University of Illinois at Chicago
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Stretch Injury Alters Calcium Dynamics and Actin Organization in Human Astrocytes
No full textTraumatic brain injury (TBI) is a major public health concern, linked to long-term
neurodegenerative conditions such as Alzheimer’s disease. Although neurons are often
the primary focus of TBI research, growing evidence highlights the critical role of
astrocytes—especially their calcium signaling dynamics—in driving post-injury
pathology. This thesis investigates how human induced pluripotent stem cell (hiPSC)-
derived astrocytes respond to mechanical strain, aiming to reproduce changes in
astrocyte behavior that can initiate neurodegeneration. In this research, a two-
dimensional in vitro stretch model was used to apply controlled mechanical strain to
monolayers of hiPSC-derived astrocytes cultured on flexible PDMS substrates. Cellular
responses were assessed using a combination of live calcium imaging and
immunofluorescence. Results showed a strain-dependent suppression of spontaneous
calcium activity post-injury, mitochondrial dysfunction, and decline in cell viability.
Additionally, injury induced changes in cytoskeleton structure and overexpression of
Piezo1, a mechanosensitive ion channel. These findings support the hypothesis that
astrocytic calcium dysfunction links immediate trauma pathology to chronic
neurodegenerative pathology. Overall, this work captures consequential changes in
astrocytes after trauma in an in vitro system that enables detailed investigation of them
An Optogenetic Toolbox for the Interrogation of Dynamic Events During Angiogenesis
No full textCharacterization of highly dynamic processes such as the induction of angiogenesis has historically been limited by a lack of tools with sufficient spatiotemporal control. To address the spatiotemporal requirements for VEGF-induced tip cell development, we designed an optogenetic VEGFR2 utilizing the LightR regulatory system. We demonstrated that LightR regulation of RTKs provides spatiotemporal control over VEGFR2 signaling. Furthermore, we demonstrated that previous VVD modifications conserved their effect in RTK regulation. Despite these successes, LightR-VEGFR2 regulation was perturbed in endothelial cells. These findings highlight the challenges in the creation of LightR-RTKs but also provide a logical foundation for their creation. Another immediate consequence of angiogenic stimulation is rapid alteration of the endothelial barrier. VE-Cadherin is an essential junctional protein involved in the maintenance of the endothelial barrier. While many of the components involved in these processes have been characterized, the dynamic changes in VE cadherin interactome induced by angiogenic stimuli have not been fully established. To address this, we generated an optogenetic biotin proximity ligase termed LightR-TurboID. Fusion of LightR-TurboID to VE-Cadherin resulted in a construct capable of labelling known interacting partners with sub-minute resolution. Furthermore, the stability of biotinylation also enables the capture of weak or transient binders that may dissociate from the VE-Cadherin complex. Proteomics investigations have further supported the specificity of VE-Cadherin-LiTurboID towards endothelial and junctional complexes. We aim to utilize these characteristics to identify novel components of junctional machinery and further develop the short-timescale mechanism of barrier alteration following angiogenic stimulation. Together, these optogenetic tools have expanded our toolbox for the investigation of highly dynamic events with unparalleled spatiotemporal resolution
Exact Solutions to Electromagnetic Scattering Problems with Multi-Layered Oblate Spheroidal Geometry
No full textComputational electromagnetic methods (CEM) play a critical role in the design and analysis of complex electromagnetic (EM) systems. CEM, such as the finite element method (FEM), finite-difference time-domain (FDTD), and method of moments (MoM), allow researchers and engineers to solve practical EM problems with complex geometries and materials. However, the reliability of these numerical methods requires validation to ensure their accuracy and robustness. One of the primary validation methods involves comparisons with exact solutions derived from canonical EM scattering problems. These analytical solutions serve as benchmarks, providing reference results that numerical solutions must match under the same conditions. The novelty of this work lies in developing a new analytical exact solution for electromagnetic scattering, designed to serve as a critical benchmark for validating numerical approaches in computational electromagnetics. We investigate a semi-oblate spheroidal cavity placed under a double-negative half-space. The cavity has two layers: one is made of double-positive material (DPS), the other is made of double-negative (DNG) metamaterial. The source of the incident field is an electric or magnetic dipole placed in the DNG region (inside or above the cavity). The solution is expressed in terms of series expansions of eigenfunctions, making it a canonical reference for evaluating the accuracy and stability of computational methods. Unlike simpler canonical problems, this configuration includes features such as a cavity and sharp edges, which can be difficult for numerical solvers to handle accurately. We consider exact solutions that can be computed as the sum of series expansions, where the expansion coefficients can be derived analytically and do not require, for example, the approximate solution of a system of equations. After the expansion coefficients are found, we express electric and magnetic fields in all three regions of the considered geometry (two layers of the cavity and the upper half-space). We plot fields with various geometry parameters, investigate some particular cases, and compare results with the full wave numerical model simulation. We also discuss the nuances of series convergence and the possibility of series acceleration
Schooling in the Afterlives: Neoliberalism, Anti-Blackness, and Fugitive Pedagogies in Black Education
No full textThe COVID-19 pandemic in the summer of 2020 coincided with significant social unrest following the state killings of George Floyd and Breonna Taylor. This period saw wide-spread protests against systemic racism and police brutality, leading to various corporate and institutional responses, including those in the public education sector. Many charter schools that once upheld harmful punitive rules and policies known as “no excuses” did away with them, rebranding and adopting anti-racist policies and practices as a step to-ward repair. Schooling in the Afterlives: Neoliberalism, Anti-Blackness, and Fugitive Ped-agogies in Black Education examines how Black students and their teachers in an urban charter school on the South Side of Chicago navigate the neoliberal considerations and challenges of a charter school in the wake of anti-Black policies and practices. Given the current political moment and ongoing legislative attacks on educational freedoms, there is a need to understand how Black students and their teachers in urban contexts create liberatory spaces amidst ideological changes in education while also confronting the leg-acies of anti-Blackness that shape them. This study seeks to contribute to existing knowledge on educational reform, particularly those affecting Black (liberatory) learning spaces, while also proposing strategies/possible models for creating more equitable, supportive, and transformational educational environments
Machine Learning Driven Source Identification, State Estimation and Sensor Optimization in Water Systems
No full textWater distribution and sewer systems are vital infrastructure systems that play a key role in safeguarding public health and environmental quality. In water distribution systems, operators face difficulty maintaining disinfectant residuals within a narrow range to ensure microbiological safety while avoiding toxic disinfection byproducts. Meanwhile, sewer system operators are challenged by identifying and eliminating illicit discharges that could compromise wastewater treatment processes or contaminate receiving water bodies. In general, both systems share a fundamental constraint: limited coverage of water quality sensors, resulting from their high deployment costs, technical challenges, and logistical constraints. This constraint hinders comprehensive knowledge of systems’ parameters and obstructs operational decision-making.
This dissertation addresses these challenges by introducing innovative machine learning (ML) driven frameworks to tackle three problems: i) Source Identification (SI) of contaminants in sewer systems, ii) State Estimation (SE) of water quality parameters in water distribution systems, and iii) Sensor Placement Optimization (SPO) in both systems. Traditional methods proposed to address these problems have been constrained by a number of simplifying assumptions that limit their practical applicability, such as considering only single, non-reactive contamination sources or static sensor configurations. In addition, these methods typically rely on computationally intensive physics-based models like EPA-SWMM and EPA-NET. As a result, these conventional methods lack the scalability required for real-world applications.
This dissertation advances sewer system monitoring by introducing a novel Multi-Layer Perceptron Neural Network (MLP-NN) surrogate model that effectively emulates the physics-based EPASWMM, enabling computationally efficient water quality simulations. This MLP-NN model is then integrated within a Genetic Algorithm (GA) optimization framework to enable real-time SI with high identification accuracy. The SI model is subsequently incorporated into an SPO framework that introduces two key performance metrics: observability and reliability, providing valuable insights into the inherent trade-offs between the system's ability to detect contamination events (observability) and source characterization accuracy (reliability). The SPO framework demonstrates how these metrics vary with sensor location and network configuration. In addition, the developed SPO framework offers practical solutions for optimal sensor deployment.
In the field of water distribution systems, this dissertation presents one of the first attempts to apply Graph Neural Networks (GNN) to estimate water quality parameters at unmonitored junctions. This was achieved by developing two GNN models. The first is a Static Prediction GNN (SP-GNN) model, which provides accurate state estimation for fixed sensor configurations. The second is a Dynamic Prediction GNN (DP-GNN) model, which achieves generalized state estimation across any sensor configurations without the need for retraining. The DP-GNN model enables the implementation of one universal model to diverse sensor designs and lays the foundation for GNN application in SPO. This dissertation also proposes an advanced Temporal Graph Neural Networks (TGNN) model that integrates Long Short-Term Memory (LSTM) within GNNs to assimilate data from mobile sensors. This TGNN model enables comprehensive spatiotemporal coverage, which is required to perform unsteady state estimation of chlorine concentrations resulting from dynamic water demand patterns.
This dissertation advances water and wastewater infrastructure research by demonstrating the capabilities of utilizing ML techniques in water quality monitoring in drinking water and sewer networks. Additionally, this thesis contributes to the practical application of ML techniques in real-life water systems by bridging the gap between academic research and field deployment, reducing computational costs, and considering real-life challenges. Generally, the frameworks developed in this dissertation provide water operators with practical tools for enhanced system monitoring while establishing a foundation for future research. Ultimately, this work represents a step toward safer, more efficient, and resilient water systems
Fair Scheduling and Resource Allocation for Public Services
No full textPublic services (like health inspections, mail delivery, and street sweeping) are essential operations that a government provides to address the needs and well-being of communities. Efficient and fair outcomes of such operations are needed to ensure the resources are well-utilized and their benefit is distributed fairly. In this thesis, we study the tradeoffs between efficiency and fairness for prediction, allocation, and ranking tasks. First, we dive deep into the real-world application of a predictive model used by the Chicago Department of Public Health to schedule restaurant inspections and uncover the cause of unfairness, constituting geographic inequities in inspection health outcomes linked to the sanitarians conducting the inspection. Next, we delve into the broader context of efficiency and fairness in resource allocation problems, recognizing their inherent conflict. Drawing motivation from the effect of resources on group utilities, we frame our study within the context of homogeneous functions. We explore the intricate tradeoff between efficiency, fairness, and resources. Independent of allocation mechanisms, we find that the choice of evaluation measures leads to widely diverse conclusions about the effect of resources on efficiency and fairness. Finally, we show the inherent gaps in scoring-based ranking methods for achieving the optimal tradeoff between efficiency and fairness. We propose heuristic methods that perform better at varying tradeoff levels and propose a new direction for future research
Unexplained Exercise Intolerance and Dyspnea in Long COVID
No full textLong COVID is characterized by persistent dyspnea, fatigue, and exercise intolerance despite normal cardiopulmonary findings. This study examined whether peripheral impairments—specifically respiratory muscle dysfunction and endothelial impairment—contribute to these symptoms. Thirty COVID-19 convalescent adults (20 with long COVID, 10 controls) underwent respiratory muscle testing, flow-mediated dilation (FMD), heart-rate variability (HRV), and cardiopulmonary exercise testing (CPET). Participants with long COVID exhibited marked reductions in inspiratory muscle performance—MIP (−52%), SMIP (−71%), and FIT (−79%)—and lower aerobic capacity (VO₂ peak = 21.1 ± 7.2 vs. 30.2 ± 11.0 mL·kg⁻¹·min⁻¹; p = 0.01). FMD was significantly reduced (5.3% vs 8.4%; p < 0.001), whereas ventilatory efficiency and autonomic indices were preserved. VO₂ peak correlated strongly with SMIP (r = 0.79) and MIP (r = 0.73), while FMD correlated only with MIP (r = 0.65, p < 0.001). A prediction model that included MIP accounted for 79% of the variance in VO₂ peak (VO₂ peak = 57.6 + 0.168 [MIP] − 0.271 [HR] − 0.520 [BMI] − 0.225 [Age] − 1.956 [Sex]), showing that inspiratory muscle strength is a good predictor of functional aerobic performance. MIP < 63 cmH₂O distinguished long COVID with 90% sensitivity and 93% specificity (AUC = 0.93). These findings highlight that exercise intolerance in long COVID arises primarily from peripheral mechanisms involving respiratory muscle and endothelial dysfunction rather than central cardiopulmonary limitation
Empathy in Clinical Nutrition: A Qualitative Study of Students, Interns, and Dietitians’ Perspectives
No full textIntroduction: Empathy is the cornerstone of patient-centered care. Empathy is not included in the competencies leading to the Registered Dietitian exam. It has been widely studied in various fields of healthcare but has yet to be thoroughly explored and described in the context of clinical nutrition. This study aimed to understand how empathy currently functions in the context of clinical nutrition, through the education, training, and early practice continuum.
Methods: This constructivist-interpretivist qualitative study of semi-structured interviews employed content and narrative methods to analyze the perspectives of students (n=7), interns (n=10), and early-career dietitians (n=12) on the construction and function of empathy in clinical nutrition.
Results: Themes formed in this study encompassed empathy components (affective, cognitive, moral, and behavioral), empathy growth during didactic courses and internships, the roles of positive and negative empathy in burnout, and the need for additional empathy instruction and experience throughout the education, training, and practice continuum.
Discussion: Despite prior research indicating that empathy declines during the education and training of pre-health professions, clinical students and interns prioritize and utilize different components along the education and training continuum, and dietitians use all empathy components in their practice. Empathy should be included in the education and training of future dietitians to both improve their communication skills and to protect against potential burnout
Shared Care and Teaching: A Longitudinal Study of Combined Family Medicine and Surgery Skin Clinic
No full textBACKGROUND: Procedural competency in skin cancer management remains an essential yet inconsistently achieved skill within Family Medicine training. This gap is magnified by increasing skin cancer incidence and lengthening wait times for tertiary-level cancer care.
STUDY AIMS: This study aimed to develop, implement, and evaluate an interdisciplinary educational intervention to enhance procedural skills and self-efficacy among second-year Family Medicine residents, to examine system-level outcomes in skin cancer care.
METHODS: A novel curriculum integrating a structured workshop and hands-on clinical sessions was designed using Kern’s Six-Step Curriculum Development model and deliberate practice principles. Residents performed skin biopsies and excisions under dual supervision from Surgery and Family Medicine faculty, receiving real-time feedback. Quantitative evaluation employed validated measures of knowledge, self-efficacy, and practice attitudes, administered pre-, post-, and longitudinally at 8–10 years post-training. Outcomes were assessed using Kirkpatrick’s four-level evaluation model. Complementary descriptive qualitative analysis of semi-structured interviews with current Family Physicians with a designated focused practice in dermatology explored experiential and career-shaping factors.
RESULTS: Participants demonstrated significant improvements in diagnostic and procedural knowledge, practice attitudes, and self-efficacy. Thematic analysis of qualitative data identified six career-shaping factors: variability in training exposure, self-directed learning, mentorship, delayed procedural confidence, experiential motivation, and opportunities for curriculum renewal. The program’s impact was assessed across Kirkpatrick Levels 1–4, incorporating measures of satisfaction, knowledge acquisition, self-efficacy, clinical behavior change, and system-level outcomes.
DISCUSSION: This study found sustained gains in knowledge and self-efficacy in skin cancer management with integration of skin procedures into independent practice. Over half of the program’s graduates continued to practice regionally, performing biopsies and excisions within community settings (Kirkpatrick Level 3 Behavior). System-level analysis of a longitudinal study showed stabilization of tertiary skin cancer referrals, despite rising oncology volumes, and notable improvements in timely access to consultations (Kirkpatrick Level 4). Findings demonstrate that an interdisciplinary, feedback-rich educational model can translate into sustained behavioral change, improved referral volumes, and enhanced service capacity.
CONCLUSIONS: Aligning educational innovation with system needs can achieve measurable impacts. The shared-care clinic model represents a scalable approach to advance procedural training and strengthen primary care’s role in dermatologic care
Some Clustering Approaches for High-dimensional Data
No full textThis dissertation is inspired by previous clustering research in recurrent event data and genomic expression data, with the goal of incorporating new features to extend existing methods and derive meaningful conclusions. In the first framework, we proposed a nested clustering model for recurrent event data to capture the heterogeneity structure in both patients and multi-type events while adjusting for confounding covariates. We also develop a computationally efficient algorithm for fast model estimation based on variational inference. In our second framework, We achieve temporal gene expression profile clustering based on treatment control differences and account for the covariance structure between repeated measurements of the same gene over time and between biological replicates. An Rcpp implementation of these methods is provided for efficient computation. For each framework, simulation studies demonstrate that our methods accurately recover clustering structure and outperform existing alternatives. In real-world applications, we apply the recurrent-event clustering approach to ICU electronic health-record dataset and the temporal profile clustering approach to a published microarray transcriptomic dataset—each yielding interpretable and meaningful results
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