Abstract
BackgroundTobacco smoke is the main cause of preventable mortality worldwide. Smoking increases the risk of developing many diseases and has been proposed as an aging accelerator. Yet, the molecular mechanisms driving smoking-related health decline and aging acceleration in most tissues remain unexplored.MethodsHere, we use data from the Genotype-Tissue Expression Project (GTEx) to perform a characterization of the effect of cigarette smoking across human tissues. We perform a multi-tissue analysis across 46 human tissues. Our multi-omics characterization includes analysis of gene expression, alternative splicing, DNA methylation, and histological alterations. We further analyze ex-smoker samples to assess the reversibility of these molecular alterations upon smoking cessation.ResultsWe show that smoking impacts tissue architecture and triggers systemic inflammation. We find that in many tissues, the effects of smoking significantly overlap those of aging. Specifically, both age and smoking upregulate inflammatory genes and drive hypomethylation at enhancers (odds ratio (OR) = 2). In addition, we observe widespread smoking-driven hypermethylation at target regions of the Polycomb repressive complex (OR = 2), which is a well-known aging effect. Smoking-induced epigenetic changes overlap causal aging CpGs, suggesting that these methylation changes may directly mediate the aging acceleration observed in smokers. Finally, we find that smoking effects that are shared with aging are more persistent over time.ConclusionOverall, our multi-tissue and multi-omic analysis of the effects of cigarette smoking provides an extensive characterization of the impact of tobacco smoke across tissues and unravels the molecular mechanisms driving smoking-induced tissue homeostasis decline and aging acceleration.

Abstract

Abstract
Effective selection of forecasting algorithms for time series data is a challenge in machine learning, impacting both predictive accuracy and efficiency. Metalearning, using features extracted from time series, offers a strategic approach to optimize algorithm selection. The utility of this approach depends on the amount of information the features contain about the behavior of the algorithms. Although there are several methods for systematic time series feature extraction, they have never been compared. This paper empirically analyzes the performance of each feature extraction method for algorithm selection and its impact on forecasting accuracy. Our study reveals that TSFRESH, TSFEATURES, and TSFEL exhibit comparable performance at algorithm selection accuracy, adeptly capturing time series characteristics essential for accurate algorithm selection. In contrast, Catch22 is found to be less effective for this purpose. In particular, TSFEL is identified as the most efficient method, balancing dimensionality and predictive performance. These findings provide insights for enhancing forecasting accuracy and efficiency through judicious selection of meta-feature extractors. © 2025 Elsevier B.V., All rights reserved.

Abstract
Predictive Maintenance (PdM) aims to prevent failures through early detection, yet lacks explainability to support decision-making. Current PdM models often identify failures, but fail to explain their root causes, especially in real-world scenarios, with complex and limited labeled data. This study proposes an interpretable framework that combines LSTM-based Anomaly Detection with a dual-layered Root Cause Analysis (RCA) based on SHAP attributions. Applied to a real-world dataset, the method detects degradation transitions, tracks failure patterns over time, and provides interpretable information without explicit root cause labels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract

Abstract
Detecting anomalies in Waste transportation networks is vital for uncovering illegal or unsafe activities, that can have serious environmental and regulatory consequences. Identifying anomalies in such networks presents a significant challenge due to the limited availability of labeled data and the subtle nature of illicit activities. Moreover, traditional anomaly detection methods relying solely on individual transaction data may overlook deeper, network-level irregularities that arise from complex interactions between entities, especially in the absence of labeled data. This study explores anomaly detection in a waste transport network using unsupervised learning, enhanced by limited supervision and enriched with network structure information. Initially, unsupervised models like Isolation Forest, K-Means, LOF, and Autoencoders were applied using statistical and graph-based features. These models detected outliers without prior labels. Later, information on a few confirmed anomalous users enabled weak supervision, guiding feature selection through statistical tests like Kolmogorov-Smirnov and Anderson-Darling. Results show that models trained on a reduced, graph-focused feature set improved anomaly detection, particularly under extreme class imbalance. Isolation Forest notably ranked known anomalies highly. Ego network visualizations supported these findings, demonstrating the value of integrating structural features and limited labels for identifying subtle, relational anomalies.