9,538 research outputs found

    Principles of generalization for sensorimotor cerebellar learning

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    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Graduate Catalog of Studies, 2023-2024

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    Adaptations in physiological and neuronal function during diet-induced obesity

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    Obesity significantly increases the risk of developing chronic conditions including type II diabetes, cardiovascular disease, and some cancers. The rate of obesity has tripled globally since 1975, which is in part due to the sudden prevalence and overconsumption of palatable high-fat diets (HFDs). Obesity profoundly perturbs the neural control of energy balance, affecting diverse cell types within the hypothalamus. However, an incomplete understanding of how HFD impacts the regulation of energy balance hinders our ability to more effectively treat obesity. In this thesis, I describe the physiological and neuronal response to HFD feeding in rodents. We identified that HFD exposure elevates the body weight set point, which is initially driven by a transient hyperphagia. This hyperphagia coincides with increased excitatory transmission to lateral hypothalamic orexin (ORX) neurons, which regulate acute food intake. This suggests that ORX neurons may be involved in the initial hyperphagia, implicating them in the development of obesity. As HFD prolongs, body weight gain slows and reaches a new steady state regardless of age at the start, duration of feeding, or palatability of the diet. This sustained weight coincides with increased synaptic contacts to melanin-concentrating hormone (MCH) neurons, which promote weight gain and food intake, likely contributing to the maintenance of obesity. The molecular mechanism underlying the establishment of a new set point remains elusive. During HFD feeding, the presence of a chronic low-grade hypothalamic inflammation exacerbates weight gain, therefore we reasoned that inflammatory factors could modulate appetite-promoting neurons to maintain a new set point. We found that the inflammatory mediator prostaglandin E2 (PGE2) activate MCH neurons via its EP2 receptor (EP2R). Suppressing PGE2-EP2R on MCH neurons partially protects against excess weight gain and fat accumulation in the liver during HFD feeding. This mechanism could contribute to the maintenance of an elevated body weight set point in during diet-induced obesity. Without long-term treatment options in face of the increasing rates of obesity, we are in desperate need of novel interventions. In the future, we hope that targeting EP2R on MCH neurons can lower body weight set point and aid in combatting obesity

    Derogatory, Racist, and Discriminatory Speech (DRDS) in Video Gaming

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    Video games have been examined for their effects on cognition, learning, health, and physiological arousal, yet research on social dynamics within video gaming is limited. Studies have documented the presence of derogation, racism, and discrimination in this anonymous medium. However, gamers‟ firsthand experiences are typically examined qualitatively. Thus, this study aimed to establish a quantitative baseline for the frequency of derogatory, racist, and discriminatory speech (DRDS) in gaming. DRDS frequency, sexual harassment, and hate speech measures were administered to 150 individuals from online forums and social media groups. Descriptive and inferential analyses were used to gauge which factors affected DRDS rates. Sex, intergroup and fast-paced game types, time played with others, and identity portrayal showed positive correlations with DRDS. Results indicate an array of complex social and developmental factors contribute to experiencing, perceiving, and personally using DRDS. Implications include psychosocial health impacts similar to everyday harassment, with women being at a higher risk and age as a contributing factor

    SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient

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    Many deep learning applications benefit from using large models with billions of parameters. Training these models is notoriously expensive due to the need for specialized HPC clusters. In this work, we consider alternative setups for training large models: using cheap "preemptible" instances or pooling existing resources from multiple regions. We analyze the performance of existing model-parallel algorithms in these conditions and find configurations where training larger models becomes less communication-intensive. Based on these findings, we propose SWARM parallelism, a model-parallel training algorithm designed for poorly connected, heterogeneous and unreliable devices. SWARM creates temporary randomized pipelines between nodes that are rebalanced in case of failure. We empirically validate our findings and compare SWARM parallelism with existing large-scale training approaches. Finally, we combine our insights with compression strategies to train a large Transformer language model with 1B shared parameters (approximately 13B before sharing) on preemptible T4 GPUs with less than 200Mb/s network.Comment: Accepted to International Conference on Machine Learning (ICML) 2023. 25 pages, 8 figure

    Early Shame, Self-Esteem, and Christian Women

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    The purpose of this phenomenological study was to gain a deeper understanding of the lived experiences surrounding the self-esteem of evangelical Christian women with early shame experiences. The theory guiding this study was attachment theory as it explains self-esteem being rooted in early childhood through trust building, unconditional love, and security. The theoretical framework further illustrates the impact of self-esteem as life progresses due to a combination of positive and negative self-evaluations. Early shame experiences affect one’s self-esteem adversely. Low self-esteem could breed isolation once people fail to see their self-worthy in establishing meaningful connections, without which individuals lack the nurture needed to grow and sustain their well-being. Data were collected through an unstructured interview. The study findings revealed that (1) poor parental bonds, (2) environmental stressors, and (3) identification with God were significant shared experiences. This study provided implications for community stakeholders in the field of education, healthcare, and ministry. It implied having an early secure attachment and a positive relationship with God as mitigators to adverse mental health, as they facilitate healthy coping among individuals who have faced trauma such as early shame experiences

    Multi-dimensional omics approaches to dissect natural immune control mechanisms associated with RNA virus infections

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    In recent decades, global health has been challenged by emerging and re-emerging viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), human immunodeficiency viruses (HIV-1), and Crimean–Congo hemorrhagic fever virus (CCHFV). Studies have shown dysregulations in the host metabolic processes against SARS-CoV2 and HIV-1 infections, and the research on CCHFV infection is still in the infant stage. Hence, understanding the host metabolic re-programming on the reaction level in infectious disease has therapeutic importance. The thesis uses systems biology methods to investigate the host metabolic alterations in response to SARS-CoV2, HIV-1, and CCHFV infections. The three distinct viruses induce distinct effects on human metabolism that, nevertheless, show some commonalities. We have identified alterations in various immune cell types in patients during the infections of the three viruses. Further, differential expression analysis identified that COVID-19 causes disruptions in pathways related to antiviral response and metabolism (fructose mannose metabolism, oxidative phosphorylation (OXPHOS), and pentose phosphate pathway). Up-regulation of OXPHOS and ROS pathways with most changes in OXPHOS complexes I, III, and IV were identified in people living with HIV on treatment (PLWHART). The acute phase of CCHFV infection is found to be linked with OXPHOS, glycolysis, N-glycan biosynthesis, and NOD-like receptor signaling pathways. The dynamic nature of the metabolic process and adaptive immune response in CCHFV-pathogenesis are also observed. Further, we have identified different metabolic flux in reactions transporting TCA cycle intermediates from the cytosol to mitochondria in COVID-19 patients. Genes such as monocarboxylate transporter (SLC16A6) and nucleoside transporter (SLC29A1) and metabolites such as α-ketoglutarate, succinate, and malate were found to be linked with COVID-19 disease response. Metabolic reactions associated with amino acid, carbohydrate, and energy metabolism pathways and various transporter reactions were observed to be uniquely disrupted in PLWHART along with increased production of αketoglutarate (αKG) and ATP molecules. Changes in essential (leucine and threonine) and non-essential (arginine, alanine, and glutamine) amino acid transport were found to be caused by acute CCHFV infection. The altered flux of reactions involving TCA cycle compounds such as pyruvate, isocitrate, and alpha-ketoglutarate was also observed in CCHFV infection. The research described in the thesis displayed dysregulations in similar metabolic processes against the three viral Infections. But further downstream analysis unveiled unique alterations in several metabolic reactions specific to each virus in the same metabolic pathways showing the importance of increasing the resolution of knowledge about host metabolism in infectious diseases

    Runway Safety Improvements Through a Data Driven Approach for Risk Flight Prediction and Simulation

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    Runway overrun is one of the most frequently occurring flight accident types threatening the safety of aviation. Sensors have been improved with recent technological advancements and allow data collection during flights. The recorded data helps to better identify the characteristics of runway overruns. The improved technological capabilities and the growing air traffic led to increased momentum for reducing flight risk using artificial intelligence. Discussions on incorporating artificial intelligence to enhance flight safety are timely and critical. Using artificial intelligence, we may be able to develop the tools we need to better identify runway overrun risk and increase awareness of runway overruns. This work seeks to increase attitude, skill, and knowledge (ASK) of runway overrun risks by predicting the flight states near touchdown and simulating the flight exposed to runway overrun precursors. To achieve this, the methodology develops a prediction model and a simulation model. During the flight training process, the prediction model is used in flight to identify potential risks and the simulation model is used post-flight to review the flight behavior. The prediction model identifies potential risks by predicting flight parameters that best characterize the landing performance during the final approach phase. The predicted flight parameters are used to alert the pilots for any runway overrun precursors that may pose a threat. The predictions and alerts are made when thresholds of various flight parameters are exceeded. The flight simulation model simulates the final approach trajectory with an emphasis on capturing the effect wind has on the aircraft. The focus is on the wind since the wind is a relatively significant factor during the final approach; typically, the aircraft is stabilized during the final approach. The flight simulation is used to quickly assess the differences between fight patterns that have triggered overrun precursors and normal flights with no abnormalities. The differences are crucial in learning how to mitigate adverse flight conditions. Both of the models are created with neural network models. The main challenges of developing a neural network model are the unique assignment of each model design space and the size of a model design space. A model design space is unique to each problem and cannot accommodate multiple problems. A model design space can also be significantly large depending on the depth of the model. Therefore, a hyperparameter optimization algorithm is investigated and used to design the data and model structures to best characterize the aircraft behavior during the final approach. A series of experiments are performed to observe how the model accuracy change with different data pre-processing methods for the prediction model and different neural network models for the simulation model. The data pre-processing methods include indexing the data by different frequencies, by different window sizes, and data clustering. The neural network models include simple Recurrent Neural Networks, Gated Recurrent Units, Long Short Term Memory, and Neural Network Autoregressive with Exogenous Input. Another series of experiments are performed to evaluate the robustness of these models to adverse wind and flare. This is because different wind conditions and flares represent controls that the models need to map to the predicted flight states. The most robust models are then used to identify significant features for the prediction model and the feasible control space for the simulation model. The outcomes of the most robust models are also mapped to the required landing distance metric so that the results of the prediction and simulation are easily read. Then, the methodology is demonstrated with a sample flight exposed to an overrun precursor, and high approach speed, to show how the models can potentially increase attitude, skill, and knowledge of runway overrun risk. The main contribution of this work is on evaluating the accuracy and robustness of prediction and simulation models trained using Flight Operational Quality Assurance (FOQA) data. Unlike many studies that focused on optimizing the model structures to create the two models, this work optimized both data and model structures to ensure that the data well capture the dynamics of the aircraft it represents. To achieve this, this work introduced a hybrid genetic algorithm that combines the benefits of conventional and quantum-inspired genetic algorithms to quickly converge to an optimal configuration while exploring the design space. With the optimized model, this work identified the data features, from the final approach, with a higher contribution to predicting airspeed, vertical speed, and pitch angle near touchdown. The top contributing features are altitude, angle of attack, core rpm, and air speeds. For both the prediction and the simulation models, this study goes through the impact of various data preprocessing methods on the accuracy of the two models. The results may help future studies identify the right data preprocessing methods for their work. Another contribution from this work is on evaluating how flight control and wind affect both the prediction and the simulation models. This is achieved by mapping the model accuracy at various levels of control surface deflection, wind speeds, and wind direction change. The results saw fairly consistent prediction and simulation accuracy at different levels of control surface deflection and wind conditions. This showed that the neural network-based models are effective in creating robust prediction and simulation models of aircraft during the final approach. The results also showed that data frequency has a significant impact on the prediction and simulation accuracy so it is important to have sufficient data to train the models in the condition that the models will be used. The final contribution of this work is on demonstrating how the prediction and the simulation models can be used to increase awareness of runway overrun.Ph.D

    Eating Behavior In-The-Wild and Its Relationship to Mental Well-Being

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    The motivation for eating is beyond survival. Eating serves as means for socializing, exploring cultures, etc. Computing researchers have developed various eating detection technologies that can leverage passive sensors available on smart devices to automatically infer when and, to some extent, what an individual is eating. However, despite their significance in eating literature, crucial contextual information such as meal company, type of food, location of meals, the motivation of eating episodes, the timing of meals, etc., are difficult to detect through passive means. More importantly, the applications of currently developed automated eating detection systems are limited. My dissertation addresses several of these challenges by combining the strengths of passive sensing technologies and EMAs (Ecological Momentary Assessment). EMAs are a widely adopted tool used across a variety of disciplines that can gather in-situ information about individual experiences. In my dissertation, I demonstrate the relationship between various eating contexts and the mental well-being of college students and information workers through naturalistic studies. The contributions of my dissertation are four-fold. First, I develop a real-time meal detection system that can detect meal-level episodes and trigger EMAs to gather contextual data about one’s eating episode. Second, I deploy this system in a college student population to understand their eating behavior during day-to-day life and investigate the relationship of these eating behaviors with various mental well-being outcomes. Third, based on the limitations of passive sensing systems to detect short and sporadic chewing episodes present in snacking, I develop a snacking detection system and operationalize the definition of snacking in this thesis. Finally, I investigate the causal relationship between stress levels experienced by remote information workers during their workdays and its effect on lunchtime. This dissertation situates the findings in an interdisciplinary context, including ubiquitous computing, psychology, and nutrition.Ph.D
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