Publications by authors named "Taoyang Wang"

Nano-/microplastics accumulate in aquatic bodies and raise increasing threats to ecosystems and human health. The limitation of existing water cleanup strategies, especially in the context of nano-/microplastics, primarily arises from their complexity (morphological, compositional, and dimensional). Here, highly efficient and bio-based flowthrough capturing materials (bioCap) are reported to remove a broad spectrum of nano-/microplastics from water: polyethylene terephthalate (anionic, irregular shape), polyethylene (net neutral, irregular shape), polystyrene (anionic and cationic, spherical shape), and other anionic and spherical shaped particles (polymethyl methacrylate, polypropylene, and polyvinyl chloride).

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Traditional techniques for pollutant removal (e.g., static absorption and membrane nanofiltration) are either time consuming or energy intensive with limited permeances.

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A better understanding of the molecular mechanism involving the lncRNA-miRNA-mRNA network underlying radiation damage can be beneficial for radioprotection. This study was designed to investigate the potential role of lncRNA NEAT1, miR-147 and Phosphoinositide Dependent Protein Kinase 1 (PDPK1) interaction in radioprotection by troxerutin (TRT). We first demonstrated that NEAT1 sponged miR-147, and PDPK1 mRNA was the primary target of miR-147.

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Background: In response to radiation injury, p65 becomes activated. The formation of p65 is one target of Onjisaponin B (OB), but it has not been studied in radioprotection. In addition, there is a binding site for p65 in the promoter region of Cas3.

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This study aimed to evaluate the role of FRT in ROS/DNA regulation with or without PARP-1 in radiation-injured thymus cells. The administration of FRT to PARP-1-/- (KO) mice demonstrated that FRT significantly increased the viability of thymus cells and decreased their rate of apoptosis through PARP-1. Radiation increased the levels of ROS, γ-H2AX and 53BP1, and induced DNA double strand breaks.

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Deep learning-based aircraft detection methods have been increasingly implemented in recent years. However, due to the multi-resolution imaging modes, aircrafts in different images show very wide diversity on size, view and other visual features, which brings great challenges to detection. Although standard deep convolution neural networks (DCNN) can extract rich semantic features, they destroy the bottom-level location information.

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The Gaofen-3 (GF-3) satellite is the first C-band multi-polarization synthetic aperture radar (SAR) with the ability of high-accuracy mapping in China. However, the Ground Control Points (GCPs) are essential to ensure the accuracy of mapping for GF-3 SAR imagery at present. In this paper, we analyze the error sources that affect the geometric processing and propose a new block adjustment method without GCPs for GF-3 SAR imagery.

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The evaluation of geometric accuracy of high-resolution satellite images (HRSIs) has been increasingly recognized in recent years. The traditional approach is to verify each satellite individually. It is difficult to directly compare the difference in their accuracy.

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The GaoFen-3 (GF-3) satellite is the first C-band multi-polarization synthetic aperture radar (SAR) imaging satellite with a resolution up to 1 m in China. It is also the only SAR satellite of the High-Resolution Earth Observation System designed for civilian use. There are 12 different imaging models to meet the needs of different industry users.

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