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We report results on the ratio of midrapidity antiproton-to-proton yields in Au+Au collisions at sqrt[sNN] = 130 GeV per nucleon pair as measured by the STAR experiment at RHIC. Within the rapidity and transverse momentum range of | y|<0.5 and 0.4<pt<1.0 GeV/c, the ratio is essentially independent of either transverse momentum or rapidity, with an average of 0.65±0.01(stat)±0.07(syst) for minimum bias collisions. Within errors, no strong centrality dependence is observed. The results indicate that at this RHIC energy, although the p-p-bar pair production becomes important at midrapidity, a significant excess of baryons over antibaryons is still present.
Data from the first physics run at the Relativistic Heavy-Ion Collider at Brookhaven National Laboratory, Au+Au collisions at sqrt[sNN]=130 GeV, have been analyzed by the STAR Collaboration using three-pion correlations with charged pions to study whether pions are emitted independently at freeze-out. We have made a high-statistics measurement of the three-pion correlation function and calculated the normalized three-particle correlator to obtain a quantitative measurement of the degree of chaoticity of the pion source. It is found that the degree of chaoticity seems to increase with increasing particle multiplicity.
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.
Web spiders connect silk proteins, so-called spidroins, into fibers of extraordinary toughness. The spidroin N-terminal domain (NTD) plays a pivotal role in this process: it polymerizes spidroins through a complex mechanism of dimerization. Here we analyze sequences of spidroin NTDs and find an unusually high content of the amino acid methionine. We simultaneously mutate all methionines present in the hydrophobic core of a spidroin NTD from a nursery web spider’s dragline silk to leucine. The mutated NTD is strongly stabilized and folds at the theoretical speed limit. The structure of the mutant is preserved, yet its ability to dimerize is substantially impaired. We find that side chains of core methionines serve to mobilize the fold, which can thereby access various conformations and adapt the association interface for tight binding. Methionine in a hydrophobic core equips a protein with the capacity to dynamically change shape and thus to optimize its function.