Bioculture Journal
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The neural dynamics and intrinsic properties of heterogeneous ventral tegmental area populations in motivated behavior
No full textThesis (Ph.D.)--University of Washington, 2024Ventral tegmental area (VTA) dopamine neurons regulate reward-related associative learning and reward-driven motivated behaviors, but how these processes are coordinated by distinct VTA neuronal subpopulations remains unresolved. Here we examine the neural correlates of reward-related prediction-error, action, cue, and outcome encoding as well as effort exertion and reward anticipation during reward-seeking behaviors. We compare the contribution of two primarily dopaminergic and largely non-overlapping VTA subpopulations, all VTA dopamine neurons, and VTA GABAergic neurons of the mouse midbrain to these processes. The dopamine subpopulation that projects to the nucleus accumbens (NAc) core preferentially encodes prediction-error and reward-predictive cues. In contrast, the dopamine subpopulation that projects to the NAc shell preferentially encodes goal-directed actions and reflects relative reward anticipation. VTA GABA neuron activity strongly contrasts VTA dopamine population activity and preferentially encodes reward outcome and retrieval. Electrophysiology, targeted optogenetics, and whole-brain input mapping reveal heterogeneity among VTA dopamine subpopulations. Our results demonstrate that VTA subpopulations carry distinct reward-related learning and motivation signals and reveal a striking pattern of functional heterogeneity among projection-defined VTA dopamine neuron populations
Fire Regimes: Rhetoric and the Local Climate Politics of Wildfire
No full textThesis (Ph.D.)--University of Washington, 2024Fire history, topography, climate, and vegetation make up a fire regime: an ecological tool usedto determine a general pattern of wildfire in a particular ecosystem over time. By situating
clashes between local politics and federal and state projects to scale policies regulating fire
regimes, this project looks at how the ecological is impacted by the rhetorical through public
debate. Here, I look at the role of science in mediating, further aggravating, and sometimes
creating some understanding in relations between the state and locals around wildfire through
definitions. First, I demonstrate how definitional rhetoric was instrumental in the United States
Forest Service gaining control over the management of our nation’s forests. I then move 100
years in the future to the Oregon Labor Day Fires of 2020 where public officials (mis)used
definitional rhetoric to rhetorically maneuver political arson rumors while evacuating residents.
Lastly, I analyze the controversial Oregon Wildfire Risk Map created in the aftermath of the
2020 fires, showing how the public contested the state’s definition of “risk” and how scientists
and public officials recovered the map by using the more scientifically specific definition of
“hazard.” Together, I weave together a story of how tensions between the government and local
residents came to shape and be shaped by wildfires in the American West
Uncovering the mechanistic basis of intracellular Raf inhibitor sensitivity reveals synergistic cotreatment strategies
No full textThesis (Ph.D.)--University of Washington, 2024Raf kinases are crucial effectors in the Ras-Raf-Mek-Erk signaling pathway, making them important targets for the development of cancer therapeutics. This study investigates the variable potency of DFG-out-stabilizing Raf inhibitors in mutant KRas-expressing cell lines. We demonstrate that inhibitor potency correlates with basal Raf activity, with more active Raf being more susceptible to inhibition. We further show that DFG-out-stabilizing inhibitors disrupt high-affinity Raf-Mek interactions, promoting the formation of inhibited Raf dimers. Furthermore, we identify cobimetinib as a Mek inhibitor that uniquely sensitizes Raf to DFG-out inhibitors by disrupting autoinhibited Raf-Mek complexes. Building on this insight, we developed cobimetinib analogs with enhanced sensitization properties. Our findings provide a mechanistic framework for understanding the cellular determinants of DFG-out-stabilizing inhibitor sensitivity and offer strategies for optimizing synergistic Raf-Mek inhibitor combinations
Effects of Spinal Cord Stimulation on Neuromechanics of Gait for Children with Cerebral Palsy
No full textThesis (Ph.D.)--University of Washington, 2024Cerebral palsy (CP) is one of the largest causes of motor disability in children. Due to an injury in the central nervous system around the time of birth, children with CP have altered motor control and function that affects their movement. Common interventions to support mobility in children with CP often target secondary complications such as bone deformities, muscle contracture, and spasticity. Interventions are needed that can non-invasively support mobility in children with CP while targeting the underlying nervous system injury. Expertise in engineering, biomechanics, neuroscience, and rehabilitation can help to design and evaluate novel interventions for children with CP. This dissertation investigates how three novel interventions: spinal stimulation, interval treadmill training, and exoskeletons impact movement for children with CP.Transcutaneous spinal cord stimulation (tSCS) is a novel technique for modulating neural activity. Previous research suggests that tSCS can boost sensory feedback as it enters the spinal cord and may be effective for improving motor output when applied during rehabilitation. The evidence thus far for how tSCS may impact movement for children with CP is minimal but suggests that tSCS may improve whole-body motor function and coordination of muscle activity, even after one session of use. We enrolled four children with CP in a pilot study where they received 24 sessions each of short-burst interval treadmill training (SBLTT) only and SBLTT with tSCS. We found that tSCS+SBLTT reduced spasticity while maintaining walking function and reducing self-reported fatigue more than SBLTT only. However, we are continuing to understand the underlying neural and biomechanical changes that drive these functional improvements, as well as more about how these changes translate to community mobility.
Increased sensory information from tSCS+SBLTT may change how the body controls movement. Understanding the biomechanical changes with tSCS+SBLTT can elucidate the mechanisms driving functional improvements. In the same study of four children with CP, we quantified changes in muscle activity and joint kinematics. We found that participants walked in a more upright posture, with more knee and hip extension, after tSCS+SBLTT. Muscle co-contraction was also reduced, primarily in the thigh. Participants also had a reduction in motor control complexity after SBLTT only, but not after tSCS+SBLTT, despite reductions in spasticity. These results suggest that tSCS+SBLTT may improve coordination of movement and lead to more energy efficient walking patterns in children with CP.
One challenge when implementing novel rehabilitation techniques is tracking individual progress. Understanding why and how someone’s walking changes with rehabilitation is important for determining the best method for reaching their movement goals. This can be challenging to quantify due to the natural variability in movement, nonlinear rehabilitation progression, and additional factors that can mask change. We developed a causal modeling and machine learning paradigm to measure the direct effect of SBLTT on step length in children with CP. Using a virtual dataset, we validated that this paradigm can accurately capture nonlinear changes in step length with simulated training data. We then applied the causal modeling and machine learning paradigm to show that three of four children with CP improved step length with SBLTT, even after controlling for changes like treadmill speed and incline. This framework can be used to track individual therapy progression and determine how an intervention is affecting an individual's movement, remaining accurate even when there is high variability in the data.
Another aspect of translating novel techniques into rehabilitative care is understanding how they affect muscle fatigue during training. Overexertion of muscles that causes fatigue can limit motor learning of new tasks. Children with CP fatigue faster than peers, making fatigue an important consideration when developing rehabilitation programs. We quantified how tSCS and a resistive ankle exoskeleton, designed to increase muscle engagement, affected fatigue in nine children with CP. Each participant did 20-minutes of walking on separate days with no devices, tSCS only, bilateral resistive ankle exoskeletons (Exo), and tSCS+Exo. We found that the Exo session had the greatest rate of fatigue within the first 5-minutes of training, while there was an increase in muscle engagement with minimal signs of fatigue during the tSCS+Exo training. These findings suggest that the resistive exoskeleton may be more fatiguing on muscles, but that tSCS reduces the rate of fatigue. The use of these tools together may be beneficial for optimizing engagement in rehabilitation programs while supporting neuroplasticity.
This dissertation contributes to the fields of mechanical engineering, rehabilitation engineering, and neuroscience through a detailed investigation into how novel rehabilitation strategies affect movement for children with CP. We employ methods across these fields to comprehensively deepen our understanding of human movement and evaluating individual responses to rehabilitation. This work will support future translation of novel, non-invasive rehabilitation strategies into clinical care with tools to support how we can optimize and personalize their implementation
Deep learning frameworks for modeling how neural circuits learn
No full textThesis (Ph.D.)--University of Washington, 2024The brain's prowess in learning and adapting remains an enigma, particularly in its approach to the 'temporal credit assignment' problem. How do neural circuits determine which specific states and connections contribute to future outcomes, and subsequently adjust these for enhanced learning? My thesis addresses this by combining insights from the latest large-scale neuroscience data and recent deep learning theoretical tools. The first two projects introduce novel learning rules inspired by the Allen Institute's transcriptomics data, which revealed widespread and intricate cell-type-specific interactions among neuromodulatory molecules. This rule enables neurons to propagate credit information efficiently, enhancing learning performance beyond that of biologically plausible predecessors. Extensive computational experiments confirm the significant role of local neuromodulatory signals in learning, offering new perspectives on neural information processing. My third project assesses the generalization capabilities of bio-plausible learning rules through the lens of deep learning theory, particularly focusing on the curvature of the loss landscape via the loss’ Hessian eigenspectrum. Our findings reveal that these rules often settle in high-curvature regions of the loss landscape, indicating suboptimal generalization. This analysis led to a mathematical theorem linking synaptic weight update dynamics to landscape curvature, proposing neuromodulator-driven adjustments as a potential enhancement for learning rule performance. Given how initial conditions can greatly influence a system’s future trajectory, the fourth project delves into the impact of initial connectivity structures on learning dynamics in neural circuits. By examining various connectivity patterns derived from neuroscience data, including recent electron microscopy data, we analyze how these structures influence learning regimes, implicating metabolic costs and risks of catastrophic forgetting. Our findings suggest that high-rank initializations utilize pre-existing high-dimensional input expansion to facilitate input decoding, leading to minimal changes post-training and increasing the propensity for lazy learning. These specific initializations thus predispose networks toward certain learning behaviors, critically affecting their ability to adapt and generalize
Sources of Bias in Naturalistic Decision Making Under Risk
No full textThesis (Ph.D.)--University of Washington, 2024Severe weather situations such as tornadoes and droughts require people to take protective action even when the probability of the severe weather is low because the consequence of not protecting is very serious. In naturalistic decision experiments based on these situations, people are risk-seeking such that they often do not take protective actions when it is economically rational to do so. This project studied this phenomenon from a signal detection theory perspective. A random likelihood model was introduced to estimate the subjective criterion which is the likelihood above which one takes protection actions. This model separates the subjective criterion from subjective likelihood, which is participants’ perception of the probability of the weather event. Three experiments manipulated the gain-loss framing and the economically rational criterion (the criterion based on expected value theory) to examine their effect on the subjective criterion. When the gain-loss framing was manipulated, the subjective criterion was higher in a loss frame than a gain frame. When the economically rational criterion was manipulated, the subjective criterion was between the economically rational criterion and the center of the possible likelihood range (50%). Neither manipulation affected subjective likelihood. In addition, participants showed an overestimation in subjective likelihood in all conditions. The shifted subjective criterion overcame this overestimation and resulted in risk-seeking decisions in some conditions. Thus, the random likelihood model analysis suggests that shift of the subjective criterion is the source of risk-seeking decisions in naturalistic decision tasks. Potential interventions are discussed with the aim to improve the placement of the subjective criterion
Mapping the Chinese Community of Western Washington
No full textThesis (Master's)--University of Washington, 2024The Chinese community is one of the oldest and most established communities on the West Coast in addition to one of the most influential communities in founding current day Washington state’s landscape, economy, and historical businesses. In Western Washington, the Chinese community is largely concentrated in the Seattle area, but originated with immigrants who settled all across the Pacific Northwest in the mid-late 19th century. This project enhances the awareness of the Chinese community in Western Washington within the Eastside Heritage Center, highlighting this community’s experiences while moving through the history of the region. The map I created highlights notable locations relevant to the history of the Chinese community in Western Washington and provides visitors with an expansive and comprehensive view of the community’s historical and continued impacts in the region, allowing visitors to discover the influence and history of this community through historic neighborhoods, businesses, and key individuals. The map is available in a digital file and physical copies can be picked up at the Eastside Heritage Center
Psychology of Disposal and its Influence on Consumer Behavior
No full textThesis (Ph.D.)--University of Washington, 2024Consumption is a multi-stage process encompassing acquisition, usage, and disposal, and the issues surrounding consumers' disposal are as complex and far-reaching as those of acquisition and usage. Yet research on disposal remains limited, making it imperative to study related issues not only to offer a richer perspective on disposal but also because mindless disposal has contributed to dire environmental and social problems. My dissertation aims to enhance the understanding of disposal by illuminating the psychology behind it, providing insights into how consumers conceptualize disposal and how this influences decision-making at various consumption stages. Chapters 1 and 2 consolidate past research on disposal and provide an integrative framework to help researchers make novel predictions and ask new questions. This framework incorporates ways in which disposal exerts influence across the entire consumption cycle and identifies illustrative questions that can guide the ongoing development of disposal research. This presents an opportunity for consumer research to claim ownership in a domain that is, by definition, a key aspect of consumption. Chapter 3 then demonstrates an example of how the integrative framework can be implemented to broaden the disposal literature by investigating how referencing disposal as part of product information (i.e., disposal reference) influences consumers' product evaluations at purchase. Across eight studies, I show that highlighting disposal at the point of product acquisition negatively impacts consumers' product evaluations by affecting perceptions of the product's wastefulness. I also demonstrate that morality underlies this disposal reference effect. Referencing disposal imbues the product with moral significance, an effect amplified among consumers with a strong moral identity. In documenting a novel effect of considering disposal on product evaluations at purchase, these findings advance our understanding of disposal, wastefulness, and morality. In the concluding chapter, I delineate disposal’s substantive import and discuss further research directions on disposal-related phenomena and areas of inquiry. Overall, my dissertation theoretically and empirically expands our understanding of disposal while highlighting its importance across consumers, businesses, society, and the ecosystem
Solvation Meta Predictor
No full textThesis (Master's)--University of Washington, 2024Predicting the solubility of aqueous mixtures is a critical task in cheminformatics, impacting fields such as drug discovery, chemical engineering, and environmental science. This study aims to enhance the predictive accuracy of machine learning models for solubility by employing advanced ensemble techniques. We evaluated the performance of three individual models: SMI, MDM, and GNN, and compared them to ensemble methods including simple averaging and an Optuna-optimized ensemble. Our results indicate that the Optuna-optimized ensemble model achieved the highest predictive accuracy, with an R2 value of 0.8117, outperforming individual models and simple ensemble techniques. To further improve model performance, we propose the implementation of the Mixture of Experts (MoE) approach. This advanced ensemble technique leverages specialized experts and a gating network to optimize model predictions based on input features. MoE promises to enhance model flexibility, scalability, and specialization, making it a robust tool for handling complex and heterogeneous datasets. Future work will involve integrating additional models and exploring other ensemble strategies to further improve predictive accuracy. The findings of this study highlight the potential of ensemble methods to significantly improve the prediction of solubility in aqueous mixtures, offering valuable insights for various scientific and industrial applications
