An adaptive single-step FDR procedure with applications to DNA microarray analysis

The use of multiple hypothesis testing procedures has been receiving a lot of attention recently by statisticians in DNA microarray analysis. The traditional FWER controlling procedures are not very useful in this situation since the experiments are exploratory by nature and researchers are more interested in controlling the rate of false positives rather than controlling the probability of making a single erroneous decision. This has led to increased use of FDR (False Discovery Rate) controlling procedures. Genovese and Wasserman proposed a single-step FDR procedure that is an asymptotic approximation to the original Benjamini and Hochberg stepwise procedure. In this paper, we modify the Genovese-Wasserman procedure to force the FDR control closer to the level alpha in the independence setting. Assuming that the data comes from a mixture of two normals, we also propose to make this procedure adaptive by first estimating the parameters using the EM algorithm and then using these estimated parameters into the above modification of the Genovese-Wasserman procedure. We compare this procedure with the original Benjamini-Hochberg and the SAM thresholding procedures. The FDR control and other properties of this adaptive procedure are verified numerically.     

Novel optimal temperature profile for acidification process of <Emphasis Type="Italic">Lactobacillus bulgaricus</Emphasis> and <Emphasis Type="Italic">Streptococcus thermophilus</Emphasis> in yoghurt fermentation using artificial neural network and genetic algorithm

The acidification behavior of Lactobacillus bulgaricus and Streptococcus thermophilus for yoghurt production was investigated along temperature profiles within the optimal window of 38–44 °C. For the optimal acidification temperature profile search, an optimization engine module built on a modular artificial neural network (ANN) and genetic algorithm (GA) was used. Fourteen batches of yoghurt fermentations were evaluated using different temperature profiles in order to train and validate the ANN sub-module. The ANN captured the nonlinear relationship between temperature profiles and acidification patterns on training data after 150 epochs. This served as an evaluation function for the GA. The acidification slope of the temperature profile was the performance index. The GA sub-module iteratively evolved better temperature profiles across generations using GA operations. The stopping criterion was met after 11 generations. The optimal profile showed an acidification slope of 0.06117 compared to an initial value of 0.0127 and at a set point sequence of 43, 38, 44, 43, and 39 °C. Laboratory evaluation of three replicates of the GA suggested optimum profile of 43, 38, 44, 43, and 39 °C gave an average slope of 0.04132. The optimization engine used (to be published elsewhere) could effectively search for optimal profiles of different physico-chemical parameters of fermentation processes.     

Exploring zipping and assembly as a protein folding principle

It has been proposed that proteins fold by a process called "Zipping and Assembly" (Z&A). Zipping refers to the growth of local substructures within the chain, and assembly refers to the coming together of already-formed pieces. Our interest here is in whether Z&A is a general method that can fold most of sequence space, to global minima, efficiently. Using the HP model, we can address this question by enumerating full conformation and sequence spaces. We find that Z&A reaches the global energy minimum native states, even though it searches only a very small fraction of conformational space, for most sequences in the full sequence space. We find that Z&A, a mechanism-based search, is more efficient in our tests than the replica exchange search method. Folding efficiency is increased for chains having: (a) small loop-closure steps, consistent with observations by Plaxco et al. 1998;277;985-994 that folding rates correlate with contact order, (b) neither too few nor too many nucleation sites per chain, and (c) assembly steps that do not occur too early in the folding process. We find that the efficiency increases with chain length, although our range of chain lengths is limited. We believe these insights may be useful for developing faster protein conformational search algorithms.     

Predicting disulfide connectivity patterns

Disulfide bonds play an important role in stabilizing protein structure and regulating protein function. Therefore, the ability to infer disulfide connectivity from protein sequences will be valuable in structural modeling and functional analysis. However, to predict disulfide connectivity directly from sequences presents a challenge to computational biologists due to the nonlocal nature of disulfide bonds, i.e., the close spatial proximity of the cysteine pair that forms the disulfide bond does not necessarily imply the short sequence separation of the cysteine residues. Recently, Chen and Hwang (Proteins 2005;61:507-512) treated this problem as a multiple class classification by defining each distinct disulfide pattern as a class. They used multiple support vector machines based on a variety of sequence features to predict the disulfide patterns. Their results compare favorably with those in the literature for a benchmark dataset sharing less than 30% sequence identity. However, since the number of disulfide patterns grows rapidly when the number of disulfide bonds increases, their method performs unsatisfactorily for the cases of large number of disulfide bonds. In this work, we propose a novel method to represent disulfide connectivity in terms of cysteine pairs, instead of disulfide patterns. Since the number of bonding states of the cysteine pairs is independent of that of disulfide bonds, the problem of class explosion is avoided. The bonding states of the cysteine pairs are predicted using the support vector machines together with the genetic algorithm optimization for feature selection. The complete disulfide patterns are then determined from the connectivity matrices that are constructed from the predicted bonding states of the cysteine pairs. Our approach outperforms the current approaches in the literature.     

Coding processes involved in the cortical representation of complex tactile stimuli

To understand how information is coded in the primary somatosensory cortex (S1) we need to decipher the relationship between neural activity and tactile stimuli. Such a relationship can be formally measured by mutual information. The present study was designed to determine how S1 neuronal populations code for the multidimensional kinetic features (i.e. random, time-varying patterns of force) of complex tactile stimuli, applied at different locations of the rat forepaw. More precisely, the stimulus localization and feature extraction were analyzed as two independent processes, using both rate coding and temporal coding strategies. To model the process of stimulus kinetic feature extraction, multidimensional stimuli were projected onto lower dimensional subspace and then clustered according to their similarity. Different combinations of stimuli clustering were applied to differentiate each stimulus identification process. Information analyses show that both processes are synergistic, this synergy is enhanced within the temporal coding framework. The stimulus localization process is faster than the stimulus feature extraction process. The latter provides more information quantity with rate coding strategy, whereas the localization process maximizes the mutual information within the temporal coding framework. Therefore, combining mutual information analysis with robust clustering of complex stimuli provides a framework to study neural coding mechanisms related to complex stimuli discrimination.     

Fast numerical methods for simulating large-scale integrate-and-fire neuronal networks

We discuss numerical methods for simulating large-scale, integrate-and-fire (I&F) neuronal networks. Important elements in our numerical methods are (i) a neurophysiologically inspired integrating factor which casts the solution as a numerically tractable integral equation, and allows us to obtain stable and accurate individual neuronal trajectories (i.e., voltage and conductance time-courses) even when the I&F neuronal equations are stiff, such as in strongly fluctuating, high-conductance states; (ii) an iterated process of spike-spike corrections within groups of strongly coupled neurons to account for spike-spike interactions within a single large numerical time-step; and (iii) a clustering procedure of firing events in the network to take advantage of localized architectures, such as spatial scales of strong local interactions, which are often present in large-scale computational models—for example, those of the primary visual cortex. (We note that the spike-spike corrections in our methods are more involved than the correction of single neuron spike-time via a polynomial interpolation as in the modified Runge-Kutta methods commonly used in simulations of I&F neuronal networks.) Our methods can evolve networks with relatively strong local interactions in an asymptotically optimal way such that each neuron fires approximately once in operations, where N is the number of neurons in the system. We note that quantifications used in computational modeling are often statistical, since measurements in a real experiment to characterize physiological systems are typically statistical, such as firing rate, interspike interval distributions, and spike-triggered voltage distributions. We emphasize that it takes much less computational effort to resolve statistical properties of certain I&F neuronal networks than to fully resolve trajectories of each and every neuron within the system. For networks operating in realistic dynamical regimes, such as strongly fluctuating, high-conductance states, our methods are designed to achieve statistical accuracy when very large time-steps are used. Moreover, our methods can also achieve trajectory-wise accuracy when small time-steps are used. Action Editor: Nicolas Brunel     

Macro-invertebrates in a dynamic river environment: analysis of time series from artificial substrates,using a ‘white box’ neural network modelling method

New statistical modelling methods, such as neural networks (NNs), allow us to take a step further in the understanding of complex relations in aquatic ecosystems. In this paper the results from the analysis of macro-invertebrate communities in a complex riverine environment are presented. We attempted to explain observed changes in species composition and abundance with neural network modelling methods and compared the results to linear regression. The NN method used is an improved form of the RF5 algorithm, developed to effectively discover numeric laws from data. RF5 uses Product Unit Networks (PUNs), which are in effect multivariate non-discrete power functions. The data set consisted of a 10-year time series of monthly samples of macro-invertebrates on artificial substrates in the rivers Rhine and Meuse in the Netherlands. During this period the invertebrate community has largely changed coinciding with the␣invasion of Ponto-Caspian crustaceans. We used physical–chemical data and data on the abundance of the invasive taxa Corophium curvispinum and Dikerogammarus villosis to explain the observed changes in the resident invertebrate community. The analyses showed temperature, abundance of invasive taxa and peak discharges as important factors. Comparison of the results from NN modelling to linear regression revealed that the factors temperature and abundance of Dikerogammarus villosis explained equally well in both cases. Only the neural network was able to use information on peak discharge and timing of the peak in the previous winter to improve model performances. Neural networks are known to yield excellent modelling results, a drawback however is their lack of transparency or their ‘black box’ character. The use of relatively easy interpretable (white box) PUNs allows us to investigate the extracted relations in more detail and can enhance our understanding of ecosystem functioning. Our results show that peak discharges might be an important factor structuring invertebrate communities in rivers and hint on the existence of interacting effects from invasive species and discharge peaks. They finally show the value of biological data sets that are collected over a long period and in a highly standardised way.     

Research on WNN Greenhouse Temperature Prediction Method Based on GA

Temperature in agricultural production has a direct impact on the growth of crops. The emergence of greenhouses has improved the impact of the original unpredictable changes in temperature, but the temperature modeling of greenhouses is still the main direction at present. Neural network modeling relies on sufficient actual data to model greenhouses, but there is a widening gap in the application of different neural networks. This paper proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm (GA-WNN). With the simple network structure and the nonlinear adaptability of the wavelet basis function, wavelet neural network (WNN) improved model training speed and accuracy of prediction results compared with back propagation neural networks (BPNN), which was conducive to the prediction and control of short-term greenhouse temperature fluctuations. At the same time, the genetic algorithm (GA) was introduced to globally optimize the initial weights of the original model, which improved the insensitivity of the model to the initial weights and thresholds, and improved the training speed and stability of the model. Finally, simulation results for the greenhouse showed that the model training speed, prediction results accuracy and model stability of the GA-WNN in the greenhouse were improved in comparison to results obtained by the WNN and BPNN in the greenhouse.     

植物适应酸铝胁迫机理的研究进展

酸铝胁迫是限制植物正常生长发育的重要非生物胁迫因子,严重制约了我国酸性土壤地区的农业生产水平。植物抵御酸铝胁迫的形式复杂多样,如分泌有机酸、提高根际pH、分泌黏液、细胞壁对Al³⁺的固定、有机酸对细胞溶质中Al³⁺的螯合与液泡区隔化等。现有研究多集中于常规生理特征分析,缺乏深入的分子生物学解析。基于此,本文对国内外植物适应酸铝胁迫机理的相关研究进行了归纳和总结,从酸铝胁迫对植物生长与生理代谢的影响、植物适应酸铝胁迫最主要的两种生理机制(Al排除机制、Al耐受机制)以及分子水平上调控相关耐铝基因进行了综述。最后针对现有研究的不足提出了展望,以期为深入揭示植物适应酸铝胁迫的机理以及挖掘适于酸土生长的优质作物资源提供理论依据。