Abstract: As computer simulations continue to grow in size and complexity, they provide a particularly challenging example of big data. Many application areas are moving toward exascale (i.e. 10 ^18 FLOPS, or FLoating-point Operations Per Second). Analyzing these simulations is difficult because their output may exceed both the storage capacity and the bandwidth required for transfer to storage. One approach is to embed some level of analysis in the simulation while the simulation is running, often called in situ analysis. This talk describes an online in situ method for approximating a complex simulation using piecewise linear fitting. The immediate goal is to identify important time steps of the simulation. We then use those time steps and the linear fits both to significantly reduce the data transfer and storage requirements and to facilitate post processing and reconstruction of the simulation. We illustrate the method using an example that tracks the development of evolving simulation behavior by monitoring various aspects of the simulation over time.
Abstract: Constructing a useful mental representation of physics situations is integral to success in problem solving. It is known that experts identify/perceive meaningful patters and/or changes in visual stimuli related to their domain of expertise. We present data from an experiment using the "flicker" technique, in which subjects with a high level of physics knowledge viewed nearly identical pairs of diagrams that are representative of typical introductory physics situations. The two diagrams in each pair contained a subtle difference that either does, or does not change the underlying physics depicted in the diagram. We present results on how the speed of noticing physics-relevant changes in the diagram pairs is faster than noticing the physics-irrelevant changes, even when the visual salience of the change is taken into account. We discuss the cognitive implications of our findings.
Abstract: The termination of RNA transcription is a well-known phenomenon in the scientific community. While the exact mechanism behind transcription termination differs between prokaryotes and eukaryotes, in prokaryotes termination is often dependent on the actions of Rho. Rho is an ATP dependent helicase that acts to release the RNA from the rest of the transcription machinery upon completing of transcription. Even though it is broadly understood, the exact role Rho protein plays, during transcription, as well as its energy requirements have yet to be elucidated.
This study attempts to better establish the energy requirement for Rho during transcription termination. The Fluorescence Resonance Energy Transfer (FRET) was used, which allowed us to better visualize the protein-nucleic acid interaction and molecular movement as a whole. During this study, Rho was analyzed under various concentrations of adenosine triphosphate (ATP). Results indicate that approximately 1mM of ATP is optimal to propel Rho along the RNA strand. However, adding a higher concentration of ATP did not increase Rho's rate at which it moved along the RNA molecule. These finding not only gives insight into Rho's minimal energy requirement but contributes to further understanding gene expression and regulation.