Diurnal Variations in Neural Activity of Healthy Human Brain Decoded with Resting-State Blood Oxygen Level Dependent fMRI

It remains an ongoing investigation about how the neural activity alters with the diurnal rhythms in human brain. Resting-state functional magnetic resonance imaging (RS-fMRI) reflects spontaneous activities and/or the endogenous neurophysiological process of the human brain. In the present study, we applied the ReHo (regional homogeneity) and ALFF (amplitude of low frequency fluctuation) based on RS-fMRI to explore the regional differences in the spontaneous cerebral activities throughout the entire brain between the morning and evening sessions within a 24-h time cycle. Wide spread brain areas were found to exhibit diurnal variations, which may be attributed to the internal molecular systems regulated by clock genes, and the environmental factors including light-dark cycle, daily activities and homeostatic sleep drive. Notably, the diurnal variation of default mode network (DMN) suggests that there is an adaptation or compensation response within the subregions of DMN, implying a balance or a decoupling of regulation between these regions.National Natural Science Foundation of China [81371359]; National Basic Research Program of China [2015CB755500]; Basic Research Program of Shenzhen [JCYJ20160429191938883]SCI(E)[email protected]

A new species of the genus Zographetus Watson, 1893 from China (Lepidoptera: Hesperiidae)

Xue, Guoxi, Li, Meng, Li, Xiaojuan, Xie, Guoguang, Chen, Keda, Li, Jialing (2019): A new species of the genus Zographetus Watson, 1893 from China (Lepidoptera: Hesperiidae). Zootaxa 4629 (2): 255-262, DOI: https://doi.org/10.11646/zootaxa.4629.2.

Adaptive Recursive Least Squares Denoising in Ventricular Fibrillation ECG Signals

Abstract Cardiac arrest is a fatal and urgent disease in humans. A high‐quality electrocardiogram (ECG) has a positive guide to the success of defibrillation and resuscitation. However, because of artificial motion interference and ambient noise, reliable ECG signals can be obtained only during chest compression (CC) pauses. To address this issue, the adaptive recursive least squares (RLS) denoising approach is proposed. First, the ECG signals of porcine are divided into three groups: CC, without CC, and both with and without CC. Then, five Gaussian noises with different signals‐to‐noise ratios (SNR) and five noises with different distribution types are added, respectively. Furthermore, RLS is compared with six other different denoising approaches. Experimental results demonstrate significant differences between RLS and the other six algorithms in main metrics. SNR and related factors are larger, while the root mean square error is smaller. In conclusion, RLS can significantly eliminate many types of ambient noise, and improve the quality of ECG signals during cardiopulmonary resuscitation