The sweetpotato weevil, Cylas formicarius (Fabricius), is a serious pest of sweetpotato. Olfaction-based approaches, such as use of synthetic sex pheromones to monitor populations and the bait-and-kill method to eliminate males, have been applied successfully for population management of C. formicarius. However, the molecular basis of olfaction in C. formicarius remains unknown. In this study, we produced antennal transcriptomes from males and females of C. formicarius using high-throughput sequencing to identify gene families associated with odorant detection. A total of 54 odorant receptors (ORs), 11 gustatory receptors (GRs), 15 ionotropic receptors (IRs), 3 sensory neuron membrane proteins (SNMPs), 33 odorant binding proteins (OBPs), and 12 chemosensory proteins (CSPs) were identified. Tissue-specific expression patterns revealed that all 54 ORs and 11 antennal IRs, one SNMP, and three OBPs were primarily expressed in antennae, suggesting their putative roles in olfaction. Sex-specific expression patterns of these antenna-predominant genes suggest that they have potential functions in sexual behaviors. This study provides a framework for understanding olfaction in coleopterans as well as future strategies for controlling the sweetpotato weevil pest.

The males of many Bactrocera species (Diptera: Tephritidae) respond strongly to plant-derived chemicals (male lures) and can be divided into cue lure/raspberry ketone (CL/RK) responders, methyl eugenol (ME) responders and non-responders. Representing a non-responders, Bactrocera minax display unique olfactory sensory characteristics compared with other Bactrocera species. The chemical senses of insects mediate behaviors that are associated with survival and reproduction. Here, we report the generation of transcriptomes from antennae and the rectal glands of both male and female adults of B. minax using Illumina sequencing technology, and annotated gene families potentially responsible for chemosensory. We developed four transcriptomes from different tissues of B. minax and identified a set of candidate genes potentially responsible for chemosensory by analyzing the transcriptomic data. The candidates included 40 unigenes coding for odorant receptors (ORs), 30 for ionotropic receptors (IRs), 17 for gustatory receptors (GRs), three for sensory neuron membrane proteins (SNMPs), 33 for odorant-binding proteins (OBPs), four for chemosensory proteins (CSPs). Sex- and tissue-specific expression profiles for candidate chemosensory genes were analyzed via transcriptomic data analyses, and expression profiles of all ORs and antennal IRs were investigated by real-time quantitative PCR (RT-qPCR). Phylogenetic analyses were also conducted on gene families and paralogs from other insect species together. A large number of chemosensory genes were identified from transcriptomic data. Identification of these candidate genes and their expression profiles in various tissues provide useful information for future studies towards revealing their function in B. minax.

Antennal and abdominal transcriptomes of males and females of the coconut hispine beetle Brontispa longissima were sequenced to identify and compare the expression patterns of genes involved in odorant reception and detection. Representative proteins from the chemosensory gene families likely essential for insect olfaction were identified. These include 48 odorant receptors (ORs), 19 ionotropic receptors (IRs), 4 sensory neuron membrane proteins (SNMPs), 34 odorant binding proteins (OBPs) and 16 chemosensory proteins (CSPs). Phylogenetic analysis revealed the evolutionary relationship of these proteins with homologs from Coleopterans or other insects, and led to the identification of putative aggregation pheromone receptors in B. longissima. Comparative expression analysis performed by calculating FPKM values were also validated using quantitative real time-PCR (qPCR). The results revealed that all ORs and antennal IRs, two IR co-receptors (BlonIR8a and BlonIR25a) and one SNMP (BlonSNMP1a) were predominantly expressed in antennae when compared to abdomens, and approximately half of the OBPs (19) and CSPs (7) were enriched in antennae. These findings for the first time reveal the identification of key molecular components in B. longissima olfaction and provide a valuable resource for future functional analyses of olfaction, and identification of potential targets to control this quarantine pest.

The oriental fruit fly, Bactrocera dorsalis s.s., is one of the most important quarantine pests in many countries, including China. Although the oriental fruit fly has been investigated extensively, its origins and genetic structure remain disputed. In this study, the NADH dehydrogenase subunit 1 (ND1) gene was used as a genetic marker to examine the genetic diversity, population structure, and gene flow of B. dorsalis s.s. throughout its range in China and southeast Asia.

Citrus Huanglongbing (HLB), also known as citrus greening, is a severe disease that has caused substantial economic damage to the global citrus industry. Early detection is challenging due to the lack of distinctive early symptoms, making current diagnostic methods often ineffective. Therefore, there is an urgent need for an intelligent and timely detection system for HLB. This study leverages multispectral imagery acquired via unmanned aerial vehicles (UAVs) and deep convolutional neural networks. This study introduce a novel model, MGA-UNet, specifically designed for HLB recognition. This image segmentation model enhances feature transmission by integrating channel attention and spatial attention within the skip connections. Furthermore, this study evaluate the comparative effectiveness of high-resolution and multispectral images in HLB detection, finding that multispectral imagery offers superior performance. To address data imbalance and augment the dataset, this study employ a generative model, DCGAN, for data augmentation, significantly boosting the model’s recognition accuracy. Our proposed model achieved a mIoU of 0.89, a mPA of 0.94, a precision of 0.95, and a recall of 0.94 in identifying diseased trees. The intelligent monitoring method for HLB presented in this study offers a cost-effective and highly accurate solution, holding considerable promise for the early warning of this disease.