Functional magnetic resonance imaging (fMRI) can be used to assess functional activity in the brain and connectivity between different regions of interest (ROIs), and a functional connectome is a map of the interactions between ROIs. Previous research has shown that a functional connectome contains enough unique characteristics, not unlike a fingerprint, that it can be used for accurate identification of an individual subject from a large group. RC is working with the UVA Functional Neuroradiology Lab to perform this fingerprinting analysis for a wide variety of populations and to develop innovative ways to visualize the results.
PI: Jason Druzgal (Radiology and Medical Imaging)
Coronary artery disease (CAD) is the major cause of morbidity and mortality worldwide. Recent genome wide association studies (GWAS) have revealed more than 50 genomic loci that are associated with increased risk for CAD. However, the pathological mechanisms for majority of the GWAS loci leading to increased susceptibility to this complex disorder are still unclear. RC is working with Redouane Aherrahrou (CPHG) who aims to study the impact of the CAD-associated genetic factors on the cellular and molecular SMC phenotypes. Support for this project has included preparation of scripts for programmatic data analyses, data visualization, statistical modeling, and assistance with use of the Rivanna high-performance computing cluster.
Researchers are using sonomicrometry to study the biomechanics of the human brain. While at times the signals collected do not require any preprocessing, more frequently they do require some denoising or are too noisy to analyze. Currently, researchers are manually categorizing the quality of thousands of these sonomicrometry signals and preprocessing them individually. RC is helping researchers develop a machine learning model to classify the signals and to determine the necessary preprocessing steps.
Preliminary sonomicrometry data have been collected, and RC is working to classify, prepare, and normalize the data for use in a machine learning model. RC is currently developing preliminary models to classify the data by signal quality and preprocess automation techniques that will later be applied to noisy signals.
Two important measures of the in vivo interaction of transcription factors with chromatin are the search time and the residence time. The former refers to the time it takes a factor to find its binding location, while the latter is the time the factor physically attaches to the chromatin. By quantifying the interaction dynamics of transcription factors, researchers hope to understand the role of these factors in basic cellular processes such as transcription and gene regulation. The RC team is working with collaborators from UVA and the NIH to understand the dynamics of the Gal4 protein in yeast. The project involves quantitatively analyzing ChIP-qPCR data, writing and running non-linear regression and statistical routines in Mathematica, and developing numerical simulations to determine the error bounds on the kinetic parameters.