The origin of multiple polypeptides of molecular weight below 110 000 encoded by tobacco mosaic virus RNA in the messenger-dependent rabbit reticulocyte lysate

Multiple polypeptides encoded by tobacco mosaic virus (TMV) RNA in the messenger-dependent rabbit reticulocyte lysate are not attributable to contaminating 3′-coterminal RNA fragments, multiple leaky termination codons or endonuclease activity opening-up legitimate or spurious internal initiation sites. Quantitative analysis of polypeptides encoded over a range of added RNA concentrations from 0.09 μg·ml^?1 to 180 μg·ml^?1 compared wi preparation, or with RNA extracted from the alkali-stable fraction of TMV suggest that apart from four legitimate virus-coded products of apparent M_r approx. 165 000, 110 000, 30 000 and 17 500 all other polypeptides arise from the overlapping 5′-proximal cistrons either by (i) site-selective endonucleolytic cleavage, (ii) sense codon misreading, or (iii) specific regions of secondary structure on TMV RNA which impede ribosome translocation.     

The whole alignment and nothing but the alignment: the problem of spurious alignment flanks

Pairwise sequence alignment is a ubiquitous tool for inferring the evolution and function of DNA, RNA and protein sequences. It is therefore essential to identify alignments arising by chance alone, i.e. spurious alignments. On one hand, if an entire alignment is spurious, statistical techniques for identifying and eliminating it are well known. On the other hand, if only a part of the alignment is spurious, elimination is much more problematic. In practice, even the sizes and frequencies of spurious subalignments remain unknown. This article shows that some common scoring schemes tend to overextend alignments and generate spurious alignment flanks up to hundreds of base pairs/amino acids in length. In the UCSC genome database, e.g. spurious flanks probably comprise >18% of the human–fugu genome alignment. To evaluate the possibility that chance alone generated a particular flank on a particular pairwise alignment, we provide a simple ‘overalignment’ P-value. The overalignment P-value can identify spurious alignment flanks, thereby eliminating potentially misleading inferences about evolution and function. Moreover, by explicitly demonstrating the tradeoff between over- and under-alignment, our methods guide the rational choice of scoring schemes for various alignment tasks.     

A general population-genetic model for the production by population structure of spurious genotype-phenotype associations in discrete, admixed or spatially distributed populations

In linkage disequilibrium mapping of genetic variants causally associated with phenotypes, spurious associations can potentially be generated by any of a variety of types of population structure. However, mathematical theory of the production of spurious associations has largely been restricted to population structure models that involve the sampling of individuals from a collection of discrete subpopulations. Here, we introduce a general model of spurious association in structured populations, appropriate whether the population structure involves discrete groups, admixture among such groups, or continuous variation across space. Under the assumptions of the model, we find that a single common principle--applicable to both the discrete and admixed settings as well as to spatial populations--gives a necessary and sufficient condition for the occurrence of spurious associations. Using a mathematical connection between the discrete and admixed cases, we show that in admixed populations, spurious associations are less severe than in corresponding mixtures of discrete subpopulations, especially when the variance of admixture across individuals is small. This observation, together with the results of simulations that examine the relative influences of various model parameters, has important implications for the design and analysis of genetic association studies in structured populations.     

When is a correlation between non‐independent variables “spurious”?

Correlations which are artifacts of various types of data transformations can be said to be spurious. This study considers four common types of analyses where the X and Y variables are not independent; these include regressions of the form X/Z vs Y/Z, X×Z vs Y×Z, X vs Y/X, and X+Y vs Y. These analyses were carried out using a series of Monte Carlo simulations while varying sample size and sample variability. The impact of disparities in variability between the shared and non-shared terms and measurement error for the shared term on the magnitude of the spurious correlations was also considered. The accuracy of equations previously derived to predict the magnitude of spurious correlations was also assessed. These results show the risk of producing spurious correlations when analyzing non-independent variables is very large. Spurious correlations occurred in all cases assessed, the mean spurious coefficient of determination (r²) frequently exceeded 0.50, and in some cases the 90% confidence interval for these simulations included all large r² values. The magnitude of spurious correlations was sensitive to differences in the variability of the shared and non-shared terms, with large spurious correlations obtained when the variability for the shared term was larger. Sample size had only a modest impact on the magnitude of spurious correlations. When measurement error for the shared variable was smaller than one half the coefficient of variation for that variable, which is generally the case, the measurement error did not generate large spurious correlations. The equations available to predict expected spurious correlations provided accurate predictions for the case of X×Z vs Y×Z, variable predictions for the case of X vs Y/X, and poor predictions for most cases of X/Z vs Y/Z, and X+Y vs Y.     

A graphical approach for evaluating effective connectivity in neural systems

The identification of effective connectivity from time-series data such as electroencephalogram (EEG) or time-resolved function magnetic resonance imaging (fMRI) recordings is an important problem in brain imaging. One commonly used approach to inference effective connectivity is based on vector autoregressive models and the concept of Granger causality. However, this probabilistic concept of causality can lead to spurious causalities in the presence of latent variables. Recently, graphical models have been used to discuss problems of causal inference for multivariate data. In this paper, we extend these concepts to the case of time-series and present a graphical approach for discussing Granger-causal relationships among multiple time-series. In particular, we propose a new graphical representation that allows the characterization of spurious causality and, thus, can be used to investigate spurious causality. The method is demonstrated with concurrent EEG and fMRI recordings which are used to investigate the interrelations between the alpha rhythm in the EEG and blood oxygenation level dependent (BOLD) responses in the fMRI. The results confirm previous findings on the location of the source of the EEG alpha rhythm.     

Beyond Correlation in the Detection of Climate Change Impacts: Testing a Mechanistic Hypothesis for Climatic Influence on Sockeye Salmon (Oncorhynchus nerka) Productivity

Detecting the biological impacts of climate change is a current focus of ecological research and has important applications in conservation and resource management. Owing to a lack of suitable control systems, measuring correlations between time series of biological attributes and hypothesized environmental covariates is a common method for detecting such impacts. These correlative approaches are particularly common in studies of exploited fish species because rich biological time-series data are often available. However, the utility of species-environment relationships for identifying or predicting biological responses to climate change has been questioned because strong correlations often deteriorate as new data are collected. Specifically stating and critically evaluating the mechanistic relationship(s) linking an environmental driver to a biological response may help to address this problem. Using nearly 60 years of data on sockeye salmon from the Kvichak River, Alaska we tested a mechanistic hypothesis linking water temperatures experienced during freshwater rearing to population productivity by modeling a series of intermediate, deterministic relationships and evaluating temporal trends in biological and environmental time-series. We found that warming waters during freshwater rearing have profoundly altered patterns of growth and life history in this population complex yet there has been no significant correlation between water temperature and metrics of productivity commonly used in fisheries management. These findings demonstrate that pairing correlative approaches with careful consideration of the mechanistic links between populations and their environments can help to both avoid spurious correlations and identify biologically important, but not statistically significant relationships, and ultimately producing more robust conclusions about the biological impacts of climate change.     

Candidate gene analysis for quantitative traits using the transmission disequilibrium test: the example of the melanocortin 4-receptor in pigs

Population-wide associations between loci due to linkage disequilibrium can be used to map quantitative trait loci (QTL) with high resolution. However, spurious associations between markers and QTL can also arise as a consequence of population stratification. Statistical methods that cannot differentiate between loci associations due to linkage disequilibria from those caused in other ways can render false-positive results. The transmission-disequilibrium test (TDT) is a robust test for detecting QTL. The TDT exploits within-family associations that are not affected by population stratification. However, some TDTs are formulated in a rigid form, with reduced potential applications. In this study we generalize TDT using mixed linear models to allow greater statistical flexibility. Allelic effects are estimated with two independent parameters: one exploiting the robust within-family information and the other the potentially biased between-family information. A significant difference between these two parameters can be used as evidence for spurious association. This methodology was then used to test the effects of the fourth melanocortin receptor (MC4R) on production traits in the pig. The new analyses supported the previously reported results; i.e., the studied polymorphism is either causal or in very strong linkage disequilibrium with the causal mutation, and provided no evidence for spurious association.     

Minimizing Cognitive Errors in Site-Specific Causal Assessments

Interest in causal investigations in aquatic systems has been a natural outgrowth of the increased use of biological monitoring to characterize the condition of resources. Although biological monitoring approaches are critical tools for detecting whether effects are occurring, they do not identify the cause of the observed effects. Formal approaches to causal evaluation can provide a mechanism to build on expert knowledge, increasing the likelihood that remedial efforts will achieve the desired environmental improvement. This paper examines how formal approaches to causal investigations minimize common errors. We reviewed common cognitive errors reported in the literature, and compared them with considerations suggested for strength-of-evidence approaches. Many of the causal considerations are directed toward distinguishing spurious correlations from true causal relationships. However, this is only one type of error; others include hypothesis dependence, confirmation bias, hypothesis tenacity and anchoring. We suggest three general principles for minimizing error in site-specific investigations: (1) Conduct the causal evaluation as a fair, transparent comparison among alternatives; (2) Carefully describe and quantify the conjunction of cause and effect; and (3) Consider that conjunction between cause and effect is spurious, or that a real conjunction was masked.     

Color vision models: Some simulations,a general n‐dimensional model,and the colourvision R package

The development of color vision models has allowed the appraisal of color vision independent of the human experience. These models are now widely used in ecology and evolution studies. However, in common scenarios of color measurement, color vision models may generate spurious results. Here I present a guide to color vision modeling (Chittka (1992, Journal of Comparative Physiology A, 170, 545) color hexagon, Endler & Mielke (2005, Journal Of The Linnean Society, 86, 405) model, and the linear and log‐linear receptor noise limited models (Vorobyev & Osorio 1998, Proceedings of the Royal Society B, 265, 351; Vorobyev et al. 1998, Journal of Comparative Physiology A, 183, 621)) using a series of simulations, present a unified framework that extends and generalize current models, and provide an R package to facilitate the use of color vision models. When the specific requirements of each model are met, between‐model results are qualitatively and quantitatively similar. However, under many common scenarios of color measurements, models may generate spurious values. For instance, models that log‐transform data and use relative photoreceptor outputs are prone to generate spurious outputs when the stimulus photon catch is smaller than the background photon catch; and models may generate unrealistic predictions when the background is chromatic (e.g. leaf reflectance) and the stimulus is an achromatic low reflectance spectrum. Nonetheless, despite differences, all three models are founded on a similar set of assumptions. Based on that, I provide a new formulation that accommodates and extends models to any number of photoreceptor types, offers flexibility to build user‐defined models, and allows users to easily adjust chromaticity diagram sizes to account for changes when using different number of photoreceptors.     

Association mapping in structured populations

The use, in association studies, of the forthcoming dense genomewide collection of single-nucleotide polymorphisms (SNPs) has been heralded as a potential breakthrough in the study of the genetic basis of common complex disorders. A serious problem with association mapping is that population structure can lead to spurious associations between a candidate marker and a phenotype. One common solution has been to abandon case-control studies in favor of family-based tests of association, such as the transmission/disequilibrium test (TDT), but this comes at a considerable cost in the need to collect DNA from close relatives of affected individuals. In this article we describe a novel, statistically valid, method for case-control association studies in structured populations. Our method uses a set of unlinked genetic markers to infer details of population structure, and to estimate the ancestry of sampled individuals, before using this information to test for associations within subpopulations. It provides power comparable with the TDT in many settings and may substantially outperform it if there are conflicting associations in different subpopulations.     

Relating species density to environmental variables in presence of spatial autocorrelation: a study case on soil nematodes distribution

The relationships between soil nematodes and soil texture, carbon content and depth were assessed in both a cultivated and uncultivated plots. Simple correlation coefficient analyses indicated a negative linear correlation between the plant-parasitic species Helicotylenchus retnsus Siddiqui and Brown and soil clay content in the uncultivated site. However, Mantel Lests showed this correlation to be spurious and only due to the presence of a common spatial pattern in the variables distribution. Both H. retusus density and soil clay content appeared to be independently driven by a common, unknown cause. The use of suitable statistical methods to analyse spatially dependent variables proved to be particularly fruitful in that they prevent spurious correlations and allow us to test different causal models involving the set of variables at hand.     

Is sexual organ reciprocity related to legitimate pollen deposition in distylous Pulmonaria (Boraginaceae)?

Heterostyly, i.e. the reciprocal positioning of anthers and stigmas, is a floral polymorphism that is thought to promote disassortative (i.e. between‐morph) pollination and to maintain genetic diversity within populations. Recent research, however, has shown several cases of heterostylous plant species in which the reciprocal positioning of the sexual organs varies, which may affect the likelihood of ‘legitimate’ pollination between compatible morphs, and hence morph fitness and ultimately the stability of this floral polymorphism. In this study, we investigated floral morphology and stigmatic pollen deposition patterns in nine Pulmonaria species (Boraginaceae) across Europe to test the hypothesis that sexual organ reciprocity is related to legitimate pollen deposition. We used a recently developed adaptive accuracy concept to assess the level of reciprocity and to relate inaccuracies in positioning of sexual organs to legitimate pollen deposition measured on stigmas in the field. The nine investigated Pulmonaria species showed substantial deviations from perfect reciprocity, with total inaccuracies varying between 2.9 and 20.3% relative to the squared mean of all anther and stigma heights in the population. In almost all cases, most of the inaccuracy was generated by the high‐level organs. Total pollen deposition was always higher on stigmas of long‐styled individuals, but stigmas of short‐styled individuals received significantly more legitimate pollen than stigmas of long‐styled individuals (71.23% and 38.75% respectively). We also found a weak but significant relationship between measures of inaccuracy and the level of disassortative pollination. Under these conditions of pollen flow, the distylous mating system is predicted to persist in only eleven (27%) of all sampled populations. Overall, these results indicate that deviations from perfect reciprocal herkogamy are common in Pulmonaria and have an impact on legitimate pollen deposition.     

Fine-Scale Patterns of Population Stratification Confound Rare Variant Association Tests

Advances in next-generation sequencing technology have enabled systematic exploration of the contribution of rare variation to Mendelian and complex diseases. Although it is well known that population stratification can generate spurious associations with common alleles, its impact on rare variant association methods remains poorly understood. Here, we performed exhaustive coalescent simulations with demographic parameters calibrated from exome sequence data to evaluate the performance of nine rare variant association methods in the presence of fine-scale population structure. We find that all methods have an inflated spurious association rate for parameter values that are consistent with levels of differentiation typical of European populations. For example, at a nominal significance level of 5%, some test statistics have a spurious association rate as high as 40%. Finally, we empirically assess the impact of population stratification in a large data set of 4,298 European American exomes. Our results have important implications for the design, analysis, and interpretation of rare variant genome-wide association studies.     

基于模拟退火法由脑磁图推测电流偶极子参数

利用模拟退火（Ｓｉｍｕｌａｔｅｄ Ａｎｎｅａｌｉｎｇ） 算法,由脑磁图（ ＭＥＧ） 数据反演脑内作为磁源的单电流偶极子参数,可以得到理想的结果。在上述工作的基础上,对脑内多电流偶极子参数的反演,则呈现如下状况：即以少于实际源数目的偶极子为源假设反演,目标函数得不到极小优化。反之,目标函数可以得到极小优化, 但出现多余的伪偶极子, 且这些伪偶极子在多次不同条件的反演结果中,处于不稳定状态。若将多次反演结果中处于不稳定状态的偶极子作为伪偶极子的判据而将其排除,则可以得到一种判断磁源偶极子数目的方法     

Congruence of Morphological and Molecular Phylogenies

When phylogenetic trees constructed from morphological and molecular evidence disagree (i.e. are incongruent) it has been suggested that the differences are spurious or that the molecular results should be preferred a priori. Comparing trees can increase confidence (congruence), or demonstrate that at least one tree is incorrect (incongruence). Statistical analyses of 181 molecular and 49 morphological trees shows that incongruence is greater between than within the morphological and molecular partitions, and this difference is significant for the molecular partition. Because the level of incongruence between a pair of trees gives a minimum bound on how much error is present in the two trees, our results indicate that the level of error may be underestimated by congruence within partitions. Thus comparisons between morphological and molecular trees are particularly useful for detecting this incongruence (spurious or otherwise). Molecular trees have higher average congruence than morphological trees, but the difference is not significant, and both within- and between-partition incongruence is much lower than expected by chance alone. Our results suggest that both molecular and morphological trees are, in general, useful approximations of a common underlying phylogeny and thus, when molecules and morphology clash, molecular phylogenies should not be considered more reliable a priori.     

Detecting consistent common lines in cryo-EM by voting

The single-particle reconstruction problem of electron cryo-microscopy (cryo-EM) is to find the three-dimensional structure of a macromolecule given its two-dimensional noisy projection images at unknown random directions. Ab initio estimates of the 3D structure are often obtained by the “Angular Reconstitution” method, in which a coordinate system is established from three projections, and the orientation of the particle giving rise to each image is deduced from common lines among the images. However, a reliable detection of common lines is difficult due to the low signal-to-noise ratio of the images. In this paper we describe a global self-correcting voting procedure in which all projection images participate to decide the identity of the consistent common lines. The algorithm determines which common line pairs were detected correctly and which are spurious. We show that the voting procedure succeeds at relatively low detection rates and that its performance improves as the number of projection images increases. We demonstrate the algorithm for both simulative and experimental images of the 50S ribosomal subunit.     

Social Geomatics: Participatory Forest Mapping to Mediate Resource Conflict in the Bolivian Amazon

This paper describes a participatory mapping method field tested with agro-extractive settlements in the Bolivian Amazon. A regional transition from customary to formal property rights resulting from sweeping 1996 land tenure reforms has led to confusion and conflicts over resource rights, a problem compounded by recent high market prices for Brazil nuts. In response to community requests to clarify resource rights to Brazil nut trees, CIFOR offered to train community members to map trees, trails and other key features themselves. This experience indicates that local residents can map their resources in an effective and efficient way and in the process gather necessary information to mediate competing claims, demonstrate their legitimate resource claims to external stakeholders and make management decisions. We argue that maps and properties are more likely to be seen as legitimate reflections of de facto rights if local stakeholders are involved as a group from the outset.     

Can the problem of hybridization in threatened species be evaluated using a fieldwork research? A case study in snapdragons

Hybridization, natural or artificial, is considered disadvantageous for species biodiversity when it threatens the population integrity of endangered species. Frequently, studies investigating whether hybridization poses a legitimate risk to rare species are based on genetic data obtained in molecular biology laboratories. In this study, we used field research to approach the problem that hybridization could cause for the viability of a population of a rare species and to be able to propose the most appropriate initial conservation strategy. Specifically, using the model genus Antirrhinum, the reproductive barriers between the rare A. pulverulentum and its common congener A. litigiosum have been analysed under the reproductive isolation index (RI). A. pulverulentum had a high value of total RI, indicating that there are barriers to gene flow from A. litigiosum towards this species, and also had a high value for the intrinsic RI, reflecting a low inherent capacity for production of hybrid plants; in addition, the possibility of successful backcrosses between this species with hybrids produced from A. litigiosum ovules were low, given the high intrinsic RI of A. litigiosum. These data indicate the current existence of strong and permanent barriers to hybridization between the two species, suggesting that hybridization does not seem to be a serious problem for the conservation of A. pulverulentum in the studied population, nor for the near future. This study shows how the RI index can provide useful information for conservation purposes and proposes different management recommendations.     

Revisiting the importance of model fitting for model-based fMRI: It does matter in computational psychiatry

Computational modeling has been applied for data analysis in psychology, neuroscience, and psychiatry. One of its important uses is to infer the latent variables underlying behavior by which researchers can evaluate corresponding neural, physiological, or behavioral measures. This feature is especially crucial for computational psychiatry, in which altered computational processes underlying mental disorders are of interest. For instance, several studies employing model-based fMRI—a method for identifying brain regions correlated with latent variables—have shown that patients with mental disorders (e.g., depression) exhibit diminished neural responses to reward prediction errors (RPEs), which are the differences between experienced and predicted rewards. Such model-based analysis has the drawback that the parameter estimates and inference of latent variables are not necessarily correct—rather, they usually contain some errors. A previous study theoretically and empirically showed that the error in model-fitting does not necessarily cause a serious error in model-based fMRI. However, the study did not deal with certain situations relevant to psychiatry, such as group comparisons between patients and healthy controls. We developed a theoretical framework to explore such situations. We demonstrate that the parameter-misspecification can critically affect the results of group comparison. We demonstrate that even if the RPE response in patients is completely intact, a spurious difference to healthy controls is observable. Such a situation occurs when the ground-truth learning rate differs between groups but a common learning rate is used, as per previous studies. Furthermore, even if the parameters are appropriately fitted to individual participants, spurious group differences in RPE responses are observable when the model lacks a component that differs between groups. These results highlight the importance of appropriate model-fitting and the need for caution when interpreting the results of model-based fMRI.