• Tuberculosis and Respiratory Diseases
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• v.62 no.5
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• pp.406-416
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• 2007

Background: Single nucleotide polymorphisms (SNPs), which consist of a substitution of a single nucleotide pair, are the most abundant form of genetic variations occurring with a frequency of approximately 1 per 1000 base pairs. SNPs by themselves do not cause disease but can predispose humans to disease, modify the extent or severity of the disease or influence the drug response and treatment efficacy. Single nucleotide polymorphisms (SNPs), particularly those within the regulatory regions of the genes often influence the expression levels and can modify the disease. Studies examining the associations between SNP and the disease outcome have provided valuable insight into the disease etiology and potential therapeutic intervention. Traditionally, the genotyping of SNPs has been carried out using polymerase chain reaction-restriction fragment length polymorphism(PCR-RFLP), which is a low throughput technique not amenable for use in large-scale SNP studies. Recently, TaqMan real-time PCR chemistry was adapted for use in allelic discrimination assays. This study validated the accuracy and utility of real-time PCR technology for SNPs genotyping Methods: The SNPs in promoter sequence (-37 and -524) of lung cancer suppressor gene, RRM1 (ribonucleotide reductase M1 subunit) with the genomic DNA samples of 89 subjects were genotyped using both real-time PCR and PCR-RFLP. Results: The discordance rates were 2.2% (2 mismatches) in -37 and 16.3% (15 mismatches) in -524. Auto-direct sequencing of all the mismatched samples(17 cases) were in accord with the genotypes read by real-time PCR. In addition, 138 genomic DNAs were genotyped using real-time PCR in a duplicate manner (two separated assays). Ninety-eight percent of the samples showed concordance between the two assays. Conclusion: Real-time PCR allelic discrimination assays are amenable to high-throughput genotyping and overcome many of the problematic features associated with PCR-RFLP.

• Journal of Veterinary Clinics
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• v.27 no.6
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• pp.704-712
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• 2010

Porcine circovirus-2 (PCV2) has been implicated in many clinical diseases/syndromes that are now referred to as PCV-associated diseases (PCVAD). Due to significant economic losses caused by PCVAD, many swine operations have launched extensive monitoring programs for PCV2. Traditional serum sampling is, however, rather expensive and laborious, hampering effective large scale pathogen surveillance. A field-based longitudinal study was conducted to assess the utility of pen-based oral fluid sample as an alternative to serum for herd PCV2 testing. Six pens (25 pigs/pen) at each of 3 different sites were used in the study. One oral fluid and 5 random serum samples per pen were collected at 3, 5, 8, 12, and 16 weeks of age, and the sera were pooled by pen for testing. All samples were tested for PCV2 by real-time PCR and for antibodies by indirect fluorescent antibody test (for both anti-PCV2 IgG and IgA) and 3 ELISA assays (blocking ELISA, indirect ELISA, and IgG/IgM sandwich ELISA). PCV2 DNA was detected in oral fluid samples sporadically until 8 weeks and in all pens at 16 weeks. PCV2-specific IgG was detected in oral fluid samples at 3 weeks and persisted until 5 to 8 weeks in all sites. Anti-PCV2 IgG and IgA were detectable in oral fluid samples collected at 16 weeks from all of the pens at 1 site. The detection of PCV2 and anti-PCV2 antibody in oral fluid samples correlated positively with results on pooled sera, suggesting that oral fluids can be a cost-effective alternative to serum for herd monitoring of PCV2 infection.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1522-1522
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• 2001

Beverages based on fruit juices are among the most popular commercially available drinks. There is an ever-increasing demand for these juices in the market. Orange juice is one of the most common as well as most favorite flavor. The fruit processing industries have a tremendous responsibility of quality control. For quality evaluation estimation of various components of the juice is necessary. Sucrose, glucose, fructose, citric acid and malic acid are the prime components of orange juice. Little information is available on analysis of orange juice. However, conventional and general wet chemistry procedures are currently being used which are no longer desired by the industry owing to the time involved, labor input and harmful chemicals required for each analysis. Need to replace these techniques with new, highly specific and automated sophisticated techniques viz. HPLC and spectroscopy has been realized since long time. Potential of Near Infrared Spectroscopy in quantitative analysis of different components of food samples has also been well established. A rapid, non-destructive and accurate technique based on Near Infrared Spectroscopy for determination of sugars and organic acids in orange juice will be highly useful. The current study is an investigation into the potential of Near Infrared Diffuse Reflectance Spectroscopy for rapid quantitative analysis of sucrose, glucose, fructose citric acid and malic acid in orange juice. All the Near Infrared measurements were peformed on a dispersive NIR spectrophotometer (ELICO 153) in diffuse reflectance mode. The spectral region from 1100 to 2500nm has been explored. The calibration has been performed on synthetic samples that are mixtures of sucrose, glucose, fructose, citric acid and malic acid in different concentration ranges typically encountered real orange juice. These synthetic samples are therefore considered to be representatives of natural juices. All the Near Infrared spectra of synthetic samples were subjected to mathematical analysis using Partial Least Square (PLS) algorithm. After the validation, calibration was applied to commercially available real samples and freshly squeezed natural juice samples. The actual concentrations were compared with those predicted from calibration curve. A good correlation is obtained between actual and predicted values as indicated by correlation coefficient ($R^2$) value, which is close to unity, showing the feasibility of the technique.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.521-524
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• 2000

Nearinfra-red(NIR) reflectance NIR a spectra (400 to 2,100 nm) were collected on 32 beef samples to find feasibility of predicting beef tenderness. The study to predict beef tenderness was accomplished with the stepwise second differential data of the collected NIR spectra. Beef tenderness was measured by Warner-Bratzler(WB) shear force using a Universal Testing Machine(UTM). After modeling the relation between Warner-Bratzler shear force and NIR spectrum of 19 samples among the 32 beef samples, the verification was carried out through predicting the other 13 samples. The SEC and R$^2$ values in the prediction equation were 9.07(N) and 0.6463, respectively. The SEP and R$^2$ were 14.8(N) and 0.7082 (wave length 552 nm, 1988 nm) respectively. The result implied that it was possible to predict the beef tenderness using NIR spectrum and that the tenderness could be predicted non-destructively in real time.

• Mass Spectrometry Letters
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• v.4 no.2
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• pp.25-29
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• 2013

Proteomics for biomarker validation needs high throughput instrumentation to analyze huge set of clinical samples for quantitative and reproducible analysis at a minimum time without manual experimental errors. Sample preparation, a vital step in proteomics plays a major role in identification and quantification of proteins from biological samples. Tryptic digestion a major check point in sample preparation for mass spectrometry based proteomics needs to be more accurate with rapid processing time. The present study focuses on establishing a high throughput automated online system for proteolytic digestion and desalting of proteins from biological samples quantitatively and qualitatively in a reproducible manner. The present study compares online protein digestion and desalting of BSA with conventional off-line (in-solution) method and validated for real time sample for reproducibility. Proteins were identified using SEQUEST data base search engine and the data were quantified using IDEALQ software. The present study shows that the online system capable of handling high throughput samples in 96 well formats carries out protein digestion and peptide desalting efficiently in a reproducible and quantitative manner. Label free quantification showed clear increase of peptide quantities with increase in concentration with much linearity compared to off line method. Hence we would like to suggest that inclusion of this online system in proteomic pipeline will be effective in quantification of proteins in comparative proteomics were the quantification is really very crucial.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.727-729
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• 2002

In this paper, we proposed quad variable rates ADPCM coding method and its implementation on C6000 DSP, which is modified from the standard ADPCM of ITU G.726 for speech quality improvement considering the environmental noise Four coding rates, 16Kbps, 24Kbps, 32Kbps and 40Kbps are used for speech window samples and the rate decision threshold is decided by the environmental noise level. The object of the proposed method is to reduce the coding rate while retaining the speech quality and the speech quality is considerably close to 40Kbps single rate coder with the coding rate close to 16Kbps single rate coder under the environmental noise. The environmental noise level affects the coding rate and the noise level is calculated per every speech window samples. At high noise level, more samples are coded at higher rates to enhance the quality, but at low noise level, only the big speech signals are coded at higher rates and more speech samples are coded at lower coding rates to reduce the coding rates. The influence of the noise on tile speech signal is considerably high for small signals and the small signal has the higher ZCR (zero crossing rate). The method is simulated in PC and to be implemented on C6000 floating point DSP board in real time operations.

• Smart Media Journal
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• v.9 no.3
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• pp.80-89
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• 2020

Out - of - distribution (OOD) samples are frequently encountered when deploying a classification model in plenty of real-world machine learning-based applications. Those samples are normally sampling far away from the training distribution, but many classifiers still assign them high reliability to belong to one of the training categories. In this study, we address the problem of removing OOD examples by estimating marginal density estimation using variational autoencoder (VAE). We also investigate other proper methods, such as temperature scaling, Gaussian discrimination analysis, and label smoothing. We use Chonnam National University (CNU) weeds dataset as the in - distribution dataset and CIFAR-10, CalTeach as the OOD datasets. Quantitative results show that the proposed framework can reject the OOD test samples with a suitable threshold.

• Bulletin of the Korean Chemical Society
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• v.19 no.10
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• pp.1036-1042
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• 1998

The preconcentration and determination of trace elements in water samples were studied by a solvent sublation utilizing dithizonate complexation. After metal dithizonates were formed, trace amounts of cadmium, cobalt, copper and lead were floated and extracted into small volume of a water-immiscible organic solvent on the surface of sample solution and determined in the solvent directly by GF-AAS. Several experimental conditions as formation condition of metal-dithizonate complexes, pH of solution, amount of dithizone, stirring time, the type and amount of surfactants, N2 bubbling rate and so on were optimized for the complete formation and effective flotation of the complexes. And also four kinds of light solvents were compared each other to extract the floated complexes, effectively. After the pH was adjusted to 4.0 with 5 M HNO3, 8.0 mL of 0.05% acetone solution of dithizone was added to 1.00 L water sample. The dithizonate complexes were flotated and extracted into the upper methyl isobutylketone (MIBK) layer by the addition of 2.0 mL 0.2% ethanolic sodium lauryl sulfate solution and with the aid of small nitrogen gas bubbles. And this solvent sublation method was applied to the analysis of real water samples and good results of more than 85% recoveries were obtained in spiked samples.

• Bulletin of the Korean Chemical Society
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• v.16 no.7
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• pp.582-588
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• 1995

The preconcentration and determination of trace Cd(Ⅱ), Cu(Ⅱ), Pb(Ⅱ), Mn(Ⅱ) and Zn(Ⅱ) in water samples were studied by the precipitate flotation using La(OH)3 as a coprecipitant. The analytes were quantitatively coprecipitated by adding 3.0 mL of 0.1 M La(Ⅲ) solution in a 1,000 mL water sample and adjusting the pH to 9.5 with NaOH solution. After the addition of the 1:8 mixed surfactant solution of each 0.1% sodium oleate and sodium lauryl sulfate, the solution was stirred with a magnetic stirrer for 10 minutes. The precipitates were floated to the surface by bubbling with nitrogen gas and collected in a small sampling bottle. The precipitates were dissolved in nitric acid and then the solutions were diluted to 25.00 mL with a deionized water. The analytes were determined by flame atomic absorption spectrometry. This procedure was applied to the waste water analysis. This technique was simple, convenient and especially rapid for the analysis of a large volume of sample. And also, from the recoveries of better than 92% which were obtained from real samples, this method could be judged to be applicable to the preconcentration and quantitative determination of trace elements in water samples.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.637-639
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• 2003

As the number of spectral bands of high spectral resolution data increases, the capability to detect more detailed classes should also increase, and the classification accuracy should increase as well. Often, it is impossible to access enough training pixels for supervise classification. For this reason, the performance of traditional classification methods isn't useful. In this paper, we propose a new model for classification that operates based on decision fusion. In this classifier, learning is performed at two steps. In first step, only training samples are used and in second step, this classifier utilizes semilabeled samples in addition to original training samples. At the beginning of this method, spectral bands are categorized in several small groups. Information of each group is used as a new source and classified. Each of this primary classifier has special characteristics and discriminates the spectral space particularly. With using of the benefits of all primary classifiers, it is made sure that the results of the fused local decisions are accurate enough. In decision fusion center, some rules are used to determine the final class of pixels. This method is applied to real remote sensing data. Results show classification performance is improved, and this method may solve the limitation of training samples in the high dimensional data and the Hughes phenomenon may be mitigated.