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David Smith

As seen in: Emerging Infectious Diseases, Nature, Frontiers, MDPI, The BMJ, Thorax, ACS Publications, Irish Examiner, The Astronomical Journal, Physical Review B and
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Articles

A deep learning-based framework for fundus image classification of inherited retinal degeneration in dogs

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By David SMith, David Smith, Valérie L. Dufour, Gustavo Aguirre Verified, William Beltrán Verified, Hippolyte G. J. Beltran-Grémaud
| Nature Verified
Abstract Inherited retinal degenerations (IRDs) are a frequent cause of vision impairment in numerous breeds of dogs. Veterinary ophthalmologists frequently suspect retinal degeneration when funduscopic examinations reveal increased tapetal reflectivity and retinal vascular attenuation; however, these clinical signs can be subtle, especially in early disease. In this study, we evaluated three deep learning architectures using fundus images to detect degenerative retinal disease in dogs.
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Explainable tokamak-agnostic forecasting of fusion plasma instability via megahertz turbulent fluctuations

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By Jaewook Kim, David SMith, David Smith, K. S. Gill, G. R. McKee, Z. Yan, B. Geiger, R. Coffee, E. Kolemen, Semin Joung, A. Jalalvand
| Nature Verified
Abstract Scientific applications of artificial intelligence (AI) often remain limited by device-specific training and unexplained “black-box” approaches, creating fundamental barriers to cross-system generalization. This challenge is critical for nuclear fusion, where future reactors will have limited operational data for AI training.
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Addendum: Detection of colinear blocks and synteny and evolutionary analyses based on utilization of MCScanX

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By Xi Zhang, Yupeng Wang, Paule V. Joseph, Andrew H. Paterson, David SMith, David Smith
| Nature Verified
You have full access to this article via your institution. Addendum to: Nature Protocols https://doi.org/10.1038/s41596-024-00968-2, published online 15 March 2024. The bioinformatics protocol by Wang et al.1 outlines the steps for efficiently identifying colinear blocks in intra- and inter-species BLASTP outputs using the Multiple Colinearity Scan Toolkit Version X (MCScanX)2.
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