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Institute
The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p-Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.
The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p–Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.
Long-range angular correlations on the near and away side in p–Pb collisions at √sNN=5.02 TeV
(2013)
Angular correlations between charged trigger and associated particles are measured by the ALICE detector in p–Pb collisions at a nucleon–nucleon centre-of-mass energy of 5.02 TeV for transverse momentum ranges within 0.5<pT,assoc<pT,trig<4 GeV/c. The correlations are measured over two units of pseudorapidity and full azimuthal angle in different intervals of event multiplicity, and expressed as associated yield per trigger particle. Two long-range ridge-like structures, one on the near side and one on the away side, are observed when the per-trigger yield obtained in low-multiplicity events is subtracted from the one in high-multiplicity events. The excess on the near-side is qualitatively similar to that recently reported by the CMS Collaboration, while the excess on the away-side is reported for the first time. The two-ridge structure projected onto azimuthal angle is quantified with the second and third Fourier coefficients as well as by near-side and away-side yields and widths. The yields on the near side and on the away side are equal within the uncertainties for all studied event multiplicity and pT bins, and the widths show no significant evolution with event multiplicity or pT. These findings suggest that the near-side ridge is accompanied by an essentially identical away-side ridge.
Biodiversity is a cornerstone of human health and well-being. However, while evidence of the contributions of nature to human health is rapidly building, research into how biodiversity relates to human health remains limited in important respects. In particular, a better mechanistic understanding of the range of pathways through which biodiversity can influence human health is needed. These pathways relate to both psychological and social processes as well as biophysical processes. Building on evidence from across the natural, social and health sciences, we present a conceptual framework organizing the pathways linking biodiversity to human health. Four domains of pathways—both beneficial as well as harmful—link biodiversity with human health: (i) reducing harm (e.g. provision of medicines, decreasing exposure to air and noise pollution); (ii) restoring capacities (e.g. attention restoration, stress reduction); (iii) building capacities (e.g. promoting physical activity, transcendent experiences); and (iv) causing harm (e.g. dangerous wildlife, zoonotic diseases, allergens). We discuss how to test components of the biodiversity-health framework with available analytical approaches and existing datasets. In a world with accelerating declines in biodiversity, profound land-use change, and an increase in non-communicable and zoonotic diseases globally, greater understanding of these pathways can reinforce biodiversity conservation as a strategy for the promotion of health for both people and nature. We conclude by identifying research avenues and recommendations for policy and practice to foster biodiversity-focused public health actions.
We measured the Coulomb dissociation of 16O into 4He and 12C at the R3B setup in a first campaign within FAIR Phase 0 at GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt. The goal was to improve the accuracy of the experimental data for the 12C(α,γ)16O fusion reaction and to reach lower center-ofmass energies than measured so far.
The experiment required beam intensities of 109 16O ions per second at an energy of 500 MeV/nucleon. The rare case of Coulomb breakup into 12C and 4He posed another challenge: The magnetic rigidities of the particles are so close because of the same mass-to-charge-number ratio A/Z = 2 for 16O, 12C and 4He. Hence, radical changes of the R3B setup were necessary. All detectors had slits to allow the passage of the unreacted 16O ions, while 4He and 12C would hit the detectors' active areas depending on the scattering angle and their relative energies. We developed and built detectors based on organic scintillators to track and identify the reaction products with sufficient precision.
Purpose: Collaborative care is effective in improving symptoms of patients with depression. The aims of this study were to characterize symptom trajectories in patients with major depression during one year of collaborative care and to explore associations between baseline characteristics and symptom trajectories.
Methods: We conducted a cluster-randomized controlled trial in primary care. The collaborative care intervention comprised case management and behavioral activation. We used the Patient Health Questionnaire-9 (PHQ-9) to assess symptom severity as the primary outcome. Statistical analyses comprised latent growth mixture modeling and a hierarchical binary logistic regression model.
Results: We included 74 practices and 626 patients (310 intervention and 316 control recipients) at baseline. Based on a minimum of 12 measurement points for each intervention recipient, we identified two latent trajectories, which we labeled "fast improvers" (60.5%) and "slow improvers" (39.5%). At all measurements after baseline, "fast improvers" presented higher PHQ mean values than "slow improvers". At baseline, "fast improvers" presented fewer physical conditions, higher health-related quality of life, and had made fewer suicide attempts in their history.
Conclusions: A notable proportion of 39.5% of patients improved only "slowly" and probably needed more intense treatment. The third follow-up in month two could well be a sensible time to adjust treatment to support "slow improvers".