• Fisheries and Aquatic Sciences
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• v.19 no.5
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• pp.26.1-26.8
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• 2016

Background: The nematode species belonging to genus Anisakis occur at their third larval stage in numerous marine teleost fish species worldwide and known to cause accidental human infection through the ingestion of raw or undercooked fish or squids. They may also draw the attention of consumers because of the visual impact of both alive and dead worms. Therefore, the information on their geographical distribution and clear species identification is important for epidemiological survey and further prevention of human infection. Results: For identification of anisakid nematodes species isolated from largehead hairtail (Trichiurus japonicus), polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis of internal transcribed spacers of ribosomal DNA were conducted. Mitochondrial cytochrome c oxidase subunit 2 gene was also sequenced, and phylogenetic analysis was conducted. From the largehead hairtail (n = 9), 1259 nematodes were isolated in total. Most of the nematodes were found encapsulated throughout the viscera (56.2 %, 708/1259) or moving freely in the body cavity (41.5 %, 523/1259), and only 0.3 % (4/1259) was found in the muscles. By PCR-RFLP, three different nematode species were identified. Anisakis pegreffii was the most dominantly found (98.7 %, 1243/1259) from the largehead hairtail, occupying 98.7 % (699/708) of the nematodes in the mesenteries and 98.1 % (513/523) in the body cavity. Hybrid genotype (Anisakis simplex ${\times}$ A. pegreffii) occupied 0.5 %, and Hysterothylacium sp. occupied 0.2 % of the nematodes isolated in this study. Conclusions: The largehead hairtail may not significantly contribute accidental human infection of anisakid nematode third stage larvae because most of the nematodes were found from the viscera or body cavity, which are not consumed raw. But, a high prevalence of anisakid nematode larvae in the largehead hairtail is still in concern because they may raise food safety problems to consumers. Immediate evisceration or freezing of fish after catch will be necessary before consumption.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.6-11
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• 2011

In this paper, we propose a gait-based human identification system using eigenfeature regularization and extraction (ERE). First, a gait feature for human identification which is called gait energy image (GEI) is generated from walking sequences acquired from a camera sensor. In training phase, regularized transformation matrix is obtained by applying ERE to the gallery GEI dataset, and the gallery GEI dataset is projected onto the eigenspace to obtain galley features. In testing phase, the probe GEI dataset is projected onto the eigenspace created in training phase and determine the identity by using a nearest neighbor classifier. Experiments are carried out on the CASIA gait dataset A to evaluate the performance of the proposed system. Experimental results show that the proposed system is better than previous works in terms of correct classification rate.

• Anatomy and Cell Biology
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• v.56 no.2
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• pp.161-165
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• 2023

The depressor anguli oris (DAO) muscle is a thin, superficial muscle located below the corner of the mouth. It is the target for botulinum neurotoxin (BoNT) injection therapy, aimed at treating drooping mouth corners. Hyperactivity of the DAO muscle can lead to a sad, tired, or angry appearance in some patients. However, it is difficult to inject BoNT into the DAO muscle because its medial border overlaps with the depressor labii inferioris and its lateral border is adjacent to the risorius, zygomaticus major, and platysma muscles. Moreover, a lack of knowledge of the anatomy of the DAO muscle and the properties of BoNT can lead to side effects, such as asymmetrical smiles. Anatomical-based injection sites were provided for the DAO muscle, and the proper injection technique was reviewed. We proposed optimal injection sites based on the external anatomical landmarks of the face. The aim of these guidelines is to standardize the procedure and maximize the effects of BoNT injections while minimizing adverse events, all by reducing the dose unit and injection points.

• Molecular & Cellular Toxicology
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• v.4 no.3
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• pp.253-259
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• 2008

Human follicular fluid (HFF) is the in vivo microenvironment for oocyte maturation and includes a variety of proteins that could be involved in oocyte development and fertilization. We therefore used a proteomic approach to identify new HFF proteins. HFF from mature human follicles was obtained from five women following oocyte collection for in vitro fertilization (IVF). Ethanol-precipitated HFF run on two-dimensional gel electrophoresis (2DE) produced approximately 250 Coomassie brilliant blue-stained spots, 64 of which were identified using matrix-assisted laser desorption/ionization-mass spectrometry (MALDIMS). In this study, several proteins including complement factor H, inter-${\alpha}$ (globulin) inhibitor H4, inter-${\alpha}$-trypsin inhibitor heavy chain H4 precursor, human zinc-${\alpha}$-2-glycoprotein chain B, PRO2619, PRO02044, and complex-forming glycoprotein HC were new proteins that have not been previously reported in HFF using proteomic methods. Additionally, we identified alloalbumin venezia for the first time from trichloroacetic acid (TCA)-precipitated HFF. These HFF proteins could serve as new biomarkers for important human reproductive processes.

• Journal of Korean Society for Quality Management
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• v.20 no.2
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• pp.94-108
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• 1992

This study involves an experiment for the cognitive experiment design and the human reliability in software engineering. Its overall objectives are to analyze common-cause human domain error and reliability in human-software interaction. A laboratory study was performed to analyze software engineers' task behavior in software production and to identify software design factors contributing to the effects in common cause failure redundancy. Common-cause model and its function were developed, then the main experiment using programming experts was conducted in order to define a new cognitive paradigm, in the aspects of identification, pattern recognition, and behavior domain for human reliability and quality control in software development. The results and analytical procedures developed in this research can be applied to reliability improvement and cost reduction in software development for many applications. Results are also expected to provide guidelines for software engineering quality control and for more effective design of human-software interface system.

• Journal of agricultural medicine and community health
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• v.44 no.1
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• pp.39-42
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• 2019

Zoonoses are the diseases that are transmitted to human being from vertebrate animals either from livestock animals or from wildlife. Recently, zoonoses are increasingly common as a result of incremental human-animal contact. Propagative infections in wild animals and livestock are transmitted to human beings who are encountered with them. In general, wild animals can transmit infectious agents to livestock, and then livestock further transmit them to human being is a simple model of on how zoonotic diseases get transmitted to human being. This model emphasizes the importance of early detection of zoonoses by surveillance at its incipient stage. Cooperation between the respective ministries plays an important role in the identification of zoonoses and planning for the formulation of better preventive and control policy and strategy. We will be able to predict the occurrence of zoonotic diseases in human on the basis of disease trends in wildlife and livestock once when we obtain the surveillance data and data generated by respective ministries through sound cooperation and collaboration.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3E
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• pp.109-117
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• 2009

Identity recognition in real environment with a reliable mode is a key issue in human computer interaction (HCI). In this paper, we present a robust person identification system considering score-based optimal reliability measure of audio-visual modalities. We propose an extension of the modified reliability function by introducing optimizing parameters for both of audio and visual modalities. For degradation of visual signals, we have applied JPEG compression to test images. In addition, for creating mismatch in between enrollment and test session, acoustic Babble noises and artificial illumination have been added to test audio and visual signals, respectively. Local PCA has been used on both modalities to reduce the dimension of feature vector. We have applied a swarm intelligence algorithm, i.e., particle swarm optimization for optimizing the modified convection function's optimizing parameters. The overall person identification experiments are performed using VidTimit DB. Experimental results show that our proposed optimal reliability measures have effectively enhanced the identification accuracy of 7.73% and 8.18% at different illumination direction to visual signal and consequent Babble noises to audio signal, respectively, in comparison with the best classifier system in the fusion system and maintained the modality reliability statistics in terms of its performance; it thus verified the consistency of the proposed extension.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.3
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• pp.127-133
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• 2009

The pre-existing medical treatment was done in person between doctors and patients. EMR (Electronic Medical Record) System computerizing medical history of patients has been proceed and has raised concerns in terms of violation of human right for private information. Which integrates "Identification information" containing patients' personal details as well as "Medical records" such as the medical history of patients and computerizes all the records processed in hospital. Therefore, all medical information should be protected from misuse and abuse since it is very important for every patient. Particularly the right to privacy of medical record for each patient should be surely secured. Medical record means what doctors put down during the medical examination of patients. In this paper, we applies fingerprint identification to EMR system login to raise the quality of personal identification when user access to EMR System. The system implemented in this paper consists of embedded module to carry out fingerprint identification, web server and web site. Existing carries out it in client. And the confidence of hospital service is improved because login is forbidden without fingerprint identification success.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.64-79
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• 2017

Identifying disease genes from human genome is a critical task in biomedical research. Important biological features to distinguish the disease genes from the non-disease genes have been mainly selected based on traditional feature selection approaches. However, the traditional feature selection approaches unnecessarily consider many unimportant biological features. As a result, although some of the existing classification techniques have been applied to disease gene identification, the prediction performance was not satisfactory. A small set of the most important biological features can enhance the accuracy of disease gene identification, as well as provide potentially useful knowledge for biologists or clinicians, who can further investigate the selected biological features as well as the potential disease genes. In this paper, we propose a new stepwise random forests (SRF) approach for biological feature selection and disease gene identification. The SRF approach consists of two stages. In the first stage, only important biological features are iteratively selected in a forward selection manner based on one-dimensional random forest regression, where the updated residual vector is considered as the current response vector. We can then determine a small set of important biological features. In the second stage, random forests classification with regard to the selected biological features is applied to identify disease genes. Our extensive experiments show that the proposed SRF approach outperforms the existing feature selection and classification techniques in terms of biological feature selection and disease gene identification.

• Smart Structures and Systems
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• v.6 no.1
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• pp.39-56
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• 2010

Structural Control relies, with a great deal, on the ability of the control algorithm to identify the current state of the system, at any given point in time. When such algorithms are designed to perform in a smart manner, several smart technologies/devices are called upon to perform tasks that involve pattern recognition and control. Smart pattern recognition is proposed to replace/enhance traditional state identification techniques, which require the extensive manipulation of intricate mathematical equations. Smart pattern recognition techniques attempt to emulate the behavior of the human brain when performing abstract pattern identification. Since these techniques are largely heuristic in nature, it is reasonable to ensure their reliability under real life situations. In this paper, a neural network pattern recognition scheme is explored. The pattern identification of three structural systems is considered. The first is a single bay three-story frame. Both the second and the third models are variations on benchmark problems, previously published for control strategy evaluation purposes. A Neural Network was developed and trained to identify the deformed shape of structural systems under earthquake excitation. The network was trained, for each individual model system, then tested under the effect of a different set of earthquake records. The proposed smart pattern identification scheme is considered an integral component of a Smart Structural System. The Reliability assessment of such component represents an important stage in the evaluation of an overall reliability measure of Smart Structural Systems. Several studies are currently underway aiming at the identification of a reliability measure for such smart pattern recognition technique.