Zhejiang Gongshang University

About: Zhejiang Gongshang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 8258 authors who have published 7670 publications receiving 90296 citations. The organization is also known as: Zhèjiāng Gōngshāng Dàxué.

Mechanisms of Nickel Uptake and Hyperaccumulation by Plants and Implications for Soil Remediation

Abstract: Soil contamination by heavy metals like Ni was originally restricted to metalliferous soils but in modern time it has become a general problem due to increasing anthropogenic activities. Because of the characteristics of cost-effectiveness, environmental friendliness, and fewer side-effects, the development of plant based remediation technologies for the cleanup of Ni-contaminated soils has attracted much attention. Nickel is an essential micronutrient, but is toxic to plants at excessive levels. Some plant species can accumulate Ni in the shoots at a high concentration, these plants are called hyperaccumulators. In the past two decades, researchers have endeavored to understand the physiological and molecular mechanisms of Ni uptake, transport, and detoxification in the Ni-hyperaccumulator plants. This is the basis of creating ideal plants for phytoremediation through cell and genetic engineering technologies, which may subsequently improve phytoremediation efficiency for decontaminating Ni-contaminated soils. Both rhizosphere microorganisms and endophytes can play a role in phytoremediation. Optimizing plant and soil management practices, particularly the correction of soil pH and additions of amendments of exogenous chelates and fertilizers, can also enhance phytoremediation of Ni-contaminated soils. The primary objective of this review is to discuss the recent progresses in basic and applied research relevant to phytoremediation of Ni-contaminated soils.

Distance Measurement System Based on Binocular Stereo Vision

Abstract: This system is modeled on the distance perception methods of human eyes. It uses DSP to process the images obtained by two CCD cameras and calculate the distance by the principle of parallax. This paper describes the working principle and hardware platform design. It focuses on the image matching methods based on Canny edge detection and Harris corner detection. The results of the experiment show that the system is efficient and reliable.

Genome shuffling of Streptomyces viridochromogenes for improved production of avilamycin.

Abstract: Avilamycin is one of EU-approved antimicrobial agents in feed industry to inhibit the growth of multidrug-resistant Gram-positive bacteria. Here, we applied a process of combining ribosome engineering and genome shuffling to achieve rapid improvement of avilamycin production in Streptomyces viridochromogenes AS 4.126. The starting mutant population was generated by 60Co γ-irradiation treatments of the spores. After five rounds of protoplast fusion with streptomycin-resistance screening, an improved recombinant E-219 was obtained and its yield of avilamycin reached 1.4 g/L, which was increased by 4.85-fold and 36.8-fold in comparison with that of the shuffling starter Co γ-316 and the ancestor AS 4.126. Furthermore, the mechanism for the improvement of shuffled strains was investigated. Recombinants with enhanced streptomycin resistance exhibited significantly higher avilamycin production and product resistance, probably due to the mutations in the ribosome protein S12. The morphological difference between the parent mutant and shuffled recombinant was observed in conidiospore, and hyphae pellets. The presence of genetic diversity among shuffled populations with varied avilamycin productivity was confirmed by randomly amplified polymorphic DNA analysis. In summary, our results demonstrated that genome shuffling combined with ribosome engineering was a powerful approach for molecular breeding of high-yield industrial strains.

A Unified Model for System Reliability Evaluation Under Dynamic Operating Conditions

Abstract: The working conditions of multicomponent systems are usually dynamic and stochastic. Reliability evaluation of such systems is challenging, since the components are generally positively correlated. Based on the cumulative exposure principle, we model the effects of the dynamic environments on the component lifetimes by a common stochastic time scale, and exponential dispersion process is utilized to describe the stochastic time scale. Then, the component lifetimes are shown to be positively quadrant dependent, and the joint survival function of the component lifetimes is derived, which includes the results of [1] as special cases. In this article, we show that neglecting either the effects of dynamic environments or the correlation among component lifetimes would underestimate the reliability of series systems and overestimate the reliability of parallel systems. We also investigate the problem of parameter redundancy of the model, and give some suggestions for data analysis. Simulation studies show that the unified model is flexible and useful for suggesting an optimal model given observed data.