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Logistic mixture of multivariate regressions for analysis of water quality impacted by agrochemicals
Joo, Yongsung,Lee, Keunbaik,Min, Joong-Hyuk,Yun, Seong-Taek,Park, Trevor WILEY 2007 Environmetrics Vol. No.
<P>In this paper, we study the impacts of two representative agricultural activities, fertilizers and lime application, on water quality. Because of heavy usage of nitrogen fertilizers, nitrate (NO<EM><SUB>3</SUB><SUP>−</SUP></EM>) concentration in water is considered as one of the best indicators for agricultural pollution. The mixture of normal distributions has been widely applied with (NO<EM><SUB>3</SUB><SUP>−</SUP></EM>) concentrations to cluster water samples into two environmentally interested groups (water impacted by agrochemicals and natural background water groups). However, this method fails to yield satisfying results because it cannot distinguish low-level fertilizer impact and natural background noise. To improve performance of cluster analysis, we introduce the logistic mixture of multivariate regressions model (LMMR). In this approach, water samples are clustered based on the relationships between major element concentrations and physicochemical variables, which are different in impacted water and natural background water. Copyright © 2006 John Wiley & Sons, Ltd.</P>
Joo, Yongsung,Kim, Dalho,Lee, Keunbaik,Yun, Seong-Taek,Kim, Kyoung-Ho,Mercante, Donald John Wiley Sons, Ltd. 2009 Environmetrics Vol.20 No.3
<P>It is well known that groundwater is a valuable but vulnerable natural resource. To set forth a proper strategy for conservation and sustainable use of groundwater resources, we need precise evaluations of the human impact on groundwater quality. In this paper, we develop a Bayesian contamination model that clusters the sampling locations of groundwater into polluted and unpolluted groups and simultaneously estimates the average amount of human impact. Among major dissolved ions in groundwater, NO<SUB>3</SUB><SUP>−</SUP>, Ca<SUP>2+</SUP>, SO<SUB>4</SUB><SUP>2−</SUP>, Cl<SUP>−</SUP>, and Na<SUP>+</SUP> were documented as useful variables describing the hydrochemical characteristics of anthropogenically polluted groundwater. Increased concentrations of these ions indicate that overused agrochemicals (particularly nitrogen fertilizers) and domestic sewage are the most important causes of groundwater pollution to considerable depths in the studied region. Our proposed model can be used to identify effective measures for groundwater quality management such as source control. Copyright © 2008 John Wiley & Sons, Ltd.</P>
Seung Yeon Lee,Joo Hyun Kim,Sun Shin Yi,Hyeon-Gu Yeo,Youngjeon Lee,Yongsung Hwang,Jin Woo Lee 한국공업화학회 2023 Journal of Industrial and Engineering Chemistry Vol.124 No.-
Osteomyelitis is one of the most common inflammatory bone diseases caused by gram-positive bacteria. Although cefazolin is widely used as an osteomyelitis antibiotic against Staphylococcus aureus, its thermolabileproperties may decrease its antibiotic activity when incorporated into bone cement, such as polymethylmethacrylate. Thus, to fully characterize the loss of antibiotic activity during the fused depositionmodeling 3D-printing processes, we systematically evaluated the antibacterial activity of a cefazolinloadedpolycaprolactone (PCL)-based scaffold at varying 3D printing temperatures against S. aureus. Invitro antibacterial activity analysis revealed that cefazolin-loaded PCL scaffolds printed between 60 Cand 120 C could maintain the antibacterial activity, whereas the antibacterial activity of scaffoldsprinted between 140 C and 160 C was significantly decreased. In addition, the therapeutic potentialof the 3D-printed, scaffolds was assessed based on serum interleukin-6 (IL-6) levels, micro-computedtomography (micro-CT), and histological evaluation using a rabbit model of S. aureus-induced chronicosteomyelitis. Our in vivo results demonstrated that the serum level of IL-6 decreased in the cefazolinloadedPCL scaffold-transplanted group. Furthermore, micro-CT and histological analyses confirmedthe decreased deformation and overgrowth of the bone. The utilization of a 3D-printable, cefazolinloadedPCL scaffold offers a promising and distinct approach for the treatment of osteomyelitis.
Kim, Jin Hwi,Lee, Dong Hoon,Joo, Yongsung,Zoh, Kyung Duk,Ko, Gwangpyo,Kang, Joo-Hyon Elsevier BV 2016 Science of the Total Environment Vol.569 No.-
<P><B>Abstract</B></P> <P>Although norovirus outbreaks are well-recognized to have strong winter seasonality relevant to low temperature and humidity, the role of artificial human-made features within geographical areas in norovirus outbreaks has rarely been studied. The aim of this study is to assess the natural and human-made environmental factors favoring the occurrence of norovirus outbreaks using nationwide surveillance data. We used a geographic information system and binary response models to examine whether the norovirus outbreaks are spatially patterned and whether these patterns are associated with specific environmental variables including service levels of water supply and sanitation systems and land-use types. The results showed that small-scale low-tech local sewage treatment plants and winter sports areas were statistically significant factors favoring norovirus outbreaks. Compactness of the land development also affected the occurrence of norovirus outbreaks; transportation, water, and forest land-uses were less favored for effective transmission of norovirus, while commercial areas were associated with an increased rate of norovirus outbreaks. We observed associations of norovirus outbreaks with various outcomes of human activities, including discharge of poorly treated sewage, overcrowding of people during winter season, and compactness of land development, which might help prioritize target regions and strategies for the management of norovirus outbreaks.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A GIS and binary response models were used to predict norovirus outbreaks' patterns. </LI> <LI> Strong winter seasonality of norovirus outbreaks was confirmed. </LI> <LI> Local sewage treatment plants favored norovirus outbreaks. </LI> <LI> Compactness of the land development affected the occurrence of norovirus outbreaks. </LI> <LI> Overcrowding of people and cold temperature synergistically increased norovirus outbreaks. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Estimating monotone convex functions via sequential shape modification
Lee, Sanghan,Lim, Johan,Kim, Seung-Jean,Joo, Yongsung Taylor Francis 2009 JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION Vol.79 No.8
<P> We propose a sequential method to estimate monotone convex functions that consists of: (i) monotone regression via solving a constrained least square (LS) problem and (ii) convexification of the monotone regression estimate via solving a uniform approximation problem with associated constraints. We show that this method is faster than the constrained LS method. The ratio of computation time increases as data size increases. Moreover, we show that, under an appropriate smoothness condition, the uniform convergence rate achieved by the proposed method is nearly comparable to the best achievable rate for a non-parametric estimate which ignores the shape constraint. Simulation studies show that our method is comparable to the constrained LS method in estimation error. We illustrate our method by analysing ground water level data of wells in Korea.</P>
Flexible marginalized models for bivariate longitudinal ordinal data
Lee, Keunbaik,Daniels, Michael J.,Joo, Yongsung Oxford University Press 2013 Biostatistics Vol.14 No.3
<P>Random effects models are commonly used to analyze longitudinal categorical data. Marginalized random effects models are a class of models that permit direct estimation of marginal mean parameters and characterize serial correlation for longitudinal categorical data via random effects (Heagerty, 1999). Marginally specified logistic-normal models for longitudinal binary data. <I>Biometrics</I> <B>55</B>, 688–698; Lee and Daniels, 2008. Marginalized models for longitudinal ordinal data with application to quality of life studies. <I>Statistics in Medicine</I> <B>27</B>, 4359–4380). In this paper, we propose a Kronecker product (KP) covariance structure to capture the correlation between processes at a given time <I>and</I> the correlation within a process over time (serial correlation) for bivariate longitudinal ordinal data. For the latter, we consider a more general class of models than standard (first-order) autoregressive correlation models, by re-parameterizing the correlation matrix using partial autocorrelations (Daniels and Pourahmadi, 2009). Modeling covariance matrices via partial autocorrelations. <I>Journal of Multivariate Analysis</I> <B>100</B>, 2352–2363). We assess the reasonableness of the KP structure with a score test. A maximum marginal likelihood estimation method is proposed utilizing a quasi-Newton algorithm with quasi-Monte Carlo integration of the random effects. We examine the effects of demographic factors on metabolic syndrome and C-reactive protein using the proposed models.</P>
A Bayesian Spatial Contamination Model
Jonghyun Na,Taekseon Ryu,Joonmyoung Kim,Hansuk Kim,Manjae Kwon,Yongsung Joo 한국자료분석학회 2022 Journal of the Korean Data Analysis Society Vol.24 No.3
In environmental research, it is often the case that to cluster observations into environmentally polluted and natural groups is an important issue. The Bayesian contamination model which adopts a multivariate mixture regression model has been developed in that it aims to cluster observations and estimate the average amount of pollution. However, because the Bayesian contamination model does not take spatial correlations between observations into consideration, a Bayesian spatial contamination model is proposed. A simulation study was conducted showing that the proposed model has an advantage over the Bayesian contamination model in terms of biases and RMSE of estimators of the logistic regression parameters. We applied the proposed model into environmental data and confirmed the improvement on the model fit. Also, the clustering was reasonably performed from the environmental perspective, which was coherent with the fact that the underground water flows from the southwest side to the northeast side. This model is expected to be utilized effectively to monitor the quality of a ground or groundwater and capture the heterogeneity in it which is suspected of environmental pollution especially when the interested site consists of areas with strong spatial dependency.