[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123599-en":3,"doc-seo-123599-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123599,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Interpretable machine learning-accelerated seed treatment by nanomaterials for environmental stress alleviation","Crops face constant environmental pressures, and nanomaterial-based seed nanopriming offers a cost-effective, environmentally friendly strategy to mitigate stress. A study evaluates 56 seed nanopriming treatments in maize and identifies seven treatments that significantly raise the stress resistance index under salinity and combined heat-drought conditions. Metabolomics highlights ZnO regulation of amino acid metabolism, secondary metabolite synthesis, carbohydrate metabolism, and translation. Interpretable machine learning with an ISAR framework is used to predict and explain stress mitigation effects, combining post hoc and model-based interpretations, while nanoparticle concentration, size, and zeta potential are linked to root dry weight.","Interpretable machine learning-accelerated seed treatment by nanomaterials for environmental stress alleviation  \nHengjie Yua, b, Dan Luoc, Sam F. Y. Lid, Maozhen Qua, b, Da Liua, b, Yingchao Hea, b, and Fang Chenga, b* .  \naCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China  \nbKey Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China  \nc Department of Biological and Environmental Engineering, Cornell University, Ithaca, New York 14853, USA  \nd Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore  \n*Email: [fcheng@zju.edu.cn](fcheng@zju.edu.cn)  \nAbstract  \nCrops are constantly challenged by different environmental conditions. Seed treatment by nanomaterials is a cost-effective and environmentally-friendly solution for environmental stress mitigation in crop plants. Here, 56 seed nanopriming treatments are used to alleviate environmental stresses in maize. Seven selected nanopriming treatments significantly increase the stress resistance index (SRI) by 13.9% and 12.6% under salinity stress and combined heat-drought stress, respectively. Metabolomics data reveals that ZnO nanopriming treatment, with the highest SRI value, mainly regulates the pathways of amino acid metabolism, secondary metabolite synthesis,  \ncarbohydrate metabolism, and translation. Understanding the mechanism of seed nanopriming is still difficult due to the variety of nanomaterials and the complexity of interactions between nanomaterials and plants. Using the nanopriming data, we present an interpretable structureactivity relationship (ISAR) approach based on interpretable machine learning for predicting and understanding its stress mitigation effects. The post hoc and model-based interpretation approaches of machine learning are combined to provide complementary benefits and give researchers or policymakers more illuminating or trustworthy results. The concentration, size, and zeta potential of nanoparticles are identified as dominant factors for correlating root dry weight under salinity stress, and their effects and interactions are explained. Additionally, a web-based interactive tool is developed for offering prediction-level interpretation and gathering more details about specific nanopriming treatments. This work offers a promising framework for accelerating the agricultural applications of nanomaterials and may profoundly contribute to nanosafety assessment.  \nKeywords: environmental stress, metal oxide nanomaterials, seed nanopriming, machine learning, model interpretation  \n1. Introduction  \nCrops are constantly challenged by different environmental conditions, such as drought, salinity, and extreme temperatures (Ahuja et al. , 2010; Ioannou et al. , 2020) . Various field applications of nanoparticles were tested to mitigate environmental stresses (Kah et al. , 2019; Zhao et al. , 2020) , such as drought stress in maize (Cu nanoparticles) (Van Nguyen et al. , 2022) , high-temperature stress in grain sorghum (Se nanoparticles) (Djanaguiraman et al. , 2018) , and salinity stress in broad bean (TiO2 nanoparticles) (Abdel Latef et al. , 2018) . Compared with traditional analogs, it was estimated that the median efficiency of nanoagrochemicals increased by ~20-30%(Kah et al. , 2018) . Seed priming by nanomaterials is a novel approach that is cost-effective and environmentally-friendly due to the lower amount of nanomaterials and minimized environmental exposure compared to field applications (De La Torre-Roche et al. , 2020; Hofmann et al. , 2020) . Seed nanopriming could stimulate the growth of crop plants under environmental stresses by  \nincreasing photosynthetic pigments levels (Abdel Latef et al. , 2017) , enhancing antioxidant enzyme activities (Shah et al. , 2021) , reducing reactive oxygen species (ROS) production (Rai-Kalal et al. , 2021) , and regulating the H2O2 signaling network (Rai-Kalal","cbCaimOXS1kGLJib","https://ap.wps.com/l/cbCaimOXS1kGLJib","pdf",5820017,1,30,"English","en",105,"# Introduction\n## Background: environmental stresses and nanoagrochemical effects\n## Nanomaterial seed priming as a mitigation strategy\n## From QSAR to machine learning and interpretability","[{\"question\":\"What is the document’s main goal regarding seed nanopriming?\",\"answer\":\"It aims to alleviate environmental stress in crops using nanomaterial-based seed nanopriming and to explain the mechanisms behind stress mitigation using interpretable machine learning.\"},{\"question\":\"Which experimental focus and outcomes are reported for maize?\",\"answer\":\"The study tests 56 nanopriming treatments in maize and finds seven selected treatments that significantly increase the stress resistance index under salinity stress and combined heat-drought stress.\"},{\"question\":\"How does interpretable machine learning contribute to understanding the treatment effects?\",\"answer\":\"It uses an ISAR approach and combines post hoc and model-based interpretation to predict and provide trustworthy, mechanistic-level insights into how treatments mitigate stress.\"}]","Interpretable machine learning-accelerated seed treatment by nanomaterials for environmental stress alleviation | 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is the document’s main goal regarding seed nanopriming?","Question",{"text":75,"@type":76},"It aims to alleviate environmental stress in crops using nanomaterial-based seed nanopriming and to explain the mechanisms behind stress mitigation using interpretable machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which experimental focus and outcomes are reported for maize?",{"text":80,"@type":76},"The study tests 56 nanopriming treatments in maize and finds seven selected treatments that significantly increase the stress resistance index under salinity stress and combined heat-drought stress.",{"name":82,"@type":73,"acceptedAnswer":83},"How does interpretable machine learning contribute to understanding the treatment effects?",{"text":84,"@type":76},"It uses an ISAR approach and combines post hoc and model-based interpretation to predict and provide trustworthy, mechanistic-level insights into how treatments mitigate 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