Search Results (4,231)

With the proliferation of the Internet of Things (IoT), gesture recognition has attracted attention as a core technology in human–computer interaction (HCI). In particular, mmWave frequency-modulated continuous-wave (FMCW) radar has emerged as an alternative to vision-based approaches due to its robustness to illumination changes and advantages in privacy. However, in real-world human–machine interface (HMI) environments, hand gestures are inevitably accompanied by torso- and arm-related reflections, which can also contain gesture-relevant variations. To effectively capture these variations without discarding them, we propose a preprocessing method called Adaptive Top-K Selection, which leverages vector entropy to summarize and preserve informative signals from both hand and body reflections. In addition, we present a Multi-Stream EfficientNetV2 architecture that jointly exploits temporal range and Doppler trajectories, together with radar-specific data augmentation and a training optimization strategy. In experiments on the publicly available FMCW gesture dataset released by the Karlsruhe Institute of Technology, the proposed method achieved an average accuracy of 99.5%. These results show that the proposed approach enables accurate and reliable gesture recognition even in realistic HMI environments with co-existing body reflections. Full article

Power-system safety is fundamental to protecting lives and ensuring reliable grid operation. Yet, hierarchical text classification (HTC) methods struggle with domain-dense accident narratives that require cross-sentence reasoning, often yielding limited fine-grained recognition, inconsistent label paths, and weak evidence traceability. We propose EG-HPT (Evidence-Grounded Hierarchy-Aware Prompt Tuning), which augments hierarchical prompt tuning with Global Pointer-based nested-entity recognition and a sentence–entity heterogeneous graph to aggregate cross-sentence cues; label-aware attention selects Top-k evidence nodes and a weighted InfoNCE objective aligns label and evidence representations, while a hierarchical separation loss and an ancestor-completeness constraint regularize the taxonomy. On a HFACS-based power-accident corpus, EG-HPT consistently outperforms strong baselines in Micro-F1, Macro-F1, and path-constrained Micro-F1 (C-Micro-F1), with ablations confirming the contributions of entity evidence and graph aggregation. These results indicate a deployable, interpretable solution for automated risk factor analysis, enabling auditable evidence chains and supporting multi-granularity accident intelligence in safety-critical operations. Full article

The SETD6 protein lysine methyltransferase monomethylates specific lysine residues in a diverse set of substrates which contain the target lysine residue in a highly variable amino acid sequence context. To investigate the mechanism underlying this multispecificity, we analyzed SETD6 substrate recognition using AlphaFold 3 docking and peptide SPOT array methylation experiments. Structural modeling of the SETD6–E2F1 complex suggested that substrate binding alone is insufficient to restrict SETD6 activity to only one lysine residue, pointing to additional sequence readout at the target site. Methylation of mutational scanning peptide SPOT arrays derived from four different SETD6 substrates (E2F1 K117, H2A.Z K7, RELA K310, and H4 K12) revealed sequence preferences of SETD6 at positions −1, +2, and +3 relative to the target lysine. Notably, glycine or large aliphatic residues were favored at −1, isoleucine/valine at +2, and lysine at +3. These preferences, however, were sequence context dependent and variably exploited among different substrates, indicating conformational variability of the enzyme–substrate interface. Mutation of SETD6 residue L260, which forms a contact with the +2 site in the available SETD6-RELA structure, further demonstrated substrate-specific differences in recognition at the +2/+3 sites. Together, these findings reveal a versatile mode of peptide recognition in which the readout of each substrate position depends on the overall substrate peptide sequence. These findings can explain the multispecificity of SETD6 and similar mechanisms may underlie substrate selection in other protein methyltransferases. Full article

Background: Most immunologically “cold” tumors do not respond durably to checkpoint blockade because tumor antigen (TA) release and presentation are insufficient to prime effective T-cell immunity. While prior work demonstrated synergy between cisplatin and a TLR7/8/9 agonist (CR108) in 4T1 tumors, the underlying mechanism—particularly whether chemotherapy functions as a broad antigen-releasing agent enabling TLR-driven immune amplification—remained undefined. Methods: Using murine models of breast (4T1), melanoma (B16-F10), and colorectal cancer (CT26), we tested multiple chemotherapeutic classes combined with CR108. We quantified intratumoral and systemic soluble TAs, antigen presentation and cross-priming by antigen-presenting cells, tumor-infiltrating lymphocytes, and cytokine production by flow cytometry/ICS. T-cell receptor β (TCRβ) repertoire dynamics in tumor-draining lymph nodes were profiled to assess amplitude and breadth. Tumor microenvironment remodeling was analyzed, and public datasets (e.g., TCGA basal-like breast cancer) were interrogated for expression of genes linked to TA generation/processing and peptide loading. Results: Using cisplatin + CR108 in 4T1 as a benchmark, we demonstrate that diverse chemotherapies—especially platinum agents—broadly increase the repertoire of soluble tumor antigens available for immune recognition. Across regimens, chemotherapy combined with CR108 increased T-cell recognition of candidate TAs and enhanced IFN-γ⁺ CD8⁺ responses, with platinum agents producing the largest expansions in soluble TAs. TCRβ sequencing revealed increased clonal amplitude without loss of repertoire breadth, indicating focused yet diverse antitumor T-cell expansion. Notably, therapeutic efficacy was not predicted by canonical damage-associated molecular pattern (DAMP) signatures but instead correlated with antigen availability and processing capacity. In human basal-like breast cancer, higher expression of genes involved in TA generation and antigen processing/presentation correlated with improved survival. Conclusions: Our findings establish an antigen-centric mechanism underlying chemo–TLR agonist synergy: chemotherapy liberates a broadened repertoire of tumor antigens, which CR108 then leverages via innate immune activation to drive potent, T-cell-mediated antitumor immunity. This framework for rational selection of chemotherapy partners for TLR7/8/9 agonism and support clinical evaluation to convert “cold” tumors into immunologically responsive disease. Full article

Objective evaluation of pilot fatigue is crucial for enhancing aviation safety. Although electroencephalography (EEG) is regarded as an effective tool for recognizing pilot fatigue, the direct application of deep learning models to raw EEG signals faces significant challenges due to issues such as massive data volume, excessively long training time, and model overfitting. Moreover, existing feature-based methods often suffer from data redundancy due to the lack of effective feature and channel selections, which compromises the model’s recognition efficiency and accuracy. To address these issues, this paper proposes a framework, named ASFT-Transformer, for fast and accurate detection of pilot fatigue. This framework first extracts time-domain and frequency-domain features from the four EEG frequency bands. Subsequently, it introduces a feature and channel selection strategy based on one-way analysis of variance and support vector machine (ANOVA-SVM) to identify the most fatigue-relevant features and pivotal EEG channels. Finally, the FT-Transformer (Feature Tokenizer + Transformer) model is employed for classification based on the selected features, transforming the fatigue recognition problem into a tabular data classification task. EEG data is collected from 32 pilots before and after actual simulator training to validate the proposed method. The results show that ASFT-Transformer achieved average accuracies of 97.24% and 87.72% based on cross-clip data partitioning and cross-subject data partitioning, which were significantly superior to several mainstream machine learning and deep learning models. Under the two types of cross-validation, the proposed feature and channel selection strategy not only improved the average accuracy by 2.45% and 8.07%, respectively, but also drastically reduced the average training time from above 1 h to under 10 min. This study offers civil aviation authorities and airline operators a tool to manage pilot fatigue objectively and effectively, thereby contributing to flight safety. Full article

Background: Vertigo and dizziness have been observed in patients with atrial fibrillation (AF), yet their role as possible early manifestations of AF remains insufficiently understood. Methods: A systematic review of five relevant studies, comprising a total of 2102 patients, was conducted to evaluate the relationship between vestibular symptoms and AF. Results: Dizziness and vertigo were frequently reported, particularly in acute presentations, although they were rarely considered primary symptoms. Evidence suggests a potential correlation between vestibular manifestations and AF, with vertigo possibly acting as an autonomic or inflammatory trigger in selected cases. Conclusions: The findings highlight the clinical relevance of including vertigo and dizziness in the routine assessment of patients with AF. Proper recognition of these symptoms may reduce underdiagnosis, improve treatment adherence, and prevent complications associated with misattribution of vertigo to non-cardiac causes. Full article

This paper proposes TWISS (TOPSIS + Wrapper Incremental Subset Selection), a novel hybrid feature selection framework designed for electromyographic (EMG) pattern recognition in upper-limb prosthetic control. TWISS integrates the multi-criteria decision-making method TOPSIS with a forward wrapper search strategy, enabling subject-specific feature optimization based on a ranking that combines filter metrics, including Chi-squared, ANOVA, and Mutual Information. Unlike conventional static feature sets, such as the Hudgins configuration (48 features: four per channel, 12 channels) or All Features (192 features: 16 per channel, 12 channels), TWISS dynamically adapts feature subsets to each subject, addressing inter-subject variability and classification robustness challenges in EMG systems. The proposed algorithm was evaluated on the publicly available Ninapro DB7 dataset, comprising both intact and transradial amputee participants, and implemented in an open-source, fully reproducible environment. Two Google Colab tools were developed to support diverse workflows: one for end-to-end feature extraction and selection, and another for selection on precomputed feature sets. Experimental results demonstrated that TWISS achieved a median F1-macro score of 0.6614 with Logistic Regression, outperforming the All Features set (0.6536) and significantly surpassing the Hudgins set (0.5626) while reducing feature dimensionality. TWISS offers a scalable and computationally efficient solution for feature selection in biomedical signal processing and beyond, promoting the development of personalized, low-cost prosthetic control systems and other resource-constrained applications. Full article

Hepatocellular carcinoma (HCC) is a hypervascular malignancy commonly treated with transarterial chemoembolization (TACE), in which success relies on the accurate identification and embolization of tumor feeding arteries while sparing the nontumorous liver parenchyma. This review introduces the concept of selective angiographic roadmap analysis (SARA), a systematic and stepwise approach to evaluating hepatic arterial supply in HCC, with the aim of standardizing angiographic planning and improving TACE outcomes. SARA emphasizes recognition of typical and variant hepatic arterial anatomy, systematic identification of accessory and extrahepatic feeders, and integration with intraprocedural cone-beam computed tomography (CBCT) to enhance feeder detection and reduce nontarget embolization. Although primarily applied in TACE, the principles of SARA are equally relevant to transarterial radioembolization (TARE) where precise arterial mapping is critical. Embolization strategies are discussed across different levels of selectivity, from lobar to superselective techniques. The complementary role of advanced imaging modalities, such as CT angiography (CTA), MR angiography (MRA), and artificial intelligence-assisted vessel tracking, is also explored. Adopting the SARA framework in conjunction with these technologies may improve technical success and tumor control and preserve liver function in patients undergoing intra-arterial therapies. Full article

The investigation into the complementary roles of essential oils (EOs) and organic acids in enhancing rumen physiology is increasingly gaining recognition within the field of animal nutrition. Essential oils are known for their antimicrobial effects, which can specifically target certain microbial populations in the rumen, thereby impacting fermentation processes, methane output, and nutrient digestion. In addition, the integration of organic acids plays a crucial role in stabilizing rumen pH and steering the metabolic activities of bacterial populations toward propionate production, a process essential for energy metabolism in ruminants. The concurrent use of essential oils and organic acids may yield synergistic benefits that could further optimize ruminal fermentation efficiency, enhance feed conversion rates, and lower methane emissions. This systematic review used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The literature search was meticulously designed to encompass parameters related to ruminant species, feed additives, essential oils, organic acids, synergistic effects, and rumen physiology. The efficacy of both organic acids and essential oils is highly dependent on their concentration and the specific combinations utilized. When certain essential oils are used in conjunction with selected organic acids, they may mitigate any potential negative effects on fermentation, thereby fostering a more favorable environment for the proliferation of beneficial microbial communities. Understanding the relationship between essential oils and organic acids is essential for the formulation of diets that enhance rumen health while concurrently reducing environmental pressures through diminished methane emissions. Future research should prioritize long-term in vivo investigations to gain more comprehensive insights into the interactions among these dietary components and identify the optimal combinations for ruminant feeding strategies. Full article

Dihydroorotase (DHOase) catalyzes the reversible cyclization of N-carbamoyl-L-aspartate to dihydroorotate, a key step in de novo pyrimidine biosynthesis. A flexible active site loop in DHOase undergoes conformational switching between loop-in and loop-out states, influencing substrate binding, catalysis, and inhibitor recognition. In this study, we identified 5-fluoroorotate (5-FOA) and myricetin as inhibitors of Saccharomyces cerevisiae DHOase and systematically analyzed 97 crystal structures and AlphaFold 3.0 models of DHOases from 16 species representing types I, II, and III. Our results demonstrate that loop conformation is not universally ligand-dependent and varies markedly across DHOase types, with type II enzymes showing the greatest flexibility. Notably, S. cerevisiae DHOase consistently adopted the loop-in state, even with non-substrate ligands, restricting accessibility for docking-based inhibitor screening. Docking experiments with 5-FOA and myricetin confirmed that the loop-in conformation prevented productive active-site docking. These findings highlight the importance of selecting appropriate loop conformations for structure-based drug design and underscore the need to account for loop dynamics in inhibitor screening. Full article

Molecularly imprinted polymer (MIP) biosensors offer an attractive strategy for selective biomolecule detection, yet imprinting proteins with structural fidelity remains a major challenge. In this work, we present a rationally designed electrochemical biosensor for matrix metal-loproteinase-8 (MMP-8), a key salivary biomarker of periodontal disease. By integrating graphene oxide (GO) with electropolymerized poly(eriochrome black T, EBT) films on screen-printed carbon electrodes, the partially reduced GO interface enhanced electrical conductivity and facilitated the formation of well-defined poly(EBT) films with re-designed polymerization route, while template extraction generated artificial antibody-like sites capable of specific protein binding. The MIP-based electrodes were comprehensively validated through morphological, spectroscopic, and electrochemical analyses, demonstrating stable and selective recognition of MMP-8 against structurally similar interferents. Complementary density functional theory (DFT) modeling revealed energetically favorable interactions between the EBT monomer and catalytic residues of MMP-8, providing molecular-level insights into imprinting specificity. These experimental and computational findings highlight the importance of rational monomer selection and nanomaterial-assisted polymerization in achieving selective protein imprinting. This work presents a systematic approach that integrates electrochemical engineering, nanomaterial interfaces, and computational validation to address long-standing challenges in protein-based MIP biosensors. By bridging molecular design with practical sensing performance, this study advances the translational potential of MIP-based electrochemical biosensors for point-of-care applications. Full article

Municipal solid waste (MSW) refers to solid and semi-solid waste generated during human production and daily activities. The process of incinerating such waste, known as municipal solid waste incineration (MSWI), serves as a critical method for reducing waste volume and recovering resources. Automatic online recognition of flame combustion status during MSWI is a key technical approach to ensuring system stability, addressing issues such as high pollution emissions, severe equipment wear, and low operational efficiency. However, when manually selecting optimized features and hyperparameters based on empirical experience, the MSWI flame combustion state recognition model suffers from high time consumption, strong dependency on expertise, and difficulty in adaptively obtaining optimal solutions. To address these challenges, this article proposes a method for constructing a flame combustion state recognition model optimized based on reinforcement learning (RL), long short-term memory (LSTM), and parallel differential evolution (PDE) algorithms, achieving collaborative optimization of deep features and model hyperparameters. First, the feature selection and hyperparameter optimization problem of the ViT-IDFC combustion state recognition model is transformed into an encoding design and optimization problem for the PDE algorithm. Then, the mutation and selection factors of the PDE algorithm are used as modeling inputs for LSTM, which predicts the optimal hyperparameters based on PDE outputs. Next, during the PDE-based optimization of the ViT-IDFC model, a policy gradient reinforcement learning method is applied to determine the parameters of the LSTM model. Finally, the optimized combustion state recognition model is obtained by identifying the feature selection parameters and hyperparameters of the ViT-IDFC model. Test results based on an industrial image dataset demonstrate that the proposed optimization algorithm improves the recognition performance of both left and right grate recognition models, with the left grate achieving a 0.51% increase in recognition accuracy and the right grate a 0.74% increase. Full article

Background/Objectives: Glioblastoma (GBM) is an aggressive primary brain tumor with limited therapeutic options despite multimodal treatment. Small interfering RNA (siRNA)-based therapeutics can silence tumor-promoting genes, but achieving efficient and tumor-specific delivery remains challenging. Lipid nanoparticles (LNPs) are promising siRNA carriers; however, conventional antibody conjugation can impair antigen recognition and complicate manufacturing. This study aimed to establish a modular Fc-binding peptide (FcBP)-mediated post-insertion strategy to enable PD-L1-targeted delivery of VEGF-siRNA via LNPs for GBM therapy. Methods: Preformed VEGF-siRNA-loaded LNPs were functionalized with FcBP–lipid conjugates, enabling non-covalent anchoring of anti-PD-L1 antibodies through Fc interactions. Particle characteristics were analyzed using dynamic light scattering and encapsulation efficiency assays. Targeted cellular uptake and VEGF gene silencing were evaluated in PD-L1-positive GL261 glioma cells. Anti-tumor efficacy was assessed in a subcutaneous GL261 tumor model following repeated intratumoral administration using tumor volume and bioluminescence imaging as endpoints. Results: FcBP post-insertion preserved LNP particle size (125.2 ± 1.3 nm), polydispersity, zeta potential, and siRNA encapsulation efficiency. Anti-PD-L1–FcBP-LNPs significantly enhanced cellular uptake (by ~50-fold) and VEGF silencing in PD-L1-expressing GL261 cells compared to controls. In vivo, targeted LNPs reduced tumor volume by 65% and markedly suppressed bioluminescence signals without inducing weight loss. Final tumor weight was reduced by 63% in the anti-PD-L1–FcBP–LNP group (656.9 ± 125.4 mg) compared to the VEGF-siRNA LNP group (1794.1 ± 103.7 mg). The FcBP-modified LNPs maintained antibody orientation and binding activity, enabling rapid functionalization with targeting antibodies. Conclusions: The FcBP-mediated post-insertion strategy enables site-specific, modular antibody functionalization of LNPs without compromising physicochemical integrity or antibody recognition. PD-L1-targeted VEGF-siRNA delivery demonstrated potent, selective anti-tumor effects in GBM murine models. This platform offers a versatile approach for targeted nucleic acid therapeutics and holds translational potential for treating GBM. Full article

In the last decades, the introduction of intermittent renewable energy sources has transformed the operation of power systems. In this framework, ancillary service markets (ASMs) play an important role, due to their contribution in supporting system operators to balance demand and supply and managing real-time contingencies. Usually, ASMs require that energy is committed before actual participation, hence scheduling systems of plants and microgrids are required to compute the dispatching program and bidding strategy before needs of the market are revealed. Since possible ASM requirements are given as input to scheduling systems, the chance of accessing accurate estimates may be helpful to define reliable dispatching programs and effective bidding strategies. Within this context, this paper proposes a methodology to estimate the revenue range of energy exchange proposals in the ASM. To this end, the possible revenues are discretized into ranges and a classification pattern recognition algorithm is implemented. Modeling is performed using extreme gradient boosting. Input data to be fed to the algorithm are selected because of relationships with the production unit making the proposal, with the location and temporal indication, with the grid power dispatch and with the market regulations. Different tests are set up using historical data referred to the Italian ASM. Results show that the model can appropriately estimate rejection and the revenue range of awarded bids and offers, respectively, in more than 82% and 70% of cases. Full article

A surface molecularly imprinted ratiometric fluorescent sensor (Eu/CDs@SiO₂@IIPs) was constructed for the selective and visual detection of Cu²⁺. The sensor integrates blue-emitting carbon dots as an internal reference and a custom-designed Eu(III) complex, Eu(MAA)₂(2,9-phen), as both the functional and fluorescent monomer within a surface-imprinted polymer layer, enabling efficient ratiometric fluorescence response. This structural design ensured that all fluorescent monomers were located at the recognition sites, thereby reducing background fluorescence interference and enhancing the accuracy of signal changes. Under optimized conditions, the sensor exhibited a detection limit of 2.79 nM, a wide linear range of 10–100 nM, and a rapid response time of 3.0 min. Moreover, the uncoordinated nitrogen atoms in the phenanthroline ligand improved resistance to interference from competing ions, significantly enhancing selectivity. Practical applicability was validated by spiked recovery tests in deionized and river water, with results showing good agreement with ICP-MS analysis. These findings highlight the potential of Eu/CDs@SiO₂@IIPs as a sensitive, selective, and portable sensing platform for on-site monitoring of Cu²⁺ in complex water environments. Full article