Mason Journals (George Mason Univ.)
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Analysis of the spatial-temporal patterns of vegetation, precipitation, land surface temperature, and soil moisture across the Sahara-Sahel Great Green Wall region using remote sensing data.
No full textThe Great Green Wall initiative, starting in 2007 and in development up until now, aims to combat desertification andenhance sustainability 8000 km across Africa’s Sahel-Sahara region encompassing 18 key countries associated with the initiative—Djibouti, Eritrea, Ethiopia, Sudan, Chad, Niger, Nigeria, Mali, Burkina Faso, Mauritania, and Senegal—that have all joined to combat land degradation and restore native plant life to the landscape (Schleeter et. al, 2023). This study aims to utilize satellite remote sensing data to analyze the temporal trends and spatial patterns of the driving forces sustaining life in the environmentally critical region. In terms of vegetation trends, the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data products from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) measurements are key to benchmarking the change of green vegetation over time. Land surface temperature data also harvested from MODIS aims to document both the daytime and nighttime temperature of the study area, a vital indication of land fertility. Precipitation data harvested from the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) was analyzed alongside the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) dataset in order to investigate the impact the Great Green Wall has on evapotranspiration levels. By employing the statistical analysis technique and identifying trends and correlations through regression models for these four key parameters, an assessment is made to help understand the effectiveness of the Great Green Wall initiative in meeting its development goals
Evaluating the Impact of Different Funding and Governance Models on GitHub Activity of OSS Projects
No full textThrough the use of GitHub, developers are able to improve and access Open-Source Software (OSS) projects. Due to the rise in blockchain and other new technologies, there has been plenty of development of new OSS projects. These projects can be managed through different governance models and may use different methods of funding. As a result, the methods of governance and funding can impact the developmental activity of these projects. This study analyzes data gathered from approximately 600 OSS projects using BigQuery. The data retrieved through BigQuery from each project’s respective GitHub repositories was done with the use of an SQL script. The script pulled the dates, actor lDs, and other data relating to developmental activity that ranged from 2013 to present. Once the data for each project was collected, each project’s background was researched. Information such as its funding model, governance mode, and project type was found. The developmental activity on GitHub indicates the success of an OSS project, with more activity deeming a project more successful and impactful. The study seeks to identify a relationship between the method of funding (Ex. Venture capitalist investments, token sales, donations), governance model (Ex. Non-profit organization, private company, DAO) and success for an OSS project.
This abstract is part of a collection in which the overarching large project under Dr. Jiasun Li was subdivided into discrete critical tasks that were carried out by multiple individuals or smaller teams. Abstracts in this collection read similarly given the shared project goals, but represent distinct tasks completed by the abstract authors towards finalizing the described analysis
Characterizing the small molecule Pyr-4MDM for modulation of the LTA4H enzyme as a potential anti-inflammatory drug
No full textLeukotriene A4 hydrolase (LTA4H) is an enzyme that affects neutrophil infiltration in emphysematous chronic obstructive pulmonary disease (COPD). There are two different enzymatic pathways for LTA4H: the pro-inflammatory epoxy hydrolase (EH) pathway, where the LTA4H converts LTA4 to LTB4; and the anti-inflammatory aminopeptidase (AP) pathway, where the LTA4H cleaves proline-glycine-proline to proline and gly-pro. Many studies that focus on EH inhibitors of LTA4H have been conducted to inhibit pro-inflammatory effects but did not show clinical benefit. Our strategy is to activate the LTA4H AP pathway to promote anti-inflammatory effects. Our hypothesis for this study was that a novel compound, Pyr-4MDM, synthesized in our labs will activate the LTA4H AP activity. We used Ala-pNA as a reporter group for AP activity. To determine whether Pyr-4MDM was either an activator or an inhibitor of LTA4H AP activity, we treated recombinant LTA4H with escalating concentrations of Pyr-4MDM and measured the rate of Ala-pNA cleavage, which was monitored at 405 nm. We also characterized 4MDM or bestatin as positive controls for LTA4H activation or inhibition, measuring the corresponding AC50 and IC50 values, respectively. We then analyzed the activity of our Pyr-4MDM to determine whether it was an activator or inhibitor of LTA4H. Preliminary research shows that Pyr-4MDM is an activator of LTA4H’s AP and is able to cleave Ala-pNA successfully, supporting our hypothesis. Next steps will be to test this compound in a mouse model for COPD to see if the compound is efficacious
Development of Software Tools for Satellite Observations and Near-Infrared Imaging in Support of the NASA Landolt Mission
No full textThe brightness of stars can currently only be measured to ≈2.5% uncertainty, limiting astronomers’ understanding of stellar characterization and dark energy parameters. The NASA Landolt mission, planned to launch in 2029, will use calibrated lasers mounted on a satellite in geostationary orbit to improve measurements of the absolute flux of >60 target stars to <0.5% uncertainty at visible and near-infrared (NIR) wavelengths. The 32-inch telescope at the George Mason University Observatory will serve as one of the ground stations for the mission and will observe the Landolt satellite to obtain flux data. We developed software to automate satellite observing using the telescope in the three Landolt observing modes: (1) tracking the satellite with the stars streaking; (2) tracking the stars at sidereal rate with the satellite streaking; and (3) tracking at half-sidereal rate with both the satellite and stars streaking to produce identical point-spread functions. The software was tested through observations of INTELSAT-40E, a geosynchronous communications satellite. Additionally, we developed software for imaging using the GMU C-RED2 high-speed low-noise NIR camera and integrated it with the satellite observation software. These tools enable novel satellite tracking methods and C-RED2 camera functionalities for use in the Landolt mission
Modeling changes in climatic variables in Morocco using remote sensing data and statistical techniques
No full textIn the arid Rhamna region of Morocco, climate change has taken a significant toll on the vegetation in the area; severe drought since 2018 has heavily impacted the agricultural sector of the nation, which makes up 39% of the labor workforce. Existing major crops, such as olive trees, require more water than can be sustainably provided, and will deplete the groundwater of the region in the coming decades if they continue to be grown. In order to accelerate the transition towards more sustainable cropping systems, it is crucial to understand the changes in key climatic variables in the region, including spatial patterns and temporal trends. In order to understand these changes, statistical analyses of time-series data for soil moisture, precipitation, land surface temperature, and evapotranspiration were conducted, modeling the changes over time from the year 2012 to 2022. The data used is available to the public in Google Earth Engine, and processing was done locally using Python libraries. Preliminary analyses on 8-day aggregates of the data yield an R² value of 0.6, suggesting that seasonal trends in the Rhamna region can be accurately approximated by these mathematical models and therefore aid in decision-making
FlightList: Automating Self-Assessment Checklists for Optimal Compliance and Efficiency in Air Force Units
No full textSelf-assessment checklists (SACs) are used in the Air Force to evaluate the safety of the internal controls in each unit, ensuring they meet the required standards. However, a significant challenge arises with SACs due to the constant changes in regulations necessitating frequent updates to the checklist. Air Force workers, commanders, and other compliance officers spend roughly hundreds of hours a month going through Air Force regulation documentation, spanning over 74 pages. As a result, creating checklist questions based on these requirements can be extremely time-consuming when done manually. The objective of our research was to build an Artificial Intelligence-based tool to address this inefficiency. We did this by creating an algorithm, in Python, that first goes through a PDF, which in our case was an Air Force regulation document, and extracts sentences that contain the keywords "shall", "will", and "must". After extracting these sentences, we then normalized the text by replacing newline characters with spaces. Lastly, we split the normalized text into potential sentences. Then the algorithm can phrase these sentences into question form, to automatically generate questions. Going by the name of FlightList, our product aims to assist Air Force workers who have to manually create these SACs, allowing them to put their time and effort into less monotonous tasks that require more manpower. We hope this prototype can be integrated into the Air Force to make the process of turning regulations into checklists more efficient
Analyzing Historical Snow Water Equivalent (SWE) Data: Trends, Economic and Social Impacts, and Prediction Accuracy Assessment
No full textThe accumulation and melting of snow water significantly impacts water resources, energy systems, agriculturalproductivity, and ecological balances across diverse regions. This study analyzes trends and variability in Snow WaterEquivalent (SWE) over the past decade, focusing on its effects on agriculture, social dynamics, and economic factors in theWestern United States. Analysis of SWE data collected over 10 years (2013-2023) reveals significant shifts in SWEpatterns, with earlier declines in spring and more severe reductions during summer, driven by accelerated temperatureincreases and altered precipitation dynamics. Although SWE variability has known effects on various sectors, there is asignificant gap in integrated studies that combine agricultural, social, and economic impacts, particularly in the WesternU.S. This research addresses this gap by employing advanced machine-learning techniques to analyze the effect of SWEvariability on agricultural productivity and associated economic outcomes. Regression models and machine learningtechniques were utilized to analyze 10 years of SWE data, crop yields, and water availability, focusing on the Western US.We employed statistical methods and machine learning algorithms based on SWE variability to identify patterns andrelationships within the data and seek actionable insights into agricultural outcomes and their economic impacts,offering strategies for better water resource management and improved economic resilience in agriculture. We identifiedstrong correlations between SWE variability and crop yields, with specific impacts on water-intensive crops. This researchprovides actionable insights for agricultural planning and water resource management in the face of changing SWEpatterns
Identifying Genetic Predispositions in Gastro-esophageal Disease Subtypes as Precursors to Esophageal Cancer
No full textGastro-esophageal reflux disease (GRD) includes subtypes such as Barrett’s esophagus (BE), functional heartburn (FH), erosive reflux disease (ERD) and non-erosive reflux disease (NERD). These conditions are not only debilitating but can also lead to the development of esophageal cancer (ESCA). This progression is driven by chronic inflammation, changes in the cellular environment, and genetic factors. While previous research has demonstrated that all GRD subtypes could be predisposing factors to esophageal cancer, there is a significant lack of data on the perturbed gene pathways in GRD patients that are implicated in esophageal cancer and could play a role in its development. This research work therefore focused on elucidating gene signatures and notable biological processes in GRD sub-types, that could act as precursor factors to esophageal cancer using differential gene and pathway analysis, comparative condition-to-condition analysis and protein-protein interaction mapping. The results revealed a 47-gene signature including MUC17, AZGP1, TFF2 and CNN1, which is not only associated with all GRD subtypes but also implicated in esophageal cancer. Consistent with previous research, this study underscores the importance of MUC17, AZGP1, CLMP and CNN1 as key genes that drive the transition of GRD subtypes to esophageal cancer, offering potential pathways for improved diagnosis, monitoring, and treatment in GRD patients
Caring connection: Strengthening communication between parents/guardians and inclusive postsecondary education program staff
No full textThis practice article describes one inclusive postsecondary education program's pilot intervention to increase communication amongst caregivers and inclusive postsecondary staff. This pilot intervention aimed to address and alleviate the difficulty caregivers might experience as their young adult with an intellectual or developmental disability transitions into postsecondary schooling, as channels of communication and advocacy shift from the caregiver to the young adult themselves while attempting to utilize postsecondary staff resources efficiently. This article briefly overviews the planning process for a monthly virtual parent/guardian meeting throughout the academic year and reviews outcomes from this pilot intervention
Correction to Evaluating Self-Determination and Academic Enabling Behaviors in Students with Intellectual Disabilities in Inclusive Postsecondary Education Programs
No full textCorrection to Potts et al. (2024)
In the article “Evaluating Self-Determination and Academic Enabling Behaviors in Students with Intellectual Disabilities in Inclusive Postsecondary Education Programs,” by Ellen E. Potts, Andrew T. Roach, Allison Wayne, Erin Vinoski Thomas, and Daniel Crimmins (Journal of Inclusive Postsecondary Education, 2024, Vol. 6, No 1. https://doi.org/10.13021/jipe.2024.3279, published on June 12, 2024, several referenced tables were omitted. Table 2 and Table 3 have since been added to the body of the article.
https://doi.org/10.13021/jipe.2024.417