The chromosome level whole genome sequence and the seconary matabolism gene cluster prediction of Fusarium meridionale, the pathogen causing maize ear rot.

Fusarium meridionale is one of the pathogens causing maize ear rot, it produce bioactive secondary metabolites may threaten humans food safty, however, the production mechanism of the secondary metabolites and their interaction with maize ear remains poorly understood. To facilitate related studies, we sequenced and assembled the genome of F. meridionale strain JX18-4. The size of F. meridionale JX18-4 genome is 37.11 Mbp, include four nuclear chromosome contigs that consists of 11920 coding genes and one mitochondrial contig. 95.64% gene synteny collinearity was found between the assembly and the reference genomes F. graminearum strain PH-1. Compared to the sequences of seconary matabolism gene clusters sequences reported previously, the stain JX18-4 was predicted potential producing 8 clusters, including nivalenol, zearalenone, aurofusarin, fusarielin, fusaristatin A, fusarin, fusarubin and butenolide. This study aims to reveal the molecular mechanism of secondary metabolites producing, and the genomic information of JX18-4 will provide resources for the study of biological control mechanisms and plant-microbe interactions.

A Glove-Wearing Detection Algorithm Based on Improved YOLOv8.

Wearing gloves during machinery operation in workshops is essential for preventing accidental injuries, such as mechanical damage and burns. Ensuring that workers are wearing gloves is a key strategy for accident prevention. Consequently, this study proposes a glove detection algorithm called YOLOv8-AFPN-M-C2f based on YOLOv8, offering swifter detection speeds, lower computational demands, and enhanced accuracy for workshop scenarios. This research innovates by substituting the head of YOLOv8 with the AFPN-M-C2f network, amplifying the pathways for feature vector propagation, and mitigating semantic discrepancies between non-adjacent feature layers. Additionally, the introduction of a superficial feature layer enriches surface feature information, augmenting the model's sensitivity to smaller objects. To assess the performance of the YOLOv8-AFPN-M-C2f model, this study conducted multiple experiments using a factory glove detection dataset compiled for this study. The results indicate that the enhanced YOLOv8 model surpasses other network models. Compared to the baseline YOLOv8 model, the refined version shows a 2.6% increase in mAP@50%, a 63.8% rise in FPS, and a 13% reduction in the number of parameters. This research contributes an effective solution for the detection of glove adherence.