Geographical origin discriminant of Panax japonicus based on stable isotope ratios analysis / 中草药

Objective: The isotopic ratios of light elements (C/N/H/O) in Panax japonicus from six producing areas were determined with isotope ratio mass spectrometry.Methods: Three methods, including linear discrimination (LD), gaussian kernel support vector machine (SVM), and the back-propagation learning algorithm of pattern recognition based on neural network toolbox (BPN) were employed to establish a model for P. japonicus geographical origin discrimination. Results: The results showed that stable isotope carbon (δ 13C) had obvious regional characteristics, which will be used to effectively distinguish the origin of P. japonicus. The methods of LD and BPN could classify the geographical origin of P. japonicus from six producing areas, both of which showed that the accuracy rates were 100% using training dataset. Conclusion: Therefore, the stable isotope technique combined with LD and BPN method can effectively trace the origin of P. japonicus.

Research on the separability of steady-state visual evoked potential features modulated by different visual attentional states / 生物医学工程学杂志

Attention can concentrate our mental resources on processing certain interesting objects, which is an important mental behavior and cognitive process. Recognizing attentional states have great significance in improving human's performance and reducing errors. However, it still lacks a direct and standardized way to monitor a person's attentional states. Based on the fact that visual attention can modulate the steady-state visual evoked potential (SSVEP), we designed a go/no-go experimental paradigm with 10 Hz steady state visual stimulation in background to investigate the separability of SSVEP features modulated by different visual attentional states. The experiment recorded the EEG signals of 15 postgraduate volunteers under high and low visual attentional states. High and low visual attentional states are determined by behavioral responses. We analyzed the differences of SSVEP signals between the high and low attentional levels, and applied classification algorithms to recognize such differences. Results showed that the discriminant canonical pattern matching (DCPM) algorithm performed better compared with the linear discrimination analysis (LDA) algorithm and the canonical correlation analysis (CCA) algorithm, which achieved up to 76% in accuracy. Our results show that the SSVEP features modulated by different visual attentional states are separable, which provides a new way to monitor visual attentional states.