Crossa, José; Burgueño, Juan; Dreisigacker, Susanne; Vargas, Mateo; Herrera-Foessel, Sybil A; Lillemo, Morten; Singh, Ravi P; Trethowan, Richard; Warburton, Marilyn; Franco, Jorge; Reynolds, Matthew; Crouch, Jonathan H; Ortiz, Rodomiro
2007-11-01
Linkage disequilibrium can be used for identifying associations between traits of interest and genetic markers. This study used mapped diversity array technology (DArT) markers to find associations with resistance to stem rust, leaf rust, yellow rust, and powdery mildew, plus grain yield in five historical wheat international multienvironment trials from the International Maize and Wheat Improvement Center (CIMMYT). Two linear mixed models were used to assess marker-trait associations incorporating information on population structure and covariance between relatives. An integrated map containing 813 DArT markers and 831 other markers was constructed. Several linkage disequilibrium clusters bearing multiple host plant resistance genes were found. Most of the associated markers were found in genomic regions where previous reports had found genes or quantitative trait loci (QTL) influencing the same traits, providing an independent validation of this approach. In addition, many new chromosome regions for disease resistance and grain yield were identified in the wheat genome. Phenotyping across up to 60 environments and years allowed modeling of genotype x environment interaction, thereby making possible the identification of markers contributing to both additive and additive x additive interaction effects of traits.
Reid, Jane M; Arcese, Peter; Keller, Lukas F; Losdat, Sylvain
2014-08-01
Ongoing evolution of polyandry, and consequent extra-pair reproduction in socially monogamous systems, is hypothesized to be facilitated by indirect selection stemming from cross-sex genetic covariances with components of male fitness. Specifically, polyandry is hypothesized to create positive genetic covariance with male paternity success due to inevitable assortative reproduction, driving ongoing coevolution. However, it remains unclear whether such covariances could or do emerge within complex polyandrous systems. First, we illustrate that genetic covariances between female extra-pair reproduction and male within-pair paternity success might be constrained in socially monogamous systems where female and male additive genetic effects can have opposing impacts on the paternity of jointly reared offspring. Second, we demonstrate nonzero additive genetic variance in female liability for extra-pair reproduction and male liability for within-pair paternity success, modeled as direct and associative genetic effects on offspring paternity, respectively, in free-living song sparrows (Melospiza melodia). The posterior mean additive genetic covariance between these liabilities was slightly positive, but the credible interval was wide and overlapped zero. Therefore, although substantial total additive genetic variance exists, the hypothesis that ongoing evolution of female extra-pair reproduction is facilitated by genetic covariance with male within-pair paternity success cannot yet be definitively supported or rejected either conceptually or empirically.
Careau, Vincent; Wolak, Matthew E.; Carter, Patrick A.; Garland, Theodore
2015-01-01
Given the pace at which human-induced environmental changes occur, a pressing challenge is to determine the speed with which selection can drive evolutionary change. A key determinant of adaptive response to multivariate phenotypic selection is the additive genetic variance–covariance matrix (G). Yet knowledge of G in a population experiencing new or altered selection is not sufficient to predict selection response because G itself evolves in ways that are poorly understood. We experimentally evaluated changes in G when closely related behavioural traits experience continuous directional selection. We applied the genetic covariance tensor approach to a large dataset (n = 17 328 individuals) from a replicated, 31-generation artificial selection experiment that bred mice for voluntary wheel running on days 5 and 6 of a 6-day test. Selection on this subset of G induced proportional changes across the matrix for all 6 days of running behaviour within the first four generations. The changes in G induced by selection resulted in a fourfold slower-than-predicted rate of response to selection. Thus, selection exacerbated constraints within G and limited future adaptive response, a phenomenon that could have profound consequences for populations facing rapid environmental change. PMID:26582016
Careau, Vincent; Wolak, Matthew E; Carter, Patrick A; Garland, Theodore
2015-11-22
Given the pace at which human-induced environmental changes occur, a pressing challenge is to determine the speed with which selection can drive evolutionary change. A key determinant of adaptive response to multivariate phenotypic selection is the additive genetic variance-covariance matrix ( G: ). Yet knowledge of G: in a population experiencing new or altered selection is not sufficient to predict selection response because G: itself evolves in ways that are poorly understood. We experimentally evaluated changes in G: when closely related behavioural traits experience continuous directional selection. We applied the genetic covariance tensor approach to a large dataset (n = 17 328 individuals) from a replicated, 31-generation artificial selection experiment that bred mice for voluntary wheel running on days 5 and 6 of a 6-day test. Selection on this subset of G: induced proportional changes across the matrix for all 6 days of running behaviour within the first four generations. The changes in G: induced by selection resulted in a fourfold slower-than-predicted rate of response to selection. Thus, selection exacerbated constraints within G: and limited future adaptive response, a phenomenon that could have profound consequences for populations facing rapid environmental change.
REFINING GENETICALLY INFERRED RELATIONSHIPS USING TREELET COVARIANCE SMOOTHING.
Crossett, Andrew; Lee, Ann B; Klei, Lambertus; Devlin, Bernie; Roeder, Kathryn
2013-06-27
Recent technological advances coupled with large sample sets have uncovered many factors underlying the genetic basis of traits and the predisposition to complex disease, but much is left to discover. A common thread to most genetic investigations is familial relationships. Close relatives can be identified from family records, and more distant relatives can be inferred from large panels of genetic markers. Unfortunately these empirical estimates can be noisy, especially regarding distant relatives. We propose a new method for denoising genetically-inferred relationship matrices by exploiting the underlying structure due to hierarchical groupings of correlated individuals. The approach, which we call Treelet Covariance Smoothing, employs a multiscale decomposition of covariance matrices to improve estimates of pairwise relationships. On both simulated and real data, we show that smoothing leads to better estimates of the relatedness amongst distantly related individuals. We illustrate our method with a large genome-wide association study and estimate the "heritability" of body mass index quite accurately. Traditionally heritability, defined as the fraction of the total trait variance attributable to additive genetic effects, is estimated from samples of closely related individuals using random effects models. We show that by using smoothed relationship matrices we can estimate heritability using population-based samples. Finally, while our methods have been developed for refining genetic relationship matrices and improving estimates of heritability, they have much broader potential application in statistics. Most notably, for error-in-variables random effects models and settings that require regularization of matrices with block or hierarchical structure.
REFINING GENETICALLY INFERRED RELATIONSHIPS USING TREELET COVARIANCE SMOOTHING1
Crossett, Andrew; Lee, Ann B.; Klei, Lambertus; Devlin, Bernie; Roeder, Kathryn
2013-01-01
Recent technological advances coupled with large sample sets have uncovered many factors underlying the genetic basis of traits and the predisposition to complex disease, but much is left to discover. A common thread to most genetic investigations is familial relationships. Close relatives can be identified from family records, and more distant relatives can be inferred from large panels of genetic markers. Unfortunately these empirical estimates can be noisy, especially regarding distant relatives. We propose a new method for denoising genetically—inferred relationship matrices by exploiting the underlying structure due to hierarchical groupings of correlated individuals. The approach, which we call Treelet Covariance Smoothing, employs a multiscale decomposition of covariance matrices to improve estimates of pairwise relationships. On both simulated and real data, we show that smoothing leads to better estimates of the relatedness amongst distantly related individuals. We illustrate our method with a large genome-wide association study and estimate the “heritability” of body mass index quite accurately. Traditionally heritability, defined as the fraction of the total trait variance attributable to additive genetic effects, is estimated from samples of closely related individuals using random effects models. We show that by using smoothed relationship matrices we can estimate heritability using population-based samples. Finally, while our methods have been developed for refining genetic relationship matrices and improving estimates of heritability, they have much broader potential application in statistics. Most notably, for error-in-variables random effects models and settings that require regularization of matrices with block or hierarchical structure. PMID:24587841
The phenotypic and genetic covariance structure of drosphilid wings.
McGuigan, Katrina; Blows, Mark W
2007-04-01
Evolutionary constraint results from the interaction between the distribution of available genetic variation and the position of selective optima. The availability of genetic variance in multitrait systems, as described by the additive genetic variance-covariance matrix (G), has been the subject of recent attempts to assess the prevalence of genetic constraints. However, evolutionary constraints have not yet been considered from the perspective of the phenotypes available to multivariate selection, and whether genetic variance is present in all phenotypes potentially under selection. Determining the rank of the phenotypic variance-covariance matrix (P) to characterize the phenotypes available to selection, and contrasting it with the rank of G, may provide a general approach to determining the prevalence of genetic constraints. In a study of a laboratory population of Drosophila bunnanda from northern Australia we applied factor-analytic modeling to repeated measures of individual wing phenotypes to determine the dimensionality of the phenotypic space described by P. The phenotypic space spanned by the 10 wing traits had 10 statistically supported dimensions. In contrast, factor-analytic modeling of G estimated for the same 10 traits from a paternal half-sibling breeding design suggested G had fewer dimensions than traits. Statistical support was found for only five and two genetic dimensions, describing a total of 99% and 72% of genetic variance in wing morphology in females and males, respectively. The observed mismatch in dimensionality between P and G suggests that although selection might act to shift the intragenerational population mean toward any trait combination, evolution may be restricted to fewer dimensions.
Genetically determined phenotype covariation networks control bone strength.
Jepsen, Karl J; Courtland, Hayden-William; Nadeau, Joseph H
2010-07-01
To identify genes affecting bone strength, we studied how genetic variants regulate components of a phenotypic covariation network that was previously shown to accurately characterize the compensatory trait interactions involved in functional adaptation during growth. Quantitative trait loci (QTLs) regulating femoral robustness, morphologic compensation, and mineralization (tissue quality) were mapped at three ages during growth using AXB/BXA Recombinant Inbred (RI) mouse strains and adult B6-i(A) Chromosome Substitution Strains (CSS). QTLs for robustness were identified on chromosomes 8, 12, 18, and 19 and confirmed at all three ages, indicating that genetic variants established robustness postnatally without further modification. A QTL for morphologic compensation, which was measured as the relationship between cortical area and body weight, was identified on chromosome 8. This QTL limited the amount of bone formed during growth and thus acted as a setpoint for diaphyseal bone mass. Additional QTLs were identified from the CSS analysis. QTLs for robustness and morphologic compensation regulated bone structure independently (ie, in a nonpleiotropic manner), indicating that each trait may be targeted separately to individualize treatments aiming to improve strength. Multiple regression analyses showed that variation in morphologic compensation and tissue quality, not bone size, determined femoral strength relative to body weight. Thus an individual inheriting slender bones will not necessarily inherit weak bones unless the individual also inherits a gene that impairs compensation. This systems genetic analysis showed that genetically determined phenotype covariation networks control bone strength, suggesting that incorporating functional adaptation into genetic analyses will advance our understanding of the genetic basis of bone strength.
Natural variation and genetic covariance in adult hippocampal neurogenesis
Kempermann, Gerd; Chesler, Elissa J; Lu, Lu; Williams, Robert; Gage, Fred
2006-01-01
Adult hippocampal neurogenesis is highly variable and heritable among laboratory strains of mice. Adult neurogenesis is also remarkably plastic and can be modulated by environment and activity. Here, we provide a systematic quantitative analysis of adult hippocampal neurogenesis in two large genetic reference panels of recombinant inbred strains (BXD and AXB?BXA, n ? 52 strains). We combined data on variation in neurogenesis with a new transcriptome database to extract a set of 190 genes with expression patterns that are also highly variable and that covary with rates of (i) cell proliferation, (ii) cell survival, or the numbers of surviving (iii) new neurons, and (iv) astrocytes. Expression of a subset of these neurogenesis-associated transcripts was controlled in cis across the BXD set. These self-modulating genes are particularly interesting candidates to control neurogenesis. Among these were musashi (Msi1h) and prominin1?CD133 (Prom1), both of which are linked to stem-cell maintenance and division. Twelve neurogenesis-associated transcripts had significant cis-acting quantitative trait loci, and, of these, six had plausible biological association with adult neurogenesis (Prom1, Ssbp2, Kcnq2, Ndufs2, Camk4, and Kcnj9). Only one cis- cting candidate was linked to both neurogenesis and gliogenesis, Rapgef6, a downstream target of ras signaling. The use of genetic reference panels coupled with phenotyping and global transcriptome profiling thus allowed insight into the complexity of the genetic control of adult neurogenesis.
Genetic covariance structure of incisor crown size in twins.
Dempsey, P J; Townsend, G C; Martin, N G; Neale, M C
1995-07-01
Previous studies of tooth size in twins and their families have suggested a high degree of genetic control, although there have been difficulties separating the various genetic and environmental effects. A genetic analysis of variation in crown size of the permanent incisors of South Australian twins was carried out, with structural equation modeling used to determine the relative contributions of genetic and environmental factors. Maximum mesiodistal crown dimensions of maxillary and mandibular permanent incisors were recorded from dental models of 298 pairs of twins, including 149 monozygous (MZ) and 149 dizygous (DZ) pairs. The analysis revealed that: (i) an adequate fit required additive genetic and unique environmental components; (ii) augmenting the model with non-additive genetic variation did not lead to a significant improvement in fit; (iii) there was evidence of shared environmental influences in the upper central incisors of males; (iv) the additive genetic component constituted a general factor loading on all eight teeth, with group factors loading on antimeric pairs of teeth; (v) unique environmental effects were mostly variable-specific; (vi) most factor loadings on antimeric tooth pairs could be constrained to be equal, indicating a symmetry of genetic and environmental influences between left and right sides; and (vii) estimated heritability of the incisor mesiodistal dimensions varied from 0.81 to 0.91.
Berger, David; Postma, Erik; Blanckenhorn, Wolf U; Walters, Richard J
2013-08-01
Although the potential to adapt to warmer climate is constrained by genetic trade-offs, our understanding of how selection and mutation shape genetic (co)variances in thermal reaction norms is poor. Using 71 isofemale lines of the fly Sepsis punctum, originating from northern, central, and southern European climates, we tested for divergence in juvenile development rate across latitude at five experimental temperatures. To investigate effects of evolutionary history in different climates on standing genetic variation in reaction norms, we further compared genetic (co)variances between regions. Flies were reared on either high or low food resources to explore the role of energy acquisition in determining genetic trade-offs between different temperatures. Although the latter had only weak effects on the strength and sign of genetic correlations, genetic architecture differed significantly between climatic regions, implying that evolution of reaction norms proceeds via different trajectories at high latitude versus low latitude in this system. Accordingly, regional genetic architecture was correlated to region-specific differentiation. Moreover, hot development temperatures were associated with low genetic variance and stronger genetic correlations compared to cooler temperatures. We discuss the evolutionary potential of thermal reaction norms in light of their underlying genetic architectures, evolutionary histories, and the materialization of trade-offs in natural environments.
Bezdjian, Serena; Raine, Adrian; Tuvblad, Catherine; Baker, Laura A.
2011-01-01
The present study investigated the genetic and environmental covariance between psychopathic personality traits with reactive and proactive aggression in 9- to 10-year-old twins (N = 1,219). Psychopathic personality traits were assessed with the Child Psychopathy Scale (D. R. Lynam, 1997), while aggressive behaviors were assessed using the Reactive Proactive Questionnaire (A. Raine et al., 2006). Significant common genetic influences were found to be shared by psychopathic personality traits and aggressive behaviors using both caregiver (mainly mother) and child self-reports. Significant genetic and nonshared environmental influences specific to psychopathic personality traits and reactive and proactive aggression were also found, suggesting etiological independence among these phenotypes. Additionally, the genetic relation between psychopathic personality traits and aggression was significantly stronger for proactive than reactive aggression when using child self-reports. PMID:21557742
Selection and genetic (co)variance in bighorn sheep.
Coltman, David W; O'Donoghue, Paul; Hogg, John T; Festa-Bianchet, Marco
2005-06-01
Genetic theory predicts that directional selection should deplete additive genetic variance for traits closely related to fitness, and may favor the maintenance of alleles with antagonistically pleiotropic effects on fitness-related traits. Trait heritability is therefore expected to decline with the degree of association with fitness, and some genetic correlations between selected traits are expected to be negative. Here we demonstrate a negative relationship between trait heritability and association with lifetime reproductive success in a wild population of bighorn sheep (Ovis canadensis) at Ram Mountain, Alberta, Canada. Lower heritability for fitness-related traits, however, was not wholly a consequence of declining genetic variance, because those traits showed high levels of residual variance. Genetic correlations estimated between pairs of traits with significant heritability were positive. Principal component analyses suggest that positive relationships between morphometric traits constitute the main axis of genetic variation. Trade-offs in the form of negative genetic or phenotypic correlations among the traits we have measured do not appear to constrain the potential for evolution in this population.
The genetic covariance between life cycle stages separated by metamorphosis.
Aguirre, J David; Blows, Mark W; Marshall, Dustin J
2014-08-07
Metamorphosis is common in animals, yet the genetic associations between life cycle stages are poorly understood. Given the radical changes that occur at metamorphosis, selection may differ before and after metamorphosis, and the extent that genetic associations between pre- and post-metamorphic traits constrain evolutionary change is a subject of considerable interest. In some instances, metamorphosis may allow the genetic decoupling of life cycle stages, whereas in others, metamorphosis could allow complementary responses to selection across the life cycle. Using a diallel breeding design, we measured viability at four ontogenetic stages (embryo, larval, juvenile and adult viability), in the ascidian Ciona intestinalis and examined the orientation of additive genetic variation with respect to the metamorphic boundary. We found support for one eigenvector of G: (gobsmax ), which contrasted larval viability against embryo viability and juvenile viability. Target matrix rotation confirmed that while gobsmax shows genetic associations can extend beyond metamorphosis, there is still considerable scope for decoupled phenotypic evolution. Therefore, although genetic associations across metamorphosis could limit that range of phenotypes that are attainable, traits on either side of the metamorphic boundary are capable of some independent evolutionary change in response to the divergent conditions encountered during each life cycle stage.
An alternative covariance estimator to investigate genetic heterogeneity in populations
Technology Transfer Automated Retrieval System (TEKTRAN)
Genomic predictions and GWAS have used mixed models for identification of associations and trait predictions. In both cases, the covariance between individuals for performance is estimated using molecular markers. Mixed model properties indicate that the use of the data for prediction is optimal if ...
Smith, Rachel A.; Greenberg, Marisa; Parrott, Roxanne L.
2014-01-01
With a growing interest in using genetic information to motivate young adults’ health behaviors, audience segmentation is needed for effective campaign design. Using latent class analysis, this study identifies segments based on young adults’ (N = 327) beliefs about genetic threats to their health and personal efficacy over genetic influences on their health. A four-class model was identified. The model indicators fit the risk perception attitude framework (Rimal & Real, 2003), but the covariates (e.g., current health behaviors) did not. In addition, opinion leader qualities covaried with one profile: those in this profile engaged in fewer preventative behaviors and more dangerous treatment options, and also liked to persuade others, making them a particularly salient group for campaign efforts. The implications for adult-onset disorders, like alpha-1 antitrypsin deficiency are discussed. PMID:24111749
Houle, D; Meyer, K
2015-08-01
We explore the estimation of uncertainty in evolutionary parameters using a recently devised approach for resampling entire additive genetic variance-covariance matrices (G). Large-sample theory shows that maximum-likelihood estimates (including restricted maximum likelihood, REML) asymptotically have a multivariate normal distribution, with covariance matrix derived from the inverse of the information matrix, and mean equal to the estimated G. This suggests that sampling estimates of G from this distribution can be used to assess the variability of estimates of G, and of functions of G. We refer to this as the REML-MVN method. This has been implemented in the mixed-model program WOMBAT. Estimates of sampling variances from REML-MVN were compared to those from the parametric bootstrap and from a Bayesian Markov chain Monte Carlo (MCMC) approach (implemented in the R package MCMCglmm). We apply each approach to evolvability statistics previously estimated for a large, 20-dimensional data set for Drosophila wings. REML-MVN and MCMC sampling variances are close to those estimated with the parametric bootstrap. Both slightly underestimate the error in the best-estimated aspects of the G matrix. REML analysis supports the previous conclusion that the G matrix for this population is full rank. REML-MVN is computationally very efficient, making it an attractive alternative to both data resampling and MCMC approaches to assessing confidence in parameters of evolutionary interest.
Immunity Traits in Pigs: Substantial Genetic Variation and Limited Covariation
Flori, Laurence; Gao, Yu; Laloë, Denis; Lemonnier, Gaëtan; Leplat, Jean-Jacques; Teillaud, Angélique; Cossalter, Anne-Marie; Laffitte, Joëlle; Pinton, Philippe; de Vaureix, Christiane; Bouffaud, Marcel; Mercat, Marie-José; Lefèvre, François; Oswald, Isabelle P.; Bidanel, Jean-Pierre; Rogel-Gaillard, Claire
2011-01-01
Background Increasing robustness via improvement of resistance to pathogens is a major selection objective in livestock breeding. As resistance traits are difficult or impossible to measure directly, potential indirect criteria are measures of immune traits (ITs). Our underlying hypothesis is that levels of ITs with no focus on specific pathogens define an individual's immunocompetence and thus predict response to pathogens in general. Since variation in ITs depends on genetic, environmental and probably epigenetic factors, our aim was to estimate the relative importance of genetics. In this report, we present a large genetic survey of innate and adaptive ITs in pig families bred in the same environment. Methodology/Principal Findings Fifty four ITs were studied on 443 Large White pigs vaccinated against Mycoplasma hyopneumoniae and analyzed by combining a principal component analysis (PCA) and genetic parameter estimation. ITs include specific and non specific antibodies, seric inflammatory proteins, cell subsets by hemogram and flow cytometry, ex vivo production of cytokines (IFNα, TNFα, IL6, IL8, IL12, IFNγ, IL2, IL4, IL10), phagocytosis and lymphocyte proliferation. While six ITs had heritabilities that were weak or not significantly different from zero, 18 and 30 ITs had moderate (0.1
Van Hulle, Carol A; Moore, Mollie N; Shirtcliff, Elizabeth A; Lemery-Chalfant, Kathryn; Goldsmith, H Hill
2015-05-01
Although several studies have shown that pubertal tempo and timing are shaped by genetic and environmental factors, few studies consider to what extent endocrine triggers of puberty are shaped by genetic and environmental factors. Doing so moves the field from examining correlated developmentally-sensitive biomarkers toward understanding what drives those associations. Two puberty related hormones, dehydroepiandrosterone and testosterone, were assayed from salivary samples in 118 MZ (62 % female), 111 same sex DZ (46 % female) and 103 opposite-sex DZ twin pairs, aged 12-16 years (M = 13.1, SD = 1.3). Pubertal status was assessed with a composite of mother- and self-reports. We used biometric models to estimate the genetic and environmental influences on the variance and covariance in testosterone and DHEA, with and without controlling for their association with puberty, and to test for sex differences. In males, the variance in testosterone and pubertal status was due to shared and non-shared environmental factors; variation in DHEA was due to genetic and non-shared environmental factors. In females, variance in testosterone was due to genetic and non-shared environmental factors; genetic, shared, and non-shared environmental factors contributed equally to variation in DHEA. In males, the testosterone-DHEA covariance was primarily due to shared environmental factors that overlapped with puberty as well as shared and non-shared environmental covariation specific to testosterone and DHEA. In females, the testosterone-DHEA covariance was due to genetic factors overlapping with pubertal status, and shared and non-shared environmental covariation specific to testosterone and DHEA.
Evolutionary response when selection and genetic variation covary across environments.
Wood, Corlett W; Brodie, Edmund D
2016-10-01
Although models of evolution usually assume that the strength of selection on a trait and the expression of genetic variation in that trait are independent, whenever the same ecological factor impacts both parameters, a correlation between the two may arise that accelerates trait evolution in some environments and slows it in others. Here, we address the evolutionary consequences and ecological causes of a correlation between selection and expressed genetic variation. Using a simple analytical model, we show that the correlation has a modest effect on the mean evolutionary response and a large effect on its variance, increasing among-population or among-generation variation in the response when positive, and diminishing variation when negative. We performed a literature review to identify the ecological factors that influence selection and expressed genetic variation across traits. We found that some factors - temperature and competition - are unlikely to generate the correlation because they affected one parameter more than the other, and identified others - most notably, environmental novelty - that merit further investigation because little is known about their impact on one of the two parameters. We argue that the correlation between selection and genetic variation deserves attention alongside other factors that promote or constrain evolution in heterogeneous landscapes.
Genetic diversity and species diversity of stream fishes covary across a land-use gradient.
Blum, Michael J; Bagley, Mark J; Walters, David M; Jackson, Suzanne A; Daniel, F Bernard; Chaloud, Deborah J; Cade, Brian S
2012-01-01
Genetic diversity and species diversity are expected to covary according to area and isolation, but may not always covary with environmental heterogeneity. In this study, we examined how patterns of genetic and species diversity in stream fishes correspond to local and regional environmental conditions. To do so, we compared population size, genetic diversity and divergence in central stonerollers (Campostoma anomalum) to measures of species diversity and turnover in stream fish assemblages among similarly sized watersheds across an agriculture-forest land-use gradient in the Little Miami River basin (Ohio, USA). Significant correlations were found in many, but not all, pair-wise comparisons. Allelic richness and species richness were strongly correlated, for example, but diversity measures based on allele frequencies and assemblage structure were not. In-stream conditions related to agricultural land use were identified as significant predictors of genetic diversity and species diversity. Comparisons to population size indicate, however, that genetic diversity and species diversity are not necessarily independent and that variation also corresponds to watershed location and glaciation history in the drainage basin. Our findings demonstrate that genetic diversity and species diversity can covary in stream fish assemblages, and illustrate the potential importance of scaling observations to capture responses to hierarchical environmental variation. More comparisons according to life history variation could further improve understanding of conditions that give rise to parallel variation in genetic diversity and species diversity, which in turn could improve diagnosis of anthropogenic influences on aquatic ecosystems.
Genetic diversity and species diversity of stream fishes covary across a land-use gradient
Blum, M.J.; Bagley, M.J.; Walters, D.M.; Jackson, S.A.; Daniel, F.B.; Chaloud, D.J.; Cade, B.S.
2012-01-01
Genetic diversity and species diversity are expected to covary according to area and isolation, but may not always covary with environmental heterogeneity. In this study, we examined how patterns of genetic and species diversity in stream fishes correspond to local and regional environmental conditions. To do so, we compared population size, genetic diversity and divergence in central stonerollers (Campostoma anomalum) to measures of species diversity and turnover in stream fish assemblages among similarly sized watersheds across an agriculture-forest land-use gradient in the Little Miami River basin (Ohio, USA). Significant correlations were found in many, but not all, pair-wise comparisons. Allelic richness and species richness were strongly correlated, for example, but diversity measures based on allele frequencies and assemblage structure were not. In-stream conditions related to agricultural land use were identified as significant predictors of genetic diversity and species diversity. Comparisons to population size indicate, however, that genetic diversity and species diversity are not necessarily independent and that variation also corresponds to watershed location and glaciation history in the drainage basin. Our findings demonstrate that genetic diversity and species diversity can covary in stream fish assemblages, and illustrate the potential importance of scaling observations to capture responses to hierarchical environmental variation. More comparisons according to life history variation could further improve understanding of conditions that give rise to parallel variation in genetic diversity and species diversity, which in turn could improve diagnosis of anthropogenic influences on aquatic ecosystems. ?? 2011 Springer-Verlag.
ERIC Educational Resources Information Center
Bezdjian, Serena; Tuvblad, Catherine; Raine, Adrian; Baker, Laura A.
2011-01-01
The present study investigated the genetic and environmental covariance between psychopathic personality traits with reactive and proactive aggression in 9- to 10-year-old twins (N = 1,219). Psychopathic personality traits were assessed with the Child Psychopathy Scale (D. R. Lynam, 1997), while aggressive behaviors were assessed using the…
The evolutionary stability of cross-sex, cross-trait genetic covariances.
Gosden, Thomas P; Chenoweth, Stephen F
2014-06-01
Although knowledge of the selective agents behind the evolution of sexual dimorphism has advanced considerably in recent years, we still lack a clear understanding of the evolutionary durability of cross-sex genetic covariances that often constrain its evolution. We tested the relative stability of cross-sex genetic covariances for a suite of homologous contact pheromones of the fruit fly Drosophila serrata, along a latitudinal gradient where these traits have diverged in mean. Using a Bayesian framework, which allowed us to account for uncertainty in all parameter estimates, we compared divergence in the total amount and orientation of genetic variance across populations, finding divergence in orientation but not total variance. We then statistically compared orientation divergence of within-sex (G) to cross-sex (B) covariance matrices. In line with a previous theoretical prediction, we find that the cross-sex covariance matrix, B, is more variable than either within-sex G matrix. Decomposition of B matrices into their symmetrical and nonsymmetrical components revealed that instability is linked to the degree of asymmetry. We also find that the degree of asymmetry correlates with latitude suggesting a role for spatially varying natural selection in shaping genetic constraints on the evolution of sexual dimorphism.
Sarmento, J L R; Torres, R A; Sousa, W H; Lôbo, R N B; Albuquerque, L G; Lopes, P S; Santos, N P S; Bignard, A B
2016-06-20
Polynomial functions of different orders were used to model random effects associated with weight of Santa Ines sheep from birth to 196 days. Fixed effects included in the models were contemporary groups, age of ewe at lambing, and fourth-order Legendre polynomials for age to represent the average growth curve. In the random part, functions of different orders were included to model variances associated with direct additive and maternal genetic effects and with permanent environmental effects of the animal and mother. Residual variance was fitted by a sixth-order ordinary polynomial for age. The higher the order of the functions, the better the model fit the data. According to the Akaike information criterion and likelihood ratio test, a continuous function of order, five, five, seven, and three for direct additive genetic, maternal genetic, animal permanent environmental, and maternal permanent environmental effects (k = 5573), respectively, was sufficient to model changes in (co)variances with age. However, a more parsimonious model of order three, three, five, and three (k = 3353) was suggested based on Schwarz's Bayesian information criterion for the same effects. Since it was a more flexible model, model k = 5573 provided inconsistent genetic parameter estimates when compared to the biologically expected result. Predicted breeding values obtained with models k = 3353 and k = 5573 differed, especially at young ages. Model k = 3353 adequately fit changes in variances and covariances with time, and may be used to describe changes in variances with age in the Santa Ines sheep studied.
Mohammadi, Hossein; Shahrebabak, Mohammad Moradi; Vatankhah, Mahmood; Shahrebabak, Hossein Moradi
2013-01-01
Genetic parameters and genetic trends for birth weight (BW), weaning weight (WW), 6-month weight (6MW), and yearling weight (YW) traits were estimated by using records of 5,634 Makooei lambs, descendants of 289 sires and 1,726 dams, born between 1996 and 2009 at the Makooei sheep breeding station, West Azerbaijan, Iran. The (co)variance components were estimated with different animal models using a restricted maximum likelihood procedure and the most appropriate model for each trait was determined by Akaike's Information Criterion. Breeding values of animals were predicted with best linear unbiased prediction methodology under multi-trait animal models and genetic trends were estimated by regression mean breeding values on birth year. The most appropriate model for BW was a model including direct and maternal genetic effects, regardless of their covariance. The model for WW and 6MW included direct additive genetic effects. The model for YW included direct genetic effects only. Direct heritabilities based on the best model were estimated 0.15 ± 0.04, 0.16 ± 0.03, 0.21 ± 0.04, and 0.22 ± 0.06 for BW, WW, 6MW, and YW, respectively, and maternal heritability obtained 0.08 ± 0.02 for BW. Genetic correlations among the traits were positive and varied from 0.28 for BW-YW to 0.66 for BW-WW and phenotypic correlations were generally lower than the genetic correlations. Genetic trends were 8.1 ± 2, 67.4 ± 5, 38.7 ± 4, and 47.6 ± 6 g per year for BW, WW, 6MW, and YW, respectively.
Additional Considerations in the Definition and Renormalization of Non-Covariant Gauges
NASA Astrophysics Data System (ADS)
Joglekar, Satish D.
In this work, we pursue further consequences of a general formalism for non-covariant gauges developed in an earlier work (hep-th/0205042). We carry out further analysis of the additional restrictions on renormalizations noted in that work. We use the example of the axial gauge A3 = 0. We find that if multiplicative renormalization together with ghost-decoupling is to hold, the ``prescription-term'' (that defines a prescription) cannot be chosen arbitrarily but has to satisfy certain nontrivial conditions (over and above those implied by the validity of power counting) arising from the WT identitites associated with the residual gauge invariance. We also give a restricted class of solutions to these conditions.
Vitezica, Zulma G.; Varona, Luis; Legarra, Andres
2013-01-01
Genomic evaluation models can fit additive and dominant SNP effects. Under quantitative genetics theory, additive or “breeding” values of individuals are generated by substitution effects, which involve both “biological” additive and dominant effects of the markers. Dominance deviations include only a portion of the biological dominant effects of the markers. Additive variance includes variation due to the additive and dominant effects of the markers. We describe a matrix of dominant genomic relationships across individuals, D, which is similar to the G matrix used in genomic best linear unbiased prediction. This matrix can be used in a mixed-model context for genomic evaluations or to estimate dominant and additive variances in the population. From the “genotypic” value of individuals, an alternative parameterization defines additive and dominance as the parts attributable to the additive and dominant effect of the markers. This approach underestimates the additive genetic variance and overestimates the dominance variance. Transforming the variances from one model into the other is trivial if the distribution of allelic frequencies is known. We illustrate these results with mouse data (four traits, 1884 mice, and 10,946 markers) and simulated data (2100 individuals and 10,000 markers). Variance components were estimated correctly in the model, considering breeding values and dominance deviations. For the model considering genotypic values, the inclusion of dominant effects biased the estimate of additive variance. Genomic models were more accurate for the estimation of variance components than their pedigree-based counterparts. PMID:24121775
Garant, Dany; Hadfield, Jarrod D; Kruuk, Loeske E B; Sheldon, Ben C
2008-01-01
Global warming has had numerous effects on populations of animals and plants, with many species in temperate regions experiencing environmental change at unprecedented rates. Populations with low potential for adaptive evolutionary change and plasticity will have little chance of persistence in the face of environmental change. Assessment of the potential for adaptive evolution requires the estimation of quantitative genetic parameters, but it is as yet unclear what impact, if any, global warming will have on the expression of genetic variances and covariances. Here we assess the impact of a changing climate on the genetic architecture underlying three reproductive traits in a wild bird population. We use a large, long-term, data set collected on great tits (Parus major) in Wytham Woods, Oxford, and an 'animal model' approach to quantify the heritability of, and genetic correlations among, laying date, clutch size and egg mass during two periods with contrasting temperature conditions over a 40-year period (1965-1988 [cooler] vs. 1989-2004 [warmer]). We found significant additive genetic variance and heritability for all traits under both temperature regimes. We also found significant negative genetic covariances and correlations between clutch size and egg weight during both periods, and among laying date and clutch size in the colder years only. The overall G matrix comparison among periods, however, showed only a minor difference among periods, thus suggesting that genotype by environment interactions are negligible in this context. Our results therefore suggest that despite substantial changes in temperature and in mean laying date phenotype over the last decades, and despite the large sample sizes available, we are unable to detect any significant change in the genetic architecture of the reproductive traits studied.
Covariate-Adjusted Precision Matrix Estimation with an Application in Genetical Genomics
Cai, T. Tony; Li, Hongzhe; Liu, Weidong; Xie, Jichun
2017-01-01
Summary Motivated by analysis of genetical genomics data, we introduce a sparse high dimensional multivariate regression model for studying conditional independence relationships among a set of genes adjusting for possible genetic effects. The precision matrix in the model specifies a covariate-adjusted Gaussian graph, which presents the conditional dependence structure of gene expression after the confounding genetic effects on gene expression are taken into account. We present a covariate-adjusted precision matrix estimation method using a constrained ℓ1 minimization, which can be easily implemented by linear programming. Asymptotic convergence rates in various matrix norms and sign consistency are established for the estimators of the regression coefficients and the precision matrix, allowing both the number of genes and the number of the genetic variants to diverge. Simulation shows that the proposed method results in significant improvements in both precision matrix estimation and graphical structure selection when compared to the standard Gaussian graphical model assuming constant means. The proposed method is also applied to analyze a yeast genetical genomics data for the identification of the gene network among a set of genes in the mitogen-activated protein kinase pathway.
Estimation of genetic (co)variances of Gompertz growth function parameters in pigs.
Coyne, J M; Matilainen, K; Berry, D P; Sevon-Aimonen, M-L; Mäntysaari, E A; Juga, J; Serenius, T; McHugh, N
2017-04-01
The objective of this study was to estimate genetic (co)variances for the Gompertz growth function parameters, asymptotic mature weight (A), the ratio of mature weight to birthweight (B) and rate of maturation (k), using alternative modelling approaches. The data set consisted of 51 893 live weight records from 10 201 growing pigs. The growth of each pig was modelled using the Gompertz model employing either a two-step fixed effect or mixed model approach or a one-step mixed model approach using restricted maximum likelihood for the estimation of genetic (co)variance. Heritability estimates for the Gompertz growth function parameters, A (0.40), B (0.69) and k (0.45), were greatest for the one-step approach, compared with the two-step fixed effects approach, A (0.10), B (0.33) and k (0.13), and the two-step mixed model approach, A (0.17), B (0.32) and k (0.18). Inferred genetic correlations (i.e. correlations of estimated breeding values) between growth function parameters within models ranged from -0.78 to 0.76, and across models ranged from 0.28 to 0.73 for parameter A, 0.75 to 0.88 for parameter B and 0.09 to 0.37 for parameter k. Correlations between predicted daily sire live weights based on the Gompertz growth curve parameters' estimated breeding values from 60 to 200 days of age between all three modelled approaches were moderately to strongly correlated (0.75 to 0.95). Results from this study provide heritability estimates for biologically interpretable parameters of pig growth through the quantification of genetic (co)variances, thereby facilitating the estimation of breeding values for inclusion in breeding objectives to aid in breeding and selection decisions.
Fu, C.Y.; Hetrick, D.M.
1982-01-01
Recent ratio data, with carefully evaluated covariances, were combined with eleven of the ENDF/B-V dosimetry cross sections using the generalized least-squares method. The purpose was to improve these evaluated cross sections and covariances, as well as to generate values for the cross-reaction covariances. The results represent improved cross sections as well as realistic and usable covariances. The latter are necessary for meaningful intergral-differential comparisons and for spectrum unfolding.
Zhang, Zhe; Erbe, Malena; He, Jinlong; Ober, Ulrike; Gao, Ning; Zhang, Hao; Simianer, Henner; Li, Jiaqi
2015-02-09
Obtaining accurate predictions of unobserved genetic or phenotypic values for complex traits in animal, plant, and human populations is possible through whole-genome prediction (WGP), a combined analysis of genotypic and phenotypic data. Because the underlying genetic architecture of the trait of interest is an important factor affecting model selection, we propose a new strategy, termed BLUP|GA (BLUP-given genetic architecture), which can use genetic architecture information within the dataset at hand rather than from public sources. This is achieved by using a trait-specific covariance matrix ( T: ), which is a weighted sum of a genetic architecture part ( S: matrix) and the realized relationship matrix ( G: ). The algorithm of BLUP|GA (BLUP-given genetic architecture) is provided and illustrated with real and simulated datasets. Predictive ability of BLUP|GA was validated with three model traits in a dairy cattle dataset and 11 traits in three public datasets with a variety of genetic architectures and compared with GBLUP and other approaches. Results show that BLUP|GA outperformed GBLUP in 20 of 21 scenarios in the dairy cattle dataset and outperformed GBLUP, BayesA, and BayesB in 12 of 13 traits in the analyzed public datasets. Further analyses showed that the difference of accuracies for BLUP|GA and GBLUP significantly correlate with the distance between the T: and G: matrices. The new strategy applied in BLUP|GA is a favorable and flexible alternative to the standard GBLUP model, allowing to account for the genetic architecture of the quantitative trait under consideration when necessary. This feature is mainly due to the increased similarity between the trait-specific relationship matrix ( T: matrix) and the genetic relationship matrix at unobserved causal loci. Applying BLUP|GA in WGP would ease the burden of model selection.
Rapid divergence of genetic variance-covariance matrix within a natural population.
Doroszuk, Agnieszka; Wojewodzic, Marcin W; Gort, Gerrit; Kammenga, Jan E
2008-03-01
The matrix of genetic variances and covariances (G matrix) represents the genetic architecture of multiple traits sharing developmental and genetic processes and is central for predicting phenotypic evolution. These predictions require that the G matrix be stable. Yet the timescale and conditions promoting G matrix stability in natural populations remain unclear. We studied stability of the G matrix in a 20-year evolution field experiment, where a population of the cosmopolitan parthenogenetic soil nematode Acrobeloides nanus was subjected to drift and divergent selection (benign and stress environments). Selection regime did not influence the level of absolute genetic constraints: under both regimes, two genetic dimensions for three life-history traits were identified. A substantial response to selection in principal components structure and in general matrix pattern was indicated by three statistical methods. G structure was also influenced by drift, with higher divergence under benign conditions. These results show that the G matrix might evolve rapidly in natural populations. The observed high dynamics of G structure probably represents the general feature of asexual species and limits the predictive power of G in phenotypic evolution analyses.
Runcie, Daniel E.; Mukherjee, Sayan
2013-01-01
Quantitative genetic studies that model complex, multivariate phenotypes are important for both evolutionary prediction and artificial selection. For example, changes in gene expression can provide insight into developmental and physiological mechanisms that link genotype and phenotype. However, classical analytical techniques are poorly suited to quantitative genetic studies of gene expression where the number of traits assayed per individual can reach many thousand. Here, we derive a Bayesian genetic sparse factor model for estimating the genetic covariance matrix (G-matrix) of high-dimensional traits, such as gene expression, in a mixed-effects model. The key idea of our model is that we need consider only G-matrices that are biologically plausible. An organism’s entire phenotype is the result of processes that are modular and have limited complexity. This implies that the G-matrix will be highly structured. In particular, we assume that a limited number of intermediate traits (or factors, e.g., variations in development or physiology) control the variation in the high-dimensional phenotype, and that each of these intermediate traits is sparse – affecting only a few observed traits. The advantages of this approach are twofold. First, sparse factors are interpretable and provide biological insight into mechanisms underlying the genetic architecture. Second, enforcing sparsity helps prevent sampling errors from swamping out the true signal in high-dimensional data. We demonstrate the advantages of our model on simulated data and in an analysis of a published Drosophila melanogaster gene expression data set. PMID:23636737
Estimates of (co)variance components and genetic parameters for growth traits of Avikalin sheep.
Prince, Leslie Leo L; Gowane, Gopal R; Chopra, Ashish; Arora, Amrit L
2010-08-01
(Co)variance components and genetic parameters for various growth traits of Avikalin sheep maintained at Central Sheep and Wool Research Institute, Avikanagar, Rajasthan, India, were estimated by Restricted Maximum Likelihood, fitting six animal models with various combinations of direct and maternal effects. Records of 3,840 animals descended from 257 sires and 1,194 dams were taken for this study over a period of 32 years (1977-2008). Direct heritability estimates (from best model as per likelihood ratio test) for weight at birth, weaning, 6 and 12 months of age, and average daily gain from birth to weaning, weaning to 6 months, and 6 to 12 months were 0.28 +/- 0.03, 0.20 +/- 0.03, 0.28 +/- 0.07, 0.15 +/- 0.04, 0.21 +/- 0.03, 0.16 and 0.03 +/- 0.03, respectively. Maternal heritability for traits declined as animal grows older and it was not at all evident at adult age and for post-weaning daily gain. Maternal permanent environmental effect (c(2)) declined significantly with advancement of age of animal. A small effect of c(2) on post-weaning weights was probably a carryover effect of pre-weaning maternal influence. A significant large negative genetic correlation was observed between direct and maternal genetic effects for all the traits, indicating antagonistic pleiotropy, which needs special care while formulating breeding plans. A fair rate of genetic progress seems possible in the flock by selection for all traits, but direct and maternal genetic correlation needs to be taken in to consideration.
Colautti, Robert I; Barrett, Spencer C H
2011-09-01
Evolution during biological invasion may occur over contemporary timescales, but the rate of evolutionary change may be inhibited by a lack of standing genetic variation for ecologically relevant traits and by fitness trade-offs among them. The extent to which these genetic constraints limit the evolution of local adaptation during biological invasion has rarely been examined. To investigate genetic constraints on life-history traits, we measured standing genetic variance and covariance in 20 populations of the invasive plant purple loosestrife (Lythrum salicaria) sampled along a latitudinal climatic gradient in eastern North America and grown under uniform conditions in a glasshouse. Genetic variances within and among populations were significant for all traits; however, strong intercorrelations among measurements of seedling growth rate, time to reproductive maturity and adult size suggested that fitness trade-offs have constrained population divergence. Evidence to support this hypothesis was obtained from the genetic variance-covariance matrix (G) and the matrix of (co)variance among population means (D), which were 79.8% (95% C.I. 77.7-82.9%) similar. These results suggest that population divergence during invasive spread of L. salicaria in eastern North America has been constrained by strong genetic correlations among life-history traits, despite large amounts of standing genetic variation for individual traits.
Explaining additional genetic variation in complex traits
Robinson, Matthew R.; Wray, Naomi R.; Visscher, Peter M.
2015-01-01
Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of complex traits, discovering >6000 variants associated with >500 quantitative traits and common complex diseases in humans. The associations identified so far represent only a fraction of those which influence phenotype, as there are likely to be very many variants across the entire frequency spectrum, each of which influences multiple traits, with only a small average contribution to the phenotypic variance. This presents a considerable challenge to further dissection of the remaining unexplained genetic variance within populations, which limits our ability to predict disease risk, identify new drug targets, improve and maintain food sources, and understand natural diversity. This challenge will be met within the current framework through larger sample size, better phenotyping including recording of non-genetic risk factors, focused study designs, and an integration of multiple sources of phenotypic and genetic information. The current evidence supports the application of quantitative genetic approaches, and we argue that one should retain simpler theories until simplicity can be traded for greater explanatory power. PMID:24629526
Ontogeny of additive and maternal genetic effects: lessons from domestic mammals.
Wilson, Alastair J; Reale, Denis
2006-01-01
Evolution of size and growth depends on heritable variation arising from additive and maternal genetic effects. Levels of heritable (and nonheritable) variation might change over ontogeny, increasing through "variance compounding" or decreasing through "compensatory growth." We test for these processes using a meta-analysis of age-specific weight traits in domestic ungulates. Generally, mean standardized variance components decrease with age, consistent with compensatory growth. Phenotypic convergence among adult sheep occurs through decreasing environmental and maternal genetic variation. Maternal variation similarly declines in cattle. Maternal genetic effects are thus reduced with age (both in absolute and relative terms). Significant trends in heritability (decreasing in cattle, increasing in sheep) result from declining maternal and environmental components rather than from changing additive variation. There was no evidence for increasing standardized variance components. Any compounding must therefore be masked by more important compensatory processes. While extrapolation of these patterns to processes in natural population is difficult, our results highlight the inadequacy of assuming constancy in genetic parameters over ontogeny. Negative covariance between direct and maternal genetic effects was common. Negative correlations with additive and maternal genetic variances indicate that antagonistic pleiotropy (between additive and maternal genetic effects) may maintain genetic variance and limit responses to selection.
Unnatural reactive amino acid genetic code additions
Deiters, Alexander; Cropp, T Ashton; Chin, Jason W; Anderson, J Christopher; Schultz, Peter G
2011-02-15
This invention provides compositions and methods for producing translational components that expand the number of genetically encoded amino acids in eukaryotic cells. The components include orthogonal tRNAs, orthogonal aminoacyl-tRNA synthetases, orthogonal pairs of tRNAs/synthetases and unnatural amino acids. Proteins and methods of producing proteins with unnatural amino acids in eukaryotic cells are also provided.
Unnatural reactive amino acid genetic code additions
Deiters, Alexander; Cropp, Ashton T; Chin, Jason W; Anderson, Christopher J; Schultz, Peter G
2013-05-21
This invention provides compositions and methods for producing translational components that expand the number of genetically encoded amino acids in eukaryotic cells. The components include orthogonal tRNAs, orthogonal aminoacyl-tRNA synthetases, pairs of tRNAs/synthetases and unnatural amino acids. Proteins and methods of producing proteins with unnatural amino acids in eukaryotic cells are also provided.
Unnatural reactive amino acid genetic code additions
Deiters, Alexander; Cropp, T. Ashton; Chin, Jason W.; Anderson, J. Christopher; Schultz, Peter G.
2011-08-09
This invention provides compositions and methods for producing translational components that expand the number of genetically encoded amino acids in eukaryotic cells. The components include orthogonal tRNAs, orthogonal aminoacyl-tRNAsyn-thetases, pairs of tRNAs/synthetases and unnatural amino acids. Proteins and methods of producing proteins with unnatural amino acids in eukaryotic cells are also provided.
Unnatural reactive amino acid genetic code additions
Deiters, Alexander; Cropp, T. Ashton; Chin, Jason W.; Anderson, J. Christopher; Schultz, Peter G.
2014-08-26
This invention provides compositions and methods for producing translational components that expand the number of genetically encoded amino acids in eukaryotic cells. The components include orthogonal tRNAs, orthogonal aminoacyl-tRNA synthetases, orthogonal pairs of tRNAs/synthetases and unnatural amino acids. Proteins and methods of producing proteins with unnatural amino acids in eukaryotic cells are also provided.
Jang, K L; Hu, S; Livesley, W J; Angleitner, A; Riemann, R; Ando, J; Ono, Y; Vernon, P A; Hamer, D H
2001-08-01
The Revised NEO Personality Inventory domains of Neuroticism and Agreeableness are considered factorially distinct despite several intercorrelations between these domains. The genetic correlation, an index of the degree to which these intercorrelations are caused by genetic influences, was estimated using data from 913 monozygotic and 562 dizygotic volunteer twin pairs from Canada, Germany, and Japan. The serotonin transporter gene, 5-HTTLPR, was assayed in a sample of 388 nontwin sibling pairs from the United States to determine the contribution of the serotonin transporter locus to the covariation between the Neuroticism and Agreeableness scales. In all four samples, genetic influences contributed to the covariance of Neuroticism and Agreeableness, with the serotonin transporter gene accounting for 10% of the relationship between these domains.
ERIC Educational Resources Information Center
Tuvblad, Catherine; Zheng, Mo; Raine, Adrian; Baker, Laura A.
2009-01-01
Previous studies examining the covariation among Attention Deficit Hyperactivity Disorder (ADHD), Oppositional Defiant Disorder (ODD) and Conduct Disorder (CD) have yielded inconsistent results. Some studies have concluded that the covariation among these symptoms is due to common genetic influences, whereas others have found a common…
Estimates of (co)variance components and genetic parameters for growth traits in Sirohi goat.
Gowane, Gopal R; Chopra, Ashish; Prakash, Ved; Arora, A L
2011-01-01
Data were collected over a period of 21 years (1988-2008) to estimate (co)variance components for birth weight (BWT), weaning weight (WWT), 6-month weight (6WT), 9-month weight (9WT), 12-month weight (12WT), average daily gain from birth to weaning (ADG1), weaning to 6WT (ADG2), and from 6WT to 12WT (ADG3) in Sirohi goats maintained at the Central Sheep and Wool Research Institute, Avikanagar, Rajasthan, India. Analyses were carried out by restricted maximum likelihood, fitting six animal models with various combinations of direct and maternal effects. The best model was chosen after testing the improvement of the log-likelihood values. Heritability estimates for BWT, WWT, 6WT, 9WT, 12WT, ADG1, ADG2, and ADG3 were 0.39 ± 0.05, 0.09 ± 0.03, 0.06 ± 0.02, 0.09 ± 0.03, 0.11 ± 0.03, 0.10 ± 0.3, 0.04 ± 0.02, and 0.01 ± 0.01, respectively. For BWT and ADG1, only direct effects were significant. Estimate of maternal permanent environmental effect were important for body weights from weaning to 12WT and also for ADG2 and ADG3. However, direct maternal effects were not significant throughout. Estimate of c (2) were 0.06 ± 0.02, 0.03 ± 0.02, 0.06 ± 0.02, 0.05 ± 0.02, 0.02 ± 0.02, and 0.02 ± 0.02 for 3WT, 6WT, 9WT, 12WT, ADG2, and ADG3, respectively. The estimated repeatabilities across years of ewe effects on kid body weights were 0.10, 0.08, 0.05, 0.08, and 0.08 at birth, weaning, 6, 9, and 12 months of age, respectively. Results suggest possibility of modest rate of genetic progress for body weight traits and ADG1 through selection, whereas only slow progress will be possible for post-weaning gain. Genetic and phenotypic correlations between body weight traits were high and positive. High genetic correlation between 6WT and 9WT suggests that selection of animals at 6 months can be carried out instead of present practice of selection at 9 months.
The Expression of Additive and Nonadditive Genetic Variation under Stress
Blows, M. W.; Sokolowski, M. B.
1995-01-01
Experimental lines of Drosophila melanogaster derived from a natural population, which had been isolated in the laboratory for ~70 generations, were crossed to determine if the expression of additive, dominance and epistatic genetic variation in development time and viability was associated with the environment. No association was found between the level of additive genetic effects and environmental value for either trait, but nonadditive genetic effects increased at both extremes of the environmental range for development time. The expression of high levels of dominance and epistatic genetic variation at environmental extremes may be a general expectation for some traits. The disruption of the epistatic gene complexes in the parental lines resulted in hybrid breakdown toward faster development and there was some indication of hybrid breakdown toward higher viability. A combination of genetic drift and natural selection had therefore resulted in different epistatic gene complexes being selected after ~70 generations from a common genetic base. After crossing, the hybrid populations were observed for 10 generations. Epistasis contributed on average 12 hr in development time. Fluctuating asymmetry in sternopleural bristle number also evolved in the hybrid populations, decreasing by >18% in the first seven generations after hybridization. PMID:7672585
Huchard, E; Charmantier, A; English, S; Bateman, A; Nielsen, J F; Clutton-Brock, T
2014-09-01
Individual variation in growth is high in cooperative breeders and may reflect plastic divergence in developmental trajectories leading to breeding vs. helping phenotypes. However, the relative importance of additive genetic variance and developmental plasticity in shaping growth trajectories is largely unknown in cooperative vertebrates. This study exploits weekly sequences of body mass from birth to adulthood to investigate sources of variance in, and covariance between, early and later growth in wild meerkats (Suricata suricatta), a cooperative mongoose. Our results indicate that (i) the correlation between early growth (prior to nutritional independence) and adult mass is positive but weak, and there are frequent changes (compensatory growth) in post-independence growth trajectories; (ii) among parameters describing growth trajectories, those describing growth rate (prior to and at nutritional independence) show undetectable heritability while associated size parameters (mass at nutritional independence and asymptotic mass) are moderately heritable (0.09 ≤ h(2) < 0.3); and (iii) additive genetic effects, rather than early environmental effects, mediate the covariance between early growth and adult mass. These results reveal that meerkat growth trajectories remain plastic throughout development, rather than showing early and irreversible divergence, and that the weak effects of early growth on adult mass, an important determinant of breeding success, are partly genetic. In contrast to most cooperative invertebrates, the acquisition of breeding status is often determined after sexual maturity and strongly impacted by chance in many cooperative vertebrates, who may therefore retain the ability to adjust their morphology to environmental changes and social opportunities arising throughout their development, rather than specializing early.
ERIC Educational Resources Information Center
McAdams, Tom; Rowe, Richard; Rijsdijk, Fruhling; Maughan, Barbara; Eley, Thalia C.
2012-01-01
Multivariate genetic studies have revealed genetic correlations between antisocial behavior (ASB) and substance use (SU). However, ASB is heterogeneous, and it remains unclear whether all forms are similarly related to SU. The present study examines links between cannabis use, alcohol consumption, and aggressive and delinquent forms of ASB using a…
WANG, PAN; GAO, YU; ISEN, JOSHUA; TUVBLAD, CATHERINE; RAINE, ADRIAN; BAKER, LAURA A.
2015-01-01
The genetic architecture of the association between psychopathic traits and reduced skin conductance responses (SCRs) is poorly understood. By using 752 twins aged 9–10 years, this study investigated the heritability of two SCR measures (anticipatory SCRs to impending aversive stimuli and unconditioned SCRs to the aversive stimuli themselves) in a countdown task. The study also investigated the genetic and environmental sources of the covariance between these SCR measures and two psychopathic personality traits: impulsive/disinhibited (reflecting impulsive–antisocial tendencies) and manipulative/deceitful (reflecting the affective–interpersonal features). For anticipatory SCRs, 27%, 14%, and 59% of the variation was due to genetic, shared environmental, and nonshared environmental effects, respectively, while the percentages for unconditioned SCRs were 44%, 2%, and 54%. The manipulative/deceitful (not impulsive/disinhibited) traits were negatively associated with both anticipatory SCRs (r = −.14, p < .05) and unconditioned SCRs (r = −.17, p < .05) in males only, with the former association significantly accounted for by genetic influences (rg = −.72). Reduced anticipatory SCRs represent a candidate endophenotype for the affective–interpersonal facets of psychopathic traits in males. PMID:26439076
Mandal, A; Neser, F W C; Roy, R; Rout, P K; Notter, D R
2009-02-01
Variance components and genetic parameters for greasy fleece weights of Muzaffarnagari sheep maintained at the Central Institute for Research on Goats, Makhdoom, Mathura, India, over a period of 29 years (1976 to 2004) were estimated by restricted maximum likelihood (REML), fitting six animal models including various combinations of maternal effects. Data on body weights at 6 (W6) and 12 months (W12) of age were also included in the study. Records of 2807 lambs descended from 160 rams and 1202 ewes were used for the study. Direct heritability estimates for fleece weight at 6 (FW6) and 12 months of age (FW12), and total fleece weights up to 1 year of age (TFW) were 0.14, 0.16 and 0.25, respectively. Maternal genetic and permanent environmental effects did not significantly influence any of the traits under study. Genetic correlations among fleece weights and body weights were obtained from multivariate analyses. Direct genetic correlations of FW6 with W6 and W12 were relatively large, ranging from 0.61 to 0.67, but only moderate genetic correlations existed between FW12 and W6 (0.39) and between FW12 and W12 (0.49). The genetic correlation between FW6 and FW12 was very high (0.95), but the corresponding phenotypic correlation was much lower (0.28). Heritability estimates for all traits were at least 0.15, indicating that there is potential for their improvement by selection. The moderate to high positive genetic correlations between fleece weights and body weights at 6 and 12 months of age suggest that some of the genetic factors that influence animal growth also influence wool growth. Thus selection to improve the body weights or fleece weights at 6 months of age will also result in genetic improvement of fleece weights at subsequent stages of growth.
Additive and nonadditive genetic variation in avian personality traits.
van Oers, K; Drent, P J; de Jong, G; van Noordwijk, A J
2004-11-01
Individuals of all vertebrate species differ consistently in their reactions to mildly stressful challenges. These typical reactions, described as personalities or coping strategies, have a clear genetic basis, but the structure of their inheritance in natural populations is almost unknown. We carried out a quantitative genetic analysis of two personality traits (exploration and boldness) and the combination of these two traits (early exploratory behaviour). This study was carried out on the lines resulting from a two-directional artificial selection experiment on early exploratory behaviour (EEB) of great tits (Parus major) originating from a wild population. In analyses using the original lines, reciprocal F(1) and reciprocal first backcross generations, additive, dominance, maternal effects ands sex-dependent expression of exploration, boldness and EEB were estimated. Both additive and dominant genetic effects were important determinants of phenotypic variation in exploratory behaviour and boldness. However, no sex-dependent expression was observed in either of these personality traits. These results are discussed with respect to the maintenance of genetic variation in personality traits, and the expected genetic structure of other behavioural and life history traits in general.
Converse, Sarah J.; Royle, J. Andrew; Urbanek, Richard P.
2012-01-01
Inbreeding depression is frequently a concern of managers interested in restoring endangered species. Decisions to reduce the potential for inbreeding depression by balancing genotypic contributions to reintroduced populations may exact a cost on long-term demographic performance of the population if those decisions result in reduced numbers of animals released and/or restriction of particularly successful genotypes (i.e., heritable traits of particular family lines). As part of an effort to restore a migratory flock of Whooping Cranes (Grus americana) to eastern North America using the offspring of captive breeders, we obtained a unique dataset which includes post-release mark-recapture data, as well as the pedigree of each released individual. We developed a Bayesian formulation of a multi-state model to analyze radio-telemetry, band-resight, and dead recovery data on reintroduced individuals, in order to track survival and breeding state transitions. We used studbook-based individual covariates to examine the comparative evidence for and degree of effects of inbreeding, genotype, and genotype quality on post-release survival of reintroduced individuals. We demonstrate implementation of the Bayesian multi-state model, which allows for the integration of imperfect detection, multiple data types, random effects, and individual- and time-dependent covariates. Our results provide only weak evidence for an effect of the quality of an individual's genotype in captivity on post-release survival as well as for an effect of inbreeding on post-release survival. We plan to integrate our results into a decision-analytic modeling framework that can explicitly examine tradeoffs between the effects of inbreeding and the effects of genotype and demographic stochasticity on population establishment.
Logan, Jessica; Petrill, Stephen A.; Flax, Judy; Justice, Laura M.; Hou, Liping; Bassett, Anne S.; Tallal, Paula; Brzustowicz, Linda M.
2011-01-01
Specific language impairment is a developmental language disorder characterized by failure to develop language normally in the absence of a specific cause. Previous twin studies have documented the heritability of reading and language measures as well as the genetic correlation between those measures. This paper presents results from an alternative to the classical twin designs by estimating heritability from extended pedigrees. These pedigrees were previously studied as part of series of molecular genetic studies of specific language impairment where the strongest genetic findings were with reading phenotypes rather than language despite selecting pedigrees based on language impairments. To explore the relationship between reading and language in these pedigrees, variance components estimates of heritability of reading and language measures were conducted showing general agreement with the twin literature, as were genetics correlations between reading and language. Phonological short-term memory, phonological awareness and auditory processing were evaluated as candidate mediators of the reading-language genetic correlations. Only phonological awareness showed significant genetic correlations with all reading measures and several language measures while phonological short-term memory and auditory processing did not. PMID:21193955
Plasse, Dieter; Verde, Omar; Arango, Jesús; Camaripano, Luis; Fossi, Hugo; Romero, Rafael; Rodriguez, Carlos M; Rumbos, José L
2002-12-31
(Co)variance components and genetic parameters were estimated for body weights of an elite Brahman herd under a designed, supervised management and genetic program, including strategic artificial insemination (AI). Restricted maximum likelihood methods were used with a univariate animal model for birth weight (BW) and a bivariate model for weaning weight (205-day weight, 205W) and 18-month weight (548-day weight, 548W). Models included random animal direct and maternal genetic effects, maternal permanent environmental effect (c2), and sex-year-month of birth-age of dam and genetic group (identified and unidentified paternity), as fixed effects. Analysis A1 included all calves and analysis A2 included only those with identified sires. Of the 8,066 calves born, 36% were progeny of AI, 11% from single sire and 53% from multi-sire herds. They were born from 1985 to 1998, from 2559 dams and 146 sires (78 identified). Estimates of direct, maternal and total heritabilities from A1 for BW, 205W and 548W were: 0.23, 0.07 and 0.30; 0.08, 0.14 and 0.16; 0.16, 0.04 and 0.28, respectively. Corresponding estimates of direct maternal genetic correlations were 0.22, 0.07 and 0.86, and c2 estimates were 0.04, 0.14 and 0.04, respectively. Estimates of direct and maternal genetic, and permanent environmental correlations between 205W and 548W were: 0.66, 0.70 and 1.00. Variances and genetic parameters from A1 and A2 were, in general, very similar. Estimates of phenotypic, and direct and maternal genetic trends per year from A1 were: 0.393, 0.004 and 0.003 kg (BW), 3.367, 0.142 and 0.115 kg (205W), 1.813, 0.263 and 0.095 kg (548W). Estimates of direct and maternal genetic trends from A2 were: 0.033 and -0.002 kg (BW); 0.186 and 0.276 kg (205W); 0.471 and 0.136 kg (548W). The modern selection methods that have been used recently should be continued, with emphasis on the improvement of cow efficiency for sustainable beef production on floodable savanna combined with improved pasture.
Uesugi, Akane; Connallon, Tim; Kessler, André; Monro, Keyne
2017-04-10
Insect herbivores are important mediators of selection on traits that impact plant defense against herbivory and competitive ability. Although recent experiments demonstrate a central role for herbivory in driving rapid evolution of defense and competition-mediating traits, whether and how herbivory shapes heritable variation in these traits remains poorly understood. Here, we evaluate the structure and evolutionary stability of the G matrix for plant metabolites that are involved in defense and allelopathy in the tall goldenrod, Solidago altissima. We show that G has evolutionarily diverged between experimentally-replicated populations that evolved in the presence versus the absence of ambient herbivory, providing direct evidence for the evolution of G by natural selection. Specifically, evolution in an herbivore-free habitat altered the orientation of G, revealing a negative genetic covariation between defense- and competition-related metabolites that is typically masked in herbivore-exposed populations. Our results may be explained by predictions of classical quantitative genetic theory, as well as the theory of acquisition-allocation trade-offs. The study provides compelling evidence that herbivory drives the evolution of plant genetic architecture. This article is protected by copyright. All rights reserved.
Colour ornamentation in the blue tit: quantitative genetic (co)variances across sexes.
Charmantier, A; Wolak, M E; Grégoire, A; Fargevieille, A; Doutrelant, C
2017-02-01
Although secondary sexual traits are commonly more developed in males than females, in many animal species females also display elaborate ornaments or weaponry. Indirect selection on correlated traits in males and/or direct sexual or social selection in females are hypothesized to drive the evolution and maintenance of female ornaments. Yet, the relative roles of these evolutionary processes remain unidentified, because little is known about the genetic correlation that might exist between the ornaments of both sexes, and few estimates of sex-specific autosomal or sex-linked genetic variances are available. In this study, we used two wild blue tit populations with 9 years of measurements on two colour ornaments: one structurally based (blue crown) and one carotenoid based (yellow chest). We found significant autosomal heritability for the chromatic part of the structurally based colouration in both sexes, whereas carotenoid chroma was heritable only in males, and the achromatic part of both colour patches was mostly non heritable. Power limitations, which are probably common among most data sets collected so far in wild populations, prevented estimation of sex-linked genetic variance. Bivariate analyses revealed very strong cross-sex genetic correlations in all heritable traits, although the strength of these correlations was not related to the level of sexual dimorphism. In total, our results suggest that males and females share a majority of their genetic variation underlying colour ornamentation, and hence the evolution of these sex-specific traits may depend greatly on correlated responses to selection in the opposite sex.
Genetic (Co)Variation for Life Span in Rhabditid Nematodes: Role of Mutation, Selection, and History
Upadhyay, Ambuj; Salomon, Matthew P.; Grigaltchik, Veronica; Baer, Charles F.
2009-01-01
The evolutionary mechanisms maintaining genetic variation in life span, particularly post-reproductive life span, are poorly understood. We characterized the effects of spontaneous mutations on life span in the rhabditid nematodes Caenorhabditis elegans and C. briggsae and standing genetic variance for life span and correlation of life span with fitness in C. briggsae. Mutations decreased mean life span, a signature of directional selection. Mutational correlations between life span and fitness were consistently positive. The average selection coefficient against new mutations in C. briggsae was approximately 2% when homozygous. The pattern of phylogeographic variation in life span is inconsistent with global mutation–selection balance (MSB), but MSB appears to hold at the local level. Standing genetic correlations in C. briggsae reflect mutational correlations at a local scale but not at a broad phylogeographic level. At the local scale, results are broadly consistent with predictions of the “mutation accumulation” hypothesis for the evolution of aging. PMID:19671885
Additional mechanisms conferring genetic susceptibility to Alzheimer’s disease
Calero, Miguel; Gómez-Ramos, Alberto; Calero, Olga; Soriano, Eduardo; Avila, Jesús; Medina, Miguel
2015-01-01
Familial Alzheimer’s disease (AD), mostly associated with early onset, is caused by mutations in three genes (APP, PSEN1, and PSEN2) involved in the production of the amyloid β peptide. In contrast, the molecular mechanisms that trigger the most common late onset sporadic AD remain largely unknown. With the implementation of an increasing number of case-control studies and the upcoming of large-scale genome-wide association studies there is a mounting list of genetic risk factors associated with common genetic variants that have been associated with sporadic AD. Besides apolipoprotein E, that presents a strong association with the disease (OR∼4), the rest of these genes have moderate or low degrees of association, with OR ranging from 0.88 to 1.23. Taking together, these genes may account only for a fraction of the attributable AD risk and therefore, rare variants and epistastic gene interactions should be taken into account in order to get the full picture of the genetic risks associated with AD. Here, we review recent whole-exome studies looking for rare variants, somatic brain mutations with a strong association to the disease, and several studies dealing with epistasis as additional mechanisms conferring genetic susceptibility to AD. Altogether, recent evidence underlines the importance of defining molecular and genetic pathways, and networks rather than the contribution of specific genes. PMID:25914626
Heritability and genetic covariation of sensitivity to PROP, SOA, quinine HCl, and caffeine.
Hansen, Jonathan L; Reed, Danielle R; Wright, Margaret J; Martin, Nicholas G; Breslin, Paul A S
2006-06-01
The perceived bitterness intensity for bitter solutions of propylthiouracil (PROP), sucrose octa-acetate (SOA), quinine HCl and caffeine were examined in a genetically informative sample of 392 females and 313 males (mean age of 17.8 +/- 3.1 years), including 62 monozygotic and 131 dizygotic twin pairs and 237 sib pairs. Broad-sense heritabilities were estimated at 0.72, 0.28, 0.34, and 0.30 for PROP, SOA, quinine, and caffeine, respectively, for perceived intensity measures. Modeling showed 1) a group factor which explained a large amount of the genetic variation in SOA, quinine, and caffeine (22-28% phenotypic variation), 2) a factor responsible for all the genetic variation in PROP (72% phenotypic variation), which only accounted for 1% and 2% of the phenotypic variation in SOA and caffeine, respectively, and 3) a modest specific genetic factor for quinine (12% phenotypic variation). Unique environmental influences for all four compounds were due to a single factor responsible for 7-22% of phenotypic variation. The results suggest that the perception of PROP and the perception of SOA, quinine, and caffeine are influenced by two distinct sets of genes.
Colour ornamentation in the blue tit: quantitative genetic (co)variances across sexes
Charmantier, A; Wolak, M E; Grégoire, A; Fargevieille, A; Doutrelant, C
2017-01-01
Although secondary sexual traits are commonly more developed in males than females, in many animal species females also display elaborate ornaments or weaponry. Indirect selection on correlated traits in males and/or direct sexual or social selection in females are hypothesized to drive the evolution and maintenance of female ornaments. Yet, the relative roles of these evolutionary processes remain unidentified, because little is known about the genetic correlation that might exist between the ornaments of both sexes, and few estimates of sex-specific autosomal or sex-linked genetic variances are available. In this study, we used two wild blue tit populations with 9 years of measurements on two colour ornaments: one structurally based (blue crown) and one carotenoid based (yellow chest). We found significant autosomal heritability for the chromatic part of the structurally based colouration in both sexes, whereas carotenoid chroma was heritable only in males, and the achromatic part of both colour patches was mostly non heritable. Power limitations, which are probably common among most data sets collected so far in wild populations, prevented estimation of sex-linked genetic variance. Bivariate analyses revealed very strong cross-sex genetic correlations in all heritable traits, although the strength of these correlations was not related to the level of sexual dimorphism. In total, our results suggest that males and females share a majority of their genetic variation underlying colour ornamentation, and hence the evolution of these sex-specific traits may depend greatly on correlated responses to selection in the opposite sex. PMID:27577691
Ratterman, Nicholas L.; Rosenthal, Gil G.; Carney, Ginger E.; Jones, Adam G.
2013-01-01
How mating preferences evolve remains one of the major unsolved mysteries in evolutionary biology. One major impediment to the study of ornament-preference coevolution is that many aspects of the theoretical literature remain loosely connected to empirical data. Theoretical models typically streamline mating preferences by describing preference functions with a single parameter, a modeling convenience that may veil important aspects of preference evolution. Here, we use a high-throughput behavioral assay in Drosophila melanogaster to quantify attractiveness and multiple components of preferences in both males and females. Females varied genetically with respect to how they ranked males in terms of attractiveness as well as the extent to which they discriminated among different males. Conversely, males showed consistent preferences for females, suggesting that D. melanogaster males tend to rank different female phenotypes in the same order in terms of attractiveness. Moreover, we reveal a heretofore undocumented positive genetic correlation between male attractiveness and female choosiness, which is a measure of the variability in a female’s response to different male phenotypes. This genetic correlation sets the stage for female choosiness to evolve via a correlated response to selection on male traits and potentially adds a new dimension to the Fisherian sexual selection process. PMID:24212081
Kuja-Halkola, Ralf; D'Onofrio, Brian M; Larsson, Henrik; Lichtenstein, Paul
2014-09-01
Maternal smoking during pregnancy (SDP) has been associated with several psychiatric outcomes in the offspring; studies have questioned whether the associations are causal, however. We analyzed all children born in Sweden between 1983 and 2009 to investigate the effect of SDP on multiple indicators of adverse outcomes in three areas: pregnancy outcomes (birth weight, preterm birth and being born small for gestational age), long-term cognitive abilities (low academic achievement and general cognitive ability) and externalizing behaviors (criminal conviction, violent criminal conviction and drug misuse). SDP was associated with all outcomes. Within-family analyses of the pregnancy outcomes were consistent with a causal interpretation as the associations persisted when siblings discordant for SDP were compared. For the cognitive and externalizing outcomes, the results were not consistent with causal effects; when comparing differentially exposed siblings none of the associations remained significant. In quantitative genetic models genetic factors explained the majority of the associations between SDP and cognitive and externalizing outcomes. The results suggest that the associations between SDP in mothers and cognition and externalizing behaviors in their offspring is primarily due to genetic effects that influence the behaviors in both generations.
Lobach, Iryna; Mallick, Bani; Carroll, Raymond J
2011-01-01
Case-control studies are widely used to detect gene-environment interactions in the etiology of complex diseases. Many variables that are of interest to biomedical researchers are difficult to measure on an individual level, e.g. nutrient intake, cigarette smoking exposure, long-term toxic exposure. Measurement error causes bias in parameter estimates, thus masking key features of data and leading to loss of power and spurious/masked associations. We develop a Bayesian methodology for analysis of case-control studies for the case when measurement error is present in an environmental covariate and the genetic variable has missing data. This approach offers several advantages. It allows prior information to enter the model to make estimation and inference more precise. The environmental covariates measured exactly are modeled completely nonparametrically. Further, information about the probability of disease can be incorporated in the estimation procedure to improve quality of parameter estimates, what cannot be done in conventional case-control studies. A unique feature of the procedure under investigation is that the analysis is based on a pseudo-likelihood function therefore conventional Bayesian techniques may not be technically correct. We propose an approach using Markov Chain Monte Carlo sampling as well as a computationally simple method based on an asymptotic posterior distribution. Simulation experiments demonstrated that our method produced parameter estimates that are nearly unbiased even for small sample sizes. An application of our method is illustrated using a population-based case-control study of the association between calcium intake with the risk of colorectal adenoma development.
Mallick, Bani; Carroll, Raymond J.
2011-01-01
Case-control studies are widely used to detect gene-environment interactions in the etiology of complex diseases. Many variables that are of interest to biomedical researchers are difficult to measure on an individual level, e.g. nutrient intake, cigarette smoking exposure, long-term toxic exposure. Measurement error causes bias in parameter estimates, thus masking key features of data and leading to loss of power and spurious/masked associations. We develop a Bayesian methodology for analysis of case-control studies for the case when measurement error is present in an environmental covariate and the genetic variable has missing data. This approach offers several advantages. It allows prior information to enter the model to make estimation and inference more precise. The environmental covariates measured exactly are modeled completely nonparametrically. Further, information about the probability of disease can be incorporated in the estimation procedure to improve quality of parameter estimates, what cannot be done in conventional case-control studies. A unique feature of the procedure under investigation is that the analysis is based on a pseudo-likelihood function therefore conventional Bayesian techniques may not be technically correct. We propose an approach using Markov Chain Monte Carlo sampling as well as a computationally simple method based on an asymptotic posterior distribution. Simulation experiments demonstrated that our method produced parameter estimates that are nearly unbiased even for small sample sizes. An application of our method is illustrated using a population-based case-control study of the association between calcium intake with the risk of colorectal adenoma development. PMID:21949562
Efficient Improvement of Silage Additives by Using Genetic Algorithms
Davies, Zoe S.; Gilbert, Richard J.; Merry, Roger J.; Kell, Douglas B.; Theodorou, Michael K.; Griffith, Gareth W.
2000-01-01
The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of genetic algorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh ryegrass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e., no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a “fitness” value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a “cost” element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a genetic algorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments, each of which comprised 50 treatments, there was a steady increase in the amount of lactate that accumulated; the best treatment combination was that used in the last experiment, which produced 4.6 times more lactate than the untreated silage. The additive combinations that were found to yield the highest fitness values in the final (fifth) experiment were assessed to determine a range of biochemical and microbiological quality parameters during full-term silage
Efficient improvement of silage additives by using genetic algorithms.
Davies, Z S; Gilbert, R J; Merry, R J; Kell, D B; Theodorou, M K; Griffith, G W
2000-04-01
The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of genetic algorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh ryegrass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e. , no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a "fitness" value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a "cost" element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a genetic algorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments, each of which comprised 50 treatments, there was a steady increase in the amount of lactate that accumulated; the best treatment combination was that used in the last experiment, which produced 4.6 times more lactate than the untreated silage. The additive combinations that were found to yield the highest fitness values in the final (fifth) experiment were assessed to determine a range of biochemical and microbiological quality parameters during full-term silage fermentation. We
NASA Astrophysics Data System (ADS)
Chartin, Caroline; Stevens, Antoine; van Wesemael, Bas
2015-04-01
Providing spatially continuous Soil Organic Carbon data (SOC) is needed to support decisions regarding soil management, and inform the political debate with quantified estimates of the status and change of the soil resource. Digital Soil Mapping techniques are based on relations existing between a soil parameter (measured at different locations in space at a defined period) and relevant covariates (spatially continuous data) that are factors controlling soil formation and explaining the spatial variability of the target variable. This study aimed at apply DSM techniques to recent SOC content measurements (2005-2013) in three different landuses, i.e. cropland, grassland, and forest, in the Walloon region (Southern Belgium). For this purpose, SOC databases of two regional Soil Monitoring Networks (CARBOSOL for croplands and grasslands, and IPRFW for forests) were first harmonized, totalising about 1,220 observations. Median values of SOC content for croplands, grasslands, and forests, are respectively of 12.8, 29.0, and 43.1 g C kg-1. Then, a set of spatial layers were prepared with a resolution of 40 meters and with the same grid topology, containing environmental covariates such as, landuses, Digital Elevation Model and its derivatives, soil texture, C factor, carbon inputs by manure, and climate. Here, in addition to the three classical texture classes (clays, silt, and sand), we tested the use of clays + fine silt content (particles < 20 µm and related to stable carbon fraction) as soil covariate explaining SOC variations. For each of the three land uses (cropland, grassland and forest), a Generalized Additive Model (GAM) was calibrated on two thirds of respective dataset. The remaining samples were assigned to a test set to assess model performance. A backward stepwise procedure was followed to select the relevant environmental covariates using their approximate p-values (the level of significance was set at p < 0.05). Standard errors were estimated for each of
Rettew, David C; Rebollo-Mesa, Irene; Hudziak, James J; Willemsen, Gonneke; Boomsma, Dorret I
2008-05-01
The influence of non-additive genetic influences on personality traits has been increasingly reported in adult populations. Less is known, however, with respect to younger samples. In this study, we examine additive and non-additive genetic contributions to the personality trait of extraversion in 1,689 Dutch twin pairs, 1,505 mothers and 1,637 fathers of the twins. The twins were on average 15.5 years (range 12-18 years). To increase statistical power to detect non-additive genetic influences, data on extraversion were also collected in parents and simultaneously analyzed. Genetic modeling procedures incorporating age as a potential modifier of heritability showed significant influences of additive (20-23%) and non-additive genetic factors (31-33%) in addition to unshared environment (46-48%) for adolescents and for their parents. The additive genetic component was slightly and positively related to age. No significant sex differences were found for either extraversion means or for the magnitude of the genetic and environmental influences. There was no evidence of non-random mating for extraversion in the parental generation. Results show that in addition to additive genetic influences, extraversion in adolescents is influenced by non-additive genetic factors.
Koivula, M; Sevón-Aimonen, M-L; Strandén, I; Matilainen, K; Serenius, T; Stalder, K J; Mäntysaari, E A
2008-06-01
This paper's objectives were to estimate the genetic (co)variance components of the Gompertz growth curve parameters and to evaluate the relationship of estimated breeding values (EBV) based on average daily gain (ADG) and Gompertz growth curves. Finnish Yorkshire central test station performance data was obtained from the Faba Breeding (Vantaa, Finland). The final data set included 121,488 weight records from 10,111 pigs. Heritability estimates for the Gompertz growth parameters mature weight (alpha), logarithm of mature weight to birth weight ratio (beta) and maturation rate (kappa) were 0.44, 0.55 and 0.31, respectively. Genotypic and phenotypic correlations between the growth curve parameters were high and mainly negative. The only positive relationship was found between alpha and beta. Pearson and Spearman rank correlation coefficients between EBV for ADG and daily gain calculated from Gompertz growth curves were 0.79. The Spearman rank correlation between the sire EBV for ADG and Gompertz growth curve parameter-based ADG for all sires with at least 15 progeny was 0.86. Growth curves differ significantly between individuals and this information could be utilized for selection purposes when improving growth rate in pigs.
Hur, Yoon-Mi
2009-04-01
A growing literature suggests that personality traits may be endophenotype markers for psychiatric illnesses. Although the phenotypic relationships between obsessive-compulsive disorder (OCD) and high neuroticism and low extraversion have been well documented, underlying genetic and environmental contributions to these associations have not been explored previously. Five hundred and twenty-four monozygoitc (MZ) and 228 dizygotic (DZ) pairs of adolescent and young adult twins (aged 13-24 years) drawn from the South Korean Twin Registry completed the Maudsley Obsessive Compulsive Inventory (MOCI) and the Neuroticism and Extraversion scale of the Eysenck Personality Scale by mail. The total score of MOCI (MOCIT) was significantly and positively correlated with Neuroticism (r = .44), but only weakly and negatively related to Extraversion (r = -.10). A trivariate Cholesky model was applied to the data. The additive genetic correlations in the best-fitting model were .51 between Neuroticism and MOCIT and -.17 between Extraversion and MOCIT, suggesting that additive genetic factors that lead to high neuroticism and low extraversion overlap with those genetic factors influencing high OC symptoms. These findings add to the cumulative evidence of the shared genetic etiology for the associations between a personality profile of high neuroticism and low extraversion and mental illnesses.
NASA Astrophysics Data System (ADS)
Dehipawala, Sunil; Nguyen, A.; Tremberger, G.; Cheung, E.; Holden, T.; Lieberman, D.; Cheung, T.
2013-09-01
The evolutionary rate co-variation in meiotic proteins has been reported for yeast and mammal using phylogenic branch lengths which assess retention, duplication and mutation. The bioinformatics of the corresponding DNA sequences could be classified as a diagram of fractal dimension and Shannon entropy. Results from biomedical gene research provide examples on the diagram methodology. The identification of adaptive selection using entropy marker and functional-structural diversity using fractal dimension would support a regression analysis where the coefficient of determination would serve as evolutionary pathway marker for DNA sequences and be an important component in the astrobiology community. Comparisons between biomedical genes such as EEF2 (elongation factor 2 human, mouse, etc), WDR85 in epigenetics, HAR1 in human specificity, clinical trial targeted cancer gene CD47, SIRT6 in spermatogenesis, and HLA-C in mosquito bite immunology demonstrate the diagram classification methodology. Comparisons to the SEPT4-XIAP pair in stem cell apoptosis, testesexpressed taste genes TAS1R3-GNAT3 pair, and amyloid beta APLP1-APLP2 pair with the yeast-mammal DNA sequences for meiotic proteins RAD50-MRE11 pair and NCAPD2-ICK pair have accounted for the observed fluctuating evolutionary pressure systematically. Regression with high R-sq values or a triangular-like cluster pattern for concordant pairs in co-variation among the studied species could serve as evidences for the possible location of common ancestors in the entropy-fractal dimension diagram, consistent with an example of the human-chimp common ancestor study using the FOXP2 regulated genes reported in human fetal brain study. The Deinococcus radiodurans R1 Rad-A could be viewed as an outlier in the RAD50 diagram and also in the free energy versus fractal dimension regression Cook's distance, consistent with a non-Earth source for this radiation resistant bacterium. Convergent and divergent fluctuating evolutionary
Dick, Danielle M; Viken, Richard J; Kaprio, Jaakko; Pulkkinen, Lea; Rose, Richard J
2005-04-01
Conduct disorder (CD), attention deficit hyperactivity disorder (ADHD), and oppositional defiant disorder (ODD) are common childhood externalizing disorders that frequently co-occur. However, the causes of their comorbidity are not well understood. To address that question, we analyzed data from >600 Finnish twin pairs, who completed standardized interviews at age 14. Behavior genetic methods were used to examine how genetic/environmental factors contribute to each disorder's symptoms and to their covariation. We found significant genetic effects on each disorder with only modest evidence of shared environmental influences. Our data suggest the comorbidity among CD, ADHD, and ODD is primarily explained by shared genetic influences; however, each disorder was also under unique genetic influence, supporting the distinction of each disorder.
Tatar, M.; Promislow, D.E.L.; Khazaeli, A.A.; Curtsinger, J.W.
1996-06-01
Under the mutation accumulation model of senescence, it was predicted that the additive genetic variance (V{sub A}) for fitness traits will increase with age. We measured age-specific mortality and fecundity from 65,134 Drosophila melanogaster and estimated genetic variance components, based on reciprocal crosses of extracted second chromosome lines. Elsewhere we report the results for mortality. Here, for fecundity, we report a biomodal pattern for V{sub A} with peaks at 3 days and at 17-31 days. Under the antagonistic pleiotropy model of senescence, it was predicted that negative correlations will exist between early and late life history traits. For fecundity itself we find positive genetic correlations among age classes >3 days but negative nonsignificant correlations between fecundity at 3 days and at older age classes. For fecundity vs. age-specific mortality, we find positive fitness correlations (negative genetic correlations) among the traits at all ages >3 days but a negative fitness correlation between fecundity at 3 days and mortality at the oldest ages (positive genetic correlations). For age-specific mortality itself we find overwhelmingly positive genetic correlations among all age classes. The data suggest that mutation accumulation may be a major source of standing genetic variance for senescence. 75 refs., 4 figs., 1 tab.
Chang, Yu-Tzu; Hsu, Shih-Wei; Tsai, Shih-Jen; Chang, Ya-Ting; Huang, Chi-Wei; Liu, Mu-En; Chen, Nai-Ching; Chang, Wen-Neng; Hsu, Jung-Lung; Lee, Chen-Chang; Chang, Chiung-Chih
2017-03-25
The 677 C to T transition in the MTHFR gene is a genetic determinant for hyperhomocysteinemia. We investigated whether this polymorphism modulates gray matter (GM) structural covariance networks independently of white-matter integrity in patients with Alzheimer's disease (AD). GM structural covariance networks were constructed by 3D T1-magnetic resonance imaging and seed-based analysis. The patients were divided into two genotype groups: C homozygotes (n = 73) and T carriers (n = 62). Using diffusion tensor imaging and white-matter parcellation, 11 fiber bundle integrities were compared between the two genotype groups. Cognitive test scores were the major outcome factors. The T carriers had higher homocysteine levels, lower posterior cingulate cortex GM volume, and more clusters in the dorsal medial lobe subsystem showing stronger covariance strength. Both posterior cingulate cortex seed and interconnected peak cluster volumes predicted cognitive test scores, especially in the T carriers. There were no between-group differences in fiber tract diffusion parameters. The MTHFR 677T polymorphism modulates posterior cingulate cortex-anchored structural covariance strength independently of white matter integrities. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.
Galilean covariant harmonic oscillator
NASA Technical Reports Server (NTRS)
Horzela, Andrzej; Kapuscik, Edward
1993-01-01
A Galilean covariant approach to classical mechanics of a single particle is described. Within the proposed formalism, all non-covariant force laws defining acting forces which become to be defined covariantly by some differential equations are rejected. Such an approach leads out of the standard classical mechanics and gives an example of non-Newtonian mechanics. It is shown that the exactly solvable linear system of differential equations defining forces contains the Galilean covariant description of harmonic oscillator as its particular case. Additionally, it is demonstrated that in Galilean covariant classical mechanics the validity of the second Newton law of dynamics implies the Hooke law and vice versa. It is shown that the kinetic and total energies transform differently with respect to the Galilean transformations.
Martin, Nicolas W; Hansell, Narelle K; Wainwright, Mark A; Shekar, Sri N; Medland, Sarah E; Bates, Timothy C; Burt, Jennifer S; Martin, Nicholas G; Wright, Margaret J
2009-07-01
The Author Recognition Test (ART) measures print exposure and is a unique predictor of phonological and orthographic processes in reading. In a sample of adolescent and young adult twins and siblings (216 MZ/430 DZ pairs, 307 singletons; aged 11-29 years) ART scores were moderately heritable (67%) and correlated with reading and verbal abilities, with genes largely accounting for the covariance. We also examine whether high (and low) (i.e. 1SD above the mean) represents a quantitative extreme of the normal distribution. Heritability for high ART was of similar magnitude to the full sample, but, a specific genetic factor, independent from both low ART performance and high reading ability, accounted for 53-58% of the variance. This suggests a distinct genetic etiology for high ART ability and we speculate that the specific genetic influence is on orthographical processing, a critical factor in developing word recognition skills.
Greenwood, Tiffany A.; Lazzeroni, Laura C.; Calkins, Monica E.; Freedman, Robert; Green, Michael F.; Gur, Raquel E.; Gur, Ruben C.; Light, Gregory A.; Nuechterlein, Keith H.; Olincy, Ann; Radant, Allen D.; Seidman, Larry J.; Siever, Larry J.; Silverman, Jeremy M.; Stone, William S.; Sugar, Catherine A.; Swerdlow, Neal R.; Tsuang, Debby W.; Tsuang, Ming T.; Turetsky, Bruce I.; Braff, David L.
2015-01-01
The Consortium on the Genetics of Schizophrenia Family Study (COGS-1) has previously reported our efforts to characterize the genetic architecture of 12 primary endophenotypes for schizophrenia. We now report the characterization of 13 additional measures derived from the same endophenotype test paradigms in the COGS-1 families. Nine of the measures were found to discriminate between schizophrenia patients and controls, were significantly heritable (31 to 62%), and were sufficiently independent of previously assessed endophenotypes, demonstrating utility as additional endophenotypes. Genotyping via a custom array of 1536 SNPs from 94 candidate genes identified associations for CTNNA2, ERBB4, GRID1, GRID2, GRIK3, GRIK4, GRIN2B, NOS1AP, NRG1, and RELN across multiple endophenotypes. An experiment-wide p value of 0.003 suggested that the associations across all SNPs and endophenotypes collectively exceeded chance. Linkage analyses performed using a genome-wide SNP array further identified significant or suggestive linkage for six of the candidate endophenotypes, with several genes of interest located beneath the linkage peaks (e.g., CSMD1, DISC1, DLGAP2, GRIK2, GRIN3A, and SLC6A3). While the partial convergence of the association and linkage likely reflects differences in density of gene coverage provided by the distinct genotyping platforms, it is also likely an indication of the differential contribution of rare and common variants for some genes and methodological differences in detection ability. Still, many of the genes implicated by COGS through endophenotypes have been identified by independent studies of common, rare, and de novo variation in schizophrenia, all converging on a functional genetic network related to glutamatergic neurotransmission that warrants further investigation. PMID:26597662
Additive genetic contribution to symptom dimensions in major depressive disorder.
Pearson, Rahel; Palmer, Rohan H C; Brick, Leslie A; McGeary, John E; Knopik, Valerie S; Beevers, Christopher G
2016-05-01
Major depressive disorder (MDD) is a phenotypically heterogeneous disorder with a complex genetic architecture. In this study, genomic-relatedness-matrix restricted maximum-likelihood analysis (GREML) was used to investigate the extent to which variance in depression symptoms/symptom dimensions can be explained by variation in common single nucleotide polymorphisms (SNPs) in a sample of individuals with MDD (N = 1,558) who participated in the National Institute of Mental Health Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. A principal components analysis of items from the Hamilton Rating Scale for Depression (HRSD) obtained prior to treatment revealed 4 depression symptom components: (a) appetite, (b) core depression symptoms (e.g., depressed mood, anhedonia), (c) insomnia, and (d) anxiety. These symptom dimensions were associated with SNP-based heritability (hSNP2) estimates of 30%, 14%, 30%, and 5%, respectively. Results indicated that the genetic contribution of common SNPs to depression symptom dimensions were not uniform. Appetite and insomnia symptoms in MDD had a relatively strong genetic contribution whereas the genetic contribution was relatively small for core depression and anxiety symptoms. While in need of replication, these results suggest that future gene discovery efforts may strongly benefit from parsing depression into its constituent parts. (PsycINFO Database Record
Harvey, J B J; Goff, Lynda J
2010-01-01
The fungal endophyte Haloguignardia irritans induces gall formation on the brown algal genera Cystoseira and Halidrys occurring from Oregon to Baja California, Mexico. Here we examine genetic covariation and compare rDNA phylogenies to investigate the coevolutionary histories of H. irritans and its algal hosts. Despite recognition of H. irritans as a single morphological species, internal transcribed spacer rDNA sequences representative of its geographic range are characterized by sequence variation at the intraspecific to intrageneric levels. An assessment of parallel cladogenesis between endophyte and host phylogenies provides evidence for a combination of independent fungal divergence and host jumping, similar to that observed in terrestrial lichens. Our results suggest that reduced gene flow due to geographic isolation is a major contributing factor to more concerted covariation observed at one island site, rather than to differences among algal host species alone. Because geography and its effects on gene flow can create heterogeneous mosaics of coevolution for symbioses in terrestrial environments, our results support the notion that conservation efforts toward the maintenance of genetic diversity in marine environments should likewise consider geographic complexity and its effects on coevolving marine species.
Griffin, Robert M; Schielzeth, Holger; Friberg, Urban
2016-12-07
Theory makes several predictions concerning differences in genetic variation between the X chromosome and the autosomes due to male X hemizygosity. The X chromosome should: (i) typically show relatively less standing genetic variation than the autosomes, (ii) exhibit more variation in males compared to females because of dosage compensation, and (iii) potentially be enriched with sex-specific genetic variation. Here, we address each of these predictions for lifespan and aging in Drosophila melanogaster To achieve unbiased estimates of X and autosomal additive genetic variance, we use 80 chromosome substitution lines; 40 for the X chromosome and 40 combining the two major autosomes, which we assay for sex-specific and cross-sex genetic (co)variation. We find significant X and autosomal additive genetic variance for both traits in both sexes (with reservation for X-linked variation of aging in females), but no conclusive evidence for depletion of X-linked variation (measured through females). Males display more X-linked variation for lifespan than females, but it is unclear if this is due to dosage compensation since also autosomal variation is larger in males. Finally, our results suggest that the X chromosome is enriched for sex-specific genetic variation in lifespan but results were less conclusive for aging overall. Collectively, these results suggest that the X chromosome has reduced capacity to respond to sexually concordant selection on lifespan from standing genetic variation, while its ability to respond to sexually antagonistic selection may be augmented.
Griffin, Robert M.; Schielzeth, Holger; Friberg, Urban
2016-01-01
Theory makes several predictions concerning differences in genetic variation between the X chromosome and the autosomes due to male X hemizygosity. The X chromosome should: (i) typically show relatively less standing genetic variation than the autosomes, (ii) exhibit more variation in males compared to females because of dosage compensation, and (iii) potentially be enriched with sex-specific genetic variation. Here, we address each of these predictions for lifespan and aging in Drosophila melanogaster. To achieve unbiased estimates of X and autosomal additive genetic variance, we use 80 chromosome substitution lines; 40 for the X chromosome and 40 combining the two major autosomes, which we assay for sex-specific and cross-sex genetic (co)variation. We find significant X and autosomal additive genetic variance for both traits in both sexes (with reservation for X-linked variation of aging in females), but no conclusive evidence for depletion of X-linked variation (measured through females). Males display more X-linked variation for lifespan than females, but it is unclear if this is due to dosage compensation since also autosomal variation is larger in males. Finally, our results suggest that the X chromosome is enriched for sex-specific genetic variation in lifespan but results were less conclusive for aging overall. Collectively, these results suggest that the X chromosome has reduced capacity to respond to sexually concordant selection on lifespan from standing genetic variation, while its ability to respond to sexually antagonistic selection may be augmented. PMID:27678519
Lohr, Jennifer N; Haag, Christoph R
2015-12-01
Reduced population size is thought to have strong consequences for evolutionary processes as it enhances the strength of genetic drift. In its interaction with selection, this is predicted to increase the genetic load, reduce inbreeding depression, and increase hybrid vigor, and in turn affect phenotypic evolution. Several of these predictions have been tested, but comprehensive studies controlling for confounding factors are scarce. Here, we show that populations of Daphnia magna, which vary strongly in genetic diversity, also differ in genetic load, inbreeding depression, and hybrid vigor in a way that strongly supports theoretical predictions. Inbreeding depression is positively correlated with genetic diversity (a proxy for Ne ), and genetic load and hybrid vigor are negatively correlated with genetic diversity. These patterns remain significant after accounting for potential confounding factors and indicate that, in small populations, a large proportion of the segregation load is converted into fixed load. Overall, the results suggest that the nature of genetic variation for fitness-related traits differs strongly between large and small populations. This has large consequences for evolutionary processes in natural populations, such as selection on dispersal, breeding systems, ageing, and local adaptation.
Revell, L J; Mahler, D L; Sweeney, J R; Sobotka, M; Fancher, V E; Losos, J B
2010-02-01
The pattern of genetic variances and covariances among characters, summarized in the additive genetic variance-covariance matrix, G, determines how a population will respond to linear natural selection. However, G itself also evolves in response to selection. In particular, we expect that, over time, G will evolve correspondence with the pattern of multivariate nonlinear natural selection. In this study, we substitute the phenotypic variance-covariance matrix (P) for G to determine if the pattern of multivariate nonlinear selection in a natural population of Anolis cristatellus, an arboreal lizard from Puerto Rico, has influenced the evolution of genetic variances and covariances in this species. Although results varied among our estimates of P and fitness, and among our analytic techniques, we find significant evidence for congruence between nonlinear selection and P, suggesting that natural selection may have influenced the evolution of genetic constraint in this species.
Covariant Bardeen perturbation formalism
NASA Astrophysics Data System (ADS)
Vitenti, S. D. P.; Falciano, F. T.; Pinto-Neto, N.
2014-05-01
In a previous work we obtained a set of necessary conditions for the linear approximation in cosmology. Here we discuss the relations of this approach with the so-called covariant perturbations. It is often argued in the literature that one of the main advantages of the covariant approach to describe cosmological perturbations is that the Bardeen formalism is coordinate dependent. In this paper we will reformulate the Bardeen approach in a completely covariant manner. For that, we introduce the notion of pure and mixed tensors, which yields an adequate language to treat both perturbative approaches in a common framework. We then stress that in the referred covariant approach, one necessarily introduces an additional hypersurface choice to the problem. Using our mixed and pure tensors approach, we are able to construct a one-to-one map relating the usual gauge dependence of the Bardeen formalism with the hypersurface dependence inherent to the covariant approach. Finally, through the use of this map, we define full nonlinear tensors that at first order correspond to the three known gauge invariant variables Φ, Ψ and Ξ, which are simultaneously foliation and gauge invariant. We then stress that the use of the proposed mixed tensors allows one to construct simultaneously gauge and hypersurface invariant variables at any order.
Forest, Adriana R; Semeniuk, Christina A D; Heath, Daniel D; Pitcher, Trevor E
2016-08-01
Chinook salmon, Oncorhynchus tshawytscha, exhibit alternative reproductive tactics (ARTs) where males exist in two phenotypes: large "hooknose" males and smaller "jacks" that reach sexual maturity after only 1 year in seawater. The mechanisms that determine "jacking rate"-the rate at which males precociously sexually mature-are known to involve both genetics and differential growth rates, where individuals that become jacks exhibit higher growth earlier in life. The additive genetic components have been studied and it is known that jack sires produce significantly more jack offspring than hooknose sires, and vice versa. The current study was the first to investigate both additive and non-additive genetic components underlying jacking through the use of a full-factorial breeding design using all hooknose sires. The effect of dams and sires descendant from a marker-assisted broodstock program that identified "high performance" and "low performance" lines using growth- and survival-related gene markers was also studied. Finally, the relative growth of jack, hooknose, and female offspring was examined. No significant dam, sire, or interaction effects were observed in this study, and the maternal, additive, and non-additive components underlying jacking were small. Differences in jacking rates in this study were determined by dam performance line, where dams that originated from the low performance line produced significantly more jacks. Jack offspring in this study had a significantly larger body size than both hooknose males and females starting 1 year post-fertilization. This study provides novel information regarding the genetic architecture underlying ARTs in Chinook salmon that could have implications for the aquaculture industry, where jacks are not favoured due to their small body size and poor flesh quality.
The intraclass covariance matrix.
Carey, Gregory
2005-09-01
Introduced by C.R. Rao in 1945, the intraclass covariance matrix has seen little use in behavioral genetic research, despite the fact that it was developed to deal with family data. Here, I reintroduce this matrix, and outline its estimation and basic properties for data sets on pairs of relatives. The intraclass covariance matrix is appropriate whenever the research design or mathematical model treats the ordering of the members of a pair as random. Because the matrix has only one estimate of a population variance and covariance, both the observed matrix and the residual matrix from a fitted model are easy to inspect visually; there is no need to mentally average homologous statistics. Fitting a model to the intraclass matrix also gives the same log likelihood, likelihood-ratio (LR) chi2, and parameter estimates as fitting that model to the raw data. A major advantage of the intraclass matrix is that only two factors influence the LR chi2--the sampling error in estimating population parameters and the discrepancy between the model and the observed statistics. The more frequently used interclass covariance matrix adds a third factor to the chi2--sampling error of homologous statistics. Because of this, the degrees of freedom for fitting models to an intraclass matrix differ from fitting that model to an interclass matrix. Future research is needed to establish differences in power-if any--between the interclass and the intraclass matrix.
ERIC Educational Resources Information Center
Hart, Sara A.; Petrill, Stephen A.; DeThorne, Laura S.; Deater-Deckard, Kirby; Thompson, Lee A.; Schatschneider, Chris; Cutting, Laurie E.
2009-01-01
Background: Despite the well-replicated relationship between the home literacy environment and expressive vocabulary, few studies have examined the extent to which the home literacy environment is associated with the development of early vocabulary ability in the context of genetic influences. This study examined the influence of the home literacy…
ERIC Educational Resources Information Center
Roisman, Glenn I.; Fraley, R. Chris
2008-01-01
A number of relatively small-sample, genetically sensitive studies of infant attachment security have been published in the past several years that challenge the view that all psychological phenotypes are heritable and that environmental influences on child development--to the extent that they can be detected--serve to make siblings dissimilar.…
ERIC Educational Resources Information Center
Miller, Geoffrey F.; Penke, Lars
2007-01-01
Most theories of human mental evolution assume that selection favored higher intelligence and larger brains, which should have reduced genetic variance in both. However, adult human intelligence remains highly heritable, and is genetically correlated with brain size. This conflict might be resolved by estimating the coefficient of additive genetic…
Gowane, G R; Chopra, A; Prince, L L L; Paswan, C; Arora, A L
2010-03-01
(Co)variance components and genetic parameters of weight at birth (BWT), weaning (3WT), 6, 9 and 12 months of age (6WT, 9WT and 12WT, respectively) and first greasy fleece weight (GFW) of Bharat Merino sheep, maintained at Central Sheep and Wool Research Institute, Avikanagar, Rajasthan, India, were estimated by restricted maximum likelihood, fitting six animal models with various combinations of direct and maternal effects. Data were collected over a period of 10 years (1998 to 2007). A log-likelihood ratio test was used to select the most appropriate univariate model for each trait, which was subsequently used in bivariate analysis. Heritability estimates for BWT, 3WT, 6WT, 9WT and 12WT and first GFW were 0.05 ± 0.03, 0.04 ± 0.02, 0.00, 0.03 ± 0.03, 0.09 ± 0.05 and 0.05 ± 0.03, respectively. There was no evidence for the maternal genetic effect on the traits under study. Maternal permanent environmental effect contributed 19% for BWT and 6% to 11% from 3WT to 9WT and 11% for first GFW. Maternal permanent environmental effect on the post-3WT was a carryover effect of maternal influences during pre-weaning age. A low rate of genetic progress seems possible in the flock through selection. Direct genetic correlations between body weight traits were positive and ranged from 0.36 between BWT and 6WT to 0.94 between 3WT and 6WT and between 6WT and 12WT. Genetic correlations of 3WT with 6WT, 9WT and 12WT were high and positive (0.94, 0.93 and 0.93, respectively), suggesting that genetic gain in post-3WT will be maintained if selection age is reduced to 3 months. The genetic correlations of GFW with live weights were 0.01, 0.16, 0.18, 0.40 and 0.32 for BWT, 3WT, 6WT, 9WT and 12WT, respectively. Correlations of permanent environmental effects of the dam across different traits were high and positive for all the traits (0.45 to 0.98).
Shokrollahi, B; Baneh, H
2012-02-08
The objective of the present study was to estimate genetic parameters for body weight at different ages in Arabi sheep using data collected from 1999 to 2009. Investigated traits consisted of birth weight (N = 2776), weaning weight (N = 2002) and weight at six months of age (N = 1885). The data were analyzed using restricted maximum likelihood analysis, by fitting univariate and multivariate animal models. All three weight traits were significantly influenced by birth year, sex and birth type. Age of dam only significantly affected birth weight. Log-likelihood ratio tests were conducted to determine the most suitable model for each growth trait in univariate analyses. Direct and total heritability estimates for birth weight, weaning weight and weight at six months of age (based on the best model) were 0.42 and 0.16 (model 4), 0.38 and 0.13 (model 4) and 0.14 and 0.14 (model 1), respectively. Estimation of maternal heritability for birth weight and weaning weight was 0.22 and 0.18, respectively. Genetic and phenotypic correlations among these traits were positive. Phenotypic correlations among traits were low to moderate. Genetic correlations among traits were positive and higher than the corresponding phenotypic correlations. Weaning weight had a strong and significant correlation with weight at six months of age (0.99). We conclude that selection can be made in animals based on weaning weight instead of the present practice of selection based on weight at six months.
Accounting for additive genetic mutations on litter size in Ripollesa sheep.
Casellas, J; Caja, G; Piedrafita, J
2010-04-01
Little is known about mutational variability in livestock, among which only a few mutations with relatively large effects have been reported. In this manuscript, mutational variability was analyzed in 1,765 litter size records from 404 Ripollesa ewes to characterize the magnitude of this genetic source of variation and check the suitability of including mutational effects in genetic evaluations of this breed. Threshold animal models accounting for additive genetic mutations were preferred to models without mutational contributions, with an average difference in the deviance information criterion of more than 5 units. Moreover, the statistical relevance of the additive genetic mutation term was checked through a Bayes factor approach, which showed that the models with mutational variability were 8.5 to 22.7 times more probable than the others. The mutational heritability (percentage of the phenotypic variance accounted for by mutational variance) was 0.6 or 0.9%, depending on whether genetic dominance effects were accounted for by the analytical model. The inclusion of mutational effects in the genetic model for evaluating litter size in Ripollesa ewes called for some minor modifications in the genetic merit order of the individuals evaluated, which suggested that the continuous uploading of new additive mutations could be taken into account to optimize the selection scheme. This study is the first attempt to estimate mutational variances in a livestock species and thereby contribute to better characterization of the genetic background of productive traits of interest.
2011-01-01
Physiological and environmental variables, or covariates, can account for an important portion of the variability observed in behavioural/physiological results from different laboratories even when using the same type of animals and phenotyping procedures. We present the results of a behavioural study with a sample of 1456 genetically heterogeneous N/Nih-HS rats, including males and females, which are part of a larger genome-wide fine-mapping QTL (Quantitative Trait Loci) study. N/Nih-HS rats have been derived from 8 inbred strains and provide very small distance between genetic recombinations, which makes them a unique tool for fine-mapping QTL studies. The behavioural test battery comprised the elevated zero-maze test for anxiety, novel-cage (open-field like) activity, two-way active avoidance acquisition (related to conditioned anxiety) and context-conditioned freezing (i.e. classically conditioned fear). Using factorial analyses of variance (ANOVAs) we aimed to analyse sex differences in anxiety and fear in this N/Nih-HS rat sample, as well as to assess the effects of (and interactions with) other independent factors, such as batch, season, coat colour and experimenter. Body weight was taken as a quantitative covariate and analysed by covariance analysis (ANCOVA). Obliquely-rotated factor analyses were also performed separately for each sex, in order to evaluate associations among the most relevant variables from each behavioural test and the common dimensions (i.e. factors) underlying the different behavioural responses. ANOVA analyses showed a consistent pattern of sex effects, with females showing less signs of anxiety and fear than males across all tests. There were also significant main effects of batch, season, colour and experimenter on almost all behavioural variables, as well as "sex × batch", "sex × season" and "sex × experimenter" interactions. Body weight showed significant effects in the ANCOVAs of most behavioural measures, but sex effects were
Mulligan, Megan K.; Wang, Xusheng; Adler, Adrienne L.; Mozhui, Khyobeni; Lu, Lu; Williams, Robert W.
2012-01-01
GABA type-A receptors are essential for fast inhibitory neurotransmission and are critical in brain function. Surprisingly, expression of receptor subunits is highly variable among individuals, but the cause and impact of this fluctuation remains unknown. We have studied sources of variation for all 19 receptor subunits using massive expression data sets collected across multiple brain regions and platforms in mice and humans. Expression of Gabra1, Gabra2, Gabrb2, Gabrb3, and Gabrg2 is highly variable and heritable among the large cohort of BXD strains derived from crosses of fully sequenced parents—C57BL/6J and DBA/2J. Genetic control of these subunits is complex and highly dependent on tissue and mRNA region. Remarkably, this high variation is generally not linked to phenotypic differences. The single exception is Gabrb3, a locus that is linked to anxiety. We identified upstream genetic loci that influence subunit expression, including three unlinked regions of chromosome 5 that modulate the expression of nine subunits in hippocampus, and that are also associated with multiple phenotypes. Candidate genes within these loci include, Naaa, Nos1, and Zkscan1. We confirmed a high level of coexpression for subunits comprising the major channel—Gabra1, Gabrb2, and Gabrg2—and identified conserved members of this expression network in mice and humans. Gucy1a3, Gucy1b3, and Lis1 are novel and conserved associates of multiple subunits that are involved in inhibitory signaling. Finally, proximal and distal regions of the 3′ UTRs of single subunits have remarkably independent expression patterns in both species. However, corresponding regions of different subunits often show congruent genetic control and coexpression (proximal-to-proximal or distal-to-distal), even in the absence of sequence homology. Our findings identify novel sources of variation that modulate subunit expression and highlight the extraordinary capacity of biological networks to buffer 4–100 fold
Porto, A; Sebastião, H; Pavan, S E; VandeBerg, J L; Marroig, G; Cheverud, J M
2015-04-01
We tested the hypothesis that the rate of marsupial cranial evolution is dependent on the distribution of genetic variation in multivariate space. To do so, we carried out a genetic analysis of cranial morphological variation in laboratory strains of Monodelphis domestica and used estimates of genetic covariation to analyse the morphological diversification of the Monodelphis brevicaudata species group. We found that within-species genetic variation is concentrated in only a few axes of the morphospace and that this strong genetic covariation influenced the rate of morphological diversification of the brevicaudata group, with between-species divergence occurring fastest when occurring along the genetic line of least resistance. Accounting for the geometric distribution of genetic variation also increased our ability to detect the selective regimen underlying species diversification, with several instances of selection only being detected when genetic covariances were taken into account. Therefore, this work directly links patterns of genetic covariation among traits to macroevolutionary patterns of morphological divergence. Our findings also suggest that the limited distribution of Monodelphis species in morphospace is the result of a complex interplay between the limited dimensionality of available genetic variation and strong stabilizing selection along two major axes of genetic variation.
Estimation of Additive, Dominance, and Imprinting Genetic Variance Using Genomic Data
Lopes, Marcos S.; Bastiaansen, John W. M.; Janss, Luc; Knol, Egbert F.; Bovenhuis, Henk
2015-01-01
Traditionally, exploration of genetic variance in humans, plants, and livestock species has been limited mostly to the use of additive effects estimated using pedigree data. However, with the development of dense panels of single-nucleotide polymorphisms (SNPs), the exploration of genetic variation of complex traits is moving from quantifying the resemblance between family members to the dissection of genetic variation at individual loci. With SNPs, we were able to quantify the contribution of additive, dominance, and imprinting variance to the total genetic variance by using a SNP regression method. The method was validated in simulated data and applied to three traits (number of teats, backfat, and lifetime daily gain) in three purebred pig populations. In simulated data, the estimates of additive, dominance, and imprinting variance were very close to the simulated values. In real data, dominance effects account for a substantial proportion of the total genetic variance (up to 44%) for these traits in these populations. The contribution of imprinting to the total phenotypic variance of the evaluated traits was relatively small (1–3%). Our results indicate a strong relationship between additive variance explained per chromosome and chromosome length, which has been described previously for other traits in other species. We also show that a similar linear relationship exists for dominance and imprinting variance. These novel results improve our understanding of the genetic architecture of the evaluated traits and shows promise to apply the SNP regression method to other traits and species, including human diseases. PMID:26438289
Dochtermann, Ned A; Schwab, Tori; Sih, Andrew
2015-01-07
Individual animals frequently exhibit repeatable differences from other members of their population, differences now commonly referred to as 'animal personality'. Personality differences can arise, for example, from differences in permanent environmental effects--including parental and epigenetic contributors--and the effect of additive genetic variation. Although several studies have evaluated the heritability of behaviour, less is known about general patterns of heritability and additive genetic variation in animal personality. As overall variation in behaviour includes both the among-individual differences that reflect different personalities and temporary environmental effects, it is possible for personality to be largely genetically influenced even when heritability of behaviour per se is quite low. The relative contribution of additive genetic variation to personality variation can be estimated whenever both repeatability and heritability are estimated for the same data. Using published estimates to address this issue, we found that approximately 52% of animal personality variation was attributable to additive genetic variation. Thus, while the heritability of behaviour is often moderate or low, the heritability of personality is much higher. Our results therefore (i) demonstrate that genetic differences are likely to be a major contributor to variation in animal personality and (ii) support the phenotypic gambit: that evolutionary inferences drawn from repeatability estimates may often be justified.
Genetic algorithm-guided discovery of additive combinations that direct quantum dot assembly.
Bawazer, Lukmaan A; Ihli, Johannes; Comyn, Timothy P; Critchley, Kevin; Empson, Christopher J; Meldrum, Fiona C
2015-01-14
The use of combinations of organic additives to control crystallization, as occurs in biomineralization, is rarely investigated due to the vast potential reaction space. It is demonstrated here that combinatorial approaches led by genetic algorithm heuristics can enable identification of active additive combinations, and four key organic molecules are rapidly identified, which generate highly fluorescent CdS quantum dot superstructures.
Covariance Manipulation for Conjunction Assessment
NASA Technical Reports Server (NTRS)
Hejduk, M. D.
2016-01-01
The manipulation of space object covariances to try to provide additional or improved information to conjunction risk assessment is not an uncommon practice. Types of manipulation include fabricating a covariance when it is missing or unreliable to force the probability of collision (Pc) to a maximum value ('PcMax'), scaling a covariance to try to improve its realism or see the effect of covariance volatility on the calculated Pc, and constructing the equivalent of an epoch covariance at a convenient future point in the event ('covariance forecasting'). In bringing these methods to bear for Conjunction Assessment (CA) operations, however, some do not remain fully consistent with best practices for conducting risk management, some seem to be of relatively low utility, and some require additional information before they can contribute fully to risk analysis. This study describes some basic principles of modern risk management (following the Kaplan construct) and then examines the PcMax and covariance forecasting paradigms for alignment with these principles; it then further examines the expected utility of these methods in the modern CA framework. Both paradigms are found to be not without utility, but only in situations that are somewhat carefully circumscribed.
Monir, Md. Mamun; Zhu, Jun
2017-01-01
Most of the genome-wide association studies (GWASs) for human complex diseases have ignored dominance, epistasis and ethnic interactions. We conducted comparative GWASs for total cholesterol using full model and additive models, which illustrate the impacts of the ignoring genetic variants on analysis results and demonstrate how genetic effects of multiple loci could differ across different ethnic groups. There were 15 quantitative trait loci with 13 individual loci and 3 pairs of epistasis loci identified by full model, whereas only 14 loci (9 common loci and 5 different loci) identified by multi-loci additive model. Again, 4 full model detected loci were not detected using multi-loci additive model. PLINK-analysis identified two loci and GCTA-analysis detected only one locus with genome-wide significance. Full model identified three previously reported genes as well as several new genes. Bioinformatics analysis showed some new genes are related with cholesterol related chemicals and/or diseases. Analyses of cholesterol data and simulation studies revealed that the full model performs were better than the additive-model performs in terms of detecting power and unbiased estimations of genetic variants of complex traits. PMID:28079101
NASA Technical Reports Server (NTRS)
Smalheer, C. V.
1973-01-01
The chemistry of lubricant additives is discussed to show what the additives are chemically and what functions they perform in the lubrication of various kinds of equipment. Current theories regarding the mode of action of lubricant additives are presented. The additive groups discussed include the following: (1) detergents and dispersants, (2) corrosion inhibitors, (3) antioxidants, (4) viscosity index improvers, (5) pour point depressants, and (6) antifouling agents.
Product versus additive threshold models for analysis of reproduction outcomes in animal genetics.
David, I; Bodin, L; Gianola, D; Legarra, A; Manfredi, E; Robert-Granié, C
2009-08-01
The phenotypic observation of some reproduction traits (e.g., insemination success, interval from lambing to insemination) is the result of environmental and genetic factors acting on 2 individuals: the male and female involved in a mating couple. In animal genetics, the main approach (called additive model) proposed for studying such traits assumes that the phenotype is linked to a purely additive combination, either on the observed scale for continuous traits or on some underlying scale for discrete traits, of environmental and genetic effects affecting the 2 individuals. Statistical models proposed for studying human fecundability generally consider reproduction outcomes as the product of hypothetical unobservable variables. Taking inspiration from these works, we propose a model (product threshold model) for studying a binary reproduction trait that supposes that the observed phenotype is the product of 2 unobserved phenotypes, 1 for each individual. We developed a Gibbs sampling algorithm for fitting a Bayesian product threshold model including additive genetic effects and showed by simulation that it is feasible and that it provides good estimates of the parameters. We showed that fitting an additive threshold model to data that are simulated under a product threshold model provides biased estimates, especially for individuals with high breeding values. A main advantage of the product threshold model is that, in contrast to the additive model, it provides distinct estimates of fixed effects affecting each of the 2 unobserved phenotypes.
Common genetic variants, acting additively, are a major source of risk for autism
2012-01-01
Background Autism spectrum disorders (ASD) are early onset neurodevelopmental syndromes typified by impairments in reciprocal social interaction and communication, accompanied by restricted and repetitive behaviors. While rare and especially de novo genetic variation are known to affect liability, whether common genetic polymorphism plays a substantial role is an open question and the relative contribution of genes and environment is contentious. It is probable that the relative contributions of rare and common variation, as well as environment, differs between ASD families having only a single affected individual (simplex) versus multiplex families who have two or more affected individuals. Methods By using quantitative genetics techniques and the contrast of ASD subjects to controls, we estimate what portion of liability can be explained by additive genetic effects, known as narrow-sense heritability. We evaluate relatives of ASD subjects using the same methods to evaluate the assumptions of the additive model and partition families by simplex/multiplex status to determine how heritability changes with status. Results By analyzing common variation throughout the genome, we show that common genetic polymorphism exerts substantial additive genetic effects on ASD liability and that simplex/multiplex family status has an impact on the identified composition of that risk. As a fraction of the total variation in liability, the estimated narrow-sense heritability exceeds 60% for ASD individuals from multiplex families and is approximately 40% for simplex families. By analyzing parents, unaffected siblings and alleles not transmitted from parents to their affected children, we conclude that the data for simplex ASD families follow the expectation for additive models closely. The data from multiplex families deviate somewhat from an additive model, possibly due to parental assortative mating. Conclusions Our results, when viewed in the context of results from genome
Chowdhury, Susmita; Henneman, Lidewij; Dent, Tom; Hall, Alison; Burton, Alice; Pharoah, Paul; Pashayan, Nora; Burton, Hilary
2015-01-01
There is growing evidence that inclusion of genetic information about known common susceptibility variants may enable population risk-stratification and personalized prevention for common diseases including cancer. This would require the inclusion of genetic testing as an integral part of individual risk assessment of an asymptomatic individual. Front line health professionals would be expected to interact with and assist asymptomatic individuals through the risk stratification process. In that case, additional knowledge and skills may be needed. Current guidelines and frameworks for genetic competencies of non-specialist health professionals place an emphasis on rare inherited genetic diseases. For common diseases, health professionals do use risk assessment tools but such tools currently do not assess genetic susceptibility of individuals. In this article, we compare the skills and knowledge needed by non-genetic health professionals, if risk-stratified prevention is implemented, with existing competence recommendations from the UK, USA and Europe, in order to assess the gaps in current competences. We found that health professionals would benefit from understanding the contribution of common genetic variations in disease risk, the rationale for a risk-stratified prevention pathway, and the implications of using genomic information in risk-assessment and risk management of asymptomatic individuals for common disease prevention. PMID:26068647
Chowdhury, Susmita; Henneman, Lidewij; Dent, Tom; Hall, Alison; Burton, Alice; Pharoah, Paul; Pashayan, Nora; Burton, Hilary
2015-06-09
There is growing evidence that inclusion of genetic information about known common susceptibility variants may enable population risk-stratification and personalized prevention for common diseases including cancer. This would require the inclusion of genetic testing as an integral part of individual risk assessment of an asymptomatic individual. Front line health professionals would be expected to interact with and assist asymptomatic individuals through the risk stratification process. In that case, additional knowledge and skills may be needed. Current guidelines and frameworks for genetic competencies of non-specialist health professionals place an emphasis on rare inherited genetic diseases. For common diseases, health professionals do use risk assessment tools but such tools currently do not assess genetic susceptibility of individuals. In this article, we compare the skills and knowledge needed by non-genetic health professionals, if risk-stratified prevention is implemented, with existing competence recommendations from the UK, USA and Europe, in order to assess the gaps in current competences. We found that health professionals would benefit from understanding the contribution of common genetic variations in disease risk, the rationale for a risk-stratified prevention pathway, and the implications of using genomic information in risk-assessment and risk management of asymptomatic individuals for common disease prevention.
Additive genetic breeding values correlate with the load of partially deleterious mutations.
Tomkins, Joseph L; Penrose, Marissa A; Greeff, Johan; LeBas, Natasha R
2010-05-14
The mutation-selection-balance model predicts most additive genetic variation to arise from numerous mildly deleterious mutations of small effect. Correspondingly, "good genes" models of sexual selection and recent models for the evolution of sex are built on the assumption that mutational loads and breeding values for fitness-related traits are correlated. In support of this concept, inbreeding depression was negatively genetically correlated with breeding values for traits under natural and sexual selection in the weevil Callosobruchus maculatus. The correlations were stronger in males and strongest for condition. These results confirm the role of existing, partially recessive mutations in maintaining additive genetic variation in outbred populations, reveal the nature of good genes under sexual selection, and show how sexual selection can offset the cost of sex.
McGuigan, Katrina; Aguirre, J. David; Blows, Mark W.
2015-01-01
How new mutations contribute to genetic variation is a key question in biology. Although the evolutionary fate of an allele is largely determined by its heterozygous effect, most estimates of mutational variance and mutational effects derive from highly inbred lines, where new mutations are present in homozygous form. In an attempt to overcome this limitation, middle-class neighborhood (MCN) experiments have been used to assess the fitness effect of new mutations in heterozygous form. However, because MCN populations harbor substantial standing genetic variance, estimates of mutational variance have not typically been available from such experiments. Here we employ a modification of the animal model to analyze data from 22 generations of Drosophila serrata bred in an MCN design. Mutational heritability, measured for eight cuticular hydrocarbons, 10 wing-shape traits, and wing size in this outbred genetic background, ranged from 0.0006 to 0.006 (with one exception), a similar range to that reported from studies employing inbred lines. Simultaneously partitioning the additive and mutational variance in the same outbred population allowed us to quantitatively test the ability of mutation-selection balance models to explain the observed levels of additive and mutational genetic variance. The Gaussian allelic approximation and house-of-cards models, which assume real stabilizing selection on single traits, both overestimated the genetic variance maintained at equilibrium, but the house-of-cards model was a closer fit to the data. This analytical approach has the potential to be broadly applied, expanding our understanding of the dynamics of genetic variance in natural populations. PMID:26384357
Gasparini, Clelia; Devigili, Alessandro; Dosselli, Ryan; Pilastro, Andrea
2013-01-01
In polyandrous species, a male's reproductive success depends on his fertilization capability and traits enhancing competitive fertilization success will be under strong, directional selection. This leads to the prediction that these traits should show stronger condition dependence and larger genetic variance than other traits subject to weaker or stabilizing selection. While empirical evidence of condition dependence in postcopulatory traits is increasing, the comparison between sexually selected and ‘control’ traits is often based on untested assumption concerning the different strength of selection acting on these traits. Furthermore, information on selection in the past is essential, as both condition dependence and genetic variance of a trait are likely to be influenced by the pattern of selection acting historically on it. Using the guppy (Poecilia reticulata), a livebearing fish with high levels of multiple paternity, we performed three independent experiments on three ejaculate quality traits, sperm number, velocity, and size, which have been previously shown to be subject to strong, intermediate, and weak directional postcopulatory selection, respectively. First, we conducted an inbreeding experiment to determine the pattern of selection in the past. Second, we used a diet restriction experiment to estimate their level of condition dependence. Third, we used a half-sib/full-sib mating design to estimate the coefficients of additive genetic variance (CVA) underlying these traits. Additionally, using a simulated predator evasion test, we showed that both inbreeding and diet restriction significantly reduced condition. According to predictions, sperm number showed higher inbreeding depression, stronger condition dependence, and larger CVA than sperm velocity and sperm size. The lack of significant genetic correlation between sperm number and velocity suggests that the former may respond to selection independently one from other ejaculate quality traits
Evolution of sexual dimorphism in phenotypic covariance structure in Phymata.
Punzalan, David; Rowe, Locke
2015-06-01
Sexual dimorphism is a consequence of both sex-specific selection and potential constraints imposed by a shared genetic architecture underlying sexually homologous traits. However, genetic architecture is expected to evolve to mitigate these constraints, allowing the sexes to approach their respective optimal mean phenotype. In addition, sex-specific selection is expected to generate sexual dimorphism of trait covariance structure (e.g., the phenotypic covariance matrix, P), but previous empirical work has not fully addressed this prediction. We compared patterns of phenotypic divergence, for three traits in seven taxa in the insect genus Phymata (Reduviidae), to ask whether sexual dimorphism in P is common and whether its magnitude relates to the extent of sexual dimorphism in trait means. We found that sexual dimorphism in both mean and covariance structure was pervasive but also that the multivariate distance between sex-specific means was correlated with sex differences in the leading eigenvector of P, while accounting for uncertainty in phylogenetic relationships. Collectively, our findings suggest that sexual dimorphism in covariance structure may be a common but underappreciated feature of dioecious populations.
Kariuki, C M; Ilatsia, Evans D; Kosgey, Isaac S; Kahi, Alexander K
2010-03-01
Genetic and phenotypic parameters were estimated for lamb growth traits for the Dorper sheep in semi-arid Kenya using an animal model. Data on lamb growth performance were extracted from available performance records at the Sheep and Goats Station in Naivasha, Kenya. Growth traits considered were body weights at birth (BW0, kg), at 1 month (BW1, kg), at 2 months (BW2, kg), at weaning (WW, kg), at 6 months (BW6, kg), at 9 months (BW9, kg) and at yearling (YW, kg), average daily gain from birth to 6 months (ADG(0-6), gm) and from 6 months to 1 year (ADG(6-12), gm). Direct heritability estimates were, correspondingly, 0.18, 0.36, 0.32, 0.28, 0.21, 0.14, 0.29, 0.12 and 0.30 for BW0, BW1, BW2, WW, BW6, BW9, YW, ADG(0-6) and ADG(6-12). The corresponding maternal genetic heritability estimates for body weights up to 9 months were 0.16, 0.10, 0.10, 0.19, 0.21 and 0.18. Direct-maternal genetic correlations were negative and high ranging between -0.47 to -0.94. Negative genetic correlations were observed for ADG(0-6)-ADG(6-12), BW2-ADG(6-12), WW-ADG(6-12) and BW6-ADG(6-12). Phenotypic correlations ranged from 0.15 to 0.96. Maternal effects are important in the growth performance of the Dorper sheep though a negative correlation exists between direct and maternal genetic effects. The current study has provided important information on the extent of additive genetic variation in the existing flocks that could now be used in determining the merit of breeding rams and ewes for sale to the commercial flocks. The estimates provided would form the basis of designing breeding schemes for the Dorper sheep in Kenya. Implications of the study to future Dorper sheep breeding programmes are also discussed.
de Candia, Teresa R; Lee, S Hong; Yang, Jian; Browning, Brian L; Gejman, Pablo V; Levinson, Douglas F; Mowry, Bryan J; Hewitt, John K; Goddard, Michael E; O'Donovan, Michael C; Purcell, Shaun M; Posthuma, Danielle; Visscher, Peter M; Wray, Naomi R; Keller, Matthew C
2013-09-05
To investigate the extent to which the proportion of schizophrenia's additive genetic variation tagged by SNPs is shared by populations of European and African descent, we analyzed the largest combined African descent (AD [n = 2,142]) and European descent (ED [n = 4,990]) schizophrenia case-control genome-wide association study (GWAS) data set available, the Molecular Genetics of Schizophrenia (MGS) data set. We show how a method that uses genomic similarities at measured SNPs to estimate the additive genetic correlation (SNP correlation [SNP-rg]) between traits can be extended to estimate SNP-rg for the same trait between ethnicities. We estimated SNP-rg for schizophrenia between the MGS ED and MGS AD samples to be 0.66 (SE = 0.23), which is significantly different from 0 (p(SNP-rg = 0) = 0.0003), but not 1 (p(SNP-rg = 1) = 0.26). We re-estimated SNP-rg between an independent ED data set (n = 6,665) and the MGS AD sample to be 0.61 (SE = 0.21, p(SNP-rg = 0) = 0.0003, p(SNP-rg = 1) = 0.16). These results suggest that many schizophrenia risk alleles are shared across ethnic groups and predate African-European divergence.
Kirkpatrick, Robert M; McGue, Matt; Iacono, William G
2015-03-01
The present study of general cognitive ability attempts to replicate and extend previous investigations of a biometric moderator, family-of-origin socioeconomic status (SES), in a sample of 2,494 pairs of adolescent twins, non-twin biological siblings, and adoptive siblings assessed with individually administered IQ tests. We hypothesized that SES would covary positively with additive-genetic variance and negatively with shared-environmental variance. Important potential confounds unaddressed in some past studies, such as twin-specific effects, assortative mating, and differential heritability by trait level, were found to be negligible. In our main analysis, we compared models by their sample-size corrected AIC, and base our statistical inference on model-averaged point estimates and standard errors. Additive-genetic variance increased with SES-an effect that was statistically significant and robust to model specification. We found no evidence that SES moderated shared-environmental influence. We attempt to explain the inconsistent replication record of these effects, and provide suggestions for future research.
Kirkpatrick, Robert M.; McGue, Matt; Iacono, William G.
2015-01-01
The present study of general cognitive ability attempts to replicate and extend previous investigations of a biometric moderator, family-of-origin socioeconomic status (SES), in a sample of 2,494 pairs of adolescent twins, non-twin biological siblings, and adoptive siblings assessed with individually administered IQ tests. We hypothesized that SES would covary positively with additive-genetic variance and negatively with shared-environmental variance. Important potential confounds unaddressed in some past studies, such as twin-specific effects, assortative mating, and differential heritability by trait level, were found to be negligible. In our main analysis, we compared models by their sample-size corrected AIC, and base our statistical inference on model-averaged point estimates and standard errors. Additive-genetic variance increased with SES—an effect that was statistically significant and robust to model specification. We found no evidence that SES moderated shared-environmental influence. We attempt to explain the inconsistent replication record of these effects, and provide suggestions for future research. PMID:25539975
Keller, Matthew C; Coventry, William L; Heath, Andrew C; Martin, Nicholas G
2005-11-01
Studies using the classical twin design often conclude that most genetic variation underlying personality is additive in nature. However, studies analyzing only twins are very limited in their ability to detect non-additive genetic variation and are unable to detect sources of variation unique to twins, which can mask non-additive genetic variation. The current study assessed 9672 MZ and DZ twin individuals and 3241 of their siblings to investigate the environmental and genetic architecture underlying eight dimensions of personality: four from Eysenck's Personality Questionnaire and four from Cloninger's Temperament and Character Inventory. Broad-sense heritability estimates from best-fitting models were two to three times greater than the narrow-sense heritability estimates for Harm Avoidance, Novelty Seeking, Reward Dependence, Persistence, Extraversion, and Neuroticism. This genetic non-additivity could be due to dominance, additive-by-additive epistasis, or to additive genetic effects combined with higher-order epistasis. Environmental effects unique to twins were detected for both Lie and Psychoticism but accounted for little overall variation. Our results illustrate the increased sensitivity afforded by extending the classical twin design to include siblings, and may provide clues to the evolutionary origins of genetic variation underlying personality.
NASA Astrophysics Data System (ADS)
Edenhofer, Peter; Ulamec, Stephan
2015-04-01
The paper is devoted to results of doctoral research work at University of Bochum as applied to the radar transmission experiment CONSERT of the ESA cometary mission Rosetta. This research aims at achieving the limits of optimum spatial (and temporal) resolution for radar remote sensing by implementation of covariance informations concerned with error-balanced control as well as coherence of wave propagation effects through random composite media involved (based on Joel Franklin's approach of extended stochastic inversion). As a consequence the well-known inherent numerical instabilities of remote sensing are significantly reduced in a robust way by increasing the weight of main diagonal elements of the resulting composite matrix to be inverted with respect to off-diagonal elements following synergy relations as to the principle of correlation receiver in wireless telecommunications. It is shown that the enhancement of resolution for remote sensing holds for an integral and differential equation approach of inversion as well. In addition to that the paper presents a discussion on how the efficiency of inversion for radar data gets achieved by an overall optimization of inversion due to a novel neuro-genetic approach. Such kind of approach is in synergy with the priority research program "Organic Computing" of DFG / German Research Organization. This Neuro-Genetic Optimization (NGO) turns out, firstly, to take into account more detailed physical informations supporting further improved resolution such as the process of accretion for cometary nucleus, wave propagation effects from rough surfaces, ground clutter, nonlinear focusing, etc. as well as, secondly, to accelerate the computing process of inversion in a really significantly enhanced and fast way, e.g., enabling online-control of autonomous processes such as detection of unknown objects, navigation, etc. The paper describes in some detail how this neuro-genetic approach of optimization is incorporated into the
A covariance NMR toolbox for MATLAB and OCTAVE.
Short, Timothy; Alzapiedi, Leigh; Brüschweiler, Rafael; Snyder, David
2011-03-01
The Covariance NMR Toolbox is a new software suite that provides a streamlined implementation of covariance-based analysis of multi-dimensional NMR data. The Covariance NMR Toolbox uses the MATLAB or, alternatively, the freely available GNU OCTAVE computer language, providing a user-friendly environment in which to apply and explore covariance techniques. Covariance methods implemented in the toolbox described here include direct and indirect covariance processing, 4D covariance, generalized indirect covariance (GIC), and Z-matrix transform. In order to provide compatibility with a wide variety of spectrometer and spectral analysis platforms, the Covariance NMR Toolbox uses the NMRPipe format for both input and output files. Additionally, datasets small enough to fit in memory are stored as arrays that can be displayed and further manipulated in a versatile manner within MATLAB or OCTAVE.
Thepparat, Mongkol; Boonkum, Wuttigrai; Duangjinda, Monchai; Tumwasorn, Sornthep; Nakavisut, Sansak; Thongchumroon, Thumrong
2015-07-01
The objectives of this study were to compare covariance functions (CF) and estimate the heritability of milk yield from test-day records among exotic (Saanen, Anglo-Nubian, Toggenburg and Alpine) and crossbred goats (Thai native and exotic breed), using a random regression model. A total of 1472 records of test-day milk yield were used, collected from 112 does between 2003 and 2006. CF of the study were Wilmink function, second- and third-order Legendre polynomials, and linear splines 4 knots located at 5, 25, 90 and 155 days in milk (SP25-90) and 5, 35, 95 and 155 of days in milk (SP35-95). Variance components were estimated by restricted maximum likelihood method (REML). Goodness of fit, Akaike information criterion (AIC), percentage of squared bias (PSB), mean square error (MSE), and empirical correlation (RHO) between the observed and predicted values were used to compare models. The results showed that CF had an impact on (co)variance estimation in random regression models (RRM). The RRM with splines 4 knots located at 5, 25, 90 and 155 of days in milk had the lowest AIC, PSB and MSE, and the highest RHO. The heritability estimated throughout lactation obtained with this model ranged from 0.13 to 0.23.
McFarlane, S Eryn; Gorrell, Jamieson C; Coltman, David W; Humphries, Murray M; Boutin, Stan; McAdam, Andrew G
2014-01-01
A trait must genetically correlate with fitness in order to evolve in response to natural selection, but theory suggests that strong directional selection should erode additive genetic variance in fitness and limit future evolutionary potential. Balancing selection has been proposed as a mechanism that could maintain genetic variance if fitness components trade off with one another and has been invoked to account for empirical observations of higher levels of additive genetic variance in fitness components than would be expected from mutation–selection balance. Here, we used a long-term study of an individually marked population of North American red squirrels (Tamiasciurus hudsonicus) to look for evidence of (1) additive genetic variance in lifetime reproductive success and (2) fitness trade-offs between fitness components, such as male and female fitness or fitness in high- and low-resource environments. “Animal model” analyses of a multigenerational pedigree revealed modest maternal effects on fitness, but very low levels of additive genetic variance in lifetime reproductive success overall as well as fitness measures within each sex and environment. It therefore appears that there are very low levels of direct genetic variance in fitness and fitness components in red squirrels to facilitate contemporary adaptation in this population. PMID:24963372
All covariance controllers for linear discrete-time systems
NASA Technical Reports Server (NTRS)
Hsieh, Chen; Skelton, Robert E.
1990-01-01
The set of covariances that a linear discrete-time plant with a specified-order controller can have is characterized. The controllers that assign such covariances to any linear discrete-time system are given explicitly in closed form. The freedom in these covariance controllers is explicit and is parameterized by two orthogonal matrices. By appropriately choosing these free parameters, additional system objectives can be achieved without altering the state covariance, and the stability of the closed-loop system is guaranteed.
Covariant mutually unbiased bases
NASA Astrophysics Data System (ADS)
Carmeli, Claudio; Schultz, Jussi; Toigo, Alessandro
2016-06-01
The connection between maximal sets of mutually unbiased bases (MUBs) in a prime-power dimensional Hilbert space and finite phase-space geometries is well known. In this article, we classify MUBs according to their degree of covariance with respect to the natural symmetries of a finite phase-space, which are the group of its affine symplectic transformations. We prove that there exist maximal sets of MUBs that are covariant with respect to the full group only in odd prime-power dimensional spaces, and in this case, their equivalence class is actually unique. Despite this limitation, we show that in dimension 2r covariance can still be achieved by restricting to proper subgroups of the symplectic group, that constitute the finite analogues of the oscillator group. For these subgroups, we explicitly construct the unitary operators yielding the covariance.
Covariant Noncommutative Field Theory
Estrada-Jimenez, S.; Garcia-Compean, H.; Obregon, O.; Ramirez, C.
2008-07-02
The covariant approach to noncommutative field and gauge theories is revisited. In the process the formalism is applied to field theories invariant under diffeomorphisms. Local differentiable forms are defined in this context. The lagrangian and hamiltonian formalism is consistently introduced.
Peng, Donghai; Zhou, Chenfei; Chen, Shouwen; Ruan, Lifang; Yu, Ziniu; Sun, Ming
2008-01-01
The aim of the present study is to evaluate the toxicology safety to mammals of a genetically modified (GM) Bacillus thuringiensis with an additional N-acyl homoserine lactones gene (aiiA), which possesses insecticidal activity together with restraint of bacterial pathogenicity and is intended for use as a multifunctional biopesticide. Safety assessments included an acute oral toxicity test and 28-d animal feeding study in Wistar rats, primary eye and dermal irritation in Zealand White rabbits, and delayed contact hypersensitivity in guinea pigs. Tests were conducted using spray-dried powder preparation. This GM product showed toxicity neither in oral acute toxicity test nor in 28-d animal feeding test at a dose of 5,000 mg/kg body weight. During the animal feeding test, there were no significant differences in growth, food and water consumption, hematology, blood biochemical indices, organ weights, and histopathology finding between rats in controls and tested groups. Tested animals in primary eye and dermal irritation and delayed contact hypersensitivity test were also devoid of any toxicity compared to controls. All the above results demonstrated that the GM based multifunctional B. thuringiensis has low toxicity and low eye and dermal irritation and would not cause hypersensitivity to laboratory mammals and therefore could be regarded as safe for use as a pesticide.
Concordance between criteria for covariate model building.
Hennig, Stefanie; Karlsson, Mats O
2014-04-01
When performing a population pharmacokinetic modelling analysis covariates are often added to the model. Such additions are often justified by improved goodness of fit and/or decreased in unexplained (random) parameter variability. Increased goodness of fit is most commonly measured by the decrease in the objective function value. Parameter variability can be defined as the sum of unexplained (random) and explained (predictable) variability. Increase in magnitude of explained parameter variability could be another possible criterion for judging improvement in the model. The agreement between these three criteria in diagnosing covariate-parameter relationships of different strengths and nature using stochastic simulations and estimations as well as assessing covariate-parameter relationships in four previously published real data examples were explored. Total estimated parameter variability was found to vary with the number of covariates introduced on the parameter. In the simulated examples and two real examples, the parameter variability increased with increasing number of included covariates. For the other real examples parameter variability decreased or did not change systematically with the addition of covariates. The three criteria were highly correlated, with the decrease in unexplained variability being more closely associated with changes in objective function values than increases in explained parameter variability were. The often used assumption that inclusion of covariates in models only shifts unexplained parameter variability to explained parameter variability appears not to be true, which may have implications for modelling decisions.
The use of a covariate reduces experimental error in nutrient digestion studies in growing pigs
Technology Transfer Automated Retrieval System (TEKTRAN)
Covariance analysis limits error, the degree of nuisance variation, and overparameterizing factors to accurately measure treatment effects. Data dealing with growth, carcass composition, and genetics often utilize covariates in data analysis. In contrast, nutritional studies typically do not. The ob...
Construction of Covariance Functions with Variable Length Fields
NASA Technical Reports Server (NTRS)
Gaspari, Gregory; Cohn, Stephen E.; Guo, Jing; Pawson, Steven
2005-01-01
This article focuses on construction, directly in physical space, of three-dimensional covariance functions parametrized by a tunable length field, and on an application of this theory to reproduce the Quasi-Biennial Oscillation (QBO) in the Goddard Earth Observing System, Version 4 (GEOS-4) data assimilation system. These Covariance models are referred to as multi-level or nonseparable, to associate them with the application where a multi-level covariance with a large troposphere to stratosphere length field gradient is used to reproduce the QBO from sparse radiosonde observations in the tropical lower stratosphere. The multi-level covariance functions extend well-known single level covariance functions depending only on a length scale. Generalizations of the first- and third-order autoregressive covariances in three dimensions are given, providing multi-level covariances with zero and three derivatives at zero separation, respectively. Multi-level piecewise rational covariances with two continuous derivatives at zero separation are also provided. Multi-level powerlaw covariances are constructed with continuous derivatives of all orders. Additional multi-level covariance functions are constructed using the Schur product of single and multi-level covariance functions. A multi-level powerlaw covariance used to reproduce the QBO in GEOS-4 is described along with details of the assimilation experiments. The new covariance model is shown to represent the vertical wind shear associated with the QBO much more effectively than in the baseline GEOS-4 system.
Khovaev, A A
2008-01-01
In this article analysis questions of using genetically modified microorganisms in manufacture food production, present new GMM used in manufacture -food ferments; results of medical biological appraisal/ microbiological and genetic expert examination/ of food, getting by use microorganisms or there producents with indication modern of control methods.
Planning additional drilling campaign using two-space genetic algorithm: A game theoretical approach
NASA Astrophysics Data System (ADS)
Kumral, Mustafa; Ozer, Umit
2013-03-01
Grade and tonnage are the most important technical uncertainties in mining ventures because of the use of estimations/simulations, which are mostly generated from drill data. Open pit mines are planned and designed on the basis of the blocks representing the entire orebody. Each block has different estimation/simulation variance reflecting uncertainty to some extent. The estimation/simulation realizations are submitted to mine production scheduling process. However, the use of a block model with varying estimation/simulation variances will lead to serious risk in the scheduling. In the medium of multiple simulations, the dispersion variances of blocks can be thought to regard technical uncertainties. However, the dispersion variance cannot handle uncertainty associated with varying estimation/simulation variances of blocks. This paper proposes an approach that generates the configuration of the best additional drilling campaign to generate more homogenous estimation/simulation variances of blocks. In other words, the objective is to find the best drilling configuration in such a way as to minimize grade uncertainty under budget constraint. Uncertainty measure of the optimization process in this paper is interpolation variance, which considers data locations and grades. The problem is expressed as a minmax problem, which focuses on finding the best worst-case performance i.e., minimizing interpolation variance of the block generating maximum interpolation variance. Since the optimization model requires computing the interpolation variances of blocks being simulated/estimated in each iteration, the problem cannot be solved by standard optimization tools. This motivates to use two-space genetic algorithm (GA) approach to solve the problem. The technique has two spaces: feasible drill hole configuration with minimization of interpolation variance and drill hole simulations with maximization of interpolation variance. Two-space interacts to find a minmax solution
Sparse estimation of a covariance matrix.
Bien, Jacob; Tibshirani, Robert J
2011-12-01
We suggest a method for estimating a covariance matrix on the basis of a sample of vectors drawn from a multivariate normal distribution. In particular, we penalize the likelihood with a lasso penalty on the entries of the covariance matrix. This penalty plays two important roles: it reduces the effective number of parameters, which is important even when the dimension of the vectors is smaller than the sample size since the number of parameters grows quadratically in the number of variables, and it produces an estimate which is sparse. In contrast to sparse inverse covariance estimation, our method's close relative, the sparsity attained here is in the covariance matrix itself rather than in the inverse matrix. Zeros in the covariance matrix correspond to marginal independencies; thus, our method performs model selection while providing a positive definite estimate of the covariance. The proposed penalized maximum likelihood problem is not convex, so we use a majorize-minimize approach in which we iteratively solve convex approximations to the original nonconvex problem. We discuss tuning parameter selection and demonstrate on a flow-cytometry dataset how our method produces an interpretable graphical display of the relationship between variables. We perform simulations that suggest that simple elementwise thresholding of the empirical covariance matrix is competitive with our method for identifying the sparsity structure. Additionally, we show how our method can be used to solve a previously studied special case in which a desired sparsity pattern is prespecified.
NASA Astrophysics Data System (ADS)
Frasinski, Leszek J.
2016-08-01
Recent technological advances in the generation of intense femtosecond pulses have made covariance mapping an attractive analytical technique. The laser pulses available are so intense that often thousands of ionisation and Coulomb explosion events will occur within each pulse. To understand the physics of these processes the photoelectrons and photoions need to be correlated, and covariance mapping is well suited for operating at the high counting rates of these laser sources. Partial covariance is particularly useful in experiments with x-ray free electron lasers, because it is capable of suppressing pulse fluctuation effects. A variety of covariance mapping methods is described: simple, partial (single- and multi-parameter), sliced, contingent and multi-dimensional. The relationship to coincidence techniques is discussed. Covariance mapping has been used in many areas of science and technology: inner-shell excitation and Auger decay, multiphoton and multielectron ionisation, time-of-flight and angle-resolved spectrometry, infrared spectroscopy, nuclear magnetic resonance imaging, stimulated Raman scattering, directional gamma ray sensing, welding diagnostics and brain connectivity studies (connectomics). This review gives practical advice for implementing the technique and interpreting the results, including its limitations and instrumental constraints. It also summarises recent theoretical studies, highlights unsolved problems and outlines a personal view on the most promising research directions.
Technology Transfer Automated Retrieval System (TEKTRAN)
Objective: To determine the extent to which the risk for incident coronary heart disease (CHD) increases in relation to a genetic risk score (GRS) that additively integrates the influence of high-risk alleles in nine documented single nucleotide polymorphisms (SNPs) for CHD, and to examine whether t...
Covariance Applications with Kiwi
NASA Astrophysics Data System (ADS)
Mattoon, C. M.; Brown, D.; Elliott, J. B.
2012-05-01
The Computational Nuclear Physics group at Lawrence Livermore National Laboratory (LLNL) is developing a new tool, named `Kiwi', that is intended as an interface between the covariance data increasingly available in major nuclear reaction libraries (including ENDF and ENDL) and large-scale Uncertainty Quantification (UQ) studies. Kiwi is designed to integrate smoothly into large UQ studies, using the covariance matrix to generate multiple variations of nuclear data. The code has been tested using critical assemblies as a test case, and is being integrated into LLNL's quality assurance and benchmarking for nuclear data.
Kumar, Satish; Molloy, Claire; Muñoz, Patricio; Daetwyler, Hans; Chagné, David; Volz, Richard
2015-01-01
The nonadditive genetic effects may have an important contribution to total genetic variation of phenotypes, so estimates of both the additive and nonadditive effects are desirable for breeding and selection purposes. Our main objectives were to: estimate additive, dominance and epistatic variances of apple (Malus × domestica Borkh.) phenotypes using relationship matrices constructed from genome-wide dense single nucleotide polymorphism (SNP) markers; and compare the accuracy of genomic predictions using genomic best linear unbiased prediction models with or without including nonadditive genetic effects. A set of 247 clonally replicated individuals was assessed for six fruit quality traits at two sites, and also genotyped using an Illumina 8K SNP array. Across several fruit quality traits, the additive, dominance, and epistatic effects contributed about 30%, 16%, and 19%, respectively, to the total phenotypic variance. Models ignoring nonadditive components yielded upwardly biased estimates of additive variance (heritability) for all traits in this study. The accuracy of genomic predicted genetic values (GEGV) varied from about 0.15 to 0.35 for various traits, and these were almost identical for models with or without including nonadditive effects. However, models including nonadditive genetic effects further reduced the bias of GEGV. Between-site genotypic correlations were high (>0.85) for all traits, and genotype-site interaction accounted for <10% of the phenotypic variability. The accuracy of prediction, when the validation set was present only at one site, was generally similar for both sites, and varied from about 0.50 to 0.85. The prediction accuracies were strongly influenced by trait heritability, and genetic relatedness between the training and validation families. PMID:26497141
Forsberg, Simon K. G.; Andreatta, Matthew E.; Huang, Xin-Yuan; Danku, John; Salt, David E.; Carlborg, Örjan
2015-01-01
Genome-wide association (GWA) analyses have generally been used to detect individual loci contributing to the phenotypic diversity in a population by the effects of these loci on the trait mean. More rarely, loci have also been detected based on variance differences between genotypes. Several hypotheses have been proposed to explain the possible genetic mechanisms leading to such variance signals. However, little is known about what causes these signals, or whether this genetic variance-heterogeneity reflects mechanisms of importance in natural populations. Previously, we identified a variance-heterogeneity GWA (vGWA) signal for leaf molybdenum concentrations in Arabidopsis thaliana. Here, fine-mapping of this association reveals that the vGWA emerges from the effects of three independent genetic polymorphisms that all are in strong LD with the markers displaying the genetic variance-heterogeneity. By revealing the genetic architecture underlying this vGWA signal, we uncovered the molecular source of a significant amount of hidden additive genetic variation or “missing heritability”. Two of the three polymorphisms underlying the genetic variance-heterogeneity are promoter variants for Molybdate transporter 1 (MOT1), and the third a variant located ~25 kb downstream of this gene. A fourth independent association was also detected ~600 kb upstream of MOT1. Use of a T-DNA knockout allele highlights Copper Transporter 6; COPT6 (AT2G26975) as a strong candidate gene for this association. Our results show that an extended LD across a complex locus including multiple functional alleles can lead to a variance-heterogeneity between genotypes in natural populations. Further, they provide novel insights into the genetic regulation of ion homeostasis in A. thaliana, and empirically confirm that variance-heterogeneity based GWA methods are a valuable tool to detect novel associations of biological importance in natural populations. PMID:26599497
Generalized Linear Covariance Analysis
NASA Technical Reports Server (NTRS)
Carpenter, James R.; Markley, F. Landis
2014-01-01
This talk presents a comprehensive approach to filter modeling for generalized covariance analysis of both batch least-squares and sequential estimators. We review and extend in two directions the results of prior work that allowed for partitioning of the state space into solve-for'' and consider'' parameters, accounted for differences between the formal values and the true values of the measurement noise, process noise, and textita priori solve-for and consider covariances, and explicitly partitioned the errors into subspaces containing only the influence of the measurement noise, process noise, and solve-for and consider covariances. In this work, we explicitly add sensitivity analysis to this prior work, and relax an implicit assumption that the batch estimator's epoch time occurs prior to the definitive span. We also apply the method to an integrated orbit and attitude problem, in which gyro and accelerometer errors, though not estimated, influence the orbit determination performance. We illustrate our results using two graphical presentations, which we call the variance sandpile'' and the sensitivity mosaic,'' and we compare the linear covariance results to confidence intervals associated with ensemble statistics from a Monte Carlo analysis.
29 CFR 2590.702-1 - Additional requirements prohibiting discrimination based on genetic information.
Code of Federal Regulations, 2010 CFR
2010-07-01
... diabetes. A begins to experience excessive sweating, thirst, and fatigue. A's physician examines A and... adult onset diabetes mellitus (Type 2 diabetes). (ii) Conclusion. In this Example 1, A has been... involved. The diagnosis is not based principally on genetic information. Thus, Type 2 diabetes...
45 CFR 146.122 - Additional requirements prohibiting discrimination based on genetic information.
Code of Federal Regulations, 2010 CFR
2010-10-01
... diabetes. A begins to experience excessive sweating, thirst, and fatigue. A's physician examines A and... adult onset diabetes mellitus (Type 2 diabetes). (ii) Conclusion. In this Example 1, A has been... involved. The diagnosis is not based principally on genetic information. Thus, Type 2 diabetes...
Evaluation of the Covariance Matrix of Estimated Resonance Parameters
NASA Astrophysics Data System (ADS)
Becker, B.; Capote, R.; Kopecky, S.; Massimi, C.; Schillebeeckx, P.; Sirakov, I.; Volev, K.
2014-04-01
In the resonance region nuclear resonance parameters are mostly obtained by a least square adjustment of a model to experimental data. Derived parameters can be mutually correlated through the adjustment procedure as well as through common experimental or model uncertainties. In this contribution we investigate four different methods to propagate the additional covariance caused by experimental or model uncertainties into the evaluation of the covariance matrix of the estimated parameters: (1) including the additional covariance into the experimental covariance matrix based on calculated or theoretical estimates of the data; (2) including the uncertainty affected parameter in the adjustment procedure; (3) evaluation of the full covariance matrix by Monte Carlo sampling of the common parameter; and (4) retroactively including the additional covariance by using the marginalization procedure of Habert et al.
Brier, Matthew R; Mitra, Anish; McCarthy, John E; Ances, Beau M; Snyder, Abraham Z
2015-11-01
Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity.
Brier, Matthew R.; Mitra, Anish; McCarthy, John E.; Ances, Beau M.; Snyder, Abraham Z.
2015-01-01
Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity. PMID:26208872
Covariate analysis of bivariate survival data
Bennett, L.E.
1992-01-01
The methods developed are used to analyze the effects of covariates on bivariate survival data when censoring and ties are present. The proposed method provides models for bivariate survival data that include differential covariate effects and censored observations. The proposed models are based on an extension of the univariate Buckley-James estimators which replace censored data points by their expected values, conditional on the censoring time and the covariates. For the bivariate situation, it is necessary to determine the expectation of the failure times for one component conditional on the failure or censoring time of the other component. Two different methods have been developed to estimate these expectations. In the semiparametric approach these expectations are determined from a modification of Burke's estimate of the bivariate empirical survival function. In the parametric approach censored data points are also replaced by their conditional expected values where the expected values are determined from a specified parametric distribution. The model estimation will be based on the revised data set, comprised of uncensored components and expected values for the censored components. The variance-covariance matrix for the estimated covariate parameters has also been derived for both the semiparametric and parametric methods. Data from the Demographic and Health Survey was analyzed by these methods. The two outcome variables are post-partum amenorrhea and breastfeeding; education and parity were used as the covariates. Both the covariate parameter estimates and the variance-covariance estimates for the semiparametric and parametric models will be compared. In addition, a multivariate test statistic was used in the semiparametric model to examine contrasts. The significance of the statistic was determined from a bootstrap distribution of the test statistic.
Using Analysis of Covariance (ANCOVA) with Fallible Covariates
ERIC Educational Resources Information Center
Culpepper, Steven Andrew; Aguinis, Herman
2011-01-01
Analysis of covariance (ANCOVA) is used widely in psychological research implementing nonexperimental designs. However, when covariates are fallible (i.e., measured with error), which is the norm, researchers must choose from among 3 inadequate courses of action: (a) know that the assumption that covariates are perfectly reliable is violated but…
Covariant deformed oscillator algebras
NASA Technical Reports Server (NTRS)
Quesne, Christiane
1995-01-01
The general form and associativity conditions of deformed oscillator algebras are reviewed. It is shown how the latter can be fulfilled in terms of a solution of the Yang-Baxter equation when this solution has three distinct eigenvalues and satisfies a Birman-Wenzl-Murakami condition. As an example, an SU(sub q)(n) x SU(sub q)(m)-covariant q-bosonic algebra is discussed in some detail.
The Bayesian Covariance Lasso.
Khondker, Zakaria S; Zhu, Hongtu; Chu, Haitao; Lin, Weili; Ibrahim, Joseph G
2013-04-01
Estimation of sparse covariance matrices and their inverse subject to positive definiteness constraints has drawn a lot of attention in recent years. The abundance of high-dimensional data, where the sample size (n) is less than the dimension (d), requires shrinkage estimation methods since the maximum likelihood estimator is not positive definite in this case. Furthermore, when n is larger than d but not sufficiently larger, shrinkage estimation is more stable than maximum likelihood as it reduces the condition number of the precision matrix. Frequentist methods have utilized penalized likelihood methods, whereas Bayesian approaches rely on matrix decompositions or Wishart priors for shrinkage. In this paper we propose a new method, called the Bayesian Covariance Lasso (BCLASSO), for the shrinkage estimation of a precision (covariance) matrix. We consider a class of priors for the precision matrix that leads to the popular frequentist penalties as special cases, develop a Bayes estimator for the precision matrix, and propose an efficient sampling scheme that does not precalculate boundaries for positive definiteness. The proposed method is permutation invariant and performs shrinkage and estimation simultaneously for non-full rank data. Simulations show that the proposed BCLASSO performs similarly as frequentist methods for non-full rank data.
Khondker, Zakaria S; Zhu, Hongtu; Chu, Haitao; Lin, Weili; Ibrahim, Joseph G.
2012-01-01
Estimation of sparse covariance matrices and their inverse subject to positive definiteness constraints has drawn a lot of attention in recent years. The abundance of high-dimensional data, where the sample size (n) is less than the dimension (d), requires shrinkage estimation methods since the maximum likelihood estimator is not positive definite in this case. Furthermore, when n is larger than d but not sufficiently larger, shrinkage estimation is more stable than maximum likelihood as it reduces the condition number of the precision matrix. Frequentist methods have utilized penalized likelihood methods, whereas Bayesian approaches rely on matrix decompositions or Wishart priors for shrinkage. In this paper we propose a new method, called the Bayesian Covariance Lasso (BCLASSO), for the shrinkage estimation of a precision (covariance) matrix. We consider a class of priors for the precision matrix that leads to the popular frequentist penalties as special cases, develop a Bayes estimator for the precision matrix, and propose an efficient sampling scheme that does not precalculate boundaries for positive definiteness. The proposed method is permutation invariant and performs shrinkage and estimation simultaneously for non-full rank data. Simulations show that the proposed BCLASSO performs similarly as frequentist methods for non-full rank data. PMID:24551316
Genetic variation at the TPH2 gene influences impulsivity in addition to eating disorders.
Slof-Op't Landt, Margarita C T; Bartels, Meike; Middeldorp, Christel M; van Beijsterveldt, Catherina E M; Slagboom, P Eline; Boomsma, Dorret I; van Furth, Eric F; Meulenbelt, Ingrid
2013-01-01
Genes are involved in eating disorders (EDs) and self-induced vomiting (SV), a key symptom of different types of EDs. Perfectionism and impulsivity are potential risk factors for EDs. TPH2 (tryptophan hydroxylase 2) SNP rs1473473 was previously associated with anorexia nervosa and EDs characterized by SV. Could perfectionism or impulsivity be underlying the association between rs1473473 and EDs? Genetic association between TPH2 SNP rs1473473 and perfectionism or impulsivity was first evaluated in a random control group (N = 512). The associations obtained in this control group were subsequently tested in a group of patients with an ED (N = 267). The minor allele of rs1473473 (OR = 1.49) was more frequent in impulsive controls, but also in impulsive patients with an ED (OR = 1.83). The largest effect was found in the patients with an ED characterized by SV (OR = 2.51, p = 0.02). Genetic variation at the TPH2 gene appeared to affect impulsivity which, in turn, might predispose to the SV phenotype.
Brazzola, Gregory; Chèvre, Nathalie; Wedekind, Claus
2014-11-01
The evolutionary potential of natural populations to adapt to anthropogenic threats critically depends on whether there exists additive genetic variation for tolerance to the threat. A major problem for water-dwelling organisms is chemical pollution, and among the most common pollutants is 17α-ethinylestradiol (EE2), the synthetic estrogen that is used in oral contraceptives and that can affect fish at various developmental stages, including embryogenesis. We tested whether there is variation in the tolerance to EE2 within Alpine whitefish. We sampled spawners from two species of different lakes, bred them in vitro in a full-factorial design each, and studied growth and mortality of embryos. Exposure to EE2 turned out to be toxic in all concentrations we tested (≥1 ng/L). It reduced embryo viability and slowed down embryogenesis. We found significant additive genetic variation in EE2-induced mortality in both species, that is, genotypes differed in their tolerance to estrogen pollution. We also found maternal effects on embryo development to be influenced by EE2, that is, some maternal sib groups were more susceptible to EE2 than others. In conclusion, the toxic effects of EE2 were strong, but both species demonstrated the kind of additive genetic variation that is necessary for an evolutionary response to this type of pollution.
Brazzola, Gregory; Chèvre, Nathalie; Wedekind, Claus
2014-01-01
The evolutionary potential of natural populations to adapt to anthropogenic threats critically depends on whether there exists additive genetic variation for tolerance to the threat. A major problem for water-dwelling organisms is chemical pollution, and among the most common pollutants is 17α-ethinylestradiol (EE2), the synthetic estrogen that is used in oral contraceptives and that can affect fish at various developmental stages, including embryogenesis. We tested whether there is variation in the tolerance to EE2 within Alpine whitefish. We sampled spawners from two species of different lakes, bred them in vitro in a full-factorial design each, and studied growth and mortality of embryos. Exposure to EE2 turned out to be toxic in all concentrations we tested (≥1 ng/L). It reduced embryo viability and slowed down embryogenesis. We found significant additive genetic variation in EE2-induced mortality in both species, that is, genotypes differed in their tolerance to estrogen pollution. We also found maternal effects on embryo development to be influenced by EE2, that is, some maternal sib groups were more susceptible to EE2 than others. In conclusion, the toxic effects of EE2 were strong, but both species demonstrated the kind of additive genetic variation that is necessary for an evolutionary response to this type of pollution. PMID:25553069
A sparse Ising model with covariates.
Cheng, Jie; Levina, Elizaveta; Wang, Pei; Zhu, Ji
2014-12-01
There has been a lot of work fitting Ising models to multivariate binary data in order to understand the conditional dependency relationships between the variables. However, additional covariates are frequently recorded together with the binary data, and may influence the dependence relationships. Motivated by such a dataset on genomic instability collected from tumor samples of several types, we propose a sparse covariate dependent Ising model to study both the conditional dependency within the binary data and its relationship with the additional covariates. This results in subject-specific Ising models, where the subject's covariates influence the strength of association between the genes. As in all exploratory data analysis, interpretability of results is important, and we use ℓ1 penalties to induce sparsity in the fitted graphs and in the number of selected covariates. Two algorithms to fit the model are proposed and compared on a set of simulated data, and asymptotic results are established. The results on the tumor dataset and their biological significance are discussed in detail.
Lebigre, Christophe; Arcese, Peter; Reid, Jane M
2013-07-01
Age-specific variances and covariances in reproductive success shape the total variance in lifetime reproductive success (LRS), age-specific opportunities for selection, and population demographic variance and effective size. Age-specific (co)variances in reproductive success achieved through different reproductive routes must therefore be quantified to predict population, phenotypic and evolutionary dynamics in age-structured populations. While numerous studies have quantified age-specific variation in mean reproductive success, age-specific variances and covariances in reproductive success, and the contributions of different reproductive routes to these (co)variances, have not been comprehensively quantified in natural populations. We applied 'additive' and 'independent' methods of variance decomposition to complete data describing apparent (social) and realised (genetic) age-specific reproductive success across 11 cohorts of socially monogamous but genetically polygynandrous song sparrows (Melospiza melodia). We thereby quantified age-specific (co)variances in male within-pair and extra-pair reproductive success (WPRS and EPRS) and the contributions of these (co)variances to the total variances in age-specific reproductive success and LRS. 'Additive' decomposition showed that within-age and among-age (co)variances in WPRS across males aged 2-4 years contributed most to the total variance in LRS. Age-specific (co)variances in EPRS contributed relatively little. However, extra-pair reproduction altered age-specific variances in reproductive success relative to the social mating system, and hence altered the relative contributions of age-specific reproductive success to the total variance in LRS. 'Independent' decomposition showed that the (co)variances in age-specific WPRS, EPRS and total reproductive success, and the resulting opportunities for selection, varied substantially across males that survived to each age. Furthermore, extra-pair reproduction increased
Schwabe, Inga; Boomsma, Dorret I; Zeeuw, Eveline L de; Berg, Stéphanie M van den
2016-07-01
The often-used ACE model which decomposes phenotypic variance into additive genetic (A), common-environmental (C) and unique-environmental (E) parts can be extended to include covariates. Collection of these variables however often leads to a large amount of missing data, for example when self-reports (e.g. questionnaires) are not fully completed. The usual approach to handle missing covariate data in twin research results in reduced power to detect statistical effects, as only phenotypic and covariate data of individual twins with complete data can be used. Here we present a full information approach to handle missing covariate data that makes it possible to use all available data. A simulation study shows that, independent of missingness scenario, number of covariates or amount of missingness, the full information approach is more powerful than the usual approach. To illustrate the new method, we applied it to test scores on a Dutch national school achievement test (Eindtoets Basisonderwijs) in the final grade of primary school of 990 twin pairs. The effects of school-aggregated measures (e.g. school denomination, pedagogical philosophy, school size) and the effect of the sex of a twin on these test scores were tested. None of the covariates had a significant effect on individual differences in test scores.
Evolutionary quantitative genetics of nonlinear developmental systems.
Morrissey, Michael B
2015-08-01
In quantitative genetics, the effects of developmental relationships among traits on microevolution are generally represented by the contribution of pleiotropy to additive genetic covariances. Pleiotropic additive genetic covariances arise only from the average effects of alleles on multiple traits, and therefore the evolutionary importance of nonlinearities in development is generally neglected in quantitative genetic views on evolution. However, nonlinearities in relationships among traits at the level of whole organisms are undeniably important to biology in general, and therefore critical to understanding evolution. I outline a system for characterizing key quantitative parameters in nonlinear developmental systems, which yields expressions for quantities such as trait means and phenotypic and genetic covariance matrices. I then develop a system for quantitative prediction of evolution in nonlinear developmental systems. I apply the system to generating a new hypothesis for why direct stabilizing selection is rarely observed. Other uses will include separation of purely correlative from direct and indirect causal effects in studying mechanisms of selection, generation of predictions of medium-term evolutionary trajectories rather than immediate predictions of evolutionary change over single generation time-steps, and the development of efficient and biologically motivated models for separating additive from epistatic genetic variances and covariances.
Earth Observing System Covariance Realism
NASA Technical Reports Server (NTRS)
Zaidi, Waqar H.; Hejduk, Matthew D.
2016-01-01
The purpose of covariance realism is to properly size a primary object's covariance in order to add validity to the calculation of the probability of collision. The covariance realism technique in this paper consists of three parts: collection/calculation of definitive state estimates through orbit determination, calculation of covariance realism test statistics at each covariance propagation point, and proper assessment of those test statistics. An empirical cumulative distribution function (ECDF) Goodness-of-Fit (GOF) method is employed to determine if a covariance is properly sized by comparing the empirical distribution of Mahalanobis distance calculations to the hypothesized parent 3-DoF chi-squared distribution. To realistically size a covariance for collision probability calculations, this study uses a state noise compensation algorithm that adds process noise to the definitive epoch covariance to account for uncertainty in the force model. Process noise is added until the GOF tests pass a group significance level threshold. The results of this study indicate that when outliers attributed to persistently high or extreme levels of solar activity are removed, the aforementioned covariance realism compensation method produces a tuned covariance with up to 80 to 90% of the covariance propagation timespan passing (against a 60% minimum passing threshold) the GOF tests-a quite satisfactory and useful result.
Impact of the 235U Covariance Data in Benchmark Calculations
Leal, Luiz C; Mueller, Don; Arbanas, Goran; Wiarda, Dorothea; Derrien, Herve
2008-01-01
The error estimation for calculated quantities relies on nuclear data uncertainty information available in the basic nuclear data libraries such as the U.S. Evaluated Nuclear Data File (ENDF/B). The uncertainty files (covariance matrices) in the ENDF/B library are generally obtained from analysis of experimental data. In the resonance region, the computer code SAMMY is used for analyses of experimental data and generation of resonance parameters. In addition to resonance parameters evaluation, SAMMY also generates resonance parameter covariance matrices (RPCM). SAMMY uses the generalized least-squares formalism (Bayes method) together with the resonance formalism (R-matrix theory) for analysis of experimental data. Two approaches are available for creation of resonance-parameter covariance data. (1) During the data-evaluation process, SAMMY generates both a set of resonance parameters that fit the experimental data and the associated resonance-parameter covariance matrix. (2) For existing resonance-parameter evaluations for which no resonance-parameter covariance data are available, SAMMY can retroactively create a resonance-parameter covariance matrix. The retroactive method was used to generate covariance data for 235U. The resulting 235U covariance matrix was then used as input to the PUFF-IV code, which processed the covariance data into multigroup form, and to the TSUNAMI code, which calculated the uncertainty in the multiplication factor due to uncertainty in the experimental cross sections. The objective of this work is to demonstrate the use of the 235U covariance data in calculations of critical benchmark systems.
Chiou, Jeng-Min; Liang, Kung-Yee; Chiu, Yen-Feng
2005-01-01
Multipoint linkage analysis using sibpair designs remains a common approach to help investigators to narrow chromosomal regions for traits (either qualitative or quantitative) of interest. Despite its popularity, the success of this approach depends heavily on how issues such as genetic heterogeneity, gene-gene, and gene-environment interactions are properly handled. If addressed properly, the likelihood of detecting genetic linkage and of efficiently estimating the location of the trait locus would be enhanced, sometimes drastically. Previously, we have proposed an approach to deal with these issues by modeling the genetic effect of the target trait locus as a function of covariates pertained to the sibpairs. Here the genetic effect is simply the probability that a sibpair shares the same allele at the trait locus from their parents. Such modeling helps to divide the sibpairs into more homogeneous subgroups, which in turn helps to enhance the chance to detect linkage. One limitation of this approach is the need to categorize the covariates so that a small and fixed number of genetic effect parameters are introduced. In this report, we take advantage of the fact that nowadays multiple markers are readily available for genotyping simultaneously. This suggests that one could estimate the dependence of the generic effect on the covariates nonparametrically. We present an iterative procedure to estimate (1) the genetic effect nonparametrically and (2) the location of the trait locus through estimating functions developed by Liang et al. ([2001a] Hum Hered 51:67-76). We apply this new method to the linkage study of schizophrenia to illustrate how the onset ages of each sibpair may help to address the issue of genetic heterogeneity. This analysis sheds new light on the dependence of the trait effect on onset ages from affected sibpairs, an observation not revealed previously. In addition, we have carried out some simulation work, which suggests that this method provides
Covariant magnetic connection hypersurfaces
NASA Astrophysics Data System (ADS)
Pegoraro, F.
2016-04-01
> In the single fluid, non-relativistic, ideal magnetohydrodynamic (MHD) plasma description, magnetic field lines play a fundamental role by defining dynamically preserved `magnetic connections' between plasma elements. Here we show how the concept of magnetic connection needs to be generalized in the case of a relativistic MHD description where we require covariance under arbitrary Lorentz transformations. This is performed by defining 2-D magnetic connection hypersurfaces in the 4-D Minkowski space. This generalization accounts for the loss of simultaneity between spatially separated events in different frames and is expected to provide a powerful insight into the 4-D geometry of electromagnetic fields when .
Additional studies of sheep haemopexin: genetic control, frequencies and postnatal development.
Stratil, A; Bobák, P; Margetín, M; Glasnák, V
1989-01-01
This study presents evidence that sheep haemopexin phenotypes are genetically controlled by three alleles, HpxA, HpxB1 and HpxB2, of a single autosomal locus. Frequencies of two alleles, HpxA and HpxB (HpxB encompasses two isoalleles, HpxB1 and HpxB2), were studied in eight sheep breeds in Czechoslovakia. The frequency of the HpxA allele was highest (ranging from 0.81 in Merino to 1.0 in East Friesian sheep). Qualitative and quantitative changes in haemopexin during postnatal development were studied by starch gel electrophoresis and rocket immunoelectrophoresis respectively. In electrophoresis, 1- or 2-day-old lambs had two very weak zones corresponding in mobility to two slower zones of adult animals. Later, the third more anodic zone appeared and gradually increased in intensity. In 1-month-old lambs the patterns were practically identical with those of adult animals. Using rocket immunoelectrophoresis, the level of haemopexin shortly after birth was practically zero. It rose sharply till the sixth day of life; then the level continued to rise slowly till about 1 month of age. The mean haemopexin level in adult sheep was 64.5 +/- 18.26 (SD) mg/100ml serum, ranging from 30.5 to 116.5 mg/100ml.
A Nonparametric Prior for Simultaneous Covariance Estimation.
Gaskins, Jeremy T; Daniels, Michael J
2013-01-01
In the modeling of longitudinal data from several groups, appropriate handling of the dependence structure is of central importance. Standard methods include specifying a single covariance matrix for all groups or independently estimating the covariance matrix for each group without regard to the others, but when these model assumptions are incorrect, these techniques can lead to biased mean effects or loss of efficiency, respectively. Thus, it is desirable to develop methods to simultaneously estimate the covariance matrix for each group that will borrow strength across groups in a way that is ultimately informed by the data. In addition, for several groups with covariance matrices of even medium dimension, it is difficult to manually select a single best parametric model among the huge number of possibilities given by incorporating structural zeros and/or commonality of individual parameters across groups. In this paper we develop a family of nonparametric priors using the matrix stick-breaking process of Dunson et al. (2008) that seeks to accomplish this task by parameterizing the covariance matrices in terms of the parameters of their modified Cholesky decomposition (Pourahmadi, 1999). We establish some theoretic properties of these priors, examine their effectiveness via a simulation study, and illustrate the priors using data from a longitudinal clinical trial.
Genetic evidence for an additional function of phage T4 gene 32 protein: interaction with ligase.
Mosig, G; Breschkin, A M
1975-04-01
Gene 32 of bacteriophage T4 is essential for DNA replication, recombination, and repair. In an attempt to clarify the role of the corresponding gene product, we have looked for mutations that specifically inactivate one but not all of its functions and for compensating suppressor mutations in other genes. Here we describe a gene 32 ts mutant that does not produce progeny, but in contrast to an am mutant investigated by others, is capable of some primary and secondary DNA replication and of forming "joint" recombinational intermediates after infection of Escherichia coli B at the restrictive temperature. However, parental and progeny DNA strands are not ligated to covalently linked "recombinant" molecules, and single strands of vegetative DNA do not exceed unit length. Progeny production as well as capacity for covalent linkage in this gene 32 ts mutant are partially restored by additional rII mutations. Suppression by rII depends on functioning host ligase [EC 6.5.1.2; poly(deoxyribonucleotide):poly(deoxyribonucleotide) ligase (AMP-forming, NMN-forming)]. This gene 32 ts mutation (unlike some others) in turn suppresses the characteristic plaque morphology of rII mutants. We conclude that gene 32 protein, in addition to its role in DNA replication and in the formation of "joint" recombinational intermediates, interacts with T4 ligase [EC 6.5.1.1; poly(deoxyribonucleotide):poly(deoxyribonucleotide) ligase (AMP-forming)] when recombining DNA strands are covalently linked. The protein of the mutant that we describe here is mainly defective in this interaction, thus inactivating T4 ligase in recombination. Suppressing rII mutations facilitate substitution of host ligase. There is suggestive evidence that these interactions occur at the membrane.
Deriving covariant holographic entanglement
NASA Astrophysics Data System (ADS)
Dong, Xi; Lewkowycz, Aitor; Rangamani, Mukund
2016-11-01
We provide a gravitational argument in favour of the covariant holographic entanglement entropy proposal. In general time-dependent states, the proposal asserts that the entanglement entropy of a region in the boundary field theory is given by a quarter of the area of a bulk extremal surface in Planck units. The main element of our discussion is an implementation of an appropriate Schwinger-Keldysh contour to obtain the reduced density matrix (and its powers) of a given region, as is relevant for the replica construction. We map this contour into the bulk gravitational theory, and argue that the saddle point solutions of these replica geometries lead to a consistent prescription for computing the field theory Rényi entropies. In the limiting case where the replica index is taken to unity, a local analysis suffices to show that these saddles lead to the extremal surfaces of interest. We also comment on various properties of holographic entanglement that follow from this construction.
Stardust Navigation Covariance Analysis
NASA Technical Reports Server (NTRS)
Menon, Premkumar R.
2000-01-01
The Stardust spacecraft was launched on February 7, 1999 aboard a Boeing Delta-II rocket. Mission participants include the National Aeronautics and Space Administration (NASA), the Jet Propulsion Laboratory (JPL), Lockheed Martin Astronautics (LMA) and the University of Washington. The primary objective of the mission is to collect in-situ samples of the coma of comet Wild-2 and return those samples to the Earth for analysis. Mission design and operational navigation for Stardust is performed by the Jet Propulsion Laboratory (JPL). This paper will describe the extensive JPL effort in support of the Stardust pre-launch analysis of the orbit determination component of the mission covariance study. A description of the mission and it's trajectory will be provided first, followed by a discussion of the covariance procedure and models. Predicted accuracy's will be examined as they relate to navigation delivery requirements for specific critical events during the mission. Stardust was launched into a heliocentric trajectory in early 1999. It will perform an Earth Gravity Assist (EGA) on January 15, 2001 to acquire an orbit for the eventual rendezvous with comet Wild-2. The spacecraft will fly through the coma (atmosphere) on the dayside of Wild-2 on January 2, 2004. At that time samples will be obtained using an aerogel collector. After the comet encounter Stardust will return to Earth when the Sample Return Capsule (SRC) will separate and land at the Utah Test Site (UTTR) on January 15, 2006. The spacecraft will however be deflected off into a heliocentric orbit. The mission is divided into three phases for the covariance analysis. They are 1) Launch to EGA, 2) EGA to Wild-2 encounter and 3) Wild-2 encounter to Earth reentry. Orbit determination assumptions for each phase are provided. These include estimated and consider parameters and their associated a-priori uncertainties. Major perturbations to the trajectory include 19 deterministic and statistical maneuvers
Covariance Manipulation for Conjunction Assessment
NASA Technical Reports Server (NTRS)
Hejduk, M. D.
2016-01-01
Use of probability of collision (Pc) has brought sophistication to CA. Made possible by JSpOC precision catalogue because provides covariance. Has essentially replaced miss distance as basic CA parameter. Embrace of Pc has elevated methods to 'manipulate' covariance to enable/improve CA calculations. Two such methods to be examined here; compensation for absent or unreliable covariances through 'Maximum Pc' calculation constructs, projection (not propagation) of epoch covariances forward in time to try to enable better risk assessments. Two questions to be answered about each; situations to which such approaches are properly applicable, amount of utility that such methods offer.
Gamal El-Dien, Omnia; Ratcliffe, Blaise; Klápště, Jaroslav; Porth, Ilga; Chen, Charles; El-Kassaby, Yousry A.
2016-01-01
The open-pollinated (OP) family testing combines the simplest known progeny evaluation and quantitative genetics analyses as candidates’ offspring are assumed to represent independent half-sib families. The accuracy of genetic parameter estimates is often questioned as the assumption of “half-sibling” in OP families may often be violated. We compared the pedigree- vs. marker-based genetic models by analysing 22-yr height and 30-yr wood density for 214 white spruce [Picea glauca (Moench) Voss] OP families represented by 1694 individuals growing on one site in Quebec, Canada. Assuming half-sibling, the pedigree-based model was limited to estimating the additive genetic variances which, in turn, were grossly overestimated as they were confounded by very minor dominance and major additive-by-additive epistatic genetic variances. In contrast, the implemented genomic pairwise realized relationship models allowed the disentanglement of additive from all nonadditive factors through genetic variance decomposition. The marker-based models produced more realistic narrow-sense heritability estimates and, for the first time, allowed estimating the dominance and epistatic genetic variances from OP testing. In addition, the genomic models showed better prediction accuracies compared to pedigree models and were able to predict individual breeding values for new individuals from untested families, which was not possible using the pedigree-based model. Clearly, the use of marker-based relationship approach is effective in estimating the quantitative genetic parameters of complex traits even under simple and shallow pedigree structure. PMID:26801647
Gamal El-Dien, Omnia; Ratcliffe, Blaise; Klápště, Jaroslav; Porth, Ilga; Chen, Charles; El-Kassaby, Yousry A
2016-01-22
The open-pollinated (OP) family testing combines the simplest known progeny evaluation and quantitative genetics analyses as candidates' offspring are assumed to represent independent half-sib families. The accuracy of genetic parameter estimates is often questioned as the assumption of "half-sibling" in OP families may often be violated. We compared the pedigree- vs. marker-based genetic models by analysing 22-yr height and 30-yr wood density for 214 white spruce [Picea glauca (Moench) Voss] OP families represented by 1694 individuals growing on one site in Quebec, Canada. Assuming half-sibling, the pedigree-based model was limited to estimating the additive genetic variances which, in turn, were grossly overestimated as they were confounded by very minor dominance and major additive-by-additive epistatic genetic variances. In contrast, the implemented genomic pairwise realized relationship models allowed the disentanglement of additive from all nonadditive factors through genetic variance decomposition. The marker-based models produced more realistic narrow-sense heritability estimates and, for the first time, allowed estimating the dominance and epistatic genetic variances from OP testing. In addition, the genomic models showed better prediction accuracies compared to pedigree models and were able to predict individual breeding values for new individuals from untested families, which was not possible using the pedigree-based model. Clearly, the use of marker-based relationship approach is effective in estimating the quantitative genetic parameters of complex traits even under simple and shallow pedigree structure.
Covariant harmonic oscillators: 1973 revisited
NASA Technical Reports Server (NTRS)
Noz, M. E.
1993-01-01
Using the relativistic harmonic oscillator, a physical basis is given to the phenomenological wave function of Yukawa which is covariant and normalizable. It is shown that this wave function can be interpreted in terms of the unitary irreducible representations of the Poincare group. The transformation properties of these covariant wave functions are also demonstrated.
Covariance hypotheses for LANDSAT data
NASA Technical Reports Server (NTRS)
Decell, H. P.; Peters, C.
1983-01-01
Two covariance hypotheses are considered for LANDSAT data acquired by sampling fields, one an autoregressive covariance structure and the other the hypothesis of exchangeability. A minimum entropy approximation of the first structure by the second is derived and shown to have desirable properties for incorporation into a mixture density estimation procedure. Results of a rough test of the exchangeability hypothesis are presented.
Hoffmann, A.A.; Parsons, P.A. )
1989-08-01
Previously we found that Drosophila melanogaster lines selected for increased desiccation resistance have lowered metabolic rate and behavioral activity levels, and show correlated responses for resistance to starvation and a toxic ethanol level. These results were consistent with a prediction that increased resistance to many environmental stresses may be genetically correlated because of a reduction in metabolic energy expenditure. Here we present experiments on the genetic basis of the selection response and extend the study of correlated responses to other stresses. The response to selection was not sex-specific and involved X-linked and autosomal genes acting additively. Activity differences contributed little to differences in desiccation resistance between selected and control lines. Selected lines had lower metabolic rates than controls in darkness when activity was inhibited. Adults from selected lines showed increased resistance to a heat shock, {sup 60}Co-gamma-radiation, and acute ethanol and acetic acid stress. The desiccation, ethanol and starvation resistance of isofemale lines set up from the F2s of a cross between one of the selected and one of the control lines were correlated. Selected and control lines did not differ in ether-extractable lipid content or in resistance to acetone, ether or a cold shock.
David, Dezsö; Ribeiro, Sofia; Ferrão, Lénia; Gago, Teresa; Crespo, Francisco
2004-06-01
Antithrombin (AT), the most important coagulation serine proteases inhibitor, plays an important role in maintaining the hemostatic balance. Inherited AT deficiency, mainly characterized by predisposition to recurrent venous thromboembolism, is transmitted in an autosomal dominant manner. In this study, we analyzed the underlying genetic alterations in 12 unrelated Portuguese thrombophilic families with AT deficiency. At the same time, the modulating effect of the FV Leiden mutation, PT 20210A, PAI-1 4G, and MTHFR 677T allelic variants, on the thrombotic risk of AT deficient patients was also evaluated. Three novel frameshift alterations, a 4-bp deletion in exon 4 and two 1-bp insertions in exon 6, were identified in six unrelated type I AT deficient families. A novel missense mutation in exon 3a, which changes the highly conserved F147 residue, and a novel splice site mutation in the invariant acceptor AG dinucleotide of intron 2 were also identified in unrelated type I AT deficient families. In addition to these, two previously reported missense mutations changing the AT reactive site bond (R393-S394) and leading to type II-RS deficiency, and a previously reported cryptic splice site mutation (IVS4-14G-->A), were also identified. In these families, increased thrombotic risk associated with co-inheritance of the FV Leiden mutation and of the PAI-1 4G variant was also observed. In conclusion, we present the first data regarding the underlying genetic alterations in Portuguese thrombophilic families with AT deficiency, and confirm that the FV Leiden mutation and probably the PAI-1 4G variant represent additional thrombotic risk factors in these families.
2010-01-01
Background Hypertriglyceridemia (HTG) is a well-established independent risk factor for cardiovascular disease and the influence of several genetic variants in genes related with triglyceride (TG) metabolism has been described, including LPL, APOA5 and APOE. The combined analysis of these polymorphisms could produce clinically meaningful complementary information. Methods A subgroup of the ICARIA study comprising 1825 Spanish subjects (80% men, mean age 36 years) was genotyped for the LPL-HindIII (rs320), S447X (rs328), D9N (rs1801177) and N291S (rs268) polymorphisms, the APOA5-S19W (rs3135506) and -1131T/C (rs662799) variants, and the APOE polymorphism (rs429358; rs7412) using PCR and restriction analysis and TaqMan assays. We used regression analyses to examine their combined effects on TG levels (with the log-transformed variable) and the association of variant combinations with TG levels and hypertriglyceridemia (TG ≥ 1.69 mmol/L), including the covariates: gender, age, waist circumference, blood glucose, blood pressure, smoking and alcohol consumption. Results We found a significant lowering effect of the LPL-HindIII and S447X polymorphisms (p < 0.0001). In addition, the D9N, N291S, S19W and -1131T/C variants and the APOE-ε4 allele were significantly associated with an independent additive TG-raising effect (p < 0.05, p < 0.01, p < 0.001, p < 0.0001 and p < 0.001, respectively). Grouping individuals according to the presence of TG-lowering or TG-raising polymorphisms showed significant differences in TG levels (p < 0.0001), with the lowest levels exhibited by carriers of two lowering variants (10.2% reduction in TG geometric mean with respect to individuals who were homozygous for the frequent alleles of all the variants), and the highest levels in carriers of raising combinations (25.1% mean TG increase). Thus, carrying two lowering variants was protective against HTG (OR = 0.62; 95% CI, 0.39-0.98; p = 0.042) and having one single raising polymorphism (OR
Covariance Models for Hydrological Applications
NASA Astrophysics Data System (ADS)
Hristopulos, Dionissios
2014-05-01
This methodological contribution aims to present some new covariance models with applications in the stochastic analysis of hydrological processes. More specifically, we present explicit expressions for radially symmetric, non-differentiable, Spartan covariance functions in one, two, and three dimensions. The Spartan covariance parameters include a characteristic length, an amplitude coefficient, and a rigidity coefficient which determines the shape of the covariance function. Different expressions are obtained depending on the value of the rigidity coefficient and the dimensionality. If the value of the rigidity coefficient is much larger than one, the Spartan covariance function exhibits multiscaling. Spartan covariance models are more flexible than the classical geostatatistical models (e.g., spherical, exponential). Their non-differentiability makes them suitable for modelling the properties of geological media. We also present a family of radially symmetric, infinitely differentiable Bessel-Lommel covariance functions which are valid in any dimension. These models involve combinations of Bessel and Lommel functions. They provide a generalization of the J-Bessel covariance function, and they can be used to model smooth processes with an oscillatory decay of correlations. We discuss the dependence of the integral range of the Spartan and Bessel-Lommel covariance functions on the parameters. We point out that the dependence is not uniquely specified by the characteristic length, unlike the classical geostatistical models. Finally, we define and discuss the use of the generalized spectrum for characterizing different correlation length scales; the spectrum is defined in terms of an exponent α. We show that the spectrum values obtained for exponent values less than one can be used to discriminate between mean-square continuous but non-differentiable random fields. References [1] D. T. Hristopulos and S. Elogne, 2007. Analytic properties and covariance functions of
... Inheritance; Heterozygous; Inheritance patterns; Heredity and disease; Heritable; Genetic markers ... The chromosomes are made up of strands of genetic information called DNA. Each chromosome contains sections of ...
Hawking radiation and covariant anomalies
Banerjee, Rabin; Kulkarni, Shailesh
2008-01-15
Generalizing the method of Wilczek and collaborators we provide a derivation of Hawking radiation from charged black holes using only covariant gauge and gravitational anomalies. The reliability and universality of the anomaly cancellation approach to Hawking radiation is also discussed.
A New Approach for Nuclear Data Covariance and Sensitivity Generation
Leal, L.C.; Larson, N.M.; Derrien, H.; Kawano, T.; Chadwick, M.B.
2005-05-24
Covariance data are required to correctly assess uncertainties in design parameters in nuclear applications. The error estimation of calculated quantities relies on the nuclear data uncertainty information available in the basic nuclear data libraries, such as the U.S. Evaluated Nuclear Data File, ENDF/B. The uncertainty files in the ENDF/B library are obtained from the analysis of experimental data and are stored as variance and covariance data. The computer code SAMMY is used in the analysis of the experimental data in the resolved and unresolved resonance energy regions. The data fitting of cross sections is based on generalized least-squares formalism (Bayes' theory) together with the resonance formalism described by R-matrix theory. Two approaches are used in SAMMY for the generation of resonance-parameter covariance data. In the evaluation process SAMMY generates a set of resonance parameters that fit the data, and, in addition, it also provides the resonance-parameter covariances. For existing resonance-parameter evaluations where no resonance-parameter covariance data are available, the alternative is to use an approach called the 'retroactive' resonance-parameter covariance generation. In the high-energy region the methodology for generating covariance data consists of least-squares fitting and model parameter adjustment. The least-squares fitting method calculates covariances directly from experimental data. The parameter adjustment method employs a nuclear model calculation such as the optical model and the Hauser-Feshbach model, and estimates a covariance for the nuclear model parameters. In this paper we describe the application of the retroactive method and the parameter adjustment method to generate covariance data for the gadolinium isotopes.
Covariance Generation Using CONRAD and SAMMY Computer Codes
Leal, Luiz C; Derrien, Herve; De Saint Jean, C; Noguere, G; Ruggieri, J M
2009-01-01
Covariance generation in the resolved resonance region can be generated using the computer codes CONRAD and SAMMY. These codes use formalisms derived from the R-matrix methodology together with the generalized least squares technique to obtain resonance parameter. In addition, resonance parameter covariance is also obtained. Results of covariance calculations for a simple case of the s-wave resonance parameters of 48Ti in the energy region 10-5 eV to 300 keV are compared. The retroactive approach included in CONRAD and SAMMY was used.
RNA sequence analysis using covariance models.
Eddy, S R; Durbin, R
1994-01-01
We describe a general approach to several RNA sequence analysis problems using probabilistic models that flexibly describe the secondary structure and primary sequence consensus of an RNA sequence family. We call these models 'covariance models'. A covariance model of tRNA sequences is an extremely sensitive and discriminative tool for searching for additional tRNAs and tRNA-related sequences in sequence databases. A model can be built automatically from an existing sequence alignment. We also describe an algorithm for learning a model and hence a consensus secondary structure from initially unaligned example sequences and no prior structural information. Models trained on unaligned tRNA examples correctly predict tRNA secondary structure and produce high-quality multiple alignments. The approach may be applied to any family of small RNA sequences. Images PMID:8029015
Bottleneck Effects on Genetic Variance for Courtship Repertoire
Meffert, L. M.
1995-01-01
Bottleneck effects on evolutionary potential in mating behavior were addressed through assays of additive genetic variances and resulting phenotypic responses to drift in the courtship repertoires of six two-pair founder-flush lines and two control populations of the housefly. A simulation addressed the complication that an estimate of the genetic variance for a courtship trait (e.g., male performance vigor or the female requirement for copulation) must involve assays against the background behavior of the mating partners. The additive ``environmental'' effect of the mating partner's phenotype simply dilutes the net parent-offspring covariance for a trait. However, if there is an interaction with this ``environmental'' component, negative parent-offspring covariances can result under conditions of high incompatibility between the population's distributions for male performance and female choice requirements, despite high levels of genetic variance. All six bottlenecked lines exhibited significant differentiation from the controls in at least one measure of the parent-offspring covariance for male performance or female choice (estimated by 50 parent-son and 50 parent-daughter covariances for 10 courtship traits per line) which translated to significant phenotypic drift. However, the average effect across traits or across lines did not yield a significant net increase in genetic variance due to bottlenecks. Concerted phenotypic differentiation due to the founder-flush event provided indirect evidence of directional dominance in a subset of traits. Furthermore, indirect evidence of genotype-environment interactions (potentially producing genotype-genotype effects) was found in the negative parent-offspring covariances predicted by the male-female interaction simulation and by the association of the magnitude of phenotypic drift with the absolute value of the parent-offspring covariance. Hence, nonadditive genetic effects on mating behavior may be important in
Covariate-free and Covariate-dependent Reliability.
Bentler, Peter M
2016-12-01
Classical test theory reliability coefficients are said to be population specific. Reliability generalization, a meta-analysis method, is the main procedure for evaluating the stability of reliability coefficients across populations. A new approach is developed to evaluate the degree of invariance of reliability coefficients to population characteristics. Factor or common variance of a reliability measure is partitioned into parts that are, and are not, influenced by control variables, resulting in a partition of reliability into a covariate-dependent and a covariate-free part. The approach can be implemented in a single sample and can be applied to a variety of reliability coefficients.
Levy Matrices and Financial Covariances
NASA Astrophysics Data System (ADS)
Burda, Zdzislaw; Jurkiewicz, Jerzy; Nowak, Maciej A.; Papp, Gabor; Zahed, Ismail
2003-10-01
In a given market, financial covariances capture the intra-stock correlations and can be used to address statistically the bulk nature of the market as a complex system. We provide a statistical analysis of three SP500 covariances with evidence for raw tail distributions. We study the stability of these tails against reshuffling for the SP500 data and show that the covariance with the strongest tails is robust, with a spectral density in remarkable agreement with random Lévy matrix theory. We study the inverse participation ratio for the three covariances. The strong localization observed at both ends of the spectral density is analogous to the localization exhibited in the random Lévy matrix ensemble. We discuss two competitive mechanisms responsible for the occurrence of an extensive and delocalized eigenvalue at the edge of the spectrum: (a) the Lévy character of the entries of the correlation matrix and (b) a sort of off-diagonal order induced by underlying inter-stock correlations. (b) can be destroyed by reshuffling, while (a) cannot. We show that the stocks with the largest scattering are the least susceptible to correlations, and likely candidates for the localized states. We introduce a simple model for price fluctuations which captures behavior of the SP500 covariances. It may be of importance for assets diversification.
Jamei, Masoud; Dickinson, Gemma L; Rostami-Hodjegan, Amin
2009-01-01
An increasing number of failures in clinical stages of drug development have been related to the effects of candidate drugs in a sub-group of patients rather than the 'average' person. Expectation of extreme effects or lack of therapeutic effects in some subgroups following administration of similar doses requires a full understanding of the issue of variability and the importance of identifying covariates that determine the exposure to the drug candidates in each individual. In any drug development program the earlier these covariates are known the better. An important component of the drive to decrease this failure rate in drug development involves attempts to use physiologically-based pharmacokinetics 'bottom-up' modeling and simulation to optimize molecular features with respect to the absorption, distribution, metabolism and elimination (ADME) processes. The key element of this approach is the separation of information on the system (i.e. human body) from that of the drug (e.g. physicochemical characteristics determining permeability through membranes, partitioning to tissues, binding to plasma proteins or affinities toward certain enzymes and transporter proteins) and the study design (e.g. dose, route and frequency of administration, concomitant drugs and food). In this review, the classical 'top-down' approach in covariate recognition is compared with the 'bottom-up' paradigm. The determinants and sources of inter-individual variability in different stages of drug absorption, distribution, metabolism and excretion are discussed in detail. Further, the commonly known tools for simulating ADME properties are introduced.
Technology Transfer Automated Retrieval System (TEKTRAN)
Streptococcus (S.) iniae and S. agalactiae are both economically important Gram positive bacterial pathogens affecting the globally farmed tilapia (Oreochromis spp.). Historically control of these bacteria in tilapia culture has included biosecurity, therapeutants and vaccination strategies. Genet...
Palmer, Rohan H C; Nugent, Nicole R; Brick, Leslie A; Bidwell, Cinnamon L; McGeary, John E; Keller, Matthew C; Knopik, Valerie S
2016-06-01
Exposure to traumatic experiences is associated with an increased risk for drug dependence and poorer response to substance abuse treatment (Claus & Kindleberger, 2002; Jaycox, Ebener, Damesek, & Becker, 2004). Despite this evidence, the reasons for the observed associations of trauma and the general tendency to be dependent upon drugs of abuse remain unclear. Data (N = 2,596) from the Study of Addiction: Genetics and Environment were used to analyze (a) the degree to which commonly occurring single nucleotide polymorphisms (SNPs; minor allele frequency > 1%) in the human genome explains exposure to interpersonal traumatic experiences, and (b) the extent to which additive genetic effects on trauma are shared with additive genetic effects on drug dependence. Our results suggested moderate additive genetic influences on interpersonal trauma, h(2) SNP-Interpersonal = .47, 95% confidence interval (CI) [.10, .85], that are partially shared with additive genetic effects on generalized vulnerability to drug dependence, h(2) SNP-DD = .36, 95% CI [.11, .61]; rG-SNP = .49, 95% CI [.02, .96]. Although the design/technique does not exclude the possibility that substance abuse causally increases risk for traumatic experiences (or vice versa), these findings raise the possibility that commonly occurring SNPs influence both the general tendency towards drug dependence and interpersonal trauma.
Holmans, Peter; Moskvina, Valentina; Jones, Lesley; Sharma, Manu; Vedernikov, Alexey; Buchel, Finja; Saad, Mohamad; Sadd, Mohamad; Bras, Jose M; Bettella, Francesco; Nicolaou, Nayia; Simón-Sánchez, Javier; Mittag, Florian; Gibbs, J Raphael; Schulte, Claudia; Durr, Alexandra; Guerreiro, Rita; Hernandez, Dena; Brice, Alexis; Stefánsson, Hreinn; Majamaa, Kari; Gasser, Thomas; Heutink, Peter; Wood, Nicholas W; Martinez, Maria; Singleton, Andrew B; Nalls, Michael A; Hardy, John; Morris, Huw R; Williams, Nigel M
2013-03-01
Parkinson's disease (PD) is the second most common neurodegenerative disease affecting 1-2% in people >60 and 3-4% in people >80. Genome-wide association (GWA) studies have now implicated significant evidence for association in at least 18 genomic regions. We have studied a large PD-meta analysis and identified a significant excess of SNPs (P < 1 × 10(-16)) that are associated with PD but fall short of the genome-wide significance threshold. This result was independent of variants at the 18 previously implicated regions and implies the presence of additional polygenic risk alleles. To understand how these loci increase risk of PD, we applied a pathway-based analysis, testing for biological functions that were significantly enriched for genes containing variants associated with PD. Analysing two independent GWA studies, we identified that both had a significant excess in the number of functional categories enriched for PD-associated genes (minimum P = 0.014 and P = 0.006, respectively). Moreover, 58 categories were significantly enriched for associated genes in both GWA studies (P < 0.001), implicating genes involved in the 'regulation of leucocyte/lymphocyte activity' and also 'cytokine-mediated signalling' as conferring an increased susceptibility to PD. These results were unaltered by the exclusion of all 178 genes that were present at the 18 genomic regions previously reported to be strongly associated with PD (including the HLA locus). Our findings, therefore, provide independent support to the strong association signal at the HLA locus and imply that the immune-related genetic susceptibility to PD is likely to be more widespread in the genome than previously appreciated.
Covariance expressions for eigenvalue and eigenvector problems
NASA Astrophysics Data System (ADS)
Liounis, Andrew J.
There are a number of important scientific and engineering problems whose solutions take the form of an eigenvalue--eigenvector problem. Some notable examples include solutions to linear systems of ordinary differential equations, controllability of linear systems, finite element analysis, chemical kinetics, fitting ellipses to noisy data, and optimal estimation of attitude from unit vectors. In many of these problems, having knowledge of the eigenvalue and eigenvector Jacobians is either necessary or is nearly as important as having the solution itself. For instance, Jacobians are necessary to find the uncertainty in a computed eigenvalue or eigenvector estimate. This uncertainty, which is usually represented as a covariance matrix, has been well studied for problems similar to the eigenvalue and eigenvector problem, such as singular value decomposition. There has been substantially less research on the covariance of an optimal estimate originating from an eigenvalue-eigenvector problem. In this thesis we develop two general expressions for the Jacobians of eigenvalues and eigenvectors with respect to the elements of their parent matrix. The expressions developed make use of only the parent matrix and the eigenvalue and eigenvector pair under consideration. In addition, they are applicable to any general matrix (including complex valued matrices, eigenvalues, and eigenvectors) as long as the eigenvalues are simple. Alongside this, we develop expressions that determine the uncertainty in a vector estimate obtained from an eigenvalue-eigenvector problem given the uncertainty of the terms of the matrix. The Jacobian expressions developed are numerically validated with forward finite, differencing and the covariance expressions are validated using Monte Carlo analysis. Finally, the results from this work are used to determine covariance expressions for a variety of estimation problem examples and are also applied to the design of a dynamical system.
Gaskins, J T; Daniels, M J
2016-01-02
The estimation of the covariance matrix is a key concern in the analysis of longitudinal data. When data consists of multiple groups, it is often assumed the covariance matrices are either equal across groups or are completely distinct. We seek methodology to allow borrowing of strength across potentially similar groups to improve estimation. To that end, we introduce a covariance partition prior which proposes a partition of the groups at each measurement time. Groups in the same set of the partition share dependence parameters for the distribution of the current measurement given the preceding ones, and the sequence of partitions is modeled as a Markov chain to encourage similar structure at nearby measurement times. This approach additionally encourages a lower-dimensional structure of the covariance matrices by shrinking the parameters of the Cholesky decomposition toward zero. We demonstrate the performance of our model through two simulation studies and the analysis of data from a depression study. This article includes Supplementary Material available online.
A class of covariate-dependent spatiotemporal covariance functions.
Reich, Brian J; Eidsvik, Jo; Guindani, Michele; Nail, Amy J; Schmidt, Alexandra M
2011-12-01
In geostatistics, it is common to model spatially distributed phenomena through an underlying stationary and isotropic spatial process. However, these assumptions are often untenable in practice because of the influence of local effects in the correlation structure. Therefore, it has been of prolonged interest in the literature to provide flexible and effective ways to model non-stationarity in the spatial effects. Arguably, due to the local nature of the problem, we might envision that the correlation structure would be highly dependent on local characteristics of the domain of study, namely the latitude, longitude and altitude of the observation sites, as well as other locally defined covariate information. In this work, we provide a flexible and computationally feasible way for allowing the correlation structure of the underlying processes to depend on local covariate information. We discuss the properties of the induced covariance functions and discuss methods to assess its dependence on local covariate information by means of a simulation study and the analysis of data observed at ozone-monitoring stations in the Southeast United States.
Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices.
Cai, Tony; Ma, Zongming; Wu, Yihong
2015-04-01
This paper considers a sparse spiked covariancematrix model in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minimax rank detection. The optimal rate of convergence for estimating the spiked covariance matrix under the spectral norm is established, which requires significantly different techniques from those for estimating other structured covariance matrices such as bandable or sparse covariance matrices. We also establish the minimax rate under the spectral norm for estimating the principal subspace, the primary object of interest in principal component analysis. In addition, the optimal rate for the rank detection boundary is obtained. This result also resolves the gap in a recent paper by Berthet and Rigollet [2] where the special case of rank one is considered.
Progress of Covariance Evaluation at the China Nuclear Data Center
Xu, R.; Zhang, Q.; Zhang, Y.; Liu, T.; Ge, Z.; Lu, H.; Sun, Z.; Yu, B.; Tang, G.
2015-01-15
Covariance evaluations at the China Nuclear Data Center focus on the cross sections of structural materials and actinides in the fast neutron energy range. In addition to the well-known Least-squares approach, a method based on the analysis of the sources of experimental uncertainties is especially introduced to generate a covariance matrix for a particular reaction for which multiple measurements are available. The scheme of the covariance evaluation flow is presented, and an example of n+{sup 90}Zr is given to illuminate the whole procedure. It is proven that the accuracy of measurements can be properly incorporated into the covariance and the long-standing small uncertainty problem can be avoided.
Welch, Allison M; Smith, Michael J; Gerhardt, H Carl
2014-06-01
Genetic variation in sexual displays is crucial for an evolutionary response to sexual selection, but can be eroded by strong selection. Identifying the magnitude and sources of additive genetic variance underlying sexually selected traits is thus an important issue in evolutionary biology. We conducted a quantitative genetics experiment with gray treefrogs (Hyla versicolor) to investigate genetic variances and covariances among features of the male advertisement call. Two energetically expensive traits showed significant genetic variation: call duration, expressed as number of pulses per call, and call rate, represented by its inverse, call period. These two properties also showed significant genetic covariance, consistent with an energetic constraint to call production. Combining the genetic variance-covariance matrix with previous estimates of directional sexual selection imposed by female preferences predicts a limited increase in call duration but no change in call rate despite significant selection on both traits. In addition to constraints imposed by the genetic covariance structure, an evolutionary response to sexual selection may also be limited by high energetic costs of long-duration calls and by preferences that act most strongly against very short-duration calls. Meanwhile, the persistence of these preferences could be explained by costs of mating with males with especially unattractive calls.
Covariation Neglect among Novice Investors
ERIC Educational Resources Information Center
Hedesstrom, Ted Martin; Svedsater, Henrik; Garling, Tommy
2006-01-01
In 4 experiments, undergraduates made hypothetical investment choices. In Experiment 1, participants paid more attention to the volatility of individual assets than to the volatility of aggregated portfolios. The results of Experiment 2 show that most participants diversified even when this increased risk because of covariation between the returns…
Condition Number Regularized Covariance Estimation.
Won, Joong-Ho; Lim, Johan; Kim, Seung-Jean; Rajaratnam, Bala
2013-06-01
Estimation of high-dimensional covariance matrices is known to be a difficult problem, has many applications, and is of current interest to the larger statistics community. In many applications including so-called the "large p small n" setting, the estimate of the covariance matrix is required to be not only invertible, but also well-conditioned. Although many regularization schemes attempt to do this, none of them address the ill-conditioning problem directly. In this paper, we propose a maximum likelihood approach, with the direct goal of obtaining a well-conditioned estimator. No sparsity assumption on either the covariance matrix or its inverse are are imposed, thus making our procedure more widely applicable. We demonstrate that the proposed regularization scheme is computationally efficient, yields a type of Steinian shrinkage estimator, and has a natural Bayesian interpretation. We investigate the theoretical properties of the regularized covariance estimator comprehensively, including its regularization path, and proceed to develop an approach that adaptively determines the level of regularization that is required. Finally, we demonstrate the performance of the regularized estimator in decision-theoretic comparisons and in the financial portfolio optimization setting. The proposed approach has desirable properties, and can serve as a competitive procedure, especially when the sample size is small and when a well-conditioned estimator is required.
Technology Transfer Automated Retrieval System (TEKTRAN)
An additive genetic risk score (GRS) for coronary heart disease (CHD) has previously been associated with incident CHD in the population-based Greek European Prospective Investigation into Cancer and nutrition (EPIC) cohort. In this study, we explore GRS-‘environment’ joint actions on CHD for severa...
Propensity score methods and unobserved covariate imbalance: comments on "squeezing the balloon".
Ali, M Sanni; Groenwold, Rolf H H; Klungel, Olaf H
2014-06-01
In their recent Health Services Research article titled "Squeezing the Balloon: Propensity Scores and Unmeasured Covariate Balance," Brooks and Ohsfeldt (2013) addressed an important topic on the balancing property of the propensity score (PS) with respect to unmeasured covariates. They concluded that PS methods that balance measured covariates between treated and untreated subjects exacerbate imbalance in unmeasured covariates that are unrelated to measured covariates. Furthermore, they emphasized that for PS algorithms, an imbalance on unmeasured covariates between treatment and untreated subjects is a necessary condition to achieve balance on measured covariates between the groups. We argue that these conclusions are the results of their assumptions on the mechanism of treatment allocation. In addition, we discuss the underlying assumptions of PS methods, their advantages compared with multivariate regression methods, as well as the interpretation of the effect estimates from PS methods.
AFCI-2.0 Neutron Cross Section Covariance Library
Herman, M.; Herman, M; Oblozinsky, P.; Mattoon, C.M.; Pigni, M.; Hoblit, S.; Mughabghab, S.F.; Sonzogni, A.; Talou, P.; Chadwick, M.B.; Hale, G.M.; Kahler, A.C.; Kawano, T.; Little, R.C.; Yount, P.G.
2011-03-01
materials and fission products, and 20 actinides. Covariances are given in 33-energy groups, from 10?5 eV to 19.6 MeV, obtained by processing with LANL processing code NJOY using 1/E flux. In addition to these 110 files, the library contains 20 files with nu-bar covariances, 3 files with covariances of prompt fission neutron spectra (238,239,240-Pu), and 2 files with mu-bar covariances (23-Na, 56-Fe). Over the period of three years several working versions of the library have been released and tested by ANL and INL reactor analysts. Useful feedback has been collected allowing gradual improvements of the library. In addition, QA system was developed to check basic properties and features of the whole library, allowing visual inspection of uncertainty and correlations plots, inspection of uncertainties of integral quantities with independent databases, and dispersion of cross sections between major evaluated libraries. The COMMARA-2.0 beta version of the library was released to ANL and INL reactor analysts in October 2010. The final version, described in the present report, was released in March 2011.
Modelling the random effects covariance matrix in longitudinal data.
Daniels, Michael J; Zhao, Yan D
2003-05-30
A common class of models for longitudinal data are random effects (mixed) models. In these models, the random effects covariance matrix is typically assumed constant across subject. However, in many situations this matrix may differ by measured covariates. In this paper, we propose an approach to model the random effects covariance matrix by using a special Cholesky decomposition of the matrix. In particular, we will allow the parameters that result from this decomposition to depend on subject-specific covariates and also explore ways to parsimoniously model these parameters. An advantage of this parameterization is that there is no concern about the positive definiteness of the resulting estimator of the covariance matrix. In addition, the parameters resulting from this decomposition have a sensible interpretation. We propose fully Bayesian modelling for which a simple Gibbs sampler can be implemented to sample from the posterior distribution of the parameters. We illustrate these models on data from depression studies and examine the impact of heterogeneity in the covariance matrix on estimation of both fixed and random effects.
NASA Astrophysics Data System (ADS)
Agilan, V.; Umamahesh, N. V.
2017-03-01
Present infrastructure design is primarily based on rainfall Intensity-Duration-Frequency (IDF) curves with so-called stationary assumption. However, in recent years, the extreme precipitation events are increasing due to global climate change and creating non-stationarity in the series. Based on recent theoretical developments in the Extreme Value Theory (EVT), recent studies proposed a methodology for developing non-stationary rainfall IDF curve by incorporating trend in the parameters of the Generalized Extreme Value (GEV) distribution using Time covariate. But, the covariate Time may not be the best covariate and it is important to analyze all possible covariates and find the best covariate to model non-stationarity. In this study, five physical processes, namely, urbanization, local temperature changes, global warming, El Niño-Southern Oscillation (ENSO) cycle and Indian Ocean Dipole (IOD) are used as covariates. Based on these five covariates and their possible combinations, sixty-two non-stationary GEV models are constructed. In addition, two non-stationary GEV models based on Time covariate and one stationary GEV model are also constructed. The best model for each duration rainfall series is chosen based on the corrected Akaike Information Criterion (AICc). From the findings of this study, it is observed that the local processes (i.e., Urbanization, local temperature changes) are the best covariate for short duration rainfall and global processes (i.e., Global warming, ENSO cycle and IOD) are the best covariate for the long duration rainfall of the Hyderabad city, India. Furthermore, the covariate Time is never qualified as the best covariate. In addition, the identified best covariates are further used to develop non-stationary rainfall IDF curves of the Hyderabad city. The proposed methodology can be applied to other situations to develop the non-stationary IDF curves based on the best covariate.
ERIC Educational Resources Information Center
Moeyaert, Mariola; Ugille, Maaike; Ferron, John M.; Beretvas, S. Natasha; Van den Noortgate, Wim
2016-01-01
The impact of misspecifying covariance matrices at the second and third levels of the three-level model is evaluated. Results indicate that ignoring existing covariance has no effect on the treatment effect estimate. In addition, the between-case variance estimates are unbiased when covariance is either modeled or ignored. If the research interest…
Understanding covariate shift in model performance
McGaughey, Georgia; Walters, W. Patrick; Goldman, Brian
2016-01-01
Three (3) different methods (logistic regression, covariate shift and k-NN) were applied to five (5) internal datasets and one (1) external, publically available dataset where covariate shift existed. In all cases, k-NN’s performance was inferior to either logistic regression or covariate shift. Surprisingly, there was no obvious advantage for using covariate shift to reweight the training data in the examined datasets. PMID:27803797
Disintegrating the fly: A mutational perspective on phenotypic integration and covariation.
Haber, Annat; Dworkin, Ian
2017-01-01
The structure of environmentally induced phenotypic covariation can influence the effective strength and magnitude of natural selection. Yet our understanding of the factors that contribute to and influence the evolutionary lability of such covariation is poor. Most studies have either examined environmental variation without accounting for covariation, or examined phenotypic and genetic covariation without distinguishing the environmental component. In this study, we examined the effect of mutational perturbations on different properties of environmental covariation, as well as mean shape. We use strains of Drosophila melanogaster bearing well-characterized mutations known to influence wing shape, as well as naturally derived strains, all reared under carefully controlled conditions and with the same genetic background. We find that mean shape changes more freely than the covariance structure, and that different properties of the covariance matrix change independently from each other. The perturbations affect matrix orientation more than they affect matrix eccentricity or total variance. Yet, mutational effects on matrix orientation do not cluster according to the developmental pathway that they target. These results suggest that it might be useful to consider a more general concept of "decanalization," involving all aspects of variation and covariation.
Covariance Modifications to Subspace Bases
Harris, D B
2008-11-19
Adaptive signal processing algorithms that rely upon representations of signal and noise subspaces often require updates to those representations when new data become available. Subspace representations frequently are estimated from available data with singular value (SVD) decompositions. Subspace updates require modifications to these decompositions. Updates can be performed inexpensively provided they are low-rank. A substantial literature on SVD updates exists, frequently focusing on rank-1 updates (see e.g. [Karasalo, 1986; Comon and Golub, 1990, Badeau, 2004]). In these methods, data matrices are modified by addition or deletion of a row or column, or data covariance matrices are modified by addition of the outer product of a new vector. A recent paper by Brand [2006] provides a general and efficient method for arbitrary rank updates to an SVD. The purpose of this note is to describe a closely-related method for applications where right singular vectors are not required. This note also describes the SVD updates to a particular scenario of interest in seismic array signal processing. The particular application involve updating the wideband subspace representation used in seismic subspace detectors [Harris, 2006]. These subspace detectors generalize waveform correlation algorithms to detect signals that lie in a subspace of waveforms of dimension d {ge} 1. They potentially are of interest because they extend the range of waveform variation over which these sensitive detectors apply. Subspace detectors operate by projecting waveform data from a detection window into a subspace specified by a collection of orthonormal waveform basis vectors (referred to as the template). Subspace templates are constructed from a suite of normalized, aligned master event waveforms that may be acquired by a single sensor, a three-component sensor, an array of such sensors or a sensor network. The template design process entails constructing a data matrix whose columns contain the
Are Maxwell's equations Lorentz-covariant?
NASA Astrophysics Data System (ADS)
Redžić, D. V.
2017-01-01
It is stated in many textbooks that Maxwell's equations are manifestly covariant when written down in tensorial form. We recall that tensorial form of Maxwell's equations does not secure their tensorial contents; they become covariant by postulating certain transformation properties of field functions. That fact should be stressed when teaching about the covariance of Maxwell's equations.
Lorentz-covariant dissipative Lagrangian systems
NASA Technical Reports Server (NTRS)
Kaufman, A. N.
1985-01-01
The concept of dissipative Hamiltonian system is converted to Lorentz-covariant form, with evolution generated jointly by two scalar functionals, the Lagrangian action and the global entropy. A bracket formulation yields the local covariant laws of energy-momentum conservation and of entropy production. The formalism is illustrated by a derivation of the covariant Landau kinetic equation.
A covariant Fokker-Planck equation for a simple gas from relativistic kinetic theory
Chacon-Acosta, Guillermo; Dagdug, Leonardo; Morales-Tecotl, Hugo A.
2010-12-14
A manifestly covariant Fokker-Planck differential equation is derived for the case of a relativistic simple gas by taking a small momentum transfer approximation within the collision integral of the relativistic Boltzmann equation. We follow closely previous work, with the main difference that we keep manifest covariance at every stage of the analysis. In addition, we use the covariant Juettner distribution function to find a relativistic generalization of the Einstein's fluctuation-dissipation relation.
Covariance Evaluation Methodology for Neutron Cross Sections
Herman,M.; Arcilla, R.; Mattoon, C.M.; Mughabghab, S.F.; Oblozinsky, P.; Pigni, M.; Pritychenko, b.; Songzoni, A.A.
2008-09-01
We present the NNDC-BNL methodology for estimating neutron cross section covariances in thermal, resolved resonance, unresolved resonance and fast neutron regions. The three key elements of the methodology are Atlas of Neutron Resonances, nuclear reaction code EMPIRE, and the Bayesian code implementing Kalman filter concept. The covariance data processing, visualization and distribution capabilities are integral components of the NNDC methodology. We illustrate its application on examples including relatively detailed evaluation of covariances for two individual nuclei and massive production of simple covariance estimates for 307 materials. Certain peculiarities regarding evaluation of covariances for resolved resonances and the consistency between resonance parameter uncertainties and thermal cross section uncertainties are also discussed.
Livshits, G; Yakovenko, K; Ginsburg, E; Kobyliansky, E
1998-01-01
The present study utilized pedigree data from three ethnically different populations of Kirghizstan, Turkmenia and Chuvasha. Principal component analysis was performed on a matrix of genetic correlations between 22 measures of adiposity, including skinfolds, circumferences and indices. Findings are summarized as follows: (1) All three genetic matrices were not positive definite and the first four factors retained even after exclusion RG > or = 1.0, explained from 88% to 97% of the total additive genetic variation in the 22 trials studied. This clearly emphasizes the massive involvement of pleiotropic gene effects in the variability of adiposity traits. (2) Despite the quite natural differences in pairwise correlations between the adiposity traits in the three ethnically different samples under study, factor analysis revealed a common basic pattern of covariability for the adiposity traits. In each of the three samples, four genetic factors were retained, namely, the amount of subcutaneous fat, the total body obesity, the pattern of distribution of subcutaneous fat and the central adiposity distribution. (3) Genetic correlations between the retained four factors were virtually non-existent, suggesting that several independent genetic sources may be governing the variation of adiposity traits. (4) Variance decomposition analysis on the obtained genetic factors leaves no doubt regarding the substantial familial and (most probably genetic) effects on variation of each factor in each studied population. The similarity of results in the three different samples indicates that the findings may be deemed valid and reliable descriptions of the genetic variation and covariation pattern of adiposity traits in the human species.
EvolQG - An R package for evolutionary quantitative genetics
Melo, Diogo; Garcia, Guilherme; Hubbe, Alex; Assis, Ana Paula; Marroig, Gabriel
2016-01-01
We present an open source package for performing evolutionary quantitative genetics analyses in the R environment for statistical computing. Evolutionary theory shows that evolution depends critically on the available variation in a given population. When dealing with many quantitative traits this variation is expressed in the form of a covariance matrix, particularly the additive genetic covariance matrix or sometimes the phenotypic matrix, when the genetic matrix is unavailable and there is evidence the phenotypic matrix is sufficiently similar to the genetic matrix. Given this mathematical representation of available variation, the \\textbf{EvolQG} package provides functions for calculation of relevant evolutionary statistics; estimation of sampling error; corrections for this error; matrix comparison via correlations, distances and matrix decomposition; analysis of modularity patterns; and functions for testing evolutionary hypotheses on taxa diversification. PMID:27785352
Phase-covariant quantum benchmarks
Calsamiglia, J.; Aspachs, M.; Munoz-Tapia, R.; Bagan, E.
2009-05-15
We give a quantum benchmark for teleportation and quantum storage experiments suited for pure and mixed test states. The benchmark is based on the average fidelity over a family of phase-covariant states and certifies that an experiment cannot be emulated by a classical setup, i.e., by a measure-and-prepare scheme. We give an analytical solution for qubits, which shows important differences with standard state estimation approach, and compute the value of the benchmark for coherent and squeezed states, both pure and mixed.
Technology Transfer Automated Retrieval System (TEKTRAN)
The genus Capsicum represents one of several well characterized Solanaceous genera. A wealth of classical and molecular genetics research is available for the genus. Information gleaned from its cultivated relatives, tomato and potato, provide further insight for basic and applied studies. Early ...
Technology Transfer Automated Retrieval System (TEKTRAN)
Maintaining genetic variation in wild populations of Arctic organisms is fundamental to the long-term persistence of high latitude biodiversity. Variability is important because it provides options for species to respond to changing environmental conditions and novel challenges such as emerging path...
WAIS-IV subtest covariance structure: conceptual and statistical considerations.
Ward, L Charles; Bergman, Maria A; Hebert, Katina R
2012-06-01
D. Wechsler (2008b) reported confirmatory factor analyses (CFAs) with standardization data (ages 16-69 years) for 10 core and 5 supplemental subtests from the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV). Analyses of the 15 subtests supported 4 hypothesized oblique factors (Verbal Comprehension, Working Memory, Perceptual Reasoning, and Processing Speed) but also revealed unexplained covariance between Block Design and Visual Puzzles (Perceptual Reasoning subtests). That covariance was not included in the final models. Instead, a path was added from Working Memory to Figure Weights (Perceptual Reasoning subtest) to improve fit and achieve a desired factor pattern. The present research with the same data (N = 1,800) showed that the path from Working Memory to Figure Weights increases the association between Working Memory and Matrix Reasoning. Specifying both paths improves model fit and largely eliminates unexplained covariance between Block Design and Visual Puzzles but with the undesirable consequence that Figure Weights and Matrix Reasoning are equally determined by Perceptual Reasoning and Working Memory. An alternative 4-factor model was proposed that explained theory-implied covariance between Block Design and Visual Puzzles and between Arithmetic and Figure Weights while maintaining compatibility with WAIS-IV Index structure. The proposed model compared favorably with a 5-factor model based on Cattell-Horn-Carroll theory. The present findings emphasize that covariance model comparisons should involve considerations of conceptual coherence and theoretical adherence in addition to statistical fit.
Pilacinski, W; Crawford, A; Downey, R; Harvey, B; Huber, S; Hunst, P; Lahman, L K; MacIntosh, S; Pohl, M; Rickard, C; Tagliani, L; Weber, N
2011-01-01
Crop varieties with multiple GM events combined by conventional breeding have become important in global agriculture. The regulatory requirements in different countries for such products vary considerably, placing an additional burden on regulatory agencies in countries where the submission of additional data is required and delaying the introduction of innovative products to meet agricultural needs. The process of conventional plant breeding has predictably provided safe food and feed products both historically and in the modern era of plant breeding. Thus, previously approved GM events that have been combined by conventional plant breeding and contain GM traits that are not likely to interact in a manner affecting safety should be considered to be as safe as their conventional counterparts. Such combined GM event crop varieties should require little, if any, additional regulatory data to meet regulatory requirements.
Evaluation of Tungsten Nuclear Reaction Data with Covariances
Trkov, A. Capote, R.; Kodeli, I.; Leal, L.
2008-12-15
As a follow-up of the work presented at the ND-2007 conference in Nice, additional fast reactor benchmarks were analyzed. Adjustment to the cross sections in the keV region was necessary. Evaluated neutron cross section data files for {sup 180,182,183,184,186}W isotopes were produced. Covariances were generated for all isotopes except {sup 180}W. In the resonance range the retro-active method was used. Above the resolved resonance range the covariance prior was generated by the Monte Carlo technique from nuclear model calculations with the Empire-II code. Experimental data were taken into account through the GANDR system using the generalized least-squares technique. Introducing experimental data results in relatively small changes in the cross sections, but greatly constrains the uncertainties. The covariance files are currently undergoing testing.
Evaluation of Tungsten Nuclear Reaction Data with Covariances
Trkov, A.; Capote, R.; Kodeli, I.; Leal, Luiz C.
2008-12-01
As a follow-up of the work presented at the ND-2007 conference in Nice, additional fast reactor benchmarks were analyzed. Adjustment to the cross sections in the keV region was necessary. Evaluated neutron cross section data files for 180,182,183,184,186W isotopes were produced. Covariances were generated for all isotopes except 180W. In the resonance range the retro-active method was used. Above the resolved resonance range the covariance prior was generated by the Monte Carlo technique from nuclear model calculations with the Empire-II code. Experimental data were taken into account through the GANDR system using the generalized least-squares technique. Introducing experimental data results in relatively small changes in the cross sections, but greatly constrains the uncertainties. The covariance files are currently undergoing testing.
Relativistic covariance of Ohm's law
NASA Astrophysics Data System (ADS)
Starke, R.; Schober, G. A. H.
2016-04-01
The derivation of Lorentz-covariant generalizations of Ohm's law has been a long-term issue in theoretical physics with deep implications for the study of relativistic effects in optical and atomic physics. In this article, we propose an alternative route to this problem, which is motivated by the tremendous progress in first-principles materials physics in general and ab initio electronic structure theory in particular. We start from the most general, Lorentz-covariant first-order response law, which is written in terms of the fundamental response tensor χμ ν relating induced four-currents to external four-potentials. By showing the equivalence of this description to Ohm's law, we prove the validity of Ohm's law in every inertial frame. We further use the universal relation between χμ ν and the microscopic conductivity tensor σkℓ to derive a fully relativistic transformation law for the latter, which includes all effects of anisotropy and relativistic retardation. In the special case of a constant, scalar conductivity, this transformation law can be used to rederive a standard textbook generalization of Ohm's law.
Neutron Cross Section Covariances for Structural Materials and Fission Products
NASA Astrophysics Data System (ADS)
Hoblit, S.; Cho, Y.-S.; Herman, M.; Mattoon, C. M.; Mughabghab, S. F.; Obložinský, P.; Pigni, M. T.; Sonzogni, A. A.
2011-12-01
We describe neutron cross section covariances for 78 structural materials and fission products produced for the new US evaluated nuclear reaction library ENDF/B-VII.1. Neutron incident energies cover full range from 10 eV to 20 MeV and covariances are primarily provided for capture, elastic and inelastic scattering as well as (n,2n). The list of materials follows priorities defined by the Advanced Fuel Cycle Initiative, the major application being data adjustment for advanced fast reactor systems. Thus, in addition to 28 structural materials and 49 fission products, the list includes also 23Na which is important fast reactor coolant. Due to extensive amount of materials, we adopted a variety of methodologies depending on the priority of a specific material. In the resolved resonance region we primarily used resonance parameter uncertainties given in Atlas of Neutron Resonances and either applied the kernel approximation to propagate these uncertainties into cross section uncertainties or resorted to simplified estimates based on integral quantities. For several priority materials we adopted MF32 covariances produced by SAMMY at ORNL, modified by us by adding MF33 covariances to account for systematic uncertainties. In the fast neutron region we resorted to three methods. The most sophisticated was EMPIRE-KALMAN method which combines experimental data from EXFOR library with nuclear reaction modeling and least-squares fitting. The two other methods used simplified estimates, either based on the propagation of nuclear reaction model parameter uncertainties or on a dispersion analysis of central cross section values in recent evaluated data files. All covariances were subject to quality assurance procedures adopted recently by CSEWG. In addition, tools were developed to allow inspection of processed covariances and computed integral quantities, and for comparing these values to data from the Atlas and the astrophysics database KADoNiS.
Moreira, X; Zas, R; Sampedro, L
2013-05-01
The apparent failure of invasions by alien pines in Europe has been explained by the co-occurrence of native pine congeners supporting herbivores that might easily recognize the new plants as hosts. Previous studies have reported that exotic pines show reduced tolerance and capacity to induce resistance to those native herbivores. We hypothesize that limited genetic variation in resistance to native herbivores and the existence of evolutionary trade-offs between growth and resistance could represent additional potential constraints on the evolution of invasiveness of exotic pines outside their natural range. In this paper, we examined genetic variation for constitutive and induced chemical defences (measured as non-volatile resin in the stem and total phenolics in the needles) and resistance to two major native generalist herbivores of pines in cafeteria bioassays (the phloem-feeder Hylobius abietis and the defoliator Thaumetopoea pityocampa) using half-sib families drawn from a sample of the population of Pinus radiata introduced to Spain in the mid-19th century. We found (i) significant genetic variation, with moderate-to-high narrow-sense heritabilities for both the production of constitutive non-volatile resin and induced total phenolics, and for constitutive resistance against T. pityocampa in bioassays, (ii) no evolutionary trade-offs between plant resistance and growth traits or between the production of different quantitative chemical defences and (iii) a positive genetic correlation between constitutive resistance to the two studied herbivores. Overall, results of our study indicate that the exotic pine P. radiata has limited genetic constraints on the evolution of resistance against herbivores in its introduced range, suggesting that, at least in terms of interactions with these enemies, this pine species has potential to become invasive in the future.
COVARIANCE ASSISTED SCREENING AND ESTIMATION.
Ke, By Tracy; Jin, Jiashun; Fan, Jianqing
2014-11-01
Consider a linear model Y = X β + z, where X = Xn,p and z ~ N(0, In ). The vector β is unknown and it is of interest to separate its nonzero coordinates from the zero ones (i.e., variable selection). Motivated by examples in long-memory time series (Fan and Yao, 2003) and the change-point problem (Bhattacharya, 1994), we are primarily interested in the case where the Gram matrix G = X'X is non-sparse but sparsifiable by a finite order linear filter. We focus on the regime where signals are both rare and weak so that successful variable selection is very challenging but is still possible. We approach this problem by a new procedure called the Covariance Assisted Screening and Estimation (CASE). CASE first uses a linear filtering to reduce the original setting to a new regression model where the corresponding Gram (covariance) matrix is sparse. The new covariance matrix induces a sparse graph, which guides us to conduct multivariate screening without visiting all the submodels. By interacting with the signal sparsity, the graph enables us to decompose the original problem into many separated small-size subproblems (if only we know where they are!). Linear filtering also induces a so-called problem of information leakage, which can be overcome by the newly introduced patching technique. Together, these give rise to CASE, which is a two-stage Screen and Clean (Fan and Song, 2010; Wasserman and Roeder, 2009) procedure, where we first identify candidates of these submodels by patching and screening, and then re-examine each candidate to remove false positives. For any procedure β̂ for variable selection, we measure the performance by the minimax Hamming distance between the sign vectors of β̂ and β. We show that in a broad class of situations where the Gram matrix is non-sparse but sparsifiable, CASE achieves the optimal rate of convergence. The results are successfully applied to long-memory time series and the change-point model.
COVARIANCE ASSISTED SCREENING AND ESTIMATION
Ke, By Tracy; Jin, Jiashun; Fan, Jianqing
2014-01-01
Consider a linear model Y = X β + z, where X = Xn,p and z ~ N(0, In). The vector β is unknown and it is of interest to separate its nonzero coordinates from the zero ones (i.e., variable selection). Motivated by examples in long-memory time series (Fan and Yao, 2003) and the change-point problem (Bhattacharya, 1994), we are primarily interested in the case where the Gram matrix G = X′X is non-sparse but sparsifiable by a finite order linear filter. We focus on the regime where signals are both rare and weak so that successful variable selection is very challenging but is still possible. We approach this problem by a new procedure called the Covariance Assisted Screening and Estimation (CASE). CASE first uses a linear filtering to reduce the original setting to a new regression model where the corresponding Gram (covariance) matrix is sparse. The new covariance matrix induces a sparse graph, which guides us to conduct multivariate screening without visiting all the submodels. By interacting with the signal sparsity, the graph enables us to decompose the original problem into many separated small-size subproblems (if only we know where they are!). Linear filtering also induces a so-called problem of information leakage, which can be overcome by the newly introduced patching technique. Together, these give rise to CASE, which is a two-stage Screen and Clean (Fan and Song, 2010; Wasserman and Roeder, 2009) procedure, where we first identify candidates of these submodels by patching and screening, and then re-examine each candidate to remove false positives. For any procedure β̂ for variable selection, we measure the performance by the minimax Hamming distance between the sign vectors of β̂ and β. We show that in a broad class of situations where the Gram matrix is non-sparse but sparsifiable, CASE achieves the optimal rate of convergence. The results are successfully applied to long-memory time series and the change-point model. PMID:25541567
Logistics for Working Together to Facilitate Genomic/Quantitative Genetic Prediction
Technology Transfer Automated Retrieval System (TEKTRAN)
The incorporation of DNA tests into the national cattle evaluation system will require estimation of variances of and covariances among the additive genetic components of the DNA tests and the phenotypic traits they are intended to predict. Populations with both DNA test results and phenotypes will ...
Computation of transform domain covariance matrices
NASA Technical Reports Server (NTRS)
Fino, B. J.; Algazi, V. R.
1975-01-01
It is often of interest in applications to compute the covariance matrix of a random process transformed by a fast unitary transform. Here, the recursive definition of fast unitary transforms is used to derive recursive relations for the covariance matrices of the transformed process. These relations lead to fast methods of computation of covariance matrices and to substantial reductions of the number of arithmetic operations required.
Shrinkage approach for EEG covariance matrix estimation.
Beltrachini, Leandro; von Ellenrieder, Nicolas; Muravchik, Carlos H
2010-01-01
We present a shrinkage estimator for the EEG spatial covariance matrix of the background activity. We show that such an estimator has some advantages over the maximum likelihood and sample covariance estimators when the number of available data to carry out the estimation is low. We find sufficient conditions for the consistency of the shrinkage estimators and results concerning their numerical stability. We compare several shrinkage schemes and show how to improve the estimator by incorporating known structure of the covariance matrix.
Frailty models with missing covariates.
Herring, Amy H; Ibrahim, Joseph G; Lipsitz, Stuart R
2002-03-01
We present a method for estimating the parameters in random effects models for survival data when covariates are subject to missingness. Our method is more general than the usual frailty model as it accommodates a wide range of distributions for the random effects, which are included as an offset in the linear predictor in a manner analogous to that used in generalized linear mixed models. We propose using a Monte Carlo EM algorithm along with the Gibbs sampler to obtain parameter estimates. This method is useful in reducing the bias that may be incurred using complete-case methods in this setting. The methodology is applied to data from Eastern Cooperative Oncology Group melanoma clinical trials in which observations were believed to be clustered and several tumor characteristics were not always observed.
Lorentz covariant {kappa}-Minkowski spacetime
DaPbrowski, Ludwik; Godlinski, Michal; Piacitelli, Gherardo
2010-06-15
In recent years, different views on the interpretation of Lorentz covariance of noncommuting coordinates have been discussed. By a general procedure, we construct the minimal canonical central covariantization of the {kappa}-Minkowski spacetime. Here, undeformed Lorentz covariance is implemented by unitary operators, in the presence of two dimensionful parameters. We then show that, though the usual {kappa}-Minkowski spacetime is covariant under deformed (or twisted) Lorentz action, the resulting framework is equivalent to taking a noncovariant restriction of the covariantized model. We conclude with some general comments on the approach of deformed covariance.
Balancing continuous covariates based on Kernel densities.
Ma, Zhenjun; Hu, Feifang
2013-03-01
The balance of important baseline covariates is essential for convincing treatment comparisons. Stratified permuted block design and minimization are the two most commonly used balancing strategies, both of which require the covariates to be discrete. Continuous covariates are typically discretized in order to be included in the randomization scheme. But breakdown of continuous covariates into subcategories often changes the nature of the covariates and makes distributional balance unattainable. In this article, we propose to balance continuous covariates based on Kernel density estimations, which keeps the continuity of the covariates. Simulation studies show that the proposed Kernel-Minimization can achieve distributional balance of both continuous and categorical covariates, while also keeping the group size well balanced. It is also shown that the Kernel-Minimization is less predictable than stratified permuted block design and minimization. Finally, we apply the proposed method to redesign the NINDS trial, which has been a source of controversy due to imbalance of continuous baseline covariates. Simulation shows that imbalances such as those observed in the NINDS trial can be generally avoided through the implementation of the new method.
Gao, H; Wu, Y; Zhang, T; Wu, Y; Jiang, L; Zhan, J; Li, J; Yang, R
2014-12-01
Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate independent 'super traits' from the original multivariate phenotypic traits for the univariate analysis. However, parameter estimates in this framework may not be the same as those from the joint analysis of all traits, leading to spurious linkage results. In this paper, we propose to perform the PCA for residual covariance matrix instead of the phenotypical covariance matrix, based on which multiple traits are transformed to a group of pseudo principal components. The PCA for residual covariance matrix allows analyzing each pseudo principal component separately. In addition, all parameter estimates are equivalent to those obtained from the joint multivariate analysis under a linear transformation. However, a fast least absolute shrinkage and selection operator (LASSO) for estimating the sparse oversaturated genetic model greatly reduces the computational costs of this procedure. Extensive simulations show statistical and computational efficiencies of the proposed method. We illustrate this method in a GWAS for 20 slaughtering traits and meat quality traits in beef cattle.
Gao, H; Zhang, T; Wu, Y; Wu, Y; Jiang, L; Zhan, J; Li, J; Yang, R
2014-01-01
Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate independent ‘super traits' from the original multivariate phenotypic traits for the univariate analysis. However, parameter estimates in this framework may not be the same as those from the joint analysis of all traits, leading to spurious linkage results. In this paper, we propose to perform the PCA for residual covariance matrix instead of the phenotypical covariance matrix, based on which multiple traits are transformed to a group of pseudo principal components. The PCA for residual covariance matrix allows analyzing each pseudo principal component separately. In addition, all parameter estimates are equivalent to those obtained from the joint multivariate analysis under a linear transformation. However, a fast least absolute shrinkage and selection operator (LASSO) for estimating the sparse oversaturated genetic model greatly reduces the computational costs of this procedure. Extensive simulations show statistical and computational efficiencies of the proposed method. We illustrate this method in a GWAS for 20 slaughtering traits and meat quality traits in beef cattle. PMID:24984606
Identifying sources of uncertainty using covariance analysis
NASA Astrophysics Data System (ADS)
Hyslop, N. P.; White, W. H.
2010-12-01
Atmospheric aerosol monitoring often includes performing multiple analyses on a collected sample. Some common analyses resolve suites of elements or compounds (e.g., spectrometry, chromatography). Concentrations are determined through multi-step processes involving sample collection, physical or chemical analysis, and data reduction. Uncertainties in the individual steps propagate into uncertainty in the calculated concentration. The assumption in most treatments of measurement uncertainty is that errors in the various species concentrations measured in a sample are random and therefore independent of each other. This assumption is often not valid in speciated aerosol data because some errors can be common to multiple species. For example, an error in the sample volume will introduce a common error into all species concentrations determined in the sample, and these errors will correlate with each other. Measurement programs often use paired (collocated) measurements to characterize the random uncertainty in their measurements. Suites of paired measurements provide an opportunity to go beyond the characterization of measurement uncertainties in individual species to examine correlations amongst the measurement uncertainties in multiple species. This additional information can be exploited to distinguish sources of uncertainty that affect all species from those that only affect certain subsets or individual species. Data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) program are used to illustrate these ideas. Nine analytes commonly detected in the IMPROVE network were selected for this analysis. The errors in these analytes can be reasonably modeled as multiplicative, and the natural log of the ratio of concentrations measured on the two samplers provides an approximation of the error. Figure 1 shows the covariation of these log ratios among the different analytes for one site. Covariance is strongest amongst the dust element (Fe, Ca, and
Connolly, Eric J; Beaver, Kevin M
2016-04-01
Emerging evidence from longitudinal research suggests that bullied children are more likely to develop antisocial tendencies and mental health problems later in life. Less research, however, has used genetically sensitive research designs to control for genetic confounding and examine whether the well-supported association between bullying victimization and maladaptive development is partially accounted for by common genetic and environmental influences. Using sibling data from the National Longitudinal Survey of Youth 1997, the current study used a series of bivariate liability-threshold models to disentangle the genetic and environmental influences on observed covariance between repeated bullying victimization, delinquent involvement, and symptoms of depression/anxiety. Results revealed that common additive genetic and nonshared environmental effects accounted for the covariance in liability between bullying victimization and delinquent involvement as well as bullying victimization and symptoms of depression/anxiety. The results suggest the presence of genotype-environment correlation (rGE) between repeated victimization and maladaptive development.
Informed conditioning on clinical covariates increases power in case-control association studies.
Zaitlen, Noah; Lindström, Sara; Pasaniuc, Bogdan; Cornelis, Marilyn; Genovese, Giulio; Pollack, Samuela; Barton, Anne; Bickeböller, Heike; Bowden, Donald W; Eyre, Steve; Freedman, Barry I; Friedman, David J; Field, John K; Groop, Leif; Haugen, Aage; Heinrich, Joachim; Henderson, Brian E; Hicks, Pamela J; Hocking, Lynne J; Kolonel, Laurence N; Landi, Maria Teresa; Langefeld, Carl D; Le Marchand, Loic; Meister, Michael; Morgan, Ann W; Raji, Olaide Y; Risch, Angela; Rosenberger, Albert; Scherf, David; Steer, Sophia; Walshaw, Martin; Waters, Kevin M; Wilson, Anthony G; Wordsworth, Paul; Zienolddiny, Shanbeh; Tchetgen, Eric Tchetgen; Haiman, Christopher; Hunter, David J; Plenge, Robert M; Worthington, Jane; Christiani, David C; Schaumberg, Debra A; Chasman, Daniel I; Altshuler, David; Voight, Benjamin; Kraft, Peter; Patterson, Nick; Price, Alkes L
2012-01-01
Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low-BMI cases are larger than those estimated from high-BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1 × 10(-9)). The improvement varied across diseases with a 16% median increase in χ(2) test statistics and a
Singh, Sudhanshu; Mackill, David J.; Ismail, Abdelbagi M.
2014-01-01
1 lines. This suggests the possibility of further improvements in submergence tolerance by incorporating additional traits present in FR13A or other similar landraces. PMID:25281725
Singh, Sudhanshu; Mackill, David J; Ismail, Abdelbagi M
2014-10-03
1 lines. This suggests the possibility of further improvements in submergence tolerance by incorporating additional traits present in FR13A or other similar landraces.
Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R
2012-08-01
compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.
Covariance Structure Analysis of Ordinal Ipsative Data.
ERIC Educational Resources Information Center
Chan, Wai; Bentler, Peter M.
1998-01-01
Proposes a two-stage estimation method for the analysis of covariance structure models with ordinal ipsative data (OID). A goodness-of-fit statistic is given for testing the hypothesized covariance structure matrix, and simulation results show that the method works well with a large sample. (SLD)
Quality Quantification of Evaluated Cross Section Covariances
Varet, S.; Dossantos-Uzarralde, P.
2015-01-15
Presently, several methods are used to estimate the covariance matrix of evaluated nuclear cross sections. Because the resulting covariance matrices can be different according to the method used and according to the assumptions of the method, we propose a general and objective approach to quantify the quality of the covariance estimation for evaluated cross sections. The first step consists in defining an objective criterion. The second step is computation of the criterion. In this paper the Kullback-Leibler distance is proposed for the quality quantification of a covariance matrix estimation and its inverse. It is based on the distance to the true covariance matrix. A method based on the bootstrap is presented for the estimation of this criterion, which can be applied with most methods for covariance matrix estimation and without the knowledge of the true covariance matrix. The full approach is illustrated on the {sup 85}Rb nucleus evaluations and the results are then used for a discussion on scoring and Monte Carlo approaches for covariance matrix estimation of the cross section evaluations.
Group Theory of Covariant Harmonic Oscillators
ERIC Educational Resources Information Center
Kim, Y. S.; Noz, Marilyn E.
1978-01-01
A simple and concrete example for illustrating the properties of noncompact groups is presented. The example is based on the covariant harmonic-oscillator formalism in which the relativistic wave functions carry a covariant-probability interpretation. This can be used in a group theory course for graduate students who have some background in…
Position Error Covariance Matrix Validation and Correction
NASA Technical Reports Server (NTRS)
Frisbee, Joe, Jr.
2016-01-01
In order to calculate operationally accurate collision probabilities, the position error covariance matrices predicted at times of closest approach must be sufficiently accurate representations of the position uncertainties. This presentation will discuss why the Gaussian distribution is a reasonable expectation for the position uncertainty and how this assumed distribution type is used in the validation and correction of position error covariance matrices.
Adjoints and Low-rank Covariance Representation
NASA Technical Reports Server (NTRS)
Tippett, Michael K.; Cohn, Stephen E.
2000-01-01
Quantitative measures of the uncertainty of Earth System estimates can be as important as the estimates themselves. Second moments of estimation errors are described by the covariance matrix, whose direct calculation is impractical when the number of degrees of freedom of the system state is large. Ensemble and reduced-state approaches to prediction and data assimilation replace full estimation error covariance matrices by low-rank approximations. The appropriateness of such approximations depends on the spectrum of the full error covariance matrix, whose calculation is also often impractical. Here we examine the situation where the error covariance is a linear transformation of a forcing error covariance. We use operator norms and adjoints to relate the appropriateness of low-rank representations to the conditioning of this transformation. The analysis is used to investigate low-rank representations of the steady-state response to random forcing of an idealized discrete-time dynamical system.
Wolfe, Marnin D; Kulakow, Peter; Rabbi, Ismail Y; Jannink, Jean-Luc
2016-08-31
In clonally propagated crops, non-additive genetic effects can be effectively exploited by the identification of superior genetic individuals as varieties. Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop that feeds hundreds of millions. We quantified the amount and nature of non-additive genetic variation for three key traits in a breeding population of cassava from sub-Saharan Africa using additive and non-additive genome-wide marker-based relationship matrices. We then assessed the accuracy of genomic prediction for total (additive plus non-additive) genetic value. We confirmed previous findings based on diallel populations, that non-additive genetic variation is significant for key cassava traits. Specifically, we found that dominance is particularly important for root yield and epistasis contributes strongly to variation in CMD resistance. Further, we showed that total genetic value predicted observed phenotypes more accurately than additive only models for root yield but not for dry matter content, which is mostly additive or for CMD resistance, which has high narrow-sense heritability. We address the implication of these results for cassava breeding and put our work in the context of previous results in cassava, and other plant and animal species.
Flowing on Riemannian manifold: domain adaptation by shifting covariance.
Cui, Zhen; Li, Wen; Xu, Dong; Shan, Shiguang; Chen, Xilin; Li, Xuelong
2014-12-01
Domain adaptation has shown promising results in computer vision applications. In this paper, we propose a new unsupervised domain adaptation method called domain adaptation by shifting covariance (DASC) for object recognition without requiring any labeled samples from the target domain. By characterizing samples from each domain as one covariance matrix, the source and target domain are represented into two distinct points residing on a Riemannian manifold. Along the geodesic constructed from the two points, we then interpolate some intermediate points (i.e., covariance matrices), which are used to bridge the two domains. By utilizing the principal components of each covariance matrix, samples from each domain are further projected into intermediate feature spaces, which finally leads to domain-invariant features after the concatenation of these features from intermediate points. In the multiple source domain adaptation task, we also need to effectively integrate different types of features between each pair of source and target domains. We additionally propose an SVM based method to simultaneously learn the optimal target classifier as well as the optimal weights for different source domains. Extensive experiments demonstrate the effectiveness of our method for both single source and multiple source domain adaptation tasks.
Robust Kronecker Product PCA for Spatio-Temporal Covariance Estimation
NASA Astrophysics Data System (ADS)
Greenewald, Kristjan; Hero, Alfred O.
2015-12-01
Kronecker PCA involves the use of a space vs. time Kronecker product decomposition to estimate spatio-temporal covariances. In this work the addition of a sparse correction factor is considered, which corresponds to a model of the covariance as a sum of Kronecker products of low (separation) rank and a sparse matrix. This sparse correction extends the diagonally corrected Kronecker PCA of [Greenewald et al 2013, 2014] to allow for sparse unstructured "outliers" anywhere in the covariance matrix, e.g. arising from variables or correlations that do not fit the Kronecker model well, or from sources such as sensor noise or sensor failure. We introduce a robust PCA-based algorithm to estimate the covariance under this model, extending the rearranged nuclear norm penalized LS Kronecker PCA approaches of [Greenewald et al 2014, Tsiligkaridis et al 2013]. An extension to Toeplitz temporal factors is also provided, producing a parameter reduction for temporally stationary measurement modeling. High dimensional MSE performance bounds are given for these extensions. Finally, the proposed extension of KronPCA is evaluated on both simulated and real data coming from yeast cell cycle experiments. This establishes the practical utility of robust Kronecker PCA in biological and other applications.
Treatment decisions based on scalar and functional baseline covariates.
Ciarleglio, Adam; Petkova, Eva; Ogden, R Todd; Tarpey, Thaddeus
2015-12-01
The amount and complexity of patient-level data being collected in randomized-controlled trials offer both opportunities and challenges for developing personalized rules for assigning treatment for a given disease or ailment. For example, trials examining treatments for major depressive disorder are not only collecting typical baseline data such as age, gender, or scores on various tests, but also data that measure the structure and function of the brain such as images from magnetic resonance imaging (MRI), functional MRI (fMRI), or electroencephalography (EEG). These latter types of data have an inherent structure and may be considered as functional data. We propose an approach that uses baseline covariates, both scalars and functions, to aid in the selection of an optimal treatment. In addition to providing information on which treatment should be selected for a new patient, the estimated regime has the potential to provide insight into the relationship between treatment response and the set of baseline covariates. Our approach can be viewed as an extension of "advantage learning" to include both scalar and functional covariates. We describe our method and how to implement it using existing software. Empirical performance of our method is evaluated with simulated data in a variety of settings and also applied to data arising from a study of patients with major depressive disorder from whom baseline scalar covariates as well as functional data from EEG are available.
Button, Tanya M M; Hewitt, John K; Rhee, Soo Hyun; Young, Susan E; Corley, Robin P; Stallings, Michael C
2006-02-01
Conduct disorder (CD) symptoms and substance dependence commonly co-occur. Both phenotypes are highly heritable and a common genetic influence on the covariation has been suggested. The aim of this study was to determine the extent to which genes and environment contribute to the covariance between CD and drug dependence using twins from the Colorado Longitudinal Twin Sample and the Colorado Twin Registry. A total of 880 twin pairs (237 monozygotic [MZ] female, 195 MZ male, 116 dizygotic [DZ] female, 118 DZ male and 214 DZ opposite-sex) aged 13 to 18 (mean = 15.65) were included in the analysis. CD was assessed by lifetime Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) symptom count and a polysubstance dependence vulnerability index was developed from responses to the Composite International Diagnostic Interview--Substance Abuse Module. A bivariate Cholesky Decomposition model was used to partition the cause of variation and covariation of the two phenotypes. No sex-limitation was observed in our data, and male and female parameter estimates were constrained to be equal. Both CD symptoms and dependence vulnerability were significantly heritable, and genes, shared environment and nonshared environment all contributed to the covariation between them. Genes contributed 35% of the phenotypic covariance, shared environment contributed 46%, and nonshared environmental influences contributed the remaining 19% to the phenotypic covariance. Therefore, there appears to be pleiotropic genetic influence on CD symptoms and dependence vulnerability.
Covariate-Adjusted Linear Mixed Effects Model with an Application to Longitudinal Data
Nguyen, Danh V.; Şentürk, Damla; Carroll, Raymond J.
2009-01-01
Linear mixed effects (LME) models are useful for longitudinal data/repeated measurements. We propose a new class of covariate-adjusted LME models for longitudinal data that nonparametrically adjusts for a normalizing covariate. The proposed approach involves fitting a parametric LME model to the data after adjusting for the nonparametric effects of a baseline confounding covariate. In particular, the effect of the observable covariate on the response and predictors of the LME model is modeled nonparametrically via smooth unknown functions. In addition to covariate-adjusted estimation of fixed/population parameters and random effects, an estimation procedure for the variance components is also developed. Numerical properties of the proposed estimators are investigated with simulation studies. The consistency and convergence rates of the proposed estimators are also established. An application to a longitudinal data set on calcium absorption, accounting for baseline distortion from body mass index, illustrates the proposed methodology. PMID:19266053
Collet, J M; Blows, M W
2014-11-01
After choosing a first mate, polyandrous females have access to a range of opportunities to bias paternity, such as repeating matings with the preferred male, facilitating fertilization from the best sperm or differentially investing in offspring according to their sire. Female ability to bias paternity after a first mating has been demonstrated in a few species, but unambiguous evidence remains limited by the access to complex behaviours, sperm storage organs and fertilization processes within females. Even when found at the phenotypic level, the potential evolution of any mechanism allowing females to bias paternity other than mate choice remains little explored. Using a large population of pedigreed females, we developed a simple test to determine whether there is additive genetic variation in female ability to bias paternity after a first, chosen, mating. We applied this method in the highly polyandrous Drosophila serrata, giving females the opportunity to successively mate with two males ad libitum. We found that despite high levels of polyandry (females mated more than once per day), the first mate choice was a significant predictor of male total reproductive success. Importantly, there was no detectable genetic variance in female ability to bias paternity beyond mate choice. Therefore, whether or not females can bias paternity before or after copulation, their role on the evolution of sexual male traits is likely to be limited to their first mate choice in D. serrata.
Exploring Eddy-Covariance Measurements Using a Spatial Approach: The Eddy Matrix
NASA Astrophysics Data System (ADS)
Engelmann, Christian; Bernhofer, Christian
2016-10-01
Taylor's frozen turbulence hypothesis states that "standard" eddy-covariance measurements of fluxes at a fixed location can replace a spatial ensemble of instantaneous values at multiple locations. For testing this hypothesis, a unique turbulence measurement set-up was used for two measurement campaigns over desert (Namibia) and grassland (Germany) in 2012. This "Eddy Matrix" combined nine ultrasonic anemometer-thermometers and 17 thermocouples in a 10 m × 10 m regular grid with 2.5-m grid distance. The instantaneous buoyancy flux derived from the spatial eddy covariance of the Eddy Matrix was highly variable in time (from -0.3 to 1 m K s^{-1}). However, the 10-min average reflected 83 % of the reference eddy-covariance flux with a good correlation. By introducing a combined eddy-covariance method (the spatial eddy covariance plus the additional flux of the temporal eddy covariance of the spatial mean values), the mean flux increases by 9 % relative to the eddy-covariance reference. Considering the typical underestimation of fluxes by the standard eddy-covariance method, this is seen as an improvement. Within the limits of the Eddy Matrix, Taylor's hypothesis is supported by the results.
Rasul, Golam; Glover, Karl D; Krishnan, Padmanaban G; Wu, Jixiang; Berzonsky, William A; Ibrahim, Amir M H
2015-12-01
Elevated level of late maturity α-amylase activity (LMAA) can result in low falling number scores, reduced grain quality, and downgrade of wheat (Triticum aestivum L.) class. A mating population was developed by crossing parents with different levels of LMAA. The F2 and F3 hybrids and their parents were evaluated for LMAA, and data were analyzed using the R software package 'qgtools' integrated with an additive-dominance genetic model and a mixed linear model approach. Simulated results showed high testing powers for additive and additive × environment variances, and comparatively low powers for dominance and dominance × environment variances. All variance components and their proportions to the phenotypic variance for the parents and hybrids were significant except for the dominance × environment variance. The estimated narrow-sense heritability and broad-sense heritability for LMAA were 14 and 54%, respectively. High significant negative additive effects for parents suggest that spring wheat cultivars 'Lancer' and 'Chester' can serve as good general combiners, and that 'Kinsman' and 'Seri-82' had negative specific combining ability in some hybrids despite of their own significant positive additive effects, suggesting they can be used as parents to reduce LMAA levels. Seri-82 showed very good general combining ability effect when used as a male parent, indicating the importance of reciprocal effects. High significant negative dominance effects and high-parent heterosis for hybrids demonstrated that the specific hybrid combinations; Chester × Kinsman, 'Lerma52' × Lancer, Lerma52 × 'LoSprout' and 'Janz' × Seri-82 could be generated to produce cultivars with significantly reduced LMAA level.
Kushwaha, B P; Mandal, A; Arora, A L; Kumar, R; Kumar, S; Notter, D R
2009-08-01
Estimates of (co)variance components were obtained for weights at birth, weaning and 6, 9 and 12 months of age in Chokla sheep maintained at the Central Sheep and Wool Research Institute, Avikanagar, Rajasthan, India, over a period of 21 years (1980-2000). Records of 2030 lambs descended from 150 rams and 616 ewes were used in the study. Analyses were carried out by restricted maximum likelihood (REML) fitting an animal model and ignoring or including maternal genetic or permanent environmental effects. Six different animal models were fitted for all traits. The best model was chosen after testing the improvement of the log-likelihood values. Direct heritability estimates were inflated substantially for all traits when maternal effects were ignored. Heritability estimates for weight at birth, weaning and 6, 9 and 12 months of age were 0.20, 0.18, 0.16, 0.22 and 0.23, respectively in the best models. Additive maternal and maternal permanent environmental effects were both significant at birth, accounting for 9% and 12% of phenotypic variance, respectively, but the source of maternal effects (additive versus permanent environmental) at later ages could not be clearly identified. The estimated repeatabilities across years of ewe effects on lamb body weights were 0.26, 0.14, 0.12, 0.13, and 0.15 at birth, weaning, 6, 9 and 12 months of age, respectively. These results indicate that modest rates of genetic progress are possible for all weights.
CMB lens sample covariance and consistency relations
NASA Astrophysics Data System (ADS)
Motloch, Pavel; Hu, Wayne; Benoit-Lévy, Aurélien
2017-02-01
Gravitational lensing information from the two and higher point statistics of the cosmic microwave background (CMB) temperature and polarization fields are intrinsically correlated because they are lensed by the same realization of structure between last scattering and observation. Using an analytic model for lens sample covariance, we show that there is one mode, separately measurable in the lensed CMB power spectra and lensing reconstruction, that carries most of this correlation. Once these measurements become lens sample variance dominated, this mode should provide a useful consistency check between the observables that is largely free of sampling and cosmological parameter errors. Violations of consistency could indicate systematic errors in the data and lens reconstruction or new physics at last scattering, any of which could bias cosmological inferences and delensing for gravitational waves. A second mode provides a weaker consistency check for a spatially flat universe. Our analysis isolates the additional information supplied by lensing in a model-independent manner but is also useful for understanding and forecasting CMB cosmological parameter errors in the extended Λ cold dark matter parameter space of dark energy, curvature, and massive neutrinos. We introduce and test a simple but accurate forecasting technique for this purpose that neither double counts lensing information nor neglects lensing in the observables.
Covariance Spectroscopy for Fissile Material Detection
Rusty Trainham, Jim Tinsley, Paul Hurley, Ray Keegan
2009-06-02
Nuclear fission produces multiple prompt neutrons and gammas at each fission event. The resulting daughter nuclei continue to emit delayed radiation as neutrons boil off, beta decay occurs, etc. All of the radiations are causally connected, and therefore correlated. The correlations are generally positive, but when different decay channels compete, so that some radiations tend to exclude others, negative correlations could also be observed. A similar problem of reduced complexity is that of cascades radiation, whereby a simple radioactive decay produces two or more correlated gamma rays at each decay. Covariance is the usual means for measuring correlation, and techniques of covariance mapping may be useful to produce distinct signatures of special nuclear materials (SNM). A covariance measurement can also be used to filter data streams because uncorrelated signals are largely rejected. The technique is generally more effective than a coincidence measurement. In this poster, we concentrate on cascades and the covariance filtering problem.
Using Incidence Sampling to Estimate Covariances.
ERIC Educational Resources Information Center
Knapp, Thomas R.
1979-01-01
This paper presents the generalized symmetric means approach to the estimation of population covariances, complete with derivations and examples. Particular attention is paid to the problem of missing data, which is handled very naturally in the incidence sampling framework. (CTM)
Covariation bias in panic-prone individuals.
Pauli, P; Montoya, P; Martz, G E
1996-11-01
Covariation estimates between fear-relevant (FR; emergency situations) or fear-irrelevant (FI; mushrooms and nudes) stimuli and an aversive outcome (electrical shock) were examined in 10 high-fear (panic-prone) and 10 low-fear respondents. When the relation between slide category and outcome was random (illusory correlation), only high-fear participants markedly overestimated the contingency between FR slides and shocks. However, when there was a high contingency of shocks following FR stimuli (83%) and a low contingency of shocks following FI stimuli (17%), the group difference vanished. Reversal of contingencies back to random induced a covariation bias for FR slides in high- and low-fear respondents. Results indicate that panic-prone respondents show a covariation bias for FR stimuli and that the experience of a high contingency between FR slides and aversive outcomes may foster such a covariation bias even in low-fear respondents.
Conformally covariant parametrizations for relativistic initial data
NASA Astrophysics Data System (ADS)
Delay, Erwann
2017-01-01
We revisit the Lichnerowicz-York method, and an alternative method of York, in order to obtain some conformally covariant systems. This type of parametrization is certainly more natural for non constant mean curvature initial data.
Combining biomarkers for classification with covariate adjustment.
Kim, Soyoung; Huang, Ying
2017-03-09
Combining multiple markers can improve classification accuracy compared with using a single marker. In practice, covariates associated with markers or disease outcome can affect the performance of a biomarker or biomarker combination in the population. The covariate-adjusted receiver operating characteristic (ROC) curve has been proposed as a tool to tease out the covariate effect in the evaluation of a single marker; this curve characterizes the classification accuracy solely because of the marker of interest. However, research on the effect of covariates on the performance of marker combinations and on how to adjust for the covariate effect when combining markers is still lacking. In this article, we examine the effect of covariates on classification performance of linear marker combinations and propose to adjust for covariates in combining markers by maximizing the nonparametric estimate of the area under the covariate-adjusted ROC curve. The proposed method provides a way to estimate the best linear biomarker combination that is robust to risk model assumptions underlying alternative regression-model-based methods. The proposed estimator is shown to be consistent and asymptotically normally distributed. We conduct simulations to evaluate the performance of our estimator in cohort and case/control designs and compare several different weighting strategies during estimation with respect to efficiency. Our estimator is also compared with alternative regression-model-based estimators or estimators that maximize the empirical area under the ROC curve, with respect to bias and efficiency. We apply the proposed method to a biomarker study from an human immunodeficiency virus vaccine trial. Copyright © 2017 John Wiley & Sons, Ltd.
Breeding curvature from extended gauge covariance
NASA Astrophysics Data System (ADS)
Aldrovandi, R.
1991-05-01
Independence between spacetime and “internal” space in gauge theories is related to the adjoint-covariant behaviour of the gauge potential. The usual gauge scheme is modified to allow a coupling between both spaces. Gauging spacetime translations produce field equations similar to Einstein equations. A curvature-like quantity of mixed differential-algebraic character emerges. Enlarged conservation laws are present, pointing to the presence of an covariance.
Noncommutative Gauge Theory with Covariant Star Product
Zet, G.
2010-08-04
We present a noncommutative gauge theory with covariant star product on a space-time with torsion. In order to obtain the covariant star product one imposes some restrictions on the connection of the space-time. Then, a noncommutative gauge theory is developed applying this product to the case of differential forms. Some comments on the advantages of using a space-time with torsion to describe the gravitational field are also given.
Covariant action for type IIB supergravity
NASA Astrophysics Data System (ADS)
Sen, Ashoke
2016-07-01
Taking clues from the recent construction of the covariant action for type II and heterotic string field theories, we construct a manifestly Lorentz covariant action for type IIB supergravity, and discuss its gauge fixing maintaining manifest Lorentz invariance. The action contains a (non-gravitating) free 4-form field besides the usual fields of type IIB supergravity. This free field, being completely decoupled from the interacting sector, has no physical consequence.
Phase-covariant quantum cloning of qudits
Fan Heng; Imai, Hiroshi; Matsumoto, Keiji; Wang, Xiang-Bin
2003-02-01
We study the phase-covariant quantum cloning machine for qudits, i.e., the input states in a d-level quantum system have complex coefficients with arbitrary phase but constant module. A cloning unitary transformation is proposed. After optimizing the fidelity between input state and single qudit reduced density operator of output state, we obtain the optimal fidelity for 1 to 2 phase-covariant quantum cloning of qudits and the corresponding cloning transformation.
Engelen, Bert; Meinken, Kristin; von Wintzingerode, Friedrich; Heuer, Holger; Malkomes, Hans-Peter; Backhaus, Horst
1998-01-01
Herbogil (dinoterb), a reference herbicide, the mineral oil Oleo (paraffin oil used as an additive to herbicides), and Goltix (metamitron) were taken as model compounds for the study of impacts on microbial soil communities. After the treatment of soil samples, effects on metabolic sum parameters were determined by monitoring substrate-induced respiration (SIR) and dehydrogenase activity, as well as carbon and nitrogen mineralization. These conventional ecotoxicological testing procedures are used in pesticide registration. Inhibition of biomass-related activities and stimulation of nitrogen mineralization were the most significant effects caused by the application of Herbogil. Even though Goltix and Oleo were used at a higher dosage (10 times higher), the application of Goltix resulted in smaller effects and the additive Oleo was the least-active compound, with minor stimulation of test parameters at later observation times. The results served as a background for investigation of the power of “fingerprinting” methods in microbial ecology. Changes in catabolic activities induced by treatments were analyzed by using the 95 carbon sources provided by the BIOLOG system. Variations in the complex metabolic fingerprints demonstrated inhibition of many catabolic pathways after the application of Herbogil. Again, the effects of the other compounds were expressed at much lower levels and comprised stimulations as well as inhibitions. Testing for significance by a multivariate t test indicated that the sensitivity of this method was similar to the sensitivities of the conventional testing procedures. The variation of sensitive carbon sources, as determined by factor weights at different observation times, indicated the dynamics of the community shift induced by the Herbogil treatment in more detail. DNA extractions from soil resulted in a collection of molecules representing the genetic composition of total bacterial communities. Distinct and highly reproducible
Lorentz covariance of loop quantum gravity
NASA Astrophysics Data System (ADS)
Rovelli, Carlo; Speziale, Simone
2011-05-01
The kinematics of loop gravity can be given a manifestly Lorentz-covariant formulation: the conventional SU(2)-spin-network Hilbert space can be mapped to a space K of SL(2,C) functions, where Lorentz covariance is manifest. K can be described in terms of a certain subset of the projected spin networks studied by Livine, Alexandrov and Dupuis. It is formed by SL(2,C) functions completely determined by their restriction on SU(2). These are square-integrable in the SU(2) scalar product, but not in the SL(2,C) one. Thus, SU(2)-spin-network states can be represented by Lorentz-covariant SL(2,C) functions, as two-component photons can be described in the Lorentz-covariant Gupta-Bleuler formalism. As shown by Wolfgang Wieland in a related paper, this manifestly Lorentz-covariant formulation can also be directly obtained from canonical quantization. We show that the spinfoam dynamics of loop quantum gravity is locally SL(2,C)-invariant in the bulk, and yields states that are precisely in K on the boundary. This clarifies how the SL(2,C) spinfoam formalism yields an SU(2) theory on the boundary. These structures define a tidy Lorentz-covariant formalism for loop gravity.
Low-dimensional Representation of Error Covariance
NASA Technical Reports Server (NTRS)
Tippett, Michael K.; Cohn, Stephen E.; Todling, Ricardo; Marchesin, Dan
2000-01-01
Ensemble and reduced-rank approaches to prediction and assimilation rely on low-dimensional approximations of the estimation error covariances. Here stability properties of the forecast/analysis cycle for linear, time-independent systems are used to identify factors that cause the steady-state analysis error covariance to admit a low-dimensional representation. A useful measure of forecast/analysis cycle stability is the bound matrix, a function of the dynamics, observation operator and assimilation method. Upper and lower estimates for the steady-state analysis error covariance matrix eigenvalues are derived from the bound matrix. The estimates generalize to time-dependent systems. If much of the steady-state analysis error variance is due to a few dominant modes, the leading eigenvectors of the bound matrix approximate those of the steady-state analysis error covariance matrix. The analytical results are illustrated in two numerical examples where the Kalman filter is carried to steady state. The first example uses the dynamics of a generalized advection equation exhibiting nonmodal transient growth. Failure to observe growing modes leads to increased steady-state analysis error variances. Leading eigenvectors of the steady-state analysis error covariance matrix are well approximated by leading eigenvectors of the bound matrix. The second example uses the dynamics of a damped baroclinic wave model. The leading eigenvectors of a lowest-order approximation of the bound matrix are shown to approximate well the leading eigenvectors of the steady-state analysis error covariance matrix.
Estimation of genetic parameters and genetic changes for growth characteristics of Santa Ines sheep.
Aguirre, E L; Mattos, E C; Eler, J P; Barreto Neto, A D; Ferraz, J B
2016-08-19
Studying genetic parameters and genetic changes in Santa Ines sheep is important, because it is the commonest breed in Brazil. This study obtained genetic data from 37,735 pedigree records of lambs over 12 years (2003-2014) from 33 flocks in 10 Brazilian States; 11,851 records of performance were available. (Co)variance components, genetic parameters and breeding values estimates were obtained by derivative-free restricted maximum likelihood in a univariate analysis that included maternal additive genetic and maternal permanent environmental effects. Birth weight, weaning weight, weight at 180 days of age, weight at 270 days of age, average daily weight gain in the following states: from birth to weaning, from weaning to 6 months, from 6 months to 9 months, and from weaning to 9 months; presence of hair in fur and leg muscularity were assessed. (Co)variance component values increased in the weight traits with age. A significant maternal effect was found in the pre-weaned stage that decreased in the post-weaned stage. High values were estimated for the maternal permanent environmental effect, possibly because of the extensive grassland that was available. High total heritability values were estimated for all of the traits evaluated. Significant, positive correlations were found between direct and maternal additive genetic traits with a gradual decrease as the lambs gained independence from their mothers. The genetic trends observed were irregular and incremental. Significant genetic variance suggests that direct selection for pre-weaning traits results in indirect selection of maternal abilities, and individual selection of any post-weaning trait results in rapid genetic improvement.
NASA Astrophysics Data System (ADS)
Kim, Sung Yong; Terrill, Eric; Cornuelle, Bruce
2007-06-01
Surface currents measured by high-frequency radars are objectively mapped using covariance matrices computed from hourly surface current vectors spanning two years. Since retrievals of surface radial velocities are inherently gappy in space and time, the irregular density of surface current data leads to negative eigenvalues in the sample covariance matrix. The number and the magnitude of the negative eigenvalues depend on the degree of data continuity used in the matrix computation. In a region of 90% data coverage, the negative eigenvalues of the sample covariance matrix are small enough to be removed by adding a noise term to the diagonal of the matrix. The mapping is extended to regions of poorer data coverage by applying a smoothed covariance matrix obtained by spatially averaging the sample covariance matrix. This approach estimates a stable covariance matrix of surface currents for regions with the intermittent radar coverage. An additional benefit is the removal of baseline errors that often exist between two radar sites. The covariance matrices and the correlation functions of the surface currents are exponential in space rather than Gaussian, as is often assumed in the objective mapping of oceanographic data sets. Patterns in the decorrelation length scale provide the variabilities of surface currents and the insights on the influence of topographic features (bathymetry and headlands). The objective mapping approach presented herein lends itself to various applications, including the Lagrangian transport estimates, dynamic analysis through divergence and vorticity of current vectors, and statistical models of surface currents.
Convex Banding of the Covariance Matrix.
Bien, Jacob; Bunea, Florentina; Xiao, Luo
2016-01-01
We introduce a new sparse estimator of the covariance matrix for high-dimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator which tapers the sample covariance matrix by a Toeplitz, sparsely-banded, data-adaptive matrix. As a result of this adaptivity, the convex banding estimator enjoys theoretical optimality properties not attained by previous banding or tapered estimators. In particular, our convex banding estimator is minimax rate adaptive in Frobenius and operator norms, up to log factors, over commonly-studied classes of covariance matrices, and over more general classes. Furthermore, it correctly recovers the bandwidth when the true covariance is exactly banded. Our convex formulation admits a simple and efficient algorithm. Empirical studies demonstrate its practical effectiveness and illustrate that our exactly-banded estimator works well even when the true covariance matrix is only close to a banded matrix, confirming our theoretical results. Our method compares favorably with all existing methods, in terms of accuracy and speed. We illustrate the practical merits of the convex banding estimator by showing that it can be used to improve the performance of discriminant analysis for classifying sound recordings.
Upper and lower covariance bounds for perturbed linear systems
NASA Technical Reports Server (NTRS)
Xu, J.-H.; Skelton, R. E.; Zhu, G.
1990-01-01
Both upper and lower bounds are established for state covariance matrices under parameter perturbations of the plant. The motivation for this study lies in the fact that many robustness properties of linear systems are given explicitly in terms of the state covariance matrix. Moreover, there exists a theory for control by covariance assignment. The results provide robustness properties of these covariance controllers.
Yuan, Ke-Hai; Hayashi, Kentaro; Yanagihara, Hirokazu
2007-01-01
Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given sample size, also provides more accurate results than those based on standard asymptotics. But the procedure needs a matrix to play the role of the population covariance matrix. The closer the matrix is to the true population covariance matrix, the more valid the bootstrap inference is. The current paper proposes a class of covariance matrices by combining theory and data. Thus, a proper matrix from this class is closer to the true population covariance matrix than those constructed by any existing methods. Each of the covariance matrices is easy to generate and also satisfies several desired properties. An example with nine cognitive variables and a confirmatory factor model illustrates the details for creating population covariance matrices with different misspecifications. When evaluating the substantive model, bootstrap or simulation procedures based on these matrices will lead to more accurate conclusion than that based on artificial covariance matrices.
Progress on Nuclear Data Covariances: AFCI-1.2 Covariance Library
Oblozinsky,P.; Oblozinsky,P.; Mattoon,C.M.; Herman,M.; Mughabghab,S.F.; Pigni,M.T.; Talou,P.; Hale,G.M.; Kahler,A.C.; Kawano,T.; Little,R.C.; Young,P.G
2009-09-28
Improved neutron cross section covariances were produced for 110 materials including 12 light nuclei (coolants and moderators), 78 structural materials and fission products, and 20 actinides. Improved covariances were organized into AFCI-1.2 covariance library in 33-energy groups, from 10{sup -5} eV to 19.6 MeV. BNL contributed improved covariance data for the following materials: {sup 23}Na and {sup 55}Mn where more detailed evaluation was done; improvements in major structural materials {sup 52}Cr, {sup 56}Fe and {sup 58}Ni; improved estimates for remaining structural materials and fission products; improved covariances for 14 minor actinides, and estimates of mubar covariances for {sup 23}Na and {sup 56}Fe. LANL contributed improved covariance data for {sup 235}U and {sup 239}Pu including prompt neutron fission spectra and completely new evaluation for {sup 240}Pu. New R-matrix evaluation for {sup 16}O including mubar covariances is under completion. BNL assembled the library and performed basic testing using improved procedures including inspection of uncertainty and correlation plots for each material. The AFCI-1.2 library was released to ANL and INL in August 2009.
ERIC Educational Resources Information Center
Zeytun, Aysel Sen; Cetinkaya, Bulent; Erbas, Ayhan Kursat
2010-01-01
Various studies suggest that covariational reasoning plays an important role on understanding the fundamental ideas of calculus and modeling dynamic functional events. The purpose of this study was to investigate a group of mathematics teachers' covariational reasoning abilities and predictions about their students. Data were collected through…
Defining habitat covariates in camera-trap based occupancy studies
Niedballa, Jürgen; Sollmann, Rahel; Mohamed, Azlan bin; Bender, Johannes; Wilting, Andreas
2015-01-01
In species-habitat association studies, both the type and spatial scale of habitat covariates need to match the ecology of the focal species. We assessed the potential of high-resolution satellite imagery for generating habitat covariates using camera-trapping data from Sabah, Malaysian Borneo, within an occupancy framework. We tested the predictive power of covariates generated from satellite imagery at different resolutions and extents (focal patch sizes, 10–500 m around sample points) on estimates of occupancy patterns of six small to medium sized mammal species/species groups. High-resolution land cover information had considerably more model support for small, patchily distributed habitat features, whereas it had no advantage for large, homogeneous habitat features. A comparison of different focal patch sizes including remote sensing data and an in-situ measure showed that patches with a 50-m radius had most support for the target species. Thus, high-resolution satellite imagery proved to be particularly useful in heterogeneous landscapes, and can be used as a surrogate for certain in-situ measures, reducing field effort in logistically challenging environments. Additionally, remote sensed data provide more flexibility in defining appropriate spatial scales, which we show to impact estimates of wildlife-habitat associations. PMID:26596779
Bilinear covariants and spinor fields duality in quantum Clifford algebras
Abłamowicz, Rafał; Gonçalves, Icaro; Rocha, Roldão da
2014-10-15
Classification of quantum spinor fields according to quantum bilinear covariants is introduced in a context of quantum Clifford algebras on Minkowski spacetime. Once the bilinear covariants are expressed in terms of algebraic spinor fields, the duality between spinor and quantum spinor fields can be discussed. Thus, by endowing the underlying spacetime with an arbitrary bilinear form with an antisymmetric part in addition to a symmetric spacetime metric, quantum algebraic spinor fields and deformed bilinear covariants can be constructed. They are thus compared to the classical (non quantum) ones. Classes of quantum spinor fields classes are introduced and compared with Lounesto's spinor field classification. A physical interpretation of the deformed parts and the underlying Z-grading is proposed. The existence of an arbitrary bilinear form endowing the spacetime already has been explored in the literature in the context of quantum gravity [S. W. Hawking, “The unpredictability of quantum gravity,” Commun. Math. Phys. 87, 395 (1982)]. Here, it is shown further to play a prominent role in the structure of Dirac, Weyl, and Majorana spinor fields, besides the most general flagpoles and flag-dipoles. We introduce a new duality between the standard and the quantum spinor fields, by showing that when Clifford algebras over vector spaces endowed with an arbitrary bilinear form are taken into account, a mixture among the classes does occur. Consequently, novel features regarding the spinor fields can be derived.
Defining habitat covariates in camera-trap based occupancy studies.
Niedballa, Jürgen; Sollmann, Rahel; bin Mohamed, Azlan; Bender, Johannes; Wilting, Andreas
2015-11-24
In species-habitat association studies, both the type and spatial scale of habitat covariates need to match the ecology of the focal species. We assessed the potential of high-resolution satellite imagery for generating habitat covariates using camera-trapping data from Sabah, Malaysian Borneo, within an occupancy framework. We tested the predictive power of covariates generated from satellite imagery at different resolutions and extents (focal patch sizes, 10-500 m around sample points) on estimates of occupancy patterns of six small to medium sized mammal species/species groups. High-resolution land cover information had considerably more model support for small, patchily distributed habitat features, whereas it had no advantage for large, homogeneous habitat features. A comparison of different focal patch sizes including remote sensing data and an in-situ measure showed that patches with a 50-m radius had most support for the target species. Thus, high-resolution satellite imagery proved to be particularly useful in heterogeneous landscapes, and can be used as a surrogate for certain in-situ measures, reducing field effort in logistically challenging environments. Additionally, remote sensed data provide more flexibility in defining appropriate spatial scales, which we show to impact estimates of wildlife-habitat associations.
NASA Astrophysics Data System (ADS)
Hui, Yi; Law, Siu Seong; Ku, Chiu Jen
2017-02-01
Covariance of the auto/cross-covariance matrix based method is studied for the damage identification of a structure with illustrations on its advantages and limitations. The original method is extended for structures under direct white noise excitations. The auto/cross-covariance function of the measured acceleration and its corresponding derivatives are formulated analytically, and the method is modified in two new strategies to enable successful identification with much fewer sensors. Numerical examples are adopted to illustrate the improved method, and the effects of sampling frequency and sampling duration are discussed. Results show that the covariance of covariance calculated from responses of higher order modes of a structure play an important role to the accurate identification of local damage in a structure.
Incorporating covariates in skewed functional data models.
Li, Meng; Staicu, Ana-Maria; Bondell, Howard D
2015-07-01
We introduce a class of covariate-adjusted skewed functional models (cSFM) designed for functional data exhibiting location-dependent marginal distributions. We propose a semi-parametric copula model for the pointwise marginal distributions, which are allowed to depend on covariates, and the functional dependence, which is assumed covariate invariant. The proposed cSFM framework provides a unifying platform for pointwise quantile estimation and trajectory prediction. We consider a computationally feasible procedure that handles densely as well as sparsely observed functional data. The methods are examined numerically using simulations and is applied to a new tractography study of multiple sclerosis. Furthermore, the methodology is implemented in the R package cSFM, which is publicly available on CRAN.
FAST NEUTRON COVARIANCES FOR EVALUATED DATA FILES.
HERMAN, M.; OBLOZINSKY, P.; ROCHMAN, D.; KAWANO, T.; LEAL, L.
2006-06-05
We describe implementation of the KALMAN code in the EMPIRE system and present first covariance data generated for Gd and Ir isotopes. A complete set of covariances, in the full energy range, was produced for the chain of 8 Gadolinium isotopes for total, elastic, capture, total inelastic (MT=4), (n,2n), (n,p) and (n,alpha) reactions. Our correlation matrices, based on combination of model calculations and experimental data, are characterized by positive mid-range and negative long-range correlations. They differ from the model-generated covariances that tend to show strong positive long-range correlations and those determined solely from experimental data that result in nearly diagonal matrices. We have studied shapes of correlation matrices obtained in the calculations and interpreted them in terms of the underlying reaction models. An important result of this study is the prediction of narrow energy ranges with extremely small uncertainties for certain reactions (e.g., total and elastic).
Covariance based outlier detection with feature selection.
Zwilling, Chris E; Wang, Michelle Y
2016-08-01
The present covariance based outlier detection algorithm selects from a candidate set of feature vectors that are best at identifying outliers. Features extracted from biomedical and health informatics data can be more informative in disease assessment and there are no restrictions on the nature and number of features that can be tested. But an important challenge for an algorithm operating on a set of features is for it to winnow the effective features from the ineffective ones. The powerful algorithm described in this paper leverages covariance information from the time series data to identify features with the highest sensitivity for outlier identification. Empirical results demonstrate the efficacy of the method.
Sparse Covariance Matrix Estimation With Eigenvalue Constraints.
Liu, Han; Wang, Lie; Zhao, Tuo
2014-04-01
We propose a new approach for estimating high-dimensional, positive-definite covariance matrices. Our method extends the generalized thresholding operator by adding an explicit eigenvalue constraint. The estimated covariance matrix simultaneously achieves sparsity and positive definiteness. The estimator is rate optimal in the minimax sense and we develop an efficient iterative soft-thresholding and projection algorithm based on the alternating direction method of multipliers. Empirically, we conduct thorough numerical experiments on simulated datasets as well as real data examples to illustrate the usefulness of our method. Supplementary materials for the article are available online.
Parametric number covariance in quantum chaotic spectra.
Vinayak; Kumar, Sandeep; Pandey, Akhilesh
2016-03-01
We study spectral parametric correlations in quantum chaotic systems and introduce the number covariance as a measure of such correlations. We derive analytic results for the classical random matrix ensembles using the binary correlation method and obtain compact expressions for the covariance. We illustrate the universality of this measure by presenting the spectral analysis of the quantum kicked rotors for the time-reversal invariant and time-reversal noninvariant cases. A local version of the parametric number variance introduced earlier is also investigated.
Covariance Analysis of Gamma Ray Spectra
Trainham, R.; Tinsley, J.
2013-01-01
The covariance method exploits fluctuations in signals to recover information encoded in correlations which are usually lost when signal averaging occurs. In nuclear spectroscopy it can be regarded as a generalization of the coincidence technique. The method can be used to extract signal from uncorrelated noise, to separate overlapping spectral peaks, to identify escape peaks, to reconstruct spectra from Compton continua, and to generate secondary spectral fingerprints. We discuss a few statistical considerations of the covariance method and present experimental examples of its use in gamma spectroscopy.
Covariance analysis of gamma ray spectra
Trainham, R.; Tinsley, J.
2013-01-15
The covariance method exploits fluctuations in signals to recover information encoded in correlations which are usually lost when signal averaging occurs. In nuclear spectroscopy it can be regarded as a generalization of the coincidence technique. The method can be used to extract signal from uncorrelated noise, to separate overlapping spectral peaks, to identify escape peaks, to reconstruct spectra from Compton continua, and to generate secondary spectral fingerprints. We discuss a few statistical considerations of the covariance method and present experimental examples of its use in gamma spectroscopy.
NASA Astrophysics Data System (ADS)
Hohm, Olaf; Samtleben, Henning
2013-09-01
We extend the techniques of double field theory to more general gravity theories and U-duality symmetries, having in mind applications to the complete D = 11 supergravity. In this paper we work out a (3 + 3)-dimensional `U-duality covariantization' of D = 4 Einstein gravity, in which the Ehlers group SL(2, ) is realized geometrically, acting in the 3 representation on half of the coordinates. We include the full (2 + 1)-dimensional metric, while the `internal vielbein' is a coset representative of SL(2, )/SO(2) and transforms under gauge transformations via generalized Lie derivatives. In addition, we introduce a gauge connection of the `C-bracket', and a gauge connection of SL(2, ), albeit subject to constraints. The action takes the form of (2 + 1)-dimensional gravity coupled to a Chern-Simons-matter theory but encodes the complete D = 4 Einstein gravity. We comment on generalizations, such as an ` E 8(8) covariantization' of M-theory.
Wählby, Ulrika; Jonsson, E Niclas; Karlsson, Mats O
2002-01-01
The aim of this study was to compare 2 stepwise covariate model-building strategies, frequently used in the analysis of pharmacokinetic-pharmacodynamic (PK-PD) data using nonlinear mixed-effects models, with respect to included covariates and predictive performance. In addition, the effects of stepwise regression on the estimated covariate coefficients were assessed. Using simulated and real PK data, covariate models were built applying (1) stepwise generalized additive models (GAM) for identifying potential covariates, followed by backward elimination in the computer program NONMEM, and (2) stepwise forward inclusion and backward elimination in NONMEM. Different versions of these procedures were tried (eg, treating different study occasions as separate individuals in the GAM, or fixing a part of the parameters when the NONMEM procedure was used). The final covariate models were compared, including their ability to predict a separate data set or their performance in cross-validation. The bias in the estimated coefficients (selection bias) was assessed. The model-building procedures performed similarly in the data sets explored. No major differences in the resulting covariate models were seen, and the predictive performances overlapped. Therefore, the choice of model-building procedure in these examples could be based on other aspects such as analyst- and computer-time efficiency. There was a tendency to selection bias in the estimates, although this was small relative to the overall variability in the estimates. The predictive performances of the stepwise models were also reasonably good. Thus, selection bias seems to be a minor problem in this typical PK covariate analysis.
Genetic parameters and trends in the Chilean multibreed dairy cattle population.
Elzo, M A; Jara, A; Barria, N
2004-05-01
Estimates of additive and nonadditive multibreed co-variance components, genetic parameters, and predicted genetic values for first lactation 305-d mature equivalent (ME) milk yield, fat yield, and protein yield were computed using data from a sample of 3316 cows from the Chilean Holstein-other breeds multibreed population. Variances and covariances were estimated by 2-trait REML analyses using a Generalized Expectation-Maximization algorithm applied to multibreed populations. Multiple estimates of additive genetic, nonadditive genetic, and environmental variances from 2-trait analyses were averaged to yield a single variance estimate for each trait and effect. Heritabilities were moderate for all traits in Holstein, other, and Holstein x other crossbred groups. Interbreed interactibilities (ratio of nonadditive genetic to phenotypic variances) were all near zero. Multibreed additive, nonadditive, and total genetic trends were estimated using the complete dataset (56,277 cows). Upward trends between 1990 and 2000 existed for all traits, genetic effects, and breed groups, except for 305-d ME protein yield in 1/4 Holstein, indicating that Chilean dairy producers were successful in choosing progressively better semen and sires from imported and local sources over time.
Observed Score Linear Equating with Covariates
ERIC Educational Resources Information Center
Branberg, Kenny; Wiberg, Marie
2011-01-01
This paper examined observed score linear equating in two different data collection designs, the equivalent groups design and the nonequivalent groups design, when information from covariates (i.e., background variables correlated with the test scores) was included. The main purpose of the study was to examine the effect (i.e., bias, variance, and…
Economical phase-covariant cloning of qudits
Buscemi, Francesco; D'Ariano, Giacomo Mauro; Macchiavello, Chiara
2005-04-01
We derive the optimal N{yields}M phase-covariant quantum cloning for equatorial states in dimension d with M=kd+N, k integer. The cloning maps are optimal for both global and single-qudit fidelity. The map is achieved by an 'economical' cloning machine, which works without ancilla.
Neutron Multiplicity: LANL W Covariance Matrix for Curve Fitting
Wendelberger, James G.
2016-12-08
In neutron multiplicity counting one may fit a curve by minimizing an objective function, χ$2\\atop{n}$. The objective function includes the inverse of an n by n matrix of covariances, W. The inverse of the W matrix has a closed form solution. In addition W^{-1} is a tri-diagonal matrix. The closed form and tridiagonal nature allows for a simpler expression of the objective function χ$2\\atop{n}$. Minimization of this simpler expression will provide the optimal parameters for the fitted curve.
Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies
Zaitlen, Noah; Lindström, Sara; Pasaniuc, Bogdan; Cornelis, Marilyn; Genovese, Giulio; Pollack, Samuela; Barton, Anne; Bickeböller, Heike; Bowden, Donald W.; Eyre, Steve; Freedman, Barry I.; Friedman, David J.; Field, John K.; Groop, Leif; Haugen, Aage; Heinrich, Joachim; Henderson, Brian E.; Hicks, Pamela J.; Hocking, Lynne J.; Kolonel, Laurence N.; Landi, Maria Teresa; Langefeld, Carl D.; Le Marchand, Loic; Meister, Michael; Morgan, Ann W.; Raji, Olaide Y.; Risch, Angela; Rosenberger, Albert; Scherf, David; Steer, Sophia; Walshaw, Martin; Waters, Kevin M.; Wilson, Anthony G.; Wordsworth, Paul; Zienolddiny, Shanbeh; Tchetgen, Eric Tchetgen; Haiman, Christopher; Hunter, David J.; Plenge, Robert M.; Worthington, Jane; Christiani, David C.; Schaumberg, Debra A.; Chasman, Daniel I.; Altshuler, David; Voight, Benjamin; Kraft, Peter; Patterson, Nick; Price, Alkes L.
2012-01-01
Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1×10−9). The improvement varied across diseases with a 16% median increase in χ2 test statistics and a
Yadav, Anupama; Dhole, Kaustubh
2016-01-01
Cryptic genetic variation (CGV) refers to genetic variants whose effects are buffered in most conditions but manifest phenotypically upon specific genetic and environmental perturbations. Despite having a central role in adaptation, contribution of CGV to regulation of quantitative traits is unclear. Instead, a relatively simplistic architecture of additive genetic loci is known to regulate phenotypic variation in most traits. In this paper, we investigate the regulation of CGV and its implication on the genetic architecture of quantitative traits at a genome-wide level. We use a previously published dataset of biparental recombinant population of Saccharomyces cerevisiae phenotyped in 34 diverse environments to perform single locus, two-locus, and covariance mapping. We identify loci that have independent additive effects as well as those which regulate the phenotypic manifestation of other genetic variants (variance QTL). We find that whereas additive genetic variance is predominant, a higher order genetic interaction network regulates variation in certain environments. Despite containing pleiotropic loci, with effects across environments, these genetic networks are highly environment specific. CGV is buffered under most allelic combinations of these networks and perturbed only in rare combinations resulting in high phenotypic variance. The presence of such environment specific genetic networks is the underlying cause of abundant gene–environment interactions. We demonstrate that overlaying identified molecular networks on such genetic networks can identify potential candidate genes and underlying mechanisms regulating phenotypic variation. Such an integrated approach applied to human disease datasets has the potential to improve the ability to predict disease predisposition and identify specific therapeutic targets. PMID:28172852
Janes, Holly; Pepe, Margaret S
2009-06-01
Recent scientific and technological innovations have produced an abundance of potential markers that are being investigated for their use in disease screening and diagnosis. In evaluating these markers, it is often necessary to account for covariates associated with the marker of interest. Covariates may include subject characteristics, expertise of the test operator, test procedures or aspects of specimen handling. In this paper, we propose the covariate-adjusted receiver operating characteristic curve, a measure of covariate-adjusted classification accuracy. Nonparametric and semiparametric estimators are proposed, asymptotic distribution theory is provided and finite sample performance is investigated. For illustration we characterize the age-adjusted discriminatory accuracy of prostate-specific antigen as a biomarker for prostate cancer.
On the regularity of the covariance matrix of a discretized scalar field on the sphere
NASA Astrophysics Data System (ADS)
Bilbao-Ahedo, J. D.; Barreiro, R. B.; Herranz, D.; Vielva, P.; Martínez-González, E.
2017-02-01
We present a comprehensive study of the regularity of the covariance matrix of a discretized field on the sphere. In a particular situation, the rank of the matrix depends on the number of pixels, the number of spherical harmonics, the symmetries of the pixelization scheme and the presence of a mask. Taking into account the above mentioned components, we provide analytical expressions that constrain the rank of the matrix. They are obtained by expanding the determinant of the covariance matrix as a sum of determinants of matrices made up of spherical harmonics. We investigate these constraints for five different pixelizations that have been used in the context of Cosmic Microwave Background (CMB) data analysis: Cube, Icosahedron, Igloo, GLESP and HEALPix, finding that, at least in the considered cases, the HEALPix pixelization tends to provide a covariance matrix with a rank closer to the maximum expected theoretical value than the other pixelizations. The effect of the propagation of numerical errors in the regularity of the covariance matrix is also studied for different computational precisions, as well as the effect of adding a certain level of noise in order to regularize the matrix. In addition, we investigate the application of the previous results to a particular example that requires the inversion of the covariance matrix: the estimation of the CMB temperature power spectrum through the Quadratic Maximum Likelihood algorithm. Finally, some general considerations in order to achieve a regular covariance matrix are also presented.
Das, Kiranmoy; Daniels, Michael J
2014-03-01
Estimation of the covariance structure for irregular sparse longitudinal data has been studied by many authors in recent years but typically using fully parametric specifications. In addition, when data are collected from several groups over time, it is known that assuming the same or completely different covariance matrices over groups can lead to loss of efficiency and/or bias. Nonparametric approaches have been proposed for estimating the covariance matrix for regular univariate longitudinal data by sharing information across the groups under study. For the irregular case, with longitudinal measurements that are bivariate or multivariate, modeling becomes more difficult. In this article, to model bivariate sparse longitudinal data from several groups, we propose a flexible covariance structure via a novel matrix stick-breaking process for the residual covariance structure and a Dirichlet process mixture of normals for the random effects. Simulation studies are performed to investigate the effectiveness of the proposed approach over more traditional approaches. We also analyze a subset of Framingham Heart Study data to examine how the blood pressure trajectories and covariance structures differ for the patients from different BMI groups (high, medium, and low) at baseline.
Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.
Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A
2016-01-01
Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.
Background error covariance modelling for convective-scale variational data assimilation
NASA Astrophysics Data System (ADS)
Petrie, R. E.
An essential component in data assimilation is the background error covariance matrix (B). This matrix regularizes the ill-posed data assimilation problem, describes the confidence of the background state and spreads information. Since the B-matrix is too large to represent explicitly it must be modelled. In variational data assimilation it is essentially a climatological approximation of the true covariances. Such a conventional covariance model additionally relies on the imposition of balance conditions. A toy model which is derived from the Euler equations (by making appropriate simplifications and introducing tuneable parameters) is used as a convective-scale system to investigate these issues. Its behaviour is shown to exhibit large-scale geostrophic and hydrostatic balance while permitting small-scale imbalance. A control variable transform (CVT) approach to modelling the B-matrix where the control variables are taken to be the normal modes (NM) of the linearized model is investigated. This approach is attractive for convective-scale covariance modelling as it allows for unbalanced as well as appropriately balanced relationships. Although the NM-CVT is not applied to a data assimilation problem directly, it is shown to be a viable approach to convective-scale covariance modelling. A new mathematically rigorous method to incorporate flow-dependent error covariances with the otherwise static B-matrix estimate is also proposed. This is an extension to the reduced rank Kalman filter (RRKF) where its Hessian singular vector calculation is replaced by an ensemble estimate of the covariances, and is known as the ensemble RRKF (EnRRKF). Ultimately it is hoped that together the NM-CVT and the EnRRKF would improve the predictability of small-scale features in convective-scale weather forecasting through the relaxation of inappropriate balance and the inclusion of flow-dependent covariances.
Gao, Feng; Manatunga, Amita K; Chen, Shande
2007-02-20
Often in many biomedical and epidemiologic studies, estimating hazards function is of interest. The Breslow's estimator is commonly used for estimating the integrated baseline hazard, but this estimator requires the functional form of covariate effects to be correctly specified. It is generally difficult to identify the true functional form of covariate effects in the presence of time-dependent covariates. To provide a complementary method to the traditional proportional hazard model, we propose a tree-type method which enables simultaneously estimating both baseline hazards function and the effects of time-dependent covariates. Our interest will be focused on exploring the potential data structures rather than formal hypothesis testing. The proposed method approximates the baseline hazards and covariate effects with step-functions. The jump points in time and in covariate space are searched via an algorithm based on the improvement of the full log-likelihood function. In contrast to most other estimating methods, the proposed method estimates the hazards function rather than integrated hazards. The method is applied to model the risk of withdrawal in a clinical trial that evaluates the anti-depression treatment in preventing the development of clinical depression. Finally, the performance of the method is evaluated by several simulation studies.
Lorentz Covariant Distributions with Spectral Conditions
Zinoviev, Yury M.
2007-11-14
The properties of the vacuum expectation values of products of the quantum fields are formulated in the book [1]. The vacuum expectation values of quantum fields products would be the Fourier transforms of the Lorentz covariant tempered distributions with supports in the product of the closed upper light cones. Lorentz invariant distributions are studied in the papers [2]--[4]. The authors of these papers wanted to describe Lorentz invariant distributions in terms of distributions given on the Lorentz group orbit space. This orbit space has a complicated structure. It is noted [5] that a tempered distribution with support in the closed upper light cone may be represented as the action of the wave operator in some power on a differentiable function with support in the closed upper light cone. For the description of the Lorentz covariant differentiable functions the boundary of the closed upper light cone is not important. The measure of this boundary is zero.
Chiral four-dimensional heterotic covariant lattices
NASA Astrophysics Data System (ADS)
Beye, Florian
2014-11-01
In the covariant lattice formalism, chiral four-dimensional heterotic string vacua are obtained from certain even self-dual lattices which completely decompose into a left-mover and a right-mover lattice. The main purpose of this work is to classify all right-mover lattices that can appear in such a chiral model, and to study the corresponding left-mover lattices using the theory of lattice genera. In particular, the Smith-Minkowski-Siegel mass formula is employed to calculate a lower bound on the number of left-mover lattices. Also, the known relationship between asymmetric orbifolds and covariant lattices is considered in the context of our classification.
On covariance structure in noisy, big data
NASA Astrophysics Data System (ADS)
Paffenroth, Randy C.; Nong, Ryan; Du Toit, Philip C.
2013-09-01
Herein we describe theory and algorithms for detecting covariance structures in large, noisy data sets. Our work uses ideas from matrix completion and robust principal component analysis to detect the presence of low-rank covariance matrices, even when the data is noisy, distorted by large corruptions, and only partially observed. In fact, the ability to handle partial observations combined with ideas from randomized algorithms for matrix decomposition enables us to produce asymptotically fast algorithms. Herein we will provide numerical demonstrations of the methods and their convergence properties. While such methods have applicability to many problems, including mathematical finance, crime analysis, and other large-scale sensor fusion problems, our inspiration arises from applying these methods in the context of cyber network intrusion detection.
Low-Fidelity Covariances: Neutron Cross Section Covariance Estimates for 387 Materials
The Low-fidelity Covariance Project (Low-Fi) was funded in FY07-08 by DOEÆs Nuclear Criticality Safety Program (NCSP). The project was a collaboration among ANL, BNL, LANL, and ORNL. The motivation for the Low-Fi project stemmed from an imbalance in supply and demand of covariance data. The interest in, and demand for, covariance data has been in a continual uptrend over the past few years. Requirements to understand application-dependent uncertainties in simulated quantities of interest have led to the development of sensitivity / uncertainty and data adjustment software such as TSUNAMI [1] at Oak Ridge. To take full advantage of the capabilities of TSUNAMI requires general availability of covariance data. However, the supply of covariance data has not been able to keep up with the demand. This fact is highlighted by the observation that the recent release of the much-heralded ENDF/B-VII.0 included covariance data for only 26 of the 393 neutron evaluations (which is, in fact, considerably less covariance data than was included in the final ENDF/B-VI release).[Copied from R.C. Little et al., "Low-Fidelity Covariance Project", Nuclear Data Sheets 109 (2008) 2828-2833] The Low-Fi covariance data are now available at the National Nuclear Data Center. They are separate from ENDF/B-VII.0 and the NNDC warns that this information is not approved by CSEWG. NNDC describes the contents of this collection as: "Covariance data are provided for radiative capture (or (n,ch.p.) for light nuclei), elastic scattering (or total for some actinides), inelastic scattering, (n,2n) reactions, fission and nubars over the energy range from 10(-5{super}) eV to 20 MeV. The library contains 387 files including almost all (383 out of 393) materials of the ENDF/B-VII.0. Absent are data for (7{super})Li, (232{super})Th, (233,235,238{super})U and (239{super})Pu as well as (223,224,225,226{super})Ra, while (nat{super})Zn is replaced by (64,66,67,68,70{super})Zn
Torsion and geometrostasis in covariant superstrings
Zachos, C.
1985-01-01
The covariant action for freely propagating heterotic superstrings consists of a metric and a torsion term with a special relative strength. It is shown that the strength for which torsion flattens the underlying 10-dimensional superspace geometry is precisely that which yields free oscillators on the light cone. This is in complete analogy with the geometrostasis of two-dimensional sigma-models with Wess-Zumino interactions. 13 refs.
Discrete symmetries in covariant loop quantum gravity
NASA Astrophysics Data System (ADS)
Rovelli, Carlo; Wilson-Ewing, Edward
2012-09-01
We study time-reversal and parity—on the physical manifold and in internal space—in covariant loop gravity. We consider a minor modification of the Holst action which makes it transform coherently under such transformations. The classical theory is not affected but the quantum theory is slightly different. In particular, the simplicity constraints are slightly modified and this restricts orientation flips in a spin foam to occur only across degenerate regions, thus reducing the sources of potential divergences.
Linear Covariance Analysis for a Lunar Lander
NASA Technical Reports Server (NTRS)
Jang, Jiann-Woei; Bhatt, Sagar; Fritz, Matthew; Woffinden, David; May, Darryl; Braden, Ellen; Hannan, Michael
2017-01-01
A next-generation lunar lander Guidance, Navigation, and Control (GNC) system, which includes a state-of-the-art optical sensor suite, is proposed in a concept design cycle. The design goal is to allow the lander to softly land within the prescribed landing precision. The achievement of this precision landing requirement depends on proper selection of the sensor suite. In this paper, a robust sensor selection procedure is demonstrated using a Linear Covariance (LinCov) analysis tool developed by Draper.
Inverse covariance simplification for efficient uncertainty management
NASA Astrophysics Data System (ADS)
Jalobeanu, A.; Gutiérrez, J. A.
2007-11-01
When it comes to manipulating uncertain knowledge such as noisy observations of physical quantities, one may ask how to do it in a simple way. Processing corrupted signals or images always propagates the uncertainties from the data to the final results, whether these errors are explicitly computed or not. When such error estimates are provided, it is crucial to handle them in such a way that their interpretation, or their use in subsequent processing steps, remain user-friendly and computationally tractable. A few authors follow a Bayesian approach and provide uncertainties as an inverse covariance matrix. Despite its apparent sparsity, this matrix contains many small terms that carry little information. Methods have been developed to select the most significant entries, through the use of information-theoretic tools for instance. One has to find a Gaussian pdf that is close enough to the posterior pdf, and with a small number of non-zero coefficients in the inverse covariance matrix. We propose to restrict the search space to Markovian models (where only neighbors can interact), well-suited to signals or images. The originality of our approach is in conserving the covariances between neighbors while setting to zero the entries of the inverse covariance matrix for all other variables. This fully constrains the solution, and the computation is performed via a fast, alternate minimization scheme involving quadratic forms. The Markovian structure advantageously reduces the complexity of Bayesian updating (where the simplified pdf is used as a prior). Moreover, uncertainties exhibit the same temporal or spatial structure as the data.
Covariant quantization of the CBS superparticle
NASA Astrophysics Data System (ADS)
Grassi, P. A.; Policastro, G.; Porrati, M.
2001-07-01
The quantization of the Casalbuoni-Brink-Schwarz superparticle is performed in an explicitly covariant way using the antibracket formalism. Since an infinite number of ghost fields are required, within a suitable off-shell twistor-like formalism, we are able to fix the gauge of each ghost sector without modifying the physical content of the theory. The computation reveals that the antibracket cohomology contains only the physical degrees of freedom.
Twisted covariant noncommutative self-dual gravity
Estrada-Jimenez, S.; Garcia-Compean, H.; Obregon, O.; Ramirez, C.
2008-12-15
A twisted covariant formulation of noncommutative self-dual gravity is presented. The formulation for constructing twisted noncommutative Yang-Mills theories is used. It is shown that the noncommutative torsion is solved at any order of the {theta} expansion in terms of the tetrad and some extra fields of the theory. In the process the first order expansion in {theta} for the Plebanski action is explicitly obtained.
Development of covariance capabilities in EMPIRE code
Herman,M.; Pigni, M.T.; Oblozinsky, P.; Mughabghab, S.F.; Mattoon, C.M.; Capote, R.; Cho, Young-Sik; Trkov, A.
2008-06-24
The nuclear reaction code EMPIRE has been extended to provide evaluation capabilities for neutron cross section covariances in the thermal, resolved resonance, unresolved resonance and fast neutron regions. The Atlas of Neutron Resonances by Mughabghab is used as a primary source of information on uncertainties at low energies. Care is taken to ensure consistency among the resonance parameter uncertainties and those for thermal cross sections. The resulting resonance parameter covariances are formatted in the ENDF-6 File 32. In the fast neutron range our methodology is based on model calculations with the code EMPIRE combined with experimental data through several available approaches. The model-based covariances can be obtained using deterministic (Kalman) or stochastic (Monte Carlo) propagation of model parameter uncertainties. We show that these two procedures yield comparable results. The Kalman filter and/or the generalized least square fitting procedures are employed to incorporate experimental information. We compare the two approaches analyzing results for the major reaction channels on {sup 89}Y. We also discuss a long-standing issue of unreasonably low uncertainties and link it to the rigidity of the model.
Using Covariance Analysis to Assess Pointing Performance
NASA Technical Reports Server (NTRS)
Bayard, David; Kang, Bryan
2009-01-01
A Pointing Covariance Analysis Tool (PCAT) has been developed for evaluating the expected performance of the pointing control system for NASA s Space Interferometry Mission (SIM). The SIM pointing control system is very complex, consisting of multiple feedback and feedforward loops, and operating with multiple latencies and data rates. The SIM pointing problem is particularly challenging due to the effects of thermomechanical drifts in concert with the long camera exposures needed to image dim stars. Other pointing error sources include sensor noises, mechanical vibrations, and errors in the feedforward signals. PCAT models the effects of finite camera exposures and all other error sources using linear system elements. This allows the pointing analysis to be performed using linear covariance analysis. PCAT propagates the error covariance using a Lyapunov equation associated with time-varying discrete and continuous-time system matrices. Unlike Monte Carlo analysis, which could involve thousands of computational runs for a single assessment, the PCAT analysis performs the same assessment in a single run. This capability facilitates the analysis of parametric studies, design trades, and "what-if" scenarios for quickly evaluating and optimizing the control system architecture and design.
Shrinkage covariance matrix approach for microarray data
NASA Astrophysics Data System (ADS)
Karjanto, Suryaefiza; Aripin, Rasimah
2013-04-01
Microarray technology was developed for the purpose of monitoring the expression levels of thousands of genes. A microarray data set typically consists of tens of thousands of genes (variables) from just dozens of samples due to various constraints including the high cost of producing microarray chips. As a result, the widely used standard covariance estimator is not appropriate for this purpose. One such technique is the Hotelling's T2 statistic which is a multivariate test statistic for comparing means between two groups. It requires that the number of observations (n) exceeds the number of genes (p) in the set but in microarray studies it is common that n < p. This leads to a biased estimate of the covariance matrix. In this study, the Hotelling's T2 statistic with the shrinkage approach is proposed to estimate the covariance matrix for testing differential gene expression. The performance of this approach is then compared with other commonly used multivariate tests using a widely analysed diabetes data set as illustrations. The results across the methods are consistent, implying that this approach provides an alternative to existing techniques.
Technology Transfer Automated Retrieval System (TEKTRAN)
A high density genetic linkage map plays important roles in understanding genome structure of tetraploid cotton, dissecting economically important traits, identifying molecular markers associated with a trait, and cloning a gene of interest through map-based cloning strategy. Four hundred fifty f...
Factorization of the Discrete Noise Covariance Matrix for Plans,
1991-02-01
rapport prdsente la formulation exacte de la matrice de covariance Qk necessaire pour la propagation de la matrice de covariance du filtre Kalman ...approximation la d6composition necessaire pour utiliser la formulation Biermann-Agee-Turner du filtre Kalman . Cette decomposition approximative est...form of the discrete driving noise covariance matrix Qk which is needed to propagate the covariance matrix in the Kalman filter used by PLANS. It is
Petersen, Ashley; Witten, Daniela; Simon, Noah
2016-01-01
We consider the problem of predicting an outcome variable using p covariates that are measured on n independent observations, in a setting in which additive, flexible, and interpretable fits are desired. We propose the fused lasso additive model (FLAM), in which each additive function is estimated to be piecewise constant with a small number of adaptively-chosen knots. FLAM is the solution to a convex optimization problem, for which a simple algorithm with guaranteed convergence to a global optimum is provided. FLAM is shown to be consistent in high dimensions, and an unbiased estimator of its degrees of freedom is proposed. We evaluate the performance of FLAM in a simulation study and on two data sets. Supplemental materials are available online, and the R package flam is available on CRAN. PMID:28239246
Hidden Covariation Detection Produces Faster, Not Slower, Social Judgments
ERIC Educational Resources Information Center
Barker, Lynne A.; Andrade, Jackie
2006-01-01
In P. Lewicki's (1986b) demonstration of hidden covariation detection (HCD), responses of participants were slower to faces that corresponded with a covariation encountered previously than to faces with novel covariations. This slowing contrasts with the typical finding that priming leads to faster responding and suggests that HCD is a unique type…
Earth Observation System Flight Dynamics System Covariance Realism
NASA Technical Reports Server (NTRS)
Zaidi, Waqar H.; Tracewell, David
2016-01-01
This presentation applies a covariance realism technique to the National Aeronautics and Space Administration (NASA) Earth Observation System (EOS) Aqua and Aura spacecraft based on inferential statistics. The technique consists of three parts: collection calculation of definitive state estimates through orbit determination, calculation of covariance realism test statistics at each covariance propagation point, and proper assessment of those test statistics.
Covariate Selection in Propensity Scores Using Outcome Proxies
ERIC Educational Resources Information Center
Kelcey, Ben
2011-01-01
This study examined the practical problem of covariate selection in propensity scores (PSs) given a predetermined set of covariates. Because the bias reduction capacity of a confounding covariate is proportional to the concurrent relationships it has with the outcome and treatment, particular focus is set on how we might approximate…
Covariate Imbalance and Precision in Measuring Treatment Effects
ERIC Educational Resources Information Center
Liu, Xiaofeng Steven
2011-01-01
Covariate adjustment can increase the precision of estimates by removing unexplained variance from the error in randomized experiments, although chance covariate imbalance tends to counteract the improvement in precision. The author develops an easy measure to examine chance covariate imbalance in randomization by standardizing the average…
Familial covariation of facial emotion recognition and IQ in schizophrenia.
Andric, Sanja; Maric, Nadja P; Mihaljevic, Marina; Mirjanic, Tijana; van Os, Jim
2016-12-30
Alterations in general intellectual ability and social cognition in schizophrenia are core features of the disorder, evident at the illness' onset and persistent throughout its course. However, previous studies examining cognitive alterations in siblings discordant for schizophrenia yielded inconsistent results. Present study aimed to investigate the nature of the association between facial emotion recognition and general IQ by applying genetically sensitive cross-trait cross-sibling design. Participants (total n=158; patients, unaffected siblings, controls) were assessed using the Benton Facial Recognition Test, the Degraded Facial Affect Recognition Task (DFAR) and the Wechsler Adult Intelligence Scale-III. Patients had lower IQ and altered facial emotion recognition in comparison to other groups. Healthy siblings and controls did not significantly differ in IQ and DFAR performance, but siblings exhibited intermediate angry facial expression recognition. Cross-trait within-subject analyses showed significant associations between overall DFAR performance and IQ in all participants. Within-trait cross-sibling analyses found significant associations between patients' and siblings' IQ and overall DFAR performance, suggesting their familial clustering. Finally, cross-trait cross-sibling analyses revealed familial covariation of facial emotion recognition and IQ in siblings discordant for schizophrenia, further indicating their familial etiology. Both traits are important phenotypes for genetic studies and potential early clinical markers of schizophrenia-spectrum disorders.
Chaste, Pauline; Klei, Lambertus; Sanders, Stephan J.; Murtha, Michael T.; Hus, Vanessa; Lowe, Jennifer K.; Willsey, A. Jeremy; Moreno-De-Luca, Daniel; Yu, Timothy W.; Fombonne, Eric; Geschwind, Daniel; Grice, Dorothy E.; Ledbetter, David H.; Lord, Catherine; Mane, Shrikant M.; Martin, Christa Lese; Martin, Donna M.; Morrow, Eric M.; Walsh, Christopher A.; Sutcliffe, James S.; State, Matthew W.; Devlin, Bernie; Cook, Edwin H.; Kim, Soo-Jeong
2013-01-01
BACKGROUND Brain development follows a different trajectory in children with Autism Spectrum Disorders (ASD) than in typically developing children. A proxy for neurodevelopment could be head circumference (HC), but studies assessing HC and its clinical correlates in ASD have been inconsistent. This study investigates HC and clinical correlates in the Simons Simplex Collection cohort. METHODS We used a mixed linear model to estimate effects of covariates and the deviation from the expected HC given parental HC (genetic deviation). After excluding individuals with incomplete data, 7225 individuals in 1891 families remained for analysis. We examined the relationship between HC/genetic deviation of HC and clinical parameters. RESULTS Gender, age, height, weight, genetic ancestry and ASD status were significant predictors of HC (estimate of the ASD effect=0.2cm). HC was approximately normally distributed in probands and unaffected relatives, with only a few outliers. Genetic deviation of HC was also normally distributed, consistent with a random sampling of parental genes. Whereas larger HC than expected was associated with ASD symptom severity and regression, IQ decreased with the absolute value of the genetic deviation of HC. CONCLUSIONS Measured against expected values derived from covariates of ASD subjects, statistical outliers for HC were uncommon. HC is a strongly heritable trait and population norms for HC would be far more accurate if covariates including genetic ancestry, height and age were taken into account. The association of diminishing IQ with absolute deviation from predicted HC values suggests HC could reflect subtle underlying brain development and warrants further investigation. PMID:23746936
Covariance Between Genotypic Effects and its Use for Genomic Inference in Half-Sib Families
Wittenburg, Dörte; Teuscher, Friedrich; Klosa, Jan; Reinsch, Norbert
2016-01-01
In livestock, current statistical approaches utilize extensive molecular data, e.g., single nucleotide polymorphisms (SNPs), to improve the genetic evaluation of individuals. The number of model parameters increases with the number of SNPs, so the multicollinearity between covariates can affect the results obtained using whole genome regression methods. In this study, dependencies between SNPs due to linkage and linkage disequilibrium among the chromosome segments were explicitly considered in methods used to estimate the effects of SNPs. The population structure affects the extent of such dependencies, so the covariance among SNP genotypes was derived for half-sib families, which are typical in livestock populations. Conditional on the SNP haplotypes of the common parent (sire), the theoretical covariance was determined using the haplotype frequencies of the population from which the individual parent (dam) was derived. The resulting covariance matrix was included in a statistical model for a trait of interest, and this covariance matrix was then used to specify prior assumptions for SNP effects in a Bayesian framework. The approach was applied to one family in simulated scenarios (few and many quantitative trait loci) and using semireal data obtained from dairy cattle to identify genome segments that affect performance traits, as well as to investigate the impact on predictive ability. Compared with a method that does not explicitly consider any of the relationship among predictor variables, the accuracy of genetic value prediction was improved by 10–22%. The results show that the inclusion of dependence is particularly important for genomic inference based on small sample sizes. PMID:27402363
Covariance Between Genotypic Effects and its Use for Genomic Inference in Half-Sib Families.
Wittenburg, Dörte; Teuscher, Friedrich; Klosa, Jan; Reinsch, Norbert
2016-09-08
In livestock, current statistical approaches utilize extensive molecular data, e.g., single nucleotide polymorphisms (SNPs), to improve the genetic evaluation of individuals. The number of model parameters increases with the number of SNPs, so the multicollinearity between covariates can affect the results obtained using whole genome regression methods. In this study, dependencies between SNPs due to linkage and linkage disequilibrium among the chromosome segments were explicitly considered in methods used to estimate the effects of SNPs. The population structure affects the extent of such dependencies, so the covariance among SNP genotypes was derived for half-sib families, which are typical in livestock populations. Conditional on the SNP haplotypes of the common parent (sire), the theoretical covariance was determined using the haplotype frequencies of the population from which the individual parent (dam) was derived. The resulting covariance matrix was included in a statistical model for a trait of interest, and this covariance matrix was then used to specify prior assumptions for SNP effects in a Bayesian framework. The approach was applied to one family in simulated scenarios (few and many quantitative trait loci) and using semireal data obtained from dairy cattle to identify genome segments that affect performance traits, as well as to investigate the impact on predictive ability. Compared with a method that does not explicitly consider any of the relationship among predictor variables, the accuracy of genetic value prediction was improved by 10-22%. The results show that the inclusion of dependence is particularly important for genomic inference based on small sample sizes.
Bromley, C M; Snowder, G D; Van Vleck, L D
2000-04-01
Genetic parameters for Columbia, Polypay, Rambouillet, and Targhee sheep were estimated using REML with animal models for prolificacy, weight, and wool traits. All bivariate analyses included a covariance between additive genetic effects for the two traits plus appropriate additional covariances. Number of observations by breed ranged from 5,140 to 7,095 for prolificacy traits, from 7,750 to 9,530 for weight traits, and from 4,603 to 34,746 for wool traits. Heritability estimates ranged from .03 to .11 for prolificacy traits (litter size at birth and litter size at weaning), from .09 to .26 for weight traits (birth weight and average daily gain), and from .25 to .53 for wool traits (fleece weight, fleece grade and staple length). Estimates of direct genetic correlations among prolificacy and among weight traits were positive and ranged from .58 to 1.00 and .18 to 1.00, respectively. Estimates of direct genetic correlation between fleece weight and staple length were positive (.50 to .70) but were negative between fleece weight and fleece grade (-.60 to -.34) and between staple length and fleece grade (-.72 and -.40). Prolificacy and wool traits were essentially uncorrelated. Weight and prolificacy traits were slightly positively correlated. Weight traits had a moderate positive direct genetic correlation with fleece weight and staple length, but were uncorrelated with fleece grade. These estimates of genetic parameters between prolificacy, weight, and wool traits can be used to construct multiple-trait selection indexes for dual-purpose sheep.
Linear Covariance Analysis and Epoch State Estimators
NASA Technical Reports Server (NTRS)
Markley, F. Landis; Carpenter, J. Russell
2012-01-01
This paper extends in two directions the results of prior work on generalized linear covariance analysis of both batch least-squares and sequential estimators. The first is an improved treatment of process noise in the batch, or epoch state, estimator with an epoch time that may be later than some or all of the measurements in the batch. The second is to account for process noise in specifying the gains in the epoch state estimator. We establish the conditions under which the latter estimator is equivalent to the Kalman filter.
Cosmology of a covariant Galilean field.
De Felice, Antonio; Tsujikawa, Shinji
2010-09-10
We study the cosmology of a covariant scalar field respecting a Galilean symmetry in flat space-time. We show the existence of a tracker solution that finally approaches a de Sitter fixed point responsible for cosmic acceleration today. The viable region of model parameters is clarified by deriving conditions under which ghosts and Laplacian instabilities of scalar and tensor perturbations are absent. The field equation of state exhibits a peculiar phantomlike behavior along the tracker, which allows a possibility to observationally distinguish the Galileon gravity from the cold dark matter model with a cosmological constant.
Minimal covariant observables identifying all pure states
NASA Astrophysics Data System (ADS)
Carmeli, Claudio; Heinosaari, Teiko; Toigo, Alessandro
2013-09-01
It has been recently shown by Heinosaari, Mazzarella and Wolf (2013) [1] that an observable that identifies all pure states of a d-dimensional quantum system has minimally 4d-4 outcomes or slightly less (the exact number depending on d). However, no simple construction of this type of minimal observable is known. We investigate covariant observables that identify all pure states and have minimal number of outcomes. It is shown that the existence of this kind of observables depends on the dimension of the Hilbert space.
Linear Covariance Analysis and Epoch State Estimators
NASA Technical Reports Server (NTRS)
Markley, F. Landis; Carpenter, J. Russell
2014-01-01
This paper extends in two directions the results of prior work on generalized linear covariance analysis of both batch least-squares and sequential estimators. The first is an improved treatment of process noise in the batch, or epoch state, estimator with an epoch time that may be later than some or all of the measurements in the batch. The second is to account for process noise in specifying the gains in the epoch state estimator. We establish the conditions under which the latter estimator is equivalent to the Kalman filter.
Covariant harmonic oscillators and coupled harmonic oscillators
NASA Technical Reports Server (NTRS)
Han, Daesoo; Kim, Young S.; Noz, Marilyn E.
1995-01-01
It is shown that the system of two coupled harmonic oscillators shares the basic symmetry properties with the covariant harmonic oscillator formalism which provides a concise description of the basic features of relativistic hadronic features observed in high-energy laboratories. It is shown also that the coupled oscillator system has the SL(4,r) symmetry in classical mechanics, while the present formulation of quantum mechanics can accommodate only the Sp(4,r) portion of the SL(4,r) symmetry. The possible role of the SL(4,r) symmetry in quantum mechanics is discussed.
Covariant change of signature in classical relativity
NASA Astrophysics Data System (ADS)
Ellis, G. F. R.
1992-10-01
This paper gives a covariant formalism enabling investigation of the possibility of change of signature in classical General Relativity, when the geometry is that of a Robertson-Walker universe. It is shown that such changes are compatible with the Einstein field equations, both in the case of a barotropic fluid and of a scalar field. A criterion is given for when such a change of signature should take place in the scalar field case. Some examples show the kind of resulting exact solutions of the field equations.
Spatiotemporal noise covariance estimation from limited empirical magnetoencephalographic data.
Jun, Sung C; Plis, Sergey M; Ranken, Doug M; Schmidt, David M
2006-11-07
The performance of parametric magnetoencephalography (MEG) and electroencephalography (EEG) source localization approaches can be degraded by the use of poor background noise covariance estimates. In general, estimation of the noise covariance for spatiotemporal analysis is difficult mainly due to the limited noise information available. Furthermore, its estimation requires a large amount of storage and a one-time but very large (and sometimes intractable) calculation or its inverse. To overcome these difficulties, noise covariance models consisting of one pair or a sum of multi-pairs of Kronecker products of spatial covariance and temporal covariance have been proposed. However, these approaches cannot be applied when the noise information is very limited, i.e., the amount of noise information is less than the degrees of freedom of the noise covariance models. A common example of this is when only averaged noise data are available for a limited prestimulus region (typically at most a few hundred milliseconds duration). For such cases, a diagonal spatiotemporal noise covariance model consisting of sensor variances with no spatial or temporal correlation has been the common choice for spatiotemporal analysis. In this work, we propose a different noise covariance model which consists of diagonal spatial noise covariance and Toeplitz temporal noise covariance. It can easily be estimated from limited noise information, and no time-consuming optimization and data-processing are required. Thus, it can be used as an alternative choice when one-pair or multi-pair noise covariance models cannot be estimated due to lack of noise information. To verify its capability we used Bayesian inference dipole analysis and a number of simulated and empirical datasets. We compared this covariance model with other existing covariance models such as conventional diagonal covariance, one-pair and multi-pair noise covariance models, when noise information is sufficient to estimate them. We
Graff, Mariaelisa; Ngwa, Julius S; Workalemahu, Tsegaselassie; Homuth, Georg; Schipf, Sabine; Teumer, Alexander; Völzke, Henry; Wallaschofski, Henri; Abecasis, Goncalo R; Edward, Lakatta; Francesco, Cucca; Sanna, Serena; Scheet, Paul; Schlessinger, David; Sidore, Carlo; Xiao, Xiangjun; Wang, Zhaoming; Chanock, Stephen J; Jacobs, Kevin B; Hayes, Richard B; Hu, Frank; Van Dam, Rob M; Crout, Richard J; Marazita, Mary L; Shaffer, John R; Atwood, Larry D; Fox, Caroline S; Heard-Costa, Nancy L; White, Charles; Choh, Audrey C; Czerwinski, Stefan A; Demerath, Ellen W; Dyer, Thomas D; Towne, Bradford; Amin, Najaf; Oostra, Ben A; Van Duijn, Cornelia M; Zillikens, M Carola; Esko, Tõnu; Nelis, Mari; Nikopensius, Tit; Metspalu, Andres; Strachan, David P; Monda, Keri; Qi, Lu; North, Kari E; Cupples, L Adrienne; Gordon-Larsen, Penny; Berndt, Sonja I
2013-09-01
Genetic loci for body mass index (BMI) in adolescence and young adulthood, a period of high risk for weight gain, are understudied, yet may yield important insight into the etiology of obesity and early intervention. To identify novel genetic loci and examine the influence of known loci on BMI during this critical time period in late adolescence and early adulthood, we performed a two-stage meta-analysis using 14 genome-wide association studies in populations of European ancestry with data on BMI between ages 16 and 25 in up to 29 880 individuals. We identified seven independent loci (P < 5.0 × 10⁻⁸) near FTO (P = 3.72 × 10⁻²³), TMEM18 (P = 3.24 × 10⁻¹⁷), MC4R (P = 4.41 × 10⁻¹⁷), TNNI3K (P = 4.32 × 10⁻¹¹), SEC16B (P = 6.24 × 10⁻⁹), GNPDA2 (P = 1.11 × 10⁻⁸) and POMC (P = 4.94 × 10⁻⁸) as well as a potential secondary signal at the POMC locus (rs2118404, P = 2.4 × 10⁻⁵ after conditioning on the established single-nucleotide polymorphism at this locus) in adolescents and young adults. To evaluate the impact of the established genetic loci on BMI at these young ages, we examined differences between the effect sizes of 32 published BMI loci in European adult populations (aged 18-90) and those observed in our adolescent and young adult meta-analysis. Four loci (near PRKD1, TNNI3K, SEC16B and CADM2) had larger effects and one locus (near SH2B1) had a smaller effect on BMI during adolescence and young adulthood compared with older adults (P < 0.05). These results suggest that genetic loci for BMI can vary in their effects across the life course, underlying the importance of evaluating BMI at different ages.
Graff, Mariaelisa; Ngwa, Julius S.; Workalemahu, Tsegaselassie; Homuth, Georg; Schipf, Sabine; Teumer, Alexander; Völzke, Henry; Wallaschofski, Henri; Abecasis, Goncalo R.; Edward, Lakatta; Francesco, Cucca; Sanna, Serena; Scheet, Paul; Schlessinger, David; Sidore, Carlo; Xiao, Xiangjun; Wang, Zhaoming; Chanock, Stephen J.; Jacobs, Kevin B.; Hayes, Richard B.; Hu, Frank; Van Dam, Rob M.; Crout, Richard J.; Marazita, Mary L.; Shaffer, John R; Atwood, Larry D.; Fox, Caroline S.; Heard-Costa, Nancy L.; White, Charles; Choh, Audrey C.; Czerwinski, Stefan A.; Demerath, Ellen W.; Dyer, Thomas D.; Towne, Bradford; Amin, Najaf; Oostra, Ben A.; Van Duijn, Cornelia M.; Zillikens, M. Carola; Esko, Tõnu; Nelis, Mari; Nikopensius, Tit; Metspalu, Andres; Strachan, David P.; Monda, Keri; Qi, Lu; North, Kari E.; Cupples, L. Adrienne; Gordon-Larsen, Penny; Berndt, Sonja I.
2013-01-01
Genetic loci for body mass index (BMI) in adolescence and young adulthood, a period of high risk for weight gain, are understudied, yet may yield important insight into the etiology of obesity and early intervention. To identify novel genetic loci and examine the influence of known loci on BMI during this critical time period in late adolescence and early adulthood, we performed a two-stage meta-analysis using 14 genome-wide association studies in populations of European ancestry with data on BMI between ages 16 and 25 in up to 29 880 individuals. We identified seven independent loci (P < 5.0 × 10−8) near FTO (P = 3.72 × 10−23), TMEM18 (P = 3.24 × 10−17), MC4R (P = 4.41 × 10−17), TNNI3K (P = 4.32 × 10−11), SEC16B (P = 6.24 × 10−9), GNPDA2 (P = 1.11 × 10−8) and POMC (P = 4.94 × 10−8) as well as a potential secondary signal at the POMC locus (rs2118404, P = 2.4 × 10−5 after conditioning on the established single-nucleotide polymorphism at this locus) in adolescents and young adults. To evaluate the impact of the established genetic loci on BMI at these young ages, we examined differences between the effect sizes of 32 published BMI loci in European adult populations (aged 18–90) and those observed in our adolescent and young adult meta-analysis. Four loci (near PRKD1, TNNI3K, SEC16B and CADM2) had larger effects and one locus (near SH2B1) had a smaller effect on BMI during adolescence and young adulthood compared with older adults (P < 0.05). These results suggest that genetic loci for BMI can vary in their effects across the life course, underlying the importance of evaluating BMI at different ages. PMID:23669352
Klimentidis, Y C; Miller, G F; Shriver, M D
2009-01-01
Previous studies have shown a relationship between health-related phenotypes and the degree of African, European, or Native American genetic admixture, indicating that there may be a genetic component to these phenotypes. However, these relationships may be driven to a large extent by the environmental differences that co-vary with admixture differences between and within groups. In this study, we examine the relationship between genetic admixture and two phenotypic measurements that are potentially related to health: body mass index (BMI) and percent body fat (PBF). In addition to admixture proportions, we attempt to assess the influence of some environmental covariates by examining how the phenotypes vary with self-reported household income, education of parents, and physical activity level. Genetic, anthropometric, and environmental data were collected from 170 self-reported Hispanic and Native American university students in Albuquerque, NM. We examine the relationships between genetic admixture, phenotype, and environment in both the full sample, as well as in Hispanics and Native Americans separately. Among Hispanics, we find no significant relationship between genetic admixture and body composition. Among Native Americans, despite a small sample size, we find a statistically significant, negative relationship between European genetic admixture and PBF and BMI, after adjusting for other predictor variables. We compare our findings to previous research, and discuss their implications for understanding health disparities within and between ethnic groups.
Mitteroecker, Philipp; Bookstein, Fred
2009-03-01
Many classic quantitative genetic theories assume the covariance structure among adult phenotypic traits to be relatively static during evolution. But the cross-sectional covariance matrix arises from the joint variation of a large range of developmental processes and hence is not constant over the period during which a population of developing organisms is actually exposed to selection. To examine how development shapes the phenotypic covariance structure, we ordinate the age-specific covariance matrices of shape coordinates for craniofacial growth in rats and humans. The metric that we use for this purpose is given by the square root of the summed squared log relative eigenvalues. This is the natural metric on the space of positive-definite symmetric matrices, which we introduce and justify in a biometric context. In both species, the covariance matrices appear to change continually throughout the full period of postnatal development. The resulting ontogenetic trajectories alter their direction at major changes of the developmental programs whereas they are fairly straight in between. Consequently, phenotypic covariance matrices--and thus also response to selection--should be expected to vary both over ontogenetic and phylogenetic time scales as different phenotypes are necessarily produced by different developmental pathways.
Application of copulas to improve covariance estimation for partial least squares.
D'Angelo, Gina M; Weissfeld, Lisa A
2013-02-20
Dimension reduction techniques, such as partial least squares, are useful for computing summary measures and examining relationships in complex settings. Partial least squares requires an estimate of the covariance matrix as a first step in the analysis, making this estimate critical to the results. In addition, the covariance matrix also forms the basis for other techniques in multivariate analysis, such as principal component analysis and independent component analysis. This paper has been motivated by an example from an imaging study in Alzheimer's disease where there is complete separation between Alzheimer's and control subjects for one of the imaging modalities. This separation occurs in one block of variables and does not occur with the second block of variables resulting in inaccurate estimates of the covariance. We propose the use of a copula to obtain estimates of the covariance in this setting, where one set of variables comes from a mixture distribution. Simulation studies show that the proposed estimator is an improvement over the standard estimators of covariance. We illustrate the methods from the motivating example from a study in the area of Alzheimer's disease.
Li, Baoyue; Bruyneel, Luk; Lesaffre, Emmanuel
2014-05-20
A traditional Gaussian hierarchical model assumes a nested multilevel structure for the mean and a constant variance at each level. We propose a Bayesian multivariate multilevel factor model that assumes a multilevel structure for both the mean and the covariance matrix. That is, in addition to a multilevel structure for the mean we also assume that the covariance matrix depends on covariates and random effects. This allows to explore whether the covariance structure depends on the values of the higher levels and as such models heterogeneity in the variances and correlation structure of the multivariate outcome across the higher level values. The approach is applied to the three-dimensional vector of burnout measurements collected on nurses in a large European study to answer the research question whether the covariance matrix of the outcomes depends on recorded system-level features in the organization of nursing care, but also on not-recorded factors that vary with countries, hospitals, and nursing units. Simulations illustrate the performance of our modeling approach.
Retrospective Likelihood Based Methods for Analyzing Case-Cohort Genetic Association Studies
Shen, Yuanyuan; Cai, Tianxi; Chen, Yu; Yang, Ying; Chen, Jinbo
2016-01-01
Summary The case cohort (CCH) design is a cost effective design for assessing genetic susceptibility with time-to-event data especially when the event rate is low. In this work, we propose a powerful pseudo score test for assessing the association between a single nucleotide polymorphism (SNP) and the event time under the CCH design. The pseudo score is derived from a pseudo likelihood which is an estimated retrospective likelihood that treats the SNP genotype as the dependent variable and time-to-event outcome and other covariates as independent variables. It exploits the fact that the genetic variable is often distributed independent of covariates or only related to a low-dimensional subset. Estimates of hazard ratio parameters for association can be obtained by maximizing the pseudo likelihood. A unique advantage of our method is that it allows the censoring distribution to depend on covariates that are only measured for the CCH sample while not requiring the knowledge of follow up or covariate information on subjects not selected into the CCH sample. In addition to these flexibilities, the proposed method has high relative efficiency compared with commonly used alternative approaches. We study large sample properties of this method and assess its finite sample performance using both simulated and real data examples. PMID:26177343
Shape covariation between the craniofacial complex and first molars in humans
Polychronis, Georgios; Halazonetis, Demetrios J
2014-01-01
The occurrence of mutual genetic loci in morphogenesis of the face and teeth implies shape covariation between these structures. However, teeth finalize their shape at an early age, whereas the face grows and is subjected to environmental influences for a prolonged period; it is therefore conceivable that covariation might modulate with age. Here we investigate the extent of this covariation in humans by measuring the 3D shape of the occlusal surface of the permanent first molars and the shape of the craniofacial complex from lateral radiographs, at two maturations stages. A sample of Greek subjects was divided into two groups (110 adult, 110 prepubertal) with equally distributed gender. The occlusal surfaces of the right first molars were 3D scanned from dental casts; 265 and 274 landmarks (including surface and curve semilandmarks) were digitized on the maxillary and mandibular molars, respectively. The corresponding lateral cephalometric radiographs were digitized with 71 landmarks. Geometric morphometric methods were used to assess shape variation and covariation. The vertical dimension of the craniofacial complex was the main parameter of shape variation, followed by anteroposterior deviations. The male craniofacial complex was larger (4.0–5.7%) and was characterized by a prominent chin and clockwise rotation of the cranial base (adult group only). Allometry was weak and statistically significant only when examined for the sample as a whole (percent variance explained: 2.1%, P = 0.0002). Covariation was statistically significant only between the lower first molar and the craniofacial complex (RV = 14.05%, P = 0.0099, and RV = 12.31%, P = 0.0162, for the prepubertal and adult groups, respectively). Subtle age-related covariation differences were noted, indicating that environmental factors may influence the pattern and strength of covariation. However, the main pattern was similar in both groups: a class III skeletal pattern (relative maxillary retrusion and
Noisy covariance matrices and portfolio optimization
NASA Astrophysics Data System (ADS)
Pafka, S.; Kondor, I.
2002-05-01
According to recent findings [#!bouchaud!#,#!stanley!#], empirical covariance matrices deduced from financial return series contain such a high amount of noise that, apart from a few large eigenvalues and the corresponding eigenvectors, their structure can essentially be regarded as random. In [#!bouchaud!#], e.g., it is reported that about 94% of the spectrum of these matrices can be fitted by that of a random matrix drawn from an appropriately chosen ensemble. In view of the fundamental role of covariance matrices in the theory of portfolio optimization as well as in industry-wide risk management practices, we analyze the possible implications of this effect. Simulation experiments with matrices having a structure such as described in [#!bouchaud!#,#!stanley!#] lead us to the conclusion that in the context of the classical portfolio problem (minimizing the portfolio variance under linear constraints) noise has relatively little effect. To leading order the solutions are determined by the stable, large eigenvalues, and the displacement of the solution (measured in variance) due to noise is rather small: depending on the size of the portfolio and on the length of the time series, it is of the order of 5 to 15%. The picture is completely different, however, if we attempt to minimize the variance under non-linear constraints, like those that arise e.g. in the problem of margin accounts or in international capital adequacy regulation. In these problems the presence of noise leads to a serious instability and a high degree of degeneracy of the solutions.
Covariant perturbations in a multifluid cosmological medium
NASA Astrophysics Data System (ADS)
Dunsby, Peter K. S.; Bruni, Marco; Ellis, George F. R.
1992-08-01
In a series of recent papers, a new covariant formalism was introduced to treat inhomogeneities in any spacetime. The variables introduced in these papers are gauge-invariant with respect to a Robertson-Walker background spacetime because they vanish identically in such models, and they have a transparent physical meaning. Exact evolution equations were found for these variables, and the linearized form of these equations were obtained, showing that they give the standard results for a barotropic perfect fluid. In this paper we extend this formalism to the general case of multicomponent fluid sources with interactions between them. We show, using the tilted formalism of King and Ellis, (1973) that choosing either the energy frame or the particle frame gives rise to a set of physically well-defined covariant and gauge-invariant variables which describe density and velocity perturbations, both for the total fluid and its constituent components. We then derive a complete set of equations for these variables and show, through harmonic analysis, that they are equivalent to those of Bardeen (1980) and of Kodama and Sasaki (1984). We discuss a number of interesting applications, including the case where the universe is filled with a mixture of baryons and radiation, coupled through Thomson scattering, and we derive solutions for the density and velocity perturbations in the large-scale limit. We also correct a number of errors in the previous literature.
Modeling Covariance Matrices via Partial Autocorrelations
Daniels, M.J.; Pourahmadi, M.
2009-01-01
Summary We study the role of partial autocorrelations in the reparameterization and parsimonious modeling of a covariance matrix. The work is motivated by and tries to mimic the phenomenal success of the partial autocorrelations function (PACF) in model formulation, removing the positive-definiteness constraint on the autocorrelation function of a stationary time series and in reparameterizing the stationarity-invertibility domain of ARMA models. It turns out that once an order is fixed among the variables of a general random vector, then the above properties continue to hold and follows from establishing a one-to-one correspondence between a correlation matrix and its associated matrix of partial autocorrelations. Connections between the latter and the parameters of the modified Cholesky decomposition of a covariance matrix are discussed. Graphical tools similar to partial correlograms for model formulation and various priors based on the partial autocorrelations are proposed. We develop frequentist/Bayesian procedures for modelling correlation matrices, illustrate them using a real dataset, and explore their properties via simulations. PMID:20161018
A Class of Population Covariance Matrices in the Bootstrap Approach to Covariance Structure Analysis
ERIC Educational Resources Information Center
Yuan, Ke-Hai; Hayashi, Kentaro; Yanagihara, Hirokazu
2007-01-01
Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given…
Evaluating covariance in prognostic and system health management applications
NASA Astrophysics Data System (ADS)
Menon, Sandeep; Jin, Xiaohang; Chow, Tommy W. S.; Pecht, Michael
2015-06-01
Developing a diagnostic and prognostic health management system involves analyzing system parameters monitored during the lifetime of the system. This data analysis may involve multiple steps, including data reduction, feature extraction, clustering and classification, building control charts, identification of anomalies, and modeling and predicting parameter degradation in order to evaluate the state of health for the system under investigation. Evaluating the covariance between the monitored system parameters allows for better understanding of the trends in monitored system data, and therefore it is an integral part of the data analysis. Typically, a sample covariance matrix is used to evaluate the covariance between monitored system parameters. The monitored system data are often sensor data, which are inherently noisy. The noise in sensor data can lead to inaccurate evaluation of the covariance in data using a sample covariance matrix. This paper examines approaches to evaluate covariance, including the minimum volume ellipsoid, the minimum covariance determinant, and the nearest neighbor variance estimation. When the performance of these approaches was evaluated on datasets with increasing percentage of Gaussian noise, it was observed that the nearest neighbor variance estimation exhibited the most stable estimates of covariance. To improve the accuracy of covariance estimates using nearest neighbor-based methodology, a modified approach for the nearest neighbor variance estimation technique is developed in this paper. Case studies based on data analysis steps involved in prognostic solutions are developed in order to compare the performance of the covariance estimation methodologies discussed in the paper.
The Impact of Covariate Measurement Error on Risk Prediction
Khudyakov, Polyna; Gorfine, Malka; Zucker, David; Spiegelman, Donna
2015-01-01
In the development of risk prediction models, predictors are often measured with error. In this paper, we investigate the impact of covariate measurement error on risk prediction. We compare the prediction performance using a costly variable measured without error, along with error-free covariates, to that of a model based on an inexpensive surrogate along with the error-free covariates. We consider continuous error-prone covariates with homoscedastic and heteroscedastic errors, and also a discrete misclassified covariate. Prediction performance is evaluated by the area under the receiver operating characteristic curve (AUC), the Brier score (BS), and the ratio of the observed to the expected number of events (calibration). In an extensive numerical study, we show that (i) the prediction model with the error-prone covariate is very well calibrated, even when it is mis-specified; (ii) using the error-prone covariate instead of the true covariate can reduce the AUC and increase the BS dramatically; (iii) adding an auxiliary variable, which is correlated with the error-prone covariate but conditionally independent of the outcome given all covariates in the true model, can improve the AUC and BS substantially. We conclude that reducing measurement error in covariates will improve the ensuing risk prediction, unless the association between the error-free and error-prone covariates is very high. Finally, we demonstrate how a validation study can be used to assess the effect of mismeasured covariates on risk prediction. These concepts are illustrated in a breast cancer risk prediction model developed in the Nurses’ Health Study. PMID:25865315
NASA Astrophysics Data System (ADS)
Petersen, Arthur C.; Holtslag, Albert A. M.
1999-12-01
Covariances and fluxes of reactive species in the clear convective atmospheric boundary layer (CABL) are studied and parameterized. The covariances result from correlations between reactive species. These covariances may have a considerable influence on the modeled reaction rates in atmospheric chemistry models, but usually are neglected. To facilitate the representation of covariance effects in large-scale atmospheric chemistry models, the authors have developed a new first-order closure for covariances. The closure is based on top-hat distributions, as is common in mass-flux schemes. In addition, the authors utilize an existing nonlocal first-order closure expression for the flux, which represents the combined effects of gradient mixing and nonlocal convective mixing. The authors show how the latter also includes the impact of chemistry on the nonlocal flux contribution. The impact of the closures is illustrated first for artificial, simple chemistry cases. The results are evaluated using large-eddy simulation (LES). By comparing results for the entraining and solid-lid CABL it is established that the covariance closure works satisfactorily away from the inversion. Subsequently, the closures are evaluated against LES for a photochemical case with 10 reactions involving six modeled species. The accuracy of the modeled covariances is found to be within a factor of 2, which is sufficient to improve the modeled concentrations.
Lyapunov Exponents and Covariant Vectors for Turbulent Flow Simulations
NASA Astrophysics Data System (ADS)
Blonigan, Patrick; Murman, Scott; Fernandez, Pablo; Wang, Qiqi
2016-11-01
As computational power increases, engineers are beginning to use scale-resolving turbulent flow simulations for applications in which jets, wakes, and separation dominate. However, the chaotic dynamics exhibited by scale-resolving simulations poses problems for the conventional sensitivity analysis and stability analysis approaches that are vital for design and control. Lyapunov analysis is used to study the chaotic behavior of dynamical systems, including flow simulations. Lyapunov exponents are the growth or a decay rate of specific flow field perturbations called the Lyapunov covariant vectors. Recently, the authors have used Lyapunov analysis to study the breakdown in conventional sensitivity analysis and the cost of new shadowing-based sensitivity analysis. The current work reviews Lyapunov analysis and presents new results for a DNS of turbulent channel flow, wall-modeled channel flow, and a DNS of a low pressure turbine blade. Additionally, the implications of these Lyapunov analyses for computing sensitivities of these flow simulations will be discussed.
Coupled singular value decomposition of a cross-covariance matrix.
Kaiser, Alexander; Schenck, Wolfram; Möller, Ralf
2010-08-01
We derive coupled on-line learning rules for the singular value decomposition (SVD) of a cross-covariance matrix. In coupled SVD rules, the singular value is estimated alongside the singular vectors, and the effective learning rates for the singular vector rules are influenced by the singular value estimates. In addition, we use a first-order approximation of Gram-Schmidt orthonormalization as decorrelation method for the estimation of multiple singular vectors and singular values. Experiments on synthetic data show that coupled learning rules converge faster than Hebbian learning rules and that the first-order approximation of Gram-Schmidt orthonormalization produces more precise estimates and better orthonormality than the standard deflation method.
Orbit Determination Covariance Analysis for the Europa Clipper Mission
NASA Technical Reports Server (NTRS)
Ionasescu, Rodica; Martin-Mur, Tomas; Valerino, Powtawche; Criddle, Kevin; Buffington, Brent; McElrath, Timothy
2014-01-01
A new Jovian satellite tour is proposed by NASA, which would include numerous flybys of the moon Europa, and would explore its potential habitability by characterizing the existence of any water within and beneath Europa's ice shell. This paper describes the results of a covariance study that was undertaken on a sample tour to assess the navigational challenges and capabilities of such a mission from an orbit determination (OD) point of view, and to help establish a delta V budget for the maneuvers needed to keep the spacecraft on the reference trajectory. Additional parametric variations from the baseline case were also investigated. The success of the Europa Clipper mission will depend on the science measurements that it will enable. Meeting the requirements of the instruments onboard the spacecraft is an integral part of this analysis.
Morrissey, Michael B; Ferguson, Moira M
2011-04-01
In addition to the well-studied evolutionary parameters of (1) phenotype-fitness covariance and (2) the genetic basis of phenotypic variation, adaptive evolution by natural selection requires that (3) fitness variation is effected by heritable genetic differences among individuals and (4) phenotype-fitness covariances must be, at least in part, underlain by genetic covariances. These latter two requirements for adaptive evolutionary change are relatively unstudied in natural populations. Absence of the latter requirements could explain stasis of apparently directionally selected heritable traits. We provide complementary analyses of selection and variation at phenotypic and genetic levels for juvenile growth rate in brook charr Salvelinus fontinalis in Freshwater River, Newfoundland, Canada. Contrary to the vast majority of reports in fish, we found very little viability selection of juvenile body size. Large body size appears nonetheless to be selectively advantageous via a relationship with early maturity. Genetic patterns in evolutionary parameters largely reflected phenotypic patterns. We have provided inference of selection based on longitudinal data, which are uncommon in high fecundity organisms. Furthermore we have provided a practicable framework for further studies of the genetic basis of natural selection.
Noisy covariance matrices and portfolio optimization II
NASA Astrophysics Data System (ADS)
Pafka, Szilárd; Kondor, Imre
2003-03-01
Recent studies inspired by results from random matrix theory (Galluccio et al.: Physica A 259 (1998) 449; Laloux et al.: Phys. Rev. Lett. 83 (1999) 1467; Risk 12 (3) (1999) 69; Plerou et al.: Phys. Rev. Lett. 83 (1999) 1471) found that covariance matrices determined from empirical financial time series appear to contain such a high amount of noise that their structure can essentially be regarded as random. This seems, however, to be in contradiction with the fundamental role played by covariance matrices in finance, which constitute the pillars of modern investment theory and have also gained industry-wide applications in risk management. Our paper is an attempt to resolve this embarrassing paradox. The key observation is that the effect of noise strongly depends on the ratio r= n/ T, where n is the size of the portfolio and T the length of the available time series. On the basis of numerical experiments and analytic results for some toy portfolio models we show that for relatively large values of r (e.g. 0.6) noise does, indeed, have the pronounced effect suggested by Galluccio et al. (1998), Laloux et al. (1999) and Plerou et al. (1999) and illustrated later by Laloux et al. (Int. J. Theor. Appl. Finance 3 (2000) 391), Plerou et al. (Phys. Rev. E, e-print cond-mat/0108023) and Rosenow et al. (Europhys. Lett., e-print cond-mat/0111537) in a portfolio optimization context, while for smaller r (around 0.2 or below), the error due to noise drops to acceptable levels. Since the length of available time series is for obvious reasons limited in any practical application, any bound imposed on the noise-induced error translates into a bound on the size of the portfolio. In a related set of experiments we find that the effect of noise depends also on whether the problem arises in asset allocation or in a risk measurement context: if covariance matrices are used simply for measuring the risk of portfolios with a fixed composition rather than as inputs to optimization, the
AFCI-2.0 Library of Neutron Cross Section Covariances
Herman, M.; Herman,M.; Oblozinsky,P.; Mattoon,C.; Pigni,M.; Hoblit,S.; Mughabghab,S.F.; Sonzogni,A.; Talou,P.; Chadwick,M.B.; Hale.G.M.; Kahler,A.C.; Kawano,T.; Little,R.C.; Young,P.G.
2011-06-26
Neutron cross section covariance library has been under development by BNL-LANL collaborative effort over the last three years. The primary purpose of the library is to provide covariances for the Advanced Fuel Cycle Initiative (AFCI) data adjustment project, which is focusing on the needs of fast advanced burner reactors. The covariances refer to central values given in the 2006 release of the U.S. neutron evaluated library ENDF/B-VII. The preliminary version (AFCI-2.0beta) has been completed in October 2010 and made available to the users for comments. In the final 2.0 release, covariances for a few materials were updated, in particular new LANL evaluations for {sup 238,240}Pu and {sup 241}Am were adopted. BNL was responsible for covariances for structural materials and fission products, management of the library and coordination of the work, while LANL was in charge of covariances for light nuclei and for actinides.
Spatially covariant theories of a transverse, traceless graviton: Formalism
NASA Astrophysics Data System (ADS)
Khoury, Justin; Miller, Godfrey E. J.; Tolley, Andrew J.
2012-04-01
General relativity is a generally covariant, locally Lorentz covariant theory of two transverse, traceless graviton degrees of freedom. According to a theorem of Hojman, Kuchař, and Teitelboim, modifications of general relativity must either introduce new degrees of freedom or violate the principle of local Lorentz covariance. In this paper, we explore modifications of general relativity that retain the same graviton degrees of freedom, and therefore explicitly break Lorentz covariance. Motivated by cosmology, the modifications of interest maintain explicit spatial covariance. In spatially covariant theories of the graviton, the physical Hamiltonian density obeys an analogue of the renormalization group equation which encodes invariance under flow through the space of conformally equivalent spatial metrics. This paper is dedicated to setting up the formalism of our approach and applying it to a realistic class of theories. Forthcoming work will apply the formalism more generally.
Davies, Christopher E; Giles, Lynne C; Glonek, Gary Fv
2017-01-01
One purpose of a longitudinal study is to gain insight of how characteristics at earlier points in time can impact on subsequent outcomes. Typically, the outcome variable varies over time and the data for each individual can be used to form a discrete path of measurements, that is a trajectory. Group-based trajectory modelling methods seek to identify subgroups of individuals within a population with trajectories that are more similar to each other than to trajectories in distinct groups. An approach to modelling the influence of covariates measured at earlier time points in the group-based setting is to consider models wherein these covariates affect the group membership probabilities. Models in which prior covariates impact the trajectories directly are also possible but are not considered here. In the present study, we compared six different methods for estimating the effect of covariates on the group membership probabilities, which have different approaches to account for the uncertainty in the group membership assignment. We found that when investigating the effect of one or several covariates on a group-based trajectory model, the full likelihood approach minimized the bias in the estimate of the covariate effect. In this '1-step' approach, the estimation of the effect of covariates and the trajectory model are carried out simultaneously. Of the '3-step' approaches, where the effect of the covariates is assessed subsequent to the estimation of the group-based trajectory model, only Vermunt's improved 3 step resulted in bias estimates similar in size to the full likelihood approach. The remaining methods considered resulted in considerably higher bias in the covariate effect estimates and should not be used. In addition to the bias empirically demonstrated for the probability regression approach, we have shown analytically that it is biased in general.
Power series evaluation of transition and covariance matrices.
NASA Technical Reports Server (NTRS)
Bierman, G. J.
1972-01-01
Reexamination power series solutions to the matrix covariance differential equation and the transition differential equation. Truncation error bounds are derived which are computationally attractive and which extend previous results. Polynomial approximations are obtained by exploiting the functional equations satisfied by the transition and covariance matrices. The series-functional equation propagation technique represents a fast and accurate alternative to the numerical integration of the time-invariant transition and covariance equations.
Bayesian hierarchical model for large-scale covariance matrix estimation.
Zhu, Dongxiao; Hero, Alfred O
2007-12-01
Many bioinformatics problems implicitly depend on estimating large-scale covariance matrix. The traditional approaches tend to give rise to high variance and low accuracy due to "overfitting." We cast the large-scale covariance matrix estimation problem into the Bayesian hierarchical model framework, and introduce dependency between covariance parameters. We demonstrate the advantages of our approaches over the traditional approaches using simulations and OMICS data analysis.
Dutoit, Ludovic; Vijay, Nagarjun; Mugal, Carina F; Bossu, Christen M; Burri, Reto; Wolf, Jochen; Ellegren, Hans
2017-02-22
Closely related species may show similar levels of genetic diversity in homologous regions of the genome owing to shared ancestral variation still segregating in the extant species. However, after completion of lineage sorting, such covariation is not necessarily expected. On the other hand, if the processes that govern genetic diversity are conserved, diversity may potentially covary even among distantly related species. We mapped regions of conserved synteny between the genomes of two divergent bird species-collared flycatcher and hooded crow-and identified more than 600 Mb of homologous regions (66% of the genome). From analyses of whole-genome resequencing data in large population samples of both species we found nucleotide diversity in 200 kb windows to be well correlated (Spearman's ρ = 0.407). The correlation remained highly similar after excluding coding sequences. To explain this covariation, we suggest that a stable avian karyotype and a conserved landscape of recombination rate variation render the diversity-reducing effects of linked selection similar in divergent bird lineages. Principal component regression analysis of several potential explanatory variables driving heterogeneity in flycatcher diversity levels revealed the strongest effects from recombination rate variation and density of coding sequence targets for selection, consistent with linked selection. It is also possible that a stable karyotype is associated with a conserved genomic mutation environment contributing to covariation in diversity levels between lineages. Our observations imply that genetic diversity is to some extent predictable.
Dutoit, Ludovic; Vijay, Nagarjun; Mugal, Carina F.; Bossu, Christen M.; Burri, Reto; Wolf, Jochen
2017-01-01
Closely related species may show similar levels of genetic diversity in homologous regions of the genome owing to shared ancestral variation still segregating in the extant species. However, after completion of lineage sorting, such covariation is not necessarily expected. On the other hand, if the processes that govern genetic diversity are conserved, diversity may potentially covary even among distantly related species. We mapped regions of conserved synteny between the genomes of two divergent bird species—collared flycatcher and hooded crow—and identified more than 600 Mb of homologous regions (66% of the genome). From analyses of whole-genome resequencing data in large population samples of both species we found nucleotide diversity in 200 kb windows to be well correlated (Spearman's ρ = 0.407). The correlation remained highly similar after excluding coding sequences. To explain this covariation, we suggest that a stable avian karyotype and a conserved landscape of recombination rate variation render the diversity-reducing effects of linked selection similar in divergent bird lineages. Principal component regression analysis of several potential explanatory variables driving heterogeneity in flycatcher diversity levels revealed the strongest effects from recombination rate variation and density of coding sequence targets for selection, consistent with linked selection. It is also possible that a stable karyotype is associated with a conserved genomic mutation environment contributing to covariation in diversity levels between lineages. Our observations imply that genetic diversity is to some extent predictable. PMID:28202815
EMPIRE ULTIMATE EXPANSION: RESONANCES AND COVARIANCES.
HERMAN,M.; MUGHABGHAB, S.F.; OBLOZINSKY, P.; ROCHMAN, D.; PIGNI, M.T.; KAWANO, T.; CAPOTE, R.; ZERKIN, V.; TRKOV, A.; SIN, M.; CARSON, B.V.; WIENKE, H. CHO, Y.-S.
2007-04-22
The EMPIRE code system is being extended to cover the resolved and unresolved resonance region employing proven methodology used for the production of new evaluations in the recent Atlas of Neutron Resonances. Another directions of Empire expansion are uncertainties and correlations among them. These include covariances for cross sections as well as for model parameters. In this presentation we concentrate on the KALMAN method that has been applied in EMPIRE to the fast neutron range as well as to the resonance region. We also summarize role of the EMPIRE code in the ENDF/B-VII.0 development. Finally, large scale calculations and their impact on nuclear model parameters are discussed along with the exciting perspectives offered by the parallel supercomputing.
Covariance of lucky images: performance analysis
NASA Astrophysics Data System (ADS)
Cagigal, Manuel P.; Valle, Pedro J.; Cagigas, Miguel A.; Villó-Pérez, Isidro; Colodro-Conde, Carlos; Ginski, C.; Mugrauer, M.; Seeliger, M.
2017-01-01
The covariance of ground-based lucky images is a robust and easy-to-use algorithm that allows us to detect faint companions surrounding a host star. In this paper, we analyse the relevance of the number of processed frames, the frames' quality, the atmosphere conditions and the detection noise on the companion detectability. This analysis has been carried out using both experimental and computer-simulated imaging data. Although the technique allows us the detection of faint companions, the camera detection noise and the use of a limited number of frames reduce the minimum detectable companion intensity to around 1000 times fainter than that of the host star when placed at an angular distance corresponding to the few first Airy rings. The reachable contrast could be even larger when detecting companions with the assistance of an adaptive optics system.
Conformal killing tensors and covariant Hamiltonian dynamics
Cariglia, M.; Gibbons, G. W.; Holten, J.-W. van; Horvathy, P. A.; Zhang, P.-M.
2014-12-15
A covariant algorithm for deriving the conserved quantities for natural Hamiltonian systems is combined with the non-relativistic framework of Eisenhart, and of Duval, in which the classical trajectories arise as geodesics in a higher dimensional space-time, realized by Brinkmann manifolds. Conserved quantities which are polynomial in the momenta can be built using time-dependent conformal Killing tensors with flux. The latter are associated with terms proportional to the Hamiltonian in the lower dimensional theory and with spectrum generating algebras for higher dimensional quantities of order 1 and 2 in the momenta. Illustrations of the general theory include the Runge-Lenz vector for planetary motion with a time-dependent gravitational constant G(t), motion in a time-dependent electromagnetic field of a certain form, quantum dots, the Hénon-Heiles and Holt systems, respectively, providing us with Killing tensors of rank that ranges from one to six.
Covariant generalization of cosmological perturbation theory
Enqvist, Kari; Hoegdahl, Janne; Nurmi, Sami; Vernizzi, Filippo
2007-01-15
We present an approach to cosmological perturbations based on a covariant perturbative expansion between two worldlines in the real inhomogeneous universe. As an application, at an arbitrary order we define an exact scalar quantity which describes the inhomogeneities in the number of e-folds on uniform density hypersurfaces and which is conserved on all scales for a barotropic ideal fluid. We derive a compact form for its conservation equation at all orders and assign it a simple physical interpretation. To make a comparison with the standard perturbation theory, we develop a method to construct gauge-invariant quantities in a coordinate system at arbitrary order, which we apply to derive the form of the nth order perturbation in the number of e-folds on uniform density hypersurfaces and its exact evolution equation. On large scales, this provides the gauge-invariant expression for the curvature perturbation on uniform density hypersurfaces and its evolution equation at any order.
Covariates of Craving in Actively Drinking Alcoholics
Chakravorty, Subhajit; Kuna, Samuel T.; Zaharakis, Nikola; O’Brien, Charles P.; Kampman, Kyle M.; Oslin, David
2010-01-01
The goal of this cross-sectional study was to assess the relationship of alcohol craving with biopsychosocial and addiction factors that are clinically pertinent to alcoholism treatment. Alcohol craving was assessed in 315 treatment-seeking, alcohol dependent subjects using the PACS questionnaire. Standard validated questionnaires were used to evaluate a variety of biological, addiction, psychological, psychiatric, and social factors. Individual covariates of craving included age, race, problematic consequences of drinking, heavy drinking, motivation for change, mood disturbance, sleep problems, and social supports. In a multivariate analysis (R2 = .34), alcohol craving was positively associated with mood disturbance, heavy drinking, readiness for change, and negatively associated with age. The results from this study suggest that alcohol craving is a complex phenomenon influenced by multiple factors. PMID:20716308
Yusuf, Mohd Aslam; Sarin, Neera Bhalla
2007-02-01
Alpha-tocopherol, the most biologically active form of vitamin E, is implicated in decreasing the risk of several types of cancers, coronary heart disease and a number of degenerative human conditions, when taken in excess of the recommended daily allowance. Natural alpha-tocopherol has twice the bioavailability of the synthetic isomer. This study describes a successful attempt at fortifying human diets with natural alpha-tocopherol by taking recourse to genetic engineering of an important oilseed crop, Brassica juncea. Gamma-tocopherol methyl transferase cDNA from Arabidopsis thaliana, coding for the enzyme catalysing the conversion of the large gamma-tocopherol pool to alpha-tocopherol, was overexpressed in B. juncea plants. The successful integration of the transgene was confirmed by PCR and Southern blot analysis, while the enhanced transcript level was evident in the northern blot analysis. HPLC analysis of the seeds of the T1 transgenic lines showed a shift in tocopherol profile with the highest over-expressors having alpha-tocopherol levels as high as sixfold over the non-transgenic controls. This study discusses the production of a transgenic oilseed crop with high alpha-tocopherol levels, which can provide a feasible, innocuous, and inexpensive way of taking the beneficial effects of high alpha-tocopherol intake to the masses.
Yin, Junming; Chen, Xi; Xing, Eric P.
2016-01-01
We consider the problem of sparse variable selection in nonparametric additive models, with the prior knowledge of the structure among the covariates to encourage those variables within a group to be selected jointly. Previous works either study the group sparsity in the parametric setting (e.g., group lasso), or address the problem in the nonparametric setting without exploiting the structural information (e.g., sparse additive models). In this paper, we present a new method, called group sparse additive models (GroupSpAM), which can handle group sparsity in additive models. We generalize the ℓ1/ℓ2 norm to Hilbert spaces as the sparsity-inducing penalty in GroupSpAM. Moreover, we derive a novel thresholding condition for identifying the functional sparsity at the group level, and propose an efficient block coordinate descent algorithm for constructing the estimate. We demonstrate by simulation that GroupSpAM substantially outperforms the competing methods in terms of support recovery and prediction accuracy in additive models, and also conduct a comparative experiment on a real breast cancer dataset.
Performance of internal covariance estimators for cosmic shear correlation functions
Friedrich, O.; Seitz, S.; Eifler, T. F.; Gruen, D.
2015-12-31
Data re-sampling methods such as the delete-one jackknife are a common tool for estimating the covariance of large scale structure probes. In this paper we investigate the concepts of internal covariance estimation in the context of cosmic shear two-point statistics. We demonstrate how to use log-normal simulations of the convergence field and the corresponding shear field to carry out realistic tests of internal covariance estimators and find that most estimators such as jackknife or sub-sample covariance can reach a satisfactory compromise between bias and variance of the estimated covariance. In a forecast for the complete, 5-year DES survey we show that internally estimated covariance matrices can provide a large fraction of the true uncertainties on cosmological parameters in a 2D cosmic shear analysis. The volume inside contours of constant likelihood in the $\\Omega_m$-$\\sigma_8$ plane as measured with internally estimated covariance matrices is on average $\\gtrsim 85\\%$ of the volume derived from the true covariance matrix. The uncertainty on the parameter combination $\\Sigma_8 \\sim \\sigma_8 \\Omega_m^{0.5}$ derived from internally estimated covariances is $\\sim 90\\%$ of the true uncertainty.
Performance of internal covariance estimators for cosmic shear correlation functions
Friedrich, O.; Seitz, S.; Eifler, T. F.; ...
2015-12-31
Data re-sampling methods such as the delete-one jackknife are a common tool for estimating the covariance of large scale structure probes. In this paper we investigate the concepts of internal covariance estimation in the context of cosmic shear two-point statistics. We demonstrate how to use log-normal simulations of the convergence field and the corresponding shear field to carry out realistic tests of internal covariance estimators and find that most estimators such as jackknife or sub-sample covariance can reach a satisfactory compromise between bias and variance of the estimated covariance. In a forecast for the complete, 5-year DES survey we show that internally estimated covariance matrices can provide a large fraction of the true uncertainties on cosmological parameters in a 2D cosmic shear analysis. The volume inside contours of constant likelihood in themore » $$\\Omega_m$$-$$\\sigma_8$$ plane as measured with internally estimated covariance matrices is on average $$\\gtrsim 85\\%$$ of the volume derived from the true covariance matrix. The uncertainty on the parameter combination $$\\Sigma_8 \\sim \\sigma_8 \\Omega_m^{0.5}$$ derived from internally estimated covariances is $$\\sim 90\\%$$ of the true uncertainty.« less
PRELIMINARY CROSS SECTION AND NU-BAR COVARIANCES FOR WPEC SUBGROUP 26
ROCHMAN,D.
2007-01-31
We report preliminary cross section covariances developed for the WPEC Subgroup 26 for 45 out of 52 requested materials. The covariances were produced in 15- and 187-group representations as follows: (1) 36 isotopes ({sup 16}O, {sup 19}F, {sup 23}Na, {sup 27}Al, {sup 28}Si, {sup 52}Cr, {sup 56,56}Fe, {sup 58}Ni, {sup 90,91,92,94}Zr, {sup 166,167,168,170}Er, {sup 206,207,208}Pb, {sup 209}Bi, {sup 233,234,236}U, {sup 237}Np, {sup 238,240,241,242}Pu, {sup 241,242m,243}Am, {sup 242,243,244,245}Cm) were evaluated using the BNL-LANL methodology. For the thermal region and the resolved and unresolved resonance regions, the methodology has been based on the Atlas-Kalman approach, in the fast neutron region the Empire-Kalman method has been used; (2) 6 isotopes ({sup 155,156,157,158,160}Gd and {sup 232}Th) were taken from ENDF/B-VII.0; and (3) 3 isotopes ({sup 1}H, {sup 238}U and {sup 239}Pu) were taken from JENDL-3.3. For 6 light nuclei ({sup 4}He, {sup 6,7}Li, {sup 9}Be, {sup 10}B, {sup 12}C), only partial cross section covariance results were obtained, additional work is needed and they do not report the results here. Likewise, the cross section covariances for {sup 235}U, which they recommend to take from JENDL-3.3, will be included once the multigroup processing is successfully completed. Covariances for the average number of neutrons per fission, total {nu}-bar, are provided for 10 actinides identified as priority by SG26. Further work is needed to resolve some of the issues and to produce covariances for the full set of 52 materials.
Alfred Stadler, Franz Gross
2010-10-01
We provide a short overview of the Covariant Spectator Theory and its applications. The basic ideas are introduced through the example of a {phi}{sup 4}-type theory. High-precision models of the two-nucleon interaction are presented and the results of their use in calculations of properties of the two- and three-nucleon systems are discussed. A short summary of applications of this framework to other few-body systems is also presented.
Bryan, M.F.; Piepel, G.F.; Simpson, D.B.
1996-03-01
The high-level waste (HLW) vitrification plant at the Hanford Site was being designed to transuranic and high-level radioactive waste in borosilicate class. Each batch of plant feed material must meet certain requirements related to plant performance, and the resulting class must meet requirements imposed by the Waste Acceptance Product Specifications. Properties of a process batch and the resultlng glass are largely determined by the composition of the feed material. Empirical models are being developed to estimate some property values from data on feed composition. Methods for checking and documenting compliance with feed and glass requirements must account for various types of uncertainties. This document focuses on the estimation. manipulation, and consequences of composition uncertainty, i.e., the uncertainty inherent in estimates of feed or glass composition. Three components of composition uncertainty will play a role in estimating and checking feed and glass properties: batch-to-batch variability, within-batch uncertainty, and analytical uncertainty. In this document, composition uncertainty and its components are treated in terms of variances and variance components or univariate situations, covariance matrices and covariance components for multivariate situations. The importance of variance and covariance components stems from their crucial role in properly estimating uncertainty In values calculated from a set of observations on a process batch. Two general types of methods for estimating uncertainty are discussed: (1) methods based on data, and (2) methods based on knowledge, assumptions, and opinions about the vitrification process. Data-based methods for estimating variances and covariance matrices are well known. Several types of data-based methods exist for estimation of variance components; those based on the statistical method analysis of variance are discussed, as are the strengths and weaknesses of this approach.
Spencer, Michael
1974-01-01
Food additives are discussed from the food technology point of view. The reasons for their use are summarized: (1) to protect food from chemical and microbiological attack; (2) to even out seasonal supplies; (3) to improve their eating quality; (4) to improve their nutritional value. The various types of food additives are considered, e.g. colours, flavours, emulsifiers, bread and flour additives, preservatives, and nutritional additives. The paper concludes with consideration of those circumstances in which the use of additives is (a) justified and (b) unjustified. PMID:4467857
Adams, Dean C.; Felice, Ryan N.
2014-01-01
Morphological integration describes the degree to which sets of organismal traits covary with one another. Morphological covariation may be evaluated at various levels of biological organization, but when characterizing such patterns across species at the macroevolutionary level, phylogeny must be taken into account. We outline an analytical procedure based on the evolutionary covariance matrix that allows species-level patterns of morphological integration among structures defined by sets of traits to be evaluated while accounting for the phylogenetic relationships among taxa, providing a flexible and robust complement to related phylogenetic independent contrasts based approaches. Using computer simulations under a Brownian motion model we show that statistical tests based on the approach display appropriate Type I error rates and high statistical power for detecting known levels of integration, and these trends remain consistent for simulations using different numbers of species, and for simulations that differ in the number of trait dimensions. Thus, our procedure provides a useful means of testing hypotheses of morphological integration in a phylogenetic context. We illustrate the utility of this approach by evaluating evolutionary patterns of morphological integration in head shape for a lineage of Plethodon salamanders, and find significant integration between cranial shape and mandible shape. Finally, computer code written in R for implementing the procedure is provided. PMID:24728003
Conditional Covariance Theory and Detect for Polytomous Items
ERIC Educational Resources Information Center
Zhang, Jinming
2007-01-01
This paper extends the theory of conditional covariances to polytomous items. It has been proven that under some mild conditions, commonly assumed in the analysis of response data, the conditional covariance of two items, dichotomously or polytomously scored, given an appropriately chosen composite is positive if, and only if, the two items…
Perturbative approach to covariance matrix of the matter power spectrum
Mohammed, Irshad; Seljak, Uros; Vlah, Zvonimir
2016-06-30
We evaluate the covariance matrix of the matter power spectrum using perturbation theory up to dominant terms at 1-loop order and compare it to numerical simulations. We decompose the covariance matrix into the disconnected (Gaussian) part, trispectrum from the modes outside the survey (beat coupling or super-sample variance), and trispectrum from the modes inside the survey, and show how the different components contribute to the overall covariance matrix. We find the agreement with the simulations is at a 10\\% level up to $k \\sim 1 h {\\rm Mpc^{-1}}$. We show that all the connected components are dominated by the large-scale modes ($k<0.1 h {\\rm Mpc^{-1}}$), regardless of the value of the wavevectors $k,\\, k'$ of the covariance matrix, suggesting that one must be careful in applying the jackknife or bootstrap methods to the covariance matrix. We perform an eigenmode decomposition of the connected part of the covariance matrix, showing that at higher $k$ it is dominated by a single eigenmode. The full covariance matrix can be approximated as the disconnected part only, with the connected part being treated as an external nuisance parameter with a known scale dependence, and a known prior on its variance for a given survey volume. Finally, we provide a prescription for how to evaluate the covariance matrix from small box simulations without the need to simulate large volumes.
Alternative Multiple Imputation Inference for Mean and Covariance Structure Modeling
ERIC Educational Resources Information Center
Lee, Taehun; Cai, Li
2012-01-01
Model-based multiple imputation has become an indispensable method in the educational and behavioral sciences. Mean and covariance structure models are often fitted to multiply imputed data sets. However, the presence of multiple random imputations complicates model fit testing, which is an important aspect of mean and covariance structure…
Covariate-Based Assignment to Treatment Groups: Some Simulation Results.
ERIC Educational Resources Information Center
Jain, Ram B.; Hsu, Tse-Chi
1980-01-01
Six estimators of treatment effect when assignment to treatment groups is based on the covariate are compared in terms of empirical standard errors and percent relative bias. Results show that simple analysis of covariance estimator is not always appropriate. (Author/GK)
Handling Correlations between Covariates and Random Slopes in Multilevel Models
ERIC Educational Resources Information Center
Bates, Michael David; Castellano, Katherine E.; Rabe-Hesketh, Sophia; Skrondal, Anders
2014-01-01
This article discusses estimation of multilevel/hierarchical linear models that include cluster-level random intercepts and random slopes. Viewing the models as structural, the random intercepts and slopes represent the effects of omitted cluster-level covariates that may be correlated with included covariates. The resulting correlations between…
Performance of internal covariance estimators for cosmic shear correlation functions
NASA Astrophysics Data System (ADS)
Friedrich, O.; Seitz, S.; Eifler, T. F.; Gruen, D.
2016-03-01
Data re-sampling methods such as delete-one jackknife, bootstrap or the sub-sample covariance are common tools for estimating the covariance of large-scale structure probes. We investigate different implementations of these methods in the context of cosmic shear two-point statistics. Using lognormal simulations of the convergence field and the corresponding shear field we generate mock catalogues of a known and realistic covariance. For a survey of {˜ } 5000 ° ^2 we find that jackknife, if implemented by deleting sub-volumes of galaxies, provides the most reliable covariance estimates. Bootstrap, in the common implementation of drawing sub-volumes of galaxies, strongly overestimates the statistical uncertainties. In a forecast for the complete 5-yr Dark Energy Survey, we show that internally estimated covariance matrices can provide a large fraction of the true uncertainties on cosmological parameters in a 2D cosmic shear analysis. The volume inside contours of constant likelihood in the Ωm-σ8 plane as measured with internally estimated covariance matrices is on average ≳85 per cent of the volume derived from the true covariance matrix. The uncertainty on the parameter combination Σ _8 ˜ σ _8 Ω _m^{0.5} derived from internally estimated covariances is ˜90 per cent of the true uncertainty.
Covariate Balance in Bayesian Propensity Score Approaches for Observational Studies
ERIC Educational Resources Information Center
Chen, Jianshen; Kaplan, David
2015-01-01
Bayesian alternatives to frequentist propensity score approaches have recently been proposed. However, few studies have investigated their covariate balancing properties. This article compares a recently developed two-step Bayesian propensity score approach to the frequentist approach with respect to covariate balance. The effects of different…
The Use of Covariation as a Principle of Causal Analysis
ERIC Educational Resources Information Center
Shultz, Thomas R.; Mendelson, Rosyln
1975-01-01
This study investigated the use of covariation as a principle of causal analysis in children 3-4, 6-7, and 9-11 years of age. The results indicated that children as young as 3 years were capable of using covariation information in their attributions of simple physical effects. (Author/CS)
Covariation Is a Poor Measure of Molecular Coevolution.
Talavera, David; Lovell, Simon C; Whelan, Simon
2015-09-01
Recent developments in the analysis of amino acid covariation are leading to breakthroughs in protein structure prediction, protein design, and prediction of the interactome. It is assumed that observed patterns of covariation are caused by molecular coevolution, where substitutions at one site affect the evolutionary forces acting at neighboring sites. Our theoretical and empirical results cast doubt on this assumption. We demonstrate that the strongest coevolutionary signal is a decrease in evolutionary rate and that unfeasibly long times are required to produce coordinated substitutions. We find that covarying substitutions are mostly found on different branches of the phylogenetic tree, indicating that they are independent events that may or may not be attributable to coevolution. These observations undermine the hypothesis that molecular coevolution is the primary cause of the covariation signal. In contrast, we find that the pairs of residues with the strongest covariation signal tend to have low evolutionary rates, and that it is this low rate that gives rise to the covariation signal. Slowly evolving residue pairs are disproportionately located in the protein's core, which explains covariation methods' ability to detect pairs of residues that are close in three dimensions. These observations lead us to propose the "coevolution paradox": The strength of coevolution required to cause coordinated changes means the evolutionary rate is so low that such changes are highly unlikely to occur. As modern covariation methods may lead to breakthroughs in structural genomics, it is critical to recognize their biases and limitations.
Empirical Performance of Covariates in Education Observational Studies
ERIC Educational Resources Information Center
Wong, Vivian C.; Valentine, Jeffrey C.; Miller-Bains, Kate
2017-01-01
This article summarizes results from 12 empirical evaluations of observational methods in education contexts. We look at the performance of three common covariate-types in observational studies where the outcome is a standardized reading or math test. They are: pretest measures, local geographic matching, and rich covariate sets with a strong…
Perturbative approach to covariance matrix of the matter power spectrum
NASA Astrophysics Data System (ADS)
Mohammed, Irshad; Seljak, Uroš; Vlah, Zvonimir
2017-04-01
We evaluate the covariance matrix of the matter power spectrum using perturbation theory up to dominant terms at 1-loop order and compare it to numerical simulations. We decompose the covariance matrix into the disconnected (Gaussian) part, trispectrum from the modes outside the survey (supersample variance) and trispectrum from the modes inside the survey, and show how the different components contribute to the overall covariance matrix. We find the agreement with the simulations is at a 10 per cent level up to k ∼ 1 h Mpc-1. We show that all the connected components are dominated by the large-scale modes (k < 0.1 h Mpc-1), regardless of the value of the wave vectors k, k΄ of the covariance matrix, suggesting that one must be careful in applying the jackknife or bootstrap methods to the covariance matrix. We perform an eigenmode decomposition of the connected part of the covariance matrix, showing that at higher k, it is dominated by a single eigenmode. The full covariance matrix can be approximated as the disconnected part only, with the connected part being treated as an external nuisance parameter with a known scale dependence, and a known prior on its variance for a given survey volume. Finally, we provide a prescription for how to evaluate the covariance matrix from small box simulations without the need to simulate large volumes.
What's the best statistic for a simple test of genetic association in a case-control study?
Kuo, Chia-Ling; Feingold, Eleanor
2010-04-01
Genome-wide genetic association studies typically start with univariate statistical tests of each marker. In principle, this single-SNP scanning is statistically straightforward--the testing is done with standard methods (e.g. chi(2) tests, regression) that have been well studied for decades. However, a number of different tests and testing procedures can be used. In a case-control study, one can use a 1 df allele-based test, a 1 or 2 df genotype-based test, or a compound procedure that combines two or more of these statistics. Additionally, most of the tests can be performed with or without covariates included in the model. While there are a number of statistical papers that make power comparisons among subsets of these methods, none has comprehensively tackled the question of which of the methods in common use is best suited to univariate scanning in a genome-wide association study. In this paper, we consider a wide variety of realistic test procedures, and first compare the power of the different procedures to detect a single locus under different genetic models. We then address the question of whether or when it is a good idea to include covariates in the analysis. We conclude that the most commonly used approach to handle covariates--modeling covariate main effects but not interactions--is almost never a good idea. Finally, we consider the performance of the statistics in a genome scan context.
Choosing covariates in the analysis of clinical trials.
Beach, M L; Meier, P
1989-12-01
Much of the literature on clinical trials emphasizes the importance of adjusting the results for any covariates (baseline variables) for which randomization fails to produce nearly exact balance, but the literature is very nearly devoid of recipes for assessing the consequences of such adjustments. Several years ago, Paul Canner presented an approximate expression for the effect of a covariate adjustment, and he considered its use in the selection of covariates. With the aid of Canner's equation, using both formal analysis and simulation, the impact of covariate adjustment is further explored. Unless tight control over the analysis plans is established in advance, covariate adjustment can lead to seriously misleading inferences. Illustrations from the clinical trials literature are provided.
HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS
Fan, Jianqing; Liao, Yuan; Mincheva, Martina
2012-01-01
The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied. PMID:22661790
HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS.
Fan, Jianqing; Liao, Yuan; Mincheva, Martina
2011-01-01
The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied.
UDU/T/ covariance factorization for Kalman filtering
NASA Technical Reports Server (NTRS)
Thornton, C. L.; Bierman, G. J.
1980-01-01
There has been strong motivation to produce numerically stable formulations of the Kalman filter algorithms because it has long been known that the original discrete-time Kalman formulas are numerically unreliable. Numerical instability can be avoided by propagating certain factors of the estimate error covariance matrix rather than the covariance matrix itself. This paper documents filter algorithms that correspond to the covariance factorization P = UDU(T), where U is a unit upper triangular matrix and D is diagonal. Emphasis is on computational efficiency and numerical stability, since these properties are of key importance in real-time filter applications. The history of square-root and U-D covariance filters is reviewed. Simple examples are given to illustrate the numerical inadequacy of the Kalman covariance filter algorithms; these examples show how factorization techniques can give improved computational reliability.
Central subspace dimensionality reduction using covariance operators.
Kim, Minyoung; Pavlovic, Vladimir
2011-04-01
We consider the task of dimensionality reduction informed by real-valued multivariate labels. The problem is often treated as Dimensionality Reduction for Regression (DRR), whose goal is to find a low-dimensional representation, the central subspace, of the input data that preserves the statistical correlation with the targets. A class of DRR methods exploits the notion of inverse regression (IR) to discover central subspaces. Whereas most existing IR techniques rely on explicit output space slicing, we propose a novel method called the Covariance Operator Inverse Regression (COIR) that generalizes IR to nonlinear input/output spaces without explicit target slicing. COIR's unique properties make DRR applicable to problem domains with high-dimensional output data corrupted by potentially significant amounts of noise. Unlike recent kernel dimensionality reduction methods that employ iterative nonconvex optimization, COIR yields a closed-form solution. We also establish the link between COIR, other DRR techniques, and popular supervised dimensionality reduction methods, including canonical correlation analysis and linear discriminant analysis. We then extend COIR to semi-supervised settings where many of the input points lack their labels. We demonstrate the benefits of COIR on several important regression problems in both fully supervised and semi-supervised settings.
Generalized Covariant Gyrokinetic Dynamics of Magnetoplasmas
Cremaschini, C.; Tessarotto, M.; Nicolini, P.; Beklemishev, A.
2008-12-31
A basic prerequisite for the investigation of relativistic astrophysical magnetoplasmas, occurring typically in the vicinity of massive stellar objects (black holes, neutron stars, active galactic nuclei, etc.), is the accurate description of single-particle covariant dynamics, based on gyrokinetic theory (Beklemishev et al., 1999-2005). Provided radiation-reaction effects are negligible, this is usually based on the assumption that both the space-time metric and the EM fields (in particular the magnetic field) are suitably prescribed and are considered independent of single-particle dynamics, while allowing for the possible presence of gravitational/EM perturbations driven by plasma collective interactions which may naturally arise in such systems. The purpose of this work is the formulation of a generalized gyrokinetic theory based on the synchronous variational principle recently pointed out (Tessarotto et al., 2007) which permits to satisfy exactly the physical realizability condition for the four-velocity. The theory here developed includes the treatment of nonlinear perturbations (gravitational and/or EM) characterized locally, i.e., in the rest frame of a test particle, by short wavelength and high frequency. Basic feature of the approach is to ensure the validity of the theory both for large and vanishing parallel electric field. It is shown that the correct treatment of EM perturbations occurring in the presence of an intense background magnetic field generally implies the appearance of appropriate four-velocity corrections, which are essential for the description of single-particle gyrokinetic dynamics.
Holographic bound in covariant loop quantum gravity
NASA Astrophysics Data System (ADS)
Tamaki, Takashi
2016-07-01
We investigate puncture statistics based on the covariant area spectrum in loop quantum gravity. First, we consider Maxwell-Boltzmann statistics with a Gibbs factor for punctures. We establish formulas which relate physical quantities such as horizon area to the parameter characterizing holographic degrees of freedom. We also perform numerical calculations and obtain consistency with these formulas. These results tell us that the holographic bound is satisfied in the large area limit and the correction term of the entropy-area law can be proportional to the logarithm of the horizon area. Second, we also consider Bose-Einstein statistics and show that the above formulas are also useful in this case. By applying the formulas, we can understand intrinsic features of Bose-Einstein condensate which corresponds to the case when the horizon area almost consists of punctures in the ground state. When this phenomena occurs, the area is approximately constant against the parameter characterizing the temperature. When this phenomena is broken, the area shows rapid increase which suggests the phase transition from quantum to classical area.
General covariance from the quantum renormalization group
NASA Astrophysics Data System (ADS)
Shyam, Vasudev
2017-03-01
The quantum renormalization group (QRG) is a realization of holography through a coarse-graining prescription that maps the beta functions of a quantum field theory thought to live on the "boundary" of some space to holographic actions in the "bulk" of this space. A consistency condition will be proposed that translates into general covariance of the gravitational theory in the D +1 dimensional bulk. This emerges from the application of the QRG on a planar matrix field theory living on the D dimensional boundary. This will be a particular form of the Wess-Zumino consistency condition that the generating functional of the boundary theory needs to satisfy. In the bulk, this condition forces the Poisson bracket algebra of the scalar and vector constraints of the dual gravitational theory to close in a very specific manner, namely, the manner in which the corresponding constraints of general relativity do. A number of features of the gravitational theory will be fixed as a consequence of this form of the Poisson bracket algebra. In particular, it will require the metric beta function to be of the gradient form.
Frame Indifferent (Truly Covariant) Formulation of Electrodynamics
NASA Astrophysics Data System (ADS)
Christov, Christo
2010-10-01
The Electromagnetic field is considered from the point of view of mechanics of continuum. It is shown that Maxwell's equations are mathematically strict corollaries form the equation of motions of an elastic incompressible liquid. If the concept of frame-indifference (material invariance) is applied to the model of elastic liquid, then the partial time derivatives have to be replaced by the convective time derivative in the momentum equations, and by the Oldroyd upper-convected derivative in the constitutive relation. The convective/convected terms involve the velocity at a point of the field, and as a result, when deriving the Maxwell form of the equations, one arrives at equations which contain both the terms of Maxwell's equation and the so-called laws of motional EMF: Faraday's, Oersted--Ampere's, and the Lorentz-force law. Thus a unification of the electromagnetism is achieved. Since the new model is frame indifferent, it is truly covariant in the sense that the governing system is invariant when changing to a coordinate frame that can accelerate or even deform in time.
Correcting eddy-covariance flux underestimates over a grassland.
Twine, T. E.; Kustas, W. P.; Norman, J. M.; Cook, D. R.; Houser, P. R.; Meyers, T. P.; Prueger, J. H.; Starks, P. J.; Wesely, M. L.; Environmental Research; Univ. of Wisconsin at Madison; DOE; National Aeronautics and Space Administration; National Oceanic and Atmospheric Administrationoratory
2000-06-08
Independent measurements of the major energy balance flux components are not often consistent with the principle of conservation of energy. This is referred to as a lack of closure of the surface energy balance. Most results in the literature have shown the sum of sensible and latent heat fluxes measured by eddy covariance to be less than the difference between net radiation and soil heat fluxes. This under-measurement of sensible and latent heat fluxes by eddy-covariance instruments has occurred in numerous field experiments and among many different manufacturers of instruments. Four eddy-covariance systems consisting of the same models of instruments were set up side-by-side during the Southern Great Plains 1997 Hydrology Experiment and all systems under-measured fluxes by similar amounts. One of these eddy-covariance systems was collocated with three other types of eddy-covariance systems at different sites; all of these systems under-measured the sensible and latent-heat fluxes. The net radiometers and soil heat flux plates used in conjunction with the eddy-covariance systems were calibrated independently and measurements of net radiation and soil heat flux showed little scatter for various sites. The 10% absolute uncertainty in available energy measurements was considerably smaller than the systematic closure problem in the surface energy budget, which varied from 10 to 30%. When available-energy measurement errors are known and modest, eddy-covariance measurements of sensible and latent heat fluxes should be adjusted for closure. Although the preferred method of energy balance closure is to maintain the Bowen-ratio, the method for obtaining closure appears to be less important than assuring that eddy-covariance measurements are consistent with conservation of energy. Based on numerous measurements over a sorghum canopy, carbon dioxide fluxes, which are measured by eddy covariance, are underestimated by the same factor as eddy covariance evaporation
NASA Astrophysics Data System (ADS)
Berge, Léonie; Litaize, Olivier; Serot, Olivier; Archier, Pascal; De Saint Jean, Cyrille; Pénéliau, Yannick; Regnier, David
2016-02-01
As the need for precise handling of nuclear data covariances grows ever stronger, no information about covariances of prompt fission neutron spectra (PFNS) are available in the evaluated library JEFF-3.2, although present in ENDF/B-VII.1 and JENDL-4.0 libraries for the main fissile isotopes. The aim of this work is to provide an estimation of covariance matrices related to PFNS, in the frame of some commonly used models for the evaluated files, such as the Maxwellian spectrum, the Watt spectrum, or the Madland-Nix spectrum. The evaluation of PFNS through these models involves an adjustment of model parameters to available experimental data, and the calculation of the spectrum variance-covariance matrix arising from experimental uncertainties. We present the results for thermal neutron induced fission of 235U. The systematic experimental uncertainties are propagated via the marginalization technique available in the CONRAD code. They are of great influence on the final covariance matrix, and therefore, on the spectrum uncertainty band width. In addition to this covariance estimation work, we have also investigated the importance on a reactor calculation of the fission spectrum model choice. A study of the vessel fluence depending on the PFNS model is presented. This is done through the propagation of neutrons emitted from a fission source in a simplified PWR using the TRIPOLI-4® code. This last study includes thermal fission spectra from the FIFRELIN Monte-Carlo code dedicated to the simulation of prompt particles emission during fission.
Functional Generalized Additive Models.
McLean, Mathew W; Hooker, Giles; Staicu, Ana-Maria; Scheipl, Fabian; Ruppert, David
2014-01-01
We introduce the functional generalized additive model (FGAM), a novel regression model for association studies between a scalar response and a functional predictor. We model the link-transformed mean response as the integral with respect to t of F{X(t), t} where F(·,·) is an unknown regression function and X(t) is a functional covariate. Rather than having an additive model in a finite number of principal components as in Müller and Yao (2008), our model incorporates the functional predictor directly and thus our model can be viewed as the natural functional extension of generalized additive models. We estimate F(·,·) using tensor-product B-splines with roughness penalties. A pointwise quantile transformation of the functional predictor is also considered to ensure each tensor-product B-spline has observed data on its support. The methods are evaluated using simulated data and their predictive performance is compared with other competing scalar-on-function regression alternatives. We illustrate the usefulness of our approach through an application to brain tractography, where X(t) is a signal from diffusion tensor imaging at position, t, along a tract in the brain. In one example, the response is disease-status (case or control) and in a second example, it is the score on a cognitive test. R code for performing the simulations and fitting the FGAM can be found in supplemental materials available online.
TRANSPOSABLE REGULARIZED COVARIANCE MODELS WITH AN APPLICATION TO MISSING DATA IMPUTATION.
Allen, Genevera I; Tibshirani, Robert
2010-06-01
Missing data estimation is an important challenge with high-dimensional data arranged in the form of a matrix. Typically this data matrix is transposable, meaning that either the rows, columns or both can be treated as features. To model transposable data, we present a modification of the matrix-variate normal, the mean-restricted matrix-variate normal, in which the rows and columns each have a separate mean vector and covariance matrix. By placing additive penalties on the inverse covariance matrices of the rows and columns, these so called transposable regularized covariance models allow for maximum likelihood estimation of the mean and non-singular covariance matrices. Using these models, we formulate EM-type algorithms for missing data imputation in both the multivariate and transposable frameworks. We present theoretical results exploiting the structure of our transposable models that allow these models and imputation methods to be applied to high-dimensional data. Simulations and results on microarray data and the Netflix data show that these imputation techniques often outperform existing methods and offer a greater degree of flexibility.
Predicting the risk of toxic blooms of golden alga from cell abundance and environmental covariates
Patino, Reynaldo; VanLandeghem, Matthew M.; Denny, Shawn
2016-01-01
Golden alga (Prymnesium parvum) is a toxic haptophyte that has caused considerable ecological damage to marine and inland aquatic ecosystems worldwide. Studies focused primarily on laboratory cultures have indicated that toxicity is poorly correlated with the abundance of golden alga cells. This relationship, however, has not been rigorously evaluated in the field where environmental conditions are much different. The ability to predict toxicity using readily measured environmental variables and golden alga abundance would allow managers rapid assessments of ichthyotoxicity potential without laboratory bioassay confirmation, which requires additional resources to accomplish. To assess the potential utility of these relationships, several a priori models relating lethal levels of golden alga ichthyotoxicity to golden alga abundance and environmental covariates were constructed. Model parameters were estimated using archived data from four river basins in Texas and New Mexico (Colorado, Brazos, Red, Pecos). Model predictive ability was quantified using cross-validation, sensitivity, and specificity, and the relative ranking of environmental covariate models was determined by Akaike Information Criterion values and Akaike weights. Overall, abundance was a generally good predictor of ichthyotoxicity as cross validation of golden alga abundance-only models ranged from ∼ 80% to ∼ 90% (leave-one-out cross-validation). Environmental covariates improved predictions, especially the ability to predict lethally toxic events (i.e., increased sensitivity), and top-ranked environmental covariate models differed among the four basins. These associations may be useful for monitoring as well as understanding the abiotic factors that influence toxicity during blooms.
Ryu, Duchwan; Li, Erning; Mallick, Bani K
2011-06-01
We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves.
TRANSPOSABLE REGULARIZED COVARIANCE MODELS WITH AN APPLICATION TO MISSING DATA IMPUTATION
Allen, Genevera I.; Tibshirani, Robert
2015-01-01
Missing data estimation is an important challenge with high-dimensional data arranged in the form of a matrix. Typically this data matrix is transposable, meaning that either the rows, columns or both can be treated as features. To model transposable data, we present a modification of the matrix-variate normal, the mean-restricted matrix-variate normal, in which the rows and columns each have a separate mean vector and covariance matrix. By placing additive penalties on the inverse covariance matrices of the rows and columns, these so called transposable regularized covariance models allow for maximum likelihood estimation of the mean and non-singular covariance matrices. Using these models, we formulate EM-type algorithms for missing data imputation in both the multivariate and transposable frameworks. We present theoretical results exploiting the structure of our transposable models that allow these models and imputation methods to be applied to high-dimensional data. Simulations and results on microarray data and the Netflix data show that these imputation techniques often outperform existing methods and offer a greater degree of flexibility. PMID:26877823
Quantifying lost information due to covariance matrix estimation in parameter inference
NASA Astrophysics Data System (ADS)
Sellentin, Elena; Heavens, Alan F.
2017-02-01
Parameter inference with an estimated covariance matrix systematically loses information due to the remaining uncertainty of the covariance matrix. Here, we quantify this loss of precision and develop a framework to hypothetically restore it, which allows to judge how far away a given analysis is from the ideal case of a known covariance matrix. We point out that it is insufficient to estimate this loss by debiasing the Fisher matrix as previously done, due to a fundamental inequality that describes how biases arise in non-linear functions. We therefore develop direct estimators for parameter credibility contours and the figure of merit, finding that significantly fewer simulations than previously thought are sufficient to reach satisfactory precisions. We apply our results to DES Science Verification weak lensing data, detecting a 10 per cent loss of information that increases their credibility contours. No significant loss of information is found for KiDS. For a Euclid-like survey, with about 10 nuisance parameters we find that 2900 simulations are sufficient to limit the systematically lost information to 1 per cent, with an additional uncertainty of about 2 per cent. Without any nuisance parameters, 1900 simulations are sufficient to only lose 1 per cent of information. We further derive estimators for all quantities needed for forecasting with estimated covariance matrices. Our formalism allows to determine the sweetspot between running sophisticated simulations to reduce the number of nuisance parameters, and running as many fast simulations as possible.
Tosteson, Tor D; Buzas, Jeffrey S; Demidenko, Eugene; Karagas, Margaret
2003-04-15
Covariate measurement error is often a feature of scientific data used for regression modelling. The consequences of such errors include a loss of power of tests of significance for the regression parameters corresponding to the true covariates. Power and sample size calculations that ignore covariate measurement error tend to overestimate power and underestimate the actual sample size required to achieve a desired power. In this paper we derive a novel measurement error corrected power function for generalized linear models using a generalized score test based on quasi-likelihood methods. Our power function is flexible in that it is adaptable to designs with a discrete or continuous scalar covariate (exposure) that can be measured with or without error, allows for additional confounding variables and applies to a broad class of generalized regression and measurement error models. A program is described that provides sample size or power for a continuous exposure with a normal measurement error model and a single normal confounder variable in logistic regression. We demonstrate the improved properties of our power calculations with simulations and numerical studies. An example is given from an ongoing study of cancer and exposure to arsenic as measured by toenail concentrations and tap water samples.
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Recurrence Analysis of Eddy Covariance Fluxes
NASA Astrophysics Data System (ADS)
Lange, Holger; Flach, Milan; Foken, Thomas; Hauhs, Michael
2015-04-01
The eddy covariance (EC) method is one key method to quantify fluxes in biogeochemical cycles in general, and carbon and energy transport across the vegetation-atmosphere boundary layer in particular. EC data from the worldwide net of flux towers (Fluxnet) have also been used to validate biogeochemical models. The high resolution data are usually obtained at 20 Hz sampling rate but are affected by missing values and other restrictions. In this contribution, we investigate the nonlinear dynamics of EC fluxes using Recurrence Analysis (RA). High resolution data from the site DE-Bay (Waldstein-Weidenbrunnen) and fluxes calculated at half-hourly resolution from eight locations (part of the La Thuile dataset) provide a set of very long time series to analyze. After careful quality assessment and Fluxnet standard gapfilling pretreatment, we calculate properties and indicators of the recurrent structure based both on Recurrence Plots as well as Recurrence Networks. Time series of RA measures obtained from windows moving along the time axis are presented. Their interpretation is guided by three different questions: (1) Is RA able to discern periods where the (atmospheric) conditions are particularly suitable to obtain reliable EC fluxes? (2) Is RA capable to detect dynamical transitions (different behavior) beyond those obvious from visual inspection? (3) Does RA contribute to an understanding of the nonlinear synchronization between EC fluxes and atmospheric parameters, which is crucial for both improving carbon flux models as well for reliable interpolation of gaps? (4) Is RA able to recommend an optimal time resolution for measuring EC data and for analyzing EC fluxes? (5) Is it possible to detect non-trivial periodicities with a global RA? We will demonstrate that the answers to all five questions is affirmative, and that RA provides insights into EC dynamics not easily obtained otherwise.
Inflation in general covariant theory of gravity
NASA Astrophysics Data System (ADS)
Huang, Yongqing; Wang, Anzhong; Wu, Qiang
2012-10-01
In this paper, we study inflation in the framework of the nonrelativistic general covariant theory of the Hořava-Lifshitz gravity with the projectability condition and an arbitrary coupling constant λ. We find that the Friedmann-Robterson-Walker (FRW) universe is necessarily flat in such a setup. We work out explicitly the linear perturbations of the flat FRW universe without specifying to a particular gauge, and find that the perturbations are different from those obtained in general relativity, because of the presence of the high-order spatial derivative terms. Applying the general formulas to a single scalar field, we show that in the sub-horizon regions, the metric and scalar field are tightly coupled and have the same oscillating frequencies. In the super-horizon regions, the perturbations become adiabatic, and the comoving curvature perturbation is constant. We also calculate the power spectra and indices of both the scalar and tensor perturbations, and express them explicitly in terms of the slow roll parameters and the coupling constants of the high-order spatial derivative terms. In particular, we find that the perturbations, of both scalar and tensor, are almost scale-invariant, and, with some reasonable assumptions on the coupling coefficients, the spectrum index of the tensor perturbation is the same as that given in the minimum scenario in general relativity (GR), whereas the index for scalar perturbation in general depends on λ and is different from the standard GR value. The ratio of the scalar and tensor power spectra depends on the high-order spatial derivative terms, and can be different from that of GR significantly.
Schwinger mechanism in linear covariant gauges
NASA Astrophysics Data System (ADS)
Aguilar, A. C.; Binosi, D.; Papavassiliou, J.
2017-02-01
In this work we explore the applicability of a special gluon mass generating mechanism in the context of the linear covariant gauges. In particular, the implementation of the Schwinger mechanism in pure Yang-Mills theories hinges crucially on the inclusion of massless bound-state excitations in the fundamental nonperturbative vertices of the theory. The dynamical formation of such excitations is controlled by a homogeneous linear Bethe-Salpeter equation, whose nontrivial solutions have been studied only in the Landau gauge. Here, the form of this integral equation is derived for general values of the gauge-fixing parameter, under a number of simplifying assumptions that reduce the degree of technical complexity. The kernel of this equation consists of fully dressed gluon propagators, for which recent lattice data are used as input, and of three-gluon vertices dressed by a single form factor, which is modeled by means of certain physically motivated Ansätze. The gauge-dependent terms contributing to this kernel impose considerable restrictions on the infrared behavior of the vertex form factor; specifically, only infrared finite Ansätze are compatible with the existence of nontrivial solutions. When such Ansätze are employed, the numerical study of the integral equation reveals a continuity in the type of solutions as one varies the gauge-fixing parameter, indicating a smooth departure from the Landau gauge. Instead, the logarithmically divergent form factor displaying the characteristic "zero crossing," while perfectly consistent in the Landau gauge, has to undergo a dramatic qualitative transformation away from it, in order to yield acceptable solutions. The possible implications of these results are briefly discussed.
Quantification of Covariance in Tropical Cyclone Activity across Teleconnected Basins
NASA Astrophysics Data System (ADS)
Tolwinski-Ward, S. E.; Wang, D.
2015-12-01
Rigorous statistical quantification of natural hazard covariance across regions has important implications for risk management, and is also of fundamental scientific interest. We present a multivariate Bayesian Poisson regression model for inferring the covariance in tropical cyclone (TC) counts across multiple ocean basins and across Saffir-Simpson intensity categories. Such covariability results from the influence of large-scale modes of climate variability on local environments that can alternately suppress or enhance TC genesis and intensification, and our model also simultaneously quantifies the covariance of TC counts with various climatic modes in order to deduce the source of inter-basin TC covariability. The model explicitly treats the time-dependent uncertainty in observed maximum sustained wind data, and hence the nominal intensity category of each TC. Differences in annual TC counts as measured by different agencies are also formally addressed. The probabilistic output of the model can be probed for probabilistic answers to such questions as: - Does the relationship between different categories of TCs differ statistically by basin? - Which climatic predictors have significant relationships with TC activity in each basin? - Are the relationships between counts in different basins conditionally independent given the climatic predictors, or are there other factors at play affecting inter-basin covariability? - How can a portfolio of insured property be optimized across space to minimize risk? Although we present results of our model applied to TCs, the framework is generalizable to covariance estimation between multivariate counts of natural hazards across regions and/or across peril types.
Action recognition from video using feature covariance matrices.
Guo, Kai; Ishwar, Prakash; Konrad, Janusz
2013-06-01
We propose a general framework for fast and accurate recognition of actions in video using empirical covariance matrices of features. A dense set of spatio-temporal feature vectors are computed from video to provide a localized description of the action, and subsequently aggregated in an empirical covariance matrix to compactly represent the action. Two supervised learning methods for action recognition are developed using feature covariance matrices. Common to both methods is the transformation of the classification problem in the closed convex cone of covariance matrices into an equivalent problem in the vector space of symmetric matrices via the matrix logarithm. The first method applies nearest-neighbor classification using a suitable Riemannian metric for covariance matrices. The second method approximates the logarithm of a query covariance matrix by a sparse linear combination of the logarithms of training covariance matrices. The action label is then determined from the sparse coefficients. Both methods achieve state-of-the-art classification performance on several datasets, and are robust to action variability, viewpoint changes, and low object resolution. The proposed framework is conceptually simple and has low storage and computational requirements making it attractive for real-time implementation.
2010-01-01
The objective of the present study was to estimate genetic parameters for test-day milk, fat and protein yields and 305-day-yields in Murrah buffaloes. 4,757 complete lactations of Murrah buffaloes were analyzed. Co-variance components were estimated by the restricted maximum likelihood method. The models included additive direct genetic and permanent environmental effects as random effects, and the fixed effects of contemporary group, milking number and age of the cow at calving as linear and quadratic covariables. Contemporary groups were defined by herd-year-month of test for test-day yields and by herd-year-season of calving for 305-day yields. The heritability estimates obtained by two-trait analysis ranged from 0.15 to 0.24 for milk, 0.16 to 0.23 for protein and 0.13 to 0.22 for fat, yields. Genetic and phenotypic correlations were all positive. The observed population additive genetic variation indicated that selection might be an effective tool in changing population means in milk, fat and protein yields. PMID:21637608
Fox, Andrew; Williams, Mathew; Richardson, Andrew D.; Cameron, David; Gove, Jeffrey H.; Quaife, Tristan; Ricciuto, Daniel M; Reichstein, Markus; Tomelleri, Enrico; Trudinger, Cathy; Van Wijk, Mark T.
2009-10-01
We describe a model-data fusion (MDF) inter-comparison project (REFLEX), which compared various algorithms for estimating carbon (C) model parameters consistent with both measured carbon fluxes and states and a simple C model. Participants were provided with the model and with both synthetic net ecosystem exchange (NEE) ofCO2 and leaf area index (LAI) data, generated from the model with added noise, and observed NEE and LAI data from two eddy covariance sites. Participants endeavoured to estimate model parameters and states consistent with the model for all cases over the two years for which data were provided, and generate predictions for one additional year without observations. Nine participants contributed results using Metropolis algorithms, Kalman filters and a genetic algorithm. For the synthetic data case, parameter estimates compared well with the true values. The results of the analyses indicated that parameters linked directly to gross primary production (GPP) and ecosystem respiration, such as those related to foliage allocation and turnover, or temperature sensitivity of heterotrophic respiration,were best constrained and characterised. Poorly estimated parameters were those related to the allocation to and turnover of fine root/wood pools. Estimates of confidence intervals varied among algorithms, but several algorithms successfully located the true values of annual fluxes from synthetic experiments within relatively narrow 90% confidence intervals, achieving>80% success rate and mean NEE confidence intervals <110 gCm-2 year-1 for the synthetic case. Annual C flux estimates generated by participants generally agreed with gap-filling approaches using half-hourly data. The estimation of ecosystem respiration and GPP through MDF agreed well with outputs from partitioning studies using half-hourly data. Confidence limits on annual NEE increased by an average of 88% in the prediction year compared to the previous year, when data were available. Confidence
Hawking radiation, covariant boundary conditions, and vacuum states
Banerjee, Rabin; Kulkarni, Shailesh
2009-04-15
The basic characteristics of the covariant chiral current
Reverse attenuation in interaction terms due to covariate measurement error.
Muff, Stefanie; Keller, Lukas F
2015-11-01
Covariate measurement error may cause biases in parameters of regression coefficients in generalized linear models. The influence of measurement error on interaction parameters has, however, only rarely been investigated in depth, and if so, attenuation effects were reported. In this paper, we show that also reverse attenuation of interaction effects may emerge, namely when heteroscedastic measurement error or sampling variances of a mismeasured covariate are present, which are not unrealistic scenarios in practice. Theoretical findings are illustrated with simulations. A Bayesian approach employing integrated nested Laplace approximations is suggested to model the heteroscedastic measurement error and covariate variances, and an application shows that the method is able to reveal approximately correct parameter estimates.
Covariance matrices and applications to the field of nuclear data
Smith, D.L.
1981-11-01
A student's introduction to covariance error analysis and least-squares evaluation of data is provided. It is shown that the basic formulas used in error propagation can be derived from a consideration of the geometry of curvilinear coordinates. Procedures for deriving covariances for scaler and vector functions of several variables are presented. Proper methods for reporting experimental errors and for deriving covariance matrices from these errors are indicated. The generalized least-squares method for evaluating experimental data is described. Finally, the use of least-squares techniques in data fitting applications is discussed. Specific examples of the various procedures are presented to clarify the concepts.
The importance of covariance in nuclear data uncertainty propagation studies
Benstead, J.
2012-07-01
A study has been undertaken to investigate what proportion of the uncertainty propagated through plutonium critical assembly calculations is due to the covariances between the fission cross section in different neutron energy groups. The uncertainties on k{sub eff} calculated show that the presence of covariances between the cross section in different neutron energy groups accounts for approximately 27-37% of the propagated uncertainty due to the plutonium fission cross section. This study also confirmed the validity of employing the sandwich equation, with associated sensitivity and covariance data, instead of a Monte Carlo sampling approach to calculating uncertainties for linearly varying systems. (authors)
Genome-Wide Scan for Adaptive Divergence and Association with Population-Specific Covariates.
Gautier, Mathieu
2015-12-01
In population genomics studies, accounting for the neutral covariance structure across population allele frequencies is critical to improve the robustness of genome-wide scan approaches. Elaborating on the BayEnv model, this study investigates several modeling extensions (i) to improve the estimation accuracy of the population covariance matrix and all the related measures, (ii) to identify significantly overly differentiated SNPs based on a calibration procedure of the XtX statistics, and (iii) to consider alternative covariate models for analyses of association with population-specific covariables. In particular, the auxiliary variable model allows one to deal with multiple testing issues and, providing the relative marker positions are available, to capture some linkage disequilibrium information. A comprehensive simulation study was carried out to evaluate the performances of these different models. Also, when compared in terms of power, robustness, and computational efficiency to five other state-of-the-art genome-scan methods (BayEnv2, BayScEnv, BayScan, flk, and lfmm), the proposed approaches proved highly effective. For illustration purposes, genotyping data on 18 French cattle breeds were analyzed, leading to the identification of 13 strong signatures of selection. Among these, four (surrounding the KITLG, KIT, EDN3, and ALB genes) contained SNPs strongly associated with the piebald coloration pattern while a fifth (surrounding PLAG1) could be associated to morphological differences across the populations. Finally, analysis of Pool-Seq data from 12 populations of Littorina saxatilis living in two different ecotypes illustrates how the proposed framework might help in addressing relevant ecological issues in nonmodel species. Overall, the proposed methods define a robust Bayesian framework to characterize adaptive genetic differentiation across populations. The BayPass program implementing the different models is available at http://www1.montpellier.inra.fr/CBGP/software/baypass/.
Genome-Wide Scan for Adaptive Divergence and Association with Population-Specific Covariates
Gautier, Mathieu
2015-01-01
In population genomics studies, accounting for the neutral covariance structure across population allele frequencies is critical to improve the robustness of genome-wide scan approaches. Elaborating on the BayEnv model, this study investigates several modeling extensions (i) to improve the estimation accuracy of the population covariance matrix and all the related measures, (ii) to identify significantly overly differentiated SNPs based on a calibration procedure of the XtX statistics, and (iii) to consider alternative covariate models for analyses of association with population-specific covariables. In particular, the auxiliary variable model allows one to deal with multiple testing issues and, providing the relative marker positions are available, to capture some linkage disequilibrium information. A comprehensive simulation study was carried out to evaluate the performances of these different models. Also, when compared in terms of power, robustness, and computational efficiency to five other state-of-the-art genome-scan methods (BayEnv2, BayScEnv, BayScan, flk, and lfmm), the proposed approaches proved highly effective. For illustration purposes, genotyping data on 18 French cattle breeds were analyzed, leading to the identification of 13 strong signatures of selection. Among these, four (surrounding the KITLG, KIT, EDN3, and ALB genes) contained SNPs strongly associated with the piebald coloration pattern while a fifth (surrounding PLAG1) could be associated to morphological differences across the populations. Finally, analysis of Pool-Seq data from 12 populations of Littorina saxatilis living in two different ecotypes illustrates how the proposed framework might help in addressing relevant ecological issues in nonmodel species. Overall, the proposed methods define a robust Bayesian framework to characterize adaptive genetic differentiation across populations. The BayPass program implementing the different models is available at http://www1.montpellier
Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters.
Mathew, B; Bauer, A M; Koistinen, P; Reetz, T C; Léon, J; Sillanpää, M J
2012-10-01
Accurate and fast estimation of genetic parameters that underlie quantitative traits using mixed linear models with additive and dominance effects is of great importance in both natural and breeding populations. Here, we propose a new fast adaptive Markov chain Monte Carlo (MCMC) sampling algorithm for the estimation of genetic parameters in the linear mixed model with several random effects. In the learning phase of our algorithm, we use the hybrid Gibbs sampler to learn the covariance structure of the variance components. In the second phase of the algorithm, we use this covariance structure to formulate an effective proposal distribution for a Metropolis-Hastings algorithm, which uses a likelihood function in which the random effects have been integrated out. Compared with the hybrid Gibbs sampler, the new algorithm had better mixing properties and was approximately twice as fast to run. Our new algorithm was able to detect different modes in the posterior distribution. In addition, the posterior mode estimates from the adaptive MCMC method were close to the REML (residual maximum likelihood) estimates. Moreover, our exponential prior for inverse variance components was vague and enabled the estimated mode of the posterior variance to be practically zero, which was in agreement with the support from the likelihood (in the case of no dominance). The method performance is illustrated using simulated data sets with replicates and field data in barley.
The genetic correlation between height and IQ: shared genes or assortative mating?
Keller, Matthew C; Garver-Apgar, Christine E; Wright, Margaret J; Martin, Nicholas G; Corley, Robin P; Stallings, Michael C; Hewitt, John K; Zietsch, Brendan P
2013-04-01
Traits that are attractive to the opposite sex are often positively correlated when scaled such that scores increase with attractiveness, and this correlation typically has a genetic component. Such traits can be genetically correlated due to genes that affect both traits ("pleiotropy") and/or because assortative mating causes statistical correlations to develop between selected alleles across the traits ("gametic phase disequilibrium"). In this study, we modeled the covariation between monozygotic and dizygotic twins, their siblings, and their parents (total N = 7,905) to elucidate the nature of the correlation between two potentially sexually selected traits in humans: height and IQ. Unlike previous designs used to investigate the nature of the height-IQ correlation, the present design accounts for the effects of assortative mating and provides much less biased estimates of additive genetic, non-additive genetic, and shared environmental influences. Both traits were highly heritable, although there was greater evidence for non-additive genetic effects in males. After accounting for assortative mating, the correlation between height and IQ was found to be almost entirely genetic in nature. Model fits indicate that both pleiotropy and assortative mating contribute significantly and about equally to this genetic correlation.
Particle-vibration coupling within covariant density functional theory
Litvinova, E.; Ring, P.; Tselyaev, V.
2007-06-15
Covariant density functional theory, which has so far been applied only within the framework of static and time-dependent mean-field theory, is extended to include particle-vibration coupling (PVC) in a consistent way. Starting from a conventional energy functional, we calculate the low-lying collective vibrations in the relativistic random phase approximation (RRPA) and construct an energy-dependent self-energy for the Dyson equation. The resulting Bethe-Salpeter equation in the particle-hole (p-h) channel is solved in the time blocking approximation (TBA). No additional parameters are used, and double counting is avoided by a proper subtraction method. The same energy functional, i.e., the same set of coupling constants, generates the Dirac-Hartree single-particle spectrum, the static part of the residual p-h interaction, and the particle-phonon coupling vertices. Therefore, a fully consistent description of nuclear excited states is developed. This method is applied for an investigation of damping phenomena in the spherical nuclei with closed shells {sup 208}Pb and {sup 132}Sn. Since the phonon coupling terms enrich the RRPA spectrum with a multitude of p-hxphonon components, a noticeable fragmentation of the giant resonances is found, which is in full agreement with experimental data and with results of the semiphenomenological nonrelativistic approach.
Mutually unbiased bases as minimal Clifford covariant 2-designs
NASA Astrophysics Data System (ADS)
Zhu, Huangjun
2015-06-01
Mutually unbiased bases (MUBs) are interesting for various reasons. The most attractive example of (a complete set of) MUBs is the one constructed by Ivanović as well as Wootters and Fields, which is referred to as the canonical MUB. Nevertheless, little is known about anything that is unique to this MUB. We show that the canonical MUB in any prime power dimension is uniquely determined by an extremal orbit of the (restricted) Clifford group except in dimension 3, in which case the orbit defines a special symmetric informationally complete measurement (SIC), known as the Hesse SIC. Here the extremal orbit is the orbit with the smallest number of pure states. Quite surprisingly, this characterization does not rely on any concept that is related to bases or unbiasedness. As a corollary, the canonical MUB is the unique minimal 2-design covariant with respect to the Clifford group except in dimension 3. In addition, these MUBs provide an infinite family of highly symmetric frames and positive-operator-valued measures (POVMs), which are of independent interest.
True covariance simulation of the EUVE update filter
NASA Technical Reports Server (NTRS)
Bar-Itzhack, I. Y.; Harman, R. R.
1990-01-01
This paper presents a covariance analysis of the performance and sensitivity of the attitude determination Extended Kalman Filter (EKF) used by the On Board Computer (OBC) of the Extreme Ultra Violet Explorer (EUVE) spacecraft. The linearized dynamics and measurement equations of the error states are used in formulating the 'truth model' describing the real behavior of the systems involved. The 'design model' used by the OBC EKF is then obtained by reducing the order of the truth model. The covariance matrix of the EKF which uses the reduced order model is not the correct covariance of the EKF estimation error. A 'true covariance analysis' has to be carried out in order to evaluate the correct accuracy of the OBC generated estimates. The results of such analysis are presented which indicate both the performance and the sensitivity of the OBC EKF.
True covariance simulation of the EUVE update filter
NASA Technical Reports Server (NTRS)
Bar-Itzhack, Itzhack Y.; Harman, R. R.
1989-01-01
A covariance analysis of the performance and sensitivity of the attitude determination Extended Kalman Filter (EKF) used by the On Board Computer (OBC) of the Extreme Ultra Violet Explorer (EUVE) spacecraft is presented. The linearized dynamics and measurement equations of the error states are derived which constitute the truth model describing the real behavior of the systems involved. The design model used by the OBC EKF is then obtained by reducing the order of the truth model. The covariance matrix of the EKF which uses the reduced order model is not the correct covariance of the EKF estimation error. A true covariance analysis has to be carried out in order to evaluate the correct accuracy of the OBC generated estimates. The results of such analysis are presented which indicate both the performance and the sensitivity of the OBC EKF.
Covariance Matrix Evaluations for Independent Mass Fission Yields
Terranova, N.; Serot, O.; Archier, P.; De Saint Jean, C.
2015-01-15
Recent needs for more accurate fission product yields include covariance information to allow improved uncertainty estimations of the parameters used by design codes. The aim of this work is to investigate the possibility to generate more reliable and complete uncertainty information on independent mass fission yields. Mass yields covariances are estimated through a convolution between the multi-Gaussian empirical model based on Brosa's fission modes, which describe the pre-neutron mass yields, and the average prompt neutron multiplicity curve. The covariance generation task has been approached using the Bayesian generalized least squared method through the CONRAD code. Preliminary results on mass yields variance-covariance matrix will be presented and discussed from physical grounds in the case of {sup 235}U(n{sub th}, f) and {sup 239}Pu(n{sub th}, f) reactions.
Empirical State Error Covariance Matrix for Batch Estimation
NASA Technical Reports Server (NTRS)
Frisbee, Joe
2015-01-01
State estimation techniques effectively provide mean state estimates. However, the theoretical state error covariance matrices provided as part of these techniques often suffer from a lack of confidence in their ability to describe the uncertainty in the estimated states. By a reinterpretation of the equations involved in the weighted batch least squares algorithm, it is possible to directly arrive at an empirical state error covariance matrix. The proposed empirical state error covariance matrix will contain the effect of all error sources, known or not. This empirical error covariance matrix may be calculated as a side computation for each unique batch solution. Results based on the proposed technique will be presented for a simple, two observer and measurement error only problem.
Rudolf Keller
2004-08-10
In this project, a concept to improve the performance of aluminum production cells by introducing potlining additives was examined and tested. Boron oxide was added to cathode blocks, and titanium was dissolved in the metal pool; this resulted in the formation of titanium diboride and caused the molten aluminum to wet the carbonaceous cathode surface. Such wetting reportedly leads to operational improvements and extended cell life. In addition, boron oxide suppresses cyanide formation. This final report presents and discusses the results of this project. Substantial economic benefits for the practical implementation of the technology are projected, especially for modern cells with graphitized blocks. For example, with an energy savings of about 5% and an increase in pot life from 1500 to 2500 days, a cost savings of $ 0.023 per pound of aluminum produced is projected for a 200 kA pot.
Harrup, Mason K; Rollins, Harry W
2013-11-26
An additive comprising a phosphazene compound that has at least two reactive functional groups and at least one capping functional group bonded to phosphorus atoms of the phosphazene compound. One of the at least two reactive functional groups is configured to react with cellulose and the other of the at least two reactive functional groups is configured to react with a resin, such as an amine resin of a polycarboxylic acid resin. The at least one capping functional group is selected from the group consisting of a short chain ether group, an alkoxy group, or an aryloxy group. Also disclosed are an additive-resin admixture, a method of treating a wood product, and a wood product.
Cosmic shear covariance: the log-normal approximation
NASA Astrophysics Data System (ADS)
Hilbert, S.; Hartlap, J.; Schneider, P.
2011-12-01
Context. Accurate estimates of the errors on the cosmological parameters inferred from cosmic shear surveys require accurate estimates of the covariance of the cosmic shear correlation functions. Aims: We seek approximations to the cosmic shear covariance that are as easy to use as the common approximations based on normal (Gaussian) statistics, but yield more accurate covariance matrices and parameter errors. Methods: We derive expressions for the cosmic shear covariance under the assumption that the underlying convergence field follows log-normal statistics. We also derive a simplified version of this log-normal approximation by only retaining the most important terms beyond normal statistics. We use numerical simulations of weak lensing to study how well the normal, log-normal, and simplified log-normal approximations as well as empirical corrections to the normal approximation proposed in the literature reproduce shear covariances for cosmic shear surveys. We also investigate the resulting confidence regions for cosmological parameters inferred from such surveys. Results: We find that the normal approximation substantially underestimates the cosmic shear covariances and the inferred parameter confidence regions, in particular for surveys with small fields of view and large galaxy densities, but also for very wide surveys. In contrast, the log-normal approximation yields more realistic covariances and confidence regions, but also requires evaluating slightly more complicated expressions. However, the simplified log-normal approximation, although as simple as the normal approximation, yields confidence regions that are almost as accurate as those obtained from the log-normal approximation. The empirical corrections to the normal approximation do not yield more accurate covariances and confidence regions than the (simplified) log-normal approximation. Moreover, they fail to produce positive-semidefinite data covariance matrices in certain cases, rendering them
Explicitly covariant dispersion relations and self-induced transparency
NASA Astrophysics Data System (ADS)
Mahajan, S. M.; Asenjo, Felipe A.
2017-02-01
Explicitly covariant dispersion relations for a variety of plasma waves in unmagnetized and magnetized plasmas are derived in a systematic manner from a fully covariant plasma formulation. One needs to invoke relatively little known invariant combinations constructed from the ambient electromagnetic fields and the wave vector to accomplish the program. The implication of this work applied to the self-induced transparency effect is discussed. Some problems arising from the inconsistent use of relativity are pointed out.
Petelle, M B; Martin, J G A; Blumstein, D T
2015-10-01
Describing and quantifying animal personality is now an integral part of behavioural studies because individually distinctive behaviours have ecological and evolutionary consequences. Yet, to fully understand how personality traits may respond to selection, one must understand the underlying heritability and genetic correlations between traits. Previous studies have reported a moderate degree of heritability of personality traits, but few of these studies have either been conducted in the wild or estimated the genetic correlations between personality traits. Estimating the additive genetic variance and covariance in the wild is crucial to understand the evolutionary potential of behavioural traits. Enhanced environmental variation could reduce heritability and genetic correlations, thus leading to different evolutionary predictions. We estimated the additive genetic variance and covariance of docility in the trap, sociability (mirror image stimulation), and exploration and activity in two different contexts (open-field and mirror image simulation experiments) in a wild population of yellow-bellied marmots (Marmota flaviventris). We estimated both heritability of behaviours and of personality traits and found nonzero additive genetic variance in these traits. We also found nonzero maternal, permanent environment and year effects. Finally, we found four phenotypic correlations between traits, and one positive genetic correlation between activity in the open-field test and sociability. We also found permanent environment correlations between activity in both tests and docility and exploration in the MIS test. This is one of a handful of studies to adopt a quantitative genetic approach to explain variation in personality traits in the wild and, thus, provides important insights into the potential variance available for selection.
New capabilities for processing covariance data in resonance region
Wiarda, D.; Dunn, M. E.; Greene, N. M.; Larson, N. M.; Leal, L. C.
2006-07-01
The AMPX [1] code system is a modular system of FORTRAN computer programs that relate to nuclear analysis with a primary emphasis on tasks associated with the production and use of multi group and continuous energy cross sections. The module PUFF-III within this code system handles the creation of multi group covariance data from ENDF information. The resulting covariances are saved in COVERX format [2]. We recently expanded the capabilities of PUFF-III to include full handling of covariance data in the resonance region (resolved as well as unresolved). The new program handles all resonance covariance formats in File 32 except for the long-range covariance sub sections. The new program has been named PUFF-IV. To our knowledge, PUFF-IV is the first processing code that can address both the new ENDF format for resolved resonance parameters and the new ENDF 'compact' covariance format. The existing code base was rewritten in Fortran 90 to allow for a more modular design. Results are identical between the new and old versions within rounding errors, where applicable. Automatic test cases have been added to ensure that consistent results are generated across computer systems. (authors)
Adjusting power for a baseline covariate in linear models
Glueck, Deborah H.; Muller, Keith E.
2009-01-01
SUMMARY The analysis of covariance provides a common approach to adjusting for a baseline covariate in medical research. With Gaussian errors, adding random covariates does not change either the theory or the computations of general linear model data analysis. However, adding random covariates does change the theory and computation of power analysis. Many data analysts fail to fully account for this complication in planning a study. We present our results in five parts. (i) A review of published results helps document the importance of the problem and the limitations of available methods. (ii) A taxonomy for general linear multivariate models and hypotheses allows identifying a particular problem. (iii) We describe how random covariates introduce the need to consider quantiles and conditional values of power. (iv) We provide new exact and approximate methods for power analysis of a range of multivariate models with a Gaussian baseline covariate, for both small and large samples. The new results apply to the Hotelling-Lawley test and the four tests in the “univariate” approach to repeated measures (unadjusted, Huynh-Feldt, Geisser-Greenhouse, Box). The techniques allow rapid calculation and an interactive, graphical approach to sample size choice. (v) Calculating power for a clinical trial of a treatment for increasing bone density illustrates the new methods. We particularly recommend using quantile power with a new Satterthwaite-style approximation. PMID:12898543
[Clinical research XIX. From clinical judgment to analysis of covariance].
Pérez-Rodríguez, Marcela; Palacios-Cruz, Lino; Moreno, Jorge; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2014-01-01
The analysis of covariance (ANCOVA) is based on the general linear models. This technique involves a regression model, often multiple, in which the outcome is presented as a continuous variable, the independent variables are qualitative or are introduced into the model as dummy or dichotomous variables, and factors for which adjustment is required (covariates) can be in any measurement level (i.e. nominal, ordinal or continuous). The maneuvers can be entered into the model as 1) fixed effects, or 2) random effects. The difference between fixed effects and random effects depends on the type of information we want from the analysis of the effects. ANCOVA effect separates the independent variables from the effect of co-variables, i.e., corrects the dependent variable eliminating the influence of covariates, given that these variables change in conjunction with maneuvers or treatments, affecting the outcome variable. ANCOVA should be done only if it meets three assumptions: 1) the relationship between the covariate and the outcome is linear, 2) there is homogeneity of slopes, and 3) the covariate and the independent variable are independent from each other.
Large Covariance Estimation by Thresholding Principal Orthogonal Complements.
Fan, Jianqing; Liao, Yuan; Mincheva, Martina
2013-09-01
This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented.
Real-time probabilistic covariance tracking with efficient model update.
Wu, Yi; Cheng, Jian; Wang, Jinqiao; Lu, Hanqing; Wang, Jun; Ling, Haibin; Blasch, Erik; Bai, Li
2012-05-01
The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties, as well as their correlation, are characterized. The similarity between two covariance descriptors is measured on Riemannian manifolds. Based on the same metric but with a probabilistic framework, we propose a novel tracking approach on Riemannian manifolds with a novel incremental covariance tensor learning (ICTL). To address the appearance variations, ICTL incrementally learns a low-dimensional covariance tensor representation and efficiently adapts online to appearance changes of the target with only O(1) computational complexity, resulting in a real-time performance. The covariance-based representation and the ICTL are then combined with the particle filter framework to allow better handling of background clutter, as well as the temporary occlusions. We test the proposed probabilistic ICTL tracker on numerous benchmark sequences involving different types of challenges including occlusions and variations in illumination, scale, and pose. The proposed approach demonstrates excellent real-time performance, both qualitatively and quantitatively, in comparison with several previously proposed trackers.
A three domain covariance framework for EEG/MEG data.
Roś, Beata P; Bijma, Fetsje; de Gunst, Mathisca C M; de Munck, Jan C
2015-10-01
In this paper we introduce a covariance framework for the analysis of single subject EEG and MEG data that takes into account observed temporal stationarity on small time scales and trial-to-trial variations. We formulate a model for the covariance matrix, which is a Kronecker product of three components that correspond to space, time and epochs/trials, and consider maximum likelihood estimation of the unknown parameter values. An iterative algorithm that finds approximations of the maximum likelihood estimates is proposed. Our covariance model is applicable in a variety of cases where spontaneous EEG or MEG acts as source of noise and realistic noise covariance estimates are needed, such as in evoked activity studies, or where the properties of spontaneous EEG or MEG are themselves the topic of interest, like in combined EEG-fMRI experiments in which the correlation between EEG and fMRI signals is investigated. We use a simulation study to assess the performance of the estimator and investigate the influence of different assumptions about the covariance factors on the estimated covariance matrix and on its components. We apply our method to real EEG and MEG data sets.
Large Covariance Estimation by Thresholding Principal Orthogonal Complements
Fan, Jianqing; Liao, Yuan; Mincheva, Martina
2012-01-01
This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented. PMID:24348088
Alterations in Anatomical Covariance in the Prematurely Born.
Scheinost, Dustin; Kwon, Soo Hyun; Lacadie, Cheryl; Vohr, Betty R; Schneider, Karen C; Papademetris, Xenophon; Constable, R Todd; Ment, Laura R
2015-10-22
Preterm (PT) birth results in long-term alterations in functional and structural connectivity, but the related changes in anatomical covariance are just beginning to be explored. To test the hypothesis that PT birth alters patterns of anatomical covariance, we investigated brain volumes of 25 PTs and 22 terms at young adulthood using magnetic resonance imaging. Using regional volumetrics, seed-based analyses, and whole brain graphs, we show that PT birth is associated with reduced volume in bilateral temporal and inferior frontal lobes, left caudate, left fusiform, and posterior cingulate for prematurely born subjects at young adulthood. Seed-based analyses demonstrate altered patterns of anatomical covariance for PTs compared with terms. PTs exhibit reduced covariance with R Brodmann area (BA) 47, Broca's area, and L BA 21, Wernicke's area, and white matter volume in the left prefrontal lobe, but increased covariance with R BA 47 and left cerebellum. Graph theory analyses demonstrate that measures of network complexity are significantly less robust in PTs compared with term controls. Volumes in regions showing group differences are significantly correlated with phonological awareness, the fundamental basis for reading acquisition, for the PTs. These data suggest both long-lasting and clinically significant alterations in the covariance in the PTs at young adulthood.
The performance analysis based on SAR sample covariance matrix.
Erten, Esra
2012-01-01
Multi-channel systems appear in several fields of application in science. In the Synthetic Aperture Radar (SAR) context, multi-channel systems may refer to different domains, as multi-polarization, multi-interferometric or multi-temporal data, or even a combination of them. Due to the inherent speckle phenomenon present in SAR images, the statistical description of the data is almost mandatory for its utilization. The complex images acquired over natural media present in general zero-mean circular Gaussian characteristics. In this case, second order statistics as the multi-channel covariance matrix fully describe the data. For practical situations however, the covariance matrix has to be estimated using a limited number of samples, and this sample covariance matrix follow the complex Wishart distribution. In this context, the eigendecomposition of the multi-channel covariance matrix has been shown in different areas of high relevance regarding the physical properties of the imaged scene. Specifically, the maximum eigenvalue of the covariance matrix has been frequently used in different applications as target or change detection, estimation of the dominant scattering mechanism in polarimetric data, moving target indication, etc. In this paper, the statistical behavior of the maximum eigenvalue derived from the eigendecomposition of the sample multi-channel covariance matrix in terms of multi-channel SAR images is simplified for SAR community. Validation is performed against simulated data and examples of estimation and detection problems using the analytical expressions are as well given.
Gaussian covariance matrices for anisotropic galaxy clustering measurements
NASA Astrophysics Data System (ADS)
Grieb, Jan Niklas; Sánchez, Ariel G.; Salazar-Albornoz, Salvador; Dalla Vecchia, Claudio
2016-04-01
Measurements of the redshift-space galaxy clustering have been a prolific source of cosmological information in recent years. Accurate covariance estimates are an essential step for the validation of galaxy clustering models of the redshift-space two-point statistics. Usually, only a limited set of accurate N-body simulations is available. Thus, assessing the data covariance is not possible or only leads to a noisy estimate. Further, relying on simulated realizations of the survey data means that tests of the cosmology dependence of the covariance are expensive. With these points in mind, this work presents a simple theoretical model for the linear covariance of anisotropic galaxy clustering observations with synthetic catalogues. Considering the Legendre moments (`multipoles') of the two-point statistics and projections into wide bins of the line-of-sight parameter (`clustering wedges'), we describe the modelling of the covariance for these anisotropic clustering measurements for galaxy samples with a trivial geometry in the case of a Gaussian approximation of the clustering likelihood. As main result of this paper, we give the explicit formulae for Fourier and configuration space covariance matrices. To validate our model, we create synthetic halo occupation distribution galaxy catalogues by populating the haloes of an ensemble of large-volume N-body simulations. Using linear and non-linear input power spectra, we find very good agreement between the model predictions and the measurements on the synthetic catalogues in the quasi-linear regime.
Natural selection and quantitative genetics of life-history traits in Western women: a twin study.
Kirk, K M; Blomberg, S P; Duffy, D L; Heath, A C; Owens, I P; Martin, N G
2001-02-01
Whether contemporary human populations are still evolving as a result of natural selection has been hotly debated. For natural selection to cause evolutionary change in a trait, variation in the trait must be correlated with fitness and be genetically heritable and there must be no genetic constraints to evolution. These conditions have rarely been tested in human populations. In this study, data from a large twin cohort were used to assess whether selection will cause a change among women in a contemporary Western population for three life-history traits: age at menarche, age at first reproduction, and age at menopause. We control for temporal variation in fecundity (the "baby boom" phenomenon) and differences between women in educational background and religious affiliation. University-educated women have 35% lower fitness than those with less than seven years education, and Roman Catholic women have about 20% higher fitness than those of other religions. Although these differences were significant, education and religion only accounted for 2% and 1% of variance in fitness, respectively. Using structural equation modeling, we reveal significant genetic influences for all three life-history traits, with heritability estimates of 0.50, 0.23, and 0.45, respectively. However, strong genetic covariation with reproductive fitness could only be demonstrated for age at first reproduction, with much weaker covariation for age at menopause and no significant covariation for age at menarche. Selection may, therefore, lead to the evolution of earlier age at first reproduction in this population. We also estimate substantial heritable variation in fitness itself, with approximately 39% of the variance attributable to additive genetic effects, the remainder consisting of unique environmental effects and small effects from education and religion. We discuss mechanisms that could be maintaining such a high heritability for fitness. Most likely is that selection is now acting on
A Population Genetic Signal of Polygenic Adaptation
Berg, Jeremy J.; Coop, Graham
2014-01-01
Adaptation in response to selection on polygenic phenotypes may occur via subtle allele frequencies shifts at many loci. Current population genomic techniques are not well posed to identify such signals. In the past decade, detailed knowledge about the specific loci underlying polygenic traits has begun to emerge from genome-wide association studies (GWAS). Here we combine this knowledge from GWAS with robust population genetic modeling to identify traits that may have been influenced by local adaptation. We exploit the fact that GWAS provide an estimate of the additive effect size of many loci to estimate the mean additive genetic value for a given phenotype across many populations as simple weighted sums of allele frequencies. We use a general model of neutral genetic value drift for an arbitrary number of populations with an arbitrary relatedness structure. Based on this model, we develop methods for detecting unusually strong correlations between genetic values and specific environmental variables, as well as a generalization of comparisons to test for over-dispersion of genetic values among populations. Finally we lay out a framework to identify the individual populations or groups of populations that contribute to the signal of overdispersion. These tests have considerably greater power than their single locus equivalents due to the fact that they look for positive covariance between like effect alleles, and also significantly outperform methods that do not account for population structure. We apply our tests to the Human Genome Diversity Panel (HGDP) dataset using GWAS data for height, skin pigmentation, type 2 diabetes, body mass index, and two inflammatory bowel disease datasets. This analysis uncovers a number of putative signals of local adaptation, and we discuss the biological interpretation and caveats of these results. PMID:25102153
Genetics of grass dry matter intake, energy balance, and digestibility in grazing irish dairy cows.
Berry, D P; Horan, B; O'Donovan, M; Buckley, F; Kennedy, E; McEvoy, M; Dillon, P
2007-10-01
The objective of this study was to estimate genetic parameters for grass dry matter intake (DMI), energy balance (EB), and cow internal digestibility (IDG) in grazing Holstein-Friesian dairy cows. Grass DMI was estimated up to 4 times per lactation on 1,588 lactations from 755 cows on 2 research farms in southern Ireland. Simultaneously measured milk production and BW records were used to calculate EB. Cow IDG, measured as the ratio of feed and fecal concentrations of the natural odd carbon-chain n-alkane pentatriacontane, was available on 583 lactations from 238 cows. Random regression and multitrait animal models were used to estimate residual, additive genetic and permanent environmental (co)variances across lactations. Results were similar for both models. Heritability for DMI, EB, and IDG across lactation varied from 0.10 [8 days in milk (DIM)] to 0.30 (169 DIM), from 0.06 (29 DIM) to 0.29 (305 DIM), and from 0.08 (50 DIM) to 0.45 (305 DIM), respectively, when estimated using the random regression model. Genetic correlations within each trait tended to decrease as the interval between periods compared increased for DMI and EB, whereas the correlations with IDG in early lactation were weakest when measured midlactation. The lowest correlation between any 2 periods was 0.10, -0.36, and -0.04 for DMI, EB, and IDG, respectively, suggesting the effect of different genes at different stages of lactations. Eigenvalues and associated eigenfunctions of the additive genetic covariance matrix revealed considerable genetic variation among animals in the shape of the lactation profiles for DMI, EB, and IDG. Genetic parameters presented are the first estimates from dairy cows fed predominantly grazed grass and imply that genetic improvement in DMI, EB, and IDG in Holstein-Friesian cows fed predominantly grazed grass is possible.
Jay, Flora; François, Olivier; Blum, Michael G. B.
2011-01-01
Background The mainland of the Americas is home to a remarkable diversity of languages, and the relationships between genes and languages have attracted considerable attention in the past. Here we investigate to which extent geography and languages can predict the genetic structure of Native American populations. Methodology/Principal Findings Our approach is based on a Bayesian latent cluster regression model in which cluster membership is explained by geographic and linguistic covariates. After correcting for geographic effects, we find that the inclusion of linguistic information improves the prediction of individual membership to genetic clusters. We further compare the predictive power of Greenberg's and The Ethnologue classifications of Amerindian languages. We report that The Ethnologue classification provides a better genetic proxy than Greenberg's classification at the stock and at the group levels. Although high predictive values can be achieved from The Ethnologue classification, we nevertheless emphasize that Choco, Chibchan and Tupi linguistic families do not exhibit a univocal correspondence with genetic clusters. Conclusions/Significance The Bayesian latent class regression model described here is efficient at predicting population genetic structure using geographic and linguistic information in Native American populations. PMID:21305006
Covariant and background independent functional RG flow for the effective average action
NASA Astrophysics Data System (ADS)
Safari, Mahmoud; Vacca, Gian Paolo
2016-11-01
We extend our prescription for the construction of a covariant and background-independent effective action for scalar quantum field theories to the case where momentum modes below a certain scale are suppressed by the presence of an infrared regulator. The key step is an appropriate choice of the infrared cutoff for which the Ward identity, capturing the information from single-field dependence of the ultraviolet action, continues to be exactly solvable, and therefore, in addition to covariance, manifest background independence of the effective action is guaranteed at any scale. A practical consequence is that in this framework one can adopt truncations dependent on the single total field. Furthermore we discuss the necessary and sufficient conditions for the preservation of symmetries along the renormalization group flow.
Scale covariant gravitation. V - Kinetic theory. VI - Stellar structure and evolution
NASA Technical Reports Server (NTRS)
Hsieh, S.-H.; Canuto, V. M.
1981-01-01
A scale covariant kinetic theory for particles and photons is developed. The mathematical framework of the theory is given by the tangent bundle of a Weyl manifold. The Liouville equation is derived, and solutions to corresponding equilibrium distributions are presented and shown to yield thermodynamic results identical to the ones obtained previously. The scale covariant theory is then used to derive results of interest to stellar structure and evolution. A radiative transfer equation is derived that can be used to study stellar evolution with a variable gravitational constant. In addition, it is shown that the sun's absolute luminosity scales as L approximately equal to GM/kappa, where kappa is the stellar opacity. Finally, a formula is derived for the age of globular clusters as a function of the gravitational constant using a previously derived expression for the absolute luminosity.
Career maturity: a latent means and covariance structure analysis of Daidalos.
Dybwad, Tom-Erik
2009-04-01
A latent means and covariance structure analysis was conducted to assess the construct validity and construct comparability in the measuring of career maturity across boys and girls. The career maturity inventory has been coined Daidalos. The sample consisted of 2,443 high school students recruited from one county in Norway. Of these, 1,132 were males, and 1,311 were females. The mean age of the participants was 17 years of age. Goodness-of-fit statistics provided support for a five-factor first-order model in which the factor loadings, factor covariances and item intercepts were invariant across groups. Additionally, deltaS-B chi2 was shown to be non-significant for the testing of invariance of the measurement model. Three significant differences in latent means were detected, with boys scoring higher on negative attitudes toward school or dropout intentions, and girls scoring higher on career uncertainty and need for world-of-work information.
Area group: an example of style and paste compositional covariation in Maya pottery
Bishop, R.L.; Reents, D.J.; Harbottle, G.; Sayre, E.V.; van Zelst, L.
1983-06-12
This paper has addressed aspects of ceramic style and iconography as found in Late Classic Maya ceramic art, including the supplemental perspective afforded by the analysis of ceramic paste. The chemical data provide a means to assess the extent of stylistic-paste compositional covariation. Depending upon the strength of that covariation various inferences may be drawn about craft specialization, exchange and information flow within Maya society. At the least, it provides an empirical means of comparing stylistically similar vessels; and when they are members of a chemically homogeneous group, it permits style to be addressed in terms of its variation. Additionally, compositionally defined site or region specific reference units provide a chemical background against which the non-provenienced vessels may be compared, allowing the whole vessels to be related to the archaelogically recovered fragmentary material. Finally, this multidisciplinary approach has been illustrated by preliminary findings concerning a specific group of polychrome vessels, The Area Group.
2013-01-01
Background Intra-specific variation in melanocyte pigmentation, common in the animal kingdom, has caught the eye of naturalists and biologists for centuries. In vertebrates, dark, eumelanin pigmentation is often genetically determined and associated with various behavioral and physiological traits, suggesting that the genes involved in melanism have far reaching pleiotropic effects. The mechanisms linking these traits remain poorly understood, and the potential involvement of developmental processes occurring in the brain early in life has not been investigated. We examined the ontogeny of rapid eye movement (REM) sleep, a state involved in brain development, in a wild population of barn owls (Tyto alba) exhibiting inter-individual variation in melanism and covarying traits. In addition to sleep, we measured melanistic feather spots and the expression of a gene in the feather follicles implicated in melanism (PCSK2). Results As in mammals, REM sleep declined with age across a period of brain development in owlets. In addition, inter-individual variation in REM sleep around this developmental trajectory was predicted by variation in PCSK2 expression in the feather follicles, with individuals expressing higher levels exhibiting a more precocial pattern characterized by less REM sleep. Finally, PCSK2 expression was positively correlated with feather spotting. Conclusions We demonstrate that the pace of brain development, as reflected in age-related changes in REM sleep, covaries with the peripheral activation of the melanocortin system. Given its role in brain development, variation in nestling REM sleep may lead to variation in adult brain organization, and thereby contribute to the behavioral and physiological differences observed between adults expressing different degrees of melanism. PMID:23886007
Lopes, Fernando Brito; Magnabosco, Cláudio Ulhôa; Paulini, Fernanda; da Silva, Marcelo Corrêa; Miyagi, Eliane Sayuri; Lôbo, Raysildo Barbosa
2013-01-01
Components of (co)variance and genetic parameters were estimated for adjusted weights at ages 120 (W120), 240 (W240), 365 (W365) and 450 (W450) days of Polled Nellore cattle raised on pasture and born between 1987 and 2010. Analyses were performed using an animal model, considering fixed effects: herd-year-season of birth and calf sex as contemporary groups and the age of cow as a covariate. Gibbs Samplers were used to estimate (co)variance components, genetic parameters and additive genetic effects, which accounted for great proportion of total variation in these traits. High direct heritability estimates for the growth traits were revealed and presented mean 0.43, 0.61, 0.72 and 0.67 for W120, W240, W365 and W450, respectively. Maternal heritabilities were 0.07 and 0.08 for W120 and W240, respectively. Direct additive genetic correlations between the weight at 120, 240, 365 and 450 days old were strong and positive. These estimates ranged from 0.68 to 0.98. Direct-maternal genetic correlations were negative for W120 and W240. The estimates ranged from −0.31 to −0.54. Estimates of maternal heritability ranged from 0.056 to 0.092 for W120 and from 0.064 to 0.096 for W240. This study showed that genetic progress is possible for the growth traits we studied, which is a novel and favorable indicator for an upcoming and promising Polled Zebu breed in Tropical regions. Maternal effects influenced the performance of weight at 120 and 240 days old. These effects should be taken into account in genetic analyses of growth traits by fitting them as a genetic or a permanent environmental effect, or even both. In general, due to a medium-high estimate of environmental (co)variance components, management and feeding conditions for Polled Nellore raised at pasture in tropical regions of Brazil needs improvement and growth performance can be enhanced. PMID:24040412
Madrasi, Kumpal; Chaturvedula, Ayyappa; Haberer, Jessica E; Sale, Mark; Fossler, Michael J; Bangsberg, David; Baeten, Jared M; Celum, Connie; Hendrix, Craig W
2016-12-06
Adherence is a major factor in the effectiveness of preexposure prophylaxis (PrEP) for HIV prevention. Modeling patterns of adherence helps to identify influential covariates of different types of adherence as well as to enable clinical trial simulation so that appropriate interventions can be developed. We developed a Markov mixed-effects model to understand the covariates influencing adherence patterns to daily oral PrEP. Electronic adherence records (date and time of medication bottle cap opening) from the Partners PrEP ancillary adherence study with a total of 1147 subjects were used. This study included once-daily dosing regimens of placebo, oral tenofovir disoproxil fumarate (TDF), and TDF in combination with emtricitabine (FTC), administered to HIV-uninfected members of serodiscordant couples. One-coin and first- to third-order Markov models were fit to the data using NONMEM(®) 7.2. Model selection criteria included objective function value (OFV), Akaike information criterion (AIC), visual predictive checks, and posterior predictive checks. Covariates were included based on forward addition (α = 0.05) and backward elimination (α = 0.001). Markov models better described the data than 1-coin models. A third-order Markov model gave the lowest OFV and AIC, but the simpler first-order model was used for covariate model building because no additional benefit on prediction of target measures was observed for higher-order models. Female sex and older age had a positive impact on adherence, whereas Sundays, sexual abstinence, and sex with a partner other than the study partner had a negative impact on adherence. Our findings suggest adherence interventions should consider the role of these factors.
NASA Astrophysics Data System (ADS)
Jia, Shaoyang; Pennington, M. R.
2016-12-01
We derive the gauge covariance requirement imposed on the QED fermion-photon three-point function within the framework of a spectral representation for fermion propagators. When satisfied, such requirement ensures solutions to the fermion propagator Schwinger-Dyson equation (SDE) in any covariant gauge with arbitrary numbers of spacetime dimensions to be consistent with the Landau-Khalatnikov-Fradkin transformation (LKFT). The general result has been verified by the special cases of three and four dimensions. Additionally, we present the condition that ensures the vacuum polarization is independent of the gauge parameter. As an illustration, we show how the gauge technique dimensionally regularized in four dimensions does not satisfy the covariance requirement.
Jia, Shaoyang; Pennington, M. R.
2016-12-12
In this paper, we derive the gauge covariance requirement imposed on the QED fermion-photon three-point function within the framework of a spectral representation for fermion propagators. When satisfied, such requirement ensures solutions to the fermion propagator Schwinger-Dyson equation (SDE) in any covariant gauge with arbitrary numbers of spacetime dimensions to be consistent with the Landau-Khalatnikov-Fradkin transformation (LKFT). The general result has been verified by the special cases of three and four dimensions. Additionally, we present the condition that ensures the vacuum polarization is independent of the gauge parameter. Finally, as an illustration, we show how the gauge technique dimensionally regularized in four dimensions does not satisfy the covariance requirement.
Jia, Shaoyang; Pennington, M. R.
2016-12-12
In this paper, we derive the gauge covariance requirement imposed on the QED fermion-photon three-point function within the framework of a spectral representation for fermion propagators. When satisfied, such requirement ensures solutions to the fermion propagator Schwinger-Dyson equation (SDE) in any covariant gauge with arbitrary numbers of spacetime dimensions to be consistent with the Landau-Khalatnikov-Fradkin transformation (LKFT). The general result has been verified by the special cases of three and four dimensions. Additionally, we present the condition that ensures the vacuum polarization is independent of the gauge parameter. Finally, as an illustration, we show how the gauge technique dimensionally regularizedmore » in four dimensions does not satisfy the covariance requirement.« less
Genetic parameters for carcass traits and body size in sheep for meat production.
Figueiredo Filho, Luiz Antonio Silva; Do Ó, Alan Oliveira; Sarmento, José Lindenberg Rocha; Santos, Natanael Pereira Da Silva; Torres, Tatiana Saraiva
2016-01-01
The aim was to estimate the covariance components and genetic parameters of carcass traits and body size of meat sheep by employing animal models for single and multi-trait analyses. Data were collected from herds of adult Santa Ines sheep. The ribeye area, subcutaneous fat thickness, and rump fat thickness, evaluated in vivo by ultrasound images of the carcass; and morphometric traits: fat depth, rump height, chest circumference, chest depth, body length, and rump length were measured. The covariance components and genetic parameters for these traits were estimated by restricted maximum likelihood methodology, considering the random additive direct-genetic effects of the animal and errors associated with each observation, and the fixed effects of the contemporary group, the type of birth, and the age of the animal classes. Heritability estimates for carcass traits and animal size were higher in the three-trait analyses than in the single-trait analyses. The magnitudes of the values obtained indicate that genetic progress can be achieved by selection based on the traits studied.
Application of hierarchical genetic models to Raven and WAIS subtests: a Dutch twin study.
Rijsdijk, Frühling V; Vernon, P A; Boomsma, Dorret I
2002-05-01
Hierarchical models of intelligence are highly informative and widely accepted. Application of these models to twin data, however, is sparse. This paper addresses the question of how a genetic hierarchical model fits the Wechsler Adult Intelligence Scale (WAIS) subtests and the Raven Standard Progressive test score, collected in 194 18-year-old Dutch twin pairs. We investigated whether first-order group factors possess genetic and environmental variance independent of the higher-order general factor and whether the hierarchical structure is significant for all sources of variance. A hierarchical model with the 3 Cohen group-factors (verbal comprehension, perceptual organisation and freedom-from-distractibility) and a higher-order g factor showed the best fit to the phenotypic data and to additive genetic influences (A), whereas the unique environmental source of variance (E) could be modeled by a single general factor and specifics. There was no evidence for common environmental influences. The covariation among the WAIS group factors and the covariation between the group factors and the Raven is predominantly influenced by a second-order genetic factor and strongly support the notion of a biological basis of g.
Methane fluxes above the Hainich forest by True Eddy Accumulation and Eddy Covariance
NASA Astrophysics Data System (ADS)
Siebicke, Lukas; Gentsch, Lydia; Knohl, Alexander
2016-04-01
Understanding the role of forests for the global methane cycle requires quantifying vegetation-atmosphere exchange of methane, however observations of turbulent methane fluxes remain scarce. Here we measured turbulent fluxes of methane (CH4) above a beech-dominated old-growth forest in the Hainich National Park, Germany, and validated three different measurement approaches: True Eddy Accumulation (TEA, closed-path laser spectroscopy), and eddy covariance (EC, open-path and closed-path laser spectroscopy, respectively). The Hainich flux tower is a long-term Fluxnet and ICOS site with turbulent fluxes and ecosystem observations spanning more than 15 years. The current study is likely the first application of True Eddy Accumulation (TEA) for the measurement of turbulent exchange of methane and one of the very few studies comparing open-path and closed-path eddy covariance (EC) setups side-by-side. We observed uptake of methane by the forest during the day (a methane sink with a maximum rate of 0.03 μmol m-2 s-1 at noon) and no or small fluxes of methane from the forest to the atmosphere at night (a methane source of typically less than 0.01 μmol m-2 s-1) based on continuous True Eddy Accumulation measurements in September 2015. First results comparing TEA to EC CO2 fluxes suggest that True Eddy Accumulation is a valid option for turbulent flux quantifications using slow response gas analysers (here CRDS laser spectroscopy, other potential techniques include mass spectroscopy). The TEA system was one order of magnitude more energy efficient compared to closed-path eddy covariance. The open-path eddy covariance setup required the least amount of user interaction but is often constrained by low signal-to-noise ratios obtained when measuring methane fluxes over forests. Closed-path eddy covariance showed good signal-to-noise ratios in the lab, however in the field it required significant amounts of user intervention in addition to a high power consumption. We conclude
Estimates of genetic parameters of body weight in descendants of X-irradiated rat spermatogonia.
Gianola, D; Chapman, A B; Rutledge, J J
1977-08-01
Effects of nine generations of 450r per generation of ancestral spermatogonial X irradiation of inbred rats on genetic parameters of body weight at 3, 6, and 10 weeks of age and of weight gains between these periods were studied. Covariances among relatives were estimated by mixed model and regression techniques in randomly selected lines with (R) and without (C) radiation history. Analyses of the data were based on five linear genetic models combining additive direct, additive indirect (maternal), dominance and environmental effects. Parameters in these models were estimated by generalized least-squares. A model including direct and indirect genetic effects fit more closely to the data in both R and C lines. Overdominance of induced mutations did not seem to be present. Ancestral irradiation increased maternal additive genetic variances of body weights and gains but not direct genetic variances. Theoretically, due to a negative direct-maternal genetic correlation, within full-sib family selection would be ineffective in increasing body weight at six weeks in both R and C lines. However, progress from mass selection would be expected to be faster in the R lines.
Kimmel, Charles B.; Cresko, William A.; Phillips, Patrick C.; Ullmann, Bonnie; Currey, Mark; von Hippel, Frank; Kristjánsson, Bjarni K.; Gelmond, Ofer; McGuigan, Katrina
2014-01-01
Evolution of similar phenotypes in independent populations is often taken as evidence of adaptation to the same fitness optimum. However, the genetic architecture of traits might cause evolution to proceed more often toward particular phenotypes, and less often toward others, independently of the adaptive value of the traits. Freshwater populations of Alaskan threespine stickleback have repeatedly evolved the same distinctive opercle shape after divergence from an oceanic ancestor. Here we demonstrate that this pattern of parallel evolution is widespread, distinguishing oceanic and freshwater populations across the Pacific Coast of North America and Iceland. We test whether this parallel evolution reflects genetic bias by estimating the additive genetic variance– covariance matrix (G) of opercle shape in an Alaskan oceanic (putative ancestral) population. We find significant additive genetic variance for opercle shape and that G has the potential to be biasing, because of the existence of regions of phenotypic space with low additive genetic variation. However, evolution did not occur along major eigenvectors of G, rather it occurred repeatedly in the same directions of high evolvability. We conclude that the parallel opercle evolution is most likely due to selection during adaptation to freshwater habitats, rather than due to biasing effects of opercle genetic architecture. PMID:22276538
An Empirical State Error Covariance Matrix for Batch State Estimation
NASA Technical Reports Server (NTRS)
Frisbee, Joseph H., Jr.
2011-01-01
State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. Consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. It then follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully account for the error in the state estimate. By way of a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm, it is possible to arrive at an appropriate, and formally correct, empirical state error covariance matrix. The first specific step of the method is to use the average form of the weighted measurement residual variance performance index rather than its usual total weighted residual form. Next it is helpful to interpret the solution to the normal equations as the average of a collection of sample vectors drawn from a hypothetical parent population. From here, using a standard statistical analysis approach, it directly follows as to how to determine the standard empirical state error covariance matrix. This matrix will contain the total uncertainty in the
Covariate-adjusted confidence interval for the intraclass correlation coefficient.
Shoukri, Mohamed M; Donner, Allan; El-Dali, Abdelmoneim
2013-09-01
A crucial step in designing a new study is to estimate the required sample size. For a design involving cluster sampling, the appropriate sample size depends on the so-called design effect, which is a function of the average cluster size and the intracluster correlation coefficient (ICC). It is well-known that under the framework of hierarchical and generalized linear models, a reduction in residual error may be achieved by including risk factors as covariates. In this paper we show that the covariate design, indicating whether the covariates are measured at the cluster level or at the within-cluster subject level affects the estimation of the ICC, and hence the design effect. Therefore, the distinction between these two types of covariates should be made at the design stage. In this paper we use the nested-bootstrap method to assess the accuracy of the estimated ICC for continuous and binary response variables under different covariate structures. The codes of two SAS macros are made available by the authors for interested readers to facilitate the construction of confidence intervals for the ICC. Moreover, using Monte Carlo simulations we evaluate the relative efficiency of the estimators and evaluate the accuracy of the coverage probabilities of a 95% confidence interval on the population ICC. The methodology is illustrated using a published data set of blood pressure measurements taken on family members.
Covariant Lyapunov vectors of chaotic Rayleigh-Bénard convection
NASA Astrophysics Data System (ADS)
Xu, M.; Paul, M. R.
2016-06-01
We explore numerically the high-dimensional spatiotemporal chaos of Rayleigh-Bénard convection using covariant Lyapunov vectors. We integrate the three-dimensional and time-dependent Boussinesq equations for a convection layer in a shallow square box geometry with an aspect ratio of 16 for very long times and for a range of Rayleigh numbers. We simultaneously integrate many copies of the tangent space equations in order to compute the covariant Lyapunov vectors. The dynamics explored has fractal dimensions of 20 ≲Dλ≲50 , and we compute on the order of 150 covariant Lyapunov vectors. We use the covariant Lyapunov vectors to quantify the degree of hyperbolicity of the dynamics and the degree of Oseledets splitting and to explore the temporal and spatial dynamics of the Lyapunov vectors. Our results indicate that the chaotic dynamics of Rayleigh-Bénard convection is nonhyperbolic for all of the Rayleigh numbers we have explored. Our results yield that the entire spectrum of covariant Lyapunov vectors that we have computed are tangled as indicated by near tangencies with neighboring vectors. A closer look at the spatiotemporal features of the Lyapunov vectors suggests contributions from structures at two different length scales with differing amounts of localization.
A new look at Lorentz-covariant loop quantum gravity
NASA Astrophysics Data System (ADS)
Geiller, Marc; Lachièze-Rey, Marc; Noui, Karim
2011-08-01
In this work, we study the classical and quantum properties of the unique commutative Lorentz-covariant connection for loop quantum gravity. This connection has been found after solving the second-class constraints inherited from the canonical analysis of the Holst action without the time gauge. We show that it has the property of lying in the conjugacy class of a pure su(2) connection, a result which enables one to construct the kinematical Hilbert space of the Lorentz-covariant theory in terms of the usual SU(2) spin-network states. Furthermore, we show that there is a unique Lorentz-covariant electric field, up to trivial and natural equivalence relations. The Lorentz-covariant electric field transforms under the adjoint action of the Lorentz group, and the associated Casimir operators are shown to be proportional to the area density. This gives a very interesting algebraic interpretation of the area. Finally, we show that the action of the surface operator on the Lorentz-covariant holonomies reproduces exactly the usual discrete SU(2) spectrum of time-gauge loop quantum gravity. In other words, the use of the time gauge does not introduce anomalies in the quantum theory.
Newton law in covariant unimodular F(R) gravity
NASA Astrophysics Data System (ADS)
Nojiri, S.; Odintsov, S. D.; Oikonomou, V. K.
2016-09-01
We investigate the Newton law in the unimodular F(R) gravity. In the standard F(R) gravity, due to the extra scalar mode, there often appear the large corrections to the Newton law and such models are excluded by the experiments and/or the observations. In the unimodular F(R) gravity, however, the extra scalar mode become not to be dynamical due to the unimodular constraint and there is not any correction to the Newton law. Even in the unimodular Einstein gravity, the Newton law is reproduced but the mechanism is a little bit different from that in the unimodular F(R) gravity. We also investigate the unimodular F(R) gravity in the covariant formulation. In the covariant formulation, we include the three-form field. We show that the three-form field could not have any unwanted property, like ghost nor correction to the Newton law. In the covariant formulation, however, the above extra scalar mode becomes dynamical and could give a correction to the Newton law. We also show that there are no difference in the Friedmann-Robertson-Walker (FRW) dynamics in the non-covariant and covariant formulation.
Estimation of propensity scores using generalized additive models.
Woo, Mi-Ja; Reiter, Jerome P; Karr, Alan F
2008-08-30
Propensity score matching is often used in observational studies to create treatment and control groups with similar distributions of observed covariates. Typically, propensity scores are estimated using logistic regressions that assume linearity between the logistic link and the predictors. We evaluate the use of generalized additive models (GAMs) for estimating propensity scores. We compare logistic regressions and GAMs in terms of balancing covariates using simulation studies with artificial and genuine data. We find that, when the distributions of covariates in the treatment and control groups overlap sufficiently, using GAMs can improve overall covariate balance, especially for higher-order moments of distributions. When the distributions in the two groups overlap insufficiently, GAM more clearly reveals this fact than logistic regression does. We also demonstrate via simulation that matching with GAMs can result in larger reductions in bias when estimating treatment effects than matching with logistic regression.
ERIC Educational Resources Information Center
Forster, Fred
Statistical methods are described for diagnosing and treating three important problems in covariate tests of significance: curvilinearity, covariable effectiveness, and treatment-covariable interaction. Six major assumptions, prerequisites for covariate procedure, are discussed in detail: (1) normal distribution, (2) homogeneity of variances, (3)…
Early Risk Factors for Alcohol Use Across High School and Its Covariation With Deviant Friends
Armstrong, Jeffrey M.; Ruttle, Paula L.; Burk, Linnea R.; Costanzo, Philip R.; Strauman, Timothy J.; Essex, Marilyn J.
2013-01-01
Objective: Past research has associated childhood characteristics and experiences with alcohol use at single time points in adolescence. Other work has focused on drinking trajectories across adolescence but with risk factors typically no earlier than middle or high school. Similarly, although the connection between underage drinking and affiliation with deviant friends is well established, early risk factors for their covariation across adolescence are uncertain. The present study examines the influence of early individual and contextual factors on (a) trajectories across high school of per-occasion alcohol use and (b) the covariation of alcohol use and deviant friends over time. Method: In a longitudinal community sample (n = 374; 51% female), temperamental disinhibition, authoritarian and authoritative parenting, and parental alcohol use were assessed during childhood, and adolescents reported on alcohol use and affiliation with deviant friends in the spring of Grades 9, 10, 11, and 12. Results: Early parental alcohol use predicted the intercept of adolescent drinking. Subsequent patterns of adolescent alcohol use were predicted by sex and interactions of sex and childhood disinhibition with early authoritarian parenting. Additionally, childhood disinhibition interacted with parental alcohol use to moderate the covariation of drinking and deviant friends. Conclusions: These findings highlight early individual and contextual risk factors for alcohol use across high school, extending previous work and underscoring the importance of developmental approaches and longitudinal techniques for understanding patterns of growth in underage drinking. PMID:23948534
NASA Astrophysics Data System (ADS)
Agilan, V.; Umamahesh, N. V.
2016-10-01
Storm water management and other engineering design applications are primarily based on rainfall Intensity-Duration-Frequency (IDF) curves and the existing IDF curves are based on the concept of stationary Extreme Value Theory (EVT). However, during the last few decades, global climate change is intensifying the extreme precipitation events and creating a non-stationary component in the extreme rainfall time series. Subsequently, in recent years, advancements in the EVT helped the researchers to propose a method for developing non-stationary rainfall IDF curve by modelling trend present in the observed extreme rainfall series using covariate. But, is it capable of encompassing future rainfall changes? Towards answering this question, the Hyderabad city, India non-stationary rainfall IDF curves are compared with the IDF curves of two future time periods (2015-2056 and 2057-2098). Using 24 Global Climate Models' (GCMs') simulations and 'K' Nearest Neighbor (KNN) weather generator based downscaling method, the IDF curves are developed for two future time periods and they are compared with covariate based non-stationary rainfall IDF curves of the Hyderabad city. The results of this study indicate that the return of period of an extreme rainfall of the Hyderabad city is reducing. In addition, it is noted that the non-stationary IDF curve developed by modelling trend in the observed extreme rainfall with covariate is an appropriate choice for designing the Hyderabad city infrastructure under climate change.
On the constrained classical capacity of infinite-dimensional covariant quantum channels
Holevo, A. S.
2016-01-15
The additivity of the minimal output entropy and that of the χ-capacity are known to be equivalent for finite-dimensional irreducibly covariant quantum channels. In this paper, we formulate a list of conditions allowing to establish similar equivalence for infinite-dimensional covariant channels with constrained input. This is then applied to bosonic Gaussian channels with quadratic input constraint to extend the classical capacity results of the recent paper [Giovannetti et al., Commun. Math. Phys. 334(3), 1553-1571 (2015)] to the case where the complex structures associated with the channel and with the constraint operator need not commute. In particular, this implies a multimode generalization of the “threshold condition,” obtained for single mode in Schäfer et al. [Phys. Rev. Lett. 111, 030503 (2013)], and the proof of the fact that under this condition the classical “Gaussian capacity” resulting from optimization over only Gaussian inputs is equal to the full classical capacity. Complex structures correspond to different squeezings, each with its own normal modes, vacuum and coherent states, and the gauge. Thus our results apply, e.g., to multimode channels with a squeezed Gaussian noise under the standard input energy constraint, provided the squeezing is not too large as to violate the generalized threshold condition. We also investigate the restrictiveness of the gauge-covariance condition for single- and multimode bosonic Gaussian channels.
Regional Scaling of Airborne Eddy Covariance Flux Observation
NASA Astrophysics Data System (ADS)
Sachs, T.; Serafimovich, A.; Metzger, S.; Kohnert, K.; Hartmann, J.
2014-12-01
The earth's surface is tightly coupled to the global climate system by the vertical exchange of energy and matter. Thus, to better understand and potentially predict changes to our climate system, it is critical to quantify the surface-atmosphere exchange of heat, water vapor, and greenhouse gases on climate-relevant spatial and temporal scales. Currently, most flux observations consist of ground-based, continuous but local measurements. These provide a good basis for temporal integration, but may not be representative of the larger regional context. This is particularly true for the Arctic, where site selection is additionally bound by logistical constraints, among others. Airborne measurements can overcome this limitation by covering distances of hundreds of kilometers over time periods of a few hours. The Airborne Measurements of Methane Fluxes (AIRMETH) campaigns are designed to quantitatively and spatially explicitly address this issue: The research aircraft POLAR 5 is used to acquire thousands of kilometers of eddy-covariance flux data. During the AIRMETH-2012 and AIRMETH-2013 campaigns we measured the turbulent exchange of energy, methane, and (in 2013) carbon dioxide over the North Slope of Alaska, USA, and the Mackenzie Delta, Canada. Here, we present the potential of environmental response functions (ERFs) for quantitatively linking flux observations to meteorological and biophysical drivers in the flux footprints. We use wavelet transforms of the original high-frequency data to improve spatial discretization of the flux observations. This also enables the quantification of continuous and biophysically relevant land cover properties in the flux footprint of each observation. A machine learning technique is then employed to extract and quantify the functional relationships between flux observations and the meteorological and biophysical drivers. The resulting ERFs are used to extrapolate fluxes over spatio-temporally explicit grids of the study area. The
Affine Covariant Features for Fisheye Distortion Local Modelling.
Furnari, Antonino; Farinella, Giovanni; Bruna, Arcangelo; Battiato, Sebastiano
2016-11-10
Perspective cameras are the most popular imaging sensors used in Computer Vision. However, many application fields including automotive, surveillance and robotics, require the use of wide angle cameras (e.g., fisheye), which allow to acquire a larger portion of the scene using a single device at the cost of the introduction of noticeable radial distortion in the images. Affine covariant feature detectors have proven successful in a variety of Computer Vision applications including object recognition, image registration and visual search. Moreover, their robustness to a series of variabilities related to both the scene and the image acquisition process has been thoroughly studied in the literature. In this paper, we investigate their effectiveness on fisheye images providing both theoretical and experimental analyses. As theoretical outcome, we show that the inherently non-linear radial distortion can be locally approximated by linear functions with a reasonably small error. The experimental analysis builds on Mikolajczyk's benchmark to assess the robustness of three popular affine region detectors (i.e., Maximally Stable Extremal Regions (MSER), Harris and Hessian affine region detectors), with respect to different variabilities as well as to radial distortion. To support the evaluations, we rely on the Oxford dataset and introduce a novel benchmark dataset comprising 50 images depicting different scene categories. Experiments are carried out on rectilinear images to which radial distortion is artificially added, and on real-world images acquired using fisheye lenses. Our analysis points out that affine region detectors can be effectively employed directly on fisheye images and that the radial distortion is locally modelled as an additional affine variability.
Inverse modeling of the terrestrial carbon flux in China with flux covariance among inverted regions
NASA Astrophysics Data System (ADS)
Wang, H.; Jiang, F.; Chen, J. M.; Ju, W.; Wang, H.
2011-12-01
Quantitative understanding of the role of ocean and terrestrial biosphere in the global carbon cycle, their response and feedback to climate change is required for the future projection of the global climate. China has the largest amount of anthropogenic CO2 emission, diverse terrestrial ecosystems and an unprecedented rate of urbanization. Thus information on spatial and temporal distributions of the terrestrial carbon flux in China is of great importance in understanding the global carbon cycle. We developed a nested inversion with focus in China. Based on Transcom 22 regions for the globe, we divide China and its neighboring countries into 17 regions, making 39 regions in total for the globe. A Bayesian synthesis inversion is made to estimate the terrestrial carbon flux based on GlobalView CO2 data. In the inversion, GEOS-Chem is used as the transport model to develop the transport matrix. A terrestrial ecosystem model named BEPS is used to produce the prior surface flux to constrain the inversion. However, the sparseness of available observation stations in Asia poses a challenge to the inversion for the 17 small regions. To obtain additional constraint on the inversion, a prior flux covariance matrix is constructed using the BEPS model through analyzing the correlation in the net carbon flux among regions under variable climate conditions. The use of the covariance among different regions in the inversion effectively extends the information content of CO2 observations to more regions. The carbon flux over the 39 land and ocean regions are inverted for the period from 2004 to 2009. In order to investigate the impact of introducing the covariance matrix with non-zero off-diagonal values to the inversion, the inverted terrestrial carbon flux over China is evaluated against ChinaFlux eddy-covariance observations after applying an upscaling methodology.
Revised error propagation of 40Ar/39Ar data, including covariances
NASA Astrophysics Data System (ADS)
Vermeesch, Pieter
2015-12-01
The main advantage of the 40Ar/39Ar method over conventional K-Ar dating is that it does not depend on any absolute abundance or concentration measurements, but only uses the relative ratios between five isotopes of the same element -argon- which can be measured with great precision on a noble gas mass spectrometer. The relative abundances of the argon isotopes are subject to a constant sum constraint, which imposes a covariant structure on the data: the relative amount of any of the five isotopes can always be obtained from that of the other four. Thus, the 40Ar/39Ar method is a classic example of a 'compositional data problem'. In addition to the constant sum constraint, covariances are introduced by a host of other processes, including data acquisition, blank correction, detector calibration, mass fractionation, decay correction, interference correction, atmospheric argon correction, interpolation of the irradiation parameter, and age calculation. The myriad of correlated errors arising during the data reduction are best handled by casting the 40Ar/39Ar data reduction protocol in a matrix form. The completely revised workflow presented in this paper is implemented in a new software platform, Ar-Ar_Redux, which takes raw mass spectrometer data as input and generates accurate 40Ar/39Ar ages and their (co-)variances as output. Ar-Ar_Redux accounts for all sources of analytical uncertainty, including those associated with decay constants and the air ratio. Knowing the covariance matrix of the ages removes the need to consider 'internal' and 'external' uncertainties separately when calculating (weighted) mean ages. Ar-Ar_Redux is built on the same principles as its sibling program in the U-Pb community (U-Pb_Redux), thus improving the intercomparability of the two methods with tangible benefits to the accuracy of the geologic time scale. The program can be downloaded free of charge from
Spatial Pyramid Covariance based Compact Video Code for Robust Face Retrieval in TV-series.
Li, Yan; Wang, Ruiping; Cui, Zhen; Shan, Shiguang; Chen, Xilin
2016-10-10
We address the problem of face video retrieval in TV-series which searches video clips based on the presence of specific character, given one face track of his/her. This is tremendously challenging because on one hand, faces in TV-series are captured in largely uncontrolled conditions with complex appearance variations, and on the other hand retrieval task typically needs efficient representation with low time and space complexity. To handle this problem, we propose a compact and discriminative representation for the huge body of video data, named Compact Video Code (CVC). Our method first models the face track by its sample (i.e., frame) covariance matrix to capture the video data variations in a statistical manner. To incorporate discriminative information and obtain more compact video signature suitable for retrieval, the high-dimensional covariance representation is further encoded as a much lower-dimensional binary vector, which finally yields the proposed CVC. Specifically, each bit of the code, i.e., each dimension of the binary vector, is produced via supervised learning in a max margin framework, which aims to make a balance between the discriminability and stability of the code. Besides, we further extend the descriptive granularity of covariance matrix from traditional pixel-level to more general patchlevel, and proceed to propose a novel hierarchical video representation named Spatial Pyramid Covariance (SPC) along with a fast calculation method. Face retrieval experiments on two challenging TV-series video databases, i.e., the Big Bang Theory and Prison Break, demonstrate the competitiveness of the proposed CVC over state-of-the-art retrieval methods. In addition, as a general video matching algorithm, CVC is also evaluated in traditional video face recognition task on a standard Internet database, i.e., YouTube Celebrities, showing its quite promising performance by using an extremely compact code with only 128 bits.
Adaptive Covariance Inflation in a Multi-Resolution Assimilation Scheme
NASA Astrophysics Data System (ADS)
Hickmann, K. S.; Godinez, H. C.
2015-12-01
When forecasts are performed using modern data assimilation methods observation and model error can be scaledependent. During data assimilation the blending of error across scales can result in model divergence since largeerrors at one scale can be propagated across scales during the analysis step. Wavelet based multi-resolution analysiscan be used to separate scales in model and observations during the application of an ensemble Kalman filter. However,this separation is done at the cost of implementing an ensemble Kalman filter at each scale. This presents problemswhen tuning the covariance inflation parameter at each scale. We present a method to adaptively tune a scale dependentcovariance inflation vector based on balancing the covariance of the innovation and the covariance of observations ofthe ensemble. Our methods are demonstrated on a one dimensional Kuramoto-Sivashinsky (K-S) model known todemonstrate non-linear interactions between scales.
Realistic Covariance Prediction for the Earth Science Constellation
NASA Technical Reports Server (NTRS)
Duncan, Matthew; Long, Anne
2006-01-01
Routine satellite operations for the Earth Science Constellation (ESC) include collision risk assessment between members of the constellation and other orbiting space objects. One component of the risk assessment process is computing the collision probability between two space objects. The collision probability is computed using Monte Carlo techniques as well as by numerically integrating relative state probability density functions. Each algorithm takes as inputs state vector and state vector uncertainty information for both objects. The state vector uncertainty information is expressed in terms of a covariance matrix. The collision probability computation is only as good as the inputs. Therefore, to obtain a collision calculation that is a useful decision-making metric, realistic covariance matrices must be used as inputs to the calculation. This paper describes the process used by the NASA/Goddard Space Flight Center's Earth Science Mission Operations Project to generate realistic covariance predictions for three of the Earth Science Constellation satellites: Aqua, Aura and Terra.
Realistic Covariance Prediction For the Earth Science Constellations
NASA Technical Reports Server (NTRS)
Duncan, Matthew; Long, Anne
2006-01-01
Routine satellite operations for the Earth Science Constellations (ESC) include collision risk assessment between members of the constellations and other orbiting space objects. One component of the risk assessment process is computing the collision probability between two space objects. The collision probability is computed via Monte Carlo techniques as well as numerically integrating relative probability density functions. Each algorithm takes as inputs state vector and state vector uncertainty information for both objects. The state vector uncertainty information is expressed in terms of a covariance matrix. The collision probability computation is only as good as the inputs. Therefore, to obtain a collision calculation that is a useful decision-making metric, realistic covariance matrices must be used as inputs to the calculation. This paper describes the process used by NASA Goddard's Earth Science Mission Operations Project to generate realistic covariance predictions for three of the ESC satellites: Aqua, Aura, and Terra
A Lorentz-Covariant Connection for Canonical Gravity
NASA Astrophysics Data System (ADS)
Geiller, Marc; Lachièze-Rey, Marc; Noui, Karim; Sardelli, Francesco
2011-08-01
We construct a Lorentz-covariant connection in the context of first order canonical gravity with non-vanishing Barbero-Immirzi parameter. To do so, we start with the phase space formulation derived from the canonical analysis of the Holst action in which the second class constraints have been solved explicitly. This allows us to avoid the use of Dirac brackets. In this context, we show that there is a ''unique'' Lorentz-covariant connection which is commutative in the sense of the Poisson bracket, and which furthermore agrees with the connection found by Alexandrov using the Dirac bracket. This result opens a new way toward the understanding of Lorentz-covariant loop quantum gravity.
Data Covariances from R-Matrix Analyses of Light Nuclei
Hale, G.M. Paris, M.W.
2015-01-15
After first reviewing the parametric description of light-element reactions in multichannel systems using R-matrix theory and features of the general LANL R-matrix analysis code EDA, we describe how its chi-square minimization procedure gives parameter covariances. This information is used, together with analytically calculated sensitivity derivatives, to obtain cross section covariances for all reactions included in the analysis by first-order error propagation. Examples are given of the covariances obtained for systems with few resonances ({sup 5}He) and with many resonances ({sup 13}C ). We discuss the prevalent problem of this method leading to cross section uncertainty estimates that are unreasonably small for large data sets. The answer to this problem appears to be using parameter confidence intervals in place of standard errors.
Walsh, Stephen J.; Tardiff, Mark F.
2007-10-01
Removing background from hyperspectral scenes is a common step in the process of searching for materials of interest. Some approaches to background subtraction use spectral library data and require invertible covariance matrices for each member of the library. This is challenging because the covariance matrix can be calculated but standard methods for estimating the inverse requires that the data set for each library member have many more spectral measurements than spectral channels, which is rarely the case. An alternative approach is called shrinkage estimation. This method is investigated as an approach to providing an invertible covariance matrix estimate in the case where the number of spectral measurements is less than the number of spectral channels. The approach is an analytic method for arriving at a target matrix and the shrinkage parameter that modify the existing covariance matrix for the data to make it invertible. The theory is discussed to develop different estimates. The resulting estimates are computed and inspected on a set of hyperspectral data. This technique shows some promise for arriving at an invertible covariance estimate for small hyperspectral data sets.
Covariance Matrix Estimation for the Cryo-EM Heterogeneity Problem.
Katsevich, E; Katsevich, A; Singer, A
2015-01-22
In cryo-electron microscopy (cryo-EM), a microscope generates a top view of a sample of randomly oriented copies of a molecule. The problem of single particle reconstruction (SPR) from cryo-EM is to use the resulting set of noisy two-dimensional projection images taken at unknown directions to reconstruct the three-dimensional (3D) structure of the molecule. In some situations, the molecule under examination exhibits structural variability, which poses a fundamental challenge in SPR. The heterogeneity problem is the task of mapping the space of conformational states of a molecule. It has been previously suggested that the leading eigenvectors of the covariance matrix of the 3D molecules can be used to solve the heterogeneity problem. Estimating the covariance matrix is challenging, since only projections of the molecules are observed, but not the molecules themselves. In this paper, we formulate a general problem of covariance estimation from noisy projections of samples. This problem has intimate connections with matrix completion problems and high-dimensional principal component analysis. We propose an estimator and prove its consistency. When there are finitely many heterogeneity classes, the spectrum of the estimated covariance matrix reveals the number of classes. The estimator can be found as the solution to a certain linear system. In the cryo-EM case, the linear operator to be inverted, which we term the projection covariance transform, is an important object in covariance estimation for tomographic problems involving structural variation. Inverting it involves applying a filter akin to the ramp filter in tomography. We design a basis in which this linear operator is sparse and thus can be tractably inverted despite its large size. We demonstrate via numerical experiments on synthetic datasets the robustness of our algorithm to high levels of noise.
Covariance Matrix Estimation for the Cryo-EM Heterogeneity Problem*
Katsevich, E.; Katsevich, A.; Singer, A.
2015-01-01
In cryo-electron microscopy (cryo-EM), a microscope generates a top view of a sample of randomly oriented copies of a molecule. The problem of single particle reconstruction (SPR) from cryo-EM is to use the resulting set of noisy two-dimensional projection images taken at unknown directions to reconstruct the three-dimensional (3D) structure of the molecule. In some situations, the molecule under examination exhibits structural variability, which poses a fundamental challenge in SPR. The heterogeneity problem is the task of mapping the space of conformational states of a molecule. It has been previously suggested that the leading eigenvectors of the covariance matrix of the 3D molecules can be used to solve the heterogeneity problem. Estimating the covariance matrix is challenging, since only projections of the molecules are observed, but not the molecules themselves. In this paper, we formulate a general problem of covariance estimation from noisy projections of samples. This problem has intimate connections with matrix completion problems and high-dimensional principal component analysis. We propose an estimator and prove its consistency. When there are finitely many heterogeneity classes, the spectrum of the estimated covariance matrix reveals the number of classes. The estimator can be found as the solution to a certain linear system. In the cryo-EM case, the linear operator to be inverted, which we term the projection covariance transform, is an important object in covariance estimation for tomographic problems involving structural variation. Inverting it involves applying a filter akin to the ramp filter in tomography. We design a basis in which this linear operator is sparse and thus can be tractably inverted despite its large size. We demonstrate via numerical experiments on synthetic datasets the robustness of our algorithm to high levels of noise. PMID:25699132
Genetic parameters for buffalo milk yield and milk quality traits using Bayesian inference.
Aspilcueta-Borquis, R R; Araujo Neto, F R; Baldi, F; Bignardi, A B; Albuquerque, L G; Tonhati, H
2010-05-01
The availability of accurate genetic parameters for important economic traits in milking buffaloes is critical for implementation of a genetic evaluation program. In the present study, heritabilities and genetic correlations for fat (FY305), protein (PY305), and milk (MY305) yields, milk fat (%F) and protein (%P) percentages, and SCS were estimated using Bayesian methodology. A total of 4,907 lactations from 1,985 cows were used. The (co)variance components were estimated using multiple-trait analysis by Bayesian inference method, applying an animal model, through Gibbs sampling. The model included the fixed effects of contemporary groups (herd-year and calving season), number of milking (2 levels), and age of cow at calving as (co)variable (quadratic and linear effect). The additive genetic, permanent environmental, and residual effects were included as random effects in the model. The posterior means of heritability distributions for MY305, FY305, PY305, %F, P%, and SCS were 0.22, 0.21, 0.23, 0.33, 0.39, and 0.26, respectively. The genetic correlation estimates ranged from -0.13 (between %P and SCS) to 0.94 (between MY305 and PY305). The permanent environmental correlation estimates ranged from -0.38 (between MY305 and %P) to 0.97 (between MY305 and PY305). Residual and phenotypic correlation estimates ranged from -0.26 (between PY305 and SCS) to 0.97 (between MY305 and PY305) and from -0.26 (between MY305 and SCS) to 0.97 (between MY305 and PY305), respectively. Milk yield, milk components, and milk somatic cells counts have enough genetic variation for selection purposes. The genetic correlation estimates suggest that milk components and milk somatic cell counts would be only slightly affected if increasing milk yield were the selection goal. Selecting to increase FY305 or PY305 will also increase MY305, %P, and %F.
Neutron Resonance Parameters and Covariance Matrix of 239Pu
Derrien, Herve; Leal, Luiz C; Larson, Nancy M
2008-08-01
In order to obtain the resonance parameters in a single energy range and the corresponding covariance matrix, a reevaluation of 239Pu was performed with the code SAMMY. The most recent experimental data were analyzed or reanalyzed in the energy range thermal to 2.5 keV. The normalization of the fission cross section data was reconsidered by taking into account the most recent measurements of Weston et al. and Wagemans et al. A full resonance parameter covariance matrix was generated. The method used to obtain realistic uncertainties on the average cross section calculated by SAMMY or other processing codes was examined.
Vector order parameter in general relativity: Covariant equations
NASA Astrophysics Data System (ADS)
Meierovich, Boris E.
2010-07-01
Phase transitions with spontaneous symmetry breaking and vector order parameter are considered in multidimensional theory of general relativity. Covariant equations, describing the gravitational properties of topological defects, are derived. The topological defects are classified in accordance with the symmetry of the covariant derivative of the vector order parameter. The abilities of the derived equations are demonstrated in application to the braneworld concept. New solutions of the Einstein equations with a transverse vector order parameter are presented. In the vicinity of phase transition, the solutions are found analytically.
Realization of the optimal phase-covariant quantum cloning machine
Sciarrino, Fabio; De Martini, Francesco
2005-12-15
In several quantum information (QI) phenomena of large technological importance the information is carried by the phase of the quantum superposition states, or qubits. The phase-covariant cloning machine (PQCM) addresses precisely the problem of optimally copying these qubits with the largest attainable 'fidelity'. We present a general scheme which realizes the 1{yields}3 phase covariant cloning process by a combination of three different QI processes: the universal cloning, the NOT gate, and the projection over the symmetric subspace of the output qubits. The experimental implementation of a PQCM for polarization encoded qubits, the first ever realized with photons, is reported.
Vector order parameter in general relativity: Covariant equations
Meierovich, Boris E.
2010-07-15
Phase transitions with spontaneous symmetry breaking and vector order parameter are considered in multidimensional theory of general relativity. Covariant equations, describing the gravitational properties of topological defects, are derived. The topological defects are classified in accordance with the symmetry of the covariant derivative of the vector order parameter. The abilities of the derived equations are demonstrated in application to the braneworld concept. New solutions of the Einstein equations with a transverse vector order parameter are presented. In the vicinity of phase transition, the solutions are found analytically.
Donegan, Sarah; Williamson, Paula; D'Alessandro, Umberto; Tudur Smith, Catrin
2012-12-20
Mixed treatment comparison (MTC) meta-analysis allows several treatments to be compared in a single analysis while utilising direct and indirect evidence. Treatment by covariate interactions can be included in MTC models to explore how the covariate modifies the treatment effects. If interactions exist, the assumptions underlying MTCs may be invalidated. For conventional pair-wise meta-analysis, important benefits regarding the investigation of such interactions, gained from using individual patient data (IPD) rather than aggregate data (AD), have been described. We aim to compare IPD MTC models including patient-level covariates with AD MTC models including study-level covariates. IPD and AD random-effects MTC models for dichotomous outcomes are specified. Three assumptions are made regarding the interactions (i.e. independent, exchangeable and common interactions). The models are applied to a dataset to compare four drugs for treating malaria (i.e. amodiaquine-artesunate, dihydroartemisinin-piperaquine (DHAPQ), artemether-lumefantrine and chlorproguanil-dapsone plus artesunate) using the outcome unadjusted treatment success at day 28. The treatment effects and regression coefficients for interactions from the IPD models were more precise than those from AD models. Using IPD, assuming independent or exchangeable interactions, the regression coefficient for chlorproguanil-dapsone plus artesunate versus DHAPQ was statistically significant and assuming common interactions, the common coefficient was significant; whereas using AD, no coefficients were significant. Using IPD, DHAPQ was the best drug; whereas using AD, the best drug varied. Using AD models, there was no evidence that the consistency assumption was invalid; whereas, the assumption was questionable based on the IPD models. The AD analyses were misleading.
NASA Astrophysics Data System (ADS)
Leitão, Sofia; Stadler, Alfred; Peña, M. T.; Biernat, Elmar P.
2017-01-01
The Covariant Spectator Theory (CST) is used to calculate the mass spectrum and vertex functions of heavy-light and heavy mesons in Minkowski space. The covariant kernel contains Lorentz scalar, pseudoscalar, and vector contributions. The numerical calculations are performed in momentum space, where special care is taken to treat the strong singularities present in the confining kernel. The observed meson spectrum is very well reproduced after fitting a small number of model parameters. Remarkably, a fit to a few pseudoscalar meson states only, which are insensitive to spin-orbit and tensor forces and do not allow to separate the spin-spin from the central interaction, leads to essentially the same model parameters as a more general fit. This demonstrates that the covariance of the chosen interaction kernel is responsible for the very accurate prediction of the spin-dependent quark-antiquark interactions.
Genetic analysis of life-history constraint and evolution in a wild ungulate population.
Morrissey, Michael B; Walling, Craig A; Wilson, Alastair J; Pemberton, Josephine M; Clutton-Brock, Tim H; Kruuk, Loeske E B
2012-04-01
Trade-offs among life-history traits are central to evolutionary theory. In quantitative genetic terms, trade-offs may be manifested as negative genetic covariances relative to the direction of selection on phenotypic traits. Although the expression and selection of ecologically important phenotypic variation are fundamentally multivariate phenomena, the in situ quantification of genetic covariances is challenging. Even for life-history traits, where well-developed theory exists with which to relate phenotypic variation to fitness variation, little evidence exists from in situ studies that negative genetic covariances are an important aspect of the genetic architecture of life-history traits. In fact, the majority of reported estimates of genetic covariances among life-history traits are positive. Here we apply theory of the genetics and selection of life histories in organisms with complex life cycles to provide a framework for quantifying the contribution of multivariate genetically based relationships among traits to evolutionary constraint. We use a Bayesian framework to link pedigree-based inference of the genetic basis of variation in life-history traits to evolutionary demography theory regarding how life histories are selected. Our results suggest that genetic covariances may be acting to constrain the evolution of female life-history traits in a wild population of red deer Cervus elaphus: genetic covariances are estimated to reduce the rate of adaptation by about 40%, relative to predicted evolutionary change in the absence of genetic covariances. Furthermore, multivariate phenotypic (rather than genetic) relationships among female life-history traits do not reveal this constraint.
An Empirical State Error Covariance Matrix Orbit Determination Example
NASA Technical Reports Server (NTRS)
Frisbee, Joseph H., Jr.
2015-01-01
State estimation techniques serve effectively to provide mean state estimates. However, the state error covariance matrices provided as part of these techniques suffer from some degree of lack of confidence in their ability to adequately describe the uncertainty in the estimated states. A specific problem with the traditional form of state error covariance matrices is that they represent only a mapping of the assumed observation error characteristics into the state space. Any errors that arise from other sources (environment modeling, precision, etc.) are not directly represented in a traditional, theoretical state error covariance matrix. First, consider that an actual observation contains only measurement error and that an estimated observation contains all other errors, known and unknown. Then it follows that a measurement residual (the difference between expected and observed measurements) contains all errors for that measurement. Therefore, a direct and appropriate inclusion of the actual measurement residuals in the state error covariance matrix of the estimate will result in an empirical state error covariance matrix. This empirical state error covariance matrix will fully include all of the errors in the state estimate. The empirical error covariance matrix is determined from a literal reinterpretation of the equations involved in the weighted least squares estimation algorithm. It is a formally correct, empirical state error covariance matrix obtained through use of the average form of the weighted measurement residual variance performance index rather than the usual total weighted residual form. Based on its formulation, this matrix will contain the total uncertainty in the state estimate, regardless as to the source of the uncertainty and whether the source is anticipated or not. It is expected that the empirical error covariance matrix will give a better, statistical representation of the state error in poorly modeled systems or when sensor performance
Increased genetic risk for obesity in premature coronary artery disease.
Cole, Christopher B; Nikpay, Majid; Stewart, Alexandre F R; McPherson, Ruth
2016-04-01
There is ongoing controversy as to whether obesity confers risk for CAD independently of associated risk factors including diabetes mellitus. We have carried out a Mendelian randomization study using a genetic risk score (GRS) for body mass index (BMI) based on 35 risk alleles to investigate this question in a population of 5831 early onset CAD cases without diabetes mellitus and 3832 elderly healthy control subjects, all of strictly European ancestry, with adjustment for traditional risk factors (TRFs). We then estimated the genetic correlation between these BMI and CAD (rg) by relating the pairwise genetic similarity matrix to a phenotypic covariance matrix between these two traits. GRSBMI significantly (P=2.12 × 10(-12)) associated with CAD status in a multivariate model adjusted for TRFs, with a per allele odds ratio (OR) of 1.06 (95% CI 1.042-1.076). The addition of GRSBMI to TRFs explained 0.75% of CAD variance and yielded a continuous net recombination index of 16.54% (95% CI=11.82-21.26%, P<0.0001). To test whether GRSBMI explained CAD status when adjusted for measured BMI, separate models were constructed in which the score and BMI were either included as covariates or not. The addition of BMI explained ~1.9% of CAD variance and GRSBMI plus BMI explained 2.65% of CAD variance. Finally, using bivariate restricted maximum likelihood analysis, we provide strong evidence of genome-wide pleiotropy between obesity and CAD. This analysis supports the hypothesis that obesity is a causal risk factor for CAD.
Age-dependent genetic variance in a life-history trait in the mute swan.
Charmantier, Anne; Perrins, Christopher; McCleery, Robin H; Sheldon, Ben C
2006-01-22
Genetic variance in characters under natural selection in natural populations determines the way those populations respond to that selection. Whether populations show temporal and/or spatial constancy in patterns of genetic variance and covariance is regularly considered, as this will determine whether selection responses are constant over space and time. Much less often considered is whether characters show differing amounts of genetic variance over the life-history of individuals. Such age-specific variation, if present, has important potential consequences for the force of natural selection and for understanding the causes of variation in quantitative characters. Using data from a long-term study of the mute swan Cygnus olor, we report the partitioning of phenotypic variance in timing of breeding (subject to strong natural selection) into component parts over 12 different age classes. We show that the additive genetic variance and heritability of this trait are strongly age-dependent, with higher additive genetic variance present in young and, particularly, old birds, but little evidence of any genetic variance for birds of intermediate ages. These results demonstrate that age can have a very important influence on the components of variation of characters in natural populations, and consequently that separate age classes cannot be assumed to be equivalent, either with respect to their evolutionary potential or response.
Covariance of lichen and vascular plant floras
Bennett, J.P.; Wetmore, C.M.
1999-01-01
The geographic relationships among taxonomic groups are important to study to determine patterns of biodiversity and whether or not associations occur between large groups, e.g., birds and vascular plants. This study was undertaken to determine relationships between higher plants and lower plants, specifically vascular plant and lichen floras in nine national parks of the Great Lakes region. No significant relationship was found between vascular plant floras and lichen floras in this area, which spans 1200 km longitudinally, or between an additional 19 areas from North America that were less than 1000 km(2) in area. For areas larger than 1000 km(2), however, a significant positive relationship existed for 33 areas that span one to approximately 150 million km(2). The ratio of numbers of vascular plants to lichens appeared to average just over 6 across the 33 areas. In the Great Lakes parks, between 28-30% of either the vascular plant or lichen species were singletons (occurring in only one park), but the parks that contained the most singletons were not congruent: Isle Royale had the most singleton lichens, while Indiana Dunes had the most vascular plant singletons. Fewer lichen species (2%) than vascular plants (4%) occurred in all nine parks. Latitude appeared to explain some of the variation between the two groups: vascular plants decreased with increasing latitude, while lichens increased.
Genetic pleiotropy between asthma and obesity in a community-based sample of twins
Hallstrand, Teal S.; Fischer, Mary E.; Wurfel, Mark M.; Afari, Niloofar; Buchwald, Dedra; Goldberg, Jack
2007-01-01
Background Asthma and obesity are common conditions that are strongly associated. This association might be due to shared genetic or environmental causes. Objective We sought to determine whether a shared genetic cause is responsible for the association between asthma and obesity and to estimate the magnitude of shared genetic cause. Methods The analyses were performed with 1001 monozygotic and 383 dizygotic same-sex twin pairs within the University of Washington Twin Registry. The presence of asthma was determined by self-report of a physician diagnosis of asthma, and body mass index (BMI) was calculated by using self-reported height and weight. Obesity was defined as a BMI of 30 or greater. The association between asthma and BMI was assessed by means of mixed-effects ordinal regression. Twin correlations examined the association of asthma and obesity. Univariate and bivariate structural equation models estimated the components of variance attributable to genetic and environmental effects. Results A strong association between asthma and BMI was identified in the sample population (P < .001). Substantial heritability was detected for asthma (53%) and obesity (77%), which is indicative of additive genetic influences on each disorder. The best-fitting model of shared components of variance indicated that 8% of the genetic component of obesity is shared with asthma. Conclusion The covariation between obesity and asthma is predominantly caused by shared genetic risk factors for both conditions. PMID:16337451
Triangular covariance factorizations for. Ph.D. Thesis. - Calif. Univ.
NASA Technical Reports Server (NTRS)
Thornton, C. L.
1976-01-01
An improved computational form of the discrete Kalman filter is derived using an upper triangular factorization of the error covariance matrix. The covariance P is factored such that P = UDUT where U is unit upper triangular and D is diagonal. Recursions are developed for propagating the U-D covariance factors together with the corresponding state estimate. The resulting algorithm, referred to as the U-D filter, combines the superior numerical precision of square root filtering techniques with an efficiency comparable to that of Kalman's original formula. Moreover, this method is easily implemented and involves no more computer storage than the Kalman algorithm. These characteristics make the U-D method an attractive realtime filtering technique. A new covariance error analysis technique is obtained from an extension of the U-D filter equations. This evaluation method is flexible and efficient and may provide significantly improved numerical results. Cost comparisons show that for a large class of problems the U-D evaluation algorithm is noticeably less expensive than conventional error analysis methods.
Vertical Covariance Localization for Satellite Radiances in Ensemble Kalman Filters
2010-01-01
Vertical Covariance Localization for Satellite Radiances in Ensemble Kalman Filters WILLIAM F. CAMPBELL, CRAIG H. BISHOP, AND DANIEL HODYSS Naval...being used in the operational data assimila- tion system at Environment Canada for their ensemble Corresponding author address: Dr. William F...here. Acknowledgments. The authors thank Jeff Whitaker, Peter Houtekamer, Herschel Mitchell, and our anony- mous reviewer for their valuable comments. We
Incorporating Prior Theory in Covariance Structure Analysis: A Bayesian Approach.
ERIC Educational Resources Information Center
Fornell, Claes; Rust, Roland T.
1989-01-01
A Bayesian approach to the testing of competing covariance structures is developed. Approximate posterior probabilities are easily obtained from the chi square values and other known constants. The approach is illustrated using an example that demonstrates how the prior probabilities can alter results concerning the preferred model specification.…
Covariance matrices for use in criticality safety predictability studies
Derrien, H.; Larson, N.M.; Leal, L.C.
1997-09-01
Criticality predictability applications require as input the best available information on fissile and other nuclides. In recent years important work has been performed in the analysis of neutron transmission and cross-section data for fissile nuclei in the resonance region by using the computer code SAMMY. The code uses Bayes method (a form of generalized least squares) for sequential analyses of several sets of experimental data. Values for Reich-Moore resonance parameters, their covariances, and the derivatives with respect to the adjusted parameters (data sensitivities) are obtained. In general, the parameter file contains several thousand values and the dimension of the covariance matrices is correspondingly large. These matrices are not reported in the current evaluated data files due to their large dimensions and to the inadequacy of the file formats. The present work has two goals: the first is to calculate the covariances of group-averaged cross sections from the covariance files generated by SAMMY, because these can be more readily utilized in criticality predictability calculations. The second goal is to propose a more practical interface between SAMMY and the evaluated files. Examples are given for {sup 235}U in the popular 199- and 238-group structures, using the latest ORNL evaluation of the {sup 235}U resonance parameters.
Block diagonal representations for covariance based anomalous change detectors
Matsekh, Anna; Theiler, James
2009-01-01
Change detection methods are of crucial importance in many remote sensing applications such as monitoring and surveillance, where the goal is to identify and separate changes of interest from pervasive changes inevitably present in images taken at different times and in different environmental and illumination conditions. Anomalous change detection (ACD) methods aim to identify rare, unusual, or anomalous changes among the changes of interest. Covariance-based ACD methods provide a powerful tool for detection of unusual changes in hyper-spectral images. In this paper we study the properties of the eigenvalue spectra of a family of ACD matrices in order to better understand the algebraic and numerical behavior of the covariance-based quadratic ACD methods. We propose to use singular vectors of covariance matrices of two hyper-spectral images in whitened coordinates for obtaining block-diagonal representations of the matrices of quadratic ACD methods. SVD transformation gives an equivalent representation of ACD matrices in compact block-diagonal form. In the paper we show that the eigenvalue spectrum of a block-diagonal ACD matrix can be identified analytically as a function of the singular value spectrum of the corresponding covariance matrix in whitened coordinates.
A new eddy-covariance method using empirical mode decomposition
Technology Transfer Automated Retrieval System (TEKTRAN)
We introduce a new eddy-covariance method that uses a spectral decomposition algorithm called empirical mode decomposition. The technique is able to calculate contributions to near-surface fluxes from different periodic components. Unlike traditional Fourier methods, this method allows for non-ortho...
A Statistical Test for Comparing Nonnested Covariance Structure Models.
ERIC Educational Resources Information Center
Levy, Roy; Hancock, Gregory R.
While statistical procedures are well known for comparing hierarchically related (nested) covariance structure models, statistical tests for comparing nonhierarchically related (nonnested) models have proven more elusive. While isolated attempts have been made, none exists within the commonly used maximum likelihood estimation framework, thereby…
Determining the oxygen isotope composition of evapotranspiration with eddy covariance
Technology Transfer Automated Retrieval System (TEKTRAN)
The oxygen isotope componsition of evapotranspiration (dF) represents an important tracer in the study of biosphere-atmosphere interactions, hydrology, paleoclimate, and carbon cycling. Here we demonstrate direct measurement of dF based on eddy covariance (EC) and tunable diode laser (EC-TDL) techni...
Fast Covariance Estimation for High-dimensional Functional Data.
Xiao, Luo; Zipunnikov, Vadim; Ruppert, David; Crainiceanu, Ciprian
2016-01-01
We propose two fast covariance smoothing methods and associated software that scale up linearly with the number of observations per function. Most available methods and software cannot smooth covariance matrices of dimension J > 500; a recently introduced sandwich smoother is an exception but is not adapted to smooth covariance matrices of large dimensions, such as J = 10, 000. We introduce two new methods that circumvent those problems: 1) a fast implementation of the sandwich smoother for covariance smoothing; and 2) a two-step procedure that first obtains the singular value decomposition of the data matrix and then smoothes the eigenvectors. These new approaches are at least an order of magnitude faster in high dimensions and drastically reduce computer memory requirements. The new approaches provide instantaneous (a few seconds) smoothing for matrices of dimension J = 10,000 and very fast (< 10 minutes) smoothing for J = 100, 000. R functions, simulations, and data analysis provide ready to use, reproducible, and scalable tools for practical data analysis of noisy high-dimensional functional data.
Positive Loadings and Factor Correlations from Positive Covariance Matrices
ERIC Educational Resources Information Center
Krijnen, Wim P.
2004-01-01
In many instances it is reasonable to assume that the population covariance matrix has positive elements. This assumption implies for the single factor analysis model that the loadings and regression weights for best linear factor prediction are positive. For the multiple factor analysis model where each variable loads on a single factor and a…
Covariance, correlation matrix, and the multiscale community structure of networks.
Shen, Hua-Wei; Cheng, Xue-Qi; Fang, Bin-Xing
2010-07-01
Empirical studies show that real world networks often exhibit multiple scales of topological descriptions. However, it is still an open problem how to identify the intrinsic multiple scales of networks. In this p