dataesr/scientific-paragraphs-categorization huggingface dataset:
The results are also compared to the predictions of a Monte Carlo event generator based on the NLO QCD calculation for the short-distance scattering process matched with parton showers, followed by hadronisation. The measurement is performed with two different jet radius parameters to test the sensitivity to perturbative (higher-order corrections and parton shower) and non-perturbative effects. The outline of the paper is as follows. A brief description of the ATLAS detector is given in Section 2. The inclusive jet production cross-section is defined in Section 4. Section 3 gives an overview of the data set and Monte Carlo simulations used. The details of the experimental measurement are presented in the next sections. Section 5 describes the event and jet selection for the measurement. The jet energy calibration and the uncertainties associated with the jet energy measurements are outlined in Section 6.
Data set and Monte Carlo simulations The measurement uses proton-proton collision data at a centre-of-mass energy of √ s = 8 TeV collected by the ATLAS detector during the data-taking period of the LHC in 2012. The LHC beams were operated with proton bunches organised in "bunch trains", with bunch-crossing intervals (or bunch spacing) of 50 ns. 1 ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector and the z-axis along the beam pipe. The x-axis points from the IP to the centre of the LHC ring, and the y-axis points upwards. Cylindrical coordinates (r, φ) are used in the transverse plane, φ being the azimuthal angle around the z-axis. The pseudorapidity is defined in terms of the polar angle θ as η =ln tan(θ/2). Angular distance is measured in units of ∆R ≡ (∆y) 2 + (∆φ) 2 , where y is the jet rapidity.
The absolute luminosity measurement is derived from beam-separation scans performed in November 2012 and corresponds to 20. 2 fb -1 with an uncertainty of 1. 9%. The uncertainty in the luminosity is determined following the technique described in Refs. [29]. The average number of interactions per bunch crossing, µ , was 10 ≤ µ ≤ 36. All data events considered in this analysis have good detector status and data quality. For the simulation of the detector response to scattered particles in proton-proton collisions, events are generated with the Pythia 8  [30]  (v8. 160) Monte Carlo event generator. It uses LO QCD matrix elements for 2 → 2 processes, along with a leading-logarithmic (LL) p T -ordered parton shower  [31]  including photon radiation, underlying-event simulation with multiple parton interactions  [32] , and hadronisation with the Lund string model  [33].
The MC event generator's parameter values are set according to the AU2 underlying event tune  [34]  and the CT10 PDF set  [35]  is used. The stable particles from the generated events are passed through the ATLAS detector simulation  [36]  based on the Geant4 software toolkit  [37]  and are reconstructed using the same version of the ATLAS software as used to process the data. Effects from multiple proton-proton interactions in the same and neighbouring bunch crossings (pile-up) are included by overlaying inclusive proton-proton collision events (minimum bias), which consist of single-, double-and non-diffractive collisions generated by the Pythia 8 event generator using the A2 tune  [34]  based on the MSTW2008 LO PDF set  [38]. The Monte Carlo events are weighted such that the distribution of the generated mean number of proton-proton collisions per bunch crossing matches that of the corresponding data-taking period.
0 package  [47]. In this simulation the CT10 PDF set  [35]  is used. The simulation of parton showers, the hadronisation and the underlying event is based on Pythia 8  [30]  using the AU2 tune  [34]. These predictions are refered to as the Powheg predictions in the following. The renormalisation and factorisation scales for the fixed-order NLO prediction are set to the transverse momentum of each of the outgoing partons of the 2 → 2 process, p Born T. In addition to the hard scatter, Powheg also generates the hardest partonic emission in the event using the LO 2 → 3 matrix element or parton showers. The radiative emissions in the parton showers are limited by the matching scale µ M provided by Powheg.
4 and R = 0. 6. In the central (forward) region the total uncertainty is about 5% (10%) at medium p T of 300-600 GeV. The uncertainty increases towards both lower and higher p T reaching to 15% at low p T and 50% at high p T. The JES and JER uncertainties for jets with different sizes are rather similar at the jet level. However, at the cross-section level differences occur due to the different slopes of the distributions. The dominant systematic uncertainty source for the measurement of the inclusive jet cross-sections is related to the jet energy measurement. The jet energy scale uncertainty is larger than the jet energy resolution uncertainty. [GeV] T,jet p 70 2   The NLOJet++  [64]  (v4. 1. 3) software program is used to calculate the NLO QCD predictions for the 2 → 2 processes for the inclusive jet cross-sections.
The renormalisation and factorisation scales are set to the p T of the leading jet in the event, i. e. µ R = µ F = p jet,max T. For fast and flexible calculations with various PDFs as well as different renormalisation and factorisation scales, the APPLGRID software  [65]  is interfaced with NLOJet++. The inclusive jet cross-sections are presented for the CT14  [66] , MMHT2014  [67] , NNPDF3. 0  [68] , HERAPDF2. 0 [69]  PDF sets provided by the LHAPDF6  [70]  library. The value for the strong coupling constant α s is taken from the corresponding PDF set. Three sources of uncertainty in the NLO QCD calculation are considered: the PDFs, the choice of renormalisation and factorisation scales, and the value of α s. The PDF uncertainty is defined at 68% confidence level (CL) and is evaluated following the prescriptions given for each PDF set, as recommended by the PDF4LHC group for PDF-sensitive analyses  [71].
Qualitative comparisons of data to NLO QCD calculations The measured double-differential inclusive jet cross-sections are shown in Figure  5  and Figure  6  as a function of the jet p T for anti-k t jets with R = 0. 4 and R = 0. 6 for each jet rapidity bin. The cross-section covers 11 orders of magnitude in the central rapidity region and 9 orders of magnitude in the forward region. Jet transverse momenta above p T = 2 TeV are observed. In the most forward region the jet p T reaches about 500 GeV. Tabulated values of all observed results, with full details of uncertainties and their correlations, are also provided in the Durham HEP database  [84]. The measurement is compared to an NLO QCD prediction using the MMHT2014 PDF set  [67]  based on NLOJet++ corrected for non-perturbative and electroweak effects.
For the theoretical prediction, the uncertainties related to the scale variations, the alternative scale choice, the PDF eigenvectors, the non-perturbative corrections and the strong coupling constant are treated as separate uncertainty components. In the case of the NNPDF3. 0 PDF set, the replicas are used to evaluate a covariance matrix, from which the eigenvectors are then determined. Table  2  shows the evaluated P obs for the NLO QCD predictions corrected for non-perturbative and electroweak effects for each rapidity bin considered individually. In this case, only cross-section measurements with p T > 100 GeV are included in the quantitative comparison of data and theory to reduce the influence of non-perturbative corrections. For anti-k t jets with R = 0. 4, P obs values larger than about 4% are found for all cross-sections and PDF sets.
In addition to the quantitative comparisons of the theory and data cross-sections in individual jet rapidity bins, all data points can be considered together. Table  3  shows the χ 2 values for each PDF set, R value and scale choice, when using all the |y| bins together. Various p T ranges are tested. All the corresponding P obs are much smaller than 10 -3. If the statistical uncertainty of the η-intercalibration were treated as a single component (see Section 6), the χ 2 values computed in Table  3  would be strongly enhanced (by even more than 200 units for some configurations). Further quantitative comparisons using all the |y| bins together were performed in more restricted p T ranges ( 70 < p T < 100 GeV, 100 < p T < 240 GeV, 240 < p T < 408 GeV, 408 < p T < 642 GeV, 642 < p T < 952 GeV and p T > 952 GeV), for the CT14 PDF set.
For anti-k t jets with R = 0. 6 the Powheg prediction is higher than the fixed-order NLO QCD prediction at low p T and lower at high p T. In the most forward rapidity region the two predictions are similar. The ratio of the Powheg prediction to data is less dependent on the jet radius than the same ratio using the fixed-order NLO QCD prediction corrected for non-perturbative effects. The theory to data ratio for anti-k t jets with R = 0. 6 and the same ratio for anti-k t jets with R = 0. 4 is unity within 5% for all jet p T and rapidities while the fixed-order calculation shows deviations of up to 15% for low p T jets in the central region. This indicates the importance of parton shower effects in correctly describing the jet radius dependence. Theory/Data 0. 7 0. 8 0. 9 Only the nominal values of this ratio are indicated.
Conclusion The double-differential inclusive jet cross-sections in proton-proton collisions at √ s = 8 TeV are measured for jets reconstructed with the anti-k t algorithm with jet radius parameter values of R = 0. 4 and R = 0. 6 in the kinematic region of the jet transverse momentum from p T = 70 GeV to about 2. 5 TeV and jet rapidities |y| < 3. The measurement is based on the data collected with the ATLAS detector during LHC operation in 2012, corresponding to an integrated luminosity of 20. 2 fb -1. The cross-sections are measured double-differentially in the jet transverse momentum and rapidity. The dominant systematic uncertainty arises from the jet energy calibration. Compared to previous jet cross-section measurements a significant reduction of the uncertainties is achieved. The publication of all observed results, including uncertainties and correlations, in the Durham HEP database allows further quantitative comparisons of data and theory.
A quantitative comparison of the measurement to fixed-order NLO QCD calculations, corrected for nonperturbative and electroweak effects, shows overall fair agreement (with p-values in the percent range) when considering jet cross-sections in individual jet rapidity bins treated independently. Some tension between data and theory is observed in the central rapidity region for anti-k t jets with R = 0. 6. Strong tension between data and theory is observed when considering data points from all jet transverse momentum and rapidity regions, with a full treatment of the correlations. This tension can be reduced, but not completely resolved, using alternative correlation scenarios for the experimental and theoretical two-point systematic uncertainties. The remaining tension could be due either to the breakdown of the assumptions that need to be made in the treatment of two-point systematic uncertainty components, or to an incomplete theoretical description, such as missing higher-order corrections.
Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Région Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; CERCA Programme Generalitat de Catalunya, Generalitat Valenciana, Spain; the Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers.
Acknowledgements We honour the memory of our colleague  Hrachya Hakobyan , who made a large contribution to this work, but died 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; SRNSF, 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; 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.
, Life Technologies SAS, Saint Aubin, France). The primer sequences used for gyrB amplification (UP-1 and UP-2r) and sequencing (UP-1S and UP-2Sr) were the same as reported in  Yamamoto and Harayama (1995). BLAST analyses were conducted to compare the resulting 16S rDNA and gyrB sequences for B. subtilis CU1 to other B. subtilis strains using the National Center for Biotechnology Information (NCBI) nucleotide database. The B. subtilis CU1 genome was assembled using the bioinformatics software Velvet [version 1. 2. 10] that operates based upon de novo assembly of reads without any a priori with respect to the organism being studied  (Zerbino and Birney, 2008). A script was prepared to allow for testing of k-mers from 15 to 43 base pairs at 2base pair intervals. This script generated 93 contigs ranging from 350 to 450,000 base pairs.
This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession MOOX00000000. The version described in this paper is version MOOX01000000. Following this read assembly, BLAST version 2. 2. 26 was employed to survey the genome for genes associated with conferring antibiotic  (ermA, ermB, ermC, cfr, tetA, tetK, tetL, tetP, tetV, qnrA, qnrB, qnrD, qnrS, parC, parE, gyrA, gyrB, aac, rpoB, msrA, vga, vgB, strA, strB, aphA1, van A, vanB, vanS, vanW)  or multidrug resistance  (mdtA, mdtB, mdtC, mdtD, mdtE) , as well as genes implicated in biogenic amine (hdc, tyrA, tyrB) or enterotoxin  (cytK, bceT, hblA, hblB, hblC, hblD, nheA, nheB, nheC)  production.
Human study As outlined in the original study plan  (Lefevre et al. , 2015) , all clinical data were analyzed using SAS ® software version 9. 1. 3 Service Pack 4 (SAS Institute Inc. , Cary, U. S. ) and results were expressed as mean ± SD. Statistical significance was set at P < 0. 05. The number of subjects with at least one adverse effect, the association of adverse events with study participation, and the severity of adverse events were compared between the probiotic and placebo groups using the chi-square test or Fisher's exact test.
Species and strain identification A total of 1312 nucleotides of 16S rDNA of B. subtilis CU1 were sequenced. The obtained sequence was compared to the 16S rDNA sequences deposited in the NCBI nucleotide database, and strong homology was found between B. subtilis CU1 and Bacillus subtilis subsp. subtilis and Bacillus subtilis subsp. spizizenii (Table  1 ). Molecular identification of B. subtilis CU1 to a species level was determined by sequencing gyrB (551 base pairs), and when compared to the NCBI nucleotide database, characterization of the strain at the subspecies level as Bacillus subtilis subspecies spizizenii was suggested by the high homology to two spizizenii isolates (Table  1 ). Characterization of B. subtilis CU1 at the strain level was conducted using RAPD PCR analysis with three different primers.
Assessment of toxigenic activity The toxigenic potential of B. subtilis CU1 was evaluated by assessing its hemolytic, surfactant, and cytotoxic activity in vitro (Table  3 ). Hemolytic activity was evaluated on human erythrocytes by the addition of cell-free supernatants to fresh, viable erythrocyte cultures. B. subtilis CU1 cell-free supernatants (from LB or MH media cultured at 30 or 37 C) did not induce hemolysis of erythrocytes. When erythrocytes were incubated with cell-free supernatant from B. cereus 14579 cultures or SDS, high levels of hemoglobin were released from the erythrocytes under all experimental conditions (Fig. 3A , Table  3 ). The surfactant activity of B. subtilis CU1 was assessed by the qualitative drop-collapse method. B. subtilis 13 GR cultures (positive control) grown in liquid LB and MH media produced significant surfactant activity, and some dispersion (slightly spread out) was also observed when this strain was grown on solid LB medium (data not shown).
, 2009). Partial sequencing of the ribosomal 16S DNA, up to a maximum of 1312 base pairs, and comparison of this sequence with other Bacillus strains showed strong homology with several Bacillus species belonging to the B. subtilis group (!99%). Using nucleotide sequence analyses of gyrB, Wang and colleagues have identified gyrB as a secondary target for taxonomic identification of members of the B. subtilis complex  (Wang et al. , 2007). A sequence homology of 97% and 95% was observed for B. subtilis CU1 gyrB relative to respective gyrB sequences of Bacillus subtilis subsp. spizizenii and B. subtilis subsp. subtilis identified within the NCBI database. As reported by  Wang et al. (2007) , strains with 95. 5e100% identity with the gyrB sequence display high DNA-DNA relatedness  suggesting the strains are conspecific.
subtilis CU1 was not resistant to any of these antibiotics above defined regulatory thresholds, based upon comparison of the measured MICs with the microbiological cut-off values listed by EFSA for the Bacillus species (EFSA, 2012). B. subtilis CU1 was also evaluated for phenotypic evidence of toxigenic activity using three validated in vitro assays and the results of all tests for hemolytic and cytotoxic activity as well as biosurfactant production consistently demonstrated a lack of toxigenicity. As outlined in the safety decision tree proposed by  Pariza et al. (2015) , an important and necessary step in the safety assessment of novel microbial cultures is to sequence the genome of the new microbial strain, which can then be bioinformatically searched for genetic factors associated with pathogenicity as well   Data represent total number of adverse events followed by % in brackets.
0 1 (1. 2) 0 0 Radio-infiltration (shoulder) 1 (1. 0) 0 0 0 0 0 Rhinitis 0 1 (1. 0) 0 8 (9. 8) 0 0 Sore throat 1 (1. 0) 0 0 1 (1. 2) 0 0 Tracheitis 1 (1. 0) 0 0 0 0 0 Trouble sleeping, insomnia 2 (2. 0) 1 (1. 0) 0 2 (2. 4) 2 (2. as antibiotic resistance. In fact, the B. subtilis CU1 genome was sequenced and BLAST software was employed to survey the genome for genes associated with conferring antibiotic or multidrug resistance, as well as genes implicated in biogenic amine (i. e. , histamine, tryptamine) or enterotoxin (i. e. , cytotoxin, haemolysin) production. Overall, no genetic factors associated with antibiotic or multidrug resistance as well as biogenic amine or enterotoxin synthesis were identified in the B. subtilis CU1 genome (personal data), confirming the results of the above in vitro assays.
Acknowledgments The authors would like to thank the clinical study subjects for their interest, time, and compliance with dietary supplementation. The authors would like to acknowledge the staff at BIOFORTIS (a  M erieux NutriSciences Company, 3 route de la Chatterie, Saint-Herblain, France ),  Eurofins IDmyk  (1, rue des Vergers, 69760 Limonest, France), and  BIOCEANE  ( rue du Moulin de la Rousseli ere, 44821 Saint Herblain, France ) for their diligent work in conducting the clinical trial,  16S  and gyrB sequencing, and MIC testing, respectively.
spizizenii BCRC 17366 913 97 3 Bacillus subtilis subsp. subtilis BCRC 10255 863 95 4 Bacillus subtilis BCRC 17433 857 95 5 Bacillus subtilis BCRC 17435 852 95 6 Bacillus subtilis BCRC 14714 857 95 7 Bacillus vallismortis BCRC 17183 802 93 8 Bacillus mojavensis BCRC 17124 734 91 9 Bacillus mojavensis BCRC 17531 734 91 10 Bacillus mojavensis DHT-13 503 91 a Compared to the NCBI nucleotide database. b Out of a total of 100 homologous sequences reported.
Electrochemical Data Analysis Partial least squares-discriminant analysis (PLS-DA)  [21]  was used as the pattern recognition technique for the computation of the classification models applied to determine the predicted probability of the presence of the three targets. The performance of each classification model was evaluated on the basis of the following parameters: Once the optimal model for each target substance selected, the probability of the presence of the different target substances for any new measurement was computed. All data analysis were performed using the PLS Toolbox ver. 7. 5 and ver. 7. 8. 2 (Eigenvector Research Inc. , Wenatchee, WA, USA) and all routines were written in MATLAB© platform 7. 11 R2010b (The Mathworks Inc. , Novi, MI, USA).
Signature Definition and Data Analysis Using the individual signals of the different electrodes for each compound, an approach based on the creation of a unique signature for each target was applied so as to take full advantage of the information provided by the different electrodes on the same sample. To this aim, the signals acquired with the four electrode surfaces were merged together as a unique new signal after a block of data was scaled to unit variance. This approach led to a signature with equal importance of the information provided by the different electrodes. A schematic representation of this approach is shown in Figure  4. These signatures were acquired to generate an experimental database of target compounds in multiple aqueous environments, such as distilled water, drinking water, non-drinking water, soapy water, and artificial waste water.
This approach was used to build a stronger database than just a database produced in pure water and to insert in the model the expected variations related to potential matrix effects. In addition to the three main target substances (B01, B08, and B15), data were also acquired on additional target substances B04, B05, and B11, and on the interfering species Int09, representative of common cleaning products (once again kept as confidential). The composition of the dataset used for the calculation of the PLS-DA classification models of target B01, B08, and B15 is reported in Table  1.
This approach was used to build a stronger database than just a database produced in pure water and to insert in the model the expected variations related to potential matrix effects. In addition to the three main target substances (B01, B08, and B15), data were also acquired on additional target substances B04, B05, and B11, and on the interfering species Int09, representative of common cleaning products (once again kept as confidential). The composition of the dataset used for the calculation of the PLS-DA classification models of target B01, B08, and B15 is reported in Table  1. For explanatory purpose, the signatures obtained during the training step for each of the three target compounds are presented in Figure  5. Here, for the sake of clarity, are presented only the experiments performed in water, soapy water, and artificial waste water.