This paper proposes a scale-free highly-clustered echo state network (SHESN). We designed the SHESN to include a naturally evolving state reservoir according to incremental growth rules that account for the following features:(1) short characteristic path length, (2) high clustering coefficient, (3) scale-free distribution, and (4) hierarchical and distributed architecture. This new state reservoir contains a large number of internal neurons that are sparsely interconnected in the form of domains. Each domain comprises one backbone neuron and a number of local neurons around this backbone. Such a natural and efficient recurrent neural system essentially interpolates between the completely regular Elman network and the completely random echo state network (ESN) proposed by H. Jaeger et al. We investigated the collective characteristics of the proposed complex network model. We also successfully applied it to challenging problems such as the Mackey-Glass dynamic system and the laser time series prediction.Compared to the ESN, our experimental results show that the SHESN model has a significantly enhanced echo state property and better performance in approximating highly complex nonlinear dynamics. In a word, this large-scale dynamic complex network reflects some natural characteristics of biological neural systems in many aspects such as power law, small-world property, and hierarchical architecture. It should have strong computing power, fast signal propagation speed, and coherent synchronization. Index TermsEcho state network, local preferential attachments, recurrent neural networks, scale-free, small-world, time series prediction. Manuscript

Transmembrane-4-L-six-family-1(TM4SF1), a four-transmembrane L6 family member, is highly expressed in various pancreatic cancer cell lines and promotes cancer cells metastasis. However, the TM4SF1-associated signaling network in metastasis remains unknown. In the present study, we found that TM4SF1 affected the formation and function of invadopodia. Silencing of TM4SF1 reduced the expression of DDR1 significantly in PANC-1 and AsPC-1 cells. Through double fluorescence immuno-staining and Co-immunoprecipitation, we also found that TM4SF1 colocalized with DDR1 and had an interaction with DDR1. In addition, upregulating the expression of DDR1 rescued the inhibitory effects of cell migration and invasion, the expression of MMP2 and MMP9 and the formation and function of invadopodia when TM4SF1 silenced. In pancreatic cancer tissues, qRT-PCR and scatter plots analysis further determined that TM4SF1 had a correlation with DDR1. Collectively, our study provides a novel regulatory pathway involving TM4SF1, DDR1, MMP2 and MMP9, which promotes the formation and function of invadopodia to support cell migration and invasion in pancreatic cancer.

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In general, there are two kinds of cooperative driving strategies, planning based strategy and ad hoc negotiation based strategy, for connected and automated vehicles (CAVs) merging problems. The planning based strategy aims to find the global optimal passing order, but it is time-consuming when the number of considered vehicles is large. In contrast, the ad hoc negotiation based strategy runs fast, but it always finds a local optimal solution. In this paper, we propose a grouping based cooperative driving strategy to make a good tradeoff between time consumption and coordination performance. The key idea is to fix the passing orders for some vehicles whose intervehicle headways are small enough (e.g., smaller than the preselected grouping threshold). From the viewpoint of optimization, this method reduces the size of the solution space. A brief analysis shows that the sub-optimal passing order found by the grouping based strategy has a high probability to be close to the global optimal passing order, if the grouping threshold is appropriately chosen. A series of simulation experiments are carried out to validate that the proposed strategy can yield a satisfied coordination performance with less time consumption and is promising to be used in practice.Index Terms-Connected and Automated Vehicles (CAV), cooperative driving, merging problem, grouping based strategy.