Annually approximately 1.5 million Americans suffer from a traumatic brain injury (TBI) increasing the risk of developing a further neurological complication later in life [1-3]. The molecular drivers of the subsequent ensuing pathologies after the initial injury event are vast and include signaling processes that may contribute to neurodegenerative diseases such as Alzheimer’s Disease (AD). One such molecular signaling pathway that may link TBI to AD is necroptosis. Necroptosis is an atypical mode of cell death compared with traditional apoptosis, both of which have been demonstrated to be present post-TBI [4-6]. Necroptosis is initiated by tissue necrosis factor (TNF) signaling through the RIPK1/RIPK3/MLKL pathway, leading to cell failure and subsequent death. Prior studies in rodent TBI models report necroptotic activity acutely after injury, within 48 hours. Here, the study objective was to recapitulate prior data and characterize MLKL and RIPK1 cortical expression post-TBI with our lab’s controlled cortical impact mouse model. Using standard immunohistochemistry approaches, it was determined that the tissue sections acquired by prior lab members were of poor quality to conduct robust MLKL and RIPK1 immunostaining assessment. Therefore, the thesis focused on presenting the staining method completed. The discussion also expanded on expected results from these studies regarding the spatial distribution necroptotic signaling in this TBI model.
In the past decade, numerous methods have been developed to analyze in-vivo calcium imaging data that involves complex techniques such as overlapping signals segregation and motion artifact correction. The hypothesis used to detect calcium signal is the spatiotemporal sparsity of calcium signal, and these methods are unable to identify the passive cells that are not actively firing during the time frame in the video. Statistics regarding the percentage of cells in each frame of view can be critical for the analysis of calcium imaging data for human induced pluripotent stem cells derived neurons and astrocytes.
The objective of this research is to develop a simple and efficient semi-automated pipeline for analysis of in-vitro calcium imaging data. The region of interest (ROI) based image segmentation is used to extract the data regarding intensity fluctuation caused by calcium concentration changes in each cell. It is achieved by using two approaches: basic image segmentation approach and a machine learning approach. The intensity data is evaluated using a custom-made MATLAB that generates statistical information and graphical representation of the number of spiking cells in each field of view, the number of spikes per cell and spike height.
Blue colors are often iridescent in nature and the effect of iridescence on warning signal function was unknown. I reared B. philenor larvae under varied food deprivation treatments. Iridescent colors did not have more variation than pigment-based colors under these conditions; variation which could affect predator learning. Learning could also be affected by changes in appearance, as iridescent colors change in both hue and brightness as the angle of illuminating light and viewer change in relation to the color surface. Iridescent colors can also be much brighter than pigment-based colors and iridescent animals can statically display different hues. I tested these potential effects on warning signal learning by domestic chickens (Gallus gallus domesticus) and found that variation due to the directionality of iridescence and a brighter warning signal did not influence learning. However, blue-violet was learned more readily than blue-green. These experiments revealed that the directionality of iridescent coloration does not likely negatively affect its potential effectiveness as a warning signal.