Entomological Research最新文献

Improving complex agronomic traits such as yield, plant height, and disease resistance in rice (Oryza sativa L.) is challenging due to their polygenic nature. Conventional breeding methods, often reliant on collective parameters, face limitations in efficiently identifying superior genotypes. Phenomics, however, presents a promising, targeted approach by focusing on individual phenotypic traits. This study employs time-lapse imaging to monitor dynamic phenotypic changes in rice plants infested with the brown planthopper (BPH) (Nilaparvata lugens). Utilizing the Phlizon 6000 W LED Grow Light, which provides far-red wavelengths, and image acquisition with 12 Raspberry Pi 4 Model B units and Raspberry Pi NoIR cameras fitted with blue filters for enhanced NDVI calculations, we captured detailed imagery of plant responses. Our study revealed that NDVI values in the stem region of control plants remained stable, while leaf region values showed an increase. For infested plants, NDVI fluctuations were observed at the lamina joint in the stem region, whereas leaf region values remained consistent. Importantly, damage progression was slower at the lamina joint in resistant rice varieties compared to susceptible ones, underscoring lamina joint discoloration as a valuable parameter for evaluating BPH resistance. This phenome-based precision breeding approach holds significant potential for accelerating the development of rice varieties with enhanced resistance to this pervasive pest, offering new avenues for improving crop resilience and yield.

Insecticide resistance in mosquito populations is an important area in the insecticide-disease control equation. Two major types of insecticide responses have been well recognized: physiological resistance and behavioral avoidance. Globally, the development of physiological resistance to insecticides among insect groups and disease vectors has been widely documented. In Thailand, physiological resistance has emerged across all major classes of synthetic insecticides where the majority are synthetic pyrethroids. Interestingly, the physiological resistance remains limited in Anopheles vectors despite the long-term use of synthetic compounds. This phenomenon suggests that behavioral responses may play a key role in disease prevention while simultaneously reducing the likelihood of insecticide resistance in mosquito vectors.

Early detection of wasp hives is crucial for mitigating their impact on native species, preventing agricultural damage, and improving pest control strategies. Traditional detection methods rely on ground surveys and sensor-based tracking of individual insects, which are often labor-intensive, time-consuming, and prone to errors because of environmental constraints. The integration of artificial intelligence and drone-based imaging has the potential to revolutionize ecological monitoring by providing scalable, efficient, and noninvasive methods for detecting wasp hives. However, research on AI-assisted hive detection remains limited, with most studies focusing on large-scale wildlife monitoring rather than small-object localization. Therefore, we propose a system for searching the candidate site of a wasp hive using a small drone. In the proposed system, a small drone is equipped with a camera and takes aerial images of the error range. Subsequently, three-dimensional (3D) modeling is performed on the captured images using a 3D surveying toolkit, and deep learning–based hive detection is performed on the completed 3D model to extract the GPS information of the detected target.

This study evaluates the effects of constant and fluctuating temperatures on the development, survival, and fertility of Myzus persicae, with an emphasis on understanding how thermal variability shapes its life history traits. Moderate temperature fluctuations (10 ± 5°C, 15 ± 5°C, 20 ± 5°C, and 25 ± 5°C) significantly enhanced developmental rates and reproductive success compared to constant conditions, demonstrating increased metabolic efficiency and adaptability under variable thermal environments. In contrast, extreme fluctuations (30 ± 5°C) imposed physiological stress, leading to delayed development, reduced survival, and decreased reproductive output, indicating limited tolerance of M. persicae to high thermal variability. A temperature-dependent model revealed an optimal range around 25°C for developmental efficiency and reproductive performance. Fertility peaked at 20 ± 5°C but was markedly suppressed at 30 ± 5°C, underscoring the detrimental effects of extreme thermal fluctuations on population growth. These findings suggest that moderate thermal variability may promote population expansion of M. persicae under favorable conditions, whereas extreme fluctuations could act as natural constraints, limiting its dynamics in warmer climates. This study provides critical insights into the adaptability of M. persicae to thermal variability, offering a framework for predicting its responses to climate change. However, the observed physiological limitations under elevated temperatures highlight the need for further field-based studies that incorporate ecological complexity. Such research will be essential to improve predictions of M. persicae population dynamics and to develop informed pest management strategies in agroecosystems experiencing increasing temperature fluctuations.

Forensic entomology is a sub-discipline of entomology concerning insects and arthropods with their relationship to criminal investigations. Most important that comes out of it is the finding of an exact time of occurrence of a dead body by estimating the post-mortem interval (PMI). Our study focused on blow fly pupae, which are some of the earliest insects to colonize decomposing bodies. The pupae of the blow fly have the longest development period in the blow fly life history, but we have trouble viewing the inside of the pupae because of the puparium. It is, therefore, a developmental internal X-ray imaging stage carried out to see if such a division experiment could reveal a difference in growth based on temperatures in two different temperatures. All experiments were recorded accurately with an indoor Wi-Fi camera. Finally, the sampling individual in 20 °C and 30 °C shows a significant difference at the developmental time of 10,640 min and 5470 min, respectively. As the internal developmental stage at two different temperatures, it distinctly delimited based on their morphological characteristics. The internal developmental stages, identified by morphological characteristics, were subdivided into four distinct phases, improving the precision of post-mortem interval estimation. In this study, we attempted to delimit the pupae into developmental instars based on internal development with the help of X-ray imaging. More economical and easier than those currently in use, such as anatomical and micro-computed tomography. However, it is followed by collecting data through additional experiments at various temperatures to apply this method into actuality.

Mosquito-borne diseases remain a significant health concern amidst current microbial outbreaks. Phytochemicals offer environmentally safe, biodegradable, and targeted pest management. Nanostructure lipid carriers (NLCs), a second generation of solid lipid nanoparticles, are gaining attention as potential diagnostic and therapeutic tools. Sesbania leaves, rich in fatty acids, phenolics, and terpenes, were analyzed using gas chromatography–mass spectrometry. Magnetic nanoparticles (Se-NLC-MNPs) modified the surface of Sesbania extract, encapsulated in the NLC. The resulting nanoparticles were 129.2 and 218.5 nm in size, with zeta potentials of −6.20 and 43.9 mV, respectively. Transmission electron microscopy showed spherical and oval shapes. XRD patterns confirmed the successful decoration of the NLC with the magnetic nanoparticles. The Sesbania extract (Se) and its nanoparticle conjugates were tested for larvicidal efficacy against Culex pipiens and Musca domestica larvae, at doses ranging from 50 to 1500 ppm and 0.1 to 5 mg/mL. Se-NLC-MNPs showed higher larval mortality rates compared to their Se formulation extracts, achieving 100% mortality in third-instar larvae. Sesbania methanol extract contained more terpenes, fatty acids, and other organic compounds than the aqueous extract, making it more harmful to insect larvae. In terms of relative toxicity, Se-NLC-MNPs were more effective than Se-NLC. An in vitro cytotoxicity assay against the WI38 cell line indicated the cytotoxicity assay, suggesting the potential for these nanoparticles to develop into high-performance, environmentally acceptable therapeutics for mosquito-borne diseases.

Various miticides are being applied to apiaries to prevent mites. However, abuse of miticides could seriously damage bee health. To understand the physiological response of honey bees caused by inappropriate exposure to miticides, it is necessary to identify the marker genes whose expression alters in honey bees following exposure to miticides. Although quantitative real-time polymerase chain reaction (qRT-PCR) is widely used for gene expression analysis, selecting appropriate stably expressed reference gene(s) across various conditions is essential for accurately determining target gene expression levels. Therefore, this study assessed the expression stabilities of 10 candidate reference genes (RPS5, RPS18, GAPDH, ARF1, RAB1a, PPI, PGK, SDH, TBP, and EF1) using C_q distribution and four algorithm programs (NormFinder, BestKeeper, geNorm, and RefFinder). Subsequently, we validated various normalization methods using each of the 10 reference genes and a combination of multiple genes by calculating the expression of the target gene (SOD2). Based on the various analysis methods used in this study, RPS5 is suggested as the most optimal reference gene for qRT-PCR analysis in honey bees under multiple conditions of miticide exposure.