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Endothelial PRMT7 prevents dysfunction, promotes revascularization and enhances cardiac recovery post-myocardial infarction
No full textMyocardial infarction (MI) induces ischemic damage, triggering endothelial cell (EC) dysfunction that impairs revascularization and cardiac recovery. A key contributor to this dysfunction is excessive endoplasmic reticulum (ER) stress, which is activated by MI and exacerbates EC apoptosis and impaired angiogenesis. Here we investigate the role of endothelial-specific protein arginine methyltransferase 7 (PRMT7) in mitigating ER stress and promoting vascular homeostasis after MI. We demonstrate that PRMT7 expression is upregulated in ECs under tumor necrosis factor α or tunicamycin treatment, while its inhibition exacerbates ER stress and induces EC death. Using endothelial-specific PRMT7-knockout models, we show that PRMT7 deficiency increases apoptosis and fibrosis, impairing cardiac recovery. Transcriptomic analysis reveals that PRMT7 loss leads to the upregulation of pro-apoptotic pathways and suppression of angiogenic and proliferative signaling. Conversely, PRMT7 overexpression or treatment with the PRMT7-inducing drug bindarit restores EC function, suppresses ER stress and enhances revascularization and cardiac repair after MI. These findings establish endothelial PRMT7 as a critical regulator of EC survival and function, highlighting its potential as a therapeutic target to mitigate ER stress and improve post-MI cardiac recovery.TRUEsciescopuskc
Exploring the Sound Absorption Potential of Ecoflex™ 00-35 for Soft and Flexible Noise Reduction
No full textThis study investigates the acoustic performance of Ecoflex (TM) 00-35, a highly flexible silicone rubber, for use in soft and adaptable vibration and noise control systems. Under normal conditions, Ecoflex (TM) 00-35 consists of two components-Part A and Part B-which are mixed and cured at room temperature to form an elastomer. In this study, curing parameters such as the A/B mixing ratio, thinning agent addition, and curing pressure were varied to examine their effects on acoustic behavior. The microstructure of the prepared samples was analyzed using scanning electron microscopy (SEM), while sound absorption properties were measured using impedance tubes. Test results demonstrated that modifying curing parameters, applying vacuum, and incorporating a thinning agent increased the average cell diameter, leading to the fabrication of a moderate sound absorber with a sound absorption coefficient ranging from 0.35 to 0.60 in the low- to mid-frequency ranges. Further enhancement in low-frequency absorption was achieved by applying low pressure for a short duration, allowing cell expansion. In contrast, the addition of a thinning agent significantly improved absorption at higher frequencies. These findings highlight the influence of processing conditions on the acoustic behavior of soft silicone elastomers and provide valuable insights into their structure-property relationships. Ultimately, this study contributes to the development of advanced materials for acoustic damping and noise control applications.TRUEsciescopu
Designing organic mixed ionic-electronic conductors with low environmental footprint for bioelectronics and energy storage
No full textOrganic mixed ionic-electronic conductors (OMIECs) are touted as a highly promising sub-class of organic electronics that see application in organic energy storage, where global scale implementation is envisioned, as well as bioelectronics, where biocompatibility is an additional key requirement. Therefore, the ongoing development of new OMIECs should not just focus on developing materials of high performance in target applications, but also place increasing emphasis on developing materials of low environmental footprint, in line with the future need for sustainable electronics. To empower this direction of OMIEC research, the following review first explores the emerging applications of OMIECs in organic electrochemical transistors (OECTs) and biosensing, signal processing and neuromorphic computing, as well as organic energy storage, to distil the key materials characteristics required for high performance in each target application. A summary of the three different categories of OMIECs, which include those based on small molecules, conjugated polymers and 2D/3D covalent-organic frameworks is also provided, to highlight the key characteristics of each OMIEC and suitability for specific applications. Finally, strategies that enable the low environmental footprint synthesis and materials design diversification of OMIECs are discussed, which encompass the deployment of more environmentally benign cross-coupling and metal-free polymerisations, as well as post-synthetic modification. © 2025TRUEsciescopu
Low-temperature catalytic CO2 methanation over nickel supported on praseodymium oxide
No full textNickel (Ni)-based catalysts are widely used for CO2 methanation due to their cost-effectiveness compared to noble metals and high efficiency. However, their catalytic performance at low temperatures remains a significant challenge, primarily due to the limited activation of CO2. This study reveals that the Ni supported on praseodymium oxide (PrOx) significantly enhanced low-temperature CO2 methanation activity. This enhancement was primarily attributed to the dual role of PrOx: promoting CO2 activation and modifying the reducibility of Ni active sites. PrOx facilitated the formation of oxygen vacancies (Ov) through the valence state transition (Pr3+ Pr4+), providing electron donor sites for direct CO2 dissociation (CO2 -> CO + O*). Furthermore, metal-support interaction (MSI) between Ni and PrOx enhanced the reducibility of Ni2+ to Ni0 , inducing a higher density of hydrogen activation sites for the hydrogenation of CO2. The integration of these properties induced a high efficiency of the CO2 methanation pathway by enhancing reactant activation efficiency. These findings demonstrate that the synergistic interaction between Ni and PrOx enhances CO2 methanation by simultaneously improving Ni site reducibility and providing abundant oxygen vacancies for CO2 activation, indicating PrOx as a highly effective support material for low-temperature CO2 methanation catalysts.FALSEsciescopu
T-Cell Synaptosomes Orchestrate Long-Term Anti-Tumor Immunity via Proliferative and Metabolic Reprogramming
No full textRobust self-healing of Bessel-Gaussian beams under geometric obstructions for free-space optical communication
No full textWe experimentally generated Bessel-Gaussian (BG) beams and investigated their self-healing and speckle characteristics under various conditions. Diffracting and non-diffracting Bessel speckles were produced by scattering the beams and observing their near-field and Fourier-transformed far-field speckle patterns. The non-diffracting behavior was confirmed for both coherent and partially coherent BG beams. The topological charge of the beams, ranging from l = 1 to 10, was measured using a Mach-Zehnder interferometer, with forked interference fringes revealing the beam order. The self-healing property was analyzed by introducing geometric obstructions blocking 10%-70% of the beam, showing that the recovery distance increases from similar to 70 cm to 170 cm with the obstacle size and decreases with the axicon parameter. The recovered intensity decreases with increasing the obstruction, indicating a limit to the self-reconstruction capability. These findings demonstrate the robustness of BG beams for applications in optical manipulation, imaging, and free-space optical communication.FALSEsciescopu
ConnecToMind: Connectome-Aware fMRI Decoding for Visual Image Reconstruction
No full textRecent deep-learning approaches have achieved significant improvements in reconstructing visual images from human brain activity. However, existing methods typically represent brain activity as flattened voxel-wise signals, overlooking the detailed anatomical and functional organization of visual cortical regions. Here, we propose ConnecToMind, a novel decoding framework that employs a region-level fMRI embedding module to preserve distinct functional representations across visual cortical sub-regions, while leveraging functional connectivity (FC) derived from resting-state fMRI. Experiments on the Natural Scenes Dataset (NSD) demonstrate that ConnecToMind outperforms the MindEye in both the semantic and perceptual fidelity of reconstructed images, validating the effectiveness of preserving distinct functional representations with FC prior. Moreover, ConnecToMind shows competitive performance in image retrieval tasks. Ablation analyses further reveal that low-level (e.g., V1–V3) and high-level (e.g., Lateral Occipital, Fusiform) visual regions distinctly contribute to the reconstruction quality, highlighting the importance of region-specific embeddings in visual reconstruction. All codes for this study are publicly available at GitHub (https://github.com/aimed-gist/ConneToMind). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026
G-Universe: A Collection of In-House Technology Computer-Aided Design Simulators as a Platform for Developing New Simulation Capabilities
No full textIn this work, G-Universe, a collection of in-house Technology Computer-Aided Design (TCAD) simulators for new simulation capabilities, is briefly introduced. After discussing the motivation of in-house TCAD development, its two major building blocks, G-Process and G-Device, are introduced with examples. Future development directions are discussed. © 2025 Elsevier B.V., All rights reserved
Robust Maritime Object Detection under Adverse Conditions Via Joint Semantic Learning without Extra Computational Overhead
No full textSub-Seasonal Forecasting From Necessity to Improvement: Identifying Climate Shifts, Evaluating Predictability, and Advancing with Neural Networks
No full textAs climate change accelerates, extreme weather events are occurring with increased frequency and severity, highlighting an urgent need for improved sub-seasonal forecasting. This dissertation investigates sub-seasonal forecasting as an adaptation tool by examining the Time of Emergence (TOE) when climate change signals exceed natural variability. Specifically, the study finds that peak summer temperatures in South Korea and water storage in the western United States are nearing climate thresholds sooner than anticipated, marking the emergence of a new normal in these regions. To address current forecasting challenges, this work introduces a novel metric that combines mean state and seasonal cycles, providing an approach to evaluating forecast performance. This metric reveals the relationship between the performance in simulating the seasonal cycle and the sub-seasonal predictability of Sub-seasonal to Seasonal (S2S) models, suggesting a new direction for improving these models. Furthermore, this research applies neural network-based post-processing techniques—specifically, U-Net architectures—to refine forecasts generated by the European Centre for Medium-Range Weather Forecasts (ECMWF) model. These advanced deep-learning techniques substantially increase forecast accuracy and enable downscaling to finer spatial resolutions. However, challenges remain in reliably predicting extreme events, which require further model refinement. Despite these constraints, enhancing sub-seasonal forecasting is crucial in responding to the upcoming new normal, and further development and effort are needed.DoctorAbstract i
LIST OF FIGURES iv
LIST OF TABLES vii
I. Introduction １
1.1. Research Background and Motivation １
1.2. Objective and Overview ５
1.3. Literature Review ６
1.3.1. TOE under global warming ６
1.3.2. Evaluation of sub-seasonal predictability １０
1.3.3. Improving sub-seasonal predictability １４
II. Estimation of the TOE due to Climate Shifts １８
2.1. Introduction １８
2.2. Data and Methods ２０
2.2.1. Data ２０
2.2.2. TOE and natural climate variability ２４
2.2.3. Statistical temperature models and climate change signal ２５
2.2.4. Hydroclimate variables and aridity index ２７
2.3. Results ２９
2.3.1. Estimation of regional TOE during peak summer in Korea ２９
2.3.2. Biases in simulated natural climate variability and long-term trend ３５
2.3.3. TOE of the western United States hydrological variables ４５
2.3.4. Aridification in the western United States ５３
2.4. Discussion ５５
III. Evaluation of Sub-seasonal Predictability Using the Mean State ５７
3.1. Introduction ５７
3.2. Data and Methods ５９
3.2.2. Data ５９
3.2.2. Evaluation metrics for the mean state ６１
3.2.3. Evaluation metrics for the prediction skill ６３
3.3. Results ６４
3.3.1. Performance of the mean state of S2S models ６４
3.3.2. Weather and sub‑seasonal prediction skill of S2S models ６９
3.3.3. Relationship between the performance of mean state and prediction skill ７４
3.4. Discussion ８３
IV. Enhancing and Downscaling Sub-Seasonal Predictability Using Neural Networks ８４
4.1. Introduction ８４
4.2. Data and Methods ８６
4.2.1. Data ８６
4.2.2. U-Net architecture ８７
4.2.3. Pre-processing ８９
4.2.4. Evaluation metrics ９１
4.3. Results ９２
4.3.1. Role of sub-variables and ensemble members ９２
4.3.2. Prediction accuracy and downscaling ９５
4.3.3. General and extreme predictability at the county scale １０１
4.4. Discussion １０４
V. Summary and Conclusion １０５
VI. Reference １０７
Acknowledgments １１９
Curriculum Vitae １２