دورية أكاديمية
Toxicological assessment of agrochemicals on bees using machine learning tools.
| العنوان: | Toxicological assessment of agrochemicals on bees using machine learning tools. |
|---|---|
| المؤلفون: | Bernardes RC; Departamento de Entomologia, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil. Electronic address: bernardesrodrigoc@gmail.com., Botina LL; Departamento de Entomologia, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil., da Silva FP; Departamento de Agronomia, Universidade Federal do Espírito Santo, Alegre, Espírito Santo, Brazil., Fernandes KM; Departamento de Biologia Geral, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil., Lima MAP; Departamento de Biologia Animal, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil., Martins GF; Departamento de Biologia Geral, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil. |
| المصدر: | Journal of hazardous materials [J Hazard Mater] 2022 Feb 15; Vol. 424 (Pt A), pp. 127344. Date of Electronic Publication: 2021 Sep 27. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 9422688 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-3336 (Electronic) Linking ISSN: 03043894 NLM ISO Abbreviation: J Hazard Mater Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Amsterdam : Elsevier, |
| مواضيع طبية MeSH: | Agrochemicals*/toxicity , Insecticides*/toxicity, Animals ; Artificial Intelligence ; Bees ; Machine Learning ; Mass Gatherings ; Neonicotinoids/toxicity ; Nitro Compounds/toxicity |
| مستخلص: | 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. (Copyright © 2021 Elsevier B.V. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Artificial intelligence; Ecotoxicology; Physiological response; Pollinators; Risk assessment |
| المشرفين على المادة: | 0 (Agrochemicals) 0 (Insecticides) 0 (Neonicotinoids) 0 (Nitro Compounds) |
| تواريخ الأحداث: | Date Created: 20211004 Date Completed: 20220114 Latest Revision: 20220114 |
| رمز التحديث: | 20221213 |
| DOI: | 10.1016/j.jhazmat.2021.127344 |
| PMID: | 34607030 |
| قاعدة البيانات: | MEDLINE |
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