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Phenotype Specific Nuclear Lamina Remodeling in hiPSC Derived Cardiomyocytes Bearing TNNT2 Sarcomeric Variants
No full text[Summary] Cardiomyocytes endure physical stress from the myocardium environment while generating their own mechanical strains. The force generated by sarcomeres is transmitted both longitudinally to adjacent sarcomeres and laterally to the cytoskeleton via intermediate filaments. This mechanical stimulus impacts other organelles, including the nucleus, thus playing a vital role in sensing and signaling nuclear adaptations. However, there is limited understanding of how changes in cardiac contractility affect nuclear mechanics. Here, we sought to investigate the effects of hyper- and hypo-contractility in nuclei of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) bearing TNNT2 pathogenic variants associated with hypertrophic (HCM) or dilated (DCM) cardiomyopathies. Transcriptomics analyses of these variant bearing hiPSC-CMs confirmed that differential gene expression occurs and is associated with maladaptive and compensatory responses in HCM and DCM. Our findings show a cause-and-effect link between impaired contractility and nuclear lamina remodeling in cardiomyopathic phenotypes. Disease-induced dysfunctional contractile transients alter the expression of nucleoskeleton protein lamin A/C, influencing nuclear stiffness. These changes in stiffness were rescued by treatment with myosin modulators Mavacamten or Omecamtiv Mecarbil. This study shows that nuclear mechanics is influenced by the interaction between the sarcomere and the cytoskeletal network. Exploring the relationship between contractile dysfunction and nuclear lamina remodeling may reveal new therapeutic targets for cardiomyopathies
Demonstrating the Resilience of ePortfolios in Times of Disruption: Two International Case Studies
No full textThis chapter explores ePortfolios as a resilient practice in higher education during periods of disruption. Drawing on the Support principle from the Digital Ethics Principles in ePortfolios, developed by the Association for Authentic, Experiential, and Evidence-Based Learning’s (AEEBL’s) Digital Ethics Task Force, the authors argue that the student resilience fostered by ePortfolio practices can only be achieved successfully if ePortfolio programs are implemented and supported in a way that makes them resilient as well. Two case studies from Old Dominion University (ODU) in the United States and The University of Queensland (UQ) in Australia demonstrate how both institutions embedded ePortfolio pedagogy across curricular and co-curricular contexts through centralized, well-resourced support systems that sustained student engagement and learning continuity during the rapid transition to online and hybrid learning necessitated by the COVID-19 pandemic. Survey data from North America, Australia, and New Zealand complement these case studies, highlighting both the strengths and challenges of resilient ePortfolio implementation. While survey respondents reported diverse support roles and modalities, they also noted a lack of institutional recognition and compensation for ePortfolio professionals, underscoring the need for formal acknowledgment of the labor underpinning the resilience of ePortfolio implementations. The findings affirm that when supported by intentional pedagogical design and institutional investment, ePortfolios foster student agency, reflection, and adaptability, all of which support their resilience as learners and, in the future, as professionals. As higher education continues to face disruptions, from technological shifts to enrollment challenges, sustained support for ePortfolio ecosystems will be essential. Institutions must prioritize not only technological infrastructure but also the professional development and recognition of those who lead and sustain ePortfolio initiatives. In doing so, they cultivate ethical, inclusive, and future-ready learning environments that empower both educators and learners
Mitigating Out-of-Plane Fiber Waviness in AFP Laminates with Tow-Gaps via Selective Placement of Thermoplastic Veils
No full textFiber tow-gaps and overlaps formed during the Automated Fiber Placement (AFP) process pose a significant challenge by introducing non-uniform composite morphologies, often characterized by resin-rich regions and fiber waviness. These defects occur as deposited fibers sink into the gap regions during consolidation, with gap geometry determined during path planning. Such morphological inconsistencies can compromise structural reliability by initiating premature failure, particularly through localized out-of-plane waviness and resin accumulation. This study investigates the integration of high melting temperature thermoplastic veils, specifically polyetherimide (PEI), into fiber tow-gaps as a method to prevent ply sinking and reduce fiber waviness on both internal and external surfaces of the laminate. The PEI veils also serve to reinforce resin-rich regions by forming an interpenetrated network of high fracture toughness material within the brittle epoxy matrix. Tensile tests conducted on cross-ply laminates containing staggered gaps demonstrated that the inclusion of PEI veils modified the failure mode. The results suggest that the selective placement of thermoplastic veils within tow-gaps during AFP offers a viable strategy to mitigate manufacturing-induced non-uniform morphologies
Is It Getting Better? An Evaluation of Two Successive Generations of ChatGPT in Answering Specialized Vascular Surgery Questions
No full textPurpose: Large language models (LLMs) can generate clinically relevant text; however, their performance in highly specialized medical domains remains uncertain. This study evaluated ChatGPT-3.5 and ChatGPT-4 (OpenAI) using vascular surgery board–style questions from the Vascular Education and Self-Assessment Program, version 4 (VESAP4) and compared the two public model versions (June and November 2023).
Materials and Methods: All non-image VESAP4 questions (n=384) were presented independently three times to each model version (ChatGPT-3.5 June/November; ChatGPT-4, June/November). Outcomes included accuracy (proportion correct), consistency (same option letter across all three attempts and “consistently correct”), explanation length (word count), and modes of failure classified for a pre-specified index attempt using a multi-label taxonomy, with independent dual review and consensus. Accuracy and consistency were reported with 95% confidence intervals, and between-condition differences were compared using the chi-square (proportions) and Welch t-test (word count).
Results: Accuracy was 47.9% and 46.5% for ChatGPT-3.5 (June/November) and 62.4% and 63.8% for ChatGPT-4, respectively. No significant within-model improvement occurred between June and November, whereas ChatGPT-4 outperformed ChatGPT-3.5 in both months (P\u3c 0.0001). For consistency, ChatGPT-3.5 increased in “same-letter” (55.5% to 65.6%; P=0.004) with no change in “consistently correct” (40.6% to 40.4%; P=0.94). ChatGPT-4 decreased in “same-letter” (90.1% to 79.7%; P\u3c 0.0001) with stable “consistently correct” (60.4% to 58.6%; P=0.61). Between the models, ChatGPT-4 exceeded ChatGPT-3.5 on both consistency metrics in June and November (all P\u3c 0.0001). Explanation length shifted within models: ChatGPT-3.5 produced shorter responses in November compared with June (105.4±1.0 vs. 164.2±1.5 words; P\u3c 0.0001), whereas ChatGPT-4 produced longer responses (282.0±1.3 vs. 120.0±1.2 words; P\u3c 0.0001). Performance across VESAP4 subsections was higher for Vascular Medicine and Radiological Imaging/Radiation Safety, and lower for Dialysis Access Management. The modes of failure were predominantly due to external information retrieval errors for ChatGPT-3.5 and logical errors for ChatGPT-4.
Conclusion: ChatGPT-4 outperformed ChatGPT-3.5 on vascular surgery board–style questions yet achieved only moderate accuracy. Version updates did not consistently improve the performance in this specialized domain, emphasizing the complexity of decision-making in vascular surgery and the current limitations of LLMs in surgical education
Form and Function in Mobulids: A Comparative Analysis of Filter Morphology with Bioinspiration Applications
No full textMobulas (manta and devil rays) are large-scale ram filter feeders that separate planktonic food particles from large volumes of water with minimal clogging. This contrasts with most human-made filters that can suffer from problematic clogging requiring additional mechanisms for clearing blocked surfaces and maintaining performance. Prior studies have shown that mobulas employ a unique mechanism referred to as ricochet separation to filter feed, whereby captive vortices in filter pores cause particles to bounce off the filter surfaces and away from the filter pores. This mechanism enables the filtration of particles smaller than the pore size and reduced clogging. However, few studies have examined how the morphology of the filtering structure varies across the diversity of mobulid species, and little is known about how this variation may impact filtration efficiency or prey selectivity. This study conducts a systematic investigation of the gross morphology of the filtering structure in seven mobilid species using a combination of computed tomography and macro photography. Examination of filter anatomy suggests that some features are highly variable while others are well-conserved across species. In particular, a reconstruction of the phylogenetically corrected morphospaces indicated that the primary pore dimensions of the filter lobes are a major driver of morphological variation across species. Additionally, inspection of the gross anatomy revealed a pronounced asymmetry in the anterior and posterior filter plates of each gill arch. This asymmetry suggests that water may impinge on the filtering structures at different angles than has previously been speculated. Here, the functional ramifications of the observed morphological variations were interpreted using recent modeling studies. Most mobulid fishes have a filter morphology that should be capable of high filtration efficiency and low hydrodynamic resistance, but may also be sensitive to flow conditions. A deeper understanding of the mechanics of filter-feeding in mobulid fishes would generate needed insights into the ecology of these species and could provide a firmer framework for the development of bioinspired filtration systems. These findings highlight the value of integrating detailed anatomical studies into bioinspired design efforts and pave the way for the development of bioinspired filter systems with improved performance
Understanding PII Leakage in Large Language Models: A Systematic Survey
No full textLarge Language Models (LLMs) have demonstrated exceptional success across a variety of tasks, particularly in natural language processing, leading to their growing integration into numerous facets of daily life. However, this widespread deployment has raised substantial privacy concerns, especially regarding personally identifiable information (PII), which can be directly associated with specific individuals. The leakage of such information presents significant real-world privacy threats. In this paper, we conduct a systematic investigation into existing research on PII leakage in LLMs, encompassing commonly utilized PII datasets, evaluation metrics, and current studies on both PII leakage attacks and defensive strategies. Finally, we identify unresolved challenges in the current research landscape and suggest future research directions
Comprehensive Benchmarking of Several Machine Learning and Bayesian Models for Early-Stage Diabetes Risk Prediction: A Large-Scale Comparative Study
No full textDiabetes remains a critical global health challenge, with early detection is crucial for effective management. This study presents a comprehensive benchmarking analysis of 14 diverse machine learning and Bayesian models for early-stage diabetes risk prediction using clinical data [2] from Sylhet, Bangladesh. This research evaluated traditional methods (Logistic Regression, Decision Trees), ensemble techniques (Random Forest, XGBoost, LightGBM), Bayesian approaches (BART, Bayesian Logistic Regression), and advanced neural architectures (Deep Belief Networks) using both 70-30 train-test splits and 10-fold cross-validation. The results demonstrate that ensemble methods consistently outperformed other approaches, with Random Forest(RF) achieving the highest cross-validated AUC (0.9951) and accuracy (0.9699). The study provides valuable insights into model selection for clinical decision support systems and highlights the robustness of tree-based ensemble methods for medical diagnosis tasks
Remica Bingham-Risher: 48th Annual ODU Literary Festival
No full textRemica Bingham-Risher, a native of Phoenix, Arizona, is a Cave Canem fellow and Affrilachian Poet. She is the author of Conversion (Lotus, 2006), What We Ask of Flesh/ (Etruscan, 2013), adapted into an immersive dance and installation work by INSPIRIT Dance Company, and Starlight & Error (Diode, 2017). Her memoir, Soul Culture: Black Poets, Books and Questions That Grew Me Up, was published by Beacon Press (2022). Her newest book, Room Swept Home (Wesleyan, 2024) is a finalist for the Library of Virginia Award, was chosen as an Honor Poetry Book by the Black Caucus of the American Library Association (BCALA), and won the L.A. Times Book Prize. She is the Director of Quality Enhancement Plan Initiatives at Old Dominion University in Norfolk, VA, where she resides with her husband and children
Christal Brown: 48th Annual ODU Literary Festival
No full textChristal Brown is the founder of INSPIRIT, a dance company, and Project: BECOMING. Brown earned a BFA in Dance and a minor in Business from UNC-Greensboro, and an MFA in New Media Art and technology from Long Island Univ. She performed with Chuck Davis’ African-American Dance Ensemble, Andrea E. Woods/Souloworks and Gesel Mason Performance Projects, and apprenticed with the Liz Lerman Dance Exchange. Upon moving to NYC, Brown apprenticed with The Bill T. Jones/Arnie ZaneDance Co. before joining Urban Bush Women as a principal performer, community specialist and apprentice program coordinator. In 2018, after performing with Bebe Miller Company, Brown achieved a personal and professional milestone of dancing her way through the African diaspora
JJJJJerome Ellis: 48th Annual ODU Literary Festival
No full textJJJJJerome Ellis (any pronoun) is a disabled Grenadian-Jamaican-American artist, surfer, and person who stutters. The artist works across music, performance, writing, video, and photography. JJJJJerome has the great privilege of being married to poet-ecologist Luísa Black Ellis. They live in a monastery on a creek in traditional Chesapeake and Nansemond territory, in Norfolk, VA. JJJJJerome dreams of building a sonic bath house