2022 UMKC Dissertations - Freely Available Online
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Item Alcohol/HIV-Induced Neurodeficit and Circumvention by Neuroprotective Agent(2023) Schwartz, Daniel Christopher; Wang, JianpingAlcohol Use Disorder (AUD) remains a major problem in the United States, with usage varying between acute (binge) and chronic (heavy usage) staging. Alcohol Use Disorders affect 14.5 million people, with 9 million men and 5.5 million women. In the case of alcohol abuse, the effect of alcohol has been very well studied on the fetus but understanding of the chronic effect of alcohol abuse on neurotoxicity in the adult population, especially with comorbid conditions, such as Wernicke’s and Korsakoff’s Encephalopathy, remains more limited. Alcohol use and infection with the Human Immunodeficiency Virus-1 (HIV-1) in the United States is relatively common, with 30 to 60 % of these individuals having AUDs. These patients are exposed to different viral proteins that are known to be neurotoxic in the central nervous system. The combined effects of alcohol induced neurotoxicity with HIV-induced neurotoxicity are the interest of the current project. We have utilized the HIV-1 transactivator of transcription (Tat) protein as a model for HIV infection in an animal model. This is a widely accepted model that has been used to study aspects of HIV infection. Through use of this animal model, the work demonstrates that chronic exposure to alcohol and Tat creates a deficit in neurocognitive function with concomitant changes in receptors, cytokines and other inflammatory substances and molecules. This is important to keep in mind about changes in receptors and cytokines because the levels and changes can lead to the perpetuation of that neurodeficit. In this model, animals were treated with alcohol for 12 weeks. Tat is introduced by use of a transgenic animal line that has been treated the same way as control animals. The animals were subjected to a behavioral battery that tested different types of memory, anxiety, and motor function. We have found that treated animals exhibited additive, meaning the effects of the substances are combined, synergistic, meaning the substances work in concert to cause drug effect, or antagonistic, meaning the substances work against each other to cause no drug effects, which ultimately created an increased neurodeficit. Importantly, this work has also identified sex differences that are evident with treatment by HIV-1 Tat and alcohol, and these may be clinically relevant for treatment of patients with HIV-1 infection combined with alcohol abuse. Understanding sex difference or how drugs and other xenobiotics affect the individual is important because of development of different treatment strategies. This work evaluated the use of peroxisome proliferator activated receptor (PPAR) agonist on its ability to help alleviate the neurodeficit caused by Tat. The animals completed the described behavior battery above allowed us to identify the drug effects and potential changes in sex difference. In Chapter 1, we introduce the pharmacology and toxicology of alcohol, exposure plans and types of toxicity studies, HIV-1 Tat and HIV Associated Neurocognitive Disorders, and PPAR γ agonist pharmacology and toxicology. We also provide a detailed effort to link all of these topics together to further the study described below. In Chapter 2, we propose and carry out a two-pronged study to demonstrate prolonged exposure to alcohol in a controlled setting. We also examine synaptic protein analysis to ascertain the neurodeficit incurred from prolonged exposure to alcohol. In Chapter 3, we propose and carry out a three-pronged study to investigate prolonged exposure to alcohol, as well as to HIV-1 Tat. We also investigate how the resulting neurodeficit changes cytokine and receptors, and at how drug effect influences these changes. In Chapter 4, we propose and carry out a three-pronged study to demonstrate the neuroprotective properties of PPAR γ agonist, Rosiglitazone. We investigate the efficacy of this treatment using an animal study, and further investigate the effects on receptors and cytokines. We also evaluate the type of drug effect rosiglitazone exerts in the system. In Chapter 5, we conclude by giving an overview of our results. We also provide some commentary on the use of personalized, precision medicine approach to help with treatment of HIV-1 and alcohol abuse in populations. Further studies using animal models and three different clinical trial options for expand the goals of this work that are ultimately proposed.Item Adaptive Data-driven Optimization using Transfer Learning for Resilient, Energy-efficient, Resource-aware, and Secure Network Slicing in 5G-Advanced and 6G Wireless Systems(2022) Thantharate, Anurag; Beard, Cory5G–Advanced is the next step in the evolution of the fifth–generation (5G) technology. It will introduce a new level of expanded capabilities beyond connections and enables a broader range of advanced applications and use cases. 5G–Advanced will support modern applications with greater mobility and high dependability. Artificial intelligence and Machine Learning will enhance network performance with spectral efficiency and energy savings enhancements. This research established a framework to optimally control and manage an appropriate selection of network slices for incoming requests from diverse applications and services in Beyond 5G networks. The developed DeepSlice model is used to optimize the network and individual slice load efficiency across isolated slices and manage slice lifecycle in case of failure. The DeepSlice framework can predict the unknown connections by utilizing the learning from a developed deep-learning neural network model. The research also addresses threats to the performance, availability, and robustness of B5G networks by proactively preventing and resolving threats. The study proposed a Secure5G framework for authentication, authorization, trust, and control for a network slicing architecture in 5G systems. The developed model prevents the 5G infrastructure from Distributed Denial of Service by analyzing incoming connections and learning from the developed model. The research demonstrates the preventive measure against volume attacks, flooding attacks, and masking (spoofing) attacks. This research builds the framework towards the zero trust objective (never trust, always verify, and verify continuously) that improves resilience. Another fundamental difficulty for wireless network systems is providing a desirable user experience in various network conditions, such as those with varying network loads and bandwidth fluctuations. Mobile Network Operators have long battled unforeseen network traffic events. This research proposed ADAPTIVE6G to tackle the network load estimation problem using knowledge-inspired Transfer Learning by utilizing radio network Key Performance Indicators from network slices to understand and learn network load estimation problems. These algorithms enable Mobile Network Operators to optimally coordinate their computational tasks in stochastic and time-varying network states. Energy efficiency is another significant KPI in tracking the sustainability of network slicing. Increasing traffic demands in 5G dramatically increase the energy consumption of mobile networks. This increase is unsustainable in terms of dollar cost and environmental impact. This research proposed an innovative ECO6G model to attain sustainability and energy efficiency. Research findings suggested that the developed model can reduce network energy costs without negatively impacting performance or end customer experience against the classical Machine Learning and Statistical driven models. The proposed model is validated against the industry-standardized energy efficiency definition, and operational expenditure savings are derived, showing significant cost savings to MNOs.Item Plasma-enhanced molecular layer deposition (PEMLD) of boron carbide dielectrics from carboranes for interconnect and patterning applications(2022) Thapa, Rupak; Paquette, Michelle M.; Caruso, Anthony N.; Oyler, Nathan (Nathan Andrew)The dimensional scaling of semiconductor devices following Moore’s law has resulted in smaller, faster, and cheaper integrated circuits (IC) chips, going from ~3000 transistors in an IC chip in the early 1970s, to ~100 billion per chip today. But this has come at a price of introducing complex patterning processes for achieving feature sizes not accessible through traditional lithography. Also, to connect these huge numbers of transistors, single-level interconnect was not sufficient so multi-level interconnects were introduced that require complex process. Such processes require multiple materials that can act as diffusion barriers, copper capping layers, etch stops, hard masks, etc. Currently, silicon-based materials are being used to fulfill the majority of these needs, but complex next-generation multi-patterning schemes demand new materials and new methods to deposit them. In this work we have investigated the development of a so far unprecedented plasma-enhanced molecular layer deposition (PEMLD) process for boron carbide from carboranes. This novel method will allow for the deposition of thin films of boron carbide with molecular-level control. Boron carbide, which lies outside the silicon class of materials, has appealing chemical, mechanical, electrical, dielectric, and etch properties, making it a candidate for various patterning-support layers. We have demonstrated the deposition of monolayers from three different types of carborane derivatives: 1,2-dithiol-o-carborane on copper, 9-thiol-m-carborane on copper, and 1,2-bis(hydroxymethyl)-o-carborane on SiO₂. Monolayer formation is supported by an increase in hydrophobicity observed via contact angle measurements, an increase in thickness observed with ellipsometry, and an increase in boron coverage determined by X-ray photoelectron spectroscopy (XPS). Toward developing a PELMD process, we further investigated multilayer growths. We treated all three types of monolayers with cycles of nitrogen plasma followed by ortho-carborane-1-carbonylchloride. The 1,2-bis(hydroxymethyl)-o-carborane monolayer was treated with oxygen plasma followed by second dosing of the same precursor. All growths demonstrated continuous thickness increase observed by ellipsometry, supported by the continuous increase in the B:Cu/B:Si ratio obtained from XPS. With each half cycle of the PEMLD process, i.e., plasma treatment or exposure to precursor vapor, we observed an alternating increase and decrease in the N:B/O:B ratio, which distinguished each half cycle. Hence, we were able to develop and demonstrate two novel PEMLD process for the deposition of boron carbide films from carboranes.Item Optimization of Handover, Survivability, Multi-Connectivity and Secure Slicing in 5G Cellular Networks using Matrix Exponential Models and Machine Learning(2022) Paropkari, Rahul Arun; Beard, CoryThis works proposes optimization of cellular handovers, cellular network survivability modeling, multi-connectivity and secure network slicing using matrix exponentials and machine learning techniques. We propose matrix exponential (ME) modeling of handover arrivals with the potential to much more accurately characterize arrivals and prioritize resource allocation for handovers, especially handovers for emergency or public safety needs. With the use of a ‘B’ matrix for representing a handover arrival, we have a rich set of dimensions to model system handover behavior. We can study multiple parameters and the interactions between system events along with the user mobility, which would trigger a handoff in any given scenario. Additionally, unlike any traditional handover improvement scheme, we develop a ‘Deep-Mobility’ model by implementing a deep learning neural network (DLNN) to manage network mobility, utilizing in-network deep learning and prediction. We use the radio and the network key performance indicators (KPIs) to train our model to analyze network traffic and handover requirements. Cellular network design must incorporate disaster response, recovery and repair scenarios. Requirements for high reliability and low latency often fail to incorporate network survivability for mission critical and emergency services. Our Matrix Exponential (ME) model shows how survivable networks can be designed based on controlling numbers of crews, times taken for individual repair stages, and the balance between fast and slow repairs. Transient and the steady state representations of system repair models, namely, fast and slow repairs for networks consisting of multiple repair crews have been analyzed. Failures are exponentially modeled as per common practice, but ME distributions describe the more complex recovery processes. In some mission critical communications, the availability requirements may exceed five or even six nines (99.9999%). To meet such a critical requirement and minimize the impact of mobility during handover, a Fade Duration Outage Probability (FDOP) based multiple radio link connectivity handover method has been proposed. By applying such a method, a high degree of availability can be achieved by utilizing two or more uncorrelated links based on minimum FDOP values. Packet duplication (PD) via multi-connectivity is a method of compensating for lost packets on a wireless channel. Utilizing two or more uncorrelated links, a high degree of availability can be attained with this strategy. However, complete packet duplication is inefficient and frequently unnecessary. We provide a novel adaptive fractional packet duplication (A-FPD) mechanism for enabling and disabling packet duplication based on a variety of parameters. We have developed a ‘DeepSlice’ model by implementing Deep Learning (DL) Neural Network to manage network load efficiency and network availability, utilizing in-network deep learning and prediction. Our Neural Network based ‘Secure5G’ Network Slicing model will proactively detect and eliminate threats based on incoming connections before they infest the 5G core network elements. These will enable the network operators to sell network slicing as-a-service to serve diverse services efficiently over a single infrastructure with higher level of security and reliability.Item Development and Evaluation of 3D-Printed Cardiovascular Stents(2022) Veerubhotla, Hari Mani Krishna; Lee, Chi H. (Chi-Hyun); Van Horn, J. DavidBiodegradable stents (BDS) could be a promising alternative to the conventional metallic stents for the treatment of atherosclerosis which is a coronary artery disease (CAD). Three-dimensional (3D) printing technique has offered easy and fast fabrication of BDS with enhanced reproducibility and efficacy. Therefore, the main aim of the current study was to develop 3D-printed biodegradable cardiovascular stents. A combination of 2-hydroxy ethyl methacrylate (HEMA) and methacrylate graphene oxide (MeGO) was used to enhance the mechanical properties and loaded with resveratrol (RSL) to efficiently recover endothelial cell function. The biocompatibility of the 3D-printed stent was evaluated using in vitro cell culture studies and zebrafish embryo model. The percentage changes in volume of stents fabricated using HEMA and HEMA-0.35MeGO were 36.7±4.7% and 7.7±1.6%, respectively after 24 hr. The remaining fractions of 3D stents after 21 days and their corresponding first-order degradation rates were 95.9±0.5% and 0.30±0.10 x 10-4/hr for HEMA, and 98.2±0.3% and 0.11±0.04 x 10-4/hr for HEMA-0.35MeGO. The addition of MeGO significantly (p<0.001) enhanced the stiffness of hydrogel inks for 3D stents, indicating Young’s moduli of 0.22±0.01x10-2 MPa and 0.41±0.01x10-2 MPa for HEMA and HEMA-0.35MeGO after 21 days. 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) assay revealed that 3D stents loaded with RSL (~1 mM) exerted no cytotoxic effects on the human umbilical vein endothelial cells (HUVECs). The controlled release of RSL from the stent enhanced nitric oxide (NO) production, lowered the levels tumor necrosis factor (TNF)-α, and alleviated H2O2-induced oxidative stress in HUVECs. The RSL-loaded 3D stents displayed maximum viability of HUVECs in presence of oxidized low-density lipoprotein (ox-LDL) and resulted in similar NO production like control group (52.8±0.4 μM and 51.9±0.8 μM), whereas lipopolysaccharides (LPS) treatment (10 μg/mL) significantly displayed higher amounts of NO (82.4±1.2 μM). The amounts of TNF-α and interleukin (IL)-β released from zebrafish embryos (1.8±0.8 and 1.9±0.2) in the group treated with the RSL-loaded 3D stents were significantly lower (p<0.001) than those from the LPS alone treated group (8.1±2.3 and 23.8±3.8, respectively). It was found that stents with RSL at a dose of 1 mM indicated no mortality during the developmental stages, but stents at a dose of 2 mM resulted a lowered survival rate (93.3±3.3%), shorter length of larvae, pericardial and yolk sac edemas of 53.3±23.3% and 3.3±3.3%, respectively. The 3D-printed biodegradable stent based on HEMA-MeGO and loaded with RSL displayed excellent biocompatibility and mechanical properties. RSL released from 3D cardiovascular stents efficiently recovered the endothelial function. The 3D-printed stents with the loading dose of 1 mM RSL displayed good biocompatibility in the zebrafish embryo toxicity studies. The RSL-loaded 3D stents efficiently downregulated the pro-inflammatory cytokines, while producing minimal developmental defects in zebrafish embryos. The RSL-loaded 3D stents displayed good biocompatibility in both Raw 264.7 cells and zebrafish embryo models, guaranteeing acceptable host response in clinical application. Overall, our results demonstrated that RSL-loaded stents protect an organism against oxidative stress, while limiting developmental defects in zebrafish embryos.