Molecular Weight Controllable Degradation of Laminaria japonica Polysaccharides and Its Antioxidant Properties

In this study, molecular weight controllable degradation of algal Laminaria japonica polysaccharides(LPS) was investigated by ultrasound combined with hydrogen peroxide. Three main factors, i.e., ultrasonic power(A), ultrasonic time(B), and H_2O_2 concentration(C) were chosen for optimizing parameters by employing three-factors, three-levels BBD. The influence of degradation on structure change and antioxidant activities was also investigated. A second-order polynomial equation including molecular weight(Y) of Laminaria japonica polysaccharides and each variable parameter, i.e., ultrasonic power(A), ultrasonic time(B), and H_2O_2 concentration(C), was established: Y=20718.67-4273.13A-4000.38B-1438.75C+2333.25AB+1511.00AC+873.00BC+2838.29A^2 + 2490.79B^2+873.04C^2. The equation regression coefficient value(R^2 = 0.969) indicated that this equation was valid. The value of the adjusted determination coefficient(adjusted R^2 = 0.914) also confirmed that the model was highly significant. The results of selected experimental degradation conditions matched with the predicted value. FT-IR spectra revealed that the structures of LPS before and after degradation were not significantly changed. Antioxidant activities of LPS revealed that low Mws possessed stronger inhibitory than the original polysaccharides. The scavenging effects on superoxide radicals was the highest when IC50 of crude LPS was 4.92 mg mL^(-1) and IC50 of Mw 18.576 KDa was 1.02 mg mL^(-1), which was fourfold higher than initial polysaccharide.

Longitudinal Acceleration Tracking Control of Low Speed Heavy-Duty Vehicles

This paper presents a model matching control （MMC） method based on the sliding mode control （SMC） method for longitudinal acceleration tracking control in a vehicular stop-and-go cruise control system. The nonlinearity of the vehicle acceleration response at low speeds was analyzed to develop a transfer function model of the vehicle longitudinal dynamics using the least-mean-square system identification technique. This transfer function was then used to design the MMC controller, including an SMC feedback compensator. The system combines the advantages of the two control methods with robust control and rapid response. Simulations show that the controller enhances the rapid trackability to the vehicle acceleration and improves the system＇s robustness at low speeds compared with conventional PID MMC controllers.

SHEsis PCA: A GPU-Based Software to Correct for Population Stratification that Efficiently Accelerates the Process for Handling Genome-Wide Datasets

Population stratification is a problem in genetic association studies because it is likely to highlight loci that underlie the population structure rather than disease-related loci. At present, principal component analysis （PCA） has been proven to be an effective way to correct for population stratification. However, the conventional PCA algorithm is time-consuming when dealing with large datasets. We developed a Graphic processing unit （GPU）-based PCA software named SHEsisPCA （http：//analysis.bio-x.cn/SHEsisMain.htm） that is highly parallel with a highest speedup greater than 100 compared with its CPU version. A cluster algorithm based on X-means was also implemented as a way to detect population subgroups and to obtain matched cases and controls in order to reduce the genomic inflation and increase the power. A study of both simulated and real datasets showed that SHEsisPCA ran at an extremely high speed while the accuracy was hardly reduced. Therefore, SHEsisPCA can help correct for population stratification much more efficiently than the conventional CPU-based algorithms.