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Background/Objectives: Telomere dysfunction and the shelterin complex are implicated in cancer, yet the specific functions and interactions of telomerase and shelterin genes in hepatocellular carcinoma (HCC) tumorigenesis remain poorly understood. This study aims to investigate the clinico-biological functions and collaborative contributions of telomerase and shelterin components in hepatocarcinogenesis. Methods: We analyzed tumor and matched non-tumor tissues from 274 HCC patients who underwent hepatectomy. Telomere-related parameters, including TERT (telomerase reverse transcriptase) expression and telomere length measured by qRT-PCR, telomerase activity assessed by the Telomerase Repeated Amplification Protocol assay, and six shelterin components analyzed by RNA sequencing, were correlated with clinicopathological features. siRNA-mediated knockdown of TPP1 (POT1–TIN2 organizing protein) was performed to evaluate its regulatory effect on TERT expression. Findings were externally validated. Results: TERT and TPP1 were upregulated in tumors with increased telomerase activity and shortened telomere length. Among the shelterin components, TPP1 showed the strongest correlation with TERT, and its expression increased with tumor multiplicity and advancing stage. TPP1 expression also correlated with proliferation-associated genes, consistent with Gene Set Enrichment Analysis suggesting TPP1 involvement in proliferative activity. TPP1 knockdown suppressed TERT protein expression and inhibited HCC cell proliferation, with the strongest anti-proliferative effect observed after dual TERT–TPP1 knockdown. Clinically, high TPP1 expression was associated with significantly earlier HCC recurrence, and co-high expression of TPP1–TERT was linked to significantly worse survival after hepatectomy. Conclusions: The TERT–TPP1 axis enhances proliferative activity and is associated with aggressive features and poor outcomes in HCC. TPP1 represents a potential therapeutic target and prognostic biomarker for HCC. Full article

Cancer is a heterogeneous disease at the cellular level and analyzing the genetic and molecular profile is essential for targeted therapy. Cancer cells continue to mutate, often resulting in drug resistance. In addition, cancers such as triple-negative breast cancer (TNBC) lack the target proteins used in some of the most effective therapies. This necessitates the identification of novel target proteins and biomarkers for effective treatment strategies. Ubiquitin E3 ligases are often differentially expressed in cancer cells, and numerous anticancer agents have been developed to inhibit them. SMURF2 is an E3 ligase that is differentially expressed in multiple cancer types. Although inhibiting upregulated SMURF2 may be strategically straightforward, enhancing the downregulated gene is often difficult. In addition, because E3 ligases ubiquitinate a variety of substrate proteins, targeting SMURF2 requires detailed analysis to achieve anticancer effect. This review discusses the dual role of SMURF2 in carcinogenesis and addresses the complex context-dependent function of SMURF2 in the various cellular pathways. In addition, resistance to existing cancer therapy related to SMURF2 and sensitivity mechanisms is discussed. Lastly, theranostic strategies for anticancer agents and biomarker development are suggested. Full article

Fibromyalgia is a complex chronic disorder involving persistent widespread pain accompanied by functional limitations, cognitive impairments, and alterations in neural processing. Previous research indicates that exercise-based interventions can play a key role in alleviating symptom burden and enhancing physical performance; however, there is limited evidence regarding their impact on neurophysiological mechanisms. Creative dance, in combination with strength training, may stimulate both motor and cognitive systems, promoting brain plasticity and functional improvements. This study will analyze the effects of a six-week strength and creative dance program on physical fitness under single- and dual-task conditions in women with fibromyalgia and will explore the associated changes in brain electrical activity and autonomic modulation. Methods: This randomized controlled trial will be divided into an exercise group (n = 22) and a control group (n = 22). The 6-week supervised intervention consists of two 60-minute sessions per week, combining strength exercises and creative dance. Primary outcomes include physical fitness tests (strength, mobility, balance, and agility gait test in single-task and dual-task), fibromyalgia symptoms, and quality of life. Secondary outcomes include changes in electroencephalography, heart rate variability, physical activity level, and fear of falling. Statistical analyses will compare within- and between-group differences using non-parametric tests and effect sizes. It is hypothesized that the intervention will improve physical fitness and dual-task performance, alongside increases in brain activity power. This study may provide insights into the neurophysiological mechanisms underlying the benefits of exercise benefits in fibromyalgia. Full article

High-quality spectral filters with versatile tuning mechanisms are essential for applications in photonic integrated circuits, including sensing, laser stabilization, and spectral signal processing. We report the implementation of thermo-optic (TO) and electro-optic (EO) spectral tuning in silicon nitride Mach–Zehnder interferometers (MZIs) and micro-ring resonators (MRRs) by functionalizing the devices with a PMMA:JRD1 polymer cladding and integrating titanium tracks as heaters and electrodes. The fabricated MZIs and MRRs exhibit narrow linewidths of 25–30 pm and achieved TO tuning efficiencies of 1.7 and 13 pm/mW and EO tuning efficiencies of 0.33 and 1.6 pm/V, respectively. Closed-loop regulation using TO and EO effects enables stable half-fringe locking under environmental perturbations. This simple, broadly compatible hybrid platform demonstrates a practical approach to dual-mode spectral tuning and modulation in integrated photonic filters, providing a flexible route toward compact, reconfigurable, and environmentally robust photonic circuits. Full article

Neurocognitive and metacognitive impairments are well-documented in schizophrenia spectrum disorders (SSDs). However, the relationship between these two domains remains underexplored, despite increasing interest in their combined impact on recovery and functional outcomes. Neurocognition refers to processes such as attention, memory, and executive functioning, and the neural systems that support these processes, both of which are frequently abnormal in SSDs and contribute to significant functional difficulties. Metacognition, in contrast, refers to the capacity to reflect on and integrate thoughts, emotions, and experiences into a coherent understanding of oneself and others. Although both domains are often studied in isolation, emerging evidence suggests a potential interdependence between neurocognition and metacognition, particularly regarding their influence on outcome. This scoping review explores empirical studies examining associations between neurocognition and metacognition in individuals with SSDs, specifically in the context of functional outcomes. We aim to clarify how these domains interact and explore their combined implications for recovery-oriented interventions and clinical practice. Findings may inform more integrated models of cognition and guide the development of dual-targeted treatment approaches to improve functional recovery in SSDs. Full article

To address the clinical urgency of simultaneously monitoring multiple biomarkers in chronic wound infections, this study presents the innovative development of an electrochemical sensor based on a MWCNTs/MXene/PVA composite hydrogel. A dual-channel conductive network is constructed via the electrostatic self-assembly of the two-dimensional material MXene and multi-walled carbon nanotubes (MWCNTs). This strategy not only enhances the charge transfer efficiency but also effectively suppresses the aggregation of MWCNTs and exposes the electrocatalytic active sites. Additionally, the thermal annealing process is incorporated to facilitate the ordered arrangement of polyvinyl alcohol (PVA) nanocrystalline domains, strengthening the hydrogen bond-mediated interfacial adhesion and resolving the issues of hydrogel swelling and delamination. The detection limit (LOD) of the optimized sensor (MWCNTs_0.6/MXene_0.4/PVA) for pyocyanin (PCN) within complex biological matrices, including phosphate-buffered saline (PBS), Luria–Bertani (LB) broth, and saliva, was decreased to a range of 0.84~0.98 μM. Leveraging the disparities in the characteristic oxidation potentials (ΔE > 0.3 V) of PCN, uric acid (UA), and histamine (HA) in simulated wound exudate (SWE), the multi-component synchronous detection functionality of the non-specific sensor was expanded for the first time. This study offers a high-precision and multi-parameter integrated approach for point-of-care testing (POCT) of wound infections. Full article

Precise short-term forecasting of wind power generation is indispensable for ensuring the security and economic efficiency of power grid operations. Nevertheless, the inherent non-stationarity and stochastic nature of wind power series present significant challenges for prediction accuracy. To address these issues, this paper proposes a dual-optimized hybrid deep learning framework combining Spearman correlation analysis, RIME-VMD, and TCN-BiGRU-SA. First, Spearman correlation analysis is employed to screen meteorological factors, eliminating redundant features and reducing model complexity. Second, an adaptive Variational Mode Decomposition (VMD) strategy, optimized by the RIME algorithm based on Minimum Envelope Entropy, decomposes the non-stationary wind power series into stable intrinsic mode functions (IMFs). Third, a hybrid predictor integrating Temporal Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (BiGRU), and Self-Attention (SA) mechanisms is constructed to capture both local trends and long-term temporal dependencies. Furthermore, the RIME algorithm is utilized again to optimize the hyperparameters of the deep learning predictor to avoid local optima. The proposed framework is validated using full-year datasets from two distinct wind farms in Xinjiang and Gansu, China. Experimental results demonstrate that the proposed model achieves a Root Mean Square Error (RMSE) of 7.5340 MW on the primary dataset, significantly outperforming mainstream baseline models. The multi-dataset verification confirms the model’s superior prediction accuracy, robustness against seasonal variations, and strong generalization capability. Full article

Per- and polyfluoroalkyl substances (PFASs) are persistent and mobile contaminants of global concern, and, while granular activated carbon (GAC) is widely used for their removal, it is limited by the high regeneration and disposal costs. This study investigates surface-modified clinoptilolite zeolites as low-cost and thermally regenerable alternatives to GAC for PFAS removal from water. Natural clinoptilolite was modified through acid washing, ion exchange with Fe³⁺ or La³⁺, grafting with aminosilane (APTES) or hydrophobic silane (DTMS), dual APTES + DTMS grafting, and graphene oxide coating. The adsorption performance was evaluated for perfluorooctanoic acid (PFOA, C8) and perfluorobutanoic acid (PFBA, C4) at 100 µg L⁻¹ in single- and mixed-solute systems, with an additional high-concentration PFOA test (1 mg L⁻¹). PFAS concentrations were quantified by liquid chromatography–tandem mass spectrometry (LC–MS/MS) using a SCIEX 7500 QTRAP system coupled to a Waters ACQUITY UPLC I-Class. Raw zeolite showed limited PFOA removal (4%), whereas dual-functionalized APTES + DTMS zeolites achieved up to 93% removal, comparable to GAC (97%) and superior to single-silane or metal-exchanged variants. At lower concentrations, modified zeolites effectively removed PFOA but showed limited PFBA removal (<25%), highlighting ongoing challenges for short-chain PFASs. Overall, the results demonstrate that dual-functionalized clinoptilolite zeolites represent a promising and scalable platform for PFAS remediation, particularly for mid- to long-chain compounds, provided that strategies for enhancing short-chain PFAS binding are further developed. Full article

Integrated sensing and communication (ISAC) is expected to be a key enabler for future wireless networks, improving spectral and hardware efficiency by jointly performing radar sensing and wireless communication within a unified framework. This paper proposes BeamNet, an unsupervised deep learning framework for transmit beamforming in dual-function radar-communication systems operating over general fading with imperfect channel state information (CSI). BeamNet maps noisy estimates of the communication and sensing channels to a transmit beamforming vector and is trained end-to-end by maximizing a weighted sum of the communication rate (CR) and sensing rate (SR), thereby learning the CR–SR Pareto frontier without beamforming labels or embedded optimization solvers. Using Rayleigh fading with perfect CSI, we first show that BeamNet reproduces the analytical Pareto-optimal beamforming solutions. We then use BeamNet to characterize, for Nakagami-m and Rician fading, the CR–SR trade-off across a range of fading parameters, and to assess robustness under distribution mismatch between training and test channels. Finally, under imperfect CSI, we demonstrate that BeamNet yields CR–SR trade-offs that are consistently sandwiched between the perfect-CSI and mismatched analytical baselines, outperforming the closed-form beamformer applied to imperfect CSI and recovering part of the performance loss caused by channel estimation errors. These results indicate that unsupervised learning offers a flexible and robust approach to ISAC beamforming in fading environments with imperfect channel knowledge. Full article

The increasing need for accurate prediction of geochemical anomalies requires methods capable of capturing complex spatial patterns that traditional approaches often fail to represent adequately. For N datasets of the form (X_i,Y_i) representing the geographic coordinates of sampling points and C_i denoting the geochemical measurement, training multilayer perceptrons (MLPs) presents a challenge. The low informativeness of the input features and their weak correlation with the target variable result in excessively simplified predictions. Analysis of a baseline model trained only on geographic coordinates showed that, while the loss function converges rapidly, the resulting values become overly “compressed” and fail to reflect the actual concentration range. To address this, a preprocessing method based on anisotropy was developed to enhance the correlation between input and output variables. This approach constructs, for each prediction point, a structured informational model that incorporates the direction and magnitude of spatial variability through sectoral and radial partitioning of the nearest sampling data. The transformed features are then used in a dual-MLP architecture, where the first network produces sectoral estimates, and the second aggregates them into the final prediction. The results show that anisotropic feature transformation significantly improves neural network prediction capabilities in geochemical analysis. Full article

Managing unknown, time-varying noise and outliers presents a critical challenge in GPS applications. Variational Bayesian (VB) inference effectively estimates unknown noise statistics but lacks robustness to outliers, while robust filters such as the centered error entropy (CEE) suppress outliers but rely on fixed noise assumptions. To address both limitations, we propose the centered error entropy-based variational Bayesian extended Kalman filter (CEEVB-EKF), which integrates VB inference with the CEE criterion in a unified framework. The method estimates time-varying noise covariance via recursive VB updates and applies the CEE cost function for robustness to heavy-tailed disturbances and outliers. This dual-stage design improves adaptability and reliability, with simulations showing superior, stable state estimation, making CEEVB-EKF suitable for positioning, tracking, and autonomous navigation. Full article

The traditional mantis search algorithm (MSA) suffers from limitations such as slow convergence and a high likelihood of converging to local optima in complex optimization scenarios. This paper proposes an improved mantis search algorithm (IMSA) to overcome these issues. An adaptive probability conversion factor is designed, which adaptively controls the proportion of individuals entering the search phase and the attack phase so that the algorithm can smoothly transition from large-scale global exploration to local fine search. In the search phase, a probability update strategy based on both subspace and full space is designed, significantly improving the adaptability of the algorithm to complex problems by dynamically adjusting the search range. The elite population screening mechanism, based on Euclidean distance and fitness double criteria, is introduced to provide dual guidance for the evolution direction of the algorithm. In the attack stage, the base vector adaptive probability selection mechanism is designed, and the algorithm’s pertinence in different optimization stages is enhanced by dynamically adjusting the base vector selection strategy. Finally, in the stage of sexual cannibalism, the directed random disturbance update method of inferior individuals is adopted, and the population is directly introduced through the non-greedy replacement strategy, which effectively overcomes the loss of population diversity. The experimental results of 29 test functions on the CEC2017 test set demonstrate that the IMSA exhibits significant advantages in convergence speed, calculation accuracy, and stability compared to the original MSA and the five best meta-heuristic algorithms. Full article

The propagation of uncertainties in structural dynamic responses, arising from variations in material properties, geometry, and boundary conditions, is of critical concern to researchers in a variety of engineering instances. Conventional methods like high-fidelity Monte Carlo simulation are computationally prohibitive, while existing surrogate models can improve efficiency at the expense of accuracy. To achieve a trade-off between accuracy and efficiency, a Physics-Informed Conditional Variational Autoencoder (PI-CVAE) model is proposed. It integrates a novel dual-branch encoder for time-frequency feature extraction, a learnable frequency-filtering decoder, and a holistic physics-informed loss function so as to enable efficient generation of dynamic responses with high accuracy and adequate physics consistency. Comprehensive numerical analysis of plate structures demonstrates that the proposed approach achieves remarkable accuracy (maximum FRF error < 0.2% and $R^2$ > 0.99) and a computational speedup of 8–11 times in comparison with conventional simulation techniques. By maintaining high accuracy while efficiently propagating uncertainties, the PI-CVAE model provides a practical framework for probabilistic vibration analysis, especially during the acoustic design phase. Full article

Soil heavy metal (HM) pollution poses a severe threat to ecological security and human health. Selenium (Se) is an essential trace element for the human body and can regulate crop growth and development as well as HM uptake in HM-contaminated soils. The regulatory mechanisms of Se on HMs are mainly reflected in four aspects: Geochemical immobilization promotes the formation of metal selenide precipitates and the adsorption of HMs by soil colloids by regulating the rhizosphere redox potential (Eh) and pH value. Rhizosphere microbial remodeling drives the enrichment of functional microorganisms such as Se redox bacteria, plant growth-promoting rhizobacteria (PGPR), and arbuscular mycorrhizal fungi (AMF) through the dual selective pressure of Se toxicity and root exudates, in order to synergistically realize Se speciation transformation and HM adsorption/chelation. Root barrier reinforcement constructs physical and chemical dual defense barriers by inducing the formation of iron plaques on the root surface, remodeling root morphology and strengthening cell wall components such as lignin and polysaccharides. Intracellular transport regulation down-regulates the genes encoding HM uptake transporters, up-regulates the genes encoding HM efflux proteins, and promotes the synthesis of phytochelatins (PCs) to form HM complexes and lastly realizes vacuolar sequestration. Finally, we summarize current research gaps in the interaction mechanisms of different Se species, precise application strategies, and long-term environmental risk assessment, providing a theoretical basis and technical outlook for the green remediation of HM-contaminated farmlands and Se biofortification of crops. Full article

In recent years, the expansion of network scale and the diversification of attack methods pose dual challenges to intrusion detection systems in extracting effective features and addressing class imbalance. To address these issues, the Spatial–Temporal Equilibrium Graph Convolutional Network (STEGCN) is proposed. This model integrates Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU), leveraging GCN to extract high-order spatial features from network traffic data while capturing complex topological relationships and latent patterns. Meanwhile, GRU efficiently models the dynamic evolution of network traffic over time, accurately depicting temporal trends and anomaly patterns. The synergy of these two components provides a comprehensive representation of network behavior. To mitigate class imbalance in intrusion detection, the Equalization Loss v2 (EQLv2) is introduced. By dynamically adjusting gradient contributions, this function reduces the dominance of majority classes, thereby enhancing the model’s sensitivity to minority-class attacks. Experimental results demonstrate that STEGCN achieves superior detection performance on the UNSW-NB15 and CICIDS2017 datasets. Compared with traditional deep learning models, STEGCN shows significant improvements in accuracy and recall, particularly in detecting minority-class intrusions. Full article