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Posttraumatic Stress, Alcohol Use, and Alcohol Use Motives Among Latina Survivors of Interpersonal Trauma: Examining Associations with Anxiety Sensitivity and Distress Tolerance
No full textHazardous alcohol use, interpersonal trauma, and posttraumatic stress disorder (PTSD) symptomatology are prevalent among college students, especially women who identify as Hispanic/Latinx. However, a dearth of literature has focused on alcohol use and PTSD relations among Hispanic/Latinx college student women, specifically. Thus, research is needed to investigate malleable transdiagnostic psychological factors involved in PTSD symptoms and alcohol use and motivations for alcohol use among Hispanic/Latinx students to inform culturally-tailored, evidence-based interventions. A growing body of literature has demonstrated that anxiety sensitivity (i.e., fear of anxiety-related bodily sensations) and distress tolerance (i.e., ability to tolerate negative emotional states) are two malleable transdiagnostic mechanisms with relevance to both alcohol use and PTSD. The current project examined, among 288 Hispanic/Latina college students (Mage = 23.3, SD = 5.4) with interpersonal trauma histories, the indirect effects of PTSD symptom severity on (1) alcohol use and (2) alcohol use motives (i.e., coping, conformity, enhancement, social) through distress tolerance and anxiety sensitivity, evaluated as parallel statistical mediators. Covariates included subjective social status, country of origin, and trauma load. Results revealed anxiety sensitivity, but not distress tolerance, mediated the link between PTSD symptom severity and a) alcohol use severity; b) conformity motives for alcohol use; and c) social motives for alcohol use. Further, PTSD symptom severity was associated with coping motives for alcohol use via both anxiety sensitivity and distress tolerance. This line of research has the potential to inform and advance culturally-informed literature focused on factors that may impact co-occurring PTSD symptoms and alcohol use among an understudied population
Structured 3��� UTRs Destabilize mRNAs in Plants
No full textRNA secondary structure (RSS) represents an intricate code that goes beyond the conventional genetic information, exerting regulatory roles in various biological processes, such as transcription, RNA processing, protein synthesis, and miRNA biogenesis. The 3��� untranslated regions (3��� UTRs) of mRNA emerge as critical orchestrators in gene regulation. Nevertheless, the specific roles of RSS within 3��� UTRs on gene expression remain a subject of inconsistency across diverse organisms and/or contexts.
In our study, a serendipitous discovery came to light: the primary substrate of miR159a (pri-miR159a), when inserted into a 3��� UTR, could promote mRNA accumulation remarkably. This enhanced expression was attributed to the premature polyadenylation of the transcript within the hybrid pri-miR159a-3��� UTR, resulting in a poorly structured 3��� UTR. Notably, RNA decay assays provided insights into the regulatory role of RSS within 3��� UTR. Poorly structured 3��� UTRs could promote mRNA stability, while highly structured 3��� UTRs led to mRNA destabilization both in vitro and in vivo. Furthermore, our exploration extended beyond reporter lines, as genome-wide DMS-MaPseq revealed a consistent inverse relationship between 3��� UTRs��� RSS and transcript accumulation across the entire transcriptome of not only Arabidopsis but also rice and human.
Mechanistically, transcripts with highly structured 3��� UTRs were found to be preferentially degraded by 3������5��� exoribonuclease SUPPRESSOR OF VARICOSE (SOV) and 5������3��� EXORIBONUCLEASE 4 (XRN4), resulting in decreased expression in Arabidopsis. Finally, our findings were underscored by the engineered different structured 3��� UTRs in an endogenous FLOWERING LOCUS T (FT) gene, yielding a demonstrable earlier flowering phenotype in Arabidopsis.
In summary, our study elucidates that highly structured 3��� UTRs tend to contribute to the reduced accumulation of harbored transcripts in Arabidopsis, a phenomenon that may extend to other organisms, including rice and mammals. Beyond its fundamental insights, our research introduces a pioneering strategy involving the engineering of 3��� UTRs��� RSS for the purpose of modulating plant traits in agricultural production and enhancing mRNA stability in biotechnology applications
Data Modeling, Computing, and Generation: New Techniques by and for AI
No full textThis dissertation investigates approaches in data handling within the domain of Artificial Intelligence (AI), covering data modeling, computing, and generation. It explores four primary tasks, each addressing distinct challenges and presenting novel solutions in their respective fields.
In the realm of data modeling, the Side Information Boosted Symbolic Regression (SIBSR) and Symbolic Modeling techniques are introduced. SIBSR incorporates side information into the symbolic regression process, enhancing the search for accurate mathematical relationships in complex datasets. Symbolic Modeling extends this approach to multi-dataset scenarios, particularly in financial asset pricing, providing adaptable and interpretable models that capture the dynamics of financial markets.
For data computing, the focus shifts to neuromorphic systems with the analysis of new Analog Error-Correcting Codes (ECCs) and the design of neural network-based decoders. These advancements address the challenges of reliability and accuracy in analog data processing, marking a progression of error-correcting from digital to analog and benefits in neuromorphic computing environments.
In data generation, Reinforcement Prompting, a novel methodology that leverages Large Language Models (LLMs) for the generation of synthetic data, is proposed. This approach mitigates issues of data privacy and scarcity of labeled datasets, especially in the finance domain. This method demonstrates that models trained on the generated synthetic data maintain performance integrity comparable to those trained on real financial data.
The dissertation presents a comprehensive exploration of these methods, substantiated by experimental evaluations and theoretical analysis. The research contributes to the advancement of AI in data handling, offering new perspectives and tools in data modeling, computing, and generation. The findings underscore the transformative potential of AI in understanding, processing, and generating data more effectively and ethically across various domains
Second-hand Illegality: Bureaucratic Exclusion and Resource Inequality in College Financial Aid for U.S.-born Latina/o Children of Undocumented Parents
No full textThis study presents a systematic analysis of the bureaucratic obstacles confronted by U.S.-born Latina/o children of undocumented parents when seeking financial aid for college. It delves into the unique challenges these students face during the financial aid application process, where parental information is a pivotal factor. Methods: Employing a semi-structured interview approach, I engaged with 15 participants who shared their experiences with bureaucratic barriers when parental information was requested. The study unveils the concept of "Second-hand illegality," where participants found their own access to resources for education obstructed due to their parents' undocumented status. This phenomenon became most pronounced at three key junctures within the Free Application for Federal Student Aid (FAFSA) form: (1) when the application necessitated parental social security numbers, (2) when it required parent income details, and (3) when it demanded parent signatures for submission. Consulting these points compelled participants to employ innovative strategies to surmount the obstacles. This research underscores a fundamental structural issue within the higher education system, focusing on a demographic often overlooked in immigration literature. The strategies devised to overcome the bureaucratic hurdles posed by the FAFSA lead to outcomes mirroring those experienced by their undocumented parents, including rejection, denial, or limitations on access to crucial resources, services, and benefits. In a broader context, this study highlights the need for systemic changes and policy reform to ensure equitable access to higher education for all U.S. students, regardless of their parental immigration status
Transparency, Accuracy, & Uncertainty in Human-AI Collaborative Decision-Making for Spacecraft Anomaly Diagnosis
No full textAI agents are becoming increasingly ubiquitous in a variety of domains, from safety-critical environments to day-to-day activities. These days they are being considered more as a virtual peer rather than a decision-making tool. In the coming years, AI agents will have a key role to play in spaceflight missions that will voyage beyond low earth orbit, where communication delays with the ground control will become longer and more frequent. On-board AI agents can help the crewmembers detect, diagnose, and treat spacecraft anomalies faster, giving them more autonomy and allowing them to respond faster to emergencies, or to focus on other critical aspects of their mission.
In order for any technology to be accepted and used by the operators, a sufficient amount of trust needs to be established first. Trust in automation is a key factor that determines willingness of a human operator to rely on an AI agent. Previous research on trust in an AI agent highlights some key elements that influence its development, such as its transparency, accuracy, and reliability. However, having perfectly accurate and reliable agents may not be possible or even enough to establish trust, especially in scenarios where there is significant uncertainty in the agent���s recommendations. In light of this fact, the link between trust, accuracy, and uncertainty merits further examination. This dissertation aims to elucidate this potential link in an agent that provides explanations for its recommendations compared to one that does not explain its decisions to the user.
This thesis presents the development and use of an AI-agent, Daphne, for detecting, diagnosing, and treating spacecraft anomalies related to the Environment Control and Life Support Systems (ECLSS). We present an experiment where human operators rely on Daphne���s recommendations induced with various levels of inaccuracies and uncertainties to detect and diagnose ECLSS-based anomalies. Human performance (number of anomalies correctly diagnosed and time to diagnosis), trust, situational awareness, cognitive workload, satisfaction, and confidence in their response were measured using both objective and subjective techniques.
Our results show that the effects of automation transparency can influence operator task performance, trust, situational awareness, workload, user confidence, satisfaction, and appropriate reliance positively. Results also suggested that agent accuracy improved task performance, appropriate reliance, and partially improved user confidence, while trust, workload, and SA were not significantly affected. Results also showed that uncertainty in agent���s recommendations reduces task performance, trust, situational awareness, user confidence, satisfaction, and appropriate reliance, and increases mental workload.
Overall, this work sheds light on under-investigated issues in Human-AI Collaboration by providing insights on factors that are most likely to effect the human-AI relationship during long duration exploration missions for spacecraft anomaly diagnosis. Further, this work provides recommendations and guidelines for designers and developers of XAI systems for developing transparent AI agents to support operators in time- and safety-critical tasks and environments, such as crew members during long-duration exploration missions
Ira Greenbaum field notebook: GK2501-GK3000.pdf
No full textBound book, each page corresponds to a karyotype slide data.Data pages for GK2501-GK3000 corresponding to unique identifiers of specimens/samples examined for biological research. Specimens are primarily housed at Texas A&M University; Biodiverstiy Research and Teaching Collection
The Effectiveness of a Newly Developed Fluoride-Releasing O-Ring for Prevention of White Spot Lesions: An In-Vitro Study
No full textPreventing white spot lesions (WSLs) in orthodontic patients remains a challenge. An effective method for incorporating calcium fluoride (Ca-F) into polycaprolactone (PCL), widely used for drug delivery, has been established. With this method, an elastomeric ligature (O-ring) is transformed into a Ca-F O-ring capable of consistently releasing therapeutic levels of fluoride ion over a span of seven weeks. The aim of this study is to assess the effectiveness of Ca-F O-ring in preventing WSL in vitro.
A Ca-F solution was combined with 10% PCL to create a mixture of 5% PCL with Ca-F. O-rings were briefly immersed in this mixture to generate the Ca-F O-rings. 60 premolars with sound enamel were bonded and randomly allocated into three groups for pH cycling: 1) ordinary O-ring with topical over-the-counter fluoridated toothpaste application (O-F); 2) ordinary O-ring (O-R); 3) Ca-F O-ring (O-CaF). Acid-resistant nail polish was applied to surrounding tooth surface, leaving a 4x2 mm2 treatment window of exposed enamel adjacent to cervical aspect of the bracket. All specimens underwent a 9-day pH cycling process. Lightness (L*) value within the treatment window of each sample was evaluated using spectrophotometer at baseline (T0) and after (T1) pH cycling. Mineral densities were determined after pH cycling for the treatment window, adjacent surface enamel layer, and deep enamel layer.
No significant differences in L* values were found between groups at T0 and T1. At T1, L* in group O-CaF was lower than other groups, but not significantly. Group O-CaF had significantly lower mineral densities in treatment window regions than the other groups at T1. For all groups, mineral density increased in the treatment window after pH cycling when compared to adjacent surface enamel control regions.
Use of Ca-F O-rings for prevention of WSLs in orthodontic patients is promising. When compared to controls with and without topical fluoride, the group O-CaF exhibited potentially more effective remineralization after pH cycling and less of a hypermineralized surface seen with WSLs. Future studies with SEM analysis and with increased pH cycling time are needed to confirm the ability of these Ca-F O-rings to effectively remineralize enamel after pH cycling
A Study of Hard and Soft Materials Subjected to Ballistic and Hypervelocity Impact
No full textHypervelocity impacts (HVIs) (���2.0 km/s) can induce extremely high strain rates (>10��� s�����). Hence, developing materials to withstand HVIs is a key challenge in the effort to enhance protective infrastructure. Furthermore, computational software such as the Elastic Plastic Impact Computation (EPIC) code, can accelerate the development of these material systems.
Thermoplastics such as high-density polyethylene (HDPE) can be used to offer HVI damage resistance without compromising on weight and cost. This document reports a modeling effort to predict the HVI response of 6.35 mm HDPE plates when impacted by 10 mm Al spheres travelling 2.0���6.8 km/s. It was found that the simulation accurately predicted debris cloud velocity and shape, hole size, and mass loss for impact velocities <5.5 km/s.
Often, much denser high-performance concrete (HPC) mixtures like BBR9 are used as a HVI resistant building material. This work reports simulation and experimental efforts to predict the HVI response of BBR9 plates of varying thickness (25.4���127.0 mm) impacted by 10 mm S2 tool steel spheres travelling 1.8���3.1 km/s. Simulations reflected experimental fragmentation and energy absorption results well, and it was found that they even captured the transition region in which an increase in impact velocity resulted in a decrease in debris cloud velocity.
Layering materials to exploit their strengths can result in composites with increased kinetic energy dissipation and reduced target weight. The results reported in the HDPE and BBR9 investigations lead to their use in composite "sandwich" targets that can be optimized for key metrics. To test this principle, a multifaceted computational and experimental approach was used. By adjusting the volume fractions of the two materials in EPIC and subjecting the layered targets to a 2.0 km/s impact from a 10 mm S2 tool steel projectile, a mass-optimized, 50.8 mm thick composite target that dissipated maximum projectile kinetic energy was generated. Subsequently, the target was subjected to V������ ballistic limit testing with a 12.7 mm S2 tool steel spherical projectile, where it was found that the simulation-predicted and actual ballistic limits were within <1% of one another, justifying the idea that simulation-informed optimization is a useful tool in material design
Reducing Risky Driving Behaviors by Considering Personality Factors
No full textTraffic accidents at intersections have been a concerning issue that negatively affects driving safety, with nearly 1000 people are killed in traffic accidents involving running red lights every year. However, driving safety issues at traffic light intersection remains understudied compared with other driving safety topics such as aggressive driving or distracted driving.
Current driving studies show that driving safety and driving behaviors are affected by many individual characteristics, such as age, sex, and personality. While other individual characteristics have been deeply studied, personality, as a well-developed and widely recognized individual characteristic, has not been given equal amount of attention in the study field of driving safety and behaviors.
In this dissertation, three studies were designed and conducted to further investigate the relationship between personality and risky driving behaviors. Data analysis results showed a strong correlation (R = 0.74) between the category of extraversion/introversion personalities and risky driving behaviors, meaning that the more extraverted the person is, the more risky driving behaviors they tend to exhibit. Theory on personality suggests that this is because extraverts are not as capable in perceiving risk-related information from the driving context compared to introverts. Targeting these characteristics, this research showed that increasing information salience and redundantly displaying information can help reduce risky driving behaviors, especially among extraverts.
The findings from this dissertation provide contributions in both scientific and practical aspects. For the scientific aspect, this dissertation provides a foundation of studying the relationships between personalities and risky behaviors in fast-paced real-life decision-making scenarios, such as driving. Moreover, this dissertation helps draw research attention to some understudied aspects such as driving decisions and safety at traffic light intersections.
This research also contributes in a practical sense to the design of safe transportation systems. The results can be used to design and develop vehicle assistance technologies that target specific personalities. Furthermore, findings from this dissertation can be used in future research of machine learning and adaptive autonomous driving systems. Additionally, findings from this dissertation can be used to design driving safety courses and training programs that target people���s personalities and specific needs to help improve driving safety
Nuclear Safeguards Feasibility Study for a Molten Salt Reactor Using MCNP Modeling and Simulations
No full textThe technological developments within the last couple of decades have exponentially increased the demand for reliable and sustainable energy. The successful fulfillment of this energy demand shows a strong correlation with the Human Development Index, which is based on health, education, and income parameters. Among other energy sources, nuclear energy has become prominent in providing clean energy by protecting air quality, having a small footprint with high energy density, and being a reliable and stable option. However, considering the dual nature (peaceful and non-peaceful uses) of nuclear energy, nuclear safeguards are an important international instrument to prevent nuclear material diversion for non-peaceful purposes. This work focused on developing a nuclear safeguards monitoring approach for a generic Molten Salt Reactor (MSR) designed at Texas A&M University.
This thesis includes a comprehensive neutronics modeling of the MSR using the Monte Carlo radiation transport code, MCNP��6.2. The modeled MSR has a 300 MWth power and operates in a thermal neutron spectrum at 900 K. It uses molten fluoride salt (2LiF:BeF2) as a coolant with UF4 fuel mixture with 3.5% low-enriched uranium (LEU). The reactor core design used graphite as the neutron moderator and reflector. Non-soluble fission products (FP) were extracted through gaseous extraction, and FP removal was conducted to improve the performance. A High-Purity Germanium (HPGe) detector, a widely used Non-Destructive Assay (NDA) equipment was modeled for Special Nuclear Material (SNM) mass quantification. In this methodology, the first step was determining the relationship between the Pu amount and fuel burnup. In the second step, the fuel burnup relationships with the radioactivity of 137Cs, the radioactivity ratios of 134Cs/137Cs, and 154Eu/137Cs were established. In the last step, these relationships were used to estimate SNM mass.
The results indicate that the Pu amount relationships with 137Cs radioactivity and the ratio of 134Cs/137Cs can be utilized to quantify SNM mass at all fuel burnup levels. The ratio of 154Eu/137Cs is applicable even for very-high fuel burnup levels. However, it does not provide accurate results at ultra-high fuel burnup levels due to its saturation. The proposed safeguards monitoring approach in this thesis provides a method for estimating the Pu mass in the MSR at different fuel burnup levels so that any diversion of Pu for non-peaceful purposes can be prevented through early detection and deterrence