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Dihadron angular correlations in d + Au collisions at √sNN = 200 GeV are reported as a function of the measured zero-degree calorimeter neutral energy and the forward charged hadron multiplicity in the Au-beam direction. A finite correlated yield is observed at large relative pseudorapidity (η) on the near side (i.e. relative azimuth φ ∼ 0). This correlated yield as a function of η appears to scale with the dominant, primarily jet-related, away-side (φ ∼ π) yield. The Fourier coefficients of the φ correlation, Vn = (cosnφ), have a strong η dependence. In addition, it is found that V1 is approximately inversely proportional to the mid-rapidity event multiplicity, while V2 is independent of it with similar magnitude in the forward (d-going) and backward (Au-going) directions.
Effect of event selection on jetlike correlation measurement in d+Au collisions at √sNN = 200 GeV
(2015)
Dihadron correlations are analyzed in √sNN = 200 GeV d + Au collisions classified by forward charged particle multiplicity and zero-degree neutral energy in the Au-beam direction. It is found that the jetlike correlated yield increases with the event multiplicity. After taking into account this dependence, the non-jet contribution on the away side is minimal, leaving little room for a back-to-back ridge in these collisions.
Nutrition support is a necessary therapy for critically ill cardiac surgery patients. However, conclusive evidence for this population, consisting of well-conducted clinical trials is lacking. To clarify optimal strategies to improve outcomes, an international multidisciplinary group of 25 experts from different clinical specialties from Germany, Canada, Greece, USA and Russia discussed potential approaches to identify patients who may benefit from nutrition support, when best to initiate nutrition support, and the potential use of pharmaco-nutrition to modulate the inflammatory response to cardiopulmonary bypass. Despite conspicuous knowledge and evidence gaps, a rational nutritional support therapy is presented to benefit patients undergoing cardiac surgery.
Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences.