Reconstructed CT slices for a right medial cuneiform (entocuneiform) of Cantius mckennai (University of Michigan Museum of Paleontology catalog number UMMP VP 81820), 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.
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 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 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 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.
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.
Flanner, M. G., A. S. Gardner,S. Eckhardt, A. Stohl, and J. Perket(2014), Aerosol radiative forcing fromthe 2010 Eyjafjallajökull volcaniceruptions,J. Geophys. Res.Atmos.,119, 9481–9491,doi:10.1002/2014JD021977
The Multifaceted Optimization Algorithm (MOA) is a tool for generating corrected empirical model thermospheric densities during geomagnetic storms. It consists of a suite of Python functions that operate around the Spacecraft Orbit Characterization Kit (SpOCK), an orbital propagator developed by Charles D. Bussy-Virat, PhD, Joel Getchius, and Aaron J. Ridley, PhD at the University of Michigan, and it estimates new densities for the NRLMSISE-00 atmospheric model. MOA generates new model densities by estimating modifications to inputs to the NLRMSISE-00 model that minimize the orbit error between modeled spacecraft in SpOCK, and their actual altitudes as described in publicly-available Two-Line Element Sets (TLEs), made available online via Space-track.org.
MOA consists of three sub-process: (1) The Area Optimization Algorithm (AROPT), (2) the F10.7 Optimization Algorithm (FOPT), and (3) the Ap Optimization Algorithm (APOPT). AROPT computes the contribution to the drag of the modeled spacecraft due to their varying projected area. FOPT estimates modifications to the 10.7 cm solar radio flux in NRLMSISE-00, and APOPT estimates modifications to the Earth's magnetic activity in NRLMSISE-00. MOA finds these modifications across many spacecraft, and the medians of those modifications are then applied in NLRMSISE-00 along the orbit of another satellite to generate new densities for verification. In this instance, modifications are applied along the orbits of the Swarm spacecraft and compared to Swarm GPS-derived densities.
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
Data for "Comparison of Anorectal Function Measured using Wearable Digital Manometry and a High Resolution Manometry System." article (PLOS ONE) PONE-D-20-01826R1
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.
Jieming Li, Leyou Zhang, Alexander Johnson-Buck, Nils G. Walter. (2020) Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning. Nature Communications, In Press. 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
AutoSiM software and sample single-molecule trace data accompanying "Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning"
