Search Results (60,535)

Background: Polyploidization generates extensive gene redundancy, but how duplicated metabolic genes are retained and subsequently diversified remains poorly understood. UDP-glycosyltransferases (UGTs) provide a suitable system for examining this process because they participate in specialized metabolism, plant development, and environmental responses. This study aimed to characterize the retention, expansion, and regulatory divergence of the UGT family in allotetraploid Panax ginseng at subgenome resolution. Methods: We integrated telomere-to-telomere (T2T) genome annotation, phylogenetic and chromosomal analyses, duplication classification, collinearity and Ka/Ks analyses, promoter cis-acting element prediction, developmental co-expression networks, and transcriptomic responses to biotic and abiotic treatments. Results: A total of 212 PgUGT genes were identified, including 104 and 108 members in the A and B subgenomes, respectively. The family exhibited an overall near-mirrored retention pattern between the two subgenomes, accompanied by local copy-number asymmetry. Whole-genome and segmental duplication accounted for 64.2% of the family, and 99.5% of the gene pairs with valid Ka/Ks estimates had values below 1, suggesting pervasive purifying selection. PgUGT-containing co-expression modules were associated with bud, stem, leaf, and fruit developmental conditions, while promoter cis-acting element compositions exhibited member-specific variation. Transcriptional responses to fungal pathogens and abiotic, hormone, and chemical treatments were concentrated in particular members and local gene arrays rather than being coordinated across entire clades or subgenomes. Conclusions: The PgUGT family is characterized by extensive ancestral copy retention accompanied by local copy-number changes and copy-specific regulatory divergence. These findings provide a subgenome-resolved framework for understanding UGT family evolution in allotetraploid ginseng and prioritize candidate PgUGT genes for subsequent functional validation. Full article

Obstructive sleep apnea is a common sleep disorder characterized by chronic intermittent hypoxia (CIH), sleep fragmentation, and upper-airway collapse. Beyond respiratory issues, OSA significantly impacts the central nervous system, increasing the risk for mild cognitive impairment and dementia. This review synthesizes the dual nature of CIH-induced molecular cascades, describing pathways driving neurodegeneration, such as SUMOylation dysregulation, toll-like receptor signaling, and inflammasome overactivation (largely characterized in experimental and animal CIH models), and those activating endogenous neuroprotective adaptations. Physiological defense mechanisms, such as the upregulation of erythropoietin, shifts toward anaerobic glycolysis, and the activation of neurotrophic factors, may act to mitigate neuronal injury. Rather than acting as independent processes, these molecular responses form a dynamic balance between neuronal injury and endogenous neuroprotection. Here, we introduce the concept of ‘neuroprotective reserve’ as a novel, hypothesis-generating framework to describe this intrinsic buffering capacity against hypoxic stress. Understanding and clinically validating these pathways is critical for developing early diagnostic biomarkers and targeted adjunctive interventions that complement standard therapy to prevent irreversible neurocognitive decline. Full article

This article presents a unified stability–accuracy co-design framework for power-hardware-in-the-loop simulation (PHILS) of grid-connected inverters. PHILS enables realistic inverter evaluation by integrating a real-time simulator (RTS), power amplifier (PA), measurement sensors, and a device under test (DUT), but computation delay and hardware dynamics can significantly degrade stability and accuracy. To address these challenges, this work develops a systematic methodology incorporating PHILS component modeling, interface selection, and filtering and compensation design. Open-loop transfer-function analysis is employed to evaluate stability through gain and phase margins, while closed-loop analysis is used to quantify magnitude and phase accuracy and guide the co-design process. The framework is experimentally validated using a 500 W laboratory prototype emulating a 250 kW grid-connected inverter model. The resulting configuration achieves gain and phase margins of 11.1 dB and 90.2°, respectively, with magnitude error below 1% up to 570 Hz and phase error below 10% up to 265 Hz. Under active-power variations, the active-power error remains below 1%, while the maximum reactive-power error is 5.88%. These results demonstrate the practical applicability of the proposed stability–accuracy co-design methodology under the tested conditions. Full article

Traditional imitation learning is prone to distribution shift and trajectory divergence in highly dynamic, strongly time-varying flight tasks. To address this, we propose a temporally adaptive action-chunking framework built on temporal-derivative encoding, termed Temporal-Derivative-Encoded Adaptive Action Chunking with Transformer (TDA-ACT). First, using state temporal-derivative features as the core representation, we construct a confidence-estimation mechanism that also incorporates the latent-variable variance of the maneuver-mode representation and the action-prediction variance produced by the decoder. Second, we develop a confidence-guided adaptive temporal-ensembling strategy that uses this confidence metric to jointly adjust the fusion scope and the fusion weights of historical predictions, enabling a dynamic trade-off between long-horizon smoothing under stable conditions and high-frequency responsiveness during aggressive maneuvers. On both the Loop and AileronRoll maneuvers in JSBSim, TDA-ACT reduces action jerk and suppresses trajectory divergence relative to ACT and other imitation-learning baselines. Full article

Reducing unsprung mass in high-performance braking systems is critical for vehicle dynamics, but applying topology optimization (TO) to conventional solid discs under combined thermo-mechanical loading often causes “thermal entrapment” and structural failure. This study addresses this limitation by proposing a floating disc architecture and a two-stage computational framework. First, TO via the SIMP algorithm was applied to the Aluminum 7075-T6 inner carrier strictly under mechanical loads, decoupling artificial thermal stresses and yielding a fixed, optimized geometry. Results show the TO achieved a drastic 53% mass reduction (from 0.218 kg to 0.102 kg) specifically for the inner carrier component. Second, a high-fidelity Surrogate Model (Digital Twin) of this new geometry was developed to bypass the immense computational cost of multi-parameter, non-linear thermo-mechanical Finite Element Analysis (FEA). Utilizing Latin Hypercube Sampling (LHS) across 15 design scenarios and the Genetic Aggregation algorithm, the surrogate model was trained to predict real-time responses. Subsequently, the Digital Twin predicted stress and temperature fields with near-perfect accuracy (R² ≈ 0.9998), rapidly identifying the limit braking scenario. Fatigue analysis confirmed the final component safely withstands 10⁶ extreme braking cycles (safety factor 1.65). Full article

While the motivational dynamics of second language (L2) learning have been extensively theorized, the nonlinear, cyclical nature of third language (L3) motivation remains empirically underexplored. This study aims to develop a process-oriented model that captures the nonlinear dynamics and resilience mechanisms enabling re-engagement after interruption. Drawing upon Complex Dynamic Systems Theory, this study adopts a qualitative approach informed by constructivist grounded theory principles, rather than a strict constructivist grounded theory design, to investigate motivational dynamics and resilience construction among 30 Chinese university learners of German as a third language (L3), using in-depth narrative accounts and semi-structured interviews with five representative cases. The findings reveal a recurrent nonlinear cycle of engagement, interruption, suspension, re-engagement, and recovery. Interruptions fall into four types: external pressure-induced, internal psychological, passive structural, and rational strategic interruption, the last of which suggests that active withdrawal may itself serve as a form of resilience. Re-engagement relies on the synergy of teacher affective triggers, peer support, external pressure, and meaning restoration, unfolding predominantly as gradual affective accumulation rather than instantaneous insight. Resilience construction emerges from the interplay of five resources: teacher affective guardianship, peer emotional support, meaning reconstruction, small-step accumulation, and pressure-driven persistence. Across repeated cycles, learners may undergo a progressive meaning reconstruction, shifting from utilitarian motives to intrinsic identification. The proposed “Interruption-re-engagement” model offers a process-oriented framework for understanding the phased decline and conditional recovery of L3 motivation, addressing the explanatory gap in mainstream motivation theories regarding nonlinear motivational processes. Full article

Rapid selection of appropriate antimicrobial therapy is important for improving patient outcomes and supporting antimicrobial stewardship, particularly in the context of increasing antimicrobial resistance (AMR). Conventional antimicrobial susceptibility testing (AST), although essential for clinical decision-making, may not provide actionable susceptibility information immediately. This study develops an explainable multi-label machine learning framework for patient-specific antibiotic recommendation based on susceptibility prediction and ranked decision support using routinely collected clinical microbiology data. The framework integrates leakage-controlled preprocessing, microbiological and demographic feature representation, multi-label susceptibility encoding, One-vs-Rest ensemble learning, probability-based antibiotic ranking, statistical evaluation, temporal validation, and SHapley Additive exPlanations (SHAP). The dataset comprised 234 clinical records, of which 196 bacterial/other records were retained for the primary analysis after excluding 38 fungal records. A chronological partition produced 156 development records and 40 temporally held-out test records. Thirty-three antibiotic susceptibility labels were retained based on development-set availability. XGBoost, Random Forest, and LightGBM were evaluated using five-fold out-of-fold (OOF) validation. Random Forest was selected for the final recommendation and explainability analyses based on its overall performance, achieving a Micro-F1 of 0.4204, Macro-F1 of 0.3003, AUROC of 0.7069, AUPRC of 0.3979, and Precision@5 of 0.3962. A leakage-safe local antibiogram was additionally evaluated as a population-level ranking baseline, achieving a Precision@5 of 0.3077. Feature ablation showed that the combination of organism and age produced the highest OOF Micro-F1 (0.4635) and AUROC (0.7186), while the addition of gender and specimen type improved selected ranking measures but did not consistently improve aggregate classification performance. On the temporally held-out test cohort, Random Forest achieved a Micro-F1 of 0.4267, AUROC of 0.7447, AUPRC of 0.4892, and Precision@5 of 0.4350. SHAP analysis was completed for all 33 antibiotic-specific classifiers, providing global and antibiotic-level explanations of model behavior. The framework provides an interpretable approach for ranking potentially susceptible antibiotics at the patient level and is intended as clinical decision support rather than an autonomous prescribing system. Further external and prospective multicentre validation, including dedicated evaluation of challenging cases such as pan-drug resistance, is required before clinical deployment. Full article

Developing methods for achieving safe and efficient crowd evacuation is an important research issue. In particular, controlling pedestrian flows based on predictions from a mathematical model is important. In this paper, we propose a new method for generating route opening patterns. The target area is modeled by an undirected graph, and pedestrian flows are represented by the temporal change in density at each vertex. We assume that the density at each vertex can be observed using equipment such as IoT devices. Based on the model predictive control framework, we consider the problem of finding a route opening pattern that minimizes a safety-related penalty. To solve this problem, we propose a Transformer-based solution method that combines offline and online computations. The effectiveness of the proposed method is demonstrated through a numerical simulation. In the numerical example, a route opening pattern satisfying the Signal Temporal Logic (STL) formula was obtained using the proposed method. The evacuation rate, which represents the proportion of pedestrians whose movement is complete, was $0.8526$, the penalty was 2193, and the maximum online computation time was $8.5199$ s. In the offline computation, the training dataset for the Transformer model was generated using a genetic algorithm (GA). By combining offline and online computations, we confirmed that an appropriate route opening pattern can be generated during online computation. Full article

Permafrost warming is reshaping cold region surface water systems, where thermokarst lake drainage can abruptly alter lake abundance, hydrological connectivity and exposed thaw terrain. Yet future drainage risk trajectories remain poorly constrained for thermokarst lakes on the Mongolian Plateau (MP), a mid-latitude permafrost region undergoing strong climatic and cryospheric change. Here, we developed a future-compatible drainage risk assessment framework that combines Landsat-derived drainage mapping, environmental predictors, eXtreme Gradient Boosting (XGBoost) modelling, and fixed 2020 baseline risk thresholds with climate projections. The model was trained with 2003–2020 annual lake observations and applied to 31,786 undrained candidate lakes. Independent validation using 2021–2025 drainage events showed that 987 of 1188 events (83.1%) exceeded the fixed 2020 top 10% risk threshold, corresponding to an enrichment ratio of 8.31. Future projections showed a substantial upward shift in drainage risk levels relative to the 2020 baseline. These outputs represent relative risk levels referenced to the fixed 2020 distribution rather than calibrated probabilities of drainage within a specified future period. Under the Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5) scenario, 12,448 lakes (39.16%) exceeded the fixed top 10% threshold by 2081–2100, while 17,980 lakes (56.57%) exceeded the fixed top 25% threshold. Among the top 10% high-risk lakes, 9597 were consistently identified by all six global climate models (GCMs), indicating strong cross-model agreement. These GCM-supported high-risk lakes formed spatially coherent clusters in the northern, western and northeastern MP. The Stefan-type active layer sensitivity test retained high spatial overlap with the main projection (Jaccard = 0.917). Environmental association analysis showed that mean annual ground temperature (MAGT) was the most closely related factor, with consistently negative Spearman correlations across scenarios and projection periods (−0.45 to −0.43), followed by thawing degree days (TDD), freezing degree days (FDD), June–September precipitation and river network distance. These findings identify areas with elevated relative drainage risk levels as warming continues. Full article

Occupational accident severity is often modeled as a single ordered outcome, potentially obscuring differences between fatal and severe non-fatal injuries. This study develops a dual-pathway framework that separately analyzes fatal versus non-fatal outcomes and severe versus minor injuries among non-fatal cases. Using 48,909 accident records from the CSDataset, structured attributes were integrated with narrative-derived scenario-exposure and work-activity variables obtained through rule-constrained LLM coding; expert validation yielded Cohen’s κ = 0.742. Logistic and Firth logistic regressions were combined with machine learning models, SHAP analysis, and scenario–activity co-occurrence analysis. The two pathways shared several explanatory variables but differed in association direction and magnitude. In the fatality pathway, vehicle and mobile-equipment exposure (OR = 1.813, 95% CI: 1.668–1.971) and workplace violence (OR = 5.184, 95% CI: 4.190–6.415) showed positive associations. Among non-fatal cases, driving operation showed the strongest positive activity-level association with severe injury (OR = 2.188, 95% CI: 1.715–2.793). Co-occurrence analysis showed that frequent combinations were not necessarily outcome-enriched; vehicle and mobile-equipment exposure combined with driving operation was enriched in both pathways. These findings support pathway-specific interpretation and work-context-oriented safety management and prevention. Full article

Severe heavy metal pollution in aquatic environments demands the engineered development of structurally optimized, highly selective adsorbents. Herein, we report the intentional fabrication of a novel ternary MnO₂/NH₂-MIL-101(Fe)/g-C₃N₄ nanocomposite via an integrated in situ interfacial growth pathway. The true architectural novelty of this multi-component assembly lies in utilizing the 3D mesoporous metal–organic framework (MOF) scaffolding to structurally isolate the 2D g-C₃N₄ layers and prevent the self-aggregation of redox-active MnO₂ nanoparticles. Comprehensive characterization via PXRD, FTIR, TEM, Raman, and high-resolution XPS confirmed that this unique interfacial hybridization maximizes the spatial exposure of unblocked chemical binding sites. Batch extraction trials demonstrated a high Pb²⁺ removal efficiency of 99.5 ± 2.7% at an optimized pH of 6.0, yielding a superior maximum monolayer adsorption capacity (q_max) of 431.8 mg.g⁻¹. Competitive selectivity matrices containing co-existing ions (Cu²⁺, Cd²⁺, Ni²⁺, and Cr³⁺) revealed noticeable selectivity toward Pb²⁺ ions, driven by soft Lewis’s acid-base affinities, while background electrolyte tests identified SO₄^2- as the most influential competing anion. Non-linear isotherm modeling showed a better agreement with the Langmuir model, suggesting dominant monolayer adsorption on accessible surface sites, while kinetic data followed the pseudo-second-order model. Spectroscopic profiling proved that this heightened performance is dictated by a multi-modal integrated network operating via cooperative inner-sphere Mn-OH complexation, oxygen-vacancy trapping, exocyclic framework amine (-NH₂) chelation, and g-C₃N₄ triazine dative configurations. These findings establish the ternary system as an advanced, highly recyclable benchmark for targeted heavy metal decontamination. Full article

Gallium nitride (GaN) high-electron-mobility transistor (HEMT) biosensors have recently emerged as promising platforms for highly sensitive and label-free detection of biomolecules. Their exceptional electrical properties, including high carrier mobility, together with the wide bandgap and strong chemical bonds of GaN materials, provide excellent stability under high temperatures, ionizing radiation, and chemically harsh environments. Despite significant experimental progress, comprehensive analytical models capable of linking biomolecular recognition events to the electrical response of GaN biosensors remain limited. This work presents a physics-based, design-oriented analytical modeling framework for AlGaN/GaN HEMT biosensors. The model incorporates biomolecular binding kinetics, the dielectric properties of the hybrid system, and electrostatic coupling to the transistor channel conductivity. Numerical simulations are performed using COMSOL Multiphysics to validate the analytical model. The developed framework provides physical insight into the mechanisms governing biosensor operation and offers practical guidelines for the optimization and comparative assessment of HEMT/MIS-HEMT biosensor architectures. Full article

Deceptive path planning enables autonomous agents to obscure their true goals from observers by deviating from an expected optimal path. Prior work largely solves full-horizon, end-to-end optimization for single agents, so planning complexity grows with the path length and size of the environment, and adaptation en route requires a new optimization. We propose a unified framework for deceptive path planning onboard the agent, computing over short-horizon candidate paths within a receding-horizon loop. The method processes an agent’s position sensor information and develops a policy for the next trajectory plan. By parameterizing a user-defined cost that captures optimal planning and deception (and optionally includes constraints, trajectory smoothness, and coupling terms between agents), a Boltzmann framework yields stochastic policies that balance the tradeoff between optimal paths and deceptive deviation. Policies are updated locally and do not require learning or training. The level of deception and adherence to constraints can be dynamically tuned, enabling online adaptation to changes in goals and constraints. This step-by-step tuning opens the door to new forms of dynamic deception. In the multi-agent case, we develop a joint planner that is also tunable and can be applied as desired among all or subsets of agents. Single- and multi-agent simulation studies demonstrate the flexibility of our approach, maintaining deception while adapting as desired, avoiding the recomputation required by full-horizon methods, and supporting intuitive tuning via a small set of parameters. Full article

Head and neck squamous cell carcinoma (HNSCC) remains clinically challenging because of its marked inter- and intra-tumour heterogeneity, the dynamic emergence of therapeutic resistance, and the limited ability of current biomarkers to guide treatment adaptation over time. Recent advances in digital twin (DT) technology have motivated the development of patient-specific, continuously updated computational models capable of integrating multi-scale data to support precision oncology. However, no current DT framework for HNSCC combines molecular stratification, longitudinal monitoring of resistance, pharmacodynamic modelling, and clinical decision support within a single adaptive system. In this review, we critically examine the current state of DT-enabled approaches for targeted therapy in HNSCC and propose a conceptual framework for their future clinical implementation. We discuss how genomic and multi-omic stratification, mechanistic imaging models, pharmacokinetic/pharmacodynamic modelling, longitudinal liquid biopsy (ctDNA and exosomes), ex vivo functional testing, toxicity prediction, and artificial intelligence could be integrated into a continuously updated patient-specific model. We further examine the biological mechanisms driving resistance to targeted therapies and immunotherapy, highlighting how these dynamic processes should inform adaptive therapeutic decision-making. We discuss existing DT-like approaches and evaluated them according to their capacity to fulfil the DT criteria, while also presenting DT-enabling technologies. Among the frameworks currently available, the deep reinforcement learning-based DITTO platform represents the closest approximation to a clinically relevant HNSCC digital twin. However, it does not yet incorporate molecular signalling networks, longitudinal resistance biomarkers, or multimodal biological data. We therefore identify the integration of these complementary data layers as the principal challenge and opportunity for the next generation of DTs. Collectively, this review provides a conceptual DT framework in which four main data domains could fulfil different roles within the DT. By sharing different parameters across these layers, the framework could forecast emerging resistance and update model predictions longitudinally. Such a DT could support biomarker-guided patient stratification, adaptive treatment selection, rational combination therapies, toxicity prediction, and future clinical trial design in HNSCC. We also highlight challenges and limitations that need to be addressed for future clinical translation, including data integration, interpretability, clinical validation, workflow integration, and ethical and regulatory considerations. Full article

Target-oriented sensing image processing and analysis are important for intelligent surveillance and dynamic sensing applications, where wireless systems need to recover task-relevant visual and state information from heterogeneous sensing data. This study develops a vision–radar semantic communication system for joint target image reconstruction and dynamic state estimation. The transmitter jointly encodes scene-level visual observations and target-associated radar echoes through vision–radar semantic extraction and fusion, lightweight contextual semantic encoding, and communication-aware semantic transmission. The receiver uses a multi-task semantic decoder to reconstruct the target image and estimate normalized distance, angle, and velocity. Experiments are conducted under adaptive signal-to-noise ratio (SNR)–modulation operating points and evaluated using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and normalized state-estimation root mean square errors (RMSEs). The developed system achieves an average PSNR of 24.57 dB, SSIM of 0.8896, Normalized Distance RMSE of 0.0120, Normalized Angle RMSE of 0.0059, and Normalized Velocity RMSE of 0.0015, outperforming a continuous-channel training variant on all reported average metrics. Ablation studies show that the composite reconstruction objective and adaptive task-balancing scheme provide favorable visual-sensing tradeoffs. These results indicate that vision–radar semantic communication can support target-oriented visual sensing and dynamic state recovery within a unified sensing-oriented wireless framework. Full article