Effective extraction of natural antioxidants from cheap plant sources is still a problem. In this paper, an excellent method of ultrasound-assisted extraction of phenolic compounds from Ajuga ciliata Bunge was studied. The effects of four factors including ethanol volume fraction, ultrasonic time, ultrasonic temperature and material liquid ratio were discussed. After single factor experiments had been investigated, a 4-factor, 3-level Box-Behnken design experiment was used to obtain the model optimum conditions, which are shown as follows: ethanol volume fraction of 41%, liquid-solid ratio of 35:1 mL/g, ultrasonic temperature of 60 °C and ultrasonic time of 50 min. Under these conditions, the experimental productivity is 3.552 mg/g. The spectra of Fourier infrared and energy dispersive X-ray suggest that phenolic compounds exist in the extracts. Besides, free radical scavenging potentials of superoxide anion, hydroxyl and DPPH were measured to evaluate their antioxidant properties. This study proves that the ultrasonic-assisted extraction technique can extract phenolic compounds with antioxidant capacity from Ajuga ciliata Bunge.
For semantic segmentation of remote sensing images (RSI), trade-off between representation power and location accuracy is quite important. How to get the trade-off effectively is an open question, where current approaches of utilizing very deep models result in complex models with large memory consumption. In contrast to previous work that utilizes dilated convolutions or deep models, we propose a novel two-stream deep neural network for semantic segmentation of RSI (RSI-Net) to obtain improved performance through modeling and propagating spatial contextual structure effectively and a decoding scheme with image-level and graph-level combination. The first component explicitly models correlations between adjacent land covers and conduct flexible convolution on arbitrarily irregular image regions by using graph convolutional network, while densely connected atrous convolution network (DenseAtrousCNet) with multi-scale atrous convolution can expand the receptive fields and obtain image global information. Extensive experiments are implemented on the Vaihingen, Potsdam and Gaofen RSI datasets, where the comparison results demonstrate the superior performance of RSI-Net in terms of overall accuracy (91.83%, 93.31% and 93.67% on three datasets, respectively), F1 score (90.3%, 91.49% and 89.35% on three datasets, respectively) and kappa coefficient (89.46%, 90.46% and 90.37% on three datasets, respectively) when compared with six state-ofthe-art RSI semantic segmentation methods.
Many studies reported that spontaneous fluctuation of the blood oxygen level-dependent signal exists in multiple frequency components and changes over time. By assuming a reliable energy contrast between low- and high-frequency bands for each voxel, we developed a novel spectrum contrast mapping (SCM) method to decode brain activity at the voxel-wise level and further validated it in designed experiments. SCM consists of the following steps: first, the time course of each given voxel is subjected to fast Fourier transformation; the corresponding spectrum is divided into low- and high-frequency bands by given reference frequency points; then, the spectral energy ratio of the low- to high-frequency bands is calculated for each given voxel. Finally, the activity decoding map is formed by the aforementioned energy contrast values of each voxel. Our experimental results demonstrate that the SCM (1) was able to characterize the energy contrast of task-related brain regions; (2) could decode brain activity at rest, as validated by the eyes-closed and eyes-open resting-state experiments; (3) was verified with test-retest validation, indicating excellent reliability with most coefficients > 0.9 across the test sessions; and (4) could locate the aberrant energy contrast regions which might reveal the brain pathology of brain diseases, such as Parkinson’s disease. In summary, we demonstrated that the reliable energy contrast feature was a useful biomarker in characterizing brain states, and the corresponding SCM showed excellent brain activity-decoding performance at the individual and group levels, implying its potentially broad application in neuroscience, neuroimaging, and brain diseases.
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