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At least 649 records · Page 36Linked to original sources
PubMed2000

Effect of a concentrated "inert" macromolecular cosolute on the stability of a globular protein with respect to denaturation by heat and by chaotropes: a statistical-thermodynamic model.

An equilibrium statistical-thermodynamic model for the effect of volume exclusion arising from high concentrations of stable macromolecules upon the stability of a trace globular protein with respect to denaturation by heat and by chaotropes is presented. The stable cosolute and the native form of the trace protein are modeled by effective hard spherical particles. The denatured state of the trace protein is represented as an ensemble of substates modeled by random coils having the same contour length but different rms end-to-end distances (i.e., different degrees of compaction). The excess or nonideal chemical potential of the native state and of each denatured substate is calculated as a function of the concentration of stable cosolute, leading to an estimate of the relative abundance of each state and substate, and the ensemble average free energy of the transition between native and denatured protein. The effect of the addition of stable cosolute upon the temperature of half-denaturation and upon the concentration of chaotrope required to half-denature the tracer at constant temperature is then estimated. At high cosolute concentration (>100 g/l) these effects are predicted to be large and readily measurable experimentally, provided that an experimental system exhibiting a fully reversible unfolding equilibrium at high total macromolecular concentration can be developed.

Computer Simulation↗
PubMed1993

Lipid-cholesterol interactions in the P beta' phase. Application of a statistical mechanical model.

We describe a statistical mechanical model for lipid-cholesterol mixtures in the P beta' (ripple) phase of lipid bilayers. The model is a simple extension of an earlier model for the ripple phase in pure lipid bilayers. The extension consists of adding a degree of freedom to allow for the occupation of underlying lattice sites by cholesterol molecules, and adding a lipid-cholesterol interaction term to the model Hamiltonian. The interaction term was constructed based on numerical calculations of lipid-cholesterol energies for several different packing juxtapositions of the two molecules. Other than the lipid-cholesterol interactions, the extended model uses the same parameter set as the earlier model, so that comparison of the properties of the extended model with experimental data serves as a test of the validity of the original model. Properties of the model were calculated using the Monte Carlo method. Results are displayed as snapshots of the ripple configurations at different cholesterol concentrations. The spacing of the ripples increases with increasing cholesterol concentration and the rate of increase compares very well with experimental data. The success of this model supports the conclusion drawn earlier that frustration arising from anisotropic packing interactions is responsible for the ripple phase in lipid bilayers. In the extended model these packing interactions are responsible for the selective partitioning of cholesterol in the regions between the ripples.

Animals↗
PubMedDate not supplied

Prediction of polar surface area and drug transport processes using simple parameters and PLS statistics.

Modeling of the calculated polar surface area of drugs with rapidly derived descriptors (i.e., the number of hydrogen bonds accepting oxygen and nitrogen atoms and the number of hydrogen atoms bonded to these) using partial least squares projection to latent structures (PLS) analysis is described. The statistical analysis showed strong relationships between the hydrogen-bonding descriptors and the calculated polar surface area of five chemically diverse sets of drugs (R2>0.93 and Q2>0.69, n = 11, 20, 45, 70, and 74, respectively). The statistical models (using H-bonding descriptors and log P) of transport across Caco-2 cells (n = 11), brain-blood partitioning (two data sets, n = 45 and 70) and percent intestinal absorption (n = 20) showed R2 = 0.92, 0.72, 0.76, and 0.81 and Q2 = 0.74, 0.75, 0.71, and 0.73, respectively. The inclusion of log P improved two models, had no effect on one model, and had a slightly negative impact on one model. The combination of H-bonding descriptors with log P is similar to the Lipinski "rule-of-five" mnemonic. However, by using a multivariate statistical method (e.g., PLS), the prediction becomes quantitative instead of qualitative. Good statistical models were derived which permit fast computational screening and prioritization of virtual compound libraries.

Blood-Brain Barrier↗
PubMed2001

Beat-to-beat stroke volume estimation from aortic pressure waveform in conscious rats: comparison of models.

Several methods for estimating stroke volume (SV) were tested in conscious, freely moving rats in which ascending aortic pressure and cardiac flow were simultaneously (beat-to-beat) recorded. We compared two pulse-contour models to two new statistical models including eight parameters extracted from the pressure waveform in a multiple linear regression. Global as well as individual statistical models gave higher correlation coefficients between estimated and measured SV (model 1, r = 0.97; model 2, r = 0.96) than pulse-contour models (model 1, r = 0.83; model 2, r = 0.91). The latter models as well as statistical model 1 used the pulsatile systolic area and thus could be applied to only 47 +/- 17% of the cardiac beats. In contrast, statistical model 2 used the pressure-increase characteristics and was therefore established for all of the cardiac beats. The global statistical model 2 applied to data sets independent of those used to establish the model gave reliable SV estimates: r = 0.54 +/- 0.07, a small bias between -8% to +10%, and a mean precision of 7%. This work demonstrated the limits of pulse-contour models to estimate SV in conscious, unrestrained rats. A multivariate statistical model using eight parameters easily extracted from the aortic waveform could be applied to all cardiac beats with good precision.

Animals↗
PubMed2001

Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks.

We propose a model-driven approach for analyzing genomic expression data that permits genetic regulatory networks to be represented in a biologically interpretable computational form. Our models permit latent variables capturing unobserved factors, describe arbitrarily complex (more than pair-wise) relationships at varying levels of refinement, and can be scored rigorously against observational data. The models that we use are based on Bayesian networks and their extensions. As a demonstration of this approach, we utilize 52 genomes worth of Affymetrix GeneChip expression data to correctly differentiate between alternative hypotheses of the galactose regulatory network in S. cerevisiae. When we extend the graph semantics to permit annotated edges, we are able to score models describing relationships at a finer degree of specification.

Bayes Theorem↗
PubMedDate not supplied

What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models.

OBJECTIVE: Statistical models, such as linear or logistic regression or survival analysis, are frequently used as a means to answer scientific questions in psychosomatic research. Many who use these techniques, however, apparently fail to appreciate fully the problem of overfitting, ie, capitalizing on the idiosyncrasies of the sample at hand. Overfitted models will fail to replicate in future samples, thus creating considerable uncertainty about the scientific merit of the finding. The present article is a nontechnical discussion of the concept of overfitting and is intended to be accessible to readers with varying levels of statistical expertise. The notion of overfitting is presented in terms of asking too much from the available data. Given a certain number of observations in a data set, there is an upper limit to the complexity of the model that can be derived with any acceptable degree of uncertainty. Complexity arises as a function of the number of degrees of freedom expended (the number of predictors including complex terms such as interactions and nonlinear terms) against the same data set during any stage of the data analysis. Theoretical and empirical evidence--with a special focus on the results of computer simulation studies--is presented to demonstrate the practical consequences of overfitting with respect to scientific inference. Three common practices--automated variable selection, pretesting of candidate predictors, and dichotomization of continuous variables--are shown to pose a considerable risk for spurious findings in models. The dilemma between overfitting and exploring candidate confounders is also discussed. Alternative means of guarding against overfitting are discussed, including variable aggregation and the fixing of coefficients a priori. Techniques that account and correct for complexity, including shrinkage and penalization, also are introduced.

Computer Simulation↗
PubMed2003

Segmentation of prostate boundaries from ultrasound images using statistical shape model.

This paper presents a statistical shape model for the automatic prostate segmentation in transrectal ultrasound images. A Gabor filter bank is first used to characterize the prostate boundaries in ultrasound images in both multiple scales and multiple orientations. The Gabor features are further reconstructed to be invariant to the rotation of the ultrasound probe and incorporated in the prostate model as image attributes for guiding the deformable segmentation. A hierarchical deformation strategy is then employed, in which the model adaptively focuses on the similarity of different Gabor features at different deformation stages using a multiresolution technique, i.e., coarse features first and fine features later. A number of successful experiments validate the algorithm.

Algorithms↗
PubMed1981

Biological models and statistical interactions: an example from multistage carcinogenesis.

From the assessment of statistical interaction between risk factors it is tempting to infer the nature of the biologic interaction between the factors. However, the use of statistical analyses of epidemiologic data to infer biologic processes can be misleading. as an example, we consider the multistage model of carcinogenesis. Under this biologic model, it is shown, by means of simple hypothetical examples, that even if carcinogenic factors act independently, some pairs may fit an additive statistical model, some a multiplicative statistical model, and some neither. The elucidation of biological interactions by means of statistical models requires the imaginative and prudent use of inductive and deductive reasoning; it cannot be done mechanically.

Cell Transformation, Neoplastic↗
PubMed2006

Using habitat suitability index and particle dispersion models for early detection of marine invaders.

Eradication and control of invasive species are often possible only if populations are detected when they are small and localized. To be efficient, detection surveys should be targeted at locations where there is the greatest risk of incursions. We examine the utility of habitat suitability index (HSI) and particle dispersion models for targeting sampling for marine pests. Habitat suitability index models are a simple way to identify suitable habitat when species distribution data are lacking. We compared the performance of HSI models with statistical models derived from independent data from New Zealand on the distribution of two nonindigenous bivalves: Theora lubrica and Musculista senhousia. Logistic regression models developed using the HSI scores as predictors of the presence/absence of Theora and Musculista explained 26.7% and 6.2% of the deviance in the data, respectively. Odds ratios for the HSI scores were greater than unity, indicating that they were genuine predictors of the presence/ absence of each species. The fit and predictive accuracy of each logistic model were improved when simulated patterns of dispersion from the nearest port were added as a predictor variable. Nevertheless, the combined model explained, at best, 46.5% of the deviance in the distribution of Theora and correctly predicted 56% of true presences and 50% of all cases. Omission errors were between 6% and 16%. Although statistical distribution models built directly from environmental predictors always outperformed the equivalent HSI models, the gain in model fit and accuracy was modest. High residual deviance in both types of model suggests that the distributions realized by Theora and Musculista in the field data were influenced by factors not explicitly modeled as explanatory variables and by error in the environmental data used to project suitable habitat for the species. Our results highlight the difficulty of accurately predicting the distribution of invasive marine species that exhibit low habitat occupancy and patchy distributions in time and space. Although the HSI and statistical models had utility as predictors of the likely distribution of nonindigenous marine species, the level of spatial accuracy achieved with them may be well below expectations for sensitive surveillance programs.

Animals↗
PubMed1980

Optimal coding of electrocardiograms for epidemiological studies. The performance of human coders - astatistical model.

A statistical model is introduced to characterize the ECG coding process when multiple coding is performed by human readers. Triplicate reading and arbitration of disagreements would considerably reduce the miss rate with a concomitant increase in cost and effort. The results suggest that the use of the majority rule (acceptance of the most common code from multiple, blinded individual codings) is inappropriate for ECG coding. The model proposed gives a plausible explanation to the observed high miss rate in coding of rare events in spite of duplicate reading and arbitration of disagreements. The main problem identified is the high individual miss rate in a fraction of "difficult" ECG records suggested that efficient use of computer programs and visual verification may be required to reduce coding errors to an acceptable level.

Electrocardiography↗
PubMed1997

Comparison of population pharmacokinetic modeling methods using simulated data: results from the Population Modeling Workgroup.

Statistical modeling methods have had increasing use in drug disposition studies, both to estimate pharmacokinetic parameters and to develop regression models that relate these parameter estimates to patient characteristics. These methods are particularly flexible as they allow non-linearity and sparse within-patient information. In the past few years, multiple analysis methods have become available, but there is a lack of systematic comparisons of their estimates on the same data sets. Two simulated data sets were therefore developed by the Population Modeling Workgroup of the Biopharmaceutical Section of the American Statistical Association. We analysed these data sets using seven population modeling programs, some of which contain multiple analysis methods. Although each data set represents a single replicate from a given model and data collection design, the results suggest that the behaviour of some methods differs from that of the others.

Anti-Arrhythmia Agents↗
PubMed2004

Statistical shape modeling of low level visual area borders.

This paper proposes a statistical modeling of functional landmarks delimiting low level visual areas which are highly variable across individuals. Low level visual areas are first precisely delineated by fMRI retinotopic mapping which provides detailed information about the correspondence between the visual field and its cortical representation. The model is then built by learning the variability within a given training set. It relies on an appropriate data representation and on the definition of an intrinsic coordinate system common to all visual maps. This allows to build a consistent training set on which a principal component analysis is eventually applied. Our approach constitutes a first step toward a functional landmark-based probabilistic atlas of low level visual areas.

Adult↗
PubMed1991

Trial order affects cue interaction in contingency judgment.

Recent research on contingency judgment indicates that the judged predictiveness of a cue is dependent on the predictive strengths of other cues. Two classes of models correctly predict such cue interaction: associative models and statistical models. However, these models differ in their predictions about the effect of trial order on cue interaction. In five experiments reported here, college students viewed trial-by-trial data regarding several medical symptoms and a disease, judging the predictive strength of each symptom with respect to the disease. The results indicate that trial order influences the manner in which cues interact, but that neither the associative nor the statistical models can fully account for the data pattern. A possible variation of an associative account is discussed.

Cues↗
PubMed1995

Statistical response models for ozone exposure: their generality when applied to human spirometric and animal permeability functions of the lung.

Exposure of humans or animals to ozone (O3) alters spirometric and permeability functions of the lung. While these responses show clear concentration (C) dependency, the interactive role of exposure duration (T) has not been well defined. Ozone-induced alterations in forced expiratory volume in 1 s (FEV1) obtained from human studies and in levels of bronchoalveolar lavage fluid protein (BALP) obtained from studies of rats and guinea pigs were used to compare the utility of several proposed response models as functions of C and T. A large human-study database compiled for T = 2 h and a wide-ranging C and T study on animals were used to contrast each model. The models examined included the quadratic, logistic, log regression, and exponential models. This work suggests that models used for risk assessment should incorporate both T and C. Our results suggest that modified forms of many of these models perform well with both human and animal responses and can be additionally modified to include ventilation rate. As a simple biological model, the exponential model showed advantages. The absolute concentration rates-of-change in the exponential function of integrated physiological changes like BALP and FEV1 were equal for low O3 exposure.

Animals↗
PubMed2004

Application of a statistical dynamic model investigating the short-term cellular kinetics induced by riddelliine, a hepatic endothelial carcinogen.

In recent studies, riddelliine, a pyrrolizidine alkaloid, was found to increase rates of replication and apoptosis and induce hemangiosarcoma in the liver of rats and mice. To analyze DNA replication and apoptosis data taken from the same animals, we have developed a predictive mathematical model for describing BrdU labeling and apoptotic processes. The model allows the incorporation of simple diurnal patterns in cellular kinetics and is applied to data on hepatocytes and endothelial cells taken from riddelliine exposed rats. Predictions from the model were used with multivariable nonlinear regression techniques to estimate replication and apoptotic rate constants for both cell types and all treatment groups. Hypothesis tests were used with the predicted rates to separate the competing effects of riddelliine on replication and apoptosis of hepatocytes and endothelial cells as well as compare replication rates between cell types. That estimated replication rates were found to be significantly higher for endothelial cells supports the supposition of induction of hemangiosarcoma by riddelliine in the liver.

Algorithms↗
PubMed2006

Nonrigid 2-D/3-D registration for patient specific bronchoscopy simulation with statistical shape modeling: phantom validation.

This paper presents a nonrigid registration two-dimensional/three-dimensional (2-D/3-D) framework and its phantom validation for subject-specific bronchoscope simulation. The method exploits the recent development of five degrees-of-freedom miniaturized catheter tip electromagnetic trackers such that the position and orientation of the bronchoscope can be accurately determined. This allows the effective recovery of unknown camera rotation and airway deformation, which is modelled by an active shape model (ASM). ASM captures the intrinsic variability of the tracheo-bronchial tree during breathing and it is specific to the class of motion it represents. The method reduces the number of parameters that control the deformation, and thus greatly simplifies the optimisation procedure. Subsequently, pq-based registration is performed to recover both the camera pose and parameters of the ASM. Detailed assessment of the algorithm is performed on a deformable airway phantom, with the ground truth data being provided by an additional six degrees-of-freedom electromagnetic (EM) tracker to monitor the level of simulated respiratory motion.

Algorithms↗
PubMed2004

Robustness of spatial average equalization: a statistical reverberation model approach.

Traditionally, multiple listener room equalization is performed to improve sound quality at all listeners, during audio playback, in a multiple listener environment (e.g., movie theaters, automobiles, etc.). A typical way of doing multiple listener equalization is through spatial averaging, where the room responses are averaged spatially between positions and an inverse equalization filter is found from the spatially averaged result. However, the equalization performance, will be affected if there is a mismatch between the position of the microphones (which are used for measuring the room responses for designing the equalization filter) and the actual center of listener head position (during playback). In this paper, we will present results on the effects of microphone-listener mismatch on spatial average equalization performance. The results indicate that, for the analyzed rectangular configuration, the region of effective equalization depends on (i) the distance of a listener from the source, (ii) the amount of mismatch between the responses, and (iii) the frequency of the audio signal. We also present some convergence analysis to interpret the results.

Architecture↗