dataesr/scientific-paragraphs-categorization huggingface dataset:
Of note, all of the mould-active azoles adversely interact with immunomodulatory and antineoplastic drugs. Prophylactic fluconazole may be a worthwhile alternative to mould-active prophylaxis in alloHCT in centres practicing early diagnostics-driven therapy  [8]. It should be noted that patient-level data (from published papers), rather than (raw) data extracted from clinical study reports, were used to drive this MTC meta-analysis.
Acknowledgments The work was funded by  Pfizer International Operations. The systematic literature review was conducted by  Evidera (formerly United BioSource) , funded by  Pfizer International Operations , according to parameters defined by author consensus. Sonja Sorenson  and  Lael Cragin  are full-time employees of  Evidera (formerly United BioSource ), who were paid consultants to  Pfizer  for the development of this manuscript. Editorial support was provided by  Leigh Prevost, BSc , of  PAREXEL , and was funded by  Pfizer International Operations.
After this point, a continued rise was assumed due to disease progression, which was derived from a regression analysis of the UKPDS dataset  [13]. Similar assumptions were used for SBP and cholesterol. In clinical studies, DAPA + MET has resulted in a statistically significant reduction in body weight compared with a sulphonylurea + MET and with placebo  [15, 16]. Significant differences between DAPA + MET and DPP-4i + MET for this outcome were also demonstrated in the NMA  [6]. Hence, the effect of patient weight in terms of risk of CV complications and the impact on patient health-related quality of life (HRQoL) was incorporated into the economic model. Initially, progression of weight was established from the impact of each treatment on weight over a 12-month period. In the dapagliflozin arm, patients' weight was assumed to be maintained in year 2 based on 2-year clinical extension data  [17].
The same model has also been used to compare the costeffectiveness of DAPA + MET with sulphonylurea + MET; this analysis produced a similar magnitude of costs and QALYs for DAPA + MET as estimated in the current analysis and also concluded that DAPA + MET was a costeffective use of NHS resources in the UK setting  [7]. In addition, the cost-effectiveness of dapagliflozin as an add-on to insulin has been investigated from a Dutch healthcare perspective, where it was also found to have an ICER within acceptable cost-effectiveness limits  [8]. Although the model has been validated against UKPDS datasets and extensive scenario and sensitivity analyses have been performed, several assumptions exist which may have an impact on the robustness of the study outcomes. Firstly, the model does not include less severe health states, such as microalbuminuria and foot ulcers; however, the utility and cost impact of such health states would be expected to be minimal and therefore have a negligible impact on the final ICER.
Additionally, cost and resource use was derived from UK sources, and utility data were largely sourced from the UKPDS studies. The results may also be of interest to other countries where DPP-4 inhibitors are commonly used in clinical practice as add-ons to metformin, especially as the treatment effect data are sourced from international trials. However, available treatments and advised strategies may differ between countries, and factors such as the risk equations, utilities and costs may be subject to change. As such, extrapolation of these results to countries outside of the UK should be performed with caution and local adaptation of the economic model would be advised.
Acknowledgements The authors acknowledge the support of the entire FERMI team for the preparation and tuning of the FEL. The authors wish to thank  C. Svetina ,  M. Zangrando  and the  PADRES group  for the support on the  FERMI photon diagnostics. Thanks are also extended to  D. Garzella ,  P. Zeitoun  and  H. Merdji  for insightful discussions, and to  K. Prince  for careful reading of the manuscript. P. R. R. aacknowledges the  TALENTS UP Programme (7th Framework Programme, Specific Programme: PEOPLE Marie Curie Actions COFUND ). B. M. acknowledges the support of  LabEx PALM  ( RAMSES  project).
(B1) Immunogold labeling of TRPC6 on isolated phagosomal membranes from nontreated as well as (R)-roscovitine-treated J774 cells. (Scale bar, 100 nm. ) TRPC6 antibody staining was undetectable in trpc6 -/-AMs, demonstrating antibody specificity for TRPC6 (see Fig. S3 ). (B2) Comparative summary of immunogold labeling of isolated phagosomal membrane fragments with equivalent area. Data were analyzed for n = 11 phagosomal fragments (images) under control conditions and n = 8 fragments for cells pretreated with (R)-roscovitine before particle uptake. Significance level: **P < 0. 01 (Fisher's exact test). (C1 and C2) Calcein-monitored Ca 2+ changes in phagosomes before and after (R)-roscovitine or OAG in trpc6 +/+ and trpc6 -/-AMs: (C1) Merged confocal fluorescent and DIC images of phagocytosed zymosan particles in trpc6 +/+ (Upper) and trpc6 -/-(Lower) AMs.
Experiments were carried out on cultured AMs using Fluo-4AM as the Ca 2+ indicator dye within 2 d after isolation. (A) Representative traces showing increases in spontaneous Ca 2+ oscillation frequency in human AMs (n = 32 cells, 3 BAL human samples) following exposure to (R)-roscovitine (50 μM, 15 min). In any given field, only a subpopulation of cells exhibited oscillatory behavior. In human AMs, 35% exhibited spontaneous oscillations, whereas 18% of murine AMs showed the same behavior. The (R)-roscovitine effect was most visible on oscillating cells; therefore, we confined our analysis to cells with spontaneous oscillations. There was no obvious morphological difference between oscillating and nonoscillating cells at rest. (B) Data from murine AMs (n = 24 cells, 3 mice) are as described for human cells in A.
ACKNOWLEDGMENTS. The authors thank  Dr. Rong Ma  ( University of North Texas, Fort Worth ), who generously offered the use of his laboratory and animals in the first experiments involving the trpc6 -/-mice;  Drs. Radmila Sarac ,  Theodore Karrison , and  Olga Zaborina , for many helpful conversations over the course of the study;  Dr. G. Thinakaran  for the gift of the many vesicle-directed antibodies;  Yimei Chen  in the  Electron Microcopy Core at the University of Chicago , for his expertise;  Dr. Richard D. Ye  ( University of Illinois at Chicago ) for his help in the initial experiments examining whether (R)-roscovitine acted through the formyl peptide receptors; and  Dr. Partha Kasturi  ( University of Kansas ) for providing the initial experiments and analysis examining whether (R)-roscovitine modulated V-ATPase activity.
Introduction We report on a search for new states decaying to dijets in proton-proton (pp) collisions at a center-of-mass energy of √ s = 8 TeV. The data sample corresponds to an integrated luminosity of 19. 7 fb -1 collected with the CMS detector at the CERN LHC in 2012. This analysis extends the search for new phenomena presented in previous CMS  [1] [2] [3] [4] [5] [6]  and ATLAS  [7] [8] [9] [10] [11]  publications. A review of experimental searches for new particles in the dijet mass spectrum is presented in Ref. [12]. Many extensions of the standard model (SM) predict the existence of new massive particles that couple to quarks or antiquarks (q) and gluons (g). These new particles could produce resonant bumps in the dijet invariant mass distribution associated with strong interaction processes.
• uncertainties due to the choice of the background fit function are taken into account by the marginalization procedure described below. Using studies based on simulations, the dependence of the signal mass shapes on the number of pileup interactions is found to be negligible. Similarly, no appreciable difference in the signal acceptance is observed when different PDF sets are used. For setting upper limits on signal cross sections a Bayesian formalism  [55]  is used, with a uni-form prior for the signal cross section in the range [0,+∞]. For a given value of the resonance mass the data are fit to the background function plus a signal line shape, the signal cross section being a free parameter. The resulting fit function with the signal cross section set to zero is used as the initial background hypothesis.
Summary A search for resonances and quantum black holes has been performed in inclusive and b-tagged dijet mass spectra measured with the CMS detector at the LHC. The data set corresponds to 19. 7 fb -1 of integrated luminosity collected in proton-proton collisions at √ s = 8 TeV. The inclusive search has been conducted for narrow resonances and, for the first time, for wide resonances with relative widths up to 30% of the resonance mass. No evidence for new particle production is found. Upper limits at 95% CL on the product of the cross section, branching fraction into dijets, and acceptance are provided for all generic searches. Specific lower limits are set on the masses of string resonances, excited quarks, axigluons, colorons, color-octet scalar resonances, scalar diquarks, W and Z bosons, and RS gravitons.
Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (p T ) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (H T ), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5. 0 fb -1. The measured cross sections are compared to predictions from Monte Carlo generators, MADGRAPH+PYTHIA and SHERPA, and to next-to-leading-order calculations from BLACKHAT+SHERPA. The differential cross sections are found to be in agreement with the predictions, apart from the p T distributions of the leading jets at high p T values, the distributions of the H T at high-H T and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.
5. Clustering is performed using all particles after decay and fragmentation, excluding neutrinos and the muon from the W boson decay. Additionally, jets are required to have p T > 30 GeV and |η| < 2. 4, and to be separated from the muon by ∆R > 0. 5. The reconstructed distributions are corrected to the particle level with the method of regularised singular value decomposition (SVD)  [10]  unfolding, using the ROOUNFOLD toolkit  [43]. For each distribution, the total background, including the multijet estimate from data and all simulated processes except the W boson signal, is subtracted from the data before unfolding. A response matrix, defining the migration probability between the particle-level and reconstructed quantities, as well as the overall reconstruction efficiency, is computed using W+jets events simulated with MADGRAPH+PYTHIA.
The uncertainties in jet energy resolution are assessed by increasing the p T difference between the reconstructed and particle-level jets by an η-dependent value  [40]. The jet energy uncertainties are determined by varying the p T of the jets in data rather than in simulation. Muon momentum scale and resolution uncertainties also introduce uncertainties in the overall event acceptance. A muon momentum scale uncertainty of 0. 2% and a muon momentum resolution uncertainty of 0. 6% are assumed  [35]. The effects of these uncertainties are assessed by directly varying the momentum scale and randomly fluctuating the muon momentum in the simulation. Variations for uncertainties in the energy and momentum scales and resolutions affect the size and shape of the background distribution to be subtracted from the data distribution, as well as the acceptance of W+jets simulated events, which define the response matrix used for unfolding.
Background normalisation uncertainties are determined by varying the cross sections of the backgrounds within their theoretical uncertainties. For the Z+jets process, a normalisation uncertainty of 4. 3% is calculated as the sum in quadrature of the factorisation/renormalisation scale and PDF uncertainties calculated in FEWZ  [29]. For the diboson and single top-quark processes, uncertainties are calculated with MCFM  [30] [31] [32] [33]  to be 4% and 6%, respectively. The uncertainty in the tt modelling is assessed by taking the difference between data and simulation in a control region with two or more b-tagged jets, and is estimated to be 5 to 12% for jet multiplicities of 2 to 6. The estimate of the multijet background has an uncertainty based on the limited number of events in the multijet sample and in the control regions where the multijet sample normalisation is calculated, and other systematic variations affecting the backgrounds in the multijet control regions introduce variations in the multijet normalization and template shape.
Parton showering (PS) and hadronisation of the MADGRAPH sample is performed with PYTHIA 6. 426 using the Z2 tune. Table  1 : Ranges of uncertainties for the measurement of dσ/dp T of the nth jet in events with n or more jets. The uncertainties displayed include the statistical uncertainty in the data minus the background, propagated through the unfolding procedure (Statistical), the jet energy scale and resolution (JES, JER), the choice of generator used in the unfolding procedure (Generator), the uncertainty due to a finite number of simulated events used to construct the response matrix (MC stat. ), and all other systematic uncertainties (Other) detailed in Section 7, including pileup, integrated luminosity, background normalisation, b-tagging, muon momentum and resolution, trigger efficiency, muon identification.
Summary Measurements of the cross sections and differential cross sections for a W boson produced in association with jets in pp collisions at a centre-of-mass energy of 7 TeV have been presented. The data were collected with the CMS detector during the 2011 pp run of the LHC, and correspond to an integrated luminosity of 5. 0 fb -1. Cross sections have been determined using the muon decay mode of the W boson and were presented as functions of the jet multiplicity, the transverse momenta and pseudorapidities of the four leading jets, the difference in azimuthal angle between each jet and the muon, and the H T for jet multiplicities up to four. The results, corrected for all detector effects by means of regularised unfolding, have been compared with particle-level simulated predictions from pQCD.
The cross section measurement for the exclusive and inclusive jet multiplicities, compared to the predictions of MADGRAPH 5. 1. 1 + PYTHIA 6. 426, SHERPA 1. 4. 0, and BLACK-HAT+SHERPA (corrected for hadronisation and multiple-parton interactions). Black circular markers with the grey hatched band represent the unfolded data measurement and its uncertainty. Overlaid are the predictions together with their statistical uncertainties (Theory stat. ). The BLACKHAT+SHERPA uncertainty also contains theoretical systematic uncertainties (Theory syst. ) described in Section 8. The lower plots show the ratio of each prediction to the unfolded data. Predictions from generators, MADGRAPH+PYTHIA and SHERPA, and NLO calculations from BLACKHAT+SHERPA, describe the jet multiplicity within the uncertainties.
The distributions of ∆φ between the second-, third-, and fourth-leading jets and the muon agree with all predictions within uncertainties. No significant disagreement was found in the distributions of η of the four leading jets. The differential cross section measurement for the leading four jets' transverse momenta, compared to the predictions of MADGRAPH 5. 1. 1 + PYTHIA 6. 426, SHERPA 1. 4. 0, and BLACKHAT+SHERPA (corrected for hadronisation and multiple-parton interactions). Black circular markers with the grey hatched band represent the unfolded data measurement and its uncertainty. Overlaid are the predictions together with their statistical uncertainties (Theory stat. ). The BLACKHAT+SHERPA uncertainty also contains theoretical systematic uncertainties (Theory syst. ) described in Section 8.
The lower plots show the ratio of each prediction to the unfolded data. The differential cross section measurement for H T for inclusive jet multiplicities 1-4, compared to the predictions of MADGRAPH 5. 1. 1 + PYTHIA 6. 426, SHERPA 1. 4. 0, and BLACK-HAT+SHERPA (corrected for hadronisation and multiple-parton interactions). Black circular markers with the grey hatched band represent the unfolded data measurement and its uncertainty. Overlaid are the predictions together with their statistical uncertainties (Theory stat. ). The BLACKHAT+SHERPA uncertainty also contains theoretical systematic uncertainties (Theory syst. ) described in Section 8. The lower plots show the ratio of each prediction to the unfolded data. The differential cross section measurement for the pseudorapidity of the four leading jets, compared to the predictions of MADGRAPH 5.
1. 1 + PYTHIA 6. 426, SHERPA 1. 4. 0, and BLACKHAT+SHERPA (corrected for hadronisation and multiple-parton interactions). Black circular markers with the grey hatched band represent the unfolded data measurement and its uncertainty. Overlaid are the predictions together with their statistical uncertainties (Theory stat. ). The BLACKHAT+SHERPA uncertainty also contains theoretical systematic uncertainties (Theory syst. ) described in Section 8. The lower plots show the ratio of each prediction to the unfolded data. The differential cross section measurement in ∆φ(jet n , µ), for n = 1 -4, compared to the predictions of MADGRAPH 5. 1. 1 + PYTHIA 6. 426, SHERPA 1. 4. 0, and BLACKHAT+SHERPA (corrected for hadronisation and multiple-parton interactions). Black circular markers with the grey hatched band represent the unfolded data measurement and its uncertainty.
Figure3: The cross section measurement for the exclusive and inclusive jet multiplicities, compared to the predictions of MADGRAPH 5. 1. 1 + PYTHIA 6. 426, SHERPA 1. 4. 0, and BLACK-HAT+SHERPA (corrected for hadronisation and multiple-parton interactions). Black circular markers with the grey hatched band represent the unfolded data measurement and its uncertainty. Overlaid are the predictions together with their statistical uncertainties (Theory stat. ). The BLACKHAT+SHERPA uncertainty also contains theoretical systematic uncertainties (Theory syst. ) described in Section 8. The lower plots show the ratio of each prediction to the unfolded data.
Figure4: The differential cross section measurement for the leading four jets' transverse momenta, compared to the predictions of MADGRAPH 5. 1. 1 + PYTHIA 6. 426, SHERPA 1. 4. 0, and BLACKHAT+SHERPA (corrected for hadronisation and multiple-parton interactions). Black circular markers with the grey hatched band represent the unfolded data measurement and its uncertainty. Overlaid are the predictions together with their statistical uncertainties (Theory stat. ). The BLACKHAT+SHERPA uncertainty also contains theoretical systematic uncertainties (Theory syst. ) described in Section 8. The lower plots show the ratio of each prediction to the unfolded data.
Figure5: The differential cross section measurement for H T for inclusive jet multiplicities 1-4, compared to the predictions of MADGRAPH 5. 1. 1 + PYTHIA 6. 426, SHERPA 1. 4. 0, and BLACK-HAT+SHERPA (corrected for hadronisation and multiple-parton interactions). Black circular markers with the grey hatched band represent the unfolded data measurement and its uncertainty. Overlaid are the predictions together with their statistical uncertainties (Theory stat. ). The BLACKHAT+SHERPA uncertainty also contains theoretical systematic uncertainties (Theory syst. ) described in Section 8. The lower plots show the ratio of each prediction to the unfolded data.
Figure6: The differential cross section measurement for the pseudorapidity of the four leading jets, compared to the predictions of MADGRAPH 5. 1. 1 + PYTHIA 6. 426, SHERPA 1. 4. 0, and BLACKHAT+SHERPA (corrected for hadronisation and multiple-parton interactions). Black circular markers with the grey hatched band represent the unfolded data measurement and its uncertainty. Overlaid are the predictions together with their statistical uncertainties (Theory stat. ). The BLACKHAT+SHERPA uncertainty also contains theoretical systematic uncertainties (Theory syst. ) described in Section 8. The lower plots show the ratio of each prediction to the unfolded data.
Figure7: The differential cross section measurement in ∆φ(jet n , µ), for n = 1 -4, compared to the predictions of MADGRAPH 5. 1. 1 + PYTHIA 6. 426, SHERPA 1. 4. 0, and BLACKHAT+SHERPA (corrected for hadronisation and multiple-parton interactions). Black circular markers with the grey hatched band represent the unfolded data measurement and its uncertainty. Overlaid are the predictions together with their statistical uncertainties (Theory stat. ). The BLACK-HAT+SHERPA uncertainty also contains theoretical systematic uncertainties (Theory syst. ) described in Section 8. The lower plots show the ratio of each prediction to the unfolded data.
Acknowledgments We extend our thanks to  Daniel Maître  and  Zvi Bern  for the production of data and tools used to create the BLACKHAT+SHERPA predictions, and for the sharing of expertise and advice regarding these predictions. 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:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  MoER ,  ERC IUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  NRF  and  WCU (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  CINVESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MESTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  ThEPCenter, IPST, STAR  and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
A sequencing library was prepared with Nextera XT version 3 chemistry (Illumina). Following fragmentation, end reparation, and sample tagging, the Illumina MiSeq platform sequencer produced 300-bp paired-end reads that were obtained from 700-bp inserts, yielding an average coverage of 1,000ϫ. The program FastQC version 0. 10. 1 (http: //www. bioinformatics. babraham. ac. uk/projects/fastqc/) was applied to check the quality of the generated 16,638,445 reads. This data set was employed for the de novo sequence assembly by various assemblers, including SPAdes version 3. 0. 0 (4), Minia version 1. 6088  (5, 6) , Velvet version 1. 2. 10 (7), and MaZuRCA version 2. 2. 1  (8). The sequence contigs obtained from the assemblers were subsequently input into the Contig Integrator for Sequence Assembly (CISA) to generate an integrated set of contigs  (9).