A detection method for grass carp hemorrhagic virus (GCHV) based on a reverse transcription polymerase chain reaction

A rapid, sensitive and highly specific detection method for grass carp hemorrhagic virus (GCHV) based on a reverse transcription-polymerase chain reaction (RT-PCR) has been developed. Two pairs of PCR primers were synthesized according to the cloned cDNA sequences of the GCHV-861 strain. For each primer combination, only one specific major product was obtained when amplification was performed by using the genomic dsRNA of GCHV-861 strain. The lengths of their expected products were 320 and 223 bp, respectively. No products were obtained when nucleic acids other than GCHV-861 genomic RNA were used as RT-PCR templates. To assess the sensitivity of the method, dilutions of purified GCHV-861 dsRNA total genome (0.01 pg up to 1000 pg) were amplified and quantities of as little as 0.1 pg of purified dsRNA were detectable when the amplification product was analyzed by 1.5% agarose gel electrophoresis. This technique could detect GCHV-861 not only in infected cell culture fluids, but also in infected grass carp Ctenopharyngodon idellus and rare minnow Gobiocypris rarus with or without hemorrhagic symptoms. The results show that the RT-PCR amplification method is useful for the direct detection of GCHV.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

Performance of an embedded optical vector matrix multiplication processor architecture

An embedded architecture of optical vector matrix multiplier (OVMM) is presented. The embedded architecture is aimed at optimising the data flow of vector matrix multiplier (VMM) to promote its performance. Data dependence is discussed when the OVMM is connected to a cluster system. A simulator is built to analyse the performance according to the architecture. According to the simulation, Amdahl's law is used to analyse the hybrid opto-electronic system. It is found that the electronic part and its interaction with optical part form the bottleneck of system.

Steady-state bright-dark vector spatial solitons in biased photorefractive-photovoltaic crystals

We show that bright-dark vector solitons are possible in biased photorefractive-photovoltaic crystals under steady-state conditions, which result from both the bulk photovoltaic effect and the spatially nonuniform screening of the external bias field. The analytical solutions of these vector solitons can be obtained in the case of \sigma\ much less than 1, where sigma is the parameter controlling the intensities of the two optical beams. In the limit of -1 < sigma much less than 1, these vector solitons can also be determined by use of simple numerical integration procedures. When the bulk photovoltaic effect is neglectable, these vector solitons are bright-dark vector screening solitons studied previously in the \sigma\ much less than 1 regime, and predict bright-dark vector screening solitons in the -1 < sigma less than or equal to 1 regime. When the external bias field is absent, these vector solitons predict bright-dark vector photovoltaic solitons in closed and open circuits. (C) 2002 Elsevier Science B.V. All rights reserved.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.