Field assessments and genetic analyses on tansy aphid, Metopeurum fuscoviride Stroyan were made aiming to investigate whether a specialist aphid speci

A new betaproteobacterium, CGI-09T, was isolated from an activated sludge bioreactor which treated landfill leachate. Based on 16S rRNA gene sequence analysis, the new strain shared the highest pairwise similarity values with members of the order Burkholderiales : Derxia gummosa IAM 13946T (family Alcaligenaceae ), 93.7 % and Lautropia mirabilis DSM 11362T (family Burkholderiaceae ), 93.6 %. Cells of strain CGI-09T were rod-shaped and non-motile. The new strain was oxidase and catalase positive and capable of reducing nitrate to nitrite. The predominant fatty acids were C16 : 1 ω7c, C16 : 0, cycloC17 : 0 and C18 : 1 ω7c, the major respiratory quinone was Q-8, and the detected polar lipids were phosphatidylglycerol, diphosphatidylglycerol, phosphatidylethanolamine and an unknown phospholipid. The G+C content of the genomic DNA of strain CGI-09T was 70.2 mol%. The new bacterium can be distinguished from the members of genera Derxia and Lautropia based on its non-motile cells, arginine dihydrolase activity, its high cyclo C17 : 0 fatty acid content and the lack of hydroxy fatty acids. On the basis of the phenotypic, chemotaxonomic and molecular data, strain CGI-09T is considered to represent a new genus and species within the family Burkholderiaceae , for which the name Quisquiliibacterium transsilvanicum gen. nov., sp. nov. is proposed. The type strain is CGI-09T (=DSM 29781T=JCM 31785T).

Many recent studies shed light on the vulnerability of networks against large-scale natural disasters. The corresponding network failures, called regional failures, are manifested at failing multiple network elements that are physically close to each other. The recovery mechanisms of current backbone networks protect failures listed as Shared Risk Link Groups (SRLGs). We aim to design an algorithm for the routing engines, which can generate a reasonable list of SRLGs based on the limited geometric information available. As a first step towards this direction, in this paper, we propose a limited geographic information failure model for the network topology that enables efficient algorithms to compute the set of links that are expected to be close to each other. More precisely, we work with (1) relative node positions without knowing the real distances, (2) an area in the map defines the route of each physical cable, and (3) a regional failure is a circular disk with k=0,1, ... nodes in its interior. We describe an efficient algorithm for listing SRLGs based on our limited geographic information failure model and show that under realistic assumptions, the obtained list of SRLGs is short, having approximately 1.2 n and 2.2n elements for k=0 and k=1, respectively, where n is the number of nodes of the network.

The current best practice in survivable routing is to compute link or node disjoint paths in the network topology graph. It can protect single-point failures; however, several failure events may cause the interruption of multiple network elements. The set of network elements subject to potential failure events is called Shared Risk Link Group (SRLG), identified during network planning. Unfortunately, for any given list of SRLGs, finding two paths that can survive a single SRLG failure is NP-Complete. In this paper, we provide a polynomial-time SRLG-disjoint routing algorithm for planar network topologies and a large set of SRLGs. Namely, we focus on regional failures, where the failed network elements must not be far from each other. We use a flexible definition of regional failure, where the only restrictions are that i) the topology is a planar graph, ii) each SRLG forms a set of connected edges in the dual of the planar graph, and iii) for each node $v$ , the links incident to $v$ are part of an SRLG. The proposed algorithm is based on a max-min theorem. Through extensive simulations, we show that the algorithm scales well with the network size, and one of the paths returned by the algorithm is only $4$ % longer than the shortest path on average.