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Background Accumulating evidences show that long noncoding RNAs (lncRNA) play essential roles in the development and progression of various malignancies. However, their functions remains poorly understood and many lncRNAs have not been defined in colorectal cancer (CRC). In this study, we investigated the role of DLEU1 in CRC. Methods Quantitative real-time PCR was used to detect the expression of DLEU1 and survival analysis was adopted to explore the association between DLEU1 expression and the prognosis of CRC patients. CRC cells were stably transfected with lentivirus approach and cell proliferation, migration, invasion and cell apoptosis, as well as tumorigenesis in nude mice were performed to assess the effects of DLEU1 in BCa. Biotin-coupled probe pull down assay, RNA immunoprecipitation and Fluorescence in situ hybridization assays were conducted to confirm the relationship between DLEU1 and SMARCA1. Results Here we revealed that DLEU1 was crucial for activation of KPNA3 by recruiting SMARCA1, an essential subunit of the NURF chromatin remodeling complex, in CRC. DLEU1 was indispensible for the deposition of SMARCA1 at the promoter of KPNA3 gene. Increased expression of DLEU1 and KPNA3 was observed in human CRC tissues. And higher expression of DLEU1 or KPNA3 in patients indicates lower survival rate and poorer prognosis. DLEU1 knockdown remarkably inhibited CRC cell proliferation, migration and invasion in vitro and in vivo while overexpressing KPNA3 in the meantime reversed it. Conclusions Our results identify DLEU1 as a key regulator by a novel DLEU1/SMARCA1/KPNA3 axis in CRC development and progression, which may provide a potential biomarker and therapeutic target for the management of CRC.

Infrasound signal classification represents a critical challenge that demands immediate attention. Feature extraction stands as the core concept for enhancing classification accuracy in infrasound signal processing. However, existing feature extraction methodologies fail to meet the requirements for long-distance detection scenarios. To address these limitations, this study proposes a novel classification framework based on the spatiotemporal characteristics of infrasound signals. The proposed framework incorporates advanced signal processing techniques, signal enhancement algorithms, and deep learning architectures to achieve precise classification of infrasound signals. This paper designs three sets of comparative experiments, and the results demonstrate that the proposed method achieves a classification accuracy rate of 83.9% on chemical explosion and seismic infrasound datasets, outperforming eight other comparative classification methods. This substantiates the efficacy of the proposed approach.

Many urban rail systems operate near capacity given the rapid increase in passenger demand, and unplanned disruptions are unavoidable. From a passenger perspective, the duration of trip delays is a major concern, and passenger trip delays may be longer than the train delays. Several studies have focused on predicting train delays, but the research on the duration of the disruption impacts on passenger trips is limited given that the duration is not observed directly. This paper proposes a probabilistic method to estimate the disruption impact duration using smartcard data, explores statistical and machine learning models to predict the duration of impacts on passengers, and identifies influencing factors including incident characteristics, operating conditions, infrastructure, external factors, and demand. The results highlight that prediction accuracies are acceptable for multiple linear regression, accelerated failure time, and random forest models. Disruptions caused by power failures have longer impact durations than other causes, followed by platform screen doors. The fixed block signaling system leads to a larger disruption duration than the moving block system. The study provides, for the first time, a data-driven approach to understanding the duration of the impact of disruptions on passenger trips using smartcard data which can facilitate timely and informed decision-making under unplanned disruptions.

The classification and distinction between nuclear explosions and natural earthquake events are essential to the Comprehensive Nuclear Test Ban Treaty. Nuclear explosion data are lacking; thus, classification problems must be studied in small sample scenarios. The classification problem of the eXtreme Gradient Boosting (XGBoost) model in one small sample scenario is examined using the sparrow search algorithm (SSA) algorithm to optimize the key hyperparameters of the model automatically. The shortcomings of SSA are addressed by using a Gaussian chaotic mapping method, introducing a population proportion dynamic adjustment strategy, and proposing a step-size adjustment factor for modification. The problem of the uneven initial population distribution is addressed by constructing the (modified SSA) MSSA–XGBoost classification model, thereby reducing population diversity and affecting the convergence speed of the algorithm. The fixed proportion problem of the sparrow population, which easily falls into the local optimal solution, is solved using the aforementioned approach. The fixed update step position of the discoverer is also resolved, thus limiting the global search capability and optimization efficiency of the algorithm and realizing the independent optimization of three important hyperparameters. Furthermore, artificial feature extraction can be avoided using this approach, and the number of iterations, maximum tree depth, and learning rate can be automatically optimized, achieving excellent results in small sample seismic event classification. Experimental results reveal that the classification accuracy of the MSSA–XGBoost model is 96.37%, demonstrating its superiority to the original model (93.47%) as well as to the support vector machine and convolutional neural network. Meanwhile, a nearly 30% improvement is observed in computational efficiency.

Simulating natural ants’ foraging behavior, the ant colony optimization (ACO) algorithm performs excellently in combinational optimization problems, for example the traveling salesman problem and the quadratic assignment problem. However, the ACO is seldom used to inverted for gravitational and magnetic data. On the basis of the continuous and multi-dimensional objective function for potential field data optimization inversion, we present the node partition strategy ACO (NP-ACO) algorithm for inversion of model variables of fixed shape and recovery of physical property distributions of complicated shape models. We divide the continuous variables into discrete nodes and ants directionally tour the nodes by use of transition probabilities. We update the pheromone trails by use of Gaussian mapping between the objective function value and the quantity of pheromone. It can analyze the search results in real time and promote the rate of convergence and precision of inversion. Traditional mapping, including the ant-cycle system, weaken the differences between ant individuals and lead to premature convergence. We tested our method by use of synthetic data and real data from scenarios involving gravity and magnetic anomalies. The inverted model variables and recovered physical property distributions were in good agreement with the true values. The ACO algorithm for binary representation imaging and full imaging can recover sharper physical property distributions than traditional linear inversion methods. The ACO has good optimization capability and some excellent characteristics, for example robustness, parallel implementation, and portability, compared with other stochastic metaheuristics.

Background/Objectives: The polyphenol resveratrol (Rev) has been found to exhibit various beneficial effects including prevention of pulmonary arterial hypertension (PAH). The present study was designed to investigate the action and potential mechanism of Rev on PAH, focusing on the role of SIRT1 (Silent Information Regulator 1) in apoptosis of pulmonary artery smooth muscle cells (PASMCs). Methods: PAH rats were established by exposure to hypoxia for 21 days. Rev and SRT1720 (a selective SIRT1 activator) were used to reverse PAH by gavaging rats. PASMCs were confronted with hypoxia for 24 h or 48 h and were then treated with Rev or SRT1720 in vitro. Western blot was performed to detect the protein expression of SIRT1. CCK-8 and scratch wound experiments were carried out to verify cell proliferation. In addition, the TUNEL positive assay and flow cytometry assay were used to measure PASMC apoptosis. Mitochondrial permeability transition (mPT) was identified by confocal microscopy. Right ventricular systolic pressure (RVSP) was determined with a Gould pressure transducer, and right ventricular hypertrophy (RVH) was determined by weighing the cardiac muscle. Results: We demonstrated that Rev could reverse the remodelling of the pulmonary vasculature, thus contributing to alleviating the severity of PAH. Down-regulation of SIRT1 was observed in PAH, but administration of Rev had no obvious effect on the protein expression of SIRT1. In addition, Rev could induce mitochondrial swelling and nuclear pyknosis, leading to small, dense, and dysmorphic mitochondria in rats exposed to hypoxia alone. Rev treatment inhibited PASMC proliferation in a dose-dependent manner in vitro. Incubation with SRT1720, a specific activator of SIRT1, significantly retarded PASMC proliferation and promoted PASMC apoptosis in vitro. The mechanism could be associated with inducing mPT damage in PASMCs. Rev and SRT1720 treatment mitigated RVSP and reduced RVH. Conclusion: Rev produced a beneficial effect partially by enhancing the activation of SIRT1, thus improving RVSP and reducing RVH. SIRT1 activation increased PASMC apoptosis by inducing mPT dysfunction, which might be a novel future strategy for the treatment of PAH.

Infrasound signal classification remains a critical challenge in geophysical monitoring systems, where classification performance is fundamentally constrained by feature extraction efficacy. Existing two-dimensional feature extraction methods suffer from inadequate representation of spatiotemporal signal dynamics, leading to performance degradation in long-distance detection scenarios. To overcome these limitations, we present a novel classification framework that effectively captures spatiotemporal infrasound characteristics through Gramian Angular Field (GAF) transformation. The proposed method introduces an innovative encoding scheme that transforms one-dimensional infrasonic waveforms into two-dimensional GAF images while preserving crucial temporal dependencies. Building upon this representation, we develop an advanced hybrid deep learning architecture that integrates ConvLSTM networks to simultaneously extract and correlate spatial and spectral features. Extensive experimental validation on both chemical explosion and seismic infrasound datasets shows our approach achieves 92.4% classification accuracy, demonstrating consistent superiority over four state-of-the-art benchmark methods. These findings demonstrate the effectiveness of the proposed method.

The Tilt-depth method has been widely used to determinate the source depth of a magnetic anomaly. In the present study, we deduce similar Tilt-depth methods for both magnetic and gravity data based on the contact and sphere models and obtain the same equation for a gravity anomaly as that for a magnetic anomaly. The theoretical equations and the model tests show that the routine Tilt-depth method would result in unreliable depth estimation for deep sources. This is due to that the contact model is no longer valid for causative sources under the condition in which the depths of causative sources are significantly larger than their horizontal lengths. Accordingly, we suggest that the Tilt-depth derived from the contact model can be used to detect a shallow source, whereas the Tilt-depth derived from the sphere model can be used to detect a deep source. We propose a weighting method based on the estimated depths from both the contact model and the sphere model to estimate the depth for real data. The model tests suggest that the determined depths from the contact model and the sphere model are shallower and deeper, respectively, than the real depth, while the estimated depth from the proposed method is more close to the actual depth. In the application to the Weigang iron ore located in Jiangsu province, China, the routine Tilt-depth method results in −76% relative error, whereas the proposed method obtains the reliable depth estimation compared with the drill holes. In addition, the proposed method works well in the application for the Shijiaquan iron ore located in Shandong province, China. These results indicate that the proposed weighting equation is a general improvement.

Reflections from a hydrocarbon-saturated zone are generally expected to have a tendency to be low frequency. Previous work has shown the application of seismic spectral decomposition for low-frequency shadow detection. In this paper, we further analyse the characteristics of spectral amplitude in fractured sandstone reservoirs with different fluid saturations using the Wigner–Ville distribution (WVD)-based method. We give a description of the geometric structure of cross-terms due to the bilinear nature of WVD and eliminate cross-terms using smoothed pseudo-WVD (SPWVD) with time- and frequency-independent Gaussian kernels as smoothing windows. SPWVD is finally applied to seismic data from West Sichuan depression. We focus our study on the comparison of SPWVD spectral amplitudes resulting from different fluid contents. It shows that prolific gas reservoirs feature higher peak spectral amplitude at higher peak frequency, which attenuate faster than low-quality gas reservoirs and dry or wet reservoirs. This can be regarded as a spectral attenuation signature for future exploration in the study area.

Many sheet metal parts go through a bending operation during the manufacturing process. Compared to deep-drawing operations, failure in bending operations cannot be predicted accurately with a forming limit curve from the Nakajima or Marciniak experiment, especially in a pre-deformed state. Due to the small bending radii and the associated strong curvature, the failure only occurs with significantly higher strains for states without pre-deformation. Likewise, the failure is not caused by a localization, but by damage to the outer surface of the sample. The introduction of pre-deformation in the sheet material leads to development of texture and damage, where these mechanisms depend on the loading direction. If such pre-deformed sheet material is subsequently bent, the sample may fail unexpectedly early compared to the initial forming limit curve. The present experimental work aims at investigating the influence of pre-deformation and subsequent loading direction for different materials. Therefore, specimens have been pre-deformed in different orientations, followed by bending tests in different orientations. Different pre-deformation levels and loading directions combinations on three sheet materials were investigated. Based on the experimental results a so called bending forming limit curve (BFLC) can be derived enabling enhanced prediction of failure for bending processes after pre-deformation.