Maryland Shared Open Access Repository
MD-SOAR is a shared digital repository platform for twelve colleges and universities in Maryland. It is currently funded by the University System of Maryland and Affiliated Institutions (USMAI) Library Consortium (usmai.org) and other participating partner institutions. MD-SOAR is jointly governed by all participating libraries, who have agreed to share policies and practices that are necessary and appropriate for the shared platform. Within this broad framework, each library provides customized repository services and collections that meet local institutional needs. Please follow the links below to learn more about each library's repository services and collections.
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AMINO ACID ALPHABET (R)EVOLUTION: CHANGING THE MOLECULAR BASIS OF LIFE
(2025-01-01) Brown, Sean Michael; Freeland, Stephen J; Biological Sciences; Biological Sciences
Life on Earth has evolved to construct metabolism as a network of genetically encoded proteins. Each protein comprises a sequence of amino acids, covalently linked together. Early evolution established a single, standard library of 20 L-?-amino acids with which to build proteins since life’s last universal common ancestor (LUCA). Multiple disciplinary lenses agree, however, that a far greater diversity of amino acid structures was available to life’s origins and early evolution. Amino acids appear readily available to the planetary bodies that comprise our galaxy, have been detected within meteorites, produced by a wide range of conditions for simulating prebiotic chemistry, and have even been theorized to occur within the interstellar medium. Given that a single set has proven capable of building proteins adapted to every imaginable environment for life on Earth for more than 3.5 billion years, they are an attractive, recognized focus of astrobiology research. A decade of this research now supports the idea that the genetically encoded set of 20 amino acids exhibits highly unusual physicochemical properties as a set. The range and evenness with which they cover the chemistry space of possible volume and hydrophobicity is remarkable and non-random. This theory has matured far enough that it is both tractable and useful to now ask the question: if an independent origin of life were to build proteins using amino acids then what structures and functions would we expect, using Earth life as a guide? Here I attempt to shed light on an answer to that question through the intersection of “state-of-the-art” computational and empirical methods. This work begins to uncover whether the fundamental molecular biochemistry of life elsewhere, if using monosubstituted alpha amino acids, looks eerily similar to, or starkly different from, life as we know it here on Earth. Specifically, by (i) using a heuristic search algorithm to search for and analyze alternative amino acid alphabets (ii) designing, synthesizing, and characterizing the world’s first xeno peptides; and (iii) evaluating the reliability of state-of-the-art spectral prediction algorithms for xeno amino acids. The results primarily indicate that certain amino acids are predisposed to forming high physicochemical coverage sets, the one amino acid alphabet used by life is not the only one capable of forming peptide structures, and that current spectral prediction algorithms are insufficient when simulating xeno amino acid spectra. I conclude by identifying and calling for the further development of multiple tractable research directions so as to continue uncovering what alternative biochemistries may look like.
Interested: How Susan S. White, Shareholder Advocacy, and 50 Years of Indigenous Activism Changed the Game and the Name of the Washington Football Team
(2025-01-01) Burstrem, Jessica; Meringolo, Denise D Goings, Ramon B; Language, Literacy & Culture; Language Literacy and Culture
This qualitative, descriptive case study utilized oral history informed by Indigenous research methodologies to explore how Susan White (Oneida of Wisconsin, 1963-2018) led an intertribal coalition of asset managers to address the problem of convincing the Washington, DC, NFL team to abandon their racist former name and mascot. I describe the shareholder advocacy process in that social movement and the confluence of factors that may have enabled the effectiveness of that tactic. Black-led movements generated opportunities at both the start and end of this movement. Indigenous activists utilized a range of tactics over the decades to pressure the team. Public opinion responded to the two trademark lawsuits (one plaintiff, Norbert Hill, Jr., is a narrator in this project). In 2020, the visibility of Black Lives Matter peaked after George Floyd was lynched. Other movements also gained widespread support. Over decades, Susan built relationships, establishing a coalition of socially responsible investment (SRI) leaders that continues to support Indigenous-led initiatives, including name change movements, today. This study finds that her determination, engagement, motivation, and perseverance enabled the movement's outcome. The popularity of corporate anti-racism in summer 2020 was an opportune moment for re-engagement; then, the partnerships she had created enabled the coalition's quick response. This dissertation discusses the dependence of white heteropatriarchal masculine identity formation on stereotyped representations of Black and Indigenous masculinity. Gridiron (or American) football fandom can provide a sense of community and belonging that were lost in the invention of whiteness itself—and thus can also become a space for performing white supremacist capitalist heteropatriarchy. Challenging the usage of Indigenous mascots promotes Indigenous self-determination and resists genocide. It also achieves those goals simply by virtue of the impacts of any social movement, which is inherently successful for those who participate in it, witness it, and/or learn of it. This project intervenes in the common assumption that the U.S. Black civil rights movement was the inspiration that sparked Indigenous activism in the 1970s. While the Black Panther Party and the American Indian Movement (AIM) did offer support to each other, both started locally based on observed local needs. Likewise, Susan’s work was part of the Oneida Nation’s larger financial and cultural reclamation and revitalization projects for sovereignty and survivance. One significant factor in this story is this use of shareholder advocacy, a form of activist investing, to effect cultural change, rather than the more typical profit motive. Its history reveals that socially motivated investing is not just a recent offshoot of profit-motivated activist investing, but rather, its own form of activist investing. Another factor is the movement’s target: a privately-owned corporation. The coalition instead targeted its publicly-traded sponsors, FedEx, Nike, and PepsiCo. A further feature is the activists’ decision to invest rather than divest or boycott. They made a conscious, strategic decision to not only participate in capitalism, but also to exploit it for their own particular ends. This case study supports the claim that long-term, coalitional movements can achieve their stated goal(s). The outcome of this movement is a testament to the power of Indigenous activism, and its success strengthens ongoing Indigenous movements and shareholder advocacy work.
Awake/Dreaming
(2025-01-01) Scheiber, Alexi; Francis Parks, Corrie; Visual Arts; Imaging and Digital Arts
Awake/Dreaming is a body of work that includes animation, poetry, papermaking, installation and this written thesis. This work explores climate consciousness by breaking emotional responses into a loose binary of the Awake (anxious, grieving, conscious of systems of harm and oppression) and Dreaming (imaginative, radical possibility) modes. Artistic research started with an exploration of invasive species as a subtle and nefarious visual marker of the climate crisis in the Mid-Atlantic region. Visual research expanded into practical intersectional climate solutions that would make a tangible difference in the average person’s life. Much of the iconography of this work traces back to the Mid-Atlantic region and Baltimore specifically, though many of the solutions depicted can and are being applied elsewhere. Poetry functions as a scaffold where art and traditional essay writing felt inadequate to wholly express an idea. My original poem “The Dreaming Manifesto” is scattered throughout the work– as narration in the animation The Dreaming World, and as isolated lines on the seed paper installation of the same name.
Mathematical Modeling of Border Cell Cluster Migration in Drosophila melanogaster
(2025-01-01) Akhavan, Naghmeh; Peercy, Bradford E.; Mathematics and Statistics; Mathematics, Applied
Cell migration plays a key role in development, wound healing, immune response, and cancer metastasis. This dissertation presents mathematical models of border cell cluster migration in Drosophila melanogaster, focusing on tissue geometry and chemoattractant diffusion. We first develop a 1D hybrid model to study how extracellular egg chamber structures shape chemoattractant gradients and border cell movement. Simulations show that narrow regions enhance directional cues and migration speed, while broader regions reduce gradients. High chemoattractant concentrations can impair movement due to receptor saturation, revealing a non-monotonic link between signal strength and motility. A key contribution of this work is the development of a predictive compu-tational model that integrates chemoattractant diffusion, receptor dynamics, and force-based migration mechanics. We establish a framework capturing the interplay between signaling and physical constraints. Sensitivity analyses further reveal that alterations in tissue structure, such as genetic mutations affecting egg chamber morphology, lead to predictable shifts in migration dynamics. Next, we develop a phase field modeling framework (system of coupled partial differential equations) for multicellular cluster migration within the 2D egg chamber geometry. A key innovation is the development of a novel Tangential Interface Migration (TIM) term, representing the climbing behavior of border cells as they navigate through the nurse cell environment. This term replaces the standard chemical gradient response with a more biophysically appropriate mechanism. Importantly, with the TIM force, cluster migration depends on the presence and alignment of neighboring cells, consistent with experimental observations that collective proximity is required for successful migration. This study introduces phase field modeling as a novel approach to investigating border cell cluster migration, offering a significant alternative over traditional agent-based and continuum models. Unlike models that track individual cells or oversimplify the extracellular space, the phase field method provides a continuous representation of the migrating cluster, naturally capturing dynamic shape changes, cell-cell adhesion, and interactions with tissue structure. By incorporating chemoattractant diffusion and receptor dynamics into this framework, we create a powerful and flexible tool that enables a more precise study of collective migration in structured environments. In conclusion, this research advances our understanding of collective migration by integrating experimental observations with novel mathematical modeling. These insights support future research in development and disease modeling.
Pixel-Level Scene Recognition Under Diverse Constraints
(2025-01-01) Ahmed, Masud; Roy, Nirmalya; Information Systems; Information Systems
Recent advances in computer vision have significantly enhanced tasks such as object recognition and semantic segmentation, thereby enabling a myriad of applications in smart cities, autonomous driving, medical diagnostics, and robotics. Convolutional neural networks (CNNs) have achieved remarkable success through supervised learning; however, their performance often deteriorates when confronted with substantial domain shifts between training and real-world deployment environments. Unsupervised domain adaptation (UDA) seeks to bridge this gap by exploiting labeled source data along with unlabeled target data, yet these methods typically reach a performance plateau when the domain discrepancy is too large. Fine-tuning with a small, carefully selected subset of target data emerges as a promising strategy to overcome these limitations while reducing the burden of extensive manual annotation. In this work, we first address the fine-tuning challenge within a CNN-based framework by actively sampling high-uncertainty regions from target images and employing continual learning techniques to adapt the model incrementally. Recognizing the inherent limitations of CNNs in capturing complex and nuanced variations in real-world data, we propose a novel transformer-based semantic segmentation approach that operates in a continuous embedding space. Unlike conventional vector quantization methods that depend on discrete embeddings, our framework leverages continuous embeddings using an autoregressive (AR) generative model guided by a diffusion loss. This approach synergistically combines a CNN-based encoder for local feature extraction, a diffusion-based AR transformer to capture long-range dependencies, and a CNN-based decoder to reconstruct detailed pixel-level segmentation masks. Extensive experiments conducted on public datasets such as GTAV, Cityscapes, SemanticKITTI, ACDC, as well as our own CADEdgeTune dataset—characterized by low-angle, real-world imagery—demonstrate that our model attains impressive zero-shot domain adaptation performance. It achieves robust segmentation under adverse weather conditions and varied viewpoints, while also exhibiting strong resilience against noise. Future work will extend these concepts to LiDAR-based semantic segmentation and explore the design of large vision models that fully exploit continuous embedding representations.