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Efficient decomposition of ammonium perchlorate (AP) which is the oxidant and combustion adjuvant of the solid propellants is critical for the fast ignition and high-speed liftoff of the rocket. We synthesize Co metal-decorated carbon nanotubes (Co/CNTs) as an advanced catalyst to promote the sufficient thermal decomposition of AP under reduced temperature. The results indicate that Co/CNTs effectively accelerate the decomposition of AP by reducing the thermal decomposition temperature from 430.0 to 309.9 ℃ and achieve a 3-times higher thermal releasing than that of the pure AP. The acid-etching comparison test reveals that decreasing the cobalt content in Co/CNTs does not affect the decomposition temperature of AP but suppress the decomposition heat releasing. It is proposed that the CNTs boost fast electron transfer during the AP decomposition and the cobalt works as an active additive to increase the heat releasing. This work provides an insight into the electron-transfer mechanism for the thermal decomposition of AP and a valuable guidance for developing advanced combustion catalysts used in the solid propellants. Graphical abstract

Summary Cotton is widely cultivated globally because it provides natural fibre for the textile industry and human use. To identify quantitative trait loci (QTLs)/genes associated with fibre quality and yield, a recombinant inbred line (RIL) population was developed in upland cotton. A consensus map covering the whole genome was constructed with three types of markers (8295 markers, 5197.17 centimorgans (cM)). Six fibre yield and quality traits were evaluated in 17 environments, and 983 QTLs were identified, 198 of which were stable and mainly distributed on chromosomes 4, 6, 7, 13, 21 and 25. Thirty‐seven QTL clusters were identified, in which 92.8% of paired traits with significant medium or high positive correlations had the same QTL additive effect directions, and all of the paired traits with significant medium or high negative correlations had opposite additive effect directions. In total, 1297 genes were discovered in the QTL clusters, 414 of which were expressed in two RNA‐Seq data sets. Many genes were discovered, 23 of which were promising candidates. Six important QTL clusters that included both fibre quality and yield traits were identified with opposite additive effect directions, and those on chromosome 13 (qClu‐chr13‐2) could increase fibre quality but reduce yield; this result was validated in a natural population using three markers. These data could provide information about the genetic basis of cotton fibre quality and yield and help cotton breeders to improve fibre quality and yield simultaneously.

Aramid Fiber III was modified by oxygen plasma treatment. The fiber surface wettability before and after different oxygen plasma treatment pressure, and surface wettability aging behavior were observed by Dynamic Contact Angles Analysis (DCAA), respectively. The results showed that aramid fiber surface wettability were increased after oxygen plasma treatment. The total surface free energy increased from 37.78 mJ/m2 for untreated sample to 69.63 mJ/m2 with 40 Pa plasma treated fiber. The fiber surface wettability decreased slowly with storage time in air. The fiber surface wettability decreased from 67.79 mJ/m2 for 20 Pa plasma-treated sample to 63.85 mJ/m2 after 30 days.

The stellate ganglia play an important role in cardiac remodeling after myocardial infarction (MI). This study aimed to investigate whether adiponectin (APN), an adipokine mainly secreted by adipose tissue, could modulate the left stellate ganglion (LSG) and exert cardioprotective effects through the sympathetic nervous system (SNS) in a canine model of MI. APN microinjection and APN overexpression with recombinant adeno-associated virus vector in the LSG were performed in acute and chronic MI models, respectively. The results showed that acute APN microinjection decreased LSG function and neural activity, and suppressed ischemia-induced ventricular arrhythmia. Chronic MI led to a decrease in the effective refractory period and action potential duration at 90% and deterioration in echocardiography performance, all of which was blunted by APN overexpression. Moreover, APN gene transfer resulted in favorable heart rate variability alteration, and decreased cardiac SNS activity, serum noradrenaline and neuropeptide Y, which were augmented after MI. APN overexpression also decreased the expression of nerve growth factor and growth associated protein 43 in the LSG and peri-infarct myocardium, respectively. Furthermore, RNA sequencing of LSG indicated that 4-week MI up-regulated the mRNA levels of macrophage/microglia activation marker Iba1, chemokine ligands (CXCL10, CCL20), chemokine receptor CCR5 and pro-inflammatory cytokine IL6, and downregulated IL1RN and IL10 mRNA, which were reversed by APN overexpression. Our results reveal that APN inhibits cardiac sympathetic remodeling and mitigates cardiac remodeling after MI. APN-mediated gene therapy may provide a potential therapeutic strategy for the treatment of MI.

BackgroundDisruption of the autonomic nervous system (ANS) can lead to acute coronary syndrome (ACS). We developed a nomogram model using heart rate variability (HRV) and other data to predict major adverse cardiovascular events (MACEs) following emergency coronary angiography in patients with ACS.MethodsACS patients admitted from January 2018 to June 2020 were examined. Holter monitors were used to collect HRV data for 24 h. Coronary angiograms, clinical data, and MACEs were recorded. A nomogram was developed using the results of Cox regression analysis.ResultsThere were 439 patients in a development cohort and 241 in a validation cohort, and the mean follow-up time was 22.80 months. The nomogram considered low-frequency/high-frequency ratio, age, diabetes, previous myocardial infarction, and current smoking. The area-under-the-curve (AUC) values for 1-year MACE-free survival were 0.790 (95% CI: 0.702–0.877) in the development cohort and 0.894 (95% CI: 0.820–0.967) in the external validation cohort. The AUCs for 2-year MACE-free survival were 0.802 (95% CI: 0.739–0.866) in the development cohort and 0.798 (95% CI: 0.693–0.902) in the external validation cohort. Development and validation were adequately calibrated and their predictions correlated with the observed outcome. Decision curve analysis (DCA) showed the model had good discriminative ability in predicting MACEs.ConclusionOur validated nomogram was based on non-invasive ANS assessment and traditional risk factors, and indicated reliable prediction of MACEs in patients with ACS. This approach has potential for use as a method for non-invasive monitoring of health that enables provision of individualized treatment strategies.

Both coronary physiology and deceleration capacity (DC) showed prognostic efficacy for patients with acute coronary syndrome (ACS). This retrospective cohort study was performed to evaluate the prognostic implication of DC combined with the relative increase and final coronary physiology as detected by quantitative flow ratio (QFR) for patients with non-ST-elevation ACS (NSTE-ACS) who underwent complete and successful percutaneous coronary intervention (PCI). Patients with NSTE-ACS who underwent PCI with pre- and post-procedural QFR in our department between January 2018 and November 2019 were included. The 24-hour deceleration capacity (DC 24h) was obtained Holter monitoring. The incidence of major adverse cardiac and cerebrovascular events (MACCEs) during follow up was defined as the primary outcome. The optimal cutoffs of the relative increase, final QFR, and DC 24h for prediction of MACCEs were determined receiver operating characteristic (ROC) analysis and the predictive efficacies were evaluated with multivariate Cox regression analysis. Overall, 240 patients were included. During a mean follow up of 21.3 months, 31 patients had MACCEs. Results of multivariate Cox regression analyses showed that a higher post-PCI QFR [adjusted hazard ratio (HR): 0.318; 95% confidence interval (CI): 0.129-0.780], a higher relative QFR increase (HR: 0.161; 95% CI: 0.066-0.391], and a higher DC (HR: 0.306; 95% CI: 0.134-0.701) were all independent predictors of lower risk of MACCEs. Subsequently, incorporating low DC (≤2.42) into the risk predicting model with clinical variables, the predictive efficacies of low relative QRS increase (≤23%) and low post-PCI QFR (≤0.88) for MACCEs were both significantly improved. The DC combined with relative increase and final coronary physiology may improve the predictive efficacy of existing models based on clinical variables for MACCEs in NSTE-ACS patients who underwent complete and successful PCI.

Patients with lower extremity arteriosclerosis obliterans (LEASO) are more likely to appear to be associated with adverse cardiovascular outcomes. Currently, few studies have reported the sex-specific characteristics and risk of major cardiovascular and cerebrovascular adverse events (MACCEs) in LEASO. Our study was conducted to determine the characteristics and contributions of LEASO to MACCEs in males and females. We conducted a single-center retrospective study of consecutively enrolled patients with first-diagnosed LEASO at Renmin Hospital of Wuhan University from November 2017 to November 2019. The ratio of patients between the LEASO and control groups was 1 to 1 and based on age, sex, comorbid diabetes mellitus and hypertension, current smoking and medications. The occurrence of MACCEs was used as the primary endpoint of this observational study. A LEASO group ( = 430) and control group ( = 430) were enrolled in this study. A total of 183 patients experienced MACCEs during an average of 38.83 ± 14.28 months of follow-up. Multivariate Cox regression analysis indicated that LEASO was an independent predictor of the occurrence of MACCEs in all patients (HR: 2.448, 95% CI: 1.730-3.464, < 0.001). Subgroup analysis by sex subgroup was conducted for sex, and LEASO was also an independent predictor of the occurrence of MACCEs in both male cases (HR: 2.919, 95% CI: 1.776-4.797, < 0.001) and female cases (HR: 1.788, 95% CI: 1.110-2.880, = 0.017). Moreover, Kaplan-Meier analysis indicated no significant difference in event-free survival between patients of different sexes with LEASO (χ = 0.742, = 0.389). LEASO tended to a useful risk stratified indicator for MACCEs in both male and female patients in our study. Notably, attention should be given to patients with LEASO who should undergo comprehensive cardiovascular evaluation and intervention, even if there is a lack of traditional cardiovascular risk factors.

Mast cells (MCs) are important intermediates between the nervous and immune systems. The cardiac autonomic nervous system (CANS) crucially modulates cardiac electrophysiology and arrhythmogenesis, but whether and how MC-CANS neuroimmune interaction influences arrhythmia remain unclear. Our clinical data showed a close relationship between serum levels of MC markers and CANS activity, and then we use mast cell stabilizers (MCSs) to alter this MC-CANS communication. MCSs, which are well-known anti-allergic agents, could reduce the risk of ventricular arrhythmia (VA) after myocardial infarction (MI). RNA-sequencing (RNA-seq) analysis to investigate the underlying mechanism by which MCSs could affect the left stellate ganglion (LSG), a key therapeutic target for modulating CANS, showed that the IL-6 and γ-aminobutyric acid (GABA)-ergic system may be involved in this process. Our findings demonstrated that MCSs reduce VA risk along with revealing the potential underlying antiarrhythmic mechanisms.

Cardiac autonomic nerve imbalance has been well documented to provide a critical foundation for the development of acute coronary syndrome (ACS) but is not included in the postdischarge GRACE score. We investigated whether capturing cardiac autonomic nervous system (ANS)-related modulations by 24-h deceleration capacity (DC) could improve the capability of existing prognostic models, including the postdischarge Global Registry of Acute Coronary Events (GRACE) score, to predict prognosis after ACS. Patients with ACS were assessed with 24-h Holter monitoring in our department from June 2017 through June 2019. The GRACE score was calculated for postdischarge 6-month mortality. The patients were followed longitudinally for the incidence of major adverse cardiac events (MACEs), set as a composite of non-fatal myocardial infarction and death. To evaluate the improvement in its discriminative and reclassification capabilities, the GRACE score with DC model was compared with a model using the GRACE score only, using area under the receiver-operator characteristic curve (AUC), Akaike's information criteria, the likelihood ratio test, category-free integrated discrimination index (IDI) and continuous net reclassification improvement (NRI). Overall, 323 patients were enrolled consecutively. After the follow-up period (mean, 43.78 months), 41 patients were found to have developed MACEs, which were more frequent among patients with DC <2.5 ms. DC adjusted for the GRACE score independently predicted the occurrence of MACEs with an adjusted hazard ratio (HR) of 0.885 and 95% CI of 0.831-0.943 ( < 0.001). Moreover, adding DC to the GRACE score only model increased the discriminatory ability for MACEs, as indicated by the likelihood ratio test (χ = 9.277, 1 df; < 0.001). The model including the GRACE score combined with DC yielded a lower corrected Akaike's information criterion compared to that with the GRACE score alone. Incorporation of the DC into the existing model that uses the GRACE score enriched the net reclassification indices (NRIe 7.3%, NRIne 12.8%, NRI 0.200; = 0.003). Entering the DC into the GRACE score model enhanced discrimination (IDI of 1.04%, < 0.001). DC serves as an independent and effective predictor of long-term adverse outcomes after ACS. Integration of DC and the postdischarge GRACE score significantly enhanced the discriminatory capability and precision in the prediction of poor long-term follow-up prognosis.

Clustering data has a wide range of applications and has attracted considerable attention in data mining and artificial intelligence. However it is difficult to find a set of clusters that best fits natural partitions without any class information. In this paper, a method for detecting the optimal cluster number is proposed. The optimal cluster number can be obtained by the proposal, while partitioning the data into clusters by FCM (Fuzzy c-means) algorithm. It overcomes the drawback of FCM algorithm which needs to define the cluster number c in advance. The method works by converting the fuzzy cluster result into a weighted bipartite network and then the optimal cluster number can be detected by the improved bipartite modularity. The experimental results on artificial and real data sets show the validity of the proposed method.