Volatile organic compounds (VOCs) in plants are various organic compounds with small molecular weight and high vapor pressure. The metabolomics approach was recently introduced to analyze VOCs involved in biological processes, such as abiotic and biotic stresses, spatial and temporal distribution, and genotypic differences. In addition, this approach is widely used in combination with identification of VOCs analysis and statistical analysis using multivariate analysis, such as principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), hierarchical cluster analysis (HCA), etc. First, in this review, the current condition of the metabolomics approach to analyze VOCs synthesized in plants using head space-solid phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) is discussed. In addition, metabolomics approach, such as extraction and analysis of VOCs using HS-SPME-GC-MS, conversion, and processing of mass spectral (MS) data, a database for VOCs identification, useful statistical methods, and statistical tools and applications, are explained. Finally, multi-omics in combination with other omics techniques, such as genomics, transcriptomics, etc. are suggested as prospects of a metabolomics approach for VOC analysis in floricultural plants using HS-SPMEGC- MS. Therefore, the metabolomics approach of HS-SPMEGC- MS will facilitate our understanding of VOCs synthesized in plants. Furthermore, the multi-omics approach will help understand gene functions involved in the biosynthesis of VOCs and help develop new development cultivars with nicer floral scents by contributing to the development of the floricultural industry.

Recently, the demand for more precise and demand-oriented customized spatial information is increasing due to the 4th industrial revolution. In particular, the use of 3D spatial information and digital twins which based on spatial information, and research for solving social problems in cities by using such information are continuously conducted. Globally, non-face-to-face services are increasing due to COVID-19, and the national policy direction is also rapidly progressing digital transformation, digitization and virtualization of the Korean version of the New Deal, which means that 3D spatial information has become an important factor to support it. In this study, physical objects for cities defined by world organizations such as ISO, OGC, and ITU were selected and the target of the 3D object model was limited to buildings. Based on CityGML2.0, the data collected using a drone suitable for building a 3D model of a small area is selected to be updated through road name address and building ledger, which are administrative information related to this, and LoD2.5 data is constructed and urban space. It was intended to suggest an object update method for a 3D building among data.

Using the frequency-based decomposition, I decompose the consumption growth to explain well-known patterns of stock returns in the Korean market. To be more specific, the consumption growth is decomposed by its half-life of shocks. The component over four years of half-life is called the business-cycle consumption component, and the components with half-lives under four years are short-run components. I compute the long-run and short-run components of stock excess returns as well and use component- by-component sensitivities to price stock portfolios. As a result, the business-cycle consumption risk with half-life of over four years is useful in explaining the cross-section of size-book-to-market portfolios and size-momentum portfolios in the Korean stock market. The short-run components have their own pricing abilities with mixed direction, so that the restricted one short-term factor model is rejected. The explanatory power with short- and long-run components is comparable to that of the Fama-French three-factor model. The components with one- to four-year half-lives are also helpful in explaining the returns. The results about the long-run components emphasize the importance of long-run component in consumption growth to explain the asset returns.

This study explores multiple variables of an OTT service for discovering hidden relationship between rating and the other variables of each successful and failed content, respectively. In order to extract key variables that are strongly correlated to the rating across the contents, this work analyzes 170 Netflix original dramas and 419 movies. These contents are classified as success and failure by using the rating site IMDb, respectively. The correlation between the contents, which are classified via rating, and variables such as violence, lewdness and running time are analyzed to determine whether a certain variable appears or not in each successful and failure content. This study employs a regression analysis to discover correlations across the variables as a main analysis method. Since the correlation between independent variables should be low, check multicollinearity and select the variable. Cook's distance is used to detect and remove outliers. To improve the accuracy of the model, a variable selection based on AIC(Akaike Information Criterion) is performed. Finally, the basic assumptions of regression analysis are identified by residual diagnosis and Dubin Watson test. According to the whole analysis process, it is concluded that the more director awards exist and the less immatatable tend to be successful in movies. On the contrary, lower fear tend to be failure in movies. In case of dramas, there are close correlations between failure dramas and lower violence, higher fear, higher drugs.

Background: Although it has been reported that both self-myofascial release (SMR) with foam rolling (FR) and active static hamstring stretching (e.g., jackknife stretching) are effective in improving hamstring flexibility, no study has compared the effects of these exercises. Objectives: To compare the effects of SMR with FR and jack-knife stretching on hamstring flexibility. Design: A Randomized controlled trial. Methods: Subjects with hamstring tightness were divided into the SMR with the FR group (n=12) and the jack-knife stretching group (n=12). Subjects groups performed SMR with FR or jack-knife stretching according to group assignment. To identify changes in hamstring flexibility, the finger-to-floor distance (FFD) test, active knee extension (AKE) test, and passive straight leg raising (PSLR) test were performed at pre- and post-exercise. Results: Significant increases occurred in knee extension angle during the AKE test and hip flexion angle during the PSLR test after exercise in both groups (P<.001). Additionally, FFD and anterior pelvic tilt during the FFD test significantly increased (P<.001); however, we observed no significant interaction and main effects for the groups (P>.05). Conclusion: Both SMR with FR and jack-knife stretching are effective in improving hamstring flexibility in subjects with hamstring tightness.

The role of transient receptor potential vanilloid receptor-1 (TRPV1) has been primarily investigated in pain sensory neurons. Relatively, little research has been performed in testicular cells. TRPV1 is abundantly expressed in Leydig cells of young adult mice. This study was conducted to determine the role of the TRPV1 channel in Leydig cells. TRPV1 modulators and testosterone were treated to the mouse Leydig cell line TM3 cells for 24 h. Capsaicin, a TRPV1 activator, dose-dependently induced cell death, whereas capsazepine, a TRPV1 inhibitor, inhibited capsaicin-induced cell death. Testosterone treatment reduced capsaicin-induced cell death. High concentrations of testosterone decreased TRPV1 mRNA and protein expression levels. However, TRPV1 modulators did not affect testosterone production. These results showed that capsaicin induced cell death of Leydig cells and that testosterone reduced capsaicininduced cell death. Our findings suggest that testosterone may regulate the survival of Leydig cells in young adult mice by decreasing the expression level of TRPV1.