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Browsing by Author "Li, Zhen"

Now showing 1 - 4 of 4
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    A Deep Branched Network for Failure Mode Diagnostics and Remaining Useful Life Prediction
    Li, Zhen; Li, Yongxiang; Yue, Xiaowei; Zio, Enrico; Wu, Jianguo (IEEE, 2022-08)
    In complex systems, the operating units often suffer from multiple failure modes, and each failure mode results in distinct degradation path and service life. Thus, it is critical to perform the failure mode diagnostics and predict the remaining useful life (RUL) accordingly in modern industrial systems. However, most of the existing approaches consider the prognostic problem under a single failure mode or treat the failure mode classification and RUL prediction as two independent tasks, despite the fact that they are closely related and should be synergistically performed to enhance the generalization performance. Motivated by these issues, we propose a deep branched network (DBNet) for failure mode classification and RUL prediction. In this approach, the two tasks are jointly learned in a sequential manner, in which the feature extraction layers are shared by both tasks, while the neural network branches into individualized subnetworks for RUL prediction of each mode based on the output of the diagnostic subnetwork. Different from the traditional multitask learning-based methods, where the diagnostics and RUL prediction are performed in parallel, the proposed DBNet innovatively couples these two tasks sequentially to boost the prognostic accuracy. The effectiveness of the proposed method is thoroughly demonstrated and evaluated on an aircraft gas turbine engine with multiple failure modes.
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    Molecular epidemiology of hepatitis E virus infections in Shanghai, China
    Zhu, Yumin; Si, Fusheng; Cao, Dianjun; Yu, Xiaoming; Yu, Ruisong; Dong, Shijuan; Huang, Fenfen; Zhang, Yuanshu; Li, Zhen (2011-12-15)
    Background Hepatitis E virus (HEV) causes acute or fulminant hepatitis in humans and is an important public health concern in many developing countries. China has a high incidence of HEV epidemics, with at least three genotypes (1, 3 and 4) and nine subtypes (1b, 1c, 3b, 4a, 4b, 4d, 4g, 4h and 4i) so far identified. Since genotype 3 and the newly identified subtype 4i have been exclusively limited geographically to Shanghai and its neighboring provinces, the epidemiology of HEV infections within the municipality, a major industrial and commercial center, deserves closer attention. Findings A total of 65 sequences, 60 located within the HEV SH-SW-zs1 genome [GenBank:EF570133], together with five full-length swine and human HEV genomic sequences, all emanating from Shanghai, were retrieved from GenBank. Consistent with the primary role of genotype 4 in China overall, analysis of the sequences revealed this to have been the dominant genotype (58/65) in Shanghai. Six HEV subtypes (3b, 4a, 4b, 4d, 4h and 4i) were also represented. However, although subtype 4a is the dominant subtype throughout China, subtype 4i (29/65) was the most prevalent subtype among the Shanghai sequences, followed by subtypes 4d (10/65) and 4h (9/65). Subtypes 4h, 4i and 4d were found in both swine and humans, whereas 4b was found only in swine and subtype 4a only in humans. Conclusions Six different swine and human HEV subtypes have so far been documented in Shanghai. More molecular epidemiological investigations of HEV in swine, and particularly among the human population, should be undertaken.
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    RETICULON-LIKE PROTEIN B2 is a proviral factor co-opted for the biogenesis of viral replication organelles in plants
    Zhang, Qianshen; Wen, Zhiyan; Zhang, Xin; She, Jiajie; Wang, Xiaoling; Gao, Zongyu; Wang, Ruiqi; Zhao, Xiaofei; Su, Zhen; Li, Zhen; Li, Dawei; Wang, Xiaofeng; Zhang, Yongliang (Oxford University Press, 2023-05)
    Endomembrane remodeling to form a viral replication complex (VRC) is crucial for a virus to establish infection in a host. Although the composition and function of VRCs have been intensively studied, host factors involved in the assembly of VRCs for plant RNA viruses have not been fully explored. TurboID-based proximity labeling (PL) has emerged as a robust tool for probing molecular interactions in planta. However, few studies have employed the TurboID-based PL technique for investigating plant virus replication. Here, we used Beet black scorch virus (BBSV), an endoplasmic reticulum (ER)-replicating virus, as a model and systematically investigated the composition of BBSV VRCs in Nicotiana benthamiana by fusing the TurboID enzyme to viral replication protein p23. Among the 185 identified p23-proximal proteins, the reticulon family of proteins showed high reproducibility in the mass spectrometry data sets. We focused on RETICULON-LIKE PROTEIN B2 (RTNLB2) and demonstrated its proviral functions in BBSV replication. We showed that RTNLB2 binds to p23, induces ER membrane curvature, and constricts ER tubules to facilitate the assembly of BBSV VRCs. Our comprehensive proximal interactome analysis of BBSV VRCs provides a resource for understanding plant viral replication and offers additional insights into the formation of membrane scaffolds for viral RNA synthesis. TurboID-based proximity labeling reveals the viral replication complex structure of a plant virus, unveiling a proviral function of RETICULON-LIKE PROTEIN B2 in viral replication complex formation.
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    A Shape-Constrained Neural Data Fusion Network for Health Index Construction and Residual Life Prediction
    Li, Zhen; Wu, Jianguo; Yue, Xiaowei (IEEE, 2021-11-01)
    With the rapid development of sensor technologies, multisensor signals are now readily available for health condition monitoring and remaining useful life (RUL) prediction. To fully utilize these signals for a better health condition assessment and RUL prediction, health indices are often constructed through various data fusion techniques. Nevertheless, most of the existing methods fuse signals linearly, which may not be sufficient to characterize the health status for RUL prediction. To address this issue and improve the predictability, this article proposes a novel nonlinear data fusion approach, namely, a shape-constrained neural data fusion network for health index construction. Especially, a neural network-based structure is employed, and a novel loss function is formulated by simultaneously considering the monotonicity and curvature of the constructed health index and its variability at the failure time. A tailored adaptive moment estimation algorithm (Adam) is proposed for model parameter estimation. The effectiveness of the proposed method is demonstrated and compared through a case study using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set.