This was a small descriptive study to determine whether short chain fatty acids (SCFAs) are detectable in water. It is part of a larger study that assessed the utility of exhaled breath condensate (EBC) as a biofluid for microbiome assays.
Yue, M., Kim, J. H., Evans, C. R., Kachman, M., Erb-Downward, J. R., D’Souza, J., Foxman, B., Adar, S. D., Curtis, J. L., & Stringer, K. A. (2020). Measurement of Short-Chain Fatty Acids in Respiratory Samples: Keep Your Assay above the Water Line. American Journal of Respiratory and Critical Care Medicine, 202(4), 610–612. https://doi.org/10.1164/rccm.201909-1840LE
Reconstructed CT slices for a left dentary of Carpodaptes stonleyi (University of Michigan Museum of Paleontology catalog number UMMP VP 85286) as a series of TIFF images. Raw projections are not included in this dataset.
This archived dataset includes all of the input files that were used to run the model for all the runs in this set, including files containing model parameters and drivers. This dataset also includes all of the model output files from model runs in this set.
Yuan, Y., S. J. Sharp, J. P. Martina, K. J. Elgersma, and W. S. Currie. Sustained-flux global warming potential driven by nitrogen inflow and hydroperiod in a model of Great Lakes coastal wetlands. JGR Biogeosciences in review.
Time series dataset of adoption by year of climate action plans by 177 U.S. cities, 2010-2019, with links to plans included. This dataset is intended for use by researchers and practitioners investigating both individual climate action plans and time series patterns of adoption at the municipal level.
The intent of the project was to identify all relevant studies and data related to the topic. There are searches for the following databases: Ovid MEDLINE, Elsevier Embase, Clarivate Web of Science, and Wiley Cochrane Central Register of Controlled Trials. The searches yielded 1168 citations after duplicates were removed in Endnote X8.
DeLong MR, Tandon VJ, Bertrand AA, MacEachern M, Goldberg M, Salibian A, Pusic AL, Festekjian JH, Wilkins EG. Review of Outcomes in Prepectoral Prosthetic Breast Reconstruction with and without Surgical Mesh Assistance. Plast Reconstr Surg. 2021 Feb 1;147(2):305-315. doi: 10.1097/PRS.0000000000007586. PMID: 33177453.
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.
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
These data are TLA events identified in MACCS magnetometer data throughout 2015. These events are short-timescale (< 60 s), large -amplitude (> 6 nT/s) magnetic disturbances measured at Earth's surface that are analyzed for space weather research purposes. and The events were identified in a year's worth of magnetic field data using an algorithm developed in the MATLAB platform. The algorithm dBdt_main.m can be run using the associated scripts (clean_maccs.m, simple_dbdt.m, extremes1.m, newdbdt.m) to return the events in the 2015_AllEvents.csv file. The substorm onset delays of each event are determined with the onset_delays.m script and the substorm event list 20191127-15-56-substorms.csv (both included).
Engebretson, M. J., Pilipenko, V. A., Ahmed, L. Y., Posch, J. L., Steinmetz, E. S., Moldwin, M. B., … Vorobev, A. V. (2019). Nighttime Magnetic Perturbation Events Observed in Arctic Canada: 1. Survey and Statistical Analysis. Journal of Geophysical Research: Space Physics, 124(9), 7442–7458. https://doi.org/10.1029/2019JA026794
This publication contains anonymized time integrated activity maps for two patients. SPECT/CT scans were taken at 4 different time points in the week following a therapeutic dose of Lu-177 DOTATATE and combined into a single activity map for each patient. All images are in DICOM format.
The datasets of this archive are produced for a research project on the development of an advanced hydrologic modeling system for the St. Lawrence river basin. The outputted datasets from model simulations are in Netcdf 4 format. The author recommend using the netCDF Operators (NCO) program for data processing. For visualization and plotting, the author recommend using software like MATLAB, Python or R.