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BACKGROUND This study aims to explore the association between a healthy lifestyle and abnormal ambulatory blood pressure (ABP) in Chinese youths. METHODS A school-based sample of 1,296 college students was investigated. A lifestyle score was calculated by synthesizing 5 lifestyle factors, including smoking, alcohol consumption, diet, physical activity, and sleeping. The total score ranged from 0 to 5, with a higher score indicating a healthier lifestyle. This score was then divided into 3 categories representing low adherence to a healthy lifestyle (0–2), medium adherence (3), and high adherence (4–5). Abnormal 24-hour blood pressure (BP) was defined as systolic BP (SBP) ≥ 130 mm Hg and/or diastolic BP (DBP) ≥ 80 mm Hg. Abnormal daytime BP was determined as daytime SBP ≥ 135 mm Hg and/or DBP ≥ 85 mm Hg, while abnormal nighttime BP was characterized as nighttime SBP ≥ 120 mm Hg and/or DBP ≥ 70 mm Hg. We assessed the associations using the binomial regression model. RESULTS Mean age was 18.81 years, and 74.5% were women. The prevalence of abnormal 24-hour BP, daytime BP, and nighttime BP are 4.2%, 3.7%, and 9.0%, respectively. We found that participants with a high level of adherence to a healthy lifestyle had a significantly lower prevalence of abnormal 24-hour BP [prevalence ratios (PR) = 0.15, 95% CI: 0.05, 0.48] and abnormal daytime BP (PR = 0.16, 95%CI: 0.05, 0.52), when compared to those with a low level of adherence and after adjusting for the potential covariates. CONCLUSIONS A healthier lifestyle is associated with a better ambulatory BP profile among youths. [ABSTRACT FROM AUTHOR]

The synapse is one of the fundamental elements in human brain performing functions such as learning, memorizing, and visual processing. The implementation of synaptic devices to realize neuromorphic computing and sensing tasks is a key step to artificial intelligence, which, however, has been bottlenecked by the complex circuitry and device integration. We report a high-performance charge-trapping memory synaptic device based on two-dimensional (2D) MoS2 and high-k Ta2O5–TiO2 (TTO) composite to build efficient and reliable neuromorphic system, which can be modulated by both electrical and optical stimuli. Significant and essential synaptic behaviors including short-term plasticity, long-term potentiation, and long-term depression have been emulated. Such excellent synaptic behaviors originated from the good nonvolatile memory performance due to the high density of defect states in the engineered TTO composite. The 2D synaptic device also exhibits effective switching by incident light tuning, which further enables pattern recognition with accuracy rate reaching 100%. Such experimental demonstration paves a robust way toward a multitask neuromorphic system and opens up potential applications in future artificial intelligence and sensing technology.

The synapse is one of the fundamental elements in human brain performing functions such as learning, memorizing, and visual processing. The implementation of synaptic devices to realize neuromorphic computing and sensing tasks is a key step to artificial intelligence, which, however, has been bottlenecked by the complex circuitry and device integration. We report a high-performance charge-trapping memory synaptic device based on two-dimensional (2D) MoS2 and high-k Ta2O5–TiO2 (TTO) composite to build efficient and reliable neuromorphic system, which can be modulated by both electrical and optical stimuli. Significant and essential synaptic behaviors including short-term plasticity, long-term potentiation, and long-term depression have been emulated. Such excellent synaptic behaviors originated from the good nonvolatile memory performance due to the high density of defect states in the engineered TTO composite. The 2D synaptic device also exhibits effective switching by incident light tuning, which further enables pattern recognition with accuracy rate reaching 100%. Such experimental demonstration paves a robust way toward a multitask neuromorphic system and opens up potential applications in future artificial intelligence and sensing technology. [ABSTRACT FROM AUTHOR]