Graph decompositions for demographic loop analysis

A new approach to loop analysis is presented in which decompositions of the total elasticity of a population projection matrix over a set of life history pathways are obtained as solutions of a constrained system of linear equations. In loop analysis, life history pathways are represented by loops in the life cycle graph, and the elasticity of the loop is interpreted as a measure of the contribution of the life history pathway to the population growth rate. Associated with the life cycle graph is a vector space -- the cycle space of the graph -- which is spanned by the loops. The elasticities of the transitions in the life cycle graph can be represented by a vector in the cycle space, and a loop decomposition of the life cycle graph is then defined to be any nonnegative linear combination of the loops which sum to the vector of elasticities. In contrast to previously published algorithms for carrying out loop analysis, we show that a given life cycle graph admits of either a unique loop decomposition or an infinite set of loop decompositions which can be characterized as a bounded convex set of nonnegative vectors. Using this approach, loop decompositions which minimize or maximize a linear objective function can be obtained as solutions of a linear programming problem, allowing us to place lower and upper bounds on the contributions of life history pathways to the population growth rate. Another consequence of our approach to loop analysis is that it allows us to identify the exact tradeoffs in contributions to the population growth rate that must exist between life history pathways.     

The Decompositions for Point Symmetry Models in Two-way Contingency Tables

The decomposition for the complete point symmetry model in a rectangular contingency table is shown. Also the respective decompositions for the local point symmetry model and the reverse local point symmetry model in a square contingency table are given. Moreover test procedures for the decomposed models and an example are given.     

Insights into protein-protein binding by binding free energy calculation and free energy decomposition for the Ras-Raf and Ras-RalGDS complexes

Absolute binding free energy calculations and free energy decompositions are presented for the protein-protein complexes H-Ras/C-Raf1 and H-Ras/RalGDS. Ras is a central switch in the regulation of cell proliferation and differentiation. In our study, we investigate the capability of the molecular mechanics (MM)-generalized Born surface area (GBSA) approach to estimate absolute binding free energies for the protein-protein complexes. Averaging gas-phase energies, solvation free energies, and entropic contributions over snapshots extracted from trajectories of the unbound proteins and the complexes, calculated binding free energies (Ras-Raf: -15.0(+/-6.3)kcal mol(-1); Ras-RalGDS: -19.5(+/-5.9)kcal mol(-1)) are in fair agreement with experimentally determined values (-9.6 kcal mol(-1); -8.4 kcal mol(-1)), if appropriate ionic strength is taken into account. Structural determinants of the binding affinity of Ras-Raf and Ras-RalGDS are identified by means of free energy decomposition. For the first time, computationally inexpensive generalized Born (GB) calculations are applied in this context to partition solvation free energies along with gas-phase energies between residues of both binding partners. For selected residues, in addition, entropic contributions are estimated by classical statistical mechanics. Comparison of the decomposition results with experimentally determined binding free energy differences for alanine mutants of interface residues yielded correlations with r(2)=0.55 and 0.46 for Ras-Raf and Ras-RalGDS, respectively. Extension of the decomposition reveals residues as far apart as 25A from the binding epitope that can contribute significantly to binding free energy. These "hotspots" are found to show large atomic fluctuations in the unbound proteins, indicating that they reside in structurally less stable regions. Furthermore, hotspot residues experience a significantly larger-than-average decrease in local fluctuations upon complex formation. Finally, by calculating a pair-wise decomposition of interactions, interaction pathways originating in the binding epitope of Raf are found that protrude through the protein structure towards the loop L1. This explains the finding of a conformational change in this region upon complex formation with Ras, and it may trigger a larger structural change in Raf, which is considered to be necessary for activation of the effector by Ras.     

Theoretical study of the reaction mechanism of CH<Subscript>3</Subscript>NO<Subscript>2</Subscript> with NO<Subscript>2</Subscript>, NO and CO: the bimolecular reactions that cannot be ignored

The intriguing decompositions of nitro-containing explosives have been attracting interest. While theoretical investigations have long been concentrated mainly on unimolecular decompositions, bimolecular reactions have received little theoretical attention. In this paper, we investigate theoretically the bimolecular reactions between nitromethane (CH₃NO₂)—the simplest nitro-containing explosive—and its decomposition products, such as NO₂, NO and CO, that are abundant during the decomposition process of CH₃NO₂. The structures and potential energy surface (PES) were explored at B3LYP/6-31G(d), B3P86/6-31G(d) and MP2/6-311?+?G(d,p) levels, and energies were refined using CCSD(T)/cc-pVTZ methods. Quantum chemistry calculations revealed that the title reactions possess small barriers that can be comparable to, or smaller than, that of the initial decomposition reactions of CH₃NO₂. Considering that their reactants are abundant in the decomposition process of CH₃NO₂, we consider bimolecular reactions also to be of great importance, and worthy of further investigation. Moreover, our calculations show that NO₂ can be oxidized by CH₃NO₂ to NO₃ radical, which confirms the conclusion reached formerly by Irikura and Johnson [(2006) J Phys Chem A 110:13974–13978] that NO₃ radical can be formed during the decomposition of nitramine explosives.     

Reactive molecular dynamics simulations on the thermal decomposition of poly alpha-methyl styrene

Using molecular dynamics simulations with ReaxFF reactive force field, the thermal decomposition mechanism of poly alpha-methyl styrene (PAMS) materials and the effects of heating rate and impurity fluorobenzene on PAMS thermal decompositions are studied. The results show that: 1) Pyrolysis mechanism of PAMS consists of initiation and propagation processes. In the initiation stage, random scissions of C-C backbone produce fragments, and in the propagation stage, depolymerizing reactions generate monomers and other products. 2) Higher decomposition temperature is needed for greater heating rate. 3) The presence of impurity fluorobenzene retards thermal decomposition of PAMS.     

A flexible approach for understanding protein stability

A distance constraint model (DCM) is presented that identifies flexible regions within protein structure consistent with specified thermodynamic condition. The DCM is based on a rigorous free energy decomposition scheme representing structure as fluctuating constraint topologies. Entropy non-additivity is problematic for naive decompositions, limiting the success of heat capacity predictions. The DCM resolves non-additivity by summing over independent entropic components determined by an efficient network-rigidity algorithm. A minimal 3-parameter DCM is demonstrated to accurately reproduce experimental heat capacity curves. Free energy landscapes and quantitative stability-flexibility relationships are obtained in terms of global flexibility. Several connections to experiment are made.     

Decomposition of indwelling EMG signals.

Decomposition of indwelling electromyographic (EMG) signals is challenging in view of the complex and often unpredictable behaviors and interactions of the action potential trains of different motor units that constitute the indwelling EMG signal. These phenomena create a myriad of problem situations that a decomposition technique needs to address to attain completeness and accuracy levels required for various scientific and clinical applications. Starting with the maximum a posteriori probability classifier adapted from the original precision decomposition system (PD I) of LeFever and De Luca (25, 26), an artificial intelligence approach has been used to develop a multiclassifier system (PD II) for addressing some of the experimentally identified problem situations. On a database of indwelling EMG signals reflecting such conditions, the fully automatic PD II system is found to achieve a decomposition accuracy of 86.0% despite the fact that its results include low-amplitude action potential trains that are not decomposable at all via systems such as PD I. Accuracy was established by comparing the decompositions of indwelling EMG signals obtained from two sensors. At the end of the automatic PD II decomposition procedure, the accuracy may be enhanced to nearly 100% via an interactive editor, a particularly significant fact for the previously indecomposable trains.     

Ascorbic acid conversion to erythroascorbic acid, mediated by ubiquitin

We recently identified a microbial conversion of l-ascorbic acid (AsA) to l-erythroascorbic acid (eAsA), a five-carbon analog of AsA. In this paper, we show that ubiquitin plays a crucial role in this process. Based on an assay that determined AsA decomposition, we purified proteins that had N-terminal amino acid sequences identical to that of yeast ubiquitin. Purified ubiquitin facilitated decompositions of AsA and dehydro-AsA, accompanying a partial conversion to eAsA through C1-elimination. Acetylation or limited hydrolysis of ubiquitin abolished its activity. A mutant ubiquitin, with Lys⁶ replaced by Arg, completely lost activity, whereas a mutant, with six other Lys residues (positions at 11, 27, 29, 33, 48 and 63) substituted by Arg, retained activity. Thus, Lys⁶, which locates in close proximity to His⁶⁸, is crucial for ubiquitin activity in the AsA conversion to eAsA.     

Using eigenstructure decompositions of time-varying autoregressions in common spatial patterns-based EEG signal classification

Brain–computer interfaces based on common spatial patterns (CSP) depend on the operational frequency bands of the events to be discriminated. This problem has been addressed through sub-band decompositions of the electroencephalographic signals using filter banks, then the performance relies on the number of filters that are stacked and the criteria to select their bandwidths. Here, we propose an alternative approach based on an eigenstructure decomposition of the signals’ time-varying autoregressions (TVAR). The eigen-based decomposition of the TVAR allows for subject-specific estimation of the principal time-varying frequencies, then such principal eigencomponents can be used in the traditional CSP-based classification. We show through a series of numerical experiments that the proposed classification scheme can achieve a performance which is comparable with the one obtained through the filter bank-based approach. However, our method does not rely on a preliminary selection of a frequency band, yet good performance is achieved under realistic conditions (such as reduced number of sensors and small amount of training data) independently of the time interval selected.     

Robustness of inference of haplotype block structure.

In this report, we examine the validity of the haplotype block concept by comparing block decompositions derived from public data sets by variants of several leading methods of block detection. We first develop a statistical method for assessing the concordance of two block decompositions. We then assess the robustness of inferred haplotype blocks to the specific detection method chosen, to arbitrary choices made in the block-detection algorithms, and to the sample analyzed. Although the block decompositions show levels of concordance that are very unlikely by chance, the absolute magnitude of the concordance may be low enough to limit the utility of the inference. For purposes of SNP selection, it seems likely that methods that do not arbitrarily impose block boundaries among correlated SNPs might perform better than block-based methods.     

羊草草原枯枝落叶分解的研究——枯枝落叶分解与生态环境的关系

枯枝落叶的分解受生态环境的影响,枯枝落叶置于不同的生态环境下,其分解速率不同。例如,羊草(Leymus chinensis)在6种不同生境中的分解存在着明显差异。枯枝落叶位于地表和地下,其分解速率则不同,埋入地下的分解比位于地表的迅速。分解速率与土壤水分、地表温度和土壤pH呈指数正相关,与相对湿度呈线性正相关,它们对分解有积极的促进作用。通过生态因子对分解影响的综合分析表明,在羊草草原上,诸生态因子对枯枝落叶分解的重要性依次为:土壤水分、土壤pH、地表温度、相对湿度。     

rCUR:an R package for CUR matrix decomposition

ABSTRACT: BACKGROUND: Many methods for dimensionality reduction of large data sets such as those generated in microarray studies boil down to the Singular Value Decomposition (SVD). Although singular vectors associated with the largest singular values have strong optimality properties and can often be quite useful as a tool to summarize the data, they are linear combinations of up to all of the data points, and thus it is typically quite hard to interpret those vectors in terms of the application domain from which the data are drawn. Recently, an alternative dimensionality reduction paradigm, CUR matrix decompositions, has been proposed to address this problem and has been applied to genetic and internet data. CUR decompositions are low-rank matrix decompositions that are explicitly expressed in terms of a small number of actual columns and/or actual rows of the data matrix. Since they are constructed from actual data elements, CUR decompositions are interpretable by practitioners of the eld from which the data are drawn. RESULTS: We present an implementation to perform CUR matrix decompositions, in the form of a freely available, open source R-package called rCUR. This package will help users to perform CUR-based analysis on large-scale data, such as those obtained from different high-throughput technologies, in an interactive and exploratory manner. We show two examples that illustrate how CUR-based techniques make it possible to reduce signicantly the number of probes, while at the same time maintaining major trends in data and keeping the same classication accuracy. CONCLUSIONS: The package rCUR provides functions for the users to perform CUR-based matrix decompositions in the R environment. In gene expression studies, it gives an additional way of analysis of differential expression and discriminant gene selection based on the use of statistical leverage scores. These scores, which have been used historically in diagnostic regression analysis to identify outliers, can be used by rCUR to identify the most informative data points with respect to which to express the remaining data points.     

放牧对青藏高原东部两种典型高寒草地类型凋落物分解的影响

为认识放牧对青藏高原东部中生性的高寒草甸草地和半湿生的沼泽草地凋落物分解的影响,在这两种草地上分别设置了围栏和放牧样地,研究了其各自的混合凋落物样品和4个优势物种(发草Deschampsiacaespitos、鹅绒委陵菜Potentilla anserine、木里苔草Carexmuliensis、藏嵩草Kobresiatibetica)凋落物的分解和养分释放动态,这4个优势物种也大致代表了当地沼泽草地生态系统在放牧和气候变暖驱动下逆行演替不同阶段的优势物种类群。结果表明,各优势物种凋落物的分解速率有显著差异;放牧在总体上促进了凋落物的分解,但不同物种的响应有所不同;放牧对凋落物C的释放影响不显著或有抑制作用,但对N、P的释放具有一定促进作用。对各优势物种凋落物分解和养分释放模式的分析表明,群落逆行演替过程中,凋落物分解和C释放加速,可能促进沼泽湿地退化的正反馈效应。草甸草地的退化标志物种鹅绒委陵菜具有较高的凋落物质量和分解速度,反映了中生条件下植物应对牲畜啃食采用"逃避"而非"抵抗"策略的趋向。     

北方城市12种常见树木落花分解及其养分释放特征

城市绿化产生的树木落花量日益增加,由于落花富含养分,所以研究落花的分解及其养分释放特性,可以为落花作为施肥材料和制备生物质有机肥等农业再利用提供科学依据。以樱花(Cerasus sp.)、海棠(Malus spectabilis)、白玉兰(Magnolia denudata)、紫玉兰(Magnolia liliflora)、桃(Amygdalus persica)、红叶李(Prunus cerasiferaⅠ)、紫叶李(Prunus cerasiferaⅡ)、含笑(Michelia figo)、紫荆(Cercis chinensis)、杏梅(Prunus mume)、银杏(Ginkgo biloba)和栾树(Koelreuteria paniculate)等12种树木落花为研究对象,通过室内落花分解模拟试验,结果表明:(1)粗蛋白含量以桃花和白玉兰落花中较高(251.44—274.94 mg/g)(P<0.05),木质素含量以紫荆和银杏落花中较高(47.35—49.95μg/g)(P<0.5),N含量以桃花和白玉兰落花中较高(40.23—43.99 mg/g)(P<0.5),P含量以桃花和白玉兰落花中较高(1.37—1.58 mg/g)(P<0.5),K含量以红叶李、紫叶李和白玉兰落花中较高(4.53—4.72 mg/g)(P<0.5),Cu含量以白玉兰落花中较高(22.74μg/g),Zn含量以紫叶李落花中较高(249.06μg/g)(P<0.5),Fe含量以紫荆、樱花、紫叶李、杏梅、海棠和桃花中较高(85.81—118.34μg/g)(P<0.5),Mn含量以杏梅、樱花、桃花和海棠落花中较高(24.09—25.71μg/g)(P<0.5)。(2)12种落花中,除了银杏分解稍慢(周转期1.12 a)之外,其他落花分解均较快(周转期为1.00—1.05 a)。(3)落花养分释放速率较之分解速率表现为:N在樱花、海棠、紫玉兰、红叶李、含笑和紫荆落花中呈超速释出状态,而在其他落花中呈慢速释出状态,P在白玉兰、桃花、紫叶李、紫荆和银杏落花中呈超速释出状态,而在其他落花中呈慢速释出状态,K除了在紫荆落花中呈超速释出状态之外,在其他落花中均呈慢速释出状态。(4)相关性分析表明,落花中含有较高的粗蛋白、N、P和K含量可以加快落花分解,而落花中含有较高的木质素含量、木质素/N和N/P则导致落花分解速度减慢。研究结果将为落花的资源化利用奠定理论基础。     

Singular value decomposition analysis of the secondary structure features contributing to the circular dichroism spectra of model proteins

Amyloid fibril formation occurs in restricted environment, such as the interface between intercellular fluids and bio-membranes. Conformational interconversion from α-helix to β-structure does not progress in fluids; however, it can occur after sedimentary aggregation during amyloid fibril formation induced by heat treatment of hen egg white lysozyme (HEWL). Secondary structures of various proteins and denatured proteins titrated with 2,2,2-trifluoroethanol (TFE) were examined using their CD spectra. Gaussian peak/trough and singular value decompositions (SVD) showed that the spectral pattern of the α-helix comprised a sharp trough at wavelength 207 nm and a broad trough at 220 nm. Conversely, we distinguished two patterns for β-sheet—a spread barrel type, corresponding to ConA, and a tightly weaved type, corresponding to the soybean trypsin inhibitor. Herein, we confirmed that the spectral/conformational interconversion of the heat-treated HEWL was not observed in the dissolved fluid.     

Smoothing spline ANOVA frailty model for recurrent event data

Gap time hazard estimation is of particular interest in recurrent event data. This article proposes a fully nonparametric approach for estimating the gap time hazard. Smoothing spline analysis of variance (ANOVA) decompositions are used to model the log gap time hazard as a joint function of gap time and covariates, and general frailty is introduced to account for between-subject heterogeneity and within-subject correlation. We estimate the nonparametric gap time hazard function and parameters in the frailty distribution using a combination of the Newton-Raphson procedure, the stochastic approximation algorithm (SAA), and the Markov chain Monte Carlo (MCMC) method. The convergence of the algorithm is guaranteed by decreasing the step size of parameter update and/or increasing the MCMC sample size along iterations. Model selection procedure is also developed to identify negligible components in a functional ANOVA decomposition of the log gap time hazard. We evaluate the proposed methods with simulation studies and illustrate its use through the analysis of bladder tumor data.     

A scale-independent signal processing method for sequence analysis

In this paper, we present methods to detect and localize patternsin biologically related protein sequences (family). The patternscommon to the sequences of the family are detected by usingFourier analysis. No previous scales (codes) are needed, theyare actually produced as a result of the analysis procedure,together with the frequencies of the Fourier decompositions.Characteristic features of the family are thus expressed as(code–frequency) pairs. Various tools are proposed inorder to localize the patterns, to compare the codes, and toevaluate the proximity of an arbitrary sequence to the investigatedfamily. The general strategy is illustrated on a family composedof proteins Received on October 17, 1989; accepted on January 16, 1990     

Uncovering Community Structures with Initialized Bayesian Nonnegative Matrix Factorization

Uncovering community structures is important for understanding networks. Currently, several nonnegative matrix factorization algorithms have been proposed for discovering community structure in complex networks. However, these algorithms exhibit some drawbacks, such as unstable results and inefficient running times. In view of the problems, a novel approach that utilizes an initialized Bayesian nonnegative matrix factorization model for determining community membership is proposed. First, based on singular value decomposition, we obtain simple initialized matrix factorizations from approximate decompositions of the complex network’s adjacency matrix. Then, within a few iterations, the final matrix factorizations are achieved by the Bayesian nonnegative matrix factorization method with the initialized matrix factorizations. Thus, the network’s community structure can be determined by judging the classification of nodes with a final matrix factor. Experimental results show that the proposed method is highly accurate and offers competitive performance to that of the state-of-the-art methods even though it is not designed for the purpose of modularity maximization.