Identification of Thermoduric Leuconostoc mesenteroides BD5H That Can Grow at Over 42℃

권오식,이삼빈 계명대학교 자연과학연구소 2017 Quantitative Bio-Science Vol.36 No.1

Lactic acid bacteria was isolated from Chonggak and Baechu kimchi and characterized by a sugar fermentation analysis. Leuconostoc strains (1B12, BD5H and CH5H) growing at 40℃ were isolated and compared with L. mesenteroides subsp. mesenteroides KCTC 3722 grown at a mesophilic temperature (30℃). Among these, L. mesenteroides BD5H could grow at 42℃ (pH 4.70±0.02) and could still grow at 43℃ (pH 5.51±0.04), which indicates that is a thermoduric bacterial strain. In the fermentation of pentose, all strains had exactly the same patterns of fermentation, except L. mesenteroides CH5H, which could not ferment ribose (pH 5.92±0.04). These strains had significantly different abilities to ferment galactose, with only L. mesenteroides BD5H capable of active galactose fermentation (pH 4.94±0.03). All strains failed to ferment rhamnose compared to other hexoses (p<0.001). L. mesenteroides BD5H could ferment cellobiose (4.42± 0.02), while the remaining strains could not. L. mesenteroides BD5H could ferment raffinose (pH 4.66±0.07), but not melezitose. These fermentation characteristics are typical of L. mesenteroides. Interestingly L. mesenteroides BD5H could ferment amygdalin (pH 4.82±0.04), while other strains could not (p<0.001). Furthermore L. mesenteroides BD5H could ferment salicin (pH 4.77±0.15), while the other strains could not. According to 16S rRNA sequence analysis using primers 785F and 907R, all tested strains were L. mesenteroides, with 99~100% identity. Significant differences were observed between these strains in their ability to ferment carbohydrates, which enabled better differentiation than that afforded by 16S rRNA sequence analysis. Carbohydrate fermentation analysis allowed for subspecies-level identification of L. mesenteroides isolated from Baechu and Chonggak kimchi.

An Investment Model Based on a Head-And-Shoulder Pattern with Multiple Moving Average Technical Indicators for Future Markets

JaeHoon Oh,Piao Lingnan,YongHak Lee,SungMin Yang,SungHwan Kim 계명대학교 자연과학연구소 2022 Quantitative Bio-Science Vol.41 No.2

Future market forecasting is a challenging task in the financial time-series study field. Factors such as the economic environment, political policies, and market news affect it. Although only a few people can profit from it, many researchers and professional investors are eager to spend considerable effort studying the future market in an attempt to develop profitable methods. Certainly, many investment strategies exist in the market including traditional technical analysis and modern emerging artificial intelligence analysis. However, most methods are too complicated or heavily require investors to select and analyze many technical indicators. Moreover, most individual investors do not understand financial statements, the meaning of various technical indicators, and their influence on the future market. These non-professional investors are inevitably in need of a simple and intelligible investment method. Hence, this study proposes a head-and-shoulder combined pattern recognition model based on perceptually important point identification matching (PIP), template matching, and the floating weighted method using two basic technical indicators (5MA and 20MA). A four-year period (2017~2020) of the Crude Oil, NASDAQ100, and S&P500 index was analyzed as the experimental dataset to validate the proposed model. The experimental results show that the proposed model is effective in forecasting accuracy and can be applied to develop investment strategies.

2-(2-Aminopyrimidin-4-yl)phenol Analogs as PIM Kinase Inhibitors

Hyeonseong Choo,Mingyu Jeon,Victor Sukbong Hong,Jinho Lee 계명대학교 자연과학연구소 2022 Quantitative Bio-Science Vol.41 No.2

PIM kinases, a family of serine/threonine kinases, are major downstream effectors of the Janus kinase/signal transducer and activator of transcription signaling cascade, which drive cell growth, proliferation, and metastasis in solid tumors and hematological cancers. Therefore, PIM kinases are potential targets for anticancer therapies. 2,4-Bis(2-aminopyrimidin-4-yl) phenol is a potential anticancer agent that targets the cyclin-dependent kinase family. As this compound showed submicromolar potency in a PIM-1 kinase assay, we designed 2-(2-aminopyrimidin-4-yl)phenol analogs as PIM kinase inhibitors. Systematic modifications at the 2- and 4- positions of the phenol ring led to the discovery of PIM kinase inhibitor 16, which showed sub-micromolar potency against PIM-2 and double-digit nanomolar potency against PIM-1 and PIM-3 isoforms. Based on molecular docking studies, compound 16 was predicted to bind to the adenosine triphosphate pocket of PIM-1 kinase with two hydrogen bonding interactions and hydrophobic interactions.

Classification of Covid-19 Infection Based on Chest X-ray Pictures Using OpenCv and Convolution Neural Networks

Hyebin Park,Jeong-Soo Park 계명대학교 자연과학연구소 2022 Quantitative Bio-Science Vol.41 No.2

COVID-19 is a pathogen called SARS-CoV-2, an RNA virus that can infect various animals, including humans. As COVID-19 spread globally, the World Health Organization upgraded it to a pandemic in March 2020. In addition to solving the problem of shortage of medical personnel, rapid and accurate classification of infected patients emerged as an important issue. Therefore, we propose a deep learning-based chest X-ray image reading model that can notify the doctor whether the patient is infected. The goal is to achieve multiclass classification, which not only classifies COVID-19 infections, but also other lung diseases to help the medical community. The proposed method is a combination model. It involves pre-processing the chest X-ray image using the image augmentation method and various convolutional neural network (CNN) models. The purpose of the proposed method is to classify COVID-19, normal people, and viral pneumonia appropriately. Overall, 15,153 X-ray images were used in the study. By using the proposed method, we obtained a model with high accuracy through improved image data. Characteristically, some models tend to detect COVID-19 and pneumonia properly. Finally, an ensemble model was created using models made by the proposed method. Eventually, we obtained a high accuracy (0.981) model for detecting infections appropriately.

Synthesis and Characteristics of 1,1-Dialkyl-2,5-diethynyl-3,4-diphenylsiloles

Se Yeon Park,Young Tae Park 계명대학교 자연과학연구소 2022 Quantitative Bio-Science Vol.41 No.2

1,1-Dialkyl-2,5-diethynyl-3,4-diphenyl siloles 3a-c were synthesized in moderate yields as white solids through the desilylation reactions of 1,1-dialkyl-2,5-bis(trimethylsilylethynyl)-3,4-diphenylsiloles 2a-c. These materials were prepared from 1,1-dialkyl-2,5-dibromo-3,4-diphenylsiloles 1a-c. All the prepared structures of silole derivatives were characterized using 1H, 13C, 29Si NMR, FT-IR, UV-vis spectral data, and thermal stabilities using TGA. The synthesized siloles 2a-c and 3a-c were consistent with the structures proposed in the reaction scheme and were also studied with DFT calculations. The maximum absorption wavelengths of 2a-c and 3a-c were measured at 324~393 nm, which might be attributed to the chromophores of silolene-ethynylene originating from 2,5-bis(trimethylsilylethynyl) or -diethynyl-3,4-diphenylsiloles within the backbone of siloles prepared. 2a-c and 3a-c were stable up to 100℃ with less than 2% weight loss.

A Simple Registration Algorithm for Different Time Domains of Functional Data

Kyungmin Ahn 계명대학교 자연과학연구소 2022 Quantitative Bio-Science Vol.41 No.2

In Functional Data Analysis, functional data contains two types of variabilities: amplitude or vertical variability and phase or horizontal variability. Particularly for Functional Data Analysis, phase variation is a crucial noise and this is due to the lack of registration between peaks or valleys. Many registration or alignment algorithms have been proposed to reduce the phase variation between curves. However, these methods are restricted to the same fixed time intervals, that is, the functional observations are defined on the same fixed time domains. However, owing to the lack of synchronization, several functional data can be observed at different time intervals. In this study, we proposed a functional linear registration algorithm using a simple linear equation to align the functional data, which can also handle the different time intervals of functions. We demonstrated the framework using simulated data and real data to assess the algorithm’s effectiveness.

Subject Specific Deep Neural Network for Longitudinal Study in Pharmacokinetics and Pharmacodynamics

Changha Hwang 계명대학교 자연과학연구소 2022 Quantitative Bio-Science Vol.41 No.2

In this study we propose a subject specific deep neural network (SSDNN) model for analyzing pharmacokinetic (PK) and pharmacodynamic (PD) data. PK and PD data are obtained at subject-specific irregular time intervals, and a different number of observations are collected for each subject, based on the number of times the subject visited the hospital. The SSDNN’s performance is compared to that of the standard neural network (NN) and support vector machine (SVM) using three evaluation metrics, which are mean squared error (MSE), mean absolute error (MAE) and mean relative absolute error (MRAE). We find that the absolute values of the four measures of the proposed SSDNN are significantly lower than those of NN and SVR for PK and PD data. These findings imply that the proposed SSDNN is an appealing tool for analyzing PK and PD data.

Test for Homogeneity of the Scale Matrix of Elliptical Tail Dependence with Application to Real Data

Moosup Kim 계명대학교 자연과학연구소 2022 Quantitative Bio-Science Vol.41 No.2

In finance, random quantities such as returns frequently exhibit asymmetry in their tail behavior. The tail behavior is described using an elliptical distribution owing to its heaviness. Moreover, elliptical distributions constitute a parametric model class of multivariate regular variations, and the scale matrix is important in determining the tail dependence. For testing the asymmetry of the tail dependence, we propose a likelihood ratio test that checks the homogeneity of the scale matrix in separate regions. Moreover, the proposed test is applied to a real dataset to investigate the asymmetry of tail dependence.

Association of Matrix Metalloproteinase 3 and 11β-Hydroxysteroid Dehydrogenase Type 1 Gene Polymorphisms with Type 2 Diabetes in Koreans

김수원,유민 계명대학교 자연과학연구소 2015 Quantitative Bio-Science Vol.34 No.1

Type 2 diabetes is a typical polygenic disease complex, for which several common risk alleles have been identified. Matrix metalloproteinase-3 (MMP3) is an enzyme known to be involved in the destruction of the extracellular matrix in normal physiological processes and disease. 11β-Hydroxysteroid dehydrogenase type 1 (11β-HSD1) that catalyzes the conversion of inactive cortisone to active cortisol. is related to the development of type 2 diabetes. Therefore, we investigated the genotypes for the SNP rs522616 in MMP3 gene and rs12086634 in 11β-HSD1 gene between patients and the control group in Korean population. One hundred patients (Male 58, Female 42) with type 2 diabetes (T2D) and 100 controls (Male 36, Female 64) participated in this study. As a result, there was no association between the SNP rs522616 in MMP3 gene, rs12086634 in 11β-HSD1 gene and T2D. Further studies with larger population may be needed for the development of diagnostic methods at the genetic level.

Effects of Aroma Hand Massage Using Lavender Oil on the Improvement of Sleep Quality in Hospitalized Elderly Inpatients

김양희,김필순,김영철 계명대학교 자연과학연구소 2017 Quantitative Bio-Science Vol.36 No.1

This study was carried out to evaluate the effects of aroma hand massage using lavender oil on the improvement of sleep quality in hospitalized elderly inpatients. The patients (>65 years old) hospitalized in K care hospital were divided into two groups: the lavender group (17 people receiving aroma hand massage using lavender oil) and the jojoba group (15 people receiving hand massage using jojoba oil). Actigraphy and sleep satisfaction were used as research tools. Paired t-tests, χ2 test, and t-tests were used to analyze the data using the SPSS WIN 17.0 program. The lavender group showed a statistically significant improvement after the treatment not only in subjective measures including sleep satisfaction but also in objective measures, including actigraphic parameters such as total sleep time, wake time after sleep onset, and sleep efficiency. Based on the results, we demonstrated that aroma hand massage using lavender oil is effective in improving the sleep quality in elderly inpatients. Furthermore, our results indicated that this effect was not merely a placebo or hand massage effect, but the result of the lavender oil.