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The cold atmospheric pressure plasma, which has been widely used for biomedical applications, may potentially affect the conformation of DNA. In this letter, an atmospheric pressure plasma plume is used to investigate its effects on the conformational changes of DNA of plasmid pAHC25. It is found that the plasma plume could cause plasmid DNA topology alteration, resulting in the percentage of the supercoiled plasmid DNA form decreased while that of the open circular and linearized form of plasmid DNA increased as detected by agrose gel electrophoresis. On the other hand, further investigation by using polymerase chain reaction method shows that the atmospheric pressure plasma jet treatments under proper conditions does not affect the genes of the plasmid DNA, which may have potential application in increasing the transformation frequency by genetic engineering.

No abstract

In this paper, we aim to tackle the problem of discovering dynamic communities in weighted graph streams, especially when the underlying social behavior of individuals varies considerably over different graph regions. To tackle this problem, a novel structure termed Local Weighted-Edge-based Pattern (LWEP) Summary is proposed to describe a local homogeneous region. To efficiently compute LWEPs, some statistics need to be maintained according to the principle of preserving maximum weighted neighbor information with limited memory storage. To this end, the proposed approach is divided into online and offline components. During the online phase, we introduce some statistics, termed top-k neighbor lists and topk candidate lists, to track. The key is to maintain only the top-k neighbors with the largest link weights for each node. To allow for less active neighbors to transition into top-k neighbors, an auxiliary data structure termed top-k candidate list is used to identify emerging active neighbors. The statistics can be efficiently maintained in the online component. In the offline component, these statistics are used at each snapshot to efficiently compute LWEPs. Clustering is then performed to consolidate LWEPs into high level clusters. Finally, mapping is made between clusters of consecutive snapshots to generate temporally smooth communities. Experimental results are presented to illustrate the effectiveness and efficiency of the proposed approach.

A wide range of complex systems can be modeled as networks with corresponding constraints on the edges and nodes, which have been extensively studied in recent years. Nowadays, with the progress of information technology, systems that contain the information collected from multiple perspectives have been generated. The conventional models designed for single perspective networks fail to depict the diverse topological properties of such systems, so multilayer network models aiming at describing the structure of these networks emerge. As a major concern in network science, decomposing the networks into communities, which usually refers to closely interconnected node groups, extracts valuable information about the structure and interactions of the network. Unlike the contention of dozens of models and methods in conventional single-layer networks, methods aiming at discovering the communities in the multilayer networks are still limited. In order to help explore the community structure in multilayer networks, we propose the multilayer edge mixture model, which explores a relatively general form of a community structure evaluator from an edge combination view. As an example, we demonstrate that the multilayer modularity and stochastic blockmodels can be derived from the proposed model. We also explore the decomposition of community structure evaluators with specific forms to the multilayer edge mixture model representation, which turns out to reveal some new interpretation of the evaluators. The flexibility and performance on different networks of the proposed model are illustrated with applications on a series of benchmark networks.