The Ginkgo biloba microRNA160-ERF4 module participates in terpene trilactone biosynthesis.

Terpene trilactones (TTLs) are important secondary metabolites in ginkgo (Ginkgo biloba); however, their biosynthesis gene regulatory network remains unclear. Here, we isolated a G. biloba ethylene response factor 4 (GbERF4) involved in TTL synthesis. Overexpression of GbERF4 in tobacco (Nicotiana tabacum) significantly increased terpenoid content and upregulated the expression of key enzyme genes (3-hydroxy-3-methylglutaryl-CoA reductase [HMGR], 3-hydroxy-3-methylglutaryl-CoA synthase [HMGS], 1-deoxy-D-xylulose-5-phosphate reductoisomerase [DXR], 1-deoxy-D-xylulose-5-phosphate synthase [DXS], acetyl-CoA C-acetyltransferase [AACT], and geranylgeranyl diphosphate synthase [GGPPS]) in the terpenoid pathway in tobacco, suggesting that GbERF4 functions in regulating the synthesis of terpenoids. The expression pattern analysis and previous microRNA (miRNA) sequencing showed that gb-miR160 negatively regulates the biosynthesis of TTLs. Transgenic experiments showed that overexpression of gb-miR160 could significantly inhibit the accumulation of terpenoids in tobacco. Targeted inhibition and dual-luciferase reporter assays confirmed that gb-miR160 targets and negatively regulates GbERF4. Transient overexpression of GbERF4 increased TTL content in G. biloba, and further transcriptome analysis revealed that DXS, HMGS, CYPs, and transcription factor genes were upregulated. In addition, yeast 1-hybrid and dual-luciferase reporter assays showed that GbERF4 could bind to the promoters of the HMGS1, AACT1, DXS1, levopimaradiene synthase (LPS2), and GGPPS2 genes in the TTL biosynthesis pathway and activate their expression. In summary, this study investigated the molecular mechanism of the gb-miR160-GbERF4 regulatory module in regulating the biosynthesis of TTLs. It provides information for enriching the understanding of the regulatory network of TTL biosynthesis and offers important gene resources for the genetic improvement of G. biloba with high contents of TTLs.

Optimizing Road Safety: Advancements in Lightweight YOLOv8 Models and GhostC2f Design for Real-Time Distracted Driving Detection.

The rapid detection of distracted driving behaviors is crucial for enhancing road safety and preventing traffic accidents. Compared with the traditional methods of distracted-driving-behavior detection, the YOLOv8 model has been proven to possess powerful capabilities, enabling it to perceive global information more swiftly. Currently, the successful application of GhostConv in edge computing and embedded systems further validates the advantages of lightweight design in real-time detection using large models. Effectively integrating lightweight strategies into YOLOv8 models and reducing their impact on model performance has become a focal point in the field of real-time distracted driving detection based on deep learning. Inspired by GhostConv, this paper presents an innovative GhostC2f design, aiming to integrate the idea of linear transformation to generate more feature maps without additional computation into YOLOv8 for real-time distracted-driving-detection tasks. The goal is to reduce model parameters and computational load. Additionally, enhancements have been made to the path aggregation network (PAN) to amplify multi-level feature fusion and contextual information propagation. Furthermore, simple attention mechanisms (SimAMs) are introduced to perform self-normalization on each feature map, emphasizing feature maps with valuable information and suppressing redundant information interference in complex backgrounds. Lastly, the nine distinct distracted driving types in the publicly available SFDDD dataset were expanded to 14 categories, and nighttime scenarios were introduced. The results indicate a 5.1% improvement in model accuracy, with model weight size and computational load reduced by 36.7% and 34.6%, respectively. During 30 real vehicle tests, the distracted-driving-detection accuracy reached 91.9% during daylight and 90.3% at night, affirming the exceptional performance of the proposed model in assisting distracted driving detection when driving and contributing to accident-risk reduction.