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As the core components of permanent magnet motors, the surface defects of magnetic tiles can directly affect the working performance of the motors. There are various types of defects in magnetic tiles, such as chipping and wear. Traditional magnetic tile defect detection mainly relies on manual inspection and faces challenges like low detection accuracy and high cost. Moreover, most defects on magnetic tile surfaces are located on curved surfaces, leading to an uneven distribution of defects and tiny features, which makes accurate defect localization challenging. To solve these problems, this study proposes a novel magnetic tile defect detection algorithm called YOLO-RDM. First, we apply DOConv to the neck network. By using a lightweight convolution method, we replace the traditional convolution in the original network, thereby improving the feature extraction ability of the model and achieving lightweight processing. Second, we design an RPA Block to improve the C2f module. By introducing a parallel attention mechanism, we enhance the feature extraction ability of the algorithm. Finally, we replace the original backbone network of YOLOv8 with the MogaNet network. MogaNet is a module that aggregates contextual information, enhancing the network’s discriminative power, learning efficiency, and ability to capture defect features in images. The experimental results show that the mean average precision (mAP@0.5) of the improved model reaches 95.0%, which is 4.8% higher than that of the original model, and its inference time is less than 5.6 ms. It also has obvious performance advantages compared with other object detection models. In addition, it achieves good recognition results on the NEU metal surface defect dataset. It can be proven that the YOLO-RDM model has strong recognition and generalization abilities and can be used in practical applications of magnetic tile defect detection.

Tomato disease image recognition plays a crucial role in agricultural production. Today, while machine vision methods based on deep learning have achieved some success in disease recognition, they still face several challenges. These include issues such as imbalanced datasets, unclear disease features, small inter-class differences, and large intra-class variations. To address these challenges, this paper proposes a method for classifying and recognizing tomato leaf diseases based on machine vision. First, to enhance the disease feature details in images, a piecewise linear transformation method is used for image enhancement, and oversampling is employed to expand the dataset, compensating for the imbalanced dataset. Next, this paper introduces a convolutional block with a dual attention mechanism called DAC Block, which is used to construct a lightweight model named LDAMNet. The DAC Block innovatively uses Hybrid Channel Attention (HCA) and Coordinate Attention (CSA) to process channel information and spatial information of input images respectively, enhancing the model’s feature extraction capabilities. Additionally, this paper proposes a Robust Cross-Entropy (RCE) loss function that is robust to noisy labels, aimed at reducing the impact of noisy labels on the LDAMNet model during training. Experimental results show that this method achieves an average recognition accuracy of 98.71% on the tomato disease dataset, effectively retaining disease information in images and capturing disease areas. Furthermore, the method also demonstrates strong recognition capabilities on rice crop disease datasets, indicating good generalization performance and the ability to function effectively in disease recognition across different crops. The research findings of this paper provide new ideas and methods for the field of crop disease recognition. However, future research needs to further optimize the model’s structure and computational efficiency, and validate its application effects in more practical scenarios.

Irrational application of chemical fertilizers causes soil nutrient imbalance, reduced microbial diversity, soil diseases, and other soil quality problems and is one of the main sources of non-point pollution. The application of microbial inoculant (MI) can improve the soil environment and crop growth to reduce problems caused by irrational application of chemical fertilizers. Field experiments were carried out in high-phosphorus soils to study the effects of the addition of various MIs combined with chemical fertilizers on soil properties, wheat growth, and soil microbial composition and structure. The MIs consisted of one fungal agent: Trichoderma compound agent (TC) and five bacterial agents, namely soil remediation agent (SR), anti-repeat microbial agent (AM), microbial agent (MA), plant growth-promoting rhizobacteria (PG), and biological fertilizer agent (BF). The wheat yield increased by 15.2–33.4% with the addition of MIs, and PG with Bacillus subtilis as the core microorganism had the most obvious effect on increasing the production ( p  < 0.05). For the entire growth period of wheat, all MIs applied significantly increased the available nitrogen (AN) ( p  < 0.05) but did not significantly affect the available phosphorus (AP). BF has the best effect on increasing AN in the soil. The 16S rRNA sequencing results indicated that the dominant phyla of soil bacteria were Proteobacteria, Acidobacteria, Bacteroidetes, Actinobacteria, and Verrucomicrobia. The addition of MIs increased the relative abundance of Acidobacteria, Actinobacteria, Chloroflexi and decreased Proteobacteria and Bacteroidetes. The diversity of soil bacterial community (Chao1) was significantly higher in the soil added with TC than that added with BF ( p  < 0.05). All bacterial agents significantly enriched various genera ( p  < 0.05), while the fungal agent (TC) did not enrich the genera significantly. pH and AN, but not TP, were closely related to the dominant bacteria phylum in high-P soil. The application of MIs improved AN in soil, increased the wheat yield, and changed the relative abundance of the soil dominant phylum, and these changes were closely related to the type of MIs. The results provide a scientific basis for rational use of different types of MIs in high-P soil.

The formation of holes in epithelial tissue is essential for development, but it can also be associated with epithelial barrier dysfunction and cancer progression. Here we show that active cell contraction in epithelioid monolayer tissues derived from human embryonic stem cells can spontaneously launch a morphological transition cascade consisting of hole nucleation, coalescence and network formation. Accumulated tissue-level tensile stresses drive hole expansion from isotropic round expansion to local fracture of intercellular junctions. This is followed by fast crack propagation, which is later suppressed by the self-organized supracellular actomyosin ring and accompanied by crack blunting and a fracture-to-rounding transition. During hole coalescence, we find a fracture–slip mechanism that enables layer-by-layer breaking of the multicellular bridge but without inducing excessive cell deformation. Our multiscale theory captures these experimental observations and predicts that substrate rigidity sensing and adhesion of cells compete with cellular contraction to mediate the morphological dynamics. These findings suggest that living tissues may coordinate the mechanics across molecular, cellular and tissue scales to drive topological changes while reducing the risk of mechanical damage to cells. Active cell contraction drives hole nucleation, fracture and crack propagation in a tissue monolayer through a process reminiscent of dewetting thin films.

Rivets are usually used to connect the skin of an aircraft with joints such as frames and stringers, so the skin of the connection part is a porous structure. During the service of the aircraft, cracks appear in some difficult-to-detect parts of the skin porous structure, which causes great difficulties in the service life prediction and health monitoring of the aircraft. In this paper, a secondary development subroutine in PYTHON based on ABAQUS-XFEM is compiled to analyze the cracks that are difficult to monitor in the porous structure of aircraft skin joints. The program can automatically analyze the stress intensity factor of the crack tip with different lengths in the porous structure, and then the residual fatigue life can be deduced. For the sake of safety, the program adopts a more conservative algorithm. In comparison with the physical fatigue test results, the fatigue life of the simulation results is 16% smaller. This project provides a feasible simulation method for fatigue life prediction of porous structures. It lays a foundation for the subsequent establishment of digital twins for damage monitoring of aircraft porous structures.

Skeletal muscle and thermogenic adipose tissue are both critical for the maintenance of body temperature in mammals. However, whether these two tissues are interconnected to modulate thermogenesis and metabolic homeostasis in response to thermal stress remains inconclusive. Here, we report that human and mouse obesity is associated with elevated Musclin levels in both muscle and circulation. Intriguingly, muscle expression of Musclin is markedly increased or decreased when the male mice are housed in thermoneutral or chronic cool conditions, respectively. Beige fat is then identified as the primary site of Musclin action. Muscle-transgenic or AAV-mediated overexpression of Musclin attenuates beige fat thermogenesis, thereby exacerbating diet-induced obesity and metabolic disorders in male mice. Conversely, Musclin inactivation by muscle-specific ablation or neutralizing antibody treatment promotes beige fat thermogenesis and improves metabolic homeostasis in male mice. Mechanistically, Musclin binds to transferrin receptor 1 (Tfr1) and antagonizes Tfr1-mediated cAMP/PKA-dependent thermogenic induction in beige adipocytes. This work defines the temperature-sensitive myokine Musclin as a negative regulator of adipose thermogenesis that exacerbates the deterioration of metabolic health in obese male mice and thus provides a framework for the therapeutic targeting of this endocrine pathway. Interorgan communications play key roles in the regulation of whole-body energy metabolism. Here, the authors report the myokine Musclin as a negative regulator of beige adipose thermogenesis and systemic energy homeostasis through Tfr1/PKA signalling mediated muscle fat crosstalk.

Genetic switching driven by noise is a fundamental cellular process in genetic regulatory networks. Quantitatively characterizing this switching and its fluctuation properties is a key problem in computational biology. With an autoregulatory dimer model as a specific example, we design a general methodology to quantitatively understand the metastability of gene regulatory system perturbed by intrinsic noise. Based on the large deviation theory, we develop new analytical techniques to describe and calculate the optimal transition paths between the on and off states. We also construct the global quasi-potential energy landscape for the dimer model. From the obtained quasi-potential, we can extract quantitative results such as the stationary distributions of mRNA, protein and dimer, the noise strength of the expression state, and the mean switching time starting from either stable state. In the final stage, we apply this procedure to a transcriptional cascades model. Our results suggest that the quasi-potential energy landscape and the proposed methodology are general to understand the metastability in other biological systems with intrinsic noise.

The polycyclic aromatic hydrocarbon (PAH)-degrading strain Q8 was isolated from oilfield produced water. According to the analysis of a biochemical test, 16S rRNA gene, house-keeping genes and DNA–DNA hybridization, strain Q8 was assigned to a novel species of the genus Gordonia. The strain could not only grow in mineral salt medium (MM) and utilize naphthalene and pyrene as its sole carbon source, but also degraded mixed naphthalene, phenanthrene, anthracene and pyrene. The degradation ratio of these four PAHs reached 100%, 95.4%, 73.8% and 53.4% respectively after being degraded by Q8 for seven days. A comparative experiment found that the PAHs degradation efficiency of Q8 is higher than that of Gordonia alkaliphila and Gordonia paraffinivorans, which have the capacities to remove PAHs. Fourier transform infrared spectra, saturate, aromatic, resin and asphaltene (SARA) and gas chromatography–mass spectrometry (GC–MS) analysis of crude oil degraded by Q8 were also studied. The results showed that Q8 could utilize n-alkanes and PAHs in crude oil. The relative proportions of the naphthalene series, phenanthrene series, thiophene series, fluorene series, chrysene series, C21-triaromatic steroid, pyrene, and benz(a)pyrene were reduced after being degraded by Q8. Gordonia sp. nov. Q8 had the capacity to remediate water and soil environments contaminated by PAHs or crude oil, and provided a feasible way for the bioremediation of PAHs and oil pollution.

Irinotecan (CPT11), a broad-spectrum cytotoxic anticancer agent, induces a series of toxic side-effects. The most conspicuous side-effect is gastrointestinal mucositis, including nausea, vomiting, and diarrhea. A growing body of evidence indicates that bacteria β-glucuronidase (GUS), an enzyme expressed by intestinal microbiota, converts the inactive CPT11 metabolite SN38G to the active metabolite SN38 to ultimately induce intestinal mucositis. We sought to explore the potential efficacy and underlying mechanisms of berberine on CPT11-induced mucositis. Our study showed that berberine (50 mg/kg; i. g.) mitigated the CPT11-induced loss of mucosal architecture, ulceration, and neutrophil infiltration. Meanwhile, berberine improved mucosal barrier function by increasing the number of globlet cells, protecting trans-endothelial electrical resistance (TEER), reducing permeability and increasing tight junction proteins expression. LC-MS analysis showed that berberine decreased the content of SN38 in feces, which correlated with decreases in both GUS activity and GUS-producing bacteria. Further molecular docking and Lineweaver-Burk plots analyses suggested that berberine functions as a potential non-competitive inhibitor against GUS enzyme. Of note, berberine maintained the anti-tumor efficacy of CPT11 in a tumor xenograft model while abrogating the intestinal toxicity of CPT11. Overall, we identified for the first time the remission effects of berberine on intestinal mucositis induced by CPT11 without impairing the anti-colorectal cancer efficacy of CPT11 partially via inhibiting bacterial GUS enzyme.

Titanium alloys are extensively used in the manufacturing of key components in aerospace engines and aircraft structures due to their excellent properties. However, aircraft skins in harsh operating environments are subjected to long-term corrosion and pressure concentrations, which can lead to the formation of cracks and other defects. In this paper, a detection probe is designed based on the principle of alternating current field measurement, which can effectively detect both surface and buried defects in thin-walled titanium alloy plates. A finite element simulation model of alternating current field measurement detection for buried defects in thin-walled TC4 titanium alloy plates is established using COMSOL 5.6 software. The influence of defect length, depth, and excitation frequency on the characteristic signals is investigated, and the detection probe is optimized. Simulation and experimental results demonstrate that the proposed detection probe exhibits high detection sensitivity to varying lengths and depths of buried defects, and can detect small cracks with a length of 3 mm and a burial depth of 2 mm, as well as deep defects with a length of 10 mm and a burial depth of 4 mm. The feasibility of this probe for detecting buried defects in titanium alloy aircraft skin is confirmed.