More Like this

1. Composite quantitative knee structure metrics predict the development of accelerated knee osteoarthritis: data from the osteoarthritis initiative

   https://doi.org/10.1186/s12891-020-03338-7  

   Harkey, Matthew S.; Davis, Julie E.; Price, Lori Lyn; Ward, Robert J.; MacKay, James W.; Eaton, Charles B.; Lo, Grace H.; Barbe, Mary F.; Zhang, Ming; Pang, Jincheng; et al (December 2020, BMC Musculoskeletal Disorders)

   Abstract Background We aimed to determine if composite structural measures of knee osteoarthritis (KOA) progression on magnetic resonance (MR) imaging can predict the radiographic onset of accelerated knee osteoarthritis. Methods We used data from a nested case-control study among participants from the Osteoarthritis Initiative without radiographic KOA at baseline. Participants were separated into three groups based on radiographic disease progression over 4 years: 1) accelerated (Kellgren-Lawrence grades [KL] 0/1 to 3/4), 2) typical (increase in KL, excluding accelerated osteoarthritis), or 3) no KOA (no change in KL). We assessed tibiofemoral cartilage damage (four regions: medial/lateral tibia/femur), bone marrow lesion (BML) volume (four regions: medial/lateral tibia/femur), and whole knee effusion-synovitis volume on 3 T MR images with semi-automated programs. We calculated two MR-based composite scores. Cumulative damage was the sum of standardized cartilage damage. Disease activity was the sum of standardized volumes of effusion-synovitis and BMLs. We focused on annual images from 2 years before to 2 years after radiographic onset (or a matched time for those without knee osteoarthritis). To determine between group differences in the composite metrics at all time points, we used generalized linear mixed models with group (3 levels) and time (up to 5 levels). For our prognostic analysis, we used multinomial logistic regression models to determine if one-year worsening in each composite metric change associated with future accelerated knee osteoarthritis (odds ratios [OR] based on units of 1 standard deviation of change). Results Prior to disease onset, the accelerated KOA group had greater average disease activity compared to the typical and no KOA groups and this persisted up to 2 years after disease onset. During a pre-radiographic disease period, the odds of developing accelerated KOA were greater in people with worsening disease activity [versus typical KOA OR (95% confidence interval [CI]): 1.58 (1.08 to 2.33); versus no KOA: 2.39 (1.55 to 3.71)] or cumulative damage [versus typical KOA: 1.69 (1.14 to 2.51); versus no KOA: 2.11 (1.41 to 3.16)]. Conclusions MR-based disease activity and cumulative damage metrics may be prognostic markers to help identify people at risk for accelerated onset and progression of knee osteoarthritis. 

   more » « less
2. Biclustering reveals potential knee OA phenotypes in exploratory analyses: Data from the Osteoarthritis Initiative

   https://doi.org/10.1371/journal.pone.0266964  

   Nelson, Amanda E.; Keefe, Thomas H.; Schwartz, Todd A.; Callahan, Leigh F.; Loeser, Richard F.; Golightly, Yvonne M.; Arbeeva, Liubov; Marron, J. S. (May 2022, PLOS ONE)

   Shaharudin, Shazlin (Ed.)

   Objective To apply biclustering, a methodology originally developed for analysis of gene expression data, to simultaneously cluster observations and clinical features to explore candidate phenotypes of knee osteoarthritis (KOA) for the first time. Methods Data from the baseline Osteoarthritis Initiative (OAI) visit were cleaned, transformed, and standardized as indicated (leaving 6461 knees with 86 features). Biclustering produced submatrices of the overall data matrix, representing similar observations across a subset of variables. Statistical validation was determined using the novel SigClust procedure. After identifying biclusters, relationships with key outcome measures were assessed, including progression of radiographic KOA, total knee arthroplasty, loss of joint space width, and worsening Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, over 96 months of follow-up. Results The final analytic set included 6461 knees from 3330 individuals (mean age 61 years, mean body mass index 28 kg/m 2 , 57% women and 86% White). We identified 6 mutually exclusive biclusters characterized by different feature profiles at baseline, particularly related to symptoms and function. Biclusters represented overall better (#1), similar (#2, 3, 6), and poorer (#4, 5) prognosis compared to the overall cohort of knees, respectively. In general, knees in biclusters #4 and 5 had more structural progression (based on Kellgren-Lawrence grade, total knee arthroplasty, and loss of joint space width) but tended to have an improvement in WOMAC pain scores over time. In contrast, knees in bicluster #1 had less incident and progressive KOA, fewer total knee arthroplasties, less loss of joint space width, and stable pain scores compared with the overall cohort. Significance We identified six biclusters within the baseline OAI dataset which have varying relationships with key outcomes in KOA. Such biclusters represent potential phenotypes within the larger cohort and may suggest subgroups at greater or lesser risk of progression over time. 

   more » « less
3. Impact of a Blended Immersive and Computational Modeling Tool on Elementary Ecosystems Science Learning

   Metcalf, S.J.; Dickes, A.C.; Reilly, J.M.; Kamarainen, A.M.; Brennan, K.A.; Grotzer, T.A.; Dede, C.J. (January 2020, Annual meeting program American Educational Research Association)

   null (Ed.)

   Integrating computational thinking and scientific modeling in elementary school is challenging, but provides opportunities to meet important 21st century learning goals. Furthermore, computational modeling deepens the level of understanding of the modeling process and the phenomena being modeled, making science content more accessible. This paper presents findings from EcoMOD, a research project that blends an immersive virtual ecosystem and a 2D modeling environment to support computational modeling and ecosystem science learning in 3rd grade. As part of the study, students filled out pre- and post- surveys about science content understanding, affective measures, and scientific modeling. Findings for the overall student gains across the three dimensions of the surveys suggest that the EcoMOD curriculum was effective in learning ecosystem science content and practice, as well as developing an understanding of the value of computational modeling. 

   more » « less
4. Neocortex and Bridges-2: A High Performance AI+HPC Ecosystem for Science, Discovery, and Societal Good

   https://doi.org/10.1007/978-3-030-68035-0_15  

   Buitrago, P. A.; Nystrom, N. A. (February 2021, Communications in computer and information science)

   Nesmachnow, S.; Castro, H.; Tchernykh, A. (Ed.)

   Artificial intelligence (AI) is transforming research through analysis of massive datasets and accelerating simulations by factors of up to a billion. Such acceleration eclipses the speedups that were made possible though improvements in CPU process and design and other kinds of algorithmic advances. It sets the stage for a new era of discovery in which previously intractable challenges will become surmountable, with applications in fields such as discovering the causes of cancer and rare diseases, developing effective, affordable drugs, improving food sustainability, developing detailed understanding of environmental factors to support protection of biodiversity, and developing alternative energy sources as a step toward reversing climate change. To succeed, the research community requires a high-performance computational ecosystem that seamlessly and efficiently brings together scalable AI, general-purpose computing, and large-scale data management. The authors, at the Pittsburgh Supercomputing Center (PSC), launched a second-generation computational ecosystem to enable AI-enabled research, bringing together carefully designed systems and groundbreaking technologies to provide at no cost a uniquely capable platform to the research community. It consists of two major systems: Neocortex and Bridges-2. Neocortex embodies a revolutionary processor architecture to vastly shorten the time required for deep learning training, foster greater integration of artificial deep learning with scientific workflows, and accelerate graph analytics. Bridges-2 integrates additional scalable AI, high-performance computing (HPC), and high-performance parallel file systems for simulation, data pre- and post-processing, visualization, and Big Data as a Service. Neocortex and Bridges-2 are integrated to form a tightly coupled and highly flexible ecosystem for AI- and data-driven research. 

   more » « less
5. The Design and Implementation of a Method for Evaluating and Building Research Practice Partnerships

   https://doi.org/10.1145/1234567890  

   Rorrer, A. (January 2021, Proceedings of the 52nd ACM Technical Symposium on Computer Science Education)

   We have established a research-practice partnership (RPP) to build a computer science (CS) and computational thinking (CT)-focused STEM ecosystem at two middle schools. Creating such an ecosystem to broaden student participation in computing through an RPP approach involves all stakeholders in the research process. Borrowing upon visual participatory research methods, we developed a graphic research instrument to engage teachers in the research process and elicit their perspectives on strategies for building the ecosystem. This experience report describes our research methodology across two distinct cases to demonstrate the utility of this drawing activity as an investigative and partnership development tool. The contribution is in offering a flexible approach to other university-based RPP teams that enables a synergistic partnership development tool and data collection instrument that can be tailored to a variety of RPP contexts, facilitating more productive and equitable ways of engaging stakeholders in the research process. We describe our project contexts and share results from the pilot study with practitioner-members of our RPP teams. We discuss two cases to highlight the contribution this approach made to the development of our partnerships. 

   more » « less