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Li Weijian,Chen Gaohuang,Feng Zhenyu,Zhu Baoyi,Zhou Lilin,Zhang Yuying,Mai Junyan,Jiang Chonghe,Zeng Jianwen 한국유전학회 2021 Genes & Genomics Vol.43 No.12
Background Prostate cancer (PCa) is one of the most common malignancies in men. YTHDF1 may play an important role in promoting PCa progression, but there is no reports to date on YTHDF1 function in PCa. Objective This study explored whether YTHDF1 could regulate TRIM44 in PCa cells. Methods By querying the TCGA database, we evaluated YTHDF1 expression in PCa, the OS and DFS of YTHDF1, and the correlation between YTHDF1 and TRIM44 in PCa. We constructed vectors to interfere with YTHDF1 expression and overexpress TRIM44 to examine the role of YTHDF1 and TRIM44 in PCa cells. Diferentially expressed mRNAs were identifed by mRNA sequencing. The levels of YTHDF1, TRIM44, LGR4, SGTA, DDX20, and FZD8 were measured by qRT-PCR and WB was used to determine YTHDF1 and TRIM44 expression. A CCK-8 assay was used to assess cell proliferation. A Transwell chamber assay was used measure cell migration and invasion ability. Results YTHDF1 was highly expressed in both Pca tissues and cells. PCa patient prognosis with high YTHDF1 expression was relatively poor. Cell function experiments showed that inhibiting YTHDF1 expression decreased cell proliferation, migration, and invasion. RNA sequencing analysis revealed that YTHDF1 may promote PCa cell proliferation, migration, and invasion by modulating TRIM44 expression. Cell function experiments further verifed that YTHDF1 promoted PCa cell proliferation, migration, and invasion by regulating TRIM44. Conclusions YTHDF1 enhances PCa cell proliferation, migration, and invasion by regulating TRIM44.
On p-th Moment Exponential Stability for Stochastic Cellular Neural Networks with Distributed Delays
Changjin Xu,Lilin Chen,Peiluan Li 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.3
In this paper, a class of stochastic cellular neural networks with distributed delays are investigated. With the help of the method of variation parameter and inequality techniques, some sufficient conditions for the p-th moment exponential stability of the system are established. An example is given to illustrate the feasibility and effectiveness of our main results. Our results obtained in this paper improve and generalize some earlier works reported in the literature.
Ting Yuan,Tengfei Huo,Haie Huo,Xianjie Fang,Lilin Li,Miao Chen,Li Yu 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.11
Social stability risk posed by energy infrastructure projects can seriously affect urban sustainable development. There is thus a need to better understand how risk strategies are designed. However, there is few considerations on risk strategy design by breaking causal relationships. This research innovatively explores social stability risk variables by content mining, develops causal relationships of the social stability risk variables via Fault Tree Analysis (FTA) method, explores core risk variables and critical causal relationships by social network analysis (SNA) method, designs and validates risk strategies. The findings show that: 1) From the overall risk network perspective, the risk network contain 8 core risk variables. 2) From individual risk network perspective, there are 75 critical causal relationships. The top 3 critical causal relationships contain: projects that destroy the cultural landscape would be regarded as threating national security, which often inspires demonstrations among the local people and the line betweenness is the largest (35.589). Traffic congestion by the project is the main reason to cause local small-scale public petition, and the line betweenness is 35.075. Projects that threaten the ecological environment often bring psychological rejection of the project by the local public, and the line betweenness is 30.837. 3) Two scenarios are evaluated in terms of basic scenario and the experiment group scenario. Compared with the basic scenario, risk strategies considering causal relationships have significant effectiveness. The overall risk network density has reduced by 35.22%. The clustering coefficients has decreased by 16.20%. Intermediate central potential has reduced by 9.49%. This study offers a good reference for project managers to complete the sustainable risk control.