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.     

Tea Plantation Frost Damage Early Warning Using a Two-Fold Method for Temperature Prediction

As the source and main producing area of tea in the world, China has formed unique tea culture, and achieved remarkable economic benefits. However, frequent meteorological disasters, particularly low temperature frost damage in late spring has seriously threatened the growth status of tea trees and caused quality and yield reduction of tea industry. Thus, timely and accurate early warning of frost damage occurrence in specific tea garden is very important for tea plantation management and economic values. Aiming at the problems existing in current meteorological disaster forecasting methods, such as difficulty in obtaining massive meteorological data, large amount of calculation for predicted models and incomplete information on frost damage occurrence, this paper proposed a two-fold algorithm for short-term and real-time prediction of temperature using field environmental data, and temperature trend results from a nearest local weather station for accurate frost damage occurrence level determination, so as to achieve a specific tea garden frost damage occurrence prediction in a microclimate. Time-series meteorological data collected from a small weather station was used for testing and parameterization of a two-fold method, and another dataset acquired from Tea Experimental Base of Zhejiang University was further used to validate the capability of a two-fold model for frost damage forecasting. Results showed that compared with the results of autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR), the proposed two-fold method using a second order Furrier fitting model and a K-Nearest Neighbor model (K = 3) with three days historical temperature data exhibited excellent accuracy for frost damage occurrence prediction on consideration of both model accuracy and computation (98.46% forecasted duration of frost damage, and 95.38% for forecasted temperature at the onset time). For field test in a tea garden, the proposed method accurately predicted three times frost damage occurrences, including onset time, duration and occurrence level. These results suggested the newly-proposed two-fold method was suitable for tea plantation frost damage occurrence forecasting.     

Optimization and evaluation of a coarse-grained model of protein motion using x-ray crystal data

Simple coarse-grained models, such as the Gaussian network model, have been shown to capture some of the features of equilibrium protein dynamics. We extend this model by using atomic contacts to define residue interactions and introducing more than one interaction parameter between residues. We use B-factors from 98 ultra-high resolution (相似文献   

蓝藻水华预报模型及基于遗传算法的参数优化

蓝藻水华预报是应对水危机,保障水资源供给的一项重要工作。以太湖北部三湾(竺山湖、梅梁湾、贡湖)为研究对象,采用动态空间环境建模技术,构建了蓝藻水华预报模型,并通过实地观测建立了模拟的初始参数集。利用2008年04-09月太湖水环境、气象等实测数据,采用遗传算法优化叶绿素a浓度预报模型中敏感度较高的4个参数。研究结果表明,该模型在蓝藻水华空间分布的预报上达到了一定的精度;采用遗传算法能全面、高效地进行参数优化,降低了模拟结果的相对残差,提高了模型预报精度。     

互信息理论结合决策树算法的土壤质量预测

在充分利用土壤类型、土地利用方式、岩性类型、地形、道路、工业类型等影响土壤质量主要因素,准确获取区域土壤质量的空间分布特征的基础上,采用互信息理论对13个辅助变量（岩性类型、土地利用方式、土壤类型、到城镇的距离、到道路的距离、到工业用地的距离、到河流的距离、相对高程、坡度、坡向、平向曲率、纵向曲率和切线曲率）进行筛选,然后通过决策树See5.0预测研究区土壤质量.结果表明： 影响研究区土壤质量的主要因素包括土壤类型、土地利用方式、岩性类型、到城镇的距离、到水域的距离、相对高程、到道路的距离和到工业用地的距离;以互信息理论选取的因子为预测变量的决策树模型精度明显优于以全部因子为预测变量的决策树模型,在前者的决策树模型中,无论是决策树还是决策规则,分类预测精度均达到80%以上.互信息理论结合决策树的方法在充分利用连续型和字符型数据的基础上,不仅精简了一般决策树算法的输入参数,而且能有效地预测和评价区域土壤质量等级.     

Prediction of Interacting Protein Pairs from Sequence Using a Bayesian Method

With the development of bioinformatics, more and more protein sequence information has become available. Meanwhile, the number of known protein–protein interactions (PPIs) is still very limited. In this article, we propose a new method for predicting interacting protein pairs using a Bayesian method based on a new feature representation. We trained our model using data on 6,459 PPI pairs from the yeast Saccharomyces cerevisiae core subset. Using six species of DIP database, our model demonstrates an average prediction accuracy of 93.67%. The result showed that our method is superior to other methods in both computing time and prediction accuracy.     

Quantitative trait prediction based on genetic marker-array data, a simulation study

MOTIVATION: Using simulation studies for quantitative trait loci (QTL), we evaluate the prediction quality of regression models that include as covariates single-nucleotide polymorphism (SNP) genetic markers which did not achieve genome-wide significance in the original genome-wide association study, but were among the SNPs with the smallest P-value for the selected association test. We compare the results of such regression models to the standard approach which is to include only SNPs that achieve genome-wide significance. Using mean square prediction error as the model metric, our simulation results suggest that by using the coefficient of determination (R(2)) value as a guideline to increase or reduce the number of SNPs included in the regression model, we can achieve better prediction quality than the standard approach. However, important parameters such as trait heritability, the approximate number of QTLs, etc. have to be determined from previous studies or have to be estimated accurately.     

一种自优化RBF神经网络的叶绿素a浓度时序预测模型

藻类水华发生过程具有复杂性、非线性、时变性等特点,其准确预测一直是一个国际性难题.以天津市于桥水库为研究对象,根据2000年1月至2003年12月常规监测的水生生态数据(采样周期为10 d),提出了一种结合时序方法的可自优化RBF神经网络智能预测模型,对判断藻类水华的重要指标叶绿素a浓度进行预测.研究了训练样本量及RBF神经网络扩展速度SPREAD值的可自优化性能,以及该模型用于于桥水库叶绿素a浓度的短期变化趋势预测的可行性.结果表明,预测性能指标随SPREAD值及样本量不同发生变化,该预测模型能自动寻到最优SPREAD值,并发现至少需要约两年的训练样本量才能达到较好预测效果.当样本量为105,SPREAD值为10时,预测效果最好,精度较高,预测值与实测值的相关系数R达到0.982.该方法对水库的藻类水华预警有一定的参考价值.     

Efficiency of genomic selection for tomato fruit quality

Fruit quality is polygenic; each component has variable heritability and is difficult to assess. Genomic selection, which allows the prediction of phenotypes based on the whole-genome genotype, could vastly help to improve fruit quality. The goal of this study is to evaluate the accuracy of genomic selection for several metabolomic and quality traits by cross-validation and to estimate the impact of different factors on its accuracy. We analyzed data from 45 phenotypic traits and genotypic data obtained from a previous study of genetic association on a collection of 163 tomato accessions. We tested the influence of (1) the size of training population, (2) the number and density of molecular markers and (3) individual relatedness on the accuracy of prediction. The prediction accuracy of phenotypic values was largely related to the heritability of the traits. The size of training population increased the accuracy of predictions. Using 122 accessions and 5995 single nucleotide polymorphisms (SNPs) was the optimal condition. The density of markers and their numbers also affected the accuracy of the prediction. Using 2313 SNP markers distributed 0.1 cM or more apart from each other reduced the accuracy of prediction, and no gain in prediction accuracy was found when more markers were used in the model. Additionally, the more accessions were related, the more accurate were the predictions. Finally, the structure of the population negatively affected the prediction accuracy. In conclusion, the results obtained by cross-validation illustrated the effect of several parameters on the accuracy of prediction and revealed the potential of genomic selection in tomato breeding programs.     

应用ARIMA模型预测宝安区某街道其它感染性腹泻发病率的探讨

目的：探讨应用ARIMA模型预测宝安区某街道其它感染性腹泻发病率的可行性。方法：应用SPSS13．0软件对2005年～2009年宝安区某街道其它感染性腹泻逐月发病率进行ARIMA模型建模拟合,用所得到的模型对2010年各月发病率进行预测,并评价其预测效果。结果：宝安区某街道其它感染性腹泻发病率每年11月为发病高峰,ARIMA（0,1,1）（0,1,0）12模型是其拟合的最佳模型,其预测结果和实际值绝对误差的绝对值最大为930．47,最小为1．96,平均值214．83,平均相对误差百分比39．04％。结论：模型虽然起到一定的预测效果,但预测精度仍存在误差,可通过积累新的周期数据对ARIMA模型进行修正和重新拟合,也可尝试新的预测方法或其他模型,才能加强和保证预测的精度。     

Predicting the Perceived Sound Quality of Frequency-Compressed Speech

The performance of objective speech and audio quality measures for the prediction of the perceived quality of frequency-compressed speech in hearing aids is investigated in this paper. A number of existing quality measures have been applied to speech signals processed by a hearing aid, which compresses speech spectra along frequency in order to make information contained in higher frequencies audible for listeners with severe high-frequency hearing loss. Quality measures were compared with subjective ratings obtained from normal hearing and hearing impaired children and adults in an earlier study. High correlations were achieved with quality measures computed by quality models that are based on the auditory model of Dau et al., namely, the measure PSM, computed by the quality model PEMO-Q; the measure qc, computed by the quality model proposed by Hansen and Kollmeier; and the linear subcomponent of the HASQI. For the prediction of quality ratings by hearing impaired listeners, extensions of some models incorporating hearing loss were implemented and shown to achieve improved prediction accuracy. Results indicate that these objective quality measures can potentially serve as tools for assisting in initial setting of frequency compression parameters.     

Including dominance effects in the prediction model through locus-specific weights on heterozygous genotypes can greatly improve genomic predictive abilities

The dominance effect is considered to be a key factor affecting complex traits. However, previous studies have shown that the improvement of the model, including the dominance effect, is usually less than 1%. This study proposes a novel genomic prediction method called CADM, which combines additive and dominance genetic effects through locus-specific weights on heterozygous genotypes. To the best of our knowledge, this is the first study of weighting dominance effects for genomic prediction. This method was applied to the analysis of chicken (511 birds) and pig (3534 animals) datasets. A 5-fold cross-validation method was used to evaluate the genomic predictive ability. The CADM model was compared with typical models considering additive and dominance genetic effects (ADM) and the model considering only additive genetic effects (AM). Based on the chicken data, using the CADM model, the genomic predictive abilities were improved for all three traits (body weight at 12th week, eviscerating percentage, and breast muscle percentage), and the average improvement in prediction accuracy was 27.1% compared with the AM model, while the ADM model was not better than the AM model. Based on the pig data, the CADM model increased the genomic predictive ability for all the three pig traits (trait names are masked, here designated as T1, T2, and T3), with an average increase of 26.3%, and the ADM model did not improve, or even slightly decreased, compared with the AM model. The results indicate that dominant genetic variation is one of the important sources of phenotypic variation, and the novel prediction model significantly improves the accuracy of genomic prediction.Subject terms: Animal breeding, Quantitative trait     

Duty ratio drive prediction model of lifetime degradation for organic light emitting diode-on-silicon microdisplay

A duty ratio drive prediction (DRDP) model of luminance degradation for organic light emitting diodes (OLED) microdisplay is proposed in this paper. The traditional stretched exponential decay (SED) model is not applicable for OLED driven by duty ratio. The DRDP model introduces the duty ratio as the variables affecting the lifetime of OLED. By fitting the undetermined coefficients with the measured luminance data, the quantitative relationships among the initial luminance, duty ratio, and OLED lifetime are obtained. Meanwhile, the model quantifies the phenomenon of spontaneous luminance recovery, which occurs when OLED switches from bright to dark. Finally, the DRDP model is used to compensate the luminance degradation of OLED driven by duty ratio. The experimental results show that the average prediction accuracy of DRDP model for white, red, green, and blue (W/R/G/B) OLED degradation trend is 0.9623. The average prediction accuracy of W/R/G/B OLED lifetime is 0.6119, which is greater than that of SED model. The lifetime is extended by 89.83% after compensation.     

Evaluating the accuracy of SHAPE-directed RNA secondary structure predictions

Recent advances in RNA structure determination include using data from high-throughput probing experiments to improve thermodynamic prediction accuracy. We evaluate the extent and nature of improvements in data-directed predictions for a diverse set of 16S/18S ribosomal sequences using a stochastic model of experimental SHAPE data. The average accuracy for 1000 data-directed predictions always improves over the original minimum free energy (MFE) structure. However, the amount of improvement varies with the sequence, exhibiting a correlation with MFE accuracy. Further analysis of this correlation shows that accurate MFE base pairs are typically preserved in a data-directed prediction, whereas inaccurate ones are not. Thus, the positive predictive value of common base pairs is consistently higher than the directed prediction accuracy. Finally, we confirm sequence dependencies in the directability of thermodynamic predictions and investigate the potential for greater accuracy improvements in the worst performing test sequence.     

Optimal breeding-value prediction using a sparse selection index

Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a sparse selection index (SSI) that integrates selection index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-Best Linear Unbiased Predictor (G-BLUP) (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in 10 different environments) that the SSI can achieve significant (anywhere between 5 and 10%) gains in prediction accuracy relative to the G-BLUP.     

High-resolution structure prediction of a circular permutation loop

Methods for rapid and reliable design and structure prediction of linker loops would facilitate a variety of protein engineering applications. Circular permutation, in which the existing termini of a protein are linked by the polypeptide chain and new termini are created, is one such application that has been employed for decreasing proteolytic susceptibility and other functional purposes. The length and sequence of the linker can impact the expression level, solubility, structure and function of the permuted variants. Hence it is desirable to achieve atomic‐level accuracy in linker design. Here, we describe the use of RosettaRemodel for design and structure prediction of circular permutation linkers on a model protein. A crystal structure of one of the permuted variants confirmed the accuracy of the computational prediction, where the all‐atom rmsd of the linker region was 0.89 Å between the model and the crystal structure. This result suggests that RosettaRemodel may be generally useful for the design and structure prediction of protein loop regions for circular permutations or other structure‐function manipulations.     

基于MAXENT模型的黑龙江大兴安岭森林雷击火火险预测

黑龙江大兴安岭是森林雷击火的高发地区,急需研发精确的火险预测模型对该区森林火灾进行预测.本文基于大兴安岭地区森林雷击火灾数据及环境变量数据,采用MAXENT模型进行森林雷击火的火险预测.首先对各环境变量进行共线性诊断,再利用累积正则化增益法和Jackknife方法评价了环境变量的重要性,最后采用最大Kappa值和AUC值检测了MAXENT模型的预测精度.结果表明: 闪电能量和中和电荷量的方差膨胀因子（VIF）值分别为5.012和6.230,与其他变量之间存在共线性,不能用于模型训练.日降雨量、云地闪电数量及云地闪回击电流强度是影响森林雷击火发生的3个最重要因素,日平均风速和坡向的影响较小.随着建模数据比例的增加,最大Kappa值和AUC值均有增大趋势.最大Kappa值都大于0.75,平均值为0.772; AUC值都大于0.5,平均值为0.859.MAXENT模型的预测精度达到中等精度,可应用于大兴安岭地区的森林雷击火火险预测.     

Determination of the Optimal Training Principle and Input Variables in Artificial Neural Network Model for the Biweekly Chlorophyll-a Prediction: A Case Study of the Yuqiao Reservoir,China

Predicting the levels of chlorophyll-a (Chl-a) is a vital component of water quality management, which ensures that urban drinking water is safe from harmful algal blooms. This study developed a model to predict Chl-a levels in the Yuqiao Reservoir (Tianjin, China) biweekly using water quality and meteorological data from 1999-2012. First, six artificial neural networks (ANNs) and two non-ANN methods (principal component analysis and the support vector regression model) were compared to determine the appropriate training principle. Subsequently, three predictors with different input variables were developed to examine the feasibility of incorporating meteorological factors into Chl-a prediction, which usually only uses water quality data. Finally, a sensitivity analysis was performed to examine how the Chl-a predictor reacts to changes in input variables. The results were as follows: first, ANN is a powerful predictive alternative to the traditional modeling techniques used for Chl-a prediction. The back program (BP) model yields slightly better results than all other ANNs, with the normalized mean square error (NMSE), the correlation coefficient (Corr), and the Nash-Sutcliffe coefficient of efficiency (NSE) at 0.003 mg/l, 0.880 and 0.754, respectively, in the testing period. Second, the incorporation of meteorological data greatly improved Chl-a prediction compared to models solely using water quality factors or meteorological data; the correlation coefficient increased from 0.574-0.686 to 0.880 when meteorological data were included. Finally, the Chl-a predictor is more sensitive to air pressure and pH compared to other water quality and meteorological variables.     

New coefficient for water quality modelling in meandering rivers: Fatigue Factor

The accuracy of water quality predictions is essential, especially in countries affected by climate change and ecological water diversity. Water quality modelling in rivers is a valuable tool for enabling decision-making in surface water management because water quality prediction using sampling methods is expensive and time-consuming. The collection of technical knowledge of river characteristics and information about the sources of pollution plays a vital role in this context. This research focused on the effects of river geometry and meandering on the one-dimensional pollutant transport process. Flow velocity magnitude and direction in meandering rivers are frequently variable, leading to uncertain dispersion coefficients and massive changes in pollution concentration even over short distances of these rivers. So, the geometry of meandering rivers has a significant effect on their ecological indicators. A new coefficient called Fatigue Factor was introduced and defined in this study to consider this effect. Colidale Beck (CB) and Tyne rivers were selected for water quality modelling and implementation of the Fatigue Factor. The simulation-optimization method was employed to calculate zinc concentrations along the CB river using measured data for performance assessment of the model. The genetic algorithm performed well in predicting measured zinc concentration with high accuracy. Results of the model demonstrated that the mean effect of the Fatigue Factor in reducing the peak concentration of zinc increases by 3.8% compared to ignoring the Fatigue Factor along the CB length. With the Fatigue Factor consideration, the Mean Percentage Error between model outputs and measured data is 4%, while without it is 18%. Also, the Fatigue Factor had a greater impact on river pollution transport than the dispersion coefficient. With a 50% increase in the Fatigue Factor, the zinc concentration decreased by 6.1% more than the same increase in the dispersion coefficient. Moreover, results indicated that a 100% increment in the Fatigue Factor increases the assimilation capacity up to 3.5 times in CB.     

Improved free energy parameters for RNA pseudoknotted secondary structure prediction

Accurate prediction of RNA pseudoknotted secondary structures from the base sequence is a challenging computational problem. Since prediction algorithms rely on thermodynamic energy models to identify low-energy structures, prediction accuracy relies in large part on the quality of free energy change parameters. In this work, we use our earlier constraint generation and Boltzmann likelihood parameter estimation methods to obtain new energy parameters for two energy models for secondary structures with pseudoknots, namely, the Dirks–Pierce (DP) and the Cao–Chen (CC) models. To train our parameters, and also to test their accuracy, we create a large data set of both pseudoknotted and pseudoknot-free secondary structures. In addition to structural data our training data set also includes thermodynamic data, for which experimentally determined free energy changes are available for sequences and their reference structures. When incorporated into the HotKnots prediction algorithm, our new parameters result in significantly improved secondary structure prediction on our test data set. Specifically, the prediction accuracy when using our new parameters improves from 68% to 79% for the DP model, and from 70% to 77% for the CC model.