Random regression models using Legendre orthogonal polynomials to evaluate the milk production of Alpine goats.

PubMed

Silva, F G; Torres, R A; Brito, L F; Euclydes, R F; Melo, A L P; Souza, N O; Ribeiro, J I; Rodrigues, M T

2013-12-11

The objective of this study was to identify the best random regression model using Legendre orthogonal polynomials to evaluate Alpine goats genetically and to estimate the parameters for test day milk yield. On the test day, we analyzed 20,710 records of milk yield of 667 goats from the Goat Sector of the Universidade Federal de Viçosa. The evaluated models had combinations of distinct fitting orders for polynomials (2-5), random genetic (1-7), and permanent environmental (1-7) fixed curves and a number of classes for residual variance (2, 4, 5, and 6). WOMBAT software was used for all genetic analyses. A random regression model using the best Legendre orthogonal polynomial for genetic evaluation of milk yield on the test day of Alpine goats considered a fixed curve of order 4, curve of genetic additive effects of order 2, curve of permanent environmental effects of order 7, and a minimum of 5 classes of residual variance because it was the most economical model among those that were equivalent to the complete model by the likelihood ratio test. Phenotypic variance and heritability were higher at the end of the lactation period, indicating that the length of lactation has more genetic components in relation to the production peak and persistence. It is very important that the evaluation utilizes the best combination of fixed, genetic additive and permanent environmental regressions, and number of classes of heterogeneous residual variance for genetic evaluation using random regression models, thereby enhancing the precision and accuracy of the estimates of parameters and prediction of genetic values.

Genetic evaluation and selection response for growth in meat-type quail through random regression models using B-spline functions and Legendre polynomials.

PubMed

Mota, L F M; Martins, P G M A; Littiere, T O; Abreu, L R A; Silva, M A; Bonafé, C M

2018-04-01

The objective was to estimate (co)variance functions using random regression models (RRM) with Legendre polynomials, B-spline function and multi-trait models aimed at evaluating genetic parameters of growth traits in meat-type quail. A database containing the complete pedigree information of 7000 meat-type quail was utilized. The models included the fixed effects of contemporary group and generation. Direct additive genetic and permanent environmental effects, considered as random, were modeled using B-spline functions considering quadratic and cubic polynomials for each individual segment, and Legendre polynomials for age. Residual variances were grouped in four age classes. Direct additive genetic and permanent environmental effects were modeled using 2 to 4 segments and were modeled by Legendre polynomial with orders of fit ranging from 2 to 4. The model with quadratic B-spline adjustment, using four segments for direct additive genetic and permanent environmental effects, was the most appropriate and parsimonious to describe the covariance structure of the data. The RRM using Legendre polynomials presented an underestimation of the residual variance. Lesser heritability estimates were observed for multi-trait models in comparison with RRM for the evaluated ages. In general, the genetic correlations between measures of BW from hatching to 35 days of age decreased as the range between the evaluated ages increased. Genetic trend for BW was positive and significant along the selection generations. The genetic response to selection for BW in the evaluated ages presented greater values for RRM compared with multi-trait models. In summary, RRM using B-spline functions with four residual variance classes and segments were the best fit for genetic evaluation of growth traits in meat-type quail. In conclusion, RRM should be considered in genetic evaluation of breeding programs.

Replication of a gene-environment interaction Via Multimodel inference: additive-genetic variance in adolescents' general cognitive ability increases with family-of-origin socioeconomic status.

PubMed

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.

Replication of a Gene-Environment Interaction via Multimodel Inference: Additive-Genetic Variance in Adolescents’ General Cognitive Ability Increases with Family-of-Origin Socioeconomic Status

PubMed Central

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

Covariance functions for body weight from birth to maturity in Nellore cows.

PubMed

Boligon, A A; Mercadante, M E Z; Forni, S; Lôbo, R B; Albuquerque, L G

2010-03-01

The objective of this study was to estimate (co)variance functions using random regression models on Legendre polynomials for the analysis of repeated measures of BW from birth to adult age. A total of 82,064 records from 8,145 females were analyzed. Different models were compared. The models included additive direct and maternal effects, and animal and maternal permanent environmental effects as random terms. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of animal age (cubic regression) were considered as random covariables. Eight models with polynomials of third to sixth order were used to describe additive direct and maternal effects, and animal and maternal permanent environmental effects. Residual effects were modeled using 1 (i.e., assuming homogeneity of variances across all ages) or 5 age classes. The model with 5 classes was the best to describe the trajectory of residuals along the growth curve. The model including fourth- and sixth-order polynomials for additive direct and animal permanent environmental effects, respectively, and third-order polynomials for maternal genetic and maternal permanent environmental effects were the best. Estimates of (co)variance obtained with the multi-trait and random regression models were similar. Direct heritability estimates obtained with the random regression models followed a trend similar to that obtained with the multi-trait model. The largest estimates of maternal heritability were those of BW taken close to 240 d of age. In general, estimates of correlation between BW from birth to 8 yr of age decreased with increasing distance between ages.

Random Regression Models Using Legendre Polynomials to Estimate Genetic Parameters for Test-day Milk Protein Yields in Iranian Holstein Dairy Cattle.

PubMed

Naserkheil, Masoumeh; Miraie-Ashtiani, Seyed Reza; Nejati-Javaremi, Ardeshir; Son, Jihyun; Lee, Deukhwan

2016-12-01

The objective of this study was to estimate the genetic parameters of milk protein yields in Iranian Holstein dairy cattle. A total of 1,112,082 test-day milk protein yield records of 167,269 first lactation Holstein cows, calved from 1990 to 2010, were analyzed. Estimates of the variance components, heritability, and genetic correlations for milk protein yields were obtained using a random regression test-day model. Milking times, herd, age of recording, year, and month of recording were included as fixed effects in the model. Additive genetic and permanent environmental random effects for the lactation curve were taken into account by applying orthogonal Legendre polynomials of the fourth order in the model. The lowest and highest additive genetic variances were estimated at the beginning and end of lactation, respectively. Permanent environmental variance was higher at both extremes. Residual variance was lowest at the middle of the lactation and contrarily, heritability increased during this period. Maximum heritability was found during the 12th lactation stage (0.213±0.007). Genetic, permanent, and phenotypic correlations among test-days decreased as the interval between consecutive test-days increased. A relatively large data set was used in this study; therefore, the estimated (co)variance components for random regression coefficients could be used for national genetic evaluation of dairy cattle in Iran.

Random Regression Models Using Legendre Polynomials to Estimate Genetic Parameters for Test-day Milk Protein Yields in Iranian Holstein Dairy Cattle

PubMed Central

Naserkheil, Masoumeh; Miraie-Ashtiani, Seyed Reza; Nejati-Javaremi, Ardeshir; Son, Jihyun; Lee, Deukhwan

2016-01-01

The objective of this study was to estimate the genetic parameters of milk protein yields in Iranian Holstein dairy cattle. A total of 1,112,082 test-day milk protein yield records of 167,269 first lactation Holstein cows, calved from 1990 to 2010, were analyzed. Estimates of the variance components, heritability, and genetic correlations for milk protein yields were obtained using a random regression test-day model. Milking times, herd, age of recording, year, and month of recording were included as fixed effects in the model. Additive genetic and permanent environmental random effects for the lactation curve were taken into account by applying orthogonal Legendre polynomials of the fourth order in the model. The lowest and highest additive genetic variances were estimated at the beginning and end of lactation, respectively. Permanent environmental variance was higher at both extremes. Residual variance was lowest at the middle of the lactation and contrarily, heritability increased during this period. Maximum heritability was found during the 12th lactation stage (0.213±0.007). Genetic, permanent, and phenotypic correlations among test-days decreased as the interval between consecutive test-days increased. A relatively large data set was used in this study; therefore, the estimated (co)variance components for random regression coefficients could be used for national genetic evaluation of dairy cattle in Iran. PMID:26954192

Random regression models using Legendre polynomials or linear splines for test-day milk yield of dairy Gyr (Bos indicus) cattle.

PubMed

Pereira, R J; Bignardi, A B; El Faro, L; Verneque, R S; Vercesi Filho, A E; Albuquerque, L G

2013-01-01

Studies investigating the use of random regression models for genetic evaluation of milk production in Zebu cattle are scarce. In this study, 59,744 test-day milk yield records from 7,810 first lactations of purebred dairy Gyr (Bos indicus) and crossbred (dairy Gyr × Holstein) cows were used to compare random regression models in which additive genetic and permanent environmental effects were modeled using orthogonal Legendre polynomials or linear spline functions. Residual variances were modeled considering 1, 5, or 10 classes of days in milk. Five classes fitted the changes in residual variances over the lactation adequately and were used for model comparison. The model that fitted linear spline functions with 6 knots provided the lowest sum of residual variances across lactation. On the other hand, according to the deviance information criterion (DIC) and bayesian information criterion (BIC), a model using third-order and fourth-order Legendre polynomials for additive genetic and permanent environmental effects, respectively, provided the best fit. However, the high rank correlation (0.998) between this model and that applying third-order Legendre polynomials for additive genetic and permanent environmental effects, indicates that, in practice, the same bulls would be selected by both models. The last model, which is less parameterized, is a parsimonious option for fitting dairy Gyr breed test-day milk yield records. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Consequences of environmental and biological variances for range margins: a spatially explicit theoretical model

NASA Astrophysics Data System (ADS)

Malanson, G. P.; DeRose, R. J.; Bekker, M. F.

2016-12-01

The consequences of increasing climatic variance while including variability among individuals and populations are explored for range margins of species with a spatially explicit simulation. The model has a single environmental gradient and a single species then extended to two species. Species response to the environment is a Gaussian function with a peak of 1.0 at their peak fitness on the gradient. The variance in the environment is taken from the total variance in the tree ring series of 399 individuals of Pinus edulis in FIA plots in the western USA. The variability is increased by a multiplier of the standard deviation for various doubling times. The variance of individuals in the simulation is drawn from these same series. Inheritance of individual variability is based on the geographic locations of the individuals. The variance for P. edulis is recomputed as time-dependent conditional standard deviations using the GARCH procedure. Establishment and mortality are simulated in a Monte Carlo process with individual variance. Variance for P. edulis does not show a consistent pattern of heteroscedasticity. An obvious result is that increasing variance has deleterious effects on species persistence because extreme events that result in extinctions cannot be balanced by positive anomalies, but even less extreme negative events cannot be balanced by positive anomalies because of biological and spatial constraints. In the two species model the superior competitor is more affected by increasing climatic variance because its response function is steeper at the point of intersection with the other species and so the uncompensated effects of negative anomalies are greater for it. These theoretical results can guide the anticipated need to mitigate the effects of increasing climatic variability on P. edulis range margins. The trailing edge, here subject to increasing drought stress with increasing temperatures, will be more affected by negative anomalies.

Genetic and environmental influences on blood pressure variability: a study in twins.

PubMed

Xu, Xiaojing; Ding, Xiuhua; Zhang, Xinyan; Su, Shaoyong; Treiber, Frank A; Vlietinck, Robert; Fagard, Robert; Derom, Catherine; Gielen, Marij; Loos, Ruth J F; Snieder, Harold; Wang, Xiaoling

2013-04-01

Blood pressure variability (BPV) and its reduction in response to antihypertensive treatment are predictors of clinical outcomes; however, little is known about its heritability. In this study, we examined the relative influence of genetic and environmental sources of variance of BPV and the extent to which it may depend on race or sex in young twins. Twins were enrolled from two studies. One study included 703 white twins (308 pairs and 87 singletons) aged 18-34 years, whereas another study included 242 white twins (108 pairs and 26 singletons) and 188 black twins (79 pairs and 30 singletons) aged 12-30 years. BPV was calculated from 24-h ambulatory blood pressure recording. Twin modeling showed similar results in the separate analysis in both twin studies and in the meta-analysis. Familial aggregation was identified for SBP variability (SBPV) and DBP variability (DBPV) with genetic factors and common environmental factors together accounting for 18-40% and 23-31% of the total variance of SBPV and DBPV, respectively. Unique environmental factors were the largest contributor explaining up to 82-77% of the total variance of SBPV and DBPV. No sex or race difference in BPV variance components was observed. The results remained the same after adjustment for 24-h blood pressure levels. The variance in BPV is predominantly determined by unique environment in youth and young adults, although familial aggregation due to additive genetic and/or common environment influences was also identified explaining about 25% of the variance in BPV.

Additive-Multiplicative Approximation of Genotype-Environment Interaction

PubMed Central

Gimelfarb, A.

1994-01-01

A model of genotype-environment interaction in quantitative traits is considered. The model represents an expansion of the traditional additive (first degree polynomial) approximation of genotypic and environmental effects to a second degree polynomial incorporating a multiplicative term besides the additive terms. An experimental evaluation of the model is suggested and applied to a trait in Drosophila melanogaster. The environmental variance of a genotype in the model is shown to be a function of the genotypic value: it is a convex parabola. The broad sense heritability in a population depends not only on the genotypic and environmental variances, but also on the position of the genotypic mean in the population relative to the minimum of the parabola. It is demonstrated, using the model, that GXE interaction rectional may cause a substantial non-linearity in offspring-parent regression and a reversed response to directional selection. It is also shown that directional selection may be accompanied by an increase in the heritability. PMID:7896113

Overlapping Genetic and Child-Specific Nonshared Environmental Influences on Listening Comprehension, Reading Motivation, and Reading Comprehension

PubMed Central

Schenker, Victoria J.; Petrill, Stephen A.

2015-01-01

This study investigated the genetic and environmental influences on observed associations between listening comprehension, reading motivation, and reading comprehension. Univariate and multivariate quantitative genetic models were conducted in a sample of 284 pairs of twins at a mean age of 9.81 years. Genetic and nonshared environmental factors accounted for statistically significant variance in listening and reading comprehension, and nonshared environmental factors accounted for variance in reading motivation. Furthermore, listening comprehension demonstrated unique genetic and nonshared environmental influences but also had overlapping genetic influences with reading comprehension. Reading motivation and reading comprehension each had unique and overlapping nonshared environmental contributions. Therefore, listening comprehension appears to be related to reading primarily due to genetic factors whereas motivation appears to affect reading via child-specific, nonshared environmental effects. PMID:26321677

Overlapping genetic and child-specific nonshared environmental influences on listening comprehension, reading motivation, and reading comprehension.

PubMed

Schenker, Victoria J; Petrill, Stephen A

2015-01-01

This study investigated the genetic and environmental influences on observed associations between listening comprehension, reading motivation, and reading comprehension. Univariate and multivariate quantitative genetic models were conducted in a sample of 284 pairs of twins at a mean age of 9.81 years. Genetic and nonshared environmental factors accounted for statistically significant variance in listening and reading comprehension, and nonshared environmental factors accounted for variance in reading motivation. Furthermore, listening comprehension demonstrated unique genetic and nonshared environmental influences but also had overlapping genetic influences with reading comprehension. Reading motivation and reading comprehension each had unique and overlapping nonshared environmental contributions. Therefore, listening comprehension appears to be related to reading primarily due to genetic factors whereas motivation appears to affect reading via child-specific, nonshared environmental effects. Copyright © 2015 Elsevier Inc. All rights reserved.

The Genetic and Environmental Etiologies of the Relations between Cognitive Skills and Components of Reading Ability

PubMed Central

Christopher, Micaela E.; Keenan, Janice M.; Hulslander, Jacqueline; DeFries, John C.; Miyake, Akira; Wadsworth, Sally J.; Willcutt, Erik; Pennington, Bruce; Olson, Richard K.

2016-01-01

While previous research has shown cognitive skills to be important predictors of reading ability in children, the respective roles for genetic and environmental influences on these relations is an open question. The present study explored the genetic and environmental etiologies underlying the relations between selected executive functions and cognitive abilities (working memory, inhibition, processing speed, and naming speed) with three components of reading ability (word reading, reading comprehension, and listening comprehension). Twin pairs drawn from the Colorado Front Range (n = 676; 224 monozygotic pairs; 452 dizygotic pairs) between the ages of eight and 16 (M = 11.11) were assessed on multiple measures of each cognitive and reading-related skill. Each cognitive and reading-related skill was modeled as a latent variable, and behavioral genetic analyses estimated the portions of phenotypic variance on each latent variable due to genetic, shared environmental, and nonshared environmental influences. The covariance between the cognitive skills and reading-related skills was driven primarily by genetic influences. The cognitive skills also shared large amounts of genetic variance, as did the reading-related skills. The common cognitive genetic variance was highly correlated with the common reading genetic variance, suggesting that genetic influences involved in general cognitive processing are also important for reading ability. Skill-specific genetic variance in working memory and processing speed also predicted components of reading ability. Taken together, the present study supports a genetic association between children’s cognitive ability and reading ability. PMID:26974208

Effects of social contact and zygosity on 21-y weight change in male twins.

PubMed

McCaffery, Jeanne M; Franz, Carol E; Jacobson, Kristen; Leahey, Tricia M; Xian, Hong; Wing, Rena R; Lyons, Michael J; Kremen, William S

2011-08-01

Recent evidence indicates that social contact is related to similarities in weight gain over time. However, no studies have examined this effect in a twin design, in which genetic and other environmental effects can also be estimated. We determined whether the frequency of social contact is associated with similarity in weight change from young adulthood (mean age: 20 y) to middle age (mean age: 41 y) in twins and quantified the percentage of variance in weight change attributable to social contact, genetic factors, and other environmental influences. Participants were 1966 monozygotic and 1529 dizygotic male twin pairs from the Vietnam-Era Twin Registry. Regression models tested whether frequency of social contact and zygosity predicted twin pair similarity in body mass index (BMI) change and weight change. Twin modeling was used to partition the percentage variance attributable to social contact, genetic, and other environmental effects. Twins gained an average of 3.99 BMI units, or 13.23 kg (29.11 lb), over 21 y. In regression models, both zygosity (P < 0.001) and degree of social contact (P < 0.02) significantly predicted twin pair similarity in BMI change. In twin modeling, social contact between twins contributed 16% of the variance in BMI change (P < 0.001), whereas genetic factors contributed 42%, with no effect of additional shared environmental factors (1%). Similar results were obtained for weight change. Frequency of social contact significantly predicted twin pair similarity in BMI and weight change over 21 y, independent of zygosity and other shared environmental influences.

Environmental stress, inbreeding, and the nature of phenotypic and genetic variance in Drosophila melanogaster.

PubMed Central

Fowler, Kevin; Whitlock, Michael C

2002-01-01

Fifty-two lines of Drosophila melanogaster founded by single-pair population bottlenecks were used to study the effects of inbreeding and environmental stress on phenotypic variance, genetic variance and survivorship. Cold temperature and high density cause reduced survivorship, but these stresses do not cause repeatable changes in the phenotypic variance of most wing morphological traits. Wing area, however, does show increased phenotypic variance under both types of environmental stress. This increase is no greater in inbred than in outbred lines, showing that inbreeding does not increase the developmental effects of stress. Conversely, environmental stress does not increase the extent of inbreeding depression. Genetic variance is not correlated with environmental stress, although the amount of genetic variation varies significantly among environments and lines vary significantly in their response to environmental change. Drastic changes in the environment can cause changes in phenotypic and genetic variance, but not in a way reliably predicted by the notion of 'stress'. PMID:11934358

Active commuting patterns at a large, midwestern college campus.

PubMed

Bopp, Melissa; Kaczynski, Andrew; Wittman, Pamela

2011-01-01

To understand patterns and influences on active commuting (AC) behavior. Students and faculty/staff at a university campus. In April-May 2008, respondents answered an online survey about mode of travel to campus and influences on commuting decisions. Hierarchical regression analyses predicted variance in walking and biking using sets of demographic, psychological, and environmental variables. Of 898 respondents, 55.7% were female, 457 were students (50.4%). Students reported more AC than faculty/staff. For students, the models explained 36.2% and 29.1% of the variance in walking and biking, respectively. Among faculty/staff, the models explained 45% and 25.8% of the variance in walking and biking. For all models, the psychological set explained the greatest amount of variance. With current economic and ecological concerns, AC should be considered a behavior to target for campus health promotion.

Multichannel Singular Spectrum Analysis in the Estimates of Common Environmental Effects Affecting GPS Observations

NASA Astrophysics Data System (ADS)

Gruszczynska, Marta; Rosat, Severine; Klos, Anna; Gruszczynski, Maciej; Bogusz, Janusz

2018-03-01

We described a spatio-temporal analysis of environmental loading models: atmospheric, continental hydrology, and non-tidal ocean changes, based on multichannel singular spectrum analysis (MSSA). We extracted the common annual signal for 16 different sections related to climate zones: equatorial, arid, warm, snow, polar and continents. We used the loading models estimated for a set of 229 ITRF2014 (International Terrestrial Reference Frame) International GNSS Service (IGS) stations and discussed the amount of variance explained by individual modes, proving that the common annual signal accounts for 16, 24 and 68% of the total variance of non-tidal ocean, atmospheric and hydrological loading models, respectively. Having removed the common environmental MSSA seasonal curve from the corresponding GPS position time series, we found that the residual station-specific annual curve modelled with the least-squares estimation has the amplitude of maximum 2 mm. This means that the environmental loading models underestimate the seasonalities observed by the GPS system. The remaining signal present in the seasonal frequency band arises from the systematic errors which are not of common environmental or geophysical origin. Using common mode error (CME) estimates, we showed that the direct removal of environmental loading models from the GPS series causes an artificial loss in the CME power spectra between 10 and 80 cycles per year. When environmental effect is removed from GPS series with MSSA curves, no influence on the character of spectra of CME estimates was noticed.

Multichannel Singular Spectrum Analysis in the Estimates of Common Environmental Effects Affecting GPS Observations

NASA Astrophysics Data System (ADS)

Gruszczynska, Marta; Rosat, Severine; Klos, Anna; Gruszczynski, Maciej; Bogusz, Janusz

2018-05-01

We described a spatio-temporal analysis of environmental loading models: atmospheric, continental hydrology, and non-tidal ocean changes, based on multichannel singular spectrum analysis (MSSA). We extracted the common annual signal for 16 different sections related to climate zones: equatorial, arid, warm, snow, polar and continents. We used the loading models estimated for a set of 229 ITRF2014 (International Terrestrial Reference Frame) International GNSS Service (IGS) stations and discussed the amount of variance explained by individual modes, proving that the common annual signal accounts for 16, 24 and 68% of the total variance of non-tidal ocean, atmospheric and hydrological loading models, respectively. Having removed the common environmental MSSA seasonal curve from the corresponding GPS position time series, we found that the residual station-specific annual curve modelled with the least-squares estimation has the amplitude of maximum 2 mm. This means that the environmental loading models underestimate the seasonalities observed by the GPS system. The remaining signal present in the seasonal frequency band arises from the systematic errors which are not of common environmental or geophysical origin. Using common mode error (CME) estimates, we showed that the direct removal of environmental loading models from the GPS series causes an artificial loss in the CME power spectra between 10 and 80 cycles per year. When environmental effect is removed from GPS series with MSSA curves, no influence on the character of spectra of CME estimates was noticed.

Personal and environmental characteristics predicting burnout among certified athletic trainers at National Collegiate Athletic Association institutions.

PubMed

Kania, Michelle L; Meyer, Barbara B; Ebersole, Kyle T

2009-01-01

Recent research in the health care professions has shown that specific personal and environmental characteristics can predict burnout, which is a negative coping strategy related to stressful situations. Burnout has been shown to result in physiologic (eg, headaches, difficulty sleeping, poor appetite), psychological (eg, increased negative self-talk, depression, difficulty in interpersonal relationships), and behavioral (eg, diminished care, increased absenteeism, attrition) symptoms. To examine the relationship between selected personal and environmental characteristics and burnout among certified athletic trainers (ATs). Cross-sectional survey. A demographic survey that was designed for this study and the Maslach Burnout Inventory-Human Services Survey. A total of 206 ATs employed at National Collegiate Athletic Association (NCAA) institutions as clinical ATs volunteered. We assessed personal and environmental characteristics of ATs with the demographic survey and measured burnout using the Maslach Burnout Inventory-Human Services Survey. Multiple regression analyses were performed to examine relationships between specific personal and environmental characteristics and each of the 3 subscales of burnout (emotional exhaustion, depersonalization, personal accomplishment). Most ATs we surveyed experienced low to average levels of burnout. Personal characteristics predicted 45.5% of the variance in emotional exhaustion (P < .001), 21.5% of the variance in depersonalization (P < .001), and 24.8% of the variance in personal accomplishment (P < .001). Environmental characteristics predicted 16.7% of the variance in emotional exhaustion (P = .005), 14.4% of the variance in depersonalization (P = .024), and 10.4% of the variance in personal accomplishment (P = .209). Stress level and coaches' pressure to medically clear athletes predicted ratings on all 3 subscales of burnout. Our findings were similar to those of other studies of burnout among NCAA Division I ATs, coaches, and coach-teachers. The results also support the Cognitive-Affective Model of Athletic Burnout proposed by Smith. Finally, these results indicate new areas of concentration for burnout research and professional practice.

Developing and Evaluating New Methods for Assessing Concurrent Environmental Exposures

EPA Science Inventory

Summary of purpose and scope (no longer than 200 words): One limitation to current environmental health research is the focus on single contaminant exposures. Each exposure estimated in epidemiologic models accounts for a relatively small proportion of observed variance in health...

A fully-stochasticized, age-structured population model for population viability analysis of fish: Lower Missouri River endangered pallid sturgeon example

USGS Publications Warehouse

Wildhaber, Mark L.; Albers, Janice; Green, Nicholas; Moran, Edward H.

2017-01-01

We develop a fully-stochasticized, age-structured population model suitable for population viability analysis (PVA) of fish and demonstrate its use with the endangered pallid sturgeon (Scaphirhynchus albus) of the Lower Missouri River as an example. The model incorporates three levels of variance: parameter variance (uncertainty about the value of a parameter itself) applied at the iteration level, temporal variance (uncertainty caused by random environmental fluctuations over time) applied at the time-step level, and implicit individual variance (uncertainty caused by differences between individuals) applied within the time-step level. We found that population dynamics were most sensitive to survival rates, particularly age-2+ survival, and to fecundity-at-length. The inclusion of variance (unpartitioned or partitioned), stocking, or both generally decreased the influence of individual parameters on population growth rate. The partitioning of variance into parameter and temporal components had a strong influence on the importance of individual parameters, uncertainty of model predictions, and quasiextinction risk (i.e., pallid sturgeon population size falling below 50 age-1+ individuals). Our findings show that appropriately applying variance in PVA is important when evaluating the relative importance of parameters, and reinforce the need for better and more precise estimates of crucial life-history parameters for pallid sturgeon.

Vulnerability of US thermoelectric power generation to climate change when incorporating state-level environmental regulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Lu; Hejazi, Mohamad; Li, Hongyi

This study explores the interactions between climate and thermoelectric generation in the U.S. by coupling an Earth System Model with a thermoelectric power generation model. We validated model simulations of power production for selected power plants (~44% of existing thermoelectric capacity) against reported values. In addition, we projected future usable capacity for existing power plants under two different climate change scenarios. Results indicate that climate change alone may reduce average thermoelectric generating capacity by 2%-3% by the 2060s. Reductions up to 12% are expected if environmental requirements are enforced without waivers for thermal variation. This study concludes that the impactmore » of climate change on the U.S. thermoelectric power system is less than previous estimates due to an inclusion of a spatially-disaggregated representation of environmental regulations and provisional variances that temporarily relieve power plants from permit requirements. This work highlights the significance of accounting for legal constructs in which the operation of power plants are managed, and underscores the effects of provisional variances in addition to environmental requirements.« less

Does education confer a culture of healthy behavior? Smoking and drinking patterns in Danish twins.

PubMed

Johnson, Wendy; Kyvik, Kirsten Ohm; Mortensen, Erik L; Skytthe, Axel; Batty, G David; Deary, Ian J

2011-01-01

More education is associated with healthier smoking and drinking behaviors. Most analyses of effects of education focus on mean levels. Few studies have compared variance in health-related behaviors at different levels of education or analyzed how education impacts underlying genetic and environmental sources of health-related behaviors. This study explored these influences. In a 2002 postal questionnaire, 21,522 members of the Danish Twin Registry, born during 1931-1982, reported smoking and drinking habits. The authors used quantitative genetic models to examine how these behaviors' genetic and environmental variances differed with level of education, adjusting for birth-year effects. As expected, more education was associated with less smoking, and average drinking levels were highest among the most educated. At 2 standard deviations above the mean educational level, variance in smoking and drinking was about one-third that among those at 2 standard deviations below, because fewer highly educated people reported high levels of smoking or drinking. Because shared environmental variance was particularly restricted, one explanation is that education created a culture that discouraged smoking and heavy drinking. Correlations between shared environmental influences on education and the health behaviors were substantial among the well-educated for smoking in both sexes and drinking in males, reinforcing this notion.

Genetic influences on the difference in variability of height, weight and body mass index between Caucasian and East Asian adolescent twins.

PubMed

Hur, Y-M; Kaprio, J; Iacono, W G; Boomsma, D I; McGue, M; Silventoinen, K; Martin, N G; Luciano, M; Visscher, P M; Rose, R J; He, M; Ando, J; Ooki, S; Nonaka, K; Lin, C C H; Lajunen, H R; Cornes, B K; Bartels, M; van Beijsterveldt, C E M; Cherny, S S; Mitchell, K

2008-10-01

Twin studies are useful for investigating the causes of trait variation between as well as within a population. The goals of the present study were two-fold: First, we aimed to compare the total phenotypic, genetic and environmental variances of height, weight and BMI between Caucasians and East Asians using twins. Secondly, we intended to estimate the extent to which genetic and environmental factors contribute to differences in variability of height, weight and BMI between Caucasians and East Asians. Height and weight data from 3735 Caucasian and 1584 East Asian twin pairs (age: 13-15 years) from Australia, China, Finland, Japan, the Netherlands, South Korea, Taiwan and the United States were used for analyses. Maximum likelihood twin correlations and variance components model-fitting analyses were conducted to fulfill the goals of the present study. The absolute genetic variances for height, weight and BMI were consistently greater in Caucasians than in East Asians with corresponding differences in total variances for all three body measures. In all 80 to 100% of the differences in total variances of height, weight and BMI between the two population groups were associated with genetic differences. Height, weight and BMI were more variable in Caucasian than in East Asian adolescents. Genetic variances for these three body measures were also larger in Caucasians than in East Asians. Variance components model-fitting analyses indicated that genetic factors contributed to the difference in variability of height, weight and BMI between the two population groups. Association studies for these body measures should take account of our findings of differences in genetic variances between the two population groups.

Genetic influences on the difference in variability of height, weight and body mass index between Caucasian and East Asian adolescent twins

PubMed Central

Hur, Y-M; Kaprio, J; Iacono, WG; Boomsma, DI; McGue, M; Silventoinen, K; Martin, NG; Luciano, M; Visscher, PM; Rose, RJ; He, M; Ando, J; Ooki, S; Nonaka, K; Lin, CCH; Lajunen, HR; Cornes, BK; Bartels, M; van Beijsterveldt, CEM; Cherny, SS; Mitchell, K

2008-01-01

Objective Twin studies are useful for investigating the causes of trait variation between as well as within a population. The goals of the present study were two-fold: First, we aimed to compare the total phenotypic, genetic and environmental variances of height, weight and BMI between Caucasians and East Asians using twins. Secondly, we intended to estimate the extent to which genetic and environmental factors contribute to differences in variability of height, weight and BMI between Caucasians and East Asians. Design Height and weight data from 3735 Caucasian and 1584 East Asian twin pairs (age: 13–15 years) from Australia, China, Finland, Japan, the Netherlands, South Korea, Taiwan and the United States were used for analyses. Maximum likelihood twin correlations and variance components model-fitting analyses were conducted to fulfill the goals of the present study. Results The absolute genetic variances for height, weight and BMI were consistently greater in Caucasians than in East Asians with corresponding differences in total variances for all three body measures. In all 80 to 100% of the differences in total variances of height, weight and BMI between the two population groups were associated with genetic differences. Conclusion Height, weight and BMI were more variable in Caucasian than in East Asian adolescents. Genetic variances for these three body measures were also larger in Caucasians than in East Asians. Variance components model-fitting analyses indicated that genetic factors contributed to the difference in variability of height, weight and BMI between the two population groups. Association studies for these body measures should take account of our findings of differences in genetic variances between the two population groups. PMID:18779828

Genotype by environment (climate) interaction improves genomic prediction for production traits in US Holstein cattle.

PubMed

Tiezzi, F; de Los Campos, G; Parker Gaddis, K L; Maltecca, C

2017-03-01

Genotype by environment interaction (G × E) in dairy cattle productive traits has been shown to exist, but current genetic evaluation methods do not take this component into account. As several environmental descriptors (e.g., climate, farming system) are known to vary within the United States, not accounting for the G × E could lead to reranking of bulls and loss in genetic gain. Using test-day records on milk yield, somatic cell score, fat, and protein percentage from all over the United States, we computed within herd-year-season daughter yield deviations for 1,087 Holstein bulls and regressed them on genetic and environmental information to estimate variance components and to assess prediction accuracy. Genomic information was obtained from a 50k SNP marker panel. Environmental effect inputs included herd (160 levels), geographical region (7 levels), geographical location (2 variables), climate information (7 variables), and management conditions of the herds (16 total variables divided in 4 subgroups). For each set of environmental descriptors, environmental, genomic, and G × E components were sequentially fitted. Variance components estimates confirmed the presence of G × E on milk yield, with its effect being larger than main genetic effect and the environmental effect for some models. Conversely, G × E was moderate for somatic cell score and small for milk composition. Genotype by environment interaction, when included, partially eroded the genomic effect (as compared with the models where G × E was not included), suggesting that the genomic variance could at least in part be attributed to G × E not appropriately accounted for. Model predictive ability was assessed using 3 cross-validation schemes (new bulls, incomplete progeny test, and new environmental conditions), and performance was compared with a reference model including only the main genomic effect. In each scenario, at least 1 of the models including G × E was able to perform better than the reference model, although it was not possible to find the overall best-performing model that included the same set of environmental descriptors. In general, the methodology used is promising in accounting for G × E in genomic predictions, but challenges exist in identifying a unique set of covariates capable of describing the entire variety of environments. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Aquatic community structure in Mediterranean edge-of-field waterbodies as explained by environmental factors and the presence of pesticide mixtures.

PubMed

Pereira, Ana Santos; Dâmaso-Rodrigues, Maria Luísa; Amorim, Ana; Daam, Michiel A; Cerejeira, Maria José

2018-06-16

Studies addressing the predicted effects of pesticides in combination with abiotic and biotic factors on aquatic biota in ditches associated with typical Mediterranean agroecosystems are scarce. The current study aimed to evaluate the predicted effects of pesticides along with environmental factors and biota interactions on macroinvertebrate, zooplankton and phytoplankton community compositions in ditches adjacent to Portuguese maize and tomato crop areas. Data was analysed with the variance partitioning procedure based on redundancy analysis (RDA). The total variance in biological community composition was divided into the variance explained by the multi-substance potentially affected fraction [(msPAF) arthropods and primary producers], environmental factors (water chemistry parameters), biotic interactions, shared variance, and unexplained variance. The total explained variance reached 39.4% and the largest proportion of this explained variance was attributed to msPAF (23.7%). When each group (phytoplankton, zooplankton and macroinvertebrates) was analysed separately, biota interactions and environmental factors explained the largest proportion of variance. Results of this study indicate that besides the presence of pesticide mixtures, environmental factors and biotic interactions also considerably influence field freshwater communities. Subsequently, to increase our understanding of the risk of pesticide mixtures on ecosystem communities in edge-of-field water bodies, variations in environmental and biological factors should also be considered.

Toward a Bioecological Model of School Engagement: A Biometric Analysis of Gene and Environmental Factors

PubMed Central

Maynard, Brandy R.; Beaver, Kevin M.; Vaughn, Michael G.; DeLisi, Matthew; Roberts, Gregory

2014-01-01

School disengagement is associated with poor academic achievement, dropout, and risk behaviors such as truancy, delinquency, and substance use. Despite empirical research identifying risk correlates of school disengagement across the ecology, it is unclear from which domain these correlates arise. To redress this issue, the current study used intraclass correlation and DeFries-Fulker analyses to longitudinally decompose variance in three domains of engagement (academic, behavioral, and emotional) using data from the National Longitudinal Study of Adolescent Health. Findings suggest that nonshared environmental factors (that is, environmental contexts and experiences that are unique to each sibling) account for approximately half of the variance in indicators of school disengagement when controlling for genetic influences, and that this variance increases as adolescents grow older and rely less on their immediate family. The present study contributes new evidence on the biosocial underpinnings of school engagement and highlights the importance of interventions targeting factors in the nonshared environment. PMID:25525321

Detection of mastitis in dairy cattle by use of mixture models for repeated somatic cell scores: a Bayesian approach via Gibbs sampling.

PubMed

Odegård, J; Jensen, J; Madsen, P; Gianola, D; Klemetsdal, G; Heringstad, B

2003-11-01

The distribution of somatic cell scores could be regarded as a mixture of at least two components depending on a cow's udder health status. A heteroscedastic two-component Bayesian normal mixture model with random effects was developed and implemented via Gibbs sampling. The model was evaluated using datasets consisting of simulated somatic cell score records. Somatic cell score was simulated as a mixture representing two alternative udder health statuses ("healthy" or "diseased"). Animals were assigned randomly to the two components according to the probability of group membership (Pm). Random effects (additive genetic and permanent environment), when included, had identical distributions across mixture components. Posterior probabilities of putative mastitis were estimated for all observations, and model adequacy was evaluated using measures of sensitivity, specificity, and posterior probability of misclassification. Fitting different residual variances in the two mixture components caused some bias in estimation of parameters. When the components were difficult to disentangle, so were their residual variances, causing bias in estimation of Pm and of location parameters of the two underlying distributions. When all variance components were identical across mixture components, the mixture model analyses returned parameter estimates essentially without bias and with a high degree of precision. Including random effects in the model increased the probability of correct classification substantially. No sizable differences in probability of correct classification were found between models in which a single cow effect (ignoring relationships) was fitted and models where this effect was split into genetic and permanent environmental components, utilizing relationship information. When genetic and permanent environmental effects were fitted, the between-replicate variance of estimates of posterior means was smaller because the model accounted for random genetic drift.

A twin study of body dysmorphic concerns.

PubMed

Monzani, B; Rijsdijk, F; Anson, M; Iervolino, A C; Cherkas, L; Spector, T; Mataix-Cols, D

2012-09-01

Dysmorphic concern refers to an excessive preoccupation with a perceived or slight defect in physical appearance. It lies on a continuum of severity from no or minimal concerns to severe concerns over one's appearance. The present study examined the heritability of dysmorphic concerns in a large sample of twins. Twins from the St Thomas UK twin registry completed a valid and reliable self-report measure of dysmorphic concerns, which also includes questions about perceived body odour and malfunction. Twin modelling methods (female twins only, n=3544) were employed to decompose the variance in the liability to dysmorphic concerns into additive genetic, shared and non-shared environmental factors. Model-fitting analyses showed that genetic factors accounted for approximately 44% [95% confidence intervals (CI) 36-50%] of the variance in dysmorphic concerns, with non-shared environmental factors and measurement error accounting for the remaining variance (56%; 95% CI 50-63%). Shared environmental factors were negligible. The results remained unchanged when excluding individuals reporting an objective medical condition/injury accounting for their concern in physical appearance. Over-concern with a perceived or slight defect in physical appearance is a heritable trait, with non-shared environmental factors also playing an important role in its causation. The results are relevant for various psychiatric disorders characterized by excessive concerns in body appearance, odour or function, including but not limited to body dysmorphic disorder.

Social cognitive predictors of first- and non-first-generation college students' academic and life satisfaction.

PubMed

Garriott, Patton O; Hudyma, Aaron; Keene, Chesleigh; Santiago, Dana

2015-04-01

The present study tested Lent's (2004) social-cognitive model of normative well-being in a sample (N = 414) of first- and non-first-generation college students. A model depicting relationships between: positive affect, environmental supports, college self-efficacy, college outcome expectations, academic progress, academic satisfaction, and life satisfaction was examined using structural equation modeling. The moderating roles of perceived importance of attending college and intrinsic goal motivation were also explored. Results suggested the hypothesized model provided an adequate fit to the data while hypothesized relationships in the model were partially supported. Environmental supports predicted college self-efficacy, college outcome expectations, and academic satisfaction. Furthermore, college self-efficacy predicted academic progress while college outcome expectations predicted academic satisfaction. Academic satisfaction, but not academic progress predicted life satisfaction. The structural model explained 44% of the variance in academic progress, 56% of the variance in academic satisfaction, and 28% of the variance in life satisfaction. Mediation analyses indicated several significant indirect effects between variables in the model while moderation analyses revealed a 3-way interaction between academic satisfaction, intrinsic motivation for attending college, and first-generation college student status on life satisfaction. Results are discussed in terms of applying the normative model of well-being to promote first- and non-first-generation college students' academic and life satisfaction. (c) 2015 APA, all rights reserved).

A study of changes in genetic and environmental influences on weight and shape concern across adolescence.

PubMed

Wade, Tracey D; Hansell, Narelle K; Crosby, Ross D; Bryant-Waugh, Rachel; Treasure, Janet; Nixon, Reginald; Byrne, Susan; Martin, Nicholas G

2013-02-01

The goal of the current study was to examine whether genetic and environmental influences on an important risk factor for disordered eating, weight and shape concern, remained stable over adolescence. This stability was assessed in 2 ways: whether new sources of latent variance were introduced over development and whether the magnitude of variance contributing to the risk factor changed. We examined an 8-item WSC subscale derived from the Eating Disorder Examination (EDE) using telephone interviews with female adolescents. From 3 waves of data collected from female-female same-sex twin pairs from the Australian Twin Registry, a subset of the data (which included 351 pairs at Wave 1) was used to examine 3 age cohorts: 12 to 13, 13 to 15, and 14 to 16 years. The best-fitting model contained genetic and environmental influences, both shared and nonshared. Biometric model fitting indicated that nonshared environmental influences were largely specific to each age cohort, and results suggested that latent shared environmental and genetic influences that were influential at 12 to 13 years continued to contribute to subsequent age cohorts, with independent sources of both emerging at ages 13 to 15. The magnitude of all 3 latent influences could be constrained to be the same across adolescence. Ages 13 to 15 were indicated as a time of risk for the development of high levels of WSC, given that most specific environmental risk factors were significant at this time (e.g., peer teasing about weight, adverse life events), and indications of the emergence of new sources of latent genetic and environmental variance over this period. 2013 APA, all rights reserved

Genetic and environmental variances of bone microarchitecture and bone remodeling markers: a twin study.

PubMed

Bjørnerem, Åshild; Bui, Minh; Wang, Xiaofang; Ghasem-Zadeh, Ali; Hopper, John L; Zebaze, Roger; Seeman, Ego

2015-03-01

All genetic and environmental factors contributing to differences in bone structure between individuals mediate their effects through the final common cellular pathway of bone modeling and remodeling. We hypothesized that genetic factors account for most of the population variance of cortical and trabecular microstructure, in particular intracortical porosity and medullary size - void volumes (porosity), which establish the internal bone surface areas or interfaces upon which modeling and remodeling deposit or remove bone to configure bone microarchitecture. Microarchitecture of the distal tibia and distal radius and remodeling markers were measured for 95 monozygotic (MZ) and 66 dizygotic (DZ) white female twin pairs aged 40 to 61 years. Images obtained using high-resolution peripheral quantitative computed tomography were analyzed using StrAx1.0, a nonthreshold-based software that quantifies cortical matrix and porosity. Genetic and environmental components of variance were estimated under the assumptions of the classic twin model. The data were consistent with the proportion of variance accounted for by genetic factors being: 72% to 81% (standard errors ∼18%) for the distal tibial total, cortical, and medullary cross-sectional area (CSA); 67% and 61% for total cortical porosity, before and after adjusting for total CSA, respectively; 51% for trabecular volumetric bone mineral density (vBMD; all p < 0.001). For the corresponding distal radius traits, genetic factors accounted for 47% to 68% of the variance (all p ≤ 0.001). Cross-twin cross-trait correlations between tibial cortical porosity and medullary CSA were higher for MZ (rMZ  = 0.49) than DZ (rDZ  = 0.27) pairs before (p = 0.024), but not after (p = 0.258), adjusting for total CSA. For the remodeling markers, the data were consistent with genetic factors accounting for 55% to 62% of the variance. We infer that middle-aged women differ in their bone microarchitecture and remodeling markers more because of differences in their genetic factors than differences in their environment. © 2014 American Society for Bone and Mineral Research.

Global Sensitivity Analysis for Process Identification under Model Uncertainty

NASA Astrophysics Data System (ADS)

Ye, M.; Dai, H.; Walker, A. P.; Shi, L.; Yang, J.

2015-12-01

The environmental system consists of various physical, chemical, and biological processes, and environmental models are always built to simulate these processes and their interactions. For model building, improvement, and validation, it is necessary to identify important processes so that limited resources can be used to better characterize the processes. While global sensitivity analysis has been widely used to identify important processes, the process identification is always based on deterministic process conceptualization that uses a single model for representing a process. However, environmental systems are complex, and it happens often that a single process may be simulated by multiple alternative models. Ignoring the model uncertainty in process identification may lead to biased identification in that identified important processes may not be so in the real world. This study addresses this problem by developing a new method of global sensitivity analysis for process identification. The new method is based on the concept of Sobol sensitivity analysis and model averaging. Similar to the Sobol sensitivity analysis to identify important parameters, our new method evaluates variance change when a process is fixed at its different conceptualizations. The variance considers both parametric and model uncertainty using the method of model averaging. The method is demonstrated using a synthetic study of groundwater modeling that considers recharge process and parameterization process. Each process has two alternative models. Important processes of groundwater flow and transport are evaluated using our new method. The method is mathematically general, and can be applied to a wide range of environmental problems.

Genetic parameters of legendre polynomials for first parity lactation curves.

PubMed

Pool, M H; Janss, L L; Meuwissen, T H

2000-11-01

Variance components of the covariance function coefficients in a random regression test-day model were estimated by Legendre polynomials up to a fifth order for first-parity records of Dutch dairy cows using Gibbs sampling. Two Legendre polynomials of equal order were used to model the random part of the lactation curve, one for the genetic component and one for permanent environment. Test-day records from cows registered between 1990 to 1996 and collected by regular milk recording were available. For the data set, 23,700 complete lactations were selected from 475 herds sired by 262 sires. Because the application of a random regression model is limited by computing capacity, we investigated the minimum order needed to fit the variance structure in the data sufficiently. Predictions of genetic and permanent environmental variance structures were compared with bivariate estimates on 30-d intervals. A third-order or higher polynomial modeled the shape of variance curves over DIM with sufficient accuracy for the genetic and permanent environment part. Also, the genetic correlation structure was fitted with sufficient accuracy by a third-order polynomial, but, for the permanent environmental component, a fourth order was needed. Because equal orders are suggested in the literature, a fourth-order Legendre polynomial is recommended in this study. However, a rank of three for the genetic covariance matrix and of four for permanent environment allows a simpler covariance function with a reduced number of parameters based on the eigenvalues and eigenvectors.

A Multi-factorial Model for Examining Racial and Ethnic Disparities in Acute Asthma Visits by Children

PubMed Central

Feldman, Jonathan M.; Serebrisky, Denise; Spray, Amanda

2012-01-01

Background Causes of children’s asthma health disparities are complex. Parents’ asthma illness representations may play a role. Purpose The study aims to test a theoretically based, multi-factorial model for ethnic disparities in children’s acute asthma visits through parental illness representations. Methods Structural equation modeling investigated the association of parental asthma illness representations, sociodemographic characteristics, health care provider factors, and social–environmental context with children’s acute asthma visits among 309 White, Puerto Rican, and African American families was conducted. Results Forty-five percent of the variance in illness representations and 30% of the variance in acute visits were accounted for. Statistically significant differences in illness representations were observed by ethnic group. Approximately 30% of the variance in illness representations was explained for whites, 23% for African Americans, and 26% for Puerto Ricans. The model accounted for >30% of the variance in acute visits for African Americans and Puerto Ricans but only 19% for the whites. Conclusion The model provides preliminary support that ethnic heterogeneity in asthma illness representations affects children’s health outcomes. PMID:22160799

Personal and Environmental Characteristics Predicting Burnout Among Certified Athletic Trainers at National Collegiate Athletic Association Institutions

PubMed Central

Kania, Michelle L; Meyer, Barbara B; Ebersole, Kyle T

2009-01-01

Context: Recent research in the health care professions has shown that specific personal and environmental characteristics can predict burnout, which is a negative coping strategy related to stressful situations. Burnout has been shown to result in physiologic (eg, headaches, difficulty sleeping, poor appetite), psychological (eg, increased negative self-talk, depression, difficulty in interpersonal relationships), and behavioral (eg, diminished care, increased absenteeism, attrition) symptoms. Objective: To examine the relationship between selected personal and environmental characteristics and burnout among certified athletic trainers (ATs). Design: Cross-sectional survey. Setting: A demographic survey that was designed for this study and the Maslach Burnout Inventory–Human Services Survey. Patients or Other Participants: A total of 206 ATs employed at National Collegiate Athletic Association (NCAA) institutions as clinical ATs volunteered. Main Outcome Measure(s): We assessed personal and environmental characteristics of ATs with the demographic survey and measured burnout using the Maslach Burnout Inventory–Human Services Survey. Multiple regression analyses were performed to examine relationships between specific personal and environmental characteristics and each of the 3 subscales of burnout (emotional exhaustion, depersonalization, personal accomplishment). Results: Most ATs we surveyed experienced low to average levels of burnout. Personal characteristics predicted 45.5% of the variance in emotional exhaustion (P < .001), 21.5% of the variance in depersonalization (P < .001), and 24.8% of the variance in personal accomplishment (P < .001). Environmental characteristics predicted 16.7% of the variance in emotional exhaustion (P  =  .005), 14.4% of the variance in depersonalization (P  =  .024), and 10.4% of the variance in personal accomplishment (P  =  .209). Stress level and coaches' pressure to medically clear athletes predicted ratings on all 3 subscales of burnout. Conclusions: Our findings were similar to those of other studies of burnout among NCAA Division I ATs, coaches, and coach-teachers. The results also support the Cognitive-Affective Model of Athletic Burnout proposed by Smith. Finally, these results indicate new areas of concentration for burnout research and professional practice. PMID:19180220

A rapid generalized least squares model for a genome-wide quantitative trait association analysis in families.

PubMed

Li, Xiang; Basu, Saonli; Miller, Michael B; Iacono, William G; McGue, Matt

2011-01-01

Genome-wide association studies (GWAS) using family data involve association analyses between hundreds of thousands of markers and a trait for a large number of related individuals. The correlations among relatives bring statistical and computational challenges when performing these large-scale association analyses. Recently, several rapid methods accounting for both within- and between-family variation have been proposed. However, these techniques mostly model the phenotypic similarities in terms of genetic relatedness. The familial resemblances in many family-based studies such as twin studies are not only due to the genetic relatedness, but also derive from shared environmental effects and assortative mating. In this paper, we propose 2 generalized least squares (GLS) models for rapid association analysis of family-based GWAS, which accommodate both genetic and environmental contributions to familial resemblance. In our first model, we estimated the joint genetic and environmental variations. In our second model, we estimated the genetic and environmental components separately. Through simulation studies, we demonstrated that our proposed approaches are more powerful and computationally efficient than a number of existing methods are. We show that estimating the residual variance-covariance matrix in the GLS models without SNP effects does not lead to an appreciable bias in the p values as long as the SNP effect is small (i.e. accounting for no more than 1% of trait variance). Copyright © 2011 S. Karger AG, Basel.

Whole tree xylem sap flow responses to multiple environmental variables in a wet tropical forest

Treesearch

J.J. O' Brien; S.F. Oberbauer; D.B. Clark

2004-01-01

In order to quantify and characterize the variance in rain-forest tree physiology, whole tree sap flow responses to local environmental conditions were investigated in 10 species of trees with diverse traits at La Selva Biological Station, Costa Rica. A simple model was developed to predict tree sap flow responses to a synthetic environmental variable generated by a...

Sampling design optimisation for rainfall prediction using a non-stationary geostatistical model

NASA Astrophysics Data System (ADS)

Wadoux, Alexandre M. J.-C.; Brus, Dick J.; Rico-Ramirez, Miguel A.; Heuvelink, Gerard B. M.

2017-09-01

The accuracy of spatial predictions of rainfall by merging rain-gauge and radar data is partly determined by the sampling design of the rain-gauge network. Optimising the locations of the rain-gauges may increase the accuracy of the predictions. Existing spatial sampling design optimisation methods are based on minimisation of the spatially averaged prediction error variance under the assumption of intrinsic stationarity. Over the past years, substantial progress has been made to deal with non-stationary spatial processes in kriging. Various well-documented geostatistical models relax the assumption of stationarity in the mean, while recent studies show the importance of considering non-stationarity in the variance for environmental processes occurring in complex landscapes. We optimised the sampling locations of rain-gauges using an extension of the Kriging with External Drift (KED) model for prediction of rainfall fields. The model incorporates both non-stationarity in the mean and in the variance, which are modelled as functions of external covariates such as radar imagery, distance to radar station and radar beam blockage. Spatial predictions are made repeatedly over time, each time recalibrating the model. The space-time averaged KED variance was minimised by Spatial Simulated Annealing (SSA). The methodology was tested using a case study predicting daily rainfall in the north of England for a one-year period. Results show that (i) the proposed non-stationary variance model outperforms the stationary variance model, and (ii) a small but significant decrease of the rainfall prediction error variance is obtained with the optimised rain-gauge network. In particular, it pays off to place rain-gauges at locations where the radar imagery is inaccurate, while keeping the distribution over the study area sufficiently uniform.

Effect of Body Composition Methodology on Heritability Estimation of Body Fatness

PubMed Central

Elder, Sonya J.; Roberts, Susan B.; McCrory, Megan A.; Das, Sai Krupa; Fuss, Paul J.; Pittas, Anastassios G.; Greenberg, Andrew S.; Heymsfield, Steven B.; Dawson-Hughes, Bess; Bouchard, Thomas J.; Saltzman, Edward; Neale, Michael C.

2014-01-01

Heritability estimates of human body fatness vary widely and the contribution of body composition methodology to this variability is unknown. The effect of body composition methodology on estimations of genetic and environmental contributions to body fatness variation was examined in 78 adult male and female monozygotic twin pairs reared apart or together. Body composition was assessed by six methods – body mass index (BMI), dual energy x-ray absorptiometry (DXA), underwater weighing (UWW), total body water (TBW), bioelectric impedance (BIA), and skinfold thickness. Body fatness was expressed as percent body fat, fat mass, and fat mass/height2 to assess the effect of body fatness expression on heritability estimates. Model-fitting multivariate analyses were used to assess the genetic and environmental components of variance. Mean BMI was 24.5 kg/m2 (range of 17.8–43.4 kg/m2). There was a significant effect of body composition methodology (p<0.001) on heritability estimates, with UWW giving the highest estimate (69%) and BIA giving the lowest estimate (47%) for fat mass/height2. Expression of body fatness as percent body fat resulted in significantly higher heritability estimates (on average 10.3% higher) compared to expression as fat mass/height2 (p=0.015). DXA and TBW methods expressing body fatness as fat mass/height2 gave the least biased heritability assessments, based on the small contribution of specific genetic factors to their genetic variance. A model combining DXA and TBW methods resulted in a relatively low FM/ht2 heritability estimate of 60%, and significant contributions of common and unique environmental factors (22% and 18%, respectively). The body fatness heritability estimate of 60% indicates a smaller contribution of genetic variance to total variance than many previous studies using less powerful research designs have indicated. The results also highlight the importance of environmental factors and possibly genotype by environmental interactions in the etiology of weight gain and the obesity epidemic. PMID:25067962

Childhood problem behavior and parental divorce: evidence for gene-environment interaction.

PubMed

Robbers, Sylvana; van Oort, Floor; Huizink, Anja; Verhulst, Frank; van Beijsterveldt, Catharina; Boomsma, Dorret; Bartels, Meike

2012-10-01

The importance of genetic and environmental influences on children's behavioral and emotional problems may vary as a function of environmental exposure. We previously reported that 12-year-olds with divorced parents showed more internalizing and externalizing problems than children with married parents, and that externalizing problems in girls precede and predict later parental divorce. The aim of the current study was to investigate as to whether genetic and environmental influences on internalizing and externalizing problems were different for children from divorced versus non-divorced families. Maternal ratings on internalizing and externalizing problems were collected with the Child Behavior Checklist in 4,592 twin pairs at ages 3 and 12 years, of whom 367 pairs had experienced a parental divorce between these ages. Variance in internalizing and externalizing problems at ages 3 and 12 was analyzed with biometric models in which additive genetic and environmental effects were allowed to depend on parental divorce and sex. A difference in the contribution of genetic and environmental influences between divorced and non-divorced groups would constitute evidence for gene-environment interaction. For both pre- and post-divorce internalizing and externalizing problems, the total variances were larger for children from divorced families, which was mainly due to higher environmental variances. As a consequence, heritabilities were lower for children from divorced families, and the relative contributions of environmental influences were higher. Environmental influences become more important in explaining variation in children's problem behaviors in the context of parental divorce.

Psychopathic personality development from ages 9 to 18: Genes and environment.

PubMed

Tuvblad, Catherine; Wang, Pan; Bezdjian, Serena; Raine, Adrian; Baker, Laura A

2016-02-01

The genetic and environmental etiology of individual differences was examined in initial level and change in psychopathic personality from ages 9 to 18 years. A piecewise growth curve model, in which the first change score (G1) influenced all ages (9-10, 11-13, 14-15, and 16-18 years) and the second change score (G2) only influenced ages 14-15 and 16-18 years, fit the data better did than the standard single slope model, suggesting a turning point from childhood to adolescence. The results indicated that variations in levels and both change scores were mainly due to genetic (A) and nonshared environmental (E) influences (i.e., AE structure for G0, G1, and G2). No sex differences were found except on the mean values of level and change scores. Based on caregiver ratings, about 81% of variance in G0, 89% of variance in G1, and 94% of variance in G2 were explained by genetic factors, whereas for youth self-reports, these three proportions were 94%, 71%, and 66%, respectively. The larger contribution of genetic variance and covariance in caregiver ratings than in youth self-reports may suggest that caregivers considered the changes in their children to be more similar as compared to how the children viewed themselves.

Sex Differences in Sources of Resilience and Vulnerability to Risk for Delinquency.

PubMed

Newsome, Jamie; Vaske, Jamie C; Gehring, Krista S; Boisvert, Danielle L

2016-04-01

Research on adolescent risk factors for delinquency has suggested that, due to genetic differences, youth may respond differently to risk factors, with some youth displaying resilience and others a heightened vulnerability. Using a behavioral genetic design and data from the National Longitudinal Study of Adolescent to Adult Health, this study examines whether there are sex differences in the genetic and environmental factors that influence the ways in which adolescents respond to cumulative risk for violent, nonviolent, and overall delinquency in a sample of twins (152 MZ male, 155 MZ female, 140 DZ male, 130 DZ female, and 204 DZ opposite-sex twin pairs). The results revealed that males tended to show greater vulnerability to risk for all types of delinquency, and females exhibited greater resilience. Among males, additive genetic factors accounted for 41, 29, and 43 % of the variance in responses to risk for violent, nonviolent, and overall delinquency, respectively. The remaining proportion of variance in each model was attributed to unique environmental influences, with the exception of 11 % of the variance in nonviolent responses to risk being attributed to common environmental factors. Among females, no significant genetic influences were observed; however, common environmental contributions to differences in the ways females respond to risk for violent, nonviolent, and overall delinquency were 44, 42, and 45 %, respectively. The remaining variance was attributed to unique environmental influences. Overall, genetic factors moderately influenced males' responses to risk while environmental factors fully explain variation in females' responses to risk. The implications of these findings are discussed in the context of improving the understanding of relationships between risks and outcomes, as well as informing policy and practice with adolescent offenders.

Genetic modelling of test day records in dairy sheep using orthogonal Legendre polynomials.

PubMed

Kominakis, A; Volanis, M; Rogdakis, E

2001-03-01

Test day milk yields of three lactations in Sfakia sheep were analyzed fitting a random regression (RR) model, regressing on orthogonal polynomials of the stage of the lactation period, i.e. days in milk. Univariate (UV) and multivariate (MV) analyses were also performed for four stages of the lactation period, represented by average days in milk, i.e. 15, 45, 70 and 105 days, to compare estimates obtained from RR models with estimates from UV and MV analyses. The total number of test day records were 790, 1314 and 1041 obtained from 214, 342 and 303 ewes in the first, second and third lactation, respectively. Error variances and covariances between regression coefficients were estimated by restricted maximum likelihood. Models were compared using likelihood ratio tests (LRTs). Log likelihoods were not significantly reduced when the rank of the orthogonal Legendre polynomials (LPs) of lactation stage was reduced from 4 to 2 and homogenous variances for lactation stages within lactations were considered. Mean weighted heritability estimates with RR models were 0.19, 0.09 and 0.08 for first, second and third lactation, respectively. The respective estimates obtained from UV analyses were 0.14, 0.12 and 0.08, respectively. Mean permanent environmental variance, as a proportion of the total, was high at all stages and lactations ranging from 0.54 to 0.71. Within lactations, genetic and permanent environmental correlations between lactation stages were in the range from 0.36 to 0.99 and 0.76 to 0.99, respectively. Genetic parameters for additive genetic and permanent environmental effects obtained from RR models were different from those obtained from UV and MV analyses.

Shared versus distinct genetic contributions of mental wellbeing with depression and anxiety symptoms in healthy twins.

PubMed

Routledge, Kylie M; Burton, Karen L O; Williams, Leanne M; Harris, Anthony; Schofield, Peter R; Clark, C Richard; Gatt, Justine M

2016-10-30

Mental wellbeing and mental illness symptoms are typically conceptualized as opposite ends of a continuum, despite only sharing about a quarter in common variance. We investigated the normative variation in measures of wellbeing and of depression and anxiety in 1486 twins who did not meet clinical criteria for an overt diagnosis. We quantified the shared versus distinct genetic and environmental variance between wellbeing and depression and anxiety symptoms. The majority of participants (93%) reported levels of depression and anxiety symptoms within the healthy range, yet only 23% reported a wellbeing score within the "flourishing" range: the remainder were within the ranges of "moderate" (67%) or "languishing" (10%). In twin models, measures of wellbeing and of depression and anxiety shared 50.09% of variance due to genetic factors and 18.27% due to environmental factors; the rest of the variance was due to unique variation impacting wellbeing or depression and anxiety symptoms. These findings suggest that an absence of clinically-significant symptoms of depression and anxiety does not necessarily indicate that an individual is flourishing. Both unique and shared genetic and environmental factors may determine why some individuals flourish in the absence of symptoms while others do not. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The Relationship Between the Genetic and Environmental Influences on Common Externalizing Psychopathology and Mental Wellbeing

PubMed Central

Kendler, Kenneth S.; Myers, John M.; Keyes, Corey L. M.

2012-01-01

To determine the relationship between the genetic and environmental risk factors for externalizing psychopathology and mental wellbeing, we examined detailed measures of emotional, social and psychological wellbeing, and a history of alcohol-related problems and smoking behavior in the last year in 1,386 individual twins from same-sex pairs from the MIDUS national US sample assessed in 1995. Cholesky decomposition analyses were performed with the Mx program. The best fit model contained one highly heritable common externalizing psychopathology factor for both substance use/abuse measures, and one strongly heritable common factor for the three wellbeing measures. Genetic and environmental risk factors for externalizing psychopathology were both negatively associated with levels of mental wellbeing and accounted for, respectively, 7% and 21% of its genetic and environmental influences. Adding internalizing psychopathology assessed in the last year to the model, genetic risk factors unique for externalizing psychopathology were now positively related to levels of mental wellbeing, although accounting for only 5% of the genetic variance. Environmental risk factors unique to externalizing psychopathology continued to be negatively associated with mental wellbeing, accounting for 26% of the environmental variance. When both internalizing psychopathology and externalizing psychopathology are associated with mental wellbeing, the strongest risk factors for low mental wellbeing are genetic factors that impact on both internalizing psychopathology and externalizing psychopathology, and environmental factors unique to externalizing psychopathology. In this model, genetic risk factors for externalizing psychopathology predict, albeit weakly, higher levels of mental wellbeing. PMID:22506307

Mathematical Ability and Socio-Economic Background: IRT Modeling to Estimate Genotype by Environment Interaction.

PubMed

Schwabe, Inga; Boomsma, Dorret I; van den Berg, Stéphanie M

2017-12-01

Genotype by environment interaction in behavioral traits may be assessed by estimating the proportion of variance that is explained by genetic and environmental influences conditional on a measured moderating variable, such as a known environmental exposure. Behavioral traits of interest are often measured by questionnaires and analyzed as sum scores on the items. However, statistical results on genotype by environment interaction based on sum scores can be biased due to the properties of a scale. This article presents a method that makes it possible to analyze the actually observed (phenotypic) item data rather than a sum score by simultaneously estimating the genetic model and an item response theory (IRT) model. In the proposed model, the estimation of genotype by environment interaction is based on an alternative parametrization that is uniquely identified and therefore to be preferred over standard parametrizations. A simulation study shows good performance of our method compared to analyzing sum scores in terms of bias. Next, we analyzed data of 2,110 12-year-old Dutch twin pairs on mathematical ability. Genetic models were evaluated and genetic and environmental variance components estimated as a function of a family's socio-economic status (SES). Results suggested that common environmental influences are less important in creating individual differences in mathematical ability in families with a high SES than in creating individual differences in mathematical ability in twin pairs with a low or average SES.

Variance and covariance estimates for weaning weight of Senepol cattle.

PubMed

Wright, D W; Johnson, Z B; Brown, C J; Wildeus, S

1991-10-01

Variance and covariance components were estimated for weaning weight from Senepol field data for use in the reduced animal model for a maternally influenced trait. The 4,634 weaning records were used to evaluate 113 sires and 1,406 dams on the island of St. Croix. Estimates of direct additive genetic variance (sigma 2A), maternal additive genetic variance (sigma 2M), covariance between direct and maternal additive genetic effects (sigma AM), permanent maternal environmental variance (sigma 2PE), and residual variance (sigma 2 epsilon) were calculated by equating variances estimated from a sire-dam model and a sire-maternal grandsire model, with and without the inverse of the numerator relationship matrix (A-1), to their expectations. Estimates were sigma 2A, 139.05 and 138.14 kg2; sigma 2M, 307.04 and 288.90 kg2; sigma AM, -117.57 and -103.76 kg2; sigma 2PE, -258.35 and -243.40 kg2; and sigma 2 epsilon, 588.18 and 577.72 kg2 with and without A-1, respectively. Heritability estimates for direct additive (h2A) were .211 and .210 with and without A-1, respectively. Heritability estimates for maternal additive (h2M) were .47 and .44 with and without A-1, respectively. Correlations between direct and maternal (IAM) effects were -.57 and -.52 with and without A-1, respectively.

Genetic and Environmental Influences on Individual Differences in Frequency of Play with Pets among Middle-Aged Men: A Behavioral Genetic Analysis

PubMed Central

Jacobson, Kristen C.; Hoffman, Christy L.; Vasilopoulos, Terrie; Kremen, William S.; Panizzon, Matthew S.; Grant, Michael D.; Lyons, Michael J.; Xian, Hong; Franz, Carol E.

2014-01-01

There is growing evidence that pet ownership and human–animal interaction (HAI) have benefits for human physical and psychological well-being. However, there may be pre-existing characteristics related to patterns of pet ownership and interactions with pets that could potentially bias results of research on HAI. The present study uses a behavioral genetic design to estimate the degree to which genetic and environmental factors contribute to individual differences in frequency of play with pets among adult men. Participants were from the ongoing longitudinal Vietnam Era Twin Study of Aging (VETSA), a population-based sample of 1,237 monozygotic (MZ) and dizygotic (DZ) twins aged 51–60 years. Results demonstrate that MZ twins have higher correlations than DZ twins on frequency of pet play, suggesting that genetic factors play a role in individual differences in interactions with pets. Structural equation modeling revealed that, according to the best model, genetic factors accounted for as much as 37% of the variance in pet play, although the majority of variance (63–71%) was due to environmental factors that are unique to each twin. Shared environmental factors, which would include childhood exposure to pets, overall accounted for <10% of the variance in adult frequency of pet play, and were not statistically significant. These results suggest that the effects of childhood exposure to pets on pet ownership and interaction patterns in adulthood may be mediated primarily by genetically-influenced characteristics. PMID:25580056

Genetic and Environmental Influences on Individual Differences in Frequency of Play with Pets among Middle-Aged Men: A Behavioral Genetic Analysis.

PubMed

Jacobson, Kristen C; Hoffman, Christy L; Vasilopoulos, Terrie; Kremen, William S; Panizzon, Matthew S; Grant, Michael D; Lyons, Michael J; Xian, Hong; Franz, Carol E

2012-12-01

There is growing evidence that pet ownership and human-animal interaction (HAI) have benefits for human physical and psychological well-being. However, there may be pre-existing characteristics related to patterns of pet ownership and interactions with pets that could potentially bias results of research on HAI. The present study uses a behavioral genetic design to estimate the degree to which genetic and environmental factors contribute to individual differences in frequency of play with pets among adult men. Participants were from the ongoing longitudinal Vietnam Era Twin Study of Aging (VETSA), a population-based sample of 1,237 monozygotic (MZ) and dizygotic (DZ) twins aged 51-60 years. Results demonstrate that MZ twins have higher correlations than DZ twins on frequency of pet play, suggesting that genetic factors play a role in individual differences in interactions with pets. Structural equation modeling revealed that, according to the best model, genetic factors accounted for as much as 37% of the variance in pet play, although the majority of variance (63-71%) was due to environmental factors that are unique to each twin. Shared environmental factors, which would include childhood exposure to pets, overall accounted for <10% of the variance in adult frequency of pet play, and were not statistically significant. These results suggest that the effects of childhood exposure to pets on pet ownership and interaction patterns in adulthood may be mediated primarily by genetically-influenced characteristics.

Age-related variation in genetic control of height growth in Douglas-fir.

PubMed

Namkoong, G; Usanis, R A; Silen, R R

1972-01-01

The development of genetic variances in height growth of Douglas-fir over a 53-year period is analyzed and found to fall into three periods. In the juvenile period, variances in environmental error increase logarithmically, genetic variance within populations exists at moderate levels, and variance among populations is low but increasing. In the early reproductive period, the response to environmental sources of error variance is restricted, genetic variance within populations disappears, and populational differences strongly emerge but do not increase as expected. In the later period, environmental error again increases rapidly, but genetic variance within populations does not reappear and population differences are maintained at about the same level as established in the early reproductive period. The change between the juvenile and early reproductive periods is perhaps associated with the onset of ecological dominance and significant allocations of energy to reproduction.

Per-pixel bias-variance decomposition of continuous errors in data-driven geospatial modeling: A case study in environmental remote sensing

NASA Astrophysics Data System (ADS)

Gao, Jing; Burt, James E.

2017-12-01

This study investigates the usefulness of a per-pixel bias-variance error decomposition (BVD) for understanding and improving spatially-explicit data-driven models of continuous variables in environmental remote sensing (ERS). BVD is a model evaluation method originated from machine learning and have not been examined for ERS applications. Demonstrated with a showcase regression tree model mapping land imperviousness (0-100%) using Landsat images, our results showed that BVD can reveal sources of estimation errors, map how these sources vary across space, reveal the effects of various model characteristics on estimation accuracy, and enable in-depth comparison of different error metrics. Specifically, BVD bias maps can help analysts identify and delineate model spatial non-stationarity; BVD variance maps can indicate potential effects of ensemble methods (e.g. bagging), and inform efficient training sample allocation - training samples should capture the full complexity of the modeled process, and more samples should be allocated to regions with more complex underlying processes rather than regions covering larger areas. Through examining the relationships between model characteristics and their effects on estimation accuracy revealed by BVD for both absolute and squared errors (i.e. error is the absolute or the squared value of the difference between observation and estimate), we found that the two error metrics embody different diagnostic emphases, can lead to different conclusions about the same model, and may suggest different solutions for performance improvement. We emphasize BVD's strength in revealing the connection between model characteristics and estimation accuracy, as understanding this relationship empowers analysts to effectively steer performance through model adjustments.

Pedigree- and SNP-Associated Genetics and Recent Environment are the Major Contributors to Anthropometric and Cardiometabolic Trait Variation.

PubMed

Xia, Charley; Amador, Carmen; Huffman, Jennifer; Trochet, Holly; Campbell, Archie; Porteous, David; Hastie, Nicholas D; Hayward, Caroline; Vitart, Veronique; Navarro, Pau; Haley, Chris S

2016-02-01

Genome-wide association studies have successfully identified thousands of loci for a range of human complex traits and diseases. The proportion of phenotypic variance explained by significant associations is, however, limited. Given the same dense SNP panels, mixed model analyses capture a greater proportion of phenotypic variance than single SNP analyses but the total is generally still less than the genetic variance estimated from pedigree studies. Combining information from pedigree relationships and SNPs, we examined 16 complex anthropometric and cardiometabolic traits in a Scottish family-based cohort comprising up to 20,000 individuals genotyped for ~520,000 common autosomal SNPs. The inclusion of related individuals provides the opportunity to also estimate the genetic variance associated with pedigree as well as the effects of common family environment. Trait variation was partitioned into SNP-associated and pedigree-associated genetic variation, shared nuclear family environment, shared couple (partner) environment and shared full-sibling environment. Results demonstrate that trait heritabilities vary widely but, on average across traits, SNP-associated and pedigree-associated genetic effects each explain around half the genetic variance. For most traits the recently-shared environment of couples is also significant, accounting for ~11% of the phenotypic variance on average. On the other hand, the environment shared largely in the past by members of a nuclear family or by full-siblings, has a more limited impact. Our findings point to appropriate models to use in future studies as pedigree-associated genetic effects and couple environmental effects have seldom been taken into account in genotype-based analyses. Appropriate description of the trait variation could help understand causes of intra-individual variation and in the detection of contributing loci and environmental factors.

Genetic variance of tolerance and the toxicant threshold model.

PubMed

Tanaka, Yoshinari; Mano, Hiroyuki; Tatsuta, Haruki

2012-04-01

A statistical genetics method is presented for estimating the genetic variance (heritability) of tolerance to pollutants on the basis of a standard acute toxicity test conducted on several isofemale lines of cladoceran species. To analyze the genetic variance of tolerance in the case when the response is measured as a few discrete states (quantal endpoints), the authors attempted to apply the threshold character model in quantitative genetics to the threshold model separately developed in ecotoxicology. The integrated threshold model (toxicant threshold model) assumes that the response of a particular individual occurs at a threshold toxicant concentration and that the individual tolerance characterized by the individual's threshold value is determined by genetic and environmental factors. As a case study, the heritability of tolerance to p-nonylphenol in the cladoceran species Daphnia galeata was estimated by using the maximum likelihood method and nested analysis of variance (ANOVA). Broad-sense heritability was estimated to be 0.199 ± 0.112 by the maximum likelihood method and 0.184 ± 0.089 by ANOVA; both results implied that the species examined had the potential to acquire tolerance to this substance by evolutionary change. Copyright © 2012 SETAC.

Heritable Environmental Variance Causes Nonlinear Relationships Between Traits: Application to Birth Weight and Stillbirth of Pigs

PubMed Central

Mulder, Herman A.; Hill, William G.; Knol, Egbert F.

2015-01-01

There is recent evidence from laboratory experiments and analysis of livestock populations that not only the phenotype itself, but also its environmental variance, is under genetic control. Little is known about the relationships between the environmental variance of one trait and mean levels of other traits, however. A genetic covariance between these is expected to lead to nonlinearity between them, for example between birth weight and survival of piglets, where animals of extreme weights have lower survival. The objectives were to derive this nonlinear relationship analytically using multiple regression and apply it to data on piglet birth weight and survival. This study provides a framework to study such nonlinear relationships caused by genetic covariance of environmental variance of one trait and the mean of the other. It is shown that positions of phenotypic and genetic optima may differ and that genetic relationships are likely to be more curvilinear than phenotypic relationships, dependent mainly on the environmental correlation between these traits. Genetic correlations may change if the population means change relative to the optimal phenotypes. Data of piglet birth weight and survival show that the presence of nonlinearity can be partly explained by the genetic covariance between environmental variance of birth weight and survival. The framework developed can be used to assess effects of artificial and natural selection on means and variances of traits and the statistical method presented can be used to estimate trade-offs between environmental variance of one trait and mean levels of others. PMID:25631318

Extended Twin Study of Alcohol Use in Virginia and Australia.

PubMed

Verhulst, Brad; Neale, Michael C; Eaves, Lindon J; Medland, Sarah E; Heath, Andrew C; Martin, Nicholas G; Maes, Hermine H

2018-06-01

Drinking alcohol is a normal behavior in many societies, and prior studies have demonstrated it has both genetic and environmental sources of variation. Using two very large samples of twins and their first-degree relatives (Australia ≈ 20,000 individuals from 8,019 families; Virginia ≈ 23,000 from 6,042 families), we examine whether there are differences: (1) in the genetic and environmental factors that influence four interrelated drinking behaviors (quantity, frequency, age of initiation, and number of drinks in the last week), (2) between the twin-only design and the extended twin design, and (3) the Australian and Virginia samples. We find that while drinking behaviors are interrelated, there are substantial differences in the genetic and environmental architectures across phenotypes. Specifically, drinking quantity, frequency, and number of drinks in the past week have large broad genetic variance components, and smaller but significant environmental variance components, while age of onset is driven exclusively by environmental factors. Further, the twin-only design and the extended twin design come to similar conclusions regarding broad-sense heritability and environmental transmission, but the extended twin models provide a more nuanced perspective. Finally, we find a high level of similarity between the Australian and Virginian samples, especially for the genetic factors. The observed differences, when present, tend to be at the environmental level. Implications for the extended twin model and future directions are discussed.

Environmental impact on young children's participation in home-based activities.

PubMed

Albrecht, Erin C; Khetani, Mary A

2017-04-01

To test the effect of child, family, and environmental factors on young children's participation in home-based activities. Caregivers of young children were recruited using convenience and snowball sampling. Participants were 395 caregivers of children (222 males, 173 females) aged from 1 month to 5 years and 11 months. Demographic items and the home section of the Young Children's Participation and Environment Measure were administered online, followed by completion of the daily activities, mobility, and social/cognitive domains of the Pediatric Evaluation of Disability Inventory Computer Adaptive Test by telephone interview. A structural equation model fitted the data well (comparative fit index=0.91) and explained 31.2% of the variance in perceived environmental support and 42.5% of the variance in home involvement. Functional limitations and performance had an indirect effect on young children's participation through their effect on perceived environmental support. Specifically, fewer functional limitations and higher task performance were associated with greater environmental support, which in turn predicted higher levels of home involvement. Results suggest the importance of a young child's functional abilities and task performance on caregiver perceptions of environmental support at home, and the impact of environmental support on a child's participation in home-based activities during the early childhood period. Results warrant replication with more diverse samples to evaluate model generalizability. © 2016 The Authors. Developmental Medicine & Child Neurology published by John Wiley & Sons Ltd on behalf of Mac Keith Press.

Modeling genetic and environmental factors to increase heritability and ease the identification of candidate genes for birth weight: a twin study.

PubMed

Gielen, M; Lindsey, P J; Derom, C; Smeets, H J M; Souren, N Y; Paulussen, A D C; Derom, R; Nijhuis, J G

2008-01-01

Heritability estimates of birth weight have been inconsistent. Possible explanations are heritability changes during gestational age or the influence of covariates (e.g. chorionicity). The aim of this study was to model birth weights of twins across gestational age and to quantify the genetic and environmental components. We intended to reduce the common environmental variance to increase heritability and thereby the chance of identifying candidate genes influencing the genetic variance of birth weight. Perinatal data were obtained from 4232 live-born twin pairs from the East Flanders Prospective Twin Survey, Belgium. Heritability of birth weights across gestational ages was estimated using a non-linear multivariate Gaussian regression with covariates in the means model and in covariance structure. Maternal, twin-specific, and placental factors were considered as covariates. Heritability of birth weight decreased during gestation from 25 to 42 weeks. However, adjusting for covariates increased the heritability over this time period, with the highest heritability for first-born twins of multipara with separate placentas, who were staying alive (from 52% at 25 weeks to 30% at 42 weeks). Twin-specific factors revealed latent genetic components, whereas placental factors explained common and unique environmental factors. The number of placentas and site of the insertion of the umbilical cord masked the effect of chorionicity. Modeling genetic and environmental factors leads to a better estimate of their role in growth during gestation. For birth weight, mainly environmental factors were explained, resulting in an increase of the heritability and thereby the chance of finding genes influencing birth weight in linkage and association studies.

Psychopathic personality traits in 5 year old twins: the importance of genetic and shared environmental influences.

PubMed

Tuvblad, Catherine; Fanti, Kostas A; Andershed, Henrik; Colins, Olivier F; Larsson, Henrik

2017-04-01

There is limited research on the genetic and environmental bases of psychopathic personality traits in children. In this study, psychopathic personality traits were assessed in a total of 1189 5-year-old boys and girls drawn from the Preschool Twin Study in Sweden. Psychopathic personality traits were assessed with the Child Problematic Traits Inventory, a teacher-report measure of psychopathic personality traits in children ranging from 3 to 12 years old. Univariate results showed that genetic influences accounted for 57, 25, and 74 % of the variance in the grandiose-deceitful, callous-unemotional, and impulsive-need for stimulation dimensions, while the shared environment accounted for 17, 48 and 9 % (n.s.) in grandiose-deceitful and callous-unemotional, impulsive-need for stimulation dimensions, respectively. No sex differences were found in the genetic and environmental variance components. The non-shared environment accounted for the remaining 26, 27 and 17 % of the variance, respectively. The three dimensions of psychopathic personality were moderately correlated (0.54-0.66) and these correlations were primarily mediated by genetic and shared environmental factors. In contrast to research conducted with adolescent and adult twins, we found that both genetic and shared environmental factors influenced psychopathic personality traits in early childhood. These findings indicate that etiological models of psychopathic personality traits would benefit by taking developmental stages and processes into consideration.

Effects of data structure on the estimation of covariance functions to describe genotype by environment interactions in a reaction norm model

PubMed Central

Calus, Mario PL; Bijma, Piter; Veerkamp, Roel F

2004-01-01

Covariance functions have been proposed to predict breeding values and genetic (co)variances as a function of phenotypic within herd-year averages (environmental parameters) to include genotype by environment interaction. The objective of this paper was to investigate the influence of definition of environmental parameters and non-random use of sires on expected breeding values and estimated genetic variances across environments. Breeding values were simulated as a linear function of simulated herd effects. The definition of environmental parameters hardly influenced the results. In situations with random use of sires, estimated genetic correlations between the trait expressed in different environments were 0.93, 0.93 and 0.97 while simulated at 0.89 and estimated genetic variances deviated up to 30% from the simulated values. Non random use of sires, poor genetic connectedness and small herd size had a large impact on the estimated covariance functions, expected breeding values and calculated environmental parameters. Estimated genetic correlations between a trait expressed in different environments were biased upwards and breeding values were more biased when genetic connectedness became poorer and herd composition more diverse. The best possible solution at this stage is to use environmental parameters combining large numbers of animals per herd, while losing some information on genotype by environment interaction in the data. PMID:15339629

A twin study of early-childhood asthma in Puerto Ricans.

PubMed

Bunyavanich, Supinda; Silberg, Judy L; Lasky-Su, Jessica; Gillespie, Nathan A; Lange, Nancy E; Canino, Glorisa; Celedón, Juan C

2013-01-01

The relative contributions of genetics and environment to asthma in Hispanics or to asthma in children younger than 3 years are not well understood. To examine the relative contributions of genetics and environment to early-childhood asthma by performing a longitudinal twin study of asthma in Puerto Rican children ≤ 3 years old. 678 twin infants from the Puerto Rico Neo-Natal Twin Registry were assessed for asthma at age 1 year, with follow-up data obtained for 624 twins at age 3 years. Zygosity was determined by DNA microsatellite profiling. Structural equation modeling was performed for three phenotypes at ages 1 and 3 years: physician-diagnosed asthma, asthma medication use in the past year, and ≥ 1 hospitalization for asthma in the past year. Models were additionally adjusted for early-life environmental tobacco smoke exposure, sex, and age. The prevalences of physician-diagnosed asthma, asthma medication use, and hospitalization for asthma were 11.6%, 10.8%, 4.9% at age 1 year, and 34.1%, 40.1%, and 8.5% at 3 years, respectively. Shared environmental effects contributed to the majority of variance in susceptibility to physician-diagnosed asthma and asthma medication use in the first year of life (84%-86%), while genetic effects drove variance in all phenotypes (45%-65%) at age 3 years. Early-life environmental tobacco smoke, sex, and age contributed to variance in susceptibility. Our longitudinal study in Puerto Rican twins demonstrates a changing contribution of shared environmental effects to liability for physician-diagnosed asthma and asthma medication use between ages 1 and 3 years. Early-life environmental tobacco smoke reduction could markedly reduce asthma morbidity in young Puerto Rican children.

Psychopathic personality development from ages 9 to 18: Genes and environment

PubMed Central

TUVBLAD, CATHERINE; WANG, PAN; BEZDJIAN, SERENA; RAINE, ADRIAN; BAKER, LAURA A.

2015-01-01

The genetic and environmental etiology of individual differences was examined in initial level and change in psychopathic personality from ages 9 to 18 years. A piecewise growth curve model, in which the first change score (G1) influenced all ages (9–10, 11–13, 14–15, and 16–18 years) and the second change score (G2) only influenced ages 14–15 and 16–18 years, fit the data better did than the standard single slope model, suggesting a turning point from childhood to adolescence. The results indicated that variations in levels and both change scores were mainly due to genetic (A) and nonshared environmental (E) influences (i.e., AE structure for G0, G1, and G2). No sex differences were found except on the mean values of level and change scores. Based on caregiver ratings, about 81% of variance in G0, 89% of variance in G1, and 94% of variance in G2 were explained by genetic factors, whereas for youth self-reports, these three proportions were 94%, 71%, and 66%, respectively. The larger contribution of genetic variance and covariance in caregiver ratings than in youth self-reports may suggest that caregivers considered the changes in their children to be more similar as compared to how the children viewed themselves. PMID:25990131

Fetal Environment Is a Major Determinant of the Neonatal Blood Thyroxine Level: Results of a Large Dutch Twin Study.

PubMed

Zwaveling-Soonawala, Nitash; van Beijsterveldt, Catharina E M; Mesfum, Ertirea T; Wiedijk, Brenda; Oomen, Petra; Finken, Martijn J J; Boomsma, Dorret I; van Trotsenburg, A S Paul

2015-06-01

The interindividual variability in thyroid hormone function parameters is much larger than the intraindividual variability, suggesting an individual set point for these parameters. There is evidence to suggest that environmental factors are more important than genetic factors in the determination of this individual set point. This study aimed to quantify the effect of genetic factors and (fetal) environment on the early postnatal blood T4 concentration. This was a classical twin study comparing the resemblance of neonatal screening blood T4 concentrations in 1264 mono- and 2566 dizygotic twin pairs retrieved from the population-based Netherlands Twin Register. Maximum-likelihood estimates of variance explained by genetic and environmental influences were obtained by structural equation modeling in data from full-term and preterm twin pairs. In full-term infants, genetic factors explained 40%/31% of the variance in standardized T4 scores in boys/girls, and shared environment, 27%/22%. The remaining variance of 33%/47% was due to environmental factors not shared by twins. For preterm infants, genetic factors explained 34%/0% of the variance in boys/girls, shared environment 31%/57%, and unique environment 35%/43%. In very preterm twins, no significant contribution of genetic factors was observed. Environment explains a large proportion of the resemblance of the postnatal blood T4 concentration in twin pairs. Because we analyzed neonatal screening results, the fetal environment is the most likely candidate for these environmental influences. Genetic influences on the T4 set point diminished with declining gestational age, especially in girls. This may be due to major environmental influences such as immaturity and nonthyroidal illness in very preterm infants.

40 CFR 190.11 - Variances for unusual operations.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 26 2013-07-01 2013-07-01 false Variances for unusual operations. 190.11 Section 190.11 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) RADIATION PROTECTION PROGRAMS ENVIRONMENTAL RADIATION PROTECTION STANDARDS FOR NUCLEAR POWER OPERATIONS Environmental...

40 CFR 190.11 - Variances for unusual operations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 40 Protection of Environment 25 2011-07-01 2011-07-01 false Variances for unusual operations. 190.11 Section 190.11 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) RADIATION PROTECTION PROGRAMS ENVIRONMENTAL RADIATION PROTECTION STANDARDS FOR NUCLEAR POWER OPERATIONS Environmental...

40 CFR 190.11 - Variances for unusual operations.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 25 2014-07-01 2014-07-01 false Variances for unusual operations. 190.11 Section 190.11 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) RADIATION PROTECTION PROGRAMS ENVIRONMENTAL RADIATION PROTECTION STANDARDS FOR NUCLEAR POWER OPERATIONS Environmental...

40 CFR 190.11 - Variances for unusual operations.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 26 2012-07-01 2011-07-01 true Variances for unusual operations. 190.11 Section 190.11 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) RADIATION PROTECTION PROGRAMS ENVIRONMENTAL RADIATION PROTECTION STANDARDS FOR NUCLEAR POWER OPERATIONS Environmental...

40 CFR 190.11 - Variances for unusual operations.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 24 2010-07-01 2010-07-01 false Variances for unusual operations. 190.11 Section 190.11 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) RADIATION PROTECTION PROGRAMS ENVIRONMENTAL RADIATION PROTECTION STANDARDS FOR NUCLEAR POWER OPERATIONS Environmental...

Clock Genes Explain a Large Proportion of Phenotypic Variance in Systolic Blood Pressure and This Control Is Not Modified by Environmental Temperature.

PubMed

Dashti, Hassan S; Aslibekyan, Stella; Scheer, Frank A J L; Smith, Caren E; Lamon-Fava, Stefania; Jacques, Paul; Lai, Chao-Qiang; Tucker, Katherine L; Arnett, Donna K; Ordovás, José M

2016-01-01

Diurnal variation in blood pressure (BP) is regulated, in part, by an endogenous circadian clock; however, few human studies have identified associations between clock genes and BP. Accounting for environmental temperature may be necessary to correct for seasonal bias. We examined whether environmental temperature on the day of participants' assessment was associated with BP, using adjusted linear regression models in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) (n = 819) and the Boston Puerto Rican Health Study (BPRHS) (n = 1,248) cohorts. We estimated phenotypic variance in BP by 18 clock genes and examined individual single-nucleotide polymorphism (SNP) associations with BP using an additive genetic model, with further consideration of environmental temperature. In GOLDN, each additional 1 °C increase in environmental temperature was associated with 0.18 mm Hg lower systolic BP [SBP; β ± SE = -0.18 ± 0.05 mm Hg; P = 0.0001] and 0.10mm Hg lower diastolic BP [DBP; -0.10 ± 0.03 mm Hg; P = 0.001]. Similar results were seen in the BPRHS for SBP only. Clock genes explained a statistically significant proportion of the variance in SBP [V G/V P ± SE = 0.071 ± 0.03; P = 0.001] in GOLDN, but not in the BPRHS, and we did not observe associations between individual SNPs and BP. Environmental temperature did not influence the identified genetic associations. We identified clock genes that explained a statistically significant proportion of the phenotypic variance in SBP, supporting the importance of the circadian pathway underlying cardiac physiology. Although temperature was associated with BP, it did not affect results with genetic markers in either study. Therefore, it does not appear that temperature measures are necessary for interpreting associations between clock genes and BP. Trials related to this study were registered at clinicaltrials.gov as NCT00083369 (Genetic and Environmental Determinants of Triglycerides) and NCT01231958 (Boston Puerto Rican Health Study). © Published by Oxford University Press on behalf of American Journal of Hypertension Ltd 2015. This work is written by (a) US Government employees(s) and is in the public domain in the US.

GIS-based niche modeling for mapping species' habitats

USGS Publications Warehouse

Rotenberry, J.T.; Preston, K.L.; Knick, S.

2006-01-01

Ecological a??niche modelinga?? using presence-only locality data and large-scale environmental variables provides a powerful tool for identifying and mapping suitable habitat for species over large spatial extents. We describe a niche modeling approach that identifies a minimum (rather than an optimum) set of basic habitat requirements for a species, based on the assumption that constant environmental relationships in a species' distribution (i.e., variables that maintain a consistent value where the species occurs) are most likely to be associated with limiting factors. Environmental variables that take on a wide range of values where a species occurs are less informative because they do not limit a species' distribution, at least over the range of variation sampled. This approach is operationalized by partitioning Mahalanobis D2 (standardized difference between values of a set of environmental variables for any point and mean values for those same variables calculated from all points at which a species was detected) into independent components. The smallest of these components represents the linear combination of variables with minimum variance; increasingly larger components represent larger variances and are increasingly less limiting. We illustrate this approach using the California Gnatcatcher (Polioptila californica Brewster) and provide SAS code to implement it.

A behavioral-genetic investigation of bulimia nervosa and its relationship with alcohol use disorder

PubMed Central

Trace, Sara Elizabeth; Thornton, Laura Marie; Baker, Jessica Helen; Root, Tammy Lynn; Janson, Lauren Elizabeth; Lichtenstein, Paul; Pedersen, Nancy Lee; Bulik, Cynthia Marie

2013-01-01

Bulimia nervosa (BN) and alcohol use disorder (AUD) frequently co-occur and may share genetic factors; however, the nature of their association is not fully understood. We assessed the extent to which the same genetic and environmental factors contribute to liability to BN and AUD. A bivariate structural equation model using a Cholesky decomposition was fit to data from 7,241 women who participated in the Swedish Twin study of Adults: Genes and Environment. The proportion of variance accounted for by genetic and environmental factors for BN and AUD and the genetic and environmental correlations between these disorders were estimated. In the best-fitting model, the heritability estimates were 0.55 (95% CI: 0.37; 0.70) for BN and 0.62 (95% CI: 0.54; 0.70) for AUD. Unique environmental factors accounted for the remainder of variance for BN. The genetic correlation between BN and AUD was 0.23 (95% CI: 0.01; 0.44), and the correlation between the unique environmental factors for the two disorders was 0.35 (95% CI: 0.08; 0.61), suggesting moderate overlap in these factors. Findings from this investigation provide additional support that some of the same genetic factors may influence liability to both BN and AUD. PMID:23790978

Causes of individual differences in adolescent optimism: a study in Dutch twins and their siblings.

PubMed

Mavioğlu, Rezan Nehir; Boomsma, Dorret I; Bartels, Meike

2015-11-01

The aim of this study was to investigate the degree to which genetic and environmental influences affect variation in adolescent optimism. Optimism (3 items and 6 items approach) and pessimism were assessed by the Life Orientation Test-Revised (LOT-R) in 5,187 adolescent twins and 999 of their non-twin siblings from the Netherlands Twin Register (NTR). Males reported significantly higher optimism scores than females, while females score higher on pessimism. Genetic structural equation modeling revealed that about one-third of the variance in optimism and pessimism was due to additive genetic effects, with the remaining variance being explained by non-shared environmental effects. A bivariate correlated factor model revealed two dimensions with a genetic correlation of -.57 (CI -.67, -.47), while the non-shared environmental correlation was estimated to be -.21 (CI -.25, -.16). Neither an effect of shared environment, non-additive genetic influences, nor quantitative sex differences was found for both dimensions. This result indicates that individual differences in adolescent optimism are mainly accounted for by non-shared environmental factors. These environmental factors do not contribute to the similarity of family members, but to differences between them. Familial resemblance in optimism and pessimism assessed in adolescents is fully accounted for by genetic overlap between family members.

Genetic basis of between-individual and within-individual variance of docility.

PubMed

Martin, J G A; Pirotta, E; Petelle, M B; Blumstein, D T

2017-04-01

Between-individual variation in phenotypes within a population is the basis of evolution. However, evolutionary and behavioural ecologists have mainly focused on estimating between-individual variance in mean trait and neglected variation in within-individual variance, or predictability of a trait. In fact, an important assumption of mixed-effects models used to estimate between-individual variance in mean traits is that within-individual residual variance (predictability) is identical across individuals. Individual heterogeneity in the predictability of behaviours is a potentially important effect but rarely estimated and accounted for. We used 11 389 measures of docility behaviour from 1576 yellow-bellied marmots (Marmota flaviventris) to estimate between-individual variation in both mean docility and its predictability. We then implemented a double hierarchical animal model to decompose the variances of both mean trait and predictability into their environmental and genetic components. We found that individuals differed both in their docility and in their predictability of docility with a negative phenotypic covariance. We also found significant genetic variance for both mean docility and its predictability but no genetic covariance between the two. This analysis is one of the first to estimate the genetic basis of both mean trait and within-individual variance in a wild population. Our results indicate that equal within-individual variance should not be assumed. We demonstrate the evolutionary importance of the variation in the predictability of docility and illustrate potential bias in models ignoring variation in predictability. We conclude that the variability in the predictability of a trait should not be ignored, and present a coherent approach for its quantification. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Field heritability of a plant adaptation to fire in heterogeneous landscapes.

PubMed

Castellanos, M C; González-Martínez, S C; Pausas, J G

2015-11-01

The strong association observed between fire regimes and variation in plant adaptations to fire suggests a rapid response to fire as an agent of selection. It also suggests that fire-related traits are heritable, a precondition for evolutionary change. One example is serotiny, the accumulation of seeds in unopened fruits or cones until the next fire, an important strategy for plant population persistence in fire-prone ecosystems. Here, we evaluate the potential of this trait to respond to natural selection in its natural setting. For this, we use a SNP marker approach to estimate genetic variance and heritability of serotiny directly in the field for two Mediterranean pine species. Study populations were large and heterogeneous in climatic conditions and fire regime. We first estimated the realized relatedness among trees from genotypes, and then partitioned the phenotypic variance in serotiny using Bayesian animal models that incorporated environmental predictors. As expected, field heritability was smaller (around 0.10 for both species) than previous estimates under common garden conditions (0.20). An estimate on a subset of stands with more homogeneous environmental conditions was not different from that in the complete set of stands, suggesting that our models correctly captured the environmental variation at the spatial scale of the study. Our results highlight the importance of measuring quantitative genetic parameters in natural populations, where environmental heterogeneity is a critical aspect. The heritability of serotiny, although not high, combined with high phenotypic variance within populations, confirms the potential of this fire-related trait for evolutionary change in the wild. © 2015 John Wiley & Sons Ltd.

Which early life events or current environmental and lifestyle factors influence lung function in adolescents? - results from the GINIplus & LISAplus studies.

PubMed

Luzak, Agnes; Fuertes, Elaine; Flexeder, Claudia; Standl, Marie; von Berg, Andrea; Berdel, Dietrich; Koletzko, Sibylle; Heinrich, Joachim; Nowak, Dennis; Schulz, Holger

2017-07-12

Various factors may affect lung function at different stages in life. Since investigations that simultaneously consider several factors are rare, we examined the relative importance of early life, current environmental/lifestyle factors and allergic diseases on lung function in 15-year-olds. Best subset selection was performed for linear regression models to investigate associations between 21 diverse early life events and current factors with spirometric parameters (forced vital capacity, forced expiratory volume in 1 s and maximal mid-expiratory flow (FEF 25-75 )) in 1326 participants of the German GINIplus and LISAplus birth cohorts. To reduce model complexity, one model for each spirometric parameter was replicated 1000 times in random subpopulations (N = 884). Only those factors that were included in >70% of the replication models were retained in the final analysis. A higher peak weight velocity and early lung infections were the early life events prevalently associated with airflow limitation and FEF 25-75 . Current environmental/lifestyle factors at age 15 years and allergic diseases that were associated with lung function were: indoor second-hand smoke exposure, vitamin D concentration, body mass index (BMI) and asthma status. Sex and height captured the majority of the explained variance (>75%), followed by BMI (≤23.7%). The variance explained by early life events was comparatively low (median: 4.8%; range: 0.2-22.4%), but these events were consistently negatively associated with airway function. Although the explained variance was mainly captured by well-known factors included in lung function prediction equations, our findings indicate early life and current factors that should be considered in studies on lung health among adolescents.

Spectral analysis of a two-species competition model: Determining the effects of extreme conditions on the color of noise generated from simulated time series

NASA Astrophysics Data System (ADS)

Golinski, M. R.

2006-07-01

Ecologists have observed that environmental noise affects population variance in the logistic equation for one-species growth. Interactions between deterministic and stochastic dynamics in a one-dimensional system result in increased variance in species population density over time. Since natural populations do not live in isolation, the present paper simulates a discrete-time two-species competition model with environmental noise to determine the type of colored population noise generated by extreme conditions in the long-term population dynamics of competing populations. Discrete Fourier analysis is applied to the simulation results and the calculated Hurst exponent ( H) is used to determine how the color of population noise for the two species corresponds to extreme conditions in population dynamics. To interpret the biological meaning of the color of noise generated by the two-species model, the paper determines the color of noise generated by three reference models: (1) A two-dimensional discrete-time white noise model (0⩽ H<1/2); (2) A two-dimensional fractional Brownian motion model (H=1/2); and (3) A two-dimensional discrete-time model with noise for unbounded growth of two uncoupled species (1/2< H⩽1).

Home Environmental and Behavioral Risk Indices for Reading Achievement.

PubMed

Taylor, Jeanette; Ennis, Chelsea R; Hart, Sara A; Mikolajewski, Amy J; Schatschneider, Christopher

2017-07-01

The goal of this study was to identify home environmental and temperament/behavior variables that best predict standardized reading comprehension scores among school-aged children. Data from 269 children aged 9-16 ( M = 12.08; SD = 1.62) were used in discriminant function analyses to create the Home and Behavior indices. Family income was controlled in each index. The final Home and Behavior models each classified around 75% of cases correctly (reading comprehension at grade level vs. not). Each index was then used to predict other outcomes related to reading. Results showed that Home and/or Behavior accounted for 4-7% of the variance in reading fluency and spelling and 20-35% of the variance in parent-rated problems in math, social anxiety, and other dimensions. These metrics show promise as environmental and temperament/behavior risk scores that could be used to predict and potentially screen for further assessment of reading related problems.

Environmental Influences on Well-Being: A Dyadic Latent Panel Analysis of Spousal Similarity

ERIC Educational Resources Information Center

Schimmack, Ulrich; Lucas, Richard E.

2010-01-01

This article uses dyadic latent panel analysis (DLPA) to examine environmental influences on well-being. DLPA requires longitudinal dyadic data. It decomposes the observed variance of both members of a dyad into a trait, state, and an error component. Furthermore, state variance is decomposed into initial and new state variance. Total observed…

Deletion Diagnostics for the Generalised Linear Mixed Model with independent random effects

PubMed Central

Ganguli, B.; Roy, S. Sen; Naskar, M.; Malloy, E. J.; Eisen, E. A.

2015-01-01

The Generalised Linear Mixed Model (GLMM) is widely used for modelling environmental data. However, such data are prone to influential observations which can distort the estimated exposure-response curve particularly in regions of high exposure. Deletion diagnostics for iterative estimation schemes commonly derive the deleted estimates based on a single iteration of the full system holding certain pivotal quantities such as the information matrix to be constant. In this paper, we present an approximate formula for the deleted estimates and Cook’s distance for the GLMM which does not assume that the estimates of variance parameters are unaffected by deletion. The procedure allows the user to calculate standardised DFBETAs for mean as well as variance parameters. In certain cases, such as when using the GLMM as a device for smoothing, such residuals for the variance parameters are interesting in their own right. In general, the procedure leads to deleted estimates of mean parameters which are corrected for the effect of deletion on variance components as estimation of the two sets of parameters is interdependent. The probabilistic behaviour of these residuals is investigated and a simulation based procedure suggested for their standardisation. The method is used to identify influential individuals in an occupational cohort exposed to silica. The results show that failure to conduct post model fitting diagnostics for variance components can lead to erroneous conclusions about the fitted curve and unstable confidence intervals. PMID:26626135

Moderation of genetic and environmental influences on diurnal preference by age in adult twins.

PubMed

Barclay, Nicola L; Watson, Nathaniel F; Buchwald, Dedra; Goldberg, Jack

2014-03-01

Diurnal preference changes across the lifespan. However, the mechanisms underlying this age-related shift are poorly understood. The aim of this twin study was to determine the extent to which genetic and environmental influences on diurnal preference are moderated by age. Seven hundred and sixty-eight monozygotic and 674 dizygotic adult twin pairs participating in the University of Washington Twin Registry completed the reduced Morningness-Eveningness Questionnaire as a measure of diurnal preference. Participants ranged in age from 19 to 93 years (mean = 36.23, SD = 15.54) and were categorized on the basis of age into three groups: younger adulthood (19-35 years, n = 1715 individuals), middle adulthood (36-64 years, n = 1003 individuals) and older adulthood (65+ years, n = 168 individuals). Increasing age was associated with an increasing tendency towards morningness (r = 0.42, p < 0.001). Structural equation modeling techniques parsed the variance in diurnal preference into genetic and environmental influences for the total sample as well as for each age group separately. Additive genetic influences accounted for 52%[46-57%], and non-shared environmental influences 48%[43-54%], of the total variance in diurnal preference. In comparing univariate genetic models between age groups, the best-fitting model was one in which the parameter estimates for younger adults and older adults were equated, in comparison with middle adulthood. For younger and older adulthood, additive genetic influences accounted for 44%[31-49%] and non-shared environmental influences 56%[49-64%] of variance in diurnal preference, whereas for middle adulthood these estimates were 34%[21-45%] and 66%[55-79%], respectively. Therefore, genetic influences on diurnal preference are attenuated in middle adulthood. Attenuation is likely driven by the increased importance of work and family responsibilities during this life stage, in comparison with younger and older adulthood when these factors may be less influential in determining sleep-wake timing. These findings have implications for studies aimed at identifying specific non-shared environmental influences, as well as molecular genetic studies aimed at identifying specific polymorphisms associated with diurnal preference.

Gene, environment and cognitive function: a Chinese twin ageing study.

PubMed

Xu, Chunsheng; Sun, Jianping; Duan, Haiping; Ji, Fuling; Tian, Xiaocao; Zhai, Yaoming; Wang, Shaojie; Pang, Zengchang; Zhang, Dongfeng; Zhao, Zhongtang; Li, Shuxia; Gue, Matt Mc; Hjelmborg, Jacob V B; Christensen, Kaare; Tan, Qihua

2015-05-01

the genetic and environmental contributions to cognitive function in the old people have been well addressed for the Western populations using twin modelling showing moderate to high heritability. No similar study has been conducted in the world largest and rapidly ageing Chinese population living under distinct environmental condition as the Western populations. this study aims to explore the genetic and environmental impact on normal cognitive ageing in the Chinese twins. cognitive function was measured on 384 complete twin pairs with median age of 50 years for seven cognitive measurements including visuospatial, linguistic skills, naming, memory, attention, abstraction and orientation abilities. Data were analysed by fitting univariate and bivariate twin models to estimate the genetic and environmental components in the variance and co-variance of the cognitive assessments. intra-pair correlation on cognitive measurements was low to moderate in monozygotic twins (0.23-0.41, overall 0.42) and low in dizygotic twins (0.05-0.30, overall 0.31) with the former higher than the latter for each item. Estimate for heritability was moderate for overall cognitive function (0.44, 95% CI: 0.34-0.53) and low to moderate for visuospatial, naming, attention and orientation abilities ranging from 0.28 to 0.38. No genetic contribution was estimated to linguistic skill, abstraction and memory which instead were under low to moderate control by shared environmental factors accounting for 23-33% of the total variances. In contrast, all cognitive performances showed moderate to high influences by the unique environmental factors. genetic factor and common family environment have a limited contribution to cognitive function in the Chinese adults. Individual unique environment is likely to play a major role in determining the levels of cognitive performance. © The Author 2015. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Evolution of phenotypic plasticity and environmental tolerance of a labile quantitative character in a fluctuating environment.

PubMed

Lande, R

2014-05-01

Quantitative genetic models of evolution of phenotypic plasticity are used to derive environmental tolerance curves for a population in a changing environment, providing a theoretical foundation for integrating physiological and community ecology with evolutionary genetics of plasticity and norms of reaction. Plasticity is modelled for a labile quantitative character undergoing continuous reversible development and selection in a fluctuating environment. If there is no cost of plasticity, a labile character evolves expected plasticity equalling the slope of the optimal phenotype as a function of the environment. This contrasts with previous theory for plasticity influenced by the environment at a critical stage of early development determining a constant adult phenotype on which selection acts, for which the expected plasticity is reduced by the environmental predictability over the discrete time lag between development and selection. With a cost of plasticity in a labile character, the expected plasticity depends on the cost and on the environmental variance and predictability averaged over the continuous developmental time lag. Environmental tolerance curves derived from this model confirm traditional assumptions in physiological ecology and provide new insights. Tolerance curve width increases with larger environmental variance, but can only evolve within a limited range. The strength of the trade-off between tolerance curve height and width depends on the cost of plasticity. Asymmetric tolerance curves caused by male sterility at high temperature are illustrated. A simple condition is given for a large transient increase in plasticity and tolerance curve width following a sudden change in average environment. © 2014 The Author. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Jensen's Inequality Predicts Effects of Environmental Variation

Treesearch

Jonathan J. Ruel; Matthew P. Ayres

1999-01-01

Many biologists now recognize that environmental variance can exert important effects on patterns and processes in nature that are independent of average conditions. Jenson's inequality is a mathematical proof that is seldom mentioned in the ecological literature but which provides a powerful tool for predicting some direct effects of environmental variance in...

Strong Genetic Overlap Between Executive Functions and Intelligence

PubMed Central

Engelhardt, Laura E.; Mann, Frank D.; Briley, Daniel A.; Church, Jessica A.; Harden, K. Paige; Tucker-Drob, Elliot M.

2016-01-01

Executive functions (EFs) are cognitive processes that control, monitor, and coordinate more basic cognitive processes. EFs play instrumental roles in models of complex reasoning, learning, and decision-making, and individual differences in EFs have been consistently linked with individual differences in intelligence. By middle childhood, genetic factors account for a moderate proportion of the variance in intelligence, and these effects increase in magnitude through adolescence. Genetic influences on EFs are very high, even in middle childhood, but the extent to which these genetic influences overlap with those on intelligence is unclear. We examined genetic and environmental overlap between EFs and intelligence in a racially and socioeconomically diverse sample of 811 twins ages 7-15 years (M = 10.91, SD = 1.74) from the Texas Twin Project. A general EF factor representing variance common to inhibition, switching, working memory, and updating domains accounted for substantial proportions of variance in intelligence, primarily via a genetic pathway. General EF continued to have a strong, genetically-mediated association with intelligence even after controlling for processing speed. Residual variation in general intelligence was influenced only by shared and nonshared environmental factors, and there remained no genetic variance in general intelligence that was unique of EF. Genetic variance independent of EF did remain, however, in a more specific perceptual reasoning ability. These results provide evidence that genetic influences on general intelligence are highly overlapping with those on EF. PMID:27359131

Genetic and environmental influences on thin-ideal internalization across puberty and preadolescent, adolescent, and young adult development.

PubMed

Suisman, Jessica L; Thompson, J Kevin; Keel, Pamela K; Burt, S Alexandra; Neale, Michael; Boker, Steven; Sisk, Cheryl; Klump, Kelly L

2014-11-01

Mean-levels of thin-ideal internalization increase during adolescence and pubertal development, but it is unknown whether these phenotypic changes correspond to developmental changes in etiological (i.e., genetic and environmental) risk. Given the limited knowledge on risk for thin-ideal internalization, research is needed to guide the identification of specific types of risk factors during critical developmental periods. The present twin study examined genetic and environmental influences on thin-ideal internalization across adolescent and pubertal development. Participants were 1,064 female twins (ages 8-25 years) from the Michigan State University Twin Registry. Thin-ideal internalization and pubertal development were assessed using self-report questionnaires. Twin moderation models were used to examine if age and/or pubertal development moderate genetic and environmental influences on thin-ideal internalization. Phenotypic analyses indicated significant increases in thin-ideal internalization across age and pubertal development. Twin models suggested no significant differences in etiologic effects across development. Nonshared environmental influences were most important in the etiology of thin-ideal internalization, with genetic, shared environmental, and nonshared environmental accounting for approximately 8%, 15%, and 72%, respectively, of the total variance. Despite mean-level increases in thin-ideal internalization across development, the relative influence of genetic versus environmental risk did not differ significantly across age or pubertal groups. The majority of variance in thin-ideal internalization was accounted for by environmental factors, suggesting that mean-level increases in thin-ideal internalization may reflect increases in the magnitude/strength of environmental risk across this period. Replication is needed, particularly with longitudinal designs that assess thin-ideal internalization across key developmental phases. © 2014 Wiley Periodicals, Inc.

Genetic and Environmental Influences on Thin-Ideal Internalization across Puberty and Pre-Adolescent, Adolescent, and Young Adult Development

PubMed Central

Suisman, Jessica L.; Thompson, J. Kevin; Keel, Pamela K.; Burt, S. Alexandra; Neale, Michael; Boker, Steven; Sisk, Cheryl; Klump, Kelly L.

2014-01-01

Objective Mean-levels of thin-ideal internalization increase during adolescence and pubertal development, but it is unknown whether these phenotypic changes correspond to developmental changes in etiological (i.e., genetic and environmental) risk. Given the limited knowledge on risk for thin-ideal internalization, research is needed to guide the identification of specific types of risk factors during critical developmental periods. The present twin study examined genetic and environmental influences on thin-ideal internalization across adolescent and pubertal development. Method Participants were 1,064 female twins (ages 8–25 years) from the Michigan State University Twin Registry. Thin-ideal internalization and pubertal development were assessed using self-report questionnaires. Twin moderation models were used to examine if age and/or pubertal development moderate genetic and environmental influences on thin-ideal internalization. Results Phenotypic analyses indicated significant increases in thin-ideal internalization across age and pubertal development. Twin models suggested no significant differences in etiologic effects across development. Nonshared environmental influences were most important in the etiology of thin-ideal internalization, with genetic, shared environmental, and nonshared environmental accounting for approximately 8%, 15%, and 72%, respectively, of the total variance. Discussion Despite mean-level increases in thin-ideal internalization across development, the relative influence of genetic versus environmental risk did not differ significantly across age or pubertal groups. The majority of variance in thin-ideal internalization was accounted for by environmental factors, suggesting that mean-level increases in thin-ideal internalization may reflect increases in the magnitude/strength of environmental risk across this period. Replication is needed, particularly with longitudinal designs that assess thin-ideal internalization across key developmental phases. PMID:24962440

Education and alcohol use: A study of gene-environment interaction in young adulthood.

PubMed

Barr, Peter B; Salvatore, Jessica E; Maes, Hermine; Aliev, Fazil; Latvala, Antti; Viken, Richard; Rose, Richard J; Kaprio, Jaakko; Dick, Danielle M

2016-08-01

The consequences of heavy alcohol use remain a serious public health problem. Consistent evidence has demonstrated that both genetic and social influences contribute to alcohol use. Research on gene-environment interaction (GxE) has also demonstrated that these social and genetic influences do not act independently. Instead, certain environmental contexts may limit or exacerbate an underlying genetic predisposition. However, much of the work on GxE and alcohol use has focused on adolescence and less is known about the important environmental contexts in young adulthood. Using data from the young adult wave of the Finnish Twin Study, FinnTwin12 (N = 3402), we used biometric twin modeling to test whether education moderated genetic risk for alcohol use as assessed by drinking frequency and intoxication frequency. Education is important because it offers greater access to personal resources and helps determine one's position in the broader stratification system. Results from the twin models show that education did not moderate genetic variance components and that genetic risk was constant across levels of education. Instead, education moderated environmental variance so that under conditions of low education, environmental influences explained more of the variation in alcohol use outcomes. The implications and limitations of these results are discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Education and Alcohol Use: A Study of Gene-Environment Interaction in Young Adulthood

PubMed Central

Barr, Peter B.; Salvatore, Jessica E.; Maes, Hermine; Aliev, Fazil; Latvala, Antti; Viken, Richard; Rose, Richard J.; Kaprio, Jaakko; Dick, Danielle M.

2016-01-01

The consequences of heavy alcohol use remain a serious public health problem. Consistent evidence has demonstrated that both genetic and social influences contribute to alcohol use. Research on gene-environment interaction (GxE) has also demonstrated that these social and genetic influences do not act independently. Instead, certain environmental contexts may limit or exacerbate an underlying genetic predisposition. However, much of the work on GxE and alcohol use has focused on adolescence and less is known about the important environmental contexts in young adulthood. Using data from the young adult wave of the Finnish Twin Study, FinnTwin12 (N=3,402), we used biometric twin modeling to test whether education moderated genetic risk for alcohol use as assessed by drinking frequency and intoxication frequency. Education is important because it offers greater access to personal resources and helps determine one’s position in the broader stratification system. Results from the twin models show that education did not moderate genetic variance components and that genetic risk was constant across levels of education. Instead, education moderated environmental variance so that under conditions of low education, environmental influences explained more of the variation in alcohol use outcomes. The implications and limitations of these results are discussed. PMID:27367897

A Bayesian Network Based Global Sensitivity Analysis Method for Identifying Dominant Processes in a Multi-physics Model

NASA Astrophysics Data System (ADS)

Dai, H.; Chen, X.; Ye, M.; Song, X.; Zachara, J. M.

2016-12-01

Sensitivity analysis has been an important tool in groundwater modeling to identify the influential parameters. Among various sensitivity analysis methods, the variance-based global sensitivity analysis has gained popularity for its model independence characteristic and capability of providing accurate sensitivity measurements. However, the conventional variance-based method only considers uncertainty contribution of single model parameters. In this research, we extended the variance-based method to consider more uncertainty sources and developed a new framework to allow flexible combinations of different uncertainty components. We decompose the uncertainty sources into a hierarchical three-layer structure: scenario, model and parametric. Furthermore, each layer of uncertainty source is capable of containing multiple components. An uncertainty and sensitivity analysis framework was then constructed following this three-layer structure using Bayesian network. Different uncertainty components are represented as uncertain nodes in this network. Through the framework, variance-based sensitivity analysis can be implemented with great flexibility of using different grouping strategies for uncertainty components. The variance-based sensitivity analysis thus is improved to be able to investigate the importance of an extended range of uncertainty sources: scenario, model, and other different combinations of uncertainty components which can represent certain key model system processes (e.g., groundwater recharge process, flow reactive transport process). For test and demonstration purposes, the developed methodology was implemented into a test case of real-world groundwater reactive transport modeling with various uncertainty sources. The results demonstrate that the new sensitivity analysis method is able to estimate accurate importance measurements for any uncertainty sources which were formed by different combinations of uncertainty components. The new methodology can provide useful information for environmental management and decision-makers to formulate policies and strategies.

Gender Differences in Marital Status Moderation of Genetic and Environmental Influences on Subjective Health.

PubMed

Finkel, Deborah; Franz, Carol E; Horwitz, Briana; Christensen, Kaare; Gatz, Margaret; Johnson, Wendy; Kaprio, Jaako; Korhonen, Tellervo; Niederheiser, Jenae; Petersen, Inge; Rose, Richard J; Silventoinen, Karri

2015-10-14

From the IGEMS Consortium, data were available from 26,579 individuals aged 23 to 102 years on 3 subjective health items: self-rated health (SRH), health compared to others (COMP), and impact of health on activities (ACT). Marital status was a marker of environmental resources that may moderate genetic and environmental influences on subjective health. Results differed for the 3 subjective health items, indicating that they do not tap the same construct. Although there was little impact of marital status on variance components for women, marital status was a significant modifier of variance in all 3 subjective health measures for men. For both SRH and ACT, single men demonstrated greater shared and nonshared environmental variance than married men. For the COMP variable, genetic variance was greater for single men vs. married men. Results suggest gender differences in the role of marriage as a source of resources that are associated with subjective health.

Nurture Net of Nature: Re-Evaluating the Role of Shared Environments in Academic Achievement and Verbal Intelligence

PubMed Central

Daw, Jonathan; Guo, Guang; Harris, Kathie Mullan

2016-01-01

Prominent authors in the behavioral genetics tradition have long argued that shared environments do not meaningfully shape intelligence and academic achievement. However, we argue that these conclusions are erroneous due to large violations of the additivity assumption underlying behavioral genetics methods – that sources of genetic and shared and nonshared environmental variance are independent and non-interactive. This is compounded in some cases by the theoretical equation of the effective and objective environments, where the former is defined by whether siblings are made more or less similar, and the latter by whether siblings are equally subject to the environmental characteristic in question. Using monozygotic twin fixed effects models, which compare outcomes among genetically identical pairs, we show that many characteristics of objectively shared environments significantly moderate the effects of nonshared environments on adolescent academic achievement and verbal intelligence, violating the additivity assumption of behavioral genetic methods. Importantly, these effects would be categorized as nonshared environmental influences in standard twin models despite their roots in shared environments. These findings should encourage caution among those who claim that the frequently trivial variance attributed to shared environments in behavioral genetic models means that families, schools, and neighborhoods do not meaningfully influence these outcomes. PMID:26004471

Nurture net of nature: Re-evaluating the role of shared environments in academic achievement and verbal intelligence.

PubMed

Daw, Jonathan; Guo, Guang; Harris, Kathie Mullan

2015-07-01

Prominent authors in the behavioral genetics tradition have long argued that shared environments do not meaningfully shape intelligence and academic achievement. However, we argue that these conclusions are erroneous due to large violations of the additivity assumption underlying behavioral genetics methods - that sources of genetic and shared and nonshared environmental variance are independent and non-interactive. This is compounded in some cases by the theoretical equation of the effective and objective environments, where the former is defined by whether siblings are made more or less similar, and the latter by whether siblings are equally subject to the environmental characteristic in question. Using monozygotic twin fixed effects models, which compare outcomes among genetically identical pairs, we show that many characteristics of objectively shared environments significantly moderate the effects of nonshared environments on adolescent academic achievement and verbal intelligence, violating the additivity assumption of behavioral genetic methods. Importantly, these effects would be categorized as nonshared environmental influences in standard twin models despite their roots in shared environments. These findings should encourage caution among those who claim that the frequently trivial variance attributed to shared environments in behavioral genetic models means that families, schools, and neighborhoods do not meaningfully influence these outcomes. Copyright © 2015. Published by Elsevier Inc.

Contemporary group estimates adjusted for climatic effects provide a finer definition of the unknown environmental challenges experienced by growing pigs.

PubMed

Guy, S Z Y; Li, L; Thomson, P C; Hermesch, S

2017-12-01

Environmental descriptors derived from mean performances of contemporary groups (CGs) are assumed to capture any known and unknown environmental challenges. The objective of this paper was to obtain a finer definition of the unknown challenges, by adjusting CG estimates for the known climatic effects of monthly maximum air temperature (MaxT), minimum air temperature (MinT) and monthly rainfall (Rain). As the unknown component could include infection challenges, these refined descriptors may help to better model varying responses of sire progeny to environmental infection challenges for the definition of disease resilience. Data were recorded from 1999 to 2013 at a piggery in south-east Queensland, Australia (n = 31,230). Firstly, CG estimates of average daily gain (ADG) and backfat (BF) were adjusted for MaxT, MinT and Rain, which were fitted as splines. In the models used to derive CG estimates for ADG, MaxT and MinT were significant variables. The models that contained these significant climatic variables had CG estimates with a lower variance compared to models without significant climatic variables. Variance component estimates were similar across all models, suggesting that these significant climatic variables accounted for some known environmental variation captured in CG estimates. No climatic variables were significant in the models used to derive the CG estimates for BF. These CG estimates were used to categorize environments. There was no observable sire by environment interaction (Sire×E) for ADG when using the environmental descriptors based on CG estimates on BF. For the environmental descriptors based on CG estimates of ADG, there was significant Sire×E only when MinT was included in the model (p = .01). Therefore, this new definition of the environment, preadjusted by MinT, increased the ability to detect Sire×E. While the unknown challenges captured in refined CG estimates need verification for infection challenges, this may provide a practical approach for the genetic improvement of disease resilience. © 2017 Blackwell Verlag GmbH.

The heritability of psychopathic personality in 14- to 15-year-old twins: a multirater, multimeasure approach.

PubMed

Tuvblad, Catherine; Bezdjian, Serena; Raine, Adrian; Baker, Laura A

2014-09-01

Until now, no study has examined the genetic and environmental influences on psychopathic personality across different raters and method of assessment. Participants were part of a community sample of male and female twins born between 1990 and 1995. The Child Psychopathy Scale and the Antisocial Process Screening Device were administered to the twins and their parents when the twins were 14-15 years old. The Psychopathy Checklist: Youth Version (PCL:YV) was administered and scored by trained testers. Results showed that a 1-factor common pathway model was the best fit for the data. Genetic influences explained 69% of the variance in the latent psychopathic personality factor, while nonshared environmental influences explained 31%. Measurement-specific genetic effects accounted for between 9% and 35% of the total variance in each of the measures, except for PCL:YV, where all genetic influences were in common with the other measures. Measure-specific nonshared environmental influences were found for all measures, explaining between 17% and 56% of the variance. These findings provide further evidence of the heritability in psychopathic personality among adolescents, although these effects vary across the ways in which these traits are measured, in terms of both informant and instrument used. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Genetic and environmental effects on same-sex sexual behavior: a population study of twins in Sweden.

PubMed

Långström, Niklas; Rahman, Qazi; Carlström, Eva; Lichtenstein, Paul

2010-02-01

There is still uncertainty about the relative importance of genes and environments on human sexual orientation. One reason is that previous studies employed self-selected, opportunistic, or small population-based samples. We used data from a truly population-based 2005-2006 survey of all adult twins (20-47 years) in Sweden to conduct the largest twin study of same-sex sexual behavior attempted so far. We performed biometric modeling with data on any and total number of lifetime same-sex sexual partners, respectively. The analyses were conducted separately by sex. Twin resemblance was moderate for the 3,826 studied monozygotic and dizygotic same-sex twin pairs. Biometric modeling revealed that, in men, genetic effects explained .34-.39 of the variance, the shared environment .00, and the individual-specific environment .61-.66 of the variance. Corresponding estimates among women were .18-.19 for genetic factors, .16-.17 for shared environmental, and 64-.66 for unique environmental factors. Although wide confidence intervals suggest cautious interpretation, the results are consistent with moderate, primarily genetic, familial effects, and moderate to large effects of the nonshared environment (social and biological) on same-sex sexual behavior.

Random regression models on Legendre polynomials to estimate genetic parameters for weights from birth to adult age in Canchim cattle.

PubMed

Baldi, F; Albuquerque, L G; Alencar, M M

2010-08-01

The objective of this work was to estimate covariance functions for direct and maternal genetic effects, animal and maternal permanent environmental effects, and subsequently, to derive relevant genetic parameters for growth traits in Canchim cattle. Data comprised 49,011 weight records on 2435 females from birth to adult age. The model of analysis included fixed effects of contemporary groups (year and month of birth and at weighing) and age of dam as quadratic covariable. Mean trends were taken into account by a cubic regression on orthogonal polynomials of animal age. Residual variances were allowed to vary and were modelled by a step function with 1, 4 or 11 classes based on animal's age. The model fitting four classes of residual variances was the best. A total of 12 random regression models from second to seventh order were used to model direct and maternal genetic effects, animal and maternal permanent environmental effects. The model with direct and maternal genetic effects, animal and maternal permanent environmental effects fitted by quadric, cubic, quintic and linear Legendre polynomials, respectively, was the most adequate to describe the covariance structure of the data. Estimates of direct and maternal heritability obtained by multi-trait (seven traits) and random regression models were very similar. Selection for higher weight at any age, especially after weaning, will produce an increase in mature cow weight. The possibility to modify the growth curve in Canchim cattle to obtain animals with rapid growth at early ages and moderate to low mature cow weight is limited.

How Participatory Should Environmental Governance Be? Testing the Applicability of the Vroom-Yetton-Jago Model in Public Environmental Decision-Making

NASA Astrophysics Data System (ADS)

Lührs, Nikolas; Jager, Nicolas W.; Challies, Edward; Newig, Jens

2018-02-01

Public participation is potentially useful to improve public environmental decision-making and management processes. In corporate management, the Vroom-Yetton-Jago normative decision-making model has served as a tool to help managers choose appropriate degrees of subordinate participation for effective decision-making given varying decision-making contexts. But does the model recommend participatory mechanisms that would actually benefit environmental management? This study empirically tests the improved Vroom-Jago version of the model in the public environmental decision-making context. To this end, the key variables of the Vroom-Jago model are operationalized and adapted to a public environmental governance context. The model is tested using data from a meta-analysis of 241 published cases of public environmental decision-making, yielding three main sets of findings: (1) The Vroom-Jago model proves limited in its applicability to public environmental governance due to limited variance in its recommendations. We show that adjustments to key model equations make it more likely to produce meaningful recommendations. (2) We find that in most of the studied cases, public environmental managers (implicitly) employ levels of participation close to those that would have been recommended by the model. (3) An ANOVA revealed that such cases, which conform to model recommendations, generally perform better on stakeholder acceptance and environmental standards of outputs than those that diverge from the model. Public environmental management thus benefits from carefully selected and context-sensitive modes of participation.

How Participatory Should Environmental Governance Be? Testing the Applicability of the Vroom-Yetton-Jago Model in Public Environmental Decision-Making.

PubMed

Lührs, Nikolas; Jager, Nicolas W; Challies, Edward; Newig, Jens

2018-02-01

Public participation is potentially useful to improve public environmental decision-making and management processes. In corporate management, the Vroom-Yetton-Jago normative decision-making model has served as a tool to help managers choose appropriate degrees of subordinate participation for effective decision-making given varying decision-making contexts. But does the model recommend participatory mechanisms that would actually benefit environmental management? This study empirically tests the improved Vroom-Jago version of the model in the public environmental decision-making context. To this end, the key variables of the Vroom-Jago model are operationalized and adapted to a public environmental governance context. The model is tested using data from a meta-analysis of 241 published cases of public environmental decision-making, yielding three main sets of findings: (1) The Vroom-Jago model proves limited in its applicability to public environmental governance due to limited variance in its recommendations. We show that adjustments to key model equations make it more likely to produce meaningful recommendations. (2) We find that in most of the studied cases, public environmental managers (implicitly) employ levels of participation close to those that would have been recommended by the model. (3) An ANOVA revealed that such cases, which conform to model recommendations, generally perform better on stakeholder acceptance and environmental standards of outputs than those that diverge from the model. Public environmental management thus benefits from carefully selected and context-sensitive modes of participation.

Possibility of modifying the growth trajectory in Raeini Cashmere goat.

PubMed

Ghiasi, Heydar; Mokhtari, M S

2018-03-27

The objective of this study was to investigate the possibility of modifying the growth trajectory in Raeini Cashmere goat breed. In total, 13,193 records on live body weight collected from 4788 Raeini Cashmere goats were used. According to Akanke's information criterion (AIC), the sing-trait random regression model included fourth-order Legendre polynomial for direct and maternal genetic effect; maternal and individual permanent environmental effect was the best model for estimating (co)variance components. The matrices of eigenvectors for (co)variances between random regression coefficients of direct additive genetic were used to calculate eigenfunctions, and different eigenvector indices were also constructed. The obtained results showed that the first eigenvalue explained 79.90% of total genetic variance. Therefore, changing the body weights applying the first eigenfunction will be obtained rapidly. Selection based on the first eigenvector will cause favorable positive genetic gains for all body weight considered from birth to 12 months of age. For modifying the growth trajectory in Raeini Cashmere goat, the selection should be based on the second eigenfunction. The second eigenvalue accounted for 14.41% of total genetic variance for body weights that is low in comparison with genetic variance explained by the first eigenvalue. The complex patterns of genetic change in growth trajectory observed under the third and fourth eigenfunction and low amount of genetic variance explained by the third and fourth eigenvalues.

Soil moisture sensitivity of autotrophic and heterotrophic forest floor respiration in boreal xeric pine and mesic spruce forests

NASA Astrophysics Data System (ADS)

Ťupek, Boris; Launiainen, Samuli; Peltoniemi, Mikko; Heikkinen, Jukka; Lehtonen, Aleksi

2016-04-01

Litter decomposition rates of the most process based soil carbon models affected by environmental conditions are linked with soil heterotrophic CO2 emissions and serve for estimating soil carbon sequestration; thus due to the mass balance equation the variation in measured litter inputs and measured heterotrophic soil CO2 effluxes should indicate soil carbon stock changes, needed by soil carbon management for mitigation of anthropogenic CO2 emissions, if sensitivity functions of the applied model suit to the environmental conditions e.g. soil temperature and moisture. We evaluated the response forms of autotrophic and heterotrophic forest floor respiration to soil temperature and moisture in four boreal forest sites of the International Cooperative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) by a soil trenching experiment during year 2015 in southern Finland. As expected both autotrophic and heterotrophic forest floor respiration components were primarily controlled by soil temperature and exponential regression models generally explained more than 90% of the variance. Soil moisture regression models on average explained less than 10% of the variance and the response forms varied between Gaussian for the autotrophic forest floor respiration component and linear for the heterotrophic forest floor respiration component. Although the percentage of explained variance of soil heterotrophic respiration by the soil moisture was small, the observed reduction of CO2 emissions with higher moisture levels suggested that soil moisture response of soil carbon models not accounting for the reduction due to excessive moisture should be re-evaluated in order to estimate right levels of soil carbon stock changes. Our further study will include evaluation of process based soil carbon models by the annual heterotrophic respiration and soil carbon stocks.

Comparison of random regression test-day models for Polish Black and White cattle.

PubMed

Strabel, T; Szyda, J; Ptak, E; Jamrozik, J

2005-10-01

Test-day milk yields of first-lactation Black and White cows were used to select the model for routine genetic evaluation of dairy cattle in Poland. The population of Polish Black and White cows is characterized by small herd size, low level of production, and relatively early peak of lactation. Several random regression models for first-lactation milk yield were initially compared using the "percentage of squared bias" criterion and the correlations between true and predicted breeding values. Models with random herd-test-date effects, fixed age-season and herd-year curves, and random additive genetic and permanent environmental curves (Legendre polynomials of different orders were used for all regressions) were chosen for further studies. Additional comparisons included analyses of the residuals and shapes of variance curves in days in milk. The low production level and early peak of lactation of the breed required the use of Legendre polynomials of order 5 to describe age-season lactation curves. For the other curves, Legendre polynomials of order 3 satisfactorily described daily milk yield variation. Fitting third-order polynomials for the permanent environmental effect made it possible to adequately account for heterogeneous residual variance at different stages of lactation.

Approximation of reliabilities for multiple-trait model with maternal effects.

PubMed

Strabel, T; Misztal, I; Bertrand, J K

2001-04-01

Reliabilities for a multiple-trait maternal model were obtained by combining reliabilities obtained from single-trait models. Single-trait reliabilities were obtained using an approximation that supported models with additive and permanent environmental effects. For the direct effect, the maternal and permanent environmental variances were assigned to the residual. For the maternal effect, variance of the direct effect was assigned to the residual. Data included 10,550 birth weight, 11,819 weaning weight, and 3,617 postweaning gain records of Senepol cattle. Reliabilities were obtained by generalized inversion and by using single-trait and multiple-trait approximation methods. Some reliabilities obtained by inversion were negative because inbreeding was ignored in calculating the inverse of the relationship matrix. The multiple-trait approximation method reduced the bias of approximation when compared with the single-trait method. The correlations between reliabilities obtained by inversion and by multiple-trait procedures for the direct effect were 0.85 for birth weight, 0.94 for weaning weight, and 0.96 for postweaning gain. Correlations for maternal effects for birth weight and weaning weight were 0.96 to 0.98 for both approximations. Further improvements can be achieved by refining the single-trait procedures.

Evidence for the Trait-Impulsivity Etiological Model in a Clinical Sample: Bifactor Structure and Its Relation to Impairment and Environmental Risk.

PubMed

Rodenacker, Klaas; Hautmann, Christopher; Görtz-Dorten, Anja; Döpfner, Manfred

2018-05-01

The trait-impulsivity etiological model assumes that a general factor (trait-impulsivity) underlies attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and other externalizing disorders. We investigated the plausibility of this assumption by testing the factor structure of ADHD and ODD in a bifactor framework for a clinical sample of 1420 children between 6 and 18 years of age (M = 9.99, SD = 3.34; 85% male). Further, the trait-impulsivity etiological model assumes that ODD emerges only if environmental risk factors are present. Our results support the validity of the trait-impulsivity etiological model, as they confirm that ADHD and ODD share a strong general factor of disruptive behavior (DB) in this clinical sample. Furthermore, unlike the subdimensions of ADHD, we found that the specific ODD factor explained as much true score variance as the general DB factor. This suggests that a common scale of ADHD and ODD may prove to be as important as a separate ODD subscale to assess externalizing problems in school-age children. However, all other subscales of ADHD may not explain sufficient true score variance once the impact of the general DB factor has been taken into consideration. In accordance with the trait-impulsivity model, we also showed that all factors, but predominantly the general factor and specific inattention factor, predicted parent-rated impairment, and that predominantly ODD and impulsivity are predicted by environmental risk factors.

40 CFR 52.1390 - Missoula variance provision.

Code of Federal Regulations, 2014 CFR

2014-07-01

... provision. The Missoula City-County Air Pollution Control Program's Chapter X, Variances, which was adopted by the Montana Board of Health and Environmental Sciences on June 28, 1991 and submitted by the... Section 52.1390 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.1390 - Missoula variance provision.

Code of Federal Regulations, 2013 CFR

2013-07-01

... provision. The Missoula City-County Air Pollution Control Program's Chapter X, Variances, which was adopted by the Montana Board of Health and Environmental Sciences on June 28, 1991 and submitted by the... Section 52.1390 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.1390 - Missoula variance provision.

Code of Federal Regulations, 2012 CFR

2012-07-01

... provision. The Missoula City-County Air Pollution Control Program's Chapter X, Variances, which was adopted by the Montana Board of Health and Environmental Sciences on June 28, 1991 and submitted by the... Section 52.1390 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.1390 - Missoula variance provision.

Code of Federal Regulations, 2011 CFR

2011-07-01

... provision. The Missoula City-County Air Pollution Control Program's Chapter X, Variances, which was adopted by the Montana Board of Health and Environmental Sciences on June 28, 1991 and submitted by the... Section 52.1390 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

40 CFR 52.1390 - Missoula variance provision.

Code of Federal Regulations, 2010 CFR

2010-07-01

... provision. The Missoula City-County Air Pollution Control Program's Chapter X, Variances, which was adopted by the Montana Board of Health and Environmental Sciences on June 28, 1991 and submitted by the... Section 52.1390 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...

Visual Basic, Excel-based fish population modeling tool - The pallid sturgeon example

USGS Publications Warehouse

Moran, Edward H.; Wildhaber, Mark L.; Green, Nicholas S.; Albers, Janice L.

2016-02-10

The model presented in this report is a spreadsheet-based model using Visual Basic for Applications within Microsoft Excel (http://dx.doi.org/10.5066/F7057D0Z) prepared in cooperation with the U.S. Army Corps of Engineers and U.S. Fish and Wildlife Service. It uses the same model structure and, initially, parameters as used by Wildhaber and others (2015) for pallid sturgeon. The difference between the model structure used for this report and that used by Wildhaber and others (2015) is that variance is not partitioned. For the model of this report, all variance is applied at the iteration and time-step levels of the model. Wildhaber and others (2015) partition variance into parameter variance (uncertainty about the value of a parameter itself) applied at the iteration level and temporal variance (uncertainty caused by random environmental fluctuations with time) applied at the time-step level. They included implicit individual variance (uncertainty caused by differences between individuals) within the time-step level.The interface developed for the model of this report is designed to allow the user the flexibility to change population model structure and parameter values and uncertainty separately for every component of the model. This flexibility makes the modeling tool potentially applicable to any fish species; however, the flexibility inherent in this modeling tool makes it possible for the user to obtain spurious outputs. The value and reliability of the model outputs are only as good as the model inputs. Using this modeling tool with improper or inaccurate parameter values, or for species for which the structure of the model is inappropriate, could lead to untenable management decisions. By facilitating fish population modeling, this modeling tool allows the user to evaluate a range of management options and implications. The goal of this modeling tool is to be a user-friendly modeling tool for developing fish population models useful to natural resource managers to inform their decision-making processes; however, as with all population models, caution is needed, and a full understanding of the limitations of a model and the veracity of user-supplied parameters should always be considered when using such model output in the management of any species.

Strong genetic overlap between executive functions and intelligence.

PubMed

Engelhardt, Laura E; Mann, Frank D; Briley, Daniel A; Church, Jessica A; Harden, K Paige; Tucker-Drob, Elliot M

2016-09-01

Executive functions (EFs) are cognitive processes that control, monitor, and coordinate more basic cognitive processes. EFs play instrumental roles in models of complex reasoning, learning, and decision making, and individual differences in EFs have been consistently linked with individual differences in intelligence. By middle childhood, genetic factors account for a moderate proportion of the variance in intelligence, and these effects increase in magnitude through adolescence. Genetic influences on EFs are very high, even in middle childhood, but the extent to which these genetic influences overlap with those on intelligence is unclear. We examined genetic and environmental overlap between EFs and intelligence in a racially and socioeconomically diverse sample of 811 twins ages 7 to 15 years (M = 10.91, SD = 1.74) from the Texas Twin Project. A general EF factor representing variance common to inhibition, switching, working memory, and updating domains accounted for substantial proportions of variance in intelligence, primarily via a genetic pathway. General EF continued to have a strong, genetically mediated association with intelligence even after controlling for processing speed. Residual variation in general intelligence was influenced only by shared and nonshared environmental factors, and there remained no genetic variance in general intelligence that was unique of EF. Genetic variance independent of EF did remain, however, in a more specific perceptual reasoning ability. These results provide evidence that genetic influences on general intelligence are highly overlapping with those on EF. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

40 CFR 142.307 - What terms and conditions must be included in a small system variance?

Code of Federal Regulations, 2012 CFR

2012-07-01

... improvements to comply with the small system variance technology, secure an alternative source of water, or... included in a small system variance? 142.307 Section 142.307 Protection of Environment ENVIRONMENTAL... IMPLEMENTATION Variances for Small System Review of Small System Variance Application § 142.307 What terms and...

40 CFR 142.307 - What terms and conditions must be included in a small system variance?

Code of Federal Regulations, 2013 CFR

2013-07-01

... improvements to comply with the small system variance technology, secure an alternative source of water, or... included in a small system variance? 142.307 Section 142.307 Protection of Environment ENVIRONMENTAL... IMPLEMENTATION Variances for Small System Review of Small System Variance Application § 142.307 What terms and...

40 CFR 142.307 - What terms and conditions must be included in a small system variance?

Code of Federal Regulations, 2014 CFR

2014-07-01

... improvements to comply with the small system variance technology, secure an alternative source of water, or... included in a small system variance? 142.307 Section 142.307 Protection of Environment ENVIRONMENTAL... IMPLEMENTATION Variances for Small System Review of Small System Variance Application § 142.307 What terms and...

40 CFR 142.307 - What terms and conditions must be included in a small system variance?

Code of Federal Regulations, 2011 CFR

2011-07-01

... improvements to comply with the small system variance technology, secure an alternative source of water, or... included in a small system variance? 142.307 Section 142.307 Protection of Environment ENVIRONMENTAL... IMPLEMENTATION Variances for Small System Review of Small System Variance Application § 142.307 What terms and...

Examining individual, interpersonal, and environmental influences on children's physical activity levels.

PubMed

Wilk, Piotr; Clark, Andrew F; Maltby, Alana; Smith, Christine; Tucker, Patricia; Gilliland, Jason A

2018-04-01

The purpose of this study was to explore individual-level socio-demographic factors and interpersonal-level factors related to social support, as well as the potential role of neighborhood and school environments that may influence the physical activity (PA) levels of children (ages 9-11). Child and parent questionnaires included individual and interpersonal factors, and PA behaviour. Home postal codes were used to determine the neighbourhood the child resides within, as well as their geographic accessibility to recreation opportunities. The models were assessed using a series of cross-classified random-intercept multi-level regression models as children's PA may be affected by both the school they attend and the neighbourhood in which they live. In the unadjusted model, PA varied significantly across school environments (γ = 0.023; CI: 0.003-0.043), but not across neighbourhoods (γ = 0.007; CI: -0.008 to 0.021). Boys were found to be more active compared to girls (b = 0.183; CI: 0.092-0.275), while the level of PA was lower for children whose fathers achieved post-secondary education (b = - 0.197; CI: -0.376 to 0.018) than for those whose parents completed only high school. The addition of the individual-level correlates did not have a substantial effect on level 2 variances and the level 2 variance associated with school environment remained statistically significant. At the interpersonal level, children's perception of parental support (b = 0.117; CI: 0.091-0.143) and peer support (b = 0.111; CI: 0.079-0.142) were positively related to PA. The level 2 variance for the school environment became statistically non-significant when the interpersonal factors were added to the model. At the environmental level, geographic accessibility did not have a significant association with PA and they did not significantly affect level 1 or 2 variance. As many children do not accrue sufficient levels of PA, identifying modifiable determinants is necessary to develop effective strategies to increase PA.

Sex-specific genetic effects in physical activity: results from a quantitative genetic analysis.

PubMed

Diego, Vincent P; de Chaves, Raquel Nichele; Blangero, John; de Souza, Michele Caroline; Santos, Daniel; Gomes, Thayse Natacha; dos Santos, Fernanda Karina; Garganta, Rui; Katzmarzyk, Peter T; Maia, José A R

2015-08-01

The objective of this study is to present a model to estimate sex-specific genetic effects on physical activity (PA) levels and sedentary behaviour (SB) using three generation families. The sample consisted of 100 families covering three generations from Portugal. PA and SB were assessed via the International Physical Activity Questionnaire short form (IPAQ-SF). Sex-specific effects were assessed by genotype-by-sex interaction (GSI) models and sex-specific heritabilities. GSI effects and heterogeneity were tested in the residual environmental variance. SPSS 17 and SOLAR v. 4.1 were used in all computations. The genetic component for PA and SB domains varied from low to moderate (11% to 46%), when analyzing both genders combined. We found GSI effects for vigorous PA (p = 0.02) and time spent watching television (WT) (p < 0.001) that showed significantly higher additive genetic variance estimates in males. The heterogeneity in the residual environmental variance was significant for moderate PA (p = 0.02), vigorous PA (p = 0.006) and total PA (p = 0.001). Sex-specific heritability estimates were significantly higher in males only for WT, with a male-to-female difference in heritability of 42.5 (95% confidence interval: 6.4, 70.4). Low to moderate genetic effects on PA and SB traits were found. Results from the GSI model show that there are sex-specific effects in two phenotypes, VPA and WT with a stronger genetic influence in males.

Estimating non-genetic and genetic parameters of pre-weaning growth traits in Raini Cashmere goat.

PubMed

Barazandeh, Arsalan; Moghbeli, Sadrollah Molaei; Vatankhah, Mahmood; Mohammadabadi, Mohammadreza

2012-04-01

Data and pedigree information used in the present study were 3,022 records of kids obtained from the breeding station of Raini goat. The studied traits were birth weight (BW), weaning weight (WW), average daily gain from birth to weaning (ADG) and Kleiber ratio at weaning (KR). The model included the fixed effects of sex of kid, type of birth, age of dam, year of birth, month of birth, and age of kid (days) as covariate that had significant effects, and random effects direct additive genetic, maternal additive genetic, maternal permanent environmental effects and residual. (Co) variance components were estimated using univariate and multivariate analysis by WOMBAT software applying four animal models including and ignoring maternal effects. Likelihood ratio test used to determine the most appropriate models. Heritability (h(a)(2)) estimates for BW, WW, ADG, and KR according to suitable model were 0.12 ± 0.05, 0.08 ± 0.06, 0.10 ± 0.06, and 0.06 ± 0.05, respectively. Estimates of the proportion of maternal permanent environmental effect to phenotypic variance (c(2)) were 0.17 ± 0.03, 0.07 ± 0.03, and 0.07 ± 0.03 for BW, WW, and ADG, respectively. Genetic correlations among traits were positive and ranged from 0.53 (BW-ADG) to 1.00 (WW-ADG, WW-KR, and ADG-KR). The maternal permanent environmental correlations between BW-WW, BW-ADG, and WW-ADG were 0.54, 0.48, and 0.99, respectively. Results indicated that maternal effects, especially maternal permanent environmental effects are an important source of variation in pre-weaning growth trait and ignoring those in the model redound incorrect genetic evaluation of kids.

Comparison of random regression models with Legendre polynomials and linear splines for production traits and somatic cell score of Canadian Holstein cows.

PubMed

Bohmanova, J; Miglior, F; Jamrozik, J; Misztal, I; Sullivan, P G

2008-09-01

A random regression model with both random and fixed regressions fitted by Legendre polynomials of order 4 was compared with 3 alternative models fitting linear splines with 4, 5, or 6 knots. The effects common for all models were a herd-test-date effect, fixed regressions on days in milk (DIM) nested within region-age-season of calving class, and random regressions for additive genetic and permanent environmental effects. Data were test-day milk, fat and protein yields, and SCS recorded from 5 to 365 DIM during the first 3 lactations of Canadian Holstein cows. A random sample of 50 herds consisting of 96,756 test-day records was generated to estimate variance components within a Bayesian framework via Gibbs sampling. Two sets of genetic evaluations were subsequently carried out to investigate performance of the 4 models. Models were compared by graphical inspection of variance functions, goodness of fit, error of prediction of breeding values, and stability of estimated breeding values. Models with splines gave lower estimates of variances at extremes of lactations than the model with Legendre polynomials. Differences among models in goodness of fit measured by percentages of squared bias, correlations between predicted and observed records, and residual variances were small. The deviance information criterion favored the spline model with 6 knots. Smaller error of prediction and higher stability of estimated breeding values were achieved by using spline models with 5 and 6 knots compared with the model with Legendre polynomials. In general, the spline model with 6 knots had the best overall performance based upon the considered model comparison criteria.

A New Look at Genetic and Environmental Architecture on Lipids Using Non-Normal Structural Equation Modeling in Male Twins: The NHLBI Twin Study.

PubMed

Wu, Sheng-Hui; Ozaki, Koken; Reed, Terry; Krasnow, Ruth E; Dai, Jun

2017-07-01

This study examined genetic and environmental influences on the lipid concentrations of 1028 male twins using the novel univariate non-normal structural equation modeling (nnSEM) ADCE and ACE models. In the best fitting nnSEM ADCE model that was also better than the nnSEM ACE model, additive genetic factors (A) explained 4%, dominant genetic factors (D) explained 17%, and common (C) and unique (E) environmental factors explained 47% and 33% of the total variance of high-density lipoprotein cholesterol (HDL-C). The percentage of variation explained for other lipids was 0% (A), 30% (D), 34% (C) and 37% (E) for low-density lipoprotein cholesterol (LDL-C); 30, 0, 31 and 39% for total cholesterol; and 0, 31, 12 and 57% for triglycerides. It was concluded that additive and dominant genetic factors simultaneously affected HDL-C concentrations but not other lipids. Common and unique environmental factors influenced concentrations of all lipids.

Environmental drivers of the distribution of nitrogen functional genes at a watershed scale.

PubMed

Tsiknia, Myrto; Paranychianakis, Nikolaos V; Varouchakis, Emmanouil A; Nikolaidis, Nikolaos P

2015-06-01

To date only few studies have dealt with the biogeography of microbial communities at large spatial scales, despite the importance of such information to understand and simulate ecosystem functioning. Herein, we describe the biogeographic patterns of microorganisms involved in nitrogen (N)-cycling (diazotrophs, ammonia oxidizers, denitrifiers) as well as the environmental factors shaping these patterns across the Koiliaris Critical Zone Observatory, a typical Mediterranean watershed. Our findings revealed that a proportion of variance ranging from 40 to 80% of functional genes abundance could be explained by the environmental variables monitored, with pH, soil texture, total organic carbon and potential nitrification rate being identified as the most important drivers. The spatial autocorrelation of N-functional genes ranged from 0.2 to 6.2 km and prediction maps, generated by cokriging, revealed distinct patterns of functional genes. The inclusion of functional genes in statistical modeling substantially improved the proportion of variance explained by the models, a result possibly due to the strong relationships that were identified among microbial groups. Significant relationships were set between functional groups, which were further mediated by land use (natural versus agricultural lands). These relationships, in combination with the environmental variables, allow us to provide insights regarding the ecological preferences of N-functional groups and among them the recently identified clade II of nitrous oxide reducers. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Who is reducing their material consumption and why? A cross-cultural analysis of dematerialization behaviours

PubMed Central

Capstick, Stuart; Nash, Nicholas

2017-01-01

The environmental and economic imperatives to dematerialize economies, or ‘do more with less’, have been established for some years. Yet, to date, little is known about the personal drivers associated with dematerializing. This paper explores the prevalence and profile of those who are taking action to reduce consumption in different cultural contexts (UK and Brazil) and considers influences on dematerialization behaviours. We find that exemplar behaviours (avoiding buying new things and avoiding packaging) are far less common than archetypal environmental behaviours (e.g. recycling), but also that cultural context is important (Brazilians are more likely to reduce their material consumption than people in the UK). We also find that the two dematerialization behaviours are associated with different pro-environmental actions (more radical action versus green consumption, respectively); and have distinct, but overlapping, psychological (e.g. identity) and socio-demographic (e.g. education) predictors. Comparing a more traditional value-identity model of pro-environmental behaviour with a motivation-based (self-determination) model, we find that the latter explains somewhat more variance than the former. However, overall, little variance is explained, suggesting that additional factors at the personal and structural levels are important for determining these consumption behaviours. We conclude by outlining policy implications and avenues for further research. This article is part of the themed issue ‘Material demand reduction’. PMID:28461440

Shared Genetics of Temporomandibular Disorder Pain and Neck Pain: Results of a Twin Study.

PubMed

Visscher, Corine M; Schouten, Maarten J; Ligthart, Lannie; van Houtem, Caroline Mhh; de Jongh, Ad; Boomsma, Dorret I

2018-03-06

(1) To examine the heritability of TMD pain and of neck pain; and (2) to estimate the potential overlap in genetic and environmental factors influencing TMD pain and neck pain. Data from 2,238 adult female twins who completed a survey on TMD pain and neck pain were analyzed. The total variance of TMD pain and neck pain was decomposed into variance attributable to additive genetic effects and nonshared environmental effects. Bivariate structural equation modeling was applied to estimate trait-specific and genetic effects shared between traits. The prevalence of TMD pain and neck pain was 8.6% and 46.8%, respectively, while 6.7% of the twins reported both TMD pain and neck pain. The phenotypic correlation between TMD pain and neck pain, based on a liability threshold model, was 0.43 (95% confidence interval [CI] 0.34 to 0.51). The heritability for TMD was 0.35 (0.17 to 0.51), and for neck pain was 0.33 (0.23 to 0.43). The genetic correlation between TMD pain and neck pain was 0.64 (0.35 to 1.00), and the environmental correlation was 0.32 (0.14 to 0.48). This study shows that variation in TMD pain and neck pain can in part be attributed to genes. The comorbidity between them is partly explained by genes that influence both traits and partly by the same environmental factors.

Random regression analyses using B-spline functions to model growth of Nellore cattle.

PubMed

Boligon, A A; Mercadante, M E Z; Lôbo, R B; Baldi, F; Albuquerque, L G

2012-02-01

The objective of this study was to estimate (co)variance components using random regression on B-spline functions to weight records obtained from birth to adulthood. A total of 82 064 weight records of 8145 females obtained from the data bank of the Nellore Breeding Program (PMGRN/Nellore Brazil) which started in 1987, were used. The models included direct additive and maternal genetic effects and animal and maternal permanent environmental effects as random. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of age (cubic regression) were considered as random covariate. The random effects were modeled using B-spline functions considering linear, quadratic and cubic polynomials for each individual segment. Residual variances were grouped in five age classes. Direct additive genetic and animal permanent environmental effects were modeled using up to seven knots (six segments). A single segment with two knots at the end points of the curve was used for the estimation of maternal genetic and maternal permanent environmental effects. A total of 15 models were studied, with the number of parameters ranging from 17 to 81. The models that used B-splines were compared with multi-trait analyses with nine weight traits and to a random regression model that used orthogonal Legendre polynomials. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most appropriate and parsimonious model to describe the covariance structure of the data. Selection for higher weight, such as at young ages, should be performed taking into account an increase in mature cow weight. Particularly, this is important in most of Nellore beef cattle production systems, where the cow herd is maintained on range conditions. There is limited modification of the growth curve of Nellore cattle with respect to the aim of selecting them for rapid growth at young ages while maintaining constant adult weight.

Aging Trajectories in Different Body Systems Share Common Environmental Etiology: The Healthy Aging Twin Study (HATS).

PubMed

Moayyeri, Alireza; Hart, Deborah J; Snieder, Harold; Hammond, Christopher J; Spector, Timothy D; Steves, Claire J

2016-02-01

Little is known about the extent to which aging trajectories of different body systems share common sources of variance. We here present a large twin study investigating the trajectories of change in five systems: cardiovascular, respiratory, skeletal, morphometric, and metabolic. Longitudinal clinical data were collected on 3,508 female twins in the TwinsUK registry (complete pairs:740 monozygotic (MZ), 986 dizygotic (DZ), mean age at entry 48.9 ± 10.4, range 18-75 years; mean follow-up 10.2 ± 2.8 years, range 4-17.8 years). Panel data on multiple age-related variables were used to estimate biological ages for each individual at each time point, in linear mixed effects models. A weighted average approach was used to combine variables within predefined body system groups. Aging trajectories for each system in each individual were then constructed using linear modeling. Multivariate structural equation modeling of these aging trajectories showed low genetic effects (heritability), ranging from 2% in metabolic aging to 22% in cardiovascular aging. However, we found a significant effect of shared environmental factors on the variations in aging trajectories in cardiovascular (54%), skeletal (34%), morphometric (53%), and metabolic systems (53%). The remainder was due to environmental factors unique to each individual plus error. Multivariate Cholesky decomposition showed that among aging trajectories for various body systems there were significant and substantial correlations between the unique environmental latent factors as well as shared environmental factors. However, there was no evidence for a single common factor for aging. This study, the first of its kind in aging, suggests that diverse organ systems share non-genetic sources of variance for aging trajectories. Confirmatory studies are needed using population-based twin cohorts and alternative methods of handling missing data.

40 CFR 142.302 - Who can issue a small system variance?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 22 2010-07-01 2010-07-01 false Who can issue a small system variance? 142.302 Section 142.302 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER... General Provisions § 142.302 Who can issue a small system variance? A small system variance under this...

Genetic and environmental variance in content dimensions of the MMPI.

PubMed

Rose, R J

1988-08-01

To evaluate genetic and environmental variance in the Minnesota Multiphasic Personality Inventory (MMPI), I studied nine factor scales identified in the first item factor analysis of normal adult MMPIs in a sample of 820 adolescent and young adult co-twins. Conventional twin comparisons documented heritable variance in six of the nine MMPI factors (Neuroticism, Psychoticism, Extraversion, Somatic Complaints, Inadequacy, and Cynicism), whereas significant influence from shared environmental experience was found for four factors (Masculinity versus Femininity, Extraversion, Religious Orthodoxy, and Intellectual Interests). Genetic variance in the nine factors was more evident in results from twin sisters than those of twin brothers, and a developmental-genetic analysis, using hierarchical multiple regressions of double-entry matrixes of the twins' raw data, revealed that in four MMPI factor scales, genetic effects were significantly modulated by age or gender or their interaction during the developmental period from early adolescence to early adulthood.

Most of the genetic covariation between major depressive and alcohol use disorders is explained by trait measures of negative emotionality and behavioral control.

PubMed

Ellingson, J M; Richmond-Rakerd, L S; Statham, D J; Martin, N G; Slutske, W S

2016-10-01

Mental health disorders commonly co-occur, even between conceptually distinct syndromes, such as internalizing and externalizing disorders. The current study investigated whether phenotypic, genetic, and environmental variance in negative emotionality and behavioral control account for the covariation between major depressive disorder (MDD) and alcohol use disorder (AUD). A total of 3623 members of a national twin registry were administered structured diagnostic telephone interviews that included assessments of lifetime histories of MDD and AUD, and were mailed self-report personality questionnaires that assessed stress reactivity (SR) and behavioral control (CON). A series of biometric models were fitted to partition the proportion of covariance between MDD and AUD into SR and CON. A statistically significant proportion of the correlation between MDD and AUD was due to variance specific to SR (men = 0.31, women = 0.27) and CON (men = 0.20, women = 0.19). Further, genetic factors explained a large proportion of this correlation (0.63), with unique environmental factors explaining the rest. SR explained a significant proportion of the genetic (0.33) and environmental (0.23) overlap between MDD and AUD. In contrast, variance specific to CON accounted for genetic overlap (0.32), but not environmental overlap (0.004). In total, SR and CON accounted for approximately 70% of the genetic and 20% of the environmental covariation between MDD and AUD. This is the first study to demonstrate that negative emotionality and behavioral control confer risk for the co-occurrence of MDD and AUD via genetic factors. These findings are consistent with the aims of NIMH's RDoC proposal to elucidate how transdiagnostic risk factors drive psychopathology.

Multivariate Cholesky models of human female fertility patterns in the NLSY.

PubMed

Rodgers, Joseph Lee; Bard, David E; Miller, Warren B

2007-03-01

Substantial evidence now exists that variables measuring or correlated with human fertility outcomes have a heritable component. In this study, we define a series of age-sequenced fertility variables, and fit multivariate models to account for underlying shared genetic and environmental sources of variance. We make predictions based on a theory developed by Udry [(1996) Biosocial models of low-fertility societies. In: Casterline, JB, Lee RD, Foote KA (eds) Fertility in the United States: new patterns, new theories. The Population Council, New York] suggesting that biological/genetic motivations can be more easily realized and measured in settings in which fertility choices are available. Udry's theory, along with principles from molecular genetics and certain tenets of life history theory, allow us to make specific predictions about biometrical patterns across age. Consistent with predictions, our results suggest that there are different sources of genetic influence on fertility variance at early compared to later ages, but that there is only one source of shared environmental influence that occurs at early ages. These patterns are suggestive of the types of gene-gene and gene-environment interactions for which we must account to better understand individual differences in fertility outcomes.

The genetic variance but not the genetic covariance of life-history traits changes towards the north in a time-constrained insect.

PubMed

Sniegula, Szymon; Golab, Maria J; Drobniak, Szymon M; Johansson, Frank

2018-06-01

Seasonal time constraints are usually stronger at higher than lower latitudes and can exert strong selection on life-history traits and the correlations among these traits. To predict the response of life-history traits to environmental change along a latitudinal gradient, information must be obtained about genetic variance in traits and also genetic correlation between traits, that is the genetic variance-covariance matrix, G. Here, we estimated G for key life-history traits in an obligate univoltine damselfly that faces seasonal time constraints. We exposed populations to simulated native temperatures and photoperiods and common garden environmental conditions in a laboratory set-up. Despite differences in genetic variance in these traits between populations (lower variance at northern latitudes), there was no evidence for latitude-specific covariance of the life-history traits. At simulated native conditions, all populations showed strong genetic and phenotypic correlations between traits that shaped growth and development. The variance-covariance matrix changed considerably when populations were exposed to common garden conditions compared with the simulated natural conditions, showing the importance of environmentally induced changes in multivariate genetic structure. Our results highlight the importance of estimating variance-covariance matrixes in environments that mimic selection pressures and not only trait variances or mean trait values in common garden conditions for understanding the trait evolution across populations and environments. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.

Variation in Seed Germination of 134 Common Species on the Eastern Tibetan Plateau: Phylogenetic, Life History and Environmental Correlates

PubMed Central

Xu, Jing; Li, Wenlong; Zhang, Chunhui; Liu, Wei; Du, Guozhen

2014-01-01

Seed germination is a crucial stage in the life history of a species because it represents the pathway from adult to offspring, and it can affect the distribution and abundance of species in communities. In this study, we examined the effects of phylogenetic, life history and environmental factors on seed germination of 134 common species from an alpine/subalpine meadow on the eastern Tibetan Plateau. In one-way ANOVAs, phylogenetic groups (at or above order) explained 13.0% and 25.9% of the variance in germination percentage and mean germination time, respectively; life history attributes, such as seed size, dispersal mode, explained 3.7%, 2.1% of the variance in germination percentage and 6.3%, 8.7% of the variance in mean germination time, respectively; the environmental factors temperature and habitat explained 4.7%, 1.0% of the variance in germination percentage and 13.5%, 1.7% of the variance in mean germination time, respectively. Our results demonstrated that elevated temperature would lead to a significant increase in germination percentage and an accelerated germination. Multi-factorial ANOVAs showed that the three major factors contributing to differences in germination percentage and mean germination time in this alpine/subalpine meadow were phylogenetic attributes, temperature and seed size (explained 10.5%, 4.7% and 1.4% of the variance in germination percentage independently, respectively; and explained 14.9%, 13.5% and 2.7% of the variance in mean germination time independently, respectively). In addition, there were strong associations between phylogenetic group and life history attributes, and between life history attributes and environmental factors. Therefore, germination variation are constrained mainly by phylogenetic inertia in a community, and seed germination variation correlated with phylogeny is also associated with life history attributes, suggesting a role of niche adaptation in the conservation of germination variation within lineages. Meanwhile, selection can maintain the association between germination behavior and the environmental conditions within a lineage. PMID:24893308

Environmental and biological monitoring of benzene during self-service automobile refueling.

PubMed Central

Egeghy, P P; Tornero-Velez, R; Rappaport, S M

2000-01-01

Although automobile refueling represents the major source of benzene exposure among the nonsmoking public, few data are available regarding such exposures and the associated uptake of benzene. We repeatedly measured benzene exposure and uptake (via benzene in exhaled breath) among 39 self-service customers using self-administered monitoring, a technique rarely used to obtain measurements from the general public (130 sets of measurements were obtained). Benzene exposures averaged 2.9 mg/m(3) (SD = 5.8 mg/m(3); median duration = 3 min) with a range of < 0.076-36 mg/m(3), and postexposure breath levels averaged 160 microg/m(3) (SD = 260 microg/m(3)) with a range of < 3.2-1,400 microg/m(3). Log-transformed exposures and breath levels were significantly correlated (r = 0.77, p < 0.0001). We used mixed-effects statistical models to gauge the relative influences of environmental and subject-specific factors on benzene exposure and breath levels and to investigate the importance of various covariates obtained by questionnaire. Model fitting yielded three significant predictors of benzene exposure, namely, fuel octane grade (p = 0.0011), duration of exposure (p = 0.0054), and season of the year (p = 0.032). Likewise, another model yielded three significant predictors of benzene concentration in breath, specifically, benzene exposure (p = 0.0001), preexposure breath concentration (p = 0.0008), and duration of exposure (p = 0.038). Variability in benzene concentrations was remarkable, with 95% of the estimated values falling within a 274-fold range, and was comprised entirely of the within-person component of variance (representing exposures of the same subject at different times of refueling). The corresponding range for benzene concentrations in breath was 41-fold and was comprised primarily of the within-person variance component (74% of the total variance). Our results indicate that environmental rather than interindividual differences are primarily responsible for benzene exposure and uptake during automobile refueling. The study also demonstrates that self-administered monitoring can be efficiently used to measure environmental exposures and biomarkers among the general public. PMID:11133401

Estimation of test-day model (co)variance components across breeds using New Zealand dairy cattle data.

PubMed

Vanderick, S; Harris, B L; Pryce, J E; Gengler, N

2009-03-01

In New Zealand, a large proportion of cows are currently crossbreds, mostly Holstein-Friesians (HF) x Jersey (JE). The genetic evaluation system for milk yields is considering the same additive genetic effects for all breeds. The objective was to model different additive effects according to parental breeds to obtain first estimates of correlations among breed-specific effects and to study the usefulness of this type of random regression test-day model. Estimates of (co)variance components for purebred HF and JE cattle in purebred herds were computed by using a single-breed model. This analysis showed differences between the 2 breeds, with a greater variability in the HF breed. (Co)variance components for purebred HF and JE and crossbred HF x JE cattle were then estimated by using a complete multibreed model in which computations of complete across-breed (co)variances were simplified by correlating only eigenvectors for HF and JE random regressions of the same order as obtained from the single-breed analysis. Parameter estimates differed more strongly than expected between the single-breed and multibreed analyses, especially for JE. This could be due to differences between animals and management in purebred and non-purebred herds. In addition, the model used only partially accounted for heterosis. The multibreed analysis showed additive genetic differences between the HF and JE breeds, expressed as genetic correlations of additive effects in both breeds, especially in linear and quadratic Legendre polynomials (respectively, 0.807 and 0.604). The differences were small for overall milk production (0.926). Results showed that permanent environmental lactation curves were highly correlated across breeds; however, intraherd lactation curves were also affected by the breed-environment interaction. This result may indicate the existence of breed-specific competition effects that vary through the different lactation stages. In conclusion, a multibreed model similar to the one presented could optimally use the environmental and genetic parameters and provide breed-dependent additive breeding values. This model could also be a useful tool to evaluate crossbred dairy cattle populations like those in New Zealand. However, a routine evaluation would still require the development of an improved methodology. It would also be computationally very challenging because of the simultaneous presence of a large number of breeds.

40 CFR 260.33 - Procedures for variances from classification as a solid waste, for variances to be classified as...

Code of Federal Regulations, 2013 CFR

2013-07-01

... classification as a solid waste, for variances to be classified as a boiler, or for non-waste determinations. 260.33 Section 260.33 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) HAZARDOUS WASTE MANAGEMENT SYSTEM: GENERAL Rulemaking Petitions § 260.33 Procedures for variances...

40 CFR 260.33 - Procedures for variances from classification as a solid waste, for variances to be classified as...

Code of Federal Regulations, 2014 CFR

2014-07-01

... classification as a solid waste, for variances to be classified as a boiler, or for non-waste determinations. 260.33 Section 260.33 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) HAZARDOUS WASTE MANAGEMENT SYSTEM: GENERAL Rulemaking Petitions § 260.33 Procedures for variances...

40 CFR 260.33 - Procedures for variances from classification as a solid waste, for variances to be classified as...

Code of Federal Regulations, 2012 CFR

2012-07-01

... classification as a solid waste, for variances to be classified as a boiler, or for non-waste determinations. 260.33 Section 260.33 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) HAZARDOUS WASTE MANAGEMENT SYSTEM: GENERAL Rulemaking Petitions § 260.33 Procedures for variances...

Coping with daily thermal variability: behavioural performance of an ectotherm model in a warming world.

PubMed

Rojas, José M; Castillo, Simón B; Folguera, Guillermo; Abades, Sebastián; Bozinovic, Francisco

2014-01-01

Global climate change poses one of the greatest threats to species persistence. Most analyses of the potential biological impacts have focused on changes in mean temperature, but changes in thermal variance will also impact organisms and populations. We assessed the effects of acclimation to daily variance of temperature on dispersal and exploratory behavior in the terrestrial isopod Porcellio laevis in an open field. Acclimation treatments were 24 ± 0, 24 ± 4 and 24 ± 8 °C. Because the performance of ectotherms relates nonlinearly to temperature, we predicted that animals acclimated to a higher daily thermal variation should minimize the time exposed in the centre of open field, --i.e. increase the linearity of displacements. Consistent with our prediction, isopods acclimated to a thermally variable environment reduce their exploratory behaviour, hypothetically to minimize their exposure to adverse environmental conditions. This scenario as well as the long latency of animals after releases acclimated to variable environments is consistent with this idea. We suggested that to develop more realistic predictions about the biological impacts of climate change, one must consider the interactions between the mean and variance of environmental temperature on animals' performance.

Coping with Daily Thermal Variability: Behavioural Performance of an Ectotherm Model in a Warming World

PubMed Central

Rojas, José M.; Castillo, Simón B.; Folguera, Guillermo; Abades, Sebastián; Bozinovic, Francisco

2014-01-01

Global climate change poses one of the greatest threats to species persistence. Most analyses of the potential biological impacts have focused on changes in mean temperature, but changes in thermal variance will also impact organisms and populations. We assessed the effects of acclimation to daily variance of temperature on dispersal and exploratory behavior in the terrestrial isopod Porcellio laevis in an open field. Acclimation treatments were 24±0, 24±4 and 24±8°C. Because the performance of ectotherms relates nonlinearly to temperature, we predicted that animals acclimated to a higher daily thermal variation should minimize the time exposed in the centre of open field, – i.e. increase the linearity of displacements. Consistent with our prediction, isopods acclimated to a thermally variable environment reduce their exploratory behaviour, hypothetically to minimize their exposure to adverse environmental conditions. This scenario as well as the long latency of animals after releases acclimated to variable environments is consistent with this idea. We suggested that to develop more realistic predictions about the biological impacts of climate change, one must consider the interactions between the mean and variance of environmental temperature on animals' performance. PMID:25207653

Maternal pre-pregnancy weight and externalising behaviour problems in preschool children: a UK-based twin study.

PubMed

Antoniou, Evangelia E; Fowler, Tom; Reed, Keith; Southwood, Taunton R; McCleery, Joseph P; Zeegers, Maurice P

2014-10-14

To estimate the heritability of child behaviour problems and investigate the association between maternal pre-pregnancy overweight and child behaviour problems in a genetically sensitive design. Observational cross-sectional study. The Twins and Multiple Births Association Heritability Study (TAMBAHS) is an online UK-wide volunteer-based study investigating the development of twins from birth until 5 years of age. A total of 443 (16% of the initial registered members) mothers answered questions on pre-pregnancy weight and their twins' internalising and externalising problems using the Child Behavior Checklist and correcting for important covariates including gestational age, twins' birth weight, age and sex, mother's educational level and smoking (before, during and after pregnancy). The heritability of behaviour problems and their association with maternal pre-pregnancy weight. The genetic analysis suggested that genetic and common environmental factors account for most of the variation in externalising disorders (an ACE model was the most parsimonious with genetic factors (A) explaining 46% (95% CI 33% to 60%) of the variance, common environment (C) explaining 42% (95% CI 27% to 54%) and non-shared environmental factors (E) explaining 13% (95% CI 10% to 16%) of the variance. For internalising problems, a CE model was the most parsimonious model with the common environment explaining 51% (95% CI 44% to 58%) of the variance and non-shared environment explaining 49% (95% CI 42% to 56%) of the variance. Moreover, the regression analysis results suggested that children of overweight mothers showed a trend (OR=1.10, 95% CI 0.58% to 2.06) towards being more aggressive and exhibit externalising behaviours compared to children of normal weight mothers. Maternal pre-pregnancy weight may play a role in children's aggressive behaviour. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Shared genetic and environmental influences on early temperament and preschool psychiatric disorders in Hispanic twins

PubMed Central

Silberg, Judy L.; Gillespie, Nathan; Moore, Ashlee A.; Eaves, Lindon J.; Bates, John; Aggen, Steven; Pfister, Elizabeth; Canino, Glorisa

2015-01-01

Objective Despite an increasing recognition that psychiatric disorders can be diagnosed as early as preschool, little is known how early genetic and environmental risk factors contribute to the development of psychiatric disorders during this very early period of development. Method We assessed infant temperament at age 1, and attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and separation anxiety disorder (SAD) at ages 3 through 5 years in a sample of Hispanic twins. Genetic, shared, and non-shared environmental effects were estimated for each temperamental construct and psychiatric disorder using the statistical program MX. Multivariate genetic models were fitted to determine whether the same or different sets of genes and environments account for the co-occurrence between early temperament and preschool psychiatric disorders. Results Additive genetic factors accounted for 61% of the variance in ADHD, 21% in ODD, and 28% in SAD. Shared environmental factors accounted for 34% of the variance in ODD and 15% of SAD. The genetic influence on difficult temperament was significantly associated with preschool ADHD, SAD, and ODD. The association between ODD and SAD was due to both genetic and family environmental factors. The temperamental trait of resistance to control was entirely accounted for by the shared family environment. Conclusions There are different genetic and family environmental pathways between infant temperament and psychiatric diagnoses in this sample of Puerto Rican preschool age children. PMID:25728588

Shared genetic and environmental influences on early temperament and preschool psychiatric disorders in Hispanic twins.

PubMed

Silberg, Judy L; Gillespie, Nathan; Moore, Ashlee A; Eaves, Lindon J; Bates, John; Aggen, Steven; Pfister, Elizabeth; Canino, Glorisa

2015-04-01

Despite an increasing recognition that psychiatric disorders can be diagnosed as early as preschool, little is known how early genetic and environmental risk factors contribute to the development of psychiatric disorders during this very early period of development. We assessed infant temperament at age 1, and attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and separation anxiety disorder (SAD) at ages 3 through 5 years in a sample of Hispanic twins. Genetic, shared, and non-shared environmental effects were estimated for each temperamental construct and psychiatric disorder using the statistical program MX. Multivariate genetic models were fitted to determine whether the same or different sets of genes and environments account for the co-occurrence between early temperament and preschool psychiatric disorders. Additive genetic factors accounted for 61% of the variance in ADHD, 21% in ODD, and 28% in SAD. Shared environmental factors accounted for 34% of the variance in ODD and 15% of SAD. The genetic influence on difficult temperament was significantly associated with preschool ADHD, SAD, and ODD. The association between ODD and SAD was due to both genetic and family environmental factors. The temperamental trait of resistance to control was entirely accounted for by the shared family environment. There are different genetic and family environmental pathways between infant temperament and psychiatric diagnoses in this sample of Puerto Rican preschool age children.

The Environmental Reward Observation Scale (EROS): development, validity, and reliability.

PubMed

Armento, Maria E A; Hopko, Derek R

2007-06-01

Researchers acknowledge a strong association between the frequency and duration of environmental reward and affective mood states, particularly in relation to the etiology, assessment, and treatment of depression. Given behavioral theories that outline environmental reward as a strong mediator of affect and the unavailability of an efficient, reliable, and valid self-report measure of environmental reward, we developed the Environmental Reward Observation Scale (EROS) and examined its psychometric properties. In Experiment 1, exploratory factor analysis supported a unidimensional 10-item measure with strong internal consistency and test-retest reliability. When administered to a replication sample, confirmatory factor analysis suggested an excellent fit to the 1-factor model and convergent/discriminant validity data supported the construct validity of the EROS. In Experiment 2, further support for the convergent validity of the EROS was obtained via moderate correlations with the Pleasant Events Schedule (PES; MacPhillamy & Lewinsohn, 1976). In Experiment 3, hierarchical regression supported the ecological validity of the EROS toward predicting daily diary reports of time spent in highly rewarding behaviors and activities. Above and beyond variance accounted for by depressive symptoms (BDI), the EROS was associated with significant incremental variance in accounting for time spent in both low and high reward behaviors. The EROS may represent a brief, reliable and valid measure of environmental reward that may improve the psychological assessment of negative mood states such as clinical depression.

Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks

NASA Astrophysics Data System (ADS)

Mishra, U.; Riley, W. J.

2015-01-01

The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing heterogeneity of terrestrial hydrological and biogeochemical processes in earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a dataset with reasonable fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales (s = 100, 200, 500 m, 1, 2, 5, 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions (R2 = 0.83-0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98% of variability in the variance of SOC stocks. We found moderately-accurate linear relationships between mean and higher-order moments of predicted SOC stocks (R2 ~ 0.55-0.63). Current ESMs operate at coarse spatial scales (50-100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks can improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.

Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks

NASA Astrophysics Data System (ADS)

Mishra, U.; Riley, W. J.

2015-07-01

The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing the heterogeneity of terrestrial hydrological and biogeochemical processes in Earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a data set with reasonable fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales (s = 100, 200, and 500 m and 1, 2, 5, and 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions (R2 = 0.83-0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 m to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98 % of variability in the variance of SOC stocks. We found moderately accurate linear relationships between mean and higher-order moments of predicted SOC stocks (R2 ∼ 0.55-0.63). Current ESMs operate at coarse spatial scales (50-100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks could improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.

Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks

DOE PAGES

Mishra, U.; Riley, W. J.

2015-07-02

The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing the heterogeneity of terrestrial hydrological and biogeochemical processes in Earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a data setmore » with reasonable fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales ( s = 100, 200, and 500 m and 1, 2, 5, and 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions ( R 2 = 0.83–0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 m to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98 % of variability in the variance of SOC stocks. We found moderately accurate linear relationships between mean and higher-order moments of predicted SOC stocks ( R 2 ∼ 0.55–0.63). Current ESMs operate at coarse spatial scales (50–100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks could improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.« less

Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks

DOE PAGES

Mishra, U.; Riley, W. J.

2015-01-01

The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing heterogeneity of terrestrial hydrological and biogeochemical processes in earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a dataset with reasonablemore » fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales ( s = 100, 200, 500 m, 1, 2, 5, 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions ( R 2 = 0.83–0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98% of variability in the variance of SOC stocks. We found moderately-accurate linear relationships between mean and higher-order moments of predicted SOC stocks ( R 2 ~ 0.55–0.63). Current ESMs operate at coarse spatial scales (50–100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks can improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.« less

40 CFR 142.41 - Variance request.

Code of Federal Regulations, 2011 CFR

2011-07-01

....41 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS IMPLEMENTATION Variances Issued by the Administrator Under Section 1415(a) of the Act § 142.41 Variance request. A supplier of water may request the granting of a...

40 CFR 142.41 - Variance request.

Code of Federal Regulations, 2010 CFR

2010-07-01

....41 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS IMPLEMENTATION Variances Issued by the Administrator Under Section 1415(a) of the Act § 142.41 Variance request. A supplier of water may request the granting of a...

40 CFR 142.301 - What is a small system variance?

Code of Federal Regulations, 2011 CFR

2011-07-01

....301 Section 142.301 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS IMPLEMENTATION Variances for Small System... procedures and criteria for obtaining these variances. The regulations in this subpart shall take effect on...

40 CFR 142.301 - What is a small system variance?

Code of Federal Regulations, 2012 CFR

2012-07-01

....301 Section 142.301 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS IMPLEMENTATION Variances for Small System... procedures and criteria for obtaining these variances. The regulations in this subpart shall take effect on...

40 CFR 142.301 - What is a small system variance?

Code of Federal Regulations, 2010 CFR

2010-07-01

....301 Section 142.301 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS IMPLEMENTATION Variances for Small System... procedures and criteria for obtaining these variances. The regulations in this subpart shall take effect on...

40 CFR 142.301 - What is a small system variance?

Code of Federal Regulations, 2013 CFR

2013-07-01

....301 Section 142.301 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS IMPLEMENTATION Variances for Small System... procedures and criteria for obtaining these variances. The regulations in this subpart shall take effect on...

40 CFR 142.301 - What is a small system variance?

Code of Federal Regulations, 2014 CFR

2014-07-01

....301 Section 142.301 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS IMPLEMENTATION Variances for Small System... procedures and criteria for obtaining these variances. The regulations in this subpart shall take effect on...

Making (up) the grade? estimating the genetic and environmental influences of discrepancies between self-reported grades and official GPA scores.

PubMed

Schwartz, Joseph A; Beaver, Kevin M

2015-05-01

Academic achievement has been found to have a pervasive and substantial impact on a wide range of developmental outcomes and has also been implicated in the critical transition from adolescence into early adulthood. Previous research has revealed that self-reported grades tend to diverge from official transcript grade point average (GPA) scores, with students being more likely to report inflated scores. Making use of a sample of monozygotic twin (N = 282 pairs), dizygotic twin (N = 441 pairs), and full sibling (N = 1,757 pairs) pairs from the National Longitudinal Study of Adolescent Health (Add Health; 65 % White; 50 % male; mean age = 16.14), the current study is the first to investigate the role that genetic and environmental factors play in misreporting grade information. A comparison between self-reported GPA (mean score of 2.86) and official transcript GPA scores (mean score of 2.44) revealed that self-reported scores were approximately one-half letter grade greater than official scores. Liability threshold models revealed that additive genetic influences explained between 40 and 63 % of the variance in reporting inflated grades and correctly reporting GPA, with the remaining variance explained by the nonshared environment. Conversely, 100 % of the variance in reporting deflated grade information was explained by nonshared environmental influences. In an effort to identify specific nonshared environmental influences on reporting accuracy, multivariate models that adequately control for genetic influences were estimated and revealed that siblings with lower transcript GPA scores were significantly less likely to correctly report their GPA and significantly more likely to report inflated GPA scores. Additional analyses revealed that verbal IQ and self-control were not significantly associated with self-reported GPA accuracy after controlling for genetic influences. These findings indicate that previous studies that implicate verbal IQ and self-control as significant predictors of misreporting grade information may have been the result of model misspecification and genetic confounding. The findings from the current study indicate that genetic influences play a crucial role in the accuracy in which grade information is reported, but that nonshared environmental influences also play a significant role in specific circumstances. The theoretical and methodological implications of the results are discussed.

Different Slopes for Different Folks: Genetic Influences on Growth in Delinquent Peer Association and Delinquency During Adolescence.

PubMed

Connolly, Eric J; Schwartz, Joseph A; Nedelec, Joseph L; Beaver, Kevin M; Barnes, J C

2015-07-01

An extensive line of research has identified delinquent peer association as a salient environmental risk factor for delinquency, especially during adolescence. While previous research has found moderate-to-strong associations between exposure to delinquent peers and a variety of delinquent behaviors, comparatively less scholarship has focused on the genetic architecture of this association over the course of adolescence. Using a subsample of kinship pairs (N = 2379; 52% female) from the National Longitudinal Survey of Youth-Child and Young Adult Supplement (CNLSY), the present study examined the extent to which correlated individual differences in starting levels and developmental growth in delinquent peer pressure and self-reported delinquency were explained by additive genetic and environmental influences. Results from a series of biometric growth models revealed that 37% of the variance in correlated growth between delinquent peer pressure and self-reported delinquency was explained by additive genetic effects, while nonshared environmental effects accounted for the remaining 63% of the variance. Implications of these findings for interpreting the nexus between peer effects and adolescent delinquency are discussed.

Two dynamic regimes in the human gut microbiome

PubMed Central

Smillie, Chris S.; Alm, Eric J.

2017-01-01

The gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial time series is non-autoregressive. In addition, we show how community state-clustering is flawed when it comes to characterizing within-host dynamics and that more continuous methods are required. Most organisms exhibited stable, mean-reverting behavior suggestive of fixed carrying capacities and abundant taxa were largely shared across individuals. This mean-reverting behavior allowed us to apply sparse vector autoregression (sVAR)—a multivariate method developed for econometrics—to model the autoregressive component of gut community dynamics. We find a strong phylogenetic signal in the non-autoregressive co-variance from our sVAR model residuals, which suggests niche filtering. We show how changes in diet are also non-autoregressive and that Operational Taxonomic Units strongly correlated with dietary variables have much less of an autoregressive component to their variance, which suggests that diet is a major driver of microbial dynamics. Autoregressive variance appears to be driven by multi-day recovery from frequent facultative anaerobe blooms, which may be driven by fluctuations in luminal redox. Overall, we identify two dynamic regimes within the human gut microbiota: one likely driven by external environmental fluctuations, and the other by internal processes. PMID:28222117

Two dynamic regimes in the human gut microbiome.

PubMed

Gibbons, Sean M; Kearney, Sean M; Smillie, Chris S; Alm, Eric J

2017-02-01

The gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial time series is non-autoregressive. In addition, we show how community state-clustering is flawed when it comes to characterizing within-host dynamics and that more continuous methods are required. Most organisms exhibited stable, mean-reverting behavior suggestive of fixed carrying capacities and abundant taxa were largely shared across individuals. This mean-reverting behavior allowed us to apply sparse vector autoregression (sVAR)-a multivariate method developed for econometrics-to model the autoregressive component of gut community dynamics. We find a strong phylogenetic signal in the non-autoregressive co-variance from our sVAR model residuals, which suggests niche filtering. We show how changes in diet are also non-autoregressive and that Operational Taxonomic Units strongly correlated with dietary variables have much less of an autoregressive component to their variance, which suggests that diet is a major driver of microbial dynamics. Autoregressive variance appears to be driven by multi-day recovery from frequent facultative anaerobe blooms, which may be driven by fluctuations in luminal redox. Overall, we identify two dynamic regimes within the human gut microbiota: one likely driven by external environmental fluctuations, and the other by internal processes.

Random regression analyses using B-splines functions to model growth from birth to adult age in Canchim cattle.

PubMed

Baldi, F; Alencar, M M; Albuquerque, L G

2010-12-01

The objective of this work was to estimate covariance functions using random regression models on B-splines functions of animal age, for weights from birth to adult age in Canchim cattle. Data comprised 49,011 records on 2435 females. The model of analysis included fixed effects of contemporary groups, age of dam as quadratic covariable and the population mean trend taken into account by a cubic regression on orthogonal polynomials of animal age. Residual variances were modelled through a step function with four classes. The direct and maternal additive genetic effects, and animal and maternal permanent environmental effects were included as random effects in the model. A total of seventeen analyses, considering linear, quadratic and cubic B-splines functions and up to seven knots, were carried out. B-spline functions of the same order were considered for all random effects. Random regression models on B-splines functions were compared to a random regression model on Legendre polynomials and with a multitrait model. Results from different models of analyses were compared using the REML form of the Akaike Information criterion and Schwarz' Bayesian Information criterion. In addition, the variance components and genetic parameters estimated for each random regression model were also used as criteria to choose the most adequate model to describe the covariance structure of the data. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most adequate to describe the covariance structure of the data. Random regression models using B-spline functions as base functions fitted the data better than Legendre polynomials, especially at mature ages, but higher number of parameters need to be estimated with B-splines functions. © 2010 Blackwell Verlag GmbH.

40 CFR 260.33 - Procedures for variances from classification as a solid waste, for variances to be classified as...

Code of Federal Regulations, 2011 CFR

2011-07-01

... classification as a solid waste, for variances to be classified as a boiler, or for non-waste determinations. 260.33 Section 260.33 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES... from classification as a solid waste, for variances to be classified as a boiler, or for non-waste...

40 CFR 260.33 - Procedures for variances from classification as a solid waste, for variances to be classified as...

Code of Federal Regulations, 2010 CFR

2010-07-01

... classification as a solid waste, for variances to be classified as a boiler, or for non-waste determinations. 260.33 Section 260.33 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES... from classification as a solid waste, for variances to be classified as a boiler, or for non-waste...

Genetic Variance in Homophobia: Evidence from Self- and Peer Reports.

PubMed

Zapko-Willmes, Alexandra; Kandler, Christian

2018-01-01

The present twin study combined self- and peer assessments of twins' general homophobia targeting gay men in order to replicate previous behavior genetic findings across different rater perspectives and to disentangle self-rater-specific variance from common variance in self- and peer-reported homophobia (i.e., rater-consistent variance). We hypothesized rater-consistent variance in homophobia to be attributable to genetic and nonshared environmental effects, and self-rater-specific variance to be partially accounted for by genetic influences. A sample of 869 twins and 1329 peer raters completed a seven item scale containing cognitive, affective, and discriminatory homophobic tendencies. After correction for age and sex differences, we found most of the genetic contributions (62%) and significant nonshared environmental contributions (16%) to individual differences in self-reports on homophobia to be also reflected in peer-reported homophobia. A significant genetic component, however, was self-report-specific (38%), suggesting that self-assessments alone produce inflated heritability estimates to some degree. Different explanations are discussed.

Genetic parameters for test-day yield of milk, fat and protein in buffaloes estimated by random regression models.

PubMed

Aspilcueta-Borquis, Rúsbel R; Araujo Neto, Francisco R; Baldi, Fernando; Santos, Daniel J A; Albuquerque, Lucia G; Tonhati, Humberto

2012-08-01

The test-day yields of milk, fat and protein were analysed from 1433 first lactations of buffaloes of the Murrah breed, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, born between 1985 and 2007. For the test-day yields, 10 monthly classes of lactation days were considered. The contemporary groups were defined as the herd-year-month of the test day. Random additive genetic, permanent environmental and residual effects were included in the model. The fixed effects considered were the contemporary group, number of milkings (1 or 2 milkings), linear and quadratic effects of the covariable cow age at calving and the mean lactation curve of the population (modelled by third-order Legendre orthogonal polynomials). The random additive genetic and permanent environmental effects were estimated by means of regression on third- to sixth-order Legendre orthogonal polynomials. The residual variances were modelled with a homogenous structure and various heterogeneous classes. According to the likelihood-ratio test, the best model for milk and fat production was that with four residual variance classes, while a third-order Legendre polynomial was best for the additive genetic effect for milk and fat yield, a fourth-order polynomial was best for the permanent environmental effect for milk production and a fifth-order polynomial was best for fat production. For protein yield, the best model was that with three residual variance classes and third- and fourth-order Legendre polynomials were best for the additive genetic and permanent environmental effects, respectively. The heritability estimates for the characteristics analysed were moderate, varying from 0·16±0·05 to 0·29±0·05 for milk yield, 0·20±0·05 to 0·30±0·08 for fat yield and 0·18±0·06 to 0·27±0·08 for protein yield. The estimates of the genetic correlations between the tests varied from 0·18±0·120 to 0·99±0·002; from 0·44±0·080 to 0·99±0·004; and from 0·41±0·080 to 0·99±0·004, for milk, fat and protein production, respectively, indicating that whatever the selection criterion used, indirect genetic gains can be expected throughout the lactation curve.

Genomic Prediction Accounting for Residual Heteroskedasticity

PubMed Central

Ou, Zhining; Tempelman, Robert J.; Steibel, Juan P.; Ernst, Catherine W.; Bates, Ronald O.; Bello, Nora M.

2015-01-01

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. PMID:26564950

An examination of environmental and genetic contributions to the determinants of suicidal behavior among male twins

PubMed Central

Smith, April Rose; Ribeiro, Jessica; Mikolajewski, Amy; Taylor, Jeanette; Joiner, Thomas; Iacono, William G.

2012-01-01

The purpose of the present study was to examine the relative association of genetic and environmental factors with individual differences in each of the proximal, jointly necessary, and sufficient causes for suicidal behavior, according to the Interpersonal-Psychological Theory of Suicide (IPTS; Joiner, 2005). We examined data on derived scales measuring acquired capability, belongingness, and burdensomeness (the determinants of suicidal behavior, according to theory) from 348 adolescent male twins. Univariate biometrical models were used to estimate the magnitude of additive genetic (A), non-additive genetic (D), shared environmental (C), and nonshared environmental (E) effects associated with the variance in acquired capability, belongingness, and burdensomeness. The best fitting model for the acquired capability allowed for additive genetic and environmental effects, whereas the best fitting model for burdensomeness and belongingness allowed for shared and nonshared environmental effects. The present research extends prior work by specifying the environmental and genetic contributions to the components of the IPTS, and our findings suggest that belongingness and burdensomeness may be more appropriate targets for clinical intervention than acquired capability as these factors may be more malleable or amenable to change. PMID:22417928

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mishra, U.; Riley, W. J.

The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing the heterogeneity of terrestrial hydrological and biogeochemical processes in Earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a data setmore » with reasonable fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales ( s = 100, 200, and 500 m and 1, 2, 5, and 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions ( R 2 = 0.83–0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 m to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98 % of variability in the variance of SOC stocks. We found moderately accurate linear relationships between mean and higher-order moments of predicted SOC stocks ( R 2 ∼ 0.55–0.63). Current ESMs operate at coarse spatial scales (50–100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks could improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.« less

Genetic evolution, plasticity, and bet-hedging as adaptive responses to temporally autocorrelated fluctuating selection: A quantitative genetic model.

PubMed

Tufto, Jarle

2015-08-01

Adaptive responses to autocorrelated environmental fluctuations through evolution in mean reaction norm elevation and slope and an independent component of the phenotypic variance are analyzed using a quantitative genetic model. Analytic approximations expressing the mutual dependencies between all three response modes are derived and solved for the joint evolutionary outcome. Both genetic evolution in reaction norm elevation and plasticity are favored by slow temporal fluctuations, with plasticity, in the absence of microenvironmental variability, being the dominant evolutionary outcome for reasonable parameter values. For fast fluctuations, tracking of the optimal phenotype through genetic evolution and plasticity is limited. If residual fluctuations in the optimal phenotype are large and stabilizing selection is strong, selection then acts to increase the phenotypic variance (bet-hedging adaptive). Otherwise, canalizing selection occurs. If the phenotypic variance increases with plasticity through the effect of microenvironmental variability, this shifts the joint evolutionary balance away from plasticity in favor of genetic evolution. If microenvironmental deviations experienced by each individual at the time of development and selection are correlated, however, more plasticity evolves. The adaptive significance of evolutionary fluctuations in plasticity and the phenotypic variance, transient evolution, and the validity of the analytic approximations are investigated using simulations. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

Individual Differences in Exercise Behavior: Stability and Change in Genetic and Environmental Determinants From Age 7 to 18.

PubMed

Huppertz, Charlotte; Bartels, Meike; de Zeeuw, Eveline L; van Beijsterveldt, Catharina E M; Hudziak, James J; Willemsen, Gonneke; Boomsma, Dorret I; de Geus, Eco J C

2016-09-01

Exercise behavior during leisure time is a major source of health-promoting physical activity and moderately tracks across childhood and adolescence. This study aims to investigate the absolute and relative contribution of genes and the environment to variance in exercise behavior from age 7 to 18, and to elucidate the stability and change of genetic and shared environmental factors that underlie this behavior. The Netherlands Twin Register collected data on exercise behavior in twins aged approximately 7, 10, 12, 14, 16 and 18 years (N = 27,332 twins; 48 % males; 47 % with longitudinal assessments). Three exercise categories (low, middle, high) were analyzed by means of liability threshold models. First, a univariate model was fitted using the largest available cross-sectional dataset with linear and quadratic effects of age as modifiers on the means and variance components. Second, a simplex model was fitted on the longitudinal dataset. Heritability was low in 7-year-olds (14 % in males and 12 % in females), but gradually increased up to age 18 (79 % in males and 49 % in females), whereas the initially substantial relative influence of the shared environment decreased with age (from 80 to 4 % in males and from 80 to 19 % in females). This decrease was due to a large increase in the genetic variance. The longitudinal model showed the genetic effects in males to be largely stable and to accumulate from childhood to late adolescence, whereas in females, they were marked by both transmission and innovation at all ages. The shared environmental effects tended to be less stable in both males and females. In sum, the clear age-moderation of exercise behavior implies that family-based interventions might be useful to increase this behavior in children, whereas individual-based interventions might be better suited for adolescents. We showed that some determinants of individual differences in exercise behavior are stable across childhood and youth, whereas others come into play at specific ages. In view of the many benefits of regular exercise, identifying these determinants at specific ages should be a public health priority.

Genetic Architecture of Micro-Environmental Plasticity in Drosophila melanogaster.

PubMed

Morgante, Fabio; Sørensen, Peter; Sorensen, Daniel A; Maltecca, Christian; Mackay, Trudy F C

2015-05-06

Individuals of the same genotype do not have the same phenotype for quantitative traits when reared under common macro-environmental conditions, a phenomenon called micro-environmental plasticity. Genetic variation in micro-environmental plasticity is assumed in models of the evolution of phenotypic variance, and is important in applied breeding and personalized medicine. Here, we quantified genetic variation for micro-environmental plasticity for three quantitative traits in the inbred, sequenced lines of the Drosophila melanogaster Genetic Reference Panel. We found substantial genetic variation for micro-environmental plasticity for all traits, with broad sense heritabilities of the same magnitude or greater than those of trait means. Micro-environmental plasticity is not correlated with residual segregating variation, is trait-specific, and has genetic correlations with trait means ranging from zero to near unity. We identified several candidate genes associated with micro-environmental plasticity of startle response, including Drosophila Hsp90, setting the stage for future genetic dissection of this phenomenon.

A twin study exploring the association between childhood emotional and behaviour problems and specific psychotic experiences in a community sample of adolescents.

PubMed

Shakoor, Sania; McGuire, Philip; Cardno, Alastair G; Freeman, Daniel; Ronald, Angelica

2018-05-01

Childhood emotional and behaviour problems are antecedents for later psychopathology. This study investigated genetic and environmental influences shaping the longitudinal association between childhood emotional and behaviour problems and specific PEs. In a community-based twin sample, parents reported on emotional and behaviour problems when twins were ages 7 and 12 years. At age 16 years, specific PEs were measured using self-reports and parent reports. Structural equation model-fitting was conducted. Childhood emotional and behaviour problems were significantly associated with paranoia, cognitive disorganisation and parent-rated negative symptoms in adolescence (mean r = .15-.38), and to a lesser extent with hallucinations, grandiosity and anhedonia (mean r = .04-.12). Genetic influences on childhood emotional and behaviour problems explained significant proportions of variance in adolescent paranoia (4%), cognitive disorganisation (8%) and parent-rated negative symptoms (3%). Unique environmental influences on childhood emotional and behaviour problems explained ≤1% of variance in PEs. Common environmental influences were only relevant for the relationship between childhood emotional and behaviour problems and parent-rated negative symptoms (explaining 28% of variance) and are partly due to correlated rater effects. Childhood emotional and behaviour problems are significantly, if weakly, associated with adolescent PEs. These associations are driven in part by common genetic influences underlying both emotional and behaviour problems and PEs. However, psychotic experiences in adolescence are largely influenced by genetic and environmental factors that are independent of general childhood emotional and behaviour problems, suggesting they are not merely an extension of childhood emotional and behaviour problems. © 2017 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.

Genetic and environmental influences on Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5) maladaptive personality traits and their connections with normative personality traits.

PubMed

Wright, Zara E; Pahlen, Shandell; Krueger, Robert F

2017-05-01

The Diagnostic and Statistical Manual for Mental Disorders-Fifth Edition (DSM-5) proposes an alternative model for personality disorders, which includes maladaptive-level personality traits. These traits can be operationalized by the Personality Inventory for the DSM-5 (PID-5). Although there has been extensive research on genetic and environmental influences on normative level personality, the heritability of the DSM-5 traits remains understudied. The present study addresses this gap in the literature by assessing traits indexed by the PID-5 and the International Personality Item Pool NEO (IPIP-NEO) in adult twins (N = 1,812 individuals). Research aims include (a) replicating past findings of the heritability of normative level personality as measured by the IPIP-NEO as a benchmark for studying maladaptive level traits, (b) ascertaining univariate heritability estimates of maladaptive level traits as measured by the PID-5, (c) establishing how much variation in personality pathology can be attributed to the same genetic components affecting variation in normative level personality, and (d) determining residual variance in personality pathology domains after variance attributable to genetic and environmental components of general personality has been removed. Results revealed that PID-5 traits reflect similar levels of heritability to that of IPIP-NEO traits. Further, maladaptive and normative level traits that correlate at the phenotypic level also correlate at the genotypic level, indicating overlapping genetic components contribute to variance in both. Nevertheless, we also found evidence for genetic and environmental components unique to maladaptive level personality traits, not shared with normative level traits. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Combat Exposure Severity as a Moderator of Genetic and Environmental Liability to Posttraumatic Stress Disorder

PubMed Central

Wolf, Erika J.; Mitchell, Karen S.; Koenen, Karestan C.; Miller, Mark W.

2014-01-01

Background Twin studies of veterans and adults suggest that approximately 30–46% of the variance in posttraumatic stress disorder (PTSD) is attributable to genetic factors. The remaining variance is attributable to the non-shared environment, which, by definition, includes combat exposure. This study used a gene by measured environment twin design to examine if the effect of genetic and environmental factors that contribute to the etiology PTSD were dependent on level of combat exposure. Methods The sample was drawn from the Vietnam Era Twin Registry and included 620 male-male twin pairs who served in the U.S. Military in South East Asia during the Vietnam War era. Analyses were based on data from a clinical diagnostic interview of lifetime PTSD symptoms and a self-report measure of combat exposure. Results Biometric modeling revealed that the effect of genetic and non-shared environment factors on PTSD varied as a function of level of combat exposure such that the association between these factors and PTSD was stronger at higher levels of combat exposure. Conclusions Combat exposure may act as a catalyst that augments the impact of hereditary and environmental contributions to PTSD. Individuals with the greatest exposure to combat trauma were at increased risk for PTSD as a function of both genetic and other environmental factors. Additional work is needed to determine the biological and environmental mechanisms driving these associations. PMID:24001428

Combat exposure severity as a moderator of genetic and environmental liability to post-traumatic stress disorder.

PubMed

Wolf, E J; Mitchell, K S; Koenen, K C; Miller, M W

2014-05-01

Twin studies of veterans and adults suggest that approximately 30-46% of the variance in post-traumatic stress disorder (PTSD) is attributable to genetic factors. The remaining variance is attributable to the non-shared environment, which, by definition, includes combat exposure. This study used a gene by measured environment twin design to determine whether the effects of genetic and environmental factors that contribute to the etiology of PTSD are dependent on the level of combat exposure. The sample was drawn from the Vietnam Era Twin Registry (VETR) and included 620 male-male twin pairs who served in the US Military in South East Asia during the Vietnam War era. Analyses were based on data from a clinical diagnostic interview of lifetime PTSD symptoms and a self-report measure of combat exposure. Biometric modeling revealed that the effects of genetic and non-shared environment factors on PTSD varied as a function of level of combat exposure such that the association between these factors and PTSD was stronger at higher levels of combat exposure. Combat exposure may act as a catalyst that augments the impact of hereditary and environmental contributions to PTSD. Individuals with the greatest exposure to combat trauma were at increased risk for PTSD as a function of both genetic and environmental factors. Additional work is needed to determine the biological and environmental mechanisms driving these associations.

Multilevel modelling of somatotype components: the Portuguese sibling study on growth, fitness, lifestyle and health.

PubMed

Pereira, Sara; Katzmarzyk, Peter T; Gomes, Thayse Natacha; Souza, Michele; Chaves, Raquel N; Santos, Fernanda K Dos; Santos, Daniel; Hedeker, Donald; Maia, José A R

2017-06-01

Somatotype is a complex trait influenced by different genetic and environmental factors as well as by other covariates whose effects are still unclear. To (1) estimate siblings' resemblance in their general somatotype; (2) identify sib-pair (brother-brother (BB), sister-sister (SS), brother-sister (BS)) similarities in individual somatotype components; (3) examine the degree to which between and within variances differ among sib-ships; and (4) investigate the effects of physical activity (PA) and family socioeconomic status (SES) on these relationships. The sample comprises 1058 Portuguese siblings (538 females) aged 9-20 years. Somatotype was calculated using the Health-Carter method, while PA and SES information was obtained by questionnaire. Multi-level modelling was done in SuperMix software. Older subjects showed the lowest values for endomorphy and mesomorphy, but the highest values for ectomorphy; and more physically active subjects showed the highest values for mesomorphy. In general, the familiality of somatotype was moderate (ρ = 0.35). Same-sex siblings had the strongest resemblance (endomorphy: ρ SS > ρ BB > ρ BS ; mesomorphy: ρ BB = ρ SS > ρ BS ; ectomorphy: ρ BB > ρ SS > ρ BS ). For the ectomorphy and mesomorphy components, BS pairs showed the highest between sib-ship variance, but the lowest within sib-ship variance; while for endomorphy BS showed the lowest between and within sib-ship variances. These results highlight the significant familial effects on somatotype and the complexity of the role of familial resemblance in explaining variance in somatotypes.

Genetic Background Has a Major Impact on Differences in Sleep Resulting from Environmental Influences in Drosophila

PubMed Central

Zimmerman, John E.; Chan, May T.; Jackson, Nicholas; Maislin, Greg; Pack, Allan I.

2012-01-01

Study Objectives: To determine the effect of different genetic backgrounds on demographic and environmental interventions that affect sleep and evaluate variance of these measures; and to evaluate sleep and variance of sleep behaviors in 6 divergent laboratory strains of common origin. Design: Assessment of the effects of age, sex, mating status, food sources, and social experience using video analysis of sleep behavior in 2 different strains of Drosophila, white1118ex (w1118ex) and white Canton-S (wCS10). Sleep was also determined for 6 laboratory strains of Canton-S and 3 inbred lines. The variance of total sleep was determined for all groups and conditions. Measurements and Results: The circadian periods and the effects of age upon sleep were the same between w1118ex and wCS10 strains. However, the w1118ex and wCS10 strains demonstrated genotype-dependent differences in the effects upon sleep of sex, mating status, social experience, and being on different foods. Variance of total sleep was found to differ in a genotype dependent manner for interventions between the w1118ex and wCS10 strains. Six different laboratory Canton-S strains were found to have significantly different circadian periods (P < 0.001) and sleep phenotypes (P < 0.001). Three inbred lines showed reduced variance for sleep measurements. Conclusions: One must control environmental conditions in a rigorously consistent manner to ensure that sleep data may be compared between experiments. Genetic background has a significant impact upon changes in sleep behavior and variance of behavior due to demographic factors and environmental interventions. This represents an opportunity to discover new genes that modify sleep/wake behavior. Citation: Zimmerman JE; Chan MT; Jackson N; Maislin G; Pack AI. Genetic background has a major impact on differences in sleep resulting from environmental influences in Drosophila. SLEEP 2012;35(4):545-557. PMID:22467993

Variance partitioning of stream diatom, fish, and invertebrate indicators of biological condition

USGS Publications Warehouse

Zuellig, Robert E.; Carlisle, Daren M.; Meador, Michael R.; Potapova, Marina

2012-01-01

Stream indicators used to make assessments of biological condition are influenced by many possible sources of variability. To examine this issue, we used multiple-year and multiple-reach diatom, fish, and invertebrate data collected from 20 least-disturbed and 46 developed stream segments between 1993 and 2004 as part of the US Geological Survey National Water Quality Assessment Program. We used a variance-component model to summarize the relative and absolute magnitude of 4 variance components (among-site, among-year, site × year interaction, and residual) in indicator values (observed/expected ratio [O/E] and regional multimetric indices [MMI]) among assemblages and between basin types (least-disturbed and developed). We used multiple-reach samples to evaluate discordance in site assessments of biological condition caused by sampling variability. Overall, patterns in variance partitioning were similar among assemblages and basin types with one exception. Among-site variance dominated the relative contribution to the total variance (64–80% of total variance), residual variance (sampling variance) accounted for more variability (8–26%) than interaction variance (5–12%), and among-year variance was always negligible (0–0.2%). The exception to this general pattern was for invertebrates at least-disturbed sites where variability in O/E indicators was partitioned between among-site and residual (sampling) variance (among-site  =  36%, residual  =  64%). This pattern was not observed for fish and diatom indicators (O/E and regional MMI). We suspect that unexplained sampling variability is what largely remained after the invertebrate indicators (O/E predictive models) had accounted for environmental differences among least-disturbed sites. The influence of sampling variability on discordance of within-site assessments was assemblage or basin-type specific. Discordance among assessments was nearly 2× greater in developed basins (29–31%) than in least-disturbed sites (15–16%) for invertebrates and diatoms, whereas discordance among assessments based on fish did not differ between basin types (least-disturbed  =  16%, developed  =  17%). Assessments made using invertebrate and diatom indicators from a single reach disagreed with other samples collected within the same stream segment nearly ⅓ of the time in developed basins, compared to ⅙ for all other cases.

Genetic parameter estimation for pre- and post-weaning traits in Brahman cattle in Brazil.

PubMed

Vargas, Giovana; Buzanskas, Marcos Eli; Guidolin, Diego Gomes Freire; Grossi, Daniela do Amaral; Bonifácio, Alexandre da Silva; Lôbo, Raysildo Barbosa; da Fonseca, Ricardo; Oliveira, João Ademir de; Munari, Danísio Prado

2014-10-01

Beef cattle producers in Brazil use body weight traits as breeding program selection criteria due to their great economic importance. The objectives of this study were to evaluate different animal models, estimate genetic parameters, and define the most fitting model for Brahman cattle body weight standardized at 120 (BW120), 210 (BW210), 365 (BW365), 450 (BW450), and 550 (BW550) days of age. To estimate genetic parameters, single-, two-, and multi-trait analyses were performed using the animal model. The likelihood ratio test was verified between all models. For BW120 and BW210, additive direct genetic, maternal genetic, maternal permanent environment, and residual effects were considered, while for BW365 and BW450, additive direct genetic, maternal genetic, and residual effects were considered. Finally, for BW550, additive direct genetic and residual effects were considered. Estimates of direct heritability for BW120 were similar in all analyses; however, for the other traits, multi-trait analysis resulted in higher estimates. The maternal heritability and proportion of maternal permanent environmental variance to total variance were minimal in multi-trait analyses. Genetic, environmental, and phenotypic correlations were of high magnitude between all traits. Multi-trait analyses would aid in the parameter estimation for body weight at older ages because they are usually affected by a lower number of animals with phenotypic information due to culling and mortality.

40 CFR 142.20 - State-issued variances and exemptions under Section 1415(a) and Section 1416 of the Act.

Code of Federal Regulations, 2012 CFR

2012-07-01

... the Act; and (C) Ownership changes, physical consolidation with another public water system, or other... ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER... responsibility may issue variances to public water systems (other than small system variances) from the...

40 CFR 142.20 - State-issued variances and exemptions under Section 1415(a) and Section 1416 of the Act.

Code of Federal Regulations, 2010 CFR

2010-07-01

... State must consider the availability of an alternative source of water, including the feasibility of... ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER... responsibility may issue variances to public water systems (other than small system variances) from the...

40 CFR 142.20 - State-issued variances and exemptions under Section 1415(a) and Section 1416 of the Act.

Code of Federal Regulations, 2011 CFR

2011-07-01

... the Act; and (C) Ownership changes, physical consolidation with another public water system, or other... ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER... responsibility may issue variances to public water systems (other than small system variances) from the...

Does the school food environment influence the dietary behaviours of Norwegian 11-year-olds? The HEIA study.

PubMed

Gebremariam, Mekdes K; Andersen, Lene F; Bjelland, Mona; Klepp, Knut-Inge; Totland, Torunn H; Bergh, Ingunn H; Lien, Nanna

2012-07-01

The aim of the study is to investigate the influence of the school food environment on the dietary behaviours of 11-year-old Norwegian children in elementary schools. Baseline data from a school-based intervention study: the Health In Adolescents study was used. A total of 1425 11-year-old children from 35 schools from the eastern part of Norway were included. School administrators provided information on the physical, political, and sociocultural school food environment and students reported their intake of fruits, vegetables, sugar-sweetened beverages (SSB), and snacks. Multilevel modelling was used to assess the school-level variance in dietary behaviours and to investigate the association of school food environmental factors with these dietary behaviours. After adjustment for student characteristics, the school level accounted for a small proportion (1.1%-3.0%) of the variance in the dietary behaviours investigated. None of the investigated school food environmental factors were found to be related to the children's reported intake of fruits, vegetables, snacks or SSB. Most of the variance in the dietary behaviours investigated was at the personal level. Thus in this sample, the investigated school-level factors do not appear to exert a strong influence on the dietary behaviours of children. Longitudinal studies using validated measures of the school food environment are needed.

Epistasis and inheritance of plant habit and fruit quality traits in ornamental pepper (Capsicum annuum L.).

PubMed

Santos, R M C; do Rêgo, E R; Borém, A; Nascimento, M F; Nascimento, N F F; Finger, F L; Rêgo, M M

2014-10-31

Two accessions of ornamental pepper Capsicum annuum L., differing in most of the characters studied, were crossed, resulting in the F1 generation, and the F2 generation was obtained through self-fertilization of the F1 generation. The backcross generations RC1 and RC2 were obtained through crossing between F1 and the parents P1 and P2, respectively. Morpho-agronomic characterization was performed based on the 19 quantitative descriptors of Capsicum. The data obtained were subjected to generation analysis, in which the means and additive variance (σa(2)), variance due to dominance deviation (σd(2)), phenotypic variance (σf(2)), genetic variance (σg(2)) and environmental variance (σm(2)) were calculated. For the full model, we estimated the mean effects of all possible homozygotes, additives, dominants, and epistatics: additive-additive, additive-dominant, and dominant-dominant. For the additive-dominant model, we estimated the additive effects, dominant effects and mean effects of possible homozygotes. The character fruit dry matter had the lowest value for broad sense heritability (0.42), and the highest values were found for fresh matter and fruit weight, 0.91 and 0.92, respectively. The lowest value for narrow sense heritability was for the minor fruit diameter character (0.33), and the highest values were found for seed yield per fruit and fresh matter, 0.87 and 0.84, respectively. The additive-dominant model explained only the variation found in plant height, canopy width, stem length, corolla diameter, leaf width, and pedicel length, but in the other characters, the epistatic effects showed significant values.

Climate and Human Pressure Constraints Co-Explain Regional Plant Invasion at Different Spatial Scales

PubMed Central

García-Baquero, Gonzalo; Caño, Lidia; Biurrun, Idoia; García-Mijangos, Itziar; Loidi, Javier; Herrera, Mercedes

2016-01-01

Alien species invasion represents a global threat to biodiversity and ecosystems. Explaining invasion patterns in terms of environmental constraints will help us to assess invasion risks and plan control strategies. We aim to identify plant invasion patterns in the Basque Country (Spain), and to determine the effects of climate and human pressure on that pattern. We modeled the regional distribution of 89 invasive plant species using two approaches. First, distance-based Moran’s eigenvector maps were used to partition variation in the invasive species richness, S, into spatial components at broad and fine scales; redundancy analysis was then used to explain those components on the basis of climate and human pressure descriptors. Second, we used generalized additive mixed modeling to fit species-specific responses to the same descriptors. Climate and human pressure descriptors have different effects on S at different spatial scales. Broad-scale spatially structured temperature and precipitation, and fine-scale spatially structured human population density and percentage of natural and semi-natural areas, explained altogether 38.7% of the total variance. The distribution of 84% of the individually tested species was related to either temperature, precipitation or both, and 68% was related to either population density or natural and semi-natural areas, displaying similar responses. The spatial pattern of the invasive species richness is strongly environmentally forced, mainly by climate factors. Since individual species responses were proved to be both similarly constrained in shape and explained variance by the same environmental factors, we conclude that the pattern of invasive species richness results from individual species’ environmental preferences. PMID:27741276

The infinitesimal model: Definition, derivation, and implications.

PubMed

Barton, N H; Etheridge, A M; Véber, A

2017-12-01

Our focus here is on the infinitesimal model. In this model, one or several quantitative traits are described as the sum of a genetic and a non-genetic component, the first being distributed within families as a normal random variable centred at the average of the parental genetic components, and with a variance independent of the parental traits. Thus, the variance that segregates within families is not perturbed by selection, and can be predicted from the variance components. This does not necessarily imply that the trait distribution across the whole population should be Gaussian, and indeed selection or population structure may have a substantial effect on the overall trait distribution. One of our main aims is to identify some general conditions on the allelic effects for the infinitesimal model to be accurate. We first review the long history of the infinitesimal model in quantitative genetics. Then we formulate the model at the phenotypic level in terms of individual trait values and relationships between individuals, but including different evolutionary processes: genetic drift, recombination, selection, mutation, population structure, …. We give a range of examples of its application to evolutionary questions related to stabilising selection, assortative mating, effective population size and response to selection, habitat preference and speciation. We provide a mathematical justification of the model as the limit as the number M of underlying loci tends to infinity of a model with Mendelian inheritance, mutation and environmental noise, when the genetic component of the trait is purely additive. We also show how the model generalises to include epistatic effects. We prove in particular that, within each family, the genetic components of the individual trait values in the current generation are indeed normally distributed with a variance independent of ancestral traits, up to an error of order 1∕M. Simulations suggest that in some cases the convergence may be as fast as 1∕M. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Spatial and seasonal distribution of adult Oithona similis in the Southern Ocean: Predictions using boosted regression trees

NASA Astrophysics Data System (ADS)

Pinkerton, Matt H.; Smith, Adam N. H.; Raymond, Ben; Hosie, Graham W.; Sharp, Ben; Leathwick, John R.; Bradford-Grieve, Janet M.

2010-04-01

We applied a multivariate statistical modelling technique called boosted regression trees to derive relationships between environmental conditions and the distribution of the adult stage of the cyclopoid copepod Oithona similis in the Southern Ocean. Nearly 20 000 samples from the Southern Ocean Continuous Plankton Recorder survey (87% from East Antarctica) were used to model the probability of detection (presence) and relative abundance of adults of this zooplankton species in surface waters. We demonstrate that it is possible to obtain reasonable models for both the presence (area under the Receiver Operating Characteristic curve of 0.77) and relative abundance (28-35% variance explained) of adult O. similis between November and March in much of the Southern Ocean. No investigation was possible where the environmental characteristics were not well represented by the SO-CPR dataset, namely, the Argentine shelf, Weddell Sea, and the frontal region north of the Amundsen Sea, or under sea-ice. Our analyses support the hypothesis that adult O. similis abundance is related to environmental conditions in a broadly similar way throughout the Southern Ocean. Compared to a compilation of net-haul data from the literature, the abundance model explained 34% of the variance in surface concentrations of adult stages of this species, and 23-59% of the variance in depth-integrated abundance of copepodite and adult stages combined. The models show higher occurrence and elevated abundances in a broad circumpolar band between the Antarctic Polar Front and the southern boundary of the Antarctic Circumpolar Current (approximately 54-64°S). Evidence of diel vertical migration by adults of this species north of 65°S was found, with surface abundances 20% higher at night than during the day. There was no evidence of diel migration south of 65°S. Five potential "hotspots" of adult O. similis were identified: in the southern Scotia Sea, two areas off east Antarctica, in the frontal zone north of the Amundsen Sea, and a small area in the outer Bellingshausen Sea. We recommend that a database of all available net-haul data on Oithona similis in the Southern Ocean be created to facilitate further investigations on the circumpolar distribution of this species.

Genomic Prediction Accounting for Residual Heteroskedasticity.

PubMed

Ou, Zhining; Tempelman, Robert J; Steibel, Juan P; Ernst, Catherine W; Bates, Ronald O; Bello, Nora M

2015-11-12

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. Copyright © 2016 Ou et al.

Addressing the impact of environmental uncertainty in plankton model calibration with a dedicated software system: the Marine Model Optimization Testbed (MarMOT 1.1 alpha)

NASA Astrophysics Data System (ADS)

Hemmings, J. C. P.; Challenor, P. G.

2012-04-01

A wide variety of different plankton system models have been coupled with ocean circulation models, with the aim of understanding and predicting aspects of environmental change. However, an ability to make reliable inferences about real-world processes from the model behaviour demands a quantitative understanding of model error that remains elusive. Assessment of coupled model output is inhibited by relatively limited observing system coverage of biogeochemical components. Any direct assessment of the plankton model is further inhibited by uncertainty in the physical state. Furthermore, comparative evaluation of plankton models on the basis of their design is inhibited by the sensitivity of their dynamics to many adjustable parameters. Parameter uncertainty has been widely addressed by calibrating models at data-rich ocean sites. However, relatively little attention has been given to quantifying uncertainty in the physical fields required by the plankton models at these sites, and tendencies in the biogeochemical properties due to the effects of horizontal processes are often neglected. Here we use model twin experiments, in which synthetic data are assimilated to estimate a system's known "true" parameters, to investigate the impact of error in a plankton model's environmental input data. The experiments are supported by a new software tool, the Marine Model Optimization Testbed, designed for rigorous analysis of plankton models in a multi-site 1-D framework. Simulated errors are derived from statistical characterizations of the mixed layer depth, the horizontal flux divergence tendencies of the biogeochemical tracers and the initial state. Plausible patterns of uncertainty in these data are shown to produce strong temporal and spatial variability in the expected simulation error variance over an annual cycle, indicating variation in the significance attributable to individual model-data differences. An inverse scheme using ensemble-based estimates of the simulation error variance to allow for this environment error performs well compared with weighting schemes used in previous calibration studies, giving improved estimates of the known parameters. The efficacy of the new scheme in real-world applications will depend on the quality of statistical characterizations of the input data. Practical approaches towards developing reliable characterizations are discussed.

Genetic analyses of protein yield in dairy cows applying random regression models with time-dependent and temperature x humidity-dependent covariates.

PubMed

Brügemann, K; Gernand, E; von Borstel, U U; König, S

2011-08-01

Data used in the present study included 1,095,980 first-lactation test-day records for protein yield of 154,880 Holstein cows housed on 196 large-scale dairy farms in Germany. Data were recorded between 2002 and 2009 and merged with meteorological data from public weather stations. The maximum distance between each farm and its corresponding weather station was 50 km. Hourly temperature-humidity indexes (THI) were calculated using the mean of hourly measurements of dry bulb temperature and relative humidity. On the phenotypic scale, an increase in THI was generally associated with a decrease in daily protein yield. For genetic analyses, a random regression model was applied using time-dependent (d in milk, DIM) and THI-dependent covariates. Additive genetic and permanent environmental effects were fitted with this random regression model and Legendre polynomials of order 3 for DIM and THI. In addition, the fixed curve was modeled with Legendre polynomials of order 3. Heterogeneous residuals were fitted by dividing DIM into 5 classes, and by dividing THI into 4 classes, resulting in 20 different classes. Additive genetic variances for daily protein yield decreased with increasing degrees of heat stress and were lowest at the beginning of lactation and at extreme THI. Due to higher additive genetic variances, slightly higher permanent environment variances, and similar residual variances, heritabilities were highest for low THI in combination with DIM at the end of lactation. Genetic correlations among individual values for THI were generally >0.90. These trends from the complex random regression model were verified by applying relatively simple bivariate animal models for protein yield measured in 2 THI environments; that is, defining a THI value of 60 as a threshold. These high correlations indicate the absence of any substantial genotype × environment interaction for protein yield. However, heritabilities and additive genetic variances from the random regression model tended to be slightly higher in the THI range corresponding to cows' comfort zone. Selecting such superior environments for progeny testing can contribute to an accurate genetic differentiation among selection candidates. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

40 CFR 142.303 - Which size public water systems can receive a small system variance?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 22 2010-07-01 2010-07-01 false Which size public water systems can receive a small system variance? 142.303 Section 142.303 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS IMPLEMENTATION Variances for Small System General...

40 CFR 142.303 - Which size public water systems can receive a small system variance?

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 23 2014-07-01 2014-07-01 false Which size public water systems can receive a small system variance? 142.303 Section 142.303 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS IMPLEMENTATION Variances for Small System General...

Does where you shop or who you are predict what you eat?: The role of stores and individual characteristics in dietary intake.

PubMed

Vaughan, Christine A; Collins, Rebecca; Ghosh-Dastidar, Madhumita; Beckman, Robin; Dubowitz, Tamara

2017-07-01

Interventions to address diet, a modifiable risk factor for diabetes, cancer, and cardiovascular disease, have increasingly emphasized the influence of the physical environment on diet, while more traditional approaches have focused on individual characteristics. We examined environmental and individual influences on diet to understand the role of both. Household interviews were conducted in 2011 with 1372 individuals randomly selected from two low-income, predominantly African American neighborhoods in Pittsburgh, PA. Participants reported their sociodemographic characteristics, food shopping behavior, and dietary intake. Both food shopping frequency at different types of food stores and sociodemographic characteristics showed significant associations with diet in adjusted regression models. More frequent shopping at convenience and neighborhood stores and being younger, male, without a college degree, and receiving SNAP benefits were associated with greater intake of sugar-sweetened beverages (SSBs), added sugars, and discretionary fats. Being older, male, and having a college degree were associated with greater intake of fruits and vegetables. However, while food shopping behavior and sociodemographic characteristics accounted for similar amounts of nonoverlapping variance in fruit and vegetable intake, food shopping behavior accounted for much less variance, and little unique variance, in SSBs, added sugars, and discretionary fats in models with sociodemographic characteristics. The current study reinforces the need for policies and interventions at both the environmental and individual levels to improve diet in food desert residents. Individual interventions to address food choices associated with certain sociodemographic characteristics might be particularly important for curbing intake of SSBs, added sugars, and discretionary fats. Copyright © 2017 Elsevier Inc. All rights reserved.

Environmental and genetic sources of diversification in the timing of seed germination: implications for the evolution of bet hedging.

PubMed

Simons, Andrew M; Johnston, Mark O

2006-11-01

Environmental variation that is not predictably related to cues is expected to drive the evolution of bet-hedging strategies. The high variance observed in the timing of seed germination has led to it being the most cited diversification strategy in the theoretical bet-hedging literature. Despite this theoretical focus, virtually nothing is known about the mechanisms responsible for the generation of individual-level diversification. Here we report analyses of sources of variation in timing of germination within seasons, germination fraction over two generations and three sequential seasons, and the genetic correlation structure of these traits using almost 10,000 seeds from more than 100 genotypes of the monocarpic perennial Lobelia inflata. Microenvironmental analysis of time to germination suggests that extreme sensitivity to environmental gradients, or microplasticity, even within a homogeneous growth chamber, may act as an effective individual-level diversification mechanism and explains more than 30% of variance in time to germination. The heritability of within-season timing of germination was low (h(2) = 0.07) but significant under homogeneous conditions. Consistent with individual-level diversification, this low h(2) was attributable not to low additive genetic variance, but to an unusually high coefficient of residual variation in time to germination. Despite high power to detect additive genetic variance in within-season diversification, it was low and indistinguishable from zero. Restricted maximum likelihood detected significant genetic variation for germination fraction (h(2) = 0.18) under homogeneous conditions. Unexpectedly, this heritability was positive when measured within a generation by sibling analysis and negative when measured across generations by offspring-on-parent regression. The consistency of dormancy fraction over multiple delays, a major premise of Cohen's classic model, was supported by a strong genetic correlation (r = 0.468) observed for a cohort's germination fraction over two seasons. We discuss implications of the results for the evolution of bet hedging and highlight the need for further empirical study of the causal components of diversification.

Modelling heterogeneity variances in multiple treatment comparison meta-analysis--are informative priors the better solution?

PubMed

Thorlund, Kristian; Thabane, Lehana; Mills, Edward J

2013-01-11

Multiple treatment comparison (MTC) meta-analyses are commonly modeled in a Bayesian framework, and weakly informative priors are typically preferred to mirror familiar data driven frequentist approaches. Random-effects MTCs have commonly modeled heterogeneity under the assumption that the between-trial variance for all involved treatment comparisons are equal (i.e., the 'common variance' assumption). This approach 'borrows strength' for heterogeneity estimation across treatment comparisons, and thus, ads valuable precision when data is sparse. The homogeneous variance assumption, however, is unrealistic and can severely bias variance estimates. Consequently 95% credible intervals may not retain nominal coverage, and treatment rank probabilities may become distorted. Relaxing the homogeneous variance assumption may be equally problematic due to reduced precision. To regain good precision, moderately informative variance priors or additional mathematical assumptions may be necessary. In this paper we describe four novel approaches to modeling heterogeneity variance - two novel model structures, and two approaches for use of moderately informative variance priors. We examine the relative performance of all approaches in two illustrative MTC data sets. We particularly compare between-study heterogeneity estimates and model fits, treatment effect estimates and 95% credible intervals, and treatment rank probabilities. In both data sets, use of moderately informative variance priors constructed from the pair wise meta-analysis data yielded the best model fit and narrower credible intervals. Imposing consistency equations on variance estimates, assuming variances to be exchangeable, or using empirically informed variance priors also yielded good model fits and narrow credible intervals. The homogeneous variance model yielded high precision at all times, but overall inadequate estimates of between-trial variances. Lastly, treatment rankings were similar among the novel approaches, but considerably different when compared with the homogenous variance approach. MTC models using a homogenous variance structure appear to perform sub-optimally when between-trial variances vary between comparisons. Using informative variance priors, assuming exchangeability or imposing consistency between heterogeneity variances can all ensure sufficiently reliable and realistic heterogeneity estimation, and thus more reliable MTC inferences. All four approaches should be viable candidates for replacing or supplementing the conventional homogeneous variance MTC model, which is currently the most widely used in practice.

Collinearity and Causal Diagrams: A Lesson on the Importance of Model Specification.

PubMed

Schisterman, Enrique F; Perkins, Neil J; Mumford, Sunni L; Ahrens, Katherine A; Mitchell, Emily M

2017-01-01

Correlated data are ubiquitous in epidemiologic research, particularly in nutritional and environmental epidemiology where mixtures of factors are often studied. Our objectives are to demonstrate how highly correlated data arise in epidemiologic research and provide guidance, using a directed acyclic graph approach, on how to proceed analytically when faced with highly correlated data. We identified three fundamental structural scenarios in which high correlation between a given variable and the exposure can arise: intermediates, confounders, and colliders. For each of these scenarios, we evaluated the consequences of increasing correlation between the given variable and the exposure on the bias and variance for the total effect of the exposure on the outcome using unadjusted and adjusted models. We derived closed-form solutions for continuous outcomes using linear regression and empirically present our findings for binary outcomes using logistic regression. For models properly specified, total effect estimates remained unbiased even when there was almost perfect correlation between the exposure and a given intermediate, confounder, or collider. In general, as the correlation increased, the variance of the parameter estimate for the exposure in the adjusted models increased, while in the unadjusted models, the variance increased to a lesser extent or decreased. Our findings highlight the importance of considering the causal framework under study when specifying regression models. Strategies that do not take into consideration the causal structure may lead to biased effect estimation for the original question of interest, even under high correlation.

Improving the Navy’s Passive Underwater Acoustic Monitoring of Marine Mammal Populations

DTIC Science & Technology

2013-09-30

passive acoustic monitoring: Correcting humpback whale call detections for site-specific and time-dependent environmental characteristics ,” JASA Exp...marine mammal species using passive acoustic monitoring, with application to obtaining density estimates of transiting humpback whale populations in...minimize the variance of the density estimates, 3) to apply the numerical modeling methods for humpback whale vocalizations to understand distortions

Estimates of genetic and environmental (co)variances for first lactation on milk yield, survival, and calving interval.

PubMed

Dong, M C; van Vleck, L D

1989-03-01

Variance and covariance components for milk yield, survival to second freshening, calving interval in first lactation were estimated by REML with the expectation and maximization algorithm for an animal model which included herd-year-season effects. Cows without calving interval but with milk yield were included. Each of the four data sets of 15 herds included about 3000 Holstein cows. Relationships across herds were ignored to enable inversion of the coefficient matrix of mixed model equations. Quadratics and their expectations were accumulated herd by herd. Heritability of milk yield (.32) agrees with reports by same methods. Heritabilities of survival (.11) and calving interval(.15) are slightly larger and genetic correlations smaller than results from different methods of estimation. Genetic correlation between milk yield and calving interval (.09) indicates genetic ability to produce more milk is lightly associated with decreased fertility.

Portfolio optimization with skewness and kurtosis

NASA Astrophysics Data System (ADS)

Lam, Weng Hoe; Jaaman, Saiful Hafizah Hj.; Isa, Zaidi

2013-04-01

Mean and variance of return distributions are two important parameters of the mean-variance model in portfolio optimization. However, the mean-variance model will become inadequate if the returns of assets are not normally distributed. Therefore, higher moments such as skewness and kurtosis cannot be ignored. Risk averse investors prefer portfolios with high skewness and low kurtosis so that the probability of getting negative rates of return will be reduced. The objective of this study is to compare the portfolio compositions as well as performances between the mean-variance model and mean-variance-skewness-kurtosis model by using the polynomial goal programming approach. The results show that the incorporation of skewness and kurtosis will change the optimal portfolio compositions. The mean-variance-skewness-kurtosis model outperforms the mean-variance model because the mean-variance-skewness-kurtosis model takes skewness and kurtosis into consideration. Therefore, the mean-variance-skewness-kurtosis model is more appropriate for the investors of Malaysia in portfolio optimization.

Working Memory and Parent-Rated Components of Attention in Middle Childhood: A Behavioral Genetic Study

PubMed Central

Deater-Deckard, Kirby; Cutting, Laurie; Thompson, Lee A.; Petrill, Stephen A.

2012-01-01

The purpose of the current study was to investigate potential genetic and environmental correlations between working memory and three behavioral aspects of the attention network (i.e., executive, alerting, and orienting) using a twin design. Data were from 90 monozygotic (39% male) and 112 same-sex dizygotic (41% male) twins. Individual differences in working memory performance (digit span) and parent-rated measures of executive, alerting, and orienting attention included modest to moderate genetic variance, modest shared environmental variance, and modest to moderate nonshared environmental variance. As hypothesized, working memory performance was correlated with executive and alerting attention, but not orienting attention. The correlation between working memory, executive attention, and alerting attention was completely accounted for by overlapping genetic covariance, suggesting a common genetic mechanism or mechanisms underlying the links between working memory and certain parent-rated indicators of attentive behavior. PMID:21948215

Evidence for heritability of adult men's sexual interest in youth under age 16 from a population-based extended twin design.

PubMed

Alanko, Katarina; Salo, Benny; Mokros, Andreas; Santtila, Pekka

2013-04-01

Sexual interest in children resembles sexual gender orientation in terms of early onset and stability across the life span. Although a genetic component to sexual interest in children seems possible, no research has addressed this question to date. Prior research showing familial transmission of pedophilia remains inconclusive about shared environmental or genetic factors. Studies from the domains of sexual orientation and sexually problematic behavior among children pointed toward genetic components. Adult men's sexual interest in youthfulness-related cues may be genetically influenced. The aim of the present study was to test whether male sexual interest in children and youth under age 16 involves a heritable component. The main outcome measure was responses in a confidential survey concerning sexual interest, fantasies, or activity pertaining to children under the age of 16 years during the previous 12 months. The present study used an extended family design within behavioral genetic modeling to estimate the contributions of genetic and environmental factors in the occurrence of adult men's sexual interest in children and youth under age 16. Participants were male twins and their male siblings from a population-based Finnish cohort sample aged 21-43 years (N = 3,967). The incidence of sexual interest in children under age was 3%. Twin correlations were higher for monozygotic than for dizygotic twins. Behavioral genetic model fitting indicated that a model including genetic effects as well as nonshared environmental influences (including measurement error), but not common environmental influences, fits the data best. The amount of variance attributable to nonadditive genetic influences (heritability) was estimated at 14.6%. The present study provides the first indication that genetic influences may play a role in shaping sexual interest toward children and adolescents among adult men. Compared with the variance attributable to nonshared environmental effects (plus measurement error), the contribution of any genetic factors seems comparatively weak. Future research should address the possible interplay of genetic with environmental risk factors, such as own sexual victimization in childhood. © 2013 International Society for Sexual Medicine.

The cumulative effects of life event, personal and social resources on subjective well-being of elderly widowers.

PubMed

Balaswamy, S; Richardson, V E

2001-01-01

A multidimensional Life Stress Model was used to test the independent contributions of background characteristics, personal resources, life event, and environmental influences on 200 widowers' levels of well-being, measured by the Affect Balance Scale. Stepwise regression analyses revealed that environmental resources were unrelated to negative affect which is influenced more by the life event and personal resource variables. The environmental resource variables, particularly interactions with friends and neighbors, mostly influenced positive affect. The explanatory model for well-being included multiple variables and explained 33 percent of the variance. Although background characteristics had the greatest impact, absence of hospitalization, higher mastery, higher self-esteem, contacts with friends, and interaction with neighbors enhanced well-being. The results support previous speculations on the importance of positive exchanges for positive affect. African-American widowers showed higher levels of well-being than Caucasian widowers did. The results advance knowledge about differences among elderly men.

How Many Environmental Impact Indicators Are Needed in the Evaluation of Product Life Cycles?

PubMed

Steinmann, Zoran J N; Schipper, Aafke M; Hauck, Mara; Huijbregts, Mark A J

2016-04-05

Numerous indicators are currently available for environmental impact assessments, especially in the field of Life Cycle Impact Assessment (LCIA). Because decision-making on the basis of hundreds of indicators simultaneously is unfeasible, a nonredundant key set of indicators representative of the overall environmental impact is needed. We aimed to find such a nonredundant set of indicators based on their mutual correlations. We have used Principal Component Analysis (PCA) in combination with an optimization algorithm to find an optimal set of indicators out of 135 impact indicators calculated for 976 products from the ecoinvent database. The first four principal components covered 92% of the variance in product rankings, showing the potential for indicator reduction. The same amount of variance (92%) could be covered by a minimal set of six indicators, related to climate change, ozone depletion, the combined effects of acidification and eutrophication, terrestrial ecotoxicity, marine ecotoxicity, and land use. In comparison, four commonly used resource footprints (energy, water, land, materials) together accounted for 84% of the variance in product rankings. We conclude that the plethora of environmental indicators can be reduced to a small key set, representing the major part of the variation in environmental impacts between product life cycles.

Estimates of direct and maternal (co)variance components as well as genetic parameters of growth traits in Nellore sheep.

PubMed

I, Satish Kumar; C, Vijaya Kumar; G, Gangaraju; Nath, Sapna; A K, Thiruvenkadan

2017-10-01

In the present study, (co)variance components and genetic parameters in Nellore sheep were obtained by restricted maximum likelihood (REML) method using six different animal models with various combinations of direct and maternal genetic effects for birth weight (BW), weaning weight (WW), 6-month weight (6MW), 9-month weight (9MW) and 12-month weight (YW). Evaluated records of 2075 lambs descended from 69 sires and 478 dams over a period of 8 years (2007-2014) were collected from the Livestock Research Station, Palamaner, India. Lambing year, sex of lamb, season of lambing and parity of dam were the fixed effects in the model, and ewe weight was used as a covariate. Best model for each trait was determined by log-likelihood ratio test. Direct heritability for BW, WW, 6MW, 9MW and YW were 0.08, 0.03, 0.12, 0.16 and 0.10, respectively, and their corresponding maternal heritabilities were 0.07, 0.10, 0.09, 0.08 and 0.11. The proportions of maternal permanent environment variance to phenotypic variance (Pe 2 ) were 0.07, 0.10, 0.07, 0.06 and 0.10 for BW, WW, 6MW, 9MW and YW, respectively. The estimates of direct genetic correlations among the growth traits were positive and ranged from 0.44(BW-WW) to 0.96(YW-9MW), and the estimates of phenotypic and environmental correlations were found to be lower than those of genetic correlations. Exclusion of maternal effects in the model resulted in biased estimates of genetic parameters in Nellore sheep. Hence, to implement optimum breeding strategies for improvement of traits in Nellore sheep, maternal effects should be considered.

Associations between perceived neighbourhood environmental attributes and self-reported sitting time in patients with schizophrenia: a pilot study.

PubMed

Vancampfort, Davy; De Hert, Marc; De Herdt, Amber; Soundy, Andrew; Stubbs, Brendon; Bernard, Paquito; Probst, Michel

2014-01-30

Sitting behaviours may, independent of physical activity behaviours, be a distinct risk factor for multiple adverse health outcomes in patients with schizophrenia. In order to combat sitting behaviours health care providers and policy makers require further understanding of its determinants in this population group. The aim of the present study was to investigate the variance in sitting time explained by a wide range of community design and recreational environmental variables, above and beyond the variance accounted for by demographic variables. One hundred and twenty-three patients (42♀) with schizophrenia (mean age=41.5 ± 12.6 years) were included in the final analysis. The built environment was rated using the Instruments for Assessing Levels of Physical Activity and Fitness environmental questionnaire and sitting time was assessed using the International Physical Activity Questionnaire-short (IPAQ) version. Regression analysis showed that environmental variables were related to sitting time. The body mass index (BMI) and disease stage explained 8.4% of the variance in sitting, while environmental correlates explained an additional 16.8%. Clinical practice guidelines should incorporate strategies targeting changes in sitting behaviours, from encouraging environmental changes to the availability of exercise equipment. © 2013 Published by Elsevier Ireland Ltd.

Genetic Architecture of Micro-Environmental Plasticity in Drosophila melanogaster

PubMed Central

Morgante, Fabio; Sørensen, Peter; Sorensen, Daniel A.; Maltecca, Christian; Mackay, Trudy F. C.

2015-01-01

Individuals of the same genotype do not have the same phenotype for quantitative traits when reared under common macro-environmental conditions, a phenomenon called micro-environmental plasticity. Genetic variation in micro-environmental plasticity is assumed in models of the evolution of phenotypic variance, and is important in applied breeding and personalized medicine. Here, we quantified genetic variation for micro-environmental plasticity for three quantitative traits in the inbred, sequenced lines of the Drosophila melanogaster Genetic Reference Panel. We found substantial genetic variation for micro-environmental plasticity for all traits, with broad sense heritabilities of the same magnitude or greater than those of trait means. Micro-environmental plasticity is not correlated with residual segregating variation, is trait-specific, and has genetic correlations with trait means ranging from zero to near unity. We identified several candidate genes associated with micro-environmental plasticity of startle response, including Drosophila Hsp90, setting the stage for future genetic dissection of this phenomenon. PMID:25943032

Study on common seasonal signals in GPS time series and environmental loadings using Multichannel Singular Spectrum Analysis

NASA Astrophysics Data System (ADS)

Gruszczynska, Marta; Rosat, Severine; Klos, Anna; Bogusz, Janusz

2017-04-01

Seasonal oscillations in the GPS position time series can arise from real geophysical effects and numerical artefacts. According to Dong et al. (2002) environmental loading effects can account for approximately 40% of the total variance of the annual signals in GPS time series, however using generally acknowledged methods (e.g. Least Squares Estimation, Wavelet Decomposition, Singular Spectrum Analysis) to model seasonal signals we are not able to separate real from spurious signals (effects of mismodelling aliased into annual period as well as draconitic). Therefore, we propose to use Multichannel Singular Spectrum Analysis (MSSA) to determine seasonal oscillations (with annual and semi-annual periods) from GPS position time series and environmental loading displacement models. The MSSA approach is an extension of the classical Karhunen-Loève method and it is a special case of SSA for multivariate time series. The main advantage of MSSA is the possibility to extract common seasonal signals for stations from selected area and to investigate the causality between a set of time series as well. In this research, we explored the ability of MSSA application to separate real geophysical effects from spurious effects in GPS time series. For this purpose, we used GPS position changes and environmental loading models. We analysed the topocentric time series from 250 selected stations located worldwide, delivered from Network Solution obtained by the International GNSS Service (IGS) as a contribution to the latest realization of the International Terrestrial Reference System (namely ITRF2014, Rebishung et al., 2016). We also researched atmospheric, hydrological and non-tidal oceanic loading models provided by the EOST/IPGS Loading Service in the Centre-of-Figure (CF) reference frame. The analysed displacements were estimated from ERA-Interim (surface pressure), MERRA-land (soil moisture and snow) as well as ECCO2 ocean bottom pressure. We used Multichannel Singular Spectrum Analysis to determine common seasonal signals in two case studies with adopted a 3-years lag-window as the optimal window size. We also inferred the statistical significance of oscillations through the Monte Carlo MSSA method (Allen and Robertson, 1996). In the first case study, we investigated the common spatio-temporal seasonal signals for all stations. For this purpose, we divided selected stations with respect to the continents. For instance, for stations located in Europe, seasonal oscillations accounts for approximately 45% of the GPS-derived data variance. Much higher variance of seasonal signals is explained by hydrological loadings of about 92%, while the non-tidal oceanic loading accounted for 31% of total variance. In the second case study, we analysed the capability of the MSSA method to establish a causality between several time series. Each of estimated Principal Component represents pattern of the common signal for all analysed data. For ZIMM station (Zimmerwald, Switzerland), the 1st, 2nd and 9th, 10th Principal Components, which accounts for 35% of the variance, corresponds to the annual and semi-annual signals. In this part, we applied the non-parametric MSSA approach to extract the common seasonal signals for GPS time series and environmental loadings for each of the 250 stations with clear statement, that some part of seasonal signal reflects the real geophysical effects. REFERENCES: 1. Allen, M. and Robertson, A.: 1996, Distinguishing modulated oscillations from coloured noise in multivariate datasets. Climate Dynamics, 12, No. 11, 775-784. DOI: 10.1007/s003820050142. 2. Dong, D., Fang, P., Bock, Y., Cheng, M.K. and Miyazaki, S.: 2002, Anatomy of apparent seasonal variations from GPS-derived site position time series. Journal of Geophysical Research, 107, No. B4, 2075. DOI: 10.1029/2001JB000573. 3. Rebischung, P., Altamimi, Z., Ray, J. and Garayt, B.: 2016, The IGS contribution to ITRF2014. Journal of Geodesy, 90, No. 7, 611-630. DOI:10.1007/s00190-016-0897-6.

A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling

NASA Astrophysics Data System (ADS)

Dai, Heng; Chen, Xingyuan; Ye, Ming; Song, Xuehang; Zachara, John M.

2017-05-01

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study, we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multilayer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially distributed input variables.

A Geostatistics-Informed Hierarchical Sensitivity Analysis Method for Complex Groundwater Flow and Transport Modeling

NASA Astrophysics Data System (ADS)

Dai, H.; Chen, X.; Ye, M.; Song, X.; Zachara, J. M.

2017-12-01

Sensitivity analysis is an important tool for development and improvement of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study we developed a new sensitivity analysis method that integrates the concept of variance-based method with a hierarchical uncertainty quantification framework. Different uncertain inputs are grouped and organized into a multi-layer framework based on their characteristics and dependency relationships to reduce the dimensionality of the sensitivity analysis. A set of new sensitivity indices are defined for the grouped inputs using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially-distributed input variables.

Modelling heterogeneity variances in multiple treatment comparison meta-analysis – Are informative priors the better solution?

PubMed Central

2013-01-01

Background Multiple treatment comparison (MTC) meta-analyses are commonly modeled in a Bayesian framework, and weakly informative priors are typically preferred to mirror familiar data driven frequentist approaches. Random-effects MTCs have commonly modeled heterogeneity under the assumption that the between-trial variance for all involved treatment comparisons are equal (i.e., the ‘common variance’ assumption). This approach ‘borrows strength’ for heterogeneity estimation across treatment comparisons, and thus, ads valuable precision when data is sparse. The homogeneous variance assumption, however, is unrealistic and can severely bias variance estimates. Consequently 95% credible intervals may not retain nominal coverage, and treatment rank probabilities may become distorted. Relaxing the homogeneous variance assumption may be equally problematic due to reduced precision. To regain good precision, moderately informative variance priors or additional mathematical assumptions may be necessary. Methods In this paper we describe four novel approaches to modeling heterogeneity variance - two novel model structures, and two approaches for use of moderately informative variance priors. We examine the relative performance of all approaches in two illustrative MTC data sets. We particularly compare between-study heterogeneity estimates and model fits, treatment effect estimates and 95% credible intervals, and treatment rank probabilities. Results In both data sets, use of moderately informative variance priors constructed from the pair wise meta-analysis data yielded the best model fit and narrower credible intervals. Imposing consistency equations on variance estimates, assuming variances to be exchangeable, or using empirically informed variance priors also yielded good model fits and narrow credible intervals. The homogeneous variance model yielded high precision at all times, but overall inadequate estimates of between-trial variances. Lastly, treatment rankings were similar among the novel approaches, but considerably different when compared with the homogenous variance approach. Conclusions MTC models using a homogenous variance structure appear to perform sub-optimally when between-trial variances vary between comparisons. Using informative variance priors, assuming exchangeability or imposing consistency between heterogeneity variances can all ensure sufficiently reliable and realistic heterogeneity estimation, and thus more reliable MTC inferences. All four approaches should be viable candidates for replacing or supplementing the conventional homogeneous variance MTC model, which is currently the most widely used in practice. PMID:23311298

Personal and Environmental Resources Mediate the Positivity-Emotional Dysfunction Relationship.

PubMed

Lehrer, H Matthew; Janus, Katherine C; Gloria, Christian T; Steinhardt, Mary A

2017-03-01

We investigated the relationships among positivity, perceived personal and environmental resources, and emotional dysfunction in adolescent girls. We hypothesized that perceived resources would mediate the relationship between positivity and emotional dysfunction. Participants (N = 510) attending an all-girls public school completed a survey assessing emotional dysfunction (depressive symptoms and perceived stress), positivity (positive/negative emotions), and personal/ environmental resources (resilience, hope, percent adaptive coping, community connectedness, social support, and school connectedness). Perceived resources were combined into one latent variable, and structural equation modeling tested the mediating effect of perceived resources on the relationship between positivity and emotional dysfunction. The model accounted for 63% of the variance in emotional dysfunction. Positivity exerted a significant direct effect on emotional dysfunction (β = -.14, p < .01), but its influence was primarily mediated through perceived resources (indirect effect: β = -.43, p < .001). The impact of positivity on emotional dysfunction is primarily but not entirely mediated by perceived personal and environmental resources. Schools should consider strategies to enhance experiences of positive emotions and/or decrease experiences of negative emotions, in conjunction with encouraging student awareness and development of personal and environmental resources.

Speeding Up Microevolution: The Effects of Increasing Temperature on Selection and Genetic Variance in a Wild Bird Population

PubMed Central

Husby, Arild; Visser, Marcel E.; Kruuk, Loeske E. B.

2011-01-01

The amount of genetic variance underlying a phenotypic trait and the strength of selection acting on that trait are two key parameters that determine any evolutionary response to selection. Despite substantial evidence that, in natural populations, both parameters may vary across environmental conditions, very little is known about the extent to which they may covary in response to environmental heterogeneity. Here we show that, in a wild population of great tits (Parus major), the strength of the directional selection gradients on timing of breeding increased with increasing spring temperatures, and that genotype-by-environment interactions also predicted an increase in additive genetic variance, and heritability, of timing of breeding with increasing spring temperature. Consequently, we therefore tested for an association between the annual selection gradients and levels of additive genetic variance expressed each year; this association was positive, but non-significant. However, there was a significant positive association between the annual selection differentials and the corresponding heritability. Such associations could potentially speed up the rate of micro-evolution and offer a largely ignored mechanism by which natural populations may adapt to environmental changes. PMID:21408101

Regional climates in the GISS global circulation model - Synoptic-scale circulation

NASA Technical Reports Server (NTRS)

Hewitson, B.; Crane, R. G.

1992-01-01

A major weakness of current general circulation models (GCMs) is their perceived inability to predict reliably the regional consequences of a global-scale change, and it is these regional-scale predictions that are necessary for studies of human-environmental response. For large areas of the extratropics, the local climate is controlled by the synoptic-scale atmospheric circulation, and it is the purpose of this paper to evaluate the synoptic-scale circulation of the Goddard Institute for Space Studies (GISS) GCM. A methodology for validating the daily synoptic circulation using Principal Component Analysis is described, and the methodology is then applied to the GCM simulation of sea level pressure over the continental United States (excluding Alaska). The analysis demonstrates that the GISS 4 x 5 deg GCM Model II effectively simulates the synoptic-scale atmospheric circulation over the United States. The modes of variance describing the atmospheric circulation of the model are comparable to those found in the observed data, and these modes explain similar amounts of variance in their respective datasets. The temporal behavior of these circulation modes in the synoptic time frame are also comparable.

Familial resemblance and shared latent familial variance in recurrent fall risk in older women

PubMed Central

Cauley, Jane A.; Roth, Stephen M.; Kammerer, Candace; Stone, Katie; Hillier, Teresa A.; Ensrud, Kristine E.; Hochberg, Marc; Nevitt, Michael C.; Zmuda, Joseph M.

2010-01-01

Background: A possible familial component to fracture risk may be mediated through a genetic liability to fall recurrently. Methods: Our analysis sample included 186 female sibling-ships (n = 401) of mean age 71.9 yr (SD = 5.0). Using variance component models, we estimated residual upper-limit heritabilities in fall-risk mobility phenotypes (e.g., chair-stand time, rapid step-ups, and usual-paced walking speed) and in recurrent falls. We also estimated familial and environmental (unmeasured) correlations between pairs of fall-risk mobility phenotypes. All models were adjusted for age, height, body mass index, and medical and environmental factors. Results: Residual upper-limit heritabilities were all moderate (P < 0.05), ranging from 0.27 for usual-paced walking speed to 0.58 for recurrent falls. A strong familial correlation between usual-paced walking speed and rapid step-ups of 0.65 (P < 0.01) was identified. Familial correlations between usual-paced walking speed and chair-stand time (−0.02) and between chair-stand time and rapid step-ups (−0.27) were both nonsignificant (P > 0.05). Environmental correlations ranged from 0.35 to 0.58 (absolute values), P < 0.05 for all. Conclusions: There exists moderate familial resemblance in fall-risk mobility phenotypes and recurrent falls among older female siblings, which we expect is primarily genetic given that adult siblings live separate lives. All fall-risk mobility phenotypes may be coinfluenced at least to a small degree by shared latent familial or environmental factors; however, up to approximately one-half of the covariation between usual-paced walking speed and rapid step-ups may be due to a common set of genes. PMID:20167680

40 CFR 142.311 - What procedures allow the Administrator to object to a proposed small system variance or overturn...

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 22 2010-07-01 2010-07-01 false What procedures allow the Administrator to object to a proposed small system variance or overturn a granted small system variance for a public water system serving 3,300 or fewer persons? 142.311 Section 142.311 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER...

40 CFR 142.311 - What procedures allow the Administrator to object to a proposed small system variance or overturn...

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 23 2014-07-01 2014-07-01 false What procedures allow the Administrator to object to a proposed small system variance or overturn a granted small system variance for a public water system serving 3,300 or fewer persons? 142.311 Section 142.311 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER...

40 CFR 142.42 - Consideration of a variance request.

Code of Federal Regulations, 2010 CFR

2010-07-01

... water source, the Administrator shall consider such factors as the following: (1) The availability and.... 142.42 Section 142.42 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS IMPLEMENTATION Variances Issued by the...

Are there common familial influences for major depressive disorder and an overeating-binge eating dimension in both European American and African American female twins?

PubMed

Munn-Chernoff, Melissa A; Grant, Julia D; Agrawal, Arpana; Koren, Rachel; Glowinski, Anne L; Bucholz, Kathleen K; Madden, Pamela A F; Heath, Andrew C; Duncan, Alexis E

2015-05-01

Although prior studies have demonstrated that depression is associated with an overeating-binge eating dimension (OE-BE) phenotypically, little research has investigated whether familial factors contribute to the co-occurrence of these phenotypes, especially in community samples with multiple racial/ethnic groups. We examined the extent to which familial (i.e., genetic and shared environmental) influences overlapped between Major Depressive Disorder (MDD) and OE-BE in a population-based sample and whether these influences were similar across racial/ethnic groups. Participants included 3,226 European American (EA) and 550 African American (AA) young adult women from the Missouri Adolescent Female Twin Study. An adaptation of the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) was administered to assess lifetime DSM-IV MDD and OE-BE. Quantitative genetic modeling was used to estimate familial influences between both phenotypes; all models controlled for age. The best-fitting model, which combined racial/ethnic groups, found that additive genetic influences accounted for 44% (95% CI: 34%, 53%) of the MDD variance and 40% (25%, 54%) for OE-BE, with the remaining variances due to non-shared environmental influences. Genetic overlap was substantial (rg  = .61 [.39, .85]); non-shared environmental influences on MDD and OE-BE overlapped weakly (re  = .26 [.09, .42]). Results suggest that common familial influences underlie MDD and OE-BE, and the magnitude of familial influences contributing to the comorbidity between MDD and OE-BE is similar between EA and AA women. If racial/ethnic differences truly exist, then larger sample sizes may be needed to fully elucidate familial risk for comorbid MDD and OE-BE across these groups. © 2014 Wiley Periodicals, Inc.

Are There Common Familial Influences for Major Depressive Disorder and an Overeating-Binge Eating Dimension in both European-American and African-American Female Twins?

PubMed Central

Munn-Chernoff, Melissa A.; Grant, Julia D.; Agrawal, Arpana; Koren, Rachel; Glowinski, Anne L.; Bucholz, Kathleen K.; Madden, Pamela A. F.; Heath, Andrew C.; Duncan, Alexis E.

2014-01-01

Objective Although prior studies have demonstrated that depression is associated with an overeating-binge eating dimension (OE-BE), phenotypically, little research has investigated whether familial factors contribute to the co-occurrence of these phenotypes, especially in community samples with multiple racial/ethnic groups. We examined the extent to which familial (i.e., genetic and shared environmental) influences overlapped between Major Depressive Disorder (MDD) and OE-BE in a population-based sample and whether these influences were similar across racial/ethnic groups Method Participants included 3226 European-American (EA) and 550 African-American (AA) young adult women from the Missouri Adolescent Female Twin Study. An adaptation of the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) was administered to assess lifetime DSM-IV MDD and OE-BE. Quantitative genetic modeling was used to estimate familial influences between both phenotypes; all models controlled for age. Results The best-fitting model, which combined racial/ethnic groups, found that additive genetic influences accounted for 44% (95% CI: 34%, 53%) of the MDD variance and 40% (25%, 54%) for OE-BE, with the remaining variances due to non-shared environmental influences. Genetic overlap was substantial (rg = .61 [.39, .85]); non-shared environmental influences on MDD and OE-BE overlapped weakly (re = .26 [.09, .42]) Discussion Results suggest that common familial influences underlie MDD and OE-BE, and the magnitude of familial influences contributing to the comorbidity between MDD and OE-BE is similar between EA and AA women. If racial/ethnic differences truly exist, then larger sample sizes may be needed to fully elucidate familial risk for comorbid MDD and OE-BE across these groups. PMID:24659561

Romantic Relationship Satisfaction Moderates the Etiology of Adult Personality.

PubMed

South, Susan C; Krueger, Robert F; Elkins, Irene J; Iacono, William G; McGue, Matt

2016-01-01

The heritability of major normative domains of personality is well-established, with approximately half the proportion of variance attributed to genetic differences. In the current study, we examine the possibility of gene × environment interaction (G×E) for adult personality using the environmental context of intimate romantic relationship functioning. Personality and relationship satisfaction are significantly correlated phenotypically, but to date no research has examined how the genetic and environmental components of variance for personality differ as a function of romantic relationship satisfaction. Given the importance of personality for myriad outcomes from work productivity to psychopathology, it is vital to identify variables present in adulthood that may affect the etiology of personality. In the current study, quantitative models of G×E were used to determine whether the genetic and environmental influences on personality differ as a function of relationship satisfaction. We drew from a sample of now-adult twins followed longitudinally from adolescence through age 29. All participants completed the Multidimensional Personality Questionnaire (MPQ) and an abbreviated version of the Dyadic Adjustment Scale. Biometric moderation was found for eight of the eleven MPQ scales examined: well-being, social potency, negative emotionality, alienation, aggression, constraint, traditionalism, and absorption. The pattern of findings differed, suggesting that the ways in which relationship quality moderates the etiology of personality may depend on the personality trait.

Energy, water and large-scale patterns of reptile and amphibian species richness in Europe

NASA Astrophysics Data System (ADS)

Rodríguez, Miguel Á.; Belmontes, Juan Alfonso; Hawkins, Bradford A.

2005-07-01

We used regression analyses to examine the relationships between reptile and amphibian species richness in Europe and 11 environmental variables related to five hypotheses for geographical patterns of species richness: (1) productivity; (2) ambient energy; (3) water-energy balance, (4) habitat heterogeneity; and (5) climatic variability. For reptiles, annual potential evapotranspiration (PET), a measure of the amount of atmospheric energy, explained 71% of the variance, with variability in log elevation explaining an additional 6%. For amphibians, annual actual evapotranspiration (AET), a measure of the joint availability of energy and water in the environment, and the global vegetation index, an estimate of plant biomass generated through satellite remote sensing, both described similar proportions of the variance (61% and 60%, respectively) and had partially independent effects on richness as indicated by multiple regression. The two-factor environmental models successfully removed most of the statistically detectable spatial autocorrelation in the richness data of both groups. Our results are consistent with reptile and amphibian environmental requirements, where the former depend strongly on solar energy and the latter require both warmth and moisture for reproduction. We conclude that ambient energy explains the reptile richness pattern, whereas for amphibians a combination of water-energy balance and productivity best explain the pattern.

Modeling Heterogeneous Variance-Covariance Components in Two-Level Models

ERIC Educational Resources Information Center

Leckie, George; French, Robert; Charlton, Chris; Browne, William

2014-01-01

Applications of multilevel models to continuous outcomes nearly always assume constant residual variance and constant random effects variances and covariances. However, modeling heterogeneity of variance can prove a useful indicator of model misspecification, and in some educational and behavioral studies, it may even be of direct substantive…

Prediction of episodic acidification in North-eastern USA: An empirical/mechanistic approach

USGS Publications Warehouse

Davies, T.D.; Tranter, M.; Wigington, P.J.; Eshleman, K.N.; Peters, N.E.; Van Sickle, J.; DeWalle, David R.; Murdoch, Peter S.

1999-01-01

Observations from the US Environmental Protection Agency's Episodic Response Project (ERP) in the North-eastern United States are used to develop an empirical/mechanistic scheme for prediction of the minimum values of acid neutralizing capacity (ANC) during episodes. An acidification episode is defined as a hydrological event during which ANC decreases. The pre-episode ANC is used to index the antecedent condition, and the stream flow increase reflects how much the relative contributions of sources of waters change during the episode. As much as 92% of the total variation in the minimum ANC in individual catchments can be explained (with levels of explanation >70% for nine of the 13 streams) by a multiple linear regression model that includes pre-episode ANC and change in discharge as independent variable. The predictive scheme is demonstrated to be regionally robust, with the regional variance explained ranging from 77 to 83%. The scheme is not successful for each ERP stream, and reasons are suggested for the individual failures. The potential for applying the predictive scheme to other watersheds is demonstrated by testing the model with data from the Panola Mountain Research Watershed in the South-eastern United States, where the variance explained by the model was 74%. The model can also be utilized to assess 'chemically new' and 'chemically old' water sources during acidification episodes.Observations from the US Environmental Protection Agency's Episodic Response Project (ERP) in the Northeastern United States are used to develop an empirical/mechanistic scheme for prediction of the minimum values of acid neutralizing capacity (ANC) during episodes. An acidification episode is defined as a hydrological event during which ANC decreases. The pre-episode ANC is used to index the antecedent condition, and the stream flow increase reflects how much the relative contributions of sources of waters change during the episode. As much as 92% of the total variation in the minimum ANC in individual catchments can be explained (with levels of explanation >70% for nine of the 13 streams) by a multiple linear regression model that includes pre-episode ANC and change in discharge as independent variables. The predictive scheme is demonstrated to be regionally robust, with the regional variance explained ranging from 77 to 83%. The scheme is not successful for each ERP stream, and reasons are suggested for the individual failures. The potential for applying the predictive scheme to other watersheds is demonstrated by testing the model with data from the Panola Mountain Research Watershed in the South-eastern United States, where the variance explained by the model was 74%. The model can also be utilized to assess `chemically new' and `chemically old' water sources during acidification episodes.

40 CFR 142.43 - Disposition of a variance request.

Code of Federal Regulations, 2012 CFR

2012-07-01

... no access to an alternative raw water source, and can effect or anticipate no adequate improvement of....43 Section 142.43 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAMS (CONTINUED) NATIONAL PRIMARY DRINKING WATER REGULATIONS IMPLEMENTATION Variances Issued by the...

Handling nonnormality and variance heterogeneity for quantitative sublethal toxicity tests.

PubMed

Ritz, Christian; Van der Vliet, Leana

2009-09-01

The advantages of using regression-based techniques to derive endpoints from environmental toxicity data are clear, and slowly, this superior analytical technique is gaining acceptance. As use of regression-based analysis becomes more widespread, some of the associated nuances and potential problems come into sharper focus. Looking at data sets that cover a broad spectrum of standard test species, we noticed that some model fits to data failed to meet two key assumptions-variance homogeneity and normality-that are necessary for correct statistical analysis via regression-based techniques. Failure to meet these assumptions often is caused by reduced variance at the concentrations showing severe adverse effects. Although commonly used with linear regression analysis, transformation of the response variable only is not appropriate when fitting data using nonlinear regression techniques. Through analysis of sample data sets, including Lemna minor, Eisenia andrei (terrestrial earthworm), and algae, we show that both the so-called Box-Cox transformation and use of the Poisson distribution can help to correct variance heterogeneity and nonnormality and so allow nonlinear regression analysis to be implemented. Both the Box-Cox transformation and the Poisson distribution can be readily implemented into existing protocols for statistical analysis. By correcting for nonnormality and variance heterogeneity, these two statistical tools can be used to encourage the transition to regression-based analysis and the depreciation of less-desirable and less-flexible analytical techniques, such as linear interpolation.

Heritability of physical activity traits in Brazilian families: the Baependi Heart Study

PubMed Central

2011-01-01

Background It is commonly recognized that physical activity has familial aggregation; however, the genetic influences on physical activity phenotypes are not well characterized. This study aimed to (1) estimate the heritability of physical activity traits in Brazilian families; and (2) investigate whether genetic and environmental variance components contribute differently to the expression of these phenotypes in males and females. Methods The sample that constitutes the Baependi Heart Study is comprised of 1,693 individuals in 95 Brazilian families. The phenotypes were self-reported in a questionnaire based on the WHO-MONICA instrument. Variance component approaches, implemented in the SOLAR (Sequential Oligogenic Linkage Analysis Routines) computer package, were applied to estimate the heritability and to evaluate the heterogeneity of variance components by gender on the studied phenotypes. Results The heritability estimates were intermediate (35%) for weekly physical activity among non-sedentary subjects (weekly PA_NS), and low (9-14%) for sedentarism, weekly physical activity (weekly PA), and level of daily physical activity (daily PA). Significant evidence for heterogeneity in variance components by gender was observed for the sedentarism and weekly PA phenotypes. No significant gender differences in genetic or environmental variance components were observed for the weekly PA_NS trait. The daily PA phenotype was predominantly influenced by environmental factors, with larger effects in males than in females. Conclusions Heritability estimates for physical activity phenotypes in this sample of the Brazilian population were significant in both males and females, and varied from low to intermediate magnitude. Significant evidence for heterogeneity in variance components by gender was observed. These data add to the knowledge of the physical activity traits in the Brazilian study population, and are concordant with the notion of significant biological determination in active behavior. PMID:22126647

Heritability of physical activity traits in Brazilian families: the Baependi Heart Study.

PubMed

Horimoto, Andréa R V R; Giolo, Suely R; Oliveira, Camila M; Alvim, Rafael O; Soler, Júlia P; de Andrade, Mariza; Krieger, José E; Pereira, Alexandre C

2011-11-29

It is commonly recognized that physical activity has familial aggregation; however, the genetic influences on physical activity phenotypes are not well characterized. This study aimed to (1) estimate the heritability of physical activity traits in Brazilian families; and (2) investigate whether genetic and environmental variance components contribute differently to the expression of these phenotypes in males and females. The sample that constitutes the Baependi Heart Study is comprised of 1,693 individuals in 95 Brazilian families. The phenotypes were self-reported in a questionnaire based on the WHO-MONICA instrument. Variance component approaches, implemented in the SOLAR (Sequential Oligogenic Linkage Analysis Routines) computer package, were applied to estimate the heritability and to evaluate the heterogeneity of variance components by gender on the studied phenotypes. The heritability estimates were intermediate (35%) for weekly physical activity among non-sedentary subjects (weekly PA_NS), and low (9-14%) for sedentarism, weekly physical activity (weekly PA), and level of daily physical activity (daily PA). Significant evidence for heterogeneity in variance components by gender was observed for the sedentarism and weekly PA phenotypes. No significant gender differences in genetic or environmental variance components were observed for the weekly PA_NS trait. The daily PA phenotype was predominantly influenced by environmental factors, with larger effects in males than in females. Heritability estimates for physical activity phenotypes in this sample of the Brazilian population were significant in both males and females, and varied from low to intermediate magnitude. Significant evidence for heterogeneity in variance components by gender was observed. These data add to the knowledge of the physical activity traits in the Brazilian study population, and are concordant with the notion of significant biological determination in active behavior.

Genetic and nonshared environmental factors affect the likelihood of being charged with driving under the influence (DUI) and driving while intoxicated (DWI).

PubMed

Beaver, Kevin M; Barnes, J C

2012-12-01

Driving under the influence (DUI) and driving while intoxicated (DWI) are related to a range of serious health, legal, and financial costs. Given the costs to society of DUIs and DWIs, there has been interest in identifying the causes of DUIs and DWIs. The current study added to this existing knowledge base by estimating genetic and environmental effects on DUIs and DWIs in a sample of twins drawn from the National Longitudinal Study of Adolescent Health (Add Health). The results of the analyses revealed that genetic factors explained 53% of the variance in DUIs/DWIs and the nonshared environment explained 47% of the variance. Shared environmental factors explained none of the variance in DUIs/DWIs. We conclude with a discussion of the results, the limitations of the study, and how the findings might be compatible with policies designed to reduce DUIs and DWIs. Copyright © 2012 Elsevier Ltd. All rights reserved.

Genetic and environmental mediation between measures of personality and family environment in twins reared together.

PubMed

Kandler, Christian; Riemann, Rainer; Kämpfe, Nicole

2009-01-01

In this study we analyzed the etiology of the relationship between personality traits and retrospectively recalled family environment. The data of 226 identical and 168 fraternal twin pairs reared together from the Jena twin study of social attitudes were available. Personality traits were measured using the self- and peer report versions of the German NEO-personality inventory-revised. A German version of Blocks Environmental Questionnaire was applied to measure two broad dimensions of the family environment retrospectively: support and organization. We could replicate earlier findings that retrospective reports of these family environment dimensions were in part genetically influenced. A total of 66% of the genetic variance in support and 24% in organization could be accounted for by heritable variance in self-rated personality. That was replicated by using peer reports of personality, 41% explained genetic variance in support and 17% in organization. Environmental mediations were negligible. This indicates that the relationship between personality and retrospectively recalled family environment is largely genetically mediated.

Environmental factors controlling spatial variation in sediment yield in a central Andean mountain area

NASA Astrophysics Data System (ADS)

Molina, Armando; Govers, Gerard; Poesen, Jean; Van Hemelryck, Hendrik; De Bièvre, Bert; Vanacker, Veerle

2008-06-01

A large spatial variability in sediment yield was observed from small streams in the Ecuadorian Andes. The objective of this study was to analyze the environmental factors controlling these variations in sediment yield in the Paute basin, Ecuador. Sediment yield data were calculated based on sediment volumes accumulated behind checkdams for 37 small catchments. Mean annual specific sediment yield (SSY) shows a large spatial variability and ranges between 26 and 15,100 Mg km - 2 year - 1 . Mean vegetation cover (C, fraction) in the catchment, i.e. the plant cover at or near the surface, exerts a first order control on sediment yield. The fractional vegetation cover alone explains 57% of the observed variance in ln(SSY). The negative exponential relation (SSY = a × e- b C) which was found between vegetation cover and sediment yield at the catchment scale (10 3-10 9 m 2), is very similar to the equations derived from splash, interrill and rill erosion experiments at the plot scale (1-10 3 m 2). This affirms the general character of an exponential decrease of sediment yield with increasing vegetation cover at a wide range of spatial scales, provided the distribution of cover can be considered to be essentially random. Lithology also significantly affects the sediment yield, and explains an additional 23% of the observed variance in ln(SSY). Based on these two catchment parameters, a multiple regression model was built. This empirical regression model already explains more than 75% of the total variance in the mean annual sediment yield. These results highlight the large potential of revegetation programs for controlling sediment yield. They show that a slight increase in the overall fractional vegetation cover of degraded land is likely to have a large effect on sediment production and delivery. Moreover, they point to the importance of detailed surface vegetation data for predicting and modeling sediment production rates.

Moderation of Harsh Parenting on Genetic and Environmental Contributions to Child and Adolescent Deviant Peer Affiliation: A Longitudinal Twin Study.

PubMed

Li, Mengjiao; Chen, Jie; Li, Xinying; Deater-Deckard, Kirby

2015-07-01

Affiliation with deviant peers is associated with biologically influenced personal attributes, and is itself a major contributor to growth in antisocial behavior over childhood and adolescence. Several studies have shown that variance in child and adolescent deviant peer affiliation includes genetic and non-genetic influences, but none have examined longitudinal genetic and environmental stability or change within the context of harsh parenting. To address this gap, we tested the moderating role of harsh parenting on genetic and environmental stability or change of deviant peer affiliation in a longitudinal (spanning one and a half years) study of Chinese child and adolescent twin pairs (N = 993, 52.0% female). Using multiple informants (child- and parent-reports) and measurement methods to minimize rater bias, we found that individual differences in deviant peer affiliation at each assessment were similarly explained by moderate genetic and nonshared environmental variance. The longitudinal stability and change of deviant peer affiliation were explained by genetic and nonshared environmental factors. The results also revealed that the genetic variance for deviant peer affiliation is higher in the families with harsher parenting. This amplified genetic risk underscores the role of harsh parenting in the selection and socialization process of deviant peer relationships.

Motor impulsivity during childhood and adolescence: a longitudinal biometric analysis of the go/no-go task in 9- to 18-year-old twins.

PubMed

Bezdjian, Serena; Tuvblad, Catherine; Wang, Pan; Raine, Adrian; Baker, Laura A

2014-11-01

In the present study, we investigated genetic and environmental effects on motor impulsivity from childhood to late adolescence using a longitudinal sample of twins from ages 9 to 18 years. Motor impulsivity was assessed using errors of commission (no-go errors) in a visual go/no-go task at 4 time points: ages 9-10, 11-13, 14-15, and 16-18 years. Significant genetic and nonshared environmental effects on motor impulsivity were found at each of the 4 waves of assessment with genetic factors explaining 22%-41% of the variance within each of the 4 waves. Phenotypically, children's average performance improved across age (i.e., fewer no-go errors during later assessments). Multivariate biometric analyses revealed that common genetic factors influenced 12%-40% of the variance in motor impulsivity across development, whereas nonshared environmental factors common to all time points contributed to 2%-52% of the variance. Nonshared environmental influences specific to each time point also significantly influenced motor impulsivity. Overall, results demonstrated that although genetic factors were critical to motor impulsivity across development, both common and specific nonshared environmental factors played a strong role in the development of motor impulsivity across age. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

A Variance Distribution Model of Surface EMG Signals Based on Inverse Gamma Distribution.

PubMed

Hayashi, Hideaki; Furui, Akira; Kurita, Yuichi; Tsuji, Toshio

2017-11-01

Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force. Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force.

A Genetic Epidemiological Mega Analysis of Smoking Initiation in Adolescents

PubMed Central

Prom-Wormley, Elizabeth; Eaves, Lindon J.; Rhee, Soo Hyun; Hewitt, John K.; Young, Susan; Corley, Robin; McGue, Matt; Iacono, William G.; Legrand, Lisa; Samek, Diana R.; Murrelle, E. Lenn; Silberg, Judy L.; Miles, Donna R.; Schieken, Richard M.; Beunen, Gaston P.; Thomis, Martine; Rose, Richard J.; Dick, Danielle M.; Boomsma, Dorret I.; Bartels, Meike; Vink, Jacqueline M.; Lichtenstein, Paul; White, Victoria; Kaprio, Jaakko; Neale, Michael C.

2017-01-01

Abstract Introduction: Previous studies in adolescents were not adequately powered to accurately disentangle genetic and environmental influences on smoking initiation (SI) across adolescence. Methods: Mega-analysis of pooled genetically informative data on SI was performed, with structural equation modeling, to test equality of prevalence and correlations across cultural backgrounds, and to estimate the significance and effect size of genetic and environmental effects according to the classical twin study, in adolescent male and female twins from same-sex and opposite-sex twin pairs (N = 19 313 pairs) between ages 10 and 19, with 76 358 longitudinal assessments between 1983 and 2007, from 11 population-based twin samples from the United States, Europe, and Australia. Results: Although prevalences differed between samples, twin correlations did not, suggesting similar etiology of SI across developed countries. The estimate of additive genetic contributions to liability of SI increased from approximately 15% to 45% from ages 13 to 19. Correspondingly, shared environmental factors accounted for a substantial proportion of variance in liability to SI at age 13 (70%) and gradually less by age 19 (40%). Conclusions: Both additive genetic and shared environmental factors significantly contribute to variance in SI throughout adolescence. The present study, the largest genetic epidemiological study on SI to date, found consistent results across 11 studies for the etiology of SI. Environmental factors, especially those shared by siblings in a family, primarily influence SI variance in early adolescence, while an increasing role of genetic factors is seen at later ages, which has important implications for prevention strategies. Implications: This is the first study to find evidence of genetic factors in liability to SI at ages as young as 12. It also shows the strongest evidence to date for decay of effects of the shared environment from early adolescence to young adulthood. We found remarkable consistency of twin correlations across studies reflecting similar etiology of liability to initiate smoking across different cultures and time periods. Thus familial factors strongly contribute to individual differences in who starts to smoke with a gradual increase in the impact of genetic factors and a corresponding decrease in that of the shared environment. PMID:27807125

Relative importance of local habitat complexity and regional factors for assemblages of oribatid mites (Acari: Oribatida) in Sphagnum peat bogs.

PubMed

Minor, M A; Ermilov, S G; Philippov, D A; Prokin, A A

2016-11-01

We investigated communities of oribatid mites in five peat bogs in the north-west of the East European plain. We aimed to determine the extent to which geographic factors (latitude, separation distance), local environment (Sphagnum moss species, ground water level, biogeochemistry) and local habitat complexity (diversity of vascular plants and bryophytes in the surrounding plant community) influence diversity and community composition of Oribatida. There was a significant north-to-south increase in Oribatida abundance. In the variance partitioning, spatial factors explained 33.1 % of variability in abundance across samples; none of the environmental factors were significant. Across all bogs, Oribatida species richness and community composition were similar in Sphagnum rubellum and Sphagnum magellanicum, but significantly different and less diverse in Sphagnum cuspidatum. Sphagnum microhabitat explained 52.2 % of variability in Oribatida species richness, whereas spatial variables explained only 8.7 %. There was no distance decay in community similarity between bogs with increased geographical distance. The environmental variables explained 34.9 % of the variance in community structure, with vascular plants diversity, bryophytes diversity, and ground water level all contributing significantly; spatial variables explained 15.1 % of the total variance. Overall, only 50 % of the Oribatida community variance was explained by the spatial structure and environmental variables. We discuss relative importance of spatial and local environmental factors, and make general inferences about the formation of fauna in Sphagnum bogs.

A comparison between Poisson and zero-inflated Poisson regression models with an application to number of black spots in Corriedale sheep

PubMed Central

Naya, Hugo; Urioste, Jorge I; Chang, Yu-Mei; Rodrigues-Motta, Mariana; Kremer, Roberto; Gianola, Daniel

2008-01-01

Dark spots in the fleece area are often associated with dark fibres in wool, which limits its competitiveness with other textile fibres. Field data from a sheep experiment in Uruguay revealed an excess number of zeros for dark spots. We compared the performance of four Poisson and zero-inflated Poisson (ZIP) models under four simulation scenarios. All models performed reasonably well under the same scenario for which the data were simulated. The deviance information criterion favoured a Poisson model with residual, while the ZIP model with a residual gave estimates closer to their true values under all simulation scenarios. Both Poisson and ZIP models with an error term at the regression level performed better than their counterparts without such an error. Field data from Corriedale sheep were analysed with Poisson and ZIP models with residuals. Parameter estimates were similar for both models. Although the posterior distribution of the sire variance was skewed due to a small number of rams in the dataset, the median of this variance suggested a scope for genetic selection. The main environmental factor was the age of the sheep at shearing. In summary, age related processes seem to drive the number of dark spots in this breed of sheep. PMID:18558072

Genetic contribution to patent ductus arteriosus in the premature newborn.

PubMed

Bhandari, Vineet; Zhou, Gongfu; Bizzarro, Matthew J; Buhimschi, Catalin; Hussain, Naveed; Gruen, Jeffrey R; Zhang, Heping

2009-02-01

The most common congenital heart disease in the newborn population, patent ductus arteriosus, accounts for significant morbidity in preterm newborns. In addition to prematurity and environmental factors, we hypothesized that genetic factors play a significant role in this condition. The objective of this study was to quantify the contribution of genetic factors to the variance in liability for patent ductus arteriosus in premature newborns. A retrospective study (1991-2006) from 2 centers was performed by using zygosity data from premature twins born at < or =36 weeks' gestational age and surviving beyond 36 weeks' postmenstrual age. Patent ductus arteriosus was diagnosed by echocardiography at each center. Mixed-effects logistic regression was used to assess the effect of specific covariates. Latent variable probit modeling was then performed to estimate the heritability of patent ductus arteriosus, and mixed-effects probit modeling was used to quantify the genetic component. We obtained data from 333 dizygotic twin pairs and 99 monozygotic twin pairs from 2 centers (Yale University and University of Connecticut). Data on chorioamnionitis, antenatal steroids, gestational age, body weight, gender, respiratory distress syndrome, patent ductus arteriosus, necrotizing enterocolitis, oxygen supplementation, and bronchopulmonary dysplasia were comparable between monozygotic and dizygotic twins. We found that gestational age, respiratory distress syndrome, and institution were significant covariates for patent ductus arteriosus. After controlling for specific covariates, genetic factors or the shared environment accounted for 76.1% of the variance in liability for patent ductus arteriosus. Preterm patent ductus arteriosus is highly familial (contributed to by genetic and environmental factors), with the effect being mainly environmental, after controlling for known confounders.

REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits.

PubMed

Olivoto, T; Nardino, M; Carvalho, I R; Follmann, D N; Ferrari, M; Szareski, V J; de Pelegrin, A J; de Souza, V Q

2017-03-22

Methodologies using restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) in combination with sequential path analysis in maize are still limited in the literature. Therefore, the aims of this study were: i) to use REML/BLUP-based procedures in order to estimate variance components, genetic parameters, and genotypic values of simple maize hybrids, and ii) to fit stepwise regressions considering genotypic values to form a path diagram with multi-order predictors and minimum multicollinearity that explains the relationships of cause and effect among grain yield-related traits. Fifteen commercial simple maize hybrids were evaluated in multi-environment trials in a randomized complete block design with four replications. The environmental variance (78.80%) and genotype-vs-environment variance (20.83%) accounted for more than 99% of the phenotypic variance of grain yield, which difficult the direct selection of breeders for this trait. The sequential path analysis model allowed the selection of traits with high explanatory power and minimum multicollinearity, resulting in models with elevated fit (R 2 > 0.9 and ε < 0.3). The number of kernels per ear (NKE) and thousand-kernel weight (TKW) are the traits with the largest direct effects on grain yield (r = 0.66 and 0.73, respectively). The high accuracy of selection (0.86 and 0.89) associated with the high heritability of the average (0.732 and 0.794) for NKE and TKW, respectively, indicated good reliability and prospects of success in the indirect selection of hybrids with high-yield potential through these traits. The negative direct effect of NKE on TKW (r = -0.856), however, must be considered. The joint use of mixed models and sequential path analysis is effective in the evaluation of maize-breeding trials.

Evidence for Gender-Dependent Genotype by Environment Interaction in Adult Depression.

PubMed

Molenaar, Dylan; Middeldorp, Christel M; Willemsen, Gonneke; Ligthart, Lannie; Nivard, Michel G; Boomsma, Dorret I

2015-10-14

Depression in adults is heritable with about 40 % of the phenotypic variance due to additive genetic effects and the remaining phenotypic variance due to unique (unshared) environmental effects. Common environmental effects shared by family members are rarely found in adults. One possible explanation for this finding is that there is an interaction between genes and the environment which may mask effects of the common environment. To test this hypothesis, we investigated genotype by environment interaction in a large sample of female and male adult twins aged 18-70 years. The anxious depression subscale of the Adult Self Report from the Achenbach System of Empirically Based Assessment (Achenbach and Rescorla in Manual for the ASEBA adult: forms and profiles, 2003) was completed by 13,022 twins who participate in longitudinal studies of the Netherlands Twin Register. In a single group analysis, we found genotype by unique environment interaction, but no genotype by common environment interaction. However, when conditioning on gender, we observed genotype by common environment interaction in men, with larger common environmental variance in men who are genetically less at risk to develop depression. Although the effect size of the interaction is characterized by large uncertainty, the results show that there is at least some variance due to the common environment in adult depression in men.

The More, the Better? Curvilinear Effects of Job Autonomy on Well-Being From Vitamin Model and PE-Fit Theory Perspectives.

PubMed

Stiglbauer, Barbara; Kovacs, Carrie

2017-12-28

In organizational psychology research, autonomy is generally seen as a job resource with a monotone positive relationship with desired occupational outcomes such as well-being. However, both Warr's vitamin model and person-environment (PE) fit theory suggest that negative outcomes may result from excesses of some job resources, including autonomy. Thus, the current studies used survey methodology to explore cross-sectional relationships between environmental autonomy, person-environment autonomy (mis)fit, and well-being. We found that autonomy and autonomy (mis)fit explained between 6% and 22% of variance in well-being, depending on type of autonomy (scheduling, method, or decision-making) and type of (mis)fit operationalization (atomistic operationalization through the separate assessment of actual and ideal autonomy levels vs. molecular operationalization through the direct assessment of perceived autonomy (mis)fit). Autonomy (mis)fit (PE-fit perspective) explained more unique variance in well-being than environmental autonomy itself (vitamin model perspective). Detrimental effects of autonomy excess on well-being were most evident for method autonomy and least consistent for decision-making autonomy. We argue that too-much-of-a-good-thing effects of job autonomy on well-being exist, but suggest that these may be dependent upon sample characteristics (range of autonomy levels), type of operationalization (molecular vs. atomistic fit), autonomy facet (method, scheduling, or decision-making), as well as individual and organizational moderators. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Chlorophyll a and inorganic suspended solids in backwaters of the upper Mississippi River system: Backwater lake effects and their associations with selected environmental predictors

USGS Publications Warehouse

Rogala, James T.; Gray, Brian R.

2006-01-01

The Long Term Resource Monitoring Program (LTRMP) uses a stratified random sampling design to obtain water quality statistics within selected study reaches of the Upper Mississippi River System (UMRS). LTRMP sampling strata are based on aquatic area types generally found in large rivers (e.g., main channel, side channel, backwater, and impounded areas). For hydrologically well-mixed strata (i.e., main channel), variance associated with spatial scales smaller than the strata scale is a relatively minor issue for many water quality parameters. However, analysis of LTRMP water quality data has shown that within-strata variability at the strata scale is high in off-channel areas (i.e., backwaters). A portion of that variability may be associated with differences among individual backwater lakes (i.e., small and large backwater regions separated by channels) that cumulatively make up the backwater stratum. The objective of the statistical modeling presented here is to determine if differences among backwater lakes account for a large portion of the variance observed in the backwater stratum for selected parameters. If variance associated with backwater lakes is high, then inclusion of backwater lake effects within statistical models is warranted. Further, lakes themselves may represent natural experimental units where associations of interest to management may be estimated.

Informing the Human Plasma Protein Binding of ...

EPA Pesticide Factsheets

The free fraction of a xenobiotic in plasma (Fub) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data is scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict Fub for environmentally relevant chemicals via machine learning techniques. Quantitative structure-activity relationship (QSAR) models were constructed with k nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals. The models were then evaluated with independent test sets of pharmaceuticals (200 compounds) and environmentally relevant ToxCast chemicals (406 total, in two groups of 238 and 168 compounds). The selection of a minimal feature set of 10-15 2D molecular descriptors allowed for both informative feature interpretation and practical applicability domain assessment via a bounded box of descriptor ranges and principal component analysis. The diverse pharmaceutical and environmental chemical sets exhibit similarities in terms of chemical space (99-82% overlap), as well as comparable bias and variance in constructed learning curves. All the models exhibit significant predictability with mean absolute errors (MAE) in the range of 0.10-0.18 Fub. The models performed best for highly bound chemicals (MAE 0.07-0.12), neutrals (MAE 0

Alcohol use by undergraduate students on their 21st birthday: predictors of actual consumption, anticipated consumption, and normative beliefs.

PubMed

Day-Cameron, Jennifer M; Muse, Lauren; Hauenstein, Jennifer; Simmons, Lisa; Correia, Christopher J

2009-12-01

Recent research has identified celebration of a 21st birthday as an environmental event during which many college students engage in risky levels of alcohol consumption. The current study examined the relationship between personality and different aspects of alcohol use during 21st birthday celebrations: actual amount consumed for those who had turned 21, anticipated amount consumed for those under the age of 21, and normative beliefs regarding the amount other students consume on their 21st birthdays. Sensation seeking and impulsivity both displayed significant bivariate relationships with all three aspects of 21st birthday drinking. Personality traits did not contribute unique variance to actual 21st birthday drinking after the effects of typical alcohol consumption were accounted for in the models. Impulsivity contributed unique variance to models accounting for anticipated drinking and normative beliefs. Additional research is necessary to better understand the role personality variables play on alcohol consumption during 21st birthday celebrations. Copyright 2009 APA

A social relations model of observed family negativity and positivity using a genetically informative sample.

PubMed

Rasbash, Jon; Jenkins, Jennifer; O'Connor, Thomas G; Tackett, Jennifer; Reiss, David

2011-03-01

The goal of this study was to investigate individual and relationship influences on expressions of negativity and positivity in families. Parents and adolescents were observed in a round-robin design in a sample of 687 families. Data were analyzed using a multilevel social relations model. In addition, genetic contributions were estimated for actor effects. Children showed higher mean levels of negativity and lower mean levels of positivity as actors than did parents. Mothers were found to express and elicit higher mean levels of positivity and negativity than fathers. Actor effects were much stronger than partner effects, accounting for between 18%-39% of the variance depending on the actor and the outcome. Genetic (35%) and shared environmental (19%) influences explained a substantial proportion of the actor effect variance for negativity. Dyadic reciprocities were lowest in dyads with a high power differential (i.e., parent-child dyads) and highest for dyads with equal power (sibling and marital dyads). (c) 2011 APA, all rights reserved

Risk modelling in portfolio optimization

NASA Astrophysics Data System (ADS)

Lam, W. H.; Jaaman, Saiful Hafizah Hj.; Isa, Zaidi

2013-09-01

Risk management is very important in portfolio optimization. The mean-variance model has been used in portfolio optimization to minimize the investment risk. The objective of the mean-variance model is to minimize the portfolio risk and achieve the target rate of return. Variance is used as risk measure in the mean-variance model. The purpose of this study is to compare the portfolio composition as well as performance between the optimal portfolio of mean-variance model and equally weighted portfolio. Equally weighted portfolio means the proportions that are invested in each asset are equal. The results show that the portfolio composition of the mean-variance optimal portfolio and equally weighted portfolio are different. Besides that, the mean-variance optimal portfolio gives better performance because it gives higher performance ratio than the equally weighted portfolio.

Genetic influences on alcohol-related hangover.

PubMed

Slutske, Wendy S; Piasecki, Thomas M; Nathanson, Lisa; Statham, Dixie J; Martin, Nicholas G

2014-12-01

To quantify the relative contributions of genetic and environmental factors to alcohol hangover. Biometric models were used to partition the variance in hangover phenotypes. A community-based sample of Australian twins. Members of the Australian Twin Registry, Cohort II who reported consuming alcohol in the past year when surveyed in 2004-07 (n = 4496). Telephone interviews assessed participants' frequency of drinking to intoxication and frequency of hangover the day after drinking. Analyses examined three phenotypes: hangover frequency, hangover susceptibility (i.e. residual variance in hangover frequency after accounting for intoxication frequency) and hangover resistance (a dichotomous variable defined as having been intoxicated at least once in the past year with no reported hangovers). Genetic factors accounted for 45% [95% confidence interval (CI) = 37-53%] and 40% (95% CI = 33-48%) of the variation in hangover frequency in men and women, respectively. Most of the genetic variation in hangover frequency overlapped with genetic contributions to intoxication frequency. Genetic influences accounted for 24% (95% CI = 14-35%) and 16% (95% CI = 8-25%) of the residual hangover susceptibility variance in men and women, respectively. Forty-three per cent (95% CI = 22-63%) of the variation in hangover resistance was explained by genetic influences, with no evidence for significant sex differences. There was no evidence for shared environmental influences for any of the hangover phenotypes. Individual differences in the propensity to experience a hangover and of being resistant to hangover at a given level of alcohol use are genetically influenced. © 2014 Society for the Study of Addiction.

On the application of multilevel modeling in environmental and ecological studies

USGS Publications Warehouse

Qian, Song S.; Cuffney, Thomas F.; Alameddine, Ibrahim; McMahon, Gerard; Reckhow, Kenneth H.

2010-01-01

This paper illustrates the advantages of a multilevel/hierarchical approach for predictive modeling, including flexibility of model formulation, explicitly accounting for hierarchical structure in the data, and the ability to predict the outcome of new cases. As a generalization of the classical approach, the multilevel modeling approach explicitly models the hierarchical structure in the data by considering both the within- and between-group variances leading to a partial pooling of data across all levels in the hierarchy. The modeling framework provides means for incorporating variables at different spatiotemporal scales. The examples used in this paper illustrate the iterative process of model fitting and evaluation, a process that can lead to improved understanding of the system being studied.

Spawning stock and recruitment in North Sea cod shaped by food and climate

PubMed Central

Olsen, Esben Moland; Ottersen, Geir; Llope, Marcos; Chan, Kung-Sik; Beaugrand, Grégory; Stenseth, Nils Chr.

2011-01-01

In order to provide better fisheries management and conservation decisions, there is a need to discern the underlying relationship between the spawning stock and recruitment of marine fishes, a relationship which is influenced by the environmental conditions. Here, we demonstrate how the environmental conditions (temperature and the food availability for fish larvae) influence the stock–recruitment relationship and indeed what kind of stock–recruitment relationship we might see under different environmental conditions. Using unique zooplankton data from the Continuous Plankton Recorder, we find that food availability (i.e. zooplankton) in essence determines which model applies for the once large North Sea cod (Gadus morhua) stock. Further, we show that recruitment is strengthened during cold years and weakened during warm years. Our combined model explained 45 per cent of the total variance in cod recruitment, while the traditional Ricker and Beverton–Holt models only explained about 10 per cent. Specifically, our approach predicts that a full recovery of the North Sea cod stock might not be expected until the environment becomes more favourable. PMID:20810442

Use of random regression to estimate genetic parameters of temperament across an age continuum in a crossbred cattle population.

PubMed

Littlejohn, B P; Riley, D G; Welsh, T H; Randel, R D; Willard, S T; Vann, R C

2018-05-12

The objective was to estimate genetic parameters of temperament in beef cattle across an age continuum. The population consisted predominantly of Brahman-British crossbred cattle. Temperament was quantified by: 1) pen score (PS), the reaction of a calf to a single experienced evaluator on a scale of 1 to 5 (1 = calm, 5 = excitable); 2) exit velocity (EV), the rate (m/sec) at which a calf traveled 1.83 m upon exiting a squeeze chute; and 3) temperament score (TS), the numerical average of PS and EV. Covariates included days of age and proportion of Bos indicus in the calf and dam. Random regression models included the fixed effects determined from the repeated measures models, except for calf age. Likelihood ratio tests were used to determine the most appropriate random structures. In repeated measures models, the proportion of Bos indicus in the calf was positively related with each calf temperament trait (0.41 ± 0.20, 0.85 ± 0.21, and 0.57 ± 0.18 for PS, EV, and TS, respectively; P < 0.01). There was an effect of contemporary group (combinations of season, year of birth, and management group) and dam age (P < 0.001) in all models. From repeated records analyses, estimates of heritability (h2) were 0.34 ± 0.04, 0.31 ± 0.04, and 0.39 ± 0.04, while estimates of permanent environmental variance as a proportion of the phenotypic variance (c2) were 0.30 ± 0.04, 0.31 ± 0.03, and 0.34 ± 0.04 for PS, EV, and TS, respectively. Quadratic additive genetic random regressions on Legendre polynomials of age were significant for all traits. Quadratic permanent environmental random regressions were significant for PS and TS, but linear permanent environmental random regressions were significant for EV. Random regression results suggested that these components change across the age dimension of these data. There appeared to be an increasing influence of permanent environmental effects and decreasing influence of additive genetic effects corresponding to increasing calf age for EV, and to a lesser extent for TS. Inherited temperament may be overcome by accumulating environmental stimuli with increases in age, especially after weaning.

40 CFR 260.30 - Non-waste determinations and variances from classification as a solid waste.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 40 Protection of Environment 27 2012-07-01 2012-07-01 false Non-waste determinations and variances from classification as a solid waste. 260.30 Section 260.30 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) HAZARDOUS WASTE MANAGEMENT SYSTEM: GENERAL Rulemaking...

40 CFR 260.30 - Non-waste determinations and variances from classification as a solid waste.

Code of Federal Regulations, 2010 CFR

2010-07-01

... from classification as a solid waste. 260.30 Section 260.30 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) HAZARDOUS WASTE MANAGEMENT SYSTEM: GENERAL Rulemaking Petitions § 260.30 Non-waste determinations and variances from classification as a solid waste. In...

40 CFR 260.30 - Non-waste determinations and variances from classification as a solid waste.

Code of Federal Regulations, 2011 CFR

2011-07-01

... from classification as a solid waste. 260.30 Section 260.30 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) HAZARDOUS WASTE MANAGEMENT SYSTEM: GENERAL Rulemaking Petitions § 260.30 Non-waste determinations and variances from classification as a solid waste. In...

40 CFR 260.30 - Non-waste determinations and variances from classification as a solid waste.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 40 Protection of Environment 27 2013-07-01 2013-07-01 false Non-waste determinations and variances from classification as a solid waste. 260.30 Section 260.30 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) HAZARDOUS WASTE MANAGEMENT SYSTEM: GENERAL Rulemaking...

40 CFR 260.30 - Non-waste determinations and variances from classification as a solid waste.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 26 2014-07-01 2014-07-01 false Non-waste determinations and variances from classification as a solid waste. 260.30 Section 260.30 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) HAZARDOUS WASTE MANAGEMENT SYSTEM: GENERAL Rulemaking...

Evaluating a Model of Youth Physical Activity

PubMed Central

Heitzler, Carrie D.; Lytle, Leslie A.; Erickson, Darin J.; Barr-Anderson, Daheia; Sirard, John R.; Story, Mary

2011-01-01

Objective To explore the relationship between social influences, self-efficacy, enjoyment, and barriers and physical activity. Methods Structural equation modeling examined relationships between parent and peer support, parent physical activity, individual perceptions, and objectively measured physical activity using accelerometers among a sample of youth aged 10–17 years (N=720). Results Peer support, parent physical activity, and perceived barriers were directly related to youth activity. The proposed model accounted for 14.7% of the variance in physical activity. Conclusions The results demonstrate a need to further explore additional individual, social, and environmental factors that may influence youth’s regular participation in physical activity. PMID:20524889

Genetic and environmental influences on adolescents' smoking involvement: a multi-informant twin study.

PubMed

Seglem, Karoline Brobakke; Waaktaar, Trine; Ask, Helga; Torgersen, Svenn

2015-03-01

Studying monozygotic and dizygotic adolescent twin pairs of both sexes reared together, the present study examined the extent to which the variance in smoking involvement is attributable to genetic and environmental effects, and to what extent there are sex differences in the etiology. Questionnaire data on how often the adolescent had ever smoked tobacco was collected from a population-based twin sample consisting of seven national birth cohorts (ages 12-18), their mothers, and their fathers (N = 1,394 families). The data was analyzed with multivariate genetic modeling, using a multi-informant design. The etiological structure of smoking involvement was best represented in an ACE common pathway model, with smoking defined as a latent factor loading onto all three informants' reports. Estimates could be set equal across sexes. Results showed that adolescent lifetime smoking involvement was moderately heritable (37 %). The largest influence was from the shared environment (56 %), while environmental effects unique to each twin had minimal influence (7 %).

Multifactorial inheritance with cultural transmission and assortative mating. II. a general model of combined polygenic and cultural inheritance.

PubMed Central

Cloninger, C R; Rice, J; Reich, T

1979-01-01

A general linear model of combined polygenic-cultural inheritance is described. The model allows for phenotypic assortative mating, common environment, maternal and paternal effects, and genic-cultural correlation. General formulae for phenotypic correlation between family members in extended pedigrees are given for both primary and secondary assortative mating. A FORTRAN program BETA, available upon request, is used to provide maximum likelihood estimates of the parameters from reported correlations. American data about IQ and Burks' culture index are analyzed. Both cultural and genetic components of phenotypic variance are observed to make significant and substantial contributions to familial resemblance in IQ. The correlation between the environments of DZ twins is found to equal that of singleton sibs, not that of MZ twins. Burks' culture index is found to be an imperfect measure of midparent IQ rather than an index of home environment as previously assumed. Conditions under which the parameters of the model may be uniquely and precisely estimated are discussed. Interpretation of variance components in the presence of assortative mating and genic-cultural covariance is reviewed. A conservative, but robust, approach to the use of environmental indices is described. PMID:453202

Testing the psychometric properties of the Environmental Attitudes Inventory on undergraduate students in the Arab context: A test-retest approach.

PubMed

AlMenhali, Entesar Ali; Khalid, Khalizani; Iyanna, Shilpa

2018-01-01

The Environmental Attitudes Inventory (EAI) was developed to evaluate the multidimensional nature of environmental attitudes; however, it is based on a dataset from outside the Arab context. This study reinvestigated the construct validity of the EAI with a new dataset and confirmed the feasibility of applying it in the Arab context. One hundred and forty-eight subjects in Study 1 and 130 in Study 2 provided valid responses. An exploratory factor analysis (EFA) was used to extract a new factor structure in Study 1, and confirmatory factor analysis (CFA) was performed in Study 2. Both studies generated a seven-factor model, and the model fit was discussed for both the studies. Study 2 exhibited satisfactory model fit indices compared to Study 1. Factor loading values of a few items in Study 1 affected the reliability values and average variance extracted values, which demonstrated low discriminant validity. Based on the results of the EFA and CFA, this study showed sufficient model fit and suggested the feasibility of applying the EAI in the Arab context with a good construct validity and internal consistency.

Testing the psychometric properties of the Environmental Attitudes Inventory on undergraduate students in the Arab context: A test-retest approach

PubMed Central

2018-01-01

The Environmental Attitudes Inventory (EAI) was developed to evaluate the multidimensional nature of environmental attitudes; however, it is based on a dataset from outside the Arab context. This study reinvestigated the construct validity of the EAI with a new dataset and confirmed the feasibility of applying it in the Arab context. One hundred and forty-eight subjects in Study 1 and 130 in Study 2 provided valid responses. An exploratory factor analysis (EFA) was used to extract a new factor structure in Study 1, and confirmatory factor analysis (CFA) was performed in Study 2. Both studies generated a seven-factor model, and the model fit was discussed for both the studies. Study 2 exhibited satisfactory model fit indices compared to Study 1. Factor loading values of a few items in Study 1 affected the reliability values and average variance extracted values, which demonstrated low discriminant validity. Based on the results of the EFA and CFA, this study showed sufficient model fit and suggested the feasibility of applying the EAI in the Arab context with a good construct validity and internal consistency. PMID:29758021

Genes, Culture and Conservatism-A Psychometric-Genetic Approach.

PubMed

Schwabe, Inga; Jonker, Wilfried; van den Berg, Stéphanie M

2016-07-01

The Wilson-Patterson conservatism scale was psychometrically evaluated using homogeneity analysis and item response theory models. Results showed that this scale actually measures two different aspects in people: on the one hand people vary in their agreement with either conservative or liberal catch-phrases and on the other hand people vary in their use of the "?" response category of the scale. A 9-item subscale was constructed, consisting of items that seemed to measure liberalism, and this subscale was subsequently used in a biometric analysis including genotype-environment interaction, correcting for non-homogeneous measurement error. Biometric results showed significant genetic and shared environmental influences, and significant genotype-environment interaction effects, suggesting that individuals with a genetic predisposition for conservatism show more non-shared variance but less shared variance than individuals with a genetic predisposition for liberalism.

Longitudinal Effects on Early Adolescent Language: A Twin Study

PubMed Central

DeThorne, Laura Segebart; Smith, Jamie Mahurin; Betancourt, Mariana Aparicio; Petrill, Stephen A.

2016-01-01

Purpose We evaluated genetic and environmental contributions to individual differences in language skills during early adolescence, measured by both language sampling and standardized tests, and examined the extent to which these genetic and environmental effects are stable across time. Method We used structural equation modeling on latent factors to estimate additive genetic, shared environmental, and nonshared environmental effects on variance in standardized language skills (i.e., Formal Language) and productive language-sample measures (i.e., Productive Language) in a sample of 527 twins across 3 time points (mean ages 10–12 years). Results Individual differences in the Formal Language factor were influenced primarily by genetic factors at each age, whereas individual differences in the Productive Language factor were primarily due to nonshared environmental influences. For the Formal Language factor, the stability of genetic effects was high across all 3 time points. For the Productive Language factor, nonshared environmental effects showed low but statistically significant stability across adjacent time points. Conclusions The etiology of language outcomes may differ substantially depending on assessment context. In addition, the potential mechanisms for nonshared environmental influences on language development warrant further investigation. PMID:27732720

Romantic Relationship Satisfaction Moderates the Etiology of Adult Personality

PubMed Central

South, Susan C.; Krueger, Robert F.; Elkins, Irene; Iacono, William G.; McGue, Matt

2015-01-01

The heritability of major normative domains of personality is well-established, with approximately half the proportion of variance attributed to genetic differences. In the current study, we examine the possibility of gene x environment interaction (GxE) for adult personality using the environmental context of intimate romantic relationship functioning. Personality and relationship satisfaction are significantly correlated phenotypically, but to date no research has examined how the genetic and environmental components of variance for personality differ as a function of romantic relationship satisfaction. Given the importance of personality for myriad outcomes from work productivity to psychopathology, it is vital to identify variables present in adulthood that may affect the etiology of personality. In the current study, quantitative models of GxE were used to determine whether the genetic and environmental influences on personality differ as a function of relationship satisfaction. We drew from a sample of now-adult twins followed longitudinally from adolescence through age 29. All participants completed the Multidimensional Personality Questionnaire (MPQ) and an abbreviated version of the Dyadic Adjustment Scale (DAS). Biometric moderation was found for eight of the eleven MPQ scales examined: Well-Being, Social Potency, Negative Emotionality, Alienation, Aggression, Constraint, Traditionalism, and Absorption. The pattern of findings differed, suggesting that the ways in which relationship quality moderates the etiology of personality may depend on the personality trait. PMID:26581694

Glycotoxin and Autoantibodies Are Additive Environmentally Determined Predictors of Type 1 Diabetes

PubMed Central

Beyan, Huriya; Riese, Harriette; Hawa, Mohammed I.; Beretta, Guisi; Davidson, Howard W.; Hutton, John C.; Burger, Huibert; Schlosser, Michael; Snieder, Harold; Boehm, Bernhard O.; Leslie, R. David

2012-01-01

In type 1 diabetes, diabetes-associated autoantibodies, including islet cell antibodies (ICAs), reflect adaptive immunity, while increased serum Nε-carboxymethyl-lysine (CML), an advanced glycation end product, is associated with proinflammation. We assessed whether serum CML and autoantibodies predicted type 1 diabetes and to what extent they were determined by genetic or environmental factors. Of 7,287 unselected schoolchildren screened, 115 were ICA+ and were tested for baseline CML and diabetes autoantibodies and followed (for median 7 years), whereas a random selection (n = 2,102) had CML tested. CML and diabetes autoantibodies were determined in a classic twin study of twin pairs discordant for type 1 diabetes (32 monozygotic, 32 dizygotic pairs). CML was determined by enzyme-linked immunosorbent assay, autoantibodies were determined by radioimmunoprecipitation, ICA was determined by indirect immunofluorescence, and HLA class II genotyping was determined by sequence-specific oligonucleotides. CML was increased in ICA+ and prediabetic schoolchildren and in diabetic and nondiabetic twins (all P < 0.001). Elevated levels of CML in ICA+ children were a persistent, independent predictor of diabetes progression, in addition to autoantibodies and HLA risk. In twins model fitting, familial environment explained 75% of CML variance, and nonshared environment explained all autoantibody variance. Serum CML, a glycotoxin, emerged as an environmentally determined diabetes risk factor, in addition to autoimmunity and HLA genetic risk, and a potential therapeutic target. PMID:22396204

r- and K-selection in fluctuating populations is determined by the evolutionary trade-off between two fitness measures: Growth rate and lifetime reproductive success.

PubMed

Engen, Steinar; Saether, Bernt-Erik

2017-01-01

In a stable environment, evolution maximizes growth rates in populations that are not density regulated and the carrying capacity in the case of density regulation. In a fluctuating environment, evolution maximizes a function of growth rate, carrying capacity and environmental variance, tending to r-selection and K-selection under large and small environmental noise, respectively. Here we analyze a model in which birth and death rates depend on density through the same function but with independent strength of density dependence. As a special case, both functions may be linear, corresponding to logistic dynamics. It is shown that evolution maximizes a function of the deterministic growth rate r 0 and the lifetime reproductive success (LRS) R 0 , both defined at small densities, as well as the environmental variance. Under large noise this function is dominated by r 0 and average lifetimes are small, whereas R 0 dominates and lifetimes are larger under small noise. Thus, K-selection is closely linked to selection for large R 0 so that evolution tends to maximize LRS in a stable environment. Consequently, different quantities (r 0 and R 0 ) tend to be maximized at low and high densities, respectively, favoring density-dependent changes in the optimal life history. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

Genetic and environmental influences on thin-ideal internalization.

PubMed

Suisman, Jessica L; O'Connor, Shannon M; Sperry, Steffanie; Thompson, J Kevin; Keel, Pamela K; Burt, S Alexandra; Neale, Michael; Boker, Steven; Sisk, Cheryl; Klump, Kelly L

2012-12-01

Current research on the etiology of thin-ideal internalization focuses on psychosocial influences (e.g., media exposure). The possibility that genetic influences also account for variance in thin-ideal internalization has never been directly examined. This study used a twin design to estimate genetic effects on thin-ideal internalization and examine if environmental influences are primarily shared or nonshared in origin. Participants were 343 postpubertal female twins (ages: 12-22 years; M = 17.61) from the Michigan State University Twin Registry. Thin-ideal internalization was assessed using the Sociocultural Attitudes toward Appearance Questionnaire-3. Twin modeling suggested significant additive genetic and nonshared environmental influences on thin-ideal internalization. Shared environmental influences were small and non-significant. Although prior research focused on psychosocial factors, genetic influences on thin-ideal internalization were significant and moderate in magnitude. Research is needed to investigate possible interplay between genetic and nonshared environmental factors in the development of thin-ideal internalization. Copyright © 2012 Wiley Periodicals, Inc.

Markowitz portfolio optimization model employing fuzzy measure

NASA Astrophysics Data System (ADS)

Ramli, Suhailywati; Jaaman, Saiful Hafizah

2017-04-01

Markowitz in 1952 introduced the mean-variance methodology for the portfolio selection problems. His pioneering research has shaped the portfolio risk-return model and become one of the most important research fields in modern finance. This paper extends the classical Markowitz's mean-variance portfolio selection model applying the fuzzy measure to determine the risk and return. In this paper, we apply the original mean-variance model as a benchmark, fuzzy mean-variance model with fuzzy return and the model with return are modeled by specific types of fuzzy number for comparison. The model with fuzzy approach gives better performance as compared to the mean-variance approach. The numerical examples are included to illustrate these models by employing Malaysian share market data.

Integral projection models for finite populations in a stochastic environment.

PubMed

Vindenes, Yngvild; Engen, Steinar; Saether, Bernt-Erik

2011-05-01

Continuous types of population structure occur when continuous variables such as body size or habitat quality affect the vital parameters of individuals. These structures can give rise to complex population dynamics and interact with environmental conditions. Here we present a model for continuously structured populations with finite size, including both demographic and environmental stochasticity in the dynamics. Using recent methods developed for discrete age-structured models we derive the demographic and environmental variance of the population growth as functions of a continuous state variable. These two parameters, together with the expected population growth rate, are used to define a one-dimensional diffusion approximation of the population dynamics. Thus, a substantial reduction in complexity is achieved as the dynamics of the complex structured model can be described by only three population parameters. We provide methods for numerical calculation of the model parameters and demonstrate the accuracy of the diffusion approximation by computer simulation of specific examples. The general modeling framework makes it possible to analyze and predict future dynamics and extinction risk of populations with various types of structure, and to explore consequences of changes in demography caused by, e.g., climate change or different management decisions. Our results are especially relevant for small populations that are often of conservation concern.

Genetic and Environmental Influences on Global Family Conflict

PubMed Central

Horwitz, Briana N.; Neiderhiser, Jenae M.; Ganiban, Jody M.; Spotts, Erica L.; Lichtenstein, Paul; Reiss, David

2010-01-01

This study examined genetic and environmental influences on global family conflict. The sample comprised 872 same-sex pairs of twin parents, their spouses/partners and one adolescent child per twin from the Twin and Offspring Study in Sweden (TOSS). The twins, spouses and child each reported on the degree of family conflict, and there was significant agreement among the family members’ ratings. These shared perspectives were explained by one common factor, indexing global family conflict. Genetic influences explained 36% of the variance in this common factor, suggesting that twins’ heritable characteristics contribute to family conflict, via genotype-environment correlation. Nonshared environmental effects explained the remaining 64% of this variance, indicating that twins’ unique childhood and/or current family experiences also play an important role. PMID:20438198

Behavioral and Environmental Modification of the Genetic Influence on Body Mass Index: A Twin Study.

PubMed

Horn, Erin E; Turkheimer, Eric; Strachan, Eric; Duncan, Glen E

2015-07-01

Body mass index (BMI) has a strong genetic basis, with a heritability around 0.75, but is also influenced by numerous behavioral and environmental factors. Aspects of the built environment (e.g., environmental walkability) are hypothesized to influence obesity by directly affecting BMI, by facilitating or inhibiting behaviors such as physical activity that are related to BMI, or by suppressing genetic tendencies toward higher BMI. The present study investigated relative influences of physical activity and walkability on variance in BMI using 5079 same-sex adult twin pairs (70 % monozygotic, 65 % female). High activity and walkability levels independently suppressed genetic variance in BMI. Estimating their effects simultaneously, however, suggested that the walkability effect was mediated by activity. The suppressive effect of activity on variance in BMI was present even with a tendency for low-BMI individuals to select into environments that require higher activity levels. Overall, our results point to community- or macro-level interventions that facilitate individual-level behaviors as a plausible approach to addressing the obesity epidemic among US adults.

Behavioral and environmental modification of the genetic influence on body mass index: A twin study

PubMed Central

Horn, Erin E.; Turkheimer, Eric; Strachan, Eric; Duncan, Glen E.

2015-01-01

Body mass index (BMI) has a strong genetic basis, with a heritability around 0.75, but is also influenced by numerous behavioral and environmental factors. Aspects of the built environment (e.g., environmental walkability) are hypothesized to influence obesity by directly affecting BMI, by facilitating or inhibiting behaviors such as physical activity that are related to BMI, or by suppressing genetic tendencies toward higher BMI. The present study investigated relative influences of physical activity and walkability on variance in BMI using 5,079 same-sex adult twin pairs (70% monozygotic, 65% female). High activity and walkability levels independently suppressed genetic variance in BMI. Estimating their effects simultaneously, however, suggested that the walkability effect was mediated by activity. The suppressive effect of activity on variance in BMI was present even with a tendency for low-BMI individuals to select into environments that require higher activity levels. Overall, our results point to community- or macro-level interventions that facilitate individual-level behaviors as a plausible approach to addressing the obesity epidemic among U.S. adults. PMID:25894925

Estimating variation in stomatal frequency at intra-individual, intra-site, and inter-taxonomic levels in populations of the Leonardoxa africana (Fabaceae) complex over environmental gradients in Cameroon

NASA Astrophysics Data System (ADS)

Finsinger, Walter; Dos Santos, Thibaut; McKey, Doyle

2013-07-01

Variation of stomatal frequency (stomatal density and stomatal index) includes genetically-based, potentially-adaptive variation, and variation due to phenotypic plasticity, the degree of which may be fundamental to the ability to maintain high water-use efficiency and thus to deal with environmental change. We analysed stomatal frequency and morphology (pore length, pore width) in leaves from several individuals from nine populations of four sub-species of the Leonardoxa africana complex. The dataset represents a hierarchical sampling wherein factors are nested within each level (leaves in individuals, individuals in sites, etc.), allowing estimation of the contribution of different levels to overall variation, using variance-component analysis. SI showed significant variation among sites ("site" is largely confounded with "sub-species"), being highest in the sub-species localized in the highest-elevation site. However, most of the observed variance was accounted for at intra-site and intra-individual levels. This variance could reflect great phenotypic plasticity, presumably in response to highly local variation in micro-environmental conditions.

The effects of r- and K-selection on components of variance for two quantitative traits.

PubMed

Long, T; Long, G

1974-03-01

The genetic and environmental components of variance for two quantitative characters were measured in the descendants of Drosophila melanogaster populations which had been grown for several generations at densities of 100, 200, 300, and 400 eggs per vial. Populations subject to intermediate densities had a greater proportion of phenotypic variance available for selection than populations from either extreme. Selection on either character would be least effective under pure r-selection, a frequent attribute of selection programs.

Comparing estimates of genetic variance across different relationship models.

PubMed

Legarra, Andres

2016-02-01

Use of relationships between individuals to estimate genetic variances and heritabilities via mixed models is standard practice in human, plant and livestock genetics. Different models or information for relationships may give different estimates of genetic variances. However, comparing these estimates across different relationship models is not straightforward as the implied base populations differ between relationship models. In this work, I present a method to compare estimates of variance components across different relationship models. I suggest referring genetic variances obtained using different relationship models to the same reference population, usually a set of individuals in the population. Expected genetic variance of this population is the estimated variance component from the mixed model times a statistic, Dk, which is the average self-relationship minus the average (self- and across-) relationship. For most typical models of relationships, Dk is close to 1. However, this is not true for very deep pedigrees, for identity-by-state relationships, or for non-parametric kernels, which tend to overestimate the genetic variance and the heritability. Using mice data, I show that heritabilities from identity-by-state and kernel-based relationships are overestimated. Weighting these estimates by Dk scales them to a base comparable to genomic or pedigree relationships, avoiding wrong comparisons, for instance, "missing heritabilities". Copyright © 2015 Elsevier Inc. All rights reserved.

Evaluation and recommendation of sensitivity analysis methods for application to Stochastic Human Exposure and Dose Simulation models.

PubMed

Mokhtari, Amirhossein; Christopher Frey, H; Zheng, Junyu

2006-11-01

Sensitivity analyses of exposure or risk models can help identify the most significant factors to aid in risk management or to prioritize additional research to reduce uncertainty in the estimates. However, sensitivity analysis is challenged by non-linearity, interactions between inputs, and multiple days or time scales. Selected sensitivity analysis methods are evaluated with respect to their applicability to human exposure models with such features using a testbed. The testbed is a simplified version of a US Environmental Protection Agency's Stochastic Human Exposure and Dose Simulation (SHEDS) model. The methods evaluated include the Pearson and Spearman correlation, sample and rank regression, analysis of variance, Fourier amplitude sensitivity test (FAST), and Sobol's method. The first five methods are known as "sampling-based" techniques, wheras the latter two methods are known as "variance-based" techniques. The main objective of the test cases was to identify the main and total contributions of individual inputs to the output variance. Sobol's method and FAST directly quantified these measures of sensitivity. Results show that sensitivity of an input typically changed when evaluated under different time scales (e.g., daily versus monthly). All methods provided similar insights regarding less important inputs; however, Sobol's method and FAST provided more robust insights with respect to sensitivity of important inputs compared to the sampling-based techniques. Thus, the sampling-based methods can be used in a screening step to identify unimportant inputs, followed by application of more computationally intensive refined methods to a smaller set of inputs. The implications of time variation in sensitivity results for risk management are briefly discussed.

Socio-demographic, psychosocial and home-environmental attributes associated with adults' domestic screen time.

PubMed

Van Dyck, Delfien; Cardon, Greet; Deforche, Benedicte; Owen, Neville; De Cocker, Katrien; Wijndaele, Katrien; De Bourdeaudhuij, Ilse

2011-08-25

Sedentary behaviors (involving prolonged sitting time) are associated with deleterious health consequences, independent of (lack of) physical activity. To inform interventions, correlates of prevalent sedentary behaviors need to be identified. We examined associations of socio-demographic, home-environmental and psychosocial factors with adults' TV viewing time and leisure-time Internet use; and whether psychosocial and environmental correlates differed according to gender, age and educational attainment. This cross-sectional study was conducted in Ghent, Belgium, between March and May 2010. Respondents to a mail-out survey (n = 419; 20-65 years; mean age 48.5 [12.1] years; 47.3% men) completed a questionnaire on sedentary behaviors and their potential socio-demographic, psychosocial and home environmental correlates. Statistical analyses were performed using multiple linear regression models. The independent variables explained 31% of the variance in TV viewing time and 38% of the variance in leisure-time Internet use. Higher education, greater perceived pros of and confidence about reducing TV time were negatively associated with TV viewing time; older age, higher body mass index, larger TV set size and greater perceived cons of reducing TV time showed positive associations. Perceived pros of and confidence about reducing Internet use were negatively associated with leisure-time Internet use; higher education, number of computers in the home, positive family social norms about Internet use and perceived cons of reducing Internet use showed positive associations. None of the socio-demographic factors moderated these associations. Educational level, age, self-efficacy and pros/cons were the most important correlates identified in this study. If further cross-sectional and longitudinal research can confirm these findings, tailored interventions focusing on both psychosocial and environmental factors in specific population subgroups might be most effective to reduce domestic screen time.

Socio-demographic, psychosocial and home-environmental attributes associated with adults' domestic screen time

PubMed Central

2011-01-01

Background Sedentary behaviors (involving prolonged sitting time) are associated with deleterious health consequences, independent of (lack of) physical activity. To inform interventions, correlates of prevalent sedentary behaviors need to be identified. We examined associations of socio-demographic, home-environmental and psychosocial factors with adults' TV viewing time and leisure-time Internet use; and whether psychosocial and environmental correlates differed according to gender, age and educational attainment. Methods This cross-sectional study was conducted in Ghent, Belgium, between March and May 2010. Respondents to a mail-out survey (n = 419; 20-65 years; mean age 48.5 [12.1] years; 47.3% men) completed a questionnaire on sedentary behaviors and their potential socio-demographic, psychosocial and home environmental correlates. Statistical analyses were performed using multiple linear regression models. Results The independent variables explained 31% of the variance in TV viewing time and 38% of the variance in leisure-time Internet use. Higher education, greater perceived pros of and confidence about reducing TV time were negatively associated with TV viewing time; older age, higher body mass index, larger TV set size and greater perceived cons of reducing TV time showed positive associations. Perceived pros of and confidence about reducing Internet use were negatively associated with leisure-time Internet use; higher education, number of computers in the home, positive family social norms about Internet use and perceived cons of reducing Internet use showed positive associations. None of the socio-demographic factors moderated these associations. Conclusions Educational level, age, self-efficacy and pros/cons were the most important correlates identified in this study. If further cross-sectional and longitudinal research can confirm these findings, tailored interventions focusing on both psychosocial and environmental factors in specific population subgroups might be most effective to reduce domestic screen time. PMID:21864412

75 FR 3726 - Agency Information Collection Activities; Submission to OMB for Review and Approval; Comment...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-22

...- Migration' Variances (Renewal), EPA ICR Number 1353.09, OMB Control Number 2050-0062 AGENCY: Environmental... docket, go to http://www.regulations.gov . Title: Land Disposal Restrictions `No-Migration' Variances... migration.'' The applicant must demonstrate that hazardous wastes can be managed safely in a particular land...

Levels of metals in hair of young children as an indicator of environmental pollution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wibowo, A.A.; Herber, R.F.; Das, H.A.

In 1982 the levels of lead (Pb), cadmium (Cd), vanadium (V), copper (Cu), and selenium (Se) were determined in hair of 231 four- to five-year-old children. The objective was to explore the feasibility of using metal-in-hair levels in groups of children as an indicator of environmental pollution. The study was carried out in four areas, which were assumed to differ in ambient pollution by metals. A questionnaire on personal data, socioeconomic status, intake of beverages, and life-style was completed by the parents. The metal-in-hair levels covered a large range. The variables pertaining to location together with sex, presence of amore » garden, and drinking of coffee and/or tea explained 32% of the variance of Pb, 24% of the variance of Cd, and 21% of the variance of V. The total variance explained by all measured questionnaire items was at best 38%. The location was the most important factor. Cu and Se levels did not differ between the locations.« less

Climate change, species distribution models, and physiological performance metrics: predicting when biogeographic models are likely to fail.

PubMed

Woodin, Sarah A; Hilbish, Thomas J; Helmuth, Brian; Jones, Sierra J; Wethey, David S

2013-09-01

Modeling the biogeographic consequences of climate change requires confidence in model predictions under novel conditions. However, models often fail when extended to new locales, and such instances have been used as evidence of a change in physiological tolerance, that is, a fundamental niche shift. We explore an alternative explanation and propose a method for predicting the likelihood of failure based on physiological performance curves and environmental variance in the original and new environments. We define the transient event margin (TEM) as the gap between energetic performance failure, defined as CTmax, and the upper lethal limit, defined as LTmax. If TEM is large relative to environmental fluctuations, models will likely fail in new locales. If TEM is small relative to environmental fluctuations, models are likely to be robust for new locales, even when mechanism is unknown. Using temperature, we predict when biogeographic models are likely to fail and illustrate this with a case study. We suggest that failure is predictable from an understanding of how climate drives nonlethal physiological responses, but for many species such data have not been collected. Successful biogeographic forecasting thus depends on understanding when the mechanisms limiting distribution of a species will differ among geographic regions, or at different times, resulting in realized niche shifts. TEM allows prediction of the likelihood of such model failure.

Environmental rather than genetic factors determine the variation in the age of the infancy to childhood transition: a twins study.

PubMed

German, Alina; Livshits, Gregory; Peter, Inga; Malkin, Ida; Dubnov, Jonathan; Akons, Hannah; Shmoish, Michael; Hochberg, Ze'ev

2015-03-01

Using a twins study, we sought to assess the contribution of genetic against environmental factor as they affect the age at transition from infancy to childhood (ICT). The subjects were 56 pairs of monozygotic twins, 106 pairs of dizygotic twins, and 106 pairs of regular siblings (SBs), for a total of 536 children. Their ICT was determined, and a variance component analysis was implemented to estimate components of the familial variance, with simultaneous adjustment for potential covariates. We found substantial contribution of the common environment shared by all types of SBs that explained 27.7% of the total variance in ICT, whereas the common twin environment explained 9.2% of the variance, gestational age 3.5%, and birth weight 1.8%. In addition, 8.7% was attributable to sex difference, but we found no detectable contribution of genetic factors to inter-individual variation in ICT age. Developmental plasticity impacts much of human growth. Here we show that of the ∼50% of the variance provided to adult height by the ICT, 42.2% is attributable to adaptive cues represented by shared twin and SB environment, with no detectable genetic involvement. Copyright © 2015 Elsevier Inc. All rights reserved.

Origins of individual differences in imitation: links with language, pretend play, and socially insightful behavior in two-year-old twins.

PubMed

McEwen, Fiona; Happé, Francesca; Bolton, Patrick; Rijsdijk, Fruhling; Ronald, Angelica; Dworzynski, Katharina; Plomin, Robert

2007-01-01

Imitation, vocabulary, pretend play, and socially insightful behavior were investigated in 5,206 same- and opposite-sex 2-year-old twin pairs in the United Kingdom. Individual differences in imitative ability were due to modest heritability (30%), while environmental factors shared between twins (42%) and unique to each twin (28%) also made significant contributions to the variance. Imitation correlated significantly, although modestly, with vocabulary, pretend play, and socially insightful behavior, and the strongest relationship was with vocabulary. A model that represented the covariance between the variables as being due to correlated latent genetic and environmental factors fitted the data well, with shared environmental factors influencing most of the covariance. Parents who encourage imitation may also tend to foster the development of language, pretence, and socially insightful behavior.

Genetic effects of heat stress on milk yield of Thai Holstein crossbreds.

PubMed

Boonkum, W; Misztal, I; Duangjinda, M; Pattarajinda, V; Tumwasorn, S; Sanpote, J

2011-01-01

The threshold for heat stress on milk yield of Holstein crossbreds under climatic conditions in Thailand was investigated, and genetic effects of heat stress on milk yield were estimated. Data included 400,738 test-day milk yield records for the first 3 parities from 25,609 Thai crossbred Holsteins between 1990 and 2008. Mean test-day milk yield ranged from 12.6 kg for cows with <87.5% Holstein genetics to 14.4 kg for cows with ≥93.7% Holstein genetics. Daily temperature and humidity data from 26 provincial weather stations were used to calculate a temperature-humidity index (THI). Test-day milk yield varied little with THI for first parity except above a THI of 82 for cows with ≥93.7% Holstein genetics. For third parity, test-day milk yield started to decline after a THI of 74 for cows with ≥87.5% Holstein genetics and declined more rapidly after a THI of 82. A repeatability test-day model with parities as correlated traits was used to estimate heat stress parameters; fixed effects included herd-test month-test year and breed groups, days in milk, calving age, and parity; random effects included 2 additive genetic effects, regular and heat stress, and 2 permanent environment, regular and heat stress. The threshold for effect of heat stress on test-day milk yield was set to a THI of 80. All variance component estimates increased with parity; the largest increases were found for effects associated with heat stress. In particular, genetic variance associated with heat stress quadrupled from first to third parity, whereas permanent environmental variance only doubled. However, permanent environmental variance for heat stress was at least 10 times larger than genetic variance. Genetic correlations among parities for additive effects without heat stress considered ranged from 0.88 to 0.96. Genetic correlations among parities for additive effects of heat stress ranged from 0.08 to 0.22, and genetic correlations between effects regular and heat stress effects ranged from -0.21 to -0.33 for individual parities. Effect of heat stress on Thai Holstein crossbreds increased greatly with parity and was especially large after a THI of 80 for cows with a high percentage of Holstein genetics (≥93.7%). Individual sensitivity to heat stress was more environmental than genetic for Thai Holstein crossbreds. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Improving the precision of lake ecosystem metabolism estimates by identifying predictors of model uncertainty

USGS Publications Warehouse

Rose, Kevin C.; Winslow, Luke A.; Read, Jordan S.; Read, Emily K.; Solomon, Christopher T.; Adrian, Rita; Hanson, Paul C.

2014-01-01

Diel changes in dissolved oxygen are often used to estimate gross primary production (GPP) and ecosystem respiration (ER) in aquatic ecosystems. Despite the widespread use of this approach to understand ecosystem metabolism, we are only beginning to understand the degree and underlying causes of uncertainty for metabolism model parameter estimates. Here, we present a novel approach to improve the precision and accuracy of ecosystem metabolism estimates by identifying physical metrics that indicate when metabolism estimates are highly uncertain. Using datasets from seventeen instrumented GLEON (Global Lake Ecological Observatory Network) lakes, we discovered that many physical characteristics correlated with uncertainty, including PAR (photosynthetically active radiation, 400-700 nm), daily variance in Schmidt stability, and wind speed. Low PAR was a consistent predictor of high variance in GPP model parameters, but also corresponded with low ER model parameter variance. We identified a threshold (30% of clear sky PAR) below which GPP parameter variance increased rapidly and was significantly greater in nearly all lakes compared with variance on days with PAR levels above this threshold. The relationship between daily variance in Schmidt stability and GPP model parameter variance depended on trophic status, whereas daily variance in Schmidt stability was consistently positively related to ER model parameter variance. Wind speeds in the range of ~0.8-3 m s–1 were consistent predictors of high variance for both GPP and ER model parameters, with greater uncertainty in eutrophic lakes. Our findings can be used to reduce ecosystem metabolism model parameter uncertainty and identify potential sources of that uncertainty.

Genetic variation in personality traits explains genetic overlap between borderline personality features and substance use disorders

PubMed Central

Few, Lauren R.; Grant, Julia D; Trull, Timothy J.; Statham, Dixie J.; Martin, Nicholas G.; Lynskey, Michael T.; Agrawal, Arpana

2014-01-01

Aims To examine the genetic overlap between borderline personality features (BPF) and substance use disorders (SUDs) and the extent to which variation in personality traits contributes to this covariance. Design Genetic structural equation modelling was used to partition the variance in and covariance between personality traits, BPF, and SUDs into additive genetic, shared, and individual-specific environmental factors. Setting All participants were registered with the Australian Twin Registry. Participants A total of 3,127 Australian adult twins participated in the study. Measurements Diagnoses of DSM-IV alcohol and cannabis abuse/dependence (AAD; CAD), and nicotine dependence (ND) were derived via computer-assisted telephone interview. BPF and five-factor model personality traits were derived via self-report questionnaires. Findings Genetic factors were responsible for 49% (95%CI: 42%–55%) of the variance in BPF, 38–42% (95%CI range: 32%–49%) for personality traits and 47% (95%CI: 17%–77%), 54% (95%CI: 43%–64%), and 78% (67%–86%) for ND, AAD and CAD, respectively. Genetic and individual-specific environmental correlations between BPF and SUDs ranged from .33–.56 (95%CI range: .19–.74) and .19–.32 (95%CI range: .06–.43), respectively. Overall, there was substantial support for genetic influences that were specific to AAD, ND and CAD (31%–69%). Finally, genetic variation in personality traits was responsible for 11% (Extraversion for CAD) to 59% (Neuroticism for AAD) of the correlation between BPF and SUDs. Conclusions Both genetic and individual-specific environmental factors contribute to comorbidity between borderline personality features and substance use disorders. A substantial proportion of this comorbidity can be attributed to variation in normal personality traits, particularly Neuroticism. PMID:25041562

Patterns in Temporal Variability of Temperature, Oxygen and pH along an Environmental Gradient in a Coral Reef

PubMed Central

Guadayol, Òscar; Silbiger, Nyssa J.; Donahue, Megan J.; Thomas, Florence I. M.

2014-01-01

Spatial and temporal environmental variability are important drivers of ecological processes at all scales. As new tools allow the in situ exploration of individual responses to fluctuations, ecologically meaningful ways of characterizing environmental variability at organism scales are needed. We investigated the fine-scale spatial heterogeneity of high-frequency temporal variability in temperature, dissolved oxygen concentration, and pH experienced by benthic organisms in a shallow coastal coral reef. We used a spatio-temporal sampling design, consisting of 21 short-term time-series located along a reef flat-to-reef slope transect, coupled to a long-term station monitoring water column changes. Spectral analyses revealed sharp gradients in variance decomposed by frequency, as well as differences between physically-driven and biologically-reactive parameters. These results highlight the importance of environmental variance at organismal scales and present a new sampling scheme for exploring this variability in situ. PMID:24416364

Resilience of the Nexus of Competitive Water Consumption between Human Society and Environment Development: Regime Shifts and Early Warning Signals

NASA Astrophysics Data System (ADS)

Li, Z.; Liu, P.; Feng, M.; Zhang, J.

2017-12-01

Based on the modeling of the water supply, power generation and environment (WPE) nexus by Feng et al. (2016), a refined theoretical model of competitive water consumption between human society and environment has been presented in this study, examining the role of technology advancement and social environmental awareness growth-induced pollution mitigation to the environment as a mechanism for the establishment and maintenance of the coexistence of both higher social water consumption and improved environment condition. By coupling environmental and social dynamics, both of which are represented by water consumption quantity, this study shows the possibility of sustainable situation of the social-environmental system when the benefit of technology offsets the side effect (pollution) of social development to the environment. Additionally, regime shifts could be triggered by gradually increased pollution rate, climate change-induced natural resources reduction and breakdown of the social environmental awareness. Therefore, in order to foresee the pending abrupt regime shifts of the system, early warning signals, including increasing variance and autocorrelation, have been examined when the system is undergoing stochastic disturbance. ADDIN EN.REFLIST Feng, M. et al., 2016. Modeling the nexus across water supply, power generation and environment systems using the system dynamics approach: Hehuang Region, China. J. Hydrol., 543: 344-359.

Heritability of refractive error and ocular biometrics: the Genes in Myopia (GEM) twin study.

PubMed

Dirani, Mohamed; Chamberlain, Matthew; Shekar, Sri N; Islam, Amirul F M; Garoufalis, Pam; Chen, Christine Y; Guymer, Robyn H; Baird, Paul N

2006-11-01

A classic twin study was undertaken to assess the contribution of genes and environment to the development of refractive errors and ocular biometrics in a twin population. A total of 1224 twins (345 monozygotic [MZ] and 267 dizygotic [DZ] twin pairs) aged between 18 and 88 years were examined. All twins completed a questionnaire consisting of a medical history, education, and zygosity. Objective refraction was measured in all twins, and biometric measurements were obtained using partial coherence interferometry. Intrapair correlations for spherical equivalent and ocular biometrics were significantly higher in the MZ than in the DZ twin pairs (P < 0.05), when refraction was considered as a continuous variable. A significant gender difference in the variation of spherical equivalent and ocular biometrics was found (P < 0.05). A genetic model specifying an additive, dominant, and unique environmental factor that was sex limited was the best fit for all measured variables. Heritability of spherical equivalents of 88% and 75% were found in the men and women, respectively, whereas, that of axial length was 94% and 92%, respectively. Additive genetic effects accounted for a greater proportion of the variance in spherical equivalent, whereas the variance in ocular biometrics, particularly axial length was explained mostly by dominant genetic effects. Genetic factors, both additive and dominant, play a significant role in refractive error (myopia and hypermetropia) as well as in ocular biometrics, particularly axial length. The sex limitation ADE model (additive genetic, nonadditive genetic, and environmental components) provided the best-fit genetic model for all parameters.

A class of multi-period semi-variance portfolio for petroleum exploration and development

NASA Astrophysics Data System (ADS)

Guo, Qiulin; Li, Jianzhong; Zou, Caineng; Guo, Yujuan; Yan, Wei

2012-10-01

Variance is substituted by semi-variance in Markowitz's portfolio selection model. For dynamic valuation on exploration and development projects, one period portfolio selection is extended to multi-period. In this article, a class of multi-period semi-variance exploration and development portfolio model is formulated originally. Besides, a hybrid genetic algorithm, which makes use of the position displacement strategy of the particle swarm optimiser as a mutation operation, is applied to solve the multi-period semi-variance model. For this class of portfolio model, numerical results show that the mode is effective and feasible.

Environmental Variation Generates Environmental Opportunist Pathogen Outbreaks.

PubMed

Anttila, Jani; Kaitala, Veijo; Laakso, Jouni; Ruokolainen, Lasse

2015-01-01

Many socio-economically important pathogens persist and grow in the outside host environment and opportunistically invade host individuals. The environmental growth and opportunistic nature of these pathogens has received only little attention in epidemiology. Environmental reservoirs are, however, an important source of novel diseases. Thus, attempts to control these diseases require different approaches than in traditional epidemiology focusing on obligatory parasites. Conditions in the outside-host environment are prone to fluctuate over time. This variation is a potentially important driver of epidemiological dynamics and affect the evolution of novel diseases. Using a modelling approach combining the traditional SIRS models to environmental opportunist pathogens and environmental variability, we show that epidemiological dynamics of opportunist diseases are profoundly driven by the quality of environmental variability, such as the long-term predictability and magnitude of fluctuations. When comparing periodic and stochastic environmental factors, for a given variance, stochastic variation is more likely to cause outbreaks than periodic variation. This is due to the extreme values being further away from the mean. Moreover, the effects of variability depend on the underlying biology of the epidemiological system, and which part of the system is being affected. Variation in host susceptibility leads to more severe pathogen outbreaks than variation in pathogen growth rate in the environment. Positive correlation in variation on both targets can cancel the effect of variation altogether. Moreover, the severity of outbreaks is significantly reduced by increase in the duration of immunity. Uncovering these issues helps in understanding and controlling diseases caused by environmental pathogens.

Why risk is not variance: an expository note.

PubMed

Cox, Louis Anthony Tony

2008-08-01

Variance (or standard deviation) of return is widely used as a measure of risk in financial investment risk analysis applications, where mean-variance analysis is applied to calculate efficient frontiers and undominated portfolios. Why, then, do health, safety, and environmental (HS&E) and reliability engineering risk analysts insist on defining risk more flexibly, as being determined by probabilities and consequences, rather than simply by variances? This note suggests an answer by providing a simple proof that mean-variance decision making violates the principle that a rational decisionmaker should prefer higher to lower probabilities of receiving a fixed gain, all else being equal. Indeed, simply hypothesizing a continuous increasing indifference curve for mean-variance combinations at the origin is enough to imply that a decisionmaker must find unacceptable some prospects that offer a positive probability of gain and zero probability of loss. Unlike some previous analyses of limitations of variance as a risk metric, this expository note uses only simple mathematics and does not require the additional framework of von Neumann Morgenstern utility theory.

Differences in genetic and environmental variation in adult BMI by sex, age, time period, and region: an individual-based pooled analysis of 40 twin cohorts.

PubMed

Silventoinen, Karri; Jelenkovic, Aline; Sund, Reijo; Yokoyama, Yoshie; Hur, Yoon-Mi; Cozen, Wendy; Hwang, Amie E; Mack, Thomas M; Honda, Chika; Inui, Fujio; Iwatani, Yoshinori; Watanabe, Mikio; Tomizawa, Rie; Pietiläinen, Kirsi H; Rissanen, Aila; Siribaddana, Sisira H; Hotopf, Matthew; Sumathipala, Athula; Rijsdijk, Fruhling; Tan, Qihua; Zhang, Dongfeng; Pang, Zengchang; Piirtola, Maarit; Aaltonen, Sari; Öncel, Sevgi Y; Aliev, Fazil; Rebato, Esther; Hjelmborg, Jacob B; Christensen, Kaare; Skytthe, Axel; Kyvik, Kirsten O; Silberg, Judy L; Eaves, Lindon J; Cutler, Tessa L; Ordoñana, Juan R; Sánchez-Romera, Juan F; Colodro-Conde, Lucia; Song, Yun-Mi; Yang, Sarah; Lee, Kayoung; Franz, Carol E; Kremen, William S; Lyons, Michael J; Busjahn, Andreas; Nelson, Tracy L; Whitfield, Keith E; Kandler, Christian; Jang, Kerry L; Gatz, Margaret; Butler, David A; Stazi, Maria A; Fagnani, Corrado; D'Ippolito, Cristina; Duncan, Glen E; Buchwald, Dedra; Martin, Nicholas G; Medland, Sarah E; Montgomery, Grant W; Jeong, Hoe-Uk; Swan, Gary E; Krasnow, Ruth; Magnusson, Patrik Ke; Pedersen, Nancy L; Dahl Aslan, Anna K; McAdams, Tom A; Eley, Thalia C; Gregory, Alice M; Tynelius, Per; Baker, Laura A; Tuvblad, Catherine; Bayasgalan, Gombojav; Narandalai, Danshiitsoodol; Spector, Timothy D; Mangino, Massimo; Lachance, Genevieve; Burt, S Alexandra; Klump, Kelly L; Harris, Jennifer R; Brandt, Ingunn; Nilsen, Thomas S; Krueger, Robert F; McGue, Matt; Pahlen, Shandell; Corley, Robin P; Huibregtse, Brooke M; Bartels, Meike; van Beijsterveldt, Catharina Em; Willemsen, Gonneke; Goldberg, Jack H; Rasmussen, Finn; Tarnoki, Adam D; Tarnoki, David L; Derom, Catherine A; Vlietinck, Robert F; Loos, Ruth Jf; Hopper, John L; Sung, Joohon; Maes, Hermine H; Turkheimer, Eric; Boomsma, Dorret I; Sørensen, Thorkild Ia; Kaprio, Jaakko

2017-08-01

Background: Genes and the environment contribute to variation in adult body mass index [BMI (in kg/m 2 )], but factors modifying these variance components are poorly understood. Objective: We analyzed genetic and environmental variation in BMI between men and women from young adulthood to old age from the 1940s to the 2000s and between cultural-geographic regions representing high (North America and Australia), moderate (Europe), and low (East Asia) prevalence of obesity. Design: We used genetic structural equation modeling to analyze BMI in twins ≥20 y of age from 40 cohorts representing 20 countries (140,379 complete twin pairs). Results: The heritability of BMI decreased from 0.77 (95% CI: 0.77, 0.78) and 0.75 (95% CI: 0.74, 0.75) in men and women 20-29 y of age to 0.57 (95% CI: 0.54, 0.60) and 0.59 (95% CI: 0.53, 0.65) in men 70-79 y of age and women 80 y of age, respectively. The relative influence of unique environmental factors correspondingly increased. Differences in the sets of genes affecting BMI in men and women increased from 20-29 to 60-69 y of age. Mean BMI and variances in BMI increased from the 1940s to the 2000s and were greatest in North America and Australia, followed by Europe and East Asia. However, heritability estimates were largely similar over measurement years and between regions. There was no evidence of environmental factors shared by co-twins affecting BMI. Conclusions: The heritability of BMI decreased and differences in the sets of genes affecting BMI in men and women increased from young adulthood to old age. The heritability of BMI was largely similar between cultural-geographic regions and measurement years, despite large differences in mean BMI and variances in BMI. Our results show a strong influence of genetic factors on BMI, especially in early adulthood, regardless of the obesity level in the population. © 2017 American Society for Nutrition.

Temporal patterns of phyto- and bacterioplankton and their relationships with environmental factors in Lake Taihu, China.

PubMed

Su, Xiaomei; Steinman, Alan D; Xue, Qingju; Zhao, Yanyan; Tang, Xiangming; Xie, Liqiang

2017-10-01

Phytoplankton and bacterioplankton are integral components of aquatic food webs and play essential roles in the structure and function of freshwater ecosystems. However, little is known about how phyto- and bacterioplankton may respond synchronously to changing environmental conditions. Thus, we analyzed simultaneously the composition and structure of phyto- and bacterioplankton on a monthly basis over 12 months in cyanobacteria-dominated areas of Lake Taihu and compared their responses to changes in environmental factors. Metric multi-dimensional scaling (mMDS) revealed that the temporal variations of phyto- and bacterioplankton were significant. Time lag analysis (TLA) indicated that the temporal pattern of phytoplankton tended to exhibit convergent dynamics while bacterioplankton showed highly stable or stochastic variation. A significant directional change was found for bacterioplankton at the genus level and the slopes (rate of change) and regression R 2 (low stochasticity or stability) were greater if Cyanobacteria were included, suggesting a higher level of instability in the bacterial community at lower taxonomy level. Consequently, phytoplankton responded more rapidly to the change in environmental conditions than bacterioplankton when analyzed at the phylum level, while bacterioplankton were more sensitive at the finer taxonomic resolution in Lake Taihu. Redundancy analysis (RDA) results showed that environmental variables collectively explained 51.0% variance of phytoplankton and 46.7% variance of bacterioplankton, suggesting that environmental conditions have a significant influence on the temporal variations of phyto- and bacterioplankton. Furthermore, variance partitioning indicated that the bacterial community structure was largely explained by water temperature and nitrogen, suggesting that these factors were the primary drivers shaping bacterioplankton. Copyright © 2017. Published by Elsevier Ltd.

Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability.

PubMed

Ingle, Brandall L; Veber, Brandon C; Nichols, John W; Tornero-Velez, Rogelio

2016-11-28

The free fraction of a xenobiotic in plasma (F ub ) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data are scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict F ub for environmentally relevant chemicals via machine learning techniques. Quantitative structure-activity relationship (QSAR) models were constructed with k nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals. The models were then evaluated with independent test sets of pharmaceuticals (200 compounds) and environmentally relevant ToxCast chemicals (406 total, in two groups of 238 and 168 compounds). The selection of a minimal feature set of 10-15 2D molecular descriptors allowed for both informative feature interpretation and practical applicability domain assessment via a bounded box of descriptor ranges and principal component analysis. The diverse pharmaceutical and environmental chemical sets exhibit similarities in terms of chemical space (99-82% overlap), as well as comparable bias and variance in constructed learning curves. All the models exhibit significant predictability with mean absolute errors (MAE) in the range of 0.10-0.18F ub . The models performed best for highly bound chemicals (MAE 0.07-0.12), neutrals (MAE 0.11-0.14), and acids (MAE 0.14-0.17). A consensus model had the highest accuracy across both pharmaceuticals (MAE 0.151-0.155) and environmentally relevant chemicals (MAE 0.110-0.131). The inclusion of the majority of the ToxCast test sets within the AD of the consensus model, coupled with high prediction accuracy for these chemicals, indicates the model provides a QSAR for F ub that is broadly applicable to both pharmaceuticals and environmentally relevant chemicals.

Variance analysis of forecasted streamflow maxima in a wet temperate climate

NASA Astrophysics Data System (ADS)

Al Aamery, Nabil; Fox, James F.; Snyder, Mark; Chandramouli, Chandra V.

2018-05-01

Coupling global climate models, hydrologic models and extreme value analysis provides a method to forecast streamflow maxima, however the elusive variance structure of the results hinders confidence in application. Directly correcting the bias of forecasts using the relative change between forecast and control simulations has been shown to marginalize hydrologic uncertainty, reduce model bias, and remove systematic variance when predicting mean monthly and mean annual streamflow, prompting our investigation for maxima streamflow. We assess the variance structure of streamflow maxima using realizations of emission scenario, global climate model type and project phase, downscaling methods, bias correction, extreme value methods, and hydrologic model inputs and parameterization. Results show that the relative change of streamflow maxima was not dependent on systematic variance from the annual maxima versus peak over threshold method applied, albeit we stress that researchers strictly adhere to rules from extreme value theory when applying the peak over threshold method. Regardless of which method is applied, extreme value model fitting does add variance to the projection, and the variance is an increasing function of the return period. Unlike the relative change of mean streamflow, results show that the variance of the maxima's relative change was dependent on all climate model factors tested as well as hydrologic model inputs and calibration. Ensemble projections forecast an increase of streamflow maxima for 2050 with pronounced forecast standard error, including an increase of +30(±21), +38(±34) and +51(±85)% for 2, 20 and 100 year streamflow events for the wet temperate region studied. The variance of maxima projections was dominated by climate model factors and extreme value analyses.

Examining Genetic and Environmental Effects on Reactive versus Proactive Aggression

ERIC Educational Resources Information Center

Brendgen, Mara; Vitaro, Frank; Boivin, Michel; Dionne, Ginette; Perusse, Daniel

2006-01-01

This study compared the contribution of genes and environment to teacher-rated reactive and proactive aggression in 6-year-old twin pairs (172 pairs: 55 monozygotic girls, 48 monozygotic boys, 33 dizygotic girls, 36 dizygotic boys). Genetic effects accounted for 39% of the variance of reactive aggression and for 41% of the variance of proactive…

Portfolio optimization with mean-variance model

NASA Astrophysics Data System (ADS)

Hoe, Lam Weng; Siew, Lam Weng

2016-06-01

Investors wish to achieve the target rate of return at the minimum level of risk in their investment. Portfolio optimization is an investment strategy that can be used to minimize the portfolio risk and can achieve the target rate of return. The mean-variance model has been proposed in portfolio optimization. The mean-variance model is an optimization model that aims to minimize the portfolio risk which is the portfolio variance. The objective of this study is to construct the optimal portfolio using the mean-variance model. The data of this study consists of weekly returns of 20 component stocks of FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI). The results of this study show that the portfolio composition of the stocks is different. Moreover, investors can get the return at minimum level of risk with the constructed optimal mean-variance portfolio.

Understanding the covariation of tics, attention-deficit/hyperactivity, and obsessive-compulsive symptoms: A population-based adult twin study.

PubMed

Pinto, Rebecca; Monzani, Benedetta; Leckman, James F; Rück, Christian; Serlachius, Eva; Lichtenstein, Paul; Mataix-Cols, David

2016-10-01

Chronic tic disorders (TD), attention-deficit/hyperactivity-disorder (ADHD), and obsessive-compulsive disorder (OCD) frequently co-occur in clinical and epidemiological samples. Family studies have found evidence of shared familial transmission between TD and OCD, whereas the familial association between these disorders and ADHD is less clear. This study aimed to investigate to what extent liability of tics, attention-deficit/hyperactivity, and obsessive-compulsive symptoms is caused by shared or distinct genetic or environmental influences, in a large population-representative sample of Swedish adult twins (n = 21,911). Tics, attention-deficit/hyperactivity, and obsessive-compulsive symptoms showed modest, but significant covariation. Model fitting suggested a latent liability factor underlying the three phenotypes. This common factor was relatively heritable, and explained significantly less of the variance of attention-deficit/hyperactivity symptom liability. The majority of genetic variance was specific rather than shared. The greatest proportion of total variance in liability of tics, attention-deficit/hyperactivity, and obsessive-compulsive symptoms was attributed to specific non-shared environmental influences. Our findings suggest that the co-occurrence of tics and obsessive-compulsive symptoms, and to a lesser extent attention-deficit/hyperactivity symptoms, can be partly explained by shared etiological influences. However, these phenotypes do not appear to be alternative expressions of the same underlying genetic liability. Further research examining sub-dimensions of these phenotypes may serve to further clarify the association between these disorders and identify more genetically homogenous symptom subtypes. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Comparative ecological transcriptomics and the contribution of gene expression to the evolutionary potential of a threatened fish.

PubMed

Brauer, Chris J; Unmack, Peter J; Beheregaray, Luciano B

2017-12-01

Understanding whether small populations with low genetic diversity can respond to rapid environmental change via phenotypic plasticity is an outstanding research question in biology. RNA sequencing (RNA-seq) has recently provided the opportunity to examine variation in gene expression, a surrogate for phenotypic variation, in nonmodel species. We used a comparative RNA-seq approach to assess expression variation within and among adaptively divergent populations of a threatened freshwater fish, Nannoperca australis, found across a steep hydroclimatic gradient in the Murray-Darling Basin, Australia. These populations evolved under contrasting selective environments (e.g., dry/hot lowland; wet/cold upland) and represent opposite ends of the species' spectrum of genetic diversity and population size. We tested the hypothesis that environmental variation among isolated populations has driven the evolution of divergent expression at ecologically important genes using differential expression (DE) analysis and an anova-based comparative phylogenetic expression variance and evolution model framework based on 27,425 de novo assembled transcripts. Additionally, we tested whether gene expression variance within populations was correlated with levels of standing genetic diversity. We identified 290 DE candidate transcripts, 33 transcripts with evidence for high expression plasticity, and 50 candidates for divergent selection on gene expression after accounting for phylogenetic structure. Variance in gene expression appeared unrelated to levels of genetic diversity. Functional annotation of the candidate transcripts revealed that variation in water quality is an important factor influencing expression variation for N. australis. Our findings suggest that gene expression variation can contribute to the evolutionary potential of small populations. © 2017 John Wiley & Sons Ltd.

Genetic and environmental influences on global family conflict.

PubMed

Horwitz, Briana N; Neiderhiser, Jenae M; Ganiban, Jody M; Spotts, Erica L; Lichtenstein, Paul; Reiss, David

2010-04-01

This study examined genetic and environmental influences on global family conflict. The sample comprised 872 same-sex pairs of twin parents, their spouses/partners, and one adolescent child per twin from the Twin and Offspring Study in Sweden. The twins, spouses, and child each reported on the degree of family conflict, and there was significant agreement among the family members' ratings. These shared perspectives were explained by one common factor, indexing global family conflict. Genetic influences explained 36% of the variance in this common factor, suggesting that twins' heritable characteristics contribute to family conflict, via genotype-environment correlation. Nonshared environmental effects explained the remaining 64% of this variance, indicating that twins' unique childhood and/or current family experiences also play an important role. 2010 APA, all rights reserved

Genetic background in partitioning of metabolizable energy efficiency in dairy cows.

PubMed

Mehtiö, T; Negussie, E; Mäntysaari, P; Mäntysaari, E A; Lidauer, M H

2018-05-01

The main objective of this study was to assess the genetic differences in metabolizable energy efficiency and efficiency in partitioning metabolizable energy in different pathways: maintenance, milk production, and growth in primiparous dairy cows. Repeatability models for residual energy intake (REI) and metabolizable energy intake (MEI) were compared and the genetic and permanent environmental variations in MEI were partitioned into its energy sinks using random regression models. We proposed 2 new feed efficiency traits: metabolizable energy efficiency (MEE), which is formed by modeling MEI fitting regressions on energy sinks [metabolic body weight (BW 0.75 ), energy-corrected milk, body weight gain, and body weight loss] directly; and partial MEE (pMEE), where the model for MEE is extended with regressions on energy sinks nested within additive genetic and permanent environmental effects. The data used were collected from Luke's experimental farms Rehtijärvi and Minkiö between 1998 and 2014. There were altogether 12,350 weekly MEI records on 495 primiparous Nordic Red dairy cows from wk 2 to 40 of lactation. Heritability estimates for REI and MEE were moderate, 0.33 and 0.26, respectively. The estimate of the residual variance was smaller for MEE than for REI, indicating that analyzing weekly MEI observations simultaneously with energy sinks is preferable. Model validation based on Akaike's information criterion showed that pMEE models fitted the data even better and also resulted in smaller residual variance estimates. However, models that included random regression on BW 0.75 converged slowly. The resulting genetic standard deviation estimate from the pMEE coefficient for milk production was 0.75 MJ of MEI/kg of energy-corrected milk. The derived partial heritabilities for energy efficiency in maintenance, milk production, and growth were 0.02, 0.06, and 0.04, respectively, indicating that some genetic variation may exist in the efficiency of using metabolizable energy for different pathways in dairy cows. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Sleep reactivity and insomnia: genetic and environmental influences.

PubMed

Drake, Christopher L; Friedman, Naomi P; Wright, Kenneth P; Roth, Thomas

2011-09-01

Determine the genetic and environmental contributions to sleep reactivity and insomnia. Population-based twin cohort. 1782 individual twins (988 monozygotic or MZ; 1,086 dizygotic or DZ), including 744 complete twin pairs (377 MZ and 367 DZ). Mean age was 22.5 ± 2.8 years; gender distribution was 59% women. Sleep reactivity was measured using the Ford Insomnia Response to Stress Test (FIRST). The criterion for insomnia was having difficulty falling asleep, staying asleep, or nonrefreshing sleep "usually or always" for ≥ 1 month, with at least "somewhat" interference with daily functioning. The prevalence of insomnia was 21%. Heritability estimates for sleep reactivity were 29% for females and 43% for males. The environmental variance for sleep reactivity was greater for females and entirely due to nonshared effects. Insomnia was 43% to 55% heritable for males and females, respectively; the sex difference was not significant. The genetic variances in insomnia and FIRST scores were correlated (r = 0.54 in females, r = 0.64 in males), as were the environmental variances (r = 0.32 in females, r = 0.37 in males). In terms of individual insomnia symptoms, difficulty staying asleep (25% to 35%) and nonrefreshing sleep (34% to 35%) showed relatively more genetic influences than difficulty falling asleep (0%). Sleep reactivity to stress has a substantial genetic component, as well as an environmental component. The finding that FIRST scores and insomnia symptoms share genetic influences is consistent with the hypothesis that sleep reactivity may be a genetic vulnerability for developing insomnia.

The quantitative genetics of maximal and basal rates of oxygen consumption in mice.

PubMed Central

Dohm, M R; Hayes, J P; Garland, T

2001-01-01

A positive genetic correlation between basal metabolic rate (BMR) and maximal (VO(2)max) rate of oxygen consumption is a key assumption of the aerobic capacity model for the evolution of endothermy. We estimated the genetic (V(A), additive, and V(D), dominance), prenatal (V(N)), and postnatal common environmental (V(C)) contributions to individual differences in metabolic rates and body mass for a genetically heterogeneous laboratory strain of house mice (Mus domesticus). Our breeding design did not allow the simultaneous estimation of V(D) and V(N). Regardless of whether V(D) or V(N) was assumed, estimates of V(A) were negative under the full models. Hence, we fitted reduced models (e.g., V(A) + V(N) + V(E) or V(A) + V(E)) and obtained new variance estimates. For reduced models, narrow-sense heritability (h(2)(N)) for BMR was <0.1, but estimates of h(2)(N) for VO(2)max were higher. When estimated with the V(A) + V(E) model, the additive genetic covariance between VO(2)max and BMR was positive and statistically different from zero. This result offers tentative support for the aerobic capacity model for the evolution of vertebrate energetics. However, constraints imposed on the genetic model may cause our estimates of additive variance and covariance to be biased, so our results should be interpreted with caution and tested via selection experiments. PMID:11560903

Model selection for integrated pest management with stochasticity.

PubMed

Akman, Olcay; Comar, Timothy D; Hrozencik, Daniel

2018-04-07

In Song and Xiang (2006), an integrated pest management model with periodically varying climatic conditions was introduced. In order to address a wider range of environmental effects, the authors here have embarked upon a series of studies resulting in a more flexible modeling approach. In Akman et al. (2013), the impact of randomly changing environmental conditions is examined by incorporating stochasticity into the birth pulse of the prey species. In Akman et al. (2014), the authors introduce a class of models via a mixture of two birth-pulse terms and determined conditions for the global and local asymptotic stability of the pest eradication solution. With this work, the authors unify the stochastic and mixture model components to create further flexibility in modeling the impacts of random environmental changes on an integrated pest management system. In particular, we first determine the conditions under which solutions of our deterministic mixture model are permanent. We then analyze the stochastic model to find the optimal value of the mixing parameter that minimizes the variance in the efficacy of the pesticide. Additionally, we perform a sensitivity analysis to show that the corresponding pesticide efficacy determined by this optimization technique is indeed robust. Through numerical simulations we show that permanence can be preserved in our stochastic model. Our study of the stochastic version of the model indicates that our results on the deterministic model provide informative conclusions about the behavior of the stochastic model. Copyright © 2017 Elsevier Ltd. All rights reserved.

Socioeconomic Status (SES) and Children's Intelligence (IQ): In a UK-Representative Sample SES Moderates the Environmental, Not Genetic, Effect on IQ

PubMed Central

Hanscombe, Ken B.; Trzaskowski, Maciej; Haworth, Claire M. A.; Davis, Oliver S. P.; Dale, Philip S.; Plomin, Robert

2012-01-01

Background The environment can moderate the effect of genes - a phenomenon called gene-environment (GxE) interaction. Several studies have found that socioeconomic status (SES) modifies the heritability of children's intelligence. Among low-SES families, genetic factors have been reported to explain less of the variance in intelligence; the reverse is found for high-SES families. The evidence however is inconsistent. Other studies have reported an effect in the opposite direction (higher heritability in lower SES), or no moderation of the genetic effect on intelligence. Methods Using 8716 twin pairs from the Twins Early Development Study (TEDS), we attempted to replicate the reported moderating effect of SES on children's intelligence at ages 2, 3, 4, 7, 9, 10, 12 and 14: i.e., lower heritability in lower-SES families. We used a twin model that allowed for a main effect of SES on intelligence, as well as a moderating effect of SES on the genetic and environmental components of intelligence. Results We found greater variance in intelligence in low-SES families, but minimal evidence of GxE interaction across the eight ages. A power calculation indicated that a sample size of about 5000 twin pairs is required to detect moderation of the genetic component of intelligence as small as 0.25, with about 80% power - a difference of 11% to 53% in heritability, in low- (−2 standard deviations, SD) and high-SES (+2 SD) families. With samples at each age of about this size, the present study found no moderation of the genetic effect on intelligence. However, we found the greater variance in low-SES families is due to moderation of the environmental effect – an environment-environment interaction. Conclusions In a UK-representative sample, the genetic effect on intelligence is similar in low- and high-SES families. Children's shared experiences appear to explain the greater variation in intelligence in lower SES. PMID:22312423

Estimation of genetic parameters and response to selection for a continuous trait subject to culling before testing.

PubMed

Arnason, T; Albertsdóttir, E; Fikse, W F; Eriksson, S; Sigurdsson, A

2012-02-01

The consequences of assuming a zero environmental covariance between a binary trait 'test-status' and a continuous trait on the estimates of genetic parameters by restricted maximum likelihood and Gibbs sampling and on response from genetic selection when the true environmental covariance deviates from zero were studied. Data were simulated for two traits (one that culling was based on and a continuous trait) using the following true parameters, on the underlying scale: h² = 0.4; r(A) = 0.5; r(E) = 0.5, 0.0 or -0.5. The selection on the continuous trait was applied to five subsequent generations where 25 sires and 500 dams produced 1500 offspring per generation. Mass selection was applied in the analysis of the effect on estimation of genetic parameters. Estimated breeding values were used in the study of the effect of genetic selection on response and accuracy. The culling frequency was either 0.5 or 0.8 within each generation. Each of 10 replicates included 7500 records on 'test-status' and 9600 animals in the pedigree file. Results from bivariate analysis showed unbiased estimates of variance components and genetic parameters when true r(E) = 0.0. For r(E) = 0.5, variance components (13-19% bias) and especially (50-80%) were underestimated for the continuous trait, while heritability estimates were unbiased. For r(E) = -0.5, heritability estimates of test-status were unbiased, while genetic variance and heritability of the continuous trait together with were overestimated (25-50%). The bias was larger for the higher culling frequency. Culling always reduced genetic progress from selection, but the genetic progress was found to be robust to the use of wrong parameter values of the true environmental correlation between test-status and the continuous trait. Use of a bivariate linear-linear model reduced bias in genetic evaluations, when data were subject to culling. © 2011 Blackwell Verlag GmbH.

A Genetic Epidemiological Mega Analysis of Smoking Initiation in Adolescents.

PubMed

Maes, Hermine H; Prom-Wormley, Elizabeth; Eaves, Lindon J; Rhee, Soo Hyun; Hewitt, John K; Young, Susan; Corley, Robin; McGue, Matt; Iacono, William G; Legrand, Lisa; Samek, Diana R; Murrelle, E Lenn; Silberg, Judy L; Miles, Donna R; Schieken, Richard M; Beunen, Gaston P; Thomis, Martine; Rose, Richard J; Dick, Danielle M; Boomsma, Dorret I; Bartels, Meike; Vink, Jacqueline M; Lichtenstein, Paul; White, Victoria; Kaprio, Jaakko; Neale, Michael C

2017-04-01

Previous studies in adolescents were not adequately powered to accurately disentangle genetic and environmental influences on smoking initiation (SI) across adolescence. Mega-analysis of pooled genetically informative data on SI was performed, with structural equation modeling, to test equality of prevalence and correlations across cultural backgrounds, and to estimate the significance and effect size of genetic and environmental effects according to the classical twin study, in adolescent male and female twins from same-sex and opposite-sex twin pairs (N = 19 313 pairs) between ages 10 and 19, with 76 358 longitudinal assessments between 1983 and 2007, from 11 population-based twin samples from the United States, Europe, and Australia. Although prevalences differed between samples, twin correlations did not, suggesting similar etiology of SI across developed countries. The estimate of additive genetic contributions to liability of SI increased from approximately 15% to 45% from ages 13 to 19. Correspondingly, shared environmental factors accounted for a substantial proportion of variance in liability to SI at age 13 (70%) and gradually less by age 19 (40%). Both additive genetic and shared environmental factors significantly contribute to variance in SI throughout adolescence. The present study, the largest genetic epidemiological study on SI to date, found consistent results across 11 studies for the etiology of SI. Environmental factors, especially those shared by siblings in a family, primarily influence SI variance in early adolescence, while an increasing role of genetic factors is seen at later ages, which has important implications for prevention strategies. This is the first study to find evidence of genetic factors in liability to SI at ages as young as 12. It also shows the strongest evidence to date for decay of effects of the shared environment from early adolescence to young adulthood. We found remarkable consistency of twin correlations across studies reflecting similar etiology of liability to initiate smoking across different cultures and time periods. Thus familial factors strongly contribute to individual differences in who starts to smoke with a gradual increase in the impact of genetic factors and a corresponding decrease in that of the shared environment. © The Author 2017. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Genetic Liability to Disability Pension in Women and Men: A Prospective Population-Based Twin Study

PubMed Central

Narusyte, Jurgita; Ropponen, Annina; Silventoinen, Karri; Alexanderson, Kristina; Kaprio, Jaakko; Samuelsson, Åsa; Svedberg, Pia

2011-01-01

Background Previous studies of risk factors for disability pension (DP) have mainly focused on psychosocial, or environmental, factors, while the relative importance of genetic effects has been less studied. Sex differences in biological mechanisms have not been investigated at all. Methods The study sample included 46,454 Swedish twins, consisting of 23,227 complete twin pairs, born 1928–1958, who were followed during 1993–2008. Data on DP, including diagnoses, were obtained from the National Social Insurance Agency. Within-pair similarity in liability to DP was assessed by calculating intraclass correlations. Genetic and environmental influences on liability to DP were estimated by applying discrete-time frailty modeling. Results During follow-up, 7,669 individuals were granted DP (18.8% women and 14.1% men). Intraclass correlations were generally higher in MZ pairs than DZ pairs, while DZ same-sexed pairs were more similar than opposite-sexed pairs. The best-fitting model indicated that genetic factors contributed 49% (95% CI: 39–59) to the variance in DP due to mental diagnoses, 35% (95% CI: 29–41) due to musculoskeletal diagnoses, and 27% (95% CI: 20–33) due to all other diagnoses. In both sexes, genetic effects common to all ages explained one-third, whereas age-specific factors almost two-thirds, of the total variance in liability to DP irrespective of diagnosis. Sex differences in liability to DP were indicated, in that partly different sets of genes were found to operate in women and men, even though the magnitude of genetic variance explained was equal for both sexes. Conclusions The findings of the study suggest that genetic effects are important for liability to DP due to different diagnoses. Moreover, genetic contributions to liability to DP tend to differ between women and men, even though the overall relative contribution of genetic influences does not differ by sex. Hence, the pathways leading to DP might differ between women and men. PMID:21850258

A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

NASA Astrophysics Data System (ADS)

Gruszczynski, Maciej; Klos, Anna; Bogusz, Janusz

2018-04-01

For the first time, we introduced the probabilistic principal component analysis (pPCA) regarding the spatio-temporal filtering of Global Navigation Satellite System (GNSS) position time series to estimate and remove Common Mode Error (CME) without the interpolation of missing values. We used data from the International GNSS Service (IGS) stations which contributed to the latest International Terrestrial Reference Frame (ITRF2014). The efficiency of the proposed algorithm was tested on the simulated incomplete time series, then CME was estimated for a set of 25 stations located in Central Europe. The newly applied pPCA was compared with previously used algorithms, which showed that this method is capable of resolving the problem of proper spatio-temporal filtering of GNSS time series characterized by different observation time span. We showed, that filtering can be carried out with pPCA method when there exist two time series in the dataset having less than 100 common epoch of observations. The 1st Principal Component (PC) explained more than 36% of the total variance represented by time series residuals' (series with deterministic model removed), what compared to the other PCs variances (less than 8%) means that common signals are significant in GNSS residuals. A clear improvement in the spectral indices of the power-law noise was noticed for the Up component, which is reflected by an average shift towards white noise from - 0.98 to - 0.67 (30%). We observed a significant average reduction in the accuracy of stations' velocity estimated for filtered residuals by 35, 28 and 69% for the North, East, and Up components, respectively. CME series were also subjected to analysis in the context of environmental mass loading influences of the filtering results. Subtraction of the environmental loading models from GNSS residuals provides to reduction of the estimated CME variance by 20 and 65% for horizontal and vertical components, respectively.

Inferring genetic parameters of lactation in Tropical Milking Criollo cattle with random regression test-day models.

PubMed

Santellano-Estrada, E; Becerril-Pérez, C M; de Alba, J; Chang, Y M; Gianola, D; Torres-Hernández, G; Ramírez-Valverde, R

2008-11-01

This study inferred genetic and permanent environmental variation of milk yield in Tropical Milking Criollo cattle and compared 5 random regression test-day models using Wilmink's function and Legendre polynomials. Data consisted of 15,377 test-day records from 467 Tropical Milking Criollo cows that calved between 1974 and 2006 in the tropical lowlands of the Gulf Coast of Mexico and in southern Nicaragua. Estimated heritabilities of test-day milk yields ranged from 0.18 to 0.45, and repeatabilities ranged from 0.35 to 0.68 for the period spanning from 6 to 400 d in milk. Genetic correlation between days in milk 10 and 400 was around 0.50 but greater than 0.90 for most pairs of test days. The model that used first-order Legendre polynomials for additive genetic effects and second-order Legendre polynomials for permanent environmental effects gave the smallest residual variance and was also favored by the Akaike information criterion and likelihood ratio tests.

A python framework for environmental model uncertainty analysis

USGS Publications Warehouse

White, Jeremy; Fienen, Michael N.; Doherty, John E.

2016-01-01

We have developed pyEMU, a python framework for Environmental Modeling Uncertainty analyses, open-source tool that is non-intrusive, easy-to-use, computationally efficient, and scalable to highly-parameterized inverse problems. The framework implements several types of linear (first-order, second-moment (FOSM)) and non-linear uncertainty analyses. The FOSM-based analyses can also be completed prior to parameter estimation to help inform important modeling decisions, such as parameterization and objective function formulation. Complete workflows for several types of FOSM-based and non-linear analyses are documented in example notebooks implemented using Jupyter that are available in the online pyEMU repository. Example workflows include basic parameter and forecast analyses, data worth analyses, and error-variance analyses, as well as usage of parameter ensemble generation and management capabilities. These workflows document the necessary steps and provides insights into the results, with the goal of educating users not only in how to apply pyEMU, but also in the underlying theory of applied uncertainty quantification.

Visual Attention Allocation Between Robotic Arm and Environmental Process Control: Validating the STOM Task Switching Model

NASA Technical Reports Server (NTRS)

Wickens, Christopher; Vieanne, Alex; Clegg, Benjamin; Sebok, Angelia; Janes, Jessica

2015-01-01

Fifty six participants time shared a spacecraft environmental control system task with a realistic space robotic arm control task in either a manual or highly automated version. The former could suffer minor failures, whose diagnosis and repair were supported by a decision aid. At the end of the experiment this decision aid unexpectedly failed. We measured visual attention allocation and switching between the two tasks, in each of the eight conditions formed by manual-automated arm X expected-unexpected failure X monitoring- failure management. We also used our multi-attribute task switching model, based on task attributes of priority interest, difficulty and salience that were self-rated by participants, to predict allocation. An un-weighted model based on attributes of difficulty, interest and salience accounted for 96 percent of the task allocation variance across the 8 different conditions. Task difficulty served as an attractor, with more difficult tasks increasing the tendency to stay on task.

Repeatability of circadian behavioural variation revealed in free-ranging marine fish.

PubMed

Alós, Josep; Martorell-Barceló, Martina; Campos-Candela, Andrea

2017-02-01

Repeatable between-individual differences in the behavioural manifestation of underlying circadian rhythms determine chronotypes in humans and terrestrial animals. Here, we have repeatedly measured three circadian behaviours, awakening time, rest onset and rest duration, in the free-ranging pearly razorfish, Xyrithchys novacula , facilitated by acoustic tracking technology and hidden Markov models. In addition, daily travelled distance, a standard measure of daily activity as fish personality trait, was repeatedly assessed using a State-Space Model. We have decomposed the variance of these four behavioural traits using linear mixed models and estimated repeatability scores ( R ) while controlling for environmental co-variates: year of experimentation, spatial location of the activity, fish size and gender and their interactions. Between- and within-individual variance decomposition revealed significant R s in all traits suggesting high predictability of individual circadian behavioural variation and the existence of chronotypes. The decomposition of the correlations among chronotypes and the personality trait studied here into between- and within-individual correlations did not reveal any significant correlation at between-individual level. We therefore propose circadian behavioural variation as an independent axis of the fish personality, and the study of chronotypes and their consequences as a novel dimension in understanding within-species fish behavioural diversity.

Hedging Your Bets by Learning Reward Correlations in the Human Brain

PubMed Central

Wunderlich, Klaus; Symmonds, Mkael; Bossaerts, Peter; Dolan, Raymond J.

2011-01-01

Summary Human subjects are proficient at tracking the mean and variance of rewards and updating these via prediction errors. Here, we addressed whether humans can also learn about higher-order relationships between distinct environmental outcomes, a defining ecological feature of contexts where multiple sources of rewards are available. By manipulating the degree to which distinct outcomes are correlated, we show that subjects implemented an explicit model-based strategy to learn the associated outcome correlations and were adept in using that information to dynamically adjust their choices in a task that required a minimization of outcome variance. Importantly, the experimentally generated outcome correlations were explicitly represented neuronally in right midinsula with a learning prediction error signal expressed in rostral anterior cingulate cortex. Thus, our data show that the human brain represents higher-order correlation structures between rewards, a core adaptive ability whose immediate benefit is optimized sampling. PMID:21943609

High stability lasers for lidar and remote sensing

NASA Astrophysics Data System (ADS)

Heine, Frank; Lange, Robert; Seel, Stefan; Smutny, Berry

2017-11-01

Tesat-Spacecom is currently building a set flight models of frequency stabilized lasers for the ESA Missions AEOLUS and LTP. Lasers with low intensity noise in the kHz region and analogue tuning capabilities for frequency and output power are developed for the on board metrology of the LTP project, the precursor mission for LISA. This type of laser is internally stabilized by precise temperature control, approaching an ALLAN variance of 10-9 for 100 sec. It can be easily locked to external frequency references with <50kHz bandwidth. The Seed laser for the AEOLUS mission (wind LIDAR) is used as the master frequency reference and is stabilized internally by a optical cavity. It shows a 3* 10-11 Allan variance from time intervals 1 sec - 1000 sec. Furthermore it is step-tunable for calibration of the receiver instrument with a speed of GHz / sec by a digital command interface. Performance and environmental test results will be presented.

Bayesian model evidence as a model evaluation metric

NASA Astrophysics Data System (ADS)

Guthke, Anneli; Höge, Marvin; Nowak, Wolfgang

2017-04-01

When building environmental systems models, we are typically confronted with the questions of how to choose an appropriate model (i.e., which processes to include or neglect) and how to measure its quality. Various metrics have been proposed that shall guide the modeller towards a most robust and realistic representation of the system under study. Criteria for evaluation often address aspects of accuracy (absence of bias) or of precision (absence of unnecessary variance) and need to be combined in a meaningful way in order to address the inherent bias-variance dilemma. We suggest using Bayesian model evidence (BME) as a model evaluation metric that implicitly performs a tradeoff between bias and variance. BME is typically associated with model weights in the context of Bayesian model averaging (BMA). However, it can also be seen as a model evaluation metric in a single-model context or in model comparison. It combines a measure for goodness of fit with a penalty for unjustifiable complexity. Unjustifiable refers to the fact that the appropriate level of model complexity is limited by the amount of information available for calibration. Derived in a Bayesian context, BME naturally accounts for measurement errors in the calibration data as well as for input and parameter uncertainty. BME is therefore perfectly suitable to assess model quality under uncertainty. We will explain in detail and with schematic illustrations what BME measures, i.e. how complexity is defined in the Bayesian setting and how this complexity is balanced with goodness of fit. We will further discuss how BME compares to other model evaluation metrics that address accuracy and precision such as the predictive logscore or other model selection criteria such as the AIC, BIC or KIC. Although computationally more expensive than other metrics or criteria, BME represents an appealing alternative because it provides a global measure of model quality. Even if not applicable to each and every case, we aim at stimulating discussion about how to judge the quality of hydrological models in the presence of uncertainty in general by dissecting the mechanism behind BME.

Estimation of genetic parameters for heat stress, including dominance gene effects, on milk yield in Thai Holstein dairy cattle.

PubMed

Boonkum, Wuttigrai; Duangjinda, Monchai

2015-03-01

Heat stress in tropical regions is a major cause that strongly negatively affects to milk production in dairy cattle. Genetic selection for dairy heat tolerance is powerful technique to improve genetic performance. Therefore, the current study aimed to estimate genetic parameters and investigate the threshold point of heat stress for milk yield. Data included 52 701 test-day milk yield records for the first parity from 6247 Thai Holstein dairy cattle, covering the period 1990 to 2007. The random regression test day model with EM-REML was used to estimate variance components, genetic parameters and milk production loss. A decline in milk production was found when temperature and humidity index (THI) exceeded a threshold of 74, also it was associated with the high percentage of Holstein genetics. All variance component estimates increased with THI. The estimate of heritability of test-day milk yield was 0.231. Dominance variance as a proportion to additive variance (0.035) indicated that non-additive effects might not be of concern for milk genetics studies in Thai Holstein cattle. Correlations between genetic and permanent environmental effects, for regular conditions and due to heat stress, were - 0.223 and - 0.521, respectively. The heritability and genetic correlations from this study show that simultaneous selection for milk production and heat tolerance is possible. © 2014 Japanese Society of Animal Science.

Predicting invertebrate assemblage composition from harvesting pressure and environmental characteristics on tropical reef flats

NASA Astrophysics Data System (ADS)

Jimenez, H.; Dumas, P.; Ponton, D.; Ferraris, J.

2012-03-01

Invertebrates represent an essential component of coral reef ecosystems; they are ecologically important and a major resource, but their assemblages remain largely unknown, particularly on Pacific islands. Understanding their distribution and building predictive models of community composition as a function of environmental variables therefore constitutes a key issue for resource management. The goal of this study was to define and classify the main environmental factors influencing tropical invertebrate distributions in New Caledonian reef flats and to test the resulting predictive model. Invertebrate assemblages were sampled by visual counting during 2 years and 2 seasons, then coupled to different environmental conditions (habitat composition, hydrodynamics and sediment characteristics) and harvesting status (MPA vs. non-MPA and islets vs. coastal flats). Environmental conditions were described by a principal component analysis (PCA), and contributing variables were selected. Permutational analysis of variance (PERMANOVA) was used to test the effects of different factors (status, flat, year and season) on the invertebrate assemblage composition. Multivariate regression trees (MRT) were then used to hierarchically classify the effects of environmental and harvesting variables. MRT model explained at least 60% of the variation in structure of invertebrate communities. Results highlighted the influence of status (MPA vs. non-MPA) and location (islet vs. coastal flat), followed by habitat composition, organic matter content, hydrodynamics and sampling year. Predicted assemblages defined by indicator families were very different for each environment-exploitation scenario and correctly matched a calibration data matrix. Predictions from MRT including both environmental variables and harvesting pressure can be useful for management of invertebrates in coral reef environments.

Genetic and environmental bases of the interplay between magical ideation and personality.

PubMed

Brambilla, Paolo; Fagnani, Corrado; Cecchetto, Filippo; Medda, Emanuela; Bellani, Marcella; Salemi, Miriam; Picardi, Angelo; Stazi, Maria Antonietta

2014-02-28

Sub-threshold psychotic symptoms are quite commonly present in general population. Among these, Magical Ideation (MI) has been proved to be a valid predictor of psychosis. However, the genetic and environmental influences on the interplay between MI and personality have not fully been explored. A total of 534 adult twins from the population-based Italian Twin Register were assessed for MI using the MI Scale (MIS) and for personality with the temperament and character inventory (TCI). A Multivariate Cholesky model was applied with Mx statistical program. The best-fitting model showed that additive genetic and unshared environmental factors explain approximately the same proportion of variance in MI, whereas a less strong genetic influence on personality traits emerged. Relevant correlations between MI and specific personality traits (novelty seeking, cooperativeness, self-directedness, self-transcendence) were found, suggesting shared influences for MI and these traits. Both genetic and environmental factors explained these correlations, with genetic factors playing a predominant role. Moderate-to-substantial genetic effects on MI and personality were found. Shared genetic and environmental effects underlie the phenotypic correlation between MI (psychosis-proneness) and personality traits, i.e. self-directedness (negative association) and self-transcendence (positive association), potentially representing predictive markers of psychosis liability related to schizotypy and personality. © 2013 Published by Elsevier Ireland Ltd.

Sleep Duration and Area-Level Deprivation in Twins.

PubMed

Watson, Nathaniel F; Horn, Erin; Duncan, Glen E; Buchwald, Dedra; Vitiello, Michael V; Turkheimer, Eric

2016-01-01

We used quantitative genetic models to assess whether area-level deprivation as indicated by the Singh Index predicts shorter sleep duration and modifies its underlying genetic and environmental contributions. Participants were 4,218 adult twin pairs (2,377 monozygotic and 1,841 dizygotic) from the University of Washington Twin Registry. Participants self-reported habitual sleep duration. The Singh Index was determined by linking geocoding addresses to 17 indicators at the census-tract level using data from Census of Washington State and Census Tract Cartographic Boundary Files from 2000 and 2010. Data were analyzed using univariate and bivariate genetic decomposition and quantitative genetic interaction models that assessed A (additive genetics), C (common environment), and E (unique environment) main effects of the Singh Index on sleep duration and allowed the magnitude of residual ACE variance components in sleep duration to vary with the Index. The sample had a mean age of 38.2 y (standard deviation [SD] = 18), and was predominantly female (62%) and Caucasian (91%). Mean sleep duration was 7.38 h (SD = 1.20) and the mean Singh Index score was 0.00 (SD = 0.89). The heritability of sleep duration was 39% and the Singh Index was 12%. The uncontrolled phenotypic regression of sleep duration on the Singh Index showed a significant negative relationship between area-level deprivation and sleep length (b = -0.080, P < 0.001). Every 1 SD in Singh Index was associated with a ∼4.5 min change in sleep duration. For the quasi-causal bivariate model, there was a significant main effect of E (b(0E) = -0.063; standard error [SE] = 0.30; P < 0.05). Residual variance components unique to sleep duration were significant for both A (b(0Au) = 0.734; SE = 0.020; P < 0.001) and E (b(0Eu) = 0.934; SE = 0.013; P < 0.001). Area-level deprivation has a quasi-causal association with sleep duration, with greater deprivation being related to shorter sleep. As area-level deprivation increases, unique genetic and nonshared environmental residual variance in sleep duration increases. © 2016 Associated Professional Sleep Societies, LLC.

Inter-individual Differences in the Effects of Aircraft Noise on Sleep Fragmentation.

PubMed

McGuire, Sarah; Müller, Uwe; Elmenhorst, Eva-Maria; Basner, Mathias

2016-05-01

Environmental noise exposure disturbs sleep and impairs recuperation, and may contribute to the increased risk for (cardiovascular) disease. Noise policy and regulation are usually based on average responses despite potentially large inter-individual differences in the effects of traffic noise on sleep. In this analysis, we investigated what percentage of the total variance in noise-induced awakening reactions can be explained by stable inter-individual differences. We investigated 69 healthy subjects polysomnographically (mean ± standard deviation 40 ± 13 years, range 18-68 years, 32 male) in this randomized, balanced, double-blind, repeated measures laboratory study. This study included one adaptation night, 9 nights with exposure to 40, 80, or 120 road, rail, and/or air traffic noise events (including one noise-free control night), and one recovery night. Mixed-effects models of variance controlling for reaction probability in noise-free control nights, age, sex, number of noise events, and study night showed that 40.5% of the total variance in awakening probability and 52.0% of the total variance in EEG arousal probability were explained by inter-individual differences. If the data set was restricted to nights (4 exposure nights with 80 noise events per night), 46.7% of the total variance in awakening probability and 57.9% of the total variance in EEG arousal probability were explained by inter-individual differences. The results thus demonstrate that, even in this relatively homogeneous, healthy, adult study population, a considerable amount of the variance observed in noise-induced sleep disturbance can be explained by inter-individual differences that cannot be explained by age, gender, or specific study design aspects. It will be important to identify those at higher risk for noise induced sleep disturbance. Furthermore, the custom to base noise policy and legislation on average responses should be re-assessed based on these findings. © 2016 Associated Professional Sleep Societies, LLC.

Applying an economical scale-aware PDF-based turbulence closure model in NOAA NCEP GCMs.

NASA Astrophysics Data System (ADS)

Belochitski, A.; Krueger, S. K.; Moorthi, S.; Bogenschutz, P.; Cheng, A.

2017-12-01

A novel unified representation of sub-grid scale (SGS) turbulence, cloudiness, and shallow convection is being implemented into the NOAA NCEP Global Forecasting System (GFS) general circulation model. The approach, known as Simplified High Order Closure (SHOC), is based on predicting a joint PDF of SGS thermodynamic variables and vertical velocity, and using it to diagnose turbulent diffusion coefficients, SGS fluxes, condensation, and cloudiness. Unlike other similar methods, comparatively few new prognostic variables needs to be introduced, making the technique computationally efficient. In the base version of SHOC it is SGS turbulent kinetic energy (TKE), and in the developmental version — SGS TKE, and variances of total water and moist static energy (MSE). SHOC is now incorporated into a version of GFS that will become a part of the NOAA Next Generation Global Prediction System based around NOAA GFDL's FV3 dynamical core, NOAA Environmental Modeling System (NEMS) coupled modeling infrastructure software, and a set novel physical parameterizations. Turbulent diffusion coefficients computed by SHOC are now used in place of those produced by the boundary layer turbulence and shallow convection parameterizations. Large scale microphysics scheme is no longer used to calculate cloud fraction or the large-scale condensation/deposition. Instead, SHOC provides these quantities. Radiative transfer parameterization uses cloudiness computed by SHOC. An outstanding problem with implementation of SHOC in the NCEP global models is excessively large high level tropical cloudiness. Comparison of the moments of the SGS PDF diagnosed by SHOC to the moments calculated in a GigaLES simulation of tropical deep convection case (GATE), shows that SHOC diagnoses too narrow PDF distributions of total cloud water and MSE in the areas of deep convective detrainment. A subsequent sensitivity study of SHOC's diagnosed cloud fraction (CF) to higher order input moments of the SGS PDF demonstrated that CF is improved if SHOC is provided with correct variances of total water and MSE. Consequently, SHOC was modified to include two new prognostic equations for variances of total water and MSE, and coupled with the Chikira-Sugiyama parameterization of deep convection to include effects of detrainment on the prognostic variances.

A test of the cross-scale resilience model: Functional richness in Mediterranean-climate ecosystems

USGS Publications Warehouse

Wardwell, D.A.; Allen, Craig R.; Peterson, G.D.; Tyre, A.J.

2008-01-01

Ecological resilience has been proposed to be generated, in part, in the discontinuous structure of complex systems. Environmental discontinuities are reflected in discontinuous, aggregated animal body mass distributions. Diversity of functional groups within body mass aggregations (scales) and redundancy of functional groups across body mass aggregations (scales) has been proposed to increase resilience. We evaluate that proposition by analyzing mammalian and avian communities of Mediterranean-climate ecosystems. We first determined that body mass distributions for each animal community were discontinuous. We then calculated the variance in richness of function across aggregations in each community, and compared observed values with distributions created by 1000 simulations using a null of random distribution of function, with the same n, number of discontinuities and number of functional groups as the observed data. Variance in the richness of functional groups across scales was significantly lower in real communities than in simulations in eight of nine sites. The distribution of function across body mass aggregations in the animal communities we analyzed was non-random, and supports the contentions of the cross-scale resilience model. ?? 2007 Elsevier B.V. All rights reserved.

Estimation of Mangrove Net Primary Production and Carbon Sequestration service using Light Use Efficiency model in the Sunderban Biosphere region, India

NASA Astrophysics Data System (ADS)

Sannigrahi, Srikanta; Sen, Somnath; Paul, Saikat

2016-04-01

Net Primary Production (NPP) of mangrove ecosystem and its capacity to sequester carbon from the atmosphere may be used to quantify the regulatory ecosystem services. Three major group of parameters has been set up as BioClimatic Parameters (BCP): (Photosynthetically Active Radiation (PAR), Absorbed PAR (APAR), Fraction of PAR (FPAR), Photochemical Reflectance Index (PRI), Light Use Efficiency (LUE)), BioPhysical Parameters (BPP) :(Normalize Difference Vegetation Index (NDVI), scaled NDVI, Enhanced Vegetation Index (EVI), scaled EVI, Optimised and Modified Soil Adjusted Vegetation Index (OSAVI, MSAVI), Leaf Area Index (LAI)), and Environmental Limiting Parameters (ELP) (Temperature Stress (TS), Land Surface Water Index (LSWI), Normalize Soil Water Index (NSWI), Water Stress Scalar (WS), Inversed WS (iWS) Land Surface Temperature (LST), scaled LST, Vapor Pressure Deficit (VPD), scaled VPD, and Soil Water Deficit Index (SWDI)). Several LUE models namely Carnegie Ames Stanford Approach (CASA), Eddy Covariance - LUE (EC-LUE), Global Production Efficiency Model (GloPEM), Vegetation Photosynthesis Model (VPM), MOD NPP model, Temperature and Greenness Model (TG), Greenness and Radiation model (GR) and MOD17 was adopted in this study to assess the spatiotemporal nature of carbon fluxes. Above and Below Ground Biomass (AGB & BGB) was calculated using field based estimation of OSAVI and NDVI. Microclimatic zonation has been set up to assess the impact of coastal climate on environmental limiting factors. MODerate Resolution Imaging Spectroradiometer (MODIS) based yearly Gross Primary Production (GPP) and NPP product MOD17 was also tested with LUE based results with standard model validation statistics: Root Mean Square of Error (RMSE), Mean Absolute Error (MEA), Bias, Coefficient of Variation (CV) and Coefficient of Determination (R2). The performance of CASA NPP was tested with the ground based NPP with R2 = 0.89 RMSE = 3.28 P = 0.01. Among the all adopted models, EC-LUE and VPM models has explained the maximum variances (>80%) in comparison to the other model. Study result has also showed that the BPP has explained the maximum model variances (>93%) followed by BCP (>65%) and ELP (>50%). Scaled WS, iWS, LST, VPD, NDVI was performed better in a minimum ELP condition whereas surface moisture and wetness was highly correlated with the AGB and NPP (R2 = 0.86 RMSE = 1.83). During this study period (2000-2013), it was found that there was a significantly declining trend (R2 = 0.32 P = 0.05) of annual NPP and the maximum decrease was found in the eastern part where built-up area was mainly accounted for reduction of NPP. BCP are explained higher variances (>80%) in the optimum climatic condition exist along the coastal stretches in comparison to the landward extent (>45%).

Testing life history predictions in a long-lived seabird: A population matrix approach with improved parameter estimation

USGS Publications Warehouse

Doherty, P.F.; Schreiber, E.A.; Nichols, J.D.; Hines, J.E.; Link, W.A.; Schenk, G.A.; Schreiber, R.W.

2004-01-01

Life history theory and associated empirical generalizations predict that population growth rate (λ) in long-lived animals should be most sensitive to adult survival; the rates to which λ is most sensitive should be those with the smallest temporal variances; and stochastic environmental events should most affect the rates to which λ is least sensitive. To date, most analyses attempting to examine these predictions have been inadequate, their validity being called into question by problems in estimating parameters, problems in estimating the variability of parameters, and problems in measuring population sensitivities to parameters. We use improved methodologies in these three areas and test these life-history predictions in a population of red-tailed tropicbirds (Phaethon rubricauda). We support our first prediction that λ is most sensitive to survival rates. However the support for the second prediction that these rates have the smallest temporal variance was equivocal. Previous support for the second prediction may be an artifact of a high survival estimate near the upper boundary of 1 and not a result of natural selection canalizing variances alone. We did not support our third prediction that effects of environmental stochasticity (El Niño) would most likely be detected in vital rates to which λ was least sensitive and which are thought to have high temporal variances. Comparative data-sets on other seabirds, within and among orders, and in other locations, are needed to understand these environmental effects.

Vegetation greenness impacts on maximum and minimum temperatures in northeast Colorado

USGS Publications Warehouse

Hanamean, J. R.; Pielke, R.A.; Castro, C. L.; Ojima, D.S.; Reed, Bradley C.; Gao, Z.

2003-01-01

The impact of vegetation on the microclimate has not been adequately considered in the analysis of temperature forecasting and modelling. To fill part of this gap, the following study was undertaken.A daily 850–700 mb layer mean temperature, computed from the National Center for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis, and satellite-derived greenness values, as defined by NDVI (Normalised Difference Vegetation Index), were correlated with surface maximum and minimum temperatures at six sites in northeast Colorado for the years 1989–98. The NDVI values, representing landscape greenness, act as a proxy for latent heat partitioning via transpiration. These sites encompass a wide array of environments, from irrigated-urban to short-grass prairie. The explained variance (r2 value) of surface maximum and minimum temperature by only the 850–700 mb layer mean temperature was subtracted from the corresponding explained variance by the 850–700 mb layer mean temperature and NDVI values. The subtraction shows that by including NDVI values in the analysis, the r2 values, and thus the degree of explanation of the surface temperatures, increase by a mean of 6% for the maxima and 8% for the minima over the period March–October. At most sites, there is a seasonal dependence in the explained variance of the maximum temperatures because of the seasonal cycle of plant growth and senescence. Between individual sites, the highest increase in explained variance occurred at the site with the least amount of anthropogenic influence. This work suggests the vegetation state needs to be included as a factor in surface temperature forecasting, numerical modeling, and climate change assessments.

Prediction-error variance in Bayesian model updating: a comparative study

NASA Astrophysics Data System (ADS)

Asadollahi, Parisa; Li, Jian; Huang, Yong

2017-04-01

In Bayesian model updating, the likelihood function is commonly formulated by stochastic embedding in which the maximum information entropy probability model of prediction error variances plays an important role and it is Gaussian distribution subject to the first two moments as constraints. The selection of prediction error variances can be formulated as a model class selection problem, which automatically involves a trade-off between the average data-fit of the model class and the information it extracts from the data. Therefore, it is critical for the robustness in the updating of the structural model especially in the presence of modeling errors. To date, three ways of considering prediction error variances have been seem in the literature: 1) setting constant values empirically, 2) estimating them based on the goodness-of-fit of the measured data, and 3) updating them as uncertain parameters by applying Bayes' Theorem at the model class level. In this paper, the effect of different strategies to deal with the prediction error variances on the model updating performance is investigated explicitly. A six-story shear building model with six uncertain stiffness parameters is employed as an illustrative example. Transitional Markov Chain Monte Carlo is used to draw samples of the posterior probability density function of the structure model parameters as well as the uncertain prediction variances. The different levels of modeling uncertainty and complexity are modeled through three FE models, including a true model, a model with more complexity, and a model with modeling error. Bayesian updating is performed for the three FE models considering the three aforementioned treatments of the prediction error variances. The effect of number of measurements on the model updating performance is also examined in the study. The results are compared based on model class assessment and indicate that updating the prediction error variances as uncertain parameters at the model class level produces more robust results especially when the number of measurement is small.

Aggression at Age 5 as a Function of Prenatal Exposure to Cocaine, Gender, and Environmental Risk

PubMed Central

Bendersky, Margaret; Bennett, David; Lewis, Michael

2006-01-01

Objective To examine childhood aggression at age 5 in a multiple risk model that includes cocaine exposure, environmental risk, and gender as predictors. Methods Aggression was assessed in 206 children by using multiple methods including teacher report, parent report, child’s response to hypothetical provocations, and child’s observed behavior. Also examined was a composite score that reflected high aggression across contexts. Results Multiple regression analyses indicated that a significant amount of variance in each of the aggression measures and the composite was explained by the predictors. The variables that were independently related differed depending on the outcome. Cocaine exposure, gender, and environmental risk were all related to the composite aggression score. Conclusions Cocaine exposure, being male, and a high-risk environment were all predictive of aggressive behavior at 5 years. It is this group of exposed boys at high environmental risk that is most likely to show continued aggression over time. PMID:15827351

Persistent Genetic and Family-Wide Environmental Contributions to Early Number Knowledge and Later Achievement in Mathematics.

PubMed

Garon-Carrier, Gabrielle; Boivin, Michel; Kovas, Yulia; Feng, Bei; Brendgen, Mara; Vitaro, Frank; Séguin, Jean R; Tremblay, Richard E; Dionne, Ginette

2017-12-01

This study investigated the stable and transient genetic and environmental contributions to individual differences in number knowledge in the transition from preschool (age 5) to Grade 1 (age 7) and to the predictive association between early number knowledge and later math achievement (age 10-12). We conducted genetic simplex modeling across these three time points. Genetic variance was transmitted from preschool number knowledge to late-elementary math achievement; in addition, significant genetic innovation (i.e., new influence) occurred at ages 10 through 12 years. The shared and nonshared environmental contributions decreased during the transition from preschool to school entry, but shared and nonshared environment contributed to the continuity across time from preschool number knowledge to subsequent number knowledge and math achievement. There was no new environmental contribution at time points subsequent to preschool. Results are discussed in light of their practical implications for children who have difficulties with mathematics, as well as for preventive intervention.

Random sex determination: When developmental noise tips the sex balance.

PubMed

Perrin, Nicolas

2016-12-01

Sex-determining factors are usually assumed to be either genetic or environmental. The present paper aims at drawing attention to the potential contribution of developmental noise, an important but often-neglected component of phenotypic variance. Mutual inhibitions between male and female pathways make sex a bistable equilibrium, such that random fluctuations in the expression of genes at the top of the cascade are sufficient to drive individual development toward one or the other stable state. Evolutionary modeling shows that stochastic sex determinants should resist elimination by genetic or environmental sex determinants under ecologically meaningful settings. On the empirical side, many sex-determination systems traditionally considered as environmental or polygenic actually provide evidence for large components of stochasticity. In reviewing the field, I argue that sex-determination systems should be considered within a three-ends continuum, rather than the classical two-ends continuum. © 2016 WILEY Periodicals, Inc.

A Twin Study on Perceived Stress, Depressive Symptoms, and Marriage.

PubMed

Beam, Christopher R; Dinescu, Diana; Emery, Robert; Turkheimer, Eric

2017-03-01

Marriage is associated with reductions in both perceived stress and depressive symptoms, two constructs found to be influenced by common genetic effects. A study of sibling twins was used to test whether marriage decreases the proportion of variance in depressive symptoms accounted for by genetic and environmental effects underlying perceived stress. The sample consisted of 1,612 male and female twin pairs from the University of Washington Twin Registry. The stress-buffering role of marriage was tested relative to two unmarried groups: the never married and the divorced. Multivariate twin models showed that marriage reduced genetic effects of perceived stress on depressive symptoms but did not reduce environmental effects. The findings suggest a potential marital trade-off for women: access to a spouse may decrease genetic effects of perceived stress on depressive symptoms, although marital and family demands may increase environmental effects of perceived stress on depressive symptoms.

A Twin Study on Perceived Stress, Depressive Symptoms, and Marriage

PubMed Central

Beam, Christopher R.; Dinescu, Diana; Emery, Robert E.; Turkheimer, Eric

2017-01-01

Marriage is associated with reductions in both perceived stress and depressive symptoms, two constructs found to be influenced by common genetic effects. A study of sibling twins was used to test whether marriage decreases the proportion of variance in depressive symptoms accounted for by genetic and environmental effects underlying perceived stress. The sample consisted of 1,612 male and female twin pairs from the University of Washington Twin Registry. The stress-buffering role of marriage was tested relative to two unmarried groups: the never married and the divorced. Multivariate twin models showed that marriage reduced genetic effects of perceived stress on depressive symptoms, but did not reduce environmental effects. The findings suggest a potential marital trade-off for women: Access to a spouse may decrease genetic effects of perceived stress on depressive symptoms, although marital and family demands may increase environmental effects of perceived stress on depressive symptoms. PMID:28661771

Genetic and environmental influences on residential location in the U.S

PubMed Central

Duncan, Glen E.; Dansie, Elizabeth J.; Strachan, Eric; Munsell, Melissa; Huang, Ruizhu; Moudon, Anne Vernez; Goldberg, Jack; Buchwald, Dedra

2012-01-01

We used a classical twin design and measures of neighborhood walkability and social deprivation, using each twin’s street address, to examine genetic and environmental influences on the residential location of 1,389 same-sex pairs from a U.S. community-based twin registry. Within-pair correlations and structural equation models estimated these influences on walkability among younger (ages 18–24.9) and older (ages 25+) twins. Adjusting for social deprivation, walkability of residential location was primarily influenced by common environment with lesser contributions of unique environment and genetic factors among younger twins, while unique environment most strongly influenced walkability, with small genetic and common environment effects, among older twins. Thus, minimal variance in walkability was explained by shared genetic effects in younger and older twins, and confirms the importance of environmental factors in walkability of residential locations. PMID:22377617

Application of factor analysis to the water quality in reservoirs

NASA Astrophysics Data System (ADS)

Silva, Eliana Costa e.; Lopes, Isabel Cristina; Correia, Aldina; Gonçalves, A. Manuela

2017-06-01

In this work we present a Factor Analysis of chemical and environmental variables of the water column and hydro-morphological features of several Portuguese reservoirs. The objective is to reduce the initial number of variables, keeping their common characteristics. Using the Factor Analysis, the environmental variables measured in the epilimnion and in the hypolimnion, together with the hydromorphological characteristics of the dams were reduced from 63 variables to only 13 factors, which explained a total of 83.348% of the variance in the original data. After performing rotation using the Varimax method, the relations between the factors and the original variables got clearer and more explainable, which provided a Factor Analysis model for these environmental variables using 13 varifactors: Water quality and distance to the source, Hypolimnion chemical composition, Sulfite-reducing bacteria and nutrients, Coliforms and faecal streptococci, Reservoir depth, Temperature, Location, among other factors.

Operating and environmental characteristics of Sigma Tau hydrogen masers used in the Very Long Baseline Array (VLBA)

NASA Technical Reports Server (NTRS)

Tucker, T. K.

1989-01-01

Presented here are the results obtained from performance evaluation of a pair of Sigma Tau Standards Corporation Model VLBA-112 active hydrogen maser frequency standards. These masers were manufactured for the National Radio Astronomy Observatory (NRAO) for use on the Very Long Baseline Array (VLBA) project and were furnished to the Jet Propulsion Laboratory (JPL) for the purpose of these tests. Tests on the two masers were performed in the JPL Frequency Standards Laboratory (FSL) and included the characterization of output frequency stability versus environmental factors such as temperature, humidity, magnetic field, and barometric pressure. The performance tests also included the determination of phase noise and Allan variance using both FSL and Sigma Tau masers as references. All tests were conducted under controlled laboratory conditions, with only the desired environmental and operational parameters varied to determine sensitivity to external environment.

Structural changes and out-of-sample prediction of realized range-based variance in the stock market

NASA Astrophysics Data System (ADS)

Gong, Xu; Lin, Boqiang

2018-03-01

This paper aims to examine the effects of structural changes on forecasting the realized range-based variance in the stock market. Considering structural changes in variance in the stock market, we develop the HAR-RRV-SC model on the basis of the HAR-RRV model. Subsequently, the HAR-RRV and HAR-RRV-SC models are used to forecast the realized range-based variance of S&P 500 Index. We find that there are many structural changes in variance in the U.S. stock market, and the period after the financial crisis contains more structural change points than the period before the financial crisis. The out-of-sample results show that the HAR-RRV-SC model significantly outperforms the HAR-BV model when they are employed to forecast the 1-day, 1-week, and 1-month realized range-based variances, which means that structural changes can improve out-of-sample prediction of realized range-based variance. The out-of-sample results remain robust across the alternative rolling fixed-window, the alternative threshold value in ICSS algorithm, and the alternative benchmark models. More importantly, we believe that considering structural changes can help improve the out-of-sample performances of most of other existing HAR-RRV-type models in addition to the models used in this paper.

Sex Differences in Genetic and Environmental Influences on Longitudinal Change in Functional Ability in Late Adulthood.

PubMed

Finkel, Deborah; Ernsth-Bravell, Marie; Pedersen, Nancy L

2015-09-01

To determine the extent to which genetic and environmental factors contribute to individual and gender differences in aging of functional ability. Twenty assessments of functional ability are collected as part of the longitudinal Swedish Adoption/Twin Study of Aging from 859 twins aged 50-88 at the first wave. Participants completed up to 6 assessments covering a 19-year period. Factor analysis was used to create 3 factors: flexibility, fine motor skills, and balance. Latent growth curve analysis demonstrated increasing disability and variability after age 70. For flexibility, results indicated significant sex differences in mean change trajectories but no sex differences in components of variance. No sex differences were found for fine motor movement. For balance, there were no sex differences in mean change trajectories; however, there was significant genetic variance for changes in balance in women after age 70 but not for men. Although idiosyncratic environmental influences account for a large part of increasing variance, correlated and shared rearing environmental effects were also evident. Thus, both microenvironmental (individual) and macroenvironmental (family and cultural) effects, as well as genetic factors, affect maintenance of functional ability in late adulthood. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Analyzing and modelling the effect of long-term fertilizer management on crop yield and soil organic carbon in China.

PubMed

Zhang, Jie; Balkovič, Juraj; Azevedo, Ligia B; Skalský, Rastislav; Bouwman, Alexander F; Xu, Guang; Wang, Jinzhou; Xu, Minggang; Yu, Chaoqing

2018-06-15

This study analyzes the influence of various fertilizer management practices on crop yield and soil organic carbon (SOC) based on the long-term field observations and modelling. Data covering 11 years from 8 long-term field trials were included, representing a range of typical soil, climate, and agro-ecosystems in China. The process-based model EPIC (Environmental Policy Integrated Climate model) was used to simulate the response of crop yield and SOC to various fertilization regimes. The results showed that the yield and SOC under additional manure application treatment were the highest while the yield under control treatment was the lowest (30%-50% of NPK yield) at all sites. The SOC in northern sites appeared more dynamic than that in southern sites. The variance partitioning analysis (VPA) showed more variance of crop yield could be explained by the fertilization factor (42%), including synthetic nitrogen (N), phosphorus (P), potassium (K) fertilizers, and fertilizer NPK combined with manure. The interactive influence of soil (total N, P, K, and available N, P, K) and climate factors (mean annual temperature and precipitation) determine the largest part of the SOC variance (32%). EPIC performs well in simulating both the dynamics of crop yield (NRMSE = 32% and 31% for yield calibration and validation) and SOC (NRMSE = 13% and 19% for SOC calibration and validation) under diverse fertilization practices in China. EPIC can assist in predicting the impacts of different fertilization regimes on crop growth and soil carbon dynamics, and contribute to the optimization of fertilizer management for different areas in China. Copyright © 2018. Published by Elsevier B.V.

A weighted least squares estimation of the polynomial regression model on paddy production in the area of Kedah and Perlis

NASA Astrophysics Data System (ADS)

Musa, Rosliza; Ali, Zalila; Baharum, Adam; Nor, Norlida Mohd

2017-08-01

The linear regression model assumes that all random error components are identically and independently distributed with constant variance. Hence, each data point provides equally precise information about the deterministic part of the total variation. In other words, the standard deviations of the error terms are constant over all values of the predictor variables. When the assumption of constant variance is violated, the ordinary least squares estimator of regression coefficient lost its property of minimum variance in the class of linear and unbiased estimators. Weighted least squares estimation are often used to maximize the efficiency of parameter estimation. A procedure that treats all of the data equally would give less precisely measured points more influence than they should have and would give highly precise points too little influence. Optimizing the weighted fitting criterion to find the parameter estimates allows the weights to determine the contribution of each observation to the final parameter estimates. This study used polynomial model with weighted least squares estimation to investigate paddy production of different paddy lots based on paddy cultivation characteristics and environmental characteristics in the area of Kedah and Perlis. The results indicated that factors affecting paddy production are mixture fertilizer application cycle, average temperature, the squared effect of average rainfall, the squared effect of pest and disease, the interaction between acreage with amount of mixture fertilizer, the interaction between paddy variety and NPK fertilizer application cycle and the interaction between pest and disease and NPK fertilizer application cycle.

General and substance-specific predictors of young adult nicotine dependence, alcohol use disorder, and problem behavior: replication in two samples.

PubMed

Bailey, J A; Samek, D R; Keyes, M A; Hill, K G; Hicks, B M; McGue, M; Iacono, W G; Epstein, M; Catalano, R F; Haggerty, K P; Hawkins, J D

2014-05-01

This paper presents two replications of a heuristic model for measuring environment in studies of gene-environment interplay in the etiology of young adult problem behaviors. Data were drawn from two longitudinal, U.S. studies of the etiology of substance use and related behaviors: the Raising Healthy Children study (RHC; N=1040, 47% female) and the Minnesota Twin Family Study (MTFS; N=1512, 50% female). RHC included a Pacific Northwest, school-based, community sample. MTFS included twins identified from state birth records in Minnesota. Both studies included commensurate measures of general family environment and family substance-specific environments in adolescence (RHC ages 10-18; MTFS age 18), as well as young adult nicotine dependence, alcohol and illicit drug use disorders, HIV sexual risk behavior, and antisocial behavior (RHC ages 24, 25; MTFS age 25). Results from the two samples were highly consistent and largely supported the heuristic model proposed by Bailey et al. (2011). Adolescent general family environment, family smoking environment, and family drinking environment predicted shared variance in problem behaviors in young adulthood. Family smoking environment predicted unique variance in young adult nicotine dependence. Family drinking environment did not appear to predict unique variance in young adult alcohol use disorder. Organizing environmental predictors and outcomes into general and substance-specific measures provides a useful way forward in modeling complex environments and phenotypes. Results suggest that programs aimed at preventing young adult problem behaviors should target general family environment and family smoking and drinking environments in adolescence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Modeling rainfall-runoff relationship using multivariate GARCH model

NASA Astrophysics Data System (ADS)

Modarres, R.; Ouarda, T. B. M. J.

2013-08-01

The traditional hydrologic time series approaches are used for modeling, simulating and forecasting conditional mean of hydrologic variables but neglect their time varying variance or the second order moment. This paper introduces the multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) modeling approach to show how the variance-covariance relationship between hydrologic variables varies in time. These approaches are also useful to estimate the dynamic conditional correlation between hydrologic variables. To illustrate the novelty and usefulness of MGARCH models in hydrology, two major types of MGARCH models, the bivariate diagonal VECH and constant conditional correlation (CCC) models are applied to show the variance-covariance structure and cdynamic correlation in a rainfall-runoff process. The bivariate diagonal VECH-GARCH(1,1) and CCC-GARCH(1,1) models indicated both short-run and long-run persistency in the conditional variance-covariance matrix of the rainfall-runoff process. The conditional variance of rainfall appears to have a stronger persistency, especially long-run persistency, than the conditional variance of streamflow which shows a short-lived drastic increasing pattern and a stronger short-run persistency. The conditional covariance and conditional correlation coefficients have different features for each bivariate rainfall-runoff process with different degrees of stationarity and dynamic nonlinearity. The spatial and temporal pattern of variance-covariance features may reflect the signature of different physical and hydrological variables such as drainage area, topography, soil moisture and ground water fluctuations on the strength, stationarity and nonlinearity of the conditional variance-covariance for a rainfall-runoff process.

On the additive and dominant variance and covariance of individuals within the genomic selection scope.

PubMed

Vitezica, Zulma G; Varona, Luis; Legarra, Andres

2013-12-01

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.

Sleep Reactivity and Insomnia: Genetic and Environmental Influences

PubMed Central

Drake, Christopher L.; Friedman, Naomi P.; Wright, Kenneth P.; Roth, Thomas

2011-01-01

Study Objectives: Determine the genetic and environmental contributions to sleep reactivity and insomnia. Design: Population-based twin cohort. Participants: 1782 individual twins (988 monozygotic or MZ; 1,086 dizygotic or DZ), including 744 complete twin pairs (377 MZ and 367 DZ). Mean age was 22.5 ± 2.8 years; gender distribution was 59% women. Measurements: Sleep reactivity was measured using the Ford Insomnia Response to Stress Test (FIRST). The criterion for insomnia was having difficulty falling asleep, staying asleep, or nonrefreshing sleep “usually or always” for ≥ 1 month, with at least “somewhat” interference with daily functioning. Results: The prevalence of insomnia was 21%. Heritability estimates for sleep reactivity were 29% for females and 43% for males. The environmental variance for sleep reactivity was greater for females and entirely due to nonshared effects. Insomnia was 43% to 55% heritable for males and females, respectively; the sex difference was not significant. The genetic variances in insomnia and FIRST scores were correlated (r = 0.54 in females, r = 0.64 in males), as were the environmental variances (r = 0.32 in females, r = 0.37 in males). In terms of individual insomnia symptoms, difficulty staying asleep (25% to 35%) and nonrefreshing sleep (34% to 35%) showed relatively more genetic influences than difficulty falling asleep (0%). Conclusions: Sleep reactivity to stress has a substantial genetic component, as well as an environmental component. The finding that FIRST scores and insomnia symptoms share genetic influences is consistent with the hypothesis that sleep reactivity may be a genetic vulnerability for developing insomnia. Citation: Drake CL; Friedman NP; Wright KP; Roth T. Sleep reactivity and insomnia: genetic and environmental influences. SLEEP 2011;34(9):1179-1188. PMID:21886355

Northern Russian chironomid-based modern summer temperature data set and inference models

NASA Astrophysics Data System (ADS)

Nazarova, Larisa; Self, Angela E.; Brooks, Stephen J.; van Hardenbroek, Maarten; Herzschuh, Ulrike; Diekmann, Bernhard

2015-11-01

West and East Siberian data sets and 55 new sites were merged based on the high taxonomic similarity, and the strong relationship between mean July air temperature and the distribution of chironomid taxa in both data sets compared with other environmental parameters. Multivariate statistical analysis of chironomid and environmental data from the combined data set consisting of 268 lakes, located in northern Russia, suggests that mean July air temperature explains the greatest amount of variance in chironomid distribution compared with other measured variables (latitude, longitude, altitude, water depth, lake surface area, pH, conductivity, mean January air temperature, mean July air temperature, and continentality). We established two robust inference models to reconstruct mean summer air temperatures from subfossil chironomids based on ecological and geographical approaches. The North Russian 2-component WA-PLS model (RMSEPJack = 1.35 °C, rJack2 = 0.87) can be recommended for application in palaeoclimatic studies in northern Russia. Based on distinctive chironomid fauna and climatic regimes of Kamchatka the Far East 2-component WAPLS model (RMSEPJack = 1.3 °C, rJack2 = 0.81) has potentially better applicability in Kamchatka.

A Multilevel AR(1) Model: Allowing for Inter-Individual Differences in Trait-Scores, Inertia, and Innovation Variance.

PubMed

Jongerling, Joran; Laurenceau, Jean-Philippe; Hamaker, Ellen L

2015-01-01

In this article we consider a multilevel first-order autoregressive [AR(1)] model with random intercepts, random autoregression, and random innovation variance (i.e., the level 1 residual variance). Including random innovation variance is an important extension of the multilevel AR(1) model for two reasons. First, between-person differences in innovation variance are important from a substantive point of view, in that they capture differences in sensitivity and/or exposure to unmeasured internal and external factors that influence the process. Second, using simulation methods we show that modeling the innovation variance as fixed across individuals, when it should be modeled as a random effect, leads to biased parameter estimates. Additionally, we use simulation methods to compare maximum likelihood estimation to Bayesian estimation of the multilevel AR(1) model and investigate the trade-off between the number of individuals and the number of time points. We provide an empirical illustration by applying the extended multilevel AR(1) model to daily positive affect ratings from 89 married women over the course of 42 consecutive days.

Spectral analysis of the Earth's topographic potential via 2D-DFT: a new data-based degree variance model to degree 90,000

NASA Astrophysics Data System (ADS)

Rexer, Moritz; Hirt, Christian

2015-09-01

Classical degree variance models (such as Kaula's rule or the Tscherning-Rapp model) often rely on low-resolution gravity data and so are subject to extrapolation when used to describe the decay of the gravity field at short spatial scales. This paper presents a new degree variance model based on the recently published GGMplus near-global land areas 220 m resolution gravity maps (Geophys Res Lett 40(16):4279-4283, 2013). We investigate and use a 2D-DFT (discrete Fourier transform) approach to transform GGMplus gravity grids into degree variances. The method is described in detail and its approximation errors are studied using closed-loop experiments. Focus is placed on tiling, azimuth averaging, and windowing effects in the 2D-DFT method and on analytical fitting of degree variances. Approximation errors of the 2D-DFT procedure on the (spherical harmonic) degree variance are found to be at the 10-20 % level. The importance of the reference surface (sphere, ellipsoid or topography) of the gravity data for correct interpretation of degree variance spectra is highlighted. The effect of the underlying mass arrangement (spherical or ellipsoidal approximation) on the degree variances is found to be crucial at short spatial scales. A rule-of-thumb for transformation of spectra between spherical and ellipsoidal approximation is derived. Application of the 2D-DFT on GGMplus gravity maps yields a new degree variance model to degree 90,000. The model is supported by GRACE, GOCE, EGM2008 and forward-modelled gravity at 3 billion land points over all land areas within the SRTM data coverage and provides gravity signal variances at the surface of the topography. The model yields omission errors of 9 mGal for gravity (1.5 cm for geoid effects) at scales of 10 km, 4 mGal (1 mm) at 2-km scales, and 2 mGal (0.2 mm) at 1-km scales.

Environmental and Genetic Contribution to Hypertension Prevalence: Data from an Epidemiological Survey on Sardinian Genetic Isolates

PubMed Central

Biino, Ginevra; Parati, Gianfranco; Concas, Maria Pina; Adamo, Mauro; Angius, Andrea; Vaccargiu, Simona; Pirastu, Mario

2013-01-01

Background and Objectives Hypertension represents a major cause of cardiovascular morbidity and mortality worldwide but its prevalence has been shown to vary in different countries. The reasons for such differences are still matter of debate, the relative contributions given by environmental and genetic factors being still poorly defined. We estimated the current prevalence, distribution and determinants of hypertension in isolated Sardinian populations and also investigated the environmental and genetic contribution to hypertension prevalence taking advantage of the characteristics of such populations. Methods and Results An epidemiological survey with cross-sectional design was carried out measuring blood pressure in 9845 inhabitants of 10 villages of Ogliastra region between 2002 and 2008. Regression analysis for assessing blood pressure determinants and variance component models for estimating heritability were performed. Overall 38.8% of this population had hypertension, its prevalence varying significantly by age, sex and among villages taking into account age and sex structure of their population. About 50% of hypertensives had prior cardiovascular disease. High blood pressure was independently associated with age, obesity related factors, heart rate, total cholesterol, alcohol consumption, low education and smoking status, all these factors contributing more in women than in men. Heritability was 27% for diastolic and 36% for systolic blood pressure, its contribution being significantly higher in men (57%) than in women (46%). Finally, the genetic correlation between systolic and diastolic blood pressure was 0.74, indicating incomplete pleiotropy. Conclusion Genetic factors involved in the expression of blood pressure traits account for about 30% of the phenotypic variance, but seem to play a larger role in men; comorbidities and environmental factors remain of predominant importance, but seem to contribute much more in women. PMID:23527229

Widespread covariation of early environmental exposures and trait-associated polygenic variation.

PubMed

Krapohl, E; Hannigan, L J; Pingault, J-B; Patel, H; Kadeva, N; Curtis, C; Breen, G; Newhouse, S J; Eley, T C; O'Reilly, P F; Plomin, R

2017-10-31

Although gene-environment correlation is recognized and investigated by family studies and recently by SNP-heritability studies, the possibility that genetic effects on traits capture environmental risk factors or protective factors has been neglected by polygenic prediction models. We investigated covariation between trait-associated polygenic variation identified by genome-wide association studies (GWASs) and specific environmental exposures, controlling for overall genetic relatedness using a genomic relatedness matrix restricted maximum-likelihood model. In a UK-representative sample ( n = 6,710), we find widespread covariation between offspring trait-associated polygenic variation and parental behavior and characteristics relevant to children's developmental outcomes-independently of population stratification. For instance, offspring genetic risk for schizophrenia was associated with paternal age ( R 2 = 0.002; P = 1e-04), and offspring education-associated variation was associated with variance in breastfeeding ( R 2 = 0.021; P = 7e-30), maternal smoking during pregnancy ( R 2 = 0.008; P = 5e-13), parental smacking ( R 2 = 0.01; P = 4e-15), household income ( R 2 = 0.032; P = 1e-22), watching television ( R 2 = 0.034; P = 5e-47), and maternal education ( R 2 = 0.065; P = 3e-96). Education-associated polygenic variation also captured covariation between environmental exposures and children's inattention/hyperactivity, conduct problems, and educational achievement. The finding that genetic variation identified by trait GWASs partially captures environmental risk factors or protective factors has direct implications for risk prediction models and the interpretation of GWAS findings.

Genetic and Environmental Influences on Frontal EEG Asymmetry and Alpha Power in 9–10 Year Old Twins

PubMed Central

Gao, Yu; Tuvblad, Catherine; Raine, Adrian; Lozano, Dora I.; Baker, Laura A.

2008-01-01

Modest genetic influences on frontal EEG asymmetry have been found in adults, but little is known about its genetic origins in children. Resting frontal asymmetry and alpha power were examined in 951 9–10-year-old twins. Results showed that in both males and females: (1) a modest but significant amount of variance in frontal asymmetry was accounted for by genetic factors (11–27%) with the remainder accounted for by non-shared environmental influences, and (2) alpha power were highly heritable, with 70–85% of the variance accounted for by genetic factors. Results suggest that the genetic architecture of frontal asymmetry and alpha power in late childhood are similar to that in adulthood and that the high non-shared environmental influences on frontal asymmetry may reflect environmentally-influenced individual differences in the maturation of frontal cortex as well as state-dependent influences on specific measurements. PMID:19386046

Genetic variation of the weaning weight of beef cattle as a function of accumulated heat stress.

PubMed

Santana, M L; Bignardi, A B; Eler, J P; Ferraz, J B S

2016-04-01

The objective of this study was to identify the genetic variation in the weaning weight (WW) of beef cattle as a function of heat stress. The WWs were recorded at approximately 205 days of age in three Brazilian beef cattle populations: Nelore (93,616), Brangus (18,906) and Tropical Composite (62,679). In view of the cumulative nature of WW, the effect of heat stress was considered as the accumulation of temperature and humidity index units (ACTHI) from the animal's birth to weaning. A reaction norm model was used to estimate the (co)variance components of WW across the ACTHI scale. The accumulation of THI units from birth to weaning negatively affected the WW. The definition of accumulated THI units as an environmental descriptor permitted to identify important genetic variation in the WW as a function of heat stress. As evidence of genotype by environment interaction, substantial heterogeneity was observed in the (co)variance components for WW across the environmental gradient. In this respect, the best animals in less stressful environments are not necessarily the best animals in more stressful environments. Furthermore, the response to selection for WW is expected to be lower in more stressful environments. © 2015 Blackwell Verlag GmbH.

A stochastic hybrid model for pricing forward-start variance swaps

NASA Astrophysics Data System (ADS)

Roslan, Teh Raihana Nazirah

2017-11-01

Recently, market players have been exposed to the astounding increase in the trading volume of variance swaps. In this paper, the forward-start nature of a variance swap is being inspected, where hybridizations of equity and interest rate models are used to evaluate the price of discretely-sampled forward-start variance swaps. The Heston stochastic volatility model is being extended to incorporate the dynamics of the Cox-Ingersoll-Ross (CIR) stochastic interest rate model. This is essential since previous studies on variance swaps were mainly focusing on instantaneous-start variance swaps without considering the interest rate effects. This hybrid model produces an efficient semi-closed form pricing formula through the development of forward characteristic functions. The performance of this formula is investigated via simulations to demonstrate how the formula performs for different sampling times and against the real market scenario. Comparison done with the Monte Carlo simulation which was set as our main reference point reveals that our pricing formula gains almost the same precision in a shorter execution time.

Mixed model approaches for diallel analysis based on a bio-model.

PubMed

Zhu, J; Weir, B S

1996-12-01

A MINQUE(1) procedure, which is minimum norm quadratic unbiased estimation (MINQUE) method with 1 for all the prior values, is suggested for estimating variance and covariance components in a bio-model for diallel crosses. Unbiasedness and efficiency of estimation were compared for MINQUE(1), restricted maximum likelihood (REML) and MINQUE theta which has parameter values for the prior values. MINQUE(1) is almost as efficient as MINQUE theta for unbiased estimation of genetic variance and covariance components. The bio-model is efficient and robust for estimating variance and covariance components for maternal and paternal effects as well as for nuclear effects. A procedure of adjusted unbiased prediction (AUP) is proposed for predicting random genetic effects in the bio-model. The jack-knife procedure is suggested for estimation of sampling variances of estimated variance and covariance components and of predicted genetic effects. Worked examples are given for estimation of variance and covariance components and for prediction of genetic merits.

Cue Integration in Categorical Tasks: Insights from Audio-Visual Speech Perception

PubMed Central

Bejjanki, Vikranth Rao; Clayards, Meghan; Knill, David C.; Aslin, Richard N.

2011-01-01

Previous cue integration studies have examined continuous perceptual dimensions (e.g., size) and have shown that human cue integration is well described by a normative model in which cues are weighted in proportion to their sensory reliability, as estimated from single-cue performance. However, this normative model may not be applicable to categorical perceptual dimensions (e.g., phonemes). In tasks defined over categorical perceptual dimensions, optimal cue weights should depend not only on the sensory variance affecting the perception of each cue but also on the environmental variance inherent in each task-relevant category. Here, we present a computational and experimental investigation of cue integration in a categorical audio-visual (articulatory) speech perception task. Our results show that human performance during audio-visual phonemic labeling is qualitatively consistent with the behavior of a Bayes-optimal observer. Specifically, we show that the participants in our task are sensitive, on a trial-by-trial basis, to the sensory uncertainty associated with the auditory and visual cues, during phonemic categorization. In addition, we show that while sensory uncertainty is a significant factor in determining cue weights, it is not the only one and participants' performance is consistent with an optimal model in which environmental, within category variability also plays a role in determining cue weights. Furthermore, we show that in our task, the sensory variability affecting the visual modality during cue-combination is not well estimated from single-cue performance, but can be estimated from multi-cue performance. The findings and computational principles described here represent a principled first step towards characterizing the mechanisms underlying human cue integration in categorical tasks. PMID:21637344

Quantile-based Bayesian maximum entropy approach for spatiotemporal modeling of ambient air quality levels.

PubMed

Yu, Hwa-Lung; Wang, Chih-Hsin

2013-02-05

Understanding the daily changes in ambient air quality concentrations is important to the assessing human exposure and environmental health. However, the fine temporal scales (e.g., hourly) involved in this assessment often lead to high variability in air quality concentrations. This is because of the complex short-term physical and chemical mechanisms among the pollutants. Consequently, high heterogeneity is usually present in not only the averaged pollution levels, but also the intraday variance levels of the daily observations of ambient concentration across space and time. This characteristic decreases the estimation performance of common techniques. This study proposes a novel quantile-based Bayesian maximum entropy (QBME) method to account for the nonstationary and nonhomogeneous characteristics of ambient air pollution dynamics. The QBME method characterizes the spatiotemporal dependence among the ambient air quality levels based on their location-specific quantiles and accounts for spatiotemporal variations using a local weighted smoothing technique. The epistemic framework of the QBME method can allow researchers to further consider the uncertainty of space-time observations. This study presents the spatiotemporal modeling of daily CO and PM10 concentrations across Taiwan from 1998 to 2009 using the QBME method. Results show that the QBME method can effectively improve estimation accuracy in terms of lower mean absolute errors and standard deviations over space and time, especially for pollutants with strong nonhomogeneous variances across space. In addition, the epistemic framework can allow researchers to assimilate the site-specific secondary information where the observations are absent because of the common preferential sampling issues of environmental data. The proposed QBME method provides a practical and powerful framework for the spatiotemporal modeling of ambient pollutants.

Host nutrition alters the variance in parasite transmission potential

PubMed Central

Vale, Pedro F.; Choisy, Marc; Little, Tom J.

2013-01-01

The environmental conditions experienced by hosts are known to affect their mean parasite transmission potential. How different conditions may affect the variance of transmission potential has received less attention, but is an important question for disease management, especially if specific ecological contexts are more likely to foster a few extremely infectious hosts. Using the obligate-killing bacterium Pasteuria ramosa and its crustacean host Daphnia magna, we analysed how host nutrition affected the variance of individual parasite loads, and, therefore, transmission potential. Under low food, individual parasite loads showed similar mean and variance, following a Poisson distribution. By contrast, among well-nourished hosts, parasite loads were right-skewed and overdispersed, following a negative binomial distribution. Abundant food may, therefore, yield individuals causing potentially more transmission than the population average. Measuring both the mean and variance of individual parasite loads in controlled experimental infections may offer a useful way of revealing risk factors for potential highly infectious hosts. PMID:23407498

Host nutrition alters the variance in parasite transmission potential.

PubMed

Vale, Pedro F; Choisy, Marc; Little, Tom J

2013-04-23

The environmental conditions experienced by hosts are known to affect their mean parasite transmission potential. How different conditions may affect the variance of transmission potential has received less attention, but is an important question for disease management, especially if specific ecological contexts are more likely to foster a few extremely infectious hosts. Using the obligate-killing bacterium Pasteuria ramosa and its crustacean host Daphnia magna, we analysed how host nutrition affected the variance of individual parasite loads, and, therefore, transmission potential. Under low food, individual parasite loads showed similar mean and variance, following a Poisson distribution. By contrast, among well-nourished hosts, parasite loads were right-skewed and overdispersed, following a negative binomial distribution. Abundant food may, therefore, yield individuals causing potentially more transmission than the population average. Measuring both the mean and variance of individual parasite loads in controlled experimental infections may offer a useful way of revealing risk factors for potential highly infectious hosts.

Modeling Multiplicative Error Variance: An Example Predicting Tree Diameter from Stump Dimensions in Baldcypress

Treesearch

Bernard R. Parresol

1993-01-01

In the context of forest modeling, it is often reasonable to assume a multiplicative heteroscedastic error structure to the data. Under such circumstances ordinary least squares no longer provides minimum variance estimates of the model parameters. Through study of the error structure, a suitable error variance model can be specified and its parameters estimated. This...

Understanding and comparisons of different sampling approaches for the Fourier Amplitudes Sensitivity Test (FAST)

PubMed Central

Xu, Chonggang; Gertner, George

2013-01-01

Fourier Amplitude Sensitivity Test (FAST) is one of the most popular uncertainty and sensitivity analysis techniques. It uses a periodic sampling approach and a Fourier transformation to decompose the variance of a model output into partial variances contributed by different model parameters. Until now, the FAST analysis is mainly confined to the estimation of partial variances contributed by the main effects of model parameters, but does not allow for those contributed by specific interactions among parameters. In this paper, we theoretically show that FAST analysis can be used to estimate partial variances contributed by both main effects and interaction effects of model parameters using different sampling approaches (i.e., traditional search-curve based sampling, simple random sampling and random balance design sampling). We also analytically calculate the potential errors and biases in the estimation of partial variances. Hypothesis tests are constructed to reduce the effect of sampling errors on the estimation of partial variances. Our results show that compared to simple random sampling and random balance design sampling, sensitivity indices (ratios of partial variances to variance of a specific model output) estimated by search-curve based sampling generally have higher precision but larger underestimations. Compared to simple random sampling, random balance design sampling generally provides higher estimation precision for partial variances contributed by the main effects of parameters. The theoretical derivation of partial variances contributed by higher-order interactions and the calculation of their corresponding estimation errors in different sampling schemes can help us better understand the FAST method and provide a fundamental basis for FAST applications and further improvements. PMID:24143037

Understanding and comparisons of different sampling approaches for the Fourier Amplitudes Sensitivity Test (FAST).

PubMed

Xu, Chonggang; Gertner, George

2011-01-01

Fourier Amplitude Sensitivity Test (FAST) is one of the most popular uncertainty and sensitivity analysis techniques. It uses a periodic sampling approach and a Fourier transformation to decompose the variance of a model output into partial variances contributed by different model parameters. Until now, the FAST analysis is mainly confined to the estimation of partial variances contributed by the main effects of model parameters, but does not allow for those contributed by specific interactions among parameters. In this paper, we theoretically show that FAST analysis can be used to estimate partial variances contributed by both main effects and interaction effects of model parameters using different sampling approaches (i.e., traditional search-curve based sampling, simple random sampling and random balance design sampling). We also analytically calculate the potential errors and biases in the estimation of partial variances. Hypothesis tests are constructed to reduce the effect of sampling errors on the estimation of partial variances. Our results show that compared to simple random sampling and random balance design sampling, sensitivity indices (ratios of partial variances to variance of a specific model output) estimated by search-curve based sampling generally have higher precision but larger underestimations. Compared to simple random sampling, random balance design sampling generally provides higher estimation precision for partial variances contributed by the main effects of parameters. The theoretical derivation of partial variances contributed by higher-order interactions and the calculation of their corresponding estimation errors in different sampling schemes can help us better understand the FAST method and provide a fundamental basis for FAST applications and further improvements.

A robust damage-detection technique with environmental variability combining time-series models with principal components

NASA Astrophysics Data System (ADS)

Lakshmi, K.; Rama Mohan Rao, A.

2014-10-01

In this paper, a novel output-only damage-detection technique based on time-series models for structural health monitoring in the presence of environmental variability and measurement noise is presented. The large amount of data obtained in the form of time-history response is transformed using principal component analysis, in order to reduce the data size and thereby improve the computational efficiency of the proposed algorithm. The time instant of damage is obtained by fitting the acceleration time-history data from the structure using autoregressive (AR) and AR with exogenous inputs time-series prediction models. The probability density functions (PDFs) of damage features obtained from the variances of prediction errors corresponding to references and healthy current data are found to be shifting from each other due to the presence of various uncertainties such as environmental variability and measurement noise. Control limits using novelty index are obtained using the distances of the peaks of the PDF curves in healthy condition and used later for determining the current condition of the structure. Numerical simulation studies have been carried out using a simply supported beam and also validated using an experimental benchmark data corresponding to a three-storey-framed bookshelf structure proposed by Los Alamos National Laboratory. Studies carried out in this paper clearly indicate the efficiency of the proposed algorithm for damage detection in the presence of measurement noise and environmental variability.

Comparing Mapped Plot Estimators

Treesearch

Paul C. Van Deusen

2006-01-01

Two alternative derivations of estimators for mean and variance from mapped plots are compared by considering the models that support the estimators and by simulation. It turns out that both models lead to the same estimator for the mean but lead to very different variance estimators. The variance estimators based on the least valid model assumptions are shown to...

Comment on Hoffman and Rovine (2007): SPSS MIXED can estimate models with heterogeneous variances.

PubMed

Weaver, Bruce; Black, Ryan A

2015-06-01

Hoffman and Rovine (Behavior Research Methods, 39:101-117, 2007) have provided a very nice overview of how multilevel models can be useful to experimental psychologists. They included two illustrative examples and provided both SAS and SPSS commands for estimating the models they reported. However, upon examining the SPSS syntax for the models reported in their Table 3, we found no syntax for models 2B and 3B, both of which have heterogeneous error variances. Instead, there is syntax that estimates similar models with homogeneous error variances and a comment stating that SPSS does not allow heterogeneous errors. But that is not correct. We provide SPSS MIXED commands to estimate models 2B and 3B with heterogeneous error variances and obtain results nearly identical to those reported by Hoffman and Rovine in their Table 3. Therefore, contrary to the comment in Hoffman and Rovine's syntax file, SPSS MIXED can estimate models with heterogeneous error variances.

Genetic and environmental influences on the comorbidity between depression, panic disorder, agoraphobia and social phobia: A twin study

PubMed Central

Mosing, Miriam A.; Gordon, Scott D.; Medland, Sarah E.; Statham, Dixie J.; Nelson, Elliot C.; Heath, Andrew C.; Martin, Nicholas G.; Wray, Naomi R.

2011-01-01

Background Major depression (MD) and anxiety disorders such as panic disorder (PD), agoraphobia (AG) and social phobia (SP) are heritable and highly comorbid. However, the relative importance of genetic and environmental aetiology of the covariation between these disorders, particularly the relationship between PD and AG is less clear. Methods The present study measured MD, PD and AG in a population sample of 5440 twin pairs and 1245 single twins, about 45% of whom were also scored for SP. Prevalences, within individual comorbidity and twin odds ratios for comorbidity are reported. A behavioural genetic analysis of the four disorders using the classical twin design was conducted. Results Odds ratios for MD, PD, AG, and SP in twins of individuals diagnosed with one of the four disorders were increased. Heritability estimates under a threshold-liability model for MD, PD, AG, and SP respectively were 0.33 (CI:0.30–0.42), 0.38 (CI:0.24–0.55), 0.48 (CI:0.37–0.65) of, and 0.39 (CI:0.16–0.65), with no evidence for any variance explained by the common environment shared by twins. We find that a common genetic factor explains a moderate proportion of variance in these four disorders. The genetic correlation between PD and AG was 0.83. Conclusion MD, PD, AG, and SP strongly co-aggregate within families and common genetic factors explain a moderate proportion of variance in these four disorders. The high genetic correlation between PD and AG and the increased odds ratio for PD and AG in siblings of those with AG without PD suggests a common genetic aetiology for PD and AG. PMID:19750555

Classification of Global Urban Centers Using ASTER Data: Preliminary Results From the Urban Environmental Monitoring Program

NASA Astrophysics Data System (ADS)

Stefanov, W. L.; Stefanov, W. L.; Christensen, P. R.

2001-05-01

Land cover and land use changes associated with urbanization are important drivers of global ecologic and climatic change. Quantification and monitoring of these changes are part of the primary mission of the ASTER instrument, and comprise the fundamental research objective of the Urban Environmental Monitoring (UEM) Program. The UEM program will acquire day/night, visible through thermal infrared ASTER data twice per year for 100 global urban centers over the duration of the mission (6 years). Data are currently available for a number of these urban centers and allow for initial comparison of global city structure using spatial variance texture analysis of the 15 m/pixel visible to near infrared ASTER bands. Variance texture analysis highlights changes in pixel edge density as recorded by sharp transitions from bright to dark pixels. In human-dominated landscapes these brightness variations correlate well with urbanized vs. natural land cover and are useful for characterizing the geographic extent and internal structure of cities. Variance texture analysis was performed on twelve urban centers (Albuquerque, Baghdad, Baltimore, Chongqing, Istanbul, Johannesburg, Lisbon, Madrid, Phoenix, Puebla, Riyadh, Vancouver) for which cloud-free daytime ASTER data are available. Image transects through each urban center produce texture profiles that correspond to urban density. These profiles can be used to classify cities into centralized (ex. Baltimore), decentralized (ex. Phoenix), or intermediate (ex. Madrid) structural types. Image texture is one of the primary data inputs (with vegetation indices and visible to thermal infrared image spectra) to a knowledge-based land cover classifier currently under development for application to ASTER UEM data as it is acquired. Collaboration with local investigators is sought to both verify the accuracy of the knowledge-based system and to develop more sophisticated classification models.

Genetic and environmental influences on the co-morbidity between depression, panic disorder, agoraphobia, and social phobia: a twin study.

PubMed

Mosing, Miriam A; Gordon, Scott D; Medland, Sarah E; Statham, Dixie J; Nelson, Elliot C; Heath, Andrew C; Martin, Nicholas G; Wray, Naomi R

2009-01-01

Major depression (MD) and anxiety disorders such as panic disorder (PD), agoraphobia (AG), and social phobia (SP) are heritable and highly co-morbid. However, the relative importance of genetic and environmental etiology of the covariation between these disorders, particularly the relationship between PD and AG, is less clear. This study measured MD, PD, and AG in a population sample of 5,440 twin pairs and 1,245 single twins, about 45% of whom were also scored for SP. Prevalences, within individual co-morbidity and twin odds ratios for co-morbidity, are reported. A behavioral genetic analysis of the four disorders using the classical twin design was conducted. Odds ratios for MD, PD, AG, and SP in twins of individuals diagnosed with one of the four disorders were increased. Heritability estimates under a threshold-liability model for MD, PD, AG, and SP respectively were .33 (CI: 0.30-0.42), .38 (CI: 0.24-0.55), .48 (CI: 0.37-0.65), and .39 (CI: 0.16-0.65), with no evidence for any variance explained by the common environment shared by twins. We find that a common genetic factor explains a moderate proportion of variance in these four disorders. The genetic correlation between PD and AG was .83. MD, PD, AG, and SP strongly co-aggregate within families and common genetic factors explain a moderate proportion of variance in these four disorders. The high genetic correlation between PD and AG and the increased odds ratio for PD and AG in siblings of those with AG without PD suggests a common genetic etiology for PD and AG.

Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications

PubMed Central

Bouvet, J-M; Makouanzi, G; Cros, D; Vigneron, Ph

2016-01-01

Hybrids are broadly used in plant breeding and accurate estimation of variance components is crucial for optimizing genetic gain. Genome-wide information may be used to explore models designed to assess the extent of additive and non-additive variance and test their prediction accuracy for the genomic selection. Ten linear mixed models, involving pedigree- and marker-based relationship matrices among parents, were developed to estimate additive (A), dominance (D) and epistatic (AA, AD and DD) effects. Five complementary models, involving the gametic phase to estimate marker-based relationships among hybrid progenies, were developed to assess the same effects. The models were compared using tree height and 3303 single-nucleotide polymorphism markers from 1130 cloned individuals obtained via controlled crosses of 13 Eucalyptus urophylla females with 9 Eucalyptus grandis males. Akaike information criterion (AIC), variance ratios, asymptotic correlation matrices of estimates, goodness-of-fit, prediction accuracy and mean square error (MSE) were used for the comparisons. The variance components and variance ratios differed according to the model. Models with a parent marker-based relationship matrix performed better than those that were pedigree-based, that is, an absence of singularities, lower AIC, higher goodness-of-fit and accuracy and smaller MSE. However, AD and DD variances were estimated with high s.es. Using the same criteria, progeny gametic phase-based models performed better in fitting the observations and predicting genetic values. However, DD variance could not be separated from the dominance variance and null estimates were obtained for AA and AD effects. This study highlighted the advantages of progeny models using genome-wide information. PMID:26328760

Distribution of kriging errors, the implications and how to communicate them

NASA Astrophysics Data System (ADS)

Li, Hong Yi; Milne, Alice; Webster, Richard

2016-04-01

Kriging in one form or another has become perhaps the most popular method for spatial prediction in environmental science. Each prediction is unbiased and of minimum variance, which itself is estimated. The kriging variances depend on the mathematical model chosen to describe the spatial variation; different models, however plausible, give rise to different minimized variances. Practitioners often compare models by so-called cross-validation before finally choosing the most appropriate for their kriging. One proceeds as follows. One removes a unit (a sampling point) from the whole set, kriges the value there and compares the kriged value with the value observed to obtain the deviation or error. One repeats the process for each and every point in turn and for all plausible models. One then computes the mean errors (MEs) and the mean of the squared errors (MSEs). Ideally a squared error should equal the corresponding kriging variance (σK2), and so one is advised to choose the model for which on average the squared errors most nearly equal the kriging variances, i.e. the ratio MSDR = MSE/σK2 ≈ 1. Maximum likelihood estimation of models almost guarantees that the MSDR equals 1, and so the kriging variances are unbiased predictors of the squared error across the region. The method is based on the assumption that the errors have a normal distribution. The squared deviation ratio (SDR) should therefore be distributed as χ2 with one degree of freedom with a median of 0.455. We have found that often the median of the SDR (MedSDR) is less, in some instances much less, than 0.455 even though the mean of the SDR is close to 1. It seems that in these cases the distributions of the errors are leptokurtic, i.e. they have an excess of predictions close to the true values, excesses near the extremes and a dearth of predictions in between. In these cases the kriging variances are poor measures of the uncertainty at individual sites. The uncertainty is typically under-estimated for the extreme observations and compensated for by over estimating for other observations. Statisticians must tell users when they present maps of predictions. We illustrate the situation with results from mapping salinity in land reclaimed from the Yangtze delta in the Gulf of Hangzhou, China. There the apparent electrical conductivity (ECa) of the topsoil was measured at 525 points in a field of 2.3 ha. The marginal distribution of the observations was strongly positively skewed, and so the observed ECas were transformed to their logarithms to give an approximately symmetric distribution. That distribution was strongly platykurtic with short tails and no evident outliers. The logarithms were analysed as a mixed model of quadratic drift plus correlated random residuals with a spherical variogram. The kriged predictions that deviated from their true values with an MSDR of 0.993, but with a medSDR=0.324. The coefficient of kurtosis of the deviations was 1.45, i.e. substantially larger than 0 for a normal distribution. The reasons for this behaviour are being sought. The most likely explanation is that there are spatial outliers, i.e. points at which the observed values that differ markedly from those at their their closest neighbours.

Distribution of kriging errors, the implications and how to communicate them

NASA Astrophysics Data System (ADS)

Li, HongYi; Milne, Alice; Webster, Richard

2015-04-01

Kriging in one form or another has become perhaps the most popular method for spatial prediction in environmental science. Each prediction is unbiased and of minimum variance, which itself is estimated. The kriging variances depend on the mathematical model chosen to describe the spatial variation; different models, however plausible, give rise to different minimized variances. Practitioners often compare models by so-called cross-validation before finally choosing the most appropriate for their kriging. One proceeds as follows. One removes a unit (a sampling point) from the whole set, kriges the value there and compares the kriged value with the value observed to obtain the deviation or error. One repeats the process for each and every point in turn and for all plausible models. One then computes the mean errors (MEs) and the mean of the squared errors (MSEs). Ideally a squared error should equal the corresponding kriging variance (σ_K^2), and so one is advised to choose the model for which on average the squared errors most nearly equal the kriging variances, i.e. the ratio MSDR=MSE/ σ_K2 ≈1. Maximum likelihood estimation of models almost guarantees that the MSDR equals 1, and so the kriging variances are unbiased predictors of the squared error across the region. The method is based on the assumption that the errors have a normal distribution. The squared deviation ratio (SDR) should therefore be distributed as χ2 with one degree of freedom with a median of 0.455. We have found that often the median of the SDR (MedSDR) is less, in some instances much less, than 0.455 even though the mean of the SDR is close to 1. It seems that in these cases the distributions of the errors are leptokurtic, i.e. they have an excess of predictions close to the true values, excesses near the extremes and a dearth of predictions in between. In these cases the kriging variances are poor measures of the uncertainty at individual sites. The uncertainty is typically under-estimated for the extreme observations and compensated for by over estimating for other observations. Statisticians must tell users when they present maps of predictions. We illustrate the situation with results from mapping salinity in land reclaimed from the Yangtze delta in the Gulf of Hangzhou, China. There the apparent electrical conductivity (EC_a) of the topsoil was measured at 525 points in a field of 2.3~ha. The marginal distribution of the observations was strongly positively skewed, and so the observed EC_as were transformed to their logarithms to give an approximately symmetric distribution. That distribution was strongly platykurtic with short tails and no evident outliers. The logarithms were analysed as a mixed model of quadratic drift plus correlated random residuals with a spherical variogram. The kriged predictions that deviated from their true values with an MSDR of 0.993, but with a medSDR=0.324. The coefficient of kurtosis of the deviations was 1.45, i.e. substantially larger than 0 for a normal distribution. The reasons for this behaviour are being sought. The most likely explanation is that there are spatial outliers, i.e. points at which the observed values that differ markedly from those at their their closest neighbours.

Genetic and Environmental Influences on Individual Differences in Attitudes Toward Homosexuality: An Australian Twin Study

PubMed Central

Shekar, Sri N.; Zietsch, Brendan P.; Eaves, Lindon J.; Bailey, J. Michael; Boomsma, Dorret I.; Martin, Nicholas G.

2008-01-01

Previous research has shown that many heterosexuals hold negative attitudes toward homosexuals and homosexuality (homophobia). Although a great deal of research has focused on the profile of homophobic individuals, this research provides little theoretical insight into the aetiology of homophobia. To examine genetic and environmental influences on variation in attitudes toward homophobia, we analysed data from 4,688 twins who completed a questionnaire concerning sexual behaviour and attitudes, including attitudes toward homosexuality. Results show that, in accordance with literature, males have significantly more negative attitudes toward homosexuality than females and non-heterosexuals are less homophobic than heterosexuals. In contrast with some earlier findings, age had no significant effect on the homophobia scores in this study. Genetic modelling showed that variation in homophobia scores could be explained by additive genetic (36%), shared environmental (18%) and unique environmental factors (46%). However, corrections based on previous findings show that the shared environmental estimate may be almost entirely accounted for as extra additive genetic variance arising from assortative mating for homophobic attitudes. The results suggest that variation in attitudes toward homosexuality is substantially inherited, and that social environmental influences are relatively minor. PMID:18347968

Gene-environment interaction and suicidal behavior.

PubMed

Roy, Alec; Sarchiopone, Marco; Carli, Vladimir

2009-07-01

Studies have increasingly shown that gene-environment interactions are important in psychiatry. Suicidal behavior is a major public health problem. Suicide is generally considered to be a multi-determined act involving various areas of proximal and distal risk. Genetic risk factors are estimated to account for approximately 30% to 40% of the variance in suicidal behavior. In this article, the authors review relevant studies concerning the interaction between the serotonin transporter gene and environmental variables as a model of gene-environment interactions that may have an impact on suicidal behavior. The findings reviewed here suggest that there may be meaningful interactions between distal and proximal suicide risk factors that may amplify the risk of suicidal behavior. Future studies of suicidal behavior should examine both genetic and environmental variables and examine for gene-environment interactions.

Atlas of susceptibility to pollution in marinas. Application to the Spanish coast.

PubMed

Gómez, Aina G; Ondiviela, Bárbara; Fernández, María; Juanes, José A

2017-01-15

An atlas of susceptibility to pollution of 320 Spanish marinas is provided. Susceptibility is assessed through a simple, fast and low cost empirical method estimating the flushing capacity of marinas. The Complexity Tidal Range Index (CTRI) was selected among eleven empirical methods. The CTRI method was selected by means of statistical analyses because: it contributes to explain the system's variance; it is highly correlated to numerical model results; and, it is sensitive to marinas' location and typology. The process of implementation to the Spanish coast confirmed its usefulness, versatility and adaptability as a tool for the environmental management of marinas worldwide. The atlas of susceptibility, assessed through CTRI values, is an appropriate instrument to prioritize environmental and planning strategies at a regional scale. Copyright © 2016 Elsevier Ltd. All rights reserved.

A two step Bayesian approach for genomic prediction of breeding values.

PubMed

Shariati, Mohammad M; Sørensen, Peter; Janss, Luc

2012-05-21

In genomic models that assign an individual variance to each marker, the contribution of one marker to the posterior distribution of the marker variance is only one degree of freedom (df), which introduces many variance parameters with only little information per variance parameter. A better alternative could be to form clusters of markers with similar effects where markers in a cluster have a common variance. Therefore, the influence of each marker group of size p on the posterior distribution of the marker variances will be p df. The simulated data from the 15th QTL-MAS workshop were analyzed such that SNP markers were ranked based on their effects and markers with similar estimated effects were grouped together. In step 1, all markers with minor allele frequency more than 0.01 were included in a SNP-BLUP prediction model. In step 2, markers were ranked based on their estimated variance on the trait in step 1 and each 150 markers were assigned to one group with a common variance. In further analyses, subsets of 1500 and 450 markers with largest effects in step 2 were kept in the prediction model. Grouping markers outperformed SNP-BLUP model in terms of accuracy of predicted breeding values. However, the accuracies of predicted breeding values were lower than Bayesian methods with marker specific variances. Grouping markers is less flexible than allowing each marker to have a specific marker variance but, by grouping, the power to estimate marker variances increases. A prior knowledge of the genetic architecture of the trait is necessary for clustering markers and appropriate prior parameterization.

DairyWise, a whole-farm dairy model.

PubMed

Schils, R L M; de Haan, M H A; Hemmer, J G A; van den Pol-van Dasselaar, A; de Boer, J A; Evers, A G; Holshof, G; van Middelkoop, J C; Zom, R L G

2007-11-01

A whole-farm dairy model was developed and evaluated. The DairyWise model is an empirical model that simulated technical, environmental, and financial processes on a dairy farm. The central component is the FeedSupply model that balanced the herd requirements, as generated by the DairyHerd model, and the supply of homegrown feeds, as generated by the crop models for grassland and corn silage. The output of the FeedSupply model was used as input for several technical, environmental, and economic submodels. The submodels simulated a range of farm aspects such as nitrogen and phosphorus cycling, nitrate leaching, ammonia emissions, greenhouse gas emissions, energy use, and a financial farm budget. The final output was a farm plan describing all material and nutrient flows and the consequences on the environment and economy. Evaluation of DairyWise was performed with 2 data sets consisting of 29 dairy farms. The evaluation showed that DairyWise was able to simulate gross margin, concentrate intake, nitrogen surplus, nitrate concentration in ground water, and crop yields. The variance accounted for ranged from 37 to 84%, and the mean differences between modeled and observed values varied between -5 to +3% per set of farms. We conclude that DairyWise is a powerful tool for integrated scenario development and evaluation for scientists, policy makers, extension workers, teachers and farmers.

Stochastic variation in avian survival rates: Life-history predictions, population consequences, and the potential responses to human perturbations and climate change

USGS Publications Warehouse

Schmutz, Joel A.; Thomson, David L.; Cooch, Evan G.; Conroy, Michael J.

2009-01-01

Stochastic variation in survival rates is expected to decrease long-term population growth rates. This expectation influences both life-history theory and the conservation of species. From this expectation, Pfister (1998) developed the important life-history prediction that natural selection will have minimized variability in those elements of the annual life cycle (such as adult survival rate) with high sensitivity. This prediction has not been rigorously evaluated for bird populations, in part due to statistical difficulties related to variance estimation. I here overcome these difficulties, and in an analysis of 62 populations, I confirm her prediction by showing a negative relationship between the proportional sensitivity (elasticity) of adult survival and the proportional variance (CV) of adult survival. However, several species deviated significantly from this expectation, with more process variance in survival than predicted. For instance, projecting the magnitude of process variance in annual survival for American redstarts (Setophaga ruticilla) for 25 years resulted in a 44% decline in abundance without assuming any change in mean survival rate. For most of these species with high process variance, recent changes in harvest, habitats, or changes in climate patterns are the likely sources of environmental variability causing this variability in survival. Because of climate change, environmental variability is increasing on regional and global scales, which is expected to increase stochasticity in vital rates of species. Increased stochasticity in survival will depress population growth rates, and this result will magnify the conservation challenges we face.

An Ecological Analysis of Environmental Correlates of Active Commuting in Urban U.S.

PubMed Central

Fan, Jessie X.; Wen, Ming; Kowaleski-Jones, Lori

2014-01-01

We conduct a cross-sectional ecological analysis to examine environmental correlates of active commuting in 39,660 urban tracts using data from the 2010 Census, 2007-2011 American Community Survey, and other sources. The five-year average (2007-2011) prevalence is 3.05% for walking, 0.63% for biking, and 7.28% for public transportation to work, with higher prevalence for all modes in lower-income tracts. Environmental factors account for more variances in public transportation to work but economic and demographic factors account for more variances in walking and biking to work. Population density, median housing age, street connectivity, tree canopy, distance to parks, air quality, and county sprawl index are associated with active commuting, but the association can vary in size and direction for different transportation mode and for higher-income and lower-income tracts. PMID:25460907

An ecological analysis of environmental correlates of active commuting in urban U.S.

PubMed

Fan, Jessie X; Wen, Ming; Kowaleski-Jones, Lori

2014-11-01

We conduct a cross-sectional ecological analysis to examine environmental correlates of active commuting in 39,660 urban tracts using data from the 2010 Census, 2007-2011 American Community Survey, and other sources. The five-year average (2007-2011) prevalence is 3.05% for walking, 0.63% for biking, and 7.28% for public transportation to work, with higher prevalence for all modes in lower-income tracts. Environmental factors account for more variances in public transportation to work but economic and demographic factors account for more variances in walking and biking to work. Population density, median housing age, street connectivity, tree canopy, distance to parks, air quality, and county sprawl index are associated with active commuting, but the association can vary in size and direction for different transportation mode and for higher-income and lower-income tracts. Copyright © 2014 Elsevier Ltd. All rights reserved.

Genetic parameters for uniformity of harvest weight and body size traits in the GIFT strain of Nile tilapia.

PubMed

Marjanovic, Jovana; Mulder, Han A; Khaw, Hooi L; Bijma, Piter

2016-06-10

Animal breeding programs have been very successful in improving the mean levels of traits through selection. However, in recent decades, reducing the variability of trait levels between individuals has become a highly desirable objective. Reaching this objective through genetic selection requires that there is genetic variation in the variability of trait levels, a phenomenon known as genetic heterogeneity of environmental (residual) variance. The aim of our study was to investigate the potential for genetic improvement of uniformity of harvest weight and body size traits (length, depth, and width) in the genetically improved farmed tilapia (GIFT) strain. In order to quantify the genetic variation in uniformity of traits and estimate the genetic correlations between level and variance of the traits, double hierarchical generalized linear models were applied to individual trait values. Our results showed substantial genetic variation in uniformity of all analyzed traits, with genetic coefficients of variation for residual variance ranging from 39 to 58 %. Genetic correlation between trait level and variance was strongly positive for harvest weight (0.60 ± 0.09), moderate and positive for body depth (0.37 ± 0.13), but not significantly different from 0 for body length and width. Our results on the genetic variation in uniformity of harvest weight and body size traits show good prospects for the genetic improvement of uniformity in the GIFT strain. A high and positive genetic correlation was estimated between level and variance of harvest weight, which suggests that selection for heavier fish will also result in more variation in harvest weight. Simultaneous improvement of harvest weight and its uniformity will thus require index selection.

Genetic and environmental effects on body mass index from infancy to the onset of adulthood: an individual-based pooled analysis of 45 twin cohorts participating in the COllaborative project of Development of Anthropometrical measures in Twins (CODATwins) study.

PubMed

Silventoinen, Karri; Jelenkovic, Aline; Sund, Reijo; Hur, Yoon-Mi; Yokoyama, Yoshie; Honda, Chika; Hjelmborg, Jacob vB; Möller, Sören; Ooki, Syuichi; Aaltonen, Sari; Ji, Fuling; Ning, Feng; Pang, Zengchang; Rebato, Esther; Busjahn, Andreas; Kandler, Christian; Saudino, Kimberly J; Jang, Kerry L; Cozen, Wendy; Hwang, Amie E; Mack, Thomas M; Gao, Wenjing; Yu, Canqing; Li, Liming; Corley, Robin P; Huibregtse, Brooke M; Christensen, Kaare; Skytthe, Axel; Kyvik, Kirsten O; Derom, Catherine A; Vlietinck, Robert F; Loos, Ruth Jf; Heikkilä, Kauko; Wardle, Jane; Llewellyn, Clare H; Fisher, Abigail; McAdams, Tom A; Eley, Thalia C; Gregory, Alice M; He, Mingguang; Ding, Xiaohu; Bjerregaard-Andersen, Morten; Beck-Nielsen, Henning; Sodemann, Morten; Tarnoki, Adam D; Tarnoki, David L; Stazi, Maria A; Fagnani, Corrado; D'Ippolito, Cristina; Knafo-Noam, Ariel; Mankuta, David; Abramson, Lior; Burt, S Alexandra; Klump, Kelly L; Silberg, Judy L; Eaves, Lindon J; Maes, Hermine H; Krueger, Robert F; McGue, Matt; Pahlen, Shandell; Gatz, Margaret; Butler, David A; Bartels, Meike; van Beijsterveldt, Toos Cem; Craig, Jeffrey M; Saffery, Richard; Freitas, Duarte L; Maia, José Antonio; Dubois, Lise; Boivin, Michel; Brendgen, Mara; Dionne, Ginette; Vitaro, Frank; Martin, Nicholas G; Medland, Sarah E; Montgomery, Grant W; Chong, Youngsook; Swan, Gary E; Krasnow, Ruth; Magnusson, Patrik Ke; Pedersen, Nancy L; Tynelius, Per; Lichtenstein, Paul; Haworth, Claire Ma; Plomin, Robert; Bayasgalan, Gombojav; Narandalai, Danshiitsoodol; Harden, K Paige; Tucker-Drob, Elliot M; Öncel, Sevgi Y; Aliev, Fazil; Spector, Timothy; Mangino, Massimo; Lachance, Genevieve; Baker, Laura A; Tuvblad, Catherine; Duncan, Glen E; Buchwald, Dedra; Willemsen, Gonneke; Rasmussen, Finn; Goldberg, Jack H; Sørensen, Thorkild Ia; Boomsma, Dorret I; Kaprio, Jaakko

2016-08-01

Both genetic and environmental factors are known to affect body mass index (BMI), but detailed understanding of how their effects differ during childhood and adolescence is lacking. We analyzed the genetic and environmental contributions to BMI variation from infancy to early adulthood and the ways they differ by sex and geographic regions representing high (North America and Australia), moderate (Europe), and low levels (East Asia) of obesogenic environments. Data were available for 87,782 complete twin pairs from 0.5 to 19.5 y of age from 45 cohorts. Analyses were based on 383,092 BMI measurements. Variation in BMI was decomposed into genetic and environmental components through genetic structural equation modeling. The variance of BMI increased from 5 y of age along with increasing mean BMI. The proportion of BMI variation explained by additive genetic factors was lowest at 4 y of age in boys (a(2) = 0.42) and girls (a(2) = 0.41) and then generally increased to 0.75 in both sexes at 19 y of age. This was because of a stronger influence of environmental factors shared by co-twins in midchildhood. After 15 y of age, the effect of shared environment was not observed. The sex-specific expression of genetic factors was seen in infancy but was most prominent at 13 y of age and older. The variance of BMI was highest in North America and Australia and lowest in East Asia, but the relative proportion of genetic variation to total variation remained roughly similar across different regions. Environmental factors shared by co-twins affect BMI in childhood, but little evidence for their contribution was found in late adolescence. Our results suggest that genetic factors play a major role in the variation of BMI in adolescence among populations of different ethnicities exposed to different environmental factors related to obesity. © 2016 American Society for Nutrition.

Personal and environmental factors contributing to participation in romantic relationships and sexual activity of young adults with cerebral palsy.

PubMed

Wiegerink, Diana J H G; Stam, Henk J; Ketelaar, Marjolijn; Cohen-Kettenis, Peggy T; Roebroeck, Marij E

2012-01-01

To study determinants of romantic relationships and sexual activity of young adults with cerebral palsy (CP), focusing on personal and environmental factors. A cohort study was performed with 74 young adults (46 men; 28 women) aged 20-25 years (SD 1.4) with CP (49% unilateral CP, 76% GMFCS level I, 85% MACS level I). All participants were of normal intelligence. Romantic relationships, sexual activity (outcome measures), personal and environmental factors (associated factors) were assessed. Associations were analyzed using logistic regression analyses. More females than males with CP were in a current romantic relationship. Self-esteem, sexual esteem and feelings of competence regarding self-efficacy contributed positively to having current romantic relationships. A negative parenting style contributed negatively. Age and gross motor functioning explained 20% of the variance in experience with intercourse. In addition, sexual esteem and taking initiative contributed significantly to intercourse experience. For young adults with CP personal factors (20-35% explained variances) seem to contribute more than environmental factors (9-12% explained variances) to current romantic relationships and sexual experiences. We advice parents and professionals to focus on self-efficacy, self-esteem and sexual self-esteem in development of young adults with CP. [ • The severity of gross motor functioning contributed somewhat to sexual activities, but not to romantic relationships.• High self-efficacy, self-esteem and sexual self-esteem can facilitate involvement in romantic and sexual relationships for young adults with CP.

Equality in Educational Policy and the Heritability of Educational Attainment

PubMed Central

Colodro-Conde, Lucía; Rijsdijk, Frühling; Tornero-Gómez, María J.; Sánchez-Romera, Juan F.; Ordoñana, Juan R.

2015-01-01

Secular variation in the heritability of educational attainment are proposed to be due to the implementation of more egalitarian educational policies leading to increased equality in educational opportunities in the second part of the 20th century. The action of effect is hypothesized to be a decrease of shared environmental (e.g., family socioeconomic status or parents’ education) influences on educational attainment, giving more room for genetic differences between individuals to impact on the variation of the trait. However, this hypothesis has not yet found consistent evidence. Support for this effect relies mainly on comparisons between countries adopting different educational systems or between different time periods within a country reflecting changes in general policy. Using a population-based sample of 1271 pairs of adult twins, we analyzed the effect of the introduction of a specific educational policy in Spain in 1970. The shared-environmental variance decreased, leading to an increase in heritability in the post-reform cohort (44 vs. 67%) for males. Unstandardized estimates of genetic variance were of a similar magnitude (.56 vs. .57) between cohorts, while shared environmental variance decreased from .56 to .04. Heritability remained in the same range for women (40 vs. 34%). Our results support the role of educational policy in affecting the relative weight of genetic and environmental factors on educational attainment, such that increasing equality in educational opportunities increases heritability estimates by reducing variation of non-genetic familial origin. PMID:26618539

Robust Means Modeling: An Alternative for Hypothesis Testing of Independent Means under Variance Heterogeneity and Nonnormality

ERIC Educational Resources Information Center

Fan, Weihua; Hancock, Gregory R.

2012-01-01

This study proposes robust means modeling (RMM) approaches for hypothesis testing of mean differences for between-subjects designs in order to control the biasing effects of nonnormality and variance inequality. Drawing from structural equation modeling (SEM), the RMM approaches make no assumption of variance homogeneity and employ robust…

Thermospheric mass density model error variance as a function of time scale

NASA Astrophysics Data System (ADS)

Emmert, J. T.; Sutton, E. K.

2017-12-01

In the increasingly crowded low-Earth orbit environment, accurate estimation of orbit prediction uncertainties is essential for collision avoidance. Poor characterization of such uncertainty can result in unnecessary and costly avoidance maneuvers (false positives) or disregard of a collision risk (false negatives). Atmospheric drag is a major source of orbit prediction uncertainty, and is particularly challenging to account for because it exerts a cumulative influence on orbital trajectories and is therefore not amenable to representation by a single uncertainty parameter. To address this challenge, we examine the variance of measured accelerometer-derived and orbit-derived mass densities with respect to predictions by thermospheric empirical models, using the data-minus-model variance as a proxy for model uncertainty. Our analysis focuses mainly on the power spectrum of the residuals, and we construct an empirical model of the variance as a function of time scale (from 1 hour to 10 years), altitude, and solar activity. We find that the power spectral density approximately follows a power-law process but with an enhancement near the 27-day solar rotation period. The residual variance increases monotonically with altitude between 250 and 550 km. There are two components to the variance dependence on solar activity: one component is 180 degrees out of phase (largest variance at solar minimum), and the other component lags 2 years behind solar maximum (largest variance in the descending phase of the solar cycle).

10 CFR 503.36 - State or local requirements.

Code of Federal Regulations, 2011 CFR

2011-01-01

... zoning law; (2) The petitioner has made a good faith effort to obtain a variance from the State or local... petitioner is not entitled to an exemption for lack of alternate fuel supply, site limitation, environmental... supply, site limitation, environmental requirements, or inability to obtain adequate capital, the...

Genetic and Environmental Contributions to Educational Attainment in Australia.

ERIC Educational Resources Information Center

Miller, Paul; Mulvey, Charles; Martin, Nick

2001-01-01

Data from a large sample of Australian twins indicate that 50 to 65 percent of variance in educational attainments can be attributed to genetic endowments. Only about 25 to 40 percent may be due to environmental factors, depending on adjustments for measurement error and assortative mating. (Contains 51 references.) (MLH)

ROBUST ESTIMATION OF MEAN AND VARIANCE USING ENVIRONMENTAL DATA SETS WITH BELOW DETECTION LIMIT OBSERVATIONS

EPA Science Inventory

Scientists, especially environmental scientists often encounter trace level concentrations that are typically reported as less than a certain limit of detection, L. Type 1, left-censored data arise when certain low values lying below L are ignored or unknown as they cannot be mea...

Latent class models for joint analysis of disease prevalence and high-dimensional semicontinuous biomarker data.

PubMed

Zhang, Bo; Chen, Zhen; Albert, Paul S

2012-01-01

High-dimensional biomarker data are often collected in epidemiological studies when assessing the association between biomarkers and human disease is of interest. We develop a latent class modeling approach for joint analysis of high-dimensional semicontinuous biomarker data and a binary disease outcome. To model the relationship between complex biomarker expression patterns and disease risk, we use latent risk classes to link the 2 modeling components. We characterize complex biomarker-specific differences through biomarker-specific random effects, so that different biomarkers can have different baseline (low-risk) values as well as different between-class differences. The proposed approach also accommodates data features that are common in environmental toxicology and other biomarker exposure data, including a large number of biomarkers, numerous zero values, and complex mean-variance relationship in the biomarkers levels. A Monte Carlo EM (MCEM) algorithm is proposed for parameter estimation. Both the MCEM algorithm and model selection procedures are shown to work well in simulations and applications. In applying the proposed approach to an epidemiological study that examined the relationship between environmental polychlorinated biphenyl (PCB) exposure and the risk of endometriosis, we identified a highly significant overall effect of PCB concentrations on the risk of endometriosis.

The genetic relationship between cannabis and tobacco cigarette use in European- and African-American female twins and siblings.

PubMed

Agrawal, Arpana; Grant, Julia D; Lynskey, Michael T; Madden, Pamela A F; Heath, Andrew C; Bucholz, Kathleen K; Sartor, Carolyn E

2016-06-01

Use of cigarettes and cannabis frequently co-occurs. We examine the role of genetic and environmental influences on variation in and covariation between tobacco cigarette and cannabis use across European-American (EA) and African-American (AA) women. Data on lifetime cannabis and cigarette use were drawn from interviews of 956 AA and 3557 EA young adult female twins and non-twin same sex female full siblings. Twin modeling was used to decompose variance in and covariance between cigarette and cannabis use into additive genetic, shared, special twin and non-shared environmental sources. Cigarette use was more common in EAs (75.3%, 95% C.I. 73.8-76.7%) than AAs (64.2%, 95% C.I. 61.2-67.2%) while cannabis use was marginally more commonly reported by AAs (55.5%, 95% C.I. 52.5-58.8%) than EAs (52.4%, 95% C.I. 50.7-54.0%). Additive genetic factors were responsible for 43-66% of the variance in cigarette and cannabis use. Broad shared environmental factors (shared+special twin) played a more significant role in EA (23-29%) than AA (2-15%) women. In AA women, the influence of non-shared environment was more pronounced (42-45% vs. 11-19% in EA women). There was strong evidence for the same familial influences underlying use of both substances (rA=0.82-0.89; rC+T=0.70-0.75). Non-shared environmental factors were also correlated but less so (rE=0.48-0.66). No racial/ethnic differences were apparent in these sources of covariation. Heritability of cigarette and cannabis use is comparable across racial/ethnic groups. Differences in the contribution of shared and non-shared environmental influences indicate that different factors may shape substance use in EA and AA women. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Importance of spatial autocorrelation in modeling bird distributions at a continental scale

USGS Publications Warehouse

Bahn, V.; O'Connor, R.J.; Krohn, W.B.

2006-01-01

Spatial autocorrelation in species' distributions has been recognized as inflating the probability of a type I error in hypotheses tests, causing biases in variable selection, and violating the assumption of independence of error terms in models such as correlation or regression. However, it remains unclear whether these problems occur at all spatial resolutions and extents, and under which conditions spatially explicit modeling techniques are superior. Our goal was to determine whether spatial models were superior at large extents and across many different species. In addition, we investigated the importance of purely spatial effects in distribution patterns relative to the variation that could be explained through environmental conditions. We studied distribution patterns of 108 bird species in the conterminous United States using ten years of data from the Breeding Bird Survey. We compared the performance of spatially explicit regression models with non-spatial regression models using Akaike's information criterion. In addition, we partitioned the variance in species distributions into an environmental, a pure spatial and a shared component. The spatially-explicit conditional autoregressive regression models strongly outperformed the ordinary least squares regression models. In addition, partialling out the spatial component underlying the species' distributions showed that an average of 17% of the explained variation could be attributed to purely spatial effects independent of the spatial autocorrelation induced by the underlying environmental variables. We concluded that location in the range and neighborhood play an important role in the distribution of species. Spatially explicit models are expected to yield better predictions especially for mobile species such as birds, even in coarse-grained models with a large extent. ?? Ecography.

Assessment of agricultural groundwater users in Iran: a cultural environmental bias

NASA Astrophysics Data System (ADS)

Salehi, Saeid; Chizari, Mohammad; Sadighi, Hassan; Bijani, Masoud

2018-02-01

Many environmental problems are rooted in human behavior. This study aimed to explore the causal effect of cultural environmental bias on `sustainable behavior' among agricultural groundwater users in Fars province, Iran, according to Klockner's comprehensive model. A survey-based research project was conducted to gathering data on the paradigm of environmental psychology. The sample included agricultural groundwater users ( n = 296) who were selected at random within a structured sampling regime involving study areas that represent three (higher, medium and lower) bounds of the agricultural-groundwater-vulnerability spectrum. Results showed that the "environment as ductile (EnAD)" variable was a strong determinant of sustainable behavior as it related to groundwater use, and that EnAE had the highest causal effect on the behavior of agricultural groundwater users. The adjusted model explained 41% variance of "groundwater sustainable behavior". Based on the results, the groundwater sustainable behaviors of agricultural groundwater users were found to be affected by personal and subjective norm variables and that they are influenced by casual effects of the "environment as ductile (EnAD)" variable. The conclusions reflect the Fars agricultural groundwater users' attitude or worldview on groundwater as an unrecoverable resource; thus, it is necessary that scientific disciplines like hydrogeology and psycho-sociology be considered together in a comprehensive approach for every groundwater study.

Predicting biological condition in southern California streams

USGS Publications Warehouse

Brown, Larry R.; May, Jason T.; Rehn, Andrew C.; Ode, Peter R.; Waite, Ian R.; Kennen, Jonathan G.

2012-01-01

As understanding of the complex relations among environmental stressors and biological responses improves, a logical next step is predictive modeling of biological condition at unsampled sites. We developed a boosted regression tree (BRT) model of biological condition, as measured by a benthic macroinvertebrate index of biotic integrity (BIBI), for streams in urbanized Southern Coastal California. We also developed a multiple linear regression (MLR) model as a benchmark for comparison with the BRT model. The BRT model explained 66% of the variance in B-IBI, identifying watershed population density and combined percentage agricultural and urban land cover in the riparian buffer as the most important predictors of B-IBI, but with watershed mean precipitation and watershed density of manmade channels also important. The MLR model explained 48% of the variance in B-IBI and included watershed population density and combined percentage agricultural and urban land cover in the riparian buffer. For a verification data set, the BRT model correctly classified 75% of impaired sites (B-IBI < 40) and 78% of unimpaired sites (B-IBI = 40). For the same verification data set, the MLR model correctly classified 69% of impaired sites and 87% of unimpaired sites. The BRT model should not be used to predict B-IBI for specific sites; however, the model can be useful for general applications such as identifying and prioritizing regions for monitoring, remediation or preservation, stratifying new bioassessments according to anticipated biological condition, or assessing the potential for change in stream biological condition based on anticipated changes in population density and development in stream buffers.

Indoor radio channel modeling and mitigation of fading effects using linear and circular polarized antennas in combination for smart home system at 868 MHz

NASA Astrophysics Data System (ADS)

Wunderlich, S.; Welpot, M.; Gaspard, I.

2014-11-01

The markets for smart home products and services are expected to grow over the next years, driven by the increasing demands of homeowners considering energy monitoring, management, environmental controls and security. Many of these new systems will be installed in existing homes and offices and therefore using radio based systems for cost reduction. A drawback of radio based systems in indoor environments are fading effects which lead to a high variance of the received signal strength and thereby to a difficult predictability of the encountered path loss of the various communication links. For that reason it is necessary to derive a statistical path loss model which can be used to plan a reliable and cost effective radio network. This paper presents the results of a measurement campaign, which was performed in six buildings to deduce realistic radio channel models for a high variety of indoor radio propagation scenarios in the short range devices (SRD) band at 868 MHz. Furthermore, a potential concept to reduce the variance of the received signal strength using a circular polarized (CP) patch antenna in combination with a linear polarized antenna in an one-to-one communication link is presented.

Genetic and Environmental Influences on Smoking Behavior across Adolescence and Young Adulthood in the Virginia Twin Study of Adolescent Behavioral Development and the Transitions to Substance Abuse Follow-Up

PubMed Central

Do, Elizabeth K.; Prom-Wormley, Elizabeth C.; Eaves, Lindon J.; Silberg, Judy L.; Miles, Donna R.; Maes, Hermine H.

2016-01-01

Little is known regarding the underlying relationship between smoking initiation and current quantity smoked during adolescence into young adulthood. It is possible that the influences of genetic and environmental factors on this relationship vary across sex and age. To investigate this further, the current study applied a common causal contingency model to data from a Virginia-based twin study to determine: (1) if the same genetic and environmental factors are contributing to smoking initiation and current quantity smoked; (2) whether the magnitude of genetic and environmental factor contributions are the same across adolescence and young adulthood; and (3) if qualitative and quantitative differences in the sources of variance between males and females exist. Study results found no qualitative or quantitative sex differences in the relationship between smoking initiation and current quantity smoked, though relative contributions of genetic and environmental factors changed across adolescence and young adulthood. More specifically, smoking initiation and current quantity smoked remain separate constructs until young adulthood, when liabilities are correlated. Smoking initiation is explained by genetic, shared, and unique environmental factors in early adolescence and by genetic and unique environmental factors in young adulthood; while current quantity smoked is explained by shared environmental and unique environmental factors until young adulthood, when genetic and unique environmental factors play a larger role. PMID:25662421

Genetic and Environmental Influences on Smoking Behavior across Adolescence and Young Adulthood in the Virginia Twin Study of Adolescent Behavioral Development and the Transitions to Substance Abuse Follow-Up.

PubMed

Do, Elizabeth K; Prom-Wormley, Elizabeth C; Eaves, Lindon J; Silberg, Judy L; Miles, Donna R; Maes, Hermine H

2015-02-01

Little is known regarding the underlying relationship between smoking initiation and current quantity smoked during adolescence into young adulthood. It is possible that the influences of genetic and environmental factors on this relationship vary across sex and age. To investigate this further, the current study applied a common causal contingency model to data from a Virginia-based twin study to determine: (1) if the same genetic and environmental factors are contributing to smoking initiation and current quantity smoked; (2) whether the magnitude of genetic and environmental factor contributions are the same across adolescence and young adulthood; and (3) if qualitative and quantitative differences in the sources of variance between males and females exist. Study results found no qualitative or quantitative sex differences in the relationship between smoking initiation and current quantity smoked, though relative contributions of genetic and environmental factors changed across adolescence and young adulthood. More specifically, smoking initiation and current quantity smoked remain separate constructs until young adulthood, when liabilities are correlated. Smoking initiation is explained by genetic, shared, and unique environmental factors in early adolescence and by genetic and unique environmental factors in young adulthood; while current quantity smoked is explained by shared environmental and unique environmental factors until young adulthood, when genetic and unique environmental factors play a larger role.

Uncertainty importance analysis using parametric moment ratio functions.

PubMed

Wei, Pengfei; Lu, Zhenzhou; Song, Jingwen

2014-02-01

This article presents a new importance analysis framework, called parametric moment ratio function, for measuring the reduction of model output uncertainty when the distribution parameters of inputs are changed, and the emphasis is put on the mean and variance ratio functions with respect to the variances of model inputs. The proposed concepts efficiently guide the analyst to achieve a targeted reduction on the model output mean and variance by operating on the variances of model inputs. The unbiased and progressive unbiased Monte Carlo estimators are also derived for the parametric mean and variance ratio functions, respectively. Only a set of samples is needed for implementing the proposed importance analysis by the proposed estimators, thus the computational cost is free of input dimensionality. An analytical test example with highly nonlinear behavior is introduced for illustrating the engineering significance of the proposed importance analysis technique and verifying the efficiency and convergence of the derived Monte Carlo estimators. Finally, the moment ratio function is applied to a planar 10-bar structure for achieving a targeted 50% reduction of the model output variance. © 2013 Society for Risk Analysis.

Correlating bilayer tablet delamination tendencies to micro-environmental thermodynamic conditions during pan coating.

PubMed

Zacour, Brian M; Pandey, Preetanshu; Subramanian, Ganeshkumar; Gao, Julia Z; Nikfar, Faranak

2014-06-01

The objective of this study was to determine the impact that the micro-environment, as measured by PyroButton data loggers, experienced by tablets during the pan coating unit operation had on the layer adhesion of bilayer tablets in open storage conditions. A full factorial design of experiments (DOE) with three center points was conducted to study the impact of final tablet hardness, film coating spray rate and film coating exhaust temperature on the delamination tendencies of bilayer tablets. PyroButton data loggers were placed (fixed) at various locations in a pan coater and were also allowed to freely move with the tablet bed to measure the micro-environmental temperature and humidity conditions of the tablet bed. The variance in the measured micro-environment via PyroButton data loggers accounted for 75% of the variance in the delamination tendencies of bilayer tablets on storage (R(2 )= 0.75). A survival analysis suggested that tablet hardness and coating spray rate significantly impacted the delamination tendencies of the bilayer tablets under open storage conditions. The coating exhaust temperature did not show good correlation with the tablets' propensity to crack indicating that it was not representative of the coating micro-environment. Models created using data obtained from the PyroButton data loggers outperformed models created using primary DOE factors in the prediction of bilayer tablet strength, especially upon equipment or scale transfers. The coating micro-environment experienced by tablets during the pan coating unit operation significantly impacts the strength of the bilayer interface of tablets on storage.

Impact of an equality constraint on the class-specific residual variances in regression mixtures: A Monte Carlo simulation study

PubMed Central

Kim, Minjung; Lamont, Andrea E.; Jaki, Thomas; Feaster, Daniel; Howe, George; Van Horn, M. Lee

2015-01-01

Regression mixture models are a novel approach for modeling heterogeneous effects of predictors on an outcome. In the model building process residual variances are often disregarded and simplifying assumptions made without thorough examination of the consequences. This simulation study investigated the impact of an equality constraint on the residual variances across latent classes. We examine the consequence of constraining the residual variances on class enumeration (finding the true number of latent classes) and parameter estimates under a number of different simulation conditions meant to reflect the type of heterogeneity likely to exist in applied analyses. Results showed that bias in class enumeration increased as the difference in residual variances between the classes increased. Also, an inappropriate equality constraint on the residual variances greatly impacted estimated class sizes and showed the potential to greatly impact parameter estimates in each class. Results suggest that it is important to make assumptions about residual variances with care and to carefully report what assumptions were made. PMID:26139512

Population age and initial density in a patchy environment affect the occurrence of abrupt transitions in a birth-and-death model of Taylor's law

USGS Publications Warehouse

Jiang, Jiang; DeAngelis, Donald L.; Zhang, B.; Cohen, J.E.

2014-01-01

Taylor's power law describes an empirical relationship between the mean and variance of population densities in field data, in which the variance varies as a power, b, of the mean. Most studies report values of b varying between 1 and 2. However, Cohen (2014a) showed recently that smooth changes in environmental conditions in a model can lead to an abrupt, infinite change in b. To understand what factors can influence the occurrence of an abrupt change in b, we used both mathematical analysis and Monte Carlo samples from a model in which populations of the same species settled on patches, and each population followed independently a stochastic linear birth-and-death process. We investigated how the power relationship responds to a smooth change of population growth rate, under different sampling strategies, initial population density, and population age. We showed analytically that, if the initial populations differ only in density, and samples are taken from all patches after the same time period following a major invasion event, Taylor's law holds with exponent b=1, regardless of the population growth rate. If samples are taken at different times from patches that have the same initial population densities, we calculate an abrupt shift of b, as predicted by Cohen (2014a). The loss of linearity between log variance and log mean is a leading indicator of the abrupt shift. If both initial population densities and population ages vary among patches, estimates of b lie between 1 and 2, as in most empirical studies. But the value of b declines to ~1 as the system approaches a critical point. Our results can inform empirical studies that might be designed to demonstrate an abrupt shift in Taylor's law.

Job acquisition for people with severe mental illness enrolled in supported employment programs: a theoretically grounded empirical study.

PubMed

Corbière, Marc; Zaniboni, Sara; Lecomte, Tania; Bond, Gary; Gilles, Pierre-Yves; Lesage, Alain; Goldner, Elliot

2011-09-01

The main purpose of this study was to test a conceptual model based on the theory of planned behaviour (TPB) to explain competitive job acquisition of people with severe mental disorders enrolled in supported employment programs. Using a sample of 281 people with severe mental disorders participating in a prospective study design, the authors examined the contribution of the TPB in a model including clinical (e.g., severity of symptoms), psychosocial (e.g., self-esteem) and work related variables (e.g., length of time absent from the workplace) as predictors of job acquisition. Path analyses were used to test two conceptual models: (1) the model of job acquisition for people with mental illness adapted from the TPB, and (2) the extended TPB including clinical, psychosocial, and work related variables recognized in the literature as significant determinants of competitive employment. Findings revealed that both models presented good fit indices. In total, individual factors predicted 26% of the variance in job search behaviours (behavioural actions). However, client characteristics explained only 8% of variance in work outcomes, suggesting that environmental variables (e.g., stigma towards mental disorders) play an important role in predicting job acquisition. About 56% (N = 157) of our sample obtained competitive employment. Results suggest that employment specialists can be guided in their interventions by the concepts found in the extended model of work integration since most of these are modifiable, such as perceived barriers to employment, self-efficacy, and self-esteem.

The bud break process and its variation among local populations of boreal black spruce.

PubMed

Rossi, Sergio; Bousquet, Jean

2014-01-01

Phenology of local populations can exhibit adaptations to the current environmental conditions resulting from a close interaction between climate and genotype. The bud break process and its variations among populations were analyzed in greenhouse by monitoring the growth resumption in black spruce [Picea mariana (Mill.) BSP] seedlings originating from seeds of five stands across the closed boreal forest in Quebec, Canada. Bud break lasted 15 days and occurred earlier and quicker in northern provenances. Provenance explained between 10.2 and 32.3% of the variance in bud break, while the families accounted for a smaller but still significant part of the variance. The late occurrence of one phenological phase corresponded to a delayed occurrence of the others according to linear relationships. A causal model was proposed in the form of a chain of events with each phase of bud break being related to the previous and successive one, while no link was observed between non-adjacent phases. The adaptation of black spruce populations along the latitudinal gradient points toward a strategy based on rapid physiological processes triggered by temperature increase inducing high metabolic activity. The variation observed in bud break reflects an evolutionary trade-off between maximization of security and taking advantage of the short growing season. This work provides evidence of the phenological adaptations of black spruce to its local environmental conditions while retaining sizeable genetic diversity within populations. Because of the multigenic nature of phenology, this diversity should provide some raw material for adaptation to changing local environmental conditions.

Narrow-sense heritability estimation of complex traits using identity-by-descent information.

PubMed

Evans, Luke M; Tahmasbi, Rasool; Jones, Matt; Vrieze, Scott I; Abecasis, Gonçalo R; Das, Sayantan; Bjelland, Douglas W; de Candia, Teresa R; Yang, Jian; Goddard, Michael E; Visscher, Peter M; Keller, Matthew C

2018-03-28

Heritability is a fundamental parameter in genetics. Traditional estimates based on family or twin studies can be biased due to shared environmental or non-additive genetic variance. Alternatively, those based on genotyped or imputed variants typically underestimate narrow-sense heritability contributed by rare or otherwise poorly tagged causal variants. Identical-by-descent (IBD) segments of the genome share all variants between pairs of chromosomes except new mutations that have arisen since the last common ancestor. Therefore, relating phenotypic similarity to degree of IBD sharing among classically unrelated individuals is an appealing approach to estimating the near full additive genetic variance while possibly avoiding biases that can occur when modeling close relatives. We applied an IBD-based approach (GREML-IBD) to estimate heritability in unrelated individuals using phenotypic simulation with thousands of whole-genome sequences across a range of stratification, polygenicity levels, and the minor allele frequencies of causal variants (CVs). In simulations, the IBD-based approach produced unbiased heritability estimates, even when CVs were extremely rare, although precision was low. However, population stratification and non-genetic familial environmental effects shared across generations led to strong biases in IBD-based heritability. We used data on two traits in ~120,000 people from the UK Biobank to demonstrate that, depending on the trait and possible confounding environmental effects, GREML-IBD can be applied to very large genetic datasets to infer the contribution of very rare variants lost using other methods. However, we observed apparent biases in these real data, suggesting that more work may be required to understand and mitigate factors that influence IBD-based heritability estimates.

The bud break process and its variation among local populations of boreal black spruce

PubMed Central

Rossi, Sergio; Bousquet, Jean

2014-01-01

Phenology of local populations can exhibit adaptations to the current environmental conditions resulting from a close interaction between climate and genotype. The bud break process and its variations among populations were analyzed in greenhouse by monitoring the growth resumption in black spruce [Picea mariana (Mill.) BSP] seedlings originating from seeds of five stands across the closed boreal forest in Quebec, Canada. Bud break lasted 15 days and occurred earlier and quicker in northern provenances. Provenance explained between 10.2 and 32.3% of the variance in bud break, while the families accounted for a smaller but still significant part of the variance. The late occurrence of one phenological phase corresponded to a delayed occurrence of the others according to linear relationships. A causal model was proposed in the form of a chain of events with each phase of bud break being related to the previous and successive one, while no link was observed between non-adjacent phases. The adaptation of black spruce populations along the latitudinal gradient points toward a strategy based on rapid physiological processes triggered by temperature increase inducing high metabolic activity. The variation observed in bud break reflects an evolutionary trade-off between maximization of security and taking advantage of the short growing season. This work provides evidence of the phenological adaptations of black spruce to its local environmental conditions while retaining sizeable genetic diversity within populations. Because of the multigenic nature of phenology, this diversity should provide some raw material for adaptation to changing local environmental conditions. PMID:25389430

Undue influence of weight and shape: is it distinct from body dissatisfaction and concern about weight and shape?

PubMed

Wade, T D; Zhu, G; Martin, N G

2011-04-01

Three cognitive constructs are risk factors for eating disorders: undue influence of weight and shape, concern about weight and shape, and body dissatisfaction (BD). Undue influence, a diagnostic criterion for eating disorders, is postulated to be closely associated with self-esteem whereas BD is postulated to be closely associated with body mass index (BMI). We understand less about the relationships with concern about weight and shape. The aim of the current investigation was examine the degree of overlap across these five phenotypes in terms of latent genetic and environmental risk factors in order to draw some conclusions about the similarities and differences across the three cognitive variables. A sample of female Australian twins (n=1056, including 348 complete pairs), mean age 35 years (S.D.=2.11, range 28-40), completed a semi-structured interview about eating pathology and self-report questionnaires. An independent pathways model was used to investigate the overlap of genetic and environmental risk factors for the five phenotypes. In terms of variance that was not shared with other phenotypes, self-esteem emerged as being separate, with 100% of its variance unshared with the other phenotypes, followed by undue influence (51%) and then concern (34%), BD (28%) and BMI (32%). In terms of shared genetic risk, undue influence and concern were more closely related than BD, whereas BMI and BD were found to share common sources of risk. With respect to environmental risk factors, concern, BMI and BD were more closely related to each other than to undue influence.

Shared genetic influences on negative emotionality and major depression/conduct disorder comorbidity.

PubMed

Tackett, Jennifer L; Waldman, Irwin D; Van Hulle, Carol A; Lahey, Benjamin B

2011-08-01

To investigate whether genetic contributions to major depressive disorder and conduct disorder comorbidity are shared with genetic influences on negative emotionality. Primary caregivers of 2,022 same- and opposite-sex twin pairs 6 to 18 years of age comprised a population-based sample. Participants were randomly selected across five regions in Tennessee, with stratification by age and geographic location. Face-to-face structured interviews were conducted with the primary caregiver of a representative sample of twins. After accounting for genetic influences on negative emotionality, genetic influences on major depressive disorder/conduct disorder comorbidity were nonsignficant, but only in male twins. Specifically, 19% of the variance in the two disorders was accounted for by genetic factors shared with negative emotionality in male twins. Although the full hypothesis could not be tested in female twins, 10% to 11% of the variance in the two disorders was also accounted for by genetic factors shared with negative emotionality. Common shared environmental and nonshared environmental influences were found for major depressive disorder/conduct disorder comorbidity in male and female twins. Negative emotionality represents an important dispositional trait that may explain genetic influences on major depressive disorder/conduct disorder comorbidity, at least for boys. Models of major depressive disorder/conduct disorder comorbidity must simultaneously measure common and specific genetic and environmental factors for a full understanding of this phenomenon. Gender differences require specific research attention in dispositional factors and developmental progression. Copyright © 2011 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Spectral decomposition of internal gravity wave sea surface height in global models

NASA Astrophysics Data System (ADS)

Savage, Anna C.; Arbic, Brian K.; Alford, Matthew H.; Ansong, Joseph K.; Farrar, J. Thomas; Menemenlis, Dimitris; O'Rourke, Amanda K.; Richman, James G.; Shriver, Jay F.; Voet, Gunnar; Wallcraft, Alan J.; Zamudio, Luis

2017-10-01

Two global ocean models ranging in horizontal resolution from 1/12° to 1/48° are used to study the space and time scales of sea surface height (SSH) signals associated with internal gravity waves (IGWs). Frequency-horizontal wavenumber SSH spectral densities are computed over seven regions of the world ocean from two simulations of the HYbrid Coordinate Ocean Model (HYCOM) and three simulations of the Massachusetts Institute of Technology general circulation model (MITgcm). High wavenumber, high-frequency SSH variance follows the predicted IGW linear dispersion curves. The realism of high-frequency motions (>0.87 cpd) in the models is tested through comparison of the frequency spectral density of dynamic height variance computed from the highest-resolution runs of each model (1/25° HYCOM and 1/48° MITgcm) with dynamic height variance frequency spectral density computed from nine in situ profiling instruments. These high-frequency motions are of particular interest because of their contributions to the small-scale SSH variability that will be observed on a global scale in the upcoming Surface Water and Ocean Topography (SWOT) satellite altimetry mission. The variance at supertidal frequencies can be comparable to the tidal and low-frequency variance for high wavenumbers (length scales smaller than ˜50 km), especially in the higher-resolution simulations. In the highest-resolution simulations, the high-frequency variance can be greater than the low-frequency variance at these scales.

Cross-Lagged Analysis of Interplay Between Differential Traits in Sibling Pairs: Validation and Application to Parenting Behavior and ADHD Symptomatology.

PubMed

Moscati, Arden; Verhulst, Brad; McKee, Kevin; Silberg, Judy; Eaves, Lindon

2018-01-01

Understanding the factors that contribute to behavioral traits is a complex task, and partitioning variance into latent genetic and environmental components is a useful beginning, but it should not also be the end. Many constructs are influenced by their contextual milieu, and accounting for background effects (such as gene-environment correlation) is necessary to avoid bias. This study introduces a method for examining the interplay between traits, in a longitudinal design using differential items in sibling pairs. The model is validated via simulation and power analysis, and we conclude with an application to paternal praise and ADHD symptoms in a twin sample. The model can help identify what type of genetic and environmental interplay may contribute to the dynamic relationship between traits using a cross-lagged panel framework. Overall, it presents a way to estimate and explicate the developmental interplay between a set of traits, free from many common sources of bias.

The Genetic Liability to Disability Retirement: A 30-Year Follow-Up Study of 24,000 Finnish Twins

PubMed Central

Harkonmäki, Karoliina; Silventoinen, Karri; Levälahti, Esko; Pitkäniemi, Janne; Huunan-Seppälä, Antti; Klaukka, Timo; Koskenvuo, Markku; Kaprio, Jaakko

2008-01-01

Background No previous studies on the effect of genetic factors on the liability to disability retirement have been carried out. The main aim of this study was to investigate the contribution of genetic factors on disability retirement due to the most common medical causes, including depressive disorders. Methods The study sample consisted of 24 043 participants (49.7% women) consisting of 11 186 complete same-sex twin pairs including 3519 monozygotic (MZ) and 7667dizygotic (DZ) pairs. Information on retirement events during 1.1.1975–31.12.2004, including disability pensions (DPs) with diagnoses, was obtained from the Finnish nationwide official pension registers. Correlations in liability for MZ and DZ twins and discrete time correlated frailty model were used to investigate the genetic liability to age at disability retirement. Results The 30 year cumulative incidence of disability retirement was 20%. Under the best fitting genetic models, the heritability estimate for DPs due to any medical cause was 0.36 (95% CI 0.32–0.40), due to musculoskeletal disorders 0.37 (0.30–0.43), cardiovascular diseases 0.48 (0.39–0.57), mental disorders 0.42 (0.35–0.49) and all other reasons 0.24 (0.17–0.31). The effect of genetic factors decreased with increasing age of retirement. For DP due to depressive disorders, 28% of the variance was explained by environmental factors shared by family members (95% CI 21–36) and 58% of the variance by the age interval specific environmental factors (95% CI 44–71). Conclusions A moderate genetic contribution to the variation of disability retirement due to any medical cause was found. The genetic effects appeared to be stronger at younger ages of disability retirement suggesting the increasing influence of environmental factors not shared with family members with increasing age. Familial aggregation in DPs due to depressive disorders was best explained by the common environmental factors and genetic factors were not needed to account for the pattern of familial aggregation. PMID:18923678

Disentangling the drivers of taxonomic and phylogenetic beta diversities in disturbed and undisturbed subtropical forests

NASA Astrophysics Data System (ADS)

Liu, Jinliang; Qian, Hong; Jin, Yi; Wu, Chuping; Chen, Jianhua; Yu, Shuquan; Wei, Xinliang; Jin, Xiaofeng; Liu, Jiajia; Yu, Mingjian

2016-10-01

Understanding the relative importance of dispersal limitation and environmental filtering processes in structuring the beta diversities of subtropical forests in human disturbed landscapes is still limited. Here we used taxonomic (TBD) and phylogenetic (PBD), including terminal PBD (PBDt) and basal PBD (PBDb), beta diversity indices to quantify the taxonomic and phylogenetic turnovers at different depths of evolutionary history in disturbed and undisturbed subtropical forests. Multiple linear regression model and distance-based redundancy analysis were used to disentangle the relative importance of environmental and spatial variables. Environmental variables were significantly correlated with TBD and PBDt metrics. Temperature and precipitation were major environmental drivers of beta diversity patterns, which explained 7-27% of the variance in TBD and PBDt, whereas the spatial variables independently explained less than 1% of the variation for all forests. The relative importance of environmental and spatial variables differed between disturbed and undisturbed forests (e.g., when Bray-Curtis was used as a beta diversity metric, environmental variable had a significant effect on beta diversity for disturbed forests but had no effect on undisturbed forests). We conclude that environmental filtering plays a more important role than geographical limitation and disturbance history in driving taxonomic and terminal phylogenetic beta diversity.

Disentangling the drivers of taxonomic and phylogenetic beta diversities in disturbed and undisturbed subtropical forests

PubMed Central

Liu, Jinliang; Qian, Hong; Jin, Yi; Wu, Chuping; Chen, Jianhua; Yu, Shuquan; Wei, Xinliang; Jin, Xiaofeng; Liu, Jiajia; Yu, Mingjian

2016-01-01

Understanding the relative importance of dispersal limitation and environmental filtering processes in structuring the beta diversities of subtropical forests in human disturbed landscapes is still limited. Here we used taxonomic (TBD) and phylogenetic (PBD), including terminal PBD (PBDt) and basal PBD (PBDb), beta diversity indices to quantify the taxonomic and phylogenetic turnovers at different depths of evolutionary history in disturbed and undisturbed subtropical forests. Multiple linear regression model and distance-based redundancy analysis were used to disentangle the relative importance of environmental and spatial variables. Environmental variables were significantly correlated with TBD and PBDt metrics. Temperature and precipitation were major environmental drivers of beta diversity patterns, which explained 7–27% of the variance in TBD and PBDt, whereas the spatial variables independently explained less than 1% of the variation for all forests. The relative importance of environmental and spatial variables differed between disturbed and undisturbed forests (e.g., when Bray-Curtis was used as a beta diversity metric, environmental variable had a significant effect on beta diversity for disturbed forests but had no effect on undisturbed forests). We conclude that environmental filtering plays a more important role than geographical limitation and disturbance history in driving taxonomic and terminal phylogenetic beta diversity. PMID:27775021

Historical and future land use effects on N2O and NO emissions using an ensemble modeling approach: Costa Rica's Caribbean lowlands as an example

USGS Publications Warehouse

Reiners, William A.; Liu, S.; Gerow, K.G.; Keller, M.; Schimel, D.S.

2002-01-01

[1] The humid tropical zone is a major source area for N2O and NO emissions to the atmosphere. Local emission rates vary widely with local conditions, particularly land use practices which swiftly change with expanding settlement and changing market conditions. The combination of wide variation in emission rates and rapidly changing land use make regional estimation and future prediction of biogenic trace gas emission particularly difficult. This study estimates contemporary, historical, and future N2O and NO emissions from 0.5 million ha of northeastern Costa Rica, a well-documented region in the wet tropics undergoing rapid agricultural development. Estimates were derived by linking spatially distributed environmental data with an ecosystem simulation model in an ensemble estimation approach that incorporates the variance and covariance of spatially distributed driving variables. Results include measures of variance for regional emissions. The formation and aging of pastures from forest provided most of the past temporal change in N2O and NO flux in this region; future changes will be controlled by the degree of nitrogen fertilizer application and extent of intensively managed croplands.

Brain Signal Variability is Parametrically Modifiable

PubMed Central

Garrett, Douglas D.; McIntosh, Anthony R.; Grady, Cheryl L.

2014-01-01

Moment-to-moment brain signal variability is a ubiquitous neural characteristic, yet remains poorly understood. Evidence indicates that heightened signal variability can index and aid efficient neural function, but it is not known whether signal variability responds to precise levels of environmental demand, or instead whether variability is relatively static. Using multivariate modeling of functional magnetic resonance imaging-based parametric face processing data, we show here that within-person signal variability level responds to incremental adjustments in task difficulty, in a manner entirely distinct from results produced by examining mean brain signals. Using mixed modeling, we also linked parametric modulations in signal variability with modulations in task performance. We found that difficulty-related reductions in signal variability predicted reduced accuracy and longer reaction times within-person; mean signal changes were not predictive. We further probed the various differences between signal variance and signal means by examining all voxels, subjects, and conditions; this analysis of over 2 million data points failed to reveal any notable relations between voxel variances and means. Our results suggest that brain signal variability provides a systematic task-driven signal of interest from which we can understand the dynamic function of the human brain, and in a way that mean signals cannot capture. PMID:23749875

Historical and future land use effects on N2O and NO emissions using an ensemble modeling approach: Costa Rica's Caribbean lowlands as an example

NASA Astrophysics Data System (ADS)

Reiners, W. A.; Liu, S.; Gerow, K. G.; Keller, M.; Schimel, D. S.

2002-12-01

The humid tropical zone is a major source area for N2O and NO emissions to the atmosphere. Local emission rates vary widely with local conditions, particularly land use practices which swiftly change with expanding settlement and changing market conditions. The combination of wide variation in emission rates and rapidly changing land use make regional estimation and future prediction of biogenic trace gas emission particularly difficult. This study estimates contemporary, historical, and future N2O and NO emissions from 0.5 million ha of northeastern Costa Rica, a well-documented region in the wet tropics undergoing rapid agricultural development. Estimates were derived by linking spatially distributed environmental data with an ecosystem simulation model in an ensemble estimation approach that incorporates the variance and covariance of spatially distributed driving variables. Results include measures of variance for regional emissions. The formation and aging of pastures from forest provided most of the past temporal change in N2O and NO flux in this region; future changes will be controlled by the degree of nitrogen fertilizer application and extent of intensively managed croplands.

Plant identity and shallow soil moisture are primary drivers of stomatal conductance in the savannas of Kruger National Park.

PubMed

Tobin, Rebecca L; Kulmatiski, Andrew

2018-01-01

Our goal was to describe stomatal conductance (gs) and the site-scale environmental parameters that best predict gs in Kruger National Park (KNP), South Africa. Dominant grass and woody species were measured over two growing seasons in each of four study sites that represented the natural factorial combination of mean annual precipitation [wet (750 mm) or dry (450 mm)] and soil type (clay or sand) found in KNP. A machine-learning (random forest) model was used to describe gs as a function of plant type (species or functional group) and site-level environmental parameters (CO2, season, shortwave radiation, soil type, soil moisture, time of day, vapor pressure deficit and wind speed). The model explained 58% of the variance among 6,850 gs measurements. Species, or plant functional group, and shallow (0-20 cm) soil moisture had the greatest effect on gs. Atmospheric drivers and soil type were less important. When parameterized with three years of observed environmental data, the model estimated mean daytime growing season gs as 68 and 157 mmol m-2 sec-1 for grasses and woody plants, respectively. The model produced here could, for example, be used to estimate gs and evapotranspiration in KNP under varying climate conditions. Results from this field-based study highlight the role of species identity and shallow soil moisture as primary drivers of gs in savanna ecosystems of KNP.

A Robust Crowdsourcing-Based Indoor Localization System.

PubMed

Zhou, Baoding; Li, Qingquan; Mao, Qingzhou; Tu, Wei

2017-04-14

WiFi fingerprinting-based indoor localization has been widely used due to its simplicity and can be implemented on the smartphones. The major drawback of WiFi fingerprinting is that the radio map construction is very labor-intensive and time-consuming. Another drawback of WiFi fingerprinting is the Received Signal Strength (RSS) variance problem, caused by environmental changes and device diversity. RSS variance severely degrades the localization accuracy. In this paper, we propose a robust crowdsourcing-based indoor localization system (RCILS). RCILS can automatically construct the radio map using crowdsourcing data collected by smartphones. RCILS abstracts the indoor map as the semantics graph in which the edges are the possible user paths and the vertexes are the location where users may take special activities. RCILS extracts the activity sequence contained in the trajectories by activity detection and pedestrian dead-reckoning. Based on the semantics graph and activity sequence, crowdsourcing trajectories can be located and a radio map is constructed based on the localization results. For the RSS variance problem, RCILS uses the trajectory fingerprint model for indoor localization. During online localization, RCILS obtains an RSS sequence and realizes localization by matching the RSS sequence with the radio map. To evaluate RCILS, we apply RCILS in an office building. Experiment results demonstrate the efficiency and robustness of RCILS.

A Robust Crowdsourcing-Based Indoor Localization System

PubMed Central

Zhou, Baoding; Li, Qingquan; Mao, Qingzhou; Tu, Wei

2017-01-01

WiFi fingerprinting-based indoor localization has been widely used due to its simplicity and can be implemented on the smartphones. The major drawback of WiFi fingerprinting is that the radio map construction is very labor-intensive and time-consuming. Another drawback of WiFi fingerprinting is the Received Signal Strength (RSS) variance problem, caused by environmental changes and device diversity. RSS variance severely degrades the localization accuracy. In this paper, we propose a robust crowdsourcing-based indoor localization system (RCILS). RCILS can automatically construct the radio map using crowdsourcing data collected by smartphones. RCILS abstracts the indoor map as the semantics graph in which the edges are the possible user paths and the vertexes are the location where users may take special activities. RCILS extracts the activity sequence contained in the trajectories by activity detection and pedestrian dead-reckoning. Based on the semantics graph and activity sequence, crowdsourcing trajectories can be located and a radio map is constructed based on the localization results. For the RSS variance problem, RCILS uses the trajectory fingerprint model for indoor localization. During online localization, RCILS obtains an RSS sequence and realizes localization by matching the RSS sequence with the radio map. To evaluate RCILS, we apply RCILS in an office building. Experiment results demonstrate the efficiency and robustness of RCILS. PMID:28420108

Variation and Heritability in Hair Diameter and Curvature in an Australian Twin Sample.

PubMed

Ho, Yvonne Y W; Brims, Mark; McNevin, Dennis; Spector, Timothy D; Martin, Nicholas G; Medland, Sarah E

2016-08-01

Hair diameter and curvature are two characteristics of human scalp hair used in forensic contexts. While previous data show that subjective categorization of hair curvature is highly heritable, the heritability of objectively measured curvature and diameter, and variability of hair characteristics within each individual have not yet been studied. The present study measured hair diameter and curvature using an optical fiber diameter analyzer in a sample of 2,332 twins and siblings. Heritability was estimated using maximum likelihood structural equation modeling. Results show sex differences in the magnitude of genetic influence for mean diameter and curvature, with the vast majority of the variance accounted for by genetic effects in males (diameter = 86%, curvature = 53%) and females (diameter = 77%, curvature = 61%). The consistency of diameter (variance within an individual) was also highly heritable, but did not show sex limitation, with 68% of the variance accounted for by genetic factors. Moderate phenotypic correlations were seen between diameter and consistency (r = 0.3) but there was little correlation between diameter and curvature (r = -0.13). A bivariate Cholesky analysis was used to estimate the genetic and environmental correlations between hair diameter and consistency, yielding genetic correlations of r gF = 0.27 for females and r gM = 0.25 for males.

Infrastructure features outperform environmental variables explaining rabbit abundance around motorways.

PubMed

Planillo, Aimara; Malo, Juan E

2018-01-01

Human disturbance is widespread across landscapes in the form of roads that alter wildlife populations. Knowing which road features are responsible for the species response and their relevance in comparison with environmental variables will provide useful information for effective conservation measures. We sampled relative abundance of European rabbits, a very widespread species, in motorway verges at regional scale, in an area with large variability in environmental and infrastructure conditions. Environmental variables included vegetation structure, plant productivity, distance to water sources, and altitude. Infrastructure characteristics were the type of vegetation in verges, verge width, traffic volume, and the presence of embankments. We performed a variance partitioning analysis to determine the relative importance of two sets of variables on rabbit abundance. Additionally, we identified the most important variables and their effects model averaging after model selection by AICc on hypothesis-based models. As a group, infrastructure features explained four times more variability in rabbit abundance than environmental variables, being the effects of the former critical in motorway stretches located in altered landscapes with no available habitat for rabbits, such as agricultural fields. Model selection and Akaike weights showed that verge width and traffic volume are the most important variables explaining rabbit abundance index, with positive and negative effects, respectively. In the light of these results, the response of species to the infrastructure can be modulated through the modification of motorway features, being some of them manageable in the design phase. The identification of such features leads to suggestions for improvement through low-cost corrective measures and conservation plans. As a general indication, keeping motorway verges less than 10 m wide will prevent high densities of rabbits and avoid the unwanted effects that rabbit populations can generate in some areas.

[Structural Equation Modeling on Smoking Cessation Intention in Male Technical High School Students].

PubMed

Do, Eun Su; Choi, Eunsuk

2017-04-01

This study was done to develop and test a structural model on smoking cessation intention in technical high school men. The conceptual model was based on the theory of reasoned action and health promotion model. From May 29 to April 13, 2015, 413 technical high school students who smoked completed a structured questionnaire. Data were analyzed to calculate the direct and indirect effects of factors affecting smoking cessation intention. The SPSS WIN 20.0 and AMOS 21.0 programs were used. The hypothetical model was a good fit for the data. The model fit indices were χ²/df=2.36, GFI=.95, AGFI=.92, NFI=0.97, and RMSEA=.05. Self-esteem had direct and indirect effects on smoking cessation intention. Attitude, subjective norm, and self-efficacy had direct effects on smoking cessation intention. Smoking knowledge and environmental factor had indirect effects on smoking cessation intention. This model explained 87.0% of the variance in smoking cessation intention. These results indicate that technical high school students' intention to stop smoking can be improved through an increase in self-esteem, negative environmental factors, attitude toward smoking cessation, subjective norm about smoking cessation, and self-efficacy for smoking cessation. © 2017 Korean Society of Nursing Science

Developing an Integrated Model Framework for the Assessment of Sustainable Agricultural Residue Removal Limits for Bioenergy Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

David Muth, Jr.; Jared Abodeely; Richard Nelson

Agricultural residues have significant potential as a feedstock for bioenergy production, but removing these residues can have negative impacts on soil health. Models and datasets that can support decisions about sustainable agricultural residue removal are available; however, no tools currently exist capable of simultaneously addressing all environmental factors that can limit availability of residue. The VE-Suite model integration framework has been used to couple a set of environmental process models to support agricultural residue removal decisions. The RUSLE2, WEPS, and Soil Conditioning Index models have been integrated. A disparate set of databases providing the soils, climate, and management practice datamore » required to run these models have also been integrated. The integrated system has been demonstrated for two example cases. First, an assessment using high spatial fidelity crop yield data has been run for a single farm. This analysis shows the significant variance in sustainably accessible residue across a single farm and crop year. A second example is an aggregate assessment of agricultural residues available in the state of Iowa. This implementation of the integrated systems model demonstrates the capability to run a vast range of scenarios required to represent a large geographic region.« less

Metabolomics fingerprint of coffee species determined by untargeted-profiling study using LC-HRMS.

PubMed

Souard, Florence; Delporte, Cédric; Stoffelen, Piet; Thévenot, Etienne A; Noret, Nausicaa; Dauvergne, Bastien; Kauffmann, Jean-Michel; Van Antwerpen, Pierre; Stévigny, Caroline

2018-04-15

Coffee bean extracts are consumed all over the world as beverage and there is a growing interest in coffee leaf extracts as food supplements. The wild diversity in Coffea (Rubiaceae) genus is large and could offer new opportunities and challenges. In the present work, a metabolomics approach was implemented to examine leaf chemical composition of 9 Coffea species grown in the same environmental conditions. Leaves were analyzed by LC-HRMS and a comprehensive statistical workflow was designed. It served for univariate hypothesis testing and multivariate modeling by PCA and partial PLS-DA on the Workflow4Metabolomics infrastructure. The first two axes of PCA and PLS-DA describes more than 40% of variances with good values of explained variances. This strategy permitted to investigate the metabolomics data and their relation with botanic and genetic informations. Finally, the identification of several key metabolites for the discrimination between species was further characterized. Copyright © 2017 Elsevier Ltd. All rights reserved.

Inter-individual Differences in the Effects of Aircraft Noise on Sleep Fragmentation

PubMed Central

McGuire, Sarah; Müller, Uwe; Elmenhorst, Eva-Maria; Basner, Mathias

2016-01-01

Study Objectives: Environmental noise exposure disturbs sleep and impairs recuperation, and may contribute to the increased risk for (cardiovascular) disease. Noise policy and regulation are usually based on average responses despite potentially large inter-individual differences in the effects of traffic noise on sleep. In this analysis, we investigated what percentage of the total variance in noise-induced awakening reactions can be explained by stable inter-individual differences. Methods: We investigated 69 healthy subjects polysomnographically (mean ± standard deviation 40 ± 13 years, range 18–68 years, 32 male) in this randomized, balanced, double-blind, repeated measures laboratory study. This study included one adaptation night, 9 nights with exposure to 40, 80, or 120 road, rail, and/or air traffic noise events (including one noise-free control night), and one recovery night. Results: Mixed-effects models of variance controlling for reaction probability in noise-free control nights, age, sex, number of noise events, and study night showed that 40.5% of the total variance in awakening probability and 52.0% of the total variance in EEG arousal probability were explained by inter-individual differences. If the data set was restricted to nights (4 exposure nights with 80 noise events per night), 46.7% of the total variance in awakening probability and 57.9% of the total variance in EEG arousal probability were explained by inter-individual differences. The results thus demonstrate that, even in this relatively homogeneous, healthy, adult study population, a considerable amount of the variance observed in noise-induced sleep disturbance can be explained by inter-individual differences that cannot be explained by age, gender, or specific study design aspects. Conclusions: It will be important to identify those at higher risk for noise induced sleep disturbance. Furthermore, the custom to base noise policy and legislation on average responses should be re-assessed based on these findings. Citation: McGuire S, Müller U, Elmenhorst EM, Basner M. Inter-individual differences in the effects of aircraft noise on sleep fragmentation. SLEEP 2016;39(5):1107–1110. PMID:26856901

A time dependent mixing model to close PDF equations for transport in heterogeneous aquifers

NASA Astrophysics Data System (ADS)

Schüler, L.; Suciu, N.; Knabner, P.; Attinger, S.

2016-10-01

Probability density function (PDF) methods are a promising alternative to predicting the transport of solutes in groundwater under uncertainty. They make it possible to derive the evolution equations of the mean concentration and the concentration variance, used in moment methods. The mixing model, describing the transport of the PDF in concentration space, is essential for both methods. Finding a satisfactory mixing model is still an open question and due to the rather elaborate PDF methods, a difficult undertaking. Both the PDF equation and the concentration variance equation depend on the same mixing model. This connection is used to find and test an improved mixing model for the much easier to handle concentration variance. Subsequently, this mixing model is transferred to the PDF equation and tested. The newly proposed mixing model yields significantly improved results for both variance modelling and PDF modelling.

40 CFR 142.312 - What EPA action is necessary when a State proposes to grant a small system variance to a public...

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 22 2010-07-01 2010-07-01 false What EPA action is necessary when a State proposes to grant a small system variance to a public water system serving a population of more than 3,300 and fewer than 10,000 persons? 142.312 Section 142.312 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAM...

40 CFR 142.312 - What EPA action is necessary when a State proposes to grant a small system variance to a public...

Code of Federal Regulations, 2014 CFR

2014-07-01

... 40 Protection of Environment 23 2014-07-01 2014-07-01 false What EPA action is necessary when a State proposes to grant a small system variance to a public water system serving a population of more than 3,300 and fewer than 10,000 persons? 142.312 Section 142.312 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) WATER PROGRAM...

Neural Mechanisms and Children's Intellectual Development: Multiple Impacts of Environmental Factors.

PubMed

Takeuchi, Hikaru; Kawashima, Ryuta

2016-12-01

Human psychometric intelligence can predict a number of important social and academic outcomes. Substantial parts of the variances of human intelligence and the brain volume supporting those abilities are explained by environmental factors, and during childhood, human brains have higher plasticity and also 60% of variance of intelligence that is explained by environmental factors. Here, we review the representative environmental factors known to affect human intellectual development during each developmental stage. We describe what is (and what is not) being investigated to determine how these factors affect human brain development through analyses of volumetrical and cortical structures. In conclusion, environmental factors that affect children's intellectual development lead to three patterns of brain structural change. The first is global change in the brain structure, observed more often in the earlier phase of development. The second is structural changes concentrated in the medial prefrontal and adjacent areas and medial temporal areas, which are likely to be induced by stress in many cases. The third is sporadic region-specific change, likely to be primarily caused by use-dependent plasticity of the areas that is often observed in the later phase of development. These changes may underlie the alterations in children's intellectual development that is induced by environmental factors. © The Author(s) 2015.

Perceived Family Cohesion Moderates Environmental Influences on Prosocial Behavior in Nigerian Adolescent Twins.

PubMed

Hur, Yoon-Mi; Taylor, Jeanette; Jeong, Hoe-Uk; Park, Min-Seo; Haberstick, Brett C

2017-06-01

Research shows that perceived family cohesion is positively related to prosocial behavior in adolescents. In this study, we investigated heritability of prosocial behavior (PB) and perceived family cohesion (FC) among Nigerian twins attending public schools in Lagos State, Nigeria (mean age = 14.7 years, SD = 1.7 years), and explored the issue of whether children's perception of cohesive family environment moderated genetic and environmental influences on (PB). The PB scale of the Strengths and Difficulties Questionnaire and the FC scale of the Family Adaptability and Cohesion Evaluation Scale III were completed by 2,376 twins (241 monozygotic (MZ) male, 354 MZ female, 440 dizygotic (DZ) male, 553 DZ female, and 788 opposite-sex DZ twins). A general sex-limitation and the bivariate genotype by environment interaction (G×E) models were applied to the data. The general sex-limitation model showed no significant sex differences, indicating that additive genetic and non-shared environmental influences were, 38% (95% CI = 31, 46) and 62% (95% CI = 54, 69) for PB and 33% (95% CI = 24, 40) and 67% (95% CI = 60, 76) for FC in both sexes. These estimates were similar to those found in Western and Asian twin studies to date. The correlation between PB and FC was 0.36. The best-fitting bivariate G×E model indicated that FC significantly moderated non-shared environmental influence unique to PB (E×E interaction). Specifically, non-shared environmental contributions to PB were highest when FC was lowest, and decreased as the levels of FC increased. However, genetic variances in PB were stable across all levels of FC. These findings suggest that FC reduces individual differences in PB by changing non-shared environmental experiences rather than genetic factors in PB.

On the Likely Utility of Hybrid Weights Optimized for Variances in Hybrid Error Covariance Models

NASA Astrophysics Data System (ADS)

Satterfield, E.; Hodyss, D.; Kuhl, D.; Bishop, C. H.

2017-12-01

Because of imperfections in ensemble data assimilation schemes, one cannot assume that the ensemble covariance is equal to the true error covariance of a forecast. Previous work demonstrated how information about the distribution of true error variances given an ensemble sample variance can be revealed from an archive of (observation-minus-forecast, ensemble-variance) data pairs. Here, we derive a simple and intuitively compelling formula to obtain the mean of this distribution of true error variances given an ensemble sample variance from (observation-minus-forecast, ensemble-variance) data pairs produced by a single run of a data assimilation system. This formula takes the form of a Hybrid weighted average of the climatological forecast error variance and the ensemble sample variance. Here, we test the extent to which these readily obtainable weights can be used to rapidly optimize the covariance weights used in Hybrid data assimilation systems that employ weighted averages of static covariance models and flow-dependent ensemble based covariance models. Univariate data assimilation and multi-variate cycling ensemble data assimilation are considered. In both cases, it is found that our computationally efficient formula gives Hybrid weights that closely approximate the optimal weights found through the simple but computationally expensive process of testing every plausible combination of weights.

A hotspot model for leaf canopies

NASA Technical Reports Server (NTRS)

Jupp, David L. B.; Strahler, Alan H.

1991-01-01

The hotspot effect, which provides important information about canopy structure, is modeled using general principles of environmental physics as driven by parameters of interest in remote sensing, such as leaf size, leaf shape, leaf area index, and leaf angle distribution. Specific examples are derived for canopies of horizontal leaves. The hotspot effect is implemented within the framework of the model developed by Suits (1972) for a canopy of leaves to illustrate what might occur in an agricultural crop. Because the hotspot effect arises from very basic geometrical principles and is scale-free, it occurs similarly in woodlands, forests, crops, rough soil surfaces, and clouds. The scaling principles advanced are also significant factors in the production of image spatial and angular variance and covariance which can be used to assess land cover structure through remote sensing.

Nonshared Environmental Influences on Teacher-Reported Behaviour Problems: Monozygotic Twin Differences in Perceptions of the Classroom

ERIC Educational Resources Information Center

Oliver, Bonamy R.; Pike, Alison; Plomin, Robert

2008-01-01

Background: The identification of specific nonshared environments responsible for the variance in behaviour problems is a key challenge. Methods: Nonshared environmental influences on teacher-reported behaviour problems were explored independently of genetics using the monozygotic (MZ) twin differences design. Six aspects of classroom environment…

Environmental Considerations: Home and School Comparison of Spanish-English Speakers' Vocalizations

ERIC Educational Resources Information Center

Jackson, Carla W.; Callender, Maya F.

2014-01-01

This study examined differences in the quantity of child vocalizations (CVs) between preschool and home environments using the Language Environmental Analysis (LENA). The sample included monolingual English-speaking children (n = 27) and Spanish-English speaking dual language learners (n = 30). A two-way mixed effects analysis of variance with one…

Understanding Genetic and Environmental Influences on the Development of Reading: Reaching for Higher Fruit

ERIC Educational Resources Information Center

Wagner, Richard K.

2005-01-01

The transition from first-generation to second-generation studies of genetic and environmental influences on the development of reading is underway. The first generation of quantitative genetic studies yielded an extraordinary conclusion: Fifty percent or more of the variance in most constructs, including reading, is attributable to genetic…

Environmental Moderators of Genetic Influences on Adolescent Delinquent Involvement and Victimization

ERIC Educational Resources Information Center

Beaver, Kevin M.

2011-01-01

A growing body of empirical research reveals that genetic factors account for a substantial amount of variance in measures of antisocial behaviors. At the same time, evidence is also emerging indicating that certain environmental factors moderate the effects that genetic factors have on antisocial outcomes. Despite this line of research, much…

No-migration variance petition. Appendices C--J: Volume 5, Revision 1

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1990-03-01

Volume V contains the appendices for: closure and post-closure plans; RCRA ground water monitoring waver; Waste Isolation Division Quality Program Manual; water quality sampling plan; WIPP Environmental Procedures Manual; sample handling and laboratory procedures; data analysis; and Annual Site Environmental Monitoring Report for the Waste Isolation Pilot Plant.

Sex-specific selection under environmental stress in seed beetles.

PubMed

Martinossi-Allibert, I; Arnqvist, G; Berger, D

2017-01-01

Sexual selection can increase rates of adaptation by imposing strong selection in males, thereby allowing efficient purging of the mutation load on population fitness at a low demographic cost. Indeed, sexual selection tends to be male-biased throughout the animal kingdom, but little empirical work has explored the ecological sensitivity of this sex difference. In this study, we generated theoretical predictions of sex-specific strengths of selection, environmental sensitivities and genotype-by-environment interactions and tested them in seed beetles by manipulating either larval host plant or rearing temperature. Using fourteen isofemale lines, we measured sex-specific reductions in fitness components, genotype-by-environment interactions and the strength of selection (variance in fitness) in the juvenile and adult stage. As predicted, variance in fitness increased with stress, was consistently greater in males than females for adult reproductive success (implying strong sexual selection), but was similar in the sexes in terms of juvenile survival across all levels of stress. Although genetic variance in fitness increased in magnitude under severe stress, heritability decreased and particularly so in males. Moreover, genotype-by-environment interactions for fitness were common but specific to the type of stress, sex and life stage, suggesting that new environments may change the relative alignment and strength of selection in males and females. Our study thus exemplifies how environmental stress can influence the relative forces of natural and sexual selection, as well as concomitant changes in genetic variance in fitness, which are predicted to have consequences for rates of adaptation in sexual populations. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

Using the PLUM procedure of SPSS to fit unequal variance and generalized signal detection models.

PubMed

DeCarlo, Lawrence T

2003-02-01

The recent addition of aprocedure in SPSS for the analysis of ordinal regression models offers a simple means for researchers to fit the unequal variance normal signal detection model and other extended signal detection models. The present article shows how to implement the analysis and how to interpret the SPSS output. Examples of fitting the unequal variance normal model and other generalized signal detection models are given. The approach offers a convenient means for applying signal detection theory to a variety of research.

Sleep Duration and Area-Level Deprivation in Twins

PubMed Central

Watson, Nathaniel F.; Horn, Erin; Duncan, Glen E.; Buchwald, Dedra; Vitiello, Michael V.; Turkheimer, Eric

2016-01-01

Study Objectives: We used quantitative genetic models to assess whether area-level deprivation as indicated by the Singh Index predicts shorter sleep duration and modifies its underlying genetic and environmental contributions. Methods: Participants were 4,218 adult twin pairs (2,377 monozygotic and 1,841 dizygotic) from the University of Washington Twin Registry. Participants self-reported habitual sleep duration. The Singh Index was determined by linking geocoding addresses to 17 indicators at the census-tract level using data from Census of Washington State and Census Tract Cartographic Boundary Files from 2000 and 2010. Data were analyzed using univariate and bivariate genetic decomposition and quantitative genetic interaction models that assessed A (additive genetics), C (common environment), and E (unique environment) main effects of the Singh Index on sleep duration and allowed the magnitude of residual ACE variance components in sleep duration to vary with the Index. Results: The sample had a mean age of 38.2 y (standard deviation [SD] = 18), and was predominantly female (62%) and Caucasian (91%). Mean sleep duration was 7.38 h (SD = 1.20) and the mean Singh Index score was 0.00 (SD = 0.89). The heritability of sleep duration was 39% and the Singh Index was 12%. The uncontrolled phenotypic regression of sleep duration on the Singh Index showed a significant negative relationship between area-level deprivation and sleep length (b = −0.080, P < 0.001). Every 1 SD in Singh Index was associated with a ∼4.5 min change in sleep duration. For the quasi-causal bivariate model, there was a significant main effect of E (b0E = −0.063; standard error [SE] = 0.30; P < 0.05). Residual variance components unique to sleep duration were significant for both A (b0Au = 0.734; SE = 0.020; P < 0.001) and E (b0Eu = 0.934; SE = 0.013; P < 0.001). Conclusions: Area-level deprivation has a quasi-causal association with sleep duration, with greater deprivation being related to shorter sleep. As area-level deprivation increases, unique genetic and nonshared environmental residual variance in sleep duration increases. Citation: Watson NF, Horn E, Duncan GE, Buchwald D, Vitiello MV, Turkheimer E. Sleep duration and area-level deprivation in twins. SLEEP 2016;39(1):67– 77. PMID:26285009

GENETIC AND ENVIRONMENTAL CONTRIBUTIONS TO THE CO-OCCURRENCE OF DEPRESSIVE PERSONALITY DISORDER AND DSM-IV PERSONALITY DISORDERS

PubMed Central

Ørstavik, Ragnhild E.; Kendler, Kenneth S.; Røysamb, Espen; Czajkowski, Nikolai; Tambs, Kristian; Reichborn-Kjennerud, Ted

2012-01-01

One of the main controversies with regard to depressive personality disorder (DPD) concerns the co-occurrence with the established DSM-IV personality disorders (PDs). The main aim of this study was to examine to what extent DPD and the DSM-IV PDs share genetic and environmental risk factors, using multivariate twin modeling. The DSM-IV Structured Interview for Personality was applied to 2,794 young adult twins. Paranoid PD from Cluster A, borderline PD from Cluster B, and all three PDs from Cluster C were independently and significantly associated with DPD in multiple regression analysis. The genetic correlations between DPD and the other PDs were strong (.53–.83), while the environmental correlations were moderate (.36–.40). Close to 50% of the total variance in DPD was disorder specific. However, only 5% was due to disorder-specific genetic factors, indicating that a substantial part of the genetic vulnerability to DPD also increases the vulnerability to other PDs. PMID:22686231

Genetic and environmental contributions to the co-occurrence of depressive personality disorder and DSM-IV personality disorders.

PubMed

Ørstavik, Ragnhild E; Kendler, Kenneth S; Røysamb, Espen; Czajkowski, Nikolai; Tambs, Kristian; Reichborn-Kjennerud, Ted

2012-06-01

One of the main controversies with regard to depressive personality disorder (DPD) concerns the co-occurrence with the established DSM-IV personality disorders (PDs). The main aim of this study was to examine to what extent DPD and the DSM-IV PDs share genetic and environmental risk factors, using multivariate twin modeling. The DSM-IV Structured Interview for Personality was applied to 2,794 young adult twins. Paranoid PD from Cluster A, borderline PD from Cluster B, and all three PDs from Cluster C were independently and significantly associated with DPD in multiple regression analysis. The genetic correlations between DPD and the other PDs were strong (.53-.83), while the environmental correlations were moderate (.36-.40). Close to 50% of the total variance in DPD was disorder specific. However, only 5% was due to disorder-specific genetic factors, indicating that a substantial part of the genetic vulnerability to DPD also increases the vulnerability to other PDs.

Hotspots of uncertainty in land-use and land-cover change projections: a global-scale model comparison.

PubMed

Prestele, Reinhard; Alexander, Peter; Rounsevell, Mark D A; Arneth, Almut; Calvin, Katherine; Doelman, Jonathan; Eitelberg, David A; Engström, Kerstin; Fujimori, Shinichiro; Hasegawa, Tomoko; Havlik, Petr; Humpenöder, Florian; Jain, Atul K; Krisztin, Tamás; Kyle, Page; Meiyappan, Prasanth; Popp, Alexander; Sands, Ronald D; Schaldach, Rüdiger; Schüngel, Jan; Stehfest, Elke; Tabeau, Andrzej; Van Meijl, Hans; Van Vliet, Jasper; Verburg, Peter H

2016-12-01

Model-based global projections of future land-use and land-cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity. © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Geographic Model and Biomarker-Derived Measures of Pesticide Exposure and Parkinson’s Disease

PubMed Central

RITZ, BEATE; COSTELLO, SADIE

2013-01-01

For more than two decades, reports have suggested that pesticides and herbicides may be an etiologic factor in idiopathic Parkinson’s disease (PD). To date, no clear associations with any specific pesticide have been demonstrated from epidemiological studies perhaps, in part, because methods of reliably estimating exposures are lacking. We tested the validity of a Geographic Information Systems (GIS)-based exposure assessment model that estimates potential environmental exposures at residences from pesticide applications to agricultural crops based on California Pesticide Use Reports (PUR). Using lipid-adjusted dichlorodiphenyldichloroethylene (DDE) serum levels as the “gold standard” for pesticide exposure, we conducted a validation study in a sample taken from an ongoing, population-based case–control study of PD in Central California. Residential, occupational, and other risk factor data were collected for 22 cases and 24 controls from Kern county, California. Environmental GIS–PUR-based organochlorine (OC) estimates were derived for each subject and compared to lipid-adjusted DDE serum levels. Relying on a linear regression model, we predicted log-transformed lipid-adjusted DDE serum levels. GIS–PUR-derived OC measure, body mass index, age, gender, mixing and loading pesticides by hand, and using pesticides in the home, together explained 47% of the DDE serum level variance (adjusted r2 = 0.47). The specificity of using our environmental GIS–PUR-derived OC measures to identify those with high-serum DDE levels was reasonably good (87%). Our environmental GIS–PUR-based approach appears to provide a valid model for assessing residential exposures to agricultural pesticides. PMID:17119217

A longitudinal twin study of callous-unemotional traits during childhood.

PubMed

Henry, Jeffrey; Dionne, Ginette; Viding, Essi; Petitclerc, Amélie; Feng, Bei; Vitaro, Frank; Brendgen, Mara; Tremblay, Richard E; Boivin, Michel

2018-05-01

Previous research indicates that genetic factors largely account for the stability of callous-unemotional (CU) traits in adolescence. However, the genetic-environmental etiology of the development of CU traits has not been extensively investigated in childhood, despite work showing the reliable measurement and stability of CU traits from a young age. The aim of this study was to investigate the temporal pattern of genetic and environmental etiology of CU traits across primary school, from school entry (7 years) to middle (9 and 10 years) and late childhood (12 years). Data were collected in a population sample of twins composed of 662 twin pairs (Quebec Newborn Twin Study). CU traits were reported by teachers and analyzed using a biometric latent growth curve model and a Cholesky decomposition model. Latent growth curve analyses revealed that genetic factors explain most of the variance in the intercept of CU traits. Individual differences in change over time were not significant. The Cholesky model revealed that genetic factors at 7 years had enduring contributions to CU traits at 9, 10, and 12 years. New, modest genetic contributions appeared at 9 and 10 years. Nonshared environmental contributions were generally age-specific. No shared environmental contributions were detected. In sum, both modeling approaches showed that genetic factors underlie CU traits during childhood. Initial and new genetic contributions arise during this period. Environments have substantial contributions, over and above genetic factors. Future research should investigate the source of genetic risk associated with CU traits. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Development of a winter wheat adjustable crop calendar model

NASA Technical Reports Server (NTRS)

Baker, J. R. (Principal Investigator)

1978-01-01

The author has identified the following significant results. After parameter estimation, tests were conducted with variances from the fits, and on independent data. From these tests, it was generally concluded that exponential functions have little advantage over polynomials. Precipitation was not found to significantly affect the fits. The Robertson's triquadratic form, in general use for spring wheat, was found to show promise for winter wheat, but special techniques and care were required for its use. In most instances, equations with nonlinear effects were found to yield erratic results when utilized with daily environmental values as independent variables.

Nature connection, outdoor play, and environmental stewardship in residential environmental education

NASA Astrophysics Data System (ADS)

Andrejewski, Robert G.

A lack of exposure to the natural world has led to a generation of children disconnected from nature. This phenomenon has profound negative implications for the physical and psychological well being of today's youth. Residential environmental education provides one avenue to connect children to nature. One purpose of this study was to investigate the role of Outdoor School, a residential environmental education program, on ecological knowledge, children's connection to nature, school belonging, outdoor play attitude, environmental stewardship attitude, outdoor play behavior, and environmental stewardship behavior, as reported by participants. A quasi-experimental research design was utilized in the study. A total of 228 fifth grade students (156 treatment, 72 control) from central Pennsylvania participated. The results of the program evaluation indicated that Outdoor School was successful in achieving significant, positive gains in the areas of ecological knowledge, connection to nature, outdoor play behavior, and environmental stewardship behavior. No change was found from pretest to post-test in outdoor play attitudes, environmental stewardship attitudes, and school belonging. Additionally, the study addressed gaps in the literature regarding the relationship between connection to nature, environmental stewardship, and outdoor play using two different approaches. An adaptation of the Theory of Planned Behavior (TPB) was used to predict outdoor play behavior in children. In this model, favorable attitudes, subjective norms, and perceived behavioral control lead to intentions to perform a given behavior. Intention to perform the behavior is the best predictor for behavior performance. For this study, participants' feeling of connection to nature was added as an affective independent variable. This model explained 45% of the variance in outdoor play. The hypothesis that a connection to nature would be a significant predictor of both attitudes toward outdoor play was supported by testing of the model. Finally, nature connection was tested as a full mediator of the relationship between outdoor play and environmental stewardship. There is support for the idea that direct experience in the outdoors facilitates environmental behaviors, but more research is needed to understand this relationship. Testing of the model failed to demonstrate that nature connection fully mediated the relationship between outdoor play and environmental stewardship; however, a feeling of connectedness to nature augmented the influence that outdoor play behavior exerts on environmental stewardship behavior.

Removing an intersubject variance component in a general linear model improves multiway factoring of event-related spectral perturbations in group EEG studies.

PubMed

Spence, Jeffrey S; Brier, Matthew R; Hart, John; Ferree, Thomas C

2013-03-01

Linear statistical models are used very effectively to assess task-related differences in EEG power spectral analyses. Mixed models, in particular, accommodate more than one variance component in a multisubject study, where many trials of each condition of interest are measured on each subject. Generally, intra- and intersubject variances are both important to determine correct standard errors for inference on functions of model parameters, but it is often assumed that intersubject variance is the most important consideration in a group study. In this article, we show that, under common assumptions, estimates of some functions of model parameters, including estimates of task-related differences, are properly tested relative to the intrasubject variance component only. A substantial gain in statistical power can arise from the proper separation of variance components when there is more than one source of variability. We first develop this result analytically, then show how it benefits a multiway factoring of spectral, spatial, and temporal components from EEG data acquired in a group of healthy subjects performing a well-studied response inhibition task. Copyright © 2011 Wiley Periodicals, Inc.

Modeling the subfilter scalar variance for large eddy simulation in forced isotropic turbulence

NASA Astrophysics Data System (ADS)

Cheminet, Adam; Blanquart, Guillaume

2011-11-01

Static and dynamic model for the subfilter scalar variance in homogeneous isotropic turbulence are investigated using direct numerical simulations (DNS) of a lineary forced passive scalar field. First, we introduce a new scalar forcing technique conditioned only on the scalar field which allows the fluctuating scalar field to reach a statistically stationary state. Statistical properties, including 2nd and 3rd statistical moments, spectra, and probability density functions of the scalar field have been analyzed. Using this technique, we performed constant density and variable density DNS of scalar mixing in isotropic turbulence. The results are used in an a-priori study of scalar variance models. Emphasis is placed on further studying the dynamic model introduced by G. Balarac, H. Pitsch and V. Raman [Phys. Fluids 20, (2008)]. Scalar variance models based on Bedford and Yeo's expansion are accurate for small filter width but errors arise in the inertial subrange. Results suggest that a constant coefficient computed from an assumed Kolmogorov spectrum is often sufficient to predict the subfilter scalar variance.

A Random Forest Approach to Predict the Spatial Distribution ...

EPA Pesticide Factsheets

Modeling the magnitude and distribution of sediment-bound pollutants in estuaries is often limited by incomplete knowledge of the site and inadequate sample density. To address these modeling limitations, a decision-support tool framework was conceived that predicts sediment contamination from the sub-estuary to broader estuary extent. For this study, a Random Forest (RF) model was implemented to predict the distribution of a model contaminant, triclosan (5-chloro-2-(2,4-dichlorophenoxy)phenol) (TCS), in Narragansett Bay, Rhode Island, USA. TCS is an unregulated contaminant used in many personal care products. The RF explanatory variables were associated with TCS transport and fate (proxies) and direct and indirect environmental entry. The continuous RF TCS concentration predictions were discretized into three levels of contamination (low, medium, and high) for three different quantile thresholds. The RF model explained 63% of the variance with a minimum number of variables. Total organic carbon (TOC) (transport and fate proxy) was a strong predictor of TCS contamination causing a mean squared error increase of 59% when compared to permutations of randomized values of TOC. Additionally, combined sewer overflow discharge (environmental entry) and sand (transport and fate proxy) were strong predictors. The discretization models identified a TCS area of greatest concern in the northern reach of Narragansett Bay (Providence River sub-estuary), which was validated wi

Online Estimation of Allan Variance Coefficients Based on a Neural-Extended Kalman Filter

PubMed Central

Miao, Zhiyong; Shen, Feng; Xu, Dingjie; He, Kunpeng; Tian, Chunmiao

2015-01-01

As a noise analysis method for inertial sensors, the traditional Allan variance method requires the storage of a large amount of data and manual analysis for an Allan variance graph. Although the existing online estimation methods avoid the storage of data and the painful procedure of drawing slope lines for estimation, they require complex transformations and even cause errors during the modeling of dynamic Allan variance. To solve these problems, first, a new state-space model that directly models the stochastic errors to obtain a nonlinear state-space model was established for inertial sensors. Then, a neural-extended Kalman filter algorithm was used to estimate the Allan variance coefficients. The real noises of an ADIS16405 IMU and fiber optic gyro-sensors were analyzed by the proposed method and traditional methods. The experimental results show that the proposed method is more suitable to estimate the Allan variance coefficients than the traditional methods. Moreover, the proposed method effectively avoids the storage of data and can be easily implemented using an online processor. PMID:25625903

Impact of an equality constraint on the class-specific residual variances in regression mixtures: A Monte Carlo simulation study.

PubMed

Kim, Minjung; Lamont, Andrea E; Jaki, Thomas; Feaster, Daniel; Howe, George; Van Horn, M Lee

2016-06-01

Regression mixture models are a novel approach to modeling the heterogeneous effects of predictors on an outcome. In the model-building process, often residual variances are disregarded and simplifying assumptions are made without thorough examination of the consequences. In this simulation study, we investigated the impact of an equality constraint on the residual variances across latent classes. We examined the consequences of constraining the residual variances on class enumeration (finding the true number of latent classes) and on the parameter estimates, under a number of different simulation conditions meant to reflect the types of heterogeneity likely to exist in applied analyses. The results showed that bias in class enumeration increased as the difference in residual variances between the classes increased. Also, an inappropriate equality constraint on the residual variances greatly impacted on the estimated class sizes and showed the potential to greatly affect the parameter estimates in each class. These results suggest that it is important to make assumptions about residual variances with care and to carefully report what assumptions are made.

Does an uneven sample size distribution across settings matter in cross-classified multilevel modeling? Results of a simulation study.

PubMed

Milliren, Carly E; Evans, Clare R; Richmond, Tracy K; Dunn, Erin C

2018-06-06

Recent advances in multilevel modeling allow for modeling non-hierarchical levels (e.g., youth in non-nested schools and neighborhoods) using cross-classified multilevel models (CCMM). Current practice is to cluster samples from one context (e.g., schools) and utilize the observations however they are distributed from the second context (e.g., neighborhoods). However, it is unknown whether an uneven distribution of sample size across these contexts leads to incorrect estimates of random effects in CCMMs. Using the school and neighborhood data structure in Add Health, we examined the effect of neighborhood sample size imbalance on the estimation of variance parameters in models predicting BMI. We differentially assigned students from a given school to neighborhoods within that school's catchment area using three scenarios of (im)balance. 1000 random datasets were simulated for each of five combinations of school- and neighborhood-level variance and imbalance scenarios, for a total of 15,000 simulated data sets. For each simulation, we calculated 95% CIs for the variance parameters to determine whether the true simulated variance fell within the interval. Across all simulations, the "true" school and neighborhood variance parameters were estimated 93-96% of the time. Only 5% of models failed to capture neighborhood variance; 6% failed to capture school variance. These results suggest that there is no systematic bias in the ability of CCMM to capture the true variance parameters regardless of the distribution of students across neighborhoods. Ongoing efforts to use CCMM are warranted and can proceed without concern for the sample imbalance across contexts. Copyright © 2018 Elsevier Ltd. All rights reserved.

Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison

DOE PAGES

Prestele, Reinhard; Alexander, Peter; Rounsevell, Mark D. A.; ...

2016-05-02

Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, modelmore » structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Furthermore, current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.« less

Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prestele, Reinhard; Alexander, Peter; Rounsevell, Mark D. A.

Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, modelmore » structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Furthermore, current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.« less

Thermal infrared sounding observations of lower atmospheric variances at Mars and their implications for gravity wave activity: a preliminary examination

NASA Astrophysics Data System (ADS)

Heavens, N. G.

2017-12-01

It has been recognized for over two decades that the mesoscale statistical variance observed by Earth-observing satellites at temperature-sensitive frequencies above the instrumental noise floor is a measure of gravity wave activity. These types of observation have been made by a variety of satellite instruments have been an important validation tool for gravity wave parameterizations in global and mesoscale models. At Mars, the importance of topographic and non-topographic sources of gravity waves for the general circulation is now widely recognized and the target of recent modeling efforts. However, despite several ingenious studies, gravity wave activity near hypothetical lower atmospheric sources has been poorly and unsystematically characterized, partly because of the difficulty of separating the gravity wave activity from baroclinic wave activity and the thermal tides. Here will be presented a preliminary analysis of calibrated radiance variance at 15.4 microns (635-665 cm-1) from nadir, off-nadir, and limb observations by the Mars Climate Sounder on board Mars Reconnaissance Orbiter. The overarching methodology follows Wu and Waters (1996, 1997). Nadir, off-nadir, and lowest detector limb observations should sample variability with vertical weighting functions centered high in the lower atmosphere (20-30 km altitude) and full width half maximum (FWHM) 20 km but be sensitive to gravity waves with different horizontal wavelengths and slightly different vertical wavelengths. This work is supported by NASA's Mars Data Analysis Program (NNX14AM32G). References Wu, D.L. and J.W. Waters, 1996, Satellite observations of atmospheric variances: A possible indication of gravity waves, GRL, 23, 3631-3634. Wu D.L. and J.W. Waters, 1997, Observations of Gravity Waves with the UARS Microwave Limb Sounder. In: Hamilton K. (eds) Gravity Wave Processes. NATO ASI Series (Series I: Environmental Change), vol 50. Springer, Berlin, Heidelberg.

Genetic influences of sports participation in Portuguese families.

PubMed

Seabra, André F; Mendonça, Denisa M; Göring, Harald H H; Thomis, Martine A; Maia, José A

2014-01-01

To estimate familial aggregation and quantify the genetic and environmental contribution to the phenotypic variation on sports participation (SP) among Portuguese families. The sample consisted of 2375 nuclear families (parents and two offspring each) from different regions of Portugal with a total of 9500 subjects. SP assessment was based on a psychometrically established questionnaire. Phenotypes used were based on the participation in sports (yes/no), intensity of sport, weekly amount of time in SP and the proportion of the year in which a sport was regularly played. Familial correlations were calculated using family correlations (FCOR) in the SAGE software. Heritability was estimated using variance-components methods implemented in Sequential Oligogenic Linkage Analysis Routines (SOLAR) software. Subjects of the same generation tend to be more similar in their SP habits than the subjects of different generations. In all SP phenotypes studied, adjusted for the effects of multiple covariates, the proportion of phenotypic variance due to additive genetic factors ranged between 40% and 50%. The proportion of variance attributable to environmental factors ranged from 50% for the participation in sports to 60% for intensity of sport. In this large population-based family study, there was significant familial aggregation on SP. These results highlight that the variation on SP phenotypes have a significant genetic contribution although environmental factors are also important in the familial resemblance of SP.

Effects of stage of pregnancy on variance components, daily milk yields and 305-day milk yield in Holstein cows, as estimated by using a test-day model.

PubMed

Yamazaki, T; Hagiya, K; Takeda, H; Osawa, T; Yamaguchi, S; Nagamine, Y

2016-08-01

Pregnancy and calving are elements indispensable for dairy production, but the daily milk yield of cows decline as pregnancy progresses, especially during the late stages. Therefore, the effect of stage of pregnancy on daily milk yield must be clarified to accurately estimate the breeding values and lifetime productivity of cows. To improve the genetic evaluation model for daily milk yield and determine the effect of the timing of pregnancy on productivity, we used a test-day model to assess the effects of stage of pregnancy on variance component estimates, daily milk yields and 305-day milk yield during the first three lactations of Holstein cows. Data were 10 646 333 test-day records for the first lactation; 8 222 661 records for the second; and 5 513 039 records for the third. The data were analyzed within each lactation by using three single-trait random regression animal models: one model that did not account for the stage of pregnancy effect and two models that did. The effect of stage of pregnancy on test-day milk yield was included in the model by applying a regression on days pregnant or fitting a separate lactation curve for each days open (days from calving to pregnancy) class (eight levels). Stage of pregnancy did not affect the heritability estimates of daily milk yield, although the additive genetic and permanent environmental variances in late lactation were decreased by accounting for the stage of pregnancy effect. The effects of days pregnant on daily milk yield during late lactation were larger in the second and third lactations than in the first lactation. The rates of reduction of the 305-day milk yield of cows that conceived fewer than 90 days after the second or third calving were significantly (P<0.05) greater than that after the first calving. Therefore, we conclude that differences between the negative effects of early pregnancy in the first, compared with later, lactations should be included when determining the optimal number of days open to maximize lifetime productivity in dairy cows.

The variance of angle-of-arrival fluctuation of partially coherent Gaussian-Schell Model beam propagations in slant atmospheric turbulence

NASA Astrophysics Data System (ADS)

Tan, Zhenkun; Ke, Xizheng

2017-10-01

The variance of angle-of-arrival fluctuation of the partially coherent Gaussian-Schell Model (GSM) beam propagations in the slant path, based on the extended Huygens-Fresnel principle and the model of atmospheric refraction index structural constant proposed by the international telecommunication union-radio (ITU-R), has been investigated under the modified Hill turbulence model. The expression of that has been obtained. Firstly, the effects of optical wavelength, the inner-and-outer scale of the turbulence and turbulence intensity on the variance of angle-of-arrival fluctuation have been analyzed by comparing with the partially coherent GSM beam and the completely coherent Gaussian beam. Secondly, the variance of angle-of-arrival fluctuation has been compared with the von Karman spectrum and the modified Hill spectrum under the partially coherent GSM beam. Finally, the effects of beam waist radius and partial coherence length on the variance of angle-of-arrival of the collimated (focused) beam have been analyzed under the modified Hill turbulence model. The results show that the influence of the variance of angle-of-arrival fluctuation for the inner scale effect is larger than that of the outer scale effect. The variance of angle-of-arrival fluctuation under the modified Hill spectrum is larger than that of the von Karman spectrum. The influence of the waist radius on the variance of angle-of-arrival for the collimated beam is less than focused the beam. This study will provide a necessary theoretical basis for the experiments of partially coherent GSM beam propagation through atmosphere turbulence.

A consistent transported PDF model for treating differential molecular diffusion

NASA Astrophysics Data System (ADS)

Wang, Haifeng; Zhang, Pei

2016-11-01

Differential molecular diffusion is a fundamentally significant phenomenon in all multi-component turbulent reacting or non-reacting flows caused by the different rates of molecular diffusion of energy and species concentrations. In the transported probability density function (PDF) method, the differential molecular diffusion can be treated by using a mean drift model developed by McDermott and Pope. This model correctly accounts for the differential molecular diffusion in the scalar mean transport and yields a correct DNS limit of the scalar variance production. The model, however, misses the molecular diffusion term in the scalar variance transport equation, which yields an inconsistent prediction of the scalar variance in the transported PDF method. In this work, a new model is introduced to remedy this problem that can yield a consistent scalar variance prediction. The model formulation along with its numerical implementation is discussed, and the model validation is conducted in a turbulent mixing layer problem.

Psychotic experiences are linked to cannabis use in adolescents in the community because of common underlying environmental risk factors.

PubMed

Shakoor, Sania; Zavos, Helena M S; McGuire, Philip; Cardno, Alastair G; Freeman, Daniel; Ronald, Angelica

2015-06-30

Cannabis users are more likely to have psychotic experiences (PEs). The degree to which these associations are driven by genetic or environmental influences in adolescence is unknown. This study estimated the genetic and environmental contributions to the relationship between cannabis use and PEs. Specific PEs were measured in a community-based twin sample (4830 16-year-old pairs) using self-reports and parent-reports. Adolescents reported on ever using cannabis. Multivariate liability threshold structural equation model-fitting was conducted. Cannabis use was significantly correlated with PEs. Modest heritability (37%), common environmental influences (55%) and unique environment (8%) were found for cannabis use. For PEs, modest heritability (27-54%), unique environmental influences (E=12-50%) and little common environmental influences (11-20%), with the exception of parent-rated Negative Symptoms (42%), were reported. Environmental influences explained all of the covariation between cannabis use and paranoia, cognitive disorganization and parent-rated negative symptoms (bivariate common environment=69-100%, bivariate unique environment=28-31%), whilst the relationship between cannabis use and hallucinations indicated familial influences. Cannabis use explains 2-5% of variance in positive, cognitive, and negative PEs. Cannabis use and psychotic experience co-occur due to environmental factors. Focus on specific environments may reveal why adolescent cannabis use and psychotic experiences tend to 'travel together'. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Psychotic experiences are linked to cannabis use in adolescents in the community because of common underlying environmental risk factors

PubMed Central

Shakoor, Sania; Zavos, Helena M.S.; McGuire, Philip; Cardno, Alastair G.; Freeman, Daniel; Ronald, Angelica

2015-01-01

Cannabis users are more likely to have psychotic experiences (PEs). The degree to which these associations are driven by genetic or environmental influences in adolescence is unknown. This study estimated the genetic and environmental contributions to the relationship between cannabis use and PEs. Specific PEs were measured in a community-based twin sample (4830 16-year-old pairs) using self-reports and parent-reports. Adolescents reported on ever using cannabis. Multivariate liability threshold structural equation model-fitting was conducted. Cannabis use was significantly correlated with PEs. Modest heritability (37%), common environmental influences (55%) and unique environment (8%) were found for cannabis use. For PEs, modest heritability (27–54%), unique environmental influences (E=12–50%) and little common environmental influences (11–20%), with the exception of parent-rated Negative Symptoms (42%), were reported. Environmental influences explained all of the covariation between cannabis use and paranoia, cognitive disorganization and parent-rated negative symptoms (bivariate common environment=69–100%, bivariate unique environment=28–31%), whilst the relationship between cannabis use and hallucinations indicated familial influences. Cannabis use explains 2–5% of variance in positive, cognitive, and negative PEs. Cannabis use and psychotic experience co-occur due to environmental factors. Focus on specific environments may reveal why adolescent cannabis use and psychotic experiences tend to ‘travel together’. PMID:25912376

Analysis of overdispersed count data: application to the Human Papillomavirus Infection in Men (HIM) Study.

PubMed

Lee, J-H; Han, G; Fulp, W J; Giuliano, A R

2012-06-01

The Poisson model can be applied to the count of events occurring within a specific time period. The main feature of the Poisson model is the assumption that the mean and variance of the count data are equal. However, this equal mean-variance relationship rarely occurs in observational data. In most cases, the observed variance is larger than the assumed variance, which is called overdispersion. Further, when the observed data involve excessive zero counts, the problem of overdispersion results in underestimating the variance of the estimated parameter, and thus produces a misleading conclusion. We illustrated the use of four models for overdispersed count data that may be attributed to excessive zeros. These are Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial models. The example data in this article deal with the number of incidents involving human papillomavirus infection. The four models resulted in differing statistical inferences. The Poisson model, which is widely used in epidemiology research, underestimated the standard errors and overstated the significance of some covariates.

A General Definition of the Heritable Variation That Determines the Potential of a Population to Respond to Selection

PubMed Central

Bijma, Piter

2011-01-01

Genetic selection is a major force shaping life on earth. In classical genetic theory, response to selection is the product of the strength of selection and the additive genetic variance in a trait. The additive genetic variance reflects a population’s intrinsic potential to respond to selection. The ordinary additive genetic variance, however, ignores the social organization of life. With social interactions among individuals, individual trait values may depend on genes in others, a phenomenon known as indirect genetic effects. Models accounting for indirect genetic effects, however, lack a general definition of heritable variation. Here I propose a general definition of the heritable variation that determines the potential of a population to respond to selection. This generalizes the concept of heritable variance to any inheritance model and level of organization. The result shows that heritable variance determining potential response to selection is the variance among individuals in the heritable quantity that determines the population mean trait value, rather than the usual additive genetic component of phenotypic variance. It follows, therefore, that heritable variance may exceed phenotypic variance among individuals, which is impossible in classical theory. This work also provides a measure of the utilization of heritable variation for response to selection and integrates two well-known models of maternal genetic effects. The result shows that relatedness between the focal individual and the individuals affecting its fitness is a key determinant of the utilization of heritable variance for response to selection. PMID:21926298

A general definition of the heritable variation that determines the potential of a population to respond to selection.

PubMed

Bijma, Piter

2011-12-01

Genetic selection is a major force shaping life on earth. In classical genetic theory, response to selection is the product of the strength of selection and the additive genetic variance in a trait. The additive genetic variance reflects a population's intrinsic potential to respond to selection. The ordinary additive genetic variance, however, ignores the social organization of life. With social interactions among individuals, individual trait values may depend on genes in others, a phenomenon known as indirect genetic effects. Models accounting for indirect genetic effects, however, lack a general definition of heritable variation. Here I propose a general definition of the heritable variation that determines the potential of a population to respond to selection. This generalizes the concept of heritable variance to any inheritance model and level of organization. The result shows that heritable variance determining potential response to selection is the variance among individuals in the heritable quantity that determines the population mean trait value, rather than the usual additive genetic component of phenotypic variance. It follows, therefore, that heritable variance may exceed phenotypic variance among individuals, which is impossible in classical theory. This work also provides a measure of the utilization of heritable variation for response to selection and integrates two well-known models of maternal genetic effects. The result shows that relatedness between the focal individual and the individuals affecting its fitness is a key determinant of the utilization of heritable variance for response to selection.

The relationship between observational scale and explained variance in benthic communities

PubMed Central

Flood, Roger D.; Frisk, Michael G.; Garza, Corey D.; Lopez, Glenn R.; Maher, Nicole P.

2018-01-01

This study addresses the impact of spatial scale on explaining variance in benthic communities. In particular, the analysis estimated the fraction of community variation that occurred at a spatial scale smaller than the sampling interval (i.e., the geographic distance between samples). This estimate is important because it sets a limit on the amount of community variation that can be explained based on the spatial configuration of a study area and sampling design. Six benthic data sets were examined that consisted of faunal abundances, common environmental variables (water depth, grain size, and surficial percent cover), and sonar backscatter treated as a habitat proxy (categorical acoustic provinces). Redundancy analysis was coupled with spatial variograms generated by multiscale ordination to quantify the explained and residual variance at different spatial scales and within and between acoustic provinces. The amount of community variation below the sampling interval of the surveys (< 100 m) was estimated to be 36–59% of the total. Once adjusted for this small-scale variation, > 71% of the remaining variance was explained by the environmental and province variables. Furthermore, these variables effectively explained the spatial structure present in the infaunal community. Overall, no scale problems remained to compromise inferences, and unexplained infaunal community variation had no apparent spatial structure within the observational scale of the surveys (> 100 m), although small-scale gradients (< 100 m) below the observational scale may be present. PMID:29324746

Genomic scan as a tool for assessing the genetic component of phenotypic variance in wild populations.

PubMed

Herrera, Carlos M

2012-01-01

Methods for estimating quantitative trait heritability in wild populations have been developed in recent years which take advantage of the increased availability of genetic markers to reconstruct pedigrees or estimate relatedness between individuals, but their application to real-world data is not exempt from difficulties. This chapter describes a recent marker-based technique which, by adopting a genomic scan approach and focusing on the relationship between phenotypes and genotypes at the individual level, avoids the problems inherent to marker-based estimators of relatedness. This method allows the quantification of the genetic component of phenotypic variance ("degree of genetic determination" or "heritability in the broad sense") in wild populations and is applicable whenever phenotypic trait values and multilocus data for a large number of genetic markers (e.g., amplified fragment length polymorphisms, AFLPs) are simultaneously available for a sample of individuals from the same population. The method proceeds by first identifying those markers whose variation across individuals is significantly correlated with individual phenotypic differences ("adaptive loci"). The proportion of phenotypic variance in the sample that is statistically accounted for by individual differences in adaptive loci is then estimated by fitting a linear model to the data, with trait value as the dependent variable and scores of adaptive loci as independent ones. The method can be easily extended to accommodate quantitative or qualitative information on biologically relevant features of the environment experienced by each sampled individual, in which case estimates of the environmental and genotype × environment components of phenotypic variance can also be obtained.

Modeling the Etiology of Individual Differences in Early Reading Development: Evidence for Strong Genetic Influences

PubMed Central

Christopher, Micaela E.; Hulslander, Jacqueline; Byrne, Brian; Samuelsson, Stefan; Keenan, Janice M.; Pennington, Bruce; DeFries, John C.; Wadsworth, Sally J.; Willcutt, Erik; Olson, Richard K.

2012-01-01

We explored the etiology of individual differences in reading development from post-kindergarten to post-4th grade by analyzing data from 487 twin pairs tested in Colorado. Data from three reading measures and one spelling measure were fit to biometric latent growth curve models, allowing us to extend previous behavioral genetic studies of the etiology of early reading development at specific time points. We found primarily genetic influences on individual differences at post-1st grade for all measures. Genetic influences on variance in growth rates were also found, with evidence of small, nonsignificant, shared environmental influences for two measures. We discuss our results, including their implications for educational policy. PMID:24489459

Maternal-by-environment but not genotype-by-environment interactions in a fish without parental care.

PubMed

Vega-Trejo, Regina; Head, Megan L; Jennions, Michael D; Kruuk, Loeske E B

2018-01-01

The impact of environmental conditions on the expression of genetic variance and on maternal effects variance remains an important question in evolutionary quantitative genetics. We investigate here the effects of early environment on variation in seven adult life history, morphological, and secondary sexual traits (including sperm characteristics) in a viviparous poeciliid fish, the mosquitofish Gambusia holbrooki. Specifically, we manipulated food availability during early development and then assessed additive genetic and maternal effects contributions to the overall phenotypic variance in adults. We found higher heritability for female than male traits, but maternal effects variance for traits in both sexes. An interaction between maternal effects variance and rearing environment affected two adult traits (female age at maturity and male size at maturity), but there was no evidence of trade-offs in maternal effects across environments. Our results illustrate (i) the potential for pre-natal maternal effects to interact with offspring environment during development, potentially affecting traits through to adulthood and (ii) that genotype-by-environment interactions might be overestimated if maternal-by-environment interactions are not accounted for, similar to heritability being overestimated if maternal effects are ignored. We also discuss the potential for dominance genetic variance to contribute to the estimate of maternal effects variance.

Safety climate in the federal fire management community: Influences of organizational, environmental, group, and individual characteristics (Abstract)

Treesearch

Brooke Baldauf McBride; Anne E. Black

2012-01-01

This study examined the effects of organizational, environmental, group and individual characteristics on five components of safety climate in the US federal fire management community (HRO Practices, Leadership, Group Culture, Learning Orientation and Mission Clarity). Multiple analyses of variance revealed that all types of characteristics had a significant effect on...

Genetic and Environmental Effects on Stuttering: A Twin Study from Finland

ERIC Educational Resources Information Center

Rautakoski, Pirkko; Hannus, Therese; Simberg, Susanna; Sandnabba, N. Kenneth; Santtila, Pekka

2012-01-01

The present study explored the prevalence of self-reported stuttering in a Finnish twin population and examined the extent to which the variance in liability to stuttering was attributable to genetic and environmental effects. We analyzed data of 1728 Finnish twins, born between 1961 and 1989. The participants were asked to complete a…

Clock genes explain large proportion of phenotypic variance in systolic blood pressure and this control is not modified by environmental temperature

USDA-ARS?s Scientific Manuscript database

BACKGROUND: Diurnal variation in blood pressure (BP) is regulated, in part, by an endogenous circadian clock; however, few human studies have identified associations between clock genes and BP. Accounting for environmental temperature may be necessary to correct for seasonal bias. METHODS: We examin...

Stability of Playfulness across Environmental Settings: A Pilot Study

ERIC Educational Resources Information Center

Rigby, Patricia; Gaik, Sandy

2007-01-01

The Test of Playfulness (ToP) was used in this pilot study to examine the stability of playfulness of 16 children with cerebral palsy (CP), aged 4-8 years, across three environmental settings: home, community, and school. Each videotaped play segment was scored using the ToP. The ANOVA statistic demonstrated a significant variance (p less than…

Photosynthetic performance of restored and natural mangroves under different environmental constraints.

PubMed

Rovai, André Scarlate; Barufi, José Bonomi; Pagliosa, Paulo Roberto; Scherner, Fernando; Torres, Moacir Aluísio; Horta, Paulo Antunes; Simonassi, José Carlos; Quadros, Daiane Paula Cunha; Borges, Daniel Lázaro Gallindo; Soriano-Sierra, Eduardo Juan

2013-10-01

We hypothesized that the photosynthetic performance of mangrove stands restored by the single planting of mangroves species would be lowered due to residual stressors. The photosynthetic parameters of the vegetation of three planted mangrove stands, each with a different disturbance history, were compared to reference sites and correlated with edaphic environmental variables. A permutational analysis of variance showed significant interaction when the factors were compared, indicating that the photosynthetic parameters of the restoration areas differed from the reference sites. A univariate analysis of variance showed that all the photosynthetic parameters differed between sites and treatments, except for photosynthetic efficiency (αETR). The combination of environmental variables that best explained the variations observed in the photosynthetic performance indicators were Cu, Pb and elevation disruptions. Fluorescence techniques proved efficient in revealing important physiological differences, representing a powerful tool for rapid analysis of the effectiveness of initiatives aimed at restoring coastal environments. Copyright © 2013 Elsevier Ltd. All rights reserved.

Genetic analysis of longitudinal measurements of performance traits in selection lines for residual feed intake in Yorkshire swine.

PubMed

Cai, W; Kaiser, M S; Dekkers, J C M

2011-05-01

A 5-generation selection experiment in Yorkshire pigs for feed efficiency consists of a line selected for low residual feed intake (LRFI) and a random control line (CTRL). The objectives of this study were to use random regression models to estimate genetic parameters for daily feed intake (DFI), BW, backfat (BF), and loin muscle area (LMA) along the growth trajectory and to evaluate the effect of LRFI selection on genetic curves for DFI and BW. An additional objective was to compare random regression models using polynomials (RRP) and spline functions (RRS). Data from approximately 3 to 8 mo of age on 586 boars and 495 gilts across 5 generations were used. The average number of measurements was 85, 14, 5, and 5 for DFI, BW, BF, and LMA. The RRP models for these 4 traits were fitted with pen × on-test group as a fixed effect, second-order Legendre polynomials of age as fixed curves for each generation, and random curves for additive genetic and permanent environmental effects. Different residual variances were used for the first and second halves of the test period. The RRS models were fitted with the same fixed effects and residual variance structure as the RRP models and included genetic and permanent environmental random effects for both splines and linear Legendre polynomials of age. The RRP model was used for further analysis because the RRS model had erratic estimates of phenotypic variance and heritability, despite having a smaller Bayesian information criterion than the RRP model. From 91 to 210 d of age, estimates of heritability from the RRP model ranged from 0.10 to 0.37 for boars and 0.14 to 0.26 for gilts for DFI, from 0.39 to 0.58 for boars and 0.55 to 0.61 for gilts for BW, from 0.48 to 0.61 for boars and 0.61 to 0.79 for gilts for BF, and from 0.46 to 0.55 for boars and 0.63 to 0.81 for gilts for LMA. In generation 5, LRFI pigs had lower average genetic curves than CTRL pigs for DFI and BW, especially toward the end of the test period; estimated line differences (CTRL-LRFI) for DFI were 0.04 kg/d for boars and 0.12 kg/d for gilts at 105 d and 0.20 kg/d for boars and 0.24 kg/d for gilts at 195 d. Line differences for BW were 0.17 kg for boars and 0.69 kg for gilts at 105 d and 3.49 kg for boars and 8.96 kg for gilts at 195 d. In conclusion, selection for LRFI has resulted in a lower feed intake curve and a lower BW curve toward maturity.

Linear reaction norm models for genetic merit prediction of Angus cattle under genotype by environment interaction.

PubMed

Cardoso, F F; Tempelman, R J

2012-07-01

The objectives of this work were to assess alternative linear reaction norm (RN) models for genetic evaluation of Angus cattle in Brazil. That is, we investigated the interaction between genotypes and continuous descriptors of the environmental variation to examine evidence of genotype by environment interaction (G×E) in post-weaning BW gain (PWG) and to compare the environmental sensitivity of national and imported Angus sires. Data were collected by the Brazilian Angus Improvement Program from 1974 to 2005 and consisted of 63,098 records and a pedigree file with 95,896 animals. Six models were implemented using Bayesian inference and compared using the Deviance Information Criterion (DIC). The simplest model was M(1), a traditional animal model, which showed the largest DIC and hence the poorest fit when compared with the 4 alternative RN specifications accounting for G×E. In M(2), a 2-step procedure was implemented using the contemporary group posterior means of M(1) as the environmental gradient, ranging from -92.6 to +265.5 kg. Moreover, the benefits of jointly estimating all parameters in a 1-step approach were demonstrated by M(3). Additionally, we extended M(3) to allow for residual heteroskedasticity using an exponential function (M(4)) and the best fitting (smallest DIC) environmental classification model (M(5)) specification. Finally, M(6) added just heteroskedastic residual variance to M(1). Heritabilities were less at harsh environments and increased with the improvement of production conditions for all RN models. Rank correlations among genetic merit predictions obtained by M(1) and by the best fitting RN models M(3) (homoskedastic) and M(5) (heteroskedastic) at different environmental levels ranged from 0.79 and 0.81, suggesting biological importance of G×E in Brazilian Angus PWG. These results suggest that selection progress could be optimized by adopting environment-specific genetic merit predictions. The PWG environmental sensitivity of imported North American origin bulls (0.046 ± 0.009) was significantly larger (P < 0.05) than that of local sires (0.012 ± 0.013). Moreover, PWG of progeny of imported sires exceeded that of native sires in medium and superior production levels. On the other hand, Angus cattle locally selected in Brazil tended to be more robust to environmental changes and hence be more suitable when production environments for potential progeny is uncertain.

Gender differences in patterns of spatial ability, environmental cognition, and math and English achievement in late adolescence.

PubMed

Pearson, J L; Ferguson, L R

1989-01-01

Relationships were explored among three measures of spatial ability--the Embedded Figures Test (EFT), the Mental Rotations Test (MRT), and the Differential Aptitude Spatial Relations subtest (DAT)--an environmental cognition task (MAP), American College Testing (ACT) math and English achievement, and gender in a sample of 282 undergraduates. Variance attributable to gender among the spatial tasks ranged from 0.5% in the EFT to 12% in the MRT. Gender accounted for only 1% of the variance in the MAP task. Gender differences were noted in regression analyses; women's math and English achievement scores were both predictive of spatial ability, while for men, only math achievement was predictive of spatial ability. The results were interpreted as substantiating sex role socialization theory of cognitive abilities.

Establishing Functional Relationships between Abiotic Environment, Macrophyte Coverage, Resource Gradients and the Distribution of Mytilus trossulus in a Brackish Non-Tidal Environment.

PubMed

Kotta, Jonne; Oganjan, Katarina; Lauringson, Velda; Pärnoja, Merli; Kaasik, Ants; Rohtla, Liisa; Kotta, Ilmar; Orav-Kotta, Helen

2015-01-01

Benthic suspension feeding mussels are an important functional guild in coastal and estuarine ecosystems. To date we lack information on how various environmental gradients and biotic interactions separately and interactively shape the distribution patterns of mussels in non-tidal environments. Opposing to tidal environments, mussels inhabit solely subtidal zone in non-tidal waterbodies and, thereby, driving factors for mussel populations are expected to differ from the tidal areas. In the present study, we used the boosted regression tree modelling (BRT), an ensemble method for statistical techniques and machine learning, in order to explain the distribution and biomass of the suspension feeding mussel Mytilus trossulus in the non-tidal Baltic Sea. BRT models suggested that (1) distribution patterns of M. trossulus are largely driven by separate effects of direct environmental gradients and partly by interactive effects of resource gradients with direct environmental gradients. (2) Within its suitable habitat range, however, resource gradients had an important role in shaping the biomass distribution of M. trossulus. (3) Contrary to tidal areas, mussels were not competitively superior over macrophytes with patterns indicating either facilitative interactions between mussels and macrophytes or co-variance due to common stressor. To conclude, direct environmental gradients seem to define the distribution pattern of M. trossulus, and within the favourable distribution range, resource gradients in interaction with direct environmental gradients are expected to set the biomass level of mussels.

Same genetic components underlie different measures of sweet taste preference.

PubMed

Keskitalo, Kaisu; Tuorila, Hely; Spector, Tim D; Cherkas, Lynn F; Knaapila, Antti; Silventoinen, Karri; Perola, Markus

2007-12-01

Sweet taste preferences are measured by several often correlated measures. We examined the relative proportions of genetic and environmental effects on sweet taste preference indicators and their mutual correlations. A total of 663 female twins (324 complete pairs, 149 monozygous and 175 dizygous pairs) aged 17-80 y rated the liking and intensity of a 20% (wt/vol) sucrose solution, reported the liking and the use-frequency of 6 sweet foods (sweet desserts, sweets, sweet pastry, ice cream, hard candy, and chocolate), and completed a questionnaire on cravings of sweet foods. The estimated contributions of genetic factors, environmental factors shared by a twin pair, and environmental factors unique to each twin individual to the variance and covariance of the traits were obtained with the use of linear structural equation modeling. Approximately half of the variation in liking for sweet solution and liking and use-frequency of sweet foods (49-53%) was explained by genetic factors, whereas the rest of the variation was due to environmental factors unique to each twin individual. Sweet taste preference-related traits were correlated. Tetravariate modeling showed that the correlation between liking for the sweet solution and liking for sweet foods was due to genetic factors (genetic r = 0.27). Correlations between liking, use-frequency, and craving for sweet foods were due to both genetic and unshared environmental factors. Detailed information on the associations between preference measures is an important intermediate goal in the determination of the genetic components affecting sweet taste preferences.

Community assembly processes underlying phytoplankton and bacterioplankton across a hydrologic change in a human-impacted river.

PubMed

Isabwe, Alain; Yang, Jun R; Wang, Yongming; Liu, Lemian; Chen, Huihuang; Yang, Jun

2018-07-15

Although the influence of microbial community assembly processes on aquatic ecosystem function and biodiversity is well known, the processes that govern planktonic communities in human-impacted rivers remain largely unstudied. Here, we used multivariate statistics and a null model approach to test the hypothesis that environmental conditions and obstructed dispersal opportunities, dictate a deterministic community assembly for phytoplankton and bacterioplankton across contrasting hydrographic conditions in a subtropical mid-sized river (Jiulong River, southeast China). Variation partitioning analysis showed that the explanatory power of local environmental variables was larger than that of the spatial variables for both plankton communities during the dry season. During the wet season, phytoplankton community variation was mainly explained by local environmental variables, whereas the variance in bacterioplankton was explained by both environmental and spatial predictors. The null model based on Raup-Crick coefficients for both planktonic groups suggested little evidences of the stochastic processes involving dispersal and random distribution. Our results showed that hydrological change and landscape structure act together to cause divergence in communities along the river channel, thereby dictating a deterministic assembly and that selection exceeds dispersal limitation during the dry season. Therefore, to protect the ecological integrity of human-impacted rivers, watershed managers should not only consider local environmental conditions but also dispersal routes to account for the effect of regional species pool on local communities. Copyright © 2018 Elsevier B.V. All rights reserved.

Establishing Functional Relationships between Abiotic Environment, Macrophyte Coverage, Resource Gradients and the Distribution of Mytilus trossulus in a Brackish Non-Tidal Environment

PubMed Central

Kotta, Jonne; Oganjan, Katarina; Lauringson, Velda; Pärnoja, Merli; Kaasik, Ants; Rohtla, Liisa; Kotta, Ilmar; Orav-Kotta, Helen

2015-01-01

Benthic suspension feeding mussels are an important functional guild in coastal and estuarine ecosystems. To date we lack information on how various environmental gradients and biotic interactions separately and interactively shape the distribution patterns of mussels in non-tidal environments. Opposing to tidal environments, mussels inhabit solely subtidal zone in non-tidal waterbodies and, thereby, driving factors for mussel populations are expected to differ from the tidal areas. In the present study, we used the boosted regression tree modelling (BRT), an ensemble method for statistical techniques and machine learning, in order to explain the distribution and biomass of the suspension feeding mussel Mytilus trossulus in the non-tidal Baltic Sea. BRT models suggested that (1) distribution patterns of M. trossulus are largely driven by separate effects of direct environmental gradients and partly by interactive effects of resource gradients with direct environmental gradients. (2) Within its suitable habitat range, however, resource gradients had an important role in shaping the biomass distribution of M. trossulus. (3) Contrary to tidal areas, mussels were not competitively superior over macrophytes with patterns indicating either facilitative interactions between mussels and macrophytes or co-variance due to common stressor. To conclude, direct environmental gradients seem to define the distribution pattern of M. trossulus, and within the favourable distribution range, resource gradients in interaction with direct environmental gradients are expected to set the biomass level of mussels. PMID:26317668

Genetic and environmental contributions to the associations between intraindividual variability in reaction time and cognitive function.

PubMed

Finkel, Deborah; Pedersen, Nancy L

2014-01-01

Intraindividual variability (IIV) in reaction time has been related to cognitive decline, but questions remain about the nature of this relationship. Mean and range in movement and decision time for simple reaction time were available from 241 individuals aged 51-86 years at the fifth testing wave of the Swedish Adoption/Twin Study of Aging. Cognitive performance on four factors was also available: verbal, spatial, memory, and speed. Analyses indicated that range in reaction time could be used as an indicator of IIV. Heritability estimates were 35% for mean reaction and 20% for range in reaction. Multivariate analysis indicated that the genetic variance on the memory, speed, and spatial factors is shared with genetic variance for mean or range in reaction time. IIV shares significant genetic variance with fluid ability in late adulthood, over and above and genetic variance shared with mean reaction time.

Heritability of insomnia symptoms in youth and their relationship to depression and anxiety.

PubMed

Gehrman, Philip R; Meltzer, Lisa J; Moore, Melisa; Pack, Allan I; Perlis, Michael L; Eaves, Lindon J; Silberg, Judy L

2011-12-01

Insomnia is a highly prevalent sleep disorder yet little is known about the role of genetic factors in its pathophysiology. The aim of this study was to examine the relative contributions of genetic and environmental factors in explaining variability in insomnia symptoms. Traditional twin design. Academic medical center. 1412 twin pairs aged 8-16 years (48.8% MZ, 47.2% DZ, 4.0% indeterminate). None. Ratings of insomnia symptoms, depression, and overanxious disorder were made by trained interviewers based on DSM-III-R criteria. ACE models were conducted using Mx statistical software. Insomnia symptoms were prevalent in this sample based both on parental (6.6%) and youth (19.5%) reports. The overall heritability of insomnia symptoms was modest (30.7%), with the remaining variance attributed to unique environmental effects. There was no evidence of sex differences in the prevalence of insomnia symptoms or in the contribution of genetic and environmental effects. In multivariate models, there was support for insomnia-specific unique environmental effects over and above overlapping effects with depression and overanxious disorder, but no evidence for insomnia-specific genetic effects. Genetic factors play a modest role in the etiology of insomnia symptoms in 8-16 year-olds. These effects overlap with the genetics of depression and overanxious disorder. Further work is needed to determine which genes confer risk for all three disorders.

Performance of time-varying predictors in multilevel models under an assumption of fixed or random effects.

PubMed

Baird, Rachel; Maxwell, Scott E

2016-06-01

Time-varying predictors in multilevel models are a useful tool for longitudinal research, whether they are the research variable of interest or they are controlling for variance to allow greater power for other variables. However, standard recommendations to fix the effect of time-varying predictors may make an assumption that is unlikely to hold in reality and may influence results. A simulation study illustrates that treating the time-varying predictor as fixed may allow analyses to converge, but the analyses have poor coverage of the true fixed effect when the time-varying predictor has a random effect in reality. A second simulation study shows that treating the time-varying predictor as random may have poor convergence, except when allowing negative variance estimates. Although negative variance estimates are uninterpretable, results of the simulation show that estimates of the fixed effect of the time-varying predictor are as accurate for these cases as for cases with positive variance estimates, and that treating the time-varying predictor as random and allowing negative variance estimates performs well whether the time-varying predictor is fixed or random in reality. Because of the difficulty of interpreting negative variance estimates, 2 procedures are suggested for selection between fixed-effect and random-effect models: comparing between fixed-effect and constrained random-effect models with a likelihood ratio test or fitting a fixed-effect model when an unconstrained random-effect model produces negative variance estimates. The performance of these 2 procedures is compared. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Accounting for the measurement error of spectroscopically inferred soil carbon data for improved precision of spatial predictions.

PubMed

Somarathna, P D S N; Minasny, Budiman; Malone, Brendan P; Stockmann, Uta; McBratney, Alex B

2018-08-01

Spatial modelling of environmental data commonly only considers spatial variability as the single source of uncertainty. In reality however, the measurement errors should also be accounted for. In recent years, infrared spectroscopy has been shown to offer low cost, yet invaluable information needed for digital soil mapping at meaningful spatial scales for land management. However, spectrally inferred soil carbon data are known to be less accurate compared to laboratory analysed measurements. This study establishes a methodology to filter out the measurement error variability by incorporating the measurement error variance in the spatial covariance structure of the model. The study was carried out in the Lower Hunter Valley, New South Wales, Australia where a combination of laboratory measured, and vis-NIR and MIR inferred topsoil and subsoil soil carbon data are available. We investigated the applicability of residual maximum likelihood (REML) and Markov Chain Monte Carlo (MCMC) simulation methods to generate parameters of the Matérn covariance function directly from the data in the presence of measurement error. The results revealed that the measurement error can be effectively filtered-out through the proposed technique. When the measurement error was filtered from the data, the prediction variance almost halved, which ultimately yielded a greater certainty in spatial predictions of soil carbon. Further, the MCMC technique was successfully used to define the posterior distribution of measurement error. This is an important outcome, as the MCMC technique can be used to estimate the measurement error if it is not explicitly quantified. Although this study dealt with soil carbon data, this method is amenable for filtering the measurement error of any kind of continuous spatial environmental data. Copyright © 2018 Elsevier B.V. All rights reserved.

Foraminifera Record the Good Years More than the Bad

NASA Astrophysics Data System (ADS)

Hull, P. M.

2014-12-01

Past ocean conditions are primarily discerned from geochemical and community-based analyses of fossilized taxa, each of which have unique environmental niches and dynamics. A key requirement of such paleoceanographic studies is that some unbiased or well-constrained record of the living ecosystem and climate is deposited on the sea floor and preserved through the post-depositional processes that act to distort them. It is widely known that foraminiferal species exhibit varying seasonal preferences and that seasonality is a key variable to account for in paleoceanographic reconstructions. However, on longer time scales (> year), it is generally assumed that species record the 'average' environmental conditions or typical variance (e.g., El Nino intensity) that existed in a given, time-averaged sediment sample. Here I examine planktonic foraminiferal population dynamics on yearly and longer time scales, in order to quantify their effect on paleoceanographic reconstructions. Using a previously published record of >250 years of population dynamics in the Santa Barbara Basin sediments, I find that the majority of individuals in a given species lived during a small subset of the total years (~15- 37% of years depending on the species). Populations of shallow, mixed layer species primarily represent the warmest, youngest years, while thermocline species primarily represent the cooler, older years. Importantly, the seasonality of species does not always predict their interannual dynamics. The general importance of long time-scale population dynamics on paleoceanographic reconstructions will also be considered in a theoretical model parameterized with temporally explicit species co-variances and temperature variability. Such modeling is needed to constrain the relative impact that a very good year can have on our interpretation of the 'average' of hundreds to thousands of years.

Vertebral Artery Diameter and Flow: Nature or Nurture.

PubMed

Tarnoki, Adam Domonkos; Fejer, Bence; Tarnoki, David Laszlo; Littvay, Levente; Lucatelli, Pierleone; Cirelli, Carlo; Fanelli, Fabrizio; Sacconi, Beatrice; Fagnani, Corrado; Medda, Emanuela; Farina, Filippo; Meneghetti, Giorgio; Horvath, Tamas; Pucci, Giacomo; Schillaci, Giuseppe; Stazi, Maria Antonietta; Baracchini, Claudio

2017-09-01

In contrast with the carotid arteries, the vertebral arteries (VAs) show considerable variation in length, caliber, and vessel course. This study investigated whether the variation in diameter and flow characteristics of the VAs might be inherited. A total of 172 Italian twins from Padua, Perugia, and Terni (54 monozygotic, 32 dizygotic) recruited from the Italian Twin Registry underwent B-mode and pulsed-wave Doppler ultrasound assessment of their VAs. VA diameters, peak systolic velocity (PSV) and end diastolic velocity (EDV) were assessed at the level of a horizontal V2 segment. Univariate quantitative genetic modeling was performed. Fourteen percent of the sample had VA hypoplasia. Within pair correlation in monozygotic twins was higher than in dizygotics (.552 vs. .229) for VA diameter. Age- and sex-adjusted genetic effect, under the most parsimonious model, accounted for 54.7% (95% CI: 42.2-69.1%) of the variance of VA diameter, and unshared environmental effect for 45.3% (95% CI: 30.9-57.8%). No heritability was found for the PSV of VA, but shared (34.1%; 95% CI: 16.7-53.7%) and unshared (65.9%; 95% CI: 45.9-83.1%) environmental factors determined the variance. EDV of VA is moderately genetically influenced (42.4%; 95% CI: 16.1-64.9%) and also determined by the unshared environment (57.6%; 95% CI: 34.7-83.7%). The diameter of the VAs is moderately genetically determined. Different factors influence the PSV and EDV of VAs, which may highlight the complex hemodynamic background of VA flow and help to understand the vertebral flow anomalies found by ultrasound. Copyright © 2017 by the American Society of Neuroimaging.

Technical and biological variance structure in mRNA-Seq data: life in the real world

PubMed Central

2012-01-01

Background mRNA expression data from next generation sequencing platforms is obtained in the form of counts per gene or exon. Counts have classically been assumed to follow a Poisson distribution in which the variance is equal to the mean. The Negative Binomial distribution which allows for over-dispersion, i.e., for the variance to be greater than the mean, is commonly used to model count data as well. Results In mRNA-Seq data from 25 subjects, we found technical variation to generally follow a Poisson distribution as has been reported previously and biological variability was over-dispersed relative to the Poisson model. The mean-variance relationship across all genes was quadratic, in keeping with a Negative Binomial (NB) distribution. Over-dispersed Poisson and NB distributional assumptions demonstrated marked improvements in goodness-of-fit (GOF) over the standard Poisson model assumptions, but with evidence of over-fitting in some genes. Modeling of experimental effects improved GOF for high variance genes but increased the over-fitting problem. Conclusions These conclusions will guide development of analytical strategies for accurate modeling of variance structure in these data and sample size determination which in turn will aid in the identification of true biological signals that inform our understanding of biological systems. PMID:22769017