The data show an association of leukocyte telomere length shortening with increased arterial stiffness and cardiovascular burden, suggesting that telomere length is a biomarker of large artery elasticity and CAD. Further studies are warranted to study the role of LTL dynamics in the pathogenesis of atherosclerosis.

Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

No abstract

This paper studies the H∞$$ {H}_{\infty } $$ control problem of discrete‐time delayed switched systems with average dwell time switching signals using event‐triggered mechanism. The event‐triggered mechanism can reduce redundant information transmission. First, employing state‐feedback controller, two sufficient conditions are proposed to guaranteeing (i) exponential stability of the disturbance‐free closed‐loop system and (ii) H∞$$ {H}_{\infty } $$ performance of the closed‐loop system with disturbance. Moreover, the controller gain is explicitly computed by solving a set of linear matrix inequalities. Second, results obtained for state‐feedback controller are extended to those for observer‐based state‐feedback controller, and both the controller and observer gains are explicitly computed. All conditions are derived by utilizing a properly constructed decay‐rate‐dependent Lyapunov functional. Finally, a numerical example illustrates the validity of the obtained theoretical results.