cylindrical volumes were created into the palatal side of the virtual incisor to create a retention area (Fig. 3a  and b ). In the same software, limits of the denture base were virtually designed by using the point and click tool. The denture was generated with a volume (average thickness of 2. 5 mm) corresponding to a replica of the patient palatal surface (Fig. 3c  and d ). Data generated were then exported to a 5-axis milling machine (DWX 52 DC, Roland, Japan) and the resin artificial incisor was made by milling a stratified ivory disk of PolyMethylMethAcrylate (PMMA) (Trilux, Vipi, Italy).
Sequence analysis Metagenomic sequencing was performed on six teeth from six subjects. For comparison and control of bacterial communities coming from the soil with the buccal bacterial community, three bones from three other subjects were also sequenced as environmental/soil controls (S1 Table ). The DNA sequence data generated in this study have been deposited on the NCBI database (Bioproject PRJNA302605, SRA accession numbers SRR5581849, SRR5581851-58 and SRR6785785-87). Methods for the read sequencing process, for the analysis of nuclear and mitochondrial human genomes and aDNA damage are described in S1 Appendix. The identification method of microbial communities by MetaPhlAn and MALT software are also described in S1 Appendix. Finally, dental pathogen genomes were selected for their involvement in oral pathologies such as caries, periapical abscesses and periodontal diseases according to our macroscopic and radiologic analyses.
Metagenomic profiling Microbial taxonomic profiling was performed using both the methodology described by Schubert and colleagues  [46]  and based on the MetaPhlAn specific database, and MALT  [47]  applied to the NCBI nucleotide database (https://www. ncbi. nlm. nih. gov/nucleotide). One Differences were observed between the taxonomic read assignments derived from MetaPh-lAn and MALT, especially pertaining to the identification of three oral bacteria (P. alactolyticus, C. rectus and A. viscosus), which were identified in MetaPhlAn (from 48% to 0. 4% in S4  ). Reciprocally, MALT revealed the presence of the dental pathogen Tannerella forsythia in 213 sample at a particularly-high abundance level (34%) and in sample 306 (9%; S4 and S6 Tables with complete hits from MALT-MEGAN6), but this pathogen remained undetected by MetaPhlAn.
This confirms that different computational tools presently available The frequencies of all possible mismatches observed between the human nuclear genome (hg19), the P. gingivalis and S. mutans chromosomes and their mapped reads, respectively, are reported in gray according to the distance from 5' end (left panel, first 25 nucleotides sequenced) and distance to 3'end (right panel, last 25 nucleotides sequenced). The typical DNA damage mutations C>T (5') and G>A (3') are reported in the dotted and solid lines, respectively. https://doi. org/10. 1371/journal. pone. 0196482. g002 for carrying out a taxonomic identification of past microbial communities show different performances, probably due to various sensitivity and specificity levels, as well as differences in their underlying database  [44].
gingivalis and P. alactolyticus in high numbers. Concerning these dental pathogens, we noticed that 1) high coverage was found for several pathogen genomes, from 0. 5 to 7X. For example, in samples 213 and 306, we found P. alactolyticus at 4. 4X and 7X, T. Forsythia at 2X and 0. 98X respectively; in 306, O. uli at 4. 8X; in 406, R. dentocariosa at 1X; in 213, P. gingivalis at 0. 6X and, in 309, S. mutans at 0. 5X; 2). The mapped sequences covered positions in the genome at least once, with a minimum of 35%: from 35 to 50% for P. gingivalis, R. dentocariosa and S. mutans, 75% for T. forsythia, 82% for P. alactolyticus, and 93% for O. uli; 3) a specific pathogen signature was associated with the tooth of a given individual as seen in the detailed S4 Table  and    these results were confirmed by displaying them with the IGV software, which showed a uniform distribution of reads along the bacterial chromosomes.
Pictures realized and assembled by C. Willmann. https://doi. org/10. 1371/journal. pone. 0196482. g001 output of MetaPhlAn software was the relative abundances of reads showing significant matches to microbial genome databases. Fig 3 illustrates specific microbial abundances (%) detected in MetaPhlAn database in teeth and bones libraries. It was noted that the bone libraries 312 and 307 were different from 702, with a specific pattern demonstrating no contamination by bacterial pathogen DNA in the laboratory steps. Bone libraries were essentially composed of bacteria from the soil: mainly of Arthrobacter sp. for sample 307; of Nitrobacter sp. and of Bacillus haludorans for sample 312; of Nitrobacter sp. and of Rhodococcus erythropolis for sample 702. The MetaPhlAn analysis of the bacterial composition of tooth libraries showed the presence of dental pathogens (Fig 3): Actinomyces viscosus, Campylobacter rectus, Olsenella uli, Parvimonas micra, Porphyromonas gingivalis, Pseudoramicter alactolyticus, Streptococcus mutans/sanguinis, Treponema denticola, and Rothia dentocariosa, which were identified in subjects 213, 306, 309, 403, and 406.
aDNA analyses were funded by the  Institut National de Recherches Archeologiques Preventives (INRAP) ,  France. C. Willmann 's laboratory PhD work was funded by the  Institut Francais pour la Recherche Odontologique (IFRO). This work was performed in collaboration with the  GeT core facility, Toulouse, France  (http://get. genotoul. fr),and was supported by  France Genomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The radiological protocol was I-CAT CBCT, 0. 3 mm slice thickness, 8 cm × 16 cm field of view, 120 kVp, and 18 mAs. A total of 36 teeth were extracted from 14 patients between the ages of 18 and 68 years. Using 3D Slicer software, segmentations were performed twice by 2 independent observers, with a 1-month time period between the 2 segmentations to study intra-and inter-observer repeatability and reproducibility. Four windowing protocols (level/window) were applied: 1. 1131/1858, 2. 2224/4095, 3. 1131/4095, and 4. AUTO, an automatic protocol provided by default by the software. A total of 576 segmentations were performed. Tooth volumes were automatically calculated using the software. To compare the volumes obtained from CBCT segmentations with a gold-standard method, we laser-scanned the extracted teeth.
10 Two independent observers manually segmented the 24 premolars twice with Amira 3D software (FEI Visualization Sciences Group, Hillsboro, USA), applying subjective modifications of the windowing of CBCT images during the segmentation process. The reference method was based on water displacement: A subjective visual assessment of the lower level of meniscus (0. 1 mL gradation) was performed when the tooth was placed in a graduated cylinder. 10 Liu et al. concluded that there was a significant difference between the physical volume measurements of the extracted teeth and the CBCT measurements (-4% to +7%; p < 0. 05). The authors also stated that surface smoothing reduced the volume by 3-12%, and that no specifications could be given at that time regarding how to accurately segment teeth from CBCT.
Spin-Neto et al. suggested a 1131/1858 protocol for the best tooth visualization. 17 Lee et al. used a 2224/4095 protocol to generate 3D virtual surface models of the maxillary and mandibular dental arches that include whole with the roots. 18 However, the protocols by Spin-Neto et al. and Lee et al. have not been validated. Therefore, we proposed to search for the potentially most accurate in vivo windowing-based tooth segmentation protocol from CBCT. We wanted to compare the 2 protocols found in the literature with 2 proposed by our research team: 1. automatic adjustment of the windowing by the 3D Slicer software AUTO protocol, and 2. a mixed approach based on the protocols found in the literature: 1131/4095. 17, 18 The null hypothesis was that all 4 windowing protocols were equivalent for in vivo tooth volume measurement from CBCT.
Prior to the start of the study, the two observers had trained with manual segmentation on 10 other teeth that were then excluded from the main study. The segmentations were performed using 3D Slicer 3. 6. 1 open source software (SPL, Harvard Medical School, Boston, USA). 19 Each segmentation consisted of a contour of the tooth image (enamel, dentin, crown and roots), sliceby-slice, by looking at the slices on the computer screen, using a mouse, and using the Editor module of the 3D Slicer software tools, with such functions as: 1. "paint", 2. "level tracing" and 3. "draw". The segmentations were mainly performed on axial slices, and some adjustments were then performed on sagittal and coronal views (Fig. 1 ). Four different windowing protocols (level/window) were applied: 1. 1131/1858,  16  2.
2224/4095,  18  3. a mixed protocol based on protocols 1 and 2: 1131/4095, and 4. AUTO, an automatic protocol provided by default by the software (Fig. 2 ). In total, 576 segmentations were performed. Three-dimensional reconstruction of each tooth was automatically performed using 3D Slicer software, and the tooth volume was automatically calculated by the software. No smoothing function was used, as smoothing may modify the entire volume of the tooth. 10 The volume was also automatically measured in mm 3 using the Statistics module of the 3D Slicer software. To compare the volumes obtained from CBCT segmentations with a gold-standard method, we used the laser scanning method (Ceramill Map300, Amann Girrbach AG, Koblach, Austria), which is based on structural light. The 36 teeth were laser-scanned according to the manufacturer's instructions, within an error of 20 µm (Tables  1 2 3 4 ).
Inter-dental contact makes segmentation and the interpretation of the external limits of dental crowns difficult. The teeth appear to be merged together on 2D slices. Parameters specific to the observer that may affect the results of segmentation include: 1. the observer's experience using segmentation software; 2. his/her knowledge of dental anatomy, which is important when the observer needs to contour the apex region with complex root anatomy; and 3. any tiredness on the observer's part during the manual segmentation process, which may decrease the accuracy of segmentation, particularly with complex root anatomy, and when the time spent on one segmentation is excessively long. Parameters specific to the software used (3D Slicer) are 1. the tools used for manual segmentation, and 2. the contrast/windowing control.
We mainly used the "paint", "draw" and "level tracing" tools in the 3D Slicer software. The "level tracing" tool is well suited for tooth segmentation with clear limits, but if the limits are not precise or if there is a structure with a density close to that of the tooth, the "level tracing" tool is not the best option (Fig. 5 ). Other parameters that may modify the results of manual segmentation are related to the CBCT radiological protocol, such as mAs, kVp, slice thickness, field of view and voxel size, and also due to movement artifacts in the CBCT device. 26, 27 he limitations of this study are related to the CBCT radiological protocol. We tested the windowing protocols in only one type of radiological protocol, without modification of the type of CBCT device, mAs, kVp, slice thickness, field of view or voxel size.
We also did not compare windowing protocol results between different types of CBCT devices. Therefore, our findings are strictly limited to the use of 3D Slicer software and to I-CAT CBCT, with a 0. 3 mm slice thickness, 8 cm × 16 cm field of view, 120 kVp and 18 mAs. Also, we used only manual segmentation, as this technique still represents a gold standard. ] [30]  In conclusion, the null hypothesis was rejected. Compared with the other windowing protocols we investigated, the 1131/1858 windowing protocol allows for significantly closer 3D in vivo segmentation of a tooth from I-CAT CBCT, with excellent intra-observer repeatability and inter-observer reproducibility.
of the  Australian Academy of Science. MLC gratefully acknowledges an  ARC Laureate Fellowship  ( FL170100041 ), financial support from the  ARC Centre of Excellence for Electromaterials Science  ( CE140100012 ), and supercomputing time on the National Facility of the  National Computational Infrastructure. T. W. S. acknowledges the  Australian Research Council  for a  Future Fellowship  ( FT130100177 ). This work was supported by the  Australian Research Council (Centre of Excellence in Exciton Science   CE170100026 ).
Statistical analyses Data analyses were performed using R software (3. 0. 2). Satisfaction of energy and protein requirements data were arcsin square-root transformed then analysed with two linear mixed models including diet as fixed effect and horse as random effect. Strongyle faecal egg excretion data were also analysed with a linear mixed model including diet, sampling date (FEC-24, FEC-4, FEC4, FEC18) and diet × sampling date interaction as fixed effects and horse as random effect. Individual was the experimental unit. Linear mixed models were performed using the lme function of the nlme package. We tested for differences between sampling dates using the glht function of multcomp package (Tukey's correction). We also tested for between-diet differences in estimated worm count, juvenile-to-adult strongyle ratio, and estimated female fecundity (i.
Acknowledgements The PhD thesis of Claire Collas was funded by  IFCE  and the  Animal Physiology and Livestock System (PHASE) division of INRA. The experiment was funded by  IFCE  and by the  Integrated Management of Animal Health meta-programme  ( GISA-Strep ). The authors thank  Multifolia and Mg2Mix  for providing them with sainfoin pellets. They also thank the staff of the  IFCE experimental farm -Patrice Dupuy ,  Cédric Dubois ,  Jacques Boulanger ,  Claude Larry ,  Joseph Bellonie  and  Patrick Paucardfor  their technical assistance,  Aline Le Morvan  and  Angélique Quereuil  from  INRA   UMR1213 , and  Hélène Macé , student at  Angers Agriculture School (ESA).
2 ). Figure  2  displays the predictions from the selected model for a subset of individuals with representative home range compositions to illustrate the highly variable functional response in relation to the relative availability of the different habitat types in the individual home ranges (see Material and Methods for further information on how these "representative" animals were chosen). Note that the reader can draw this plot for any other animal of our dataset using the function "margin-alityDots" of our package "roedeer3sites" provided on github. In particular, see the section 5 of the examples on the help page of this function). At CH and LPP, the selection of shrubs changed in relation to their availability, which indicated a functional response in habitat selection. Below an availability of 20%, shrubs were avoided (i.
By comparing the outcome of the models with and without these three individuals, we safely concluded that these three particular individuals did not influence the results (see the help page of the dataset "coefficientsModel2" in the package "roedeer3sites" provided on github to reproduce this comparison). Predicted posterior distribution of the marginality in the ecological triangle for 8 female roe deer selected to be "representative" of the available environmental context experienced in each study area. The left column corresponds to females monitored at Chizé (animal name ending in "_ch"), the central column corresponds to females monitored at La Petite Pierre (animal name ending in "_lpp"), and the right column corresponds to females monitored at Trois-Fontaines (animal name ending in "_tf ").
We used the deviance information criterion (DIC)  50  to compare f 1 and f 2 and selected the most parsimonious model. We checked the convergence of the MCMC chains both visually and using the diagnostic of Gelman and Rubin 51. These diagnostics did not reveal any problem of convergence of the MCMC. We examined the fit of the model using the approach recommended by Gelman and Meng  52. For every value θ r of the vector of parameters sampled by MCMC, we simulated a hypothetical replication of the dataset using equation (1), i. e. , we simulated a number of locations in each habitat type for each monitored individual. Therefore, for each animal and each habitat type, we simulated 1,000,000 values of the number of locations that could have been observed if this model was true. We then compared the observed number of relocations of each roe deer in each habitat type with the statistical distribution for this number expected under our model (by checking that the observed number falls within the limits of the 95% credible interval derived from this distribution).
All analyses were carried out using the R software  54. We packed the data and R code used for the analysis in the R package "roedeer3sites", hosted on github at the following URL: https://github. com/ClementCalenge/roedeer-3sites. The package "roedeer3sites" can be installed in R with the function install_github() provided in the R package "devtools", by typing at the command prompt (after installation of the package devtools): library(devtools) install_github("ClementCalenge/roedeer3sites") The help page describing the package, accessed by typing help("roedeer3sites-package") at the R command prompt, provide detailed information on how to reproduce the results presented in this paper.
Acknowledgements This study was funded by the  Office National de la Chasse et de la Faune Sauvage. We are grateful to the wildlife technicians ( Jean-Luc  and  Monique Hamann ,  Marc Marchi ,  Thibaut Amblard ,  Olivier Widmer ,  Daniel Delorme  and  Guy Van Laere) , the foresters ( Hubert Holveck ,  Hubert Georg ,  Philippe Mortz ,  Eric Muller ,  Denis Dagneaux  and  Eric Krauser ), the 79, 52 and 67 Departemental Services and the many volunteers for their help in the capture of roe deer. We also warmly thank  François Débias ,  Julie Lorand ,  Emmanuelle Richard ,  David Storms  and all the volunteers who helped us to collect vegetation data. We would like to thank  Simon Chamaillé-Jammes  and  François Klein  for constructive comments on previous drafts of this manuscript.
The extent of binding by biotinylated peptide was then detected by incubation with streptavidin-alkaline phosphatase conjugate (GE-healthcare, Saclay, France), and after washing, by addition of 4-methylumbelliferyl phosphate substrate (Sigma, France). Fluorescence was assayed at 450 nm, with excitation at 365 nm. The peptide concentration that prevented binding of 50% of the labeled peptide (IC50) was evaluated. Data are expressed as relative affinity, as a ratio of the IC50 of test peptide to the IC50 of a reference peptide chosen as a high binder to the corresponding HLA-DR heterodimer. Mean + SEM were calculated from three independent experiments and relative affinities of <20 were considered high binders and relative affinities of 20-100, moderate binders.
resUlTs immunoprevalent T-cell epitopes recognized by seropositive healthy Donors We initially addressed human T-cell epitope identification using overlapping peptide libraries  (27, 28). Protein sequences of 20 key, serodominant, Bp proteins  (13) , previously reported by our group, were retrieved from the NCBI protein database. Peptide libraries were synthesized for each candidate as 20 mers with an overlap of 10. The details of peptide libraries are shown in Table  1  and the strategy for identification of T-cell epitopes is shown in supplementary data (Figure  S1  in Supplementary Material). We screened for induction of T-cell IFNγ responses against each peptide, totaling 739 peptides from 20 Bp antigens; for this we used PBMC from healthy, seropositive donors from an endemic area. Demographic data and HLA frequency distribution are shown in Table  S1  in Supplementary Material.
acKnOWleDgMenTs We acknowledge the help from medical staff and blood-bank donors at Khon Kaen Hospital and Ms. Jeerawan Dhanasen for collecting buffy coat samples. The authors wish to thank patients and medical staff at Srinagarind Hospital for melioidosis cases enrollment, and Prof. Gregory J. Bancroft for scientific comments. We would like to thank George Ralis and Timothy Gondre-Lewis at the NIH/NIAID and our colleagues at IEDB.