• Ocean and Polar Research
• /
• 제41권4호
• /
• pp.311-318
• /
• 2019

We analyzed the preliminary survey data (2014-2016) of macrobenthic community biomass (n = 112) from the wind farm area located in the southern part of the west coast of Korea and compared this data with data from the entire west coast (n = 369; 2006-2008). Modal classes from frequency distributions were 6 times higher in the latter (5 vs. 32 g/㎡). The mean and median values of the latter were 1.3 and 1.7 times higher (mean, 20.7 vs. 27.8 g/㎡; median, 17.1 vs. 29.5 g/㎡), and the maximum value was 3.4 times higher. Mood's median test showed significant difference at p-value = 0.01. We estimated the biomass-to-depth relationships from each data set by using Akaike Information Criterion and regarded the non-overlap of the 95% confidence intervals as indicating significant difference. The biomass was different from a 10 m depth below, and 3 times higher in the west coast at around 20 m compared with the maximum depth of the wind farm area. A local event of catastrophic sedimentation ranging from 1 to 2 m was observed in the wind farm during winter surveys. This could be a probable source of the lower biomass, but information on biomass seasonality and a natural experimental approach seem to be needed for the conduct of further studies. This study is meaningful in that it provided the background to assess future changes by understanding the lower level of benthic productivity in the area. We expect this study will contribute to the preparation of measures that can remove or mitigate the source of the lower biomass and improve the productivity of fishery resources in the area.

• 한방재활의학과학회지
• /
• 제24권4호
• /
• pp.155-162
• /
• 2014

Objectives Advanced researches on the relationship between obesity and heart rate variability (HRV), heretofore, focused on characteristics of HRV depending on the state of obesity. However, the previous researches have not quantified predictive power of HRV toward the obesity-related variables, which is rather more meaningful for clinicians who regularly treat obese patients. Hence, we designed a research to investigate whether HRV could predict serum levels of obesity-related metabolites. Methods Ninety obese premenopausal women meeting the inclusion criteria were recruited. The HRV test, blood sampling, and measurement of physical traits were conducted. Multiple regression analysis of the measurement data was carried out, putting obesity-related metabolites (insulin, glucose, triglyceride, hs-CRP, HDL, LDL, total cholesterol) as outcome variables and the others as predictors. To select appropriate predictive variables, the Akaike's Information Criterion (AIC) was applied. Normality and homoskedasticity of residuals for each model were tested to identify if there were any violations of the regression analysis's basic assumption. Logarithm transformation was used for the values of the concentration of metabolites and the HRV. Results The regression model including Total Power (TP) value and BMI had significant predictive power for serum insulin concentration (F(2, 88)=835.7, p<0.001, $R^2=0.95$). The regression coefficient of ln (TP) was -0.1002. However, it was not sure if the HRV could predict concentrations of other metabolites. Conclusions The results suggest that the Total Power (TP) value of the HRV can predict the level of serum insulin. If the BMI could be assumed as being constant, when the TP value is multiplied by n, the predicted change of insulin could be drawn by multiplying $n^{-0.1002}$. The uncertainty of this model can be assumed as approximately 5%.