Integrating machine learning interpretation methods for investigating nanoparticle uptake during seed priming and its biological effects

Autor:	Hengjie Yu, Zhilin Zhao, Da Liu, Fang Cheng
Rok vydání:	2022
Předmět:	Machine Learning Seedlings Seeds Nanoparticles Germination General Materials Science
Zdroj:	Nanoscale. 14:15305-15315
ISSN:	2040-3372 2040-3364
DOI:	10.1039/d2nr01904c
Popis:	Seed priming by nanoparticles is an environmentally-friendly solution for alleviating malnutrition, promoting crop growth, and mitigating environmental stress. However, there is a knowledge gap regarding the nanoparticle uptake and the underlying physiological mechanism. Machine learning has great potential for understanding the biological effects of nanoparticles. However, its interpretability is a challenge for building trust and providing insights into the learned relationships. Herein, we systematically investigated how the factors influence nanoparticle uptake during seed priming by ZnO nanoparticles and its effects on seed germination. The properties of the nanoparticles, priming solution, and seeds were considered.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab2b26375538c091529e9dc76fd670da https://doi.org/10.1039/d2nr01904c Zobrazit plný text záznamu