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Enhancing Intercultural Competence through a Six-Week Study Abroad Program
No full textConference presentation on the impact of a study abroad program on intercultural competenc
The Association between Occupation and Anti-Black Attitudes among Working White Women
No full textFew sociological studies investigate the association between occupation and whites’ racial attitudes. However, they do not examine variation in racial attitude levels among working white women exclusive of working white men. Overall, sociological studies rarely compare whites’ attitudes across multiple occupations, and few studies do so whilst focusing on gender. Using nine years of General Social Survey data, this study increments white racial attitudes literature by: (1) examining the association between occupation and anti-black attitudes among working white women; (2) invoking occupation as context as the theoretical framework; (3) investigating percentage of white women in an occupation, occupation contact level, occupational precarity as factors contributing to occupational differences in anti-black attitudes among working white women. Findings include significant anti-black attitude level differences across occupations, but raise new questions about specific features of occupational context
Bayesian Tensor Modeling of High-Dimensional Neuroimaging Data
No full textNeuroimaging has played a pivotal role in advancing the understanding of neurological and psychiatric conditions by providing comprehensive insights into structural and functional brain changes. Techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET), and diffusion tensor imaging (DTI) generate high-dimensional data that can be used to identify biomarkers indicative of disease progression. While these data offer valuable opportunities for early diagnosis and personalized treatment, their sheer dimensionality and spatial complexity present significant challenges, including the risk of overfitting, increased computational demands, and the curse of dimensionality. In this thesis, we contribute to research in high-dimensional neuroimaging data by developing several novel statistical approaches to (1) classify disease phenotype, (2) predict median or the tails of the distribution of cognitive scores, and (3) investigate brain voxels that are associated with different parts of the distribution of cognitive performance scores, bringing insights into the longitudinal trajectory of the disease progression.
First, we introduce a data augmentation-based Bayesian classification model incorporating tensor-valued covariates that achieves dimension reduction and the preservation of spatial information. We propose two data augmentations: a support vector machine (SVM) type of classifier, and a logistic regression classifier. Implementation follows an efficient Markov chain Monte Carlo (MCMC). After assessing the classification accuracy and parameter estimation through simulation studies, we further illustrate our method in a neuroimaging application using cortical thickness MRI data from the Alzheimer’s Disease Neuroimaging Initiative.
Second, we introduce a novel Bayesian tensor quantile regression for high-dimensional longitudinal imaging data with the aim of investigating how brain-behavior associations change over time. The model estimates both effects that are consistent across visits and patterns unique to each visit that contribute to the overall longitudinal trajectory. A tensor decomposition is employed on the tensor coefficients to reduce dimensionality and preserve spatial configuration. We incorporate multiway shrinkage priors to model the visit-invariant tensor coefficients and variable selection priors on the tensor margins of the visit-specific effects. A Markov chain Monte Carlo sampling algorithm is developed. We examine the model performance in parameter estimation, feature selection, and prediction through simulation studies. In the end, we bring new insights into the analysis of Alzheimer's disease data by providing a fuller picture of the relationship between the imaging voxels and different parts of the distributions of the cognitive scores.
Finally, we conclude the thesis by summarizing the research performed and discussing future work
Applications of Organic Electrochemical Transistors in Subsea Environments
No full textOrganic electrochemical transistors (OECTs) are an emerging sensor technology that offer significant advantages for subsea contaminant detection due to their stability in aqueous environments, ability to sense in complex solutions, and capability to amplify signals. This thesis investigates three applications of OECT sensing in subsea environments. The first focuses on the development and optimization of an OECT for detecting crude oil pipeline leaks, demonstrating the stability of OECTs in synthetic seawater and their ability to rapidly detect crude oil even in the presence of other organics. The second explores the development and optimization of an OECT for detecting carbon dioxide leaks, emphasizing the potential of OECTs as gas sensors and examining the effects of surface charges on the OECT channel. Finally, the third presents preliminary results for the development of an OECT to detect hydrocarbon gas leaks. Together, this work seeks to broaden the application of OECTs in complex subsea environments, offering novel and functional solutions to sensing challenges
Development of Extrachromosomal Genetic Technologies for Persistent and Tunable Expression of Therapeutic Payloads
No full textEffective cell and gene therapies (CGT) require sustained expression of therapeutic payloads, typically achieved through genomic integration of transgenic DNA via genome editing (e.g., CRISPR\Cas9) or integrating viral vectors (e.g., retrovirus, lentivirus). Integration-based approaches risk insertional mutagenesis and oncogene activation, yielding unpredictable, irreversible consequences. Inspired by latent DNA viruses with genomes persisting as episomes, we developed an extrachromosomal genetic technology (EGT). Our EGT, featuring an engineered origin of replication and tether protein, maintained non-integrated transgene expression in proliferative cells over 12 weeks. Despite this ability, our EGT’s large size limits therapeutic applications. We focused on expanding carrying capacity by investigating accommodating delivery methods—dual integrase-deficient lentiviruses (IDLVs), high-capacity adenovirus (HC AdV), lipid nanoparticles (LNPs)—and by developing miniaturized systems with alternative tether protein DNA-binding domains, such as Tet Repressor (TetR) or Zinc Finger (ZF) proteins. Optimized EGTs will enable the production of persistent, tunable CGTs without disrupting the endogenous genome
Safety Climate in the Houston Fire Department
Get PDFIn summer 2023, the Houston Fire Department (HFD) partnered with Rice University’s Kinder Institute for Urban Research in order to better understand the safety climate of its department. In the workplace, safety climate refers to employees’ shared perceptions of their organization’s safety policies, procedures, and practices, and the types of behaviors that are supported and rewarded by leadership. Safety climate is predictive of actual safety behaviors in the workplace as well as safety-related outcomes, such as injuries, fatalities, and near-misses (Jiang et al., 2018; Beus et al., 2010). By better understanding the climate at its stations, HFD sought to take steps to improve its safety culture in order to create a safer workplace for its first responders, whose jobs often place them in unsafe situations
Electronic Transport on Aligned Carbon Nanotube Assemblies
No full textIndividual carbon nanotubes (CNTs) offer high electrical conductivity, tensile strength, and flexibility, but these properties are diminished in randomly oriented structures. Aligned CNT fibers retain good conductivity (up to 10.9 MS/m) but are still inferior to individual CNTs. We have investigated electronic transport phenomena in these fibers through temperature- and magnetic field-dependent measurements, finding that the conductivity decreases with decreasing temperature at low temperatures due to quantum conductance corrections. Using a combination of 3D and 1D weak localization (WL) models, we explained the observed magnetoresistance and discuss their dimensionality in detail.
Low-temperature studies on individual CNT bundles showed significant quantum corrections, with WL and universal conductance fluctuations (UCF) providing consistent phase coherence length estimates (tens of nanometers). However, UCF amplitude and magnetic field asymmetry suggest a coherence length scale similar to the few-micron distance between the voltage probes.
This study enhances the understanding of electronic transport mechanisms in aligned CNT fibers, essential for improving conductivity for various applications
Are Depositors Passive or Active? Evidence from SEC Filing Information Acquisition by Bank Depositors
No full textThis paper investigates how depositors acquire information, focusing on whether they actively use SEC filings to monitor banks. While the literature on depositor discipline often assumes such behavior, direct evidence remains limited. This study addresses this gap by leveraging EDGAR log files and the ARIN database to track depositor searches, distinguishing them from other market participants, and identifying uninsured depositors based on IP address location. The findings reveal a significant positive correlation between uninsured deposits and SEC search activity, particularly for timely and frequent filings such as Form 8-K and Form 10-Q. This behavior is pronounced in banks with lower performance (measured by ROE), higher risk-taking (indicated by NPLand Z-score), and a greater proportion of long-term time deposits. This study examines behavior around bank failure events to further corroborate active depositor monitoring. Uninsured depositors intensify their information acquisition efforts before a failure, with a notable decrease afterward, supporting the notion of proactive monitoring. In contrast, insured depositors exhibit increased search activity post-failure. Additionally, depositor monitoring appears to moderate regulatory leniency. This study contributes to the literature on depositor discipline, information acquisition, and bank financial distress dynamics, particularly in the context of uninsured depositor monitoring
Accurate Inference of Phylogenetic Trees From Single-Cell DNA Copy Number Data
Get PDFCopy number aberrations (CNAs) are ubiquitous in many types of cancer. Inferring CNAs from cancer genomic data could help shed light on the initiation, progression, and potential cancer treatment. Single-cell DNA sequencing (scDNAseq) allows for the inference of CNAs at the single-cell level, providing unparalleled insights into cellular variations and genetic diversity within tumors.
In this thesis, we address several computational challenges associated with analyzing datasets of single-cell DNA sequencing to better understand cancer evolution. Firstly, we introduce NestedBD, a Bayesian birth-death model designed for inferring evolutionary trees from single-cell data. NestedBD accurately infers topologies and branch lengths, providing new insights into the evolutionary histories of cancer cells. The next part of this thesis introduces a scalable divide-and-conquer approach that partitions large single-cell sequencing datasets into subsets, constructs rooted trees for each subset, computes representative genotypes from inferred subtrees, and assembles these trees into a comprehensive evolutionary map. This method is flexible, allowing for recursive application and various tree construction and genotype inference techniques, with tree-based genotype inference proving more effective than consensus genotyping and robust against tree reconstruction errors. Finally, we extend NestedBD by developing NestedBD-Long, a novel methodology incorporating temporal data from longitudinally sampled single-cell DNA sequencing into phylogenetic inference. By explicitly mapping real-world sampling times onto inferred evolutionary trees, NestedBD-Long enhances the accuracy and interpretability of cancer evolution reconstructions. Evaluations demonstrate that NestedBD-Long consistently outperforms conventional methods, with accuracy notably improving as more temporal samples become available. This methodological advancement provides a reliable analytical framework for exploring clinical questions, including tumor progression dynamics, treatment resistance mechanisms, and patterns of metastatic dissemination
Swinging, Fast and Slow: Interpreting variation in baseball swing tracking metrics [Data and Models]
No full textThis zipped file comprises data and model files for Powers & Yurko (2025) "Swinging, Fast and Slow: Interpreting variation in baseball swing tracking metrics". Code and other details of the work are available at https://github.com/saberpowers/swinging-fast-and-slow