This study was performed to evaluate the antioxidative potential and quality of the breast meat of broiler chickens fed a dietary medicinal herb extract mix (MHEM, consisting of mulberry leaf, Japanese honeysuckle, and goldthread at a ratio of 48.5:48.5:3.0). A total of 480 one-day-old male Cobb broiler chicks were randomly allotted to 12 pens, with 40 birds per pen (replicate), and reared for 35 d. Dietary treatments consisted of a corn-soybean meal basal diet (control); a basal diet with 0.3% MHEM (T1); and a basal diet with 1% (T2) MHEM. At the end of the feeding trial, breast meat samples were excised and stored in a refrigerator at 4 degrees C to be analyzed at d 0, 3, and 7. The MHEM did not affect proximate composition of the breast meat. Total phenols content of the breast meats in the T1 and T2 diets was approximately 2 times greater than that of the control diet (P < 0.05). 1,1-Diphenyl-2-picrylhy-drazyl radical-scavenging activity and 2,2-azinobis-(3 ethylbenzothiazoline-6-sulfonic acid) cation-reduction activity were greater in the T2 diet at d 0 and in the T1 diet at d 3 compared with the control diet (P < 0.05). 2-Thiobarbituric acid-reactive substance values in the T1 and T2 diets were lower than in the control diet at d 3 and 7 and did not increase during storage, whereas the value in the control diet increased significantly. The pH of the T1 diet was significantly greater than that of the control diet at d 0 and 3. In a sensory test, panelists preferred the T1 breast meat throughout the 7-d storage period. This research indicates that dietary MHEM could increase the antioxidative potential and overall preference of breast meat during cold storage.

The highly polymorphic swine leucocyte antigen (SLA) genes are among the most important determinants of swine immune responses to disease and vaccines. Accurate and effective SLA genotyping methods are required to understand how SLA gene polymorphisms affect immunity, especially in outbred pigs with diverse genetic backgrounds. In this study, we present a simple and rapid molecular-based typing system for characterizing SLA class II alleles of the DRB1, DQB1 and DQA loci. This system utilizes a set of 47 sequence-specific PCR primers developed to differentiate alleles by groups that share similar sequence motifs. We applied this typing method to investigate the SLA class II diversity in four populations of outbred pigs (n = 206) and characterized a total of 19 SLA class II haplotypes, six of which were shared by at least three of the sampled pig populations. We found that Lr-0.1 (DRB1*01XX-DQB1*01XX-DQA*01XX) was the most prevalent haplotype with a combined frequency of 16.0%, followed by Lr-0.2 (DRB1*02XX-DQB1*02XX-DQA*02XX) with 14.6% and Lr-0.15b (DRB1*04XX-DQB1*0202-DQA*02XX) with 14.1%. Over 70% of the pigs (n = 147) had at least one copy of one of these three haplotypes. The PCR-based typing system described in this study demonstrates a reliable and unambiguous detection method for SLA class II alleles. It will be a valuable tool for studying the influence of SLA diversity on various immunological, pathological and physiological traits in outbred pigs.

An improved understanding of the factors affecting the indoor PM2.5 concentration levels can lead to the development of an efficient management strategy to control health risks from exposure to indoor PM2.5 and related toxic components. A comparison of our comprehensive data sets indicated that most indoor PM2.5 and associated elemental species were strongly enriched by indoor source activities along with infiltration of ambient outdoor air for a naturally ventilated building.

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Soil carbon is an important factor in the process of mitigating climate change and solving greenhouse gas problems. However, the previous technology for soil carbon content analysis required a lot of labor, time, and expensive equipment (i.e. an elemental analyzer). In this study, the disadvantages of previous analysis method were secured by using smartphone images and multiple regression analysis. To predict the soil carbon content, the color variables (e.g., RGB, CIE-L * a * b * , CIE-L * c * h * , and CIE-L * u * v * ), gravimetric water content, and bulk density were used as statistical data. After Pearson's correlation analysis, several variables that had high correlations were removed and then used. In addition, the result of variance inflation factor (VIF) analysis indicated that all variables should not cause multicollinearity problems. The predictive model was classified based on land use, and the predictive model for the entire sample was also derived. The adjusted coefficient of determination (Adj. R 2 ), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used to verify the predictive model. When the verification method was substituted for each predictive model, the reliability of the predictive model classified based on land use was high. Therefore, in order to predict the carbon content in the agricultural soil, it is efficient to assign each prediction model after classifying agricultural land.