Cyanobium is a genus of picoprokaryotic cyanophytes, which includes species worldwide. The present study investigated the morphology, ultrastructure, and molecular phylogeny of the unrecorded genus Cyanobium Rippka & Cohen-Bazire 1983 and species Cyanobium gracile Rippka & Cohen-Bazire 1983. A C. gracile culture from a freshwater sample collected from the Adongji pond was established by singlecell isolation. Morphological data were analyzed using light and transmission electron microscopy. C. gracile lives as solitary cells without gelatinous envelopes and is ovate, oval, or shortly rod-shaped. Thylakoids are laid along the cell walls, with three thylakoid membranes parallel to each other. Nucleoplasm was observed in the center of the cell. Molecular phylogeny performed with data from 16S small subunit ribosomal DNA gene (SSU rDNA) sequences showed that the three strains of C. gracile, including the type strain (PCC6307) and a newly recorded strain (Adong101619), formed a distinct clade with a high supporting value (maximum-likelihood=100, pp=1.00). Based on morphology and molecular data, we report the newly recorded C. gracile in Korea.

Feral cats are widely considered to be leading the potential impacts on public health. This study aimed to provide estimates of vital data for feral cats relating Trap-Neuter-Return (TNR) to establish strategies effectively to manage feral cats in Pyeongtaek. Thus, this study focused on estimating feral cat population in Pyeongtaek and conducted a comparative analysis of the data for feral cats in Seoul (2013). The number of feral cats was estimated from 23,069 to 26,655 in Pyeongtaek, 2019. In relation to human population, when comparing the number of feral cats of Pyeongtaek and Seoul, it ranged from 4.57% to 5.28%, and from 1.97% to 2.55% respectively. This showed that Pyeongtaek was higher than Seoul. Fewer kittens were found in high-density areas, which the TNR project is believed to be generally effective in controlling the number of feral cats. In conclusion, in urban and rural complexes such as Pyeongtaek City, the number of feral cats compared to the population was higher than that of Seoul City, and the TNR program is believed to be somewhat effective in controlling the number of feral cats. When implementing TNR, it is necessary periodically to investigate the population and reflect them in policymaking.

Canine mammary tumors account for ~30% of all tumors in the female dogs and approximately 50% of the tumors are malignant. Exosomes have been the focus of great interest, as they appear to be involved in numerous important cellular processes. In this study, we examined the anti-tumor effects of canine mesenchymal stem cells-derived exosomes (MSC-exosomes) in an experimental murine mammary tumor model using canine mammary carcinoma cells, REM134. The MSC-exosomes were injected tumor site and tail vein of REM134 xenografted mice. We found that tumor size of the MSC-exosomes-treated group decreased compared to those of the only tumor group in REM134-driven tumorigenic mouse model. In addition, the MSC-exosomes-treated tumor group showed meaningfully reduced expression levels of the MMP-3, IL-1β, IL-6, and TNF-α compared to those in the tumor group. Specifically, we confirmed that the expression level of the CD133, potent cancer stem cell (CSC) markers, decreased in the MSC-exosomes-treated tumor group compared to the tumor group. This study suggests that the MSC-exosomes exhibited anti-tumor effects through downregulating CSC-related markers in the canine mammary tumor murine model. Further study is needed in the future, and we are conducting research on the detailed anti-tumor mechanism of the MSC-exosomes.

Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.