Further improvements to a method for analysing stage-frequency data

An iterative procedure for correcting stage-frequency data is described to allow for situations where the period during which a population is sampled begins after some individuals have entered stage 2 or ends before all individuals are dead. The reason for correcting data in this way is to enableKiritani andNakasuji's method for estimating stage-specific survival rates, with extensions proposed byManly (1976, 1977), to be used to analyse the data. The proposed procedure is illustrated on data obtained by sampling a population of the grasshopper Chorthippus brunneus passing through four instar stages to reach the adult stage.     

Analysis of exponential data using a noniterative technique: application to surface plasmon experiments

The analysis of experimental data of exponential type plays a central role in many biophysical applications. We introduce a novel noniterative algorithm to analyze the association phase and dissociation phase of surface plasmon resonance experiments. It is shown that this algorithm can determine kinetic constants with a high level of accuracy in the presence of significant levels of noise. This algorithm should provide a valuable alternative to existing data analysis techniques.     

Parameter estimation for a leaky integrate-and-fire neuronal model from ISI data

The Ornstein-Uhlenbeck process has been proposed as a model for the spontaneous activity of a neuron. In this model, the firing of the neuron corresponds to the first passage of the process to a constant boundary, or threshold. While the Laplace transform of the first-passage time distribution is available, the probability distribution function has not been obtained in any tractable form. We address the problem of estimating the parameters of the process when the only available data from a neuron are the interspike intervals, or the times between firings. In particular, we give an algorithm for computing maximum likelihood estimates and their corresponding confidence regions for the three identifiable (of the five model) parameters by numerically inverting the Laplace transform. A comparison of the two-parameter algorithm (where the time constant tau is known a priori) to the three-parameter algorithm shows that significantly more data is required in the latter case to achieve comparable parameter resolution as measured by 95% confidence intervals widths. The computational methods described here are a efficient alternative to other well known estimation techniques for leaky integrate-and-fire models. Moreover, it could serve as a template for performing parameter inference on more complex integrate-and-fire neuronal models.     

A Probabilistic Framework for Peptide and Protein Quantification from Data-Dependent and Data-Independent LC-MS Proteomics Experiments

Abstract A probability-based quantification framework is presented for the calculation of relative peptide and protein abundance in label-free and label-dependent LC-MS proteomics data. The results are accompanied by credible intervals and regulation probabilities. The algorithm takes into account data uncertainties via Poisson statistics modified by a noise contribution that is determined automatically during an initial normalization stage. Protein quantification relies on assignments of component peptides to the acquired data. These assignments are generally of variable reliability and may not be present across all of the experiments comprising an analysis. It is also possible for a peptide to be identified to more than one protein in a given mixture. For these reasons the algorithm accepts a prior probability of peptide assignment for each intensity measurement. The model is constructed in such a way that outliers of any type can be automatically reweighted. Two discrete normalization methods can be employed. The first method is based on a user-defined subset of peptides, while the second method relies on the presence of a dominant background of endogenous peptides for which the concentration is assumed to be unaffected. Normalization is performed using the same computational and statistical procedures employed by the main quantification algorithm. The performance of the algorithm will be illustrated on example data sets, and its utility demonstrated for typical proteomics applications. The quantification algorithm supports relative protein quantification based on precursor and product ion intensities acquired by means of data-dependent methods, originating from all common isotopically-labeled approaches, as well as label-free ion intensity-based data-independent methods.     

防护措施下一类高传染性多潜伏期传染病的高维动态模型

传染病给人们的生命带来了极大的威胁,对于高传染性的疾病,政府总会采取一些防护措施．本文针对防护措施下的高传染性且具有潜伏期等特性的一类传染病,结合传染病模型,在一定假设条件下给出了这类疾病单日新收治的直接确诊病例及疑似病例的高维动态模型,并使用最小二乘法进行了参数辨识．最后以SARS为例,利用网上公布的SARS数据给出了5月22日-5月31日的预测结果并将预测结果和实际数据进行了比较,说明了模型的有效性．     

A self-paced brain interface system that uses movement related potentials and changes in the power of brain rhythms

Movement execution results in the simultaneous generation of movement-related potentials (MRP) as well as changes in the power of Mu and Beta rhythms. This paper proposes a new self-paced multi-channel BI that combines features extracted from MRPs and from changes in the power of Mu and Beta rhythms. We developed a new algorithm to classify the high-dimensional feature space. It uses a two-stage multiple-classifier system (MCS). First, an MCS classifies each neurological phenomenon separately using the information extracted from specific EEG channels (EEG channels are selected by a genetic algorithm). In the second stage, another MCS combines the outputs of MCSs developed in the first stage. Analysis of the data of four able-bodied subjects shows the superior performance of the proposed algorithm compared with a scheme where the features were all combined in a single feature vector and then classified.     

A THREE-STAGE ALGORITHM FOR FILTERING ERRONEOUS ARGOS SATELLITE LOCATIONS

Several methods have been used to identify erroneous animal locations based on Argos satellite data. Using 15,987 satellite locations for 37 gray seals ( Haliockoerus grypus ), we tested a three-stage filtering algorithm designed to address shortcomings of other filters. In stage 1, for each location, four rates of travel were calculated—the rate to each of the two previous locations and the two subsequent locations. If all four rates exceeded 2 m/sec (95th percentile of our data), the location was removed (7.25% of total locations). Stage 2 incorporated the filtering algorithm developed by McConnell et al. (1992) resulting in the rejection of 22.75% of total locations based on reasonable assumptions of straight-line travel. At stage 3, the remaining data were evaluated against a distance threshold, defined as the 99th percentile of realized distance traveled over a period of seven days. Locations exceeding this threshold-were rejected (0.69% of total locations). Overall, the three-stage filter eliminated fewer locations (30.7 ± 1.62%), than the stage 2 filter alone. Most standard locations were retained, but 85.7% of location class 0, 76.6% of A, and 41.9% of B were also retained. These location classes account for most of data routinely collected but not used.     

Protein-protein docking by simulating the process of association subject to biochemical constraints

We present a computational procedure for modeling protein-protein association and predicting the structures of protein-protein complexes. The initial sampling stage is based on an efficient Brownian dynamics algorithm that mimics the physical process of diffusional association. Relevant biochemical data can be directly incorporated as distance constraints at this stage. The docked configurations are then grouped with a hierarchical clustering algorithm into ensembles that represent potential protein-protein encounter complexes. Flexible refinement of selected representative structures is done by molecular dynamics simulation. The protein-protein docking procedure was thoroughly tested on 10 structurally and functionally diverse protein-protein complexes. Starting from X-ray crystal structures of the unbound proteins, in 9 out of 10 cases it yields structures of protein-protein complexes close to those determined experimentally with the percentage of correct contacts >30% and interface backbone RMSD <4 A. Detailed examination of all the docking cases gives insights into important determinants of the performance of the computational approach in modeling protein-protein association and predicting of protein-protein complex structures.     

数量性状主基因＋多基因混合遗传分析中鉴定多基因存在的IECM算法

为了进行２对主基因＋多基因混合遗传分析中的主基因存在的鉴定和多基因存在的鉴定以及多世代的联合遗传分析的分布参数估计,在ＥＣＭ算法和剖分成分分布方差为主基因变异组分、多基因变异组分和误差变异组分三部分基础上,提出了计算简便的迭代ＥＣＭ算法,简称ＩＥＣＭ算法,以利用 Ｐ_１、Ｆ_１、Ｐ_２和 Ｆ_（２：３）家系世代鉴定多基因存在为例阐明该算法．它的 ＣＭ步包含迭代ＣＭ_１、迭代ＣＭ_２和迭代ＣＭ_３步,在固定其它参数的情况下分别求分布平均数、多基因方差组分和误差方差的极大似然估计．通过１１３８－２ｘ邳县天鹅蛋杂交组合的Ｐ_１、Ｐ_２、Ｆ_１和Ｆ_（２：３）家系群体研究了大豆豆秆黑潜蝇的遗传规律．结果表明,它受 １对主基因的控制并有多基因的修饰．     

Quantification of two-dimensional NOE spectra via a combined linear and nonlinear least-squares fit

Summary Determining the volumes of peaks in 2D NMR spectra can be prohibitively difficult in cases of overlapping, broad lines. Deconvolution and parameter estimation can be attempted on either the time-domain or the frequency-domain data. We present a method of estimating spectral parameters from frequency-domain data, using a combination of Lorentzian and Gaussian lineshapes for reference lines. This approach combines a previously published method of projecting the data on a linear space spanned by reference lines with a nonlinear least-squares fitting algorithm. Comparison of this method with other published methods of frequency-domain deconvolution shows that it is both more precise and more accurate when estimating 2D volumes.     

Parameter estimation in cardiac ionic models

We examine the problem of parameter estimation in mathematical models of excitable cell cardiac electrical activity using the well-known Beeler–Reuter (1977) ionic equations for the ventricular action potential. The estimation problem can be regarded as equivalent to the accurate reconstruction of ionic current kinetics and amplitudes in an excitable cell model, given only action potential experimental data. We show that in the Beeler–Reuter case, all ionic currents may be reasonably reconstructed using an experimental design consisting of action potential recordings perturbed by pseudo-random injection currents.

The Beeler–Reuter model was parameterised into 63 parameters completely defining all membrane current amplitudes and kinetics. Total membrane current was fitted to model-generated experimental data using a ‘data-clamp’ protocol. The experimental data consisted of a default action-potential waveform and an optional series of perturbed waveforms generated by current injections. Local parameter identifiability was ascertained from the reciprocal condition value (1/λ) of the Hessian at the known solution. When fitting to a single action potential waveform, the model was found to be over-determined, having a 1/λ value of 3.6e−14. This value improved slightly to 1.4e−10 when an additional 2 perturbed waveforms were included in the fitting process, suggesting that the additional data did not overly improve the identifiability problem. The additional data, however, did allow the accurate reconstruction of all ionic currents. This indicates that by appropriate experimental design, it may be possible to infer the properties of underlying membrane currents from observation of transmembrane potential waveforms perturbed by pseudo-random currents.     

On the development and application of a continuous-discrete recursive prediction error algorithm

Recursive state and parameter reconstruction is a well-established field in control theory. In the current paper we derive a continuous-discrete version of recursive prediction error algorithm and apply the filter in an environmental and biological setting as a possible alternative to the well-known extended Kalman filter. The framework from which the derivation is started is the so-called 'innovations-format' of the (continuous time) system model, including (discrete time) measurements. After the algorithm has been motivated and derived, it is subsequently applied to hypothetical and 'real-life' case studies including reconstruction of biokinetic parameters and parameters characterizing the dynamics of a river in the United Kingdom. Advantages and characteristics of the method are discussed.     

Regularized EM algorithm for sparse parameter estimation in nonlinear dynamic systems with application to gene regulatory network inference

Parameter estimation in dynamic systems finds applications in various disciplines, including system biology. The well-known expectation-maximization (EM) algorithm is a popular method and has been widely used to solve system identification and parameter estimation problems. However, the conventional EM algorithm cannot exploit the sparsity. On the other hand, in gene regulatory network inference problems, the parameters to be estimated often exhibit sparse structure. In this paper, a regularized expectation-maximization (rEM) algorithm for sparse parameter estimation in nonlinear dynamic systems is proposed that is based on the maximum a posteriori (MAP) estimation and can incorporate the sparse prior. The expectation step involves the forward Gaussian approximation filtering and the backward Gaussian approximation smoothing. The maximization step employs a re-weighted iterative thresholding method. The proposed algorithm is then applied to gene regulatory network inference. Results based on both synthetic and real data show the effectiveness of the proposed algorithm.     

A new genotype calling method for affymetrix SNP arrays

Current genotype-calling methods such as Robust Linear Model with Mahalanobis Distance Classifier (RLMM) and Corrected Robust Linear Model with Maximum Likelihood Classification (CRLMM) provide accurate calling results for Affymetrix Single Nucleotide Polymorphisms (SNP) chips. However, these methods are computationally expensive as they employ preprocess procedures, including chip data normalization and other sophisticated statistical techniques. In the small sample case the accuracy rate may drop significantly. We develop a new genotype calling method for Affymetrix 100 k and 500 k SNP chips. A two-stage classification scheme is proposed to obtain a fast genotype calling algorithm. The first stage uses unsupervised classification to quickly discriminate genotypes with high accuracy for the majority of the SNPs. And the second stage employs a supervised classification method to incorporate allele frequency information either from the HapMap data or from a self-training scheme. Confidence score is provided for every genotype call. The overall performance is shown to be comparable to that of CRLMM as verified by the known gold standard HapMap data and is superior in small sample cases. The new algorithm is computationally simple and standalone in the sense that a self-training scheme can be used without employing any other training data. A package implementing the calling algorithm is freely available at http://www.sfs.ecnu.edu.cn/teachers/xuj_en.html.     

一种获得非线性生长函数参数初始值的新方法:杂种遗传算法

本研究旨在杂种遗传算法应用于非线性生长函数的参数估计.提出了杂种遗传算法估计非线性生长函数参数的数学模型.5种非线性生长函数Gompertz、Logistic、von Bertalanffy、Richards、Brody分别拟合一个较大型的、群体类型差异大的番鸭体重生长资料,利用杂种遗传算法获得了有效初始值,在lsqcurvefit与proc nlin中获得了一致最优解的结果.表明杂种遗传算法估计非线性函数参数的实际可行性.     

TOUCHSTONEX: protein structure prediction with sparse NMR data

TOUCHSTONEX, a new method for folding proteins that uses a small number of long-range contact restraints derived from NMR experimental NOE (nuclear Overhauser enhancement) data, is described. The method employs a new lattice-based, reduced model of proteins that explicitly represents C(alpha), C(beta), and the sidechain centers of mass. The force field consists of knowledge-based terms to produce protein-like behavior, including various short-range interactions, hydrogen bonding, and one-body, pairwise, and multibody long-range interactions. Contact restraints were incorporated into the force field as an NOE-specific pairwise potential. We evaluated the algorithm using a set of 125 proteins of various secondary structure types and lengths up to 174 residues. Using N/8 simulated, long-range sidechain contact restraints, where N is the number of residues, 108 proteins were folded to a C(alpha)-root-mean-square deviation (RMSD) from native below 6.5 A. The average RMSD of the lowest RMSD structures for all 125 proteins (folded and unfolded) was 4.4 A. The algorithm was also applied to limited experimental NOE data generated for three proteins. Using very few experimental sidechain contact restraints, and a small number of sidechain-main chain and main chain-main chain contact restraints, we folded all three proteins to low-to-medium resolution structures. The algorithm can be applied to the NMR structure determination process or other experimental methods that can provide tertiary restraint information, especially in the early stage of structure determination, when only limited data are available.     

Two models of the recognition and detection of texture-defined letters compared

We quantified texture segregation by measuring psychophysically the percentage correct detection scores for each of a set of 10 texture-defined (TD) letters using the temporal two-alternative forced choice method, and at the same time quantified spatial discrimination of the TD form of measuring psychophysically the percentage correct letter recognition scores for the 10 letters. Ten levels of task difficulty were created by adding noise dots to the texture patterns. The resulting psychophysical data were used to test and compare models of the detection and recognition of texture-defined letters. Each model comprised a sequence of physiologically plausible stages in early visual processing. Each had the same first, second and third stages, namely linear orientation-tuned spatial filters followed by rectification and smoothing. Model 1 had only one non-linear stage. Model 2 had two non-linear stages. In model 2 the second non-linear stage was cross-orientation inhibition. This second non-linear stage enhanced the texture borders by, in effect, comparing textures at different locations in the texture pattern. In both models, the last stage modelled either letter detection or letter recognition. Letter recognition was modelled as follows. We passed a given letter stimulus through the first several stages of a model and, in 10 separate calculations, cross-correlated the output with a template of each of the 10 letters. From these 10 correlations we obtained a predicted percentage correct letter recognition score for the given letter stimulus. The predicted recognition scores closely agreed with the experimental data at all 10 levels of task difficulty for model 2, but not for model 1. We conclude that a borderenhancing algorithm is necessary to model letter recognition. The letter-detection algorithm modelled detection of part of a letter (a single letter stroke) in terms of the signal-to-noise ratio of a letter-segment detector. The predicted letter detection scores fitted the data closely for both models.     

A New Class of Completely Self‐Designing Clinical Trials

A flexible method is proposed for group sequentially performed clinical trials which allows for an adaptive, data‐driven sample size reassessment at each stage. By also adaptively assigning different weights to the several stages the total number of study parts can be steered to an intended early or late end of the trial in dependence on all information available prior to a stage. Although at each stage the null hypothesis is tested on rejection, the full level‐α‐test is preserved at the end of the study. The proposed method is not restricted to normally distributed responses. The discussed adaptive designing is a useful tool provided that a priori information about parameters involved in the trial are not available or subject to uncertainty. The presented learning algorithm enables the complete self‐designing of a study.     

An optimized predictive strategy for interactome mapping

We present an optimized experimental strategy that can accelerate progress toward identifying the majority of pairwise protein interactions. Our method involves applying a predictive algorithm, based on the existing data, to identify protein pairs likely to interact and prioritizing these for screening. The approach is iterative as additional data allows one to refine predictions directing the next stage of experimentation.