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Prediction of bone adaptation using damage accumulation.

The adaptation of bones to a change in function has been recognised for many centuries, but only recently have mathematical laws been proposed to describe it. One proposed mathematical law is based on the hypothesis that, after a change in load, the strain in the bone microstructure is regulated to a homeostatic equilibrium. Strain-adaptive remodelling has been used successfully to simulate bone adaptation around orthopaedic implants but the predictive capabilities are constrained because many empirical constants are required in the remodelling law (a reference stimulus, a zone of equilibrium stresses or 'lazy zone' and a parameter transducing macroscopic stresses to a tissue level stimulus). An alternative approach has been proposed. It is that bone adapts to attain an optimal strength by regulating the damage generated in its microstructural elements. The question is raised whether or not a mathematical law to predict the time course of bone adaptation can be derived for damage-adaptive remodelling in a similar way to the mathematical laws based on a strain stimulus. In the present study, the hypotheses required to develop damage-adaptive remodelling laws are proposed and a remodelling law to predict the time course of bone adaptation is derived. It is shown that this is an integral remodelling law which accounts naturally for the stress history to which the tissue has been exposed since formation. A simulation of the adaptive response of a bone diaphysis under a change in torsional load shows that the law gives physically reasonable predictions. The initial remodelling prediction is similar to strain-adaptive remodelling. However, in the later stages of remodelling, the predictions differ from strain-adaptive remodelling in that direct convergence to a homeostatic strain is not predicted. Instead, undershoot (in the case of a reduction in load) and overshoot (in the case of an increase in load) are predicted.

Adaptation, Physiological↗

Improvements in a secondary structure prediction method based on a search for local sequence homologies and its use as a model building tool.

This report describes an optimised version of a secondary structure prediction method based on local homologies, using a new data base. A 63% prediction accuracy, for three states, was obtained after elimination of the protein to be predicted and all proteins with a percentage identity greater than 22% from the data base. This corresponds to a 5% increase in accuracy on the original method (Levin et al. FEBS Lett. 205 (1986) 303-308). The flexibility of the method to the incorporation of information extraneous to the prediction was demonstrated by the prediction of the homologous proteins in the data base. Using the percentage identity with the protein to be predicted, to weight the relative importance of each protein, for all proteins with a percentage identity greater than 30%, the mean correct prediction per chain was 87%. As a result this algorithm can be used during the molecular modelling process, both to give an idea of the structural similarity between two proteins and as an aid in the determination of the best alignment. Incorporation of the result of a protein folding type assignment based on the global amino-acid composition increased the overall prediction to 66%.

Algorithms↗

Evaluation of a predictive model for the shelf life of cod (Gadus morhua) fillets stored in two different atmospheres at varying temperatures.

The shelf life of fish and food in general is difficult to predict especially if stored at varying temperatures. Shelf life models were constructed (Einarsson, 1992) for cod fillets stored at constant temperatures. The aim of this study was to evaluate if these models could be used to predict spoilage and bacterial growth in cod fillets stored in air and modified atmosphere at constant and varying temperatures. Fresh fillets were packed and stored at constant or varying temperatures between -2 degrees C and 5 degrees C. Samples were taken at regular intervals for bacteriological and sensory evaluation. The results showed that fish stored at +0.6 degrees C in air had a shelf life (assessed by sensory analysis) of 11 days which is close to what could be expected and predicted. The increase in bacterial number observed was generally less than predicted. For fish fillets, stored in air at +5 degrees C for 3 days, then at +0.6 degrees C for 3 days and finally at -2 degrees C, the shelf life was found to be 7 days which was in good agreement with the predicted shelf life. The shelf life of fillets stored at same the temperatures in modified atmosphere was found to be 9 days but by prediction 11 to 12 days. The models for predicting changes in sensory score were more accurate than those predicting changes in bacterial numbers.

Air↗

Predictive microbiology.

Predictive microbiology is based upon the premise that the responses of populations of microorganisms to environmental factors are reproducible, and that by considering environments in terms of identifiable dominating constraints it is possible, from past observations, to predict the responses of those microorganisms. Proponents claim that predictive microbiology offers many benefits to the practice of food microbiology, and there is growing interest internationally. This review considers the origins, benefits and approaches to predictive microbiology and critically considers limitations and potential solutions. It is suggested that the traditional delineation between kinetic and probabilistic models is artificial, and that the two approaches represent the opposite ends of a spectrum of modelling needs. It is concluded: that despite the complexity of many food systems predictive modelling can be successfully applied; that strategies based on predictive models can simplify problems and allow useful predictions and analyses to be made; that the full potential of the technique has not yet been realised; and that "predictive microbiology" may be seen as providing a rational framework for understanding the microbial ecology of food.

Bacteria↗

PPS-87: a new event oriented solar proton prediction model.

A new event-oriented solar proton prediction model has been developed and implemented at the USAF Space Environment forecast facility. This new model generates predicted solar proton time-intensity profiles for a number of user adjustable energy ranges and is also capable of making predictions for the heavy ion flux. The computer program is designed so a forecaster can select inputs based on the data available in near real-time at the forecast center as the solar flare is occurring. The predicted event amplitude is based on the electromagnetic emission parameters of the solar flare (either microwave or soft X-ray emission) and the solar flare position on the sun. The model also has an update capability where the forecaster can normalize the prediction to actual spacecraft observations of spectral slope and particle flux as the event is occurring in order to more accurately predict the future time-intensity profile of the solar particle flux. Besides containing improvements in the accuracy of the predicted energetic particle event onset time and magnitude, the new model converts the predicted solar particle flux into an expected radiation dose that might be experienced by an astronaut during EVA activities or inside the space shuttle.

Computer Simulation↗

Prediction of ultrasonic field propagation through layered media using the extended angular spectrum method.

The angular spectrum method is a powerful technique for modeling the propagation of acoustic fields. The technique can predict an acoustic pressure field distribution over a plane, based upon knowledge of the pressure field distribution at a parallel plane. Predictions in both the forward and backward propagation directions are possible. In addition to predicting the effects of diffraction, the model also includes the effects of attenuation, refraction, dispersion, phase distortion, and the effects of finite amplitude acoustic propagation. No other model currently exists which can predict the propagation of wideband acoustic fields produced by sources of arbitrary geometry including all of the above propagation effects. Prior investigations have focused on using backward propagation predictions to analyze the surface vibration patterns of acoustic radiators. In contrast, the current effort has placed particular emphasis on verifying the model in the forward propagation case. In this paper, both forward and backward predictions are presented which demonstrate the ability of the model to characterize a three-dimensional acoustic field based upon measurements at a single plane. Results are also presented which examine the ability of the extended model to predict acoustic propagation through media composed of stacked homogeneous layers. The model has immediate applications in the study of acoustic phenomena and in the field of acoustic transducer design. Additionally, significant progress has been made toward the ultimate goal of predicting the degradation of acoustic transducer performance due to propagation through inhomogeneous, nonlinear, tissue-like media.

Acoustics↗

Comparison of three methods of profile change prediction in the adult orthodontic patient.

Potential changes in the contour of the facial profile that accompany tooth movement can be important considerations in developing an orthodontic treatment plan. The objective of this study was to compare the accuracy of three different methods of predicting horizontal soft-tissue changes. Eighty-three nongrowing, orthodontically treated patients comprised the sample. Pretreatment and posttreatment lateral cephalometric hard- and soft-tissue landmark points were digitized. A coordinate system was defined, landmark coordinates were corrected for magnification, and then hard- and soft-tissue, angular, and linear measures were calculated by computer programs developed on a DEC PDP 11/44. The accuracy of prediction of soft-tissue landmark changes was compared for three prediction methods: (1) use of ratios of means of soft-tissue changes to corresponding hard-tissue changes, (2) use of a bivariate regression equation on corresponding hard-tissue landmark changes, and (3) use of stepwise multiple regression with hard-tissue changes and initial hard- and soft-tissue facial characteristics as predictor variables. For predicting changes of four soft-tissue points, multiple regression equations were slightly more accurate than ratio of means predictions. The standard errors of the estimate ranged from 0.7 to 1.1 mm for the multiple regression predictions; these were from 0.2 to 1.4 mm lower than those obtained for ratio of means predictions. The accuracy of the bivariate regression prediction technique fell between that of the other two methods. Examination of the residuals showed that the multiple regression equations consistently underpredicted the most extreme soft-tissue facial changes.

Adolescent↗

A method for predicting protein structure from sequence.

BACKGROUND: The ability to predict the native conformation of a globular protein from its amino-acid sequence is an important unsolved problem of molecular biology. We have previously reported a method in which reduced representations of proteins are folded on a lattice by Monte Carlo simulation, using statistically-derived potentials. When applied to sequences designed to fold into four-helix bundles, this method generated predicted conformations closely resembling the real ones. RESULTS: We now report a hierarchical approach to protein-structure prediction, in which two cycles of the above-mentioned lattice method (the second on a finer lattice) are followed by a full-atom molecular dynamics simulation. The end product of the simulations is thus a full-atom representation of the predicted structure. The application of this procedure to the 60 residue, B domain of staphylococcal protein A predicts a three-helix bundle with a backbone root mean square (rms) deviation of 2.25-3 A from the experimentally determined structure. Further application to a designed, 120 residue monomeric protein, mROP, based on the dimeric ROP protein of Escherichia coli, predicts a left turning, four-helix bundle native state. Although the ultimate assessment of the quality of this prediction awaits the experimental determination of the mROP structure, a comparison of this structure with the set of equivalent residues in the ROP dime- crystal structure indicates that they have a rms deviation of approximately 3.6-4.2 A. CONCLUSION: Thus, for a set of helical proteins that have simple native topologies, the native folds of the proteins can be predicted with reasonable accuracy from their sequences alone. Our approach suggest a direction for future work addressing the protein-folding problem.

Journal Article↗

Predicting maximum heart rate among patients with coronary heart disease receiving beta-adrenergic blockade therapy.

BACKGROUND: The use of beta-adrenergic blockade (BB) therapy is common among patients with coronary heart disease (CHD), and as a result, these patients often undergo exercise testing while taking these medications. However, evaluation of maximal voluntary effort during exercise testing is often in question because current equations to predict maximum heart rate (HR(max); eg, 220 - age) are based on subjects without heart disease or BB therapy. The purpose of this study was to develop and validate an age-specific equation to predict HR(max) in patients with CHD who are receiving BB therapy. METHODS: We queried the Henry Ford Preventive Cardiology Outcomes database for patients with a history of myocardial infarction or revascularization procedure; preserved left ventricular function; age, 40 to 80 years; sinus rhythm; and a graded treadmill test with a respiratory exchange ratio > or =1.10. Data were split, based on date, such that tests performed between November 1996 and April 2001 were used as the BB prediction equation development group (n = 334; 73% men) and those performed between May 2001 and April 2002 were used as the BB cross-validation group (n = 94; 84% men). Linear regression was used to develop the equation to predict HR(max), based on age, and to calculate the correlation coefficient of the prediction equation among the cross-validation group. RESULTS: The resultant prediction equation was HR(max) = 164 - 0.7 x age (r2 = 0.13), with a standard error of the estimate of 18 per minute. Among the cross-validation group, mean predicted HR(max) was not significantly different from mean measured HR(max) (P = .7). The mean error of prediction was -0.4 +/- 2.0 per minute (mean +/- SEM), and the correlation was r = 0.38. CONCLUSIONS: This new equation provides a better estimate of HR(max) for patients with CHD receiving BB therapy than previously reported equations. Additional variables may improve the equation but may not be as convenient to use.

Adrenergic beta-Antagonists↗

External validation of a percutaneous coronary intervention mortality prediction model in patients with acute coronary syndromes.

BACKGROUND: The recently published Michigan outcome prediction model (MM) for inhospital mortality was developed and validated on a series of consecutive patients undergoing percutaneous coronary intervention (PCI). Our purpose was to externally validate the performance of the MM in 2 separate cohorts of patients with acute coronary syndrome (ACS) undergoing PCI in Canada. METHODS: A validation of the MM and development of an extended MM were performed on data describing 10,050 patients from the APPROACH prospective cohort study between January 1995 and December 2000. Performance of both models was assessed on an external data set of 3259 PCI cases from the British Columbia Cardiac Registries. Only patients with a diagnosis of ACS were included in the study. RESULTS: The original MM predicted death rates ranging from 0.1% to 60.6%, but lacked accuracy to predict inhospital mortality as severity increased. The extended MM predicted death rates more widely from 0.0% to a high of 91.0% with better accuracy to predict inhospital death in patients with ACS undergoing PCI. The areas under the receiver operating characteristic curve for the MM and the extended MM on the external validation data set were 0.93 and 0.95, respectively. CONCLUSION: The MM predicts death after PCI in patients with ACS and identifies a clear gradient of risk. However, the enhanced MM developed specifically for the subset of patients with ACS demonstrated better prediction and cross-validated performance. These prediction rules can be useful for risk-adjustment analyses and for prognostication for individual patients.

Aged↗

Development and testing of multilevel models for longitudinal craniofacial growth prediction.

INTRODUCTION: The aims of this study were to (1) develop longitudinal growth curves that would allow individual variations to be accurately modeled and (2) use these models to predict craniofacial growth changes in children with varying amounts of longitudinal data available. METHODS: Based on a sample of 159 girls (994 cephalograms) and 128 boys (947 cephalograms), multilevel population models were derived. Polynomial models of the population's growth curve were derived for the measurements MPA, Me-X, Me-theta, Me-Y, and Me-R. Angular and horizontal measures (MPA, Me-X, and Me-theta) were described by simpler, second-order models, and vertical measures (Me-Y and Me-R) were described by more complex, fifth-order models. RESULTS: Decreases in MPA during childhood and increases in Me-theta during adolescence could be explained by the relative contributions of the horizontal (Me-X) and vertical (Me-Y) movements of menton. There was greater anterior movement of menton during childhood and greater inferior movement during the adolescent growth spurt. By using varying numbers of longitudinal cephalograms between 6 and 10 years of age, the models were used to predict subjects' craniofacial growth changes from ages 10 to 15. Based on correlations, root mean squared error, and percent accuracy, individual growth predictions for the various measures were found to be highly accurate on an independent subsample drawn from the larger sample and on an independent validation sample. Correlations between predicted and actual values on the sample used to develop the models ranged from 0.81 to 0.95. Accuracy was best for the measurements that changed the most during the prediction period (Me-Y and Me-R), with accuracies between 83% and 90%. More longitudinal data did not increase the predictive accuracy for all measurements. The models that were least accurate (Me-X, MPA, and Me-theta) showed the greatest improvement in prediction accuracy with more longitudinal data. These improvements ranged from 1.6% to 15%. CONCLUSIONS: Longitudinal growth curves based on multilevel procedures can accurately describe population and individual growth curves, and 5-year predictions with this method are highly accurate and externally valid.

Adolescent↗

Bayesian network multi-classifiers for protein secondary structure prediction.

Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure prediction becomes essential for future development of the whole field of protein research. In this work we present several multi-classifiers that combine the predictions of the best current classifiers available on Internet. Our results prove that combining the predictions of a set of classifiers by creating composite classifiers is a fruitful one. We have created multi-classifiers that are more accurate than any of the component classifiers. The multi-classifiers are based on Bayesian networks. They are validated with 9 different datasets. Their predictive accuracy results outperform the best secondary structure predictors by 1.21% on average. Our main contributions are: (i) we improved the best know predictive accuracy by 1.21%, (ii) our best results have been obtained with a new semi naïve Bayes approach named Pazzani-EDA and (iii) our multi-classifiers combine results of previously build classifiers predictions obtained through Internet, thanks to our development of a Java application.

Bayes Theorem↗

Highly accurate and consistent method for prediction of helix and strand content from primary protein sequences.

OBJECTIVE: One of interesting computational topics in bioinformatics is prediction of secondary structure of proteins. Over 30 years of research has been devoted to the topic but we are still far away from having reliable prediction methods. A critical piece of information for accurate prediction of secondary structure is the helix and strand content of a given protein sequence. Ability to accurately predict content of those two secondary structures has a good potential to improve accuracy of prediction of the secondary structure. Most of the existing methods use composition vector to predict the content. Their underlying assumption is that the vector can be used to provide functional mapping between primary sequence and helix/strand content. While this is true for small sets of proteins we show that for larger protein sets such mapping are inconsistent, i.e. the same composition vectors correspond to different contents. To this end, we propose a method for prediction of helix/strand content from primary protein sequences that is fundamentally different from currently available methods. METHODS AND MATERIAL: Our method is accurate and uses a novel approach to obtain information from primary sequence based on a composition moment vector, which is a measure that includes information about both composition of a given primary sequence and the position of amino acids in the sequence. In contrast to the composition vector, we show that it provides functional mapping between primary sequence and the helix/strand content. RESULTS: A set of benchmarks involving a large protein dataset consisting of over 11,000 protein sequences from Protein Data Bank was performed to validate the method. Prediction done by a neural network had average accuracy of 91.5% for the helix and 94.5% for the strand contents. We also show that using the new measure results in about 40% reduction of error rates when compared with the composition vector results. CONCLUSIONS: The developed method has much better accuracy when compared with other existing methods, as shown on a large body of proteins, in contrast to other reported results that often target small sets of specific protein types, such as globular proteins.

Amino Acid Sequence↗

Artificial neural network modeling to predict the plasma concentration of aminoglycosides in burn patients.

The goal was to use an artificial neural network model to predict the plasma concentration of aminoglycosides in burn patients and identify patients whose plasma antibiotic concentration would be sub-therapeutic based on the patients' physiological data and taking into account burn severity. Physiological data and some indicators of burn severity were collected from 30 burn patients who received arbekacin. A three-layer artificial neural network with five neurons in the hidden layer was used to predict the plasma concentration of arbekacin. Linear modeling for prediction of plasma concentration and logistic regression modeling for the classification of patients were also used and the predictive performance was compared to results from the artificial neural network model. Dose, body mass index, serum creatinine concentration and amount of parenteral fluid were selected as covariates for the plasma concentration of arbekacin. Area of burn after skin graft was a good covariate for indicating burn severity. Predictive performance of the artificial neural network model including burn severity was much better than linear modeling and logistic regression analysis. An artificial neural network model should be helpful for the prediction of plasma concentration using patients' physiological data, and burn severity should be included for improved prediction in burn patients. Because the relationship between burn severity and plasma concentration of aminoglycosides is thought to be nonlinear, it is not surprising that the artificial neural network model showed better predictive performance compared to the linear or logistic regression models.

Adult↗

The importance of outlier detection and training set selection for reliable environmental QSAR predictions.

Empirical QSAR models are only valid in the domain they were trained and validated. Application of the model to substances outside the domain of the model can lead to grossly erroneous predictions. Partial least squares (PLS) regression provides tools for prediction diagnostics that can be used to decide whether or not a substance is within the model domain, i.e. if the model prediction can be trusted. QSAR models for four different environmental end-points are used to demonstrate the importance of appropriate training set selection and how the reliability of QSAR predictions can be increased by outlier diagnostics. All models showed consistent results; test set prediction errors were very similar in magnitude to training set estimation errors when prediction outlier diagnostics were used to detect and remove outliers in the prediction data. Test set prediction errors for substances classified as outliers were much larger. The difference in the number of outliers between models with a randomly and systematically selected training illustrates well the need of representative training data.

Animals↗

A resampling approach for adjustment in prediction models for covariate measurement error.

Recent works on covariate measurement errors focus on the possible biases in model coefficient estimates. Usually, measurement error in a covariate tends to attenuate the coefficient estimate for the covariate, i.e., a bias toward the null occurs. Measurement error in another confounding or interacting variable typically results in incomplete adjustment for that variable. Hence, the coefficient for the covariate of interest may be biased either toward or away from the null. This paper presents a new method based on a resampling technique to deal with covariate measurement errors in the context of prediction modeling. Prediction accuracy is our primary parameter of interest. Prediction accuracy of a model is defined as the success rate of prediction when the model predicts new response. We call our method bootstrap regression calibration (BRC). We study logistic regression with interacting covariates as our prediction model. We measure the prediction accuracy of a model by receiver operating characteristic (ROC) method. Results from simulations show that bootstrap regression calibration offers consistent enhancement over the commonly used regression calibration (RC) method in terms of improving prediction accuracy of the model and reducing bias in the estimated coefficients.

Calibration↗

Importance of RNA secondary structure information for yeast donor and acceptor splice site predictions by neural networks.

Previously, Patterson et al. showed that mRNA structure information aids splice site prediction in human genes [Patterson, D.J., Yasuhara, K., Ruzzo, W.L., 2002. Pre-mRNA secondary structure prediction aids splice site prediction. Pac. Symp. Biocomput. 7, 223-234]. Here, we have attempted to predict splice sites in selected genes of Saccharomyces cerevisiae using the information obtained from the secondary structures of corresponding mRNAs. From Ares database, 154 genes were selected and their structures were predicted by Mfold. We selected a 20-nucleotide window around each site, each containing 4 nucleotides in the exon region. Based on whether the nucleotide is in a stem or not, the conventional four-letter nucleotide alphabet was translated into an eight-letter alphabet. Two different three-layer-based perceptron neural networks were devised to predict the 5' and 3' splice sites. In case of 5' site determination, a network with 3 neurons at the hidden layer was chosen, while in case of 3' site 20 neurons acted more efficiently. Both neural nets were trained applying Levenberg-Marquardt backpropagation method, using half of the available genes as training inputs and the other half for testing and cross-validations. Sequences with GUs and AGs non-sites were used as negative controls. The correlation coefficients in the predictions of 5' and 3' splice sites using eight-letter alphabet were 98.0% and 69.6%, respectively, while these values were 89.3% and 57.1% when four-letter alphabet is applied. Our results suggest that considering the secondary structure of mRNA molecules positively affects both donor and acceptor site predictions by increasing the capacity of neural networks in learning the patterns.

Exons↗

Does EuroSCORE predict length of stay and specific postoperative complications after cardiac surgery?

OBJECTIVE: To evaluate the performance of EuroSCORE in the prediction of in-hospital postoperative length of stay and specific major postoperative complications after cardiac surgery. METHODS: Data on 5051 consecutive patients (isolated [74.4%] or combined coronary artery bypass grafting [11.1%], valve surgery [12.0%] and thoracic aortic surgery [2.5%]) were prospectively collected. The EuroSCORE model (standard and logistic) was used to predict in-hospital mortality, 3-month mortality, prolonged length of stay (>12 days) and major postoperative complications (intraoperative stroke, stroke over 24 h, postoperative myocardial infarction, deep sternal wound infection, re-exploration for bleeding, sepsis and/or endocarditis, gastrointestinal complications, postoperative renal failure and respiratory failure). A C statistic (or the area under the receiver operating characteristic curve) was used to test the discrimination of the EuroSCORE. The calibration of the model was assessed by the Hosmer-Lemeshow goodness-of-fit statistic. RESULTS: In-hospital mortality was 3.9% and 16.1% of patients had one or more major complications. Standard EuroSCORE showed very good discriminatory ability and good calibration in predicting in-hospital mortality (C statistic: 0.76, Hosmer-Lemeshow: P=0.449) and postoperative renal failure (C statistic: 0.79, Hosmer-Lemeshow: P=0.089) and good discriminatory ability in predicting sepsis and/or endocarditis (C statistic: 0.74, Hosmer-Lemeshow: P=0.653), 3-month mortality (C statistic: 0.73, Hosmer-Lemeshow: P=0.097), prolonged length of stay (C statistic: 0.71, Hosmer-Lemeshow: P=0.051) and respiratory failure (C statistic: 0.71, Hosmer-Lemeshow: P=0.714). There were no differences in terms of the discriminatory ability in predicting these outcomes between standard and logistic EuroSCORE. However, logistic EuroSCORE showed no calibration (Hosmer-Lemeshow: P<0.05) except for sepsis and/or endocarditis (Hosmer-Lemeshow: P=0.078). EuroSCORE was unable to predict other major complications such as intraoperative stroke, stroke over 24 h, postoperative myocardial infarction, deep sternal wound infection, gastrointestinal complications and re-exploration for bleeding. CONCLUSIONS: EuroSCORE can be used to predict not only in-hospital mortality, for which it was originally designed, but also 3-month mortality, prolonged length of stay and specific postoperative complications such as renal failure, sepsis and/or endocarditis and respiratory failure in the whole context of cardiac surgery. These outcomes can be predicted accurately using the standard EuroSCORE which is very simple and easy in its calculation.

Aged↗