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Identification of feedback loops in neural networks based on multi-step Granger causality.
Dong, Chao-Yi,Shin, Dongkwan,Joo, Sunghoon,Nam, Yoonkey,Cho, Kwang-Hyun Oxford University Press 2012 Bioinformatics Vol.28 No.16
<P>Feedback circuits are crucial network motifs, ubiquitously found in many intra- and inter-cellular regulatory networks, and also act as basic building blocks for inducing synchronized bursting behaviors in neural network dynamics. Therefore, the system-level identification of feedback circuits using time-series measurements is critical to understand the underlying regulatory mechanism of synchronized bursting behaviors.</P>
Hybrid machine learning with mode shape assessment for damage identification of plates
Zhi Chao Ong,Pei Yi Siow,Shin Yee Khoo,Kok-Sing Lim,Bee Teng Chew 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.31 No.5
Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires precollected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.
Deubiquitination and Stabilization of PD-L1 by CSN5
Lim, Seung-Oe,Li, Chia-Wei,Xia, Weiya,Cha, Jong-Ho,Chan, Li-Chuan,Wu, Yun,Chang, Shih-Shin,Lin, Wan-Chi,Hsu, Jung-Mao,Hsu, Yi-Hsin,Kim, Taewan,Chang, Wei-Chao,Hsu, Jennifer L.,Yamaguchi, Hirohito,Ding Elsevier 2016 Cancer cell Vol.30 No.6
<P><B>Summary</B></P> <P>Pro-inflammatory cytokines produced in the tumor microenvironment lead to eradication of anti-tumor immunity and enhanced tumor cell survival. In the current study, we identified tumor necrosis factor alpha (TNF-α) as a major factor triggering cancer cell immunosuppression against T cell surveillance via stabilization of programmed cell death-ligand 1 (PD-L1). We demonstrated that COP9 signalosome 5 (CSN5), induced by NF-κB p65, is required for TNF-α-mediated PD-L1 stabilization in cancer cells. CSN5 inhibits the ubiquitination and degradation of PD-L1. Inhibition of CSN5 by curcumin diminished cancer cell PD-L1 expression and sensitized cancer cells to anti-CTLA4 therapy.</P> <P><B>Highlights</B></P> <P> <UL> <LI> TNF-α stabilizes cancer cell PD-L1 in response to chronic inflammation </LI> <LI> Activation of NF-κB by TNF-α induces CSN5 expression leading to PD-L1 stabilization </LI> <LI> CSN5 enzyme activity controls T cell suppression via PD-L1 deubiquitination </LI> <LI> Destabilization of PD-L1 by CSN5 inhibitor curcumin benefits anti-CTLA4 therapy </LI> </UL> </P> <P><B>Graphical Abstract</B></P> <P>[DISPLAY OMISSION]</P>
Delay of Surgery for Spinal Metastasis due to the COVID-19 Outbreak Affected Patient Outcomes
Chia-Jung Hsieh,Chun-Yu Wu,Yen-Heng Lin,Yu-Cheng Huang,Wen-Chi Yang,Tom Wei-Wu Chen,Wei-Li Ma,Wei-Hsin Lin,Feng-Ming Hsu,Furen Xiao,Shih-Hung Yang,Dar-Ming Lai,Chang-Mu Chen,Shin-Yi Chao,Fon-Yih Tsuan 대한척추신경외과학회 2023 Neurospine Vol.20 No.4
Objective: The present study is to analyze the effects of the coronavirus disease 2019 (COVID 2019) outbreak and the subsequent lockdown on the outcomes of spinal metastasis patients. Methods: The study was a retrospective analysis of data from a prospective cohort study. All patients underwent surgical intervention for spinal metastases between January 2019 and December 2021 and had at least 3 months of postoperative follow-up. The primary outcome was overall mortality during the 4 different stages (pre-COVID-19 era, COVID-19 pandemic except in Taiwan, national lockdown, lifting of the lockdown). The secondary outcomes were the oncological severity scores, medical/surgical accessibility, and patient functional outcome during the 4 periods as well as survival/mortality. Results: A total of 233 patients were included. The overall mortality rate was 41.20%. During the Taiwan lockdown, more patients received palliative surgery than other surgical methods, and no total en bloc spondylectomy was performed. The time from surgeon visit to operation was approximately doubled after the COVID-19 outbreak in Taiwan (75.97, 86.63, 168.79, and 166.91 hours in the 4 periods, respectively). The estimated survival probability was highest after the national lockdown was lifted and lowest during the lockdown. In the multivariate analysis, increased risk of mortality was observed with delay of surgery, with emergency surgery having a higher risk with delays above 33 hours, urgent surgery (below 59 and above 111 hours), and elective surgery (above 332 hours). Conclusion: The COVID-19 pandemic and related policies have altered daily clinical practice and negatively impacted the survival of patients with spinal metastases.