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Physical interactions between vascular endothelial growth factor (VEGF), a central player in blood endothelial cell biology, and fibronectin, a major fibrillar protein of the extracellular matrix, are important determinants of angiogenic activity in health and disease. Conditions signaling the need for new blood vessel growth, such as hypoxia and low extracellular pH, increase VEGF–fibronectin interactions. These interactions can be further fine-tuned through changes in the availability of the VEGF-binding sites on fibronectin, regulated by conformational changes induced by heparin and heparan sulfate chains within the extracellular matrix. These interactions may alter VEGF bioavailability, generate gradients, or alter the way VEGF is recognized by and activates its cell-surface receptors. Here, using equilibrium and kinetic studies, we discovered that fibronectin can also interact with the extracellular domain of the VEGF receptor 2 (VEGFR2). The VEGFR2-binding sites on fibronectin show great similarity to the VEGF-binding sites, as they were also exposed upon heparin-induced conformational changes in fibronectin, and the interaction was enhanced at acidic pH. Kinetic parameters and affinities for VEGF and VEGFR2 binding to fibronectin were determined by surface plasmon resonance measurements, revealing two populations of fibronectin-binding sites for each molecule. Our data also suggest that a VEGF/VEGFR2/fibronectin triple complex may be formed by VEGF or VEGFR2 first binding to fibronectin and subsequently recruiting the third binding partner. The formation of such a complex may lead to the activation of distinct angiogenic signaling pathways, offering new possibilities for clinical applications that target angiogenesis., Journal of Biological Chemistry, 296, ISSN:0021-9258, ISSN:1083-351X

Accelerators are used in about 13% of the current Top500 List. Supercomputers leveraging accelerators grew by a factor of 2.2x in 2012 and are expected to completely dominate the Top500 by 2015. Though most of these deployments use NVIDIA GPGPU accelerators, Intel's Xeon Phi architecture will likely grow in popularity in the coming years. Unfortunately, there are few studies analyzing the performance and energy efficiency of systems leveraging the Intel Xeon Phi. We extend our systemic measurement methodology to isolate system power by component including accelerators. We use this methodology to present a detailed study of the performance-energy tradeoffs of the Xeon Phi architecture. We demonstrate the portability of our approach by comparing our Xeon Phi results to the Intel multicore Sandy Bridge host processor and the NVIDIA Tesla GPU for a wide range of HPC applications. Our results help explain limitations in the power-performance scalability of HPC applications on the current Intel Xeon Phi architecture.

Legere is a work of critical technology-art that examines the intersection between novels and visual media as two different forms of entertainment. It is set to mimic television -- the program, displayed on an old television set, has a set number of channels that the user can flip through with a remote. Each channel concurrently plays a long-running audiobook, and using speech-recognition, the program flashes the book's text at the user in sync with the narration. The exhibit is meant to mock a living room atmosphere by adding a couch, coffee table, and other peripherals like a rug, to the project space.

Accelerators offer a substantial increase in efficiency for high-performance systems offering speedups for computational applications that leverage hardware support for highly-parallel codes. However, the power use of some accelerators exceeds 200 watts at idle which means use at exascale comes at a significant increase in power at a time when we face a power ceiling of about 20 megawatts. Despite the growing domination of accelerator-based systems in the Top500 and Green500 lists of fastest and most efficient supercomputers, there are few detailed studies comparing the power and energy use of common accelerators. In this work, we conduct detailed experimental studies of the power usage and distribution of Xeon-Phi-based systems in comparison to the NVIDIA Tesla and an Intel Sandy Bridge multicore host processor. In contrast to previous work, we focus on separating individual component power and correlating power use to code behavior. Our results help explain the causes of power-performance scalability for a set of HPC applications.