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Dual Regulation of Mitochondrial Fusion by Parkin—PINK1 and OMA1
No full textMitochondrial stress pathways protect mitochondrial health from cellular insults1-8. However, their role under physiological conditions is largely unknown. Here, using 18 single, double and triple whole-body and tissue-specific knockout and mutant mice, along with systematic mitochondrial morphology analysis, untargeted metabolomics and RNA sequencing, we discovered that the synergy between two stress-responsive systems-the ubiquitin E3 ligase Parkin and the metalloprotease OMA1-safeguards mitochondrial structure and genome by mitochondrial fusion, mediated by the outer membrane GTPase MFN1 and the inner membrane GTPase OPA1. Whereas the individual loss of Parkin or OMA1 does not affect mitochondrial integrity, their combined loss results in small body size, low locomotor activity, premature death, mitochondrial abnormalities and innate immune responses. Thus, our data show that Parkin and OMA1 maintain a dual regulatory mechanism that controls mitochondrial fusion at the two membranes, even in the absence of extrinsic stress
Proso Millet in US High Plains Dryland Agriculture: Systems Resilience, Phenology, and Consumer Preferences for Alternative Grains
No full textDryland crop production in Nebraska\u27s semi-arid High Plains faces challenges from fluctuating weather impacts of climate change, including extreme temperatures, short growing seasons, and recurring droughts. Proso millet is an alternative crop used to build a more robust three-year rotation in the region, rather than the traditional two-year wheat-fallow cycle. This dissertation provides a systems-level assessment of proso millet, a climate-resilient crop with great potential to support sustainable cropping systems in the US High Plains. Using a field-to-consumer approach, it highlights its agronomic and environmental advantages in dryland rotations, develops a phenological framework to aid future research on production, crop modeling, and breeding, and explores advertising as a marketing strategy to promote broader adoption of this alternative grain in the US.
Chapter 1 examines crop rotation data from Nebraska’s High Plains Agricultural Laboratory to compare wheat–millet–fallow with wheat–corn–fallow systems in western Nebraska. Results show that overall, millet-based rotation provides greater resilience than corn, mainly through lower nitrogen requirements, decreased N2O emissions, and more consistent performance during stress years.
Chapter 2 investigates the phenological growth stages (using Zadok’s scale) and the cumulative growing degree-day (CGDD) requirements of proso millet in western Nebraska. The results indicate that the total thermal requirement for proso millet’s full growth cycle is approximately 1950 GDD. Under current growing conditions, proso millet also needs higher CGDD for stem elongation and anthesis than previously reported.
Chapter 3 explores consumer acceptance of alternative grains (AG), including millet, in the US through a nationwide survey. Results show that advertising, highlighting the nutritional and environmental benefits of these grains, significantly increases the likelihood that consumers choose AG-containing products.
Collectively, these chapters offer a comprehensive assessment of proso millet from the field to the consumer and demonstrate its agronomic, environmental, and market potential in the US. This research provides evidence that proso millet is a promising climate-smart crop with strong potential to support sustainable food and feed systems in the High Plains.
Advisors: Dipak Santra and Christopher Gustafso
CAZyme Gene Cluster Diversity in Human Gut Microbiome
No full textIn gut microbiome research, carbohydrate-active enzyme gene clusters (CGCs) have emerged as key functional units for understanding microbial glycan degradation. Unlike taxonomic or broad pathway annotations, CGCs offer gene-cluster-level resolution and capture substrate-specific microbial functions. However, their diversity and distribution in relation to host metabolic phenotypes, such as obesity, remain poorly characterized. This study tests the hypothesis that the composition and abundance of fiber-targeting CGCs vary between obese and healthy human gut microbiomes, reflecting distinct microbial carbohydrate utilization strategies. To examine this, we constructed a high-quality reference CGC dataset comprising 94,019 clusters from the Unified Human Gastrointestinal Genome and profiled shotgun gut metagenomes from 40 individuals (20 obese, 20 healthy). We observed notable differences in both the abundance and substrate preferences of CGCs between the two groups. Specifically, 11 CGCs were significantly enriched in healthy individuals, while 21 were significantly enriched in obese individuals. Obese-associated CGCs displayed elevated abundance of glycoside hydrolase families such as GH13, GH57, and GH3, suggesting increased ability to metabolize readily digestible polysaccharides, including starch and β-glucan. In contrast, healthy-associated CGCs showed enrichment in enzymes targeting host-derived and mucosal glycans, including GH2, GH92, and GT2, and exhibited significantly higher abundance of arabinogalactan-associated clusters. These patterns suggest differing microbial strategies for carbohydrate metabolism, potentially contributing to host metabolic phenotypes. By establishing a scalable and reproducible workflow for CGC profiling, this study offers new insights into the functional dynamics of the gut microbiome and highlights the potential of CGC-based analyses for understanding diet-microbe-host interactions in metabolic health. Our findings lay the groundwork for future research into microbiome-mediated metabolic modulation and the development of precision dietary or microbial interventions.
Advisor
Catching a Fever: A Comparison of \u3cem\u3eVachellia xanthophloea\u3c/em\u3e Populations in the Limpopo and Luvuvhu River Floodplains of the Makuleke Contractual Park
No full textThis study investigates patterns of stand structure regeneration, growth characteristics, and coarse woody debris (CWD) patterns in Vachellia xanthophloea (fever tree) stands within the Makuleke Contractual Park (MCP), a semi-arid savanna system in northern Kruger National Park (KNP), South Africa. Fieldwork was conducted across two stand types: a monospecific fever tree stand in Rietbok Vlei and a mixed-species stand in the Western Nhlangaluwe Floodplain where fever tree is established with Faidherbia albida (ana tree). Data were collected from 20 total 1/4-acre (1,011 m2) circular plots between both stands in 2024 and 2025, including seedling root collar diameter (RCD), mature tree diameter at breast height (DBH) and height, damage class, CWD accumulation and decay class, volumetric water content (VWC), canopy cover, elevation, and understory composition. The mixed-species stand supported higher seedling abundance in 2024 and 2025, along with greater mean RCD. This stand also exhibited higher soil moisture, more consistent canopy cover, and greater understory species richness, indicating more favorable microsite conditions for regeneration. Mature fever trees in the mixed-species stand exhibited a greater DBH, while height did not differ meaningfully between stands. The monospecific stand had a greater variation in elevation and contained more CWD overall, showing higher frequencies of advanced decay classes and suggesting elevated mortality and possible even-aged senescence. Damage observations revealed greater disturbance in the monospecific stand, particularly from elephants, insects, and ungulates. A pulse event in January 2025 rainfall spurred a 57.4% increase in seedling abundance at Rietbok Vlei in 2025. This suggests that recruitment and succession is disturbance-driven in the monospecific stand. These data reflect differences in regeneration, growth, and stand senescence decline between stand types and provide insight into the biotic and abiotic factors shaping fever tree populations in the MCP. Observed patterns align with existing models of episodic recruitment, patch dynamics, and species bottlenecks, and may support future long-term forest health monitoring in the MCP.
Advisor: Dr. Lord Ameya
Interpolation in Weighted Projective Spaces
No full textOver an algebraically closed field, the double point interpolation problem asks for the vector space dimension of the projective hypersurfaces of degree singular at a given set of points.
After being open for 90 years, a series of papers by J. Alexander and A. Hirschowitz in 1992–1995 settled this question in what is referred to as the Alexander-Hirschowitz theorem. In this thesis, we use commutative algebra to prove analogous statements in the weighted projective space, a natural generalization of the projective space.
A main contribution of this work is the careful adaption of several classical algebro-geometric techniques to the setting of weighted projective geometry. The introduction is dedicated to non-technical remarks about interpolation including its history. Chapter 2 summarizes the results. Chapter 3 includes the set up and most of the necessary background. Chapters 4–8 contain the main results and Chapter 9 contains some open problems.
Advisor: Alexandra Secelean
The Significance of Snow in Hydrometeorological Extremes
No full textSnow is the largest natural reservoir on Earth. It accumulates water during the cold and dry months and releases it during warm and dry months. Societies, especially in the extratropical zone, heavily depend on snow as their freshwater resources, resulting in the massive economic value of snow. The recent changes in snowpacks and associated processes are concerning. We are susceptible to disruptions in snow processes, which can have disastrous implications. Yet the importance of snow is often not realized by a wider population. There are limited studies quantifying snow-related extremes, partly stemming from the limitations of existing methodologies.
This dissertation investigates different aspects of snow-related extreme monitoring by studying their mechanistic drivers, improving tools to detect them, and quantifying their impacts on water availability. Firstly, we study snow-induced flooding by implementing a prototype flood forecasting system for Nebraska using a distributed conceptual rainfall runoff model. We quantify the role of snow in flood generation, taking the historic 2019 flood in the Midwest as an example case. Secondly, we examine the hydrometeorological drivers of rain-on-snow events, a phenomenon that often leads to flash flooding. Using causal inference analysis on station measurements, we found the leading drivers of these events in North America. Thirdly, we developed a novel snow drought detection framework incorporating machine learning and information theory techniques. We validate the method on recorded events and reveal the major drivers utilizing the explainable nature of the framework. Finally, we quantify the impacts of snow droughts on the water availability in rivers worldwide. Our analysis highlights the rivers experiencing worsening snow droughts and the rivers highly susceptible to snow droughts.
The outcomes of these studies progress our current knowledge regarding snow-related extreme events. They also improve our ability to predict such events and respond to them efficiently. Additionally, our results aid efficient water resource management and policymaking, especially in snow-dependent regions. Overall, the outcomes achieve the overarching goal of this dissertation: to emphasize the significance of snow to humanity.
Advisor: Tirthankar Ro
Intelligent Multi-layer Optical Network Design and Network Softwarization
No full textThe growing demand for high-capacity, low-latency services has placed significant pressure on the design and operation of optical transport networks. Multi-layer optical network design—which coordinates the physical layer with higher-layer protocols—has emerged as a critical strategy to enhance resource efficiency, service flexibility, and fault resilience. Enabled by advancements in software-defined networking (SDN) and network softwarization, intelligent multi-layer architectures allow for adaptive, cross-layer control of routing, grooming, and protection mechanisms, ultimately reducing both capital and operational expenditures.
This dissertation investigates the intelligent design and simulation of multi-layer optical networks through the integration of SDN, machine learning, and high-fidelity physical-layer modeling. We first introduce a novel service mesh architecture that supports dynamic, cross-layer service provisioning, improving both Quality of Service (QoS) and resource utilization. Second, we enhance the SimEON optical network simulator by integrating Deep Reinforcement Learning (DRL) to address the Routing, Modulation, and Spectrum Assignment (RMSA) problem in Elastic Optical Networks (EONs). Our results show that DRL-based approaches significantly outperform traditional heuristics under dynamic traffic conditions. To bridge control-plane decisions with physical-layer constraints, we develop a unified simulation framework by integrating SIMON, an optical network simulator, with GNPy, the Optical Route Planning Library. This hybrid approach enables accurate modeling of impairments such as non-linearities and dispersion, while supporting SNR-constrained dynamic routing. Finally, in collaboration with IIT-Madras, we propose a novel node architecture that incorporates optical phase conjugators (OPCs) and regenerators. Our coordinated OPC-regenerator placement strategy mitigates physical-layer impairments, reduces Digital Signal Processing (DSP) complexity, and enhances energy efficiency in long-haul Wavelength Division Multiplexing (WDM) networks.
By combining multi-layer intelligence, machine learning, and realistic simulation, this work contributes a scalable, adaptive, and performance-aware foundation for the next generation of optical transport networks.
Advisor: Byrav Ramamurth
Mass Effects on Energy Transfer Paths in Nonlinear Vibrating Systems
No full textThis dissertation examines the role of mass in nonlinear systems, uncovering its role in enabling passive energy redistribution and robust vibration control in both idealized and real-world structures. Focusing on a strongly nonlinear two-degree-of-freedom system, it investigates how changes in mass ratio influence the dynamics of energy transfer, nonlinear normal modes (NNMs), and dissipation behavior.
A number of significant contributions are introduced in this work beginning with the introduction of the frequency-energy-peaks (FE-pks) plot, a novel tool that visualizes how energy flows through the system, revealing transient resonance orbits, internal resonance effects, and effectively capturing the different nonlinear phenomena with ease. This method proved highly sensitive and efficient, even at low excitation levels and large mass ratios, capable of detecting subtle nonlinear behaviors that might otherwise be misinterpreted as noise in experimental data. Another key finding is the establishment of settling time as a practical and interpretable metric. It not only quantifies energy transfer efficiency but also serves multiple diagnostic roles: distinguishing between regular and chaotic behavior, pinpointing the energy threshold for the 1:3 internal resonance loop of the first NNM, and enabling the identification of NNMs when used for different sets of initial conditions. Another novel contribution is the isolation of early frequency content as a predictor for the onset of chaos, providing a new window for early detection and control of instabilities.
The study reveals promising future directions. Investigating the frequency content governing chaos onset could improve early prediction and control in nonlinear systems. Using image processing techniques to analyze the FE-pks plots may allow automated detection and tracking of transient resonance orbits, enhancing understanding of their behavior and role in energy transfer. Analyzing different system configurations and parameter variations could broaden the understanding of nonlinear behavior. Building a structured database of dynamic regimes, energy paths, and critical thresholds could guide the design of systems for targeted energy transfer. Additionally, further exploration of the forced response is recommended to complement free-damped analysis and reveal resonance-driven behaviors under harmonic excitation.
Advisors: Joseph Turner and Keegan J. Moor
Multivariate Mixture Regression Models with Known Group Membership and Informative Priors
No full textWe introduced couple different novel approaches to incorporate latent variable information to multivariate mixture regression models with both Gaussian and count data. We also evaluated the performance of these models with existing best approaches with simulated data from various sampling structures and also evaluated one of the models performance with rice metabolite data that provided some novel insights as well as validating existing literature about performance and behavior of these metabolites. We validated the method using extensive simulations and a real-world application. In both quantitative covariate designs and complex treatment design simulations, our method consistently outperformed established tools like limma, edgeR, and DESeq2, demonstrating a superior balance of sensitivity and precision. Application to a rice metabolite dataset confirmed its practical utility, successfully identifying key, biologically-relevant compounds. Through comprehensive simulations, we show this method consistently outperforms established tools in challenging low-replicate studies and complex, multi-group (ANOVA-type) designs, demonstrating superior sensitivity and overall performance.
Advisors: Stephen Kachman and Qi Zhan
Examining the Willingness to Utilize Telehealth among Women Who Own a Smartphone and Live in Urban Areas of Nigeria
No full textIn Nigeria, women face significant barriers to accessing healthcare, including long distances to healthcare facilities, lack of autonomy, stigma surrounding reproductive and sexual health concerns, and the unavailability of primary and specialty care. Telehealth (i.e., the use of information and communication technologies for the purpose of advancing the health of individuals and their communities) holds a promise of facilitating healthcare access by eliminating physical distance barriers to obtaining quality healthcare and contributing ease and privacy. Yet, limited research has been conducted on its use in Nigeria, particularly how it is perceived and how it might be received. Guided by the Theoretical Framework of Acceptability (TFA), the study sought to address this gap by exploring Nigerian women’s perceptions towards telehealth and assessing for factors that might influence willingness to use telehealth. Two hundred and eighty-eight adult women living in urban areas of Nigeria completed an online survey. Analysis revealed that most respondents reported willingness to use telehealth (93.0%), with 69 (25.3%) reporting to be very willing and 152 (55.7%) reporting to be willing. Although 50.0% of the respondents reported having heard about telehealth, only 74 (27.2%) reported prior experience with using telehealth. All seven behavioral constructs of the TFA were correlated with willingness to use telehealth. However, only positive view of telehealth burden (i.e., not perceiving telehealth use to be a burden) (β = .19, p = .03) and self-efficacy (β = .20, p = .015) were significant unique predictors of willingness. This exploratory analysis provides insight into how well telehealth might be adopted by Nigerian women and suggests that although only a few respondents have previously utilized telehealth and only half of the respondents had been aware of telehealth before the survey, most are willing to utilize it to meet their healthcare needs.
Advisor: Katelyn Cobur