The recent emergence of various types of flat-panel x-ray detectors and C-arm gantries now enables the construction of novel imaging platforms for a wide variety of clinical applications. Many of these applications require interactive 3D image generation, which cannot be satisfied with inexpensive PC-based solutions using the CPU. We present a solution based on commodity graphics hardware (GPUs) to provide these capabilities. While GPUs have been employed for CT reconstruction before, our approach provides significant speedups by exploiting the various built-in hardwired graphics pipeline components for the most expensive CT reconstruction task, backprojection. We show that the timings so achieved are superior to those obtained when using the GPU merely as a multi-processor, without a drop in reconstruction quality. In addition, we also show how the data flow across the graphics pipeline can be optimized, by balancing the load among the pipeline components. The result is a novel streaming CT framework that conceptualizes the reconstruction process as a steady flow of data across a computing pipeline, updating the reconstruction result immediately after the projections have been acquired. Using a single PC equipped with a single high-end commodity graphics board (the Nvidia 8800 GTX), our system is able to process clinically-sized projection data at speeds meeting and exceeding the typical flat-panel detector data production rates, enabling throughput rates of 40-50 projections s(-1) for the reconstruction of 512(3) volumes.
Although vast amounts of information are conveyed by photons in optical fibers, the majority of data processing is performed electronically, creating the infamous 'information bottleneck' and consuming energy at an increasingly unsustainable rate. The potential for photonic devices to directly manipulate light remains unfulfilled due largely to a lack of materials with strong, fast optical nonlinearities. In this paper, we show that small-signal amplifier, summator and invertor functions for optical signals may be realized using a four-port device that exploits the coherent interaction of beams on a planar plasmonic metamaterial, assuming no intrinsic nonlinearity. The redistribution of energy among ports can provide nonlinear input-output signal dependencies and may be coherently controlled at very low intensity levels, with multi-THz bandwidth and without introducing signal distortion, thereby presenting powerful opportunities for novel optical data processing architectures, complexity oracles and the locally coherent networks that are becoming part of the mainstream telecommunications agenda.
We report on the demonstration of a femtosecond all-optical modulator providing, without nonlinearity and therefore at arbitrarily low intensity, ultrafast light-by-light control. The device engages the coherent interaction of optical waves on a metamaterial nanostructure only 30 nm thick to efficiently control absorption of near-infrared (750–1040 nm) femtosecond pulses, providing switching contrast ratios approaching 3:1 with a modulation bandwidth in excess of 2 THz. The functional paradigm illustrated here opens the path to a broad family of meta-devices for ultrafast optical data processing in coherent networks.
Vanadium dioxide (VO2) is a well-known thermochromic material since it exhibits a notable optical variation in the near-infrared region from transmitting to reflecting upon the semiconductor-to-metal phase transition (SMT).
As cellular models for in vitro liver cancer and toxicity studies, HepG2 and Hep3B are the two most frequently used liver cancer cell lines. Because of their similarities they are often treated as the same in experimental studies. However, there are many differences that have been largely over-sighted or ignored between them. In this review, we summarize the differences between HepG2 and Hep3B cell lines that can be found in the literature based on PubMed search. We particularly focus on the differential gene expression, differential drug responses (chemosensitivity, cell cycle and growth inhibition, and gene induction), signaling pathways associated with these differences, as well as the factors in governing these differences between HepG2 and Hep3B cell lines. Based on our analyses of the available data, we suggest that neither HBx nor p53 may be the crucial factor to determine the differences between HepG2 and Hep3B cell lines although HBx regulates the expression of the majority of genes that are differentially expressed between HepG2 and Hep3B. Instead, the different maturation stages in cancer development of the original specimen between HepG2 and Hep3B may be responsible for the differences between them. This review provides insight into the molecular mechanisms underlying the differences between HepG2 and Hep3B and help investigators especially the beginners in the areas of liver cancer research and drug metabolism to fully understand, and thus better use and interpret the data from these two cell lines in their studies.
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