Heterogeneous silver-polyaniline nanocomposites with tunable morphology and controllable catalytic properties

National Natural Science Foundation of China [51173153, U1205113]; Special Program for Key Research of Chinese National Basic Research Program [2011CB612303]; Xiamen Science and Technology Committee [3502Z20121021, 3502Z20120015]This paper introduces not only a simple hydrothermal route to silver-polyaniline (Ag-PANI) nanocomposites with controllable morphology, but also a type of catalyst possessing tunable and switchable catalytic capability. Ag-PANI Janus nanoparticles (NPs) and Ag@PANI core-shell NPs have been constructed successfully at different hydrothermal temperatures. The diameter of both Ag and PANI hemispheres of Janus NPs, as well as the PANI shell thickness of core-shell NPs, was finely tuned via adjustment of the feed ratio. We also gained a deeper insight into the functionalities of PANI components in the catalytic capability of the heterogeneous catalysts, choosing catalytic reductions of nitrobenzene (NB) and 4-nitrophenol (4-NP) as model reactions. Our results showed that the catalytic capability of the nanocomposites was dependent on the PANI morphology and hydrophobicity. The PANI shell coating on Ag NPs can concentrate the lipophilic NB, thus leading to an enhanced catalytic capability of Ag@PANI core-shell NPs. However, this enhanced catalytic capability was not observed for Ag-PANI Janus NPs when catalytically reducing NB. More importantly, the catalytic capability of the core-shell NPs in the reduction of hydrophilic 4-NP is switchable by varying the PANI shell from an undoped to a doped state

Attention-Mechanism-Containing Neural Networks for High-Resolution Remote Sensing Image Classification

A deep neural network is suitable for remote sensing image pixel-wise classification because it effectively extracts features from the raw data. However, remote sensing images with higher spatial resolution exhibit smaller inter-class differences and greater intra-class differences; thus, feature extraction becomes more difficult. The attention mechanism, as a method that simulates the manner in which humans comprehend and perceive images, is useful for the quick and accurate acquisition of key features. In this study, we propose a novel neural network that incorporates two kinds of attention mechanisms in its mask and trunk branches; i.e., control gate (soft) and feedback attention mechanisms, respectively, based on the branches’ primary roles. Thus, a deep neural network can be equipped with an attention mechanism to perform pixel-wise classification for very high-resolution remote sensing (VHRRS) images. The control gate attention mechanism in the mask branch is utilized to build pixel-wise masks for feature maps, to assign different priorities to different locations on different channels for feature extraction recalibration, to apply stress to the effective features, and to weaken the influence of other profitless features. The feedback attention mechanism in the trunk branch allows for the retrieval of high-level semantic features. Hence, additional aids are provided for lower layers to re-weight the focus and to re-update higher-level feature extraction in a target-oriented manner. These two attention mechanisms are fused to form a neural network module. By stacking various modules with different-scale mask branches, the network utilizes different attention-aware features under different local spatial structures. The proposed method is tested on the VHRRS images from the BJ-02, GF-02, Geoeye, and Quickbird satellites, and the influence of the network structure and the rationality of the network design are discussed. Compared with other state-of-the-art methods, our proposed method achieves competitive accuracy, thereby proving its effectiveness

GAN-Assisted Two-Stream Neural Network for High-Resolution Remote Sensing Image Classification

Using deep learning to improve the capabilities of high-resolution satellite images has emerged recently as an important topic in automatic classification. Deep networks track hierarchical high-level features to identify objects; however, enhancing the classification accuracy from low-level features is often disregarded. We therefore proposed a two-stream deep-learning neural network strategy, with a main stream utilizing fine spatial-resolution panchromatic images to retain low-level information under a supervised residual network structure. An auxiliary line employed an unsupervised net to extract high-level abstract and discriminative features from multispectral images to supplement the spectral information in the main stream. Various feature extraction types from the neural network were selected and jointed in the novel net, as the combined high- and low-level features could provide a superior solution to image classification. In traditional convolutional neural networks, increased network depth might not influence the network performance perceptibly; however, we introduced a residual neural network to develop the expressive ability of the deeper net, increasing the role of net depth in feature extraction. To enhance feature robustness, we proposed a novel consolidation part in feature extraction. An adversarial net improved the feature extraction capabilities and aided digging the inherent and discriminative features from data, with increased extraction efficacy. Tests on satellite images indicated the high overall accuracy of our novel net, verifying that net depth or number of convolution kernels affected the classification capability. Various comparative tests proved the structural rationality for our two-stream structure

Smart Grid Research and Educational Kit to Enable the Control of Power Electronic-based Systems from Simulations to Experiments in Hours

Control and power electronics are two major enablers for the paradigm shift of power systems from centralized generation to distributed generation, the electrification of transportation, and the transformation of billions of lives in third-world countries. Experimental validations of control algorithms for these systems play a vital role. However, setting up a suitable experimental system requires time, effort, and a broad range of expertise. This demonstration session aims to help researchers, graduate students, and engineers remove the barriers to go real from simulations to experiments for various power electronic-based systems. It shows that it is possible to obtain experimental results within hours after completing simulations by adopting the SYNDEM Smart Grid Research and Educational Kit, which is a reconfigurable, open-source, multifunctional power electronic converter with the capability of directly downloading codes from Matlab/Simulink. This will maximize the strengths of the control community in developing control algorithms, minimize the efforts on developing hardware systems and programming control algorithms, and improve the efficiency and productivity of research and learning

Go Real: Power Electronics From Simulations to Experiments in Hours: Versatile Experimental Tool for Next Generation Engineers

The only constant is change-power systems worldwide are going through a paradigm change from centralized generation to distributed generation; transportation systems are being electrified; and billions of lives in third-world countries are awaiting low-cost sustainable electricity. Control and power electronic technologies are two common enablers to address these grand challenges. Empowering next-generation engineers with hands-on skills in control and power electronics has become a priority for global higher education. However, setting up a suitable experimental system requires time, effort, and a broad range of expertise. This article aims to help researchers, university professors, graduate students, and engineers lower the barriers to go real from simulations to experiments for various power electronic systems and improve the efficiency and productivity of research, development, and education. It shows that it is possible to obtain experimental results within hours after completing simulations by adopting the SYNDEM Smart Grid Research and Educational Kit, which is a reconfigurable, opensource, multifunctional power electronic converter with the capability of directly downloading codes from MATLAB/Simulink. This minimizes the time, cost, and efforts needed to develop hardware systems and removes the burden of coding. After briefly introducing the SYNDEM kit and highlighting the automatic code generation capability, two case studies will be illustrated: an ac motor drive and a dc-dc-ac converter for an integrated PV-storage system

Mining the Roles of Cucumber DUF966 Genes in Fruit Development and Stress Response

DUF966 genes are widely found in monocotyledons, dicotyledons, mosses, and other species. Current evidence strongly suggests that they are involved in growth regulation and stress tolerance in crops. However, their functions in cucumbers remain unexplored. Here, cucumber CsDUF966 was systemically identified and characterized using bioinformatics. Eight CsDUF966 genes were identified in the cucumber genome. These were phylogenetically separated into three groups. All CsDUF966 proteins were hydrophilic and localized to the nucleus. Moreover, three acidic and five basic proteins were identified. Evolutionary analysis of DUF966 between cucumber and 33 other Cucurbitaceae species/cultivars revealed that most CsDUF966 genes were conserved, whereas CsDUF966_4.c and CsDUF966_7.c were positively selected among the five cucumber cultivars. Expression profiling analysis showed that CsDUF966 had variable expression patterns, and that miRNA164, miRNA166, and Csa-novel-35 were involved in the post-transcriptional regulation of CsDUF966_4.c and CsDUF966_7.c. The expression of CsDUF966_4.c and CsDUF966_7.c, which were under strong neofunctionalization selection, was strictly regulated in fruit and tissues, including seeds, pericarps, peels, and spines, suggesting that these genes are fruit growth regulators and were strongly selected during the cucumber breeding program. In conclusion, the results reveal the roles of CsDUF966s in regulating cucumber fruit development and lay the foundation for further functional studies

HCG supplement did not accelerate tunica albuginea remodeling to facilitate penile growth

Abstract Penile size is closely concerned and short penis contributes serious sexual dysfunction and tremendous psychological problems to couples. Androgen is essential for penile development and testosterone replacement is recommended to patients with micropenis. We previously proved that inhibiting activity of lysyl oxidase (Anti-lysyl oxidase, Anti-LOX) combined with vacuum erectile device (VED) lengthened penis by remodeling tunica albuginea. We thus explored whether HCG supplement could accelerate tunica albuginea remodeling (induced by Anti-LOX + VED) to promote penile growth. Forty-two SD male rats (4 weeks old) were purchased and divided into 7 groups: control, Anti-LOX, HCG, VED (with a negative aspirated pressure of − 300 mmHg), Anti-LOX + VED, HCG + VED, and Anti-LOX + HCG + VED. After an intervention for 4 weeks, all rats’ penile length, exposed penile length, and erectile function were measured. Serum samples were collected to detect hormone levels and penile corpus cavernosum were harvested for histo-pathological analysis. All intervention groups showed significantly longer penis than controlled rats. Anti-LOX sharply increased penile length and exposed length by 15% and 9% respectively, this lengthening effect was more obvious in Anti-LOX + VED group (26% and 19%, respectively). Although HCG promoted penile length by 8%, this effect was slight for exposed length (3%). Moreover, Anti-LOX + HCG + VED dramatically increased penile length and exposed length by 22% and 18%, respectively, which was similar with that in Anti-LOX + VED (26% and 19%, respectively). HCG dramatically stimulated testosterone and dihydrotestosterone secretions than control group, whether with or without Anti-LOX and VED; while it induced more AR expression than other groups. Finally, all procedures did not improve or deteriorate normal erectile function. Although we verified that Anti-LOX + VED lengthened penis by inducing tunica albuginea remodeling, however, HCG supplement did not synergize with Anti-LOX + VED to accelerate albuginea remodeling to facilitate penile growth

Comparison of hepatotoxicity and mechanisms induced by triclosan (TCS) and methyl-triclosan (MTCS) in human liver hepatocellular HepG2 cells

Triclosan (TCS) is used as an antimicrobial agent and has been widely dispersed and detected in the environment and organisms including human samples. Methyl-triclosan (MTCS) is the predominant bacterial TCS metabolite. At present, the toxicological effects and mechanism of TCS and MTCS are still not fully understood. In this study, the cytotoxic effects of TCS and MTCS in HepG2 cells were investigated in terms of cell proliferation, comet assay, cell cycle, and apoptosis. In addition, the expressions of related proteins were detected with western blotting analysis. The results showed that TCS could significantly inhibit cell proliferation, while MTCS had no obvious effect on cell growth. Both TCS and MTCS caused oxidative injury associated with HO-1 induction and increased DNA strand breaks, which consequently initiated the damage repair process via up-regulation of DNA-PKcs. In addition, TCS blocked the HepG2 cells in S and G2/M phases of cell cycle through down-regulation of cyclin A2 and CDK; while MTCS induced cell cycle arrest at the S phase through up-regulation of cyclin A2 and CDK. Furthermore, TCS activated p53 mediated apoptosis in HepG2 cells in a caspase-independent manner, while MTCS induced apoptosis was dependent on caspase. Moreover, TCS exposure exhibited more severe toxicity in HepG2 cells as compared with MTCS exposure, indicating that the replacement of the ionizable proton in TCS by the methyl group in MTCS is correlated with the cellular toxicity and the molecular mechanism