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Designing a Hydrogel Micropatch for Phosphatase-Exclusion-Driven Stimulation of CD8+ T Cells for Clinical Applicability
No full textAdoptive T cell therapies have emerged as a promising approach for targeting tumors. They involve reintroducing tumor-specific cytotoxic T cells into cancer patients, thus enabling the recognition, targeting, and destruction of tumor cells. Sustained activity of these cells is required for these adoptive cell therapies to be effective. However, re-introduced cells lose their function rapidly with time after injection into patients. Commercially available products, such as Dynabeads that are functionalized with αCD3 and αCD28, have been used for ex vivo activation. However, they have numerous drawbacks, creating the need to be physically separated from the cells before re-administration.
To address these clinical problems, this project aims to develop a biocompatible hydrogel micropatch (hMP) for phosphatase exclusion that can maintain functional T cells, with the ability to increase their metabolic and cytotoxic potential. This project began by optimizing the fabrication process of hyaluronic acid-based hMPs to increase the binding efficiency, with surface decoration and the inclusion of a monoclonal adhesive layer yielding the highest adhesion rate. In vitro studies to measure the impact that the hMPs had on physiological processes were evaluated. These studies revealed that hMPs are biocompatible and do not affect transendothelial migration. Furthermore, preliminary studies quantifying the stimulatory capabilities of hMPs revealed that hMPs can drive stimulation through phosphatase exclusion and increased metabolic activity. Altogether, this study reports a biocompatible microparticle that can stimulate cytotoxic T cells, thus providing a potential avenue to retain an activated phenotype for adoptive cell therapies post ex vivo activation.Engineering Sciences S
Advancing Data Science Methods for Environmental Health Policy Design and Evaluation
No full textTo date, the field of environmental health has been primarily focused on characterizing the public health impacts of environmental exposures such as air pollution and temperature. However, beyond studying impacts, there is an increasingly recognized need for designing data-driven policies or strategies to (a) reduce both the overall health burden and health disparities associated with current environmental factors and (b) adapt to emergent threats such as climate change. Meanwhile, diverse and growing data sources, paired with modern data science methods, hold the potential for expanding the types of environmental health science and policy questions that can be answered.
This dissertation employs a wide range of data sources and novel analytic techniques to break ground on the frontier of data-driven environmental health policy design, which often involves evaluating existing policies along the way. Meanwhile, an overarching theme of this work is the bridging of different scientific domains and methodological areas, such as decision science with environmental justice, artificial intelligence with climate & health, and causal inference with remote sensing of both human activity and environmental factors.
Chapter 1 proposes a Monte Carlo-based methodology to compare realistic strategies for measuring and reporting daily air quality information. Specifically, we investigate the usefulness of low-cost air quality sensors, which are often lauded for their potential to fill spatiotemporal gaps in air quality information – especially for underserved populations, making them a central tool in environmental justice efforts. However, because many of these sensors are purchased and deployed by concerned citizens with financial means, the extra measurements are skewed towards more privileged areas. Also, low-cost sensors have lower accuracy compared to reference-grade monitors. To characterize these tradeoffs, we design and implement a simulation study based closely on real data to evaluate the accuracy and equity of information from individuals’ nearest air quality instrument (sensor or reference monitor) under both real and hypothetical low-cost sensor deployment scenarios. By varying the number of sensors deployed, the amount of sensor measurement error (noise), and the relative placement of the sensors (e.g. at schools, near major roads, and in communities with environmental justice concerns), we are able to analyze and make recommendations for how a local or regional government or organization might deploy the most effective and equitable network of low-cost air quality sensors, given their budget constraints. Further, the demonstrated simulation methodology can be adapted for other environmental monitoring objectives.
Chapter 2 develops a framework with which reinforcement learning (RL) can be used to optimize the issuance of heat alerts. Heat alerts are a practical and low-cost intervention to mitigate the public health impacts of extreme heat. However, current practice for issuing heat alerts does not take advantage of modern data science methods to optimize local alert criteria. To fill this gap, we harness RL (a branch of artificial intelligence) to build a model for whether to issue a heat alert on a given day, accounting for sequential dependence – which in the heat alert setting is due to both alert fatigue and finite resources/ability (of individuals/communities) to take health-protective measures. To use RL, we have to overcome several major incompatibilities between standard RL methods and the heat alert setting, which extend to environmental health / climate & health more generally. First, the relatively small and easily confounded signal in heat-alert-health relationships challenges the ability of RL algorithms to identify relevant effects, much less to optimize heat alert issuance. Second, mainstream RL methods are not suitable for settings with significant spatial heterogeneity, which is a known feature of heat alert health impacts. To address these challenges, we use a combination of statistical modeling, cutting-edge RL techniques, and conceptually simple yet effective modifications such as restricting alerts to extremely hot days, to learn heat alert issuance policies that reduce the adverse health impacts of extreme heat compared to the current U.S. National Weather Service policy. We also prioritize interpretable characterization of the RL results, offering intuitive insights about which counties across the nation stand to benefit the most from implementing heat alert-RL. A major contribution of this project is establishing a connection between the environmental health and artificial intelligence communities.
Chapter 3 combines spatiotemporal causal inference methods and remotely sensed data to quantify the air quality impacts of national-level plastic waste policies, via the mechanism of trash burning at open dump sites. Especially in low- and middle-income countries (LMICs) without adequate solid waste management infrastructure, trash burning is a huge environmental public health problem that is difficult to quantify on large scales due to its distributed, intermittent, and not-infrequently covert nature. This burden is compounded by many high-income countries exporting massive amounts of plastic waste, both with and without LMICs’ consent. Using Indonesia as a case study, we develop a strategy to quantify the local air quality impacts of China’s 2018 waste import ban (and subsequent diversion of waste to other Southeast Asian countries), using a collection of remotely sensed data products to overcome the lack of ground-level monitoring and to strengthen the causal argument. Methodologically, we combine two strains of causal inference, ultimately considering the proximity to ports (from which international plastic waste enters the country) as an induced continuous exposure at locations where open dumping has been detected, and using past years of data as controls, conditional on meteorologic variation. Additionally, we extend past work’s uncertainty quantification strategy to account for residual spatial dependence. This project not only reveals a statistically significant increase in Indonesian air pollution attributable to imported plastic waste, but also lays groundwork for future environmental policy evaluations in data-scarce settings.Biostatistic
Dammed Landscapes: Sovereignty and Infrastructure along Haha Wakpa / Gichi-ziibi
No full textThe U.S. Army Corps of Engineers has constructed locks and dams throughout North America to achieve hegemonic control over riparian landscapes and the people occupying those spaces. Deployed liberally throughout Haha Wakpa / Gichi-ziibi (the Dakota and Ojibwe names for what settlers call the Mississippi River), these infrastructural projects were frequently built on Native land and designed in such a way that actively disenfranchises Native people. This dissertation examines how the U.S. government and Native nations have engaged with river infrastructure to assert sovereignty along the Upper Mississippi River Watershed, both historically and today. Understanding infrastructure as texts that can be read for underlying socio-political agendas, this project will consider how locks, dams, and reservoirs in the regions of the Upper Mississippi River Watershed that no longer serve navigational purposes could be Indigenized through redesigning or removing locks and dams, informed by Dakota and Ojibwe perspectives.
By observing governance and activism tactics, the research compares how historic settler-Native relationships have played out at river infrastructure operated by the U.S. Army Corps of Engineers, and how contemporary Dakota and Ojibwe land and water management practices establish a new framework for the redesign, management, or removal of these locks and dams, as well as the landscapes that surround them. The research is grounded in six months of fieldwork on Dakota and Ojibwe homelands, which included participant observation at public events and interviews with non-profit organizers, as well as several canoe and kayak trips to observe and photograph lock and dam sites along the river. Additionally, archival research at the Army Corps of Engineers St. Paul District Library and the Gale Family Library at the Minnesota Historical Society provided a wealth of historical material to contextualize the governance tactics at play along the river today.
Based on these experiences, the dissertation concludes with a collection of Indigenous infrastructures for stewarding rivers that address riparian health and wellbeing in ways that are overlooked by current settler river management practices. In contrast to rigid material infrastructures deployed by settlers, Indigenous infrastructures include grounded socio-political tactics to care for Haha Wakpa / Gichi-ziibi, such as storytelling, mapping for rematriation, lobbying for land back, and designing “in a good way.” Through these practices, Dakota and Ojibwe leaders are inscribing a path towards strengthened reciprocal relationships between humans and rivers. While the projects discussed in this dissertation are specifically attuned to the conditions on Dakota and Ojibwe homelands, they serve as a meaningful example of Indigenous governance tactics that can be adapted to other riparian landscapes. Ultimately, as more settler infrastructure wanes, these Indigenous infrastructures present a contemporary approach for designing and stewarding rivers and their surrounding landscapes in the future.Advanced Studies Progra
Literal and Figurative Implications in Literary-Exegetical Theory: Majāz and Rhetoric in al-Zamakhsharī’s al-Kashshāf
No full textThis project studies the intellectual development of Islamic exegesis and rhetoric/literary theory (tafsīr and balāgha) during the classical period (9th – 12th century). The dissertation traces the evolution of a contested term, “majāz” often translated as “metaphor” or “figurative speech.” Through a comparative linguistic study, the dissertation challenges this conventional reading and argue that misapprehensions of the term have obscured its uniquely metonymic quality which far better accounts for its nuanced usage during this critical juncture in Arab-Islamic literary theory. The project is both diachronic and synchronic in nature: I trace uses of majāz in the classical period and its implications for Qurʾānic interpretation with a particular focus on Abū Qāsim al-Zamakhsharī’s (d. 539 H/1143 CE) seminal commentary, al-Kashshāf ʿan Ḥaqāʿiq al-Tanzīl (The Unveiler of Revealed Truths). A primary objective of this research study is to shed light on a major scholar of classical Islam whose significance in the domain of literary exegesis in Western scholarship has arguably been given insufficient due. I attempt to situate the Kashshāf more firmly in the context of the “literary” exegeses of the fifth/eleventh century, and highlight the particular branches of ʿilm al-maʿānī and bayān, central features of the Kashshāf’s literary identity as evidenced through its author’s stated reliance on them. My methodology incorporates tools in digital humanities including the use of frequency analysis to count the occurrence of specific terms such as majāz in classical tafasīr. This approach allows for a quantitative examination of textual patterns, providing insights into linguistic trends and thematic emphasis within the data. The project thus integrates philological study with data analysis.Near Eastern Languages and Civilization
The Relationships Between Perioral Musculature, Natural Tooth Position, and the Supporting Periodontium
No full textPrecision medicine aims to prevent and treat disease using patient-specific data, including
genetic and environmental factors. While precision medicine continues to improve the quality of
care for patients worldwide, this customized approach to care is beginning to emerge within
dentistry. The advancement of three-dimensional imaging and printing technology, artificial
intelligence, and smart appliances have created a platform for precision orthodontics. However,
there is still a lack of data in the literature regarding the effects of intrinsic patient forces on the
outcome and stability of orthodontic treatment. This study aimed to explore the relationships
between the natural position of teeth, the condition of their supporting periodontal tissue, and
contraction forces of adjacent perioral musculature. There were three null hypotheses in this study:
1) there is no direct relationship between increased lip force surrounding the dentition and
intercanine width, 2) there is no direct relationship between increased tongue force surrounding
the dentition and incisor position, and 3) there is no direct relationship between the position of the
mandibular incisors and the health of their supporting periodontium. The position and angulation
of incisors for orthodontically untreated adult patients (N= 24, average age 24.5) were measured
on lateral cephalograms and compared to maximum contraction forces of lip and tongue muscles
obtained using a novel intraoral pressure gauge (TongueometerTM, E2 Scientific). Ultrasound
images of the four mandibular incisors were also captured to examine the height of their facial
alveolar bone. The null hypotheses were accepted. We could not identify any significant
correlation between the maximum contraction force of the lip or tongue musculature on the
inclination or sagittal positioning of the incisors. However, increased intercanine widths were
positively correlated with increased lip muscle contraction strength and incisor-mandibular plane
angle measurements were negatively correlated with the facial bone height of mandibular incisors.
These preliminary findings may add further support to the existing literature that claims 1)
increasing intercanine widths during orthodontic treatment may result in poor stability due to
increased lip forces, and 2) individuals with increased incisor-mandibular plane angles lack hard
periodontal support and are at greater risk for periodontal tissue damage. Future studies with an
increased numbers of participants are necessary to identify other components of the dental
equilibrium, such as resting perioral tissue pressures.Orthodontic
The Strategic Enhancement of Micro-plastic Biodegradation: A Blueprint of Microbial Triple Consortia
No full textThe application of computational biology, synthetic biology and bioengineering techniques has surged within the last decade enhancing agriculture, textiles, medicine, and consumer goods. Yet, the fields of pollution and waste management, particularly plastic waste disposal, still depend on mechanical and chemical solutions. Progress in these sectors has been painfully slow as current methodologies focuses on manipulating biological systems for either bioremediation and/or sequestration. Micro-plastic pollution, an unintended consequences from the wide spread usage of plastics in cosmetics and industrial production, now poses a significant health risks to the biodiversity of aquatic and terrestrial ecosystems. Research conducted by Leslie et al., in 2022 discovered traces of micro-plastics within human blood and tissues. The persistent nature of micro-plastics can make them vectors for harmful and debilitating “forever chemicals” and pathogens. Micro-plastics are not homogeneous; they are comprised of a collection of various materials like polyethylene terephthalate (PET), polyethylene (PE), polystyrene (PS) and polyurethane (PU), complicating any potential cleanup efforts. In 2016, a Kyoto-based research team discovered that the bacterium Idellonella sakaiensis, could metabolize PET as a source of biochemical energy. In 2021, a second research team, Peller et al., published a discovery of a seed protein recreated by the algae Cladophora, identified as Moringa oleifera. This seed protein, which can aggregate micro-plastics, allowed the algae to sequester the particles. The primary challenges remain in developing a novel methodology combining these three discovers to remediate the ongoing issues caused by micro-plastic contamination.Extension Studie
Increasing Durable Packaging Circularity in Chicago
No full textSustainable packaging concerns and solutions should be addressed more holistically by policymakers. Focusing only on its direct environmental impact is insufficient and possibly counterproductive. Manufacturers have been replacing plastic packaging with alternative materials that are more readily recyclable than plastic. Some U.S. states are considering various policies to increase packaging sustainability, including bans or restrictions. Most policies and studies, however, only consider the direct environmental impact of packaging without considering its overall effect on the packaged product.
This comparative lifecycle assessment (LCA) compared the total carbon footprint (CFP) effect of expanded polystyrene (EPS) and molded fiber (MF) packaging stabilizers used to secure a television in a cardboard box shipped from South Korea to Chicago. The study included the direct CFP of both the packaging and product. I aimed to identify the conditions under which each material can result in a lower comprehensive CFP. The LCAs compared different scenarios, including varying amounts of packaging materials, energy use, and end-of-life treatments. Data were gathered from commercially available lifecycle inventories, LCAs, packaging textbooks, trade associations, and company information to create this model.
The results showed that EPS packaging stabilizers resulted in a lower CFP than MF under various scenarios. The baseline comparison resulted in an EPS CFP of 5.1 kg of carbon dioxide equivalent (CO2e) compared to 108.2 for MF. Since available data suggested that 96% of MF’s CFP relates to energy, the amount and carbon intensity of MF energy was varied. Under these energy scenarios, energy CFP was not sufficiently reduced to outperform EPS. EPS still outperformed MF even with the addition impact of increasing EPS mass or including carbon intensive recycling. For example, it takes about three times the mass of MF to pack a television compared to EPS in this model. Applying the MF to EPS packaging ratio added 24 kg of CO2e to EPS and carbon intensive recycling added 9.9. Both variations were less than MF’s baseline CFP. However, MF had a lighter CFP when EPS exceeded 13% product loss and MF had no product loss. This reflected both the carbon-intensive nature of MF and of a carbon-intensive product – a television. There was a significant data gap in the available MF production and packaging performance and optimization that could alter these results.
Given this, policymakers should consider creating incentives that reduce overall CPF by considering comprehensive CPF that includes product loss and not just packaging impact. Additionally, policy should avoid making assumptions about packaging material performance since optimizing CFP is fact and circumstance dependent. Lastly, policy should increase publicly available data on all materials, especially MF and product loss. These suggestions will help policymakers and other stakeholders make more informed decisions, avoid unintended consequences, and decrease overall climate impact.Extension Studie
Estimation and Inference in Causal Models and Multi-Modal Knowledge Graph Integration
No full textThis dissertation examines the estimation and inference of causal parameters in two different frameworks: the generalized method of moments framework and the dynamic optimal treatment regime framework.
* For generalized method of moments framework: Chapter 1 shows that when the auxiliary estimators satisfy a leave-one-out stability condition, debiased machine learning can achieve root-n consistency and asymptotic normality without requiring sample splitting or cross-fitting. This enables more efficient sample reuse, especially in moderate-sample regimes.
* For dynamic optimal treatment regimes: Chapter 2 analyzes the statistical properties of a softmax approximation to the optimal policy. It demonstrates that under a suitable growing scheme of the temperature parameter, this softmax approach yields valid inference for the value and structural parameters associated with the true optimal regime.
Apart from causal parameter estimation and inference, this dissertation also advances causal understanding by integrating biomedical knowledge to uncover biological drivers of clinical outcomes and to inform clinical decision-making:
* Biomedical knowledge graph integration: Chapter 3 constructs a heterogeneous, multi-modal knowledge graph using a Relational Graph Convolutional Network (R-GCN) to embed clinical and biological entities, including disease, drugs, genes, and single nucleotide polymorphisms (SNPs), into a unified representation that supports various link prediction tasks and downstream applications.Statistic
Biomanufacturing of Kidney Organoids, Perfusable Proximal Tubules, and Kidney Tissues
No full textHuman kidneys are vital organs that filter blood, regulate electrolyte homeostasis, and produce urine. These complex processes are carried out by nephron subunits composed of glomeruli and tubular segments that are responsible for filtration and reabsorption, respectively. While considerable efforts have focused on the fabrication of in vitro renal models that recapitulate nephron structure and function for studying nephrotoxicity and renal development, progress remains limited. Kidney organoids derived from human induced pluripotent stem cells (hiPSCs) are three dimensional (3D), multicellular structures that contain many of the cell types and architectures present in human kidneys. There is a growing interest in using kidney organoids as a platform for improved drug screening and as organ building blocks (OBBs) for the biomanufacturing of functional kidney tissues for modeling tissue development, and ultimately, renal replacement.
The overarching goal of this Ph.D. thesis is to generate kidney organoids and explore their use as building blocks for biofabricating perfusable proximal tubules, and bulk kidney tissues. Specifically, this research focuses on the scalable 3D differentiation of kidney organoids in stirred bioreactors (STRs), the development of organoid-derived perfusable proximal tubules-on-chip, and the biofabrication of 3D kidney tissues for in vitro and in vivo assessment. We first investigated the effect of hiPSC seeding density and stir rate on embryoid body (EB) formation and differentiation efficiency. Their initial differentiation efficiency was roughly 70%, which motivated the development of an optical-based screening method to rapidly predict differentiation success. Next, the concentration and timing of differentiation reagents along with improved media preparation methods were implemented to further enhance the differentiation efficiency of nephron-rich kidney organoids to roughly 95%. The STR-generated kidney organoids exhibited glomerular, proximal tubule, distal tubule, stromal and vascular cell types and architectures. Compared to kidney organoids differentiated in static conditions, STR-generated kidney organoids demonstrated increased expression of tubular, ciliary, and vascular cell types. Most importantly, kidney organoid differentiation in stirred bioreactors greatly increased the organoid volume produced in a given time period relative to 2D (static) differentiation methods.
Next, we developed an organoid-derived proximal tubule epithelial cell (OPTEC)-on-chip model that exhibits improved drug uptake compared to those based on tert1-immortalized proximal tubule (PTEC-TERT) cells. First, lotus tetragonolobus lectin (LTL+) proximal tubule cells are isolated from mature kidney organoids, that were dissociated into individual cells, using magnetic activated cell sorting (MACS) and expanded in vitro. These OPTECs are then seeded into cylindrical channels embedded within an optimized extracellular matrix (ECM) composed of gelatin-fibrin, where they form a confluent monolayer. A second bare channel is introduced adjacent to this 3D tubule within reusable multiplexed chips to mimic basolateral drug uptake. Our 3D OPTEC-on-chip model exhibits significant upregulation and improved polarization of organic cation 2 (OCT2) and organic anion 1/3 (OAT1/3) transporters, which resulted in higher drug uptake compared to PTEC-TERT-on-chip controls. Consequently, OPTEC-on-chip models also exhibited a higher normalized lactate dehydrogenase (LDH) release compared to those controls when exposed to known nephrotoxins, cisplatin and aristolochic acid. Importantly, LDH release could be diminished by adding known OCT2 and OAT1/3 inhibitors. This integrated multifluidic OPTEC platform paves the way for personalized kidney-on-chip models for drug screening and disease modeling.
Finally, we investigated the biofabrication of 3D kidney tissues from OBBs with the goal of modeling in vitro tissue development and assessing their host integration in vivo. Kidney organoids differentiated from hiPSCs in STRs were mixed in a fibrinogen solution and compacted to form a cellularly dense tissue matrix. Sacrificial writing into functional tissue (SWIFT) is then used to print sacrificial ink channels into the OBB-ECM matrix. The SWIFT kidney tissues are perfused in vitro for 10 days, during which their fusion and longitudinal maturation are assessed. SWIFT kidney tissues maintained proper expression of glomerular, proximal tubule, distal tubule, stromal, and vascular cell types and architectures. Additionally, a progressive increase in nephron gene expression is observed via Nanostring analysis, highlighting the ability of SWIFT kidney tissues to undergo further maturation in vitro under flow. To explore their host integration and immune response, kidney tissue discs are fabricated by depositing the same OBB-ECM solution used for SWIFT into cylindrical molds. The kidney discs are cultured in vitro for 7 days to promote fusion, then implanted into NSG mice reconstituted with human allogeneic immune cells. Allogeneic immune cells infiltrated the discs and attacked nephron cell types within the transplanted tissues. We find that in vivo immune response towards the transplanted tissue discs exhibit a gene signature akin to clinical acute cellular rejection. Collectively, this work provides a foundation for the biofabrication and development of kidney tissues constructed from OBBs, insight into the immunological challenge of implanting OBB-based tissues, and a platform for future immunosuppressant drug development.
In summary, a scalable approach for creating kidney organoids, perfusable organoid-derived proximal tubules, and bulk kidney tissues derived from human induced pluripotent stem cells has been established. The utility of each of these moieties (organoids, tubules, and tissues) have been validated through a combination of in vitro and in vivo studies. This PhD research provides a foundation for generating patient-specific kidney tissues for drug testing and therapeutic applications.Engineering and Applied Sciences - Engineering Science
SYNthia: An Interface Concept for Writing With Large Language Models
No full textArtificial intelligence (AI)-infused systems can offer valuable assistance to writers, but they may also produce imperfect or unsatisfactory suggestions that require efficient correction. Word choice presents a challenge for writers that can be addressed by several tools, but these systems typically require users to switch browser tabs or tools and break their flow of thinking, or otherwise fail to incorporate the context associated with users' writing or their intentions, leaving them with subpar or unrelated suggestions. We present SYNthia, a word-suggestion interface that allows users to be directly involved in the suggestion generation process by providing natural language feedback. We performed two pilot qualitative studies, finding that SYNthia provided users with a more practical interface that (1) allowed them to receive their target word more efficiently, (2) eliminated the need to for users switch contexts (e.g. switching tabs or devices), and (3) improved users' perceived quality of writing. In addition, we performed a formal user study comparing how novice and expert writers interact with SYNthia different, ultimately concluding that the writing level had no quantitatively significant impact on interactions with the tool, raising more questions for further study. However, the qualitative study surfaced several interesting observations regarding how writers interact with an AI-powered thesaurus, making progress towards the greater goal of integrating AI in the writing process while maintaining human agency and ownership. All code for this project can be found at the Github repository: https://github.com/AEst2002/word-suggester/tree/thesis.Computer Scienc