592 research outputs found
Marker Controlled Superpixel Nuclei Segmentation and Automatic Counting on Immunohistochemistry Staining Images
No full textEffects of electron donors on anaerobic microbial debromination of polybrominated diphenyl ethers (PBDEs)
No full textAchieving high performance corrosion and wear resistant epoxy coatings via incorporation of noncovalent functionalized graphene
No full textGraphene(G)-based polymer nanocomposites have attracted great interest owing to their superior physicochemical properties over polymers. However, the tendency of graphene sheets to aggregate makes it difficult to achieve homogenous dispersion in polymer matrix. Herein, by utilization of poly(2-butylaniline) (P2BA) as a dispersing agent, stable dispersion of graphene in organic solvents was achieved via non-covalent pi-pi interactions between P2BA and graphene nanosheets. The exfoliated graphene nanosheets were then integrated with coating matrix by curing reaction of epoxy resin with P2BA functionalized graphene (P2BA-G) and amine hardener. Embedding a small percentage of well-dispersed graphene nanosheets (P2BA(0.5%)-G(0.5%)) in epoxy coating remarkably improved anticorrosion performance and wear resistance properties, which was attributed to the synergistic effects of the redox catalytic capability of P2BA, high mechanical and barrier properties of well-dispersed graphene nanosheets in the epoxy matrix. The present study provides a promise strategy for development of graphene reinforced organic coatings with superior physical-mechanical properties for metal protection. (C) 2016 Elsevier Ltd. All rights reserved
Anticorrosion Performance of Epoxy Coating Containing Reactive Poly (o-phenylenediamine) Nanoparticles
No full textThe free amine-containing poly(o-phenylenediamine) (PoPD) nanoparticles with significant dispersibility in organic solvents have been synthesized by chemical oxidative polymerization of o-phenylenediamine mono-hydrochloride salt. The epoxy coatings with different contents of PoPD nanoparticles (0.5 wt%, 1 wt% and 2 wt%) were then prepared by curing reaction of epoxy resin, amine hardener and amine containing PoPD nanoparticles. The corrosion protection properties of the as prepared coatings on Q235 steel were investigated by potentiodynamic polarization, open circuit potential (OCP) and electrochemical impedance spectroscopy (EIS) technique in 3.5 wt % NaCl aqueous solution for 90 days. The results indicate that the coatings with 0.5 wt% PoPD nanoparticles (0.5-PDEP) exhibits high anticorrosive performance, which is attributed to the improved barrier effect of the nano-fillers and redox catalytic capability of embedded PoPD nanoparticles with the evidence of scanning electron microscope (SEM) and XRD. This novel amine-containing PoPD nanoparticles give a promising way to enhance the anticorrosion performance of epoxy coatings and potentially have a wider range of applications in anticorrosion related engineering applications
A Fast Algorithm for Constructing Image Identification
No full textthrough an image database which contains a large number of different images, only a small number of which are of interest. In order to select the desired image, the user may have to download (from a remote site) the complete image itself. Instead of downloading the complete image, it would save time and network bandwidth by first sending a much compressed version of the image (its identification image) that gives sufficient information to allow the user visually classify the image and then transmit the complete version of the desired image. In this work, we present a computationally very simple approach to create image identification for viewing classification purpose. The new scheme compresses and transmits only the information in and around the edges of the image and a simple interpolation technique is used to construct the ID image from those compressed features at the user's computer. The technique described herein may be useful in tele-browsing image databases
Semi-supervised Learning based on Bayesian Networks and Optimization for Interactive Image Retrieval
No full textIn this paper, we present a novel interactive image retrieval technique using semi-supervised learning. Recently, Guan and Qiu [8, 9] have shown that by constructing a Bayesian Network where the nodes represent the (continuous) class membership scores and arcs represent the dependence relations of the data points, the (semi-supervised) classification problem can be formulated as a quadratic optimization problem; and by using the labeled data as linear constraints, the optimization problem yields a large, sparse system of linear equations which can be solved very efficiently using standard methods. In this work, we show that this semi-supervised learning method can be naturally adopted as a computational tool to incorporate users feedbacks for interactive image retrieval. We present experimental results to show the effectiveness of our new interactive image retrieval method. We also show that semisupervised learning can have advantages over supervised and unsupervised learning in image retrieval applications.
Comparative genomic and transcriptomic analysis revealed genetic characteristics related to solvent formation and xylose utilization in <it>Clostridium acetobutylicum </it>EA 2018
No full textAbstract Background Clostridium acetobutylicum, a gram-positive and spore-forming anaerobe, is a major strain for the fermentative production of acetone, butanol and ethanol. But a previously isolated hyper-butanol producing strain C. acetobutylicum EA 2018 does not produce spores and has greater capability of solvent production, especially for butanol, than the type strain C. acetobutylicum ATCC 824. Results Complete genome of C. acetobutylicum EA 2018 was sequenced using Roche 454 pyrosequencing. Genomic comparison with ATCC 824 identified many variations which may contribute to the hyper-butanol producing characteristics in the EA 2018 strain, including a total of 46 deletion sites and 26 insertion sites. In addition, transcriptomic profiling of gene expression in EA 2018 relative to that of ATCC824 revealed expression-level changes of several key genes related to solvent formation. For example, spo0A and adhEII have higher expression level, and most of the acid formation related genes have lower expression level in EA 2018. Interestingly, the results also showed that the variation in CEA_G2622 (CAC2613 in ATCC 824), a putative transcriptional regulator involved in xylose utilization, might accelerate utilization of substrate xylose. Conclusions Comparative analysis of C. acetobutylicum hyper-butanol producing strain EA 2018 and type strain ATCC 824 at both genomic and transcriptomic levels, for the first time, provides molecular-level understanding of non-sporulation, higher solvent production and enhanced xylose utilization in the mutant EA 2018. The information could be valuable for further genetic modification of C. acetobutylicum for more effective butanol production.</p
Luminance Adaptive Biomarker Detection in Digital Pathology Images
No full textAbstractDigital pathology is set to revolutionise traditional approaches diagnosing and researching diseases. To realise the full potential of digital pathology, accurate and robust computer techniques for automatically detecting biomarkers play an important role. Traditional methods transform the colour histopathology images into a gray scale image and apply a single threshold to separate positively stained tissues from the background. In this paper, we show that the colour distribution of the positive immunohis-tochemical stains varies with the level of luminance and that a single threshold will be impossible to separate positively stained tissues from other tissues, regardless how the colour pixels are transformed. Based on this, we propose two novel luminance adaptive biomarker detection methods. We present experimental results to show that the luminance adaptive approach significantly improves biomarker detection accuracy and that random forest based techniques have the best performances
Bipartite Graph Partitioning and Content-based Image Clustering
No full textThis paper presents a method to model the images and their content descriptors in large image databases using bipartite graphs. A graph partitioning algorithm is then developed to cluster the images and their content description features simultaneously such that each cluster is automatically associated with the set of features that best describes its visual contents. The association of features with image clusters enables semantic based search of image databases and the division of the database into visually aligned hierarchical groups facilitates fast content-based image retrieval
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