Understanding the internet topology evolution dynamics

The internet structure is extremely complex. The Positive-Feedback Preference (PFP) model is a recently introduced internet topology generator. The model uses two generic algorithms to replicate the evolution dynamics observed on the internet historic data. The phenomenological model was originally designed to match only two topology properties of the internet, i.e. the rich-club connectivity and the exact form of degree distribution. Whereas numerical evaluation has shown that the PFP model accurately reproduces a large set of other nontrivial characteristics as well. This paper aims to investigate why and how this generative model captures so many diverse properties of the internet. Based on comprehensive simulation results, the paper presents a detailed analysis on the exact origin of each of the topology properties produced by the model. This work reveals how network evolution mechanisms control the obtained topology properties and it also provides insights on correlations between various structural characteristics of complex networks.Comment: 15 figure

The future of China’s U.S. listed firms: legal and political perspectives on possible decoupling

There is a long history of Chinese firms raising capital on leading U.S. exchanges and these shares are estimated at $1 trillion value. Yet these investments are now on shaky ground on both sides of the U.S.-China faultline. Chinese firms have benefited from U.S. capital without complying with investor protections, particularly audit inspections, owing to differences in approaches to company data and opaque structures that have misled some investors. As a result, U.S. listed Chinese companies also face the looming threat of delistings under the Holding Foreign Companies Accountable Act (HFCAA) and China is unlikely to come to the rescue of all. Chinese listings of nonstate firms had depended on a largely laissez-faire approach by China to the expansion of this sector through foreign listings but heightened levels of state involvement in the affairs of nonstate companies are now bringing strain, as Communist Party policies have changed dramatically in recent years. The HFCAA was the result of a geopolitical standoff following prolonged disputes over access to financial audits of U.S. listed Chinese companies, which had been resisted by Chinese agencies for data security reasons and those data concerns have not gone away. These concerns, together with some other Chinese state strategies have partly been allayed by an agreement as to inspections. Uncertainties remain and there may yet be the potential for future delistings, presenting risks for U.S. investors, as well as slowing the stream of U.S. listings by Chinese companies. It will not yet amount to a decoupling, but investors should be wary

Continuous-action reinforcement learning for memory allocation in virtualized servers

In a virtualized computing server (node) with multiple Virtual Machines (VMs), it is necessary to dynamically allocate memory among the VMs. In many cases, this is done only considering the memory demand of each VM without having a node-wide view. There are many solutions for the dynamic memory allocation problem, some of which use machine learning in some form. This paper introduces CAVMem (Continuous-Action Algorithm for Virtualized Memory Management), a proof-of-concept mechanism for a decentralized dynamic memory allocation solution in virtualized nodes that applies a continuous-action reinforcement learning (RL) algorithm called Deep Deterministic Policy Gradient (DDPG). CAVMem with DDPG is compared with other RL algorithms such as Q-Learning (QL) and Deep Q-Learning (DQL) in an environment that models a virtualized node. In order to obtain linear scaling and be able to dynamically add and remove VMs, CAVMem has one agent per VM connected via a lightweight coordination mechanism. The agents learn how much memory to bid for or return, in a given state, so that each VM obtains a fair level of performance subject to the available memory resources. Our results show that CAVMem with DDPG performs better than QL and a static allocation case, but it is competitive with DQL. However, CAVMem incurs significant less training overheads than DQL, making the continuous-action approach a more cost-effective solution.This research is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 754337 (EuroEXA) and the European Union’s 7th Framework Programme under grant agreement number 610456 (Euroserver). It also received funding from the Spanish Ministry of Science and Technology (project TIN2015-65316-P), Generalitat de Catalunya (contract 2014-SGR-1272), and the Severo Ochoa Programme (SEV-2015-0493) of the Spanish Government.Peer ReviewedPostprint (author's final draft

Polo-like kinase 1 siRNA-607 induces mitotic arrest and apoptosis in human nasopharyngeal carcinoma cells

Polo-like kinase (Plk) 1 is overexpressed in many human malignancies including nasopharyngeal carcinoma, indicating its potential as a therapeutic target. Recently, using a simple cellular morphologybased strategy, we have identified several novel effective siRNAs against Plk1 including Plk1 siRNA- 607. In this study, we further investigated the effects of Plk1 siRNA-607 in human nasopharyngeal carcinoma cell line, HNE-1. Real time RT-PCR and Western blot indicated that Plk1 siRNA-607 transfection resulted in a significant inhibition in Plk1 expression in the HNE-1 cells. Furthermore, cell cycle, cell growth and apoptosis analysis clearly indicated that Plk1 siRNA-607 caused a dramatic mitotic cell cycle arrest followed by massive apoptotic cell death, and eventually resulted in a significant decrease in growth and viability of the nasopharyngeal carcinoma cells. Given that Plk1 has been widely accepted as a novel efficient target for cancer therapy, these results suggested that Plk1 siRNA-607 could be further developed for the treatment of human nasopharyngeal carcinoma.Key words: Nasopharyngeal carcinoma, Plk1, RNA silencing, cell cycle, apoptosis

Upregulated sirtuin 1 by miRNA-34a is required for smooth muscle cell differentiation from pluripotent stem cells

© 2015 Macmillan Publishers Limited. All rights reserved. microRNA-34a (miR-34a) and sirtuin 1 (SirT1) have been extensively studied in tumour biology and longevityaging, but little is known about their functional roles in smooth muscle cell (SMC) differentiation from pluripotent stem cells. Using well-established SMC differentiation models, we have demonstrated that miR-34a has an important role in SMC differentiation from murine and human embryonic stem cells. Surprisingly, deacetylase sirtuin 1 (SirT1), one of the top predicted targets, was positively regulated by miR-34a during SMC differentiation. Mechanistically, we demonstrated that miR-34a promoted differentiating stem cells' arrest at G0G1 phase and observed a significantly decreased incorporation of miR-34a and SirT1 RNA into Ago2-RISC complex upon SMC differentiation. Importantly, we have identified SirT1 as a transcriptional activator in the regulation of SMC gene programme. Finally, our data showed that SirT1 modulated the enrichment of H3K9 tri-methylation around the SMC gene-promoter regions. Taken together, our data reveal a specific regulatory pathway that miR-34a positively regulates its target gene SirT1 in a cellular context-dependent and sequence-specific manner and suggest a functional role for this pathway in SMC differentiation from stem cells in vitro and in vivo