X-Ray Fluoroscopy Measurements and CFD Simulation of Hydrodynamics in a Two Dimensional Gas-Solids Fluidized Bed

X-ray fluoroscopy measurements and CFD simulation were used to characterize the hydrodynamics in a pseudo 2-D gas-solids bubbling fluidized bed using polyethylene resin and glass beads. Bubble properties, such as bubble frequency, bubble size, bubble number distribution and bubble diameter distribution, were estimated from X-ray images and compared to those from CFD simulation

c-Lysozyme promotes proliferation of chicken embryonic fibroblast through bFGF pathway

The egg white (EW) contains the majority of bioactive components which maintain embryo growth and differentiation. The discovery of new growth promoting factor in egg white will provide vital clue to understand the developmental regulation of early chicken embryo. The egg white heated with different temperatures (63.5, 70 and 95°C) underwent testing on its growth-promoting effect on chicken fibroblast in vitro. The purified c-lysozyme and the expression of related genes in basic fibroblast growth factor (bFGF) pathway were analyzed to ascertain its growth-promoting mechanism. 13 h after egg white treatment, more fibroblast synchronized with serum starvation transited into S phrase from G0/G1 in EW group than in the control group (CM) and reached the phase of peak proliferation at 15 h after treatment. It was found that c-lysozyme had the function of promoting cells growth and was decided by gradient heat inactivation of egg white. The addition of more than 0.25 mg/ml c-lysozyme produced significant increase in the cellular proliferation during 48 to 72 h of culture. At 13 h after c-lysozyme treatment, the bFGF, cyclin D, cyclin A and CDK2 were up-regulated significantly and promoted the transition from G0/G1 into S phrase and the accurate completion of S phrase. C-Lysozyme contains a growth-activating domain to promote the cell proliferation besides its anti-microbe domain.Key words: c-Lysozyme, fibroblast, fibroblast growth factor receptor (FGFR), cell cycle

HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration

Robust vision restoration for an underwater image remains a challenging problem. For the lack of aligned underwater-terrestrial image pairs, the unsupervised method is more suited to this task. However, the pure data-driven unsupervised method usually has difficulty in achieving realistic color correction for lack of optical constraint. In this paper, we propose a data- and physics-driven unsupervised architecture that learns underwater vision restoration from unpaired underwater-terrestrial images. For sufficient domain transformation and detail preservation, the underwater degeneration needs to be explicitly constructed based on the optically unambiguous physics law. Thus, we employ the Jaffe-McGlamery degradation theory to design the generation models, and use neural networks to describe the process of underwater degradation. Furthermore, to overcome the problem of invalid gradient when optimizing the hybrid physical-neural model, we fully investigate the intrinsic correlation between the scene depth and the degradation factors for the backscattering estimation, to improve the restoration performance through physical constraints. Our experimental results show that the proposed method is able to perform high-quality restoration for unconstrained underwater images without any supervision. On multiple benchmarks, we outperform several state-of-the-art supervised and unsupervised approaches. We also demonstrate that our methods yield encouraging results on real-world applications