A Note on Balanced Sampling and Robust Genetic Predictors

The concept of balanced sampling is applied to prediction in finite samples using model based inference procedures. Necessary and sufficient conditions are derived for a general linear model with arbitrary covariance structure to yield the expansion estimator as the best linear unbiased predictor for the mean. The analysis is extended to produce a robust estimator for the mean squared error under balanced sampling and the results are discussed in the context of statistical genetics where appropriate sampling produces simple efficient and robust genetic predictors free from unnecessary genetic assumptions.     

Geographical and taxonomic biases in invasion ecology

Invasive alien species come from most taxonomic groups, and invasion biology is searching for robust cross-taxon generalizations and principles. An analysis of 2,670 papers dealing with 892 invasive species showed that all major groups of invaders are well studied, but that most information on the mechanisms of invasion has emerged from work on a limited number of the most harmful invaders. A strong geographical bias, with Africa and Asia understudied, inhibits a balanced understanding of invasion, because we might be lacking knowledge of specific invasion mechanisms from poorly studied, regionally specific habitats. International cooperation is required to achieve a more geographically balanced picture of biological invasions. Invasive species with the greatest impact are best studied, but more studies of species that are naturalized but not (yet) invasive are needed to improve understanding of the mechanisms acting during the naturalization phase of invasions and leading to successful invasion.     

The Sustainability Balanced Scorecard as a Framework for Eco-efficiency Analysis

To provide valuable support for successful decision-making, managers need a balanced set of financial and nonfinancial measures that represent different requirements, strategic goals, strategies, resources, and capabilities and the causal relationships between these domains. The balanced scorecard is such a measurement system. As an open system the balanced scorecard facilitates the consideration of sustainability issues. But enhanced balanced scorecards require a new type of data. This is where eco-efficiency analysis comes into play.
This article discusses the relationship between so-called sustainability balanced scorecards and eco-efficiency analysis. Eco-efficiency analysis not only provides a data source for sustainability balanced scorecards; in the perspective of environmental information systems it also serves as a link between the balanced scorecard and corporate environmental accounting systems so that eco-efficiency as a component of an environmental information system becomes an adapter with two interfaces, which are characterized in this article. The main focus is on the principle of cause and effect, its different forms, and the implications for the design of appropriate information system components.     

Ecosystem oceanography for global change in fisheries

Overexploitation and climate change are increasingly causing unanticipated changes in marine ecosystems, such as higher variability in fish recruitment and shifts in species dominance. An ecosystem-based approach to fisheries attempts to address these effects by integrating populations, food webs and fish habitats at different scales. Ecosystem models represent indispensable tools to achieve this objective. However, a balanced research strategy is needed to avoid overly complex models. Ecosystem oceanography represents such a balanced strategy that relates ecosystem components and their interactions to climate change and exploitation. It aims at developing realistic and robust models at different levels of organisation and addressing specific questions in a global change context while systematically exploring the ever-increasing amount of biological and environmental data.     

The Effect of Categorization on Balanced Incomplete Blocks and Latin Squares

Robustness in the various analysis of variance models should be assessed with regard to all the assumptions. Here we assess the effect of the continuity assumption. The models considered were chosen because they are in a sense fragile, and the effects might be anticipated to be more dramatic here. We identify at least one of the balanced incomplete block models for which severe categorization leads to tests with unacceptable sizes and aberrant power curves. Otherwise most of the models examined are generally satisfactory – that is, robust with respect to the continuity assumption.     

Robust estimation of multivariate covariance components

In many settings, such as interlaboratory testing, small area estimation in sample surveys, and heritability studies, investigators are interested in estimating covariance components for multivariate measurements. However, the presence of outliers can seriously distort estimates obtained using standard procedures such as maximum likelihood. We propose a procedure based on M-estimation for robustly estimating multivariate covariance components in the presence of outliers; the procedure applies to balanced and unbalanced data. We present an algorithm for computing the robust estimates and examine the performance of the estimator through a simulation study. The estimator is used to find covariance components and identify outliers in a study of variability of egg length and breadth measurements of American coots.     

Design and analysis of a robust genetic Muller C-element

This paper presents results on the design and analysis of a robust genetic Muller C-element. The Muller C-element is a standard logic gate commonly used to synchronize independent processes in most asynchronous electronic circuits. Synthetic biological logic gates have been previously demonstrated, but there remain many open issues in the design of sequential (state-holding) logic operations. Three designs are considered for the genetic Muller C-element: a majority gate, a toggle switch, and a speed-independent implementation. While the three designs are logically equivalent, each design requires different assumptions to operate correctly. The majority gate design requires the most timing assumptions, the speed-independent design requires the least, and the toggle switch design is a compromise between the two. This paper examines the robustness of these designs as well as the effects of parameter variation using stochastic simulation. The results show that robustness to timing assumptions does not necessarily increase reliability, suggesting that modifications to existing logic design tools are going to be necessary for synthetic biology. Parameter variation simulations yield further insights into the design principles necessary for building robust genetic gates. The results suggest that high gene count, cooperativity of at least two, tight repression, and balanced decay rates are necessary for robust gates. Finally, this paper presents a potential application of the genetic Muller C-element as a quorum-mediated trigger.     

When Do Allocations and Constructs Respect Material,Energy, Financial,and Production Balances in LCA and EEIO?

Conservation of mass and energy are essential to physical accounting, just as price and market balances are essential to economic accounting. These principles guide data collection and inventory compilation in industrial ecology. The resulting balanced surveys, however, can rarely be used directly for life cycle assessment (LCA) or environmentally extended input‐output (EEIO) analysis; some modeling is necessary to recast coproductions by multifunctional activities as monofunctional unit processes (a.k.a. Leontief production functions or technical “recipes”). This modeling is done with allocations in LCA and constructs in input‐output. In this article, we ask how these models respect or perturb the balances of the original inventory. Which allocations or constructs, applied to what type of data set, have the potential to simultaneously respect its multiple physical, financial, and market balances? Our analysis builds upon the recent harmonization of allocations and constructs and the ongoing development of multilayered supply and use inventory tables. We derive the necessary and sufficient conditions for balanced models, investigate the role of data aggregation, and clarify these models' relation to system expansion. We find that none of the modeling families in LCA and EEIO are balanced in general, but special data characteristics can allow for the respect of multiple balances. An analysis of these special cases allows for clear guidance for data compilation and methods integration.     

Development of an estuarine multi-metric fish index and its application to Irish transitional waters

An estuarine multi-metric fish index (EMFI) was developed and applied to Irish transitional waters. The index comprised a balanced and complimentary set of 14 metrics that represent four fish community attributes: species diversity and composition, species abundance, estuarine utilisation, and trophic composition. Reference conditions and metric scoring thresholds were developed using a combination of historical records, best available data, and expert judgement. The index was applied using representative and robust fish monitoring data collected using a suite of methods designed to cover a range of habitats and conditions. To ensure consistency and comparability, all systems were considered at the whole estuary level. A sensitivity analysis was carried out to assess the response of the EMFI under various scenarios of metric change; five metrics were consistently among the most influential on the EMFI in all scenarios of metric manipulation. The overall EMFI was significantly correlated with environmental condition as measured by two separate indicators of ecological state. Ecological status classes were also established based on the relationship between the EMFI and an index of human pressure. The EMFI provides a robust, sensitive, and integrated measure of the ecological status of fishes in transitional waters and meets the requirements of the EU Water Framework Directive.     

Application of "robust" methods to the analysis of collaborative trial data using bacterial colony counts

We describe techniques for the application of two methods, robust to the presence of "outliers", to the hierarchical analysis of variance of bacterial count data from collaborative trials. The techniques are tested against both artificially-generated data with known distributional parameters and actual trial results containing outliers. The relative merits of the robust methods are discussed in comparison with conventional ANOVA techniques.     

Robust PCA and classification in biosciences

MOTIVATION: Principal components analysis (PCA) is a very popular dimension reduction technique that is widely used as a first step in the analysis of high-dimensional microarray data. However, the classical approach that is based on the mean and the sample covariance matrix of the data is very sensitive to outliers. Also, classification methods based on this covariance matrix do not give good results in the presence of outlying measurements. RESULTS: First, we propose a robust PCA (ROBPCA) method for high-dimensional data. It combines projection-pursuit ideas with robust estimation of low-dimensional data. We also propose a diagnostic plot to display and classify the outliers. This ROBPCA method is applied to several bio-chemical datasets. In one example, we also apply a robust discriminant method on the scores obtained with ROBPCA. We show that this combination of robust methods leads to better classifications than classical PCA and quadratic discriminant analysis. AVAILABILITY: All the programs are part of the Matlab Toolbox for Robust Calibration, available at http://www.wis.kuleuven.ac.be/stat/robust.html.     

The use of mass balances to test and improve the estimates of carbon fluxes in an ecosystem

A method is presented to analyse the estimates of carbon fluxes in an ecosystem by demanding that (1) the carbon budget of each ecological group should be balanced and that (2) every carbon flux in these balanced budgets should fall within the range of experimentally obtained values. It is shown that the problem of finding an optimal balanced solution is equivalent to the solution of an overdetermined linear system in the Chebyshev norm. A second problem is to find the minimum and maximum of every flux that may still yield balanced budgets, under the condition that the remaining fluxes are within their estimated ranges. Both problems may be solved by linear programming. The method is illustrated with a small prototype model. Some results are shown of the analysis of a large data set from the Oosterschelde estuary, S.W. Netherlands. The results indicate that the method is powerful in reducing the uncertainties of fluxes. The method proves to be a useful tool both for decreasing the uncertainty of estimated fluxes without additional field work and (by indicating inconsistent or inconclusive data) for directing further research. In particular, consistency is a prerequisite for any simulation modeling effort.     

Functional analysis beyond enrichment: non-redundant reciprocal linkage of genes and biological terms

Functional analysis of large sets of genes and proteins is becoming more and more necessary with the increase of experimental biomolecular data at omic-scale. Enrichment analysis is by far the most popular available methodology to derive functional implications of sets of cooperating genes. The problem with these techniques relies in the redundancy of resulting information, that in most cases generate lots of trivial results with high risk to mask the reality of key biological events. We present and describe a computational method, called GeneTerm Linker, that filters and links enriched output data identifying sets of associated genes and terms, producing metagroups of coherent biological significance. The method uses fuzzy reciprocal linkage between genes and terms to unravel their functional convergence and associations. The algorithm is tested with a small set of well known interacting proteins from yeast and with a large collection of reference sets from three heterogeneous resources: multiprotein complexes (CORUM), cellular pathways (SGD) and human diseases (OMIM). Statistical Precision, Recall and balanced F-score are calculated showing robust results, even when different levels of random noise are included in the test sets. Although we could not find an equivalent method, we present a comparative analysis with a widely used method that combines enrichment and functional annotation clustering. A web application to use the method here proposed is provided at http://gtlinker.cnb.csic.es.     

Sparse balance: Excitatory-inhibitory networks with small bias currents and broadly distributed synaptic weights

Cortical circuits generate excitatory currents that must be cancelled by strong inhibition to assure stability. The resulting excitatory-inhibitory (E-I) balance can generate spontaneous irregular activity but, in standard balanced E-I models, this requires that an extremely strong feedforward bias current be included along with the recurrent excitation and inhibition. The absence of experimental evidence for such large bias currents inspired us to examine an alternative regime that exhibits asynchronous activity without requiring unrealistically large feedforward input. In these networks, irregular spontaneous activity is supported by a continually changing sparse set of neurons. To support this activity, synaptic strengths must be drawn from high-variance distributions. Unlike standard balanced networks, these sparse balance networks exhibit robust nonlinear responses to uniform inputs and non-Gaussian input statistics. Interestingly, the speed, not the size, of synaptic fluctuations dictates the degree of sparsity in the model. In addition to simulations, we provide a mean-field analysis to illustrate the properties of these networks.     

Design and analysis issues in quantitative proteomics studies

Quantitative proteomics is the comparison of distinct proteomes which enables the identification of protein species which exhibit changes in expression or post-translational state in response to a given stimulus. Many different quantitative techniques are being utilized and generate large datasets. Independent of the technique used, these large datasets need robust data analysis to ensure valid conclusions are drawn from such studies. Approaches to address the problems that arise with large datasets are discussed to give insight into the types of statistical analyses of data appropriate for the various experimental strategies that can be employed by quantitative proteomic studies. This review also highlights the importance of employing a robust experimental design and highlights various issues surrounding the design of experiments. The concepts and examples discussed within will show how robust design and analysis will lead to confident results that will ensure quantitative proteomics delivers.     

An Extended ANOVA F‐Test with Applications to the Heterogeneity Problem in Meta‐Analysis

The classical F‐test in the one‐way random effects ANOVA model is extended to solve the long outstanding problem of testing the between‐group variance on values also different from zero. This is done first for homoscedastic and heteroscedastic cases in not necessarily balanced models and secondly for balanced homoscedastic models. By simulation, the tests are shown to attain acceptable significance levels and high power even in data that do not follow the usual ANOVA model. An important application of the tests is given by the heterogeneity questions concerning the treatment effects across studies in meta‐analysis.