dataesr/scientific-paragraphs-categorization huggingface dataset:
The uncertainty associated with E miss T is calculated by propagating the energy scale and resolution systematic uncertainties to all jets and leptons in the E miss T calculation. Additional E miss T uncertainties arising from energy deposits not associated with any reconstructed objects are also included  [64]. The uncertainty due to the finite size of the MC simulated samples are evaluated by varying the content of the migration matrix with a Poisson distribution. The standard deviation of the ensemble of results unfolded with the varied matrices is taken as the uncertainty. The effect is more significant in the 7 TeV analysis (up to 3% in high-m tt and high-p T,tt bins), due to the smaller size of the MC simulation sample available at 7 TeV. In the 8 TeV analysis, while the MC statistical uncertainty is less significant (subpercent overall), an additional uncertainty is included to account for the bias introduced by the unfolding procedure due to the observed deviation between data and the predicted t t events.
The typical size of the bias is less than 1%, and increases towards higher m tt , p T,tt , and |y tt | up to about 4%. The bias in the 7 TeV analysis is taken into account by choosing an unfolding parameter based on the level of bias for an observable, which is reflected in the data statistical uncertainty and thus not included as a systematic uncertainty. The uncertainty in the integrated luminosity is estimated to be 1. 8% for √ s = 7 TeV  [17]  and 1. 9% for √ s = 8 TeV  [18]. The effect of the uncertainty is substantially reduced in the normalized differential cross-sections due to large bin-to-bin correlations.
Predictions from Powheg+Pythia with h damp = m t , MC@NLO+Herwig, Powheg+Pythia with h damp = ∞, and Powheg+Herwig are used for comparison with data. In the 7 TeV analysis, Alpgen+Herwig is also used for the comparison, as it was the default sample used in the differential measurement in the +jets channel by ATLAS  [8]. Both NLO generators (Powheg and MC@NLO) use the NLO CT10  [26]  PDF set, while Alpgen+Herwig uses the LO CTEQ6L1  [78]  PDF set. Most of the generators agree with data in a wide kinematic range of the distributions. The m tt spectrum is well described by most of the generators at both 7 TeV and 8 TeV, except for Powheg+Pythia in the highest m tt bin in the 7 TeV analysis. For p T,tt , agreement with Powheg+Pythia with h damp = ∞ is particularly bad due to a harder p T,tt spectrum than data at both 7 TeV and 8 TeV.
Better agreement with data is obtained from Powheg+Pythia with h damp = m t. This is consistent with the studies in Refs. [27, 28]  using data from the √ s = 7 TeV ATLAS parton-level measurement in the +jets channel  [8]. In both the 7 TeV and 8 TeV analyses, MC@NLO+Herwig describes the p T,tt spectrum well also. Similar good agreement is also observed in 7 TeV and 8 TeV parton-level measurements by ATLAS in the +jets channel  [8, 11]. For |y tt |, all the generators show fair agreement with data in the 7 TeV analysis, while at 8 TeV, none of the generators provides an adequate description of |y tt |. This difference in the level of agreement is due to the improved statistical precision and finer binning in |y tt | for the 8 TeV analysis. The increasing discrepancy between data and MC prediction with increasing |y tt | is also observed at the reconstructed level for both energies, as shown in Figure  1  and Figure  2.
All generators use the NLO CT10  [26]  PDF, except for Alpgen+Herwig using the LO CTEQ6L1 PDF. The NLO+NNLL calculations use the MSTW2008nnlo PDF. The light (dark) gray band includes the total (data statistical) uncertainty in the data in each bin. The uncertainties on the two data measurements do not account for the correlations of the systematic uncertainties between the two channels.
Conclusions Normalized differential t t production cross-sections have been measured as a function of the invariant mass, the transverse momentum, and the rapidity of the t t system in √ s = 7 TeV and 8 TeV proton-proton collisions using the dilepton channel. The data correspond to an integrated luminosity of 4. 6 fb -1 and 20. 2 fb -1 for √ s = 7 TeV and 8 TeV, respectively, collected by the ATLAS detector at the CERN LHC. The results complement the other ATLAS measurements in the lepton+jets channel using the 7 TeV and 8 TeV datasets. The predictions from Monte Carlo and QCD calculations generally agree with data in a wide range of the kinematic distributions. Most of the generators describe the m tt spectrum fairly well in 7 TeV and 8 TeV data. The p T,tt spectrum in both 7 TeV and 8 TeV data is well described by Powheg+Pythia with h damp = m t and MC@NLO+Herwig, but is particularly poorly described by Powheg+Pythia with h damp = ∞.
Figure11: Ratio of different theoretical predictions and the lepton+jets measurement [8]  to the measurement of the normalized t t differential cross-sections in the dilepton channel for (a) invariant mass (m tt ) (b) transverse momentum (p T,tt ) and (c) absolute value of the rapidity (|y tt |) of the t t system at √ s = 7 TeV. Theoretical QCD calculations at NLO+NNLL level are also included in m tt and p T,tt. All generators use the NLO CT10 [26]  PDF, except for Alpgen+Herwig using the LO CTEQ6L1 PDF. The NLO+NNLL calculations use the MSTW2008nnlo PDF. The light (dark) gray band includes the total (data statistical) uncertainty in the data in each bin. The uncertainties on the two data measurements do not account for the correlations of the systematic uncertainties between the two channels.
Acknowledgements We honor the memory of our colleague  Irene Vichou , who made a large contribution to this work, but died shortly before its completion. We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC, China ;  COLCIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  HGF , and  MPG, Germany ;  GSRT, Greece; RGC, Hong Kong SAR, China ;  ISF, I-CORE  and  Benoziyo Center , Israel; INFN, Italy;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  RCN, Norway ;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland; MOST, Taiwan ;  TAEK, Turkey; STFC, United Kingdom ;  DOE  and  NSF ,  United States of America.
Major Allele Frequency (MAF), Number of private alleles (Np), and Polymorphic Information Content (PIC) were calculated with the software PowerMarker version 3. 25 (NC State University, Raleigh, NC, USA)  [38]. In addition, Analysis of Molecular Variance (AMOVA) was done using GENALEX 6. 4 (The Australian National University, Canberra, Australia)  [39] , in order to estimate the genetic structure between and among geographical regions and continents. Since sesame populations from North Africa, West Asia, and Southeast Africa have very low sample sizes compared to other populations, they were not considered in the AMOVA analysis for geographical regions in order to avoid bias in the analyses. However, all accessions, according to their continent of origin (Asia or Africa), were included in the AMOVA analysis for continents.
The significance of variance components was tested by permuting the DNA marker data 999 times. The 33 markers used in this study were mapped onto the 16 Linkage Groups (LGs) of sesame genome according to their physical positions using MapChart 2. 3 (Wageningen UR, Wageningen, Netherlands)  [40]. To identify the pair-wise genetic relationships between the 96 accessions, a genetic distance matrix was calculated with GENALEX 6. 4  [39]. Principal component analysis (PCA) based on genotype data of SSR markers was performed using GENALEX 6. 4. A Neighbor-Joining (NJ) tree based on Nei's genetic distance  [41]  was also drawn in MEGA version 6. 06 (Temple University, Philadelphia, PA, USA)  [42]. Additionally, the population structure was inferred using the Bayesian clustering method implemented in the program STRUCTURE 2.
2 (Stanford University, Stanford, CA, USA)  [43]. The software was run with the admixture model and correlated allele frequencies. Five runs were performed for each k (1 to 10) representing the number of clusters considered. The burn-in number and iterations for each run were both set to 100,000 and the true k was determined according to the method described by  [44]. Sesame accessions with membership probabilities ě0. 60 were assigned to the corresponding subgroup and accessions with membership probabilities <0. 60 were assigned to a mixed subgroup. All accessions were mapped according to their geographical coordinates with ArcGIS software version 9. 3 (Esri, Redlands, CA, USA). The geographical coordinates were obtained from the China National Genebank and for some accessions from Africa the originating country geographical coordinates were assigned.
Population Structure The model-based approach implemented in STRUCTURE was performed to examine the relatedness among the 96 sesame accessions, using the genotypic data for 33 polymorphic SSRs. To facilitate the determination of the exact k value corresponding to the genetic groups, the ad hoc quantity ∆k was used. The highest value of ∆k for the 96 sesame accessions was for k = 2 (Figure  3 ). (probability of membership <0. 6) shared among the two genetic groups. G1 gathered together 39 accessions, including 31 from Asia, and G2 included in total 51 accessions, with 42 from Africa. The six accessions assigned to the admixed group include an accession from North Africa, three accessions from South Asia, and two accessions from East Asia (Table  S3 ). It was thus suggested that based on genetic difference, the two major groups might be related to the two continents (Africa and Asia).
Acknowledgments: We thank  Xiaodong Zhu  and  Shi-yang Jiang  for their laboratory assistance,  Roger Bayala  for helping to map sesame accessions, and  Kyky Ganyo  and  Bassiaka Ouattara  for their helpful suggestions on the manuscript. Finally, we are sincerely grateful to the anonymous reviewers for their helpful comments. This work was funded by the  International Advanced Agriculture Science and Technology Introduction program  ( 948 Program , No. 2011-G1 (5)-03 ), the  Agricultural Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences  ( CAAS-ASTIP-2013-OCRI ), and the  Deutscher Akademischer Austausch Dienst (DAAD)  award.
Susceptibility testing was done with the recommended primary antimicrobials (amikacin, amoxicillin-clavulanic acid, ciprofloxacin, imipenem, trimethoprim-sulfamethoxazole, and tobramycin) and two secondary antimicrobials (cefotaxime and gentamicin). The plates were incubated at 37 • C for 72 h and read manually with a mirrored box. The MICs were interpreted according to CLSI. 15  The species level identification was done by sequencing a 606 bp long fragment of the 16S rRNA gene. DNA extraction, PCR amplification, and sequencing were carried out by using primers Noc1 (5 -GCT TAA ACC ATG CAA GTCG-3 ) and Noc2 (5 -GAA TTC CAG TCT CCC CTG-3 ) as described previously. 16 ach strain was sequenced in both the senses in order to determine a consensus sequence. To determine the percentage of similarity to the closest type strain, each sequence was used as a query using BLAST search against the GenBank database.
Temperature and humidity of each pen were recorded before obtaining images and entered as parameters in the image analysis software (ThermaCam Pro 2. 10 © 1997-2010; FLIR Systems, AB, Sweden). Limits of wing, shank and comb areas were drawn manually. Shank area was drawn from hock joint and included toes. Wing area included wing shoulder, bow, bar, primaries and secondaries. Comb area included all parts of the comb from base, points to blade. The mean shank, comb and wing temperature and standard deviations of the temperature of all pixels in each zone were extracted from the software. The emissivity of featherless areas (comb and shank) was set at 0. 980, which is the reference value provided by the software for bare skin. For the feathered wing area, we measured surface temperature with a contact thermometer, taken simultaneously with a thermographic image on a sample of 20 birds, and adjusted emissivity value to obtain the closest possible values between the two temperatures.
The pedigree file included 1502 animals (1115 hens with data and their 387 parents). Traits recorded under thermo-neutral and hot conditions were analysed as separate traits in multivariate analyses to estimate the genetic correlations between both traits. VCE6 software (Neumaier and Groeneveld, 1998;  Groeneveld et al. , 2010) was used for the genetic analyses. As not all traits could be included in a single analysis, a series of 76 analyses was run. Each analysis included four traits, that is, one temperature trait and one egg quality or feathering trait at hot and thermo-neutral conditions, in order to test each combination of traits at least once. When the same parameter appeared in different analyses, the results presented below are the average of estimated genetic parameters and the average of the standard errors.
Stereoanalysis We use the commercial stereo mapping software package SOCET SET®  (Miller and Walker, 1993; 1995; Zhang et al. , 2006)  from BAE Systems to produce DTMs from the images simulated. This software, which we use for a wide range of planetary mapping projects, is state-of-the-art and a world leader in terms of number of licenses sold. Results from different image matching algorithms can be expected to differ slightly, but those from SOCET SET should be representative, particularly in terms of the degradation of quality factors with illumination. Controlling the images, usually a time consuming step of DTM production, is not necessary because the simulated camera stations are exactly known. Manual editing of the DTMs, the other time-consuming step, can also be omitted because the objective is to evaluate the success of automated image matching.
Although we argue that these simulations are unlikely to reflect the true behavior of high resolution Europa images, the results are likely to be relevant to stereo imaging of other targets that are known to have little local albedo contrast, such as the martian polar layered deposits. The key result of this investigation is that the EIS WAC will be able to produce stereo DTMs of Europa with near-optimal quality for incidence angles between about 40° and 85°, and very probably for incidence angles between 0° and 85°. This result alleviates the concern that the WAC data would only support analysis of a fraction of the REASON sounding data set. In addition, we have placed approximate bounds on the illumination mismatches that lead to unacceptable degradation of DTM quality that will be invaluable for planning EIS NAC stereo observations.
FUTURE WORK The primary issue driving future work is the extent to which the specific results (imaging guidelines and quality rules of thumb) derived from analysis of Europa simulations with SOCET SET will apply more generally to other targets and other stereoanalysis packages. Therefore one of our goals for the future is to investigate DTMs produced from the images described here with the Ames Stereo Pipeline  (Moratto et al. , 2010) , a package in widespread use in the planetary community that uses an automated matching algorithm developed independently from the SOCET SET matcher. Such tests should shed light on whether the best DTM resolution achieved here, around 15 pixels, is a limitation of the data used or of the stereo software. A second goal is to perform similar simulations for other types of planetary images.
Observations and data reduction The data presented in this paper were primarily collected with HST/STIS (program 13388) and NOT/FIES  (program 48-103). Table  1  gives the log of observations. The HST/STIS program consisted in a non-disruptive Target of Opportunity (ToO) for the early observation of bright Galactic novae. Each visit was just one orbit long. The instrument setup was chosen to cover the whole UV range from 1200 to 2900 Å. In addition, the choice of the echelle resolutions (E230H or E230M) was driven by the target magnitude, as was the aperture (0. 2×0. 2 arcsec in all cases), satisfying the requirements of high-spectral-resolution and instrument safety constraints. Each HST/STIS observation was triggered on the basis of the SWIFT UV magnitude obtained from UVOT and X-ray evolution complemented with the optical light curves, thus to cover the most important evolutionary phases of the ejecta.
X-ray observations with Swift We include a brief description of the nova's X-ray development during our period of observation, since the central white dwarf is the photoionization source that drives the spectroscopic evolution of the ejecta in the optical and ultraviolet (cf. Woudt & Ribeiro 2014). Swift observations of V339 Del began on 2013 Aug 14. 96 UT. Starting at only nine hours after its discovery, the follow-up for V339 Del was one of the most rapid responses to a nova performed by Swift. Observations were obtained regularly until the nova entered the Sun constraint for Swift at 144. 5 days after discovery. Data collection resumed on day 202. A weak X-ray source was first detected by the XRT (X-ray Telescope) (see  Burrows et al. 2005)  35. 6 days after the nova was discovered. The source then brightened by almost five orders of magnitude, passing through a period of large-amplitude flux variability before reaching a peak count rate of ∼90 count s -1 (0.
Conclusions In its basic properties, V339 Del is not remarkable for a CO nova. For the quantity, quality and timing of the multiwavelength observations, and the enormous spectroscopic archive amassed, however, it surpasses even the optical spectroscopic database for the classical nova DQ Her  (Stratton & Manning 1939; Payne-Gaposchkin 1957). Our aim was to systematically sample the range of spectroscopic diagnostics at critical stages during the evolution of the outburst. By combining X-ray, ultraviolet, and optical spectroscopy and spectrophotometry, we have obtained a set of benchmark parameters for this nova. Abundances for key elements were not derived at this stage. We intend to perform a complete analysis once the SSS phase has definitively ended and the ejecta are in freeze-out (e. g.
2×10 21 cm -2 that is consistent with the last fit value from Swift X-ray spectrophotometry during the late SSS phase. -The recombination-dominated transitions, such as He II 1640Å, 4686Å, and the Balmer lines, followed the same temporal trend as the electron density, varying as t -3 during the nebular stages. -The filling factor was between 0. 1 and 0. 3. The ejecta were consistent with chemical homogeneity, but this conclusion is tentative pending a full abundance analysis. -The derived mass of the ejecta was (2-3)×10 -5 M. Our program of observations is continuing into the late nebular stage and will be treated in the next paper in this series. Table  3. Sample line integrated flux from NOT/FIES set of spectra. Note that the fluxes are in erg s -1 cm -2 and no reddening correction has been applied.
Acknowledgements. Based on observations made with the Nordic Optical Telescope, operated by the  Nordic Optical Telescope Scientific Association at the Observatorio del Roque de los Muchachos, La Palma, Spain , of the  Instituto de Astrofisica de Canarias. The research leading to these results has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement No. 312430  (OPTICON). Sumner Starrfield acknowledges partial support from  NASA  and  NSF  grants to ASU. CEW acknowledges partial support from  NASA Swift  grant  NNX14AC54G. Simone Scaringi  acknowledges funding from the  Alexander von Humboldt Foundation. Based on observations made with the  NASA/ESA Hubble Space Telescope , obtained [from the Data Archive] at the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc.
Then, the elements were compared with publicly known cis-acting sequences of IRESite database  [38]  that allowed us to identify common sequence motifs of possible functional importance. We selected a hairpin sequence within a previously reported domain containing a putative IRES, which has been identified before in the TRβ1-5'UTRs  [23] , and a sequence conserved among all TRβ1 5'UTR variants with multiple alternate AUGs  [22]  (Fig  2 ). To check functional properties of the putative IRES site, we performed a simple test in Caki-2 cells, cultured in serum-deprived medium, which has been reported to switch between cap-dependent and cap-independent (IRES-mediated) translation  [5, 12]. We found that protein synthesis rates after serum starvation resulted in higher luciferase activity from plasmid containing the TRβ1 IRES site (pGL3-A)  [22]  compared to the control (pGL3-control)  [22]  without the IRES sequence (S2 Fig).
Protein up-regulation induced by p16 5'UTR-specific dGoligos To test whether our approach could be applied to enhance expression of another gene, we used published sequence data  [21]  as well as dGenhancer calculator to design dGs, specifically targeting p16 INK4a 5'UTR (NCBI Ac. : NM_000077. 4), a transcript of CDKN2A tumor suppressor. In this in vitro study, dG-mediated regulation of protein synthesis was tested using PCR-amplified linear expression construct containing T7 promoter, p16 INK4a 5'UTR and the coding sequence of luciferase allowing for fast and reliable measurements of protein levels (S1 Appendix). The effects of each DNA-based dGs dG1p16-dG6p16 (S3 Table) were measured using coupled in vitro transcription-translation assay (Fig  5 ). Results from semi-quantitative realtime PCR, performed with luciferase specific primers (S4 Table ), and measurements of luciferase activity revealed that negative control (dG-), scrambled control (dGscp16), dG5p16 and microRNA-like dG3p16 and dG4p16 had no effects neither on transcription nor translation efficiency.
dGoligo controls All dGs were tested as complementary sense and antisense sequences (S3 Table ). dG5, dG6, dG7 and dG8 were synthesized as mismatched controls containing a 3 nucleotide-long mismatched insertion in the middle of the oligonucleotides (Fig  2 ). An additional scrambled control oligonucleotide (dGsc) with an irrelevant (random) sequence was as designed with GeneScript software (S3 Table ).
Bioinformatic analysis Total Gibbs energy prediction (ΔG = ΔH-TΔS) of 5'UTR secondary structures was performed using RNAstructure version 5. 2  [37]. NCBI-BLASTN program and IRESite database  [38]  were used for comparative sequence analysis towards evolutionary conserved 5'UTR domains such as IRES consensus sequences. The dGenhancer calculator was used to determine translation regulating elements (S1 Appendix).
Statistics At least three independent experiments were carried out for each assay and measured in triplicate. Normality of data distribution was estimated using Shapiro-Wilk test and in each case data were analyzed by ANOVA followed by Dunnett's multiple comparison test. p<0. 001 was considered statistically significant. Correlation of Gibbs energy and translation efficiency (Fig  1c ) was estimated by r-squared value of the Pearson product-moment correlation coefficient. Logarithmically transformed data of translation efficiency were analyzed with the Gibbs energies by linear regression. p<0. 05 was considered statistically significant.