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CD49a+ NK cells promote M2 polarization and are associated with poor pathological response in NSCLC

Three-dimensional dynamic response of infinite plate resting on multilayered orthotropic foundation under moving loads

In response to the dynamic response of layered foundation pavement structures caused by traffic loads, a three-dimensional coupled model of an infinite elastic plate on an orthotropic layered foundation under moving loads is established. Based on elastodynamic theory and Kirchhoff thin-plate theory, governing equations are derived by introducing a moving coordinate system and incorporating orthotropic constitutive relations. Through double Fourier transforms and matrix analysis, a single-layer foundation transfer matrix is formulated. The global stiffness matrix is assembled using interlayer continuity conditions, ultimately yielding an analytical solution for the dynamic response of the plate and foundation in the integral transform domain. Combined with ABAQUS finite element simulations, the effects of foundation stratification, soil orthotropy, load parameters, and plate parameters on the dynamic response are systematically studied. The results indicate that the layering of the foundation significantly affects the plate deflection. The orthogonal anisotropy of the first soil layer has a more significant impact on deflection compared to isotropic conditions, and this characteristic needs to be considered in practical engineering. The increase in load speed leads to an increase in peak deflection, while the increase in load frequency results in a decrease in peak deflection. Optimization of plate parameters can effectively suppress deformation, and the amplitude of the deflection curve decreases with the increase of the elastic modulus or thickness of the plate.

Adaptive collaborative feature fusion and shape-aware optimization for multi-scale chest lesion detection

Abstract Chest diseases remain a major cause of global morbidity and mortality, and accurate detection from chest X-ray images is critical for early diagnosis and clinical decision-making. However, large variations in lesion scale, morphology, and spatial distribution pose significant challenges for automated detection systems, particularly in identifying small lesions and achieving precise localization. To address these issues, we propose a multi-scale chest lesion detection method based on adaptive collaborative feature fusion and shape-aware optimization. The method enhances multi-scale feature modeling and introduces shape structural constraints to improve detection accuracy and localization robustness in complex anatomical environments. Specifically, a Context-Embedded Feature Enhancement Network is designed to jointly capture global anatomical context and local lesion characteristics, strengthening lesion representation. An Adaptive Feature Focusing Network further improves multi-scale feature representation through adaptive spatial feature aggregation, enabling more effective detection of small lesions. In addition, a shape-aware optimization strategy integrating normalized Wasserstein distance with shape-weighted constraints improves localization stability and bounding box regression accuracy for irregular lesions. Compared with state-of-the-art methods, the proposed method achieves improvements of 6.3% in mAP , 6.4% in small-lesion mAP , and 8.6% in mean recall on VinDr-CXR, as well as improvements of 4.2% in mAP and 7.8% in mean recall on ChestX-ray8, demonstrating its effectiveness and generalization capability for multi-scale chest lesion detection.

An optimization method for flexible interconnection planning based on improved CNN-LSTM prediction and tunable relative entropy-driven chaotic evolution

As power systems evolve towards greater intelligence and flexibility, flexible interconnection technology has emerged as a critical means to enhance operational reliability and economic performance. This paper presents a data-model dual-driven planning methodology for flexible interconnection systems, integrating a multi-scale spatio-temporal cross-enhanced CNN-LSTM model for load forecasting with a Chaotic Evolutionary Optimization (CEO) algorithm to optimize system design. The proposed framework first constructs an improved CNN-LSTM hybrid architecture, trained on historical load data and simulated feature sets, to predict future load profiles. A novel Tunable Relative Entropy (TRE) metric is introduced as a complementarity quantification index, forming a multi-objective function that incorporates system balance, reliability, economy, and spatio-temporal complementarity. The CEO algorithm is then employed to solve the optimization model, determining the optimal system configuration and operational parameters. Experimental evaluations demonstrate that the forecasting module achieves high accuracy, with a Mean Squared Error (MSE) of 0.000368 and a Mean Absolute Error (MAE) of 0.006334. Moreover, the TRE index improves complementarity efficiency by 3.8%. By leveraging the predictive capability of the hybrid neural network and the CEO algorithm’s optimization efficacy, the proposed approach not only reduces load fluctuation indices but also enhances planning efficiency and operational economy, offering a viable pathway for intelligent power system development.

Interpretable machine learning distinguishes skip from continuous metastasis in N1b papillary thyroid carcinoma

Comparative analysis of isokinetic ratios between hamstrings and quadriceps in wrestlers: Resting state versus post-fatigue evaluation

Introduction Wrestling demands high-intensity competition, and tournament structures often require athletes to compete in multiple matches in a single day. This results in accumulated neuromuscular fatigue that may disrupt the balance between hamstring and quadriceps strength, potentially increasing the risk of knee injuries. Therefore, this study aimed to determine whether the conventional hamstring-quadriceps ratio (H: Q Conv ) and functional hamstring-quadriceps ratio (H: Q Func ) are more indicative in resting and post-fatigue states, examine the H: Q Conv and H: Q Func isokinetic strength ratios, and analyze the contributions of hamstring and quadriceps strength changes to these ratios. Materials and methods The study involved 16 wrestlers who achieved national championship rankings. Their average age was 15.0 ± 1.2 years, with an average height of 171.6 ± 8.6 cm and weight of 65.1 ± 16.6 kg. Their BMI was 21.8 ± 4.0 kg/m², and they had an average of 4.3 ± 1.0 years of training experience. The athletes participated in isokinetic assessments both at rest and after fatigue. The concentric and eccentric strengths of the hamstrings and concentric strength of the quadriceps were measured at angular velocities of 60°/s and 180°/s, respectively. Results Two-factor repeated-measures ANOVA revealed a highly significant main effect of time on quadriceps concentric (Q CON )(F (1,15) =106.789, p < .001, η²ₚ = .877), and angular velocity and interaction were not significant. For H CON , there was no time effect, but the velocity effect was significant (60°/s > 180°/s; F (1,15) =31.771, p < .001, η²ₚ = .679). For hamstring eccentric (H ECC ), the time and interaction effects were insignificant, and the velocity remained at the threshold (F (1,15) =3.654, p = .075). The H: Q Conv ratio generally increased after fatigue (F = 17.492, p = .001), and there was a level difference between velocities (F = 9.147, p = .009), but the interaction was insignificant. Both time (F = 59.489, p < .001) and velocity (F = 6.716, p = .020) were significant for the H: Q Func ratio, as was the time × velocity interaction (F = 4.708, p = .046); the increase was particularly reliable at 60°/s (Δ=+0.131, 95% CI [0.044, 0.218], p = .006). In the component decompositions, the increase in the time effect was shared similarly by the hamstrings and quadriceps, whereas the hamstrings’ contribution significantly drove the angular velocity difference. Conclusion Fatigue significantly reduced the concentric torque of the quadriceps, whereas the concentric/eccentric outputs of the hamstrings were relatively preserved; consequently, the H: Q balance increased, particularly at 60°/s. The time effect was strong for H: Q Func , and a time×velocity interaction was present: the increase was reliable at 60°/s but not statistically confirmed at 180°/s. Component decomposition showed that the time-related change was shared to a similar extent by both muscles, whereas the hamstring contribution predominantly explained the angular velocity difference.

Multifactorial drivers of spatial variation in human body form across modern China

Beggar thy neighbour or befriend thy neighbour? Asymmetric spillovers of China’s double world-class policy

Higher education policies commonly engender certain spillover effects, which frequently remain unaccounted for within the formal policy evaluation framework. Focusing on China’s Double World-Class (DWC) strategy, this paper investigates, from both theoretical and empirical perspectives, whether and how the policy is associated with the research output of non-selected disciplines. Theoretically, this paper puts forward two possible causes of spillovers: reputation enhancement and resource dilution. Based on discipline-level empirical data in Chinese universities from 2014 to 2023 and combined with a panel-regression model, this paper examines whether this policy is associated with changes in the quantity and quality of research outputs among Chinese- and English-language publications across different areas. The results show that the World-Class Discipline (WCD) policy is associated with considerable asymmetric spillover effects on non-selected disciplines. This policy variable is associated with a reduction of 0.62% in the quantity and 1.77% in the quality of research outputs published in Chinese core journals in non-selected disciplines. Meanwhile, it is positively associated with an increase of 5.57% in quantity and 5.06% in quality of research outputs published in English journals. The results of mechanism tests indicate that the enhancement of university reputation and the dilution of educational resources play important roles in the asymmetric spillover effects of WCD policy. The heterogeneity analysis results show that the spillover effects of this policy change across different regions and disciplinary areas. Additionally, this paper integrates the Socialformation Paradigm theory and the Sustainable Social Development theory to further discuss the “delocalization” findings of WCD policy.

Lipase from Stenotrophomonas maltophilia strain HO5 for efficient biodiesel synthesis using non-edible plant oils

Cross-dataset benchmarking of machine learning models for marine and atmospheric environmental prediction

Accurate prediction of marine and atmospheric environmental variables is important for climate adaptation, ecosystem management, and operational decision-making, yet practitioners still lack clear guidance on which machine-learning models are reliable across heterogeneous environmental tasks. We therefore developed a unified, leakage-aware benchmark across nine datasets, of which seven passed quality checks for modeling, spanning chlorophyll-a, wind speed, hydrographic observations, biotoxins, and bathymetry, and compared representative linear, tree-based, and sequence models under a common evaluation framework. Results show strong heterogeneity across tasks and model classes: tree ensembles are robust baselines for tabular problems, LSTM-based recurrent sequence modeling is most useful when temporal structure is central, and predictive skill depends more on target structure and covariate quality than on model complexity alone. Within the observational settings represented in this benchmark—predominantly Chinese coastal/estuarine and regional marine datasets, plus one atmospheric reanalysis wind task and one global cast archive—quality-controlled chlorophyll-a is comparatively predictable, whereas event-driven biotoxins and bathymetry inversion remain difficult under the current predictors. These findings provide practical guidance for researchers and environmental monitoring practitioners working in similar data regimes, but they should not be assumed to transfer automatically to untested regions such as the North Atlantic, the Mediterranean, or tropical open-ocean systems without further validation.

Development and content validation of a quantitative informatics framework for hardware assessment in health information systems

Molecular characterization of superficial zone chondrocytes under pro-inflammatory and biomechanical stress conditions

Osteoarthritis (OA) is a chronic degenerative joint disease characterized by pathological features such as chondrocyte loss and cartilage matrix degradation. Superficial zone chondrocytes (SFC), located in the outermost layer of articular cartilage and in direct contact with synovial fluid, are the first to respond to mechanical stress and friction. In this study, SFC were isolated and identified in vitro, and their proliferatives and anti-apoptotic properties were examined. Additionally, an early OA inflammatory environment was successfully simulated in cell experiments, demonstrating that inflammatory conditions reduce stemness-associated marker expression in SFC, activate multiple inflammatory pathways, and promote MMP3 expression. When SFC were subjected to cyclic mechanical stretching under inflammatory conditions, increased expression of the mechanosensitive channel Piezo1, enhanced calcium-associated mechanotransduction sensitivity, and disruption of the cytoskeleton were associated with aggravated catabolic responses and apoptosis under inflammatory mechanical stimulation. These findings elucidate the role of SFC in early OA pathogenesis and provide insight into early OA pathogenesis and suggest potential directions for mechanism-based intervention.

The influence of students’ perceptions of teacher criticism styles on their intention to improve disruptive classroom behavior in physical education: A structural equation model based on academic emotion mechanisms

Cardiologists’ perspectives on pharmacogenomics implementation in a hybrid health system: A qualitative study from the United Arab Emirates

Background Pharmacogenomics (PGx) can optimise cardiovascular therapy, yet routine integration in cardiology remains limited. In the United Arab Emirates, a hybrid public–private health system, the real-world PGx use is still emerging. However, there is limited understanding of how cardiologists perceive and navigate PGx implementation within such complex health system contexts. Objective To examine cardiologists’ perspectives on the feasibility, barriers, and facilitators of implementing PGx using the Consolidated Framework for Implementation Research (CFIR). Methods A qualitative study using an abductive analytical approach was conducted through semi-structured interviews with 15 cardiologists from public and private institutions. Participants were recruited via purposive, convenience, and snowball sampling. Interviews were transcribed verbatim and thematically analysed in NVivo. The CFIR guided the analysis across intervention characteristics, outer setting, inner setting, individual characteristics, and process. Results Clinicians expressed strong conceptual support for PGx, especially in higher-risk scenarios, but reported limited hands-on exposure and confidence. Barriers included perceived test complexity, cost, and lack of reimbursement; insufficient laboratory capacity and EHR integration; unclear workflows and role ownership; and turnaround times misaligned with acute care. Outer-setting constraints (ambiguous policy signals and payer criteria) and inner-setting variability (resources, leadership engagement, and communication pathways) further limited uptake. Reported facilitators included multidisciplinary service models (with input from pharmacists and genetics), targeted case-based training, initial deployment in non-acute contexts, and the structured capture of results with EHR-embedded clinical decision support. Conclusions PGx implementation in cardiology within the UAE is shaped by structural, organisational, and workforce-level gaps. Addressing these through targeted clinical guidance, improved training, stronger reimbursement mechanisms, enhanced laboratory capacity, and integrated digital decision support may enable more equitable and scalable adoption. These findings provide actionable insights for health systems seeking to operationalise PGx within diverse or hybrid healthcare contexts.

Heterogeneous relationships between the multisensory content of aphantasics’ dreams and their volitional waking imagined experiences

Remote medical system driven by medical big models: Dynamic defense model for network security threats

As telemedicine systems become increasingly interconnected and medical big models are more widely deployed, remote medical infrastructures face growing cybersecurity risks. Traditional static defense mechanisms rely on predefined rule libraries and delayed patching cycles, which makes them inadequate for fast-evolving attacks in medical environments. This creates persistent risks such as model parameter leakage, insufficient privacy protection, and single-point failures in network architecture. We hypothesize that a dynamic defense framework integrating intelligent decision-making, trusted coordination, and hardware acceleration can better balance security, privacy, and real-time performance in medical scenarios. To test this hypothesis, we develop a reinforcement learning (RL)-driven adaptive dynamic defense strategy as the core decision-making module and integrate it with three supporting components: a security-enhanced model protection architecture based on an improved Shamir threshold scheme, adversarial training, and differential privacy; a blockchain-based verification mechanism using improved PBFT; and FPGA-based hardware acceleration using the Xilinx XC7K325T platform. The framework is implemented and evaluated using NS-3, Python 3.8 with PyTorch 1.12, Hyperledger Fabric 2.4, and the publicly available Synthetic IoMT Security Dataset. Across the evaluated regional medical alliance and emergency ambulance scenarios, the proposed system increases the zero-day attack blocking rate from 68.5% to 99.3%, improves medical image encryption throughput from 120 Mbps to 450 Mbps, reduces CPU peak utilization by 47.8%, eliminates privacy leakage incidents during cross-institutional data sharing, and stabilizes core clinical service latency within 35 ms. These results indicate that the proposed framework can enhance the security and operational resilience of remote medical systems under the evaluated conditions. Simulated results and deployment-based observations are distinguished in the corresponding sections.

Real-world decision support in hospitality using pessimistic multi-granulation roughness of cubic intuitionistic fuzzy soft sets

Identification of candidate sex hormone-associated genes and immune infiltration characteristics in osteoarthritis based on bioinformatics analysis and machine learning

Background Sex hormones play critical roles in the pathogenesis and progression of osteoarthritis (OA), yet the hormone-related molecular networks remain poorly defined. This study aimed to identify candidate sex hormone-associated genes in OA and to explore their potential functional enrichment and immune-related characteristics using bioinformatics analysis. Methods OA gene expression data were obtained from the GEO database and integrated with candidate sex hormone-associated genes retrieved from GeneCards. The R package “limma” was then used to identify differentially expressed genes (DEGs) and sex hormone-associated DEGs (SADEGs). OA-associated SADEGs, termed OA-SADEGs, were selected using weighted gene co-expression network analysis (WGCNA), and their potential biological functions and pathways were explored by GO and KEGG enrichment analyses. Hub genes were identified using three machine learning models. xCell analysis was used to estimate immune infiltration and its associations with hub genes, and hub gene expression was further evaluated in external datasets and peripheral blood samples. Results We identified 32 sex hormone-associated genes in OA, enriched in extracellular matrix remodeling, receptor signaling, and antigen presentation pathways. Three candidate hub genes (LOXL1, HLA-DRA, and CYBB) were consistently upregulated in OA and showed significant correlations with immune infiltration scores. xCell analysis identified 13 differentially enriched immune cell types, of which three were associated with hub genes. External dataset analysis and peripheral blood qRT-PCR showed upregulation of LOXL1, HLA-DRA, and CYBB in OA samples. Conclusion This study integrated bioinformatics and immune analyses to identify candidate sex hormone-associated genes in OA. These findings provide associative bioinformatics evidence for sex hormone-associated molecular features in OA.

Application of particle filter algorithm based on chaotic sequences and improved t-distribution in UWB indoor positioning

Importance of developmental stage and microenvironment control in Zebrafish larvae cardiovascular studies

Zebrafish ( Danio rerio ) are widely used as models in cardiovascular research due to their rapid development, optical transparency, and genetic similarity to humans. However, the lack of standardized experimental conditions, particularly regarding developmental stage and microenvironmental parameters, limits reproducibility across studies. This study aimed to characterize cardiovascular function in Zebrafish larvae and evaluate the impact of developmental stage and environmental factors. Wild-type AB embryos were maintained under standard conditions, and heart rate (HR), cardiac output (CO), and ejection fraction (EF) were measured at 24, 30, 48, 52, 56, 72, 78, and 80 hours post-fertilization (hpf). The effects of variations in temperature (27.0, 27.5, and 28.0 °C) and pH (7.0, 7.4, and 8.0) were also assessed. Results showed a progressive increase in HR from 24 to 72 hpf, stabilizing thereafter. CO exhibited two phases of elevation: an early rise between 24–48 hpf and a stronger increase between 48–56 hpf. EF remained generally stable, with a transient reduction at 48 hpf. Cardiovascular performance reached a physiologically stable state after 72 hpf, defining a reliable window for functional studies. Environmental conditions modulated these parameters: temperature variation induced approximately 20% difference in HR and reduced EF, while CO was minimally affected. In contrast, pH variations within the physiological range had no significant impact on HR, CO, or EF. These findings highlight developmental and environmental variables that may influence cardiovascular measurements in Zebrafish larvae and support the development of more consistent experimental approaches in cardiovascular and toxicological research.