Although single-atomically dispersed metal-Nx on carbon support (M-NC) has great potential in heterogeneous catalysis, the scalable synthesis of such single-atom catalysts (SACs) with high-loading metal-Nx is greatly challenging since the loading and single-atomic dispersion have to be balanced at high temperature for forming metal-Nx. Herein, we develop a general cascade anchoring strategy for the mass production of a series of M-NC SACs with a metal loading up to 12.1 wt%. Systematic investigation reveals that the chelation of metal ions, physical isolation of chelate complex upon high loading, and the binding with N-species at elevated temperature are essential to achieving high-loading M-NC SACs. As a demonstration, high-loading Fe-NC SAC shows superior electrocatalytic performance for O2 reduction and Ni-NC SAC exhibits high electrocatalytic activity for CO2 reduction. The strategy paves a universal way to produce stable M-NC SAC with high-density metal-Nx sites for diverse high-performance applications.

Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution aerial and satellite data, owing to the similarity of these data to the large datasets in computer vision. Accordingly, this study presents a detailed investigation of state-of-the-art deep learning tools for classification of complex wetland classes using multispectral RapidEye optical imagery. Specifically, we examine the capacity of seven well-known deep convnets, namely DenseNet121, InceptionV3, VGG16, VGG19, Xception, ResNet50, and InceptionResNetV2, for wetland mapping in Canada. In addition, the classification results obtained from deep CNNs are compared with those based on conventional machine learning tools, including Random Forest and Support Vector Machine, to further evaluate the efficiency of the former to classify wetlands. The results illustrate that the full-training of convnets using five spectral bands outperforms the other strategies for all convnets. InceptionResNetV2, ResNet50, and Xception are distinguished as the top three convnets, providing state-of-the-art classification accuracies of 96.17%, 94.81%, and 93.57%, respectively. The classification accuracies obtained using Support Vector Machine (SVM) and Random Forest (RF) are 74.89% and 76.08%, respectively, considerably inferior relative to CNNs. Importantly, InceptionResNetV2 is consistently found to be superior compared to all other convnets, suggesting the integration of Inception and ResNet modules is an efficient architecture for classifying complex remote sensing scenes such as wetlands.

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TFAP2A is a transcription factor that orchestrates a variety of cell processes, including cell growth and tissue differentiation. However, the regulation of TFAP2A in human nasopharyngeal carcinoma tumorigenesis and its precise mechanism of action remain largely unknown. In this study, we investigated the biologic role and clinical significance of TFAP2A in nasopharyngeal carcinoma growth and progression and identified the underlying molecular mechanisms. We found that TFAP2A was highly expressed in various nasopharyngeal carcinoma cell lines and tumor tissue specimens and was significantly correlated with hypoxia-inducible factor-1a (HIF-1a) expression. A positive correlation of TFAP2A overexpression with advanced tumor stage, local invasion, clinical progression, and poor prognosis of patients with nasopharyngeal carcinomas were also observed. Moreover, we found that knockdown of TFAP2A expression by siRNA significantly inhibited tumor cell growth in nasopharyngeal carcinoma cell lines and in a subcutaneous xenograft mouse model by targeting the HIF-1a-mediated VEGF/pigment epithelium-derived factor (PEDF) signaling pathway. Treatment of nasopharyngeal carcinoma cells with TFAP2A siRNA dramatically inhibited the expression and the release of VEGF protein but did not change the level of PEDF protein, resulting in a significant reduction of the ratio of VEGF/PEDF. Pretreatment with a HIF-1a siRNA did not significantly change the TFAP2A siRNA-mediated inhibition in cell viability. Our results indicate that TFAP2A regulates nasopharyngeal carcinoma growth and survival through the modulation of the HIF-1a-mediated VEGF/PEDF signaling pathway, and suggest that TFAP2A could be a potential prognostic biomarker and therapeutic target for nasopharyngeal carcinoma treatment. Cancer Prev Res; 7(2); 266-77. Ó2013 AACR.

Soft structures in nature, such as supercoiled DNA and proteins, can organize into complex hierarchical architectures through multiple noncovalent molecular interactions. Identifying new classes of natural building blocks capable of facilitating long-range hierarchical structuring has remained an elusive goal. We report the bottom-up synthesis of a hierarchical metal-phenolic mesocrystal where self-assembly proceeds on different length scales in a spatiotemporally controlled manner. Phenolic-based coordination complexes organize into supramolecular threads that assemble into tertiary nanoscale filaments, lastly packing into quaternary mesocrystals. The hierarchically ordered structures are preserved after thermal conversion into a metal-carbon hybrid framework and can impart outstanding performance to sodium ion batteries, which affords a capability of 72.5 milliampere hours per gram at an ultrahigh rate of 200 amperes per gram and a 90% capacity retention over 15,000 cycles at a current density of 5.0 amperes per gram. This hierarchical structuring of natural polyphenols is expected to find widespread applications.

The effects of defects on the interfacial mechanical properties of graphene/epoxy are systematically investigated by molecular dynamic simulations.

BackgroundAs an important factor affecting meat quality, intramuscular fat (IMF) content is a topic of worldwide concern. Emerging evidences indicate that microRNAs play important roles in adipocyte differentiation. However, miRNAome has neither been studied during porcine intramuscular preadipocyte differentiation, nor compared with subcutaneous preadipocytes. The objectives of this study were to identify porcine miRNAs involved in adipogenesis in primary preadipocytes, and to determine whether intramuscular and subcutaneous adipocytes differ in the expression and regulation of miRNAs.ResultsmiRNAomes in primary intramuscular and subcutaneous adipocytes during differentiation were first sequenced using the Solexa deep sequencing method. The sequences and relative expression levels of 224 known (98.2% in miRbase 18.0) and 280 potential porcine miRNAs were identified. Fifty-four of them changed in similar pattern between intramuscular vascular stem cells (IVSC) and subcutaneous vascular stem cells (SVSC) differentiation, such as miR-210, miR-10b and miR-99a. Expression levels of 10 miRNAs were reversely up-or down-regulated between IVSC and SVSC differentiation, 19 were up-or down-regulated only during IVSC differentiation and 55 only during SVSC differentiation. Additionally, 30 miRNAs showed fat-depot specific expression pattern (24 in cells of intramuscular origin and 6 in cells of subcutaneous origin). These adipogenesis-related miRNAs mainly functioned by targeting similar pathways in adipogenesis, obesity and syndrome.ConclusionComparison of miRNAomes in IVSC and SVSC during differentiation revealed that many different miRNAs are involved in adipogenesis, and they regulate SVSC and IVSC differentiation through similar pathways. These miRNAs may serve as biomarkers or targets for enhancing IMF content, and uncovering their function in IMF development will be of great value in the near future.