Analyzing patterns between regular secondary structures using short structural building blocks defined by a hidden Markov model

Hidden Markov models were used to identify recurrent short 3D structural building blocks (SSBBs) describing protein backbones. Polypeptide chains were broken down into successive short segments defined by their inter-alpha-carbon distances. Fitting the model to a database of nonredundant proteins identified 12 distinct SSBBs and described the preferred pathways by which SSBBs were assembled to form the 3D structure of the proteins. Protein backbones were labelled in terms of these SSBBs. The observed SSBB preferences for fragments located between regular secondary structures suggested that they depended more on the following regular structure than on the preceding one. Extraction of repeated series of SSBBs between regular secondary structures showed some structural specificity within different connection types. These results confirm that SSBBs can be used as building blocks for analyzing protein structures, and can yield new information on the structures of the coils flanking secondary structures. Received: 14 May 1998 / Accepted: 4 August 1998 / Published online: 16 November 1998     

Development and validation of high-performance thin-layer chromatographic method for determination of amygdalin

ABSTRACT

A new method for the extraction and quantitative determination of amygdalin has been proposed. Accelerated solvent extraction was applied for the extraction, and reversed-phase high-performance thin-layer chromatography method was developed, validated, and applied for the determination of amygdalin in the extracts of apricot, plum, almond, and peach kernels. The chromatographic system used was RP-18 silica, as stationary phase and acetonitrile/water (50:50, v/v), as mobile phase. Densitometric scanning was performed at 210 nm. The method was validated with respect to specificity, linearity, precision, and accuracy. The results showed that the peak area responses were linear within the concentration range of 2.5–50.0 µg/spot (R² = 0.9984). The limit of quantification was 4.28 µg/spot, and the detection limit 1.28 µg/spot. The intra-day and inter-day reproducibility, in terms of %RSD, were in the range of 0.81–1.15 and 1.32–1.89, respectively. The accuracy data were in the range from 99.98 to 100.56%. The method is linear, quantitative and reproducible, and could be used as an efficient and economical green chromatographic procedure for the determination of amygdalin in the fruit kernel.     

Random frog: An efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification

The identification of disease-relevant genes represents a challenge in microarray-based disease diagnosis where the sample size is often limited. Among established methods, reversible jump Markov Chain Monte Carlo (RJMCMC) methods have proven to be quite promising for variable selection. However, the design and application of an RJMCMC algorithm requires, for example, special criteria for prior distributions. Also, the simulation from joint posterior distributions of models is computationally extensive, and may even be mathematically intractable. These disadvantages may limit the applications of RJMCMC algorithms. Therefore, the development of algorithms that possess the advantages of RJMCMC methods and are also efficient and easy to follow for selecting disease-associated genes is required. Here we report a RJMCMC-like method, called random frog that possesses the advantages of RJMCMC methods and is much easier to implement. Using the colon and the estrogen gene expression datasets, we show that random frog is effective in identifying discriminating genes. The top 2 ranked genes for colon and estrogen are Z50753, U00968, and Y10871_at, Z22536_at, respectively. (The source codes with GNU General Public License Version 2.0 are freely available to non-commercial users at: http://code.google.com/p/randomfrog/.)     

Quantitative evaluation of thin-layer chromatograms. 5. Calculation of reflectance and transmission using Markov chains

Changes of reflected and transmitted light in cases where a band lies in different sublayers of a thin-layer chromatogram are calculated by the use of Markov chains. The problem of a “random walk with absorbing barriers” is described and compared with the problem of transmission and reflectance of light in a layer of sorbent.     

可列值随机变量序列的一个小偏差定理

本文得到了可列值随机变量序列的用不等式表示的强极限定理,即小偏差定理,包含了［１］的结果．［１］中研究的是相对于齐次Ｍａｒｋｏｖ 链的偏差,而本文允许非齐次的情形．     

Renormalization group for Markov chains and application to metastability

In this paper we introduce a new renormalization group method for the study of the long-time behavior of Markov chains with finite state space and with transition probabilities exponentially small in an external parameter. A general approach of metastability problems emerges from this analysis and is discussed in detail in the case of a two-dimensional Ising system at low temperature,     

Integro-differential equations of hyperbolic type with positive definite kernels

The main purpose of this work is to study the damping effect of memory terms associated with singular convolution kernels on the asymptotic behavior of the solutions of second order evolution equations in Hilbert spaces. For kernels that decay exponentially at infinity and possess strongly positive definite primitives, the exponential stability of weak solutions is obtained in the energy norm. It is also shown that this theory applies to several examples of kernels with possibly variable sign, and to a problem in nonlinear viscoelasticity.     

Zero Expectile Processes and Bayesian Spatial Regression

We introduce new classes of stationary spatial processes with asymmetric, sub-Gaussian marginal distributions using the idea of expectiles. We derive theoretical properties of the proposed processes. Moreover, we use the proposed spatial processes to formulate a spatial regression model for point-referenced data where the spatially correlated errors have skewed marginal distribution. We introduce a Bayesian computational procedure for model fitting and inference for this class of spatial regression models. We compare the performance of the proposed method with the traditional Gaussian process-based spatial regression through simulation studies and by applying it to a dataset on air pollution in California.     

Geometric interpretation of Hamiltonian cycles problem via singularly perturbed Markov decision processes

We consider the Hamiltonian cycle problem (HCP) embedded in a singularly perturbed Markov decision process (MDP). More specifically, we consider the HCP as an optimization problem over the space of long-run state-action frequencies induced by the MDP's stationary policies. We also consider two quadratic functionals over the same space. We show that when the perturbation parameter, ? is sufficiently small the Hamiltonian cycles of the given directed graph are precisely the maximizers of one of these quadratic functionals over the frequency space intersected with an appropriate (single) contour of the second quadratic functional. In particular, all these maximizers have a known Euclidean distance of z _m (?) from the origin. Geometrically, this means that Hamiltonian cycles, if any, are the points in the frequency polytope where the circle of radius z _m (?) intersects a certain ellipsoid.