The semiconductor industry is increasingly of the view that Moore's law-which predicts the biennial doubling of the number of transistors per microprocessor chip-is nearing its end. Consequently, the pursuit of alternative semiconducting materials for nanoelectronic devices, including single-walled carbon nanotubes (SWNTs), continues. Arrays of horizontal nanotubes are particularly appealing for technological applications because they optimize current output. However, the direct growth of horizontal SWNT arrays with controlled chirality, that would enable the arrays to be adapted for a wider range of applications and ensure the uniformity of the fabricated devices, has not yet been achieved. Here we show that horizontal SWNT arrays with predicted chirality can be grown from the surfaces of solid carbide catalysts by controlling the symmetries of the active catalyst surface. We obtained horizontally aligned metallic SWNT arrays with an average density of more than 20 tubes per micrometre in which 90 per cent of the tubes had chiral indices of (12, 6), and semiconducting SWNT arrays with an average density of more than 10 tubes per micrometre in which 80 per cent of the nanotubes had chiral indices of (8, 4). The nanotubes were grown using uniform size MoC and WC solid catalysts. Thermodynamically, the SWNT was selectively nucleated by matching its structural symmetry and diameter with those of the catalyst. We grew nanotubes with chiral indices of (2m, m) (where m is a positive integer), the yield of which could be increased by raising the concentration of carbon to maximize the kinetic growth rate in the chemical vapour deposition process. Compared to previously reported methods, such as cloning, seeding and specific-structure-matching growth, our strategy of controlling the thermodynamics and kinetics offers more degrees of freedom, enabling the chirality of as-grown SWNTs in an array to be tuned, and can also be used to predict the growth conditions required to achieve the desired chiralities.

BackgroundMesenchymal stem cells (MSCs) are well known having beneficial effects on intracerebral hemorrhage (ICH) in previous studies. The therapeutic mechanisms are mainly to investigate proliferation, differentiation, and immunomodulation. However, few studies have used MSCs to treat blood–brain barrier (BBB) leakage after ICH. The influence of MSCs on the BBB and its related mechanisms were investigated when MSCs were transplanted into rat ICH model in this study.MethodsAdult male Sprague–Dawley (SD) rats were randomly divided into sham-operated group, PBS-treated (ICH + PBS) group, and MSC-treated (ICH + MSC) group. ICH was induced by injection of IV collagenase into the rats’ brains. MSCs were transplanted intravenously into the rats 2 h after ICH induction in MSC-treated group. The following factors were compared: inflammation, apoptosis, behavioral changes, inducible nitric oxide synthase (iNOS), matrix metalloproteinase 9 (MMP-9), peroxynitrite (ONOO−), endothelial integrity, brain edema content, BBB leakage, TNF-α stimulated gene/protein 6 (TSG-6), and nuclear factor-κB (NF-κB) signaling pathway.ResultsIn the ICH + MSC group, MSCs decreased the levels of proinflammatory cytokines and apoptosis, downregulated the density of microglia/macrophages and neutrophil infiltration at the ICH site, reduced the levels of iNOS and MMP-9, attenuated ONOO− formation, and increased the levels of zonula occludens-1 (ZO-1) and claudin-5. MSCs also improved the degree of brain edema and BBB leakage. The protective effect of MSCs on the BBB in ICH rats was possibly invoked by increased expression of TSG-6, which may have suppressed activation of the NF-κB signaling pathway. The levels of iNOS and ONOO−, which played an important role in BBB disruption, decreased due to the inhibitory effects of TSG-6 on the NF-κB signaling pathway.ConclusionsOur results demonstrated that intravenous transplantation of MSCs decreased the levels of ONOO− and degree of BBB leakage and improved neurological recovery in a rat ICH model. This strategy may provide a new insight for future therapies that aim to prevent breakdown of the BBB in patients with ICH and eventually offer therapeutic options for ICH.

Single-walled carbon nanotube (SWNT)-based electronics have been regarded as one of the most promising candidate technologies to replace or supplement silicon-based electronics in the future. These applications require high-density horizontally aligned SWNT arrays. During the past decade, significant efforts have been directed towards growth of high-density SWNT arrays. However, obtaining SWNT arrays with suitable density and quality still remains a big challenge. Herein, we develop a rational approach to grow SWNT arrays with ultra-high density using Trojan catalysts. The density can be as high as 130 SWNTs mm À 1 . Field-effect transistors fabricated with our SWNT arrays exhibit a record drive current density of À 467.09 mA mm À 1 and an on-conductance of 233.55 mS mm À 1 . Radio frequency transistors fabricated on these samples exhibit high intrinsic f T and f MAX of 6.94 and 14.01 GHz, respectively. These results confirm our high-density SWNT arrays are strong candidates for applications in electronics.

A primary concern in practical engineering design is ensuring high system reliability throughout a product's lifecycle, which is subject to time-variant operating conditions and component deteriorations. Thus, the capability of dealing with time-dependent probabilistic constraints in reliability-based design optimization (RBDO) is of vital importance in practical engineering design applications. This paper presents a nested extreme response surface (NERS) approach to efficiently carry out time-dependent reliability analysis and determine the optimal designs. This approach employs the kriging model to build a nested response surface of time corresponding to the extreme value of the limit state function. The efficient global optimization (EGO) technique is integrated with the NERS approach to extract the extreme time responses of the limit state function for any given system design. An adaptive response prediction and model maturation (ARPMM) mechanism is developed based on the mean square error (MSE) to concurrently improve the accuracy and computational efficiency of the proposed approach. With the nested response surface of time, the time-dependent reliability analysis can be converted into the time-independent reliability analysis, and existing advanced reliability analysis and design methods can be used. The NERS approach is compared with existing time-dependent reliability analysis approaches and integrated with RBDO for engineered system design with time-dependent probabilistic constraints. Two case studies are used to demonstrate the efficacy of the proposed NERS approach.

It has been reported that mouse Lbh (limb-bud and heart) can regulate cardiac gene expression by modulating the combinatorial activities of key cardiac transcription factors, as well as their individual functions in cardiogenesis. Here we report the cloning and characterization of the human homolog of mouse Lbh gene, hLBH, from a human embryonic heart cDNA library. The cDNA of hLBH is 2927 bp long, encoding a protein product of 105 amino acids. The protein is highly conserved in evolution across different species from zebra fish, to mouse, to human. Northern blot analysis indicates that a 2.9 kb transcript specific for hLBH is most abundantly expressed in both embryonic and adult heart tissue. In COS-7 cells, hLBH proteins are localized to both the nucleus and the cytoplasm. hLBH is a transcription activator when fused to Gal-4 DNA-binding domain. Deletion analysis indicates that both the N-terminal containing proline-dependent serine/threonine kinase group and the C-terminal containing ERK D-domain motif are required for transcriptional activation. Overexpression of hLBH in COS-7 cells activates the transcriptional activities of activator protein-1 (AP-1) and serum response element (SRE). These results suggest that hLBH proteins may act as a transcriptional activator in mitogen-activated protein kinase signaling pathway to mediate cellular functions.

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Time-variant reliability measures the probability that an engineering system successfully performs intended functions over a certain period of time under various sources of uncertainty. In practice, it is computationally prohibitive to propagate uncertainty in time-variant reliability assessment based on expensiv or complex numerical models. This paper presents an equivalent stochastic process transformation approach for cost-effective prediction of reliability deterioration over the life cycle of an engineering system. To reduce the high dimensionality, a time-independent reliability model is developed by translating random processes and time parameters into random parametersin order to equivalently cover all potential failures that may occur during the time interval of interest. With the timeindependent reliability model, an instantaneous failure surface is attained by using a Kriging-based surrogate model to identify all potential failure events. To enhance the efficacy of failure surface identification, a maximum confidence enhancement method is utilized to update the Kriging model sequentially. Then, the time-variant reliability is approximated using Monte Carlo simulations of the Kriging model where system failures over a time interval are predicted by the instantaneous failure surface. The results of two case studies demonstrate that the proposed approach is able to accurately predict the time evolution of system reliability while requiring much less computational efforts compared with the existing analytical approach.

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