The inorganic scintillator used in gamma spectroscopy must have good efficiency in converting the kinetic energy of charged particles into light as well as high light output and high light detection efficiency. Accordingly, various studies have been conducted to enhance the net-efficiency. One way to improve the light yield has been studied by coating scintillators with various nanoparticles, so that the scintillation light can undergo resonance on surface between scintillators and nanoparticles resulting in higher light yield. In this study, an inorganic scintillator coated with CsPbBr3 perovskite nanocrystals using dip coating technique was proposed to improve scintillation light yield. The experiment was carried out by measuring scintillation light output, as the result of interaction between inorganic scintillator coated with CsPbBr3 perovskite nanocrystals and gamma-ray emitted from Cs-137 gamma source. The experimental results show that the channel corresponding to 662 keV full energy peak in the Cs-137 spectrum shifted to the right by 14.37%. Further study will be conducted to investigate the detailed relationships between the scintillation light yield and the characteristics of coated perovskite nanoparticles, such as diameter of nanoparticles, coated area ratio and width of coated region.

In this study, we evaluate artificial neural network (ANN) models that estimate the positions of gamma-ray sources from plastic scintillating fiber (PSF)-based radiation detection systems using different filtering ratios. The PSF-based radiation detection system consists of a single-stranded PSF, two photomultiplier tubes (PMTs) that transform the scintillation signals into electric signals, amplifiers, and a data acquisition system (DAQ). The source used to evaluate the system is Cs-137, with a photopeak of 662 keV and a dose rate of about 5 μSv/h. We construct ANN models with the same structure but different training data. For the training data, we selected a measurement time of 1 minute to secure a sufficient number of data points. Conversely, we chose a measurement time of 10 seconds for extracting time-difference data from the primary data, followed by filtering. During the filtering process, we identified the peak heights of the gaussian-fitted curves obtained from the histogram of the time-difference data, and extracted the data located above the height which is equal to the peak height multiplied by a predetermined percentage. We used percentage values of 0, 20, 40, and 60 for the filtering. The results indicate that the filtering has an effect on the position estimation error, which we define as the absolute value of the difference between the estimated source position and the actual source position. The estimation of the ANN model trained with raw data for the training data shows a total average error of 1.391 m, while the ANN model trained with 20%-filtered data for the training data shows a total average error of 0.263 m. Similarly, the 40%-filtered data result shows a total average error of 0.119 m, and the 60%-filtered data result shows a total average error of 0.0452 m. From the perspective of the total average error, it is clear that the more data are filtered, the more accurate the result is. Further study will be conducted to optimize the filtering ratio for the system and measuring time by evaluating stabilization time for position estimation of the source.

Urokinas type plasminogen activator (uPA) has been used as a therapeutic agent for treating human diseases such as thrombosis. Attempts to transgenically overexpress the uPA in animal bioreactors have been hampered due to side effects associated with this functional protein hormone on homeostasis. Recently, chicken has been emerged as a potential candidate for use as bioreactor to produce proteins of pharmaceutical importance. Since this species has low homology uPA sequence with mammals, we hypothesized that chicken could be used as a potential bioreactor for production of human uPA. In this study, using replication‐defective Murine Leukemia Virus (MLV)‐based retrovirus vectors encapsidated with Vesicular Stomatitis Virus G Glycoprotein (VSV‐G), we attempted to make transgenic chicken expressing human uPA (huPA). The recombinant retrovirus was injected beneath the blastoderm of non‐incubated chicken embryos (stage X, at laying). After 21 days of incubation (at hatching), all of the 38 living chicks that assayed, were found to express the vector‐encoded huPA gene in various organs and tissues, which was under the control of the Rous Sarcoma Virus (RSV) or Cytomegalovirus (CMV) promoter. Using specific primer set for huPA, PCR and RTPCR analyses of gDNA isolated from these samples demonstrated these chickens were transgenic for huPA. Furthermore, successful germ line transmission of huPA transgene was confirmed and next generation whole body huPA transgenic chickens were also produced. We also assayed huPA protein titer in blood (17.1 IU/ml) and eggs (4.4 IU/ml) of whole body huPA transgenic chicken. Thus, our results demonstrated that chicken could be used as bioreactors to produce huPA.

In the present study, we have developed a high-frequency plant regeneration system for Italian ryegrass via callus culture using mature seeds as explants. Optimal embryogenic callus induction was found to occur in MS medium containing 5 ㎎ 1?¹ 2,4-D, 0.5 ㎎ 1?¹ BA, 500 ㎎ 1?¹ L-proline, 1 g 1?¹ casein hydrolysate, 30 g 1?¹ sucrose, 7 ㎎ 1?¹ AgNO₃, 2 ㎎ 1?¹ CuSO₄ and solidified with 3 g 1?¹ Gelrite. The highest regeneration rate was obtained in MS medium containing 1 ㎎ 1?¹ 2,4-D, 5 ㎎ 1?¹ BA, 500 ㎎ 1?¹ L-proline, 1 g 1?¹ casein hydrolysate, 1 ㎎ 1?¹ thiamine-HCl, 30 g 1?¹ sucrose, 7 ㎎ 1?¹ AgNO₃, 2 ㎎ 1?¹ CuSO₄ and solidified with 3 g 1?¹ Gelrite. By using the most effective treatment determined for each parameter, the highest rates of embryogenic callus formation (48.9%) and regeneration (47.6%) were obtained with the Hwasan 101 cultivar. The overall plant regeneration rates of the examined cultivars ranged from 7.5% to 23.2%. Thus, optimization of regeneration frequency using mature seeds as explant material may offer a simple and efficient protocol for Italian ryegrass that may improve molecular breeding of this species.

Italian ryegrass (Lolium multiflorum Lam.) is one of important forage crop grass widely cultivated in Korea. Progress in breeding using conventional selection procedure is very slow, since Italian ryegrass is highly self-infertile. Biotechnological approaches, therefore, may contribute to the development of improved cultivars for forage crops. In an effort to optimize tissue culture responses of Italian ryegrass (Lolium multiflorum Lam.) for future genetic manipulations to improve forage characteristics, the effects of culture medium supplements on tissue culture responses were investigated with mature seeds of Korean Italian ryegrass (Lolium multiflorum Lam.) seven cultivars as explant tissues.