• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.155-162
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• 2017

In this paper we propose influence measures for two basic goodness-of-fit statistics, the coefficient of determination $R^2$ and test statistic F in the linear regression model using the deletion method. Some useful lemmas are provided. We also express the influence measures in terms of basic building blocks such as residual, leverage, and deviation that showed them as increasing function of residuals and a decreasing function of deviation. Further, the proposed measure reduces computational burden from O(n) to O(1). As illustrative examples, we applied the proposed measures to the stackloss data sets. We verified that deletion of one or few influential observations may result in big change in $R^2$ and F-statistic.

• BMB Reports
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• v.41 no.11
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• pp.778-783
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• 2008

Turnip yellow mosaic virus (TYMV) RNA has two hairpins in its 5' untranslated region (5'-UTR). To investigate the role of the hairpins in replication of TYMV, mutants lacking one or both of the two hairpins were constructed. The TYMV constructs were introduced into Chinese cabbage by an Agrobacterium-mediated T-DNA transfer method, called agroinfiltration. Analysis of total RNA from agroinfiltrated leaves showed that replication of the mutant TYMV RNA lacking both hairpins was about 1/100 of wild type. This mutant was also impaired in systemic spread. Deletion analysis of each hairpin revealed that both hairpins were needed for maximal replication. The deletion analysis along with sequence modification of the hairpin structure indicates that the second hairpin plays a role in efficient long-distance systemic movement of TYMV.

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.74-88
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• 1995

This article introduces a unified method of choosing the most explanatory and significant multiway partitions for classification tree design and analysis. The method is derived on the impurity reduction (IR) measure of divergence, which is proposed to extend the proportional-reduction-in-error (PRE) measure in the decision-theory context. For the method derivation, the IR measure is analyzed to characterize its statistical properties which are used to consistently handle the subjects of feature formation, feature selection, and feature deletion required in the associated classification tree construction. A numerical example is considered to illustrate the proposed approach.

• Journal of Broadcast Engineering
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• v.22 no.6
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• pp.734-743
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• 2017

In this paper, we introduce a technique to detect the video forgery using coding pattern analysis. In the proposed method, the recently developed standard HEVC codec, which is expected to be widely used in the future, is used. First, HEVC coding patterns of the forged and the original videos are analyzed to select the discriminative features, and the selected feature vectors are learned through the machine learning technique to model the classification criteria between two groups. Experimental results show that the proposed method is more effective to detect frame deletions for HEVC-coded videos than existing works.

• The Plant Pathology Journal
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• v.32 no.3
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• pp.173-181
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• 2016

Gene disruption by homologous recombination is widely used to investigate and analyze the function of genes in Fusarium fujikuroi, a fungus that causes bakanae disease and root rot symptoms in rice. To generate gene deletion constructs, the use of conventional cloning methods, which rely on restriction enzymes and ligases, has had limited success due to a lack of unique restriction enzyme sites. Although strategies that avoid the use of restriction enzymes have been employed to overcome this issue, these methods require complicated PCR steps or are frequently inefficient. Here, we introduce a cloning system that utilizes multi-fragment assembly by In-Fusion to generate a gene disruption construct. This method utilizes DNA fragment fusion and requires only one PCR step and one reaction for construction. Using this strategy, a gene disruption construct for Fusarium cyclin C1 (FCC1), which is associated with fumonisin B1 bio-synthesis, was successfully created and used for fungal transformation. In vivo and in vitro experiments using confirmed fcc1 mutants suggest that fumonisin production is closely related to disease symptoms exhibited by F. fujikuroi strain B14. Taken together, this multi-fragment assembly method represents a simpler and a more convenient process for targeted gene disruption in fungi.

• The Korean Journal of Applied Statistics
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• v.10 no.1
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• pp.137-149
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• 1997

In this paper we propose a logdensity estimation based on a Fourier expansion. The basis functions consisting of trigonometric functions are determinded by stepwise addition and deletion and the Bayes Information Criterion, where the maximum likelihood method is used to estimate the parameters. Numericla examples using real data and simulated data are provided to show the performance of proposed method.

• Journal of Genetic Medicine
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• v.13 no.1
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• pp.36-40
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• 2016

Tuberous sclerosis complex (TSC, MIM#191100) is an autosomal dominant neurocutaneous syndrome caused by mutation or deletion of TSC1 encoding hamartin or TSC2 encoding tuberin and characterized by seizure, mental retardation, and multiple hamartomas or benign tumors in the skin, brain, retina, heart, kidney, and lungs. The TSC2 gene on chromosome 16p13.3 lies adjacent to the PKD1 gene which is responsible for autosomal dominant polycystic kidney disease (MIM#173900). The TSC2/PKD1 contiguous gene syndrome (TSC2/PKD1 CGDS, MIM#600273) is caused by deletion of both TSC2 and PKD1 gene. We recently experienced a 15 month-old boy and a 26 month-old girl with TSC2/PKD1 CGDS confirmed by multiplex ligation-dependent probe amplification (MLPA) analysis. They showed not only typical neurologic manifestations of TSC such as epilepsy, subependymal nodules, and subcortical tubers, but also polycystic kidney disease. The contiguous gene syndrome involving PKD1 and TSC2 should be suspected in children with enlarged polycystic kidneys and TSC. MLPA analysis is a useful method for the genetic confirmation of TSC2/PKD1 CGDS.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1607-1621
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• 2018

The financial industry, which guarantees a credible transaction and can not permit forgery of its transaction information, has hitherto adhered to a traditional centralized ledger management method. However, the blockchain technology has a decentralization which has been regarded as unsafe for the time being, and the more reliable transaction agreement and data integrity are guaranteed The world's financial industry and the IT world is causing the wave. Nevertheless, the inherent characteristics of the blockchain, such as the irreversibility of block information within a blockchain and the sharing of blocks between blockchain participants, can not avoid conflicts with the privacy laws. The purpose of this study is to investigate the problems related to deletion and the third party supply of personal information by focusing on these characteristics of the blockchain and to suggest the technical alternatives of the applicable blockchain and the improvement direction of the personal information protection law for using of blockchain technology.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.886-895
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• 2018

In this paper, we introduce a technique to detect the video forgery by using the regularity that occurs in the video compression process. The proposed method uses the hierarchical regularity lost by the video double compression and the frame deletion. In order to extract such irregularities, the depth information of CU and TU, which are basic units of HEVC, is used. For improving performance, we make a depth map of CU and TU using local information, and then create input data by grouping them in GoP units. We made a decision whether or not the video is double-compressed and forged by using a general three-dimensional convolutional neural network. Experimental results show that it is more effective to detect whether or not the video is forged compared with the results using the existing machine learning algorithm.

• Journal of Korean Society of Transportation
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• v.24 no.5 s.91
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• pp.77-88
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• 2006

The purpose of this study is to Propose the time dependent K-least time path algorithm applicable to a real-time based operation strategy in multi-modal transportation network. For this purpose, we developed the extended method based on entire path deletion method which was used in the static K-least time path algorithm. This method was applied to time dependent K-least time path algorithm to find k least time paths in order based on both time dependant mode-link travel time and transfer cost In particular, this algorithm find the optimal solution, easily describing transfer behavior, such as walking and waiting for transfer by applying a link-based time dependent label. Finally, we examined the verification and application of the Proposed algorithm through case study.