Copy Number Variations of Glycoside Hydrolase 45 Genes in Bursaphelenchus xylophilus and Their Impact on the Pathogenesis of Pine Wilt Disease

Comprehensive Analysis of Copy Number Variations on Glycoside Hydrolase 45 Genes among Different Bursaphelenchus xylophilus Strains

Bursaphelenchus xylophilus is considered the most dangerous quarantine pest in China. It causes enormous economic and ecological losses in many countries from Asia and Europe. The glycoside hydrolase 45 gene family has been demonstrated in early studies to contribute to the cell wall degradation ability of B. xylophilus during its infection. However, the copy number variation (CNV) of the GH45 gene and its association with B. xylophilus pathogenicity were not fully elucidated. In this study, we found that the GH45 gene with two copies is the most predominant type among 259 B. xylophilus strains collected from China and Japan. Additionally, 18 strains are identified as GH45 genes with a single copy, and only two strains are verified to have three copies. Subsequent expression analysis and inoculation test suggest that the copy numbers of the GH45 gene are correlated with gene expression as well as the B. xylophilus pathogenicity. B. xylophilus strains with more copies of the GH45 gene usually exhibit more abundant expression and cause more severe wilt symptoms on pine trees. The aforementioned results indicated the potential regulatory effects of CNV in B. xylophilus and provided novel information to better understand the molecular pathogenesis of this devastating pest.

Evaluation of Deep Learning Segmentation Models for Detection of Pine Wilt Disease in Unmanned Aerial Vehicle Images

Pine wilt disease (PWD) is a serious threat to pine forests. Combining unmanned aerial vehicle (UAV) images and deep learning (DL) techniques to identify infected pines is the most efficient method to determine the potential spread of PWD over a large area. In particular, image segmentation using DL obtains the detailed shape and size of infected pines to assess the disease’s degree of damage. However, the performance of such segmentation models has not been thoroughly studied. We used a fixed-wing UAV to collect images from a pine forest in Laoshan, Qingdao, China, and conducted a ground survey to collect samples of infected pines and construct prior knowledge to interpret the images. Then, training and test sets were annotated on selected images, and we obtained 2352 samples of infected pines annotated over different backgrounds. Finally, high-performance DL models (e.g., fully convolutional networks for semantic segmentation, DeepLabv3+, and PSPNet) were trained and evaluated. The results demonstrated that focal loss provided a higher accuracy and a finer boundary than Dice loss, with the average intersection over union (IoU) for all models increasing from 0.656 to 0.701. From the evaluated models, DeepLLabv3+ achieved the highest IoU and an F1 score of 0.720 and 0.832, respectively. Also, an atrous spatial pyramid pooling module encoded multiscale context information, and the encoder–decoder architecture recovered location/spatial information, being the best architecture for segmenting trees infected by the PWD. Furthermore, segmentation accuracy did not improve as the depth of the backbone network increased, and neither ResNet34 nor ResNet50 was the appropriate backbone for most segmentation models.