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Abstract Background Due to a special hemodynamic feature, pulmonary vascular disease in pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD) has two stages: reversible and irreversible. So far, the mechanism involved in the transition from reversible to irreversible stage is elusive. Moreover, no recognized and reliable assessments to distinguish these two stages are available. Furthermore, we found that compared with control and reversible PAH, thrombospondin-4 (THBS4) was significantly upregulated in irreversible group by bioinformatic analysis. Hence, we further verify and investigate the expression and role of THBS4 in PAH-CHD. Methods We established the monocrotaline plus aorto-cava shunt-induced (MCT-AV) rat model. We measured the expression of THBS4 in lung tissues from MCT-AV rats. Double immunofluorescence staining of lung tissue for THBS4 and α-SMA (biomarker of smooth muscle cells) or vWF (biomarker of endothelial cells) to identify the location of THBS4 in the pulmonary artery. Primary pulmonary artery smooth muscle cells (PASMCs) were cultivated, identified, and used in this study. THBS4 was inhibited and overexpressed by siRNA and plasmid, respectively, to explore the effect of THBS4 on phenotype transformation, proliferation, apoptosis, and migration of PASMCs. The effect of THBS4 on pulmonary vascular remodeling was evaluated in vivo by adeno-associated virus which suppressed THBS4 expression. Circulating level of THBS4 in patients with PAH-CHD was measured by ELISA. Results THBS4 was upregulated in the lung tissues of MCT-AV rats, and was further upregulated in severe pulmonary vascular lesions. And THBS4 was expressed mainly in PASMCs. When THBS4 was inhibited, contractile markers α-SMA and MYH11 were upregulated, while the proliferative marker PCNA was decreased, the endothelial-mensenchymal transition marker N-cad was downregulated, proapototic marker BAX was increased. Additionally, proliferation and migration of PASMCs was inhibited and apoptosis was increased. Conversely, THBS4 overexpression resulted in opposite effects. And the impact of THBS4 on PASMCs was probably achieved through the regulation of the PI3K/AKT pathway. THBS4 suppression attenuated pulmonary vascular remodeling. Furthermore, compared with patients with simple congenital heart disease and mild PAH-CHD, the circulating level of THBS4 was higher in patients with severe PAH-CHD. Conclusions THBS4 is a promising biomarker to distinguish reversible from irreversible PAH-CHD before repairing the shunt. THBS4 is a potential treatment target in PAH-CHD, especially in irreversible stage.

ABSTRACTDigital twin railway is a pivotal foundation for the intelligent construction and maintenance of railway engineering projects within extensive open spaces. Its essence is the integrated representation and association management of multi-granularity spatiotemporal data, executable analysis models, and professional knowledge. These elements are characterized by the prominent characteristics of multi-source, heterogeneity, and massive volume. However, current decentralized and independent management strategies often neglect the dynamic coupling relationships between them, and numerous multi-path joins and conversion aggregation operations exist across various spatial scale applications. Consequently, this results in challenges such as the inability to dynamically couple data-model-knowledge and conduct global association retrieval, thereby limiting the potential for real-time analysis and intelligent application capabilities. To address these problems, we first constructed a tripartite graph model ([Formula: see text]) that explicitly associates temporal, spatial, and interactive relationships. Subsequently, an association management architecture was proposed, accompanied by a global association graph index ([Formula: see text]) and a global-local indexing mechanism. Finally, a prototype system for railway data-model-knowledge association management was developed. The effectiveness of the distributed association management method was demonstrated by employing a case study of high-temperature safety risk analysis in railway tunnel engineering with multi-physics field coupling.

Atrial septal aneurysm (ASA) is defined as excursion of the atrial septum exceeding 10 mm beyond the atrial septum into the right or left atrium, or a combined total excursion of 15 mm on the right and left sides during the cardiac cycle. According to previous studies, 20–40% of patent foramen ovale (PFO) cases are accompanied by ASAs. ASA is associated with the presence of PFO, left atrial dysfunction, cryptogenic stroke, migraine, and arterial embolism, thus making closure of PFO in patients with concomitant ASA necessary but challenging. The anatomy of ASAs associated with PFO has crucial effects on complications after the closure procedure; therefore, several factors must be considered. Herein, we review the clinical implications of concomitant PFO and ASA; discuss the complications occurring after the closure procedure; and provide practical guidance for the closure of concomitant PFO and ASA.

Reliable assessment of landslide susceptibility in broad areas of terrain remains challenging due to complex topography and poor representation of randomly selected negative samples. Assessment in broad areas is now primarily based on grid units, which do not have a clear physical meaning like slope units, and their accuracy is not ideal. Nevertheless, the large amount of manual editing, due to the incorrectly generated horizontal and vertical lines during slope unit partitioning, limits using slope units for rapid assessment over large areas. Hence, this paper proposes a reliable susceptibility assessment approach to solve this problem based on optimal slope units and negative samples involving prior knowledge. Precisely, an algorithm to automatically extract slope units is designed to eliminate fragmented and erroneous units. Second, a samples labeling index (SLI) is defined based on the certainty factors model to select negative samples reasonably. Sichuan Province, China is selected for experimental analysis, with the results demonstrate that the optimized slope unit and the negative samples selection strategy consider prior knowledge achieve better results in the random forest model, support vector machine model, and artificial neural network model. In particular, the composite performance index AUC of artificial neural network model improved from 0.81 to 0.90.

IntroductionCurrent guidelines recommended patent foramen ovale (PFO) occlusion as the preferred treatment for PFO-related cryptogenic stroke (CS); however, finding the causative foramen ovale remains challenging. This study aimed to identify predictors and establish a scoring system by assessing PFO morphology and stroke-related factors.MethodsBased on a prospective multicenter registered clinical trial, we compared data mainly derived from transesophageal echocardiography (TEE) and clinical history in patients with PFO-related CS and those without CS (non-CS) with incidental PFO. Subsequently, we explored independent predictors using logistic analysis, established a scoring system based on the results, and finally evaluated the scoring system using receiver operating characteristic (ROC) analysis and internal validation.Results75 patients with PFO-related CS and 147 non-CS patients were enrolled. Multivariate logistic analysis showed that the change in PFO height, large PFO, atrial septal aneurysm (ASA), and sustained right-to-left shunt (RLS) had independent relationships with CS. Based on the odds ratio value of each independent factor, a scoring system was built: change in PFO height ≥ 1.85 mm (3 points), large PFO (2 points), ASA (5 points), sustained RLS (2 points). 0–2 points correspond to low-risk PFO, 3–5 points medium-risk PFO, and 7–12 points high-risk PFO. ROC analysis showed an area under the curve of 0.80 to predict CS. The proportion of patients with CS is increasing based on these points.ConclusionsOur study screened out the change in PFO height as an independent predictor of CS. A simple and convenient scoring system can provide constructive guidance for identifying whether the PFO is causal and consequently selecting patients more likely to benefit from closure.

Predicting a landslide susceptibility map (LSM) is essential for risk recognition and disaster prevention. Despite the successful application of data-driven approaches for LSM prediction, most methods generally apply a single global model to predict the LSM for an entire target region. However, in large-scale areas with significant environmental change, various parts of the region hold different landslide-inducing environments, and therefore, should be predicted with respective models. This study first segmented target scenarios into blocks for individual analysis. Then, the critical problem is that in each block with limited samples, conducting training and testing a model is impossible for a satisfactory LSM prediction, especially in dangerous mountainous areas where landslide surveying is costly. To solve the problem, we trained an intermediate representation by the meta-learning paradigm, which is superior for capturing information valuable for few-shot adaption from LSM tasks. We hypothesized that there are more general and vital concepts concerning landslide causes and are sensitive to variations in input features. Thus, we can quickly adapt the models from the intermediate representation for different blocks or even unseen tasks using very few exemplar samples. Experimental results on the two study areas demonstrated the validity of our block-wise analysis in large scenarios and revealed the top few-shot adaption performances of the proposed methods.

Direction of arrival (DOA) estimation for non-Gaussian signals using three-level nested array (THL-NA) is investigated in this paper. Motivation from larger consecutive degree of freedom (DOF) and array aperture, the THL-NA is proposed, which can take full advantages of the consecutive coarrays of TL-NA and has the closed-form expression of DOF. Specifically, firstly, the array aperture is expanded by the second order sum coarray (2-SC) of the proposed array, secondly, the nested relationship between subarrays is employed to obtain the difference coarray of 2-SC (2-DCSC), finally, a consecutive virtual array with large array aperture is obtained. Besides, a successive SS-MUSIC algorithm is proposed, which employs the spatial smoothing estimating signal parameter via rotational invariance techniques (SS-ESPRIT) algorithm and partial spectrum searching multiple signal classification (PSS-MUSIC) to obtain initial estimations and fine estimations, respectively, resulting in a better balance between computational complexity and estimation accuracy.

In this paper, we investigate the problem of direction of arrival (DOA) and polarization estimation for non-Gaussian signal in polarization-sensitive augmented coprime array. Instead of the second-order cumulant statistics, the fourth-order cumulant statistics of the received signals are used for parameter estimation because they can detect more information of the non-Gaussian signal. First, polarization-sensitive augmented coprime array is designed, where each sensor element is equipped with a pair of orthogonal electric dipoles. Furthermore, a low-complexity reduced-dimensional Capon algorithm which uses the fourth-order cumulant of the received array signal is proposed for DOA and polarization estimation. Only one-dimensional peak search is required by reconstructing peak search function. Theoretical analysis has proven the effectiveness of the algorithm, and simulation results demonstrate that the proposed fourth-order cumulant reduced-dimensional Capon algorithm outperforms the other algorithms.

Objectives: Idiopathic pulmonary arterial hypertension (IPAH) is a rare but severe lung disorder, which may lead to heart failure and early mortality. However, little is known about the etiology of IPAH. Thus, the present study aimed to establish the differentially expressed genes (DEGs) between IPAH and normal tissues, which may serve as potential prognostic markers in IPAH. Furthermore, we utilized a versatile computational method, CIBERSORT to identify immune cell infiltration characteristics in IPAH.Materials and Methods: The GSE117261 and GSE48149 datasets were obtained from the Gene Expression Omnibus database. The GSE117261 dataset was adopted to screen DEGs between IPAH and the control groups with the criterion of |log2 fold change| ≥ 1, adjusted P < 0.05, and to further explore their potential biological functions via Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes Pathway analysis, and Gene Set Enrichment Analysis. Moreover, the support vector machine (SVM)-recursive feature elimination and the least absolute shrinkage and selection operator regression model were performed jointly to identify the best potential biomarkers. Then we built a regression model based on these selected variables. The GSE48149 dataset was used as a validation cohort to appraise the diagnostic efficacy of the SVM classifier by receiver operating characteristic (ROC) analysis. Finally, immune infiltration was explored by CIBERSORT in IPAH. We further analyzed the correlation between potential biomarkers and immune cells.Results: In total, 75 DEGs were identified; 40 were downregulated, and 35 genes were upregulated. Functional enrichment analysis found a significantly enrichment in heme binding, inflammation, chemokines, cytokine activity, and abnormal glycometabolism. HBB, RNASE2, S100A9, and IL1R2 were identified as the best potential biomarkers with an area under the ROC curve (AUC) of 1 (95%CI = 0.937–1.000, specificity = 100%, sensitivity = 100%) in the discovery cohort and 1(95%CI = 0.805–1.000, specificity = 100%, sensitivity = 100%) in the validation cohort. Moreover, immune infiltration analysis by CIBERSORT showed a higher level of CD8+ T cells, resting memory CD4+ T cells, gamma delta T cells, M1 macrophages, resting mast cells, as well as a lower level of naïve CD4+ T cells, monocytes, M0 macrophages, activated mast cells, and neutrophils in IPAH compared with the control group. In addition, HBB, RNASE2, S100A9, and IL1R2 were correlated with immune cells.Conclusion:HBB, RNASE2, S100A9, and IL1R2 were identified as potential biomarkers to discriminate IPAH from the control. There was an obvious difference in immune infiltration between patient with IPAH and normal groups.

Landslide deformation is the most intuitive and effective characterization of the evolution of landslides and reveals the inherent risk of landslides. Considering the inadequacy of existing deformation monitoring data for early warnings regarding landslide hazards, resulting in insufficient disaster response times, this paper proposes a time-domain correlation model. Based on the process of rainfall-induced landslide deformation, the time-domain correlation between regional rainfall and landslide deformation is proposed, which can reflect the temporal characteristics of landslide responses to rainfall, and the calculation method of the impulse response function is designed to quantitatively model and calculate the correlation. Furthermore, rainfall monitoring data are used to optimize the landslide deformation monitoring indicator system for early warnings regarding landslide instability. The feasibility of the method proposed in this paper is verified by analyzing the historical monitoring data of rainfall and landslide deformation at nine typical locations in five landslide hazard areas in Fengjie County, Chongqing city. (1) The correlation models for the XP landslide involve a delayed rainfall response time of 5 for deformation, respectively, as well as the existence of a cycle of 55–56 days, which means that the above area can advance the landslide warning by one lag time based on the cycle; (2) The correlation models for the OT landslide show consistent correlations under a 48–50-day cycle, which means that the deformation in the above areas can be predicted based on rainfall accumulation. (3) The HJWC landslide presents a turbulence correlation, which means that other monitoring data need to be supplemented and analyzed.

The features of quasi-stationary signals (QSS) are considered to be in a direct position determination (DPD) framework, and a real-valued DPD algorithm of QSS for nested arrays is proposed. By stacking the vectorization form of the signal’s covariance for different frames and further eliminating noise, a new noise-eliminated received signal matrix is obtained first. Then, the combination of the Khatri–Rao subspace method and subspace data fusion method was performed to form the cost function. High complexity can be reduced by matrix reconstruction, including the modification of the dimension-reduced matrix and unitary transformation. Ultimately, the advantage of lower complexity, compared with the previous algorithm, is verified by complexity analysis, and the superiority over the existing algorithms, in terms of the maximum number of identifiable sources, estimation accuracy, and resolution, are corroborated by some simulation results.

In recent years, direct position determination (DPD) with multiple arrays for non-circular (NC) signals is a hot topic to research. Conventional DPD techniques with spectral peak search methods have high computational complexity and are sensitive to the locations of the observation stations. Besides, there will be loss when the signal propagates in the air, which leads to different received signal-to-noise ratios (SNRs) for each observation station. To attack the problems mentioned above, this paper derives direct position determination of non-circular sources for multiple arrays via weighted Euler estimating signal parameters viarotational invariance techniques (ESPRIT) data fusion (NC-Euler-WESPRIT) method. Firstly, elliptic covariance information of NC signals and Euler transformation are used to extend the received signal. Secondly, ESPRIT is applied to avoid the high-dimensional spectral function search problem of each observation station. Then, we combine the information of all observation stations to construct a spectral function without complex multiplication to reduce the computational complexity. Finally, the data of each observation station is weighted to compensate for the projection error. The consequence of simulation indicates that the proposed NC-Euler-WESPRIT algorithm not only improves the estimation performance, but also greatly reduces the computational complexity compared with subspace data fusion (SDF) technology and NC-ESPRIT algorithm.

In this study, a magnetic solid-phase extraction (MSPE) method coupled with High-Performance Liquid Chromatography Mass Spectrometry (HPLC–MS/MS) for the determination of illegal basic dyes in food samples was developed and validated. This method was based on Magnetic sulfonated reduced graphene oxide (M-S-RGO), which was sensitive and selective to analytes with structure of multiaromatic rings and negatively charged ions. Several factors affecting MSPE efficiency such as pH and adsorption time were optimized. Under the optimum conditions, the calibration curves exhibited good linearity, ranging from 5 to 60 µg/g with correlation coefficients >0.9950. The limits of detection of 16 basic dyes were in the range of 0.01–0.2 µg/L. The recoveries ranged from 70% to 110% with RSD% < 10%. The results indicate that M-S-RGO is an efficient and selective adsorbent for the extraction and cleanup of basic dyes. Due to the MSPE procedures, matrix effect and interference were eliminated in the analysis of HPLC–MS/MS without the matrix-matched standards. Thus, validation data showed that the proposed MSPE–HPLC–MS/MS method was rapid, efficient, selective, and sensitive for the determination of illegal basic dyes in foods.

Automatic road extraction from remote-sensing imagery plays an important role in many applications. However, accurate and efficient extraction from very high-resolution (VHR) images remains difficult because of, for example, increased data size and superfluous details, the spatial and spectral diversity of road targets, disturbances (e.g., vehicles, shadows of trees, and buildings), the necessity of finding weak road edges while avoiding noise, and the fast-acquisition requirement of road information for crisis response. To solve these difficulties, a two-stage method combining edge information and region characteristics is presented. In the first stage, convolutions are executed by applying Gabor wavelets in the best scale to detect Gabor features with location and orientation information. The features are then merged into one response map for connection analysis. In the second stage, highly complete, connected Gabor features are used as edge constraints to facilitate stable object segmentation and limit region growing. Finally, segmented objects are evaluated by some fundamental shape features to eliminate nonroad objects. The results indicate the validity and superiority of the proposed method to efficiently extract accurate road targets from VHR remote-sensing images.