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Mitigation of Blast-Induced Hearing Damage Associated with Mild-TBI Chinchillas using Liraglutide
Get PDFEven with the use of hearing protection devices (HPDs), hearing damage caused by blast exposure dominates service-related disabilities faced by active service members and Veterans. Epidemiology studies have revealed that this hearing damage is associated with traumatic brain injury (TBI). There is a need to further investigate the mechanisms of the formation and prevention of auditory hearing damage. Liraglutide, a GLP-1R agonist, has been found to be a potential treatment for TBI-induced memory deficits. Our previous studies have focused on the therapeutic effect of liraglutide in the prevention and recovery from repeated low-intensity blast exposure. This thesis focuses on the therapeutic function of liraglutide after exposure to higher-level blasts associated with TBI using the chinchilla animal model with HPDs.
In this study, chinchillas were separated into 3 groups: pre-blast treatment, post-blast treatment, and blast control. All groups were exposed to 3 blasts at the blast overpressure (BOP) level equivalent to mild-TBI (15-20 psi or 103-138 kPa) on Day 1 with their ears protected with HPDs (e.g., earplugs). Chinchillas were observed for either 14 or 28 days after blast. To determine the state of the auditory system, hearing function tests including auditory brainstem response (ABR) and distortion product otoacoustic emission (DPOAE) were conducted prior to blast exposure, after blast exposure, and on Days 4, 7, 14, and 28. Upon the completion of the experiment, the cochlea and brain tissues were collected for immunofluorescence studies.
The measurements collected from ABR and DPOAE recordings as well as immunofluorescence results indicated that liraglutide was able to significantly prevent acute blast-induced hearing damage and potentially aid in recovery post-blast exposure.
The work presented in this thesis improves our understanding of the effect of higher-level blast exposure on the auditory system and the therapeutic effect of liraglutide. Future work includes improving statistical analyses and investigation of the mechanisms by which liraglutide works in auditory injury prevention and restoration
Misogyny and murder: crime fiction by women from 1947-1959
Get PDFThis project demonstrates that American women authors from 1947-1959 repurposed the crime genre to critique and engage with misogyny and sexism of the day. Hardboiled crime fiction, which was at its peak popularity in midcentury America, is almost synonymous with a tough, hyper-masculine detective who solves crime through violence, kissing beautiful and marginally consenting women along the way. The women authors in my project subverted this masculinist genre during the late 1940s and 1950s, when the postwar reconversion of the economy reinstated sexist social values. My project recuperates novels by women that were popular with critics and readers at publication, but are largely overlooked now because scholarly examinations of crime fiction of this period are confined to novels written by men. I argue that three female crime authors each use a formal feature—perspective, free indirect discourse, and character—to subvert the genre and question and critique misogyny and sexism. These novels cumulatively point to the harm caused by larger patriarchal society that is specific to the time but also touch on broader, timeless harm caused by patriarchy. In Chapter One, I study how Dorothy B. Hughes critiques misogyny specific to the postwar period by limiting the narrative perspective to Dix Steele, who functions much like a classic hardboiled detective except he is a serial killer. The end of the novel demonstrates retroactively that women characters have used their insight into systemic sexism and misogyny to capture Dix, yet this was behind the scenes the entire time, much like the women who perform unpaid or underpaid and underappreciated work under a patriarchal system. In Chapter Two, I examine how Evelyn Piper’s domestic crime novels protest the pathologization of unmarried and overindulging mothers in the 1950s, specifically through the use of free indirect discourse. Piper uses narration to make the reader complicit in a misogynistic view of mothers before condemning that view. In Chapter Three, I study how character identification functions in Patricia Highsmith’s first novel, Strangers On A Train. Highsmith dispenses with the heavy focus on plot and empirical logic that is so foundational to the crime genre and makes readers identify with a morally ambiguous character. This raises questions about queerness and morality in the heteronormativity-obsessed 1950s.
Each chapter includes study of a film noir adaptation of these novels. Chapter One demonstrates how Dorothy B. Hughes’s serial killer, played by Humphrey Bogart in the film adaptation, becomes a sympathetic hero falsely accused of murder by the women around him. In Chapter Two, I demonstrate how Otto Preminger’s adaptation of Evelyn Piper’s work completely removes her critique of the pathologization of mothers by diminishing the mother character and focusing on the men around her. In Chapter Three, I examine Alfred Hitchcock’s adaptation of Strangers On A Train, which follows a more conventional 1950s understanding of queerness than the novel and encourages the audience to identify with its solidly heterosexual and morally upstanding character. All of these films nullify the questions and critiques the women authors raise about sexism and misogyny by changing key plot and character elements to fit the stories into the masculinist film noir genre.
My conclusion jumps to the contemporary era, where crime fiction often features women detectives who work for pay but still function in the same way as their male counterparts by solving crimes alone. However, Tana French’s bestselling series Dublin Murder Squad is not centered around a single detective. Rather, each of French’s novels has a new protagonist who was a minor character from a previous novel and each new perspective shows flaws with the previous novel’s perspective. I argue that this formal innovation challenges the genre’s tradition of a masculinist lone intelligence, showing that women crime authors continue to innovate through formal features
“Why Would They Bury Her When She Can Still Walk Around” : Examining an Indigenous Gothic
No full textIn “Why Would They Bury Her When She Can Still Walk Around” : Examining an Indigenous Gothic, Tatiana Rosillo adapts the genre conventions of the literary Gothic to examine Indigenous genre media and analyze what an Indigenous American Gothic
typically entails in three central texts: Jeff Barnaby’s Rhymes For Young Ghouls, Cherie Dimaline’s Empire of Wild, and Stephen Graham Jones’ Only Good Indians. Rosillo proposes that these three texts reveal a spectrum of an Indigenous Gothic with specific commonalities. By focusing her analysis on common aspects of the Gothic — atmosphere, protagonists, and cultural anxieties — Rosillo seeks to complicate the traditional Euro-American Gothic and Horror conventions. Rosillo asserts that acknowledging the relationships and culturally-specific ways of knowing which appear in these texts brings much needed diversity to the Gothic tradition
A Simulation Study Comparing the Use of Supervised Machine Learning Variable Selection Methods in the Psychological Sciences
Get PDFWhen specifying a predictive model for classification, variable selection (or subset selection) is one of the most important steps for researchers to consider. Reducing the necessary number of variables in a prediction model is vital for many reasons, including reducing the burden of data collection and increasing model efficiency and generalizability. The pool of variable selection methods from which to choose is large, and researchers often struggle to identify which method they should use given the specific features of their data set. Yet, there is a scarcity of literature available to guide researchers in their choice; the literature centers on comparing different implementations of a given method rather than comparing different methodologies under vary data features. Through the implementation of a large-scale Monte Carlo simulation and the application to three psychological datasets, we evaluated the prediction error rates, area under the receiver operating curve, number of variables selected, computation times, and true positive rates of five different variable selection methods using R under varying parameterizations (i.e., default vs. grid tuning): the genetic algorithm (ga), LASSO (glmnet), Elastic Net (glmnet), Support Vector Machines (svmfs), and random forest (Boruta). Performance measures did not converge upon a single best method; as such, researchers should guide their method selection based on what measure of performance they deem most important.
Results do, however, indicate that the genetic algorithm is the most widely applicable method, exhibiting minimum error rates in hold-out samples when compared to other variable selection methods. Thus, if little is known of the format of the data by the researcher, choosing to implement the genetic algorithm will provide strong results
Developing and Applying CAD-generated Image Markers to Assist Disease Diagnosis and Prognosis Prediction
Get PDFDeveloping computer-aided detection and/or diagnosis (CAD) schemes has been an active research topic in medical imaging informatics (MII) with promising results in assisting clinicians in making better diagnostic and/or clinical decisions in the last two decades. To build robust CAD schemes, we need to develop state-of-the-art image processing and machine learning (ML) algorithms to optimize each step in the CAD pipeline, including detection and segmentation of the region of interest, optimal feature generation, followed by integration to ML classifiers. In my dissertation, I conducted multiple studies investigating the feasibility of developing several novel CAD schemes in the field of medicine concerning different purposes.
The first study aims to investigate how to optimally develop a CAD scheme of contrast-enhanced digital mammography (CEDM) images to classify breast masses. CEDM includes both low energy (LE) and dual-energy subtracted (DES) images. A CAD scheme was applied to segment mass regions depicting LE and DES images separately. Optimal segmentation results generated from DES images were also mapped to LE images or vice versa. After computing image features, multilayer perceptron-based ML classifiers integrated with a correlation-based feature subset evaluator and leave-one-case-out cross-validation method were built to classify mass regions. The study demonstrated that DES images eliminated the overlapping effect of dense breast tissue, which helps improve mass segmentation accuracy. By mapping mass regions segmented from DES images to LE images, CAD yields significantly improved performance.
The second study aims to develop a new quantitative image marker computed from the pre-intervention computed tomography perfusion (CTP) images and evaluate its feasibility to predict clinical outcome among acute ischemic stroke (AIS) patients undergoing endovascular mechanical thrombectomy after diagnosis of large vessel occlusion. A CAD scheme is first developed to pre-process CTP images of different scanning series for each study case, perform image segmentation, quantify contrast-enhanced blood volumes in bilateral cerebral hemispheres, and compute image features related to asymmetrical cerebral blood flow patterns based on the cumulative cerebral blood flow curves of two hemispheres. Next, image markers based on a single optimal feature and ML models fused with multi-features are developed and tested to classify AIS cases into two classes of good and poor prognosis based on the Modified Rankin Scale. The study results show that ML model trained using multiple features yields significantly higher classification performance than the image marker using the best single feature (p<0.01). This study demonstrates the feasibility of developing a new CAD scheme to predict the prognosis of AIS patients in the hyperacute stage, which has the potential to assist clinicians in optimally treating and managing AIS patients.
The third study aims to develop and test a new CAD scheme to predict prognosis in aneurysmal subarachnoid hemorrhage (aSAH) patients using brain CT images. Each patient had two sets of CT images acquired at admission and prior to discharge. CAD scheme was applied to segment intracranial brain regions into four subregions, namely, cerebrospinal fluid (CSF), white matter (WM), gray matter (GM), and extraparenchymal blood (EPB), respectively. CAD then computed nine image features related to 5 volumes of the segmented sulci, EPB, CSF, WM, GM, and four volumetrical ratios to sulci. Subsequently, 16 ML models were built using multiple features computed either from CT images acquired at admission or prior to discharge to predict eight prognosis related parameters. The results show that ML models trained using CT images acquired at admission yielded higher accuracy to predict short-term clinical outcomes, while ML models trained using CT images acquired prior to discharge had higher accuracy in predicting long-term clinical outcomes. Thus, this study demonstrated the feasibility of predicting the prognosis of aSAH patients using new ML model-generated quantitative image markers.
The fourth study aims to develop and test a new interactive computer-aided detection (ICAD) tool to quantitatively assess hemorrhage volumes. After loading each case, the ICAD tool first segments intracranial brain volume, performs CT labeling of each voxel. Next, contour-guided image-thresholding techniques based on CT Hounsfield Unit are used to estimate and segment hemorrhage-associated voxels (ICH). Next, two experienced neurology residents examine and correct the markings of ICH categorized into either intraparenchymal hemorrhage (IPH) or intraventricular hemorrhage (IVH) to obtain the true markings. Additionally, volumes and maximum two-dimensional diameter of each sub-type of hemorrhage are also computed for understanding ICH prognosis. The performance to segment hemorrhage regions between semi-automated ICAD and the verified neurology residents’ true markings is evaluated using dice similarity coefficient (DSC). The data analysis results in the study demonstrate that the new ICAD tool enables to segment and quantify ICH and other hemorrhage volumes with higher DSC.
Finally, the fifth study aims to bridge the gap between traditional radiomics and deep learning systems by comparing and assessing these two technologies in classifying breast lesions. First, one CAD scheme is applied to segment lesions and compute radiomics features. In contrast, another scheme applies a pre-trained residual net architecture (ResNet50) as a transfer learning model to extract automated features. Next, the principal component algorithm processes both initially computed radiomics and automated features to create optimal feature vectors. Then, several support vector machine (SVM) classifiers are built using the optimized radiomics or automated features. This study indicates that (1) CAD built using only deep transfer learning yields higher classification performance than the traditional radiomic-based model, (2) SVM trained using the fused radiomics and automated features does not yield significantly higher AUC, and (3) radiomics and automated features contain highly correlated information in lesion classification.
In summary, in all these studies, I developed and investigated several key concepts of CAD pipeline, including (i) pre-processing algorithms, (ii) automatic detection and segmentation schemes, (iii) feature extraction and optimization methods, and (iv) ML and data analysis models. All developed CAD models are embedded with interactive and visually aided graphical user interfaces (GUIs) to provide user functionality. These techniques present innovative approaches for building quantitative image markers to build optimal ML models. The study results indicate the underlying CAD scheme's potential application to assist radiologists in clinical settings for their assessments in diagnosing disease and improving their overall performance
Three Essays in Corporate Finance
No full textThis dissertation comprises of three essays. The first essay (Chapter 1) examines the short- and long-term stock market reactions to the Made in China 2025 industrial policy both in China and the United States. The second essay (Chapter 2) investigates the impact of venture capitalist directors on CEO compensation incentives at publicly listed and non-venture backed companies. The last essay (Chapter 3) investigates the impact of political connections on corporate insiders’ trading behavior.
While governments have been practicing varying forms of industrial policy throughout recorded time, especially in eastern Asian countries during the post-WWII period, the evidence on how financial market investors assess government industrial policy announcements is relatively scant. In Chapter 1, we study the link between industrial policy and asset prices by using the Made in China 2025 industrial policy, announced in May 2015, as an external shock. We track Chinese firms and U.S. firms in ten high-tech industries targeted by the policy. In the short run, stock prices, measured by cumulative abnormal returns (CARs), increase significantly for both Chinese and U.S. firms, by 9.9% and 1.4%, respectively. However, in the long run, Chinese firms’ CARs drop heavily, while U.S. firms’ CARs continually increase. We further find that after the policy announcement, Chinese firms’ profitability declines dramatically by an average of 52.9% and firms’ leverage increase significantly, but they do not receive additional government subsidies. We conclude that the policy only boosts market reaction in the short run but does not promote targeted industries longer term.
The value-added role of venture capital on startups has been widely examined. Our understanding is, however, minimal on whether the presence of board members with venture capital experience in publicly listed companies impacts executive compensation contracts. In Chapter 2, we examine the effect of board members with venture capital experience (i.e., VC directors) on executive incentives at non-venture-backed public firms. VC directors serving on the compensation committee are associated with greater CEO risk-taking incentives (i.e., vega) and pay-for-performance sensitivity (i.e., delta). These effects are more substantial if VC directors are from highly reputable VC firms. Using direct flight to VC hub cities indicators and VC dry powder as instruments, we show that these results are causal. In addition, VC directors are more focused on growth performance goals in CEO compensation contracts. We also document that prior finding of greater research intensity and innovation when VC directors serve on boards of public firms is partly explained by increased risk-taking incentives of the CEO instilled by such directors. Lastly, we find that having VC directors on nominating and/or governance committees is associated with a higher likelihood of forced CEO turnover.
The impact of political connections on firm value has been widely examined. However, there is not much evidence on how political connections affect corporate insider behaviors, especially insider stock transactions. In Chapter 3, I study the impact of political connections on corporate insiders’ trading behavior using corporate insiders’ employment data. I find that purchases (sales) by politically connected corporate insiders are associated with lower (higher) abnormal returns compared with non-politically connected insiders, indicating that politically connected insiders in general are cautious about potential legal risk. This effect is more significant among purchases. I also find that politically connected insiders are more likely to have longer trading horizons and are more likely to make routine trades. The Stop Trading on Congressional Knowledge (STOCK) Act passed in April 2012 effectively decreases (increases) the abnormal returns associated with insider purchases (sales) made by Congress members and staff in short horizons
Observations and Simulations of a Multistatic Weather Radar Network
Get PDFMultistatic radar architectures have the potential to provide a cost-effective source of 3D wind information from both operational and research radars, owing to a system design of one transmitter and several receivers. A prototype multistatic network consisting of two passive receivers and the KTLX WSR-88D has been constructed in the Oklahoma City metropolitan area. To achieve sufficiently precise Doppler frequency estimates while reducing cost, transmitter/receiver synchronization is done through measurements of the WSR-88D’s sidelobe radiation, rather than an expensive GPS-based system. This yields an exceptionally simple system capable of producing bistatic moment data with virtually no cooperation from the transmitting radar system.
However, the main factor inhibiting the usage of such systems in 3D dual-Doppler wind retrievals is sidelobe contamination arising from the use of low-gain antennae with broad receive beams. Therefore, mitigation of sidelobe contamination should be paramount for those seeking to use this type of radar system. To this end, simulations of multistatic radar systems with varying receiver network layouts and transmitting techniques are performed to evaluate several strategies for reducing the effects of sidelobe contamination. One such strategy is to simply increase the number of receivers, which is shown to improve retrieval quality, albeit with diminishing returns. Another strategy is sidelobe whitening, which uses varying sidelobe phases to greatly reduce the coherent signal from the sidelobes. This technique alone is shown to markedly improve measured Doppler velocities and subsequent retrievals, especially in simulations of convective systems. Since sidelobe whitening can only be done with a phased array weather radar, the potential associated with a phased array-bistatic radar system is tremendous, particularly when coupled with the rapid-scan capabilities intrinsic to phased array systems.
Since the initial deployment of the prototype multistatic system, several datasets of severe convection have been collected, including several instances of quasi-linear convective systems (QLCSs) and supercells. Multi-Doppler retrievals done with the multistatic data are able to resolve important structures in the horizontal and vertical wind fields, including mesocyclones and horizontal rotors. These retrievals are shown to be comparable in accuracy to simultaneous multi-Doppler retrievals done with only monostatic radar data, though the deleterious effects of sidelobe contamination are apparent in the multistatic retrievals in some cases
Socioeconomic status and physical activity levels in obese and non-obese African Americans
Get PDFA lack of physical activity has become a major problem in the United States with no signs of relief coming anytime soon. More specifically, the African American community continues to remain among the most inactive. While many factors may contribute to inactivity, major contributors include genetics, diet, socioeconomic status, and obesity. The purpose of this study is to evaluate the differences in socioeconomic status and physical activity between obese and non-obese individuals in the African American community. The research hypothesis states that both socioeconomic status and physical activity will be lower among obese African Americans when compared to non-obese African Americans. Forty African American participants completed an online survey to determine their current height, weight, physical activity levels, household income, education level, and additional demographic information. The results indicate that obese African Americans do have significantly lower levels of physical activity than non-obese African Americans. No difference in income or education was observed between obese and non-obese African Americans. Thus, the hypothesis was partially supported. These results indicate that obese African Americans should be targeted for physical activity interventions. Future research should focus on the design and implementation of effective physical activity interventions for obese African Americans