• The Research Journal of the Costume Culture
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• v.10 no.2
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• pp.146-159
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• 2002

This study examines the textile pattern design of Italian fashion brands and their brand extensions by comparing their images. Five Italian fashion brands are chosen and the textile pattern design of their brand extensions, which were presented during the eight collection. Then their design style is compared with the design style of their main brands. The five main brands and their brand extensions are as fellows: Anna Molinari-Blumarine, Dolce & Gabbana-D&G, Girogio Armani-Emporio Armani, Gian Franco Ferre'-GFF, and Prada-MiuMiu. Their color, motive type, motive layout, motive expression, and pattern drawing technique are examined and compared. Results suggest that most brand extensions generally use color, motive type motive layout. and motive expression similar to their main brands. In particular, their pattern drawing technique is a painting style white their main brands use a graphic style. This result suggests that to create and develop new brand extensions, Italian fashion (main brand) firms in general employ color, motive type, motive layout, and motive expression technique similar to main brands, but different drawing technique to differentiate from their main brands. The results of this study suggest that textile pattern design plays an important role in developing new brand extensions and thus should be considered as a crucial part of the product.

• The Korea Journal of Herbology
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• v.27 no.4
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• pp.33-44
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• 2012

Objectives : There is a pressing need to determine the clinical and scientific validity of herbal therapies for animal model with atopic dermatitis since some differences in systemic cytokine polarization between in animal model and in patients with atopic dermatitis has been reported. New studies for tang, medicinal herb itself or effective ingradients of medicinal herb showing anti-atopic dermatitis effectiveness are reviewed in terms of cytokine regulation. Methods : Those herbal therapies used to treat atopic dermatitis in animal model were introduced and the expression pattern of cytokine and the activity of mast cell were compared in both animal model and patients with atopic dermatitis. Results : In case of atopic dermatitis in human, there is a biphasic pattern of cytokine expression in atopic dermatitis, with acute skin inflammation associated with a predominance of IL-4 and IL-13 expression from Th2 cells, and chronic inflammation associated with increased IL-5 from Th2-cells and IFN-${\gamma}$ from Th1-cells. However, a pattern of cytokine expression in animal model with atopic dermatitis is not matched well to the biphasic pattern of cytokine expression in patients with atopic dermatitis. In addition, a kind of cytokine is different by animal model with atopic dermatitis. These differences would make herbal medicines, showing their effectiveness on atopic dermatitis, difficult to apply to patients with atopic dermatitis. Conclusion : The pattern of local cytokine expression plays an important role in modulating tissue inflammation, and in atopic dermatitis this pattern depends on the acuity or duration of the skin lesion. Thus, in order to develop medicinal herb itself or effective ingradients of medicinal herb showing anti-atopic dermatitis effectiveness, biphasic pattern of cytokine expression should be considered in animal model with atopic dermatitis.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.269-280
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• 2020

Time course gene expression data is a large amount of data observed over time in microarray experiments. This data can also simultaneously identify the level of gene expression. However, the experiment process is complex, resulting in frequent missing values due to various causes. In this paper, we propose a pattern consistency index adaptive nearest neighbors as a method of missing value imputation. This method combines the adaptive nearest neighbors (ANN) method that reflects local characteristics and the pattern consistency index that considers consistent degree for gene expression between observations over time points. We conducted a Monte Carlo simulation study to evaluate the usefulness of proposed the pattern consistency index adaptive nearest neighbors (PANN) method for two yeast time course data.

• Journal of Periodontal and Implant Science
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• v.41 no.4
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• pp.167-175
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• 2011

Purpose: Periodontal ligament (PDL) cell differentiation into osteoblasts is important in bone formation. Bone formation is a complex biological process and involves several tightly regulated gene expression patterns of bone-related proteins. The expression patterns of bone related proteins are regulated in a temporal manner both in vivo and in vitro. The aim of this study was to observe the gene expression profile in PDL cell proliferation, differentiation, and mineralization in vitro. Methods: PDL cells were grown until confluence, which were then designated as day 0, and nodule formation was induced by the addition of 50 ${\mu}g$/mL ascorbic acid, 10 mM ${\beta}$-glycerophosphate, and 100 nM dexamethasone to the medium. The dishes were stained with Alizarin Red S on days 1, 7, 14, and 21. Real-time polymerase chain reaction was performed for the detection of various genes on days 0, 1, 7, 14, and 21. Results: On day 0 with a confluent monolayer, in the active proliferative stage, c-myc gene expression was observed at its maximal level. On day 7 with a multilayer, alkaline phosphatase, bone morphogenetic protein (BMP)-2, and BMP-4 gene expression had increased and this was followed by maximal expression of osteocalcin on day 14 with the initiation of nodule mineralization. In relationship to apoptosis, c-fos gene expression peaked on day 21 and was characterized by the post-mineralization stage. Here, various genes were regulated in a temporal manner during PDL fibroblast proliferation, extracellular matrix maturation, and mineralization. The gene expression pattern was similar. Conclusions: We can speculate that the gene expression pattern occurs during PDL cell proliferation, differentiation, and mineralization. On the basis of these results, it might be possible to understand the various factors that influence PDL cell proliferation, extracellular matrix maturation, and mineralization with regard to gene expression patterns.

• Genomics & Informatics
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• v.1 no.1
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• pp.32-39
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• 2003

We present a latent variable model-based approach to the analysis of gene expression patterns, coupled with topographic clustering. Aspect model, a latent variable model for dyadic data, is applied to extract latent patterns underlying complex variations of gene expression levels. Then a topographic clustering is performed to find coherent groups of genes, based on the extracted latent patterns as well as individual gene expression behaviors. Applied to cell cycle­regulated genes of the yeast Saccharomyces cerevisiae, the proposed method could discover biologically meaningful patterns related with characteristic expression behavior in particular cell cycle phases. In addition, the display of the variation in the composition of these latent patterns on the cluster map provided more facilitated interpretation of the resulting cluster structure. From this, we argue that latent variable models, coupled with topographic clustering, are a promising tool for explorative analysis of gene expression data.

• Journal of the Korean Home Economics Association
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• v.47 no.6
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• pp.1-10
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• 2009

This study investigated the influence of children's emotional expression and sociability, and their mothers' communication pattern on their prosocial behavior. The participants were 65 preschool children aged between 5 and 6, and their mothers. Each child-mother dyad was observed for 30 minutes in a lab setting, which was designed to evaluate the child's socioemotional competence and the mother's socialization behavior. Videotaped data were analyzed by two coders for aspects of sharing behavior, the expression of happiness, sadness, anger, anxiety, and sociability for children, and mothers' communication strategies. Results showed that children's anger and anxiety expression were the most significant predictors for their prosocial behavior. Mothers' punitive communication pattern negatively affected children's prosocial behavior. However, when compared to the children's emotional expression, its' accountability were not significant. The influence of negative emotions, and its' adverse role in interpersonal interactions are discussed.

• ETRI Journal
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• v.32 no.5
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• pp.784-794
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• 2010

Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance-based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well-known machine learning methods, template matching and support vector machine, are used for classification using the Cohn-Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance-based feature descriptors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.1B
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• pp.47-55
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• 2009

In recent network intrusion detection systems, regular expressions are used to represent malicious packets. In order to process incoming packets through high speed networks in real time, we should perform hardware-based pattern matching using the configurable device such as FPGAs. However, operating speed of FPGAs is slower than giga-bit speed network and so, multi-byte processing per clock cycle may be needed. In this paper, we propose a hardware architecture of multi-byte based regular expression pattern matching and implement the pattern matching circuit generator. The throughput improvements in four-byte based pattern matching circuit synthesized in FPGA for several Snort rules are $2.62{\sim}3.4$ times.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.275-283
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• 2018

Cancer show distinct pattern of gene expression when it is compared to normal. This difference results malignant characteristic of cancer. Many cancer drugs are targeting this difference so that it can selectively kill cancer cells. One of the recent demand for personalized treating cancer is retrieving normal tissue from a patient so that the gene expression difference between cancer and normal be assessed. However, in most clinical situation it is hard to retrieve normal tissue from a patient. This is because biopsy of normal tissues may cause damage to the organ function or a risk of infection or side effect what a patient to take. Thus, there is a challenge to estimate normal cell's gene expression where cancers are originated from without taking additional biopsy. In this paper, we propose in-silico based prediction of normal cell's gene expression from gene expression data of a tumor sample. We call this challenge as reverting the cancer into normal. We divided this challenge into two parts. The first part is making a generator that is able to fool a pretrained discriminator. Pretrained discriminator is from the training of public data (9,601 cancers, 7,240 normals) which shows 0.997 of accuracy to discriminate if a given gene expression pattern is cancer or normal. Deceiving this pretrained discriminator means our method is capable of generating very normal-like gene expression data. The second part of the challenge is to address whether generated normal is similar to true reverse form of the input cancer data. We used, cycle-consistent adversarial networks to approach our challenges, since this network is capable of translating one domain to the other while maintaining original domain's feature and at the same time adding the new domain's feature. We evaluated that, if we put cancer data into a cycle-consistent adversarial network, it could retain most of the information from the input (cancer) and at the same time change the data into normal. We also evaluated if this generated gene expression of normal tissue would be the biological reverse form of the gene expression of cancer used as an input.

• Journal of Radiation Industry
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• v.5 no.1
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• pp.47-54
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• 2011

In this study, we investigated to evaluate differential expression of genes encoding lipid transfer proteins (LTP) by acute and chronic gamma irradiation in rice. After acute and chronic gamma irradiation by 100 Gy and 400 Gy to rice plant, necrotic lesion was observed in the leaf blade and anthocyanin contents were increased. We isolated a total of 21 rice lipid transfer protein (LTP) genes in the TIGR database, and these genes were divided into four different groups on the basis of nucleotide sequences. The LTP genes also were classified as different four classes according to expression pattern using RT-PCR. Group A, B contained genes with increased expression and decreased expression in acute and chronic, respectively. Group C contained genes with contrasted expression pattern. Group D wasn't a regular pattern. But the specific affinity was not obtained between two grouping.