Plant lignin is a component of the cell wall, and plays important roles in the transport potential of water and mineral nutrition and plant defence against biotic stresses. Therefore, it is necessary to identify lignin biosynthesis-related genes and dissect their functions and underlying mechanisms. Here, we characterised a cotton LAC, GhLAC4, which participates in lignin biosynthesis and plant resistance against Verticillium dahliae. According to degradome sequencing and GUS reporter analysis, ghr-miR397 was identified to directedly cleave the GhLAC4 transcript through base complementary. GhLAC4 knockdown and ghr-miR397 overexpression significantly reduced basal lignin content compared to the control, whereas ghr-miR397 silencing significantly increased basal lignin levels. Based on staining patterns and GC/MS analysis, GhLAC4 acted in G-lignin biosynthesis. Under V. dahliae infection, we found that G-lignin content in ghr-miR397-knockdowned plants significantly increased, compared to these plants under the mock treatment, while G-lignin contents in GhLAC4-silenced plants and ghr-miR397-overexpressed plants treated with pathogen were comparable with these plants treated with mock, indicating that GhLAC4 participates in defence-induced G-lignin biosynthesis in the cell wall. Knockdown of ghr-miR397 in plants inoculated with V. dahliae promoted lignin accumulation and increased plant resistance. The overexpression of ghr-miR397 and knockdown of GhLAC4 reduced lignin content and showed higher susceptibility of plants to the fungal infection compared to the control. The extract-free stems of ghr-miR397-knockdowned plants lost significantly less weight when treated with commercial cellulase and V. dahliae secretion compared to the control, while the stems of ghr-miR397-overexpressed and GhLAC4-silenced plants showed significantly higher loss of weight. These results suggest that lignin protects plant cell walls from degradation mediated by cellulase or fungal secretions. In summary, the ghr-miR397-GhLAC4 module regulates both basal lignin and defence-induced lignin biosynthesis and increases plant resistance against infection by V. dahliae.

A visual ratiometric fluorescent sensor based on blue carbon dot/Rhodamine B is used to selectively detect NO ₂ ⁻ in the environment.

Magnetic fly ash composites (Fe₃O₄/FA) were synthesized and mixed with pig manure in different ratios to study their effects on biogas production and metal passivation during anaerobic digestion.

Remote sensing technology applications for water quality inversion in large rivers are common. However, their application to medium/small-sized water bodies within rural areas is limited due to the low spatial resolution of remote sensing images. In this work, a typical small rural river was selected, and high-resolution unmanned aerial vehicle (UAV) multispectral images and ground monitoring data of the river were obtained. Then, a comparative analysis of three univariate regression models and nine machine learning models (Ridge Regression (RR), Support Vector Regression (SVR), Grid Search Support Vector Regression (GS-SVR), Random Forest (RF), Grid Search Random Forest (GS-RF), eXtreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Catboost Regression (CBR)) for their accuracy in the prediction of turbidity (TUB), total nitrogen (TN), and total phosphorus (TP) was performed. TUB can be achieved by simple statistical regression models. The CBR model exhibited the best performance for the three index inversions on the test set evaluation metrics: R² (0.90~0.92), RMSE (7.57 × 10⁻³~1.59 mg/L), MAE (0.01~1.30 mg/L), RPD (3.21~3.56), and NSE (0.84~0.92). The water pollution of the study area was closely related to its land-use pattern, excessive and irrational fertilizer application, and distribution of pollutant outlets.