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Spontaneous synchronization over large networks is ubiquitous in nature, ranging from inanimate to biological systems. In the human brain, neuronal synchronization and de-synchronization occur during sleep, with the greatest degree of neuronal synchronization during slow wave sleep (SWS). The current sleep classification schema is based on electroencephalography and provides common criteria for clinicians and researchers to describe stages of non-rapid eye movement (NREM) sleep as well as rapid eye movement (REM) sleep. These sleep stage classifications have been based on convenient heuristic criteria, with little consideration of the accompanying normal physiological changes across those same sleep stages. To begin to resolve those inconsistencies, first focusing only on NREM sleep, we propose a simple cluster synchronization model to explain the emergence of SWS in healthy people without sleep disorders. We apply the empirical mode decomposition (EMD) analysis to quantify slow wave activity in electroencephalograms, and provide quantitative evidence to support our model. Based on this synchronization model, NREM sleep can be classified as SWS and non-SWS, such that NREM sleep can be considered as an intrinsically bistable process. Finally, we develop an automated algorithm for SWS classification. We show that this new approach can unify brain wave dynamics and their corresponding physiologic changes.

Tungsten disulfide (WS2) as one of transition metal dichalcogenides exhibits excellent catalytic activity. However, its catalytic performances in aqueous phase reactions are limited by its hydrophobicity. Here, the natural hydrophilic two-dimensional clay was used to enhance the dispersibility of WS2 in aqueous phase. WS2/montmorillonite (WS2/MMT) composite nanosheets were prepared via hydrothermal synthesis of WS2 on the surface of montmorillonite from WCl6 and CH3CSNH2. The microstructure and morphology show that WS2 nanosheets are assembled parallelly on the montmorillonite with the interface interaction. Through the support of montmorillonite, WS2/MMT possesses higher photocatalytic ability for aqueous phase reactions than WS2, which could be due to the synergistic effect of higher adsorption property, higher hydrophilicity, dispersibility and more catalytic reaction site. The strategy could provide new ideas for obtaining novel hydrophilic photocatalyst with excellent performance.

The refrigerant circulation heat can be enhanced through the mutual transformation between thermal energy and surface energy during the adsorption and separation process of fluid molecules in porous materials. In this paper, the adsorption and energy storage of R1234ze(z), R1234yf, R32 and R134a, as well as their mixed refrigerants in Mg-MOF-74 and Ni-MOF-74 nanoparticles were investigated by means of molecular dynamics simulations and grand canonical Monte Carlo simulations. The results suggested that, in the case of pure refrigerant adsorption, the adsorption quantities of R32 and R134a in MOFs were higher than those of R1234yf and R1234ze(z). However, in the case of saturation adsorption, the desorption heat of R32 was lower than that of R1234yf and R1234ze(z). The addition of MOF-74 nanoparticles (NPs) could enhance the energy storage capacity of the pure refrigerant; besides, R1234yf and R1234ze(z) nanofluids had superior enhancement effect to that of R32 nanofluid. In mixed refrigerant adsorption, the adsorption quantities of R1234ze(z) and R1234yf were lower than those of R32 and R134a; with the increase in temperature, the adsorption of R1234ze(z) and R1234yf showed a gradually increasing trend, while that of R32 was gradually decreased.

The refrigerant circulation heat can be enhanced through the mutual transformation between thermal energy and surface energy during the adsorption and separation process of fluid molecules in porous materials. In this paper, the adsorption and energy storage of R1234ze(z), R1234yf, R32 and R134a, as well as their mixed refrigerants in Mg-MOF-74 and Ni-MOF-74 nanoparticles were investigated by means of molecular dynamics simulations and grand canonical Monte Carlo simulations. The results suggested that, in the case of pure refrigerant adsorption, the adsorption quantities of R32 and R134a in MOFs were higher than those of R1234yf and R1234ze(z). However, in the case of saturation adsorption, the desorption heat of R32 was lower than that of R1234yf and R1234ze(z). The addition of MOF-74 nanoparticles (NPs) could enhance the energy storage capacity of the pure refrigerant; besides, R1234yf and R1234ze(z) nanofluids had superior enhancement effect to that of R32 nanofluid. In mixed refrigerant adsorption, the adsorption quantities of R1234ze(z) and R1234yf were lower than those of R32 and R134a; with the increase in temperature, the adsorption of R1234ze(z) and R1234yf showed a gradually increasing trend, while that of R32 was gradually decreased.

Heart failure (HF) is a major cardiovascular disease worldwide, and the early detection and diagnosis remain challenges. Recently, heart rhythm complexity analysis, derived from non-linear heart rate variability (HRV) analysis, has been proposed as a non-invasive method to detect diseases and predict outcomes. In this study, we aimed to investigate the diagnostic value of heart rhythm complexity in HF patients. We prospectively analyzed 55 patients with symptomatic HF with impaired left ventricular ejection fraction and 97 participants without HF symptoms and normal LVEF as controls. Traditional linear HRV parameters and heart rhythm complexity including detrended fluctuation analysis (DFA) and multiscale entropy (MSE) were analyzed. The traditional linear HRV, MSE parameters and DFAα1 were significantly lower in HF patients compared with controls. In regression analysis, DFAα1 and MSE scale 5 remained significant predictors after adjusting for multiple clinical variables. Among all HRV parameters, MSE scale 5 had the greatest power to differentiate the HF patients from the controls in receiver operating characteristic curve analysis (area under the curve: 0.844). In conclusion, heart rhythm complexity appears to be a promising tool for the detection and diagnosis of HF.

Pulmonary hypertension is a fatal disease, however reliable prognostic tools are lacking. Heart rhythm complexity analysis is derived from non-linear heart rate variability (HRV) analysis and has shown excellent performance in predicting clinical outcomes in several cardiovascular diseases. However, heart rhythm complexity has not previously been studied in pulmonary hypertension patients. We prospectively analyzed 57 patients with pulmonary hypertension (31 with pulmonary arterial hypertension and 26 with chronic thromboembolic pulmonary hypertension) and compared them to 57 age- and sex-matched control subjects. Heart rhythm complexity including detrended fluctuation analysis (DFA) and multiscale entropy (MSE) and linear HRV parameters were analyzed. The patients with pulmonary hypertension had significantly lower mean RR, SDRR, pNN20, VLF, LF, LF/HF ratio, DFAα1, MSE slope 5, scale 5, area 1–5 and area 6–20 compared to the controls. Receiver operating characteristic curve analysis showed that heart rhythm complexity parameters were better than traditional HRV parameters to predict pulmonary hypertension. Among all parameters, scale 5 had the greatest power to differentiate the pulmonary hypertension patients from controls (AUC: 0.845, P

Measures characterizing the complexity of heart rate (HR) dynamics have been informative in predicting age- and disease-related decline in cardiovascular health, but few studies have evaluated whether mind-body exercise can impact HR complexity. This study evaluated the effects of long-term Tai Chi (TC) practice on the complexity of HR dynamics using an observational comparison of TC experts and age- and gender-matched TC-naïve individuals. Shorter-term effects of TC were assessed by randomly assigning TC-naïve participants to either TC group to receive six months of TC training or to a waitlist control group. 23 TC experts (age = 63.3 ± 8.0 y; 24.6 ± 12.0 y TC experience) and 52 TC-naïve (age = 64.3 ± 7.7 y) were enrolled. In cross-sectional analyses, TC experts had a higher overall complexity index (CI, p = 0.004) and higher entropy at multiple individual time scales (p

Tungsten disulfide (WS2) as one of transition metal dichalcogenides exhibits excellent catalytic activity. However, its catalytic performances in aqueous phase reactions are limited by its hydrophobicity. Here, the natural hydrophilic two-dimensional clay was used to enhance the dispersibility of WS2 in aqueous phase. WS2/montmorillonite (WS2/MMT) composite nanosheets were prepared via hydrothermal synthesis of WS2 on the surface of montmorillonite from WCl6 and CH3CSNH2. The microstructure and morphology show that WS2 nanosheets are assembled parallelly on the montmorillonite with the interface interaction. Through the support of montmorillonite, WS2/MMT possesses higher photocatalytic ability for aqueous phase reactions than WS2, which could be due to the synergistic effect of higher adsorption property, higher hydrophilicity, dispersibility and more catalytic reaction site. The strategy could provide new ideas for obtaining novel hydrophilic photocatalyst with excellent performance.

Abdominal aorta calcification (AAC) has been associated with clinical outcomes in peritoneal dialysis (PD) patients. Heart rhythm complexity analysis has been shown to be a promising tool to predict outcomes in patients with cardiovascular disease. In this study, we aimed to analyze the association between heart rhythm complexity and AAC in PD patients. We prospectively analyzed 133 PD patients. Heart rhythm complexity including detrended fluctuation analysis and multiscale entropy was performed. In linear analysis, the patients in the higher AAC group (AAC ≥15%) had a significantly lower standard deviation of normal RR intervals, very low frequency, low frequency, high frequency and low/high frequency ratio. In non-linear analysis, DFAα1, slope 1–5, scale 5 and area 6–20 were significantly lower in the patients with higher AAC. Receiver operating characteristic curve analysis showed that DFAα1 had the greatest discriminatory power to differentiate these two groups. Multivariate logistic regression analysis showed that DFAα1 and HbA1c were significantly associated with higher AAC ratio. Adding DFAα1 significantly improved the discriminatory power of the linear parameters in both net reclassification improvement and integrated discrimination improvement models. In conclusion, DFAα1 is highly associated with AAC and a potential cardiovascular marker in PD patients.

Natural kaolinite clays with different dimensionalities (including kaolinite nanoflakes and nanorods) supported TiO2 nanoparticles were successfully prepared via a facile sol-gel method. Moreover, comparisons between FK/TiO2 and RK/TiO2 nanocomposites are conducted in terms of matrix morphology, surface property, energy band structure and interfacial interaction. The effects of kaolinite microstructure, morphology and dimensionality on the interfacial characteristics and photocatalytic properties of the nanocomposites were investigated in detail. The results showed that TiO2 nanoparticles are more easily attached on the kaolinite nanoflakes, and possess more uniform distribution and smaller particle size than that of kaolinite nanorods. In particular, the FK/TiO2 nanocatalysts exhibit higher photocatalytic activity for the degradation of tetracycline hydrochloride than that of RK/TiO2 and bare TiO2, which is attributed to the stronger surface adsorptivity, higher loading efficiency and smaller grain size. Additionally, FK/TiO2 composites show excellent stability, which is ascribed to the intimate interfacial contact between two-dimensional kaolinite nanoflakes and TiO2 nanoparticles. Overall, the enhanced catalytic performance for FK/TiO2 composites is the synergistic effect of two-dimensional morphology, better adsorption capability and more active photocatalysis TiO2 species.

Metabolic syndrome (MetS) has been associated with chronic damage to the cardiovascular system. This study aimed to evaluate early stage cardiac autonomic dysfunction with electrocardiography (ECG)-based measures in MetS subjects. During 2012–2013, 175 subjects with MetS and 226 healthy controls underwent ECG recordings of at least 4 hours starting in the morning with ambulatory one-lead ECG monitors. MetS was diagnosed using the criteria defined in the Adult Treatment Panel III, with a modification of waist circumference for Asians. Conventional heart rate variability (HRV) analysis, and complexity index (CI1–20) calculated from 20 scales of entropy (multiscale entropy, MSE), were compared between subjects with MetS and controls. Compared with the healthy controls, subjects with MetS had significantly reduced HRV, including SDNN and pNN20 in time domain, VLF, LF and HF in frequency domain, as well as SD2 in Poincaré analysis. MetS subjects have significantly lower complexity index (CI1–20) than healthy subjects (1.69 ± 0.18 vs. 1.77 ± 0.12, p 1–20 (r = −0.27, p

Drug resistance caused by excessive and indiscriminate antibiotic usage has become a serious public health problem. The need of finding new antibacterial drugs is more urgent than ever before. Tyrosyl-tRNA synthase was proved to be a potent target in combating drug-resistant bacteria. In silico methodologies including molecular docking and 3D-QSAR were employed to investigate a series of newly reported tyrosyl-tRNA synthase inhibitors of furanone derivatives. Both internal and external cross-validation were conducted to obtain high predictive and satisfactory CoMFA model (q2 = 0.611, r2 pred = 0.933, r2 m = 0.954) and CoMSIA model (q2 = 0.546, r2 pred = 0.959, r2 m = 0.923). Docking results, which correspond with CoMFA/CoMSIA contour maps, gave the information for interactive mode exploration. Ten new molecules designed on the basis of QSAR and docking models have been predicted more potent than the most active compound 3-(4-hydroxyphenyl)-4-(2-morpholinoethoxy)furan-2(5H)-one (15) in the literatures. The results expand our understanding of furanones as inhibitors of tyrosyl-tRNA synthase and could be helpful in rationally designing of new analogs with more potent inhibitory activities.

Heart rhythm complexity analysis has been shown to have good prognostic power in patients with cardiovascular disease. The aim of this study was to analyze serial changes in heart rhythm complexity from the acute to chronic phase of acute myocardial infarction (MI). We prospectively enrolled 27 patients with anterior wall ST segment elevation myocardial infarction (STEMI) and 42 control subjects. In detrended fluctuation analysis (DFA), the patients had significantly lower DFAα2 in the acute stage (within 72 hours) and lower DFAα1 at 3 months and 12 months after MI. In multiscale entropy (MSE) analysis, the patients had a lower slope 5 in the acute stage, which then gradually increased during the follow-up period. The areas under the MSE curves for scale 1 to 5 (area 1–5) and 6 to 20 (area 6–20) were lower throughout the chronic stage. Area 6–20 had the greatest discriminatory power to differentiate the post-MI patients (at 1 year) from the controls. In both the net reclassification improvement and integrated discrimination improvement models, MSE parameters significantly improved the discriminatory power of the linear parameters to differentiate the post-MI patients from the controls. In conclusion, the patients with STEMI had serial changes in cardiac complexity.

Perovskite LaFeO3/montmorillonite nanocomposites (LaFeO3/MMT) have been successfully prepared via assembling LaFeO3 nanoparticles on the surface of montmorillonite with citric acid assisted sol-gel method. The results indicated that the uniform LaFeO3 nanoparticles were densely deposited onto the surface of montmorillonite, mainly ranging in diameter from 10 nm to 15 nm. The photocatalytic activity of LaFeO3/MMT was evaluated by the degradation of Rhodamine B (RhB) under visible light irradiation, indicating that LaFeO3/MMT exhibited remarkable adsorption efficiency and excellent photocatalytic activity with the overall removal rate of RhB up to 99.34% after visible light irradiation lasting for 90 min. The interface characteristic and possible degradation mechanism were explored. The interface characterization of LaFeO3/MMT suggested that LaFeO3 nanoparticles could be immobilized on the surface of montmorillonite with the Si-O-Fe bonds. The abundant hydroxyl groups of montmorillonite, semiconductor photocatalysis of LaFeO3 and Fenton-like reaction could enhance the photocatalytic degradation through a synergistic effect. Therefore, the LaFeO3/MMT is a very promising photocatalyst in future industrial application to treat effectively wastewater of dyes.

Cardiovascular disease is one of the leading causes of death in patients with advanced renal disease. The objective of this study was to investigate impairments in heart rhythm complexity in patients with end-stage renal disease. We prospectively analyzed 65 patients undergoing peritoneal dialysis (PD) without prior cardiovascular disease and 72 individuals with normal renal function as the control group. Heart rhythm analysis including complexity analysis by including detrended fractal analysis (DFA) and multiscale entropy (MSE) were performed. In linear analysis, the PD patients had a significantly lower standard deviation of normal RR intervals (SDRR) and percentage of absolute differences in normal RR intervals greater than 20 ms (pNN20). Of the nonlinear analysis indicators, scale 5, area under the MSE curve for scale 1 to 5 (area 1–5) and 6 to 20 (area 6–20) were significantly lower than those in the control group. In DFA anaylsis, both DFA α1 and DFA α2 were comparable in both groups. In receiver operating characteristic curve analysis, scale 5 had the greatest discriminatory power for two groups. In both net reclassification improvement model and integrated discrimination improvement models, MSE parameters significantly improved the discriminatory power of SDRR, pNN20, and pNN50. In conclusion, PD patients had worse cardiac complexity parameters. MSE parameters are useful to discriminate PD patients from patients with normal renal function.

The hierarchical interaction between electrical signals of the brain and heart is not fully understood. We hypothesized that the complexity of cardiac electrical activity can be used to predict changes in encephalic electricity after stress. Most methods for analyzing the interaction between the heart rate variability (HRV) and electroencephalography (EEG) require a computation-intensive mathematical model. To overcome these limitations and increase the predictive accuracy of human relaxing states, we developed a method to test our hypothesis. In addition to routine linear analysis, multiscale entropy and detrended fluctuation analysis of the HRV were used to quantify nonstationary and nonlinear dynamic changes in the heart rate time series. Short-time Fourier transform was applied to quantify the power of EEG. The clinical, HRV and EEG parameters of postcatheterization EEG alpha waves were analyzed using change-score analysis and generalized additive models. In conclusion, the complexity of cardiac electrical signals can be used to predict EEG changes after stress.

Excess aldosterone secretion in patients with primary aldosteronism (PA) impairs their cardiovascular system. Heart rhythm complexity analysis, derived from heart rate variability (HRV), is a powerful tool to quantify the complex regulatory dynamics of human physiology. We prospectively analyzed 20 patients with aldosterone producing adenoma (APA) that underwent adrenalectomy and 25 patients with essential hypertension (EH). The heart rate data were analyzed by conventional HRV and heart rhythm complexity analysis including detrended fluctuation analysis (DFA) and multiscale entropy (MSE). We found APA patients had significantly decreased DFAα2 on DFA analysis and decreased area 1–5, area 6–15 and area 6–20 on MSE analysis (all p

Complexity in physiological outputs is believed to be a hallmark of healthy physiological control. How to accurately quantify the degree of complexity in physiological signals with outliers remains a major barrier for translating this novel concept of nonlinear dynamic theory to clinical practice. Here we propose a new approach to estimate the complexity in a signal by analyzing the irregularity of the sign time series of its coarse-grained time series at different time scales. Using surrogate data, we show that the method can reliably assess the complexity in noisy data while being highly resilient to outliers. We further apply this method to the analysis of human heartbeat recordings. Without removing any outliers due to ectopic beats, the method is able to detect a degradation of cardiac control in patients with congestive heart failure and a more degradation in critically ill patients whose life continuation relies on extracorporeal membrane oxygenator (ECMO). Moreover, the derived complexity measures can predict the mortality of ECMO patients. These results indicate that the proposed method may serve as a promising tool for monitoring cardiac function of patients in clinical settings.