dataesr/scientific-paragraphs-categorization huggingface dataset:
Results of thePCA are represented by a two-dimensional projection of the two first principal components (PC1 vs. PC2), which describe the statistical correlations between the investigated variables on a correlation circle. Classical PCA has been applied to the log-transformed LA-ICP-MS dataset of trace element contents in tourmaline (n=116 spot analyses). A total of 18 variables has been selected for the PCA including the main trace elements (Li, Sc, Ti, V, Cr, Co, Zn, Sr, Y, Zr, Nb, Sn, LREE, HREE, W, Pb, Th, U) determined by LA-ICP-MS. Other trace elements at very low concentrations (<1 ppm) or below the limits of detection were not included. PCA applied to each individual generation of tourmaline led to the same group correlations than PCA applied to the complete tourmaline dataset. The projection of the PC1 vs.
Acknowledgements This work was supported by the  French National Research Agency  through the national  LABX-21-RESSOURCES21 , and has also taken part of the project  ERAMIN "New Ores ". The authors would like to thank  Dr. Hossein Ahmadzadeh  and the  Musée Minéralogique de l'Ecole des Mines d'Alès (France)  for supplying samples of collection from the Puy-les-Vignes mine. We also thank  Sandrine Mathieu ,  Dr. Andreï Lecomte ,  Dr. Olivier Rouer , and  Dr. Chantal Peiffert  ( GeoRessources, Vandoeuvre-lès-Nancy, France ) for technical support in providing analytical data with SEM, EMPA, and LA-ICP-MS. We are also grateful to Dr. Andrey Gurenko  and  Dr. Etienne Deloule  ( CRPG-CNRS, Vandoeuvre-lès-Nancy, France ) for their help during the SIMS analyses. Constructive reviews and suggestions by  Dr.
Mass Spectrometry Analysis LC-MS/MS analysis was performed on an LTQ-Orbitrap Velos mass spectrometer (ThermoFisher Scientific). A sample was loaded by an Easy n-LC II nanoflow system (ThermoFisher Scientific) on a 15 cm fused silica emitter (New Objective) packed in-house with reversed phase material (Reprusil-Pur C18-AQ, 3 µm) at a flow rate of 500 nl/min. The bound peptides were eluted by 140 min runs comprised of a segmented gradient from 10 to 30% of solvent B (80% acetonitrile, 0. 5% acetic acid) over 96 min, 30 to 50% of solvent B over 12 min and 50 to 60% of solvent B over 7 min at a flow rate of 200 nl/min. Peptides were sprayed directly into the mass spectrometer using a nanoelectrospray ion source (ProxeonBiosystems). The mass spectrometer was operated in the positive ion mode with application of a data dependent mode to automatically switch between MS and MS/MS acquisition and a dynamic exclusion for the subsequent 90 s of ions once selected for fragmentation.
Data Processing MS raw files were collectively processed with the MaxQuant software suite version 1. 2. 0. 32 (60). Peak lists were searched against the mouse swissprot database version 2011_07 using the Andromeda search engine (61). Trypsin was selected as proteolytic enzyme and we required a minimal peptide length of 6 amino acids and maximal 2 missed cleavage sites were allowed. Carbamidomethylation of cysteine residues was set as fixed modification while oxidation of methionine and protein Nacetylation were allowed as variable modifications. For peptide quantification the "match between runs" function was enabled with a 2 min time window.
In a study where 584 trauma patients were examined within 4 h of injury, GFAP and/or UCH-L1 were both significantly higher in CT negative mTBI patients than   1 ). Randomized groups of mice were sacrificed at each of the designated time points throughout the 12 month study and the brain cortex was dissected and frozen. Extracts of the cortical brain regions were prepared followed by T-tau and P-tau quantitation by EIMAF. Biomarker concentrations were determined and data plotted as mean ± SEM. Analysis of statistical significance was performed by two-way ANOVA with Tukey's post-hoc test for: sham P-tau vs. rmCHI P-tau ( #### p < 0. 0001), rmCHI P-tau vs. rmCHI+drug P-tau (****p < 0. 0001), and sham T-tau vs. rmCHI T-tau ( $$$$ p < 0. 0001). in patients with orthopedic injuries  (86, 92). In contrast, using a different assay Posti et al.
ACKNOWLEDGMENTS The authors would like to thank  Dr. Peter Davies  for the antitau antibodies. This study is supported in part by the Office of the Assistant Secretary of Defense for Health Affairs through the  Department of Defense (DOD) Broad Agency Announcement  under Award number  W81XWH-14-2-0166  (RR). This study was also supported in part by the  Fonds Unique Interministériel (FUI)   TRIAD  project (LM),  Fondation Jérôme Lejeune (LM) ,  NIA  grant  AG08051  (TW) and the  SUNY Downstate Medical Center. MC is recipient of a CIFRE/ManRos Therapeutics Ph. D. fellowship. LV is a Master 2 student from the  Université Technologique de Compiègne (UTC).
Cryo-Transmission Electron Microscopy (Cryo-TEM) Specimen Preparation and Imaging For cryo-TEM, 3 µL of unfixed suspensions containing ALNs were deposited onto glow-discharged Lacey Carbon 200-mesh grids and loaded into the thermostatic chamber of a Leica EM-GP automatic plunge freezer, set at 20 • C, and 95% humidity. Excess solution was blotted for 1" with a Whatman filter paper No. 1, and the grid was immediately flash-frozen in liquid ethane cooled at-185 • C. Specimens were then transferred onto a Gatan 626 cryo-holder, and cryo-TEM was carried out on a Jeol 2100 microscope, equipped with a LaB 6 cathode and operating at 200 kV, under low-dose conditions. Images were acquired using SerialEM software  (Mastronarde, 2005) , with defocus ranging of 1,000 nm, on a Gatan US4000 CCD camera.
Calculated background image was subtracted from the post-edge image to give an elemental map, in which changes in background shape were taken into account. Maps were calculated for C, N and O K-edges and Ca L 2,3 edges using a 20-eV-wide (for C) or 30-eV-wide (for N, O, Ca) energy window for pre-edge and post-edge. Zero-loss images were obtained by selecting elastically scattered electrons only. EELS spectra were acquired using a dispersion of 0. 2 eV/channel to record spectra in the range 270-600 eV. Energy resolution was 1. 3 eV as measured by the full width at half maximum of the zeroloss peak. Dwell time was optimized to acquire sufficient signal intensity and limit beam damage. Spectra were corrected from plural scattering using the Egerton procedure available with the EL/P program (Gatan).
PKH26 was excited at 561 nm and fluorescence was collected with a 582/15 BP. Targeted particles were visualized on a "marker fluorescence vs. side scatter" dotplot. Data were acquired and processed using FACSDivA 8 software (BD Biosciences). Characterization of ALNs and VLPs from samples processed for cytometric analyses was carried out by TEM as previously described. Plots were compared with those of a similarly-processed VLPs community obtained from Lake Pavin (see site description in  Borrel et al. , 2012)  on October 24th 2017. FC sorting was performed on samples stained with SYBR Green I in un-heated conditions for optimal preservation of ALNs morphology and reliable morphotype diagnosis. Commonly described "viral fractions"  (Brussaard, 2004)  were gated on SYBR Green I fluorescence and sorted out using the continuous "Purity" mode.
Sequence Assembly and Annotation The 1,930,845 raw PacBio reads (4. 1 Kb in average) were assembled using the SMRT Analysis software and the Hierarchical Genome Assembly Process (HGAP) workflow  (Chin et al. , 2013). This procedure includes pre-assembly error correction, assembly and polishing. The circular nature of HGAP derived contigs was assessed via the dot-plotting tool Gepard  (Krumsiek et al. , 2007)  and circular genome sequences were derived through an alignment approach and manual curation. The 5,162 corrected long reads (12. 6 Kb in average) produced after the pre-assembly error correction process were utilized to determine the coverage of each contig using BLASTn (threshold of 90% on the identity percent)  (Altschul et al. , 1997). These corrected reads and contigs were compared using BLASTn to the SILVA 16S rRNA gene reference database (version 132)  (Quast et al.
, 2013). The 233 contigs were also compared to the UniProt (February 2019) (UniProt Consortium, 2019) protein database using Diamond (sensitive mode)  (Buchfink et al. , 2015). Gene-calling was performed on contigs through the Prodigal software  (Hyatt et al. , 2010)  and proteins were also compared to Uniprot using Diamond. Genomic data are presented in Supplementary Data Sheet 1.
Development of a specific protocol to purified ALN enrichedcultures will be a critical point as soon as the exact nature of ALNs will be determined. Overall, our data on the atypical morphology, the reduced biovolume, the suspected dominant organic nature, the sensibility to biocide treatments, and the ability to develop in the absence of cells indicate than ALNs are new femto-entities which, at the moment, cannot be classified in any known category of femto-entities previously described in environmental samples.
ACKNOWLEDGMENTS This study was supported by the  CYSTEM platform UCA-PARTNER  ( University of Clermont Auvergne UCA), Clermont-Ferrand, France ) and the Microorganisms:  Genome and Environment laboratory (LMGE ,  UMR6023   CNRS - UCA, Clermont-Ferrand, France ). This work also benefitted from the assistance of the Multiscale Electron imaging platform (METi) at the  CBI (Toulouse, France ), from the  Technological Center for Microstructures (CTµ) (Villeurbanne, France ) and from the  GENTYANE Sequencing Platform (Clermont-Ferrand, France ). The authors thank the  UCA Centre Imagerie Cellulaire Santé  ( UCA, Clermont-Ferrand ) for help with microscopy intercalibration. We thank  Anne Catherine Lehours  (LMGE) and  Guillaume Borrel  ( BECM, Institut Pasteur, Paris, France ) for helpful comments and discussions on the manuscript and  Matthieu Legendre  ( IGS, UMR7256 Aix Marseille Université-CNRS, Marseille, France ) for his assistance with the assembly of the sequence data.
FUNDING FE was supported by the  EUed Horizon 2020 Framework Programme for Research and Innovation (Virus-X , project no. 685778 ). This study is a contribution to the C NO  LIMIT  project funded by the  Interdisciplinary Mission of the French National Center of Scientific Research (CNRS)  Program X-life, 2018 edition. Funding for sampling at  Halong Bay  was obtained through the  French-Vietnamese Hubert Curien Partnership  (Contract No. 23971TK ) and the  Ministry of Science and Technology of Vietnam  (Contract No. 46/2012/HD-NDT ).
Real-time PCR 1 μg of the RNA from the extracted liver samples was reverse transcribed (GoScript Reverse transcription system, Promega Ltd. , Southampton, UK; cat no A5001). Real-time PCRs were conducted using the SensiMix real-time PCR kit (Bioline, London, UK; cat no. QT650-05) and carried out on 384 well plates using the Roche Lightcycler 480. B2M and ACTB were identified as the most stable housekeeping genes from a group of 5 housekeeping genes tested using both GeNorm and NormFinder software (Fig. S1A ). Primers for the genes of interest were designed using Primer Blast (http://www. ncbi. nlm. nih. gov/tools/primer-blast/) to span exons in order to avoid amplicons from contaminating genomic DNA. Absence of amplicon generation from genomic DNA was confirmed by testing the primers on mock cDNA preparations lacking reverse transcriptase enzyme.
e. 12. 5 μg/group) to assist spot mapping. 2D-PAGE was performed and gels were scanned using an Ettan DIGE Imager (GE Healthcare). Samespots software was used to detect spots and quantify fluorescent spot volumes. Selected spots were excised from gels and subjected to LC-MS/MS spot identification as previously described  (Bellingham et al. , 2013; Filis et al. , 2015).
Statistics and ingenuity pathway analysis Statistical analysis of quantified ECs, transcripts, proteins and lipids was performed using 1-way ANOVA (control vs treatment for the exposure effect in females and males respectively; control females vs control males for sex differences) in R statistical software (V3. 4. 0). Data were log-transformed in those cases where model residuals departed from Normality (visually assessed by quantile-quantile residual plots) and/or showed heteroscedasticity (visually assessed by scatter plot of residuals vs fitted model values). Statistical significance threshold was P < 0. 05. Spot volumes from gel-based proteomics were analyzed in R (V3. 4. 0) using the limma package following the same statistical approach as above. P-values were adjusted using the Benjamini-Hochberg False Discovery Rate to control for multiple testing.
Statistical significance threshold was chosen to adjusted P values of < 0. 05. Multidimensional scaling was produced using the plotMDS function in R. Ingenuity Pathway Analysis (IPA) V9. 0 (Ingenuity Systems, http:// www. ingenuity. com) was used to assign affected proteins and transcripts from transcript measurements, Western blots, and proteomics to Disease or Functions Pathways  (Bellingham et al. , 2013; Filis et al. , 2015). Statistical significance was calculated by the right-tailed Fisher Exact Test with a threshold of P < 0. 05). Only those Disease & Functions Pathways that were predicted on the basis of at least 10 molecules or yielded activation scores above ± 1. 5 were considered relevant.
Acknowledgements and funding information This work was supported by the  SRF Academic Scholarship Award 2013  (to PAF, PJOS, PF), the  Wellcome Trust  ( 080388  to PAF, CC, SMR,  RMS, NPE)  and the  European Community 's  Seventh Framework Programme  (FP7/2007-2013 to PAF,  SMR, CC)  under grant agreement no. 212885. The authors would like to thank  Ms. Margaret Fraser  and the  Proteomics Core Facility  at the  University of Aberdeen  and  Ms. Carol E. Kyle  and as  Dr. Stewart Rhind  (deceased) at  James Hutton Institute, Aberdeen , for their important contributions to this study.
2 µM final concentrations), with an annealing temperature of 50 • C for 30 cycles. PCR products were purified using Agencourt AMPure XP magnetic beads (Beckman Coulter, Brea, CA, United States), quantified according to GenoScreen's protocol, and mixed in an equimolar amount. Sequencing was performed using 250-bp paired-end sequencing chemistry on the Illumina MiSeq platform (Illumina, San Diego, CA, United States) at GenoScreen (Lille, France). The sequence information was deposited to NCBI Sequence Read Archive (SRA) under accession numbers SRP160122.
Bioinformatic Analysis of 16S rRNA Gene Sequence Data Raw paired-end reads were demultiplexed per sample and subjected to the following process: (1) search and removal of both forward and reverse primers using CutAdapt, with no mismatches allowed in the primers sequences; (2) quality-filtering using the PRINSEQ-lite PERL script  (Schmieder and Edwards, 2011) , by truncating bases at the 3 end with Phred quality score <30; (3) paired-end read assembly using FLASH  (Magoč and Salzberg, 2011) , with a minimum overlap of 30 bases and >97% overlap identity. Taxonomic and diversity analysis were performed with the Metabiote Online v2. 0 pipeline (GenoScreen, Lille, France) which is based in part on QIIME software v 1. 9. 1  (Caporaso et al. , 2010). Following pre-processing, full-length 16S rRNA gene sequences were checked for chimera sequences (in-house method based on Usearch 6.
1). Similar sequences with a nucleic identity defined threshold (97% identity for an affiliation at the genus level on the V4-V5 regions of the 16S rRNA gene) were clustered with Uclust v1. 2. 22q  (Edgar, 2010)  through an open-reference Operational Taxonomic Units (OTU) picking process and complete-linkage method, finally generating groups of sequences or "Operational Taxonomic Units" (OTUs). An OTU cleaning step involving elimination of singletons was performed. The most abundant sequence of each OTU was considered as the reference sequence of its OTU and taxonomically compared to a reference database included in the Greengenes database (release 13_8 1 ;  DeSantis et al. , 2006)  by the RDP classifier method v2. 2  (Cole et al. , 2014). Alpha-diversity metrics (Chao1 index) within samples were computed using QIIME v 1.
Bacterial Community Composition Analysis Univariate statistical analyses (Student test, ANOVA) were performed with XLSTAT (Version 2016. 02. 27390). Multivariate analyses [principal component analysis (PCA)] were performed within R 2. Bacterial community composition data obtained from Illumina MiSeq sequencing were analyzed by PCA ordination. Data were first normalized using Hellinger transformation  (Legendre and Gallagher, 2001; Ramette, 2007). Explanatory variables consisted of quantitative variables including: CEs concentrations (and sum of total CE) (mg • L -1 ), pH, redox potential (mV) temperature ( • C), sulfate, chloride and acetate concentrations (mg • L -1 ), gene copies number (copies • mL -1 ) pceA(Dhc), pceA(Dhb), vcrA and bvcA to 16S rRNA gene ratios (%). Explanatory variables were standardized to provide dimensionless variables and remove undue influence of magnitude differences between scales or units.
Bacterial Community During Organohalide Respiration of CEs Changes in bacterial community were first assessed by CE-SSCP fingerprinting analyses for all microcosm experiments (i. e. , with/without acetate, sodium sulfide and/or PCE). Only minor variations were apparent during the first 19 days. Distinct changes were then observed after 34 days (Supplementary Figure  S5 ), depending on the PCE spiking dose. Principal component analysis of CE-SSCP fingerprints confirmed the observed variations (Supplementary Figure  S6 ). The first two principal components explained 38% of the variability, the first component (24%) being mainly associated with SSCP peaks only present during the first stages of the incubation. A posteriori fitting of the main physico-chemical variables and gene abundance data onto the PCA ordination plot suggested that these changes were associated to the decrease of PCE, cis-DCE and VC concentrations, and with the increase of pceA(Dhc) and vcrA dehalogenase gene abundances over time (Supplementary Figure  S6 ).
Relative abundance of Acetobacterium taxa, also associated with reductive dehalogenation  (Terzenbach and Blaut, 1994) , remained stable (1. 0% average abundance) at all sampling times and for both PCE amendments. In contrast, other taxa putatively associated to reductive dechlorination (all affiliated to the class of Dehalococcoidetes) were only found in minor proportions (<0. 3% abundance) throughout the experiment (data not shown). Sequences associated with prominent dehalogenating taxa Dehalobacter and Desulfitobacterium were not found. One of the most abundant taxa identified in microcosms is affiliated to Dehalococcoides, and more specifically to the Dehalococcoides Pinellas group (Figure  7 )  (Hug and Edwards, 2013). As Miseq sequencing do not allow accurate taxa affiliation down to genus level, the affiliation to Pinellas was confirmed by 16S rRNA gene sequence phylogeny on 923 nucleotides of the corresponding OTU (Genbank MK312632).
ACKNOWLEDGMENTS We wish to thank  Cécile Grand  ( ADEME ) for providing access to the Themeroil site and scientific discussions. We are also grateful to  Kevin Kuntze  and  Yvonne Nijenhuis [Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany ] for providing plasmids with tceA, vcrA and bvcA reference genes, and to  Julien Maillard  ( Environmental Engineering Institute, EPF Lausanne, Switzerland ) for the plasmid with the pceA reference gene. We also thank the  LIGAN-PM Genomics platform (Lille, France)  supported by the  ANR   Equipex 2010 session  ( ANR-10-EQPX-07-01 ; ' LIGAN-PM '),  FEDER , and the  Region Nord-Pasde-Calais-Picardie , for their contribution to the high-throughput sequencing analysis.
However, caution should be taken when requesting these images due to its radiation dose. 3D cephalometric tools for clinical diagnosis as well as visualization of the 3D images have been available in numerous commercial software such as Mimics (Materialise, Leuven, Belgium), OsiriX (Pixmeo, Geneva, Switzerland), Dolphin3D (Dolphin Imaging & Management Solutions, Chatsworth, California, USA), InVivo Dental (Anatomage, San Jose, California, USA), and Ondemand3D (CyberMed, Seoul, Korea). The visualization tools allow for the assessment of CBCT images by not only showing axial, coronal and sagittal images, but also creating a 3D reformatted image. 5] [16] [17] [18] [19] [20]  Most of these studies use CBCT to visualize a specific region, but still perform an overall overview of the patient using reformatted 2D images.
The use of 3D planes to quantify the craniofacial morphology proportions or measure angles between planes of anatomical structures pose mathematical challenges. It is important to understand that in 3D analysis a plane is defined by three points that may not lie at the same level; that the angle between two planes are determined by the normal vector of the planes; and clinicians need to become familiar with which angle to measure in the 3D space, since complementary angles are calculated. The main purpose is to present three new methods from an open-source software (SlicerCMF) to calculate anatomical angles in virtual surface models (constructed from CBCT images) in the three planes of space and assess the reproducibility of measuring the angles: (1) using 3D pre-existing planes, (2) creating a 3D plane based on landmarks, and (3) using lines created from landmarks.
Anatomical angles were then measured in different views of the 3D space, defined as follows: 1) FMA (Frankfurt-mandibular plane): The Frankfurt Horizontal plane passes by the most superior portion of the "Porion" at both sides and by the most inferior portion of the "Orbitale" at both sides. In cases of asymmetry, the plane was positioned in-between the most inferior portion of the left and right sides. When placing the landmarks, the software is able to create a mid-point between the right-and left-most  (Figure  3b ). c) Angle between lines from landmarks: Using the Q3DC module in SlicerCMF 3. 1 (www. slicer. org), landmarks were placed at specific anatomical locations (Figure  1 ), in order to create lines for the representation of the planes. When assessing bilateral structures, a mid-point was used as representative of both sides (Figure  3c  and 3d ).
Identification and Phylogenetic Analysis of Dehydrins in Fabaceae The complete genomes of eight species (Arachis duranensis, A. ipaënsis, Cicer arietinum, Cajanus cajan, Glycine max, Lotus japonicus, Medicago truncatula, and Phaseolus vulgaris) belonging to the Fabaceae family were downloaded from their respective public databases (Supplementary Table  1 ). To identify the putative DHN proteins in these eight proteomes, we used two independent approaches. First, the PFAM  (Finn et al. , 2016)  dehydrin domain (PF00257) was used as a query for the program hmmsearch, from the HMMER3 suite  (Mistry et al. , 2013)  against the predicted proteome of each species. In the second approach, the K-segment described by  Malik et al. (2017)  was used as input for the Find Individual Motif Occurrences (FIMO) software  (Grant et al.
, 2011) , applying a threshold q-value of < 0. 05. The proteins found using both approaches were verified for their motifs, presence and organization. Only proteins with more than half of the total length of the PF00257 domain (168 aa) and with a canonical dehydrin motif organization were considered in the present study. All the identified putative DHN protein sequences were aligned using the MAFFT software with automatic detection of the most appropriate alignment strategy  (Katoh et al. , 2002). The poorly aligned regions (more than 10% of gaps) were eliminated with the trimAl software  (Capella-Gutiérrez et al. , 2009). We performed the phylogenetic analysis of the selected protein sequences using RAxML software  (Stamatakis, 2006) , with automatic search of the fittest evolutionary model and parameters and a bootstrap search automatically stopped upon congruence.