Young Joo Yoon

Penalized regression models with autoregressive error terms

Penalized regression methods have recently gained enormous attention in statistics and the field of machine learning due to their ability of reducing the prediction error and identifying important variables at the same time. Numerous studies have been conducted for penalized regression, but most of them are limited to the case when the data are independently observed. In this paper, we study a variable selection problem in penalized regression models with autoregressive (AR) error terms. We consider three estimators, adaptive least absolute shrinkage and selection operator, bridge, and smoothly clipped absolute deviation, and propose a computational algorithm that enables us to select a relevant set of variables and also the order of AR error terms simultaneously. In addition, we provide their asymptotic properties such as consistency, selection consistency, and asymptotic normality. The performances of the three estimators are compared with one another using simulated and real examples.

Dietary Anthocyanins against Obesity and Inflammation

Chronic low-grade inflammation plays a pivotal role in the pathogenesis of obesity, due to its associated chronic diseases such as type II diabetes, cardiovascular diseases, pulmonary diseases and cancer. Thus, targeting inflammation is an attractive strategy to counter the burden of obesity-induced health problems. Recently, food-derived bioactive compounds have been spotlighted as a regulator against various chronic diseases due to their low toxicity, as opposed to drugs that induce severe side effects. Here we describe the beneficial effects of dietary anthocyanins on obesity-induced metabolic disorders and inflammation. Red cabbage microgreen, blueberry, blackcurrant, mulberry, cherry, black elderberry, black soybean, chokeberry and jaboticaba peel contain a variety of anthocyanins including cyanidins, delphinidins, malvidins, pelargonidins, peonidins and petunidins, and have been reported to alter both metabolic markers and inflammatory markers in cells, animals, and humans. This review discusses the interplay between inflammation and obesity, and their subsequent regulation via the use of dietary anthocyanins, suggesting an alternative dietary strategy to ameliorate obesity and obesity associated chronic diseases.

Workplace Bullying, Job Stress, Intent to Leave, and Nurses’ Perceptions of Patient Safety in South Korean Hospitals

BackgroundNegative work environments influence the ability of nurses to provide optimal patient care in a safe environment. AimThe purpose of the study was to test a model linking workplace bullying (WPB) and lateral violence (LV) with job stress, intent to leave, and, subsequently, nurse-assessed patient adverse outcomes (safety issues). DesignThis descriptive-correlational study examined the relationships between study variables and used a structural equation model to test the validity of the proposed theoretical framework. MethodsA convenience sample of 508 clinical nurses working in eight general hospitals in Daejeon, South Korea, completed a questionnaire on measures of WPB, LV, job stress, intent to leave, and nurse-assessed patient safety. Analysis of moment structures was used to estimate a set of three models with competing measurement structures for WPB and LV and the same structural model. Akaike Information Criterion was used for model selection. ResultsAmong the three proposed models, the model with complex factor loadings was selected (WPB and LV were both associated with verbal abuse and physical threat). WPB directly and indirectly influenced nurse-assessed patient safety. Job stress directly influenced intent to leave, and intent to leave directly influenced nurse-assessed patient safety. ConclusionsThe results of the study support the proposition that WPB, job stress, and intent to leave may be associated with nurse-perceived adverse outcomes (patient safety issues) in hospitals. Nurse perceptions of WPB were associated with nurse-assessed patient safety outcomes (adverse events) directly and through mediating job stress and intent to leave. LV was not associated with the mediators or nurse-assessed adverse outcomes (safety).

Factors affecting workplace bullying and lateral violence among clinical nurses in Korea: descriptive study

BACKGROUND & AIM Workplace bullying and lateral violence are serious issues affecting the work life of hospital nurses. The purpose of this study was to identify the selected individual and institutional characteristics for workplace bullying and lateral violence using a conceptual framework. METHODS A descriptive survey design was used. A convenience sample of 255 nurses in tertiary hospitals, who had a minimum of 6 months clinical experience, completed the survey. Regression analysis was used to determine factors significantly associated with workplace bullying and lateral violence. The Negative Acts Questionnaire-Revised and the Lateral Violence scale were used to measure workplace bullying and lateral violence. RESULTS A negative affect, individualism and working in hospital specialty units predicted workplace bullying. Individualism, a negative affect, affiliated hospital and working hours predicted verbal abuse whereas the place of employment was significantly associated with lateral violence. CONCLUSION The results of this study identified factors that are associated with bullying and violence but did not fully support the conceptual framework. The individual characteristic negative affect was significantly associated with most types of workplace bullying and lateral violence bully whereas the place of employment was an important factor in lateral violence. IMPLICATIONS FOR NURSING MANAGEMENT Nurse managers need to be aware that both individual and institutional factors may impact levels of workplace bullying and lateral violence in their hospitals and need to prepare specific strategies to address these multiple factors.

Adaptive lasso for linear regression models with ARMA-GARCH errors

ABSTRACT The linear regression models with the autoregressive moving average (ARMA) errors (REGARMA models) are often considered, in order to reflect a serial correlation among observations. In this article, we focus on an adaptive least absolute shrinkage and selection operator (LASSO) (ALASSO) method for the variable selection of the REGARMA models and extend it to the linear regression models with the ARMA-generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) errors (REGARMA-GARCH models). This attempt is an extension of the existing ALASSO method for the linear regression models with the AR errors (REGAR models) proposed by Wang et al. in 2007. New ALASSO algorithms are proposed to determine important predictors for the REGARMA and REGARMA-GARCH models. Finally, we provide the simulation results and real data analysis to illustrate our findings.

Enhanced Antioxidant Activity of Bioactives in Colored Grains by Nano-Carriers in Human Lens Epithelial Cells

The use of phytochemicals for preventing chronic diseases associated with oxidative stress such as cataracts is hindered by their low bioavailability. The effects of nano-carriers on the antioxidant activities of extracts of black rice with giant embryo (BRGEx) and soybeans (SBx) have been determined in human lens epithelial B3 cells. Scanning (SEM) and transmission electron microscopy (TEM) demonstrated that rGO (reduced graphene oxide) has a flat surface unlike GO (graphene oxide), which has a distinctive wrinkled structure with defects. UPLC analysis revealed 41.9 μg/100 g of γ-oryzanols in water extract of BRGE, and 111.8 μg /100 g of lutein, 757.7 μg/100 g of γ-tocotrienol, 4071.4 μg/100 g of γ-tocopherol in 40% ethanol extract of soybeans, respectively. Even though a low concentration of BRGEx alone did not show any antioxidant activity in B3 cells, co-treatment of BRGEx with rGO together substantially reduced hydrogen peroxide and methylglyoxal-induced DNA damage, as determined by phosphorylated γH2AX. In addition, SBx with rGO also attenuated DNA damage. Furthermore, intracellular reactive oxygen species were significantly decreased by combining extracts of these colored grains with rGO. These results suggest a potential application of nanocarriers for enhancing the bioavailability of phytochemicals.

Black soybeans protect human keratinocytes from oxidative stress-induced cell death

Abstract Black soybeans are functional foods containing a variety of bioactives such as isoflavones, carotenoids, tocopherols, phenolic acid as well as anthocyanins. Here, we examined whether Cheongja#3 black soybean extract has a protective effect on oxidative stress‐induced cell death in human keratinocytes HaCaT. First, we identified fat‐soluble bioactives in three varieties of soybean extracts (Saedanbaek, Daechan, and Cheongja#3). In particular, black soybean Cheongja#3 had high amounts of lutein than other varieties. We demonstrated that Cheongja#3 extract reduced intracellular reactive oxygen species levels in HaCaT cells. Furthermore, Cheongja#3 protected cells from hydrogen peroxide (H2O2)‐induced oxidative stress and triggered cell death determined by cell viabilities and apoptotic caspase activities. Next, we identified the underlying mechanism is due to increased Nrf2 antioxidant system by Cheongja#3, thus increasing the expression of heme oxygenases (HO)‐1. These results indicated that Cheongja#3 soybean extract has protective role against oxidative stress by upregulating the Nrf‐2 antioxidant system in human keratinocyte HaCaT cells.

Group variable selection in cardiopulmonary cerebral resuscitation data for veterinary patients

Cardiopulmonary cerebral resuscitation (CPCR) is a procedure to restore spontaneous circulation in patients with cardiopulmonary arrest (CPA). While animals with CPA generally have a lower success rate of CPCR than people do, CPCR studies in veterinary patients have been limited. In this paper, we construct a model for predicting success or failure of CPCR, and identifying and evaluating factors that affect the success of CPCR in veterinary patients. Due to reparametrization using multiple dummy variables or close proximity in nature, many variables in the data form groups, and thus a desirable method should take this grouping feature into account in variable selection. To accomplish these goals, we propose an adaptive group bridge method for a logistic regression model. The performance of the proposed method is evaluated under different simulated setups and compared with several other regression methods. Using the logistic group bridge model, we analyze data from a CPCR study for veterinary patients and discuss their implications on the practice of veterinary medicine.