Control of the whitefly Bemisia tabaci (Genn.) agricultural pest and plant virus vector relies on the use of chemical insecticides. RNA-interference (RNAi) is a homology-dependent innate immune response in eukaryotes, including insects, which results in degradation of the corresponding transcript following its recognition by a double-stranded RNA (dsRNA) that shares 100% sequence homology. In this study, six whitefly ‘gut’ genes were selected from an in silico-annotated transcriptome library constructed from the whitefly alimentary canal or ‘gut’ of the B biotype of B. tabaci, and tested for knock down efficacy, post-ingestion of dsRNAs that share 100% sequence homology to each respective gene target. Candidate genes were: Acetylcholine receptor subunit α, Alpha glucosidase 1, Aquaporin 1, Heat shock protein 70, Trehalase1, and Trehalose transporter1. The efficacy of RNAi knock down was further tested in a gene-specific functional bioassay, and mortality was recorded in 24 hr intervals, six days, post-treatment. Based on qPCR analysis, all six genes tested showed significantly reduced gene expression. Moderate-to-high whitefly mortality was associated with the down-regulation of osmoregulation, sugar metabolism and sugar transport-associated genes, demonstrating that whitefly survivability was linked with RNAi results. Silenced Acetylcholine receptor subunit α and Heat shock protein 70 genes showed an initial low whitefly mortality, however, following insecticide or high temperature treatments, respectively, significantly increased knockdown efficacy and death was observed, indicating enhanced post-knockdown sensitivity perhaps related to systemic silencing. The oral delivery of gut-specific dsRNAs, when combined with qPCR analysis of gene expression and a corresponding gene-specific bioassay that relates knockdown and mortality, offers a viable approach for functional genomics analysis and the discovery of prospective dsRNA biopesticide targets. The approach can be applied to functional genomics analyses to facilitate, species-specific dsRNA-mediated control of other non-model hemipterans.

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The data quality of low-cost sensors has received considerable attention and has also led to PM2.5 warnings. However, the calibration of low-cost sensor measurements in an environment with high relative humidity is critical. This study proposes an efficient calibration and mapping approach based on real-time spatial model. The study carried out spatial calibration, which automatically collected measurements of low-cost sensors and the regulatory stations, and investigated the spatial varying pattern of the calibrated low-cost sensor data. The low-cost PM2.5 sensors are spatially calibrated based on reference-grade measurements at regulatory stations. Results showed that the proposed spatial regression approach can explain the variability of the biases from the low-cost sensors with an R-square value of 0.94. The spatial calibration and mapping algorithm can improve the bias and decrease to 39% of the RMSE when compared to the nonspatial calibration model. This spatial calibration and real-time mapping approach provide a useful way for local communities and governmental agencies to adjust the consistency of the sensor network for improved air quality monitoring and assessment.