Human uric acid transporter 1 (hURAT1): an inhibitor structure-activity relationship (SAR) study

The current study describes the chemical synthesis of a series of (2-ethylbenzofuran-3-yl)(substituted-phenyl)methanone compounds and their subsequent in vitro testing via oocytes expressing hURAT1. The experimental data support the notion that a potent hURAT1 inhibitor requires an anion (i.e., a formal negative charge) to interact with the positively charged hURAT1 binding pocket. An anion appears to be a primary requirement in order to be a hURAT1 substrate (i.e., urate) or inhibitor. We discuss the inhibitor structure-activity relationship and how electronically donating or withdrawing groups attached to the B-ring can decrease or increase inhibitory potency, respectively.     

On Variograms in Point Process Statistics

Variograms, which are frequently used in geostatistics, are of value also in the statistics of marked point processes. When the marks come from a random field which is independent of the point process, ideas of geostatistics suffice for the interpretation of point process variograms. When this model is not appropriate, interactions between the points lead to point process variograms having forms which are unusual in geostcistics. This is shown by three theoretical examples and one from forestry.     

Phylogenetic signal in tooth wear dietary niche proxies

In the absence of independent observational data, ecologists and paleoecologists use proxies for the Eltonian niches of species (i.e., the resource or dietary axes of the niche). Some dietary proxies exploit the fact that mammalian teeth experience wear during mastication, due to both tooth‐on‐tooth and food‐on‐tooth interactions. The distribution and types of wear detectible at micro‐ and macroscales are highly correlated with the resource preferences of individuals and, in turn, species. Because methods that quantify the distribution of tooth wear (i.e., analytical tooth wear methods) do so by direct observation of facets and marks on the teeth of individual animals, dietary inferences derived from them are thought to be independent of the clade to which individuals belong. However, an assumption of clade or phylogenetic independence when making species‐level dietary inferences may be misleading if phylogenetic niche conservatism is widespread among mammals. Herein, we test for phylogenetic signal in data from numerous analytical tooth wear studies, incorporating macrowear (i.e., mesowear) and microwear (i.e., low‐magnification microwear and dental microwear texture analysis). Using two measures of phylogenetic signal, heritability (H²) and Pagel's λ, we find that analytical tooth wear data are not independent of phylogeny and failing to account for such nonindependence leads to overestimation of discriminability among species with different dietary preferences. We suggest that morphological traits inherited from ancestral clades (e.g., tooth shape) influence the ways in which the teeth wear during mastication and constrain the foods individuals of a species can effectively exploit. We do not suggest that tooth wear is simply phylogeny in disguise; the tooth wear of individuals and species likely varies within some range that is set by morphological constraints. We therefore recommend the use of phylogenetic comparative methods in studies of mammalian tooth wear, whenever possible.     

Has the "Equal Environments" assumption been tested in twin studies?

A recurring criticism of the twin method for quantifying genetic and environmental components of human differences is the necessity of the so-called "equal environments assumption" (EEA) (i.e., that monozygotic and dizygotic twins experience equally correlated environments). It has been proposed to test the EEA by stratifying twin correlations by indices of the amount of shared environment. However, relevant environments may also be influenced by genetic differences. We present a model for the role of genetic factors in niche selection by twins that may account for variation in indices of the shared twin environment (e.g., contact between members of twin pairs). Simulations reveal that stratification of twin correlations by amount of contact can yield spurious evidence of large shared environmental effects in some strata and even give false indications of genotype x environment interaction. The stratification approach to testing the equal environments assumption may be misleading and the results of such tests may actually be consistent with a simpler theory of the role of genetic factors in niche selection.     

The design and analysis of microarray experiments: applications in parasitology

Microarray experiments can generate enormous amounts of data, but large datasets are usually inherently complex, and the relevant information they contain can be difficult to extract. For the practicing biologist, we provide an overview of what we believe to be the most important issues that need to be addressed when dealing with microarray data. In a microarray experiment we are simply trying to identify which genes are the most "interesting" in terms of our experimental question, and these will usually be those that are either overexpressed or underexpressed (upregulated or downregulated) under the experimental conditions. Analysis of the data to find these genes involves first preprocessing of the raw data for quality control, including filtering of the data (e.g., detection of outlying values) followed by standardization of the data (i.e., making the data uniformly comparable throughout the dataset). This is followed by the formal quantitative analysis of the data, which will involve either statistical hypothesis testing or multivariate pattern recognition. Statistical hypothesis testing is the usual approach to "class comparison," where several experimental groups are being directly compared. The best approach to this problem is to use analysis of variance, although issues related to multiple hypothesis testing and probability estimation still need to be evaluated. Pattern recognition can involve "class prediction," for which a range of supervised multivariate techniques are available, or "class discovery," for which an even broader range of unsupervised multivariate techniques have been developed. Each technique has its own limitations, which need to be kept in mind when making a choice from among them. To put these ideas in context, we provide a detailed examination of two specific examples of the analysis of microarray data, both from parasitology, covering many of the most important points raised.     

A simple and flexible Holm gatekeeping procedure

Major objectives of a clinical trial are commonly stated in a hierarchical order as primary and secondary. The parallel gatekeeping testing strategy provides an opportunity to assess secondary objectives when all or partial primary objectives are achieved. The current available gatekeeping procedures have different pros and cons so users either need to justify the assumption associated with some procedures or tolerate suboptimal power performance of other procedures. By applying the Holm test with a flexible alpha splitting technique, we propose a procedure which (1) is powerful for assessing the primary objectives, (2) can be used when no assumption can be made on the dependency structure of test statistics, and (3) has the full flexibility to allocate user-preferred alpha to assess the secondary objectives based on the number of primary objectives achieved. A real clinical trial example is used for illustration of the proposed procedure.     

A general framework for detecting disease associations with rare variants in sequencing studies

Biological and empirical evidence suggests that rare variants account for a large proportion of the genetic contributions to complex human diseases. Recent technological advances in high-throughput sequencing platforms have made it possible for researchers to generate comprehensive information on rare variants in large samples. We provide a general framework for association testing with rare variants by combining mutation information across multiple variant sites within a gene and relating the enriched genetic information to disease phenotypes through appropriate regression models. Our framework covers all major study designs (i.e., case-control, cross-sectional, cohort and family studies) and all common phenotypes (e.g., binary, quantitative, and age at onset), and it allows arbitrary covariates (e.g., environmental factors and ancestry variables). We derive theoretically optimal procedures for combining rare mutations and construct suitable test statistics for various biological scenarios. The allele-frequency threshold can be fixed or variable. The effects of the combined rare mutations on the phenotype can be in the same direction or different directions. The proposed methods are statistically more powerful and computationally more efficient than existing ones. An application to a deep-resequencing study of drug targets led to a discovery of rare variants associated with total cholesterol. The relevant software is freely available.     

Assessing isotropy for spatial point processes

A common assumption while analyzing spatial point processes is direction invariance, i.e., isotropy. In this article, we propose a formal nonparametric approach to test for isotropy based on the asymptotic joint normality of the sample second-order intensity function. We derive an L(2) consistent subsampling estimator for the asymptotic covariance matrix of the sample second-order intensity function and use this to construct a test statistic with a chi(2) limiting distribution. We demonstrate the efficacy of the approach through simulation studies and an application to a desert plant data set, where our approach confirms suspected directional effects in the spatial distribution of the desert plant species.     

Biosynthesis and metabolic pathways of pivalic acid

Occurrence, biosynthesis, and biodegradation of pivalic acid and other compounds, having a quaternary carbon atom by different bacteria, are described. We have summarized the relevant data that have so far been published, presenting them in a graphical form, i.e., as biodegradation pathways including B(12)-dependent isomerization and desaturation that lead to the degradation of pivalic acid and similar compounds to products with other than quaternary carbon atoms, i.e., compounds whose catabolism is well known.     

Group testing for case identification with correlated responses

This article examines group testing procedures where units within a group (or pool) may be correlated. The expected number of tests per unit (i.e., efficiency) of hierarchical- and matrix-based procedures is derived based on a class of models of exchangeable binary random variables. The effect on efficiency of the arrangement of correlated units within pools is then examined. In general, when correlated units are arranged in the same pool, the expected number of tests per unit decreases, sometimes substantially, relative to arrangements that ignore information about correlation.     

Character coupling for taxa discrimination: a critical appraisal of quadratic assignment procedures (QAP)1

Taxa can be characterized by character coupling represented in similarity matrices. The customary methods of testing equality of variance-covariance matrices are based upon the multinormality assumption which is, however, frequently unacceptable in reality. Quadratic assignment procedures (QAP) have proved to be an alternative. They represent a type of computer-based test and utilize a random-permutation strategy to discover significant pattern correspondences between matrices. A comparison of the applicability of both testing methods requires an example with underlying multinormality. The samples of two species of land snails (Pulmonata, Helicidae), i. e. Arianta arbustorum (n = 104) and Arianta chamaeleon (n = 36), fulfil this requirement. Four parameters of shape and two parameters of spiral change were determined in each shell. The data serve as the basis for similarity matrices (variance-covariance, product-moment and rank order correlations). The inspection of methods reveals that QAP are suitable for correlation matrices, but can be applied for variance-covariance matrices with limitations only. Nevertheless, they are recommended procedures in taxonomy and evolutionary biology. Straightforward application, independence from distributional assumptions, and the possibility to test hypotheses of character coupling are advantageous features. The snail species are significantly discriminated by character coupling. Also, their parameters of shape and spiral change are morphologically integrated in a different way.     

Too Many) Mathematical Models of Circadian Clocks (?

Many mathematical models derived from the principles obtained from empirical observations in chronobiology have been proposed and explored. They cover several organisms and phenomena, and utilize quite different formal approaches. These models can be divided into the ones that intend to describe pacemaker core function, such the Goodwin-oscillator family, non-genetic approaches or purely mathematical (i.e., without clear biochemical correlations) models, and the ones that represent events depending on pacemaker activity, i.e., photoperiodic phenomena. We aim to illustrate the diversity of mathematical and methodological approaches to describe circadian systems and related matters.     

(Too Many) Mathematical Models of Circadian Clocks (?)

Many mathematical models derived from the principles obtained from empirical observations in chronobiology have been proposed and explored. They cover several organisms and phenomena, and utilize quite different formal approaches. These models can be divided into the ones that intend to describe pacemaker core function, such the Goodwin-oscillator family, non-genetic approaches or purely mathematical (i.e., without clear biochemical correlations) models, and the ones that represent events depending on pacemaker activity, i.e., photoperiodic phenomena. We aim to illustrate the diversity of mathematical and methodological approaches to describe circadian systems and related matters.     

Enabling richer statistical MANET simulations through cluster computing

The wide-scale adoption of modern smart phones and other multi-radio mobile devices, has begun to provide pragmatic deployment environments for non-cellular mobile ad hoc network (MANET) services (i.e., for disaster recovery scenarios, peered mobile games, social networking applications, etc.). User perceptions of the quality of such MANET services will be driven, in part, by standard network-level quality of service (QoS) metrics such as delay, jitter, throughput, etc. Much of the existing MANET literature has explored these issues, as well as MANET routing protocol design, through single computer Monte Carlo simulations (e.g., via ns-2, ns-3, OMNeT++, or OpNet). Results are then reported as the averages of these Monte Carlo runs. As is well known from probability and statistics, such averaging is only meaningful when applied across statistically ergodic data (i.e., data drawn from the same underlying distribution). But, assessing the validity of this underlying ergodic assumption requires transitioning to more rigorous cluster-based MANET simulation frameworks. This work highlights the theoretical rationale for such ergodicity testing, the developments of a cluster-based framework, the STARs framework, to support such testing, and the results and insights obtained by using this framework to evaluate the popular DYMO and OLSR MANET routing protocols. This work also discusses why the insights ergodic testing provides are of interest to potential real-world MANET deployments.     

An analytical approach to spatio-temporal dynamics of neutral community models

We propose a spatial version of the neutral community model on a network of interconnected patches. The dynamical equations for the abundances and higher order moments of the abundances are established. Due to the neutrality assumption these equations are autonomous, enabling an exact analysis of spatio-temporal dynamics. We compute local (i.e., inside a patch) and global (i.e., between patches) diversities, and illustrate our results with two examples: (1) a non-spatial community, for which we recover previous results, and (2) a model with a finite number of patches which are all connected to each other with equal migration intensity. We discuss the relevance of this model for experiments in microbial ecology.     

Three-Dimensional Array-Based Group Testing Algorithms

Summary .  We derive the operating characteristics of three-dimensional array-based testing algorithms for case identification in the presence of testing error. The operating characteristics investigated include efficiency (i.e., expected number of tests per specimen) and error rates (e.g., sensitivity, specificity, positive, and negative predictive values). The methods are illustrated by comparing the proposed algorithms with previously studied hierarchical and two-dimensional array algorithms for detecting recent HIV infections in North Carolina. Our results indicate that three-dimensional array-based algorithms can be more efficient and accurate than previously proposed algorithms in settings with test error and low prevalence.     

Testing random effects in linear mixed‐effects models with serially correlated errors

In linear mixed‐effects models, random effects are used to capture the heterogeneity and variability between individuals due to unmeasured covariates or unknown biological differences. Testing for the need of random effects is a nonstandard problem because it requires testing on the boundary of parameter space where the asymptotic chi‐squared distribution of the classical tests such as likelihood ratio and score tests is incorrect. In the literature several tests have been proposed to overcome this difficulty, however all of these tests rely on the restrictive assumption of i.i.d. measurement errors. The presence of correlated errors, which often happens in practice, makes testing random effects much more difficult. In this paper, we propose a permutation test for random effects in the presence of serially correlated errors. The proposed test not only avoids issues with the boundary of parameter space, but also can be used for testing multiple random effects and any subset of them. Our permutation procedure includes the permutation procedure in Drikvandi, Verbeke, Khodadadi, and Partovi Nia (2013) as a special case when errors are i.i.d., though the test statistics are different. We use simulations and a real data analysis to evaluate the performance of the proposed permutation test. We have found that random slopes for linear and quadratic time effects may not be significant when measurement errors are serially correlated.     

Multitrait least squares for quantitative trait loci detection

A multiple-trait QTL mapping method using least squares is described. It is presented as an extension of a single-trait method for use with three-generation, outbred pedigrees. The multiple-trait framework allows formal testing of whether the same QTL affects more than one trait (i.e., a pleiotropic QTL) or whether more than one linked QTL are segregating. Several approaches to the testing procedure are presented and their suitability discussed. The performance of the method is investigated by simulation. As previously found, multitrait analyses increase the power to detect a pleiotropic QTL and the precision of its location estimate. With enough information, discrimination between alternative genetic models is possible.     

Guidelines for the design and statistical analysis of experiments using laboratory animals

For ethical and economic reasons, it is important to design animal experiments well, to analyze the data correctly, and to use the minimum number of animals necessary to achieve the scientific objectives---but not so few as to miss biologically important effects or require unnecessary repetition of experiments. Investigators are urged to consult a statistician at the design stage and are reminded that no experiment should ever be started without a clear idea of how the resulting data are to be analyzed. These guidelines are provided to help biomedical research workers perform their experiments efficiently and analyze their results so that they can extract all useful information from the resulting data. Among the topics discussed are the varying purposes of experiments (e.g., exploratory vs. confirmatory); the experimental unit; the necessity of recording full experimental details (e.g., species, sex, age, microbiological status, strain and source of animals, and husbandry conditions); assigning experimental units to treatments using randomization; other aspects of the experiment (e.g., timing of measurements); using formal experimental designs (e.g., completely randomized and randomized block); estimating the size of the experiment using power and sample size calculations; screening raw data for obvious errors; using the t-test or analysis of variance for parametric analysis; and effective design of graphical data.