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Cerebrospinal fluid creatine kinase as a biomarker in dogs with neurologic disease
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Taylor Hanson, accepted the attached license on 2025-02-26 at 10:42.The student, Taylor Hanson, submitted this Thesis for approval on 2025-02-26 at 10:47.This Thesis was approved for publication on 2025-02-28 at 16:04.DSpace SAF Submission Ingestion Package generated from Vireo submission #21651 on 2025-10-19 at 18:09:05While the primary utility of creatine kinase (CK) measurement in clinical practice thus far has been detection of skeletal muscle injury, it has been documented that elevated CK within cerebrospinal fluid (CSF) can similarly serve as a marker of tissue damage within the central nervous system. Furthermore, there has been much interest in the possible value of CSF CK as a prognostic indicator in cases of neurologic injury, and also as a predictor of disease etiology in cases of neurologic dysfunction. While studies in human and veterinary medicine have documented prognostic utility in specific cases, the literature remains somewhat conflicted about the value of CSF (or serum) CK as an etiologic differentiator in cases of neurologic disease. Clarity in this regard has also historically been limited by the lack of known reference intervals for CK in the CSF of veterinary species in health (though a reference interval for CSF CK in healthy dogs has recently been proposed). This study aimed first to document the magnitude of CSF CK in a small group of healthy dogs for use as a control population. Next, CSF CK was measured in a cohort of dogs with various neurologic diseases to investigate whether the magnitude of CK within the CSF could serve as a predictor of the etiologic agent of disease. Secondary investigations included evaluation of other standard CSF analytes – for example, total protein – and their association to CSF CK. No significant difference in CSF CK level was identified between the normal dogs and the neurologic dogs as a whole. Dogs with neurologic signs due to metabolic disease had significantly higher CSF CK levels than all other groups evaluated, though this finding should be interpreted with caution as only two dogs with metabolic disease were present in the study. Additionally, both age and CSF total protein were identified as significant predictors of CSF CK, with older dogs tending to have lower CSF CK and CSF CK tending to increase alongside CSF total protein. Finally, CSF CK was not found to be a significant predictor of serum CK in neurologic dogs. This research raises the possibility that dogs with neurologic disease of metabolic origin may have significantly different levels of CSF CK activity than dogs with other etiologies of neurologic disease. However, further studies – ideally with much larger sample sizes – would be required to confirm these findings and better evaluate what, if any, diagnostic benefit this biomarker may confer in a clinical setting
Essays on macroeconomic and financial risks and uncertainties
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Minyoung Cho, accepted the attached license on 2025-03-17 at 14:00.The student, Minyoung Cho, submitted this Dissertation for approval on 2025-03-17 at 14:05.This Dissertation was approved for publication on 2025-03-24 at 11:17.DSpace SAF Submission Ingestion Package generated from Vireo submission #21681 on 2025-10-19 at 18:09:10This dissertation consists of three chapters that study topics on various macroeconomic and financial risks and uncertainties. Chapter 1 investigates the dynamic relationship between tornado outbreaks and regional housing market uncertainty in the United States, using a novel methodological approach that integrates panel quantile regression with two-way fixed effects and local projection techniques. Specifically, it analyzes the dispersion between tail conditional quantiles and central quantiles in the predicted house price index growth distribution within each Metropolitan Statistical Area (MSA). The findings reveal that weaker tornadoes are predominantly associated with decreased housing demand, while stronger and more destructive tornadoes are more linked to decreased housing supply. Furthermore, the results show that these strong tornadoes are associated with increased dispersion of the regional house price growth distributions, signaling heightened uncertainty in these markets. This underscores the potential for extreme weather events, particularly tornadoes intensified by climate change, to raise uncertainty levels in regional housing markets, highlighting the need for targeted policy interventions to maintain market stability. In Chapter 2, Ji Hyung Lee and I examine the impact of monetary policy shocks on inflationary risks in the United States using a quantile local projection approach. We focus on the predictive effects of contractionary monetary policy on the distribution of future inflation, with Inflation-at-Risk (IaR) used to measure the tail behavior of inflation rates. Our key findings show that inflationary risks increase following a positive monetary policy shock, with the most pronounced effects observed in the lower tail of the inflation distribution, indicating heightened disinflationary or deflationary risks. These results are robust across multiple empirical specifications, reinforcing the importance of considering the broader distributional impacts of monetary policy beyond the mean. Based on these findings, we suggest caution in adopting overly aggressive tightening policies, as they may exacerbate downside risks to inflation, with potential implications for economic stability. Chapter 3 investigates the flight-to-safety behavior from the US stock market to the US Treasury bond market in response to increases in stock market uncertainty, using a Mixed-Frequency Vector Autoregression (MF-VAR) approach. The MF-VAR model allows for the incorporation of both macroeconomic and financial variables with different data frequencies, providing a more accurate assessment of the timing and dynamics of flight-to-safety. The key findings reveal that flight-to-safety is acute but short-lived, lasting for less than a week. Additionally, significant and persistent responses from macroeconomic variables, such as output growth and inflation, are observed, following a shock to stock market uncertainty. These results highlight the importance of considering macroeconomic conditions in understanding capital flows during periods of financial stress. The paper demonstrates the usefulness of MF-VAR in capturing these dynamics and offers a potential solution for future research requiring variables with different data frequencies
Clinical validation of SARS-CoV-2 RT-LAMP in animals
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Aimee Pepper, accepted the attached license on 2025-03-18 at 11:45.The student, Aimee Pepper, submitted this Thesis for approval on 2025-03-18 at 11:54.This Thesis was approved for publication on 2025-03-20 at 10:36.DSpace SAF Submission Ingestion Package generated from Vireo submission #21683 on 2025-10-19 at 18:09:10The wide host range, pathogenicity, and zoonotic potential of SARS-CoV-2 infection in animals highlights the need for additional surveillance strategies. Shedding of SARS-CoV-2 RNA within the intestinal tract is prolonged during animal infection, suggesting that surveillance could be accomplished non-invasively through nucleic acid amplification of animal feces. We validated a commercial, pH-based, colorimetric, RT-LAMP assay for the detection of SARS-CoV-2 RNA in animal feces, with comparison to the gold standard assay, rRT-PCR. The limit of detection of the RT-LAMP assay was 72 genome copies per reaction. RT-LAMP was highly specific for SARS-CoV-2 and did not detect other human or animal coronaviruses. RT-LAMP was robust, with valid results generated for incubation lengths of 30 to 45 minutes, incubation temperatures of 60 to 70°C, and reaction volumes of 10 to 25 µL. The diagnostic sensitivity was 100% for clinical fecal samples with high viral loads (Ct ≤25), 97.4% for samples with moderate-to-high viral loads (Ct ≤33), and 62% overall (Ct ≤40). The diagnostic specificity was 97.9% overall. Blinded method testing organized by an independent laboratory confirmed the reproducibility of the assay. SARS-CoV-2 RNA could still be detected by RT-LAMP following storage of fecal suspensions with moderate-to-high or high viral loads at -80°C, -20°C, 4°C, or room temperature for up to 28 days. To our knowledge, this study represents the first clinical evaluation of RT-LAMP for SARS-CoV-2 RNA detection in animal samples. RT-LAMP testing could detect SARS-CoV-2 infection more rapidly and at the point-of-care in animals with moderate-to-high viral loads that are likely to be infectious, allowing for earlier implementation of quarantine and control measures to limit viral spread and therapeutic interventions to reduce animal mortality
Synthetic network generation with realistic cluster connectivity
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Lahari Anne, accepted the attached license on 2025-03-20 at 21:48.The student, Lahari Anne, submitted this Thesis for approval on 2025-03-20 at 22:07.This Thesis was approved for publication on 2025-03-24 at 10:55.DSpace SAF Submission Ingestion Package generated from Vireo submission #21686 on 2025-10-19 at 18:09:11Evaluating the effectiveness of community detection methods is challenging due to the scarcity of real-world networks with known ground-truth communities. To address this, synthetic networks with predefined communities serve as valuable benchmarks. Among various synthetic network generators, Stochastic Block Models (SBMs) are widely used as they can approximate real-world network properties when provided with input parameters derived from real-world networks. However, SBMs often generate disconnected clusters, even when the input clustering exhibits fully connected communities, leading to structural inconsistencies that may affect the accuracy of performance evaluations for community detection algorithms. In this study, we introduce the REalistic Cluster Connectivity Simulator (RECCS), a post-processing framework designed to enhance SBM-generated networks by improving their fit to the cluster edge connectivity observed in real-world networks. RECCS modifies the synthetic network structure to better capture intra-cluster connectivity while preserving other essential network and clustering properties. This approach is evaluated on large-scale real-world networks containing up to 13.9 million nodes. The results show that RECCS generally improves the alignment of synthetic networks with empirical cluster connectivity, with some minimal trade-offs observed in other network properties. These findings suggest that RECCS offers a useful solution for generating synthetic benchmarks that more closely reflect real-world community structures, highlighting both its potential and limitations for community detection research
The effects of shocks and cash transfers on decision-making in developing countries
No full textSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Abigail Stocker, accepted the attached license on 2025-04-02 at 08:53.The student, Abigail Stocker, submitted this Dissertation for approval on 2025-04-02 at 09:28.This Dissertation was approved for publication on 2025-04-04 at 15:49.DSpace SAF Submission Ingestion Package generated from Vireo submission #21708 on 2025-10-19 at 18:09:14This dissertation contains three essays in development economics, which study how decisions made by individuals or households regarding child marriage, education, and transportation respond to economic shocks and policy changes. Chapter 1 examines how child marriage rates for both boys and girls respond to exogenous shocks to rainfall, temperatures, and conflict. I develop a theoretical household bargaining model, which predicts that negative shocks to income or to child marriage preferences reduce child marriage rates. Using individual-level data from India, Indonesia, and Nepal, I empirically estimate the effects of shocks on child marriage. Low rainfall and high temperatures, which reduce income, decrease the annual probability of child marriage for boys and girls by 1-8%. Exposure to conflict, which increases the risk of experiencing conflict-related violence, decreases child marriage for boys and increases it for girls by up to 30% and 3%, respectively. Effects are similar regardless of the child’s age, spousal age gap, or direction of the marriage transfer. These findings suggest a perverse relationship between income and child marriage, which is relevant for policymakers seeking to simultaneously reduce child marriage and poverty. Chapter 2 studies the effects of a maternity benefits program for women in the informal sector in India. Globally, many women in the informal sector are not eligible for traditional maternity benefits programs, and this could affect the health and later-life outcomes of their children. This paper investigates the impact of IGMSY, a unique maternity benefits program in India, on children’s education later in life. The program was launched in 2011, piloted in 52 out of India’s 640 districts, and provided cash transfers to women for their first and second live births regardless of employment status. Using a difference-in-differences approach across districts and cohorts, I find that the program increased school enrollment by 9 percentage points for the youngest cohorts (preschool-age children) but did not increase enrollment, reading, or math competency for older cohorts. The effects on enrollment are strongest for children from poorer households. This intervention likely had impacts on enrollment both through improving health-related outcomes and increasing income. Chapter 3 examine how transportation decisions are affected by weather and transportation costs. The future of travel will be characterized by changes in weather patterns and changes in transportation technology. How will these forces interact? This paper explores this question by utilizing a unique randomized experiment with Uber riders in Cairo, Egypt. We consider how very hot days (>35°C/95°F) affect transportation choices, how a sizeable price decrease (simulating a future with autonomous vehicles and access to cheaper transportation) changes travel, and how the interaction of these two elements affect choices. We find that while travel will increase significantly in response to the price decrease, extreme weather dampens this effect by 21%. Individuals receiving subsidies also shift away from public transportation modes and towards private transportation modes, except when the public transit option is air conditioned. These results provide important insights for policymakers when considering optimal travel policy in the face of climate change. Overall, these essays shed light on how economic shocks and policy decisions influence household behavior and long-term outcomes, offering crucial guidance for policymakers aiming to improve the well-being of vulnerable populations
Ionospheric outflow: From fundamental theory to modern modeling frameworks
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Soo Min Kimm, accepted the attached license on 2025-04-13 at 00:01.The student, Soo Min Kimm, submitted this Thesis for approval on 2025-04-13 at 00:08.This Thesis was approved for publication on 2025-04-16 at 10:42.DSpace SAF Submission Ingestion Package generated from Vireo submission #21752 on 2025-10-19 at 18:09:19The interaction between Earth and its surrounding space environment involves a continuous exchange of plasma, primarily characterized by the outflow of charged particles from the ionosphere. The thesis presents a comprehensive explanation of the ionosphere, detailing its theoretical foundations, mechanisms, and historical advancement in modeling. Starting with an overview of Earth’s ionosphere and magnetosphere, the study examines the dynamics of ionospheric plasma and its interaction with solar and geomagnetic activities. Special emphasis is placed on the processes facilitating ion escape, including ambipolar electric fields, centrifugal forces, and wave particle interactions. The work further delves into classical and non-classical polar wind models, comparing hydrodynamic, kinetic, and generalized transport approaches, each offering unique perspectives on ion behavior at varying altitudes and conditions. The significance of satellite missions like Cluster and THEMIS is highlighted, underscoring their contributions to high-resolution data collection that has refined space weather simulations. Despite advancements, the complexity of ionospheric outflow continues to present challenges, necessitating enhanced predictive models for improved understanding and forecasting. This thesis aims to contribute to this effort by synthesizing existing research and proposing areas for future investigation
Physically realistic video editing from natural language instructions
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Hao-Yu Hsu, accepted the attached license on 2025-04-13 at 13:44.The student, Hao-Yu Hsu, submitted this Thesis for approval on 2025-04-13 at 13:54.This Thesis was approved for publication on 2025-04-15 at 05:21.DSpace SAF Submission Ingestion Package generated from Vireo submission #21755 on 2025-10-19 at 18:09:20Modern visual effects (VFX) software has made it possible for skilled artists to create imagery of virtually anything. However, the creation process remains laborious, complex, and largely inaccessible to everyday users. In this work, we present AutoVFX, a framework that automatically creates realistic and dynamic VFX videos from a single video and natural language instructions. By carefully integrating neural scene modeling, LLM-based code generation, and physical simulation, AutoVFX is able to provide physically-grounded, photorealistic editing effects that can be controlled directly using natural language instructions. We conduct extensive experiments to validate AutoVFX’s efficacy across a diverse spectrum of videos and instructions. Quantitative and qualitative results suggest that AutoVFX outperforms all competing methods by a large margin in generative quality, instruction alignment, editing versatility, and physical plausibility
Establishing a joint 2D/3D high throughput approach for evaluation of the effects of Wnt signaling and microenvironemtnal cues on liver progenitor cell differentiation
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Brock Grenci, accepted the attached license on 2025-04-15 at 17:27.The student, Brock Grenci, submitted this Thesis for approval on 2025-04-15 at 17:36.This Thesis was approved for publication on 2025-04-16 at 07:54.DSpace SAF Submission Ingestion Package generated from Vireo submission #21782 on 2025-10-19 at 18:09:23Liver development is a complex process mediated by intrinsic cell genetics and the surrounding microenvironment. The Wnt/β-catenin pathway plays a crucial role in early liver development and homeostasis. Dysregulation of various parts of this pathway can cause disease, including many types of cancer. The mechanisms behind how these genetic alterations affect cell fate or spatial patterning in combination with microenvironmental cues is not fully understood, though. Here we demonstrate the effect of Wnt signaling alterations in combination with microenvironmental cues on liver progenitor cell fate and spatial patterning using well defined high throughput 2D and 3D culture platforms. Microcontact printing is used to create protein islands on polyacrylamide gels of varying stiffness, allowing for modulation of the 2D cellular microenvironment. Similarly, a high throughput PEG-acrylate microwell system is employed to study microenvironmental effects on liver progenitor cell differentiation in 3D. This is used in conjunction with shRNA mediated knockdowns to study combinatorial effects of intrinsic and extrinsic cellular factors on liver cell differentiation. We demonstrate that impairing the destruction complex increases biliary differentiation and decreases hepatic differentiation while also shifting spatial patterning of the cells. These changes in differentiation can be further tuned by the tissue microenvironment and show that recapitulating a diseased microenvironment can affect differentiation
Effects of live Bacillus pumilus or a Lacticaseibacillus paracasei postbiotic on apparent total tract nutrient digestibility and the fecal characteristics and metabolites, immunity, and microbiota of healthy adult dogs
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Jocelyn Wren, accepted the attached license on 2025-04-16 at 09:08.The student, Jocelyn Wren, submitted this Thesis for approval on 2025-04-16 at 09:21.This Thesis was approved for publication on 2025-04-21 at 12:50.DSpace SAF Submission Ingestion Package generated from Vireo submission #21792 on 2025-10-19 at 18:09:26Probiotics and postbiotics have the potential to shift the gut microbiota, support gastrointestinal health, and enhance immune function, but must be tested for safety and efficacy in the target species. The Bacillus and Lacticaseibacillus genera have been shown to positively influence microbial balance and enhance immune response in humans and livestock. The objective of this study was to determine the effects of live Bacillus pumilus SG-154 and a Lacticaseibacillus paracasei 327 postbiotic on dietary apparent total tract digestibility and the hematology, serum metabolites, fecal characteristics, metabolites, and microbiota, and skin and nasal microbiota of healthy adult dogs. Twelve healthy adult English pointer dogs (age = 6.38 ± 2.75 yr; body weight = 23.98 ± 4.61 kg) were used in a replicated 3x3 Latin square design to test the following treatments administered via gelatin capsules: 1) placebo (control; 250 mg maltodextrin/day); 2) live B. pumilus [5 x 109 colony-forming units (CFU)/day]; 3) L. paracasei postbiotic (derived from 2 x 109 CFU/day). Each experimental period was 28 days in length, including a 22-day adaptation phase, 5-day fecal collection phase, and 1 day for blood collection, nasal swabs, and skin swabs. Data were analyzed using the Mixed Model procedure of SAS, with P<0.05 being significant and P<0.10 being trends. Neither B. pumilus nor L. paracasei influenced nutrient digestibility, food intake, fecal output, or fecal characteristics. Based on 16S rRNA gene sequencing, the relative abundance of fecal Actinobacteriota tended to be higher (P<0.10) and the relative abundance of fecal Collinsella spp. was higher (P<0.05) in dogs given B. pumilus than those given L. paracasei and controls. Treatments appeared to shift skin bacteria as well, with the relative abundance of skin Erysipelotrichaceae UCG-003 being higher (P<0.05) in dogs given L. paracasei than dogs given B. pumilus. Skin Ligilactobacillus relative abundance was lower (P<0.05) in dogs given B. pumilus than controls. The relative abundance of skin Peptoclostridium was higher (P<0.05) in dogs given L. paracasei than controls. Most hematology measures were within the reference ranges for adult dogs and unaffected by treatment. Overall, our results demonstrate that consumption of the B. pumilus SG-154 and L. paracasei 327 tested are well-tolerated and do not influence nutrient digestibility or fecal characteristics in healthy adult dogs. Further studies testing their potential benefits on host health are required
Adaptive deep learning under data scarcity
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo termsThe student, Dou Hoon Kwark, accepted the attached license on 2025-04-17 at 12:29.The student, Dou Hoon Kwark, submitted this Thesis for approval on 2025-04-17 at 12:30.This Thesis was approved for publication on 2025-04-17 at 13:33.DSpace SAF Submission Ingestion Package generated from Vireo submission #21822 on 2025-10-19 at 18:09:31Deep learning has catalyzed transformative breakthroughs in computer vision and related fields, but these advances often rely on large-scale datasets that are neither readily accessible nor cost-effective in many real-world contexts. In many practical domains—such as medical imaging and geological mapping—collecting large-scale, expertly annotated datasets is prohibitively expensive. This thesis investigates a range of strategies designed to alleviate the pervasive challenge of data scarcity. Through comprehensive studies on both discriminative and generative tasks—including segmentation, super-resolution, modality translation, and inpainting—we demonstrate novel frameworks that preserve strong predictive performance despite limited training data. Our central goal is to show that deep learning under constrained data can still deliver robust results, provided the modeling pipelines are carefully adapted to the problem at hand. First, we propose a hierarchical diffusion-based approach that synthesizes pseudo-healthy medical images with enhanced 3D consistency but moderate computational overhead. Second, we present a fusion strategy that integrates multiple 2D diffusion models into a lightweight 3D representation, improving volumetric realism when data points are limited. Finally, we explore a multi-encoder pipeline that leverages color-space transformations to better segment complex maps, demonstrating its utility in settings like geological digitization. Taken together, these contributions illustrate that addressing data scarcity does not require sacrificing performance. Rather, it calls for more nuanced model design—incorporating domain-specific insights, ensemble architectures, and complementary data transformations. Our experiments show consistent improvements across diverse tasks, highlighting the promise of deep learning solutions that are nimble enough to excel in resource-constrained environments