Fibrosis assessment using FibroMeter combined to first generation tests in hepatitis C

AIM: To evaluate the performance of FibroMeter combined to the first generation tests aspartate aminotransferase-to-platelet ratio index (APRI) or Forns index to assess significant fibrosis in chronic hepatitis C (CHC). METHODS: First generation tests APRI or Forns were initially applied in a derivation population from Rio de Janeiro in Brazil considering cut-offs previously reported in the literature to evaluate significant fibrosis. FibroMeter was sequentially applied to unclassified cases from APRI or Forns. Accuracy of non-invasive combination of tests, APRI plus FibroMeter and Forns plus FibroMeter was evaluated in the Brazilian derivation population. APRI plus FibroMeter combination was validated in a population of CHC patients from Angers in France. All patients were submitted to liver biopsy staged according to METAVIR score by experienced hepatopathologists. Significant fibrosis was considered as METAVIR F ≥ 2. The fibrosis stage classification was used as the reference for accuracy evaluation of non-invasive combination of tests. Blood samples for the calculation of serum tests were collected on the same day of biopsy procedure or within a maximum 3 mo interval and stored at -70 °C. RESULTS: Seven hundred and sixty CHC patients were included (222 in the derivation population and 538 in the validation group). In the derivation population, the FibroMeter AUROC was similar to APRI AUROC (0.855 0.815, = 0.06) but higher than Forns AUROC (0.769, < 0.001). The best FibroMeter cut-off to discriminate significant fibrosis was 0.61 (80% diagnostic accuracy; 75% in the validation population, = 0.134). The sequential combination of APRI or Forns with FibroMeter in derivation population presented similar performance compared to FibroMeter used alone (79% 78% 80%, respectively, = 0.791). Unclassified cases of significant fibrosis after applying APRI and Forns corresponded to 49% and 54%, respectively, of the total sample. However, the combination of APRI or Forns with FibroMeter allowed 73% and 77%, respectively, of these unclassified cases to be correctly evaluated. Moreover, this combination resulted in a reduction of FibroMeter requirement in approximately 50% of the entire sample. The stepwise combination of APRI and FibroMeter applied to the validation population correctly identified 74% of patients with severe fibrosis (F ≥ 3). CONCLUSION: The stepwise combination of APRI or Forns with FibroMeter may represent an accurate lower cost alternative when evaluating significant fibrosis, with no need for liver biopsy

Measurement of the azimuthal anisotropy of Y(1S) and Y(2S) mesons in PbPb collisions at √S^{S}NN = 5.02 TeV

The second-order Fourier coefficients (υ$_{2}$) characterizing the azimuthal distributions of Υ(1S) and Υ(2S) mesons produced in PbPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV are studied. The Υmesons are reconstructed in their dimuon decay channel, as measured by the CMS detector. The collected data set corresponds to an integrated luminosity of 1.7 nb$^{-1}$. The scalar product method is used to extract the υ$_{2}$ coefficients of the azimuthal distributions. Results are reported for the rapidity range |y| < 2.4, in the transverse momentum interval 0 < p$_{T}$ < 50 GeV/c, and in three centrality ranges of 10–30%, 30–50% and 50–90%. In contrast to the J/ψ mesons, the measured υ$_{2}$ values for the Υ mesons are found to be consistent with zero

Measurement of prompt D0^{0} and D‾\overline{D}0^{0} meson azimuthal anisotropy and search for strong electric fields in PbPb collisions at root SNN\sqrt{S_{NN}} = 5.02 TeV

The strong Coulomb field created in ultrarelativistic heavy ion collisions is expected to produce a rapiditydependent difference (Av2) in the second Fourier coefficient of the azimuthal distribution (elliptic flow, v2) between D0 (uc) and D0 (uc) mesons. Motivated by the search for evidence of this field, the CMS detector at the LHC is used to perform the first measurement of Av2. The rapidity-averaged value is found to be (Av2) = 0.001 ? 0.001 (stat)? 0.003 (syst) in PbPb collisions at ?sNN = 5.02 TeV. In addition, the influence of the collision geometry is explored by measuring the D0 and D0mesons v2 and triangular flow coefficient (v3) as functions of rapidity, transverse momentum (pT), and event centrality (a measure of the overlap of the two Pb nuclei). A clear centrality dependence of prompt D0 meson v2 values is observed, while the v3 is largely independent of centrality. These trends are consistent with expectations of flow driven by the initial-state geometry. ? 2021 The Author. Published by Elsevier B.V. This is an open access article under the CC BY licens

Performance of reconstruction and identification of τ leptons decaying to hadrons and vτ in pp collisions at √s=13 TeV

The algorithm developed by the CMS Collaboration to reconstruct and identify τ leptons produced in proton-proton collisions at √s=7 and 8 TeV, via their decays to hadrons and a neutrino, has been significantly improved. The changes include a revised reconstruction of π⁰ candidates, and improvements in multivariate discriminants to separate τ leptons from jets and electrons. The algorithm is extended to reconstruct τ leptons in highly Lorentz-boosted pair production, and in the high-level trigger. The performance of the algorithm is studied using proton-proton collisions recorded during 2016 at √s=13 TeV, corresponding to an integrated luminosity of 35.9 fb¯¹. The performance is evaluated in terms of the efficiency for a genuine τ lepton to pass the identification criteria and of the probabilities for jets, electrons, and muons to be misidentified as τ leptons. The results are found to be very close to those expected from Monte Carlo simulation

Performance of the CMS Level-1 trigger in proton-proton collisions at √s = 13 TeV

At the start of Run 2 in 2015, the LHC delivered proton-proton collisions at a center-of-mass energy of 13\TeV. During Run 2 (years 2015–2018) the LHC eventually reached a luminosity of 2.1× 10$^{34}$ cm$^{-2}$s$^{-1}$, almost three times that reached during Run 1 (2009–2013) and a factor of two larger than the LHC design value, leading to events with up to a mean of about 50 simultaneous inelastic proton-proton collisions per bunch crossing (pileup). The CMS Level-1 trigger was upgraded prior to 2016 to improve the selection of physics events in the challenging conditions posed by the second run of the LHC. This paper describes the performance of the CMS Level-1 trigger upgrade during the data taking period of 2016–2018. The upgraded trigger implements pattern recognition and boosted decision tree regression techniques for muon reconstruction, includes pileup subtraction for jets and energy sums, and incorporates pileup-dependent isolation requirements for electrons and tau leptons. In addition, the new trigger calculates high-level quantities such as the invariant mass of pairs of reconstructed particles. The upgrade reduces the trigger rate from background processes and improves the trigger efficiency for a wide variety of physics signals

Pileup mitigation at CMS in 13 TeV data

With increasing instantaneous luminosity at the LHC come additional reconstruction challenges. At high luminosity, many collisions occur simultaneously within one proton-proton bunch crossing. The isolation of an interesting collision from the additional "pileup" collisions is needed for effective physics performance. In the CMS Collaboration, several techniques capable of mitigating the impact of these pileup collisions have been developed. Such methods include charged-hadron subtraction, pileup jet identification, isospin-based neutral particle "δβ" correction, and, most recently, pileup per particle identification. This paper surveys the performance of these techniques for jet and missing transverse momentum reconstruction, as well as muon isolation. The analysis makes use of data corresponding to 35.9 fb$^{-1}$ collected with the CMS experiment in 2016 at a center-of-mass energy of 13 TeV. The performance of each algorithm is discussed for up to 70 simultaneous collisions per bunch crossing. Significant improvements are found in the identification of pileup jets, the jet energy, mass, and angular resolution, missing transverse momentum resolution, and muon isolation when using pileup per particle identification

Measurement of the Y(1S) pair production cross section and search for resonances decaying to Y(1S)μ⁺μ⁻ in proton-proton collisions at √s = 13 TeV

The fiducial cross section for Y(1S)pair production in proton-proton collisions at a center-of-mass energy of 13TeVin the region where both Y(1S)mesons have an absolute rapidity below 2.0 is measured to be 79 ± 11 (stat) ±6 (syst) ±3 (B)pbassuming the mesons are produced unpolarized. The last uncertainty corresponds to the uncertainty in the Y(1S)meson dimuon branching fraction. The measurement is performed in the final state with four muons using proton-proton collision data collected in 2016 by the CMS experiment at the LHC, corresponding to an integrated luminosity of 35.9fb$^{-1}$. This process serves as a standard model reference in a search for narrow resonances decaying to Y(1S)μ$^{+}$μ$^{-}$ in the same final state. Such a resonance could indicate the existence of a tetraquark that is a bound state of two bquarks and two b̅ antiquarks. The tetraquark search is performed for masses in the vicinity of four times the bottom quark mass, between 17.5 and 19.5GeV, while a generic search for other resonances is performed for masses between 16.5 and 27GeV. No significant excess of events compatible with a narrow resonance is observed in the data. Limits on the production cross section times branching fraction to four muons via an intermediate Y(1S)resonance are set as a function of the resonance mass

Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at √s = 13TeV, corresponding to an integrated luminosity of 35.9 fb−1. Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency