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The role of inulin in alleviating obesity-related disorders has been documented; yet, its underlying mechanisms still need to be further investigated. This study attempted to elucidate the causative link between the gut microbiota and the beneficial effect of inulin on obesity-related disorders via transferring the fecal microbiota from inulin-dosed mice to high-fat diet (HFD)-induced obese recipient mice. The results show that inulin supplementation can decrease body weight, fat accumulation, and systemic inflammation and can also enhance glucose metabolism in HFD-induced obese mice. Treatment with inulin reshaped the structure and composition of the gut microbiota in HFD-induced obese mice, as characterized by increases in the relative abundances of Bifidobacterium and Muribaculum and decreases in unidentified_Lachnospiraceae and Lachnoclostridium. In addition, we found that these favorable effects of inulin could be partially transferable by fecal microbiota transplantation, and Bifidobacterium and Muribaculum might be the key bacterial genera. Therefore, our results suggest that inulin ameliorates obesity-related disorders by targeting the gut microbiota.

As an essential branch of artificial intelligence, recommendation systems have gradually penetrated people’s daily lives. It is the active recommendation of goods or services of potential interest to users based on their preferences. Many recommendation methods have been proposed in both industry and academia. However, there are some limitations of previous recommendation methods: (1) Most of them do not consider the cross-correlation between data. (2) Many treat the recommendation process as a one-time act and do not consider the continuity of the recommendation system. To overcome these limitations, we propose a recommendation framework based on deep reinforcement learning techniques, known as DDRCN: a deep deterministic policy gradient recommendation framework incorporating deep cross networks. We use a Deep network and a Cross network to fit the cross relationships between the data, to obtain a representation of the user interaction data. The Actor-Critic network is designed to simulate the continuous interaction behavior of users through a greedy strategy. A deep deterministic policy gradient network is also used to train the recommendation model. Finally, we conduct experiments with two publicly available datasets and find that our proposed recommendation framework outperforms the baseline approach in the recall and ranking phases of recommendations.

Traditional Chinese medicine (TCM) has been used to treat diabetes for a long time, but its application has not been widely accepted due to unstandardized product quality and complex pharmacological mechanisms. The modernization of TCM is crucial for its further development, and in recent years the metabolomics technique has largely driven its modernization. This review focuses on the application of NMR-based metabolomics in diabetic therapy using TCM. We identified a series of metabolic pathways that altered significantly after TCM treatment, providing a better understanding of the metabolic mechanisms of TCM for diabetes care.

Legal judgment prediction (LJP) is a crucial task in legal intelligence to predict charges, law articles and terms of penalties based on case fact description texts. Although existing methods perform well, they still have many shortcomings. First, the existing methods have significant limitations in understanding long documents, especially those based on RNNs and BERT. Secondly, the existing methods are not good at solving the problem of similar charges and do not fully and effectively integrate the information of law articles. To address the above problems, we propose a novel LJP method. Firstly, we improve the model’s comprehension of the whole document based on a graph neural network approach. Then, we design a graph attention network-based law article distinction extractor to distinguish similar law articles. Finally, we design a graph fusion method to fuse heterogeneous graphs of text and external knowledge (law article group distinction information). The experiments show that the method could effectively improve LJP performance. The experimental metrics are superior to the existing state of the art.

A pectin/chitosan matrix-loaded curcumin film (PCCF) with a deep eutectic solvent (DES) as the solvent and plasticizer was prepared in this study. Different quantities of curcumin (identified as PCCF-0, PCCF-1, PCCF-2. PCCF-3) were loaded on the pectin/chitosan film in order to evaluate their effects on the film properties. Results showed that curcumin could interact with the pectin/chitosan matrix and form a complex three-dimensional network structure. PCCF could promote the thickness, tensile strength, thermal properties, antioxidant and antiseptic capacities, but deteriorate the light transmission and elongation at the same time. The addition of curcumin would change the color of the film, without significantly affecting the moisture content. The tensile strength of PCCF-3 reached the maximum value of 3.75 MPa, while the elongation decreased to 10%. Meanwhile, the water-resistance properties of PCCF-3 were significantly promoted by 8.6% compared with that of PCCF-0. Furthermore, PCCF showed remarkable sustained antioxidant activities in a dose-dependent manner. PCCF-3 could inhibit DPPH and ABTS free radicals by 58.66% and 29.07%, respectively. It also showed antiseptic capacity on fresh pork during storage. Therefore, curcumin addition could improve the barrier, mechanical, antioxidant and antiseptic properties of the polysaccharide-based film and PCCF has the potential to be used as a new kind of food packaging material in the food industry.