Optimization of Microwave-Assisted Extraction Saponins from Sapindus mukorossi Pericarps and an Evaluation of Their Inhibitory Activity on Xanthine Oxidase

A microwave-assisted extraction (MAE) method was applied to separate saponins from Sapindus mukorossi pericarps. The most important factors of the six extraction parameters were selected using Plackett–Burman designs; therefore, the further extraction procedure was optimized using the Box–Behnken designs; meanwhile, the optimum processing parameters and well-pleasing saponins extraction rate were inferred. The final operation conditions were the ethanol concentration of 40%, soaking time of 3 h, particle size of 80–100 meshes, extraction time of 13 min, solvent-solid ratio of 19 mL/g, and microwave power of 425 W. Based on the optimal extraction parameters, the extraction rate of the saponins by means of MAE technique reached 280.55 ± 6.81 mg/g, which exceeds yields acquired using conventional manners. Saponins from S. mukorossi have obvious xanthine oxidase inhibitory properties in vitro compared with allopurinol. The saponins displayed a type of competitive inhibition of xanthine oxidase. In conclusion, a MAE technique in association with a response surface design provides an efficient extraction tactics, which could sufficiently isolate saponins from S. mukorossi pericarps; further, this technique could be applied to the dissociation of other bioactive substances from plant sources. In addition, the saponins may be a promising alternative to conventional medicine to treat gout and other inflammation-associated disorders to mitigate the side effects of traditional drugs

Application of a Combined Homogenate and Ultrasonic Cavitation System for the Efficient Extraction of Flavonoids from Cinnamomum camphora Leaves and Evaluation of Their Antioxidant Activity In Vitro

A free-of-dust pollution extraction method combined-homogenate and ultrasonic cavitation system, namely, homogenate-combined ultrasonic cavitation synergistic extraction (HUCSE), was proposed for the efficient extraction of flavonoids from Cinnamomum camphora leaves. Response surface methodology of Box–Behnken design was employed to optimize the HUCSE process, and the optimum operation conditions attained with an extraction yield of 7.95 ± 0.27 mg/g were ethanol concentration 76%, homogenate/ultrasonic time 25 min, solvent-to-solid ratio 22 mL/g, and ultrasonic power 240 W. A second-order kinetic mathematical methodology was performed to depict the behaviors of HUCSE and heat reflux extraction method. The results suggested that the developed HUCSE is an efficient and green method for the extraction of C. camphora flavonoids or other plant natural products, where the obvious higher parameters of extraction capacity at saturation, second-order extraction rate constant, and original extraction rate were obtained when compared to the heat reflux method. The antioxidant activity assays in vitro showed that the C. camphora flavonoids possessed strong antioxidant activity and are promising to be applied as a natural alternative antioxidant

Graph Cut Energy Driven Earthquake-damaged Building Detection from High-resolution Remote Sensing Images

In order to make full use of the detail information provided by high-resolution remote sensing images to improve the detection accuracy of damaged buildings during earthquake,combining the shape, edge and corner characteristics of buildings, a novel method based on graph cut frame for damaged building detection is proposed in this paper. Firstly, the local image containing single building used to model the energy function is constructed by digital line graphic data. And the bound terms of the energy function are defined by the location, shape, edge and corner of buildings, respectively. Then, the energy function is minimized through max-flow/min-cut method, and the similarity of the buildings in the pre-and post-event images is measured by the minimum cut energy. Finally, the EM algorithm is exploited to select the classification threshold value of the minimum cut energy, and post-processing is performed according to the misclassification rate estimation to obtain the final detection result. Images taken in Ishinomaki before and after the 2011 off the Pacific coast of Tohoku Earthquake are used in this paper. The experimental results show that the proposed method can effectively detect the damaged buildings

Fuzzy Optimal Control for a Class of Discrete-Time Switched Nonlinear Systems

International audienceThis paper investigates the optimal tracking problem for discrete-time (DT) autonomous nonlinear switched systems with the switching cost. To avoid excessive switching frequency, the switching cost between modes is considered in the performance index, which means that the optimal switching policy is not only related to the tracking error but also the mode applied at the previous instant. The objective is to make the system state track the reference signal while minimizing the defined performance function. A model-free Q-learning algorithm that learns the optimal switching policy from real system data is developed. Furthermore, it is proved by mathematical induction that the iterative Q-functions generated by the proposed Qlearning algorithm will converge to the optimum. To implement the Q-learning algorithm, fuzzy logic systems (FLSs) are applied to approximate the iterative Q-functions. A novel structure of FLSs is designed to ensure the validity of Q-function approximation. Finally, simulation results demonstrate the effectiveness and advantages of the algorithm

Combined Saliency with Multi-Convolutional Neural Network for High Resolution Remote Sensing Scene Classification

The scene information existing in high resolution remote sensing images is important for image interpretation and understanding of the real world. Traditional scene classification methods often use middle and low-level artificial features, but high resolution images have rich information and complex scene configuration, which need high-level feature to express. A joint saliency and multi-convolutional neural network method is proposed in this paper. Firstly, we obtain meaningful patches that include dominant image information by saliency sampling. Secondly, these patches will be set as a sample input to the convolutional neural network for training, obtain feature expression on different levels. Finally, we embed the multi-layer features into the support vector machine (SVM) for image classification. Experiments using two high resolution image scene data show that saliency sampling can effectively get the main target, weaken the impact of other unrelated targets, and reduce data redundancy; convolutional neural network can automatically learn the high-level feature, compared to existing methods, the proposed method can effectively improve the classification accuracy

An Improved Anti-Interference Precoding of Large-Scale Fading System Based on Channel Inversion

Recently, a large-scale fading precoding (LSFP) for the wireless massive multiple-input, multiple-output (MIMO) systems has been proposed. In this precoding, the channel information of all the cells using re-use pilot sequences is processed jointly, and pilot contamination and interference due to a certain number of antennas are effectively eliminated. Additionally, recent studies have found that research in the asymptotic field can be applied to the wireless large-scale MIMO systems. In the LSFP, pilot contamination and signal interference will be completely eliminated when a number of antennas at a base station tend to be unlimited. In this research found that the LSFP method can eliminate most pilot contamination and interference in practical applications only when the number of antennas of the base station reaches hundreds of orders, which greatly increases the equipment construction cost. On the other hand, channel inversion denotes a multi-user channel modulation technology, where a vector signal generated between a user and a base station is used to form an inverse channel matrix so that the channels of each user are balanced during the transmission. In this paper, the channel inversion technology is used in the LSFP. The improved LSFP can effectively reduce the number of antennas required by the base station without affecting the performance of eliminating the pilot contamination and interference. It is shown that when the number of antennas of a base station tends to be unlimited, the improved LSFP can eliminate pilot contamination and signal interference. The simulation results show that in the same practical scenario, when the base station is equipped with the same number of antennas, the improved method can more effectively improve the anti-contamination and anti-interference performance over conventional LSFP

Screening the Efficacy and Safety of Molluscicides from Three Leaf Extracts of <i>Chimonanthus</i> against the Invasive Apple Snail, <i>Pomacea canaliculata</i>

Pomacea canaliculata, the invasive snail, is a host of the parasitic nematode Angiostrongylus cantonensis, which has adverse effects on the agriculture system and human health. This work evaluated the molluscicidal activity of petroleum ether extracts (PEEs) from three species of Chimonanthus against the snail P. canaliculate. Pcp (PEE of C. praecox) showed the most effective molluscicide activity. Sixty-one compounds were identified by GC-MS and the main components were terpenoids and fatty acids. The half-lethal concentration (LC50) of Pcp at 24 h (0.27 mg/mL) and 48 h (0.19 mg/mL) was used to evaluate the biochemical alterations in snail tissue. These sublethal concentrations caused the levels of alkaline phosphatase (ALP), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) activity to increase, while acetylcholinesterase (AChE) activity decreased. Also, under LC50 treatment, several histological changes were observed in the hepatopancreas and foot of the snail compared with the control group. Moreover, the toxic test in rice demonstrated that Pcp has low toxicity. These results suggest that Pcp could be developed as an effective molluscicide for P. canaliculata control

Hyperspectral Image Classification Based on the Combination of Spatial-spectral Feature and Sparse Representation

In order to avoid the problem of being over-dependent on high-dimensional spectral feature in the traditional hyperspectral image classification, a novel approach based on the combination of spatial-spectral feature and sparse representation is proposed in this paper. Firstly, we extract the spatial-spectral feature by reorganizing the local image patch with the first <i>d</i> principal components(PCs) into a vector representation, followed by a sorting scheme to make the vector invariant to local image rotation. Secondly, we learn the dictionary through a supervised method, and use it to code the features from test samples afterwards. Finally, we embed the resulting sparse feature coding into the support vector machine(SVM) for hyperspectral image classification. Experiments using three hyperspectral data show that the proposed method can effectively improve the classification accuracy comparing with traditional classification methods

A Building Extraction Method via Graph Cuts Algorithm by Fusion of LiDAR Point Cloud and Orthoimage

An automatic building extraction method based on graph cuts algorithm fusing LiDAR point cloud and orthoimage is proposed.Firstly,three geometric features are computed from LiDAR points including flatness,distribution of normal vector and GLCM (grey level co-occurrence matrix) homogeneity of normalized height.NDVI is simultaneously calculated from orthoimage.After that,both kinds of features are combined to construct the data term of energy function,then DSM and NDVI is combined to construct smooth term.Thereafter,graph cuts algorithm is applied to obtain the initial building extraction results.Finally,foreground and background segmentation method is employed to optimize the building boundary based on the orthoimage color information in certain range of the initially detected building boundary.ISPRS Vaihingen dataset is used to evaluate the proposed method.The results reveal that the proposed method can obtain high accuracy of the detection building area