dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary Combinations of ATLAS and CMS measurements of the tt charge asymmetry in lepton+jets final states are reported using proton-proton collision data collected at the LHC at centreof-mass energies of 7 and 8 TeV and corresponding to integrated luminosities for each experiment of up to 5 and 20 fb -1 , respectively. Inclusive charge asymmetry results are combined at 7 and 8 TeV, and differential measurements of the charge asymmetry as a function of the invariant mass of the tt system at 8 TeV are combined. Detailed studies of the correlations between the different measurements and systematic uncertainties have been performed. The precision of the resulting combinations is significantly improved with respect to the corresponding individual measurements. The individual results and the combinations are in agreement with standard model calculations at next-to-leadingorder and next-to-next-to-leading-order precision and also compatible with zero asymmetry.
They uniquely restrict the phase space of possible new physics phenomena which would produce asymmetries larger than the standard model ones. In addition, individual groups and members have received support from BELSPO, FRIA, and IWT (Belgium); BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust (Canada); the Leventis Foundation (Cyprus); MEYS (Czech Republic); EPLANET, ERC, ERDF, FP7, Horizon 2020, and Marie Sk lodowska-Curie Actions (European Union); Investissements d'Avenir Labex and Idex, ANR, Région Auvergne and Fondation Partager le Savoir (France); AvH Foundation (Germany); the Herakleitos, Thales, and Aristeia programmes co-financed by EU-ESF and the Greek NSRF (Greece); CSIR (India); BSF, GIF, and Minerva (Israel); BRF (Norway); the HOMING PLUS programme of the FPS, co-financed from the EU Regional Development Fund, the Mobility Plus programme of the MSHE, and the OPUS programme of the NSC (Poland); the NPRP by Qatar NRF (Qatar); Generalitat de Catalunya, Generalitat Valenciana, and the Programa Clarín-COFUND del Principado de Asturias (Spain); the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University, and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Royal Society and Leverhulme Trust (United Kingdom); the A.
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS and CMS could not be operated efficiently. The work presented in this paper is performed in the context of the LHC top physics working group and benefits from its important contributions. We acknowledge the support of  ANPCyT (Argentina) ;  YerPhI (Armenia) ;  ARC (Australia) ;  BMWFW  and  FWF (Austria) ;  ANAS (Azerbaijan) ;  SSTC (Belarus) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  NSERC ,  NRC , and  CFI (Canada) ;  CERN ;  CONICYT (Chile) ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  MSMT CR, MPO CR , and  VSC CR (Czech Republic) ;  DNRF  and  DNSRC (Denmark) ;  SENESCYT (Ecuador) ;  MoER ,  ERC IUT , and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  SRNSF (Georgia) ;  BMBF  ,  DFG ,  HGF , and  MPG (Germany) ;  GSRT (Greece) ;  RGC (Hong Kong SAR, China) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  ISF, I-CORE , and  Benoziyo Center (Israel) ;  INFN (Italy) ;  MEXT  and  JSPS (Japan) ;  MSIP , and  NRF (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CIN-VESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  CNRST (Morocco) ;  NWO.
That study showed clear evidence of the consumption of DDAs, Trichodesmium, and unicellular cyanobacteria by calanoid copepods  (Weber et al. , 2017; Conroy et al. , 2017). In another recent paper,  Azimuddin et al. (2016)  presented data analysis to understand the diversity and abundance of potentially diazotrophic microorganisms associated with marine zooplankton, especially copepods. That study was based on the nifH gene in zooplankton samples, mainly copepods, collected at 12 locations in the Pacific Ocean, four stations in the subarctic and subtropical North Pacific, including the ALOHA station, and eight sta-tions in the tropical and subtropical areas of the South Pacific. If we consult the "Copepod database" (https://www. st. nmfs. noaa. gov/copepod/, last access: November 2018), the tropical South Pacific Ocean is among the least sampled regions in the world ocean for zooplankton investigation.
Study site and sampling strategy The OUTPACE survey was performed aboard the RV L'Atalante during austral summer conditions between 18 February and 3 April 2015 in the WTSP Ocean, from New Caledonia (western part of the Melanesian archipelago) to French Polynesia, along a west-east transect covering ca. 4000 km between 17 and 22 • S (Fig. 1 ). This region is impacted by ENSO, known to be the most important mode of sea surface temperature (SST) variability on interannual to decadal timescales  (Sarmiento and Gruber, 2006). The year 2015 was classified as an El Niño event, which was reflected in SST and Chl a satellite data  (Moutin et al. , 2017). Along this transect, two types of stations were sampled (Fig. 1 ): 15 short-duration stations (SD1 to SD15, 8 h) dedicated to a large-scale description and 3 long-duration stations for Lagrangian process studies.
Identification, abundance, and individual size and weight of the zooplankton taxa The taxonomic composition was determined for each formalin sample. Samples were split using a Motoda box, and at least 100 individuals of the most abundant taxa were counted in each subsample under a dissecting microscope, a LE-ICA MZ6. Species/genus identification was done according to  Rose (1933 ), Tregouboff and Rose (1957 ), and Razouls et al. (2005 -2017). The abundance of the various taxa (groups, genera, or species) was divided by the sample volume to determine the concentration of individuals per cubic meter (ind. m -3 ). The diversity of the zooplankton was determined using the Shannon-Weaver index  (Shannon and Weaver, 1949). Approximates of the individual size (total length) and relative dimensions (length/width) of the different taxa were computed from literature values: summarized data for copepod species from  Razouls et al.
The resulting samples were poured onto the scanning cell, and zooplankton organisms were manually separated with a wooden spine in order to avoid overlapping organisms. After scanning, each image was processed using Zooprocess and the image analysis software Image-J  (Grosjean et al. , 2004; Gorsky et al. , 2010). Only objects having an equivalent circular diameter (ECD) of > 300 µm were detected and processed. Finally, the Plankton Identifier software (http://www. obs-vlfr. fr/~gaspari/Plankton_Identifier/index. php, last access: November 2018) was used for automatic classification of zooplankton into 12 categories. Among them, two categories of non-zooplankton organisms, aggregates, and fibers were grouped as detritus. A training set of about 1000 objects selected automatically from different scans was used to discriminate between and classify organisms, aggregates, and fibers.
Statistical methods Principal component analysis (PCA) was used to explore spatial patterns of the environmental variables data characterizing the zooplankton habitat: temperature, salinity, chlorophyll a, percentage of chlorophyll a (ratio Chl-a/Chl-a + Phae) (average values of chlorophyll a and phaeophytin between 0 and 200 m depth, to be consistent with the net haul depth), and MLD. The data were normalized before the analyses were run using the Primer 6. 0 software. One-way or two-way analyses of variance were run, to explore the differences between day and night samples and between stations or zones for the environmental and zooplankton parameters. Post hoc Scheffé tests were performed to analyze paired differences. Spearman's rank correlations (R s ) were computed to test relationships between zooplankton variables and environmental parameters.
Diversity was calculated for zooplankton and copepod taxa using the Shannon-Wiener diversity index. Spatial variations in the zooplankton community composition were investigated using multivariate analysis, specifically nonmetric multidimensional scaling (NMDS). A Bray-Curtis matrix "species -stations" of square root transformed abundance data was used to estimate station similarity. The similarity matrix was then ordinated using NMDS. A SIM-PER (percentage of similarity) analysis was performed to identify the species contributing most to similarity or dissimilarity between stations for the station groups identified by NMDS. Finally, to select the environmental variables "best explaining" community patterns, we used the BEST procedure with the BIOENV algorithm, which maximizes a rank correlation between the environmental and zooplankton resemblance matrices.
Letters below the mean values indicate homogeneous groups between zones (small letters) or LD stations (capital letters) according to post hoc Scheffé tests. Zooplankton stocks are estimated from the cumulated biovolume of binocular-counted organisms. Swimmer biomass in sediment trap from  Caffin et al. (2018a    Sediment trap data at day 5 cannot be considered for the analysis (see  Caffin et al. , 2018a). ton biomass decreased to an average of 22 % and showed a declining trend from west to east, with the lowest value of 7 % occurring at SD-15. The integrated phytoplankton standing stock derived from the water-column-integrated content of total chlorophyll a within the euphotic layer (Table  6 ) was highest at the LD-B and the lowest in the GY, although the stock generally decreased from west to east along the transect, as seen in Fig.
Data availability. All data and metadata are available at the French INSU/CNRS LEFE CYBER database (scientific coordinator: Hervé Claustre; data manager, webmaster: Catherine Schmechtig) at the following web address: http://www. obs-vlfr. fr/proof/php/ outpace/outpace. php (last access: December 2018), INSU/CNRS LEFE CYBER (2018). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-15-7273-2018-supplement. Author contributions. FC and MP conceived and designed the investigation and wrote the paper with contributions from all coauthors. KD and VV participated in the collection of samples; LG and OG participated in the analysis of samples. FC, MP, VV, and BPVH participated in the data analysis. Competing interests. The authors declare that they have no conflict of interest.
Acknowledgements. This is a contribution of the  OUTPACE (Oligotrophy from Ultra-oligoTrophyPACific Experiment ) project funded by the  French national research agency  ( ANR-14-CE01-0007-01 ), the  LEFE-CyBER program  ( CNRS-INSU) , the  GOPS program (IRD) , and the  CNES  ( BC T23 ,  ZBC 4500048836 ) and by the  European FEDER Fund  under project  1166-39417  The  OUT-PACE  cruise (https://doi. org/10. 17600/15000900) was managed by  Thierry Moutin  and  Sophie Bonnet  from the  MIO (Mediterranean Institute of Oceanography). The authors thank to the crew of the  R/V L'Atalante  for outstanding shipboard operation. This work was supported by the  Comisión Nacional de Investigación Científicas y Tecnológicas (CONICYT)  through grants FONDECYT no. 1130511  and  1150891  and  Instituto Milenio de Oceanografía (IMO)  grant  IC120019.
Valentina Valdés  and Katty Donoso were funded by a  CONICYT scholarship. Brian P. V. Hunt  was funded in part from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement no. 302010  -project  ISOZOO. The authors thank  France Van Wambeke ,  Cécile Dupouy , and  Marcus Stenegren  for sharing their data on primary production chlorophyll and diazotroph abundance, which helped in interpretation and comparison with our results. The authors thank both referees and the scientific associate editor for their many constructive suggestions and  Michael Paul  for improvement of the English. Edited by: Sophie Bonnet Reviewed by:  Antonio Bode  and one anonymous referee.
Statistical analyses. Community analyses were performed using Bayesian mixed-effect generalized linear models run in R 56 using the package MCMCglmm  57. Mixed-effects analysis accounts for the lack of independence (and hence reduced degrees of freedom) between branches from the same tree crowns, as well as pairs of branches in contact, which would be expected to be more similar than random if some exchange of individuals had occurred. A weak prior was employed, with the default prior variance (V) = 1, and 'degree of belief ' parameter (nu) = 0. 002 for all fixed and random effects. Models were run for 10k iterations after a burn-in of 3k, and with a thinning interval of 10 as standard. For several traits with relatively low sampling efficiency for certain variables, iterations were increased to 90k after a burn-in of 10k.
Acknowledgements The work was supported by funds of  Région Bretagne  to AP, RB and FM. Olivier Jambon  provided technical support. We thank the  Office National des Forêts  for collaboration. FM was funded by the  Estonian Science Foundation  (grant  9215 , group grant  IUT20-33 ), the  European Regional Development Fund (Centre of Excellence FIBIR) , the  Région Bretagne  (ACOMB and SAD to AP),  CNRS  ( ATIP grant  to AP), and  The Government of India (INSPIRE Faculty  Award number  DST/INSPIRE/04/2013/000476  to  U. Kodandaramaiah ). The manuscript profited strongly from comments by several anonymous referees.
F1 sperm DNA methylation To test if methylation patterns were altered in the sperm of F1 offspring, we carried out genome-wide methylated DNA immunoprecipitation followed by semiconductor sequencing (MeDIP-SC-seq) on four individuals per group  [29]. As a comparison, we compared these patterns to those in a tissue with markedly different methylation patterns (liver) using recently published genome-wide datasets  [30, 31]. Analysis of global methylation patterns through Pearson correlation analysis with Euclidian hierarchical clustering confirms that the sperm methylome differs dramatically from that of the liver (Fig. 2a ). However, there was no clear stratification between the two groups of sperm samples (Fig. 2a ). Differential signal analysis from average 5mC patterns between Veh-and Dex-exposed sperm revealed little difference in methylation across the entire genome (Fig.
Samples were sequenced to between 24 and 31 M reads. Sperm sRNAs and ChIP DNA were sent for next-generation sequencing at Source Biosciences (Nottingham, UK). Single-end 50-bp sequencing was performed on a HiSeq 2500 machine. We obtained 32. 5-62. 5 million ChIP-Seq reads and 8. 6-20. 9 million sRNA-Seq reads. All sequencing data can be accessed through the European Nucleotide Archive, accession number PRJEB14719  [72].
Bioinformatics Analyses of sRNA-Seq, ChIP-Seq and ERRBS data were performed with bespoke CGAT pipelines (https://github. com/TomSmithCGAT/Trans_of_gluco_effects_pipelines) utilising the CGAT code collection  [73] , CGAT pipelines repository (https://github. com/CGATOxford/CGATPipelines) and open-source software as detailed below. Analysis of MeDIP-Seq was performed using a previously reported approach  [29]  as detailed below.
MeDIP-SC-seq analysis Reads were mapped to the reference genome using the Torrent TMAP software. The data were then binned into 200-bp windows across the genome and data normalised first by read count and relative to a matched input sequence. These read count and input normalised datasets were then used for all subsequent analyses. Signals were then mapped to one of five unique genomic compartments ("promoter core", TSS ± 100 bp; "promoter proximal", TSS + 1 kb; "promoter distal", TSS + 1 kb to + 2 kb; "genic" or "non-genic", not associated with any of the above) using annotated Refgene_mm9 data supplied by the UCSC genome browser. Global MeDIP-SCseq analysis was carried out by plotting Pearson correlation scores and representing these through heatmap visualisation with Euclidian clustering.
DESeq2  [34]  was used to identify significantly differentially expressed sRNAs between the four Dex and Veh replicates. The DESeq2 rlog transformation was used to generate normalised counts, which were used for clustering and data exploration. Hierarchical clustering of samples based on expression of miRNA, piRNA or tRNA genes was performed using the R package pvclust, with 1000 bootstraps and the distance measure set as 1 -Spearman's correlation coefficient. To estimate our statistical power to detect differential expression we simulated in silico "spike-in" sRNA genes with differences in expression. To achieve this we shuffled the expression values between the sRNA genes for the Dex replicates whilst retaining the replicate structure to maintain the within-group variance. Spike-ins were binned by the mean expression and the induced fold change and randomly sampled to ensure even coverage over a range of expression values and fold changes.
Spike-ins with fold changes greater than fourfold or expression greater than 1024 counts were discarded. Bins with fewer than 100 spike-ins were discarded and all remaining bins were downsampled to 100 spike-ins. In total, 2500 spike-ins were retained and added to the real sRNA expression data and the DESeq2 analysis repeated. Statistical power for a given bin was calculated as n/100, where n is the number of differentially expressed spike-ins detected. Following personal communication with Oliver Rando (University of Massachusetts), we also quantified sRNA expression using an iterative approach to assign reads to sRNA. In addition, rather than quantifying against all annotated tRNA loci, we quantified tRNAs based on their 5′ 18-nucleotide sequence since the majority of reads aligning to tRNAs aligned to just the 5′ end of tRNA sequences, which is non-unique between tRNA loci.
To compute the meta-profile over gene models, we used the CGAT script bam2geneprofile. py which counts the reads overlapping the gene model, normalising each individual transcript profile by the maximum coverage and normalising the meta-profile to make the area underneath the curve equal to 1. To compute the enrichment of IP over input over the gene-model we divided the IP meta-profile by the meta-profile for their respective H3 input. We utilised SICER  [77]  to call peaks in the histone modification samples relative to their respective H3 input sample, following the author's recommendation to call peaks in both "narrow" and "broad" modes and keeping the peak calls from the two modes separate. Narrow peak calling was performed with the following options: The enrichment of modified H3 IP over unmodified H3 IP for all high-confidence peaks observed in at least one sample was calculated for sample and peak clustering.
Acknowledgements We thank  Will Mungal  for assistance with animal husbandry and  Fiona Rossi ,  Shonna Johnston  and  William Ramsay  at the  Queen's Medical Research Institute Flow Cytometry Facility  for assistance with FACS purification of germ cells. We also thank  Professor Robert Hammer  of  University of Texas Southwestern Medical Center  for supplying the GCS-eGFP line. We thank  Oliver Rando  for his advice and discussion regarding iterative mapping of sRNA-Seq reads.
Funding This work was supported by grant  MR/K018310/1  to AJD and RRM from the  UK Medical Research Council , by a  British Heart Foundation  PhD studentship to CMR ( FS/10/49/28675 ). RRM is supported by the  Medical Research Council  and work in RRM's lab is also supported by the  BBSRC. CGAT (TS and AH) is funded by the  UK Medical Research Council  (grant number  G1000902 ). The funding bodies played no role in the study design, analysis or interpretation of data or in writing the manuscript.
Availability of data and materials All sequencing data can be accessed through the European Nucleotide Archive, accession code PRJEB14719  [72]. CGAT code collection and CGAT pipelines are available through the respective Github repositories (https://github. com/ CGATOxford/cgat and https://github. com/CGATOxford/CGATPipelines). Archive versions are available at https://doi. org/10. 5281/zenodo. 1117857 and DOI: https://doi. org/10. 5281/zenodo. 1185001, respectively. Additional bespoke pipelines for sRNA-Seq histone ChIP-Seq and eRRBS analyses are available from https://github. com/TomSmithCGAT/Trans_of_gluco_effects_pipelines (archived at DOI: https://doi. org/10. 5281/zenodo. 1184584).
Age validation To validate the pipeline's age estimates, we generate a mock catalogue of RAVE-like stars using the population synthesis code Galaxia  (Sharma et al. 2011). As Galaxia uses PARSEC  (Bressan et al. 2012)  isochrones to produce stars with perfectly-known ages and atmospheric parameters (T eff , log g, [M/H]), 2MASS photometry, and distances, it offers a suitable test sample for assessing internal uncertainties on the output of the distance pipeline.
Applying uncertainties to our mock catalogue We then scattered the mock data for T eff , log g, and [M/H] by RAVE-like uncertainties. The standard deviations used in this process were the quadrature sums of RAVE's internal and external uncertainties for a star with the given true data (for more detail see Figure  5  of  Wojno et al. 2017). The true apparent magnitudes J, H and Ks in the mock data were then scattered by a Gaussian error distribution with dispersion 0. 025 mag, which is a typical 2MASS uncertainty. Finally, the true parallaxes were scattered by a Gaussian error distribution with a dispersion of 0. 3 mas, which is a typical TGAS uncertainty  (Gaia Collaboration et al. 2016; Lindegren et al. 2016).
Input versus output age comparison The mock data were then fed into the updated RAVE distance pipeline and the resulting ages compared with the 'true' ages given by Galaxia. As a preliminary sanity check the values of T eff , log g, [M/H] and distance from the pipeline were compared with the original values from Galaxia and found to agree within the errors. Figure  1  shows the input and output ages subdivided by regions within the T eff -log g  plane. The top row shows the data for giant stars (T eff < 5500 K, log g < 3. 5), the middle row shows the data for main-sequence stars (log g > 4. 25), and the bottom row shows the data for turnoff stars (5500 K < T eff < 7100 K, 3. 5 < log g < 4. 25). Panels in the left column are colourcoded by fractional age uncertainty, while panels on the right are colour-coded by absolute age uncertainty.
DISCUSSION AND CONCLUSIONS The release of Gaia-based parallaxes for the majority of RAVE stars has significantly enhanced the value of the RAVE catalogue by reducing errors in distances and proper motions, particularly for main-sequence and turnoff stars. The new parallaxes also make it possible to determine credible ages for stars in the turnoff region. It is well established that the kinematics of a stellar population is correlated with age and chemistry. Since these correlations arise from the secular evolution of the Galaxy's mass distribution, starformation rate and non-axisymmetric features (e. g. Aumer et al. 2016; Schönrich & McMillan 2017) , they must encode valuable information about our Galaxy's history. Hence it is interesting to re-examine the kinematics-age-metallicity nexus for turnoff stars that appear in both the RAVE and TGAS catalogs.