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Patatin from potato fruit juice was purified by a combination of ultrafiltration and chromatographic techniques. The in vitro antioxidant and antiproliferative activity against mouse melanoma B16 cells of patatin were investigated. The results showed that the monosaccharide composition of patatin included rhamnose, mannose, glucose, and galactose with a molar ratio of 41 : 30 : 21 : 8, and patatin consisted of (1 → 3) linked α-mannose, (1 → 4) linked α-galactose, (1 → 4) linked β-glucose, and (1 → 2) linked α-rhamnose. Furthermore, patatin possessed significant antioxidant activities measured by scavenging of the DPPH and superoxide free radicals, notable reducing power, protective effects against hydroxyl radical-induced oxidative DNA damage and lipid peroxidation inhibitory. Moreover, patatin was identified as a potent antiproliferative agent against mouse melanoma B16 cells, causing cell cycle arrest in the G1 phase. Assays of apoptotic cells also showed that patatin treatment at concentrations of 20 mg mL(-1) resulted in a marked reduction of viable cells. These results obtained in in vitro models suggested that patatin may have potential application as a cancer chemopreventive agent and food ingredient.

Human video motion transfer (HVMT) aims to synthesize videos that one person imitates other persons' actions. Although existing GAN-based HVMT methods have achieved great success, they either fail to preserve appearance details due to the loss of spatial consistency between synthesized and exemplary images, or generate incoherent video results due to the lack of temporal consistency among video frames. In this paper, we propose Coarse-to-Fine Flow Warping Network (C2F-FWN) for spatial-temporal consistent HVMT. Particularly, C2F-FWN utilizes coarse-to-fine flow warping and Layout-Constrained Deformable Convolution (LC-DConv) to improve spatial consistency, and employs Flow Temporal Consistency (FTC) Loss to enhance temporal consistency. In addition, provided with multi-source appearance inputs, C2F-FWN can support appearance attribute editing with great flexibility and efficiency. Besides public datasets, we also collected a large-scale HVMT dataset named SoloDance for evaluation. Extensive experiments conducted on our SoloDance dataset and the iPER dataset show that our approach outperforms state-of-art HVMT methods in terms of both spatial and temporal consistency. Source code and the SoloDance dataset are available at https://github.com/wswdx/C2F-FWN.

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