National Science Foundation and Office of Science and Technology (OSTP) veteran, Professor Manish Parashar, a distinguished professor of computer science at Rutgers University, will join SCI on January 1, 2021.
“We are thrilled to have a leader like Professor Parashar take the helm at the Institute,” said Dan Reed, senior vice president for Academic Affairs. “He brings an unparalleled depth and breadth of experience in cyberinfrastructure and computer and computational science that will advance SCI as it continues to innovate, grow, and build research collaborations across the entire University of Utah campus.”
SCI is a campus research center where over 185 faculty, staff, and students work together to shape the future of advanced computing. Since its founding, more than 100 undergraduates and 400 graduate students and postdoctoral fellows have worked on SCI research projects.
“I also want to acknowledge the tremendous contributions of Professor Chris Johnson, SCI’s founding director,” Reed said. “Chris built SCI into an internationally recognized center of excellence in scientific computing, imaging, and visualization.”
Over more than two decades, the SCI Institute has established itself as a recognized leader in visualization, scientific and biomedical computing and image analysis. Computer Science Rankings places the university at No. 2 in visualization work internationally.
“SCI has established itself as a pioneer and an international leader in computational and data-enabled science and engineering research and education—from developing new methods and technologies for data-driven scientific exploration to pioneering new structures for multidisciplinary research,” Parashar said. “ SCI is well poised to take on a leadership role in this scientific revolution.
“I look forward to working with the outstanding faculty, staff and students at SCI to a future of even greater achievements and transformative impact on science and society.”
Parashar is currently on loan to the National Science Foundation (NSF) and office director of its Office of Advanced Cyberinfrastructure, and he leads the NSF’s strategic vision for a National Cyberinfrastructure Ecosystem for 21st Century Science and Engineering. He also is on detail to the Office of Science and Technology Policy (OSTP) and currently serves as assistant director for Strategic Computing. Parashar co-led the committee that developed the National Strategic Computing Update on the future of computing. At OSTP, Parashar is leading the development of the national strategic plan for the Future Advanced Computing Ecosystem. He will continue his role at NSF, spending time each week at the university, until the end of his temporary NSF appointment.
He replaces interim SCI Director Mike Kirby, who will continue to lead the university’s Informatics Initiative (UI2).
ShapeWorks 5.5 Released
We are excited to announce the new release of our software, ShapeWorks 5.5!
New ShapeWorks API: Consolidation of image-based and segmentation-based grooming tools that creates a stable and reusable API making it much easier and more flexible for users to groom their datasets. This includes a full complement of unit tests. See ShapeWorks API for more details.
DeepSSM Python package: A Python package has been added for a deep learning framework that estimates statistical representations of shape directly from unsegmented images once trained. See SSMs Directly from Images for more details.
New DeepSSM use case: We added a new use case called deep_ssm that demonstrates data augmentation and deep learning on the femur data. See Femur SSM Directly from Images for more details.
New Features Added to Studio
Feature maps support: Studio supports the integration of 3d volume feature maps to map imaging data to the optimized shape model. See New in ShapeWorksStudio for more details.
New interface for group analysis: Studio supports group definitions from spreadsheets. The new interface supports multiple group sets within the same project file and categorical groups compared to the old binary groups (i.e., yes/no) setting. See New in ShapeWorksStudio for more details.
User notes in Studio: Studio stores/loads a rich text notes section in the spreadsheet.
Improved Use Support
Revamped documentation:New documentation to support both end-users and open-source developer community in one easily navigable place. This documentation includes background information about statistical shape modeling, the scientific premise of ShapeWorks, and how to get started. It also demonstrates the latest software features, exemplar use cases, and instructions to build/install ShapeWorks.
Optimized shape models for use cases: All datasets on the ShapeWorks Data Portal now have the shape model output from running the use cases with a corresponding analyze.xml for launching Studio. Users can cd to where the data is extracted and call ShapeWorksStudio analyze.xml to visualize these shape models.
We are pleased to announce the recipient of The Leonardo Award 2020 is Chris Johnson Ph.D. of the SCI Institute at the University of Utah for his curiosity, creativity and vision. Due to these unprecedented times, the Gala event was held virtually.
Wilson Good Wins Young Investigator's Award at Computing in Cardiology
Congratulations to Wilson Good on winning the Rosanna Degani Young Investigators’ Award competition at the international Computing in Cardiology conference, Rimini, 16th September 2020.
Tendon Injury and Collagen Mechanics
Accumulation of collagen molecular unfolding is the mechanism of cyclic fatigue damage and failure in collagenous tissues
In understanding the failure of dense collagenous soft tissues over multiple loading cycles, the predominant hypothesis for development of overuse injuries is that repeated subfailure loading causes accumulation of “micro-damage”, and when this micro-damage accumulates at a rate that is faster than can be repaired, this results in injury in a clinical sense (tissue failure and resulting pain from the injury and overload of surrounding structures). However the specific nature of this micro-damage has remained unknown. In this study, we demonstrate that the micro-damage is actually collagen molecular unfolding, which accumulates with repeated cyclic loading. Our results provide a convincing explanation for the micro-damage hypothesis: Molecular-level collagen damage is generated by tissue-level loading, and the ability to repair this damage determines whether the applied loading leads to tissue failure.
(A) Rat tail tendon fascicles were loaded in creep-fatigue to 40% of the ultimate tensile strength (UTS) until tissue failure. Incremental levels of fatigue were defined as the peak cyclic (creep) strain at 20, 50, and 80% of cycles to failure. (B) To label and quantify denatured collagen, we stained mechanically loaded fascicles with fluorescent CHP, which hybridizes to unfolded collagen α chains. The amount of denatured collagen was quantified on microplate via the fluorescence of bound F-CHP. Computational simulations were used to investigate the potential mechanisms of fascicle- and molecular-level fatigue behavior. (C) Biphasic finite-element simulations were used to study the potential role of fluid flow on strain rate–dependent fatigue behavior at the fascicle level. (D) MD simulations of collagen model peptides were used to identify molecular mechanisms of fatigue damage accumulation and strain rate dependence. GPO, glycine-proline-hydroxyproline.
Bei Wang Receives DOE Award
University of Utah School of Computing assistant professor Bei Wang was awarded more than $832,000 from the U.S. Department of Energy’s Early Career Research Program, one of only 75 scientists in the nation and the only faculty member from the U to earn the award this year.
Wang’s project, titled “Topology-Preserving Data Sketching for Scientific Visualization,” will conduct a study of topology-preserving data sketching techniques to improve visual exploration and understanding of large scientific data.
As scientific simulations generate a large amount of data while the simulation is running, it has become challenging to keep track of interesting phenomena and apply appropriate actions such as storage, analysis, and visualization.
Data sketching uses ideas from statistics, geometry, and linear algebra to generate an approximation of each data instance for fast and efficient processing. At the same time, visualization plays an important role in a data processing pipeline. Topology-based methods in visualization provide powerful tools to summarize and present large and complex data in a simple and easy-to-understand way.
Wang’s project combines ideas from data sketching with topological techniques in visualization. The multidisciplinary project will be universally applicable in many scientific areas, including but not limited to computational fluid dynamics and materials science.
Wang, who is also a faculty member in the U’s Scientific Computing and Imaging Institute, is focused on the analysis and visualization of large and complex data. Her research interests include topological data analysis, data visualization, computational topology, computational geometry, machine learning, and data mining. She received a bachelor’s in computer science and mathematics from the University of Bridgeport and a doctorate in computer science from Duke University.
The Early Career Research Program, now in its eleventh year, is designed to “bolster the nation’s scientific workforce by providing support to exceptional researchers during crucial early career years,” according to the DOE. Awards are given to projects related to advanced scientific computing, basic energy sciences, biological and environmental research, fusion energy sciences, high energy physics and nuclear physics.
ShapeWorks 5.4 Released
We are excited to announce the new release of our software, ShapeWorks 5.4. ShapeWorks is now faster and uses less memory, with a scalable graphic user interface for large cohorts and a flexible, user-friendly project file format.
Genome-wide Pattern Found in Tumors from Brain Cancer Patients Predicts Life Expectancy
Proof of principle study highlights mathematical methods that are uniquely suited for personalized medicine
For the past 70 years, the best indicator of life expectancy for a patient with glioblastoma (GBM) — the most common and the most aggressive brain cancer — has simply been age at diagnosis. Now, an international team of scientists has experimentally validated a predictor that is not only more accurate but also more clinically relevant: a pattern of co-occurring changes in DNA abundance levels, or copy numbers, at hundreds of thousands of sites across the whole tumor genome.
Conferences may be a little different this year, but that hasn't stopped SCI students from showing what they're made of. This week four publications were selected as finalists in two seperate conferences. Adam Rauff and Steven LaBelle were selected as finalists for the (virtual) student PhD paper competition at the Summer Biomechanics, Bioengineering and Biotransport Conference in June (SB3C). At this same conference Jason Manning was selected as a finalist in the undergraduate student paper competition.