Many engineering optimization problems can be state as function optimization with constrained, intelligence optimization algorithm can solve these problems well. Particle Swarm Optimization (PSO) algorithm was developed under the inspiration of behavior laws of bird flocks, fish schools and human communities. In this paper, aim at the disadvantages of standard Particle Swarm Optimization algorithm like being trapped easily into a local optimum, we improve the standard PSO and propose a new algorithm to solve the overcomes of the standard PSO. The new algorithm keeps not only the fast convergence speed characteristic of PSO, but effectively improves the capability of global searching as well. Experiment results reveal that the proposed algorithm can find better solution when compared to other heuristic methods and is a powerful optimization algorithm for constrained engineering optimization problems.
Nanocrystalline diamond has extensive application in biology modified material aspects of human implants, because it has good mechanical properties such as corrosion resistance and biocompatibility. This study investigated the surface characteristics and biocompatibility of pure titanium whose surface was modified with nanocrystalline diamond by Microwave Plasma Chemical Vapor Deposition. Scanning electron microscope (SEM), Spectruman analysis ray diffraction and X-Ray photoelectron spectroscopy were used to analyze the chemical composition and surface topography of nanocrystalline diamond. We found that the particle size of the diamond on the titanium disc is nanocrystalline, and is without impure elements. In the experiment, we used Human Osteoblast-like cells MG63 to investigate the bioactivity of nanocrystalline diamond film. The attachment and proliferation of the cells were tested by NAPI, CCK-8. The result showed that oxygen terminated NCD groups had a significant advantage compared with hydrogen terminated and titanium groups on attachment and proliferation of MG63.
Inflammatory responses mediated by microglia are essential contributors to the pathogenesis of manganese (Mn)-induced neurotoxicity, which results in neurodegeneration and cognitive dysfunction. Inhibition of microglia-mediated inflammation has been shown to alleviate Mn-induced neurotoxicity. Sesamol, derived from sesame, has neuroprotective properties in various disease models, including neurological diseases. Whether sesamol protects against Mn-induced neurological injuries has yet to be determined. Here, both in vivo and in vitro Mn exposure models were established to address the beneficial effects of sesamol on Mn-induced neurotoxicity. We showed that administration of sesamol mitigated learning and memory deficits in Mn-treated mice. Furthermore, sesamol reduced Mn-induced microglial activation and expression of proinflammatory mediators (TNF-α, iNOS, and Cxcl10), while exerting a marginal effect on anti-inflammation and microglial phagocytosis. Mn exposure activated the microglial cGAS-STING pathway and sesamol inhibited this pathway by reducing the phosphorylation of STING and NF-κB, concomitantly decreasing IFN-α and IFN-β synthesis. In summary, our novel results indicated that sesamol exerted its protective effects on Mn-induced neuroinflammation and cognitive impairment via the microglial cGAS-STING / NF-κB pathway, providing evidence that sesamol may serve as an effective therapeutic for preventing and treating Mn-induced neurotoxicity.
Image classification has always been an important research topic in the field of computer vision. By designing different CNN network models, an increasing number of image classification applications have undergone significant changes, such as crop species recognition in agriculture, medical image recognition in the medical field, and vehicle recognition in transportation. However, most existed CNNs only use single model and rigid classification module to encode features and classify objects in the images, which resulting in semantic wasting and trapped in a fixed feature extraction pattern. Based on this, this article focuses on how to solve the problem of extracting features from insufficient attention regions in CNN network models by using deep learning to solve image classification problems. A dual network feature fusion model (DNFFM) is proposed to improve image classification results. DNFFM has a dual backbone networks, which extracts complementary non-redundant information from the feature layer of the backbone network through the fusion module of DNFFM, so that the entire network model has a broader and richer effective attention area, thus improving the accuracy of classification. DNFFM has achieved better results on CIFAR10, CIFAR100 and SVHN than a single backbone network. Reached 97.6%, 85.7% and 98.1% respectively. Compared with the original single network with the same backbone network, 2.4%, 2.9%, 1.6% and 2.2%, 3.2%, 1.3% are improved respectively. DNFFM has the following advantages: it is an end-to-end network that can extract more feature information when the data are the same ones, and has better classification results than a single network.
Microwave structured PA-6/PMIA NFN membrane can filter airborne particles with high filtration efficiency, low pressure drop, and large dust-holding capacity.
Currently, most of the commercial dental implants are made from inert pure titanium which facilitates osteointegration but without bioactive properties like osteo-conductivity and osteo-inductivity. Current attempts to improve dental implant bioactivity are limited by their single action and low efficiency. These methods either just add chemical coating on implants surface or simply change surface roughness instead of combining chemical and mechanical changes together. Here, we developed an advanced titanium implants by electro-depostion of a zinc-doped calcium phosphate (Zn-HA) coating on the 3D printed porous dental implants which optimize biomechanical and biological microenvironment for new bone formation. The bioactive titanium implants enable osteoblast-like cells to attach on implants, render high proliferation days 4 and 7 after attaching (P <0.001) and the following osteogenic differentiation indicated by increase alkaline phosphatase activity at 7 days post-seeding (P <0.001), total cellular protein expression at 14 days (P <0.05), osteogenic gene mRNA expression like OCN, RUNX2, and Osterix at days 9 and 14 (P <0.001) and protein bone sialoprotein and collagen I excretion at days 9, 14 and 21. Also we found out the optimal molar ratio of Zn/(Zn+Ca) for osteointergration around implants. Overall, Zn-HA on 3D printed porous titanium has good osteoconductivity, especially when Zn/(Zn+Ca) is 20%.
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