University of Illinois at Chicago
University of Illinois at Chicago: UIC INDIGO (INtellectual property in DIGital form available online in an Open environment)Not a member yet
21439 research outputs found
Sort by
Health Risks from On-Road Truck Emissions in Little Village, Chicago Pre- and Post-COVID Pandemic
No full textExposure to fine particulate matter (PM2.5) poses significant health risks, including increased mortality, and truck emissions are a major contributor to urban PM2.5 pollution. As freight activity intensifies in densely populated cities, understanding the impact of truck-related emissions on air quality and public health becomes critical, particularly in environmental justice communities. This thesis presents an integrated modeling framework, building upon the extant literature in the field of air pollution modeling and health impact assessment by applying U.S. EPA's modeling suite of MOVES5, AERMOD, and BenMAP-CE to quantify truck-related PM2.5 emissions, associated health outcomes, and economic impacts in Little Village, a Chicago neighborhood with historically high truck activity.
The study compares conditions in 2019 and 2023 to evaluate changes before and after the COVID-19 pandemic. Results show substantial reductions in truck-related PM2.5 emissions during this period. After incorporating relevant background concentrations, all 24-hour average PM2.5 levels in the studied months remained below the NAAQS short-term exposure limit of 35 µg/m³. However, maximum long-term average PM2.5 concentrations from AERMOD (excluding background) exceeded both the previous (12 µg/m³) and revised (9 µg/m³) NAAQS thresholds, indicating localized pollution hotspots. Although median truck-related long-term PM2.5 levels from AERMOD remained below regulatory limits, and showed a decreasing trend, several pockets within the community still exceeded safe levels. BenMAP grid outputs, which included background concentrations, further showed that even median long-term PM2.5 exposures surpassed both the old and new standards in 2019 and 2023.
Health impact assessments estimate approximately 0.64 premature deaths annually among adults aged 35 and older attributable to truck-related PM2.5 exposure, corresponding to an annual economic burden of approximately $5.5 million. The findings imply that localized reductions in truck emissions may lead to improvements in air quality and public health, highlighting the importance of well-targeted emission control strategies, especially in environmental justice communities disproportionately burdened by freight-related pollution
Engineered Anti-CD40 Agonist Antibody for Cancer Vaccine Delivery
No full textCancer vaccines targeting patient-derived neoantigens offer great promise for personalized cancer therapy but face challenges in achieving targeted delivery to antigen-presenting cells (APCs) to elicit robust and durable cancer-specific immune responses. We synthesized an anti-mouse CD40 agonistic–monovalent streptavidin fusion antibody (αCD40-mSAs), which enables targeted delivery of biotinylated neoantigen peptides to APCs in draining lymph nodes (dLNs). αCD40-mSAs were validated for mSA expression and demonstrated strong binding affinities to mouse CD40 and biotin. Advanced imaging demonstrated that αCD40-mSAs enhances homing to dLNs and intracellular delivery of neoantigen peptides to critical APC subsets, such as cDC1. The potent agonistic effects of αCD40-mSAs on dendritic cell maturation, activation, and antigen presentation were verified through in vitro assays. Vaccination with αCD40-mSAs elicited robust cancer-specific CD8⁺ T cell responses, leading to significant tumor regression and prevention in a mouse tumor model. These results support αCD40-mSAs as an 'all-in-one' vaccine delivery platform with multifunctional immunopharmacological advantages and strong translational potential for personalized cancer vaccination
CX3CR1 Neuroimmune Microglial Cell Forkhead Box Class O1 (FoxO1) Contributions to Metabolic Homeostasis
No full textIn the central nervous system (CNS), microglial cells are neuroimmune cells that act as the brain’s sentinel macrophages to protect and maintain a homeostatic functional state for a healthy brain. In this thesis, I will discuss how neuroimmune microglial cells contribute to systemic metabolic homeostasis. Specifically, loss of a Forkhead box class O1 transcription factor (FoxO1) in the (CX3C Chemokine Receptor 1) CX3CR1-microglial cell population leads to suppression &/or resistance to high-fat diet (HFD) diet induced obesity (DIO) metabolic consequences in a sexually-dimorphic manner in an CX3CR1 FoxO1-KO in vivo model
Oxygen Atom Transfer Reactions Catalyzed by Molybdenum and Tungsten Oxo Transferase Mimics
No full textThe ability to mimic enzymatic oxidation reactions with synthetic catalysts is essential for advancing selective and sustainable chemical transformations. Nature employs molybdenum and tungsten enzymes to mediate oxygen atom transfer (OAT) reactions, which are critical in biological redox processes. Despite their efficiency, the development of synthetic analogues that replicate these functions remains an evolving field with vast potential in oxidation catalysis, environmental remediation, and chemical defense. Here, I explore the catalytic properties of molybdenum and tungsten bis(dithiolene) complexes, inspired by their biological counterparts, for controlled oxygen atom transfer reactions.
These enzyme-inspired catalysts enable the selective oxidation of sulfides to sulfoxides with high efficiency, achieving over 90% selectivity while preventing overoxidation to sulfones. Unlike conventional oxidation systems, these catalysts leverage oxo-peroxo intermediates, allowing precise control over the reaction pathway. Additionally, an unexpected reactivity pattern was observed in olefin oxidation, where instead of forming epoxides, the reaction led to carbon-carbon double bond cleavage in aryl-substituted alkenes. Computational studies suggest that O₂ transfer from a molybdenum oxo/peroxo intermediate facilitates the formation of 1,2-dioxetane species, which subsequently decompose into benzophenone and benzaldehyde derivatives.
By integrating experimental synthesis, mechanistic studies, and computational modeling, this research provides new insights into bio-inspired OAT catalysis and its potential applications. These findings expand the utility of molybdenum- and tungsten-based catalysts, paving the way for innovations in selective oxidation processes, green chemistry, and catalytic strategies for environmental and industrial applications
Methodological Insights From an Experience-Based Co-Design Method Applied to a Study of Older Adults Living with HIV’s Perspectives on Virtual Geriatric Care
No full textThis paper outlines the application of Experience-Based Co-Design (EBCD) to explore the perspectives of older adults living with HIV regarding virtual geriatric care. The study focuses on identifying the unique needs, challenges, and preferences of this population in the context of remote healthcare delivery models. EBCD, a participatory research method, engages service users, healthcare providers, and stakeholders in co-designing solutions to improve healthcare services. By involving participants throughout the research process, the approach ensures that resulting interventions are informed by real-world experiences, enhancing their likelihood of acceptance and effectiveness. The methodology includes in-depth interviews, focus groups, and journey mapping with older adults living with HIV to gather data. Through collaborative discussions, care gaps were identified, and key areas for improvement in virtual care were highlighted. Active participation from healthcare professionals ensured that these findings were translated into actionable solutions. Practical insights were also gained on fostering an inclusive and respectful environment for marginalized populations, ensuring that their voices were central to the co-design process. This study demonstrates that EBCD is an effective method for engaging older adults living with HIV in the design of virtual care interventions, leading to patient-centered solutions that address both clinical and psychosocial needs. Key contributions of the study include the development of a framework for applying EBCD in virtual geriatric care, identification of critical care gaps in this context, and the promotion of inclusive practices for vulnerable populations. The findings suggest that EBCD can play a significant role in advancing health equity and improving the quality of care for older adults living with HIV, especially as virtual healthcare continues to evolve.</p
Methodological Insights From an Experience-Based Co-Design Method Applied to a Study of Older Adults Living with HIV’s Perspectives on Virtual Geriatric Care
No full textThis paper outlines the application of Experience-Based Co-Design (EBCD) to explore the perspectives of older adults living with HIV regarding virtual geriatric care. The study focuses on identifying the unique needs, challenges, and preferences of this population in the context of remote healthcare delivery models. EBCD, a participatory research method, engages service users, healthcare providers, and stakeholders in co-designing solutions to improve healthcare services. By involving participants throughout the research process, the approach ensures that resulting interventions are informed by real-world experiences, enhancing their likelihood of acceptance and effectiveness. The methodology includes in-depth interviews, focus groups, and journey mapping with older adults living with HIV to gather data. Through collaborative discussions, care gaps were identified, and key areas for improvement in virtual care were highlighted. Active participation from healthcare professionals ensured that these findings were translated into actionable solutions. Practical insights were also gained on fostering an inclusive and respectful environment for marginalized populations, ensuring that their voices were central to the co-design process. This study demonstrates that EBCD is an effective method for engaging older adults living with HIV in the design of virtual care interventions, leading to patient-centered solutions that address both clinical and psychosocial needs. Key contributions of the study include the development of a framework for applying EBCD in virtual geriatric care, identification of critical care gaps in this context, and the promotion of inclusive practices for vulnerable populations. The findings suggest that EBCD can play a significant role in advancing health equity and improving the quality of care for older adults living with HIV, especially as virtual healthcare continues to evolve.</p
Fragments and Fantasies of Freedom: Queer Cultural Production during the Spanish Transition (1975-1982)
No full textThis dissertation studies queer cultural production during Spain’s Transition to
democracy (1975-1982), a period marked by both euphoric possibility and political discord. While conventional historiographies often present the Transition as a successful and peaceful transfer of power from dictatorship to democracy, this project foregrounds the voices and visions that resisted this official narrative. Each of these selected works—rooted in transgression, fantasy, and refusal—challenges the normative temporality and heteronormative structures promoted during the Transition, proposing instead alternative, queer visions and utopian horizons. In particular, this project addresses how these queer
artists envision what the future of Spain could be, as seen through their own distinctive and divergent perspectives. At its core, this project has three principal concerns: (1) how these queer and divergent individuals see Spain’s political apparatus during the Transition in terms of how it might evolve in the future; (2) how these conceptions are informed and shaped by their relationship to queerness in ways that deviate from the prevailing attitudes of the Transition period—which may be positive or negative; (3) and how shaping the queer archive of the Transition allows for a more profound understanding of Spain’s current cultural and political landscape. Through the use of a variety of media such as film, narrative, comics, performance art, and ephemera such as flyers and magazines, I examine how these interventions conceive a radical and queer future, while tracing the successes and failures of these visions. The primary source materials include the cinematographic work of film collective Els 5 QKs; Lluís Fernàndez’s novel L’anarquista nu (1979); the film Arrebato (1979) by Iván Zulueta; and Jesús Garay’s feature film Manderley (1981), featuring queer performance artist José Pérez Ocaña
Prosecuting Sexual Assault in Chicago
No full textThe criminal legal system is an often-recommended resource for those seeking help and justice after being sexually assaulted. However, the research thus far indicates that sexual assault (SA) cases that are reported to police are unlikely to be prosecuted and convicted and that such attrition does not happen at random. Indeed, there are patterns around which SA cases are ultimately prosecuted and convicted, especially as it relates to characteristics related to the survivor, offender, assault, and case. What characteristics or factors that have yet to be explored in the research though is how system and time factors, like the elected prosecutor, and the length of time between when a criminal SA case was referred for prosecution and when it reached disposition, contribute to these patterns of SA case attrition. As such, the current study was conducted using social regularities theory to document and understand “social regularities” or patterns in the prosecution of SA in Chicago. Descriptive and regression analyses was performed using publicly available administrative data previously published by the Cook County State’s Attorney Office. An initial sample of 3,179 referred SA cases were analyzed. The results indicate that SA cases reported to police were unlikely to be referred for prosecution and unlikely to result in a conviction and that both system and time factors influence whether SA cases result in a conviction, including the type of conviction. These findings have important implications for systems and social change, specifically as it relates to the work being done by the Chicago Alliance Against Sexual Exploitation (CAASE), a community-based non-profit organization providing services and advocacy for survivors of gender-based violence and a partner in the project. The findings from the current study will be provided to CAASE to inform their legal services program and their efforts to advocate for survivors as it relates to the policies and practices that impact them
Water Quality Control in Distribution Systems: Bayesian Optimization & Physics-Informed Machine Learning
No full textWater distribution systems (WDSs) play a vital role in maintaining public health by ensuring the continuous supply of safe drinking water. One of the most important operational challenges in WDSs is maintaining adequate chlorine residuals throughout the network to prevent microbial regrowth while minimizing the formation of harmful disinfection byproducts (DBPs). Achieving this balance requires optimizing chlorine dosage strategies, both in terms of injection locations and dosing schedules, under complex, dynamic, and spatially distributed network conditions.
Despite recent advancements in sensing and modeling, real-time control of the water quality (WQ) in WDSs remains difficult to achieve, primarily because of the limited WQ monitoring coverage and the intensive computational effort required for repeated WQ simulations. The latter have traditionally been performed by means of physics-based models that involve solving complex, nonlinear systems of partial differential equations (PDEs) to simulate the underlying physical processes that govern chlorine transport and decay in the WDS. This dissertation aims to address these key challenges by developing innovative WQ prediction and control frameworks to enable efficient and sustainable chlorine residual management in WDSs.
To achieve this objective, this dissertation presents novel approaches for designing advanced physics-informed machine learning (PI-ML) models for WQ prediction, and implementing the Bayesian optimization (BO) technique for optimizing the WQ in WDSs. The first thrust of this dissertation introduces a novel BO-based framework for optimizing chlorine booster scheduling in WDSs. The proposed framework integrates BO with a physics-based WQ simulation model, namely EPANET-MSX (multi-species simulation engine), to determine optimal chlorine injection rates and timing. Using Gaussian Process Regression (GPR) as a surrogate data-driven model, this study systematically investigates the effects of various acquisition functions, such as Expected Improvement (EI), Probability of Improvement (PI), Upper Confidence Bound (UCB), and Entropy Search (ES), along with multiple covariance kernels, including Matérn, squared-exponential, gamma-exponential, and rational quadratic. A comprehensive sensitivity analysis further examines the influence of key BO hyperparameters, such as the kernel length scale, initial sampling size, and exploration–exploitation trade-off. The results demonstrate that BO can efficiently identify optimal disinfection schedules with substantially fewer WQ simulations than conventional methods, with the choice of acquisition function showing the most significant impact on optimization performance.
The second thrust of this dissertation builds on the first thrust by rigorously benchmarking BO against traditional evolutionary algorithms (EAs) for multi-species WQ control. The latter involves modeling the complex interactions between chlorine and other chemical or microbiological species, offering a more realistic yet computationally challenging representation of water chemistry. A comprehensive comparative study was conducted to assess BO against two widely used EAs), namely the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), for optimizing multi-species chlorine dosage in a benchmark WDS. The results reveal that BO achieves superior efficiency, requiring substantially fewer simulations and less computation time per iteration while maintaining or exceeding the optimization performance of EAs. Moreover, BO exhibited lower sensitivity to variations in the constraints imposed on species concentrations, highlighting its robustness and adaptability to complex operational scenarios.
The third thrust of this dissertation transitions from optimization to prediction by introducing a novel Physics-Informed Machine Learning (PI-ML) framework for simulating WQ dynamics in WDSs. While traditional physics-based models (PBMs) rely on solving partial differential equations (PDEs) representing advection, dispersion, and reaction processes, they are computationally expensive and limited in scalability. Conversely, purely data-driven ML models, although efficient, lack physical interpretability and generalizability across networks. To address this gap, this study develops an ensemble PI-ML framework that integrates fundamental physical principles into the architecture of ML models. The PI-ML ensemble effectively captures the key transport and reaction mechanisms governing the WQ dynamics while retaining computational efficiency.
Overall, this dissertation advances the field of drinking water quality management by introducing a suite of computationally efficient, scalable, and physics-aware frameworks for chlorine residual prediction and optimization. The proposed BO-based and PI-ML approaches bridge the gap between physical modeling and data-driven learning, offering practical and generalizable tools for real-time water quality control. These contributions lay the groundwork for a paradigm shift towards intelligent, adaptive, and resilient water supply systems that ensure safe and efficient water delivery under varying operational conditions
Data sources:
Access Repository Dashboard
Do you manage Open Research Online? Become a CORE Member to access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard! 👇