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CONFIRMATION OF MHC CLASS II ANTIBODY REACTIVITY IN SOLID PHASE BEAD ASSAYS USING AN HLA EXPRESSING CELL LINE
Get PDFThesis (M.S.)--Michigan State University. Clinical Laboratory Sciences - Master of Science, 2025In order to assess compatibility between donors and recipients for organ transplantation, the recipient\u2019s anti-HLA antibody profile is compared to the donor\u2019s HLA typing. Determination of the recipient\u2019s anti-HLA antibody profile is done through use of a single antigen bead assay. Unfortunately, false positive reactivity within the assay is one of its major limitations. While not as dangerous as a false negative, a false positive result can greatly reduce a recipient\u2019s chances of receiving an organ. For example, a DRB4 false positive result in the MHC class II single antigen bead assay, a patient is considered non-compatible with 50% of the population. One of the primary ways false reactivity is identified is through surrogate flow cytometric crossmatching, where a donor expressing the antigen of interest is incubated with the recipient\u2019s serum to see if a reaction occurs. While surrogate flow cytometric crossmatches are useful in determining the true reactivity of a recipient, finding an acceptable donor can often be difficult if the patient is highly sensitized. Therefore, it was hypothesized that a cell line expressing an MHC antigen of interest could be used in place of a surrogate donor. This study focused on using T2 cell lines expressing either DRB4 or DQA1*05:01/DQB1*02:01, two common MHC class II false positive results within the single antigen bead assay, as surrogate donors for a flow cytometric crossmatch. Results showed that the T2 cell line could be used as a surrogate donor. When compared to clinical surrogate crossmatches, the T2 cell line surrogate flow cytometric crossmatches showed concordance rates of 95% and 92% for DRB4 and DQA1*05:01/DQB1*02:01 respectively. In conclusion, it was determined that the T2 cell line could be used as a surrogate donor for ruling out false positive results in the single antigen bead assay. While further testing is still necessary, the success of this experiment opens the door for further investigation. It is likely that other MHC class II antigens could be transduced into the T2 cell line to rule out more false positive results, or another cell line could be used to allow MHC class I false positives to be ruled out. Overall, the success of this experiment shows promise for the future as new ways are found to improve patients\u2019 chances of receiving a transplant, and improve overall outcomes in the field of transplantation.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
Improving best practice guidelines for Xylazine-related wounds : a DNP project
No full textProblem: Xylazine, an alpha 2-agonist used in veterinary medicine, is increasingly found in illicit drugs, leading to severe cutaneous injuries among substance use disorder (SUD) patients. These wounds are often misidentified and improperly treated due to a lack of awareness and guidelines among healthcare providers. Purpose: This project aims to develop and implement best practice guidelines for the identification, prevention, and treatment of xylazine-related wounds in SUD patients, particularly those accessing care through mobile health clinics (MHCs). Methods: A comprehensive literature review was conducted to identify existing evidence on xylazine-related wounds. A screening tool and wound care guidelines were developed and implemented in a nonprofit mobile clinic (NPMC) in southeastern Michigan. Patients identified as "at-risk" were provided with one-on-one education sessions, and healthcare providers received training on the new guidelines. Results: Implementing the screening tool and wound care guidelines improved the identification and management of xylazine-related wounds. Patients reported increased awareness of the risks associated with xylazine use and a higher likelihood of seeking early care for new wounds. Healthcare providers reported increased confidence in identifying and treating these wounds. Conclusions: The project successfully improved the identification and management of xylazine-related wounds in SUD patients. The findings highlight the importance of targeted education and training for patients and healthcare providers to address the unique challenges posed by xylazine use.Thesis (D.N.P.)--Michigan State University. Clinical nurse specialist, 2025Includes bibliographical reference
IMAGING AND THERAPY OF OVARIAN CANCER WITH TARGETED RADIOLABELED ANTIBODIES
No full textThesis (Ph.D.)--Michigan State University. Comparative Medicine and Integrative Biology - Doctor of Philosophy, 2025Ovarian cancer, notorious for its late-stage diagnosis and high mortality rate, poses a significant challenge in oncology. Traditional therapies often fall short, necessitating more effective and targeted treatment modalities. This dissertation delves into the prospective utilization of targeted radiolabeled antibodies for ovarian cancer treatment. The research focuses on Pb-214/Bi-214-TCMC-Trastuzumab and Pb-212-TCMC-hAnnA1, assessing their efficacy in preclinical mouse models. The first section of the thesis demonstrates the inhibitory effect of Pb-214/Bi-214-TCMC-Trastuzumab on human epidermal growth factor receptor positive (HER2-positive) ovarian cancer growth, leveraging the targeted alpha therapy (TAT) approach. The compound's therapeutic potential, biodistribution, and toxicity profile are systematically evaluated, presenting a promising avenue for advanced ovarian cancer treatment. Subsequent chapters introduce the first-in-class humanized Annexin A1 (hAnnA1) antibody, evaluated for its specificity in targeting ovarian cancer cells. The use of this antibody, when conjugated with the alpha-emitting radionuclide Pb-212, shows a substantial reduction in tumor burden, reinforcing the potential of TAT in a clinical setting. This dissertation underscores the complexity of ovarian cancer treatment and presents evidence for the efficacy of novel radiolabeled antibodies in its management. The findings pave the way for future clinical trials and the development of new therapeutic strategies that could offer hope to patients facing this devastating disease. The concluding chapters highlight the necessity for further research, emphasizing combination therapies, personalized medicine, and advancements in radiolabeling techniques. The promising results of Pb-214/Bi-214-TCMC-Trastuzumab and Pb-212-TCMC-hAnnA1 in preclinical models warrant their continued investigation with the goal of improving patient outcomes and expanding treatment options in the realm of ovarian cancer therapy.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
Search for a Heavy-philic W' Boson using Proton-Proton Collisions at Center-of-Mass Energy of 13 TeV using the ATLAS Detector
No full textThesis (Ph.D.)--Michigan State University. Physics - Doctor of Philosophy, 2025This thesis presents a search for a new, hypothetical particle predicted by theories extending the Standard Model of particle physics. This heavy boson interacts only with the heaviest known quarks, top and bottom (heavy-philic). Such a particle could provide insight into the fundamental forces of nature and be the first hint at extra dimensions or a composite Higgs. The boson is produced in high-energy proton-proton collisions, mainly through gluon fusion. Its decay leads to a distinctive final state: . This search uses data collected by the ATLAS detector during Run 2 at the Large Hadron Collider, focusing on events with a single charged lepton, at least five jets, and at least three jets identified as originating from bottom quarks. To improve sensitivity to this rare process, advanced machine learning techniques are applied. A profile likelihood fit to the machine learning output is used to evaluate the data. No significant excess above the Standard Model background is observed, and exclusion limits are set at the 95\% confidence level on the production cross-section of the heavy-philic boson.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
DEVELOPMENT OF A NOVEL ENERGY LOSS OPTICAL SCINTILLATION SYSTEM FOR HEAVY-ION PARTICLE IDENTIFICATION
No full textThesis (Ph.D.)--Michigan State University. Physics - Doctor of Philosophy, 2025The Energy Loss Optical Scintillation System (ELOSS) is a novel optical readout-basedgaseous detector currently under development at the Facility for Rare Isotope Beams (FRIB). It is designed to enable rapid Z-identification of rare, short-lived atomic nuclei within the S800 spectrograph. Together, the S800 spectrometer and focal plane detector system employ a B\u3c1-\u394E-ToF method to identify nuclear reaction residues. Currently, energy loss (\u394E) measurements are obtained using a gaseous transmission detector (ionization chamber), while time-of-flight (ToF) measurements are recorded with plastic scintillators positioned at two distinct locations. The atomic number (Z) is deduced according to the Bethe-Bloch equation using the \u394E and ToF. ELOSS is anticipated to significantly advance the particle identification (PID) capabilities of the S800 by offering a two-fold improvement in energy loss resolution and a detection rate increase greater than ten-fold. The ELOSS detector comprises a large volume filled with Xenon gas. As charged particles cross the ELOSS volume they deposit energy in Xenon, producing scintillation light (approximately 175 nm) along their track. Arrays of photomultiplier tubes (PMTs), with sensitivity optimized for the Xenon emission spectrum, surround the detector\u2019s effective volume to capture the scintillation light. The high electron density of Xenon, combined with its exceptional scintillation yield (approximately 20 ph/keV for heavy ions), results in low collisional straggling and a high signal-to-noise ratio, estimated to achieve an energy resolution between 0.3% and 0.9% \u3c3 (Z 3c 50). The full emission spectrum of Xenon is emitted within a few hundred nanoseconds, characterized by decay constants of 6 ns (singlet state) and 99 ns (triplet state), allowing for a fast detector response and a detection rate of approximately 50 kHz, primarily limited by the data acquisition system (DAQ). This thesis will detail the design and construction of the main ELOSS components, including the PMT-based optical readout responsible for recording scintillation light from excited Xenon gas, the implementation of an external decoupling circuit for simultaneous high voltage (HV) supply and anode signal readout, the development of a suitable DAQ for processing the PMT signals, and the assembly of the mechanical support for the optical readout. Additionally, the development of a cryogenic rare gas recovery and storage system to support ELOSS operation with ultra-expensive Xenon gas is discussed. A signal processing method using a deep neural network algorithm to correct position-dependent energy loss measurements will be explored. Position dependence arises from non-uniform light collection efficiency, which is typical of large-area scintillation detectors. This also involves the introduction of a systematic calibration of PMT anode sensitivities to correct tube-to-tube variations and achieve a deviation of less than 1% throughout the entire array. Finally, results from two fast beam tests of the ELOSS detector and its current status will be presented.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
IDENTIFICATION AND CHARACTERIZATION OF NEUROMEDIN-S EXPRESSING CELLS IN THE VENTRAL TEGMENTAL AREA AND THEIR ROLE IN MORPHINE-ELICITED BEHAVIOR
No full textThesis (Ph.D.)--Michigan State University. Neuroscience - Doctor of Philosophy, 2025Opioid addiction is a major health and economic burden, but our limited understanding of the underlying neurobiology limits better interventions. Alteration in the activity and output of dopamine (DA) neurons in the ventral tegmental area (VTA) is known to contribute to drug effects, but the mechanisms underlying these changes remain incompletely understood. Our lab previously found that Neuromedin S (NMS) expression is robustly increased by chronic morphine in VTA DA neurons. However, the potential functional impact of VTA NMS neurons hadn't been determined. For my research aims, I\u2019ve used a combination of viral labeling, chemogenetics, and cell type-specific CRISPR gene editing to provide novel insight into which VTA neurons express NMS and the role of VTA NMS neuronal activity in morphine behaviors. Using Cre-dependent retrograde viral vectors and immunohistochemistry in NMS-Cre mice, I found that NMS neurons comprise a subpopulation of VTA DA neurons that project to the nucleus accumbens, where the NMS receptor (NMUR2) is expressed, supporting the potential of a functional NMS circuit. Additionally, using chemogenetics to manipulate VTA NMS neuronal activity, I found that activation and inhibition of VTA NMS neurons promotes and attenuates morphine-elicited behavior, respectively. Interestingly, these effects appear specific to morphine, as modulation of VTA NMS activity did not affect cocaine behaviors. For my final aim, I developed novel tools to investigate the necessity of VTA NMS expression for morphine behaviors. I designed a Cre-dependent CRISPR/Cas9 viral vector to knockout (KO) NMS expression in VTA DA neurons. My results suggest that NMS KO from VTA DA neurons increases morphine behaviors. To complement these data, we also developed a constitutive NMS KO mouse line. Using these mice, I assessed the effect of whole-body NMS deletion on morphine responses. Finally, we developed an NMS floxed mouse line, in combination with viral-mediated delivery of Cre recombinase in VTA, allows us to determine the role of VTA NMS expression in morphine behaviors. Together, these studies have defined a novel opioid-responsive subpopulation of VTA neurons. These data highlight the potential for NMS signaling as an innovative target for opioid use disorder. Further, the dissemination of NMS KO tools will allow for the determination of the additional NMS brain circuits and role of NMS signaling in other motivated behaviors.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
Towards Faster Supernovae Simulations : Enhancing Convergence in TARDIS with Adaptive Damping
No full textThesis (M.S.)--Michigan State University. Computer Science - Master of Science, 2025This thesis investigates numerical techniques to improve the iterative convergence of TARDIS, an open-source one-dimensional Monte Carlo radiative transfer code for simulating supernova spectra. During each TARDIS simulation, a fixed-point iteration adjusts the ejecta\u2019s plasma state (e.g., radiative temperature and dilution factor) to satisfy radiative equilibrium, but naive full updates can lead to slow or oscillatory convergence due to Monte Carlo noise and the non-linear plasma\u2013radiation coupling. We implement and evaluate two convergence enhancement strategies within TARDIS\u2019s iterative framework: static damping (constant under-relaxation of state updates) and an adaptive damping technique that dynamically tunes the relaxation factor based on convergence history. Detailed benchmarks are conducted on multiple supernova ejecta models (SN Ia, SN Ibc, and SN Iax) to assess the performance of these methods. Convergence behavior is quantified using key metrics including shell-by-shell radiative temperature, dilution factor, and the inner boundary temperature that governs luminosity. The adaptive damping scheme modulates the update step to prevent overshoot and oscillations caused by estimator noise, while accelerating convergence when changes become small. We find that both damping strategies substantially improve the stability of the fixed-point iteration and reduce the number of iterations needed to reach a converged solution (compared to the default undamped approach), with the adaptive scheme achieving the fastest convergence and greater robustness against noise-induced fluctuations. We also analyze noise sensitivity, confirming that the adaptive method maintains reliable convergence even with varying Monte Carlo sampling. This study enhances the convergence methodology of TARDIS, contributing to more efficient and robust supernova spectral synthesis.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
Efficient Multi-Commodity Satellite Scheduling and Routing Algorithms for Earth Observation Applications
No full textThesis (M.S.)--Michigan State University. Computer Science - Master of Science, 2025Earth Observation Low Earth Orbit (EOLEO) satellites orbit the Earth, collecting hundreds of gigabytes of data per day, while traveling up to 27,000 km/h. This combination of high velocity and data volume poses a challenge when transmitting the collected data back to Earth. Current state-of-the-art techniques rely on graph solvers, column-generation algorithms, or greedy algorithms to schedule and route data in single-commodity scenarios. A single-commodity scenario only views a single commodity at a time, rather than multiple commodities in a multi-commodity scenario. A multi-commodity scenario can properly model what happens when multiple companies, each with their own commodity, use different algorithms to schedule and route data to the same ground station. We show that while graph solvers can obtain a perfect solution for a defined state, its computational overhead explodes in multi-commodity scenarios, and the algorithm is NP-complete in multi-commodity scenarios. Column generation algorithms can avoid NP-complete restrictions but leak proprietary satellite information. A Greedy algorithm, while fast, gives substantially worse data throughput. We introduce the Multi-commodity Scheduling and Routing Algorithm to solve the computational issue, preserve privacy, and operate in multi-commodity scenarios. The Multi-Commodity Scheduling and Routing algorithm utilizes dynamic weighted Voronoi diagrams to prevent state explosion, preserve privacy, and reduce computational overhead. We demonstrate improvements in data throughput of up to 5.5% in multi-commodity scenarios, and reduced computation time by as much as 800% compared to state-of-the-art algorithms. This is all implemented in, as far as we can find, a first-of-its-kind multi-commodity satellite network simulator.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
COMPUTATION CACHING FOR EFFICIENT MOBILE CONVOLUTIONAL NEURAL NETWORK INFERENCE
No full textThesis (Ph.D.)--Michigan State University. Computer Science - Doctor of Philosophy, 2025Computer vision on smartphones is commonly achieved through the use of convolutional neural networks (CNNs). CNNs offer accurate image classification, but struggle with latency when run with resource constraints, such as on low-powered mobile devices. Additionally, older CNN models tend to struggle with image classification on smartphones. This dissertation remedies the concerns with real-time image classification on smartphones through an intuitive, systems-based approach that requires no re-training of any model. Our aim of providing faster CNN inference with no required model modifications enables our advancements to be widely used, even by those with little CNN experience.We note that traditional CNN execution during inference produces vast amounts of data from the network's multitude of internal convolutional layers. These convolutional layers are made up of many 'filters', each of which is used as a feature extractor for a given input. We identify that the output from these convolutional layers shows certain patterns for a given input. We leverage these observations and design novel caching paradigms to allow for computations to be reused between many runs of the same CNN. First, we introduce a system that takes advantage of the predictability of CNNs, as well as the inherent mobility of smartphones to enable users in the same physical area to share patterns of CNN execution with each other, allowing for computation reuse. Using a smartphone's inertial characteristics, and device-to-device communication, multiple users in proximity with each other can share valuable information about their environment with each other, allowing devices to make quicker classifications.Second, we explore caching for CNN early-exit strategies. Based on known patterns in CNN execution, we explore if we can confidently predict the class of an image without computing the entire CNN. If an image's class can be found, then we 'early exit' the CNN, and return the classification without finishing the inference. We explore novel ways of finding patterns, both online and offline, requiring no deep models. This will allow us to provide latency reduction without much computation overhead. This enables us to offer some of the first CNN early-exit schemes that are specifically designed and optimized for mobile CNN execution.Third, we discuss a class-aware caching scheme that improves on traditional filter-pruning techniques for CNNs. We offer a novel approach to filter pruning, by selectively choosing what filters to compute, and which to pull from a cache based on the predicted class of an image. We develop fast ways of approximating an image's class, and based on thorough offline profiling decide which filters are most valuable for which classes. At runtime, we only compute a subset of a CNN's filters, saving significant computation time.Lastly, we explore online caching during CNN inference. Online caching strategies are inherently difficult during CNN execution, as the ground truth class of something run through an CNN is not known. This causes many issues, including improperly labeled data in the cache, which leads to significant accuracy loss. We explore novel strategies to enable online caching, such as cache refresh, and online cache entry labeling with confidence. Our advances make online cache replacement feasible on mobile CNNs, while enabling CNN computation reuse and early-exit for significant latency reduction. This dissertation addresses the challenges that many applications face in mobile image classification, ensuring that classification results can be returned in a timely manner to allow for a good user experience. The research discussed in this dissertation is all focused on solving this same problem, with novel approaches and innovations that can work together to achieve fast and efficient mobile image classification. This dissertation focuses on methods and techniques that are accessible to all developers, requiring no outside infrastructure, no model architecture changes, and no training/retraining of any deep neural networks. In conclusion, these contributions enable image classification to be viable on even the most resource-constrained devices running pretrained CNN models of many varying architectures.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
MARKET POTENTIAL FOR HIGH-OLEIC SOYBEANS IN THE MICHIGAN DAIRY INDUSTRY : A CASE STUDY OF MICHIGAN STAKEHOLDER PERSPECTIVES
No full textThesis (M.S.)--Michigan State University. Agricultural, Food and Resource Economics - Master of Science, 2025This study explores the adoption of high-oleic soybeans (HOS) as both a specialty crop and a livestock feed ingredient in Michigan\u2019s soybean and dairy supply chains. HOS varieties offer improved oil stability, health benefits, and unique nutritional advantages for dairy herds, though adoption remains limited despite industry promotion and promising research. To understand the barriers and opportunities shaping real-world adoption decisions, we conducted semi-structured interviews with 12 soybean farmers, 12 dairy producers, and 9 dairy nutritionists in the spring and summer of 2024.The findings reveal that stakeholders generally recognize the potential of HOS to enhance feed efficiency, reduce reliance on imported fat supplements, and contribute to more sustainable, localized supply chains. However, market growth is constrained by economic uncertainty, infrastructure gaps, legal restrictions tied to patented soybean traits, and market fragmentation. At the farm level, soybean growers perceive a mismatch between the financial risks of adopting HOS and the available price premiums. Dairy producers struggle with the cost of on-farm roasting and storage infrastructure required to safely incorporate HOS into cattle rations. Nutritionists largely affirm the feed value of HOS but emphasize that economic feasibility, rather than nutritional merit drives adoption decisions on dairy farms. Despite these barriers, the study highlights several emerging opportunities for cross-industry collaboration. Direct partnerships between soybean growers and dairy producers, cooperative investments in roasting and storage infrastructure, and the integration of sustainability certifications into HOS supply chains all present potential pathways to expand adoption. In addition, the potential to substitute high-oleic soybean oil as a feed supplement offers a flexible alternative, particularly in markets where whole roasted beans remain scarce. Ultimately, the research demonstrates that technical solutions alone are insufficient to scale HOS adoption. Building trust, improving access to localized agronomic and economic data, and realigning incentives across the soybean and dairy sectors are essential to unlocking the crop\u2019s full potential as both a specialty oilseed and a livestock feed source.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references