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342 results on '"Huang, Qiuyan"'

13. Studying on the hypoglycemic activity of green tea polyphenols from Camellia ptilophylla Chang using high-resolution mass spectrometry and network pharmacology

Author

HUANG Qiuyan, LI Bin, LIN Xiaorong, LU Sen, CHEN Zhongzheng, and ZHANG Yuanyuan

Subjects
cocoa tea, tea polyphenols, hypoglycemia, high-resolution mass spectrometry, network pharmacology, Food processing and manufacture, TP368-456
Abstract

Objective: This study aimed to explore the composition and the functional bioactivity of tea polyphenols from cocoa tea. Methods: Chemical components of crude green tea polyphenol extracts from cocoa tea were analyzed and identified using high resolution mass spectrometry. Additionally, their potential functional effects were predicted by network pharmacological analysis. Results: The in vitro hyperglycemic potential of crude green tea polyphenol from cocoa tea was preliminarily confirmed based on their strong inhibition on α-glucosidase and α-amylase. Six key targets including GAPDH and AKT and multiple pathways such as PI3K-AKT were predicted to be involved in the underying mechanisms behind the hyperglycemic effects of crude green tea polyphenol from cocoa tea through the GO enrichment and KEGG enrichment analysis. Ten polyphenol components including myricetin, anthocyanin, etc. were suggested to play a key role in the hyperglycemic activities of crude green tea polyphenol from cocoa tea via the construction of a “component-target-pathway” network combined with the molecular docking between these components and starch hydrolase. Conclusion: Crude green tea polyphenols from cocoa tea exerted a high hypoglycemic effect by regulating multi-targets through multi-pathways via multi-components.

Published
2023
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36. Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models.

Author

Luo, Mi, Anees, Shoaib Ahmad, Huang, Qiuyan, Qin, Xin, Qin, Zhihao, Fan, Jianlong, Han, Guangping, Zhang, Liguo, and Shafri, Helmi Zulhaidi Mohd

Subjects
BIOMASS estimation, FOREST biomass, BIOMASS conversion, MACHINE learning, FOREST management, CARBON cycle, RANDOM forest algorithms, REMOTE sensing
Abstract

The accurate estimation of forest above-ground biomass (AGB) is crucial for sustainable forest management and tracking the carbon cycle of forest ecosystem. Machine learning algorithms have been proven to have great potential in forest AGB estimation with remote sensing data. Though many studies have demonstrated that a single machine learning model can produce highly accurate estimations of forest AGB in many situations, efforts are still required to explore the possible improvement in forest AGB estimation for a specific scenario under study. This study aims to investigate the performance of novel ensemble machine learning methods for forest AGB estimation and analyzes whether these methods are affected by forest types, independent variables, and spatial autocorrelation. Four well-known machine learning models (CatBoost, LightGBM, random forest (RF), and XGBoost) were compared for forest AGB estimation in the study using eight scenarios devised on the basis of two study regions, two variable types, and two validation strategies. Subsequently, a hybrid model combining the strengths of these individual models was proposed for forest AGB estimation. The findings indicated that no individual model outperforms the others in all scenarios. The RF model demonstrates superior performance in scenarios 5, 6, and 7, while the CatBoost model shows the best performance in the remaining scenarios. Moreover, the proposed hybrid model consistently has the best performance in all scenarios in spite of some uncertainties. The ensemble strategy developed in this study for the hybrid model substantially improves estimation accuracy and exhibits greater stability, effectively addressing the challenge of model selection encountered in the forest AGB forecasting process. [ABSTRACT FROM AUTHOR]

Published
2024
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