Search Results (5,633)

Stropharia rugosoannulata, an ecologically valuable and economically important edible mushroom, faces challenges in strain-level identification and breeding due to limited genomic resources and the lack of high-resolution molecular markers. In this study, we generated high-quality genomic data for 105 S. rugosoannulata strains and identified over 2.7 million SNPs, unveiling substantial genetic diversity within the species. Using core gene-associated multiple nucleotide polymorphism (cgMNP) markers, we developed an efficient and transferable framework for strain discrimination. The analysis revealed pronounced genetic differentiation among cultivars, clustering them into two distinct phylogenetic groups. Nucleotide diversity (π) across 83 core genes varied significantly, highlighting both highly conserved loci under purifying selection and highly variable loci potentially associated with adaptive evolution. Phylogenetic analysis of the most variable gene, Phosphatidate cytidylyltransferase mitochondrial, identified 865 SNPs, enabling precise differentiation of all 85 cultivars. Our findings underscore the utility of cgMNP markers in addressing challenges posed by horizontal gene transfer and phylogenetic noise, demonstrating their robustness in cross-species applications. By providing insights into genetic diversity, evolutionary dynamics, and marker utility, this study establishes a foundation for advancing breeding programs, conservation strategies, and functional genomics in S. rugosoannulata. Furthermore, the adaptability of cgMNP markers offers a universal tool for high-resolution strain identification across diverse fungal taxa, contributing to broader fungal phylogenomics and applied mycology. Full article

We present a corrosion-resistant quadrupole ion trap mass spectrometer (QIT-MS) designed for trace detection of volatiles in sulfuric acid aerosols, with a specific focus on phosphine (PH₃). Here, we detail the gas calibration methodology using permeation tube technology for generating certified ppb-level PH₃/H₂S/CO₂ mixtures, and report results from mass spectra with sufficient resolution to distinguish isotopic envelopes that validate the detection of PH₃ at a concentration of 62 ppb. Fragmentation patterns for PH₃ and H₂S agree with NIST data, and signal-to-noise performance confirms ppb sensitivity over 2.6 h acquisition periods. We further assess spectral interferences from oxygen isotopes and propose a detection scheme based on isolated phosphorus ions (P⁺) to enable specific and interference-resistant identification of PH₃ and other reduced phosphorus species of astrobiological interest in Venus-like environments. This work extends the capabilities of QIT-MS for trace gas analysis in chemically aggressive atmospheric conditions. Full article

Gravity and gravity gradient data serve as fundamental inputs for geophysical resource exploration and geological structure analysis. However, traditional denoising methods—including wavelet transforms, moving averages, and low-pass filtering—exhibit signal loss and limited adaptability under complex, non-stationary noise conditions. To address these challenges, this study proposes an improved U-Net deep learning framework that integrates multi-scale feature extraction and attention mechanisms. Furthermore, a Laplace consistency constraint is introduced into the loss function to enhance denoising performance and physical interpretability. Notably, the datasets used in this study are generated by the authors, involving simulations of subsurface prism distributions with realistic density perturbations (±20% of typical rock densities) and the addition of controlled Gaussian noise (5%, 10%, 15%, and 30%) to simulate field-like conditions, ensuring the diversity and physical relevance of training samples. Experimental validation on these synthetic datasets and real field datasets demonstrates the superiority of the proposed method over conventional techniques. For noise levels of 5%, 10%, 15%, and 30% in test sets, the improved U-Net achieves Peak Signal-to-Noise Ratios (PSNR) of 59.13 dB, 52.03 dB, 48.62 dB, and 48.81 dB, respectively, outperforming wavelet transforms, moving averages, and low-pass filtering by 10–30 dB. In multi-component gravity gradient denoising, our method excels in detail preservation and noise suppression, improving Structural Similarity Index (SSIM) by 15–25%. Field data tests further confirm enhanced identification of key geological anomalies and overall data quality improvement. In summary, the improved U-Net not only delivers quantitative advancements in gravity data denoising but also provides a novel approach for high-precision geophysical data preprocessing. Full article

With the increasing sophistication of network attacks, machine learning (ML)-based methods have showcased promising performance in attack detection. However, ML-based methods often suffer from high false rates when tackling encrypted malicious traffic. To break through these bottlenecks, we propose EFTransformer, an encrypted flow transformer framework which inherits semantic perception and multi-scale feature fusion, can robustly and efficiently detect encrypted malicious traffic, and make up for the shortcomings of ML in the context of modeling ability and feature adequacy. EFTransformer introduces a channel-level extraction mechanism based on quintuples and a noise-aware clustering strategy to enhance the recognition ability of traffic patterns; adopts a dual-channel embedding method, using Word2Vec and FastText to capture global semantics and subword-level changes; and uses a Transformer-based classifier and attention pooling module to achieve dynamic feature-weighted fusion, thereby improving the robustness and accuracy of malicious traffic detection. Our systematic experiments on the ISCX2012 dataset demonstrate that EFTransformer achieves the best detection performance, with an accuracy of up to 95.26%, a false positive rate (FPR) of 6.19%, and a false negative rate (FNR) of only 5.85%. These results show that EFTransformer achieves high detection performance against encrypted malicious traffic. Full article

Infrared small-target detection remains a critical challenge in military reconnaissance, environmental monitoring, forest-fire prevention, and search-and-rescue operations, owing to the targets’ extremely small size, sparse texture, low signal-to-noise ratio, and complex background interference. Traditional convolutional neural networks (CNNs) struggle to detect such weak, low-contrast objects due to their limited receptive fields and insufficient feature extraction capabilities. To overcome these limitations, we propose a Multi-Scale Edge-Aware Convolution (MEAC) module that enhances feature representation for small infrared targets without increasing parameter count or computational cost. Specifically, MEAC fuses (1) original local features, (2) multi-scale context captured via dilated convolutions, and (3) high-contrast edge cues derived from differential Gaussian filters. After fusing these branches, channel and spatial attention mechanisms are applied to adaptively emphasize critical regions, further improving feature discrimination. The MEAC module is fully compatible with standard convolutional layers and can be seamlessly embedded into various network architectures. Extensive experiments on three public infrared small-target datasets (SIRSTD-UAVB, IRSTDv1, and IRSTD-1K) demonstrate that networks augmented with MEAC significantly outperform baseline models using standard convolutions. When compared to eleven mainstream convolution modules (ACmix, AKConv, DRConv, DSConv, LSKConv, MixConv, PConv, ODConv, GConv, and Involution), our method consistently achieves the highest detection accuracy and robustness. Experiments conducted across multiple versions, including YOLOv10, YOLOv11, and YOLOv12, as well as various network levels, demonstrate that the MEAC module achieves stable improvements in performance metrics while slightly increasing computational and parameter complexity. These results validate the MEAC module’s significant advantages in enhancing the detection of small and weak objects and suppressing interference from complex backgrounds. These results validate MEAC’s effectiveness in enhancing weak small-target detection and suppressing complex background noise, highlighting its strong generalization ability and practical application potential. Full article

Background: Cardiovascular diseases are the leading cause of global mortality, with 80% of coronary heart disease in patients over 65. Understanding aging cardiovascular structures is crucial. Photon-counting computed tomography (PCCT) offers improved spatial and temporal resolution and better signal-to-noise ratio, enabling texture analysis in clinical routines. Detecting structural changes in aging left-ventricular myocardium may help predict cardiovascular risk. Methods: In this retrospective, single-center, IRB-approved study, 90 patients underwent ECG-gated contrast-enhanced cardiac CT using dual-source PCCT (NAEOTOM Alpha, Siemens). Patients were divided into two age groups (50–60 years and 70–80 years). The left ventricular myocardium was segmented semi-automatically, and radiomics features were extracted using pyradiomics to compare myocardial texture features. Epicardial adipose tissue (EAT) density, thickness, and other clinical parameters were recorded. Statistical analysis was conducted with R and a Python-based random forest classifier. Results: The study assessed 90 patients (50–60 years, n = 54, and 70–80 years, n = 36) with a mean age of 63.6 years. No significant differences were found in mean Agatston score, gender distribution, or conditions like hypertension, diabetes, hypercholesterolemia, or nicotine abuse. EAT measurements showed no significant differences. The Random Forest Classifier achieved a training accuracy of 0.95 and a test accuracy of 0.74 for age group differentiation. Wavelet-HLH_glszm_GrayLevelNonUniformity was a key differentiator. Conclusions: Radiomics texture features of the left ventricular myocardium outperformed conventional parameters like EAT density and thickness in differentiating age groups, offering a potential imaging biomarker for myocardial aging. Radiomics analysis of left ventricular myocardium offers a unique opportunity to visualize changes in myocardial texture during aging and could serve as a cardiac risk predictor. Full article

Growing concern over anthropogenic underwater noise, highlighted by initiatives like the Marine Strategy Framework Directive (MSFD) and its Technical Group on Underwater Noise (TG Noise), emphasizes regions like the Western Black Sea, where increasing activities threaten marine habitats. This region is experiencing rapid growth in maritime traffic and resource exploitation, which is intensifying concerns over the noise impacts on its unique marine habitats. While machine learning offers promising solutions, a research gap persists in comprehensively evaluating diverse ML models within an integrated framework for complex underwater acoustic data, particularly concerning real-world data limitations like class imbalance. This paper addresses this by presenting a multi-faceted framework using passive acoustic monitoring (PAM) data from fixed locations (50–100 m depth). Acoustic data are processed using advanced signal processing (broadband Sound Pressure Level (SPL), Power Spectral Density (PSD)) for feature extraction (Mel-spectrograms for deep learning; PSD statistical moments for classical/unsupervised ML). The framework evaluates Convolutional Neural Networks (CNNs), Random Forest, and Support Vector Machines (SVMs) for noise event classification, alongside Gaussian Mixture Models (GMMs) for anomaly detection. Our results demonstrate that the CNN achieved the highest classification accuracy of 0.9359, significantly outperforming Random Forest (0.8494) and SVM (0.8397) on the test dataset. These findings emphasize the capability of deep learning in automatically extracting discriminative features, highlighting its potential for enhanced automated underwater acoustic monitoring. Full article

Despite the well-documented health risks of noise pollution, its impact remains overlooked mainly in life cycle assessment (LCA). This study introduces a methodological innovation by integrating both traffic and construction noise into the LCA framework for concrete construction, providing a more holistic and realistic evaluation of environmental and health impacts. By combining building information modeling (BIM) with LCA, the method automates material quantification and assesses both environmental and noise-related health burdens. A key advancement is the inclusion of health-based indicators, such as annoyance and sleep disturbance, quantified through disability-adjusted life years (DALYs). Two scenarios are examined: (1) a comparative analysis of concrete versus timber flooring and (2) end-of-life options (reuse vs. landfill). The results reveal that concrete has up to 7.4 times greater environmental impact than timber, except in land use. When noise is included, its contribution ranges from 7–33% in low-density regions (Darwin) and 62–92% in high-density areas (NSW), underscoring the critical role of local context. Traffic noise emerged as the dominant source, while equipment-related noise was minimal (0.3–1.5% of total DALYs). Timber slightly reduced annoyance but showed similar sleep disturbance levels. Material reuse reduced midpoint environmental impacts by 67–99.78%. Sensitivity analysis confirmed that mitigation measures like double glazing can cut noise-related impacts by 2–10% in low-density settings and 31–45% in high-density settings, validating the robustness of this framework. Overall, this study establishes a foundation for integrating noise into LCA, supporting sustainable material choices, environmentally responsible construction, and health-centered policymaking, particularly in noise-sensitive urban development. Full article

Using graphs as a data structure is a simple way to represent relationships between objects. Consequently, it has raised the need for algorithms to process, analyze, and extract meaningful information from graphs. Therefore, frequent subgraph mining (FSM) algorithms have been reported in the literature to discover interesting, unexpected, and useful patterns in graph databases. Frequent subgraph mining involves discovering subgraphs that appear no less than a user-specified threshold; this can be performed exactly or approximately. Although several algorithms for mining frequent approximate subgraphs exist, mining this type of subgraph in graph collections has scarcely been addressed. Thus, we propose AGCM-SLV, an algorithm for mining frequent approximate subgraphs within a graph collection that allows structural and label variations. Unlike other FSM approaches, our proposed algorithm tracks subgraph occurrences and their structural dissimilarities, allowing user-defined partial similarities between node and edge labels, and captures frequent approximate subgraphs (patterns) that would otherwise be overlooked. Experiments on real-world datasets demonstrate that our algorithm identifies more patterns than the most similar state-of-the-art algorithm with a shorter runtime. We also present experiments in which we add white noise to the graph collection at different levels, revealing that over 99% of the patterns extracted without noise are preserved under noisy conditions, making the proposed algorithm noise-tolerant. Full article

Land desertification represents a significant and sensitive global ecological issue. In the Inner Mongolia region of China, soil desertification and salinization are widespread, resulting from the combined effects of extreme drought conditions and human activities. Using Gaofen 5B AHSI imagery as our data source, we collected spectral data for seven distinct land cover types: lush vegetation, yellow sand, white sand, saline soil, saline shell, saline soil with saline vegetation, and sandy soil. We applied Particle Swarm Optimization (PSO) to fine-tune the Wavelet Packet (WP) decomposition levels, thresholds, and wavelet basis function, ensuring optimal spectral decomposition and reconstruction. Subsequently, PSO was deployed to optimize key hyperparameters of the Random Forest algorithm and compare its performance with the ResNet-Transformer model. Our results indicate that PSO effectively automates the search for optimal WP decomposition parameters, preserving essential spectral information while efficiently reducing high-frequency spectral noise. The Genetic Algorithm (GA) was also found to be effective in extracting feature bands relevant to land desertification, which enhances the classification accuracy of the model. Among all the models, integrating wavelet packet denoising, genetic algorithm feature selection, the first-order differential (FD), and the hybrid architecture of the ResNet-Transformer, the WP-GA-FD-ResNet-Transformer model achieved the highest accuracy in extracting soil sandification and salinization, with Kappa coefficients and validation set accuracies of 0.9746 and 97.82%, respectively. This study contributes to the field by advancing hyperspectral desertification monitoring techniques and suggests that the approach could be valuable for broader ecological conservation and land management efforts. Full article

Automatic modulation classification (AMC) is a fundamental technique in wireless communication systems, as it enables the identification of modulation schemes at the receiver without prior knowledge, thereby promoting efficient spectrum utilization. Recent advancements in deep learning (DL) have significantly enhanced classification performance by enabling neural networks (NNs) to learn complex decision boundaries directly from raw signal data. However, many existing NN-based AMC methods employ deep or specialized network architectures, which, while effective, tend to involve substantial structural complexity. To address this issue, we present a simple NN architecture that utilizes features derived from Hankelized matrices to extract informative signal representations. In the proposed approach, received signals are first transformed into Hankelized matrices, from which informative features are extracted using singular value decomposition (SVD). These features are then fed into a compact, fully connected (FC) NN for modulation classification across a wide range of signal-to-noise ratio (SNR) levels. Despite its architectural simplicity, the proposed method achieves competitive performance, offering a practical and scalable solution for AMC tasks at the receiver in diverse wireless environments. Full article

Background/Objectives: Neuronal oscillations play a key role in the symptoms of Parkinson’s disease (PD). This study investigates the effects of random synaptic inputs, their correlations, and the interaction with synaptic dynamics and spike timing-dependent plasticity (STDP) on the membrane potential and firing patterns of subthalamic nucleus (STN) neurons, both in healthy and PD-affected states. Methods: We used a modified Hodgkin–Huxley model with a Langevin stochastic framework to study how synaptic conductance, random input fluctuations, and STDP affect STN neuron firing and membrane potential, including sensitivity to refractory period and synaptic depression variability. Results: Our results show that random inputs significantly affect the firing patterns of STN neurons, both in healthy cells and those with PD under DBS treatment. STDP, along with random refractory periods and fluctuating input currents, increases the irregularity of inter-spike intervals (ISIs) in output neuron spike trains. Sensitivity analyses highlight the key role of synaptic depression and refractory period variability in shaping firing patterns. Combining random inputs with STDP boosts the correlation between neuron activities. Furthermore, at fixed input noise levels, the model’s output closely matches experimental firing rate and ISI variability data from PD patients and animals, with statistical tests confirming significant effects of STDP on firing regularity. Conclusions: The findings suggest that the stochastic dynamics of STN neurons, combined with STDP, are crucial for shaping neuronal firing patterns in both healthy and PD-affected states. These insights improve our understanding of how noise and plasticity contribute to neural function and dysfunction, with implications for PD symptom management. Full article

The use of high-resolution sea level anomaly (SLA) data in climate change research and ocean forecasting has become increasingly important. However, existing datasets often lack the fine spatial resolution required for capturing mesoscale ocean processes accurately. This has led to the development of conventional deep learning models for SLA spatial downscaling, but these models often overlook spatial disparities between land and ocean regions and do not adequately address the spatial structures of SLA data. As a result, their accuracy and structural consistency are suboptimal. To address these issues, we propose a Deep Separable Distillation Network (DSDN) that integrates Depthwise Separable Distillation Blocks (DSDB) and a Landmask Contextual Attention Mechanism (M_CAMB) to achieve efficient and accurate spatial downscaling. The M_CAMB employs geographically-informed land masks to enhance the attention mechanism, prioritizing ocean regions. Additionally, we introduce a novel Pixel-Structure Loss (PSLoss) to enforce spatial structure constraints, significantly improving the structural fidelity of the SLA downscaling results. Experimental results demonstrate that DSDN achieves a root mean square error (RMSE) of 0.062 cm, a peak signal-to-noise ratio (PSNR) of 42.22 dB, and a structural similarity index (SSIM) of 0.976 in SLA downscaling. These results surpass those of baseline models and highlight the superior precision and structural consistency of DSDN. Full article

Background: Many patients harbor minimal residual disease (MRD)—small clusters of residual tumor cells that survive therapy and evade conventional detection but drive recurrence. Although advances in molecular and computational methods have improved circulating tumor DNA (ctDNA)-based MRD detection, these approaches face challenges: ctDNA shedding fluctuates widely across tumor types, disease stages, and histological features. Additionally, low levels of driver mutations originating from healthy tissues can create background noise, complicating the accurate identification of bona fide tumor-specific signals. These limitations underscore the need for refined technologies to further enhance MRD detection beyond DNA sequences in solid malignancies. Methods: Profiling circulating cell-free mRNA (cfmRNA), which is hyperactive in tumor and non-tumor microenvironments, could address these limitations to inform postoperative surveillance and treatment strategies. This study reported the development of OncoMRD BREAST, a customized, gene signature-informed cfmRNA assay for residual disease monitoring in breast cancer. OncoMRD BREAST introduces several advanced technologies that distinguish it from the existing ctDNA-MRD tests. It builds on the patient-derived gene signature for capturing tumor activities while introducing significant upgrades to its liquid biopsy transcriptomic profiling, digital scoring systems, and tracking capabilities. Results: The OncoMRD BREAST test processes inputs from multiple cutting-edge biomarkers—tumor and non-tumor microenvironment—to provide enhanced awareness of tumor activities in real time. By fusing data from these diverse intra- and inter-cellular networks, OncoMRD BREAST significantly improves the sensitivity and reliability of MRD detection and prognosis analysis, even under challenging and complex conditions. In a proof-of-concept real-world pilot trial, OncoMRD BREAST’s rapid quantification of potential tumor activity helped reduce the risk of incorrect treatment strategies, while advanced predictive analytics contributed to the overall benefits and improved outcomes of patients. Conclusions: By tailoring the assay to individual tumor profiles, we aimed to enhance early identification of residual disease and optimize therapeutic decision-making. OncoMRD BREAST is the world’s first and only gene signature-powered test for monitoring residual disease in solid tumors. Full article

Star sensors, as the most precise attitude measurement devices currently available, play a crucial role in spacecraft attitude estimation. However, traditional frame-based cameras tend to suffer from target blur and loss under high-dynamic maneuvers, which severely limit the applicability of conventional star sensors in complex space environments. In contrast, event cameras—drawing inspiration from biological vision—can capture brightness changes at ultrahigh speeds and output a series of asynchronous events, thereby demonstrating enormous potential for space detection applications. Based on this, this paper proposes an event data extraction method for weak, high-dynamic space targets to enhance the performance of event cameras in detecting space targets under high-dynamic maneuvers. In the target denoising phase, we fully consider the characteristics of space targets’ motion trajectories and optimize a classical spatiotemporal correlation filter, thereby significantly improving the signal-to-noise ratio for weak targets. During the target extraction stage, we introduce the DBSCAN clustering algorithm to achieve the subpixel-level extraction of target centroids. Moreover, to address issues of target trajectory distortion and data discontinuity in certain ultrahigh-dynamic scenarios, we construct a camera motion model based on real-time motion data from an inertial measurement unit (IMU) and utilize it to effectively compensate for and correct the target’s trajectory. Finally, a ground-based simulation system is established to validate the applicability and superior performance of the proposed method in real-world scenarios. Full article