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BIOMOLECULAR ENGINEERING FOR THE DESIGN AND CHARACTERIZATION OF IMMUNE MODULATING PROTEINS
No full textBiomolecular engineering can be used both to design new molecules with sophisticated fit-for-purpose properties and/or to manipulate and subsequently characterize molecules in ways that provide insight into the behaviors of the native protein. Here, the use of biomolecular engineering for both the design and characterization of immune-modulating proteins of therapeutic potential is described.
In a design example, a novel interleukin-2 (IL-2) immunocytokine (IC) aimed at improving the tolerability and efficacy of an IL-2 therapy in cancer is described. Specifically, an IL-2-based cytokine/antibody fusion protein (immunocytokine) was engineered to augment the ratio of immune effector cells to regulatory T cells by altering affinities of the IL-2/IL-2 receptor interface as well as increase the intratumoral bioavailability of IL-2 by combining molecularly engineered collagen binding domains and intratumoral administration. The bioactivity and bioavailability of the engineered IC was localized to and retained in the tumor, thereby minimizing IL-2 toxicity. Furthermore, the immunocytokine exhibited single-agent therapeutic efficacy, elicited an abscopal response, and was synergistic with immune checkpoint blockade in mouse tumors models.
In a characterization example, biomolecular engineering was used to manipulate the native interaction between interleukin-7 (IL-7) and an anti-IL-7 neutralizing antibody in an effort to study the mechanism through which the neutralizing antibody paradoxically potentiates IL-7 activity. Specifically, the creation of a single-chain fusion protein between IL-7 and a neutralizing anti-IL-7 antibody and its subsequent characterization alongside the native cytokine, antibody, and cytokine/antibody complexes in biophysical, functional, and crystallographic studies is described. These studies elucidated a probable mechanism through which the activity of IL-7 is potentiated by the anti-IL-7 neutralizing antibody, and ongoing work evaluating the therapeutic potential of the single-chain fusion protein and cytokine/antibody complex in chimeric antigen receptor T (CAR-T) cell therapy and sepsis-induced lymphopenia is presented.
Finally, published collaborative work is appended that describes the design of recombinant class II major histocompatibility complex monomers that were incorporated into an ex vivo nanoparticle-based artificial antigen presenting cell platform to promote the expansion of antigen-specific T cells for use in adoptive cell transfer applications in cancer immunotherapy. Collectively, the work described herein demonstrates the use of biomolecular engineering for the design and characterization of immune-modulating proteins of therapeutic potential
On Wondering: The Epistemology of A Questioning Attitude
Get PDFAn emerging trend in contemporary epistemology departs from the traditional preoccupation with the nature of knowledge, belief, evidence, justification, and the problems of skepticism. This trend focuses instead on the nature of inquiry itself and especially on the role of questions and questioning attitudes that arise in and define that activity. Naturally, this emerging trend calls for a philosophical exploration of the nature of questioning attitudes like curiosity and wondering, and of the various epistemological considerations pertaining to them. Consequently, this project primarily addresses two questions: what does it mean to wonder? And what is required to wonder well?
The project is thus both descriptive and normative, aiming to pin down the place that wondering has in our ontology of epistemologically significant mental states and to determine what kinds of prescriptive norms it is subject to in the course of rational inquiry
ENHANCING CLOUD SYSTEM RUNTIME TO ADDRESS COMPLEX FAILURES
Get PDFAs the reliance on cloud systems intensifies in our progressively digital world, understanding and reinforcing their reliability becomes more crucial than ever. Despite impressive advancements in augmenting the resilience of cloud systems, the growing incidence of complex failures now poses a substantial challenge to the availability of these systems. With cloud systems continuing to scale and increase in complexity, failures not only become more elusive to detect but can also lead to more catastrophic consequences. Such failures question the foundational premises of conventional fault-tolerance designs, necessitating the creation of novel system designs to counteract them.
This dissertation aims to enhance distributed systems’ capabilities to detect, localize, and react to complex failures at runtime. To this end, this dissertation makes contributions to address three emerging categories of failures in cloud systems. The first part delves into the investigation of partial failures, introducing OmegaGen, a tool adept at generating tailored checkers for detecting and localizing such failures. The second part grapples with silent semantic failures prevalent in cloud systems, showcasing our study findings, and introducing Oathkeeper, a tool that leverages past failures to infer rules and expose these silent issues. The third part explores solutions to slow failures via RESIN, a framework specifically designed to detect, diagnose, and mitigate memory leaks in cloud-scale infrastructures, developed in collaboration with Microsoft Azure. The dissertation concludes by offering insights into future directions for the construction of reliable cloud systems
HOW THE COVID PANDEMIC HAS INFLUENCED THE PROCESS OF PRIMARY HEALTH CARE REFORM PRIORITY SETTING: A CASE STUDY FROM PAKISTAN
Get PDFBackground: Primary health care is a critical component of health systems, but there is a relative paucity of information on the indirect impact the COVID pandemic may have on primary health care systems in low and low-middle income countries using a policy process lens.
Objective: This case study from Khyber Pakhtunkhwa in Pakistan is of a PHC reform agenda from 2020-2023 and aims to contribute to the understanding of the varying effects that the wide-ranging shock of COVID had on prioritization processes within primary healthcare reforms in the province, and early implementation of that reform.
Methods and conceptual framework: This analysis draws on 14 in-depth semi-structured interviews from key officials across the provincial government and the wider policy community, complemented with a document analysis. Results were analyzed using health systems themes drawing from a resilient health systems framework, coupled with a process tracing approach to describe the reform process. The outputs of reform analysis were then examined through a modified multiple streams framework/policy feedback theory framework to describe how well the framework could describe the drivers of the reform.
Results: The health systems analysis illustrates the breadth of health systems components relevant during the pandemic. Process tracing describes how a reactive public sector primary health care agenda pre-pandemic driven by external priorities evolved into a new reform agenda after the emergency phase of the COVID response. This evolution was contributed to by an increased ability to influence the allocation of resources due to increased political power from perceived successful management of the pandemic amongst health system leadership, a wider appreciation for gaps in health system performance, and sharing of learnings from the reform experience of the neighboring province of Punjab.
Conclusion: This case study illustrates the mechanics of how a ‘window of opportunity’ for reform in in LMIC primary health care systems may have been opened due to the COVID pandemic, and how understanding the experience within the system of the pandemic could help inform a reform agenda using policy frameworks to help understand drivers of reform
Imparting Electrochemical Functionality into Extended Solids for Next-Generation Batteries
No full textAs global energy storage demands grow exponentially, exploitation of fossil fuels grows correspondingly leading to dangerous levels of anthropogenic climate change. These global trends have necessitated developing new forms of renewable energy storage, and lithium-ion batteries have been pioneers in making that an attainable reality. However, lithium-ion batteries are approaching their theoretical capacity, leading to an impending crisis in which energy storage demands may not be met.
To meet that demand, we focus on lithium-sulfur batteries as a promising contender for next-generation batteries. Compared to lithium-ion batteries, lithium-sulfur batteries offer over 4 times the amount of charge by mass and almost twice the amount of charge by volume. Moreover, sulfur is naturally abundant, making it a highly attractive material for battery technology. Limiting their implementation, however, is the polysulfide shuttle, a phenomenon in which the intermediate polysulfides are lost to the electrolyte during battery cycling leading to eventual battery failure. Additionally, the elemental sulfur at the cathode is insulating, an inherent issue in a device reliant on the movement of electrons.
To improve battery performance, we employ a materials chemistry approach to improve both charge transfer and limit the polysulfide shuttle. We begin by using metal-organic frameworks (MOFs) which are porous, tunable materials composed of metal nodes and organic linkers which come together form 1D, 2D, and 3D nets. MOFs can also be modified post-synthetically for greater functionality. However, MOFs are often insulating, so, to improve charge transfer inherent to MOFs, we design a Zr-based MOF with a molecular oligosilane to instill new charge donor abilities. To interrupt the polysulfide shuttle, we post-synthetically modify another Zr-based MOF with thiophosphates to tether polysulfides and, in turn, improve battery performance. We then apply these findings to carbon nanotubes as both a more commercially available material and to better elucidate the role of the thiophosphate group and porosity on battery performance.
These works offer a fundamental approach to practical implementation of next-generation batteries. Through a deeper understanding of charge transfer and the design of better cathode materials, we observe widespread improvements in battery performance, making a fossil-free future one step closer to reality
HOUSING QUALITY AMONG OCCUPIED U.S. HOUSEHOLDS: IMPLICATIONS FOR POPULATION HEALTH
Get PDFProblem Statement: A large body of research underscores that housing quality is considered a key driver of health status, but publicly available housing quality data is scant and not codified. As a construct, housing quality is multidimensional and can be defined using several factors such as physical adequacy, housing cost burden, and neighborhood quality. Currently, neighborhood-level housing quality data is not publicly available.
Methods: This quantitative study leveraged national datasets and predictive analytics to better understand neighborhood-level housing quality in the United States. To fill the data gap discussed in the problem statement above, we developed the Housing Quality Index (HQI), a dataset that quantifies multiple dimensions of housing quality across three levels of geography: state, county, and tract. The resulting data product, the Housing Quality Index (HQI), can be used to predict the likelihood that a jurisdiction contains a large share of poor-quality housing units. Manuscript 1 describes the development of the Housing Quality Index (HQI) and discusses high-level findings. Manuscript 2 examines how housing quality dimensions interact at the neighborhood level and assesses concordance with the Environmental Justice Index (EJI), another national index that aggregates socioeconomic and environmental factors. Lastly, Manuscript 3 analyzes the neighborhood-level relationship between housing quality and population health.
Results: Manuscript 1 found that in the U.S., approximately 63.3% (SE: 0.27) of occupied housing units, or approximately 81 million occupied households, experience at least one dimension of poor housing quality. State-level and county-level predicted estimates revealed key geographic patterns. County-level and tract-level HQI estimates provide researchers, advocates, and practitioners with a tool to better understand housing quality in a specific jurisdiction. Manuscript 2 found that approximately 42.1% of census tracts scored in the top national quartile for any poor housing quality dimension. Despite this large overall prevalence, each poor housing quality dimension captures a distinct construct, suggesting housing quality dimensions can be viewed independently or cumulatively. Manuscript 3 found a significant association between census tracts with large shares of poor housing quality and census tracts with large shares of adults self-reporting negative health status.
Conclusion: The Housing Quality Index (HQI) provides state-level, county-level, and tract-level predicted estimates of housing quality among the occupied U.S. housing stock. HQI estimates allow researchers, administrators, and policymakers to better understand housing quality nationally and within defined jurisdictions. Given that various dimensions of poor housing quality are closely linked to adverse population health outcomes, the HQI can ensure limited resources are adequately targeted for potential interventions
Essays on China’s Population Policies: Impacts and Methodological Innovations
Get PDFFamily planning policies have a large impact on households' and individuals' welfare, not only through the impact on fertility outcomes but also through other indirect channels such as the labor market and intrahousehold bargaining power. In addition, the effect of family planning policies tends to be heterogeneous among different people. Making fertility endogenous has led to significant identification and estimation issues, especially taking into consideration the bargaining between husband and wife. Taking advantage of the huge family planning policy changes that happened in China, my dissertation thoroughly examines the policy impact of the policy change on couples from different perspectives, especially emphasizing how it affects gender inequality in society. To deal with the challenge of estimating complex dynamic fertility models, I also make contributions on the technical front, where I propose a new estimation strategy for dynamic models using machine learning.
In the first essay, I look at how China's Two-Child Policy affects gender inequality and social welfare in China. I start with a difference-in-difference method to show that the Two-Child Policy increased the gender wage gap between women and men and negatively affected the intrahousehold bargaining power of women. Motivated by this empirical pattern, I then build and estimate a dynamic collective household model to quantify the welfare impact of the new policy on both genders using a novel machine learning method and indirect inference. The results suggest that the welfare cost of the Two-Child Policy for women is equivalent to 6.00% of lifetime consumption, while the welfare benefit of the policy for men is equivalent to 7.23% of lifetime consumption. Policy experiments suggest that implementing anti-discrimination laws for women in the labor market significantly improves women's welfare while providing public childcare subsidies is most effective in stimulating fertility in the post-policy era.
To deal with the problem that dynamic discrete choice models (DDCs) pose significant computational challenges, especially when dealing with high-dimensional state spaces and unobserved heterogeneity, my second essay, coauthored with Professor Yingyao Hu, introduces a unified estimation framework that integrates policy gradient methods from reinforcement learning with the established indirect inference approach. By directly parametrizing and estimating the policy function using the policy gradient method in the inner loop of the full model solution, this method builds a mapping from deep structural parameters to optimal policy function parameters, significantly reducing the computational burden of the estimation. Notably, the framework is adaptable to models with partially observed state variables, demonstrating efficacy in estimating models with various types of unobserved state variables under specific conditions. Using this method, discretizing the unobserved state variable is no longer necessary, making the estimation of DDCs with continuous, time-varying unobserved state variables tractable. Empirical validation across various model specifications confirms the effectiveness of the proposed framework, with estimates closely aligning with true parameter values. By enhancing computational efficiency and accuracy, the proposed framework facilitates more comprehensive analyses of dynamic decision-making processes.
My third essay employs a self-reported survey measure of the ideal number of children to assess the One-Child Policy's impact on couples' actual childbearing. I take advantage of the novel feature of this self-reported measure, which asks about couples' preferences in a counterfactual setting, to help identify the treatment effect of the One-Child Policy. The study estimates the policy's treatment effect by using couples' pre-policy ideal child numbers in 2014 as a counterfactual index and comparing these to their responses in the post-policy period, under the assumption that the conditional distribution of the ideal number of children given the actual number of children is stationary if there is no policy constraint. Findings indicate a significant average reduction of 0.2115 children per couple in 2014 due to the policy. Variations in policy effects are explored across educational, urban/rural, and occupational groups, with highly educated urban women in government jobs experiencing the most pronounced impact. Regional variations are also noted, reflecting policy stringency differences among provinces
SYNERGIZING CAUSAL INFERENCE AND MACHINE LEARNING FOR ACTIONABLE INFERENCE
Get PDFThe rapid development of storage systems and data-processing technologies in recent years has enabled the collection and analysis of various modalities of data generated in settings such as healthcare, social media, and genomics. This has led to a heightened interest in applying data-driven inference algorithms to automate decision-making or provide decision support. Supervised machine learning and causal inference are two important classes of data-driven inference algorithms that have received considerable attention. However, both have shortcomings that hamper their ability to provide actionable inference. The lack of explainability of supervised machine learning algorithms reduces trust in their predictions, thereby decreasing their chances of real-world deployment. Next, algorithms are often deployed in settings different from those in which they were trained, leading to inference results that may not generalize to other domains. Additionally, not every inference task involves prediction, for example, reasoning about the effect of interventions or attributing causes to the observed effects. Although causal approaches have shown promise in remedying each of these shortcomings, they make additional assumptions and often require modeling high-dimensional nuisance parameters, thereby diminishing their ability to provide actionable inference.
In this dissertation, I present methods that illustrate the synergy between supervised machine learning and causal inference and how they can address each other’s limitations. To demonstrate how causal inference can address some shortcomings of machine learning algorithms, I first provide inference algorithms for shift interventions that have applications in robust machine learning, healthcare, and social sciences. I then provide an inference framework that utilizes causal inference to estimate causal effects in systems with equilibrium dynamics. Next, I present a technique for obtaining causal explanations for the behavior of black-box algorithms by utilizing causal discovery algorithms. Subsequently, I present approaches to tighten the bounds on the Probabilities of Causation, which are useful for causal attribution and policy evaluation. To demonstrate how machine learning can help causal inference, I present an approach for estimating mediation effects in the presence of real-valued treatments using flexible machine learning algorithms. This dissertation concludes by providing closing remarks and directions for future research
Investigating Osteopontin’s Effects on the Memory and Cognition of HIV-Infected Humanized Mice by Behavior Tests
Get PDFHIV-associated neurocognitive disorder (HAND) is defined as a spectrum of neurological disorders in HIV patients, with memory and cognitive decline being characteristic symptoms. HAND occurs when HIV infiltrates through the blood-brain barrier (BBB) into the brain by infecting monocytes, monocyte-derived macrophages, and CD4+ T-cells in the bloodstream, which will be recruited into the brain by brain-resident microglia. Previous studies showed that the protein Osteopontin (OPN/SPP1), proinflammatory cytokines secreted by multiple types of immune cells, including microglia, was elevated in the brains of HIV-infected patients. We have demonstrated that although OPN/SPP1 showed protective effects on HIV-infected cultured neurons and mouse brains, it could also exacerbate the neurocognitive symptoms in patients. However, the relationship between OPN/SPP1 expression in the brain and the manifestation of behavioral symptoms in HAND or NeuroHIV is unclear. Therefore, we want to investigate whether OPN/SPP1’s expression has any impact on the memory and cognitive functions. This was tested by the novel object recognition test (NORT) and the object location test (OLT). While the results failed to reach statistical significance, several trends suggest that HIV infection led to worsened memory, which can be partially remedied by OPN/SPP1 knockdown. The findings from this study will be invaluable in directing the design of future planned studies; further adjustments are needed for future experiments
Outpatient Physical Therapy Use Following Tibial Fractures: A Retrospective Commercial Claims Analysis
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Abstract Objective The purpose of this study was to characterize outpatient physical therapy (OPT) use following tibial fractures and examine the variability of OPT attendance, time of initiation, number of visits, and length of care by patient, injury, and treatment factors. In the absence of clinical guidelines, results will guide future efforts to optimize OPT following tibial fractures. Methods This study used 2016 to 2017 claims from the IBM MarketScan Commercial Claims Research Database. The cohort included 9079 patients with International Classification of Diseases: Tenth Revision (ICD-10) diagnosis codes for tibial fractures. Use in the year following initial fracture management was determined using Current Procedural Terminology codes. Differences in use were examined using χ2 tests, t tests, and Kruskal-Wallace tests. Results Sixty-seven percent of patients received OPT the year following fracture. OPT attendance was higher in female patients, in patients with 1 or no major comorbidity, and in the western United States. Attendance was higher in patients with upper tibial fractures, moderate-severity injuries, and treatment with external fixation and in patients discharged to an inpatient rehabilitation facility. Patients started OPT on average [SD] 50 [52.6] days after fracture and attended 18 [16.1] visits over the course of 101 [86.4] days. The timing of OPT, the number of visits attended, and the length of OPT care varied by patient, injury, and treatment-level factors. Conclusions One-third of insured patients do not receive OPT following tibial fracture. The timing of OPT initiation, the length of OPT care, and the number of visits attended by patients with tibial fractures were highly variable. Further research is needed to standardize referral and prescription practices for OPT following tibial fractures. Impact OPT use varies based on patient, injury, and treatment-level factors following tibial fractures. Results from this study can be used to inform future efforts to optimize rehabilitation care for patients with tibial fractures.
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