48670 research outputs found
Sort by
On the Ethics of Neuroenhancements and the Use of Race Theory in Biomedical Ethics
No full textDrawing from normative ethics and analytic philosophy of race, this dissertation focuses on the ethical, legal, and social implications (ELSI) of emerging biotechnologies and novel clinical practices in relation to potentially vulnerable populations. The first two chapters challenge presumptive duties in favor of moral bioenhancements (MBs). Here, MBs are understood as medical interventions that alter human moral cognition – e.g., the use of non-invasive brain stimulation to mitigate aggressive behavior. In Chapter 1, I argue that there is no universal moral obligation to utilize MBs, because the mass utilization of MBs may undermine the moral dispositions they seek to promote through unwarranted differential treatment between enhanced individuals and unenhanced individuals. Chapter 2 zooms in to focus on the obligations owed to and by individuals with psychiatric disorders. I argue that when an individual with a psychiatric disorder can act autonomously and make informed decisions, and when there are viable alternatives to MBs for the prevention of harm, the person with a psychiatric disorder is not obligated to utilize MBs. The second pair of chapters demonstrate how race theory from the analytic philosophy tradition can and should inform discourse in medical ethics and public health policy. In Chapter 3, I use a virtue theoretic framework to construct a decision tree to determine when, if ever, it is morally permissible to use a biological racial classification in medicine. In Chapter 4, I offer a modified version of Jorge L.A. Garcia’s volitional account of racism (VAR) and argue that my modified account is a superior alternative to competing theories of racism when considered in a healthcare context because of its accuracy and comprehensiveness. Though seemingly disparate topics, each chapter aims to promote the just and benevolent treatment of all humans contending with life, death, and health – so, everyone
Selection Bias In Lung Allocation: Influence On Lung Allocation Score And Physician Decision-Making
Get PDFIn the U.S., donor lungs are allocated to recipients based on a lung allocation score (LAS). While the statistical models used to construct the LAS control for patients’ demographic and clinical values, they do not account for selection bias, which arises because: (1) individuals are removed from the waitlist once they receive transplant (dependent censoring), and (2) in order to receive transplant, individuals must survive on the waitlist long enough for a suitable lung to become available (survivor bias). Failure to account for selection bias can lead to inaccurate predicted probabilities and suboptimal organ allocation. The goal of this dissertation is to improve the predictive accuracy of the LAS by mitigating selection bias so that lungs are allocated to the appropriate patients in the appropriate order. This goal was accomplished via three aims. First, we proposed a weighted estimation strategy to mitigate selection bias in the pre- and post-transplant LAS models, constructed a modified LAS score using these weights, and compared its performance to that of the existing LAS. Second, we examined the clinical impact of our modified LAS in both observed data and through simulations. Third, we conducted qualitative semi-structured interviews with lung transplant surgeons and pulmonologists throughout the U.S. to examine respondents’ understanding of selection bias and how it may affect the LAS and organ distribution. We found that our modified LAS exhibited better discrimination and calibration than the existing LAS and led to changes in patient prioritization. Diagnosis group, six-minute walk distance, continuous mechanical ventilation, functional status, and age exhibited the largest impact on prioritization changes. Simulations suggest that one-year waitlist survival may improve under the modified LAS, while one-year post-transplant and overall survival remain comparable to that under the existing LAS. Finally, our qualitative study demonstrates that selection bias can arise at several points along the transplantation pathway. To address such bias, transplant centers must consider both patient health and program health within constraints imposed by donor organ scarcity. We hope that this work can inform future revisions of the LAS and other prediction models in organ transplantation to ensure more equitable allocation of donor organs
Beyond Classical Statistics: Optimality In Transfer Learning And Distributed Learning
Get PDFDuring modern statistical learning practice, statisticians are dealing with increasingly huge, complicated and structured data sets. New opportunities can be found during the learning process with better structured data sets as well as powerful data analytic resources. Also, there are more and more challenges we need to address when dealing with large data sets, due to limitation of computation, communication resources or privacy concerns. Under decision-theoretical framework, statistical optimality should be reconsidered with new type of data or new constraints. Under the framework of minimax theory, this thesis aims to address the following four problems:1. The first part of this thesis aims to develop an optimality theory for transfer learning for nonparametric classification. An near optimal adaptive classifier is also established. 2. In the second part, we study distributed Gaussian mean estimation with known vari- ance under communication constraints. The exact distributed minimax rate of con- vergence is derived under three different communication protocols. 3. In the third part, we study distributed Gaussian mean estimation with unknown vari- ance under communication constraints. The results show that the amount of additional communication cost depends on the type of underlying communication protocol. 4. In the fourth part, we investigate the minimax optimality and communication cost of adaptation for distributed nonparametric function estimation under communication constraints
Morphologies, Phase Behavior, And Applications Of Precise Ion-Containing Multiblock Copoylmers
No full textThe capability of block copolymers to self-assemble into nanometer-scale ordered morphologies has greatly advanced their applications in a variety of areas of nanotechnology. To reach sub-10 nm and even smaller length scales, a deeper understanding of ordered morphologies and phase behavior in block copolymers is required. This thesis focuses on self-assembled morphologies and phase behavior of precise ion-containing multiblock copolymers, and their applications as single-ion conducting polymers. By systematically modifying the block chemistry in precise ion-containing multiblock copolymers, ordered morphologies such as layered, double gyroid, and hexagonally-packed cylinder morphologies are produced with sub-3 nm domain spacings. The morphology – conductivity relationship in these precise ion-containing multiblock copolymers reveals that the bicontinuous double gyroid morphology exhibits faster ion transport than the isotropic hexagonally-packed cylinder morphology. The phase behavior in these ion-containing polymers provides insight into the formation of ordered morphologies in bulk and thin films. The origin of microphase separation at sub-3 nm length scales is attributed to the presence of ionic groups and alternating multiblock architecture. The fundamental understanding of phase behavior in these ion-containing polymers provides criteria for designing double gyroid and other ordered morphologies in alternating multiblock copolymers. The ionic layers of precise ion-containing polyesters are selectively solvated to enhance ion transport. The ionic conductivity of solvated ionic layers is \u3e 104 times higher than in the dried state. The increase in ionic conductivity is due to faster structural relaxation and a larger number of charge carriers in solvated ionic layers. This study provides a useful strategy to tune the ionic domains and increase the ionic conductivity of single-ion conducting polymers. A new series of precise ion-containing polyamide sulfonates are synthesized via step-growth polymerization. These polymers contain lithiated phenyl sulfonate in polar blocks that precisely alternate with a fixed number of x aliphatic carbons (x = 4, 5, 10, or 16). These precise ion-containing polyamides exhibit layered ionic aggregates with crystalline polymer backbone when x = 16. The ionic layers and crystalline polymer backbone persist at 250 °C, indicating great thermal stability. This study expands the accessible block chemistry of precise ion-containing multiblock copolymers by using step-growth polymerization
Essays On The Use Of A/b Testing Among E-Commerce Practitioners
Get PDFRandomized experiments – often called A/B tests in industrial settings – are an increasingly important element in the management of many organizations. While some firms have long had both the managerial and technical know-how to use experiments for making key decisions, new forms of software and internet infrastructure have dramatically lowered the cost of conducting A/B tests online, opening up the practice to an entirely new set of organizations. This dissertation studies the practice of A/B testing among this new wave of practitioners, characterized primarily as e-commerce businesses that have adopted new forms of low cost, easy to use, third-party experimentation software. The first two chapters of this document study A/B testing as its own distinct phenomena in digital business, answering questions about the prevalence of p-hacking among e-commerce practitioners and the nature of how firms use A/B testing software in the real world. The final chapter demonstrates how e-commerce firms can use A/B tests and recent developments in causal machine learning for improved customer targeting and price discrimination. As a whole, this work demonstrates the growing importance of A/B testing and causal reasoning as a key factor in the future of managerial decision making
Honey Over Vinegar: Alignment Decisions Of Smaller States And U.s.-China Competition For Influence In Southeast Asia
No full textCommitments of alignment from smaller states are central to the efforts of major powers to manipulate the regional and international systems in which they operate, and contestation over such commitments increasingly represents a persistent source of tension among major powers. Largely overlooked in the literature has been the active role of the smaller states and their agency in decisions of alignment. In my analysis, I highlight situations in which 1) there is explicit competition for alignment commitments between at least two major powers vying, through investment of time and resources, for influence over policy behavior, and 2) the smaller state is actively engaged in this competition. Smaller states face a simplified choice between three options: alignment with Major Power A, alignment with Major Power B, or nonalignment through hedging. Alignment comes with material benefits to the smaller state but also a loss of policy autonomy; through a strategy of nonalignment through hedging, on the other hand, the smaller state retains a greater degree of policy freedom but loses the diplomatic, economic, and security support that comes with alignment. I argue that smaller states select major power patrons based on the strategy for building authority employed by the major power: inducement or coercion. I contend that inducements are the more effective strategy, as they offer ruling regimes in smaller states material benefits that bolster their survival prospects. This framework, in which such regimes can leverage their positions to their own benefit amidst major power competition, is better able to explain alignment outcomes than traditional theories focused on the threats to national or internal security or regime-type affinity. I apply my framework to investigate alignment decisions in Southeast Asia amid growing U.S.-China competition. Through primary and secondary sources, open-source research, and cross- and between-case comparison of alignment decisions in Myanmar, Thailand, Vietnam, Cambodia, and the Philippines, I show the importance of inducements and coercion in smaller state alignment decisions
Gravitational Imagination: Picturing Suspension From Eadweard Muybridge To The Space Age
Get PDFResisting gravity holds an allure. Situating that appeal within the realm of art history, my dissertation charts modern aesthetic efforts to channel and challenge gravitational force—casting suspension as vital to modernism. I contend that new modes of pictorial time—and, in turn, novel possibilities for embodied engagement—emerged once photographic technology accelerated enough to catch airborne bodies and hold them aloft in the space of an image—documenting a potential which was actualized in the Space Age, when humans first experienced sustained weightlessness. Tracing an ungrounded sensibility that emerged between these nodal points, my project offers a thematic account of how gravitational disruption coheres in pictorial composition and perceptual effects. Drawing upon a range of interdisciplinary sources and period voices, my chapters posit the rise of a form of suspended viewership—which does not presume grounded-ness or fixed coordinates, either within artworks or on our part. From Eadweard Muybridge’s photographs of figures held in momentary flight to artists such as Helen Frankenthaler and Marcel Duchamp enacting an “aerial gesture” that employs and subverts gravity, and from Claude Monet’s “upside down” waterlily paintings to Aaron Siskind’s levitational midcentury imagery, my case studies explore increasingly unbound aesthetic terrain. Once gravity became dislodged in visual representation, I argue, formal axes were opened to more symbolic creative dimensions. With that metaphoric tenor, this dissertation defines a pictorial suspension ripe with potential—and charged with the power to resist seemingly inexorable forces. Materializing a stillness that arose in the face of modern momentum, the objects at its core open space for a “gravitational imagination”—founded in the world but also challenging its limits
Exploring Novel Materials To Be Used As Dual-Energy Mammography Contrast Agents For Breast Cancer Detection
Get PDFDual-energy mammography (DEM) is a recently FDA-approved x-ray imaging technology developed for breast cancer screening, especially advantageous for women with dense breasts. While studies have reported the benefits of DEM in breast cancer screening, thus far, no DEM-specific contrast agents have been approved. Therefore, clinics use iodinated contrast agents, which can have suboptimal contrast in DEM, short circulation half-life, and adverse effects on patients. Nanoparticle-based contrast agents can be designed to address some of these limitations. This thesis explores different materials to develop DEM-specific nanoparticle-based contrast agents with high contrast and biocompatibility. Based on previous research highlighting elements that produce high DEM contrast, we explored materials such as silver, tellurium, and molybdenum to develop DEM-specific nanoparticle-based contrast agents. First, 8 nm silver telluride nanoparticles (Ag2Te NPs) were developed as DEM-specific contrast agents. Ag2Te NPs are composed of two DEM high-contrast generating materials and thus, provide superior contrast than iodinated molecules, both in in vitro and in vivo settings. Additionally, by coating these with mPEG-SH 5K, we prolonged their circulation, tumor accumulation, and colloidal stability while maintaining biocompatibility. Next, to further improve the likelihood of clinical translation of Ag2Te NPs, we designed them to be 3 nm in size to achieve renal clearance. These 3 nm Ag2Te NPs provided similar contrast and biocompatibility to the larger Ag2Te NPs, even when studied for a longer term in vivo. Furthermore, 93% of the injected dose was excreted from the main organs in 24 hours, 95% in 7 days, and 97% in 28 days. This excretion is among the highest reported thus far for any nanoparticle type. Lastly, we developed 2 nm molybdenum disulfide nanoparticles (MoS2 NPs) with different coatings and explored them as DEM-specific contrast agents. Our findings suggest that MoS2 NPs can produce higher contrast than iodinated molecules and be biocompatible in vitro. Together, this work presents an advancement in the development of DEM-specific contrast agents and their potential progression towards clinical translation
Geometry and Topology: Building Machine Learning Surrogate Models with Graphic Statics Method
No full textThis dissertation aims at developing a machine learning workflow in solving design-related problems, taking a data-driven structural design method with topological data using graphic statics as an example. It shows the advantages of building machine learning surrogate models for learning the design topology – the relationship of design elements. It reveals a future tendency of the coexistence of the human designer and the machine, in which the machine learns the appearance and correlation between design data, while the human supervises the learning process. Theoretically, with the commencement of the age of Big Data and Artificial Intelligence, the usage of machine learning in solving design problems is widely applied. The existing research mainly focuses on the machine learning of the geometric data, however, the internal logic of a design is represented as the topology, which describes the relationship between each design element. The topology can not be easily represented for the human designer to understand, however it\u27s readable and understandable by the machine, which suggests a method of using machine learning techniques to learn the intrinsic logic of a design as the topology. Technically, we propose to use machine learning as a framework and graphic statics as a supporting method to provide training data, suggesting a new design methodology by the machine learning of the topology. Different from previous geometry-based design, in which only the design geometry is presented and considered, in this new topology-based design, the human designer employs the machine and provides training materials showing the topology of a design to train the machine. The machine finds the design rules related to the topology and applies the trained machine learning models to generate new design cases as both the geometry and the topology
On The Orbital Rigidity Conjecture And Sustained P-Divisible Groups
Get PDFThe orbital rigidity phenomenon for p-divisible groups was first discovered by Ching-LiChai, motivated by the Hecke orbit conjecture. Later, the general orbital rigidity conjecture was formulated and the second case of this conjecture was proved by Ching-Li Chai and Frans Oort. In this thesis we prove a third case of this conjecture