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Surficial geology of Russellville Quadrangle, Crawford and Lawrence Counties, Illinois and Knox County, Indiana
No full textUSGS National Cooperative Geologic Mapping Program under StateMap award number G21AC10861, 202
Alternative diet formats and novel protein ingredients for use in canine and feline diets
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01The student, Meredith Smola, accepted the attached license on 2025-04-23 at 10:19.The student, Meredith Smola, submitted this Dissertation for approval on 2025-04-23 at 10:41.This Dissertation was approved for publication on 2025-04-24 at 14:22.DSpace SAF Submission Ingestion Package generated from Vireo submission #21913 on 2025-10-19 at 19:15:55As the global population continues to grow, food sustainability is an increasingly important challenge. Along with the search for alternative proteins, including fermented ingredients, insects, and brewed yeast-based proteins, there is increased demand for innovative pet food formats that undergo different processing methods. Because there are 10 indispensable amino acids (AA) for dogs and cats, dietary protein sources must not only provide nitrogen but also the necessary amounts of each AA. As alternative proteins and processing methods reach the pet food industry, it is important to conduct sufficient testing so that diet quality and pet health are maintained. The purpose of this dissertation was to evaluate alternative proteins and diet formats for pet foods by using the precision-fed cecectomized rooster model and conducting canine nutrition studies. The first aim was to determine the apparent total tract macronutrient digestibility (ATTD) of raw and kibble diets, and test their effects on the serum metabolites, hematology, and fecal characteristics, metabolites, and microbiota of adult dogs. Adult beagle dogs were used in a replicated 4x4 Latin square design (n = 12/treatment) to test: Frozen Stella’s Super Beef Dinner (FD), Stella & Chewy’s Red Meat Raw Blend Kibble (RBK), Purina Pro Plan Adult Complete Essentials Shredded Blend Beef & Rice (PP), and Blue Wilderness Rocky Mountain Recipe with Red Meat (BW). Greater (P90%, with the exception of histidine (89.55%) and valine (89.36%) for TMc. Histidine digestibility was higher (P80%, with the exception of histidine (79.32%), lysine (73.47%), and valine (79.02%) for ASB. All AA digestibility values were different, with FSBP being the most digestible and ASB being the least digestible. The DIAAS-like values were highest for FSBP, which met the criteria for a high-quality protein source for adult cats and a good-quality protein source for growing puppies, with DIAAS-like values for other ingredients being lower. For adult and growing dogs, methionine, methionine + cystine, or threonine were the first limiting AA depending on reference guideline and life stage. For adult and growing cats, phenylalanine + tyrosine, threonine, methionine, or methionine + cystine were first limiting. The fourth aim was to evaluate the safety, efficacy, gastrointestinal tolerance, ATTD and palatability of brewed chicken protein (BCP; Saccharomyces cerevisiae expressing a chicken protein). Adult beagle dogs were used in a completely randomized design (n = 8/treatment). After a 2-wk acclimation phase, baseline measurements were collected and then dogs were allotted to the following treatments and fed for 26 wk: control diet based on chicken by-product meal and brewers rice (0% BCP; Control), 15% BCP (Low), 30% BCP (Medium), or 40% BCP (High). Palatability was assessed by comparing dry diets coated 0% vs. 1% BCP in 20 adult dogs. Consumption of BCP did not affect BW, body condition score, physical examination parameters, food intake, serum chemistry, hematology, and urinalysis parameters. The ATTD of dry matter, organic matter, and crude protein were greater (P85% with the exception of lysine (72.45%) for CGM. There were no differences between measured and predicted AA digestibility values for all protein mixtures, except histidine (2.92% difference) and serine (2.63% difference) in CGM25. These data demonstrate that AA digestibilities obtained from individual ingredients are additive and predictive of those in ingredient mixtures when using the cecectomized rooster assay
Label-free optical signatures of extracellular vesicles and their applications in disease diagnosis and therapy response
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01The student, Jaena Park, accepted the attached license on 2025-04-24 at 12:24.The student, Jaena Park, submitted this Dissertation for approval on 2025-04-24 at 12:27.This Dissertation was approved for publication on 2025-04-25 at 11:43.DSpace SAF Submission Ingestion Package generated from Vireo submission #21931 on 2025-10-19 at 19:16:06Extracellular vesicles (EVs) have emerged as critical mediators of cell-to-cell communication, particularly in the tumor microenvironment, where they modulate hypoxia response, drug resistance, and metastatic progression. Beyond these roles, EVs are promising candidates for noninvasive cancer diagnostics and therapy monitoring through liquid biopsy. However, a fundamental barrier to clinical translation lies in the difficulty of characterizing EVs in situ while preserving their biochemical composition, spatial origin, and functional state. Traditional EV analyses often rely on bulk or destructive molecular profiling, limiting their diagnostic specificity and obscuring biologically relevant heterogeneity. More critically, it remains unclear whether the metabolic content of EVs reflects the state of their parental cells. To address these challenges, this thesis integrates label-free intravital and ex vivo optical imaging to investigate the metabolic characteristics of EVs across multiple oncologic conditions, including hypoxia, chemotherapy, and radiotherapy. Simultaneous label-free autofluorescence-multiharmonic (SLAM) microscopy enables high-resolution imaging and real-time metabolic profiling of EVs using endogenous optical contrast from coenzymes found in EVs. Across tumor models and biofluid-derived EV populations, optical redox ratio (ORR) imaging was used to assess metabolic reprogramming associated with disease state and treatment response. The findings demonstrate that autofluorescence-based EV profiling supports noninvasive, functionally informative liquid biopsy for cancer screening, prognostication, and therapy assessment by capturing dynamic metabolic shifts reflective of parent cell states
Development and application of innovative technologies to unravel neuronal systems: From optogenetics to neural interfaces to spatial transcriptomics
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01The student, Huaxun Fan, accepted the attached license on 2025-04-24 at 17:47.The student, Huaxun Fan, submitted this Dissertation for approval on 2025-04-25 at 09:17.This Dissertation was approved for publication on 2025-04-27 at 20:33.DSpace SAF Submission Ingestion Package generated from Vireo submission #21965 on 2025-10-19 at 19:16:12The neuronal system represents one of the final frontiers of biological research, as it underpinning cognitive function and behavior. However, the immense complexity of neuronal systems has hindered our ability to fully understand the underlying mechanisms. This dissertation presents the development and application of innovative technologies, including optogenetics, neural interfaces and spatial transcriptomics, to provide deeper understanding of cell signaling pathways, neural communication, and the intricate architecture of brain function. Cell signaling pathways constitute the molecular machinery that regulates cellular behavior. Among them, the Raf/ERK and AKT pathways play key roles in neuronal development, survival, regeneration, and plasticity. We developed optogenetic systems, termed optoRaf and optoAKT, to enable precise control of corresponding signaling pathways in both invertebrate and vertebrate models. In Drosophila, optoRaf or optoAKT activation not only enhanced axon regeneration in both regeneration-competent and incompetent sensory neurons in the peripheral nervous system (PNS) but also enabled temporal modulation and spatial guidance of axon regrowth. Importantly in the central nervous system (CNS), activation of these systems promoted axon regrowth and functional recovery of thermonociceptive behavior. We further engineered the optoRaf system to be compatible with the adeno-associate virus (AAV) delivery system, facilitating mammalian applications. Non-invasive light delivery through transcranial window successfully activates optoRaf system in the mouse motor cortex. Optical activation of ERK signaling pathway increased calcium activity frequency and amplitude, inducing neuronal excitability. Importantly, ambient illumination in freely moving animals trigger similar activation, highlighting the potential of the optoRaf system for studying ERK-dependent neuronal plasticity in freely behaving animals and across diverse behavioral contexts. Neural communication is the foundation for cognitive function, the efficiency of neuronal information processing is orders of magnitude higher than the most advanced silicon-based semiconductors. We developed a transformative bionanotechnology platform that enables the guided formation of cultured neural networks with complex and well-defined 3D topologies. Meanwhile, these neuronal cells are seamlessly interfaced with advanced electronic devices, allowing us to administer and monitor the neuronal and synaptic activities with unprecedented high spatiotemporal resolution. Finally, the spatial context of gene expression is critical for understanding the organization and function of neuronal systems. To address this, we developed Single Nuclei Imaging-guided sPatial transcriptomics for Enhanced genome-wide coveRage (SNIPER), a technology capable of capturing whole-transcriptome profiles at single-cell resolution. This methodology offers unparalleled precision and enables comprehensive characterization of neuronal heterogeneity and spatial architecture. Collectively, these innovative technologies significantly advance our ability to investigate neuronal systems at the molecular, cellular, and network levels. Beyond providing fundamental insights into neuronal function, these tools hold potential for therapeutic applications and broader interdisciplinary research. It is our aspiration that these technological innovations will equip researchers to advance the frontiers of neuroscience and biology
"We're all Big Ten archives, but none of us quite look the same": Examining current audiovisual preservation practices in Big Ten university archives
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01The student, Katie Higley, accepted the attached license on 2025-04-28 at 18:31.The student, Katie Higley, submitted this Thesis for approval on 2025-04-28 at 18:50.This Thesis was approved for publication on 2025-04-29 at 11:27.DSpace SAF Submission Ingestion Package generated from Vireo submission #22025 on 2025-10-19 at 19:16:22This study investigates the sociotechnical aspects of digitally preserving and curating audiovisual (A/V) collections within university archives. This study was conducted through sixteen semi-structured interviews with archivists working in Big Ten university archives. The interview questions included the participants' backgrounds and experiences with A/V archiving, their institution's A/V preservation workflows, and their broader connections and collaborations within the field. The paper highlights these interviews' findings and offers recommendations to support the long-term preservation of A/V materials. It encourages participants to advocate for their A/V materials by sharing collection data with their administrators, openly sharing their experiences working with A/V, visibly collaborating, and offering meaningful opportunities to engage with A/V to students and professionals
Three essays on the economics of nutrition-sensitive programs
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01The student, Anissa Collishaw, accepted the attached license on 2025-04-29 at 09:11.The student, Anissa Collishaw, submitted this Dissertation for approval on 2025-04-29 at 09:23.This Dissertation was approved for publication on 2025-04-29 at 17:08.DSpace SAF Submission Ingestion Package generated from Vireo submission #22068 on 2025-10-19 at 19:16:41This dissertation consists of three chapters that explore the economics of nutrition-sensitive interventions in low-income countries. Chapter 1 reviews the existing evidence regarding the impact of livestock interventions, drawing primarily from the literature on livestock transfer programs, graduation programs, and livestock insurance. Livestock contribute to the welfare of rural poor households in many ways. Livestock products can be consumed, utilized, or sold for income. If consumed, livestock facilitate improved food security and nutrition. Income from livestock sales can be used for consumption – indirectly contributing to food security and nutrition – or invested. In the absence of developed financial markets, livestock also provide a savings mechanism, potentially enhancing resilience. Livestock ownership can be particularly important for rural women. Although livestock interventions have the potential to improve child nutrition outcomes, livestock intensification may pose risks related to water, sanitation and hygiene (WASH) conditions. Chapter 2 assesses the impact of SELEVER, a nutrition- and gender-sensitive poultry intervention, with and without added WASH focus, on hygiene practices, morbidity and anthropometric indices of nutrition in children aged 2-4y in Burkina Faso. We implement a 3-year cluster randomized controlled trial in 120 villages in 60 communes (districts) supported by the SELEVER project. Communes were randomly assigned using restricted randomisation to one of three groups: (1) SELEVER intervention (n=446 households); (2) SELEVER plus WASH intervention (n=432 households); and (3) control without intervention (n=899 households). Our study population included women aged 15-49y with an index child aged 2-4y. We assess effects 1.5y (WASH sub-study) and 3y (endline) post-intervention on child morbidity and child anthropometry outcomes. Participation in intervention activities was low in the SELEVER groups, ranging from 25% at 1.5y and 10% at endline. At endline, households in the SELEVER groups had higher caregivers knowledge on WASH-livestock risks and were more likely to keep children separated from poultry than in the control group. No differences were found for other hygiene practices, child morbidity symptoms or anthropometry indicators. Integrating livestock WASH interventions alongside poultry and nutrition interventions can increase knowledge of livestock-related risks and improve livestock-hygiene related practices, yet may not be sufficient to improve morbidity and nutritional status of young children. Chapter 3 explores the impact of a cash+ program in Malawi on lean season food security. Rural households in sub-Saharan Africa face high levels of poverty and annually recurring periods of lean season food insecurity. We conduct a randomized controlled trial in rural Malawi to assess the impact of coupling unconditional cash transfers of either 43/month with a nutrition behavior change intervention on the diets and food security of households during the lean season. We find evidence of protective effects, but only when the intensity of treatment is high. When combined with the behavior change intervention, the large monthly cash transfer of 43/month invested in agricultural inputs and assets, allowing them to produce and store more maize in the preceding harvest and partially insulating them against negative food price shocks. We do not find evidence of similar effects of the nutrition behavior change intervention alone or in conjunction with a smaller transfer of $17/month
Spatial and temporal variation in virtual water transfers on the U.S. electric grid
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01The student, Jennifer Nugent, accepted the attached license on 2025-04-29 at 14:28.The student, Jennifer Nugent, submitted this Dissertation for approval on 2025-04-29 at 14:28.This Dissertation was approved for publication on 2025-04-30 at 21:03.DSpace SAF Submission Ingestion Package generated from Vireo submission #22091 on 2025-10-19 at 19:16:43Water consumed by power plants is transferred virtually between balancing authorities from producers to consumers on the U.S. electric grid. A balancing authority is an entity that is responsible for meeting electricity demands in a region by managing electricity generation and inter-region transfers. This network of virtual water transfers follows seasonal trends, as energy demand, fuel mix, and water availability vary seasonally. Blue water footprints were estimated based on hydropower reservoir evaporation rates and reported thermoelectric cooling water consumption, while grey water footprints were estimated based on thermoelectric cooling water discharge and local thermal discharge regulations. The central research goal of this dissertation is to analyze the sub-annual and spatial variations in virtual water transfers on the U.S. electric grid and their relationships with changes in season, climate, and infrastructure. Objective 1 quantifies monthly virtual water transfers between balancing authorities based on hourly electricity interchange data reported to the Energy Information Administration (EIA) in Form EIA-930 for 2016–2021. Virtual water footprints were calculated using thermoelectric water consumption volumes reported in Form EIA-923, power plant data from Form EIA-860, and water consumption factors from literature. This timescale is limited by a lack of sub-annual electricity transfer data available before July 2015. Objective 2 analyzes the seasonality of virtual water transfers on a longer historical timeline by calculating net electricity transfers for each balancing authority at a monthly timescale from 2010–2020. Electricity data (generation, demand, and interchange) were compiled, cleaned, and aligned to analyze discrepancies between historical annual and modern monthly data. A consistent time series of generation and demand was produced to determine the net electricity transfers, and subsequently the net virtual blue and grey water exports and imports. The results of Objective 1 and Objective 2 show that the water footprint of hydropower generation dominates virtual water transfers. To further understand the impacts of hydropower generation, Objective 3 leverages the historical net electricity transfer data to analyze the impacts of drought on hydroelectric power generation, electricity interchange, and virtual blue water transfers. The results indicate that virtual water transfers follow seasonal trends. Virtual blue water transfers are dominated by evaporation from hydropower reservoirs in arid, high-evaporation regions, and when virtual water transfers peak depends on the methodology for calculating evaporation from reservoirs. Objective 3 illustrates the correlation between drought severity and coverage and the energy-water nexus. Drought has varying impacts on balancing authorities both spatially and seasonally. Hydropower generation sees the greatest impacts in the form of generation reduction, which most heavily impacts balancing authorities with large reliance on hydropower generation. Increased renewable energy development, particularly wind power, could mitigate drought impacts on the grid and associated water resources. Understanding the spatial and temporal transfer of water resources has important policy, water management, and equity implications for understanding burden shifts between regions. By quantifying the virtual water transfers on the electric grid on a longer historic timeline, seasonality and water consumption are analyzed in response to dynamic changes over time, such as long-term drought and changes in grid fuel mix. The results show the complexities of working with disparate data sources to provide consistent data over historical timelines. The discrepancies between existing data sources highlight the importance of high-quality data collection, organization, and availability
A phenology-guided deep learning framework for advanced soybean yield prediction in the Americas
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01The student, Chishan Zhang, accepted the attached license on 2025-04-29 at 16:24.The student, Chishan Zhang, submitted this Dissertation for approval on 2025-04-29 at 16:39.This Dissertation was approved for publication on 2025-05-01 at 11:53.DSpace SAF Submission Ingestion Package generated from Vireo submission #22097 on 2025-10-19 at 19:16:48Soybeans play a vital role in global food security and sustainable agriculture, particularly in North and South America, which account for over 86% of global production. However, these regions are increasingly threatened by climate change-induced extreme weather events, necessitating advanced monitoring and predictive capabilities to safeguard soybean yields. This PhD research enhances large-scale soybean yield estimation under varying climatic conditions by leveraging innovative remote sensing (RS) and deep learning (DL) techniques. The dissertation makes three key contributions: First, this study aims to develop a novel Phenology-guided Bayesian Neural Network (PB-CNN) framework for county-level yield estimation and uncertainty quantification in the US Corn Belt. This framework integrates phenological data with Bayesian neural networks to evaluate yield response to environmental stresses within different growing stages. The developed PB-CNN framework demonstrated improved accuracy and provided valuable uncertainty estimates compared to benchmark models. Feature importance analysis revealed that satellite-based predictors and reproductive growth stages contribute most significantly to yield formation, while soil predictors and early growth stages introduce greater uncertainty. Second, this study introduces a comprehensive approach to analyzing domain shifts in crop yield prediction and evaluating transfer learning strategies through combined crop model simulations and empirical analysis. It demonstrates that agricultural systems face unique challenges in transfer learning as environmental variations, cultivar adaptations, and management practices create multiple, simultaneous domain shifts. Comparative evaluation showed that Model-Agnostic Meta-Learning (MAML) achieved superior performance across various domain shift types, while Fine-tuning Learning (FTL) provided an efficient solution with moderate amounts of target data. Third, this study incorporates the Madden-Julian Oscillation (MJO) into the deep learning framework to assess the impact of MJO-driven extreme events on soybean production. By quantifying MJO teleconnections across different phases and ENSO conditions, it revealed regional patterns of temperature stress and soil moisture responses. Integrating projected MJO and ENSO information with within-season environmental variables reduced average prediction error, with significant improvements in major soybean-producing states during critical growth periods. By enabling accurate soybean yield prediction across the Americas, where the crop covers over 100 million hectares and contributes to $200 billion in annual global trade, this framework will allow rapid responses to potential food crises. The proposed framework supports rapid governmental and humanitarian responses to potential food crises while informing commodity pricing, crop insurance, trade decisions, and economic planning, helping to mitigate the adverse effects of climate change on a critical global food resource
Supervisory control with online learning for stabilization and near-optimal performance of time-varying linear systems
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01The student, Dhritiman Roy, accepted the attached license on 2025-05-01 at 13:19.The student, Dhritiman Roy, submitted this Thesis for approval on 2025-05-01 at 13:31.This Thesis was approved for publication on 2025-05-08 at 16:27.DSpace SAF Submission Ingestion Package generated from Vireo submission #22152 on 2025-10-19 at 19:16:58Model-based control methods are widely used in robotics because they use system equations to compute efficient control actions. However, these methods often struggle in real-world situations where the system model is not perfect or where there are unexpected disturbances. In addition, solving nonlinear optimization problems in real time can be too slow or too demanding for systems with limited onboard computing power. To address these challenges, this study proposes a hybrid control approach that combines classical control, optimal planning and online learning. The system we focus on is a 2D quadrotor, modeled as a six-dimensional system controlled using force and torque inputs. At the lower level, we use three different types of controllers: a basic Proportional-Derivative (PD) controller, a trajectory planner using nonlinear programming (NLP), and a control law based on Pontryagin’s Maximum Principle (PMP), which we implement using PyTorch. At the higher level, we add a Multi-Armed Bandit (MAB) layer using the EXP3 algorithm. This layer learns over time which controller performs best based on feedback like tracking error and energy usage. It allows the system to switch between controllers depending on how well they are working at each moment. Our results show that this combination of planning and learning can make the system more reliable and adaptive, even in uncertain environments. While we apply this to a quadrotor, the same idea can be used for many other types of robotic systems
Enhancing computational notebooks with code+data space versioning
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01The student, Hanxi Fang, accepted the attached license on 2025-05-07 at 17:26.The student, Hanxi Fang, submitted this Thesis for approval on 2025-05-07 at 17:33.This Thesis was approved for publication on 2025-05-09 at 16:23.DSpace SAF Submission Ingestion Package generated from Vireo submission #22261 on 2025-10-19 at 19:17:14There is a significant gap between how people explore data and how Jupyter-like computational notebooks are designed. People explore data nonlinearly, using execution undos, branching, and/or complete reverts, whereas computational notebooks are designed for sequential exploration only. Recent works like ForkIt are still insufficient to support these multiple modes of nonlinear exploration in a unified way. In this work, we address the challenge by proposing two-dimensional code+data space versioning for computational notebooks and verifying its effectiveness using our prototype, Kishuboard, which seamlessly integrates with Jupyter. By adjusting code and data knobs, users of Kishuboard can intuitively manage the state of computational notebooks in a flexible way, thereby achieving both execution rollbacks and checkouts across complex multi-branch exploration history. Moreover, this two-dimensional versioning mechanism can easily be presented along with a friendly one-dimensional history. Human-subject and LLM-agent-based studies indicate that Kishuboard can significantly enhance user productivity in various data science task