• Korean Journal of Health Education and Promotion
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• v.8 no.2
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• pp.74-86
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• 1991

This study tried to clear the characteristics of height growth pattern depending on Peak Height Velocity age in early, average and late groups. And in it, height growth tendency of girl students in age 7 to 18 years old was compared and investigated in order to know influencing factors, menarcheal age relation. The samples were senior girl students at high school in Seoul. Longitudinal data and survey data were collected in August, 1990. The results could be summarized as follows: 1. On distance curve, the height growth line in early group tended to be the highest and in late group the lowest. On the other hand, on velocity curve late group showed the highest peak and early group showed the lowest peak. In late group, velocity curve was too steep. Of course, these early, average and late groups were classified by PHV age. 2. In these three groups, late group showed the most growth amount per year. However, on distance curve, early group showed higher line than late group. Perhaps this means that peak growth amount in late group might have an effect on mean. 3. Growth amount of adolescence spurt age in these three groups was 6.86cm at age 9(early group), 7.27cm at l1(average group) and 7.65cm at 13(late group). In early group, because. PHV age came too early, it was difficult to find exact adolescence spurt period. In early group, the adolescence spurt period is considered to come at about age 7 to 9. In average group, at 9 to 11 and at 12 to 13 in late group. Especially, spurt of late group was remarkable. 4. When the growth amount of PA, before PA and after PA was compared, growth amount of PA in all three groups was about 20%. In early group, growth amount of APV tended to be large and in late group, that of APV tended to be large and in late group, that of BPV was large. In average group, growth amount of BPV was larger than that of APV. 5. For the purpose of comparing total height growth amount of these groups at age 18, the height growth was assumed to be over. And then, the difference of three groups was studied but it's not significant. 6. Although the difference between height growth and family environment, dietary habits, exercise, disease history in these three groups was investigated, only the income was significant. The significance of all the other factors was not approved. 7. When menarcheal age was compared with PA, generally we know menarche appeared after 1∼2 years of PHV age. But in case of early group, the difference between PHV age and menarcheal age was 5.34 years. In average group, 2.45 years and 0.82 years in late group.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.31 no.4
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• pp.214-224
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• 2020

Objectives: This study aimed to examine the trend in diagnostic prevalence of psychiatric disorders in children and adolescents. Methods: Individuals aged 0-18 years were included in the study based on the National Health Insurance Claims Data. To investigate the trends in diagnosis and diagnostic prevalence of psychiatric disorders reflecting the decrease in the birth rate, data were analyzed from 2010 as a reference year to 2015. Results: The number of patients diagnosed with psychiatric disorder decreased annually, from 23,412 on 2010 to 18,821 on 2015. The most common disorder was hyperkinetic disorder in male and depressive episode in female. Although there was no significant change in overall diagnostic prevalence rate of psychiatric disorders, age groups <10 years and some disorders had significant changes in prevalence rate. This study classified the diagnostic prevalence by age into two unique patterns: group in which the diagnosis rate increases with age and group in which the diagnosis rate peaks at a certain age and then decreases. Conclusion: Diagnostic prevalence of psychiatric disorders was different according to age and sex. These patterns should be reflected in the formulation of policies related to mental health and in medical practice for pediatric patients. It is urgent to identify how these patterns change in young adults.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.7
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• pp.923-927
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• 2008

The objective of this study was to compare random regression model and multiple trait animal model estimates of the (co) variance of total sperm cells over the active lifetime of AI boars. Data were provided by Smithfield Premium Genetics (Rose Hill, NC). Total number of records and animals for the random regression model were 19,629 and 1,736, respectively. Data for multiple trait animal model analyses were edited to include only records produced at 9, 12, 15, 18, 21, 24, and 27 months of age. For the multiple trait method estimates of genetic and residual variance for total sperm cells were heterogeneous among age classifications. When comparing multiple trait method to random regression, heritability estimates were similar except for total sperm cells at 24 months of age. The multiple trait method also resulted in higher estimates of heritability of total sperm cells at every age when compared to random regression results. Random regression analysis provided more detail with regard to changes of variance components with age. Random regression methods are the most appropriate to analyze semen traits as they are longitudinal data measured over the lifetime of boars.

• Genomics & Informatics
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• v.15 no.4
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• pp.156-161
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• 2017

Transcriptome analysis has been widely used to make biomarker panels to diagnose cancers. In breast cancer, the age of the patient has been known to be associated with clinical features. As clinical transcriptome data have accumulated significantly, we classified all human genes based on age-specific differential expression between normal and breast cancer cells using public data. We retrieved the values for gene expression levels in breast cancer and matched normal cells from The Cancer Genome Atlas. We divided genes into two classes by paired t test without considering age in the first classification. We carried out a secondary classification of genes for each class into eight groups, based on the patterns of the p-values, which were calculated for each of the three age groups we defined. Through this two-step classification, gene expression was eventually grouped into 16 classes. We showed that this classification method could be applied to establish a more accurate prediction model to diagnose breast cancer by comparing the performance of prediction models with different combinations of genes. We expect that our scheme of classification could be used for other types of cancer data.

• IE interfaces
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• v.15 no.2
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• pp.199-208
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• 2002

This study was performed to measure the various dimensions of the head and face for Korean adults. Three hundred and eighteen males and two hundred and sixty females, age ranged 18 to 60, participated for this study. Thirty-six dimensions were selected to measure. Subjects were divided into three age groups - 18 to 29, 30 to 39, and 40 to 60 - for each sex. The data were analyzed ta see the differences between the age groups and sex using SAS program. Also, the results of this study were compared to the data of Japanese and US. army. The results showed that the 'ear length', 'bigonial breadth' and 'bitragion submandibular arc' increased as the age increased(p<0.01). However, not much of differences were shown between the age groups in most of other dimensions. Males were significantly bigger than females in every dimensions. The comparison between Korea and Japanese showed significant differences in many dimensions. Due to this reason, it is considered that more caution has to be exercised in using Japanese data for the Korean. The Americans showed to be significantly bigger than Korean in most dimensions. It showed that Koreans have more roundish face and wider nose ridge than Americans. The results of this study can be used to design the products that related to the head and face.

• Journal of the Korean Society of Clothing and Textiles
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• v.22 no.2
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• pp.241-249
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• 1998

Somatotypes in the lower torso and legs of adult females were classified and discriminated. Then their distribution according to the age groups was studied. The subjects were 343 females of 18 to 49 year-old. 36 anthropometric and 21 photographic measurements were taken to each subject. The results were as follows: 1. The somatotypes in the lower torso and legs were classified into 4 types and their differences were shown by analysing factor scores and anthropometric values according to each somatotype. 2. The shape characteristic of tile classified somatotypes was represented by the rating scale of Heath-Carter. 3. The lateral silhouettes of 4 types were compared with balanced type which is type 3 in this study. 4. Individual somatotype in the lower torso and legs could be discriminated from the measured anthropometric data without modifying the data. Anthropometric data, which are needed for discriminating individual somatotype, are waist circumference, posterior waist height, and hip circumference. 5. The distribution of the somatotypes in each age group showed that the dominant somatotype of each age group was different and any somatotype was shown in a specified age group but rarely in other age group.

• Journal of the Korean Society of Costume
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• v.46
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• pp.31-48
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• 1999

The purpose of this study is to offer fundamental data for classification of somatotype for boys of elementary school age. The subject were 458 elementary school boys aged from 7 to 12 living in Pusan. Data were collected by 56 anthropometric 11 photoraphic measurements and 41 body indices. 56 anthropometric measurements were analyzed by ANOVA and method of Duncan-test as Multiple Range Test among 6 grade groups and then total 108 measurments were analyzed by statistical data processing. The results are as followings: 1, In Duncan-test boys were significantly different in most of height and length items but were not significantly different in main items for clothing construction among 1.2/3.4/5.6 grades so that the period of elementary school age were divided with 3 periods. 2. According to increase school age most of item's means increased gradually and personal difference for each items increased too. In case of height and length items the items of lower half were greater than the items of upper half in personal difference and depth items were greater than the breadth items in personal difference. And weight and angle items were very great personal difference is specially.

• Korea journal of population studies
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• v.24 no.2
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• pp.149-177
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• 2001

This is a study on age-crime curve in Korea. Three data was used in this study as following: First is the crime statistics as aggregated data. Second is the police record(N=3.541 offences) of the male ex-offenders(N=988) who have been released in eleven prisons in 1987 as individual data. Third is the self-reported group-interview data(N=10.198 offences) administered to the male prisoners(N=979) in ten correctional facilities including eight adult prisons, one juvenile prison and one juvenile training center as another individual data. Generally, the right-skewness of age-crime curve has been explained through the difference of crime rate between early starters and late starters. Moffitt explains that this is because of the higher participation rate of the juvenile period of adolescence-limited offenders, but Godttfredson and Hirschi explain that this is because of a similar distribution in the crime rate of both early starters and late starters. the analysis of this study shows that Godttfredson and Hirschi’s explanation on the generality of age-crime-curve distribution is correct, but this can be modified by various factors like a economic crisis. And the peak age of juvenile period is consistent with the Moffitt’s hypothesis that the peak age is contributed to the increase of crime rate of late starters, not with Godttfredson and Hirschi’s one.

• 한국노년학
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• v.40 no.1
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• pp.73-89
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• 2020

This study validates an abridged version of the age integration scale by Chung et al. (2015), which constitutes sub-dimensions of age flexibility and age heterogeneity-from Riley et al. (1994)-and observes disparities in the perception of age integration among different socio-cultural characteristics. This study analyzed 1,433 people in the "Age Integration and Generation Integration Survey" data, with the support of the Social Science Competency Enhancement Project (SSK) (supported by the Korea Research Foundation). This paper analyzed the data using exploratory factor analysis, confirmatory factor analysis, multiple-group factor analysis, and a compare means test. The results are as follows. First, the scale was abridged to 13 items from 28, of which eight items concern age heterogeneity and five concern age flexibility. Second, the multi-group analysis did not show a significant difference among younger, middle, and older age groups, therefore allowing the scale to be used among all age groups. Third, when sociocultural characteristics were observed, younger adults showed differences in their levels of age integration, age heterogeneity, and age flexibility regarding their residential area. Middle-aged adults showed dif f erences in their levels of age integration, age heterogeneity, and age flexibility regarding their residential area and social status. Older adults showed differences in their level of age integration regarding their residential area, education level, and social status. They also showed differences in the level of age flexibility regarding their residential area and social status and differences in the level of age heterogeneity regarding their residential area and education. Based on the results of the study, political and practical measures to promote perception on age integration in the future were suggested.

• Annals of Laboratory Medicine
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• v.38 no.6
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• pp.503-511
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• 2018

Background: Sampling a healthy reference population to generate reference intervals (RIs) for complete blood count (CBC) parameters is not common for pediatric and geriatric ages. We established age- and sex-specific RIs for CBC parameters across pediatric, adult, and geriatric ages using secondary data, evaluating patterns of changes in CBC parameters. Methods: The reference population comprised 804,623 health examinees (66,611 aged 3-17 years; 564,280 aged 18-59 years; 173,732 aged 60-99 years), and, we excluded 22,766 examinees after outlier testing. The CBC parameters (red blood cell [RBC], white blood cell [WBC], and platelet parameters) from 781,857 examinees were studied. We determined statistically significant partitions of age and sex, and calculated RIs according to the CLSI C28-A3 guidelines. Results: RBC parameters increased with age until adulthood and decreased with age in males, but increased before puberty and then decreased with age in females. WBC and platelet counts were the highest in early childhood and decreased with age. Sex differences in each age group were noted: WBC count was higher in males than in females during adulthood, but platelet count was higher in females than in males from puberty onwards (P <0.001). Neutrophil count was the lowest in early childhood and increased with age. Lymphocyte count decreased with age after peaking in early childhood. Eosinophil count was the highest in childhood and higher in males than in females. Monocyte count was higher in males than in females (P <0.001). Conclusions: We provide comprehensive age- and sex-specific RIs for CBC parameters, which show dynamic changes with both age and sex.