Increasing numbers of explanatory variables tend to result in information redundancy and “dimensional disaster” in the quantitative remote sensing of forest aboveground biomass (AGB). Feature selection of model factors is an effective method for improving the accuracy of AGB estimates. Machine learning algorithms are also widely used in AGB estimation, although little research has addressed the use of the categorical boosting algorithm (CatBoost) for AGB estimation. Both feature selection and regression for AGB estimation models are typically performed with the same machine learning algorithm, but there is no evidence to suggest that this is the best method. Therefore, the present study focuses on evaluating the performance of the CatBoost algorithm for AGB estimation and comparing the performance of different combinations of feature selection methods and machine learning algorithms. AGB estimation models of four forest types were developed based on Landsat OLI data using three feature selection methods (recursive feature elimination (RFE), variable selection using random forests (VSURF), and least absolute shrinkage and selection operator (LASSO)) and three machine learning algorithms (random forest regression (RFR), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost)). Feature selection had a significant influence on AGB estimation. RFE preserved the most informative features for AGB estimation and was superior to VSURF and LASSO. In addition, CatBoost improved the accuracy of the AGB estimation models compared with RFR and XGBoost. AGB estimation models using RFE for feature selection and CatBoost as the regression algorithm achieved the highest accuracy, with root mean square errors (RMSEs) of 26.54 Mg/ha for coniferous forest, 24.67 Mg/ha for broad-leaved forest, 22.62 Mg/ha for mixed forests, and 25.77 Mg/ha for all forests. The combination of RFE and CatBoost had better performance than the VSURF–RFR combination in which random forests were used for both feature selection and regression, indicating that feature selection and regression performed by a single machine learning algorithm may not always ensure optimal AGB estimation. It is promising to extending the application of new machine learning algorithms and feature selection methods to improve the accuracy of AGB estimates.

The solubility of isomalt in the water + ethanol solvent system was measured with the mole fraction of water ranging from 0.00 to 1.00 at (288.15, 298.15, 308.15, and 318.15) K by using the laser method that is timesaving and the gravimetric method that is more reliable. The data were fitted using the simplified Jouyban−Acree model. In this study, the solubility data obtained by these two methods were compared. The mean relative deviations for isomalt solubility determined by the laser method and the gravimetric method were less than 25 % and 17 %, respectively.

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Microbially synthesized silver nanoparticles (AgNPs) with high stability and bioactivity have recently shown considerable promise in biomedical research and application. In this study, AgNPs prepared by Penicillium aculeatum Su1 exhibited effective antibacterial action by inhibiting bacterial growth and destroying cellular structure. Meanwhile, their assessed increased in fold area (IFA) through the Kirby-Bauer disc diffusion method proved that, the AgNPs showed synergistic antibacterial effect on different bacteria when combined with antibiotics, especially for drug-resistant P. aeruginosa (4.58∼6.36-fold) and B. subtilis (4.2-fold). Moreover, the CCK-8 assay and flow cytometric analysis were used to evaluate the cytotoxic effects of AgNPs on normal cells (HBE) and lung cancer cells (HTB-182), which confirmed that they presented higher biocompatibility towards HBE cells when compared with silver ions, but high cytotoxicity in a dosedependent manner with an IC50 values of 35.00 μg/mL towards HTB-182 cells by raising intracellular reactive oxygen species (ROS) levels, hindering cell proliferation, and ultimately leading to cell cycle arrest and cell apoptosis. These results demonstrate that, the biosynthesized AgNPs could be a potential candidate for future therapies of infection caused by drug-resistant bacteria, as well as lung squamous cell carcinoma.