dataesr/scientific-paragraphs-categorization huggingface dataset:
A bit score value of 86 in the resulting plot of the sensitivity and specificity distributions for this threshold sweep gave a specificity of 99. 35% and a sensitivity of 99. 85% respectively. Hence, this cut off was used for all further analysis of the sequences against the SILVA database and also in MEGAN to make taxonomic assignments (using a minimum support of 5 and a 10% score range for its LCA algorithm)  [101]. The cluster representative sequences without a hit in the SILVA database were compared against the non-redundant database from NCBI using BLASTx with a bit score cut off of 35. The top hit of each BLASTx-search was used for the functional assignment of the cluster representatives. The cluster representative sequences without a hit in the SILVA database were further scanned with profile hidden Markov models of the ModEnzA EC groups  [69]  and the Pfam protein families  [70].
The Pfam-hits were converted to EC numbers and along with the ModEnzA EC-hits mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) reference pathways using the FROMP pathway mapping and visualization tool  [96]. For ease of data analysis all cluster representative sequences, including clustering information from Cd-hit (cluster identification number, cluster size and all clustered nucleotide sequences), results from BLAST-, Pfam-and EC-searches as well as the taxonomic assignment from MEGAN were added to a MySQL database  [96].
From this pool, 54% (DNA) and 53% (RNA) of the sequences could be identified as protein-coding (matching either the nonredundant database of NCBI or profile hidden Markov model scans of the ModEnzA EC groups or the Pfam protein families); the remainder could not be assigned. Of all protein-coding sequences, 82% of the DNA and 99% of the RNA sequences could be taxonomically identified (matched the non-redundant database of NCBI).
Hierarchical clustering of sequences using the taxonomic profiles The taxonomic profiles of the metagenomes and the metatranscriptomes (the occurrence of the microbial taxa in the samples as a percentage of the total number of sequences having a BLASTxhit) as presented in Figure  3  were used for a hierarchical clustering with the PRIMER 6 program  [102]. The samples were grouped into 6 categories according to the depths and a multivariate statistical test (ANOSIM) was used to determine if the groupings were distinct from each other in terms of their microbial communities. The relationships between the depth groups were visualized in a non-parametric Multidimensional Scaling plot using PRIMER 6.
Sequence recruitment onto reference genomes The sequence data was recruited onto the reference genomes of the five most abundant organisms (as detected with BLASTsearches) SUP05, R. magnifica, V. okutanii, Sulfurovum and D. autotrophicum using the MUMmer program  [103]. For SUP05, the draft genome (the metagenome with an ordered assembly of the contigs) as provided by Walsh and colleagues  [41]  was used and treated like the other genomes. The recruited sequences were re-assessed using a BLAST-search against the reference genomes. Sequences that hit more than one genome with a bit score difference of less than 5% between the first and second hits were discarded, giving rise to a non-overlapping set of sequences for each genome. These sequences were then recruited onto the genomes again to calculate the average coverage over nonoverlapping windows of 300 base pairs.
Detection limits for NH 4 + , NO 2 2 , NO 3 2 and PO 4 32 were 10, 10, 100 and 100 nmol l 21 , respectively. Dissolved N 2 O concentrations were determined on board in triplicates measurements using the GC headspace equilibration method as described elsewhere  [106]. H 2 S concentrations were measured continuously on water sampled from the pump-CTD using a custom-built microsensor with a detection limit of ,0. 5 mM  [107]. Chemically determined H 2 S concentration (both H 2 S and HS 2 ; detection limit ,0. 5 mM) on discrete water samples were used to calibrate the microsensor  [108]. Although the sulfidic waters contained a composite of H 2 S and HS 2 , we use the term H 2 S throughout the manuscript to avoid unnecessary complexity. Using a 125 m depth cut off and a grid resolution of 19 (bathymetrical data was obtained from the National Geophysical Data Center) along the ,200 km cruise track on the Peruvian shelf where H 2 S was detected in the water column, a total area of ,5500 km 2 was calculated to be affected by the sulfidic plume.
Flux calculations The density was calculated using the data processing program SeaSoft (Sea-Bird Electronics). The stability of the water column was expressed using the Brunt-Va ¨isa ¨la ¨frequency N, defined as: N 2 = 2(g/r)6(hr/hz) where g is the gravitational acceleration, r the water density and z the water depth. The density gradient was calculated over 4 m bins. The turbulent diffusivity Ez was calculated as described earlier  [109]  from the Brunt-Va ¨isa ¨la frequency and the dissipation rate of turbulent kinetic energy e: Ez = ce/N 2 with the mixing coefficient c = 0. 2. We applied a mean e of 1. 85610 29 W kg 21 [110]. This value was measured for the open-ocean thermocline  [110]  and was applied in several rate diffusion models  [111, 112]. Vertical concentration gradients for O 2 , H 2 S, and NO 3 2 were calculated over 4 m bins.
Satellite images Data of the sensors MODIS (Moderate Resolution Imaging Spectroradiometer) aboard the satellites Aqua and Terra (NASA) as well as MERIS (Medium Resolution Imaging Spectrometer) aboard the satellite Envisat (ESA) were implemented to study milky turquoise discolouration in waters off the Peruvian coast. Cloudy weather north of Pisco during most of our cruise made remote sensing difficult, but turquoise discolorations were observed off Lima on January 20-21 st (Figure  S1A ) and 27-28 th (Figure  S1B ). The estimation of the extent of the plumes requires data of higher spatial resolution; the algorithm for the identification of colloidal S 0 plumes is based on the high spectral resolution of MERIS data  [57]. Since full resolution MERIS data is not available for this region we present a high resolution MODIS true colour image in our main figure (Figure  1E ).
Accession numbers of sequence data Metagenomic and metatranscriptomic sequences have been deposited in the metagenomics analysis server (MG-RAST) under accession numbers 4460677. 3, 4450892. 3, 4450891. 3, 4460736. 3, 4461588. 3, 4460676. 3, 4452038. 3, 4460734. 3, 4452039. 3, 4452042. 3, 4460735. 3, 4460734. 3 and 4452043. 3.
Acknowledgments We thank the government of Peru for permitting research in their territorial waters, the chief scientist  Martin Frank  ( GEOMAR, Kiel ) and the captain and crew of  RV Meteor  for their support at sea. We acknowledge  Gabriele Klockgether ,  Andreas Ellrott  ( MPIMM, Bremen ),  Violeta V Leon Ferna  ´ndez (IMARPE, Callao) and  Hermann Bange  ( GEOMAR, Kiel ) for technical and analytical assistance. Additionally, we would like to thank  Mary Ann Moran ,  James T Hollibaugh ,  Nasreen Bano  ( University of Georgia, Athens ) and  Rachel S Poretsky  ( Georgia Institute of Technology, Atlanta ) for methodological help with the nucleic acid sample preparation.
Funding was provided by the  WGL-PAKT project 'REAL' (Leibniz Association ), the  Max Planck Society  and the  Helmholtz Association. The  Cluster of Excellence ''The Future Ocean '' provided structural support. This work is a contribution of the  Collaborative Research Centre 754  ''climate -biogeochemistry interactions in the tropical oceans'' (www. sfb754. de),which is supported by the  German Research Association. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Illumina Microarray For each of the three tissues, RNA samples from 3 animals at room temperature and 3 animals in the cold environment (total 18 samples handled separately), were labeled and amplified using the Illumina RNA Amplification Kit, and then hybridized to the MouseWG-6_V1 array using standard protocols. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  [15]  and are accessible through GEO Series accession number GSE44138 (http://www. ncbi. nlm. nih. gov/ geo/query/acc. cgi?acc = GSE44138)''. All data were analysed in Genespring GX (version 10, Agilent Tehnologies). Normalized signal values for individual probes were standardized to the median signal value for that probe across all samples. Probes were further filtered based on a flag call of present or marginal in 2 of 17 samples (lower cut-off for present = 0.
Fluorescence microscopy analysis Strains were grown overnight in LB, diluted 300 times in minimal medium A supplemented with either 0. 25% citrate or 0. 12% casamino acids and 0. 5% glucose (when looking at chromosomal tags, 0. 5 mM IPTG was added to growth medium) until they reach an OD600 comprised between 0. 05 and 0. 1. Cells were then spread on agarose pads and immediately observed using a Leica DM6000 microscope, a coolsnap HQ CCD camera (Roper) and Metamorph software. Image analysis was performed using the MATLAB-based software MicrobeTracker Suite  [44]. S2 ). For each locus, the upper panel represents the position of the focus in one focus cells, and the lower panel represents the position of the 2 foci in 2 foci cells (in red the focus proximal to the new pole of the cell). The x-axis in.
Acknowledgments We thank  Dr. Atsushi Miyawaki  from the  RIKEN Brain Science Institute  for providing DNA encoding the Dronpa protein,  F-X Barre  for sharing Matlab functions,  Marie-Agnes Petit  for preliminary bioinformatics analysis, and past and present members of the  Boccard laboratory  for helpful discussions. We also thank  B. Michel ,  O. Espeli ,  S. Duigou , and  C. Possoz  for their valuable comments on the manuscript.
The decays B + c → J/ψ D + s and B + c → J/ψ D * + s are observed for the first time using a dataset, corresponding to an integrated luminosity of 3 fb -1 , collected by the LHCb experiment in proton-proton collisions at centre-of-mass energies of √ s = 7 and 8 TeV. The statistical significance for both signals is in excess of 9 standard deviations. The following ratios of branching fractions are measured to be where the first uncertainties are statistical and the second systematic. The mass of the B + c meson is measured to be m B + c = 6276. 28 ± 1. 44 (stat) ± 0. 36 (syst) MeV/c 2 , using the B + c → J/ψ D + s decay mode.
The low Q-value for the B + c → J/ψ D + s decay mode allows the B + c mass to be precisely measured. This makes use of the D + s mass value, evaluated in Sect. 3, taking correctly into account the correlations between the measurements. The calibration of the momentum scale for the dataset used here is detailed in Refs. [28, 34]. It is based upon large calibration samples of B + → J/ψ K + and J/ψ → µ + µ -decays and leads to an accuracy in the momentum scale of 3 × 10 -4. This translates into an uncertainty of 0. 30 MeV/c 2 on the B + c meson mass. A further uncertainty of 0. 11 MeV/c 2 arises from the knowledge of the detector material distribution  [28, 29, 34, 35]   5 Normalization to the B + c → J/ψ π + decay mode A large sample of B + c → J/ψ π + decays serves as a normalization channel to measure the ratio of branching fractions for the B + c → J/ψ D + s and B + c → J/ψ π + modes.
Acknowledgements We thank  A. Luchinsky  and  A. K. Likhoded  for advice on aspects of B + c physics. We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  NSFC (China) ;  CNRS/IN2P3  and  Region Auvergne (France) ;  BMBF ,  DFG ,  HGF  and  MPG (Germany) ;  SFI (Ireland) ;  INFN (Italy) ;  FOM  and  NWO  (The Netherlands); SCSR (Poland);  ANCS/IFA (Romania) ;  MinES, Rosatom ,  RFBR  and  NRC "Kurchatov Institute" (Russia) ;  MinECo, XuntaGal and GENCAT (Spain) ;  SNSF  and  SER (Switzerland) ;  NAS Ukraine (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA).
VISTA Alignments The sequenced intergenic sequences were aligned against their orthologs in mouse (MMinter12, MMinter34 and MMinter56) and zebrafish (DRinter1a2a, intergenic region between dlx1a and dlx2a; DRinter3b4b, intergenic region between dlx3b and dlx4b; DRin-ter5a6a, intergenic region between dlx5a and dlx6a) retrieved from the Ensembl genome browser data (respectively NCBIM37 and Zv9). The alignment was done using the mVISTA tool from the Vista Genome Browser  [78] , with a threshold of 70% similarity over 100 bp.
And of the 335 papers cited by the two articles together, just 18 are cited by both. This offers at least a prima facie case for claiming that the two research areas have not overlapped to the extent one might have expected. Looking more widely at the impressive literature dealing with these two research areas confirms that MI and AO have been largely studied in relative isolation from each other. For instance, over the last 20 years, both MI and AO have been shown to contribute to improve motor performance and facilitate motor recovery, but few researchers have investigated whether MI and AO might be combined or considered in a common framework. We performed a literature search from the PubMed database by selecting indexed articles related to (i) motor/movement/action imagery and (ii) motor/movement/AO and action imitation.
A large sample of 2172 references (including review papers) met the topical inclusion criteria (note that a substantial number of sportrelated references do not appear in the Pubmed database and were therefore not considered in this illustrative overview of MI and AO research areas. This may explain the unexpectedly small number of MI studies in Sport psychology). 1203 articles investigated MI while 969 focused on AO. Each reference was then categorized as a study on either brain computer interface (BCI), cognitive psychology, rehabilitation, or sport psychology (Figure  2 ). The lack of AO research in BCI is basically expected and trivial. A larger number of AO studies than of MI studies was found in cognitive psychology, while the reverse was true in rehabilitation, which makes sense. The higher number of AO studies in sport psychology is more surprising, but the list of references retrieved from our chosen database is not exhaustive in this specific area.
Decision-making. The RGT requires successive choices among four options in an operant cage  [10, 11]. Two of the four options are associated with a higher immediate gain, but are disadvantageous in the long run due to higher unpredictable penalties (time-outs). The experiments were performed in twelve polyvalent conditioning boxes (Imetronic, Pessac, France; 28630634 cm). Boxes were equipped with four nose-poke holes, dimly illuminated within the hole with a white LED. These holes were located on a curved wall on one side of the box, equidistant to a food magazine situated on the opposite wall. Each hole was equipped with an infrared detector connected to an external dispenser delivering food pellets (45 mg, formula P, Sandow scientific, USA). Data collection was automated using a control software (Imetronic, Pessac, France) running on a computer outside the testing room.
A program (Imetronic, Pessac) controlled the chambers and collected the data on a computer situated outside the testing room. Training and test: During FI, the house light was on and the first lever press after a designated time-interval was reinforced by a food pellet. A light above the lever was on when the pellet was available until the rat visited the tray. During EXT (5 min), the house light was off and no pellet was delivered. During each session, the FI and EXT components operated twice in alternation. Rats were first trained with four sessions with a 30s FI-EXT schedule. Then, rats were trained for four sessions on a 1 min FI-EXT schedule followed by three sessions with a 2 min FI-EXT schedule. A maximum of 7 pellets per FI (14 pellets in total) were delivered during the 1 and 2 min FI conditions.
Finally, rats were tested for four sessions on a 1 min FI-EXT schedule to assess adaptability to a change for a shorter FI phase. This latter condition has been chosen for analysis. Data measure: The mean number of lever presses during each FI and each EXT conditions was recorded. As previously described  [16] , data from the initial FI after the start of the session, as well as that from the first interval following the first EXT were excluded because the behavior during these intervals might deviate from those during the other intervals. The total mean number of lever press, the number of visits to the empty tray as well as the speed in collecting pellets were also measured for FI and EXT. Impulsive actions: premature responses. The Fixed Consecutive Number of 16 lever press schedule (FCN16) measures behavioral inhibition in operant chambers by testing the rat's ability to carry out a long chain of sequential lever presses before obtaining a reward  [17].
Data measure: Only data from the third session of FCN16 were analyzed because they revealed the largest inter-individual behavioral differences between good and poor decision makers. Impulsivity in this task is reflected by a low percentage of rewarded chains (,70%). Among rewarded chains, some were just as long as necessary (16 presses) and reflect high response efficiency, whereas some others exceeded the number of presses required and reflect low response efficiency. Thus, response efficiency was estimated by the number of FCN lever presses divided by the total number of food pellet consumed. The number of sessions needed to reach the test phase (learning score) and response rate (total number of each lever responses per min) were also considered. The distribution of the mean number of chain of lever presses according to their length was analyzed.
The duration of the time-out was adjusted such that the duration of each trial was the same whichever lever was chosen. Test: During the test phase, a press on L1 immediately delivered one food pellet, and was followed by a 60s time-out, whereas a delay was inserted between L5 pressing and the delivery of the five pellets. During this delay, the light above L5 lever remained on until the pellets were delivered, then a time-out (60s minus the length of the delay) immediately followed food delivery. The delay was fixed for a given daily session and increased progressively over the days by 10s from 0 to 40s according to a criterion of stability: scores over two consecutive sessions should not vary by more than 10%. All sessions ended when 100 pellets had been delivered. Data measures: Percentage of L5 choice, total mean number of lever presses, and presses during the delay and time-out periods were measured.
Rats were tested in the middle of the dark phase between 10:00a. m and 1:00p. m. Data measures: From rat first entrance in the dark box, the latency to emerge from this compartment to the illuminated one was recorded (600s cut-off). Risk assessments were evaluated by number of body stretching and by head protruding in the light compartment, with at least the hind limb remaining in the safe compartment. Because these two parameters are correlated with the number of visits of the extremity of the open arms of an elevated plus-maze, which is the more risky area of this task (see  [11] ), we considered them as a measure of risk-taking. Proportions of visits and time spent in the dark compartment (%) were also measured. Analysis of individual differences. For each test, the proportion of rats with scores above or below the median of the whole population was recorded.
g. for a r = 2. 73, MC permutation test p =. 0002, see Figure  6D ). Decision-making and reward seeking. Reward seeking behavior was modeled by allowing the perceived magnitude of the rewards to be greater than the actual reward. In the model, consistent with experimental data (Figure  4B ), all poor DMm except one showed high reward seeking, whereas less than 29% of modeled good DM (good DMm) showed this trait (Figure  6B ). The reward seeking parameter estimated from the model correlated significantly with the corresponding behavioural measure of reward sensitivity (r = 2. 4014, MC permutation test p =. 0479, see Figure  7E ). Decision-making and risk seeking. Risk seeking was implemented by adding a risk-related reward contribution  [28]  to the actual rewards (see Methods). In the model, as in the experiments (Figure  4E ), poor DMm were characterized by higher levels of risk sensitivity than good DMm (Figure  6E ).
This work was supported and funded by  CNRS ,  Univ. of Bordeaux, Conseil Regional d'Aquitaine , a fellowship from the  French Government  to MR, Doctoral Training Center of the  Univ. of Edinburgh to VV, through the  Medical Research Council , the  Biology and Biological Sciences Research Council  and the  Engineering and Physical Sciences Research Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Charge conjugation of an initial state quark is implied in both diagrams. Permutation of the quark isospin is implied for the contact interaction diagram. This study uses proton proton collisions data at √ s = 7 TeV, corresponding to an integrated luminosity of 5. 0 fb -1 recorded by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC). The central feature of the CMS apparatus is a superconducting solenoid, of 6 m internal diameter, providing a field of 3. 8 T. Inside the magnet coil are the silicon pixel and strip tracker, the lead tungstate crystal electromagnetic calorimeter, and the brass/scintillator hadron calorimeter. Muons are detected in gas ionization detectors embedded in the magnet steel return yoke. In addition to the barrel and endcap detectors, CMS has extensive forward calorimetry.
3  Changes in DNA content reflecting chromosomal damages were estimated from blood samples by flow cytometry. We assumed that high coefficients of variation of blood cell DNA content (CV) are linked to high variation in the DNA content in blood cells of a single individual, i. e. to high chromosomal damages induced by chemicals. 3  DNA extractions were performed on muscle samples collected. cDNA and gene characterizations, as well as the PCR-SSCP (Single Strand Conformation Polymorphism) technique used to detect genetic polymorphism were previously explained. 4  A Hill and Smith synthetic analysis 8 was carried out on mature female data using the ADE-4 software,  9  computing both genetic and physiological variables, in order to explore possible relationships between qualitative data (e. g.
Accession numbers and the URL for data presented herein are as follows: Online Mendelian Inheritance in Man (OMIM), http://www. ncbi. nlm. nih. gov/Omim/ (for type I SMA [MIM 253300], type II SMA [MIM 253550], type III SMA [MIM 253400], SMN1 [MIM 600354], and SMN2 [MIM 601627]).
Overexpression of GmMSRBs Confers Oxidative Stress Tolerance in Yeast We previously showed that overexpression of MSRs could protect the yeast strain lacking all three MSRs against oxidative stress  [10]. Thus, mutant yeast strains overexpressing GmMSRBs were tested for their viability in the presence of hydrogen peroxide. As shown in Fig. 4 , overexpression of either soybean or yeast MSRBs protected cells from H 2 O 2 -induced stress, and the protection was higher in cells overexpressing either GmMSRB2 or GmMSRB4, while overexpressing yeast fRMSR alone did not confer significant protection under conditions of our study. Overall, these data suggest that the presence of a GmMSRB possessing a novel activity for free MetO could provide better protection against oxidative stress than MSRBs lacking such activity or fRMSR.