Mason Journals (George Mason Univ.)
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Novel TAR-Targeting Small Molecules Inhibit HIV-1 Transcription
No full textAs of 2023, 39.9 million people were living with Human Immunodeficiency Virus type 1 (HIV-1), with630,000 deaths per year¹. HIV-1 weakens the immune system, leading to opportunistic infections andAcquired Immune Deficiency Syndrome (AIDS)². Combination antiretroviral therapy (cART) is a currenttherapy which inhibits multiple steps of the viral life cycle, significantly reducing viral loads³. However,cART lacks a transcriptional inhibitor, allowing the presence of viral proteins, as the HIV-1 transactivatorprotein Tat continues interacting with TAR, a non-coding stem loop structure at the 5’ LTR of the HIV-1genome⁴. Thus, this study investigated small, TAR-targeting molecules, identifying two candidates, 102 FAand 110 FA, that inhibit viral transcription in monocytes and T-cells, respectively.
Since the effects of 102 FA and 110 FA have been observed separately, we explored their combined effects.We treated HIV-1 infected cells from J1.1 (T-cells) and U1 (monocytic) cell lines, with and without induction,using a combined treatment of 102 FA and 110 FA, and analyzed the effects using western blot analysis forviral proteins. 102 FA and 110 FA, combined, did not significantly reduce viral proteins compared to whenthey were used separately. Viral protein counts, per our densitometry analysis, also remained similar whenlatent cells were induced. Therefore, our data indicates that the drugs remain more effective when usedseparately than when combined, even on induced latent cells. Future studies should explore the various facetsof combination drug treatments to create more targeted HIV-1 treatments.
1. What are HIV and AIDS? (2023). HIV.gov. Retrieved July 26, 2024, fromhttps://www.hiv.gov/hiv-basics/overview/about-hiv-and-aids/what-are-hiv-and-aids2. HIV. (2023). World Health Organization. Retrieved July 26, 2024, fromhttps://www.who.int/data/gho/data/themes/hiv-aids#:~:text=Global%20 situation%20and%20trends%3A,at%20the%20end%20of%2020223. Simonetti, F. R., & Kearney, M. F. (2015). Review: Influence of ART on HIV genetics. Current opinion in HIV andAIDS, 10(1), 49–54. https://doi.org/10.1097/COH.00000000000001204. Das, A. T., Harwig, A., & Berkhout, B. (2011). The HIV-1 Tat protein has a versatile role in activating viraltranscription. Journal of virology, 85(18), 9506–9516. https://doi.org/10.1128/JVI.00650-11
Investigating the Effect of Funding and Governance Models on GitHub Development Activities of Open-Source Software Projects
No full textOpen-Source Software (OSS) projects allow developers to access, modify, and distribute source code freely, allowing cumulative aid in development. Examples of OSS projects are NFTs, Decentralized Finance projects, Blockchains, Cryptocurrencies, and other DApps whose code can be accessed in their respective GitHub Repository. Despite the significant role OSS projects hold in the tech industry, the impact of different funding and governance models on GitHub development activities remains majorly underexplored. This study addresses this gap by analyzing 500+ OSS projects to understand how these models influence project success as measured by development activity on GitHub. Using Google Big Query and data cleaning techniques, extensive GitHub data from 2013 to 2024 was processed for each project, including repository information, activity dates, actor IDs, and details on what activities occurred. We collected additional data on governance tokens, associated communities, decentralization details, token reserves, DAOs, and governance model changes by researching through its GitHub, personal websites, and CoinMarketCap page. The three main components researched were project type, the funding model, and the governance mode. All information found is key to determine what correlation exists between funding and governance models on GitHub development activities of Open-Source Software Projects.
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
Exploring the relationship between GitHub activity of an OSS and business model to determine a given projects success
No full textOpen-Sourced Software, or OSS, is a common form of software that comes with the benefit of allowing those besides the creators to aid in its development. By making the code readily available, the public can modify and utilize the code as they please, which in turn leads to more developed software. By tracking the activity of user development/interaction on GitHub, which is the industry standard place to share OSS, one can get an idea of the level of success of the. This work is meant to gather data on various types of projects and map their success under the assumption that a more successful project will have more interactions over a longer period. More specifically, to determine if and what correlation exists between a project’s success and its business model. For example, taking project X, which is a cryptocurrency, and project Y which is a crypto wallet, is there a reliable correlation between the level of each project’s success and its business model or is it mainly based on outside factors. By using an SQL script to gather data from approximately 600 OSS GitHub repositories, the analysis aims to accurately assess the relationship between business models and project success.
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
MRIO Modeling for Environmental Analysis of Broadband Infrastructure Expansion
No full textThe deployment of universal broadband infrastructure is heralded as a catalyst for societal progress and economic development. However, coupled with rapid globalization and industrialization in the past few decades, the environmental implications of infrastructure expansion remain largely unstudied. This paper employs multi-regional input-output (MRIO) modeling to quantify environmental effects across global regions in order to elucidate upon the effects and implications of various broadband expansion policies. By integrating sector-specific data from the internationally compiled Eora dataset and environmental accounts, we construct a comprehensive MRIO network to trace direct and indirect environmental impacts through global supply chains. Utilizing the Leontief inverse matrix, we furthermore calculate the total requirements of each sector, capturing both the immediate and secondary environmental burdens associated with broadband infrastructure expansion. By applying scenario analysis, we assess the implications of different broadband expansion policies under varying assumptions and conditions. Ultimately, this paper seeks to provide policymakers with empirically grounded insight to optimize future investments in infrastructure whilst fostering environmental productivity and responsibility
Measuring the Velocities of Coronal Loops
No full textThe Sun’s core is heated to temperature over fifteen million degrees Kelvin via nuclear fusion. Asenergy flows out to the surface, photosphere, the gasses cool down to 5000 degrees Kelvin. However,above the photosphere the gas heats up again to over a million degrees. The science question is how thathappens. Near the surface, the gas begins to convect, which creates strong magnetic fields. Thesemagnetic fields create Sunspots and magnetic loops in the Corona. These loops are known to causeCoronal Mass Ejections, a process where large amounts of the Sun’s magnetic matter is shot out from theCorona at very high temperatures, often millions of degrees Kelvin. These loops get ejected frequentlyand can cause problems for our technology on earth. The loops are responsible for sights like the AuroraBorealis but also have been known to mess with computer memory and even change what is stored on it.This research aims to collect data to more properly understand the magnetic loops and what causesthem to be so much hotter than the surface. It is also unknown why the Coronal Mass Ejections occur andwhy they happen where they do. To answer these questions, scientists use a combination of basic physicsequations and computers. Collecting real data helps to validate or invalidate already existing computermodels to determine which is the best. The data that we’ve measured comes from Python analysis ofimages from the spectrometer aboard the Hinode Satellite. We measured the wavelength shift ofspectral lines at different temperatures and our code utilized the Doppler effect to calculate thevelocities. The results of our measurements will be presented
An High Resolution and Efficient System for Labeling Solar Coronal Hole Images
No full textThe ClassX project is based on the development of an automatic training dataset labeling tool and online service to fill the gap of missing high-quality training image datasets. Novel spatiotemporal AI/ML-based capabilities are being developed to automatically classify, label, store, and share training datasets among a group of needed users. It is currently being expanded to the heliophysics domain. In the field of solar physics, accurately identifying and labeling coronal holes in solar images is crucial for understanding solar dynamics and space weather prediction. However, manual labeling of these features is time-consuming and tedious. To address this, we present an automated image labeling tool that leverages the Mask Region-based Convolutional Neural Network (Mask R-CNN) model to detect and generate masks for coronal holes in solar images. We trained this model on a dataset of solar images curated by segmenting images from NASA’s Solar Dynamics Observatory using ClassX’s Quickshift Segmentation implementation and then labeling them manually. Our results demonstrated that the Mask R-CNN model achieved acceptable performance in coronal hole labeling. Overall, accuracy of this model compares favorably to the U-NET model. This performance underscores the potential of implementing Mask R-CNN in reducing the manual labor required for dataset labeling and ensuring consistency across large datasets. The successful implementation of these models in labeling coronal holes will not only advance the field of solar physics but also highlight the broader applicability of ClassX in other scientific domains, fostering the development of precise and reliable machine-learning models for scientific and engineering purposes
Optimizing Positional and Rotational Data Collection and Transmission in VR Headsets to Observe Disruption of Internal Sensors
No full textWith the development of Virtual Reality (VR) technologies, security issues have become a primary concern forusers. Disruptions of sensors in headsets can affect virtual properties such as virtual boundaries that may poseto be physically dangerous for the wearer. To observe and test varying techniques of disrupting internalsensors in headsets, developing an app that can obtain and transmit sensor data effectively and efficiently isvital. This project aims to address three different difficulties in creating such an app: (1) Maximizing the samplerate of the sensor data, (2) Transmitting said data in realtime to a server, and (3) Visualizing the data. Using aMeta Quest 2, we collect 3D coordinates as well as quaternion values to describe the positional and rotationalstate of the headset. We then transmit data through a WebSocket server and graph the data with PyQt. Theapp seeks to effectively promote the enhancement of the security of VR headset sensors by allowing forexperimental testing of different methodologies to disrupt internal sensors and assess current design flaws
Polarization Dependent Photoelectrochemistry of Anisotropic Rhenium Disulfide
No full textTwo-dimensional (2D) transition metal dichalcogenides (TMDs) have garnered significant attention as promising photocatalysts due to their high surface area and tunability. Rhenium disulfide (ReS2) is a distinctive member of the TMD family, notable for its unique in-plane anisotropy resulting from diamond-shaped rhenium chains along the crystal's b-axis. In this study, we examined the effects of linearly polarized light on the photoelectrochemical response of layered ReS2. The crystals were mechanically exfoliated and placed onto a conductive indium tin oxide-coated substrate. The b-axis orientation was identified by the longer crystal edge observed in the optical micrographs of individual ReS2 flakes. Atomic force microscopy (AFM) was used to determine the thickness of the flakes. The photoelectrochemical response was probed directly using scanning electrochemical cell microscopy (SECCM). Under linearly polarized light excitation, the photocurrent associated with the iodide/triiodide redox reaction at ReS2 showed a strong dependence on the polarization angle. Specifically, maximum photocurrent was obtained when the electric field orientation was along the b-axis, while lowest current was observed with the electric field orientation perpendicular to the b-axis. Additionally, the sensitivity of the photo response to polarization diminished as the thickness increased from 15 nm to 90 nm. These insights provide a direct correlation between structural orientation and performance of anisotropic photocatalysts
Life Cycle Assessment Comparison of LiFePO4 and Li-NMC batteries and Transportation Methods for Electric Vehicle Applications
No full textElectric vehicles (EVs) are considered environmentally friendly compared to internal combustion engine vehicles (ICEVs). However, EV battery manufacturing and transport poses notable environmental risks such as global warming potential. In the current EV market, lithium iron phosphate (LFP) batteries and lithium nickel-manganese-cobalt (NMC) batteries have been used, though a new wave of LFP vehicles have recently started production, leading us to inquire about their environmental impacts. In this study we use OpenLCA to perform a life cycle assessment (LCA) and compare the environmental impacts of LFP and NMC batteries as well as marine and aviation modes of transport using manufacturing and transport data from the 2023 IDEMAT database. It is important to note that the current database is limited in both the range of available products for analysis and the customization options for simulations. Based on the publicly available information, we output 1 kg of battery cells, 4.41x108 tons-km (tkm) of the container ship, and 4.42x108 tkm of air traffic. We compared three impact categories for the battery cells from the ReCiPe Midpoint (H) impact assessment method: PM formation, mineral resource scarcity, and global warming impact (GWI, potential CO2 emissions), but only GWI for the transportation phase. LFP batteries formed four times less particulate matter and had almost six times less mineral resource scarcity than NMC batteries, but more than eight times greater GWI. Additionally, Air traffic showed 108 times greater GWI than marine transport did. Based on the limited database online, we find LFP batteries and air transport to contribute more to the most concerning issue of the three, global warming, than their counterparts. With more databases coming, a more comprehensive analysis of the batteries could be conducted, along with the development of other simulation strategies, databases, and impact assessment methods to provide a thorough comparison among different batteries and transport methods