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Tunnel opening or excavation in the strain-softening rock masses primarily leads to initial stress release and rock deformation. The increasing plastic shear strain induces a nonlinear variation of dilatancy in rock masses. Therefore, tunnel stability is strongly dependent on the stress state and the strain-softening and dilatancy behaviors of rock masses. In view of this, this paper proposes an elastoplastic finite difference solution based on the triple-shear element (TS) unified strength criterion (USC) and the nonassociative flow law; thereafter, some typical examples are referred to verify its validity. This solution allows for the description of the intermediate principal stress effect, the nonlinear strain-softening and dilatancy behaviors of the rock mass. Parametric analysis finally determines the influence characteristics of the intermediate principal stress effect (b), the critical softening parameter (η∗), softening factor (δ), and support force (pi) on the dilation angle (ψ) of the rock mass in the plastic zone. The findings confirm that the intermediate principal stress mainly affects peak values of ψ, which increases as b grows. The rate of change of ψ mainly depends on η∗, which tends to be slower with increasing η∗. The variation pattern of ψ in the residual zone under different δ is generally consistent, while the variability of ψ in the softening zone is significant. For a given intermediate principal stress state and strain-softening behavior of the rock mass, the values and the developed form of ψ in the plastic zone is independent of pi.

Foam concrete (FC) has the potential of being an alternative to ordinary concrete, as it reduces dead loads on the structure and foundation, contributes to energy conservation, and lowers the cost of production and labor cost during the construction and transportation. The paper reports a state-of-the-art review of foam concrete in terms of its components, manufacturing and material properties like drying shrinkage, compressive strength, stability and pore structure, etc. In view of the significance of the FC in engineering construction, it also includes a state-of-the-practice review of foam concrete in tunnel and underground engineering. Some shortcomings and technical limitations as well as emerging direction for performance enhancement of FC are also discussed. Current review concludes that the long-term performance and enhancement-associated properties need to be deeply investigated. This study can help alleviate consumer concerns and further encourage the wider application of FC in civil engineering.

In smart ranch management, cattle behavior recognition and tracking play a crucial role in evaluating animal welfare. To address the issues of missed and false detections caused by inter-cow occlusions and infrastructure obstructions in the barn environment, this paper proposes a multi-object tracking method called YOLO-BoT. Built upon YOLOv8, the method first integrates dynamic convolution (DyConv) to enable adaptive weight adjustments, enhancing detection accuracy in complex environments. The C2f-iRMB structure is then employed to improve feature extraction efficiency, ensuring the capture of essential features even under occlusions or lighting variations. Additionally, the Adown downsampling module is incorporated to strengthen multi-scale information fusion, and a dynamic head (DyHead) is used to improve the robustness of detection boxes, ensuring precise identification of rapidly changing target positions. To further enhance tracking performance, DIoU distance calculation, confidence-based bounding box reclassification, and a virtual trajectory update mechanism are introduced, ensuring accurate matching under occlusion and minimizing identity switches. Experimental results demonstrate that YOLO-BoT achieves a mean average precision (mAP) of 91.7% in cattle detection, with precision and recall increased by 4.4% and 1%, respectively. Moreover, the proposed method improves higher order tracking accuracy (HOTA), multi-object tracking accuracy (MOTA), multi-object tracking precision (MOTP), and IDF1 by 4.4%, 7%, 1.7%, and 4.3%, respectively, while reducing the identity switch rate (IDS) by 30.9%. The tracker operates in real-time at an average speed of 31.2 fps, significantly enhancing multi-object tracking performance in complex scenarios and providing strong support for long-term behavior analysis and contactless automated monitoring.

Background Breast cancer is the most common cancer type in female. As microRNAs play vital role in breast cancer, this study aimed to explore the molecular mechanism and clinical value of miR-21 in breast cancer. Methods qRT-PCR was performed to detect miR-21 levels in plasma of 127 healthy controls, 82 benign breast tumor, 252 breast cancer patients, as well as in breast cancer cell lines. Transwell and wound healing assay were used to analyze breast cancer metastasis in response to miR-21 inhibitor. Colony formation and eFluor™ 670 based flow cytometric analysis were used to test breast cancer proliferation following miR-21 inhibitor treatment. Leucine zipper transcription factor-like 1 (LZTFL1), the target gene of miR-21 was predicted by MIRDB, TargetScan 5.1, PicTar and miRanda. Survival analysis of LZTFL1 levels in breast cancer prognosis was estimated with the Kaplan–Meier method by log-rank test according to data from the Cancer Genome Atlas. Luciferase activity assay was performed to confirm the regulation of miR-21 on LZTFL1. LZTFL1 siRNA and miR-21 inhibitor were co-transfected to breast cancer cells, then cell proliferation, migration and epithelial–mesenchymal transition (EMT) makers were tested. BALB/c nude mice were injected in situ with Hs578T cells stably overexpressing miR-21. Breast tumor growth, metastasis and the expression of EMT markers or LZTFL1 were detected in vivo. Results Plasma miR-21 levels were elevated in breast cancer patients compared with healthy controls and benign breast tumor patients, and the miR-21 levels were significantly decreased after surgery comparing with pre operation in 44 patients. Inhibition of miR-21 suppressed cell proliferation and metastasis in breast cancer cells. LZTFL1 was identified as a novel target gene of miR-21. Knockdown of LZTFL1 overcame the suppression of miR-21 inhibitor on cell proliferation, metastasis and the expression of EMT markers in breast cancer cells. miR-21 overexpression promoted breast cancer cell proliferation and metastasis in vivo. Conclusions These results indicate that plasma miR-21 level is a crucial biomarker for breast cancer diagnosis and targeting miR-21–LZTFL1–EMT axis might be a promising strategy in breast cancer therapy. Trial registration Retrospectively registered.

The operational status of photovoltaic modules directly impacts power generation efficiency, making rapid and precise fault detection crucial for intelligent operation and maintenance of Photovoltaic (PV) power plants. Addressing the perceptual limitations of single-modal images in complex environments, this study constructs an RGBIRPV multimodal dataset tailored for centralized PV power plants and proposes an RFE-YOLO model. This model enhances detection performance through three core mechanisms: The RC module employs a CBAM-based attention mechanism for multi-parameter feature extraction, utilizing heterogeneous RC_V and RC_I architectures to achieve differentiated feature enhancement for visible and infrared modalities. The lightweight adaptive fusion FA module introduces learnable modality balance and attention cascading mechanisms to optimize multimodal information fusion. Concurrently, the multi-scale enhanced EVG module based on GSConv achieves synergistic representation of shallow details and deep semantics with low computational overhead. The experiment employed an 8:1:1 data partitioning scheme. Compared to the YOLOv11n model employing feature-level mid-fusion, the model proposed in this study achieves improvements of 2.9%, 1.8%, and 1.5% in precision, mAP@50, and F1 score, respectively. It effectively meets the demand for rapid and accurate detection of PV module failures in real power plant environments, providing an effective technical solution for intelligent operation and maintenance of photovoltaic power plants.

Photovoltaic (PV) module faults, such as hotspots, diode short circuits, occlusions, and shadows, degrade power generation efficiency and safety. Existing manual inspection and single-modality methods show limited robustness under complex conditions, especially with illumination variations and weak thermal responses, while most deep learning approaches fail to exploit the complementarity of visible and infrared modalities. To address this issue, a dual-modality visible-infrared fusion framework based on YOLO11 is proposed, integrating a multi-scale pyramid pooling and dilated convolution module (MSPPD), a gradient-aware fusion module (GAFusion), and a dynamic convolution and element-wise scaling detection head (Detect-DEhead). GAFusion enhances cross-modal structural consistency and reduces feature misalignment and information loss during fusion by introducing gradient-aware feature interaction. Shape-IoU loss is employed to improve localization accuracy. The proposed method improves mean average precision (mAP)@0.5 from 86.7% to 88.1%, while reducing parameters, computational cost, and model size from 4.3 M to 3.7 M, 11.42 GFLOPs to 9.37 GFLOPs, and 9.1 MB to 7.9 MB, respectively. With Shape-IoU, mAP@0.5 reaches 88.4%, and recall increases from 78.5% to 84.9%. Experiments on the FLIR Thermal dataset achieve gains of 2.2%, 1.6%, and 2.7% in precision, recall, and mAP@0.5. The method achieves an effective trade-off between accuracy and efficiency for intelligent PV module inspection.

WRKY transcription factors constitute a very large family of proteins in plants and participate in modulating plant biological processes, such as growth, development and stress responses. However, the exact roles of WRKY proteins are unclear, particularly in non-model plants. In this study, Gossypium hirsutum WRKY41 (GhWRKY41) was isolated and transformed into Nicotiana benthamiana. Our results showed that overexpression of GhWRKY41 enhanced the drought and salt stress tolerance of transgenic Nicotiana benthamiana. The transgenic plants exhibited lower malondialdehyde content and higher antioxidant enzyme activity, and the expression of antioxidant genes was upregulated in transgenic plants exposed to osmotic stress. A β-glucuronidase (GUS) staining assay showed that GhWRKY41 was highly expressed in the stomata when plants were exposed to osmotic stress, and plants overexpressing GhWRKY41 exhibited enhanced stomatal closure when they were exposed to osmotic stress. Taken together, our findings demonstrate that GhWRKY41 may enhance plant tolerance to stress by functioning as a positive regulator of stoma closure and by regulating reactive oxygen species (ROS) scavenging and the expression of antioxidant genes.

From a global perspective, karst geological disaster has great influence on the tunnel construction. Once it is not handled properly, the karst problems in the tunnel area will cause great disaster, such as water inrush, mud gushing and collapse, etc. These karst disasters seriously affect the construction progress of the tunnel, resulting in equipment damage, casualties, and so on. The construction of Yichang–Wanzhou (YW) railway tunnel has created a miracle in the field of Chinese tunneling. The geology of tunnel area is quite complex, the karst disaster is remarkable, and the engineering construction is extremely difficult. In this paper, the karst characteristics, unfavorable geological disasters, and treatment methods of YW railway tunnel were analyzed systematically. The YW railway tunnel and Zhengzhou–Wanzhou (ZW) railway tunnel are both located in the Qinling-Daba (QB) mountainous area, and the regional geological conditions of them are very similar. Therefore, the research in this paper can be extended to the QB mountainous area, thus providing a reference for the construction of ZW railway tunnel. The investigation can also provide reference for the similar engineering.

The YOLOv4 approach has gained significant popularity in industrial object detection due to its impressive real-time processing speed and relatively favorable accuracy. However, it has been observed that YOLOv4 faces challenges in accurately detecting small objects. Its bounding box regression strategy is rigid and fails to effectively leverage the asymmetric characteristics of objects, limiting its ability to enhance object detection accuracy. This paper proposes an enhanced version of YOLOv4 called KR–AL–YOLO (keypoint regression strategy and angle loss based YOLOv4). The KR–AL–YOLO approach introduces two customized modules: an keypoint regression strategy and an angle-loss function. These modules contribute to improving the algorithm’s detection accuracy by enabling more precise localization of objects. Additionally, KR–AL–YOLO adopts an improved feature fusion technique, which facilitates enhanced information flow within the network, thereby further enhancing accuracy performance. Experimental evaluations conducted on the COCO2017 dataset demonstrate the effectiveness of the proposed method. KR–AL–YOLO achieves an average precision of 45.6%, surpassing both YOLOv4 and certain previously developed one-stage detectors. The utilization of keypoint regression strategy and the incorporation of robust feature fusion contribute to superior object detection accuracy in KR–AL–YOLO compared to YOLOv4.

A significant number of silent biosynthetic gene clusters (BGCs) within the Burkholderia genome remain uncharacterized, representing a valuable opportunity for the discovery of new natural products. In this research, the recombineering system ETh1h2e_yi23, which facilitates recombination in Burkholderia and was developed in our previous study, was used for mining the BGCs of B. plantarii DSM9509. By using this recombineering system, the constitutive promoter was precisely inserted into the genome, resulting in the activation of the silent pla BGC, which led to the production of a new lipopeptide named plantariitin A. A distinctive characteristic of this lipopeptide is the incorporation of a non-proteinogenic amino acid residue, i.e., amino-1,2,3,6-tetrahydro-2,6-dioxo-4-pyrimidinepropanoic acid (ATDPP), which has not been identified in other natural products. A biological activity assay demonstrated that plantariitin A exhibits anti-inflammatory activity. This study further substantiates the notion that the in situ activation of silent BGCs is a crucial strategy for the discovery of new natural products within the genus Burkholderia. With the increasing availability of genomic data and the development of bioinformatics tools, Burkholderia is poised to emerge as a prominent source for the development of new lipopeptides.