Manipulation of Glutamatergic Neuronal Activity in the Primary Motor Cortex Regulates Cardiac Function in Normal and Myocardial Infarction Mice.

Cardiac function is under neural regulation; however, brain regions in the cerebral cortex responsible for regulating cardiac function remain elusive. In this study, retrograde trans-synaptic viral tracing is used from the heart to identify a specific population of the excitatory neurons in the primary motor cortex (M1) that influences cardiac function in mice. Optogenetic activation of M1 glutamatergic neurons increases heart rate, ejection fraction, and blood pressure. By contrast, inhibition of M1 glutamatergic neurons decreased cardiac function and blood pressure as well as tyrosine hydroxylase (TH) expression in the heart. Using viral tracing and optogenetics, the median raphe nucleus (MnR) is identified as one of the key relay brain regions in the circuit from M1 that affect cardiac function. Then, a mouse model of cardiac injury is established caused by myocardial infarction (MI), in which optogenetic activation of M1 glutamatergic neurons impaired cardiac function in MI mice. Moreover, ablation of M1 neurons decreased the levels of norepinephrine and cardiac TH expression, and enhanced cardiac function in MI mice. These findings establish that the M1 neurons involved in the regulation of cardiac function and blood pressure. They also help the understanding of the neural mechanisms underlying cardiovascular regulation.

A content-aware corpus-based model for analysis of marine accidents.

In the past decades, marine accidents brought the serious loss of life and property and environmental contamination. With the accumulation of marine accident data, especially accident investigation reports, compared with subjective reasoning based on expert experience, data-driven methods for analysis and accident prevention are more comprehensive and objective. This paper aims to develop a content-aware corpus-based model for the analysis of marine accidents to mine the accident semantic features. The general research framework is established to combine accident data, expert prior knowledge, and semi-automated natural language processing (NLP) technology. The NLP models are optimized, fused, and applied to the case study of ship collision accidents. The results show that the proposed model can accurately and quickly extract hazards, accident causes, and scenarios from the accident reports, and perform semantic analysis for the latent relationships between them to extend the accident causation theory. This study can provide a powerful and innovative analysis tool for marine accidents for maritime traffic safety management departments and relevant research institutions.