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KG-SLomics: Synthetic Lethality Prediction Using Knowledge Graph and Cancer Type-Specific Multiomics Integrated Graph Neural Network
No full textSynthetic lethality (SL) is a phenomenon in which the simultaneous alterations of two genes evoke cell death, whereas a mutation of either gene alone does not adversely affect cell survival. After the clinical application of PARP inhibitors, SL has been a promising strategy for the undruggable cancer mutations by targeting their alternative partner genes. While various statistical and computational methods can predict SL pairs, they often overlook key challenges, including variation across cancer types and reliance on outdated networks or gene-specific data that fail to capture cancer-specific features. Recent progress has addressed these gaps, but it struggles to generalize across multiple cancer types. In this paper, we propose KG-SLomics, a relational graph attention network-based model that predicts SL using an extensively updated knowledge graph (KG) and multiple cancer cell line data. We construct a comprehensive KG incorporating newly curated biological entities, tripling its size compared to previous versions. Pre-trained KG embeddings are combined with multiomics data to capture topological and cancer-specific features. Through relational message passing, KG-SLomics calculates SL probabilities with high accuracy, allocating high attention scores to the relevant entities in KG. It outperformed advanced baselines in various evaluations and suggested novel therapeutic targets, underscoring its clinical potential. © 2025 IEEE.TRUEscieforeig
WANCDR: Wasserstein Adversarial Network for Cancer Drug Response
No full textPredicting patient-specific drug responses from preclinical cell-line data remains challenging due to significant heterogeneity between preclinical (cell-line) and clinical (patient) gene expression profiles. In this study, we propose WANCDR, a novel adversarial neural network framework designed to improve the generalization of drug-response predictions by aligning latent representations across preclinical and clinical domains. Specifically, we introduce a domain alignment module trained adversarially, which enforces the encoder to generate domain-invariant latent embeddings. Extensive experiments conducted on preclinical (GDSC) and clinical (TCGA) datasets demonstrate that WANCDR achieves robust predictive performance on preclinical data, while substantially outperforming existing approaches in clinical generalization, particularly when classifying responses for previously unseen drugs. Qualitative analyses via UMAP visualization further validate the superior domain alignment capability of WANCDR. Collectively, these results highlight the potential of WANCDR to bridge the translational gap from preclinical insights to clinical applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026
Real-time observation of the spin Hall effect of light using metasurface-enabled single-shot weak measurements
No full textThe spin Hall effect of light (SHEL), the transverse splitting of light into two circularly polarized components via refraction or reflection, offers high-precision, nondestructive inspection of unknown interfaces when combined with a signal amplification technique called weak measurement. However, its application in detecting dynamics is limited due to its multistep process. Here, we condense the procedure into a single step, enabling calibration-free, single-shot measurement of the SHEL by replacing one component of the conventional setup with a polarization beamsplitting metasurface. Our approach allows for instantaneous evaluation of the SHEL, even with fluctuations in the original beam position. As proof of concept, we apply metasurface-assisted weak measurements to both static and dynamic scenarios, where the experimental results obtained from a single captured image demonstrate nice agreement with theory. This real-time observation of the SHEL highlights its potential for high-precision monitoring of dynamic processes such as biomedical sensing and chemical analysis. © The Author(s) 2025.TRUEsciescopu
Comparing efficiency of literature-based estimation of general biomass using Length-Weight Relationships (LWRs) between freshwater and seawater fishes
No full textFish biomass estimation is crucial for understanding aquatic ecosystem dynamics and managing fisheries resources effectively. This study evaluated the applicability of estimating fish biomass in Korean aquatic ecosystems using literature-derived General Total Length (GTL) and length–weight relationships (LWRs). We compared the estimated biomasses with measured field biomasses from literatures for freshwater and seawater fish species. Data on individual biomass, habitat, and length-weight coefficients were collected from the literature and databases for 245 fish species. Biomass was estimated using the GTL and representative LWR coefficients and then they were compared to the measured field biomasses from literature. The results showed that this estimation method was applicable only to freshwater species (R2 = 0.7133), whereas the estimates for seawater species showed a poor correlation (negative R2 values). The removal of outliers (Q > 6) improved the estimation accuracy for the freshwater species. This study demonstrates that literature-based biomass estimation using GTL and LWRs is appropriate for freshwater fish in Korea, but not for seawater species. These findings contribute to the generation of fundamental biomass data for ecosystem modeling and highlight the need for habitat-specific approaches for biomass estimation. Since habitat specific biomass data is deficient, future research should explore biomass estimation through species extrapolation to address data gaps in aquatic ecological studies. © 2025 Yeom, Kim. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.TRUEsciescopu
Simulation-Guided Subset Aggregation for Large-Scale Tacton Similarity Ratings
No full textExploring perceptual dissimilarity spaces of large-scale Tactons (i.e., Tactile icons) can inform the design of distinguishable haptic feedback. Yet, collecting pairwise similarity ratings for entire Tacton sets becomes costly as set size increases, prompting the need for alternative methods like subset aggregation. Despite previous efforts, little systematic investigation exists on efficient subset size or participant number needed to estimate large-scale Tacton perceptual spaces within a bounded error threshold. We address this gap by introducing a model that simulates between-subject variability in similarity perception. The model explores various distributions under different conditions, including total Tacton numbers and subset-to-total ratios, to guide user studies. Guided by these simulations, we evaluated subset aggregation with three small-scale Tacton sets (12 or 14 patterns) and one large-scale set (48 patterns). Study 1 revealed that initial simulations underestimated real-world variability. We refined the model, ran simulations for larger-scale conditions, and validated them in subsequent studies. The updated model closely matched reality, showing that designers can use our subset aggregation method to prototype perceptual spaces for large-scale Tacton sets. Notably, 4-7 observations were sufficient to achieve ρ ≥ 0.6, compared to the typical 12 required for generalization. We discuss the efficacy of subset aggregation and future research directions. © 2025 Elsevier B.V., All rights reserved
Characteristics of synaptic device based on van der Waals heterosturcture
No full textThe Digital Transformation Era demands Internet of Things(IoT) devices capable of real-time data acquisition, computation, and processing at the sensor to overcome limitations of conventional cloud-based architectures, such as latency, data loss, and security vulnerabilities. To adress these challenges, a neuromorphic in-sensor computing device leveraging a ReS2/h-BN/InSe/h-BN van der Waals heterostructure is presented. A novel light-triggered self-erasure mechanism is demonstrated that effectively erase stored data without the high-power consumption or sustainability issues associated with existing self-erasure technologies by inducing a rapid conductance reduction when exposed to 430 nm wavelength light. This work demonstrates the potential of light-triggered self-erasure synaptic devices to enhance the security, efficiency, and performance of next-generation smart IoT systems.MasterAbstract
contents
List of Figures
Chapter 1. Introduction
1. 1. Neuromorphic device
1. 2. Self-destructive in-sensor computing device
1. 3. Van der Waals materials
1. 4. Summary
Chapter 2. Experiment
2. 1. Fabrication process
2. 2. Electrical measurements
2. 3. Optical measurements
Chapter 3. Results and discussions
3. 1. Characterization of the ReS2 and InSe
3. 2. Device structure
3. 3. Flash memory characteristics
3. 4. Synaptic characteristics
3. 5. Light-triggered weight removable characteristics
3. 6. Hardware
Chapter 4. Conclusion
Referenc
Strain engineering of van Hove singularity and coupled itinerant ferromagnetism in quasi-2D oxide superlattices
No full textEngineering van Hove singularities (vHss) near the Fermi level, if feasible, offers a powerful route to control exotic quantum phases in electronic and magnetic behaviors. However, conventional approaches rely primarily on chemical and electrical doping and focus mainly on local electrical or optical measurements, limiting their applicability to coupled functionalities. In this study, a vHs-induced insulator-metal transition coupled with a ferromagnetic phase transition was empirically achieved in atomically designed quasi-2D SrRuO3 (SRO) superlattices via epitaxial strain engineering, which has not been observed in conventional 3D SRO systems. Theoretical calculations revealed that epitaxial strain effectively modulates the strength and energy positions of vHs of specific Ru orbitals, driving correlated phase transitions in the electronic and magnetic ground states. X-ray absorption spectroscopy confirmed the anisotropic electronic structure of quasi-2D SRO modulated by epitaxial strain. Magneto-optic Kerr effect and electrical transport measurements demonstrated modulated magnetic and electronic phases. Furthermore, magneto-electrical measurements detected significant anomalous Hall effect signals and ferromagnetic magnetoresistance, indicating the presence of magnetically coupled charge carriers in the 2D metallic regime. This study establishes strain engineering as a promising platform for tuning vHss and resultant itinerant ferromagnetism of low-dimensional correlated quantum systems. © 2025 Elsevier B.V., All rights reserved.FALSEsciescopu
Streamlining the cell flow: Feasibility of acoustically driven cell alignment for in vivo flow cytometry
No full textIn vivo flow cytometry (IVFC) utilizes blood vessels as natural conduits for real-time and noninvasive monitoring of circulating cells. However, conventional IVFC systems are primarily limited to superficial vessels, restricting analytical throughput and diagnostic sensitivity. Here, we propose a novel acoustic-based cell alignment strategy that allows IVFC to be applied in a broader range of vascular locations. We developed a dual ultrasound transducer (DUST) system in which two transducers are positioned face-to-face at the same angle. This configuration generates an interference-based acoustic field containing periodically arranged pressure nodes and antinodes within the vessel. The resulting field aligns flowing cells into multiple parallel streamlines, concentrating their movement within a confined region and enhancing the consistency and efficiency of signal detection. Blood vessel mimicking phantom experiments demonstrated that a dual ultrasound (DUS) enables stable multiple parallel streamlines of microbeads in a vessel while maintaining uniform flow velocity. Furthermore, fluorescent beads modeling rare cells exhibited approximately a 9-fold increase in signal-to-noise ratio (SNR) under DUS application compared to the non-aligned condition. Signal intensity fluctuations at the detection point were also significantly reduced, enabling more stable and reliable signal analysis. This approach demonstrates strong potential for highly sensitive, single-cell-level diagnostics in vivo. It also enables seamless integration with photoacoustic or fluorescence-based detection systems for future multimodal single-cell analysis.FALSEsciescopu
Energy harvesting with magneto-mechano-electric harvester for AC circular magnetic fields
No full textAs the industry on Internet of Things continues to expand, the demand for sustainable energy solutions to power numerous sensors and devices increases. One promising approach is energy harvesting from stray magnetic fields. In this paper, we investigated an energy harvesting with a magneto-mechano-electric (MME) energy harvester, designed for circular magnetic fields. Magnetic simulation using 3D finite element method yields optimum design for the operation at a frequency, 60 Hz. Under the influence of a 5.39 G AC circular magnetic field, the energy harvester successfully harvested energy at a frequency of 60 Hz, and operating with a maximum power of 1.92 mW and a power density of 25.2 mW/cm3. Additionally, the performance has been enhanced by placing the magnetic flux concentrator parallel and even perpendicular to the piezoelectric sheet. This study underlines the necessity for further research on the structural design of MME harvesters in environments with various types of stray magnetic fields. © 2025TRUEsciescopu
Improvement of Catalytic Methane Oxidation by Nitric Acid Treatment on Pt/TiO2
Get PDFThis study investigates the effect of nitric acid (Pt:NA) treatment on the catalytic performance of Pt/TiO2 catalysts in methane oxidation. By varying the molar ratio of Pt to nitric acid (NA) from 1:0.1 to 1:10 of Pt:NA, a series of Pt/NA-TiO2 catalysts are synthesized. The structural and catalytic properties are identified using X-Ray photoelectron spectroscopy, transmission electron microscopy, and diffuse reflectance infrared Fourier transform spectroscopy. The NA treatment during the catalyst synthesis promotes the formation of acidic sites, enhancing the uniform adsorption of the Pt precursor, which in turn improves Pt dispersion and catalytic activity. However, excessive NA treatment above Pt:NA ratio of 1:5induces the formation of larger Pt particles due to competitive nitrate adsorption, leading to decreased Pt dispersion and diminished catalytic performance. These findings provide a simple method for synthesizing active Pt supported on TiO2 catalysts with NA treatment having well-dispersed Pt particles, thereby maximizing the catalytic activity of the methane oxidation. © 2024 The Author(s). Advanced Energy and Sustainability Research published by Wiley-VCH GmbH.TRUEscopu