• Korean Journal of Veterinary Research
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• v.51 no.3
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• pp.249-252
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• 2011

Cell culture systems for the protozoan Eimeria are not yet available. The present study was conducted to develop an animal model system by inoculating animals with a live Eimeria vaccine. This study was conducted on 3-day-old chickens (n = 20) pretreated with cyclophosphamide. The chickens were divided into 2 groups: the control group (n = 10) and the inoculated group that received the live Eimeria vaccine (n = 10). During the study period, we compared the clinical signs, changes in body weight, and number of oocysts shed in the feces of the control and inoculated group. This study showed that oocyst shedding was significantly higher in the chickens inoculated with live Eimeria oocysts than in the control chickens. Moreover, body weight gain was lesser in the animals in the inoculated group than in the control animals. Fecal oocyst shedding was observed in the inoculated animals. On the basis of these findings, we suggest that live Eimeria vaccination with cyclophosphamide pretreatment may be used to obtain an effective animal model for studying protozoan infections. This animal study model may eliminate the need for a tedious continuous animal inoculation process every 6 months because the live coccidiosis vaccine contains live oocysts.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.9
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• pp.1196-1200
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• 2000

First lactation records of 683 Murrah buffaloes maintained at National Dairy Research Institute, Karnal, were used for comparing the sire evaluation for age at first calving, first lactation 305-day or less milk yield and first service period. The sires were evaluated using Simple daughters average, Contemporary comparison, Least-squares and BLUP methods. The BLUP evaluations were obtained under single-, two- and three-trait individual animal models. The results revealed that for taking a decision regarding the method of sire evaluation to be used for selecting sires with high breeding values, criteria of the rank correlation could be misleading and comparison of the selected sires is likely to give a veritable picture. The Best Linear Unbiased Prediction method under multi-trait animal model incorporating first lactation milk yield with first service period as a covariable and age at first calving in the model was found to be more efficient and accurate for sire selection in Murrah buffaloes.

• Asian-Australasian Journal of Animal Sciences
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• v.8 no.4
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• pp.365-368
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• 1995

Lactation curves of dairy cows were generated using three models, namely; incomplete gamma function (model 1), polynomial inverse function (model 2) and non-linear regression (model 3). Secondary milk yield data of 27 cows which had completed 6 lactations were used in this study. Milk yield records (once a week) throughout the lactation and from the first three months of lactation were fitted to the models. Estimation of total milk yield by model 3 using the data once a week throughout the lactation resulted in smaller % bias and standard error than those generated from model 1 and 2. But, model 2 was more accurate in predicting the 305-day milk yield equivalent closer to actual yields with smaller bias % and error using partial records up to 3 months. Also, model 2 was able to estimate the time to reach peak yield close to the actual data using partial records and model 2 could be used as a tool to advise farmers on appropriate feeding and management practices to be adopted.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.1
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• pp.61-70
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• 2020

To estimate the ventilation volume of mechanically ventilated swine farms, various regression models were applied, and errors were compared to select the regression model that can best simulate actual data. Linear regression, linear spline, polynomial regression (degrees 2 and 3), logistic curve, generalized additive model (GAM), and gompertz curve were compared. Overfitting models were excluded even when the error rate was small. The evaluation criteria were root mean square error (RMSE) and mean absolute percentage error (MAPE). The evaluation results indicated that degree 3 exhibited the lowest error rate; however, an overestimation contradiction was observed in a certain section. The logistic curve was the most stable and superior to all the models. In the estimation of ventilation volume by all of the models, the estimated ventilation volume of the logistic curve was the smallest except for the model with a large error rate and the overestimated model.

• Korean Journal of Psychosomatic Medicine
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• v.2 no.1
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• pp.46-51
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• 1994

Model' is one of the well-used, but poorly understood word in the neurobehavioral research. After Darwin's evolutionary theory, it has been generally believed that human is different from animals in terms of the complexity, not of the essential. This notion could be applied to the mind as well as body. Therefore, it became possible to establish animal models in the scientific field of mind. Experimental analysis of the animal behavior becomes an important area for establishing an animal model of human psychopathology because behavior is the ambassador of the mind. A model emphasizes a structural correspondence between sets of causally related variables in two different domains such as the animal and the human. The first selection of elements of the two domains in correspondence called the initial analogy. Once the initial analogy is formed. causally related variables in the two domains are examined and arrayed The structural parallel is the formal analogy of a model, and similarities between corresponding variables are called material analogy. Models may serve any of three major functions ; heuristic, evidential and representative. In many cases, utilizing models may be more practical than directly assessing the domain of primary interest, since technical and/or ethical problems are more serious in the human domain. Although modeling is important to study human psychopathology, rare animal models approved to be a good model for the human psychopathology up to now. Developing the appropriate model is urgent to solve many problems raised from human psychopathology.

• Journal of the Korean Statistical Society
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• v.31 no.2
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• pp.177-198
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• 2002

The estimation of variance components or variance ratios in linear model is an important issue in plant or animal breeding fields, and various estimation methods have been devised to estimate variance components or variance ratios. However, many traits of economic importance in those fields are observed as dichotomous or polychotomous outcomes. The usual estimation methods might not be appropriate for these cases. Recently threshold linear model is considered as an important tool to analyze discrete traits specially in animal breeding field. In this note, we consider a hierarchical Bayesian method for the threshold animal model. Gibbs sampler for making full Bayesian inferences about random effects as well as fixed effects is described to analyze jointly discrete traits and continuous traits. Numerical example of the model with two discrete ordered categorical traits, calving ease of calves from born by heifer and calving ease of calf from born by cow, and one normally distributed trait, birth weight, is provided.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.8
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• pp.1085-1090
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• 2002

Genetic parameters for birth weights (BWT), calving ease scores observed from calves born by heifers (CEH), and calving ease scores observed from calves born by cows (CEC) were estimated using Bayesian methodology with Gibbs sampling in different threshold animal models. Data consisted of 77,458 records for calving ease scores and birth weights in Gelbvieh cattle. Gibbs samplers were used to obtain the parameters of interest for the categorical traits in two univariate threshold animal models, a bivariate threshold animal model, and a three-trait linear-threshold animal model. Samples of heritabilities and genetic correlations were calculated from the posterior means of dispersion parameters. In a univariate threshold animal model with CEH (model 1), the posterior means of heritabilities for calving ease was 0.35 for direct genetic effects and 0.18 for maternal genetic effects. In the other univariate threshold model with CEC (model 2), the posterior means of heritabilities of CEC was 0.28 for direct genetic effects and 0.18 for maternal genetic effects. In a bivariate threshold model with CEH and CEC (model 3), heritability estimates were similar to those in unvariate threshold models. In this model, genetic correlation between heifer calving ease and cow calving ease was 0.89 and 0.87 for direct genetic effect and maternal genetic effects, respectively. In a three-trait animal model, which contained two categorical traits (CEH and CEC) and one continuous trait (BWT) (model 4), heritability estimates of CEH and CEC for direct (maternal) genetic effects were 0.40 (0.23) and 0.23 (0.13), respectively. In this model, genetic correlation estimates between CEH and CEC were 0.89 and 0.66 for direct genetic effects and maternal effects, respectively. These estimates were greater than estimates between BWT and CEH (0.82 and 0.34) or BWT and CEC (0.85 and 0.26). This result indicates that CEH and CEC should be high correlated rather than estimates between calving ease and birth weight. Genetic correlation estimates between direct genetic effects and maternal effects were -0.29, -0.31 and 0.15 for BWT, CEH and CEC, respectively. Correlation for permanent environmental effects between BWT and CEC was -0.83 in model 4. This study can provide genetic evaluation for calving ease with other continuous traits jointly with assuming that calving ease from first calving was a same trait to calving ease from later parities calving. Further researches for reliability of dispersion parameters would be needed even if the more correlated traits would be concerned in the model, the higher reliability could be obtained, especially on threshold model with property that categorical traits have little information.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.350-356
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• 2017

This study was conducted to examine the influence of the maternal genetic effect of swine on their economic traits through the estimation of their genetic parameters, breeding value and genetic trends using an animal model. The data on Duroc pigs, Korean Native Pigs and Synthetic pigs (Duroc ${\times}$ Korean Native Pig) from 2000 to 2015 were obtained from the National Institute of Animal Science in Korea and used to estimate the genetic parameters for the average daily gain (ADG) and backfat thickness (BFT). Model 1 included the additive genetic effect of the animals, Model 2 consisted of Model 1 + the maternal genetic effect and Model 3 consisted of Model 2 + the maternal permanent environment effect. The heritability calculated by estimating the additive genetic effect was higher than that calculated by estimating the maternal genetic effect using the maternal animal model. The estimated genetic correlations between the additive and maternal genetic effects for the ADG and BF were strongly negative. Thus, the estimation of the breeding value can be used to select the most appropriate individuals and make an optimal breeding scheme.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.1
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• pp.7-12
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• 2004

Real time ultrasonic measurements for 13th rib fat thickness (LBF), longissimus muscle area (LEMA) and marbling score (LMS) of live animal at pre-harvest and subsequent carcass measurements for fat thickness (BF), longissimus muscle area (EMA), marbling score (MS) as well as body weight of live animal, carcass weight (CW), dressing percentage (DP), and total merit index (TMI) on 755 Korean beef steers were analyzed to estimate genetic parameters. Data were analyzed using multivariate animal models with an EM-REML algorithm. Models included fixed effects for year-season of birth, location of birth, test station, age of dam, linear and quadratic covariates for age or body weight at slaughter and random animal and residual effects. The heritability estimates for LEMA, LBF and LMS on RTU scans were 0.17, 0.41 and 0.55 in the age-adjusted model (Model 1) and 0.20, 0.52 and 0.55 in the weight-adjusted model (Model 2), respectively. The Heritability estimates for subsequent traits on carcass measures were 0.20, 0.38 and 0.54 in Model 1 and 0.23, 0.46 and 0.55 in Model 2, respectively. Genetic correlation estimate between LEMA and EMA was 0.81 and 0.79 in Model 1 and Model 2, respectively. Genetic correlation estimate between LBF and BF were high as 0.97 in Model 1 and 0.98 in Model 2. Real time ultrasonic marbling score were highly genetically correlated to carcass MS of 0.89 in Model 1 and 0.92 in Model 2. These results indicate that RTU scans would be alterative to carcass measurement for genetic evaluation of meat quality in a designed progeny-testing program in Korean beef cattle.

• Animal Bioscience
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• v.34 no.10
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• pp.1623-1631
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• 2021

Objective: This study aimed to validate and evaluate the dry matter (DM) intake prediction model of the Korean feeding standards for dairy cattle (KFSD). Methods: The KFSD DM intake (DMI) model was developed using a database containing the data from the Journal of Dairy Science from 2006 to 2011 (1,065 observations 287 studies). The development (458 observations from 103 studies) and evaluation databases (168 observations from 74 studies) were constructed from the database. The body weight (kg; BW), metabolic BW (BW^0.75, MBW), 4% fat-corrected milk (FCM), forage as a percentage of dietary DM, and the dietary content of nutrients (% DM) were chosen as possible explanatory variables. A random coefficient model with the study as a random variable and a linear model without the random effect was used to select model variables and estimate parameters, respectively, during the model development. The best-fit equation was compared to published equations, and sensitivity analysis of the prediction equation was conducted. The KFSD model was also evaluated using in vivo feeding trial data. Results: The KFSD DMI equation is 4.103 (±2.994)+0.112 (±0.022)×MBW+0.284 (±0.020)×FCM-0.119 (±0.028)×neutral detergent fiber (NDF), explaining 47% of the variation in the evaluation dataset with no mean nor slope bias (p>0.05). The root mean square prediction error was 2.70 kg/d, best among the tested equations. The sensitivity analysis showed that the model is the most sensitive to FCM, followed by MBW and NDF. With the in vivo data, the KFSD equation showed slightly higher precision (R² = 0.39) than the NRC equation (R² = 0.37), with a mean bias of 1.19 kg and no slope bias (p>0.05). Conclusion: The KFSD DMI model is suitable for predicting the DMI of lactating dairy cows in practical situations in Korea.