Search Results (31,002)

RNA N7-methylguanosine (m7G) is an important post-transcriptional epigenetic modification that participates in key biological processes, including RNA processing, stability maintenance, and translational regulation. Medical research has shown that m7G modification and its related regulatory factors are closely related to many neurological diseases and tumors. The accurate prediction of m7G sites is thus critical for understanding their biological functions in diseases. In this work, we propose BF-m7GPred, a dual-branch deep learning framework that integrates single-nucleotide-level embeddings and motif-level embeddings for m7G modification site prediction. Our proposed context-aware module tokenizes RNA sequences using byte-pair encoding and encodes sequences with the pretrained foundation biological model DNABERT2. In parallel, the proposed feature fusion module transforms sequences into multiple feature matrices using multiple traditional encoders. We introduce a feature selection strategy tailored to the encoding characteristics of the two branches. On a benchmark dataset collected from m7G-Hub v2.0, BF-m7GPred achieves superior performance on the independent test set against existing methods. Furthermore, its generalization capability is validated through comparative experiments on 10 diverse RNA modification datasets. Full article

In X-ray security inspection imagery, hazardous object detection is challenged by severe object overlap/occlusion, ambiguous boundaries of small objects, and complex texture representations caused by material diversity. Although YOLOv8-seg provides real-time instance segmentation capability, it still has clear limitations in this application scenario. Specifically, the original SPPF module has limited ability to model long-range spatial dependencies, making it difficult to accurately separate boundaries of densely overlapped objects, while the C2f module is insufficient for multi-scale feature parsing of hazardous items with diverse sizes and materials and introduces feature redundancy, which degrades segmentation accuracy in occluded scenes. To address these issues, this paper proposes an improved YOLOv8-seg framework for X-ray hazardous object detection, termed LM-YOLOv8. For feature enhancement, an SPPF-LSKA module is constructed by integrating large-kernel separable attention with dynamic receptive-field adjustment, thereby improving global contextual modeling and alleviating boundary ambiguity. For multi-scale feature fusion, a C2f-MSC module is designed by combining multi-branch dilated convolutions with the C2f structure to enhance complex contour parsing and cross-scale feature interaction. Experiments on the PIDray dataset show that the proposed method achieves 84.8% mAP50 in instance segmentation, representing an improvement of approximately 4.0 percentage points over the baseline YOLOv8-seg. In addition, the method demonstrates stronger robustness on challenging hard/hidden subsets, validating its effectiveness for X-ray security inspection hazardous object detection. Full article

Bacterial poly(A) polymerases (PAPs) play an important role in RNA metabolism but remain poorly characterized outside Gammaproteobacteria. Here, we cloned and biochemically characterized the first PAP from Alphaproteobacteria, specifically from Marinobacter lipolyticus (Mli PAP). Using homology-based screening against E. coli PAP-1, we identified Mli PAP, sharing 54.8% sequence identity with its E. coli counterpart. The enzyme was expressed in E. coli but formed insoluble inclusion bodies; the active enzyme was purified as a fusion protein with the DsbA protein and used for functional assays. Mli PAP exhibited optimal activity at 30 °C and similar thermostability to E. coli PAP-1. ATP was the preferred substrate, with K_m comparable to E. coli PAP-1 (1.61 mM and 1.70 mM, respectively), and Mg²⁺ (10 mM) was identified as the optimal cofactor. Mli PAP displayed salt-dependent activity, with the most effective polyadenylation in KCl and inhibition by NaCl and ammonium salts, contrasting with the halophilic nature of its host. This study provides the first functional insights into PAPs from Alphaproteobacteria, broadening the understanding of PAP diversity and biochemical properties, as well as the potential applications of PAPs in biotechnology. Full article

UAVs are widely used for all-weather, round-the-clock security inspections in urban and industrial areas. However, pure visible-light systems fail at night or in adverse weather conditions, while pure infrared methods are limited by thermal noise, low spatial resolutions, and high false alarm rates. Multispectral images render the task of object detection highly reliable and robust by providing complementary target feature information. This study suggests a frequency-based cross-attention transformer (FCAT) for multispectral object detection as a solution to this issue. This approach collects cross-modal complementary characteristics, effectively learns and integrates global contextual information via the cross-attention mechanism, and greatly increases multispectral object detection accuracy. At the same time, spatial-domain features are mapped to the frequency domain via the Fourier transform, and the scaled dot product attention is estimated via element-wise product operations, which break through the limitation of traditional spatial-domain matrix multiplication and effectively reduce the computational cost of the model. Additionally, this study independently builds a multi-scene multi-time climate visible–infrared dataset (OPVM-VIRD), which contains 20,025 target instances, to address the issue of the lack of all-weather cross-spectral data in object detection tasks from the perspective of UAVs. Experimental findings from the OPVM-VIRD, M3FD, and FLIR datasets demonstrate that our proposed approach outperforms prevailing state-of-the-art multispectral object detection algorithms on public benchmarks, while the FCAT model achieves an mAP50 score of 94.7% on our custom-built dataset—10.8% higher than ICAF. At the same time, the number of FCAT parameters is 85.26 M, which is significantly lower than that of mainstream models, such as ICAF. Therefore, the FCAT is a change detection strategy with strong model generalization abilities, and it has important application value in the all-day and all-weather security patrol of cities and industrial parks carried out by UAVs. Full article

Object pose estimation is fundamental for robotic manipulation, autonomous driving, and augmented reality, yet recovering the full 9-DoF state (rotation, translation, and anisotropic 3D scale) from RGB-D observations remains challenging for previously unseen objects. Existing methods either rely on instance-specific CAD models, predefined category boundaries, or suffer from scale ambiguity under sparse observations. We propose ProM-Pose, a unified cross-modal temporal perception framework for zero-shot 9-DoF object pose estimation. By integrating language-conditioned generative 3D shape priors as canonical geometric references, an asymmetric cross-modal attention mechanism for spatially aware fusion, and a decoupled pose decoding strategy with temporal refinement, ProM-Pose constructs metrically consistent and semantically grounded representations without relying on category-specific pose priors or instance-level CAD supervision. Extensive experiments on CAMERA25 and REAL275 benchmarks demonstrate that ProM-Pose achieves competitive or superior performance compared to category-level methods, with mAP of $75.0\%$ at $5^{°},2\mathrm{cm}$ and $90.5\%$ at ${10}^{°},5\mathrm{cm}$ on CAMERA25, and $42.2\%$ at $5^{°},2\mathrm{cm}$ and $76.0\%$ at ${10}^{°},5\mathrm{cm}$ on REAL275 under zero-shot cross-domain evaluation. Qualitative results on real-world logistics scenarios further validate temporal stability and robustness under occlusion and lighting variations. ProM-Pose effectively bridges semantic grounding and metric geometric reasoning within a unified formulation, enabling stable and scale-aware 9-DoF pose estimation for previously unseen objects under open-vocabulary conditions. Full article

The increasing risk to Vulnerable Road Users (VRUs) at urban intersections necessitates advanced safety mechanisms capable of operating effectively under diverse conditions, including adverse weather like heavy rain. While optical sensors such as cameras and LiDAR often degrade in poor visibility, Radio Frequency (RF)-based systems offer resilient, all-weather tracking. This paper presents a novel approach to enhancing VRU protection by fusing two RF modalities: radar sensors and Ultra-Wideband (UWB) technology, a strong candidate for Joint Communication and Sensing (JCS). The research, conducted as part of the VIDETEC-2 project, addresses the limitations of existing vehicle-based and infrastructure-based systems, particularly in scenarios involving occlusions and blind spots. By leveraging radar’s environmental robustness alongside UWB’s precise, cost-effective short-range communication and localization, the proposed system delivers the framework for continuous vehicle and VRU tracking. The fusion of these sensor modalities, managed through a hybrid Kalman filter approach integrating an Unscented Kalman Filter (UKF) and an Extended Kalman Filter (EKF), allows reliable VRU tracking even in challenging urban scenarios. The experimental results demonstrate a reduction in tracking uncertainty and highlight the system’s potential to serve as a more accurate and responsive safety mechanism for VRUs at intersections. This work contributes to the development of intelligent road infrastructures, laying the foundation for future advancements in urban traffic safety. Full article

Cylinders are common in both industrial and daily settings. Accurate geometric fitting of their parameters, including position, orientation, and radius, is important in real-world perception tasks and industrial applications. At present, consumer-level RGB-D cameras provide three-dimensional (3D) point cloud data with acceptable accuracy and are widely adopted in various sensing applications. Consequently, this task is typically formulated as a geometric fitting problem based on point cloud data. However, point cloud data acquired from such sensors often contain noise, particularly when scanning curved surfaces, which directly degrades the performance of point cloud-based fitting methods. In this paper, we propose an edge–point cloud fusion approach for the geometric fitting of cylinder parameters from single-view RGB-D data. Our approach leverages two-dimensional (2D) image-domain edge constraints together with point cloud data, then fuses them in a unified formulation to jointly optimize cylinder parameters. By explicitly incorporating reliable edge information, our method effectively mitigates the effects of noise in point cloud data. We evaluate the proposed method using real-world RGB-D data, and the experimental results show that our approach achieves significant improvements in both accuracy and robustness. Full article

In this study, 10 popular commercial oat milk samples were analyzed for sensory quality and flavor chemistry using the Ideal Profile Method (IPM), electronic nose (E-nose), and gas chromatography-mass spectrometry (GC-MS). Based on consumer cognitive mapping of ideal products, samples were classified into “Ideal-like” and “Ideal-exceeding” categories. Ideal-like products exhibited light white appearance, pronounced oatiness, moderate sweetness and burntness, and low graininess, presenting a balanced flavor profile, whereas Ideal-exceeding samples surpassed consumer expectations in sweetness or graininess intensity, delivering stronger sensory stimulation. Furthermore, sensory differentiation among categories primarily stemmed from synergistic effects of lipid oxidation levels (e.g., 3,5-octadien-2-one) and physical stability (fiber and protein content affecting particle size distribution). This classification framework reveals that ideal sensory quality can be achieved through diverse physicochemical pathways in commercial oat milk, providing theoretical guidance for product formulation optimization and quality standardization. Full article

Accurate assessment of cotton emergence rates is essential for precision agriculture management, and unmanned aerial vehicle (UAV) imagery provides a scalable means for field-level monitoring. However, cotton seedling detection from UAV images faces persistent challenges: individual seedlings appear as small targets with diverse morphologies across varying flight altitudes; strong plastic film reflections, weeds, and soil cracks introduce substantial background interference; and “missing seedling” targets, which manifest as negative space features, exhibit high similarity to background noise. Existing CNN–Transformer hybrid detection architectures are limited by fixed convolutional receptive fields that cannot adapt to multi-scale target variations, attention mechanisms that lack explicit directional geometric modeling, and interpolation-based upsampling that attenuates high-frequency edge details of small targets. To address these issues, this paper proposes DDF-DETR (Dynamic-Direction-Frequency Detection Transformer), a multi-scale spatial context detection method based on RT-DETR. The method incorporates three components: a Dynamic Gated Mixer Block (DGMB) for adaptive multi-scale feature extraction with background noise suppression, a Direction-Aware Adaptive Transformer Encoder (DAATE) for directional geometric feature modeling at linear computational complexity, and a Frequency-Aware Sub-pixel Upsampling Network (FASN) for high-frequency detail recovery in the feature pyramid. On the self-constructed Xinjiang cotton field dataset, DDF-DETR achieves 83.72% mAP@0.5 and 63.46% mAP@0.5:0.95, representing improvements of 2.38% and 5.28% over the baseline RT-DETR-R18, while reducing the parameter count by 30.6% and computational cost to 42.8 GFLOPs. Generalization experiments on the VisDrone2019 and TinyPerson datasets further validate the robustness of the proposed method for small target detection across different scenarios. Full article

Transmission lines traversing forested areas pose significant fire risks, necessitating timely and efficient inspection mechanisms. Traditional manual patrols and cloud-based UAV inspections suffer from high latency, bandwidth dependence, and delayed response times. To address these challenges, this study proposes an integrated, real-time UAV-edge computing system for the early identification of fire risks and structural hazards along transmission corridors. The system integrates a DJI M300 RTK UAV with a Manifold 2-G edge computing unit (based on NVIDIA Jetson TX2), deploying a lightweight, TensorRT-optimized YOLOv8 model. By leveraging FP16 precision quantization and operator fusion, the system achieves a real-time inference speed of 32 FPS on the embedded platform. Furthermore, a custom Payload SDK integration ensures automated image acquisition and closed-loop data transmission via a dual-mode (4G/5G + Wi-Fi) communication link. Field experiments demonstrate that the system significantly reduces data transmission latency while maintaining high detection accuracy (mAP > 94%), providing a robust and replicable solution for intelligent power grid maintenance in resource-constrained environments. Full article

Accurate alfalfa biomass prediction is crucial for pasture management and sustainable livestock production. However, traditional methods often perform poorly under complex field conditions. To address the limited prediction accuracy of traditional methods under complex planting environments, this study proposes an alfalfa biomass prediction method combining multispectral and LiDAR data with ensemble learning model. Based on the multispectral images acquired by unmanned aerial vehicle (UAV) and airborne LiDAR data, the spectral features, three-dimensional structural features, and their interaction features are systematically extracted at the quadrat scale, and a high-quality modeling dataset is constructed by feature selection. Secondly, an ensemble model for alfalfa biomass prediction was constructed, which was composed of random forest, extra trees, and histogram gradient boosting. After model training, the coefficient of determination (R²) of the integrated model on the test set reached 0.813, and the root mean square error (RMSE) and mean absolute error (MAE) were 0.178 kg m⁻² and 0.146 kg m⁻², which were significantly better than those of similar single models. Under feature combinations, the fusion model was better than that of spectral indices only (R² = 0.773) and LiDAR traits only (R² = 0.576), and the model achieved the highest accuracy from bud emergence to early flowering (R² = 0.917). The overall prediction error of the model was approximately normal distribution, and the absolute error of more than 65% of the samples was less than 0.2. However, there was still a trend of underestimation in the high biomass interval. This research showed that the multimodal data fusion and ensemble learning method could achieve high-precision prediction of alfalfa biomass, which provided reliable technical support for pasture resources monitoring and precision agriculture management. Full article

Although recent studies have achieved remarkable progress in remote sensing image understanding by fusing spatial- and frequency-domain features to leverage their complementary strengths, they still face two key limitations: frequency modeling remains rigid due to static constraints, limiting adaptability, and spatial–frequency fusion often suffers from poor generalization and instability across tasks and network depths. Our experiments reveal that the relative importance of low- and high-frequency components varies dynamically across feature hierarchies and training stages, indicating that frequency information is inherently task-dependent and stage-aware. Motivated by these observations, we propose the Frequency–Spatial Self-Calibrated Network (FSSC-Net), a task-driven framework for adaptive frequency modeling and collaborative spatial–frequency fusion. FSSC-Net incorporates a lightweight, plug-and-play self-calibrated frequency modeling mechanism, comprising a Dynamic Frequency Selection Module and a Task-Guided Calibration Fusion Module. This mechanism adaptively modulates frequency responses via soft masks, enabling dynamic extraction of task-relevant low- and high-frequency components and effective alignment between spatial- and frequency-domain features. Moreover, we present a systematic analysis of frequency importance across tasks and training stages, providing quantitative evidence for the necessity of task-calibrated frequency modeling. Extensive experiments on various benchmarks demonstrate that FSSC-Net consistently outperforms state-of-the-art methods, exhibiting strong task adaptability and robust cross-task generalization. Full article

Accurate carbon price forecasting is vital for risk management but is hindered by high volatility and sensitivity to external shocks. Existing multivariate models typically overlook unstructured news sentiment, failing to capture irrational fluctuations driven by market public opinion. To address this, this paper proposes VBN-Net, a hybrid model integrating carbon-specific news sentiment with Variational Mode Decomposition (VMD). Two core innovations are presented: First, a multi-modal input mechanism combines structured financial data with unstructured carbon news sentiment to effectively capture policy-driven shocks. Second, a Sequential Beluga Whale Optimization strategy is designed to adaptively optimize feature engineering in steps. Unlike conventional approaches, the VBN-Net first employs VMD for denoising and frequency decomposition, and then optimizes the fusion weights of news sentiment across different frequency components derived from multi-source news. This strategy effectively overcomes the subjectivity of manual parameter selection, providing high-quality features for a fixed CNN-BiLSTM backbone. By integrating VMD-based denoising with optimized multi-source news fusion, the model achieves consistent performance improvements across multiple evaluation metrics. The empirical findings validate the effectiveness of the proposed model in enhancing forecasting performance, thereby providing a reliable analytical tool for participants in the carbon market. Full article

Surface defect detection is a core of industrial product quality control, vital for ensuring product reliability and production efficiency. However, due to diverse types and significant size variations of industrial surface defects—especially minute or complex ones—accurate feature extraction and efficient detection remain major challenges, and existing You-Only-Look-Once (YOLO) methods struggle to meet high-precision demands. This paper proposes a symmetry-aware YOLOv13-based industrial surface defect detection network. First, a Multi-level Feature Enhancement Module (MFEM) is designed, combining a star-shaped architecture with the CBAM attention mechanism to enhance defect feature discriminability via multi-branch feature interaction and nonlinear expression, while compensating for detail loss from multi-layer depth-wise separable convolutions (DSConv). The symmetric dual-branch structure in MFEM improves feature balance and structural consistency. Second, the Spatial Pixel Global Attention (SPGA) module is introduced to supplement detail information during feature pyramid transmission and enhance multi-scale feature fusion efficiency, while maintaining symmetric feature distribution. Third, the HyperACE module is improved using a multi-branch hypergraph structure to enhance long-range dependency modeling and local feature representation. On the GC10-DET dataset, the improved model achieved 69.6% Precision, 66.1% Recall, and 67.0% mAP@50, demonstrating superior performance while maintaining real-time capability. Full article

The effectiveness of unmanned aerial vehicle (UAV) swarms in complex and dynamic environments relies heavily on real-time and consistent situational awareness throughout the network. Effective event-driven correction mechanisms must meet two essential requirements: they must robustly handle uncertainties inherent in challenging situations and ensure strict commutativity between weighting and fusion operations to allow for distributed implementation. To tackle the critical issue of uncertain information processing, this work adopts Dempster–Shafer evidence theory because of its advantages in representing and managing epistemic uncertainty. However, the traditional discounting operation in evidence theory does not satisfy commutativity with the combination rule, which poses a significant barrier to distributed implementation. To address this limitation, we introduce a novel evidence weakening operation that is rigorously proven to be commutative with Dempster’s combination rule. This theoretical advancement enables the design of a distributed protocol that supports efficient propagation and parallel computation of corrections. Simulation results demonstrate that the proposed protocol achieves a zero correction error rate, along with approximately 40% reduction in latency and 35% savings in communication overhead compared to conventional serial discounting methods, while maintaining sublinear scalability. This approach provides a feasible solution for robust and efficient information fusion in dynamic multi-agent systems. Full article