Enhancing speed of pinning synchronizability: low-degree nodes with high feedback gains

Controlling complex networks is of paramount importance in science and engineering. Despite recent efforts to improve controllability and synchronous strength, little attention has been paid to the speed of pinning synchronizability (rate of convergence in pinning control) and the corresponding pinning node selection. To address this issue, we propose a hypothesis to restrict the control cost, then build a linear matrix inequality related to the speed of pinning controllability. By solving the inequality, we obtain both the speed of pinning controllability and optimal control strength (feedback gains in pinning control) for all nodes. Interestingly, some low-degree nodes are able to achieve large feedback gains, which suggests that they have high influence on controlling system. In addition, when choosing nodes with high feedback gains as pinning nodes, the controlling speed of real systems is remarkably enhanced compared to that of traditional large-degree and large-betweenness selections. Thus, the proposed approach provides a novel way to investigate the speed of pinning controllability and can evoke other effective heuristic pinning node selections for large-scale systems

Variant-Depth Neural Networks for Deblurring Traffic Images in Intelligent Transportation Systems

Intelligent transportation systems (ITS) with surveillance cameras capture traffic images or videos. However, images or videos in ITS often encounter blurs due to various reasons. Considering resource limitations, although recent technologies make progress in image-deblurring, there are still challenges in applying image-deblurring models in practical transportation systems: the model size and the running time. This work proposes an artful variant-depth network (VDN) to address the challenges. We design variant-depth sub-networks in a coarse-to-fine manner to improve the deblurring effect. We also adopt a new connection namely stack connection to connect all sub-networks to reduce the running time and model size while maintaining high deblurring quality. We evaluate the proposed VDN with the state-of-the-art (SOTA) methods on several typical datasets. Results on Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) show that the VDN outperforms SOTA image-deblurring methods. Furthermore, the VDN also has the shortest running time and the smallest model size

Aurora-A contributes to cisplatin resistance and lymphatic metastasis in non-small cell lung cancer and predicts poor prognosis

BACKGROUND: Platinum-based chemotherapy improves survival among patients with non-small cell lung cancer (NSCLC), but the efficiency is limited due to resistance. In this study, we aimed to identify the expression of Aurora-A and its correlation with cisplatin resistance and prognosis in NSCLC. METHODS: We used immunohistochemical analysis to determine the expression of Aurora-A protein in 102 NSCLC patients treated by surgery and adjuvant cisplatin-based chemotherapy. The prognostic significances were assessed by Kaplan-Meier survival estimates and Cox models. The potential role of Aurora-A in the regulation of cisplatin resistance in NSCLC cells was examined by transfections using expression vector and small interfering RNA or using small-molecule inhibitors. RESULTS: Aurora-A expression was significantly associated with clinical stage (p = 0.018), lymph node metastasis (p = 0.038) and recurrence (p = 0.005), and was an independent prognostic parameter in multivariate analysis. High level of Aurora-A expression predicted poorer overall survival (OS) and progression-free survival (PFS). In vitro data showed that Aurora-A expression was elevated in cisplatin-resistant lung cancer cells, and overexpression or knockdown of Aurora-A resulted in increased or decreased cellular resistance to cisplatin. Furthermore, inhibition of Aurora-A reversed the migration ability of cisplatin-resistant cells. CONCLUSIONS: The current findings suggest that high Aurora-A expression is correlated with cisplatin-based chemotherapeutic resistance and predicts poor patient survival in NSCLC. Aurora-A might serve as a predictive biomarker of drug response and therapeutic target to reverse chemotherapy resistance

Scaling and memory in recurrence intervals of Internet traffic

By studying the statistics of recurrence intervals, τ, between volatilities of Internet traffic rate changes exceeding a certain threshold q, we find that the probability distribution functions, Pq(τ), for both byte and packet flows, show scaling property as $P_{q}(\tau)=\frac{1}{\overline{\tau}}f(\frac{\tau}{\overline{\tau}})$. The scaling functions for both byte and packet flows obey the same stretching exponential form, f(x)=Aexp (-Bxβ), with β≈0.45. In addition, we detect a strong memory effect that a short (or long) recurrence interval tends to be followed by another short (or long) one. The detrended fluctuation analysis further demonstrates the presence of long-term correlation in recurrence intervals

An Allosteric Interaction Links USP7 to Deubiquitination and Chromatin Targeting of UHRF1

The protein stability and chromatin functions of UHRF1 (ubiquitin-like, containing PHD and RING finger domains, 1) are regulated in a cell-cycle-dependent manner. We report a structural characterization of the complex between UHRF1 and the deubiquitinase USP7. The first two UBL domains of USP7 bind to the polybasic region (PBR) of UHRF1, and this interaction is required for the USP7-mediated deubiquitination of UHRF1. Importantly, we find that the USP7-binding site of the UHRF1 PBR overlaps with the region engaging in an intramolecular interaction with the N-terminal tandem Tudor domain (TTD). We show that the USP7-UHRF1 interaction perturbs the TTD-PBR interaction of UHRF1, thereby shifting the conformation of UHRF1 from a TTD- occluded state to a state open for multivalent histone binding. Consistently, introduction of a USP7-interaction-defective mutation to UHRF1 significantly reduces its chromatin association. Together, these results link USP7 interaction to the dynamic deubiquitination and chromatin association of UHRF1

Scaling behavior of an artificial traffic model on scale-free networks

In this article, we investigate an artificial traffic model on scale-free networks. Instead of using the routing strategy of the shortest path, a generalized routing algorithm is introduced to improve the transportation throughput, which is measured by the value of the critical point disjoining the free-flow phase and the congested phase. By using the detrended fluctuation analysis, we found that the traffic rate fluctuation near the critical point exhibits the $1/f$-type scaling in the power spectrum. The simulation results agree very well with the empirical data, thus the present model may contribute to the understanding of the underlying mechanism of network traffics.Comment: 6 pages, 5 figure