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  • Science Shouldn't Be A Luxury

    Media thumbnail: Science Shouldn't Be A Luxury
    Type:
    Media
    Description/Abstract:
    What happens when the biggest barrier to scientific discovery isn’t a lack of ideas, but the cost of the tools themselves? In this episode, Jarin Earle, a junior in Mechanical Engineering at North Carolina A & T State University, shares how a summer research project designing a low-cost viscometer changed the way he thinks about engineering; not as a way to build machines, but as a way to expand who gets the opportunity to do science.
    Creator/Author:
    Earle, Jarin O.; Yavitt, Benjamin M., and Payton, Eric J.
    Submitter:
    Eric Payton
    Date Uploaded:
    07/24/2026
    Date Modified:
    08/15/2026
    Date Created:
    2026-07-24
    License:
    Attribution-NonCommercial 4.0 International

  • Bubbles and Biases: Demystifying Research and Sustainability

    Media thumbnail: Bubbles and Biases: Demystifying Research and Sustainability
    Type:
    Media
    Description/Abstract:
    Through my college journey, the terms “research” and “zero-emissions” have become central to my academic identity. Not just because they reflect both my lab experience and keen interest in sustainable tech, but because they evoke a personal question that many high school seniors and college underclassmen grapple with: what does it mean to be a scientist? I’m Trevon Collins, a mechanical engineering student at Tennessee Technological University. In this podcast episode, I’ll share the answers I’ve found through investigating age-old secrets of phase change and their implications for the green transition.
    Creator/Author:
    Collins, Trevon L; Yasin, Ayaaz; Bellur, Kishan, and Payton, Eric J
    Submitter:
    Eric Payton
    Date Uploaded:
    07/24/2026
    Date Modified:
    07/25/2026
    Date Created:
    2026-07-24
    License:
    Attribution-NonCommercial 4.0 International

  • Do Not Fear Failure

    Media thumbnail: Do Not Fear Failure
    Type:
    Media
    Description/Abstract:
    Kennedy Whiters lives by "Do not fear failure; rather, fear not trying." She participated in the REALIZE REU when she was a junior at Albany State University. Kennedy performed research on methanogens, a type of anaerobic bacteria that consumes carbon dioxide and turns it into natural gas, which we can use as a source of energy in power plants. In this episode, she describes her experience working in the lab of Prof Annie Rowe, where she found the lab group to be a good source of positive support, like her family.
    Creator/Author:
    Whiters, Kennedy D.; Rowe, Annette R., and Payton, Eric J.
    Submitter:
    Eric Payton
    Date Uploaded:
    07/19/2026
    Date Modified:
    07/24/2026
    Date Created:
    2024-07-28
    License:
    Attribution-NonCommercial 4.0 International

  • TEst for June 29

    Generic Work thumbnail: TEst for June 29
    Type:
    Generic Work
    Description/Abstract:
    test
    Creator/Author:
    Langsam, Walter
    Submitter:
    Walter Langsam
    Date Uploaded:
    06/30/2026
    Date Modified:
    06/30/2026
    License:
    Attribution 4.0 International

  • Test for Office Document

    Generic Work thumbnail: Test for Office Document
    Type:
    Generic Work
    Description/Abstract:
    tst
    Creator/Author:
    Langsam, Walter
    Submitter:
    Walter Langsam
    Date Uploaded:
    06/27/2026
    Date Modified:
    06/27/2026
    License:
    Attribution-ShareAlike 4.0 International

  • Test for server 2

    Generic Work thumbnail: Test for server 2
    Type:
    Generic Work
    Description/Abstract:
    tset
    Creator/Author:
    Langsam, Walter
    Submitter:
    Walter Langsam
    Date Uploaded:
    06/27/2026
    Date Modified:
    06/27/2026
    License:
    Attribution 4.0 International

  • Test for SideKIQ

    Generic Work thumbnail: Test for SideKIQ
    Type:
    Generic Work
    Description/Abstract:
    tst
    Creator/Author:
    Langsam, Walter
    Submitter:
    Walter Langsam
    Date Uploaded:
    06/27/2026
    Date Modified:
    06/27/2026
    License:
    Attribution 4.0 International

  • Activation of a branched chain amino acid rheostat restores replication-dependent hematopoietic stem cell fitness

    Article thumbnail: Activation of a branched chain amino acid rheostat restores replication-dependent hematopoietic stem cell fitness
    Type:
    Article
    Description/Abstract:
    Adult hematopoietic stem cells (HSCs) sustain the lifelong production of all mature blood and immune cells. HSCs possess extensive regenerative potential, but their self-renewal is limited. A long-standing question has been why replicative history negatively impacts HSC functions. We found that accrued divisions alter HSC production; generating low-output bone-marrow landscapes that are highly variable in lineage contribution and transcriptionally divergent within individual lineages. Division-driven HSC functional alterations arise from redirecting branched chain amino acid (BCAA) usage from catabolic towards anabolic activity, causing faster HSC cell cycle kinetics. Adding a BCAA transamination product overcomes the BCAA catabolic checkpoint and slows down the cell cycle, durably rescuing balanced lineage output of HSCs with accrued divisions. Hence, our study suggests the paradigm whereby replicative history causes metabolic and transcriptional drift, generating divergent HSC output. Division-dependent HSC functional drift can be restored by metabolite replacement, which has long-term therapeutic implications for HSC regenerative medicine.
    Creator/Author:
    H. Leighton Grimes; Sydney Treichel; Madeline Frangiosa; Waseem Nasr; Marie-Dominique Filippi; Juying Xu; Nathan Salomonis; Baobao Song; Andrew Harley; Devyani Sharma; James Bartram; Angelo D’Alessandro, and Travis Nemkov
    Submitter:
    Marie-Dominique Filippi
    Date Uploaded:
    06/26/2026
    Date Modified:
    06/26/2026
    License:
    Attribution-ShareAlike 4.0 International

  • Computers as Brains A Robot’s Tale

    Document thumbnail: Computers as Brains A Robot’s Tale
    Type:
    Document
    Description/Abstract:
    The computer metaphor of mind and brain states broadly that the brain is the control organ for the body. This implies that brain (including mind) and physical body are separable from each other and the physical and social environment. Given the dominance of computing technology in daily life, many brain researchers and subsequently also engineers are furthermore compelled to take brain-computer analogies not as metaphors but as literal descriptions of brain function. These two fundamental assumptions manifest as overwhelming challenges when pursuing synthetic rather than analytic approaches, i.e., when we attempt to computationally control artificial bodies such as robots, especially when co-located with humans. I will discuss the computational brain metaphor from the perspective of bodies for whom computational control is a reality – robots, and their creators – engineers. Rather than presenting new metaphors, I will use evidence from control engineering and human-robot interaction to argue for a shift of thought: if we can enrich how engineers approach robotic control, new robots could offer powerful momentum to shifting the scientific opinion towards embracing a less dualistic, more holistic view of the brain’s embedding in body and world.
    Creator/Author:
    Lorenz, Tamara
    Submitter:
    Tamara Lorenz
    Date Uploaded:
    06/15/2026
    Date Modified:
    06/15/2026
    Date Created:
    2026
    License:
    Attribution-NonCommercial-NoDerivs 4.0 International

  • Targeted After Generative (TAG) Learning for Causal Inference with Latent Variables

    Article thumbnail: Targeted After Generative (TAG) Learning for Causal Inference with Latent Variables
    Type:
    Article
    Description/Abstract:
    Latent variables and causal inference are central to implementing empirical research and advancing substantive theory across the social sciences. Conventional latent variable frameworks such as structural equation modeling (SEM) are constrained by several simplifying assumptions (e.g., linearity, additivity, correct model specification). In this study, we relax key SEM assumptions by building data adaptive estimation methods for causal inference with latent variables. We developed a theory-driven Targeted After Generative (TAG) learning framework that integrates SEM-informed variational autoencoders (SEM VAEs) with targeted machine learning to estimate the average treatment effect. On the generative side, the SEM VAE encodes theory aligned neural networks that learn latent variable distributions through flexible joint modeling of measurement and structural parameters. On the targeted learning side, we construct an efficient influence function for the treatment effect with the SEM-VAE implied posterior distribution of the latent variables to estimate the average effect. Across simulations, the TAG learning approach outperforms alternative approaches and demonstrates low to no bias even in adverse conditions. TAG learning offers a theory-driven flexible approach with methodological promise for causally interpretable data-adaptive analysis with latent variables. More practically, the framework allows estimation of causal effects involving latent variables even in the presence of unknown functional relationships in the measurement and structural models (e.g., data adaptive effect estimation for nonlinear/nonadditive SEMs).
    Creator/Author:
    Kelcey, Ben; Ataneka, Amota, and Habarurema, Jean-Baptiste
    Submitter:
    Ben Kelcey
    Date Uploaded:
    06/02/2026
    Date Modified:
    06/03/2026
    Date Created:
    05-20-26
    License:
    Attribution 4.0 International