Phylogeography of the seaweed Ishige okamurae (Phaeophyceae): evidence for glacial refugia in the northwest Pacific region

Although benthic marine algae are essential components of marine coastal systems that have been influenced profoundly by past and present climate change, our knowledge of seaweed phylogeography is limited. The brown alga Ishige okamurae Yendo occurs in the northwest Pacific, where it occupies a characteristic belt in the exposed intertidal zone. To understand the patterns of genetic diversity and the evolutionary history of this species, we analyzed mitochondrial cox3 from 14 populations (221 individuals) throughout its range. The 17 haplotypes found in this study formed five distinct clades, indicating significant genetic structure. The high differentiation and number of unique (private) haplotypes may result from the recolonization of the species from glacial refugia. Three putative refugia, each with high genetic diversity, were identified: southern Korea (including Jeju Island), northern Taiwan, and central Pacific Japan. Recolonization of I. okamurae was probably determined by ocean currents and changes in sea level during the last glacial period.     

Distribution and hydrodynamic model of the Keumdong oil spill in Kwangyang Bay, Korea

Oil concentrations in the seawater, seabed sediments, and shoreline sediments were measured from the samples collected 165 days after the Keumdong oil spill accident, which had occurred in Kwangyang Bay of the South Sea, Korea in September 1993. Distribution patterns of the concentrations in the seabed and shoreline sediments have allowed us to hindcast the transport and fate of the spilled oil. Overall, the patterns agree better with the southward (seaward) surface water circulation than with the northward (landward) bottom water circulation over the region. Rapid, initial dispersal of the spilled oil to the south and its subsequent grounding onto the intertidal seabed sediments, as well as onto the shorelines, appear to have made ineffective the subsurface oil transport by the bottom current.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Accelerating preliminary eco-innovation design for products that integrates case-based reasoning and TRIZ method

While the innovativeness and market potential of a new product is normally emphasized, its environmental impact is often neglected. Despite their ability to help designers reduce the environmental impact of products throughout their life cycle, many eco-design methods focus on the redesign of existing products, necessitating a more innovative design capacity. This work describes a novel model to accelerate the preliminary eco-innovation product design by integrating the advantages of case-based reasoning and the TRIZ method. Previous cases from a database support a novel design to satisfy functional performances under the CBR framework. The innovative principles and evolution patterns of the TRIZ method can enhance the design level of new products to achieve eco-innovation. Four eco-innovation concepts for a PC mouse demonstrate the effectiveness of the proposed model.     

Compositional data analysis of smoke emissions from debris piles with low-density polyethylene

ABSTRACT

Data describing the composition of smoke are inherently multivariate and always non-negative parts of a whole. The data are relative and the information is contained in the ratios between parts of the composition. A prior analysis of smoke emissions produced from the burning of manzanita wood mixed with low-density polyethylene plastic applied traditional statistical methods to the compositional data and found no effect. The current paper applies compositional data techniques to these smoke emissions to determine if the prior analysis was accurate. Analysis of variance of the isometric log-ratios showed that LDPE significantly affected the CO₂ emission ratio for 8 of the 191 trace gases; this analysis showed none of the gases identified in the previous analysis were affected by LDPE. LDPE did not affect the CO₂ emission ratios for the alkanes, alkenes, alkynes, aldehydes, cycloalkanes, cycloalkenes, diolefins, ketones, MAHs, and PAHs. Compositional data analysis should be used to analyze smoke emissions data. Burning contaminant-free LDPE should produce emissions like wood.     

First-trimester screening for Down syndrome; the role of nasal bone assessment in the Korean population

Objectives The aim of this study was to evaluate the role of nasal bone assessment in first-trimester screening for Down syndrome (DS) in the Korean population. Methods From July 2004 to March 2006, we prospectively evaluated the fetal nasal bones at 11–14 weeks' gestation in the Korean population. Results A successful evaluation was possible in 6490 of 6787 fetuses (95.6%). Absent, hypoechoic, and short nasal bones were seen in 4 (26.7%), 4 (26.7%), and 1 (6.7%) of 15 fetuses with DS, respectively, whereas in 5 (0.1%), 11 (0.2%), and 246 (3.8%) of 6456 normal fetuses. The incidence of absent and hypoechoic nasal bone showed significant differences between normal fetuses and fetuses with DS (P < 0.0005, both). Screening for DS using an absent or hypoechoic nasal bone resulted in a sensitivity of 53.3%, a specificity of 99.8%, a positive likelihood ratio of 215.2, and a negative likelihood ratio of 0.5. Conclusion Our study showed that nasal bone abnormality at 11–14 weeks of gestation had a high association with DS in the Korean population. This suggests that nasal bone assessment can be used to supplement the current first-trimester screening for DS in the Korean population. Copyright © 2007 John Wiley & Sons, Ltd.     

Changes in microcystin content and environmental parameters over the course of a toxic cyanobacteria bloom in a hypertrophic regulated river, South Korea

Cyanobacterial blooms and associated microcystins in hypertrophic stagnant West-Nakdong River were investigated at weekly intervals from April to August, 1999. Microcystis spp. accounted for over 85% of the numeric abundance of total phytoplankton. Microcystins were present in the blooms sampled between May and August. The peak of total microcystin (microcystin-LR + -RR) levels (maximum level; 612 microg g(-1)) was detected in the initial stage of the bloom (mid May), at the same time as high N/P ratio and high Microcystis biomass. This study indicated that total microcystins of this regulated river was positively correlated to Microcystis biomass, absolute nutrient concentration and TN/TP ratio.     

Relevance of element content of bark for the distribution of epiphytic lichens in a montane spruce forest affected by forest dieback

Element content in the bark on Norway spruce (Picea abies) was measured in a montane forest heavily affected by forest dieback and compared to that in a nearby intact stand. Bark contained less S, K, Fe, Mn, Pb, Cu, and H+ and more N, Ca, Mg, and Zn in the dieback-affected stand than in the intact one. Diversity of epiphytic lichen vegetation was higher in the dieback-affected stand than in the intact one. Cover of the foliose lichen Hypogymnia physodes was negatively correlated with Mn and Cu content of bark. Cover of the extremely acidophytic species Lecanora conizaeoides decreased with increasing Mg and increased with increasing Cu content of bark. The measurements support the hypothesis that chemical site factors are decisive for the high lichen diversity in dieback-affected montane spruce forests.