Mason Journals (George Mason Univ.)
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The Optimization of Synovial Fluid Protein Quantification with Hyaluronidase
No full textSynovial fluid (SF) is critical for joint health as it provides lubrication via high molecularweight carbohydrates like hyaluronic acid (HA) and contains signaling molecules to respond toinjury1. HA, known for its high viscosity, sequesters inflammatory modulating cytokines in SF,reducing inflammation. This property of HA interferes with accurate protein quantification usingimmunoassays to study joint inflammation2. Various protocols exist to address HA interferencebut yield inconsistent results3,4,5. One approach treats synovial fluid with hyaluronidase for 0.5-1hour at 37℃ or 20℃1. However, commercially available hyaluronidases are contaminated with proteases6, and synovial fluid contains proteases7. Although protease inhibitors can be used,some also inhibit hyaluronidase8. The research goals were to determine the optimum HAdigestion protocol, protease contamination of three hyaluronidase products, and whetherprotease inhibitors interfere with hyaluronidase. The optimal digestion protocol was determinedby measuring the viscosity of high molecular weight HA after digestion at 20℃ or 37℃ for 0.5,1, or 24 hours. Inhibition of hyaluronidase by protease inhibitors was also determined bymeasuring viscosity as a proxy for HA concentration. To assess protease contamination incommercial hyaluronidases, bovine serum albumin was treated using the HA digest protocol,and protein was quantified using Bradford Assay and spectrophotometry. Preliminary resultsindicate that hyaluronidase treatment of HA is optimal at 37℃ for 30 minutes. Proteaseinhibitors did not interfere with the digestion of HA. Initial results showed contamination ofhyaluronidase, but this research is ongoing. These results aim to provide an optimized protocolto prepare synovial fluid for immunoassays in orthopedic research.
1 Necas, J., Bartosikova, L., Brauner, P., & Kolar, J. (2008). Hyaluronic acid (hyaluronan): A review.
Veterinary Medicine, 53(8), 397–411. Faculty of Medicine and Dentistry, Palacky University, Olomouc, Czech Republic; Faculty of Pharmacy, University of Veterinary and Pharmaceutical Sciences, Brno, Czech Republic.
2 Anderson, J. R., Phelan, M. M., Rubio-Martinez, L. M., Fitzgerald, M. M., Jones, S. W., Clegg, P. D., & Peffers, M. J. (2020). Optimization of synovial fluid collection and processing for NMR metabolomics and LC-MS/MS proteomics. *Journal of Proteome Research, 19*(7), 2585-2597. https://doi.org/10.1021/acs.jproteome.0c00035
3 Keiser, H. D., & Hatcher, V. B. (1979). The Effect of Contaminant Proteases in Testicular Hyaluronidase Preparations on the Immunological Properties of Bovine Nasal Cartilage Proteoglycan. Connective Tissue Research, 6(4), 229–233. https://doi.org/10.3109/03008207909152325
4Jayadev, C., Rout, R., Jackson, W., Price, A., & Hulley, P. (2012, April). Synovial fluid preparation to improve immunoassay precision for biomarker research using multiplex platforms. *Osteoarthritis and Cartilage*, 20(S1), S169-S170. https://doi.org/10.1016/j.joca.2012.02.077
5 Keiser, H. D., & Hatcher, V. B. (1977). A Comparison of Bovine Nasal Cartilage Proteoglycan Core Protein Produced by Chondroitinase and Hyaluronidase: The Possible Role of Protease Contaminants. Connective Tissue Research, 5(3), 147–155. https://doi.org/10.3109/03008207709152265
6 Keiser, H. D., & Hatcher, V. B. (1977). A Comparison of Bovine Nasal Cartilage Proteoglycan Core Protein Produced by Chondroitinase and Hyaluronidase: The Possible Role of Protease Contaminants. Connective Tissue Research, 5(3), 147–155. https://doi.org/10.3109/03008207709152265
7 Marco Maiotti, Giovanni Monteleone, Umberto Tarantino, Giovanni F. Fasciglione, Stefano Marini, Massimiliano Coletta,Correlation between osteoarthritic cartilage damage and levels of proteinases and proteinase inhibitors in synovial fluid from the knee joint, Arthroscopy: The Journal of Arthroscopic & Related Surgery, Volume 16, Issue 5, 2000, Pages 522-526, ISSN 0749-8063, https://doi.org/10.1053/jars.2000.4632.
8 Mio, K., & Stern, R. (2001). Inhibitors of the hyaluronidases. Matrix Biol. 2002 Jan;21(1):31-7. doi: 10.1016/s0945-053x(01)00185-8
Engineering an ATP Binding Pocket in the Kirbac3.1 Inward Rectifier Potassium Channel Scaffold using De Novo Design
No full textDe novo design seeks to engineer brand-new proteins rather than modify existing ones. This specific and innovative approach allows researchers to advance medicine and biotechnology through designed proteins with unique properties (folds, functions, amino-acid sequence) absent in naturally occurring proteins. One key assumption in design is that binding sites can be engineered into any site if the underlying interactions are well understood. We sought to engineer a binding pocket for adenosine triphosphate (ATP) within the fold of the Kirbac3.1 inward rectifier potassium channel using RFDiffusion. This fold offers a great challenge because it is a membrane-bound protein we look to re-engineer into a water-soluble protein. It also has well-defined binding pockets that require transformation. We discuss the challenges in transforming a protein and the further issues in transferring binding pockets
Measuring Acceleration of WebGPU for JavaScript
No full textIn the field of computer science, optimizing the execution time of computational tasks is essential for enhancingthe performance of web applications. This study investigates the performance of WebGPU, a cross-platform graphics API designed for efficient GPU utilization. Traditional JavaScript and JavaScript leveraging WebGPU (JS + WebGPU) arecompared for various computational tasks. The primary focus is on matrix multiplication, vector addition, andmatrix-vector multiplication, where execution time is a critical factor. Benchmarking tests were conducted to evaluate the execution times of JavaScript and JS + WebGPU across different input sizes. Our measurement results showed for two matrix multiplication operations (2mm), JS + WebGPU exhibited a substantial performance improvement for larger inputs, reducing execution time from 6.836 seconds to 1.227 seconds for large (L) inputs and from 100.78 seconds to 1.621 seconds for extra-large (XL) inputs, compared to JavaScript alone. Similarly, for vector addition, JS + WebGPU outperformed JavaScript significantly for larger inputs, with execution times decreasing from 7.364 seconds to 1.082 seconds for XL inputs. In matrix-vector multiplication, JS + WebGPU also demonstrated superior performance, reducing execution times from 3.291 seconds to 0.752 seconds for L inputs and from 51.358 seconds to 1.011 seconds for XL inputs. These results highlight the potential of WebGPU to enhance computational efficiency, particularly for large-scale operations, by leveraging GPU capabilities. The study's findings suggest that integrating WebGPU with JavaScript can lead to substantial reductions in execution time with input sizes (growth in dimensions of matrices and vectors), offering significant implications for the development of high-performance web applications
Evaluating the Performance of Automated Speech Recognition Systems for Black Users
No full textAutomated Speech Recognition (ASR) systems understand and convert human speech into written text andeven transform it into its own speech, as found in applications like Siri or Alexa. While these technologies havethe potential to benefit the greater society, studies have shown they may not provide that benefit equitablyacross demographic groups. To better understand the extent to which ASR technologies support the diasporaof potential Black users, this systematic literature review covers existing research studies, performanceevaluations, and technical reports related to ASR systems. Based on an analysis of articles discussing thistopic, it was revealed that while efforts exist that aim to support Black users of ASR, there remain significantdifferences in the performance of ASR for Black and African American users. This could be because theprimary focus of efforts thus far has been on the underrepresentation of African American Vernacular English(AAVE) in existing datasets and the need for exposure to diverse linguistic patterns during training. This reviewhighlights important gaps in existing efforts to support the development of ASR technologies for the Blackcommunity, such as the lack of diverse data collection efforts
Kinases from HIV-1 Infected Exosomes Drive Cell Cycle Progression in Recipient Cells
No full textHuman immunodeficiency virus type 1 (HIV-1) is a retrovirus that depletes CD4+ cells and progresses into acquired immunodeficiency syndrome (AIDS) if left untreated [1]. As of 2022, HIV-1 caused around 88.4 million infections and 42.3 million deaths [2]. Currently, combination antiretroviral therapy (cART) is an effective therapeutic, which prevents AIDS by blocking various stages of the HIV-1 life cycle; however, it is not a cure, as low levels of viral production persist in cART patients [3]. This is partially due to exosomes, which are membrane-bound vesicles that transport proteins and nucleic acids between cells for communication [4]. When infected, exosomes carry viral and host materials that instigate infection, such as exosome-associated host kinases CDK10, GSK-3B, and MAPK8 [4, 5]. In this study, we examined how the kinases influence cell cycle progression, a key aspect of HIV-1 pathogenesis. Exosomes were isolated from infectious ACH2 and U1 cells, treated with kinase-suppressing drugs, and administered to U937 and CEM cells in G0. Levels of cell cycle proteins were then assessed through western blot to determine cycle status. Cyclins D and E, which engage in G1/S transition, decreased in CEM cells treated with ACH2 exosomes on CDK10-inhibiting NVP2, suggesting inhibition causes G1 arrest. Conversely, Cyclin E and CDK2 increased in U937 cells treated with U1 exosomes on GSK-3B-inhibiting AZD2858 and MAPK8-inhibiting DB07268, indicating inhibition allows for G1/S progression. These findings enhance our understanding of host protein impact in HIV-1 proliferation and can be considered in the development of novel therapies.
Dornadula, G. (1999). Residual hiv-1 RNA in blood plasma of patients taking suppressive highly active antiretroviral therapy. JAMA, 282(17), 1627. https://doi.org/10.1001/jama.282.17.1627
HIV. (n.d.). World Health Organization. https://www.who.int/data/gho/data/themes/hiv-aids#:~:text=Globally%2C%2039.0%20million%20%5B33.1%E2%80%93,considerably%20between%20countries%20and%20regions
HIV and AIDS: The Basics. (n.d.). HIVinfo.NIH.gov. https://hivinfo.nih.gov/understanding-hiv/fact-sheets/hiv-and-aids-basics#:~:text=The%20human%20immunodeficiency%20virus%20(HIV,advanced%20stage%20of%20HIV%20infection.
Madison, M., & Okeoma, C. (2015). Exosomes: Implications in HIV-1 pathogenesis. Viruses, 7(7), 4093-4118. https://doi.org/10.3390/v7072810
Mensah, G. et al. (2024). Effect of Kinases in Extracellular Vesicles from HIV-1-Infected Cells on Bystander Cells. Cells, 13, x. https://doi.org/10.3390/xxxxx. In preparation. 
Analyzing the Impact of Different Funding and Governance Models on an OSS Project’s Success
No full textWith the rise of both Bitcoin and Ethereum, many people have started to develop projects to improve the blockchain. These projects ranged from improving the security of crypto to facilitating the exchange of different cryptocurrencies. To further improve a project, developers often use Open-Source Software (OSS), a computer software that allows the public to use and modify a project. GitHub is an extremely popular platform where developers share OSS and can be used to find how commonly the project is updated. This study is aimed to find what factors lead to a successful project, with a specific focus on how a project's major source of funding and organizational structure impacts its success. By utilizing both an SQL and Python script, data on the activity of 20 different OSS projects from 2013 to the present were compiled. Each project was then labeled with specific information on its functionality, governance model, funding, etc. These projects were then aggregated to a data collection of over 600 projects to find the correlations between their funding models, organizational structure, and general 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.
Analyzing Open-Source Software (OSS) GitHub Projects to Determine the Correlation Between Funding/Governance Models and GitHub Development Activities
No full textGitHub, a service for software development and collaboration, hosts millions of Open-Source Software (OSS) projects. Blockchain, a complex distributed ledger technology with immense emphasis on security and transparency, is the foundation of Fintech that is built on OSS projects. OSS projects with different funding/governance models may have different GitHub development activities (e.g. watch/commit ratios). The GitHub Archive database, containing all GitHub activities, was analyzed using Google BigQuery to search Blockchain project repositories for over 25 different GitHub events. The aggregated and collected data was over 20 terabytes and had data points from the creation to present-day activities. We used Open-Source Intelligence (OSINT) to collect funding and governance models from various sources, including GitHub repositories, project websites, etc. The data was then partitioned based on parameters such as project type, funding model, and governance model. Comparing the aggregated data provides an understanding of how different business governance and funding models, used by blockchain organizations, impact the business’ 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
Investigation on predictors of an open-source software project’s success through GitHub archive
No full textWith bitcoin’s price surging over seventy thousand dollars in 2024 and more people becoming aware of cryptocurrency and the blockchain, the number of open-source software (code that the public can improve upon) projects relating to the crypto world has increased. The purposes of these projects range from expanding the functionalities of cryptocurrency platforms to improving security as well as financial inclusion. However, with such a diverse range of projects being developed, predicting their success becomes challenging. To this end, six hundred and sixty of these projects were analyzed by extracting their data. This was achieved through a series of SQL (structured query language) queries in Big Query to extract the data of these projects from GitHub Archive. Data extracted included the number of commits, watches, pull requests, and more from 2013 to the present. Subsequently, each project’s website and social media was browsed to classify the funding and business models for each of the projects. When the analysis is finished, it will reveal which activities are associated with certain project types and trends in activity that correlate with success or if there is no correlation at all.
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