IL28B Is Associated with Outcomes of Chronic HBV Infection

Xiaodong Shi,Junqi Niu,Xiumei Chi,Yu Pan,Yanhang Gao,Wanyu Li,Chen Yang,Jin Zhong,Damo Xu,Manna Zhang,Gerald Minuk,Jing Jiang 연세대학교의과대학 2015 Yonsei medical journal Vol.56 No.3

Purpose: The role of IL28B gene variants and expression in hepatitis B virus (HBV) infections are not well understood. Here, we evaluated whether IL28B gene expression and rs12979860 variations are associated with HBV outcomes. Materialsand Methods: IL28B genetic variations (rs12979860) were genotyped by pyrosequencingof DNA samples from 137 individuals with chronic HBV infection [50 inactive carriers (IC), 34 chronic hepatitis B (CHB), 27 cirrhosis, 26 hepatocellular carcinoma (HCC)], and 19 healthy controls. IL28A/B mRNA expression in peripheralblood mononuclear cells was determined by qRT-PCR, and serum IL28B proteinwas measured by ELISA. Results: Patients with IL28B C/C genotype had greater IL28A/B mRNA expression and higher IL28B protein levels than C/T patients. Within the various disease stages, compared to IC and healthy controls, IL28B expression was reduced in the CHB, cirrhosis, and HCC cohorts (CHB vs. IC, p=0.02; cirrhosis vs. IC, p=0.01; HCC vs. IC, p=0.001; CHB vs. controls, p<0.01; cirrhosis vs. controls, p<0.01; HCC vs. controls, p<0.01). When stratified with respect to serum HBV markers in the IC and CHB cohorts, IL28B mRNA and protein levels were higher in HBeAg-positive than negative individuals (p=0.01). Logistic regression analysis revealed that factors associated with high IL28B proteinlevels were C/C versus C/T genotype [p=0.016, odds ratio (OR)=0.25, 95% confidence interval (CI)=0.08‒0.78], high alanine aminotransferase values (p<0.001, OR=8.02, 95% CI=2.64‒24.4), and the IC stage of HBV infection (p<0.001). Conclusion: Our data suggest that IL28B genetic variations may play an important role in long-term development of disease in chronic HBV infections.

Fuzzy adaptive control particle swarm optimization based on T-S fuzzy model of maglev vehicle suspension system

Chen Chen,Junqi Xu,Guobin Lin,Yougang Sun,Dinggang Gao 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.1

At present, with the gradual promotion of Maglev vehicles, the stability of the suspension system has gradually become a hotspot. During the operation of Maglev vehicles, vibration may be caused by external disturbances such as track irregularity, non-directional wind load and load variation. When the vibration amplitude is within the controllable range of the current parameters, the restraint effect can be achieved and the stable convergence can be formed. However, when the vibration amplitude exceeds the current controllable range, the maglev vehicle may break the track or even lose stability. In order to solve the possible adverse effects of external disturbances on the stability of the system, a T-S fuzzy model considering both parameter uncertainties and external disturbances is constructed, and a relatively mature fuzzy adaptive control method is used for suspension control. However, considering the tracking performance of the system control parameters and the response speed of the parameter changes when the external disturbance changes, the particle swarm optimization (PSO) algorithm is used to optimize the system. The effectiveness of the optimized fuzzy adaptive control law in coordinating the closed-loop stability of the suspension system is proved in terms of response speed and convergence performance. Based on linear matrix inequality (LMI), the control response region satisfying the control performance after optimization is defined, and Lyapunov method is adopted to prove the stability of the optimized algorithm in controlling vehicle fluctuation operation. The simulation and experimental results show that the fuzzy adaptive control algorithm optimized by particle swarm optimization can further improve the speed of parameter optimization and the tracking performance of the system in the face of external disturbances and internal system parameter perturbations within a given range of control parameters. Compared with previous control strategies, the controller can greatly improve the response speed and the closed-loop information updating ability of the system in the face of disturbances, so that the system has stronger robustness and faster dynamic response.

Impeller inlet geometry effect on performance improvement for centrifugal pumps

Xianwu Luo,Yao Zhang,Junqi Peng,Hongyuan Xu,Weiping Yu 대한기계학회 2008 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.22 No.10

This research treats the effect of impeller inlet geometry on performance improvement for a boiler feed pump, who is a centrifugal pump having specific speed of 183 m⋅m3min-1⋅min-1 and close type impeller with exit diameter of 450 mm. The hydraulic performance and cavitation performance of the pump have been tested experimentally. In order to improve the pump, five impellers have been considered by extending the blade leading edge or applying much larger blade angle at impeller inlet compared with the original impeller. The 3-D turbulent flow inside those pumps has been analyzed basing on RNG k-ε turbulence model and VOF cavitation model. It is noted that the numerical results are fairly good compared with the experiments. Based on the experimental test and numerical simulation, the following conclusions can be drawn: (1) Impeller inlet geometry has important influence on performance improvement in the case of centrifugal pump. Favorite effects on performance improvement have been achieved by both extending the blade leading edge and applying much larger blade angle at impeller inlet; (2) It is suspected that the extended leading edge have favorite effect for improving hydraulic performance, and the much larger blade angle at impeller inlet have favorite effect for improving cavitation performance for the test pump; (3) Uniform flow upstream of impeller inlet is helpful for improving cavitation performance of the pump.

Multiple damage detection of maglev rail joints using time-frequency spectrogram and convolutional neural network

Yi-Qing Ni,Su-Mei Wang,Gao-Feng Jiang,Yang Lu,Guobin Lin,Hong-Liang Pan,Junqi Xu,Shuo Hao 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.4

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-loosenesscaused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFSCNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.