The liver progenitor cells could form a potential target cell population fore both tumor-initiating and -promoting chemicals. Induction of drug-metabolizing and antioxidant enzymes, including AhR-dependent CYP1A1, NQO-1 and AKR1C9, was detected in the rat liver epithelial WB-F344 "stem-like" cells. Additionally, WB-F344 cells express a functional, wild-type form of p53 protein, a biomarker of genotoxic events, and connexin 43, a basic structural unit of gap junctions forming an important type of intercellular communication. In this cellular model, two complementary assays have been established for detection of the modes of action associated with tumor promotion: inhibition of gap junctional intercellular communication (GJIC) and proliferative activity in confluent cells. We found that the PAHs and PCBs, which are AhR agonists, released WB-F344 cells from contact inhibition, increasing both DNA synthesis and cell numbers. Genotoxic effects of some PAHs that lead to apoptosis and cell cycle delay might interfere with the proliferative activity of PAHs. Contrary to that, the nongenotoxic low-molecular-weight PAHs and non-dioxin-like PCB congeners, abundant in the environment, did not significantly affect cell cycle and cell proliferation; however both groups of compounds inhibited GJIC in WB-F344 cells. The release from contact inhibiton by a mechanism that possibly involves the AhR activation, inhibition of GJIC and genotoxic events induced by environmental contaminants are three important modes of action that could play an important role in carcinogenic effects of toxic compounds. The relative potencies to inhibit GJIC, to induce AhR-mediated activity, and to release cells from contact inhibition were determined for a large series of PAHs and PCBs and their metabolites. In vitro bioassays based on detection of events on cellular level (deregulation of GJIC and/or proliferation) or determination of receptor-mediated activities in both ?$stem-like^{\circ}{\times}$ and hepatocyte-like liver cellular models are valuable tools for detection of modes of action of polyaromatic hydrocarbons. They may serve, together with concentration data, as a first step in their risk assessment.
Purpose: The purpose of this study was to investigate bone mineral density(BMD) and fear of falling and falls efficacy in the middle and old aged women over 50 years. Methods: The subjects consisted of 409 women. One-way ANOVA, Pearson's correlations and multiple regression were used to test the BMD, fear of falling and falls efficacy scale by using SPSSWIN 12.0. The BMD of the calcaneus were measured with peripheral dual energy x-ray absorptiometry(DEXA). Results: The average age was 63 years old and the average T-score was -3.21 in patient with osteoporosis, -1.72 with osteopenia, and .13 with normal. There were significant differences in the status of the BMD according to age(p=.000), height(p=.000), weight(p=.000), married status(p=.000), age of menarche(p=.002), and menopause(p=.002). The fear of falling was related with falls efficacy(r=-.247, p=.01), BMD(r=-.337, p=.01). Falls efficacy($\beta$=-.21, p=.000)and BMD($\beta$=-.26, p=.000) were predicting variables of fear of falling. The model explained 13% of the variance in fear of falling(F=27.38, p=.000). Conclusion: Fear of falling and falls efficacy were related with the bone mineral density. Falls efficacy and BMD may be useful for the predicting fear of falling for women in middle and old age. Further studies with assessment of fall-related risk-factors and a longitudinal study are necessary to assess with falls efficacy, and BMD with age.
International Journal of Computer Science & Network Security
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v.23
no.5
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pp.148-162
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2023
Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.
Elevated serum cholesterol is a main risk factor for heart disorders. Most probiotic products administered to lower cholesterol are dairy products which are not suitable for lactose-intolerant individuals. In this study, we assessed the cholesterol-lowering efficacy of LAB isolated from traditionally fermented drinks in diet-induced rats and determine their efficacy in the production of non-dairy, probiotic formulations using papaya juice. LAB were isolated from palm wine and corn beer on MRS agar using a pour-plate technique. Identification was carried out using 16S rRNA gene sequencing. A hypercholesterolemia model in which diet-induced Wistar albino rats were assigned into four groups was established. Oral gavage was carried out for 30 days. On the 31st day, the rats were dissected and the serum lipid profile was analyzed using biochemical kits. A 106 cfu/ml of a 24-h-old culture of selected lactobacilli was used to inoculate papaya juice and incubated at 37℃. Microbial and chemical changes were assessed during papaya fermentation and after four weeks of cold storage. Two selected isolates (Pw1 and Cb4) had in vitro cholesterol reduction of > 80%. These two isolates lowered lipid profile (triglyceride, total cholesterol, LDL-c) significantly, and increased HDL-c levels (p < 0.5) in the rat sera. Phylogenetic analysis showed that Pw1 was 98.86% similar to Limosilactobacillus fermentum, while Cb4 was 99.54% similar to Enteroccocus faecium. Both strains fermented papaya juice with cell viability reaching 8.92 × 108 cfu/ml and 25.3 × 108 cfu/ml respectively, and were still viable after 4 weeks of cold storage.
Proceedings of the Korea Water Resources Association Conference
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2022.05a
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pp.305-305
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2022
A better approach for assessing meteorological drought occurrences is increasingly important in mitigating and adapting to the impacts of climate change, as well as strategies for developing early warning systems. The present study defines meteorological droughts as a period with an abnormal precipitation deficit based on monthly precipitation data of 18 gauging stations for the Han River watershed in the past (1974-2015). This study utilizes a Bayesian parameter estimation approach to analyze the effects of climate change on future drought (2025-2065) in the Han River Basin using the Coupled Model Intercomparison Project Phase 6 (CMIP6) with four bias-corrected general circulation models (GCMs) under the Shared Socioeconomic Pathway (SSP)2-4.5 scenario. Given that drought is defined by several dependent variables, the evaluation of this phenomenon should be based on multivariate analysis. Two main characteristics of drought (severity and duration) were extracted from precipitation anomalies in the past and near-future periods using the copula function. Three parameters of the Archimedean family copulas, Frank, Clayton, and Gumbel copula, were selected to fit with drought severity and duration. The results reveal that the lower parts and middle of the Han River basin have faced severe drought conditions in the near future. Also, the bivariate analysis using copula showed that, according to both indicators, the study area would experience droughts with greater severity and duration in the future as compared with the historical period.
International Journal of Computer Science & Network Security
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v.24
no.2
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pp.158-168
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2024
There is hardly any person in modern times who has not taken soft drinks instead of drinking water. The rate of people taking soft drinks being surprisingly high, researchers around the world have cautioned from time to time that these drinks lead to weight gain, raise the risk of non-communicable diseases and so on. Therefore, in this work an image-based tool is developed to monitor the nutritional information of soft drinks by using deep convolutional neural network with transfer learning. At first, visual saliency, mean shift segmentation, thresholding and noise reduction technique, collectively known as 'pre-processing' are adopted to extract the location of drinks region. After removing backgrounds and segment out only the desired area from image, we impose Discrete Wavelength Transform (DWT) based resolution enhancement technique is applied to improve the quality of image. After that, transfer learning model is employed for the classification of drinks. Finally, nutrition value of each drink is estimated using Bag-of-Feature (BoF) based classification and Euclidean distance-based ratio calculation technique. To achieve this, a dataset is built with ten most consumed soft drinks in Bangladesh. These images were collected from imageNet dataset as well as internet and proposed method confirms that it has the ability to detect and recognize different types of drinks with an accuracy of 98.51%.
The Journal of the Convergence on Culture Technology
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v.10
no.1
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pp.551-557
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2024
Hydrogen is a renewable energy source with various characteristics such as clean, carbon-free and high-energy, and is internationally recognized as a "future energy". With the rapid development of the hydrogen energy industry, more hydrogen infrastructure is needed to meet the demand for hydrogen. However, hydrogen infrastructure accidents have been occurring frequently, hindering the development of the hydrogen industry. HyRAM+, developed by Sandia National Laboratories, is a software toolkit that integrates data and methods related to hydrogen safety assessments for various storage applications, including hydrogen refueling stations. HyRAM+'s physics mode simulates hydrogen leak results depending on the hydrogen refueling station components, graphing gas plume dispersion, jet frame temperature and trajectory, and radiative heat flux. In this paper, hydrogen leakage data was extracted from a hydrogen refueling station in Samcheok, Gangwon-do, using HyRAM+ software. A hydrogen leakage simulator was developed using data extracted from HyRAM+. It was implemented as a dashboard that shows the data generated by the simulator using a database and Grafana.
Recent studies have evaluated the association between specific beverage intake and metabolic risks in adults. However, more evidence is needed to examine the association between the Healthy Beverage Index (HBI) and metabolic factors. Therefore, this study investigated the relationship between HBI and metabolic factors in adults. In this cross-sectional study, 338 overweight and obese individuals living in Tabriz, Iran were selected. Data on beverage consumption, demographics, physical activity, and anthropometric characteristics were evaluated using validated standard protocols. The predefined HBI was calculated based on previous studies. The mean value of HBI index among all of the participants was 59.76 ± 6.51. Those at the higher HBI scores had significantly lower waist circumference, waist-to-hip ratio, fat mass, and weight (p < 0.05). HBI and triglyceride scores also had a significant relationship. It has been shown that at higher HBI scores compared to lower scores, high-density lipoprotein cholesterol levels increase while homeostatic model assessment for insulin resistance, low-density lipoprotein cholesterol, total cholesterol, and blood pressure decrease. HBI scores higher among Iranian adults were associated with a better chance of losing weight and weight loss and a better lipid profile, and lower blood pressure. Therefore, HBI can be a useful and helpful tool for assessing the overall quality of beverages adults consume. However, further studies are warranted to confirm the possible health effects of healthy beverage index.
Recent years have witnessed the increased usage of flammable metals, such as aluminum or magnesium, in wide range of high-tech industries. These metals are indispensable for the improvement of physical properties of materials as well as the design capability of the final product. During the process, unwanted metal dusts could be released to the environment. This can lead to an occupational health and safety issues. Due to their flammable nature, more serious problem of an explosion can happen in extreme cases. The explosion is the combustion of tiny solid particles and vapor mixture, caused by pyrolysis. This complex composition makes engineering analysis more difficult, compared to simple gas explosions or vapor cloud combustions. The study was conducted to assess this light metal dust explosion in an effort to provide the bases for a risk assessment. Dust explosion characteristics of each material was carefully evaluated and an appropriate analysis tool was developed. A comprehensive database was also constructed and utilized for the calibration of the developed response model and the verification for its accuracy. Subsequently, guidelines were provided to prevent dust explosions that could occur in top-notch industrial processes.
KSCE Journal of Civil and Environmental Engineering Research
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v.40
no.4
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pp.429-435
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2020
North Korea declared itself complete with nuclear force after its sixth nuclear test in 2017. Despite efforts at home and abroad to denuclearize the Korean Peninsula, the prospects for the denuclearization are not bright. Along with political and diplomatic efforts to deter NK's WMD threats, the government is required to strengthen its consequence management capabilities against 'catastrophic situations' expected in case of emergency. Accordingly, this study was conducted to present measures to strengthen follow-up management against CBRN threats. The research model was partially supplemented and utilized by the THIRA process adopted and utilized by the U.S. Department of Homeland Security among national-level disaster management plan development models. Korea's consequence management (CM) system encompasses risk and crisis management on disaster condition. The system has been carried out in the form of a civil, government and military integrated defense operations for the purpose of curbing the spread or use of CBRNs, responding to threats, and minimizing expected damages. The preventive stage call for the incorporation of CBRN concept and CM procedures into the national management system, supplementing the integrated alarm systems, preparation of evacuation facilities, and establishment of the integrated training systems. In the preparation phase, readjustment of relevant laws and manuals, maintenance of government organizations, developing performance procedures, establishing the on-site support systems, and regular training are essential. In the response phase, normal operations of the medical support system for first aid and relief, installation and operation of facilities for decontamination, and development of regional damage assessment and control guidelines are important. In the recovery phase, development of stabilization evaluation criteria and procedures, securing and operation of resources needed for damage recovery, and strengthening of regional damage recovery capabilities linked to local defense forces, reserve forces and civil defense committees are required.
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