The gastrointestinal (GI) stability of three flavonoids, dihydromyricetin (DMY), myricetin (MYR), and myricitrin (MYT), was examined in simulated physiological fluids. Several factors that may influence the degradation rate of theses flavonoids were evaluated, including pH and the presence of pepsin and pancreatin enzymes. We found that GI stability followed the order of MYT > DMY > MYR. These flavonoids were stable in simulated gastric fluids and buffer solutions (pH 1.2), but encountered a pseudo-first-order kinetic degradation in simulated intestinal fluids and buffer solutions (pH 6.8). We conclude that it is the pH, rather than the presence of pepsin or pancreatin, which most strongly influences the stability of these three flavonoids. Further study of the stability of the compounds using a pH range (1.0-8.0) indicated potential instability in the duodenum, small intestine, and colon. Therefore, we conclude that the low bioavailability of these flavonoids may be due to their poor stability in the GI tract.

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In order to acquire a high resolution multispectral (HRMS) image with the same spectral resolution as multispectral (MS) image and the same spatial resolution as panchromatic (PAN) image, pansharpening, a typical and hot image fusion topic, has been well researched. Various pansharpening methods that are based on convolutional neural networks (CNN) with different architectures have been introduced by prior works. However, different scale information of the source images is not considered by these methods, which may lead to the loss of high-frequency details in the fused image. This paper proposes a pansharpening method of MS images via multi-scale deep residual network (MSDRN). The proposed method constructs a multi-level network to make better use of the scale information of the source images. Moreover, residual learning is introduced into the network to further improve the ability of feature extraction and simplify the learning process. A series of experiments are conducted on the QuickBird and GeoEye-1 datasets. Experimental results demonstrate that the MSDRN achieves a superior or competitive fusion performance to the state-of-the-art methods in both visual evaluation and quantitative evaluation.

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