Power will be the key limiter to system scalability as interconnection networks take up an increasingly significant portion of system power. In this paper, we propose an architectural leakage power modeling methodology that achieves 95-98% accuracy against HSPICE estimates. When applied to interconnection networks, combined with previous proposed dynamic power models, we gain valuable insights on total network power consumption. Our modeling shows router buffers to be a prime candidate for leakage power optimization. We thus investigate the design space of power-aware buffer policies, propose a suite of policies, and explore the impact of various circuits mechanisms on these policies. Simulations show power-aware buffers saving up to 96.6% of total buffer leakage power.
Power will be the key limiter to system scalability as interconnection networks take up an increasingly significant portion of system power. In this paper, we propose an architectural leakage power modeling methodology that achieves 95-98% accuracy against HSPICE estimates. When applied to interconnection networks, combined with previous proposed dynamic power models, we gain valuable insights on total network power consumption. Our modeling shows router buffers to be a prime candidate for leakage power optimization. We thus investigate the design space of power-aware buffer policies, propose a suite of policies, and explore the impact of various circuits mechanisms on these policies . Simulations show power-aware buffers saving up to 96.6% of total buffer leakage power.
Spinal cord injury (SCI) leads to reactive inflammation and other harmful events that limit spinal cord regeneration. We propose an approach for studying the mechanisms at the levels of network topology, gene ontology, signaling pathways, and disease inference. We treated inflammatory mediators as toxic chemicals and retrieved the genes and interacting proteins associated with them via a set of biological medical databases and software. We identified >10,000 genes associated with SCI. Tumor necrosis factor (TNF) had the highest scores, and the top 30 were adopted as core data. In the core interacting protein network, TNF and other top 10 nodes were the major hubs. The core members were involved in cellular responses and metabolic processes, as components of the extracellular space and regions, in protein-binding and receptor-binding functions, as well as in the TNF signaling pathway. In addition, both seizures and SCI were highly associated with TNF levels; therefore, for achieving a better curative effect on SCI, TNF and other major hubs should be targeted together according to the theory of network intervention, rather than a single target such as TNF alone. Furthermore, certain drugs used to treat epilepsy could be used to treat SCI as adjuvants.
A major factor in the failure of central nervous system (CNS) axon regeneration is the formation of glial scar after the injury of CNS. Glial scar generates a dense barrier which the regenerative axons cannot easily pass through or by. In this paper, a mathematical model was established to explore how the regenerative axons grow along the surface of glial scar or bypass the glial scar. This mathematical model was constructed based on the spinal cord injury (SCI) repair experiments by transplanting Schwann cells as bridge over the glial scar. The Lattice Boltzmann Method (LBM) was used in this model for three-dimensional numerical simulation. The advantage of this model is that it provides a parallel and easily implemented algorithm and has the capability of handling complicated boundaries. Using the simulated data, two significant conclusions were made in this study: (1) the levels of inhibitory factors on the surface of the glial scar are the main factors affecting axon elongation and (2) when the inhibitory factor levels on the surface of the glial scar remain constant, the longitudinal size of the glial scar has greater influence on the average rate of axon growth than the transverse size. These results will provide theoretical guidance and reference for researchers to design efficient experiments.
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