Unbalancedness and Efficiency in Estimating Components of Variance: MINQUE and ANOVA Procedure

We consider the one-way classification random model. Using a measure of unbalancedness some MINQ-Estimation procedures and ANOVA-procedure have been investigated with respect to their sensibility to unbalancedness. Further if we define the efficiency of a design N as the ratio of the MSE (.)'s of the balanced and the unbalanced case we obtain for three MINQE-procedures upper and lower bounds of the minimum efficiency.     

On the Estimation of Heritability from Unbalanced Data

The distribution and moments, of ANOVA estimator of heritability are given under unbalanced random model. These expressions are used to investigate the effect of unbalancedness on the bias and variance/MSE of the estimator and also the validity of certain approximations for its variance, numerically. The computed results reveal that the unbalancedness increases both the bias and variance/MSE of the estimator and the Smith-approximation for the variance of the estimator provides better accuracy.     

On the Effect of Unbalancedness and Heteroscedasticity on the ANOVA Estimator of Group Variance Component in One-Way Random Model

The expression for rth cumulant of ANOVA estimator of group variance component is derived in the One-way unbalanced random model under heteroscedasticity. The expression is used to study the effect of unbalancedness and heteroscedasticity on the mean and variance of the estimator, numerically. The computed results reveal that the unbalancedness and heteroscedasticity have a combined effect on the mean and variance of the estimator. For certain situations of unequal group sizes and error variances, the mean and variance of the estimator are increased and for certain other situations the values are decreased.     

Effect of loss of balance on biomechanics platform measures of sway: influence of stance and a method for adjustment

This paper describes a method for adjusting biomechanics platform measures of sway for loss of balance. Area and velocity measures of sway were determined in forty-seven elderly women, in double and single leg stance, first with their eyes open, then closed. Subjects were rarely able to complete 10 s trials during single leg stances. Therefore, a method was developed for eliminating data associated with loss of balance. Monitoring changes in vertical force and velocity by computer, those points exceeding trial specific thresholds associated with loss of balance were truncated. In double leg stances, loss of balance increased area measures by 0.3%, but did not effect velocity measures. In contrast, the loss of balance increased area measures by 0-3%, but did not effect velocity measures. In contrast, the loss of balance experienced by most subjects in single leg stance exaggerated area measures by 16-38%, and velocity measures by up to 10%. In double leg stances the correlations between unadjusted area measures and area measures adjusted for loss of balance ranged from 0.98 to 1.00. In single leg stances, the correlations for the area measures ranged from 0.69 to 0.89. The correlations between adjusted and unadjusted velocity measures were 1.00 and 0.93 for the double and single leg stances respectively. Although the question of which sway measure is best remains unanswered, this study provides useful data for future research. First, it demonstrates a method for modifying area representations of the center of pressure excursions for the effects of loss of balance.(ABSTRACT TRUNCATED AT 250 WORDS)     

Baseline measures are altered in biomechanical studies

The purpose of this investigation was to examine if baseline measures are altered between conditions in biomechanical studies and to determine the need for baseline measurements in biomechanics. Ten runners were asked to run at varying speeds and obstacle heights. Baseline measures were acquired between all conditions. Right lower extremity kinematic and kinetic data were collected for all baseline trials and evaluated by both a group and a single subject analysis. The group analysis revealed significant differences between baselines only for the obstacle perturbation. The single subject analysis indicated that baseline measures are altered in a greater degree for kinematics than kinetics. These findings suggested that baseline measures are altered between conditions in biomechanical studies, and they should be used when a repeated measures or a single subject experimental design is being utilized.     

Correlations among measures of quality in HIV care in the United States: cross sectional study

Objective To determine whether a selected set of indicators can represent a single overall quality construct.Design Cross sectional study of data abstracted during an evaluation of an initiative to improve quality of care for people with HIV.Setting 69 sites in 30 states.Data sources Medical records of 9020 patients.Main outcome measures Adjusted performance rates at site level for eight measures of quality of care specific to HIV and a site level summary performance score (the number of measures for which the site was in the top quarter of the distribution).Results Of 28 site level correlations between measures, two were greater than 0.40, two were between 0.30 and 0.39, four were between 0.20 and 0.29, and the 20 remaining were all less than 0.20. One site was in the top quarter for seven measures, but no sites were in the top quarter for six or eight of the measures. Across the eight quality measures, sites were in the top quarter no more often than predicted by a chance (binomial) distribution.Conclusions The quality suggested by one measured indicator cannot necessarily be generalised to unmeasured indicators, even if this might be expected for clinical or other reasons.     

Seasonal Variation in the Capacity for Plant Trait Measures to Predict Grassland Carbon and Water Fluxes

There is a need for accurate predictions of ecosystem carbon (C) and water fluxes in field conditions. Previous research has shown that ecosystem properties can be predicted from community abundance-weighted means (CWM) of plant functional traits and measures of trait variability within a community (FDvar). The capacity for traits to predict carbon (C) and water fluxes, and the seasonal dependency of these trait-function relationships has not been fully explored. Here we measured daytime C and water fluxes over four seasons in grasslands of a range of successional ages in southern England. In a model selection procedure, we related these fluxes to environmental covariates and plant biomass measures before adding CWM and FDvar plant trait measures that were scaled up from measures of individual plants grown in greenhouse conditions. Models describing fluxes in periods of low biological activity contained few predictors, which were usually abiotic factors. In more biologically active periods, models contained more predictors, including plant trait measures. Field-based plant biomass measures were generally better predictors of fluxes than CWM and FDvar traits. However, when these measures were used in combination traits accounted for additional variation. Where traits were significant predictors their identity often reflected seasonal vegetation dynamics. These results suggest that database derived trait measures can improve the prediction of ecosystem C and water fluxes. Controlled studies and those involving more detailed flux measurements are required to validate and explore these findings, a worthwhile effort given the potential for using simple vegetation measures to help predict landscape-scale fluxes.     

Multivariate measures of similarity and niche overlap

Niche overlap measures are used to assess the similarity in resource use by two species. Recently researchers have used niche overlap measures as summary measures and for making inferences, typically about competition for resources. The problem of estimating niche overlap when the niches are multivariate normal distributions with equal covariance matrices has previously been studied. In this work, the assumption of equal covariance matrices is relaxed. Two general measures of similarity are evaluated assuming general multivariate normal distributions. Commonly used measures of overlap are given as special cases of these two general measures. The question of bias in estimating these measures is discussed and shown to be a potential problem, especially when there are many redundant variables or if sample sizes are small.     

Connections between classical and parametric network entropies

This paper explores relationships between classical and parametric measures of graph (or network) complexity. Classical measures are based on vertex decompositions induced by equivalence relations. Parametric measures, on the other hand, are constructed by using information functions to assign probabilities to the vertices. The inequalities established in this paper relating classical and parametric measures lay a foundation for systematic classification of entropy-based measures of graph complexity.     

Reliability and Minimum Detectable Change of Temporal-Spatial,Kinematic, and Dynamic Stability Measures during Perturbed Gait

Temporal-spatial, kinematic variability, and dynamic stability measures collected during perturbation-based assessment paradigms are often used to identify dysfunction associated with gait instability. However, it remains unclear which measures are most reliable for detecting and tracking responses to perturbations. This study systematically determined the between-session reliability and minimum detectable change values of temporal-spatial, kinematic variability, and dynamic stability measures during three types of perturbed gait. Twenty young healthy adults completed two identical testing sessions two weeks apart, comprised of an unperturbed and three perturbed (cognitive, physical, and visual) walking conditions in a virtual reality environment. Within each session, perturbation responses were compared to unperturbed walking using paired t-tests. Between-session reliability and minimum detectable change values were also calculated for each measure and condition. All temporal-spatial, kinematic variability and dynamic stability measures demonstrated fair to excellent between-session reliability. Minimal detectable change values, normalized to mean values ranged from 1–50%. Step width mean and variability measures demonstrated the greatest response to perturbations with excellent between-session reliability and low minimum detectable change values. Orbital stability measures demonstrated specificity to perturbation direction and sensitivity with excellent between-session reliability and low minimum detectable change values. We observed substantially greater between-session reliability and lower minimum detectable change values for local stability measures than previously described which may be the result of averaging across trials within a session and using velocity versus acceleration data for reconstruction of state spaces. Across all perturbation types, temporal-spatial, orbital and local measures were the most reliable measures with the lowest minimum detectable change values, supporting their use for tracking changes over multiple testing sessions. The between-session reliability and minimum detectable change values reported here provide an objective means for interpreting changes in temporal-spatial, kinematic variability, and dynamic stability measures during perturbed walking which may assist in identifying instability.     

Comparison of Profile Similarity Measures for Genetic Interaction Networks

Analysis of genetic interaction networks often involves identifying genes with similar profiles, which is typically indicative of a common function. While several profile similarity measures have been applied in this context, they have never been systematically benchmarked. We compared a diverse set of correlation measures, including measures commonly used by the genetic interaction community as well as several other candidate measures, by assessing their utility in extracting functional information from genetic interaction data. We find that the dot product, one of the simplest vector operations, outperforms most other measures over a large range of gene pairs. More generally, linear similarity measures such as the dot product, Pearson correlation or cosine similarity perform better than set overlap measures such as Jaccard coefficient. Similarity measures that involve L₂-normalization of the profiles tend to perform better for the top-most similar pairs but perform less favorably when a larger set of gene pairs is considered or when the genetic interaction data is thresholded. Such measures are also less robust to the presence of noise and batch effects in the genetic interaction data. Overall, the dot product measure performs consistently among the best measures under a variety of different conditions and genetic interaction datasets.     

A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data

Similarity or distance measures are core components used by distance-based clustering algorithms to cluster similar data points into the same clusters, while dissimilar or distant data points are placed into different clusters. The performance of similarity measures is mostly addressed in two or three-dimensional spaces, beyond which, to the best of our knowledge, there is no empirical study that has revealed the behavior of similarity measures when dealing with high-dimensional datasets. To fill this gap, a technical framework is proposed in this study to analyze, compare and benchmark the influence of different similarity measures on the results of distance-based clustering algorithms. For reproducibility purposes, fifteen publicly available datasets were used for this study, and consequently, future distance measures can be evaluated and compared with the results of the measures discussed in this work. These datasets were classified as low and high-dimensional categories to study the performance of each measure against each category. This research should help the research community to identify suitable distance measures for datasets and also to facilitate a comparison and evaluation of the newly proposed similarity or distance measures with traditional ones.     

Development of a symptom based outcome measure for asthma.

Measuring symptom specific health outcome is complex, but the methodologies now exist to develop measures with the appropriate properties. As one element of a major programme to develop multidomain health outcome measures for chronic disease, a symptom based measure for asthma care has been developed for use in general practice and outpatient departments. This article outlines the development process, which used a framework recently described in the theoretical literature to show the constraints that scientific criteria place on the development of outcome measures and the means of overcoming such limiting factors. Although substantial effort is required to undertake a rigorous process of development, useful tools are the result. Two five item, symptom based outcome measures for adult asthma are described.     

Algorithms to detect multiprotein modularity conserved during evolution

Detecting essential multiprotein modules that change infrequently during evolution is a challenging algorithmic task that is important for understanding the structure, function, and evolution of the biological cell. In this paper, we define a measure of modularity for interactomes and present a linear-time algorithm, Produles, for detecting multiprotein modularity conserved during evolution that improves on the running time of previous algorithms for related problems and offers desirable theoretical guarantees. We present a biologically motivated graph theoretic set of evaluation measures complementary to previous evaluation measures, demonstrate that Produles exhibits good performance by all measures, and describe certain recurrent anomalies in the performance of previous algorithms that are not detected by previous measures. Consideration of the newly defined measures and algorithm performance on these measures leads to useful insights on the nature of interactomics data and the goals of previous and current algorithms. Through randomization experiments, we demonstrate that conserved modularity is a defining characteristic of interactomes. Computational experiments on current experimentally derived interactomes for Homo sapiens and Drosophila melanogaster, combining results across algorithms, show that nearly 10 percent of current interactome proteins participate in multiprotein modules with good evidence in the protein interaction data of being conserved between human and Drosophila.     

Expected Shannon Entropy and Shannon Differentiation between Subpopulations for Neutral Genes under the Finite Island Model

Shannon entropy H and related measures are increasingly used in molecular ecology and population genetics because (1) unlike measures based on heterozygosity or allele number, these measures weigh alleles in proportion to their population fraction, thus capturing a previously-ignored aspect of allele frequency distributions that may be important in many applications; (2) these measures connect directly to the rich predictive mathematics of information theory; (3) Shannon entropy is completely additive and has an explicitly hierarchical nature; and (4) Shannon entropy-based differentiation measures obey strong monotonicity properties that heterozygosity-based measures lack. We derive simple new expressions for the expected values of the Shannon entropy of the equilibrium allele distribution at a neutral locus in a single isolated population under two models of mutation: the infinite allele model and the stepwise mutation model. Surprisingly, this complex stochastic system for each model has an entropy expressable as a simple combination of well-known mathematical functions. Moreover, entropy- and heterozygosity-based measures for each model are linked by simple relationships that are shown by simulations to be approximately valid even far from equilibrium. We also identify a bridge between the two models of mutation. We apply our approach to subdivided populations which follow the finite island model, obtaining the Shannon entropy of the equilibrium allele distributions of the subpopulations and of the total population. We also derive the expected mutual information and normalized mutual information (“Shannon differentiation”) between subpopulations at equilibrium, and identify the model parameters that determine them. We apply our measures to data from the common starling (Sturnus vulgaris) in Australia. Our measures provide a test for neutrality that is robust to violations of equilibrium assumptions, as verified on real world data from starlings.     

Patterns of sensitivity of association measures in numerical taxonomy

Numerical taxonomy and the related methods of multidimensional scaling (MDS) have been applied in a wide range of disciplines in the last twenty-five years. A common feature has been the initial derivation of a measure of association or proximity among pairs of objects from a multivariate data matrix. Although a large number of measures are in use, there has been little systematic study of the sensitivities of these measures to different aspects of character data. I report here a general model, for a class of these measures, that reveals basic patterns of sensitivity that underlie a wide variety of common measures and shows that there is a continuum of potential association measures which exhibit useful combinations of sensitivities. I distinguish between “separation” sensitive and “minimum value” sensitive measures and describe a new measure that is intermediate in exhibiting both minimum value and separation sensitivity. The utility of this new type of measure in three disciplines—ecology, psychology, and systematics—is briefly described.     

Saccadic Eye Movement Characteristics in Adult Niemann-Pick Type C Disease: Relationships with Disease Severity and Brain Structural Measures

Niemann-Pick Type C disease (NPC) is a rare genetic disorder of lipid metabolism. A parameter related to horizontal saccadic peak velocity was one of the primary outcome measures in the clinical trial assessing miglustat as a treatment for NPC. Neuropathology is widespread in NPC, however, and could be expected to affect other saccadic parameters. We compared horizontal saccadic velocity, latency, gain, antisaccade error percentage and self-paced saccade generation in 9 adult NPC patients to data from 10 age-matched controls. These saccadic measures were correlated with appropriate MRI-derived brain structural measures (e.g., dorsolateral prefrontal cortex, frontal eye fields, supplemental eye fields, parietal eye fields, pons, midbrain and cerebellar vermis) and with measures of disease severity and duration. The best discriminators between groups were reflexive saccade gain and the two volitional saccade measures. Gain was also the strongest correlate with disease severity and duration. Most of the saccadic measures showed strongly significant correlations with neurophysiologically appropriate brain regions. While our patient sample is small, the apparent specificity of these relationships suggests that as new diagnostic methods and treatments become available for NPC, a broader range of saccadic measures may be useful tools for the assessment of disease progression and treatment efficacy.     

Longitudinal assessment of antisaccades in patients with multiple sclerosis

We have previously demonstrated that assessment of antisaccades (AS) provides not only measures of motor function in multiple sclerosis (MS), but measures of cognitive control processes in particular, attention and working memory. This study sought to demonstrate the potential for AS measures to sensitively reflect change in functional status in MS. Twenty-four patients with relapsing-remitting MS and 12 age-matched controls were evaluated longitudinally using an AS saccade task. Compared to control subjects, a number of saccade parameters changed significantly over a two year period for MS patients. These included saccade error rates, latencies, and accuracy measures. Further, for MS patients, correlations were retained between OM measures and scores on the PASAT, which is considered the reference task for the cognitive evaluation of MS patients. Notably, EDSS scores for these patients did not change significantly over this period. These results demonstrate that OM measures may reflect disease evolution in MS, in the absence of clinically evident changes as measured using conventional techniques. With replication, these markers could ultimately be developed into a cost-effective, non-invasive, and well tolerated assessment tool to assist in confirming progression early in the disease process, and in measuring and predicting response to therapy.     

Information Content-Based Gene Ontology Functional Similarity Measures: Which One to Use for a Given Biological Data Type?

The current increase in Gene Ontology (GO) annotations of proteins in the existing genome databases and their use in different analyses have fostered the improvement of several biomedical and biological applications. To integrate this functional data into different analyses, several protein functional similarity measures based on GO term information content (IC) have been proposed and evaluated, especially in the context of annotation-based measures. In the case of topology-based measures, each approach was set with a specific functional similarity measure depending on its conception and applications for which it was designed. However, it is not clear whether a specific functional similarity measure associated with a given approach is the most appropriate, given a biological data set or an application, i.e., achieving the best performance compared to other functional similarity measures for the biological application under consideration. We show that, in general, a specific functional similarity measure often used with a given term IC or term semantic similarity approach is not always the best for different biological data and applications. We have conducted a performance evaluation of a number of different functional similarity measures using different types of biological data in order to infer the best functional similarity measure for each different term IC and semantic similarity approach. The comparisons of different protein functional similarity measures should help researchers choose the most appropriate measure for the biological application under consideration.     

Supply Curves for Eco-efficient Environmental Improvements Using Different Weighting Methods

Eco-efficiency implies environmental improvement at the lowest possible cost. When several environmental measures are possible, these can be ranked according to their cost per unit of environmental improvement, after which an eco-efficient set of measures can be selected that yields a given level of environmental improvement at least cost. This procedure can be visualized as a supply curve for environmental improvement. Such a curve plots cumulative cost or cost per unit of environmental improvement against cumulative environmental improvement, with measures ordered according to increasing cost per unit of environmental improvement. This paper presents supply curves for environmental improvement on the basis of a set of possible environmental measures for a company in the Dutch oil and gas producing industry.
To measure aggregated potential environmental improvement for a given measure, different environmental impacts are lumped together using weighting factors. We compare five methods for weighting environmental impacts, covering a wide range of current practices.
The supply curves that were determined for each of the five weighting methods show relatively small differences, except for one (the distance-to-target method). The ranking of measures differs significantly, though, and as a result, so do the measures that are selected if total costs are restricted to a certain budget. Also, the consequent reduction in emissions of specific substances, in particular nitrogen oxides (NO x ), depends on the weighting method selected.