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

Abstract

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 underlying...

Averaging Strategy for Interpretable Machine Learning on Small Datasets to Understand Element Uptake after Seed Nanotreatment

“…The importance of nanoparticle parameters on nano-plant interactions has been demonstrated by many studies. The concentration-dependent uptake and biological effects have been demonstrated. , Concentration was the most important factor that affected the seed’s uptake of nanoparticles during seed priming …”

“…Furthermore, it has been demonstrated that zeta potential and hydrodynamic diameter controlled the foliar delivery efficiency of nanoparticles to plant cells and organelles 44 and would affect shoot fresh weight after seed nanopriming. 23 Relationships between Important Factors and the RMC. The interpretable relationship between the important factor and RMC provides a valuable tool to understand the uptake and translocation of nanoparticles in seedlings after seed priming.…”

“…Then seeds were incubated in nanosuspensions (concentrations of 25, 50, 100, and 200 mg/L) that had been thoroughly dispersed by an ultrasonic vibration (240 W, 40 kHz for 30 min) at a ratio of 1:5 g/mL in centrifuge tubes for 12 h under dark conditions while placed horizontally in a shaker (150 r/min, 26 ± 1 °C). The selection of concentration ranges was based on a review of previous studies. ,− After planting eight seeds at a depth of 2 cm in a square pot, 100 mL of 1/4 Hogland nutrient solution was initially applied. Subsequently, 40 mL of 1/4 Hogland nutrient solution was added daily for the next three days, followed by 1/2 Hogland nutrient solution.…”

“…Therefore, the combination of post-hoc and model-based interpretation methods could be ideal. In our previous studies, their combination was used to investigate the root uptake of metal-oxide nanoparticles in the soil environment, and two ways of the integration of post-hoc and model-based interpretation methods were proposed to study nanoparticle uptake during seed priming and its biological effects …”

Interpretable machine learning for investigating complex nanomaterial–plant–soil interactions

“…33 The post hoc interpretation and model-based interpretation were integrated to understand nanoparticle uptake during seed priming and its biological effects. 34 Model-based interpretation usually comes from simple models, which means the machine learning model is interpretable on the construction of the model. Model-based interpretation is the best choice if interpretable machine learning models can achieve reasonable predictive accuracy.…”