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Object tracking is one of the most important components in numerous applications of computer vision. Remote sensing videos provided by commercial satellites make it possible to extend this topic into the earth observation domain. In satellite videos, typical moving targets like vehicles and planes only cover a small area of pixels, and they could easily be confused with surrounding complex ground scenes. Similar objects nearby in satellite videos can hardly be differed by appearance details due to the resolution constraint. Thus, tracking drift caused by distractions is also a thorny problem. Facing challenges, traditional tracking methods such as correlation filters with hand-crafted visual features achieve unsatisfactory results in satellite videos. Methods based on deep neural networks have demonstrated their superiority in various ordinary visual tracking benchmarks, but their results on satellite videos remain unexplored. In this article, deep learning technologies are applied to object tracking in satellite videos for better performance. A simple regression network is used to combine a regression model with convolutional layers and a gradient descent algorithm. The regression network fully exploits the abundant background context to learn a robust tracker. Instead of handcrafted features, both appearance features and motion features, which are extracted by pretrained deep neural networks, are used for accurate object tracking. In cases when the tracker encounters ambiguous appearance information, the motion features could provide complementary and discriminative information to improve tracking performances. Experimental results on various satellite videos show that the proposed method achieves better tracking performance than other state-of-the-arts.

A colon delivery system has been used to improve the bioavailability of glycyrrhizin, a glycoside of glycyrrhetic acid. The bioavailability of glycyrrhizin is low when administered in conventional oral galenic dosage forms because glycyrrhizin is enzymatically hydrolysed both in the stomach and in the intestine. It was reasoned that if large amounts of glycyrrhizin were directly delivered to the colon, enzymatic activity should be reduced due to saturation so that intact glycyrrhizin could be absorbed into the systemic circulation. Based on this assumption, pressure-controlled colon delivery capsules (PCDCs) were used as a colon delivery system. Eight types of glycyrrhizin solution were prepared and were introduced into PCDCs. After oral administration of the test PCDCs to beagle dogs, blood samples were obtained over 24 h and plasma glycyrrhizin concentrations were measured by an HPLC method. With PCDCs containing aqueous glycyrrhizin and propylene glycol solutions, plasma glycyrrhizin levels were extremely low and the bioavailabilities of glycyrrhizin were 0.6% and 0.4%, respectively. When Labrasol was added to both types of glycyrrhizin solution, the bioavailability was improved to 4.6% for aqueous solution and 3.8% for propylene glycol solution. When a surfactant, Polysorbate 80, was added in combination with Labrasol, synergistic effects were not obtained. Furthermore, dose-dependent effects of Polysorbate 80 were not obtained. Labrasol, which is a component of self-emulsifying drug delivery systems (SEDDS), has been shown to strongly improve the bioavailability of glycyrrhizin from the colon.

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