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The purpose is to apply the multifractal analysis method to the research of human physiology in the process of sports. The mass index spectrum is used for multifractal analysis of heart rate variability signal based on the heart rate variability signal analysis theory and fractal theory. Finally, 10 healthy college students are selected as the experimental subjects. RAC-3003 portable electronic measuring instrument is used to collect heart rate signals in different exercise stages. Finally, the data are analyzed by [Formula: see text] and Lo-[Formula: see text] analysis methods. The results show that the lowest value of ln[Formula: see text] is 3.1 and the highest value is 7.0 in different stages in the morning, and the lowest value is 3.3 and the highest value is 7.5 in different stages in the afternoon. The average value of random signal ln[Formula: see text] gradually increases from 2.6 to 3.7; whether in the morning or in the afternoon, the average Hurst exponent during exercise is lower than that before and after exercise, and the average Hurst exponent after exercise is slightly higher than that before exercise; the long-range correlation index of heart rate variability signal in each exercise stage first increases and then decreases, and the changes of short-range correlation index and long-range correlation index are opposite; the average of fitting intercept of [Formula: see text] curve is lower than that of Lo-[Formula: see text] curve in the first and third stages; the fractal coefficient of the original data in the first and third stages of exercise is significantly higher than that in the second stage, which indicates that the overall fractal degree of heart rate variability signal before and after exercise is higher.

To address the problems of scientific theory of brain and mind, common technology and engineering application of nature-inspired intelligence, this paper is focused on research in the semantic-oriented computing framework design for multimedia and multimodal information. The multimedia neural cognitive computing model of brain-inspired computing was designed based on the brain mechanism of nervous system and mind architecture of cognitive system. Furthermore, the semantic-oriented hierarchical cross-modal cognitive neural computing framework of brain-like computing was proposed based on multimedia neural cognitive computing model. Furthermore, the formal description and analysis of cross-modal cognitive neural computing framework was given. It would effectively improve the performance of semantic processing of multimedia and cross-modal information such as target detection, classification and recognition in high-resolution remote sensing image, and has far-reaching significance for exploration and realization brain and mind inspired computing.

Background Autosomal dominant lateral temporal epilepsy (ADLTE) is an inherited syndrome caused by mutations in the leucine-rich glioma inactivated 1 (LGI1) gene. It is known that functional LGI1 is secreted by excitatory neurons, GABAergic interneurons, and astrocytes, and regulates AMPA-type glutamate receptor-mediated synaptic transmission by binding ADAM22 and ADAM23. However, > 40 LGI1 mutations have been reported in familial ADLTE patients, more than half of which are secretion-defective. How these secretion-defective LGI1 mutations lead to epilepsy is unknown. Results We identified a novel secretion-defective LGI1 mutation from a Chinese ADLTE family, LGI1-W183R. We specifically expressed mutant LGI1W183R in excitatory neurons lacking natural LGI1, and found that this mutation downregulated Kv1.1 activity, led to neuronal hyperexcitability and irregular spiking, and increased epilepsy susceptibility in mice. Further analysis revealed that restoring Kv1.1 in excitatory neurons rescued the defect of spiking capacity, improved epilepsy susceptibility, and prolonged the life-span of mice. Conclusions These results describe a role of secretion-defective LGI1 in maintaining neuronal excitability and reveal a new mechanism in the pathology of LGI1 mutation-related epilepsy.