dataesr/scientific-paragraphs-categorization huggingface dataset:
Signal and background modelling The tt signal process is simulated using the matrix element event generator MADGRAPH (version 5. 1. 5. 11)  [21] , together with the MADSPIN  [22]  package for the modelling of spin correlations. The PYTHIA6 program (version 6. 426)  [23]  is used to model parton showering and hadronization. In the signal simulation, the mass of the top quark, m t , is fixed to 172. 5 GeV. The proton structure is described by the CTEQ6L1 PDF set  [24]. The same programs are used to model dependencies on the renormalization and factorization scales, µ r and µ f , respectively, the matching threshold between jets produced at the matrix-element level and via parton showering, and m t. The cross sections obtained in this paper are also compared to theoretical calculations obtained with the NLO event generators POWHEG (version 1.
Among assignments with equal number of b-tagged jets, the one with the highest average weight is chosen. Events with no solution after smearing are discarded. The method yields an average reconstruction efficiency of ≈95%, which is determined in simulation as the fraction of selected signal events (which include only direct tt decays via the e ± µ ∓ channel, i. e. excluding cascade decays via τ leptons) passing the kinematic reconstruction. The overall difference in this efficiency between data and simulation is estimated to be ≈1%, and a corresponding correction factor is applied to the simulation  [47]. A more detailed description of the kinematic reconstruction procedure can be found in Ref. [47]. In total, 38 569 events are selected in the data. The signal contribution to the event sample is 79.
Signal extraction and unfolding The number of signal events, N sig i , is extracted from the data in the ith bin of the reconstructed observables using N sig i = N sel i -N bkg i , 1 ≤ i ≤ n, (1) where n denotes the total number of bins, N sel i is the number of selected events in the ith bin, and N bkg i corresponds to the expected number of background events in this bin, except for tt final states other than the signal. The latter are dominated by events in which one or both of the intermediate W bosons decay into τ leptons with subsequent decay into an electron or muon. Since these events arise from the same tt production process as the signal, the normalisation of this background is fixed to that of the signal. The expected signal fraction is defined as the ratio of the number of selected tt signal events to the total number of selected tt events (i.
Cross section determination The normalized double-differential cross sections of tt production are measured in the full tt kinematic phase space at parton level. The number of unfolded signal events Munf ij in bin i of variable x and bin j of variable y is used to define the normalized double-differential cross sections of the tt production process, 1 σ d 2 σ dx dy ij = 1 σ 1 ∆x i 1 ∆y j Munf ij B L , ( 3 ) where σ is the total cross section, which is evaluated by integrating (d 2 σ/dx dy) ij over all bins. The branching fraction of tt into eµ final state is taken to be B = 2. 3%  [51] , and L is the integrated luminosity of the data sample. The bin widths of the x and y variables are denoted by ∆x i and ∆y j , respectively. The bin widths are chosen based on the resolution, such that the purity and the stability of each bin is generally above 30%.
For all generators there is a discrepancy between the data and simulation for the medium M(tt)     bins, where the predicted ∆η(t, t) values are too low. The disagreement is the strongest for MADGRAPH+PYTHIA6. ] -1 [GeV (t)dy(t) T dp ) t (t σ Figures  6  and 7  illustrate the comparison of the distributions of p T (tt) and ∆φ(t, t) in the same M(tt) ranges to the MC models. For the p T (tt) distribution (Fig. 6 ), which is sensitive to radiation, none of the MC generators provide a good description. The largest differences are observed between the data and POWHEG+PYTHIA6 for the highest values of p T (tt), where the predictions lie above the data. For the ∆φ(t, t) distribution (Fig. 7 ), all MC models describe the data reasonably well. In order to perform a quantitative comparison of the measured cross sections to all considered MC generators, χ 2 values are calculated as follows: χ 2 = R T N-1 Cov -1 N-1 R N-1 , (4) where R N-1 is the column vector of the residuals calculated as the difference of the measured cross sections and the corresponding predictions obtained by discarding one of the N bins, and Cov N-1 is the (N -1) × (N -1) submatrix obtained from the full covariance matrix by discarding the corresponding row and column.
The predictions are calculated using the MCFM program (version 6. 8)  [59]  and a number of the latest PDF sets, namely: ABM11  [60] , CJ15  [61] , CT14  [62] , HERAPDF2. 0 [63] , JR14  [64] , MMHT2014  [65] , and NNPDF3. 0 [66] , available via the LHAPDF interface (version 6. 1. 5) [67]. The number of active flavours is set to n f = 5 and the top quark pole mass m t = 172. 5 GeV is used. The effect of using n f = 6 in the PDF evolution, i. e. treating the top quark as a massless parton above threshold (as was done, e. g. in HERAPDF2. 0 [63] ), has been checked and the differences were found to be <0. 1% (also see the corresponding discussion in Ref. [66] ). The renormalization and factorization scales are chosen to be µ r = µ f = √ m 2 t + [p 2 T (t) + p 2 T (t) ]/2, whereas α s is set to the value used for the corresponding PDF extraction.
The theoretical uncertainty is estimated by varying µ r and µ f independently up and down by a factor of 2, subject to the additional restriction that the ratio µ r /µ f be between 0. 5 and 2  [68]  (referred to hereafter as scale uncertainties). These uncertainties are supposed to estimate the missing higher-order corrections. The PDF uncertainties are taken into account in the theoretical predictions for each PDF set. The PDF uncertainties of CJ15  [61]  and CT14  [62] , evaluated at 90% CL, are rescaled to the 68% CL. The uncertainties in the normalized tt cross sections originating from α s and m t are found to be negligible (<1%) compared to the current data precision and thus are not considered. For the double-differential cross section as a function of p T (t) and y(t), approximate NNLO predictions  [19]  are obtained using the DIFFTOP program  [4, 69, 70].
Theoretical uncertainties from scale variations are not included in this χ 2 calculation. The NLO predictions with recent global PDFs using LHC data, namely MMHT2014, CT14, and NNPDF3. 0, are found to describe the p T (t), y(t), and y(tt) cross sections reasonably, as illustrated by the χ 2 values. The CJ15 PDF set also provides a good description of these cross sections, although it does not include LHC data  [61]. The ABM11, JR14, and HERAPDF2. 0 sets yield a poorer description of the data. Large differences between the data and the nominal NLO calculations are observed for the ∆η(t, t), p T (tt), and ∆φ(t, t) cross sections. It is noteworthy that the scale uncertainties in the predictions, which are of comparable size or exceed the experimental uncertainties, are not taken into account in the  s ), thus resulting in large NLO scale uncertainties  [18].
For the p T (t), y(t), and y(tt) cross sections, the scale uncertainties in the predictions reach 4% at maximum. They increase to 8% for the ∆η(t, t) cross section, and vary within 20-50% for the p T (tt) and ∆φ(t, t) cross sections, where larger differences between data and predictions are observed. For the p T (t), y(t), and y(tt) cross sections, the PDF uncertainties as estimated from the CT14 PDF set are of the same size or larger than the scale uncertainties. The HERAPDF2. 0 predictions are mostly outside the total CT14 uncertainty band, showing also some visible shape differences with respect to CT14. The approximate NNLO predictions provide an improved description of the p T (t) shape. The data-to-theory comparisons illustrate the power of the measured normalized cross sections as a function of [p T (t), y(t)], [y(t), M(tt)], and [y(tt), M(tt)] to eventually distinguish between modern PDF sets.
Such a study is performed on these data and described in the next section. The remaining measured normalized cross sections as a function of [∆η(t, t), M(tt)], [p T (tt), M(tt)], and [∆φ(t, t), M(tt)] could be used for this purpose as well, once higher-order QCD calculations become publicly available to match the data precision. Moreover, since the latter distributions are more sensitive to QCD radiation, they will provide additional input in testing improvements to the perturbative calculations. s ) (DIFFTOP) prediction calculated with CT14. The inner vertical bars on the data points represent the statistical uncertainties and the full bars include also the systematic uncertainties added in quadrature. The light band shows the scale uncertainties (µ) for the NLO predictions using CT14, while the dark band includes also the PDF uncertainties added in quadrature (µ + PDF).
The PDF fit The double-differential normalized tt cross sections are used in a PDF fit at NLO, together with the combined HERA inclusive deep inelastic scattering (DIS) data  [63]  and the CMS measurement of the W ± boson charge asymmetry at √ s = 8 TeV  [72]. The fitted PDFs are also compared to the ones obtained in the recently published CMS measurement of inclusive jet production at 8 TeV  [8]. The XFITTER program (formerly known as HERAFITTER)  [73]  (version 1. 2. 0), an open-source QCD fit framework for PDF determination, is used. The precise HERA DIS data, obtained from the combination of individual H1 and ZEUS results, are directly sensitive to the valence and sea quark distributions and probe the gluon distribution through scaling violations. Therefore, these data form the core of all PDF fits.
Details of the PDF fit The scale evolution of partons is calculated through DGLAP equations  [74] [75] [76] [77] [78] [79] [80]  at NLO, as implemented in the QCDNUM program  [81]  (version 17. 01. 11). The Thorne-Roberts  [82] [83] [84]  variable-flavour number scheme at NLO is used for the treatment of the heavy-quark contributions. The number of flavours is set to 5, with c and b quark mass parameters M c = 1. 47 GeV and M b = 4. 5 GeV  [63]. The theoretical predictions for the W ± boson charge asymmetry data are calculated at NLO  [85]  using the MCFM program, which is interfaced with APPLGRID (ver-sion 1. 4. 70) [86] , as described in Ref. [72]. For the DIS and W ± boson charge asymmetry data µ r and µ f are set to Q, which denotes the four-momentum transfer in the case of the DIS data, and the mass of the W ± boson in the case of the W ± boson charge asymmetry.
Summary A measurement of normalized double-differential tt production cross sections in pp collisions at √ s = 8 TeV has been presented. The measurement is performed in the e ± µ ∓ final state, using   data collected with the CMS detector at the LHC, corresponding to an integrated luminosity of 19. 7 fb -1. The normalized tt cross section is measured in the full phase space as a function of different pairs of kinematic variables describing the top quark or tt system. None of the tested MC models is able to correctly describe all the double-differential distributions. The data exhibit a softer transverse momentum p T (t) distribution, compared to the Monte Carlo predictions, as was reported in previous single-differential tt cross section measurements. The double-differential studies reveal a broader distribution of rapidity y(t) at high tt invariant mass M(tt) and a larger pseudorapidity separation ∆η(t, t) at moderate M(tt) in data compared to simulation.
The data are in reasonable agreement with next-to-leading-order predictions of quantum chromodynamics using recent sets of parton distribution functions (PDFs). The measured double-differential cross sections have been incorporated into a PDF fit, together with other data from HERA and the LHC. Including the tt data, one observes a significant reduction in the uncertainties in the gluon distribution at large values of parton momentum fraction x, in particular when using the double-differential tt cross section as a function of y(tt) and M(tt). The constraints provided by these data are competitive with those from inclusive jet data. This improvement exceeds that from using single-differential tt cross section data, thus strongly suggesting the use of the double-differential tt measurements in PDF fits.
B PDF fit of single-differential tt measurement at NNLO Approximate NNLO predictions  [19]  for the y(t) single-differential cross section are obtained using the DIFFTOP program, which is interfaced to FASTNLO  [90]  (version 2. 1). The results are used in a PDF fit at NNLO. The procedure follows the determination of the PDFs at NLO described in Section 9. 1. In the NNLO fit, the scales for tt production are set to µ r = µ f = m t , with m t = 173 GeV being the top quark pole mass. The scale evolution of partons is calculated through the DGLAP equations at NNLO. The DIS and W ± boson charge asymmetry theoretical predictions are calculated at NNLO accuracy. For the W ± boson charge asymmetry predictions, the NNLO corrections are obtained by using K-factors, defined as the ratios of the predictions at NNLO to the ones at NLO, both calculated with the FEWZ  [91]  program (version 3.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: the  Austrian Federal Ministry of Science, Research and Economy  and the  Austrian Science Fund ; the  Belgian Fonds de.
Interaction effect screening in ECLIPSE To illustrate these effects in real data, we conducted a two-step screening approach to identify interaction effects between cis-eQTL SNPs and candidate TFs in a genome-wide expression dataset from 121 subjects in the ECLIPSE Study  [36]. Interaction triplets (SNP, TF and target gene) were defined based on an a priori list of TFs and significant SNP-gene pairs from the unmeasured exposure E, an interaction between G and E, and in 50% of the replicates a measured exposure Z. In the framework of this analysis, Z and E are considered as measured and unmeasured TF mRNA level, respectively. The validity of five tests is evaluated by comparing the observed -log 10 (pvalue) against the expected -log 10 (p-value) when testing for the null interaction between a G and Z.
e. , no rank-based inverse normal transformation). Step one consisted of testing all SNPs in the vicinity of a gene for cis-effect on the expression of that gene assuming an additive genetic effect and using a score test as implemented in the GGTools software  [37] , and to select the most significant cis-eQTL as candidate SNPs for interaction effect testing. Among the 18,834,685 gene-SNP pairs tested, we identified 132,074 SNPs with a q-value for association less than or equal to 0. 01. From these results we selected the most significant SNP for each Affymetrix probe set, resulting in a total of 2,982 cis-eQTL SNPs associated with 3,334 target probe sets. As observed in other studies, the variance of the residualized expression phenotypes explained by cis-eQTL SNPs can be much larger than those observed for quantitative phenotype in human GWAS  [38].
The ECLIPSE data Study participants included 121 COPD cases genotyped as part of the ECLIPSE study  [36]  using the genome-wide Illumina HumanHap550 BeadChip. Each participant had data on ~6. 1 million SNPs, either directly genotyped or imputed using the 1000 Genomes EUR reference panel (March 2010)  [48]. Details on quality control assessment, filtering of the SNPs and genotype imputation have been described elsewhere  [49, 50]. Gene expression was measured from whole blood samples on the Affymetrix HGU 133Plus 2. 0 chip, and eQTL analysis was performed as previously described  [8]. Gene expression data were log-transformed and quantile normalized using the RMA function in the "affy" R package  [51]  (step1 in  Fig 1). We then regressed expression values on age, gender, the first principal components derived from the genotype data on all ECLIPSE participants  [52] , and the first 13 principal components from gene expression, retaining residuals from this regression for further eQTL analysis (step2 in  Fig 1).
kdr mutation detection DNA was extracted from a leg of each specimen as previously described  [38]. Amplification of a 300 bp segment of the voltage-gated sodium channel (VGSC) gene flanking the 1014 position was performed using the primer pair Ag-F kdr (5'-GAC CAT GAT CTG CCA AGA TGG AAT-3') and An-kdr-R2 (5'-GAG GAT GAA CCG AAA TTG GAC- The PCR product was sequenced by Macrogen™ (Seoul, South-Korea) using both primers. Each sequence was checked and cleaned manually using the Bioedit software version 7. 1. 9 (http://www. mbio. ncsu. edu//BioEdit/bioedit. html). A consensus sequence was generated for each specimen using the CAPS3 sequence assembly program  [39]  and then aligned using the Clustal Omega multiple sequence alignment program  [40] [41] [42]  (GenBank accession numbers KY677707-KY677716).
Molecular identification of Anopheles by ITS2 sequencing Amplification of the ITS2 was performed using the primer pair ITS2A (5'-TGT GAA CTG CAG GAC ACA T-3') and ITS2B (5'-ATG CTT AAA TTY AGG GGG T-3') described by Beebe et al. [43]. The PCR mix was composed of 1 unit of Goldstar DNA polymerase (Euro-gentec™, Seraing, Belgium) and 400 μM of each primer. The PCR was conducted in a total reaction volume of 25 μl (4 μl of DNA template and 21 μl of PCR mix). The thermocycling protocol consisted in an initial activation step of 1 min at 94 °C, followed by 40 amplifcation cycles of 20 s at 94 °C, 20 s at 51 °C and 30 s at 72 °C, and a final elongation step of 30 s at 72 °C. The PCR product was sequenced by Macrogen™ (Seoul, South-Korea) using the ITS2A primer. Anopheles species was determined using the blastn algorithm of the online BLAST™ software  [44]  (accession numbers KY677698-KY677706).
Data analysis Adult mortality rate was corrected by the formula of Abbott  [45]  in the case of mortality > 5% in the control. Tests with > 20% mortality in the control were excluded from the analysis. Knock-down time 50 (KDT50) was determined by the log-probit method described by Finney  [46]  using R software  [47]. The R code used to determine the knock-down time 50 (KDT50) and its confidence interval were adapted from Johnson et al. 2013  [48].
Discussion In this study, we assessed the susceptibility status of several anopheline taxa to three public health insecticides (deltamethrin, permethrin and DDT) along the TMB. To our knowledge, this is the first study reporting the susceptibility status of Anopheles mosquitoes to pesticides used for malaria control in this area. These data are important in the context of malaria elimination to select the most appropriate insecticides for vector control. Bioassays were performed following the WHO guidelines for insecticide resistance monitoring in malaria vectors  [37]. However, the WHO recommends the use of unfed 2-5 days old female Anopheles to assess the susceptibility of wild malaria vectors populations to insecticides. This implies to collect a sufficient number of larvae, which was not possible in the present study considering the difficulty of accessing to the collection sites.
Alive female Anopheles were exposed during 1 h to insecticides (deltamethrin 0. 05%, permethrin 0. 75% and DDT 4%). KD rate was recorded at the end of the exposition period; the mortality rate was recorded after a 24 exposition period. Abbreviation: NA, not available and at least 20 mosquitoes for the control). Thus the interpretation of the must take into account the sample size and the corresponding error bars. In this study, suspected resistance to deltamethrin was detected in An. minimus (s. s. ). Resistance to DDT was previously reported in Cambodia, Vietnam and Thailand  [19, 20, 52]  whereas resistance to pyrethroids (permethrin, alpha-cypermethrin and lambda-cyhalothrin) was only detected in Vietnam  [19]. Few data have been collected so far on the susceptibility of An. maculatus (s.
Therefore we this mechanism to be involved in the resistance of An. hyrcanus (s. l. ) to the insecticides tested. We reported the occurrence of the L1014S kdr mutation in An. hyrcanus (s. l. ) at a low frequency in the specimens surviving insecticide exposure. Specimens carrying a kdr mutation were further identified as An. peditaeniatus using molecular methods (data not shown) (Accession numbers: KY677698-KY677706). This finding confirms the previous report of the L1014S mutation in An. peditaeniatus populations from Southern Vietnam and Cambodia  [23]. However, the occurrence of kdr mutations in other positions cannot be ruled out. For example, another kdr mutation (N1575Y) occurring within the main VIII of the VGSG gene was found in An. gambiae from West Africa occurring in a V1014F haplotypic background  [61].
Acknowledgements We acknowledge the  UMR-224 (MIVEGEC)  of the  Institut de Recherche pour le Développement (IRD) , the  Shoklo Malaria Research Unit (SMRU)  and the  Centre Hospitalier Régional Universitaire (CHRU) de Montpellier  for providing human resources to the project. We also thank the  Thailand International Development Cooperation Agency (TICA)  from the  Ministry of Foreign Affairs (MoFA)  through the  STOPVEC programme  for their technical and administrative assistance. This work was partially supported by the  Centre for Advanced Studies for Agriculture and Food, Institute for Advanced Studies, Kasetsart University  under the  Higher Education Research Promotion and National Research University Project of Thailand, Office of the Higher Education Commission, Ministry of Education, Thailand.
Results were processed using FlowJo (Tree Star, Inc, Ashland, OR, USA) and CellQuest software (BD Biosciences, Le Pont de Claix, France). Protein detection was performed with both intracellular and extracellular staining. For extracellular staining about 5 Â 10 5 -1 Â 10 6 LMS cells were washed, trypsinised and resuspended in 1 Â PBS. A dead cell control was prepared by placing 1 Â 10 5 cells at 56 1C for 15 min. All cells were first stained with the Live/Dead Fixable Red stain (Invitrogen, Cergy Pontoise, France), which ensure accurate assessment of cell viability in samples after fixation and/or permeabilisation. All cells were then stained with the following goat polyclonal primary antibodies: anti-TYRO3 (AF859); anti-AXL (AF154); anti-Mer (AF891) all from R&D Systems; or goat IgG isotype control (Novus Biologicals, Cambridge, UK), and a secondary donkey anti-goat IgG Alexa Fluor 488 antibody (Invitrogen).
Following the cells were fixed and permeabilised using the eBioscience Fixation & Permeabilization Kit (eBioscience, Hatfield, UK), as detailed by the supplier's and stained with the same primary and secondary antibodies again. Cells were analysed on a flow cytometer FACScalibur (BD Biosciences). Resultant data were analysed by FlowJo Software (Tree Star, Inc. ). Western blotting. Whole-cell lysates were prepared in RIPA lysis buffer supplemented with protease and phosphatase inhibitors (Bio-Rad factors 1 and 2). Protein concentrations were determined by DC protein assay (Bio-Rad, Marnes-la-Coquette, France) and equal amounts were analysed by SDS-polyacrylamide gel electrophoresis and immunoblotting. Primary antibodies against human TYRO3 (#5585; Cell Signalling, Saint Quentin Yvelines, France), AXL (ABN275; Millipore, Molsheim, France), phosphoAXL (Y779-AF2228; R&D Systems) and actin (A54441; Sigma-Aldrich) were used, followed by a secondary HRP-linked antibody.