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The Role of Non-Functional Overreaching and Neuromuscular Fatigue in Traumatic Injuries in NCAA Division-I Football
No full textThis series of studies explored the relationship between neuromuscular fatigue (NMF) and countermovement jump (CMJ) performance in NCAA Division I football athletes. Understanding NMF's impact on performance is crucial for reducing injury risk and optimizing performance. We used Exploratory Factor Analysis (EFA) to simplify CMJ data, uncovering performance constructs. Multi-Group Confirmatory Factor Analysis (MGCFA) tested these factors' stability across different fatigue states, challenging assumptions about fatigue's effect on CMJ performance. Further analysis focused on the relationship between salivary testosterone and cortisol ratio (TC ratio) and NMF throughout a season. Despite changes in self-reported fatigue and soreness, no significant alterations were found in the TC ratio or CMJ factor scores. Linear mixed models (LMMs) indicated that CMJ measures might not fully capture NMF nuances. Although there were significant changes in self-reported fatigue and salivary biomarkers over time, no significant associations with NMF were detected. These findings suggest the need for more comprehensive assessments to detect Non-Functional Overreaching (NFOR), as NMF alone may not be sufficient. Additionally, we examined the relationship between NFOR-induced NMF and traumatic Lower Extremity Injuries (LEIs). Analyzing CMJ data over four seasons, we aimed to construct a model for traumatic LEIs, considering various covariates. Results showed differences in baseline CMJ performances between injured and uninjured athletes, with those later suffering LEIs demonstrating greater baseline CMJ performances in certain groups. Our analysis revealed insights, including reduced odds of traumatic LEI over time and increased odds associated with
NFOR. In conclusion, these studies highlight the importance of monitoring NFOR-induced changes in neuromuscular performance to assess injury risk. The findings underscore the need for standardized assessment protocols and larger, more diverse samples to better understand the longitudinal association between NFOR and traumatic LEIs in this population
Utilization of EEG-based BCI System in Autistic Individuals
No full textAutism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impaired social interaction and communication, restricted interests, and repetitive behaviors. ASD is often accompanied by impaired emotional regulations (ER) defined as the ability to modify one’s arousal and emotional state to promote adaptive behavior. There has been increasing emphasis on developing technology-based intervention tools to improve ER in
ASD. Electroencephalography (EEG) has shown success in reducing ASD symptoms when used in neurofeedback-based interventions.
In this dissertation work, we develop a novel electroencephalography (EEG)-based brain-
computer interface (BCI) system that enables us to identify different stress levels and the
brain responses of autistic individuals for different stress levels and monitor the participant
responses through the analysis of certain EEG patterns associated with ER. In order to help
cultivate emotional self-awareness, support generalization of ER skills, we develop techniques
to identify and extract EEG features that are markers for real world communication or social
interaction skills
Exploring and Measuring Nature Connection in Early Childhood
No full textHumanity’s future depends on raising generations of children who feel connected to and value the natural environment and can translate that connection into sustainable attitudes and behaviors (Myers & Frumkin, 2020; Rosa et al., 2018). The cultivation of nature connection starts in early childhood when children begin to form environmental attitudes and behaviors that will last a lifetime (Green et al., 2016). Yet researchers’ understanding of early nature connection is challenged by the absence of a unified framework that incorporates children’s perspectives and limited measures for assessing nature connection in early childhood. In the present dissertation, I conducted two studies to explore and measure nature connection in Icelandic preschool children (N=117). Iceland was chosen because of its emphasis on nature and sustainability in the national preschool curriculum. In Study 1, I explored the cognitive, affective, valuative, sociocultural, and biosystem features of early nature connection through drawings and interviews with 21 Icelandic preschool children. I found that young children have working theories of nature and expressed varying environmental values rooted in their microsystem contexts and shaped by sociocultural factors related to people, place, and activities. In Study 2, I created the Early Nature Connection Scale, a self-report scale of early nature connection for children aged 4-6 years. I tested its psychometric properties with 117 Icelandic preschool children and found it was reliable (α=.76), and evidence supported its content and construct validity. Exploratory factor analysis revealed a two-factor structure explaining 90% of the variance. The two factors were described as “yummy nature connection” and “meh or yucky nature connection.” The findings of this dissertation provide an avenue for future research to explore how young children form relationships with nature, the impact of sustainability education on early nature connection, and the relationship between early nature connection and child and environmental well-being
A DEEP REINFORCEMENT LEARNING APPROACH FOR FAST FREQUENCY CONTROL IN ELASTIC POWER SYSTEM
No full textAs power systems transition towards sustainable energy sources, the integration of renewables poses several challenges that necessitate innovative management and control strategies. This dissertation addresses the urgent challenges faced by modern power systems due to the high penetration of renewable energy sources, increased demand, and the integration of diverse market participants. These factors introduce significant unpredictability and complexity, leading to voltage and frequency stability issues and necessitating the construction of new power infrastructure, which imposes operational, financial and environmental burdens.
To overcome these obstacles, this dissertation explores the innovative application of Deep Learning (DL) and Reinforcement Learning (RL) to develop real-time, adaptive control strategies that can cope with the non-linear and stochastic nature of today's power grids. Specifically, it presents a model-free frequency control scheme utilizing Deep Reinforcement Learning (DRL) that focuses on enhancing primary and secondary frequency control mechanisms through the Deep Deterministic Policy Gradient (DDPG) method. This approach demonstrates significant potential in mitigating the adverse effects of grid stochasticity, thereby bolstering system stability.
Additionally, the dissertation delves into the optimization of grid-interactive efficient buildings using the Soft Actor-Critic (SAC) algorithm, optimizing a cluster of energy storage systems performance for improved peak shaving, valley filling, and grid self-sustainability. The experimental results demonstrate that the central controller achieves high performance at both local and district wide operational evaluation indices.
Lastly, we tackle the low inertia challenge in zero-carbon grids through the application of Graph Neural Networks (GNNs), particularly the Graph Attention Network (GAT), for effective inertia estimation. This innovative method aids in the precise management of grid resources post-disturbance, highlighting the critical role of attention mechanisms in enhancing decision-making processes for system operators.
The findings underscore the transformative impact of DL and RL in advancing power system control and management, especially amidst the complexities introduced by renewable energy integration and the transition to low-inertia grids. The proposed solutions not only pave the way for advanced real-time control strategies but also signify a leap towards sustainable and resilient power systems in the era of green energy
A Case Review on the Independence Health System Food Institute
No full textThe Food Institute was established with a mission to enhance public health by promoting education and increasing access to nutritious foods, thereby addressing food insecurity. As the Institute nears its third anniversary, this paper evaluates its effectiveness in fulfilling its public health objectives within the community. Through a comprehensive examination of the Institute's programs, initiatives, and outreach efforts, this study assesses the extent to which it has contributed to mitigating food insecurity. By analyzing quantitative data collected from program participants as well as national and local data, alongside gathering additional qualitative insights from program participants, community members, and stakeholders, this paper aims to provide valuable insights into the public health relevance and importance of the Food Institute's endeavors
Beyond Human Limits: The Introduction of Artificial Intelligence Into Screening Mammography to Improve Breast Cancer Detection
No full textRadiologists can greatly improve their ability to detect breast cancer, with the integration of artificial intelligence (AI) into the breast cancer screening process. The use of AI within screening mammography can help minimize the frequency of false negative diagnoses of breast cancer, by calling out areas of concern that may otherwise be overlooked. By maximizing the ability to detect breast cancer at an earlier stage, the patient can maximize their ability to receive treatment at a time to control if not eradicate the breast cancer. The early detection of breast cancer can potentially reduce healthcare costs by treating patients at an early stage and avoiding the costs for treatment of cancer that has metastasized.
In the United States, breast cancer is the second leading cause of cancer death in women and is estimated to kill 42,250 women in just 2024 alone (Breast Cancer Statistics, n.d.). Breast cancer can be treated effectively when detected early on, which is why the American Cancer Society (ACS) recommends that women receive a screening mammogram each year. This recommendation has helped to reduce the breast cancer death rate 43% since 1989 (Breast Cancer Statistics, n.d.). Although this is a drastic improvement, not all women are able to receive the intended benefit of a yearly screening due to an epidemic of misdiagnosis.
Upwards of 30% of mammograms are misdiagnosed, causing women to forego treatment that may ultimately save their lives. 85% of the misdiagnoses can be attributed directly to human error, and could have been avoided (Ganesan, Karthikeyan, et al., 2013). In order to reduce human error, we need to provide greater support to radiologists and improve screening mammography. AI has the ability to diagnose patients and provide explanations simple enough for both physicians and patients to understand. While this technology is new and currently still being tested, integration is imperative to ensure that women receive treatment for breast cancer as soon as it’s detectable
Optimizing comparative effectiveness evidence from transfusion medicine trials: informing clinical practice and study designs
No full textBlood transfusions are frequently used interventions to prevent life-threatening anemia but can cause complications and cost U.S. hospitals almost $3 billion per year. There is a debate regarding optimal transfusion strategies for patients with acute myocardial infarction (MI) and for high-mortality risk patients with sickle cell disease (SCD). Applications of contemporary statistical methods may enhance recommendations for transfusion practices in these populations. The objectives of this dissertation were to produce evidence for transfusion recommendations to treat anemia in patients with acute MI and to determine a trial endpoint that attains sufficient statistical power for discriminating effectiveness of automated red cell exchange from standard of care in patients with SCD. In aim 1, we emulated a target trial of four transfusion strategies with hemoglobin thresholds between 7g/dL to 10g/dL in patients with acute MI and anemia using data from the Myocardial Ischemia and Transfusion (MINT) trial. The risk of 30-day death or MI increased progressively as hemoglobin transfusion thresholds decreased; a threshold of <9-10g/dL may be preferred for this population. In aim 2, we created individualized treatment rules (ITRs) to determine the optimal transfusion strategy for patients with acute MI and anemia. We did not identify treatment effect modifiers for 30-day death or MI, nor for 30-day death. An interpretable ITR for 30-day death, MI, revascularization, readmission, or heart failure led to reduced risk of this outcome compared to assigning all MINT participants a liberal or a restrictive strategy. In aim 3, we simulated a 150-patient randomized trial, Sickle Cell Disease and Cardiovascular Risk - Red Cell Exchange trial, to determine scenarios in which a count, time-to-event, or prioritized rank endpoint achieved sufficient statistical power to discriminate the effect of automated red cell exchange from standard of care. In most scenarios, a count endpoint analyzed with a negative binomial regression achieved higher power than the other endpoint analyses. Our results highlight the importance of informed endpoint selection in small treatment trials for populations with rare diseases. Together, our findings contribute valuable information for formulating transfusion practice guidelines and for designing studies to improve public health for clinical populations with anemia
Assessing Patient Perceptions and Understandings of Genetic Testing After Recurrent Pregnancy Loss
No full textGenetic testing can help to provide an explanation for many couples experiencing recurrent pregnancy loss (RPL). However, there is limited data regarding the experiences of RPL patients undergoing genetic testing. This study assessed patient experiences, perceptions, and understandings of genetic testing for RPL. An online questionnaire was developed and distributed in relevant clinics as well as through social media and support groups. In total, 115 respondents met inclusion criteria, 105 completed the survey, and all responses were analyzed. Despite ACOG and ASRM practice recommendations, only 63.7% of RPL patients were offered genetic testing on the products of conception (POC). Furthermore, only 67.9% and 54.5% were offered genetic testing for themselves and their partners, respectively. Overall, respondents recognized the potential of genetic testing to provide explanations for RPL and help plan for future pregnancies; 93.52% indicated that they would do/have done genetic testing to find an explanation for a miscarriage. Respondents who found an explanation for RPL through genetic testing were more likely to recognize the utility and limitations of genetic testing than those who did not do genetic testing or who received uninformative/negative results. Outcomes of genetic testing did not significantly impact respondents’ likelihood of utilizing reproductive technologies, additional prenatal screenings, egg/sperm donation, or adoption. Finally, 20.4% of respondents indicated that they would blame themselves if they were found to have a genetic change that explained their history of miscarriage, while only 8.3% indicated that they would blame their partner. The findings of this study indicate the need for additional patient education and resources regarding the risks, benefits, and limitations of genetic testing for RPL as well as the need for systematic implementation of current guidelines into clinical practice. This study contributes to the field of public health by identifying ways that genetic counselors can improve patient education and support more standardized and equitable implementation of guidelines
Targeting Serpin B9 via Gemcitabine-based siRNA/drug Dual-Nanocarrier for Improved Pancreatic Cancer Therapy
No full textGranzyme B is a potent cytotoxic molecule produced by cytotoxic T cells and NK cells to kill cancer cells. Serpinb9 is an endogenous inhibitor of granzyme B and is co-expressed in cytotoxic T cells to protect themselves from self-inflicted damage. Interestingly, many types of cancer cells also co-express serpinb9 and granzyme B. In this study, we showed that serpinb9 expression is correlated with gemcitabine resistance in pancreatic cancer. Based on this finding, we proposed to develop a novel therapeutic approach that combines gemcitabine chemotherapy with siRNA-mediated serpinb9 knockdown. We have developed a gemcitabine prodrug nanoparticle system capable of co-delivery of gemcitabine and siSBP9. Our nanoparticles are small in size and close to neutral in surface charge. These nanoparticles were highly effective in tumor-targeting in tumor-bearing mice. Moreover, our combined therapy showed significantly enhanced anti-tumor effect and improved tumor immune microenvironment
Procurement and use of pasteurized donor human milk in the outpatient setting: retrospective analysis and case study
No full textBackground/Significance: Due to the importance of human milk for infant health, organizations including the American Academy of Pediatrics recommend the use of pasteurized donor human milk (PDHM) if parents’ milk supply is unavailable for low birthweight infants. PDHM, like parental milk, contains macro- and micronutrients and other factors (hormones, immune components) that are essential for infant growth, development, and optimal physiologic functioning. Human milk banks screen and pasteurize raw, donated milk from milk donors to ensure safety and preserve nutritional quality. The use of PDHM has been linked to the reduction of serious neonate conditions, such as necrotizing enterocolitis (NEC). For this reason, hospitalized infants with critical illness and/or prematurity who are at risk of NEC, have been prioritized for PDHM, with infrastructure to support cost coverage. Outpatient PDHM use for other infants with medical conditions who could potentially benefit from PDHM is constrained by the associated costs of procurement and processing and insurance coverage barriers, but there is a growing interest in PDHM beyond the hospital setting. Infants discharged from pediatric hospital settings often have ongoing serious health issues and could benefit from continued PDHM until they can transition to human milk substitutes (e.g., commercial infant formula) or solid foods. Currently, patterns of procurement and distribution of PDHM and familial experiences with PDHM in the outpatient setting are understudied.
Purpose: To describe patterns and experiences with outpatient PDHM distribution/use, including indications for dispensation, cost coverage, volumes, periods of use, and perceived value and barriers to use and procurement.
Methods: This study was a retrospective analysis conducted on de-identified data from infants receiving outpatient PDHM provided by a single milk bank over a period of 5 years. In addition, we interviewed and reviewed health records of a mother whose infant had received outpatient PDHM as a case study. For the retrospective analysis, summary statistics were computed.
Results: The analytic sample for the retrospective analysis included 423 infants who received outpatient PDHM from the milk bank. On average, outpatient PDHM was dispensed for under a month (n=57; 42%) with most families not having any of the costs covered by insurance. Families paid a mean of 4,386, Range: 49,515). The most common type of PDHM prescribed was term infant milk (n=400; mean=23,970 mL) followed by dairy-free milk (n=42; mean=43,646 mL) and low dairy milk (n=26; mean=9,627 mL). The case study illustrated that there was poor awareness of outpatient PDHM as an infant feeding within the healthcare community, and that a major barrier to the procurement of outpatient PDHM was advocating for and coordinating insurance coverage. The case study highlighted that a high level of organization and commitment was needed from the parent to obtain insurance approval and that there were significant delays in obtaining the milk. The case study highlighted the financial and time costs that accessing outpatient PDHM places on the recipient family.
Conclusion: PDHM is a lifesaving intervention that can be critical to infant/child health in the inpatient setting, with limited current evidence to support use in outpatient settings. However, families who access and utilize PDHM in the outpatient setting with a $1,500 average cost for a month’s supply of PDHM access are largely limited to those who have high incomes, private insurance that covers PDHM, or residents of Pennsylvania under the Medicaid program. Finally, there are a lack of federal public healthcare programs and policies that enable access to PDHM on an outpatient basis. Further research needs to be conducted into the benefits of and access to outpatient PDHM use in different racial, ethnic, and socioeconomic groups