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The antagonistic coculture with tea phytopathogen Colletotrichum pseudomajusinduces antifungal cryptic metabolites from isogenesis endophyte Daldinia eschscholtziiagainst tea phytopathogens. Sixteen new polyketides with six structural frameworks including ten cryptic ones, named coldaldols A–C (1–3), collediol (5), and daldinrins A–L (10–20and 23), were found from the coculture of C. pseudomajusand D. eschscholtziiby different culture methods. The unique framework of compounds 11and 12featured a benzopyran-C7polyketone hybrid, and compounds 13–16were characterized by the novel benzopyran dimer. The structures were determined mainly by spectroscopic methods, including extensive one-dimensional (1D), two-dimensional (2D) NMR, high resolution electrospray ionisation mass spectroscopy (HRESIMS), ECD calculation, and single-crystal X-ray diffraction. The configuration of acyclic compounds 5and 18were determined by application of the universal NMR database. Most compounds showed significant antifungal activities against the tea pathogens C. pseudomajusand Alternariasp. with MICs of 1–8 μg/mL. Compound 12had stronger antifungal activity than that of positive drug nystatin. The ether bond at C-4 of the benzopyran derivative increased the antifungal activity. Compounds 4–9and 11–23showed antifeedant activities against silkworms with feeding deterrence indices of 15–100% at the concentration of 50 μg/cm2.

This study investigates the seismic performance of a two-bay, two-story reinforced concrete (RC) framed structure and proposes a retrofitting strategy by removing a column and strengthening the adjoining beams and columns using RC jacketing and post-tensioning tendons. Experimental testing and numerical simulations are conducted to assess the effectiveness of the retrofitting method. The experimental results indicate that the retrofitted RC frame exhibits enhanced lateral stiffness and shear force resistance, resulting in reduced shear deformation compared to the existing RC frame. However, the two frames’ ductility and energy dissipation characteristics remain similar. Notably, failure in the retrofitted frame occurs primarily in the second-story beam–column joint, whereas the original frame experiences failure at the ground-floor beam–column joints. Furthermore, a numerical macro-based finite element model is developed in OpenSEES, validated with experimental data, and utilized for a parametric study. The numerical findings suggest that retrofitting is suitable when the axial compression ratio of the second story’s side columns is kept below 0.52. Attention should be given to the size of the retrofitted columns to avoid compromising the upper floor’s structural integrity, as the retrofitting process may result in a softer upper floor. It is essential to regulate the dimensions of retrofitted columns and ensure that the elastic lateral stiffness of the retrofitted story is greater than the upper story. In this case, a 350 and 300 mm column depth had acceptable performance. Overall, this study provides valuable insights into the seismic performance of retrofitted RC-framed structures. The findings emphasize the importance of appropriate column sizing and maintaining adequate lateral stiffness to enhance retrofitted buildings’ structural integrity and seismic resistance.

Free water available in calcium aluminate cement (CAC)-bonded castables is crucial for the hydration of CAC and the conversion of hydration products in the curing and drying processes, as both the hydration and conversion reactions are dissolution–precipitation reactions. To elucidate the effect of different levels of free water loss upon the hydration of CAC, the conversion of hydration products and the mechanical strength of the CAC-bonded castables, the CAC-bonded castables were subjected to sealed and unsealed curing conditions at 50 °C and drying at 110 °C. The results demonstrate that the fast removal of free water during unsealed curing would hinder the conversion from 2CaO·Al2O3·8H2O to 3CaO·Al2O3·6H2O and consequently prevent the deterioration of strength. As a comparison, although sealed-cured samples have less water loss and high degree of hydration of CAC, they still show lower strength than the unsealed samples after curing. The following drying process further accelerates the hydration of residual calcium aluminate clinkers for both the sealed and unsealed samples, but still does not favor the conversion from 2CaO·Al2O3·8H2O to 3CaO·Al2O3·6H2O in the unsealed-cured samples.

As a critical component of a tunnel boring machine (TBM), the precise condition monitoring and fault analysis of the main bearing is essential to guarantee the safety and efficiency of the TBM cutter drive. Currently, under conditions of strong noise and complex working environments, traditional signal decomposition and machine learning methods struggle to extract weak fault features and achieve high fault classification accuracy. To address these issues, we propose a novel residual denoising and multiscale attention-based weighted domain adaptation network (RDMA-WDAN) for TBM main bearing fault diagnosis. Our approach skillfully designs a deep feature extractor incorporating residual denoising and multiscale attention modules, achieving better domain adaptation despite significant domain interference. The residual denoising component utilizes a convolutional block to extract noise features, removing them via residual connections. Meanwhile, the multiscale attention module uses a 4-branch convolution and 3 pooling strategy-based channel-spatial attention mechanism to extract multiscale features, concentrating on deep fault features. During training, a weighting mechanism is introduced to prioritize domain samples with clear fault features. This optimizes the deep feature extractor to obtain common features, enhancing domain adaptation. A low-speed and heavy-loaded bearing testbed was built, and fault data sets were established to validate the proposed method. Comparative experiments show that in noise domain adaptation tasks, proposed the RDMA–WDAN significantly improves target domain classification accuracy by 42.544%, 23.088%, 43.133%, 16.344%, 5.022%, and 9.233% over dense connection network (DenseNet), squeeze-excitation residual network (SE-ResNet), antinoise multiscale convolutional neural network (ANMSCNN), multiscale attention module-based convolutional neural network (MSAMCNN), domain adaptation network, and hybrid weighted domain adaptation (HWDA). In combined noise and working condition domain adaptation tasks, the RDMA–WDAN improves the accuracy by 45.672%, 23.188%, 43.266%, 16.077%, 5.716%, and 9.678% compared with baseline models.

Fetal Magnetic Resonance Imaging (MRI) is challenged by fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion frequently occurs in the acquisition of spatially adjacent slices. Motion correction for each slice is thus critical for the reconstruction of 3D fetal brain MRI. In this paper, we propose a novel multi-task learning framework that adopts a coarse-to-fine strategy to jointly learn the pose estimation parameters for motion correction and tissue segmentation map of each slice in fetal MRI. Particularly, we design a regression-based segmentation loss as a deep supervision to learn anatomically more meaningful features for pose estimation and segmentation. In the coarse stage, a U-Net-like network learns the features shared for both tasks. In the refinement stage, to fully utilize the anatomical information, signed distance maps constructed from the coarse segmentation are introduced to guide the feature learning for both tasks. Finally, iterative incorporation of the signed distance maps further improves the performance of both regression and segmentation progressively. Experimental results of cross-validation across two different fetal datasets acquired with different scanners and imaging protocols demonstrate the effectiveness of the proposed method in reducing the pose estimation error and obtaining superior tissue segmentation results simultaneously, compared with state-of-the-art methods.

Gray leaf spot (GLS), caused by the fungal pathogens Cercospora zeae-maydisand Cercospora zeina, is a major foliar disease of maize worldwide (Zea maysL.). Here we demonstrate that ZmWAKLencoding cell-wall-associated receptor kinase-like protein is the causative gene at the major quantitative disease resistance locus against GLS. The ZmWAKLYprotein, encoded by the resistance allele, can self-associate and interact with a leucine-rich repeat immune-related kinase ZmWIK on the plasma membrane. The ZmWAKLY/ZmWIK receptor complex interacts with and phosphorylates the receptor-like cytoplasmic kinase (RLCK) ZmBLK1, which in turn phosphorylates its downstream NADPH oxidase ZmRBOH4. Upon pathogen infection, ZmWAKLYphosphorylation activity is transiently increased, initiating immune signaling from ZmWAKLY, ZmWIK, ZmBLK1 to ZmRBOH4, ultimately triggering a reactive oxygen species burst. Our study thus uncovers the role of the maize ZmWAKL–ZmWIK–ZmBLK1–ZmRBOH4 receptor/signaling/executor module in perceiving the pathogen invasion, transducing immune signals, activating defense responses and conferring increased resistance to GLS.