Publications

Traffic flow prediction method for the intelligent marine meteorological sensor network

Machine Learning-Optimized Microplastic Adsorption Kinetics in Marine Environments with Edge Computing

PEST sequences mediate heat shock factor 2 turnover by interacting with the Cul3 subunit of the Cul3-RING ubiquitin ligase

Cullin-RING ubiquitin ligases promote the polyubiquitination and degradation of many important cellular proteins, which previous studies indicated can be targeted for degradation via interaction with BTB domain-containing subunits of this E3 ligase complex. PEST domains are known to promote the degradation of proteins that contain them. However, the molecular mechanism by which PEST sequences promote degradation of these proteins is not understood. Here we show that the PEST sequences of a short-lived protein called HSF2 interact with Cullin3, a subunit of a Cullin-RING E3 ubiquitin ligase, and that this interaction mediates the Cul3-dependent ubiquitination and degradation of HSF2. These results indicate how, at the molecular level, PEST sequences can promote the proteolysis of proteins that contain them. They also expand understanding of the mechanisms by which substrates can be recruited to Cullin-RING E3 ubiquitin ligases to include interactions between PEST sequences and Cul3.

Network intrusion detection based on DNA spatial information

Research on the Seismic Response Law of Complete Morphology of Butted Well Salt Cavern for Large-Scale Underground Energy Storage

The conversion of abandoned butted well salt cavities into underground storage facilities holds immense significance for safeguarding energy security and improving the ecological environment. A significant barrier to the reconstruction of these old cavities is the limited comprehension of their complete morphology, caused by residue coverage. The three-dimensional seismic techniques excel in identifying complex geological structures but have a limited understanding of underground old salt cavity morphology, thus the seismic forward simulation method is utilized to study their seismic response patterns. Based on 3D seismic data, well logging data, and measured cavity shape parameters from the Yexian salt mine region in Henan Province, China, a geological model and observation system were established. The seismic response characteristics of the butted well salt cavern model, encompassing five distinct morphological attributes such as cavity spacing, cavity diameter, cavity height, sediment height, and horizontal connection channel height, were thoroughly investigated. The findings show that the cavity roof exhibits a distinctive “two peaks sandwiching a strong valley” feature, with the positions of the valley and roof remaining aligned and serving as a reliable indicator for identifying the cavity’s top surface. The width of the roof waveform exhibits an exponential amplification effect relative to the cavern width. The residue’s top surface presents an “upward-opening arc” wave peak with a downward shift that diminishes as the residue’s height increases. This peak forms a circular feature with the cavity roof reflection waveform, and the residue’s top surface is always located in the upper half of this circular waveform. The horizontal connection channel’s top and bottom surfaces exhibit contrasting reflection patterns, with the top position aligning with the reflection trough and the bottom reflection waveform shifting downward as the channel height increases. The brine cavern, residue, and bottom of the salt cavern mainly exhibit chaotic reflections. There are distinct identification characteristics on the cavity top, residue top, and connecting channel top in forward simulation. The research findings provide valuable guidance for identifying the morphology of the underground real butted well salt cavity based on 3D seismic data and accelerating the construction of underground energy storage facilities.

Automata theory based on complete residuated lattice-valued logic: Pushdown automata

A study on the variation of asexually propagated strains of Pleurotus sapldus protoplasts

HSF1-TPR Interaction Facilitates Export of Stress-induced HSP70 mRNA

Stress conditions inhibit mRNA export, but mRNAs encoding heat shock proteins continue to be efficiently exported from the nucleus during stress. How HSP mRNAs bypass this stress-associated export inhibition was not known. Here, we show that HSF1, the transcription factor that binds HSP promoters after stress to induce their transcription, interacts with the nuclear pore-associating TPR protein in a stress-responsive manner. TPR is brought into proximity of the HSP70 promoter after stress and preferentially associates with mRNAs transcribed from this promoter. Disruption of the HSF1-TPR interaction inhibits the export of mRNAs expressed from the HSP70 promoter, both endogenous HSP70 mRNA and a luciferase reporter mRNA. These results suggest that HSP mRNA export escapes stress inhibition via HSF1-mediated recruitment of the nuclear pore-associating protein TPR to HSP genes, thereby functionally connecting the first and last nuclear steps of the gene expression pathway, transcription and mRNA export.

Small Target Detection Based on Multi-fractal Characteristics of Sea Clutter FRFT Spectrum

A New QRS Detection Algorithm Based on Empirical Mode Decomposition

A new QRS complex detection algorithm based on the empirical mode decomposition (EMD) is proposed in this paper. The EMD can first decompose the ECG signal into a series of oscillatory components called intrinsic mode functions (IMFs). Then with the soft- threshold denoising method on the first three IMFs, we construct the detection layer that is suitable for QRS detection. Using the corresponding relationship between the feature points of QRS complex and the modulus maxima of the detection layer, the QRS complex detection is realized. The proposed EMD-based method was validated through experiments on the MIT-BIH arrhythmia database and a QRS detection rate of 99.34% was achieved.

miR-505 Downregulates 6-Phosphofructo-2-Kinase/ Fructose-2,6-Biphosphatase 4 to Promote Cell Death in Glioblastoma

Aim: The glycolytic enzyme, 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 4 (PFKFB4), mediates shifts in glycolytic flux and is important for glioblastoma cell survival. This study aimed to identify micro-RNAs that alter PFKFB4 expression to regulate glioblastoma cell survival. Methods: Western blot analyses, luciferase reporter assays, and public database analyses were used to predict and validate the regulation of PFKFB4 mRNA expression by miR-505 in glioblastoma. Cell growth and apoptosis assays were performed to determine the effects of miR-505 on the growth and survival of primary glioblastoma stem-like cells (GSCs) and established glioblastoma cell lines. In addition, the correlations between patient survival and the expression of miR-505 and PFKFB4 mRNA in glioblastoma specimens were examined. Results: Using micro-RNA target prediction programs, a miR-505 binding site in the 3'-UTR of the PFKFB4 mRNA transcript was identified, and query of a public CLIP-Seq database indicated that PFKFB4 binds this site in living cells. It was found that fusion of the PFKFB4 3'-UTR to luciferase conferred regulation of luciferase activity by PFKFB4. In addition, Western blots revealed that miR-505 significantly decreased PFKFB4 protein expression in established glioblastoma cell lines and primary GSCs. Enforced PFKFB4 overexpression increased the growth of primary GSCs and established glioblastoma multiforme cell lines, and miR-505 antagonized this effect. By downregulating PFKFB4, miR-505 increased production of reactive oxygen species, thereby repressing glioblastoma cell growth and promoting glioblastoma cell death. Importantly, patient survival was positively correlated with miR-505 expression and negatively correlated with PFKFB4 mRNA expression in primary glioblastoma specimens. Conclusion: It was showed that miR-505 downregulates PFKFB4 expression in glioblastoma, thereby decreasing glioblastoma cell survival. Targeting PFKFB4 via miR-505 may represent a promising therapeutic approach in glioblastoma.

Design of Function Generator Based on DDS 9834

This paper mainly introduces a high resolution and high stability signal generator design based on CPLD,MCU control technology and Direct Digital Synthesis(DDS).Hardware circuit figure,flow chart of procedure and frequency control word transmission are illustrated in this paper.Method of CPLD′s communication with MCU is utilized in the design to generate high accuracy and wide frequency range signal.The system achieves concise structure,convenience usage and stable and reliable performance.

KAP regulates ROCK2 and Cdk2 in an RNA-activated glioblastoma invasion pathway

Some new entropy measures for interval-valued intuitionistic fuzzy sets based on distances and their relationships with similarity and inclusion measures

Novel dual-modal flexible liquid crystal sensor: capable of synchronously detecting temperature and curvature

Thunderstorm Detection and Prewarning via Multisource Fusion of Precipitation and Atmospheric Electric Field Features

Traditional detection and prewarning methods based on a single atmospheric electric field (AEF) data source often fail to accurately assess thunderstorm weather conditions. This article proposes a thunderstorm detection method that employs weighted multisource fusion of precipitation and AEF features, achieving a high-precision evaluation through multimodal feature fusion and machine learning model optimization. Based on the detection range of AEF apparatus, spatially matched radar chart precipitation data are extracted to construct physically meaningful precipitation statistic features and time-varying AEF features. Using feature importance analysis, the physical contribution of each feature is determined, and these features are weighted and fused accordingly to generate enhanced features. Furthermore, we establish an improved gradient boosting decision tree model that enhances classification performance in the weighted feature space by adaptively adjusting the learning rate. Experimental results demonstrate that this method achieves a highly competitive F1-score of 0.92 in thunderstorm recognition tasks, representing a 35.3% improvement over the traditional AEF threshold method. The proposed weighted fusion framework provides a novel solution for thunderstorm detection and prewarning through the synergistic use of ground-based AEF and radar-based precipitation data.

Study on Stress Concentration and Normal Magnetic Memory Signal Gradient of Q235 Steel

The metal magnetic memory testing technique was used to detect the magnetic properties of Q235 steel specimens under uniaxial tensile load. In this work, a new gradient Kxy was proposed, which is normal component of magnetic memory signal in the two directions where length and width of the specimen. The results show that it has a good correspondence with the stress concentration area in the elastic stage, the location of the stress concentration of the tested specimen can be determined by Kxy, it solves one problem that the position of the stress concentration is not accurate by zero crossing point of the normal component of magnetic memory signal curve of a single track line on the surface of the specimen.

3DAEFA-Based Thunderstorm Prediction System With Higher Performance

This article presents a thunderstorm prediction system with a 3-D atmospheric electric field (AEF) apparatus (3DAEFA), wherein the data source is the high-resolution 3-D AEF (3DAEF) values. The system is mainly composed of 3DAEF sensor design and calibration, 3DAEF acquisition and processing, and thunderstorm prediction, localization, and imaging. First, a self-made single-axis rotary vane 3DAEFA is employed to measure 3DAEFs. Second, a calibration device is developed to calibrate 3DAEF directions while completing intensity calibration. Considering that the AEF signal (AEFS) is disturbed by low-frequency noises, full-frequency-domain AEFS denoising is conducted through Savitzky–Golay (SG) filtering, and baseline estimation and denoising with sparsity (BEADS). A prediction model is built based on the bidirectional long short-term memory (BiLSTM) network. After inputting denoised AEFS spatial features into the model, which is extracted by a convolutional neural network (CNN), a CNN-BiLSTM model is formed. The proposed system is assessed in different weathers. There is a significant improvement in AEFS's signal-to-noise ratio (SNR) measured by this system, especially in thunderstorm weather, compared with the original AEFS (OAEFS). Meanwhile, determining coefficients are more than 95%, showing better prediction effects. The deviation between predicted values and real values is smaller than that of OAEFS, which makes it easier to analyze AEFS characteristics. Comparisons with radar charts demonstrate that the proposed system can effectively predict thunderstorms.