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5,069 results on '"Cui, Jie"'

121. circ_0004904 regulates the trophoblast cell in preeclampsia via miR-19b-3p/ARRDC3 axis

Author

Cao Chenyuan, Cui Jie, and Liu Guiling

Subjects
preeclampsia, circ_0004904, mir-19b-3p, arrdc3, Medicine
Abstract

Circular RNAs have been demonstrated to act as vital participants in various diseases, including preeclampsia (PE). This study aimed to research the effects of circ_0004904 on PE. The contents of circ_0004904, microRNA-19b-3p (miR-19b-3p) and arrestin domain containing 3 (ARRDC3) were quantified by quantitative real-time PCR and western blot. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide and 5-ethynyl-2′-deoxyuridine assays were enforced to assess cell proliferation. The transwell assay and flow cytometry were applied to detect the cell migration, invasion, and apoptosis. The liaison between miR-19b-3p and circ_0004904 or ARRDC3 was demonstrated by dual-luciferase reporter assay. Thereafter, circ_0004904 and ARRDC3 were augmented, and miR-19b-3p was restrained in PE. Circ_0004904 silencing contributed to cell proliferation, migration, and invasion, but restrained cell apoptosis in trophoblast cells. Further, miR-19b-3p was a target of circ_0004904, and miR-19b-3p could target ARRDC3. Additionally, circ_0004904 accelerated PE evolution via changing ARRDC3 level by binding to miR-19b-3p. In all, circ_0004904 encouraged PE progress via miR-19b-3p/ARRDC3 axis.

Published
2023
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123. Manipulating Spin Chirality of Magnetic Skyrmion Bubbles by In-Plane Re-versed Magnetic Fields in (Mn$_{1-x}$Ni$_x$)$_{65}$Ga$_{35}$ ($x = 0.45$) magnet

Author

Ding, Bei, Cui, Jie, Xu, Guizhou, Hou, Zhipeng, Li, Hang, Liu, Enke, Wu, Guangheng, Yao, Yuan, and Wang, Wenhong

Subjects
Physics - Applied Physics, Condensed Matter - Materials Science
Abstract

Understanding the dynamics of the magnetic skyrmion, a particle-like topologically stable spin texture, and its response dynamics to external fields are indis-pensable for the applications in spintronic devices. In this letter, the Lorentz transmis-sion electron microscopy (LTEM) was used to investigate the spin chirality of the mag-netic skyrmion bubbles (SKBs) in the centrosymmetric magnet MnNiGa at room tem-perature. The reversal of SKBs excited by the in-plane magnetic field has been revealed. Moreover, the collective behavior of interacting spin chirality can be manipulated by reversing the directions of the magnetic fields on a wedge-shaped thin plate. The dy-namic behavior of the bubbles at different position of the thin plate has been explored with the micromagnetic simulation, indicating a non-uniform and nontrivial dynamic magnetization on the surfaces and center of the thin plate during the spin chirality re-versal. The results suggest that the controllable symmetry breaking of the SKBs arising from thickness variation provides an ability to manipulate the collective behavior of the spin chirality with small external fields, leading to a promising application in nonvola-tile spintronic devices for magnetic skyrmions.

Published
2019
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141. Survey of Membership Inference Attacks for Machine Learning

Author

CHEN Depeng, LIU Xiao, CUI Jie, HE Daojing

Subjects
machine learning, privacy-preserving, membership inference attack, defense mechanism, Computer software, QA76.75-76.765, Technology (General), T1-995
Abstract

Artificial intelligence has been integrated into all aspects of people's daily lives with the continuous development of machine learning,especially in the deep learning area.Machine learning models are deployed in various applications,enhancing the intelligence of traditional applications.However,in recent years,research has pointed out that personal data used to train machine learning models is vulnerable to the risk of privacy disclosure.Membership inference attacks(MIAs) are significant attacks against the machine learning model that threatens users' privacy.MIA aims to judge whether user data samples are used to train the target model.When the data is closely related to the individual,such as in medical,financial,and other fields,it directly interferes with the user's private information.This paper first introduces the background knowledge of membership inference attacks.Then,we classify the existing MIAs according to whether the attacker has a shadow model.We also summarize the threats of MIAs in different fields.Also,this paper points out the defense means against MIAs.The existing defense mechanisms are classified and summarized according to the strategies for preventing model overfitting,model-based compression,and disturbance.Finally,this paper analyzes the advantages and disadvantages of the current MIAs and defense mechanisms and proposes possible research directions for future MIAs.

Published
2023
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