The FDA-approved drug bexarotene has been predominantly utilized for the treatment of cutaneous T-cell lymphoma (CTLC), but has shown promise as an off label treatment for various other cancers as well as Alzheimer's disease (AD). However, harmful side effects such as hypothyroidism have catalyzed a search for alternative rexinoids which retain similar levels of RXR agonism while reducing the undesirable effects incurred by bexarotene. This honors thesis outlines the steps taken to design and synthesize novel analogues of the selective retinoid-X-receptor (RXR) agonist 4-[1-(3,5,5,8,8-pentamethyl-5,6,7,8-tetrahydro-2-naphthyl)ethynyl]benzoic acid (bexarotene). Corresponding NMR spectra indicates the successful construction of four novel compounds which are structurally similar to known, biologically-evaluated rexinoids that have induced fewer side effects while stimulating greater levels of RXR selectivity as compared to bexarotene. Future In vitro analyses of these four analogues coupled with the recognized efficacy of their parent compounds demonstrate the chemotherapeutic potential of structurally modified bexarotene analogues
Visual analytics provides methods for data exploration, pattern recognition, and knowledge discovery. However, despite the long history of geovisualizations and network visual analytics, little work has been done to develop visual analytics tools that focus specifically on geographically networked phenomena. This thesis develops a variety of visualization methods to present data values and geospatial network relationships, which enables users to interactively explore the data. Users can investigate the connections in both virtual networks and geospatial networks and the underlying geographical context can be used to improve knowledge discovery. The focus of this thesis is on social media analysis and geographical hotspots optimization. A framework is proposed for social network analysis to unveil the links between social media interactions and their underlying networked geospatial phenomena. This will be combined with a novel hotspot approach to improve hotspot identification and boundary detection with the networks extracted from urban infrastructure. Several real world problems have been analyzed using the proposed visual analytics frameworks. The primary studies and experiments show that visual analytics methods can help analysts explore such data from multiple perspectives and help the knowledge discovery process.