Bio-inspired Design and Fabrication of Super-Strong and Multifunctional Carbon Nanotube Composites

Carbon nanotubes (CNTs) are ideal scaffolds to design and architect high-performance composites at high CNT volume fractions. In these composites, the CNT alignment determines the level of aggregation and the structure morphology, and thus the load transfer efficiency between neighboring CNTs. Here, we discuss two major solutions to produce high-volume fraction CNT composites, namely the layer-by-layer stacking of aligned CNT sheets and the stretching of entangled CNT webs (networks). As inspired by the growth procedure of natural composites, the aggregation of CNTs can be well controlled during the assembling process. As a result, the CNTs can be highly packed, aligned, and importantly unaggregated, with the impregnated polymers acting as interfacial adhesion or mortars to build up the composite structure. The CNT/bismaleimide composites can yield a super-high tensile strength up to 6.27–6.94 GPa and a modulus up to 315 GPa

Vibration Damping of Carbon Nanotube Assembly Materials

Vibration reduction is of great importance in various engineering applications, and a material that exhibits good vibration damping along with high strength and modulus has become more and more vital. Owing to the superior mechanical property of carbon nanotube (CNT), new types of vibration damping material can be developed. This paper presents recent advancements, including our progresses, in the development of high-damping macroscopic CNT assembly materials, such as forests, gels, films, and fibers. In these assemblies, structural deformation of CNTs, zipping and unzipping at CNT connection nodes, strengthening and welding of the nodes, and sliding between CNTs or CNT bundles are playing important roles in determining the viscoelasticity, and elasticity as well. Towards the damping enhancement, strategies for micro-structure and interface design are also discussed

Severe Maternal Hyperglycemia Exacerbates the Development of Insulin Resistance and Fatty Liver in the Offspring on High Fat Diet

Background. Adverse maternal environments may predispose the offspring to metabolic syndrome in adulthoods, but the underlying mechanism has not been fully understood. Methods. Maternal hyperglycemia was induced by streptozotocin (STZ) injection while control (CON) rats received citrate buffer. Litters were adjusted to eight pups per dam and then weaned to standard diet. Since 13 weeks old, a subset of offspring from STZ and CON dams were switched to high fat diet (HFD) for another 13 weeks. Glucose and insulin tolerance tests (GTT and ITT) and insulin secretion assay were performed; serum levels of lipids and leptin were measured. Hepatic fat accumulation and islet area were evaluated through haematoxylin and eosin staining. Results. STZ offspring exhibited lower survival rate, lower birth weights, and growth inhibition which persisted throughout the study. STZ offspring on HFD showed more severe impairment in GTT and ITT, and more profound hepatic steatosis and more severe hyperlipidemia compared with CON-HFD rats. Conclusions. Offspring from diabetic dams would be prone to exhibit low birth weight and postnatal growth inhibition, but could maintain normal glucose tolerance and insulin sensitivity. HFD accelerates development of insulin resistance in the offspring of diabetic dams mainly via a compensatory response of islets

Rottlerin inhibits cell growth and invasion via down-regulation of Cdc20 in glioma cells

Rottlerin, isolated from a medicinal plant Mallotus phillippinensis, has been demonstrated to inhibit cellular growth and induce cytoxicity in glioblastoma cell lines through inhibition of calmodulin-dependent protein kinase III. Emerging evidence suggests that rottlerin exerts its antitumor activity as a protein kinase C inhibitor. Although further studies revealed that rottlerin regulated multiple signaling pathways to suppress tumor cell growth, the exact molecular insight on rottlerin-mediated tumor inhibition is not fully elucidated. In the current study, we determine the function of rottlerin on glioma cell growth, apoptosis, cell cycle, migration and invasion. We found that rottlerin inhibited cell growth, migration, invasion, but induced apoptosis and cell cycle arrest. Mechanistically, the expression of Cdc20 oncoprotein was measured by the RT-PCR and Western blot analysis in glioma cells treated with rottlerin. We observed that rottlerin significantly inhibited the expression of Cdc20 in glioma cells, implying that Cdc20 could be a novel target of rottlerin. In line with this, over-expression of Cdc20 decreased rottlerin-induced cell growth inhibition and apoptosis, whereas down-regulation of Cdc20 by its shRNA promotes rottlerin-induced anti-tumor activity. Our findings indicted that rottlerin could exert its tumor suppressive function by inhibiting Cdc20 pathway which is constitutively active in glioma cells. Therefore, down-regulation of Cdc20 by rottlerin could be a promising therapeutic strategy for the treatment of glioma

An Overview of the Prediction of Protein DNA-Binding Sites

Interactions between proteins and DNA play an important role in many essential biological processes such as DNA replication, transcription, splicing, and repair. The identification of amino acid residues involved in DNA-binding sites is critical for understanding the mechanism of these biological activities. In the last decade, numerous computational approaches have been developed to predict protein DNA-binding sites based on protein sequence and/or structural information, which play an important role in complementing experimental strategies. At this time, approaches can be divided into three categories: sequence-based DNA-binding site prediction, structure-based DNA-binding site prediction, and homology modeling and threading. In this article, we review existing research on computational methods to predict protein DNA-binding sites, which includes data sets, various residue sequence/structural features, machine learning methods for comparison and selection, evaluation methods, performance comparison of different tools, and future directions in protein DNA-binding site prediction. In particular, we detail the meta-analysis of protein DNA-binding sites. We also propose specific implications that are likely to result in novel prediction methods, increased performance, or practical applications

Analysis and Control of the Singular System Model of Aphid Ecosystems

Considering the change of the parameter related to the natural enemy population and the impact on the aphid populations in the fold catastrophe manifold, the singular system model of aphid ecosystems is proposed. Combining singular system theory with catastrophe theory, the corresponding dynamics behaviors and the existence conditions of the impasse points are given by using the qualitative analysis. The biological significance of the analytical results is also discussed. The controllers are designed to make the aphid populations stabilize the refuge level by releasing natural enemy. Some numerical simulations are carried out to prove the results