Our proposed method suggests that revealing and visualizing the dynamic changes of brain conditions can help clinicians and even patients themselves better understand such conditions.
SUMMARYThe distribution of knowledge (by scientists) and data sources (advanced scientific instruments), and the need for large-scale computational resources for analyzing massive scientific data are two major problems commonly observed in scientific disciplines. Two popular scientific disciplines of this nature are brain science and high-energy physics. The analysis of brain-activity data gathered from the MEG (magnetoencephalography) instrument is an important research topic in medical science since it helps doctors in identifying symptoms of diseases. The data needs to be analyzed exhaustively to efficiently diagnose and analyze brain functions and requires access to large-scale computational resources. The potential platform for solving such resource intensive applications is the Grid. This paper presents the design and development of MEG data analysis system by leveraging Grid technologies, primarily Nimrod-G, Gridbus, and Globus. It describes the composition of the neuroscience (brain-activity analysis) application as parameter-sweep application and its on-demand deployment on global Grids for distributed execution. The results of economic-based scheduling of analysis jobs for three different optimizations scenarios on the world-wide Grid testbed resources are presented along with their graphical visualization.
The drug discovery process in general is a very resource intensive undertaking that has existed for a very long time. In the last two decades, performing molecular simulations that determine the level of interaction between a protein and ligand have been refined to the point where they are now an essential part of the drug discovery process. These simulations serve to reduce the time to discovery and improve the positive "hit" rates when screening for molecule with biological activity. As a result, the chemical search space is greatly reduced in silico, prior to any in vitro experiments that validate the results. Recently, there have been many advances in computer science technologies that have improved the virtual screening process. This paper will give a brief overview of the virtual screening process and then summarize the current state-of-the-art technologies applied to virtual screenings. Both biomedical researchers and computer scientists can use this review as a guide to the implementation requirements for computational resources of virtual screening.
Due to the recent advancement of networking and high-performance computing technologies, scientists can easily access large-scale data captured by scientific measurement devices through a network, and use huge computational power harnessed on the Internet for their analyses of scientific data. However, visualization technology, which plays a role of great importance for scientists to intuitively understand the analysis results of such scientific data, is not fully utilized so that it can seamlessly benefit from recent high-performance and networking technologies. One of such visualization technologies is SAGE (Scalable Adaptive Graphics Environment), which allows people to build an arbitrarily sized tiled display wall and is expected to be applied to scientific research. In this paper, we present a multi-application controller for SAGE, which we have developed, in the hope that it will help scientists efficiently perform scientific research requiring high-performance computing and visualization. The evaluation in this paper indicates that the efficiency of completing a comparison task among multiple data is increased by our system.
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