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Black chokeberry (Aronia melanocarpa L.) is rich in polyphenols with various physiological and pharmacological activities. However, the relationship between the modulation effect of black chokeberry polyphenols on obesity and the alteration of lipid metabolism is not clearly understood. This study aimed to investigate the beneficial effects of the black chokeberry polyphenols (BCPs) treatment on the structure of gut microbiota, lipid metabolism, and associated mechanisms in high-fat diet (HFD)-induced obese rats. Here, we found that a high-fat diet promoted body weight gain and lipid accumulation in rats, while oral BCPs supplementation reduced body weight, liver, and white adipose tissue weight and alleviated dyslipidemia and hepatic steatosis in HFD-induced obese rats. In addition, BCPs supplementation prevented gut microbiota dysbiosis by increasing the relative abundance of Bacteroides, Prevotella, Romboutsia, and Akkermansia and decreasing the relative abundance of Desulfovibrio and Clostridium. Furthermore, 64 lipids were identified as potential lipid biomarkers through lipidomics analysis after BCPs supplementation, especially PE (16:0/22:6), PE (18:0/22:6), PC (20:3/19:0), LysoPE (24:0), LysoPE (24:1), and LysoPC (20:0). Moreover, our studies provided new evidence that composition of gut microbiota was closely related to the alteration of lipid profiles after BCPs supplementation. Additionally, BCPs treatment could ameliorate the disorder of lipid metabolism by regulating the mRNA and protein expression of genes related to the glycerophospholipid metabolism signaling pathway in HFD-induced obese rats. The mRNA and protein expression of PPARα, CPT1α, EPT1, and LCAT were significantly altered after BCPs treatment. In conclusion, the results of this study indicated that BCPs treatment alleviated HFD-induced obesity by modulating the composition and function of gut microbiota and improving the lipid metabolism disorder via the glycerophospholipid metabolism signaling pathway.

Cryptography traditionally relies on uniformly distributed and precisely reproducible random strings for its secrets, however, it is difficult to create, store, and reliably retrieve such strings. To securely derive cryptographic keys from a noisy input such as biometric data, two primitives are introduced by Y. Dodis et.al.: a secure sketch which allows recovery of a noisy input given a “close” approximation thereof, and a fuzzy extractor which extracts a uniformly distributed string from this noisy input in an error-tolerant manner. Both primitives work by constructing a “public” string which is used to encode the information needed for error-tolerant reconstruction of the original input and subsequent extraction. The public string, when transmitted over an insecure channel or stored in an insecure storage, can be modified by an adversary. A robust secure sketch and a robust fuzzy extractor mean that the public string can be authenticated by a receiver, if the authenticity of public string is verified by using, in some way, recovered string in secure sketch or extracted string in fuzzy extractor, we say that the authentication has circularity in it. In this paper, we propose a fully robust fuzzy extractor based on an error-correcting code, in which the public string can be authenticated by the receiver using his own a part of string that is close to sender's string under some metric, thus the circularity in authentication can be broken fully. The security is proven in standard model.