Resolvability of fluorescence lifetime distributions using phase fluorometry.

The analysis of the fluorescence decay using discrete exponential components assumes that a small number of species is present. In the absence of a definite kinetic model or when a large number of species is present, the exponential analysis underestimates the uncertainty of the recovered lifetime values. A different approach to determine the lifetime of a population of molecules is the use of probability density functions and lifetime distributions. Fluorescence decay data from continuous distributions of exponentially decaying components were generated. Different magnitudes of error were added to the data to simulate experimental conditions. The resolvability of the distributional model was studied by fitting the simulated data to one and two exponentials. The maximum width of symmetric distributions (uniform, gaussian, and lorentzian), which cannot be distinguished from single and double exponential fits for statistical errors of 1 and 0.1%, were determined. The width limits are determined by the statistical error of the data. It is also shown that, in the frequency domain, the discrete exponential analysis does not uniformly weights all the components of a distribution. This systematic error is less important when probability and distribution functions are used to recover the decay. Finally, it is shown that real lifetime distributions can be proved using multimodal probability density functions. In the companion paper that follows we propose a physical approach, which provides lifetime distribution functions for the tryptophan decay in proteins. In the third companion paper (Alcala, J.R., E. Gratton, and F.J. Prendergast, 1987, Biophys. J., in press) we use the distribution functions obtained to fit data from the fluorescence decay of single tryptophan proteins.     

The MIAmaxent R package: Variable transformation and model selection for species distribution models

The widely used “Maxent” software for modeling species distributions from presence‐only data (Phillips et al., Ecological Modelling, 190, 2006, 231) tends to produce models with high‐predictive performance but low‐ecological interpretability, and implications of Maxent's statistical approach to variable transformation, model fitting, and model selection remain underappreciated. In particular, Maxent's approach to model selection through lasso regularization has been shown to give less parsimonious distribution models—that is, models which are more complex but not necessarily predictively better—than subset selection. In this paper, we introduce the MIAmaxent R package, which provides a statistical approach to modeling species distributions similar to Maxent's, but with subset selection instead of lasso regularization. The simpler models typically produced by subset selection are ecologically more interpretable, and making distribution models more grounded in ecological theory is a fundamental motivation for using MIAmaxent. To that end, the package executes variable transformation based on expected occurrence–environment relationships and contains tools for exploring data and interrogating models in light of knowledge of the modeled system. Additionally, MIAmaxent implements two different kinds of model fitting: maximum entropy fitting for presence‐only data and logistic regression (GLM) for presence–absence data. Unlike Maxent, MIAmaxent decouples variable transformation, model fitting, and model selection, which facilitates methodological comparisons and gives the modeler greater flexibility when choosing a statistical approach to a given distribution modeling problem.     

A New Distribution-Free Approach to Constructing the Confidence Region for Multiple Parameters

Construction of confidence intervals or regions is an important part of statistical inference. The usual approach to constructing a confidence interval for a single parameter or confidence region for two or more parameters requires that the distribution of estimated parameters is known or can be assumed. In reality, the sampling distributions of parameters of biological importance are often unknown or difficult to be characterized. Distribution-free nonparametric resampling methods such as bootstrapping and permutation have been widely used to construct the confidence interval for a single parameter. There are also several parametric (ellipse) and nonparametric (convex hull peeling, bagplot and HPDregionplot) methods available for constructing confidence regions for two or more parameters. However, these methods have some key deficiencies including biased estimation of the true coverage rate, failure to account for the shape of the distribution inherent in the data and difficulty to implement. The purpose of this paper is to develop a new distribution-free method for constructing the confidence region that is based only on a few basic geometrical principles and accounts for the actual shape of the distribution inherent in the real data. The new method is implemented in an R package, distfree.cr/R. The statistical properties of the new method are evaluated and compared with those of the other methods through Monte Carlo simulation. Our new method outperforms the other methods regardless of whether the samples are taken from normal or non-normal bivariate distributions. In addition, the superiority of our method is consistent across different sample sizes and different levels of correlation between the two variables. We also analyze three biological data sets to illustrate the use of our new method for genomics and other biological researches.     

Heterogeneity versus homogeneity: A conceptual and mathematical theory in terms of scale-invariant and scale-covariant distributions

Ecologists often use the words heterogeneity and homogeneity when they are describing the distribution of a variable, yet there has been no formal exploration into the relation of these two states to one another. This work formalizes their relationship within statistical theory with a conceptual framework that includes five parameters: L, K, S, D, and R. These parameters provide an increasing degree of specificity about a distribution as they are enumerated and accord with measurements of system extent, richness, evenness, variance, and scale-covariance. Moreover, a mathematical and physical basis for understanding heterogeneity and homogeneity is outlined in terms of Brownian motion, where the Hurst exponent H and the notion of variance are utilized to delimit these two states. Spatial and temporal patterns are quantified and classified according to the mathematical and physical basis at multiple scales, as well as within the conceptual framework that can be applied to other metrics. Concepts of scale-invariance and scale-covariance are discussed in terms of hierarchy theory. It is argued that in hierarchical distributions, we should expect more than simple scale-covariance; we should expect division by a transitional state that divides heterogeneity from homogeneity at some scale. By revisiting the underlying statistical theory behind these concepts, a more efficient approach to quantifying ecological distributions can result.     

Superstatistics analysis of the ion current distribution function: Met3PbCl influence study

A novel analysis of ion current time series is proposed. It is shown that higher (second, third and fourth) statistical moments of the ion current probability distribution function (PDF) can yield new information about ion channel properties. The method is illustrated on a two-state model where the PDF of the compound states are given by normal distributions. The proposed method was applied to the analysis of the SV cation channels of vacuolar membrane of Beta vulgaris and the influence of trimethyllead chloride (Met₃PbCl) on the ion current probability distribution. Ion currents were measured by patch-clamp technique. It was shown that Met₃PbCl influences the variance of the open-state ion current but does not alter the PDF of the closed-state ion current. Incorporation of higher statistical moments into the standard investigation of ion channel properties is proposed.     

Statistical formulae of the energy distribution among a globular protein structure ensemble

In prediction of a protein main-chain structure into which a query sequence of amino acids folds, one evaluates the relative stability of a candidate structure against reference structures. We developed a statistical theory for calculating the energy distribution over a main-chain structure ensemble, only with an amino acid composition given as a single argument. Then, we obtained a statistical formulae of the ensemble mean and ensemble variance V[E] of the reference structural energies, as explicit functions of the amino acid composition. The mean and the variance V[E] calculated from the formulae were well or roughly consistent with those resulting from a gapless threading simulation. We can use the formulae not only to perform the high-through-put screening of sequences in the inverse folding problem, but also to handle the problem analytically.     

There Is No Need To Be Normal: Generalized Linear Models of Natural Variation

Both ecological field studies and attempts to extrapolate from laboratory experiments to natural populations generally encounter the high degree of natural variability and chaotic behavior that typify natural ecosystems. Regardless of this variability and non-normal distribution, most statistical models of natural systems use normal error which assumes independence between the variance and mean. However, environmental data are often random or clustered and are better described by probability distributions which have more realistic variance to mean relationships. Until recently statistical software packages modeled only with normal error and researchers had to assume approximate normality on the original or transformed scale of measurement and had to live with the consequences of often incorrectly assuming independence between the variance and mean. Recent developments in statistical software allow researchers to use generalized linear models (GLMs) and analysis can now proceed with probability distributions from the exponential family which more realistically describe natural conditions: binomial (even distribution with variance less than mean), Poisson (random distribution with variance equal mean), negative binomial (clustered distribution with variance greater than mean). GLMs fit parameters on the original scale of measurement and eliminate the need for obfuscating transformations, reduce bias for proportions with unequal sample size, and provide realistic estimates of variance which can increase power of tests. Because GLMs permit modeling according to the non-normal behavior of natural systems and obviate the need for normality assumptions, they will likely become a widely used tool for analyzing toxicity data. To demonstrate the broad-scale utility of GLMs, we present several examples where the use of GLMs improved the statistical power of field and laboratory studies to document the rapid ecological recovery of Prince William Sound following the Exxon Valdez oil spill.     

Application of public-domain statistical analysis software for evaluation and comparison of comet assay data

A novel approach for statistical analysis of comet assay data (i.e.: tail moment) is proposed, employing public-domain statistical software, the R system. The analytical strategy takes into account that the distribution of comet assay data, like the tail moment, is usually skewed and do not follow a normal distribution. Probability distributions used to model comet assay data included: the Weibull, the exponential, the logistic, the normal, the log normal and log-logistic distribution. In this approach it was also considered that heterogeneity observed among experimental units is a random feature of the comet assay data. This statistical model can be characterized with a location parameter m(ij), a scale parameter r and a between experimental units variability parameter theta. In the logarithmic scale, the parameter m(ij) depends additively on treatment and random effects, as follows: log(m(ij)) = a0 + a1x(ij) + b(i), where exp(a0) represents approximately the mean value of the control group, exp(a1) can be interpreted as the relative risk of damage with respect to the control group, x(ij) is an indicator of experimental group and exp(b(i)) is the individual risk effects assume to follows a Gamma distribution with mean 1 and variance theta. Model selection is based on Akaike's information criteria (AIC). Real data coming from comet analysis of blood samples taken from the flounder Paralichtys orbignyanus (Teleostei: Paralichtyidae) and from samples of cells suspension obtained from the estuarine polychaeta Laeonereis acuta (Nereididae) were employed. This statistical approach showed that the comet assay data should be analyzed under a modeling framework that take into account the important features of these measurements. Model selection and heterogeneity between experimental units play central points in the analysis of these data.     

FLEXURAL RIGIDITY OF CHIVE AND ITS RESPONSE TO WATER POTENTIAL

The biomechanical relationship between the ability of a plant organ to resist bending and the extent to which tissues are hydrated is illustrated for the cylindrical leaves of chive (Allium schoenoprasnum var. schoenoprasnum L.). The flexural rigidity (EI), which measures the ability to resist bending, is maximum when leaves are fully turgid and decreases monotonically as a function of water potential (r² = 0.99). Dehydration results in a reduction in the elastic modulus (E) of leaves. Reductions in E are correlated with geometric distortion in the transverse geometry of leaves which influences their second moment of inertia (I). The traditional theory of elastic stability (developed on the basis of the mechanical behavior of nonbiological systems) is shown to be inadequate to distinguish the behavior of E as plant organs geometrically distort during dehydration. This inadequacy results from the violation of a principal assumption made by the theory (= uniform cross-sectional geometry). A derivation is presented that accommodates the localized geometric distortions in cylindrical plant organs and permits a valid estimate of reductions in E as tissues dehydrate. Based on this derivation, the Young's modulus of chive leaves just before mechanical failure due to buckling is shown to be less than 50% of that calculated for fully turgid leaves.     

Long-term response to artificial selection with multiple alleles--study by simulations

The effect of multiple alleles on long-term response to selection is examined by simulations using a pseudosampling technique to simulate the multidimensional diffusion process. The effects of alleles are independently drawn from a normal distribution and the initial frequencies of alleles are assumed either to be equal or to be drawn from a neutral equilibrium population. With these two initial gene frequency distributions we examined various properties of the selection response process for the effects of number of alleles and selection intensity. For neutral initial frequencies the effects of multiple alleles compared with two alleles are minor on the ratio of final to initial response (E(R infinity/E(R1)) and the half life of response (t0.5), but are significant on the variance of response. Under certain conditions the variance of the selection limit can even increase as selection gets stronger. For equal initial frequencies the effects of multiple alleles are, however, minor on the ratio of the variance of the selection limit to the initial genetic variance, but E(R infinity/E(R1) and t0.5 increase as the number of alleles increases. The results show that for certain statistics the effects of multiple alleles can be minimized by an appropriate transformation of parameters for given initial gene frequencies, but the effects cannot, in general, be removed by any single transformation or reparameterization of parameters.     

Variation in human limb joint articular morphology

Objectives

Synovial joints in human limbs strike a balance between mobility, stability, and articular fit, yet little is known about how these conflicting demands pattern intraspecific variation in articular shape. In this study, we use geometric morphometrics to establish the apportionment and magnitude of morphological variance of the articular surfaces of the human shoulder, elbow, hip, and knee. We hypothesize that variances will be comparable between articulating surfaces within a joint and will be larger in joints with smaller ranges of motion, given their plurality of functional demands.

Materials and Methods

Three-dimensional landmarks were taken on the articular surfaces of the glenohumeral, humeroulnar, acetabulofemoral, and tibiofemoral joints from CT scans of 200 skeletons from the University of Tennessee Donated Skeletal Collection (84 females, 116 males). Root mean-squared distances between articulations calculated from Procrustes shape coordinates were used to determine variance distributions.

Results

We found no difference in variances for each articular surface between the sexes or between left and right articular surfaces. A high range of motion is associated with greater morphological variance; however, this pattern is largely driven by the concave articular surfaces of each joint, which consistently exhibit statistically greater variance than their convex counterparts.

Discussion

The striking pattern of differential variance between articulating morphologies points to potential disparities in development between them. Consistently higher variance in concave surfaces may relate to chondral modeling theory for the formation of joints. Establishing intraspecific morphological variance patterns is a first step in understanding coordinated evolution among articular features.     

A Mathematical Model for Evolution and SETI

Darwinian evolution theory may be regarded as a part of SETI theory in that the factor f_l in the Drake equation represents the fraction of planets suitable for life on which life actually arose. In this paper we firstly provide a statistical generalization of the Drake equation where the factor f_l is shown to follow the lognormal probability distribution. This lognormal distribution is a consequence of the Central Limit Theorem (CLT) of Statistics, stating that the product of a number of independent random variables whose probability densities are unknown and independent of each other approached the lognormal distribution when the number of factors increased to infinity. In addition we show that the exponential growth of the number of species typical of Darwinian Evolution may be regarded as the geometric locus of the peaks of a one-parameter family of lognormal distributions (b-lognormals) constrained between the time axis and the exponential growth curve. Finally, since each b-lognormal distribution in the family may in turn be regarded as the product of a large number (actually “an infinity”) of independent lognormal probability distributions, the mathematical way is paved to further cast Darwinian Evolution into a mathematical theory in agreement with both its typical exponential growth in the number of living species and the Statistical Drake Equation.     

Maximum Entropy Principle Based Estimation of Performance Distribution in Queueing Theory

In related research on queuing systems, in order to determine the system state, there is a widespread practice to assume that the system is stable and that distributions of the customer arrival ratio and service ratio are known information. In this study, the queuing system is looked at as a black box without any assumptions on the distribution of the arrival and service ratios and only keeping the assumption on the stability of the queuing system. By applying the principle of maximum entropy, the performance distribution of queuing systems is derived from some easily accessible indexes, such as the capacity of the system, the mean number of customers in the system, and the mean utilization of the servers. Some special cases are modeled and their performance distributions are derived. Using the chi-square goodness of fit test, the accuracy and generality for practical purposes of the principle of maximum entropy approach is demonstrated.     

Particle sizes from sectional data

Summary .  We propose a new statistical method for obtaining information about particle size distributions from sectional data without specific assumptions about particle shape. The method utilizes recent advances in local stereology. We show how to estimate separately from sectional data the variance due to the local stereological estimation procedure and the variance due to the variability of particle sizes in the population. Methods for judging the difference between the distribution of estimated particle sizes and the distribution of true particle sizes are also provided.     

A new demographic function maximized by life-history evolution

A goal of life-history theory has been to understand what combination of demographic traits is maximized by natural selection. In practice, researchers usually choose either density-independent population growth rate, lambda, or lifetime reproductive success, R0 (expected number of offspring produced in a lifetime). Others have shown that the maxima of density-independent lambda and R0 are evolutionarily stable strategies under specific density-dependent conditions: population regulation by equal density dependence among all age classes for lambda and by density dependence on a single age class for R0. Here I extend these connections between density-independent optimization models and density-dependent invasion function models in two ways. First, I derive a new demographic function for which a maximum corresponds to attainability of the equilibrium strategy or stability of the mean rather than stability of the variance of the strategy distribution. Second, I show explicitly a continuous range of cases with maxima between those for the lambda and R0. Graphical and biological interpretations are given for an example model. Finally, exceptions to a putative life-history generality (from lambda and R0 models), that high early-life mortality selects for high iteroparity, are shown.     

A finite mixture distribution model for data collected from twins.

Most analyses of data collected from a classical twin study of monozygotic (MZ) and dizygotic (DZ) twins assume that zygosity has been diagnosed without error. However, large scale surveys frequently resort to questionnaire-based methods of diagnosis which classify twins as MZ or DZ with less than perfect accuracy. This article describes a mixture distribution approach to the analysis of twin data when zygosity is not perfectly diagnosed. Estimates of diagnostic accuracy are used to weight the likelihood of the data according to the probability that any given pair is either MZ or DZ. The performance of this method is compared to fully accurate diagnosis, and to the analysis of samples that include some misclassified pairs. Conventional analysis of samples containing misclassified pairs yields biased estimates of variance components, such that additive genetic variance (A) is underestimated while common environment (C) and specific environment (E) components are overestimated. The bias is non-trivial; for 10% misclassification, true values of Additive genetic: Common environment: Specific Environment variance components of.6:.2:.2 are estimated as.48:.29:.23, respectively. The mixture distribution yields unbiased estimates, while showing relatively little loss of statistical precision for misclassification rates of 15% or less. The method is shown to perform quite well even when no information on zygosity is available, and may be applied when pair-specific estimates of zygosity probabilities are available.     

Distributed delays stabilize neural feedback systems

We consider the effect of distributed delays in neural feedback systems. The avian optic tectum is reciprocally connected with the isthmic nuclei. Extracellular stimulation combined with intracellular recordings reveal a range of signal delays from 3 to 9 ms between isthmotectal elements. This observation together with prior mathematical analysis concerning the influence of a delay distribution on system dynamics raises the question whether a broad delay distribution can impact the dynamics of neural feedback loops. For a system of reciprocally connected model neurons, we found that distributed delays enhance system stability in the following sense. With increased distribution of delays, the system converges faster to a fixed point and converges slower toward a limit cycle. Further, the introduction of distributed delays leads to an increased range of the average delay value for which the system's equilibrium point is stable. The system dynamics are determined almost exclusively by the mean and the variance of the delay distribution and show only little dependence on the particular shape of the distribution.     

Statistical evaluation of ion-channel gating models based on distributions of log-likelihood ratios

The distributions of log-likelihood ratios (DeltaLL) obtained from fitting ion-channel dwell-time distributions with nested pairs of gating models (Xi, full model; Xi(R), submodel) were studied both theoretically and using simulated data. When Xi is true, DeltaLL is asymptotically normally distributed with predictable mean and variance that increase linearly with data length (n). When Xi(R) is true and corresponds to a distinct point in full parameter space, DeltaLL is Gamma-distributed (2DeltaLL is chi-square). However, when data generated by an l-component multiexponential distribution are fitted by l+1 components, Xi(R) corresponds to an infinite set of points in parameter space. The distribution of DeltaLL is a mixture of two components, one identically zero, the other approximated by a Gamma-distribution. This empirical distribution of DeltaLL, assuming Xi(R), allows construction of a valid log-likelihood ratio test. The log-likelihood ratio test, the Akaike information criterion, and the Schwarz criterion all produce asymmetrical Type I and II errors and inefficiently recognize Xi, when true, from short datasets. A new decision strategy, which considers both the parameter estimates and DeltaLL, yields more symmetrical errors and a larger discrimination power for small n. These observations are explained by the distributions of DeltaLL when Xi or Xi(R) is true.     

Variance stabilization and normalization for one-color microarray data using a data-driven multiscale approach

MOTIVATION: Many standard statistical techniques are effective on data that are normally distributed with constant variance. Microarray data typically violate these assumptions since they come from non-Gaussian distributions with a non-trivial mean-variance relationship. Several methods have been proposed that transform microarray data to stabilize variance and draw its distribution towards the Gaussian. Some methods, such as log or generalized log, rely on an underlying model for the data. Others, such as the spread-versus-level plot, do not. We propose an alternative data-driven multiscale approach, called the Data-Driven Haar-Fisz for microarrays (DDHFm) with replicates. DDHFm has the advantage of being 'distribution-free' in the sense that no parametric model for the underlying microarray data is required to be specified or estimated; hence, DDHFm can be applied very generally, not just to microarray data. RESULTS: DDHFm achieves very good variance stabilization of microarray data with replicates and produces transformed intensities that are approximately normally distributed. Simulation studies show that it performs better than other existing methods. Application of DDHFm to real one-color cDNA data validates these results. AVAILABILITY: The R package of the Data-Driven Haar-Fisz transform (DDHFm) for microarrays is available in Bioconductor and CRAN.