High Performance Geospatial Analysis on Emerging Parallel Architectures

Geographic information systems (GIS) are performing increasingly sophisticated analyses on growing data sets. These analyses demand high performance. At the same time, modern computing platforms increasingly derive their performance from several forms of parallelism. This dissertation explores the available parallelism in several GIS-applied algorithms: viewshed calculation, image feature transform, and feature analysis. It presents implementations of these algorithms that exploit parallel processing to reduce execution time, and analyzes the effectiveness of the implementations in their use of parallel processing

Accelerating SIFT on Parallel Architectures

SIFT is a widely-used algorithm that extracts features from images; using it to extract information from hundreds of terabytes of aerial and satellite photographs requires parallelization in order to be feasible. We explore accelerating an existing serial SIFT implementation with OpenMP parallelization and GPU execution

Accelerating Image Feature Comparisons using CUDA on Commodity Hardware

Given multiple images of the same scene, image registration is the process of determining the correct transformation to bring the images into a common coordinate system—i.e., how the images fit together. Feature based registration applies a transformation function to the input images before performing the correlation step. The result of that transformation, also called feature extraction, is a list of significant points in the images, and the registration process will attempt to correlate these points, rather than directly comparing the input images

Research Reproducibility & Replicability Webinar

This webinar focuses on the topic of clinical informatics in the midst of the global pandemic. Kevin Sexton, M.D., University of Arkansas for Medical Sciences (UAMS) gives the keynote presentation Clinical Informatics as an Ally for Data Reproducibility & Replicability in COVID-19 Research. Next, Jason Ramage, director of research compliance, and Lora Lennertz, university data services librarian, provide a survey of research integrity (as applied to R&R). The webinar concludes with a panel discussion with campus experts Seth Warn (assistant professor of geosciences and a CAST affiliate), Josh McGee (assistant professor of education reform and chief data officer for the state of Arkansas), and members of the Reproducibility & Replicability Committee

The long non-coding RNA NEAT1 is responsive to neuronal activity and is associated with hyperexcitability states

Despite their abundance, the molecular functions of long non-coding RNAs in mammalian nervous systems remain poorly understood. Here we show that the long non-coding RNA, NEAT1, directly modulates neuronal excitability and is associated with pathological seizure states. Specifically, NEAT1 is dynamically regulated by neuronal activity in vitro and in vivo, binds epilepsy-associated potassium channel-interacting proteins including KCNAB2 and KCNIP1, and induces a neuronal hyper-potentiation phenotype in iPSC-derived human cortical neurons following antisense oligonucleotide knockdown. Next generation sequencing reveals a strong association of NEAT1 with increased ion channel gene expression upon activation of iPSC-derived neurons following NEAT1 knockdown. Furthermore, we show that while NEAT1 is acutely down-regulated in response to neuronal activity, repeated stimulation results in NEAT1 becoming chronically unresponsive in independent in vivo rat model systems relevant to temporal lobe epilepsy. We extended previous studies showing increased NEAT1 expression in resected cortical tissue from high spiking regions of patients suffering from intractable seizures. Our results indicate a role for NEAT1 in modulating human neuronal activity and suggest a novel mechanistic link between an activity-dependent long non-coding RNA and epilepsy