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Elucidating the Role of mTOR in Dysregulated Protein Metabolism of Diabetic Myotubes
No full textType 2 diabetes (T2D) is characterized by elevated blood glucose and insulin in circulation, and dysregulated skeletal muscle metabolism. At the nexus of control of skeletal muscle lies the protein kinase known as mTOR. When active, skeletal muscle is anabolic, often at the expense of insulin sensitivity. In fact, skeletal muscle of rodents with T2D have elevated mTOR activity. Paradoxically, the elevated levels of anabolic activity do not lead to increased muscle mass with T2D, suggesting that mTOR���s control of autophagy has been lost under these conditions. Therefore, the purposes of this dissertation were first, to characterize myotubes from commercially available myoblasts harvested from healthy and type 2 diabetic donors; second, to describe anabolic and autophagic contributions to FSR of diabetic myotubes; and third, to modulate protein metabolism of skeletal myotubes in vitro using CRISPR-Cas9 gene editing. Results: FSR (%/h) of diabetic myotubes was significantly greater than healthy myotubes in the low (1.55 �� 0.03; 0.85 �� 0.01) mid (1.83 �� 0.08; 1.15 �� 0.05) and high (1.68 �� 0.11; 1.04 �� 0.04) glucose groups. Protein content of autophagic markers LC3B I and LC3B II was significantly greater in the healthy myotubes in high glucose media compared to diabetic myotubes in the same media. Inhibition of mTORC1, mTORC1&2, or inhibition of autophagy did not affect the FSR of healthy myotubes in euglycemic media. Additionally, inhibition of mTORC1, or mTORC1&2 did not affect FSR of diabetic myotubes in euglycemic media. However, inhibition of autophagy significantly decreased FSR of diabetic myotubes compared to diabetic controls. Finally, DEPTOR over-expression (DEPOVX) resulted in significantly lower FSR of C2C12 myotubes compared to red fluorescent protein (RFP) control myotubes; and electrical pulse stimulation (EPS) and insulin were able to affect FSR of both RFP and DEPOVX myotubes. In conclusion, diabetic myotubes present with dysregulated protein metabolism compared to healthy myotubes, and it appears that autophagy is a significant contributor to elevated FSR of diabetic myotubes. Finally, DEPTOR over-expression is feasible avenue for restoring anabolic control that still responds to canonical activation
Active Filtering Method for Grid Connected PV Farms
No full textIn this thesis a topology of an active filter is developed to mitigate harmonics in grid-connected renewables. The proposed active inductor design replace passive inductors used to filter grid connected inverter harmonics. The active inductor is expected to emulate an inductance in a smaller footprint using a Voltage Source Inverter (VSI). The VSI consists of an H bridge, a small passive inductor and a DC link capacitor. To control the proposed active inductor a two loop control is used. The inner loop consist of Hysteresis control to shape the current of the VSI in such a way that the current of the converter emulates the current of an inductor. Additionally, the outer loop of the control is in charge to regulate the DC voltage of the VSI to an specific value. This filter design will provide a more compact filter compared to passive filters used in conventional PV farms. A prototype of the active inductor is built using Gallium Nitride switches to reduce switching losses. The design is tested with 60 Hz for sine wave and square wave input voltages in simulation and hardware. A resistor was connected in series with the inductor to protect the design from current overshoots. The emulated inductance is lowered as the frequency increased to have current ripple in 5A - 10A range to observe the behavior of the system properly
The Applications and Evaluation of Different Exploratory Analyses in Online Learning Research
No full textThe prevalence of online learning as an alternative format for higher education has significantly increased in recent years. Following the COVID-19 pandemic in 2021, a staggering 59% of students enrolled in at least one online course per semester (IPEDS; National Center for Education Statistics, n.d.). However, challenges have surfaced with the increased adoption of online education. This dissertation aims to identify students who are at risk, establish a model for understanding their emotional states during online learning, and examine the potential risk factors that influence learning outcomes.
In Study 1, stepwise regression and random forest techniques was employed to develop a screening tool for identifying at-risk learners before their classes. The findings revealed that at-risk students often engaged in activities related to eating while learning. Additionally, they displayed a tendency to avoid creating a conducive learning environment, relied on instructor reminders for homework deadlines and requirements. Notably, the random forest model outperformed stepwise regression in accuracy using fewer questions.
Study 2 involved clustering online learners based on their readiness for online learning and investigating their emotional states, including anxiety, boredom, and satisfaction. A mediation model of online learning readiness, emotional states, expectations, and outcomes demonstrated significant differences in emotional experiences between ready and not-ready learners. Furthermore, satisfaction with the learning experience related both to instructors and course design significantly mediated the positive association between readiness and online learning expectations. Learning outcomes were found to be highly influenced by expectations.
Based on the insights from Studies 1 and 2, Study 3 analyzed the trajectory of eating frequency and focus time during online lectures over a semester, as well as their relationship and impact on learning outcomes. The findings indicated a significant negative relationship between changes in eating frequency and attentiveness over the study period. Students who ate less while learning likely have improved level of attentiveness over time, which positively correlated with learning outcomes.
In conclusion, this dissertation contributes to understanding of online learning by identifying at-risk students, examining emotional states, and investigating the dynamics between eating while learning, attentiveness, and learning outcomes. The findings have implications for educational institutions and instructors on how to increase students��� online learning readiness and enhance their online learning experiences and overall academic success
