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Item Development and analysis of lipidomics procedures for the causal investigation of Alzheimer's disease(Montana State University - Bozeman, College of Letters & Science, 2022) Koch, Max Richard; Chairperson, Graduate Committee: Edward Dratz; This is a manuscript style paper that includes co-authored chapters.Uncovering sets of molecular features which cause a healthy metabolic state to transition to one of disease, requires extensive experimentation and often presents a difficult analysis. In the case of neurodegenerative diseases, such as Alzheimer's Disease, simply obtaining suitable samples can be a challenging endeavor. Many current 'Omics' techniques excel at profiling a vast array of molecules, such as water-soluble metabolites, lipids, and proteins, in order to compare groups of samples from healthy and diseased organisms. Such approaches primarily use various associations between molecules and disease to identify biomarkers. However, these 'omics' experiments frequently result in intriguing biological hypotheses, but to date have rarely provided mechanistic explanations. How then, can mechanistic explanations be recovered from metabolite or lipid profile data? In our work, we applied these methods to 6 Alzheimer's diseased brain samples and 6 age matched controls. When analyzed via mass spectrometry, lipids which differed significantly between control and disease were identified, but this information was not able to'provide mechanistic insight. The beginning of any 'omics' based experiment starts with the extraction of the desired molecules. In order to assess the efficiency of three different lipid extraction methods, a lipid standard was extracted from a matrix composed of rat liver tissue and analyzed by mass spectrometry. The classic Folch extraction was found to be best at reproducibly extracting a wide range of lipids. Several of the lipids identified from human brains showed oxidative damage. Lastly, 5 statistical measures of dependence and 3 network algorithms were investigated for their ability to reconstruct mechanistic relationships in a dynamic model of arachidonic acid metabolism. Many of the metabolites of arachidonic acid are oxidation products. Under conditions of high noise and relatively few samples, standard measures of correlation, such as Pearson's correlation, Spearman's correlation and Kendall's Tau were found to perform the best. Metrics which incorporate nonlinear metabolic relations and network algorithms were found to be applicable, when sample size is large and the signal to noise ratio is close to l.Item Oregon promise: a look at institutions and decisions made as a result of Oregon Promise Policy(Montana State University - Bozeman, College of Education, Health & Human Development, 2020) Rivenes, Teresa Renee; Chairperson, Graduate Committee: Carrie B. MyersHow do free college initiatives, such as the Oregon Promise, impact decision-making at mid-sized community colleges? How have community colleges leveraged free college initiatives to increase and provide systemic support to vulnerable students? The purpose of this multiple case study was to understand the decision-making process as expressed by community college leadership and to explore the process of change. The study examined four mid-sized Oregon community colleges which constituted the entire population of mid-sized community colleges per the Carnegie classification system of size, in the state of Oregon. The participants in the study included seasoned Vice Presidents whose primary role was to implement initiatives, policies, procedures and oversee student success at their campus. The information provided serves to inform change in higher education. Attention was given to Neo-Institutionalism and Tierney's Decision-making theories as well as social-constructionist and critical social frameworks. The results indicate that system change is far more difficult than one might imagine given the multiple stakeholders, vision of shared governance, and competing interests. This study concludes with suggestions for implementing system change and the need for further research.Item The emergence of collective behavior on social and biological networks(Montana State University - Bozeman, College of Letters & Science, 2018) Wilander, Adam Troy Charles; Chairperson, Graduate Committee: Scott McCallla; Dissertation contains an article of which Adam Troy Charles Wilander is not the main author.In this thesis, we broadly examine collective behaviors in various social and biological contexts. Aggregation, for instance, is a natural phenomenon that occurs in a variety of contexts; it is observed in schools of fish, flocks of birds, and colonies of bacteria, among others. This behavior can be found in some agent-based models, where it is typically assumed every pair of individuals interact according to a simple set of rules. In the first half of this thesis, we study a particular, well-understood aggregation model upon relaxation of the assumption that every individual interacts with every other. We review prior results on this topic -- when the underlying structure of interactions is an Erdos-Renyi graph. Seeking to incorporate community structure into the network, we establish the analogous problem under a class of networks called stochastic block graphs; a particular aspect of the system's metastable dynamics is explored upon varying the graph's connection densities. Finally, we evaluate the potential to leverage this system's dynamics in order to recover community structure (given a known graph as input). In the second half of this thesis, we similarly explore the aggregate behaviors of synchronization and desynchronization, appearing in diverse settings such as the study of metabolic oscillations and cell behaviors over time, respectively. Previous studies have leveraged a model in which repressilator entities are connected by a diffusive quorum sensing mechanism; these have shown (numerically) that the complex composition of observable behaviors depends upon the insertion point of the upregulating protein in the feedback loop. We rigorously prove a version of this; for negative feedback, negative signaling (Nf-Ns) systems we find only a unique stable equilibrium or a stable oscillation is possible. Additionally, we observe (numerically) the complex multistable dynamics that arise when a positive signal is included in the feedback loop and characterize this shift as a saddle node bifurcation of a cubic curve.Item On the usability of continuous time bayesian networks: improving scalability and expressiveness(Montana State University - Bozeman, College of Engineering, 2017) Perreault, Logan Jared; Chairperson, Graduate Committee: John SheppardThe Continuous Time Bayesian Network (CTBN) is a model capable of compactly representing the behavior of discrete state systems that evolve in continuous time. This is achieved by factoring a Continuous Time Markov Process using the structure of a directed graph. Although CTBNs have proven themselves useful in a variety of applications, adoption of the model for use in real-world problems can be difficult. We believe this is due in part to limitations relating to scalability as well as representational power and ease of use. This dissertation attempts to address these issues. First, we improve the expressiveness of CTBNs by providing procedures that support the representation of non-exponential parametric distributions. We also propose the Continuous Time Decision Network (CTDN) as a framework for representing decision problems using CTBNs. This new model supports optimization of a utility value as a function of a set of possible decisions. Next, we address the issue of scalability by providing two distinct methods for compactly representing CTBNs by taking advantage of similarities in the model parameters. These compact representations are able to mitigate the exponential growth in parameters that CTBNs exhibit, allowing for the representation of more complex processes. We then introduce another approach to managing CTBN model complexity by introducing the concept of disjunctive interaction for CTBNs. Disjunctive interaction has been used in Bayesian networks to provide significant reductions in the number of parameters, and we have adapted this concept to provide the same benefits within the CTBN framework. Finally, we demonstrate how CTBNs can be applied to the real-world task of system prognostics and diagnostics. We show how models can be built and parameterized directly using information that is readily available for diagnostic models. We then apply these model construction techniques to build a CTBN describing a vehicle system. The vehicle model makes use of some of the newly introduced algorithms and techniques, including the CTDN framework and disjunctive interaction. This extended application not only demonstrates the utility of the novel contributions presented in this work, but also serves as a template for applying CTBNs to other real-world problems.Item System analysis of the environmental impact of recreation : the dynamics of the fishing ecosystem(Montana State University - Bozeman, College of Engineering, 1973) Bjerke, Ardine Leslie