analysis-of-scap-n-glycosylation-and-trafficking-in-human-cells

Analysis of SCAP <em>N</em>-glycosylation and Trafficking in Human Cells

Elevated lipogenesis is a common characteristic of cancer and metabolic diseases. Sterol regulatory element-binding proteins (SREBPs), a family of ...

The Ohio State University Comprehensive Cancer Center and College of Medicine

Conventional MAC protocols for wireless sensor network perform poorly when faced with a delay-tolerant mobile network environment. Characterized by a highly dynamic and sparse topology, poor network connectivity as well as data delay-tolerance, delay-tolerant mobile sensor networks exacerbate the severe power constraints and memory limitations of nodes. This paper proposes an energy-efficient MAC protocol using dynamic queue management (EQ-MAC) for power saving and data queue management. Via data transfers initiated by the target sink and the use of a dynamic queue management strategy based on priority, EQ-MAC effectively avoids untargeted transfers, increases the chance of successful data transmission, and makes useful data reach the target terminal in a timely manner. Experimental results show that EQ-MAC has high energy efficiency in comparison with a conventional MAC protocol. It also achieves a 46% decrease in packet drop probability, 79% increase in system throughput, and 25% decrease in mean packet delay.

An energy-efficient MAC protocol using dynamic queue management for delay-tolerant mobile sensor networks.

Magnetic resonance imaging has been benefited from compressed sensing in improving imaging speed. But the computation time of compressed sensing magnetic resonance imaging (CS-MRI) is relatively long due to its iterative reconstruction process. Recently, a patch-based nonlocal operator (PANO) has been applied in CS-MRI to significantly reduce the reconstruction error by making use of self-similarity in images. But the two major steps in PANO, learning similarities and performing 3D wavelet transform, require extensive computations. In this paper, a parallel architecture based on multicore processors is proposed to accelerate computations of PANO. Simulation results demonstrate that the acceleration factor approaches the number of CPU cores and overall PANO-based CS-MRI reconstruction can be accomplished in several seconds.

Parallel computing of patch-based nonlocal operator and its application in compressed sensing MRI.

Compressed sensing MRI (CS-MRI) is a promising technology to accelerate magnetic resonance imaging. Both improving the image quality and reducing the computation time are important for this technology. Recently, a patch-based directional wavelet (PBDW) has been applied in CS-MRI to improve edge reconstruction. However, this method is time consuming since it involves extensive computations, including geometric direction estimation and numerous iterations of wavelet transform. To accelerate computations of PBDW, we propose a general parallelization of patch-based processing by taking the advantage of multicore processors. Additionally, two pertinent optimizations, excluding smooth patches and pre-arranged insertion sort, that make use of sparsity in MR images are also proposed. Simulation results demonstrate that the acceleration factor with the parallel architecture of PBDW approaches the number of central processing unit cores, and that pertinent optimizations are also effective to make further accelerations. The proposed approaches allow compressed sensing MRI reconstruction to be accomplished within several seconds.

Article	Year
An energy-efficient MAC protocol using dynamic queue management for delay-tolerant mobile sensor networks. Sensors (Basel, Switzerland)\| PubMed ID: 22319385	2011
Parallel computing of patch-based nonlocal operator and its application in compressed sensing MRI. Computational and mathematical methods in medicine\| PubMed ID: 24963335	2014
Accelerating patch-based directional wavelets with multicore parallel computing in compressed sensing MRI. Magnetic resonance imaging\| PubMed ID: 25620521	2015

Qiyue Li

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