The goal of this model is to create and to implement qualitative model of the signal track of olfactory sensory neuron, including the feedback with a focus on diffusion of substances that allows to conduct more simulations for the better understanding of dynamics of the signal track. This model is expected to be used for the simulation of influencing during the activation of two receptors in firstly defined distance. Model was created and therefore implemented in a programming language Python with the use of library STEPS. Then I have conducted sensitivity analysis by a method Morris OAT on the model, together with an optimization with the usage of change of individual parameters with a great importance on the output of the model. Model is conducting good and biologically comparable results when there are from 10 to 100 active receptors at the beginning of the track. Unfortunately with a lower numbers, the results are not valid and therefore not to be used for the simulation of influencing of two activated receptors. Despite this is a main benefit of the work the model of signal transduction for the whole signal track with an included feedback and emphasis on diffusion. Another benefit is a set of scripts for the sensitivity analysis by a method Morris OAT and optimization.

Indiana University-Purdue University Indianapolis (IUPUI) / The voltage-gated sodium channels (VGSCs) are critical determinants of neuronal excitability. They set the threshold for action potential generation. The VGSC isoform Nav1.6 participates in various physiological processes and contributes to distinct pathological conditions, but how Nav1.6 function is differentially regulated in different cell types and subcellular locations is not clearly understood. Some VGSC isoforms are subject to S-palmitoylation and exhibit altered functional properties in different S-palmitoylation states. This dissertation investigates the role of S-palmitoylation in Nav1.6 regulation and explores the consequences of S-palmitoylation in modulating neuronal excitability in physiological and pathological conditions. The aims of this dissertation were to 1) provide biochemical and electrophysiological evidence of Nav1.6 regulation by S-palmitoylation and identify specific S-palmitoylation sites in Nav1.6 that are important for excitability modulation, 2) determine the biophysical consequences of epilepsy-associated mutations in Nav1.6 and employ computational models for excitability prediction and 3) test the modulating effects of S-palmitoylation on aberrant Nav1.6 activity incurred by epilepsy mutations. To address these aims, an acyl-biotin exchange assay was used for Spalmitoylation detection and whole-cell electrophysiology was used for channel characterization and excitability examination. The results demonstrate that 1) Nav1.6 is biochemically modified and functionally regulated by S-palmitoylation in an isoform- and site-specific manner and altered S-palmitoylation status of the channel results in substantial changes of neuronal excitability, 2) epilepsy associated Nav1.6 mutations affect different aspects of channel function, but may all converge to gain-of-function alterations with enhanced resurgent currents and increased neuronal excitability and 3) S-palmitoylation can target specific Nav1.6 properties and could possibly be used to rescue abnormal channel function in diseased conditions. Overall, this dissertation reveals S-palmitoylation as a new mechanism for Nav1.6 regulation. This knowledge is critical for understanding the potential role of S-palmitoylation in isoform-specific regulation for VGSCs and providing potential targets for the modulation of excitability disorders. / 2022-05-06

Plasma turbulence associated with the creation of an artificial dust layer in the earth's ionosphere is investigated. The Charged Aerosol Release Experiment (CARE) aims to understand the mechanisms for enhanced radar scatter from plasma irregularities embedded in dusty plasmas in space. Plasma irregularities embedded in a artificial dusty plasma in space may shed light on understanding the mechanism for enhanced radar scatter in Noctilucent Clouds (NLCs) and Polar Mesospheric Summer Echoes (PMSEs) in the earth's mesosphere. Artificially created, charged-particulate layers also have strong impact on radar scatter as well as radio signal propagation in communication and surveillance systems. The sounding rocket experiment was designed to develop theories of radar scatter from artificially created plasma turbulence in charged dust particle environment. Understanding plasma irregularities embedded in a artificial dusty plasma in space will also contribute to addressing possible effects of combustion products in rocket/space shuttle exhaust in the ionosphere. In dusty space plasmas, plasma irregularities and instabilities can be generated during active dust aerosol release experiments. Small scale irregularities (several tens of centimeter to meters) and low frequency waves (in the ion/dust scale time in the order of second) are studied in this work, which can be measured by High Frequency (HF), Very High Frequency (VHF) and Ultra High Frequency (UHF) radars. The existence of dust aerosol particles makes computational modeling of plasma irregularities extremely challenging not only because of multiple spatial and temporal scale issue but also due to complexity of dust aerosol particles. This work will provide theoretical and computational models to study plasma irregularities driven by dust aerosol release for the purpose of designing future experiments with combined ground radar, optical and in-situ measurement. In accordance with linear analysis, feasible hybrid computational models are developed to study nonlinear evolution of plasma instabilities in artificially created dusty space plasmas. First of all, the ion acoustic (IA) instability and dust acoustic (DA) instability in homogenous unmagnetized plasmas are investigated by a computational model using a Boltzmann electron assumption. Such acoustic-type instabilities are attributed to the charged dust and ion streaming along the geomagnetic field. Secondly, in a homogenous magnetized dusty plasma, lower-hybrid (LH) streaming instability will be generated by dust streaming perpendicular to the background geomagnetic field. The magnetic field effect on lower-hybrid streaming instabilities is investigated by including the ratio of electron plasma frequency and electron gyro frequency in this model. The instability in weakly magnetized circumstances agree well with that for the ion acoustic (IA) instability by a Boltzmann model. Finally, in an inhomogeneous unmagnetized/magnetized dust boundary layer, possible instabilities will be addressed, including dust acoustic (DA) wave due to flow along the boundary and lower-hybrid (LH) sheared instability due to flow cross the boundary. With applications to active rocket experiments, plasma irregularity features in a linear/nonlinear saturated stage are characterized and predicted. Important parameters of the dust aerosol clouds that impact the evolution of waves will be also discussed for upcoming dust payload generator design. These computational models, with the advantage of following nonlinear wave-particle interaction, could be used for space dusty plasmas as well as laboratory dusty plasmas. / Ph. D.

Believable Non-Player Characters (NPCs) help motivate player engagement with narrative-driven games. An important aspect of believable characters is their contextually-relevant reactions to changing situations, which emotion often drives in humans. Therefore, giving NPCs "emotion" should enhance their believability. For adoption in industry, it is important to create processes for developing tools to build NPCs "with emotion" that fit with current development practices. Psychological validity—the grounding in affective science—is a necessary quality for plausible emotion-driven NPC behaviours. Computational Models of Emotion (CMEs) are one solution because they use at least one affective theory/model in their design. However, CME development tends to be insufficiently documented such that its processes seem unsystematic and poorly defined. This makes it difficult to reuse a CME’s components, extend or scale them, or compare it to other CMEs. This work draws from software engineering to propose three methods for acknowledging and limiting subjectivity in CME development to improve their reusability, maintainability, and verifiability: a systematic, document analysis-based methodology for choosing a CME’s underlying affective theories/models using its high-level design goals and design scope, which critically influence a CME’s functional requirements; an approach for transforming natural language descriptions of affective theories into a type-based formal model using an intermediate, second natural language description refining the original descriptions and showing where and what assumptions informed the formalization; and a literary character analysis-based methodology for developing acceptance test cases with known believable characters from professionally-crafted stories that do not rely on specific CME designs. Development of EMgine, a game development CME for generating NPC emotions, shows these methods in practice. / Dissertation / Doctor of Philosophy (PhD) / Video games can deeply engage players using characters that appear to have emotionally-driven behaviours. One way that developers encode and carry knowledge between projects is by creating development tools, allowing them to focus on how they use that knowledge and create new knowledge. This work draws from software engineering to propose three methods for creating development tools for game characters “with emotion”: a process for analyzing academic emotion literature so that the tool’s functions are plausible with respect to real-life emotion; a process for translating academic emotion literature into mathematical notation; and a process for creating tests to evaluate these kinds of development tools using narrative characters. The development of an example tool for creating game characters "with emotion", EMgine, demonstrates these methods and serves as an example of good development practices.

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Categorization is a complex decision-making process that requires observers to collect information about stimuli using their senses. While research on visual or auditory categorization is extensive, there has been little attention given to tactile categorization. Here we developed a paradigm for studying tactile categorization using 3D-printed objects. Furthermore, we derived a categorization model using Bayesian inference and tested its performance against human participants in our categorization task. This model accurately predicted participant performance in our task but consistently outperformed them, even after extending the learning period for our participants. Through theoretical exploration and simulations, we demonstrated that the presence of sensory measurement noise could account for this performance gap, which we determined was a present factor in participants undergoing our task through a follow-up experiment. Including measurement noise led to a better-fitting model that was able to match the performance of our participants much more closely. Overall, the work in this thesis provides evidence for the efficacy of a tactile categorization experimental paradigm, demonstrates that a Bayesian model is a good fit and predictor for human categorization performance, and underscores the importance of accounting for sensory measurement noise in categorization models. / Dissertation / Doctor of Philosophy (PhD) / The process of categorization is an essential part of our daily life as we encounter various things in the world. Here we explore a model that attempts to explain this process. This model is derived using Bayesian inference and was applied to human behavioural data in a categorization task. We found that the model accounted for most of the performance of our participants but consistently outperformed them. We conducted simulations to explore and demonstrate that this difference is primarily due to the presence of sensory noise in participants. Once we accounted for this noise, we found that our model predicted human performance even more accurately. The work in this thesis demonstrates that a Bayesian Categorization Model which accounts for sensory noise is a good fit and predictor for human performance on categorization tasks.

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