Morphology of placentome in Korean water deer Hydropotes inermis argropus.

The placenta of the Korean water deer was anatomically examined to accumulate basic information regarding its reproductive system. The convex placentomes with five to nine well-developed pedicles were observed in the whole uterine horns, and therefore, the placenta was classified as oligocotyledonary. The evidence indicating the migration of binucleate cells (BNCs) from trophectoderm to the uterine epithelium led to the histological classification of the placenta as synepitheliochorial. The number of fetuses was markedly higher than that in other ruminant species. However, the number of placentomes was found to be similar to the other Cervidae species. Therefore, these results suggest that the Korean water deer may possess special mechanisms or structures at the fetus attachment site to maintain this unusally high number of fetuses.

Predicting occurrence of errors during a Go/No-Go task from EEG signals using Support Vector Machine.

Human error often becomes a serious problem in dairy life. Recent studies have shown that failures of attention and motor errors can be captured before they actually occur in the alpha, theta, and beta-band powers of electroencephalograms (EEGs), suggesting the possibility that errors in motor responses can be predicted. The goal of this study was to use single-trial offline classification to examine how accurately EEG signals recorded before motor responses can predict subsequent errors. Ten subjects performed a Go/No-Go task, and the accuracy of error classification by a Support Vector Machine (SVM) was investigated 1000 ms before presenting the Go/No-Go cue. The resulting mean classification accuracy was 62%, and strong increases and decreases in activities associated with errors were observed in occipital and frontal alpha-band powers. This result suggests the possibility that future errors can be predicted using EEG.