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- Creator:
- Dariya, Malyarenko, Tariq, Humera, Kushwaha, Aman, Mourad, Rami, Heist, Kevin, Chenevert, Thomas L, Ross, Brian D, Chen, Heang-Ping, and Hadjiiski, Lubomir
- Description:
- The 3D GRE MRI data for murine model of myelofbifrosis with expert segmentations of mouse tibia was used to train Attention UNET model to automate bone marrow segmentation for measurements of imaging biomarkers. This dataset consists of three archives: (1) containing the source MRI images in Meta-image-header (MHD) format with resulting segmentation labels by two experts and four UNET models with different training scenarios; (2) corresponding training models; and (3) deep-learning (DL)-based segmentation tools for application to future murine tibia MRI data. and The MHD images are an ITK compatible format that can be viewed in standard image viewer, like 3D Slicer. The image archive is structured with a directory tree that contains \"mouseID"\"scan-date"\"segmentaion-scenario"\. The "training model" archive containes DL-model labeled by the data subset, and "deployment" archive containes the DL-segmentation software.
- Keyword:
- deep-learning segmentation, preclinical MRI, murine tibia, and mouse model of myelofibrosis
- Citation to related publication:
- Kushwaha A, Mourad RF, Heist K, Tariq H, Chan HP, Ross BD, Chenevert TL, Malyarenko D, Hadjiiski LM. Improved Repeatability of Mouse Tibia Volume Segmentation in Murine Myelofibrosis Model Using Deep Learning. Tomography. 2023 Mar 7;9(2):589-602. doi: 10.3390/tomography9020048. PMID: 36961007; PMCID: PMC10037585. and https://github.com/dumichgh/MFJK1_Segmentation_MHDs
- Discipline:
- Health Sciences
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- Creator:
- Lee, Shih Kuang, Tsai, Sun Ting, and Glotzer, Sharon C.
- Description:
- The trajectory data and codes were generated for our work "Classification of complex local environments in systems of particle shapes through shape-symmetry encoded data augmentation" (amidst peer review process). The data sets contain trajectory data in GSD file format for 7 test systems, including cubic structures, two-dimensional and three-dimensional patchy particle shape systems, hexagonal bipyramids with two aspect ratios, and truncated shapes with two degrees of truncation. Besides, the corresponding Python code and Jupyter notebook used to perform data augmentation, MLP classifier training, and MLP classifier testing are included.
- Keyword:
- Machine Learning, Colloids Self-Assembly, Crystallization, and Order Parameter
- Citation to related publication:
- https://doi.org/10.48550/arXiv.2312.11822
- Discipline:
- Other, Science, and Engineering
-
- Creator:
- Katz, Sarah A., Levin, Naomi E., Abbott, Mark B., Rodbell, Donald T., Passey, Benjamin H., DeLuca, Nicole N., Larsen, Darren J., and Woods, Arielle
- Description:
- This dataset presents stable isotope data (d13C, d18O, D47, D17O) from Holocene lake cores from three lakes in the Peruvian Andes (Lakes Junin, Pumacocha, and Mehcocha). We also present new radiocarbon (14C) data and core age models for Lakes Junin and Mehcocha. We use these data to explore trends in lake water temperatures and evaporative state (i.e., water balance) over the Holocene. Our clumped isotope (D47) results suggest lake water temperatures at all three lakes were stable over the Holocene and similar to present day lake temperatures. Our triple oxygen isotope (D’17O) results illustrate that lake water balance at all three lakes was variable over the Holocene and tracks changes in austral summertime insolation, suggesting a connection between central Andean water balance and the South American summer monsoon (SASM).
- Keyword:
- Holocene, Andes, temperature, water balance, lacustrine carbonate, lake hydrology, triple oxygen isotopes, and clumped isotopes
- Citation to related publication:
- Katz, S.A., Levin, N.E., Abbott, M.B., Rodbell, D.T., Passey, B.H., DeLuca, N.M., Larsen, D.J., Woods, A. "Holocene temperature and water stress in the Peruvian Andes: insights from lake carbonate clumped and triple oxygen isotopes," in review. and Katz, S.A., (2024) Andean interglacial climate and hydrology over the last 650,000 years. [PhD Thesis, University of Michigan]
- Discipline:
- Science
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- Creator:
- Malyarenko, Dariya, Chenevert, Thomas L, Heist, Kevin, Bonham, Christopher, and Ross, Brian
- Description:
- The imaging data was used to measure repeatability and temporal trends of quantitative imaging biomarkers of myelofibrosis in bone marrow based on apparent diffusion coefficient, fat fraction and magnetization transfer ratio. The dataset consists of time series of the MRI Meta-image-header (MHD) images of wild type and diseased mice combined by the imaging time point. The MHD images are an ITK compatible format that can be viewed in standard image viewer, like 3D Slicer. Each time point image archive is structured with a directory tree that contains ./././"mouseID"/"scan-date"/"acquisition type"/
- Keyword:
- murine tibia MRI, bone marrow imaging, apparent diffusion coefficient (ADC), proton density fat fraction (PDFF), magnetization transfer ratio (MTR), and pre-clinical model of myelofibrosis
- Citation to related publication:
- Ross BD, Malyarenko D, Heist K, Amouzandeh G, Jang Y, Bonham CA, Amirfazli C, Luker GD, Chenevert TL. Repeatability of Quantitative Magnetic Resonance Imaging Biomarkers in the Tibia Bone Marrow of a Murine Myelofibrosis Model. Tomography. 2023 Feb 28;9(2):552-566. doi: 10.3390/tomography9020045. PMID: 36961004; PMCID: PMC10037563.
- Discipline:
- Health Sciences
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- Creator:
- Bautista-Arredondo, Luis F., Muñoz-Rocha, T. Verenice, Figueroa, José L., Téllez-Rojo, Martha M., Torres-Olascoaga, Libni A., Cantoral, Alejandra, Arboleda-Merino, Laura C., Leung, Cindy, Peterson, Karen E., and Lamadrid-Figueroa, Héctor
- Description:
- Data was collected from participants of the Early Life Exposures in Mexico to ENvironmental Toxicants (ELEMENT) study, which consists of three sequentially-enrolled birth cohorts of pregnant women. Research protocols of this study were approved by the Institutional Review Board at University of Michigan and the Mexico National Institute of Public Health. We obtained informed consent from study participants prior to enrollment.
- Keyword:
- Food Insecurity, COVID-19 Pandemic, Mexico, Cohort
- Citation to related publication:
- Bautista-Arredondo LF, Verenice Muñoz-Rocha T, Figueroa JL, Téllez-Rojo MM, Torres-Olascoaga LA, Cantoral A, Arboleda-Merino L, Leung L, Peterson KE, and Lamadrid-Figueroa H. A surge in food insecurity during the COVID-19 pandemic in a cohort in Mexico City. 2022. Article in process of publication.
- Discipline:
- Health Sciences
-
- Creator:
- Sheppard, Anja, Sethuraman, Advaith V, Bagoren, Onur, Pinnow, Christopher, Anderson, Jamey, Havens, Timothy C, and Skinner, Katherine A
- Description:
- The AI4Shipwrecks dataset contains sidescan sonar images of shipwrecks and corresponding binary labels collected during 2022 and 2023 at the NOAA Thunder Bay National Marine Sanctuary in Alpena, MI. The data collection platform was an Iver3 Autonomous Underwater Vehicle (AUV) equipped with an EdgeTech 2205 dual-frequency ultra-high resolution sidescan sonar and 3D bathymetric system. The labels were compiled from reference labels created by experts in marine archaeology. The intended use of this dataset is to encourage development of semantic segmentation, object detection, or anomaly detection algorithms in the computer vision field. Comparisons of state-of-the-art segmentation networks on our dataset are shown in the paper. , The file structure is organized as described in the README.txt file, where images in 'images' directories are the waterfall product of sidescan sonar surveys, and images in 'labels' directories are binary representations of expert labels. Images across the 'images' and 'labels' directories are correlated by having identical filenames. In the label images, a pixel value of '0' represents the non-shipwreck/other class and '1' represents the shipwreck class for the correspondingly named image (<wreck_name>_<##>.png) in the images directory. , and The project webpage can be found at: https://umfieldrobotics.github.io/ai4shipwrecks/
- Keyword:
- machine learning, computer vision, field robotics, marine robotics, underwater robotics, sidescan sonar, semantic segmentation, and object detection
- Discipline:
- Engineering
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- Creator:
- Xiantong Wang
- Description:
- Bursty bulk flows (BBFs) are identified as the fast earthward-propagating flows from magnetic reconnection in Earth's magnetotail. BBFs are related to particle energization process reported by satellite observations. For the first time, we use a novel numerical model that simulates kinetic physics directly in a global model. The energization of the electrons associated with BBF is demonstrated by the model. The electron velocity distribution functions (VDFs) extracted from multiple locations associated with BBF demonstrate good agreements with the observations. The energy-dependent electron pitch angle distribution at the leading part of the BBF can be explained by the enhancement of the local magnetic field.
- Discipline:
- Science
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- Creator:
- Zhang, Yingxiao MI
- Description:
- We developed a new model framework based on WRF-Chem, simulating primary biological aerosol particle emissions and their interaction with clouds. We have designed different sensitivity tests to evaluate the effects of pollen and sub-pollen particles (SPPs), respectively. Our results show that SPPs have a larger effect on cloud microphysics and precipitation than whole pollen grains.
- Keyword:
- Aerosol-cloud interactions, Primary biological aerosol particles, Ice nucleating particles, Microphyscis scheme, and Pollen
- Discipline:
- Science
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- Creator:
- Skinner, Katherine A., Vasudevan, Ram, Ramanagopal, Manikandasriram S., Ravi, Radhika, Carmichael, Spencer, and Buchan, Austin D.
- Description:
- This dataset is part of a collection created to facilitate research in the use of novel sensors for autonomous vehicle perception. , The dataset collection platform is a Ford Fusion vehicle with a roof-mounted novel sensing suite, which specifically consists of forward-facing stereo uncooled thermal cameras (FLIR 40640U050-6PAAX), event cameras (iniVation DVXplorer), monochrome cameras (FLIR BFS-PGE-16S2M), and RGB cameras (FLIR BFS-PGE-50S5C) time synchronized with ground truth poses from a high precision navigation system. , and Further information and resources (such as software tools for converting, managing, and viewing data files) are available on the project website: https://umautobots.github.io/nsavp
- Keyword:
- novel sensing, perception, autonomous vehicles, thermal sensing, neuromorphic imaging, and event cameras
- Citation to related publication:
- https://umautobots.github.io/nsavp, https://github.com/umautobots/nsavp_tools, and https://sites.google.com/umich.edu/novelsensors2023
- Discipline:
- Engineering
-
- Creator:
- Skinner, Katherine A., Vasudevan, Ram, Ramanagopal, Manikandasriram S., Ravi, Radhika, Carmichael, Spencer, and Buchan, Austin D.
- Description:
- This dataset is part of a collection created to facilitate research in the use of novel sensors for autonomous vehicle perception., The dataset collection platform is a Ford Fusion vehicle with a roof-mounted novel sensing suite, which specifically consists of forward-facing stereo uncooled thermal cameras (FLIR 40640U050-6PAAX), event cameras (iniVation DVXplorer), monochrome cameras (FLIR BFS-PGE-16S2M), and RGB cameras (FLIR BFS-PGE-50S5C) time synchronized with ground truth poses from a high precision navigation system. , and Further information and resources (such as software tools for converting, managing, and viewing data files) are available on the project website: https://umautobots.github.io/nsavp
- Keyword:
- novel sensing, perception, autonomous vehicles, thermal sensing, neuromorphic imaging, and event cameras
- Citation to related publication:
- https://umautobots.github.io/nsavp, https://github.com/umautobots/nsavp_tools, and https://sites.google.com/umich.edu/novelsensors2023
- Discipline:
- Engineering