• Journal of Microbiology and Biotechnology
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• 제17권9호
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• pp.1558-1562
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• 2007

To elucidate the influence of pipe materials on the VBNC (viable but nonculturable) state and bacterial numbers in drinking water, biofilm and effluent from stainless steel, galvanized iron, and polyvinyl chloride pipe wafers were analyzed. Although no HPC (heterotrophic plate count) was detected in the chlorinated influent of the model system, a DVC (direct viable count) still existed in the range between 3- and 4-log cells/ml. Significantly high numbers of HPC and DVC were found both in biofilm and in the effluent of the model system. The pipe material, exposure time, and the season were all relevant to the concentrations of VBNC and HPC bacteria detected. These findings indicate the importance of determining the number of VBNC cells and the type of pipe materials to estimate the HPC concentration in water distribution systems and thus the need of determining a DVC in evaluating disinfection efficiency.

• Journal of the Korean Data and Information Science Society
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• 제28권4호
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• pp.927-936
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• 2017

To analyze longitudinal count data, Poisson linear mixed models are commonly used. In the models the random effects covariance matrix explains both within-subject variation and serial correlation of repeated count outcomes. When the random effects covariance matrix is assumed to be misspecified, the estimates of covariates effects can be biased. Therefore, we propose reasonable and flexible structures of the covariance matrix using autoregressive and moving average Cholesky decomposition (ARMACD). The ARMACD factors the covariance matrix into generalized autoregressive parameters (GARPs), generalized moving average parameters (GMAPs) and innovation variances (IVs). Positive IVs guarantee the positive-definiteness of the covariance matrix. In this paper, we use the ARMACD to model the random effects covariance matrix in Poisson loglinear mixed models. We analyze epileptic seizure data using our proposed model.

• Communications for Statistical Applications and Methods
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• 제31권1호
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• pp.55-64
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• 2024

In this study, we consider the sample size determination problem for clustered count data with many zeros. In general, zero-inflated Poisson and binomial models are commonly used for zero-inflated data; however, in real data the assumptions that should be satisfied when using each model might be violated. We calculate the required sample size based on a discrete Weibull regression model that can handle both underdispersed and overdispersed data types. We use the Monte Carlo simulation to compute the required sample size. With our proposed method, a unified model with a low failure risk can be used to cope with the dispersed data type and handle data with many zeros, which appear in groups or clusters sharing a common variation source. A simulation study shows that our proposed method provides accurate results, revealing that the sample size is affected by the distribution skewness, covariance structure of covariates, and amount of zeros. We apply our method to the pancreas disorder length of the stay data collected from Western Australia.

• Asian Pacific Journal of Cancer Prevention
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• 제14권1호
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• pp.63-68
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• 2013

Background: Tumor angiogenesis correlates with recurrence and appears to be a prognostic factor for both breast and prostate cancers. In the present study, we aimed to investigate the correlation of microvessel density (MVD), a measure of angiogenesis, with nuclear pleomorphism, mitotic count, and vascular invasion in breast and prostate cancers at preclinical and clinical levels. Methods: Samples from xenograft tumors of luminal B breast cancer and prostate adenocarcinoma, established by BT-474 and PC-3 cell lines, respectively, and commensurate human paraffin-embedded blocks were obtained. To determine MVD, specimens were immunostained for CD-34. Nuclear pleomorphism, mitotic count, and vascular invasion were determined using hematoxylin and eosin (H&E)-stained slides. Results: MVD showed significant correlations with nuclear pleomorphism (r=0.68, P=0.03) and vascular invasion (r=0.77, P=0.009) in breast cancer. In prostate cancer, MVD was significantly correlated with nuclear pleomorphism (r=0.75, P=0.013) and mitotic count (r=0.75, P=0.012). In the breast cancer xenograft model, a significant correlation was observed between MVD and vascular invasion (r=0.87, P=0.011). In the prostate cancer xenograft model, MVD was significantly correlated with all three parameters (nuclear pleomorphism, r=0.95, P=0.001; mitotic count, r=0.91, P=0.001; and vascular invasion, r=0.79, P=0.017; respectively). Conclusions: Our results demonstrate that MVD is correlated with nuclear pleomorphism, mitotic count, and vascular invasion at both preclinical and clinical levels. This study therefore supports the predictive value of MVD in breast and prostate cancers.

• Communications for Statistical Applications and Methods
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• 제25권2호
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• pp.173-184
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• 2018

In medical or public health research, it is common to encounter clustered or longitudinal count data that exhibit excess zeros. For example, health care utilization data often have a multi-modal distribution with excess zeroes as well as a multilevel structure where patients are nested within physicians and hospitals. To analyze this type of data, zero-inflated count models with mixed effects have been developed where a count response variable is assumed to be distributed as a mixture of a Poisson or negative binomial and a distribution with a point mass of zeros that include random effects. However, no study has considered a situation where data are also censored due to the finite nature of the observation period or follow-up. In this paper, we present a weighted version of zero-inflated Poisson model with random effects accounting for variable individual follow-up times. We suggested two different types of weight function. The performance of the proposed model is evaluated and compared to a standard zero-inflated mixed model through simulation studies. This approach is then applied to Medicaid data analysis.

• Korean Journal of Radiology
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• 제22권3호
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• pp.344-353
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• 2021

Objective: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on deep learning algorithm. Materials and Methods: Abdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were retrospectively collected for the development of a deep learning classification algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an experienced radiologist. The postoperative pathological mitotic count was considered as the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the basis of the VGG16 convolutional neural network, using the CT images with the training set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both, the image level and the patient level. The receiver operating characteristic curves were generated on the basis of the model prediction results and the area under curves (AUCs) were calculated. The risk categories of the tumors were predicted according to the Armed Forces Institute of Pathology criteria. Results: At the image level, the classification prediction results of the mitotic counts in the test cohort were as follows: sensitivity 85.7% (95% confidence interval [CI]: 0.834-0.877), specificity 67.5% (95% CI: 0.636-0.712), PPV 82.1% (95% CI: 0.797-0.843), NPV 73.0% (95% CI: 0.691-0.766), and AUC 0.771 (95% CI: 0.750-0.791). At the patient level, the classification prediction results in the test cohort were as follows: sensitivity 90.0% (95% CI: 0.541-0.995), specificity 70.0% (95% CI: 0.354-0.919), PPV 75.0% (95% CI: 0.428-0.933), NPV 87.5% (95% CI: 0.467-0.993), and AUC 0.800 (95% CI: 0.563-0.943). Conclusion: We developed and preliminarily verified the GIST mitotic count binary prediction model, based on the VGG convolutional neural network. The model displayed a good predictive performance.

• Asian-Australasian Journal of Animal Sciences
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• 제11권6호
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• pp.642-647
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• 1998

A Poisson error model as a generalized linear mixed model (GLMM) has been suggested for genetic analysis of counted observations. One of the assumptions in this model is the normality for random effects. Since this assumption is not always appropriate, a more flexible model is needed. For count traits, a Poisson hierarchical generalized linear model (HGLM) that does not require the normality for random effects was proposed. In this paper, a Poisson-Gamma HGLM was examined along with corresponding analytical methods. While a difficulty arises with Poisson GLMM in making inferences to the expected values of observations, it can be avoided with the Poisson-Gamma HGLM. A numerical example with simulated embryo yield data is presented.

• Communications for Statistical Applications and Methods
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• 제25권1호
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• pp.29-42
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• 2018

We combine the integer-valued GARCH(1, 1) model with a generalized regime-switching model to propose a dynamic count time series model. Our model adopts Markov-chains with time-varying dependent transition probabilities to model dynamic count time series called the generalized regime-switching integer-valued GARCH(1, 1) (GRS-INGARCH(1, 1)) models. We derive a recursive formula of the conditional probability of the regime in the Markov-chain given the past information, in terms of transition probabilities of the Markov-chain and the Poisson parameters of the INGARCH(1, 1) process. In addition, we also study the forecasting of the Poisson parameter as well as the cumulative impulse response function of the model, which is a measure for the persistence of volatility. A Monte-Carlo simulation is conducted to see the performances of volatility forecasting and behaviors of cumulative impulse response coefficients as well as conditional maximum likelihood estimation; consequently, a real data application is given.

• Communications for Statistical Applications and Methods
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• 제25권1호
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• pp.61-70
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• 2018

Modeling of the random effects covariance matrix in generalized linear mixed models (GLMMs) is an issue in analysis of longitudinal categorical data because the covariance matrix can be high-dimensional and its estimate must satisfy positive-definiteness. To satisfy these constraints, we consider the autoregressive and moving average Cholesky decomposition (ARMACD) to model the covariance matrix. The ARMACD creates a more flexible decomposition of the covariance matrix that provides generalized autoregressive parameters, generalized moving average parameters, and innovation variances. In this paper, we analyze longitudinal count data with overdispersion using GLMMs. We propose negative binomial loglinear mixed models to analyze longitudinal count data and we also present modeling of the random effects covariance matrix using the ARMACD. Epilepsy data are analyzed using our proposed model.

• 한국통신학회논문지
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• 제29권2B호
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• pp.203-209
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• 2004

OBS 네크워크에서 contention으로 인한 burst의 drop은 TCP의 성능에 중요한 영향을 끼치는 요소이나, 기존의 drop policy에서는 이를 고려하지 않으며 TCP에 대한 연구로는 burst의 assembling이 주를 이루고 있다. 본 논문에서는 OBS 네트워크에서 TCP의 재전송 문제를 drop policy와 연계하여 TCP의 성능을 향상시키고자 한다. 본 논문에서 제안하는 drop policy는 burst의 재전송 횟수가 drop을 결정하는데 있어서 priority로 작용하는 Retransmission Count-based DP(RC-based DP)이다. RC-based DP 모델과 general DP 모델의 성능을 ns-2를 이용한 시뮬레이션을 통해 평가하며, 이 때 시간의 변화에 따른 TCP throughput, 목적지에서 수신한 최고 Sequence 번호, 패킷의 drop rate을 비교 분석한다.