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Articles by A Kiely
Total Records ( 2 ) for A Kiely
  M. D Jordan , A Anderson , D Begum , C Carraher , A Authier , S. D.G Marshall , A Kiely , L. N Gatehouse , D. R Greenwood , D. L Christie , A. V Kralicek , S. C Trowell and R. D. Newcomb
 

Moths recognize a wide range of volatile compounds, which they use to locate mates, food sources, and oviposition sites. These compounds are recognized by odorant receptors (OR) located within the dendritic membrane of sensory neurons that extend into the lymph of sensilla, covering the surface of insect antennae. We have identified 3 genes encoding ORs from the tortricid moth, Epiphyas postvittana, a pest of horticulture. Like Drosophila melanogaster ORs, they contain 7 transmembrane helices with an intracellular N-terminus, an orientation in the plasma membrane opposite to that of classical GPCRs. EpOR2 is orthologous to the coreceptor Or83b from D. melanogaster. EpOR1 and EpOR3 both recognize a range of terpenoids and benzoates produced by plants. Of the compounds tested, EpOR1 shows the best sensitivity to methyl salicylate [EC50 = 1.8 x 10–12 M], a common constituent of floral scents and an important signaling compound produced by plants when under attack from insects and pathogens. EpOR3 best recognizes the monoterpene citral to low concentrations [EC50 = 1.1 x 10–13 M]. Citral produces the largest amplitude electrophysiological responses in E. postvittana antennae and elicits repellent activity against ovipositing female moths. Orthologues of EpOR3 were found across 6 families within the Lepidoptera, suggesting that the ability to recognize citral may underpin an important behavior.

  A Kiely , M Xu , W. Z Song , R Huang and B. Shirazi
 

We present a lightweight lossless compression algorithm for realtime sensor networks. Our proposed adaptive linear filtering compression (ALFC) algorithm performs predictive compression using adaptive linear filtering to predict sample values followed by entropy coding of prediction residuals, encoding a variable number of samples into fixed-length packets. Adaptive prediction eliminates the need to determine prediction coefficients a priori and, more importantly, allows compression to dynamically adjust to a changing source. The algorithm requires only integer arithmetic operations and thus is compatible with sensor platforms that do not support floating-point operations. Significant robustness to packets losses is provided by including small but sufficient overhead data to allow each packet to be independently decoded. Real-world evaluations on seismic data from a wireless sensor network testbed show that ALFC provides more effective compression and uses less resources than an alternative recent work of lossless compression, S-LZW. Experiments in a multi-hop sensor network also show that ALFC can significantly improve raw data throughput and energy efficiency. We also implement the algorithm in our real sensor network, and show that our linear prediction based compression algorithm significantly improves data reliability and network efficiency.

 
 
 
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