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Assessing the Prevalence of COVID-19 and Post-COVID Condition in Adults with Disabilities in the United States: A Systematic Review
No full textWe conducted a systematic review of the literature to assess the prevalence of COVID-19 and post-COVID condition among individuals with disabilities compared to the general population in the United States for 2020 through June 2024. Through a search of seven academic databases, hand searching of nine health services research and public health journals, and the bibliographies of included manuscripts, we identified nine articles that compared COVID-19 (n=8) or post-COVID condition (n=1) for individuals with disabilities to the general population. Of these, COVID-19 prevalence or the case rate was higher among individuals in 5 of 8 manuscripts and the prevalence of post-COVID condition was higher among individuals with disabilities in the one manuscript that examined post-COVID condition. There was notable heterogeneity by type of disability, disability severity and study setting. Only one of 9 studies examined differences by sociodemographic factors such as race, ethnicity, gender and socioeconomic status. As COVID-19 and post-COVID condition continue to impact the health of individuals, additional research using nationally representative data is warranted.The research reported herein was performed pursuant to a grant from the US Social Security Administration SSA funded as part of the Retirement and Disability Research Consortiumhttps://rdrc.umbc.edu/working-papers
Advancements in Electrochemical Biosensors for Comprehensive Glycosylation Assessment of Biotherapeutics
No full textProteins represent a significant portion of the global therapeutics market, surpassing hundreds of billions of dollars annually. Among the various post-translational modifications, glycosylation plays a crucial role in influencing protein structure, stability, and function. This modification is especially important in biotherapeutics, where the precise characterization of glycans is vital for ensuring product efficacy and safety. Although mass spectrometry-based techniques have become essential tools for glycomic analysis due to their high sensitivity and resolution, their complexity and lengthy processing times limit their practical application. In contrast, electrochemical methods provide a rapid, cost-effective, and sensitive alternative for glycosylation assessment, enabling the real-time analysis of glycan structures on biotherapeutic proteins. These electrochemical techniques, often used in conjunction with complementary methods, offer valuable insights into the glycosylation profiles of both isolated glycoproteins and intact cells. This review examines the latest advancements in electrochemical biosensors for glycosylation analysis, highlighting their potential in enhancing the characterization of biotherapeutics and advancing the field of precision medicine.This research was funded by the Maryland Innovation Initiative MII TEDCO grant number 0923 0003https://www.mdpi.com/1424-8220/25/7/206
Reconsidering Lease Impacts: A Spatial Ecology Analysis of Aquaculture–Habitat Interactions
No full textThis project analyzes how oyster aquaculture affects submerged aquatic vegetation (SAV) structure in Maryland’s Chesapeake Bay using spatial modeling, seascape ecology, and multi-decadal GIS data to assess habitat fragmentation, connectivity, and potential for ecosystem coexistence.Oyster aquaculture and submerged aquatic vegetation (SAV) are both critical components of coastal ecosystem function, yet their spatial and ecological interactions remain poorly understood at landscape scales. This study presents a multi-decadal, spatially explicit analysis of SAV structure in relation to oyster aquaculture leases in Maryland’s mid-Chesapeake Bay. Using GIS-based modeling and statistical approaches including generalized additive models, factorial linear models within a BACI (Before–After Control–Impact) design and Bayesian hierarchical modeling, I tested three hypotheses related to temporal alterations in SAV structure, the influence of lease proximity and the role of lease configuration. My findings reveal that while SAV area has increased over time, fragmentation and patch isolation have also intensified, particularly on the Eastern Shore, suggesting potential declines in ecosystem resilience. Surprisingly, SAV patches near active oyster leases exhibited greater cohesion and complexity post-activation in several cases, challenging the assumption that aquaculture infrastructure inherently degrades seagrass habitats. Lease configuration variables, including cage density and alignment, had weak and inconsistent effects compared to broader site-level environmental conditions. These results emphasize the need for long-term, spatially explicit monitoring and suggest that aquaculture and habitat restoration goals may be compatible under certain environmental contexts. Adaptive permitting frameworks that account for local biophysical settings, rather than rigid exclusion zones, may better support both sustainable aquaculture development and coastal ecosystem recovery
Greenland Ice Sheet Wide Supraglacial Lake Evolution and Dynamics: Insights From the 2018 and 2019 Melt Seasons
No full textSupraglacial lakes on the Greenland Ice Sheet (GrIS) can impact both the ice sheet surface mass balance and ice dynamics. Thus, understanding the evolution and dynamics of supraglacial lakes is important to provide improved parameterizations for ice sheet models to enable better projections of future GrIS changes. In this study, we utilize the growing inventory of optical and microwave satellite imagery to automatically determine the fate of Greenland-wide supraglacial lakes during 2018 and 2019; low and high melt seasons respectively. We develop a novel time series classification method to categorize lakes into four classes: (a) Refreezing, (b) rapidly draining, (c) slowly draining, and (d) buried. Our findings reveal significant interannual variability between the two melt seasons, with a notable increase in the proportion of draining lakes, and a particular dominance of slowly draining lakes, in 2019. We also find that as mean lake depth increases, so does the percentage of lakes that drain, indicating that lake depth may influence hydrofracture potential. We further observe rapidly draining lakes at higher elevations than the previously hypothesized upper-elevation hydrofracture limit (1,600 m), and that non-draining lakes are generally deeper during the lower melt 2018 season. Our automatic classification approach and the resulting 2-year ice-sheet-wide data set provide new insights into GrIS supraglacial lake dynamics and evolution, offering a valuable resource for future research.DD, ACS, EH, MOG and AFB weresupported by the iHARP HDR Institute(NSF award #2118285). We acknowledgehigh?performance computing support fromCheyenne (https://doi.org/10.5065/D6RX99HX) provided by NCAR'sComputational and Information SystemsLaboratory, sponsored by the NSF. Thiswork also utilized the Summitsupercomputer, which is supported by theNSF (awards ACI?1532235 and ACI?1532236) and is a joint effort of the University of Colorado Boulder, and Colorado State University. Earth and Space Science 10.1029/2024EA003793DUNMIRE ET AL. 18 of 20https://onlinelibrary.wiley.com/doi/abs/10.1029/2024EA00379
Polyurethane Nanocapsules Incorporating Epigallocatechin Gallate, A Green Tea Extract
No full textExplosions cause 79% of combat-related injuries, often leading to traumatic brain injury (TBI) and hemorrhage. Epigallocatechin gallate (EGCG), a green tea polyphenol, aids neuroprotection and wound healing. In this work, we sought to investigate the fabrication and characterization of polyurethane nanocapsules encapsulating EGCG, demonstrating controlled, on-demand release, and highlighting their potential for targeted therapeutic delivery in trauma care.This work was supported in part by FY21 Defense Health Agency, DHARestoral funding, project # DS21RES15.https://onlinelibrary.wiley.com/doi/abs/10.1002/anbr.20240020
Evaluating Causal AI Techniques for Health Misinformation Detection
No full textWorkshop on Causal AI for Robust Decision Making (CARD 2025), held in conjunction with 23rd International Conference on Pervasive Computing and Communications (PerCom 2025)The proliferation of health misinformation on social media, particularly regarding chronic conditions such as diabetes, hypertension, and obesity, poses significant public health risks. This study evaluates the feasibility of leveraging Natural Language Processing (NLP) techniques for real-time misinformation detection and classification, focusing on Reddit discussions. Using logistic regression as a baseline model, supplemented by Latent Dirichlet Allocation (LDA) for topic modeling and K-Means clustering, we identify clusters prone to misinformation. While the model achieved a 73% accuracy rate, its recall for misinformation was limited to 12%, reflecting challenges such as class imbalance and linguistic nuances. The findings underscore the importance of advanced NLP models, such as transformer based architectures like BERT, and propose the integration of causal reasoning to enhance the interpretability and robustness of AI systems for public health interventions.https://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detectio
Tensor-decomposition-based A Priori Surrogate (TAPS) modeling for ultra large-scale simulations
No full textA data-free, predictive scientific AI model, Tensor-decomposition-based A Priori Surrogate (TAPS), is proposed for tackling ultra large-scale engineering simulations with significant speedup, memory savings, and storage gain. TAPS can effectively obtain surrogate models for high-dimensional parametric problems with equivalent zetta-scale (10²¹) degrees of freedom (DoFs). TAPS achieves this by directly obtaining reduced-order models through solving governing equations with multiple independent variables such as spatial coordinates, parameters, and time. The paper first introduces an AI-enhanced finite element-type interpolation function called convolution hierarchical deep-learning neural network (C-HiDeNN) with tensor decomposition (TD). Subsequently, the generalized space-parameter-time Galerkin weak form and the corresponding matrix form are derived. Through the choice of TAPS hyperparameters, an arbitrary convergence rate can be achieved. To show the capabilities of this framework, TAPS is then used to simulate a large-scale additive manufacturing process as an example and achieves around 1,370x speedup, 14.8x memory savings, and 955x storage gain compared to the finite difference method with 3.46 billion spatial degrees of freedom (DoFs). As a result, the TAPS framework opens a new avenue for many challenging ultra large-scale engineering problems, such as additive manufacturing and integrated circuit design, among others.https://www.sciencedirect.com/science/article/pii/S0045782525003731#d1e293
Towards Trust and Time-sharing Task Allocation Scheme in Mobile Crowdsensing
No full textAssigning tasks to reliable workers to obtain reliable data is a critical issue in Mobile CrowdSensing (MCS). The challenge is compounded by the problem of Information Elicitation Without Verification (IEWV), which renders traditional data quality evaluation methods ineffective. While some studies attempt to address this, they often struggle to assess workers’ dynamic trustworthiness, resulting in unreliable data. To overcome these challenges, we propose the Trust and Time-sharing Task Allocation based Truth Discovery (TTTA-TD) scheme, designed to ensure reliable data collection in MCS. This scheme includes three components: (a) Classification-based Trust Evaluation (CTE) that classifies workers based on behavior and applies tailored penalties—lenient for honest workers and stricter for malicious ones, (b) Trust-based Truth Data Discovery (TTDD), which improves truth data accuracy by integrating trust scores, and (c) Trust and Time-sharing Task Allocation (TTTA) which allocates tasks to ensure data reliability and minimize time-sharing disparities. Experimental results show that the TTTA algorithm reduces average time-sharing by 93.95%. The TTDD algorithm improves truth estimates across all dataset qualities, and the TTTA-TD scheme enhances data reliability by 0.35%, 2.06%, and 7.41% in high, medium, and low-quality datasets respectively.This work was supported in part by the National Natural Science Foundation of China (62402067), Hunan Provincial Natural Science Foundation of China (2024JJ6091) (*Corresponding author: Neal N. Xiong).https://ieeexplore.ieee.org/abstract/document/1119693
Enemies and Allies: Christian and Islamic Principalities in the Twelfth-Century Levant
No full textThroughout the course of the early to mid-twelfth century, both Christians and Muslims repeatedly joined with the other to fight their co-religionists, even while both maintained deep religious zeal and hatred for the religion of the other. While religious prejudice was still an important and indispensable factor in Christian and Muslim societies at the time, it was not always the most critical element in determining policy, and could willingly be set aside when material interest so dictated. These alliances, made primarily out of political necessity, disintegrated in the face of changing circumstances, a development that proved to be detrimental both to Outremer’s Christians and to smaller Islamic states trying to maintain their independence
Building Pathways: Integrating Systems to Support K-16 Credentialing
No full text1EdTech Digital Credentials Summit 2025, March 3-5, 2025, Phoenix, Arizonahttps://docs.google.com/presentation/d/1IwnAL0wYRiE8F7WUqaCLCn0yHlnjxGE7zvNBh0XNKA