<p>Brain connectome network visualization could help the neurologists inspect the brain structure easily and quickly. In the thesis, the model of the brain connectome network is visualized in both three dimensions (3D) environment and two dimensions (2D) environment. One is named “Brain Explorer for Connectomic Analysis” (BECA) developed by the previous research already. It could present the 3D model of brain structure with region of interests (ROIs) in different colors [5]. The other is mainly for the information visualization of brain connectome in 2D. It adopts the force-directed layout to visualize the network. However, the brain network visualization could not bring the user intuitively ideas about brain structure. Sometimes, with the increasing scales of ROIs (nodes), the visualization would bring more visual clutter for readers [3]. So, brain connectome network properties visualization becomes a useful complement to brain network visualization. For a better understanding of the effect of Alzheimer’s disease on the brain nerves, the thesis introduces several methods about the brain graph properties visualization. There are the five selected graph properties discussed in the thesis. The degree and closeness are node properties. The shortest path, maximum flow, and clique are edge properties. Except for clique, the other properties are visualized in both 3D and 2D. The clique is visualized only in 2D. For the clique, a new hypergraph visualization method is proposed with three different algorithms. Instead of using an extra node to present a clique, the thesis uses a “belt” to connect all nodes within the same clique. The methods of node connections are based on the traveling salesman problem (TSP) and Law of cosines. In addition, the thesis also applies the result of the clique to adjust the force-directed layout of brain graph in 2D to dramatically eliminate the visual clutter. Therefore, with the support of the graph properties visualization, the brain connectome network visualization tools become more flexible.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / Brain connectome network visualization could help the neurologists inspect the brain structure easily and quickly. In the thesis, the model of the brain connectome network is visualized in both three dimensions (3D) environment and two dimensions (2D) environment. One is named “Brain Explorer for Connectomic Analysis” (BECA) developed by the previous research already. It could present the 3D model of brain structure with region of interests (ROIs) in different colors [5]. The other is mainly for the information visualization of brain connectome in 2D. It adopts the force-directed layout to visualize the network. However, the brain network visualization could not bring the user intuitively ideas about brain structure. Sometimes, with the increasing scales of ROIs (nodes), the visualization would bring more visual clutter for readers [3]. So, brain connectome network properties visualization becomes a useful complement to brain network visualization. For a better understanding of the effect of Alzheimer’s disease on the brain nerves, the thesis introduces several methods about the brain graph properties visualization. There are the five selected graph properties discussed in the thesis. The degree and closeness are node properties. The shortest path, maximum flow, and clique are edge properties. Except for clique, the other properties are visualized in both 3D and 2D. The clique is visualized only in 2D. For the clique, a new hypergraph visualization method is proposed with three different algorithms. Instead of using an extra node to present a clique, the thesis uses a “belt” to connect all nodes within the same clique. The methods of node connections are based on the traveling salesman problem (TSP) and Law of cosines. In addition, the thesis also applies the result of the clique to adjust the force-directed layout of brain graph in 2D to dramatically eliminate the visual clutter. Therefore, with the support of the graph properties visualization, the brain connectome network visualization tools become more flexible.

Abdominal aortic aneurysms are a leading cause of death in the U.S. where 14,000 people die from aneurysm rupture and 178,000 are diagnosed each year. A novel alternative treatment for abdominal aortic aneurysms has been proposed, where a biodegradable polymer scaffold is photopolymerized in situ around the exterior of the aneurysm. This scaffold will mechanically constrain the aneurysm from further expansion, and will deliver a drug, doxycycline, to treat the underlying biological cause of the disease. In order for device development, a suitable polymer must be designed with appropriate mechanical properties, degradation rate, polymerization, and elution rate. Poly(β-amino ester) networks have been proposed as the material of choice; however, many of their structure-property relationships have yet to be determined. Therefore, the overall goal of this work is to determine the structure-property relationships of the poly(β-amino ester) networks in order to advance the design of the treatment, and has been divided into three objectives: (1) understand the structure-property relationships of poly(β-amino ester) networks, specifically the polymerization, degradation rate, and thermo-mechanical properties, (2) determine the impact of doxycycline incorporation on degradation rate and mechanical properties, (3) evaluate the effect of simulated physiological conditions on degradation rate and mechanical properties. In the initial chapters, the fundamental structure-property relationships are established between reactant chemical structure, step-growth polymerization, photopolymerization, thermo-mechanical properties, and degradation rate using a systematic approach of two homologous series of reactants. Further tailoring of degradation rate, water content, and modulus in vitro was performed by using a copolymer network. Doxycycline inhibited photopolymerization due to overlapping absorbance spectra with the photoinitiator, but full network formation occurred by increasing the photoinitiator concentration. Networks displayed varying controlled release rates, and the underlying release mechanism was determined for each network using established methods. In order to increase mechanical properties, a co-monomer, methyl methacrylate, was added to the network to increase the glass transition temperature, toughness, and deformation capacity. These co-networks displayed temporal-control of mechanical properties in simulated physiological conditions, since degradation caused a shift in the glass transition temperature, which changed the mechanical behavior of the network. The temporal-control of mechanical properties was further investigated under degradation conditions in vitro and in vivo. Due to the mechanically active loading environment in vivo, networks displayed a decrease in toughness, yet maintained mechanical properties similar to native biological tissues. These networks establish a multifunctional biomaterials platform with materials that can be easily synthesized, photopolymerized into various geometries, and sustain mechanical properties while undergoing degradation and therapeutic agent release.

<p> Studies to date involving the network properties of drainage systems have been theoretical in nature; and the environmental implications of these network characteristics have not been exploited to the extent that would appear warranted. This situation exists due to the lack of an accurate data source. Many studies have recognized this. inadequacy of the conventional data sources to meet the necessary requirements of efficiency (in data production and handling), accuracy, consistency and uniformity. </p> <p> The present study demonstrates that high altitude, small scale colour infrared photography is capable of providing drainage network data that fulfill all these basic requirements. Data derived from the three drainage basins, mapped from a variety of data sources, demonstrate three important points. The level of detail obtained from the small scale colour infrared photography far exceeds that available from more traditional data sources. Secondly, these network data are statistica+ly consistent with the traditional data sources. Thirdly, the basin characteristics derived from the high altitude data source show a marked association with the known surficial environments and an expected variation from one surficial environment to another. </p> / Thesis / Master of Arts (MA)