• Asian-Australasian Journal of Animal Sciences
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• v.27 no.2
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• pp.161-168
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• 2014

This study aimed to investigate dietary concentrate:forage ratios (C:F) and undegraded dietary protein (UDP) on nitrogen balance and urinary excretion of purine derivatives (PD) in lambs. Four Dorper${\times}$thin-tailed Han crossbred castrated lambs with $62.3{\pm}1.9$ kg body weight at 10 months of age were randomly assigned to four dietary treatments in a $2{\times}2$ factorial arrangement of two levels of C:F (40:60 and 60:40) and two levels of UDP (35% and 50% of CP), according to a complete $4{\times}4$ Latin-square design. Each experimental period lasted for 19 d. After a 7-d adaptation period, lambs were moved into individual metabolism crates for 12 d including 7 d of adaption and 5 d of metabolism trial. During the metabolism trial, total urine was collected for 24 h and spot urine samples were also collected at different times. Urinary PD was measured using a colorimetric method and creatinine was measured using an automated analyzer. Intake of dry matter (DM) (p<0.01) and organic matter (OM) (p<0.01) increased as the level of UDP decreased. Fecal N was not affected by dietary treatment (p>0.05) while urinary N increased as the level of UDP decreased (p<0.05), but decreased as dietary C:F increased (p<0.05). Nitrogen retention increased as dietary C:F increased (p<0.05). As dietary C:F increased, urinary excretion of PD increased (p<0.05), but was not affected by dietary UDP (p>0.05) or interaction between dietary treatments (p>0.05). Daily excretion of creatinine was not affected by dietary treatments (p<0.05), with an average value of $0.334{\times}0.005$ mmol/kg $BW^{0.75}$. A linear correlation was found between total PD excretion and PDC index ($R^2$ = 0.93). Concentrations of creatinine and PDC index in spot urine were unaffected by sampling time (p>0.05) and a good correlation was found between the PDC index (average value of three times) of spot urine and daily excretion of PD ($R^2$ = 0.88). These results suggest that for animals fed ad libitum, the PDC index in spot urine is effective to predict daily excretion of PD. In order to improve the accuracy of the spot sampling technique, an appropriate lag phase between the time of feeding and sampling should be determined so that the sampling time can coincide with the peak concentration of PD in the urine.

• Horticultural Science & Technology
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• v.32 no.1
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• pp.105-114
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• 2014

We established a high throughput screening system of African yam tuber lines which contain high contents of total carotenoids, flavonoids, and phenolic compounds using ultraviolet-visible (UV-VIS) spectroscopy and Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. The total carotenoids contents from 62 African yam tubers varied from 0.01 to $0.91{\mu}g{\cdot}g^{-1}$ dry weight (wt). The total flavonoids and phenolic compounds also varied from 12.9 to $229{\mu}g{\cdot}g^{-1}$ and from 0.29 to $5.2mg{\cdot}g^{-1}$dry wt. FT-IR spectra confirmed typical spectral differences between the frequency regions of 1,700-1,500, 1,500-1,300 and $1,100-950cm^{-1}$, respectively. These spectral regions were reflecting the quantitative and qualitative variations of amide I, II from amino acids and proteins ($1,700-1,500cm^{-1}$), phosphodiester groups from nucleic acid and phospholipid ($1,500-1,300cm^{-1}$) and carbohydrate compounds ($1,100-950cm^{-1}$). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate the 62 African yam tuber lines into three separate clusters corresponding to their taxonomic relationship. The quantitative prediction modeling of total carotenoids, flavonoids, and phenolic compounds from African yam tuber lines were established using partial least square regression algorithm from FT-IR spectra. The regression coefficients ($R^2$) between predicted values and estimated values of total carotenoids, flavonoids and phenolic compounds were 0.83, 0.86, and 0.72, respectively. These results showed that quantitative predictions of total carotenoids, flavonoids, and phenolic compounds were possible from FT-IR spectra of African yam tuber lines with higher accuracy. Therefore we suggested that quantitative prediction system established in this study could be applied as a rapid selection tool for high yielding African yam lines.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.6 no.1
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• pp.24-31
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• 2003

Purpose: As diagnostic tools for Hirschsprung's disease (HD), barium enema and rectal biopsy have radiation exposure and invasiveness respectively; however anorectal manometry does not have these disadvantages. We therefore performed this study to evaluate the diagnostic efficacy of anorectal manometry. Methods: We reviewed medical records of infants with one or two symptoms of vomiting, abdominal distension, chronic diarrhea or constipation who had a anorectal manometry followed by barium enema and/or biopsy from July 1995 to May 2002. We evaluated the sensitivity, specificity and predictive value of anorectal manometry and barium enema for diagnosis of HD. We also measured sphincter length, median value of balloon volume at which rectoanal inhibitory reflex (RAIR) occurred. Results: All 61 patients received anorectal manometry, 33 of 61 received barium enema. 18 of 61 were diagnosed as HD according to histology and 43 of 61 were evaluated as a control. The sensitivity, specificity, positive predictive value, negative predictive value of anorectal manometry and barium enema for diagnosis of HD were 1.00, 0.91, 0.82, 1.00 and 0.93, 0.67, 0.70, 0.92 respectively. The mean value of sphincter length in control was $1.68{\pm}0.67$ cm and correlated with age, weight and significantly longitudinal length. The median value of balloon volume at which RAIR occurred was 10 mL and did not correlated with age, weight and longitudinal length. Conclusion: This study suggests that anorectal manometry is an excellent initial screening test for Hirschsprung's disease because of its safety and accuracy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.28-35
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• 2006

Statistical models of shape variability based on active shape models (ASMs) have been successfully utilized to perform segmentation and recognition tasks in two-dimensional (2D) images. Three-dimensional (3D) model-based approaches are more promising than 2D approaches since they can bring in more realistic shape constraints for recognizing and delineating the object boundary. For 3D model-based approaches, however, building the 3D shape model from a training set of segmented instances of an object is a major challenge and currently it remains an open problem in building the 3D shape model, one essential step is to generate a point distribution model (PDM). Corresponding landmarks must be selected in all1 training shapes for generating PDM, and manual determination of landmark correspondences is very time-consuming, tedious, and error-prone. In this paper, we propose a novel automatic method for generating 3D statistical shape models. Given a set of training 3D shapes, we generate a 3D model by 1) building the mean shape fro]n the distance transform of the training shapes, 2) utilizing a tetrahedron method for automatically selecting landmarks on the mean shape, and 3) subsequently propagating these landmarks to each training shape via a distance labeling method. In this paper, we investigate the accuracy and compactness of the 3D model for the human liver built from 50 segmented individual CT data sets. The proposed method is very general without such assumptions and can be applied to other data sets.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• Journal of Korean Society of Forest Science
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• v.85 no.3
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• pp.453-461
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• 1996

Three Phyllostachys stands of P. pubescens, P. bambusoides and P. nigra var, henonis in Sunchon were studied to investigate biomass, net production and nutrient distribution. Five $10m{\times}10m$ quadrats were set up and 20 sample culms of 2 years and over were harvested for dimension analysis in each stand. One year old culms and subterranean parts were estimated by the harvested quadrat method. The largest mean DBH, height and basal area were shown in P. pubescens stand, and followed by P. nigra var. henonis stand and P. bambusoides stand. There was little difference in accuracy among three allometric biomass regression models of logWt=A+B1ogD, $logWt=A+BlogD^2H$ and logWt=A+BlogD+ClogH, where Wt, D and H were dry weight, DBH and height, respectively. Analysis of covariance showed that there were significant differences in intercept among the linear allometric biomass regressons of three Phyllostachys species. Biomass included subterranean parts was the largest in P. pubescens stand(103.621t/ha), and followed by P. nigra var. henonis stand(86.447t/ha) and P. bambusoides stand(36.767t/ha). Leaf biomass was 6.3% to 7.8% of total biomass in each stands. The ratio of aboveground biomass and subterranean biomass in each stand was 1.87 to 2.26. Net production included subterranean parts was the greatest in P. pubescens stand(6.115t/ha/yr), and followed by P. nigra var. henonis stand(5.609t/ha/yr) and P, bambusoides stand(3.252t/ha/yr). The highest net assimilation ratio was estimated in P. pubescens stand(2.979), and followed by P. nigra var. henonis stand(2.752) and P. bambusoides stand(2.187). Biomass accumulation ratio of each stand was 2.679 to 5.358. Concentrations of N, P and Mg were the highest in leaves, and followed by subterranean parts, and culms+branches in all three species. Concentration of Ca was the highest in leaves, and followed by culms+branches, and subterranean parts in all three species. The difference in biomass among three species stands was caused by their culm size, leaf biomass, net assimilation ratio, and efficiency of leaves to produce culms.

• Analytical Science and Technology
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• v.33 no.3
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• pp.151-158
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• 2020

Alcohol, which can easily be obtained in the same way as ordinary beverages, is harmful enough to cause death due to excessive drinking and chronic alcohol intake, so it is important to maintain a proper amount of drinking and healthy drinking habits. In addition, the incidence of behavioral disturbances and impaired judgments that can be caused by chronic alcohol drinking of more than adequate amounts of alcohol is also significant. Accordingly it is very useful for forensic science to check whether the person involved is drunken or is alcoholism state in various accidents. Currently, in Korea, alcohol consumption is determined by detecting the level of alcohol or alcohol metabolism 'ethyl glucuronide (EtG)' in blood or urine samples. However, analysis of alcohol or EtG in blood or urine can only provide information about the current state of alcohol consumption because of a narrow window of detection time. Therefore, it is important to analyze the EtG as a long-term direct alcohol metabolite bio-marker in human hair and to investigate relationship between alcohol consumption and EtG concentration for the evaluation of chronic ethanol consumption. In this study, we established an analytical method for the detection of EtG in Korean hair efficiently and validated selectivity, linearity, limits of detection (LOD), limits of quantification (LOQ), matrix effect, recovery, process efficiency, accuracy and precision using liquid chromatography tandem mass spectrometry (LC-MS/MS). In addition, the assay performance was evaluated in Korean social drinker's hair and the postmortem hair of a chronic alcoholism. The results of this study can be useful in monitoring the alcohol abuse of Korean in clinical cases and legal procedures related to custody and provide a useful tool to evaluate postmortem diagnosis of alcoholic ketoacidosis in forensics.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.3
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• pp.358-367
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• 2017

Preservatives, as food additives, are occasionally intrinsic to natural raw materials or sometimes generated during the fermentation process as reported in many research articles. Preservative compounds in raw food materials may persist in the final food product, which is not supposed to include such preservative compounds. In this study, we validated an analytical method for preservative compounds in raw materials of functional foods. Quantification of benzoic acid and sorbic acid was determined using HPLC-PDA analysis after distillation, whereas propionic acid was quantified with GC-FID. A significant set of validation data (accuracy, precision, linearity, recovery, etc) was acquired. A total of 212 samples were collected for analysis of naturally occurred preservatives, and preservatives were detected in 85 samples. Most of the detected samples showed less 10 mg/kg of preservatives. The results of this study provide fundamental data on naturally occurring preservatives in raw materials of functional foods. Moreover, building up a database of naturally occurring preservatives could solve problems in the current scientific data.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.1
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• pp.143-155
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• 2018

The Natural Landscape Rating System was introduced in the amendment of the NATURAL ENVIRONMENT CONSERVATION ACT in 2006. For landscape preservation, the system aims to consider the effects of development projects or plans implemented in a natural landscape on skylines, scenic resources, and view corridors. Currently, a lack of consistency in standards for determining Landscape Control Points (LCP) to assess landscape impact lowers the accuracy and reliability of the assessment results. As the perception of and the impact on a landscape varies, depending on the location of the LCP, it is necessary to establish a reasonable set of criteria to select viewpoints and avoid unreliability in the assessment due to unclear criteria. The intent of this study is to propose an objective and reasonable set of criteria for LCP selection to effectively measure the impact on the landscape from development projects that anticipate a change in the landscape and, ultimately, to suggest basic analysis methods to assess the landscape impact of development projects and to monitor the landscape in the future. Among the development projects affecting natural landscapes, as reported in the statement of the environmental impact assessment, cases of construction of a single building or other small-scale development projects were studied. Four spot development projects were analyzed in depth for their landscape impacts, in order to make recommendations for the LCP selection procedure, which aims to widen the scope of selection according to the direction of viewpoints from the target site. The existing results of analysis based on LCP have limitations because they failed to cover the viewshed of the target buildings when there are topographical changes in the surroundings. As a solution to this problem, a new viewshed analysis method has been proposed, with a focus on the development site and target buildings, rather than viewpoints, as used in past analysis.