Development Of A Behavioral Paradigm To Monitor Seizure Susceptibility And Severity In A Zebrafish Model Of Cblx Syndrome

Methylmalonic acidemia and homocysteinemia cblX type (cblX) (MIM#309541) is a rare, X-linked recessive disorder caused by a mutation in the HCFC1 gene. This disorder is characterized by multiple congenital anomalies which include intellectual disability, brain malformations, intractable epilepsy, microcephaly, and facial dysmorphia. The most severe symptom of cblX is intractable epilepsy which is 100% penetrant. Our lab developed a zebrafish model with a mutation in the zebrafish hcfc1a ortholog to study the mechanisms underlying seizure phenotypes in cblX. Our laboratory previously showed that mutation of hcfc1a results in increased number and proliferation of neural precursor cells (NPCs) and an increase in AKT/mTOR signaling. Previous studies in the field have confirmed that hyperactivation of mTOR signaling (mammalian target of rapamycin) is associated with seizures. Therefore, we hypothesized that zebrafish with a mutation in hcfc1a are more susceptible to seizures with increased severity relative to their wild-type siblings. To test this, we exposed mutant and wildtype type larvae to pentylenetetrazole (PTZ), a GABA antagonist, with and without inhibition of mTOR. We used Zebrabox technology to record behavioral parameters associated with seizure-like behavior. Suboptimal doses of PTZ did not lead to increased susceptibility in mutant animals, but treatment with mTOR inhibitors was able to reduce the severity of PTZ. We used western blot technology to verify the effects of mTOR inhibition. Our results suggest that hyperactivation of mTOR is partially responsible for seizure severity in hcfc1a mutant larvae but does not enhance seizure susceptibility

Saving Lives: Changing Perceptions Of Naloxone Distribution For Opioid Overdoses

This study investigated the impact and community perceptions of Narcan® (naloxone) distribution in El Paso County, Texas, as a harm reduction strategy amid the ongoing opioid epidemic. Drawing from a mixed-methods approach, the research surveyed 368 participants comprised of opioid users, family members, first responders, and community members who engaged in Narcan® training facilitated by two nonprofit organizations. The study explored four main questions: perceptions of Narcan® among different community stakeholders, patterns of Narcan® administration, whether the availability of Narcan encouraged drug use, and its effectiveness in reducing overdose deaths. Findings revealed widespread support for Narcan® distribution, although some community members expressed concerns about the false sense of safety it may provide to users. The majority of users reported prior overdose experiences and Narcan® administration, often by peers or first responders. Despite some stigma and logistical challenges, the research underscored Naloxone\u27s vital role in overdose prevention and suggested that broader community-based training and distribution may improve public health outcomes. Implications for policy, future research, and overdose intervention strategies are discussed

Computational Framework for Integrating Single Nucleotide Variant Scores to Identify Novel Genes in Cancers

A quantitative integrated scoring function, iQ(G) was developed to assess the cumulative effects of nonsynonymous single nucleotide variants (SNVs) on the protein-coding genes with the goal to find novel candidate cancer-related genes from patients with acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), and ovarian cancer (OC). With Genomic Data Commons as primary data resource for this project, whole-exome SNV data were extracted on patients with one of these three cancers. For each specific cancer, the iQ(G) function sums up the deleterious effects of individual SNVs with respect to the transcripts of the gene G in which they occur, weighted by the occurrence frequency difference between tumor and normal samples among patients and accounting for transcript lengths, to provide an overall cumulative pathogenic score for the gene. After obtaining the iQ(G) scores, the genes can be ranked accordingly, and the top-ranking genes are considered likely to be associated with the cancer. In this study, we applied iQ(G) scoring using four established SNV effect analyzers, namely FATHMM-XF, SIFT, PolyPhen, and CADD, as well as their averages. With a compiled list of known genes for each cancer type, we assessed the performance of iQ(G) when used with the individual analyzers, and with two integrative approaches that averaged the variant effects. The assessment results suggested that the integrated average approach had an overall advantage over using individual analyzers. Downstream bioinformatics analysis, including protein-protein interaction, gene ontology, and pathway analysis, performed on the top-scoring genes revealed similar carcinogenic pathways between the three cancers. This computational framework can be easily adapted to analyze SNV datasets for other cancers and to accommodate new SNV effect analyzers as they are developed in the future

Dynamic Modeling And Optimal Planning For The Simultaneous Integration Of Electric Vehicles And Renewable Energy Sources Into The Traffic-Power System

The large-scale adoption of electric vehicles (EVs) is regarded as an effective strategy to reduce greenhouse gas emissions from the transportation sector. Although EVs do not produce tailpipe emissions, their widespread adoption may place significant pressure on the power system - a critical aspect that has often been overlooked in policy and planning. Furthermore, the actual environmental benefits of EVs depend on the source of their electricity; renewable energy sources are more environmentally friendly compared to conventional fossil fuels. Taking into account the power demand induced by EV charging, we develop a continuous-time dynamic model for the optimal planning of the simultaneous adoption of EVs and the integration of renewable energy sources into the power system. The interactions between EVs and the power system (termed the traffic-power system) are explicitly considered within our mathematical model, based on the well-known Lotka-Volterra equations. This model effectively describes the relationship between competing entities, such as EVs versus legacy vehicles (LVs) and renewable versus conventional energy sources. We then formulate a control problem to determine an optimal planning policy aimed at achieving a desired market penetration rate (MPR) of EVs. This policy optimizes EV subsidies, infrastructure investment, and the rates for renewable integration and fossil fuel retirement, while minimizing costs and balancing energy demand and supply. The nonlinear optimization problem is solved using the Pontryagin minimum principle, ensuring optimality. We present a series of numerical results to demonstrate the effectiveness of the proposed approach, including extensive analyses on various aspects of the planning policy, such as different planning horizons and desired MPRs of EVs. Additionally, we conduct a cost-benefit analysis to assess the economic feasibility of selecting one set of planning goals over others. The simulation results provide valuable managerial insights for policymakers involved in the long-term planning of the increasingly interconnected traffic-power system

Decoding Anisotropic Porous Medium: A Synergy Of Lattice Boltzmann Modelling And Operator Learning To Predict Permeability As A Function Of Orientation

Understanding the directional properties of porous media is essential for accurately predicting flow behavior, reactive transport, and fluid-solid interactions in systems ranging from geothermal reservoirs to energy storage devices and biological tissues. Directional variations in permeability - reflecting a medium\u27s response to flow at different angular orientations - are particularly important for complex, inherently anisotropic geometries. In this study, we employ a Lattice Boltzmann (LBM) model to calculate directional permeabilities from porous media images subjected to varying flow inlet angles. Three classes of porous media were investigated: (1) synthetic media with circular grains, serving as isotropic baselines; (2) synthetic media with elliptical grains to introduce controlled anisotropy; and (3) micro-CT images of sandstone, characterized by naturally irregular grain distributions. For the synthetic cases, key geometric parameters were varied to diversify the medium structures. Using our LBM flow simulations, permeability was evaluated at 10° intervals across 360°, yielding 36 data points per sample. This systematic approach produced a comprehensive dataset capturing unique functional relationships between flow angle and permeability for each media class. Our primary goal is to analyze the anisotropy of these porous structures. While linear transformations of principal permeabilities can predict directional permeability in isotropic media, their applicability to anisotropic cases remains underexplored. We further aim to train machine learning models to predict permeability as a function of inlet flow angle given an image of the porous medium, and to compare model performance across the three classes of obstruction patterns. These findings deepen our understanding of how geometric anisotropy influences directional transport in porous media and pave the way for more accurate predictive models. Such advances have broad implications for filtration, energy storage, and subsurface fluid flow applications

Rethinking Iterative Proportional Fitting: Scalable And Hybrid Approaches To Joint Distribution Fitting

The Iterative Proportional Fitting (IPF) algorithm is widely used in contingency table estimation, survey weighting, and synthetic population generation due to its simplicity and strong theoretical foundation for matching observed marginal distributions. However, in high-dimensional settings, IPF faces substantial computational and memory demands, as well as statistical instability caused by sparse contingency tables. Moreover, IPF is less useful in modern population synthesis tasks that require both scalability and realism because, despite its superiority in matching known marginal distributions, it cannot produce realistic out-of-sample data points. To address these limitations, we first propose a blockwise IPF framework, in which the feature space is partitioned into smaller, correlated groups and IPF is applied independently within each group. This design significantly enhances computational efficiency while ensuring alignment with marginal distributions and preserving inter-variable dependencies. Second, we develop a hybrid framework to integrate IPF-derived weights into machine learning-based generative models. Two strategies are explored: (1) pre-sampling, where training data is reweighted using IPF weights to match marginal targets, and (2) weighted learning, where these weights are directly incorporated into the model\u27s training objective. While the framework is model-agnostic, we use Bayesian networks as a case study. Extensive simulation studies and real-world synthetic population generation experiments demonstrate that the proposed blockwise IPF framework scales efficiently to high-dimensional settings, maintaining statistical accuracy while offering substantial reductions in computational time. These experiments further show that the hybrid strategy produces synthetic data with greater sample diversity and improved alignment with marginal distributions. Finally, we introduce early-stage work on a neural network-based approach for estimating the joint distribution of a contingency table given expected marginals. Preliminary results suggest that this new paradigm holds significant promise for addressing several fundamental limitations of IPF

Ghrelin and Decision-Making: Exploring its Role in Food Reward, Novelty-Seeking, and Cost Valuation

When low energy levels are detected, Ghrelin, a peptide hormone, is produced in the fundus of the stomach. Referred to as the hunger hormone , Ghrelin is traditionally associated with increased food intake and blood sugar levels. A key modulator in the gut-brain interaction, Ghrelin\u27s growth hormone secretagogue receptor can be found widely across the cortico-striosomal circuit. Critical for decision-making, Ghrelin-based manipulation of this circuit can directly affect reward and cost valuation. While Ghrelin is uniformly considered to increase the valuation of food-based rewards, little research has explored its impact on non-food-based rewards or its effects on cost valuation. Therefore, to examine Ghrelin\u27s effects on behavioral decision-making, rodents performed a series of fifteen behavioral tasks categorized into three contexts: food reward, novelty-seeking, and cost. Notably, in food-based tasks Ghrelin did not increase food-seeking but altered preference patterns, suggesting its role in modulating reward salience over hunger- driven motivation. Ghrelin did, however, increase novelty-seeking behavior in novelty tasks. This preference for novel rewards was retained even in the presence of well-known and novel food rewards, suggesting Ghrelin plays an active role in regulating exploratory behavior. Indicating its role in cost valuation, Ghrelin consistently increased cost aversion and competitive behaviors. A culmination of these results questions the traditional view of Ghrelin as a hunger signal. As demonstrated here, while Ghrelin drives reward-seeking, its effects are highly context-dependent and potentially alter risk sensitivity and persistence in goal-directed behavior. Elucidating Ghrelin\u27s effects on decision-making has vast implications in studying the gut-brain interaction and its impact on metabolic or psychiatric disorders. Disorders such as depression, stress, obesity, and substance abuse commonly use overlapping circuitry and demonstrate altered reward processing, shifted cost valuation, and modified expression of Ghrelin. Future research aims to explore the neuronal mechanisms by which Ghrelin perturbed decision-making to better understand potential therapeutic targets for these disorders

Line Outage Impact Factors: A New Approach To Line Outage Detection With Machine Learning

Electric power systems have become one of our most critical infrastructures as we\u27ve grown dependent on electricity for everyday tasks. Ensuring power systems provide reliable service is a priority that can be affected by disturbance events. A common event is transmission line outages, where a line in the system becomes disconnected due to varying forms of physical damage. If an outage isn\u27t detected in time, other lines in the system may overload, causing cascading failures that leave many customers without power. Therefore, having a power system that can automatically detect outages is crucial for reliability, as it promotes real-time response and recovery. Various methods for line outage detection have been developed over the years, focusing on issues such as minimum deployment of Phasor Measurement Units (PMU) and detecting outages with partial data. Furthermore, machine learning has gained traction for its improvements in line outage detection. In this thesis, we developed two machine learning-based methods for detecting transmission line outages using K-Nearest Neighbors. Our first method, we showed the potential of Line Outage Distribution Factors (LODF) as a feature or data observation point selection tool. Identifying critical observation points through LODF enables the detection of outages with limited data by monitoring power flow from one transmission line while accounting for load uncertainty to estimate the status of another line. Our second method introduces a new set of factors called Line Outage Impact Factors (LOIF), a modified version of the LODF, which solves some concerns we have when using LODF for feature selection. Instead of showing the distribution of power from an outage line, LOIF shows the change in power flow of a line due to the outage of another. We develop a feature-selection method that focuses on determining the outages that provide significant and distinct changes in power flow

3D Printing Metal Horn Antennas Using Periodic and Aperiodic Perforated Designs for Directed Energy Applications

In the past decade, Additive Manufacturing (AM) has proven to be groundbreaking yet reliable technology. Not only has it revolutionized the way we approach problems in research, design, and production but it has enabled burgeoning discoveries and methods due to the technologyâ??s efficiency, speed, and low manufacturing cost. One area that benefits greatly from this includes radio frequency (RF) devices like horn antennas. At the moment, most horn antennas produced by conventional manufacturing are expensive, heavy, and limited in customization, yet they are necessary for various systems like satellite communication, radar, radio astronomy, and more. 3D printing technology would alleviate some of these issues since they can be constructed at varying levels of complexity at lower the cost, mass, and amount of time. One type of customization that has already been explored in previous research includes metal printing perforation patterns on the walls of the horn antenna using Powder Bed Fusion (PBF) technology. The purpose of this design reduces mass and surface roughness of the material without significantly altering the signal. However, this research aims to study the effects of various geometric periodic perforations and compare them to an aperiodic design known as Einstein Tiles. By comparing the electrical performances and mechanical robustness, we can determine which design is best while also analyzing how much the predicted 3D model matches the physical antenna