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Machine learning predictions of concentration-specific aggregate hazard scores of inorganic nanomaterials in embryonic zebrafish
This work has employed advanced Machine Learning techniques to create nano-QSAR tools for modeling the toxicity of metallic and metal oxide nanomaterials, both coated and uncoated and with a variety of different core compositions, and successfully correlated these properties with the associated biological responses observed in zebrafish.
Iridoid glucosides in the genus Sutera (Scrophulariaceae) as chemotaxonomic markers in tribe Limoselleae.