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Traditional Chinese medicine has been used to treat disease in China for more than five thousand years. Over the last few decades it has been used increasingly in other countries as well. As its use has spread, interest in the adverse effects of traditional Chinese medicine, including epilepsy and epileptic seizures, has grown. To date, four types of traditional Chinese medicine have been found to induce epileptic seizures. In this review we will summarize the current knowledge about possible epileptogenic mechanisms of nux vomica, illicium henryi, betelnut and mulberry drawing on botanical, phytochemical, toxicological and animal studies.

Tetramine, a banned rodenticide, is repeatedly reported to induce epileptic seizures in healthy people. Because both doctors and patients are often not aware of earlier tetramine contact, the occurrence of seizures is easily misdiagnosed as primary epilepsy. In this study, 8 cases in 2 families with generalized tonic-clonic seizures, most probably induced by tetramine, are presented. The clinical manifestation, electroencephalogram characteristics, and treatment measures of tetramine poisoning are summarized. There is the possibility of tetramine toxicity as a cause of epidemic epileptic seizures.

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In this paper, a new decadal resolution stalagmite δ18O record covering 10.4–6.5 ka BP from Kulishu cave in Beijing, north China is presented in combination with the published stalagmite δ18O record covering 10.4–14.0 ka BP in the same cave. Five significant monsoon collapses were identified around 11.5, 11.0, 10.0, 9.4, and 8.2 ka BP as well as three smaller ones around 10.3, 9.0, and 8.6 ka BP. The weak monsoon episodes around 8.6 and 8.2 ka BP form the two-step structure of the 8.2 ka event. All monsoon collapses, coeval with the cooling in northern high-latitude records, are correlated with Lakes Agassiz-Ojibway outbursts. Thus, our data support the idea of freshwater forcing of abrupt climate anomalies during the early Holocene. Nevertheless, the decreased irradiance together with freshwater outburst may account for the 9.2/9.3 ka event, which is expressed more significantly in low-latitude records.

Helicopters are complex high-order and time-varying nonlinear systems, strongly coupling with aerodynamic forces, engine dynamics, and other phenomena. Therefore, it is a great challenge to investigate system identification for dynamic modeling and adaptive control for helicopters. In this paper, we address the system identification problem as dynamic regression and propose to represent the uncertainties and the hidden states in the system dynamic model with a deep convolutional neural network. Particularly, the parameters of the network are directly learned from the real flight data of aerobatic helicopter. Since the deep convolutional model has a good performance for describing the dynamic behavior of the hidden states and uncertainties in the flight process, the proposed identifier manifests strong robustness and high accuracy, even for untrained aerobatic maneuvers. The effectiveness of the proposed method is verified by various experiments with the real-world flight data from the Stanford Autonomous Helicopter Project. Consequently, an adaptive flight control scheme including a deep convolutional identifier and a backstepping-based controller is presented. The stability of the flight control scheme is rigorously proved by the Lyapunov theory. It reveals that the tracking errors for both the position and attitude of unmanned helicopter asymptotic converge to a small neighborhood of the origin.

No abstract

Connectivity between fish stocks is fundamental to the understanding of population dynamics and the implementation of sustainable fisheries management. Otolith microchemistry is a promising tool as it can provide information on the continuous growth of otoliths and the environmental effects on otolith composition. Such elemental fingerprints can help distinguish different stocks or life history stages, identify the origins or nursery areas of fish, and assess population structure. In this study, we examined the stock discrimination and spatial connectivity of cage-cultured and wild stocks of yellowfin seabream (Acanthopagrus latus) from the coastal waters of Shantou, Yangjiang, and Zhanjiang in China southern province Guangdong during 2012-2014, based on otolith trace-elemental signatures using multivariate statistical analysis and machine learning approaches. The concentrations of 13 elements (Li, Na,Mg, Ca,Mn, Fe,Co, Ni,Cu, Zn,Sr, Sb, andBa) in the natal spot of fish otoliths, representing the embryonic and paralarval stages of fish, were analyzed using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Stepwise discriminant analysis and random forests were used to distinguish the cultured and wild stocks of yellowfin seabream, and non-metric multidimensional scaling (NMDS) and cluster analysis were used to determine the spatial variation and connectivity of yellowfin seabream stocks. Overall, the cultured and wild stocks of yellowfin seabream could be identified with classification accuracy of 80.7% and 99.2% by using stepwise discriminant analysis and random forests respectively. When we compared site difference between cultured and wild stocks (site × stock interactions), the classification success was 60.4% for stepwise discriminant analysis and 85.7% for random forests. The misclassification of cultured and wild stocks within the three sites suggested the spatial connectivity between stocks and among sampling locations. Our findings suggested that the three wild stocks of yellowfin seabream from Guangdong coastal waters could be considered as one unit for management, and the difference between cultured and wild stocks was significant for yellowfin seabream from Shantou and Yangjiang, but less significant for yellowfin seabream from Zhanjiang. This study demonstrated that otolith elemental fingerprints can help improve our knowledge on the spatial connectivity, population structure, and life history of fish stocks, and random forests can be a useful tool for identifying cultured and wild stocks compared to the traditional stepwise discriminant analysis.