Recently, machine learning has been used in every possible field to leverage its amazing power. For a long time, the net-working and distributed computing system is the key infrastructure to provide efficient computational resource for machine learning. Networking itself can also benefit from this promising technology. This article focuses on the application of Machine Learning techniques for Networking (MLN), which can not only help solve the intractable old network questions but also stimulate new network applications. In this article, we summarize the basic workflow to explain how to apply the machine learning technology in the networking domain. Then we provide a selective survey of the latest representative advances with explanations on their design principles and benefits. These advances are divided into several network design objectives and the detailed information of how they perform in each step of MLN workflow is presented. Finally, we shed light on the new opportunities on networking design and community building of this new inter-discipline. Our goal is to provide a broad research guideline on networking with machine learning to help and motivate researchers to develop innovative algorithms, standards and frameworks.Comment: 8 pages, 2 figure

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A B S T R A C TThe dried roots of Isatis tinctoria L. are highly traded in the pharmaceutical industry due to their notable antiinfluenza efficacy. For the first time, I. tinctoria hairy root cultures (ITHRCs) were co-cultured with two immobilized live GRAS (Generally Recognized as Safe) fungi, i.e. Aspergillus niger and Aspergillus niger, for the elevated production of pharmacologically active flavonoids. Immobilized A. niger (IAN) was exhibited as the superior elicitor in the plant-fungus co-cultivation system. The highest flavonoid production (3018.31 ± 48.66 μg/g DW) were achieved in IAN-treated ITHRCs under the optimal conditions of IAN spore concentration ca.10 4 spores/mL, temperature 30°C, initial pH value of media 7.0 and time 72 h, which remarkably increased 6.83-fold relative to non-treated control (441.91 ± 7.35 μg/g DW). Also, this study revealed that IAN elicitation could trigger the sequentially transient accumulation of signal molecules and intensify the oxidative stress in ITHRCs, which both contributed to the up-regulated expression of associated genes involved in flavonoid biosynthetic pathway. Moreover, IAN could be reused at least five cycles with satisfactory performance. Overall, the coupled culture of IAN and ITHRCs is a promising and effective approach for the enhanced production of flavonoids, which allows for the improved applicability of these valuable compounds in pharmaceutical fields.