[Electroacupuncture at acupoints of yangming meridians for sarcopenia: a randomized controlled trial].

OBJECTIVE: To observe the clinical effect of electroacupuncture at acupoints of yangming meridians for sarcopenia. METHODS: A total of 60 patients with sarcopenia were randomized into an observation group and a control group, 30 cases in each group. In the control group, conventional nutrition intervention for sarcopenia was adopted. In the observation group, on the basis of the treatment in the control group, acupuncture was applied at bilateral Binao (LI 14), Quchi (LI 11), Zusanli (ST 36), Yanglingquan (GB 34), etc.,ipsilateral Quchi (LI 11) and Zusanli (ST 36) were connected to electroacupuncture, with discontinuous wave, 2 Hz in frequency, 1-10 mA in intensity, 2 times a week, with a interval of 3 days. A total of 12-week treatment was required in the two groups. Before and after treatment, the appendicular skeletal muscle mass index (ASMI), grip strength, 6 m-walking time, body fat percentage and body moisture percentage were observed in the two groups. RESULTS: Compared with those before treatment, after treatment, ASMI and grip strength were increased while 6 m-walking time was shortened in the two groups (P<0.05); body fat percentage was decreased while body moisture percentage was increased in the observation group (P<0.05). After treatment, in the observation group, ASMI, grip strength and body moisture percentage were increased (P<0.05), 6 m-walking time was shortened and body fat percentage was decreased (P<0.05) compared with those in the control group. CONCLUSION: Electroacupuncture at acupoints of yangming meridians can effectively improve the skeletal muscle mass, muscle function, body fat percentage and body moisture percentage in patients with sarcopenia, and make the distribution of muscle and fat more reasonable.

Protective effects of fowl-origin cadmium-tolerant lactobacillus against sub-chronic cadmium-induced toxicity in chickens.

Cadmium (Cd) directly endangers poultry health and indirectly causes harm to human health by food chain. Numerous studies have focused on removing Cd using lactic acid bacteria (LAB). However, there is still a lack of in vivo studies to validate whether Cd can be absorbed successfully by LAB to alleviate Cd toxicity. Here, we aimed to isolated and screened poultry-derived Cd-tolerant LAB with the strongest adsorption capacity in vitro and investigate the protective effect of which on sub-chronic Cd toxicity in chickens. First, nine Cd-tolerant LAB strains were selected preliminarily by isolating, screening, and identifying from poultry farms. Next, four strains with the strongest adsorption capacity were used to explore the influence of different physical and chemical factors on the ability of LAB to adsorb Cd as well as its probiotic properties in terms of acid tolerance, bile salt tolerance, drug resistance, and antibacterial effects. Resultantly, the CLF9-1 strain with the best comprehensive ability was selected for further animal protection test. The Cd-tolerant LAB treatment promoted the growth performance of chickens and reduced the Cd-elevated liver and kidney coefficients. Moreover, Cd-induced liver, kidney, and duodenum injuries were alleviated significantly by high-dose LAB treatment. Furthermore, LAB treatment also increased the elimination of Cd in feces and markedly reduced the Cd buildup in the liver and kidney. In summary, these findings determine that screened Cd-tolerant LAB strain exerts a protective effect on chickens against sub-chronic cadmium poisoning, thus providing an essential guideline for the public health and safety of livestock and poultry.

Hierarchical structure in the world's largest high-speed rail network.

Presently, China has the largest high-speed rail (HSR) system in the world. However, our understanding of the network structure of the world's largest HSR system remains largely incomplete due to the limited data available. In this study, a publicly available data source, namely, information from a ticketing website, was used to collect an exhaustive dataset on the stations and routes within the Chinese HSR system. The dataset included all 704 HSR stations that had been built as of June, 2016. A classical set of frequently used metrics based on complex network theory were analyzed, including degree centrality, betweenness centrality, and closeness centrality. The frequency distributions of all three metrics demonstrated highly consistent bimodal-like patterns, suggesting that the Chinese HSR network consists of two distinct regimes. The results indicate that the Chinese HSR system has a hierarchical structure, rather than a scale-free structure as has been commonly observed. To the best of our knowledge, such a network structure has not been found in other railway systems, or in transportation systems in general. Follow-up studies are needed to reveal the formation mechanisms of this hierarchical network structure.

Dynamic Cluster Formation Game for Attributed Graph Clustering.

Besides the topological structure, there are additional information, i.e., node attributes, on top of the plain graphs. Usually, these systems can be well modeled by attributed graphs, where nodes represent component actors, a set of attributes describe users' portraits and edges indicate their connections. An elusive question associated with attributed graphs is to study how clusters with common internal properties form and evolve in real-world networked systems with great individual diversity, which leads to the so-called problem of attributed graph clustering (AGC). In this paper, we comprehended AGC naturally as a dynamic cluster formation game (DCFG), where each node's feasible action set can be constrained by every cluster in a discrete-time dynamical system. Specifically, we carried out a deep research on a special case of finite dynamic games, named dynamic social game (DSG), the convergence of the finite Nash equilibrium sequence in a DSG was also proved strictly. By carefully defining the feasible action set and the utility function associated with each node, the proposed DCFG can be well related to a DSG; and we showed that a balanced solution of AGC could be found by solving a finite set of coupled static Nash equilibrium problems in the related DCFG. We, finally, proposed a self-learning algorithm, which can start from any arbitrary initial cluster configuration, and, finally, find the corresponding balanced solution of AGC, where all nodes and clusters are satisfied with the final cluster configuration. Extensive experiments were applied on real-world social networks to demonstrate both effectiveness and scalability of the proposed approach by comparing with the state-of-the-art graph clustering methods in the literature.

Global vs local modularity for network community detection.

Community structures are ubiquitous in various complex networks, implying that the networks commonly be composed of groups of nodes with more internal links and less external links. As an important topic in network theory, community detection is of importance for understanding the structure and function of the networks. Optimizing statistical measures for community structures is one of most popular strategies for community detection in complex networks. In the paper, by using a type of self-loop rescaling strategy, we introduced a set of global modularity functions and a set of local modularity functions for community detection in networks, which are optimized by a kind of the self-consistent method. We carefully compared and analyzed the behaviors of the modularity-based methods in community detection, and confirmed the superiority of the local modularity for detecting community structures on large-size and heterogeneous networks. The local modularity can more quickly eliminate the first-type limit of modularity, and can eliminate or alleviate the second-type limit of modularity in networks, because of the use of the local information in networks. Moreover, we tested the methods in real networks. Finally, we expect the research can provide useful insight into the problem of community detection in complex networks.

Critical analysis of (Quasi-)Surprise for community detection in complex networks.

Module or community structures widely exist in complex networks, and optimizing statistical measures is one of the most popular approaches for revealing and identifying such structures in real-world applications. In this paper, we focus on critical behaviors of (Quasi-)Surprise, a type of statistical measure of interest for community structure, accompanied by a series of comparisons with other measures. Specially, the effect of various network parameters on the measures is thoroughly investigated. The critical number of dense subgraphs in partition transition is derived, and a kind of phase diagrams is provided to display and compare the phase transitions of the measures. The effect of "potential well" for (Quasi-)Surprise is revealed, which may be difficult to get across by general greedy (agglomerative or divisive) algorithms. Finally, an extension of Quasi-Surprise is introduced for the study of multi-scale structures. Experimental results are of help for understanding the critical behaviors of (Quasi-)Surprise, and may provide useful insight for the design of effective tools for community detection.

Social significance of community structure: statistical view.

Community structure analysis is a powerful tool for social networks that can simplify their topological and functional analysis considerably. However, since community detection methods have random factors and real social networks obtained from complex systems always contain error edges, evaluating the significance of a partitioned community structure is an urgent and important question. In this paper, integrating the specific characteristics of real society, we present a framework to analyze the significance of a social community. The dynamics of social interactions are modeled by identifying social leaders and corresponding hierarchical structures. Instead of a direct comparison with the average outcome of a random model, we compute the similarity of a given node with the leader by the number of common neighbors. To determine the membership vector, an efficient community detection algorithm is proposed based on the position of the nodes and their corresponding leaders. Then, using a log-likelihood score, the tightness of the community can be derived. Based on the distribution of community tightness, we establish a connection between p-value theory and network analysis, and then we obtain a significance measure of statistical form . Finally, the framework is applied to both benchmark networks and real social networks. Experimental results show that our work can be used in many fields, such as determining the optimal number of communities, analyzing the social significance of a given community, comparing the performance among various algorithms, etc.

Potts model based on a Markov process computation solves the community structure problem effectively.

The Potts model is a powerful tool to uncover community structure in complex networks. Here, we propose a framework to reveal the optimal number of communities and stability of network structure by quantitatively analyzing the dynamics of the Potts model. Specifically we model the community structure detection Potts procedure by a Markov process, which has a clear mathematical explanation. Then we show that the local uniform behavior of spin values across multiple timescales in the representation of the Markov variables could naturally reveal the network's hierarchical community structure. In addition, critical topological information regarding multivariate spin configuration could also be inferred from the spectral signatures of the Markov process. Finally an algorithm is developed to determine fuzzy communities based on the optimal number of communities and the stability across multiple timescales. The effectiveness and efficiency of our algorithm are theoretically analyzed as well as experimentally validated.