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- Creators: Harrington Bioengineering Program
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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.
This honors thesis explores using machine learning technology to assist a patient's return to activity following a significant injury, specifically an anterior cruciate ligament (ACL) tear. The goal of the project was to determine if a machine learning model trained with ACL reconstruction (ACLR) applicable injury data would be able to correctly predict which phase of return to sport a patient would be classified in when introduced to a new data set.
The following paper builds upon version one of The Women’s Power and Influence Index (WPI). The WPI Index is a product created by The Difference Engine, a center at ASU, to address gender inequality in the workplace. The WPI Index ranks Fortune 500 companies on various criteria and releases the information to the public in an easy-to-understand manner. Following the first release in 2021, we aim to help the WPI Index continue to grow by researching social movements that can inspire the Index, suggesting additional criteria for version 1.5, and raising awareness through events and social media. Part I of the paper details how social movements have utilized social pressure and social media to create broad change, setting the stage for the WPI Index’s public rankings to incentivize change. Part II provides research on new criteria we propose to be added to the Index for the next release. Lastly, part III covers how we used Tik Tok, events, and partnerships to help the Index gain notoriety. Altogether the paper suggests new directions and provides scientific research to further the goals of the WPI Index.
The following paper builds upon version one of The Women’s Power and Influence Index (WPI). The WPI Index is a product created by The Difference Engine, a center at ASU, to address gender inequality in the workplace. The WPI Index ranks Fortune 500 companies on various criteria and releases the information to the public in an easy-to-understand manner. Following the first release in 2021, we aim to help the WPI Index continue to grow by researching social movements that can inspire the Index, suggesting additional criteria for version 1.5, and raising awareness through events and social media. Part I of the paper details how social movements have utilized social pressure and social media to create broad change, setting the stage for the WPI Index’s public rankings to incentivize change. Part II provides research on new criteria we propose to be added to the Index for the next release. Lastly, part III covers how we used TikTok, events, and partnerships to help the Index gain notoriety. Altogether the paper suggests new directions and provides scientific research to further the goals of the WPI Index.
This thesis examines the interpretations derived from the Kac Ring Model, and the adding of a modification to the original model via “kick backs,” which can be interpreted to represent time reversals in the individual Kac rings. The results of this modification are analyzed, and their implications explored. There are three main parts to this thesis. Part 1 is a literature review which explains the working principles of the original Kac ring and explores its numerous applications. Part 2 describes the software and the theoretical & computational methodology used to implement the model and gather data. Part 3 analyzes the data gathered and makes a conclusion about its implications. There is an appendix included which contains some figures from Part 3 in a larger size, as it wasn’t possible to make the figures bigger within the text due to formatting.