• Asian-Australasian Journal of Animal Sciences
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• v.33 no.12
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• pp.2021-2030
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• 2020

Objective: Quantitative polymerase chain reaction (qPCR) has been extensively used in the field of mesenchymal stem cell (MSC) research to elucidate their characteristics and clinical potential by normalization of target genes against reference genes (RGs), which are believed to be stably expressed irrespective of various experimental conditions. However, the expression of RGs is also variable depending on the experimental conditions, which may lead to false or contradictory conclusions upon normalization. Due to the current lack of information for a clear list of stable RGs in bovine MSCs, we conducted this study to identify suitable RGs in bovine MSCs. Methods: The cycle threshold values of ten traditionally used RGs (18S ribosomal RNA [18S], beta-2-microglobulin [B2M], H2A histone family, member Z [H2A], peptidylprolyl isomerase A [PPIA], ribosomal protein 4 [RPL4], succinate dehydrogenase complex, subunit A [SDHA], beta actin [ACTB], glyceraldehyde-3-phosphate dehydrogenase [GAPDH], TATA box binding protein [TBP], and hypoxanthine phosphoribosyltrasnfrase1 [HPRT1]) in bovine bone marrow-derived MSCs (bBMMSCs) were validated for their stabilities using three types of RG evaluation algorithms (geNorm, Normfinder, and Bestkeeper). The effect of validated RGs was then verified by normalization of lineage-specific genes (fatty acid binding protein 4 [FABP4] and osteonectin [ON]) expressions during differentiations of bBMMSCs or POU class 5 homeobox 1 (OCT4) expression between bBMMSCs and dermal skins. Results: Based on the results obtained for the three most stable RGs from geNorm (TBP, RPL4, and H2A), Normfinder (TBP, RPL4, and SDHA), and Bestkeeper (TBP, RPL4, and SDHA), it was comprehensively determined that TBP and RPL4 were the most stable RGs in bBMMSCs. However, traditional RGs were suggested to be the least stable (18S) or moderately stable (GAPDH and ACTB) in bBMMSCs. Normalization of FABP4 or ON against TBP, RPL4, and 18S presented significant differences during differentiation of bBMMSCs. However, although significantly low expression of OCT4 was detected in dermal skins compared to that in bBMMSCs when TBP and RPL4 were used in normalization, normalization against 18S exhibited no significance. Conclusion: This study proposes that TBP and RPL4 were suitable as stable RGs for qPCR study in bovine MSCs.

• Proceedings of the Acoustical Society of Korea Conference
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• 1993.06a
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• pp.238-241
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• 1993

We present a new method to normalize spectral variations of different speakers based on physiological studies of hearing. The proposed method uses the cochlear frequency map to warp the input speech spectra by interpolation or decimation. Using this normalization method, we can obtain much improved recognition results for speaker independent speech recognition.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.585-594
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• 2009

Researchers of microarray experiment transpose processed images of raw data to possible data of statistical analysis: it is preprocessing. Preprocessing of microarray has image filtering, imputation and normalization. There have been studied about several different methods of normalization and imputation, but there was not further study on the order of the procedures. We have no further study about which things put first on our procedure between normalization and imputation. This study is about the identification of differentially expressed genes(DEG) on the order of the preprocessing steps using two-dye cDNA microarray in colon cancer and gastric cancer. That is, we check for compare which combination of imputation and normalization steps can detect the DEG. We used imputation methods(K-nearly neighbor, Baysian principle comparison analysis) and normalization methods(global, within-print tip group, variance stabilization). Therefore, preprocessing steps have 12 methods. We identified concordance measure of DEG using the datasets to which the 12 different preprocessing orders were applied. When we applied preprocessing using variance stabilization of normalization method, there was a little variance in a sensitive way for detecting DEG.

• The Journal of the Acoustical Society of Korea
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• v.29 no.4
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• pp.278-285
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• 2010

The difference between training and test environments is one of the major performance degradation factors in noisy speech recognition and many silence feature normalization methods were proposed to solve this inconsistency. Conventional silence feature normalization method represents higher classification performance in higher SNR, but it has a problem of performance degradation in low SNR due to the low accuracy of speech/silence classification. On the other hand, cepstral distance represents well the characteristic distribution of speech/silence (or noise) in low SNR. In this paper, we propose a Cepstral distance and Log-energy based Silence Feature Normalization (CLSFN) method which uses both log-energy and cepstral euclidean distance to classify speech/silence for better performance. Because the proposed method reflects both the merit of log energy being less affected with noise in high SNR and the merit of cepstral distance having high discrimination accuracy for speech/silence classification in low SNR, the classification accuracy will be considered to be improved. The experimental results showed that our proposed CLSFN presented the improved recognition performances comparing with the conventional SFN-I/II and CSFN methods in all kinds of noisy environments.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.175-181
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• 2002

cDNA microarray experiments permit us to investigate the expression levels of thousands of genes simultaneously and to make it easy to compare gene expression from different populations. However, researchers are asked to be cautious in interpreting the results because of the unexpected sources of variation such as systematic errors from the microarrayer and the difference of cDNA dye intensity. And the scanner itself calculates both of mean and median of the signal and background pixels, so it follows a selection which raw data will be used in analysis. In this paper, we compare the results in each case of using mean and median from the raw data and normalization methods in reducing the systematic errors with arm's skin cells of old and young males. Using median is preferable to mean because the distribution of the test statistic (t-statistic) from the median is more close to normal distribution than that from mean. Scaled print tip normalization is better than global or lowess normalization due to the distribution of the test-statistic.

• The Journal of the Acoustical Society of Korea
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• v.39 no.1
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• pp.64-68
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• 2020

In a conventional speech recognition system using Gaussian Mixture Model-Hidden Markov Model (GMM-HMM), the cepstral feature normalization method based on pole filtering was effective in improving the performance of recognition of short utterances in noisy environments. In this paper, the usefulness of this method for the state-of-the-art speech recognition system using Deep Neural Network (DNN) is examined. Experimental results on AURORA 2 DB show that the cepstral mean and variance normalization based on pole filtering improves the recognition performance of very short utterances compared to that without pole filtering, especially when there is a large mismatch between the training and test conditions.

• Journal of Korean Society for Quality Management
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• v.41 no.4
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• pp.709-724
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• 2013

Purpose: The purpose of this study is to examine five evaluation models constructed by different normalization and aggregation methods in terms of the volatility of rankings and rank reversals. We also explore how the volatility of rankings of the five models changes and how often the rank reversals occur when the outliers are removed. Methods: We used data published in the Complete University Guide 2014. Two universities with missing values were excluded from the data. The university rankings were derived by using the five models, and then each model's volatility of rankings was measured. The box-plot was used to detect outliers. Results: Model 1 has the lowest volatility among the five models whether or not the outliers are included. Model 5 has the lowest number of rank reversals. Model 3, which has been used by many institutions, appears to be in the middle among the five in terms of the volatility and the rank reversals. Conclusion: The university rankings vary from one evaluation model to another depending on what normalization and aggregation methods are used. No single model exhibits clear superiority over others in both the volatility and the rank reversal. The findings of this study are expected to provide a stepping stone toward a superior model which is both reliable and robust.

• Journal of the Korean Institute of Oriental Medical Informatics
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• v.15 no.2
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• pp.1-6
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• 2009

The discipline of terminology is based on its own theoretical principles and consists primarily of the following aspects: analysing the concepts and concept structures used in a field or domain of activity, identifying the terms assigned to the concepts, in the case of bilingual or multilingual terminology, establishing correspondences between terms in the various languages, creating new terms, as required. The word properties has syntax, morphology and orthography. The syntax is that how words are put together. The morphology is consist of inflection, derivation, and compounding. The orthography is spelling. Otherwise, the terms of TKM(Traditional Korean Medicine) is two important element of visual character and phonetic notation. A visual character consist of spell, sort words, stop words, etc. For example, that is a case of sort words in which this '다한', '한다', '多汗', '汗多' as same. A phonetic notation consist of palatalization, initial law, etc. For example, that is a case of palatalization in which this '수족랭', '수족냉', '手足冷', '手足冷' as same. Therefore, to enlarged reference terminology is a method by term's normalization. For such a reason, TKM's terms of normalization is necessary.

• Genomics & Informatics
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• v.1 no.2
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• pp.94-100
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• 2003

Motivation: Many have observed a nonlinear relationship between the signal intensity and the transcript abundance in microarray data. The first step in analyzing the data is to normalize it properly, and this should include a correction for the nonlinearity. The commonly used linear normalization schemes do not address this problem. Results: Nonlinearity is present in both cDNA and oligonucleotide arrays, but we concentrate on the latter in this paper. Across a set of chips, we identify those genes whose within-chip ranks are relatively constant compared to other genes of similar intensity. For each gene, we compute the sum of the squares of the differences in its within-chip ranks between every pair of chips as our statistic and we select a small fraction of the genes with the minimal changes in ranks at each intensity level. These genes are most likely to be non-differentially expressed and are subsequently used in the normalization procedure. This method is a generalization of the rank-invariant normalization (Li and Wong, 2001), using all available chips rather than two at a time to gather more information, while using the chip that is least likely to be affected by nonlinear effects as the reference chip. The assumption in our method is that there are at least a small number of non­differentially expressed genes across the intensity range. The normalized expression values can be substantially different from the unnormalized values and may result in altered down-stream analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11C
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• pp.1052-1061
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• 2003

This paper proposes a novel normalization scheme in the state metric calculation unit for the Block-wise MAP Turbo decoder. The proposed scheme subtracts one of four metrics from the state metrics in a trellis stage and shifts, if necessary, those metrics for normalization. The proposed architecture can reduce power consumption and memory requirement by reducing the number of the state metrics by one in a trellis stage in the Block-wise MAP decoder which requires an intensive state metric calculations. Simulation results show that dynamic power has been reduced by 17.9% and area has been reduced by 6.6% in the Turbo decoder employing the proposed normalization scheme, when compared to the conventional Block-wise MAP Turbo decoders.