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Unfolding Particle Detector Acceptance in High Energy Physics with Generative AI
No full textThe ``acceptance problem\u27\u27 in high energy physics (HEP) refers to the challenge of accurately modeling detector acceptance to ensure the precision of measurements. This study explores the application of Generative AI to address the acceptance problem in HEP. By training a Generative Adversarial Network (GAN) on simulated detector data (pseudo-data), we demonstrate its capability to learn detector responses and generate synthetic data that closely match measured distributions. A key component of our methodology is a custom generator loss function that incorporates physics-informed principles to improve training. This custom loss function penalizes deviations from the true distribution of event components, ensuring that the generated samples adhere to the underlying physics. Additionally, we trained a binary classifier to distinguish between different topological states (measured and unmeasured events) within the generated Monte Carlo pseudodata, further refining the model\u27s accuracy. Our approach preserves correlations between kinematic variables across multiple dimensions, providing an accurate representation of the underlying physics. Validation with Monte Carlo pseudodata demonstrates the method\u27s ability to recover true distributions even in regions with limited detector sensitivity, establishing a solid foundation for applying our framework to real experimental data. Our results highlight the feasibility and advantages of using generative AI in HEP, paving the way for broader applications in the field
Sense of Safety, Belonging and Third Space in ODU Students
No full textA sense of safety and belonging is essential for college students undergoing major life transitions, as these factors can impact their decision to remain at or leave an institution. This significance extends to college administrators who strive to foster a supportive environment. The concept of Third Space examines physical environments where individuals can experience belonging outside their home and workplace. We aim to find insights from this concept to broaden the holistic understanding of the factors contributing to a sense of belonging. Our study will use a survey instrument to explore the personal experiences of Old Dominion University (ODU) students concerning these themes.
The purpose of this survey is to explore how comfortable and safe students feel at ODU by identifying specific areas, locations, and aspects of ODU’s campus that contribute to or reduce students’ feelings of inclusiveness, sense of belonging, and safety while on campus. Findings from this survey will contribute to the expansion of common knowledge. Additionally, our hope is that the results of this survey can inform initiatives and other program and policy developments that would support student success and making a meaningful impact on the ODU community during their time here
The Impact of Maternal Oral Health Practices during Pregnancy on Birth Outcome: Preliminary Findings from Pregnancy Risk Assessment Monitoring System (PRAMS) Data Analysis
No full textBackground: Preterm birth (PTB) (delivery prior to 37 weeks of pregnancy) and low birth weight (LBW) (weighing less than 2500 grams at birth) present a major public health challenge. Both conditions lead to neonatal morbidity, mortality, and long-term complications, placing economic and emotional burdens on families and healthcare systems. There is strong biological plausibility that high periodontal bacterial loads from periodontal disease (PD) in pregnant women may increase the risk of PTB and LBW through bacterial transmission and inflammatory pathways affecting the placenta. This study aims to analyze the association between maternal oral health-related experiences during pregnancy and PTB/LBW using 10-year data from PRAMS Virginia.
Methods: This study is a secondary data analysis of Virginia PRAMS Phases 7 and 8, representing women surveyed within the state between 2012 and 2022. PRAMS is a state-based surveillance system in which women are selected after delivery from the birth certificates to provide a representative sample. Univariable logistic regression models were used to calculate crude odds ratios (COR) and their 95% confidence intervals (CIs) to examine associations between maternal oral health-related experiences, such as dental cleaning and dental insurance coverage, and PTB/LBW. The complex structure of PRAMS, including stratification and weighting, was taken into account during this analysis. All statistical analyses were conducted using SAS, with significance set at P \u3c 0.05.
Results: This analysis included 8,820 women who gave live birth in Virginia from 2012-2022. The prevalence of PTB and LBW was 9.07% and 7.13%, respectively. In univariable analysis, the receipt of dental cleaning during pregnancy was associated with lower odds of PTB (COR=0.80, 95% CI: 0.64–0.99, p=0.037) and LBW (COR=0.79, 95% CI: 0.65–0.96, p=0.016). Similarly, having dental insurance decreased the odds of PTB (COR=0.75, 95% CI: 0.59–0.95, p=0.018) and LBW (COR=0.80, 95% CI: 0.64–0.98, p=0.035).
Conclusion: Preliminary findings suggest that the receipt of dental cleaning and having dental insurance during pregnancy may decrease the risk of PTB and LBW. These results highlight the importance of integrating oral health services into prenatal care and expanding access to affordable dental care to improve maternal and infant health outcomes. Further analysis of these data will deal with measured and unmeasured confounding through complex statistical approaches
Quantum-Enhanced Multi-Modal Cardiac Biomarker Analysis for Cardiovascular Disease Risk Prediction
No full textCardiovascular disease (CVD) remains a major global health challenge, necessitating advanced predictive models for early detection and risk assessment. This paper presents a quantum-enhanced machine learning framework for multi-modal cardiac biomarker analysis. The model integrates quantum feature encoding with a deep learning-based classification pipeline. Multi-modal data from imaging, electrophysiological signals, and genetic biomarkers are mapped into quantum-enhanced feature spaces using Cirq. These transformed features are then fed into a hybrid quantum-classical neural network (QNN) to improve prediction accuracy. The proposed approach is benchmarked against classical machine learning methods such as Support Vector Machines (SVM) and Random Forest (RF). Results show the potential of quantum computing in advancing precision medicine by leveraging high-dimensional feature transformations
Reaction Time as a Predictor of Cognitive Function in Collegiate Athletes with Concussion History
No full textBackground
Cognitive-motor impairments are a persistent concern in athletes with a history of concussion. While reaction time (RT) is often used as a performance metric, its role in predicting broader cognitive function remains unclear. This study examines the relationship between RT and key cognitive domains, including executive function, psychomotor speed, and memory, in previously concussed collegiate athletes.
Methods
112 college-aged athletes from a NCAA division I institution with a self-reported history of concussion were included in this cross-sectional study. Reaction Time Composite (RT-Comp), executive function, and psychomotor speed were assessed using Concussion Vital Signs (CNS Vital Signs LLC), a computerized neurocognitive assessment. Immediate memory and delayed recall were evaluated using the Sport Concussion Office Assessment Tool – 6th edition (SCOAT-6). Correlations were analyzed using Pearson’s correlation to assess the strength of associations. A priori p-value was set at p \u3c .05.
Results
RT-Comp demonstrated significant negative correlations with executive function (r = -0.430, 95% CI [-0.572, -0.265], p \u3c 0.001), indicating that slower reaction times were associated with reduced cognitive flexibility and decision-making abilities. A similar negative correlation was observed with psychomotor speed (r = -0.355, 95% CI [-0.508, -0.184], p \u3c 0.001), suggesting impairments in motor response efficiency. Additionally, slower RT was associated with lower scores in immediate memory (r = -0.307, 95% CI [-0.469, -0.127], p = 0.001) and delayed recall (r = -0.233, 95% CI [-0.403, -0.043], p = 0.013), highlighting potential long-term cognitive consequences of concussion.
Conclusion
Reaction time appears to be a strong indicator of cognitive function in post-concussion athletes, correlating with executive function, psychomotor performance, and memory recall. These findings suggest that RT assessments should be integrated into concussion evaluation protocols to enhance return-to-play decisions. Further research is needed to explore whether reaction time improvements correspond with cognitive recovery over time
Generative AI for Correcting Detector Effects in Particle and Nuclear Physics: A Comprehensive Survey
No full textIn particle and nuclear physics, experimental data are often distorted by detector effects such as smearing, acceptance, and inefficiencies, necessitating correction through unfolding techniques. Recent advancements in generative AI have introduced powerful approaches for addressing these challenges, offering scalability to high-dimensional data, the ability to model complex detector responses, and support for event-level analysis. This survey explores the application of generative AI models—such as Generative Adversarial Networks, Variational Autoencoders, Normalizing Flows, and Diffusion Models—for unfolding detector effects. We provide a systematic review of existing frameworks, methodologies, and implementations, highlighting key developments, practical challenges, and areas for further improvement. Additionally, we discuss critical aspects such as latent space representations, uncertainty quantification, physics-informed constraints, and background subtraction. By synthesizing recent progress in the field, we illustrate how generative AI can significantly enhance data reconstruction and analysis in particle and nuclear physics experiments
Medical Malpractice Trends Amongst Virginia Plastic Surgeons
No full textLike other surgical specialties, plastic surgeons tend to face a high rate of medical malpractice. Medical malpractice is a significant concern for practicing physicians as lawsuits cost involved parties time and money and potentially jeopardize the physician’s career. Currently, minimal research exists investigating the most common medical malpractice charges against plastic surgeons. Thus, medical malpractice charges were investigated for board-certified plastic surgeons practicing in Virginia. Collected data included the action taken against the physician and whether a claim was paid. Out of the 190 total board-certified plastic surgeons in Virginia, 9 (4.7%) had convictions, 10 (5.3%) had paid claims, and 4 (2.1%) had both convictions and paid claims. The most common reasons for malpractice charges were postoperative complications, authorizing subordinates to provide out-of-scope care, and inappropriate handling and prescription of drugs, specifically narcotics. Physicians in Virginia can minimize their risk of malpractice by ensuring practitioners operate within the scope of their role and responsibly managing the prescription and handling of controlled substances
The Combined Effects of Cocaine and Opiate Drugs on Lipid Metabolism in Mg \u3ci\u3ein Vivo\u3c/i\u3e and \u3ci\u3ein Vitro\u3c/i\u3e
No full textCocaine is known to dysregulate lipid metabolism and promote lipid droplet-associated microglia (LDAM), which are characterized by sustained activation, defective phagocytosis, and a role in promoting addiction. Polydrug use, such as the combined administration of cocaine and morphine ( speedball ), is known to produce a more addictive phenotype than either drug alone. However, whether speedball induces higher levels of LDAM and lipid dysregulation in vivo and in vitro remains unclear.
This study aims to investigate the combined effects of cocaine and morphine on lipid metabolism and microglial activation through two specific aims. In Aim 1, C57BL/6 mice were administered cocaine and morphine (15 mg/kg each) for 15 days. Brain tissues were collected for cryosectioning and western blotting (WBs) to assess lipid metabolism pathways and lipid droplet formation. Microglial activation status was evaluated using IBA1 immunostaining. Bodipy staining was performed to assess lipid droplet formation. In Aim 2, BV2 microglial cells were exposed to cocaine and morphine (5 µM each) for 24 hours. Lipid synthesis and degradation pathways were examined through western blotting for markers such as CATB, CATD, LC3B, p21, TFEB, and p62.
Results from both in vivo and in vitro studies demonstrated that cocaine and morphine co-exposure led to significant lipid dysregulation, correlating with increased microglial activation.
These findings provide strong evidence that speedball promotes more profound lipid metabolism disturbances in microglia, contributing to exaggerated neuroinflammation. This research offers valuable insights into the mechanisms of polydrug-induced addiction and highlights lipid metabolism as a potential therapeutic target
Building Elementary Teachers\u27 Capacity for Computer Science Instruction Through Professional Development: A Randomized Control Trial
No full textThis randomized control trial evaluated the impact of a year-long professional development (PD) program on elementary teachers\u27 CS content knowledge, self-efficacy, and CS implementation. It also investigated teachers\u27 PD experiences and expectations. The findings suggest that treatment teachers\u27 self-efficacy for teaching CS significantly improved as a result of the PD, and a statistically significant difference was found between treatment and control teachers\u27 self-efficacy by the end of the PD. A higher percentage of treatment teachers than control teachers reported implementing CS-integrated lessons in their classrooms, and treatment teachers identified effective activities to engage students in learning CS and several barriers they experienced in CS integration. Treatment teachers\u27 CS content knowledge remained the same from before to the end of the PD year, but a statistically significant decrease was observed for control teachers\u27 CS content knowledge from pre- to end of year. Between group year-end differences were not statistically significant. At the end of the PD year, more treatment teachers demonstrated greater CS knowledge on 3 of 5 items than control teachers. Findings highlight the potential need for continual, sustained, and relevant CS PD over the academic year. Further study is underway with additional cohorts of the PD model
Differentiating Opioid Use Disorder from Healthy Controls via ML Analysis of RS-fMRI Networks
Get PDFObjectives/Goals: This work aims to identify functional brain networks that differentiate opioid use disorder (OUD) subjects from healthy controls (HC) using machine learning (ML) analysis of resting-state fMRI (rs-fMRI). We investigate the default mode network (DMN), salience network (SN), and executive control network (ECN), as well as demographic features. Methods/Study Population: This work uses high-resolution rs-fMRI data from a National Institute on Drug Abuse study (IRB #HM20023630) with 31 OUD and 45 HC subjects. We extract rs-fMRI blood oxygenation level-dependent (BOLD) features from the DMN, SN, and ECN. The Boruta ML algorithm identifies statistically significant features and brain activity mapping visualizes regions of heightened neural activity for OUD. We conduct fivefold cross-validation classification experiments (OUD vs. HC) to assess the discriminative power of functional network features with and without incorporating demographic features. Demographic features are ranked based on ML classification importance. Follow-up Boruta analysis is performed to study the medial prefrontal cortex (mPFC), posterior cingulate cortex, and temporoparietal junctions in the DMN. Results/Anticipated Results: Boruta ML analysis identifies the DMN as the most salient functional network for differentiating OUD from HC, with 33% of DMN features found significant (p \u3c 0.05), compared to 10% and 0% for the SN and ECN, respectively. The Boruta ML algorithm identifies age and education as the most significant demographic features. Brain activity mapping shows heightened neural activity in the DMN for OUD. The DMN exhibits the greatest discriminative power, with a mean AUC of 69.74%, compared to 47.14% and 54.15% for the SN and ECN, respectively. Fusing DMN BOLD features with the most important demographic features improves the mean AUC to 80.91% and the F1 score to 73.97%. Follow-up Boruta analysis highlights the mPFC as the most important functional hub within the DMN, with 65% significant features. Discussion/Significance of Impact: Our study enhances the understanding of OUD neurobiology, identifying the DMN as the most significant network using ML rs-fMRI BOLD feature analysis. Ethnicity, education, and age rank are the most important demographic features and the mPFC emerges as a key functional hub for OUD. Future research can build on these findings to inform treatment of OUD