ABSTRACT
Background: This study investigated the effects of purple sweet potato anthocyanins (PSPA) in a type 2 diabetes mellitus (T2DM) mouse model. Methods: Sixty-five male mice were randomly divided into one control group and four experimental groups, which were fed with a high-fat diet and intraperitoneally injected with streptozotocin (STZ) to induce T2DM. The model mice were treated with 0 (M), 227.5 (LP), 455 (MP), or 910 (HP) mg/kg PSPA for ten days. ELISA, 16S rRNA sequencing, and hematoxylin and eosin staining were used to assess blood biochemical parameters, gut microbial composition, and liver tissue structure, respectively. Results: The FBG concentration was significantly decreased in the LP (6.32 ± 1.05 mmol/L), MP (6.32 ± 1.05 mmol/L), and HP (5.65 ± 0.83 mmol/L) groups; the glycosylated hemoglobin levels were significantly decreased in the HP group (14.43 ± 7.12 pg/mL) compared with that in the M group (8.08 ± 1.04 mmol/L; 27.20 ± 7.72 pg/mL; P < 0.05). The PSPA treated groups also increased blood glutathione levels compared with M. PSPA significantly affected gut microbial diversity. The Firmicutes/Bacteroidetes ratio decreased by 38.9 %, 49.2 %, and 15.9 % in the LP, MP, and HP groups compared with that in the M group (0.62). The PSPAs treated groups showed an increased relative abundance of Lachnospiraceae_Clostridium, Butyricimonas, and Akkermansia and decreased abundance of nine bacterial genera, including Staphylococcus. Conclusion: PSPA reduced blood glucose levels, increased serum antioxidant enzymes, and optimized the diversity and structure of the gut microbiota in mice with T2DM.
ABSTRACT
Abstract Vehicle re-id play a very import role in recent public safety, it has received more and more attention. The local features (e.g. hanging decorations and stickers) are widely used for vehicle re-id, but the same local feature exists in one perspective, but not exactly exists in other perspectives. In this paper, we firstly use experiments to verify that there is a low linear correlation between different dimension global features. Then we propose a new technique which uses global features instead of local features to distinguish the nuances between different vehicles. We design a vehicle re-identification method named a generated multi branch feature fusion method (GMBFF) to make full use of the complementarity between global features with different dimensions. All branches of the proposed GMBFF model are derived from the same model and there are only slight differences among those branches. Each of those branches can extract highly discriminative features with different dimensions. Finally, we fuse the features extracted by these branches. Existing research uses the fusing features for fusion and we use the global vehicle features for fusion. We also propose two different feature fusion methods which are single fusion method (SFM) and multi fusion method (MFM). In SFM, features for fusion with larger dimension occupy more weight in fused features. MFM overcomes the disadvantage of SFM. Finally, we carry out a lot of experiments on two widely used datasets which are VeRi-776 dataset and Vehicle ID dataset. The experimental results show that our proposed method is much better than the state-of-the-art vehicle re-identification methods.