Development of indirect competitive fluorescence immunoassay for 2,2′,4,4′-tetrabromodiphenyl ether using DNA/dye conjugate as antibody multiple labels

An indirect competitive fluorescence immunoassay using a DNA/dye conjugate as antibody multiple labels was developed on 96-well plates for the identification and quantification of 2,2＇,4,4＇-tetrabromodiphenyl ether （BDE-47） in aqueous samples. A hapten, 2,4,2＇- tribromodiphenyl ether-4＇-aldehyde, was synthesized, and was conjugated to bovine serum albumin to form a coating antigen. Specific recognition of the antigen by anti-PBDE antiserum was confirmed by a surface plasmon resonance measurement. In the immunoassay, the coating antigen was adsorbed on a 96-well plate first, and a sample, antiserum and biotinylated goat anti-rabbit secondary antibody were then added and reacted sequentially. A biotinylated, double-stranded DNA with 219 base pairs was attached to the secondary antibody by using streptavidin as a molecular bridge. In situ multiple labeling of the antibody was accomplished after addition of a DNA-binding fluorescent dye, SYBR Green I. The working range of the immunoassay for the BDE-47 standard was 3.1-390 ~tg/L, with an IC50 value of 15.6 Ixg/L. The calculated LOD of the immunoassay is 0.73 Ixg/L. The immunoassay demonstrated relatively high selectivity for BDE-47, showing very low cross-reactivity （〈 3%） with BDE-15, BDE-153 and BDE-209. With a spiked river water sample containing 50 Izg/L BDE-47, quantification by the immunoassay was 41.9 ~tg/L, which compared well with the standard GC-ECD method （45.7 Ixg/L）. The developed immunoassay provides a rapid screening tool for polybrominated diphenyl ethers in environmental samples.

Interactive group decision making procedure based on uncertain multiplicative linguistic preference relations

Group decision making problems are investigated with uncertain multiplicative linguistic preference relations.An unbalanced multiplicative linguistic label set is introduced,which can be used by the experts to express their linguistic preference information over alternatives.The uncertain linguistic weighted geometric mean operator is utilized to aggregate all the individual uncertain multiplicative linguistic preference relations into a collective one,and then a simple approach is developed to determine the experts＇ weights by utilizing the consensus degrees among the individual uncertain multiplicative linguistic preference relations and the collective uncertain multiplicative linguistic preference relations.Furthermore,a practical interactive procedure for group decision making is proposed based on uncertain multiplicative linguistic preference relations,in which a possibility degree formula and a complementary matrix are used to rank the given alternatives.Finally,the proposed procedure is applied to solve the group decision making problem of a manufacturing company searching the best global supplier for one of its most critical parts used in assembling process.

Active dynamic pre-protection mechanism for MPLS-TP network

The active-fault-alarm （AFA） technologies and the dynamic protection mechanism of the optical network controlled by the multi-protocol label switching transport profile （MPLS-TP） are both studied in this article. On this basis, an active dynamic pre-protection （ADPP） mechanism is proposed. This active dynamic pre-protection mechanism is allowed to establish a so called temporary protection path （TPP） for the work path before a potential fault occurs and to switch to TPP only after the fault occurs. It keeps the TPP flexibly only when it is in low- quality or fault state to realize dynamic protection even better and to achieve better resource utilization. Simulation results show that the proposed new mechanism has better performance in terms of the flexibility and the efficiency from the perspective of time.

Multiple hypergraph ranking for video concept detection

This paper tackles the problem of video concept detection using the multi-modality fusion method. Motivated by multi-view learning algorithms, multi-modality features of videos can be represented by multiple graphs. And the graph-based semi-supervised learning methods can be extended to multiple graphs to predict the semantic labels for unlabeled video data. However, traditional graphs represent only homogeneous pairwise linking relations, and therefore the high-order correlations inherent in videos, such as high-order visual similarities, are ignored. In this paper we represent heterogeneous features by multiple hypergraphs and then the high-order correlated samples can be associated with hyperedges. Furthermore, the multi-hypergraph ranking (MHR) algorithm is proposed by defining Markov random walk on each hypergraph and then forming the mixture Markov chains so as to perform transductive learning in multiple hypergraphs. In experiments on the TRECVID dataset, a triple-hypergraph consisting of visual, textual features and multiple labeled tags is constructed to predict concept labels for unlabeled video shots by the MHR framework. Experimental results show that our approach is effective.