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Research on load forecasting based on CEEMDAN SE VMD and SelfAttention TCN fusion model
A novel prognostic model based on migrasome-related LncRNAs for gastric cancer
Abstract Gastric cancer (GC) represents a substantial public health challenge, characterized by elevated morbidity and mortality rates. Migrasomes, a newly discovered type of extracellular vesicle, have been highlighted as important contributors to cancer progression, though their specific role in GC remains unclear. To address this issue, we developed the first prognostic model utilizing migrasome-related long non-coding RNAs (MRLs). This model aims to deepen the understanding of GC pathogenesis and improve patient outcomes. Clinical and transcriptional data for 407 GC patients from TCGA were classified as training and testing sets. Through Pearson correlation analysis, 537 MRLs were recognized, and LASSO and Cox regression analyses further refined the list to four key lncRNAs (AC012055.1, LINC01150, AC053503.4, AC107021.2) for constructing the prognostic model. Kaplan-Meier survival analysis indicated a significantly poorer prognosis for the high-risk group. PCA confirmed the model’s robustness, and univariate and multivariate analyses validated it as an independent predictor of clinical outcomes. The ROC curve and C-index evaluations further affirmed the model’s predictive power. We developed a nomogram combining the MRLs signature with clinical parameters to enhance prognostic accuracy. GO, KEGG and GSEA were performed on migrasome-related genes associated with GC. Furthermore, high-risk patients exhibited increased immune cell infiltration and reduced tumor mutation burden, both associated with poorer outcomes. Additionally, twenty-nine potential therapeutic agents were identified. This novel MRLs-based model provides crucial insights into GC biology and represents a valuable tool for improving patient management and therapeutic strategies.
Electrostatic solitary wave modeling in lunar wake plasma
Increased risk of young-onset ovarian cancer in women with non-alcoholic fatty liver disease: A nationwide cohort study of 2.3 million women aged 20–39 years
Frontispiece: Enabling Unconventional “Alternating‐Distal” N<sub>2</sub> Reduction Pathway for Efficient Ammonia Electrosynthesis
Effects of tianeptine on mTORC1-mediated neuronal autophagy in primary rat hippocampal neurons under nutrient deprivation
Striatal functional connectivity associated with Sahaja Yoga meditation
First report of fire blight on Cotoneaster in China
Author Correction: Effect of dietary tall oil fatty acids and hydrolysed yeast in SNP2-positive and SNP2-negative piglets challenged with F4 enterotoxigenic Escherichia coli
A cubic-quadratic phenomenological model explains the spiking, chaotic and bursting behaviors of neuron
Abstract In this manuscript, we present a two-dimensional phenomenological spiking neuron model. By analyzing the bifurcation diagram and phase portraits of the two-dimensional model, Andronov-Hopf bifurcation, saddle-point bifurcation and saddle-point on invariant circle bifurcation are discussed in detail. Based on the above analysis, a periodic input current is designed to simulate the basic firing mode of a single neuron. With the change of the input current frequency, the model can reproduce rich dynamical behaviors such as spike, bursts, and chaos.
Epidemiological and genetic characteristics of enteroviruses associated with hand, foot, and mouth disease in Jiaxing, China from 2019 to 2022
Improved multi-objective decision-making in manufacturing processes through uncertainty quantification and robust pareto front modelling
Huygens metasurface supporting quasi-bound states in the continuum for terahertz gas sensing
Abstract We investigate a terahertz (THz) gas sensing platform based on all-dielectric metasurfaces that support quasi-bound states in the continuum (quasi-BIC) with both electric and magnetic dipole resonances. The structure is designed to achieve the first Kerker condition, minimizing backscattering and maximizing light-matter interaction, which significantly enhances the sensitivity of the sensor. By optimizing structural parameters, this metasurface selectively resonates at characteristic absorption frequencies of target gases, facilitating detection even at low concentrations. We validate the approach using two gases with strong but distinct THz absorption profiles: hydrogen cyanide and sulfur dioxide ( $$\hbox {SO}_2$$ ). Furthermore, the free-standing design maximizes gas interaction on both sides of the metasurface, eliminating substrate-induced losses and enabling a reduced physical footprint. Our findings indicate that this metasurface outperforms standard THz sensing approaches in terms of compactness and sensitivity per path length unit, obtaining the same detection threshold as sensing in free space with a path length between 2 and 3 orders of magnitude shorter, underscoring its potential for industrial applications where the available space for sensing can be limited.
Telomere-related gene risk model predicts prognostic and immune microenvironment alterations in prostate cancer
Establishment and genetic characterization of zebrafish RW line
A novel, low-cost clay ceramic membrane for the separation of oil-water emulsions
Towards decentralized and sustainable water and wastewater treatment systems
A lightweight deep learning framework for transformer fault diagnosis in smart grids using multiple scale CNN features
Abstract Scheduled maintenance and condition monitoring of power transformers in smart grids is mandatory to reduce their downtimes and maintain economic benefits. However, to minimize energy losses during inspection, non-invasive fault diagnosis techniques such as thermogram imaging can enable continuous monitoring of transformer health with minimal out-of-service time. Deep learning (DL) has proven to be a fast and efficient intelligent diagnostic tool. In this paper, a DL-based thermography method is proposed called Trans-Light for transformers’ interturn faults detection and short-circuit severity identification. Trans-light extracts deep features from two deep layers of a convolutional neural network (CNN) rather than depending on one layer, thus obtaining more intricate patterns. Moreover, a Dual-tree Complex Wavelet Transform method is adopted which offers two enhancements. First, it acquires time–frequency knowledge besides the already obtained spatial information and second, it reduces the huge deep features dimensionality. Trans-light combines extracted deep features, then a feature selection process is applied to further reduce features’ size, thus decreasing computation burden and reducing classification and training time. To validate the proposed scheme’s diagnosis performance and robustness, different combinations of two CNN models, two feature selection methods, and six classifiers were tested, applying the proposed Trans-light framework, under noise-free and noise-existing conditions. Experimental results indicated that the combination of the LDA classifier, applied with the ResNet-18 CNN model and trained with merged deep features undergoing the chi-square (χ2) selection approach, attained superior performance under noise-free conditions. Compared to its counterparts in previous work, this configuration outperforms their performance since it uses the fewest features’ number yet maintains 100% classification accuracy. Besides, it attained robust performance under two different noise natures again with minimal features’ dimension, thus minimizing computational load and implementation complexity.