Ten novel microsatellite loci were isolated from blood clam Scapharca broughtonii, and the polymorphisms were examined to estimate genetic variability. The genetic variabilities varied depending on the locus. The number of alleles ranged from 11 to 23, and the observed and expected heterozygosities ranged from 0.63 to 0.93 and 0.66 to 0.95, respectively. Four loci showed significant Hardy–Weinberg disequilibrium at P < 0.05 level. The high variabilities revealed in this study suggest that microsatellites should prove useful for various genetic investigations.

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As greenhouse gases and environmental pollution become serious, the demand for alternative energy such as bioethanol has rapidly increased, and a large supply of biomass is required for bioenergy production. Lignocellulosic biomass is the most abundant on the planet and a large part of it, the second-generation biomass, has the advantage of not being a food resource. In this study, Sicyos angulatus, known as an invasive plant (harmful) species, was used as a raw material for bioethanol production. In order to improve enzymatic hydrolysis, S. angulatus was pretreated with different NaOH concentration at 121 °C for 10 min. The optimal NaOH concentration for the pretreatment was determined to be 2% (w/w), and the glucan content (GC) and enzymatic digestibility (ED) were 46.7% and 55.3%, respectively. Through NaOH pretreatment, the GC and ED of S. angulatus were improved by 2.4-fold and 2.5-fold, respectively, compared to the control (untreated S. angulatus). The hydrolysates from S. angulatus were applied to a medium for bioethanol fermentation of Saccharomyces cerevisiae K35. Finally, the maximum ethanol production was found to be 41.3 g based on 1000 g S. angulatus, which was 2.4-fold improved than the control group.

A wide range of environmental factors heavily impact aquatic ecosystems, in turn, affecting human health. Toxic organic compounds resulting from anthropogenic activity are a source of pollution in aquatic ecosystems. To evaluate these contaminants, current approaches mainly rely on acute and chronic toxicity tests, but cannot provide explicit insights into the causes of toxicity. As an alternative, genome-wide gene expression systems allow the identification of contaminants causing toxicity by monitoring the organisms’ response to toxic substances. In this study, we selected 22 toxic organic compounds, classified as pesticides, herbicides, or industrial chemicals, that induce environmental problems in aquatic ecosystems and affect human-health. To identify toxic organic compounds using gene expression data from Daphnia magna, we evaluated the performance of three machine learning based feature-ranking algorithms (Learning Vector Quantization, Random Forest, and Support Vector Machines with a Linear kernel), and nine classifiers (Linear Discriminant Analysis, Classification And Regression Trees, K-nearest neighbors, Support Vector Machines with a Linear kernel, Random Forest, Boosted C5.0, Gradient Boosting Machine, eXtreme Gradient Boosting with tree, and eXtreme Gradient Boosting with DART booster). Our analysis revealed that a combination of feature selection based on feature-ranking and a random forest classification algorithm had the best model performance, with an accuracy of 95.7%. This is a preliminary study to establish a model for the monitoring of aquatic toxic substances by machine learning. This model could be an effective tool to manage contaminants and toxic organic compounds in aquatic systems.

Freshwater ecosystems contain a large diversity of microeukaryotes that play important roles in maintaining their structure. Microeukaryote communities vary in composition and abundance on the basis of temporal and environmental variables and may serve as useful bioindicators of environmental changes. In the present study, 18S rRNA metabarcoding was employed to investigate the seasonal diversity of microeukaryote communities during four seasons in the Han River, Korea. In total, 882 unique operational taxonomic units (OTUs) were detected, including various diatoms, metazoans (e.g., arthropods and rotifers), chlorophytes, and fungi. Although alpha diversity revealed insignificant differences based on seasons, beta diversity exhibited a prominent variation in the community composition as per seasons. The analysis revealed that the diversity of microeukaryotes was primarily driven by seasonal changes in the prevailing conditions of environmental water temperature and dissolved oxygen. Moreover, potential indicator OTUs belonging to diatoms and chlorophytes were associated with seasonal and environmental factors. This analysis was a preliminary study that established a continuous monitoring system using metabarcoding. This approach could be an effective tool to manage the Han River along with other freshwater ecosystems in Korea.