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CGMM LASSO-type estimator for the process of Ornstein–Uhlenbeck type
Yinfeng Wang,Yanlin Tang,Xinsheng Zhang 한국통계학회 2016 Journal of the Korean Statistical Society Vol.45 No.1
In this paper, westudy the LASSO-type penalizedCGMM(GMMwith continuum of moment method) estimator for the process of Ornstein–Uhlenbeck type. This LASSO-type estimator is obtained by minimizing the summation of the CGMM object function and a LASSO-type penalty, which is included for model selection. In the proposed method, model selection and estimation are done simultaneously. Under some regularity conditions, the proposed estimator asymptotically follows a non-standard normal distribution (Caner, 2009). Simulation study shows that the proposed estimator correctly selects the true model much more frequently than the commonly used Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC).
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Minghai Han,Weixian Wang,Jianli Zhou,Xun Gong,Cunbin Xu,Yinfeng Li,Qiang Li 한국생물공학회 2020 Biotechnology and Bioprocess Engineering Vol.25 No.2
The effects of activation of the unfolded protein response (UPR) via co-expression of the HAC1i gene on the production of the recombinant Pseudomonas aeruginosa elastase (rPAE) in Pichia pastoris GS115 were evaluated in this study. The results showed that expression of the HAC1i gene significantly increased the level of Kar2p (a hallmark of UPR activation) in P. pastoris GS115, demonstrating activation of the UPR. This gene did not affect the growth of yeast in the buffered glycerol-complex medium but stimulated its growth in the buffered methanolcomplex medium. Co-expression of the HAC1i gene enhanced the expression level of the heterogeneously Nglycosylated forms of rPAE, as the caseinolytic activity in the supernatant of the various glycoforms of rPAE expressed in P. pastoris GS115/HAC1 was increased 1.8—3.9-fold compared to that in the control strain P. pastoris GS115, respectively. The stimulating effects of co-expression of the gene on rPAE production were observed when 0.5, 1.0, and 2.0% methanol were added every 24 h, as the caseinolytic activity of supernatants of P. pastoris GS115/HAC1 expressing wild-type of rPAE was increased 3.3-, 1.9-, and 1.7-fold at the corresponding methanol concentration. Further, activation of UPR via co-expression of the HAC1i gene enhanced rPAE secretion in P. pastoris at 20, 24, and 28°C, as the caseinolytic activity of supernatants of P. pastoris GS115/HAC1 expressing wild-type rPAE was increased 2.3-, 2.1-, and 2.8-fold over the tested temperatures.
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Cuicui Chen,Xiaozhu Liu,Yinfeng Li,Huankun Liang,Kangyan Li,Jiali Li,Chengwu Cheng,Xianpan Liu,Shuhai Zhong,Laiqing Li,Yan Wang 사단법인약침학회 2017 Journal of Acupuncture & Meridian Studies Vol.10 No.4
Allergic contact dermatitis (ACD) is a chronic inflammatory skin disease. Topical corticosteroids are the first-line therapy for ACD despite their significant adverse effects. Acupuncture has been widely used in the treatment of various skin diseases, but its underlying mechanism remains unrevealed. In this study, we investigated the characteristics of acupuncture treatment based on effectiveness and mechanism. BALB/c mice received 1-chloro-2,4-dinitrobenzene (DNCB) application to build AD-like model. Results showed that acupuncture was an effective treatment method in inhibiting inflammatory conditions, serum IgE levels, and expression of proinflammatory cytokine Th2 (IL-4, IL-6), and Th2 (IL-1b, TNF-a) mRNA compared with DNCB treatment. Acupuncture treatment also inhibited nuclear factor-kB p65, phosphorylation of IkBa, and phosphorylation of occludin proteins expression. Furthermore, it could improve the expression of epidermal growth factor in both mRNA and protein levels. These results suggest that acupuncture, as an alternative therapy treatment for its no significant side effects, was effective in alleviating ACD by reducing proinflammatory cytokines and changing proteins’ expression.
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A Study on Multivariable Optimization in Precision Manufacturing Using MOPSONNS
Zhaopeng He,Tielin Shi,Jianping Xuan,Su Jiang,Yinfeng Wang 한국정밀공학회 2020 International Journal of Precision Engineering and Vol.21 No.11
7075 aluminum alloy has been widely applied in the field of aerospace and marine sheet metal because of its protruding mechanical and corrosion resistance. In this paper, the problem of selecting optimal process parameters to optimize multiple processing variables had been studied in precision manufacturing. Multi-objective particle swarm optimized neural networks system was put forward to determine the optimal cutting conditions with multi-objective particle swarm algorithm and multiple neural networks as prediction models of machining variables. Precision parts manufacturing of 7075 aluminum alloy would go through two operations of material removal and surface forming. Firstly, optimal cutting conditions were determined to minimize tool wear while maximizing metal removal rate in material removal stage. Secondly, it was significant and meaningful to select optimal cutting conditions corresponding to the best surface quality and minimum root mean square of tool vibration in surface forming stage. Orthogonal experiments had been carried out to observe the relationship between machining-related variables and cutting parameters in detail. Multiple neural networks were trained to establish predictive models of cutting process from orthogonal experimental and statistical data. Maximum deviation theory sorted the Pareto solutions searched by optimization process of neural networks driven by multi-objective particle swarm algorithm. The top ranking Pareto solutions had been determined as the optimal cutting parameters combination for material removal and surface forming stages, respectively. Finally, the proposed optimization system can also be used to optimize the processing of other difficult-to-machine materials.
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