Search Results (11,947)

Oncofetal reprogramming has recently emerged as a critical concept in translational cancer research, particularly for its role in driving therapeutic resistance across a variety of malignancies. This biological process refers to a pattern of gene expression that is restricted to embryogenesis, but becomes expressed again in a subpopulation of cancer cells. These genes are typically suppressed after embryogenesis, and their aberrant re-expression in tumors endows cancer cells with stem-like properties and enhanced adaptability. The goal of this review is the following: (i) comprehensively examine the multifaceted nature of oncofetal reprogramming; (ii) elucidate its underlying molecular mechanisms, including its regulators and effectors; and (iii) evaluate its consequences for the therapeutic response in different cancer types. We comprehensively integrate the latest findings from colorectal, breast, lung, liver, and other cancers to provide a detailed understanding of how oncofetal programs interfere with tumor response to treatment. Among the candidates, YAP1 and AP-1 have emerged as central transcriptional drivers of this reprogramming process, especially in colorectal and breast cancers. We also explore the distinct expression patterns of oncofetal genes across different tumor types and how these patterns correlate with treatment outcomes and patient survival. Lastly, we propose a dual-targeting therapeutic strategy that simultaneously targets both cancer stem cells and oncofetal-reprogrammed populations as a more effective approach to overcome resistance and limit recurrence. Full article

In aquaculture production, manual or fixed-schedule feeding often fails to match the real-time feeding level of fish schools, and overfeeding can lead to feed wastage and water-quality deterioration, which has become a major bottleneck for both large-scale farming efficiency and environmental sustainability. During feeding, intense competition and jumping behaviors generate splashes of varying magnitudes, which can serve as an indirect visual proxy for hunger intensity. In this study, we constructed a frame-level splash-annotated dataset and performed data preprocessing. Building upon YOLO11 pretrained weights, we introduced a P2–P5 four-scale detection head to enhance small-splash recognition, injected EGMA into the backbone C3k2 blocks, and replaced stride-2 downsampling convolutions with a three-branch ADown operator. On the validation set, the proposed YOLO11-PEGA achieved a precision of 0.86 and a recall of 0.80, with mAP@0.5 exceeding 0.80 and mAP@0.5–0.95 exceeding 0.30. Compared with the baseline model, the parameter count was reduced by 72.3%. The results demonstrate that the proposed model maintains stable detection and evaluation performance under complex environmental conditions, providing actionable decision support for feeding-threshold setting, feeding-time determination, and feed-amount adjustment. Full article

To meet the need for efficient and precise detection of people and obstacles in the actual operating environment of a gantry crane, a detection model based on an improved YOLOv5s was proposed which incorporates the parameter-free SimAM attention mechanism to enhance obstacle feature extraction capabilities, employs the EIoU loss function to optimize bounding box regression accuracy, and utilizes preprocessing techniques to improve input image quality. Training experiments on humans and simple simulated obstacles demonstrate that the improved model achieves significantly higher recognition accuracy and speed compared to the original YOLOv5 model. The improved model was applied to the recognition experiments of reducer obstacles under varying sizes, visibility levels, and distance conditions, and the comparative experiments were conducted with mainstream YOLO models, as well as different attention mechanisms and loss functions. The results show that the mAP@0.5 of the improved model achieves 0.884 with superior recognition performance and used lower computational resource requirements, providing a reliable solution for real-time obstacle detection in crane operation scenarios. Full article

Ensuring the structural integrity of high-energy piping systems is a critical requirement for the safe operation of nuclear power plants. This paper presents the design, implementation, and three-year operational validation of a three-dimensional displacement monitoring system installed on the steam generator blowdown pipeline of the Krško Nuclear Power Plant. The system was developed to verify that the plant’s operating procedures will not induce excessive dynamic displacements during operation. The measurement system configuration utilizes three non-collinear inductive displacement transducers from Hottinger Baldwin Messtechnik (HBM WA/500 mm-L), mounted via miniature universal joints to a reference plate and to a defined observation point on the pipeline. This arrangement enables the real-time monitoring of X, Y, and Z displacements within a spherical measurement volume of approximately 0.5 m. Data are continuously acquired via an HBM QuantumX MX840B amplifier and processed using CATMAN Easy-AP software through a fiber-optic communication link between the containment and control areas. The system has operated continuously for more than three years under elevated temperature and radiation conditions, confirming its reliability and robustness. The correlation of the measured displacements with process parameters such as the flow rate, pressure, and temperature provides valuable insight into transient events and contributes to predictive maintenance strategies. The presented methodology demonstrates a practical and radiation-tolerant approach for the continuous structural monitoring of nuclear plant piping systems. Full article

Ship detection in optical images is critical for maritime supervision but faces challenges from scale variations and complex backgrounds. Existing detectors often struggle to balance global context modeling with computational efficiency. To address this, we propose Contextual Efficient Hierarchical DETR (CEH-DETR), an efficient framework for multi-scale ship detection. First, we introduce the Cross-stage Parallel State Space Hidden Mixer (CPSHM) backbone, integrating State Space Models with CNNs to capture global dependencies with linear complexity. Second, the Efficient Adaptive Feature Integration (EAFI) module reduces attention complexity to linear using Token Statistics-based Attention. Third, the Hierarchical Attention-guided Feature Pyramid Network (HAFPN) effectively fuses multi-scale features while preserving spatial details. Experiments on the ABOships dataset demonstrate that CEH-DETR achieves a superior balance between accuracy and efficiency. Relative to the baseline RT-DETR, our approach achieves a parameter reduction of 25.6% while increasing mAP@50 by 2.0 percentage points and boosting inference speed to 133.7 FPS (+112.1%), making it highly suitable for real-time maritime surveillance. Full article

Background/Objectives: Angelman syndrome is a neurodevelopmental disorder resulting from a deficiency of the maternally inherited UBE3A gene. In mature neurons, UBE3A expression is restricted to the maternal allele due to tissue-specific genomic imprinting, while the paternal allele is silenced in cis by the UBE3A antisense transcript (UBE3A-ATS). To date, numerous strategies have been employed to activate paternal UBE3A expression. In this study, we utilized RNA interference (RNAi) to investigate the downregulation of UBE3A-ATS in mouse primary neurons and human induced pluripotent stem cell (iPSC)-derived neurons. Methods: To induce paternal UBE3A expression, we employed small interfering RNA (siRNA) oligonucleotides (20 mouse candidates and 47 human candidates) and lentiviral short hairpin RNA (LV-shRNA) targeting SNORD115 to suppress UBE3A-ATS expression in both mouse primary neurons and iPSCs. Subsequently, we assessed the expression levels of Angelman syndrome-related neighboring and target genes at the transcript and, where applicable, protein levels. Results: Following treatment with siSnord115 or LV-shSnord115, we observed a reduction in Ube3a-ATS and a corresponding activation of paternal Ube3a RNA and protein expression in both Ube3a^P-YFP/m+ and Ube3a^p+/m− mouse primary neurons. A similar effect was observed upon treatment with LV-shSNORD115s in human iPSC-derived neurons. Conclusions: shRNA-mediated inhibition of Ube3a-ATS by targeting Snord115 effectively restores Ube3a/UBE3A expression in both mouse neurons and human iPSCs. While promising, the mild reduction in Snord116 raises concerns about potential off-target effects. AAV-based delivery of shRNA shows potential, but its translational applicability remains to be evaluated in vivo. Full article

To address the challenges of limited detection accuracy and the difficulty of deployment on edge devices caused by dust obstruction, low illumination, and complex background interference in coal mine conveyor belt foreign object detection, this paper proposes an improved algorithm model, RTA-YOLOv11, based on the YOLOv11 framework. First, a Receptive Field Enhancement Module (RFEM) is utilized to expand the field of view by fusing multi-scale perception paths, strengthening the network’s semantic capture capability for subtle targets. Second, a Triplet Attention mechanism is introduced to suppress environmental noise and enhance the saliency of low-contrast foreign objects through cross-dimensional joint modeling of spatial and channel information. Finally, a lightweight detection head based on MBConv is designed, utilizing inverted bottleneck structures and re-parameterization strategies to compress redundant parameters and improve deployment efficiency on edge devices. Experimental results indicate that the mAP@0.5 of the improved RTA-YOLOv11 model is 4.0 percentage points higher than that of the original YOLOv11, with an inference speed of 79 FPS and a reduction in parameters of approximately 22%. Compared with algorithms such as Faster R-CNN, SSD, and YOLOv8, this model demonstrates a superior balance between accuracy and speed, providing an efficient and practical solution for intelligent mine visual perception systems. Full article

Accurate detection of pressure-relief boreholes is crucial for evaluating drilling quality and monitoring safety in coal mine roadways. Nevertheless, the highly challenging underground environment—characterized by insufficient lighting, severe dust and water mist disturbances, and frequent occlusions—poses substantial difficulties for current object detection approaches, particularly in identifying small-scale and low-visibility targets. To effectively tackle these issues, a lightweight and robust detection framework, referred to as YOLO-DFBL, is developed using the YOLOv11n architecture. The proposed approach incorporates a DualConv-based lightweight convolution module to optimize the efficiency of feature extraction, a Frequency Spectrum Dynamic Aggregation (FSDA) module for noise-robust enhancement, and a Biformer (Bi-level Routing Transformer)-based routing attention mechanism for improved long-range dependency modeling. In addition, a Lightweight Shared Convolution Head (LSCH) is incorporated to effectively decrease the overall model complexity. Experimental results on a real coal mine roadway dataset demonstrate that YOLO-DFBL achieves an mAP@50:95 of 78.9%, with a compact model size of 1.94 M parameters, a computational complexity of 4.7 GFLOPs, and an inference speed of 157.3 FPS, demonstrating superior accuracy–efficiency trade-offs compared with representative lightweight YOLO variants and classical detectors. Field experiments under challenging low-illumination and occlusion environments confirm the robustness of the proposed approach in real mining scenarios. The developed method enables reliable visual perception for underground drilling equipment and facilitates safer and more intelligent operations in coal mine engineering. Full article

Wind turbine blades (WTBs) are inevitably exposed to harsh environmental conditions, leading to surface damages such as cracks and corrosion that compromise power generation efficiency. While UAV-based inspection offers significant potential, it frequently encounters challenges in handling irregular defect shapes and preserving fine edge details. To address these limitations, this paper proposes DMR-YOLO, an Improved Wind Turbine Blade Surface Damage Detection Method Based on YOLOv8. The proposed framework incorporates three key innovations: First, a C2f-DCNv2-MPCA module is designed to dynamically adjust feature weights, enabling the model to more effectively focus on the geometric structural details of irregular defects. Secondly, a Multi-Scale Edge Perception Enhancement (MEPE) module is introduced to extract edge textures directly within the network. This approach prevents the decoupling of edge features from global context information, effectively resolving the issue of edge information loss and enhancing the recognition of small targets. Finally, the detection head is optimized using a Re-parameterized Shared Convolution Detection Head (RSCD) strategy. By employing weight sharing combined with Diverse Branch Blocks (DBB), this design significantly reduces computational redundancy while maintaining high localization accuracy. Experimental results demonstrate that DMR-YOLO outperforms the baseline YOLOv8n, achieving a 1.8% increase in mAP@0.5 to 82.2%, with a notable 3.2% improvement in the “damage” category. Furthermore, the computational load is reduced by 9.9% to 7.3 GFLOPs, while maintaining an inference speed of 92.6 FPS, providing an effective solution for real-time wind farm defect detection. Full article

The efficacy of Underwater Camouflaged Object Detection (UCOD) is fundamentally constrained by severe boundary ambiguity, where biological mimicry blends targets into complex backgrounds and aquatic optical degradation erodes edge details. We propose a lightweight boundary perception detector named CAR-YOLO (Camouflage Ambiguity Resolution YOLO). Specifically, a frequency-domain dual-path mechanism (FRM-DWT/EG-IWT) leverages selective wavelet aggregation and dynamic injection to recover high-frequency edges. Subsequently, these high-frequency cues are synergized with low-frequency semantic information via the Low-level Adaptive Fusion (LAF) module. To further address noisy samples, an Uncertainty Calibration Head (UCH) refines supervision via prediction consistency. Finally, we constructed specialized datasets based on public data for training and evaluation, including UCOD10K and UWB-COT220. On UCOD10K, CAR-YOLO achieves 27.1% mAP50–95, surpassing several state-of-the-art (SOTA) methods while reducing parameters from 2.58 M to 2.43 M and GFLOPs from 6.3 to 5.9. On the challenging UWB-COT220 benchmark, the model attains 30.7% mAP50–95, marking a 7.7-point improvement over YOLOv11. Furthermore, cross-domain experiments on UODD demonstrate strong generalization. These results indicate that CAR-YOLO effectively mitigates boundary ambiguity, achieving an optimal balance between accuracy, robustness, and efficiency. Full article

The increasing crisis of antimicrobial resistance requires innovative therapeutic strategies that can overcome the limitations of conventional antibiotics. Based on our previous finding that AP10 (a derivative of AP29) possesses antimicrobial activity but lacks thermal stability, we rationally redesigned ten new AP10 analogues to enhance functional robustness while maintaining efficacy. Among these, AP10RW is identified as the optimal candidate due to its exceptional broad-spectrum activity against both drug-sensitive and multidrug-resistant (MDR) bacterial pathogens. Structural analysis reveals that AP10RW adopts an environmentally responsive conformation, transitioning from random coil to amphiphilic α-helix in membrane-mimicking environments, while demonstrating remarkable stability under challenges including serum exposure, varying pH, high salt concentrations, and thermal stress. Mechanistic studies indicate that AP10RW exerts its effects through multiple bactericidal mechanisms involving initial high-affinity binding to bacterial characteristic molecules (LTA, LPS and PGN), followed by rapid membrane depolarization, ultrastructural damage and the induction of lethal oxidative stress. Notably, this potent antimicrobial efficacy is coupled with exceptional biosafety, demonstrating little hemolysis and negligible cytotoxicity against mammalian cells. This systematic optimization represents a significant advancement in antimicrobial peptide engineering. We have successfully transformed a thermally unstable peptide into a robust therapeutic candidate and positioned AP10RW as a promising clinical candidate for addressing the growing threat of multidrug-resistant infections. Full article

To ensure that laser chips meet stringent reliability standards in practical applications, comprehensive limit testing and reliability verification must be performed before deployment. This paper proposes an electroluminescence (EL) imaging-based detection method for Catastrophic Optical Mirror Damage (COMD) and Catastrophic Optical Bulk Damage (COBD). A novel model, PCMBA-YOLO, is developed on the YOLOv12 framework, integrating a Multi-Branch Aided Feature Pyramid Network (MBAFPN) and a Pinwheel Convolution (PConv) structure to enhance weak-signal feature extraction and expand the receptive field with minimal parameters. Furthermore, a Shape-IoU-based regression loss is introduced to model bounding-box shape and scale, improving localization precision and convergence. Experimental results show that PCMBA-YOLO achieves 99.4% mAP@0.5, 97.6% Precision, and 98.7% Recall, with a 14% reduction in parameters compared to the baseline. The proposed method demonstrates superior accuracy, efficiency, and robust generalization, providing a high-performance solution for automated visual inspection in semiconductor manufacturing. Full article

To address the challenge of defect detection in tempered glass panel production rising from sample scarcity, this paper proposes a few-shot detection methodology that integrates an enhanced Stable Diffusion model with Mask R-CNN. Specifically, the approach utilizes a Mask Encoder to optimize the Stable Diffusion architecture, employing the Structural Similarity Index Measure (SSIM) to evaluate sample quality. This process generates high-fidelity virtual samples to construct a hybrid dataset for training data augmentation. Furthermore, a resource isolation strategy is adopted to facilitate online detection using an improved semi-supervised Mask R-CNN framework. Experimental results demonstrate that the proposed scheme effectively resolves detection difficulties for eight defect types, including edge chipping and scratches. The method achieves an mAP50 of 81.5%, representing a nearly 47% improvement over baseline methods relying solely on real samples, thereby realizing high-precision and high-efficiency industrial defect detection. Full article

Cauliflower (Brassica oleracea var. botrytis) curd formation is a highly complex developmental process governed by tightly coordinated genetic and physiological regulation. Here, we performed transcriptome sequencing of curd and peduncle tissues across multiple developmental stages, generating 171.52 Gb of high-quality data. Genes associated with photosynthesis and glucosinolate biosynthesis were strongly upregulated in the shoot apical meristem (SAM), highlighting substantial metabolic investment during the pre-initiation phase of curd morphogenesis. Key floral transition regulators, particularly AP2 and MADS-box transcription factors, were activated to drive the vegetative-to-reproductive switch and initiate curd primordia, ultimately giving rise to the arrested inflorescence architecture characteristic of cauliflower. Furthermore, hormone signaling pathways—including auxin (AUX), jasmonic acid (JA), and brassinosteroid (BR)—showed marked activation during SAM proliferation and peduncle elongation, underscoring their crucial roles in structural patterning. Collectively, our findings delineate an integrated regulatory network that links metabolic activity, hormone signaling, and developmental programs, providing novel molecular insights into curd formation and identifying potential breeding targets for the genetic improvement of Brassicaceae crops. Full article

Precise identification of weeds at the maize seedling stage is pivotal for implementing Site-Specific Weed Management and minimizing herbicide environmental pollution. However, the performance of existing lightweight detectors is severely bottlenecked by unstructured field environments, characterized by the “green-on-green” spectral similarity between crops and weeds, diminutive seedling targets, and complex mutual occlusion of leaves. To address these challenges, this study proposes SLD-YOLO11, a topology-reconstructed lightweight detection model tailored for complex field environments. First, to mitigate the feature loss of tiny targets, a Lossless Downsampling Topology based on Space-to-Depth Convolution (SPD-Conv) is constructed, transforming spatial information into depth channels to preserve fine-grained features. Second, a Decomposed Large Kernel Attention (D-LKA) mechanism is designed to mimic the wide receptive field of human vision. By modeling long-range spatial dependencies with decomposed large-kernel attention, it enhances discrimination under severe occlusion by leveraging global structural context. Third, the DySample operator is introduced to replace static interpolation, enabling content-aware feature flow reconstruction. Experimental results demonstrate that SLD-YOLO11 achieves an mAP@0.5 of 97.4% on a self-collected maize field dataset, significantly outperforming YOLOv8n, YOLOv10n, YOLOv11n, and mainstream lightweight variants. Notably, the model achieves Zero Inter-class Misclassification between maize and weeds, establishing high safety standards for weeding operations. To further bridge the gap between visual perception and precision operations, a Visual Weed-Crop Competition Index (VWCI) is innovatively proposed. By integrating detection bounding boxes with species-specific morphological correction coefficients, the VWCI quantifies field weed pressure with low cost and high throughput. Regression analysis reveals a high consistency (R² = 0.70) between the automated VWCI and manual ground-truth coverage. This study not only provides a robust detector but also offers a reliable decision-making basis for real-time variable-rate spraying by intelligent weeding robots. Full article