Chironomid communities are indicators of water pollution because of their ability to thrive under freshwater conditions. However, it is difficult to distinguish between chironomid larvae based on morphology. DNA barcoding, based on nucleotide sequences of marker genes, can be used to identify chironomid larvae. Samples of chironomid larvae were collected from Gwangju Stream and Pungyeongjeong Stream, tributaries of the Yeongsan River in South Korea. We identified 3 subfamilies, 13 genera, 16 species, and 1 cryptic species. There were 7 genera and 10 species from the subfamily Chironominae, 5 genera and 5 species from subfamily Orthocladiinae, 1 genus and 1 species from subfamily Tanipodinae, and the cryptic chironomid species of the family Chironomidae. There were 21 individuals from, 7 species and 1 cryptic species from the Gwangju Stream and 24 individuals, belonging to 10 species from the Pungyeongjeong Stream. The only species detected in both streams was Cricotopus bicinctus. The relationship between water quality and the species detected was difficult to explain, but the number of species showed a tendency to increase at sites where water quality was poor. Additional investigations and studies are needed to understand the relationship between water quality and the chironomid species occurring in these two streams.

Response surface methodology (RSM) is frequently used for optimization studies. In the present work, RSM was used to determine the antimicrobial activitiesof grapefruit seed extract (GFSE) and a lactic acid mixture (LA) against Staphylococcus aureus, Bacillus cereus, Escherichia coli, Salmonella typhimurium, Pseudomonas fluorescens, and Vibrio parahaemolyticus. A central composite design was used to investigate the effects of independent variables on dependent parameters. One set of antimicrobial preparations included mixtures of 1% (w/w) GFSE and 10% (w/w) LA, in which the relative proportions of component antimicrobials varied between 0 and 100%. In further experiments, the relative proportions were between 20% and 100%. Antimicrobial effects against various microorganisms were mathematically encoded for analysis. The codes are given in parentheses after the bacterial names, and were S. aureus (), B. cereus (), E. coli (), S. typhimurium (), P. fluorescens (), and V. parahaemolyticus (). The optimum antimicrobial activity of the 1% (w/w) GFSE:10% (w/w) LA mixture against each microorganism was obtained by superimposing contour plots ofantimicrobial activities on measures of response obtained under various conditions. The optimum rangesfor maximum antimicrobial activity of a mixture with a ratio of 1:10 (by weight) GFSE and LA were 35.73:64.27 and 56.58:43.42 (v/v), and the optimum mixture ratio was 51.70-100%. Under the tested conditions (a ratio of 1% [w/w] GFSE to 10% [w/w] LA of 40:60, and a concentration of 1% [w/w] GFSE and 10% [w/w] LA, 70% of the highest value tested), and within optimum antimicrobial activity ranges, the antimicrobial activities of the 1% (w/w) GFSE:10% (w/w) LA mixture against S. aureus (), B. cereus (), E. coli (), S. typhimurium (), P. fluorescens (), and V. parahaemolyticus () were 24.55, 25.22, 20.20, 22.49, 23.89, and 28.04 mm, respectively. The predicted values at optimum conditions were similar to experimental values.