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Abstract Background In large multiregional cohort studies, survival data is often collected at small geographical levels (such as counties) and aggregated at larger levels, leading to correlated patterns that are associated with location. Traditional studies typically analyze such data globally or locally by region, often neglecting the spatial information inherent in the data, which can introduce bias in effect estimates and potentially reduce statistical power. Method We propose a Geographically Weighted Accelerated Failure Time Model for spatial survival data to investigate spatial heterogeneity. We establish a weighting scheme and bandwidth selection based on quasi-likelihood information criteria. Theoretical properties of the proposed estimators are thoroughly examined. To demonstrate the efficacy of the model in various scenarios, we conduct a simulation study with different sample sizes and adherence to the proportional hazards assumption or not. Additionally, we apply the proposed method to analyze ovarian cancer survival data from the Surveillance, Epidemiology, and End Results cancer registry in the state of New Jersey. Results Our simulation results indicate that the proposed model exhibits superior performance in terms of four measurements compared to existing methods, including the geographically weighted Cox model, when the proportional hazards assumption is violated. Furthermore, in scenarios where the sample size per location is 20-25, the simulation data failed to fit the local model, while our proposed model still demonstrates satisfactory performance. In the empirical study, we identify clear spatial variations in the effects of all three covariates. Conclusion Our proposed model offers a novel approach to exploring spatial heterogeneity of survival data compared to global and local models, providing an alternative to geographically weighted Cox regression when the proportional hazards assumption is not met. It addresses the issue of certain counties' survival data being unable to fit the model due to limited samples, particularly in the context of rare diseases.

The rapid development of the global economy and population growth are accompanied by the production of numerous waste textiles. This leads to a waste of limited resources and serious environmental pollution problems caused by improper disposal. The rational recycling of wasted textiles and their transformation into high-value-added emerging products, such as smart wearable devices, is fascinating. Here, we propose a novel roadmap for turning waste cotton fabrics into three-dimensional elastic fiber-based thermoelectric aerogels by a one-step lyophilization process with decoupled self-powered temperature–compression strain dual-parameter sensing properties. The thermoelectric aerogel exhibits a fast compression response time of 0.2 s, a relatively high Seebeck coefficient of 43 μV·K−1, and an ultralow thermal conductivity of less than 0.04 W·m−1·K−1. The cross-linking of trimethoxy(methyl)silane (MTMS) and cellulose endowed the aerogel with excellent elasticity, allowing it to be used as a compressive strain sensor for guessing games and facial expression recognition. In addition, based on the thermoelectric effect, the aerogel can perform temperature detection and differentiation in self-powered mode with the output thermal voltage as the stimulus signal. Furthermore, the wearable system, prepared by connecting the aerogel-prepared array device with a wireless transmission module, allows for temperature alerts in a mobile phone application without signal interference due to the compressive strains generated during gripping. Hence, our strategy is significant for reducing global environmental pollution and provides a revelatory path for transforming waste textiles into high-value-added smart wearable devices.

Abstract Background Mediation analysis is a powerful tool to identify factors mediating the causal pathway of exposure to health outcomes. Mediation analysis has been extended to study a large number of potential mediators in high-dimensional data settings. The presence of confounding in observational studies is inevitable. Hence, it’s an essential part of high-dimensional mediation analysis (HDMA) to adjust for the potential confounders. Although the propensity score (PS) related method such as propensity score regression adjustment (PSR) and inverse probability weighting (IPW) has been proposed to tackle this problem, the characteristics with extreme propensity score distribution of the PS-based method would result in the biased estimation. Methods In this article, we integrated the overlapping weighting (OW) technique into HDMA workflow and proposed a concise and powerful high-dimensional mediation analysis procedure consisting of OW confounding adjustment, sure independence screening (SIS), de-biased Lasso penalization, and joint-significance testing underlying the mixture null distribution. We compared the proposed method with the existing method consisting of PS-based confounding adjustment, SIS, minimax concave penalty (MCP) variable selection, and classical joint-significance testing. Results Simulation studies demonstrate the proposed procedure has the best performance in mediator selection and estimation. The proposed procedure yielded the highest true positive rate, acceptable false discovery proportion level, and lower mean square error. In the empirical study based on the GSE117859 dataset in the Gene Expression Omnibus database using the proposed method, we found that smoking history may lead to the estimated natural killer (NK) cell level reduction through the mediation effect of some methylation markers, mainly including methylation sites cg13917614 in CNP gene and cg16893868 in LILRA2 gene. Conclusions The proposed method has higher power, sufficient false discovery rate control, and precise mediation effect estimation. Meanwhile, it is feasible to be implemented with the presence of confounders. Hence, our method is worth considering in HDMA studies.

Background Ischemic stroke (IS) accounts large amount of stroke incidence. The aim of this study was to discover the risk and prognostic factors that affecting the occurrence of IS in hypertensive patients. Method Study data were obtained from the Medical Information Mart for Intensive Care (MIMIC)-IV database. To avoid biased factors selection process, several approaches were studied including logistic regression, elastic net regression, random forest, correlation analysis, and multifactor logistic regression methods. And seven different machine-learning methods are used to construct predictive models. The performance of the developed models was evaluated using AUC (Area Under the Curve), prediction accuracy, precision, recall, F1 score, PPV (Positive Predictive Value) and NPV (Negative Predictive Value). Interaction analysis was conducted to explore potential relationships between influential factors. Results The study included 92,514 hypertensive patients, of which 1746 hypertensive patients experienced IS. The Gradient Boosted Decision Tree (GBDT) model outperformed the other prediction model terms of prediction accuracy and AUC values in both ischemic and prognosis cases. By using the SHapley Additive exPlanations (SHAP), we found that a range of factors and corresponding interactions between factors are important risk factors for IS and its prognosis in hypertensive patients. Conclusion The study identified factors that increase the risk of IS and poor prognosis in hypertensive patients, which may provide guidance for clinical diagnosis and treatment.

Flexible thermoelectric materials play an important role in smart wearables, such as wearable power generation, self-powered sensing, and personal thermal management. However, with the rapid development of Internet of Things (IoT) and artificial intelligence (AI), higher standards for comfort, multifunctionality, and sustainable operation of wearable electronics have been proposed, and it remains challenging to meet all the requirements of currently reported thermoelectric devices. Herein, we present a multifunctional, wearable, and wireless sensing system based on a thermoelectric knitted fabric with over 600 mm·s−1 air permeability and a stretchability of 120%. The device coupled with a wireless transmission system realizes self-powered monitoring of human respiration through an mobile phone application (APP). Furthermore, an integrated thermoelectric system was designed to combine photothermal conversion and passive radiative cooling, enabling the characteristics of being powered by solar-driven in-plane temperature differences and monitoring outdoor sunlight intensity through the APP. Additionally, we decoupled the complex signals of resistance and thermal voltage during deformation under solar irradiation based on the anisotropy of the knitted fabrics to enable the device to monitor and optimize the outdoor physical activity of the athlete via the APP. This novel thermoelectric fabric-based wearable and wireless sensing platform has promising applications in next-generation smart textiles.

Abstract Background Outliers, data points that significantly deviate from the norm, can have a substantial impact on statistical inference and provide valuable insights in data analysis. Multiple methods have been developed for outlier detection, however, almost all available approaches fail to consider the spatial dependence and heterogeneity in spatial data. Spatial data has diverse formats and semantics, requiring specialized outlier detection methodology to handle these unique properties. For now, there is limited research exists on robust spatial outlier detection methods designed specifically under the spatial error model (SEM) structure. Method We propose the Spatial-Θ-Iterative Procedure for Outlier Detection (Spatial-Θ-IPOD), which utilizes a mean-shift vector to identify outliers within the SEM. Our method enables an effective detection of spatial outliers while also providing robust coefficient estimates. To assess the performance of our approach, we conducted extensive simulations and applied it to a real-world empirical study using life expectancy data from multiple countries. Results Simulation results showed that the masking and JD (Joint Detection) indicators of our Spatial-Θ-IPOD method outperformed several commonly used methods, even in high-dimensional scenarios, demonstrating stable performance. Conversely, the Θ-IPOD method proved to be ineffective in detecting outliers when spatial correlation was present. Moreover, our model successfully provided reliable coefficient estimation alongside outlier detection. The proposed method consistently outperformed other models (both robust and non-robust) in most cases. In the empirical study, our proposed model successfully detected outliers and provided valuable insights in the modeling process. Conclusions Our proposed Spatial-Θ-IPOD offers an effective solution for detecting spatial outliers for SEM while providing robust coefficient estimates. Notably, our approach showcases its relative superiority even in the presence of high leverage points. By successfully identifying outliers, our method enhances the overall understanding of the data and provides valuable insights for further analysis.

IntroductionLung image segmentation plays an important role in computer-aid pulmonary disease diagnosis and treatment.MethodsThis paper explores the lung CT image segmentation method by generative adversarial networks. We employ a variety of generative adversarial networks and used their capability of image translation to perform image segmentation. The generative adversarial network is employed to translate the original lung image into the segmented image.ResultsThe generative adversarial networks-based segmentation method is tested on real lung image data set. Experimental results show that the proposed method outperforms the state-of-the-art method.DiscussionThe generative adversarial networks-based method is effective for lung image segmentation.

Pseudomonas fragi (P. fragi) is usually detected in low-temperature meat products, and seriously threatens food safety and human health. Therefore, the study investigated the antibacterial mechanism of linalool against P. fragi from membrane damage and metabolic disruption. Results from field-emission transmission electron microscopy (FETEM) and atomic force microscopy (AFM) showed that linalool damage membrane integrity increases surface shrinkage and roughness. According to Fourier transform infrared (FTIR) spectra results, the components in the membrane underwent significant changes, including nucleic acid leakage, carbohydrate production, protein denaturation and modification, and fatty acid content reduction. The data obtained from amino acid metabolomics indicated that linalool caused excessive synthesis and metabolism of specific amino acids, particularly tryptophan metabolism and arginine biosynthesis. The reduced activities of glucose 6-phosphate dehydrogenase (G6PDH), malate dehydrogenase (MDH), and phosphofructokinase (PFK) suggested that linalool impair the respiratory chain and energy metabolism. Meanwhile, genes encoding the above enzymes were differentially expressed, with pfkB overexpression and zwf and mqo downregulation. Furthermore, molecular docking revealed that linalool can interact with the amino acid residues of G6DPH, MDH and PFK through hydrogen bonds. Therefore, it is hypothesized that the mechanism of linalool against P. fragi may involve cell membrane damage (structure and morphology), disturbance of energy metabolism (TCA cycle, EMP and HMP pathway) and amino acid metabolism (cysteine, glutamic acid and citrulline). These findings contribute to the development of linalool as a promising antibacterial agent in response to the food security challenge.