Enzyme activities and glyphosate biodegradation in a riparian soil affected by simulated saltwater incursion

Soil salinization due to saltwater incursion, is a major threat to biochemical activities and thus strongly alters biogeochemical processes in a freshwater riparian of coastal estuary region. A pot incubation experiment was conducted to investigate the effects of simulated saltwater incursion on some key enzymatic activities and biodegradation dynamics of herbicide glyphosate in a riparian soil in Chongming Island located in the Yangtze River estuary, China. The results showed that saltwater addition with 10% artificial seawater significantly increased the biodegradation efficiency of glyphosate with the lowest residual concentration among all the treatments. However, glyphosate degradation was markedly decreased in the riparian soil with high levels of saltwater treatment. As compared with no saltwater treatment, the half-lives for 20% and 50% seawater treatments were prolonged by 4.9% and 21.1%, respectively. Throughout the incubation period, saltwater addition with 10% seawater stimulated the enzymatic activities in the glyphosate-spiked riparian soil, as compared to the treatment with 0% seawater. Flourescein diacetate (FDA) hydrolysis rate, dehydrogenase activity (DHA), catalase activity, and alkaline phosphatase activity in the glyphosate-spiked riparian soil treated with 10% seawater were 68.5%, 49.2%, 38.7%, and 28.6% higher than those for no saltwater treatment, respectively. The effect of 20% seawater treatment on the glyphosate-spiked riparian soil enzymatic activities fluctuated between promotion and inhibition depending on the type of enzymes. Soil enzymatic activities were severely depressed by increasing salinity level with 50% seawater treatment significantly inhibited, relative to no saltwater treatment. Especially, FDA hydrolysis rate and DHA were decreased by 73.8% and 64.8%, respectively, as compared to no saltwater treatment. Glyphosate degradation percentages were strongly positively correlated to the FDA hydrolysis rate and DHA, indicating that as compared to the other enzymes, the two enzymes contributed more to the herbicide biodegradation in the salt-affected riparian soil

Large Margin Object Tracking with Circulant Feature Maps

Structured output support vector machine (SVM) based tracking algorithms have shown favorable performance recently. Nonetheless, the time-consuming candidate sampling and complex optimization limit their real-time applications. In this paper, we propose a novel large margin object tracking method which absorbs the strong discriminative ability from structured output SVM and speeds up by the correlation filter algorithm significantly. Secondly, a multimodal target detection technique is proposed to improve the target localization precision and prevent model drift introduced by similar objects or background noise. Thirdly, we exploit the feedback from high-confidence tracking results to avoid the model corruption problem. We implement two versions of the proposed tracker with the representations from both conventional hand-crafted and deep convolution neural networks (CNNs) based features to validate the strong compatibility of the algorithm. The experimental results demonstrate that the proposed tracker performs superiorly against several state-of-the-art algorithms on the challenging benchmark sequences while runs at speed in excess of 80 frames per second. The source code and experimental results will be made publicly available