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Abstract
This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (ENMs). A workflow applying two different deep learning architectures on microscopic images of Daphnia magna is proposed that can automatically detect possible malformations, such as effects on the length of the tail, and the overall size, and uncommon lipid concentrations and lipid deposit shapes, which are due to direct or parental exposure to ENMs. Next, classification models assign specific objects (heart, abdomen/claw) to classes that depend on lipid densities and compare the results with controls. The models are statistically validated in terms of their prediction accuracy on external D. magna images and illustrate that deep learning technologies can be useful in the nanoinformatics field, because they can automate time-consuming manual procedures, accelerate the investigation of adverse effects of ENMs, and facilitate the process of designing safer nanostructures. It may even be possible in the future to predict impacts on subsequent generations from images of parental exposure, reducing the time and cost involved in long-term reproductive toxicity assays over multiple generations.
Original language | English |
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Article number | e2001080 |
Number of pages | 11 |
Journal | Small |
Volume | 16 |
Issue number | 36 |
Early online date | 17 Jun 2020 |
DOIs | |
Publication status | Published - 1 Sept 2020 |
Bibliographical note
Funding Information:This work received funding from the European Union's Horizon 2020 research and innovation programme via NanoSolveIT Project under grant agreement number 814572. The Daphnia microscopy images were generated via a NERC highlight topic grant (NE/N006569/1).
Publisher Copyright:
© 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Keywords
- deep learning
- hazard assessment
- image analysis
- machine learning
- nanoinformatics
ASJC Scopus subject areas
- Chemistry(all)
- Materials Science(all)
- Biotechnology
- Biomaterials
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