Reliability Analysis of the Groundwater Conceptual Model

The hydrologic model is the foundation of water resource management and planning. Conceptual model is the essential component of groundwater model. Due to limited understanding of natural hydrogeological conditions, the conceptual model is always constructed incompletely. Therefore, the uncertainty in the model's output is evitable when natural groundwater field is simulated by a single groundwater model. A synthetic groundwater model is built and regarded as the true model, and three alternative conceptual models are constructed by considering incomplete hydrogeological conditions. The outputs (groundwater budget terms from boundary conditions) of these groundwater models are analyzed statistically. The results show that when the conceptual model is closer to the true hydrogeological conditions, the distributions of outputs of the groundwater model are more concentrated on the true outputs. Therefore, the more reliable the structure of the conceptual model is, the more reliable the output of the groundwater model is. Moreover, the uncertainty caused by the conceptual model cannot be compensated by parameter uncertainty.     

Statistical methods for estimating doubling time in in vitro cell growth

Summary Doubling time has been widely used to represent the growth pattern of cells. A traditional method for finding the doubling time is to apply gray-scaled cells, where the logarithmic transformed scale is used. As an alternative statistical method, the log-linear model was recently proposed, for which actual cell numbers are used instead of the transformed gray-scaled cells. In this paper, I extend the log-linear model and propose the extended log-linear model. This model is designed for extra-Poisson variation, where the log-linear model produces the less appropriate estimate of the doubling time. Moreover, I compare statistical properties of the gray-scaled method, the log-linear model, and the extended log-linear model. For this purpose, I perform a Monte Carlo simulation study with three data-generating models: the additive error model, the multiplicative error model, and the overdispersed Poisson model. From the simulation study, I found that the gray-scaled method highly depends on the normality assumption of the gray-scaled cells; hence, this method is appropriate when the error model is multiplicative with the log-normally distributed errors. However, it is less efficient for other types of error distributions, especially when the error model is additive or the errors follow the Poisson distribution. The estimated standard error for the doubling time is not accurate in this case. The log-linear model was found to be efficient when the errors follow the Poisson distribution or nearly Poisson distribution. The efficiency of the log-linear model was decreased accordingly as the overdispersion increased, compared to the extended log-linear model. When the error model is additive or multiplicative with Gamma-distributed errors, the log-linear model is more efficient than the gray-scaled method. The extended log-linear model performs well overall for all three data-generating models. The loss of efficiency of the extended log-linear model is observed only when the error model is multiplicative with log-normally distributed errors, where the gray-scaled method is appropriate. However, the extended log-linear model is more efficient than the log-linear model in this case.     

从临床到基础：硝酸甘油实验性偏头痛大鼠模型信度和效度评价

具有良好信度和效度的动物模型是从实验室到临床转译研究成功的保证,为进一步应用硝酸甘油（glycerol trinitrate,GTN）偏头痛大鼠模型,对其信度和效度进行评价。效度包括表面效度、建构效度、标准关联效度。衡量表面效度的标准是症状同源性。GTN偏头痛大鼠模型行为学表现与人类偏头痛有一定的相似性。建构效度主要指动物模型对理论假说的解释度,GTN模型复制了偏头痛的神经源性炎症及痛觉增敏,具有较好的建构效度。标准关联效度即预测效度主要表现为药理学反应及其在临床的干预实验。GTN模型对典型抗偏头痛药物麦角胺的反应较敏感,但是该模型的预测力效度仍未有效建立。GTN偏头痛大鼠在不同的地区、不同的实验室均已成功复制,表明其有较好的信度。     

Patient-derived orthotopic xenograft (PDOX) mouse model of adult rhabdomyosarcoma invades and recurs after resection in contrast to the subcutaneous ectopic model

Rhabdomyosarcoma (RMS) is a rare mesenchymal tumor. The aim of the present study was to develop a patient-derived orthotopic xenograft (PDOX) mouse model of RMS and compare the PDOX model to a subcutaneous (s.c.)-transplant model. A patient RMS from a striated muscle was grown orthotopically in the right biceps femoris muscle and right quadriceps muscle of nude mice to establish a PDOX model, as well as under the skin to establish an s.c. model. PDOX tumors grew at a statistically-significant faster rate compared to the s.c. tumors. Recurrence after surgical resection occurred only in PDOX tumors, not in the s.c. model. Histologically, only the PDOX model was shown to be invasive. In conclusion, these results indicate that the PDOX model of adult RMS is malignant and the subcutaneous model is benign. These results emphasize that a proper tumor microenvironment is necessary for patient-like behavior of a tumor in a mouse model.     

Modeling Exposure Intensity and Susceptibility-Latency Effect from Retrospective Cohort Data - Application to Lung Cancer Mortality in Blackfoot Disease Endemic Area in Taiwan

It is challenge in epidemiology to characterize the temperol aspect of exposure-disease association. The authors propose a stochastic model to deal with exposures that are time-dependent and exhibit susceptibility and latency effects. The model is applied to a retrospective cohort data on lung cancer mortality in the blackfoot disease endemic area in Taiwan. The authors compare the proposed model with the multistage model, the back-calculation model, the catalytic model, and the age-period-cohort models.     

Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches

Dropouts are common in longitudinal study. If the dropout probability depends on the missing observations at or after dropout, this type of dropout is called informative (or nonignorable) dropout (ID). Failure to accommodate such dropout mechanism into the model will bias the parameter estimates. We propose a conditional autoregressive model for longitudinal binary data with an ID model such that the probabilities of positive outcomes as well as the drop‐out indicator in each occasion are logit linear in some covariates and outcomes. This model adopting a marginal model for outcomes and a conditional model for dropouts is called a selection model. To allow for the heterogeneity and clustering effects, the outcome model is extended to incorporate mixture and random effects. Lastly, the model is further extended to a novel model that models the outcome and dropout jointly such that their dependency is formulated through an odds ratio function. Parameters are estimated by a Bayesian approach implemented using the user‐friendly Bayesian software WinBUGS. A methadone clinic dataset is analyzed to illustrate the proposed models. Result shows that the treatment time effect is still significant but weaker after allowing for an ID process in the data. Finally the effect of drop‐out on parameter estimates is evaluated through simulation studies.     

Development of a constructed subsurface-flow wetland simulation model

This paper presents a mechanistic, compartmental simulation model of subsurface-flow constructed wetlands. The model consists of six submodels, including the nitrogen and carbon cycles, both autotrophic and heterotrophic bacteria growth and metabolism, and water and oxygen balances. Data from an existing constructed wetland in Maryland were used to calibrate the model. Model results reproduced seasonal trends well. Interactions between the carbon, nitrogen, and oxygen cycles were evident in model output. In general, effluent biochemical oxygen demand, organic nitrogen, ammonium and nitrate concentrations were predicted well. Because little is known about rootzone aeration by wetland plants, oxygen predictions were fair. The model is generally insensitive to changes in individual parameters. This is due to the complexity of the ecosystem and the model, as well as the numerous feedback mechanisms. The model is most sensitive to changes in parameters that affect microbial growth and substrate use directly. This dynamic, compartmental, simulation model is an effective tool for evaluating the performance of subsurface-flow constructed wetlands. The model provided insights into treatment problems at an existing constructed wetland. With further evaluation and refinement, the model will be a useful design tool for subsurface-flow constructed wetlands.     

A Population-Based Morphologically Structured Model for Hyphal Growth and Product Formation in Streptomycin Fermentation

Summary A population model discriminating the hyphae according to the hyphal length and a morphologically structured model considering the specific function of different morphological forms of a hypha are combined together to describe mycelial growth, substrate consumption and secondary metabolite formation in streptomycin fermentation. In the population model, the growth modes of hyphae with different age or length are considered, while in the morphologically structured model, the morphological forms of hyphae and their functions in growth and metabolism are described. The population model and the morphologically structured model are interrelated by a branching function and a differentiation function. In the model, the growth rate of immature apical compartment is distinguished from those of matured ones, branching is proposed to occur only in the subapical region, and the hyphal compartment is assumed to synthesize secondary metabolites. The model is successfully applied to simulate the batch fermentation process of streptomycin production. The growth characteristics of filamentous microorganisms are also discussed using the model predictions.     

Parameterization of Multivariate Random Effects Models for Categorical Data

Alternative parameterizations and problems of identification and estimation of multivariate random effects models for categorical responses are investigated. The issues are illustrated in the context of the multivariate binomial logit-normal (BLN) model introduced by Coull and Agresti (2000, Biometrics 56, 73-80). We demonstrate that the BLN model is poorly identified unless proper restrictions are imposed on the parameters. Moreover, estimation of BLN models is unduly computationally complex. In the first application considered by Coull and Agresti, an identification problem results in highly unstable, highly correlated parameter estimates and large standard errors. A probit-normal version of the specified BLN model is demonstrated to be underidentified, whereas the BLN model is empirically underidentified. Identification can be achieved by constraining one of the parameters. We show that a one-factor probit model is equivalent to the probit version of the specified BLN model and that a one-factor logit model is empirically equivalent to the BLN model. Estimation is greatly simplified by using a factor model.     

枯落物分解研究方法和模型讨论

由于研究目的、尺度范围和要求精度的不同,枯落物分解的研究方法各异：野外分解袋法是最直接和最准确的方法,但是耗时太长;室内分解培养法与野外分解袋法相似,但具有灵活设计试验方案的优越性;现量估算法方法简捷,但是只对进入稳定发展和动态平衡阶段的生态系统可以获得较好的精度;综合平衡法能反映整个生长历史时期的枯落物分解速率平均水平,但其准确性取决于对历年凋落物量预测的精度。在枯落物分解模型中,分解率概算模型只适合于林地枯落物积累达到动态平衡时的情况,所以作者提出了另外的枯落物动态平衡模型予以修正;时间衰减模型以Olson指数模型为典型代表,但由于在应用过程中存在一些问题,也提出了相应的修正办法;影响因子关系模型包括基质质量因子关系模型、非生物环境因子关系模型和生物因子关系模型等类型。作者提出构建过程模型将是未来枯落物分解模型研究的方向。     

A Stochastic Model of Oscillatory Blood Testosterone Levels

A continuous-time, discrete-state stochastic model of testosterone secretion in men is considered. Blood levels of testosterone in men fluctuate periodically with a period of 2–3 h. The deterministic model, on which the stochastic model considered here is based, is well studied and has been shown to have a globally stable fixed point. Thus, no sustained oscillations are possible in the deterministic case. However, the stochastic model does observe periodic, pulsatile behavior. This demonstrates how oscillations can occur due to a switching behavior dependent on the random degradation of testosterone molecules in the system. The Gillespie algorithm is used to simulate the hormone secretion model. Important parameters of the model are discussed and results from the model are compared to experimental observations.     

Spatial mixture multiscale modeling for aggregated health data

One of the main goals in spatial epidemiology is to study the geographical pattern of disease risks. For such purpose, the convolution model composed of correlated and uncorrelated components is often used. However, one of the two components could be predominant in some regions. To investigate the predominance of the correlated or uncorrelated component for multiple scale data, we propose four different spatial mixture multiscale models by mixing spatially varying probability weights of correlated (CH) and uncorrelated heterogeneities (UH). The first model assumes that there is no linkage between the different scales and, hence, we consider independent mixture convolution models at each scale. The second model introduces linkage between finer and coarser scales via a shared uncorrelated component of the mixture convolution model. The third model is similar to the second model but the linkage between the scales is introduced through the correlated component. Finally, the fourth model accommodates for a scale effect by sharing both CH and UH simultaneously. We applied these models to real and simulated data, and found that the fourth model is the best model followed by the second model.     

用光合-蒸散耦合模型模拟冬小麦CO2通量的日变化

根据 SPAC理论建立了一个冬小麦光合和蒸散的耦合模型。冬小麦 CO2 通量包括冠层光合、呼吸和土壤呼吸。冠层光合采用了 Farquhar光合作用生化模型 ,并通过冠层阻力的参数化将光合作用与蒸腾作用耦合起来。用涡度相关方法观测了 CO2通量 ,对模型进行了验证 ,结果显示模型可以较好地模拟 CO2 通量日变化过程。对模型的敏感性分析发现日间 CO2 通量最敏感的参数是初始量子效率。其次 ,CO2 通量对光响应曲线凸度、CO2 补偿点、凋萎点和叶面积指数的变化也有着较强的敏感性 ;夜间 CO2 通量敏感的参数是最适温度下 Rubisco催化能力和暗呼吸参数     

用光合-蒸散耦合模型模拟冬小麦CO2通量的日变化

根据SPAC理论建立了一个冬小麦光合和蒸散的耦合模型.冬小麦CO2通量包括冠层光合、呼吸和土壤呼吸.冠层光合采用了Farquhar光合作用生化模型,并通过冠层阻力的参数化将光合作用与蒸腾作用耦合起来.用涡度相关方法观测了CO2通量,对模型进行了验证,结果显示模型可以较好地模拟CO2通量日变化过程.对模型的敏感性分析发现日间CO2通量最敏感的参数是初始量子效率.其次,CO2通量对光响应曲线凸度、CO2补偿点、凋萎点和叶面积指数的变化也有着较强的敏感性;夜间CO2通量敏感的参数是最适温度下Rubisco催化能力和暗呼吸参数.     

Computation of a Stabilizing Set of Feedback Matrices bf a Large-scale Nonlinear Musculoskeletal Dynamic Model

The purpose of this study is to present a general mathematical framework to compute a set of feedback matrices which stabilize an unstable nonlinear anthropomorphic musculoskeletal dynamic model. This method is activity specific and involves four fundamental stages. First, from muscle activation data (input) and motion degrees-of-freedom (output) a dynamic experimental model is obtained using system identification schemes. Second, a nonlinear musculoskeletal dynamic model which contains the same number of muscles and degrees-of-freedom and best represents the activity being considered is proposed. Third, the nonlinear musculoskeletal model (anthropomorphic model) is replaced by a family of linear systems, parameterized by the same set of input/ output data (nominal points) used in the identification of the experimental model. Finally, a set of stabilizing output feedback matrices, parameterized again by the same set of nominal points, is computed such that when combined with the anthropomorphic model, the combined system resembles the structural form of the experimental model. The method is illustrated in regard to the human squat activity.     

连翘苷降血脂及抗氧化作用的实验研究

用高脂饲料造成营养性高脂血症模型,观察连翘苷的降血脂和抗氧化作用。结果显示,与高脂血症模型组相比较,连翘苷给药组动物的血浆总胆固醇(TC)、三酰甘油(TG),低密度脂蛋白胆固醇(LDL-C)、动脉粥样硬化指数(AI)均有不同程度下降,高密度脂蛋白胆固醇(HDL-C)有所上升,血中过氧化氢酶(CAT)、过氧化物酶(POD)活性增强,血浆中丙二醛(MDA)的含量降低,说明连翘苷具有降低营养性高脂血症小鼠血脂和抗氧化的作用。     

More Parsimonious Linar-By-Linear Associatin Model in the Analysis of Cross-Classifications Having Ordered Categories

For the analysis of cross-classifications having ordered categories, this paper proposes a model which is more parsimonious than the linear-by-linear association (LL) model (that is, which can be described in terms of fewer parameters than the LL model). In a special case, this model is more parsimonious than the uniform association (U) model. Under this model, the expected frequency on a log scale is a linear function of row and column variables for fixed column and row variables, respectively. For the well-known operation and dumping severity data, the parsimonious U model proposed here fits well, and new interpretations are added.     

Skew-normal random-effects model for meta-analysis of diagnostic test accuracy (DTA) studies

Hierarchical models are recommended for meta-analyzing diagnostic test accuracy (DTA) studies. The bivariate random-effects model is currently widely used to synthesize a pair of test sensitivity and specificity using logit transformation across studies. This model assumes a bivariate normal distribution for the random-effects. However, this assumption is restrictive and can be violated. When the assumption fails, inferences could be misleading. In this paper, we extended the current bivariate random-effects model by assuming a flexible bivariate skew-normal distribution for the random-effects in order to robustly model logit sensitivities and logit specificities. The marginal distribution of the proposed model is analytically derived so that parameter estimation can be performed using standard likelihood methods. The method of weighted-average is adopted to estimate the overall logit-transformed sensitivity and specificity. An extensive simulation study is carried out to investigate the performance of the proposed model compared to other standard models. Overall, the proposed model performs better in terms of confidence interval width of the average logit-transformed sensitivity and specificity compared to the standard bivariate linear mixed model and bivariate generalized linear mixed model. Simulations have also shown that the proposed model performed better than the well-established bivariate linear mixed model in terms of bias and comparable with regards to the root mean squared error (RMSE) of the between-study (co)variances. The proposed method is also illustrated using a published meta-analysis data.     

Simulation of bracken cover in forested areas in Victoria in response to season,overstorey and fire conditions

Abstract A computer-based model is presented that brings together the existing knowledge about the dynamics of bracken in eucalypt forests in south-eastern Australia. The average length of the growing season, the timing and extent of logging, and the timing and severity of fire are major inputs to the model. The model is predominantly deterministic, but has a stochastic environmental component. The model is validated with four independent sets of data. The model successfully predicts the trends in the data. The interactions between bracken, other understorey species and eucalypt regeneration are explored with the model. The likely effect of different silvicultural and fire management practices on the cover of bracken are investigated using the model. Year to year variation can mask the effects of disturbance if bracken cover is viewed in any one year in isolation. Bracken cover trends can only be seen over a period of 10 years or more. The model reinforces the importance of site productivity as measured by length of growing season and the degree of tree removal on bracken cover.