• BMB Reports
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• v.47 no.6
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• pp.330-335
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• 2014

Turnip yellow mosaic virus (TYMV) is a spherical plant virus that has a single 6.3 kb positive strand RNA as a genome. In this study, RNA1 sequence of Flock house virus (FHV) was inserted into the TYMV genome to test whether TYMV can accommodate and express another viral entity. In the resulting construct, designated TY-FHV, the FHV RNA1 sequence was expressed as a TYMV subgenomic RNA. Northern analysis of the Nicotiana benthamiana leaves agroinfiltrated with the TY-FHV showed that both genomic and subgenomic FHV RNAs were abundantly produced. This indicates that the FHV RNA1 sequence was correctly expressed and translated to produce a functional FHV replicase. Although these FHV RNAs were not encapsidated, the FHV RNA having a TYMV CP sequence at the 3'-end was efficiently encapsidated. When an eGFP gene was inserted into the B2 ORF of the FHV sequence, a fusion protein of B2-eGFP was produced as expected.

• Journal of Microbiology and Biotechnology
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• v.29 no.11
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• pp.1790-1798
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• 2019

Flock House virus (FHV), an insect RNA virus, has a bipartite genome. FHV RNA1 can be packaged in turnip yellow mosaic virus (TYMV) as long as the FHV RNA has a TYMV sequence at the 3'-end. The encapsidated FHV RNA1 has four additional nucleotides at the 5'-end. We investigated whether the recombinant FHV RNA1 could replicate in mammalian cells. To address this issue, we prepared in vitro transcribed FHV RNAs that mimicked the recombinant FHV RNA1, and introduced them into baby hamster kidney (BHK) cells. The result showed that the recombinant FHV RNA1 was capable of replication. An eGFP gene inserted into the frame with B2 gene of the FHV RNA1 was also successfully expressed. We also observed that eGFP expression at the protein level was strong at 28℃ but weak at 30℃. Sequence analysis showed that the 3'-ends of the RNA1 and RNA3 replication products were identical to those of the authentic FHV RNAs. This indicates that FHV replicase correctly recognized an internally-located replication signal. In contrast, the 5'-ends of recombinant FHV RNA1 frequently had deletions, indicating random initiation of (+)-strand synthesis.

• Journal of Veterinary Clinics
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• v.23 no.2
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• pp.105-110
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• 2006

A three-layer, feed-forward artificial neural network (ANN) with sixteen input neurons, three hidden neurons, and one output neuron was developed to identify the presence of infectious bronchitis (IB) infection as early as possible in laying hen flocks. Retrospective data from flocks that enrolled IB surveillance program between May 2003 and November 2005 were used to build the ANN. Data set of 86 flocks was divided randomly into two sets: 77 cases for training set and 9 cases for testing set. Input factors were 16 epidemiological findings including characteristics of the layer house, management practice, flock size, and the output was either presence or absence of IB. ANN was trained using training set with a back-propagation algorithm and test set was used to determine the network's capability to predict outcomes that it has never seen. Diagnostic performance of the trained network was evaluated by constructing receiver operating characteristic (ROC) curve with the area under the curve (AUC), which were also used to determine the best positivity criterion for the model. Several different ANNs with different structures were created. The best-fitted trained network, IBV_D1, was able to predict IB in 73 cases out of 77 (diagnostic accuracy 94.8%) in the training set. Sensitivity and specificity of the trained neural network was 95.5% (42/44, 95% CI, 84.5-99.4) and 93.9% (31/33, 95% CI, 79.8-99.3), respectively. For testing set, AVC of the ROC curve for the IBV_D1 network was 0.948 (SE=0.086, 95% CI 0.592-0.961) in recognizing IB infection status accurately. At a criterion of 0.7149, the diagnostic accuracy was the highest with a 88.9% with the highest sensitivity of 100%. With this value of sensitivity and specificity together with assumed 44% of IB prevalence, IBV_D1 network showed a PPV of 80% and an NPV of 100%. Based on these findings, the authors conclude that neural network can be successfully applied to the development of a screening model for identifying IB infection in laying hen flocks.