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Establishing the Validity of a Measure of Implicit Bias Toward People With Alcohol Use Disorders
No full textAlcohol use disorder has been identified as one of the major burdens of disease but also remains one of the disorders with the lowest treatment prevalence. For individuals who seek treatment for their alcohol use disorder, they may experience barriers that impact their treatment for alcohol use disorder. These barriers may extend to a person’s beliefs, attitudes, and stigma. While attitudes and beliefs refer to their perceived need for treatment and beliefs of self-reliance, stigma is multistep process that makes way for stereotypes, prejudice, and acts of discrimination to take place. With labels such as “alcoholic” implying negative connotation and being frequently used, the fear of being stigmatized and negatively labeled often results in delayed or entirely avoided care. Attitudes towards stigmatized and other groups have primarily been examined using explicit measures of bias. While thought to be the most convenient way to learn about a person’s attitude, these measures may be affected by introspective limits and response factors. As such, explicit measure may succumb to social desirability. Research has turned to use of implicit measures such as the Implicit Association Test (IAT) to assess an individual’s unconscious and uncontrollable associations. The IAT is a validated task asking participants to quickly associate stimulus items with one of the two contrasting categories. The IAT measures response latency such that stronger associations are easier to pair resulting in faster response times and fewer errors made. While IATs have been used to assess biases toward vulnerable groups, the present study is among the first to psychometrically assess a measure of implicit bias toward persons with an alcohol use disorder through the lens of contact theory. Participants (n=175, 54.3% male) completed a developed IAT categorizing target categories (i.e., alcoholic versus non-alcoholic) and value categories (approach versus avoid), and also completed explicit measures of stigma. The IAT D-score was calculated and used to assess construct validity. Pearson correlations were also used to assess convergent and predictive validity. Results indicated small but significantly negative IAT D-scores, indicative of negative implicit bias toward people with alcohol use disorder. Exploratory analyses indicated that drinkers and children of alcoholics had an implicit bias toward people with alcohol use disorder, not in line with contact theory which would suggest that contact with stigmatized and other groups decreases prejudice ad increases positive attitudes. The IAT was not associated with explicit measures. However, initial results of the present study provide evidence of negative implicit attitudes toward alcohol use disorder. Future research should further assess negative implicit attitudes toward persons with alcohol use disorder, test the stability of the IAT using test-retest, and use multinomial processing tree analyses to further examine conscious and unconscious biases toward people with alcohol use disorder
Modeling the Spatiotemporal Variations of the Magnetic Field in Active Regions on the Sun Using Deep Neural Networks
No full textSolar active regions are areas on the Sun’s surface that have especially strong magnetic fields. Active regions are usually linked to a number of phenomena that can have serious detrimental consequences on technology and, in turn, human life. Examples of these phenomena include solar flares and coronal mass ejections, or CMEs. The precise prediction of solar flares and coronal mass ejections is still an open problem since the fundamental processes underpinning the formation and development of active regions are still not well understood. One key area of research at the intersection of solar physics and artificial intelligence is deriving insights from the available datasets of solar activity that can help us understand solar active regions better. Some machine learning models have been employed to forecast solar flares from a 6-hour to 48-hour time span, thanks to advancements in artificial intelligence. Support Vector Machine (SVM), K-Nearest-Neighbor (KNN), Extremely Randomized Trees (ERT), and deep neural network are some of the machine learning models that have been used in forecasting solar flares, but the results are not good. This is due to the models being trained with a specific set of active region parameters and an imbalanced dataset with few positive flare cases. As a result, there is a need to understand space weather and the basis by which these events occur. In this study, we applied a deep learning architecture originally designed for video prediction to predict the changes happening on the Sun in continuous time by using time series Helioseismic and Magnetic Imager data captured by the Solar Dynamics Observatory (SDO) and compared it against a no-change baseline and a regression baseline. In addition, we expanded our study to examine the changes in active regions by incorporating the 3D viewing geometry and the sun’s rotation, which helped the models focus on the changes in the active regions. We proposed using log-scale normalization to normalize the data and using the Cascading Convolutional Neural Network to predict the changes in active regions. To improve the performance of the model, we included the gradient information and the Structural Similarity Index in the training of the model by adding them as part of the loss function. In this dissertation, we demonstrated that deep neural networks can be trained to predict changes in active regions. It is our hope that further development of this work will lead to a better understanding of various physical phenomena related to space weather
Essays In Finance
No full textIn my first chapter, I investigate corporate announcements related to the Russia-Ukraine conflict of S&P 500 firms. I observe that firms withdrawing from Russia or suspending operations possess higher cash levels. Additionally, firms with more cash seem to announce withdrawals or suspensions more promptly. These findings suggest that cash levels are pivotal in how firms respond to geopolitical events. While cash does not seem influential when firms announce donations due to the conflict, it does affect the speed of such announcements. Social media also appears to play a significant role. Examining investor reactions to donation or withdrawal/suspension announcements, I report negative returns surrounding these announcements. My paper underscores the critical role of cash reserves (i.e., financial flexibility) in shaping firm reactions to geopolitical events.
In my second chapter, I investigate on the risk averseness of founder CEOs in the context of U.S. utility industry. A growing number of public U.S. firms are headed by the CEOs who founded the firms. Founder-CEO firms often differ from non-founder-CEO firms in terms of firm valuation, investment behavior, and performance. By using a sample of utility companies from year 1990 to 2022, I investigate on the risk-taking behavior of founder-CEO firms and non-founder-CEO firms. I find that utility companies run by founder CEOs use significantly lower debt compared to the utility companies run by non-founder CEOs. By addressing potential causality between leverage and performance, I still find that utility companies with founder CEOs use less debt. When I use widely used alternative measures of risk averseness i.e. corporate cash-holding and capital expenditures (CAPEX), I find that both the cash-holding and capital expenditures (CAPEX) are higher for founder led utility companies relative to non-founder led utility companies. Together, my findings suggest that founder CEOs of utility companies are risk averse
Mechanical Adaptations To Anterior Load And Fall Risks In Nulliparous Women With Simulated Gestational Weight Gain
Get PDFApproximately twenty-five percent of pregnant women experience falls during pregnancy, posing significant risks to maternal and fetal health outcomes. However, research on falls in pregnant women remains limited. PURPOSE: This study aimed to address this gap by comparing static postural stability between pregnant and nulliparous women with simulated gestational weight gain (GWG), thus validating the use of simulated pregnancy; and to observe the mechanical influences of simulated GWG on postural stability in static and dynamic conditions. It was hypothesized that increasing simulated GWG would decrease postural stability in single-limb stance, bilateral standing, margin of stability during gait initiation, and dynamic stability during a slip perturbation. METHODS: Eleven nulliparous women completed four separate data collections while adorning an anteriorly loaded weight-vest with 0, 2.26, 9.07, and 15.88 kg. Participants performed repeated balance, gait initiation, and treadmill slip perturbations under each weighted condition. RESULTS: Pregnant women and simulated pregnant women exhibited similar postural stability characteristics. Static balance was found to only be affected in anteroposterior sway magnitude between baseline and simulated second trimester in bilateral standing. Margin of stability was not significantly different across simulated trimesters during gait initiation. Dynamic postural stability displayed significant decreases at each heel strike between baseline and second trimester, and additional significance at heel strike one and heel strike three between baseline and third trimester. CONCLUSION: Overall, these findings suggest that the mechanical influences of simulated GWG influence the compensatory performance of postural stability during dynamic movements. This study contributes to the understanding of the mechanical aspects of GWG and may translate to the pregnancy-related postural stability changes and fall-risks
Abortion Funds As Care Work: Navigating The Emotional Tolls Of The Texas Executive Order Abortion Ban And Covid-19 Pandemic
Get PDFThis qualitative study explores the experiences of abortion fund staff and volunteers amidst the unprecedented challenges posed during the early months of COVID-19 and the Texas Executive Order. Through analysis of semi-structured interviews, we aimed to identify the shared experiences of staff and volunteers during this time. Four categories of experiences were identified: (1) Reproductive Justice, (2) Emotional Support, (3) Creating a Community of Care, and (4) Emotional Burnout. Our findings revealed that to navigate the uncertainties of the pandemic and Executive Order, staff and volunteers adapted a community of care model deeply rooted within the framework of reproductive justice. Central to their approach to supporting callers, was a commitment to alleviating the emotional and logistical burdens that were exacerbated during this period for individuals seeking abortion care. However, the additional work necessary to provide this support in addition to navigating their own personal challenges because of the pandemic also led to emotional burnout. We argue that the experiences of abortion fund staff and volunteers exemplify their participation in emotional labor and engagement in feminist care ethics
Staggered Boards And Human Capital Disclosure
Get PDFThis article examines the effects of staggered boards on human capital management (HCM) disclosure quality. I find that firms with staggered boards exhibit significantly better HCM disclosure scores than non-staggered boards. Additionally, firms that transition from staggered boards to non-staggered boards are shown to experience significant decreases in their HCM disclosure scores. These results are robust to the exclusion of firms that switch to or from staggered boards to non-staggered boards, propensity score matching, and alternative disclosure quality measures. While various cross-sections and the usefulness of HCM disclosure information for analyst forecasts are explored, the results were not significant
Academic And Ethnic Identity\u27s Moderating Effects On Intergenerational Conflict, Academic Motivation And Alcohol Outcomes Relationship Within First-Year Hispanic College Students
Get PDFWhile alcohol use is a public health concern for all college students, first-year college students are at higher risk for drinking and experiencing alcohol consequences. The increased risk may be attributed to the stress of transitioning from high school to college. For many of these students, this is their first-time gaining independence and trying to find out how they fit into society. Family conflict commonly occurs when students start college because they develop their values and beliefs, especially Hispanic college students. Intergenerational conflict (IGC) is a subtype of parent-child conflict that arises when the child deviates from traditional Hispanic values, attitudes, and beliefs. Little research examines how IGC, specifically conflict within domains of family expectations (FE), education and career (EC), and dating and marriage (DM), affects alcohol use and academic motivation among first-year college students. Despite this knowledge gap, studies have shown that Hispanic college students who experience IGC are likely to have more alcohol-related problems and consume more alcohol. Further, limited research has examined potential protective factors that could buffer the adverse effects associated with IGC. Ethnic identity has been found to decrease drinking and family conflict and increase academic achievement among Hispanic college students. Additionally, academic identity fosters motivation for academic achievement among college students. The current study is among the first to examine IGC and academic identity among first-year Hispanic college students. Further, the study is among the first to investigate if academic identity could be a protective factor regarding IGC. This study aims to explore the influence of identity, including ethnic and academic identity (e.g., achieved, moratorium, foreclosed, and diffused) on the relationship of intergenerational conflict (e.g., family expectations, education/career, and dating/marriage) to academic motivation (e.g., amotivation, intrinsic, and extrinsic motivation) and alcohol (e.g., use and consequences). Participants (n=268, 78.2% female) completed a two-timepoint survey to assess intergenerational conflict, alcohol use, consequences, academic motivation, and ethnic and academic identity. Five path analysis models were used to investigate the relationships between our time point 1 predictor variables, IGC (i.e., FE, EC, and DM) and identity, as well as our time point two outcome variables, alcohol outcomes, and academic motivation. Results indicated that IGC-DM had a direct effect on alcohol use. Moreover, results suggest that when ethnic and academic identities were introduced, all aspects of IGC had relationships with alcohol use or consequences. In contrast, IGC had no direct effect on the three aspects of academic motivation (i.e., motivation, intrinsic, and extrinsic motivation). Like our alcohol outcome, academic identity had a moderating effect on motivation and intrinsic motivation. Our results show that identity and IGC are complex and that different combinations can be protective or risky toward alcohol outcomes and academic motivation. Future research should focus on developing culturally informed interventions to support Hispanic college students and other ethnic minority students navigating and balancing culture and academics
Characterization Of The Androgen Receptor H1-H3 Loop As A Putative Fkbp Regulatory Surface
Get PDFProstate Cancer (PCa) is one of the most common life-threatening malignancies diagnosed among American men. Initiation and progression of PCa are dependent upon androgen receptor (AR) regulated genes. Functional receptor conformation is influenced by the cooperation of chaperone and cochaperone proteins including the 52 and 51 kDa FK506 binding proteins (FKBP52 and FKBP51). FKBP52 is known for being a positive regulator of AR, PR (progesterone receptor), and GR (glucocorticoid receptor) activity, whereas FKBP51 negatively regulates steroid hormone receptor activity. As a result, these two proteins have become highly promising therapeutic targets for the disruption of mechanisms important in several endocrine-related diseases such as prostate cancer. Previous studies have identified the BF3 surface as an AR-specific regulatory site for FKBP52, and it is likely that there is another common FKBP regulatory surface among all the regulated receptors, like the H1-H3 loop. GR is known to be more hypersensitive to FKBP52 regulation, and mutations within the GR H1-H3 loop affect FKBP-mediated receptor activities. Thus, we conducted site-directed mutagenesis to identify the residues within the human AR H1-H3 loop that are critical for FKBP co-chaperone regulation. Taking advantage of the distinct GR hypersensitivity to FKBP52, two classes of functional mutants were generated to make the human AR H1-H3 loop more like human GR or guinea pig GR. In addition, yeast-based reporter assays were performed to assess the role and relevance of those mutations in receptor activity. Similarly, mammalian reporter assays were conducted to corroborate our findings in a higher vertebrate model system. Our current data shows that, mechanistically, the H1-H3 loop is a relevant FKBP regulatory surface for the steroid hormone receptors AR and GR, and suggests that the H1- H3 loop may represent a novel target surface for the simultaneous inhibition of AR, GR, and PR
Combining green analytical methods and machine learning to develop urinary fatty acid models for cancer detection
Get PDFThis study aimed to explore fatty acids (FAs) as non-invasive biomarkers for prostate cancer (PCa) detection and prognosis, and their potential applications in other cancers. The objectives of this study were: 1) Develop a non-invasive urinary FAs-based model for diagnosing PCa; 2) Develop a FAs-based liquid biopsy model for non-invasive monitoring of PCa progression; 3) Investigate FAs composition in periprostatic adipose tissue (PPAT) collected from PCa positive patients; 4) Investigate the potential of urinary FAs biomarkers for diagnosing clear cell renal cell carcinoma (ccRCC) and ovarian cancer (OC).For the PCa diagnostic model, urine samples from 334 biopsy-confirmed PCa-positive and 232 PCa-negative subjects were analyzed for FAs content using stir bar sorptive extraction coupled with gas chromatography/mass spectrometry (SBSE-GC/MS). The data was split into training (70%) and testing (30%) sets to develop and validate the logit models. The FAs-based model achieved an area under the curve (AUC) of 0.71 (95% CI = 0.67-0.75, sensitivity= 0.48, and specificity= 0.83). In comparison, the PSA model performed with an AUC of 0.51 (95% CI = 0.46-0.66, sensitivity= 0.44, and specificity= 0.71). The finding clearly shows that our FAs model for PCa diagnosis outperformed the current standard test (PSA). We further examined the use of FAs for differentiating patients of clinically significant (i.e. high risk) PCa from those with indolent PCa (i.e. low risk) for a non-invasive FAs-based liquid biopsy model for PCa prognosis. Urine samples collected from 390 biopsy-designated PCa positive patients were categorized based on their Grade Group (GG), 213 were classified as GG 1 (low-risk PCa) and 177 were classified as GG 2 to 5 (high-risk PCa). The FA model for PCa prognosis selected 32 compounds, 31 FAs and 1 sterol, and achieved an AUC of 0.76 (95% CI = 0.67-0.84), sensitivity of 0.87, and specificity of 0.48. As urinary FAs are shown to be promising biomarkers for PCa diagnosis and prognosis, the source of those FAs is of particular interest. We then turned to investigate the FAs composition in periprostatic adipose tissue (PPAT) collected from PCa positive patients. We analyzed 33 PPAT samples for their FAs content with SBSE-GC/MS. The FAs profile of PPAT consisted of a total of 279 FAs. We observed that among the PPAT samples the carbon-18 (C18) was the most abundant length chain. We also observed that 14 out of the 20 FAs in the urine PCa diagnostic model could be traced back to PPAT, and that 9 out of the FAs 32 in the urine PCa prognosis model could be traced back to PPAT. The findings suggest that many urinary FA biomarkers are originated from PPAT which was found to be related to PCa progression. The finding may validate the use of urinary FAs for cancer detection as a non-invasive alternative. Finally, we tested the application of urinary FAs biomarkers for detections of other cancers, such as clear cell renal cell carcinoma (ccRCC) and ovarian cancer (OC). We collected urine samples from 233 Computed Tomography (CT) designated ccRCC positive patients and 43 control patients. Following the same procedure for PCa models, the ccRCC diagnostic model was developed with 14 FAs and an AUC of 0.92 (95% CI = 0.81-1.00), sensitivity of 0.88, and specificity of 0.87. A separate urine cohort of 31 urine samples from 16 OC positive, 5 benign, and 10 patients was used for exploration of the application of urinary FAs in OC detection. The 31 samples of OC underwent a partial least squares-discriminant analysis (PLS-DA). The PLS-DA clearly showed distinguished clusters among the 3 OC sample types. And among the most significant variables, several FAs were found to be important contributors. The FAs were cis-7-Hexadecenoic acid (C16:1), and methyl stearate (C18:0) identified for OC classification. The study demonstrates that urinary FAs can serve as non-invasive biomarkers for PCa diagnosis and prognosis, as well as for other cancer types like ccRCC and OC. These findings highlight the potential of FA-based models to improve cancer detection and reduce the need for invasive procedures, ultimately enhancing patient care and reducing healthcare costs. Further research is needed to refine these models and expand their application in clinical practice
