Structure controllability of complex network based on preferential matching

Minimum driver node sets (MDSs) play an important role in studying the structural controllability of complex networks. Recent research has shown that MDSs tend to avoid high-degree nodes. However, this observation is based on the analysis of a small number of MDSs, because enumerating all of the MDSs of a network is a #P problem. Therefore, past research has not been sufficient to arrive at a convincing conclusion. In this paper, first, we propose a preferential matching algorithm to find MDSs that have a specific degree property. Then, we show that the MDSs obtained by preferential matching can be composed of high- and medium-degree nodes. Moreover, the experimental results also show that the average degree of the MDSs of some networks tends to be greater than that of the overall network, even when the MDSs are obtained using previous research method. Further analysis shows that whether the driver nodes tend to be high-degree nodes or not is closely related to the edge direction of the network

Efficient target control of complex networks based on preferential matching

Controlling a complex network towards a desire state is of great importance in many applications. Existing works present an approximate algorithm to find the driver nodes used to control partial nodes of the network. However, the driver nodes obtained by this algorithm depend on the matching order of nodes and cannot get the optimum results. Here we present a novel algorithm to find the driver nodes for target control based on preferential matching. The algorithm elaborately arrange the matching order of nodes in order to minimize the size of the driver nodes set. The results on both synthetic and real networks indicate that the performance of proposed algorithm are better than the previous one. The algorithm may have various application in controlling complex networks

Hepatitis B virus–induced imbalance of inflammatory and antiviral signaling by differential phosphorylation of STAT1 in human monocytes

It is not clear how hepatitis B virus (HBV) modulates host immunity during chronic infection. In addition to the key mediators of inflammatory response in viral infection, monocytes also express a high-level IFN-stimulated gene, CH25H, upon response to IFN-a exerting an antiviral effect. In this study, the mechanism by which HBV manipulates IFN signaling in human monocytes was investigated. We observed that monocytes from chronic hepatitis B patients express lower levels of IFN signaling/stimulated genes and higher levels of inflammatory cytokines compared with healthy donors. HBV induces monocyte production of inflammatory cytokines via TLR2/MyD88/NF-kB signaling and STAT1-Ser727 phosphorylation and inhibits IFN-a–induced stat1, stat2, and ch25h expression through the inhibition of STAT1-Tyr701 phosphorylation and in an IL-10–dependent, partially autocrine manner. Further, we found that enhancement of STAT1 activity with a small molecule (2-NP) rescued HBV-mediated inhibition of IFN signaling and counteracted the induction of inflammatory cytokines. In conclusion, HBV contributes to the monocyte inflammatory response but inhibits their IFN-a/b responsiveness to impair antiviral innate immunity. These effects are mediated via differential phosphorylation of Tyr701 and Ser727 of STAT1

A Generalized Mandelbrot Set Based On Distance Ratio

The iteration of complex function can generate beautiful fractal images. This paper presents a novel method based on the iteration of the distance ratio with two points, which generates a generalized Mandelbrot set according to distance ratio convergence times. This paper states the definition of distance ratio and its iteration. Then taking the complex function f(z)=zα+c for example, it discusses the visual structure of generalized Mandelbrot with various exponent and comparing it with Mandelbrot set generated by escape time algorithm. When exponent α>1, the outer border of DRM is same as Mandelbrot set, but has complex inner structure; when α<0, the inner border of DRM is same as Mandelbrot set, DRM is the “outer” region and complement set of Mandelbrot set, the two sets cover the whole complex plane

Efficiency analysis of the target control algorithm for two synthetic networks.

<p>(A-B). For the scale-free networks with <i>N</i> = 10⁴ and <<i>k</i>> = 5.2, we show the density of input nodes as a function of the fraction of target nodes. The results are computed based on 100 network instances with the same average degree. (A) Results of the local selection scheme and (B) Results of the random selection scheme. (C-D) For the scale-free networks with <i>N</i> = 10⁴ and <<i>k</i>> = 13, we show the density of input nodes as a function of the fraction of target nodes. (C) Results of the local selection scheme and (D) Results of the random selection scheme. For each network, we compute the fraction of input nodes <i>n</i>_<i>D</i> based on the preferential matching and the greedy algorithm. For the greedy algorithm, the <i>n</i>_<i>D</i> is computed based on the results of 100 random experiments.</p

Triptolide protects against podocyte injury in diabetic nephropathy by activating the Nrf2/HO-1 pathway and inhibiting the NLRP3 inflammasome pathway

AbstractObjectives: Diabetic nephropathy (DN) is the most common microvascular complication of diabetes mellitus. This study investigated the mechanism of triptolide (TP) in podocyte injury in DN.Methods: DN mouse models were established by feeding with a high-fat diet and injecting with streptozocin and MPC5 podocyte injury models were induced by high-glucose (HG), followed by TP treatment. Fasting blood glucose and renal function indicators, such as 24 h urine albumin (UAlb), serum creatinine (SCr), blood urea nitrogen (BUN), and kidney/body weight ratio of mice were examined. H&E and TUNEL staining were performed for evaluating pathological changes and apoptosis in renal tissue. The podocyte markers, reactive oxygen species (ROS), oxidative stress (OS), serum inflammatory cytokines, nuclear factor-erythroid 2-related factor 2 (Nrf2) pathway-related proteins, and pyroptosis were detected by Western blotting and corresponding kits. MPC5 cell viability and pyroptosis were evaluated by MTT and Hoechst 33342/PI double-fluorescence staining. Nrf2 inhibitor ML385 was used to verify the regulation of TP on Nrf2.Results: TP improved renal function and histopathological injury of DN mice, alleviated podocytes injury, reduced OS and ROS by activating the Nrf2/heme oxygenase-1 (HO-1) pathway, and weakened pyroptosis by inhibiting the nod-like receptor (NLR) family pyrin domain containing 3 (NLRP3) inflammasome pathway. In vitro experiments further verified the inhibition of TP on OS and pyroptosis by mediating the Nrf2/HO-1 and NLRP3 inflammasome pathways. Inhibition of Nrf2 reversed the protective effect of TP on MPC5 cells.Conclusions: Overall, TP alleviated podocyte injury in DN by inhibiting OS and pyroptosis via Nrf2/ROS/NLRP3 axis

The efficiency of the algorithm for different average degree <<i>k</i>>.

<p>(A-B). For a scale-free network with <i>N</i> = 10⁴ and target node fraction <i>f</i> = 0.3, we show (A) the density of input nodes versus <<i>k</i>>, based on the local selection scheme, and (B) the density of input nodes versus <<i>k</i>>, based on the random selection scheme. (C-D). For an ER random network with <i>N</i> = 10⁴ and target node fraction <i>f</i> = 0.3, we show (C) the density of input nodes versus <<i>k</i>>, based on the local selection scheme, and (D) the density of input nodes versus <<i>k</i>>, based on the random selection scheme. For each average degree <<i>k</i>>, the fraction of input nodes <i>n</i>_<i>D</i> is computed based on the average results of 100 networks.</p

Illustration of preferential matching for target control.

<p>(A). A sample network <i>G</i> with target nodes {3, 6, 7, 8}. (B). Matching graph for target nodes {3, 6, 7, 8}. (C). Matching sequence of nodes based on their counts in the matching graph. The counts for node sequence {<i>n</i>₁,<i>n</i>₂,<i>n</i>₆,<i>n</i>₄,<i>n</i>₅,<i>n</i>₇,<i>n</i>₃,<i>n</i>₈} are {4,3,3,3,2,2,1,1}.</p

Identifying node role in social network based on multiple indicators.

It is a classic topic of social network analysis to evaluate the importance of nodes and identify the node that takes on the role of core or bridge in a network. Because a single indicator is not sufficient to analyze multiple characteristics of a node, it is a natural solution to apply multiple indicators that should be selected carefully. An intuitive idea is to select some indicators with weak correlations to efficiently assess different characteristics of a node. However, this paper shows that it is much better to select the indicators with strong correlations. Because indicator correlation is based on the statistical analysis of a large number of nodes, the particularity of an important node will be outlined if its indicator relationship doesn't comply with the statistical correlation. Therefore, the paper selects the multiple indicators including degree, ego-betweenness centrality and eigenvector centrality to evaluate the importance and the role of a node. The importance of a node is equal to the normalized sum of its three indicators. A candidate for core or bridge is selected from the great degree nodes or the nodes with great ego-betweenness centrality respectively. Then, the role of a candidate is determined according to the difference between its indicators' relationship with the statistical correlation of the overall network. Based on 18 real networks and 3 kinds of model networks, the experimental results show that the proposed methods perform quite well in evaluating the importance of nodes and in identifying the node role