Explore the vast range of titles available.

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.

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.

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.

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.