Directed Flow of Identified Particles in Au plus Au Collisions at root S-NN=200 GeV at RHIC

STAR's measurements of directed flow (v(1)) around midrapidity for pi(+/-), K-+/-, K-S(0), p, and (p) over bar in Au + Au collisions at root s(NN) = 200 GeV are presented. A negative v(1) (y) slope is observed for most of produced particles (pi(+/-), K-+/-, K-S(0), p, and (p) over bar). In 5%-30% central collisions, a sizable difference is present between the v(1)(y) slope of protons and antiprotons, with the former being consistent with zero within errors. The v(1) excitation function is presented. Comparisons to model calculations (RQMD, UrQMD, AMPT, QGSM with parton recombination, and a hydrodynamics model with a tilted source) are made. For those models which have calculations of v(1) for both pions and protons, none of them can describe v(1()y) forpions and protons simultaneously. The hydrodynamics model with a tilted source as currently implemented cannot explain the centrality dependence of the difference between the v(1)(y) slopes of protons and antiprotons.

Anisotropic Flow in Event-by-Event Ideal Hydrodynamic Simulations of root s(NN) 200 GeV Au+Au Collisions

We simulate top-energy Au + Au collisions using ideal hydrodynamics in order to make the first comparison to the complete set of midrapidity flow measurements made by the PHENIX Collaboration. A simultaneous calculation of nu(2), nu(3), nu(4), and the first event-by-event calculation of quadrangular flow defined with respect to the nu(2) event plane (nu(4){Psi(2)}) gives good agreement with measured values, including the dependence on both transverse momentum and centrality. This provides confirmation that the collision system is indeed well described as a quark-gluon plasma with an extremely small viscosity and that correlations are dominantly generated from collective effects. In addition, we present a prediction for nu(5).

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.