Recombinant Adeno-Associated Virus-Mediated Gene Therapy for Treatment of Familial Hypercholesterolemia in Rabbits

Familial hypercholesterolemia (FH) is a genetic disorder characterized by abnormally high concentrations of low-density lipoprotein-cholesterol (LDLcholesterol) in the blood that can contribute to heart disease. FH can result from a defect in the gene for the LDL receptor (LDL-R). FH patients lacking functional LDL-R may benefit from viral-mediated transfer of a functional copy of the open reading frame (ORF) of the LDL-R. Since a recombinant adeno-associated virus (rAAV) is not immunogenic and can be mass-produced, it shows promise for gene therapy applications. AAV6 and AAV8 have been shown to specifically transduce hepatocytes in several species, which normally remove the majority of LDL-cholesterol from the blood via LDL-R-mediated endocytosis. Because of the potential of rAAV to treat FH by delivery of a correct LDL-R ORF to hepatocytes, the liver specificity of these two AAV serotypes was evaluated. Additionally, rabbits were chosen as the animal model for this study because a specific strain of rabbits, Watanabe heritable hyperlipidemic (WHHL), adequately mimics the pathology of FH in humans. Exposure of rabbit liver to rAAV with the marker LacZ and subsequent inspection of liver tissue showed that AAV8 transduced rabbit liver more efficiently than AAV6. To assess the feasibility of producing a rAAV capable of transferring the LDL-R ORF to rabbit hepatocytes in vivo, rAAV8-LDL-R was mass-produced by a baculovirus system in suspension grown insect cells.

CONCURRENT LOCALIZATION AND MAPPING WITH SONAR SENSORS AND CONSIDERATION OF VEHICLE MOTION

Simultaneous Localization and Mapping (SLAM) is a procedure used to determine the location of a mobile vehicle in an unknown environment, while constructing a map of the unknown environment at the same time. Mobile platforms, which make use of SLAM algorithms, have industrial applications in autonomous maintenance, such as the inspection of flaws and defects in oil pipelines and storage tanks. A typical SLAM consists of four main components, namely, experimental setup (data gathering), vehicle pose estimation, feature extraction, and filtering. Feature extraction is the process of realizing significant features from the unknown environment such as corners, edges, walls, and interior features. In this work, an original feature extraction algorithm specific to distance measurements obtained through SONAR sensor data is presented. This algorithm has been constructed by combining the SONAR Salient Feature Extraction Algorithm and the Triangulation Hough Based Fusion with point-in-polygon detection. The reconstructed maps obtained through simulations and experimental data with the fusion algorithm are compared to the maps obtained with existing feature extraction algorithms. Based on the results obtained, it is suggested that the proposed algorithm can be employed as an option for data obtained from SONAR sensors in environment, where other forms of sensing are not viable. The algorithm fusion for feature extraction requires the vehicle pose estimation as an input, which is obtained from a vehicle pose estimation model. For the vehicle pose estimation, the author uses sensor integration to estimate the pose of the mobile vehicle. Different combinations of these sensors are studied (e.g., encoder, gyroscope, or encoder and gyroscope). The different sensor fusion techniques for the pose estimation are experimentally studied and compared. The vehicle pose estimation model, which produces the least amount of error, is used to generate inputs for the feature extraction algorithm fusion. In the experimental studies, two different environmental configurations are used, one without interior features and another one with two interior features. Numerical and experimental findings are discussed. Finally, the SLAM algorithm is implemented along with the algorithms for feature extraction and vehicle pose estimation. Three different cases are experimentally studied, with the floor of the environment intentionally altered to induce slipping. Results obtained for implementations with and without SLAM are compared and discussed. The present work represents a step towards the realization of autonomous inspection platforms for performing concurrent localization and mapping in harsh environments.

REDUCING TEACHER ATTRITION: THE ROLES AND RESPONSIBILITIES OF BUILDING PRINCIPALS IN A LARGE URBAN DISTRICT TO SUPPORT TEACHER RETENTION

The high rate of teacher attrition in urban schools is well documented. While this does not seem like a problem in Carter County, this equates to hundreds of teachers that need to be replaced annually. Since school year (SY) 2007-08, Carter County has lost over 7,100 teachers, approximately half of (50.1%) of whom resigned, often going to neighboring, higher-paying jurisdictions as suggested by exit survey data (SY2016-2020 Strategic Plan). Included in this study is a range of practices principals use to retain teachers. While the role of the principal is recognized as a critical element in teacher retention, few studies explore the specific practices principals implement to retain teachers and how they use their time to accomplish this task. Through interviews, observations, document analysis and reflective notes, the study identifies the practices four elementary school principals of high and relatively low attrition schools use to support teacher retention. In doing so, the study uses a qualitative cross-case analysis approach. The researcher examined the following leadership practices of the principal and their impact on teacher retention: (a) providing leadership, (b) supporting new teachers, (c) training and mentoring teaching staff, (d) creating opportunities for collaboration, (d) creating a positive school climate, and (e) promoting teacher autonomy. The following research questions served as a foundational guide for the development and implementation of this study: 1. How do principals prioritize addressing teacher attrition or retention relative to all of their other responsibilities? How do they allocate their time to this challenge? 2. What do principals in schools with low attrition rates do to promote retention that principals in high attrition schools do not? What specific practices or interventions are principals in these two types of schools utilizing to retain teachers? Is there evidence to support their use of the practices? The findings that emerge from the data revealed the various practices principals use to influence and support teachers do not differ between the four schools.