Patrick Wang

BABAR, the detector for the SLAC PEP-II asymmetric e+e- B Factory operating at the γ resonance, was designed to allow comprehensive studies of CP-violation in B-meson decays. Charged particle tracks are measured in a multi-layer silicon vertex tracker surrounded by a cylindrical wire drift chamber. Electromagnetic showers from electrons and photons are detected in an array of CsI crystals located just inside the solenoidal coil of a superconducting magnet. Muons and neutral hadrons are identified by arrays of resistive plate chambers inserted into gaps in the steel flux return of the magnet. Charged hadrons are identified by dE/dx measurements in the tracking detectors and by a ring-imaging Cherenkov detector surrounding the drift chamber. The trigger, data acquisition and data-monitoring systems, VME- and network-based, are controlled by custom-designed online software. Details of the layout and performance of the detector components and their associated electronics and software are presented. © 2002 Elsevier Science B.V. All rights reserved.

In organisms, ribonucleic acid plays an essential role. Its function is being discovered more and more. Due to the conserved nature of RNA sequences, its function mainly depends on the RNA secondary structure. The discovery of an approximate relationship between two RNA secondary structures helps to understand their functional relationship better. It is an important and urgent task to explore structural similarities from the graphical representation of RNA secondary structures. In this paper, a novel graphical analysis method based on the triple vector curve representation of RNA secondary structures is proposed. A combinational method involving a discrete wavelet transform and fractal dimension with sliding window is introduced to analyze and compare the graphs derived from feature extraction; after that, the distance matrix is generated. Then, the distance matrix is analyzed by clustering and visualized as a clustering tree. RNA virus and noncoding RNA datasets are applied to perform experiments and analyze the clustering tree. The results show that the proposed method yields more accurate results in the comparison of RNA secondary structures.

Analysis of gene expression patterns in gastric cancer can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A network was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.