Dynamic modelling of a helical peptide in solution using NMR data: Multiple conformations and multi-spin effects

Summary Nuclear Overhauser effect (NOE) measurements on molecules in solution provide information about only the ensemble-averaged properties of these molecules. An algorithm is presented that uses a list of NOEs to produce an ensemble of molecules that on average agrees with these NOEs, taking into account the effect of surrounding spins on the buildup of each NOE (spin diffusion). A simplified molecular dynamics simulation on several copies of the molecule in parallel is restrained by forces that are derived directly from differences between calculated and measured NOEs. The algorithm is tested on experimental NOE data of a helical peptide derived from bovine pancreatic trypsin inhibitor.     

Optimized Two-Stage Ensemble Model for Mammography Mass Recognition

ObjectivesMammography mass recognition is considered as a very challenge pattern recognition problem due to the high similarity between normal and abnormal masses. Therefore, the main objective of this study is to develop an efficient and optimized two-stage recognition model to tackle this recognition task.Material and methodsBasically, the developed recognition model combines an ensemble of linear Support Vector Machine (SVM) classifiers with a Reinforcement Learning-based Memetic Particle Swarm Optimizer (RLMPSO) as RLMPSO-SVM recognition model. RLMPSO is used to construct a two-stage of an ensemble of linear SVM classifiers by performing simultaneous SVM parameters tuning, features selection, and training instances selection. The first stage of RLMPSO-SVM recognition model is responsible about recognizing the input ROI mammography masses as normal or abnormal mass pattern. Meanwhile, the second stage of RLMPSO-SVM model used to perform further recognition for abnormal ROIs as malignant or benign masses. In order to evaluate the effectiveness of RLMPSO-SVM, a total of 1187 normal ROIs, 111 malignant ROIs, and 135 benign ROIs were randomly selected from DDSM database images.ResultsReported results indicated that RLMPSO-SVM model was able to achieve performances of 97.57% sensitivity rate with 97.86% specificity rate for normal vs. abnormal recognition cases. For malignant vs. benign recognition performance it was reported of 97.81% sensitivity rate with 96.92% specificity rate.ConclusionReported results indicated that RLMPSO-SVM recognition model is an effective tool that could assist the radiologist during the diagnosis of the presented abnormalities in mammography images. The outcomes indicated that RLMPSO-SVM significantly outperformed various SVM-based models as well as other variants of computational intelligence models including multi-layer perceptron, naive Bayes classifier, and k-nearest neighbor.     

Properties of Confined Square-well Fluids using the Gibbs Ensemble Simulation Technique

In this work we have used the extension of the Gibbs ensemble simulation technique to inhomogeneous fluids [Panagiotopoulos, A.Z. (1987) "Adsorption and capillary condensation of fluid in cylindrical pores by Monte Carlo simulation in the Gibbs ensemble", Mol. Phys. , 62 (3), 701-719], which has been applied to adsorption phenomena of confined fluids. Fluid molecules are described by spherical particles interacting via a square-well potential. The fluid is confined in two types of walls: symmetrical (two hard walls) and non-symmetrical (one square-well wall and one hard wall). In order to analyze the behavior of the confined fluid by varying the potential parameters, we evaluated the bulk and confined densities, the internal energies and the density profiles for different supercritical temperatures. A variety of adsorption profiles can be obtained by using this model. The simulation data reported here complements the available simulation data for this system and can be useful in the development of inhomogeneous fluid theories. Since the square-well parameters can be related to real molecules this system can also be used to understand real adsorption systems.     

Phase Coexistence Curves for Off-lattice Polymer-Solvent Mixtures: Gibbs-Duhem Integration Simulations

The Gibbs-Duhem integration scheme is combined with the osmotic Gibbs-ensemble simulation method presented in previous work [Brennan, J.K. and Madden, W.G. "Phase coexistence curves for off-lattice polymer-solvent mixtures: Gibbs-ensemble simulations." Macromolecules , 2002, 35, 2827.] to calculate the phase coexistence of a polymer-solvent mixture. Gibbs-Duhem integration simulations are carried out at temperatures for which the osmotic Gibbs-ensemble method is not valid because the solvent-rich phase contains a significant amount of polymer. This combined strategy allows for the calculation of the full coexistence curve for polymer-solvent systems in the continuum. An alternative formulation of the Gibbs-Duhem integration algorithm is also presented. A major strength of the technique is that neither chain insertions nor deletions are required. The method allows for the calculation of the phase behavior of polymer-solvent mixtures containing long chains or branched and networked chains not previously possible.     

Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks

The ensemble modeling (EM) approach has shown promise in capturing kinetic and regulatory effects in the modeling of metabolic networks. Efficacy of the EM procedure relies on the identification of model parameterizations that adequately describe all observed metabolic phenotypes upon perturbation. In this study, we propose an optimization-based algorithm for the systematic identification of genetic/enzyme perturbations to maximally reduce the number of models retained in the ensemble after each round of model screening. The key premise here is to design perturbations that will maximally scatter the predicted steady-state fluxes over the ensemble parameterizations. We demonstrate the applicability of this procedure for an Escherichia coli metabolic model of central metabolism by successively identifying single, double, and triple enzyme perturbations that cause the maximum degree of flux separation between models in the ensemble. Results revealed that optimal perturbations are not always located close to reaction(s) whose fluxes are measured, especially when multiple perturbations are considered. In addition, there appears to be a maximum number of simultaneous perturbations beyond which no appreciable increase in the divergence of flux predictions is achieved. Overall, this study provides a systematic way of optimally designing genetic perturbations for populating the ensemble of models with relevant model parameterizations.     

Computerized classification of Mediterranean vegetation using panchromatic aerial photographs

Abstract. Historical aerial photographs are an important source for data on medium- to long-term (10 - 50 yr) vegetation changes. Older photographs are panchromatic, and manual interpretation has traditionally been used to derive vegetation data from such photographs. We present a method for computerized analysis of panchromatic aerial photographs, which enables one to create high resolution, accurate vegetation maps. Our approach is exemplified using two aerial photographs (from 1964 and 1992) of a test area on Mt. Meron, Israel. Spatial resolution (pixel size) of the geo-rectified photos was 0.30 m and spatial accuracy (RMS error) ca. 1 m. An illumination adjustment prior to classification was found to be essential in reducing misclassification error rates. Two classification approaches were employed: a standard maximum-likelihood supervised classifier, and a modification of a supervised classification, which takes into account spectral properties of individual pixels as well as their neighbourhood characteristics. Accuracy of the maximum likelihood classification was 81 % in the 1992 image and 54 % in the 1964 image. The neighbour classifier increased accuracy to 89 % and 82 % respectively. The overall results suggest that computerized analysis of sequences of panchromatic aerial photographs may serve as a valuable tool for the quantification of medium-term vegetation changes.     

A lead dependent ischemic episodes detection strategy using Hermite functions

In this work a new strategy for automatic detection of ischemic episodes is proposed. A new measure for ST deviation based on the time–frequency analysis of the ECG and the use of a reduced set of Hermite basis functions for T wave and QRS complex morphology characterization, are the key points of the proposed methodology.Usually, ischemia manifests itself in the ECG signal by ST segment deviation or by QRS complex and T wave changes in morphology. These effects might occur simultaneously. Time–frequency methods are especially adequate for the detection of small transient characteristics hidden in the ECG, such as ST segment alterations. A Wigner–Ville transform-based approach is proposed to estimate the ST shift. To characterize the alterations in the T wave and the QRS morphologies, each cardiac beat is described by expansions in Hermite functions. These demonstrated to be suitable to capture the most relevant morphologic characteristics of the signal. A lead dependent neural network classifier considers, as inputs, the ST segment deviation and the Hermite expansion coefficients. The ability of the proposed method in ischemia episodes detection is evaluated using the European Society of Cardiology ST–T database. A sensitivity of 96.7% and a positive predictivity of 96.2% reveal the capacity of the proposed strategy to perform ischemic episodes identification.     

Towards better accuracy for missing value estimation of epistatic miniarray profiling data by a novel ensemble approach

Epistatic miniarray profiling (E-MAP) is a powerful tool for analyzing gene functions and their biological relevance. However, E-MAP data suffers from large proportion of missing values, which often results in misleading and biased analysis results. It is urgent to develop effective missing value estimation methods for E-MAP. Although several independent algorithms can be applied to achieve this goal, their performance varies significantly on different datasets, indicating different algorithms having their own advantages and disadvantages. In this paper, we propose a novel ensemble approach EMDI based on the high-level diversity to impute missing values that consists of two global and four local base estimators. Experimental results on five E-MAP datasets show that EMDI outperforms all single base algorithms, demonstrating an appropriate combination providing complementarity among different methods. Comparison results between several fusion strategies also demonstrate that the proposed high-level diversity scheme is superior to others. EMDI is freely available at www.csbio.sjtu.edu.cn/bioinf/EMDI/.