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Simulation-based education is gaining attention worldwide as it is recognized as effective in fostering collaborative skills in healthcare students. We conducted a comprehensive review of simulation-based interprofessional education (IPE) to examine the current state of simulation-based IPE. This scoping review systematically analyzed studies on simulation-based IPE in South Korean healthcare education, following established guidelines. Relevant articles were comprehensively searched, and key data on simulation methods, implementations, and educational effectiveness were extracted for analysis. The present study included nine quantitative studies and one mixed-methods study. The majority of participants were undergraduate nursing and medical students. The duration of IPE interventions ranged from 2 hours to 2 weeks. Education methods included standardized patients, high-fidelity simulators, and role-playing. Educational outcomes focused on measuring IPE competencies and satisfaction levels, concentrating on Kirkpatrick levels 1 and 2. While most studies reported high satisfaction levels, there is a need for objective evaluation of educational effectiveness. As simulation-based IPE in Korean healthcare education evolves, there is a need for greater inclusivity of diverse roles, multidisciplinary respect, and scenario development allowing active participation across professions. Establishing institutional frameworks, community linkages, and a deep understanding of IPE’s purpose and essence among practitioners is crucial for its academic maturation.

Abstract Background The decision to screen for breast cancer among older adults with dementia is complex and must often be individualized, as these individuals have an elevated risk of harm from over-screening. Medicare beneficiaries with dementia are increasingly enrolling in Medicare Advantage plans, which typically promote receipt of preventive cancer screening among their enrollees. This study examined the utilization of breast cancer screening among Medicare enrollees with dementia, in Medicare Advantage and in fee-for-service Medicare. Methods We conducted a pooled cross-sectional study of women with Alzheimer’s disease and related dementias or cognitive impairment who were eligible for mammogram screening. We used Medicare Current Beneficiary Survey data to identify utilization of biennial mammogram screening between 2012 and 2019. Poisson regression models were used to estimate prevalence ratios of mammogram utilization and to calculate adjusted mammogram rates for Medicare Advantage and fee-for-service Medicare enrollees with dementia, and further stratified by rurality and by dual eligibility for Medicare and Medicaid. Results Mammogram utilization was 16% higher (Prevalence Ratio [PR] 1.16; 95% CI: 1.05, 1.29) among Medicare Advantage enrollees with dementia, compared to their counterparts in fee-for-service Medicare. Rural enrollees experienced no significant difference (PR 0.99; 95% CI: 0.72, 1.37) in mammogram use between Medicare Advantage and fee-for-service Medicare enrollees. Among urban enrollees, Medicare Advantage enrollment was associated with a 21% higher mammogram rate (PR 1.21; 95% CI: 1.09, 1.35). Dual-eligible Medicare Advantage enrollees had a 34% higher mammogram rate (PR 1.34; 95% CI: 1.10, 1.63) than dual-eligible fee-for-service Medicare enrollees. Among non-dual-eligible enrollees, adjusted mammogram rates were not significantly different (PR 1.11; 95% CI: 0.99, 1.24) between Medicare Advantage and fee-for-service Medicare enrollees. Conclusions Medicare beneficiaries age 65–74 with Alzheimer’s disease and related dementias or cognitive impairment had a higher mammogram use rate when they were enrolled in Medicare Advantage plans compared to fee-for-service Medicare, especially when they were dual-eligible or lived in urban areas. However, some Medicare Advantage enrollees with Alzheimer’s disease and related dementias or cognitive impairment may have experienced over-screening for breast cancer.

BackgroundStudies suggest that distress is associated with various health conditions such as hypertension, asthma, diabetes, and coronary heart disease. However, only few studies focused on Asian Americans and little is known about the association with multiple comorbidity.MethodsWe conducted a cross-sectional analysis among 400 Chinese and Korean American participants (aged 50-75 years) of the STOP CRC randomized controlled trial. Perceived distress was assessed using the distress thermometer scale (range 0-10). Disease diagnosis was self-reported by the participants. Multimorbidity (MM) was defined as having ≥2 chronic conditions. Complex multimorbidity (CMM) was defined as having ≥3 of the following body system disorders: circulation disorder, endocrine-metabolic disorder, cancer, anxiety or depression, breathing problem, and other health problems. We performed logistic regression for CMM and Poisson regression with robust error variance for MM to estimate associations with distress, adjusting for potential confounders.ResultsThe mean age was 58.4 years and mean distress score was 3.65. One-unit increase in distress score was associated with a 1.22-fold increase in the odds of having CMM (95% CI: 1.04-1.42). The magnitude of association slightly increased after additional adjustment for socioeconomic factors and health insurance status (OR: 1.29; 95% CI: 1.10-1.52). Higher distress score was positively associated with MM but the association was only marginally significant (PR: 1.04; 95% CI: 0.99-1.10), adjusting for socioeconomic factors and health insurance status.ConclusionOur data suggest that higher perceived distress may be associated with simultaneous dysfunction of multiple distinct body systems among Chinese and Korean American older adults.

BackgroundGiven the rapidly growing Asian populations in the U.S. due to immigration, and the aging demographic of Asian immigrants, it is crucial to understand how acculturation shapes health among older adult Asian immigrants. We study the relationship between acculturation and self-rated health (SRH) and moderating roles of age and Asian subgroup.MethodsOur cross-sectional study consisted of 200 Chinese and 200 Korean immigrants aged 49–75 living in Baltimore–Washington DC metropolitan area, who were recruited from primary care physicians' clinics in Maryland and Northern Virginia. The participants completed the survey either in-person or by phone in their preferred language. Multidimensional proxies were used to measure acculturation: years living in the U.S. (≥23 years,

Abstract Background The early onset of Alzheimer’s disease and related dementias (ADRD) before age 65 can introduce life and health care complications. Preserving an early-onset ADRD patient’s daily functioning longer and delaying declines in health from non-ADRD conditions become important preventive goals. This study examined the differences in utilization of preventive cancer screenings between patients with and without early-onset ADRD, and compared utilization of the screenings in rural versus urban areas among women with early-onset ADRD in the United States. Methods We conducted a cross-sectional study of women aged 40 to 64 years eligible for mammogram and cervical cancer screenings using commercial insurance claims from 2012 to 2018. We measured the use of biennial mammogram among women 50 to 64 years old, and the use of triennial Pap smear test among women 40 to 64 years old. We used inverse probability weighted logistic regressions to estimate the odds of receiving preventive cancer screenings by the presence of early-onset ADRD or cognitive impairments (CI). We used multivariable logistic regressions to estimate the odds of receiving preventive cancer screenings by rural or urban residence among women with early-onset ADRD/CI. Results Among 6,349,308 women in the breast cancer screening sample (mean [SD] age, 56.52 [4.03] years), 36,131 had early-onset ADRD/CI (mean [SD] age, 57.99 [3.98] years). Among 6,583,088 women in the cervical cancer screening sample (mean [SD] age, 52.37 [6.81] years), 30,919 had early-onset ADRD/CI (mean [SD] age, 55.79 [6.22] years). Having early-onset ADRD/CI was associated with lower utilization of mammogram (OR: 0.92, 95% CI: 0.90–0.95). No significant difference was observed in Pap smear screening (OR: 0.99, 95% CI: 0.96–1.02) between patients with and without early-onset ADRD/CI. Among patients with early-onset ADRD/CI, those in rural areas were less likely than those in urban areas to have mammograms (OR: 0.91, 95% CI: 0.85–0.97) and Pap smears (OR: 0.65, 95% CI: 0.61–0.71). Conclusions The observed pattern of rural-urban differences in cancer screening in our study emphasizes the need for efforts to promote evidence-based, individualized decision-making processes in the early-onset ADRD population.

BackgroundFine-needle aspiration biopsy (FNAB) is a good diagnostic tool for thyroid nodules; however, its high false-negative rate for giant nodules remains controversial. Many clinicians recommend surgical resection for nodules >4 cm owing to an increased risk of malignancy and an increased false-negative rate. This study aimed to examine the feasibility of this approach and investigate the incidence of malignancy in thyroid nodules >4 cm without suspicious cytology based on medical records in our center.MethodsThis was a retrospective analysis of 453 patients that underwent preoperative FNAB for nodules measuring >4 cm between January 2017 and August 2022 at Severance Hospital, Seoul.ResultsAmong the 453 patients, 140 nodules were benign and 119 were indeterminate. Among 259 patients, the final pathology results were divided into benign (149) and cancerous (110) groups, and the prevalence of malignancy was 38.9% in the benign group and 55.5% in the indeterminate group. Among the malignancies, follicular carcinoma and follicular variants of papillary carcinoma were observed in 83% of the cytologically benign group and 62.8% of the indeterminate group.ConclusionPreoperative FNAB had high false-negative rates and low diagnostic accuracy in patients with thyroid nodules >4 cm without suspicious cytologic features; therefore, diagnostic surgery may be considered a treatment option.

Abstract Despite widespread public interest in problematic gaming interventions, questions regarding the empirical status of treatment efficacy persist. We conducted pairwise and network meta-analyses based on 17 psychological intervention studies on excessive gaming (n = 745 participants). The pairwise meta-analysis showed that psychological interventions reduce excessive gaming more than the inactive control (standardized mean difference [SMD] = 1.70, 95% confidence interval [CI] 1.27 to 2.12) and active control (SMD = 0.88, 95% CI 0.21 to 1.56). The network meta-analysis showed that a combined treatment of Cognitive Behavioral Therapy (CBT) and Mindfulness was the most effective intervention in reducing excessive gaming, followed by a combined CBT and Family intervention, Mindfulness, and then CBT as a standalone treatment. Due to the limited number of included studies and resulting identified methodological concerns, the current results should be interpreted as preliminary to help support future research focused on excessive gaming interventions. Recommendations for improving the methodological rigor are also discussed.

Objectives Survival machine learning (ML) has been suggested as a useful approach for forecasting future events, but a growing concern exists that ML models have the potential to cause racial disparities through the data used to train them. This study aims to develop race/ethnicity-specific survival ML models for Hispanic and black women diagnosed with breast cancer to examine whether race/ethnicity-specific ML models outperform the general models trained with all races/ethnicity data.Methods We used the data from the US National Cancer Institute’s Surveillance, Epidemiology and End Results programme registries. We developed the Hispanic-specific and black-specific models and compared them with the general model using the Cox proportional-hazards model, Gradient Boost Tree, survival tree and survival support vector machine.Results A total of 322 348 female patients who had breast cancer diagnoses between 1 January 2000 and 31 December 2017 were identified. The race/ethnicity-specific models for Hispanic and black women consistently outperformed the general model when predicting the outcomes of specific race/ethnicity.Discussion Accurately predicting the survival outcome of a patient is critical in determining treatment options and providing appropriate cancer care. The high-performing models developed in this study can contribute to providing individualised oncology care and improving the survival outcome of black and Hispanic women.Conclusion Predicting the individualised survival outcome of breast cancer can provide the evidence necessary for determining treatment options and high-quality, patient-centred cancer care delivery for under-represented populations. Also, the race/ethnicity-specific ML models can mitigate representation bias and contribute to addressing health disparities.

Various types of damage, including health risks, caused by abnormal climate conditions have gradually increased in recent years, particularly in cities with a high degree of complexity. In particular, it is important to address heatwave damage risk from both technical and policy perspectives because it poses risks to older adults and/or disadvantaged individuals. This study was performed to assess the quantitative effects of two heatwave mitigation measures, cool roofs and cool pavements, in the Jangyumugye district of Gimhae, Republic of Korea. Thermal imaging cameras were used to measure surface temperatures after the installation of these measures; after the installation of cool roofs, indoor temperatures were also measured using temperature measurement data loggers. Then, a survey was conducted to obtain feedback from users of the associated facilities. From August to October 2019, surface temperatures were significantly reduced following the installation of cool roofs and cool pavements. Cool roofs constructed using slabs and panels provided average temperature reductions of 15.5 °C and 11.6 °C, respectively. Cool pavements showed a temperature reduction of ≥4 °C in both parking lot and alley sites. More than 30% of users noticed a reduction in temperature as a result of heatwave measure installation. These results may be used by the city to make informed policy decisions regarding the implementation of heatwave mitigation measures.

Study objectives: While sleep apnea has been associated with cardiovascular disease (CVD) risk factors in white individuals in the U.S., these associations in Chinese and Korean Americans are less well-understood, particularly how these associations vary by age, gender, Asian origin, obesity, chronic conditions, and daytime sleepiness. Methods: We used a sample of Chinese and Korean Americans ages 50-75 (n = 394) from the Baltimore-Washington DC Metropolitan Area to examine the associations of high risk (HR) sleep apnea with diagnoseable hypercholesterolemia and diabetes, as well as the following biomarkers: total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol/HDL-C ratio, triglycerides, and glucose (non-fasting). Poisson models included demographic factors, socioeconomic status, and body mass index (BMI). We tested for potential effect modifiers. Results: HR-sleep apnea was associated with higher LDL-C level (β = 14.56, p

Identification of areas susceptible to floods is an important issue which requires an increased attention due to the changing frequency and magnitude of floods, which is mainly a result of the ongoing climate change and increasing anthropic pressure on the landscape. In this study, the aim was to identify the areas susceptible to floods using and comparing two different approaches, namely the multi-criteria decision analysis-analytical hierarchy process (MCDA-AHP) and the machine learning-boosted classification (BCT) and boosted regression (BRT) tree. The study area was represented by the Topľa river basin, Slovakia. Altogether, seven relevant flood conditioning factors: elevation, slope, river network density, distance from river, flow accumulation, curve numbers and lithology as well as flood inventory database consisting of 107 flood locations were used. Based on the results, almost 40% of the study area is characterized by high to very high flood susceptibility using the MCDA-AHP. In case of the BCT and BRT models, the share of high and very high flood susceptibility class on the basin area is 45% and 38%, respectively. Validation of the performed flood susceptibility models confirmed generally higher accuracy of the machine learning models. The accuracy of the MCDA-AHP model was 81.33% while the accuracy of the boosted tree models was 87.70% and 91.42%, respectively, for classification and regression. The results of this study can enhance more effective preliminary flood risk assessment according to the EU Floods Directive.

Abstract Background Plants have been used as an important source of indispensable bioactive compounds in various cosmetics, foods, and medicines. However, the subsequent functional annotation of these compounds seems arduous because of the largely uncharacterized, vast metabolic repertoire of plant species with known biological phenotypes. Hence, a rapid multi-parallel screening and characterization approach is needed for plant functional metabolites. Results Fifty-one species representing three plant families, namely Asteraceae, Fabaceae, and Rosaceae, were subjected to metabolite profiling using gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) and ultrahigh-performance liquid chromatography quadrupole orbitrap ion trap tandem mass spectrometry (UHPLC-Q-orbitrap-MS/MS) as well as multivariate analyses. Partial least squares discriminant analysis (PLS-DA) of the metabolite profiling datasets indicated a distinct clustered pattern for 51 species depending on plant parts (leaves and stems) and relative phylogeny. Examination of their relative metabolite contents showed that the extracts from Fabaceae plants were abundant in amino acids, fatty acids, and genistein compounds. However, the extracts from Rosaceae had higher levels of catechin and ellagic acid derivatives, whereas those from Asteraceae were higher in kaempferol derivatives and organic acids. Regardless of the different families, aromatic amino acids, branch chain amino acids, chlorogenic acid, flavonoids, and phenylpropanoids related to the shikimate pathway were abundant in leaves. Alternatively, certain amino acids (proline, lysine, and arginine) as well as fatty acids levels were higher in stem extracts. Further, we investigated the associated phenotypes, i.e., antioxidant activities, affected by the observed spatial (leaves and stem) and intra-family metabolomic disparity in the plant extracts. Pearson’s correlation analysis indicated that ellagic acid, mannitol, catechin, epicatechin, and quercetin derivatives were positively correlated with antioxidant phenotypes, whereas eriodictyol was positively correlated with tyrosinase inhibition activity. Conclusions This work suggests that metabolite profiling, including multi-parallel approaches and integrated bioassays, may help the expeditious characterization of plant-derived metabolites while simultaneously unraveling their chemodiversity.

A metabolomics approach was used to profile metabolites of Panax notoginseng fermented with Aspergillus cristatus in two ways, liquid-state fermentation (LF-P) and solid-state fermentation (SSF-P) and examine metabolite markers representing antioxidant activity and skin anti-aging. Protopanaxadiol (PPD) and protopanaxatriol (PPT) contents were higher in SSF-P than in LF-P and showed a multiplicative increase over the fermentation period of four days. PPD and PPT levels also correlated with antioxidant and anti-aging effects in skin, based on the mRNA expression of dermal extracellular matrix components. In the bioactivity validation assays, PPD and PPT significantly improved the expression of type-I collagen, fibrillin-1, and elastin in human dermal fibroblasts from both young and old subjects; these were comparable with the effects of the SSF-P extracts. Overall, our results suggest that changes in the metabolites of P. notoginseng fermented with A. cristatus enhance the quality and availability of bioactive compounds associated with skin anti-aging.

Background: Hypertension has the characteristic that the risk of complications can be reduced through appropriate medication in daily life. Hence, it is important to consider practical measures to increase medication adherence, particularly among older patients. Methods: This study used the Korea Health Panel 2020 data (Version 2.0.1), jointly conducted by Korea Institute for Health and Social Affairs and National Health Insurance. A total of 2300 patients with high blood pressure over 65 years of age were selected. In order to identify types of medication adherence in older hypertensive patients, and examine factors that influence the types, the Latent Profile Analysis (LPA) and logistic regression were performed. Results: The types of adherence groups were classified into two groups: an ‘adherence group’ (87.1%) and a ‘non-adherence group’ (12.9%). Furthermore, age, living alone, and depressive symptoms were identified as determinants of medication adherence type among older hypertensive patients. Conclusion: The significant impact of sociodemographic status (age, living alone, and depressive symptoms) on medication adherence among older hypertensive patients indicates the need to establish more specific empirical interventions based on each type’s characteristics. It is expected that this study will provide an in-depth understanding of factors associated with medication adherence among older patients with hypertension, which can support interventions tailored to the specific needs of those who are non-adherent.

Abstract Background Checkpoint inhibitors have not been effective for prostate cancer as single agents. Durvalumab is a human IgG1-K monoclonal antibody that targets programmed death ligand 1 and is approved by the U.S. Food and Drug Administration for locally advanced or metastatic urothelial cancer and locally advanced, unresectable stage 3 non-small cell lung cancer. Olaparib, a poly (ADP-ribose) polymerase inhibitor, has demonstrated an improvement in median progression-free survival (PFS) in select patients with metastatic castration-resistant prostate cancer (mCRPC). Data from other trials suggest there may be improved activity in men with DNA damage repair (DDR) mutations treated with checkpoint inhibitors. This trial evaluated durvalumab and olaparib in patients with mCRPC with and without somatic or germline DDR mutations. Methods Eligible patients had received prior enzalutamide and/or abiraterone. Patients received durvalumab 1500 mg i.v. every 28 days and olaparib 300 mg tablets p.o. every 12 h until disease progression or unacceptable toxicity. All patients had biopsies of metastatic lesions with an evaluation for both germline and somatic mutations. Results Seventeen patients received durvalumab and olaparib. Nausea was the only nonhematologic grade 3 or 4 toxicity occurring in > 1 patient (2/17). No patients were taken off trial for toxicity. Median radiographic progression-free survival (rPFS) for all patients is 16.1 months (95% CI: 4.5–16.1 months) with a 12-month rPFS of 51.5% (95% CI: 25.7–72.3%). Activity is seen in patients with alterations in DDR genes, with a median rPFS of 16.1 months (95% CI: 7.8–18.1 months). Nine of 17 (53%) patients had a radiographic and/or PSA response. Patients with fewer peripheral myeloid-derived suppressor cells and with alterations in DDR genes were more likely to respond. Early changes in circulating tumor cell counts and in both innate and adaptive immune characteristics were associated with response. Conclusions Durvalumab plus olaparib has acceptable toxicity, and the combination demonstrates efficacy, particularly in men with DDR abnormalities. Trial registration ClinicalTrials.gov identifier: NCT02484404.

Abstract Heat shock factor 1 (HSF1) initiates a broad transcriptional response to proteotoxic stress while also mediating a cancer-specific transcriptional program. HSF1 is thought to be regulated by molecular chaperones, including Heat Shock Protein 90 (HSP90). HSP90 is proposed to sequester HSF1 in unstressed cells, but visualization of this interaction in vivo requires protein crosslinking. In this report, we show that HSP90 binding to HSF1 depends on HSP90 conformation and is only readily visualized for the ATP-dependent, N-domain dimerized chaperone, a conformation only rarely sampled by mammalian HSP90. We have used this mutationally fixed conformation to map HSP90 binding sites on HSF1. Further, we show that ATP-competitive, N-domain targeted HSP90 inhibitors disrupt this interaction, resulting in the increased duration of HSF1 occupancy of the hsp70 promoter and significant prolongation of both the constitutive and heat-induced HSF1 transcriptional activity. While our data do not support a role for HSP90 in sequestering HSF1 monomers to suppress HSF1 transcriptional activity, our findings do identify a noncanonical role for HSP90 in providing dynamic modulation of HSF1 activity by participating in removal of HSF1 trimers from heat shock elements in DNA, thus terminating the heat shock response.

Since flood frequency increases with the impact of climate change, the damage that is emphasized on flood-risk maps is based on actual flooded area data; therefore, flood-susceptibility maps for the Seoul metropolitan area, for which random-forest and boosted-tree models are used in a geographic information system (GIS) environment, are created for this study. For the flood-susceptibility mapping, flooded-area, topography, geology, soil and land-use datasets were collected and entered into spatial datasets. From the spatial datasets, 12 factors were calculated and extracted as the input data for the models. The flooded area of 2010 was used to train the model, and the flooded area of 2011 was used for the validation. The importance of the factors of the flood-susceptibility maps was calculated and lastly, the maps were validated. As a result, the distance from the river, geology and digital elevation model showed a high importance among the factors. The random-forest model showed validation accuracies of 78.78% and 79.18% for the regression and classification algorithms, respectively, and boosted-tree model showed validation accuracies of 77.55% and 77.26% for the regression and classification algorithms, respectively. The flood-susceptibility maps provide meaningful information for decision-makers regarding the identification of priority areas for flood-mitigation management.

Though varying metabolomes are believed to influence distinctive characteristics of different soy foods, an in-depth, comprehensive analysis of both soluble and volatile metabolites is largely unreported. The metabolite profiles of different soy products, including cheonggukjang, meju, doenjang, and raw soybean, were characterized using LC-MS (liquid chromatography–mass spectrometry), GC-MS (gas chromatography–mass spectrometry), and headspace solid-phase microextraction (HS-SPME) GC-MS. Principal component analysis (PCA) showed that the datasets for the cheonggukjang, meju, and doenjang extracts were distinguished from the non-fermented soybean across PC1, while those for cheonggukjang and doenjang were separated across PC2. Volatile organic compound (VOC) profiles were clearly distinct among doenjang and soybean, cheonggukjang, and meju samples. Notably, the relative contents of the isoflavone glycosides and DDMP (2,3-dihydro-2,5-dihydroxy-6-methyl-4H-pyran-4-one) conjugated soyasaponins were higher in soybean and cheonggukjang, compared to doenjang, while the isoflavone aglycones, non-DDMP conjugated soyasaponins, and amino acids were significantly higher in doenjang. Most VOCs, including the sulfur containing compounds aldehydes, esters, and furans, were relatively abundant in doenjang. However, pyrazines, 3-methylbutanoic acid, maltol, and methoxyphenol were higher in cheonggukjang, which contributed to the characteristic aroma of soy foods. We believe that this study provides the fundamental insights on soy food metabolomes, which determine their nutritional, functional, organoleptic, and aroma characteristics.

The role of forests is increasing because of rapid land use changes worldwide that have implications on ecosystems and the carbon cycle. Therefore, it is necessary to obtain accurate information about forests and build forest inventories. However, it is difficult to assess the internal structure of the forest through 2D remote sensing techniques and fieldwork. In this aspect, we proposed a method for estimating the vertical structure of forests based on full-waveform light detection and ranging (FW LiDAR) data in this study. Voxel-based tree point density maps were generated by estimating the number of canopy height points in each voxel grid from the raster digital terrain model (DTM) and canopy height points after pre-processing the LiDAR point clouds. We applied an unsupervised classification algorithm to the voxel-based tree point density maps and identified seven classes by profile pattern analysis for the forest vertical types. The classification accuracy was found to be 72.73% from the validation from 11 field investigation sites, which was additionally confirmed through comparative analysis with aerial images. Based on this pre-classification reference map, which is assumed to be ground truths, the deep neural network (DNN) model was finally applied to perform the final classification. As a result of accuracy assessment, it showed accuracy of 92.72% with a good performance. These results demonstrate the potential of vertical structure estimation for extensive forests using FW LiDAR data and that the distinction between one-storied and two-storied forests can be clearly represented. This technique is expected to contribute to efficient and effective management of forests based on accurate information derived from the proposed method.

In Korea, the Ministry of Environment and regional environment management agencies conduct environmental impact assessments (EIA) to mitigate and assess the impact of major development projects on the environment. EIA Big Data are used in conjunction with a geographical information system (GIS), and consist of indicators related to air, soil, and water that are measured before and after the development project. The impact of the development project on the environment can be evaluated through the variations of each indicator. This study analyzed trends in the environmental impacts of development projects during 2007–2016 using 21 types of EIA Big Data. A model was developed to estimate the Korean Environment Institute’s Environmental Impact Assessment Index for Development Projects (KEIDP) using a multi-layer perceptron-based artificial neural network (MLP-ANN) approach. A trend analysis of development projects in South Korea revealed that the mean value of KEIDP gradually increased over the study period. The rate of increase was 0.007 per year, with an R2 value of 0.8. In the future, it will be necessary for all management agencies to apply the KEDIP calculation model to minimize the impact of development projects on the environment and reduce deviations among development projects through continuous monitoring.

Rice koji, used as a starter for maximizing fermentation benefits, produces versatile end products depending on the inoculum microbes used. Here, we performed metabolite profiling to compare rice koji fermented with two important filamentous fungus, Aspergillus oryzae and A. cristatus, during 8 days. The multivariate analyses showed distinct patterns of primary and secondary metabolites in the two kojis. The rice koji fermented with A. oryzae (RAO) showed increased α-glucosidase activity and higher contents of sugar derivatives than the one fermented with A. cristatus (RAC). RAC showed enhanced β-glucosidase activity and increased contents of flavonoids and lysophospholipids, compared to RAO. Overall, at the final fermentation stage (8 days), the antioxidant activities and anti-aging effects were higher in RAC than in RAO, corresponding to the increased metabolites such as flavonoids and auroglaucin derivatives in RAC. This comparative metabolomic approach can be applied in production optimization and quality control analyses of koji products.

Three types of doenjang, a fermented soybean paste, were prepared by adding coriander (CR), Korean mint (KM), and peppermint (PM) and compared to the control group (CN) by studying their metabolite profiles and antioxidant activities followed by different fermentation periods (1, 30, and 150 days, respectively). The primary metabolome was analyzed by GC-TOF-MS, and 36 of metabolites were identified in four types of doenjang samples (CN, CR, KM, and PM). Samples were clustered based on the herb type and fermentation period in PCA and PLS-DA analysis. For the secondary metabolome analysis, UHPLC-Q-orbitrap-MS was used, and 26 metabolites were identified. The statistical analysis showed that the samples were clustered by herb type rather than fermentation period, and the samples containing KM and PM were located in the same group. The DPPH assay showed that PM-containing doenjang had the highest antioxidant activity. Correlation analysis indicated that organic acids such as lactic acid, malonic acid, succinic acid, uracil, vanillic acid, and quinic acid showed positive correlation with the DPPH activity. Overall, our results demonstrated that incorporating herbs in doenjang during fermentation caused significant shifts (p-value < 0.05) in the doenjang metabolites and antioxidant activity. Hence, herbs could be utilized for enhancing doenjang fermentation.

Numerous varieties of doenjang are manufactured by many food companies using different ingredients and fermentation processes, and thus, the qualities such as taste and flavor are very different. Therefore, in this study, we compared many products, specifically, 19 traditional doenjang (TD) and 17 industrial doenjang (ID). Subsequently, we performed non-targeted metabolite profiling, and multivariate statistical analysis to discover distinct metabolites in two types of doenjang. Amino acids, organic acids, isoflavone aglycones, non-DDMP (2,3-dihydro-2,5-dihydroxy-6-methyl-4H-pyran-4- one) soyasaponins, hydroxyisoflavones, and biogenic amines were relatively abundant in TD. On the contrary, contents of dipeptides, lysophospholipids, isoflavone glucosides and DDMP-conjugated soyasaponin, precursors of the above-mentioned metabolites, were comparatively higher in ID. We also observed relatively higher antioxidant, protease, and β-glucosidase activities in TD. Our results may provide valuable information on doenjang to consumers and manufacturers, which can be used while selecting and developing new products.

The eukaryotic heat shock protein 90 (Hsp90) undergoes an ATP-dependent conformational cycle that is influenced by posttranslational modifications and co-chaperones. Here, the authors show that the yeast co-chaperone Hch1 can be functionally substituted by site-specific phosphorylation in human Hsp90.

The medication regimen complexity index (MRCI), originally developed in English, is a reliable and valid tool to assess the complexity of pharmacotherapy. This study aimed to validate the Korean version of MRCI (MRCI-K). A cross-cultural methodological study comprising 335 discharged patients of a tertiary hospital in Korea was conducted. The translation process included translation into Korean by two clinical pharmacists, back translation by two native speakers, and a pretest of the tool, culminating in the Korean version of MRCI-K. Reliability analysis was assessed using inter-rater and test-retest reliability with 25 randomly selected patients. Convergent and discriminant validity analyses were conducted by correlating MRCI scores with medication number, age, sex, adverse drug reaction (ADR) reports, and length of stay. The criterion validity was confirmed through evaluation by a nine-member expert panel that subjectively ranked these regimens. The reliability analysis demonstrated excellent internal consistency (Cronbach's α = 0.977), and the intraclass correlation coefficient exceeded 0.90 for all cases. The correlation coefficient for the number of medications was 0.955 (P < 0.001). Weak significant correlations were observed with age and length of stay. The MRCI-K group with ADR reports scored higher (mean, 31.8) than the group without ADR reports (mean, 27.3). The expert panel's ranking had a stronger correlation with the MRCI ranking than the medication number ranking. MRCI-K has similar reliability and validity as MRCI and is useful for analyzing therapeutic regimens with potential applications in both practice and research in Korea.

Doenjang and ganjang are secondary fermented soybean products from meju (primary fermented product) following a complex fermentation process that separates the products into solid (doenjang) and liquid (ganjang) states. We performed a comparative study on gas chromatography mass spectrometry-(GC-MS) and liquid chromatography mass spectrometry-(LC-MS) based metabolite profiling with fungal and bacterial microbial community analysis of doenjang and ganjang during fermentation. Metabolite profiling and microbial community data showed distinct patterns, depending on the fermentation process. The relative levels of metabolic patterns were similar and most of the microorganisms produced halophilic or halotolerant microbes during the fermentation period in doenjang and ganjang. In the doenjang end products, isoflavones, soyasaponins, and amino acids were largely distributed and Debaryomyces and Staphylococcus were dominant, whereas the biogenic amine and phenylpropanoid contents were highly distributed in the ganjang end products, with higher levels of Meyerozyma and Tetragenococcus. Our results demonstrate that the quality of doenjang and ganjang is predominantly influenced by the microbiome and by metabolite changes during fermentation. Moreover, the present study provides a platform for comparing samples in different states.

This paper employs critical discourse analysis to examine the conceptualisation of disability within Developmentally Appropriate Practice (DAP) and to investigate how DAP addresses the role of early childhood educators working with children with disabilities in inclusive classrooms. The research questions guiding this study are: 1) How is disability conceptualised within the DAP? 2) How does DAP address the role of educators when working with children with disabilities in the context of inclusion? Using different models of disability and DisCrit as the theoretical lens, the study reveals that disability within DAP is associated with implicit bias, diversity, and a paradigm of fixing. The role of educators unfolds through themes such as reflecting on one's bias, creating inclusive environments by focusing on strengths and Universal Design (UD) and implementing screening processes. The findings shed light on the conflicting discourse of disability embedded in DAP, emphasising the need for educators to critically examine the notions of normalcy and the intersection of racism and ableism in the context of early childhood inclusion.

In recent years, the incidence of localized heavy rainfall has increased as abnormal weather events occur more frequently. In densely populated urban areas, this type of heavy rain can cause extreme landslide damage, so that it is necessary to estimate and analyze the susceptibility of future landslides. In this regard, deep learning (DL) methodologies have been used to identify areas prone to landslides recently. Therefore, in this study, DL methodologies, including a deep neural network (DNN), kernel-based DNN, and convolutional neural network (CNN) were used to identify areas where landslides could occur. As a detailed step for this purpose, landslide occurrence was first determined as landslide inventory through aerial photographs with comparative analysis using field survey data; a training set was built for model training through oversampling based on the landslide inventory. A total of 17 landslide influencing variables that influence the frequency of landslides by topography and geomorphology, as well as soil and forest variables, were selected to establish a landslide inventory. Then models were built using DNN, kernel-based DNN, and CNN models, and the susceptibility of landslides in the study area was determined. Model performance was evaluated through the average precision (AP) score and root mean square error (RMSE) for each of the three models. Finally, DNN, kernel-based DNN, and CNN models showed performances of 99.45%, 99.44%, and 99.41%, and RMSE values of 0.1694, 0.1806, and 0.1747, respectively. As a result, all three models showed similar performance, indicating excellent predictive ability of the models developed in this study. The information of landslides occurring in urban areas, which cause a great damage even with a small number of occurrences, can provide a basis for reference to the government and local authorities for urban landslide management.

Every year, many countries carry out landslide susceptibility analyses to establish and manage countermeasures and reduce the damage caused by landslides. Because increases in the areas of landslides lead to new landslides, there is a growing need for landslide prediction to reduce such damage. Among the various methods for landslide susceptibility analysis, statistical methods require information about the landslide occurrence point. Meanwhile, analysis based on physical slope models can estimate stability by considering the slope characteristics, which can be applied based on information about the locations of landslides. Therefore, in this study, a probabilistic method based on a physical slope model was developed to analyze landslide susceptibility. To this end, an infinite slope model was used as the physical slope model, and Monte Carlo simulation was applied based on landslide inventory including landslide locations, elevation, slope gradient, specific catchment area (SCA), soil thickness, unit weight, cohesion, friction angle, hydraulic conductivity, and rainfall intensity; deterministic analysis was also performed for the comparison. The Mt. Umyeon area, a representative case for urban landslides in South Korea where large scale human damage occurred in 2011, was selected for a case study. The landslide prediction rate and receiver operating characteristic (ROC) curve were used to estimate the prediction accuracy so that we could compare our approach to the deterministic analysis. The landslide prediction rate of the deterministic analysis was 81.55%; in the case of the Monte Carlo simulation, when the failure probabilities were set to 1%, 5%, and 10%, the landslide prediction rates were 95.15%, 91.26%, and 90.29%, respectively, which were higher than the rate of the deterministic analysis. Finally, according to the area under the curve of the ROC curve, the prediction accuracy of the probabilistic model was 73.32%, likely due to the variability and uncertainty in the input variables.

Adequate groundwater development for the rural population is essential because groundwater is an important source of drinking water and agricultural water. In this study, ensemble models of decision tree-based machine learning algorithms were used with geographic information system (GIS) to map and test groundwater yield potential in Yangpyeong-gun, South Korea. Groundwater control factors derived from remote sensing data were used for mapping, including nine topographic factors, two hydrological factors, forest type, soil material, land use, and two geological factors. A total of 53 well locations with both specific capacity (SPC) data and transmissivity (T) data were selected and randomly divided into two classes for model training (70%) and testing (30%). First, the frequency ratio (FR) was calculated for SPC and T, and then the boosted classification tree (BCT) method of the machine learning model was applied. In addition, an ensemble model, FR-BCT, was applied to generate and compare groundwater potential maps. Model performance was evaluated using the receiver operating characteristic (ROC) method. To test the model, the area under the ROC curve was calculated; the curve for the predicted dataset of SPC showed values of 80.48% and 87.75% for the BCT and FR-BCT models, respectively. The accuracy rates from T were 72.27% and 81.49% for the BCT and FR-BCT models, respectively. Both the BCT and FR-BCT models measured the contributions of individual groundwater control factors, which showed that soil was the most influential factor. The machine learning techniques used in this study showed effective modeling of groundwater potential in areas where data are relatively scarce. The results of this study may be used for sustainable development of groundwater resources by identifying areas of high groundwater potential.

Recently, due to the acceleration of global warming, an accurate understanding and management of forest carbon stocks, such as forest aboveground biomass, has become very important. The vertical structure of the forest, which is the internal structure of the forest, was mainly investigated by field surveys that are labor intensive. Recently, remote sensing techniques have been actively used to explore large and inaccessible areas. In addition, machine learning techniques that could classify and analyze large amounts of data are being used in various fields. Thus, this study aims to analyze the forest vertical structure (number of tree layers) to estimate forest aboveground biomass in Jeju Island from optical and radar satellite images using artificial neural networks (ANN). For this purpose, the eight input neurons of the forest related layers, based on remote sensing data, were prepared: normalized difference vegetation index (NDVI), normalized difference water index (NDWI), NDVI texture, NDWI texture, average canopy height, standard deviation canopy height and two types of coherence maps were created using the Kompsat-3 optical image, L-band ALOS PALSAR-1 radar images, digital surface model (DSM), and digital terrain model (DTM). The forest vertical structure data, based on field surveys, was divided into the training/validation and test data and the hyper-parameters of ANN were trained using the training/validation data. The forest vertical classification result from ANN was evaluated by comparison to the test data. It showed about a 65.7% overall accuracy based on the error matrix. This result shows that the forest vertical structure map can be effectively generated from optical and radar satellite images and existing DEM and DTM using the ANN approach, especially for national scale mapping.

As global warming accelerates in recent years, the frequency of droughts has increased and water management at the national level has become very important. In particular, accurate understanding and management of the forest is essential as the water storage capacity of forest is determined by forest structure. Typically, data on forest vertical structure have been constructed from field surveys that are both costly and time-consuming. In addition, machine learning techniques could be applied to analyze, classify, and predict the uncertainties of internal structures in forest. Therefore, this study aims to map the forest vertical structure for estimating forest water storage capacity from multi-seasonal optical satellite image and topographic data using artificial neural network (ANN) in Gongju-si, South Korea. For this purpose, the 14 input neurons of normalized difference vegetation index (NDVI), two types of normalized difference water index (NDWI), two types of Normalized Difference Red Edge Index (NDre), principal component analysis (PCA) texture, and canopy height average and standard deviation maps were generated from Sentinel-2 optical images obtained in spring and fall season and topographic height maps such as digital terrain models (DTM) and digital surface models (DSM). The training/validation and test datasets for the ANN model were derived from forest vertical structures based on field surveys. Finally, the forest vertical classification map, the result of ANN application, was evaluated by creating an error matrix compared with the field survey results. The result showed an overall test accuracy of ~65.7% based on the number of pixels. The result shows that forest vertical structure in Gong-ju, Korea can be efficiently classified by using multi-seasonal Sentinel-2 satellite images and the ANN approach.

Aspergillus oryzae has been commonly used to make koji, meju, and soy sauce in traditional food fermentation industries. However, the metabolic behaviors of A. oryzae during fermentation in various culture environments are largely uncharacterized. Thus, we performed time resolved (0, 4, 8, 12, 16 day) secondary metabolite profiling for A. oryzae KCCM 12698 cultivated on malt extract agar and broth (MEA and MEB) under solid-state fermentation (SSF) and submerged fermentation (SmF) conditions using the ultrahigh performance liquid chromatography-linear trap quadrupole-ion trap-mass spectrometry (UHPLC-LTQ-IT-MS/MS) followed by multivariate analyses. We observed the relatively higher proportions of coumarins and oxylipins in SSF, whereas the terpenoids were abundant in SmF. Moreover, we investigated the antimicrobial efficacy of metabolites that were extracted from SSF and SmF. The SSF extracts showed higher antimicrobial activities as compared to SmF, with higher production rates of bioactive secondary metabolites viz., ketone-citreoisocoumarin, pentahydroxy-anthraquinone, hexylitaconic acid, oxylipins, and saturated fatty acids. The current study provides the underpinnings of a metabolomic framework regarding the growth and bioactive compound production for A. oryzae under the primarily employed industrial cultivation states. Furthermore, the study holds the potentials for rapid screening and MS-characterization of metabolites helpful in determining the consumer safety implications of fermented foods involving Koji mold.

Chemotaxonomic metabolite profiling of 62 indigenous Korean plant species was performed by ultrahigh performance liquid chromatography (UHPLC)-linear trap quadrupole-ion trap (LTQ-IT) mass spectrometry/mass spectrometry (MS/MS) combined with multivariate statistical analysis. In partial least squares discriminant analysis (PLS-DA), the 62 species clustered depending on their phylogenetic family, in particular, Aceraceae, Betulaceae, and Fagaceae were distinguished from Rosaceae, Fabaceae, and Asteraceae. Quinic acid, gallic acid, quercetin, quercetin derivatives, kaempferol, and kaempferol derivatives were identified as family-specific metabolites, and were found in relatively high concentrations in Aceraceae, Betulaceae, and Fagaceae. Fagaceae and Asteraceae were selected based on results of PLS-DA and bioactivities to determine the correlation between metabolic differences among plant families and bioactivities. Quinic acid, quercetin, kaempferol, quercetin derivatives, and kaempferol derivatives were found in higher concentrations in Fagaceae than in Asteraceae, and were positively correlated with antioxidant and tyrosinase inhibition activities. These results suggest that metabolite profiling was a useful tool for finding the different metabolic states of each plant family and understanding the correlation between metabolites and bioactivities in accordance with plant family.

Zingiber species are members of the Zingiberaceae family, and are widely used for medicinal and food purposes. In this study aboveground and root parts of Zingiber mioga and Zingiber officinale were subjected to metabolite profiling by ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) in order to characterize them by species and parts and also to measure bioactivities. Both primary and secondary metabolites showed clear discrimination in the PCA score plot and PLS-DA by species and parts. Tetrahydrocurcumin, diarylheptanoid, 8-gingerol, and 8-paradol were discriminating metabolites between Z. mioga and Z. officinale that were present in different quantities. Eleven flavonoids, six amino acids, six organic acids, four fatty acids, and gingerenone A were higher in the aboveground parts than the root parts. Antioxidant activities were measured and were highest in the root part of Z. officinale. The relatively high contents of tetrahydrocurcumin, diarylheptanoid, and galanganol C in the root part of Z. officinale showed highly positive correlation with bioactivities based on correlation assay. On the basis of these results, we can suggest different usages of structurally different parts of Zingiber species as food plants.

Background: Hepatitis B virus (HBV) infection disproportionately affects Asian Americans but HBV screening rates among Asian American are substantially low. This study examines the impact of multiple recommendations and self-efficacy on HBV screening uptake among Asian Americans. Methods: Data for this study were from 872 Chinese, Korean and Vietnamese recruited for a liver cancer prevention program in the Washington D.C – Baltimore metropolitan area. Results: 410 (47%) respondents reported previous HBV screening. Only 19.8% recalled a physician recommendation. Higher level of HBV screening was reported among people who had physician recommendation, family member recommendation or friend recommendation. Perceived self-efficacy was also an important predictor to HBV screening. The effect of self-efficacy was significant in subgroup analyses among Chinese and Korean, but not for Vietnamese. Conclusion and Global Health Implications: The disproportional prevalence of HBV infection among Asian Americans is considered to be one of the most important health disparities for Asian population. Understanding the condition and screening behavior in this population is especially important. Our findings suggest that recommendation from physician and social networks should be encouraged for HBV screening among Asian Americans. Both recommendation and self-efficacy of HBV screening are important psychosocial constructs to be targeted in liver cancer prevention interventions. Key words: Hepatitis B Virus • Asian Americans • Screening • Recommendation • Self-efficacy Copyright © 2017 Lu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Many ethnic fermented soybean products (FSPs) have long been consumed as seasoning and protein sources in East Asia. To evaluate the quality of various FSPs in East Asia, non-targeted metabolite profiling with multivariate analysis of six traditional FSPs (Natto; NT, Cheonggukjang; CG, Doenjang; DJ, Miso; MS, Doubanjiang; DB, Tianmianjiang; TM) was performed. Six FSPs could be clearly distinguished by principle component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Amino acid contents were relatively higher in NT and CG, sugar and sugar alcohol contents were relatively higher in MS and TM, isoflavone glycoside contents were relatively highest in CG, isoflavone aglycon contents were the highest in DJ, and soyasaponin contents were the highest in CG. Antioxidant activity and physicochemical properties were determined to examine the relationships between the FSPs and their antioxidant activities. We observed a negative correlation between isoflavone aglycon contents and 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) activity. Furthermore, the order of ABTS activity of FSPs has a positive correlation with the order of soybean content in the six FSPs. Herein it was found that primary metabolites were affected by the main ingredients and secondary metabolites were most influenced by the fermentation time, and that soybean content contributed more to antioxidant activity than fermentation time.

Plant species have traditionally been revered for their unparalleled pharmacognostic applications. We outline a non-iterative multi-parallel metabolomic-cum-bioassay-guided methodology toward the functional characterization of ethanol extracts from the Betulaceae family plants (n = 10). We performed mass spectrometry (MS)-based multivariate analyses and bioassay-guided (ABTS antioxidant activity and cytoprotective effects against H2O2-induced cell damage) analyses of SPE fractions. A clearly distinct metabolomic pattern coupled with significantly higher bioactivities was observed for 40% methanol SPE eluate. Further, the 40% SPE eluate was subjected to preparative high-performance liquid chromatography (prep-HPLC) analysis, yielding 72 sub-fractions (1 min−1), with the highest antioxidant activities observed for the 15 min and 31 min sub-fractions. We simultaneously performed hyphenated-MS-based metabolite characterization of bioactive components for both the 40% methanol SPE fraction and its prep-HPLC sub-fraction (15 min and 31 min). Altogether, 19 candidate metabolites were mainly observed to contribute toward the observed bioactivities. In particular, ethyl gallate was mainly observed to affect the antioxidant activities of SPE and prep-HPLC fractions of Alnus firma extracts. We propose an integrated metabolomic-cum-bioassay-guided approach for the expeditious selection and characterization of discriminant metabolites with desired phenotypes or bioactivities.

BACKGROUND:This study aimed to evaluate the effect of a lay health worker (LHW) telephone intervention on completing a series of hepatitis B virus (HBV) vaccinations among foreign-born Asian Americans in the Baltimore-Washington Metropolitan area. METHODS:During the period of April 2013 and March 2014, we recruited Asian Americans who were 18 years of age and older in the community-based organizations. Of the 645 eligible participants, 600 (201 Chinese, 198 Korean, 201 Vietnamese) completed a pretest survey and received hepatitis B screening. Based on the screening results, we conducted a randomized controlled trial among those unprotected (HBsAg-/HBsAB-) by assigning them either to an intervention group (n = 124) or control group (n = 108). The intervention group received a list of resources by mails for where to get free vaccinations as well as reminder calls for vaccinations from trained LHWs, while the control group received only list of resources by mail. Seven months after mailing the HBV screening results, trained LHWs followed up with all participants by phone to ask how many of the recommended series of 3 vaccinations they had received: none, 1 or 2, or all 3 (complete). Their self-reported vaccinations were verified with the medical records. Multinomial logistic regressions were used to examine the effect of the LHW intervention. Process evaluation was conducted by asking study participants in the intervention group to evaluate the performance of the LHWs. RESULTS:After seven months, those in the intervention group were more likely to have 1 or more vaccines than the control group, compared to the no vaccination group (OR = 3.04, 95% CI, 1.16, 8.00). Also, those in the intervention group were more likely to complete a series of vaccinations than the control group, compared to the no vaccination group (OR = 7.29, 95% CI 3.39, 15.67). The most important barrier preventing them from seeking hepatitis B vaccinations was lack of time to get the vaccination. The most important promoters to getting vaccinations, among those who had vaccinations (n = 89), were our intervention program (70.8%) and self-motivation (49.4%). The majority of participants in the intervention group received the phone calls from LHWs (93%) and almost all of them got the reminder to receive vaccines (98%). CONCLUSION:The LHW intervention was successful at increasing HBV vaccinations rates among foreign-born Asian Americans. This study suggests that this culturally integrated intervention program may be useful for reducing liver cancer disparities from chronic HBV infection in high risk Asian Americans. TRIAL REGISTRATION:ClinicalTrials.gov NCT02760537.

The main objective of this study was to examine the association between four types of adverse life events (family environment, separation, social adversity, and death) and the development of depressive symptoms among Puerto Rican youth. This was a secondary analysis using three waves (2000-2004) of interview data from the Boricua Youth Study of 10-13 year old Puerto Rican youth residing in New York and Puerto Rico with no depressive symptoms at baseline (n = 977). Depressive symptoms increased with an increase in social adversity, separation, death, and death events. Youth support from parents was a significant protective factor for all adverse events and parent coping was a protective factor in social adversity events. Relying on standard diagnostic tools is ideal to identify youth meeting the criteria for a diagnosis of depression but not useful to detect youth who present with subclinical levels of depression. Youth with sub-clinical levels of depression will not get treated and are at increased risk of developing depression later in life. Adverse life events are potentially relevant to use in conjunction with other screening tools to identify Puerto Rican youth who have subclinical depression and are at risk of developing depression in later adolescence.

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics implies that annotated metabolites can serve as potential markers of the associated bioactivities of plant extracts. Firstly, we selected Aphananthe aspera and Zelkova serrata (Family: Ulmaceae) from 16 Korean plant species based on their distinct principal component analysis (PCA) patterns in LC-MS datasets and antioxidant activity assays. Further, we chose 40% solid-phase extraction (SPE) extracts of the two species displaying the highest antioxidant activities coupled with distinct PCA patterns. Examining the metabolite compositions of the 40% SPE extracts, we observed relatively higher abundances of quercetin, kaempferol, and isorhamnetin O-glucosides for A. aspera, whereas quercetin, isorhamnetin O-glucuronides, and procyanidin dimer were relatively higher in Z. serrata. These metabolites were clearly distinguished in pathway map and displayed strong positive correlations with antioxidant activity. Further, we performed preparative high-performance liquid chromatography (prep-HPLC) analysis coupled with the 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) assay to validate their functional correlations. As a result, quercetin O-sophoroside was determined as the main antioxidant in A. aspera, while isorhamnetin O-glucuronide and procyanidin dimer were the primary antioxidants in Z. serrata. The current study suggests that the LC-MS-based untargeted metabolomics strategy can be used to illuminate subtle metabolic disparities as well as compounds associated with bioactivities.

The two cytosolic/nuclear isoforms of the molecular chaperone HSP90, stress-inducible HSP90α and constitutively expressed HSP90β, fold, assemble and maintain the three-dimensional structure of numerous client proteins. Because many HSP90 clients are important in cancer, several HSP90 inhibitors have been evaluated in the clinic. However, little is known concerning possible unique isoform or conformational preferences of either individual HSP90 clients or inhibitors. In this report, we compare the relative interaction strength of both HSP90α and HSP90β with the transcription factors HSF1 and HIF1α, the kinases ERBB2 and MET, the E3-ubiquitin ligases KEAP1 and RHOBTB2, and the HSP90 inhibitors geldanamycin and ganetespib. We observed unexpected differences in relative client and drug preferences for the two HSP90 isoforms, with HSP90α binding each client protein with greater apparent affinity compared to HSP90β, while HSP90β bound each inhibitor with greater relative interaction strength compared to HSP90α. Stable HSP90 interaction was associated with reduced client activity. Using a defined set of HSP90 conformational mutants, we found that some clients interact strongly with a single, ATP-stabilized HSP90 conformation, only transiently populated during the dynamic HSP90 chaperone cycle, while other clients interact equally with multiple HSP90 conformations. These data suggest different functional requirements among HSP90 clientele that, for some clients, are likely to be ATP-independent. Lastly, the two inhibitors examined, although sharing the same binding site, were differentially able to access distinct HSP90 conformational states.

This study developed habitat potential maps for the marten (Martes flavigula) and leopard cat (Prionailurus bengalensis) in South Korea. Both species are registered on the Red List of the International Union for Conservation of Nature, which means that they need to be managed properly. Various factors influencing the habitat distributions of the marten and leopard were identified to create habitat potential maps, including elevation, slope, timber type and age, land cover, and distances from a forest stand, road, or drainage. A spatial database for each species was constructed by preprocessing Geographic Information System (GIS) data, and the spatial relationship between the distribution of leopard cats and environmental factors was analyzed using an artificial neural network (ANN) model. This process used half of the existing habitat location data for the marten and leopard cat for training. Habitat potential maps were then created considering the relationships. Using the remaining half of the habitat location data for each species, the model was validated. The results of the model were relatively successful, predicting approximately 85% for the marten and approximately 87% for the leopard cat. Therefore, the habitat potential maps can be used for monitoring the habitats of both species and managing these habitats effectively.

The application of data mining models has become increasingly popular in recent years in assessments of a variety of natural hazards such as landslides and floods. Data mining techniques are useful for understanding the relationships between events and their influencing variables. Because landslides are influenced by a combination of factors including geomorphological and meteorological factors, data mining techniques are helpful in elucidating the mechanisms by which these complex factors affect landslide events. In this study, spatial data mining approaches based on data on landslide locations in the geographic information system environment were investigated. The topographical factors of slope, aspect, curvature, topographic wetness index, stream power index, slope length factor, standardized height, valley depth, and downslope distance gradient were determined using topographical maps. Additional soil and forest variables using information obtained from national soil and forest maps were also investigated. A total of 17 variables affecting the frequency of landslide occurrence were selected to construct a spatial database, and support vector machine (SVM) and artificial neural network (ANN) models were applied to predict landslide susceptibility from the selected factors. In the SVM model, linear, polynomial, radial base function, and sigmoid kernels were applied in sequence; the model yielded 72.41%, 72.83%, 77.17% and 72.79% accuracy, respectively. The ANN model yielded a validity accuracy of 78.41%. The results of this study are useful in guiding effective strategies for the prevention and management of landslides in urban areas.

Although acculturation may have positive effects for immigrants, including better socioeconomic profiles and increased occupational opportunities, their health profiles deteriorate with longer duration in the U.S. Prior research indicates that increasing acculturation is associated with some poorer health outcomes among immigrants in the U.S. However, most of these studies have used length of stay or English language proficiency as proxies for acculturation, and have mainly examined self-reported "current" health outcomes. This study advances knowledge on associations between acculturation and health among immigrants by explicitly examining self-reported "change" in health since immigration, in relation to acculturation-related variables. We use data from the New Immigrant Survey (NIS; 2003-2004), a cross-sectional study of legal immigrants to the U.S. In addition to testing more conventionally examined proxies of acculturation (length of stay and English proficiency), we also examine English language use and self-reported change in diet. Multivariable logistic regression analyses on 5,982 participants generally supported previous literature indicating a deleterious impact of acculturation, with increasing duration of stay and greater self-reported change in diet being associated with a poorer change in health since moving to the U.S. Although English language proficiency and use were associated with greater odds of reporting a worse change in health when examined individually, they were non-significant in multivariable models including all acculturation measures. Findings from this study suggest that when taking into account multiple measures of acculturation, language may not necessarily indicate unhealthy assimilation and dietary change may be a pathway leading to declines in immigrant health. Increasing duration in the U.S. may also reflect the adoption of unhealthy behaviors, as well as greater exposure to harmful sources of psychosocial stress including racial and anti-immigrant discrimination. Our study suggests that multiple indicators of acculturation may be useful in examining the effect of acculturation on changes in health among immigrants.

Rice koji, used early in the manufacturing process for many fermented foods, produces diverse metabolites and enzymes during fermentation. Using gas chromatography time-of-flight mass spectrometry (GC-TOF-MS), ultrahigh-performance liquid chromatography linear trap quadrupole ion trap tandem mass spectrometry (UHPLC-LTQ-IT-MS/MS), and multivariate analysis we generated the metabolite profiles of rice koji produced by fermentation with Aspergillus oryzae (RK_AO) or Bacillus amyloliquefaciens (RK_BA) for different durations. Two principal components of the metabolomic data distinguished the rice koji samples according to their fermenter species and fermentation time. Several enzymes secreted by the fermenter species, including α-amylase, protease, and β-glucosidase, were assayed to identify differences in expression levels. This approach revealed that carbohydrate metabolism, serine-derived amino acids, and fatty acids were associated with rice koji fermentation by A. oryzae, whereas aromatic and branched chain amino acids, flavonoids, and lysophospholipids were more typical in rice koji fermentation by B. amyloliquefaciens. Antioxidant activity was significantly higher for RK_BA than for RK_AO, as were the abundances of flavonoids, including tricin, tricin glycosides, apigenin glycosides, and chrysoeriol glycosides. In summary, we have used MS-based metabolomics and enzyme activity assays to evaluate the effects of using different microbial species and fermentation times on the nutritional profile of rice koji.