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LiDAR-unmanned aerial system (LiDAR-UAS) technology can accurately and efficiently obtain detailed and accurate three-dimensional spatial information of objects. The classification of objects in estuarine areas is highly important for management, planning, and ecosystem protection. Owing to the presence of slopes in estuarine areas, distinguishing between dense vegetation (lawns and trees) on slopes and the ground at the tops of slopes is difficult. In addition, the imbalance in the number of point clouds also poses a challenge for accurate classification directly from point cloud data. A multifeature-assisted and multilayer fused neural network (MLF-PointNet++) is proposed for LiDAR-UAS point cloud classification in estuarine areas. First, the 3D shape features that characterize the geometric characteristics of targets and the visible-band difference vegetation index (VDVI) that can characterize vegetation distribution are used as auxiliary features to enhance the distinguishability of dense vegetation (lawns and trees) on slopes and the ground at the tops of slopes. Second, to enhance the extraction of target spatial information and contextual relationships, the feature vectors output by different layers of set abstraction in the PointNet++ model are fused to form a combined feature vector that integrates low and high-level information. Finally, the focal loss function is adopted as the loss function in the MLF-PointNet++ model to reduce the effect of imbalance in the number of point clouds in each category on the classification accuracy. A classification evaluation was conducted using LiDAR-UAS data from the Moshui River estuarine area in Qingdao, China. The experimental results revealed that MLF-PointNet++ had an overall accuracy (OA), mean intersection over union (mIOU), kappa coefficient, precision, recall, and F1-score of 0.976, 0.913, 0.960, 0.953, 0.953, and 0.953, respectively, for object classification in the three representative areas, which were better than the corresponding values for the classification methods of random forest, BP neural network, Naive Bayes, PointNet, PointNet++, and RandLA-Net. The study results provide effective methodological support for the classification of objects in estuarine areas and offer a scientific basis for the sustainable development of these areas. [ABSTRACT FROM AUTHOR]

Background: Myasthenia gravis (MG) is an autoimmune disease characterized by pathogenic antibodies that target structures of the neuromuscular junction. The evidence suggests that the regulation of long noncoding RNAs (lncRNAs) that is mediated by transcription factors (TFs) plays a key role in the pathophysiology of MG. Nevertheless, the detailed molecular mechanisms of lncRNAs in MG remain largely undetermined. Methods: Using microarray analysis, we analyzed the lncRNA levels in MG. By bioinformatics analysis, LINC01566 was found to potentially play an important role in MG. First, qRT‒PCR was performed to verify the LINC1566 expressions in MG patients. Then, fluorescence in situ hybridization was conducted to determine the localization of LINC01566 in CD4 + T cells. Finally, the impact of LINC01566 knockdown or overexpression on CD4 + T-cell function was also analyzed using flow cytometry and CCK-8 assay. A dual-luciferase reporter assay was used to validate the binding of the TF FOSL1 to the LINC01566 promoter. Results: Based on the lncRNA microarray and differential expression analyses, we identified 563 differentially expressed (DE) lncRNAs, 450 DE mRNAs and 19 DE TFs in MG. We then constructed a lncRNA-TF-mRNA network. Through network analysis, we found that LINC01566 may play a crucial role in MG by regulating T-cell-related pathways. Further experiments indicated that LINC01566 is expressed at low levels in MG patients. Functionally, LINC01566 is primarily distributed in the nucleus and can facilitate CD4 + T-cell apoptosis and inhibit cell proliferation. Mechanistically, we hypothesized that LINC01566 may negatively regulate the expressions of DUSP3, CCR2, FADD, SIRPB1, LGALS3 and SIRPB1, which are involved in the T-cell activation pathway, to further influence the cellular proliferation and apoptosis in MG. Moreover, we found that the effect of LINC01566 on CD4 + T cells in MG was mediated by the TF FOSL1, and in vitro experiments indicated that FOSL1 can bind to the promoter region of LINC01566. Conclusions: In summary, our research revealed the protective roles of LINC01566 in clinical samples and cellular experiments, illustrating the potential roles and mechanism by which FOSL1/LINC01566 negatively regulates CD4 + T-cell activation in MG. [ABSTRACT FROM AUTHOR]

Strain-hardened austenitic stainless steel (γ-SS) is extensively used in high-pressure hydrogen systems after welding. The weld may experience varying degrees of hydrogen embrittlement (HE) with different levels of pre-strain, which seriously threatens the reliability of hydrogen systems. Therefore, it is vital to research the effect of pre-strain on hydrogen-induced cracking of γ-SS welds. In this research, the impact of hydrogen on the mechanical properties of type 304 γ-SS plasma arc welding (PAW) welds with different levels of pre-strain are studied through slow strain rate tensile (SSRT) test. The pre-strain of the weld after hydrogen charging is optimized, then the causes of hydrogen-induced cracking are illustrated by microstructure analysis. The weld metal zone (WMZ) has a greater HE susceptibility than the base metal zone (BMZ). The HE susceptibility of the weld can decrease after solution-treated. The fracture of the weld with pre-strain of 5% is mainly due to dislocation and ferrite increasing the HE susceptibility. When the pre-strain is above 10%, the weld metal and the base metal have undergone a severe transformation from austenite to α′ martensite. The content of α′ martensite rises with the rising pre-strain. The HE susceptibility of the weld is greater than the base metal due to the influence of dislocations and ferrite. The brittle fracture is initiated primarily at the phase boundary. The strength of welds can be improved while keeping good HE resistance with pre-strain of 5%. [Display omitted] • The mechanical properties of the welds can be improved while keeping good resistance to hydrogen embrittlement with pre-strain of 5%. • The existence of ferrite reduces the relative elongation and relative reduction of area of the welds. • The brittle fracture of the weld is initiated primarily at the phase boundary between austenite and α′ martensite. [ABSTRACT FROM AUTHOR]

Mg2SiO4 and MgSiO3 ceramics with superior microwave dielectric properties are considered to be promising candidates for 5G applications. However, a slight deviation from the stoichiometric Mg/Si ratio will significantly influence their microwave dielectric properties, which will hinder their practical applications. In this work, the xMgO-SiO2 (x = 1~2) ceramics were synthesized by a solid-state reaction method. The influence of the Mg/Si ratio x on the crystalline phase, microstructure, and microwave dielectric properties was investigated through X-ray diffraction (XRD), a scanning electron microscope (SEM), and the resonant cavity method. The XRD patterns revealed the coexistence of Mg2SiO4 and MgSiO3 within the x range of 1~2, which was further demonstrated by the energy-dispersive X-ray spectra. The SEM images show a typical polycrystalline morphology of ceramics with an inhomogeneous grain size distribution. It is found that the microwave dielectric properties fluctuate at both sides of the x range while those remain relatively stable with minor changes at the intermediate components, indicating an obvious low composition dependence helpful for practical applications. Further, a demonstrator of a microstrip patch antenna for 5G applications using the 1.5MgO-SiO2 ceramic was designed and fabricated, and a return loss of −16.2 dB was demonstrated, which demonstrated the potential applications. [ABSTRACT FROM AUTHOR]