Search Results (109)

Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease in which environmental factors modulate genetically determined immune dysregulation. Vitamin D has emerged as a plausible modifier of disease expression because its active metabolite signals through the vitamin D receptor on innate and adaptive immune cells and influences antigen presentation, cytokine balance, and lymphocyte differentiation. This narrative review synthesizes current evidence on vitamin D status and supplementation in SLE with attention to organ-specific domains. Observational studies consistently report high rates of hypovitaminosis D in SLE and associations with less favorable clinical profiles, including higher global and renal disease activity, adverse cardiometabolic features, greater infection vulnerability, and neuropsychiatric manifestations. Preclinical models demonstrate neuroprotective and barrier-stabilizing actions of vitamin D analogs, supporting biological plausibility. Interventional trials indicate that supplementation safely corrects deficiency and shows signals of benefit for selected outcomes (e.g., modest activity reductions or fatigue in specific contexts), although effects on interferon signatures, complement, and autoantibodies are heterogeneous and often limited. Overall, current evidence supports optimization of vitamin D status as a low-risk adjunct in comprehensive SLE care while highlighting the need for adequately powered, organ-focused randomized trials using standardized measurements and prespecified endpoints to define causality, therapeutic targets, and long-term safety. Full article

Inferring causal relationships in the decision-making processes of machine learning models is essential for advancing explainable artificial intelligence. In this work, we propose a novel causality measure and a distance metric derived from Lempel–Ziv (LZ) complexity. We explore how these measures can be integrated into decision tree classifiers by enabling splits based on features that cause the most changes in the target variable. Specifically, we design (i) a causality-based decision tree, where feature selection is driven by the LZ-based causal score; (ii) a distance-based decision tree, using LZ-based distance measure. We compare these models against traditional decision trees constructed using Gini impurity and Shannon entropy as splitting criteria. While all models show comparable classification performance on standard datasets, the causality-based decision tree significantly outperforms all others on the Coupled Auto Regressive (AR) dataset, which is known to exhibit an underlying causal structure. This result highlights the advantage of incorporating causal information in settings where such a structure exists. Furthermore, based on the features selected in the LZ causality-based tree, we define a causal strength score for each input variable, enabling interpretable insights into the most influential causes of the observed outcomes. This makes our approach a promising step toward interpretable and causally grounded decision-making in AI systems. Full article

This study proposes a novel causality-structured matrix long short-term memory (C-mLSTM) model for significant wave height (SWH) forecasting. The framework incorporates a two-stage causal feature selection methodology using cointegration testing and Granger causality testing to identify long-term stable causal relationships among variables. These relationships are embedded within the C-mLSTM architecture, enabling the model to effectively capture both temporal dependencies and causal information within the data. Furthermore, the model integrates Bayesian optimization (BO) and twin delayed deep deterministic policy gradient (TD3) algorithms for synergistic optimization. This combined TD3-BO approach achieves an 11.11% improvement in the mean absolute percentage error (MAPE) on average compared to the base model without optimization. For 1–24 h SWH forecasts, the proposed TD3-BO-C-mLSTM outperforms the benchmark models TD3-BO-LSTM and TD3-BO-mLSTM in prediction accuracy. Finally, a Shapley additive explanations (SHAP) analysis was conducted on the input features of the BO-C-mLSTM model, which reveals interpretability patterns consistent with the two-stage causal feature selection methodology. This research demonstrates that integrating causal modeling with optimization strategies significantly enhances time-series forecasting performance. Full article

Mine water inrush is a significant environmental catastrophe during the coal mining process, and the timely discrimination of the source of water inrush is the key to ensuring safe production in coal mines. This work suggests a mine water inrush—belief rule base (MWI-BRB) source discrimination model to overcome the interpretability and performance issues with conventional models. MWI-BRB firstly automatically constructs the reference values of prerequisite attributes using the Sum of Squared Errors—K-means++ algorithm, which effectively combines expert knowledge and data-driven methods, and solves the limitation of the traditional belief rule base model relying on specialist knowledge. Secondly, the hierarchical incremental structure solves the rule explosion problem caused by complex features while using XGBoost to select features. Finally, in the inference process, the model adopts an evidential reasoning algorithm to realize transparent causal inference, guaranteeing the model’s interpretability and transparency. The Penalized Covariance Matrix Adaptation Evolution Strategy algorithm optimizes the model parameters to increase the discriminative accuracy of the model even more. Experimental results on a real coal mine dataset (a total of 67 samples from Hebei, China, covering four water inrush sources) demonstrate that the proposed MWI-BRB achieves 95.23% accuracy, 95.23% recall, and 95.36% F1-score under a 7:3 training–testing split with parameter tuning performed via leave-one-out cross-validation. The near-identical values across accuracy, recall, and F1-score reflect the balanced nature of the dataset and the robustness of the model across different evaluation metrics. Compared with baseline models, MWI-BRB’s accuracy and recall are 4.78% higher than BPNN and 9.52% higher than KNN, RF, and XGBoost; its F1-score is 4.85% higher than BPNN, 10.64% higher than KNN, 10.19% higher than RF, and 9.65% higher than XGBoost. Moreover, the model maintains high interpretability. In conclusion, the MWI-BRB model can realize efficient and accurate water inrush source discrimination in complex environments, which provides a feasible technical solution for the prevention and control of mine water damage. Full article

Background: Chemokines play a pivotal role in the progression of osteoarthritis (OA), but their exact mechanisms remain unclear. This study aimed to identify potential chemokine-associated biomarkers and investigate their causal relationships with OA. Methods: Transcriptome and genome-wide association study (GWAS) data were obtained from public databases, while chemokine-related genes (CRGs) were sourced from the literature. Initially, CRGs were expanded, followed by Mendelian randomization (MR) analysis, differential expression analysis, machine learning, and receiver operating characteristic (ROC) curve plotting to identify potential biomarkers. The causal relationships between these biomarkers and OA, as well as their biological functions, were further explored. Results: Fourteen candidate genes were identified for machine learning analysis, with DDIT3, CEBPB, CX3CR1, and ARHGAP25 emerging as feature genes. CEBPB and CX3CR1, which exhibited AUCs > 0.7 in the GSE55235 and GSE55457 datasets, were selected as potential biomarkers. Notably, CEBPB expression was lower, while CX3CR1 expression was elevated in the case group. Furthermore, both genes were co-enriched in spliceosome, lysosome, and cell adhesion molecule pathways. MR analysis confirmed that CEBPB and CX3CR1 were causally linked to OA and acted as protective factors (IVW model for CEBPB: OR = 0.9051, p = 0.0001; IVW model for CX3CR1: OR = 0.8141, p = 0.0282). Conclusions: CEBPB and CX3CR1 were identified as potential chemokine-related biomarkers, offering insights into OA and suggesting new avenues for further investigation. Full article

In the cybersecurity attack and defense space, the “attacker” and the “defender” form a dynamic and symmetrical adversarial pair. Their strategy iterations and capability evolutions have long been in a symmetrical game of mutual restraint. We will introduce modern Intrusion Detection Systems (IDSs) from the defender’s side to counter the techniques designed by the attacker (APT attack). One major challenge faced by IDS is to identify complex attack paths from a vast provenance graph. By constructing an attack behavior tracking graph, the interactions between system entities can be recorded, but the malicious activities of attackers are often hidden among a large number of normal system operations. Although traditional methods can identify attack behaviors, they only focus on the surface association relationships between entities and ignore the deep causal relationships, which limits the accuracy and interpretability of detection. Existing graph anomaly detection methods usually assign the same weight to all interactions, while we propose a Causal Autoencoder for Graph Explanation (CAGE) based on reinforcement learning. This method extracts feature representations from the traceability graph through a graph attention network(GAT), uses Q-learning to dynamically evaluate the causal importance of edges, and highlights key causal paths through a weight layering strategy. In the DARPA TC project, the experimental results conducted on the selected three datasets indicate that the precision of this method in the anomaly detection task remains above 97% on average, demonstrating excellent accuracy. Moreover, the recall values all exceed 99.5%, which fully proves its extremely low rate of missed detections. Full article

Background: Unlike most cancers, breast cancer poses a persistent risk of distant recurrence—often years after initial treatment—making long-term risk stratification uniquely challenging. Current tools fall short in predicting late metastatic events, particularly for early-stage patients. Methods: We present an interpretable machine learning (ML) pipeline to predict distant recurrence-free survival at 5, 10, and 15 years, integrating Bayesian network-based causal feature selection, deep feed-forward neural network models (DNMs), and SHAP-based interpretation. Using electronic health record (EHR)-based clinical data from over 6000 patients, we first applied the Markov blanket and interactive risk factor learner (MBIL) to identify minimally sufficient predictor subsets. These were then used to train optimized DNM classifiers, with hyperparameters tuned via grid search and benchmarked against models from 10 traditional ML methods and models trained using all predictors. Results: Our best models achieved area under the curve (AUC) scores of 0.79, 0.83, and 0.89 for 5-, 10-, and 15-year predictions, respectively—substantially outperforming baselines. MBIL reduced input dimensionality by over 80% without sacrificing accuracy. Importantly, MBIL-selected features (e.g., nodal status, hormone receptor expression, tumor size) overlapped strongly with top SHAP contributors, reinforcing interpretability. Calibration plots further demonstrated close agreement between predicted probabilities and observed recurrence rates. The percentage performance improvement due to grid search ranged from 25.3% to 60%. Conclusions: This study demonstrates that combining causal selection, deep learning, and grid search improves prediction accuracy, transparency, and calibration for long-horizon breast cancer recurrence risk. The proposed framework is well-positioned for clinical use, especially to guide long-term follow-up and therapy decisions in early-stage patients. Full article

We propose a novel interpretability framework that integrates instance-wise feature selection with causal reasoning to explain decisions made by black-box image classifiers. Instead of relying on feature importance or mutual information, our method identifies input regions that exert the greatest causal influence on model predictions. Causal influence is formalized using a structural causal model and quantified via a conditional mutual information term. To optimize this objective efficiently, we employ continuous subset sampling and the matrix-based Rényi’s $\alpha$-order entropy functional. The resulting explanations are compact, semantically meaningful, and causally grounded. Experiments across multiple vision datasets demonstrate that our method outperforms existing baselines in terms of predictive fidelity. Full article

This study proposes a novel LK-BP-AFPSO model for the nondestructive evaluation of wood mechanical properties, combining a backpropagation neural network (BP) with adaptive fractional-order particle swarm optimization (AFPSO) and Liang–Kleeman (LK) information flow theory. The model accurately predicts four key mechanical properties—longitudinal tensile strength (SPG), modulus of elasticity (MOE), bending strength (MOR), and longitudinal compressive strength (CSP)—using only nondestructive physical features. Tested across diverse wood types (fast-growing YKS, red-heart CSH/XXH, and iron-heart XXT), the framework demonstrates strong generalizability, achieving an average prediction accuracy (R²) of 0.986 and reducing mean absolute error (MAE) by 23.7% compared to conventional methods. A critical innovation is the integration of LK causal analysis, which quantifies feature–target relationships via information flow metrics, effectively eliminating 29.5% of spurious correlations inherent in traditional feature selection (e.g., PCA). Experimental results confirm the model’s robustness, particularly for heartwood variants, while its adaptive fractional-order optimization accelerates convergence by 2.1× relative to standard PSO. This work provides a reliable, interpretable tool for wood quality assessment, with direct implications for grading systems and processing optimization in the forestry industry. Full article

A State of Health (SOH) estimation of lithium-ion batteries is a core function of battery management systems, directly affecting the safe operation, lifetime prediction, and economic efficiency of batteries. However, existing methods still face challenges in balancing feature robustness and model generalization ability; for instance, some studies rely on features whose physical correlation with SOH lacks strict verification, or the models struggle to simultaneously capture the temporal dynamics of health factors and nonlinear mapping relationships. To address this, this paper proposes an SOH estimation method based on incremental capacity (IC) curves and a Temporal Convolutional Network—Relevance Vector Machine (TCN-RVM) model, with core innovations reflected in two aspects. Firstly, five health factors are extracted from IC curves, and the strong correlation between these features and SOH is verified using both Pearson and Spearman coefficients, ensuring the physical rationality and statistical significance of feature selection. Secondly, the TCN-RVM model is constructed to achieve complementary advantages. The dilated causal convolution of TCN is used to extract temporal local features of health factors, addressing the insufficient capture of long-range dependencies in traditional models; meanwhile, the Bayesian inference framework of RVM is integrated to enhance the nonlinear mapping capability and small-sample generalization, avoiding the overfitting tendency of complex models. Experimental validation is conducted using the lithium-ion battery dataset from the University of Maryland. The results show that the mean absolute error of the SOH estimation using the proposed method does not exceed 0.72%, which is significantly superior to comparative models such as CNN-GRU, KELM, and SVM, demonstrating higher accuracy and reliability compared with other models. Full article

As industrialization and economic growth accelerate, PM_2.5 pollution has become a critical environmental concern. Predicting PM_2.5 concentration is challenging due to its nonlinear and complex temporal dynamics, limiting the accuracy and robustness of traditional machine learning models. To enhance prediction accuracy, this study focuses on Ma’anshan City, China and proposes a novel hybrid model (QMEWOA-QCAM-BiTCN-BiLSTM) based on an “optimization first, prediction later” approach. Feature selection using Pearson correlation and RFECV reduces model complexity, while the Whale Optimization Algorithm (WOA) optimizes model parameters. To address the local optima and premature convergence issues of WOA, we introduce a quantum-enhanced multi-strategy improved WOA (QMEWOA) for global optimization. A Quantum Causal Attention Mechanism (QCAM) is incorporated, leveraging Quantum State Mapping (QSM) for higher-order feature extraction. The experimental results show that our model achieves a MedAE of 1.997, MAE of 3.173, MAPE of 10.56%, and RMSE of 5.218, outperforming comparison models. Furthermore, generalization experiments confirm its superior performance across diverse datasets, demonstrating its robustness and effectiveness in PM_2.5 concentration prediction. Full article

Recently, a network based on selective state space models (SSMs), Mamba, has emerged as a research focus in hyperspectral image (HSI) classification due to its linear computational complexity and strong long-range dependency modeling capability. Originally designed for 1D causal sequence modeling, Mamba is challenging for HSI tasks that require simultaneous awareness of spatial and spectral structures. Current Mamba-based HSI classification methods typically convert spatial structures into 1D sequences and employ various scanning patterns to capture spatial dependencies. However, these approaches inevitably disrupt spatial structures, leading to ineffective modeling of complex spatial relationships and increased computational costs due to elongated scanning paths. Moreover, the lack of neighborhood spectral information utilization fails to mitigate the impact of spatial variability on classification performance. To address these limitations, we propose a novel model, Dual-Aware Discriminative Fusion Mamba (DADFMamba), which is simultaneously aware of spatial-spectral structures and adaptively integrates discriminative features. Specifically, we design a Spatial-Structure-Aware Fusion Module (SSAFM) to directly establish spatial neighborhood connectivity in the state space, preserving structural integrity. Then, we introduce a Spectral-Neighbor-Group Fusion Module (SNGFM). It enhances target spectral features by leveraging neighborhood spectral information before partitioning them into multiple spectral groups to explore relations across these groups. Finally, we introduce a Feature Fusion Discriminator (FFD) to discriminate the importance of spatial and spectral features, enabling adaptive feature fusion. Extensive experiments on four benchmark HSI datasets demonstrate that DADFMamba outperforms state-of-the-art deep learning models in classification accuracy while maintaining low computational costs and parameter efficiency. Notably, it achieves superior performance with only 30 training samples per class, highlighting its data efficiency. Our study reveals the great potential of Mamba in HSI classification and provides valuable insights for future research. Full article

Background and Aims: Ornidazole, a nitroimidazole antibiotic, is widely used for protozoal and anaerobic infections and is generally considered safe. However, ornidazole-induced liver injury (OILI) is an underrecognized yet potentially severe adverse reaction. This multicenter study aims to characterize the clinical features, histopathology, and outcomes of OILI to improve the awareness and management of this rare entity worldwide. Methods: We conducted a retrospective analysis of 101 patients with OILI from eight tertiary centers between 2006 and 2023. Cases were included based on liver enzyme elevations temporally linked to ornidazole and the exclusion of other causes. Causality was assessed using the Roussel Uclaf Causality Assessment Method (RUCAM) score. Clinical data, laboratory parameters, autoantibody profiles, histology, treatments, and outcomes were evaluated. Results: OILI was classified as highly probable in 42.6% of cases (n = 43), probable in 51.5% of cases (n = 52), and possible in 5.9% (n = 6) of cases. The predominant pattern was acute hepatocellular injury (83.2%) (n = 84). Autoimmune-like hepatitis occurred in 5% of cases (n = 5), with ANA positivity in 16.8% of cases (n = 17). Corticosteroids were used in 24.8% of cases (n = 25) and were associated with higher ANA positivity and a 20% (n = 5) relapse rate post-discontinuation. Recovery was achieved in 87.7% of cases (n = 88), while 7.9% of cases (n = 8) required liver transplantation and 4% (n = 4) died. Conclusions: Ornidazole can cause serious idiosyncratic liver injury, including autoimmune phenotypes, and should be considered in the differential diagnosis of acute hepatitis. Given the notable risk of liver failure and death, early recognition, drug discontinuation, and close monitoring are essential. In select cases, corticosteroids and plasmapheresis may be beneficial, though the evidence remains limited. Full article

Recent advances in hyperspectral image (HSI) classification have demonstrated the effectiveness of hybrid architectures that integrate convolutional neural networks (CNNs) and Transformers, leveraging CNNs for local feature extraction and Transformers for global dependency modeling. However, existing fusion approaches face three critical challenges: (1) insufficient synergy between spectral and spatial feature learning due to rigid coupling mechanisms; (2) high computational complexity resulting from redundant attention calculations; and (3) limited adaptability to spectral redundancy and noise in small-sample scenarios. To address these limitations, we propose HyperTransXNet, a novel CNN-Transformer hybrid architecture that incorporates adaptive spectral-spatial fusion. Specifically, the proposed HyperTransXNet comprises three key modules: (1) a Hybrid Spatial-Spectral Module (HSSM) that captures the refined local spectral-spatial features and models global spectral correlations by combining depth-wise dynamic convolution with frequency-domain attention; (2) a Mixture-of-Experts Routing (MoE-R) module that adaptively fuses multi-scale features by dynamically selecting optimal experts via Top-K sparse weights; and (3) a Spatial-Spectral Tokens Enhancer (SSTE) module that ensures causality-preserving interactions between spectral bands and spatial contexts. Extensive experiments on the Indian Pines, Houston 2013, and WHU-Hi-LongKou datasets demonstrate the superiority of HyperTransXNet. Full article

Parks play a crucial role in mitigating urban heat island effects, a key challenge for urban sustainability. Park cooling intensity (PCI) mechanisms across varying canopy-layer urban heat island (CUHI) gradients remain underexplored, particularly regarding interactions with meteorological, topographical, and socio-economic factors. According to the urban-suburban air temperature difference, this study classified the city into non-, weak, and strong CUHI regions. We integrated causal inference, machine learning and a geographical detector (Geodetector) to model and interpret PCI dynamics across CUHI gradients. The results reveal that surrounding impervious surface coverage is a universal driver of PCI by enhancing thermal contrast at park boundaries. However, the dominant drivers of PCI varied significantly across CUHI gradients. In non-CUHI regions, surrounding imperviousness dominated PCI and exhibited bilaterally enhanced interaction with intra-park patch density. Weak CUHI regions relied on intra-park green coverage with nonlinear synergies between water body proportion and park area. Strong CUHI regions involved systemic urban fabric influences mediated by surrounding imperviousness, evidenced by a validated causal network. Crucially, causal inference reduces model complexity by decreasing predictor counts by 79%, 25% and 71% in non-, weak and strong CUHI regions, respectively, while maintaining comparable accuracy to full-factor models. This outcome demonstrates the efficacy of causal inference in eliminating collinear metrics and spurious correlations from traditional feature selection, ensuring retained predictors reside within causal pathways and support process-based interpretability. Our study highlights the need for context-adaptive cooling strategies and underscores the value of integrating causal–statistical approaches. This framework provides actionable insights for designing climate-resilient blue–green spaces, advancing urban sustainability goals. Future research should prioritize translating causal diagnostics into scalable strategies for sustainable urban planning. Full article