Background: Cadmium (Cd) is toxic heavy metal that accumulates in organisms after passing through their respiratory and digestive tracts. Although several studies have reported the toxic effects of Cd exposure on human health, its role in embryonic development during preimplantation stage remains unclear. We investigated the effects of Cd on porcine embryonic development and elucidated the mechanism. Methods: We cultured parthenogenetic embryos in media treated with 0, 20, 40, or 60 μM Cd for 6 days and evaluated the rates of cleavage and blastocyst formation. To investigate the mechanism of Cd toxicity, we examined intracellular reactive oxygen species (ROS) and glutathione (GSH) levels. Moreover, we examined mitochondrial content, membrane potential, and ROS. Results: Cleavage and blastocyst formation rates began to decrease significantly in the 40 μM Cd group compared with the control. During post-blastulation, development was significantly delayed in the Cd group. Cd exposure significantly decreased cell number and increased apoptosis rate compared with the control. Embryos exposed to Cd had significantly higher ROS and lower GSH levels, as well as lower expression of antioxidant enzymes, compared with the control. Moreover, embryos exposed to Cd exhibited a significant decrease in mitochondrial content, mitochondrial membrane potential, and expression of mitochondrial genes and an increase in mitochondrial ROS compared to the control. Conclusions: We demonstrated that Cd exposure impairs porcine embryonic development by inducing oxidative stress and mitochondrial dysfunction. Our findings provide insights into the toxicity of Cd exposure on mammalian embryonic development and highlight the importance of preventing Cd pollution.

Nuclear facilities present the important task related to the migration and retention of radioactive contaminants such as cesium (Cs), strontium (Sr), and cobalt (Co) for unexpected events in various environmental conditions. The distribution coefficient (Kd) is important factor for understanding these contaminants mobility, influenced by environmental variables. This study focusses the prediction of Kd values for radionuclides within solid phase groups through the application of machine-learning models trained on experimental data and open source data from Japan atomic energy agency. Three machine-learning models, such as the convolutional neural network, artificial neural network, and random forest, were trained for prediction model of the distribution coefficient (Kd). Fourteen input variables drawn from the database and experimental data, including parameters such as initial concentration, solid-phase characteristics, and solution conditions, served as the basis for model training. To enhance model performance, these variables underwent preprocessing steps involving normalization and log transformation. The performances of the models were evaluated using the coefficient of determination. These results showed that the environmental media, initial radionuclide concentration, solid phase properties, and solution conditions were significant variables for Kd prediction. These models accurately predict Kd values for different environmental conditions and can assess the environmental risk by analyzing the behavior of radionuclides in solid phase groups. The results of this study can improve safety analyses and longterm risk assessments related to waste disposal and prevent potential hazards and sources of contamination in the surrounding environment.

Radioactive contaminants, such as 137Cs, are a significant concern for long-term storage of nuclear waste. Migration and retention of these contaminants in various environmental media can pose a risk to the surrounding environment. The distribution coefficient (Kd) is a critical parameter for assessing the behavior of these contaminants and can introduce significant errors in predicting migration and remediation options. Accurate prediction of Kd values is essential to assess the behavior of radioactive contaminants and to ensure environmental safety. In this study, we present machine learning models based on the Japan Atomic Energy Agency Sorption Database (JAEA-SDB) to predict Kd values for Cs in soils. We used three different machine learning models, namely the random forest (RF), artificial neural network (ANN), and convolutional neural network (CNN), to predict Kd values. The models were trained on 14 input variables from the JAEA-SDB, including factors such as Cs concentration, solid phase properties, and solution conditions which are preprocessed by normalization and log transformation. We evaluated the performance of our models using the coefficient of determination (R2) value. The RF, ANN, and CNN models achieved R2 values of over 0.97, 0.86, and 0.88, respectively. Additionally, we analyzed the variable importance of RF using out-of-bag (OOB) and CNN with an attention module. Our results showed that the initial radionuclide concentration and properties of solid phase were important variables for Kd prediction. Our machine learning models provide accurate predictions of Kd values for different soil conditions. The Kd values predicted by our models can be used to assess the behavior of radioactive contaminants in various environmental media. This can help in predicting the potential migration and retention of contaminants in soils and the selection of appropriate site remediation options. Our study provides a reliable and efficient method for predicting Kd values that can be used in environmental risk assessment and waste management.

The Ag0-containing sorbents synthesized by Na, Al, and Si alkoxides have higher maximum iodine capture capacity and textural properties than zeolite-based Ag0-containing sorbents. However, these sorbents were prepared in the form of granules via a step for cutting cylindrical alcogels. Since asmade sorbents decreased packing density, they must be additionally crushed and then classified into an appropriate size for increasing packing density. The bead formation in the step of sol-gelation could bring about the simplification of sorbent preparation process and an improvement of packing density. In the Na, Al, and Si alkoxides as starting materials, sol solution was hydrophilic and lower density than vegetable oil, which transformed sol droplets to sol-gel beads. Thus, in these precursors, sol droplets, which must be sprayed by single nozzle placed at bottom side of oil column, can rise up through oil column. Acetic acid (HOAc) was used as the catalyst for the hydrolysis of Na alkoxide (TEOS) and gelation of the Na+AlSi-OH alcosol. For obtaining sol-gel beads, experiments were performed by the flowrate change of sol solution and HOAc at different nozzle sizes using soybean oil column of 1 m in length. At a sol/HOAc flowrate ratio of 3.85, some Na+AlSi-OH alcogel beads were obtained. After the Ag/Na ion-exchange, Ag content in Ag+AlSi-OH hydrogel was low due to reaction between Na+ and HOAc during sol-gelation and aging step. The Ag+AlSi-OH hydrogel with high Ag content could be prepared by Na addition. After the solvent exchange and drying at ambient pressure, the bead sorbents had higher Ag0 content and larger pore size than granular sorbents. However, further experiments are needed to increase yield rate in bead sorbent.

In KAERI, the nuclide management technology is currently being developed for the reduction of disposal area required for spent fuel management. Among the all fission products of interest, Cs, I, Kr, Tc are considered to be significantly removed by following mid-temperature and high-temperature treatment, however, a difficulty of spent-fuel thermal treatment experiment limits the development of such thermal treatment. In this study, we applied our previously developed two-stage diffusion release model coupled to UO2 oxidation model to the development of optima thermal treatment scenario. Since the formation of cesium pertechnetate should be avoided and the fission release behavior is considerably affected by the extent of oxygen, we obtained oxygen-content dependent model parameters for two-stage fission release model and applied the model to the evaluation of fission release behavior to different oxygen content and thermal treatment procedure. It was found that the developed fission release model closely describes the experimental behavior of fission product of interest, implying a validity of model prediction and the thermal treatment condition reducing the chemical reaction between cesium and technetium could be developed.

경기도농업기술원에서는 2016년 백색 대형 스탠다드 장미 ‘Fascinar’을 육성하였다. 2011년 ‘Splash’ 품종을 모본으로, 향기가 강한 연보라색 장미 ‘Charming Easies’ 품종을 부본으로 교배하였고 5개월 후에 성숙한 꼬투리를 획득하여 암면배지에 파종하여 후대를 얻었다. 2012년 화색, 화형, 향기 유무 등의 특성검정을 통하여 G11-338 계통을 선발하였다. 선발된 계통을 2013년부터 2016년까지 1차, 2차, 3차 특성검정을 통하여 품종의 안정성과 균일성을 확인하여 ‘Fascinar’로 명명하였고 2017년에 국립종자원에 품종보호출원(제2017-198호) 한 후 2019년에 품종보호권이 등록(제7795호)되었다. 화색은 백색(RHS, N155D)으로 대조품종보다 짙은 백색이며, 화형은 스탠다드이며 고심형인 대형 장미이다. 향기는 대조품종 ‘화이트라임’ 보다 매우 강하며 주요향기 성분은 GERMACRENE-D, Nerol 등 테르펜류와 phenylethyl Alcohol이다. 잎의 형태는 타원형이고 가시가 적은 편이다. 연간 절화수량은 172.2본이고, 꽃잎수는 38.4매이며 절화품질 중 절화장은 75.0cm 절화경경은 6.2mm 절화중은 10.6g 이다. 절화수명은 11일, 화폭 9.2cm, 화고 5.2cm이다. 기존에 육성된 품종에 비해 향기 성분 함량이 높고 화형과 화색이 우수 하여 품종으로서 가치가 높은 품종이다.

Chlorination and UV illumination are being widely applied to inactivate a number of pathogenic microbials in the environment. Here, we evaluated the inactivation efficiency of individual and combined treatments of chlorination and UV under various aqueous conditions. UV dosage was required higher in waste water than in phosphate buffer to achieve the similar disinfecting efficiency. Free chlorine generated by electrolysis of waste water was abundant enough to inactivate microbials. Based on these, hybrid system composed of sequential treatment of electrolysis-mediated chlorination and UV treatment was developed under waste water conditions. Compared to individual treatments, hybrid system inactivated bacteria (i.e., E. coli and S. typhimurium) and viruses (i.e., MS-2 bacteriophage, rotavirus, and norovirus) more efficiently. The hybrid system also mitigated the photo re-pair of UV-driven DNA damages of target bacteria. The combined results suggested the hybrid system would achieve high inactivation efficiency and safety on various pathogenic microbials in wastewater.