dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This research was supported by the  DFG research fellowship  grant  261553824  " Vertical and lateral aspects of Chinese dialect history " (JML) and by the  ERC   Synergy Project 609823 ASIA "Beyond Boundaries: Religion, Region, Language and the State " (NWH). EB and JSP are supported by the  ERC  under the European Community's  Seventh Framework Programme ,  FP7/2007-2013 Grant Agreement 615247. We are specifically indebted to  François-Joseph Lapointe  for the helpful advice regarding our statistical approach. We are very grateful to  Doug Cooper  for providing help in preparing the data by Schuessler (2007), and we thank  George Starostin ,  William Baxter ,  Laurent Sagart , and  Pan Wuyun  for providing free online access to their data.
LC-MS/MS analysis Peptides were analyzed by nanoliquid chromatography coupled to tandem mass spectrometry (Ultimate 3000 coupled to LTQ-Orbitrap Velos Pro, Thermo Scientific). Peptides were sampled on a 300 µm×5 mm PepMap C18 precolumn and separated on a 75 µm×250 mm C18 column (3 µm beads, PepMap, Thermo Scientific) using a 120 min gradient ranging from 5% to 37% acetonitrile in 0. 1% formic acid during 114 min before reaching 72% acetonitrile in 0. 1% formic acid for the last 6 min. Spray voltage and heated capillary were set at 1. 4 kV and 200 °C, respectively. MS and MS/MS data were acquired using Xcalibur (Thermo Scientific). Survey full-scan MS spectra (m/z=400-1600) were acquired in the Orbitrap with a resolution of 60,000 after accumulation of 106 ions (maximum filling time, 500 ms).
The 20 most intense ions from the preview survey scan delivered by the Orbitrap were fragmented by collision-induced dissociation (collision energy, 35%) in the LTQ after accumulation of 104 ions (maximum filling time, 100 ms). Peptides and proteins were identified through concomitant searches against Uniprot (Homo sapiens taxonomy, November 2016 version) and classical contaminants database (126 sequences, homemade) and the corresponding reversed databases using Mascot (version 2. 5. 1). The Proline software (http:// proline. profiproteomics. fr) was used to filter the results (conservation of rank 1 peptides, peptide identification FDR < 1% as calculated on peptide scores by employing the reverse database strategy, minimum peptide score of 25, and minimum of 1 specific peptide per identified protein group) before performing a compilation, grouping and comparison of the proteins from the 2 fractions.
LC-SRM analysis LC-SRM analyses were performed on a 6500 QTrap mass spectrometer (AB Sciex, Les Ulis, France) 400-1000 m/z range) equipped with a TurboV source and controlled by Analyst software (version 1. 6. 1, AB Sciex). The instrument was coupled to an Ultimate 3000 LCchromatography system (Thermo Scientific, Courtaboeuf, France). Chromatography was performed using a two-solvent system combining solvent A (2% acetonitrile, 0. 1% formic acid) and solvent B (80% acetonitrile, 0. 1% formic acid). Peptide digests were concentrated on a 1×15 mm C18 PepMap precolumn (Thermo Scientific) before separation on a Kinetex XB-C18 column (2. 1×100 mm, 1. 7 µm, 100 Å; Phenomenex, Le Pecq, France). A 40 min gradient from 3% B to 35% in 30 min and from 35% to 90% in 10 min was applied at a flow rate of 50 µL/min.
MS data were acquired in positive mode with an ion spray voltage of 4300 V and the interface heater temperature was set to 320 °C. Collision exit, declustering and entrance potentials were set to 18, 14 and 55 V, respectively. For each signature peptide, collision energy was calculated based on the following equations: CE (Volts) =0. 44*m/z+4 for doubly-charged precursors and CE (Volts)=0. 5*m/z +5 for triply-charged precursors. Scheduled SRM acquisitions were performed with Q1 and Q3 quadrupoles operating at unit resolution, the acquisition time windows and target scan time were set to 120 s and 1 s, respectively. Parameters for LC-SRM acquisitions can be found in Supplementary Table  1.
LC-SRM data analysis LC-SRM data analysis was performed using Skyline software  [16]. Peptide peak picking was performed using the mProphet algorithm and the second best peak model. A Q-value of 0. 01 (1% FDR) was set as the cut-off for peptide signal analysis. In addition to peptide signal scoring (composite signal), all transitions were individually inspected and excluded if they were found to be unsuitable for quantification (low signal to noise ratio, obvious interferences). Unlabeled/labeled peak area ratios were calculated for each SRM transition. These ratios were then used to determine the corresponding average peptide ratio, and finally the average protein ratio was calculated from the ratios for the different signature peptides. Biomarker concentration was calculated from the average protein ratio.
To assess our procedure, we first optimized the LC-SRM method for detection of the target proteins in plasma. The peptides monitored for the four target proteins were selected based on sequence specificity (BLAST search against Uniprot database) and analytical detectability. The "best-flying" signature peptides and the best SRM transitions were selected experimentally based on LC-SRM analysis of recombinant proteins spiked into glycodepleted plasma and digested with an endoLysC/trypsin mixture. Three specific peptides were retained for ADH4 and FABP1. For BHMT, two peptides shared by isoforms 1 and 2 were selected along with a peptide specific for isoform 1. The two isoforms of BHMT are expressed in the liver and they can therefore both be considered candidate liver cytolysis biomarkers. Two specific peptides were selected for ALT1, and a peptide shared by isoforms ALT1 and ALT2 was also tracked (Supplementary Table  1 ).
Acknowledgements We thank the team at EDyP for scientific discussions and technical support. We thank  Maighread Gallagher-Gambarelli  for editing services. This study was supported by grants from the  7th Framework Programme  of the  European Union  (Contract no. 262067-PRIME-XS ), by the  COST Action CliniMark  ( CA16113 ) supported by  COST (European Cooperation in Science and Technology) , by the  French National Research Agency  in the framework of the  "Investissements d′avenir" program  ( ANR-15-IDEX-02 ,  LIFE  project) and by the " Investissement d′Avenir Infrastructures Nationales en Biologie et Santé" program  ( ProFI  project,  ANR-10-INBS-08 ).
Real time PCR RNA was extracted from ES cells using a Zymo research kit. One µg of RNA was reverse-transcribed using the SuperscriptII reverse transcriptase (Invitrogen, Cergy, France) and oligodT(12-18). qPCR was performed using a Light Cycler LC 1. 5. Amplification was carried out as recommended by the manufacturer. 12 µl reaction mixture contained 10 µl of Roche SYBR Green I mix respectively (including Taq DNA polymerase, reaction buffer, deoxynucleoside trisphosphate mix, SYBR Green I dye, and 3 mM MgCl2), 0. 25 µM concentration of appropriate primer and 10 ng of diluted cDNA. Melting curves were used to determine the specificity of PCR products, which were confirmed using conventional gel electrophoresis and sequencing. Data were analysed according to Pfaffl using ATP50 as the reference gene.
Partial Least Squares Regression. The PLSR model for multiple response variables (RGB colours and total reflectance) resulted in a significant component that explained 45. 2% of the variance in the dataset. Pearson's product-moment correlation between response variables and PLSR components was high and significant (r = 0. 56, t = 14. 7, df = 458, p < 2. 2e-16; Fig. 3b ). The weights of the habitat colouration indexes (RGB and total reflectance) were positive and relatively high, indicating that the synthetic response variable describing animal colouration (derived from RGB colours and total reflectance) followed the colouration of the respective habitat of individuals (Table  1 ). Another important predictor affecting animal colourations was latitude, indicating that specimens from lower latitudes were brighter in colouration.
The phylogenetic eigenvector (EV1) and origin of the specimen (field versus museum collected) were other important predictors, explaining more than 5% variance of animal colouration. Specimens of the same species coming from different collections behave similarly (non-significant collection affiliation effect) indicating that fur characteristics were not differentially affected by taxidermic treatments and preparation, but field collected individuals were in average brighter than museum specimens (a condition therefore controlled for in all models; Table  1 ). All important predictors (all colouration traits, latitude, field origin and phylogenetic eigenvector) were also statistically significant (Table  1 ). The results derived from the dataset reduced to barcoded specimens were similar, with geographic location being relatively more important (explaining more), and phylogenetic signal relatively less important (explaining less of the variation in dependent variables; Table  S3 ).
Discussion Our results showed significant variation in both habitat and animal colouration between closely related Gerbillus species (Fig. 2a ). This first indicates that species differentiated their occupied niches, as defined here by habitat variation (exemplified by substrate colouration), and second that species evolved toward variable phenotypes, in terms of their coat colouration. We showed that habitat and phenotype colourations significantly covary across phylogenetic tree (Table  1 ; Fig. 2b ), and within three out of six species with comprehensive datasets (Fig. 1 , Table  2 ). The significant level of covariation between repeatable measures of variation in habitats and phenotypes is a strong indication of the existence of selective pressures related to camouflage in Gerbillus. The covariation is not masked by other factors, such as geographic origin of specimens, the phylogenetic relationship among species, or by differences between specimens originating from variable collection types.
Also, generalists species might find refuges from selective pressures, making them less susceptible to predation  34. Indeed, in our dataset species with an apparent lack of phenotype-habitat covariation (G. amoenus, G. pyramidum and G. tarabuli; Table  2 ) tend to occupy a wide range of habitats  38. Interspecific competition could have strengthened differences in the level of camouflage between gerbils, in cases where generalists are also dominant and if subordinates are exposed to increased foraging costs. According to the centrifugal community hypothesis proposed for Gerbillus  45  the two competing species share preference for the same primary habitat but differ in preference for less optimal, marginal habitats  46. For most of Gerbillus species optimal habitats lay along a gradient from open to shrubby areas, defining a balance between resource availability (seed production/renewal and foraging efficiency in open areas) and safety from avian predators (in shrubs)  36.
Materials and Methods Specimens and Species. Gerbillus species are arid and semi-arid habitat specialists, distributed from North and West Africa to western India through the Middle East  63. We studied samples (n = 460; Fig. 1 ) belonging to fourteen North African Gerbillus species collected during field expeditions (n = 100) or housed in various museum collections (n = 360) at: CBGP -Center of Biology for the Management of Populations, Montferrier, France; NHM -Natural History Museum, Vienna, Austria; RMCA -Royal Museum for Central Africa, Tervuren, Belgium; MNA -Museum of Natural Sciences, Brussels, Belgium; ISEA -Institute of Systematics and Evolution of Animals, Polish Academy of Sciences, Kraków, Poland (Supplementary information: Data Description). We used only museum specimens that were preserved and stored without any chemical treatment as dried skins, and for which geographic coordinates or location description existed.
Ear tissue samples were collected from live animals (n = 93) caught either with Sherman traps (Extra-Large Kangaroo Rat; placed every night in trap-line of 10-30 traps and collected every morning), with butterfly nets while walking at night, or from well preserved carcasses (n = 7). Species affiliations of all specimens were identified using external measurements (hind feet length, body mass, body and tail lengths, ear size) and other morphological features (presence of hair on foot soles, body colouration, and proportion of body parts)  38. Samples were georeferenced using the global positioning system. To confirm species identification, DNA from 60 individuals was extracted and a species specific marker, cytochrome b, was amplified and sequenced with described procedures  31, 64. Available sequences from 161 specimens from the CBGP collection were used to confirm species affiliations (Supplementary information: Data Description and Alignment.
Animal Colouration. Digital photography was used to estimate animal fur colouration  25, 30, 65. Field and museum samples were photographed alongside a ColorChecker (X-Rite, Michigan, USA) with Canon EOS400D digital camera, 18-55 mm (set to 55 mm) 1:3. 5-56 lens. Photographs were black and white-balance corrected (with GIMP 2. 8 program) using the white and black references in the ColorChecker for light condition standardization. High-resolution images (TIFF format) were analysed with Hyper-Utility 2 program (www. fujifilm. com) to quantify fur colouration from a square-shaped area on the back of animal's head (between the ears, pixel size: 40-150). To test for repeatability of colouration estimates (representativeness of dorsal colour), a total of 40 samples were randomly selected with "Sampling Design Tool" (in ArcGIS 10.
Repeatability of fur and habitat colouration was estimated with the intraclass correlation coefficient (τ) based on between and within group mean squares extracted from ANOVA/ANCOVA analysis  [71] [72] [73]. Correlations between fur and habitat colouration were tested with partial Pearson's product-moment correlations controlling for the sample origin (museum vs. field collections) in R 3. 1. 3 (http://www. r-project. org/) with "ppcor" package. Analysis of variance was used to test the significance of differences between species in average habitat and fur colourations, controlling for variation between collection/field types of specimens. Phylogenetic vector regression (PVR) is an optimal method to detect phylogenetic inertia on datasets containing a relatively small numbers of species (e.
The phylogenetic inertia is tested here by estimating sign and strength of phylogenetic signal, an area under the phylogenetic signal-representation (PSR) curve. The PSR curve is built from sequential PVR models with increasing number of phylogenetic eigenvectors included, and by plotting eigenvectors R 2 against their accumulated eigenvalues. The area under PVR curve describes deviations from Brownian motion, i. e. nonlinear curves reveal if traits evolved at a slower or higher rate than expected. We constructed PVR curves for studied traits (animal colouration)  3, 4, 29  potentially related to adaptation (estimated by co-variation with habitat colouration) invoking camouflage (animal-habitat colour matching). Due to multicollinearity in our dataset, the relationship between the response variables (animal fur reflectance and RGB colours), habitat variables (substrate reflectance and RGB colours) and phylogenetic signal (represented by phylogenetic eigenvectors) was tested on comprehensive sample size (n = 460) with partial least squares regression (PLSR), a method extremely resilient to the correlations between predictor variables  82.
Acknowledgements The study was financially supported by the  Academy of Finland  to TM (Grant No. 268670 ). ZB,  JCB , and JCC are supported by the  Portuguese Foundation for Science and Technology  ( SFRH/BPD/84822/2012 ,  IF/00459/2013 , and  SFRH/BD/87885/2012 , respectively). Field and museum sampling expeditions were supported by  National Geographic Society  (grant:  GEFNE53-12 ) and  European Commission SYNTHESIS  (grants:  BE-TAF-1796  and  AT-TAF-1665 ) programs to ZB. We acknowledge  Pedro Santos Lda (Trimble GPS), Off Road Power Shop, P. N. Banc d' Arguin (Mauritania) ,  Abdeljebbar Qninba  and  Mohammed Aziz El Agbani  ( University Mohammed V Agdal, Rabat, Morocco ) for logistic support during field expeditions. We acknowledge  Carole Paleco  ( Royal Belgian Institute of Natural Sciences, Brussels ),  Patricia Mergen  and  Emmanuel Gilissen  ( Royal Museum for Central Africa, Tervuren ), and  Frank Emmanuel Zachos  ( Museum of Natural History, Vienna ) for support during the realization of SYNTHESIS museum grants, and  Emmanuelle Artige  for access to the CBGP collections.
Sequence Database Searching and Data Analysis The MS/MS spectra were searched using the MASCOT engine (Matrix Science, London, UK; version 2. 2) in the Proteome Discoverer 1. 3 (Thermo Electron, San Jose, USA. ) against the Uniprot S. suis fasta database (38,369 sequences, downloaded March 4th, 2013) and the decoy database. False discovery rates (FDR) were calculated by running all spectra against a decoy database using the MASCOT software. To identify proteins, the following options were used: Peptide mass tolerance = 20 ppm, MS/MS tolerance = 0. 1 Da, Enzyme = Trypsin, Missed cleavage = 2, Fixed modification: Carbamidomethyl (C), iTRAQ 8plex (K), iTRAQ 8plex (N-term), Variable modification: Oxidation (M). The quantification was performed based on the peak intensities of the reporter ions in the MS/MS spectra.
Sub-MIC Tylosin Inhibits Biofilm Formation and Differentially Expressed Proteins by iTRAQ A total of 1501 proteins were identified by iTRAQ. Detailed information is shown in the Supplementary File. A ratio of proteins with >1. 5 or <0. 67 (p < 0. 05) was considered to be differentially expressed. Based on this criterion, 96 differentially expressed proteins were identified in 1/4 MIC tylosin treated cells and nontreated cells. Detailed information could is shown Table  1. These proteins were detected by GO annotation. Thirty-five proteins were deleted from the database. The remaining 61 proteins were classified into biological process, molecular function and cellular component (Figure  4 ). The results regarding the biological process were as follows: single-organism process (8, 13%), response to stimulus (4, 7%), localization (4, 7%), biological adhesion (1, 2%), cellular component organization or biogenesis (6, 10%), cellular process (25, 41%), biological regulation (3, 5%), metabolic process (30, 49%).
The sequence analysis and assembly were carried out using CLC Genomics Workbench (ver. 6. 0. , Qiagen). Prediction and identification of the main components of the genome, including open reading frames (ORFs), tRNA, rRNA, ncRNA, CRISPR-Cas system, and repeat elements, were conducted using software of Glimmer 3. 02 and some other online tools. The nucleotides and predicted proteins of 1655 were compared with previously sequenced C. jejuni genomes in GenBank using updated nt and nr databases with BLAST software. The genomes used as reference sequences for the comparative genomic analysis were C. jejuni 81-176, C. jejuni NCTC11168, C. jejuni RM1221. The comparative genomic analysis was performed using BLASTN, BLASTX, Mummer and Quast. The sequence data from C. jejuni 1655 was compared to the results of antimicrobial resistance and virulence to identify genetic factors that correspond to the observed mechanisms.
Macrolides and lincosamides bind to the 50S of ribosome and block the NPET  (Tenson et al. , 2003). In our experiments, mutation in PA3297 did not alter the bacterial resistance to other antibiotics, including those binding to 30S or other parts of 50S ribosome. In addition, the expression of PA3297 was upregulated by AZM, which was abolished by ribosome protection. Therefore, PA3297 might specifically play a role in bacterial response to macrolides and lincosamides. PA3297 is also named HrpA, both in PAO1 and PA14  (Winsor et al. , 2016). According to the Profiles from GEO Expression Database at NCBI, the expression level of PA3297 was higher in biofilm than that in planktonic cells  (Anderson et al. , 2008). Isolates from CF lungs displayed higher expression levels of PA3297 levels than PAO1  (Son et al.
ACKNOWLEDGMENTS This work was supported by the  National Natural Science Foundation of China  (No. 31472231 ) and  Guangdong Technology Research Center for Traditional Chinese Veterinary Medicine and Natural Medicine  (No. 20141020002 ). We thank  Shanghai Applied   Protein Technology Co. Ltd. for the help with iTRAQ. ACKNOWLEDGMENTS We thank  Timothy J. Foster ,  Moyne Institute of Preventive Medicine, Department of Microbiology, Trinity College, Dublin, Ireland , for providing the E. coli strain DC10B. We also thank  Ralph Bertram ,  Klinikum Nürnberg Medical School GmbH, Research Department, Paracelsus Medical University, Nuremberg, Germany  for helpful discussions about S. aureus gene manipulation and persister assay. This work was supported by the  National Natural Science Foundation of China  ( 81572046  and  81471987 ), and the  Key Technologies Research and Development Program for Infectious Diseases of China  ( 2013ZX10003008-003 ).
Statistical Analysis. All results were reported as mean ± SD. Statistical analysis was carried out using 1-way ANOVA followed by Tukey-Kramer multiple comparison test. Differences between two groups were considered as significant given a p value < 0. 05. Graphs were done using GraphPad Prism 5 software (GraphPad Inc. , San Diego, CA) that was also used for statistical analysis. 2. 5. Molecular Modeling. Activation of the Nrf2 pathway occurs with inhibition of the KEAP1/Nrf2 protein-protein interaction or Keap1 dimerization. Therefore, Nrf2 inducers act by (i) disrupting the Keap1-DC domain/Nfr2 interaction  [46] ; (ii) covalent binding to cysteine residues of the Keap1-BTB domain  [47]. We investigated the binding of VP10/12 and VP10/39 at Keap1-DC and Keap1-BTB with a molecular modeling approach.
The whole human Keap1 dimer was built with the advanced molecular modeling task of Maestro© and subjected to loop optimization and two energy minimization steps: rigid body energy and all-atom energy minimization using the VSGB 2. 0 solvation model  [48]. BTB and IVR domains of Keap1 were modeled on the basis of BTB and BACK (IVR) domains of Keltch11 (PDB:3I3N); the DC domain of Keap1 was modeled using the PDB:1X2R X-ray structure as template. The quality of the model was assessed by determination of protein Ramachandran plots before and after energy minimization; the two energy minimization steps led to a significant decrease of residue dihedral violations (Figure  1S  supplemental material available online at https://doi. org/10. 1155/2017/1420892). Superimposition between the model of human Keap1 dimer with the electron microscopy reconstruction map (24 Å) of murine Keap1 dimer  [49]  was carried out with Chimera 1.
Chemical Synthesis of NO-Releasing Curcumin (VP10/12) and NO-Releasing CAPE (VP10/39). Hybrid NO-releasing molecules were obtained in high yield by using an efficient synthetic route (Figures  2  and 3 ). The alcoholic-free groups of CAPE and curcumin were firstly acylated with 4-bromobutanoyl chloride or 2-chloro-propanoyl chloride, respectively. In a subsequent step, the intermediates were converted into the corresponding nitrate derivatives by the treatment with AgNO 3 at reflux into the dark. The desired final products, VP10/12 and VP10/39, were easily obtained by flash chromatography purification. VP10/12 was obtained in a high overall yield, 70% two steps; on the contrary, VP10/39 was obtained in poor yield because it is easily hydrolyzed under purification conditions. Oxidative damage affected cellular viability of ARPE-19 cells, given a significant (p < 0 05) decrease of ATP levels in CTRL+ cells, in comparison to CTRL-cells (Figures  6(a  Overall, these data indicated that both VP10/12 and VP10/39 are capable to protect ARPE-19 cells from oxidative damage; however, VP10/39 was more effective in decreasing ROS formation and LDH release than VP10/12, which at highest concentration (100 μM) exerted a slight, still significant, toxic effect on ARPE-19 cells.