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MiR 329/449 Suppresses Cell Proliferation, Migration and Synergistically Sensitizes GBM to TMZ by Inhibiting Src/FAK, NF-kB, and Cyclin D1 Activity
No full textGlioblastoma Multiforme (GBM) is one of the most common brain tumors and is associated with aggressive tumor characteristics and extremely poor patient survival. The median survival time for GBM patients is around 12–15 months. Temozolomide (TMZ) is a key chemotherapeutic drug used in the treatment of GBM. However, at least 50% of GBM patients do not respond to TMZ, necessitating the identification of novel therapeutic strategies sensitizing patients to TMZ. In this study, we aimed to investigate the effects of two different tumor suppressor microRNAs (miR-329 and miR-449b) on cell proliferation and migration of GBM cells, and their potential for sensitizing GBM cells to TMZ. Our findings show that MiR-329/449b treatments suppressed spheroid formation and migration of GBM (LN229 and U87) cells. When miR treatments were combined with Temozolomide (TMZ), we also observed that they synergistically enhanced the suppressive effects of TMZ and inhibited the activity of clinically significant NF-KB and Src/FAK signaling pathways, making the combination therapy a viable option to treat GBM, with greater impact on patient survival
Immigrant Inclusion: Reframing Local Politics in Immigration Debates
No full textThrough a three-paper dissertation, I explore the causes and consequences of local immigrant integration policies, such as the adoption of sanctuary practices, municipal IDs, the establishment of local immigrant affairs offices, and more. While these policies have gained traction in recent years, we know little about why they emerge and what happens when they do emerge. Through my dissertation, I offer one of the first accounts of the causes and consequences of local immigrant inclusion policy environments. In my first paper, I ask whether descriptive representation leads to substantive representation, that is, whether mayors from racial and ethnic minority groups are more likely to pass inclusive policy. I find that Asian, and to some extent, Latino mayors are more likely to pass these types of policies. In my second paper, I ask whether the adoption of local inclusion policies, through policy feedback, increases the political participation of naturalized immigrants. I find that over time, immigrants have a higher likelihood of voting in cities with more inclusive policy environments. In my third paper, I ask whether the adoption of a specific policy, sanctuary policies—policies that attempt to protect immigrants from deportation—can change civic behavior among immigrant groups. While I find that the individual likelihood to volunteer remains unchanged, both naturalized and non-naturalized immigrants join more civic associations on average after the adoption of a sanctuary policy. To determine why policies are passed at the local level and to understand how these policies change urban political landscapes, I collect novel data across the 100 largest cities by population size across the U.S. from 2009 to 2020 across 14 individual policy variables. I blend this data with data on corresponding mayoral racial identity, data on voting from the Cooperative Election Study (CES), and civic engagement data from the Current Population Survey. I use a combination of OLS, logistic regression, and multilevel logistic regression models to arrive at my findings
Optimal Architectures for Electric Vehicle Fast Charging and Impacts on Grid and Battery Life
No full textWith the growing adoption of electric vehicles (EVs), the demand for public charging infrastructure is increasing to support more widespread, out-of-home recharging options. At the same time, charging power levels are rising to reduce recharging times, making them comparable to the refueling durations of internal combustion vehicles (ICVs) and minimizing wait times at EV fast-charging stations (EVFS). However, the widespread deployment of high-power fast chargers introduces several challenges, particularly concerning power quality disturbances on the grid and accelerated degradation of EV battery life. Therefore, a optimally structured approach is required to achieve scalable megawatt-scale future EVFS. This thesis proposes optimal EVFS architecture that addresses both grid and battery related challenges posed by high-power fast charging while also meeting key design criteria such as cost optimization, power density, service reliability, and fault isolation. Two optimized DC-DC converter topologies are introduced for EV charging applications. The first is an isolated ultra-wide voltage DC-DC converter to accommodate both 400 V and 800 V EV platforms. The second is a power-dense partial power converter (PPC) topology suitable for both EV charging and integration with battery energy storage systems (BESS). Its bidirectional, non-isolated configuration makes it a cost-effective and efficient solution for coupling BESS with EVFS infrastructure. Incorporating these converter designs into the proposed EVFS architecture led to improved system efficiency, reduced total power converter ratings, and enhanced power density. A multi-zonal EVFS structure is introduced to reduce fault impacts and improve charging service reliability. Additionally, operational strategies are proposed to mitigate grid-side impacts, including demand fluctuations, total harmonic distortion (THD), and supraharmonic emissions. To address battery degradation, several fast-charging protocols are reviewed, and an unbiased evaluation procedure is proposed to accurately assess their impact on lithium-ion (Li-ion) cell lifetime. The thesis includes a detailed design and operational analysis of the proposed converters, supported by simulation and experimental results. A 156-stall, 55 MW case study of the multi-zonal EVFS architecture is presented to demonstrate its scalability, performance, and grid compatibility. Finally, experimental results from cyclic testing of Li-ion cells using the proposed evaluation methodology are provided to examine the effects of fast-charging protocols on battery lifetime
Understanding the Talmud in a Korean and Korean-American Context
No full textRegarded as an important component of Judaism and Jewish culture, the Talmud is composed of religious law, and oral tradition accompanied by commentary. Curiously, the Talmud also is a Best-Seller in the Children's Education section in South Korea. Yet, Jews only compose a small portion of the South Korean population, suggesting that a larger subject of the population is responsible for this phenomenon. This summer research project aims to construct an oral history of Koreans and Korean-Americans who have read the Talmud to understand the lived experiences of their interactions with an important Jewish text.Comparative Cultural Studies, Department ofHonors Colleg
Forecasting Energy Consumption During Hurricanes Using Machine Learning
No full textThis project focuses on energy consumption forecasting during hurricanes using advanced machine learning. Integrating Long Short-Term Memory (LSTM) networks with behavioral insights through Large Language Models (LLMS), the model will predict energy consumption based on weather, energy and behavioral data. The goal is to create a specialized forecasting model to optimize energy management during hurricanes, in order to reduce carbon emissions and mitigate the high costs associated with these extreme weather events.Engineering Technology, Department ofHonors CollegeComputer Science, Department o
The Impact of Perceived Support on Food and Beverage Employee Well-Being
No full textFood and beverage employees are seeking greater workplace treatment and conditions, yet are continuously faced with a stressful working environment and the norm of hustle culture. Many researchers have explored the issues of job burnout and turnover intention within the hospitality industry and have posited organizational support as a strategy to reduce these issues. Additionally, an increasing topic in the literature is focusing on well-being and mental health in the workplace. A gap exists concerning the relationship between perceived support and workplace well-being and mental health for food and beverage employees. These two distinct but related studies will explore how perceived support can increase employee well-being and mental health in the workplace for food and beverage employees. First, the experiences of food and beverage employees regarding their workplace stressors and felt organizational support are explored. Then the interplaying relationships between leadership, perceived support, trust, and workplace well-being are tested. For study one, eighteen semi-structured interviews were conducted with beverage managers, sommeliers, bartenders, servers, and sales representatives. The interviews were structured to learn about participants' perceptions of their organizations, leadership, well-being and mental health. Content analysis was conducted to analyze the information gathered from the interviews. The data was hand coded, and then the codes were verified by and compared with the output from ATLAS.ti AI coding software. The results illustrate the importance of organizational and leadership support for the well-being of food and beverage employees and uncover how improved support may help reduce stress and increase workplace well-being. Using organizational support theory (OST) as a framework, study two examines the relationships among leadership behavior, perceived support, trust, and workplace well-being. OST assumes that employees develop a general perception concerning the extent to which their organization values their contributions and cares about their well-being (Eisenberger et al., 1986). Employee’s perceptions were collected using a self-administered survey via Qualtrics focusing on these key variables. The findings reveal a chain mediation effect of inclusive leadership and workplace well-being from perceived support and trust. Similarly, chain mediation was found for the effect of abusive supervision and workplace well-being through perceived support and trust. This study illustrates the importance of support, organization actions, and leadership behavior for the well-being of food and beverage employees
Model-Informed Drug Development of Riluzole for Neuroprotection in Acute Spinal Cord Injury: Integrating Pharmacokinetics, Pharmacodynamics, and Clinical Outcomes for Precision Medicine
No full textTraumatic spinal cord injury (SCI) is a debilitating condition with no FDA-approved pharmacological therapies. Progress in therapeutic development has been hindered by the complexity of SCI pathophysiology, considerable interindividual variability, and a relatively small patient population. Riluzole, a sodium channel blocker with established neuroprotective properties, was evaluated in the Riluzole in Spinal Cord Injury Study (RISCIS), a Phase II/III clinical trial conducted by the North American Clinical Trials Network (NACTN). This research represents the pharmacokinetic (PK) sub-study of RISCIS and investigates the therapeutic potential of riluzole to improve neurological outcomes using an integrated, model-informed framework that incorporates pharmacokinetics, pharmacodynamics (PD), and clinical outcomes (CO). By leveraging modeling and simulation, this approach provides a strategy to address clinical heterogeneity and guide individualized treatment based on drug exposure, biomarker response, and recovery profiles. The first objective involved exploring clinical responses to riluzole through comprehensive exploratory data analysis using the RISCIS PK sub-study dataset. Results indicated that individuals with incomplete cervical SCI, particularly those classified as American Spinal Injury Association Impairment Scale (AIS) C, experienced greater improvement in upper extremity motor function in the C7–T1 segments when treated with riluzole compared to placebo. Additionally, a reduction in pain scores on Day 14 suggested a potential short-term symptomatic benefit associated with riluzole treatment. The second objective focused on characterizing riluzole disposition over the 14-day dosing period through development of a time-varying one-compartment PK model, which revealed increasing clearance and volume of distribution over the time period of 2-week treatment. PK/PD models incorporating delayed drug effects and disease progression dynamics were then used to evaluate relationships of alanine aminotransferase (ALT), a marker of hepatic safety, with riluzole exposure and phosphorylated neurofilament heavy chain (pNFH), a biomarker of axonal injury. These models captured subtle exposure-related effects not evident through conventional group-level analyses, demonstrating the added value of model-based methods in uncovering interindividual variability in both efficacy and safety parameters. The third objective aimed to characterize exposure–response relationships by applying response surface methodology (RSM) to integrate PK, PD, and clinical outcome data. RSM models revealed non-linear interactions between riluzole AUC, early clinical or biomarker predictors, and six-month motor recovery. Mid-range AUC values were associated with the most favorable outcomes, suggesting the existence of an optimal therapeutic window. Among candidate predictors, Day 7 AUC emerged as the earliest reliable indicator of long-term benefit, while Day 14 motor scores provided a stable reference for recovery assessment. Based on these findings, PK simulations were conducted to evaluate individualized dosing strategies that maintain exposure within the identified target range, accounting for time-varying PK behavior and patient-specific variability. This modeling and simulation framework supports the feasibility of precision dosing in SCI and offers a path forward for tailoring interventions to individual recovery trajectories. Despite limitations related to sample size and variability, this study demonstrates the utility of an integrated PK/PD/CO modeling approach to inform exposure-guided therapy in acute SCI. The findings establish a foundation for future research to aim at optimizing riluzole dosing, incorporating biomarkers into trial design, and advancing personalized neuroprotective strategies
Review of Shape-Memory Polymer Nanocomposites and Their Applications
No full textShape-memory polymer nanocomposites (SMPNCs) have emerged as a transformative class of smart materials, combining the versatility of shape-memory polymers (SMPs) with the enhanced properties imparted by nanostructures. Integrating these nanofillers, this review explores the pivotal role of SMPNCs in addressing critical limitations of traditional SMPs, including low tensile strength, restricted actuation modes, and limited recovery stress. It comprehensively examines the integration of nanofillers, such as nanoparticles, nanotubes, and nanofibers, which augment mechanical robustness, thermal conductivity, and shape-recovery performance. It also consolidates foundational knowledge of SMPNCs, covering the principles of the shape-memory phenomenon, fabrication techniques, shape-recovery mechanisms, modeling approaches, and actuation methods, with an emphasis on the structural parameters of nanofillers and their interactions with polymer matrices. Additionally, the transformative real-world applications of SMPNCs are also highlighted, including their roles in minimally invasive medical devices, adaptive automotive systems, 4D printing, wearable electronics, and soft robotics. By providing a systematic overview of SMPNC development and applications, this review aims to serve as a comprehensive resource for scientists, engineers, and practitioners, offering a detailed roadmap for advancing smart materials and unlocking the vast potential of SMPNCs across various industries in the future
Neural Network-Based Location Determination via Digital TV Signal Characteristics and Data Quality
No full textCurrent research into indoor positioning systems primarily relies on anchor-based methods such as Wi-Fi, Bluetooth, and RFID, as Global Positioning Systems (GPS) are unable to penetrate solid structures. However, anchor-based methods necessitates certain infrastructure exist or additional be installed within the monitored environment, creating limitations where less invasion methods are required. In contrast, terrestrial digital television signals (ATSC) whose transmission stations already exist close by may offer an alternative. We investigate whether ATSC can achieve similar effects of location determination.Computer Science, Department ofHonors Colleg
Understanding Mutation Rates Under Microgravity Conditions
No full textMutations are random changes to a DNA sequence that occur continually and can alter phenotypes. Environmental conditions select against less advantageous phenotypes, allowing the more fit individuals of a population to survive and reproduce. This concept, known as the theory of natural selection, has not yet been extensively studied at the population level under space microgravity conditions. This proposed experiment aims to determine if microgravity increases mutation rates in the Drosophila melanogaster genome. 100 generations of this model organism will be tested within a Random Positioning Machine (RPM) to simulate microgravity conditions and ensure sufficient time for stable mutation rate patterns. Genome sequencing at a singular and well-researched locus, specifically the myosin heavy chain gene, and subsequent mutation rate calculations will then be conducted to compare the microgravity population with an Earth gravity population. This comparison may reveal whether microgravity conditions increase mutation rates and compromise genetic stability. Future studies could investigate how fruit flies mutated and adapted to microgravity exhibit different physiologies and behaviors compared to their Earth-adapted counterparts. Discovering increased mutation rates due to microgravity could inform the development of improved methods and equipment for protecting human DNA during space travel. These findings could have significant implications for future exploration and long-term colonization in outer space.Honors CollegeBiology and Biochemistry, Department o