Mason Journals (George Mason Univ.)
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Exploring the Use of NEXRAD Radar Data for Real-Time Fire Plume Height Estimation
No full textPlume height plays a vital role in wildfire smoke dispersion and its subsequent effects on environmental health. Satellite data (e.g., MISR, CALIOP) has been widely used to estimate fire plume height; however, the temporal and spatial resolutions of these measurements are quite limited. Previous studies have found that NEXRAD (radar) data can be used to estimate fire plume height. In this study, we explore the possibility of establishing a real-time plume height dataset using NEXRAD data. Specifically, plume heights from high-resolution 3-D NEXRAD WSR-88D reflectivity data (≥ 5 dBZ) are calculated and the radar-estimates are evaluated based on CAMS Global Fire Assimilation System (GFAS) recorded mean injection heights and plume top heights for each fire point in August 2020 over the CONUS region. Observed precipitation data is used to mitigate the factor of cloud cover. We find that plume heights estimated using radar data are comparable to satellite model plume heights. Additionally, we explore the performance of radar-estimated plume heights across different regions, fire types, land uses, and fire intensities. The study indicates that radar-estimated plume heights and model-simulated plume heights show higher correlations in the Southwestern US, for Evergreen Needleleaf Forest land uses, and stronger fire cases. These correlations exceed the average up to 12%, 20%, and 32%, respectively. These results demonstrate that NEXRAD data can be used to obtain real-time plume heights in higher resolutions, particularly if data is filtered for key geographic variables, thereby improving wildfire air quality simulations
Optimizing exothermic chemical dyes within polymeric surfaces for high efficiency laser capture microdissection (LCM)
No full textThe tumor microenvironment is a heterogeneous population of tissue and cell types that promote tumor growth. Technologies to study the tumor microenvironment typically rely on immuno-staining to identify single markers of different tissue cell subtypes without further analysis. One method to overcome these methods is Laser Capture Microdissection (LCM). LCM utilizes laser energy to melt a polymeric capture surface, or "cap", to precisely remove cells of interest from a tissue section for downstream analysis. Conventional LCM systems have an infrared (808 nm) laser. Next-generation systems contain a near-UV (405 nm) "blue" laser to obtain single-cell microdissections. To achieve high precision microdissections, a novel polymeric melting cap needed to be developed for multiple laser types. Candidate near-UV chemical dyes with absorbance near 405 nm wavelengths were dissolved within a volatile solution and mixed with a Ethylene-vinyl acetate polymeric slurry for deposition onto a cap surface body. After drying, the polymer was subjected to heat and pressure for 2 minutes followed by immediate submersion into dry ice. The cap was inspected for polymeric surface thickness, clarity, and contamination. Cap performance was measured by the AccuLift system on ovarian cancer tissue cases at various laser powers and duration settings. It was found that a combination of a near-UV absorbing dye, a photo-initiating resin, and infrared absorbing dye was required to transmit the 405 nm laser energy to melt and capture single cells (<15 micron). This next generation cap will be critical to single-cell biology research for cancer and neurodegenerative diseases such as Alzheimer’s
Investigating HER2/EGFR Signaling Dynamics and EV-Mediated Communication in Cancer Cells
No full textHER2 and EGFR are both receptors that help mediate and control cell growth. Both HER2 and EGFR play crucial roles in cancer signaling. In HER2-overexpressing cancers, HER2/EGFR heterodimers form constitutively active complexes that evade recycling, leading to persistent oncogenic signaling. This study explored the internalization dynamics of these receptors and their potential use as extracellular vesicle (EV)-based cancer markers. We used two cell lines: human epithelial-like meningioma (IOMM-LEE) and BT474 (human breast HER2-positive ductal carcinoma) which overexpressed HER2. We tracked the internalization of equimolar HER2/EGFR in meningioma cells and overexpressed HER2 in BT474 cells by using anti-phospho-HER2 and anti-phospho-EGFR antibodies. To investigate EV-mediated signaling, cells were treated with EGF and EVs were collected at 30, 60, and 120 min post-treatment. We explored two possible mechanisms through which receptor dimers drive cancer progression. 1) Activation of cancer gene expression: During endosomal trafficking, instead of recycling to the plasma membrane, receptor dimers bind to importin alpha and translocate to the nucleus, thereby directly influencing gene expression, and 2) EV-mediated signaling: EVs containing phosphorylated receptors facilitate intercellular communication, triggering signaling cascades in recipient cells. This study provides insights into the complex relationship between HER2/EGFR signaling, receptor trafficking, and EV-mediated communication in cancer cells. Our findings suggest that phosphorylated HER2 in EVs may serve as a potential biomarker for diagnostic and therapeutic strategies for HER2-positive cancers
Greenhouse Gas (GHG) Emissions Poised to Rocket: Modeling the Environmental Impact of LEO Satellite Constellations
No full textThe proliferation of satellite megaconstellations in low Earth orbit (LEO) represents a significant advancement in global broadband connectivity. However, the environmental impact, particularly greenhouse gas (GHG) emissions associated with these constellations, remains relatively underexplored. This study addresses a critical gap in modeling the GHG emissions of current and future satellite megaconstellations. To quantify the emissions, we employ a comprehensive open-source life cycle assessment (LCA) methodology to evaluate the environmental costs of producing, deploying, and maintaining satellites across various megaconstellations and launch vehicles. Our analysis reveals that the production of launch vehicles and propellant combustion during launch events contribute most significantly to overall GHG emissions, accounting for 72.6% of life cycle emissions. Among the rockets analyzed, reusable vehicles like Falcon-9 and Starship demonstrate 95.4% lower production emissions compared to non-reusable alternatives, highlighting the environmental benefits of reusability in space technology. The analysis also includes a per-subscriber emissions evaluation for each megaconstellation, which reveals substantial variation – with some constellations, such as Globalstar, exhibiting emissions per subscriber exceeding the average by over 375%. These findings underscore the importance of judicious selection of launch vehicles and satellite designs to minimize environmental impact. This study provides a critical baseline for policymakers and industry stakeholders to develop strategies for reducing the carbon footprint of satellite megaconstellations, thereby promoting sustainable growth in the space industry. Our code is available in the Open-source Rocket and Constellation Lifecycle Emissions (ORACLE) repository, allowing for transparency and facilitating further research in this field
Quantitative Isotopically Labeled Tags for Precise Proteomic Analysis via Mass Spectrometry
No full textPeptide tagging is a technique in proteomics where specific tags are attached to peptides to enhance their detection and analysis. These tags typically consist of an ionization head, a linker, and reporter group, which together improve the peptide's ionization efficiency and detectability in mass spectrometry. By facilitating more accurate identification and quantification of peptides, peptide tagging is crucial for studying protein functions and identifying biomarkers for disease diagnosis and monitoring. Conventional tags, with a mass range of 100-300 Da, often fail to ionize and recognize smaller peptides efficiently, leading to undetected low-abundance biomarkers. To address this, we developed Quantitative Isotopically Labeled tags (QUAIL) with relatively larger mass and better reporter group, significantly improving sensitivity in mass spectrometry (MS)-based diagnostics. QUAIL also enhances multiplexing efficiency, allowing for the simultaneous analysis of multiple samples, thus saving time and resources. We evaluated QUAIL tagging efficiency by experimenting different solvents, temperatures, and incubation times to optimize the tagging process. The successful tagging reaction was confirmed by direct infusion on a SCIEX QTRAP 4500 mass spectrometer. The potential of QUAIL to improve diagnosis and early disease detection is substantial, particularly in identifying minute quantities of biomarkers. For example, tagging peptides from Amyloid Beta for Alzheimer's and Hemoglobin A1c for diabetes could facilitate early diagnosis and intervention. This research highlights QUAIL's promising potential in biological research and medical diagnostics, paving the way for more efficient and accurate disease prediction
Deploying mmWave distance sensing to Jackal UGV for more accurate navigation in hazardous environments
No full textThe development of autonomous machines has been revolutionary in minimizing human risk in defense and rescue. Most Unmanned Ground Vehicles (UGVs) currently use vision systems such as LiDAR or stereo cameras. However, the downside of popular vision systems is that they are easily compromised in reduced light and vision conditions such as fog, smoke, and rain. This research explores millimeter wave (mmWave) radar as an alternative vision system due to its resilience to such occlusions. We provide a proof-of-concept mmWave radar-based navigation system on the Jackal UGV. Through this approach, the robot eliminates intermediary visualization steps, directly processing raw data captured by the radar and produces information used for movement. Controlling both the Jackal UGV and the mmWave radar through the Robot Operating System (ROS) allows for real-time autonomous and semi-autonomous control. This robot proved to be functional semi-autonomously through goal-based Simultaneous Localization and Mapping through the Rviz software. Point clouds and Range-Angle diagrams created from the mmWaveRadar data provide high accuracy environment mapping. This approach of combining mmWave radars with existing UGVs will allow them to function in most environments with minimal drops in accuracy when compared with other sensing methods
Detection and Quantification of Nile Red Stained Microplastic Particulates in Breastmilk using Fiji
No full textMicroplastics are small, water-insoluble plastic particles that measure between 1 micrometer and 1 millimeter in size, and have been found in various environmental matrices. Their small size allows them to infiltrate cells and even nuclei, posing potential risks to human health through mechanisms such as oxidative damage, DNA alteration, and disruption of biological processes. Despite their widespread presence, there is a notable gap in research on the presence of microplastics in human breast milk, with only a few studies having investigated this area of study. Recently, advanced identification is being used to detect and analyze microplastics in various environments, over traditional techniques such as visual sorting, sieving, and density separation. In this study, a modified staining technique was developed for detecting microplastics in breast milk, utilizing a combination of fluorescent staining and Fenton's reagent that was specifically selected to be digest the fats, sugars, and proteins that are found in breast milk. Quantitative analysis of microplastics was achieved via the development of a custom image processing protocol using Fiji software, which utilizes programming scripts to automate data analysis. Our findings indicate that microplastics may be present in breastmilk, however, limitations in our current methodology, particularly the inaccessibility of advanced equipment, hinder our ability to precisely identify microplastic particulates based on chemical composition Future research could benefit from utilizing techniques including Raman spectroscopy, which may offer enhanced precision in detecting and quantifying microplastics in human breast milk. This study highlights the need for further in-depth investigation to better understand the implications of microplastic presence in breastmilk
Control of Tuberculosis epidemic in South Africa using a Multi-stage Stochastic Recourse approach for resource allocation under various transmission rates
No full textTuberculosis (TB), caused by bacteria Mycobacterium tuberculosis, is one of the leading infectious diseasesglobally. Every year, 10 million people fall ill with tuberculosis, and despite being a preventable and curable disease, TBkills 1.5 million people every year. South Africa, as of 2022, is on WHO’s list of 30 countries with a high burden oftuberculosis and has an incidence rate of 615 per 100,000. The TB epidemic has proliferated in South Africa due in part tothe HIV population. HIV is one of the most significant risk factors in TB spread. This is due to individuals with HIV having agreater rate of active TB, meaning a greater chance of spreading the disease and a greater need for health resources.While this connection between HIV and TB has significantly been researched, the implications on the budget for multiplelocations have not.This work presents a comprehensive multistage stochastic recourse method applied to an epidemiccompartmental model for TB dynamics. This model is analyzed for various TB transmission rates, taking into accountvarious biological, environmental, and socioeconomic factors in South Africa. The mathematical model incorporates theprogression from latent TB infection to active disease, accounting for variation in susceptibility, infectiousness, andtreatment responses. We employ discretized differential equations to describe the interaction between susceptible,infected, and recovered populations, and incorporate stochastic transmission rates to capture the inherent randomnessin disease spread and intervention impacts. Sensitivity analyses identify key parameters influencing disease dynamics,highlighting critical intervention points for effective TB control. Another contribution of this work involves thedevelopment of a Graphical User Interface to allow users to input their own values and determine the effect of differentparameters on disease flow. Our results underscore the importance of early detection and targeted public healthstrategies. The model serves as a robust tool for policymakers to simulate various scenarios and optimize TB controlmeasures, ultimately contributing to the global efforts in eradicating this enduring public health challenge
Automating Labeling of ML Clusters using KPM Data for Interference Detection
No full textSenseORAN is a framework designed to enhance spectrum sensing and radar detection capabilities within the Citizens Broadband Radio Service (CBRS) band by leveraging the Open Radio Access Network (O-RAN) infrastructure. It aims to improve the detection of radar signals that may interfere with 5G signals by integrating AI and machine learning models deployed as xApps on Near-Real-Time RAN Intelligent Controllers (Near-RT RIC).
Wfocus on automating the process of labeling Key Performance Metrics (KPMs) data collected from a softwareized LTE network. To address this challenge, we employ k-means clustering algorithms to process the KPM data and partitions data into distinct clusters based on similarities. In our implementation, the algorithm analyzes the KPM data and automatically detects which clusters represent interference and which do not.
Our methodology involves several key steps: first, preprocessing the KPM data to ensure it is suitable for clustering, including dropping features with little predictive value and handling missing values. Next, we apply the k-means algorithm to identify clusters within the data. We then validate the clusters to ensure they accurately represent interference patterns. Finally, we integrate the labeled data into our machine learning models, which are deployed as xApps on the Near-RT RIC. This automated labeling process significantly reduces the manual effort required and provides a scalable and efficient solution for real-time interference detection in wireless communications. 
A Comparative Study of Machine Learning Models for PM2.5 Prediction
No full textPM2.5 is a predominant pollutant with significant impacts on human health and atmospheric environmental quality. Fine particles such as PM2.5 can penetrate deep into the respiratory system, leading to various health issues, including respiratory and cardiovascular diseases. Moreover, high concentrations of PM2.5 can reduce visibility and contribute to environmental degradation. Despite numerous efforts, there remains a critical gap in systematic studies that effectively pinpoint the optimal models for predicting PM2.5 levels while accounting for meteorological influences. This study addresses this gap by aiming to identify the most effective models and the sweet spot in terms of predictive accuracy and the influence of meteorological factors on PM2.5 levels. To achieve this, datasets from 14 air quality monitoring sites across six northeastern U.S. states—New York, Pennsylvania, Vermont, Massachusetts, Connecticut, and Rhode Island—are utilized to enhance the retrieval and prediction of PM2.5 concentrations. The data encompasses atmospheric variablessuch as temperature, boundary layer height, and relative humidity, which are meticulously preprocessed to eliminate irrelevant information. Various machine learning models, including Linear Regression, Random Forest, Support Vector Machines, XGBoost, Extra Trees, and Deep Learning models, are employed to identify the optimal approach for PM2.5 prediction. The datasets are divided into training and testing sets, with the training data used to train the models and the test data reserved for evaluation. Model performance is rigorously assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²) metrics. This research fills a vital gap in air quality research, providing environmental agencies with robust tools for accurate PM2.5 prediction and ultimately aiding in the development of more effective air pollution control strategies and public health policies. Additionally, the study identifies the most effective models for predicting PM2.5 levels and helps understand the influence of various meteorological factors on these predictions