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295 results on '"Zhao, Pengyue"'

20. Efficient delivery of the herbicide quinclorac by nanosuspension for enhancing deposition, uptake and herbicidal activity.

Author

Cheng, Xuejian, Wang, Aiping, Cao, Lidong, Cao, Chong, Zhao, Pengyue, Yu, Manli, Zheng, Li, and Huang, Qiliang

Subjects
WEED control, ECONOMIC efficiency, CHEMICAL industry, PESTICIDES, FOOD security, HERBICIDES, ENVIRONMENTAL risk
Abstract

BACKGROUND: The presence of barnyardgrass poses a threat to global food security by reducing rice yields. Currently, herbicides are primarily applied for weed management. However, the effectiveness of herbicide deposition and uptake on barnyardgrass is limited as a consequence of the high wax content on leaves, low water solubility and extreme lipophilicity of herbicides. Therefore, it is imperative to develop novel formulations for efficient delivery of herbicides to improve herbicidal activity and reduce dosage. RESULTS: We successfully prepared nanosuspension(s) (NS) of quinclorac through the wet media milling technique. This NS demonstrates excellent physical stability and maintains nanoscale during dose transfer. The deposition concentration and uptake concentration of NS on barnyardgrass were 3.84–4.47‐ and 2.11–2.58‐fold greater than those traditional formulations, respectively. Moreover, the NS exhibited enhanced herbicidal activity against barnyardgrass at half the dosage required by conventional formulations without compromising rice safety. CONCLUSIONS: These findings suggest that NS can effectively facilitate the delivery of hydrophobic and poorly water‐soluble herbicide active ingredients, thereby enhancing their deposition, uptake and bioactivity. This study expands the potential application of NS in pesticide delivery, which can provide valuable support for optimizing pesticide utilization, improving economic efficiency and mitigating environmental risks. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Published
2024
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26. DIMINISHED EXPRESSION OF GLS IN CD4+T CELLS SERVES AS A PROGNOSTIC INDICATOR ASSOCIATED WITH CUPROPTOSIS IN SEPTIC PATIENTS

Author

Yang, Jiaqi, Chen, Ning, Zhao, Pengyue, Yang, Xingpeng, Li, Yuxuan, Fu, Ze, Yan, Yang, Dong, Ning, Li, Songyan, Yao, Renqi, Du, Xiaohui, and Yao, Yongming

Abstract

Objectives:Sepsis is defined as a life-threatening disease associated with a dysfunctional host immune response. Stratified identification of critically ill patients might significantly improve the survival rate. The present study sought to probe molecular markers associated with cuproptosis in septic patients to aid in stratification and improve prognosis. Methods:We studied expression of cuproptosis-related genes (CRGs) using peripheral blood samples from septic patients. Further classification was made by examining levels of expression of these potential CRGs in patients. Coexpression networks were constructed using the Weighted Gene Coexpression Network Analysis (WGCNA) method to identify crucial prognostic CRGs. Additionally, we utilized immune cell infiltration analysis to further examine the immune status of septic patients with different subtypes and its association with the CRGs. scRNA-seq data were also analyzed to verify expression of key CRGs among specific immune cells. Finally, immunoblotting, flow cytometry, immunofluorescence, and CFSE analysis were used to investigate possible regulatory mechanisms. Results:We classified septic patients based on CRG expression levels and found significant differences in prognosis and gene expression patterns. Three key CRGs that may influence the prognosis of septic patients were identified. A decrease in GLS expression was subsequently verified in Jurkat cells, accompanied by a reduction in O-GlcNAc levels, and chelation of copper by tetrathiomolybdate could not rescue the reduction in GLS and O-GLcNAc levels. Moreover, immoderate chelation of copper was detrimental to mitochondrial function, cell viability, and cell proliferation, as well as the immune status of the host. Conclusion:We have identified novel molecular markers associated with cuproptosis, which could potentially function as diagnostic indicators for septic patients. The reversible nature of the observed alterations in FDX1 and LIAS was demonstrated through copper chelation, whereas the correlation between copper and the observed changes in GLS requires further investigation.

Published
2024
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38. Predicting Mechanical Properties of Boron Nitride Nanosheets Obtained from Molecular Dynamics Simulation: A Machine Learning Method.

Author

Pan, Jiansheng, Liu, Huan, Zhu, Wendong, Wang, Shunbo, Gao, Xifeng, and Zhao, Pengyue

Subjects
BORON nitride, MOLECULAR dynamics, MACHINE dynamics, MACHINE learning, TENSILE strength, NANOSTRUCTURED materials
Abstract

Obtaining the mechanical properties of boron nitride nanosheets (BNNSs) requires extensive computational atomistic simulations, so it is necessary to predict to reduce time costs. In this work, we obtained the ultimate tensile strength and Young's modulus of the BNNS material through molecular dynamics (MDs) simulations by taking into account factors, such as the BNNSs' chirality, layer number, ambient temperature, and strain rate. Subsequently, employing comprehensive training and optimization of the MDs data, we developed multiple ML models to estimate the ultimate tensile strength and Young's modulus. Among these models, the random forest model was chosen for its accurate prediction of the mechanical properties of the BNNSs, offering significant benefits for performance analysis and the engineering design of two-dimensional nanomaterials resembling BNNSs. Finally, based on the predicted results of the ML models, we propose a predictive model for the mechanical properties of the BNNSs, which serves as a valuable reference for future research endeavors. [ABSTRACT FROM AUTHOR]

Published
2024
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45. SE-U-Lite: Milling Tool Wear Segmentation Based on Lightweight U-Net Model With Squeeze-and-Excitation Module

Author

Zhao, Pengyue, Li, Ziteng, You, Zhichao, Chen, Zhigao, Huang, Tao, Guo, Kai, and Li, Duo

Abstract

Tool condition is crucial for guarantee processing efficiency and surface quality. However, the complex industrial environment, limited computational conditions, and less training data present a challenge to the accuracy and efficiency of tool condition monitoring (TCM). This article introduces a lightweight deep learning method that achieves highly accurate end-to-end image segmentation of tool wear, thereby reducing computational complexity and memory consumption. To reduce the model parameters, axial depthwise (DW) convolution is used, which combines cross-shaped and DW convolutions to expand the reception field and extract effective contextual semantic information. In addition, the squeeze-and-excitation module (SE) is introduced into the multilevel jump path to provide attention information on the tool wear region. The tool wear is then obtained through traditional image processing including morphological filtering, Canny edge detection, and Hough line detection. In situ machine vision system with industrial edge deployment is designed to capture and process the images of milling cutters throughout the tool life-cycle. The experimental results validated the proposed lightweight model that could provide precise prediction of tool wear.

Published
2024
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50. Identification and quantitative detection of two pathogenic bacteria based on a terahertz metasensor

Author

Ma, Zhaofu, primary, Jiao, Yanan, additional, Zhang, Chiben, additional, Lou, Jing, additional, Zhao, Pengyue, additional, Zhang, Bin, additional, Wang, Yujia, additional, Yu, Ying, additional, Sun, Wen, additional, Yan, Yang, additional, Yang, Xingpeng, additional, Sun, Lang, additional, Wang, Ride, additional, Chang, Chao, additional, Li, Xiru, additional, and Du, Xiaohui, additional

Published
2023
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