Hongfeng Yu

The acquisition of expression of hTERT, the catalytic subunit of the telomerase enzyme, seems to be an essential step in the development of a majority of human tumors. However, little is known about the mechanisms preventing telomerase gene expression in normal and transformed cells that do not express hTERT. Using a methylation-specific PCR-based assay, we have found that the CpG island associated with the hTERT gene is unmethylated in telomerase-negative primary tissues and nonimmortalized cultured cells, indicating that mechanisms independent of DNA methylation are sufficient to prevent hTERT expression. The hTERT CpG island is methylated in many telomerase-negative and telomerase-positive cultured cells and tumors, but the extent of methylation did not correlate with expression of hTERT. Demethylation of DNA with 5-azacytidine in two cell lines induced expression of hTERT, suggesting that DNA methylation can contribute to hTERT repression in some cells. Together, these data show that the hTERT CpG island can undergo cytosine methylation in cultured cells and tumors and that DNA methylation may contribute to the regulation of the hTERT gene, but that CpG island methylation is not responsible for repressing hTERT expression in most telomerase-negative cells.

The ability to identify and present the most essential aspects of time-varying data is critically important in many areas of science and engineering. This paper introduces an importance-driven approach to time-varying volume data visualization for enhancing that ability. By conducting a block-wise analysis of the data in the joint feature-temporal space, we derive an importance curve for each data block based on the formulation of conditional entropy from information theory. Each curve characterizes the local temporal behavior of the respective block, and clustering the importance curves of all the volume blocks effectively classifies the underlying data. Based on different temporal trends exhibited by importance curves and their clustering results, we suggest several interesting and effective visualization techniques to reveal the important aspects of time-varying data.

With the onset of extreme-scale computing, I/O constraints make it increasingly difficult for scientists to save a sufficient amount of raw simulation data to persistent storage. One potential solution is to change the data analysis pipeline from a post-process centric to a concurrent approach based on either in-situ or in-transit processing. In this context computations are considered in-situ if they utilize the primary compute resources, while in-transit processing refers to offloading computations to a set of secondary resources using asynchronous data transfers. In this paper we explore the design and implementation of three common analysis techniques typically performed on large-scale scientific simulations: topological analysis, descriptive statistics, and visualization. We summarize algorithmic developments, describe a resource scheduling system to coordinate the execution of various analysis workflows, and discuss our implementation using the DataSpaces and ADIOS frameworks that support efficient data movement between in-situ and in-transit computations. We demonstrate the efficiency of our lightweight, flexible framework by deploying it on the Jaguar XK6 to analyze data generated by S3D, a massively parallel turbulent combustion code. Our framework allows scientists dealing with the data deluge at extreme scale to perform analyses at increased temporal resolutions, mitigate I/O costs, and significantly improve the time to insight.

Parallel supercomputing has traditionally focused on the inner kernel of scientific simulations: the solver. The front and back ends of the simulation pipeline - problem description and interpretation of the output - have taken a back seat to the solver when it comes to attention paid to scalability and performance, and are often relegated to offline, sequential computation. As the largest simulations move beyond the realm of the terascale and into the petascale, this decomposition in tasks and platforms becomes increasingly untenable. We propose an end-to-end approach in which all simulation components - meshing, partitioning, solver, and visualization - are tightly coupled and execute in parallel with shared data structures and no intermediate I/O. We present our implementation of this new approach in the context of octree-based finite element simulation of earthquake ground motion. Performance evaluation on up to 2048 processors demonstrates the ability of the end-to-end approach to overcome the scalability bottlenecks of the traditional approach