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• W3204561567 endingPage "127344" @default.
• W3204561567 startingPage "127344" @default.
• W3204561567 abstract "Machine learning (ML) is a branch of artificial intelligence (AI) that enables the analysis of complex multivariate data. ML has significant potential in risk assessments of non-target insects for modeling the multiple factors affecting insect health, including the adverse effects of agrochemicals. Here, the potential of ML for risk assessments of glyphosate (herbicide; formulation) and imidacloprid (insecticide, neonicotinoid; formulation) on the stingless bee Melipona quadrifasciata was explored. The collective behavior of forager bees was analyzed after in vitro exposure to agrochemicals. ML algorithms were applied to identify the agrochemicals that the bees have been exposed to based on multivariate behavioral features. Changes in the in situ detection of different proteins in the midgut were also studied. Imidacloprid exposure leads to the greatest changes in behavior. The ML algorithms achieved a higher accuracy (up to 91%) in identifying agrochemical contamination. The two agrochemicals altered the detection of cells positive for different proteins, which can be detrimental to midgut physiology. This study provides a holistic assessment of the sublethal effects of glyphosate and imidacloprid on a key pollinator. The procedures used here can be applied in future studies to monitor and predict multiple environmental factors affecting insect health in the field." @default.
• W3204561567 created "2021-10-11" @default.
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• W3204561567 date "2022-02-01" @default.
• W3204561567 modified "2023-10-16" @default.
• W3204561567 title "Toxicological assessment of agrochemicals on bees using machine learning tools" @default.
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• W3204561567 doi "https://doi.org/10.1016/j.jhazmat.2021.127344" @default.
• W3204561567 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34607030" @default.
• W3204561567 hasPublicationYear "2022" @default.
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