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4. Insights into the synthesis, secretion and curing of barnacle cyprid adhesive via transcriptomic and proteomic analyses of the cement gland

Author

Yan, Guoyang, Sun, Jin, Wang, Zishuai, Qian, Peiyuan, He, Lisheng, Yan, Guoyang, Sun, Jin, Wang, Zishuai, Qian, Peiyuan, and He, Lisheng

Abstract

Barnacles represent one of the model organisms used for antifouling research, however, knowledge regarding the molecular mechanisms underlying barnacle cyprid cementation is relatively scarce. Here, RNA-seq was used to obtain the transcriptomes of the cement glands where adhesive is generated and the remaining carcasses of Megabalanus volcano cyprids. Comparative transcriptomic analysis identified 9060 differentially expressed genes, with 4383 upregulated in the cement glands. Four cement proteins, named Mvcp113k, Mvcp130k, Mvcp52k and Mvlcp1-122k, were detected in the cement glands. The salivary secretion pathway was significantly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the differentially expressed genes, implying that the secretion of cyprid adhesive might be analogous to that of saliva. Lysyl oxidase had a higher expression level in the cement glands and was speculated to function in the curing of cyprid adhesive. Furthermore, the KEGG enrichment analysis of the 352 proteins identified in the cement gland proteome partially confirmed the comparative transcriptomic results. These results present insights into the molecular mechanisms underlying the synthesis, secretion and curing of barnacle cyprid adhesive and provide potential molecular targets for the development of environmentally friendly antifouling compounds. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Published
2020

6. Performance assessment of distributed LQG control subject to communication delays.

Author

Yan, Guoyang, Liu, Jinfeng, Alipouri, Yousef, and Huang, Biao

Subjects
*H2 control, *TELECOMMUNICATION systems
Abstract

This paper deals with the control performance assessment of distributed networked control systems in the presence of communication delays. The best achievable steady-state performance of distributed networked control systems in the framework of distributed linear quadratic Gaussian (LQG) control is presented where communication delays serve as the most fundamental performance limitation. In the proposed distributed LQG control design, distributed controllers and distributed observers are designed simultaneously without using separation principle. And the non-applicability of separation principle is investigated and demonstrated in distributed networked control with communication delays. In order to handle time-varying communication delays, we propose to use lower and upper bounds to characterize the range of the best achievable control performance with respect to the range of the communication delays. The obtained results can be used to evaluate the potential performance improvement of an existing distributed networked control system. The proposed approach is illustrated through a numerical example. [ABSTRACT FROM AUTHOR]

Published
2021
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9. Metric learning method aided data-driven design of fault detection systems

Author

Yan, Guoyang, Mei, Jiangyuan, Yin, Shen, and Karimi, HAMID REZA

Subjects
Engineering (all), Article Subject, lcsh:TA1-2040, lcsh:Mathematics, Mathematics (all), VDP::Technology: 500::Materials science and engineering: 520, lcsh:Engineering (General). Civil engineering (General), lcsh:QA1-939
Abstract

Published version of an article in the journal: Mathematical Problems in Engineering. Also available from the publisher at: http://dx.doi.org/10.1155/2014/974758 Fault detection is fundamental to many industrial applications. With the development of system complexity, the number of sensors is increasing, which makes traditional fault detection methods lose efficiency. Metric learning is an efficient way to build the relationship between feature vectors with the categories of instances. In this paper, we firstly propose a metric learning-based fault detection framework in fault detection. Meanwhile, a novel feature extraction method based on wavelet transform is used to obtain the feature vector from detection signals. Experiments on Tennessee Eastman (TE) chemical process datasets demonstrate that the proposed method has a better performance when comparing with existing methods, for example, principal component analysis (PCA) and fisher discriminate analysis (FDA). © 2014 Guoyang Yan et al.

Published
2014

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