Bioculture Journal
Not a member yet
1309 research outputs found
Sort by
Electrochemical Engineering Models of Electroanalytical Tools for Advanced Batteries
No full textThesis (Ph.D.)--University of Washington, 2022Battery design and usage can be optimized through the use of electrochemical engineering models that detail the physicochemical processes occurring in a battery. While lithium-ion battery chemistry and models are well-established, lithium sulfur is still an active area of development and is a promising next-generation chemistry. The first part of this dissertation covers the ongoing challenges of continuum modeling of lithium sulfur, with efforts to accelerate model development using electroanalytical techniques – galvanostatic intermittent titration technique and electrochemical impedance spectroscopy (EIS). We explore the implications on thermo-kinetic parameters by modeling the thermodynamic equilibrium before adding further complexities of kinetics and transport. The second part of this work aims to improve diagnostics, sensing, and control of lithium-ion batteries by modeling a novel technique, dynamic EIS
Cognitive Mechanisms for Reasoning with Uncertainty Visualizations
No full textThesis (Ph.D.)--University of Washington, 2022The joint proliferation of data-driven interfaces in public life and data science in organizations makes reasoning with uncertainty in data visualizations critically important. Lay people and data analysts alike make visual judgments about data almost daily---whether relying on a deluge of Covid-19 visualizations to manage risks to personal and public health, or using exploratory data analysis to drive business decisions. In order to design data visualization software that supports statistically rigorous judgments in these contexts, the visualization community must understand how people reason with uncertainty visualizations. My dissertation addresses cognitive mechanisms that chart users rely on when reasoning with uncertainty: (1) automatic perceptual processing, through which the visual system makes intuitive inferences; (2) heuristic strategies, used to interpret visualizations and make consequential decisions; and (3) model-based thinking, whereby analysts compare observed patterns in data with counterfactual predictions from models (either mental or realized in software) that might explain the data. As a capstone to my thesis, I present Exploratory Visual Modeling (EVM), a prototype visual data analysis tool that deploys these cognitive mechanisms to support more rigorous exploratory data analysis. The tool enables analysts to express their provisional mental models of data generating process as formal statistical models and to check predictions from these models against observed patterns in data. I present insights from the design process of EVM, as well as considerations for evaluating the design hypothesis that the model checks enabled by EVM facilitate improvements in generative thinking during exploratory data analysis. EVM deploys automatic and heuristic cognitive mechanisms in service of model-based thinking, providing a proof-of-concept for new ways of designing visualization software
A Novel Framework for GPU-Parallelized Peridynamics with Applications in Nonlinear Elasticity and Topology Optimization
No full textThesis (Ph.D.)--University of Washington, 2022This work focuses on advancing the state-of-the-art of the peridynamic simulation method. Peridynamics is a framework for modelling continuum mechanics, similar to the finite element method (FEM). The peridynamic formulation, which involves a meshfree discretization of a continuum into material points and evaluation of force-exerting interactions between these points, allows representation of complex physical phenomena such as nonlinear mechanics, crack propagation, and material failure more naturally than gradient-based approaches like FEM. This work presents a three-pronged approach towards advancing the state-of-the-art of peridynamic simulation: Chapter 2 definitively improves treatment of the long-standing "peridynamic surface effect", which limits the theoretical accuracy of the peridynamic method due to the method's formulation assuming all material points to be in the bulk of the body. The solution developed here extends an approach which eliminates the effect in simple geometries to apply a specialized boundary treatment to arbitrary domain shapes. Chapter 3 develops a novel, high performance software package for parallelized peridynamic simulation on graphics processing units (GPUs). This approach is several hundred times faster than equivalent serial approaches, and several times faster than alternative GPU-based approaches recently developed by other researchers. Additionally, this software is capable of simulating problem sizes an order of magnitude larger than competing methods on limited GPU memory. Finally, Chapter 4 applies this highly-efficient software towards novel applications of peridynamics, particularly in nonlinear structural analysis and large scale topology optimization. Specifically, the GPU peridynamic framework is capable of evaluating large deformations 2-3 orders of magnitude faster than state-of-the-art nonlinear FEM solvers. The framework is demonstrated to produce accurate simulations of behavior in auxetic structures exhibiting geometric discontinuities and nonlinear deformations, a use case which demands nonlinear solution for reasonable results; the peridynamic solver runs 1250x faster than the GPU-accelerated commercial software ANSYS. Finally, utilizing the GPU framework for the structural analysis step of large-scale topology optimization problems produces optimal topologies 35-65x faster than state-of-the-art FEM optimizers run on the CPU, and 2-4x faster than GPU-based FEM optimizers running on identical hardware
The Value of using local data to allocate resources to fight the HIV Epidemic. Case of study in Atlanta, Georgia
No full textThesis (Ph.D.)--University of Washington, 2022Local estimates can procure means to achieve a more equitable progress and end the HIV epidemic in the United States within the next decade. The aim of this dissertation was to quantify the incremental costs and health benefits of using prevalence data at the zip code level to inform resources allocation within Atlanta, Georgia, compared to the current distribution based on supply-side criteria.This study was structured in three aims. First, I conducted a simulation-based calibration of an HIV-mathematical model to project the epidemic at the zip code level under varying circumstances. Second, the CDC reports diagnosed HIV cases by zip code, but undiagnosed cases are unknown, which I predicted based on social determinants of HIV spreading and prevalence estimates at the county level. Third, I designed a cost-effective analysis (CEA) to quantify the health and economic consequences up to 2040 of allocating resources across zip codes under three alternatives: status quo, reallocation proportional to diagnosed-only- and to total-cases. For each scenario I estimated the incremental cost-effectiveness ratio (ICER), as the cost per quality-adjusted life year (QALY) gained, compared to the status quo.
The CEA showed high variability across zip codes depending upon the direction of reallocation. Compared to the status, the reallocation alternative based on diagnoses-only were dominant among increased-coverage zip codes with costs savings of 3Million more expensive and yielded 2,019 less QALYs. The results under total-cases reallocation were the same across coverage-increased and -decreased zip codes and remarkably similar in the incremental effects.
This study provides evidence that the health production function is heterogeneous across zip codes, making the reallocation of resources a non-zero-sum game. This implies the existence of a reallocation algorithm that maximizes the generation of QALYs while minimizing additional costs. These results create opportunities for prioritization of resources at the local level. While Atlanta provides an excellent setting to highlight the benefits of resources reallocation, several other cities have high variability of HIV spreading at the zip code level and presence residential segregation. Therefore, my analytical framework, methodology, and findings could be of interest in other cities and states across the country
Impact of Autonomous and Connected Truck Platoons in the Pacific Northwest on Transportation Infrastructure
No full textThe operational characteristics of freight shipments will significantly change after implementation of autonomous and connected trucks (ACTs). This change will have major impacts on mobility, safety, and infrastructure service life. Truck platooning is one of the truck arrangements that will soon become feasible with connected vehicle technology. It will enable trucks to be connected with themselves and the surrounding infrastructure. Although truck platooning will increase fuel efficiency and improve transportation services, the platooning configuration is expected to accelerate damage to the existing infrastructure. This damage, if accumulated, will cost the country billions of dollars to fix and will affect the mobility of people and goods. This research aimed to develop a well-defined framework for assessing and data-driven solution for addressing the influence of truck platoons on existing bridges in the Pacific Northwest to be ready for the near future implementation of ACTs and to preserve the current bridge inventory. An extensive parametric study of 59,200 models considering a wide range of parameters was conducted. Four bridge cases were included: simple span, two-span, three-span, and four-span bridges. The effects of bridge continuity was demonstrated by the two-, three-, and four-span bridges. Spans varied from 20 ft. to 200 ft. (6 m to 60 m), increments of 5 ft. (1.5 m). The HS-20 design truck was arranged, according to the parametric study, to form different platooning configurations by using up to 20 trucks at headway spacings varying from 10 to 30 ft. The results were then used to provide guidelines for the optimum parameters and load rating charts for future truck platooning applications.US Department of Transportation
Northwest Pacific Transportation Consortium
National Institute for Advanced Transportation Technology
University of Idah
A Qualitative Investigation into the Impact COVID-Era Telehealth Policies Had on Organizations Ability to Deliver Care to People Experiencing Houselessness in Seattle
No full textThesis (Master's)--University of Washington, 2022As Covid-19 swept through the nation, many businesses and organizations moved to a remote setting where possible. While each business and organization, without a doubt, struggled to adapt, those who worked with vulnerable populations were disproportionately affected. Organizations with the goal of providing and connecting houseless individuals with medical care, mental health treatment, and more needed to find creative means to continue to provide those much needed services from a safe distance. This report aims were descriptive in nature, and aimed to learn more about how these organizations continued to provide services, and what lessons they are taking forward. Data was collected through semi-structured interviews with organization leaders and service providers as well as data provided by various state organizations to provide a scale for the issues raised in these interviews. The interviewees, or stakeholders, had all interacted with houseless clients via telehealth, either directly providing care, or managing staff who did. All organizations operated mostly in the greater Seattle Area, with some edge cases or clients being from elsewhere in the region. All organizations had performed a number of in person services before switching to primarily telehealth during the COVID-19 pandemic. Analysis suggests that telehealth was helpful for interacting with the houseless community however there were some important caveats. Firstly, telehealth was often aided by clients being able to use physical spaces provided by these organizations as well as access devices capable of engaging with services providers remotely while clients were at the facility. Second, some clients did not respond well to telehealth services or were unable to engage with them in a meaningful way. Thirdly, there are some real concerns with confidentiality that should be better addressed for this type of service. There are unique challenges to providing telehealth services to those experiencing houselessness that must be addressed, however, it is very much worthwhile to extend policies that allow for telehealth to continue in its current state while these issues are addressed. More research must be done to further understand the impact of these policies and on telehealth’s efficacy in this context
Assessing Uremic Toxin Binding Dynamics in Human Serum Albumin, Sudlow Site I
No full textThesis (Master's)--University of Washington, 2022In an effort to better understand the dynamics corresponding to the stabilization of uremic toxins such as indoxyl and p-cresyl sulfate, simulative modeling must be employed to observe molecular perambulations. In this investigation, the GROMACS molecular dynamics engine was used to perform a 250 ns simulation of human serum albumin complexed with these uremic toxins in its Sudlow Site I binding pocket. The trajectories were then taken and subjected to analyses designed to assess interaction energy and intermolecular distance between the amino acid residues that comprised the binding site. Intermolecular distance information was subjected to dimensionality reduction via primary component analysis, and then a mean-shift algorithm was employed to determine the most likely binding conformation, which was then cross referenced with the initial configuration from the experimentally-derived PDB file
Integrating epidemiologic and molecular methods to improve vaginal health
No full textThesis (Ph.D.)--University of Washington, 2022Challenges measuring the microbiome and limited incorporation of epidemiologic methods in microbiome science have hindered the progress of vaginal microbiome research, and major questions in the field remain unanswered despite decades of work. This dissertation seeks to address these gaps by describing measurement error and resulting bias in vaginal microbiota research; evaluating the state of epidemiologic evidence regarding the role of Lactobacillus iners, a controversial vaginal bacterial species, in various sexual health outcomes; and investigating bacterial drivers of BV symptomatology. The most popular method for characterizing the vaginal microbiota is 16S rRNA gene amplicon sequencing, which provides information on the taxonomic composition of a bacterial community. Shotgun metagenome sequencing characterizes the bacterial genes in a sample and provides information on the community’s functional potential, which is more relevant to understanding mechanisms and causal relationships between the microbiome and health outcomes than taxonomic composition. Metagenome inference methods attempt to bridge the gap between 16S rRNA gene amplicon sequencing and shotgun metagenomics by predicting a bacterial community’s metagenome based on its taxonomic composition and annotated whole genome sequences of its members. Several studies have compared metagenome inference performance in different human body sites; however, none specifically reported on the vaginal microbiome. In Chapter 2, we compared the performance of two popular metagenome inference methods, PICRUSt2 and Tax4Fun2, using paired 16S rRNA gene amplicon sequencing and shotgun metagenome sequencing data from vaginal samples from 72 pregnant individuals enrolled in the Pregnancy, Infection, and Nutrition cohort. PICRUSt2 and Tax4Fun2 performed modestly overall (median Spearman correlations between observed and predicted KEGG ortholog [KO] relative abundances 0.20 and 0.22, respectively). Both methods performed best among Lactobacillus crispatus-dominated vaginal microbiotas (median Spearman correlations 0.24 and 0.25, respectively) and worst among L. iners-dominated microbiotas (median Spearman correlations 0.06 and 0.11, respectively). Differential metagenome inference performance across vaginal microbiota community types can be considered differential measurement error, which often results in differential misclassification. As such, metagenome inference will introduce hard-to-predict bias in vaginal microbiome research. These findings demonstrate the importance of considering common epidemiologic biases in designing and evaluating novel microbiome analytic methods. The vaginal microbiota of reproductive-age individuals is often dominated by a single Lactobacillus species, most commonly L. iners or L. crispatus. While L. crispatus-dominated vaginal microbiotas are widely thought to protect against adverse sexual health outcomes, the role of L. iners-dominated microbiotas is less clear and has been debated. To better understand the role of L. iners, in Chapter 3 we conducted systematic reviews of the associations between L. iners compared to L. crispatus and the following outcomes: bacterial vaginosis (BV); Chlamydia trachomatis, Neisseria gonorrhoeae, Trichomonas vaginalis, human papillomavirus (HPV), HIV, and genital herpes simplex virus type-2 (HSV-2) infections; and cervical dysplasia. We searched four databases for epidemiologic studies of reproductive-age, nonpregnant, cisgender women that used marker gene sequencing to characterize vaginal microbiota composition and presented an effect estimate for the association between L. iners compared to L. crispatus and outcomes of interest. For outcomes with >3 eligible studies presenting the same form of effect estimate, we conducted random-effects meta-analysis. Three BV studies were included in meta-analysis, which indicated L. iners-dominated microbiotas were associated with 2.1-fold higher prevalence of BV compared to L. crispatus-dominated microbiotas (95% CI 0.9-4.9). Six C. trachomatis studies were included in meta-analysis, which showed L. iners-dominated microbiotas were associated with 3.4-fold higher odds of chlamydia compared to L. crispatus-dominated microbiotas (95% CI 2.1-5.4). L. iners-dominated vaginal microbiotas may be suboptimal compared to L. crispatus-dominated microbiotas for BV and chlamydia, which is consistent with prior in vitro, in silico, and genomic work. Evidence was sparse for other outcomes. Nearly all included studies assessed microbiota composition and outcome status cross-sectionally and were at serious risk of bias, critically limiting the quality of evidence reviewed. In contrast to Lactobacillus-dominated microbiotas, BV is a polymicrobial condition characterized by a diverse community of anaerobic and facultative bacteria. It is the most common cause of vaginal discharge worldwide, affecting approximately one quarter of reproductive-age individuals. In high-resource settings, clinical BV diagnosis is typically based on the presence of at least three of four signs and symptoms termed Amsel criteria: amine odor on addition of potassium hydroxide to vaginal fluid; clue cells on vaginal wet prep; thin, gray, homogeneous vaginal discharge; and elevated vaginal pH. Because bacterial colonization and associations with these signs and symptoms may vary between populations, in Chapter 4 we assessed relationships between vaginal bacteria and Amsel criteria among two distinct populations. We included Kenyan participants from the placebo arm of the Preventing Vaginal Infections (PVI) trial and participants from a Seattle-based cross-sectional BV study in this analysis. Amsel criteria were recorded at study visits, and the vaginal microbiota was characterized using 16S rRNA gene amplicon sequencing. We fit logistic regression models to evaluate associations between bacterial relative abundance and each Amsel criterion. BV-associated bacterium 1 (BVAB1) was positively associated with all Amsel criteria in both populations. Eggerthella type 1, Fannyhessea (Atopobium) vaginae, Gardnerella spp., Sneathia amnii, and Sneathia sanguinegens were positively associated with all Amsel criteria in the Seattle study, and all but discharge in the PVI trial. This core group of vaginal bacteria may play a key role in the manifestation of BV signs and symptoms across diverse populations, and these findings are consistent with growing evidence regarding the role of biogenic amines and extracellular enzymes in BV etiology and symptom manifestation. Finally, in Chapter 5 I discuss the implications of this dissertation work for microbiome science, medicine, and public health. I recommend avenues by which investigators can better incorporate epidemiologic methods and principles into their work, I provide a novel characterization of L. iners, and I weigh various strategies to improve BV diagnostics and treatment in high- and low-resource settings
Grounding Language by Seeing, Hearing, and Interacting
No full textThesis (Ph.D.)--University of Washington, 2022As humans, our understanding of language is grounded in a rich mental model about "how the world works." As children, we learn this mental model gradually. We take in raw perceptual input about the world through all of our senses, and learn to make sense of people and objects around us -- enough to take action in the world. Our understanding of language and vision is grounded in the world. Deep learning has made significant progress in recent years, for a variety of AI problems. Yet today's state-of-the-art models in natural language processing (NLP) and computer vision (CV) are ungrounded. They learn exclusively from text-only, or text-annotated data on the internet, making it harder for them to connect language and vision to the world beyond those modalities. In this thesis, I will present a few lines of work to bridge this gap between machines and humans. I will first discuss how we might measure grounded understanding. I will introduce a suite of approaches for constructing benchmarks, using machines in the loop to filter out spurious biases. These include benchmarking grounding through exams about written text alone, through visual scenes, as well as through interacting with humans. Then, I will introduce PIGLeT: a model that learns physical commonsense understanding by interacting with the world through simulation, using this knowledge to ground language. PIGLeT learns linguistic form and meaning – together – and outperforms text-to-text only models that are orders of magnitude larger. Finally, I will introduce MERLOT, which learns about situations in the world by watching millions of YouTube videos with transcribed speech. MERLOT is trained to jointly represent video, audio, and language, together and over time – learning multimodal and neural script knowledge representations
Essays on Health Behaviors in Developing Countries
No full textThesis (Ph.D.)--University of Washington, 2022This thesis contains three essays on how access to health care and health information affect health behaviors and health beliefs in developing countries. In the first chapter, I study why rural households in Bangladesh keep seeking health advice from untrained informal providers when mobile health services (MHS) are freely available from qualified public healthcare providers and how they can be nudged to adopt the MHS. Using a randomized controlled trial among 2900 rural households from 580 neighborhoods in Bangladesh, this paper studies whether and how the adoption of mobile health services can be improved. I find that information about the service improves households’ awareness by more than 30 percentage points but does not affect the adoption in the following two months. Among the participants who were also encouraged to call at one of the MHS phone numbers to see how the service works, 63% attempted during the intervention and 22% of them used the service in the following two months. The adoption of MHS decreases households’ health expenditure, mostly driven by the reduction in medicine consumption. This happened because households, who adopted MHS, also made fewer visits to informal providers who usually overprescribe medicine. The second chapter studies how information can affect people’s health risk beliefs and health behaviors. The local prevalence of infections and the severity of its consequences are among the key determinants of the adoption of preventive behaviors for an infectious disease. By conducting a survey among more than 2000 adults in Bangladesh, I find that most people either do not know or underestimate the local prevalence of COVID-19 infections and overestimate its fatality rate. In a randomized experiment, I give the treatment group information about the coronavirus case number in their districts and the case fatality rate in Bangladesh and worldwide. Immediately after receiving the information, the treatment group perceives higher infection risk. Nine to fifteen days after the intervention, those who received information underestimate the local prevalence less and, consequently, still perceive higher infection risk than the control group. The treatment group also updates their belief about the fatality rate downward. Potentially due to this countervailing update of risk beliefs, the information does not have any effect on the self-reported preventive behaviors.
In the final chapter, I develop a simple model which illustrates why opposition leaders can be very effective for the COVID-19 vaccination awareness campaign. To test this empirically, I also conduct an experiment in Bangladesh where 3,781 individuals in Bangladesh randomly received information about COVID-19 and its vaccines, the vaccination status of ruling and opposition leaders. While all treatments improved confidence on COVID-19 vaccines, the information about the opposition leaders’ vaccination status decreased the perceived side effects. The participants from the opposition treatment are 11 percentage points more likely to intend to get vaccinated than the participants of the information treatment