Development of a distributed geometric modeling system based on peer to peer

Distributed systems are required in industrial environments for collecting distributed information. The developed distributed CAD system has sharing of functions, which consist of a client and server as a Peer to Peer (P2P) system. It is constructed using CAD kernel and COM/DCOM technology to share functions on a network. In recent years, the distributed system has been applied to industries for combining work functions. Here, the requester is the client, and the provider is the server. The developed system in this study shares the functions among the systems through a P2P structure. All systems linked to a local network can use the functions of the other systems. The importance of constructing this kind of distributed CAD system is for transferring 3D model data for gaining access to the location of the providers. This system can perform many operations concurrently using other systems and can save time on work. Some of the operations performed and tested by the developed distributed CAD system are Boolean operation, obtaining properties, triangulation, and tessellation.     

Synthetic CD4 exocyclic peptides antagonize CD4 holoreceptor binding and T cell activation

We have developed peptide analogs to analyze precise human CD4 substructures involved in MHC class II binding. Forms of the complementarity determining-like regions (CDRs) of the D1 domain of human CD4 were reproduced as synthetic aromatically modified exocyclic (AME) analogs and tested for their ability to block CD4-MHC II interactions and T cell activation. The exocyclic derived from CDR3 (residues 82-89) of human CD4, which specifically associated with CD4 on the T cell surface to create a heteromeric CD4 complex, blocked IL-2 production and antagonized the normal function of the CD4 receptor. The approach of creating novel synthetic antagonistic receptor complexes may represent a new receptor specific pharmaceutical approach to modulate biological function.     

Structural analysis of p185c-neu and epidermal growth factor receptor tyrosine kinases: oligomerization of kinase domains

The epidermal growth factor receptor (EGFR) and p185c-neu proteins associate as dimers to create an efficient signaling assembly. Overexpression of these receptors together enhances their intrinsic kinase activity and concomitantly results in oncogenic cellular transformation. The ectodomain is able to stabilize the dimer, whereas the kinase domain mediates biological activity. Here we analyze potential interactions of the cytoplasmic kinase domains of the EGFR and p185c-neu tyrosine kinases by homology molecular modeling. This analysis indicates that kinase domains can associate as dimers and, based on intermolecular interaction calculations, that heterodimer formation is favored over homodimers. The study also predicts that the self-autophosphorylation sites located within the kinase domains are not likely to interfere with tyrosine kinase activity, but may regulate the selection of substrates, thereby modulating signal transduction. In addition, the models suggest that the kinase domains of EGFR and p185c-neu can undergo higher order aggregation such as the formation of tetramers. Formation of tetrameric complexes may explain some of the experimentally observed features of their ligand affinity and hetero-receptor internalization.     

Mining gene–sample–time microarray data: a coherent gene cluster discovery approach

Extensive studies have shown that mining microarray data sets is important in bioinformatics research and biomedical applications. In this paper, we explore a novel type of gene–sample–time microarray data sets that records the expression levels of various genes under a set of samples during a series of time points. In particular, we propose the mining of coherent gene clusters from such data sets. Each cluster contains a subset of genes and a subset of samples such that the genes are coherent on the samples along the time series. The coherent gene clusters may identify the samples corresponding to some phenotypes (e.g., diseases), and suggest the candidate genes correlated to the phenotypes. We present two efficient algorithms, namely the Sample-Gene Search and the Gene–Sample Search, to mine the complete set of coherent gene clusters. We empirically evaluate the performance of our approaches on both a real microarray data set and synthetic data sets. The test results have shown that our approaches are both efficient and effective to find meaningful coherent gene clusters. Daxin Jiang received the Ph.D. degree in computer science and engineering from the State University of New York at Buffalo in 2005. He received the B.S. degree in computer science from the University of Science and Technology of China. From 1998 to 2000, he was a M.S. student in Software Institute, Chinese Academy of Sciences. He is currently an assistant professor at the School of Computer Engineering, Nanyang Technology University, Singapore. His research interests include data mining, bioinformatics, machine learning, and information retrieval. Jian Pei received the Ph.D. degree in computing science from Simon Fraser University, Canada, in 2002, under Dr. Jiawei Han's supervision. He also received the B.Eng. and the M.Eng. degrees from Shanghai Jiao Tong University, China, in 1991 and 1993, respectively, both in Computer Science. He is currently an assistant professor of computing science at Simon Fraser University. His research interests include developing effective and efficient data analysis techniques for novel data intensive applications. He is currently interested in various techniques of data mining, data warehousing, online analytical processing, and database systems, as well as their applications in bioinformatics. His current research is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the National Science Foundation (NSF) of the United States. Since 2000, he has published over 70 research papers in refereed journals, conferences, and workshops, has served in the organization committees and the program committees of over 60 international conferences and workshops, and has been a reviewer for some leading academic journals. He is a member of the ACM, the ACM SIGMOD, and the ACM SIGKDD. Murali Ramanathan is an associate professor of pharmaceutical sciences and neurology. He received the B.Tech. (Honors) in chemical engineering from the Indian Institute of Technology, India, in 1983. After a 4-year stint in the chemical industry, he obtained the M.S. degree in chemical engineering from Iowa State University, Ames, IA, in 1987, and the Ph.D. degree in bioengineering from the University of California-San Francisco and University of California-Berkeley Joint Program in Bioengineering in 1994. Dr. Ramanathan research interests are primarily focused on the treatment of multiple sclerosis (MS), an inflammatory-demyelinating disease of the central nervous system that affects over 1 million patients worldwide. MS is a complex, variable disease that causes physical and cognitive disability and nearly 50% of patients diagnosed with MS are unable to walk after 15 years. The etiology and pathogenesis of MS remains poorly understood. Dr. Ramanathan's research interests include stochastic modeling of pharmaceutical systems and novel approaches to analyzing and using genetic and genomic data for improving patient care and optimizing therapy. Chuan Lin is currently a Ph.D. student in the Department of Computer Science and Engineering, State University of New York at Buffalo. She received the B.E. and the M.S. degrees in computer science and technology from Tsinghua University in China. Her research interests include bioinformatics, data mining, and machine learning. Chun Tang received the B.S. and M.S. degrees from Peking University, China, in 1996 and 1999, respectively, and the Ph.D. degree from State University of New York at Buffalo, USA, in 2005, all in computer science. Currently, she is a postdoctoral associate of Center for Medical Informatics, Yale University. Her research interests include bioinformatics, data mining, machine learning, database, and information retrieval. Aidong Zhang received the Ph.D. degree in computer science from Purdue University, West Lafayette, Indiana, in 1994. She was an assistant professor from 1994 to 1999, an associate professor from 1999 to 2002, and has been a professor since 2002 in the Department of Computer Science and Engineering at State University of New York at Buffalo. Her research interests include multimedia systems, content-based image retrieval, bioinformatics, and data mining. She is an author of over 140 research publications in these areas. Dr. Zhang's research has been funded by NSF, NIH, NIMA, and Xerox. Zhang serves on the editorial boards of International Journal of Bioinformatics Research and Applications (IJBRA), ACM Multimedia Systems, International Journal of Multimedia Tools and Applications, and International Journal of Distributed and Parallel Databases. She was the editor for ACM SIGMOD DiSC (Digital Symposium Collection) from 2001 to 2003. She was co-chair of the technical program committee for ACM Multimedia in 2001. She has also served on various conference program committees. Dr. Zhang is a recipient of the National Science Foundation CAREER award and SUNY Chancellor's Research Recognition award.     

Preparation of activated graphene and effect of activation parameters on electrochemical capacitance

Activation parameters such as temperature and the amount of potassium hydroxide (KOH) were varied during the synthesis of activated microwave-exfoliated graphite oxide (a-MEGO) and the effects of these parameters on the specific surface area of a-MEGO and electrochemical capacitance of a-MEGO electrodes were investigated. At 800 °C and a KOH/MEGO mass ratio of 6.5, a maximum specific surface area of 3100 m²/g was obtained and a high specific capacitance of 172 F/g (at 1 A/g constant current and 3.5 V maximal voltage) was measured in a two-electrode cell with a-MEGO electrodes in an organic electrolyte.