病毒感染研究中的分子通讯：模型， 实验数据和未来发展方向

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摘要翻译：
全世界每年有数亿人受到病毒感染的影响，然而，其中一些人在感染期间和感染后既没有疫苗，也没有有效的治疗。新冠肺炎疫情凸显了这一挑战，表明病毒如何快速传播，如何影响整个社会。必须出现引入不同学科的新技术，为抗击病毒感染以及未来可能的流行病提供前瞻性战略。在过去的十年里，一个涉及生物工程、纳米技术和信息与通信技术(ICT)的跨学科领域正在发展，被称为分子通信。这一新兴领域使用了经典通信系统的元素，并将其映射到体内外的分子信号和通信，其目的是开发可以为未来医学服务的新工具。在本文中，我们提供了一个广泛而详细的讨论如何将分子通讯整合到病毒感染性疾病模型的研究中，以及如何将分子作为信息载体来开发可能的治疗和疫苗。本文综述了现有的病毒感染的分子通讯模型（体内和体外），深入分析了它们对宿主的影响和随后对体内其他系统的通讯过程（如免疫反应），已知病毒感染的实验数据来源和如何被分子通讯界使用，以及开放的问题和未来的发展方向。由于治疗学/疫苗的发展需要以信息和通信技术为中心的跨学科方法，我们相信分子通信可以通过提供不同介质中分子传播的详细特征和操作来发挥核心作用。
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英文标题：
《Molecular Communications in Viral Infections Research: Modelling,
  Experimental Data and Future Directions》
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作者：
Michael Taynnan Barros, Mladen Veleti\'c, Masamitsu Kanada,
  Massimiliano Pierobon, Seppo Vainio, Ilangko Balasingham, Sasitharan
  Balasubramaniam
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最新提交年份：
2020
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Hundreds of millions of people worldwide are affected by viral infections each year, and yet, several of them neither have vaccines nor effective treatment during and post-infection. This challenge has been highlighted by the COVID-19 pandemic, showing how viruses can quickly spread and how they can impact society as a whole. Novel techniques that bring in different disciplines must emerge to provide forward-looking strategies to combat viral infections, as well as possible future pandemics. In the past decade, an interdisciplinary area involving bioengineering, nanotechnology and information and communication technology (ICT) has been developing, known as Molecular Communications. This new emerging area uses elements of classical communication systems and maps it to molecular signalling and communication found inside and outside the body, where the aim is to develop new tools that can serve future medicine. In this paper, we provide an extensive and detailed discussion on how Molecular Communications can be integrated into the research on viral infectious diseases modelling, and how possible treatment and vaccines can be developed considering molecules as information carriers. We provide a literature review on the existing models of Molecular Communications for viral infection (in-body and out-body), a deep analysis on their effects on the host and subsequent communication process for other systems within the body (e.g., immune response), sources of experimental data on known viral infections and how it can be used by the Molecular Communications community, as well as open issues and future directions. Since the development of therapeutics/vaccines needs an interdisciplinary approach centred around ICT, we are confident that Molecular Communications can play a central role here by providing a detail characterisation and manipulation of the propagation of molecules in different media.
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PDF链接：
https://arxiv.org/pdf/2011.00002

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关键词：发展方向 实验数据 未来发展 Applications Quantitative 信息 well Molecular 模型 通讯