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MAF-Net: Multimodal cross-attention-based fusion network for cardiovascular disease classification
Cardiovascular disease ranks among the leading causes of death globally, posing a severe threat to human health. Consequently, rapid and accurate identification of cardiovascular disease has become a critical research endeavor. Electrocardiograms (ECGs), as a non-invasive detection tool, are widely used in cardiovascular disease detection due to their convenience and effectiveness. However, existing methods are often limited to single-modality analysis, neglecting the interaction between clinical data (such as age, gender, weight, etc.) and ECG features in classification tasks, resulting in limited recognition accuracy. Integrating multimodal data is key to improving CVD diagnostic accuracy. To address this, we propose MAF-Net (Multimodal Cross-Attention-based Fusion Network), a multi-class classification model that fuses clinical data features with ECG signal features for cardiovascular disease classification. The model comprises three components: (1) X Branch (Clinical Data Processing): Generates high-order interaction features via a second-order polynomial feature cross-layer and employs channel attention-weighted selection to identify key clinical factors;(2) Y Branch (Multi-scale ECG Feature Extraction): Parallel multi-scale convolutional modules (64@7, 128@3, 256@3) capture local morphological features, while Bi-LSTM models long-range temporal dependencies, supplemented by multi-head attention to focus on pathological segments;(3) Bidirectional Modality Fusion Module: Employing a bidirectional cross-attention mechanism, it uses clinical features as Query and ECG features as Key/Value to deeply fuse clinical and ECG data features. On the dataset, experiments targeting five super-categories of arrhythmias— NORM (Normal), MI (Myocardial Infarction), STTC (ST-T Segment Changes), CD (Conduction Disturbance), HYP (Hypertrophy). showed an accuracy rate of 90.75% ± 0.32%, precision of 84.58% ± 0.41%, and recall of 87.12% ± 0.38%, with an F1 score of 0.8069 ± 0.005 and a ROC-AUC value of 0.9407 ± 0.002. Results indicate that this model outperforms existing methods across key metrics, demonstrating its potential for application in clinical decision support.
Impact performance and probabilistic reliability of natural fiber-reinforced standard and high-strength concrete
Multifaceted bioactivity of brown seaweed-derived fucoidan from the Indian Coastline: A natural health product candidate
Brown seaweeds are rich in bioactive constituents, among which fucoidan a sulphated polysaccharide is particularly noted for its broad-spectrum therapeutic properties. This study explores the multifaceted bioactivity of fucoidan extracted from six brown seaweed species ( Sargassum wightii , Sargassum tenerrimum , Sargassum cinereum , Turbinaria conoides , Padina boergesenii , and Padina tetrastromatica ) collected from the Pudumadam coast, Gulf of mannar region, Tamil Nadu, India. Fucoidan was isolated and structurally characterized using Fourier-transform infrared spectroscopy (FT-IR) and nuclear magnetic resonance (NMR) spectroscopy. The antioxidant capacity of the extracts was assessed through 2,2-diphenyl-1-picrylhydrazyl (DPPH), hydroxyl radical scavenging activity (HRSA), and ferric reducing antioxidant power (FRAP) assays, with Sargassum cinereum showing the highest antioxidant activity (IC₅₀: 0.342 ± 0.01 mg/ml for DPPH; 1.26 ± 0.02 mg/ml for HRSA; 33.17 ± 0.02 mM Fe (II)/g for FRAP). In vitro antidiabetic assays demonstrated that fucoidan from Sargassum wightii exhibited the strongest inhibition of α-amylase and α-D-glucosidase (IC₅₀: 0.042 ± 0.02 mg/ml). Additionally, fucoidan from S. wightii displayed significant cytotoxic activity against MCF-7 human breast cancer cells. These findings underscore the therapeutic promise of brown seaweed-derived fucoidan as a multifunctional agent, supporting its potential application in the formulation of natural health products.
Spatiotemporal evolution and driving mechanisms of multiple scales ecological security in Shanxi Province from the perspective of service, risk and health
Muscle activation and intermuscular coordination adaptations to early strength training during maximal force production
Previously untrained individuals tend to increase maximal and rapid strength during the initial resistance training stages. Muscle activation and coordination are neural mechanisms contributing to the increased mechanical output. However, how the force production characteristics are accompanied by changes in activation of agonist muscles as well as coordination of muscles with different functional roles is not fully understood. This study investigated the time course of adaptations following 6 weeks of resistance training, evaluating every two weeks a leg-press isometric maximum voluntary contraction. Peak force (PF), rate of force development (RFD), rate of EMG rise (RER) of the agonist muscles and intermuscular coherence between synergist or antagonist pairs of muscles were evaluated. In result of a dynamic squat program, the maximal and rapid leg-press isometric force increased after the 6-week period ( p = 0.011 and p = 0.015, respectively), although improvements at specific intervals of RFD at specific time points were observed. Regarding knee extensor activation, generally decreased RER was observed only for rectus femoris and not for the monoarticular portions of quadriceps. Additionally, intermuscular coherence analysis revealed increased coupling between rectus femoris and the monoarticular portions of quadriceps after training, and adaptations between agonist muscles acting in different joints as well as between agonist and antagonist muscle at specific time points were observed concerning specific bands. This is the first study to characterize the time course of intermuscular coordination adaptations during the early phase of strength training in previously untrained individuals, bringing new insights into the neural mechanisms of muscle recruitment following resistance training in what concerns to coordinative strategies in the control of muscles with different functional roles.
From digital sparks to performance gains: organizational resilience thrives under ESG pressure
Expression of Concern: Trends and disparities in dilated cardiomyopathy related mortality among adults in the United States: A CDC WONDER analysis (1999–2023)
Correction: Evaluating the sustainability and productivity of conventional, organic, and regenerative agriculture in maize-soybean rotations: a modelling LCA study
RETRACTED: Hybrid deep learning and feature selection approach for autism detection from rs-fMRI data
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that is primarily characterized by deficits in social communication and restricted or repetitive behavioral patterns. Although psychologists contribute significantly to the understanding of ASD, offering insights into its cognitive, emotional, and behavioral dimensions through assessments, diagnoses, therapeutic approaches, and family support, the diagnostic process remains complex. This complexity arises from the diverse manifestations of the disorder and the challenges associated with data sharing. In addition, conventional machine learning approaches for ASD detection may struggle with high-dimensional neuroimaging data and may require careful feature engineering. Consequently, this motivated us to enhance ASD diagnosis by incorporating deep learning (DL) techniques for feature extraction alongside a modified exponential-trigonometric optimization (ETO) algorithm as a feature selection (FS) technique. The modified ETO integrates the Arithmetic Optimization Algorithm (AOA) and the Guided Learning Strategy (GLS) to improve diagnostic performance. To evaluate the effectiveness of the proposed model, we utilized resting-state functional MRI (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE I). Furthermore, the performance of the proposed model was compared with that of established models. The results indicate that the proposed model achieves competitive and, in most cases, superior performance compared with the benchmark methods, demonstrating superior accuracy, sensitivity, and AUC in diagnosing ASD. On average across the three atlas-based feature sets, the proposed model has an accuracy, sensitivity, and AUC of 73%, 78%, and 79%, respectively.
Early intervention with tirzepatide or semaglutide influences anti-atherosclerotic effects in ApoE knockout mice
Abstract This study aimed to investigate the anti-atherosclerotic properties of tirzepatide, a dual glucose-dependent insulinotropic polypeptide (GIP)/glucagon-like peptide-1 (GLP-1) receptor agonist, in comparison with semaglutide, a selective GLP-1 receptor agonist. ApoE knockout mice were divided into early diabetes (dosed from 10 to 22 weeks of age), late diabetes (dosed from 18 to 30 weeks of age), and non-diabetic groups after streptozotocin treatment, and each group received semaglutide, tirzepatide, or saline for 12 weeks. In the early diabetes group, both agents significantly suppressed aortic plaque formation compared with control, while modestly improving glycemia and lipid levels. No significant vascular effects were observed in late diabetes or non-diabetic groups. Tirzepatide markedly reduced inflammatory mediators, including Mcp-1 , Il-6 , I-cam , and Cd68 , whereas semaglutide showed partial overlap. Notably, these anti-inflammatory effects were also detected in non-diabetic mice, suggesting vascular protection may involve arterial actions independently of metabolic control. Taken together, our findings demonstrate that tirzepatide exerts anti-atherosclerotic effects comparable to semaglutide, supporting the concept that GIP and GLP-1 signaling can confer vascular benefits. These results highlight the potential clinical relevance of dual incretin receptor agonism for cardiovascular risk reduction, although further studies are required to clarify the specific role of GIP signaling.
Correction: A data-driven approach to establishing cell motility patterns as predictors of macrophage subtypes and their relation to cell morphology
MRR-YOLO: an instance segmentation technique for ground-based cloud images
Rejection or support? research on labor participation strategy of older adults based on three-party evolutionary game
To address the demographic challenges posed by accelerated aging and shrinking labor force, it is crucial to develop human resources and encourage labor participation among older adults to achieve active aging. This study constructs a three-party evolutionary game model involving government departments, local enterprises, and older workers based on evolutionary game theory. It analyses the strategic choices of each party during the gaming process and their evolutionary strategies under different conditions, with numerical simulations conducted to examine the impact of parameter adjustments on these evolutionary dynamics. The findings indicate that: the effectiveness of digital government construction serves as a critical determinant for governmental support of older adults’ labor participation; the probability of enterprises actively employing older workers correlates with both the outcomes of corporate digital transformation and labor costs associated with older adults’ employment; older individuals’ likelihood of labor participation relates to employment income and age discrimination, while digital technology empowerment facilitates strategic shifts from negative to positive engagement for both government and enterprises. Based on these conclusions, policy recommendations including strengthening digital government development, accelerating enterprise digital transformation, and fostering age-friendly employment environments are proposed, thereby providing theoretical foundations for implementing national strategies addressing the labor shortage challenges due to population aging.
Association between estimated plasma volume status and all-cause mortality in critically ill patients with non-traumatic subarachnoid hemorrhage: analysis of the MIMIC-IV database
Bridging theory and behavior for healthcare accessibility modeling: A mobility-driven revision of the E2SFCA
Proximity to a healthcare supplier does not necessarily equate to meaningful access to care. Traditional healthcare accessibility models, particularly the Enhanced Two-Step Floating Catchment Area (E2SFCA) method, rely heavily on assumptions of proximity-driven behavior, fixed catchment sizes, and uniform distance decay. These simplifications often overlook the complexities of real-world healthcare-seeking behavior. This study examines the assumptions of the E2SFCA framework by integrating large-scale human mobility data in 2023, which captures anonymized, real-world visitation patterns between neighborhoods and hospitals across Pennsylvania. We revise the E2SFCA model through two key innovations: 1) replacing static catchment thresholds with dynamic, visit-weighted boundaries derived from observed travel behavior, and 2) estimating hospital-specific distance decay functions that better reflect heterogeneous patterns of attraction. These refinements result in accessibility metrics that align more closely with empirical realities. Compared to the traditional model, the revised E2SFCA demonstrates a more meaningful relationship with real-world health outcomes. Specifically, the revised model shows a stronger and statistically significant correlation with household income (r = 0.31, p = 0.011, vs. r = 0.14 in the traditional model) and a more plausible negative association with poor or fair health status (r = −0.12 vs. r = 0.17), aligning with the expectation that better accessibility corresponds to better health outcomes. Additionally, the revised model reveals significantly greater inequality in access that exposes disparities in healthcare accessibility that distance centric approaches tend to obscure. By integrating human mobility data in spatial accessibility modeling, this study offers a more realistic, equitable, and policy-relevant framework for evaluating healthcare access.
Quantifying channel width thresholds for safe inland navigation under excessive cross-flow conditions
Abstract Existing standards rely primarily on cross-flow velocity limits, whereas the cumulative influence of cross-flow length on ship drift and channel safety remains insufficiently quantified. In this study, a flow-field-driven manoeuvring assessment framework that integrates a steady two-dimensional nonuniform flow model with a standard 3-DOF MMG manoeuvring model is developed, and numerical simulations are performed for representative inland cargo ships operating in China’s Class I–V waterways under conservative upstream conditions. The key contributions of this study are as follows: (i) Introduction of the acceptable maximum safety cross-flow length (AMSCL), defined as the maximum cross-flow zone length that allows a ship to exit the zone within safety boundaries without requiring channel widening at a given excessive cross-flow velocity. (ii) Across Class I–V waterways, the AMSCL values range from 7.78 to 54.98 m for cross-flow velocities between 0.35 and 0.60 m/s, demonstrating the strong combined effects of cross-flow velocity and length on safety margins. (iii) Based on the AMSCL and simulated trajectories, chart-based criteria are developed to determine the required local channel widening and to quantify its approximately linear relationship with cross-flow velocity and length. (iv) A confluence case study (Guangping River–Pinglu Canal) confirms that the proposed widening scheme improves heading stability and reduces cross-flow-induced navigation risk. This research provides a quantitative framework for enhancing navigation safety and optimizing channel design in inland waterways subject to excessive cross-flow.
A study of location selection for large agricultural wholesale markets under the perspective of modern circulation
This study investigates the critical challenges associated with location selection for large-scale Agricultural Product Wholesale Markets (APWMs) under the traditional circulation model. It identifies and elaborates on the evolving characteristics of circulation stakeholders, supply chains, distribution channels, organizational structures, and external environments during the transition from traditional to modern circulation systems. In response to the demands of modern circulation, a comprehensive location selection evaluation framework is proposed, integrating five key criteria: location, planning, transportation, land use, and urban compatibility. Elastic and rigid evaluation standards are established according to the nature of each criterion. The framework innovatively integrates national territorial and spatial planning, road traffic planning, industrial development planning, and urban big data resources through Geographic Information System (GIS) technology, consolidating these into a unified database. To determine comprehensive weights for different functional types of APWMs, the normalized linear aggregation method is applied to combine weights derived from the Analytic Hierarchy Process (AHP) and the entropy weight method, enabling an analysis of correlation and contribution levels. Furthermore, this study introduces an innovative application of the Genetic Algorithm (GA), implemented in Python, to re-optimize the integration of subjective and objective weights through iterative computation until convergence, thereby enhancing the accuracy of comprehensive weight estimation and validating the location selection outcomes. A case study demonstrates the successful development of a five-phase location selection methodology—region screening, scope delineation, data analysis, weight optimization, and comprehensive evaluation—enabling both quantitative ranking and recommendation of candidate location and the optimal solution was ultimately selected from seven candidate schemes. This research provides practical guidance for location selection of large-scale APWMs within modern circulation contexts and offers methodological insights applicable to urban logistics planning and the siting of other large-scale infrastructure facilities.