Identification of Albula sp. (Albulidae: Albuliformes) Leptocephalus Collected from the Southern Coastal Waters of Korea using Cytochrome b DNA Sequences

A single specimen ofAlbula leptocephalus (55.7 mm SL) was collected from the southern coastal waters of Korea using an aquatic lamp. It is characterized by having a ribbonlike body with a small head and a well-forked caudal fin. Although the general appearance was similar to the leptocephalus ofA. vulpes including myomere counts and fin ray counts, the melanophore deposition was different from that ofA. vulpes. This leptocephalus specimen was confirmed withA. forsteri using the cytochrome b mtDNA (Cytb) analysis. The genetic distance ofCytb between the present leptocephalus andA. forsteri is 0.006-0.038, which falls into the cutoff point separatingAlbula species into eight deep lineages including the four valid species. Its genetic characteristic have more similarities to those of Fiji than those of Hawaii and the Northern territory of Australia.     

Chelonodontops bengalensis (Tetraodontiformes: Tetraodontidae): A New Species of Puffer Fish from the Northern Bay of Bengal Based on Morphology and DNA Barcode

Ocean Science Journal - The new puffer fish species Chelonodontops bengalensis (Pisces: Tetraodontidae) is described from two specimens collected on the southwest coast of the Bay of Bengal,...     

A subagging regression method for estimating the qualitative and quantitative state of groundwater

A subsample aggregating (subagging) regression (SBR) method for the analysis of groundwater data pertaining to trend-estimation-associated uncertainty is proposed. The SBR method is validated against synthetic data competitively with other conventional robust and non-robust methods. From the results, it is verified that the estimation accuracies of the SBR method are consistent and superior to those of other methods, and the uncertainties are reasonably estimated; the others have no uncertainty analysis option. To validate further, actual groundwater data are employed and analyzed comparatively with Gaussian process regression (GPR). For all cases, the trend and the associated uncertainties are reasonably estimated by both SBR and GPR regardless of Gaussian or non-Gaussian skewed data. However, it is expected that GPR has a limitation in applications to severely corrupted data by outliers owing to its non-robustness. From the implementations, it is determined that the SBR method has the potential to be further developed as an effective tool of anomaly detection or outlier identification in groundwater state data such as the groundwater level and contaminant concentration.