AIM: To develop a real-time reverse transcriptionpolymerase chain reaction(RT-PCR) assay to genotype rotavirus(G and P) in Alberta from January 2012 to June 2013. METHODS: We developed and validated a different approa...AIM: To develop a real-time reverse transcriptionpolymerase chain reaction(RT-PCR) assay to genotype rotavirus(G and P) in Alberta from January 2012 to June 2013. METHODS: We developed and validated a different approach to perform rotavirus G and P genotyping using a two-step SYBR green RT-PCR(rt-g PCR) by selecting genotype-specific primers of published conventional RT nested PCR(cn RT-PCR) assay and optimizing the amplification conditions. c DNA was first synthesized from total RNA with Super Script? Ⅱ reverse transcriptase kit followed by amplication step using monoplex SYBR green real-time PCR. After the PCR reaction, melting curve analysis was used to determine specific genotype. Sixteen samples previously genotyped using cn RT-PCR were tested using the new assay and the genotyping results were compared as sensitivity analysis. Assay specificity was evaluated by testing other gastroenteritis viruses with the new assay. The amplicon size of each available genotype was determined by gelelectrophoresis and DNA sequences were obtained using Sanger-sequencing method. After validation and optimization, the new assay was used to genotype 122 pediatric clinical stool samples previously tested positive for rotavirus using electron microscopy between January2012 and June 2013.RESULTS: The new rt-g PCR assay was validated and optimized. The assay detected G1 to G4, G9, G12 and P[4] and P[8] that were available as positive controls in our laboratory. A single and clear peak of melting curve was generated for each of specific G and P genotypes with a Tm ranging from 80 ℃ to 82 ℃. The sensitivity of rt-g PCR was comparable to cn RT-PCR with 100% correlation of the 16 samples with known G and P genotypes. No cross reaction was found with other gastroenteritis viruses. Using the new rt-g PCR assay, genotypes were obtained for 121 of the 122 pediatric clinical samples tested positive for rotavirus: G1P[8](42.6%), G2P[4](4.9%), G3P[8](10.7%), G9P[8](10.7%), G9P[4](6.6%), G12P[8](23.0%), and unknown GP[8](0.8%). For the first time, G12 rotavirus strains were found in Alberta and G12 was the second most common genotype during the study period. Gel electrophoresis of all the genotypes showed expected amplicon size for each genotype. The sequence data of the two G12 samples along with other genotypes were blasted in NCBI BLAST or analyzed with Rota C Genotyping tool(http://rotac.regatools.be/). All genotyping results were confirmed to be correct.CONCLUSION: rt-g PCR is a useful tool for the genotyping and characterization of rotavirus. Monitoring of rotavirus genotypes is important for the identification of emerging strains and ongoing evaluation of rotavirus vaccination programs.展开更多
Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selecti...Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.展开更多
Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification m...Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification models for this domain.This study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock indices.The input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model.CRO was chosen because it requires fewer control parameters and has a faster convergence rate.Five statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock markets.The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior.We conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be significant.This model can be used as a promising tool for financial forecasting.展开更多
The effect of synthesis temperature and reaction time on the visible-light photocatalytic activity of hierarchical network-like SnIn4S8 microspheres was investigated by the low-temperature co-precipitation strategy. T...The effect of synthesis temperature and reaction time on the visible-light photocatalytic activity of hierarchical network-like SnIn4S8 microspheres was investigated by the low-temperature co-precipitation strategy. The preparation temperature and reaction time have great influence on the photocatalytic activity of SnIn4S8, and the optimal preparation temperature and reaction time are 70℃ and 3 h, respectively.The SnIn4S8 shows good reusability and high stability with no observable decrease of photocatalytic activity in five consecutive cycles.展开更多
文摘AIM: To develop a real-time reverse transcriptionpolymerase chain reaction(RT-PCR) assay to genotype rotavirus(G and P) in Alberta from January 2012 to June 2013. METHODS: We developed and validated a different approach to perform rotavirus G and P genotyping using a two-step SYBR green RT-PCR(rt-g PCR) by selecting genotype-specific primers of published conventional RT nested PCR(cn RT-PCR) assay and optimizing the amplification conditions. c DNA was first synthesized from total RNA with Super Script? Ⅱ reverse transcriptase kit followed by amplication step using monoplex SYBR green real-time PCR. After the PCR reaction, melting curve analysis was used to determine specific genotype. Sixteen samples previously genotyped using cn RT-PCR were tested using the new assay and the genotyping results were compared as sensitivity analysis. Assay specificity was evaluated by testing other gastroenteritis viruses with the new assay. The amplicon size of each available genotype was determined by gelelectrophoresis and DNA sequences were obtained using Sanger-sequencing method. After validation and optimization, the new assay was used to genotype 122 pediatric clinical stool samples previously tested positive for rotavirus using electron microscopy between January2012 and June 2013.RESULTS: The new rt-g PCR assay was validated and optimized. The assay detected G1 to G4, G9, G12 and P[4] and P[8] that were available as positive controls in our laboratory. A single and clear peak of melting curve was generated for each of specific G and P genotypes with a Tm ranging from 80 ℃ to 82 ℃. The sensitivity of rt-g PCR was comparable to cn RT-PCR with 100% correlation of the 16 samples with known G and P genotypes. No cross reaction was found with other gastroenteritis viruses. Using the new rt-g PCR assay, genotypes were obtained for 121 of the 122 pediatric clinical samples tested positive for rotavirus: G1P[8](42.6%), G2P[4](4.9%), G3P[8](10.7%), G9P[8](10.7%), G9P[4](6.6%), G12P[8](23.0%), and unknown GP[8](0.8%). For the first time, G12 rotavirus strains were found in Alberta and G12 was the second most common genotype during the study period. Gel electrophoresis of all the genotypes showed expected amplicon size for each genotype. The sequence data of the two G12 samples along with other genotypes were blasted in NCBI BLAST or analyzed with Rota C Genotyping tool(http://rotac.regatools.be/). All genotyping results were confirmed to be correct.CONCLUSION: rt-g PCR is a useful tool for the genotyping and characterization of rotavirus. Monitoring of rotavirus genotypes is important for the identification of emerging strains and ongoing evaluation of rotavirus vaccination programs.
文摘Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.
文摘Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification models for this domain.This study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock indices.The input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model.CRO was chosen because it requires fewer control parameters and has a faster convergence rate.Five statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock markets.The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior.We conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be significant.This model can be used as a promising tool for financial forecasting.
基金supported by Natural Science Foundation of China (51741806, 51238002, 51720105001)Science Fund for Excellent Young Scholars of Jiangxi Province (20162BCB23038)Natural Science Foundation of Jiangxi Province (20161BAB206117)
文摘The effect of synthesis temperature and reaction time on the visible-light photocatalytic activity of hierarchical network-like SnIn4S8 microspheres was investigated by the low-temperature co-precipitation strategy. The preparation temperature and reaction time have great influence on the photocatalytic activity of SnIn4S8, and the optimal preparation temperature and reaction time are 70℃ and 3 h, respectively.The SnIn4S8 shows good reusability and high stability with no observable decrease of photocatalytic activity in five consecutive cycles.
