Mason Journals (George Mason Univ.)
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Sunderland, Willard, The Baron's Cloak: A History of the Russian Empire in War and Revolution
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Understanding the impact of model resolution on Atmospheric River representation over western North America
No full textAtmospheric rivers (ARs) are essential to many communities, providing a seasonal source of precipitation, however, ARs are also hazardous through flooding and landslides. The double-sided nature of ARs demands that they are well understood and are accurately modeled as we project their behavior under warming climate conditions. Projections of ARs are typically performed at low (2° or 1°) resolution, however, it remains unclear whether ARs can be resolved at such resolutions. To determine how model resolution influences the representation of ARs we analyze Preindustrial output from CESM 1.2 (Community Earth System Model) with spatial resolutions from 2° to 0.25°. We then focus on the western coast of North America as ARs are common there during winter months. We compare the output to ERA5 observation data to validate the accuracy of 2° and 0.25° simulations. Our study finds that higher resolutions (0.25°) represent ARs more accurately than 2° due to improved representation of precipitation systems over land when compared to observation data (ERA5). Looking at the IVT (integrated water vapor transport, a defining factor of ARs) anomaly between 2° and 0.25° also displays an increase around the west coast of North America with 0.25°. This increase is supported by the ERA5 data. Ultimately, this work enables future research to better understand and represent ARs and their impacts on western North America
Simulating Vaccine Decisions in an Agent-Based Model of Disease Spread
No full textAgent-based models (ABMs) are used to simulate the spread of disease. Compared to traditional mathematical disease models, ABMs can capture movement, heterogeneous characteristics, and behaviors of individuals, all of which play a role in disease dynamics. However, ABMs often oversimplify or ignore the health behavior component. Models typically use spatially aggregated data to determine a probability of adopting a behavior and apply it uniformly to the entire agent population. This ignores the underlying mechanisms that drive health decisions. Therefore, this study seeks to develop a more data-driven approach to modeling health behaviors in ABMs of disease spread. First, we generate a realistic population of agents for Virginia with characteristics including age, gender, race, income, education, and social influence. Using individual level survey data that asks questions about vaccine decisions in 2021, we train a logistic regression to predict vaccine uptake based on the characteristics above. Next, in the agent-based simulation of disease spread, the agents apply the trained model to themselves using their own characteristics as an input to determine their vaccine decision. We compare our method for simulating behavior with a uniform probability of vaccine uptake. This study advances disease simulations by allowing for data driven health behaviors
Can the cellular wiring of the brain shed light on epilepsy?
No full textSince Cajal’s and Golgi’s pioneering studies of neuronal morphology, neuroscientists have widely recognized the fundamental importance of the branching structure of brain cells in both physiological function and disease. Epilepsy, a chronic neurological disorder characterized by unprovoked seizures, is associated with abnormal neuronal discharges and hyperexcitability. Despite significant advancements in epilepsy research, the relationship between this pathology and neural morphology remains is not fully understood. NeuroMorpho.Org is an open-access repository containing over 260,000 digital reconstructions of neural morphologies from 94 species collected from nearly 1000 labs worldwide. In this work we downloaded 1,417 reconstructions from 19 NeuroMorpho.Org archives to explore morphological changes of brain cells in hippocampus and neocortex, the main epileptogenic regions. Even though NeuroMorpho.Org contained reconstructions from patients with epilepsy, the lack of proper controls makes their analysis difficult to interpret. Our preliminary results show that genetic models of epilepsy do not affect the morphology of neurons or glia in either brain region. In contrast, pharmacologically induced epilepsy mainly alters the morphology of microglial cells. Understanding the changes in cellular architecture associated with this pathology can open new opportunities to develop new treatments and improve the life quality of patients
LiDAR Drone Technology for Forensic Applications in Forested Areas Shows the Most Detail with Triple Returns
No full textLiDAR (Light Detection and Ranging) technology has become prevalent in forensic science as a way to document and investigate crime scenes. Using drone LiDAR, scientists are able to rapidly and accurately scan large areas to increase efficiency when collecting data. The data captured by drones also enables scientists to create 3D models that can be used in court. While the L1 LiDAR sensor has an accuracy of about 10 cm in open air at 165 ft, this sensor can also be used in forested areas to look through the trees to give a look at a potentially inaccessible crime scene; however, this field has not yet determined the accuracy with capturing LiDAR data in these areas. For this experiment, an object with a length of 0.61 m and width of 0.36 m was placed on the edge of a forested area. Measurements were taken with a LiDAR DJI L1 scanner with single and triple returns at an altitude of 180 ft, speed of 2.7 mph, and 70% front and side overlap. Flight data showed the object, and using a scale of the open sidewalk, the length and width were calculated at 0.50 m and 0.25 m respectively. The percent error for the length is 18%, while the percent error for the width is 31%. Given the somewhat high percent errors, the next step should be to compare this scanner with the L2 scanner or other LiDARs to further explore the accuracy in wooded areas
The Next Generation of Laser Capture Microdissection: A Comparative Study
No full textLaser Capture Microdissection (LCM) has become an essential research technique in biomarker discovery. LCM allows for the isolation of pure cell populations from heterogeneous tissue samples and coupled with Reverse Phase Protein Arrays (RPPA), molecular protein profiling of disease mechanisms of action can be achieved with high accuracy. In this study, we compare both the Pixcel IIe system and the newly introduced AccuLift LCM system —focusing on their usage, effectiveness, and results from RPPA analysis. We followed the instruments’ manufacturing recommendations to isolate tumor cells from formalin fixed paraffin embedded (FFPE) tissue sections of five individual prostate cancer biopsies. Consistent with our laboratory procedures, slides were deparaffinized, dehydrated, and hematoxylin stained prior to microdissection. LCM caps were lysed with extraction buffer, obtained lysates were then printed onto nitrocellulose slides and stained with a selected group of antibodies for comparison purposes. Slides were scanned with a laser scanner and images analyzed with MicroVigene software. Our results showed a similar trend of protein expression from the RPPA analysis of the prostate tumors microdissected using both LCM instruments. There was no distinct difference on intensity values in three of the biopsie. Two of the five biopsies differ slightly on intensity values detected by the software analysis. These differences may be due to the type of caps used, amount of isolated tumor cells and/or sample loss during processing. Further experiments are needed with a larger set of samples to confirm these results
Inhibiting the Interaction Between Cancer Immunotherapy Targets FGL-1 and LAG-3 Proteins Utilizing Small Interfering Peptides
No full textImmunotherapies are a method of cancer treatment that uses a patient’s own immune system to fight cancer cells. While immunotherapies are beneficial against numerous cancer subtypes, they are not effective within every single patient. Our study aims to inhibit the interactions between fibrinogen-like protein 1 (FGL-1), a ligand of lymphocyte-activation gene 3 (LAG-3) found on T-cells, in order to prevent inappropriate downregulation of T-cell activity. Inhibiting protein-protein interactions is typically very challenging with existing methods. Utilizing pulldowns and silver staining, we attempted to evaluate the ability of two small peptides to disrupt the protein-protein interactions between FGL-1 and LAG-3. These peptides were designed to be utilized in tandem in a multivalent inhibitor design which was validated through files from the Protein Data Bank visualized with the ChimeraX software platform. Our pulldown assay was designed to evaluate how LAG-3 and FGL-1 interact by using beads that only interact with LAG-3 and leave FGL-1 untouched. As a result, the presence of FGL-1 indicates interactions with LAG-3. We designed and validated the assay using silver stain imaging which stained FGL-1 and LAG-3 for visualization, thereby confirming the reported interaction. An additional peptide was then added to block the interaction between LAG-3 and FGL-1. This would theoretically result in absence of FGL-1 in the pulldown—however, this was not the case in our first trial. We aim to provide more accurate insights into the FGL-1/LAG-3 interaction, which is important for developing new immunotherapies. With the development of different immunotherapies, we should be able to further research and by refining our tagging methods, we enhance the reliability of our findings, contributing to the broader understanding of protein-protein interactions in immune pathways
Using Language Models to Promote Inclusive Language in Software Development Communities
No full textThe use of non-inclusive and harmful terminology in software development communities poses significant challenges in fostering an inclusive environment. The HaTe Detector project aims to address this issue by developing a tool that identifies and suggests replacements for harmful terms in computing artifacts.. Non-inclusive language can perpetuate stereotypes, reinforce biases, and create an unwelcoming atmosphere for underrepresented groups. Addressing this issue is important for promoting diversity, equality, and inclusion in tech. The HaTe Detector project uses existing research and tools focused on inclusive language, including the GitHub Inclusifier project, which offer guidelines and automated corrections for promoting inclusive language in technical and everyday contexts. We designed an experiment to evaluate several different LLMS including GPT-4, BERT, RoBERTa, T5, and DistilBERT for their ability to detect and replace harmful terms. We created specific prompts to cover detection, replacement suggestions, contextual understanding, and handling of complex scenarios. Preliminary results indicate that LLMs can effectively identify and suggest replacements for harmful terms and emphasize the potential of LLMs to support automated tools in promoting inclusive language in tech. This project contributes to ongoing efforts to foster a more inclusive tech community by building on existing literature and practicing robust evaluation methods.