Publications by authors named "M Carol Greenlee"

White-tailed deer are susceptible to scrapie (WTD scrapie) after oronasal inoculation with the classical scrapie agent from sheep. Deer affected by WTD scrapie are difficult to differentiate from deer infected with chronic wasting disease (CWD). To assess the transmissibility of the WTD scrapie agent and tissue phenotypes when further passaged in white-tailed deer, we oronasally inoculated wild-type white-tailed deer with WTD scrapie agent.

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Scrapie is a fatal, transmissible neurodegenerative disease that affects sheep and goats. Replication of PrP in the lymphoid tissue allows for the scrapie agent to be shed into the environment. Brain and retropharyngeal lymph node (RPLN) from a sheep inoculated with the classical scrapie agent was used to compare infectivity of these tissues.

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The pervasive use of information technologies (IT) has tremendously benefited our daily lives. However, unpredicted technical breakdowns and errors can lead to the experience of stress, which has been termed technostress. It remains poorly understood how people dynamically respond to unpredicted system runtime errors occurring while interacting with the IT systems on a behavioral and neuronal level.

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This study aimed to investigate the impact of eccentric-vision training on population receptive field (pRF) estimates to provide insights into brain plasticity processes driven by practice. Fifteen participants underwent functional magnetic resonance imaging (fMRI) measurements before and after behavioral training on a visual crowding task, where the relative orientation of the opening (gap position: up/down, left/right) in a Landolt C optotype had to be discriminated in the presence of flanking ring stimuli. Drifting checkerboard bar stimuli were used for pRF size estimation in multiple regions of interest (ROIs): dorsal-V1 (dV1), dorsal-V2 (dV2), ventral-V1 (vV1), and ventral-V2 (vV2), including the visual cortex region corresponding to the trained retinal location.

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In this study, an automated 2D machine learning approach for fast and precise segmentation of MS lesions from multi-modal magnetic resonance images (mmMRI) is presented. The method is based on an U-Net like convolutional neural network (CNN) for automated 2D slice-based-segmentation of brain MRI volumes. The individual modalities are encoded in separate downsampling branches without weight sharing, to leverage the specific features.

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