• Journal of Korean Medicine Rehabilitation
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• v.26 no.2
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• pp.75-83
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• 2016

Objectives This study is to review the effect of pharmacopuncture on treatment of osteoporosis in animal models reported in korean domestic journals. Methods The databases (Koreantk, KISS, NDSL) were searched with term as osteoporosis, and animal study reports on osteoporosis with pharmacopuncture were reviewed. Animal model, intervention, and osteoporosis indicator were extracted. Results 22 articles were reviewed. 11 studies used ddy mouse and 9 studies used SD rat. 20 studies used ovariectomy to induce osteoporosis. 21 studies used simple pharmacopuncture. Cervi pantotrichum cornu was most frequently used pharmacopuncture and Umgok (KI10) was most frequently used acupuncture point. Each study shows significant changes of osteoporosis indicators. Conclusions Pharmacopuncture is expected to be a positive effect on osteoporosis.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.5
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• pp.622-626
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• 2002

Growth records over six generations of 686 pigs in SD-II Swine Line were used to estimate the genetic and phenotypic covariance functions for body weight as longitudinal data. A random regression model with Legendre polynomials of age as independent variables was used to estimate the (co)variances among the regression coefficients, thus the coefficients of genetic and permanent environmental covariance functions by restricted maximum likelihood employing the average information algorithm. The results showed that, using litter effect as additional random effect, a reduced order of fit did not describe the data adequately. For all five orders of fit, however, the change trends of genetic and phenotypic (co)variances were very similar from ${\kappa}$=3 onwards.

• Asian-Australasian Journal of Animal Sciences
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• v.1 no.3
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• pp.149-152
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• 1988

Postweaning performances of steer from 11 to 18 months of age under intensive feedlot condition were examined in Brahman and Santa Gertudis cattle breeds which have been established in Paraguay. Fitting of growth data in each breed to an empirical growth model with non-linear least square analysis resulted in the following age(t; months) - weight(w; kg) function made out each breed: w=638.26($1-2.341e^{-0.010965t}$) for Brahman and w=716.38($1-2.365e^{-0.10741t}$) for Santa Gertrudis. The estimated mature size of Brahman steers(638 kg) was 11% lower than that of Santa Gertrudis steers(716 kg). However, slightly larger k value (rate of maturing) of Brahman steer in the mechanistic model suggested relatively earlier maturing tendency in the breed. No significant differences in dressing percentage (Brahman, 59.3%; Santa Gertrudis, 58.8%) of chilled carcass weight to live-weight were observed between breed.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.3
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• pp.358-363
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• 2005

Five crossbred beef cattle (Simmental${\times}$yellow cattle, Shantung Province) fitted with permanent cannulae in the rumen and T-type cannulae at the proximal duodenum and terminal ileum, were fed five different diets containing corn, cotton meal or soybean meal and ammoniated straw to determine the dry matter, crude protein and amino acid flows in duodenal and ileum digesta, and to calculate the regression equations between theoretical and experimental concentration of AA in duodenal digesta. The results showed that there was a strong correlation between experimental (g/d, y) and theoretical CP flows (g/d, x) at the proximal duodenum, the $R^2$-value regression equation of crude protein is very high (0.9636). The $R^2$-value regression equation of the limiting amino acid (such as Met or Lys) is high (0.7573 or 0.9252 respectively). This results indicated that we can formulate better diets fed to beef cattle according to the theoretical amino acid concentration. A mathematical model has been successfully constructed for predicting the supply of absorbed amino acids at the proximal duodenum in growing beef cattle.

• Asian-Australasian Journal of Animal Sciences
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• v.22 no.7
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• pp.931-936
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• 2009

Body weights of 862 Angora goats between birth and 36 months of age, recorded on a semiyearly basis from 1988 to 2000, were used to estimate genetic, permanent environmental and phenotypic covariance functions. These functions were estimated by fitting a random regression model with 6th order polynomial for direct additive genetic and animal permanent environmental effects and 4th and 5th order polynomial for maternal genetic and permanent environmental effects, respectively. A phenotypic covariance function was estimated by modelling overall animal and maternal effects. The results showed that the most variable coefficient was the intercept for both direct and maternal additive genetic effects. The direct additive genetic (co)variances increased with age and reached a maximum at about 30 months, whereas the maternal additive genetic (co)variances increased rapidly from birth and reached a maximum at weaning, and then decreased with age. Animal permanent environmental (co)variances increased with age from birth to 30 months with lower rate before 12 months and higher rate between 12 and 30 months. Maternal permanent environmental (co)variances changed little before 6 months but then increased slowly and reached a maximum at about 30 months. These results suggested that the contribution of maternal additive genetic and permanent environmental effects to growth variation differed from those of direct additive genetic and animal permanent environmental effects not only in expression time, but also in action magnitude. The phenotypic (co)variance estimates increased with age from birth to 36 months of age.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.9
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• pp.1226-1234
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• 2015

The development of effective genetic evaluations and selection of sires requires accurate estimates of genetic parameters for all economically important traits in the breeding goal. The main objective of this study was to assess the relative performance of the traditional lactation average model (LAM) against the random regression test-day model (RRM) in the estimation of genetic parameters and prediction of breeding values for Holstein Friesian herds in Ethiopia. The data used consisted of 6,500 test-day (TD) records from 800 first-lactation Holstein Friesian cows that calved between 1997 and 2013. Co-variance components were estimated using the average information restricted maximum likelihood method under single trait animal model. The estimate of heritability for first-lactation milk yield was 0.30 from LAM whilst estimates from the RRM model ranged from 0.17 to 0.29 for the different stages of lactation. Genetic correlations between different TDs in first-lactation Holstein Friesian ranged from 0.37 to 0.99. The observed genetic correlation was less than unity between milk yields at different TDs, which indicated that the assumption of LAM may not be optimal for accurate evaluation of the genetic merit of animals. A close look at estimated breeding values from both models showed that RRM had higher standard deviation compared to LAM indicating that the TD model makes efficient utilization of TD information. Correlations of breeding values between models ranged from 0.90 to 0.96 for different group of sires and cows and marked re-rankings were observed in top sires and cows in moving from the traditional LAM to RRM evaluations.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.1
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• pp.47-55
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• 2021

The objective of this study was to access the effect of climate and soil factors on alfalfa dry matter yield (DMY) by the contribution through constructing the yield prediction model in a general linear model considering climate and soil physical variables. The processes of constructing the yield prediction model for alfalfa was performed in sequence of data collection of alfalfa yield, meteorological and soil, preparation, statistical analysis, and model construction. The alfalfa yield prediction model used a multiple regression analysis to select the climate variables which are quantitative data and a general linear model considering the selected climate variables and soil physical variables which are qualitative data. As a result, the growth degree days(GDD) and growing days(GD), and the clay content(CC) were selected as the climate and soil physical variables that affect alfalfa DMY, respectively. The contributions of climate and soil factors affecting alfalfa DMY were 32% (GDD, 21%, GD 11%) and 63%, respectively. Therefore, this study indicates that the soil factor more contributes to alfalfa DMY than climate factor. However, for examming the correct contribution, the factors such as other climate and soil factors, and the cultivation technology factors which were not treated in this study should be considered as a factor in the model for future study.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.737-743
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• 2014

This experiment was conducted to compare heterogeneity for the variance in dairy cattle population and to induce homogeneity of variance using 502,228 performance test records of dairy cattle. The estimates of heritability for milk yields, fat yields and protein yields were 0.28, 0.26 and 0.24, respectively and the estimate of average breeding value by birth year was lower in HV (heterogenous variance) model than in animal model, collectively. The average breeding values of milk yields, fat yields and protein yields for 545 sire bulls applicable to the criteria of interbull MACE programme were 453.54kg, 10.75kg and 14.33kg, respectively and when the heterogeneity was adjusted they were 432.06kg, 10.15kg and 13.40kg, respectively, which were lower in all milk traits collectively. In animal model, coefficients of phenotypic correlation between dataset I and II were 0.839 in milk yields, 0.821 in fat yields, and 0.837 in protein yields, while in HV model, they were 0.841 in milk yields, 0.820 in fat yields, and 0.836 in protein yields, showing similar results in 2 models. When compared using animal model and HV model, the regression coefficient for ratio of number of daughters by calving year of milk yields increased from 15.157 to 16.105 and that of fat yields increased from =0.227 to =0.196, but that of protein yields decreased from 0.630 to 0.586.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.8
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• pp.1080-1084
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• 2002

Carcass characteristics of 241 crossbred pigs (Korean wild boars ${\times}$ Landrace sows) were analyzed to examine variations in fasted body weight (FASTWT), carcass weight (CARCWT), dressing percentage (DP), back fat thickness (BFT) and longissimus muscle weight (LMW), and to estimate genetic and phenotypic parameters using three different slaughter-end points. Covariates in the least squares full sib model were slaughter age, fasted body weight and back fat thickness of the carcass. Coefficient of variation was highest for BFT followed by LMW, CARCWT, FASTWT and DP in magnitude. Regressions of three covariates on traits were all linear. However, slaughter age was not significant as a linear covariate for five traits while FASTWT was significant for CARCWT and LMW and BFT was significant for all remaining traits. Genetic and phenotypic variation was considerably reduced by regressing FASTWT or BFT in the model. Heritability estimates of FASTWT, CARCWT, DP and BFT were 0.68, 0.61, 0.11 and 0.49, respectively, using slaughter age as covariate (model 1). Those of CARCWT, DP, BFT and LMW were 0.15, 0.15, 0.30 and 0.11, respectively, using FASTWT as covariate (model 2). Heritability estimates of the traits using LMW as covariate (model 3) were similar to the estimates from Model 1 except that the estimate of CARCWT was reduced to 0.39. Genetic or phenotypic correlations among FASTWT, CARCWT and BFT were all positive and moderate to high. Those between BFT and LMW were also positive and low to moderate. However, genetic and phenotypic correlations between DP and CARCWT were positive while those between DP and FASTWT were negative. It was suggested from this study that differences in carcass yield traits be determined using slaughter age or back fat thickness as slaughter-end point and carcass quality traits using fasted body weight as slaughter-end point.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.12
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• pp.1836-1843
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• 2019

Objective: Social genetic effects (SGE) are an important genetic component for growth, group productivity, and welfare in pigs. The present study was conducted to evaluate i) the feasibility of the single-step genomic best linear unbiased prediction (ssGBLUP) approach with the inclusion of SGE in the model in pigs, and ii) the changes in the contribution of heritable SGE to the phenotypic variance with different scaling ${\omega}$ constants for genomic relationships. Methods: The dataset included performance tested growth rate records (average daily gain) from 13,166 and 21,762 pigs Landrace (LR) and Yorkshire (YS), respectively. A total of 1,041 (LR) and 964 (YS) pigs were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel. With the BLUPF90 software package, genetic parameters were estimated using a modified animal model for competitive traits. Giving a fixed weight to pedigree relationships (${\tau}:1$), several weights (${\omega}_{xx}$, 0.1 to 1.0; with a 0.1 interval) were scaled with the genomic relationship for best model fit with Akaike information criterion (AIC). Results: The genetic variances and total heritability estimates ($T^2$) were mostly higher with ssGBLUP than in the pedigree-based analysis. The model AIC value increased with any level of ${\omega}$ other than 0.6 and 0.5 in LR and YS, respectively, indicating the worse fit of those models. The theoretical accuracies of direct and social breeding value were increased by decreasing ${\omega}$ in both breeds, indicating the better accuracy of ${\omega}_{0.1}$ models. Therefore, the optimal values of ${\omega}$ to minimize AIC and to increase theoretical accuracy were 0.6 in LR and 0.5 in YS. Conclusion: In conclusion, single-step ssGBLUP model fitting SGE showed significant improvement in accuracy compared with the pedigree-based analysis method; therefore, it could be implemented in a pig population for genomic selection based on SGE, especially in South Korean populations, with appropriate further adjustment of tuning parameters for relationship matrices.