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

Deep Learning based HEp-2 Image Classification: A Comprehensive Review

Classification of HEp-2 cell patterns plays a significant role in the indirect immunofluorescence test for identifying autoimmune diseases in the human body. Many automatic HEp-2 cell classification methods have been proposed in recent years, amongst which deep learning based methods have shown impressive performance. This paper provides a comprehensive review of the existing deep learning based HEp-2 cell image classification methods. These methods perform HEp-2 image classification at two levels, namely, cell-level and specimen-level. Both levels are covered in this review. At each level, the methods are organized with a deep network usage based taxonomy. The core idea, notable achievements, and key strengths and weaknesses of each method are critically analyzed. Furthermore, a concise review of the existing HEp-2 datasets that are commonly used in the literature is given. The paper ends with a discussion on novel opportunities and future research directions in this field. It is hoped that this paper would provide readers with a thorough reference of this novel, challenging, and thriving field.Comment: Published in Medical Image Analysi

A novel chaotic time series prediction method and its application to carrier vibration interference attitude prediction of stabilized platform

Aiming at the problems existing in previous chaos time series prediction methods, a novel chaos times series prediction method, which applies modified GM(1, 1) model with optimizing parameters to study evolution laws of phase point L1 norm in reconstructed phase space, is proposed in this paper. Phase space reconstruction theory is used to reconstruct the unobserved phase space for chaotic time series by C-C method, and L1 norm series of phase points can be obtained in the reconstructed phase space. The modified GM(1, 1) model, which is improved by optimizing background value and optimizing original condition, is used to study the change law of phase point L1 norm for forecasting. The measured data from stabilized platform experiment and three traditional chaos time series are applied to evaluate the performance of the proposed model. To test the prediction method, three accuracy evaluation standards are employed here. The empirical results of stabilized platform are encouraging and indicate that the newly proposed method is excellent in prediction of chaos time series of chaos systems

Driver Fatigue Features Extraction

Driver fatigue is the main cause of traffic accidents. How to extract the effective features of fatigue is important for recognition accuracy and traffic safety. To solve the problem, this paper proposes a new method of driver fatigue features extraction based on the facial image sequence. In this method, first, each facial image in the sequence is divided into nonoverlapping blocks of the same size, and Gabor wavelets are employed to extract multiscale and multiorientation features. Then the mean value and standard deviation of each block’s features are calculated, respectively. Considering the facial performance of human fatigue is a dynamic process that developed over time, each block’s features are analyzed in the sequence. Finally, Adaboost algorithm is applied to select the most discriminating fatigue features. The proposed method was tested on a self-built database which includes a wide range of human subjects of different genders, poses, and illuminations in real-life fatigue conditions. Experimental results show the effectiveness of the proposed method

Parallelization and I/O performance optimization of a global nonhydrostatic dynamical core using MPI

The Global ‐ Regional Integrated forecast SysTem (GRIST) is the next- generation weather and climate integrated model dynamic framework developed by Chinese Academy of Meteorological Sciences. In this paper, we present several changes made to the global nonhydrostatic dynamical (GND) core, which is part of the ongoing prototype of GRIST. The changes leveraging MPI and PnetCDF techniques were targeted at the parallelization and performance optimization to the original serial GND core. Meanwhile, some sophisticated data structures and interfaces were designed to adjust flexibly the size of boundary and halo domains according to the variable accuracy in parallel context. In addition, the I/O performance of PnetCDF decreases as the number of MPI processes increases in our experimental environment. Especially when the number exceeds 6000, it caused system-wide outages (SWO). Thus, a grouping solution was proposed to overcome that issue. Several experiments were carried out on the supercomputing platform based on Intel x86 CPUs in the National Supercomputing Center in Wuxi. The results demonstrated that the parallel GND core based on grouping solution achieves good strong scalability and improves the performance significantly, as well as avoiding the SWOs

Learning Partial Correlation based Deep Visual Representation for Image Classification

Visual representation based on covariance matrix has demonstrates its efficacy for image classification by characterising the pairwise correlation of different channels in convolutional feature maps. However, pairwise correlation will become misleading once there is another channel correlating with both channels of interest, resulting in the ``confounding'' effect. For this case, ``partial correlation'' which removes the confounding effect shall be estimated instead. Nevertheless, reliably estimating partial correlation requires to solve a symmetric positive definite matrix optimisation, known as sparse inverse covariance estimation (SICE). How to incorporate this process into CNN remains an open issue. In this work, we formulate SICE as a novel structured layer of CNN. To ensure end-to-end trainability, we develop an iterative method to solve the above matrix optimisation during forward and backward propagation steps. Our work obtains a partial correlation based deep visual representation and mitigates the small sample problem often encountered by covariance matrix estimation in CNN. Computationally, our model can be effectively trained with GPU and works well with a large number of channels of advanced CNNs. Experiments show the efficacy and superior classification performance of our deep visual representation compared to covariance matrix based counterparts.Comment: This paper is published at CVPR 202