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INVESTIGATION OF THE CHROMATIN REGULATOR SET4 IN HYPOXIC STRESS IN SACCHAROMYCES CEREVISIAE
No full textStress responses in yeast are often mediated by changes in gene expression during cellular adaptation. This genomic reprogramming is dependent on dynamic signaling events that modulate the chromatin environment, resulting in changes in transcriptional regulation. The conserved yeast protein Set4 is induced under low oxygen conditions and is a key regulator of the chromatin environment in hypoxia. Set4 is part of a subset of the SET domain lysine methyltransferases that appear to lack catalytic activity known as the Set3 SET domain subfamily. It is a paralog to the chromatin regulator Set3 in yeast, and orthologous to mammalian MLL5/KMT2E and SETD5, which have each been implicated in dysregulation of neurodevelopment and various cancers. Thus, determining the mechanisms by which these noncanonical SET domain proteins function in regulating chromatin may elucidate further insight into human disease. Though the protein interactions of the Set3 subfamily have been characterized, it remains unclear what proteins interact with Set4 and by what mechanism Set4 mediates its protective role under hypoxic stress. Here, we present our current investigation on Set4 and its involvement with chromatin modifiers and transcription regulators to control gene regulation in hypoxia. We also show potential protein interactors of Set4 and detail multiple methods used to immunoprecipitate Set4. Finally, we characterize the noncanonical SET domains of Set3 and Set4 and differentiate the roles of these paralogs in hypoxic stress. Altogether, these studies shed new light on the complex regulation of chromatin and gene expression in response to hypoxia and reveal potential new roles for this disease-associated family of proteins
Governor Moore’s Vetoes Move Him to the Middle as Reelection Looms
No full textLast week Maryland Governor Wes Moore vetoed a reparations study bill and three environment bills despite their low budget impact, angering some of his closest allies: the General Assembly’s Black Caucus and the region’s environmental groups. Sunil Dasgupta talks with Maryland Senator Charles Sydnor, regional environment leader Mike Tidwell, and bike and pedestrian safety advocate Seth Grimes, about the politics. Music by Frederick, MD,- based country-folk singer-songwriter Susanna Laird.https://open.spotify.com/episode/6oOhyeAEpvX1fRyBIuCRv
Assessing COVID-19 Lockdowns' Impacts on Global Urban PM₂.₅ Air Quality with Observations and Modeling
No full textThe regional lockdowns, implemented around the world over 2020-2022 to contain the rapid spread of the novel coronavirus disease 2019 (COVID-19), inadvertently created a natural laboratory for investigating the effect of reducing 15 anthropogenic emissions on urban air quality in unprecedentedly large temporal and spatial scales. In this study, we analyze multi-year surface PM₂.₅ observations in 21 cities around the globe to examine anomaly of daily PM₂.₅ concentrations during major COVID-19 lockdowns with respect to that in the pre-pandemic years. We then use a set of GEOS global aerosol transport modeling experiments to disentangle the effect of the lockdown emission reductions from other non-lockdown effects. Our analysis shows that no systematic reductions in PM₂.₅ are found in response to the lockdowns globally. In some locations, we 20 find the coincidences of an increasing stringency index and a decreasing of surface PM₂.₅, which often leads to the record low of PM₂.₅ over extensive period. These observations clearly suggest the positive impacts of COVID-19 lockdown-induced anthropogenic emission reductions on air quality. In other stations, however, the lockdown’s impacts could be masked by differing meteorology and the occurrence of dust and wildfire events. We also found that current satellite remote sensing of aerosol optical depth cannot be used to reliably discern the change of surface PM₂.₅ due to the COVID-19 lockdowns. Results of this study provide a preview of potential mixed effects on urban air quality when implementing air pollution control regulations such as transitioning gasoline and diesel-powered vehicles to electric vehicles.https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1750
Fusion of Vision Transformer and Convolutional Neural Network for Explainable and Efficient Histopathological Image Classification in Cyber-Physical Healthcare Systems
No full textAccurate and interpretable classification of breast cancer histopathology images is critical for early diagnosis and treatment planning. This study proposes a hybrid deep learning model that integrates convolutional neural networks (CNNs) with a Vision Transformer (ViT) to jointly capture local texture patterns and global contextual features. The fusion architecture is evaluated on two publicly available datasets: BreakHis and the invasive ductal carcinoma (IDC) dataset. Results demonstrate that the ViT+CNN model consistently outperforms standalone CNN and ViT models, achieving state-of-the-art accuracy while maintaining robustness across datasets. To assess the feasibility of deployment in real-world clinical scenarios, we benchmark inference latency and memory usage under both standard and edge-constrained environments. Although the fusion model has higher computational cost, its latency remains within acceptable thresholds for real-time diagnostic workflows. Furthermore, we enhance interpretability by combining Grad-CAM with attention rollout, allowing for transparent visual explanation of the model’s decisions. The findings support the clinical potential of hybrid transformer-convolutional models for scalable, reliable, and explainable medical image analysis.https://link.springer.com/article/10.1007/s41314-025-00079-
Latent Diffusion Unlearning: Protecting Against Unauthorized Personalization Through Trajectory Shifted Perturbations
No full text33rd ACM International Conference on Multimedia,October 27 - 31, 2025,Dublin, IrelandText-to-image diffusion models have demonstrated remarkable effectiveness in rapid and high-fidelity personalization, even when provided with only a few user images. However, the effectiveness of personalization techniques has lead to concerns regarding data privacy, intellectual property protection, and unauthorized usage. To mitigate such unauthorized usage and model replication, the idea of generating ``unlearnable'' training samples utilizing image poisoning techniques has emerged. Existing methods for this have limited imperceptibility as they operate in the pixel space which results in images with noise and artifacts. In this work, we propose a novel model-based perturbation strategy that operates within the latent space of diffusion models. Our method alternates between denoising and inversion while modifying the starting point of the denoising trajectory: of diffusion models. This trajectory-shifted sampling ensures that the perturbed images maintain high visual fidelity to the original inputs while being resistant to inversion and personalization by downstream generative models. This approach integrates unlearnability into the framework of Latent Diffusion Models (LDMs), enabling a practical and imperceptible defense against unauthorized model adaptation. We validate our approach on four benchmark datasets to demonstrate robustness against state-of-the-art inversion attacks. Results demonstrate that our method achieves significant improvements in imperceptibility (∼8% -10% on perceptual metrics including PSNR, SSIM, and FID) and robustness ( ∼10% on average across five adversarial settings), highlighting its effectiveness in safeguarding sensitive data.Prof. Lokhande thanks support provided by University at Buffalo Startup funds, Adobe Research Gift and internal funding from the University at Buffalo’s Research and Economic Development office. Dr. Tejas Gokhale was supported by UMBC’s Strategic Award for Research Transitions (START)http://arxiv.org/abs/2510.0308
COIL Multidisciplinary Global Engineering Capstone Class Impact: Faculty and Student Insights Across Four Countries
No full text2025 ASEE Annual Conference & Exposition, 22 - 25 June 2025, Montreal, CanadaEngineers today face multifaceted global challenges, climate change, health challenges andindustrial expansion, requiring more than technical expertise. The demand for globalcompetencies, such as cross-cultural sensitivity, social responsibility, and the ability tocollaborate in diverse, multicultural environments, has become increasingly critical. Researchhighlights the role of international mobility in cultivating these skills, showing that students whoparticipate in cross-border academic experiences often demonstrate heightened globalcompetence, particularly in communication and adaptability.https://peer.asee.org/coil-multidisciplinary-global-engineering-capstone-class-impact-faculty-and-student-insights-across-four-countrie
IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being
No full textSustainable Development Goals (SDGs) give the UN a road map for development with Agenda 2030 as a target. SDG3 "Good Health and Well-Being" ensures healthy lives and promotes well-being for all ages. Digital technologies can support SDG3. Burnout and even depression could be reduced by encouraging better preventive health. Due to the lack of patient knowledge and focus to take care of their health, it is necessary to help patients before it is too late. New trends such as positive psychology and mindfulness are highly encouraged in the USA. Digital Twins (DTs) can help with the continuous monitoring of emotion using physiological signals (e.g., collected via wearables). DTs facilitate monitoring and provide constant health insight to improve quality of life and well-being with better personalization. Healthcare DTs challenges are standardizing data formats, communication protocols, and data exchange mechanisms. As an example, ISO has Internet of Things (IoT) and DTs Working Group. To achieve those data integration and knowledge challenges, we designed the Mental Health Knowledge Graph (KG) (ontology and dataset) to boost mental health; which acquires knowledge from ontology-based mental health projects classified within the LOV4IoT ontology catalog (Emotion, Depression, and Mental Health). Furthermore, the KG is mapped to standards from ETSI SmartM2M such as SAREF for eHealth Ageing Well domain to represent medical devices and sensors.We want to acknowledge the Kno.e.sis research team (lead by Professor Amit Sheth) from Wright State University, Ohio, USA for fruitful discussions about related topics such as "Mental Health/Depression/Suicide", and "Semantic, Cognitive, and Perceptual Computing" and with cognitive psychologists such as Professor Valerie Shalin during Dr. Gyrard’s post-doc in 2018-2019. This work has partially received funding from the European Union’s Horizon 2020 research and innovation program under project grant agreement StandICT.eu 2026 No. 101091933 (open call). We would like to thank the project partners for their valuable comments. The opinions expressed are those of the authors and do not reflect those of the sponsors.https://www.taylorfrancis.com/chapters/edit/10.1201/9781003630685-8/iot-based-preventive-mental-health-using-knowledge-graphs-standards-better-well-being-amelie-gyrard-seyedali-mohammadi-manas-gaur-antonio-kun
Attention-Guided Deep Adversarial Temporal Subspace Clustering (A-DATSC) Model for multivariate spatiotemporal data
No full textInternational Conference on Advances in Geographic Information Systems 2025 (ACM SIGSPATIAL 2025),November 3-6, 2025, Minneapolis, MN, USADeep subspace clustering models are vital for applications such as snowmelt detection, sea ice tracking, crop health monitoring, infectious disease modeling, network load prediction, and land-use planning, where multivariate spatiotemporal data exhibit complex temporal dependencies and reside on multiple nonlinear manifolds beyond the capability of traditional clustering methods. These models project data into a latent space where samples lie in linear subspaces and exploit the self-expressiveness property to uncover intrinsic relationships. Despite their success, existing methods face major limitations: they use shallow autoencoders that ignore clustering errors, emphasize global features while neglecting local structure, fail to model long-range dependencies and positional information, and are rarely applied to 4D spatiotemporal data. To address these issues, we propose A-DATSC (Attention-Guided Deep Adversarial Temporal Subspace Clustering), a model combining a deep subspace clustering generator and a quality-verifying discriminator. The generator, inspired by U-Net, preserves spatial and temporal integrity through stacked TimeDistributed ConvLSTM2D layers, reducing parameters and enhancing generalization. A graph attention transformer based self-expressive network captures local spatial relationships, global dependencies, and both short- and long-range correlations. Experiments on three real-world multivariate spatiotemporal datasets show that A-DATSC achieves substantially superior clustering performance compared to state-of-the-art deep subspace clustering models.http://arxiv.org/abs/2510.1800
Directed Assembly of Gold Bipyramids and Quantum Dots Using Click Chemistry for Plasmon-Exciton Coupling
No full textAdvances in directed nanoparticle assembly are enabling the development of hybrid nanostructures with enhanced light-matter interaction. Among these hybrid nanostructures are those with coupled plasmonic and excitonic components. Here, we report the design and assembly of hybrid nanostructures for plasmon-exciton coupling, composed of end-to-end pairs of gold bipyramids (AuBPs) with a CdSe/CdS quantum dot (QD) between the AuBPs. The assembly is achieved through a copper-free click reaction between azide-functionalized AuBPs and dibenzocyclooctyne (DBCO)-modified QDs, providing efficient and strong linkage between nanoparticles. The functionalization and assembly of the nanoparticles was verified through infrared and visible absorption spectroscopy, fluorescence spectroscopy, zeta potential measurements, and transmission electron microscopy. The AuBPs provide concentrated electric field confinement at their tips through the excitation of longitudinal plasmon resonances, enabling interaction with excitons in QDs located near the tips. Measurements on single assemblies showed an induced transparency in the plasmon scattering spectrum, characteristic of intermediate coupling between plasmons and excitons. A coupling strength of 45 meV was achieved for single QDs at room temperature. These results highlight the potential of colloidal AuBP-QD assemblies for achieving strong plasmon-exciton coupling using a directed assembly approach enabled by an efficient click chemistry strategy.The authors gratefully acknowledge financial support from the U.S. National Science Foundation (DMR-1905135) for work related to the formation and characterization of nanoparticle assemblies. Additional support for work related to the synthesis and functionalization is gratefully acknowledged from the U.S. National Science Foundation Science and Technology Center (STC) for Integration of Modern Optoelectronic Materials on Demand (IMOD) under Cooperative Agreement No. DMR-2019444. The authors also thank Dr. Tagide de Carvalho (Keith R. Porter Imaging Facility, UMBC) for assistance with TEM imaging.https://pubs.acs.org/doi/10.1021/acsanm.5c0289
EXPLORING INTERNATIONAL STUDENTS’ CHALLENGES IN THE U.S.: INSIGHTS FOR INSTITUTIONS FROM REDDIT THREADS
No full textInternational students enrich American colleges and universities with their diverse backgrounds. As they adapt to a new country and culture, many students face challenges that could manifest as concerns and complaints. This thesis used qualitative methods to analyze 100 comments made by international students on Reddit to explore the challenges they encounter during their academic experience in the U.S. Key discussion themes include socio-cultural differences, financial struggles, challenges in social relationships, and mental health issues. Based on these findings, this study suggests that U.S. institutions expand resources in pre-departure orientations, increase financial opportunities and mental health services, and enhance reliable channels to report abuses or scams. Analyzing students’ challenges using data from social media platforms that provide anonymity can enhance and strengthen institutional support services and programs. These initiatives can address issues that students do not typically share with institutions or researchers