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Wheat scab can reduce wheat yield and quality. Currently, unmanned aerial vehicles (UAVs) are widely used for monitoring field crops. However, UAV is constrained by limited computational resources on-board the platforms. In addition, compared to ground images, UAV images have complex backgrounds and smaller targets. Given the aforementioned challenges, this paper proposes a lightweight wheat scab detection network based on UAV. In addition, overlapping cropping and image contrast enhancement methods are designed to preprocess UAV remote-sensing images. Additionally, this work constructed a lightweight wheat scab detection network called LWSDNet using mixed deep convolution (MixConv) to monitor wheat scab in field environments. MixConv can significantly reduce the parameters of the LWSDNet network through depthwise convolution and pointwise convolution, and different sizes of kernels can extract rich scab features. In order to enable LWSDNet to extract more scab features, a scab feature enhancement module, which utilizes spatial attention and dilated convolution, is designed to improve the ability of the network to extract scab features. The MixConv adaptive feature fusion module is designed to accurately detect lesions of different sizes, fully utilizing the semantic and detailed information in the network to enable more accurate detection by LWSDNet. During the training process, a knowledge distillation strategy that integrates scab features and responses is employed to further improve the average precision of LWSDNet detection. Experimental results demonstrate that the average precision of LWSDNet in detecting wheat scab is 79.8%, which is higher than common object detection models and lightweight object detection models. The parameters of LWSDNet are only 3.2 million (M), generally lower than existing lightweight object detection networks. [ABSTRACT FROM AUTHOR]

This review explores additive manufacturing (AM) for refractory tungsten (W) and its alloys, highlighting the primary challenges and determining factors in the AM of pure W, W alloys and composites. The challenges mainly arise from W's high melting point, low laser absorptivity, high thermal conductivity, high melt viscosity, high oxygen affinity, high ductile-to-brittle transition temperature, and inherent embrittlement, which lead to defects and anomalies in AM-produced parts. This review focuses on both processes and alloying strategies to address the issues related to densification, micro-cracking, and the resultant properties in W-based components. Cracking in additively manufactured W remains a persistent issue due to thermal stress, embrittlement, and oxide formation. Powder characteristics, process parameters, and thermal management strategies are crucial for W densification. Throughout the review, existing knowledge and insights are organized into comprehensive tables, serving as valuable resources for researchers delving deeper into this topic. Future research in W-AM should focus on understanding the interaction between AM process parameters and microstructural and material design. Advances in atomic-level understanding, thermodynamic modeling, and data analytics have the potential to significantly enhance the precision, sustainability, and applicability of W-AM. [ABSTRACT FROM AUTHOR]

At present, footprint image retrieval based on deep learning mainly focuses on complete footprint, but many of the footprints obtained in the field of public safety and criminal investigation are incomplete forms, Therefore, the feature analysis of incomplete footprint has important practical significance. Based on multiple scale features fusion, we proposed a method to solve incomplete footprints retrieval. On the basis of extracting the global feature of footprint, this method simultaneously extracts multiple local features from different stages of the backbone network to supplement the footprint feature information. The multi-scale feature orthogonal fusion module is used to reduce redundant features, improve the expression ability of footprint features, and solve the problem of incomplete footprint retrieval to a certain extent. The experiment shows that our method has certain effectiveness in retrieving problems on incomplete footprint, with a Top 1 accuracy of 87.08%, expanding the scope of footprint research. [ABSTRACT FROM AUTHOR]

Innate immunity, the first line of host defense against viral infections, recognizes viral components through different pattern-recognition receptors. Nucleic acids derived from viruses are mainly recognized by Toll-like receptors, nucleotide-binding domain leucine-rich repeat-containing receptors, absent in melanoma 2-like receptors, and cytosolic DNA sensors (e.g., Z-DNA-binding protein 1 and cyclic GMP-AMP synthase). Different types of nucleic acid sensors can recognize specific viruses due to their unique structures. PANoptosis is a unique form of inflammatory cell death pathway that is triggered by innate immune sensors and driven by caspases and receptor-interacting serine/threonine kinases through PANoptosome complexes. Nucleic acid sensors (e.g., Z-DNA-binding protein 1 and absent in melanoma 2) not only detect viruses, but also mediate PANoptosis through providing scaffold for the assembly of PANoptosomes. This review summarizes the structures of different nucleic acid sensors, discusses their roles in viral infections by driving PANoptosis, and highlights the crosstalk between different nucleic acid sensors. It also underscores the promising prospect of manipulating nucleic acid sensors as a therapeutic approach for viral infections. [ABSTRACT FROM AUTHOR]

The citrus industry is facing an unprecedented challenge from Huanglongbing (HLB). All cultivars can be affected by the HLB-associated bacterium 'Candidatus Liberibacter asiaticus' (CLas) and there is no known resistance. Insight into HLB pathogenesis is urgently needed in order to develop effective management strategies. Here, we use Sec-delivered effector 1 (SDE1), which is conserved in all CLas isolates, as a molecular probe to understand CLas virulence. We show that SDE1 directly interacts with citrus papain-like cysteine proteases (PLCPs) and inhibits protease activity. PLCPs are defense-inducible and exhibit increased protein accumulation in CLas-infected trees, suggesting a role in citrus defense responses. We analyzed PLCP activity in field samples, revealing specific members that increase in abundance but remain unchanged in activity during infection. SDE1-expressing transgenic citrus also exhibit reduced PLCP activity. These data demonstrate that SDE1 inhibits citrus PLCPs, which are immune-related proteases that enhance defense responses in plants.

PurposeTo evaluate the magnetic susceptibility properties of different anatomical structures within the knee joint using quantitative susceptibility mapping (QSM).MethodsA collagen tissue model was simulated and ex vivo animal cartilage experiments were conducted at 9.4 Tesla (T) to evaluate the B0 orientation-dependent magnetic susceptibility contrast observed in cartilage. Furthermore, nine volunteers (six healthy subjects without knee pain history and three patients with known knee injury, between 29 and 58 years old) were scanned using gradient-echo acquisitions on a high-field 7T MR scanner. Susceptibility values of different tissues were quantified and diseased cartilage and meniscus were compared against that of healthy volunteers.ResultsSimulation and ex vivo animal cartilage experiments demonstrated that collagen fibrils exhibit an anisotropic susceptibility. A gradual change of magnetic susceptibility was observed in the articular cartilage from the superficial zone to the deep zone, forming a multilayer ultrastructure consistent with anisotropy of collagen fibrils. Meniscal tears caused a clear reduction of susceptibility contrast between the injured meniscus and surrounding cartilage illustrated by a loss of the sharp boundaries between the two. Moreover, QSM showed more dramatic contrast in the focal degenerated articular cartilage than R2* mapping.ConclusionThe arrangement of the collagen fibrils is significant, and likely the most dominant source of magnetic susceptibility anisotropy. Quantitative susceptibility mapping offers a means to characterize magnetic susceptibility properties of tissues in the knee joint. It is sensitive to collagen damage or degeneration and may be useful for evaluating the status of knee diseases, such as meniscal tears and cartilage disease. Magn Reson Med 78:1933-1943, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

PurposeTo investigate the B0 orientation-dependent magnetic susceptibility of collagen fibrils within the articular cartilage and to determine whether susceptibility tensor imaging (STI) can detect the 3D collagen network within cartilage.MethodsMultiecho gradient echo datasets (100-μm isotropic resolution) were acquired from fixed porcine articular cartilage specimens at 9.4 T. The susceptibility tensor was calculated using phase images acquired at 12 or 15 different orientations relative to B0 . The susceptibility anisotropy of the collagen fibril was quantified and diffusion tensor imaging (DTI) was compared against STI. 3D tractography was performed to visualize and track the collagen fibrils with DTI and STI.ResultsSTI experiments showed the distinct and significant anisotropic magnetic susceptibility of collagen fibrils within the articular cartilage. STI can be used to measure and quantify susceptibility anisotropy maps. Furthermore, STI provides orientation information of the underlying collagen network via 3D tractography.ConclusionThe findings of this study demonstrate that STI can characterize the orientation variation of collagen fibrils where diffusion anisotropy fails. We believe that STI could serve as a sensitive and noninvasive marker to study the collagen fibrils microstructure. Magn Reson Med 78:1683-1690, 2017. © 2017 International Society for Magnetic Resonance in Medicine.