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The aim of this study was to evaluate the effect of ligustrazine on the apoptosis of A549 cells and clarify the mechanism of ligustrazine-induced apoptosis.Ligustrazine was prepared with medium according to the gradient concentration. Based on a cytotoxicity test, 3 different concentrations of ligustrazine were selected to form low, medium, and high groups, with a 0 mg/mL dose used as the control. The apoptosis degree and Fas (Fas cell surface death receptor) and Fas-L (Fas Ligand) expression were detected by flow cytometry and quantitative polymerase chain reaction (qPCR), respectively; meanwhile, the activity of caspase 8 and caspase 3 was analyzed by enzyme-linked immunosorbent assay (ELISA) and qPCR, respectively.After 24 hours of ligustrazine administration, the survival rate of A549 cells decreased with the increase of drug concentration, while the rate of apoptosis increased with the increase of drug concentration. Meanwhile, Fas and Fas-L expression was found to be significantly increased at both the gene and protein level, which was positively correlated with drug concentration. Furthermore, the expression of caspase 8 and caspase 3 was positively correlated with the concentration of ligustrazine, and there was significant difference compared with the control group.Ligustrazine can induce the apoptosis of A549 cells via the upregulation of Fas- and caspase-activating death receptor pathway expression.

This work provides quantified explanations for the thermodynamic and kinetic characteristics of esterification in aqueous phase, and how phase transfer catalysts improve water phase esterification of fatty acids in a computational-experimental way. Self-catalyzed reaction mode with or without solvation effects, water participated reaction mode, and catalytic reaction mode (catalyzed by p-dodecyl benzene sulfonic acid, DBSA) are discussed. Our results show that the initial self-catalytic reaction mode undergoes the energy barrier of 100.1 kJ/mol, and rises to 148.9 kJ/mol when water molecule is involved, which hinders the esterification reaction. With the DBSA catalyst, this energy barrier will drop to 97.5 kJ/mol and the water phase esterification is successfully promoted with the yield of 81%. The key kinetic factor of binding energy is discussed as that water molecule has a strong reactant binding competitiveness (with the binding energy of -57.9 kJ/mol, and the value for the non-aqueous phase mode is 3.0 kJ/mol) and DBSA has the binding energy with the value of -45.3 kJ/mol, so it can compete with water to form reactant complexes. This work is a successful practice of a computation-experiment combined scheme, and provides a quantitative basis for the improvement of phase transfer catalysts on water phase esterification reactions. The calculation mode and method of aqueous esterification make it possible to convert bio-based fatty acids into fatty acid esters in fermentation broth.

With the improvement of medical consumption level, patients have more and more demand for the prediction of treatment costs. However, the prediction accuracy of existing research methods is low when the amount of data is small. In order to solve this problem, a weighted lasso regression method is proposed to predict the treatment cost based on the electronic medical record. Firstly, a set of transformation method of text-based medical record data is established, and the missing values are supplemented according to the clustering distance to realize the data representation of medical records. Then, in view of the low prediction accuracy of the traditional regression model, the lasso regression model with local weighting is established by introducing the data feature weight into lasso regression method. Finally, the model is verified by the medical record data provided by the hospital, and the results show that the model has higher prediction accuracy.

It was the constant pursuit of researchers to explore catalysts with higher catalytic activity, and the use of co-catalysts to modify the performance of photocatalytic materials had a significant effect on charge separation. In this paper, the non-precious metal NiS-modified ZnCo2O4 composite catalytic material prepared by hydrothermal method had high-efficiency catalytic performance. When the weight ratio of NiS to ZnCo2O4 was 12%, the optimal catalytic activity is 3.57 mmol g−1 h−1, which was more than twice that of ZnCo2O4. The presence of NiS not only improved the specific surface area of the catalyst and its ability to respond to light, but the close interface formed by the combination of the two monomer phases accelerated the transfer and the utilization of the photocharge on ZnCo2O4 to NiS, thereby promoting the separation of electron holes and improving the photocatalytic activity of the catalyst. According to the research results, the mechanism of hydrogen production in the photocatalytic system was revealed. In this paper, NiS was used to modify the bimetallic oxide with cubic appearance, which provided a new strategy for the development of new photocatalysts.

A high-precision measurement method, based on multiple sampling, is proposed for the time interval of two signals in this study. A time interval measurement circuit integrated into the time-to-digital converter (TDC), is designed based on this high-precision measurement method. In the TDC, the authors use two identical delay lines as the holding module to ensure the two signals with a constant time interval. The TDC samples the two signals multiple times by a clock signal, whose period is shorter than that of the delay line. Consequently, the problem of limited resolution caused by a mismatch between delay lines in the delay-line structure can be avoided, and the precision of the output can be improved. The proposed TDC is designed and simulated in Semiconductor Manufacturing International Corporation (SMIC) 0.18 μm complementary metal-oxide-semiconductor process. Simulation results show that the differential non-linearity and the integral non-linearity are always less than one least significant bits. The proposed TDC achieves input dynamic range of 0-32.13 ns and time resolution of 9 ps.

Charging according to disease is an important way to effectively promote the reform of medical insurance mechanism, reasonably allocate medical resources and reduce the burden of patients, and it is also an important direction of medical development at home and abroad. The cost forecast of single disease can not only find the potential influence and driving factors, but also estimate the active cost, and tell the management and reasonable allocation of medical resources. In this paper, a method of Bayesian network combined with regression analysis is proposed to predict the cost of treatment based on the patient's electronic medical record when the amount of data is small. Firstly, a set of text-based medical record data conversion method is established, and in the clustering method, the missing value interpolation is carried out by weighted method according to the distance, which completes the data preparation and processing for the realization of data prediction. Then, aiming at the problem of low prediction accuracy of traditional regression model, this paper establishes a prediction model combined with local weight regression method after Bayesian network interpretation and classification of patients' treatment process. Finally, the model is verified with the medical record data provided by the hospital, and the results show that the model has higher prediction accuracy.

The paper aims to understand how the fractal dimension and growth time of electrical trees change with temperature and moisture. The fractal dimension of final electrical trees was estimated using 2-D box-counting method. Four groups of electrical trees were grown at variable moisture and temperature. The relation between growth time and fractal dimension of electrical trees were summarized. The results indicate the final electrical trees can have similar fractal dimensions via similar tree growth time at different combinations of moisture level and temperature conditions.

In this paper, a fusion algorithm of background difference and Sobel is proposed. This algorithm can not only accurately detect the moving target, but also well describe the appearance and contour of the target, and also eliminate a lot of irrelevant noise. The detection and tracking system is based on the FPGA development platform of DE2-115 development board. Firstly, video images are collected by TRDB-D5M camera, raw image format is converted into RGB format and stored in SDRAM, and image acquisition is realized. The image is read from SDRAM, and then the median filter is carried out after the gray-scale transformation to realize the image preprocessing. Then we use the fusion algorithm of background difference and Sobel edge detection mentioned above, as well as the centroid tracking algorithm to detect and track the moving target. Finally, we display the real-time image and tracking results through VGA. After many times of debugging, the system can process 640 * 480 @ 60Hz image, realize the real-time tracking of moving target, and the tracking rate can reach higher than 90%. The system realizes the expected function and meets the requirements of real-time, and has a large potential for progress and application prospects.

This paper first introduces the principle of target detection based on Gaussian mixture model, and then introduces the improved method of the original Gaussian mixture model to detect moving targets in detail. The main content is the improvement of initialization method. In this paper, the fast initialization method is used, and the learning rate in the training stage is adjusted, so that it can quickly adapt to the changes in the environment in the training stage. In the aspect of parameter updating, the concept of change rate is introduced, which aims to describe the size of changes in the current frame and the previous frame, so as to adjust the learning rate adaptively In terms of the total number of Gaussian components, this paper adopts the method of initializing a single Gaussian model to increase the Gaussian components with the complexity of description, and the method of combining the removal of Gaussian components and the combination of the removal of Gaussian components in each frame. The removal of Gaussian components can directly remove the Gaussian components with small weight and reduce unnecessary calculation, while the combination of Gaussian components can reduce calculation to describe the background more precisely. In the last part of the improvement, the frame difference method is combined with the Gaussian mixture model to make the edge detection more accurate. Finally, a GUI interface is designed.

Available rotor kinetic energies from doubly-fed induction generators (DFIGs) in the upstream of a wind farm are different from DFIGs in the downstream due to the non-negligible wake effect. Thus, a variable gain of a droop control method is required for system inertia support because the constant-control gain method weakens the inertia support capability during disturbance. This paper presents an improved droop control method from a wind farm to efficiently utilize the rotating masses of DFIGs for system inertia support considering rotor kinetic energy-based variable-droop characteristics from temporal and spatial viewpoint. DFIGs in the upstream of a wind farm with large rotor speeds release more kinetic energy to the grid for system inertia support; DFIGs in the downstream with low rotor speeds release less kinetic energy to prevent rotor speed from stalling. Simulation results clearly indicate that the proposed method provides better performance in terms of improving the frequency nadir, nadir-based frequency response, and preventing DFIGs from stalling. Therefore, the proposed method improves the capability for wind power integration and facilitates to wind energy accommodation.

This paper proposes a novel composite power system reliability evaluating approach based on a splitting method. The essential concept of this method is to replicate target realizations of the sampling process to increase the occurrence frequency of the rare events, thereby improving the assessment efficiency of sampling rare failure events. In the traditional Monte Carlo simulation approach, the power system samples are classified into secure and failure states. The secure states are further quantitatively evaluated by a redundant capacity index in the splitting method to enable an adaptive design of gradual failure domains. Gibbs sampling is introduced to generate states conditional on given domains. The results indicate that the proposed method can improve the efficiency of the power system reliability evaluation, especially in the case of high-reliable power systems.

A hybrid methodology is presented to predict equivalent circulating density (ECD), which combines autoregressive integrated moving average (ARIMA) and back propagation neural network (BPNN) models. Research results are compared to previously published ECD prediction method that is based on theoretical calculation of hydraulic parameters. The hybrid methodology is based on data analysis theory. It uses ARIMA model to capture the linear trends of ECD, and then the BP neural network is used to predict the nonlinear and stochastic change law of ECD. Finally BP neural network prediction results are used to correct the prediction error of ARIMA to get the ECD prediction results. With a deepwater well in the South China Sea, a simulation experiment is carried out to verify the comprehensiveness and accuracy of the method presented in this paper. The prediction results are similar to those of the traditional hydraulics model, which considers the effect of temperature and pressure on drilling fluid density and rheological parameters. Comparisons are also provided for three classical time series prediction models, including the support vector machine, multiple linear regression and grey prediction. The root mean square error (RMSE) and the mean absolute deviation (MAD) were selected as evaluation indicators. The comparison results show that the hybrid model can reflect the variation law of ECD more accurately. Therefore, relative to the traditional methods of prediction, the hybrid methodology has the advantages of advanced modeling thought and simple operation, and it can be selected for prediction of ECD. Because of the effects of high temperature, high pressure and uncertain factors, the accurate prediction of ECD is very difficult. This paper provides a novel idea for accurately predicting ECD by analyzing the implicit relationship of ECD series data through data mining.

A novel Contingency Analysis algorithm has been proposed for Security Analysis (SA) to incorporate Congestion Revenue Right (CRR) options. By replacing fast forward and full backward operations with basic linear algebra operations – Multiplications and Additions, the performance can be improved. The new algorithm is easy to perform in parallel through compiler support of simultaneous multithreading (SMT) and OpenMP. The new algorithm has been implemented in ISO Day Ahead Market (DAM) and performance is improved by more than three times. The same technique can be applied to CRR auction markets, which typically contain abundant number of CRR options.

This paper presents a method of modeling the offensive-defensive competition process of the SAS’s cybersecurity based on Markov decision process (MDP). It takes into account the key characteristics of the target substation as well as the technical capabilities of both the attacker and defender. The optimal action strategies of attacker and defender are obtained by solving the offensive-defensive MDP model. They are helpful for evaluating the cybersecurity of an SAS or the whole power cyber-physical system (CPS) objectively. In addition, they provide the theoretical basis for the substation operators to upgrade the defensive system of the SASs accordingly.

This paper proposes a novel composite network reliability evaluating approach using a line sampling method. The essential concept of this method is to replace the random points with lines to explore the failure regions of high-dimensional systems of the sampling process to increase the occurrence frequency of the rare events, thereby improving the assessment efficiency of sampling rare failure events. In the traditional Monte Carlo simulation approach, the power system samples are randomly selected from the sampling space. The sampling space are further reshaped by a normal distribution in the line sampling method to enable an adaptive design of line sampling space. An important sampling direction is introduced to generate sampling states on given direction. The IEEE-RTS 79, modified IEEE-RTS-79, and IEEE-RTS-96 test systems are used to analyse the computational performance of the proposed approach. The results indicate that the proposed method can improve the efficiency of the power system reliability evaluation, especially in the case of high-reliable power systems.

Background Free-range cattle are common in the Northeast China area, which have close contact with farmers and may carry virus threatening to cattle and farmers. Methods Using viral metagenomics we analyzed the virome in plasma samples collected from 80 cattle from the forested region of Northeast China. Results The virome of cattle plasma is composed of the viruses belonging to the families including Parvoviridae, Papillomaviridae, Picobirnaviridae, and divergent viral genomes showing sequence similarity to circular Rep-encoding single stranded (CRESS) DNA viruses. Five such CRESS-DNA genomes were full characterized, with Rep sequences related to circovirus and gemycircularvirus. Three bovine parvoviruses belonging to two different genera were also characterized. Conclusion The virome in plasma samples of cattle from the forested region of Northeast China was revealed, which further characterized the diversity of viruses in cattle plasma. Electronic supplementary material The online version of this article (10.1186/s12985-018-0923-9) contains supplementary material, which is available to authorized users.

Dear editor: Bufavirus (BuV) belongs to the genus Protoparvovirus within the family Parvoviridae [1–4] and was firstly discovered in the stools of diarrhoeic children in 2012 in Burkina Faso [1] wh...

The classification problem of imbalanced data has become a very important issue in the fields of machine learning and data mining. At present, relatively effective oversampling methods for processing imbalanced data include SMOTE, Borderline-SMOTE, and ADASYN. These algorithms have their own advantages; however, they do not adequately consider the distance factor, which is an important factor for balancing data precisely and reducing the misclassification probability of a minority boundary sample. Therefore, a new algorithm, αDistance Borderline-ADASYN-SMOTE algorithm, is proposed in the paper by combining the optimized Borderline-SMOTE algorithm with the optimized ADASYN algorithm. In the new algorithm, both the amount and the distance distribution of the nearest neighbor samples are considered. A few formulas are created to realize the algorithm. After being balanced by the algorithm, the data obtained from ADNI data set is trained, verified and tested by the Dense Convolutional Network. The experimental results show that the new model improves the classification performance of the Alzheimer’s disease.