• Journal of Animal Science and Technology
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• v.64 no.3
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• pp.588-598
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• 2022

Despite vaccination, equine influenza virus (EIV) and equine herpesvirus (EHV) infections still cause highly contagious respiratory diseases in horses. Recently, concurrent vaccination with EIV and EHV was suggested as a new approach; however, there have been no reports of concurrent vaccination with recombinant canarypox EIV and inactivated EHV vaccines. In this study, we aimed to compare the EIV-specific immune responses induced by concurrent administrations of a recombinant canarypox EIV vaccine and an inactivated bivalent EHV vaccine with those induced by a single recombinant canarypox EIV vaccine in experimental horse and mouse models. Serum and peripheral blood mononuclear cells (PBMCs) were collected from immunized animals after vaccination. EIV-specific serum antibody levels, serum hemagglutinin inhibition (HI) titers, and interferon-gamma (IFN-γ) levels were measured by enzyme-linked immunosorbent assay, HI assay, and quantitative polymerase chain reaction, respectively. Concurrent EIV and EHV vaccine administration significantly increased IFN-γ production, without compromising humoral responses. Our data demonstrate that concurrent vaccination with EIV and EHV vaccines can enhance EIV-specific cellular responses in horses.

• Korean Journal of Veterinary Service
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• v.46 no.4
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• pp.263-268
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• 2023

Despite regular vaccinations, equine influenza virus (EIV) and Streptococcus equi subsp. equi (strangles) are the cause of highly contagious respiratory infections in horses. Many recent studies have reported that the concurrent administration of two vaccines could simplify horse management and minimize veterinary expenses. However, there is little information available regarding the efficacy of concurrent vaccinations against EIV and strangles. In this study, we evaluated EIV-specific antibody responses following the single EIV vaccination with the recombinant viral-vectored EIV vaccine or concurrent vaccination with the EIV and inactivated strangles vaccines. Blood samples were collected at 1-, 2-, 4-, and 8 weeks post-immunization (wpi) from each group. EIV-specific antibodies were evaluated by enzyme-linked immunosorbent assay (ELISA) and hemagglutination inhibition (HAI) assay. Both single and concurrent vaccination showed similar levels of EIV-specific serum immunoglobulin g (IgG) at 1 and 2 wpi. However, at 4 to 8 wpi, the EIV-only vaccination group showed significantly higher serum IgG levels than those from the concurrently vaccinated group. The HAI titers showed similar trends as the ELISA data, except at 8 wpi when both groups presented HAI titers with no significant differences. These data demonstrate that the concurrent vaccination against EIV and strangles could compromise the humoral immune response to equine influenza between vaccination intervals, which suggests the use of the consecutive vaccination protocol for EIV and strangles rather than concurrent vaccination.

• 전기의세계
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• v.36 no.12
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• pp.892-898
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• 1987

본고에서는 일반적으로 많이 사용하는 방법들인 ELS, RML, EIV 등을 소개하고 더 정확한 추정을 위해서 과결정 확장기구 변수법(OEIV;Overdetermined EIV)을 소개하며 이를 격자구조를 이용해서 실현한 격자구조 확장기구변수법(LEIV;Lattice EIV)을 소개하였다.

• Journal of Microbiology and Biotechnology
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• v.25 no.3
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• pp.317-320
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• 2015

H3N8 equine influenza virus (EIV) causes respiratory diseases in the horse population, and it has been demonstrated that EIV can transmit into dogs owing to its availability on receptors of canine respiratory epithelial cells. Recently, we isolated H3N8 EIV from an EIV-vaccinated horse that showed symptoms of respiratory disease, and which has a partially truncated nonstructural gene (NS). However, it is not clear that the NS-truncated EIV has an ability to cross the host species barrier from horses to dogs as well. Here, we experimentally infected the NS-truncated H3N8 EIV into dogs, and monitored their clinical signs and viral load in respiratory organs to determine the virus's transmissibility.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.887-895
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• 2014

Drowsy driving is a large proportion of the total car accidents. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. Many researches have been published that to measure electroencephalogram(EEG) signals is the effective way in order to be aware of fatigue and drowsiness of drivers. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, transition, and drowsiness. This paper proposes a drowsiness detection system using errors-in-variables(EIV) for extraction of feature vectors and multilayer perceptron (MLP) for classification. The proposed method evaluates robustness for noise and compares to the previous one using linear predictive coding (LPC) combined with MLP. From evaluation results, we conclude that the proposed scheme outperforms the previous one in the low signal-to-noise ratio regime.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.11
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• pp.1020-1028
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• 2011

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying artificial neural network. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes a neural-network-based fault diagnosis system using AR coefficients as feature vectors by LPC(linear predictive coding) and EIV(errors-in variables) analysis. We extracted feature vectors from sound, vibration and current faulty signals and evaluated the suitability of feature vectors depending on the classification results and training error rates by changing AR order and adding noise. From experimental results, we conclude that classification results using feature vectors by EIV analysis indicate more than 90 % stably for less than 10 orders and noise effect comparing to LPC.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.3
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• pp.312-320
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• 1990

Analysis and lattice implementation of Extended Instrumental Variable (EIV) methods for high resolution spectral analysis are presented. The performance of EIV is improved by using prefilters and the unbiasness of EIV is proved by using the fact that residual processes are white. We derive the order and time update formulas for the covariance lattice algorithm which is particularly useful in case of short data or nonstationary processes. The ARMA model can be modeled as two channel AR processes. Using this model, the lattice algorithms of EIV are derived. Computer simulations are performed to show the usefulness of the proposed algorithms.

• Korean Journal of Veterinary Research
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• v.59 no.3
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• pp.151-155
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• 2019

Equine influenza (EI) is the main cause of respiratory illness in equines across the globe and is caused by equine influenza A virus (EIV-A), which has impacted the equine industry internationally because of the marginal mortality and high morbidity. In the present study, the immune responses after equine influenza vaccination were evaluated in 4,144 horses in Korea using the hemagglutination inhibition (HI) assay. The equine influenza virus (EIV), A/equine/South Africa/4/03 (H3N8), was used as the antigen in the HI assay. The mean seropositive rates were 89.2% (97.4% in 2016, 77.6% in 2017, and 92.4% in 2018). This paper highlights the advances in understanding the effects of vaccines and control strategies for mitigating the emerging menace by EIV.

• Resources Recycling
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• v.10 no.2
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• pp.50-53
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• 2001

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1731-1733
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• 2015

Since electroencephalogram(EEG) has non-linear and non-stationary properties, it is effective to analyze the characteristic of EEG with time-frequency method rather than spectrum method. In this letter, we propose the modified drowsiness detection system using discrete wavelet transform combined with errors-in-variables and multilayer perceptron methods. For the comparison of the proposed scheme with the previous one, the state 'others' is added to the previous states of drivers: 'alertness,' 'transition,' and 'drowsiness.' From the computer simulation using machine learning, we confirm that the proposed scheme outperforms the previous one for some conditions.