The Bayesian algorithm model is a model algorithm that calculates probabilities based on input data and is mainly used for complex disasters, water quality management, the ecological structure between living things or living-non-living factors. In this study, we analyzed the main factors affected Korean Estuary Trophic Diatom Index (KETDI) change based on the Bayesian network analysis using the diatom community and physicochemical factors in the domestic estuarine aquatic ecosystem. For Bayesian analysis, estuarine diatom habitat data and estuarine aquatic diatom health (2008~2019) data were used. Data were classified into habitat, physical, chemical, and biological factors. Each data was input to the Bayesian network model (GeNIE model) and performed estuary aquatic network analysis along with the nationwide and each coast. From 2008 to 2019, a total of 625 taxa of diatoms were identified, consisting of 2 orders, 5 suborders, 18 families, 141 genera, 595 species, 29 varieties, and 1 species. Nitzschia inconspicua had the highest cumulative cell density, followed by Nitzschia palea, Pseudostaurosira elliptica and Achnanthidium minutissimum. As a result of analyzing the ecological network of diatom health assessment in the estuary ecosystem using the Bayesian network model, the biological factor was the most sensitive factor influencing the health assessment score was. In contrast, the habitat and physicochemical factors had relatively low sensitivity. The most sensitive taxa of diatoms to the assessment of estuarine aquatic health were Nitzschia inconspicua, N. fonticola, Achnanthes convergens, and Pseudostaurosira elliptica. In addition, the ratio of industrial area and cattle shed near the habitat was sensitively linked to the health assessment. The major taxa sensitive to diatom health evaluation differed according to coast. Bayesian network analysis was useful to identify major variables including diatom taxa affecting aquatic health even in complex ecological structures such as estuary ecosystems. In addition, it is possible to identify the restoration target accurately when restoring the consequently damaged estuary aquatic ecosystem.

In the present study, we investigated the protective effect of various grain methanolic extracts against UVB-induced photo-aging in human skin fibroblasts. Various grain methanolic extracts were evaluated for their antioxidant compounds and activities. 2,2-Ddiphenyl-1-picryhydrazyl radical (DPPH) and ABTS 2,2-azino-bris-(3-ethylbenzoth iazoline-6-sulphonic acid) radical cation scavenging activities have been used to measure the relative antioxidant activities of extracts from grains. The content of total polyphenolics in the extracts were evaluated using spectrophotometric methods. Human skin fibroblast (Hs68) cells were pretreated with various grain methanolic extracts (25 μg/mL). Skin toxicity was simulated by exposing the cells to UVB (30 mJ/cm2) irradiation. In response to the UVB-irradiation, an increased amount of matrix metalloproteinases (MMPs) release was observed, whereas pretreatment of various grain methanolic extracts significantly inhibited the production of MMP-1 in Hs68 cells. We also found that pretreatment of the extracts significantly decreased UVB-induced reactive oxygen species and significantly increased total collagen content in Hs68 cells. These results provide that grains could be regarded as a potential ingredient in natural cosmetics, used for UVB protection.