Traces from single-molecule fluorescence microscopy (SMFM) experiments exhibit photophysical artifacts that typically necessitate human expert screening, which is time-consuming and introduces potential for user-dependent expectation bias. Here, we have used deep learning to develop a rapid, automatic SMFM trace selector, termed AutoSiM, that improves the sensitivity and specificity of an assay for a DNA point mutation based on single-molecule recognition through equilibrium Poisson sampling (SiMREPS). The improved performance of AutoSiM is based on accepting both more true positives and fewer false positives than the conventional approach of hidden Markov modeling (HMM) followed by thresholding. As a second application, the selector was used for automated screening of single-molecule Förster resonance energy transfer (smFRET) data to identify high-quality traces for further analysis, and achieves ~90% concordance with manual selection while requiring less processing time. AutoSiM can be adapted readily to novel datasets, requiring only modest Transfer Learning.
Li, J., Zhang, L., Johnson-Buck, A., & Walter, N. G. (2020). Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning. Nature Communications, 11(1), 5833. https://doi.org/10.1038/s41467-020-19673-1 and Hayward, S., Lund, P., Kang, Q., Johnson-Buck, A., Tewari, M., Walter, N. (2018). Single-molecule microscopy image data and analysis files for "Ultra-specific and Amplification-free Quantification of Mutant DNA by Single-molecule Kinetic Fingerprinting" [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/Z2CZ35DF
This project evaluated the binding of antibody fragments to membrane proteins fused to a short epitope sequence (“MPER”). This dataset includes atomic coordinates (.pdb files) for bioinformatic models of antibody fragment binding to an MPER epitope – membrane protein fusion.
McIlwain, B. C., Erwin, A. L., Davis, A. R., Ben Koff, B., Chang, L., Bylund, T., Chuang, G.-Y., Kwong, P. D., Ohi, M. D., Lai, Y.-T., & Stockbridge, R. B. (2021). N-terminal Transmembrane-Helix Epitope Tag for X-ray Crystallography and Electron Microscopy of Small Membrane Proteins. Journal of Molecular Biology, 166909. https://doi.org/10.1016/j.jmb.2021.166909
Please refer to the "README.txt" for more details., MATLAB R2018a (Mathworks, Natick, MA, USA) was used to process this data., and Excel (Microsoft Office) was used to store survey data on the comfort of both systems and also to provide absolute and relative intraobserver variablities for the DM device.
Comparison of anorectal function measured using wearable digital manometry and a high resolution manometry system Attari A, Chey WD, Baker JR, Ashton-Miller JA (2020) Comparison of anorectal function measured using wearable digital manometry and a high resolution manometry system. PLOS ONE 15(9): e0228761. https://doi.org/10.1371/journal.pone.0228761
This dataset includes spectrally-resolved optical properties for volcanic ash particles from the 2010 Eyjafjallajökull volcanic eruptions. These properties were used in the climate simulations described by Flanner et al. (2014, doi:10.1002/2014JD021977) to quantify ash radiative forcing from the eruptions.
We collected hours of functional magnetic resonance imaging data from human subjects listening to natural stories. We developed a predictive model of the voxel-wise response and further applied it to thousands of new words to understand how the brain stores and connects different concepts. and This is a dataset for the paper:
Zhang, Y., Han, K., Worth, R., & Liu, Z. (2020). Connecting concepts in the brain by mapping cortical representations of semantic relations. Nature communications, 11(1), 1-13. https://doi.org/10.1038/s41467-020-15804-w. This project is also documented at https://osf.io/eq2ba/.
Zhang, Y., Han, K., Worth, R., & Liu, Z. (2020). Connecting concepts in the brain by mapping cortical representations of semantic relations. Nature communications, 11(1), 1-13. https://doi.org/10.1038/s41467-020-15804-w
Reconstructed CT slices for a right proximal metatarsal 1 of the Cantius trigonodus (University of Michigan Museum of Paleontology catalog number UMMP VP 81822), as a series of TIFF images. Raw projections are not included in this dataset.
Reconstructed CT slices for a right cuboid of Cantius mckennai (University of Michigan Museum of Paleontology catalog number UMMP VP 81823), as a series of TIFF images. Raw projections are not included in this dataset.
Reconstructed CT slices for a right calcaneum of Cantius mckennai (University of Michigan Museum of Paleontology catalog number UMMP VP 81821), as a series of TIFF images. Raw projections are not included in this dataset.
Reconstructed CT slices for a right astragalar [astragalus] body of Cantius mckennai (University of Michigan Museum of Paleontology catalog number UMMP VP 81827), as a series of TIFF images. Raw projections are not included in this dataset.
Reconstructed CT slices for a right navicular of Cantius mckennai (University of Michigan Museum of Paleontology catalog number UMMP VP 81831), as a series of TIFF images. Raw projections are not included in this dataset.