• Asian-Australasian Journal of Animal Sciences
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• v.20 no.6
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• pp.983-988
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• 2007

The aim of this study was to examine the correlation between bovine casein (CN) mRNA expression levels in mammary epithelial cells and lactation period, the yields of milk proteins and other parameters. The cells were collected from each cow's milk, which contained somatic cell counts (SCC) of less than 100,000 cells/ml. The levels of ${\alpha}s1-$, ${\alpha}s2-$, ${\beta}$- and ${\kappa}$-CN mRNA expression were significantly correlated with each other in mammary epithelial cells (p<0.01). All cows produced either less than 30 kg/day/cow or a over 30 kg/day/cow level of milk yield (MY). It was shown that the CN mRNA expression levels decreased gradually from the calving period to late lactation, when MY was over 30 kg/day/cow. The SCC tended to increase gradually during the course of lactation, but it was negatively correlated with milk protein and CN yields (p<0.01) when MY was less than 30 kg/day/cow. Moreover, there was a tendency for a negative correlation between SCC and ${\alpha}s1$-CN and ${\beta}$-CN mRNA expression level, when MY was less than 30 kg/day/cow (p<0.05).

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.10
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• pp.1397-1401
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• 2003

The aim of this study was to determine genotypes of four genetic markers and to investigate their association with milk production traits in Brown Swiss cattle imported to Slovakia. The bovine $\kappa$-casein, $\beta$-lactoglobulin, growth hormone and prolactin genotypes of 107 cows were identified by polymerase chain reaction. Effects all four genetic markers on milk, fat, protein and lactose yields and fat, protein and lactose percentage were estimated from a data set of 249 lactations. The frequency of desirable B allele of $\kappa$-casein gene to milk production was 0.46, alleles A of $\beta$-lactoglobulin gene was 0.55, allele and L of growth hormone gene was 0.45 and allele A and B of bovine prolactin gene were 0.61 and 0.39. The results of milk production obtained in our work showed that BB genotypes of $\kappa$-CN gene, AA genotypes of $\beta$-LG gene, LL genotypes of bGH gene were significantly associated with better milk production traits, mainly about the fat content. Association of a bovine prolactin genotypes with milk production were not found.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.1
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• pp.14-22
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• 2019

Objective: The aim of the study was to find a possible association between the ${\beta}-$ and ${\kappa}-casein$ and ${\beta}-lactoglobulin$ genotypes and important milk physiochemical and technological characteristics such as acidity, alcohol stability, the contents of some minerals and the parameters of acid fermentation ability (FEA) in Czech Fleckvieh Cattle. Methods: Milk and blood samples were collected from 338 primiparous Czech Fleckvieh cows at the same stage of lactation. The genotypes of individual cows for ${\kappa}-casein$ (alleles A, B, and E) and ${\beta}-lactoglobulin$ (alleles A and B) were ascertained by polymerase chain reaction-restriction fragment length polymorphism, while their ${\beta}-casein$ (alleles $A^1$, $A^2$, $A^3$, and B) genotype was determined using melting curve genotyping analysis. The data collected were i) milk traits including active acidity (pH), titratable acidity (TA), alcohol stability (AS); calcium (Ca), phosphorus (P), sodium (Na), magnesium (Mg), and potassium (K) contents; and ii) yoghurt traits including active acidity (Y-pH), titratable acidity (Y-TA), and the counts of both Lactobacilli and Streptococci in 1 mL of yoghurt. A linear model was assumed with fixed effects of herd, year, and season of calving, an effect of the age of the cow at first calving and effects of the casein and lactoglobulin genotypes of ${\beta}-CN$ (${\beta}-casein$, CSN2), ${\kappa}-CN$ (${\kappa}-casein$, CSN3), and ${\beta}-LG$ (${\beta}-lactoglobulin$, LGB), or the three-way interaction between those genes. Results: The genetic polymorphisms were related to the milk TA, AS, content of P and Ca, Y-pH and Lactobacilli number in the fresh yoghurt. The CSN3 genotype was significantly associated with milk AS (p<0.05). The effect of the composite CSN2-CSN3-LGB genotype on the investigated traits mostly reflected the effects of the individual genes. It significantly influenced TA (p<0.01), Y-pH (p<0.05) and the log of the Lactobacilli count (p<0.05). Conclusion: Our findings indicate that the yoghurt fermentation test together with milk proteins genotyping could contribute to milk quality control and highlight new perspectives in dairy cattle selection.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.5
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• pp.732-739
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• 2002

Milk samples with different phenotype combination of $\{kappa}$-casein and ${\beta}$-lactoglobulin and different preheating temperatures of 30, 70, 75 and $80^{\circ}C$ were used for cheesemaking under laboratory conditions. For the 853 batches of cheese, mean composition was 59.64% total solids, 30.24% fat and 23.66% protein, and the whey contained 6.93% total solids, 0.30% fat and 0.87% protein. Least squares analysis of the data indicated that heating temperature of the milk and ${\kappa}$-CN/${\beta}$-LG phenotypes had significant effects on cheese and whey compositions. The total solids, fat and protein contents of cheese were negatively correlated with preheating temperatures of milk. Cheese from BB/BB phenotype milk had the highest and those from AA/AA phenotype milk had the lowest concentrations of total solids, fat and protein. Mean recoveries of milk components in the cheese were 53.71% of total solids, 87.15% of fat, and 80.32% of protein. For the 10 different types of milk, maximum recoveries of milk components in cheese occurred with preheating temperature of $70^{\circ}C$ or $75^{\circ}C$ and lowest recoveries occurred at $80^{\circ}C$. The whey averaged 6.94% total solids, 0.30% fat and 0.87% protein. Losses of milk components in the whey were lowest for milk preheated at $80^{\circ}C$ and for milk containing the BB/BB phenotype.

• Journal of the Korean Society of Food Science and Nutrition
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• v.31 no.3
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• pp.438-444
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• 2002

Interactions between meat proteins and hydrocolloids in a model system may play an important role for the improvement of textural properties in low-fat sausage mixtures. The objective of this study was to determine gel properties as affected by the type and level of hydrocolloid, various pH values of meat protein-hydrocolloid mixture before cooking, and internal cooking temperatures. The desirable heat-induced gels (HIGs) were formed at least pH values above 6.0. The addition of konjac flour (KF), kappa-carrageenan (CN) and locust bean gum (LBG) to extracted salt soluble proteins (2%) improved the gel strength with increased levels (0.5∼1.5%) and HIGs containing CN had the highest (p<0.05) gel strength. The increase of cooking temperature increased gel strength, depending on pH and type of hydrocolloid. However, the minimun internal cooking temperature to make viscoelastic HIGs was 70$^{\circ}C$. These results indicated that desirable HIGs were manufactured with each hydrocolloid concentration of 1% and minimum cooking temperature of 70$^{\circ}C$ with pH values higher than 6.0.

• Journal of the Korean Society of Food Science and Nutrition
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• v.31 no.4
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• pp.629-635
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• 2002

This study was performed to develop low-fat comminuted sausages (LFSs, < 3%) manufactured with 1% single (Konjac flour, KF; kappa-carrageenan, k-CN and Locust bean gum, LBG) or mixed hydrocolloids and to select the best combination which had similar textural characteristics to those with regular-fat (~25% fat) control. In experiment 1, LFSs were formulated with each 1% hydrocolloid, smoked and cooked to an internal temperature of 71.7$^{\circ}C$. The pH range of LFSs was 6.29 to 6.34 and approximately 23~24% of fat was removed in the final products, resulting in the higher moisture and protein contents (%) in LFSs, as compared to regular-fat control. No differences (p>0.05) in cooking loss (CL, %), expressible moisture (EM, %), and hunter color values (L, a, b) were observed with the addition of each 1% hydrocolloid. However, LFSs containing 1% k-CN had textural hardness values similar to those with low-/regular-fat controls, whereas LFSs having either KF or LBG had similar cohesiveness values to those with regular-fat counterpart. Tn experiment 2, two or three mixed hydrocolloids were added to the low-fat sausage formulation. The addition of mixed KF+LBG (KLL) and KF+CN+LBG(KCL) reduced EM and textural hardness values, as compared to low-fat control. Among the treatments, LFSs containing two or three combinations of CN with KF or/and LBG had similar textural characteristics to those with regular-fat control. These results suggested that multiple addition of CN with other hydrocolloids (KF or LBG) for the replacement of fats in LFSs would be recommended for the proper functional and textural properties.

• Journal of Korean Society of Rural Planning
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• v.21 no.2
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• pp.33-49
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• 2015

The objective of this study was to predict land use change based on the land use change scenarios for the Hwangguji river watershed, South Korea. The land use change scenario was derived from the representative concentration pathways (RCP) 4.5 and 8.5 scenarios. The CLUE (conversion of land use and its effects) model was used to simulate the land use change. The CLUE is the modeling framework to simulate land use change considering empirically quantified relations between land use types and socioeconomic and biophysical driving factors through dynamical modeling. The Hwangguji river watershed, South Korea was selected as study area. Future land use changes in 2040, 2070, and 2100 were analyzed relative to baseline (2010) under the RCP4.5 and 8.5 scenarios. Binary logistic regressions were carried out to identify the relation between land uses and its driving factors. CN (Curve number) and impervious area based on the RCP4.5 and 8.5 scenarios were calculated and analyzed using the results of future land use changes. The land use change simulation of the RCP4.5 scenario resulted that the area of urban was forecast to increase by 12% and the area of forest was estimated to decrease by 16% between 2010 and 2100. The land use change simulation of the RCP8.5 scenario resulted that the area of urban was forecast to increase by 16% and the area of forest was estimated to decrease by 18% between 2010 and 2100. The values of Kappa and multiple resolution procedure were calculated as 0.61 and 74.03%. CN (III) and impervious area were increased by 0-1 and 0-8% from 2010 to 2100, respectively. The study findings may provide a useful tool for estimating the future land use change, which is an important factor for the future extreme flood.