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Unique phenotypic and T cell receptor characteristics of CD8+ T cells accumulated in the brains of Alzheimer’s disease mice
Enhanced forecasting of friction and cohesion of augmented unsaturated soil with nanostructured quarry fines (NQF) addition
AI driven system for enhancing consumer electronics through maintenance personalization and security
Parental reporting of activities relevant for young children’s soil/dust ingestion
Broad generalisation of the ventriloquism aftereffect across sound frequencies
Abstract Humans localise sounds in the horizontal plane by processing level and timing differences between the ears. This neurocomputational process is continuously and adaptively calibrated using visual input, as seen in the ventriloquism aftereffect: a shift in sound perception toward a previously seen light. It is unknown from where in the brain this aftereffect originates; adaptation could occur at an early level in the auditory system where neurons are narrowly tuned to frequency, at a later level in the auditory system where localisation cues are extracted, or outside the auditory system at a higher-level spatial map. To investigate this, we examined how the ventriloquism aftereffect generalises across sound frequencies. Participants localised seven narrowband sounds (0.5–8 kHz), targeting different localisation cues. We found that sound localisation accuracy in darkness varied slightly with frequency. When sounds were paired with a visual stimulus that was offset by 10 deg, participants exhibited a pronounced bias toward the light of about ~ 63%, corresponding to the well-known ventriloquism effect. The bias was stronger for narrowband compared to broadband sounds. After exposure to a block of these audiovisual stimuli, a ventriloquism aftereffect in the form of a spatial bias of ~ 12% was observed across all tested frequencies, largely independent of the frequency of the exposure sound. Together with earlier reports of both frequency-specific and frequency-general recalibration, our results indicate that under conditions of a fixed and consistent audiovisual spatial offset, the ventriloquism aftereffect generalises across sound frequencies, consistent with adaptation at a frequency-independent multisensory spatial stage.
Unveiling cryptic diversity: integrative taxonomy discovers eight new species of moths and exposes biodiversity shortfalls in a Neotropical region
Reversible medical image cryptography using spatial XOR-rotation and chaos-driven permutation and diffusion schemes
Intelligent monitoring and anomaly detection for power service processes based on spatiotemporal attention mechanism
Abstract Power service process monitoring faces critical challenges in capturing complex spatiotemporal dependencies and identifying anomalies across distributed operational networks. This paper proposes an intelligent monitoring system incorporating spatiotemporal attention mechanisms to address these limitations. The system features a hierarchical attention architecture that jointly models temporal evolution patterns within service workflows and spatial correlations across regional centers, coupled with an adaptive threshold mechanism for anomaly detection. Experimental validation using real-world data from multiple power utilities demonstrates superior performance, achieving 96.84% accuracy and 96.0% recall in field deployment. The system reduces average process completion time by 20.3% and customer complaints by 31.2% across 32 service centers during a 6-month trial. Results confirm that explicit joint spatiotemporal modeling significantly outperforms conventional approaches, providing actionable insights for proactive process optimization in power utility operations.
A manta ray-bayesian optimization approach for hyperparameter-tuned convolutional neural networks in lung cancer classification
Abstract Lung cancer remains a global health challenge that is unavoidable. Despite the advances in lung cancer classification using deep learning models, the performance remains highly dependent on hyperparameter selection, whereas conventional grid or random search methods are often computationally inefficient in high-dimensional spaces. So, to address the issue, this paper presents a Convolutional Neural Network(CNN) which is hybridized by dual stage hyperparameter optimization techniques for lung cancer image classification. The approach integrates Bayesian Optimization (BO) and Manta Ray Foraging Optimization (MRFO) to efficiently explore and fine-tune a defined hyperparameter search space, including convolution filter count, learning rate, dense layer neurons, and dropout rate. Initially, Bayesian Optimization explores the search space by modeling the objective function with a Gaussian Process and selecting candidate hyperparameters via the Expected Improvement criterion. The best solution obtained is then further enhanced using MRFO, which incrementally refines the parameters through its chain, cyclone, and somersault foraging mechanisms. This two-step process strikes a balance between exploring the world and taking advantage of local resources. The CNN trained with the optimized hyperparameters achieved good accuracy in lung cancer image classification, demonstrating the potency of combining probabilistic modeling with bio-inspired optimization. Experimental results show that the proposed hybrid CNN method has a testing accuracy of 98%, which is better than that of many cutting-edge models. The results show that metaheuristic-based optimization could be useful in deep learning applications, especially in medical image analysis.
Impact of over compressing gas diffusion electrodes in alkaline zero-gap CO2 electrolyzers
Identifying time-lag effects of temperature and precipitation on vegetation growth variation in the lower Yellow River of east China
Few-shot prototype adaptation for generalizable electromyography gesture recognition
Abstract We present EMG-Adapt, a novel few-shot prototype adaptation framework designed to enhance the robustness and data efficiency of electromyography (EMG)-based gesture recognition. By integrating the representational power of prototype learning with the rapid adaptation capabilities of meta-learning, our framework introduces several technical novelties. These include a cepstrum coefficient average feature extraction method that reduces sensitivity to noise and variations, a deep prototype learning method based on hybrid loss functions for both discriminative classification and embedding space structure, and a meta-learning strategy for efficient prototype update with minimal labeled examples. Our integrated approach significantly improves few-shot gesture recognition performance, requiring substantially less calibration data than conventional methods. Extensive experiments on five public EMG datasets demonstrate state-of-the-art performance in cross-session and cross-user generalization scenarios, while maintaining computational efficiency. This work represents a significant advancement towards practical, user-friendly, and scalable EMG-based human-computer interfaces, with potential applications in prosthetics, assistive technologies, and virtual reality. Future research will explore self-supervised learning techniques and extend the framework to handle more gestures and online adaptation strategies for enhanced real-world robustness.
Demirjian’s and Cameriere’s methods for estimating the 18-year legal age threshold using third molar maturity in a Northern Thai population
Synthesis and biological evaluation of ibuprofen/o-vanillin Schiff base complexes with anti-inflammatory, anti-proliferative and anti-SARS-COV-19 activities
Abstract This study presents the synthesis and comprehensive characterization of four novel metal-complexes formed from the reaction of copper(II), nickel(II), zinc(II), and vanadium(IV) salts with the bidentate ibuprofen Schiff base ligand (HL) in 1:1 metal-to-ligand ratio. The copper complex shows the highest antibacterial and cytotoxicity efficiency, in addition to a strong binding interactions with DNA. The in-vitro anti-inflammatory and anti-COVID-19 potencies of HL and its complexes were assessed, ranking them as: CuL > NiL > ZnL > VOL > HL.
An ultra-compact and high isolated 8 × 8 MIMO antenna system for 5G NR-n46 and n79 band applications
Abstract This article presents an ultra-compact (1.02λ × 1.02λ mm 2 ) and highly isolated 8-port MIMO antenna designed for NR-n46 and n79 bands, as well as licensed assisted access (LAA). A systematic study was performed to choose an optimal antenna (Design-3) among all designs (Design-1, Design-2, and Design-3) after systematic study (parametric study and circuit theory analysis) of Ref. design-1, Ref. design-2, Ref. design-3 and Ref. design-4. An optimal and proposed antenna geometry consists of two orthogonal radiators on the top and a novel ground plane (rectangular ring, centered annular ring and plus shaped slot) at the bottom of each corner of the dielectric substrate to create a perfectly matched 8-port antenna. The proposed antenna demonstrates a wideband frequency operation of 700 MHz within the 4.75–5.45 GHz range, specifically in the sub-6 GHz 5G band. It resonates at 5.2 GHz, achieving an isolation of 33 dB, a gain of 4.7 dB, and a radiation efficiency of 92.5%. The MIMO characteristics, including ECC, DG, TARC, MEG, and CCL, were evaluated and found to be within acceptable parameters. The antenna was fabricated, tested in a laboratory setting, and its performance was validated against simulated results.
The impact of ERAP1 inhibition on metabolite homeostasis of melanoma cells
AI-enabled RF data synthesis for breast ultrasound: efficacy in quantitative ultrasound tissue characterization
Abstract Quantitative ultrasound (QUS) methods can derive insightful biomarkers from raw radiofrequency (RF) signals for tissue characterization and monitoring, but their clinical adoption is limited by the inaccessibility and storage burden of RF data. This study is the first to investigate the potential of deep generative models in synthesizing RF data from standard B-mode images and evaluate their efficacy in downstream QUS analysis. Three conditional generative adversarial networks (cGAN), namely Pix2Pix, a shallow ViT‐based cGAN, and a deep ViT‐based cGAN, were adapted and trained on a large paired dataset of RF/B‐mode frames (21,174 training, 3,456 validation, 8,919 test frames) collected from 152 patients (98 patients in the training, 16 in validation, and 38 in the test set) with suspicious breast lesions. The synthesized RF data were assessed using sample-level evaluation metrics, and via a benign-malignant lesion classification task based on the corresponding QUS features. The generative models achieved a structural similarity index measure (SSIM) of 0.82 ± 0.05 on the synthetic RF data and an average peak signal-to-noise ratio (PSNR) of about 33 dB on the corresponding B-mode images, confirming strong reconstruction fidelity. In the lesion classification experiments, a classifier trained on a selected subset of six QUS features derived from the original RF data achieved a test accuracy of 82 ± 6%. In training and testing the classifier with the same subset of QUS features derived from the synthetic RF data, the deep ViT cGAN matched the original model’s performance (accuracy = 82 ± 6%), outperforming the Pix2Pix and shallow ViT cGANs. When the feature selection and classifier training and testing were exclusively performed on the synthetic QUS parameters, the Deep ViT cGAN (accuracy = 81 ± 7%) and Pix2Pix cGAN (accuracy = 81 ± 6%) demonstrated competitive performance, while the Shallow ViT remained slightly lower (accuracy = 79 ± 6%). The promising results obtained in this study demonstrate the feasibility of RF data synthesis from B‐mode images, and therefore, is a step forward towards QUS‐based tissue characterization without the necessity of direct access to RF data.
Evaluating long-read metagenomics for bloodstream infection diagnostics: a pilot study from a Thai Tertiary Hospital
A CAM bioimaging model reveals the connection between VEGFA vascular remodeling and enhanced sarcoma progression via tumor secretome
Abstract We developed a sensitive bioimaging system for sarcoma by creating a cell line that stably expresses both Katushka2S fluorophore and NanoLuc luciferase, enabling robust dual tracking of tumor growth and metastasis in the chick chorioallantoic membrane (CAM) model. NanoLuc luciferase was notably more effective than Katushka2S for identifying tumor cell metastases in embryo tissues. Pretreating CAM with tumor cell-conditioned medium (TCM) significantly increased neovascularization, Ki67 expression, tumor volumes, and metastasis of the most difficult-to-establish low-tumorigenic and low-metastatic U2OS cells, indicating that tumor cell secretome actively alter the CAM vascular environment to aid tumor progression. The bead-based multiplex profiling of the TCM demonstrated a notable increase in pro-angiogenic factors. Neutralizing VEGFA, the most abundant factor in the TCM, effectively counteracted vascular and pro-metastatic effects of TCM. In contrast, elevating VEGFA levels brought back the pro-tumorigenic effects of TCM. This study reveals the importance of tumor cell secretomes in creating the vascular niche in CAM and points to VEGFA as a target to prevent secretome-induced angiogenesis and sarcoma development. Furthermore, our optimized CAM model permits continuous tumor growth and metastasis monitoring in embryonic development, providing a reliable platform for prognostic studies of sarcoma treatments in ongoing anti-angiogenic and multikinase inhibitors trials.