• Korean Journal of Biological Psychiatry
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• v.17 no.4
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• pp.203-209
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• 2010

Objectives : Increasing evidence suggests the presence of neurobiological bases for temperamental characteristics in humans. Brain correlates of harm avoidance(HA) have been most extensively studied using functional and structural brain imaging methods due to its potential link with anxiety and depressive disorders. To date, however, we are not aware of any reports that have examined the potential relationship between HA levels and regional cortical thickness. The aim of the current study is to examine the cortical thickness which is associated with HA temperament in healthy young subjects. Methods : Twenty-eight young, healthy individuals(13 men and 15 women, mean age, $29.4{\pm}6.3$ years) were screened for eligibility and administered the Korean version of the Cloninger's Temperament and Character Inventory and underwent high-resolution structural magnetic resonance imaging scanning. Results : HA was associated with cortical thickness in the right superior frontal cortex and in the left parietal cortex, adjusted for age and sex and corrected for multiple comparisons using the permutation testing method. Conclusion : Individual temperamental differences in HA are associated with structural variations in specific areas of the brain. The fact that these brain regions are involved in top-down modulations of subcortical fear reactions adds functional significance to current findings.

• Journal of the Korea Society of Computer and Information
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• v.10 no.3 s.35
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• pp.173-182
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• 2005

In this paper, we propose a landmark recognition method which is irrelevant to the camera viewpoint on the navigation for localization. Features in previous research is variable to camera viewpoint, therefore due to the wealth of information, extraction of visual landmarks for positioning is not an easy task. The proposed method in this paper, has the three following stages; first, extraction of features, second, learning and recognition, third, matching. In the feature extraction stage, we set the interest areas of the image. where we extract the corner points. And then, we extract features more accurate and resistant to noise through statistical analysis of a small eigenvalue. In learning and recognition stage, we form robust feature models by testing whether the feature model consisted of five corner points is an invariant feature irrelevant to viewpoint. In the matching stage, we reduce time complexity and find correspondence accurately by matching method using similarity evaluation function and Graham search method. In the experiments, we compare and analyse the proposed method with existing methods by using various indoor images to demonstrate the superiority of the proposed methods.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.11
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• pp.1537-1544
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• 2015

Meat and carcass quality attributes are of crucial importance influencing consumer preference and profitability in the pork industry. A set of 400 Berkshire pigs were collected from Dasan breeding farm, Namwon, Chonbuk province, Korea that were born between 2012 and 2013. To perform genome wide association studies (GWAS), eleven meat and carcass quality traits were considered, including carcass weight, backfat thickness, pH value after 24 hours (pH24), Commission Internationale de l'Eclairage lightness in meat color (CIE L), redness in meat color (CIE a), yellowness in meat color (CIE b), filtering, drip loss, heat loss, shear force and marbling score. All of the 400 animals were genotyped with the Porcine 62K SNP BeadChips (Illumina Inc., USA). A SAS general linear model procedure (SAS version 9.2) was used to pre-adjust the animal phenotypes before GWAS with sire and sex effects as fixed effects and slaughter age as a covariate. After fitting the fixed and covariate factors in the model, the residuals of the phenotype regressed on additive effects of each single nucleotide polymorphism (SNP) under a linear regression model (PLINK version 1.07). The significant SNPs after permutation testing at a chromosome-wise level were subjected to stepwise regression analysis to determine the best set of SNP markers. A total of 55 significant (p<0.05) SNPs or quantitative trait loci (QTL) were detected on various chromosomes. The QTLs explained from 5.06% to 8.28% of the total phenotypic variation of the traits. Some QTLs with pleiotropic effect were also identified. A pair of significant QTL for pH24 was also found to affect both CIE L and drip loss percentage. The significant QTL after characterization of the functional candidate genes on the QTL or around the QTL region may be effectively and efficiently used in marker assisted selection to achieve enhanced genetic improvement of the trait considered.