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32 results on '"ZHENG Hongling"'

1. EBV lytic proteins BZLF1 and BILF1 inhibit the expression of major histocompatibility complex in Raji cells

Author

ZHENG Hongling, GUO Qingwei, ZHANG Wei, ZHANG Yan, HE Xingbo, YE Bing

Subjects
bzlf1, bilf1, major histocompatibility complex ⅱ, major histocompatibility complex ⅰ, Medicine
Abstract

Objective To investigate whether BZLF1 and BILF1 can inhibit the expression of major histocompatibility complex(MHC)molecules in Raji cells during Epstein-Barr virus (EBV) lytic cycle. Methods Reactivation was triggered by treating Raji cells with TPA and NaB. As the marker protein during EBV lytic state, the BZLF1 protein expression was detected by Western blot. The expression of BZLF1 and BILF1 at transcription level was analyzed by RT-qPCR in EBV reactivation. The expression of MHC class Ⅱ and MHC class Ⅰ molecules was analyzed by flow cytometry. After BZLF1 and BILF1 over-expression or gene silencing via siRNA in Raji cells by electroporation, the expression of MHC class Ⅱ and MHC class Ⅰ molecules was detected by flow cytometry and Western blot respectively. Results EBV reactivation can be triggered with TPA and NaB. The BZLF1 and BILF1 gene expression was observed by inducing EBV reactivation in Raji cells. The levels of MHC class Ⅱ and MHC class Ⅰ molecules were down regulated by inducing EBV reactivation or ectopic expression of BZLF1 and BILF1 (P＜0.05). Expression of MHC class Ⅱ and MHC class Ⅰ molecules was restored by specific siRNA. Conclusions BZLF1 and BILF1 down regulate the expression of MHC class Ⅱ and MHC class Ⅰ molecules in EBV reactivation respectively.

Published
2023
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2. Learn From Model Beyond Fine-Tuning: A Survey

Author

Zheng, Hongling, Shen, Li, Tang, Anke, Luo, Yong, Hu, Han, Du, Bo, and Tao, Dacheng

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Foundation models (FM) have demonstrated remarkable performance across a wide range of tasks (especially in the fields of natural language processing and computer vision), primarily attributed to their ability to comprehend instructions and access extensive, high-quality data. This not only showcases their current effectiveness but also sets a promising trajectory towards the development of artificial general intelligence. Unfortunately, due to multiple constraints, the raw data of the model used for large model training are often inaccessible, so the use of end-to-end models for downstream tasks has become a new research trend, which we call Learn From Model (LFM) in this article. LFM focuses on the research, modification, and design of FM based on the model interface, so as to better understand the model structure and weights (in a black box environment), and to generalize the model to downstream tasks. The study of LFM techniques can be broadly categorized into five major areas: model tuning, model distillation, model reuse, meta learning and model editing. Each category encompasses a repertoire of methods and strategies that aim to enhance the capabilities and performance of FM. This paper gives a comprehensive review of the current methods based on FM from the perspective of LFM, in order to help readers better understand the current research status and ideas. To conclude, we summarize the survey by highlighting several critical areas for future exploration and addressing open issues that require further attention from the research community. The relevant papers we investigated in this article can be accessed at ., Comment: 20 pages, 9 figures

Published
2023

20. Quantitative Determination of Ethylene Using Smartphone Based Optical Fiber Sensor (SOFs) Coupled with Pyrene-Tagged Grubbs Catalyst

Author

Yang, Xin, primary, Leong, Justin Lee Kee, additional, Sun, Mingtai, additional, Jing, Linzhi, additional, Zhang, Yuannian, additional, Wang, Tian, additional, Wang, Xiang, additional, Zhan, Ningping, additional, Xu, Chunyuhang, additional, Zheng, Hongling, additional, Du, Ke, additional, Wang, Suhua, additional, and Huang, Dejian, additional

Published
2022
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29. Iron‐Catalyzed Regioselective Alkenylboration of Olefins.

Author

Yu, Xiaolong, Zheng, Hongling, Zhao, Haonan, Lee, Boon Chong, and Koh, Ming Joo

Subjects
*STEREOCHEMISTRY, *FLUORIDES, *HALIDES, *BROMIDES, *CHLORIDES, *ALKENES, *IRON chlorides, *ALKENYL group
Abstract

The first examples of an iron‐catalyzed three‐component synthesis of homoallylic boronates from regioselective union of bis(pinacolato)diboron, an alkenyl halide (bromide, chloride or fluoride), and an olefin are disclosed. Products that bear tertiary or quaternary carbon centers could be generated in up to 87 % yield as single regioisomers with complete retention of the olefin stereochemistry. With cyclopropylidene‐containing substrates, ring cleavage leading to trisubstituted E‐alkenylboronates were selectively obtained. Mechanistic studies revealed reaction attributes that are distinct from previously reported alkene carboboration pathways. [ABSTRACT FROM AUTHOR]

Published
2021
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