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THE IMPACT OF PRENATAL EXPOSURE TO INFECTION ON LATER-LIFE ALCOHOL USE
Get PDFPrenatal exposure to infection is a risk factor in the development of several neuropsychiatric disorders, including schizophrenia and depression. These disorders often co-occur with alcohol misuse which leads to adverse outcomes, including increased hospitalization and mortality. Despite these consequences, little is understood regarding the mechanisms regarding how early exposure to infection impacts brain development, which inhibits our ability to create effective therapies. We have previously demonstrated that maternal immune activation (MIA), a model of prenatal exposure to infection, combined with adolescent alcohol exposure (AE) leads to enhanced home-cage drinking and disrupts cortical-striatal oscillations in adult offspring. The current project aims to determine whether these findings extend to operant alcohol self-administration, and whether the anti-oxidant n-acetylcysteine (NAC) may be effective in preventing the effect of MIA on drinking behavior. Pregnant Sprague-Dawley rats were exposed to poly(I:C) (4mg/kg) or saline on gestational day 15 and were exposed to NAC (100 mg/kg) or saline 24 hours before and after MIA treatment. During adolescence, postnatal day (PD) 28-48,offspring were given 24-hour home cage access to 10% ethanol (AE) using a two-bottle choice technique. In adulthood (>PD 70), rats were trained to self-administer 10% ethanol for 30-minutes, 5 days per week. The results of this study show that MIA/AE increases alcohol self-administration, as measured by active lever presses and mean alcohol consumed (g/kg). This effect was more pronounced in males. Furthermore, prenatal NAC treatment prevented these increases in alcohol self-administration. These data contribute to our overall hypothesis that oxidative stress caused by MIA negatively influences synaptic development, which primes the brain to be more susceptible to the negative effects of AE, leading to increases in alcohol drinking behavior in adulthood. Ongoing work attempts to identify whether/how MIA and AE impacts synaptic plasticity in rodents, in order to determine the physical mechanism that underlies the behavioral changes we observed here. Therefore, future studies will investigate the impact of MIA and AE on dendritic spine, microglia and astrocyte densities
A Mixed-Methods Exploration of Basic Needs Staff Perspectives on Service Provision at Public Universities in Washington State
Get PDFAddressing the unmet basic needs of college students is a critical issue that can significantly influence retention and graduation rates. This study explored the experiences and perceptions of staff who implement basic needs services for college students at public higher education institutions in Washington State. It also investigated the range of services offered, how campuses utilize data, and the perceived training needs of staff. Using a convergent, mixed-methods approach, this research included in-depth interviews and a survey to collect qualitative and quantitative data. A qualitative description approach was applied to interview data and open-ended questions on the survey. Descriptive statistics were used to quantitatively assess the breadth of services offered, alongside content analysis of interview data to gain deeper insights into how these services function on campus. Food supports were the most offered services for unmet basic needs, with 100% of surveyed institutions offering a food pantry. The Social Determinants of Health theory provided a lens for understanding how colleges address basic needs to support students. Findings suggest that asking students about social and parenting supports at intake may help staff to address student needs holistically. The qualitative findings offer themes into the challenges and successes of implementing these services, including helping students navigate basic needs services on campus, staff expertise and capacity, providing a student-centered approach, and addressing institutional and policy barriers
Predictive Modeling in Cancer Cell Separation A Machine Learning Approach in DLD Devices
Get PDFDeterministic Lateral Displacement (DLD) devices serve as a powerful tool in the field of microfluidics, enabling label-free, size-based separation of particles and cells.These devices offer significant potential for cancer diagnostics, specifically in isolating circulating tumor cells (CTCs) from blood samples to facilitate early detection andimprove patient outcomes. Due to the challenge of identifying rare CTCs among the vastly larger population of blood cells, DLD technology optimizes separation through carefully designed geometric configurations, focusing on parameters such as row shift fraction, post size, and gap distance to effectively differentiate cancer cells based on their unique physical properties. This thesis explores how fine-tuning these parameters in DLD devices can lead to more precise and reliable isolation of lung cancer cells, supporting advancements in early cancer diagnostics.In addition to DLD design optimization, this study integrates machine learning models to enhance the process of parameter selection, reducing the reliance on exhaustive simulations and physical prototyping. A large dataset, generated through validated numerical models, underpins the training of various machine learning algorithms, including gradient boosting, k-nearest neighbors (kNN), random forest, and MLP regressor, each tailored to predict particle trajectories and improve separation efficiency. These models are not only instrumental in accurately predicting cell migration patterns within the DLD devices but also serve to identify optimal device configurations rapidly, thus enabling high-throughput and cost-effective cancer cell separation.The application of machine learning in this research extends beyond trajectory prediction; it systematically isolates crucial design parameters essential for advancing DLD technology in cancer research. By analyzing migration characteristics and predicting separation outcomes based on model input, the thesis provides a frame-work for automated DLD device design, offering a streamlined approach for efficient, scalable, and precise cancer cell separation. Ultimately, this predictive modeling approach, combining the strengths of machine learning and microfluidics, is poised to support early cancer detection, thereby contributing to more accessible and targeted therapeutic strategies in precision medicine
Scalable and Efficient Distributed Frameworks for Influence Maximization
Get PDFInfluence maximization is a fundamental operation among graph problems that involve simulating a stochastic diffusion process on real-world networks. Given a graph G(V, E), the objective is to identify a small set of key influential “seeds”—i.e., a fixed-size set of k nodes, which when influenced is likely to lead to the maximum number of nodes in the network getting influenced. The problem has numerous applications including (but not limited to) viral marketing in social networks, epidemic control in contact networks, and in finding influential proteins in molecular networks. Despite its importance, application of influence maximization at scale continues to pose significant challenges. The problem is NP-hard, but there exist efficient polynomial time approximations. The intuition behind the two main schools of such approaches can be distilled to either trying to answer the question “who influences whom?” or “who gets influenced by whom?”. The current state-of-the-art distributed algorithms available for these two paradigms of approximating influence maximization, namely greedy hill climbing and reverse influence sampling, involves direct parallel adaptations of sequential greedy approaches. Scaling these algorithms remains a daunting task due to the complexities associated with steps involving stochastic sampling and large-scale aggregations.This dissertation first highlights the uniqueness and dynamism of the influence maximization workload by contrasting the performance gains (or lack thereof) from a popular preprocessing step against those achieved in classical prototypical graph workloads. With this motivation in mind, the dissertation introduces two novel frameworks for approximating influence maximization. The first is aimed at optimizing the greedy hill climbing approach while the second targets the paradigm of reverse influence sampling. Both frameworks are built with the theme of concurrently processing sub-units of the overall workload and leveraging the property of submodularity that persists in the subsets representing these workloads. Experimental results show that these methods are able to increase problem size reach, reduce time to solution, maintain quality, and extend the scaling boundary compared to their state-of-the-art counterparts.We believe that the contributions from this disseration will be helpful in making influence maximization more accessible as an application promoting its adoption in large scale scientific pipelines. Additionally, parts of the work described in the disseration are also generic enough to be extended to a broader base of application settings that use submodular optimization. This has the potential to motivate and inspire research into such applications which have been hitherto impractical due to their high computational costs and/or memory footprint
2-ARACHIDONOYLGLYCEROL, BUT NOT ANANDAMIDE, IN THE BASOLATERAL AMYGDALA MODULATES THE STRENGTH OF COCAINE-PAIRED CONTEXTUAL MEMORIES
Get PDFA major impediment in treating substance use disorder (SUD) is craving following exposure to drug-associated environmental cues. Exposure to a cocaine-predictive context can trigger the retrieval of cocaine-associated memories. When cocaine-associated memories are recalled, they become unstable and need to be reconsolidated to persist. Memory reconsolidation is dependent on several mechanisms, including de novo protein synthesis and synaptic plasticity. Manipulations that disrupt unstable cocaine memories or impede their reconsolidation have been shown to reduce drug-seeking behavior; however, little is known about the mechanisms regulating drug memory strength. Therefore, understanding the neurobiological mechanisms of drug memory maintenance remains of great importance, with the long-term goal of identifying treatment approaches to prevent cocaine relapse. To that end, this thesis evaluated the roles of 2-arachidonoglycerol (2-AG) and anandamide (AEA) in contextual drug-memory reconsolidation using an instrumental model of context-induced cocaine relapse. The 2-AG and AEA systems were probed by enzyme inhibitor manipulations that targeted 2-AG or AEA synthesis ordegradation in the basolateral amygdala (BLA). Chapter One provides background information and outlines the rationale for this investigation. Chapter Two outlines the design and methodology of the experiments in this study. Chapter Three describes the behavioral effects of enzyme inhibitor manipulation. Chapter Four includes a summary of the findings and discusses their significance in the context of the literature on memory reconsolidation. Overall, this thesis provides evidence in support of the argument that 2-AG, but not AEA, signaling in the BLA regulates contextual cocaine-associated memory strength. This outcome suggests that 2-AG may be a viable therapeutic target for relapse prevention in individuals with cocaine use disorder