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116 results on '"Biling Zhang"'

1. An Integrated Transcriptomics and Lipidomics Analysis Reveals That Ergosterol Is Required for Host Defense Against Bacterial Infection in Drosophila

Author

Zihao Deng, Yanyang Yang, Jiazhen Luo, Biling Zhang, Jiyong Liu, Guanghou Shui, Renjie Jiao, and Chuanxian Wei

Subjects
Drosophila, innate immunity, lipid metabolism, lipidomics, transcriptomics, ergosterol, Immunologic diseases. Allergy, RC581-607
Abstract

Animals adjust their lipid metabolism states in response to pathogens infection. However, the underlying molecular mechanisms for how lipid metabolism responds to infection remain to be elusive. In this study, we assessed the temporal changes of lipid metabolism profiles during infection by an integrated transcriptomics and lipidomics analysis. Ergosterol is identified to be required for proper host defense to pathogens. Notably, ergosterol level is increased in the hemolymph upon bacterial infection. We show that the increase of ergosterol level by food supplement or genetic depletion of Acsl, a long-chain fatty acid-CoA synthetase, promotes host survival against bacterial challenges. Together, our results suggest a critical role of lipid metabolism adaption in the process of host defense against invading pathogens.

Published
2022
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39. A Learning Aided Long-Term User Association Scheme for Ultra-Dense Networks

Author

Jung-Lang Yu, Shaobo Liu, Biling Zhang, and Zhu Han

Subjects
Scheme (programming language), Mathematical optimization, Computational complexity theory, Computer Networks and Communications, Computer science, Association (object-oriented programming), Perspective (graphical), Aerospace Engineering, Power (physics), Term (time), Association scheme, Automotive Engineering, Electrical and Electronic Engineering, computer, Blossom algorithm, computer.programming_language
Abstract

In an ultra-dense network (UDN, how to associate the mobile user equipments (UEs with the appropriate low power nodes (LPNs is a very essential and challenging problem, especially when the UEs are moving and may suffer from frequent handovers. In this paper, the UE association problem is investigated from a long-term perspective, where the UE's mobility pattern, i.e., the moving velocity and direction, is taken into consideration. To predict the UE's positions based on their previous activity data, a practical scheme, i.e., the classification and batch online learning method (CBOM, is proposed for achieving the UE's velocity and direction transition matrices. Then the UE association problem with the objective of maximizing the UEs' expected achievable rate while minimizing the number of handovers is formulated. Since the proposed problem is a NP-hard mixed integer nonlinear programming problem, we reformulate it into a matching game, and propose a mobility-based long-term matching algorithm to find the near-optimal solutions. We prove that our proposed algorithm is convergent and has low computational complexity. Simulation results show our two proposed schemes outperform the existing schemes in terms of the achieved data rate, the number of handovers, and the overall system utility.

Published
2022

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