• Journal of Microbiology and Biotechnology
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• v.31 no.1
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• pp.163-170
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• 2021

Enzyme replacement therapy for lysosomal storage diseases usually requires recombinant enzymes containing mannose-6-phosphate (M6P) glycans for cellular uptake and lysosomal targeting. For the first time, a strategy is established here for the in vitro mannosyl-phosphorylation of high-mannose type N-glycans that utilizes a recombinant Mnn14 protein derived from Saccharomyces cerevisiae. Among a series of N-terminal- or C-terminal-deleted recombinant Mnn14 proteins expressed in Pichia pastoris, rMnn1477-935 with deletion of N-terminal 76 amino acids spanning the transmembrane domain (46 amino acids) and part of the stem region (30 amino acids), showed the highest level of mannosyl-phosphorylation activity. The optimum reaction conditions for rMnn1477-935 were determined through enzyme assays with a high-mannose type N-glycan (Man8GlcNAc2) as a substrate. In addition, rMnn1477-935 was shown to mannosyl-phosphorylate high-mannose type N-glycans (Man7-9GlcNAc2) on recombinant human lysosomal alpha-glucosidase (rhGAA) with remarkably high efficiency. Moreover, the majority of the resulting mannosyl-phosphorylated glycans were bis-form which can be converted to bis-phosphorylated M6P glycans having a superior lysosomal targeting capability. An in vitro N-glycan mannosyl-phosphorylation reaction using rMnn1477-935 will provide a flexible and straightforward method to increase the M6P glycan content for the generation of "Biobetter" therapeutic enzymes.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.496-499
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• 1999

This paper describes a medular neural network(MNN) for a vision system which tracks a given object using a sequence of images from a camera unit. The MNN is used to precisely recognize the given robot endeffector and to minize the processing time. Since the robot endeffector can be viewed in many different shapes in 3-D space, a MNN structure, which contains a set of feedforwared neural networks, co be more attractive in recognizing the given object. Each single neural network learns the endeffector with a cluster of training patterns. The training patterns for a neural network share the similar charateristics so that they can be easily trained. The trained MNN is less sensitive to noise and it shows the better performance in recognizing the endeffector. The recognition rate of MNN is enhanced by 14% over the single neural network. A vision system with the MNN can precisely recognize the endeffector and place it at the center of a display for a remote operator.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.618-626
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• 2004

In this paper, we describe a robot endeffector recognition system based on a Modular Neural Networks (MNN). The proposed recognition system can be used for vision system which track a given object using a sequence of images from a camera unit. The main objective to achieve with the designed MNN is to precisely recognize the given robot endeffector and to minimize the processing time. Since the robot endeffector can be viewed in many different shapes in 3- D space, a MNN structure, which contains a set of feedforwared neural networks, can be more attractive in recognizing the given object. Each single neural network learns the endeffector with a cluster of training patterns. The training MNN patterns for a neural network share the similar characteristics so that they can be easily trained. The trained UM is les s sensitive to noise and it shows the better performance in recognizing the endeffector. The recognition rate of MNN is enhanced by 14％ over the single neural network. A vision system with the MNN can precisely recognize the endeffector and place it at the center of a display for a remote operator.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.57-61
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• 2013

A hybrid genetic algorithm based learning method for the morphological neural networks (MNN) is proposed. The morphological neural networks are based on max-plus algebra, therefore, it is difficult to optimize the coefficients of MNN by the learning method with derivative operations. In order to solve the difficulty, a hybrid genetic algorithm based learning method to optimize the coefficients of MNN is used. Through the image compression/reconstruction experiment using test images extracted from standard image database(SIDBA), it is confirmed that the quality of the reconstructed images obtained by the proposed method is better than that obtained by the conventional neural networks.