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Taking the typical main canal of the Middle Route of South-to-North Water Transfer project as an example, the numerical simulation study on the variation law of water level before sluice under different wind conditions is carried out. First, based on numerical simulation, mathematical induction, and statistical analysis method, the influences of wind speed, wind duration and wind direction on water level fluctuation are put forward and the fast prediction formula of maximum water level fluctuation under wind influence is extracted. Then, the feasibility of the fast prediction formula is verified by the actual monitoring data. The results show that: (1) the duration of wind has little influence on the maximum water level variation, and the maximum error is 9.83 %; (2) the maximum water level amplitude increases with increasing wind speed and decreases with increasing wind source distance; (3) the whole water level variation is symmetrical about the angle α=180°, and 0° to 90° is a period of maximum water level variation; (4) the error between the calculation result of the fast prediction formula and the measured result is 1.25 %, which shows that the maximum water level prediction formula is relatively applicable to open channel water transfer project. These research results provide a scientific basis for routine dispatching of water conveyance projects.

Abstract Background To investigate the association between serum glycosylated hemoglobin (HbA1c) levels and nonalcoholic fatty liver disease (NAFLD) in an elderly Chinese population. Methods A cross-sectional study was performed among the 949 retired elderly employees of Zhenhai Refining & Chemical Company Ltd., Ningbo, China. Results A total of 257 (27.08%) subjects fulfilled the diagnostic criteria of NAFLD, and NAFLD patients had significantly higher serum HbA1c levels than controls (P vs. 25.20%; P P for trend P =0.026). Conclusions Our results suggest that serum HbA1c level is associated with NAFLD, and increased serum HbA1c level is an independent risk factor for NAFLD in elderly Chinese.

Abstract Background Although Argonaute proteins are considered to play important roles in stem cell self-renewal, RNA interference (RNAi) and translational regulation, relatively little is known about their functions in human disease. In this study, we investigated the expression of eight members of human Argonaute family in colon cancer and identified their potential roles in tumor development and progression. Methods Antibodies against human Argonaute proteins were prepared by immunizing rabbits with synthetic peptides derived from the sequences of Argonaute members. Then we constructed a tissue microarray containing 75 specimens from colon cancer and 75 specimens from adjacent non-cancer tissue, and assayed eight different proteins (EIF2C1, EIF2C2, EIF2C3, EIF2C4, PIWIL1, PIWIL2, PIWIL3 and PIWIL4) by immunohistochemistry on consecutive formalin-fixed tissue microarray sections. Results The expression of EIF2C1-4 and PIWIL1-4 was significantly higher in tumorous tissue than in adjacent tissue. Notably, a significant correlation was observed between the positive expression of EIF2C2, EIF2C3, EIF2C4, PIWIL4 and the presence of distant metastasis. Logistic regression analysis revealed that an increased expression of EIF2C1 and PIWIL2 was significantly associated with occurrence of colon cancer tissue compared with non-cancer tissue. Conclusions Argonaute proteins are overexpressed in colon cancer relative to adjacent non-cancer tissue. The expression of EIF2C2-4 and PIWIL4 appears increased in advanced tumors with distant metastasis, suggesting it may promote tumor invasion. Furthermore, EIF2C1 and PIWIL2 might represent novel colon cancer markers with early diagnostic significance.

To address the channel estimation problem for non-orthogonal multiple access (NOMA) systems under non-Gaussian impulsive noise, a joint channel and impulsive noise estimation method based on approximate message passing was proposed, by exploiting the joint sparsity of the channel and impulsive noise. Firstly, based on sparse Bayesian learning theory, a compressed sensing equation was constructed by using all subcarriers, and then a joint estimation optimization problem for the channel, impulsive noise, and data symbols was proposed. To address this hyperparameter nonlinear non-convex problem, an expectation maximization (EM) implementation algorithm based on Gaussian generalized approximation message passing and sparse Bayesian learning (SBL) theory was designed. Simulation results show that compared to the SBL method based on EM, the proposed algorithm exhibited a slight degradation in terms of mean square error (MSE) for channel and impulsive noise estimation, bit error rate (BER). However, the complexity of the proposed algorithm was reduced by one order of magnitude. [ABSTRACT FROM AUTHOR]