In this study, we investigated the possibility of using mouse embryonic stem cell conditioned medium (ESCM) and embryonic stem cell medium (ESM) for in vitro maturation in the efficient in vitro production of blastocysts from porcine follicular oocyte. Depending on the concentration of supplement of ESCM added to the NCSU-23 solution did not affect 2-cell development rates and blastocysts development. However, in particular, the survival rate (10 days of culture) of blastocyst was significantly higher than that of the control group as the additive concentration (30%) increased (p < 0.05). The survival rate of blastocysts showed a similar tendency even with addition of ESM (30%) alone. On the other hand, the duration of the addition of these additives during IVM (0-44 h) was that the IVM I period (0-22 h) were more effective than the IVM II period (22-44 h). Thus, the effect of these additives is probably due to the combination of the various physiologically active substances of ESCM or the appropriate amino acids and vitamins of ESM. In particular, these additives were more effective during the first half (IVM I) of in vitro maturation. In summary, optimization of ESCM or ESM supplementation may improve in vitro maturation of porcine oocyte and affect developmental competency. Therefore, if more efficient methods of adding ESCM or ESM to basal culture medium can be developed during in vitro maturation of porcine follicle oocytes, high quality blastocysts will be developed from low porcine follicular oocyte compared to other domestic animals.

In this study, a high-resolution daily data set of surface weather were obtained from PRIDE(PRISMbased Dynamic downscaling Error correction) model for the period of 2000 to 2017 over South Korea. The simulation data of five RCM(Regional Climate Model) were also used which are forced by the CMIP6 participating model UK-ESM as the boundary condition under historical period (2000-2014) and SSP 5-8.5 period (2015- 2017). Here we compared the RCM data and the PRIDE data with MK-PRISM data in terms of ensemble mean and ensemble spread. Results show that the PRIDE model effectively eliminates systematic error in the RCM up to 63.0% for daily average temperature, 72.2% for daily maximum temperature, 68.2% for daily minimum temperature, and 28.7% for daily precipitation when evaluated from the RMSE perspective. Overall, the ensemble spread of the PRIDE model is significantly decreased from 1.46°C to 0.36°C for daily temperature and from 2.0 mm/day to 0.72 mm/day for daily precipitation compared to the RCM ensemble spread, indicating that the largest systematic error of the RCMs is effectively removed in the PRIDE model.