Different Aspects Of Variation Research Articles

Variability in Neural Circuit Formation.

The study of neural development is usually concerned with the question of how nervous systems get put together. Variation in these processes is usually of interest as a means of revealing these normative mechanisms. However, variation itself can be an object of study and is of interest from multiple angles. First, the nature of variation in both the processes and the outcomes of neural development is relevant to our understanding of how these processes and outcomes are encoded in the genome. Second, variation in the wiring of the brain in humans may underlie variation in all kinds of psychological and behavioral traits, as well as neurodevelopmental disorders. And third, genetic variation that affects circuit development provides the raw material for evolutionary change. Here, I examine these different aspects of variation in circuit development and consider what they may tell us about these larger questions.

Repeatability of mate preference functions in Enchenopa treehoppers (Hemiptera: Membracidae)

Patterns of variation in mate preferences provide information about the processes influencing the evolution of mate choice behaviour. We measured variation in female mate preferences using preference functions, which are curves that describe female responses across variation in male signals. Over 3 weeks of testing, we repeatedly measured female Enchenopa binotata ‘Ptelea’ treehoppers (Hemiptera: Membracidae) for their preference for male signal frequency (Hz). We then calculated the consistency of differences in mate preference functions among females (i.e. repeatability) and variability across females for the preference functions. We then measured traits describing different aspects of variation in mate preferences, including peak preference and selectivity. We found repeatability in the overall preference functions, indicating a potential for preference functions to respond to selection. Furthermore, we found substantial repeatability and variability in selectivity traits and low repeatability and variability in peak preference. Finally, we found only weak covariation between peak preference and selectivity. We discuss the implications of our results within the framework of the evolution of mate preferences, both in terms of past selection shaping mate preferences, and the potential for preferences to respond to current selection.

Scale transition theory: Its aims, motivations and predictions

Scale transition theory is an approach to understanding population and community dynamics in the presence of spatial or temporal variation in environmental factors or population densities. It focuses on changes in the equations for population dynamics as the scale enlarges. These changes are explained in terms of interactions between nonlinearities and variation on lower scales, and they predict the emergence of new properties on larger scales that are not predicted by lower scale dynamics in the absence of variation on those lower scales. These phenomena can be understood in terms of statistical inequalities arising from the process of nonlinear averaging, which translates the rules for dynamics from lower to higher scales. Nonlinearities in population dynamics are expressions of the fundamental biology of the interactions between individual organisms. Variation that interacts with these nonlinearities also involves biology fundamentally in several different ways. First, there are the aspects of biology that are sensitive to variation in space or time. These determine which aspects of a nonlinear dynamical equation are affected by variation, and whether different individuals or different species are sensitive to different extents or to different aspects of variation. Second is the nature of the variation, for example, whether it is variation in the physical environment or variation in population densities. From the interplay between variation and nonlinearities in population dynamics, scale transition theory builds a theory of changes in dynamics with changes in scale. In this article, the focus is on spatial variation, and the theory is illustrated with examples relevant to the dynamics of insect communities. In these communities, one commonly occurring nonlinear relationship is a negative exponential relationship between survival of an organism and the densities of natural enemies or competitors. This negative exponential has a biological origin in terms of independent actions of many individuals. The subsequent effects of spatial variation can be represented naturally in terms of Laplace transforms and related statistical transforms to obtain both analytical solutions and an extra level of understanding. This process allows us to analyze the meaning and effects of aggregation of insects in space. Scale transition theory more generally, however, does not aim to have fully analytical solutions but partial analytical solutions applicable for circumstances too complex for full analytical solution. These partial solutions are intended to provide a framework for understanding of numerical solutions, simulations and field studies where key quantities can be estimated from empirical data.

Morphological diversity of wing structure in Rhinolophoid bats (Chiroptera, Rhinolophoidea)

The results of studies on the morphological diversity of wing structure in horseshoe bats (Rhinol� ophoidea) are considered in relation to ecological differentiation within the superfamily. Various skeletal ele� ments of the wing are shown to form several groups of mutually correlated characters. The main groups com� prise the characters that account for the size of the wing and play a role in the formation of its outer contours. The distribution of characters shows that they describe different aspects of variation and can provide a fairly complete picture of the morphological and ecological structure of the superfamily. The results obtained by means of multidimensional scaling and clustering methods primarily reflect ecological types of bats and only then characterize their taxonomic relationships. Analysis of variation in structural features of the wing within the superfamily Rhinolophoidea shows that the same wing form in different families may be accounted for by different ratios of digit elements, primarily the lengths of distal and proximal phalanges of digits III and IV.

The use of variance components for the assessment of outcome measures in rheumatology

There is current interest in the development of new or improved outcome measures for rheumatological diseases. In the early stages of development, attention is usually directed to how well the measure distinguishes between patients and whether different observers attach similar values of the measure to the same patient. An approach, based on variance components, to the assessment of outcome measures is presented. The need to assess different aspects of variation associated with a measure is stressed. The terms ‘observer reliability’ and ‘agreement’ are frequently used in the evaluation of measurement instruments, and are often used interchangeably. In this paper, we use the terms to refer to different concepts assessing different aspects of variation. They are likely to correspond well in heterogeneous populations, but not in homogeneous populations where reliability will generally be low but agreement may well be high. Results from a real patient exercise, designed to study a set of tools for assessing myositis outcomes, are used to illustrate the approach that examines both reliability and agreement, and the need to evaluate both is demonstrated. A new measure of agreement, based on the ratio of standard deviations, is presented and inference procedures are discussed. To facilitate the interpretation of the combination of measures of reliability and agreement, a classification system is proposed that provides a summary of the performance of the tools. The approach is demonstrated for discrete ordinal and continuous outcomes.

Phenology of high-elevation pelagic bacteria: the roles of meteorologic variability, catchment inputs and thermal stratification in structuring communities

Many eukaryotic communities exhibit predictable seasonality in species composition, but such phenological patterns are not well-documented in bacterial communities. This study quantified seasonal variation in the community composition of bacterioplankton in a high-elevation lake in the Sierra Nevada of California over a 3-year period of 2004-2006. Bacterioplankton exhibited consistent phenological patterns, with distinct, interannually recurring community types characteristic of the spring snowmelt, ice-off and fall-overturn periods in the lake. Thermal stratification was associated with the emergence of specific communities each summer and increased community heterogeneity throughout the water column. Two key environmental variables modulated by regional meteorologic variation, lake residence time and thermal stability, predicted the timing of occurrence of community types each year with 75% accuracy, and each corresponded with different aspects of variation in community composition (orthogonal ordination axes). Seasonal variation in dissolved organic matter source was characterized fluorometrically in 2005 and was highly correlated with overall variation in bacterial community structure (r(Mantel)=0.75, P<0.001) and with the relative contributions of specific phylotypes within the Cyanobacteria, Actinobacteria and beta-Proteobacteria. The seasonal dynamics of bacterial clades (tracked through coupling of randomized clone sequence libraries to restriction fragment length polymorphism fingerprints) matched previous results from alpine lakes and were variously related to solute inputs, thermal stability and temperature. Taken together, these results describe a phenology of high-elevation bacterioplankton communities linked to climate-driven physical and chemical lake characteristics already known to regulate eukaryotic plankton community structure.

Bayesian meta-analysis, with application to studies of ETS and lung cancer

Meta-analysis enables researchers to combine the results of several studies to assess the information they provide as whole. It has been used to give a systematic overview of many areas in which data on a possible association between an exposure and an outcome have been collected in a number of studies but where the overall picture remains obscure, both as to the existence or size of the effect. This paper outlines some innovations in meta-analysis, based on using Markov chain Monte Carlo (MCMC) techniques for implementing Bayesian hierarchical models, and compares these with a more well-known random effects (RE) model. The new techniques allow different aspects of variation to be incorporated into descriptions of the association, and in particular enable researchers to better quantify differences between studies. Both the classical and Bayesian methods are applied, in this paper, to the current collection of studies of the association between incidence of lung cancer in female never-smokers and exposure to environmental tobacco smoke (ETS), both in the home through spousal smoking and in the workplace. In this paper it is demonstrated that compared with the RE model, the Bayesian methods: (a) allow more detailed modeling of study heterogeneity to be incorporated; (b) are relatively robust against a wide choice of specifications of such information on heterogeneity; (c) allow for more detailed and satisfactory statements to be made, not only about the overall risk but about the individual studies, on the basis of the combined information. For the workplace exposure data set, the Bayesian methods give a somewhat lower overall estimate of relative risk of lung cancer associated with ETS, indicating the care that needs to be taken in using point estimates based on any one method of analysis. On the larger spousal data set the methods give similar answers. Some of the other concerns with meta-analysis are also considered. These include: consistency between different geographic areas (Asia and the United States), and our studies show that Bayesian methods permit an account of the overall picture to be taken, thus improving the ability to estimate accurately in the subgroups; and publication bias which, as shown with the spousal exposure data, may lead to an inflated excess risk.

Bayesian meta-analysis, with application to studies of ETS and lung cancer

<h2>Abstract</h2> Meta-analysis enables researchers to combine the results of several studies to assess the information they provide as whole. It has been used to give a systematic overview of many areas in which data on a possible association between an exposure and an outcome have been collected in a number of studies but where the overall picture remains obscure, both as to the existence or size of the effect. This paper outlines some innovations in meta-analysis, based on using Markov chain Monte Carlo (MCMC) techniques for implementing Bayesian hierarchical models, and compares these with a more well-known random effects (RE) model. The new techniques allow different aspects of variation to be incorporated into descriptions of the association, and in particular enable researchers to better quantify differences between studies. Both the classical and Bayesian methods are applied, in this paper, to the current collection of studies of the association between incidence of lung cancer in female never-smokers and exposure to environmental tobacco smoke (ETS), both in the home through spousal smoking and in the workplace. In this paper it is demonstrated that compared with the RE model, the Bayesian methods: (a) allow more detailed modeling of study heterogeneity to be incorporated; (b) are relatively robust against a wide choice of specifications of such information on heterogeneity; (c) allow for more detailed and satisfactory statements to be made, not only about the overall risk but about the individual studies, on the basis of the combined information. For the workplace exposure data set, the Bayesian methods give a somewhat lower overall estimate of relative risk of lung cancer associated with ETS, indicating the care that needs to be taken in using point estimates based on any one method of analysis. On the larger spousal data set the methods give similar answers. Some of the other concerns with meta-analysis are also considered. These include: consistency between different geographic areas (Asia and the United States), and our studies show that Bayesian methods permit an account of the overall picture to be taken, thus improving the ability to estimate accurately in the subgroups; and publication bias which, as shown with the spousal exposure data, may lead to an inflated excess risk.

Variation Reduction and Designed Experiments

Summary This paper considers different aspects of variation and discusses two strategies for reducing variation caused by an identified source. The strategies involve controlling the source of the variation, or finding a special type of interaction between the source and a process control factor. The latter approach is further explained by examples using either traditional designed experiments or the product arrays proposed by Taguchi.

Variation within categories

Two aspects of variation within categories, relating to different models of categorization, were investigated — frequency of dimensional values and typicality differences within values. The influence of range of typicality experienced during learning and of informational value of feedback was also studied. Finally, differential forgetting of values was examined. In the experiment subjects learned to categorize faces, and then performed a classification test task and pairwise comparisons of faces. A variety of dependent variables was employed, including the galvanic skin response (GSR). Typicality and frequency of values appeared to influence categorization performance independent of each other. It was concluded that both prototype distance models and frequency models explain different aspects of variation within the same categories, and that models of classification should account for frequency of values in contrasting categories. Results showed furthermore (1) the influence of typicality range on the extension of a category; (2) no influence of specific feedback regarding representativeness of a face; (3) less decay with more important values; and (4) a positive relationship between uncertainty reduction and GSR.