Cardiovascular disease (CVD) is the number one killer in the western world and includes heart failure (HF) after myocardial infarction (MI) and peripheral artery disease (PAD) in the limbs. There is a significant need for developing new therapies to treat patients with these cardiovascular diseases. Recently, the fields of tissue engineering and regenerative medicine have begun to develop naturally derived injectable biomaterials for treating cardiovascular diseases. These naturally derived biomaterials can be derived from a specific tissue's extracellular matrix (ECM) by applying decellularization techniques to either human or non-human tissue sources. This thesis provides crucial steps for the advancement and development of these biomaterials by investigating the importance of tissue specific and species specific sourcing, and investigating a humanized mouse model as a preclinical tool for testing naturally derived biomaterials. Included here is the development of two new human based biomaterials: a human myocardial matrix and a human umbilical cord matrix. The human myocardial matrix (HMM) was used as a tool for investigating allogeneic versus xenogeneic tissue sourcing and for studying the human immune response. The HMM was compared to a porcine myocardial matrix (PMM), which was previously developed as a potential therapy for treating HF after MI. The second material, the human umbilical cord matrix (hUC), was used for investigating the importance of developing tissue specific therapies for treating PAD. Also, the hUC was compared to a porcine skeletal muscle matrix (SKM), which was previously developed as a tissue specific therapy for treating PAD. It was shown in a preclinical animal study that the tissue specific naturally derived material has the potential for producing more desirable clinical outcomes than a non-tissue specific material. In conclusion, this thesis advanced the field of naturally derived biomaterials by presenting the humanized mouse model as a viable tool for preclinical testing and showed the potential for these biomaterials to be used as a therapy for treating PAD

Vanadium-dependent bromoperoxidase (VBPO) carries out the two-electron oxidation of halides using hydrogen peroxide to create a hypohalous acid-like intermediate, which then halogenates electron-rich organic molecules. While this enzyme is well-characterized in marine eukaryotic macroalgae, the activity and function of VBPO in prokaryotes remains vastly unexplored. A gene (sync_2681) encoding a putative VBPO was recently annotated in the genome of Synechococcus sp. CC9311, however the activity, function, and consequences of the expression of VBPO in cyanobacteria remained unknown. The first goal of this dissertation was to better characterize the activity of VBPO in CC9311, finding that the observed activity resulted from the single, expected gene product. One highly neglected aspect of studies of VBPO is the use of genetic manipulation to test natural physiological and ecological functions of this enzyme. Thus the second goal of this dissertation was to explore the function of VBPO in CC9311 through the creation of a mutant lacking a functional VBPO, then tracking activity under diverse conditions in coordination with global quantitative proteomics. The final goal of this dissertation was to explore the chemical consequences of the expression of VBPO in cyanobacteria, specifically measuring the production of halomethanes. VBPO has long been implicated in the production of polyhalogenated methanes, such as bromoform and dibromomethane through the use of eukaryotic algal protein extracts, however, this dissertation offers the first evidence that the VBPO in a marine cyanobacterium is responsible for the production of halogenated methanes in a laboratory setting

This paper compares the Act-R and Soar cognitive architectures, focusing on their theories of control. Act-R treats control (conflict resolution) as an automatic process, whereas Soar treats it as a potentially deliberate, knowledge-based process. The comparison reveals that Soar can model extremely flexible control, but has difficulty accounting for probabilistic operator selection and the independent effects of history and distance to goal on the likelihood of selecting an operator. In contrast, Act-R's control is well supported by empirical data, but has difficulty modeling task-switching, multiple interleaved tasks, and dynamic abandoning of subgoals. The comparison also reveals that many of the justifications for each architecture's control structure, such as some forms of flexible control and satisficing, are just as easily handled by both.

This paper describes and evaluates a computational model of anomalous data integration. This model makes use of three factors: entrenchment of the current theory (the amount of data explained), the relative probability of the contradictory explanations (based on conditional probabilities as part of the domain-knowledge), and the availability of alternative explanations based on learning. In an experimental study we found that the enu-enchment of a theory and the availability and likelihood of an alternative explanation influenced solution speed and the correctness of inferred causal explanations. However, in detail, the single levels of both factors were not cleariy distinguishable and did not follow the predictions. These findings suggest that entrenchment itself is not a major factor in determining the difficulty of a task. Instead, we hypothesize that task difficulty is dominated by a person's ability to construct an alternative explanation of a given situation, a factor that is only indirectly related to entrenchment.