Modeling hormesis using multivariate nonlinear regression in plant biology: A comprehensive approach to understanding dose-response relationships.

Autor: Jardim Amorim D; Universidade de Sao Paulo, ESALQ, Departamento de Ciências Exatas, Piracicaba 13418-900, Brazil., Corrêa Vieira AM; Universidade de Sao Paulo, ESALQ, Departamento de Ciências Exatas, Piracicaba 13418-900, Brazil; Syngenta Crop Protection AG, Global Biological Data Analytics, 4058 Basel, Switzerland., Fidelis CR; Universidade de Sao Paulo, ESALQ, Departamento de Ciências Exatas, Piracicaba 13418-900, Brazil., Camilo Dos Santos JC; School of Agricultural Sciences, Laboratory of Ecophysiology Applied to Agriculture, Department of Crop Production, São Paulo State University (UNESP), 18610-034 Botucatu, Brazil., de Almeida Silva M; School of Agricultural Sciences, Laboratory of Ecophysiology Applied to Agriculture, Department of Crop Production, São Paulo State University (UNESP), 18610-034 Botucatu, Brazil. Electronic address: marcelo.a.silva@unesp.br., Garcia Borges Demétrio C; Universidade de Sao Paulo, ESALQ, Departamento de Ciências Exatas, Piracicaba 13418-900, Brazil.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2023 Dec 20; Vol. 905, pp. 167041. Date of Electronic Publication: 2023 Sep 18.
DOI: 10.1016/j.scitotenv.2023.167041
Abstrakt: For over a century, ecotoxicological studies have reported the occurrence of hormesis as a significant phenomenon in many areas of science. In plant biology, hormesis research focuses on measuring morphological, physiological, biochemical, and productivity changes in plants exposed to low doses of herbicides. These studies involve multiple features that are often correlated. However, the multivariate aspect and interdependencies among components of a plant system are not considered in the adopted modeling framework. Therefore, a multivariate nonlinear modeling approach for hormesis is proposed, where information regarding correlations among response variables is taken into account through a variance-covariance matrix obtained from univariate residuals. The proposed methodology is evaluated through a Monte Carlo simulation study and an application to experimental data from safflower (Carthamus tinctorius L.) cultivation. In the simulation study, the multivariate model outperformed the univariate models, exhibiting higher precision, lower bias, and greater accuracy in parameter estimation. These results were also confirmed in the analysis of the experimental data. Using the delta method, mean doses of interest can be derived along with their associated standard errors. This is the first study to address hormesis in a multivariate context, allowing for a better understanding of the biphasic dose-response relationships by considering the interrelationships among various measured characteristics in the plant system, leading to more precise parameter estimates.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Marcelo de Almeida Silva reports financial support was provided by National Council for Scientific and Technological Development (CNPq, Brazil). Deoclecio Jardim Amorim reports financial support was provided by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Brazil).
(Copyright © 2023 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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