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Vitamin C (VC) and folic acid (FA) are the important nutrient and antioxidant in human body. In order to improve their stability, their co-loaded liposomes (VCFA-Lip) and chitosan-coated liposomes (CS-VCFA-Lip) are prepared and characterised. The mean particle size of VCFA-Lip and CS-VCFA-Lip is 138 nm and 249 nm, respectively. The encapsulation efficiencies of both drugs for CS-VCFA-Lip are much higher than those for VCFA-Lip. Furthermore, the experimental results show that the antioxidant activity of CS-VCFA-Lip is higher than that of VCFA-Lip. Moreover, the storage stability study reveals that the chitosan coating can efficiently improve the physical stability of VCFA-Lip. These results indicate that stability of VC and FA can be greatly improved after being wrapped by liposomes. In addition, the performance of CS-VCFA-Lip is better than VCFA-Lip, indicating CS-VCFA-Lip can be applied as a promising delivery system for the antioxidant defence system to the food industry and cosmetic industry.

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Over the past 4 decades, China has experienced a nutritional transition and has developed the largest population of internet users. In this study, we evaluated the impacts of internet access on the nutritional intake in Chinese rural residents. An IV-Probit-based propensity score matching method was used to determine the impact of internet access on nutritional intake. The data were collected from 10,042 rural households in six Chinese provinces. The results reveal that rural residents with internet access have significantly higher energy, protein, and fat intake than those without. Chinese rural residents with internet access consumed 1.35% (28.62 kcal), 5.02% (2.61 g), and 4.33% (3.30 g) more energy, protein, and fat, respectively. There was heterogeneity in regard to the intake of energy, protein, and fat among those in different income groups. Moreover, non-staple food consumption is the main channel through which internet access affects nutritional intake. The results demonstrate that the local population uses the internet to improve their nutritional status. Further studies are required to investigate the impact of internet use on food consumed away from home and micronutrient intake.

This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom (columns in the dictionary matrix) related to some label, we propose cross-label suppression constraint to enlarge the difference among representations for different classes. Meanwhile, we introduce group regularization to enforce representations to preserve label properties of original samples, meaning the representations for the same class are encouraged to be similar. Upon the cross-label suppression, we don't resort to frequently-used 0 -norm or 1 -norm for coding, and obtain computational efficiency without losing the discriminative power for categorization. Moreover, two simple classification schemes are also developed to take full advantage of the learnt dictionary. Extensive experiments on six data sets including face recognition, object categorization, scene classification, texture recognition and sport action categorization are conducted, and the results show that the proposed approach can outperform lots of recently presented dictionary algorithms on both recognition accuracy and computational efficiency.