Evaluation of d2, a microsatellite measure of inbreeding and outbreeding, in wolves with a known pedigree

We have evaluated a microsatellite measure proposed as an indicator of inbreeding and outbreeding using a captive wolf population with known inbreeding levels and founder sources. The measure, which is based on the difference in the repeat number for microsatellite alleles within an individual, was not more predictive of the known inbreeding coefficient than microsatellite heterozygosity (it was actually less predictive). We also found no support that the measure was predictive of the level of outbreeding. However, we could not determine if the measure was predictive of very low levels of inbreeding due to matings between remote relatives. Overall, it appears that the usefulness of this measure to identify individuals on the inbred-outbred continuum beyond that of heterozygosity and identify biologically important associations with fitness-related traits may be limited. We suggest that the measure be examined theoretically to determine when (and how much) the predictive value of the measure is different from that of heterozygosity for inbreeding or outbreeding levels in a variety of different scenarios.     

Features selection and architecture optimization in connectionist systems

In this paper, we propose a features selection measure and an architecture optimization procedure for Multi-Layer Perceptrons (MLP). The algorithm presented in this contribution employs a heuristic measure named HVS (Heuristic for Variable Selection). This new measure allows us to identify and select important variables in the features space. This can be achieved by eliminating redundant features and those which do not contain enough relevant information. The proposed measure is used in a new procedure aimed at selecting the "best" MLP architecture given an initial structure. Application results for two generic problems: regression and discrimination, demonstrates the proposed selection algorithm's effectiveness in identifying optimized connectionist models with higher accuracy. Finally, an extension of HVS, named epsilonHVS, is proposed for discriminative features detection and architecture optimization for Time Delay Neural Networks models (TDNN).     

Measures of Imbalance for Unbalanced Models

In this paper we present a procedure to measure the degree of imbalance of an unbalanced data set. The procedure is based on choosing an appropriate loglinear model for the subclass frequencies of the data. A measure of imbalance is then introduced as some function of the chi-squared statistic used in the goodness-of-fit test for the loglinear model. The proposed procedure can also be used to measure departures from certain types of balance, such as proportionality of subclass frequencies, partial balance, and last-stage uniformity.     

A simple and species-independent coding measure

We present a coding measure which is based on the statistical properties of the stop codons, and that is able to estimate accurately the variation of coding content along an anonymous sequence. As the stop codons play the same role in all the genomes (with very few exceptions) the measure turns out to be species-independent. We show results both for prokaryotic and for eukaryotic genomes, indicating, first, the accuracy of the measure, and, second, that better prediction is achieved if the measure is applied on homogeneous, isochore-like sequences than if it is applied following the standard moving window approach. Finally, we discuss on some of the possible applications of the measure.     

A new measure of time-varying association for shared frailty models with bivariate current status data

In this paper, a new measure for assessing the temporal variation in the strength of association in bivariate current status data is proposed. This novel measure is relevant for shared frailty models. We show that this measure is particularly convenient, owing to its connection with the relative frailty variance and its interpretability in suggesting appropriate frailty models. We introduce a method of estimation and standard errors for this measure. We discuss its properties and compare it to an existing measure of association applicable to current status data. Small sample performance of the measure in realistic scenarios is investigated using simulations. The methods are illustrated with bivariate serological survey data on a pair of infections, where the time-varying association is likely to represent heterogeneities in activity levels and/or susceptibility to infection.     

The repeatability of fluctuating asymmetry: a revision and extension

Fluctuating asymmetry (FA) is an imperfect measure of the developmental stability of an individual. Estimates of the heritability of FA will underestimate the heritability of developmental stability; the extent of this bias can be estimated and corrected by estimating the repeatability of developmental stability. This note corrects an error in a previous derivation of this repeatability using the absolute value of the difference between sides as the measure of FA, and derives the repeatability of developmental stability for another common measure of FA, the variance among sides within an individual.     

Of beta diversity,variance, evenness,and dissimilarity

The amount of variation in species composition among sampling units or beta diversity has become a primary tool for connecting the spatial structure of species assemblages to ecological processes. Many different measures of beta diversity have been developed. Among them, the total variance in the community composition matrix has been proposed as a single‐number estimate of beta diversity. In this study, I first show that this measure summarizes the compositional variation among sampling units after nonlinear transformation of species abundances. Therefore, it is not always adequate for estimating beta diversity. Next, I propose an alternative approach for calculating beta diversity in which variance is substituted by a weighted measure of concentration (i.e., an inverse measure of evenness). The relationship between this new measure of beta diversity and so‐called multiple‐site dissimilarity measures is also discussed.     

Weighted relative entropy for alignment-free sequence comparison based on Markov model

In this paper, we introduce a probabilistic measure for computing the similarity between two biological sequences without alignment. The computation of the similarity measure is based on the Kullback-Leibler divergence of two constructed Markov models. We firstly validate the method on clustering nine chromosomes from three species. Secondly, we give the result of similarity search based on our new method. We lastly apply the measure to the construction of phylogenetic tree of 48 HEV genome sequences. Our results indicate that the weighted relative entropy is an efficient and powerful alignment-free measure for the analysis of sequences in the genomic scale.     

A Cross Modal Performance-Based Measure of Sensory Stimuli Intricacy

We define a new measure of sensory stimuli which has the following properties: It is cross modal, performance based, robust, and well defined. We interpret this measure as the intricacy or complexity of the stimuli, yet its validity is independent of its interpretation. We tested the validity and cross modality of our measure using three olfactory and one visual experiment. In order to test the link between our measure and cognitive performance we also conducted an additional visual experiment. We found that our measure is correlated with the results of the well-established Rapid Serial Visual Presentation masking experiment. Specifically, ranking stimuli according to our measure was correlated at r = 0.75 (p < 0.002) with masking effectiveness. Thus, our novel measure of sensory stimuli provides a new quantitative tool for the study of sensory processing.     

A new method of testing for Hardy-Weinberg equilibrium and ordering populations

The assumption of Hardy-Weinberg equilibrium (HWE) among alleles in a nonevolving population is of fundamental importance in genetic studies. Deviation from HWE in a population usually indicates inbreeding, stratification and sometimes problems in genotyping. In populations of affected individuals, these deviations can also provide evidence for association. In this paper, we introduce a measure based on the Kullback-Leibler discrimination information function that quantifies the deviation from HWE in a population. We use this measure to order populations. We also propose a test for HWE based on an estimate of this measure. The test is a statistically consistent test of the null hypothesis for all alternatives and is very easy to implement. Our proposed test statistic is compared with an earlier, widely used, test. Finally, the use of the proposed new test is shown in an illustrative example.     

Transition Confidence and Modified Mean Values: Confidence Measures for Hypotheses of Character State Transition between Nodes and Ancestral State Optimizations

A measure is introduced to quantify the reliability of character state transitions and character state stasis (lack of character state change between nodes). The mean value of a character state at an internal node is altered to obtain a measure termed the modified mean. Modified mean values at connecting nodes are employed to determine the support for hypotheses of character state transition/stasis between the nodes. The lowest of the two neighboring modified mean values implied by an hypothesis of character state transition/stasis is used as a direct measure of support for that hypotheses of character state transformation or stasis between the respective nodes.     

A new distance measure for comparing sequence profiles based on path lengths along an entropy surface

We describe a new distance measure for comparing DNA sequence profiles. For this measure, columns in a multiple alignment are treated as character frequency vectors (sum of the frequencies equal to one). The distance between two vectors is based on minimum path length along an entropy surface. Path length is estimated using a random graph generated on the entropy surface and Dijkstra's algorithm for all shortest paths to a source. We use the new distance measure to analyze similarities within familes of tandem repeats in the C. elegans genome and show that this new measure gives more accurate refinement of family relationships than a method based on comparing consensus sequences.     

A tail strength measure for assessing the overall univariate significance in a dataset

We propose an overall measure of significance for a set of hypothesis tests. The 'tail strength' is a simple function of the p-values computed for each of the tests. This measure is useful, for example, in assessing the overall univariate strength of a large set of features in microarray and other genomic and biomedical studies. It also has a simple relationship to the false discovery rate of the collection of tests. We derive the asymptotic distribution of the tail strength measure, and illustrate its use on a number of real datasets.     

Identification of protein coding regions of rice genes using alternative spectral rotation measure and linear discriminant analysis

An improved method, called Alternative Spectral Rotation (ASR) measure, for predicting protein coding regions in rice DNA has been developed. The method is based on the Spectral Rotation (SR) measure proposed by Kotlar and Lavner, and its accuracy is higher than that of the SR measure and the Spectral Content (SC) measure proposed by Tiwari et al. In order to increase the identifying accuracy,we chose three different coding characters, namely the asymmetric, purine, and stop-codon variables as parameters, and an approving result was presented by the method of Linear Discriminant Analysis (LDA).     

Picking lowest-cost measurements for identifying unknowns

One way to identify an unknown item is to measure its traits and compare the measurements with those of items the unknown might be. This paper outlines a way to pick what to measure from given known traits to (1) minimize measurement cost and (2) ensure that measuring the picked traits is as good as measuring all the given traits for purposes of identifying unknowns. The paper also relates a computer program which has been written to do this, and the program's use with the Protein Atlas in minimizing the number of amino acids to measure in an experiment involving polypeptide identification.     

MTRAP: Pairwise sequence alignment algorithm by a new measure based on transition probability between two consecutive pairs of residues

Background  

Sequence alignment is one of the most important techniques to analyze biological systems. It is also true that the alignment is not complete and we have to develop it to look for more accurate method. In particular, an alignment for homologous sequences with low sequence similarity is not in satisfactory level. Usual methods for aligning protein sequences in recent years use a measure empirically determined. As an example, a measure is usually defined by a combination of two quantities (1) and (2) below: (1) the sum of substitutions between two residue segments, (2) the sum of gap penalties in insertion/deletion region. Such a measure is determined on the assumption that there is no an intersite correlation on the sequences. In this paper, we improve the alignment by taking the correlation of consecutive residues.     

The determination and measurement of functional disability in rheumatoid arthritis

Although functional outcome is frequently discussed and written about, it is often not clear what functional outcome is and how it can be measured. This paper introduces the concept of latent and observed measures of functional disability, and distinguishes between disability as a process measure and disability as an outcome measure. Using the Health Assessment Questionnaire as the main functional outcome measure in rheumatoid arthritis, we propose and discuss several methods for determining disability, and describe the implications of altering the disability course.     

An entropy-based measure of founder informativeness

Optimizing quantitative trait locus (QTL) mapping experiments requires a generalized measure of marker informativeness because variable information is obtained from different marker systems, marker distribution and pedigree types. Such a measure can be derived from the concept of Shannon entropy, a central concept in information theory. Here we introduce entropy-based founder informativeness (EFI), a new measure of information content generalized across pedigrees, maps, marker systems and mating configurations. We derived equations for inbred- and outbred-derived mapping populations. Mathematical properties of EFI include enhanced sensitivity to mapping population type and extension to any number of founders. To illustrate the use of EFI, we compared experimental designs for QTL mapping for three examples: (i) different marker systems for an F2 pedigree, (ii) different marker densities and sampling sizes for a BC1 pedigree and (iii) a comparison of haplotypic versus zygotic analyses of an outbred pedigree. As an a priori generalized measure of information content, EFI does not require phenotypic data for optimizing experimental designs for QTL mapping.     

On RASA

Relative Apparent Synapomorphy Analysis (RASA) was recently proposed as a way to measure phylogenetic signal, choose "optimal" outgroups, find long branches, and eliminate long-branch attraction. In this paper it is shown with simple examples that RASA has several problems. The null regression model used by RASA to measure phylogenetic signal does not have a straightforward relation to phylogenetic information. RASA detects long branches, but does not discriminate between long branches that mislead an analysis and those that do not. Rooted RASA, which is used for "optimal outgroup analysis," is shown to be an inappropriate measure of "+esiomorphy content".     

The fitness value of information

Communication and information are central concepts in evolutionary biology. In fact, it is hard to find an area of biology where these concepts are not used. However, quantifying the information transferred in biological interactions has been difficult. How much information is transferred when the first spring rainfall hits a dormant seed, or when a chick begs for food from its parent? One measure that is commonly used in such cases is fitness value: by how much, on average, an individual's fitness would increase if it behaved optimally with the new information, compared to its average fitness without the information. Another measure, often used to describe neural responses to sensory stimuli, is the mutual information – a measure of reduction in uncertainty, as introduced by Shannon in communication theory. However, mutual information has generally not been considered to be an appropriate measure for describing developmental or behavioral responses at the organismal level, because it is blind to function; it does not distinguish between relevant and irrelevant information. In this paper we show that there is in fact a surprisingly tight connection between these two measures in the important context of evolution in an uncertain environment. In this case, a useful measure of fitness benefit is the increase in the long‐term growth rate, or the fold increase in number of surviving lineages. We show that in many cases the fitness value of a developmental cue, when measured this way, is exactly equal to the reduction in uncertainty about the environment, as described by the mutual information.