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PubMed1991

A log-linear modeling framework for selective mixing.

Nonrandom mixing can significantly alter the diffusion path of an infectious disease such as AIDS that requires intimate contact. Recent attempts to model this effect have sought a general framework capable of representing both simple and arbitrarily complicated mixing structures, and of solving the balancing problem in a nonequilibrium multigroup population. Log-linear models are proposed here as a general framework for solving the first problem. This approach offers several additional benefits: The parameters used to govern the mixing have a simple, intuitive interpretation, the framework provides a statistically sound basis for the estimation of these parameters from mixing-matrix data, and the resulting estimates are easily integrated into compartmental models for diffusion. A modified selection model is proposed to solve the second problem of generalizing the selection process to nonequilibrium populations. The distribution of contacts under this model is derived and is found to satisfy the assumptions of statistical inference for log-linear models. Together these techniques provide an integrated and flexible framework for modeling the role of selective mixing in the spread of disease.

Acquired Immunodeficiency Syndrome↗
PubMed2005

Comparison of three models to estimate breeding values for percentage of loin intramuscular fat in Duroc swine.

Three selection models were evaluated to compare selection candidate rankings based on EBV and to evaluate subsequent effects of model-derived EBV on the selection differential and expected genetic response in the population. Data were collected from carcass- and ultrasound-derived estimates of loin i.m. fat percent (IMF) in a population of Duroc swine under selection to increase IMF. The models compared were Model 1, a two-trait animal model used in the selection experiment that included ultrasound IMF from all pigs scanned and carcass IMF from pigs slaughtered to estimate breeding values for both carcass (C1) and ultrasound IMF (U1); Model 2, a single-trait animal model that included ultrasound IMF values on all pigs scanned to estimate breeding values for ultrasound IMF (U2); and Model 3, a multiple-trait animal model including carcass IMF from slaughtered pigs and the first three principal components from a total of 10 image parameters averaged across four longitudinal ultrasound images to estimate breeding values for carcass IMF (C3). Rank correlations between breeding value estimates for U1 and C1, U1 and U2, and C1 and C3 were 0.95, 0.97, and 0.92, respectively. Other rank correlations were 0.86 or less. In the selection experiment, approximately the top 10% of boars and 50% of gilts were selected. Selection differentials for pigs in Generation 3 were greatest when ranking pigs based on C1, followed by U1, U2, and C3. In addition, selection differential and estimated response were evaluated when simulating selection of the top 1, 5, and 10% of sires and 50% of dams. Results of this analysis indicated the greatest selection differential was for selection based on C1. The greatest loss in selection differential was found for selection based on C3 when selecting the top 10 and 1% of boars and 50% of gilts. The loss in estimated response when selecting varying percentages of boars and the top 50% of gilts was greatest when selection was based on C3 (16.0 to 25.8%) and least for selection based on U1 (1.3 to 10.9%). Estimated genetic change from selection based on carcass IMF was greater than selection based on ultrasound IMF. Results show that selection based on a combination of ultrasonically predicted IMF and sib carcass IMF produced the greatest selection differentials and should lead to the greatest genetic change.

Adipose Tissue↗
PubMed2004

[Distributed modeling of nutrient transport in basin with support of remote sensing and geography information system].

Agricultural non-point source pollution has become serious in our country. Modeling the processes of nutrient(especially nitrogen and phosphorus) transport in basin and evaluating the adopted management practices are important for controlling the impact of non-point pollution on environment. A research scheme for distributed simulation of nutrient transport processes in Lianshui Basin, Xingguo County, Jiangxi Province was designed, with the support of remote sensing (RS) and geography information system (GIS). The research procedure included model selection, discretization and spatial parameterization of the basin, prediction, and validation. SWAT model was selected, and basin-subbasin-hydrological response unit discretization scheme was designed. Supported by RS and GIS and based on the topographic features of the watershed, the subwatershed discretization divided the watershed into subbasins, and each subbasin could be further partitioned into multiple hydrologic response units (HRUs), which were unique soil/land use combinations within the subbasin and modeled through statistical spatial overlay analysis. The parameters of land use were obtained from the supervised classification of TM imagery based on field training samples, and those of soil were obtained from field sampling and further interpolated through geostatistical method. The simulation was carried out by using the data from 1991 to 2000. The results showed that the simulation accuracy of annual runoff water yield and sediment yield was 89.9% and 70.2%, respectively.

Computer Simulation↗
PubMedDate not supplied

Use of the score test as a goodness-of-fit measure of the covariance structure in genetic analysis of longitudinal data.

Model selection is an essential issue in longitudinal data analysis since many different models have been proposed to fit the covariance structure. The likelihood criterion is commonly used and allows to compare the fit of alternative models. Its value does not reflect, however, the potential improvement that can still be reached in fitting the data unless a reference model with the actual covariance structure is available. The score test approach does not require the knowledge of a reference model, and the score statistic has a meaningful interpretation in itself as a goodness-of-fit measure. The aim of this paper was to show how the score statistic may be separated into the genetic and environmental parts, which is difficult with the likelihood criterion, and how it can be used to check parametric assumptions made on variance and correlation parameters. Selection of models for genetic analysis was applied to a dairy cattle example for milk production.

Animals↗
PubMed1999

Population models of genomic imprinting. I. Differential viability in the sexes and the analogy with genetic dominance.

Many single-locus, two-allele selection models of genomic imprinting have been shown to reduce formally to one-locus Mendelian models with a modified parameter for genetic dominance. One exception is the model where selection at the imprinted locus affects the sexes differently. We present two models of maternal inactivation with differential viability in the sexes, one with complete inactivation, and the other with a partial penetrance for inactivation. We show that, provided dominance relations at the imprintable locus are the same in both sexes, a globally stable polymorphism exists for a range of viabilities that is independent of the penetrance of imprinting. The conditions for a polymorphism are the same as in previous models with differential viability in the sexes but without imprinting and in a model of the paternal X-inactivation system in marsupials. The model with incomplete inactivation is used to illustrate the analogy between imprinting and dominance by comparing equilibrium bifurcation plots for fixed values of dominance and penetrance. We also derive a single expression for the dominance parameter that leaves the frequency and stability of equilibria unchanged for all levels of inactivation. Although an imprinting model with sex differences does not formally reduce to a nonimprinting scheme, close theoretical parallels clearly exist.

Animals↗
PubMed2003

Attention, spatial representation, and visual neglect: simulating emergent attention and spatial memory in the selective attention for identification model (SAIM).

The selective attention for identification model (SAIM) is presented. This uses a spatial window to select visual information for recognition, binding parts to objects and generating translation-invariant recognition. The model provides a qualitative account of both normal and disordered attention. Simulations of normal attention demonstrate 2-object costs and effects of object familiarity on selection, global precedence, spatial cueing, and inhibition of return. When lesioned, SAIM demonstrated either view- or object-centered neglect or spatial extinction, depending on the type and extent of lesion. The model provides a framework to unify (a) object- and space-based theories of normal selection, (b) dissociations within the syndrome of unilateral neglect, and (c) attentional and representational accounts of neglect.

Attention↗
PubMed2003

Evaluation of the de-selection of men who have had sex with men from blood donation in England.

BACKGROUND AND OBJECTIVES: The Blood Services of the UK permanently de-select men who have had sex with men (MSM) from donating blood. The rationale for this has been questioned. This article attempts to evaluate whether this selection criterion does contribute to blood safety. MATERIALS AND METHODS: Data about transfusion-transmissible infections, in particular about human immunodeficiency virus (HIV) infection, were used to evaluate whether de-selection of MSM meets the aims of donor selection. Models were constructed to estimate the risk of HIV-infectious donations entering the blood supply should this criterion be changed. RESULTS: Many assumptions were required to generate estimates of the risk of HIV infection entering the blood supply. The accuracy of the estimates is therefore uncertain and the probable ranges around the estimates were wide. However, by using the most probable assumptions, our models suggested that de-selection of MSM for 12 months since the last sexual contact, or complete removal of this selection criterion, would be expected to increase the risk of HIV-infectious donations entering the blood supply in England by approximately 60% (from the current risk of 0.45 per year to 0.75 per year) and 500% (to 2.5 per year), respectively. The increase in numbers of non-infected donations would be relatively small--less than 2% of donations. The probability of a relatively high frequency of other sexually transmissible blood-borne infections also currently favours maintaining permanent de-selection of MSM, irrespective of the risk of HIV-infectious donations. Current compliance with this selection criteria was estimated to be 95%. CONCLUSIONS: Based on current knowledge, accepting blood donations from MSM would probably increase the risk of transfusion-transmission of HIV and of other blood-borne infections. Good compliance with this criterion has contributed greatly to the safety of blood transfusions in England. Better communication about donor selection, to maintain and improve compliance with this and other selection criteria, is recommended. Other risk groups are gaining in relative importance for the risk of transfusion-transmitted HIV infection, and ongoing evaluation of all donor-selection criteria is also recommended.

Adolescent↗
PubMed2004

Ecological and social effects on reproduction and local recruitment in the red-backed shrike.

Numerous hypotheses have been proposed to explain variation in reproductive performance and local recruitment of animals. While most studies have examined the influence of one or a few social and ecological factors on fitness traits, comprehensive analyses jointly testing the relative importance of each of many factors are rare. We investigated how a multitude of environmental and social conditions simultaneously affected reproductive performance and local recruitment of the red-backed shrike Lanius collurio (L.). Specifically, we tested hypotheses relating to timing of breeding, parental quality, nest predation, nest site selection, territory quality, intraspecific density and weather. Using model selection procedures, predictions of each hypothesis were first analysed separately, before a full model was constructed including variables selected in the single-hypothesis tests. From 1988 to 1992, 50% of 332 first clutches produced at least one fledgling, while 38.7% of 111 replacement clutches were successful. Timing of breeding, nest site selection, predation pressure, territory quality and intraspecific density influenced nest success in the single-hypothesis tests. The full model revealed that nest success was negatively associated with laying date, intraspecific density, and year, while nest success increased with nest concealment. Number of fledglings per successful nest was only influenced by nest concealment: better-camouflaged nests produced more fledglings. Probability of local recruitment was related to timing of breeding, parental quality and territory quality in the single-hypothesis tests. The full models confirmed the important role of territory quality for recruitment probability. Our results suggest that reproductive performance, and particularly nest success, of the red-backed shrike is primarily affected by timing of breeding, nest site selection, and intraspecific density. This study highlights the importance of considering many factors at the same time, when trying to evaluate their relative contributions to fitness and life history evolution.

Altitude↗
PubMed1998

TNF receptor-deficient mice reveal striking differences between several models of thymocyte negative selection.

Central tolerance depends upon Ag-mediated cell death in developing thymocytes. However, the mechanism of induced death is poorly understood. Among the known death-inducing proteins, TNF was previously found to be constitutively expressed in the thymus. The role of TNF in thymocyte negative selection was therefore investigated using TNF receptor (TNFR)-deficient mice containing a TCR transgene. TNFR-deficient mice displayed aberrant negative selection in two models: an in vitro system in which APC are cultured with thymocytes, and a popular in vivo system in which mice are treated with anti-CD3 Abs. In contrast, TNFR-deficient mice displayed normal thymocyte deletion in two Ag-induced in vivo models of negative selection. Current models of negative selection and the role of TNFR family members in this process are discussed in light of these results.

Animals↗
PubMed2005

The victor/FRST function for model quality estimation.

Scoring functions are widely used in the final step of model selection in protein structure prediction. This is of interest both for comparative modeling targets, where it is important to select the best model among a set of many good, "correct" ones, as well as for other (fold recognition or novel fold) targets, where the set may contain many incorrect models. A novel combination of four knowledge-based potentials recognizing different features of native protein structures is introduced and tested. The pairwise, solvation, hydrogen bond, and torsion angle potentials contain largely orthogonal information. Of these, the torsion angle potential is found to show the strongest correlation with model quality. Combining these features with a linear weighting function, it was possible to construct a robust energy function capable of discriminating native-like structures on several benchmarking sets. In a recent blind test (CAFASP-4 MQAP), the scoring function ranked consistently well and was able to reliably distinguish the correct template from an ensemble of high quality decoys in 52 of 70 cases (33 of 34 for comparative modeling). An executable version of the Victor/FRST function for Linux PCs is available for download from the URL http://protein.cribi.unipd.it/frst/.

Computational Biology↗
PubMed2001

Locally regularized spatiotemporal modeling and model comparison for functional MRI.

In this work we treat fMRI data analysis as a spatiotemporal system identification problem and address issues of model formulation, estimation, and model comparison. We present a new model that includes a physiologically based hemodynamic response and an empirically derived low-frequency noise model. We introduce an estimation method employing spatial regularization that improves the precision of spatially varying noise estimates. We call the algorithm locally regularized spatiotemporal (LRST) modeling. We develop a new model selection criterion and compare our model to the SPM-GLM method. Our findings suggest that our method offers a better approach to identifying appropriate statistical models for fMRI studies.

Arousal↗
PubMed1989

Sociobiology and the structural stability of behavior patterns.

A structural stability approach to population-genetic systems and to dynamic evolutionary games is attempted in order to examine the theoretical significance of sociobiological selection models. A criterion of weak selection is derived that is not restricted to differential reproduction in polymorphic systems but describes possible directions of evolutionary change in time scales governed by genetic mutation rates. The criterion applies to the problems of how the initial mutational basis of an adaptive trait may be established and how this may happen, for analogous traits, independently in different species. Two basic sociobiological concepts are reconsidered with reference to the criterion. It is shown that W. D. Hamilton's condition of increases in inclusive fitness due to altruistic interactions among kin expresses the structural instability of populations against the evolution of altruistic behavior. Using the dynamic approach to evolutionary game theory, it is demonstrated that if a behavioral phenotype is an evolutionarily stable strategy, it is structurally stable against perturbations of the fitness payoffs, provided selection is weak. These results are applied to material problems of the evolution of animal social behavior.

Animals↗
PubMed2006

Confounder selection in environmental epidemiology: assessment of health effects of prenatal mercury exposure.

PURPOSE: The purpose of the study is to compare different approaches to the identification of confounders needed for analyzing observational data. Whereas standard analysis usually is conducted as if the confounders were known a priori, selection uncertainty also must be taken into account. METHODS: Confounders were selected by using backward elimination (BE), change in estimate (CIE) method, Akaike information criterion, Bayesian information criterion (BIC), and an empirical approach using a priori information. A modified ridge regression estimator, which shrinks effects of confounders toward zero, also was considered. For each criterion, uncertainty in the estimated exposure effect was assessed by using bootstrap simulations for which confounders were selected in each sample. These methods were illustrated by using data for mercury neurotoxicity in Faroe Islands children. Point estimates and standard errors of mercury effects on confounder-sensitive neurobehavioral outcomes were calculated for each selection procedure. RESULTS: The full model and the empirical a priori model showed approximately the same precision, and these methods were (slightly) inferior to only modified ridge regression. Lower precisions were obtained by using BE with a low cutoff level, BIC, and CIE. CONCLUSIONS: Standard analysis ignores model selection uncertainty and is likely to yield overoptimistic inferences. Thus, the traditional BE procedure with p = 5% should be avoided. If data-dependent procedures are required for confounder identification, we recommend that inferences be based on bootstrap statistics to describe the selection process.

Animals↗
PubMed2005

Model comparison for risk assessment: a case study of contaminated groundwater.

Many environmental multimedia risk assessment models have been developed and widely used along with increasing sophistication of the risk assessment method. Despite of the considerable improvement, uncertainty remains a primary threat to the credibility of and users' confidence in the model-based risk assessments. In particular, it has been indicated that scenario and model uncertainty may affect significantly the assessment outcome. Furthermore, the uncertainty resulting from choosing different models has been shown more important than that caused by parameter uncertainty. Based on the relationship between exposure pathways and estimated risk results, this study develops a screening procedure to compare the relative suitability between potential multimedia models, which would facilitate the reduction of uncertainty due to model selection. MEPAS, MMSOILS, and CalTOX models, combined with Monte Carlo simulation, are applied to a realistic groundwater-contaminated site to demonstrate the process. It is also shown that the identification of important parameters and exposure pathways, and implicitly, the subsequent design of uncertainty reduction and risk management measures, would be better-formed.

Animals↗
PubMed2005

Development of a population pharmacokinetic model for carbamazepine based on sparse therapeutic monitoring data from pediatric patients with epilepsy.

BACKGROUND: Population models can be important extensions of therapeutic drug monitoring (TDM), as they allow estimation of individual pharmacokinetic parameters based on a small number of measured drug concentrations. OBJECTIVE: This study used a Bayesian approach to explore the utility of routinely collected and sparse TDM data (1 sample per patient) for carbamazepine (CBZ) monotherapy in developing a population pharmacokinetic (PPK) model for CBZ in pediatric patients that would allow prediction of CBZ concentrations for both immediate- and controlled-release formulations. METHODS: Patient and TDM data were obtained from a pediatric neurology outpatient database. Data were analyzed using an iterative 2-stage Bayesian algorithm and a nonparametric adaptive grid algorithm. Models were compared by final log likelihood, mean error (ME) as a measure of bias, and root mean squared error (RMSE) as a measure of precision. RESULTS: Fifty-seven entries with data on CBZ monotherapy were identified from the database and used in the analysis (36 from males, 21 from females; mean [SD] age, 9.1 [4.4] years [range, 2-21 years]). Preliminary models estimating clearance (Cl) or the elimination rate constant (K(el)) gave good prediction of serum concentrations compared with measured serum concentrations, but estimates of Cl and K(el) were highly correlated with estimates of volume of distribution (V(d)). Different covariate models were then tested. The selected model had zero-order input and had age and body weight as covariates. Cl (L/h) was calculated as K(el) . V(d), where K(el) = [K(i) - (K(s) . age)] and V(d) = [V(i) + (V(s) . body weight)]. Median parameter estimates were V(i) (intercept) = 11.5 L (fixed); V(s) (slope) = 0.3957 L/kg (range, 0.01200-1.5730); K(i) (intercept) = 0.173 h(-1) (fixed); and K(s) (slope) = 0.004487 h(-1) . y(-1) (range, 0.0001800-0.02969). The fit was good for estimates of steady-state serum concentrations based on prior values (population median estimates) (R = 0.468; R(2) = 0.219) but was even better for predictions based on individual Bayesian posterior values (R(2) = 0.991), with little bias (ME = -0.079) and good precision (RMSE = 0.055). CONCLUSIONS: Based on the findings of this study, sparse TDM data can be used for PPK modeling of CBZ clearance in children with epilepsy, and these models can be used to predict Cl at steady state in pediatric patients. However, to estimate additional pharmacokinetic model parameters (eg, the absorption rate constant and V(d)), it would be necessary to combine sparse TDM data with additional well-timed samples. This would allow development of more informative PPK models that could be used as part of Bayesian dose-individualization strategies.

Adolescent↗
PubMed2006

Weak selection and recent mutational changes influence polymorphic synonymous mutations in humans.

Recent large-scale genomic and evolutionary studies have revealed the small but detectable signature of weak selection on synonymous mutations during mammalian evolution, likely acting at the level of translational efficacy (i.e., translational selection). To investigate whether weak selection, and translational selection in particular, plays any role in shaping the fate of synonymous mutations that are present today in human populations, we studied genetic variation at the polymorphic level and patterns of evolution in the human lineage after human-chimpanzee separation. We find evidence that neutral mechanisms are influencing the frequency of polymorphic mutations in humans. Our results suggest a recent increase in mutational tendencies toward AT, observed in all isochores, that is responsible for AT mutations segregating at lower frequencies than GC mutations. In all, however, changes in mutational tendencies and other neutral scenarios are not sufficient to explain a difference between synonymous and noncoding mutations or a difference between synonymous mutations potentially advantageous or deleterious under a translational selection model. Furthermore, several estimates of selection intensity on synonymous mutations all suggest a detectable influence of weak selection acting at the level of translational selection. Thus, random genetic drift, recent changes in mutational tendencies, and weak selection influence the fate of synonymous mutations that are present today as polymorphisms. All of these features, neutral and selective, should be taken into account in evolutionary analyses that often assume constancy of mutational tendencies and complete neutrality of synonymous mutations.

Animals↗
PubMed2004

Reconstruction of 3-D head geometry from digitized point sets: an evaluation study.

In this paper, we evaluate different methods to estimate patient-specific scalp, skull, and brain surfaces from a set of digitized points from the target's scalp surface. The reconstruction problem is treated as a registration problem: An a priori surface model, consisting of the scalp, skull, and brain surfaces, is registered to the digitized surface points. The surface model is generated from segmented magnetic resonance (MR) volume images. We study both affine and free-form deformation (FFD) registration, the use of average models, the averaging of individual registration results, a model selection procedure, and statistical deformation models. The registration algorithms are mainly previously published, and the objective of this paper is to evaluate these methods in this particular application with sparse data. The main interest of this paper is to generate geometric head models for biomedical applications, such as electroencephalography and magnetoencephalographic. However, the methods can also be applied to other anatomical regions and to other application areas. The methods were validated using 15 MR volume images, from which the scalp, skull, and brain were manually segmented. The best results were achieved by averaging the results of the FFD registrations of the database: the mean distance from the manually segmented target surface to a deformed a priori model surface for the studied anatomical objects was 1.68-2.08 mm, depending on the point set used. The results support the use of the evaluated methods for the reconstruction of geometric models in applications with sparse data.

Brain↗
PubMed2004

Sparse modeling using orthogonal forward regression with PRESS statistic and regularization.

The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights regression models based on an approach of directly optimizing model generalization capability. This is achieved by utilizing the delete-1 cross validation concept and the associated leave-one-out test error also known as the predicted residual sums of squares (PRESS) statistic, without resorting to any other validation data set for model evaluation in the model construction process. Computational efficiency is ensured using an orthogonal forward regression, but the algorithm incrementally minimizes the PRESS statistic instead of the usual sum of the squared training errors. A local regularization method can naturally be incorporated into the model selection procedure to further enforce model sparsity. The proposed algorithm is fully automatic, and the user is not required to specify any criterion to terminate the model construction procedure. Comparisons with some of the existing state-of-art modeling methods are given, and several examples are included to demonstrate the ability of the proposed algorithm to effectively construct sparse models that generalize well.

Journal Article↗