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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.

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.

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.

Due to manufacturing and assembly errors, clearances are inevitably present in the joints of a mechanism, which significantly compromise the motion accuracy, dynamic characteristics, and operational reliability of mechanical systems. However, current research on planar multi-link mechanisms with multiple joint clearances remains limited, especially that pertaining to the reliability and nonlinear dynamic characteristics of such mechanisms. Therefore, this paper takes a planar six-link pushing mechanism as the research object. Firstly, based on the Newton-Euler method, a dynamic modeling method suitable for planar multi-link mechanisms with multiple joint clearances is proposed, and the theoretical model is verified by simulation employing ADAMS. Secondly, the influence of the number of clearances and friction coefficients on the dynamic response of the mechanism is analyzed, and the influence laws of various factors on the dynamic response of the mechanism with clearances are revealed. On this basis, a motion-reliability analysis method is proposed, which combines the Monte Carlo algorithm and uses the failure rate as the performance index to evaluate the reliabilities of displacement, velocity and acceleration. Finally, an in-depth analysis via phase diagrams, Poincaré maps and bifurcation diagrams is conducted, and the key factors that influence the nonlinear dynamic characteristics of planar multi-link mechanism with joint clearances are revealed. The research results indicate that the developed dynamic model with clearances exhibits high accuracy, and clearances have a significant impact on the dynamic response of the mechanism. Meanwhile, the proposed motion reliability analysis method can effectively reveal the influencing factors of the mechanism’s motion reliability. In addition, increasing the clearance values and driving speeds will reduce the stability of the mechanism and further intensify its chaotic behavior. The results provide a theoretical basis for the precision manufacturing and performance improvement of multi-link mechanisms with multiple joint clearances in practical engineering applications, with high engineering application value.

Material testing and modeling is one of the cornerstones of virtual analysis of sheet metal forming processes. However, it is also becoming more and more relevant for incoming goods inspection, especially in view of the increasing amount of recycled material or frequent changes of suppliers, e.g. to provide workers, processes and/or process models with relevant information about a new batch of material. Efficient material testing and straight-forward test evaluation is essential for this. The flow curve and yield locus are central to describe the forming behavior of sheet metal materials. However, the parameters of the associated models are currently determined in various tests on different systems and with special sample geometries. The present work presents a methodology that allows the determination of a set of flow curve and yield locus parameters from three three-point bending tests only. The evaluation routine does not require finite element simulation and processes only the force-displacement information of the bending tests, which also places low demands on the measurement technology. The results were compared with a conventionally determined parameter set using a validation test, and the results are of reasonable quality, especially considering the minimal effort involved.

Gravity anomalies illuminate subsurface lithology and geological structure in three dimensions,which is vital for studies of concealed faults,sedimentary basins,basement lithology,and other geological targets.Although not all geological contacts correspond to lithological contacts,the contact mapping provides key information on structural regimes,deformation styles and trends.Many techniques for contact mapping have been developed.Here,we evaluate five methods applied to gridded data.The first two are the horizontal gradient magnitude of the gravity field （GFhgm）,and tilt （TIhgm）.The third and fourth rely on locating maxima of the analytic signal （AS） and the 3D local wavenumber （LW）.The fifth is normalized standard deviation （NSTD） method.In this article,we evaluate the use of these five methods for mapping contacts and compare the results.First,synthetic vertically-sided models are used to quantify the offsets of maxima from the true contact location due to the source effects of finite source thickness,central depth,and width.Second,the effects of contact dip are discussed.Finally,a real data set is used to evaluate the ability of each method to produce maps of coherent contact trends in the presence of noise and gridding artifacts.

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.