Prediction of the growth of wear-type rail corrugation

The growth behaviour of the vibrational wear phenomenon known as rail corrugation is investigated analytically and numerically using mathematical models. A simplified feedback model for wear-type rail corrugation that includes a wheel pass time delay is developed with an aim to analytically distil the most critical interaction occurring between the wheel/rail structural dynamics, rolling contact mechanics and rail wear. To this end, a stability analysis on the complete system is performed to determine the growth of wear-type rail corrugations over multiple wheelset passages. This analysis indicates that although the dynamical behaviour of the system is stable for each wheel passage, over multiple wheelset passages, the growth of wear-type corrugations is shown to be the result of instability due to feedback interaction between the three primary components of the model. The corrugations are shown analytically to grow for all realistic railway parameters. From this analysis an analytical expression for the exponential growth rate of corrugations in terms of known parameters is developed. This convenient expression is used to perform a sensitivity analysis to identify critical parameters that most affect corrugation growth. The analytical predictions are shown to compare well with results from a benchmarked time-domain finite element model. (C) 2004 Elsevier B.V. All rights reserved.

Are interactions in Gray's Reinforcement Sensitivity Theory proximal or distal in the prediction of religiosity: a test of the joint subsystems hypothesis

Gray's Reinforcement Sensitivity Theory (RST) consists of the Behavioural Activation System (BAS) which is the basis of Impulsivity, and Behavioural Inhibition System (BIS) which is the basis of Anxiety. In this study, Impulsivity and Anxiety were used as distal predictors of attitudes to religion in the prediction of three religious dependent variables (Church attendance, Amount of prayer, and Importance of church). We hypothesised that Impulsivity would independently predict a Rewarding attitude to the Church and that Anxiety would independently predict an Anxious attitude to the church, and that these attitudes would be proximal predictors of our dependent variables. Moreover, we predicted that interactions between predictors would be proximal. Using structural equation modelling, data from 400 participants supported the hypotheses. We also tested Eysenck's personality scales of Extraversion and Neuroticism and found a key path of the structural equation model to be non-significant. (C) 2003 Elsevier Ltd. All rights reserved.

Prediction of BRCA1 status in patients with breast cancer using estrogen receptor and basal phenotype

Purpose: To investigate the proportion of breast cancers arising inpatients with germ line BRCA1 and BRCA2 mutations expressing basal markers and developing predictive tests for identification of high-risk patients. Experimental Design: Histopathologic material from 182 tumors in BRCA1 mutation carriers, 63 BRCA2 carriers, and 109 controls, collected as part of the international Breast Cancer Linkage Consortium were immunohistochemically stained for CK14, CK5/6, CK17, epidermal growth factor receptor (EGFR), and osteonectin. Results: All five basal markers were commoner in BRCA1 tumors than in control tumors (CK14: 61% versus 12%; CK5/6: 58% versus 7%; CK17: 53% versus 10%; osteonectin: 43% versus 19%; EGFR: 67% versus 21%; P < 0.0001 in each case). In a multivariate analysis, CK14, CK5/6, and estrogen receptor (ER) remained significant predictors of BRCA1 carrier status. In contrast, the frequency of basal markers in BRCA2 tumors did not differ significant from controls. Conclusion: The use of cytokeratin staining in combination with ER and morphology provides a more accurate predictor of BRCA1 mutation status than previously available, that may be useful in selecting patients for BRCA1 mutation testing. The high percentage of BRCA1 cases positive for EGFR suggests that specific anti-tyrosine kinase therapy may be of potential benefit in these patients.

Prediction of high-pressure adsorption equilibrium of supercritical gases using density functional theory

In this paper, we present the results of the prediction of the high-pressure adsorption equilibrium of supercritical. gases (Ar, N-2, CH4, and CO2) on various activated carbons (BPL, PCB, and Norit R1 extra) at various temperatures using a density-functional-theory-based finite wall thickness (FWT) model. Pore size distribution results of the carbons are taken from our recent previous work 1,2 using this approach for characterization. To validate the model, isotherms calculated from the density functional theory (DFT) approach are comprehensively verified against those determined by grand canonical Monte Carlo (GCMC) simulation, before the theoretical adsorption isotherms of these investigated carbons calculated by the model are compared with the experimental adsorption measurements of the carbons. We illustrate the accuracy and consistency of the FWT model for the prediction of adsorption isotherms of the all investigated gases. The pore network connectivity problem occurring in the examined carbons is also discussed, and on the basis of the success of the predictions assuming a similar pore size distribution for accessible and inaccessible regions, it is suggested that this is largely related to the disordered nature of the carbon.

Prediction of total body water in infants and children

Background: In paediatric clinical practice treatment is often adjusted in relation to body size, for example the calculation of pharmacological and dialysis dosages. In addition to use of body weight, for some purposes total body water (TBW) and surface area are estimated from anthropometry using equations developed several decades previously. Whether such equations remain valid in contemporary populations is not known. Methods: Total body water was measured using deuterium dilution in 672 subjects (265 infants aged < 1 year; 407 children and adolescents aged 1-19 years) during the period 1990-2003. TBW was predicted (a) using published equations, and (b) directly from data on age, sex, weight, and height. Results: Previously published equations, based on data obtained before 1970, significantly overestimated TBW, with average biases ranging from 4% to 11%. For all equations, the overestimation of TBW was greatest in infancy. New equations were generated. The best equation, incorporating log weight, log height, age, and sex, had a standard error of the estimate of 7.8%. Conclusions: Secular trends in the nutritional status of infants and children are altering the relation between age or weight and TBW. Equations developed in previous decades significantly overestimate TBW in all age groups, especially infancy; however, the relation between TBW and weight may continue to change. This scenario is predicted to apply more generally to many aspects of paediatric clinical practice in which dosages are calculated on the basis of anthropometric data collected in previous decades.

Better prediction of protein contact number using a support vector regression analysis of amino acid sequence

Background: Protein tertiary structure can be partly characterized via each amino acid's contact number measuring how residues are spatially arranged. The contact number of a residue in a folded protein is a measure of its exposure to the local environment, and is defined as the number of C-beta atoms in other residues within a sphere around the C-beta atom of the residue of interest. Contact number is partly conserved between protein folds and thus is useful for protein fold and structure prediction. In turn, each residue's contact number can be partially predicted from primary amino acid sequence, assisting tertiary fold analysis from sequence data. In this study, we provide a more accurate contact number prediction method from protein primary sequence. Results: We predict contact number from protein sequence using a novel support vector regression algorithm. Using protein local sequences with multiple sequence alignments (PSI-BLAST profiles), we demonstrate a correlation coefficient between predicted and observed contact numbers of 0.70, which outperforms previously achieved accuracies. Including additional information about sequence weight and amino acid composition further improves prediction accuracies significantly with the correlation coefficient reaching 0.73. If residues are classified as being either contacted or non-contacted, the prediction accuracies are all greater than 77%, regardless of the choice of classification thresholds. Conclusion: The successful application of support vector regression to the prediction of protein contact number reported here, together with previous applications of this approach to the prediction of protein accessible surface area and B-factor profile, suggests that a support vector regression approach may be very useful for determining the structure-function relation between primary sequence and higher order consecutive protein structural and functional properties.

Accurate prediction of scorpion toxin functional properties from primary structures

Scorpion toxins are common experimental tools for studies of biochemical and pharmacological properties of ion channels. The number of functionally annotated scorpion toxins is steadily growing, but the number of identified toxin sequences is increasing at much faster pace. With an estimated 100,000 different variants, bioinformatic analysis of scorpion toxins is becoming a necessary tool for their systematic functional analysis. Here, we report a bioinformatics-driven system involving scorpion toxin structural classification, functional annotation, database technology, sequence comparison, nearest neighbour analysis, and decision rules which produces highly accurate predictions of scorpion toxin functional properties. (c) 2005 Elsevier Inc. All rights reserved.

Prediction of protein B-factor profiles

The polypeptide backbones and side chains of proteins are constantly moving due to thermal motion and the kinetic energy of the atoms. The B-factors of protein crystal structures reflect the fluctuation of atoms about their average positions and provide important information about protein dynamics. Computational approaches to predict thermal motion are useful for analyzing the dynamic properties of proteins with unknown structures. In this article, we utilize a novel support vector regression (SVR) approach to predict the B-factor distribution (B-factor profile) of a protein from its sequence. We explore schemes for encoding sequences and various settings for the parameters used in SVR. Based on a large dataset of high-resolution proteins, our method predicts the B-factor distribution with a Pearson correlation coefficient (CC) of 0.53. In addition, our method predicts the B-factor profile with a CC of at least 0.56 for more than half of the proteins. Our method also performs well for classifying residues (rigid vs. flexible). For almost all predicted B-factor thresholds, prediction accuracies (percent of correctly predicted residues) are greater than 70%. These results exceed the best results of other sequence-based prediction methods. (C) 2005 Wiley-Liss, Inc.

Substrate specifity of protein kinases and computational prediction of substrates

To ensure signalling fidelity, kinases must act only on a defined subset of cellular targets. Appreciating the basis for this substrate specificity is essential for understanding the role of an individual protein kinase in a particular cellular process. The specificity in the cell is determined by a combination of peptide specificity of the kinase (the molecular recognition of the sequence surrounding the phosphorylation site), substrate recruitment and phosphatase activity. Peptide specificity plays a crucial role and depends on the complementarity between the kinase and the substrate and therefore on their three-dimensional structures. Methods for experimental identification of kinase substrates and characterization of specificity are expensive and laborious, therefore, computational approaches are being developed to reduce the amount of experimental work required in substrate identification. We discuss the structural basis of substrate specificity of protein kinases and review the experimental and computational methods used to obtain specificity information. (c) 2005 Elsevier B.V. All rights reserved.

MULTIPRED: A computational system for prediction of promiscuous HLA binding peptides

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules ( proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability ( area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets termed T-cell epitope hotspots. MULTIPRED is available at http:// antigen.i2r.a-star.edu.sg/ multipred/.

Prediction of subcellular localization using sequence-biased recurrent networks

Motivation: Targeting peptides direct nascent proteins to their specific subcellular compartment. Knowledge of targeting signals enables informed drug design and reliable annotation of gene products. However, due to the low similarity of such sequences and the dynamical nature of the sorting process, the computational prediction of subcellular localization of proteins is challenging. Results: We contrast the use of feed forward models as employed by the popular TargetP/SignalP predictors with a sequence-biased recurrent network model. The models are evaluated in terms of performance at the residue level and at the sequence level, and demonstrate that recurrent networks improve the overall prediction performance. Compared to the original results reported for TargetP, an ensemble of the tested models increases the accuracy by 6 and 5% on non-plant and plant data, respectively.

How preferred ear for listening moderates emotional cognitions in the prediction of personality

Two studies investigate how cognitions of aurally presented information interact with aural preference (self-reported preferred ear for listening) in the prediction of personality. In Study 1, participants provided attractiveness cognitions of various statements after listening to aurally presented material. Aural preference x attractiveness interactions significantly predicted Extraversion and Neuroticism. In Study 2, participants provided cognitions of pleasantness from various scenarios. An aural preference x pleasantness interaction significantly predicted Neuroticism. Although other interpretations are possible, I conclude that these findings support the idea of aural preference as a useful measure of hemispheric asymmetry, such that the right hemisphere (left aural preference) provides facilitation of emotional expression, whereas the left hemisphere (right aural preference) provides suppression. My findings support a more historical view of emotional asymmetry than the more modem approach-avoidance perspective and suggest that moderating effects of hemispheric asymmetry are important to include in studies investigating emotions associated with personality.

The appetitive motivation scale and other BAS measures in the prediction of Approach and Active Avoidance

The Appetitive Motivation Scale (Jackson & Smillie, 2004) is a new trait conceptualisation of Gray's (I 970 199 1) Behavioural Activation System. In this experiment we explore relationships that the Appetitive Motivation Scale and other measures of Gray's model have with Approach and Active Avoidance responses. Using a sample of 144 undergraduate students, both Appetitive Motivation and Sensitivity to Reward (from the Sensitivity to Punishment and Sensitivity to Reward Questionnaire, SPSRQ; Torrubia, Avila, Molto, & Ceseras, 2001), were found to be significant predictors of Approach and Active Avoidance response latency. This confirms previous experimental validations of the SPSRQ (e.g., Avila, 2001) and provides the first experimental evidence for the validity of the Appetitive Motivation scale. Consistent with interactive views of Gray's model (e.g., Corr, 2001), high SPSRQ Sensitivity to Punishment diminished the relationship between Sensitivity to Reward and our BAS criteria. Measures of BIS did not however interact in this way with the appetitive motivation scale. A surprising result was the failure for any of Carver and White's (1994) BAS scales to correlate with RST criteria. Implications of these findings and potential future directions are discussed. (C) 2004 Elsevier Ltd. All rights reserved.

A glass transition temperature approach for the prediction of the surface stickiness of a drying droplet during spray drying

The development of surface stickiness of droplets of sugar and acid-rich foods during spray drying can be explained using the notion of glass transition temperature (T-g). In this work, criteria for a safe drying regime have been developed and their physical basis provided. A dimensionless time (psi) is introduced as an indicator of spray dryability and it is correlated with the recovery of powders in practical spray drying. Droplets with initial diameters of 120 mum were subjected to simulated spray drying conditions and their safe drying regime and 41 values generated. The model predicted the recovery in a pilot scale spray dryer reasonably well. (C) 2004 Elsevier B.V. All rights reserved.