To investigate the anti-atherosclerosis related mechanism of blueberries, the phenolic acids (PAs) content, antioxidant and anti-inflammatory activities, as well as the microRNA (miRNA) regulation of polyphenol fractions in blueberry samples from China were studied. Sixteen batches of blueberries including 14 commercialized cultivars (Reka, Patriot, Brigitta, Bluecrop, Berkeley, Duke, Darrow, Northland, Northblue, Northcountry, Bluesource, Southgood, O’Neal, and Misty) were used in this study. Seven PAs in the polyphenol fractions from 16 blueberry samples in China were quantified by high performance liquid chromatography/tandem mass spectrometry (HPLC/MS2). The antioxidant activities of blueberry polyphenols were tested by (1,1-diphenyl-2-picrylhydrazyl [DPPH]) assay. The anti-inflammatory (tumor necrosis factor-α [TNF-α] and interleukin-6 [IL-6]) activities of the polyphenol fractions of the blueberries were investigated by using lipopolysaccharide (LPS) induced RAW 264.7 macrophages. The correlation analysis showed that the antioxidant (1,1-diphenyl-2-picrylhydrazyl [DPPH]) and anti-inflammatory (tumor necrosis factor-α [TNF-α] and interleukin-6 [IL-6]) activities of the polyphenol fractions of the blueberries were in accordance with their PA contents. Although the polyphenol-enriched fractions of blueberries could inhibit the microRNAs (miRNAs) (miR-21, miR-146a, and miR-125b) to different extents, no significant contribution from the PAs was observed. The inhibition of these miRNAs could mostly be attributed to the other compounds present in the polyphenol-enriched fraction of the blueberries. This is the first study to evaluate the PAs content, antioxidant and anti-inflammatory activities, and miRNA regulation of Chinese blueberries.

High spatial quality (HQ) optical remote sensing images are very useful for target detection, target recognition and image classification. Due to the influence of imaging equipment accuracy and atmospheric environment, HQ images are difficult to acquire, while low spatial quality (LQ) remote sensing images are very easy to acquire. Hence, denoising and super-resolution (SR) reconstruction technology are the most important solutions to improve the quality of remote sensing images very effectively, which can lower the cost as much as possible. Most existing methods usually only employ denoising or SR technology to obtain HQ images. However, due to the complex structure and the large noise of remote sensing images, the quality of the remote sensing image obtained only by denoising method or SR method cannot meet the actual needs. To address these problems, a method of reconstructing HQ remote sensing images based on Generative Adversarial Network (GAN) named “Restoration Generative Adversarial Network with ResNet and DenseNet” (RRDGAN) is proposed, which can acquire better quality images by incorporating denoising and SR into a unified framework. The generative network is implemented by fusing Residual Neural Network (ResNet) and Dense Convolutional Network (DenseNet) in order to consider denoising and SR problems at the same time. Then, total variation (TV) regularization is used to furthermore enhance the edge details, and the idea of Relativistic GAN is explored to make the whole network converge better. Our RRDGAN is implemented in wavelet transform (WT) domain, since different frequency parts could be handled separately in the wavelet domain. The experimental results on three different remote sensing datasets shows the feasibility of our proposed method in acquiring remote sensing images.

It is challenging to restore a clear image from an atmospheric degraded sequence. The main reason for the image degradation is geometric distortion and blurring caused by turbulence. In this paper, we present a method to eliminate geometric distortion and blur and to recover a single high-quality image from the degraded sequence images. First, we use optical flow technology to register the sequence images, thereby suppressing the geometric deformation of each frame. Next, sequence images are summed by a temporal filter to obtain a single blurred image. Then, the graph Laplacian matrix is used as the cost function to construct the regularization term. The final clear image and point spread function are obtained by iteratively solving the problem. Experiments show that the method can effectively eliminate the distortion and blur, restore the image details, and significantly improve the image quality.

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