Publications

The gut microbiota plays a significant role in metabolic diseases such as obesity. We extracted and purified a new type of pectin polysaccharide (mango peel pectin, MPP) from mango (Mangifera indica L.) peel. The structural analysis results reveal that MPP has a molecular weight (Mw) of 6.76 × 105 Da and the mass fractions of the main components were galacturonic acid (21.36%), glucose (8.85%), and arabinose (5.97%). The results of methylation and NMR analyses reveal that the backbone of MPP consisted of →6)-α-D-GalpAOMe-(1→ and →4)-β-D-Glcp-(1→ linkages. Based on the above structural analysis, we further explored the therapeutic effect of MPP on high-fat diet-induced obese mice. The results demonstrate that MPP significantly suppressed body weight and dyslipidemia, reduced liver damage and lipid accumulation, attenuated changes in adipocyte hypertrophy, and improved glucose homeostasis and insulin resistance, with fasting blood glucose (FBG) levels decreasing by more than 12.8%. Furthermore, the modulatory impact of MPP on gut microbiota composition was investigated. MPP treatment significantly enhanced the levels of short-chain fatty acids (SCFAs) by decreasing the amount of Bacillota and reducing the Bacillota/Bacteroidota ratio, especially with an increase in the total SCFA content of over 64%. Meanwhile, MPP treatment encouraged beneficial bacteria to grow (e.g., Bacteroidota, Akkermansia, and Nanasyncoccus), altered the gut microbiome profiles in mice, and decreased the abundance of harmful bacteria (e.g., Paralachnospira, Coproplasma, Pseudoflavonifractor, Parabacteroides, Acetatifactor, and Phocaeicola). Overall, the findings demonstrate for the first time that MPP treats obesity by alleviating dyslipidemia, improving insulin resistance, and regulating gut microbiota to improve the intestinal environment.

The effect of two antifungals (boscalid + kresoxim-methyl and metrafenone) applied onto vines under Good Agricultural Practices (GAPs) on the volatile composition of Tempranillo and Graciano red wines was studied. Changes in aroma profile in the wines were assessed from the combined odour activity values (OAVs) for the volatile compounds in each of seven different odorant series (viz., ripe fruits, fresh fruits, lactic, floral, vinous, spicy and herbaceous). Graciano wines obtained from grapes treated with the antifungals exhibited markedly increased concentrations of varietal volatile compounds (monoterpenes and C13-norisoprenoids) and aldehydes, and decreased concentrations of acetates and aromatic alcohols. By contrast, the concentrations of volatile compounds in Tempranillo wines showed different changes depending on the fungicide applied. Also, the aroma profiles of wines obtained from treated grapes were modified, particularly the ripe fruit nuances in Graciano wines. The OAV of this odorant series underwent an increase by more than 60% with respect to the control wine as a result of the increase of β-damascenone concentration (which imparts wine a dry plum note). The aroma profile of Tempranillo red wines containing metrafenone residues exhibited marked changes relative to those from untreated grapes.

ABSTRACT New research shows that flavonoids containing a pyrogallol group in ring B react with amino acids at 37°C, forming ammoniated derivatives and aldehydes. Despite this, studies on these reactions and the activity of the resulting ammoniated flavonoids (NH 2 ‐flavonoids) are scarce. This study investigates the amination of (+)‐dihydromyricetin (DMY), (−)‐epigallocatechin (EGC), and (−)‐epigallocatechin gallate (EGCG) by reaction systems involving the composite amino acid or single phenylalanine using UPLC‐QTOF‐MS/MS. Amino acid‐induced ammoniation occurs on both the flavonoids and their degradation products, forming unstable N ‐DMY, N ‐EGC, and N ‐EGCG. A method was developed to synthesize these NH 2 ‐flavonoids and their structures were confirmed with MS, NMR, and CD. Compared to the original flavonoids, N ‐DMY, N ‐EGC, and N ‐EGCG have reduced antioxidant, anticancer, and antibacterial activities. This provides a new sight that flavonoids may regulate physiological functions by affecting amino acid levels. Moreover, the pharmacokinetics, metabolism, and broader biological activities of N ‐DMY, N ‐EGC, N ‐EGCG and other NH 2 ‐flavonoids needs to be studied in future work.

Abstract The wind power industry is increasing worldwide every year. Thus, obtaining timely and detailed information on wind farms’ number and spatial distribution is critical for quantitatively estimating wind energy utilization and for planning the construction of new wind farms. Therefore, this study proposes a method for quickly identifying wind farms in a large-scale area. Given that wind farms mainly comprise individual objects such as wind turbines and substations, we labeled sample images of wind turbines and substations on a global scale. Then, these sample images are used to train a target recognition model and an object classification model and detect the specific locations of wind turbines and substations in the study area. Additionally, we deeply analyzed the location features of the wind turbines and further improved the recognition accuracy based on these known features using geographic constraints. Based on the location information of wind turbines and substations, a clustering model organizes them effectively into complete wind farms. A comprehensive evaluation of the clustering model verifies its scientific validity and reliability. Specifically, this framework was systematically tested throughout Vietnam with remarkable results, using high-resolution historical images provided by Google Earth. Indeed, our framework achieved 90.45% recall and 95.73% accuracy for wind turbines and 81.37% recall and 78.96% accuracy for substations. Finally, we successfully obtained the spatial location and distribution of 15 completed wind farms, demonstrating that the proposed scheme can quickly and accurately identify wind power plants in a large-scale area, which supports wind power management and energy utilization planning.