Neurodegeneration in Alzheimer's disease is characterised by multiple pathologies including disrupted calcium homeostasis and elevated Zn2+ levels. Calbindin D28k (CB-D28k), which buffers Ca2+ and can bind Zn2+, was suspected to be involved in these abnormalities. The PDB structure of this EF-hand protein shows that not all hands are well formed. Docking and molecular dynamics calculations were employed to achieve the two goals in this project. The first goal was to get a better structure of CB-D28k to improve our understanding of its behavior. Calculations improved the structure protein: helix-loop-helix sequences were formed in all hands and most canonical interactions were formed in the four functional hands. The second goal was to test the Ca2+ binding capacity of Zn2+-bound CB-D28k. Analysis of calculated structures showed that the Ca2+ binding capability of Zn2+ bound protein was significantly compromised, permitting only half of the correct canonical interactions with the loop residues.

Neurodegeneration in Alzheimer's disease is characterised by multiple pathologies including disrupted calcium homeostasis and elevated Zn2+ levels. Calbindin D28k (CB-D28k), which buffers Ca2+ and can bind Zn2+, was suspected to be involved in these abnormalities. The PDB structure of this EF-hand protein shows that not all hands are well formed. Docking and molecular dynamics calculations were employed to achieve the two goals in this project. The first goal was to get a better structure of CB-D28k to improve our understanding of its behavior. Calculations improved the structure protein: helix-loop-helix sequences were formed in all hands and most canonical interactions were formed in the four functional hands. The second goal was to test the Ca2+ binding capacity of Zn2+-bound CB-D28k. Analysis of calculated structures showed that the Ca2+ binding capability of Zn2+ bound protein was significantly compromised, permitting only half of the correct canonical interactions with the loop residues.

Neurons and networks of the brain may use various strategies of computation to provide the neural substrate for sensation, perception, or cognition. To simplify the scenario, two of the most commonly cited neural codes are firing rate and temporal coding, whereby firing rates are typically measured over a longer duration of time (i.e., seconds or minutes), and temporal codes use shorter time windows (i.e., 1 to 100 ms). However, it is possible that neurons may use other strategies. Here, we highlight three methods of computation that neurons, or networks, of the brain may use to encode and/or decode incoming activity. First, we explain how single neurons of the brain can utilize a neuronal oscillation, specifically by employing a ‘spike-phase’ code wherein responses to stimuli have greater reliability, in turn increasing the ability to discriminate between stimuli. Our focus was to explore the limitations of spike-phase coding, including the assumptions of low firing rates and precise timing of action potentials. Second, we examined the ability of single neurons to track the onset of network bursting activity, namely ‘burst predictors’. In addition, we show that burst predictors were less susceptible to an in vitro model of neuronal stroke (i.e., excitotoxicity). Third, we discuss the possibility of distributed processing with neuronal networks of the brain. Specifically, we show experimental and computational evidence supporting the possibility that the population activity of cortical networks may be useful to downstream classification. Furthermore, we show that when network activity is highly variable across time, there is an increase in the ability to linearly separate the spiking activity of various networks. Overall, we use the results of both experimental and computational methods to highlight three strategies of computation that neurons and networks of the brain may employ.

Intelligent tutoring systems are effective for improving students’ learning outcomes (Bowen et al., 2013; Koedinger & Anderson, 1997; Pane et al., 2013). However, constructing tutoring systems that are pedagogically effective has been widely recognized as a challenging problem (Murray, 1999, 2003). In this thesis, I explore the use of computational models of apprentice learning, or computer models that learn interactively from examples and feedback, to support tutor development. In particular, I investigate their use for authoring expert-models via demonstrations and feedback (Matsuda et al., 2014), predicting student behavior within tutors (VanLehn et al., 1994), and for testing alternative learning theories (MacLellan, Harpstead, Patel, & Koedinger, 2016). To support these investigations, I present the Apprentice Learner Architecture, which posits the types of knowledge, performance, and learning components needed for apprentice learning and enables the generation and testing of alternative models. I use this architecture to create two models: the DECISION TREE model, which non- incrementally learns when to apply its skills, and the TRESTLE model, which instead learns incrementally. Both models both draw on the same small set of prior knowledge for all simulations (six operators and three types of relational knowledge). Despite their limited prior knowledge, I demonstrate their use for efficiently authoring a novel experimental design tutor and show that they are capable of achieving human-level performance in seven additional tutoring systems that teach a wide range of knowledge types (associations, categories, and skills) across multiple domains (language, math, engineering, and science). I show that the models are capable of predicting which versions of a fraction arithmetic and box and arrows tutors are more effective for human students’ learning. Further, I use a mixedeffects regression analysis to evaluate the fit of the models to the available human data and show that across all seven domains the TRESTLE model better fits the human data than the DECISION TREE model, supporting the theory that humans learn the conditions under which skills apply incrementally, rather than non-incrementally as prior work has suggested (Li, 2013; Matsuda et al., 2009). This work lays the foundation for the development of a Model Human Learner— similar to Card, Moran, and Newell’s (1986) Model Human Processor—that encapsulates psychological and learning science findings in a format that researchers and instructional designers can use to create effective tutoring systems.

Advancements in genetically based technologies have begun to allow us to better understand the relationships between underlying neural activity and the patterns of measurable behavior that can be reproducibly studied in the laboratory. As this field develops, there are key limitations to the currently available technologies hindering their full potential to deliver meaningful datasets. The limitations which are most critical to advancement of these technologies in behavioral neuroscience are: the temporal resolution at which physiological events can be windowed, the divergent molecular pathways in signal transduction that introduce ambiguity into the output of activity sensors, and the impractical size of the tool’s genetic material—requiring 3-4 separate AAV vectors to deliver a fully functional system into a cell. To address these limitations and help bring the potential of these types of technologies into better realization, we have engineered a nucleus localized light-sensitive Ca2+-dependent gene expression system based on AsLOV2 and the downstream responsive element antagonist modulator (DREAM). The design and engineering of each component was performed in such a way to: 1) preserve behaviorally relevant temporal dynamics, 2) preserve signal fidelity appropriate for studying experience-driven neural activity patterns and their relationship to specific animal responses, and 3) have full delivery of the system’s genetic material via a single AAV vector. The system was tested in vitro and subsequently in vivo with neural activity induced by Channelrhodopsin, and could be used in the future with behaviorally-driven neural activity. To our knowledge this is the first optogenetic tool for the practical use of linking activity-dependent gene activation in response to direct nuclear calcium transduction.

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No description available.

Adolescence is a transition period during which individuals have increasing autonomy in decision-making for themselves (Casey, Jones, and Hare, 2008), often choosing among options about which they have little knowledge and experience. This process of individuation and independence is reflected as real-world risk taking behaviors (Silveri et al., 2004), including higher motor accidents, unwanted pregnancies, sexually transmitted diseases, drug addictions, and death (Casey et al., 2008). The extent to which adolescents continue to display increased behaviors with negative consequences during this period of life depends critically on their ability to explore and learn potential consequences of actions within novel environments. This learning is not limited to the value of the outcome associated with making choices, but extends to the levels of risk taken in making those choices. While the existing adolescence literature has focused on neural substrates of risk preferences, how adolescents behaviorally and neurally learn about risks remain unknown. Success or failure to learn the potential variability of these consequences, or the risks involved, in ambiguous decisions is hypothesized to be a crucial process to allow the individuals to make decisions based on their risk preferences. The alternative in which adolescents fail to learn about the risks involved in their decisions leaves the adolescent in a state of continued exploration of the ambiguity, reflected as continued risk-taking behavior. This dissertation comprises 2 papers. The first paper is a perspective paper outlining a paradigm that risk taking behavior observed during adolescents may be a product of each adolescent's abilities to learn about risk. The second paper builds on the hypothesis of the perspective paper by first examining neural correlates of risk learning and quantifying individual risk learning abilities and then examining longitudinal risk learning developmental trajectories in relation to real-world risk-trajectories in adolescent individuals. / Doctor of Philosophy / Adolescence is a transition period during which individuals have increasing autonomy in decision-making for themselves, often choosing among options about which they have little knowledge and experience. This process of individuation and independence begins with the adolescent exploring their world and those options they are ignorant of. This is reflected as real-world risk-taking behaviors, including higher motor accidents, unwanted pregnancies, sexually transmitted diseases, drug addictions, and death. We hypothesized and tested the premise that whether adolescents who succeeded or fail to learn about the negative consequences of their actions while exploring will continue to partake in behaviors with negative consequences. This learning is not limited to the value of the outcome associated with making choices, but extends to the range of possible outcomes of the choices or the risks involved. Indeed, the failure to learn the risks involved in decisions with no known information show continued and greater risk-taking behavior, perhaps remaining in a state of continued exploration of the unknown.

CD4+ T cells mediate and orchestrate a tremendous panoply of lymphoid cell subsets in the human immune system. CD4+ T cells are able to differentiate into either effector pro-inflammatory or regulatory anti-inflammatory subsets depending on the cytokine milieu in their environment. This complex process is mediated through a variety of cytokines and soluble factors. Yet, the mechanisms of action underlying the process of differentiation and plasticity of this interesting immune subset are incompletely understood. To gain a better understanding of the CD4+ T cell differentiation and function, here we present an array of different strategies to model and validate CD4+ T cell differentiation and heterogeneity. The approaches presented here vary from ordinary-differential equation-based to agent-based simulations, from data-driven to theory-based approaches, and from intracellular mathematical to tissue-level or cellular modeling. The knowledge generated throughout this dissertation exemplifies how a combination of computational modeling with experimental immunology can efficiently advance the scene on CD4+ T cell differentiation. In this thesis I present i) an overview on CD4+ T cell differentiation and an introduction to which computational strategies have been adopted in the field to tackle with this problem, ii) ODE-based modeling and predictions on Th17 plasticity modulated by PPARγ, iii) ODE- and ABM-based cellular level modeling of immune responses towards Helicobacter pylori and the role of CD4+ T cell subsets on it, iv) Intracellular strategies to validate a potential therapeutic target within a CD4+ T cell to treat H. pylori infection, and finally v) data-driven strategies to model Th17 differentiation based on sequencing or microarray data to generate novel predictions on specific components. I present both mathematical and computational work as well as experimental work, in vitro and in vivo with animal models, to demonstrate how computational immunology and immunoinformatics can help, not only in understanding this complex process, but also in the development of immune therapeutics for infectious, allergic and immune-mediated diseases. / Ph. D.

This dissertation delves into using entropy, a fundamental concept in thermodynamics and information theory, for analyzing social dynamics. Entropy relies on a probability distribution over states, which is crucial for quantifying social systems’ complexity, unpredictability, and self-organization behavior. Through an interdisciplinary approach encompassing social physics, agent-based modeling, and sentiment analysis, the research investigates the role of entropy and its underlying probability distribution in three key areas: residential segregation, financial systems, and sentiment fluctuations in online social networks. By integrating entropy-based models that leverage the probability distribution over states, the research aims to enhance the understanding of complex social phenomena and provide practical insights for policymakers, urban planners, and social media ex- parts. The findings demonstrate the potential of entropy as a unifying framework for studying social sciences, economics, and digital social systems, highlighting the growing relevance of probability distributions in decoding patterns of social dynamics. The dissertation contributes to the theoretical basis for modeling and predicting the complexity of social networks using entropy and its associated probability distribution, with significant implications for various domains.