以小时为步长的大兴安岭典型林分地表死可燃物含水率模型预测及外推精度

地表死可燃物含水率是火险天气和火行为预报中的重要指标.本研究基于时滞平衡含水率法(Nelson和Simard方法)及气象要素回归方法,于2010年9—10月对黑龙江省大兴安岭地区盘古林场不同郁闭度的山杨-白桦混交林、红皮云杉纯林,以及采伐迹地(原1∶1樟子松-白桦混交林)地表死可燃物含水率进行以小时为步长的连续测定,建立其预测模型,得到预测误差,并使用相应的模型对其他林分地表死可燃物含水率进行外推精度分析.结果表明:采用Nelson平衡含水率法构建的地表死可燃物含水率变化模型的平均绝对误差、平均相对误差和均方误差根(0.0154、0.104和0.0226)低于Simard法(0.0185、0.117和0.0256)和气象要素回归法(0.0222、0.150和0.0331).在外推效果方面,气象要素回归法的平均绝对误差、平均相对误差和均方误差根(0.0410、0.0300和0.0740)低于Simard法(0.610、0.492和0.846),但前两者均高于Nelson法(0.034、0.021和0.0660),说明以小时为步长的时滞平衡含水率法,尤其是Nelson法适用于大兴安岭地区所测林分.外推虽不能降低误差,但有助于提高现有模型应用至不同林分条件或大尺度范围内的地表死可燃物含水率预测精度和利用率.模型建模和外推误差与不同树种和郁闭度条件差异有关,研究时应根据不同林分和地点选择合适的平衡含水率模型.     

Multi-class cancer classification via partial least squares with gene expression profiles

MOTIVATION: Discrimination between two classes such as normal and cancer samples and between two types of cancers based on gene expression profiles is an important problem which has practical implications as well as the potential to further our understanding of gene expression of various cancer cells. Classification or discrimination of more than two groups or classes (multi-class) is also needed. The need for multi-class discrimination methodologies is apparent in many microarray experiments where various cancer types are considered simultaneously. RESULTS: Thus, in this paper we present the extension to the classification methodology proposed earlier Nguyen and Rocke (2002b; Bioinformatics, 18, 39-50) to classify cancer samples from multiple classes. The methodologies proposed in this paper are applied to four gene expression data sets with multiple classes: (a) a hereditary breast cancer data set with (1) BRCA1-mutation, (2) BRCA2-mutation and (3) sporadic breast cancer samples, (b) an acute leukemia data set with (1) acute myeloid leukemia (AML), (2) T-cell acute lymphoblastic leukemia (T-ALL) and (3) B-cell acute lymphoblastic leukemia (B-ALL) samples, (c) a lymphoma data set with (1) diffuse large B-cell lymphoma (DLBCL), (2) B-cell chronic lymphocytic leukemia (BCLL) and (3) follicular lymphoma (FL) samples, and (d) the NCI60 data set with cell lines derived from cancers of various sites of origin. In addition, we evaluated the classification algorithms and examined the variability of the error rates using simulations based on randomization of the real data sets. We note that there are other methods for addressing multi-class prediction recently and our approach is along the line of Nguyen and Rocke (2002b; Bioinformatics, 18, 39-50). CONTACT: dnguyen@stat.tamu.edu; dmrocke@ucdavis.edu     

S形增长模型之比较、组合预测及应用

对５种Ｓ形增长模型进行比较,以组合模型的误差平方和最小为目标建立组合预测模型,并以实例说明其应用。     

On the Applicability of Multidimensional Classification Procedures

An example from the EEG-analysis shows that particularly in using the R-error estimation the variance analysis and discrimination might indicate contradictory results. Therefore the use of results of MANOVA for a subsequent discrimination appears not to be advisable as a rule. If the number of test persons per class does not amount to at least the five-fold of the number of characters, the error estimation according to the R-method is not applicable. Since the feature selection by the methods investigated in this paper only pretend an improvement of the classification, producing even misleading results in single cases, it should not be used, at least not with smaller random samples.     

Simulation of PSII-operating efficiency from chlorophyll fluorescence in response to light and temperature in chrysanthemum (<Emphasis Type="Italic">Dendranthema grandiflora</Emphasis>) using a multilayer leaf model

Chlorophyll fluorescence serves as a proxy photosynthesis measure under different climatic conditions. The objective of the study was to predict PSII quantum yield using greenhouse microclimate data to monitor plant conditions under various climates. Multilayer leaf model was applied to model fluorescence emission from actinic light-adapted (F') leaves, maximum fluorescence from light-adapted (Fm') leaves, PSII-operating efficiency (F_q'/F_m'), and electron transport rate (ETR). A linear function was used to approximate F' from several measurements under constant and variable light conditions. Model performance was evaluated by comparing the differences between the root mean square error (RMSE) and mean square error (MSE) of observed and predicted values. The model exhibited predictive success for Fq'/Fm' and ETR under different temperature and light conditions with lower RMSE and MSE. However, prediction of F' and Fm' was poor due to a weak relationship under constant (R² = 0.48) and variable (R² = 0.35) light.     

Human wavelength discrimination of monochromatic light explained by optimal wavelength decoding of light of unknown intensity

We show that human ability to discriminate the wavelength of monochromatic light can be understood as maximum likelihood decoding of the cone absorptions, with a signal processing efficiency that is independent of the wavelength. This work is built on the framework of ideal observer analysis of visual discrimination used in many previous works. A distinctive aspect of our work is that we highlight a perceptual confound that observers should confuse a change in input light wavelength with a change in input intensity. Hence a simple ideal observer model which assumes that an observer has a full knowledge of input intensity should over-estimate human ability in discriminating wavelengths of two inputs of unequal intensity. This confound also makes it difficult to consistently measure human ability in wavelength discrimination by asking observers to distinguish two input colors while matching their brightness. We argue that the best experimental method for reliable measurement of discrimination thresholds is the one of Pokorny and Smith, in which observers only need to distinguish two inputs, regardless of whether they differ in hue or brightness. We mathematically formulate wavelength discrimination under this wavelength-intensity confound and show a good agreement between our theoretical prediction and the behavioral data. Our analysis explains why the discrimination threshold varies with the input wavelength, and shows how sensitively the threshold depends on the relative densities of the three types of cones in the retina (and in particular predict discriminations in dichromats). Our mathematical formulation and solution can be applied to general problems of sensory discrimination when there is a perceptual confound from other sensory feature dimensions.     

Application of the backstepping method to the prediction of increase or decrease of infected population

Background

In mathematical epidemiology, age-structured epidemic models have usually been formulated as the boundary-value problems of the partial differential equations. On the other hand, in engineering, the backstepping method has recently been developed and widely studied by many authors.

Methods

Using the backstepping method, we obtained a boundary feedback control which plays the role of the threshold criteria for the prediction of increase or decrease of newly infected population. Under an assumption that the period of infectiousness is same for all infected individuals (that is, the recovery rate is given by the Dirac delta function multiplied by a sufficiently large positive constant), the prediction method is simplified to the comparison of the numbers of reported cases at the current and previous time steps.

Results

Our prediction method was applied to the reported cases per sentinel of influenza in Japan from 2006 to 2015 and its accuracy was 0.81 (404 correct predictions to the total 500 predictions). It was higher than that of the ARIMA models with different orders of the autoregressive part, differencing and moving-average process. In addition, a proposed method for the estimation of the number of reported cases, which is consistent with our prediction method, was better than that of the best-fitted ARIMA model ARIMA(1,1,0) in the sense of mean square error.

Conclusions

Our prediction method based on the backstepping method can be simplified to the comparison of the numbers of reported cases of the current and previous time steps. In spite of its simplicity, it can provide a good prediction for the spread of influenza in Japan.

     

Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.     

Analysis of Schizophrenia Data Using A Nonlinear Threshold Index Logistic Model

Genetic information, such as single nucleotide polymorphism (SNP) data, has been widely recognized as useful in prediction of disease risk. However, how to model the genetic data that is often categorical in disease class prediction is complex and challenging. In this paper, we propose a novel class of nonlinear threshold index logistic models to deal with the complex, nonlinear effects of categorical/discrete SNP covariates for Schizophrenia class prediction. A maximum likelihood methodology is suggested to estimate the unknown parameters in the models. Simulation studies demonstrate that the proposed methodology works viably well for moderate-size samples. The suggested approach is therefore applied to the analysis of the Schizophrenia classification by using a real set of SNP data from Western Australian Family Study of Schizophrenia (WAFSS). Our empirical findings provide evidence that the proposed nonlinear models well outperform the widely used linear and tree based logistic regression models in class prediction of schizophrenia risk with SNP data in terms of both Types I/II error rates and ROC curves.     

A suggestion for computing objective function in model calibration

A parameter-optimization process (model calibration) is usually required for numerical model applications, which involves the use of an objective function to determine the model cost (model-data errors). The sum of square errors (SSR) has been widely adopted as the objective function in various optimization procedures. However, ‘square error’ calculation was found to be more sensitive to extreme or high values. Thus, we proposed that the sum of absolute errors (SAR) may be a better option than SSR for model calibration. To test this hypothesis, we used two case studies—a hydrological model calibration and a biogeochemical model calibration—to investigate the behavior of a group of potential objective functions: SSR, SAR, sum of squared relative deviation (SSRD), and sum of absolute relative deviation (SARD). Mathematical evaluation of model performance demonstrates that ‘absolute error’ (SAR and SARD) are superior to ‘square error’ (SSR and SSRD) in calculating objective function for model calibration, and SAR behaved the best (with the least error and highest efficiency). This study suggests that SSR might be overly used in real applications, and SAR may be a reasonable choice in common optimization implementations without emphasizing either high or low values (e.g., modeling for supporting resources management).     

Neural network programming in bioprocess variable estimation and state prediction.

A neural network program with efficient learning ability for bioprocess variable estimation and state prediction was developed. A 3 layer, feed-forward neural network architecture was used, and the program was written in Quick C ver 2.5 for an IBM compatible computer with a 80486/33 MHz processor. A back propagation training algorithm was used based on learning by pattern and momentum in a combination as used to adjust the connection of weights of the neurons in adjacent layers. The delta rule was applied in a gradient descent search technique to minimize a cost function equal to the mean square difference between the target and the network output. A non-linear, sigmoidal logistic transfer function was used in squashing the weighted sum of the inputs of each neuron to a limited range output. A good neural network prediction model was obtained by training with a sequence of past time course data of a typical bioprocess. The well trained neural network estimated accurately and rapidly the state variables with or without noise even under varying process dynamics.     

Protein fold recognition based on error correcting output codes and SVM

A new approach based on the implementation of support vector machine (SVM) with the error correcting output codes (ECOC) is presented for recognition of multi-class protein folds. The experimental show that the proposed method can improve prediction accuracy by 4%-10% on two datasets containing 27 SCOP folds.     

Detection of pest species with different ratios in tea plant based on electronic nose

It is highly possible that tea (Camellia sinensis) plant is attacked by more than one pest species at the same time, and the determination of their proportion is of great significance to the management of tea plants. However, there are no literatures focusing on it previously. In this work, two pest species (Ectropis obliqua and Ectropis grisescens) in six different ratios (10:0, 8:2, 6:4, 4:6, 2:8 and 0:10) were applied to attack tea plants and electronic nose (E‐nose) was employed to detect them, labelled as group 10:0, 8:2, 6:4, 4:6, 2:8 and 0:10, respectively. Two prediction methods were applied to predict the ratio of E. obliqua and E. grisescens attacking tea plant and their performances were compared. The first method employed regression algorithm for prediction analysis based on the whole E‐nose data directly. The second method classified tea plants into three main classes (the first class contained group 10:0, the second class contained groups 8:2, 6:4, 4:6 and 2:8, and the third class contained group 0:10) first, then regression algorithm was applied to deal with the second class for prediction analysis. The results showed that the second method had a better performance. Its discrimination results showed 100% of the correct classification rate for training set and 93.75% for testing set. Meanwhile, its prediction results showed 0.0005 of root mean square error (RMSE) for calibration set, 0.0064 for validation set and 99.07% of fitting correlation coefficients (R²) for calibration set, 91.22% for validation set, which were acceptable for prediction analysis and proved that E‐nose was a feasible technique for pests' ratio prediction.     

Competing Risks Data Analysis with High-dimensional Covariates:An Application in Bladder Cancer

Analysis of microarray data is associated with the methodological problems of high dimension and small sample size. Various methods have been used for variable selection in highdimension and small sample size cases with a single survival endpoint. However, little effort has been directed toward addressing competing risks where there is more than one failure risks. This study compared three typical variable selection techniques including Lasso, elastic net, and likelihood-based boosting for high-dimensional time-to-event data with competing risks. The performance of these methods was evaluated via a simulation study by analyzing a real dataset related to bladder cancer patients using time-dependent receiver operator characteristic(ROC) curve and bootstrap.632+ prediction error curves. The elastic net penalization method was shown to outperform Lasso and boosting. Based on the elastic net, 33 genes out of 1381 genes related to bladder cancer were selected. By fitting to the Fine and Gray model, eight genes were highly significant(P 0.001). Among them, expression of RTN4, SON, IGF1 R, SNRPE, PTGR1, PLEK, and ETFDH was associated with a decrease in survival time, whereas SMARCAD1 expression was associated with an increase in survival time. This study indicates that the elastic net has a higher capacity than the Lasso and boosting for the prediction of survival time in bladder cancer patients.Moreover, genes selected by all methods improved the predictive power of the model based on only clinical variables, indicating the value of information contained in the microarray features.     

VPMCD: variable interaction modeling approach for class discrimination in biological systems

Data classification algorithms applied for class prediction in computational biology literature are data specific and have shown varying degrees of performance. Different classes cannot be distinguished solely based on interclass distances or decision boundaries. We propose that inter-relations among the features be exploited for separating observations into specific classes. A new variable predictive model based class discrimination (VPMCD) method is described here. Three well established and proven data sets of varying statistical and biological significance are utilized as benchmark. The performance of the new method is compared with advanced classification algorithms. The new method performs better during different tests and shows higher stability and robustness. The VPMCD is observed to be a potentially strong classification approach and can be effectively extended to other data mining applications involving biological systems.     

The Evolution of Dimorphic Traits: Predicting the Genetic Correlation between Environments

Many traits vary in a dichotomous manner, although the underlying genetic determination is polygenic. The genetic basis of such dimorphic traits can be analyzed using the threshold model, in which it is assumed that there is a continuously distributed underlying character and the phenotype is determined by whether the character is above or below a threshold. Threshold traits frequently vary with environmental variables such as photoperiod, temperature and density. This effect can be accounted for using a threshold model in which (1) there is a critical value of the environmental variable at which a genotype switches to the alternate morph, and (2) switch (threshold) points are normally distributed in the population. I term this the environmental threshold (ET) model. I show that the ET model predicts that across environments differing in only one factor the genetic correlation will be 1. This prediction is supported by data from three wing dimorphic insects. Evidence is presented that the genetic correlation between environments differing in two components (temperature and photoperiod) is less than 1.     

Fuzzy neural network based prediction model applied in primary component analysis

To solve the low accuracy and bad robustness problems in traditional water quality prediction method, this paper put forward a primary component analysis (PCA)–fuzzy neural network (FNN)–DEBP based prediction model of dissolved oxygen (DO) in aquaculture water quality. This model used PCA to extract the PC of aquaculture ecological indexes, then reduced the input vector dimension of the model, and utilized differential evolutionary algorithm to optimize the weight parameter of FNN, in order to automatically obtain the optimum parameters and build nonlinear prediction model of DO in aquaculture water quality. The model was applied in a predictive analysis on the water quality data online monitored from December 1st 2015 to December 8th 2015 in a Penaeus orientalis culture pond. The testing results show that this model has obtained a good predictive effect. Compared to BP-FNN model, in PCA–FNN–DEBP model, the absolute error of 95.8% test samples is less than 20%, and the maximum error is 0.22 mg/L, both of which are superior than BP-FNN prediction method. Due to rapid computation speed and high prediction accuracy, PCA–FNN–DEBP algorithm can provide strategic basis for the regulation and management of water quality in P. orientalis culture.     

Poisson vs normal-errors regression in Mac Nally (1996)

Abstract Mac Nally (1996), in describing the application of ‘hierarchical partitioning’ in regression modelling of species richness of breeding passerine birds with response variable the species count, rejects the use of Poisson regression in favour of normal-errors regression on an incorrect basis. Mac Nally uses a function of the residual sum of squares, the root-mean square prediction error (RMSPE), calculated from predictions from each regression and rejects the Poisson regression because its RMSPE was 20% larger. This note points out that the RMSPE will always be larger for the Poisson regression, given the same link function and linear predictor is used, even if the response is truly Poisson. References to appropriate methods of determining the most suitable response distribution and link function in the context of generalized linear models are given.