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IRIDIUM CATALYZED C\u2013H 1,2-DIBORYLATION AND 1,2,3-TRIBORYLATION OF ARENES THROUGH THE USE OF ANTI-AROMATIC PYRAZINE BASED LIGANDS AND OTHER BORON RELATED STUDIES
Full text linkThesis (Ph.D.)--Michigan State University. Chemistry - Doctor of Philosophy, 2025During the last few decades iridium catalyzed C\u2013H borylations have become an important method to access aryl boronic esters. As aryl C\u2013H borylations are governed predominately by sterics, this reaction offers a complementary regiochemical approach to traditional synthetic routes accessing aromatic boronic esters such as metal halogen exchange and Miyaura borylation. As these reactions can be reliably directed away from positions ortho to any functionality other than hydrogen, fluorine, and nitriles, iridium catalyzed borylations find ample use in industry and total synthesis. Since this reaction is directed sterically and not electronically, regiochemical selectivity can become challenging in cases where multiple activation sites are available, especially in the case of 1,2-disubstituted arenes. To overcome this challenge, we developed a method for para selective borylation of phenols and anilines utilizing alkyl ammonium cations as steric shields to direct borylation. We further show how this methodology can be expanded to utilize in situ heteroatom borylation in place of the alkyl ammonium cations to access the same para selectivity. We further utilized existing 4,4,5,5-tetramethyl-1,3,2-dioxaborolane (Bpin) functionalities to guide the regiochemistry of iridium catalyzed C\u2013H borylation ortho to itself to access 1,2-diborylated arenes as well as new 1,2,3-triborylated arenes. As borylated compounds are important synthetic intermediates, being able to study C\u2013H borylations mechanistically offers important insights into reactions utilizing these compounds. We therefore investigated developing a method to measure boron heavy atom isotope effects using fluorine reporters and NMR to observe boron isotopic distributions.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
Advancing Image Reconstruction and Restoration through Robust Supervised and Generative Models
No full textThesis (Ph.D.)--Michigan State University. Biomedical Engineering - Doctor of Philosophy, 2025Magnetic Resonance Imaging (MRI) is a critical tool in medical diagnosis and treatment planning due to its excellent soft tissue contrast and non-ionizing nature. However, MRI faces challenges like prolonged scan times and data acquisition constraints arising from patient privacy concerns and heterogeneous medical data. This thesis introduces computationally efficient deep learning algorithms to address these challenges in two parts.In Part I, we focus on MRI reconstruction under limited or no data availability. For limited data, we propose the LONDN MRI method, which trains on a small set of adaptively chosen neighboring images, achieving superior performance compared to supervised models like MoDL, with significant improvement. For data-free scenarios, we develop Self-Guided DIP and Autoencoding Sequential DIP (aSeqDIP), which leverage self-regularization and sequential U-Net architectures to improve both performance and efficiency, outperforming traditional supervised models. In Part II, we enhance the robustness and generalization capabilities of medical imaging models using a combination of randomized smoothing and diffusion-based purification. We introduce SMUG, an unrolling method that mitigates worst-case perturbations and data variations such as mask shifts and noise. Additionally, our Diffusion Purification framework effectively removes noise in biomedical lesion data, surpassing adversarial training and other robustness methods. These contributions advance MRI reconstruction and robust medical imaging, addressing critical limitations in clinical workflows.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
CHARACTERIZING AND INTEGRATING RESISTIVE INK FOR USE IN RF COMPONENTS
Full text linkThesis (M.S.)--Michigan State University. Electrical and Computer Engineering - Master of Science, 2025Aerosol jet printing (AJP) is gaining attention in additive manufacturing as a method that can be used to fabricate small and precise radio frequency (RF) components. Although work has been done to prove the usefulness of this manufacturing method up to D-band, most of that work has been done using highly conductive inks for transmission lines and radiators. Currently, no reliable method exists to fabricate resistive components for use in high-frequency circuits. The work presented here undergoes the process of characterizing a commercially available ink for that purpose. It will be shown that resistors can be fabricated small enough and with low enough resistance to be used in RF components. A Wilkinson power divider that operates in Ka band as well as one that operates in V band are fabricated. A set of loaded microstrips is fabricated to show how the printed resistors operate when they act as terminations.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
INVESTIGATING THE MALE GERM CELL AND MOLECULAR REQUIREMENT OF RNF216 ON SPERMATOGENESIS AND FERTILITY IN MICE
No full textThesis (Ph.D.)--Michigan State University. Cell and Molecular Biology - Doctor of Philosophy, 2025Infertility effects 10% of the population with half of cases attributed to the malepartner, resulting in financial, emotional, and social burdens when trying to conceive. Male infertility may be attributed to disruptions in spermatogenesis, which prevents spermatogonia from becoming mature spermatozoa necessary for fertilization. However, the mechanisms behind male infertility and contributing genetic factors are not fully elucidated. One such factor is Ring finger protein 216 (RNF216/TRIAD3), an E3 ubiquitin ligase in the RING-between-RING (RBR) subfamily with mutations identified in patients with Gordon Holmes Syndrome (GHS), a neurodegenerative disorder with male reproductive dysfunction. Additionally, global deletion of ubiquitous RNF216 in mice revealed essentiality of RNF216 in male fertility. However, the germ cell requirement and mechanism of RNF216 in male reproduction and GHS are unclear. To address this, I generated novel transgenic mouse lines to examine expression and localization of RNF216 in vivo, conditionally knockout RNF216 in male germ cells, and model the human GHS ubiquitin ligase inactivating RNF216 mutation. First, I characterized RNF216 expression in male germ cell populations that is seminiferous tubule stage-specific and localized within sub-nuclear domains. Furthermore, I discovered RNF216 is intrinsically required in male germ cells for spermatogenesis and fertility but is dispensable for spermatogonia function and survival. Finally, I determined the human GHS mutation led to progressive germ cell degeneration and infertility, demonstrating RNF216-directed ubiquitination is essential for spermatogenesis. These data definitively show RNF216 has a pivotal role in male germ cell biology, GHS reproductive etiology, and infertility.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
Multiracial identity response as a predictor of preterm birth among nulliparous, singleton birthing people in the US : An application of machine learning algorithms
Full text linkThesis (M.S.)--Michigan State University. Epidemiology - Master of Science, 2025Background: Preterm birth (PTB) is a significant cause of neurological or respiratory complications and infant death. Early identification of pregnant people at risk for PTB enables timely interventions and personalized pregnancy management to prevent potential complications. Over the past ten years, the multiracial population in the US has experienced significant growth. Multiracial disaggregation has been suggested as a factor that could help explain disparities in PTB rates, but it remains unclear whether classifying people into granular racial groups helps predict PTB. Objectives: This study aims to build four predictive models for preterm birth and to investigate which of them are important predictors of PTB across 31 race/ethnicity groups that include multiracial identities among nulliparous, singleton birthing people. Methods: We used population-based, cross-sectional data from U.S. birth records in 2019. Medical and socioeconomic factors potentially associated with PTB and race/ethnicity groups, including multiracial groups that are available within the first 16 weeks of pregnancy, were compared between nulliparous, singleton birthing people delivering preterm (<37 weeks of gestation) and term ( 6537 weeks of gestation). Logistic regression with all variables, logistic regression with selected main effect variables and two-way interaction variables, Decision Tree, and a Random Forest model were employed to build the prediction models. A Random Forest model from an oversampling dataset was utilized to assess the relative importance of risk factors. Results: 97,555 individuals experienced PTB, and 24,041 were classified as multiracial among the analytic sample (N=1,032,465). The ranges of areas under the receiver operating-characteristic curves(AUC) of all models with oversampling data were 57. The accuracy range of all models with an oversampling dataset was 62 to 65. The mean decrease in the accuracy of the importance plot indicated that some multiracial groups were important predictors of PTB compared with socioeconomic factors. Conclusions: This study's results supported the idea that several granular multiracial groups could be considered meaningful predictors of PTB.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
MACHINE INTELLIGENCE-ENABLED MULTIMODAL BIOMEDICAL IMAGING
Full text linkThesis (Ph.D.)--Michigan State University. Electrical and Computer Engineering - Doctor of Philosophy, 2025Due to the rapid development of computational technologies, deep-learning-based approaches have emerged as practical and promising remedies for a wide range of biomedical applications. This dissertation demonstrates the utilization of deep learning approaches across multiple modalities in the field of biomedical applications: histopathology image analysis, multispectral optoacoustic tomography (MSOT), computed tomography (CT), magnetic particle imaging (MPI), and Raman spectroscopy. The first deep learning application is convolutional neural networks (CNNs) for resolution enhancement and nuclei segmentation of hematoxylin and eosin (H&E) images. This deep learning-based approach could facilitate cancer diagnosis using the H&E images in a low resource setting. The second application is based on hybrid recurrent and convolutional neural networks to generate sequential cross-sectional MSOT images in order to reduce the acquisition time. Essentially, the proposed deep learning model can generate the missing sequential MSOT images in the data acquired by a large step size setting, resulting in a comparable resolution to the data acquired by a small step size setting. The third application is an efficient end-to-end deep learning model based on U-Net architecture and a multi-head attention mechanism for MPI-CT image segmentation. This proposed model can directly segment the MPI signal from the co-registered MPI-CT image with promising performance. Lastly, it is a custom-made Raman spectrometer together with computer vision-based positional tracking and monocular depth estimation using deep learning for the visualization of 2D and 3D surface-enhanced Raman Scattering (SERS) nanoparticles (NPs) imaging, respectively. The combination of Raman spectroscopy, image processing, deep learning, and SERS molecular imaging shows the robust and feasible potential for clinical applications.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
UNDERSTANDING AND QUANTIFYING IMPACTS OF THE CONTINUUM ON NUCLEAR STRUCTURE
Full text linkThesis (Ph.D.)--Michigan State University. Physics - Doctor of Philosophy, 2025With the opening of new facilities, such as the Facility for Rare Isotope Beams, exotic nuclei will be increasingly accessible. Many exotic nuclei are strongly coupled to the continuum resulting in interesting structure formations, such as halo nuclear states or nuclear resonances. In this thesis, the Gamow Shell Model framework is used to describe nuclei as Open Quantum Systems. This framework is a configuration-interaction shell model implemented in the Berggren basis which includes bound, resonant, and scattering states on equal footing. Two case studies are presented to highlight the impact of continuum effects on nuclear structure. Spectroscopic Factors are calculated for 8,9C, 8B, 8,9Li, and 8He using a traditional Shell Model approach (ignoring the continuum) and the Gamow Shell Model. The results from both methods are compared and demonstrate Spectroscopic Factors in these nuclei are dependent on the continuum. The newly discovered ephemeral nucleus 9N will be presented, which is unique with over half of its nucleons lying in the continuum. A projection method will be outlined to extract the continuum effects from the Gamow Shell Model to understand and directly quantify the effects of continuum coupling. Finally, a new outreach and community engagement demonstration, designed to educate the general public about nuclear structure and decays, will be discussed.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
EVALUATING COTTON SEED TREATMENT EFFICACY, EFFECTS ON SEEDLING DISEASES AND MICROBIAL DIVERSITY IN ARKANSAS
Full text linkThesis (M.S.)--Michigan State University. Plant Pathology - Master of Science, 2025Cotton is one of the most significant crops primarily grown worldwide for fiber, feed, and oilproduction. In the United States, it is primarily cultivated in the \u2018Cotton Belt\u2019, a region spanning from Virginia to California and covering approximately 10 million acres. In Arkansas, where cotton is typically grown from late April to October, the crop is susceptible to various fungal diseases that can reduce both lint quality and yield. Among the main diseases of economic importance, the seedling disease complex is a significant global issue affecting the establishment and production of cotton stands. It refers to a range of diseases, primarily caused by Pythium spp., Rhizoctonia solani, Fusarium spp., and Thielaviopsis basicola (Berkeleyomyces basicola), that compromise cottonseed germination and seedlings' emergence, survival, and development. Fungicide seed treatments are a key tool in managing cotton seedling diseases, offering critical protection against soilborne and seedborne pathogens. However, their effectiveness depends on the composition and prevalence of pathogen populations, which vary annually and regionally, as well as environmental conditions. The objectives of this study were to evaluate the effectiveness of four standard fungicide seed treatments in improving seedling emergence and survival across multiple years and locations in cotton fields in Arkansas. Additionally, we aimed to characterize the soil- and root-associated microbial communities in cotton, investigating how microbial composition varies by location, year, and seed treatment. For that, a field trial was conducted in Judd Hill (2019 \u2013 2023) and Marianna (2021 \u2013 2023), Arkansas. Four treatments containing a base insecticide (imidacloprid) were evaluated. Treatments consisted of no fungicide (T1), metalaxyl (T2), penflufen (T3), and a mix of prothioconazole, myclobutanil, penflufen, metalaxyl (T4). Our results suggest that the use of seed treatments is effective in controlling seedling disease complex, but their efficacy depends on environmental conditions and surrounding microbes.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
Fasahan Dabbobi : Technology, Livestock, and Environment in West Africa, 1890-1980
No full textThesis (Ph.D.)--Michigan State University. History - Doctor of Philosophy, 2025Fasahan Dabbobi argues that West African approaches to technology shaped social, animal, and environmental history. The dissertation focuses on a transregional livestock trade operating across Ghana, Burkina Faso, and Mali from 1890 to 1980. During that period, Muslim migrants built a livestock trading network through the selective usage and adaptation of technology\u2014both old and new. Their creative approach reconfigured social relationships central to the trade and altered the type of animals and animal products available in open-air markets. The dissertation broadens the definition of technology to include the perspectives of Muslim migrants who define technology in relation to human creativity and inventiveness in responding to changing historical contexts. Fasahan dabbobi, which translates from Hausa to animal technology, serves as the organizing principle for the dissertation by naming the complex historical process of incorporating technology into the livestock trade. In particular, the dissertation explores how Muslim migrants, as well as West African veterinarians, shaped the incorporation of four technologies into the trade, including vaccine production, transportation, meat preservation, and leatherworking. While West African actors led these processes, the livestock also shaped the relationships between the trade and technology. For this reason, the dissertation treats livestock as co-participants in the history and users of some of the technologies. By centering technology in scholarly approaches to livestock in Africa, the dissertation proposes an alternative model for thinking about agricultural futures on the continent and the place of livestock in the African Anthropocene.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references
TESTING THE PROFILE OF OBJECT-BASED ATTENTION
Full text linkThesis (M.A.)--Michigan State University. Psychology - Master of Arts, 2025Previous work on feature-based attention has established two prominent models of the selection profile: feature-similarity gain and surround suppression. The former predicts a monotonic decrease in task performance as the target feature becomes more different from the attended feature, whereas the latter predicts a non-monotonic performance pattern where the lowest performance occurs for targets close to the attended feature with a rebound in performance for more distant features. While support for both models have been found using simple features, it is unclear whether the selection profile for object-based attention aligns with either model. The current study assessed the selection profile for simple shapes, as a first step toward more parametric investigations of object-based attention. The study used a newly developed standardized circular shape space that allowed object difference to be quantitatively measured. In two experiments, participants were directed to attend to two target shapes that systematically varied along the shape circle. Two distractor shapes then appeared, overlapping with the target shapes, and one shape in each pair underwent a brief luminance change. Participants reported the status of each target shape (no change, dimmer, brighter). Experiment 1 used finer sampling of the shape space with a maximum target difference of 90\ub0, and Experiment 2 used a coarser sampling with maximum target difference of 180\ub0. For both experiments, performance accuracy peaked when the two target shapes matched and then decreased in a monotonic manner as the two shapes became more different. These results align more with the feature-similarity gain model and suggest that an analogous shape-similarity gain effect operates at a higher level of complexity. Such a gain effect may support object-based selection to differentiate target objects along higher-order, holistic dimensions like shape.Description based on online resource. Title from PDF t.p. (Michigan State University Fedora Repository, viewed ).Includes bibliographical references