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PubMed2006

Application of random effects to the study of resource selection by animals.

1. Resource selection estimated by logistic regression is used increasingly in studies to identify critical resources for animal populations and to predict species occurrence. 2. Most frequently, individual animals are monitored and pooled to estimate population-level effects without regard to group or individual-level variation. Pooling assumes that both observations and their errors are independent, and resource selection is constant given individual variation in resource availability. 3. Although researchers have identified ways to minimize autocorrelation, variation between individuals caused by differences in selection or available resources, including functional responses in resource selection, have not been well addressed. 4. Here we review random-effects models and their application to resource selection modelling to overcome these common limitations. We present a simple case study of an analysis of resource selection by grizzly bears in the foothills of the Canadian Rocky Mountains with and without random effects. 5. Both categorical and continuous variables in the grizzly bear model differed in interpretation, both in statistical significance and coefficient sign, depending on how a random effect was included. We used a simulation approach to clarify the application of random effects under three common situations for telemetry studies: (a) discrepancies in sample sizes among individuals; (b) differences among individuals in selection where availability is constant; and (c) differences in availability with and without a functional response in resource selection. 6. We found that random intercepts accounted for unbalanced sample designs, and models with random intercepts and coefficients improved model fit given the variation in selection among individuals and functional responses in selection. Our empirical example and simulations demonstrate how including random effects in resource selection models can aid interpretation and address difficult assumptions limiting their generality. This approach will allow researchers to appropriately estimate marginal (population) and conditional (individual) responses, and account for complex grouping, unbalanced sample designs and autocorrelation.

Animals↗

PubMed2001

Is mixed effects modeling or naïve pooled data analysis preferred for the interpretation of single sample per subject toxicokinetic data?

The purpose of this study was to evaluate whether mixed effects modeling (MEM) performs better than either noncompartmental or compartmental naïve pooled data (NPD) analysis for the interpretation of single sample per subject pharmacokinetic (PK) data. Using PK parameters determined during a toxicokinetic study in rats, we simulated data sets that might emerge from similar experiments. Data sets were simulated with varying numbers of animals at each sampling time (4-48) and the number of samples taken (1-3) from each individual. Each data set was replicated 50 times and analyzed using several variations of MEM that differed in the assumptions made regarding intraindividual error, NPD, and a graphical noncompartmental method. These analyses attempted to retrieve the underlying parameter and covariate effect values. We compared these analysis methods with respect to how well the underlying values were retrieved. All analysis methods performed poorly with single sample per subject data but MEM gave less biased estimates under the simulated conditions used here. MEM performance increased when covariate effects were sought in the analysis compared with analyses seeking only PK parameters. Decreasing the number of animals used per sampling time from 48 to 16 did not influence the quality of parameter estimates but further reductions (< 16 animals per sampling time) resulted in a reduced proportion of acceptable estimates. Parameter estimate quality improved and worsened with MEM and NPD, respectively, when additional samples were obtained from each individual. Assumptions made regarding the magnitude of intraindividual error were unimportant with single sample per subject data but influenced parameter estimates if more samples were obtained from each individual. MEM is preferable to both NPD and noncompartmental approaches for the analysis of single sample per subject data but even with MEM estimates of clearance are often biased.

Animals↗

PubMed2001

Model-free approach to the dynamic interpretation of residual dipolar couplings in globular proteins.

The effects of internal motions on residual dipolar NMR couplings of proteins partially aligned in a liquid-crystalline environment are analyzed using a 10 ns molecular dynamics (MD) computer simulation of ubiquitin. For a set of alignment tensors with different orientations and rhombicities, MD-averaged dipolar couplings are determined and subsequently interpreted for different scenarios in terms of effective alignment tensors, average orientations of dipolar vectors, and intramolecular reorientational vector distributions. Analytical relationships are derived that reflect similarities and differences between motional scaling of dipolar couplings and scaling of dipolar relaxation data (NMR order parameters). Application of the self-consistent procedure presented here to dipolar coupling measurements of biomolecules aligned in different liquid-crystalline media should allow one to extract in a "model-free" way average orientations of dipolar vectors and specific aspects of their motions.

Computer Simulation↗

PubMedDate not supplied

Markov Network Analysis: suggestions for innovations in covariance structure analysis.

Studies of aging offer special methodological challenges to the researcher in that he must often examine the change of multiple correlated variables over time. We present a set of procedures that are specifically designed to model change in such multivariate situations. These procedures, which we will call Markov Network Analysis, are directly applicable to modeling change from longitudinal or serial data. In such cases, the parameters of the model have dynamic interpretations, e.g., as coefficients in positive or negative feedback loops. In cross-sectional data, one cannot directly estimate the dynamic coefficients but the model does show how certain dynamic interpretations can be made. Statistically, maximum likelihood estimation procedures are developed and presented. In the development of the statistical model, it is shown how the bias of sequential hypothesis testing, a frequent occurrence in the estimation of complex covariance structure models, may be reduced.

Age Factors↗

PubMed2006

Dream interpretation, affect, and the theory of neuronal group selection: Freud, Winnicott, Bion, and Modell.

The author uses a dream specimen as interpreted during psychoanalysis to illustrate Modell's hypothesis that Edelman's theory of neuronal group selection (TNGS) may provide a valuable neurobiological model for Freud's dynamic unconscious, imaginative processes in the mind, the retranscription of memory in psychoanalysis, and intersubjective processes in the analytic relationship. He draws parallels between the interpretation of the dream material with keen attention to affect-laden meanings in the evolving analytic relationship in the domain of psychoanalysis and the principles of Edelman's TNGS in the domain of neurobiology. The author notes how this correlation may underscore the importance of dream interpretation in psychoanalysis. He also suggests areas for further investigation in both realms based on study of their interplay.

Brain↗

PubMed2005

Structural model of human GAD65: prediction and interpretation of biochemical and immunogenic features.

The 65 kDa human isoform of glutamate decarboxylase, GAD65, plays a central role in neurotransmission in higher vertebrates and is a typical autoantigen in several human autoimmune diseases, such as insulin-dependent diabetes mellitus (IDDM), Stiff-man syndrome and autoimmune polyendocrine syndrome type I. In autoimmune diabetes, an attack of inflammatory cells to endocrine pancreatic beta-cells leads to their complete destruction, eventually resulting in the inability to produce sufficient insulin for the body's requirements. Even though the etiology of beta-cell destruction is still a matter of debate, the role and antigenic potency of GAD65 are widely recognized. Herein a model of GAD65 is presented, which is based on the recently solved crystal structures of mammalian DOPA decarboxylase and of bacterial glutamate decarboxylase. The model provides for the first time a detailed and accurate structure of the GAD65 subunit (all three domains) and of its dimeric quaternary assembly. It reveals the structural basis for specific antibody recognition to GAD65 as opposed to GAD67, the other human isoform, which shares 81% sequence similarity with GAD65 and is much less antigenic. Literature data on monoclonal antibody binding are perfectly consistent with the detailed features of the model, which allows explanation of several findings on GAD65 immunogenicity. Importantly, by analyzing the active site, we identified the residues most likely involved in catalysis and substrate recognition, paving the way for rational mutagenesis studies of the GAD65 reaction mechanism, specificity and inhibition.

Animals↗

PubMed1973

Theory of muscular contraction extended to groups of actin sites.

It was shown in an earlier paper how to connect, in principle, the biochemical states of a cross-bridge with the mechanics of muscular contraction, by the methods of statistical mechanics. The treatment applies to cross-bridges that are able to interact with only one actin site at a time. The present paper shows that it is a straightforward matter to extend the theory to groups of actin sites (three, five, etc.), say 55 A apart, as suggested by the work of Moore, H. E. Huxley, and DeRosier. The possibility of the cross-bridge attachment slipping between sites is included. This provides an alternative molecular interpretation of the model introduced by A. F. Huxley and Simmons. A second possible interpretation is also suggested: their discrete stable angles correspond to different biochemical (attached) states. The Huxley-Simmons analysis of an example is rederived and extended somewhat (x averaging), from the point of view of the present theory. Their qualitative conclusions are left unchanged by the x averaging, but significant quantitative effects are possible. Possible consequences of fast slipping in isotonic contraction are discussed in a preliminary way.

Actins↗

PubMed2004

Statistical model with a standard Gamma distribution.

We study a statistical model consisting of N basic units which interact with each other by exchanging a physical entity, according to a given microscopic random law, depending on a parameter lambda. We focus on the equilibrium or stationary distribution of the entity exchanged and verify through numerical fitting of the simulation data that the final form of the equilibrium distribution is that of a standard Gamma distribution. The model can be interpreted as a simple closed economy in which economic agents trade money and a saving criterion is fixed by the saving propensity lambda. Alternatively, from the nature of the equilibrium distribution, we show that the model can also be interpreted as a perfect gas at an effective temperature T(lambda), where particles exchange energy in a space with an effective dimension D(lambda).

Journal Article↗

PubMed1999

Breeding biology of muscovy duck (Cairina moschata) under natural incubation: the use of the weibull function and a beta-binomial model to predict nest hatchability

In this paper we tested the Weibull function and beta-binomial distribution to analyse and predict nest hatchability, using empirical data on hatchability in Muscovy duck (Cairina moschata) eggs under natural incubation (932 successfully incubated nests and 11 822 eggs). The estimated parameters of the Weibull function and beta-binomial model were compared with the logistic regression analysis. The maximum likelihood estimation of the parameters was used to quantify simultaneously the influence of the nesting behaviour and the duration of the reproduction cycle on hatchability. The estimated parameters showed that the hatchability was not affected in natural dump nests, but in artificial dump nests and in nests with non-term eggs the hatchability was reduced by 10 and 25%, respectively. Similar results were obtained using logistic regression. Both models provided a satisfactory description of the observed data set, but the beta-binomial model proved to have more parameters with practical and biological meaningful interpretations, because this model is able to quantify and incorporate the unexplained variation in a single parameter theta (which is a variance measure). Copyright 1999 Academic Press.

Journal Article↗

PubMed1990

Contraceptive and sexual behavior of black female adolescents. A test of a social-psychological theoretical model.

To test a previous theoretical model, we studied the relationships between social, attitude, and psychological factors and adolescent contraceptive behavior over a 6-month period. A pretested questionnaire was administered to a random sample of 115 black females aged 12 to 18 years from a low socioeconomic population. Six months after the initial interview 113 of the subjects were administered a second questionnaire assessing their sexual and contraceptive behavior. Based on regression analysis, previous pregnancy and coital frequency explained 12.7% (p less than or equal to 0.0001) of the variation in engaging in unprotected coitus. When controlling for sexual activity, birth control knowledge and previous broken appointments explained an additional 5.5% of variation in the regression model (total R2 = 0.185). Coital frequency of the subjects was positively associated with Tanner stage, length of the relationship, effectiveness of previous contraceptive method(s), and degree of birth control knowledge. Coital frequency was inversely associated with a negative attitude toward pregnancy and negative parental attitudes toward adolescent pregnancy. The perceived risk of pregnancy was positively correlated with coital frequency and previous pregnancy. These findings support our model, but suggest that among low socioeconomic black female adolescents the interpretation of several model components depends on the subjects' attitude toward pregnancy and her perception of her parent's attitude toward adolescent pregnancy.

Adolescent↗

PubMed1997

Analysis of a model for the pathogenesis of AIDS.

According to a previously proposed mathematical model, the pathogenesis of acquired immunodeficiency syndrome (AIDS) could be explained by two phenomena: direct human immunodeficiency virus (HIV) infection of CD4+ T-cell populations and ongoing generation and selection of HIV mutants with increasing replicative capacity. In the present paper, the results obtained with this model are described in more detail. For different values of biologically interpretable parameters, the model predicts very different patterns of CD4+ T-cell decline after primary infection. With the assumption of a variability of 10% to 25% of three parameters between infected individuals, the model yields a realistic distribution curve of the incubation period to AIDS.

Acquired Immunodeficiency Syndrome↗

PubMed2006

Importance of the O-M-O bridges (M = V5+, Mo6+) for the spin-exchange interactions in the magnetic oxides of Cu2+ ions bridged by MO4 tetrahedra: spin-lattice models of Rb2Cu2(MoO4)3, BaCu2V2O8, and KBa3Ca4Cu3V7O28.

The spin-lattice models relevant for the magnetic oxides Rb2Cu2(MoO4)3, BaCu2V2O8, and KBa3Ca4Cu3V7O28 were determined by evaluating the relative strengths of the spin-exchange interactions between their Cu2+ ions on the basis of spin dimer analysis. Our study shows that the O-M-O bridges (M = V5+, Mo6+) between the magnetic ions Cu2+, provided by the MO4 tetrahedra, are crucial for the spin-exchange interactions and hence for deducing the spin-lattice models needed to interpret the magnetic properties of these oxides. The spin-lattice model of Rb2Cu2(MoO4)3 is not a uniform chain but two interpenetrating spin ladders that interact weakly with geometric spin frustration. The spin-lattice model of BaCu2V2O8 is an alternating chain as expected, but the spin-exchange paths responsible for it differ from those expected. With respect to the strongest spin exchange of BaCu2V2O8, the spin exchange of KBa3Ca4Cu3V7O28 is only slightly weaker, but the strongest spin exchange of Rb2Cu2(MoO4)3 is much weaker. This difference in the spin-exchange strengths is caused by the difference in the bridging modes of the MO4 tetrahedra leading to these spin-exchange interactions.

Journal Article↗

PubMed2001

15N NMR relaxation as a probe for helical intrinsic propensity: the case of the unfolded D2 domain of annexin I.

The isolated D2 domain of annexin I is unable to adopt a tertiary fold but exhibits both native and non-native residual structures. It thus constitutes an attractive model for the investigation of dynamics of partially folded states in the context of protein folding and stability. 15N relaxation parameters of the D2 domain have been acquired at three different magnetic fields, 500, 600 and 800 MHz. This enables the estimation of the contribution of conformational exchange to the relaxation parameters on the micro- to millisecond time scale, thus providing a suitable data set for the description of motions on the pico- and nanosecond time scale. The analysis of the seven spectral densities obtained (J(0), J(50 MHz), J(60 MHz), J(80 MHz), , , ) provides complementary and meaningful results on the conformational features of the D2 domain structure previously depicted by chemical shift and NOE data. Especially, residual helix segments exhibit distinct dynamical behaviors that are related to their intrinsic helical propensity. Beside the spectral density analysis, a series of models derived from the Lipari and Szabo model-free approach are investigated. Two models containing three parameters are able to reproduce equally well the experimental data within experimental errors but provide different values of order parameters and correlation times. The inability to find a unique model to describe the data emphasizes the difficulty to use and interpret the model-free parameters in the case of partially or fully unfolded proteins consisting of a wide range of interconverting conformers.

Annexin A1↗

PubMed2006

Disease mapping and spatial regression with count data.

In this paper, we provide critical reviews of methods suggested for the analysis of aggregate count data in the context of disease mapping and spatial regression. We introduce a new method for picking prior distributions, and propose a number of refinements of previously used models. We also consider ecological bias, mutual standardization, and choice of both spatial model and prior specification. We analyze male lip cancer incidence data collected in Scotland over the period 1975-1980, and outline a number of problems with previous analyses of these data. In disease mapping studies, hierarchical models can provide robust estimation of area-level risk parameters, though care is required in the choice of covariate model, and it is important to assess the sensitivity of estimates to the spatial model chosen, and to the prior specifications on the variance parameters. Spatial ecological regression is a far more hazardous enterprise for two reasons. First, there is always the possibility of ecological bias, and this can only be alleviated by the inclusion of individual-level data. For the Scottish data, we show that the previously used mean model has limited interpretation from an individual perspective. Second, when residual spatial dependence is modeled, and if the exposure has spatial structure, then estimates of exposure association parameters will change when compared with those obtained from the independence across space model, and the data alone cannot choose the form and extent of spatial correlation that is appropriate.

Epidemiologic Methods↗

PubMed1995

Prediction of adult cardiovascular multifactorial risk status from childhood risk factor levels. The Bogalusa Heart Study.

There is increasing interest in identifying children at risk for later development of cardiovascular disease. The authors studied 1,457 children who were first examined as part of the Bogalusa Heart Study in 1973 and again 15 years later as young adults. Age-, race-, and sex-specific quartiles were defined for each of three risk factor variables-ponderal index (weight/height3), systolic blood pressure, and cholesterol--for both the child and adult measures. Adults were classified as clustered if they were in the top quartile for each of the variables. Clustered adults had higher levels of several risk factor variables, in addition to the criteria variables, than did nonclustered individuals. Of children who placed in the top quartile on three factors, 21.8% were clustered as adults. Only 1.1% of those with no risk factor levels in the top quartile were clustered as adults (p < 0.0001). Logistic regression was used to predict adult cluster status from childhood variables levels. All three factors were significant predictors, with blood pressure being the most powerful. This well-fitting model is easily interpretable in terms of standard deviations and can be a useful model for identifying at-risk children.

Adolescent↗

PubMed2000

Imaging synaptic neurotransmission with in vivo binding competition techniques: a critical review.

Several groups have provided evidence that positron emission tomography (PET) and single-photon emission computed tomography (SPECT) neuroreceptor imaging techniques might be applied to measure acute fluctuations in dopamine (DA) synaptic concentration in the living human brain. Competition between DA and radioligands for binding to D2 receptor is the principle underlying this approach. This new application of neuroreceptor imaging provides a dynamic measurement of neurotransmission that is likely to be informative to our understanding of neuropsychiatric conditions. This article reviews and discusses the body of data supporting the feasibility and potential of this imaging paradigm. Endogenous competition studies performed in rodents, nonhuman primates, and humans are first summarized. After this overview, the validity of the model underlying the interpretation of these imaging data is critically assessed. The current reference model is defined as the occupancy model, since changes in radiotracer binding potential (BP) are assumed to be directly caused by changes in occupancy of D2 receptors by DA. Experimental data supporting this model are presented. The evidence that manipulation of DA synaptic levels induces change in the BP of several D2 radiotracers (catecholamines and benzamides) is unequivocal. The fact that these changes in BP are mediated by changes in DA synaptic concentration is well documented. The relationship between the magnitude of BP changes measured with PET or SPECT and the magnitude of changes in DA concentration measured by microdialysis supports the use of these noninvasive techniques to measure changes in neurotransmission. On the other hand, several observations remain unexplained. First, the amphetamine-induced changes in the BP of D2 receptor antagonists [123I]IBZM and [11C]raclopride last longer than amphetamine-induced changes in DA extracellular concentration. Second, nonbenzamide D2 receptor antagonists, such as spiperone and pimozide, are not affected by changes in DA release, or are affected in a direction opposite to that predicted by the occupancy model. Similar observations are reported with D1 radiotracers. These results suggest that the changes in BP following changes in DA concentration might not be fully accounted by a simple occupancy model. Specifically, the data are reviewed supporting that agonist-mediated receptor internalization might play an important role in characterizing receptor-ligand interactions. Finally, it is proposed that a better understanding of the mechanism underlying the effects observed with benzamides is essential to develop this imaging technique to other receptor systems.

Animals↗

PubMed2026

Research on identification of key genes and immune-metabolic mechanisms in atrial fibrillation through integrated multi-cohort transcriptomic analysis and machine learning.

This study aimed to integrate multiple datasets for the identification of atrial fibrillation (AF)-related differentially expressed genes (DEGs), analyze their underlying mechanisms through functional enrichment and machine learning, construct diagnostic models, and explore immune-metabolic interactions to provide novel biomarkers and theoretical foundations. Gene expression datasets were integrated and normalized, with batch effects removed using principal component analysis. Differential expression analysis, functional enrichment analysis (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways), and machine learning-based feature gene selection and model construction were performed. Shapley additive explanations analysis was utilized to interpret the constructed models, while gene set enrichment analysis, gene set variation analysis, and immune cell infiltration analysis were conducted to investigate the associations between feature genes and immune infiltration. After integrating and normalizing gene expression data and eliminating batch effects via principal component analysis, 6 DEGs were identified, including 4 upregulated and 2 down-regulated ones. Functional enrichment analysis showed these DEGs were significantly enriched in neuro-related biological processes and pathways, indicating their key roles in AF pathogenesis. Five key feature genes were selected using LASSO, random forest, and support vector machine-recursive feature elimination algorithms. They had significant expression differences between the AF and control groups (P < .001) and were located on distinct chromosomes. The constructed random forest and support vector machine models performed excellently (area under the curve ≥ 0.85). Shapley additive explanations analysis revealed TNNI1 contributed most to model prediction, with its expression significantly positively correlated with immune cell infiltration. Gene set enrichment analysis and gene set variation analysis analyses further showed feature genes participated in AF pathogenesis by regulating immune modulation, metabolic pathways, and autophagy. Immune cell infiltration analysis found altered proportions of T-cell subsets and M0 macrophages in the AF group, along with complex links between feature gene expression and immune cell function. This study systematically elucidated the unique gene expression patterns and key regulatory pathways associated with AF, clarifying the crucial roles of feature genes in immune regulation, metabolic imbalance, and cellular dysfunction. These findings provide a theoretical basis and potential therapeutic targets for understanding AF pathogenesis and developing targeted treatment strategies.

Atrial Fibrillation↗

PubMed2004

Breather statics and dynamics in Klein-Gordon chains with a bend.

In this paper, we examine a nonlinear model with an impurity emulating a bend. We justify the geometric interpretation of the model and connect it with earlier work on models including geometric effects. We focus on both the bifurcation and stability analysis of the modes that emerge as a function of the strength of the bend angle, but we also examine dynamical effects including the scattering of mobile localized modes (discrete breathers) off of such a geometric structure. The potential outcomes of such numerical experiments (including transmission, trapping within the bend as well as reflection) are highlighted and qualitatively explained. Such models are of interest both theoretically in understanding the interplay of breathers with curvature, but also practically in simple models of photonic crystals or of bent chains of DNA.

Journal Article↗