New control methods are necessary for stored grain pest management programs due to both the widespread problems of insecticide-resistance populations and the increasing concerns of consumers regarding pesticide residues in food products. Thus, this study evaluated the bioactivity of extracts and fractions obtained from different structures (leaves, branches, and seeds) of Annona mucosa (Annonaceae) against Sitophilus zeamais (Coleoptera: Curculionidae), which is a primary insect pest of stored cereals in tropical conditions. In the screening assay, the most promising treatments were extracts prepared from the seeds of Annona mucosa in hexane and dichloromethane (LC 90 values of 259.31 and 425.15 mg kg À1 , respectively) and, to a lesser extent, an extract prepared from the leaves in hexane (LC 90 of 1047.15 mg kg À1). Based on these results and the chromatographic profile of the bioactive crude extracts, the extract prepared from the seeds in hexane was fractionated by liquideliquid partitioning. The dichloromethane and hydroalcoholic fractions exhibited insecticidal activity against S. zeamais, and no significant difference was observed between these two fractions. The chemical analyses (1 H NMR, HPLC, and TLC) showed the presence of alkaloids and acetogenins in the bioactive fractions, which are likely related to the observed bioactivity. Thus, A. mucosa, particularly its seeds, is a promising source of compounds that can be used as a prototype model and/or a biorational insecticide for the control of S. zeamais in stored cereals.
Computer vision and reflectance-based analyses are becoming increasingly important methods to quantify and characterize phenotypic responses by whole organisms to environmental factors. Here, we present the first study of how a non-destructive and completely non-invasive method, body reflectance profiling, can be used to detect and time stress responses in adult beetles. Based on high-resolution hyperspectral imaging, we acquired time series of average reflectance profiles (70 spectral bands from 434-876 nm) from adults in two beetle species, maize weevils (Sitophilus zeamais) and larger black flour beetles (Cynaus angustus). For each species, we acquired reflectance data from untreated controls and from individuals exposed continuously to killing agents (an insecticidal plant extract applied to maize kernels or entomopathogenic nematodes applied to soil applied at levels leading to ≈100% mortality). In maize weevils (exposed to hexanic plant extract), there was no significant effect of the on reflectance profiles acquired from adult beetles after 0 and 12 hours of exposure, but a significant treatment response in spectral bands from 434 to 550 nm was detected after 36 to 144 hours of exposure. In larger black flour beetles, there was no significant effect of exposure to entomopathogenic nematodes after 0 to 26 hours of exposure, but a significant response in spectral bands from 434-480 nm was detected after 45 and 69 hours of exposure. Spectral bands were used to develop reflectance-based classification models for each species, and independent validation of classification algorithms showed sensitivity (ability to positively detect terminal stress in beetles) and specificity (ability to positively detect healthy beetles) of about 90%. Significant changes in body reflectance occurred at exposure times, which coincided with published exposure times and known physiological responses to each killing agent. The results from this study underscore the potential of hyperspectral imaging as an approach to non-destructively and non-invasively quantify stress detection in insects and other animals.
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