Search Results (6,184)

G-quadruplex (G4) DNAzymes, guanine-rich sequences that fold into four-stranded structures and bind hemin to mimic peroxidase activity, are widely used in biosensing. Split G4 DNAzymes offer conditional activation upon target recognition, enabling high specificity and modularity. However, achieving low OFF-state leakage remains a major challenge. Here, we systematically characterized four representative G4 scaffolds, C-myc, Bcl2, PS5.M, and C-kit, under standardized ABTS/H₂O₂ conditions to assess their kinetic properties and suitability for split designs. C-myc exhibited the highest sustained activity and near-linear concentration dependence, making it ideal for quantitative sensing, while Bcl2 showed durable catalysis suited for extended read windows. C-kit produced rapid bursts with early plateaus, favoring binary outputs, and PS5.M initiated quickly but inactivated rapidly, suggesting potential application of systems requiring fast response. Split-mode analysis revealed that symmetric 2:2 partitions often retained significant activity, whereas asymmetric 3:1 splits reduced but did not eliminate leakage. Among the four G4 DNAzymes, PS5.M demonstrated the most promising OFF-state suppression. Design strategies to minimize leakage including non-classical splits, loop/flank edits, and template-assisted assembly could be used to optimize biosensor functionalities. These findings identify essential factors critical for designing robust split DNAzyme biosensors, advancing applications in diagnostics and molecular logic gates. Full article

The integration of large-scale renewable energy sources has increased the complexity of operation and maintenance in modern power systems, causing on-site substation operation and maintenance activities to exhibit stronger continuity and dynamics, and thereby placing higher demands on real-time operational perception and safety judgment. However, existing behavior recognition methods have difficulty accurately identifying operational states in complex scenarios involving continuous actions, partial occlusions, and fine-grained manipulations. To address these challenges, this paper proposes a safety behavior recognition method for substation operations based on a dual-path spatiotemporal network. Personnel localization is achieved using YOLOv8, while behavior classification is performed through the SlowFast framework. In the Slow pathway, an ECA attention mechanism is integrated with residual structures to enhance the representation of sustained operational postures. In the Fast pathway, a multi-path excitation residual network is introduced to fuse temporal, channel, and motion information, improving the multi-scale representation of local action variations. Furthermore, to mitigate the issue of class imbalance in substation operation data, Focal Loss based on binary cross-entropy is incorporated to adaptively down-weight easily classified samples. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 87.77% and an F1-score of 85.56% across multiple operation scenarios. The results further indicate improved recognition stability and adaptability, supporting safe substation operation and maintenance in renewable energy-integrated power systems. Full article

Compound yeast culture (CYC) is known to enhance animal health, but its effects on hepatic immune function are unclear. This study systematically examined CYC’s regulatory effects on the liver of weaned lambs using transcriptomics and integrative bioinformatics. Ten lambs were randomly assigned to a control diet or a basal diet supplemented with 30 g/d per head of Saccharomyces cerevisiae and Kluyveromyces marxianus co-culture (CYC group) for 42 days. Histological analysis showed that CYC improved hepatocyte arrangement and sinusoidal integrity, suggesting enhanced hepatic tissue stability. Cytokine analysis revealed CYC significantly increased IL-6 and IL-1β while reducing IL-10, TGF-β1, TNF-α, and CXCL9, indicating a bidirectional modulation of the immune response. Additionally, CYC enhanced antioxidant defenses by increasing T-SOD, GSH-Px, and T-AOC activities and decreasing MDA content. Transcriptomic sequencing indicated that CYC reshaped hepatic gene expression. Upregulated genes were enriched in immune-regulatory and structural pathways, including PI3K-AKT signaling, ECM–receptor interactions, Toll-like receptor pathways, and cell adhesion molecules. Protein-level validation further confirmed activation of PI3K and AKTAKT phosphorylation with limited engagement of NF-κB signaling. Conversely, downregulated genes were mainly associated with oxidative stress and energy metabolism, such as ROS-related pathways and MAPK signaling. WGCNA identified key hub genes (PTPRC, CD86, and ITGAV), which correlate with pro-inflammatory factors and participate in immune recognition, T-cell activation, and cell adhesion. These data suggest that CYC promotes hepatic immune homeostasis by enhancing immune signaling, stabilizing tissue architecture, and modulating oxidative stress/metabolic processes. This study provides mechanistic insights into CYC’s regulation of liver immune function and supports its targeted application as a functional feed additive for ruminants. Full article

WiFi sensing relies on capturing channel state information (CSI) fluctuations induced by human activities. Accurate motion segmentation is crucial for applications ranging from intrusion detection to activity recognition. However, prevailing methods based on variance, correlation coefficients, or deep learning are often constrained by complex threshold-setting procedures and dependence on high-quality sample data. To address these limitations, this paper proposes a training-free and environment-independent motion segmentation system using commercial WiFi devices from an image-processing perspective. The system employs a novel quasi-envelope to characterize CSI fluctuations and an iterative segmentation algorithm based on an improved Otsu thresholding method. Furthermore, a dedicated motion detection algorithm, leveraging the grayscale distribution of variance images, provides a precise termination criterion for the iterative process. Real-world experiments demonstrate that our system achieves an E-FPR of 0.33% and an E-FNR of 0.20% in counting motion events, with average temporal errors of 0.26 s and 0.29 s in locating the start and end points of human activity, respectively, confirming its effectiveness and robustness. Full article

Real-time fine-grained human activity recognition (HAR) remains a challenging problem due to rapid spatial–temporal variations, subtle motion differences, and dynamic environmental conditions. Addressing this difficulty, we propose NovAc-DL, a unified deep learning framework designed to accurately classify short human-like actions, specifically, “pour” and “stir” from sequential video data. The framework integrates adaptive time-distributed convolutional encoding with temporal reasoning modules to enable robust recognition under realistic robotic-interaction conditions. A balanced dataset of 2000 videos was curated and processed through a consistent spatiotemporal pipeline. Three architectures, LRCN, CNN-TD, and ConvLSTM, were systematically evaluated. CNN-TD achieved the best performance, reaching 98.68% accuracy with the lowest test loss (0.0236), outperforming the other models in convergence speed, generalization, and computational efficiency. Grad-CAM visualizations further confirm that NovAc-DL reliably attends to motion-salient regions relevant to pouring and stirring gestures. These results establish NovAc-DL as a high-precision real-time-capable solution for deployment in healthcare monitoring, industrial automation, and collaborative robotics. Full article

Machine Learning and Knowledge Extraction have evolved from algorithmic tools for pattern recognition into a unifying foundational scientific framework underpinning virtually all of today’s groundbreaking advances, enabling systematic discovery, interpretation and understanding across domains. This paper introduces a comprehensive research agenda that defines currently the future of innovation in Artificial Intelligence. We identify ten interrelated research frontiers that collectively map the transition from data-driven learning to knowledge-centric, trustworthy, and sustainable intelligence. These frontiers span the full spectrum of future AI research: from physics-informed and hybrid architectures that embed causality and domain knowledge, to multimodal and embedded intelligence that ground AI in real-world contexts; from interpretable and responsible design principles that ensure transparency and fairness, to safe and sustainable deployment in open-world environments. Together, these directions delineate a coherent roadmap toward AI systems that not only predict but also explain, reason, and collaborate. Future AI can be seen as a new member of your research lab, an active participant in knowledge creation, driven by interdisciplinary integration, global cooperation, ethical responsibility, and human oversight. By embedding principles of transparency, sustainability, and societal alignment from the outset, we envision AI as both a catalyst for scientific discovery and a cornerstone of responsible technological progress. Full article

The intratumoral microbiota, comprising bacteria, fungi, and viruses within the tumor microenvironment, actively influences carcinogenesis. Key mechanisms include the induction of host DNA damage, modulation of critical oncogenic signaling pathways such as WNT-β-catenin, NF-κB, and PI3K, and the orchestration of inflammatory processes. The microbiome’s interaction with the host immune system is complex and bidirectional. On one hand, specific microbes can foster a pro-tumorigenic niche by suppressing the activity of cytotoxic T cells and natural killer (NK) cells or by promoting the accumulation of immunosuppressive cell types like tumor-associated macrophages (TAMs). On the other hand, microbial components can serve as neoantigens for T cell recognition or produce metabolites that reprogram the immune landscape to enhance anti-tumor responses. The composition of this microbiome is emerging as a crucial factor influencing the outcomes of immunotherapies. Prospective investigations in cancer immunotherapy ought to prioritize mechanistic inquiry employing integrative multi-omics methodologies. The execution of meticulously designed clinical trials for the validation of microbial biomarkers, and the systematic, evidence-based development of microbiome-targeted therapeutic interventions aimed at enhancing antitumor immune responses. Full article

Accurate recognition of lower limb activities is essential for wearable rehabilitation systems and assistive robotics like exoskeletons and prosthetics. This study introduces SDALLE, a custom hardware data acquisition system that integrates surface electromyography sensors (EMGs) and inertial measurement sensors (IMUs) into a wireless, portable platform for locomotor monitoring. Using this system, data were collected from nine healthy subjects performing four fundamental locomotor activities: walking, jogging, stair ascent, and stair descent. The recorded signals underwent an offline structured preprocessing pipeline consisting of time-series augmentation (jittering and scaling) to increase data diversity, followed by wavelet-based denoising to suppress high-frequency noise and enhance signal quality. A temporal one-dimensional convolutional neural network (1D-TCNN) with three convolutional blocks and fully connected layers was trained on the prepared dataset to classify the four activities. Classification using IMU sensors achieved the highest performance, with accuracies ranging from 0.81 to 0.95. The gyroscope X-axis of the left Rectus Femoris achieved the best performance (0.95), while accelerometer signals also performed strongly, reaching 0.93 for the Vastus Medialis in the Y direction. In contrast, electromyography channels showed lower discriminative capability. These results demonstrate that the combination of SDALLE hardware, appropriate data preprocessing, and a temporal CNN provides an effective offline sensing and activity classification pipeline for lower limb activity recognition and offers an open-source dataset that supports further research in human activity recognition, rehabilitation, and assistive robotics. Full article

Background and Objectives: Low back pain (LBP) is one of the most frequent complications during pregnancy, with a high and variable incidence. LBP has been associated with physical inactivity, but it has not been evaluated exclusively in physically active (PA) pregnant women. This study aimed T to estimate the incidence of LBP in PA pregnant women and describe its clinical, functional, emotional, and occupational impact. Materials and Methods: A prospective cohort of 147 women with PA pregnancies was recruited between gestational weeks 11 and 13⁺⁶. Most (92.5%) hold a university degree. All received standardized informational intervention based on international recommendations on PA during pregnancy and LBP prevention. Data were collected through an in-person interview in the first trimester and a postpartum follow-up phone interview. PA was assessed using the International Physical Activity Questionnaire (IPAQ, short version), and LBP intensity was evaluated using the Visual Analog Scale (VAS). Results: LBP occurred in 64.6% of participants, despite maintaining regular PA. Pain intensity was higher in standing position (VAS = 4.9) and lower in lateral decubitus (VAS = 2.7). More than half (55.8%) did not seek medical consultation. LBP was associated with functional limitations (work, sleep, walking), emotional distress (52.6%), and work leave (30.5%; mean 9.4 weeks). In the multivariable logistic regression analysis, standing occupational position showed a borderline association with LBP (OR = 2.14; 95% CI: 1.00–4.55; p = 0.047), while a history of LBP in a previous pregnancy showed a statistically significant association (OR = 2.89; 95% CI: 1.12–7.48; p = 0.029). Higher PA levels during pregnancy were associated with slightly lower odds of LBP (OR = 0.91 per 500 MET·min/week; 95% CI: 0.83–0.99; p = 0.032), although the magnitude of this association was small. Conclusions: LBP showed a high incidence even among PA and highly educated pregnant women. More than half of the women did not seek medical consultation, suggesting potential under-recognition of LBP. Standing occupational position and a previous pregnancy-related LBP were identified as independent risk factors associated with LBP in the multivariable model. Higher PA levels were inversely associated with LBP. Full article

Affective Computing and emotion recognition hold significant importance in healthcare, identity verification, human–computer interaction, and related fields. Accurate identification of emotion is crucial for applications in medicine, education, psychology, and military domains. Electroencephalographic (EEG) signals have gained widespread application in emotion recognition due to their inherent characteristics of being non-concealable and directly reflecting brain activity. In recent years, with the establishment of open datasets and advancements in deep learning, an increasing number of researchers have integrated EEG with deep learning methods for emotion recognition studies. This review summarizes commonly used deep learning models in EEG-based emotion recognition along with their applications in this field, including the design of different network architectures, optimization strategies, and model designs based on EEG signal features. We also discuss limitations from the perspectives of commonality–individuality (C-I) and suggest improvements. The review outlines future research directions and provided a minimal C-I framework to assess models. Through this review, we aim to provide researchers in this field with a comprehensive reference and approach to balance universality and personalization to promote the development of deep learning-based EEG emotion recognition methods. Full article

Background: Resveratrol (3,5,4′-trihydroxy-trans-stilbene, RVT) is one of the most extensively studied natural polyphenols, with numerous health benefits documented in the literature. One of its most characterized biological properties is the strong antioxidant capacity. However, its poor biopharmaceutical properties limit its in vivo applicability. In this study, we conducted a detailed comparative analysis of the antioxidant and protective capacity of pure and loaded into Pluronic micelles resveratrol. Methods: Various in vitro antioxidant assays, such as DPPH, ABTS, superoxide anion radical scavenging, ferric (FRAP), and copper-reducing power assay (CUPPRAC), and iron-induced lipid peroxidation were performed. In addition, the in vitro 6-OHDA model of neurotoxicity in brain synaptosomes and the in vivo scopolamine (Sco)-induced model of cognitive impairment in rats were also employed. The main antioxidant biomarkers—the levels of lipid peroxidation (LPO) and total glutathione (GSH), as well as activities of superoxide dismutase, catalase, and glutathione peroxidase—were measured in the cortex and hippocampus. Results: The results from the in vitro tests demonstrated better ferric-reducing power activity and better neuroprotective capacity of the micellar resveratrol (mRVT), as evidenced by preserved synaptosomal viability and maintained GSH levels in a concentration-dependent manner in 6-OHDA-induced neurotoxicity. Regarding the in vivo results, mRVT (10 µM concentration) was the most effective treatment in supporting recognition memory formation in dementia rats. Further, mRVT demonstrated better LPO protective capacity in the hippocampus and GSH preserving activity in the cortex than the pure drug. Conclusions: The incorporation of resveratrol in polymeric micelles could enhance its antioxidant and neuroprotective effects. Full article

Biometric authentication is widely regarded as more secure and reliable than conventional approaches like passwords and PINs. Nonetheless, many current systems rely on active user participation, such as fingerprint scanning or facial recognition, which can disrupt tasks, increase the likelihood of errors, and raise privacy concerns. To address these challenges, this study introduces a continuous, seamless authentication framework that utilizes vital signs for passive identity verification across various activities, including resting, walking, and running. The framework analyzes physiological indicators such as Heart Rate (HR), Heart Rate Variability (HRV), Skin Temperature, Peripheral Oxygen Saturation (${\mathrm{SpO}}_2$), and Breathing Rate to provide zero-effort authentication without requiring user intervention. Multiple machine learning algorithms, including Decision Tree, Random Forest, XGBoost, Gradient Boosting, and K-Nearest Neighbors, were implemented and compared to identify the most effective predictive model. The methodology involved data collection, preprocessing, model construction, evaluation, and comparison. Experimental results revealed that the XGBoost Classifier achieved the highest accuracy at 96%. Overall, the proposed framework demonstrates strong reliability, scalability, adaptability, and flexibility, making it suitable for practical deployment. By continuously verifying identity without interrupting user activity, it improves both security and usability, offering a modern and convenient alternative to traditional authentication methods. Full article

Emotion recognition based on EEG signals remains a challenging task due to the complex spatiotemporal properties of brain activity and substantial intersubject variability. To address these challenges, we propose the EED-CL framework, which integrates an extended EEG-Deformer (EED) with contrastive learning (CL). The proposed model incorporates a depthwise separable convolution encoder for efficient extraction of spatiotemporal EEG features, a hierarchical coarse-to-medium-to-fine (HCMFT) transformer to capture multiscale temporal patterns, and an attentive dense information purification (ADIP) module to suppress noise and refine essential latent representations. In addition, CL-based pretraining facilitates robust feature learning even in settings with limited labeled data. The extracted multiscale features are integrated and classified through a Transformer encoder and an MLP. Experiments conducted on multiple benchmark EEG datasets show that EED consistently outperforms conventional models, while EED-CL achieves further improvements under label-constrained conditions. Notably, EED-CL demonstrates strong robustness to intersubject variability and noise, enabling stable emotion classification even when labeled samples are scarce. These findings indicate that EED-CL effectively captures multiscale spatiotemporal EEG patterns and offers a scalable and reliable approach for EEG-based emotion recognition. Full article

Breast cancer remains the most commonly diagnosed malignancy among women worldwide and continues to pose significant therapeutic challenges, particularly in advanced and refractory disease. Although traditionally considered less immunogenic compared with other solid tumours, growing evidence demonstrates that subsets of breast cancer, particularly triple-negative and HER2-positive subtypes, exhibit immune-responsive features. This recognition has spurred the development and clinical evaluation of immunotherapeutic strategies, with immune checkpoint inhibitors (ICIs) emerging as the most prominent approach. This new class of drugs targeting the programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) axis has demonstrated meaningful clinical activity in select patient populations, leading to regulatory approvals in combination with chemotherapy for advanced triple-negative breast cancer. Despite these advances, response rates remain modest, and the benefits are largely restricted to patients with PD-L1-positive tumours. Ongoing studies are evaluating predictive biomarkers, optimal treatment combinations, and mechanisms of resistance to expand the efficacy of ICIs across broader breast cancer subtypes. Furthermore, novel checkpoint targets such as cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), lymphocyte-activation gene 3 (LAG-3), and T cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domains (TIGIT) are under investigation, with the potential to enhance or complement PD-1/PD-L1 blockade. This review summarises the current state of knowledge on breast cancer immunotherapy with an emphasis on ICIs, highlighting key clinical trial findings, as well as emerging biomarkers of response, and strategies to overcome therapeutic resistance, if cancer cells eventually develop resistance. By integrating preclinical insights with clinical progress, we aim to provide a comprehensive overview of the evolving role of checkpoint blockade in breast cancer and outline future directions to optimise patient outcomes. Full article

Migraine is a prevalent and disabling neurological disorder with multifactorial origins and complex clinical manifestations. While pharmacologic therapies remain the cornerstone of management, a growing body of evidence highlights the role of extracranial peripheral nerve compression as a significant contributor to migraine pathophysiology in selected patients. This recognition has expanded the therapeutic role of plastic surgery, offering anatomically targeted interventions that complement or surpass traditional medical approaches for refractory cases. From a plastic surgeon’s perspective, optimal migraine care begins with accurate identification of clinical patterns, trigger-site mapping, and the judicious use of diagnostic tools such as nerve blocks and botulinum toxin. Surgical decompression techniques, including endoscopic and open approaches, address compression of the supraorbital, supratrochlear, zygomaticotemporal, greater and lesser occipital, auriculotemporal, and intranasal contact-point trigger sites. Adjunctive strategies such as autologous fat grafting further enhance outcomes by providing neuroprotective cushioning and modulating local inflammation through adipose-derived stem cell activity. Recent advances, including neuromodulation technologies, next-generation biologics, and innovations in surgical visualization, underscore the ongoing shift toward precision-based, mechanism-driven therapy. As understanding of migraine heterogeneity deepens, the integration of surgical expertise with modern neuroscience offers a comprehensive and personalized therapeutic framework. Plastic surgeons, equipped with detailed knowledge of peripheral nerve anatomy and minimally invasive techniques, play an increasingly pivotal role in the multidisciplinary management of refractory migraine. Full article