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1-Methylxanthine enhances memory and neurotransmitter levels
No full text1-Methylxanthine (1-MX) is the major metabolite of caffeine and paraxanthine and might contribute to their activity. 1-MX is an adenosine receptor antagonist and increases the release and survivability of neurotransmitters; however, no study has addressed the potential physiological effects of 1-MX ingestion. The aim of this study was to compare the effect of 1-MX on memory and related biomarkers in rats compared to control. Memory (escape latency in the Morris water maze test), neurotransmitters (acetylcholine, dopamine, gamma-amino butyric acid (GABA)), and neurochemicals (BDNF, catalase, glutathione, Amyloid Beta and cyclic GMP) were analyzed from whole brain samples in young (8-weeks-old) and aged (16-months-old) rats following 12 days of supplementation (100 mg/d HED of 1-MX [UPLEVEL®, Ingenious Ingredients L.P., Lewisville, TX, USA]) via oral gavage. 1-MX supplementation reduced escape latency by 39% in young animals and 27% in aged animals compared to controls (both p<0.001). Additionally, 1-MX increased the levels of acetylcholine, dopamine, GABA, and cyclic GMP (all p<0.001). Furthermore, 1-MX supplementation led to reduced amyloid beta and higher catalase, BDNF and glutathione concentrations (p<0.001). Collectively, our findings suggest that 1-MX may have cognitive-enhancing and neuroprotective properties
Predicting Appropriate Educational Environments for Special Education
No full textNo explicit and objective method using historical national data sets is universally implemented in United States (US) public schools to predict the appropriate educational environments for students in special education (SPED) . How are national K-12 special education data sets of students’ background characteristics, academic proficiency, behavior, and needs related to trends in educational environments? Categorization is a causal-model theory, which explains the potential for developing an explicit and objective method for using historical national data to predict appropriate special education environments for students nationwide.
Based on a modeling of previous work examining relationships between student background characteristics and STEM pathways (Gottlieb, 2018), this study will use a logistic regression model to identify factors related to student educational environments in special education, to explore how those factors change as the educational environment changes from one of three selected placements, and to examine how these factors vary with educational environments across the identified student and district characteristics.
Casual-Model Theory showed consistent connections to previous literature to highlight the different weights of inputs for decisions about educational environments for students with disabilities. The concept of Category Stretching (Durand & Paolella, 2013) also aligned in that the weight of the student characteristics and district factors influencing placement decisions can change over time due to changes in state policies. The conceptual idea of LRE also consistently remained aligned with the methods and findings of this research, exhibiting how states approach keeping kids with disabilities in the regular classroom to the maximum extent appropriate.
As state policies change, it is plausible that, due to category stretching, the casual power of the many independent variables impacting placement decisions may fluctuate. However, based on the data from the USED on the 2018-2019 school year, and the results of this logistic regression, the independent variables of age, special education funding per pupil, reading proficiency, discipline, and disability have the greatest casual weight toward educational environments in special education
Developing Walkability Assessment for University Campuses: A Mixed-Methods Approach Using Geotechnology, Graph Theory, and Participatory Mapping
No full textUniversity campuses significantly influence students' ability to adopt healthy lifestyles. A well-designed campus-built environment can encourage walking, a vital form of active transportation. While various health initiatives exist, walking remains a crucial factor in promoting physical activity on college campuses, especially since students often face stressors that limit their time for regular exercise. Despite its importance, there is no comprehensive campus walkability assessment available to urban planners and policymakers that maps the impacts of built environments on walking behaviors. This study addresses this gap by proposing a mixed-methods approach that combines participatory GIS (PGIS) and mathematical analysis to evaluate campus walkability. The research was conducted in three phases. In the first phase, a mathematical approach using graph theory, random walk analysis, and geometrization was applied to assess the physical structure of Texas Tech University (TTU). This analysis aimed to predict areas with low pedestrian flow, accessibility challenges, navigational difficulties, isolation, and integration issues within the campus environment. In the second phase, participatory mapping was employed to capture the social and spatial dimensions of campus walkability. A two-day workshop with diverse TTU student groups, representing various racial, gender, and national backgrounds, used ArcGIS Field Maps and ArcGIS Survey123 to record walking trajectories and perceptions on a web map of the campus. This approach allowed for the integration of subjective experiences with the objective spatial network data, revealing how campus structure impacts pedestrian perceptions and behaviors. The third
phase involved capturing the broader campus community's perceptions through an IRB-approved online survey involving students, faculty, and staff. The survey included structured and open-ended questions to gather comprehensive feedback on their walking experiences and preferences. This research emphasizes the importance of combining mathematical modeling, spatial analysis, and participatory engagement to develop a robust framework for assessing campus walkability. By comparing subjective and objective data, the study highlights how a campus's spatial network and structure impact pedestrian perceptions, ultimately fostering healthier and more inclusive university environments
Toxicological Effects Of Titanium Carbide MXenes On Hyalella azteca, Zebrafish (Danio rerio), And Earthworms (Eisenia fetida).
No full textThis research investigates the environmental fate and potential toxicity of titanium carbide MXenes (Ti3C2), a class of engineered nanomaterials (ENMs), using three model organisms. MXenes are designed to be functionally 'tuned,' which enhances their capabilities, making them highly reactive nanomaterials. Although stable upon synthesis, MXenes are prone to degradation into smaller particles, which can aggregate under specific environmental conditions. To maintain stability, they are currently stored in antioxidant solutions, extending their shelf life from days to approximately six months. Due to the degradation process, it is critical to evaluate the potential environmental risks associated with MXenes before their widespread application. This research focuses on identifying potential toxic effects in Hyalella azteca, zebrafish (Danio rerio) and earthworms (Eisenia fetida).
Both computational modeling and prior research suggest that MXenes degrade over time, predominantly accumulating in aquatic sediments. This makes sediment-dwelling organisms, such as Hyalella azteca, directly exposed to these nanomaterials. H. azteca, a small freshwater amphipod, was selected for toxicity testing due to its ecological role as a primary consumer at the base of the aquatic food chain, making it a potential prey species for higher trophic levels. Zebrafish (D. rerio) was chosen as a secondary model organism to evaluate bioaccumulation, as zebrafish could feed on H. azteca and share 70% of their genome with humans, making them an established model for toxicological studies. Additionally, the earthworm (E. fetida), a sentinel species in terrestrial ecosystems, was used due to MXenes' potential applications in agriculture, allowing for an evaluation of their environmental impact beyond aquatic systems.
While each stage of MXene production, from laboratory synthesis to distribution, has been identified as potentially hazardous, there remains a significant gap in understanding the environmental fate and effects of MXenes, particularly given their anticipated widespread application. This research aims to address these gaps by investigating the toxicity and fate of Ti3C2 MXenes in aquatic ecosystems, focusing on H. azteca and D. rerio. By selecting both a sediment-dwelling invertebrate and its predator, the aim is to clarify the short- and long-term toxicological impacts of MXenes on different trophic levels, with an experimental design reflecting these temporal aspects. The inclusion of E. fetida in terrestrial studies further broadens the environmental scope, recognizing the potential agricultural applications of MXenes.
Given the intricate interconnections between human health and environmental health, understanding the behavior and risks of these nanomaterials in natural systems is crucial. This research provides essential data for risk assessments, offering real-world insights into the environmental and ecological consequences of MXenes. It is imperative that we perform this research now, before the global deployment of large-scale MXene applications, to ensure their safety and mitigate unforeseen ecological and health risks in the interconnected ecosystems upon which all life depends
Rapid isolation and recovery of Salmonella using hollow glass microspheres coated with multilayered nanofilms
Full text linkTimely isolation, recovery, and identification of Salmonella from food samples is essential for prevention and control of foodborne Salmonella outbreaks. Traditional culture-based Salmonella isolation and serotyping techniques are time consuming and labor intensive. Despite the progress of innovative microfluidic or immunomagnetic isolation techniques, sophisticated lab equipment and microfabrication are often needed. Here, we present a novel, rapid yet simple method for isolation and recovery of Salmonella from mixed bacterial populations in food matrices and blood. This method utilizes self-floating hollow glass microspheres (HGMS) coated with biodegradable layer-by-layer (LbL) films and Salmonella specific antibodies. The isolation and recovery process can be completed in less than 2 h, without any sophisticated laboratory equipment or external force. In this study, we demonstrate that Salmonella can be captured due to antigen-antibody interactions on the surface of HGMS, allowing them to float to the top. The HGMS can then be washed and subjected to enzymatic degradation of the LbL film to recover the captured bacteria. The recovered Salmonella can subsequently be grown on selective agar plates for further analysis. Recovery efficiency of up to 22 % and detection limit of 100 CFU/mL were achieved. This method is expected to provide a viable alternative to traditional isolation techniques, especially in resource limited areas
Physics-informed Feature Engineering in Machine Learning-Based Forecasting of Variable Renewable Energy
No full textThe United States aims to achieve a 100% clean energy grid by 2035, placing Variable Renewable Energy (VRE) sources such as solar and wind at the forefront of this transition. In 2023, VRE accounted for 14.1% of total energy generation in the US, and it is projected that by 2045, VRE will account for 40-70% of total energy generation. This rapid increase underscores the growing importance of VRE in the nation's energy landscape. However, integrating VRE into the grid presents significant challenges due to its inherent variability and
unpredictability. Accurate forecasting of VRE generation is crucial for effectively managing these variable resources. This dissertation addresses the urgent need for accurate, transparent, and robust forecasting models for VRE generation. Traditional Numerical Weather Prediction (NWP) models, while accurate, are time-consuming and computationally expensive, limiting their real-time applicability. In contrast, machine learning (ML) models offer faster predictions
and computational efficiency, making them crucial for the changing energy landscape. However, ML models often lack transparency and rely on extensive datasets, which can be inconsistent due to factors like curtailment and outages. To overcome these challenges, this research integrates physics-informed feature engineering into state-of-the-art ML models, hypothesizing that this integration will enhance day-ahead VRE forecasting accuracy by over 10% and reduce systematic biases in forecasting. Additionally, it examines the impact of curtailed
historical generation data and varying weather forecasting accuracy impact on ML-based VRE forecasting models. The dissertation evaluates six advanced ML models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Long- Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolution Neural Network (CNN). By incorporating physics-informed features such as clear sky irradiation models (Ineichen, Solis), cell temperature model, Bulk Richardson Number (RiB), wind gradient, sine-cosine transformations, polynomial features, and Discrete Wavelet Transformations (DWT) of wind speed, the models are tested on data from 20 solar farms and 20 wind farms across California. Results demonstrate that integrating physics-informed features significantly improves forecasting accuracy, with an over 10% increase across all case study sites. The study
also reveals that curtailed generation data impacts solar power forecasting accuracy by 3-5%, while accurate weather forecasting can lead to over 98% day-ahead VRE forecasting accuracy. Tree-based models like RF excel in day-ahead solar power forecasting, whereas neural network models like LSTM are most effective for day-ahead wind power forecasting
Hematological and inflammatory markers in beef-on-dairy neonatal calves: new insights into reference intervals
Full text linkColostrum early in life supports the development and function of the gastrointestinal tract while also influencing the endocrine and metabolic systems of the newborn calf. A complete blood count (CBC) analysis is a blood test that aids as a diagnostic tool and can provide insight into the overall health of the individual. While there are established reference intervals for adult cattle, there are no universally used intervals for neonatal or young calves.
The objectives of this thesis research project were to evaluate the pattern of blood markers of animal health in beef-on-dairy calves challenged with Salmonella; and to evaluate the impact of colostrum feeding on the physiological and immune blood parameters in beef-on-dairy calves from birth until 21 days of age. To achieve the outlined objectives, a total of 24 newborn male and female beef-on-dairy calves were enrolled in this study. Twelve calves received colostrum at the first feeding, while the other half were given milk replacer at their first feeding (n = 12/group). Following this initial feeding, all calves were subsequently fed milk-replacer. At 8 days of age, a subset of calves from each group was orally inoculated with Salmonella Typhimurium to induce an inflammatory response. On day 11 of the trial, all calves inoculated with Salmonella were humanely euthanized for tissue collection while the remaining calves were kept until 21 days of age for further evaluation of health parameters in the blood
Deciphering origins of hydrocarbon deposits by means of intramolecular carbon isotopes of propane adsorbed on sediments
Full text linkHydrocarbons are one of the important fluids within the Earth's crust, and different biotic and abitoic processes can generate hydrocarbon during geological periods. Tracing the sources and sinks of hydrocarbons can help us better understand the carbon cycle of the earth. In this study, an improved approach of adsorbed hydrocarbons extraction from sediments was established. The improved thermal desorption approach, compound-specific isotope analysis and position-specific isotope analysis were integrated to investigate the molecular and intramolecular isotope fractionation between trace hydrocarbon gases within sediments and geological hydrocarbon deposits. The isotopic compositions of the terminal position carbon of propane (δ13Cterminal) serves as a correlation indicator between trace hydrocarbon gases within sediments and geological hydrocarbon deposits. The tight sandstone gas from the Turpan-Hami Basin is a first case study for the application of this novel method to trace hydrocarbon origins. The results showed that the hydrocarbons in the tight sandstone gases in the study area most likely originated from humic organic matter (type III kerogen) at an early mature stage. δ13Cterminal values of the thermally desorbed propane gases from different source rocks were distinguishable and the values of the tight sandstone gases significantly overlap with those of the Lower Jurassic Sangonghe source rocks, suggesting their genetic relationship. Overall, the results provided novel position-specific carbon isotopic constraints on origins of hydrocarbons
Visual Analytics for Complex Multivariate Data: Techniques for Soil Science, Temporal Networks, and Interactive 3D Visualization
No full textIn an era of exponential data growth, the ability to effectively visualize and analyze complex multivariate data is crucial for scientific discovery and decision-making across numerous disciplines. This dissertation addresses key challenges in current visualization techniques for high-dimensional spatial, temporal, and network data through innovative visual analytics approaches.
The research tackles three main issues: (1) the inadequacy of traditional visualization methods in representing high-dimensional spatial data, (2) the difficulty in detecting and analyzing anomalies in multivariate time series, and (3) the challenge of visualizing evolving relationships in dynamic network data. To address these challenges, this dissertation proposes:
1. A high-dimensional spatial data visualization framework that integrates computational geometry with dimensionality reduction techniques. This novel approach combines multiple visualization methods to provide comprehensive views of spatial and temporal variations in complex datasets.
2. A dual-view framework for multivariate time series data, designed to highlight abnormal patterns through feature extraction and interactive filtering.
3. A temporal graph visualization method for evolving network relationships, incorporating quantitative metrics to track changing node importance over time.
Methodologically, this research employs a combination of computational geometry, dimensionality reduction, network analysis, and interactive visualization techniques. All proposed approaches are implemented as web-based, scalable tools capable of handling large-scale, complex datasets.
The effectiveness of these visualization techniques is demonstrated through multiple case studies. These include applications in soil science for analyzing elemental compositions, genomics for exploring gene relationship evolution, and time series analysis for detecting anomalies in various global datasets. These diverse applications highlight the wide applicability of the developed visualization methodologies across different domains with orthogonal data paradigms.
This research contributes to advancing the field of visual analytics by providing researchers with powerful tools to uncover intricate patterns and relationships in multivariate data. The proposed techniques have the potential to accelerate scientific discovery across various disciplines, from environmental science to genomics and beyond