RESUMEN
Plutella xylostella is a typical phototactic pest. LW-opsin contributes to the phototaxis of P. xylostella, but the expression changes of other genes in the phototransduction pathway caused by the mutation of LW-opsin remain unknown. In the study, the head transcriptomes of male G88 and LW-opsin mutants were compared. A GO-function annotation showed that DEGs mainly belonged to the categories of molecular functions, biological processes, and cell composition. Additionally, a KEGG-pathway analysis suggested that DEGs were significantly enriched in some classical pathways, such as the phototransduction-fly and vitamin digestion and absorption pathways. The mRNA expressions of genes in the phototransduction-fly pathway, such as Gq, ninaC, and rdgC were significantly up-regulated, and trp, trpl, inaD, cry1, ninaA and arr1 were significantly down-regulated. The expression trends of nine DEGs in the phototransduction pathway confirmed by a RT-qPCR were consistent with transcriptomic data. In addition, the influence of a cry1 mutation on the phototaxis of P. xylostella was examined, and the results showed that the male cry1 mutant exhibited higher phototactic rates to UV and blue lights than the male G88. Our results indicated that the LW-opsin mutation changed the expression of genes in the phototransduction pathway, and the mutation of cry1 enhanced the phototaxis of a P. xylostella male, providing a basis for further investigation on the phototransduction pathway in P. xylostella.
RESUMEN
Huanglongbing (HLB), or citrus greening disease, has complex and variable symptoms, making its diagnosis almost entirely reliant on subjective experience, which results in a low diagnosis efficiency. To overcome this problem, we constructed and validated a deep learning (DL)-based method for detecting citrus HLB using YOLOv5l from digital images. Three models (Yolov5l-HLB1, Yolov5l-HLB2, and Yolov5l-HLB3) were developed using images of healthy and symptomatic citrus leaves acquired under a range of imaging conditions. The micro F1-scores of the Yolov5l-HLB2 model (85.19%) recognising five HLB symptoms (blotchy mottling, "red-nose" fruits, zinc-deficiency, vein yellowing, and uniform yellowing) in the images were higher than those of the other two models. The generalisation performance of Yolov5l-HLB2 was tested using test set images acquired under two photographic conditions (conditions B and C) that were different from that of the model training set condition (condition A). The results suggested that this model performed well at recognising the five HLB symptom images acquired under both conditions B and C, and yielded a micro F1-score of 84.64% and 85.84%, respectively. In addition, the detection performance of the Yolov5l-HLB2 model was better for experienced users than for inexperienced users. The PCR-positive rate of Candidatus Liberibacter asiaticus (CLas) detection (the causative pathogen for HLB) in the samples with five HLB symptoms as classified using the Yolov5l-HLB2 model was also compared with manual classification by experts. This indicated that the model can be employed as a preliminary screening tool before the collection of field samples for subsequent PCR testing. We also developed the 'HLBdetector' app using the Yolov5l-HLB2 model, which allows farmers to complete HLB detection in seconds with only a mobile phone terminal and without expert guidance. Overall, we successfully constructed a reliable automatic HLB identification model and developed the user-friendly 'HLBdetector' app, facilitating the prevention and timely control of HLB transmission in citrus orchards.
