Does Applicability Domain Exist in Microarray-Based Genomic Research?

Constructing an accurate predictive model for clinical decision-making on the basis of a relatively small number of tumor samples with high-dimensional microarray data remains a very challenging problem. The validity of such models has been seriously questioned due to their failure in clinical validation using independent samples. Besides the statistical issues such as selection bias, some studies further implied the probable reason was improper sample selection that did not resemble the genomic space defined by the training population. Assuming that predictions would be more reliable for interpolation than extrapolation, we set to investigate the impact of applicability domain (AD) on model performance in microarray-based genomic research by evaluating and comparing model performance for samples with different extrapolation degrees. We found that the issue of applicability domain may not exist in microarray-based genomic research for clinical applications. Therefore, it is not practicable to improve model validity based on applicability domain.     

Application of spectrofluorometry to the prediction of PHB concentrations in a fed-batch process

On-line estimation of biopolymer production during fermentation would be a useful adjunct to the development of strategies for process control and optimization. This study examined the applicability of spectrofluorometry, along with other on-line measurements, for the prediction of poly-ß-hydroxybutyric acid (PHB) concentrations in a high-cell density fed-batch fermentation of Ralstonia eutropha. Models previously used for modelling PHB evolution with time are not sufficiently accurate for situations where transient intermediate accumulations or PHB degradation occur. Thus, the mass balance in the model was modified to account for these situations. An estimation algorithm was developed that is based on a hybrid model consisting of a dynamic mass balance of PHB where the main reaction coefficient was regressed with respect to spectrofluorometric data. The regression between the kinetic parameter and the spectrofluorometric data was accomplished using partial least squares (PLS) regression to avoid high sensitivity to noise expected from highly correlated data, such as the spectrofluorometric readings. The model accounts for dynamics of intermediates and in this way allows the prediction of dynamic behaviour in PHB concentrations that cannot be predicted with other reported mathematical models.     

遥感GPP模型在中亚干旱区4个典型生态系统的适用性评价

总初级生产力(GPP)是全球生态系统碳循环的重要组成部分,对全球气候变化有重要影响。目前有多种遥感模型可以模拟总初级生产力,比较不同遥感模型在中亚干旱区上的适用性对推进全球干旱区碳收支估算具有重要意义。基于涡度协相关技术观测的四个地面站数据验证MOD17、VODCA2、VPM、TG、SANIRv五种模型的模拟精度。结果表明：(1)基于光能利用率理论的MOD17、VPM模型模拟咸海荒漠植被和阜康荒漠植被GPP的精度最高(R²分别为0.52和0.80),但在模拟草地、农田生态系统生产力时存在较明显的低估(RE>20%);基于植被指数的遥感模型TG模型、SANIRv模型模拟巴尔喀什湖草地生态系统和乌兰乌苏农田生态系统GPP的精度最高(R²分别为0.91和0.81),同时模拟值与实测值的相对误差也较低;基于微波的VODCA2模型模拟各生态系统生产力的效果最差。(2)水分亏缺是限制植被GPP的主要因素,因此是否合理考虑水分胁迫是影响GPP模型在中亚干旱区适用性的重要因素。研究揭示了遥感GPP模型在中亚干旱区的应用潜力,为推进全球植被碳通量的准确估...     

Statistical inference from single channel records: two-state Markov model with limited time resolution

Though stochastic models are widely used to describe single ion channel behaviour, statistical inference based on them has received little consideration. This paper describes techniques of statistical inference, in particular likelihood methods, suitable for Markov models incorporating limited time resolution by means of a discrete detection limit. To simplify the analysis, attention is restricted to two-state models, although the methods have more general applicability. Non-uniqueness of the mean open-time and mean closed-time estimators obtained by moment methods based on single exponential approximations to the apparent open-time and apparent closed-time distributions has been reported. The present study clarifies and extends this previous work by proving that, for such approximations, the likelihood equations as well as the moment equations (usually) have multiple solutions. Such non-uniqueness corresponds to non-identifiability of the statistical model for the apparent quantities. By contrast, higher-order approximations yield theoretically identifiable models. Likelihood-based estimation procedures are developed for both single exponential and bi-exponential approximations. The methods and results are illustrated by numerical examples based on literature and simulated data, with consideration given to empirical distributions and model control, likelihood plots, and point estimation and confidence regions.     

Accelerating Computation of DCM for ERP in MATLAB by External Function Calls to the GPU

This study aims to improve the performance of Dynamic Causal Modelling for Event Related Potentials (DCM for ERP) in MATLAB by using external function calls to a graphics processing unit (GPU). DCM for ERP is an advanced method for studying neuronal effective connectivity. DCM utilizes an iterative procedure, the expectation maximization (EM) algorithm, to find the optimal parameters given a set of observations and the underlying probability model. As the EM algorithm is computationally demanding and the analysis faces possible combinatorial explosion of models to be tested, we propose a parallel computing scheme using the GPU to achieve a fast estimation of DCM for ERP. The computation of DCM for ERP is dynamically partitioned and distributed to threads for parallel processing, according to the DCM model complexity and the hardware constraints. The performance efficiency of this hardware-dependent thread arrangement strategy was evaluated using the synthetic data. The experimental data were used to validate the accuracy of the proposed computing scheme and quantify the time saving in practice. The simulation results show that the proposed scheme can accelerate the computation by a factor of 155 for the parallel part. For experimental data, the speedup factor is about 7 per model on average, depending on the model complexity and the data. This GPU-based implementation of DCM for ERP gives qualitatively the same results as the original MATLAB implementation does at the group level analysis. In conclusion, we believe that the proposed GPU-based implementation is very useful for users as a fast screen tool to select the most likely model and may provide implementation guidance for possible future clinical applications such as online diagnosis.     

Estimation of non-linear growth models by linearization: a simulation study using a Gompertz function

A method based on Taylor series expansion for estimation of location parameters and variance components of non-linear mixed effects models was considered. An attractive property of the method is the opportunity for an easily implemented algorithm. Estimation of non-linear mixed effects models can be done by common methods for linear mixed effects models, and thus existing programs can be used after small modifications. The applicability of this algorithm in animal breeding was studied with simulation using a Gompertz function growth model in pigs. Two growth data sets were analyzed: a full set containing observations from the entire growing period, and a truncated time trajectory set containing animals slaughtered prematurely, which is common in pig breeding. The results from the 50 simulation replicates with full data set indicate that the linearization approach was capable of estimating the original parameters satisfactorily. However, estimation of the parameters related to adult weight becomes unstable in the case of a truncated data set.     

Does your species have memory? Analyzing capture–recapture data with memory models

We examine memory models for multisite capture–recapture data. This is an important topic, as animals may exhibit behavior that is more complex than simple first‐order Markov movement between sites, when it is necessary to devise and fit appropriate models to data. We consider the Arnason–Schwarz model for multisite capture–recapture data, which incorporates just first‐order Markov movement, and also two alternative models that allow for memory, the Brownie model and the Pradel model. We use simulation to compare two alternative tests which may be undertaken to determine whether models for multisite capture–recapture data need to incorporate memory. Increasing the complexity of models runs the risk of introducing parameters that cannot be estimated, irrespective of how much data are collected, a feature which is known as parameter redundancy. Rouan et al. (JABES, 2009, pp 338–355) suggest a constraint that may be applied to overcome parameter redundancy when it is present in multisite memory models. For this case, we apply symbolic methods to derive a simpler constraint, which allows more parameters to be estimated, and give general results not limited to a particular configuration. We also consider the effect sparse data can have on parameter redundancy and recommend minimum sample sizes. Memory models for multisite capture–recapture data can be highly complex and difficult to fit to data. We emphasize the importance of a structured approach to modeling such data, by considering a priori which parameters can be estimated, which constraints are needed in order for estimation to take place, and how much data need to be collected. We also give guidance on the amount of data needed to use two alternative families of tests for whether models for multisite capture–recapture data need to incorporate memory.